1. Estimation

1.1 Introduction

1.2 Point estimation

1.3 Properties of point estimators

1.4 Interval estimation

2. Hypothesis testing

2.1 Introduction

2.2 Most powerful test. Neyman-Pearson Lemma

2.3 Testing of simple vs composite hypotheses

2.4 P-value

2.5 Normal populations - mean and variance testing

2.6 Normal populations - Testing equality of two populations  

2.7 Non-normal populations - Large samples results

3. Non-parametric methods

3.1 Introduction

3.2 Adjustment test

3.3 Independence test

4. Linear regression model

4.1 Introduction

4.2 Definition of the linear regression model

4.3 Basic hypotheses of the model

4.4 Coefficient estimation through the least squares method

4.5 Properties of the least squares estimator

4.6 Unbiased estimation of the error variance

4.7 Coefficient of determination

4.8 Regression through the origin

4.9 The normal linear regression model

4.10 Inference in the linear regression model

5. Further topics in the linear regression model

5.1 Dummy variables

5.2 Specification tests

5.3 Prediction

5.4  Heteroskedasticity