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 ...

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, ...

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 ...

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 ...

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, ...