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The admissions officer for Clearwater College developed the following estimated regression equation relating the final college GPA to the student's SAT mathematics score and high-schoo ŷ-1.41 +0.0235x1 +0.00486x2 where 1 high-school grade point average 2 SAT mathemathics score y final college grade point average Round test statistic values to 2 decimal places and all other values to 4 decimal places. Do not round your intermediate calculations. a. Complete the missing entries in this Excel Regression tool output. Enter negative values as negative numbers. SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations ANOVA Regression Residual Total Intercept 0.9681 0.9373 ✔ 0.9194 0.1296 df 10 2 7 9 Coefficients -1.4053 ✔ ✔ ✔ ✔ SS 1.76209 0.1179 1.88 Standard Error 0.4848 0.0086666 0.001077 X1 0.023467 X2 0.00486 b. Using a 0.05, test for overall significance. MS 0.8810 , So reject 0.0168 t Stat -2.8987 2.7078 4.5125 ✔ F 52.4405 P-value 0.017624 There exists significant relationship. c. Did the estimated regression equation provide a good fit to the data? Explain. Yes , because the R value is greater d. Use the t test and a = 0.05 to test Ho: B₁ = 0 and Ho: B2 = 0. Use t table. For B1, the p-value is less than 0.01 0.024084 0.001462 than 0.50, Significance F 0.000061 Ho B₁0. : = 0

The admissions officer for Clearwater College developed the following estimated regression equation relating the final college GPA to the student's SAT mathematics score and high-schoo ŷ-1.41 +0.0235x1 +0.00486x2 where 1 high-school grade point average 2 SAT mathemathics score y final college grade point average Round test statistic values to 2 decimal places and all other values to 4 decimal places. Do not round your intermediate calculations. a. Complete the missing entries in this Excel Regression tool output. Enter negative values as negative numbers. SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations ANOVA Regression Residual Total Intercept 0.9681 0.9373 ✔ 0.9194 0.1296 df 10 2 7 9 Coefficients -1.4053 ✔ ✔ ✔ ✔ SS 1.76209 0.1179 1.88 Standard Error 0.4848 0.0086666 0.001077 X1 0.023467 X2 0.00486 b. Using a 0.05, test for overall significance. MS 0.8810 , So reject 0.0168 t Stat -2.8987 2.7078 4.5125 ✔ F 52.4405 P-value 0.017624 There exists significant relationship. c. Did the estimated regression equation provide a good fit to the data? Explain. Yes , because the R value is greater d. Use the t test and a = 0.05 to test Ho: B₁ = 0 and Ho: B2 = 0. Use t table. For B1, the p-value is less than 0.01 0.024084 0.001462 than 0.50, Significance F 0.000061 Ho B₁0. : = 0

MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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nd i - Select your answer - less than 0.01 between 0.01 and 0.02 between 0.02 and 0.05 between 0.05 and 0.10 between 0.10 and 0.20 between 0.20 and 0.40 greater than 0.40
Transcribed Image Text:nd i - Select your answer - less than 0.01 between 0.01 and 0.02 between 0.02 and 0.05 between 0.05 and 0.10 between 0.10 and 0.20 between 0.20 and 0.40 greater than 0.40
The admissions officer for Clearwater College developed the following estimated regression equation relating the final college GPA to the student's SAT mathematics score and high-school GPA. y = -1.41 +0.0235x1 +0.00486x2 where 1 high-school grade point average 2 SAT mathemathics score y final college grade point average Round test statistic values to 2 decimal places and all other values to 4 decimal places. Do not round your intermediate calculations. a. Complete the missing entries in this Excel Regression tool output. Enter negative values as negative numbers. SUMMARY OUTPUT Multiple R R Square Adjusted R Square Standard Error Regression Statistics Observations ANOVA Regression Residual Total Intercept Yes 0.9681 0.9373 0.9194 0.1296 df 10 2 7 9 Coefficients -1.4053 0.023467 0.00486 SS 1.76209 0.1179 1.88 Standard Error 0.4848 0.0086666 0.001077 MS , SO reject 0.8810 , SO reject 0.0168 -2.8987 X1 X2 b. Using a = 0.05, test for overall significance. There exists significant relationship. c. Did the estimated regression equation provide a good fit to the data? Explain. because the value is greater d. Use the t test and a = 0.05 to test Ho: B₁ = 0 and Ho : 62 For B₁, the p-value is less than 0.01 For B2, the p-value is less than 0.01 t Stat 2.7078 4.5125 F 52.4405 = 0. Use t table. P-value 0.017624 0.024084 0.001462 than 0.50. Significance F 0.000061 Ho : B₁ = 0. Ho: B₂ = 0.
Transcribed Image Text:The admissions officer for Clearwater College developed the following estimated regression equation relating the final college GPA to the student's SAT mathematics score and high-school GPA. y = -1.41 +0.0235x1 +0.00486x2 where 1 high-school grade point average 2 SAT mathemathics score y final college grade point average Round test statistic values to 2 decimal places and all other values to 4 decimal places. Do not round your intermediate calculations. a. Complete the missing entries in this Excel Regression tool output. Enter negative values as negative numbers. SUMMARY OUTPUT Multiple R R Square Adjusted R Square Standard Error Regression Statistics Observations ANOVA Regression Residual Total Intercept Yes 0.9681 0.9373 0.9194 0.1296 df 10 2 7 9 Coefficients -1.4053 0.023467 0.00486 SS 1.76209 0.1179 1.88 Standard Error 0.4848 0.0086666 0.001077 MS , SO reject 0.8810 , SO reject 0.0168 -2.8987 X1 X2 b. Using a = 0.05, test for overall significance. There exists significant relationship. c. Did the estimated regression equation provide a good fit to the data? Explain. because the value is greater d. Use the t test and a = 0.05 to test Ho: B₁ = 0 and Ho : 62 For B₁, the p-value is less than 0.01 For B2, the p-value is less than 0.01 t Stat 2.7078 4.5125 F 52.4405 = 0. Use t table. P-value 0.017624 0.024084 0.001462 than 0.50. Significance F 0.000061 Ho : B₁ = 0. Ho: B₂ = 0.
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