Statistical Regression analysis  provides an equation that explains the nature and relationship between the predictor variables and response variables. For a linear regression analysis, following are some of the ways in which inferences can be drawn based on the output of p-values and coefficients. While interpreting the p-values in  linear regression analysis in statistics , the p-value of each term decides the coefficient which if zero becomes a null hypothesis. A low p-value of less than .05 allows you to reject the null hypothesis. This could mean that if a predictor has a low p-value, it could be an effective addition to the model as the changes in the value of the predictor are directly proportional to the changes in the response variable. On the contrary, a p-value that is larger does not affect the model as in that case, the changes in the value of the predictor and the changes in the response variable are not directly linked. If you are to take a...

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