SUMMARY OUTPUTRegression StatisticsMultiple RR-Square0.971AAdjusted R-Square BStandard Error30.462Observations51ANOVAFSignificance FRegression C 747851.57373925.79402.989.89E-31df SSMSResidual 48 D927.91Total50792391.11StandardLowerUpper95%Coefficientst Stat P-ValueError95%InterceptE62.1326.79 1.60E-301539.661789.51Price of Roses-6.68F-1.411.64E-01-16.162.81Disposable Income(M)9.730.34G1.23E-319.0410.42Determine the intercept coefficient (point E) and whether that estimate isstatistically significant at the 5 percent level.1,664.46 and statistically insignificant since the P-value is less than 5 percent2.32 and statistically insignificant since the t-statistic is less than 2 in absolutevalue2.32 and statistically significant since the t-statistic is greater than 2 in absolutevalueO 1,664.46 and statistically significant since the P-value is less than 5 percent

The t statistic 26.79 is and the standard error is 62.13.

Answered: SUMMARY OUTPUT Regression Statistics…

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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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? ANOVA Source df MS F Significance F Regression 3 406.124504 135.374835 0.849953 0.497712 Residual 10 1592.732639 159.273264 Total 13 1998.857143 Copy Data Answer 田 Tables 国 Keypad Keyboard Shortcuts O Yes O No
|After running the regression analysis between independent and dependent variable the below output is available. SUMMARY OUTPUT Regression Statistics Multiple R 0.714528 R Square 0.510551 Adjusted R Square 0.44937 Standard Error 49.60803 Observations 10 ANOVA ignificance F 1 20536.44 20536.44 8.344902 0.020238 df SS MS F Regression Residual 8 19687.66 2460.957 Total 40224.1 Coefficientsandard Err t Stat P-value Lower 95%Upper 95%ower 95.0%pper 95.0% Intercept -112.208 186.0395 -0.60314 0.563118 -541.216 316.7995 -541.216 316.7995 X Variable 0.398147 0.137827 2.888754 0.020238 0.080319 0.715976 0.080319 0.715976 a. What is the equation of the line? What can you say about the relationship between the two variables?
59
**Please complete table**
Please help interpret this SPSS output of a multiple regression analysis result? DV- To what extent the product safety incident have changed your perceptions of the involved brand(s) IV'S- Trustworthy , Reliability , I don’t know how good the company’s products will be before I buy it. , Preventability
Analysis of Variance results: Where: Age > 5Responses: AgeFactors: Sex Response statistics by factor Sex n Mean Std. Dev. Std. Error female 121 10.490083 4.2222704 0.38384277 male 155 9.0806452 3.0091656 0.241702 ANOVA table Source DF SS MS F-Stat P-value Sex 1 134.98939 134.98939 10.466692 0.0014 Error 274 3533.79 12.897044 Total 275 3668.7794 Tukey HSD results (95% level) female subtracted from Difference Lower Upper P-value male -1.4094375 -2.2670907 -0.55178425 0.0014 In one complete sentence, interpret your confidence interval. Make sure in the interpretation it is clear the direction of the difference (i.e. which gender weighs more and by how much. Explain why a confidence interval provides more information to a tiger researcher than the results of a hypothesis test
Big Time Corporation wanted to determine the relationship between its monthly operating costs and a potential cost driver, machine hours. The output of a regression analysis showed the following information (note: only a portion of the regression analysis results is presented here) SUMMARY OUTPUT Regression Statistics Multiple R 0.998041188 R Square 0.996086212 Adjusted R Square 0.994781616 Standard Error 917.1714273 Observations 5 ANOVA df SS MS F Significance F Regression 1 642276389.7 642276389.7 763.5208906 0.000104039 Residual 3 2523610.281 841203 427 Total 4 644800000 Coefficients Standard Errort Stat P-value Intercept 6679.617454 931.8558049 7.168080532 0.005592948 3714.03639 X Variable 9.796772265 0.354545968 27.63188178 0.000104039 8.66844876 What is variable cost per machine hour (rounded to the nearest cent)? OA. $6,679.62 OB. $917.17 OC. $9.80 OD. $.99 Lower 95% Upper 95.0% 9645.198517 10.92509577
The key assumptions of the ANOVA include homogeneity of variance, normality of population distributions and: Independence of observations The DV must be measured on a nominal level The IV must be measured on a continuous level Variability of residuals
Beachcomber Ltd in a local car dealership that sells used and new vehicles. The manager of the company wants to know how different variables affect the sales of his vehicles. A random sample of yearly data was taken with the view to testing the model: SALES=a+BAGE+YMIL+SENG Where SALES= amount that a vehicle is sold for($000's), AGE = age of the vehicle, MIL= the total mileage of the vehicle at the point of sale and ENG = the size of the engine. The sample of data was processed using MINITAB and the following is an extract of the output obtained:
Using the previous regression model, is there a statistically significant relationship between GPA and Salary Expectation at the 5% significance level? Enter the word "relationship" if a relationship exists and "no relationship" if no relationship exists
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.
WALKING RUNNING Mean 7.181818182 7.3636364 Variance 4.563636364 6.4545455 Observations 11 11 Pooled Variance 5.509090909 Hypothesized Mean Difference 0 df 20 t Stat -0.181668105 P(T<=t) one-tail 0.428835886 t Critical one-tail 1.724718243 P(T<=t) two-tail 0.857671772 t Critical two-tail 2.085963447 If the t-Stat value is less than the t Critical two-tail value I can reject the null, right? I am still a little confused on what a few numbers represent.

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