Dealing with multicollinearity: Factor analysis/PCA

Linear regression for machine learning

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

Create a bubble chart

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: 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 is the extension hub?

Ridge regression

Lasso and elastic net

What's next