Applied Statistics

Prerequisites:

 Probability and statistics, matrix algebra.

 Aims of the subject:

Principles of statistical thinking and statistical inference. Understanding of some statistical procedures, explanation the results of statistical evaluations. Solving problems and examples from business world and marketing. Integration theory and the use of statistical software packages (NCSS, SPSS Student Version) with decision making procedures.

 

Subject matter:

1.     Estimating population values. Point and confidence interval estimates for the population mean and population proportion

2.     Hypothesis testing: definitions and steps, type I. and II. errors

3.     Tests concerning population means (one-sample, two-samples), F-test

4.     Hypotheses about population proportions

5.     Categorical variables. Contingency tables. Chi-square test

6.     Design of experiments and ANOVA

7.     Simple linear regression, least square method, inference for regression

8.     Multiple regression: Assumptions, estimates, inference for multiple regression

9.     Analysing and forecasting time-series data. Time series, description of their components. Trend analysis, forecasting

10. Seasonality and cyclical behaviour – empirical estimates

11. Forecasting using smoothing methods. Exponential smoothing methods

12. Introduction to multivariate classification. General purpose and description of discriminant function analysis

13. General purpose and description of cluster analysis

 

Assessment strategy and method:

Solving exercises, written test, exam.

 

Indicative reading:

1.     Groebner D.F., et al: Business Statistics, A Decision – Making Approach. Prentice Hall (2008, 2005, 2001)

2.     McClave J.T., Benson P.G., Sincich T.: Statistics for Business and Economics. Prentice Hall International, Inc. Upper Saddle River, 1998

3.     Sincich T.: Business Statistics by Example. Macmillan Publishing Company. New York, 1995

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