Regression

R markdown (R) and Jupyter (python) notebooks detailing regression functionality grouped by functions. The functionality demonstrated in each language is in the following files:

1. 1_SLR_SumSqs_R_fkingfisher.* : simple linear regression model example with automatic and manual calculations for sums of squares (total, error, regression/response) and coefficient of determination (R²)
2. 2_SLR_Significance_Confidence_fkingfisher.* : simple linear regression model example with automatic and manual calculations to test model signficance (t and F test), a slope coefficient hypothesis test (t test), confidence and prediction intervals at a specific predictor variable value, and plots of prediction and interval ranges on a scatter plot.
3. 3_SLR_Adequecy_Residuals_fkingfisher.* : simple linear regression model example that tests model adequecy by examining the normality and variance of residuals
4. 4_SLR_Adequecy_PowerTransform_fkingfisher.* : simple linear regression model example that demonstrates how to generate a model with greater adequecy for analysis using the Box-Cox power transformation.
5. 5_MLR_Determination_Confidence_fkingfisher.* : multiple linear regression model example that demonstrates how to generate interaction terms, test model adequacy, evaluate the contribution of each regressor, calculate coefficient of determination (R²), calculate adjusted CoD (Adjusted R²), and coefficient confidence intervals.