Regression Statistics Multiple R 0.6906 R Square 0.4769 Adjusted R Square 0.4705 Standard Error 7.4823 Observations...
| Regression Statistics | ||||
| Multiple R | 0.6906 | |||
| R Square | 0.4769 | |||
| Adjusted R Square | 0.4705 | |||
| Standard Error | 7.4823 | |||
| Observations | 83 | |||
| Coefficients | Standard Error | t Stat | P-value | |
| Intercept | 50.5266 | 1.3369 | 37.7950 | 0.0000 |
| Age | 0.2087 | 0.0243 | **** | 0.0000 |
The Excel output above is from a regression of body length on age in a sample of bears. Use the output to answer the following questions.
- What is the value of b0? (1 dp) Answer
- What is the value of b1? (4 dp) Answer
- What is the se(b1)? (4dp) Answer
- Calculate the absolute value of the test statistic for b1. (2dp) Answer
- What is the goodness of fit (as a percentage)? (1 dp) Answer
- What is the correlation? (2 dp) Answer
- Would you (A) reject or (B) not reject the hypothesis that β1=0? Answer
Homework Answers
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Regression Statistics
Multiple R
0.6906
R Square
0.4769
Adjusted R Square
0.4705
Standard Error
7.4823
Observations...
Regression Statistics Multiple R 0.88012 R Square 0.77461 Adjusted R Square 0.77190 Standard Error 56.6927 Observations...
Regression Statistics Multiple R 0.88012 R Square 0.77461 Adjusted R Square 0.77190 Standard Error 56.6927 Observations 253 ANOVA Significance 285.2516 MS 916816.787 3214.0637 Regression Residual Total 0.000 2750450.3598 800301.8665 3550752.226 252 Intercept Income Coefficients Standard Error 70.2382 15.8338 5.45850 .2485 t Stat P-value 4.4360 0.000014 21.96960 .000 Lower 3 9.053 4.969 "pper 95% 1.4234 479 HULLU LIIS TILIR. SUMMARY OUTPUT Regression Statistics Multiple R 0.8778 R Square Adjusted R Square 0.6558 Standard Error Observations ANOVA ANOVA Significance Regression 45.3528 de...
SUMMARY OUTPUT 0.865 0.748 Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations...
SUMMARY OUTPUT 0.865 0.748 Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 0.726 5.195 50 ANOVA df SS MS F Significance F 0.0000 3605.7736 1201.9245 Regression Residual Total 1214.2264 26.3962 49 4820 P-value 0.7798 Intercept Income Coefficients Standard Error -1.6335 5.8078 0.4485 0.1137 4.2615 0.8062 -0.6517 0.4319 t Stat -0.281 3.9545 0.0003 Size 5.286 0.0001 0.1383 School -1.509 A real estate builder wishes to determine how house size (House) is influenced by family income (Income). family...
Regression Statistics Multiple R 0.896755 R Square 0.80417 Adjusted R Square 0.767452 Standard Error 51.04855 Observations...
Regression Statistics Multiple R 0.896755 R Square 0.80417 Adjusted R Square 0.767452 Standard Error 51.04855 Observations 20 ANOVA df SS MS F Significance F Regression 3 171220.5 57073.49 21.90118 6.56E-06 Residual 16 41695.28 2605.955 Total 19 212915.8 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 427.1938 59.60143 7.167509 2.24E-06 300.8444 553.5432 300.8444 553.5432 Temp (deg) -4.58266 0.772319 -5.93364 2.1E-05 -6.21991 -2.94542 -6.21991 -2.94542 Insulation (ins.) -14.8309 4.754412 -3.11939 0.006606 -24.9098 -4.75196 -24.9098 -4.75196...
REGRESSION STATISTICS Multiple R .142620229 R Square .02034053 Standard Error 20.25979924 Observations 22 Coefficients Standard Error...
REGRESSION STATISTICS Multiple R .142620229 R Square .02034053 Standard Error 20.25979924 Observations 22 Coefficients Standard Error T Stat P-Value Intercept 39.39.027309 37.24347659 1.057642216 .302826622 Attendance .340583573 .52852452 .644404489 .526635689 _______ In the table above, the proportion of the variation in "Score received on the exam" that can be explained by the variation in attendance is given by -standard error -R Square -The P-value -Intercept
Dep.= % WRK Indep.= % MGT SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R...
Dep.= % WRK Indep.= % MGT SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations ANOVA Significance df SS MS F F Regression 102.1488 148.9539 Residual Total 12.0000 Standard Coefficients Error t Stat P-value Lower 95% Upper 95% Intercept % MGT 0.4543 SE CI CI PI PI Predicted Predicted Lower Upper Lower Upper x0 Value Value 95% 95% 95% 95% 67.0000 67.8474 65.8779 69.8169 72.0000 70.1189 68.2003 72.0375 76.0000 71.9361 69.7884 74.0838 Dep.= % MGT...
Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 0.72 0.51 0.38 99.45...
Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 0.72 0.51 0.38 99.45 6 Anova df SS MS F Significance F 0.11 1 41497.60 41497.60 4.20 Regression Residual 4 39561.23 9890.31 Total 5 81058.83 t Stat P-value Coefficients Standard Error 1423.60 564.95 2.52 0.07 Intercept X Variable 1 Lower 95% Upper 95% -144.96 2992.16 -0.11 0.72 Lower 95.0% Upper 95.0% -144.96 2992.16 -0.11 0.72 0.31 0.15 2.05 0.11 Assume that Craig's Fresh and Hot Pancake Restaurant does...
Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 0.4960479...
please choose the correct answers
Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 0.496047946 0.246063564 0.205674112 0.082322859 作少 60 Intercept Mkt-RF SMB HML Coefficients Standard Error t Stat 0.026911876 0.011569784 2.326048238 0.565675211 0.381955416 1.480998008 1.588686162 0.434827534 3.653600652 0.166588781 0.456612309 0.364836377 Which of the following statements are correct based on the above regression output? Select all that apply The estimate for alpha is statistically different from zero because 10.03bo. The estimate for alpha is statistically different from...
SUMMARY OUTPUT Regression Statistics 0.99 Multiple R Square Adjusted R Square Standard Error Observations 0.97 252...
SUMMARY OUTPUT Regression Statistics 0.99 Multiple R Square Adjusted R Square Standard Error Observations 0.97 252 Coefficients Standard Emo Stat P-value Lower 95% 95% Intercept 131.92 1776 000 166.73 Price of Good - 118 -634 000 Price of Related Good 1024 097 10.60 0.00 27 1221 Income 030 0.10 300 001 The demand for your produd demands on three factors the price of your good, the price of and good and the average income of your customers Excel estimated the...
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...
Dep.- WRK Indep.- MGT SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted RSquare Standard Error...
Dep.- WRK Indep.- MGT SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted RSquare Standard Error Observations ANOVA Regression 102.1488 Residual Total 12.00001 Standard Coefficients P.valuell Lower 95 Upper 9524 LUV Upper 95 Intercept 6 MGT 0.4543 Predicted Predicted Lower Upper Lower XO Value Value 67.0000 65.8779 69.8169 72.0000 67.8474 70.1189 71.9361 68.2003 22.0375 74,0828 76.0000 69.7884 Dep.-% MGT Indep96 WRK SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations Observations ANOVA Regression 460.8873 148.9539...
Regression Statistics Multiple R 0.88012 R Square 0.77461 Adjusted R Square 0.77190 Standard Error 56.6927 Observations 253 ANOVA Significance 285.2516 MS 916816.787 3214.0637 Regression Residual Total 0.000 2750450.3598 800301.8665 3550752.226 252 Intercept Income Coefficients Standard Error 70.2382 15.8338 5.45850 .2485 t Stat P-value 4.4360 0.000014 21.96960 .000 Lower 3 9.053 4.969 "pper 95% 1.4234 479 HULLU LIIS TILIR. SUMMARY OUTPUT Regression Statistics Multiple R 0.8778 R Square Adjusted R Square 0.6558 Standard Error Observations ANOVA ANOVA Significance Regression 45.3528 de...
SUMMARY OUTPUT 0.865 0.748 Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 0.726 5.195 50 ANOVA df SS MS F Significance F 0.0000 3605.7736 1201.9245 Regression Residual Total 1214.2264 26.3962 49 4820 P-value 0.7798 Intercept Income Coefficients Standard Error -1.6335 5.8078 0.4485 0.1137 4.2615 0.8062 -0.6517 0.4319 t Stat -0.281 3.9545 0.0003 Size 5.286 0.0001 0.1383 School -1.509 A real estate builder wishes to determine how house size (House) is influenced by family income (Income). family...
Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 0.72 0.51 0.38 99.45 6 Anova df SS MS F Significance F 0.11 1 41497.60 41497.60 4.20 Regression Residual 4 39561.23 9890.31 Total 5 81058.83 t Stat P-value Coefficients Standard Error 1423.60 564.95 2.52 0.07 Intercept X Variable 1 Lower 95% Upper 95% -144.96 2992.16 -0.11 0.72 Lower 95.0% Upper 95.0% -144.96 2992.16 -0.11 0.72 0.31 0.15 2.05 0.11 Assume that Craig's Fresh and Hot Pancake Restaurant does...
please choose the correct answers
Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 0.496047946 0.246063564 0.205674112 0.082322859 作少 60 Intercept Mkt-RF SMB HML Coefficients Standard Error t Stat 0.026911876 0.011569784 2.326048238 0.565675211 0.381955416 1.480998008 1.588686162 0.434827534 3.653600652 0.166588781 0.456612309 0.364836377 Which of the following statements are correct based on the above regression output? Select all that apply The estimate for alpha is statistically different from zero because 10.03bo. The estimate for alpha is statistically different from...
SUMMARY OUTPUT Regression Statistics 0.99 Multiple R Square Adjusted R Square Standard Error Observations 0.97 252 Coefficients Standard Emo Stat P-value Lower 95% 95% Intercept 131.92 1776 000 166.73 Price of Good - 118 -634 000 Price of Related Good 1024 097 10.60 0.00 27 1221 Income 030 0.10 300 001 The demand for your produd demands on three factors the price of your good, the price of and good and the average income of your customers Excel estimated the...
Dep.- WRK Indep.- MGT SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted RSquare Standard Error Observations ANOVA Regression 102.1488 Residual Total 12.00001 Standard Coefficients P.valuell Lower 95 Upper 9524 LUV Upper 95 Intercept 6 MGT 0.4543 Predicted Predicted Lower Upper Lower XO Value Value 67.0000 65.8779 69.8169 72.0000 67.8474 70.1189 71.9361 68.2003 22.0375 74,0828 76.0000 69.7884 Dep.-% MGT Indep96 WRK SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations Observations ANOVA Regression 460.8873 148.9539...
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