NOTE: Please include excel functions, as this problem is being asked to be compl
ID: 3362846 • Letter: N
Question
NOTE: Please include excel functions, as this problem is being asked to be completed through Excel. If the person who answers this could upload an Excel Workbook of the solution that would be wonderful. Or a thorough walkthrough and steps. Thanks.
duate assistant for the Santa Clara Broncos football team has compiled the following statistics 2 The WINS AVERAGE OFFENSIVE YARDS AVERAGE INTERCEPTIONS 10 500 450 250 485 399 521 158 525 485 300 350 375 150 380 10 15 10 15 13 develop a linear regression model for wins employing average offensive yards and average interceptions b Calculate the MAD for your model. Calculate R, F, and t statistics and interpret them d. Run tests for multicollinearity, autocorrelation, and heteroscedasticity. Comment on the outcomesExplanation / Answer
Here average interceptions is dependent variable and there are two independent variables as wins and average offensive yards.
This is the problem of multiple regression.
We can do multiple regression in MINITAB.
steps :
ENTER data into MINITAB sheet --> STAT --> Regression --> Regression --> Response : average intrceptions --> Predictors : wins and average offensive yards --> Graphs --> Residual plots : select last two options --> ok --> Results : select second option --> ok --> ok
a)
The regression equation is
average interceptions = 20.4 - 0.085 wins - 0.0354 average offesive yards
c) R = 0.9659
It indicates that there is some relationship between dependent and independent varibles.
Using F test we can test the hypothesis that,
H0 : Bj = 0 Vs H1 : Bj not = 0
where Bjs population slope for jth independent variable.
Assume alpha= 0.05
F = 76.28
P-value = 0.000
Reject H0 at 5% level of significance.
Conclusion : Atleast on of the slopeis differ than 0.
Using t test we can find which variable differs.
Hypothesis for the test :
H0 : B = 0 Vs H1 : B not= 0
where B is population slope
Decision rule :
If P-value < alpha then reject H0 at 5% level of significance.
Conclusion : Corresponding variable is significant.
We see tha average offensive is significant variable and wins is insignificant variable.
Multicollinearity = 6.9
It represented as there is high multicollinearity problem.
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