Question

Can someone help me with this last question?

I cant seem to get the right answer.

Consumer Reports tested 19 different brands and models of road, fitness, and comfort bikes. Road bikes are designed for long road trips; fitness bikes are designed for regular workouts or daily commutes; and comfort bikes are designed for leisure rides on typically flat roads. The following data show the type, weight (lb.), and price (S) for the 19 bicycles tested (Consumer Reports website, February 2009). Click on the datafile logo to reference the data. DATA file Weight Price() 1800 1800 1000 1300 1320 1050 1350 Brand and Model Klein RA've v Giant OCR Composite 3 Giant OCR Specialized Roubaix Trek Pilot 2.1 Cannondale Svnapse 4 LeMond Poprad Raleigh Cadent 1.0 Giant FCR3 Schwinn Super Sport GS Fuji Absolute 2.0 Jamis Coda Comp Cannondale Road Warrior 400 21 21 24 Road Road Road Fitness Fitness Fitness 24 700 Comfort Comfort Comfort Comfort Comfort Comfort Mongoose Switchback SX Giant Sedona DX Jamis Explorer 4.0 Diamondback Wildwood Deluxe Specialized Crossroads Sport 32 32 35 350 330 31 d. To account for possible interaction between the type of bike and the weight of the bike, develop a new estimated regression equation that can be used to predict the price of the bike given the type, the weight of the bike, and any interaction between weight and each of the dummy variables defined in part (c). What estimated regression equation appears to be the best predictor of price? Please round to four decimal places. У 2596.2834 WxC 460.6952 Weight 10475.255 Type_Fitness 247.0232Type_Comfort+1000.4587 x22.6923

Answer 1

Step 1: Choose Data > Data Analysis Step 2: Select Regression and Click Ok. Step 3: Select the cells A1: A20 under Input Y Range Step 4: Select the cells B1: C20 under Input X Range Step 5: Enter 0.05 as Alpha Step 6: Click Ok. EXCEL output: SUMMARY OUTPUT n Stotistics Multiple R R Square Adjusted R Square Standard Error 0.852617391 0.726956416 0.692825968 264.6751295 19 ANOVA 5S MS 2 2984163.739 1492081.87 21.29935169 3.08927E-05 16 1120846.787 70052.9242 18 4105010.526 Residual Total Coefficients Standard Error 2145.695972 398.7365333 5.381237465 6.11814E-05 1300.412282 2990.979662 1300.412282 2990.979662 471.5712127 177.0032408-2.664195359 0.0169703 "846.8013208-96.34110457 ·846.3013208-9634110457 39.85877326 17.92372611 -2.22379950S 0.040910618 -77.85537522 1.862171287 -77.85537522 -1.862171287 t Stet p-value r 95% Lower 95.0% 95.0 Lower 95% Type From EXCEL output, the regression equation is Price -2145.6959-471.5712Type -39.8587 Weight Here, the coefficient of determination is 0.7269. That means 72.69% ofthe variance in the price is being explained by the type and weight