Registration via LPIS
This course will be held in presence mode.
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 students are allowed to miss 20% of the course which translates 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:
- Group Project (30%)
- Programming exercises (20% = 4(+1) x 5%, best out of 5)
- Participation (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)
All the material covered in the following courses is considered a prerequisite:
- Einstieg in die SBWL: Service und Digital Marketing
- Digital Marketing
- Mathematik (STEOP)
- Statistik (CBK)