1. Sampling

1.1 Mean and variance of the sample mean.

1.2 Mean and variance of the sample variance.

1.3 Distribution of the sample and sampling distributions of a statistic.

1.4 Results for Bernoulli population.

1.5 Results for Poisson population.

1.6 Results for Normal population.

2. Estimation

2.1 Introduction

2.2 Point estimation

2.3 Properties of point estimators

2.4 Interval estimation

3. Hypothesis testing

3.1 Introduction

3.2 Most powerful test. Neyman-Pearson Lemma

3.3 Testing of simple vs composite hypotheses

3.4 P-value

3.5 Normal populations - mean and variance testing

3.6 Normal populations - Testing equality of two populations  

3.7 Non-normal populations - Large samples results

4. Non-parametric methods

4.1 Introduction

4.2 Goodness-of-fit test

4.3 Independence test

5. Linear regression model

5.1 Introduction

5.2 Definition of the linear regression model

5.3 Basic hypotheses of the model

5.4 Coefficient estimation through the least squares method

5.5 Goodness-of-fit assessment

5.6 The normal linear regression model

5.7 Inference in the linear regression model