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
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.