Linear regression can be used in statistics in order to create a model out a dependable scalar value and an explanatory variable. Linear regression has applications in finance, economics and environmental science.

I want to develop a regression model for predicting YardsAllowed as a function of Takeaways, and I need to explain the statistical signifance of the model.

Ridge regression is used in linear regression to deal with multicollinearity. It reduces the MSE of the model in exchange for introducing some bias.

The value depends on the slope of the line.

in general regression model the dependent variable is continuous and independent variable is discrete type.

in genral regression model the variables are linearly related.

in logistic regression model the response varaible must be categorical type.

the relation ship between the response and explonatory variables is non-linear.