Today we encountered a question from the professor which was “what is a p value?”. So while the end of the class we got to know the meaning of p value. P value is nothing but assuming or the probability of the Null hypothesis to be true. Null hypothesis means that its treats everything equally or it can be written as null hypothesis doesn’t have any relationship with the values. Usually the significant p value is 0.05 which proves that the null hypothesis is true, but the p value can be 0,01 or 0.09. when ever we get p value as less than or greater than 0.05 we can tell that the null hypothesis is rejected.

Heteroskedasticity means the fanning out of the data points as the value on axis increases. In simple words when we draw a best fit line and if the points are moving far from the best fit then it’s called as heteroskedasticity. we even have a test for Heteroskedasticity which is called as Breusch-Pagan test. As we learned before if the significance value of p is less than 0.05 then we reject that null hypothesis, it also means that there is heteroskedasticity in that. Breusch- pagan test helps in identifying the heteroskedasticity. It has four simple steps:

1. Fit a linear regression model to obtain the residuals.

2. Calculate the squared residuals.

3. From the squared residuals fit a new regression model.

4.Calculate chi-square test Xsqaure and n*Rsquare(new), where n is number of observations and Rsquare is the new regression model which used the square residuals.