1. AGW 615 Advanced Business Statistics

2. Course Instructor: T. Ramayah & Dr. Arumugam, V
Web Page:
OBJECTIVES
Among others this course exposes students to statistical techniques for business decisions and business research.
In particular, it exposes students to use and interpret data for
decision making enhance their skills in using statistical techniques for business research, enhance their skills
and ability to use these techniques, and use of statistical
packages (SPSS etc.)
EVALUATION
This course will be evaluated by the following components:
1. Coursework %
Quizzes 20%
Project work/assignment 30%
2. Final Examination %

3. COURSE CONTENT AND OUTLINE OF SESSIONS
SESSION NO.
TOPICS
REFERENCE CHAPTERS 1
Overview to Business Statistics & Introductory Probability
1,6 2
Random Variables & Probability Distributions
7,8 3
Sampling Methods:
Sample Size and Sampling Distribution
5,9 4
Statistical Inference: Estimation and Testing of Hypothesis: Basic Principles
10,11,12 5*
Using SPSS (Lab) 6* 7
Two Samples Test 13 8
Two Samples Test: Non-parametric
21.1 –21.3 9
K Samples Test: ANOVA & Non-Parametric
15, 10
Chi-Square Test, Correlation & Simple Regression
16,17 11
Multiple Regression & Diagnostics 19 12
Discriminant Analysis
Hair et al.
Factor Analysis
Hair et al
* Session 5 & 6 will be conducted in the Lab on a Sunday

4. REFERENCE TEXT
Keller, G and Warrack, B. (2005).
Statistics for Management and Economics,
7th ed., Thomson Brooks/Cole: Australia.
Hair, J. F., Jr. Anderson R. E., Tatham,
R. L. & Black, W. C. (2005).
Multivariate Data Analysis, 5th ed.,
Prentice Hall International, Inc.:
New Jersey, USA.

5. OTHER REFERENCES
Aczel, A. D. & Sounderpandian, J. (2002). Complete Business Statistics, 5th ed., McGraw-Hill: USA.
Bowerman, B. & O’Connel, R. T. (2003). Business Statistics in Practice, 3rd ed., McGraw-Hill: USA.
Cohen, J., Cohen, P., West S. G. & Aiken, L. S. (2003). Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences, Lawrence Erlbaum Associates, Publishers: USA.
Kohler H. (2002). Statistics for Business and Economics: Minitam Enhancecd,
South-Western Thomson Learning: USA.
Levin, R. I. & Rubin D. (1998). Statistics for Management, 7th ed., Prentice Hall International, Inc: New Jersey, USA.
McClave, J. T., Benson, P. G. & Sincich, T. (2001).
Statistics for Business and Economics, 8th ed.,Prentice Hall: USA.
Bruce L. Bowerman, Richard T.O’Connell and Michael L. Hand (2002): Bussiness Statistics Practice, Irwin, USA.

6. Introduction to Statistics
Session 1 : Lesson 1
Introduction to
Statistics

7. Objectives To define statistics
To discuss the wide range of applications of statistics in business
To understand the branches of statistics
To describe the levels of measurement of data

8. What is Statistics?
Science of collecting, organizing, presenting, analyzing, and interpreting data for the purpose of assisting in making more effective decision
Branch of mathematics
Facts and figures
A subject or discipline
Collections of data
A way to get information from data

9. “Statistics is a way to get information from data”
What is Statistics?
“Statistics is a way to get information from data”
Statistics
Data
Information
Data: Facts, especially numerical facts, collected together for reference or information.
Information: Knowledge communicated concerning some particular fact.
Statistics is a tool for creating new understanding from a set of numbers.
Definitions: Oxford English Dictionary

10. Example - Stats Anxiety…
A business school student is anxious about their statistics course, since they’ve heard the course is difficult. The professor provides last term’s final exam marks to the student. What can be discerned from this list of numbers?
Statistics
Data
Information
New information about the statistics class.
List of last term’s marks. 95 89 70 65 78 57 :
E.g. Class average,
Proportion of class receiving A’s
Most frequent mark,
Marks distribution, etc.

11. Applications of Statistics in Business
Accounting – auditing and cost estimation
Finance – investments and portfolio management
Human resource – compensation, job satisfaction, performance measure
Operation – quality management, forecasting, MIS, capacity planning, materials control
Marketing - market analysis, consumer research, pricing
Economics – regional, national, and international economic performance
International Business- market and demographic analysis.

12. Key Statistical Concepts…
Population
— a population is the group of all items of interest to a statistics practitioner.
— frequently very large; sometimes infinite.
E.g. All Blue collar workers in Malaysia
Sample
— A sample is a set of data drawn from the population.
— Potentially very large, but less than the population.
E.g. a sample of 765 blue collar workers

13. Key Statistical Concepts…
Parameter
— A descriptive measure of a population.
Statistic
— A descriptive measure of a sample.

14. Key Statistical Concepts…
Population
Sample
Subset
Statistic
Parameter
Populations have Parameters,
Samples have Statistics.

15. Branches of Statistics
Descriptive Statistics
Inferential Statistics
Parametric Statistics
Non-Parametric Statistics

16. Descriptive Statistics…
…are methods of organizing, summarizing, and presenting data in a convenient and informative way. These methods include:
Graphical Techniques
Numerical Techniques
The actual method used depends on what information we would like to extract. Are we interested in…
measure(s) of central location? and/or
measure(s) of variability (dispersion)?
Descriptive Statistics helps to answer these questions…

17. Inferential Statistics…
Descriptive Statistics describe the data set that’s being analyzed, but doesn’t allow us to draw any conclusions or make any interferences about the data. Hence we need another branch of statistics: inferential statistics.
Inferential statistics is also a set of methods, but it is used to draw conclusions or inferences about characteristics of populations based on data from a sample.

18. Statistical Inference…
Statistical inference is the process of making an estimate, prediction, or decision about a population based on a sample.
Population
Sample
Inference
Statistic
Parameter
What can we infer about a Population’s Parameters
based on a Sample’s Statistics?

19. Statistical Inference…
We use statistics to make inferences about parameters.
Therefore, we can make an estimate, prediction, or decision about a population based on sample data.
Thus, we can apply what we know about a sample to the larger population from which it was drawn!

20. Statistical Inference…
Rationale:
•Large populations make investigating each member impractical and expensive.
•Easier and cheaper to take a sample and make estimates about the population from the sample.
However:
Such conclusions and estimates are not always going to be correct.
For this reason, we build into the statistical inference “measures of reliability”, namely confidence level and significance level.

21. Confidence & Significance Levels…
The confidence level is the proportion of times that an estimating procedure will be correct.
E.g. a confidence level of 95% means that, estimates based on this form of statistical inference will be correct 95% of the time.
When the purpose of the statistical inference is to draw a conclusion about a population, the significance level measures how frequently the conclusion will be wrong in the long run.
E.g. a 5% significance level means that, in the long run, this type of conclusion will be wrong 5% of the time.

22. Process of Inferential Statistics

23. Types of Data and Information
Definitions…
A variable is some characteristic of a population or sample.
E.g. student grades; workers salary
Typically denoted with a capital letter:A,A-, B+, B, B-…
The values of the variable are the range of possible values for a variable.
E.g. student marks (0..100)
Data are the observed values of a variable.
E.g. student marks: {67, 74, 71, 83, 93, 55, 48}

24. Types of Data & Information
Data (at least for purposes of Statistics) fall into three main groups:
Interval Data
Nominal Data
Ordinal Data

25. Interval Data… Interval data
• Real numbers, i.e. heights, weights, prices, etc.
• Also referred to as quantitative or numerical.
Arithmetic operations can be performed on Interval Data, thus its meaningful to talk about 2*Height, or Price + $1, and so on.

26. Nominal data are also called qualitative or categorical.
• The values of nominal data are categories.
E.g. responses to questions about marital status, coded as:
Single = 1, Married = 2, Divorced = 3, Widowed = 4
Because the numbers are arbitrary, arithmetic operations don’t make any sense (e.g. does Widowed ÷ 2 = Married?!)
Nominal data are also called qualitative or categorical.

27. Ordinal Data…
Ordinal Data appear to be categorical in nature, but their values have an order; a ranking to them:
E.g. College course rating system:
poor = 1, fair = 2, good = 3, very good = 4, excellent = 5
While its still not meaningful to do arithmetic on this data (e.g. does 2*fair = very good?!), we can say things like:
excellent > poor or fair < very good
That is, order is maintained no matter what numeric values are assigned to each category.

28. Types of Data & Information…
Categorical?
Interval Data N Y
Ordered?
Ordinal Data Y
Categorical Data N
Nominal Data

29. E.g. Representing Student Grades…
Data
Categorical?
Interval Data
e.g. {0..100} N Y
Ordered?
Ordinal Data
e.g. {F, D, C, B, A} Y
Categorical Data N
Rank order to data
Nominal Data
e.g. {Pass | Fail}
NO rank order to data

30. Calculations for Types of Data
As mentioned above,
• All calculations are permitted on interval data.
• Only calculations involving a ranking process are allowed for ordinal data.
• No calculations are allowed for nominal data, only counting the number of observations in each category is possible.
This lends itself to the following “hierarchy of data”…

31. Hierarchy of Data… Interval Values are real numbers.
All calculations are valid.
Data may be treated as ordinal or nominal.
Ordinal
Values must represent the ranked order of the data.
Calculations based on an ordering process are valid.
Data may be treated as nominal but not as interval.
Nominal
Values are the arbitrary numbers that represent categories.
Only calculations based on the frequencies of occurrence are valid.
Data may not be treated as ordinal or interval.

32. Data Level, Operations, and Statistical Methods
Nominal
Ordinal
Interval
Meaningful Operations
Classifying and Counting
All of the above plus Ranking
All of the above plus Addition, Subtraction, Multiplication, and Division
Statistical
Methods
Nonparametric
Parametric