An R-square of 0.052 means that:
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Does Job stress influence overall life satisfaction?
A sample of 7,814 people were surveyed, and this is the result of the
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|
Mean |
Std. Deviation |
N |
|
Life satisfaction |
7.97 |
1.194 |
7817 |
|
Job stress |
3.16 |
1.667 |
7817 |
|
Model Summaryb |
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|
Model |
R |
R Square |
Adjusted R Square |
Std. Error of the Estimate |
Change Statistics |
||||
|
R Square Change |
F Change |
df1 |
df2 |
Sig. F Change |
|||||
|
1 |
.228a |
.052 |
.052 |
1.162 |
.052 |
427.062 |
1 |
7815 |
.000 |
|
a. Predictors: (Constant), Job stress |
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b. Dependent Variable: Life satisfaction |
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ANOVAa |
||||||
|
Model |
Sum of Squares |
df |
Mean Square |
F |
Sig. |
|
|
1 |
Regression |
577.130 |
1 |
577.130 |
427.062 |
.000b |
|
Residual |
10561.161 |
7815 |
1.351 |
|
|
|
|
Total |
11138.291 |
7816 |
|
|
|
|
|
a. Dependent Variable: Life satisfaction |
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|
b. Predictors: (Constant), Job stress |
|
Coefficientsa |
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|
Model |
Unstandardized Coefficients |
Standardized Coefficients |
t |
Sig. |
95.0% Confidence Interval for B |
|||
|
B |
Std. Error |
Beta |
Lower Bound |
Upper Bound |
||||
|
1 |
(Constant) |
8.485 |
.028 |
|
301.319 |
.000 |
8.430 |
8.541 |
|
Job stress |
-.163 |
.008 |
-.228 |
-20.665 |
.000 |
-.178 |
-.148 |
|
|
a. Dependent Variable: Life satisfaction |
Question: An R-square of 0.052 means that:
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- Does Job stress influence overall life satisfaction? A sample of 7,814 people were surveyed, and this is the result of the regression analysis: Descriptive Statistics Mean Std. Deviation N Life satisfaction 7.97 1.194 7817 Job stress 3.16 1.667 7817 Model Summaryb Model R R Square Adjusted R Square Std. Error of the Estimate Change Statistics R Square Change F Change df1 df2 Sig. F Change 1 .228a .052 .052 1.162 .052 427.062 1 7815 .000 a. Predictors: (Constant), Job stress b. Dependent Variable: Life satisfaction ANOVAa Model Sum of Squares df Mean Square F Sig. 1 Regression 577.130 1 577.130 427.062 .000b Residual 10561.161 7815 1.351 Total 11138.291 7816 a. Dependent Variable: Life satisfaction b. Predictors: (Constant), Job stress Coefficientsa Model Unstandardized…For an ANOVA test of significance of a regression model with 10 regressor variables and 50 observations, what is the degree of freedom of the SSr? choices 11 10 39 49Paul Consultancy took a random sample of the monthly office rents per square meter and the percentage of vacancy office space in fifteen different cities. The results are shown in the table: (Use 3 decimal places) Vacancy rate Monthly rent 15 8000 12 7500 11 5300 8 6200 6 4500 10 5000 9 6000 9 6500 12 7000 20 10000 21 9500 15 9000 8 7500 9 8800 20 11000 Determine the following: a: ? b: ? r: ? r2: ? MS regression: ? MS residual: ? F ratio: ? F0.05: ?
- In an ANOVA table for a multiple regression analysis, the global test of significance is based on the _________. Select one: a. Regression mean square divided by the mean square error b. Treatment mean square and block mean square c. Treatment mean square divided by the error variation d. Block and error variationSarah is the office manager for a group of financial advisors who provide financial services forindividual clients. She would like to investigate whether a relationship exists between the numberof presentations made to prospective clients in a month and the number of new clients per month.The following table shows the number of presentations and corresponding new clients for aa random sample of six employees.Employee Presentations New Clients1 7 22 9 33 9 44 10 35 11 56 12 3Sarah would like to use simple regression analysis to estimate the number of new clients permonth based on the number of presentations made by the employee per month. The test statisticfor testing the hypothesis that the population coefficient of determination is greater than zero is________. A) 1.03 B) 1.45 C) -1.16 D) 0.35. A professor at the University of Alabama was interested in evaluating the relationship between family support and delinquency. Using data collected on 4545 families, the researcher used regression to analyze the relationship. The results are presented below. Variables Entered/Removeda Model Variables Entered Variables Removed Method 1 Family supportb . Enter a. Dependent Variable: Delinquency b. All requested variables entered. Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .249a .062 .062 1.59168 a. Predictors: (Constant), Family support ANOVAa Model Sum of Squares df Mean Square F Sig. 1 Regression 759.204 1 759.204 299.671 <.001b Residual 11479.107 4531 2.533 Total 12238.311 4532 a. Dependent Variable: Delinquency b. Predictors: (Constant), Family support…
- A professor at the University of Alabama was interested in evaluating the relationship between family support and delinquency. Using data collected on 4545 families, the researcher used regression to analyze the relationship. The results are presented below. Variables Entered/Removeda Model Variables Entered Variables Removed Method 1 Family supportb . Enter a. Dependent Variable: Delinquency b. All requested variables entered. Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .249a .062 .062 1.59168 a. Predictors: (Constant), Family support ANOVAa Model Sum of Squares df Mean Square F Sig. 1 Regression 759.204 1 759.204 299.671 <.001b Residual 11479.107 4531 2.533 Total 12238.311 4532 a. Dependent Variable: Delinquency b. Predictors: (Constant), Family support…. A professor at the University of Alabama was interested in evaluating the relationship between family support and delinquency. Using data collected on 4545 families, the researcher used regression to analyze the relationship. The results are presented below. Variables Entered/Removeda Model Variables Entered Variables Removed Method 1 Family supportb . Enter a. Dependent Variable: Delinquency b. All requested variables entered. Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .249a .062 .062 1.59168 a. Predictors: (Constant), Family support ANOVAa Model Sum of Squares df Mean Square F Sig. 1 Regression 759.204 1 759.204 299.671 <.001b Residual 11479.107 4531 2.533 Total 12238.311 4532 a. Dependent Variable: Delinquency b. Predictors: (Constant), Family support…A professor at the University of Alabama was interested in evaluating the relationship between family support and delinquency. Using data collected on 4545 families, the researcher used regression to analyze the relationship. The results are presented below. Variables Entered/Removeda Model Variables Entered Variables Removed Method 1 Family supportb . Enter a. Dependent Variable: Delinquency b. All requested variables entered. Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .249a .062 .062 1.59168 a. Predictors: (Constant), Family support ANOVAa Model Sum of Squares df Mean Square F Sig. 1 Regression 759.204 1 759.204 299.671 <.001b Residual 11479.107 4531 2.533 Total 12238.311 4532 a. Dependent Variable: Delinquency b. Predictors: (Constant), Family support…
- A professor at the University of Alabama was interested in evaluating the relationship between family support and delinquency. Using data collected on 4545 families, the researcher used regression to analyze the relationship. The results are presented below. Variables Entered/Removeda Model Variables Entered Variables Removed Method 1 Family supportb . Enter a. Dependent Variable: Delinquency b. All requested variables entered. Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .249a .062 .062 1.59168 a. Predictors: (Constant), Family support ANOVAa Model Sum of Squares df Mean Square F Sig. 1 Regression 759.204 1 759.204 299.671 <.001b Residual 11479.107 4531 2.533 Total 12238.311 4532 a. Dependent Variable: Delinquency b. Predictors: (Constant), Family support…. A researcher wants to forecast the annual sales of Walmart, based on store size. To examine the relationship between the store size in square feet and its annual sales in million dollars, a sample of 14 stores was selected shown below in the picture : Answer the following: i) Null hypothesis of correlation ii) Coefficient of Correlation and its interpretation iii) Interpret the sig value of ANOVA. iv) Coefficient of Determination and its interpretation v) Write down the Regression Model. vi) Interpret the value of ‘a’ vii) Interpret the value of ‘slope’The following data is a regression model where the U.S. Department of Transportation has tried to relate the rate of fatal traffic accidents (per 1000 licenses) to the percentage of motorists under the age of 21. Data has been collected for 42 major cities in the United States. SUMMARY OUTPUT Regression Statistics Multiple R 0.83938748 R Square 0.70457134 Adjusted R Square 0.69718562 Standard Error 0.58935028 Observations 42 ANOVA df SS MS F Regression 1 33.13441764 33.1344 95.3964 Residual 40 13.89335048 0.34733 Total 41 47.02776812 Coefficients Standard Error t Stat P-value Intercept -1.5974138 0.371671454 -4.2979 0.00010 Percent Under 21 0.28705317 0.029389769 9.76711 3.79E-9…