## Lecture 11: Refinements of the Stata Language

Graphics. Summarising data. More syntax. Looping. Reshaping data.

## Lecture 10: Survival Analysis

Censoring. Survival curves and life tables. Comparing survival curves. Parametric regression. Cox regression.

## Lecture 9: Modelling Counts

Poisson regression, constraints, overdispersion. Negative binomial regression.

## Lecture 8: Modelling Categorical Outcomes

Fitting binomial, multinomial and probit regression models for discrete and categorical responses with R.

## Lecture 7: Modelling Binary Outcomes

Limits of linear regression and how these motivate generalised linear models. Introduction to logistic regression, diagnostics, sensitivity and specificity. Alternative models.

## Lecture 6: Linear Models II

Categorical variables, interactions, confounding and variable selection for linear regression models.

## Lecture 5: Linear Models

Assumptions, interpretation, inference, goodness of fit and diagnostics for linear regression models.

## Lecture 4: Hypothesis Testing

Performing null hypothesis significance testing for means, proportions and variances with one or two samples. Using base R and community packages to do power calculations.

## Lecture 3: Sampling and Confidence Intervals

Using R to generate random numbers, compute means and standard deviations, perform Student's t-tests and calculate confidence intervals.

## Lecture 2: Summarising Data

Obtaining numerical and visual summaries of datasets in R. An introduction to the split-apply-combine approach to data analysis, using either base functions or the packages dplyr and data.table.

## Lecture 1: Essentials of the R language

A direct R translation of the introductory practical on 'Essentials of Stata'.