f 1. In a simple linear regression problem with 10 observations (sample size = n = 10). There are 6 unique observations. ANOVA table is gathered to see if simple regression equation is significant. Another ANOVA test is conducted to see if linear relationship is adequate. For these two problems degrees of freedom total, regression, error, lack of fit, pure error are: 2. 3. 3. 3 categories and 10 observations per category, SSE = 399.6, MSE = a. 133.2 b. 13.32 c. 14.8 d. 30.0 4. 5. Refer to Exhibit 14-1. The least squares estimate of equals 6. 7. Exhibit 13-1 Refer to Exhibit 13-1. The mean square within treatments (MSE) equals 8. Refer to Exhibit 13-1. The null hypothesis is to be tested at the 5% level of significance. The critical value from the table is 9. Refer to Exhibit 13-3. The test statistic to test the null hypothesis equals 10. Refer to Exhibit 13-3. The mean square between treatments (MSTR) equals 11. Refer to Exhibit 13-1. The test statistic to test the null hypothesis equals 12. Refer to Exhibit 13-1. The null hypothesis 13. 14. 15. Refer to Exhibit 14-1. The point estimate of when = 20 is Coefficient of determination = +1 16. Refer to Exhibit 14-1. Interpret the y-intercept. 17. Refer to Exhibit 14-2. The least squares estimate of equals 18
. Refer to Exhibit 14-2. The coefficient of determination equals 19. Refer to Exhibit 14-3. Based on the above estimated regression equation, if advertising is $3,000, then the point estimate for sales (in dollars) is 20. Refer to Exhibit 14-3. The statistic for testing the significance of the slope is 21. 22. 23. 24. 25. 26. 27. 28. 29. 30. 31. 32. 33. 34. 35. NEW SUBMISSION 2. 3. 4. 5. Refer to Exhibit 14-1. The least squares estimate of equals 8. Refer to Exhibit 13-1. The mean square between treatments (MSTR) equals 9. Refer to Exhibit 13-3. The mean square within treatments (MSE) equals 13. 15. Exhibit 14-1A regression analysis resulted in the following information regarding a dependent variable ( ) and an independent variable ( ). Sum (x) = 30 Sum (x ) = 104 Sum (y) = 40 Sum (y ) = 178 Sum (x)(y) = 134 Refer to Exhibit 14-1. Interpret the slope (b ) 17. Refer to Exhibit 14-2. The least squares estimate of equals 19. Refer to Exhibit 14-3. The statistic computed from the above data is 20. In an analysis of variance problem if SST = 120 and SSTR = 80, then SSE is 21. 22. Refer to Exhibit 14-1. The coefficient of determination equals 23. Refer to Exhibit 14-3. Using = 0.05, the critical value for testing the significance of the slope is