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36	0	103	444	9.06122449	3.551724138
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15	0	114	189	5.727272727	3.166666667
3	0	51	100	3.571428571	2.684210526
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23	0	203	246	5.857142857	4.613636364
0	0	4	60	3	0.2
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9	0	52	111	3.264705882	1.733333333
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45	0	205	284	11.36	5.394736842
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10	0	80	103	4.12	2.857142857
0	0	36	136	5.913043478	1.2
7	0	18	107	4.28	0.6
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42	0	111	438	7.963636364	3.580645161
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6	0	96	93	5.166666667	2
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0	0	11	201	5.289473684	0.423076923
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48	0	176	330	13.2	4.756756757
31	0	260	215	8.6	5.777777778
17	0	46	373	8.288888889	1.4375
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0	0	98	43	1.791666667	3.266666667
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33	0	153	309	6.866666667	4.78125
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9	0	74	128	3.764705882	2.176470588
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28	0	113	318	7.95	3.138888889
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3	0	33	109	4.36	1.178571429
38	0	30	486	7.967213115	2
10	0	8	102	3.4	0.296296296
27	0	173	178	5.393939394	4.675675676
41	0	326	82	4.555555556	6.791666667
27	0	203	284	8.114285714	4.06
14	0	44	176	5.5	1.76
23	0	147	322	6.44	4.323529412
38	0	133	385	9.166666667	4.15625
24	0	318	22	3.666666667	6.489795918
24	0	73	232	6.823529412	2.212121212
28	0	123	296	6.88372093	4.1
33	0	134	228	5.302325581	3.526315789
17	0	135	138	5.75	3.648648649
24	0	173	231	4.8125	5.965517241
45	0	136	505	6.733333333	4.533333333
41	0	201	396	10.7027027	4.568181818
24	0	20	405	8.265306122	1.052631579
23	0	49	401	9.780487805	1.75
31	0	132	357	9.394736842	3.473684211
28	0	218	184	12.26666667	3.824561404
24	0	127	340	7.234042553	4.535714286
17	0	115	192	6.620689655	3.285714286
33	0	115	329	6.854166667	4.791666667
56	0	201	281	10.40740741	4.674418605
49	0	226	119	6.611111111	4.264150943
55	0	283	400	9.302325581	7.075
30	0	152	255	9.107142857	3.234042553
35	0	181	325	7.738095238	5.838709677
41	0	258	138	5.52	5.733333333
59	0	379	275	17.1875	7.58
21	0	118	287	7	3.933333333
48	0	203	261	10.03846154	5.205128205
14	0	113	197	6.566666667	2.627906977
15	0	192	135	5.4	3.84
17	0	175	288	7.024390244	3.888888889
19	0	123	207	5.594594595	3.324324324
20	0	282	176	4.756756757	5.875
55	0	232	336	10.5	4.461538462
26	0	133	253	6.837837838	4.586206897
34	0	309	227	6.305555556	4.414285714
24	0	121	220	7.096774194	2.630434783
17	0	159	87	3.782608696	4.184210526
55	0	210	386	8.976744186	5.25
40	0	294	198	7.333333333	5.764705882
21	0	99	307	8.771428571	3.3
10	0	139	66	3	3.390243902
6	0	21	143	5.5	0.954545455
28	0	188	111	4.625	3.916666667
16	0	67	67	2.161290323	2.310344828
7	0	55	136	5.230769231	1.896551724
23	0	269	137	8.5625	4.803571429
31	0	33	412	10.56410256	1.03125
20	0	110	303	7.390243902	3.4375
30	0	217	359	7.039215686	5.046511628
10	0	155	177	4.214285714	4.428571429
31	0	218	176	5.866666667	4.844444444
42	0	239	222	8.88	5.085106383
45	0	311	203	7.807692308	8.405405405
27	0	220	195	7.8	4.4
38	0	184	150	6.818181818	4.279069767
31	0	150	440	9.361702128	5
28	0	275	186	11.625	6.25
28	0	55	271	8.46875	1.774193548
20	0	160	201	7.444444444	4.571428571
27	0	58	328	8.41025641	1.757575758
10	0	158	104	3.466666667	3.853658537
24	0	40	440	9.166666667	1.666666667
26	0	34	299	5.98	1.133333333
14	0	104	186	5.636363636	4.952380952
31	0	200	191	8.304347826	4.651162791
33	0	326	174	6.214285714	6.936170213
14	0	92	149	5.321428571	3.172413793
34	0	86	260	5.098039216	2.866666667
38	0	182	289	7.41025641	4.136363636
45	0	177	444	13.05882353	4.317073171
49	0	430	137	6.85	9.772727273
10	0	195	120	9.230769231	4.875
18	0	15	242	5.761904762	0.535714286
26	0	170	272	10.88	3.333333333
38	0	291	325	8.125	6.613636364
30	0	233	144	10.28571429	4.854166667
27	0	232	253	5.75	6.823529412
24	0	72	250	6.944444444	2.88
13	0	188	64	4.923076923	4.947368421
24	0	131	295	5.566037736	3.742857143
24	0	138	281	7.025	3.45
14	0	132	171	7.772727273	3.384615385
66	0	257	447	10.15909091	4.942307692
3	0	58	134	2.977777778	2.148148148
16	0	163	260	5	4.939393939
16	0	33	381	6.803571429	1.375
27	0	172	253	5.622222222	4.3
13	0	209	128	3.459459459	5.225
21	0	205	130	6.5	6.40625
13	0	136	130	3.170731707	4.533333333
48	0	153	449	9.977777778	3.326086957
13	0	205	148	4.933333333	5.256410256
44	0	212	268	6.7	5.578947368
20	0	34	291	7.275	1.416666667
30	0	156	177	6.321428571	5.571428571
51	0	265	355	7.553191489	4.568965517
14	0	-14	93	4.227272727	-0.466666667
14	0	132	233	6.297297297	3.069767442
35	0	248	227	6.676470588	8.266666667
18	0	272	196	7.259259259	5.787234043
31	0	378	18	1.8	6.75
13	0	58	218	8.074074074	2.148148148
58	0	416	151	6.04	6.603174603
59	0	366	234	8.357142857	7.625
46	0	162	254	7.470588235	3.115384615
3	0	80	108	3.176470588	4.210526316
27	0	232	156	5.379310345	4.218181818
45	0	179	270	6.75	5.59375
17	0	54	368	6.814814815	1.588235294
23	0	188	239	8.535714286	4.086956522
10	0	30	191	6.161290323	1.111111111
17	0	146	219	9.52173913	4.866666667
13	0	57	186	5.027027027	2.47826087
45	0	117	514	8.290322581	3.774193548
9	0	135	107	4.458333333	3.648648649
14	0	134	177	4.538461538	7.882352941
12	0	297	201	5.911764706	9
6	0	83	46	2.875	2.305555556
26	0	81	308	8.324324324	3.52173913
14	0	144	95	4.75	4
16	0	70	210	6.774193548	2.916666667
20	0	103	325	5.327868852	4.47826087
23	0	118	316	8.102564103	3.806451613
24	0	99	254	7.055555556	3.535714286
14	0	123	263	6.921052632	4.555555556
21	0	157	219	5.093023256	3.568181818
31	0	48	315	10.5	1.548387097
17	0	188	140	6.086956522	4.177777778
14	0	110	123	5.857142857	3.333333333
14	0	113	118	5.619047619	4.708333333
49	0	281	247	11.76190476	5.734693878
52	0	166	316	7.523809524	6.916666667
10	0	167	160	4.102564103	4.513513514
58	0	279	316	13.16666667	7.75
26	0	174	270	11.73913043	4.971428571
29	0	144	289	9.965517241	3.6
13	0	126	87	3.222222222	4.064516129
61	0	186	315	7.682926829	6.642857143
20	0	68	249	7.78125	1.619047619
13	0	203	137	4.151515152	5.638888889
15	0	167	74	3.217391304	4.911764706
26	0	81	194	8.434782609	1.760869565
17	0	62	208	10.4	2.583333333
41	0	140	310	7.209302326	3.111111111
21	0	173	130	5.2	4.119047619
10	0	83	82	4.1	2.184210526
0	0	37	130	2.954545455	1.37037037
31	0	242	312	5.379310345	4.321428571
62	0	262	445	10.85365854	6.390243902
41	0	276	180	12.85714286	5.872340426
55	0	232	473	8.924528302	6.27027027
28	0	139	380	8.636363636	6.043478261
21	0	144	251	8.096774194	4.363636364
12	0	108	204	5.828571429	3.6
42	0	135	320	11.85185185	4.090909091
0	0	141	44	2.2	3.357142857
56	0	240	383	12.76666667	4.8
13	0	101	197	6.15625	3.15625
28	0	253	192	8	5.88372093
30	0	119	272	9.379310345	3.051282051
13	0	139	131	5.458333333	3.30952381
7	0	212	101	4.391304348	5.170731707
29	0	127	169	6.259259259	3.735294118
23	0	230	245	5.975609756	4.6
40	0	179	240	8.888888889	3.140350877
3	0	69	154	4.529411765	3.136363636
0	0	119	85	3.863636364	3.71875
27	0	185	220	9.565217391	3.7
34	0	101	390	9.069767442	3.15625
52	0	249	233	8.961538462	5.790697674
45	0	77	407	8.479166667	2.655172414
27	0	270	83	4.611111111	5.510204082
10	0	189	259	5.886363636	5.25
70	0	184	478	11.11627907	5.111111111
34	0	136	494	9.320754717	3.487179487
24	0	76	265	7.571428571	2.235294118
27	0	156	402	7.730769231	4
17	0	66	196	7.259259259	2.0625
55	0	239	284	9.793103448	7.709677419
14	0	39	226	6.108108108	1.625
24	0	168	299	8.305555556	5.419354839
58	0	496	80	8.888888889	5.975903614
30	0	226	86	4.526315789	5.65
3	0	127	203	4.833333333	2.886363636
48	0	262	307	7.30952381	5.24
32	0	124	277	11.08	2.296296296
24	0	299	244	5.951219512	5.339285714
31	0	103	419	12.6969697	3.029411765
30	0	292	206	12.11764706	5.84
40	0	309	208	8.32	5.942307692
31	0	127	303	7.390243902	3.527777778
7	0	74	78	2.888888889	2.96
18	0	116	190	4.871794872	3.314285714
17	0	36	293	7.918918919	1.44
41	0	284	390	9.069767442	5.461538462
21	0	191	187	5.666666667	6.161290323
13	0	78	216	8.64	2.108108108
21	0	127	280	6.666666667	3.96875
45	0	177	317	8.128205128	3.933333333
29	0	218	445	7.672413793	4.192307692
26	0	179	199	5.527777778	4.261904762
16	0	128	97	3.592592593	4
42	0	275	197	7.296296296	5.729166667
23	0	145	162	6	3.536585366
53	0	163	442	9.822222222	4.179487179
35	0	234	233	7.516129032	5.318181818
24	0	139	212	7.851851852	4.212121212
42	0	175	262	10.07692308	4.166666667
31	0	131	264	6.947368421	2.729166667
21	0	125	237	5.780487805	4.166666667
21	0	90	230	8.214285714	2.903225806
31	0	117	188	7.833333333	2.925
13	0	103	89	4.45	2.641025641
24	0	202	256	5.688888889	4.80952381
24	0	139	376	6.372881356	3.088888889
24	0	152	233	5.974358974	3.8
19	0	174	185	5.606060606	4.046511628
31	0	327	206	5.15	7.431818182
13	0	82	234	5.2	2.277777778
60	0	268	268	10.72	7.243243243
30	0	66	523	10.25490196	3.666666667
13	0	197	164	5.655172414	6.566666667
3	0	124	45	5.625	2.818181818
7	0	145	30	2.142857143	3.020833333
3	0	79	101	4.391304348	3.291666667
48	0	382	213	5.605263158	9.317073171
30	0	109	303	7.046511628	2.725
14	0	114	262	6.238095238	3.257142857
7	0	146	81	4.263157895	3.476190476
17	0	155	174	6.444444444	3.780487805
26	0	228	120	8	4.145454545
31	0	230	297	6.6	4.791666667
17	0	102	184	4.972972973	2.833333333
36	0	170	378	7.875	4.594594595
30	0	60	352	7.489361702	1.875
45	0	374	85	17	5.666666667
26	0	311	132	7.764705882	5.980769231
28	0	103	329	7.833333333	4.291666667
20	0	192	234	5.707317073	4
38	0	143	348	8.923076923	4.205882353
34	0	191	155	6.2	4.244444444
16	0	54	128	6.736842105	2.076923077
16	0	114	260	5.098039216	3
22	0	242	200	8.695652174	4.033333333
14	0	33	281	6.244444444	1.269230769
0	0	80	111	3.580645161	3.2
28	0	163	322	8.256410256	4.657142857
41	0	236	269	6.56097561	5.756097561
41	0	334	190	7.916666667	4.985074627
25	0	43	270	7.941176471	1.791666667
23	0	165	178	6.137931034	4.230769231
48	0	141	440	9.565217391	5.035714286
20	0	277	162	8.526315789	5.12962963
65	0	181	501	13.54054054	5.171428571
31	0	268	180	7.826086957	4.620689655
28	0	75	364	7.744680851	3.260869565
34	0	247	186	8.086956522	5.488888889
17	0	142	405	7.788461538	4.176470588
24	0	148	256	7.757575758	3.441860465
27	0	129	204	7.555555556	3.486486486
17	0	225	127	3.735294118	6.081081081
21	0	112	193	5.078947368	3.733333333
44	0	123	289	6.880952381	3.617647059
20	0	146	97	5.705882353	3.244444444
21	0	99	326	7.581395349	3.09375
30	0	218	133	4.03030303	4.638297872
7	0	121	219	7.3	2.75
0	0	92	107	4.115384615	2.875
37	0	210	360	7.659574468	5.675675676
28	0	330	109	5.45	5.789473684
17	0	121	203	4.951219512	4.033333333
34	0	115	252	8.689655172	4.107142857
59	0	148	300	9.375	2.96
31	0	94	260	6.341463415	3.032258065
28	0	283	157	6.28	5.66
14	0	73	287	6.377777778	2.433333333
24	0	178	301	9.40625	3.955555556
37	0	193	221	7.892857143	4.288888889
14	0	144	124	5.166666667	4.64516129
31	0	174	188	8.952380952	4.578947368
41	0	172	471	13.45714286	4.648648649
0	0	111	98	3.62962963	3.083333333
47	0	401	110	6.470588235	7.160714286
31	0	235	144	5.76	7.833333333
7	0	107	182	5.055555556	2.891891892
17	0	79	218	4.448979592	2.548387097
35	0	154	343	9.026315789	3.208333333
45	0	326	85	12.14285714	4.939393939
30	0	223	219	7.064516129	4.054545455
24	0	204	194	5.388888889	6
38	0	213	375	7.5	5.461538462
35	0	172	330	7.5	4.777777778
44	0	241	270	7.941176471	4.547169811
34	0	212	278	8.6875	3.925925926
3	0	62	129	6.142857143	1.9375
31	0	38	388	6.689655172	1.727272727
38	0	231	205	17.08333333	6.416666667
23	0	121	259	5.395833333	3.903225806
17	0	137	165	5.322580645	5.074074074
35	0	133	301	10.75	4.586206897
31	0	299	141	8.8125	7.475
42	0	170	285	8.142857143	3.333333333
16	0	46	244	5.951219512	3.066666667
31	0	195	218	16.76923077	5
44	0	198	208	8.666666667	3.96
31	0	174	247	7.264705882	5.8
19	0	139	353	9.051282051	3.159090909
27	0	105	337	9.911764706	2.386363636
44	0	149	473	8.924528302	4.515151515
3	0	98	83	2.515151515	4.083333333
7	0	191	133	4.75	4.897435897
35	0	238	210	10.5	5.409090909
21	0	130	262	5.137254902	4.642857143
23	0	350	37	12.33333333	5.737704918
17	0	119	188	8.545454545	4.25
10	0	85	190	3.958333333	2.833333333
23	0	154	279	5.8125	4.4
13	0	39	113	4.035714286	1.21875
17	0	105	220	7.333333333	2.763157895
14	0	140	159	4.076923077	3.111111111
34	0	123	356	7.416666667	3.324324324
13	0	149	60	3	2.98
30	0	240	205	7.884615385	5.106382979
12	0	76	156	4.588235294	2.171428571
27	0	210	55	3.055555556	4.565217391
28	0	221	264	11.47826087	4.911111111
34	0	-1	355	9.342105263	-0.041666667
13	0	165	108	4.695652174	4.230769231
29	0	140	152	7.238095238	4.375
21	0	314	59	6.555555556	5.50877193
28	0	186	238	5.804878049	4.894736842
37	0	109	340	7.906976744	3.205882353
49	0	426	204	6.375	7.745454545
63	0	253	224	10.66666667	6.837837838
6	0	85	49	4.9	2.361111111
31	0	114	224	4.977777778	4.956521739
20	0	139	160	6.956521739	3.02173913
27	0	97	333	6.529411765	2.771428571
28	0	87	256	6.736842105	2.9
52	0	271	211	10.04761905	4.593220339
37	0	328	279	17.4375	6.074074074
20	0	137	268	5.826086957	3.605263158
37	0	237	261	6.214285714	5.152173913
10	0	159	125	3.676470588	4.676470588
17	0	184	324	6.352941176	4.842105263
47	0	170	385	11	3.777777778
17	0	203	91	5.6875	3.980392157
17	0	110	208	6.709677419	3.333333333
31	0	254	105	5	6.512820513
34	0	100	385	7.264150943	3.225806452
25	0	150	336	9.333333333	3.947368421
51	0	123	508	9.960784314	3.514285714
10	0	146	87	3.48	3.041666667
17	0	233	44	6.285714286	4.396226415
19	0	248	89	3.56	4.862745098
52	0	286	107	5.944444444	5.958333333
29	0	161	229	7.896551724	4.472222222
3	0	85	238	5.804878049	2.833333333
16	0	214	88	4	5.944444444
42	0	195	314	9.515151515	4.875
21	0	200	128	3.878787879	6.060606061
44	0	469	55	18.33333333	7.68852459
22	0	98	397	7.218181818	4.260869565
24	0	161	359	8.756097561	3.5
45	0	304	396	12.77419355	4.164383562
48	0	337	239	6.828571429	9.108108108
26	0	230	168	5.793103448	4.6
40	0	300	197	5.794117647	8.571428571
0	0	10	125	5.208333333	0.5
3	0	88	83	3.074074074	3.52
29	0	116	261	7.457142857	2.9
28	0	153	97	6.0625	5.464285714
21	0	81	293	6.369565217	3.24
10	0	31	288	4.965517241	2.583333333
28	0	31	242	5.902439024	1.24
31	0	147	312	17.33333333	3.868421053
48	0	293	372	9.538461538	5.745098039
31	0	295	163	7.409090909	6.020408163
44	0	281	174	7.565217391	5.203703704
28	0	113	217	6.027777778	3.53125
42	0	228	199	7.96	6
35	0	148	268	12.76190476	3.7
45	0	374	179	5.774193548	9.12195122
13	0	178	224	8.96	5.235294118
16	0	122	296	6.29787234	4.88
58	0	169	386	10.43243243	4.694444444
23	0	67	237	8.172413793	2.233333333
0	0	35	64	2.909090909	0.744680851
17	0	230	9	0.692307692	5.75
27	0	151	280	6.829268293	3.145833333
28	0	183	273	8.029411765	5.382352941
52	0	238	367	8.534883721	5.173913043
38	0	314	175	6.730769231	7.85
31	0	191	289	6.720930233	4.775
19	0	157	270	5.744680851	3.651162791
21	0	273	62	2.583333333	3.845070423
6	0	93	191	4.897435897	2.268292683
42	0	199	257	8.566666667	4.627906977
42	0	369	113	7.533333333	6.833333333
24	0	204	146	5.84	3.777777778
7	0	127	158	6.076923077	3.96875
21	0	140	227	6.135135135	4.827586207
14	0	49	258	6.789473684	1.484848485
51	0	74	284	9.466666667	2.387096774
33	0	244	188	5.529411765	4.436363636
37	0	236	279	10.33333333	5.021276596
46	0	401	255	6.710526316	9.547619048
20	0	187	69	4.3125	3.895833333
28	0	202	209	6.147058824	5.611111111
7	0	42	204	6.8	1.166666667
17	0	74	62	3.263157895	2.055555556
45	0	226	137	6.85	4.612244898
29	0	286	179	6.884615385	5.72
31	0	257	79	4.647058824	4.14516129
45	0	41	367	11.12121212	1.322580645
54	0	309	350	12.06896552	5.517857143
34	0	261	85	4.047619048	6.525
27	0	144	160	5.714285714	4.8
14	0	35	228	9.913043478	1.129032258
58	0	344	180	7.2	8.19047619
7	0	89	161	5.193548387	2.696969697
46	0	191	395	9.186046512	3.673076923
3	0	9	129	6.142857143	0.346153846
26	0	142	234	5.318181818	4.303030303
38	0	243	236	7.866666667	6.567567568
35	0	222	167	10.4375	5.842105263
36	0	299	234	9.36	8.542857143
23	0	166	281	6.69047619	3.952380952
13	0	98	287	6.522727273	3.62962963
34	0	266	235	6.351351351	5.018867925
15	0	15	165	6.875	0.714285714
35	0	89	308	8.555555556	1.711538462
23	0	156	151	7.19047619	3.714285714
17	0	154	227	7.09375	3.581395349
41	0	128	320	11.42857143	3.878787879
28	0	159	181	6.033333333	4.96875
34	0	143	346	10.8125	3.864864865
16	0	174	191	5.787878788	3.782608696
34	0	301	163	7.409090909	8.361111111
24	0	305	35	3.181818182	6.630434783
22	0	170	187	5.054054054	5.3125
37	0	142	240	6.857142857	4.4375
28	0	177	146	5.034482759	4.538461538
27	0	197	89	4.684210526	3.648148148
29	0	107	165	5.689655172	2.972222222
45	0	174	329	13.70833333	5.4375
28	0	71	315	5.625	2.84
23	0	100	182	6.066666667	2.380952381
45	0	151	461	10.02173913	4.575757576
17	0	99	236	5.619047619	3
58	0	452	82	10.25	6.647058824
55	0	277	377	9.195121951	4.946428571
31	0	115	327	8.384615385	4.423076923
42	0	207	129	10.75	3.980769231
29	0	67	319	7.780487805	4.1875
10	0	127	86	5.058823529	3.848484848
52	0	189	374	8.5	4.846153846
41	0	278	389	7.480769231	5.914893617
44	0	296	284	8.352941176	6.727272727
28	0	198	137	6.523809524	9.428571429
14	0	104	145	4.833333333	3.714285714
56	0	377	261	11.34782609	9.666666667
55	0	119	467	12.62162162	3.5
28	0	271	74	5.692307692	6.775
38	0	244	243	6.75	6.777777778
28	0	210	214	7.642857143	4.117647059
21	0	65	221	6.696969697	2.321428571
21	0	138	307	6.395833333	3.538461538
31	0	252	150	9.375	4.5
6	0	132	129	3.909090909	3.567567568
17	0	67	311	7.23255814	2.03030303
38	0	187	261	5.673913043	3.528301887
0	0	29	210	5.384615385	1.115384615
45	0	206	223	10.61904762	6.058823529
41	0	225	278	8.967741935	4.891304348
28	0	121	284	7.473684211	3.78125
20	0	63	303	6.733333333	2.1
23	0	79	163	6.52	3.590909091
41	0	186	233	6.657142857	3.875
17	0	120	281	6.69047619	3.75
43	0	144	532	10.43137255	4.363636364
45	0	138	335	13.4	3
48	1	229	338	6.377358491	5.452380952
14	1	46	249	7.323529412	1.533333333
13	1	78	265	6.30952381	2.785714286
69	1	476	77	15.4	9.153846154
17	1	254	140	5.185185185	5.404255319
20	1	105	202	10.1	2.692307692
41	1	203	388	9.7	4.833333333
12	1	168	217	6.382352941	4
17	1	144	197	5.628571429	3.692307692
47	1	312	114	5.7	5.379310345
7	1	25	246	5.857142857	1.086956522
28	1	100	202	5.315789474	2.941176471
24	1	127	210	6.363636364	3.432432432
12	1	140	110	3.333333333	4
35	1	256	133	8.3125	6.095238095
28	1	100	297	6.1875	3.571428571
31	1	232	163	7.086956522	5.948717949
24	1	18	365	7.3	0.782608696
20	1	93	299	6.953488372	3.576923077
21	1	203	166	6.916666667	3.759259259
42	1	163	229	6.361111111	4.289473684
34	1	225	217	4.822222222	5.769230769
43	1	285	422	12.05714286	5.377358491
59	1	399	205	8.913043478	6.234375
61	1	222	384	10.10526316	5.414634146
27	1	149	168	8	3.465116279
49	1	390	287	10.25	6.964285714
63	1	317	202	9.619047619	5.283333333
76	1	338	324	11.57142857	9.388888889
52	1	134	208	7.172413793	3.722222222
47	1	194	424	12.11428571	3.803921569
16	1	185	152	5.62962963	3.490566038
63	1	291	248	16.53333333	6.191489362
13	1	29	301	7.717948718	1.526315789
16	1	225	105	4.2	4.166666667
48	1	371	67	11.16666667	7.893617021
24	1	204	155	6.2	4.163265306
50	1	341	138	6.272727273	7.577777778
45	1	195	184	5.935483871	7.5
28	1	126	319	7.780487805	3.405405405
51	1	275	281	10.80769231	5.97826087
38	1	178	247	7.057142857	3.955555556
63	1	394	119	17	6.253968254
41	1	100	439	10.45238095	3.03030303
34	1	189	147	8.166666667	4.609756098
45	1	325	133	7.388888889	7.558139535
31	1	210	221	8.84	4.772727273
48	1	246	349	10.90625	5.857142857
38	1	214	313	13.60869565	4.115384615
38	1	285	291	7.275	5.588235294
26	1	205	261	11.34782609	5.125
28	1	192	235	6.351351351	5.818181818
51	1	212	255	8.793103448	4.930232558
44	1	185	359	13.80769231	4.625
51	1	243	192	13.71428571	6.394736842
38	1	185	360	13.84615385	4.404761905
49	1	251	199	7.653846154	4.327586207
34	1	256	188	5.222222222	5.446808511
34	1	88	319	9.96875	2.666666667
65	1	217	381	10.58333333	6.575757576
40	1	178	257	7.558823529	6.846153846
56	1	401	142	6.761904762	7.425925926
17	1	104	307	6.822222222	3.466666667
62	1	207	380	13.10344828	6.46875
48	1	182	308	15.4	5.352941176
26	1	281	110	5	5.017857143
49	1	222	311	8.405405405	5.162790698
70	1	242	363	11	3.903225806
33	1	224	184	7.36	7.225806452
34	1	152	351	9.486486486	4.470588235
59	1	174	437	7.666666667	5.612903226
31	1	190	137	6.523809524	4.634146341
73	1	185	471	9.612244898	4.743589744
31	1	77	233	8.034482759	2.40625
16	1	102	311	7.068181818	2.833333333
55	1	185	324	10.125	5.78125
59	1	266	370	11.5625	6.487804878
52	1	296	340	8.947368421	5.803921569
35	1	238	264	9.103448276	6.432432432
37	1	166	139	6.043478261	4.611111111
41	1	163	234	6.5	5.620689655
52	1	266	355	9.594594595	5.911111111
66	1	339	172	9.052631579	6.519230769
61	1	485	246	9.84	8.50877193
41	1	144	441	11.60526316	4.235294118
31	1	84	328	9.111111111	2.470588235
23	1	194	165	4.342105263	4.217391304
35	1	164	321	12.84	4.555555556
24	1	129	125	4.032258065	2.866666667
41	1	140	252	8.129032258	3.181818182
22	1	145	270	10.8	3.152173913
12	1	103	412	7.773584906	3.322580645
37	1	220	312	7.255813953	5.641025641
38	1	182	181	5.323529412	3.714285714
20	1	172	142	4.4375	4.195121951
65	1	439	132	13.2	7.839285714
43	1	413	7	1.75	7.942307692
56	1	270	233	8.034482759	5.510204082
32	1	164	414	10.61538462	4.685714286
57	1	254	389	9.487804878	5.906976744
31	1	70	325	7.222222222	2.692307692
35	1	225	180	6.923076923	4.6875
42	1	299	284	9.161290323	7.292682927
45	1	278	214	8.230769231	4.276923077
55	1	288	367	14.11538462	4.881355932
27	1	165	236	7.375	4.342105263
24	1	195	132	6	4.431818182
34	1	198	204	7.034482759	5.351351351
24	1	338	122	5.545454545	5.728813559
41	1	172	313	9.78125	3.822222222
21	1	229	152	5.846153846	4.403846154
35	1	199	244	8.133333333	4.145833333
69	1	229	445	10.5952381	7.633333333
22	1	195	222	8.222222222	5.735294118
52	1	239	275	6.875	7.966666667
53	1	215	248	9.185185185	4.886363636
44	1	360	187	20.77777778	8.571428571
47	1	202	391	10.28947368	5.315789474
73	1	215	392	10.59459459	6.142857143
27	1	127	289	7.605263158	4.233333333
31	1	168	205	8.2	4.421052632
27	1	156	185	5.606060606	6
34	1	192	283	9.75862069	4.173913043
20	1	93	124	4.133333333	2.066666667
20	1	168	160	4.705882353	4
66	1	412	373	9.815789474	7.357142857
38	1	251	289	8.027777778	5.577777778
70	1	224	403	10.89189189	4.977777778
44	1	125	249	8.892857143	4.464285714
7	1	42	63	2.863636364	1.75
14	1	105	295	5.9	2.692307692
34	1	278	163	6.269230769	6.043478261
49	1	218	326	13.04	6.055555556
31	1	273	171	7.125	5.150943396
63	1	316	333	15.13636364	6.32
29	1	430	32	1.882352941	7.413793103
17	1	63	247	7.057142857	2.333333333
30	1	149	221	15.78571429	4.027027027
3	1	37	192	5.189189189	1.48
20	1	223	166	4.882352941	6.757575758
44	1	147	353	6.537037037	3.675
58	1	188	263	7.305555556	4.177777778
41	1	344	152	9.5	4.985507246
48	1	390	168	7	6.610169492
27	1	200	128	4.740740741	5.128205128
76	1	347	136	9.066666667	8.675
42	1	149	128	12.8	5.321428571
36	1	205	234	7.090909091	3.942307692
32	1	84	445	8.090909091	3.652173913
26	1	130	427	8.211538462	4.193548387
19	1	43	335	6.203703704	1.653846154
35	1	72	309	10.65517241	1.846153846
59	1	284	453	12.24324324	7.675675676
31	1	197	192	6	5.324324324
21	1	224	166	7.904761905	4.765957447
3	1	181	56	2.434782609	4.209302326
37	1	428	24	2.181818182	6.202898551
35	1	248	283	8.085714286	5.511111111
48	1	258	303	7.973684211	6.972972973
31	1	130	361	12.03333333	3.170731707
17	1	19	253	7.228571429	0.678571429
36	1	132	379	9.023809524	4
52	1	286	487	12.81578947	6.085106383
17	1	123	196	5.025641026	5.590909091
52	1	291	335	10.15151515	6.191489362
45	1	415	41	5.857142857	5.533333333
47	1	147	394	10.1025641	4.2
50	1	281	310	6.739130435	7.594594595
34	1	132	317	8.567567568	4.4
14	1	186	54	18	3.444444444
52	1	246	330	13.2	6.473684211
70	1	344	280	10.76923077	7.47826087
61	1	311	228	7.35483871	5.867924528
14	1	-3	151	6.565217391	-0.142857143
15	1	171	98	3.266666667	4.071428571
48	1	254	217	8.346153846	6.864864865
35	1	77	247	10.73913043	1.833333333
39	1	248	242	6.540540541	5.391304348
38	1	284	355	13.14814815	4.813559322
55	1	210	237	8.172413793	4.285714286
41	1	198	334	9.823529412	4.604651163
24	1	218	282	7.230769231	4.448979592
62	1	275	372	20.66666667	7.432432432
30	1	215	242	8.066666667	6.71875
52	1	237	391	11.84848485	5.642857143
24	1	228	196	9.333333333	5.302325581
35	1	110	263	11.43478261	3.793103448
31	1	74	368	11.87096774	2.114285714
44	1	286	364	11.74193548	5.5
45	1	216	279	8.205882353	5.023255814
10	1	104	184	6.814814815	2.363636364
9	1	85	185	5.967741935	2.5
15	1	105	385	6.875	3.088235294
20	1	162	136	4.689655172	4.378378378
48	1	383	101	5.315789474	7.816326531
28	1	254	123	4.92	6.512820513
35	1	115	346	12.81481481	3.709677419
24	1	175	303	8.657142857	6.481481481
0	1	139	56	3.733333333	3.23255814
41	1	136	443	9.844444444	4.689655172
28	1	199	202	11.22222222	5.685714286
23	1	45	393	7.145454545	1.8
35	1	114	297	8.735294118	3.352941176
73	1	280	379	13.06896552	5.714285714
69	1	222	312	12	5.55
44	1	233	302	8.388888889	5.825
24	1	144	121	3.457142857	4.965517241
39	1	165	195	7.5	3.666666667
45	1	411	74	4.352941176	8.5625
9	1	115	126	5.25	2.804878049
52	1	344	254	9.071428571	5.931034483
10	1	177	150	3.846153846	5.057142857
33	1	220	228	6.705882353	5.365853659
30	1	165	161	9.470588235	5.5
31	1	155	272	8.5	5.535714286
28	1	330	141	7.421052632	8.048780488
24	1	124	241	6.885714286	4.275862069
38	1	205	292	9.419354839	4.880952381
35	1	303	135	5.4	6.586956522
47	1	256	285	10.17857143	7.314285714
37	1	215	288	4.881355932	5.119047619
47	1	193	272	11.33333333	6.225806452
56	1	266	454	15.13333333	6.487804878
27	1	105	258	6.45	3.620689655
27	1	140	165	6.111111111	3.684210526
31	1	75	303	6.733333333	3.75
77	1	499	293	7.512820513	11.88095238
30	1	171	138	4.928571429	5.029411765
24	1	296	106	4.608695652	7.789473684
53	1	158	412	11.44444444	4.27027027
14	1	112	173	4.675675676	4
52	1	215	392	8.711111111	5.512820513
17	1	234	92	6.571428571	3.714285714
24	1	111	275	7.638888889	3.083333333
29	1	247	189	5.558823529	4.490909091
45	1	304	297	8.027027027	5.152542373
21	1	232	225	9	5.523809524
24	1	107	152	5.428571429	3.962962963
45	1	288	274	9.785714286	6.260869565
24	1	99	264	5.5	3.09375
31	1	254	194	7.76	4.980392157
48	1	358	278	8.967741935	5.774193548
18	1	106	219	6.84375	3.419354839
36	1	193	280	7.179487179	7.72
31	1	216	184	7.36	5.023255814
35	1	182	228	11.4	4.23255814
21	1	149	338	7.347826087	4.257142857
34	1	137	166	5.1875	3.914285714
27	1	246	226	7.0625	5.857142857
62	1	457	224	8	8.960784314
62	1	210	278	12.63636364	4.88372093
7	1	111	89	2.617647059	2.265306122
38	1	223	288	8	6.558823529
27	1	140	238	4.857142857	3.58974359
14	1	40	233	7.060606061	1.212121212
36	1	242	368	10.51428571	6.205128205
27	1	312	201	8.04	5.379310345
37	1	72	328	9.111111111	2.117647059
34	1	165	114	4.56	4.583333333
45	1	232	262	8.733333333	5.272727273
30	1	181	143	7.526315789	5.027777778
31	1	279	153	8.5	8.205882353
41	1	191	377	11.42424242	5.457142857
27	1	151	321	7.642857143	3.595238095
21	1	120	249	9.222222222	3.157894737
0	1	42	133	4.75	2
24	1	127	154	4.052631579	3.96875
27	1	204	264	8.516129032	5.666666667
27	1	171	119	7	4.071428571
56	1	105	464	11.04761905	3.28125
28	1	42	366	8.714285714	1.4
9	1	201	188	6.714285714	4.02
70	1	427	366	14.64	8.056603774
28	1	77	349	8.725	2.026315789
48	1	261	271	11.29166667	7.909090909
37	1	169	308	5.923076923	3.673913043
28	1	109	283	6.581395349	3.892857143
13	1	45	213	6.085714286	1.730769231
24	1	290	256	8.258064516	5.087719298
27	1	67	280	9.655172414	2.161290323
20	1	222	178	5.933333333	5.55
49	1	291	252	10.95652174	5.017241379
80	1	517	109	7.266666667	9.574074074
28	1	326	186	7.44	6.037037037
20	1	122	281	7.805555556	3.588235294
14	1	182	272	6.181818182	5.2
59	1	366	323	13.45833333	6.777777778
30	1	353	115	4.107142857	5.983050847
34	1	167	279	8.454545455	4.911764706
3	1	223	131	4.851851852	5.068181818
17	1	178	199	7.653846154	4.139534884
10	1	85	237	7.9	2.5
24	1	163	136	4.857142857	3.880952381
6	1	86	234	6.157894737	2.529411765
30	1	174	365	6.759259259	4.243902439
24	1	267	139	7.315789474	5.680851064
23	1	62	280	7	2.214285714
33	1	298	137	6.523809524	6.622222222
29	1	83	182	5.6875	2.441176471
44	1	391	80	5.714285714	7.82
38	1	243	269	7.27027027	6.567567568
38	1	84	271	9.344827586	2.470588235
43	1	138	290	8.787878788	3.942857143
50	1	228	376	9.894736842	5.428571429
14	1	131	251	5.577777778	4.09375
23	1	40	289	7.605263158	1.333333333
9	1	33	195	5.416666667	0.942857143
27	1	184	128	6.736842105	3.607843137
63	1	368	312	13	7.076923077
44	1	107	320	12.8	3.242424242
33	1	270	117	11.7	5.294117647
20	1	133	232	6.27027027	4.03030303
35	1	234	108	10.8	4.875
13	1	45	183	6.535714286	1.5
12	1	88	231	6.078947368	2.514285714
30	1	194	322	7.853658537	4.311111111
22	1	152	348	5.898305085	4.108108108
10	1	26	236	6.378378378	0.866666667
17	1	141	202	4.590909091	4.028571429
27	1	112	212	6.057142857	2.8
20	1	108	156	8.210526316	3.176470588
49	1	333	188	7.52	6.283018868
20	1	184	136	6.476190476	4.487804878
45	1	170	225	7.75862069	3.863636364
34	1	79	390	8.666666667	2.633333333
49	1	248	325	9.027777778	7.515151515
31	1	47	232	6.444444444	1.807692308
16	1	142	215	5	3.944444444
13	1	9	91	4.55	0.272727273
31	1	249	65	5.416666667	7.323529412
31	1	249	168	8	4.220338983
33	1	11	394	13.5862069	0.785714286
13	1	34	286	5.607843137	1.47826087
24	1	132	363	8.441860465	3.384615385
18	1	119	146	4.171428571	3.606060606
24	1	130	326	7.244444444	3.023255814
0	1	29	76	2.111111111	1.115384615
36	1	49	441	7.875	1.814814815
27	1	176	226	6.277777778	3.2
34	1	55	329	7.833333333	1.527777778
10	1	193	106	3.419354839	5.078947368
24	1	199	97	4.409090909	4.738095238
6	1	59	216	4.32	2.681818182
24	1	77	231	5.5	2.138888889
55	1	279	275	9.482758621	4.728813559
55	1	314	256	11.13043478	6.826086957
21	1	305	104	8.666666667	5.083333333
38	1	401	45	2.647058824	6.573770492
31	1	114	163	7.761904762	3.257142857
49	1	214	243	13.5	4.458333333
12	1	190	156	5.379310345	4.871794872
24	1	74	209	6.966666667	3.7
52	1	265	400	11.42857143	7.794117647
38	1	207	312	7.428571429	4.3125
38	1	288	230	6.96969697	6.260869565
3	1	106	151	3.871794872	4.076923077
29	1	278	200	6.451612903	5.450980392
44	1	368	276	9.2	6.344827586
29	1	154	266	6.65	4.8125
52	1	150	368	16.72727273	4.838709677
3	1	59	166	4.256410256	2.80952381
49	1	182	317	10.56666667	3.872340426
7	1	76	122	2.975609756	2.451612903
34	1	150	278	6.95	4.054054054
43	1	202	336	10.18181818	4.926829268
45	1	125	211	14.06666667	3.787878788
13	1	60	255	5.1	4.285714286
7	1	69	158	4.388888889	1.769230769
28	1	200	158	5.448275862	5.555555556
45	1	228	157	4.757575758	4.75
26	1	141	222	9.25	4.028571429
59	1	273	363	11.34375	4.627118644
21	1	161	252	7.2	5.193548387
66	1	261	515	9.196428571	8.419354839
41	1	215	262	11.90909091	5.243902439
38	1	201	179	7.782608696	4.369565217
3	1	113	55	9.166666667	2.306122449
31	1	73	227	8.407407407	1.871794872
24	1	313	55	4.583333333	5.396551724
0	1	196	74	3.7	3.62962963
62	1	206	519	11.53333333	4.47826087
49	1	122	223	12.38888889	2.652173913
49	1	288	238	7.933333333	5.538461538
13	1	68	207	4.224489796	2.428571429
26	1	142	249	8.586206897	4.4375
27	1	134	262	7.939393939	2.913043478
21	1	196	129	4.607142857	5.297297297
45	1	167	341	8.973684211	3.88372093
41	1	178	281	9.064516129	4.45
45	1	282	259	11.77272727	6.266666667
58	1	165	428	11.56756757	4.125
26	1	256	188	5.081081081	4.196721311
35	1	351	166	8.736842105	6.5
34	1	222	341	7.577777778	3.580645161
38	1	410	123	5.125	8.2
10	1	105	151	5.033333333	2.837837838
29	1	248	291	8.818181818	6.888888889
48	1	231	260	8.387096774	5.775
24	1	141	208	8	4.40625
14	1	53	198	5.076923077	1.892857143
30	1	265	170	6.538461538	5.196078431
31	1	299	80	8	5.155172414
27	1	186	217	7.233333333	3.875
14	1	82	112	5.6	1.952380952
31	1	29	273	8.272727273	0.90625
28	1	203	228	6.514285714	5.8
27	1	138	359	8.159090909	4.181818182
56	1	310	131	8.1875	6.458333333
23	1	32	284	6.311111111	1.28
14	1	159	191	5.457142857	4.076923077
28	1	272	213	10.65	5.333333333
25	1	65	301	7.525	2.5
50	1	212	326	12.53846154	6.424242424
10	1	86	171	5.181818182	3.185185185
30	1	143	239	8.24137931	3.325581395
16	1	143	238	6.263157895	3.864864865
63	1	238	373	11.3030303	8.206896552
0	1	65	45	2.045454545	2.096774194
56	1	507	85	10.625	8.593220339
37	1	115	367	7.97826087	3.709677419
38	1	197	252	7.875	6.793103448
34	1	112	420	10	3.111111111
52	1	163	464	11.04761905	4.794117647
10	1	51	147	3.418604651	1.961538462
41	1	133	377	9.666666667	4.15625
32	1	213	343	11.06451613	4.840909091
9	1	120	178	5.5625	2.666666667
31	1	217	193	6.225806452	5.710526316
30	1	212	149	5.518518519	5.170731707
24	1	96	322	8.256410256	2.594594595
38	1	160	296	9.866666667	4.324324324
19	1	154	218	4.541666667	3.85
45	1	93	347	8.069767442	3.1
20	1	145	330	6.6	3.625
19	1	152	272	9.379310345	4.108108108
52	1	111	409	7.865384615	5.045454545
7	1	112	150	4.545454545	3.5
12	1	153	156	4.727272727	3.731707317
28	1	197	195	7.5	3.716981132
44	1	159	234	6.324324324	3.613636364
41	1	214	262	10.48	6.114285714
20	1	163	365	8.69047619	4.289473684
35	1	221	202	10.63157895	5.390243902
30	1	99	296	9.866666667	2.538461538
23	1	62	168	6.72	1.878787879
59	1	120	412	12.48484848	2.857142857
37	1	107	384	7.245283019	2.609756098
52	1	177	157	6.541666667	4.11627907
21	1	263	114	11.4	4.31147541
28	1	376	106	7.066666667	9.4
17	1	131	203	6.548387097	3.852941176
51	1	150	313	10.43333333	3.333333333
42	1	200	261	10.03846154	4.651162791
62	1	304	389	11.44117647	6.08
21	1	115	346	9.105263158	3.026315789
14	1	100	254	6.35	3.333333333
38	1	315	114	6	6.3
34	1	72	297	8.25	2.322580645
20	1	180	205	6.40625	6
31	1	128	339	7.704545455	2.976744186
7	1	35	113	4.708333333	1
37	1	306	129	6.45	5.884615385
14	1	95	283	5.339622642	2.714285714
31	1	231	202	10.1	4.8125
40	1	174	399	8.3125	4.142857143
13	1	55	278	6.465116279	4.230769231
19	1	117	247	9.148148148	2.785714286
42	1	113	421	11.69444444	2.511111111
23	1	230	241	5.87804878	4.509803922
42	1	204	382	10.61111111	5.666666667
45	1	169	301	9.121212121	4.828571429
63	1	405	212	9.636363636	7.105263158
30	1	213	189	5.558823529	4.953488372
31	1	317	181	9.05	5.561403509
37	1	285	327	9.342857143	5.816326531
0	1	55	111	4.44	2.115384615
26	1	196	226	8.692307692	5.157894737
10	1	137	120	4	3.805555556
30	1	244	128	5.565217391	4.357142857
28	1	82	291	6.0625	2.157894737
35	1	148	240	4.897959184	6.166666667
17	1	135	230	8.846153846	2.8125
38	1	255	97	4.619047619	5.795454545
17	1	149	122	4.066666667	3.921052632
36	1	319	105	4.565217391	7.418604651
38	1	165	267	8.9	4.714285714
35	1	143	278	8.6875	3.042553191
0	1	53	93	2.90625	1.827586207
10	1	97	183	6.310344828	3.233333333
54	1	213	244	7.625	4.346938776
10	1	168	136	4.857142857	3.428571429
45	1	276	209	5.5	5.632653061
43	1	182	270	5.744680851	3.433962264
41	1	161	212	6.625	4.735294118
20	1	136	217	7	5.913043478
58	1	202	381	10.58333333	4.29787234
27	1	139	271	6.948717949	3.657894737
27	1	174	315	8.076923077	3.346153846
42	1	176	307	8.297297297	5.5
23	1	274	99	6.1875	5.169811321
42	1	458	128	11.63636364	5.797468354
41	1	58	390	10	1.757575758
70	1	330	411	15.22222222	7.857142857
52	1	142	398	7.653846154	3.227272727
48	1	336	93	9.3	5.508196721
22	1	261	67	6.7	5.326530612
48	1	211	286	14.3	4.906976744
21	1	112	187	4.675	3.862068966
42	1	116	264	9.103448276	2.577777778
31	1	133	158	13.16666667	2.462962963
49	1	323	267	7.026315789	5.474576271
30	1	241	225	8.035714286	5.020833333
13	1	202	199	6.21875	4.926829268
34	1	207	211	7.535714286	4.404255319
31	1	188	290	12.08333333	3.916666667
7	1	104	189	7	3.586206897
7	1	231	161	4.735294118	4.914893617
40	1	228	388	8.083333333	5.7
28	1	226	263	8.766666667	5.794871795
19	1	153	262	6.238095238	4.135135135
24	1	124	224	6.222222222	4
37	1	503	31	5.166666667	7.507462687
55	1	344	398	7.509433962	6.254545455
19	1	40	226	7.533333333	1.25
31	1	220	184	8.761904762	5
31	1	110	183	7.625	2.5
10	1	75	143	6.80952381	2.142857143
29	1	428	16	1.777777778	6.202898551
44	1	248	184	7.076923077	4.275862069
45	1	221	354	9.315789474	6.5
48	1	209	217	7.75	5.5
39	1	120	280	8.484848485	3.243243243
35	1	138	262	7.939393939	5.307692308
7	1	35	181	5.323529412	0.972222222
17	1	163	205	7.321428571	4.939393939
27	1	100	387	7.588235294	4
63	1	291	300	15	8.083333333
27	1	233	175	6.25	5.547619048
27	1	174	198	11.64705882	3.954545455
17	1	101	180	6.428571429	3.060606061
34	1	150	221	9.608695652	2.941176471
41	1	135	254	6.195121951	5
34	1	246	277	8.393939394	6.833333333
35	1	321	223	7.964285714	5.944444444
27	1	182	258	4.961538462	4.666666667
41	1	223	358	8.136363636	5.717948718
52	1	237	489	15.28125	5.642857143
26	1	144	317	9.606060606	4.114285714
30	1	267	104	4.727272727	7.026315789
31	1	84	322	7	2.625
27	1	258	155	8.157894737	5.375
24	1	238	188	4.947368421	4.407407407
21	1	117	272	8.774193548	3.9
48	1	296	224	7.724137931	5.692307692
21	1	165	184	5.575757576	5.15625
55	1	183	274	6.372093023	4.945945946
58	1	296	296	15.57894737	6.88372093
20	1	152	275	8.59375	3.454545455
53	1	162	480	8.727272727	4.153846154
10	1	166	141	3.916666667	3.860465116
30	1	152	109	5.19047619	3.454545455
19	1	125	302	9.4375	3.048780488
31	1	229	168	6.72	4.673469388
30	1	147	195	5.735294118	3.972972973
6	1	25	252	7	1
20	1	107	188	6.266666667	3.34375
7	1	-2	180	5.142857143	-0.166666667
28	1	88	354	5.619047619	3.826086957
49	1	201	267	10.68	5.583333333
55	1	315	326	8.15	7.682926829
27	1	291	136	4.121212121	5.82
62	1	182	399	10.23076923	6.066666667
24	1	258	172	7.47826087	5.375
16	1	126	154	4.4	4.344827586
27	1	108	357	9.916666667	3.6
13	1	215	107	4.115384615	4.777777778
54	1	358	353	13.07407407	6.392857143
17	1	106	155	5	3.419354839
16	1	122	147	5.653846154	3.210526316
0	1	115	131	5.458333333	3.484848485
41	1	289	258	10.75	5.452830189
14	1	132	104	8.666666667	2.75
27	1	143	286	11	4.333333333
52	1	286	114	6	6.355555556
49	1	81	375	13.88888889	3.115384615
31	1	300	149	6.208333333	5.263157895
49	1	150	337	12.96153846	3.571428571
10	1	83	148	6.434782609	2.766666667
21	1	78	312	8	2.689655172
52	1	222	368	10.82352941	6.9375
27	1	227	180	4.864864865	5.820512821
8	1	28	257	6.945945946	0.823529412
30	1	175	223	6.96875	5.64516129
48	1	155	284	6.761904762	4.84375
23	1	227	169	5.28125	4.829787234
24	1	161	231	6.416666667	4.735294118
24	1	112	204	6	3.5
28	1	291	150	8.333333333	6.191489362
38	1	15	497	9.036363636	0.882352941
27	1	201	353	10.08571429	3.941176471
27	1	153	255	7.5	3.731707317
35	1	229	280	9.333333333	6.735294118
66	1	167	537	13.425	4.175
56	1	274	299	11.07407407	7.611111111
40	1	185	212	7.066666667	4.868421053
33	1	266	161	6.708333333	4.222222222
23	1	215	297	6.906976744	5.119047619
20	1	196	156	15.6	4.170212766
9	1	93	165	6.346153846	2.735294118
27	1	300	90	4.736842105	5.769230769
14	1	89	174	3.70212766	3.178571429
45	1	275	150	8.333333333	5.97826087
51	1	148	345	9.583333333	4.228571429
3	1	34	106	2.65	1.36
13	1	88	173	3.931818182	2.75
7	1	130	109	5.19047619	3.823529412
21	1	113	287	7.972222222	3.054054054
41	1	217	362	10.05555556	7
31	1	141	283	7.447368421	3.710526316
38	1	229	243	6.75	5.725
20	1	133	305	7.261904762	4.290322581
10	1	166	137	3.914285714	4.368421053
44	1	477	300	10.34482759	7.571428571
59	1	299	299	9.966666667	7.475
39	1	179	320	9.696969697	4.972222222
23	1	124	257	6.58974359	3.179487179
30	1	250	184	7.666666667	4.545454545
52	1	279	405	12.27272727	7.153846154
17	1	62	158	6.869565217	1.823529412
28	1	189	187	8.5	4.295454545
27	1	333	114	8.142857143	5.842105263
23	1	59	349	6.58490566	2.95
27	1	205	282	7.05	4.555555556
30	1	78	354	7.531914894	3.391304348
32	1	207	288	9.290322581	3.980769231
21	1	161	97	7.461538462	3.425531915
41	1	307	220	6.470588235	5.581818182
38	1	257	122	10.16666667	5.039215686
10	1	107	107	2.972222222	3.057142857
13	1	113	248	5.76744186	5.136363636
35	1	309	271	15.94117647	4.828125
37	1	235	174	6.692307692	4.795918367
27	1	144	253	7.441176471	3.348837209
14	1	21	180	7.2	0.913043478
24	1	59	222	7.161290323	1.638888889
21	1	135	166	6.384615385	3.214285714
31	1	150	291	6.928571429	4.838709677
59	1	274	384	8.930232558	6.85
13	1	213	62	2.296296296	5.756756757
59	1	298	189	8.590909091	6.47826087
27	1	134	243	8.379310345	2.481481481
28	1	171	111	5.842105263	5.7
24	1	237	172	6.142857143	4.9375
27	1	242	80	3.80952381	7.117647059
17	1	59	256	5.953488372	2.269230769
23	1	279	208	6.117647059	4.810344828
31	1	216	146	11.23076923	3.927272727
21	1	152	100	3.225806452	3.454545455
38	1	266	159	7.227272727	7.189189189
3	1	20	241	5.127659574	0.8
21	1	250	132	5.5	5.208333333
17	1	75	222	8.222222222	2.205882353
28	1	68	316	7.523809524	2.193548387
55	1	403	81	9	6.2
14	1	89	170	6.071428571	2.282051282
19	1	186	267	5.134615385	5.8125
52	1	289	174	6.692307692	7.41025641
20	1	56	243	7.59375	2
6	1	89	304	6.755555556	2.119047619
24	1	141	121	3.78125	3.43902439
34	1	196	283	7.648648649	5.157894737
31	1	249	162	7.363636364	6.552631579
10	1	68	144	4.965517241	3.777777778
37	1	158	302	9.4375	4.647058824
54	1	307	453	12.24324324	6.673913043
49	1	266	386	9.19047619	6.487804878
49	1	325	209	11	7.065217391
34	1	159	257	9.518518519	3.613636364
35	1	96	296	6.727272727	3.096774194
38	1	293	138	7.263157895	5.528301887
27	1	214	340	5.964912281	6.6875
36	1	202	304	7.794871795	4.29787234
44	1	326	230	10.45454545	6.791666667
41	1	304	279	6.2	5.846153846
40	1	272	234	8.666666667	5.666666667
14	1	59	271	7.742857143	1.966666667
14	1	192	245	5	4.923076923
20	1	190	244	8.714285714	3.454545455
24	1	135	506	6.746666667	3.375
28	1	94	274	5.956521739	3.133333333
48	1	332	275	12.5	5.928571429
27	1	289	158	6.869565217	6.720930233
14	1	180	98	3.769230769	5.625
7	1	101	131	4.09375	3.482758621
16	1	158	176	7.04	2.872727273
41	1	165	329	8.024390244	4.230769231
38	1	165	220	6.666666667	4.024390244
42	1	217	258	8.6	4.822222222
22	1	72	367	8.534883721	2.88
15	1	226	166	4.368421053	5.380952381
52	1	158	362	9.282051282	4.647058824
32	1	255	88	3.384615385	7.96875
33	1	171	381	9.525	4.071428571
9	1	197	110	4.230769231	4.020408163
20	1	124	129	7.166666667	2.88372093
56	1	297	163	8.578947368	5.823529412
7	1	-26	229	6.361111111	-1
27	1	130	273	7.8	2.765957447
31	1	200	261	10.03846154	4.347826087
31	1	204	109	3.75862069	4.857142857
21	1	268	58	5.8	5.36
45	1	244	232	6.823529412	5.422222222
52	1	193	235	10.2173913	5.216216216
42	1	276	235	6.911764706	5.872340426
38	1	86	300	11.53846154	3.071428571
48	1	171	407	16.28	4.5
52	1	250	315	9.84375	5.208333333
0	1	82	127	3.432432432	2.733333333
7	1	73	95	2.878787879	2.517241379
56	1	195	208	8.32	5.571428571
56	1	310	302	8.162162162	6.458333333
13	1	69	181	5.323529412	2.3
65	1	361	200	10	6.811320755
16	1	70	137	4.28125	3.181818182
7	1	146	109	4.739130435	6.347826087
30	1	141	237	4.9375	4.40625
40	1	174	213	10.65	4.461538462
34	1	190	285	7.5	5.277777778
24	1	73	311	7.068181818	2.147058824
38	1	152	280	8.235294118	4.903225806
28	1	319	175	4.375	6.787234043
14	1	86	46	2.875	2.965517241
20	1	156	159	6.625	3.627906977
21	1	261	140	23.33333333	5.019230769
42	1	143	359	10.55882353	3.666666667
0	1	125	23	1.4375	2.840909091
50	1	73	406	9.441860465	2.517241379
58	1	415	129	4.448275862	9.431818182
23	1	130	183	7.32	4.333333333
8	1	88	135	4.5	3.034482759
14	1	80	190	5.428571429	2.5
30	1	333	203	5.486486486	5.644067797
27	1	149	332	5.824561404	7.095238095
19	1	127	320	8.421052632	3.735294118
23	1	213	202	8.08	5.605263158
49	1	323	195	7.8	7.340909091
21	1	78	278	7.513513514	2.294117647
35	1	260	96	8	6.5
34	1	242	189	14.53846154	5.377777778
35	1	8	433	8.490196078	0.347826087
10	1	110	154	3.85	2.894736842
21	1	73	255	7.5	1.921052632
23	1	138	336	8.195121951	4.181818182
20	1	229	140	7.368421053	4.490196078
28	1	208	102	6	4
24	1	189	142	5.259259259	6.096774194
20	1	137	296	6.577777778	3.605263158
31	1	151	368	8.975609756	3.511627907
45	1	240	203	10.15	4.363636364
49	1	176	370	9.25	4.093023256
52	1	218	337	9.361111111	4.954545455
45	1	403	64	2.782608696	8.395833333
26	1	160	287	7.175	3.720930233
3	1	87	116	4.296296296	3.222222222
35	1	175	408	10.2	5
52	1	308	366	9.631578947	6.039215686
30	1	201	280	8.484848485	5.153846154
34	1	308	206	5.567567568	5.923076923
7	1	194	82	4.555555556	4.731707317
55	1	188	248	9.185185185	4.947368421
24	1	-2	407	7.980392157	-0.086956522
20	1	262	171	4.5	6.55
63	1	195	288	13.09090909	4.642857143
20	1	153	88	3.259259259	3.477272727
21	1	112	338	9.657142857	4.869565217
35	1	290	248	9.92	6.304347826
28	1	119	315	7.682926829	4.76
24	1	92	199	7.653846154	2.486486486
38	1	199	309	9.967741935	4.627906977
16	1	68	317	6.215686275	3.777777778
28	1	154	148	4.484848485	3.347826087
30	1	213	202	7.769230769	4.733333333
7	1	152	140	6.086956522	3.534883721
13	1	57	307	6.531914894	2.28
14	1	78	241	5.87804878	2.516129032
27	1	307	126	4.846153846	5.032786885
40	1	235	175	6.730769231	3.983050847
20	1	216	108	4.153846154	4.32
28	1	206	243	5.4	4.790697674
31	1	38	287	6.833333333	1.583333333
32	1	181	221	7.366666667	4.763157895
41	1	186	344	10.42424242	3.957446809
17	1	129	76	2.620689655	4.161290323
53	1	331	192	7.68	6.365384615
28	1	230	172	8.19047619	5
55	1	489	53	7.571428571	8.732142857
14	1	100	215	6.142857143	2.857142857
13	1	91	169	7.347826087	2.459459459
21	1	53	223	6.027027027	2.944444444
34	1	187	343	8.365853659	4.56097561
36	1	150	336	5.793103448	4.166666667
31	1	216	182	5.515151515	4.8
2	1	128	134	3.435897436	3.2
31	1	320	88	7.333333333	6.037735849
23	1	132	325	7.222222222	4.551724138
19	1	244	83	3.952380952	5.191489362
48	1	138	542	10.62745098	6.272727273
27	1	161	160	5.517241379	4.128205128
28	1	240	116	7.25	4.444444444
27	1	148	254	6.047619048	4.352941176
47	1	377	135	15	6.732142857
38	1	153	269	12.80952381	3.558139535
24	1	98	206	6.64516129	2
24	1	233	106	4.818181818	5.825
14	1	107	152	5.428571429	2.815789474
29	1	233	204	7.846153846	4.017241379
42	1	64	239	9.192307692	1.488372093
17	1	395	84	3	5.984848485
55	1	343	192	12	7.456521739
17	1	103	207	5.307692308	2.641025641
45	1	111	410	7.884615385	3.083333333
7	1	135	68	3.578947368	3.068181818
13	1	52	216	8	2.166666667
27	1	214	151	5.592592593	3.821428571
27	1	233	203	14.5	5.065217391
41	1	262	155	11.92307692	5.137254902
45	1	319	289	6.880952381	5.696428571
47	1	229	458	12.05263158	6.361111111
16	1	23	208	7.172413793	1.15
31	1	303	124	4.769230769	6.183673469
37	1	319	189	8.590909091	4.623188406
35	1	126	71	4.4375	2.680851064
21	1	131	237	5.266666667	4.517241379
35	1	290	307	9.029411765	6.444444444
32	1	150	285	9.827586207	4.545454545
27	1	116	246	7.235294118	4
7	1	-5	38	1.80952381	-0.166666667
45	1	282	331	12.25925926	6.87804878
7	1	77	202	6.516129032	2.40625
37	1	326	174	9.157894737	5.258064516
16	1	171	218	6.055555556	4.170731707
55	1	254	344	9.052631579	5.906976744
44	1	373	163	4.075	8.108695652
31	1	22	353	9.051282051	0.733333333
44	1	417	173	9.611111111	5.873239437
21	1	158	223	7.433333333	4.051282051
27	1	29	370	6.607142857	1.115384615
36	1	285	149	9.933333333	5.377358491
23	1	222	310	7.380952381	5.285714286
49	1	645	111	6.166666667	7.678571429
51	1	189	380	9.047619048	5.4
23	1	177	217	7.482758621	3.847826087
27	1	307	227	8.730769231	6.673913043
10	1	99	106	2.864864865	3.193548387
45	1	226	194	6.466666667	4.264150943
37	1	312	218	6.8125	5.379310345
24	1	16	397	8.270833333	0.695652174
38	1	154	286	9.225806452	4.4
16	1	48	239	7.242424242	1.263157895
7	1	118	155	6.2	4.37037037
44	1	285	211	6.806451613	5.277777778
48	1	207	347	9.914285714	5.594594595
45	1	254	315	12.11538462	6.047619048
47	1	174	371	7.893617021	3.70212766
24	1	166	161	7	4.048780488
42	1	102	418	8.038461538	3.1875
40	1	145	405	8.617021277	3.815789474

par5 <- '0'
par4 <- '0'
par3 <- 'No Linear Trend'
par2 <- 'Do not include Seasonal Dummies'
par1 <- '1'
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
mywarning <- ''
par1 <- as.numeric(par1)
if(is.na(par1)) {
par1 <- 1
mywarning = 'Warning: you did not specify the column number of the endogenous series! The first column was selected by default.'
}
if (par4=='') par4 <- 0
par4 <- as.numeric(par4)
if (par5=='') par5 <- 0
par5 <- as.numeric(par5)
x <- na.omit(t(y))
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'Seasonal Differences (s=12)'){
(n <- n - 12)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B12)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+12,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'First and Seasonal Differences (s=12)'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
(n <- n - 12)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B12)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+12,j] - x[i,j]
}
}
x <- x2
}
if(par4 > 0) {
x2 <- array(0, dim=c(n-par4,par4), dimnames=list(1:(n-par4), paste(colnames(x)[par1],'(t-',1:par4,')',sep='')))
for (i in 1:(n-par4)) {
for (j in 1:par4) {
x2[i,j] <- x[i+par4-j,par1]
}
}
x <- cbind(x[(par4+1):n,], x2)
n <- n - par4
}
if(par5 > 0) {
x2 <- array(0, dim=c(n-par5*12,par5), dimnames=list(1:(n-par5*12), paste(colnames(x)[par1],'(t-',1:par5,'s)',sep='')))
for (i in 1:(n-par5*12)) {
for (j in 1:par5) {
x2[i,j] <- x[i+par5*12-j*12,par1]
}
}
x <- cbind(x[(par5*12+1):n,], x2)
n <- n - par5*12
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
(k <- length(x[n,]))
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
(k <- length(x[n,]))
head(x)
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, mywarning)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,formatC(signif(mysum$coefficients[i,1],5),format='g',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,2],5),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,3],4),format='e',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4],4),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4]/2,4),format='g',flag=' '))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a,formatC(signif(sqrt(mysum$r.squared),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$adj.r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a,formatC(signif(mysum$fstatistic[1],6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[2],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[3],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a,formatC(signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a,formatC(signif(mysum$sigma,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a,formatC(signif(sum(myerror*myerror),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
if(n < 200) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,formatC(signif(x[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(x[i]-mysum$resid[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$resid[i],6),format='g',flag=' '))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,1],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,2],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,3],6),format='g',flag=' '))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant1,6))
a<-table.element(a,formatC(signif(numsignificant1/numgqtests,6),format='g',flag=' '))
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant5,6))
a<-table.element(a,signif(numsignificant5/numgqtests,6))
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant10,6))
a<-table.element(a,signif(numsignificant10/numgqtests,6))
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}
}