# 1. When you think about it, pu

1. When you think about it, putting a regression model togetheris really not that difficult. After all, I assume if you are doingthat you would have a feel for the process you are modeling and thevariables to choose for the model. But the real question is “is themodel any good”? Here is where measures of validity and reliabilitycome into play. How do we measure validity in a regressionmodel?

Measuring the validity of the regressionmodel

Check the sign and value of regression coefficient: We mustcheck the sign the regression coefficient. It must make practicalsense. For example, sales and price have an inverse relationship,hence we can expect a negative sign for the variable. Similarly,the value of the coefficient if it makes intuitive sense.

Pvalue and significance of the variable: We check the pvalue anddetermine if the pvalue is less than 0.05, then the variable issignificant.

Global Hypothesis Test: This is based on the ANOVA conducted onthe regression if the regression model hold good and it has atleast one independent variable that is significant in predictingthe dependent variables. Only if the pvalue of the ANOVA is lessthan 0.05, we can conclude that the model is significant.

Variance inflation Factor: We can calculate the VIF for eachvariable and if the value exceeds 5 or 10, then we can concludethat the variable is poorly estimated and it is unstable.

Coefficient of determination(rsqaure)It is the measure of the amount of variability in y explained by x.Its value lies between 0 and 1. Greater the value, better is themodel.

Adjusted R2 is an improved version of R2, which increases onlyif a significant variable is added to the model. It penalizes themodel for every junk or non-signficant variable that is added tothe model.R2 will be greater than adjusted R2, as adjusted R2 only considersthe significant variable.If the Adjusted R2 is lower than the R2,then it gives us an indication that there are some non-signficantvariables added ot model.

Root MSE or Root Mean square error, tells us the standarddeviation of the residuals (actual minus predicted values). Inother words, the residuals tell us how far the actual points isfrom the regression line. RMSE helps us understand the spread ofthe residuals. If the RMSE value is high it indicates that theresiduals are far away from the regression line or if it low, itindicates the actual points are very close to the regressionline.

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