a negative simple linear regression equation slope

Table 1 shows the data and predictions for the first five students in the dataset. In other words, to compute the prediction of a student's University GPA, you add up (a) their High-School GPA multiplied by 0.541, (b) their SAT multiplied by 0.008, and (c) 0.540. For these data, the best prediction equation is shown below: Where UGPA' is the predicted value of University GPA and A is a constant. As in the case of simple linear regression, we define the best predictions as the predictions that minimize the squared errors of prediction. That is, the problem is to find the values of b 1 and b 2 in the equation shown below that give the best predictions of UGPA. The basic idea is to find a linear combination of HSGPA and SAT that best predicts University GPA (UGPA). For example, in the SAT case study, you might want to predict a student's university grade point average on the basis of their High-School GPA (HSGPA) and their total SAT score (verbal + math). In multiple regression, the criterion is predicted by two or more variables. In simple linear regression, a criterion variable is predicted from one predictor variable.

State the assumptions of multiple regression and specify which aspects of the analysis require assumptions.

Test the difference between a complete and reduced model for significance.Define R 2 in terms of proportion explained.

Explain why the sum of squares explained in a multiple regression model is usually less than the sum of the sums of squares in simple regression.Explain why a regression weight is called a 'partial slope'.Explain what R is and how it is related to r.Prerequisites Simple Linear Regression, Partitioning Sums of Squares, Standard Error of the Estimate, Inferential Statistics for b and r

A negative simple linear regression equation slope