Sample data (as shown below) was collected on the weight of 5 vehicles (in lbs.) and...
Sample data (as shown below) was collected on the weight of 5 vehicles (in lbs.) and associated fuel efficiency in Miles Per Gallon (MPG). The following is a partial Excel output which was reported.
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Regression Statistics |
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|
Multiple R |
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|
R Square |
0.965997 |
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Adjusted R Square |
0.954663 |
||||
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Standard Error |
|||||
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Observations |
5 |
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ANOVA |
|||||
|
df |
SS |
MS |
F |
Significance F |
|
|
Regression |
1 |
503.4779 |
503.4779 |
85.22868 |
0.002688719 |
|
Residual |
3 |
17.72213 |
5.907376 |
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|
Total |
4 |
||||
|
Coefficients |
Standard Error |
t Stat |
P-value |
||
|
Intercept |
44.34483 |
3.094192 |
14.33163 |
0.000736 |
|
|
Weight of Car (lbs.) |
-0.00649 |
0.000703 |
-9.23194 |
0.002689 |
|
- What is the regression equation?
- Y = 7 + 1X
- Y = 1 + 7X
- Y = -1.38 + 99.3X
- Y= 44.34 - 0.0065X
Homework Answers
TOPIC: Finding the correct fitted regression equation.
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Sample data (as shown below) was collected on the weight of 5
vehicles (in lbs.) and...
Based on the below data what will be the value of mse? Regression Statistics Multiple R...
Based on the below data what will be the value of mse? Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 8 ANOVA df SS MS F Regression 1 23 23.0 11.5 Residual 6 12 2.0 Total 7 Coefficients Standard Error t Stat P-value Intercept 20 31.274666 3.984284 0.007248 Advertising (thousands of $) 41 6.19330674 1.610802 0.158349
Based on the below data what will be the value of multiple R? Regression Statistics Multiple...
Based on the below data what will be the value of multiple R? Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 8 ANOVA df SS MS F Regression 1 29 29 7 Residual 6 26 4 Total 7 Coefficients Standard Error t Stat P-value Intercept 1 31.274666 3.984284 0.007248 Advertising (thousands of S) 42 6.19330674 1.610802 0.158349 Submit Answer format: Number Round to: 2 decimal places.
You were asked by your manager to evaluate the regression tables below to decide which cost driver would be best to use...
You were asked by your manager to evaluate the regression tables below to decide which cost driver would be best to use for the production department. Since your manager is new and does not understand the regression analysis tables, you will need to explain why one set of statistics is better than the other and why you have chosen the better driver. Manufacturing Direct Labor Hours Regression Statistics Multiple R 0.799304258 R Square 0.638887297 Adjusted R Square 0.602776026 Standard Error...
5- Interpret the coefficient of determination (R-squared) and the F test. SUMMARY OUTPUT Regression Statistics Multiple...
5- Interpret the coefficient of determination (R-squared) and the F test. SUMMARY OUTPUT Regression Statistics Multiple R 0.8811 R Square 0.7764 Adjusted R Square 0.7205 Standard Error 14.7724 Observations 16 ANOVA df SS MS F Regression 3 9091.7392 3030.5797 13.8874 Residual 12 2618.7008 218.2251 Total 15 11710.44 Coefficients Standard Error t Stat P-value Intercept 29.1385 174.7427 0.1668 0.8703 PFH -2.1236 0.3405 -6.2361 0.0000 PR 1.0345 0.4667 2.2164 0.0467 M 3.0871 0.9993 3.0892 0.0094
7,10,11 Based on the following regression output, what is the equation of the regression line? Regression...
7,10,11
Based on the following regression output, what is the equation of the regression line? Regression Statistics Multiple R 0.917214 R Square 0.841282 Adjusted R Square 0.821442 Standard Error 9.385572 Observations 10 ANOVA df SS MS Significance F 1 Regression 3735.3060 3735.30600 42.40379 0.000186 8 Residual 704.7117 88.08896 9 Total 4440.0170 Coefficients Standard Error t Stat P-value Lower 95% Intercept 31.623780 10.442970 3.028236 0.016353 7.542233 X Variable 1.131661 0.173786 6.511819 0.000186 0.730910 o a. 9; = 7.542233+0.7309 Xli o b....
Calculate the 95% prediction interval of y when x=5 using the 2000 pairs Mean of x...
Calculate the 95% prediction interval of y when x=5 using the 2000 pairs Mean of x = 4.51 Regression Statistics Multiple R 0.012848 R Square 0.000165 Adjusted R Square -0.00034 Standard Error 2.869737 Observations 2000 ANOVA df SS MS F Significance F Regression 1 2.716416 2.716416 0.329847 0.565814 Residual 1998 16454.31 8.235388 Total 1999 16457.02 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 4.509054 0.119572 37.70997 1.7E-235 4.274555 4.743552574 4.274555 4.743553 X 0.012884...
4. Part of an Excel output relating X (independent variable) and Y (dependent variable) is shown...
4. Part of an Excel output relating X (independent variable) and Y (dependent variable) is shown below. Fill in all the blanks marked with "?". Summary Output Regression Statistics Multiple R ? R Square ? Adjusted R Square 0.8125 Standard Error 1.3693064 Observations 7 ANOVA df SS MS F Significance F Regression ? 50.625 ? ? ? Residual ? 9.375 ? Total 6 60 Coefficients Standard Error. t Stat P-value Lower 95% Intercept 13.75 1.398341. 9.833082 0.0001853 10.15555 x -1.125...
4- Indicate if the estimates are statistically significant at 0.1%, 1%, 5% or 10%. Regression summary...
4- Indicate if the estimates are statistically significant at 0.1%, 1%, 5% or 10%. Regression summary output using Excel is as follows. SUMMARY OUTPUT Regression Statistics Multiple R 0.8811 R Square 0.7764 Adjusted R Square 0.7205 Standard Error 14.7724 Observations 16 ANOVA df SS MS F Regression 3 9091.7392 3030.5797 13.8874 Residual 12 2618.7008 218.2251 Total 15 11710.44 Coefficients Standard Error t Stat P-value Intercept 29.1385 174.7427 0.1668 0.8703 PFH -2.1236 0.3405 -6.2361 0.0000 PR 1.0345 0.4667 2.2164 0.0467 M...
Following a regression analysis output : SUMMARY OUTPUT Regression Statistics Multiple R 0.719422 R Square Adjusted...
Following a regression analysis output : SUMMARY OUTPUT Regression Statistics Multiple R 0.719422 R Square Adjusted R Square 0.477366 Standard Error Observations 14 ANOVA df SS MS F Regression 1 3.028885709 Residual 12 2.823257148 Total 13 5.852142857 Coefficients Standard Error t Stat P-value Intercept 1.157091 0.566482479 0.063699302 Satisfaction with Speed of Execution 0.636798 0.177478218 0.003726861 Group of answer choices R Square is 0.517 Standard error is 0.386 Residuals are 2.823 F-test is 11.87 R Square is 0.517 Standard error is...
What is the coefficient? What is the standard error? What is the z-statistic? Is the coefficient...
What is the coefficient?
What is the standard error?
What is the z-statistic?
Is the coefficient sufficiently different from zero? How about
one? Explain.
SUMMARY OUTPUT Regression Statistics Multiple R 0.58175248 R Square 0.33843594 Adjusted R S 0.31393357 Standard Err 1.1991813 Observations 29 ANOVA df SS MS Significance F 0.000932269 Regression 1 19.86268888 19.86268888 13.8123745 Residual 38.82696629 27 1.438035789 Total 58.68965517 28 Coefficients Standard Error P-value t Stat Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept -0.0202247 0.223805467 -0.090367404...
Based on the below data what will be the value of multiple R? Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 8 ANOVA df SS MS F Regression 1 29 29 7 Residual 6 26 4 Total 7 Coefficients Standard Error t Stat P-value Intercept 1 31.274666 3.984284 0.007248 Advertising (thousands of S) 42 6.19330674 1.610802 0.158349 Submit Answer format: Number Round to: 2 decimal places.
7,10,11
Based on the following regression output, what is the equation of the regression line? Regression Statistics Multiple R 0.917214 R Square 0.841282 Adjusted R Square 0.821442 Standard Error 9.385572 Observations 10 ANOVA df SS MS Significance F 1 Regression 3735.3060 3735.30600 42.40379 0.000186 8 Residual 704.7117 88.08896 9 Total 4440.0170 Coefficients Standard Error t Stat P-value Lower 95% Intercept 31.623780 10.442970 3.028236 0.016353 7.542233 X Variable 1.131661 0.173786 6.511819 0.000186 0.730910 o a. 9; = 7.542233+0.7309 Xli o b....
What is the coefficient?
What is the standard error?
What is the z-statistic?
Is the coefficient sufficiently different from zero? How about
one? Explain.
SUMMARY OUTPUT Regression Statistics Multiple R 0.58175248 R Square 0.33843594 Adjusted R S 0.31393357 Standard Err 1.1991813 Observations 29 ANOVA df SS MS Significance F 0.000932269 Regression 1 19.86268888 19.86268888 13.8123745 Residual 38.82696629 27 1.438035789 Total 58.68965517 28 Coefficients Standard Error P-value t Stat Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept -0.0202247 0.223805467 -0.090367404...
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