**This is an introductory course of quantitative methods for policy analysts**

It aims to prepare researchers to intelligently apply basic statistical methods for the purposes of empirical analysis. This course is thus a practical guide to statistical application for future policy analysts and makers. However, to become effective users of statistics, the students must learn elementary statistical concepts and theory, and be aware of the various assumptions of the methodology. The course will consequently combine simple exposition to statistical theory with practical use of statistical modeling. The course will alternate between lectures and practical lab sessions where students will be encouraged to apply the material while learning to program in the statistical software package R.

### BEginnings

Lecture: Why study quantitative analysis?

R Notes: Getting started with R

R Script: Demonstration in R

### Session 2

Lecture: Research Design

### Session 4

Lecture: Inference

### Session 5

Lecture: Hypothesis testing

### Session 7

Mid-term exam correction

### Session 8

Lecture: Regression

### Session 10

Lecture: Heteroscedasticity and Multicollinearity

### Session 11

Lecture: Regression Warnings

### Session 12

Lecture: Interaction effects