An engineer is testing a new car model to determine how its fuel efficiency, measured in L/(10

The​ least-squares regression equation is y=761.7x+13,208 where y is the median income and x is the percentage of 25 years and older with at least a​ bachelor's degree in the region. The scatter diagram indicates a linear relation between the two variables with a correlation coefficient of 0.7483.   Predict the median income of a region in which 20​% of adults 25 years and older have at least a​ bachelor's degree.

The​ least-squares regression equation is y=784.6x+12,431 where y is the median income and x is the percentage of 25 years and older with at least a​ bachelor's degree in the region. The scatter diagram indicates a linear relation between the two variables with a correlation coefficient of 0.7962. Predict the median income of a region in which 25​% of adults 25 years and older have at least a​ bachelor's degree.

Let price denote a price index for the goods sold by a restaurant, advert the amount spent on advertising, sales the sales for the restaurant, and consider the following two regressions First regression: sales = B1 + B2price + B3price? + B4advert + ßsadvert? + e, Second regression: sales = B1 + B2price + B3price? + e We estimate both regressions using a sample of 105 observations. The sum of square residuals (E ê) from the first regression equals 50, while the sum of square Li=1 residuals from the second regression equals 70. Suppose we are interested in testing the null hypothesis that expected sales do not depend on advertising. What is the F- statistic for this null hypothesis? Recall the F-statistic is given by ((SSER - SSEU)/J)/(SSEy/(n – K)). O a. -15 O b. 42 Oc. 21 O d. 20 O e. All other options are incorrect.

Ankit

Consumers are often interested in the fuel efficiency of the vehicles they choose to buy, so much so that they will research the various models they consider buying. Fuel efficiency can depend on a variety of variables. In this analysis, there are 73 automobiles that are popular with consumers. A regression analysis has been performed; the dependent variable is CityMPG (EPA miles per gallon in city driving), and independent variables are Length (vehicle length in inches), Width (vehicle width in inches), Weight (vehicle weight in pounds), and ManTran (1 if manual shift transmission, 0 otherwise). The level of significance is 0.05. Use the following MegaStat output to answer questions about this regression analysis. a. State the regression equation. b. How would CityMPG be affected if the width of a vehicle increased by an inch? c. Estimate the CityMPG for a vehicle with a length of 190 inches, a width of 75 inches, a weight of 4100 pounds, and a manual. Round your answer to the nearest…

For major league baseball teams, do higher player payrolls mean more gate money? Here are data for each of the American League teams in the year 2002. The variable x denotes the player payroll (in millions of dollars) for the year 2002, and the variable y denotes the mean attendance (in thousands of fans) for the 81 home games that year. The data are plotted in the scatter plot below, as is the least-squares regression line. The equation for this line is y = 11.43 + 0.23x. Player payroll, x (in Mean attendance, y (in $1,000,000s) thousands) Anaheim 62.8 28.52 Baltimore 56.5 33.09 40- Boston 110.2 32.72 35 Chicago White Sox 54.5 20.74 30- Cleveland 74.9 32.35 25- Detroit 54.4 18.52 Kansas City 49.4 16.30 15- Minnesota 41.3 23.70 10+ New York Yankees 133.4 42.84 Oakland 41.9 26.79 20 40 60 80 100 120 140 Seattle 86.1 43.70 Player payroll, Тarmpa Bay 34.7 13.21 X (in $1,000,000s) Техas 106.9 29.01 Toronto 66.8 20.25 Send data to calculator Send data to Excel Based on the sample data and…

A recent study showed that the hours a person exercised in a week affected the individual'sresting heart rate. It was computed that r = -.68 and the least squares regression line was?̂ = 83-1.4x, where x is the hours exercised and y is the resting heart rate.   d. What percentage of variability in resting heart rate can be explained by variability inhours exercised?

A trucking company considered a multiple regression model for relating the dependent variable y = total daily travel time for one of its drivers (hours) to the predictors x₁ = distance traveled (miles) and x₂ = the number of deliveries made. Suppose that the model equation is Y = -0.800+ 0.060x₁ +0.900x₂ + e (a) What is the mean value of travel time when distance traveled is 50 miles and four deliveries are made? hr (b) How would you interpret ₁ = 0.060, the coefficient of the predictor x₁? O When the number of deliveries is constant, the average change in travel time associated with a ten-mile (i.e. one unit) increase in distance traveled is 0.060 hours. O The total daily travel time increases by 0.060 hours when the distance traveled increases by 1. O When the number of deliveries is held fixed, the average change in travel time associated with a one-mile (i.e. one unit) increase in distance traveled is 0.060 hours. O The average change in travel time associated with a one-mile (i.e.…

The​ least-squares regression equation is y=784.6x+12,431 where y is the median income and x is the percentage of 25 years and older with at least a​ bachelor's degree in the region. The scatter diagram indicates a linear relation between the two variables with a correlation coefficient of 0.7962.  In a particular​ region, 26.5 percent of adults 25 years and older have at least a​ bachelor's degree. The median income in this region is ​$29,889. Is this income higher or lower than what you would​ expect? Why?