The column label is specified * Y: dependent Variable.The column label is specified. * data : The data the model trains ... two blocks of code represent the dataset in a graph. ggplot2 library is used ...

Dummy variables can be tested for their significance and relevance in regression models by using various statistical tests and criteria, such as the T-test, F-test, R-squared, and Adjusted R-squared.

In this tutorial, we will look at three most popular non-linear regression models and how to solve them in R. This is a hands ... The first 3 lines calculate the nth degree polynomial of the ...

Regression models are powerful tools for exploring the relationships between a dependent variable and one or more independent variables. However, not all variables are equally important or ...

This code has been produced as a part of my doctoral research. Please cite the following article if you use this code in any kind: Afghari, A.P., 2019. Detecting motor vehicle crash blackspots based ...

Fortunately, the capability to use machine learning (ML) algorithms to detect patterns associated with variables that drive business has made it very useful in predicting the risk factors related to ...

At times it is desirable to have independent variables in the model that are qualitative rather than quantitative. This is easily handled in a regression framework. Regression uses qualitative ...

R-Squared vs. Adjusted R-Squared R-squared only works as intended in a simple linear regression model with one explanatory variable. With a multiple regression made up of several independent ...

Abstract: In this paper, a random forest regression model with multitype predictor variables (MTVRF) was utilized with four kinds of input variables, including surface reflectance, spectral indices, ...