🥖Linear Modeling Theory Unit 9 – Diagnostics & Remedies for Linear Regression
Linear regression is a powerful statistical tool for modeling relationships between variables. This unit covers essential diagnostic techniques and remedies to ensure accurate and reliable results, including residual analysis, assumption checking, and methods to address common issues like heteroscedasticity and multicollinearity.
The unit explores advanced techniques like generalized linear models and mixed-effects models, extending linear regression's capabilities. It emphasizes the importance of proper model specification, variable selection, and diagnostic checking for obtaining trustworthy results across various real-world applications in fields such as finance, marketing, and healthcare.
Linear regression models the relationship between a dependent variable and one or more independent variables
Ordinary least squares (OLS) estimation minimizes the sum of squared residuals to find the best-fitting line
Residuals represent the difference between observed and predicted values of the dependent variable
Coefficient of determination (R-squared) measures the proportion of variance in the dependent variable explained by the model
Adjusted R-squared accounts for the number of predictors in the model
Multicollinearity occurs when independent variables are highly correlated with each other
Heteroscedasticity refers to non-constant variance of the residuals across the range of predicted values
Outliers are data points that are far from the majority of the data and can heavily influence the regression line
Diagnostic Tools
Residual plots visualize the relationship between residuals and predicted values to check for patterns or trends
Residuals should be randomly scattered around zero with no discernible pattern
Normal probability plots (Q-Q plots) assess the normality of residuals by comparing their distribution to a theoretical normal distribution
Cook's distance measures the influence of individual observations on the regression coefficients
Variance Inflation Factor (VIF) quantifies the severity of multicollinearity for each independent variable
VIF values greater than 5 or 10 indicate problematic multicollinearity
Breusch-Pagan test checks for the presence of heteroscedasticity in the residuals
Durbin-Watson test detects autocorrelation in the residuals of a time series regression model
Common Issues in Linear Regression
Omitted variable bias occurs when a relevant predictor is not included in the model, leading to biased coefficient estimates
Misspecification of the functional form (e.g., assuming a linear relationship when it is non-linear) can lead to poor model fit and biased estimates
Measurement errors in the variables can introduce bias and reduce the precision of the estimates
Endogeneity arises when an independent variable is correlated with the error term, violating the assumption of exogeneity
Endogeneity can be caused by omitted variables, simultaneous causality, or measurement errors
Sample selection bias occurs when the sample is not representative of the population of interest, leading to biased estimates
Overfitting happens when a model is too complex and fits the noise in the data rather than the underlying relationship
Overfitted models have poor generalization performance on new data
Assumption Violations
Linearity assumption: The relationship between the dependent and independent variables is linear
Violated when the true relationship is non-linear (e.g., quadratic, exponential)
Independence of errors assumption: The residuals are uncorrelated with each other
Violated when there is autocorrelation in the residuals (common in time series data)
Homoscedasticity assumption: The variance of the residuals is constant across all levels of the predicted values
Violated when there is heteroscedasticity (non-constant variance)
Normality assumption: The residuals follow a normal distribution with a mean of zero
Violated when the residuals are skewed or have heavy tails
No multicollinearity assumption: The independent variables are not highly correlated with each other
Violated when there is significant multicollinearity among predictors
Remedial Measures
Variable transformation (e.g., logarithmic, square root) can help address non-linearity and heteroscedasticity
Adding interaction terms or polynomial terms can capture non-linear relationships between variables
Robust standard errors (e.g., White's heteroscedasticity-consistent standard errors) can be used when heteroscedasticity is present
Weighted least squares (WLS) estimation assigns different weights to observations based on the variance of the residuals to address heteroscedasticity
Ridge regression and Lasso regression are regularization techniques that can help mitigate multicollinearity by shrinking the coefficient estimates
Ridge regression adds a penalty term to the OLS objective function based on the L2 norm of the coefficients
Lasso regression uses the L1 norm penalty, which can also perform variable selection by setting some coefficients to zero
Instrumental variables (IV) estimation can address endogeneity by using an instrument that is correlated with the endogenous variable but uncorrelated with the error term
Advanced Techniques
Generalized linear models (GLMs) extend linear regression to handle non-normal response variables (e.g., logistic regression for binary outcomes, Poisson regression for count data)
Mixed-effects models (also known as hierarchical or multilevel models) account for clustered or nested data structures by incorporating random effects
Quantile regression estimates the relationship between the independent variables and specific quantiles of the dependent variable, providing a more comprehensive view of the data
Generalized additive models (GAMs) allow for non-linear relationships between the dependent and independent variables using smooth functions
GAMs are more flexible than traditional linear models and can capture complex patterns in the data
Bayesian linear regression incorporates prior knowledge about the parameters and updates the estimates based on the observed data
Bayesian methods provide a probabilistic framework for inference and can handle small sample sizes or high-dimensional data
Real-World Applications
Predicting house prices based on features such as square footage, number of bedrooms, and location
Analyzing the factors that influence customer satisfaction in a service industry
Estimating the effect of advertising expenditure on sales revenue for a company
Identifying the key drivers of employee turnover in an organization
Forecasting energy consumption based on historical data and weather variables
Assessing the impact of socioeconomic factors on health outcomes in a population
Predicting stock prices using financial indicators and market sentiment data
Key Takeaways
Diagnostic tools help identify potential issues in linear regression models, such as non-linearity, heteroscedasticity, and multicollinearity
Assumption violations can lead to biased and inefficient estimates, requiring appropriate remedial measures
Variable transformations, robust standard errors, and regularization techniques can address common issues in linear regression
Advanced techniques like GLMs, mixed-effects models, and GAMs extend the capabilities of linear regression to handle more complex data structures and relationships
Proper model specification, variable selection, and diagnostic checking are crucial for obtaining reliable and interpretable results
Linear regression has wide-ranging applications in various fields, including finance, marketing, healthcare, and social sciences
Understanding the assumptions, limitations, and remedial measures of linear regression is essential for effective data analysis and decision-making