A. Please explain how to construct the wgressuom model B. Calculate the multiple

Question

A. Please explain how to construct the wgressuom model B. Calculate the multiple coefficient of determination R^2=? C. State the null and alternative hypothesis D. Identify the value of test statistic F=? E. Explain how to find P- Value
Please show work! I am in summer classes so professors aren’t able to meet with us and show us how to do it, seeing your work along with answers helps me figure out the other similar problems! Thank you :-) of hours they studied for the exam (x2), and the number of times they were absent during the semester (K3). Use the data provided for these variables to complete parts a through f below A business statistics professor would like to develop a regression model to predict the final exam scores (y) for students based on their current GPAs (x1), the number Click the icon to view the data set. a) Construct a regression model using all three independent variables. Round to two decimal places as needed.) Score GPA Hours Absences 68 2.53 70 I 2.60 a 250-1 743.08 6.00 1.50 2.99 2.00 4.50 83 3.25 300 2.80 2.71 400 85 3.75 200 2.92 6.00 3.16 3.54 87 89 4.00 5.00 40 91 92 93 S.7y 7.00 4.00 2.98 5.00 4.00 nter your answer in the edit fields and then click Check Answer 3.48 650 1 parts remaining

Explanation / Answer

(A) the regression equation is given as using ms-excel

y^=51.52+7.97X1+1.97X2-0.87X3

(B)R^2=0.41

(C) the null hypothesi H0:

H0 : ?1 = ?2 = ... = ?k = 0

H1 at least one ?i ? 0, i = 1, ..., k

(D)F=3.63

(E) P-value=0.0358 ( using ms-excel=fdist(3.63, 3,16) )

following regression analysis has been generated using ms-excel

y=51.52+7.97X1+1.97X2-0.87X3

SUMMARY OUTPUT Regression Statistics Multiple R 0.63661154 R Square 0.405274252 Adjusted R Square 0.293763175 Standard Error 7.15738703 Observations 20 ANOVA df SS MS F Significance F Regression 3 558.5489745 186.183 3.634386 0.035806 Residual 16 819.6510255 51.22819 Total 19 1378.2 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 51.51512695 13.66967507 3.76857 0.001681 22.53671 80.49354 X Variable 1 7.968396242 4.355981138 1.8293 0.086053 -1.26587 17.20266 X Variable 2 1.965896907 1.079726198 1.820737 0.087401 -0.32302 4.254814 X Variable 3 -0.874747355 1.435014766 -0.60957 0.550703 -3.91684 2.167348

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