Descriptive Statistics

Descriptive statistics allow us to represent essential information about a large amount of data by summarizing characteristics of the data visually and numerically. They also form the basis for inferential statistics, that help us decide whether our data provide support for the research hypothesis. You will learn about the basic concepts and learn to calculate and generate these statistics yourself using freely available statistical software.

In the first part of the course we will consider the basic concepts, graphs and the most important descriptive statistics that help us to spot interesting patterns. The second part of the course is concerned with the basics of probability, necessary to understand inferential statistics. We will end the course with a short preview of inferential statistics. 

Week 1: Exploring data 

• introduction to statistics
• sample and population
• center and variability statistics
• graphical representation
• quiz and warm-up assignments (not graded)

Week 2: Association

• contingency tables
• correlation
• regression
• quiz and small assignment (graded)

Week 3: Probability

• definition of probability
• calculating probabilities
• quiz and paper on week 1 & 2 (graded)

Week 4: Probability distributions

• basic concepts
• types of distributions
• normal distribution
• binomial distribution
• quiz and small assignment (graded) 

Week 5: Sampling distributions

• variation in sample values 
• sampling distribution for proportions
• sampling distribution for means
• quiz and paper on week 3 & 4 (graded)

Week 6: Confidence intervals & significance testing

• statistical inference
• confidence interval for proportions and means
• significance tests for one proportion and one mean
• quiz and small assignment (graded)

Week 7: Study week

• time to ask your final questions
• time to work on last paper

Week 8: Exam week

• paper on week 5 & 6 (graded), final exam (graded) and course evaluation