Multiple Regression

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The general form of the multiple regression is Y= a +b1X1 +b2X2+b3X3...+bnXn + e which can be estimated by Y= a +b1X1 +b2X2+b3X3...+bnXn, where a is the intercept and bi's are the partial regression coefficients.
Coefficient of Multiple Determination: R2 which gives the % of the variance in the dependent variable that is explained by the variation in the independent variables.


Once the dependent and independent variable are selected, the method for variables to enter can be selected or to be removed using any of the methods say stepwise, Remove, Backward or Forward. When clicked on the statistics, the estimate confidence intervals and model fit are selected and in the residuals Durbin-Watson is selected. The significance of each of these is as follows.
Estimates: They give the estimated coefficients of the regression mode. The test statistics and their significances are also obtained for each regression. Here T-test is used to see whether each b differences significantly from zero.
The correlation matrix gives the Pearson correlation coefficient between every pair of variables. It also gives the one significance of each correlation. Here we observe that the correlation is significant with p<0.001 and this table also gives the number of cases contributing to each correlation. Here N=389 which indicates 389 cases contribute to each correlation.
The diagonal elements of the matrix have the correlation coefficient of 1 which indicates a perfect positive correlation since they represent the correlation of each v ...
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