which can aid you in the development of your model. In a “forward” stepwise regression analysis, the computer will begin by examining every possible simple linear regression model, and will show you ...

The main distinction between different types of regression model is that they are used for different types of outcome (eg, linear regression for a continuous outcomes and a logistic regression for a ...

In this module, we will learn how to diagnose issues with the fit of a linear regression model. In particular, we will use formal tests and visualizations to decide whether a linear model is ...

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 ...

By the end of the video, students will be able to (1) recognize the major steps involved in an OpenMx model and (2) translate implementation of a linear regression between a statistical equation, ...

Ordinary regression analysis is based on several statistical assumptions. One key assumption is that the errors are independent of each other. However, with time series data, the ordinary regression ...

Multiple regression and regression diagnostics. Generalised linear models; the exponential family, the linear predictor, link functions, analysis of deviance, parameter estimation, deviance residuals.

Common data analysis and regression techniques for application in science, business and social science. Topics include simple and multiple regression; linear models with categorical explanatory ...

Multicollinearity exists when there is a linear relationship ... Multicollinearity creates a problem in the multiple regression model because the inputs are all influencing each other.