Linear regression ... value to ensure that they are normally distributed. Multicollinearity is a problematic situation in which the independent variables in a regression model are correlated.

that in nature there never was a normal distribution ... and correlation in the errors, but it is sensitive to extreme values of both predictor and predicted. Linear regression is much more ...

Linear regression-based quantitative trait loci/association mapping methods such as least squares commonly assume normality of residuals. In genetics studies of plants or animals, some ...

Ordinary regression analysis is based on several statistical assumptions. One key assumption is that the errors are independent of each other. However, with time series data, the ordinary regression ...