This course gives an introduction to the statistical software R. R is a programming 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.

Moreover, this course covers the statistical methods most commonly used in the field of marketing:

Foundations: Dependent vs. independent variables, descriptive 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

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.

Course attendance is mandatory. Students are allowed to miss 20% of units, which equals to one (1) session.

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:

• 3 assignments (30% = 3 x 10%)
• 4 programming exercises (10% = 4 x 2.5%)
• Final exam (60% with a requirement of minimum 50%, i.e. 30 points)

The students are allowed to miss only one (1) session. 

Students have to be prepared to present their assignment solutions in class, based on their uploaded document.

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

Previous knowledge of statistical concepts and methods and programming is helpful, but not mandatory. 
Students are highly encouraged to do prior reading on the R programming language, available at short.wu.ac.at/mr2019.