1.Fill in the blanks for standard error, df regression, df total, MS regression,
ID: 3235684 • Letter: 1
Question
1.Fill in the blanks for standard error, df regression, df total, MS regression, MS error, F calc, and calculated t values (t stat)
2.What is the Global Hypothesis:
H0:
H1:
3.State the test statistic:
4.Is the model worth keeping? Why?
5.What are the independent variables that were evaluated:
6.Circle the variables that could be dropped from the regression equation?
College GPA SAT score Gender IQ
7.What is the dependent variable being evaluated?
8.What percent of the variation does the model explain? How do we calculate this?
9.After dropping the unnecessary coefficients, write the regression equation:
10.All other things being equal, what would your predicted change in salary be if your GPA was 1.0 higher?
A regression analysis analyzed factors, which may or may not contribute to job income after college. The following was the data collected and the initial regression performed. MALE (in 000s) SCORE 60 3 1 300 110 1 2.9 2300 110 88 38 1700 130 0 1200 1500Explanation / Answer
1. Filled values:
Formula:
2. Global hypothesis
H0: regression is significant (MSR>MSE)
Ha: regression is insignificant (MSR<=MSE)
3. test stat F=MSR/MSE = 42.3
4..Yes, model is worth. Based on p-value<0.05
5. independent variables that were evaluated:
6. variables that could be dropped from the regression equation:
SAT score IQ
since they are insignificant (p-value>0.05)
7. dependent variable being evaluated: Job Income
8.97.9% of the variation in Job income is explained by model based on R-sq value.
STD ERROR= 3.502407 ANOVA df SS MS F Regression 4 2079.049 519.7623 42.37126 Residual 7 85.868 12.26686 total 11 2164.917 Coefficient SE t-stat Intercept 56.385 9.358 6.025326 COLLEGE GPA 6.304 1.676 3.761337 SAT 0.004 0.003 1.333333 IQ 0.035 0.073 0.479452 Male(0) Female(1) -24.431 2.131 -11.4646Related Questions
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