Linear regression is an old statistical method of determining relationships between variables. It finds the linear function (in the 1-d case, a straight line) which ...

1 Navy Submarine Academy, Qingdao, China. 2 School of Mathematics and Statistics, Qingdao University, Qingdao, China. The error distribution testing plays an ...

Residual plots can be used to validate assumptions about the regression model. Figure 1: Residual plots are helpful in assessments of nonlinear trends and heteroscedasticity. A formal test of lack ...

Linear regression models assume that the dependent variable is a linear function of the independent variables, plus some random error. This means that the ...

In response to the challenges posed by the inherent limitations, we introduce a novel representation learning model based on linear regression. This model seamlessly integrates three essential modules ...

We focus on the federated multi-task linear regression setting, where each machine possesses its own data for individual tasks and sharing the full local data between machines is prohibited. Motivated ...

Linear regression is an algorithm used to predict, or visualize, a relationship between two different features/variables. In linear regression tasks, there are two kinds of variables being examined: ...

“The statistician knows...that in nature there never was a normal distribution, there never was a straight line, yet with normal and linear assumptions, known to be false, he can often derive ...