How to interpret the output from SAS? Please answer all questions! Thank you! 1.

Question

How to interpret the output from SAS? Please answer all questions! Thank you!

1. Describe the form, strength, and direction of the relationship between Exam 1 and Exam 2. 2. What is the least squares regression line? 3. Is there a linear relationship between Exam 1 and Exam 2? State your hypotheses, test statistic, p-value, and conclusion in terms of the problem 4. What is the percent of variation in Exam 2 explained by Exam 1. 5. What is the estimate for the standard deviation? 6. Check and comment on the (4) assumptions for regression 7. Give the 95% confidence interval for the regression coefficient for Exam 1. 8. A student received an 82% on Exam 1 . What is this student's predicted score on Exam 2? 9. The student described above actually received a 90% on Exam 2, what is their residual? The REG Procedure Model: MODEL1 Dependent Variable: exam2 Analysis of Variance Mean Sum of DF Squares Source Square F Value Pr> F Model Error Corrected Total 18071.23956 8071.2395640.86 It! Intercept 4.85586 12.76357 -0.38 0.7044 exam1 1 0.96325 0.15068 6.39

Explanation / Answer

1. From the scatter plot we see that there is positive relationship between exam1 and exam2.

2. The line of regression is of the form,

y = intercept + slope*x

y = -4.856 + 0.963*x

Here slope have positive sign also indicate that there is positive relationship between exam1 and exam2.

3. Here we have to test the hypothesis that,

H0 : B = 0 Vs H1 : B not= 0

H0 : There is no any relationship between exam1 and exam2.

H1 : There is relationship between exam1 and exam2.

where B is population slope for independent variable.

Assume alpha = level of significance = 0.05

Test statistic follows t-distribution.

Test statistic = 6.39

P-value < 0.0001 < alpha

Reject H0 at 5% level of significance.

Conclusion : There is some relationship between exam1 and exam2.

4. Here we need R square.

R square = 0.2943

Interpretation : It can expresses the proportion of variation in dependent variable which is explained by variation in independent variable.

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