Regression Techniques for Every Analysis
From simple linear relationships to complex multi-variable models, regression analysis reveals the mathematical relationships that drive your business outcomes and enables accurate predictions.
Linear Regression
Simple relationships between two variables
Analyze straight-line relationships between variables. Perfect for understanding how one factor influences another - like sales vs. marketing spend or price vs. demand.
Multiple Regression
Complex relationships with multiple factors
Analyze how multiple independent variables simultaneously influence your target outcome. Understand the relative importance of different factors and their combined effect.
Logistic Regression
Predict binary outcomes and probabilities
Analyze yes/no, success/failure, or other binary outcomes. Calculate probability of events and identify factors that influence categorical results.
Polynomial Regression
Capture curved and non-linear relationships
Model curved relationships where the effect of variables changes at different levels. Perfect for growth curves, diminishing returns, and accelerating effects.
Ridge Regression
Regularized regression for stable predictions
Handle multicollinearity and prevent overfitting with regularization. Ideal when you have many correlated variables or limited data relative to features.
Lasso Regression
Automatic feature selection and sparse models
Automatically select the most important features while setting others to zero. Perfect for high-dimensional data where you need to identify key drivers.
Stepwise Regression
Systematic variable selection process
Systematically add or remove variables based on statistical significance. Build parsimonious models that include only the most meaningful predictors.
Time Series Regression
Regression with temporal dependencies
Analyze time-dependent relationships while accounting for trends, seasonality, and autocorrelation. Perfect for forecasting with external predictors.
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