Department of Statistics - BSMRSTU (Bangabandhu … of Statistics ... and Probability, 3rd edition, Mullick& Brothers. 7. Gupta, S.P. and ... Introduction to Mathematical Statistics,

Bangabandhu Sheikh Mujibur Rahman Science and Technology University, Gopalganj – 8100

Detailed Syllabus for B. Sc. (Hons.), Session: 2015-2016

Department of Statistics

Faculty of Science

Detailed Syllabus for BSMRST University First Year

1st Sem

este

r

Course Code Course Title Credit Hours Credit STA100 Principles of Statistics I 39 3 STA102 Probability Theory I 39 3

STA140 Computer Fundamentals and Spreadsheet Software Applications on Statistics

39 3

MAT114 Basic Mathematics 39 3 MAT116 Calculus 39 3 ECO124 Principles of Economics I 26 2 STA131 Introductory Statistical Lab 26 2 STA149 Viva-Voce 1 Total 20

2nd S

emes

ter

STA150 Principles of Statistics II 39 3 STA170 Introduction to Demography 39 3 STA152 Probability Theory II 39 3 MAT164 Advanced Calculus 39 3 MAT166 Matrix Algebra 39 3 ECO174 Principles of Economics II 26 2 STA191 Principles of Statistics and Demography Lab 26 2 STA199 Viva-Voce 1

Total 20 1st Year Total 40

Second Year

1st Sem

este

r

Course Code Course Title Credit Hours Credit STA200 Probability Distribution 39 3 STA202 Sampling Distribution 26 2 STA240 Regression Analysis 39 3 STA242 Statistical Inference I 39 3 MAT224 Differential Equations with Linear Algebra 39 3 MAT226 Complex Variable and Transformations 39 3 STA231 Sampling distribution and Inference Lab 26 2 STA233 Probability distribution and Regression Lab 26 2 STA249 Viva-Voce 1

Total 22

2nd S

emes

ter

STA250 Sampling Techniques 39 3 STA252 Programming In C 39 3 STA260 Order Statistics 26 2 STA262 Operation Research 26 2 STA270 Design and Analysis of Experiments I 26 2 MAT274 Numerical Methods in C 26 2 STA281 Sampling and Agricultural Statistics Lab 26 2 STA283 C and Operation Research Lab 26 2 STA299 Viva-Voce 1

Total 19 2nd Year Total 41

Third Year 1st S

emes

ter

Course Code Course Title Credit Hours Credit STA300 Statistical Inference II 26 2 STA302 Generalized Linear Model 26 2 STA310 Basic Econometrics 39 3 STA320 Time Series Analysis 39 3 STA340 Introduction to R 26 2 STA342 Population Studies 39 3 STA331 R, Econometrics and GLM Lab 26 2 STA333 Inference, Time series and Population Studies Lab 26 2 STA349 Viva-Voce 1

Total 20

2nd S

emes

ter

STA350 Industrial Statistics 26 2 STA352 Non-parametric and Robust Methods 26 2 STA360 Epidemiology 39 3 STA362 Programming in SPSS and STATA 39 3 STA370 Fundamental Multivariate Analysis 39 3 STA372 Actuarial Statistics 26 2 STA381 Actuarial, Epidemiology and Programming Lab 26 2

STA383 Multivariate, Nonparametric and Industrial Statistics Lab 26 2

STA399 Viva-Voce 1 Total 20

3rd Year Total 40

Fourth Year

1st Sem

este

r

Course Code Course Title Credit Hours Credit STA408 Project Thesis I 26 2 STA400 Simulation and Modeling 39 3 STA402 Bio-statistics 39 3 STA422 Stochastic Process 39 3 STA430 Design and Analysis of Experiments II 39 3 STA431 Experimental Design and Social Statistics Lab 26 2 STA433 Simulation and Biostatistics Lab 26 2 STA449 Viva-Voce 1

Total 19

2nd S

emes

ter

STA458 Project Thesis II 26 2 STA452 Environmental Statistics 26 2 STA460 Advanced Econometrics 39 3 STA462 Advanced Sampling 39 3 STA470 Advanced Multivariate Analysis 39 3 CSE456 Data Structure and Database Technology 26 2 CSE485 Data Structure and Database Technology Lab 26 1 STA481 Multivariate and Environmental Statistics Lab 26 1.5 STA483 Sampling and Econometrics Lab 26 1.5 STA499 Viva-Voce 1

Total 20 4th Year Total 39

Grand Total (1st Year - 4th Year) 160

Bangabandhu Sheikh Mujibur Rahman Science and Technology University

Gopalganj – 8100, Bangladesh Faculty of SCIENCE

Department of Statistics Detailed Syllabus for B. Sc. (Hons.), Session: 2013-2014

The B.Sc. (Hons.) courses in Statistics (STA) consist of total 160 credits distributed over eight

semesters in four academic years. Each academic year will be divided into two semesters (1st Semester

& 2nd Semester). Final examinations held at the end of each semester. The medium of answer in the

examination of all written, laboratory and other courses will be English. The theoretical examination of

courses less than or equal to 2 Credits shall be of 3 hours duration and courses greater than 3 Credits

shall be of 4 hours duration. By the approval of the Academic Committee, an academic schedule for the

academic year shall be announced for general notification before the start of the academic year. No

student shall ordinarily be admitted after the class starts.

Summary of the Syllabus

Credits distribution and Percentage in Statistics disciplines for B.Sc. (Hons.)

Course Type Percentage Credits Non major 16.88 27

Major 83.12 133 Total 100 160

1st Year 1st Semester

STA100: Principles of Statistics I [70% Exam, 20% Quizzes/ Class Tests, 10% Attendance]

3 credit, 39 Contact hours, Exam. Time: 4 hours

Statistics: Definition, History, Nature, Scope and Classification of Statistics, Limitation, Uses and Abuses of Statistics. Data Collection: Population, Sample, Parameter, Statistic, Variables and Attributes, Types of Variables, Scales of Measurement: Nominal, Ordinal, Interval and Ratio; Primary and Secondary Data, Methods of Collecting Data. Data Organization: Classification and Tabulation of Data, Frequency Distribution, Graphical Presentation of Data, Stem and Leaf Display, Dot Plot, Time Series Plot, Outliers and 5-number summaries, Box Plots and their Uses. Measures of central Tendency: Mean, Median, Mode, Geometric Mean, Harmonic Mean, Trimmed mean with their properties, Quantiles with their Graphical Presentation, Application of Measures of Central Tendency. Measures of Dispersion: Absolute Measures of Dispersion: Range, Quartile Deviation, Mean Deviation, Variance and Standard Deviation. Relative Measures of Dispersion: Coefficient of Range, Coefficient of Quartile deviation, Coefficient of Mean Deviation and Coefficient of Variation. Application of Different Measures of Dispersion. Moments and Shape Characteristics of Distribution: Moments, Sheppard’s Corrections for Grouping Error, Skewness and Kurtosis with their Applications in Other Fields.

Books Recommended:

1. Anderson, A.J.B (1989): Interpreting Data, Chapman and Hall, London. 2. Hogg and Tanis (2001): Probability and Statistical Inference, 6th ed., Prentice Hall, N.J. 3. Newbold, P., Carlson, W.L. and Thome, B. (2008), Statistics for Business and Economics,Sixth

Edition, Prentice-Hall, Inc. 4. Stuart, A. and Ord, J. K. (1994): Kendall’s Advanced Theory of Statistics, Vol 1: Distribution

Theory, 6th edition, A Hodder Arnold Publication. 5. K.C. Bhuyan (2011): Methods of Statistics, Sahitya Prokashani, Dhaka. 6. Islam, M. N (2004): An Introduction to Statistics and Probability, 3rd edition, Mullick&

Brothers. 7. Gupta, S.P. and Kapoor, V.K(2009): Fundamentals of Mathematical Statistics,11th edition,

Sultan Chand &Sons. 8. Miller, I. and Miller, M. (2005): Mathematical Statistics with Applications, 7th edition, Pearson

Education. 9. Shil, R.N. and Debnath, S.C.(2009): An Introduction to the Theory of Statistics, Latest edition.

STA102: Probability Theory I

[70% Exam, 20% Quizzes/ Class Tests, 10% Attendance] 3 credit, 39 Contact hours, Exam. Time: 4hours

Set Theory: Set, Element of a Set, Describing set- List method and Roster method, Finite set, Infinite set, Universal set, Empty set and subsets. Set operations and their properties. Functions and set functions. Combinatorial Methods: Methods of counting sample points, Review of Permutations, Combinations and Series. Probability: Approaches to defining Probability: Subjective and Objective Probabilities, Classical, Empirical, Geometric and Axiomatic, Experiment, Outcomes, Equally likely outcomes, Mutually exclusive outcomes, Favourable outcomes, Exhaustive outcomes, Random experiment, Sample space and events, Event space, Union and Intersection of Events, Different Types of Events, Odds and Odds Ratio, Probability Functions, Theorems on Probability, Additive law of probability Probability Space, Application of Bose-Einestein Statistics, Total Probability, Tree Diagrams and Compound Probability, Conditional Probability, Dependence and Independence, and Bayes’ Theorem with its applications. Random Variable: Concepts of Random Variable, Discrete and Continuous Variables, Functions of Random Variables. Probability Mass Function, Probability Density Function, Distribution Function & Its Properties. Books Recommended: 1. Ross, S. M. (2008): A First Course in Probability, 8th edition, Prentice Hall. 2. Hogg and Tanis (2001): Probability and Statistical Inference, 6th ed., Prentice Hall, N.J. 3. Hogg, R.V. and Craig, A. T (2009): Introduction to Mathematical Statistics, 6th edition, Pearson

Education, Singapore. 4. Mood, A. M. and Graybill, F. A. and Boes, D.C. (1974): Introduction to the Theory of Statistics, 3rd

edition, McGraw-Hill. 5. Roy, M.K. (2008): Fundamentals of Probability and Probability Distributions, 8th edition. 6. Meyer, P. L. (1970): Introductory Probability and Statistical Applications, 2nd edition, Addison

Wesley, New York. 7. Feller, W. (1985): Introduction to Probability Theory and Its Applications, Vol.1, 3rd edition, John

Wiley & Sons, New York. 8. Mosteller, F., Rourke and Thomas (1970): Probability with Statistical Applications, 2nd edition,

Addison-Wesley, New York. 9. Rohatgi, V.K. and Saleh, A.K. Md. E. (2001): An Introduction to Probability and Statistics, John

Wiley and Sons, New York.

STA140: Computer Fundamentals and Spreadsheet Software Applications on Statistics

[70% Exam, 20% Quizzes/ Class Tests, 10% Attendance]


Computer Basics: Computer system, structure, characteristics and functions of computer, criteria of powerful computer, classifications and generations of computer, parts of computer hardware.

Number System and Codes: Decimal, Binary, Octal, Hexadecimal Number System, addition, subtraction, multiplication and division in all number system.

Programming Languages and Paradigms: Classification, assembler and translators, source and object programs.

Data Processing & Devices: Presentation and processing of data by computer, CPU-control Unit, arithmetic/logic unit and registers, factors affecting the processing speed of computer, Memory: main and auxiliary memory, storage devices, keyboard, mouse, monitor, printer, tape, etc, number system.

PC Operating System (Networking and Internet): Windows 2000 Professional, 2000/2003 Server, Windows XP, Windows NET, Vista, Linux, Windows-7 and higher version with different internal and external commands, Meaning of networking, uses and structure, basic components of data communication system, network topologies, types of network - LAN, WAN, MAN, etc., some commonly used protocols, TCP/IP, server, factors required to run internet, ISP, features of browser, search engine.

Electronic Spreadsheet (MS Excel, Word & Power Point): Meaning of spreadsheet soft-ware, spreadsheet software’s interface, entering data in worksheet, editing and formatting worksheet, word processing programs and their uses, entering and editing text, formula and function management, creating tables and others, meaning and use of power point presentation programs, presentation program’s interface, creating presentation, formatting slides.

Books Recommended:

1. Norton, P. (2006-2007),Introduction to Computers, 6th edition, Tata McGraw-Hill Publishing Company Ltd., New Delhi.

2. Rajaraman, V. (1999), Fundamentals of Computers, Prentice Hall, India. 3. Courter, G. and Marquis, A. (1999), Mastering Microsoft Office 2000, John Wiley and Sons,

Professional Edition. 4. Gallo, A. M. and Nenno, R. B. (1985), Computers and Society with Basic and Pascal, Prindle,

Weber and Schmidt, Boston. 5. Capron, H. L. (2000), Tools for an Information Age, 7th edition, Prentice Hall, USA.

MAT114: Basic Mathematics [70% Exam, 20% Quizzes/ Class Tests, 10% Attendance]


Simultaneous equations: Introduction to Simultaneous Equations, Different Worked Problems on Simultaneous Equations, Practical Problems Involving Simultaneous Equations. Quadratic and Cubic equations: Practical Problems Involving Quadratic and Cubic Equations, Solution of Linear and Quadratic Equations Simultaneously. Inequalities: Concept of Inequalities, Simple Inequalities, Inequalities Involving a Modulus, Inequalities Involving Quotients, Inequalities Involving Square Functions, Quadratic Inequalities. Geometry: Practical Problems Involving straight line graphs, Graphical solution of Simultaneous Equations, Graphical solutions of quadratic equations, Graphical solution of linear and quadratic equations simultaneously, Graphical solution of cubic equations. Introduction to logarithms, Laws of logarithms, Indicial equations, Graphs of logarithmic functions. The exponential function Evaluating exponential functions, The power series for ex, Graphs of exponential functions, Napierian logarithms, Evaluating Napierian logarithms, Laws of growth and decay. Reduction of non-linear laws to linear-form Graphs with logarithmic scales. Geometry of Conic Section. Trigonometry :Trigonometric waveforms, Cartesian and polar co-ordinates Areas of plane figures, Volumes of common solids, Irregular areas and volumes and mean values of waveforms, Triangles and some practical applications. Books Recommended: 1. John Bird (2005). Basic Engineering Mathematics, 4th edition, Elsevier, Amsterdam. 2. Graham Currell and Antony Dowman (2009): Essential Mathematics and Statistics for Science, John

Wiley & Sons, N.Y. 3. Jason Matthiopoulo (2011): How to be a Quantitative Ecologist: The 'A to R' of Green Mathematics

and Statistics, John Wiley & Sons , N.Y. 4. Andre Francis - Thomson Learning (2008): Business Mathematics and Statistics , sixth edition,,

GENGAGE Learning EMEA, N.Y. USA.

MAT116: Calculus

[70% Exam, 20% Quizzes/ Class Tests, 10% Attendance] 3 credit, 39 Contact hours, Exam. Time: 4 hours

Real Numbers: The Real Number System as a complete ordered field, Mathematical Induction, The Real Line, Topological Properties.

Functions: Function and Relation, Domain, Range, Inverse Function and Graphs of Functions Like exponential, Logarithmic, Sine, Tangent etc.

Limit and Continuity: Limits, Existence and Finding of Limits, Theorems of Limit, Sandwich Theorem, Limit Involving Infinity, , Continuity, Uniform Continuity and Lipschitz Continuity, Algebra of Conitnuous Properies, Extreme Value Theorem, Intermediate Value Theorem and Indeterminate Form. Ordinary Differentiation: Differentiability and Tangents, Differentiation as Local Linearization, Derievative Function, Algebra of Differentiable Functions, Successive Differentiation and Leibnitz Theorem, Application of the Derivative. Expansions of Functions: Rolle's Theorem, Mean Value Theorem, First Derivative Test, Concavity Test, Second Derivative Test, Taylor's and Maclaurin's Formulae, Maxima and Minima Functions of One Variable. Tangents and Normal Asymptotes, L-Hospitals Rule, Guide Line for Sketching Graph. Indefinite and Definite Integrals :Definition of Integrals, General Properties, Indefinite Integral, Fundamental Theorem of Definite Integrals, Evaluations of Definite Integrals and Reduction Formulae, Method of Substitution, Integration by Parts, Integral as Signed Measure, Integral and Probability.

Books Recommended: 1. Anton, H (2006): Calculus with Analytic Geometry, Wiley, New York. 2. Ayres, F. and Meldelson, E. (1992): Calculus, McGraw-Hill, 3rd edition, New York. 3. Bacon, H.M. (1942): Differential and Integral Calculus, 2nd edition, McGraw-Hill, New York. 4. Edwards, J, (1994): Differential Calculus, Macmillan, London 5. Lang, S. (1988): First Course in Calculus, 5th edition, Springer-Verlag, New York.

ECO124: Principles of Economics I [70% Exam, 20% Quizzes/ Class Tests, 10% Attendance]


Economics: Appropriate Meaning of Economics, Scarcity, Choice, Tradeoff and Efficiency, Production Possibility Frontier (PPF), Economic Problems.

Demand, Supply and Price: Determinants of Demand and Supply, Laws of Demand and Supply, Movements along and Shift of Demand and Supply Curves, Equilibrium Price and Quantity, Concept of Elasticity of Demand and Supply.

Utility and Preferences: Concept of Utility, Cardinal and Ordinal Utility, Law of Diminishing Marginal Utility, Principle of Equiv-Marginal Utility per Unit of Money Spent, Paradox of Thrift. Indifference Curve-Indifference Curve and Its Characteristics, Budget Line, Substitution Effect, Income Effect and Price Effect.

Cost-Output: Concept of Short-run and Long-run, Average, Marginal and Total Product, Relationship between AP and MP, Law of Diminishing Marginal Returns, Variable Cost, Fixed Cost and Total Cost, Returns to Scale, Relationship between AC and MC.

Perfect & Imperfect Competition Market: Features of Perfect Competition Market, Marginal Analysis of a Firm, Short-run and Long-run Supply Curve of a Firm, Profit Outcomes in the Short-run and Long-run, Breakeven and Shutdown Point, Efficiency of Competition, Features of Monopoly, Monopolistic Competition, and Oligopoly, Price Settings Strategies of Monopoly, Price Discrimination and its Types, Monopoly’s Output and Price, Dead Weight Loss.

Books Recommended: 1. Samuelson, P. A. and Nordhaus, W. D. (2001): Economics, 17th edition, McGraw-Hill, New

York. 2. Parkin, M (2003): Microeconomics, 6th edition, Pearson Education Inc., Australia. 3. Mankiw, N. G. (2001): Principles of Microeconomics, Latest edition, Worth Publication. 4. Lipsey, R.G. and Crystal, K.A., An Introduction to Positive Economics, Oxford University Press. 5. Ahuija, H. L. (2004) Modern Microeconomic Theory and Applications, Sultan Chand and Company

Ltd., New Delhi. 6. Ahuja, H. L. (2004): Macroeconomic Theory and Policy, Sultan Chand and Company Ltd, New

Delhi.

STA131: Introductory Statistical Lab [60% Exam, 30% Quizzes/ Class Tests, 10% Attendance]

2 credit, 26 Contact hours, Exam. Time: 5 hours Practical Problems set by course teachers (STA100, STA102 and STA140)

STA149: Viva-Voce

1st Year 2nd Semester

STA150: Principles of Statistics II [70% Exam, 20% Quizzes/ Class Tests, 10% Attendance]


Correlation and Regression: Bivariate Data, Scatter diagram, Construction of Bivariate Table. Simple Correlation, Partial and Multiple Correlation, Correlation Ratio, Rank Correlation, Spearman Rank Correlation, Kendall’s Tau, Intra-class Correlation, Serial and Bi-serial Correlation, Non-sense Correlation. Simple Regression, 3-Variable Regression, Principle of Least Squares, Principle of Minimum Perpendicular Method, Lines of Best Fit, Residual Analysis, Linearity of Regression, Three Variables Regression, Coefficient of Determination. Analysis of Attributes: Basic Ideas, Classification, Order of Classes and Class Frequencies, Ultimate Class Frequencies, Positive Attributes, Consistency, Incomplete Data, Association of Attributes, Independence, Complete Association and Disassociation, Measures of Association, Coefficient of Association, Coefficient of Colligation and Partial Association. Contingency Table, Analysis of r x c Contingency Table, Analysis of 2x2 Contingency Table by Yate’s Correction, Fisher’s Exact Test, Power Function of 2×2 Contingency Table.

Index Number: Meaning and Problems Involved in Constructing Index Number, Different Methods of Index Number, Criteria of a Good Index Number, Uses of and Limitations of Index Number, Unit Test, Time Reversal Test, Factor Reversal Test, Circular Test, Cost of Living Index Number, Wholesale Price Index Number, Consumer Price Index Number, Base Shifting, Splicing and Deflating of Index Number, Index of Industrial Production.

Books Recommended: 1. Hogg and Tanis (2001). Probability and Statistical Inference, 6th ed., Prentice Hall, N.J. 2. Newbold, P., Carlson, W.L. and Thome, B. (2008), Statistics for Business and Economics,Sixth

Edition, Prentice-Hall, Inc. 3. Gujrati,D. (2003): Basic Econometrics 4th edition, McGraw-Hill, New York. 4. Mason, D. R. and Lind, A. D. (2007), Statistical Techniques in Business and Economics, 11th

edition, Irwin. 5. Stuart, A. and Ord, J. K. (1994): Kendall’s Advanced Theory of Statistics, Vol 1: Distribution

Theory, 6th edition, A Hodder Arnold Publication. 6. K.C. Bhuyan (2011): Methods of Statistics, Sahitya Prokashani, Dhaka. 7. Islam, M. N (2004): An Introduction to Statistics and Probability, 3rd edition, Mullick& Brothers. 8. Gupta, S.P. and Kapoor, V.K(2009): Fundamentals of Mathematical Statistics,11th edition, Sultan

Chand &Sons. 9. Miller, I. and Miller, M. (2005): Mathematical Statistics with Applications, 7th edition, Pearson

Education. 10. Shil, R.N. and Debnath, S.C.(2009): An Introduction to the Theory of Statistics, Latest edition.

STA152: Probability Theory II [70% Exam, 20% Quizzes/ Class Tests, 10% Attendance]


Expectation: Meaning of Expectation, properties of expectation, expectation for discrete and continuous random variables, Mean, Variance, Moments, Expectation of Sums and Products of Random Variables, Measures of central tendency or location for both discrete and continuous random variables, Chebyshev’s Inequality, Markov Inequality, Central Limit Theorem and its applications.

Joint and Conditional Distributions: Joint Distribution Functions, Marginal Distribution and Conditional Distributions, Independence of Random Variables, Conditional Mean and Conditional Variance, Covariance and Correlation Coefficient, Cauchy-Schwarz Inequality.

Probability Distributions: Bernoulli, Binomial, Poisson, Geometric and Normal Distribution.

Generating Function: Moments, Raw moments and Central Moments, Relation between Raw and Central Moments, Moment Generating Function, Factorial Function, Characteristic Function, Probability Generating Function, Moments from the probability generating function, Cumulant Generating Function and their properties, Inversion Theorem. Application of probability Generating Function in Basic Distributions: Bernoulli, Binomial, Poisson, Geometric and Normal Distribution.

Books Recommended:

1. Hogg, R.V. and Craig, A. T (2009): Introduction to Mathematical Statistics, 6th edition, Pearson Education, Singapore.

2. Ross, S. M. (2008): A First Course in Probability, 8th edition, Prentice Hall. 3. Roy, M.K. (2008): Fundamentals of Probability and Probability Distributions, 8th edition. 4. Meyer, P. L. (1970): Introductory Probability and Statistical Applications, 2nd edition, Addison

Wesley, New York. 5. Mood, A. M. and Graybill, F. A. and Boes, D.C. (1974): Introduction to the Theory of Statistics, 3rd

edition, McGraw-Hill, New York. 6. Feller, W. (1985): Introduction to Probability Theory and Its Applications, Vol.1, 3rd edition, John

Wiley & Sons, New York. 7. Rohatgi, V.K. and Saleh, A.K. Md. E. (2001): An Introduction to Probability and Statistics, John

Wiley and Sons, New York. 8. Staurt, A. and Ord, J., K. (1994): Kendall’s Advance Theory of Statistics, Vol I, 6th edition, Weily, New

York.

STA170: Introduction to Demography


Basic: Definition, Nature and Scope of Demography, Vital Events, Demography and Population Studies, Sources of Demographic Data, Methods of Data Collection, Concept of De Facto and De Jure Population, Census, Vital Registration, Sample Survey - Definition, Essential Feature, Planning, Preparation, Basic definition: Rates, Ratio, Proportion. Fertility: Basic measures of fertility: Crude birth rate, age specific fertility rates (ASFR), general fertility rate (GFR), total fertility rate (TFR), gross reproduction rate (GRR), net reproduction rate (NRR), child women ratio and interpretation of each measures, Concept of fecundity and its relationship with fertility.Transition theory and the present situation in Bangladesh, Malthus theory and its criticism. Mortality: Basic measures of Mortality: Crude death rate (CDR), age specific death rates (ASDR), infant mortality rate, neo-natal mortality rate. Standardized death rate its need and use, Direct and indirect standardized rates, Commonly used ratio: Sex ratio, child-women ratio, dependency ratio, density of population. Fertility and Mortality in Bangladesh since 1951, Role of socio-economic development on fertility and mortality. Nuptiality: Marriage, types of marriage, age of marriage, age at marriage and its effect on fertility, celibacy, widowhood, divorce and separation, their effect on fertility and population growth. Migration: internal and international migration, sources of migration data, factors affecting both internal and international migration, impact of migration on origin and destination. Migration of Bangladeshis abroad and its impact on overall economic development of the country.

Measures of fertility, mortality, Nuptiality and migration should be applied on Bangladesh demographic and health survey data. Books Recommended: 1. Shryock, H. S., J. S. Siegel and Associates (2004): The Methods and Materials of Demography;

Latest edition, Academic Press, N.Y. 2. Nicholas P. Jewell: Statistics for Epidemiology. 3. Barclay, J.: Techniques of Population Analysis, Wiley, New York.

4. Bangladesh Bureau of Statistics (2003): 1981, 1991, 1993, 2001 Bangladesh Population Census Report, Bangladesh.

5. Biswas, S. (1988): Stochastic Process in Demography and Applications, Wiley Eastern, New Delhi.

6. Cox, D.R.: Demography. 7. Spiegelman, M.: Introduction to Demography, HarvardUniversityPress, USA. 8. Misra, B.D.: An Introduction to the Study of Population, South Asia, New Delhi. 9. Ramakumar, R. (1986): Technical Demography, Wiley Eastern, New Delhi.

MAT164: Advanced Calculus


Infinite Sequences and Series of Functions and Vectors: Sequences of Real Numbers, Cauchy Criterion of Convergence, Limit Superior and Limit inferior, Infinite Series of Constants, Sequences and Series of Functions, Uniform Convergence, Power Series, Structure of Rn, Sequences and series of vectors.

Real-Valued Functions of Several Variables: Limit, Continuous Real-Valued Function of n Variables, Partial Derivatives and the Differential, The Chain Rule and Taylor’s Theorem .

Vector-Valued Functions of Several Variables: Linear Transformations and Matrices, Continuity and Differentiability of Transformations, The Inverse Function Theorem, The Implicit Function Theorem.

Differentiation of Function of Matrices: Function of matrices. Differentiation of different type of matrix function with respect to a vector.

Integrals of Functions of Several Variables: Definition and Existence of the Multiple Integral, Iterated Integrals and Multiple Integrals, Change of Variables in Multiple Integrals.

Examples and applications in statistics

Books Recommended: 1. William F. Trench (2011): Introduction to Real Analysis, Free edition, First edition, 2003,

Pearson Publication, USA. 2. Andre I. Khuri (2003): Advanced Calculus with Applications in Statistics Second edition, John

Wiley & Sons, N.Y. 3. Jan R. Magnus and Heinz Neudecker(1999): Matrix Differential Calculus with Applications in

Statistics and Econometrics, Third edition, John Wiley & Sons, N.Y. 4. Robert Wrede and Murray R. Spiegel(2002): Theory and Problems of Advanced Calculus, second

edition, Schaum’s Outline Series, McGraw-Hill, N.Y. 5. Anton, H (2000): Calculus with Analytic Geometry, Wiley, N.Y. 6. Edwards, J, (1994): Differential Calculus, Macmillan, London. 7. Lang, S. (1988): First Course in Calculus, 5th ed., Springer-Verlag, N.Y.

MAT166: Matrix Algebra [70% Exam, 20% Quizzes/ Class Tests, 10% Attendance]


Vectors: Plane and R as Geometric and algebraic vector spaces, Their relations. Linear dependence and independence. General Vector space. Basis and dimension. Sub-space. Direct Sum, Dot product, Projection, GS orthogolanization. CS inequality.

Matrices: Basic operations. Some special types of Matrices and their properties. Trace of a matrix. Determinants and their properties. Rank of matrices and their properties. Inverse of matrices. Inverse by partitioning. Block Matrices and their multiplication, LU and QR decompositions, Solution of systems of linear equations, Characteristic equations, Latent roots and vectors. Generalized Inverse. MP inverse. Study of quadratic forms. Geometric significance of quadratic forms.

General Symmetric Matrices: Introduction, Diagonalization of general symmetric matrices. Spectral decomposition, Significance of Spectral decomposition,

Projection: Linear transformation and matrices, Projection and idempotent matrices.

Books Recommended: 1. Basilevsky, A. (2005): Applied Matrix Algebra in the Statistical Sciences Dover Publications,

N.Y. 2. David A. Harville( 1997). Matrix Algebra from a statistician’ perspective, Springer_Verlag New

York, Inc. 3. Lars Eldén (2007).Matrix Methods in Data Mining and Pattern Recognition, Society for Industrial

and Applied Mathematics(SIAM), Philadelphia, USA. 4. Lipschutz, S. (1987): Theory and Problems of Linear Algebra, McGraw-Hill. 5. George A. F. Seber (2008). A Matrix Handbook for Statisticians, John Wiley & Sons, N.J. 6. Rao,C. R. (1984): Linear Statistical Inference and its Applications, 2nd ed., Wiley, N.Y. 7. Healy, M.J.R. (1986): Matrices for Statistics, Clarendon Press, Oxford.

ECO174: Principles of Economics II [70% Exam, 20% Quizzes/ Class Tests, 10% Attendance]


Economic Situation: Inflation, unemployment and low growth rate, Macroeconomic policy & goals. National Output Concepts. Measurement of National output, rates of growth, unemployment and inflation. Determinants of Aggregate Demand-Consumption, Savings and Investment, Aggregate Supply- short run and long run. Determination of income in the very short run. Concept of Multiplier.

Money and Monetary Institutions: Types and Functions of money, Constituents of money supply. Role of Central and Commercial Bank. Multiple Expansions of Deposits Control of Money Supply, Quantity Theory of Money.

Government Role and Fiscal Policy: Objectives and Instruments of Fiscal Policy as well as Government. Budget & Deficit Budget and Surplus, Financing the deficit, Sources of Revenue, Direct and Indirect taxes, Government Purchases and Transfer Payment, Monetary and Fiscal Policy in a closed economy. Basis for Trade Theory of Comparative Advantage, Perfect mobility of capital-Determination of Interest Rate & Exchange rate. Monetary and Fiscal Policy in an Open Economy. Multipliers in the Short run and in the Long run.

Inflation & Economic Growth: Causes of Inflation, Cost Push and Demand Pull Inflation, Stagflation, Demand and Supply Side Factors of Inflation, Phenomenon of Phillips Curve, Nature of business cycles, Sources of growth. Benefits and Costs of Growth. Theories of growth

Books Recommended: 1. R.Dornbusch, S.Fischer and R.Startz (2002): Macroeconomics, 8th Edition, Tata McGraw hill. 2. R.G.Lipsey and K.A.Chrystal (2004): Economics, 10th Edition (First Indian Edition), Oxford

University Press. 3. Todaro, M. (1993): Economics for a Development World, 4th ed., Orient Longman. 4. M. Parkin (2003): Macroeconomics, 6th Edition, Addison Wesley. 5. J.E.Stiglitz (1997): Principles of Macroeconomics, 2nd Edition, Norton & Company, Inc. 6. N.G.Mankiw (1997): Macroeconomics, 3rd Edition, Worth Publishers. 7. Taslim and Chowdhury (1995): Macroeconomic Analysis, Prentice Hall. 8. W.H.Branson (1989): Macroeconomic Theory and Policy, 3rd Edition.

STA191: Principles of Statistics and Demography Lab


Practical Problems set by course teachers (STA150, STA152 and STA170)

STA199: Viva-Voce

2nd Year 1st Semester

STA200: Probability Distribution [70% Exam, 20% Quizzes/ Class Tests, 10% Attendance]

3 credit, 39 Contact hours, Exam. Time: 4 hours Univariate Discrete & Continuous Distribution: Bernoulli, Binomial, Poisson, Hyper Geometric, Geometric, Negative Binomial, Uniform or Rectangular, Multinomial, Logarithmic Series, Power Series, Truncated Binomial, Truncated Poisson, Beta Binomial. Continuous Distribution-Uniform, Exponential, Beta, Gamma, Normal, Log Normal, Pareto , Laplace Double Exponential, Cauchy , Triangular, Maxwell, Weibull, Gumbel, Erlang, and other Exponential Family of Distributions, Pearson System Curve, Inverse Gaussian Distribution, Gram Charlier Series.

Bivariate Distributions: Concept of Bivariate Probability Distribution, Marginal Distribution and Conditional Distribution, Mean and Variances, Moments, Cumulants. Moment Generating Functions and Cumulant Generating Functions, Derivation of Bivariate Normal Distribution and Study of its Properties.

Books Recommended: 1. Hogg, R.V. and Craig, A. T (2009): Introduction to Mathematical Statistics, 6th edition,

Pearson Education, Singapore. 2. Devore, J. L. (2002): Probability and Statistics for Engineering and Sciences, 5th edition, Thomson

Books/Cole, USA.

3. Rohatgi, V.K. and Saleh, A.K. Md. E. (2001): An Introduction to Probability and Statistics, John Wiley and Sons, New York.

4. Roy, M.K. (2008): Fundamentals of Probability and Probability Distributions, 8th edition. 5. Mood, A. M. and Graybill, F. A. and Boes, D.C. (1974): Introduction to the Theory of Statistics, 3rd

edition, McGraw-Hill, New York. 6. Kendall, M. and Stuart, A. (1979): The Advanced Theory of Statistics, Volume 2, 4th edition,

Macmillan Publishing Inc., New York. 7. Feller, W. (1985): Introduction to Probability Theory and Its Applications, Vol.1, 3rd edition, John

Wiley & Sons, New York. 8. Mosteller, F., Rourke and Thomas (1970): Probability with Statistical Applications, 2nd edition,

Addison-Wesley, New York.

STA202: Sampling Distribution [70% Exam, 20% Quizzes/ Class Tests, 10% Attendance]

2 credit, 26 Contact hours, Exam. Time: 3 hours Random Variable and Sampling Distribution: Random Variable, Distribution of Random Variable, Properties of Random Variable, Functions of Random Variable, their Conditional Distributions, Concept of Sampling Distribution, Methods of Obtaining Sampling Distribution, Variate Transformations: Square Root, Log, Sin Inverse, Fisher’s Z Transformation, Laplace Transformation, Exact Sampling Distribution Related to Normal Population, Distribution of Sample Mean, Sample Variance, Sample Covariance. Central and Non-Central Distribution of t, Chi square ( 2 ), Variance Ratio F, Fisher’s Z Distribution, Concept of multivariate data, examples from real life and uses of multivariate analysis.

Books Recommended:

1. Hogg, R. V. and Craig, A. T. (2002): Introduction to Mathematical Statistics, 5th edition, Pearson Education, Asia.

2. Johnson, R.A. & Wichern, D.W. (2007): Applied Multivariate Statistical Analysis, Prentice –Hall Inc.

3. Anderson, T. W. (1984), Introduction to Multivariate Analysis, 2nd edition, 1984, John Wiley, New York.

4. Hogg and Tanis (2001): Probability and Statistical Inference, 6th edition, Prentice Hall, New Jersey. 5. Mood, A. M. and Graybill, F. A. and Boes, D.C. (1974): Introduction to the Theory of Statistics, 3rd

edition, McGraw-Hill, New York. 6. Rohatgi, V. K. and Saleh, A. K. Md. (2000): An Introduction to Probability and Statistics, Second

edition, Wiley-Interscience. 7. Evans, M., N. Hasting and B. Peacock (2000): Statistical Distributions, 3rd edition, Wiley, New York. 8. Hoel, P.G. (1984): Introduction to Mathematical Statistics, 5th edition, John Wiley and Sons, New

York.

STA240: Regression Analysis


Simple Regression Model: Linear and Non-Linear Model, Regression Function, Normal and Non-normal Regression Model Building, Least Square Estimators and Their Properties, Precision of the Estimated Regression Model, Examining Regression Equation, Lack of Fit and Pure Error, Inverse Linear Regression, Confidence Interval and Testing Hypothesis in Simple Regression, Estimation Problems.

Multiple Linear Regression: General linear regression model, Matrix approach. Estimation of model Parameters by OLS Method. Properties of OLS Estimators. Confidence interval and Hypothesis testing, Restricted least squares. Polynomial regression models, Orthogonal polynomials. Bias in Regression Estimates. Inverse regression, Regression with dummy independent variables, Inadequacy of Scatter diagram in Linear Regression, Lack of fit and pure Error, Examination of Residuals, Tests for normality,

Variable selection and model building: selecting the best regression equation. Stepwise regression procedure, Criteria for model selection - Cp criterion, Akaike Information Criteria, Schwartz Criteria

Books Recommended: 1. Gujaratee D. N. (2002) Basic Econometrics, Mcgraw Hill, N.Y 2. Chatterjee, S. and Hadi, A.S. (2006): Regression Analysis by Example, 3rd ed., Wiley, N.Y. 3. Draper, N. R. and H. Smith (2003): Applied Linear Regression, 3rd ed., Wiley, N.Y. 4. Montgomery, D. C., Peck, E. and Vining, G.G. (2003): An Introduction to Linear Regression Analysis, 3rd ed., Wiley, N.Y. 5. Koutsoyiannis A. (1987) Theory of Econometrics, Second Edition, ELBS, London. 6. Bobke, P. (2001): Correlation and Regression, 2nd ed., Thousand Oaks, CA 7. Birkes, D. and Y. Dodge (1993): Alternative Methods of Regression, Wiley. 8. Dobson A.J. (1990): An Introduction to Generalized Linear Models, Chapman and Hall, N.Y. 9. Johnston, J. (1997) : Econometric Methods, 4th ed., McGraw-Hill, N.Y.

STA242: Statistical Inference I [70% Exam, 20% Quizzes/ Class Tests, 10% Attendance]


Introduction-Concept of inference, parameter space, concept of estimation, estimate and estimator,point and interval estimations and testing of hypothesis, basic concepts of point estimation, principles of point estimation. Characteristics of Point Estimators-criteria of good estimator, unbiasedness, consistency, sufficiency-completeness, Factorization theorem on sufficient statistic, Invariance property of sufficient estimator, Fisher-Neyman criterion on sufficient estimator and their application, efficiency, problems and examples. Methods of Point Estimation: Introduction, Estimation methods–moments, maximum likelihood, minimum chi-square, least squares and method of minimum variance, with their properties, Minimax estimators, Point estimators concerning Bernoulli, binomial, Poisson, geometric, uniform, normal, exponential, gamma, beta and Weibull distributions.

Interval Estimation-Concept of central and non-central confidence interval, confidence interval for parameters of normal, binomial, Poisson and exponential distribution, Method of Finding Confidence Intervals: Pivotal Quantity Method, Statistical Method, Large sample method and Neyman classical

confidence intervals, Confidence Interval for Mean, Variance, difference in means, simultaneous confidence region.

Test of Significance: Basic Ideas and Preliminary Definition, Simple hypothesis, Composite Hypothesis, Critical region, Best Critical Region, Two-sided BCR, Steps of Hypothesis Testing, p-value, Test of Mean, Variance, Proportion, Correlation, Regression Coeffiients, Test of Homogeneity in Parallel Samples.

Books Recommended:

1. Cassela, G. and Berger, R. L. (2001): Statistical Inference, Wadsworth Publishing Company, California.


3. Kendall, M.G. and Stuart, A. (2004): Advanced Theory of Statistics, 14th edition, Edward Arnold, New York.

4. Rohatgi, V.K., Saleh, A.K. Md. E., (2001): An Introduction to Probability and Statistics, John Wiley and Sons, New York.

5. Lehmann, E.L. and G. Cassela (1998): Theory of Point estimation, Springer Verlag, New York. 6. Mood, A. M. and Graybill, F. A. and Boes, D.C. (1974): Introduction to the Theory of Statistics, 3rd

edition, McGraw-Hill, New York. 7. Rohatgi, V. K. (1993): An Introduction to Probability Theory and Mathematical Statistics, Wiley Eastern. 8. Kendall, M. and Stuart, A. (1979): The Advanced Theory of Statistics, Volume 2, 4th edition,

Macmillan Publishing Inc., New York.

MAT224: Differential Equations with Linear Algebra [70% Exam, 20% Quizzes/ Class Tests, 10% Attendance]


Essentials of linear algebra: Determinants and inverse using Maple, The eigenvalue problem, Markov

chains, eigenvectors, and Google, Using Maple to find eigenvalues and eigenvectors, Generalized vectors, Bases and dimension in vector spaces, Computer graphics: geometry and linear algebra at work, Bézier curves, Discrete dynamical systems.

First-order differential equations: Motivating problems, Definitions, notation, and terminology Plotting slope fields using Maple ,Linear first-order differential equations, Applications of linear first-order differential equations, Mixing problems, Exponential growth and decay, Newton’s law of Cooling, Nonlinear first-order differential equations, Separable equations, Exact equations, Euler’s method, Implementing Euler’s method in Excel, Applications of nonlinear first-order differential Equations, The logistic equation, Torricelli’s law, Converting certain second-order des to first-order Des, How raindrops fall, Riccati’s equation, Bernoulli’s equation

Linear systems of differential equations: Motivating problems, The eigenvalue problem revisited, Homogeneous linear first-order systems, Systems with all real linearly independent eigenvectors, Plotting direction fields for systems using Maple, When a matrix lacks two real linearly independent

eigenvectors, Nonhomogeneous systems: undetermined, Coefficients, Nonhomogeneous systems: variation of parameters, Applying variation of parameters using Maple, Applications of linear systems,

Higher order differential equations: Motivating equations Homogeneous equations: distinct real roots, Homogeneous equations: repeated and complex roots, Repeated roots, Complex roots, Nonhomogeneous equations, Undetermined coefficients, Variation of parameters, Forced motion: beats and resonance,\Higher order linear differential equations, Solving characteristic equations using Maple, For further study, Damped motion, Forced oscillations with damping, The Cauchy–Euler equation, Companion systems and companion matrices,

Books recommended: 1. Matthew R. Boelkins, J. L. Goldberg, and Merle C. Potter(2009): Differential Equations

With Linear Algebra, Oxford university press, Inc. N.Y. 2. S.M. Goodge (2000): Differential Equations and Linear Algebra, Printice Hall, N.J., USA. 3. Alexander D. Poularikas ed. (2010): Transforms and Applications: Handbook, CRC Press, Taylor

and Francis Group, N.Y. 4. Ross, S.L. (1989): Differential Equations, 4th ed., Wiley, N.Y. 5. Ayres, F. (1997): Differential Equations, Schaum’s Outline Series McGraw-Hill, NY. 6. Hall, Maple and Vinograde (1959): Introduction to the Laplace Transform, Appleton-Century, N.Y. 7. Morris W. Hirsch, Stephen Smaland Robert L. Devaney(2004). Differential Equations, Dynamical

Systems, And An Introduction to Chaos Amsterdam, f Elsevier.

MAT226: Complex Variable and Transformations


Complex number: Properties of Complex Numbers, Its Differences with Real Numbers, Topological Properties.

Complex functions: Different functions, limit and continuity. Complex differentiation. and Cauchy Riemann equations.

Complex integration: Line Interg Cauchy’s integral theorem. Morera’s theorem, Liouville’s theorem, Rouches’s theorem. Taylor’s theorem, Laurant’s theorem, The residue theorem. Evaluation of integrals. Elementary conformal transformations. Characteristic functions.

Transforms:: Fourier Transforms, Laplace Transforms, Hilbert Transforms and Wavelets Transforms.. Their Applications.

Books recommended: 1. S.D. Fisher (1990). Complex variables, second edition, Dover Publications, Inc., N.Y. 2. James Ward Brown, Ruel V. Churchill(2003) Complex Variables and pplications, Seventh Edition,

McGraw-Hill, Boston, USA. 3. Spiegel, M.R. et al. (2009): Complex Variables, 2nd edition, Schaum’s Outline Series, McGraw-

Hill, N.Y. 4. Alexander D. Poularikas (2010). Transforms and Applications: Handbook, CRC Press, Taylor and

Francis Group, N.Y.

5. Andrei D. Polyanin, Alexander V. Manzhirov (2005) Handbook of Mathematics for Engineers and Scientists

6. Ross, S.L. (1989): Differential Equations, 4th ed., Wiley, N.Y. 7. Ayres, F. (1997): Differential Equations, Schaum’s Outline Series McGraw-Hill, N.Y. 8. M. R. Spiegel (1965). Theory and Problems of Laplace Transforms, Schaum’s Outline Series,

McGraw Hill, N.Y.

STA231: Sampling distribution and Inference Lab


Practical Problems set by course teachers (STA202 and STA242).

STA233: Probability Distribution and Regression Lab


STA249: Viva-Voce

2nd Year 2nd Semester

STA250: Sampling Techniques [70% Exam, 20% Quizzes/ Class Tests, 10% Attendance]


Preliminaries: Concept and Scope of Sampling, Census, Post Enumeration Check, Census versus Sample Survey, Uses of Sample Survey, Principles of Sample Survey, Principle Steps in Sample Survey, Preparation of Questionnaire, Schedules, Instruction etc, Survey Enumeration, Pilot Survey, Requirement of a Good Sample Design.

Sampling: Probability and Non-probability Sampling, Sampling with and without Replacement and with Equal Probabilities, Sampling and Non-sampling errors, Bias, Accuracy and Precision. Probability Sampling- Procedure of Drawing a Probability Sampling. Simple Random Sampling with and without Replacement, Stratified Sampling, Systematic Sampling, Cluster Sampling with Equal Probabilities. Estimation of Mean, Proportion, Ratio. Estimated Variance and Confidence Intervals, Optimum Allocation. Sample Size Determination for Estimating Mean and Proportion. Precision of Stratified, Systematic and Cluster Random Sampling Compare to Simple Random Sampling. Non-Random Sampling- Purposive/Judgment Sampling, Quota sampling, Convenience Sampling and Snowball Sampling, Merits, Demerits and their Applications.

Methods of Estimation using Auxiliary Information: Ratio, Difference, Regression and Product Methods of Estimation. Estimation for Population Total, Mean, Variance and Proportion. Comparison of Different Methods.

Books Recommended: 1. Cochran, W.G. (2002): Sampling Techniques, 4th edition, Wiley Eastern, New Delhi. 2. Raj, D. and Chandhok, P. (1998): Sample Survey Theory, Narosa Publishing House, New Delhi. 3. Tryfos, P. (1996): Sampling Methods for Applied Research, John Wiley and Sons, New York. 4. Islam, M.N.: An Introduction of Sampling Methods: Theory and Applications. 5. Mukhopadhyay, P. (2000): Theory and Methods of Survey Sampling, Prentice-Hall of India (P) Ltd.,

New Delhi. 6. Murthy, M.N. (1977): Sampling Methods, 2nd edition, Statistical Publishing Society, Calcutta. 7. Sukhatme, P.V., Sukhatme, B.V., Sukhatme, S. and Asok, C. (1984): Theory of Survey with

Applications, 2nd edition, ISAS, New Delhi. 8. Thomson, S.K. (1992): Sampling, John Wiley and Sons, New York.

STA252: Programming in C

[70% Exam,20% Quizzes/ Class Tests, 10% Attendance]


Problems Thinking: Problem solving techniques, algorithm specification and development, Programming style, debugging, documentation, Program design methodologies, structured and modular program design. Programming C: Introduction, Importance of C, sample C programs, basic structure of C programs, programming style, executive of C program.

Sequential Structure: Overview, character set, data types, classes of data, arithmetic operations, expressions, assignment statements, input and output.

Selective Structure: Overview, relational operators, logical operators, arrays, strings, pointers, structures, unions.

Decision Making and Branching: Simple IF statement, IF-ELSE statement, nested IF-ELSE statement, Switch statement, ?: operator, GOTO statement and their application.

Decision Making and Looping: The WHILE statement, DO statement, FOR statement, jumps in loops and their application.

Functions: Need for user defined functions, multifunction program, return values and their types, function calls, function declaration and category of functions.

File Management in C: Why files, opening and closing a data file, reading and writing a data file, input/output operations on files, random access to files.

Developing a C program: Program design and coding, common programming errors, program testing and debugging, program efficiency.

Books Recommended:

1. Balaguruswamy E:Programming in ANSI C 2nd edition, Tata Mcgrow-Hill pub. Co.Ltd. Delhi. 2. Byron Gottfried: Programming with C.

3. Kerighan and Ritchie: The C Programming Language. 4. Afifi, A.A. and Azen, S.P. (1979): Statistical Analysis: A Computer Oriented Approach, 2nd

edition, Academic Press, New York. 5. Law, M. A. and Kellon, W. D. (2000): Simulation Modeling and Analysis, 3rd edition, Tata

McGraw-Hill, New Delhi. 6. Ross, B. M. (1997): Simulation, 2nd edition, Academic Press, USA. 7. Rubinstein, R. Y. (1981): Simulation and the Monte Carlo Method, John Wiley and Sons, New

York.

STA260: Order Statistics [70% Exam, 20% Quizzes/ Class Tests, 10% Attendance]


Order Statistics: Definition and distribution function of order statistics for both discrete and continuous case, Asymptotic distribution, Sample Cumulative Distribution Function, Joint Distribution of n Order Statistics, Marginal Distribution of Order Statistics, Conditional Distribution of Order Statistics, Distribution of Median and Range, Exact Moments of Order Statistics, Large-Sample Approximations to Mean and Variance of rth order statistics, Simple Estimation of Parameters Based on Order Statistics, Best Linear Unbiased Estimation Based on Order Statistics, Estimation based on Selected Statistics. Rank-Order Statistics- Definition, Correlation between Variate Values and Ranks, Treatment of Ties in Rank Tests. Books Recommended: 1. Arnold B.C., Balakrishnan, N. and Nagaraja, H.N.: First Course in Order Statistics, Wilely

New York. 2. Gibbons, J.D. and Chakraborti, S Gibbons, (1992): Nonparametric Statistical Inference, Marcel

Dekker, Inc, USA. 3. Conover.W.J.: Practical Non parametric Statistics, Wiley, New York. 4. David, H.A.: Order Statistics, 2nd Ed, John Wiley, New York. 5. Hollander,M. Nonparametric Statistical Methods, Wiley, New York. 6. Krishnaith,R.R.: Non parametric Methods. 7. Wolfe, D.A.: Introduction to the theory of Non parametric Statistics.

STA262: Operation Research


Introduction: Definition, scope and limitations of operational research, problem formulation and modeling in operational research, classification of operational research and important characteristics of operational research techniques.

Linear Programming: Introduction, formulation of linear programming problem, hyper-plane, hyper-sphere, open set, closed set, convex set, convex polyhedron, convex and concave functions, basic solution, basic feasible solution, non-degenerate and degenerate basic solution, theorem related to solution, properties of solution to linear programming problem, graphical solution, generating extreme point solution, simplex methods, revised simplex method, Two-phase Method, Big-M method, Dual

Simplex Method, introduction to transportation problem, setting up of transporting problem with its solutions, concept of non-linear programming.

Game Theory: “Two person zero sum” game and non-zero sum games, pure, mixed and optimal strategy games, solution of game by graphical methods, simplex method, approximate solution of game by brown’s algorithm.

Books Recommended:

1. Gass, S.I (2010): Linear Programming Methods and Applications, 4th edition, Dover Publications/McGraw-Hill, New York.

2. Taha, H.A.: Operation Research An Introduction, Prentice-Hall New Delhi. 3. Swarup,K Gupta,,P.K and Mohan, M (2003): Operations Research, Sultan Chand and Publications,

New Delhi. 4. Hadley,G (1990): Linear Programming, Oxford and IBH. 5. Saaty, T.L.: Mathematical Methods of Operation Research, McGraw-Hill, New York. 6. Sasieni, M. and Yaspan, A.: Operation Research Methods and Problems, Wiley, New York.

STA270: Design and Analysis of Experiments I [70% Exam, 20% Quizzes/ Class Tests, 10% Attendance]


Basic Concept of Experimental Design: Definition, basic principle of experimental design, requirements of good experiments, non-experimental design and analysis of variance, linear models, analysis of variance in one-way, two-way and three-way classification with equal and unequal number of observations per cell, analysis of variance with fixed effects, mixed effects and random effects model, Model adequacy checking. Standard Design: Introduction, analysis of completely randomized design (CRD), Randomized block design (RBD) and Latin square design (LSD). Orthogonally of Design and Missing Values: Introduction, missing data in RBD and LSD, efficiency of designs. Multiple Comparisons: Concept and procedure of multiple comparisons, Fishers LSD methods, Duncan’s multiple range test, Neyman-Keul’s test, Tuckey’s test, Scheffe’s method. Books Recommended: 1. Montgomery D. C. (2005): Design and Analysis of Experiments, 6th edition, Wiley, USA. 2. Das, M. N. and N. C. Giri (1986): Design and Analysis of Experiments, 2nd Edition, Wiley Eastern,

India. 3. Bhuiyan, M. R. : Experimental Design. 4. Bhuyan, K.C.: “Bangla Book”, Part I & II. 5. Cochran, W.G. and Cox, G.M. (2000): Experimental Design, 2nd Edition, Wiley, New York. 6. Fisher, R.A. (1995): The Design of Experiments, 8th edition, Hafner, New York. 7. Hitson, A. (1995): The Analysis of Variance, 3rd edition, Wiley, New York. 8. John and Quenouille (1977): Experiments Design and Analysis, 2nd Edition, Charles Griffin, London. 9. Scheffe, H. (1959): Analysis of Variance, Wiley, New York.

MAT274: Numerical Methods in C


Numerical Techniques: Factorial Notation of Finite Differences, Shifting Operator, Difference Operator, Differential Operator and their Relationship, Difference Table, Concepts of Interpolation, Newton’s Interpolation Formula, Relationship Between Simple Difference and Divided Difference. Newton’s General Interpolation Formula, Lagrange’s Formula, Inverse Interpolation, Method of Successive Approximations or Iteration and Reversion of Series,Gauss Formula, Stirling’s Formula and Bessel’s Formula. Numerical Differentiation and Integration: General Quadrature Formula, Simpson’s Rule, Weddle’s Rule, Trapezoidal rule, Euler Maclaurin’s Formula and their Applications.Algebraic and Transcendental Equatins-Bisection Method, Method of False Position, Newton-Rapson Method, Method of Iteration, Concept of Extrapolation, Different Methods of Extrapolation. Characteristic roots and characteristic vector calculation in C.

Books Recommended: 1. Vasishtha, A. R. and Vasishtha, V. (2007): Numerical Analysis, Kedar Nath Ram Nath, New

Delhi. 2. LawM. A. and Kellon, W. D. (2000): Simulation Modeling and Analysis, 3rd edition, Tata

McGraw-Hill, New Delhi. 3. Scarborough, J. B. (1966): Numerical Mathematical Analysis, 6th edition, Johns Hopkins Press,

Baltimor. 4. Bartley, P., Fox, B. L. and Schrage, L. E. (1987): A Guide to Simulation, 2nd edition, Springer-

Verlag, New York. 5. Ross, B. M. (1997): Simulation, 2nd edition, Academic Press, USA. 6. Rubinstein, R. Y. (1981): Simulation and the Monte Carlo Method, John Wiley and Sons, New

York. 7. Narayan, S.: Mathematical Analysis, Latest edition. 8. Sastry, S. (1977): Introductory Methods of Numerical Analysis, 2nd edition, Prentice Hall, New

Delhi.

STA281: Sampling and Design Lab

[60% Exam, 30% Quizzes/ Class Tests, 10% Attendance] 2 credit, 26Contact hours, Exam. Time: 5 hours

Practical Problems set by course teachers (STA250, STA270).

STA283: C and Operation Research Lab [60% Exam, 30% Quizzes/ Class Tests, 10% Attendance]


Practical Problems set by course teachers (STA252 and STA260). STA299: Viva-Voce

3rd Year 1st Semester

STA300: Statistical Inference II [70% Exam, 20% Quizzes/ Class Tests, 10% Attendance]


Point Estimation: Concept of Efficient estimator, more efficient estimator with example, minimum variance unbiased estimators (MVUE), uniformly minimum variance unbiased estimators (UMVUE), Theorem related to MVUE, LMVUE, UMVUE, Cramer-Rao inequality, condition for the equality sign in Cramer-Rao inequality, use of Cramer-Rao inequality in finding UMVUE, asymptotically efficient estimators, consistent asymptotically normal estimators, best asymptotically normal estimators, Rao-Blackwell theorem, application of these theorem, Lehman-Scheffe Theorem, Ancillary Statistics, Minimal Sufficient Statistics, Concept of Bayesian Methods, Bayesian Approach to Estimation Theory, Prior and Posterior Distribution, Bayes Risk, Bayes Estimation, Posterior Bayes Estimator, Minimax Estimator, Admissible Estimator. Interval Estimation: Confidence Intervals for Parameters of Binomial, Poisson, Normal Distribution and Exponential Distribution, Large Sample Confidence Interval, Bayesian Interval Estimation and Neyman Classical Confidence Intervals.

Hypothesis Test: Neyman-Pearson Lemma, Most powerful test, Uniformly Most Powerful Test, Uniformly Most Powerful Unbiased Test, Locally Uniformly Most Powerful Unbiased Test, Optimal Tests in Different Situations, Randomized Tests, Consistent Tests, Unbiased Tests, Similar Region.

Likelihood Ratio Test: Likelihood Ratio Test, Distribution of LR Statistic, Asymptotic distribution of LR Statistic, LR test in linear model. Sequential Test: Sequential Analysis, Sequential Probability Ratio Test (SPRT), Efficiency of SPRT, Fundamental Identity of Sequential Analysis, OC and ASN Function. Bayesian Hypothesis Testing: Bayesian Test of Hypothesis, Test of hypothesis concerning normal and exponential distribution in predictive approach.

Books Recommended:

1. Mood, A. M. and Graybill, F. A. and Boes, D.C. (1974): Introduction to the Theory of Statistics, 3rd edition, McGraw-Hill, New York.


3. Rohatgi, V.K., Saleh, A.K. Md. E., (2001): An Introduction to Probability and Statistics, John Wiley and Sons, New York.

4. Cassela, G. and Berger, R. L. (2001): Statistical Inference, Wadsworth Publishing Company, California.

5. Kendall, M. and Stuart, A. (1979): The Advanced Theory of Statistics, Volume 2, 4th edition, Macmillan Publishing Inc., New York.

6. Kendall, M.G. and Stuart, A. (2004): Advanced Theory of Statistics, 14th edition, Edward Arnold, New York.

7. Yule, G.U. and Kendall, M.G. (1999): An Introduction to the Theory of Statistics, Universal Book Stall, New Delhi.

8. Rohatgi, V. K. (1993): An Introduction to Probability Theory and Mathematical Statistics, Wiley Eastern.

STA302: Generalized Linear Model [70% Exam, 20% Quizzes/ Class Tests, 10% Attendance]


Generalized linear models: Exponential family of distributions; Estimation: method of maximum likelihood, method of least squares, estimation of generalized linear models. Inference: Sampling distribution for scores, sampling distribution for maximum likelihood estimators, Confidence intervals for model parameters, adequacy of a model, sampling distribution for log-likelihood statistic,log-likelihood ratio statistic (deviance), Assessing goodness of fit, hypothesis testing. Models for binary and multiple responses: Probability distributions, generalized linear models, dose response models, general logistic regression, Nominal logistic regression models, ordinal logistic regression models; maximum likelihood estimation least square methods and log-likelihood ratio statistic, confidence interval of model parameters, residual analysis, hypothesis testing, other criteria for goodness of fit. Application of these models on practical data. Books Recommended:

1. Dobson, A (2001). An introduction to generalized linear models, 2nd edition. Chap-man & Hall. 2. Gujrati,D. (2010): Basic Econometrics 6th edition, McGraw-Hill, New York.

STA310: Basic Econometrics [70% Exam, 20% Quizzes/ Class Tests, 10% Attendance]


Concept: Meaning, economics and econometrics, types of Econometrics, nature, scope and sources of econometric data.

Multicollinearity: Concept of multicollinearity, estimation in presence of multicollinearity, theoretical and different consequences of multicollinearity, detection of multicollinearity, remedial measures of multicollinearity.

Heterosedasticity: Meaning of heteroscedasticity, OLS and GLS estimation in presence of heteroscedasticity, consequences of using ordinary least squares in presence of heteroscedasticity, detection of heteroscedasticity by both informal and formal methods, Park, Glejser, Spearman’s rank correlation, Goldfield-Quandt, and Breusch-Pagan-Godfrey test of heteroscedasticityt, remedial measures of heteroscedasticity.

Autocorrelation: Concept of autocorrelation, Ordinary Least Squares estimators and best linear unbiased estimators, estimation in presence of autocorrelation, consequences of using ordinary least squares in presence of autocorrelation, detection of autocorrelation by graphical methods, run test, Durbin-Watson D-test, H-test asymptotic autocorrelation, remedial measures of autocorrelation for both known and unknown p, Ceohrance-Orcutt Iterative, Durbin’s two step and EGLS methods of estimating p, concept of autoregressive conditional heteroscedasticity (ARCH) model, generalized ARCH (GARCH) model.

Dummy Variables: Meaning nature of dummy variables, regression on different combination of quantitative and qualitative variables, testing structural stability of regression model, comparing two regression by dummy variables, comparison with chow test, use of dummy variables in seasonal and piece-wise linear regression and combing time and cross sectional data, dummy variable trap, method of avoiding dummy variable trap.

Books Recommended:

1. Gujrati,D. (2010): Basic Econometrics 6th edition, McGraw-Hill, New York. 2. Greene, W.H. (2003): Econometric Analysis, 5th Ed, Pearson Education 3. Johnston, J. (1977): Econometric Methods, 4th edition, McGraw-Hill, New York. 4. Desai, M. (1976): Applied Econometrics, Oxford Publication. 5. Cameron, A. C. and Trivedi, P.K. (2005): Microeconometircs- Methdos and Application,

Cambridge University Press, UK. 6. Wooldridge, J. (2005): Introductory Econometrics: A Modern Approach, South-western College

Pub. 7. Koutsoyiannis, A. (1977): Theory of Econometrics, 2nd Edition, Palgrave Macmillan Ltd,India. 8. Klein, L.R. (1974): A Text Book of Econometrics, Evanston, Ill., Row, Peterson.

STA320: Time Series Analysis [70% Exam, 20% Quizzes/ Class Tests, 10% Attendance]


Introduction: Meaning and objectives of Time series, the different component of time series, measurement of secular trend, seasonal, cyclical and irregular component, elimination of the seasonal, cyclical and irregular components, Moving-average Method, Weighted Moving Average, Linear Trends, Least Squares Method, Non-linear Trends, Time series plot, simple time series models, stationary models. Stationary Processes: Basic properties, linear processes, AR and MA processes, properties of sample mean and autocorrelation function. ARMA Models: Fitting autoregressive, Moving average and ARMA (p, q) process, ACF and PACF of ARMA (p, q) process, Preliminary estimation, maximum likelihood estimation, diagnostic checking, fo recasting order selection, problems. Non-stationary and seasonal time series models: ARIMA models for non-stationary time series, identification techniques, unit roots in time series models, seasonal ARIMA (SARIMA) models, estimating parameters of ARIMA and SARIMA Model, regression with ARMA errors, diagnostic checking, Forecasting, Practical Problems.

Books Recommended:

1. Brockwell, P. J. and Davis, R. A. (2002): Introduction to Time Series and Forecasting; Springer New York.

2. Box, G., Jenkins, G. M. and Reinsel, G. (2008): Time Series Analysis: Forecasting and Control, 3rd edition, Wiley, New York.

3. Chris Chatfield: The Analysis of Time Series, 6th Ed, CRC PressTaylor & Francis Group 4. Cooray, T.M.J.A.: Applied Time Series, Analysis and Forecasting, Narosa Publishing House Pvt

Ltd. 5. Gujrati,D. (2003): Basic Econometrics 4th Ed, McGraw-Hill, New York. 6. Hamilton, J. D. (1994): Time Series Analysis, Princeton University Press, New Jersy. 7. Harris, R. and Robert, S. (2003): Applied Time Series, ReplicaPress Pvt, Ltd. 8. Makridakis, S., Whellwright, S. C. and Hyndman ,R. J. (1998): Forecasting Methods and

Applications, 3rd Edition, John Wiley and Sons, New York.

STA340: Introduction to R [70% Exam, 20% Quizzes/ Class Tests, 10% Attendance]


Introduction: R , R and the Window System, Downloading R, Installing R, R- A simple R Session with Some Basic R Commands, Case-Sensitivity, Recall and Correction of previous commands . Manipulation: Expressions and objects, functions and arguments, vectors, missing values, matrices and arrays, factors, lists, data frames, indexing, conditional selection, indexing of data frames, subset and transform, grouped data and data frames, sorting, implicit loops. The Graphics Subsystems: plot layout, building a plot from pieces, using par, combining plots; Session Management: the workspace, getting help, packages, built-in data, attach and detach; Data Entry: reading from a text file, the data editor, interfacing to other programs. Probability and Distributions: Random sampling, probability calculations, discrete and continuous distributions, densities, cumulative distribution functions, quantiles, random numbers; Descriptive Statistics and Graphics: summary statistics for a single group, graphical display distributions - histograms, empirical cumulative distributions, Q-Q plots, boxplots, summary statistics by groups, graphics for grouped data, generating tables, graphical display of tables (barplot, dotplot, pie chart). Parametric Hypothesis Testing: One sample test, two sample test, chi-square goodness of fit test, overall significance test. Regression and Correlation: Correlation, simple and multiple linear regression, residuals and Fitted values, prediction and confidence bands; one-way analysis of variance, pair wise comparisons and multiple testing, two-way analysis of variance.

Books Recommended:

1. Statistical Analysis With R (2010): John M. Quick

2. Venables, W.N. and Smith, D. M. , An Introduction to R

3. R Reference Manuals.

4. Stata Manuals, Stata Press.

STA342: Population Studies [70% Exam, 20% Quizzes/ Class Tests, 10% Attendance]

3 credit, 39 Contact hours, Exam. Time: 4 hours Errors and adjustment in Demographic Data: Sources and types of errors; General methods of evaluation and detection of error and de-efficiencies in data; Methods of checking completeness and other types of errors in demographic data and their adjustment, Age Heaping, Evaluation of Age and Sex Data, Myer’s Index, Whipple’s Index, United Nations Index ,Population Pyramid, Cohort and Lexis diagram.

Graduation of data: Meaning and its need, techniques of graduation, graduation of age distribution. Life table: Its concept, structure and calculation, complete life table (life table by single year of age) and abridged life table, multiple decrement life tables, working life table, Model life tables, different life table functions and inter-relationships among them, use of life table, etc. Force of mortality: idea and definition, calculation of life table with the help of force of mortality.

Population growth and measurement: Definition, Techniques to measure it, doubling time concept in demography. Population estimates and projections. Different techniques of population projection, component method, arithmetic/linear method, geometric method, exponential method, matrix method etc., need of population projections.

Stable and stationary population: Definition, characteristics and uses, Lotka's characteristics equation, intrinsic birth and death rates, effect of uniform drop in force of mortality on the growth rate, effects of changes in fertility and mortality on the age distribution of population. . Books Recommended: 1. Shryock, H. S., J. S. Siegel and Associates (2004): The Methods and Materials of Demography;

Latest edition, Academic Press, N.Y. 2. Nicholas P. Jewell: Statistics for Epidemiology. 3. Barclay, J.: Techniques of Population Analysis, Wiley, New York. 4. Bangladesh Bureau of Statistics (2003): 1981, 1991, 1993, 2001 Bangladesh Population Census

Report, Bangladesh. 5. Biswas, S. (1988): Stochastic Process in Demography and Applications, Wiley Eastern, New

Delhi. 6. Cox, D.R.: Demography. 7. Spiegelman, M.: Introduction to Demography, HarvardUniversityPress, USA. 8. Misra, B.D.: An Introduction to the Study of Population, South Asia, New Delhi. 9. Ramakumar, R. (1986): Technical Demography, Wiley Eastern, New Delhi.

STA331: R, Econometrics and GLM Lab [60% Exam, 30% Quizzes/ Class Tests, 10% Attendance]


Practical Problems set by course teachers (STA302, STA310 and STA340).

STA333: Inference, Time Series and Population Studies Lab [60% Exam, 30% Quizzes/ Class Tests, 10% Attendance]


Practical Problems set by course teachers (STA300, STA320 and STA342). STA349: Viva-Voce

3rd Year 2nd Semester

STA350: Industrial Statistics


Statistical Quality Control: Meaning of Statistical Quality Control (SQC), Quality control measures, Statistical quality control techniques, its uses and usefulness. Process Control and Product Control, Causes of Variation in Quality Product, Basic Principles of Quality Control, Control Chart Techniques, Various Attribute and Variable Control Charts, Acceptance Sampling and Sampling Inspection by Attributes and Variables, Sampling versus Screening, Industrial Statistics and Quality Control, Nature and scope, production and realization statistics, Sources of industrial statistics, labour statistics and working time, etc. Indicator of output statistics, Sampling Plans - Single, Double, Multiple, Continuous Sampling Plans and their Relative Advantages and Disadvantages. Concept of OC, ASN, AOQ, AOQL, AQL and other characteristics of Sampling Plans, Sequential Sampling Plan, OC and ASN of Sequential Sampling. Introduction to theory and application of statistical procedures used in industry: Process optimization techniques, evolutionary operations, response surface methodology, canonical and ridge analysis, method of steepest ascent, and first and second order models. Books Recommended: 1. Montgomery, D. C. (2002): Introduction to Statistical Quality Control, 4th edition, John Wiley

and Sons, New York. 2. Banks, J. (1989): Principles of Quality Control, John Wiley and Sons, New York. 3. Burr, J. (2002): Elementary Statistical Quality Control, Marcel Dekkar, USA. 4. Duncan, A. J. (1970): Quality Control and Industrial Statistics, 3rd edition, Richard D. Irwin,

Homewood, Illinoy. 5. Garrett, H. E. (1958): Statistics in Psychology and Education, Vakits, 5th edition, New York. 6. Goon, A. M., Gupta, M. K. and Dasgupta, B. (1972): Fundamentals of Statistics, Vol. II, The World

Press Private Ltd., India. 7. Grant, E. L. (1996): Statistical Quality Control, 7th edition, McGraw-Hill, New York. 8. Mukhopadhyaya, P. (1999): Applied Statistics, Books and Allied (P) Ltd., India.

STA352: Non-Parametric and Robust Methods [70% Exam, 20% Quizzes/ Class Tests, 10% Attendance]


One-Sample Problems: Introduction, Location Model, Geometry and Inference in the Location Model, Properties of Norm-Based Inference, Robustness Properties of Norm-Based Inference, Inference and the Wilcoxon Signed-Rank Norm, Inference Based on General Signed-Rank Norms, Ranked Set Sampling, L1 Interpolated Confidence Intervals.

Two-Sample Problems: Introduction, Geometric Motivation, Inference Based on the Mann-Whitney-Wilcoxon, General Rank Scores, L1 Analyses, Robustness Properties, Proportional Hazards, Two-Sample Rank Set Sampling (RSS), Two-Sample Scale Problem, Behrens-Fisher Problem, Paired Designs.

Linear Models: Introduction, Geometry of Estimation and Tests, Assumptions for Asymptotic Theory, Theory of Rank-Based Estimates, Theory of Rank-Based Tests, Implementation of the R Analysis, L1

Analysis, Diagnostics, Survival Analysis, Correlation Model, High Breakdown (HBR) Estimates, Diagnostics for Differentiating between Fits, Rank-Based Procedures for Nonlinear Models.

Books Recommended:

1. Thomas P. Hettmansperger and Joseph W. McKean, (2011): Robust Nonparametric Statistical Methods, CRC Press Taylor & Francis Group.

2. Gibbons, J.D. and Chakraborti, S Gibbons, (1992): Nonparametric Statistical Inference, Marcel Dekker, Inc, USA.

3. Conover.W.J.: Practical Non parametric Statistics, Wiley, New York. 4. Hollander,M. Nonparametric Statistical Methods, Wiley, New York. 5. Krishnaith,R.R.: Non parametric Methods. 6. Wolfe, D.A.: Introduction to the theory of Non parametric Statistics. CRC Press Taylor & Francis

Group.

STA360: Epidemiology [70% Exam, 20% Quizzes/ Class Tests, 10% Attendance]


Introduction: Definition and scope of epidemiology, uses of epidemiology, Descriptive Epidemiology, Concept of cause, establishing cause of disease.

Types of epidemiologic studies: Cross Sectional, cohort, case control, retrospective and prospective, clinical trials, community intervention and cluster randomized trials.

Measures of Disease Frequency: Incidence and prevalence rates, relation between incidence and prevalence, case fatality rate, risk ratio, rate ratio, risk difference, rate difference, mortality measures, standardized mortality ratio.

Measures of association: Relative risk, attributable risk, odds ratio, risk difference, comparison of proportion different sample.

Epidemiology and prevention: Scope of prevention, levels of prevention: primordial, primary, secondary and tertiary.

Screening: Sensitivity, specificity, negative and positive predictive values.

Books Recommended:

1. Kenneth, J. and Rothman, S. G. (2008): Modern Epidemiology, 3rd Ed, Lippincott Williams and Wilkins, USA.

2. Barker, D.J.P. and Hall, A. J. (1991): Practical Epidemiology, Churcill LIving Stone, Edinburg. 3. Kleinbaum, .G, Kupper L. L and Morgenstern (1982): Epidemiologic Research. 4. Mark Woodward: Epidemiology ,Study design and data analysis. 5. Newman, S: Biostatistical Methods in Epidemiology, Wiley, New York. 6. Nicholas P. Jewell: Statistics for Epidemiology. 7. Sahai, H. and Khurshid, A.: Statistics in Epidemiology Methods, Techniques and Applications CRC

Press, Boca Rotan, Florida.

STA362: Programming in SPSS and STATA [70% Exam, 20% Quizzes/ Class Tests, 10% Attendance]


SPSS Introduction to SPSS: Meaning, Application and Background of SPSS; Reading the Data set: Reading SPSS Data, Reading Data from Spreadsheet formats, Reading Data from Simple Database formats, Reading Data from other Statistical Programs, e.g, STATA, SAS, etc; Define the Variable: Variable type, Variable name, Variable formats, Variable Labels,Value Labels; Transformation Expressions: Numeric Expressions, Arithmetic Operations, Numeric Functions, Arithmetic Functions, Statistical Functions, Random Variable and Distribution Functions, Missing Values in Numeric Expressions, Logical Expressions, Logical Functions, Relational Operators, NOT Logical Operator, AND and OR Logical Operators, Other Functions; Working with Date and Time: Date and Time formats, Arithmetic operations with date and time variables, Date and Time functions; Working with Commands: Introduction, Syntax, Diagrams,Command Specification, Running Commands, Subcommands, Keywords, Delimiters, Command Order; Different commands in SPSS: Get, Save, Save Out_le, Split Files, Sort Cases, Add Files, Match Files, Import, Compute, Recode, If, Select If, Do If, End If, List, Aggregate, Sample selection, Report; Graphical Presentation: Simple Bar Graphs, Line Graphs, Graphs for cumulating frequency and Pie Graphs; Statistical Analysis: Univariate Analysis - Frequencies, Descriptive Statistics, Multiple Response, Bivariate Analysis, Crosstabs, Correlation; Means; Statistical Tests (Parametric and Non-parametric); ANOVA; Regression; Logistic Regression. Introduction to STATA: An example of STATA session, documentation and help files, searching for information; Data management: example commands, creating a new data set, specifying subsets of data within and if qualifiers, generating and replacing variables, using functions, converting numeric and string formats, creating new categorical and ordinal variables, importing data from other programs, combining STATA files, transporting, reshaping or collapsing data, weighting observations, creating random data and random samples, managing memory; Graphs: example commands, histograms, scatter plots, line plots, connected-line plots, other two-way plots, box plots, pie and bar charts, retrieving and combining graphs; Summary statistics and tables: example commands, summary statistics for measurement variables, exploratory data analysis, normality tests and transformations, frequency tables and two-way cross tabulations, multiple tables and multi-way cross tabulations, tables of means, medians and other summary statistics; ANOVA and other comparison methods: example commands, one-sample tests, two-sample tests, one-way and two-way analysis of variance, analysis of

covariance (ANCOVA); Linear regression analysis: example commands, regression table, multiple regression, predicted values and residuals, basic graphs for regression, correlations, hypothesis tests, dummy variables, categorical variables and interactions, stepwise regression, polynomial regression; Regression diagnostics: example commands, diagnostic plots, diagnostic case statistics.

STA370: Fundamental Multivariate Analysis [70% Exam, 20% Quizzes/ Class Tests, 10% Attendance]


Basic concept: applications of multivariate techniques; The organization of data; data display and pictorial representations; distance. Random vectors and random sampling: some basic of matrix and vector algebra; positive definite matrices; a square-root matrix; random vectors and matrices; mean vectors and covariance matrices; matrix inequalities and maximization; the geometry of the sample; random sample and expected values of sample means and covariance matrix; generalized variance; sample mean, covariance, and correlation as matrix operations; sample values of linear combinations of variables. The multivariate normal distribution: the multivariate normal density and its properties; sampling from a multivariate normal distribution and maximum likelihood estimation; sampling distribution and large sample behavior of sample mean vector and sample variance-covariance matrix; assessing the assumption of normality; detecting outliers and data cleaning; transformation to near normality. Inferences about a mean vector: the plausibility of mean vector as a value for a normal population mean; Hotelling T2 and likelihood ratio tests; confidence regions and simultaneous comparisons of component means; large sample inference about a population mean vector; inferences about mean vectors when some observations are missing; time dependence in multivariate data.

STA372: Actuarial Statistics


The meaning of actuarial science, its relationship with life insurance, role of insurance in the economy,

important uses of actuarial statistics specially in context of Bangladesh, Theory of rates of interest and

discount including theoretical continuous case of forces of interest and discount, annuities and sinking

funds including continuous case, practical and theoretical applications primarily to mortgages and

bonds, yield rates, Economics of insurance, utility theory, application of probability to problems of life

and death, determination of single premium for insurances and annuities in both discrete and continuous

case, Theory and practice of pension plan funding, assumptions, basic actuarial functions, population

theory applied to private pension, Survival distributions, life table, life insurance, life annuities, net

premium, premium series, multiple life functions, multiple decrement models, valuation theory for

pension plans, expense function and dividends.

Comparisons of several multivariate means: paired comparisons and a repeated measures design; comparing mean vectors from two populations; comparison of several multivariatepopulation means

(one-way MANOVA); simultaneous confidence intervals for treatment effects; two-way multivariate analysis of variance; pro_les analysis; repeated measures designs and growth curves;

Books Recommended:

1. Bowers, N.L., Gerber, H.V., Hickman, J.C., Jones, D.A. and Nesbitt, C. J.: Actuarial Mathematics.

2. Parmenter,M.M.: Theory of Interest and Life contingencies with pension Application. 3. Uddin, Mohammad Sohrab: An Introduction to Actuarial and Financial Mathematics.

STA381: Actuarial, Epidemiology and Programming Lab



STA383: Multivariate, Non-Parametric and Industrial Statistics Lab [60% Exam, 30% Quizzes/ Class Tests, 10% Attendance]



STA399: Viva- Voce

4th Year 1st Semester

STA408: Project Thesis I (2 credits)

Respective guide teacher will prescribe and assist the topics of the project.

STA400: Simulation and Modeling 100 Marks [70% Exam, 20% Quizzes/ Class Tests, 10% Attendance]


Introduction to Simulation: Basic concepts of systems, models, and simulation. Discrete and continuous systems simulation. Purposes of simulation. Advantages and disadvantages of simulation. Steps in a simulation study. Simulation application examples - queuing system, inventory system.

Random-Number and Random Variate Generation: Concept of random numbers. Techniques for generating random numbers. Tests for random numbers. Methods for generating random variates–inverse transformation, composition, convolution, acceptance-rejection. Comparison of the methods.

Applications of probability distributions in simulation – Uniform, Exponential, Weibull, Gamma, Normal, Binomial, Poisson. Monte Carlo sampling. Latin hyper cube sampling. Verification and Validation of Simulation Models: Introduction to modeling techniques, Problems, Models and systems, Modeling Concepts. Model building, verifications and Validations. Techniques for verification and validation of simulation models. Statistical methods for comparing real-word observations and simulation output data.

Analysis of Simulation Data: Identifying the distribution with data. Parameter estimation. Goodness-of-fit tests. Output analysis for terminating and steady state simulations, Simulation case studies.

Books recommended: 1. Brian Everitt and Torsten Hothorn (2006): A Handbook of Statistical Analysis using R.

Champman & Hall. 2. Averill. M. L. and W. Kelton (2000): Simulation Modeling and Analysis, 3rd ed, Tata McGraw-Hill,

New Delhi. 3. Michael J. Crawley (2005): Statistics: An Introduction using R, Wiley. 4. J.A. Spriet: Computer Aided Modeling and Simulation, Academic Press. 5. Narsingh Deo. (1997): System Simulation with Digital Computer. Prentice Hall, India. 6. Barley, P., B.L. Fox, and L.E. Scharge (1987): A Guide to Simulation, 2nd ed., Springer Verlag,

NY 7. Banks, J., J. S. Carson, and B. L. Nelson (1999): Discrete-Event System Simulation, 3rd ed.,

Prentice-Hall, N.Y. 8. John M. Chambers(2007): Software for data Analysis: Programing with R. Springer.

STA402: Bio-Statistics 100 Marks [70% Exam, 20% Quizzes/ Class Tests, 10% Attendance]


Survival Data: Probability density, hazard function, survival function, interrelationship, mean residual life function and median lifetime, some important lifetime distributions, Cross-sectional, Prospective and Retrospective study designs. Study of prevalence, Incidence and risk factors. Attributable risk and relative risk. Screening test. Clinical drug trials - Symptoms and cognitive factors. Randomized, Sequential and most general rules. Treatment covariate interaction,Types of censoring, Construction of likelihood function with censored data, Estimation of life parameters and their sampling variances from Exponential, Weibull, and extreme value distributions using type I and type II censored data.

Parametric Survival Distributions: Likelihood function of failure time data for different consoring mechanism exponential, two parameter exponential, weibull, lognormal, normal and gamma distributions, inference procedures (Estimation and tests for small and large samples),Non-parametric Methods.

Comparison of Survival Curves: Comparison of Two Groups, Log-rank (Mantel-Haenszel) Test; Hazard ratios, confidence interval for hazard ratios, stratified log rank test, median survival, non-proportional hazards, other tests comparing two group, comparison of more than two groups.

Proportional Hazards Model: Proportional hazards model and its characteristics, evaluating proportional hazards assumptions, estimation procedures.

Books Recommended: 1. Lawless, J.F,(2003): Statistical Models and Methodsfor Lifetime data, 2nd Ed, Wiley , New york 2. Lee.E.T.(1980): Statistical Methods For survival Data Analysis, Life Learning Publication,

Belmont, California 3. Kalbflersch, J.D. and Prentice, R.L(2002): The Statistical Analysis of failure Time Data, Wiley,

NewYork 4. Daniel W.W: Bio-statistics: A Foundation for Analysis in the Health Science, 7th Ed. John Wiley

and Sons, New York 5. Coz, D.R and Oakes, D,(1988): Analysis of Survival data, Chapman and hall 6. Joseph L. Fleiss: Statistical methods For Rates of proportions 7. Kalbflesch, J. D. and Prentice, R. L.: The Statistical Analysis of Failure Time Data, John Wiley

New York. 8. Kleinbaum, D.G, (1996): Survival Analysis, Springer,New York.

STA422: Stochastic Process [70% Exam, 20% Quizzes/ Class Tests, 10% Attendance]

3 credit, 39 Contact hours, Exam. Time: 4 hours Stochastic Process: Notion of stochastic processes, Specification of stochastic processes, Stationary processes. Markov Chains: Concept, definition, conditional probability, transition probability function, one and n-step transition probability, Higher transition probabilities, classification of states and chains, properties of communication of states, Chapman Kolmogorov equations, first entrance decomposition formula. Poisson process: the Poisson distribution, counting and Poisson process; the law of rare events; distribution associated with Poisson process; Non-homogenous Poisson processes.Continuous time Markov chains: pure birth processes; pure death processes; birth and death processes; limiting behavior of birth and death processes; birth and death process with absorbing states; finite state continuous time Markov chains. Random Walk and Ruin Problem: The classical ruin problem, probability of ruin, affect of change of state, expected duration of the game, expected game. Renewal Theory: Renewal event, Recurrent event, Delayed recurrent event, Renewal Process, Distribution of N(t), Renewal reward process, Regenerative Process, Cyclic Renewal, Alternative Renewal process, Recurrent events, Renewal equation, Delayed recurrent events.

Books Recommended:

1. Ross, S. M,(2010).: Introduction to Probability Models, 9th edition, Academic Press. 2. Mehedi, J, (1994): Stochastic Process, 2nd Ed, Wiley Eastern Ltd, New Delhi. 3. Feller, W. (1988): An introduction to the Probability and its Application, Vols. I & II. 3rd Edition,

Wiley, New York. 4. A.K. Basu : An Introduction to Stochastic Process, Narosa Publishing House Pvt Ltd. 5. Bhat, U.N. (1981): Elements of Applied Stochastic Processes, 2nd Edition, Wiley, New York. 6. Barlett, M.S: An Introduction To Stochastic Process,5th Ed. 7. Cox and Miller (1985): The Theory of Stochastic Process, 2nd edition, Chapman and Hall, London. 8. Bailey, N.T.J (1964): The Elements of Stochastic Processes, Wiley, New York.

STA430: Design and Analysis of Experiments II [70% Exam, 20% Quizzes/ Class Tests, 10% Attendance]


Factorial Experiments: Basic ideas, factorial experiments, total and partial confounding in factorial experiments. Factorial Experiments at 3 Levels and Mixed Factorial Experiment: factorial experiment in RBD, factorial experiment. Split-plot Design: Analysis of split-plot design. Nested Design: Nested design, analysis of nested design.

Incomplete Block Design: Balanced incomplete block and partially balanced incomplete block design with their construction.

Analysis of Covariance: Covariance analysis with one concomitant variable, analysis of covariance in RBD and LSD with a single covariate. Covariance analysis with two concomitant variables.

Books Recommended:

1. Cochran, W.G. and Cox, G.M. (2000): Experimental Design, 2nd Edition, Wiley, New York. 2. Das, M. N. and N. C. Giri (1986): Design and Analysis of Experiments, 2nd Edition, Wiley Eastern,

India. 3. Fisher, R.A. (1995): The Design of Experiments, 8th edition, Hafner, New York. 4. Hitson, A. (1995): The Analysis of Variance, 3rd edition, Wiley, New York. 5. John and Quenouille (1977): Experiments Design and Analysis, 2nd Edition, Charles Griffin, London. 6. K.C.Peng, Experimental Design 7. Montgomery D. C. (2005): Design and Analysis of Experiments, 6th edition, Wiley, USA. 8. Scheffe, H. (1959): Analysis of Variance, Wiley, New York.

STA431: Experimental Design and Social Statistics Lab [60% Exam, 30% Quizzes/ Class Tests, 10% Attendance]



STA433: Simulation and Biostatistics Lab [60% Exam, 30% Quizzes/ Class Tests, 10% Attendance]


Practical Problems set by course teachers (STA400, STA402).

STA449: Viva- Voce

4th Year 2nd Semester

STA458: Project Thesis-II

Continuity of the project.

STA452: Environmental Statistics [70% Exam, 20% Quizzes/ Class Tests, 10% Attendance]

2 credit, 26 Contact hours, Exam. Time: 3 hours Introduction: Concept of environmental statistics, uses and importance of environmental statistics. Stochastic process in Environment: Applications of Bernoulli. Poisson and normal processes to environmental problems. Environmental sampling: Network Sampling, composite sampling, ranked-set-sampling. Detectability of Sampling: Basic Concept of Detectability, constant detectability over region, estimating detecatability, effect of estimated detectability, detectability with simple random sampling. Diffusion and Dispersion of Pollutants: Wedge Machine, Particle Frame machine, Plume model. Dilution of Pollutants: Deterministic dilution, stochastic dilution. Theory of successive random dilution (SRD), application of SRD to Environmental phenomena: Air quality, indoor air quality, water quality, concentrations of pollutants in soils, plants and animals. Concentration in food and human tissue. Statistical Theory of Rollback: Predicting concentrations after source control, correlation, previous rollback concepts, environmental transport models in air and water. Environmental data: Concept of environmental data, difference between environmental and simple statistical data, idea of geo-statistical, point pattern or lattice and areal data.

Books Recommended:

1. Barnett, V. and Turkman, K.F (1993): Statistics For the Environment, John Wiley and Sons, Chichester.

2. Bryan, F. J.: Statistics for Environment Science and Management, 1st Ed. CRC Press. 3. Hill. M.K.: Understanding Environmental Pollutions, Cambridge University. 4. Millard, S.P and Neerchal, NK.: Environmental Statistics Using S-Plus, CRC Press. 5. Wayner .R. Ott,(2002): Environmental Statistics and Data analysis, Lewis Publishers, England.

STA460: Advanced Econometrics


Econometric Modeling and Model Selection: Average economic regression, methodology and

specification errors, types of specification error, nature, consequences and remedies of specification

errors, test of specification error, errors of measurement in dependent and explanatory variables, Monte-

Carlo experiment of specification error, Different model selection criteria.

Dynamic Econometric Model: Autoregression, distributed lagged variables, lag model, meaning of

dynamic distribution lag and autoregressive models role and reasons for lags in econometric model.

Method of estimation of lag by Adhocmollud, Koyck and Almon method of estimating distributed lag

model, median lag of different models, method of instrumental variable, detecting autocorrelation in

autoregressive model by Durbin H-test, Granger causality test.

Non-Linear Model: Non-linear model and principles of non-linear least squares estimation, numerical

method of estimating least squares, properties of non-linear regression, Cobb-Douglas and CES

production functions, estimation of Cobb-Douglas production function parameters.

Simultaneous Equation Model: Identification of a Structural Equation, Estimation of Simultaneous

Equation Systems,OLS and LS bias. Indirect Least Squares, Two- stage Least Squares,Three stage least

squares, Examples of Simultaneous – Equations Models, Supply – Demand Model, Klein-Goldberger

Model, the Revised Klein Goldberger Model, Money Demand – Supply Model.

Books Recommended:

1. Gujrati,D. (2003): Basic Econometrics 4th edition, McGraw-Hill, New York. 2. Greene, W.H. (2003): Econometric Analysis, 5th Ed, Pearson Education 3. Johnston, J. (1977): Econometric Methods, 4th edition, McGraw-Hill, New York. 4. Desai, M. (1976): Applied Econometrics, Oxford Publication. 5. Cameron, A. C. and Trivedi, P.K. (2005): Microeconometircs- Methdos and Application,

Cambridge University Press, UK. 6. Wooldridge, J. (2005): Introductory Econometrics: A Modern Approach, South-western College

Pub. 7. Koutsoyiannis, A. (1977): Theory of Econometrics, 2nd Edition, Palgrave Macmillan Ltd,India. 8. Klein, L.R. (1974): A Text Book of Econometrics, Evanston, Ill., Row, Peterson.

STA462: Advance Sampling

100 Marks [70% Exam, 20% Quizzes/ Class Tests, 10% Attendance] 3 credit, 39 Contact hours, Exam. Time: 4 hours

Sampling Strategy: Equal and unequal Probability Sampling with and without replacement. Different

sampling schemes, Horvitz-Thompson estimator, Brewers, Durbin, Raj, Murthy, Rao and Cochrans

methods, comparisons of their efficiencies.

Cluster Sampling & Multistage sampling: Unequal size and varying probability cluster sampling,

Relative Efficiency, Determination of optimum cluster size, Relative Accuracy’s, Sub-sampling with units

of Equal and Unequal sizes- Two stage, Three stage and Multistage sampling. Optimum sampling and sub-

sampling fractions, two stage and Three stage sampling, Self weighting sampling.

Double Sampling Procedures and Repetitive Surveys: Introduction, Double sampling for Stratification,

Ratio, Difference, Regression, Product and PPS method of estimation, Optimum allocation, Sampling on

Two and/or More occasions, Repetitive surveys, Multi-phase sampling.

Non-Sampling Errors: Sources and types of non-sampling errors, Effect of non-response, Technique for

adjustment of non-response: Hansen and Hurwitz technique, Politz-Semons techniques, Randomized

response technique.

Books Recommended:

1. Cochran, W.G. (2002): Sampling Techniques,4th ed. Wiley, N.Y. 2. Mukhopadhayay (2000): Theory and Methods of Survey Sampling, Prentice-Hall, New Delhi. 3. Islam, M.N.(2005): An Introduction to Sampling Methods Theory and Applications, Book Word,

Dhaka. 4. Kish, L. (1995): Survey Sampling, Wiley, N.Y. 5. Lohr, S.L(2010): Sampling: Design and Analysis 2nd ed., Books Cengage Learning, Australia. 6. Raj, D and Chandhok, P.(1998): Sample Survey Theory, Norosa publishing house, New Delhi. 7. Sarndal, C.E., Swenson, B. and Wretman, J. (1992): Modeling Assisted Survey Sampling, Springer

Verlag. 8. Singh, D. and Chaudhary, F.S.(1986):Theory and Analysis of Sample Survey Designs, Wiley

Eastern Ltd. 9. Singh, R and Naurang, S.M.(1996): Elements of Survey Sampling, Kluwer Academic Publishers,

London. 10. Sukhatme, P.V., Sukhatme, B.V., Sukhatme, S. and Ashok, C. (1997): Sampling Theory of Surveys

with Application,3nd ed., Indian Society of Agricultural Statistics

STA470: Advanced Multivariate Analysis [70% Exam, 20% Quizzes/ Class Tests, 10% Attendance]


Principal Components Analysis (PCA): Introduction to principal components in the population. ML estimator of the principal components and their variances. Sampling properties of the Sample principal components. Statistical inference. Singular Value decomposition (SVD) and its application to PCA. Application of Principal Component in Regression Analysis.

Factor Analysis (FA): Introduction. The mathematical model for factor structure. ML estimators for random orthogonal factors. Estimation for fixed factors. Testing the goodness of Fit of the factor model. Factor interpretation and transformation.

Canonical Correlation Analysis (CCA): Introduction. Canonical correlation and varieties in the population. Estimation of canonical correlation and varieties. Statistical Inference.

Cluster Analysis (CA): Introduction. Similarity measures, Hierarchical clustering methods (Single linkage, complete linkage, average linkage, Ward’s methods), Non-hierarchical clustering methods (K-means, Fuzzy and model based clustering), Correspondence analysis.

Independent Component Analysis (ICA): Introduction, Information theory (entropy, mutual information, K-L divergence, maximum entropy, negentropy), ICA by maximizing nongaussianity, ICA by maximum likelihood estimation, ICA by minimizing mutual information. Algorithms(Fast ICA, Infomax).

Books Recommended: 1. Johnson, R.A. & Wichern, D.W. (2007): Applied Multivariate Statistical Analysis, Prentice –

Hall Inc. 2. Anderson, T. W. (1984), Introduction to Multivariate Analysis, 2nd edition, 1984, John Wiley, New

York. 3. Bhuiyan, K.C, (2006) Multivariate Analysis and its Application. 4. Goldstein, M. D.: Multivariate Analysis: methods Applications. 5. Kendell. M.G.: Multivariate Analysis, New York, 6. Kshiragar, A.M: Multivariate Analysis, Marcell Dekkar Inc .New York. 7. Mardia, K.V., Kent, J.T. and Bibby, J.M. (1979): Multivariate Analysis, Academic Press, London.

CSE456: Data Structure and Database Technology

[70% Exam, 20% Quizzes/ Class Tests, 10% Attendance]


Arrays: Representation of arrays, ordered lists, data manipulation in 2D or more dimensions of array, Matrix data representation in array (such as image data). Stacks, queues and Recursion: Fundamentals, different types of stacks and queues: Circular, dequeues, etc; evaluation of expressions. Recursion: Direct and indirect recursion, depth of recursion. Trees: Basic terminology, binary trees, binary tree representations, binary tree traversal; Extended binary trees: 2-trees, internal and external path lengths, Huffman codes/algorithm. Application of Trees: Set representation, decision trees. Graphs: Introduction, definitions and terminology, graph representations, traversals, spanning trees, shortest path and transitive closure, enumerating all paths, matrix representation of graph. Sorting and Searching: Bubble sort, insertion sort, selection sort, quick sort, heap sort, how fast can we sort? Linear Search and Binary search.

Introduction to Database System: Overview, File system VS database system, Advantage of a DBMS, Describing and storing in a DBMS, Queries in a DBMS, Transaction management, Structure of a DBMS, Applications.

The Entity-Relationship Model: Basic concept, Design issue, Mapping constraints, Keys, E-R diagram, Weak entity sets, Extended E-R features, Design of an E-R database schema, Reduction of a E-R schema to tables.

Relational Model: Structure of relational databases, The relational algebra, The tuple relational calculus, the domain relational calculus, relational algebra operations, modification of the database, introduction to views.

Structured Query Language: The form of a basic SQL query, UNION, INTERSECTION and EXCEPT, nested queries, aggregate operations, null values, embedded SQL, cursors, dynamic SQL, ODBC and JDBC, triggers and active database.

Relational Database Design: Pitfalls in relational database design, Decomposition, normalization using functional dependencies, normalization using multivalued dependencies, normalization using join dependencies, domain-key normal form.

Books Recommended:

1. Seymour Lipschutz, Schaum's Outline of Theory and Problems of Data Structures, Schaum's Outlines.

2. E. Horowitz and S. Sahni, Fundamentals of Data Structures, Galgotia. 3. Robert L. Kruse, Data Structures and Program Design, 3rd Edition. 4. Niklaus Wirth, Algorithms-Data Structures-Programs, Prentice Hall of India, 1992. 5. A. Silberschatz: Database System Concepts, Mcgraw-Hill. 6. Raghu Ramakrishnan, Johannes Gehrke: Database Management System, McGraw-Hill Higher

Education. 7. James Martin: Principles of Database Management, Prentice-hall Of India Pvt Ltd. 8. Ullman: Database Management systems, Prentice-Hall Publication. 9. Abey: Oracle 8i a Beginners Guide, McGraw Hill.

CSE485: Data Structure and Database Technology Lab

100 Marks [60% Practical, 30% Quizzes/Viva-voce, 10% Attendance]

1 Credit, 26 Contact hours

Laboratory works based on CSE456, MS Access, Epi Info and FoxPro.

STA481: Multivariate and Environment Statistics Lab [60% Exam, 30% Quizzes/ Class Tests, 10% Attendance]

1.5 credit, 26 Contact hours, Exam. Time: 5 hours Practical Problems set by course teachers (STA452 and STA470)

STA483: Sampling and Econometrics Lab [60% Exam, 30% Quizzes/ Class Tests, 10% Attendance]

1.5 credit, 26 Contact hours, Exam. Time: 5 hours

Practical Problems set by course teachers (STA460 andSTA462)

STA499: Viva-Voce

Documents

Department of Statistics - BSMRSTU (Bangabandhu … of Statistics ... and Probability, 3rd edition, Mullick& Brothers. 7. Gupta, S.P. and ... Introduction to Mathematical Statistics,