Automated multiple regression model-building techniques often hide important aspects of data from the data analyst. Such features as nonlinearity, collinearity, outliers, and points with high leverage ...

First, multiple linear regression models are considered and the design matrices are allowed to be different. Second, the predictor variables are either unconstrained or constrained to finite intervals ...

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 ...

Logistic regression is a powerful statistical method that is used to model the probability that a set of explanatory (independent or predictor) variables predict data in an outcome (dependent or ...

This course focuses on statistical methods that are useful in the investigation of hypotheses in the social sciences and the analysis of public policies and programs. The bulk of the course is a ...

Learn how to graph linear regression in Excel. Use these steps to analyze the linear relationship between an independent and a dependent variable.

Multiple regression models forecast a variable using a linear combination of predictors, whereas autoregressive models use a combination of past values of the variable.