Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 0.99 0.98 0.96 5.24 16 ANOVA df 8 7 15 SS MS F significance F 10508.17 1313.521 47.76926 2.01E-05 192.4804 27.4972 10700.65 Regression Residual Total Coefficients Standard Error 20.84 4.49 4.64 3.98 -13.03 3.21 30.93 1.24 39.17 10.35 12.64 3.41 26.57 3.78 -16.03 4.18 -17.50 3.03 4.49 2.73 3.21 3.41 3.71 3.78 3.03 2.73 3.41 3.78 4.64 1.24 -3.51 9.08 10.35 4.18 9.74 -4.71 -4.62 P-value Lower 95% Upper 95% 0.00 10.21 -3.62 31.47 -21.80 0.26 22.87 0.01 30.22 0.00 5.49 48.11 0.00 19.80 33.02 0.00 20.11 -24.08 0.00 -26.45 11.57 -8.55 -4.26 Intercept Lenovo Dell Memory 6GB Memory 8GB Hard Drive 1TB Speed - 3.1GHz Price - $800 Price - $1000 What is the relative attribute important of "Memory"? A. 15.07% B. 11.20% C. 23.54% D. 34.70% E. 15.50%
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Step 1: First, we need to identify the coefficients for the "Memory 6GB" and "Memory 8GB" attributes in the regression statistics. Show more…
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The ANOVA summary table in the multiple regression model with four independent variables is complete (e). We are to determine the regression mean square (MSR) and the mean square error (MSE). MSR = MSE = (Round to four decimal places if needed) Compute the overall FSTAT test statistic. FSTAT = (Round to three decimal places if needed) Determine whether there is a significant relationship between the four independent variables at the 0.05 level of significance. State the null and alternative hypotheses. Choose the correct hypotheses below. H0: β1 = β2 = β3 = β4 = 0 H1: At least one βj ≠0, for j = 1, 2, 3, 4 The p-value is (Round to three decimal places if needed) Draw a conclusion. Choose the correct answer below. There is insufficient evidence of a significant linear relationship of the independent variables because the test statistic is less than the level of significance. There is insufficient evidence of a significant linear relationship with at least one of the independent variables because the p-value is greater than the level of significance. There is sufficient evidence of a significant linear relationship with at least one of the independent variables because the test statistic is greater than the critical value. There is sufficient evidence of a significant linear relationship with at least one of the independent variables because the test statistic is less than the critical value. Compute the coefficient of multiple determination and interpret its meaning. (Round to four decimal places if needed) Interpret the meaning of the coefficient of multiple determination. The coefficient of multiple determination indicates that (Round to two decimal places if needed) Compute the adjusted R^2. (Round to four decimal places if needed)
Adi S.
Regression Statistics Multiple R: 0.971 R-Square: A Adjusted R-Square: B Standard Error: 30.462 Observations: 51 ANOVA df: C SS: 747851.57 MS: 373925.79 F: 402.98 Significance F: 9.89E-31 Regression Residual: 48 D: 927.91 Total: 50 792391.11 Coefficients Standard Error t Stat P-Value Lower 95% Upper 95% Intercept: E 62.13 26.79 1.60E-30 1539.66 1789.51 Price of Roses: -6.68 F -1.41 1.64E-01 -16.16 2.81 Disposable Income (M): 9.73 0.34 G 1.23E-31 9.04 10.42 From the regression output, the predicted regression line is: a) QRd = 1664.46 - 6.68PR + 9.73M. b) PR = 1664.46 - 6.68QR + 9.73M. c) QRd = 2.32 - 6.68PR + 9.73M. d) There is not sufficient information to answer the question.
Ameer S.
SUMMARY OUTPUT Regression Statistics Multiple R 1 R Square 1 Adjusted R Square 65535 Standard Error 0 Observations 4 ANOVA df SS MS F Significance F Regression 3 19235.23924 6411.74641 #NUM! #NUM! Residual 0 0 65535 Total 3 19235.23924 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 1412.72036 0 65535 #NUM! 1412.72036 1412.72036 1412.72036 1412.72036 X Variable 1 -616.63372 0 65535 #NUM! -616.63372 -616.63372 -616.63372 -616.63372 X Variable 2 757.72152 0 65535 #NUM! 757.721529 757.721529 757.721529 757.721529 X Variable 3 -506.28783 0 65535 #NUM! -506.28783 -506.28783 -506.28783 -506.28783 RESIDUAL OUTPUT Observation Predicted Y Residuals Standard Residuals 1 1.653 -2.47358E-13 -0.6238259 2 3.677 3.20188E-13 0.80750171 3 167.6 5.40012E-13 1.36188915 4 19.39 1.27898E-13 0.32255269 PROBABILITY OUTPUT Percentile Y 12.5 1.653 37.5 3.677 62.5 19.39 87.5 167.6
Sri K.
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