Regression Match

Set 1: Mechanics of Regression & Correlation

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