In determining any significant relationship among X1, X2 and Y, I find that in every case, the p-value equals to 0.000. In addition, in all three cases, the t-value does not equal zero. This means because ß1 and ß2 are significant, it can be concluded that the relationship between each variable is indeed, significant.
Question #48 deals with the personnel director for Electronics Associates that has developed the following estimated regression equation relating an employee's score on a job satisfaction test to length of service and wage rate. The equation is: Y = 14.4 - 8.69X1 + 13.5X2 where X1 equals the length of service in years, X2 equals the wage rate in dollars, and y equals to job satisfaction test score meaning that higher scores indicate greater job satisfaction. The portion of the Minitab shows as follows:
(chart)
The x1 coefficient, -8.69 is located within the regression equation given in the text. The r-sq was computed by implementing the formula, SSR/SST meaning that 648.83 divided by 720.0 will result in 90.12%. The R-sq adjusted was determined by applying the following formula: 1-(1-R square) n-1/n-p-1. By substituting the values, I get 1-(1-.9012) 7/5 which gives us the answer in decimals .862. Multiply that by 100 and I get 86.2%. In the ANOVA section, the df error was determine by subtracting the total df from the regression df and I get five as our answer. The SSR was found by subtracting the total SSR from the SS error resulting in SSR as 648.83. The MSR was found by dividing the SSR from the DF.
