company that manufactures riding mowers wants to identify the best sales prospects for an intense sales campaign. In particular, the manufacturer is interested in classifying households as prospective owners or non-owners on the basis of Income (in $1000) and Lot Size (in 1000 ft2). The following table (table 1.1) gives the relevant data.

Table 1.1-Data on Riding Mowers

Income

Lot Size

Ownership

60

18.4

owner

85.5

16.8

owner

64.8

21.6

owner

61.5

20.8

owner

87

23.6

owner

110.1

19.2

owner

108

17.6

owner

82.8

22.4

owner

69

20

owner

93

20.8

owner

51

22

owner

81

20

owner

75

19.6

non-owner

52.8

20.8

non-owner

64.8

17.2

non-owner

43.2

20.4

non-owner

84

17.6

non-owner

49.2

17.6

non-owner

59.4

16

non-owner

66

18.4

non-owner

47.4

16.4

non-owner

33

18.8

non-owner

51

14

non-owner

63

14.8

non-owner

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Question 12pts

The prior probability of households in the study who are owners of riding mower is

0.65

0.55

0.50

None of the above

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Question 22pts

The overall accuracy of the model with regard to classification is

86.66%

81.54%

83%

83.33%

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Question 32pts

The Type I Error and Type II Error reveal that

Type I Error is more than Type II Error

They are perfectly balanced

They are equal

None of the above

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Question 42pts

If we increase the cut of probability to 0.6

A: Prediction accuracy of non-owner classification improves

B: Prediction accuracy of owner classification decreases

C: Prediction accuracy of owner classification remains same

Only A

Only B

Only C

A & C

A & B

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Question 52pts

If we decrease the cut of Probability to 0.4

A: Predictive accuracy of non-owner classification increases

B: Predictive accuracy of owner classification decreases

C: Predictive accuracy of non-owner classification decreases

D: Predictive accuracy of owner classification increases

Only A

only B

Only C

Only D

C & D

None of the above