In this second part of the lecture on #simplelinearregression model
We understand how to verify the four basic assumptions about the error distribution using #NormalProbabilityPlots in arriving at a
reliable simple linear regression model
We learn how to improve on the model utility by identifying the outliers and the datapoints that exert excessive influence on the regression line fit and eliminate them before arriving at the model using the #Residualplots
We understand the influence of the #SamplingVariability on the precisions of the #pointestimate and #pointprediction derived out of the model
and learn how to judge the precisions of the point estimate or prediction by calculating the #confidenceinterval and the #predictioninterval and qualify the estimate/prediction using those intervals