Learn how to model data in R, one of the most important tools available for data analysis, machine learning, and data science.
Overview
Syllabus
Introduction
- Model data with R
- Using the exercise files
- Data science with R: A case study
- Computing frequencies
- Computing descriptive statistics
- Computing correlations
- Creating contingency tables
- Conducting a principal component analysis
- Conducting an item analysis
- Conducting a confirmatory factor analysis
- Comparing proportions
- Comparing one mean to a population: One-sample t-test
- Comparing paired means: Paired samples t-test
- Comparing two means: Independent samples t-test
- Comparing multiple means: One-factor analysis of variance
- Comparing means with multiple categorical predictors: Factorial analysis of variance
- Predicting outcomes with linear regression
- Predicting outcomes with lasso regression
- Predicting outcomes with quantile regression
- Predicting outcomes with logistic regression
- Predicting outcomes with Poisson or log-linear regression
- Assessing predictions with blocked-entry models
- Grouping cases with hierarchical clustering
- Grouping cases with k-means clustering
- Classifying cases with k-nearest neighbors
- Classifying cases with decision tree analysis
- Creating ensemble models with random forest classification
- Next steps
Taught by
Barton Poulson
