Statistical Modelling with R
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'.