AGW 615 Advanced Business Statistics

1. AGW 615Advanced Business Statistics

2. Course Instructor: T. Ramayah & Dr. Arumugam, V • E-mail: ramayah@gmail.com, arumugamveeri@hotmail.com • Web Page: http://www.management.usm.my/ramayah • 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 50% • Quizzes 20% • Project work/assignment 30% • 2. Final Examination 50% ramayah@gmail.com, arumugamveeri@hotmail.com http://www.management.usm.my/ramayah

3. COURSE CONTENT AND OUTLINE OF SESSIONS * 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. 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. What is Statistics? • “Statistics is a way to get information from data” Statistics Data Information Information: Knowledge communicated concerning some particular fact. Data: Facts, especially numerical facts, collected together for reference or information. 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 ofall 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 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 processof 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 statisticsto 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 valuesof 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… Nominal Data • 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 > poororfair < very good That is, order is maintained no matter what numeric values are assigned to each category.

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