This is the guidance for Linear Regression.
Click on the plot to add more data points then click 'SEE THE REGRESSION' button to see the linear regression
Click on 'RESIDUAL PLOT' and 'COOK'S DISTANCE' buttons to see corresponding diagonal plots.
Click on 'RESET button to reset all the plots.
In statistics, linear regression is a linear approach to modeling the relationship between a scalar response (or dependent variable) and one or more explanatory variables (or independent variables). The case of one explanatory variable is called simple linear regression.
In linear regression, the relationships are modeled using linear predictor functions whose unknown model parameters are estimated from the data. Such models are called linear models. Most commonly, the conditional mean of the response given the values of the explanatory variables (or predictors) is assumed to be an affine function of those values; less commonly, the conditional median or some other quantile is used.
You can click on the plot to add more data points