Answer (a): It can be observed from the scatter plot that the data or dots makes a straight line approximately. So it can be said that the data follows the linear trend and thus the relation between variables is linear.Answer (c): The slope of the equation is 1.0817 which gives the change in sales when year of experience is increased by 1. So if experience is increased by 1 year then sales will increased by 1.0817answer from (d) The general manager of a chain of furniture stores believes that experience is the most important factor indetermining the level of success of a salesperson. To examine this belief she records last month's sales (in$1,000s) and the years of experience of 10 randomly selected salespeople. These data are listed below.SalespersoYears ofSalesЕxperience179.31020431558186.5147122087179.2030101525Scatter plot of Sales ($1000) vs Yea rs of ex perience30-252010.1020Years at expenence What does the scatter diagram above tell you about the relationship between years of experienceand sales ($1000)?b).Determine the least squares regression line.c).Interpret the value of the slope of the regression line.d).Construct the 95% confidence interval for the slope of the population regressionequation. Does it suggest there is a linear relationship between years of experience and sales?e).about the two variables.Determine the coefficient of determination and discuss what its value tells youDiscussf).Calculate the coefficient of correlation.r =What sign does it have? Why?g).Conduct a test of the population coefficient of correlation to determine at the 5%significance level whether a linear relationship exists between years of experience and sales.

“Since you have posted a question with multiple sub-parts, we will solve the first three sub-parts for you. To get the remaining sub-part solved please repost the complete question and mention the sub-parts to be solved.” (d) t statistics for Slope = 12.825. The critical value of T i.e. Tc at 95% level of significance and df = 8 is 2.306002. From the above result, we see that t statistics >> Tc, and hence we Reject the Null Hypothesis and conclude that the slope of the Years of Exp variable is significantly different from zero.(e) R2 = 0.9536, this means that 95.36% of the variability in Sales can be explained by Exp of Salesman alone. Only 4.64% of variability remains still unexplained which is quite a small portion. This also tells us that there is a strong and significant linear relationship b/w the 2 variables. (f) r = 0.9765, this value tells us that there is a high correlation b/w the Actual and Predicted Values of Sales Variables. This also tells how good our linear regression model fits our Data.