# Question: The data set below shows a sample of salaries for

The data set below shows a sample of salaries for 39 engineers employed by the Solnar Company along with each engineer's years of experience.

(a) Construct a scatter plot using Salary as the response variable and Years as the explanatory variable. Describe the shape of the scatter plot. Does it appear that a nonlinear model would be appropriate? Explain.

(b) Run the regression with Salary as the response variable and Years and YearsSq as the explanatory variables. Report the R2, Fcalc statistic, and p-value. Is the nonlinear model significant?

(c) Report the p-values for both Years and YearsSq. Are these variables significant predictors? Use 5 .10.

(a) Construct a scatter plot using Salary as the response variable and Years as the explanatory variable. Describe the shape of the scatter plot. Does it appear that a nonlinear model would be appropriate? Explain.

(b) Run the regression with Salary as the response variable and Years and YearsSq as the explanatory variables. Report the R2, Fcalc statistic, and p-value. Is the nonlinear model significant?

(c) Report the p-values for both Years and YearsSq. Are these variables significant predictors? Use 5 .10.

## Answer to relevant Questions

Observations are taken on sales of a certain mountain bike in 30 sporting goods stores. The regression model was Y = total sales (thousands of dollars), X1 = display floor space (square meters), X2 = competitors' advertising ...Which violations of regression assumptions, if any, do you see in these residual diagnostics? Explain. Use the standard error to construct an approximate prediction interval for Y. Based on the width of this prediction interval, would you say the predictions are good enough to have practical value? In a study of paint peel problems, a regression was suggested to predict defects per million (the response variable). The intended predictors were supplier (four suppliers, coded as binaries) and substrate (four materials, ...A researcher used stepwise regression to create regression models to predict CarTheft (thefts per 1,000) using four predictors: Income (per capita income), Unem (unemployment percent), Pupil/Tea (pupil-to-teacher ratio), and ...Post your question