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USING MICROSOFT EXCEL WITH BUSINESS RESEARCH METHODS
www.drjayeshpatidar.blogspot.com
TITLE BAR
MENU BAR
FORMULA BAR
STANDARD TOOLBAR
FORMATTING TOOLBAR
ACTIVE CELL
PASTE FUNCTION
TOOLS MENU
The Paste Function Provides Numerous Statistical
Operations
The Statistical Function Category
Data Analysis Dialog Box
• Click on “Tools”• Select “Data Analysis”• Select statistical operation
o such as Histogram
Functions
• Functions are predefined formulas for mathematical operations
• They perform calculations by using specific values, called arguments
• Arguments indicate data or a range of cells
• Arguments are performed, in a particular order, called the syntax.
Functions
• Functions are predefined formulas for mathematical operations
• They perform calculations by using specific values, called arguments
• Arguments are performed, in a particular order, called the syntax.
• For example, the SUM function adds values or ranges of cells
Easy to Use Paste Functions
• AVERAGE (MEAN)• MEDIAN• MODE• SUM• STANDARD DEVIATION
Functions
• The syntax of a function begins with the function name
• followed by an opening parenthesis• the arguments for the function • separated by commas• a closing parenthesis. • If the function starts a formula, an equal
sign (=) is typed before the function name.
The Equal Sign Then The Function Name And
Arguments
• =FUNCTION (Argument1)• =FUNCTION (Argument1,Argument2)
Arguments
• Typical arguments are numbers, text, arrays, and cell references.
• Arguments can also be constants, formulas, or other functions.
The AVERAGE Function Located in the Statistical Category
Data Array
• The data appear in cells A2 through 14• A2:A14• Sometimes written with dollars signs• $A$2:$A$14
Sum, Average, and Standard Deviation
• =FUNCTION (Argument1)• =SUM(A2:A9)• =AVERAGE(A2:A9)• =STDEVA(A2:A9)
SUM FunctionSales Call Example
AVERAGE (Mean) FunctionSales Call Example
Standard Deviation FunctionSales Call Example
Variance s2: (algebraic, scalable computation)
Standard deviation s is the square root of variance s2
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• Variance
• Standard deviation: the square root of the variance
– Measures spread about the mean
– It is zero if and only if all the values are equal
– Both the deviation and the variance are algebraic
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Data Dispersion Characteristics
• Motivation
– To better understand the data: central tendency, variation and spread
• Data dispersion characteristics
– median, max, min, quantiles, outliers, variance, etc.
• Numerical dimensions correspond to sorted intervals
– Data dispersion: analyzed with multiple granularities of precision
– Boxplot or quantile analysis on sorted intervals
• Dispersion analysis on computed measures
– Folding measures into numerical dimensions
– Boxplot or quantile analysis on the transformed cube
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Measuring the Central Tendency
• Mean
– Weighted arithmetic mean
• Median: A holistic measure
– Middle value if odd number of values, or average of the middle two
values otherwise
– estimated by interpolation
• Mode
– Value that occurs most frequently in the data
– Unimodal, bimodal, trimodal
– Empirical formula:
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Measuring the Dispersion of Data
• Quartiles, outliers and boxplots
– Quartiles: Q1 (25th percentile), Q3 (75th percentile)
– Inter-quartile range: IQR = Q3 – Q1
– Five number summary: min, Q1, M, Q3, max
– Boxplot: ends of the box are the quartiles, median is marked, whiskers,
and plot outlier individually
– Outlier: usually, a value higher/lower than 1.5 x IQR
• Variance and standard deviation
– Variance s2: (algebraic, scalable computation)
– Standard deviation s is the square root of variance s2
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Boxplot Analysis
• Five-number summary of a distribution:
Minimum, Q1, M, Q3, Maximum
• Boxplot
– Data is represented with a box
– The ends of the box are at the first and third quartiles, i.e., the height of the box is IRQ
– The median is marked by a line within the box
– Whiskers: two lines outside the box extend to Minimum and Maximum
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A Boxplot
A boxplot
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Visualization of Data Dispersion:
Boxplot Analysis
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Mining Descriptive Statistical Measures in Large
Databases
• Variance
• Standard deviation: the square root of the variance
– Measures spread about the mean
– It is zero if and only if all the values are equal
– Both the deviation and the variance are algebraic
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Histogram Analysis
• Graph displays of basic statistical class descriptions
– Frequency histograms
• A univariate graphical method
• Consists of a set of rectangles that reflect the counts or frequencies of
the classes present in the given data
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Quantile Plot
• Displays all of the data (allowing the user to assess both the overall behavior and unusual occurrences)
• Plots quantile information
– For a data xi data sorted in increasing order, fi indicates that approximately 100 fi% of the data are below or equal to the value xi
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Quantile-Quantile (Q-Q) Plot
• Graphs the quantiles of one univariate distribution against
the corresponding quantiles of another
• Allows the user to view whether there is a shift in going from
one distribution to another
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Scatter plot
• Provides a first look at bivariate data to see clusters of
points, outliers, etc
• Each pair of values is treated as a pair of coordinates and
plotted as points in the plane
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Loess Curve
• Adds a smooth curve to a scatter plot in order to provide
better perception of the pattern of dependence
• Loess curve is fitted by setting two parameters: a smoothing
parameter, and the degree of the polynomials that are fitted
by the regression
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Graphic Displays of Basic Statistical
Descriptions
• Histogram: (shown before)
• Boxplot: (covered before)
• Quantile plot: each value xi is paired with fi indicating that
approximately 100 fi % of data are xi
• Quantile-quantile (q-q) plot: graphs the quantiles of one
univariant distribution against the corresponding quantiles of
another
• Scatter plot: each pair of values is a pair of coordinates and
plotted as points in the plane
• Loess (local regression) curve: add a smooth curve to a
scatter plot to provide better perception of the pattern of
dependence www.drjayeshpatidar.blogspot.com
Proportion
• =COUNT• =COUNTIF• DIVIDE COUNTIF BY COUNT• =D3/D2
Frequency Distributions
• There are alternative ways of constructing frequency distributions
• COUNTIF function• HISTOGRAM function
=COUNTIF(A6:A134,1)=D4/D9*100
Histogram Function
• Tools -Data Analysis-Histogram• Bins
The bins are thefrequency categories
Insert Input and Bin Ranges
Text Labels Can Be Included or Excluded From Input Range
The Chart Wizard
The Descriptive Statistics Function
SEVERAL ROWS OF DATA ARE HIDDEN
SEVERAL ROWS OF DATA ARE HIDDEN
Correlation
Correlation Coefficient, r = .75
Regression Analysis