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