In exercise 7, a sales manager collected the following data on $ y = $ annual sales and $ x = $ years of experience. The estimated regression equation for these data is $ \hat{y} = 80 + 4x $.### Data Table| Salesperson | Years of Experience | Annual Sales ($1000s) ||-------------|---------------------|-----------------------|| 1 | 1 | 80 || 2 | 2 | 97 || 3 | 3 | 92 || 4 | 4 | 102 || 5 | 4 | 103 || 6 | 6 | 111 || 7 | 8 | 119 || 8 | 10 | 123 || 9 | 11 | 117 || 10 | 13 | 136 |### Tasksa. Compute SST, SSR, and SSE.- **SSE** (Sum of Squares for Error) \[ \_\_\_\_\_\_\_\]- **SST** (Total Sum of Squares) \[ \_\_\_\_\_\_\_\]- **SSR** (Sum of Squares for Regression) \[ \_\_\_\_\_\_\_\]b. Compute the coefficient of determination $ r^2 $.- $ r^2 $: \[ \_\_\_\_\_\_\_\] %Does this least squares line provide a good fit?- Select: [ Yes / No ]c. What is the value of the sample correlation coefficient (to 2 decimals)?- Sample correlation coefficient: \[ \_\_\_\_\_\_\_\] This information is useful for understanding the relationship between a salesperson's experience and their annual sales, as well as how well the regression model fits the observed data.

(a) Use EXCEL to determine the values of SST, SSR and SSE. EXCEL procedure: Go to EXCEL Go to Data>Data Analysis. Choose Regression. Input Y range as $A$1:$A$11. Input X range as $B$1:$B$11 Check the option Labels in First row. Click OK. EXCEL output: From the EXCEL output, The value of SSE is 170. The value of SST is 2442. The value of SSR is 2272. (b) Obtain the value of coefficient of determination. From the EXCEK output, the value of coefficient of determination is 0.9304 Thus, the value of coefficient of determination is 93.04%. Determine whether the least squares line provides the good fit or not. The regression equation provides a good fit to the data or not is determined below as follows: If the value of R square near to 1, then fit of the data model is good. If the value of R square near to 0, then fit of the data model is not good. Since, the value of R-square is 0.9304 which is very far away from 0 and very near to the value of 1. It provides a good fit as a large proportion of the variability in y that has been explained by the least squares line. Moreover the value is approaching to the value of 1 it can be concluded that the regression equation provides a good fit to the data. (c ) Determine the value of sample correlation coefficient. The value of sample correlation coefficient is obtained below as follows: The required value is, Thus, the value of sample correlation coefficient is 0.96.