Now that you've got a good sense of how to 'speak' R, let's use it with linear regression to make distinctive predictions.

When multiple variables are associated with a response, the interpretation of a prediction equation is seldom simple.

Multiple regression models with survey data Regression becomes a more useful tool when researchers want to look at multiple factors simultaneously. If we want to know whether the racial divide ...

Conclusions: Generalised linear models are attractive for the regression of cost data because they provide parametric methods of analysis where a variety of non-normal distributions can be specified ...

The literature of regression analysis with missing values of the independent variables is reviewed. Six classes of procedures are distinguished: complete case analysis, available case methods, least ...

DTSA 5011 Modern Regression Analysis in R DTSA 5011 Modern Regression Analysis in R Specialization: Statistical Modeling for Data Science Applications Instructor: Brian Zaharatos, Director, ...

In this module, we will introduce generalized linear models (GLMs) through the study of binomial data. In particular, we will motivate the need for GLMs; introduce the binomial regression model, ...

Using historical data and regression analysis has its limitations in business forecasting. For example, a significant correlation between the independent and dependent variable does not ...