/*  This is the partitioned Gatlin data set, the partitioning done in
    XLMiner using a split of 60% Training and 40% Validation and the 
    random number seed = 12345.  First 93 observations represent the
    training data set while the remaing 62 observations make up the
    validation data set. */ 

Data Gatlin;
  input case y x1 x2 x3 x7 x12 x14 x16 x19;
datalines;
1	1	48	90	20	57	42	1	1	12
4	1	39	65	8	67	29	1	1	13
5	1	52	50	5	48	48	1	2	17
6	1	52	50	5	48	52	1	2	17
9	1	61	50	12	74	41	0	3	14
10	1	42	60	15	55	47	1	2	16
12	1	43	65	8	41	57	1	5	16
17	1	47	33	4	39	53	0	1	12
18	1	42	50	3	53	55	0	2	13
19	1	40	40	6	50	59	1	3	14
20	1	40	70	11	64	53	1	2	16
21	1	40	70	11	64	50	1	2	16
23	1	48	50	3	66	42	0	2	14
26	1	41	85	3	62	33	1	1	16
27	1	44	40	5	58	54	1	2	14
29	1	53	50	9	50	52	0	4	15
30	1	39	50	14	52	57	0	5	13
31	1	42	25	17	58	60	0	3	15
32	1	41	35	3	58	46	1	2	15
35	1	40	25	2	47	66	0	1	16
36	1	39	50	1	47	62	0	2	13
37	1	43	40	3	56	54	1	4	12
38	1	40	30	3	60	50	1	2	16
39	1	53	40	4	38	36	0	2	17
41	1	39	40	4	57	58	0	2	13
43	1	47	25	7	43	48	0	5	14
44	1	42	60	1	62	44	0	2	12
45	1	43	40	5	72	57	0	2	14
48	1	42	40	12	48	62	0	3	14
49	1	56	20	6	45	38	0	2	13
51	1	40	25	4	45	50	0	4	13
52	1	42	50	4	59	57	0	2	16
53	1	51	20	25	54	43	0	2	13
54	0	47	30	1	47	40	0	3	14
55	0	45	70	1	61	36	0	3	12
56	0	48	60	6	61	38	0	2	13
57	0	49	60	1	46	46	0	4	18
58	0	42	60	6	47	51	1	1	16
62	0	51	25	1	55	56	1	4	14
64	0	45	50	4	57	49	1	5	13
65	0	43	50	6	51	44	0	3	12
66	0	43	25	6	51	41	0	3	12
67	0	37	50	7	37	49	1	2	14
68	0	37	50	8	37	53	1	2	14
69	0	40	50	5	50	53	1	2	13
71	0	46	25	2	50	51	0	2	12
73	0	48	25	2	38	45	0	4	14
75	0	48	25	2	38	45	0	4	13
76	0	42	30	1	38	50	0	2	12
77	0	39	50	1	59	50	0	4	12
81	0	57	40	1	55	37	0	4	12
82	0	51	50	3	52	48	0	6	15
84	0	48	40	3	47	50	0	1	16
85	0	42	70	4	42	43	1	1	15
87	0	41	40	2	53	52	0	2	16
89	0	43	10	1	58	52	0	2	14
90	0	43	10	1	58	52	0	2	14
92	0	40	50	6	49	39	0	3	13
94	0	42	30	1	62	57	1	5	18
95	0	40	50	7	53	45	0	3	13
96	0	40	33	2	50	45	1	3	16
97	0	40	50	1	50	57	0	3	12
98	0	46	50	1	44	48	1	5	12
100	0	39	30	4	52	49	0	2	14
101	0	38	25	2	43	52	0	3	13
102	0	39	0	3	52	49	0	4	12
104	0	54	50	1	47	57	0	4	12
105	0	38	25	2	41	52	0	3	13
108	0	40	40	3	55	55	0	2	12
110	0	40	33	2	50	45	1	3	15
111	0	40	50	1	53	57	0	3	12
119	0	39	20	7	43	55	0	3	13
121	0	47	20	3	47	50	1	3	14
122	0	46	50	2	68	47	0	5	12
123	0	41	70	4	51	44	0	2	17
125	0	42	60	7	50	45	0	1	15
127	0	40	50	3	55	55	0	3	13
128	0	41	40	14	23	46	1	1	19
129	0	67	40	1	62	31	0	2	14
131	0	40	30	1	46	42	0	4	16
133	1	43	80	7	55	39	0	2	12
136	1	56	75	19	48	38	1	1	15
138	1	45	50	2	54	41	1	4	14
139	1	48	75	5	48	43	0	4	16
140	1	47	75	25	53	61	1	3	12
143	1	43	50	1	48	67	0	3	16
144	1	49	50	10	50	55	0	4	16
147	1	40	40	6	50	55	0	3	12
149	1	41	75	12	55	54	1	4	16
150	1	46	35	13	59	38	0	3	13
151	1	62	30	8	58	41	1	2	14
152	1	58	70	6	69	44	1	2	15
154	1	42	60	7	55	47	1	2	16
2	1	99	75	8	67	23	1	1	11
3	1	76	60	11	67	35	1	2	14
7	1	76	60	11	67	27	1	2	12
8	1	59	50	5	57	38	0	2	16
11	1	43	65	9	41	57	1	5	16
13	1	52	50	5	48	36	1	2	17
14	1	67	30	5	50	36	0	2	13
15	1	42	50	3	53	59	0	2	14
16	1	40	50	4	54	57	0	2	18
22	1	50	60	6	55	54	1	3	13
24	1	37	50	5	61	59	1	5	15
25	1	51	0	11	73	37	1	3	12
28	1	48	40	6	59	55	0	4	14
33	1	40	33	19	55	52	0	2	16
34	1	40	20	2	60	60	0	1	14
40	1	48	75	5	44	43	1	4	16
42	1	39	25	5	62	53	0	3	14
46	1	42	50	1	58	47	0	4	17
47	1	43	50	7	63	52	0	3	14
50	1	40	25	3	54	45	0	3	12
59	0	43	70	2	59	77	1	2	14
60	0	58	25	1	61	45	0	2	16
61	0	49	50	9	50	35	0	3	12
63	0	40	55	3	49	55	1	3	12
70	0	42	45	2	57	55	0	2	17
72	0	46	25	2	50	49	0	2	12
74	0	47	20	3	47	50	1	3	14
78	0	45	50	2	51	47	0	2	12
79	0	43	50	1	51	39	0	3	13
80	0	51	50	3	52	48	0	6	14
83	0	48	40	3	47	50	0	1	16
86	0	42	70	4	42	43	1	1	16
88	0	43	25	9	64	42	0	1	16
91	0	49	0	16	54	48	1	1	12
93	0	42	50	4	54	55	1	3	16
99	0	40	25	4	50	48	0	2	12
103	0	40	40	3	55	60	0	2	13
106	0	38	25	2	43	52	0	3	14
107	0	43	30	4	61	36	0	2	14
109	0	40	20	4	50	48	0	2	12
112	0	46	30	1	43	39	1	1	16
113	0	49	0	14	54	48	1	1	12
114	0	43	25	9	60	42	0	1	16
115	0	43	70	1	47	43	1	3	13
116	0	39	60	1	52	45	0	3	15
117	0	42	75	7	64	55	1	3	16
118	0	39	20	7	46	55	0	3	13
120	0	45	45	5	36	57	0	4	16
124	0	39	40	2	54	57	0	3	17
126	0	41	50	4	38	39	0	4	15
130	0	47	75	8	49	42	1	2	16
132	1	45	40	4	49	49	0	3	12
134	1	46	40	1	64	42	0	2	15
135	1	54	30	13	54	45	0	1	15
137	1	39	25	5	62	53	0	3	14
141	1	62	50	2	48	33	1	2	16
142	1	40	35	2	74	57	0	5	12
145	1	39	40	10	48	53	0	1	12
146	1	62	35	6	64	42	0	2	15
148	1	38	50	4	51	52	0	2	18
153	1	41	30	2	53	49	0	2	15
155	1	61	50	12	71	28	0	3	13
;

data training;
  set gatlin;
  if _n_ <= 93;

data validation;
  set gatlin;
  if _n_ >= 94;

proc logistic data = training descending;
  model y = x1 x2 x3 x7 x12 x14 x16 x19 / selection = backward slstay = 0.10;

proc logistic data = training descending;
  model y = x1 x2 x3 x7 x12 x14 x16 x19 / selection = forward slentry = 0.10;

proc logistic data = training descending;
model y = x1 x2 x3 x7 x12 x14 x16 x19 / selection = stepwise slstay = 0.10 slentry = 0.10;

  run;