Materials for the math refresher (R, LaTeX, and Rmarkdown) and introduction to statistics course (Fall 2019)
If you find any errors or typos, or anything you want to add, please shoot me a PR! :)
- Introduction to LaTeX
- Introduction to Programming in R
- Introduction to Rmarkdown
- Rstudio projects,
data.tableobjects, working directories, subsetting, descriptive statistics, ggplot, sampling distributions - Basic Debugging, Standard Errors and Confidence Intervals, Central Limit Theorem, Population as a DGP, Unbiasedness and Consistency
- Recoding and Saving Data, One-sample and Two-sample t-tests, Crosstables and Pearson's Chi-squared Tests
- Conditional Means and Simple Linear Regression, Fitting Linear Regression Models in R
- Multiple Linear Regression I: Interpretation of Regression Coefficients, the Frish-Waugh Theorem
- Multiple Linear Regression II: Data Preparation for a Concrete Data Analysis Example
- Multiple Linear Regression III: Fitting Multiple Regression Models to Data, Interaction Effects, Heteroskedasticity-robust Covariance Matrix Estimation.
- Binary Outcomes, Maximum Likelihood Estimation, Linear Probability Model, Logistic Regression, Predicted Probabilities
- Latent Variable Formulation of the Logit/Probit Model, Ordered and Multinomial Logistic Regression, Simulation-based Confidence Intervals for Predicted Probabilities
- Very Short Introduction to Longitudinal Data
- Basic mean and variance calculations, some combinatorics, exercises in plotting via ggplot, a small simulation exercise
- Hypothesis testing, t- and chi-squared tests, statistical power, correlations and linear regression
- Assumptions of the Linear Regression Model, interactions, stripes in residuals-versus-fitted plots
- Simpson's Paradox, Fixed and Random Effects