A first part is about computational statistics. First, fundamentals of statistics in terms of the assumed data generating model and sampling uncertainty are discussed. Then Monte Carlo simulation is introduced and how to use it in the context of assessing properties of statistical estimators and hypothesis tests (control of type I error and power) is discussed. Finally, the use of cross-validation in the context of model selection and model assessment is discussed with special attention for the bias-variance trade-off in the context of prediction.

In a second part, statistical computing is covered; this is the use of computers for optimization and numerical approximation in statistical problems. Maximum likelihood and least-square estimation is introduced and the necessary basics of univariate optimization are covered with respect to descriptive statistics like sample mean and sample variance and with respect to linear regression analysis. Then, the use of numerical optimization routines (e.g., bisection and Newton methods) are discussed in relation to logistic regression analysis.

**Grading scheme**
The final grade will be based on two items:

- 4 homework assignments, the best 3 of which each count for 15% of the final grade.
- A written exam (closed book) which counts for the remaining 55% of the final grade.

If you reach less than 50% of the points on the final exam, then you will fail the course, regardless of the points you collected with the homework assignments. Your grade will be the minimum of 5 and the grade you achieved. However, you are allowed to participate in the second chance exam. The grade of the second chance exam replaces the grade for the first exam, that is, your homework assignments always count for 45% of your grade.