Unit 9: Inference for Quantitative Data: Slopes

The estimated change in the predicted Y for every one-unit increase in X, observed in our _.

The true, unknown linear relationship between X and Y in the entire _. This is the parameter we are trying to infer about.

For inference about slopes, we use a t-distribution with n-2 degrees of freedom, losing one for the slope 'b' and one for the _.

A measure of how much sample slopes 'b' would typically _ from the true population slope 'β' if many samples were taken.

A mnemonic for checking conditions for inference for slopes: Linearity, Independence, Normality of Residuals, and Equal _ of Residuals.