Linear regression (also called simple regression) is one of the most common techniques of regression analysis. Multiple regression is a broader class of regression analysis, which encompasses both ...

Estimating Coefficients and Predicting Values The equation y = mx +b represents the most basic linear regression equation: x is the predictor or independent variable y is the dependent variable or ...

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.

Simple linear regression is commonly used in forecasting and financial analysis—for a company to tell how a change in the GDP could affect sales, for example. Microsoft Excel and other software ...

A linear regression is a statistical model that attempts to show the relationship between two variables with a linear equation. A regression analysis involves graphing a line over a set of data ...

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 Canadian Journal of Statistics / La Revue Canadienne de Statistique, Vol. 5, No. 1 (1977), pp. 111-120 (10 pages) In this paper we present two methods of estimating a linear regression equation ...

Goodness-of-fit statistics for general multiple-linear-regression equations are reviewed for the case of replicated responses. A modification of the coefficient of determination is recommended. This ...