Build predictive regression models

Model relationships between variables using regression analysis

Quantifying how variables influence outcomes when you need interpretable coefficients rather than pure prediction.

• · predictor variables
• · response variable
• · numerical dataset

• · fitted model coefficients
• · R-squared value
• · residual diagnostics
• · confidence intervals

Variables should be numeric; sufficient sample size (n>10); linear relationship plausible

• · Linearity assumption violated (curved relationship)
• · Multicollinearity among predictors
• · Heteroscedastic residuals
• · Non-normal error distribution
• · Extrapolation beyond data range

• · Covers LINE diagnostic framework
• · Box-Jenkins foundational quote
• · Residual analysis emphasized over formula worship