Observations ANOVA df Regression 1 Residual 5 Total 6 Intercept 72.69 x variable 1 -5.85 O 5.85 O-72.69 O-5.85 Coefficients 72.69 SS 1945.3 150.4 2095.7 Standard Error 4.74 0.73 MS 1945.3 30.1 t Stat 15.34 -8.04 F 64.7 P-value 0.00 0.00 Significance F 0.00048117 Lower 95% 60.51 -7.72 Upper 95% 84.87 -3.98
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- Chapter 9, Section 2, Exercise 030 Use the following ANOVA table for regression to answer the questions. Analysis of Variance Source DF SS MS F P Regression 1 304.5 304.5 2.01 0.158 Residual Error 174 26361.0 151.5 Total 175 26665.5 Give the F-statistic and p-value.Enter the exact answers.The F-statistic is=The p-value is=pre-test post-test Mean Variance -2.25 0.75 9.30 13.30 Observations 12 12 Pearson Correlation 0.9478 Hypothesized Mean 0 Difference df 11 t Stat 2.076 P(T<=t) one-tail 0.035 t Critical one-tail 1.796 P(T<=t) two-tail 0.065 t Critical two-tail 2.201In regression, what is the difference between an observed value of the response variable and its predicted value called? Choose the correct answer below. R2 A standard error A residual A mean square error Leverage
- ul Sprint 11:42 AM 86% Done Attachment 11 of 12 10 Find the explained variation, unexplained variation, total variation, and coerfficient of determination 11) The paired data below consists of test scores and hours of preparation for 5 randomly selected students. The equation of the regression line is y = 44.8447 + 3.52427x. Find the total variation. 6 10 x Hours of preparation y Test score 2 9 64 48 72 73 80 A) 511.724 B) 87.4757 C) 599.2 D) 498.103A regression analysis was performed and the summary output is shown below. Regression Statistics Multiple R 0.7802268560.780226856 R Square 0.6087539470.608753947 Adjusted R Square 0.5870180550.587018055 Standard Error 6.7217061336.721706133 Observations 2020 ANOVA dfdf SSSS MSMS F� Significance F� Regression 11 1265.3871265.387 1265.3871265.387 28.006928.0069 4.9549E-054.9549E-05 Residual 1818 813.264813.264 45.18145.181 Total 1919 2078.6512078.651 Step 1 of 2: How many independent variables are included in the regression modelA regression analysis was performed and the summary output is shown below. Regression Statistics Multiple R 0.7802268560.780226856 R Square 0.6087539470.608753947 Adjusted R Square 0.5870180550.587018055 Standard Error 6.7217061336.721706133 Observations 20 ANOVA dfdf SSSS MSMS F� Significance F� Regression 11 1265.3871265.387 1265.3871265.387 28.006928.0069 4.9549E-054.9549E-05 Residual 1818 813.264813.264 45.18145.181 Total 1919 2078.6512078.651 Step 2 of 2: Which measure is appropriate for determining the proportion of variation in the dependent variable explained by the set of independent variable(s) in this model?
- The parallel trends assumption is a crucial assumption for which the following estimators? Multiple regression Differences-in-Differences First differences Instrumental VariableTreatment II III 2 N = 24 G = 72 EX2 = 292 3 M = 2 M = 3 T = 16 T= 24 T= 32 SS = 16 SS = 24 SS = 20 Use an ANOVA with a = .05 to determine whether there is a significant mean difference between the two treatments. (Round to two decimal places where needed.) Source df MS Feritical Between treatments Within treatments Total F Distribution Numerator Degrees of Freedom = 6 Denominator Degrees of Freedom = 16 0.0 1.0 3.0 4.0 5.0 6.0 7.0 9.0 11.0 120 Conclusion: Fail to reject the null hypothesis; there are no significant differences among the three treatments. Reject the null hypothesis; there are no significant differences among the three treatments. Reject the null hypothesis; there are significant differences among the three treatments. o Fail to reject the null hypothesis; there are significant differences among the three treatments.A special bumper was installed on selected vehicles in a large fleet. The dollar cost of body repairs was recorded for all vehicles that were involved in accidents over a 1-year period. Those with the special bumper are the test group and the other vehicles are the control group, shown below. Each "repair incident" is defined as an invoice (which might include more than one separate type of damage). Statistic Test Group Control Group Mean Damage x¯1x¯1 = $ 1,101 x¯2x¯2 = $ 1,766 Sample Standard Deviation s1 = $ 696 s2 = $ 838 Repair Incidents n1 = 12 n2 = 9 (a) Construct a 90 percent confidence interval for the true difference of the means assuming equal variances. (Round answers to 3 decimal places. Negative values should be indicated by a minus sign.) (b) Repeat part (a), using the assumption of unequal variances with Welch's formula for d.f. (Round answers to 2 decimal places. Negative values should be indicated by a minus sign.) (d) Construct separate…
- When there are omitted variables in your regression, then... Group of answer choices This has no effect on the estimation of the explanatory variable because the variable is omitted This will always bias the OLS estimation of the explanatory variable the estimation of the explanatory variable(s) is unaffected The OLS estimation is biased if the omitted variables are correlated with the included variable3.Which model do you think is the “best” reduced model? Discuss why you choose this model. Analysis of Variance Table (Step back model) Response: rent Df Sum Sq Mean Sq F value Pr(>F) age 1 21000 21000 17.1136 0.0003079 *** sqft 1 35364 35364 28.8196 1.134e-05 *** sd 1 5961 5961 4.8576 0.0362339 * unts 1 8678 8678 7.0722 0.0130049 * gar 1 33364 33364 27.1899 1.713e-05 *** cp 1 7641 7641 6.2269 0.0189934 * Residuals 27 33131 1227 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Step forward model: Response: rent Df Sum Sq Mean Sq F value Pr(>F) age 1 21000 21000 16.6255 0.000406 *** sqft 1 35364 35364 27.9976 1.757e-05 *** sd 1 5961 5961 4.7191 0.039517 * unts 1 8678 8678 6.8705 0.014697 * gar 1 33364 33364 26.4144 2.599e-05 *** cp 1 7641 7641 6.0493 0.021176 *…Are the heights of individuals affected by the heights of their parents. Regression Statistics Multiple R R Square Adjusted R Squar 0.631071992 0.398251859 0.365724932 Standard Error 2.914527039 Observations 40 ANOVA df MS F Significance F Regression Residual 2 208.0084392 104.0042 12.24376 8.30181E-05 37 314.2953108 8.494468 Total 39 522.30375 Coefficients Standard Error t Stat P-value Intercept Mother's Height Father's Height 9.804326378 12.39987353 0.79068 0.43417 0.657952815 0.147476295 4.461414 7.34E-05 0.200358437 0.138223638 1.449524 0.155615 1. Write the regression equation that represents the above equation. 2. Is this a good predictor equation? Why or why not (use appropriate statistics/hypothesis test to prove your point)? 3. Use the equation to predict the height of someone whose mother is 52 inches tall and whose father is 70 inches tall.