Regression Match

Set 1: Mechanics of Regression & Correlation

Set 1 of 2
Error (Residual)
Pearson's r
Covariance
Data = Model + Error
Slope (b)
Ordinary Least Squares (OLS)
Scatterplot Visualization
Correlation Significance
Intercept (a)
Zero Correlation Consequence
The value of the function when X is zero (crosses y-axis)
The uncertainty remaining after fitting the model to the data
Unstandardized measure of shared variance limited by units
Magnitude of change in f(x) resulting from a change in x
Standardized measure of association bounded between -1 and 1
The fundamental form of most statistical models
Should be judged by magnitude (effect size), not p-values
The regression model collapses to the Mean of Y (flat line)
Optimization algorithm that minimizes squared errors to estimate parameters
Necessary to detect non-linear relationships (e.g., U-shaped) missed by r