• The course will be held in distance mode in any case.

This course will be held in distance mode. The lectures are recorded and provided to you via Lecturecasts on learn@WU. I will be available to you during the times above to do exercises and answer any questions.      

This course covers the statistical methods most commonly used in the field of marketing:

Foundations: Dependent vs. independent variables, descriptive statistics & introduction to probability theory/statistics

Measurement & scaling: Types of scales and descriptive & inferential statistics, reliability & validity, measurement error

Data collection & sampling: Taking samples from a population, sampling distribution of the mean, central limit theorem, confidence intervals, sample size

Exploring data with graphs: Histogram, scatter plot etc.

Exploring assumptions: Testing whether a distribution is normal, testing for homogeneity of variance

Basics of hypothesis testing: Different types of tests and assumptions, e.g., parametric vs. non-parametric, normal vs. t-distribution, chi-2 distribution, degrees of freedom

Comparing means: t-test, analysis of variance (ANOVA)

Correlation and regression: Looking at the relationships, simple and multiple regression

This course also gives an introduction to the statistical software R. R is a language and environment for statistical computing and graphics, which provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, …) and graphical techniques, and is highly extensible. With the knowledge gained in this course, you will be ready to undertake your very first own data analysis including the statistical methods most commonly used in the field of marketing.

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. During the sessions, students will have the chance to apply methods covered utilizing R. 

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

• Individual programming assignments (20% = 4 x 5%)
• Individual statistical exercises (40% = 4 x 10%)
• Final exam (40%)

The results for all exercises should be clearly stated in the documents handed in.

For a positive grade, students have to fulfill 60% of the requirements.

For this SBWL we have the following scale:

< 60%                                fail (5)

60% bis 69,99%               sufficient (4)

70% bis 79,99%               satisfactory (3)

80% bis 89,99%               good (2)

>= 90%                              excellent (1)

Please note that copying/cheating on individual assignments (computer exercises, exam) will result in immediate exclusion from the course and a failing grade (5).