Title: Summary
Course: UGQuants (SSPS) > Section: Multiple Regression

Summary

After working through this module you should have learned...

1 | The difference between experimental control of variables and the statistical control possible with linear regression.
2 | How the coefficients in multiple linear regression measure the association between each independent variable and the dependent variable, controlling for all the other independent variables in the model.
3 | The importance of considering how best to operationalise a theoretical model with empirically measurable and available data.
4 | How we can transform variables using logs or powers to obtain a linear relationship.
5 | How to interpret the value of R-squared, the coefficient of determination, for a model.
6 | How to interpret and use the values for the constant (intercept) and unstandardized and standardized (beta) coefficients.
7 | How to consider whether a regression model works equally well for all your data, or whether it is better to use different models for different ranges of data.
8 | The importance of collinearity between independent variables and how to check for it.
9 | How to identify and deal with outliers in the data.
10 | The different techniques for entering variables in a linear regression model.
11 | How to create and use dummy variables for ordinal and nominal independent variables.
12 | How to analyse residuals, using a normal probability plot, and the Cook’s Distance measure, in order to elaborate or improve a model.