Expand your data science skills by learning how to leverage the concepts of linear regression to solve real-world problems.
Overview
Syllabus
Introduction
- Welcome
- What you should know
- Using the exercise files
- Building effective scatter plots in Chart Builder
- Adding labels and spikes to a scatter plot
- Create a 3D scatter plot
- Bubble chart with GPL
- Residuals and R2
- Calculating and interpreting regression coefficients
- Challenges and assumptions of multiple regression
- Checking assumptions visually
- Checking assumptions with Explore
- Checking assumptions: Durbin-Watson
- Checking assumptions: Levine's test
- Checking assumptions: Correlation matrix
- Checking assumptions: Residuals plot
- Checking assumptions: Summary
- Creating dummy codes
- Dummy coding with the R extension
- Detecting variable interactions
- Creating and testing interaction terms
- Three regression strategies and when to use them
- Understanding partial correlations
- Understanding part correlations
- Visualizing part and partial correlations
- Simultaneous regression: Setting up the analysis
- Simultaneous regression: Interpreting the output
- Hierarchical regression: Setting up the analysis
- Hierarchical regression: Interpreting the output
- Creating a train-test partition in SPSS
- Stepwise regression: Setting up the analysis
- Stepwise regression: Interpreting the output
- Collinearity diagnostics
- Dealing with multicollinearity: Factor analysis/PCA
- Dealing with multicollinearity: Manually combine IVs
- Diagnosing outliers and influential points
- Dealing with outliers: Studentized deleted residuals
- Dealing with outliers: Should cases be removed?
- Detecting curvilinearity
- Regression options
- Automatic linear modeling
- Regression trees
- Time series forecasting
- Categorical regression with optimal scaling
- Comparing regression to Neural Nets
- Logistic regression
- SEM
- What's next
Taught by
Keith McCormick