MRKT 365 — ANOVA & Linear Regression Study Guide

These flashcards cover key concepts and definitions related to ANOVA and Linear Regression as discussed in the lecture.

ANOVA

Analysis of Variance; compares three or more group means to determine if they differ significantly.

F-test

A statistical test that evaluates whether between-group variance is large enough to reject the null hypothesis.

Total Sum of Squares (SST)

Total variation in all observations from the overall mean.

Sum of Squares Between Groups (SSBG)

Variation between group means and the overall mean; represents explained variance.

Sum of Squares Error (SSE)

Variation within groups; represents unexplained variance or random error.

Mean Squares (MS)

Sums of Squares divided by degrees of freedom, used to compute the F-statistic.

F-statistic

A value that tests whether model variance is significantly greater than error variance.

Coefficient of Determination (R²)

Percentage of variance in the dependent variable explained by the model.

p-value

Probability of obtaining observed results if the null hypothesis is true; helps determine significance.

Normality

The assumption that residuals follow a normal distribution, tested with the Shapiro-Wilk test.

Constant Variances

Assumption that residual variance remains constant; visualized with Predicted vs. Residuals plot.

Linearity

Assumption that there is a linear relationship between the dependent and independent variables.

Multicollinearity

Assumption that independent variables are not highly correlated, assessed with correlation matrix or VIF.

Residual

The difference between observed and predicted values of Y.

Variance Inflation Factor (VIF)

A measure of how much collinearity inflates coefficient variance; acceptable VIF < 5.

Transformations

Methods used to correct violations of linear regression assumptions.