Statistics for Management and Economics 10th Keller 課后答案SME10e_SM_ch18

1、Chapter 1818.1 ab 18.2 ab18.3 a Sales = + Space + Space+ b = 41.15 and = .4068. The models fit is relatively poor.F = 7.54, p-value = .0032. However, there is enough evidence to support the validity of the model.18.4aFirstorder model: a Demand = + Price+ Secondorder model: a Demand = + Price + Price

2、+ Firstorder model:Secondorder model:c The second order model fits better because its standard error of estimate is 5.96, whereas that of the firstorder models is 13.29d .= 766.9 359.1(2.95) + 64.55(2.95)= 269.318.5aFirstorder model: a Time = + Day+ Secondorder model: a Time = + Day + Day+ b Firstor

3、der modelF = 45.48, p-value = 0. The model is valid.Secondorder modelF = 26.98, p-value = .0005. The model is valid.c The secondorder model is only slightly better because its standard error of estimate is smaller.18.6a MBA GPA= + UnderGPA + GMAT + Work + UnderGPAGMAT + bF = 18.43, p-value = 0; = .7

4、90 and = .4674. The model is valid, but the fit is relatively poor.c MBA example = .788 and = .4635. There is little difference between the fits of the two models.18.7 a (Excel output shown below)b At least on is not equal to 0F = 80.65, p-value = 0. There is enough evidence to infer that the model

5、is valid.18.8abc Both models fit equally well. The standard errors of estimate and coefficients of determination are quite similar.18.9ab At least on is not equal to 0F = 5.36, p-value = .0019. There is enough evidence to infer that the model is valid.c 00t = .12, p-value = .9086. There is not enoug

6、h evidence to infer that there is an interaction effect between faceoffs won and penalty minutes differential.18.10a Yield = + Pressure + Temperature + Pressure +Temperature+ Pressure Temperature + b c = 512 and = .6872. The models fit is good. 18.11 The number of indicator variables is m 1 = 5 1 =

7、4.18.12 a= 1 if Catholic= 0 otherwise= 1 if Protestant= 0 otherwiseb= 1 if 8:00 A.M. to 4:00 P.M.= 0 otherwise= 1 if 4:00 P.M. to midnight= 0 otherwisec= 1 if Jack Jones= 0 otherwise= 1 if Mary Brown= 0 otherwise= 1 if George Fosse= 0 otherwise18.13 a Macintoshb IBMc other18.14 1 if B.A. = 0 otherwi

8、se1 if B.B.A. = 0 otherwise1 if B.Sc. or B.Eng. = 0 otherwiseI1: t = -1.54, p-value = .1269I2: t = 2.93, p-value = .0043I3: t = .166, p-value = .8684Only I2 is statistically significant. However, this allows us to answer the question affirmatively.18.15aPrediction: MBA GPA will lie between 8.55 and

9、11.67bPrediction: MBA GPA will lie between 8.15 and 11.3118.16ab Exercise 18.10: = 3.24 + .451Mother + .411Father + .0166Gmothers + .0869GfathersThere are large differences to all the coefficients.c 00t = 5.56, p-value = 0. There is enough evidence to infer that smoking affects longevity.18.17ab At

10、least on is not equal to 0F = 20.43, p-value = 0. There is enough evidence to infer that the model is valid.c 0 0: t = 1.86, p-value = .0713: t = 1.58, p-value = .1232Weather is not a factor in attendance.d0 0t = 3.30, p-value = .0023/2 = .0012. There is sufficient evidence to infer that weekend att

11、endance is larger than weekday attendance.18.18ab0 0t = 1.43, p-value = .1589. There is not enough evidence to infer that the type of commercial affects memory test scores.c Let= 1 if humorous= 0 otherwise= 1 if musical= 0 otherwiseSee Excel output below.d 0 0I1: t = 1.61, p-value = .1130I2: t = 3.0

12、1, p-value = .0039There is enough evidence to infer that there is a difference in memory test scores between watchers of humorous and serious commercials.e The variable type of commercial in parts (a) and (b) is nominal. It is usually meaningless to conduct a regression analysis with such variables

13、without converting them to indicator variables.18.19 ab Let= 1 if morning= 0 otherwise= 1 if early afternoon= 0 otherwisec Model 1:= 6.25 and = .8525.Model 2:= 3.82 and = .9461.The second model fits better.d0 0I1: t = 4.51, p-value = 0. There is enough evidence to infer that the average time to unlo

14、ad in the morning is different from that in the late afternoon.I2: t = 4.47, p-value = .0001. There is enough evidence to infer that the average time to unload in the early afternoon is different from that in the late afternoon.18.20a Let= 1 if no scorecard= 0 otherwise= 1 if scorecard overturned mo

15、re than 10% of the time= 0 otherwisebc = 4.20 and = .5327. The models fit is mediocre.dThere is a high correlation betweenand that may distort the ttests.e =.00012; in this sample for each additional dollar lent the default rate increases by .00012 provided the other variables remain the same.= 4.08

16、; In this sample banks that dont use scorecards on average have default rates 4.08 percentage points higher than banks that overturn their scorecards less than 10% of the time.= 10.18; In this sample banks that overturn their scorecards more than 10% of the time on average have default rates 10.18 p

17、ercentage points higher than banks that overturn their scorecards less than 10% of the time.fWe predict that the banks default rate will fall between 1.39 and 18.49%.18.21 a Let= 1 if welding machine= 0 otherwise= 1 if lathe= 0 otherwiseb = 2.54; in this sample for each additional month repair costs

18、 increase on average by $2.54 provided that the other variable remains constant.= 11.76; in this sample welding machines cost on average $11.76 less to repair than stamping machines for the same age of machine.= 199.4; in this sample lathes cost on average $199.40 less to repair than stamping machin

19、es for the same age of machine.c0 0t = .60, p-value .5531/2 = .2766. There is no evidence to infer that welding machines cost less to repair than stamping machines.18.22a. The coefficient of determination in Exercise 16.107 was .3270. In this model the coefficient of determination is .6385. This mod

20、el is better.bLower prediction limit = 150.5, upper prediction limit = 174.1cLower prediction limit = 162.6, upper prediction limit = 186.3d No, because the width of the prediction intervals are far too wide.18.23ab0 0t = 3.11, p-value = .0025. There is enough evidence to infer that the availability

21、 of shiftwork affects absenteeism. c0 0t = .468, p-value = .6402/2 = .3201. There is not enough evidence to infer that people who work for themselves have lower incomes after removing the effect of age, education, and weekly hours of work.18.25 Let PARTYID3-1 = 1, if Democrat=0, if notPARTYID3-2=1,

22、if Republican= 0, if nota.H0:5 = 0H1:5 0t = 7.24, p-value = 0. There is sufficient evidence to infer that Republicans are more likely than Independents to believe that government should take no action to reduce income differences after removing the effects of age, income, education, and weekly hours

23、.18.26 Let POLVIEWS3-1 = 1, if liberal=0, if notPOLVIEWS3-2=1, if conservative= 0, if nota.H0:5 = 0H1:5 0t = 6.17, p-value = 0. There is sufficient evidence to infer that conservatives are more likely than moderates to believe that government should take no action to reduce income differences after

24、removing the effects of age, income, education, and weekly hours.18.27.H0:2 = 0H1:2 0t = -.778, p-value = .4366. There is not enough evidence to infer that men and women differ in the amount of television per day after removing the effects of age and education.18.28 Let POLVIEWS3-1 = 1, if liberal=0

25、, if notPOLVIEWS3-2=1, if conservative= 0, if nota.H0:5 = 0H1:5 0t = 4.23, p-value = 0. There is sufficient evidence to infer that conservatives are more likely than moderates to believe that people should help themselves after removing the effects of age, income, education, and weekly hours.18.29H0

26、:4 = 0H1:4 0t = 1.22, p-value = .2212. There is not enough evidence to infer that there are differences in mean income between people who work for the government and people who work for private employers after removing the effects of age, education, and weekly hours of work. 18.30 LetRACECAT1 = 1, i

27、f White=0, if notRACECAT2=1, if black= 0, if notCompare whites and othersH0:2 = 0H1:2 0t = 1.42, p-value = .1545Compare blacks and othersH0:3 = 0H1:3 0t = 5.90, p-value = 0.There is enough evidence to infer that there are differences between the blacks and others after removing the effects of age an

28、d education.18.31 Let PARTYID3-1 = 1, if Democrat=0, if notPARTYID3-2=1, if Republican= 0, if nota.H0:5 = 0H1:5 0t = 5.76, p-value = 0. There is sufficient evidence to infer that Republicans are more likely than Independents to believe that people should help themselves after removing the effects of

29、 age, income, education, and weekly hours.18.32H0:2 = 0H1:2 0t = 2.45, p-value = .0144. There is enough evidence to infer that there are differences in income between Americans born in the United States and those born elsewhere after removing the effects of age, education , and weekly hours of work.

30、18.33 = 1 if strong= 0 otherwise (i.e. not very strong)t = 6.54, p-value = 9.65E-11 0. There is enough evidence to infer that people with the same age, education, and income differ in how definite they intend to vote between those who consider themselves strong versus not very strong supporters of t

31、heir political parties.18.34a At least on is not equal to 0F = 344.04, p-value = 0. There is enough evidence to infer that the model is valid.b 0 0t = 1.72, p-value = .0879/2 = .0440. There is evidence that male professors are better paid than female professors with the same qualifications.18.35In t

32、his case maledominated jobs are paid on average $.039 (3.9 cents) less than femaledominated jobs after adjusting for the value of each job.18.36 All weights = .2In this case maledominated jobs are paid on average $.26 (26 cents) more than femaledominated jobs after adjusting for the value of each jo

33、b.18.37 The strength of this approach lies in regression analysis. This statistical technique allows us to determine whether gender is a factor in determining salaries. However, the conclusion is very much dependent upon the subjective assignment of weights. Change the value of the weights and a tot

34、ally different conclusion is achieved.18.38ab. The only variable that was significantly linearly related to EQWLTH in Exercise 17.17 was INCOME, which is the only variable in the stepwise regression.18.39ab. The only variables that were significantly linearly related to TVHOURS in Exercise 17.18 are

35、 the only variables in the stepwise regression.18.4018.4118.4218.43a Mileage = + Speed +Speed+ bc = 3.86 and = .7102. The model fits moderately well.18.44a Apply a firstorder model with interaction.bc: At least on is not equal to 0F = 54.14, p-value = 0. There is enough evidence to infer that the model is valid.18.45ab F = 32.65, p-value = 0. There is enough evidence to infer that the model is valid.18.46 a Let= 1 if ad was in newspaper= 0 othe