MADAM SITI AISYAH BINTI ZAKARIA INSTITUT MATEMATIK KEJURUTERAAN UNIVERSITI MALAYSIA PERLIS

MADAM SITI AISYAH BINTI ZAKARIA

INSTITUT MATEMATIK KEJURUTERAANUNIVERSITI MALAYSIA PERLIS

Students, like professional people, must be able to read and understand the various statistical studies performed in their fields. To have this understanding, they must be knowledgeable about the vocabulary, symbols, concepts, and statistical procedures used in these studies.

Students and professional people may be called on to conduct research in their fields, since statistical procedures are the basic to research. To accomplish this, they must be able to design experiments; collect, organize, analyze, and summarize data; and possibly make reliable predictions or forecasts for future use. They must also be able to communicate the results of the study in their own words.

Students and professional people can also use the knowledge gained from studying statistics to become better consumers and citizens. For ex. They can make intelligent decisions about what products to purchase based on consumer studies about government spending based on utilization studies and so on.

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Examples statements present some statistics in business:

• Bill Gates is the richest American with a net worth $43 billion (Forbes, September,30,2002).

• A total of 35billion transactions were handled by the Visa system during 2001 (Forbes, September 16,2002).

• On average, a household carried a credit card balance in $8562 in 2001 (Newsweek, April 1, 2002).

• On average, a wedding in America costs $20,357 (Smart Money, June 2002).

• Based on the 2000 census, 40.5 million households have two vehicles. (Source: Census Bureau)


DATA Data is the facts and figures collected, analyzed, and

summarized for presentation and interpretation A data with lot of observations usually looks non informative

that is we cannot get much information with the raw data. Raw data is also called as ungrouped data. Data refers to quantitative or qualitative attributes of a

variable or set of variables.

- Example :- the whole numbers that represents the scores of students.

Data is categorized by two :-

- quantitative data

- qualitative data Data should be summarized in more informative way such as

graphical, diagrams and charts.


Element – entities on which data are collected ( ex. In note)

Variable – is a characteristic of interest for the elements

Observation – the set of measurements obtained for a particular element

MADAM SITI AISYAH BINTI ZAKARIA 6

Measurement Levels and the Appropriate Averages

ALL DATA

Qualitative data Quantitative data

Nominal

Car makes,Days of Week,Gender

Ordinal

TV channelRanks and titleCalendar dates

Interval and Ratio

Sales ($)Accounts Receivable

Market share

Mode Median Mean


There are four measurement scales :-

i. Nominal

ii. Ordinal

iii. Interval

iv. Ratio

1) Nominal only for qualitative classification. the weakest data measurement where numbers are used to

represent an item / characteristic. each data should not be treated as numerical since relative size

has no meaning. no order or ranking can be imposed on the data.

(e.g : gender – male =1 , female = 2)8MADAM SITI AISYAH BINTI ZAKARIA

2) Ordinal it is possible to rank order all the categories according to some

criterion. classifies data into categories that can be ranked ( no precise

difference )

(e.g : grades – A,B,C,D and collegiate class – freshman, sophomore, junior, senior)

3) Interval have the property that the distances between categories are

defined by fixed and equal units. is ranks of data quantity and compare the size of difference between two

observations (precise difference do exist)

(example :For age, a change from age 21 to 22 is the same for changes age 31 to 42)


4) Ratio The highest level of measurement and allows for all basic

arithmetic operations including division and multiplication. Has the property that a zero point is naturally defined. The mode, mean, median can be used to describe interval and

ratio data. Poses all the characteristics of interval measurement. True zero exist.

(E.g : Production of 20 units per hour (ratio level) is twice the production of 10 units per hour)


Variable

Quantitative Qualitative

Discrete(e.g, number of houses,

cars accidents

Continuous (e.g., length, age, height, weight, time) e.g., gender,

marital status


1) Quantitative variable A variable that can be measured numerically. Data collected on a quantitative variable are called quantitative data. There are two types of quantitative variables:-

i. Discrete Variable

A variable whose values are countable, can assume only

certain values with no intermediate values.

ii. Continuous Variable

A variable that can assume any numerical value over a

certain interval or intervals.

2) Qualitative variable A variable that cannot assume a numerical value but can be classified into

two or more nonnumeric categories. Data collected on such a variable are called qualitative data.


1) Cross Section Data Data collected on different elements at the same point in time

or for same period of time. An example of cross-section data which is giving of six

companies for the same period (2007) :-

Company 2007 Charitable Giving (millions of

dollars)

Home Depot 42

Macy`s 35.2

Wal-Mart 337.9

Best Buy 31.8

Target 168.9

Lowe`s 27.5Table 1.2 : Charitable Givings of Six Retailers in 2007


2) Time-Series Data Data collected on the same element for the same variable at

different points in time or for different periods of time. Example, a Movieplex with 8 screens would count as 8 toward

the total number of screens.

Year Total Indoor Movie Screens

2003 35,361

2004 36,012

2005 37,092

2006 37,776

2007 38,159

2008 38,198

Table 1.3 : Number of Movie Screens


DATA SOURCES

SOURCES OF DATA

Primary Data(data collected

by the researcher)

Examples:-i.Personal Interviewii.Telephone Interviewiii.Questionnaireiv.Observations

Secondary Data(already collected/

published by someone else)

Examples:- From books, magazines,

annual report, internet


STATISTICS Numerical facts Field or discipline of study Collection of methods for planning experiments,

obtaining data and organizing, analyzing, interpreting and drawing the conclusions or making a decision.

Example : (In NOTE)


STATISTICS

DESCRIPTIVE STATISTICS

DESCRIPTIVE STATISTICS

INFERENTIAL STATISTICS

INFERENTIAL STATISTICS

Using tables, graphs & summarymeasures

Using sample result inmaking decision or predict about a population.

Also called inductive reasoning or inductive statistics.


Descriptive Statistics

a study on data summary or describes a collection, data organization (presentation of data in a more informative way such as graphical, diagrams and charts).

In general divided by two categories :-

- Data presentation (display)

- Tabular

- Charts/graphs

- Statistics


Inferential Statistics Consists of generalizing from samples to population,

performing estimations and hypothesis tests, determining relationships among variables, and making predictions.

Area statistics which are deal with decision making procedures.

Population – consists of all subjects (human or otherwise) that are being studied.

Sample – is a group of subjects selected from a population.

Example :-

- In order to find the salary of a college graduate, we may select 2000 recent college graduates, find the starting salaries and make decision based on the information.


STATISTICAL ANALYSIS USING EXCEL

Example 1.1 :-

Following table shows data for income tax returns for 1995 to 2001 that were filed electronically. Get the sum of income tax for all years and get average of the income tax for those 7 years.

i. Data is key in using Excel.

Figure 1


ii. To get sum of income tax for all years, type =SUM(.

iii. Select the range of cells (C4:C10) of numerical data, and close the bracket.

Figure 2


iv. Press Enter, the sum should appear.

Figure 3


v. To get, the average, the sum should divide by the number of

years.

vi. Type =AVERAGE(.

vii. Select the range of cells for all years (C4:C10) and close bracket.

viii. Press Enter. The average of income tax for those years should appear.

Figure 4


Population- Entire collection of individuals which are characteristic

being studied.

Sample

- Subset of population.

Population

Sample


Census - Survey includes every member of population.

Sample survey- Collecting information from a portion of population (techniques)

Element

- Specific subject or object about which information collected.

Variable- Characteristics which make different values.


Observation

- Value of variable for an element.

Data Set

- A collection of observation on one or more variables.

Name Score

Mohd Amirul bin Hamdi

90

Hashimah 78

Element

Variable

Observation/Measurement

Table 1: Student’s Score for Business Statistic


MADAM SITI AISYAH BINTI ZAKARIA 27

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MADAM SITI AISYAH BINTI ZAKARIA INSTITUT MATEMATIK KEJURUTERAAN UNIVERSITI MALAYSIA PERLIS