 Students after checking the assumptions required to be fulfilled for the MLR, now we are going to interpret the results that we got from our analysis. So in results we are going to see three things, model summary, an overtable and coefficients of the independent variables. So the first thing which we are going to see is the model summary of the MLR. So in which we are going to check this table which is made in SPSS output with the title of model summary. Here Bs as you see the footnotes it is indicated that your dependent variable is online bridging social capital and in this we have the predictor which is constant, the other motivations we need to use social media which is indicated in this. So here we do not know which predictor is more strong or which is less strong. But the most important thing for us to see in this table is the value of R-Scare which is 0.549 as I have mentioned here. So if we adjust it with the standard error then it becomes 0.456. So it means that all these independent variables actually explaining 45.6% of the variance of the dependent variable. So the dependent variable is online bridging social capital. All these predictors are explaining 45% of the variance which is a significant amount. And it seems that motivations to use social media actually play a very important role in developing your social capital in online platforms. The second table which we are going to see is the table of ANOVA. And if you see the table again now with value annexed to a dependent variable our online bridging social capital or here also predictors are explained in the game. So with the degree freedom of 7 because we have I think 7 IVs and our total sample size was 1244 the F value which is 149.782 is significant because this value is less than 0.05. It means that the way we have assumed this model it is actually statistically correct. And there is a relationship between independent and dependent variables and all these predictors actually explain the variance of the dependent variable which is online bridging social capital in this example. So this table of ANOVA is telling us that our model is theoretically correct. And the last table we are going to see is of coefficients. So this table is very important for us because in R-square overall we are told that how much variance is explained by your IVs. But here we see that which variable is significantly contributing and which variable is insignificant and whose contribution is more and whose contribution is less. So for that we see that we have beta value. Now what were we doing with all this 45% variance R-square which we have 45.6. So here we have every variable against beta value which is actually the value of correlation coefficient. So here we find that your one variable is insignificant personal information disclosure. So this variable did not contribute in the explaining of online bridging social capital. And the rest of our variables they have contributed in order to explain online bridging social capital. And out of these variables the most wrong variable was the making new social ties. Because its beta is 0.295. And after this we see that your self expression is its value 0.183. And after that seeking and sharing information. And after that maintaining existing social ties and then self documentation and recreation and entertainment is the weakest predictor of developing social capital in online social platforms. Now this data you have is theoretically very correct. We know that those who have motivation to develop social ties through social media platforms then their social capital is high. Or those who maintain it or those who tell a lot about themselves on social media platforms then their social capital is higher. And comparatively if you are only using it for recreation and entertainment or you are sharing information about yourself then your social capital is less generated. So theoretically we see that our model is the empirical results we reconfirm the theory that is in line with the theory. So how do we write this? We will see in reporting the results. So I have told you in detail that multiple linear regression analysis how we do it and how we interpret it.