There are two independent variables X1 and X2 amp; one dependent variable Y R-squared represents the percentage of data points that fit the...

There are two independent variables X1 and X2 & one dependent variable Y

R-squared represents the percentage of data points that fit the regression model; from the output 95.803% of the data point fit the model

However, since there are two independent variables the adjusted R-square should be used to assess the effectiveness of the regression model to fit the data point; hence, 95.79% of data points fit the model

Significance F this figure demonstrates the probability that the regression happened by chance; Since Significance of F is 0% then the chance that the regression happened by chance is void

The regression and residual sum of square errors are added together allowing the researcher to obtain the total sum of square; After which the residual sum of squares is divided by total sum of squares, the solution can be subtracted from 1 to obtain the R squared

Total SS=Residual SS+ Regression SS

R^2=1-Residual SS/Total SS

The coefficient for x1,x2, and y-intercept are indicated; hence Y=59.28310-28.4552X1+2.571423X2

The t statistic of the hypothesis for each of the coefficient is indicated below the t stat column

The P-value provides information on whether or not to reject the null hypothesis that adjusted coefficient are equal to zero; from the above output it is clear that null hypothesis should be reject since P-value<alpha