majiayu000 Regression Modeling
Regression models quantify the relationship between a response variable and one or more explanatory variables. The goal may be prediction ("what will Y be when X = 10?"), explanation ("how does Y change when X increases by one unit?"), or both. This skill covers the core regression toolkit from simple linear regression through logistic regression, with emphasis on the diagnostics that separate a useful model from a misleading one.
Agent affinity: box (model building, diagnostics, "all models are wrong"), pearson (correlation, regression theory), efron (computational model fitting)
Concept IDs: stat-descriptive-statistics, stat-hypothesis-testing
Simple Linear Regression
The model
Y = beta_0 + beta_1 * X + epsilon, where epsilon ~ N(0, sigma^2).
- beta_0: Y-intercept. The predicted value of Y when X = 0.
- beta_1: Slope. The change in predicted Y for a one-unit increase in X.
- epsilon: Error term. Captures everything the model does not explain.
Least squares estimation
The least squares estimates minimize the sum of squared residuals:
b_1 = sum((x_i - x-bar)(y_i - y-bar)) / sum((x_i - x-bar)^2) b_0 = y-bar - b_1 * x-bar
The fitted line y-hat = b_0 + b_1 * x passes through the point (x-bar, y-bar).
Interpretation
- b_1 = 2.3: "For each one-unit increase in X, Y increases by 2.3 units on average."
- b_0 = 15.7: "When X = 0, the predicted Y is 15.7." (Only meaningful if X = 0 is within the data range.)
- Extrapolation warning: The model is only trustworthy within the range of observed X values. Extrapolating beyond that range assumes the linear relationship continues, which may be false.
R-Squared and Model Fit
R-squared (coefficient of determination)
R^2 = 1 - SS_residual / SS_total = SS_regression / SS_total.
R^2 is the proportion of variance in Y explained by the model. Range: 0 to 1. An R^2 of 0.72 means the model explains 72% of the variability in Y.
Adjusted R-squared
R^2_adj = 1 - (SS_residual / (n-k-1)) / (SS_total / (n-1)).
Adjusted R^2 penalizes for adding predictors. It can decrease when a useless predictor is added. Use adjusted R^2, not R^2, for comparing models with different numbers of predictors.
Cautions about R-squared
- A high R^2 does not mean the model is correct. A quadratic relationship fit with a line can have moderate R^2 but systematically wrong predictions.
- A low R^2 does not mean the model is useless. In social science, R^2 = 0.10 with a clear causal mechanism is scientifically important.
- R^2 always increases (or stays the same) when you add a predictor, regardless of that predictor's relevance. This is why adjusted R^2 exists.
Multiple Regression
The model
Y = beta_0 + beta_1X_1 + beta_2X_2 + ... + beta_k*X_k + epsilon.
Each beta_j is the partial effect of X_j on Y, holding all other predictors constant.
Interpretation with multiple predictors
"Holding all other variables constant, a one-unit increase in X_2 is associated with a b_2-unit change in Y." The phrase "holding all other variables constant" is critical -- it distinguishes multiple regression from running separate simple regressions.
Multicollinearity
When predictors are highly correlated with each other, the model has difficulty separating their individual effects.
Detection:
- Correlation matrix: Pairwise correlations > 0.8 are concerning.
- Variance Inflation Factor (VIF): VIF_j = 1 / (1 - R^2_j), where R^2_j is the R^2 from regressing X_j on all other predictors. VIF > 10 is a red flag; VIF > 5 warrants attention.
Consequences: Coefficients become unstable (large standard errors), making individual predictor effects unreliable. The overall model's predictive ability may be fine.
Remedies: Remove one of the correlated predictors, combine them (e.g., principal components), or accept the instability if prediction is the only goal.
Residual Analysis and Diagnostics
"All models are wrong, but some are useful." -- George E.P. Box. The diagnostics below determine whether a model is useful enough.
Assumptions to check
- Linearity: The relationship between predictors and response is linear.
- Independence: Residuals are independent of each other.
- Homoscedasticity: Residuals have constant variance across all levels of X.
- Normality: Residuals are approximately normally distributed.
The acronym LINE captures all four.
Residual plots
| Plot | What it checks | Healthy pattern | Problem signal |
|---|---|---|---|
| Residuals vs. fitted values | Linearity, homoscedasticity | Random scatter around zero | Curved pattern (nonlinearity), funnel shape (heteroscedasticity) |
| Normal Q-Q plot | Normality | Points on the diagonal line | Systematic departures at the tails |
| Residuals vs. predictor | Linearity for each predictor | Random scatter | Curved pattern |
| Residuals vs. order | Independence | Random scatter | Patterns over time (autocorrelation) |
Influential observations
- Leverage: How far an observation's X value is from the mean of X. High leverage points have outsized potential influence.
- Cook's distance: Measures how much all fitted values change when observation i is removed. Cook's D > 4/n is a common threshold.
- DFFITS: The change in the fitted value at X_i when observation i is deleted. |DFFITS| > 2*sqrt(k/n) is flagged.
Action: Investigate influential points. They may be data entry errors, genuinely unusual observations, or indicators that the model is wrong. Do not automatically remove them.
Polynomial Regression
Y = beta_0 + beta_1X + beta_2X^2 + ... + beta_p*X^p + epsilon.
Used when the scatter plot shows curvature that a straight line cannot capture.
Cautions:
- Higher-degree polynomials can overfit, fitting noise rather than signal.
- Polynomial extrapolation is especially dangerous -- polynomial tails diverge wildly.
- Start with degree 2; go higher only with strong evidence and domain justification.
Logistic Regression
When the response is binary
When Y is 0 or 1 (success/failure, yes/no), linear regression is inappropriate (predicted values can fall outside [0, 1]). Logistic regression models the log-odds:
log(p / (1-p)) = beta_0 + beta_1X_1 + ... + beta_kX_k
where p = P(Y = 1).
Interpretation
- Odds ratio: exp(beta_j) is the multiplicative change in odds for a one-unit increase in X_j. An odds ratio of 1.5 means "50% higher odds of success."
- Probability curve: The logistic function p = 1 / (1 + exp(-(beta_0 + beta_1*X))) produces an S-shaped curve mapping the linear predictor to [0, 1].
Model fitting
Logistic regression is fit by maximum likelihood, not least squares. The log-likelihood is maximized iteratively (usually via Newton-Raphson or Fisher scoring).
Diagnostics
- Deviance residuals replace ordinary residuals.
- AIC (Akaike Information Criterion) for model comparison (lower is better).
- Hosmer-Lemeshow test for goodness-of-fit.
- ROC curve and AUC for classification performance.
Variable Selection
Methods
| Method | Approach | Strengths | Weaknesses |
|---|---|---|---|
| Forward selection | Start with no predictors, add the most significant one at each step | Simple | May miss important combinations |
| Backward elimination | Start with all predictors, remove the least significant one at each step | Considers all predictors | Requires n >> k |
| Stepwise | Combination of forward and backward | Flexible | Overfits; inflates significance |
| Best subsets | Evaluate all 2^k possible models | Exhaustive | Computationally expensive for large k |
| LASSO (L1 penalty) | Penalized regression that shrinks some coefficients to zero | Built-in variable selection, handles multicollinearity | Requires tuning of penalty parameter |
| AIC / BIC comparison | Select model with lowest information criterion | Principled tradeoff between fit and complexity | Requires fitting multiple models |
Box's dictum applied: No variable selection method guarantees finding the "true" model. Use domain knowledge first, then let statistical methods refine.
Common Mistakes
| Mistake | Why it fails | Fix |
|---|---|---|
| Interpreting correlation as causation | Regression shows association, not causation | Only causal language if the study design supports it (experiment, not observational) |
| Ignoring residual plots | The model may be systematically wrong | Always plot residuals after fitting |
| Extrapolating beyond data range | No evidence that the relationship holds outside observed X values | State the range of validity |
| Adding too many predictors | Overfitting; R^2 increases but generalization decreases | Use adjusted R^2 or cross-validation |
| Ignoring influential points | One point can change the entire regression line | Check leverage, Cook's distance, DFFITS |
| Using linear regression for binary outcomes | Predicted values outside [0, 1], violates assumptions | Use logistic regression |
Cross-References
- box agent: Model building philosophy, response surface methodology, diagnostics.
- pearson agent: Regression theory, correlation, coefficient estimation.
- efron agent: Computational fitting, cross-validation, bootstrap for regression.
- descriptive-statistics skill: Scatter plots and correlation precede regression.
- inferential-statistics skill: Hypothesis tests for regression coefficients.
- bayesian-methods skill: Bayesian regression as an alternative to frequentist fitting.
References
- Box, G. E. P. (1976). "Science and statistics." Journal of the American Statistical Association, 71(356), 791-799.
- Kutner, M. H., Nachtsheim, C. J., Neter, J., & Li, W. (2005). Applied Linear Statistical Models. 5th edition. McGraw-Hill.
- Agresti, A. (2015). Foundations of Linear and Generalized Linear Models. Wiley.
- James, G., Witten, D., Hastie, T., & Tibshirani, R. (2021). An Introduction to Statistical Learning. 2nd edition. Springer.
- Hosmer, D. W., Lemeshow, S., & Sturdivant, R. X. (2013). Applied Logistic Regression. 3rd edition. Wiley.