With now well-recognized nonnegligible model selection uncertainty, data analysts should no longer be satisfied with the output of a single final model from a model selection process, regardless of ...

The topic of variable importance in linear regression is reviewed, and a measure first justified theoretically by Pratt (1987) is examined in detail. Asymptotic variance estimates are used to ...

Dr. Chris Hillman, Global AI Lead at Teradata, joins eSpeaks to explore why open data ecosystems are becoming essential for enterprise AI success. In this episode, he breaks down how openness — in ...

Regression is a statistical tool used to understand and quantify the relation between two or more variables. Regressions range from simple models to highly complex equations. The two primary uses for ...

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 ...

Please note: This item is from our archives and was published in 2021. It is provided for historical reference. The content may be out of date and links may no longer function. When teaching cost ...

Your client, Dave’s BBQ, a local independent restaurant, is interested in determining the effect on sales revenue of certain advertising strategies. Dave has weekly data on advertising dollars spent ...

When you perform regression analysis in Microsoft Excel, you are engaging in a statistical process that helps you understand the relationship between variables. This technique is particularly useful ...

Linear models, generalized linear models, and nonlinear models are examples of parametric regression models because we know the function that describes the relationship between the response and ...