**Lecture 0**: General overview

**Lecture 1**: Probability review/overview, part 1

**Lecture 2**: Probability review/overview, part 2. Uniform, gamma and normal distributions

**Lecture 3**: Probability review/overview, part 3. Central limit theorem

**Lecture 4**: Sampling distributions and a first look at estimation of means

**Lecture 5**: Interval estimation intro and confidence intervals for the mean

**Lecture 6, part 1**: Sample variance and the chi square distribution

**Lecture 6, part 2**: The what and why of the chi square distribution

**Lecture 7**: Confidence intervals for population variance

**Lecture 8**: Normal approximation for chi square for large n

**Lecture 8.5**: Doing better than just the central limit theorem for approximating chi square for medium size n

**Lecture 9**: The t-distribution and sampling from normal populations with unknown variance

**Lecture 10**: The f-distribution

**Supplemental Lecture 10.1**: The f-distribution: Working with multiple sample variances from a single population

**Supplemental Lecture 10.2**: The f-distribution: working with ratios of variances

**Supplemental Lecture 10.3**: The f-distribution: confidence intervals for ratios of variances

**Lecture 11, part 1**: Unbiased estimators

**Lecture 11, part 2**: Efficiency of estimators

**Lecture 12**: Consistent estimators

**Lecture 13**: Sufficient estimators

**Lecture 14.1**: How do we find estimators?

**Lecture 14.2**: The method of moments

**Lecture 14.3**: Detour: Jensen's inequality

**Lecture 15**: Maximum likelihood estimators

**Lecture 16**: Bayesian estimation of parameters

**Lecture 17**: Introduction to hypothesis testing

**Lecture 18**: Hypothesis testing: basic examples

**Lecture 19**: Hypothesis testing: more examples

**Lecture 20**: Most powerful regions and the Neyman-Pearson Lemma

**Lecture 20.1**: Supplimentary examples on simple and compound hypotheses

**Lecture 21, part 1**: The power function of a test concerning composite hypotheses

**Lecture 21, part 2**: Likelihood ratio tests

**Lecture 21, part 3**: Introduction to using likelihood ratio tests

**Lecture 22**: Likelihood ratio test example featuring the t-distribution

**Lecture 23, part 1**: Regression Analysis: Basic concepts

**Lecture 23, part 2**: Regression Analysis: Normal regression analysis

**Lecture 23, part 3**: Regression Analysis: Normal correlation analysis