Multicollinearity is a problematic situation in which the independent variables in a regression model are correlated. When the independent variables in a linear regression are highly correlated ...

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, ...

Model building via linear regression models. Method of least squares, theory and practice. Checking for adequacy of a model, examination of residuals, checking outliers. Practical hand on experience ...

Microsoft Excel and other software can do all the calculations, but it's good to know how the mechanics of simple linear regression work. At the heart of a regression model is the relationship ...

Q. I was just wondering, if Puerto Rico had electoral votes, what the regression model would show for it. We can’t really say because we have neither Kerry numbers nor fundraising numbers for ...

and linear statistical models in particular. In this module, we will learn how to fit linear regression models with least squares. We will also study the properties of least squares, and describe some ...

In this investigation, we present simulations to compare the performance of linear and logistic regression models under alternative weighting schemes in a fixed-effects meta-analysis framework ...

Will Kenton is an expert on the economy and investing laws and regulations. He previously held senior editorial roles at Investopedia and Kapitall Wire and holds a MA in Economics from The New ...

xkcd #2048 is exceptionally relevant to this. Doing linear regression well with a big dataset is difficult! I do this all the time at work and honestly I often show a scatter plot without any ...