This course builds on and extends the methodology and key subjects from the first year. Students will be trained in the use of empirical analysis for decision making. Special emphasis is given to interpretation of economic and behavioral data. Students will learn to distinguish random variation from systematic variation and causality from correlation. Methodological issues are integrated with other economic subjects through examples and specific applications. While the main focus in the first year course in statistics is univariate analyses, this course also covers multivariate methods.

The following topics are covered:

1. Introduction to scientific methods in social sciences

- Methodology
- Qualitative vs quantitative analysis
- Research ethics

2. Descriptive statistics

- Population and sample
- Types of data and information
- Central location, variance and co-variance

3. Comparing two populations

- Sampling distributions
- Comparing two means
- Comparing two variances
- Comparing two proportions

4. Chi-squared tests

- Goodness-of-fit (more than two proportions)
- Test for independence in a contingency table

5. Regression analysis

- Simple regression
- Multiple regression: Modelling and residual analysis
- Panel data
- Categorical regression (logit and probit)

6. Introduction to machine learning

7. Time series