points) Consider the regression above. It we wanted to test the null hypothesis that gender has no effect on log earnings, how would write this ioint null hypothesis. Write the restricted and the unrestricted equations b. Using symbols, write the homoskedasticity-only formula for the test statistic. c. How many degrees of freedom (restrictions) are involved? tatistic comes out to be less than the critical value, do we reject or fail to reject the joint null hypothesis?
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please show all work and graphs SUMMARY OUTPUT Regression Statistics 0.989894408 Multiple R R Square 0.97989094 0.946375839 Adjusted R Square Standard Error 0.093997207 Observations ANOVA F Sign...
w Add-ins Help FormatTell me what you want to do AaBbCcD AoBb .a-| = ::: ....
w Add-ins Help FormatTell me what you want to do AaBbCcD AoBb .a-| = ::: . 으 田·LNormall1NoSpac . Heading 1 Heading 2 Title Subtitle Sut tie Subtitle Subtle i SUMMARY OUTPUT Regression Stotistics Multiple R R Square Adjusted R Square Standard Error Observations 0.989894408 0.97989094 0.946375839 0.093997207 ANOVA FSignificance F 5 1.291627011 0.258325402 29.23729686 0.009507436 Ss MS Regressiorn Residual Total 3 0026506425 0.008835475 8 1.318133436 p,value Lower 95% Upper 95% Coefficients Standard Error t Stot 2.000000 0.199040522 12.23566447 0.001175553...
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...
#9 need help all of it SUMMARY OUTPUT Regression Statistics Multiple R 0.89079322 R Square Adjusted R S 0.78995244 Standard Erro 3.04000462 Observations 0.79351257 60 ANOVA MS Significance F df Regre...
#9 need help all of it
SUMMARY OUTPUT Regression Statistics Multiple R 0.89079322 R Square Adjusted R S 0.78995244 Standard Erro 3.04000462 Observations 0.79351257 60 ANOVA MS Significance F df Regression 1 2059.8551 2059.8551222.888768 1.5799E-21 58 536.014429 9.24162808 59 2595.86953 3 Residual Total er 95% Lower 98.0% Upper 98.0% 4.70337792 0.85182782 5.52151244 8.2562E-07 2.99825928 6.40849657 2.66548423 6.74127162 ertising 2.04813433 0.13718744 14.9294597 1.5799E-21 1.77352384 2.32274483 1.7199302 2.37633847 Coefficients Standard Errot Stat P-value Lower 95% Intercept Adv 9. A marketing manager claims...
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...
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...
SUMMARY OUTPUT Regression Statistics Multiple R 0.99806038 R Square 0.996124522 Adjusted R Square 0.995155653 Standard Error...
SUMMARY OUTPUT Regression Statistics Multiple R 0.99806038 R Square 0.996124522 Adjusted R Square 0.995155653 Standard Error 387.1597665 Observations 16 ANOVA df SS MS F Significance F Regression 3 4.62E+08 1.54E+08 1028.131 9.91937E-15 Residual 12 1798712 149892.7 Total 15 4.64E+08 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 1946.802039 504.1819 3.861309 0.002263 848.2839829 3045.32 848.284 3045.32 XRay (x1) 0.038577091 0.013042 2.957935 0.011966 0.010161233 0.066993 0.010161 0.066993 BedDays (x2) 1.039391967 0.067556 15.38573 2.91E-09 0.892201042 1.186583...
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 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...
SUMMARY OUTPUT Regression Statistics Multiple R 0.985689515 R Square 0.97158382 Adjusted R Square 0.968940454 Standard Error...
SUMMARY OUTPUT Regression Statistics Multiple R 0.985689515 R Square 0.97158382 Adjusted R Square 0.968940454 Standard Error 754.6653051 Observations 48 ANOVA df SS MS F Significance F Regression 4 837320651.9 209330163 367.555599 1.23563E-32 Residual 43 24489348.08 569519.723 Total 47 861810000 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept -979.9824986 2587.408411 -0.3787506 0.70673679 -6197.988856 4238.02386 -6197.988856 4238.023859 Price (cents) -39.65930534 3.380682944 -11.731152 5.4685E-15 -46.47710226 -32.841508 -46.47710226 -32.84150842 Competitors Price (cents) 39.71320378 3.717321495 10.6832847 1.1179E-13 32.21651052 47.209897...
SUMMARY OUTPUT Regression Statistics Multiple R 0.633614748 R Square 0.401467649 Adjusted R Square 0.388732918 Standard Error...
SUMMARY OUTPUT Regression Statistics Multiple R 0.633614748 R Square 0.401467649 Adjusted R Square 0.388732918 Standard Error 7373785408 Observations ANOVA SS SS F Significance F 1 17141221.72 17141222 31.52541 1.02553E-06 4725555174.28 543727.1 48 4 2696396 1 17141221.72 17141222 3152541 Siewicowe Regression Residual Total Coefficients Standard Error Star P-value 2194.707265 332.0870736 6.608831 3.21E-08 40.870917 7279205668 5.61475 1.03E-06 Coefficients Standard Porn Photo Intercept Lower 95% Upper 95% Lower 95.096 Upper 95.0% 1526,634245 2862.780285 1526.634245 2862.780285 26.22704404 55.51478995 26.22704404 55.51478995 54 SUMMARY OUTPUT Regression...
w Add-ins Help FormatTell me what you want to do AaBbCcD AoBb .a-| = ::: . 으 田·LNormall1NoSpac . Heading 1 Heading 2 Title Subtitle Sut tie Subtitle Subtle i SUMMARY OUTPUT Regression Stotistics Multiple R R Square Adjusted R Square Standard Error Observations 0.989894408 0.97989094 0.946375839 0.093997207 ANOVA FSignificance F 5 1.291627011 0.258325402 29.23729686 0.009507436 Ss MS Regressiorn Residual Total 3 0026506425 0.008835475 8 1.318133436 p,value Lower 95% Upper 95% Coefficients Standard Error t Stot 2.000000 0.199040522 12.23566447 0.001175553...
#9 need help all of it
SUMMARY OUTPUT Regression Statistics Multiple R 0.89079322 R Square Adjusted R S 0.78995244 Standard Erro 3.04000462 Observations 0.79351257 60 ANOVA MS Significance F df Regression 1 2059.8551 2059.8551222.888768 1.5799E-21 58 536.014429 9.24162808 59 2595.86953 3 Residual Total er 95% Lower 98.0% Upper 98.0% 4.70337792 0.85182782 5.52151244 8.2562E-07 2.99825928 6.40849657 2.66548423 6.74127162 ertising 2.04813433 0.13718744 14.9294597 1.5799E-21 1.77352384 2.32274483 1.7199302 2.37633847 Coefficients Standard Errot Stat P-value Lower 95% Intercept Adv 9. A marketing manager claims...
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 0.88012 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 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...
SUMMARY OUTPUT Regression Statistics Multiple R 0.633614748 R Square 0.401467649 Adjusted R Square 0.388732918 Standard Error 7373785408 Observations ANOVA SS SS F Significance F 1 17141221.72 17141222 31.52541 1.02553E-06 4725555174.28 543727.1 48 4 2696396 1 17141221.72 17141222 3152541 Siewicowe Regression Residual Total Coefficients Standard Error Star P-value 2194.707265 332.0870736 6.608831 3.21E-08 40.870917 7279205668 5.61475 1.03E-06 Coefficients Standard Porn Photo Intercept Lower 95% Upper 95% Lower 95.096 Upper 95.0% 1526,634245 2862.780285 1526.634245 2862.780285 26.22704404 55.51478995 26.22704404 55.51478995 54 SUMMARY OUTPUT Regression...
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