1) Descriptive statistics and graphs:

• Central tendency (e.g., mean, median, ...)

• Measures of dispersion (e.g., variance, standard deviation, ...)

• Histogram

• Boxplot

• Scatterplot

2) Inferential statistics:

• Chi-square test

• Confidence Intervals

• Odds ratios

• t-test and analysis of variance (ANOVA)

• Mann-Whitney U-test and Kruskal-Wallis H-Test

3) Measures of association (i.e., Correlation, Pearson & Spearman) & Linear (i.e., univariate and multiple) regression

4) Principal Component Analysis

At the end of this course, you will be able to:

• Interpret statistical analyses used in the field of marketing.

• Learn how to perform exploratory data analysis on a data set by using the statistical software R.

All methods are deepened by practical examples in the context of typical marketing applications.

The course is taught using a combination of material presented by the lecturer and supported by practical examples.

The performance of students is assessed based on various exercises (delivery via learn@wu) and a final examination:

• Active participation in the class (20%)
• 4 assignments (50%)
• A final exam (30%)

I am happy to answer your questions so feel free to send me a short email if you would like to talk to me in person. I will also try to be available in the classroom after each class or during the breaks of each class.