An automobile rental company wants to predict the yearly maintenance expense (Y) for an automobile using the number of miles driven during the year () and the age of the car (, in years) at the beginning of the year. The company has gathered the data on 10 automobiles and run a regression analysis with the results shown below:.   Summary measures       Multiple R 0.9689       R-Square 0.9387       Adj R-Square 0.9212       StErr of Estimate 72.218                 Regression coefficients         Coefficient Std Err t-value p-value Constant 33.796 48.181 0.7014 0.5057 Miles Driven 0.0549 0.0191 2.8666 0.0241 Age of car 21.467 20.573 1.0434 0.3314 Use the information above to estimate the annual maintenance expense for a 10 years old car with 60,000 miles.

A regression was run to determine if there is a relationship between hours of TV watched per day (x) and number of situps a person can do (y).The results of the regression were:y=ax+b a=-1.309 b=28.368 r2=0.4096 r=-0.64 Use this to predict the number of situps a person who watches 3 hours of TV can do (to one decimal place)

The accompanying data are the number of wins and the earned run averages (mean number of earned runs allowed per nine innings pitched) for eight baseball pitchers in a recent season. Find the equation of the regression line. Then construct a scatter plot of the data and draw the regression line. Then use the regression equation to predict the value of y for each of the given x-values, if meaningful. If the x-value is not meaningful to predict the value of y, explain why not. (a) x = 5 wins (c) x = 19 wins (d) x = 15 wins (b)x= 10 wins Click the icon to view the table of numbers of wins and earned run average. .. The equation of the regression line is y=x+ X+ (Round to two decimal places as needed.) Construct a scatter plot of the data and draw the regression line. Choose the correct graph below. OA. B. O C. O D. AERA AERA 6+ Q AERA 6+ 4- 4- 2- 2- 0- 6 12 18 24 0 12 18 24 6 12 18 24 Wins 12 18 24 Wins Wins Wins (a) Predict the ERA for 5 wins, if it is meaningful. Select the correct…

The following table gives the data for the grades on the midterm exam and the grades on the final exam. Determine the equation of the regression line, yˆ=b0+b1x�^=�0+�1�. Round the slope and y-intercept to the nearest thousandth. Grades on Midterm and Final Exams Grades on Midterm 7171 6262 7878 9494 8383 8181 8080 9494 8585 6262 Grades on Final 8888 7979 8888 9191 8080 7070 7171 9393 6565 7777

**Based on the regression results, answer the following questions** Data are collected on the income (measured in $1000’s of $’s), house value (measured in $1000’s of $’s), years in a home, age, sex (=1 for male, =0 for female), and the amount outstanding on a mortgage (measured in $1000’s of $’s). The dependent variable is income. Based on the regression results, answer the following questions e) Are any of the explanatory (independent) variables significant at the 10% level of significance? How do you know? f) What is the predicted value of income for a person that has been in a house for 5 years, is 38 years old, is a male, has a house valued at $250,000, and has $100,000 left on their mortgage? (g) What is the effect on the income of a person being a male versus a female?

Does how long young children remain at the lunch table help predict how much they eat? Twenty toddlers were observed over several months at a nursery school. "Time" is the average number of minutes a child spent at the table when lunch was served. "Calories" is the average number of calories the child consumed during lunch, calculated from careful observation of what the child ate each day. A regression was run and the results were: = 114 + 2.4*x R² = 0.68 r = 0.822 Predict the number of calories a toddler consumes who remains at the table for 14.5 minutes (round your answer to two decimal places)

A regression was run to determine if there is a relationship between hours of TV watched per day (x) and number of situps a person can do (y).The results of the regression were: y=ax+b a=-0.609 b=27.646 r2=0.910116 r=-0.954 Use this to predict the number of situps a person who watches 12 hours of TV can do (to one decimal place)

The table below shows the average weekly wages​ (in dollars) for state government employees and federal government employees for 10 years. Construct and interpret a 98% prediction interval for the average weekly wages of federal government employees when the average weekly wages of state government employees is​ $878.The equation of the regression line is y=1.342x+55.816. Wages [state]- 712, 746, 786, 803, 841, 879, 918, 926, 954, 979 Wages [federal]- 1,024, 1,048, 1,098, 1,135, 1,195, 1,240, 1,270, 1,299, 1,340, 1,376 Construct and interpret a 98​% prediction interval for the average weekly wages of federal government employees when the average weekly wages of state government employees is $878. Select the correct choice below and fill in the answer boxes to complete your choice. (Round to the nearest cent as​ needed.)     PICK THE CORRECT ANSWER [A] We can be 98​% confident that when the average weekly wages of state government employees is $878​, the average weekly wages of…

A regression was run to determine if there is a relationship between hours of TV watched per day (x) and number of situps a person can do (y).The results of the regression were:y=ax+b a=-0.96 b=35.944 r2=0.736164 r=-0.858 Use this to predict the number of situps a person who watches 10.5 hours of TV can do (to one decimal place)

Below is some of the regression output from a regression of the amount various customers paid for a new car (expressed in dollars) versus the age of the customer (expressed in years), the number of previous cars the customer had purchased from the dealership in the past, a dummy variable indicating the gender of the customer (=1 for a Man and = 0 for a woman), and an interactive term the multiplies the age of the customer with the gender dummy variable.  Regression Statistics           Multiple R 0.963           R Square             Adjusted R Square             Standard Error             Observations 20                         ANOVA               df SS MS F Significance F Regression   24686354.49 6171589 47.8 2.3289E-08   Residual     129243       Total   26625000…