Question

Calculate the following statistics given the existing information (1 point per calculation): Regression Statistics Multiple R...

Calculate the following statistics given the existing information (1 point per calculation):

Regression Statistics

Multiple R

R Square

Adjusted R Square

0.559058

Standard Error

Observations

30

ANOVA

df

SS

MS

F

Significance F

Regression

2

3609132796

19.38411515

6.02827E-06

Residual

27

2513568062

Total

29

6122700857

Coefficients

Standard Error

t Stat

P-value

Lower 95%

Upper 95%

Intercept

-15800.8

57294.51554

-0.27578

0.784814722

CARAT

12266.83

1999.250369

6.135715

1.48071E-06

DEPTH

156.686

928.9461882

0.168671

0.867312915

Additionally interpret your results. Be sure to comment on Accuracy, significance and your coefficients. The model is predicting the price of diamonds given CARAT and DEPTH. (5 points)

Math   Statistics-And-Probability   
0 0
Add a commentTranscribed image text
Next >

Homework Answers

Answer #1

0 0
Know the answer?
Your Answer:

Post as a guest

Your Name:

What's your source?

Earn Coins

Coins can be redeemed for fabulous gifts.

Log In

Sign Up

Not the answer you're looking for?
Ask your own homework help question
Similar Questions
Regression Statistics Multiple R 0.3641 R Square 0.1325 Adjusted R Square 0.1176 Standard Error 0.0834 Observations...
Regression Statistics Multiple R 0.3641 R Square 0.1325 Adjusted R Square 0.1176 Standard Error 0.0834 Observations 60 ANOVA df SS MS F Significance F Regression 1 0.0617 0.0617 8.8622 0.0042 Residual 58 0.4038 0.0070 Total 59 0.4655 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept -0.0144 0.0110 -1.3062 0.1966 -0.0364 0.0077 X Variable 1 0.8554 0.2874 2.9769 0.0042 0.2802 1.4307 How do you interpret the above table?
Compare the two regression models. Does it make sense that spending and household debt could each...
Compare the two regression models. Does it make sense that spending and household debt could each be predicted by annual household income? Why or why not? 1. Predicting spending by household income Regression Statistics Multiple R 0.859343186 R Square 0.738470711 Adjusted R Square 0.737149856 Standard Error 1602.157625 Observations 200 ANOVA df SS MS F Significance F Regression 1 1435121315 1435121315 559.085376 1.42115E-59 Residual 198 508247993.2 2566909.056 Total 199 1943369308 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower...
SUMMARY OUTPUT Regression Statistics Multiple R 0.84508179 R Square 0.714163232 Adjusted R Square 0.704942691 Standard Error...
SUMMARY OUTPUT Regression Statistics Multiple R 0.84508179 R Square 0.714163232 Adjusted R Square 0.704942691 Standard Error 9.187149383 Observations 33 ANOVA df SS MS F Significance F Regression 1 6537.363661 6537.363661 77.4535073 6.17395E-10 Residual 31 2616.515127 84.40371378 Total 32 9153.878788 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 61.07492285 3.406335763 17.92980114 6.41286E-18 54.12765526 68.02219044 54.12765526 68.02219044 Time (Y) -0.038369095 0.004359744 -8.800767426 6.17395E-10 -0.047260852 -0.029477338 -0.047260852 -0.029477338 Using your highlighted cells, what is the equation...
Using the attached regression output, answer the following: SUMMARY OUTPUT Regression Statistics Multiple R 0.972971 R...
Using the attached regression output, answer the following: SUMMARY OUTPUT Regression Statistics Multiple R 0.972971 R Square 0.946673 Adjusted R Square 0.944355 Standard Error 76.07265 Observations 49 ANOVA df SS MS F Significance F Regression 2 4725757 2362878 408.3046 5.24E-30 Residual 46 266204.2 5787.049 Total 48 4991961 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept -0.46627 14.97924 -0.03113 0.975302 -30.6179 29.68537 X1 0.09548 0.084947 1.123997 0.266846 -0.07551 0.26647 X2 0.896042 0.205319 4.364141 7.16E-05 0.482756 1.309328 a. What...
] A partial computer output from a regression analysis using Excel’s Regression tool follows. Regression Statistics...
] A partial computer output from a regression analysis using Excel’s Regression tool follows. Regression Statistics Multiple R (1) R Square 0.923 Adjusted R Square (2) Standard Error 3.35 Observations ANOVA df SS MS F Significance F Regression (3) 1612 (7) (9) Residual 12 (5) (8) Total (4) (6) Coefficients Standard Error t Stat P-value Intercept 8.103 2.667 x1 7.602 2.105 (10) x2 3.111 0.613 (11)
SUMMARY OUTPUT Regression Statistics Multiple R 0.993709623 R Square 0.987458816 Adjusted R Square 0.987378251 Standard Error...
SUMMARY OUTPUT Regression Statistics Multiple R 0.993709623 R Square 0.987458816 Adjusted R Square 0.987378251 Standard Error 514.2440271 Observations 471 ANOVA df SS MS F Significance F Regression 3 9723795745 3241265248 12256.7707 0 Residual 467 123496711.4 264446.9194 Total 470 9847292456 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept -267.1127974 42.01832073 -6.357055513 4.8988E-10 -349.68118 -184.54441 -349.68118 -184.54441 Fuel cost (000,000) 0.449917223 0.098292092 4.577349137 6.0451E-06 0.25676768 0.64306676 0.25676768 0.64306676 Salary (000,000) -0.327915884 0.188252958 -1.741889678 0.08218614 -0.6978436...
Regression Statistics Multiple R 0.710723 R Square 0.505127 Adjusted R Square 0.450141 Standard Error 1.216847 Observations...
Regression Statistics Multiple R 0.710723 R Square 0.505127 Adjusted R Square 0.450141 Standard Error 1.216847 Observations 21 ANOVA df SS MS F Significance F Regression 2 27.20518 13.60259 9.186487 0.00178 Residual 18 26.65291 1.480717 Total 20 53.8581 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 58.74307 12.66908 4.636728 0.000205 32.12632 85.35982 32.12632 85.35982 High School Grad -0.00133 0.000311 -4.28236 0.000448 -0.00198 -0.00068 -0.00198 -0.00068 Bachelor's -0.00016 5.46E-05 -3.00144 0.007661 -0.00028 -4.9E-05 -0.00028 -4.9E-05...
(a) Present the regression output below noting the coefficients, assessing the adequacy of the model and...
(a) Present the regression output below noting the coefficients, assessing the adequacy of the model and the p-value of the model and the coefficients individually. SUMMARY OUTPUT Regression Statistics Multiple R 0.19476248 R Square 0.037932424 Adjusted R Square 0.035147858 Standard Error 12.09940236 Observations 694 ANOVA df SS MS F Significance F Regression 2 3988.511973 1994.255986 13.62238235 1.5759E-06 Residual 691 101159.3165 146.3955376 Total 693 105147.8284 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 27.88762549...
Dep.= Mileage Indep.= Cylinders SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square Standard...
Dep.= Mileage Indep.= Cylinders SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 7.0000 ANOVA Significance df SS MS F F Regression 12.4926 Residual Total 169.4286 Standard Coefficients Error t Stat P-value Lower 95% Upper 95% Intercept 38.7857 Cylinders -2.7500 SE CI CI PI PI Predicted Predicted Lower Upper Lower Upper x0 Value Value 95% 95% 95% 95% 4.0000 1.9507 6.0000 1.1763 Is there a relationship between a car's gas MILEAGE (in miles/gallon) and its...
Dep.= Mileage Indep.= Length SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square Standard...
Dep.= Mileage Indep.= Length SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 7.0000 ANOVA Significance df SS MS F F Regression 6.1135 Residual Total 169.4286 Standard Coefficients Error t Stat P-value Lower 95% Upper 95% Intercept 80.0094 Length -0.3047 SE CI CI PI PI Predicted Predicted Lower Upper Lower Upper x0 Value Value 95% 95% 95% 95% 175.0000 2.3108 210.0000 2.9335 Is there a relationship between a car's gas MILEAGE (in miles/gallon) and its...
ADVERTISEMENT
Need Online Homework Help?

Get Answers For Free
Most questions answered within 1 hours.

Ask a Question
ADVERTISEMENT