Chapter 11: Multiple Regression

regression coefficients

Values that represent the relationship between each independent variable and the dependent variable in a regression model

(e.g., in y = 2x + 5, the regression coefficient for x is 2)

multiple correlation coefficient, R

A measure of the strength of the relationship between a dependent variable and multiple independent variables in a regression model, ranging from 0 to 1

(e.g., an R of 0.8 indicates a strong relationship)

Squared correlation coefficient

Also called R² (R-squared), it represents the proportion of variance in the dependent variable explained by the independent variable(s)

(e.g., an R² of 0.75 means 75% of the variance is explained by the model)

Multicollinearity

A situation in multiple regression where independent variables are highly correlated, making it difficult to determine their individual effects on the dependent variable

(e.g., height and weight both predicting body mass)

Stepwise procedures

A regression method that adds or removes predictors one at a time based on statistical criteria to build the best model

(e.g., adding variables only if they significantly improve the prediction)