Concept explainers
The article “The Analysis and Selection of Variables in Linear Regression” (Biometrics [1976]: 1–49) reports on an analysis of data taken from issues of Motor Trend magazine. The dependent variable y was gas mileage and there were n = 32 observations. The independent variables were x1 = Engine type (1 = straight, 0 = V), x2 = number of cylinders, x3 = Transmission type (1 = manual, 0 = automatic), x4 = Number of transmission speeds, x5 = Engine size, x6 = Horsepower, x1 = Number of carburetor barrels, x8 = Final drive ratio, x9 = Weight, and x10 = Quarter-mile time. The R2 and adjusted R2 values are given in the accompanying table for the best model using k predictors for k = 1,…, 10.
Which model would you select? Explain your choice and the criteria used to reach your decision.
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Chapter 14 Solutions
Introduction To Statistics And Data Analysis
- Sam Jones has 2 years of historical sales data for his company. He is applyingfor a business loan and must supply his projections of sales by month for thenext 2 years to the bank. a. Using the data from Table 6–12, provide a regression forecast for timeperiods 25 through 48.b. Does Sam’s sales data show a seasonal pattern?arrow_forwardQ2) Convert the data in table below into information using regression approach. X 1 2 3 4 5 6 Y 6 1 9 5 17 12arrow_forward-Using the data in Table 6–11, answer the following: What is the slope? What is the intercept? Write the regression equation. Calculate a regression forecast for month 25.arrow_forward
- Create two new independent variables: Top 2–5 and Top 6–10. Top 2–5 represents the number of timesthe driver finished between second and fifth place and Top 6–10 represents the number of times thedriver finished between sixth and tenth place. Develop an estimated regression equation that can beused to predict Winnings ($)using Poles, Wins, Top 2–5, and Top 6–10. Test for individual significanceand discuss your findings and conclusions. - Please do not hand write response. thank you Driver Points Poles Wins Top 5 Top 10 Winnings ($) Tony Stewart 2403 1 5 9 19 6,529,870 Carl Edwards 2403 3 1 19 26 8,485,990 Kevin Harvick 2345 0 4 9 19 6,197,140 Matt Kenseth 2330 3 3 12 20 6,183,580 Brad Keselowski 2319 1 3 10 14 5,087,740 Jimmie Johnson 2304 0 2 14 21 6,296,360 Dale Earnhardt Jr. 2290 1 0 4 12 4,163,690 Jeff Gordon 2287 1 3 13 18 5,912,830 Denny Hamlin 2284 0 1 5 14 5,401,190 Ryan Newman 2284 3 1 9 17 5,303,020 Kurt Busch 2262 3 2 8 16 5,936,470 Kyle Busch…arrow_forwardYou work as a data scientist for a real estate company in a seaside resort town. Your boss has asked you to discover if it's possible to predict how much a home's distance from the water affects its selling price. You are going to collect a random sample of 7 recently sold homes in your town. You will note the distance each home is from the water (denoted by x, in km) and each home's selling price (denoted by y, in hundreds of thousands of dollars). You will also note the product x.y of the distance from the water and selling price for each home. (These products are written in the row labeled "xy"). (a) Click on "Take Sample" to see the results for your random sample. Distance from the water, .x (in km) Take Sample Selling price, y (in hundreds of thousands of dollars) xy Send data to calculator Based on the data from your sample, enter the indicated values in the column on the left below. Round decimal values to three decimal places. When you are done, select "Compute". (In the table…arrow_forwardA statistical program is recommended. Car manufacturers produced a variety of classic cars that continue to increase in value. Suppose the following data is based upon the Martin Rating System for Collectible Cars, and shows the rarity rating (1–20) and the high price ($1,000) for 15 classic cars. (b) Develop an estimated multiple regression equation with x = rarity rating and x2 as the two independent variables. (Round b0 and b1 to the nearest integer and b2 to one decimal place.) (c) Consider the nonlinear relationship shown by equation (16.7): E(y) = β0β1x Use logarithms to develop an estimated regression equation for this model. (Round b0 to three decimal places and b1 to four decimal places.)arrow_forward
- Create two new independent variables: Top 2–5 and Top 6–10. Top 2–5 represents the number of times the driver finished between second and fifth place and Top 6–10 represents the number of times the driver finished between sixth and tenth place. Develop an estimated regression equation that can be used to predict Winnings ($)using Poles, Wins, Top 2–5, and Top 6–10. Test for individual significance and discuss your findings and conclusions. Please do not hand write. thank you Driver Points Poles Wins Top 5 Top 10 Winnings ($) Tony Stewart 2403 1 5 9 19 6,529,870 Carl Edwards 2403 3 1 19 26 8,485,990 Kevin Harvick 2345 0 4 9 19 6,197,140 Matt Kenseth 2330 3 3 12 20 6,183,580 Brad Keselowski 2319 1 3 10 14 5,087,740 Jimmie Johnson 2304 0 2 14 21 6,296,360 Dale Earnhardt Jr. 2290 1 0 4 12 4,163,690 Jeff Gordon 2287 1 3 13 18 5,912,830 Denny Hamlin 2284 0 1 5 14 5,401,190 Ryan Newman 2284 3 1 9 17 5,303,020 Kurt Busch 2262 3 2 8 16 5,936,470 Kyle Busch 2246 1 4 14…arrow_forwardQ: The dataset posted below lists a sample of months and the advertising budget (in hundreds of dollars) for TV, radio and newspaper advertisements. Also included is whether a coupon was published for that month and the resulting sales (in thousands of dollars). a) Develop a multiple regression model predicting the sales based off the four predictor variables: TV, radio, and newspaper advertising budget and whether a coupon is used. Recode Coupon as 0 = No and 1 = Yes. Report the estimated regression equation (Solve in Excel) TV ($100) radio ($100) newspaper ($100) Coupon sales ($1000) 0.7 39.6 8.7 No 1.6 230.1 37.8 69.2 No 22.1 4.1 11.6 5.7 Yes 3.2 44.5 39.3 45.1 No 10.4 250.9 36.5 72.3 No 22.2 8.6 2.1 1 No 4.8 17.2 45.9 69.3 Yes 9.3 104.6 5.7 34.4 No 10.4 216.8 43.9 27.2 Yes 22.3 5.4 29.9 9.4 No 5.3 69 9.3 0.9 No 9.3 70.6 16 40.8 No 10.5 151.5 41.3 58.5 No 18.5 195.4 47.7 52.9 Yes 22.4 13.1 0.4 25.6 Yes 5.3 76.4 0.8…arrow_forwardThe data from the table below gives a regression that is a) reliable. b) unreliable. c) unable to determine the reliability.arrow_forward
- A highway department is studying the relationship between traffic flow and speed. The following model has been hypothesized: y = ?0 + ?1x + ? where y = traffic flow in vehicles per hour x = vehicle speed in miles per hour. The following data were collected during rush hour for six highways leading out of the city. Traffic Flow(y) Vehicle Speed(x) 1,258 35 1,329 40 1,227 30 1,336 45 1,348 50 1,125 25 In working further with this problem, statisticians suggested the use of the following curvilinear estimated regression equation. ŷ = b0 + b1x + b2x2 (a) Develop an estimated regression equation for the data of the form ŷ = b0 + b1x + b2x2.(Round b0 to the nearest integer and b1 to two decimal places and b2 to three decimal places.) ŷ = ?? (b) Use ? = 0.01 to test for a significant relationship. State the null and alternative hypotheses. -H0: One or more of the parameters is not equal to zero.Ha: b0 = b1 = b2 = 0 -H0: b0 = b1 = b2 = 0Ha: One or more…arrow_forwardFind the new data point (x,y) in which x=2 from the data points (1.3) and (4.12)arrow_forwardThe table contains data on vehicle speed (h) and fuel consumption (lt / 100km) of 5 randomly selected vehicles. Estimate the average fuel consumption of a vehicle traveling at 45 km / h using the simple linear regression equation between vehicle speed and fuel consumption. Speed 55 60 65 70 75 Consumption 11 10 9 8 7 Please choose one: a. 6 b. 5 c. 13 D. 8arrow_forward
- Functions and Change: A Modeling Approach to Coll...AlgebraISBN:9781337111348Author:Bruce Crauder, Benny Evans, Alan NoellPublisher:Cengage Learning