Lab Materials for Introduction to Statistics (Instructor: Siwei Cheng) taught in 2019 at NYU. All Rmarkdown codes for the lab instructions are freely available here.

Labs

• Lab 1: Rstudio projects, data.table objects, working directories, subsetting, descriptive statistics, ggplot, sampling distributions

• Lab 2: Basic Debugging, Standard Errors and Confidence Intervals, Central Limit Theorem, Population as a DGP, Unbiasedness and Consistency

• Lab 3: Recoding and Saving Data, One-sample and Two-sample t-tests, Crosstables and Pearson’s Chi-squared Tests

• Lab 4: Conditional Means, Simple Linear Regression, and Fitting Regression Models in R

• Lab 5: Multiple Regression I: Interpretation of Regression Coefficients, Frisch-Waugh Theorem

• Lab 6: Multiple Linear Regression II: Data Preparation for a Concrete Data Analysis Example

• Lab 7: Multiple Linear Regression III: Fitting Multiple Regression Models to Data, Interaction Effects, Huber-White Covariance Matrix Estimator

• Lab 8: Binary Outcomes, Maximum Likelihood Estimation, Linear Probability Model, Logistic Regression, Predicted Probabilities

• Lab 9: Latent Variable Formulation of the Logit/Probit Model, Ordered and Multinomial Logistic Regression, Simulation-based Confidence Intervals for Predicted Probabilities

• Lab 10: Very Brief Introduction to Longitudinal Data Analysis