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 ...

Logistic regression is a statistical method used to examine the relationship between a binary outcome variable and one or more explanatory variables. It is a special case of a regression model that ...

Robert E. Jensen, A Multiple Regression Model for Cost Control -- Assumptions and Limitations, The Accounting Review, Vol. 42, No. 2 (Apr., 1967), pp. 265-273 ...

R 2 (R-squared) is a statistical measure of the goodness of fit of a linear regression model (from 0.00 to 1.00), also known as the coefficient of determination.

This is where regression comes in. By using the regression function `svyglm ()` in R, we can conduct a regression analysis that includes party differences in the same model as race. Using `svyglm ()` ...

The first part of the demo output shows how a linear regression model is created and trained: Creating and training model Setting SGD lrnRate = 0.001 Setting SGD maxEpochs = 200 epoch = 0 MSE = 0.1095 ...