 Students in this module we are going to talk about other assumptions of our analysis of multiple linear aggression and particularly we are going to talk about normality and homosidacity in MLR. We have performed the analysis and in our analysis in our while performing the analysis we went to the options of plots and where we check the assumption of normality and we asked the SPSS to take Z predicted on X and take Z residual on Y. So this is actually the normal PP plot of regression standardized residuals it actually make a linear relationship between observed residuals and expected residuals and this is the outcome of this analysis that we ask the SPSS to do. So according to the theoretical assumption the residuals must be with the line of the linear line and in our analysis you can see that almost we have fulfilled this assumption in an ideal manner our residuals expected and observed are placed on the line, on the linear line. There is a little curve and there is a little curve in the linear line but it is a very minute difference from the linear line, deviation from the linear line. So we can neglect that deviation and we can conclude that we have fulfilled the assumption of normality. The second draft which is the draft related to the homosidacity and in homosidacity graph this is called scatter plot we check the homosidacity with the scatter plot and here we need to check the that our residuals actually the standardized residuals and predicted residuals there is no shape like this or there is no shape like this which is the shape of the cone, most of us actually these dark predicted residuals are centered so this thing indicates that there is the homosidacity in the data and this assumption is also fulfilled.