ANOVA |
|||||
df |
SS |
||||
Regression |
1 |
882 |
|||
Residual |
20 |
4000 |
|||
Total |
21 |
4882 |
|||
Coefficients |
Standard Error |
t Stat |
|||
Intercept |
5.00 |
3.56 |
|||
Variable x |
6.30 |
3.00 |
Use the ANOVA table that was provided in question 7 and Perform an F test and determine whether x and y are related. Use α = .05
Answer Options:
Since the test statistic F = 3.45 < 4.35 ,Fail to reject HO
Since the test statistic F = .45< 3.45, Fail to reject HO
Since the test statistic F = 4.41 > 4.35, reject H0.
Since the test statistic F = 4.41>4.05, reject HO.
We have F test statistic value is
F critical value is 4.35.........................by using =FINV(0.05,1,20)
F test statistic value >F critical value
i.e. 4.41 > 4.35
therefore we reject H0.
Since the test statistic F = 4.41 > 4.35, reject H0.
ANOVA df SS Regression 1 882 Residual 20 4000 Total 21 4882 Coefficients Standard Error t...
ANOVA df SS Regression 1 0.72 Residual 10 62.6 Total 11 63.32 Coefficients Std Error Intercept 14.64 146.76 No. of accounts (000) 1.99 5.87 This printout is for data relating the number of ATM withdrawals (in thousands) to the number of accounts (in thousands) at that branch. Predict the number of withdrawals if the number of accounts is 24.528 thousand. State the answer in thousands correct to two decimal places.
ANOVA DF SS MS Regression 1 0.0994 0.0985 Residual 62 0.1413 0.0025 Total 61 0.2407 Coefficients Standard Error Intercept -0.013 0.0053 S&P 500 Returns 1,2139 0.1878 Looking both at the specification of the model and at the estimated coefficient, how can you interpret the coefficient of S&P 500 Returns
Using the following information: Coefficients Intercept -12.8094 Independent variable 2.1794 ANOVA df SS MS F Regression 1 12323.56 12323.56 90.0481 Residual 8 1094.842 136.8550 Total 9 13418.4 Estimate the value of Ŷ when X = 4.
(4) A regression software output is given below. df ANOVA Source Regression Residual Total 4 SS 227,09 153,07 380,16 MS 56,8 6,1 25 29 Variables Intercept X1 X2 X3 X4 Standard Coefficients Error 68,33 8,9 0,85 0,3 -0,33 0,8 -0,81 0,2 -0,58 0,2 a. How large is the sample size? b. Write the regression equation. Interprete the coefficient of X2. c. Determine and interprete the coefficient of determination. d. Conduct a global test of hypothesis fort he meaning of the...
#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...
A regression model relating x, number of salespersons at a branch office, to y, annual sales at the office (in thousands of dollars) provided the following computer output from a regression analysis of the data. Where n total=26. a. Write the estimated regression equation (to whole number). y=_____+_____x b. Compute the F statistic and test the significance of the relationship at a .05 level of significance. (to 2 decimals) F-value ____ p-value is _______, we _________ h0 c. Compute the...
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...
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 Significance F MS 5 1.291627011 0.258325402 29.23729686 0.009507436 Regression Residual Total 3 0.026506425 0.008835475 8 1.318133436 Coefficients Standard Error Stat 2.000000 1.000000 1.000000 0.199040522 12.23566447 0.001175553 1.801957267 3.068828814 0.116964675 7.144529794 0.005645963 0.46342381 1.207891408 0.221410557 -4.794091072 0.017265888 -1.766089586 -0.356835166 0.115122597 -2.041317871 0.133876794 -0.601373298 0.131369669 0.500000 0.130770483 -4.049701664 0.027116334 0.945751481 -0.113411402 Intercept GPA Fin Major Gender...
5. Summary of regression between a dependent variable y and two independent variables X, and x2 is as follows. Please complete the table: SUMMARY OUTPUT Regression Statistics Multiple R 0.9620 R Square R2E? Adjusted R Square 0.9043 Standard Error 12.7096 Observations 10 ANOVA F Significance F F=? Overall p-value=? Regression Residual Total 2 df of SSE MS MSR=? MSE? 14052.1550 1130.7450 SSTE? MSE? 9 Coefficients -18.3683 Standard Error 17.9715 t Stat -1.0221 Intercept ty=? 2.0102 4.7378 0.2471 0.9484 P-value 0.3408...
for b. the p-value is (less than 0.01, between 0.01 and 0.025, between 0.025 and 0.05, between 0.05 and 0.10, or greater than 0.10), we (Reject, Accept) H0 for c. the p-value is (less than 0.01, between 0.01 and 0.025, between 0.025 and 0.05, between 0.05 and 0.10, or greater than 0.10), we (Reject, Accept) H0 Check My Work (3 remaining) eBook A regression model relating 2, number of salespersons at a branch office, to y, annual sales at the...