We have discussed the basis of linear regression as fitting a straight line through a plot of data. However, there may be circumstances where the relationship between the variables is non-linear (i.e.

R. Dennis Cook, Added-Variable Plots and Curvature in Linear Regression, Technometrics, Vol. 38, No. 3 (Aug., 1996), pp. 275-278 ...

I use Python 3 and Jupyter Notebooks to generate plots and equations with linear regression on Kaggle data. I checked the correlations and built a basic machine learning model with this dataset.

One option for working with survey data in R is to use the “survey” package. For an introduction on working with survey data in R, see our earlier blog post. The first step involves creating a survey ...

Residual plots can be used to validate assumptions about the regression model. Figure 1: Residual plots are helpful in assessments of nonlinear trends and heteroscedasticity. A formal test of lack ...

The R package rcssci offers an intuitive solution for visualizing Restricted Cubic Splines (RCS) in regression analyses. It automates the generation of spline plots for outcomes like odds ratios (OR), ...