Dr. James McCaffrey presents a complete end-to-end demonstration of linear regression with two-way interactions between predictor variables. Standard linear regression predicts a single numeric value ...

The critical barrier in interaction analysis for next-generation sequencing (NGS) data is that the traditional pairwise interaction analysis that is suitable for common variants is difficult to apply ...

Regression models with interaction effects have been widely used in multivariate analysis to improve model flexibility and prediction accuracy. In functional data analysis, however, due to the ...

A key challenge for monetary policymakers is to predict where inflation is headed. One promising approach involves modifying a typical Phillips curve predictive regression to include an interaction ...

In nonparametric multivariate regression analysis, we seek methods to reduce the dimensionality of the regression function to bypass the difficulty caused by the curse of dimensionality. The original ...

The standard linear regression model does not apply when the effect of one explanatory variable on the dependent variable depends on the value of another explanatory variable. In this case, the ...

The short course will illustrate how to use JMP in linear regression analysis. The three main topics will be: Exploratory data analysis, simple liner regression and polynomial regression How to fit a ...

The goal of a machine learning regression problem is to predict a single numeric value. For example, you might want to predict an employee's salary based on age, height, high school grade point ...