18 complete)Where the number in parentheses is the homoskedastic standard error for the regression coefficient B,(0.069)Suppose you wanted to test the hypothesis that B, equals zero at the 1%, 5% and 10% level. That is,Ho: B1=0 vs. H1: B1#0%3DReport the t-statistic and p-value for this test.The t-statistic is(Round your response to two decimal places)The p-value is(Round your response to three decimal places)Can you reject the null hypothesis at the 5% significance level?O A. Yes.B. No.Construct a 95% confidence interval for the slope coefficient B,.The 95% confidence interval for the slope coefficient B, is [ D(Round your response to three decimal places)Click to select your answer(s).d.DELLP Type here to search What-If Forecasta日UngAdvancedColumns cOAnalysis SheetESubData TypesSort & FilterData ToolsForecastH.一KLNSUMMARY OUTPUTRegression StatisticsMultiple R0.009746989R Square9.50038E-05Adjusted R Square-0.010108108Standard Error1.798411671Observations100ANOVAdfSSMSSignificance FRegression0.0301152560.030115256 0.0093112570.923324618Residual98316.95988473.234284538Total99316.99t StatP-valueLower 95%Upper 95%Lower 95.0% Upper 95.0%CoefficientsStandard Error13.0336395114.019421513.0336395114.01942150.2483744454.46023549 4.51479E-75Intercept13.52653051-0.0066804770.06923143 -0.096494857 0.923324618 -0.144067996 0.130707042 -0.144067996 0.130707042DistanceLL

Compute the test statistic by substituting b1=-0.0067 and s.eb1=0.0692 from the output table in the…

SUMMARY OUTPUT Regression Statistics Multiple R 0.009746989 R Square 9.50038E-05 Adjusted R Square -0.010108108 Standard Error 1.798411671 Observations 100 ANOVA df SS MS Significance F Regression 0.030115256 0.030115256 0.009311257 0.923324618 Residual 98 316.9598847 3.234284538 Total 99 316.99 Lower 95.0% Upper 95.0% 14.0194215 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 13.52653051 0.24837444 54.46023549 4.51479E-75 13.03363951 14.0194215 13.03363951 -0.006680477 0.06923143 -0.096494857 0.923324618 -0.144067996 0.130707042 -0.144067996 0.130707042 Distance

SUMMARY OUTPUT Regression Statistics Multiple R 0.009746989 R Square 9.50038E-05 Adjusted R Square -0.010108108 Standard Error 1.798411671 Observations 100 ANOVA df SS MS Significance F Regression 0.030115256 0.030115256 0.009311257 0.923324618 Residual 98 316.9598847 3.234284538 Total 99 316.99 Lower 95.0% Upper 95.0% 14.0194215 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 13.52653051 0.24837444 54.46023549 4.51479E-75 13.03363951 14.0194215 13.03363951 -0.006680477 0.06923143 -0.096494857 0.923324618 -0.144067996 0.130707042 -0.144067996 0.130707042 Distance

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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How do I explain why this is a good outcome?
The results of the regression analysis to estimate setup costs using the number of setup hours are as follows: SUMMARY OUTPUT Regression Statistics Multiple R 0.965127 R Square 0.931469 Adjusted R Square 0.921679 Standard Error 285.6803 Observations 9 ANOVA df SS MS F Regression 1 7765004 7765004 95.14395 Residual 7 571292.5 81613.21 Total 8 8336296 Coefficients Std Error t Stat P-value Intercept 2498.644 680.6304 3.671073 0.007953 X Variable 1 2.506915 0.257009 9.754176 2.52E–05 What percentage change (rounded to one decimal place) in setup costs can be explained by changes in setup hours? a.95.1% b.96.5% c.25.7% d.none of these e.93.1%
17. How many observations are there?
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 2.201
DO MALES AND FEMALES DIFFER ON ATTITUDES TOWARD CRIME ? MALES N=12 FEMALES N=13 CRIME ATTITUDE MALES 77, 97, 28, 55, 75, 43, 21, 70, 91, 22, 58, 45 FEMALES 32,31, 89, 21, 79, 67, 41, 24, 43, 87, 64, 28, 21 MEAN SD MALE FEMALE • DV= 1 •T-VALUE • df • SIGNIFICANT (Y/N). • TEST STATISTIC RAN • ACCEPT OR REJECT NULL HYPOTHESIS. • CONCLUSION:
In 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
Using the Excel output reported above, if we were to test to see whether "Attendance" is statistically significantly associated with "Score received on the exam," we would conclude that we should Regression Statistics Multiple R R Square Standard Error Observations Intercept Attendance 0.142620229 0.02034053 20.25979924 22 Coefficients Standard Error 39.39027309 37.24347659 0.340583573 0.52852452 T Stat 1.057642216 0.644404489 P-value 0.302826622 0.526635689 O Reject the null hypothesis and conclude that Attendance IS statistically significantly associated with "Score received on the exam" Reject the null hypothesis and conclude that Attendance is is NOT statistically significantly associated with "Score received on the exam" Accept the null hypothesis and conclude that Attendance IS statistically significantly associated with "Score received on the exam" Fail to reject the null hypothesis and conclude that we cannot say that Attendance is statistically significantly associated with…
Based on the ANOVA table given, is there enough evidence at the 0.05 level of significance to conclude that the linear relationship between the independent variables and the dependent variable is statistically significant? Source df SS Regression 2 984.715358 Residual 7 298.884642 Total 9 1283.600000 ANOVA MS F 492.357679 11.531217 42.697806 Significance F 0.006092 Copy Data Ĵ Based on the ANOVA table given, is there enough evidence at the 0.05 level of significance to conclude that the linear relationship between the independent variables and the dependent variable is statistically significant? Source df SS Regression 2 984.715358 Residual 7 298.884642 Total 9 1283.600000 ANOVA MS F Significance F 492.357679 11.531217 0.006092 42.697806 Copy Data
Chapter 9, Section 2, Exercise 032 Use the following ANOVA table for regression to answer the questions. Response: Y Source DF Sum Sq Mean Sq F-value Pr(>F) Regression 1 355.21 355.21 14.76 0.000 Residual Error 359 8641.13 24.07 Total 360 8996.34 Give the F-statistic and p-value.Enter the exact answers.The F-statistic is=The p-value is=
The regional manager of a franchise business is interested in understanding how income in a region affects sales. Below is a regression output for sales ($’000) regressed on the average household income of an area ($’000) Linear Fit Sales = 14.5774 + 2.9048*Income Summary of Fit RSquare 0.9683 RSquare Adj 0.9630 Root Mean Square Error 3.1083 Mean of Response 43.6250 Analysis of Variance Source DF Sum of Squares Mean Square F Ratio Model 1 1771.9048 1771.90 183.3946 Error 6 57.9702 9.66 Pro>F C. Total 7 1829.8750 ItI Intercept 14.5774 2.4101 6.05 0.0009* Income 2.9098 0.2145 13.54 < 0.0001* Answer the following questions: (i) What is the average sales across all regions? (ii) Interpret the slope of regression (iii) What is the prediction of the value of sales in a region with an average…