(Solved) You obtain the following regression statistics for the relationship between defect rate and volume at one of your plants. You have a random sample
Question: You obtain the following regression statistics for the relationship between defect rate and volume at one of your plants. You have a random sample of the results from 160 shifts at the plant.
| Model | R | R Square | Adjusted R Square | Std. Error of the estimate |
| 1 | .740 | .548 | .545 | 4.92 |
| Model | Sum of Squares | Df | Mean Square | F | Sig. | |||||
| 1 | Regression Residual Total | 4647.124 3829.839 8476.963 |
1
158 159 |
4647.124
24.239 |
191.717 | .000 | ||||
| Model | Un-standardized Coefficients | Standardized Coefficients | t | Sig. | ||
| 1 | B | Std. Error | Beta | |||
| (Constant) VOLUME | -97.073 |
7.819
.002 |
740 | 50.995 13.846 | .000 .000 | |
- What are the null and alternative hypotheses?
- What is the population of interest? What is the sample?
- On basis of the output, what can you conclude about the null hypothesis?
- Can you reject null hypothesis that the slope is 0?
- Can you reject the null hypothesis that there is no linear relationship between the dependent and independent variables?
- Can you reject the null hypothesis that the population correlation coefficient is 0?
- What would you predict the defect range to be on a day when the volume is 4200 units? What would you predict the average defect rate to be for all days with production volumes of 4200?
- In what way do the two estimates of the defect rate in question 3g differ? (Calculations not required).
Deliverable: Word Document 
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