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Obs	Category	Liquidity	Profitability	Activity
1	1	0.90	0.34	1.53
2	1	0.88	0.23	1.67
3	1	0.92	0.28	1.43
4	1	0.89	0.14	1.24
5	1	0.78	0.35	1.80
6	1	0.81	0.26	2.01
7	1	0.72	0.18	1.75
8	1	0.93	0.22	0.99
9	1	0.82	0.26	1.40
10	1	0.86	0.31	1.42
11	1	0.76	0.41	1.45
12	1	0.72	0.14	2.12
13	1	0.86	0.24	2.29
14	1	0.85	0.19	1.23
15	1	0.9	0.27	1.28
16	1	0.76	0.29	1.76
17	1	0.93	0.27	1.49
18	1	0.71	0.24	1.37
19	1	0.96	0.43	1.36
20	1	0.71	0.16	1.57
21	1	0.86	0.15	1.65
22	1	0.85	0.3	1.22
23	1	0.72	0.28	1.54
24	1	0.75	0.25	1.58
25	1	0.97	0.28	1.43
26	1	0.95	0.22	1.22
27	1	0.87	0.34	1.22
28	1	0.81	0.31	1.27
29	1	0.74	0.14	1.38
30	1	0.9	0.29	2.1
31	1	0.79	0.12	1.22
32	1	0.81	0.29	1.31
33	1	0.79	0.28	1.58
34	1	0.88	0.26	1.48
35	1	0.89	0.16	1.82
36	1	0.79	0.18	1.27
37	1	0.88	0.26	1.7
38	1	0.85	0.37	1.55
39	1	1	0.24	1.2
40	1	0.86	0.4	1.59
41	1	0.91	0.36	1.02
42	1	0.91	0.16	0.96
43	1	0.85	0.24	2.17
44	1	0.89	0.13	1.51
45	1	0.86	0.21	1.29
46	1	0.96	0.23	1.06
47	1	0.79	0.19	1.83
48	1	0.87	0.11	1.86
49	1	0.87	0.23	1.56
50	2	0.68	0.19	1.23
51	2	0.56	0.2	1.87
52	2	0.72	0.25	1.05
53	2	0.75	0.18	1.53
54	2	0.77	0.2	1.26
55	2	0.75	0.2	1.79
56	2	0.61	0.17	1.27
57	2	0.58	0.19	1.68
58	2	0.74	0.18	1.57
59	2	0.63	0.16	1.3
60	2	0.75	0.23	1.29
61	2	0.67	0.23	1.11
62	2	0.7	0.24	1.23
63	2	0.74	0.17	1.09
64	2	0.68	0.3	1.49
65	2	0.75	0.26	1.62
66	2	0.74	0.18	1.34
67	2	0.75	0.19	1.08
68	2	0.68	0.17	1.36
69	2	0.5	0.11	1.18
70	2	0.8	0.23	1.13
71	2	0.61	0.17	0.98
72	2	0.62	0.21	0.92
73	2	0.69	0.13	1.65
74	2	0.79	0.25	1.2
75	2	0.94	0.23	1.77
76	2	0.82	0.23	1.7
77	2	0.71	0.19	1.45
78	2	0.69	0.29	1.16
79	2	0.73	0.25	1.81
80	2	0.67	0.15	1.1
81	2	0.78	0.15	1.52
82	2	0.74	0.16	1.6
83	2	0.87	0.1	1.21
84	2	0.69	0.26	1.74
85	2	0.82	0.17	1.55
86	2	0.62	0.23	1.47
87	2	0.6	0.15	1.53
88	2	0.63	0.19	1.03
89	2	0.76	0.26	1.33
90	2	0.78	0.26	1.34
91	2	0.78	0.27	1.67
92	2	0.72	0.18	1.53
93	2	0.69	0.16	1.20
94	2	0.63	0.15	0.88
95	2	0.58	0.22	1.42
96	2	0.81	0.18	1.59
97	2	0.67	0.21	1.21
98	2	0.65	0.16	1.37

CRARY Profile Activity Liquidity 090 1 17 092 0 08 12 14 : 22 19 ?? 023 6 EF 127 1 95 1.71 035 0.16 115 03 2 2 LED S60 280 UNNN 01 11 8%w2mmmm El EZO nnnnnnnnnnnnnnOOMSOON 1. ter , 0 15 11 02 680 13 60 620 280 111 023 12355rbaDHR88bHmm8%%n%88%h% $8mm88%E8%TR88wmm 12 102 12: 1.0- 0.75 0.61 117 0.13 Es U 22 03 12 0 05 air 03 RUBARNEN - 1.11 CE0 2. C 690 620 D 12: 177 1. 14 610 620 690 125 3 NGONONOOMNOONG 16 LED E90 15 0.17 12 16 . EAT 2 1 12 021 The manager of the commercial loan department for a bank wants to develop a rule to use in determining whether or not to approve various requests for loans. The manager believes that three key characteristics of a company's performance are important in making this decision: liquidity, profitability, and activity. The manager measures liquidity as the ratio of current assets to current liabilities. Profitability is measured as the ratio of net profit to sales. Activity is measured as the ratio of sales to fixed assets. The manager has collected the data found in the file Loans.xlsm accompanying this book containing a sample of 98 loans that the bank has made in the past five years. These loans have been classified into two groups: (1) those that were acceptable and (2) those that should have been rejected. a. What are the coordinates of the centroids for the acceptable loans and the unacceptable loans? Round your answers to two decimal places, if necessary. Centroid Group Liquidity Profitability Activity 2 .71 .20 1.38 b. Use XL Miner's standard data partition command to partition the data into a training set (with 60% of the observations) and validation set (with 40% of the observations) using the default seed of 12345. Use discriminant analysis to create a classifier for this data. How accurate is this procedure on the training and validation data sets? Round your answers to one decimal place. Data Set Overall Error Training 20.0 % Validation 15.0 % c. Use logistic regression to create a classifier for this data. How accurate is this procedure on the training and validation data sets? Round your answers to one decimal place, Data Set Overall Error Training 20.0 % Validation 15.0 % d. Use the k-nearest neighbor technique to create a classifier for this data (with normalized inputs). What value of k seems to work best? How accurate is this procedure on the training and validation data sets? Round your answers to one decimal place and if your answer is zero, entero". Data Set Overall Error Training 20.0 % Validation 15.0 % e. Use a classification tree to create a classifier for this data (with normalized inputs and at least 4 observations per terminal node). Create a graphic depiction of the best pruned tree using the validation data. How accurate is this procedure on the training and validation data sets? Round your answers to one decimal place. Data Set Overall Error 13. 33% Training Validation 10.0 % f. Use a neural network to create a classifier for this data (use normalized inputs and a single hidden layer with 2 nodes). How accurate is this procedure on the training and validation data sets? Round your answers to one decimal place. Data Set Overall Error Training 10.0 % Validation 10.0 g. Return to the Data sheet and use the Transform, Bin Continuous Data command to create binned variables for liquidity, profitability, and activity. Use XLMiner's standard data partition command to partition the data into a training set (with 60% of the observations) and validation set (with 40% of the observations) using the default seed of 12345. Now use the nave Bayes technique to create a classifier for the data using the new binned variables for liquidity, profitability, and activity. How accurate is this procedure on the training and validation data sets? Round your answers to one decimal place. Data Set Overall Error Training 20.0 % Validation 15.0 % CRARY Profile Activity Liquidity 090 1 17 092 0 08 12 14 : 22 19 ?? 023 6 EF 127 1 95 1.71 035 0.16 115 03 2 2 LED S60 280 UNNN 01 11 8%w2mmmm El EZO nnnnnnnnnnnnnnOOMSOON 1. ter , 0 15 11 02 680 13 60 620 280 111 023 12355rbaDHR88bHmm8%%n%88%h% $8mm88%E8%TR88wmm 12 102 12: 1.0- 0.75 0.61 117 0.13 Es U 22 03 12 0 05 air 03 RUBARNEN - 1.11 CE0 2. C 690 620 D 12: 177 1. 14 610 620 690 125 3 NGONONOOMNOONG 16 LED E90 15 0.17 12 16 . EAT 2 1 12 021 The manager of the commercial loan department for a bank wants to develop a rule to use in determining whether or not to approve various requests for loans. The manager believes that three key characteristics of a company's performance are important in making this decision: liquidity, profitability, and activity. The manager measures liquidity as the ratio of current assets to current liabilities. Profitability is measured as the ratio of net profit to sales. Activity is measured as the ratio of sales to fixed assets. The manager has collected the data found in the file Loans.xlsm accompanying this book containing a sample of 98 loans that the bank has made in the past five years. These loans have been classified into two groups: (1) those that were acceptable and (2) those that should have been rejected. a. What are the coordinates of the centroids for the acceptable loans and the unacceptable loans? Round your answers to two decimal places, if necessary. Centroid Group Liquidity Profitability Activity 2 .71 .20 1.38 b. Use XL Miner's standard data partition command to partition the data into a training set (with 60% of the observations) and validation set (with 40% of the observations) using the default seed of 12345. Use discriminant analysis to create a classifier for this data. How accurate is this procedure on the training and validation data sets? Round your answers to one decimal place. Data Set Overall Error Training 20.0 % Validation 15.0 % c. Use logistic regression to create a classifier for this data. How accurate is this procedure on the training and validation data sets? Round your answers to one decimal place, Data Set Overall Error Training 20.0 % Validation 15.0 % d. Use the k-nearest neighbor technique to create a classifier for this data (with normalized inputs). What value of k seems to work best? How accurate is this procedure on the training and validation data sets? Round your answers to one decimal place and if your answer is zero, entero". Data Set Overall Error Training 20.0 % Validation 15.0 % e. Use a classification tree to create a classifier for this data (with normalized inputs and at least 4 observations per terminal node). Create a graphic depiction of the best pruned tree using the validation data. How accurate is this procedure on the training and validation data sets? Round your answers to one decimal place. Data Set Overall Error 13. 33% Training Validation 10.0 % f. Use a neural network to create a classifier for this data (use normalized inputs and a single hidden layer with 2 nodes). How accurate is this procedure on the training and validation data sets? Round your answers to one decimal place. Data Set Overall Error Training 10.0 % Validation 10.0 g. Return to the Data sheet and use the Transform, Bin Continuous Data command to create binned variables for liquidity, profitability, and activity. Use XLMiner's standard data partition command to partition the data into a training set (with 60% of the observations) and validation set (with 40% of the observations) using the default seed of 12345. Now use the nave Bayes technique to create a classifier for the data using the new binned variables for liquidity, profitability, and activity. How accurate is this procedure on the training and validation data sets? Round your answers to one decimal place. Data Set Overall Error Training 20.0 % Validation 15.0 %