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*The author of this computation has been verified*

R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Sun, 13 Dec 2009 04:04:11 -0700

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Dec/13/t1260702342dfhpgv0641rumis.htm/, Retrieved Sun, 28 Apr 2024 08:17:18 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=67220, Retrieved Sun, 28 Apr 2024 08:17:18 +0000

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

124

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RMPD  [Multiple Regression] [Seatbelt] [2009-11-12 13:54:52] [b98453cac15ba1066b407e146608df68]
-    D    [Multiple Regression] [workshop 7] [2009-11-20 08:30:37] [f1a50df816abcbb519e7637ff6b72fa0]
-    D        [Multiple Regression] [paper - multiple ...] [2009-12-13 11:04:11] [a18540c86166a2b66550d1fef0503cc2] [Current]

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Dataseries X:

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Histogram

Boxplots

100,00	100,00
103,53	102,62
108,36	107,62
115,20	103,46
123,51	103,61
132,87	106,10
130,55	107,13
136,68	108,82
140,63	112,93
143,47	109,35
124,10	108,75
111,49	110,83
119,93	110,95
131,79	114,96
136,61	120,45
141,79	122,89
142,23	120,43
146,74	121,76
154,85	122,78
148,44	125,32
154,18	128,68
149,10	127,91
152,22	125,52
149,34	127,56
160,94	127,90
176,16	130,75
195,12	133,57
186,07	135,83
200,78	135,26
208,15	135,99
209,56	139,12
203,33	137,64
198,84	138,59
200,63	138,32
206,47	135,99
196,68	136,96
203,81	137,13
190,18	138,67
187,50	143,04
187,62	143,98
168,92	144,09
164,78	144,97
175,98	147,77
174,70	149,73
166,95	153,11
161,76	151,58
149,65	149,04
137,42	154,70
142,60	154,91
146,94	159,08
152,52	168,01
147,47	164,17
146,15	163,77
152,04	163,49
144,42	166,13
138,15	166,15
125,94	170,05
112,61	167,37
111,48	164,80
95,25	169,53
105,38	168,17
109,59	172,45
99,07	177,81
92,07	175,38
89,10	175,64
86,36	178,80
95,39	180,49
95,27	182,71
98,56	185,73
101,79	183,17
102,02	182,11
98,21	185,43
104,42	185,29
105,62	188,55
109,46	191,89
110,94	190,62
113,09	190,29
109,58	193,27
111,41	194,54
109,83	195,42
110,58	198,58
109,04	197,60
107,80	194,62
109,79	199,30
110,76	199,51
112,64	203,08
114,17	204,36
115,99	206,47
119,01	206,51
117,92	208,09
115,92	210,08
120,75	212,42
124,94	231,32
129,17	231,94
128,14	228,02
134,18	231,95
131,74	233,88
134,32	235,95
137,80	242,92
141,79	240,80
142,75	240,34
144,30	241,95
145,49	246,61
138,21	247,80
139,02	250,97
141,91	248,11
144,95	243,75
146,11	248,79
150,96	247,03
148,20	250,49
152,12	260,83
154,74	256,22
150,80	255,33
152,60	259,54
158,74	260,64
161,83	262,20
162,40	267,29
156,11	265,55
154,93	258,99
157,18	265,04
159,85	262,18
154,40	265,05
151,57	268,78
133,34	265,93
131,20	261,30
124,17	265,20
133,19	263,26
130,94	265,41
119,58	268,75
118,55	261,95
119,96	258,16
108,42	265,22
95,93	267,34
88,83	269,01
84,98	272,90
81,61	278,76
72,84	278,98
74,72	281,03
83,40	285,65
87,42	287,34
86,33	294,57
94,28	294,24
98,81	295,13
100,96	299,65
99,14	303,59

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	4 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 4 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=67220&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=67220&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=67220&T=0

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	4 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 170.018050942712 -0.185865384723691X[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  170.018050942712 -0.185865384723691X[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=67220&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  170.018050942712 -0.185865384723691X[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=67220&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=67220&T=1

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 170.018050942712 -0.185865384723691X[t] + e[t]

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	170.018050942712	8.533857	19.9228	0	0
X	-0.185865384723691	0.042221	-4.4022	2.1e-05	1e-05

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & 170.018050942712 & 8.533857 & 19.9228 & 0 & 0 \tabularnewline
X & -0.185865384723691 & 0.042221 & -4.4022 & 2.1e-05 & 1e-05 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=67220&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]170.018050942712[/C][C]8.533857[/C][C]19.9228[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]X[/C][C]-0.185865384723691[/C][C]0.042221[/C][C]-4.4022[/C][C]2.1e-05[/C][C]1e-05[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=67220&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=67220&T=2

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	170.018050942712	8.533857	19.9228	0	0
X	-0.185865384723691	0.042221	-4.4022	2.1e-05	1e-05

Multiple Linear Regression - Regression Statistics
Multiple R	0.345468222898485
R-squared	0.119348293032637
Adjusted R-squared	0.113189889487411
F-TEST (value)	19.3797454415195
F-TEST (DF numerator)	1
F-TEST (DF denominator)	143
p-value	2.08334234278462e-05
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	29.6276045773387
Sum Squared Residuals	125524.678277733

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.345468222898485 \tabularnewline
R-squared & 0.119348293032637 \tabularnewline
Adjusted R-squared & 0.113189889487411 \tabularnewline
F-TEST (value) & 19.3797454415195 \tabularnewline
F-TEST (DF numerator) & 1 \tabularnewline
F-TEST (DF denominator) & 143 \tabularnewline
p-value & 2.08334234278462e-05 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 29.6276045773387 \tabularnewline
Sum Squared Residuals & 125524.678277733 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=67220&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.345468222898485[/C][/ROW]
[ROW][C]R-squared[/C][C]0.119348293032637[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.113189889487411[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]19.3797454415195[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]1[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]143[/C][/ROW]
[ROW][C]p-value[/C][C]2.08334234278462e-05[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]29.6276045773387[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]125524.678277733[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=67220&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=67220&T=3

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Regression Statistics
Multiple R	0.345468222898485
R-squared	0.119348293032637
Adjusted R-squared	0.113189889487411
F-TEST (value)	19.3797454415195
F-TEST (DF numerator)	1
F-TEST (DF denominator)	143
p-value	2.08334234278462e-05
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	29.6276045773387
Sum Squared Residuals	125524.678277733

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	100	151.431512470344	-51.4315124703439
2	103.53	150.944545162367	-47.4145451623672
3	108.36	150.015218238749	-41.6552182387489
4	115.2	150.788418239199	-35.5884182391994
5	123.51	150.760538431491	-27.2505384314908
6	132.87	150.297733623529	-17.4277336235289
7	130.55	150.106292277263	-19.5562922772635
8	136.68	149.792179777080	-13.1121797770804
9	140.63	149.028273045866	-8.39827304586606
10	143.47	149.693671123177	-6.22367112317687
11	124.1	149.805190354011	-25.7051903540111
12	111.49	149.418590353786	-37.9285903537858
13	119.93	149.396286507619	-29.4662865076190
14	131.79	148.650966314877	-16.8609663148770
15	136.61	147.630565352744	-11.0205653527439
16	141.79	147.177053814018	-5.38705381401811
17	142.23	147.634282660438	-5.40428266043839
18	146.74	147.387081698756	-0.64708169875586
19	154.85	147.197499006338	7.65250099366229
20	148.44	146.725400929140	1.71459907086046
21	154.18	146.100893236468	8.07910676353207
22	149.1	146.244009582705	2.85599041729482
23	152.22	146.688227852195	5.5317721478052
24	149.34	146.309062467358	3.03093753264154
25	160.94	146.245868236552	14.6941317634476
26	176.16	145.716151890090	30.4438481099101
27	195.12	145.192011505169	49.9279884948309
28	186.07	144.771955735694	41.2980442643065
29	200.78	144.877899004986	55.902100995014
30	208.15	144.742217274138	63.4077827258623
31	209.56	144.160458619953	65.3995413800474
32	203.33	144.435539389344	58.8944606106564
33	198.84	144.258967273856	54.5810327261438
34	200.63	144.309150927732	56.3208490722684
35	206.47	144.742217274138	61.7277827258623
36	196.68	144.561927850956	52.1180721490442
37	203.81	144.530330735553	59.2796692644473
38	190.18	144.244098043078	45.9359019569217
39	187.5	143.431866311836	44.0681336881643
40	187.62	143.257152850195	44.3628471498045
41	168.92	143.236707657876	25.6832923421241
42	164.78	143.073146119319	21.706853880681
43	175.98	142.552723042093	33.4272769579073
44	174.7	142.188426888034	32.5115731119657
45	166.95	141.560201887668	25.3897981123318
46	161.76	141.844575926295	19.9154240737046
47	149.65	142.316674003494	7.33332599650641
48	137.42	141.264675925958	-3.84467592595752
49	142.6	141.225644195166	1.37435580483446
50	146.94	140.450585540868	6.48941445913226
51	152.52	138.790807655285	13.7291923447148
52	147.47	139.504530732624	7.96546926737584
53	146.15	139.578876886514	6.57112311348637
54	152.04	139.630919194236	12.4090808057637
55	144.42	139.140234578566	5.27976542143426
56	138.15	139.136517270871	-0.986517270871246
57	125.94	138.411642270449	-12.4716422704489
58	112.61	138.909761501508	-26.2997615015083
59	111.48	139.387435540248	-27.9074355402482
60	95.25	138.508292270505	-43.2582922705052
61	105.38	138.761069193729	-33.3810691937294
62	109.59	137.965565347112	-28.375565347112
63	99.07	136.969326884993	-37.899326884993
64	92.07	137.420979769872	-45.3509797698716
65	89.1	137.372654769843	-48.2726547698434
66	86.36	136.785320154117	-50.4253201541166
67	95.39	136.471207653934	-41.0812076539335
68	95.27	136.058586499847	-40.7885864998469
69	98.56	135.497273037981	-36.9372730379814
70	101.79	135.973088422874	-34.183088422874
71	102.02	136.170105730681	-34.1501057306812
72	98.21	135.553032653399	-37.3430326533985
73	104.42	135.579053807260	-31.1590538072598
74	105.62	134.973132653061	-29.3531326530606
75	109.46	134.352342268083	-24.8923422680835
76	110.94	134.588391306683	-23.6483913066826
77	113.09	134.649726883641	-21.5597268836414
78	109.58	134.095848037165	-24.5158480371648
79	111.41	133.859798998566	-22.4497989985657
80	109.83	133.696237460009	-23.8662374600088
81	110.58	133.108902844282	-22.5289028442820
82	109.04	133.291050921311	-24.2510509213112
83	107.8	133.844929767788	-26.0449297677878
84	109.79	132.975079767281	-23.1850797672809
85	110.76	132.936048036489	-22.1760480364889
86	112.64	132.272508613025	-19.6325086130254
87	114.17	132.034600920579	-17.8646009205790
88	115.99	131.642424958812	-15.6524249588121
89	119.01	131.634990343423	-12.6249903434231
90	117.92	131.341323035560	-13.4213230355597
91	115.92	130.971450919960	-15.0514509199595
92	120.75	130.536525919706	-9.7865259197061
93	124.94	127.023670148428	-2.08367014842835
94	129.17	126.908433609900	2.26156639010033
95	128.14	127.637025918017	0.502974081983463
96	134.18	126.906574956052	7.27342504394758
97	131.74	126.547854763536	5.19214523646431
98	134.32	126.163113417158	8.15688658284233
99	137.8	124.867631685634	12.9323683143665
100	141.79	125.261666301248	16.5283336987522
101	142.75	125.347164378221	17.4028356217793
102	144.3	125.047921108816	19.2520788911845
103	145.49	124.181788416003	21.3082115839969
104	138.21	123.960608608182	14.2493913918181
105	139.02	123.371415338608	15.6485846613922
106	141.91	123.902990338918	18.0070096610824
107	144.95	124.713363416313	20.2366365836871
108	146.11	123.776601877305	22.3333981226945
109	150.96	124.103724954419	26.8562750455808
110	148.2	123.460630723275	24.7393692767248
111	152.12	121.538782645232	30.5812173547678
112	154.74	122.395622068808	32.3443779311916
113	150.8	122.561042261213	28.2389577387875
114	152.6	121.778548991526	30.8214510084742
115	158.74	121.574097068330	37.1659029316703
116	161.83	121.284147068161	40.5458529318392
117	162.4	120.338092259917	42.0619077400828
118	156.11	120.661498029336	35.4485019706636
119	154.93	121.880774953124	33.0492250468762
120	157.18	120.756289375546	36.4237106244545
121	159.85	121.287864375855	38.5621356241447
122	154.4	120.754430721698	33.6455692783017
123	151.57	120.061152836679	31.5088471633211
124	133.34	120.590869183141	12.7491308168586
125	131.2	121.451425914412	9.74857408558788
126	124.17	120.726550913990	3.44344908601028
127	133.19	121.087129760354	12.1028702396463
128	130.94	120.687519183198	10.2524808168023
129	119.58	120.066728798221	-0.486728798220618
130	118.55	121.330613414342	-2.78061341434171
131	119.96	122.035043222445	-2.0750432224445
132	108.42	120.722833606295	-12.3028336062952
133	95.93	120.328798990681	-24.398798990681
134	88.83	120.018403798192	-31.1884037981925
135	84.98	119.295387451617	-34.3153874516173
136	81.61	118.206216297136	-36.5962162971365
137	72.84	118.165325912497	-45.3253259124973
138	74.72	117.784301873814	-43.0643018738137
139	83.4	116.925603796390	-33.5256037963902
140	87.42	116.611491296207	-29.1914912962072
141	86.33	115.267684564655	-28.9376845646549
142	94.28	115.329020141614	-21.0490201416137
143	98.81	115.163599949210	-16.3535999492097
144	100.96	114.323488410259	-13.3634884102586
145	99.14	113.591178794447	-14.4511787944472

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 100 & 151.431512470344 & -51.4315124703439 \tabularnewline
2 & 103.53 & 150.944545162367 & -47.4145451623672 \tabularnewline
3 & 108.36 & 150.015218238749 & -41.6552182387489 \tabularnewline
4 & 115.2 & 150.788418239199 & -35.5884182391994 \tabularnewline
5 & 123.51 & 150.760538431491 & -27.2505384314908 \tabularnewline
6 & 132.87 & 150.297733623529 & -17.4277336235289 \tabularnewline
7 & 130.55 & 150.106292277263 & -19.5562922772635 \tabularnewline
8 & 136.68 & 149.792179777080 & -13.1121797770804 \tabularnewline
9 & 140.63 & 149.028273045866 & -8.39827304586606 \tabularnewline
10 & 143.47 & 149.693671123177 & -6.22367112317687 \tabularnewline
11 & 124.1 & 149.805190354011 & -25.7051903540111 \tabularnewline
12 & 111.49 & 149.418590353786 & -37.9285903537858 \tabularnewline
13 & 119.93 & 149.396286507619 & -29.4662865076190 \tabularnewline
14 & 131.79 & 148.650966314877 & -16.8609663148770 \tabularnewline
15 & 136.61 & 147.630565352744 & -11.0205653527439 \tabularnewline
16 & 141.79 & 147.177053814018 & -5.38705381401811 \tabularnewline
17 & 142.23 & 147.634282660438 & -5.40428266043839 \tabularnewline
18 & 146.74 & 147.387081698756 & -0.64708169875586 \tabularnewline
19 & 154.85 & 147.197499006338 & 7.65250099366229 \tabularnewline
20 & 148.44 & 146.725400929140 & 1.71459907086046 \tabularnewline
21 & 154.18 & 146.100893236468 & 8.07910676353207 \tabularnewline
22 & 149.1 & 146.244009582705 & 2.85599041729482 \tabularnewline
23 & 152.22 & 146.688227852195 & 5.5317721478052 \tabularnewline
24 & 149.34 & 146.309062467358 & 3.03093753264154 \tabularnewline
25 & 160.94 & 146.245868236552 & 14.6941317634476 \tabularnewline
26 & 176.16 & 145.716151890090 & 30.4438481099101 \tabularnewline
27 & 195.12 & 145.192011505169 & 49.9279884948309 \tabularnewline
28 & 186.07 & 144.771955735694 & 41.2980442643065 \tabularnewline
29 & 200.78 & 144.877899004986 & 55.902100995014 \tabularnewline
30 & 208.15 & 144.742217274138 & 63.4077827258623 \tabularnewline
31 & 209.56 & 144.160458619953 & 65.3995413800474 \tabularnewline
32 & 203.33 & 144.435539389344 & 58.8944606106564 \tabularnewline
33 & 198.84 & 144.258967273856 & 54.5810327261438 \tabularnewline
34 & 200.63 & 144.309150927732 & 56.3208490722684 \tabularnewline
35 & 206.47 & 144.742217274138 & 61.7277827258623 \tabularnewline
36 & 196.68 & 144.561927850956 & 52.1180721490442 \tabularnewline
37 & 203.81 & 144.530330735553 & 59.2796692644473 \tabularnewline
38 & 190.18 & 144.244098043078 & 45.9359019569217 \tabularnewline
39 & 187.5 & 143.431866311836 & 44.0681336881643 \tabularnewline
40 & 187.62 & 143.257152850195 & 44.3628471498045 \tabularnewline
41 & 168.92 & 143.236707657876 & 25.6832923421241 \tabularnewline
42 & 164.78 & 143.073146119319 & 21.706853880681 \tabularnewline
43 & 175.98 & 142.552723042093 & 33.4272769579073 \tabularnewline
44 & 174.7 & 142.188426888034 & 32.5115731119657 \tabularnewline
45 & 166.95 & 141.560201887668 & 25.3897981123318 \tabularnewline
46 & 161.76 & 141.844575926295 & 19.9154240737046 \tabularnewline
47 & 149.65 & 142.316674003494 & 7.33332599650641 \tabularnewline
48 & 137.42 & 141.264675925958 & -3.84467592595752 \tabularnewline
49 & 142.6 & 141.225644195166 & 1.37435580483446 \tabularnewline
50 & 146.94 & 140.450585540868 & 6.48941445913226 \tabularnewline
51 & 152.52 & 138.790807655285 & 13.7291923447148 \tabularnewline
52 & 147.47 & 139.504530732624 & 7.96546926737584 \tabularnewline
53 & 146.15 & 139.578876886514 & 6.57112311348637 \tabularnewline
54 & 152.04 & 139.630919194236 & 12.4090808057637 \tabularnewline
55 & 144.42 & 139.140234578566 & 5.27976542143426 \tabularnewline
56 & 138.15 & 139.136517270871 & -0.986517270871246 \tabularnewline
57 & 125.94 & 138.411642270449 & -12.4716422704489 \tabularnewline
58 & 112.61 & 138.909761501508 & -26.2997615015083 \tabularnewline
59 & 111.48 & 139.387435540248 & -27.9074355402482 \tabularnewline
60 & 95.25 & 138.508292270505 & -43.2582922705052 \tabularnewline
61 & 105.38 & 138.761069193729 & -33.3810691937294 \tabularnewline
62 & 109.59 & 137.965565347112 & -28.375565347112 \tabularnewline
63 & 99.07 & 136.969326884993 & -37.899326884993 \tabularnewline
64 & 92.07 & 137.420979769872 & -45.3509797698716 \tabularnewline
65 & 89.1 & 137.372654769843 & -48.2726547698434 \tabularnewline
66 & 86.36 & 136.785320154117 & -50.4253201541166 \tabularnewline
67 & 95.39 & 136.471207653934 & -41.0812076539335 \tabularnewline
68 & 95.27 & 136.058586499847 & -40.7885864998469 \tabularnewline
69 & 98.56 & 135.497273037981 & -36.9372730379814 \tabularnewline
70 & 101.79 & 135.973088422874 & -34.183088422874 \tabularnewline
71 & 102.02 & 136.170105730681 & -34.1501057306812 \tabularnewline
72 & 98.21 & 135.553032653399 & -37.3430326533985 \tabularnewline
73 & 104.42 & 135.579053807260 & -31.1590538072598 \tabularnewline
74 & 105.62 & 134.973132653061 & -29.3531326530606 \tabularnewline
75 & 109.46 & 134.352342268083 & -24.8923422680835 \tabularnewline
76 & 110.94 & 134.588391306683 & -23.6483913066826 \tabularnewline
77 & 113.09 & 134.649726883641 & -21.5597268836414 \tabularnewline
78 & 109.58 & 134.095848037165 & -24.5158480371648 \tabularnewline
79 & 111.41 & 133.859798998566 & -22.4497989985657 \tabularnewline
80 & 109.83 & 133.696237460009 & -23.8662374600088 \tabularnewline
81 & 110.58 & 133.108902844282 & -22.5289028442820 \tabularnewline
82 & 109.04 & 133.291050921311 & -24.2510509213112 \tabularnewline
83 & 107.8 & 133.844929767788 & -26.0449297677878 \tabularnewline
84 & 109.79 & 132.975079767281 & -23.1850797672809 \tabularnewline
85 & 110.76 & 132.936048036489 & -22.1760480364889 \tabularnewline
86 & 112.64 & 132.272508613025 & -19.6325086130254 \tabularnewline
87 & 114.17 & 132.034600920579 & -17.8646009205790 \tabularnewline
88 & 115.99 & 131.642424958812 & -15.6524249588121 \tabularnewline
89 & 119.01 & 131.634990343423 & -12.6249903434231 \tabularnewline
90 & 117.92 & 131.341323035560 & -13.4213230355597 \tabularnewline
91 & 115.92 & 130.971450919960 & -15.0514509199595 \tabularnewline
92 & 120.75 & 130.536525919706 & -9.7865259197061 \tabularnewline
93 & 124.94 & 127.023670148428 & -2.08367014842835 \tabularnewline
94 & 129.17 & 126.908433609900 & 2.26156639010033 \tabularnewline
95 & 128.14 & 127.637025918017 & 0.502974081983463 \tabularnewline
96 & 134.18 & 126.906574956052 & 7.27342504394758 \tabularnewline
97 & 131.74 & 126.547854763536 & 5.19214523646431 \tabularnewline
98 & 134.32 & 126.163113417158 & 8.15688658284233 \tabularnewline
99 & 137.8 & 124.867631685634 & 12.9323683143665 \tabularnewline
100 & 141.79 & 125.261666301248 & 16.5283336987522 \tabularnewline
101 & 142.75 & 125.347164378221 & 17.4028356217793 \tabularnewline
102 & 144.3 & 125.047921108816 & 19.2520788911845 \tabularnewline
103 & 145.49 & 124.181788416003 & 21.3082115839969 \tabularnewline
104 & 138.21 & 123.960608608182 & 14.2493913918181 \tabularnewline
105 & 139.02 & 123.371415338608 & 15.6485846613922 \tabularnewline
106 & 141.91 & 123.902990338918 & 18.0070096610824 \tabularnewline
107 & 144.95 & 124.713363416313 & 20.2366365836871 \tabularnewline
108 & 146.11 & 123.776601877305 & 22.3333981226945 \tabularnewline
109 & 150.96 & 124.103724954419 & 26.8562750455808 \tabularnewline
110 & 148.2 & 123.460630723275 & 24.7393692767248 \tabularnewline
111 & 152.12 & 121.538782645232 & 30.5812173547678 \tabularnewline
112 & 154.74 & 122.395622068808 & 32.3443779311916 \tabularnewline
113 & 150.8 & 122.561042261213 & 28.2389577387875 \tabularnewline
114 & 152.6 & 121.778548991526 & 30.8214510084742 \tabularnewline
115 & 158.74 & 121.574097068330 & 37.1659029316703 \tabularnewline
116 & 161.83 & 121.284147068161 & 40.5458529318392 \tabularnewline
117 & 162.4 & 120.338092259917 & 42.0619077400828 \tabularnewline
118 & 156.11 & 120.661498029336 & 35.4485019706636 \tabularnewline
119 & 154.93 & 121.880774953124 & 33.0492250468762 \tabularnewline
120 & 157.18 & 120.756289375546 & 36.4237106244545 \tabularnewline
121 & 159.85 & 121.287864375855 & 38.5621356241447 \tabularnewline
122 & 154.4 & 120.754430721698 & 33.6455692783017 \tabularnewline
123 & 151.57 & 120.061152836679 & 31.5088471633211 \tabularnewline
124 & 133.34 & 120.590869183141 & 12.7491308168586 \tabularnewline
125 & 131.2 & 121.451425914412 & 9.74857408558788 \tabularnewline
126 & 124.17 & 120.726550913990 & 3.44344908601028 \tabularnewline
127 & 133.19 & 121.087129760354 & 12.1028702396463 \tabularnewline
128 & 130.94 & 120.687519183198 & 10.2524808168023 \tabularnewline
129 & 119.58 & 120.066728798221 & -0.486728798220618 \tabularnewline
130 & 118.55 & 121.330613414342 & -2.78061341434171 \tabularnewline
131 & 119.96 & 122.035043222445 & -2.0750432224445 \tabularnewline
132 & 108.42 & 120.722833606295 & -12.3028336062952 \tabularnewline
133 & 95.93 & 120.328798990681 & -24.398798990681 \tabularnewline
134 & 88.83 & 120.018403798192 & -31.1884037981925 \tabularnewline
135 & 84.98 & 119.295387451617 & -34.3153874516173 \tabularnewline
136 & 81.61 & 118.206216297136 & -36.5962162971365 \tabularnewline
137 & 72.84 & 118.165325912497 & -45.3253259124973 \tabularnewline
138 & 74.72 & 117.784301873814 & -43.0643018738137 \tabularnewline
139 & 83.4 & 116.925603796390 & -33.5256037963902 \tabularnewline
140 & 87.42 & 116.611491296207 & -29.1914912962072 \tabularnewline
141 & 86.33 & 115.267684564655 & -28.9376845646549 \tabularnewline
142 & 94.28 & 115.329020141614 & -21.0490201416137 \tabularnewline
143 & 98.81 & 115.163599949210 & -16.3535999492097 \tabularnewline
144 & 100.96 & 114.323488410259 & -13.3634884102586 \tabularnewline
145 & 99.14 & 113.591178794447 & -14.4511787944472 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=67220&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]100[/C][C]151.431512470344[/C][C]-51.4315124703439[/C][/ROW]
[ROW][C]2[/C][C]103.53[/C][C]150.944545162367[/C][C]-47.4145451623672[/C][/ROW]
[ROW][C]3[/C][C]108.36[/C][C]150.015218238749[/C][C]-41.6552182387489[/C][/ROW]
[ROW][C]4[/C][C]115.2[/C][C]150.788418239199[/C][C]-35.5884182391994[/C][/ROW]
[ROW][C]5[/C][C]123.51[/C][C]150.760538431491[/C][C]-27.2505384314908[/C][/ROW]
[ROW][C]6[/C][C]132.87[/C][C]150.297733623529[/C][C]-17.4277336235289[/C][/ROW]
[ROW][C]7[/C][C]130.55[/C][C]150.106292277263[/C][C]-19.5562922772635[/C][/ROW]
[ROW][C]8[/C][C]136.68[/C][C]149.792179777080[/C][C]-13.1121797770804[/C][/ROW]
[ROW][C]9[/C][C]140.63[/C][C]149.028273045866[/C][C]-8.39827304586606[/C][/ROW]
[ROW][C]10[/C][C]143.47[/C][C]149.693671123177[/C][C]-6.22367112317687[/C][/ROW]
[ROW][C]11[/C][C]124.1[/C][C]149.805190354011[/C][C]-25.7051903540111[/C][/ROW]
[ROW][C]12[/C][C]111.49[/C][C]149.418590353786[/C][C]-37.9285903537858[/C][/ROW]
[ROW][C]13[/C][C]119.93[/C][C]149.396286507619[/C][C]-29.4662865076190[/C][/ROW]
[ROW][C]14[/C][C]131.79[/C][C]148.650966314877[/C][C]-16.8609663148770[/C][/ROW]
[ROW][C]15[/C][C]136.61[/C][C]147.630565352744[/C][C]-11.0205653527439[/C][/ROW]
[ROW][C]16[/C][C]141.79[/C][C]147.177053814018[/C][C]-5.38705381401811[/C][/ROW]
[ROW][C]17[/C][C]142.23[/C][C]147.634282660438[/C][C]-5.40428266043839[/C][/ROW]
[ROW][C]18[/C][C]146.74[/C][C]147.387081698756[/C][C]-0.64708169875586[/C][/ROW]
[ROW][C]19[/C][C]154.85[/C][C]147.197499006338[/C][C]7.65250099366229[/C][/ROW]
[ROW][C]20[/C][C]148.44[/C][C]146.725400929140[/C][C]1.71459907086046[/C][/ROW]
[ROW][C]21[/C][C]154.18[/C][C]146.100893236468[/C][C]8.07910676353207[/C][/ROW]
[ROW][C]22[/C][C]149.1[/C][C]146.244009582705[/C][C]2.85599041729482[/C][/ROW]
[ROW][C]23[/C][C]152.22[/C][C]146.688227852195[/C][C]5.5317721478052[/C][/ROW]
[ROW][C]24[/C][C]149.34[/C][C]146.309062467358[/C][C]3.03093753264154[/C][/ROW]
[ROW][C]25[/C][C]160.94[/C][C]146.245868236552[/C][C]14.6941317634476[/C][/ROW]
[ROW][C]26[/C][C]176.16[/C][C]145.716151890090[/C][C]30.4438481099101[/C][/ROW]
[ROW][C]27[/C][C]195.12[/C][C]145.192011505169[/C][C]49.9279884948309[/C][/ROW]
[ROW][C]28[/C][C]186.07[/C][C]144.771955735694[/C][C]41.2980442643065[/C][/ROW]
[ROW][C]29[/C][C]200.78[/C][C]144.877899004986[/C][C]55.902100995014[/C][/ROW]
[ROW][C]30[/C][C]208.15[/C][C]144.742217274138[/C][C]63.4077827258623[/C][/ROW]
[ROW][C]31[/C][C]209.56[/C][C]144.160458619953[/C][C]65.3995413800474[/C][/ROW]
[ROW][C]32[/C][C]203.33[/C][C]144.435539389344[/C][C]58.8944606106564[/C][/ROW]
[ROW][C]33[/C][C]198.84[/C][C]144.258967273856[/C][C]54.5810327261438[/C][/ROW]
[ROW][C]34[/C][C]200.63[/C][C]144.309150927732[/C][C]56.3208490722684[/C][/ROW]
[ROW][C]35[/C][C]206.47[/C][C]144.742217274138[/C][C]61.7277827258623[/C][/ROW]
[ROW][C]36[/C][C]196.68[/C][C]144.561927850956[/C][C]52.1180721490442[/C][/ROW]
[ROW][C]37[/C][C]203.81[/C][C]144.530330735553[/C][C]59.2796692644473[/C][/ROW]
[ROW][C]38[/C][C]190.18[/C][C]144.244098043078[/C][C]45.9359019569217[/C][/ROW]
[ROW][C]39[/C][C]187.5[/C][C]143.431866311836[/C][C]44.0681336881643[/C][/ROW]
[ROW][C]40[/C][C]187.62[/C][C]143.257152850195[/C][C]44.3628471498045[/C][/ROW]
[ROW][C]41[/C][C]168.92[/C][C]143.236707657876[/C][C]25.6832923421241[/C][/ROW]
[ROW][C]42[/C][C]164.78[/C][C]143.073146119319[/C][C]21.706853880681[/C][/ROW]
[ROW][C]43[/C][C]175.98[/C][C]142.552723042093[/C][C]33.4272769579073[/C][/ROW]
[ROW][C]44[/C][C]174.7[/C][C]142.188426888034[/C][C]32.5115731119657[/C][/ROW]
[ROW][C]45[/C][C]166.95[/C][C]141.560201887668[/C][C]25.3897981123318[/C][/ROW]
[ROW][C]46[/C][C]161.76[/C][C]141.844575926295[/C][C]19.9154240737046[/C][/ROW]
[ROW][C]47[/C][C]149.65[/C][C]142.316674003494[/C][C]7.33332599650641[/C][/ROW]
[ROW][C]48[/C][C]137.42[/C][C]141.264675925958[/C][C]-3.84467592595752[/C][/ROW]
[ROW][C]49[/C][C]142.6[/C][C]141.225644195166[/C][C]1.37435580483446[/C][/ROW]
[ROW][C]50[/C][C]146.94[/C][C]140.450585540868[/C][C]6.48941445913226[/C][/ROW]
[ROW][C]51[/C][C]152.52[/C][C]138.790807655285[/C][C]13.7291923447148[/C][/ROW]
[ROW][C]52[/C][C]147.47[/C][C]139.504530732624[/C][C]7.96546926737584[/C][/ROW]
[ROW][C]53[/C][C]146.15[/C][C]139.578876886514[/C][C]6.57112311348637[/C][/ROW]
[ROW][C]54[/C][C]152.04[/C][C]139.630919194236[/C][C]12.4090808057637[/C][/ROW]
[ROW][C]55[/C][C]144.42[/C][C]139.140234578566[/C][C]5.27976542143426[/C][/ROW]
[ROW][C]56[/C][C]138.15[/C][C]139.136517270871[/C][C]-0.986517270871246[/C][/ROW]
[ROW][C]57[/C][C]125.94[/C][C]138.411642270449[/C][C]-12.4716422704489[/C][/ROW]
[ROW][C]58[/C][C]112.61[/C][C]138.909761501508[/C][C]-26.2997615015083[/C][/ROW]
[ROW][C]59[/C][C]111.48[/C][C]139.387435540248[/C][C]-27.9074355402482[/C][/ROW]
[ROW][C]60[/C][C]95.25[/C][C]138.508292270505[/C][C]-43.2582922705052[/C][/ROW]
[ROW][C]61[/C][C]105.38[/C][C]138.761069193729[/C][C]-33.3810691937294[/C][/ROW]
[ROW][C]62[/C][C]109.59[/C][C]137.965565347112[/C][C]-28.375565347112[/C][/ROW]
[ROW][C]63[/C][C]99.07[/C][C]136.969326884993[/C][C]-37.899326884993[/C][/ROW]
[ROW][C]64[/C][C]92.07[/C][C]137.420979769872[/C][C]-45.3509797698716[/C][/ROW]
[ROW][C]65[/C][C]89.1[/C][C]137.372654769843[/C][C]-48.2726547698434[/C][/ROW]
[ROW][C]66[/C][C]86.36[/C][C]136.785320154117[/C][C]-50.4253201541166[/C][/ROW]
[ROW][C]67[/C][C]95.39[/C][C]136.471207653934[/C][C]-41.0812076539335[/C][/ROW]
[ROW][C]68[/C][C]95.27[/C][C]136.058586499847[/C][C]-40.7885864998469[/C][/ROW]
[ROW][C]69[/C][C]98.56[/C][C]135.497273037981[/C][C]-36.9372730379814[/C][/ROW]
[ROW][C]70[/C][C]101.79[/C][C]135.973088422874[/C][C]-34.183088422874[/C][/ROW]
[ROW][C]71[/C][C]102.02[/C][C]136.170105730681[/C][C]-34.1501057306812[/C][/ROW]
[ROW][C]72[/C][C]98.21[/C][C]135.553032653399[/C][C]-37.3430326533985[/C][/ROW]
[ROW][C]73[/C][C]104.42[/C][C]135.579053807260[/C][C]-31.1590538072598[/C][/ROW]
[ROW][C]74[/C][C]105.62[/C][C]134.973132653061[/C][C]-29.3531326530606[/C][/ROW]
[ROW][C]75[/C][C]109.46[/C][C]134.352342268083[/C][C]-24.8923422680835[/C][/ROW]
[ROW][C]76[/C][C]110.94[/C][C]134.588391306683[/C][C]-23.6483913066826[/C][/ROW]
[ROW][C]77[/C][C]113.09[/C][C]134.649726883641[/C][C]-21.5597268836414[/C][/ROW]
[ROW][C]78[/C][C]109.58[/C][C]134.095848037165[/C][C]-24.5158480371648[/C][/ROW]
[ROW][C]79[/C][C]111.41[/C][C]133.859798998566[/C][C]-22.4497989985657[/C][/ROW]
[ROW][C]80[/C][C]109.83[/C][C]133.696237460009[/C][C]-23.8662374600088[/C][/ROW]
[ROW][C]81[/C][C]110.58[/C][C]133.108902844282[/C][C]-22.5289028442820[/C][/ROW]
[ROW][C]82[/C][C]109.04[/C][C]133.291050921311[/C][C]-24.2510509213112[/C][/ROW]
[ROW][C]83[/C][C]107.8[/C][C]133.844929767788[/C][C]-26.0449297677878[/C][/ROW]
[ROW][C]84[/C][C]109.79[/C][C]132.975079767281[/C][C]-23.1850797672809[/C][/ROW]
[ROW][C]85[/C][C]110.76[/C][C]132.936048036489[/C][C]-22.1760480364889[/C][/ROW]
[ROW][C]86[/C][C]112.64[/C][C]132.272508613025[/C][C]-19.6325086130254[/C][/ROW]
[ROW][C]87[/C][C]114.17[/C][C]132.034600920579[/C][C]-17.8646009205790[/C][/ROW]
[ROW][C]88[/C][C]115.99[/C][C]131.642424958812[/C][C]-15.6524249588121[/C][/ROW]
[ROW][C]89[/C][C]119.01[/C][C]131.634990343423[/C][C]-12.6249903434231[/C][/ROW]
[ROW][C]90[/C][C]117.92[/C][C]131.341323035560[/C][C]-13.4213230355597[/C][/ROW]
[ROW][C]91[/C][C]115.92[/C][C]130.971450919960[/C][C]-15.0514509199595[/C][/ROW]
[ROW][C]92[/C][C]120.75[/C][C]130.536525919706[/C][C]-9.7865259197061[/C][/ROW]
[ROW][C]93[/C][C]124.94[/C][C]127.023670148428[/C][C]-2.08367014842835[/C][/ROW]
[ROW][C]94[/C][C]129.17[/C][C]126.908433609900[/C][C]2.26156639010033[/C][/ROW]
[ROW][C]95[/C][C]128.14[/C][C]127.637025918017[/C][C]0.502974081983463[/C][/ROW]
[ROW][C]96[/C][C]134.18[/C][C]126.906574956052[/C][C]7.27342504394758[/C][/ROW]
[ROW][C]97[/C][C]131.74[/C][C]126.547854763536[/C][C]5.19214523646431[/C][/ROW]
[ROW][C]98[/C][C]134.32[/C][C]126.163113417158[/C][C]8.15688658284233[/C][/ROW]
[ROW][C]99[/C][C]137.8[/C][C]124.867631685634[/C][C]12.9323683143665[/C][/ROW]
[ROW][C]100[/C][C]141.79[/C][C]125.261666301248[/C][C]16.5283336987522[/C][/ROW]
[ROW][C]101[/C][C]142.75[/C][C]125.347164378221[/C][C]17.4028356217793[/C][/ROW]
[ROW][C]102[/C][C]144.3[/C][C]125.047921108816[/C][C]19.2520788911845[/C][/ROW]
[ROW][C]103[/C][C]145.49[/C][C]124.181788416003[/C][C]21.3082115839969[/C][/ROW]
[ROW][C]104[/C][C]138.21[/C][C]123.960608608182[/C][C]14.2493913918181[/C][/ROW]
[ROW][C]105[/C][C]139.02[/C][C]123.371415338608[/C][C]15.6485846613922[/C][/ROW]
[ROW][C]106[/C][C]141.91[/C][C]123.902990338918[/C][C]18.0070096610824[/C][/ROW]
[ROW][C]107[/C][C]144.95[/C][C]124.713363416313[/C][C]20.2366365836871[/C][/ROW]
[ROW][C]108[/C][C]146.11[/C][C]123.776601877305[/C][C]22.3333981226945[/C][/ROW]
[ROW][C]109[/C][C]150.96[/C][C]124.103724954419[/C][C]26.8562750455808[/C][/ROW]
[ROW][C]110[/C][C]148.2[/C][C]123.460630723275[/C][C]24.7393692767248[/C][/ROW]
[ROW][C]111[/C][C]152.12[/C][C]121.538782645232[/C][C]30.5812173547678[/C][/ROW]
[ROW][C]112[/C][C]154.74[/C][C]122.395622068808[/C][C]32.3443779311916[/C][/ROW]
[ROW][C]113[/C][C]150.8[/C][C]122.561042261213[/C][C]28.2389577387875[/C][/ROW]
[ROW][C]114[/C][C]152.6[/C][C]121.778548991526[/C][C]30.8214510084742[/C][/ROW]
[ROW][C]115[/C][C]158.74[/C][C]121.574097068330[/C][C]37.1659029316703[/C][/ROW]
[ROW][C]116[/C][C]161.83[/C][C]121.284147068161[/C][C]40.5458529318392[/C][/ROW]
[ROW][C]117[/C][C]162.4[/C][C]120.338092259917[/C][C]42.0619077400828[/C][/ROW]
[ROW][C]118[/C][C]156.11[/C][C]120.661498029336[/C][C]35.4485019706636[/C][/ROW]
[ROW][C]119[/C][C]154.93[/C][C]121.880774953124[/C][C]33.0492250468762[/C][/ROW]
[ROW][C]120[/C][C]157.18[/C][C]120.756289375546[/C][C]36.4237106244545[/C][/ROW]
[ROW][C]121[/C][C]159.85[/C][C]121.287864375855[/C][C]38.5621356241447[/C][/ROW]
[ROW][C]122[/C][C]154.4[/C][C]120.754430721698[/C][C]33.6455692783017[/C][/ROW]
[ROW][C]123[/C][C]151.57[/C][C]120.061152836679[/C][C]31.5088471633211[/C][/ROW]
[ROW][C]124[/C][C]133.34[/C][C]120.590869183141[/C][C]12.7491308168586[/C][/ROW]
[ROW][C]125[/C][C]131.2[/C][C]121.451425914412[/C][C]9.74857408558788[/C][/ROW]
[ROW][C]126[/C][C]124.17[/C][C]120.726550913990[/C][C]3.44344908601028[/C][/ROW]
[ROW][C]127[/C][C]133.19[/C][C]121.087129760354[/C][C]12.1028702396463[/C][/ROW]
[ROW][C]128[/C][C]130.94[/C][C]120.687519183198[/C][C]10.2524808168023[/C][/ROW]
[ROW][C]129[/C][C]119.58[/C][C]120.066728798221[/C][C]-0.486728798220618[/C][/ROW]
[ROW][C]130[/C][C]118.55[/C][C]121.330613414342[/C][C]-2.78061341434171[/C][/ROW]
[ROW][C]131[/C][C]119.96[/C][C]122.035043222445[/C][C]-2.0750432224445[/C][/ROW]
[ROW][C]132[/C][C]108.42[/C][C]120.722833606295[/C][C]-12.3028336062952[/C][/ROW]
[ROW][C]133[/C][C]95.93[/C][C]120.328798990681[/C][C]-24.398798990681[/C][/ROW]
[ROW][C]134[/C][C]88.83[/C][C]120.018403798192[/C][C]-31.1884037981925[/C][/ROW]
[ROW][C]135[/C][C]84.98[/C][C]119.295387451617[/C][C]-34.3153874516173[/C][/ROW]
[ROW][C]136[/C][C]81.61[/C][C]118.206216297136[/C][C]-36.5962162971365[/C][/ROW]
[ROW][C]137[/C][C]72.84[/C][C]118.165325912497[/C][C]-45.3253259124973[/C][/ROW]
[ROW][C]138[/C][C]74.72[/C][C]117.784301873814[/C][C]-43.0643018738137[/C][/ROW]
[ROW][C]139[/C][C]83.4[/C][C]116.925603796390[/C][C]-33.5256037963902[/C][/ROW]
[ROW][C]140[/C][C]87.42[/C][C]116.611491296207[/C][C]-29.1914912962072[/C][/ROW]
[ROW][C]141[/C][C]86.33[/C][C]115.267684564655[/C][C]-28.9376845646549[/C][/ROW]
[ROW][C]142[/C][C]94.28[/C][C]115.329020141614[/C][C]-21.0490201416137[/C][/ROW]
[ROW][C]143[/C][C]98.81[/C][C]115.163599949210[/C][C]-16.3535999492097[/C][/ROW]
[ROW][C]144[/C][C]100.96[/C][C]114.323488410259[/C][C]-13.3634884102586[/C][/ROW]
[ROW][C]145[/C][C]99.14[/C][C]113.591178794447[/C][C]-14.4511787944472[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=67220&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=67220&T=4

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	100	151.431512470344	-51.4315124703439
2	103.53	150.944545162367	-47.4145451623672
3	108.36	150.015218238749	-41.6552182387489
4	115.2	150.788418239199	-35.5884182391994
5	123.51	150.760538431491	-27.2505384314908
6	132.87	150.297733623529	-17.4277336235289
7	130.55	150.106292277263	-19.5562922772635
8	136.68	149.792179777080	-13.1121797770804
9	140.63	149.028273045866	-8.39827304586606
10	143.47	149.693671123177	-6.22367112317687
11	124.1	149.805190354011	-25.7051903540111
12	111.49	149.418590353786	-37.9285903537858
13	119.93	149.396286507619	-29.4662865076190
14	131.79	148.650966314877	-16.8609663148770
15	136.61	147.630565352744	-11.0205653527439
16	141.79	147.177053814018	-5.38705381401811
17	142.23	147.634282660438	-5.40428266043839
18	146.74	147.387081698756	-0.64708169875586
19	154.85	147.197499006338	7.65250099366229
20	148.44	146.725400929140	1.71459907086046
21	154.18	146.100893236468	8.07910676353207
22	149.1	146.244009582705	2.85599041729482
23	152.22	146.688227852195	5.5317721478052
24	149.34	146.309062467358	3.03093753264154
25	160.94	146.245868236552	14.6941317634476
26	176.16	145.716151890090	30.4438481099101
27	195.12	145.192011505169	49.9279884948309
28	186.07	144.771955735694	41.2980442643065
29	200.78	144.877899004986	55.902100995014
30	208.15	144.742217274138	63.4077827258623
31	209.56	144.160458619953	65.3995413800474
32	203.33	144.435539389344	58.8944606106564
33	198.84	144.258967273856	54.5810327261438
34	200.63	144.309150927732	56.3208490722684
35	206.47	144.742217274138	61.7277827258623
36	196.68	144.561927850956	52.1180721490442
37	203.81	144.530330735553	59.2796692644473
38	190.18	144.244098043078	45.9359019569217
39	187.5	143.431866311836	44.0681336881643
40	187.62	143.257152850195	44.3628471498045
41	168.92	143.236707657876	25.6832923421241
42	164.78	143.073146119319	21.706853880681
43	175.98	142.552723042093	33.4272769579073
44	174.7	142.188426888034	32.5115731119657
45	166.95	141.560201887668	25.3897981123318
46	161.76	141.844575926295	19.9154240737046
47	149.65	142.316674003494	7.33332599650641
48	137.42	141.264675925958	-3.84467592595752
49	142.6	141.225644195166	1.37435580483446
50	146.94	140.450585540868	6.48941445913226
51	152.52	138.790807655285	13.7291923447148
52	147.47	139.504530732624	7.96546926737584
53	146.15	139.578876886514	6.57112311348637
54	152.04	139.630919194236	12.4090808057637
55	144.42	139.140234578566	5.27976542143426
56	138.15	139.136517270871	-0.986517270871246
57	125.94	138.411642270449	-12.4716422704489
58	112.61	138.909761501508	-26.2997615015083
59	111.48	139.387435540248	-27.9074355402482
60	95.25	138.508292270505	-43.2582922705052
61	105.38	138.761069193729	-33.3810691937294
62	109.59	137.965565347112	-28.375565347112
63	99.07	136.969326884993	-37.899326884993
64	92.07	137.420979769872	-45.3509797698716
65	89.1	137.372654769843	-48.2726547698434
66	86.36	136.785320154117	-50.4253201541166
67	95.39	136.471207653934	-41.0812076539335
68	95.27	136.058586499847	-40.7885864998469
69	98.56	135.497273037981	-36.9372730379814
70	101.79	135.973088422874	-34.183088422874
71	102.02	136.170105730681	-34.1501057306812
72	98.21	135.553032653399	-37.3430326533985
73	104.42	135.579053807260	-31.1590538072598
74	105.62	134.973132653061	-29.3531326530606
75	109.46	134.352342268083	-24.8923422680835
76	110.94	134.588391306683	-23.6483913066826
77	113.09	134.649726883641	-21.5597268836414
78	109.58	134.095848037165	-24.5158480371648
79	111.41	133.859798998566	-22.4497989985657
80	109.83	133.696237460009	-23.8662374600088
81	110.58	133.108902844282	-22.5289028442820
82	109.04	133.291050921311	-24.2510509213112
83	107.8	133.844929767788	-26.0449297677878
84	109.79	132.975079767281	-23.1850797672809
85	110.76	132.936048036489	-22.1760480364889
86	112.64	132.272508613025	-19.6325086130254
87	114.17	132.034600920579	-17.8646009205790
88	115.99	131.642424958812	-15.6524249588121
89	119.01	131.634990343423	-12.6249903434231
90	117.92	131.341323035560	-13.4213230355597
91	115.92	130.971450919960	-15.0514509199595
92	120.75	130.536525919706	-9.7865259197061
93	124.94	127.023670148428	-2.08367014842835
94	129.17	126.908433609900	2.26156639010033
95	128.14	127.637025918017	0.502974081983463
96	134.18	126.906574956052	7.27342504394758
97	131.74	126.547854763536	5.19214523646431
98	134.32	126.163113417158	8.15688658284233
99	137.8	124.867631685634	12.9323683143665
100	141.79	125.261666301248	16.5283336987522
101	142.75	125.347164378221	17.4028356217793
102	144.3	125.047921108816	19.2520788911845
103	145.49	124.181788416003	21.3082115839969
104	138.21	123.960608608182	14.2493913918181
105	139.02	123.371415338608	15.6485846613922
106	141.91	123.902990338918	18.0070096610824
107	144.95	124.713363416313	20.2366365836871
108	146.11	123.776601877305	22.3333981226945
109	150.96	124.103724954419	26.8562750455808
110	148.2	123.460630723275	24.7393692767248
111	152.12	121.538782645232	30.5812173547678
112	154.74	122.395622068808	32.3443779311916
113	150.8	122.561042261213	28.2389577387875
114	152.6	121.778548991526	30.8214510084742
115	158.74	121.574097068330	37.1659029316703
116	161.83	121.284147068161	40.5458529318392
117	162.4	120.338092259917	42.0619077400828
118	156.11	120.661498029336	35.4485019706636
119	154.93	121.880774953124	33.0492250468762
120	157.18	120.756289375546	36.4237106244545
121	159.85	121.287864375855	38.5621356241447
122	154.4	120.754430721698	33.6455692783017
123	151.57	120.061152836679	31.5088471633211
124	133.34	120.590869183141	12.7491308168586
125	131.2	121.451425914412	9.74857408558788
126	124.17	120.726550913990	3.44344908601028
127	133.19	121.087129760354	12.1028702396463
128	130.94	120.687519183198	10.2524808168023
129	119.58	120.066728798221	-0.486728798220618
130	118.55	121.330613414342	-2.78061341434171
131	119.96	122.035043222445	-2.0750432224445
132	108.42	120.722833606295	-12.3028336062952
133	95.93	120.328798990681	-24.398798990681
134	88.83	120.018403798192	-31.1884037981925
135	84.98	119.295387451617	-34.3153874516173
136	81.61	118.206216297136	-36.5962162971365
137	72.84	118.165325912497	-45.3253259124973
138	74.72	117.784301873814	-43.0643018738137
139	83.4	116.925603796390	-33.5256037963902
140	87.42	116.611491296207	-29.1914912962072
141	86.33	115.267684564655	-28.9376845646549
142	94.28	115.329020141614	-21.0490201416137
143	98.81	115.163599949210	-16.3535999492097
144	100.96	114.323488410259	-13.3634884102586
145	99.14	113.591178794447	-14.4511787944472

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
5	0.0543336217108625	0.108667243421725	0.945666378289137
6	0.0481150990737843	0.0962301981475685	0.951884900926216
7	0.0206389496865986	0.0412778993731972	0.979361050313401
8	0.00799378093007457	0.0159875618601491	0.992006219069925
9	0.00273576178032791	0.00547152356065582	0.997264238219672
10	0.00140573783459866	0.00281147566919732	0.998594262165401
11	0.000595578619164258	0.00119115723832852	0.999404421380836
12	0.00186585911491275	0.00373171822982551	0.998134140885087
13	0.00118440319504998	0.00236880639009996	0.99881559680495
14	0.000537723878008446	0.00107544775601689	0.999462276121992
15	0.000263010128489922	0.000526020256979843	0.99973698987151
16	0.000103358197089751	0.000206716394179502	0.99989664180291
17	3.73003912277326e-05	7.46007824554652e-05	0.999962699608772
18	1.33461383485114e-05	2.66922766970228e-05	0.999986653861652
19	5.93858773318676e-06	1.18771754663735e-05	0.999994061412267
20	2.04075595374329e-06	4.08151190748657e-06	0.999997959244046
21	6.6504338572235e-07	1.3300867714447e-06	0.999999334956614
22	2.37136201376090e-07	4.74272402752179e-07	0.999999762863799
23	7.42122166915262e-08	1.48424433383052e-07	0.999999925787783
24	2.41498551227396e-08	4.82997102454793e-08	0.999999975850145
25	9.29196852313395e-09	1.85839370462679e-08	0.999999990708031
26	1.13489341433591e-08	2.26978682867182e-08	0.999999988651066
27	1.01878887898270e-07	2.03757775796540e-07	0.999999898121112
28	6.01899630032177e-08	1.20379926006435e-07	0.999999939810037
29	2.02048673220298e-07	4.04097346440596e-07	0.999999797951327
30	8.70470962682326e-07	1.74094192536465e-06	0.999999129529037
31	1.27733751278629e-06	2.55467502557257e-06	0.999998722662487
32	1.22309891374483e-06	2.44619782748966e-06	0.999998776901086
33	8.0817365147768e-07	1.61634730295536e-06	0.999999191826348
34	6.12885517880207e-07	1.22577103576041e-06	0.999999387114482
35	1.07257126921125e-06	2.14514253842250e-06	0.99999892742873
36	8.2359946401838e-07	1.64719892803676e-06	0.999999176400536
37	1.02103442185417e-06	2.04206884370833e-06	0.999998978965578
38	8.76865649238785e-07	1.75373129847757e-06	0.99999912313435
39	1.70977582206830e-06	3.41955164413659e-06	0.999998290224178
40	3.63299791760851e-06	7.26599583521701e-06	0.999996367002082
41	3.15095786303396e-05	6.30191572606792e-05	0.99996849042137
42	0.000227058925574136	0.000454117851148271	0.999772941074426
43	0.000691531958936874	0.00138306391787375	0.999308468041063
44	0.00219679790088322	0.00439359580176643	0.997803202099117
45	0.0102322677908677	0.0204645355817354	0.989767732209132
46	0.0293131396797708	0.0586262793595416	0.97068686032023
47	0.0716507837019145	0.143301567403829	0.928349216298086
48	0.222995354377805	0.445990708755611	0.777004645622195
49	0.364080575790869	0.728161151581739	0.63591942420913
50	0.49637064025509	0.99274128051018	0.50362935974491
51	0.629249935711324	0.741500128577352	0.370750064288676
52	0.715747591003395	0.568504817993211	0.284252408996605
53	0.777301713384288	0.445396573231424	0.222698286615712
54	0.81823161353911	0.363536772921782	0.181768386460891
55	0.858407445367312	0.283185109265376	0.141592554632688
56	0.891205847357055	0.217588305285890	0.108794152642945
57	0.926312932663523	0.147374134672954	0.0736870673364772
58	0.955741725607158	0.0885165487856848	0.0442582743928424
59	0.970394207742918	0.0592115845141645	0.0296057922570823
60	0.986712207412366	0.0265755851752688	0.0132877925876344
61	0.990663141078824	0.0186737178423512	0.00933685892117559
62	0.992242198846722	0.0155156023065567	0.00775780115327834
63	0.994636541165487	0.0107269176690251	0.00536345883451255
64	0.9966628802517	0.00667423949660182	0.00333711974830091
65	0.997959082275746	0.00408183544850744	0.00204091772425372
66	0.998794491529132	0.0024110169417349	0.00120550847086745
67	0.998957315692915	0.00208536861417031	0.00104268430708515
68	0.999060368971588	0.00187926205682379	0.000939631028411894
69	0.999035234907788	0.00192953018442359	0.000964765092211796
70	0.998915973637327	0.00216805272534543	0.00108402636267272
71	0.998771823584617	0.00245635283076509	0.00122817641538255
72	0.998721436592671	0.00255712681465780	0.00127856340732890
73	0.998468406748182	0.00306318650363502	0.00153159325181751
74	0.998122421921552	0.00375515615689553	0.00187757807844777
75	0.997553120177472	0.00489375964505651	0.00244687982252825
76	0.996796461937967	0.00640707612406659	0.00320353806203329
77	0.99575663162761	0.00848673674477976	0.00424336837238988
78	0.994701910567166	0.0105961788656685	0.00529808943283426
79	0.993323775705369	0.0133524485892626	0.00667622429463132
80	0.991938066609506	0.0161238667809889	0.00806193339049445
81	0.990316761249846	0.0193664775003075	0.00968323875015375
82	0.989000812142191	0.0219983757156172	0.0109991878578086
83	0.98842637182737	0.0231472563452595	0.0115736281726298
84	0.987710013473071	0.0245799730538583	0.0122899865269291
85	0.987422983283562	0.0251540334328763	0.0125770167164381
86	0.987202504783559	0.0255949904328824	0.0127974952164412
87	0.987380116180003	0.0252397676399943	0.0126198838199971
88	0.987883273937457	0.0242334521250859	0.0121167260625429
89	0.988719352382597	0.0225612952348060	0.0112806476174030
90	0.990846139358769	0.0183077212824628	0.00915386064123138
91	0.994171846651562	0.0116563066968761	0.00582815334843803
92	0.996572837397685	0.00685432520463089	0.00342716260231545
93	0.996853100444787	0.00629379911042673	0.00314689955521336
94	0.996959448451295	0.00608110309741047	0.00304055154870524
95	0.997619633688807	0.00476073262238515	0.00238036631119258
96	0.997775851068017	0.00444829786396683	0.00222414893198341
97	0.99807375608026	0.00385248783948217	0.00192624391974109
98	0.998231567774051	0.00353686445189765	0.00176843222594882
99	0.997950965955145	0.00409806808971032	0.00204903404485516
100	0.997634697014862	0.00473060597027667	0.00236530298513833
101	0.997299118827654	0.00540176234469261	0.00270088117234631
102	0.996779666088247	0.00644066782350664	0.00322033391175332
103	0.99581600798613	0.00836798402774238	0.00418399201387119
104	0.99477665279039	0.0104466944192181	0.00522334720960907
105	0.993082319312398	0.0138353613752042	0.00691768068760212
106	0.991144521992295	0.0177109560154091	0.00885547800770456
107	0.989549969924612	0.0209000601507757	0.0104500300753878
108	0.986395757477453	0.027208485045094	0.013604242522547
109	0.982446373262624	0.0351072534747528	0.0175536267373764
110	0.976808693286236	0.0463826134275281	0.0231913067137641
111	0.971785823174737	0.0564283536505258	0.0282141768252629
112	0.964421728751552	0.0711565424968953	0.0355782712484477
113	0.953185912000697	0.0936281759986054	0.0468140879993027
114	0.942872944868137	0.114254110263726	0.0571270551318631
115	0.939563092562403	0.120873814875194	0.060436907437597
116	0.944865105865242	0.110269788269516	0.0551348941347578
117	0.962528003337122	0.074943993325756	0.037471996662878
118	0.968356250990519	0.0632874980189618	0.0316437490094809
119	0.966731389843521	0.0665372203129576	0.0332686101564788
120	0.976673444592531	0.0466531108149373	0.0233265554074687
121	0.986470700187803	0.0270585996243936	0.0135292998121968
122	0.993266193470603	0.0134676130587944	0.00673380652939718
123	0.998018238552218	0.00396352289556364	0.00198176144778182
124	0.998009465604993	0.0039810687900133	0.00199053439500665
125	0.99756868519775	0.00486262960450201	0.00243131480225101
126	0.996697764168193	0.0066044716636146	0.0033022358318073
127	0.99757314463477	0.00485371073046069	0.00242685536523034
128	0.998729601111119	0.00254079777776206	0.00127039888888103
129	0.998871907594588	0.00225618481082306	0.00112809240541153
130	0.999057645184	0.00188470963199957	0.000942354815999787
131	0.999790907700383	0.000418184599233913	0.000209092299616957
132	0.999963173478877	7.3653042245963e-05	3.68265211229815e-05
133	0.999984338999673	3.13220006532921e-05	1.56610003266460e-05
134	0.999992436194005	1.51276119891310e-05	7.56380599456552e-06
135	0.999997203443047	5.59311390682643e-06	2.79655695341322e-06
136	0.999993376158204	1.32476835912680e-05	6.62384179563402e-06
137	0.999958389802188	8.32203956239369e-05	4.16101978119684e-05
138	0.999811725498136	0.000376549003728766	0.000188274501864383
139	0.998756440558705	0.00248711888258927	0.00124355944129464
140	0.991726866379096	0.016546267241808	0.008273133620904

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
5 & 0.0543336217108625 & 0.108667243421725 & 0.945666378289137 \tabularnewline
6 & 0.0481150990737843 & 0.0962301981475685 & 0.951884900926216 \tabularnewline
7 & 0.0206389496865986 & 0.0412778993731972 & 0.979361050313401 \tabularnewline
8 & 0.00799378093007457 & 0.0159875618601491 & 0.992006219069925 \tabularnewline
9 & 0.00273576178032791 & 0.00547152356065582 & 0.997264238219672 \tabularnewline
10 & 0.00140573783459866 & 0.00281147566919732 & 0.998594262165401 \tabularnewline
11 & 0.000595578619164258 & 0.00119115723832852 & 0.999404421380836 \tabularnewline
12 & 0.00186585911491275 & 0.00373171822982551 & 0.998134140885087 \tabularnewline
13 & 0.00118440319504998 & 0.00236880639009996 & 0.99881559680495 \tabularnewline
14 & 0.000537723878008446 & 0.00107544775601689 & 0.999462276121992 \tabularnewline
15 & 0.000263010128489922 & 0.000526020256979843 & 0.99973698987151 \tabularnewline
16 & 0.000103358197089751 & 0.000206716394179502 & 0.99989664180291 \tabularnewline
17 & 3.73003912277326e-05 & 7.46007824554652e-05 & 0.999962699608772 \tabularnewline
18 & 1.33461383485114e-05 & 2.66922766970228e-05 & 0.999986653861652 \tabularnewline
19 & 5.93858773318676e-06 & 1.18771754663735e-05 & 0.999994061412267 \tabularnewline
20 & 2.04075595374329e-06 & 4.08151190748657e-06 & 0.999997959244046 \tabularnewline
21 & 6.6504338572235e-07 & 1.3300867714447e-06 & 0.999999334956614 \tabularnewline
22 & 2.37136201376090e-07 & 4.74272402752179e-07 & 0.999999762863799 \tabularnewline
23 & 7.42122166915262e-08 & 1.48424433383052e-07 & 0.999999925787783 \tabularnewline
24 & 2.41498551227396e-08 & 4.82997102454793e-08 & 0.999999975850145 \tabularnewline
25 & 9.29196852313395e-09 & 1.85839370462679e-08 & 0.999999990708031 \tabularnewline
26 & 1.13489341433591e-08 & 2.26978682867182e-08 & 0.999999988651066 \tabularnewline
27 & 1.01878887898270e-07 & 2.03757775796540e-07 & 0.999999898121112 \tabularnewline
28 & 6.01899630032177e-08 & 1.20379926006435e-07 & 0.999999939810037 \tabularnewline
29 & 2.02048673220298e-07 & 4.04097346440596e-07 & 0.999999797951327 \tabularnewline
30 & 8.70470962682326e-07 & 1.74094192536465e-06 & 0.999999129529037 \tabularnewline
31 & 1.27733751278629e-06 & 2.55467502557257e-06 & 0.999998722662487 \tabularnewline
32 & 1.22309891374483e-06 & 2.44619782748966e-06 & 0.999998776901086 \tabularnewline
33 & 8.0817365147768e-07 & 1.61634730295536e-06 & 0.999999191826348 \tabularnewline
34 & 6.12885517880207e-07 & 1.22577103576041e-06 & 0.999999387114482 \tabularnewline
35 & 1.07257126921125e-06 & 2.14514253842250e-06 & 0.99999892742873 \tabularnewline
36 & 8.2359946401838e-07 & 1.64719892803676e-06 & 0.999999176400536 \tabularnewline
37 & 1.02103442185417e-06 & 2.04206884370833e-06 & 0.999998978965578 \tabularnewline
38 & 8.76865649238785e-07 & 1.75373129847757e-06 & 0.99999912313435 \tabularnewline
39 & 1.70977582206830e-06 & 3.41955164413659e-06 & 0.999998290224178 \tabularnewline
40 & 3.63299791760851e-06 & 7.26599583521701e-06 & 0.999996367002082 \tabularnewline
41 & 3.15095786303396e-05 & 6.30191572606792e-05 & 0.99996849042137 \tabularnewline
42 & 0.000227058925574136 & 0.000454117851148271 & 0.999772941074426 \tabularnewline
43 & 0.000691531958936874 & 0.00138306391787375 & 0.999308468041063 \tabularnewline
44 & 0.00219679790088322 & 0.00439359580176643 & 0.997803202099117 \tabularnewline
45 & 0.0102322677908677 & 0.0204645355817354 & 0.989767732209132 \tabularnewline
46 & 0.0293131396797708 & 0.0586262793595416 & 0.97068686032023 \tabularnewline
47 & 0.0716507837019145 & 0.143301567403829 & 0.928349216298086 \tabularnewline
48 & 0.222995354377805 & 0.445990708755611 & 0.777004645622195 \tabularnewline
49 & 0.364080575790869 & 0.728161151581739 & 0.63591942420913 \tabularnewline
50 & 0.49637064025509 & 0.99274128051018 & 0.50362935974491 \tabularnewline
51 & 0.629249935711324 & 0.741500128577352 & 0.370750064288676 \tabularnewline
52 & 0.715747591003395 & 0.568504817993211 & 0.284252408996605 \tabularnewline
53 & 0.777301713384288 & 0.445396573231424 & 0.222698286615712 \tabularnewline
54 & 0.81823161353911 & 0.363536772921782 & 0.181768386460891 \tabularnewline
55 & 0.858407445367312 & 0.283185109265376 & 0.141592554632688 \tabularnewline
56 & 0.891205847357055 & 0.217588305285890 & 0.108794152642945 \tabularnewline
57 & 0.926312932663523 & 0.147374134672954 & 0.0736870673364772 \tabularnewline
58 & 0.955741725607158 & 0.0885165487856848 & 0.0442582743928424 \tabularnewline
59 & 0.970394207742918 & 0.0592115845141645 & 0.0296057922570823 \tabularnewline
60 & 0.986712207412366 & 0.0265755851752688 & 0.0132877925876344 \tabularnewline
61 & 0.990663141078824 & 0.0186737178423512 & 0.00933685892117559 \tabularnewline
62 & 0.992242198846722 & 0.0155156023065567 & 0.00775780115327834 \tabularnewline
63 & 0.994636541165487 & 0.0107269176690251 & 0.00536345883451255 \tabularnewline
64 & 0.9966628802517 & 0.00667423949660182 & 0.00333711974830091 \tabularnewline
65 & 0.997959082275746 & 0.00408183544850744 & 0.00204091772425372 \tabularnewline
66 & 0.998794491529132 & 0.0024110169417349 & 0.00120550847086745 \tabularnewline
67 & 0.998957315692915 & 0.00208536861417031 & 0.00104268430708515 \tabularnewline
68 & 0.999060368971588 & 0.00187926205682379 & 0.000939631028411894 \tabularnewline
69 & 0.999035234907788 & 0.00192953018442359 & 0.000964765092211796 \tabularnewline
70 & 0.998915973637327 & 0.00216805272534543 & 0.00108402636267272 \tabularnewline
71 & 0.998771823584617 & 0.00245635283076509 & 0.00122817641538255 \tabularnewline
72 & 0.998721436592671 & 0.00255712681465780 & 0.00127856340732890 \tabularnewline
73 & 0.998468406748182 & 0.00306318650363502 & 0.00153159325181751 \tabularnewline
74 & 0.998122421921552 & 0.00375515615689553 & 0.00187757807844777 \tabularnewline
75 & 0.997553120177472 & 0.00489375964505651 & 0.00244687982252825 \tabularnewline
76 & 0.996796461937967 & 0.00640707612406659 & 0.00320353806203329 \tabularnewline
77 & 0.99575663162761 & 0.00848673674477976 & 0.00424336837238988 \tabularnewline
78 & 0.994701910567166 & 0.0105961788656685 & 0.00529808943283426 \tabularnewline
79 & 0.993323775705369 & 0.0133524485892626 & 0.00667622429463132 \tabularnewline
80 & 0.991938066609506 & 0.0161238667809889 & 0.00806193339049445 \tabularnewline
81 & 0.990316761249846 & 0.0193664775003075 & 0.00968323875015375 \tabularnewline
82 & 0.989000812142191 & 0.0219983757156172 & 0.0109991878578086 \tabularnewline
83 & 0.98842637182737 & 0.0231472563452595 & 0.0115736281726298 \tabularnewline
84 & 0.987710013473071 & 0.0245799730538583 & 0.0122899865269291 \tabularnewline
85 & 0.987422983283562 & 0.0251540334328763 & 0.0125770167164381 \tabularnewline
86 & 0.987202504783559 & 0.0255949904328824 & 0.0127974952164412 \tabularnewline
87 & 0.987380116180003 & 0.0252397676399943 & 0.0126198838199971 \tabularnewline
88 & 0.987883273937457 & 0.0242334521250859 & 0.0121167260625429 \tabularnewline
89 & 0.988719352382597 & 0.0225612952348060 & 0.0112806476174030 \tabularnewline
90 & 0.990846139358769 & 0.0183077212824628 & 0.00915386064123138 \tabularnewline
91 & 0.994171846651562 & 0.0116563066968761 & 0.00582815334843803 \tabularnewline
92 & 0.996572837397685 & 0.00685432520463089 & 0.00342716260231545 \tabularnewline
93 & 0.996853100444787 & 0.00629379911042673 & 0.00314689955521336 \tabularnewline
94 & 0.996959448451295 & 0.00608110309741047 & 0.00304055154870524 \tabularnewline
95 & 0.997619633688807 & 0.00476073262238515 & 0.00238036631119258 \tabularnewline
96 & 0.997775851068017 & 0.00444829786396683 & 0.00222414893198341 \tabularnewline
97 & 0.99807375608026 & 0.00385248783948217 & 0.00192624391974109 \tabularnewline
98 & 0.998231567774051 & 0.00353686445189765 & 0.00176843222594882 \tabularnewline
99 & 0.997950965955145 & 0.00409806808971032 & 0.00204903404485516 \tabularnewline
100 & 0.997634697014862 & 0.00473060597027667 & 0.00236530298513833 \tabularnewline
101 & 0.997299118827654 & 0.00540176234469261 & 0.00270088117234631 \tabularnewline
102 & 0.996779666088247 & 0.00644066782350664 & 0.00322033391175332 \tabularnewline
103 & 0.99581600798613 & 0.00836798402774238 & 0.00418399201387119 \tabularnewline
104 & 0.99477665279039 & 0.0104466944192181 & 0.00522334720960907 \tabularnewline
105 & 0.993082319312398 & 0.0138353613752042 & 0.00691768068760212 \tabularnewline
106 & 0.991144521992295 & 0.0177109560154091 & 0.00885547800770456 \tabularnewline
107 & 0.989549969924612 & 0.0209000601507757 & 0.0104500300753878 \tabularnewline
108 & 0.986395757477453 & 0.027208485045094 & 0.013604242522547 \tabularnewline
109 & 0.982446373262624 & 0.0351072534747528 & 0.0175536267373764 \tabularnewline
110 & 0.976808693286236 & 0.0463826134275281 & 0.0231913067137641 \tabularnewline
111 & 0.971785823174737 & 0.0564283536505258 & 0.0282141768252629 \tabularnewline
112 & 0.964421728751552 & 0.0711565424968953 & 0.0355782712484477 \tabularnewline
113 & 0.953185912000697 & 0.0936281759986054 & 0.0468140879993027 \tabularnewline
114 & 0.942872944868137 & 0.114254110263726 & 0.0571270551318631 \tabularnewline
115 & 0.939563092562403 & 0.120873814875194 & 0.060436907437597 \tabularnewline
116 & 0.944865105865242 & 0.110269788269516 & 0.0551348941347578 \tabularnewline
117 & 0.962528003337122 & 0.074943993325756 & 0.037471996662878 \tabularnewline
118 & 0.968356250990519 & 0.0632874980189618 & 0.0316437490094809 \tabularnewline
119 & 0.966731389843521 & 0.0665372203129576 & 0.0332686101564788 \tabularnewline
120 & 0.976673444592531 & 0.0466531108149373 & 0.0233265554074687 \tabularnewline
121 & 0.986470700187803 & 0.0270585996243936 & 0.0135292998121968 \tabularnewline
122 & 0.993266193470603 & 0.0134676130587944 & 0.00673380652939718 \tabularnewline
123 & 0.998018238552218 & 0.00396352289556364 & 0.00198176144778182 \tabularnewline
124 & 0.998009465604993 & 0.0039810687900133 & 0.00199053439500665 \tabularnewline
125 & 0.99756868519775 & 0.00486262960450201 & 0.00243131480225101 \tabularnewline
126 & 0.996697764168193 & 0.0066044716636146 & 0.0033022358318073 \tabularnewline
127 & 0.99757314463477 & 0.00485371073046069 & 0.00242685536523034 \tabularnewline
128 & 0.998729601111119 & 0.00254079777776206 & 0.00127039888888103 \tabularnewline
129 & 0.998871907594588 & 0.00225618481082306 & 0.00112809240541153 \tabularnewline
130 & 0.999057645184 & 0.00188470963199957 & 0.000942354815999787 \tabularnewline
131 & 0.999790907700383 & 0.000418184599233913 & 0.000209092299616957 \tabularnewline
132 & 0.999963173478877 & 7.3653042245963e-05 & 3.68265211229815e-05 \tabularnewline
133 & 0.999984338999673 & 3.13220006532921e-05 & 1.56610003266460e-05 \tabularnewline
134 & 0.999992436194005 & 1.51276119891310e-05 & 7.56380599456552e-06 \tabularnewline
135 & 0.999997203443047 & 5.59311390682643e-06 & 2.79655695341322e-06 \tabularnewline
136 & 0.999993376158204 & 1.32476835912680e-05 & 6.62384179563402e-06 \tabularnewline
137 & 0.999958389802188 & 8.32203956239369e-05 & 4.16101978119684e-05 \tabularnewline
138 & 0.999811725498136 & 0.000376549003728766 & 0.000188274501864383 \tabularnewline
139 & 0.998756440558705 & 0.00248711888258927 & 0.00124355944129464 \tabularnewline
140 & 0.991726866379096 & 0.016546267241808 & 0.008273133620904 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=67220&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]5[/C][C]0.0543336217108625[/C][C]0.108667243421725[/C][C]0.945666378289137[/C][/ROW]
[ROW][C]6[/C][C]0.0481150990737843[/C][C]0.0962301981475685[/C][C]0.951884900926216[/C][/ROW]
[ROW][C]7[/C][C]0.0206389496865986[/C][C]0.0412778993731972[/C][C]0.979361050313401[/C][/ROW]
[ROW][C]8[/C][C]0.00799378093007457[/C][C]0.0159875618601491[/C][C]0.992006219069925[/C][/ROW]
[ROW][C]9[/C][C]0.00273576178032791[/C][C]0.00547152356065582[/C][C]0.997264238219672[/C][/ROW]
[ROW][C]10[/C][C]0.00140573783459866[/C][C]0.00281147566919732[/C][C]0.998594262165401[/C][/ROW]
[ROW][C]11[/C][C]0.000595578619164258[/C][C]0.00119115723832852[/C][C]0.999404421380836[/C][/ROW]
[ROW][C]12[/C][C]0.00186585911491275[/C][C]0.00373171822982551[/C][C]0.998134140885087[/C][/ROW]
[ROW][C]13[/C][C]0.00118440319504998[/C][C]0.00236880639009996[/C][C]0.99881559680495[/C][/ROW]
[ROW][C]14[/C][C]0.000537723878008446[/C][C]0.00107544775601689[/C][C]0.999462276121992[/C][/ROW]
[ROW][C]15[/C][C]0.000263010128489922[/C][C]0.000526020256979843[/C][C]0.99973698987151[/C][/ROW]
[ROW][C]16[/C][C]0.000103358197089751[/C][C]0.000206716394179502[/C][C]0.99989664180291[/C][/ROW]
[ROW][C]17[/C][C]3.73003912277326e-05[/C][C]7.46007824554652e-05[/C][C]0.999962699608772[/C][/ROW]
[ROW][C]18[/C][C]1.33461383485114e-05[/C][C]2.66922766970228e-05[/C][C]0.999986653861652[/C][/ROW]
[ROW][C]19[/C][C]5.93858773318676e-06[/C][C]1.18771754663735e-05[/C][C]0.999994061412267[/C][/ROW]
[ROW][C]20[/C][C]2.04075595374329e-06[/C][C]4.08151190748657e-06[/C][C]0.999997959244046[/C][/ROW]
[ROW][C]21[/C][C]6.6504338572235e-07[/C][C]1.3300867714447e-06[/C][C]0.999999334956614[/C][/ROW]
[ROW][C]22[/C][C]2.37136201376090e-07[/C][C]4.74272402752179e-07[/C][C]0.999999762863799[/C][/ROW]
[ROW][C]23[/C][C]7.42122166915262e-08[/C][C]1.48424433383052e-07[/C][C]0.999999925787783[/C][/ROW]
[ROW][C]24[/C][C]2.41498551227396e-08[/C][C]4.82997102454793e-08[/C][C]0.999999975850145[/C][/ROW]
[ROW][C]25[/C][C]9.29196852313395e-09[/C][C]1.85839370462679e-08[/C][C]0.999999990708031[/C][/ROW]
[ROW][C]26[/C][C]1.13489341433591e-08[/C][C]2.26978682867182e-08[/C][C]0.999999988651066[/C][/ROW]
[ROW][C]27[/C][C]1.01878887898270e-07[/C][C]2.03757775796540e-07[/C][C]0.999999898121112[/C][/ROW]
[ROW][C]28[/C][C]6.01899630032177e-08[/C][C]1.20379926006435e-07[/C][C]0.999999939810037[/C][/ROW]
[ROW][C]29[/C][C]2.02048673220298e-07[/C][C]4.04097346440596e-07[/C][C]0.999999797951327[/C][/ROW]
[ROW][C]30[/C][C]8.70470962682326e-07[/C][C]1.74094192536465e-06[/C][C]0.999999129529037[/C][/ROW]
[ROW][C]31[/C][C]1.27733751278629e-06[/C][C]2.55467502557257e-06[/C][C]0.999998722662487[/C][/ROW]
[ROW][C]32[/C][C]1.22309891374483e-06[/C][C]2.44619782748966e-06[/C][C]0.999998776901086[/C][/ROW]
[ROW][C]33[/C][C]8.0817365147768e-07[/C][C]1.61634730295536e-06[/C][C]0.999999191826348[/C][/ROW]
[ROW][C]34[/C][C]6.12885517880207e-07[/C][C]1.22577103576041e-06[/C][C]0.999999387114482[/C][/ROW]
[ROW][C]35[/C][C]1.07257126921125e-06[/C][C]2.14514253842250e-06[/C][C]0.99999892742873[/C][/ROW]
[ROW][C]36[/C][C]8.2359946401838e-07[/C][C]1.64719892803676e-06[/C][C]0.999999176400536[/C][/ROW]
[ROW][C]37[/C][C]1.02103442185417e-06[/C][C]2.04206884370833e-06[/C][C]0.999998978965578[/C][/ROW]
[ROW][C]38[/C][C]8.76865649238785e-07[/C][C]1.75373129847757e-06[/C][C]0.99999912313435[/C][/ROW]
[ROW][C]39[/C][C]1.70977582206830e-06[/C][C]3.41955164413659e-06[/C][C]0.999998290224178[/C][/ROW]
[ROW][C]40[/C][C]3.63299791760851e-06[/C][C]7.26599583521701e-06[/C][C]0.999996367002082[/C][/ROW]
[ROW][C]41[/C][C]3.15095786303396e-05[/C][C]6.30191572606792e-05[/C][C]0.99996849042137[/C][/ROW]
[ROW][C]42[/C][C]0.000227058925574136[/C][C]0.000454117851148271[/C][C]0.999772941074426[/C][/ROW]
[ROW][C]43[/C][C]0.000691531958936874[/C][C]0.00138306391787375[/C][C]0.999308468041063[/C][/ROW]
[ROW][C]44[/C][C]0.00219679790088322[/C][C]0.00439359580176643[/C][C]0.997803202099117[/C][/ROW]
[ROW][C]45[/C][C]0.0102322677908677[/C][C]0.0204645355817354[/C][C]0.989767732209132[/C][/ROW]
[ROW][C]46[/C][C]0.0293131396797708[/C][C]0.0586262793595416[/C][C]0.97068686032023[/C][/ROW]
[ROW][C]47[/C][C]0.0716507837019145[/C][C]0.143301567403829[/C][C]0.928349216298086[/C][/ROW]
[ROW][C]48[/C][C]0.222995354377805[/C][C]0.445990708755611[/C][C]0.777004645622195[/C][/ROW]
[ROW][C]49[/C][C]0.364080575790869[/C][C]0.728161151581739[/C][C]0.63591942420913[/C][/ROW]
[ROW][C]50[/C][C]0.49637064025509[/C][C]0.99274128051018[/C][C]0.50362935974491[/C][/ROW]
[ROW][C]51[/C][C]0.629249935711324[/C][C]0.741500128577352[/C][C]0.370750064288676[/C][/ROW]
[ROW][C]52[/C][C]0.715747591003395[/C][C]0.568504817993211[/C][C]0.284252408996605[/C][/ROW]
[ROW][C]53[/C][C]0.777301713384288[/C][C]0.445396573231424[/C][C]0.222698286615712[/C][/ROW]
[ROW][C]54[/C][C]0.81823161353911[/C][C]0.363536772921782[/C][C]0.181768386460891[/C][/ROW]
[ROW][C]55[/C][C]0.858407445367312[/C][C]0.283185109265376[/C][C]0.141592554632688[/C][/ROW]
[ROW][C]56[/C][C]0.891205847357055[/C][C]0.217588305285890[/C][C]0.108794152642945[/C][/ROW]
[ROW][C]57[/C][C]0.926312932663523[/C][C]0.147374134672954[/C][C]0.0736870673364772[/C][/ROW]
[ROW][C]58[/C][C]0.955741725607158[/C][C]0.0885165487856848[/C][C]0.0442582743928424[/C][/ROW]
[ROW][C]59[/C][C]0.970394207742918[/C][C]0.0592115845141645[/C][C]0.0296057922570823[/C][/ROW]
[ROW][C]60[/C][C]0.986712207412366[/C][C]0.0265755851752688[/C][C]0.0132877925876344[/C][/ROW]
[ROW][C]61[/C][C]0.990663141078824[/C][C]0.0186737178423512[/C][C]0.00933685892117559[/C][/ROW]
[ROW][C]62[/C][C]0.992242198846722[/C][C]0.0155156023065567[/C][C]0.00775780115327834[/C][/ROW]
[ROW][C]63[/C][C]0.994636541165487[/C][C]0.0107269176690251[/C][C]0.00536345883451255[/C][/ROW]
[ROW][C]64[/C][C]0.9966628802517[/C][C]0.00667423949660182[/C][C]0.00333711974830091[/C][/ROW]
[ROW][C]65[/C][C]0.997959082275746[/C][C]0.00408183544850744[/C][C]0.00204091772425372[/C][/ROW]
[ROW][C]66[/C][C]0.998794491529132[/C][C]0.0024110169417349[/C][C]0.00120550847086745[/C][/ROW]
[ROW][C]67[/C][C]0.998957315692915[/C][C]0.00208536861417031[/C][C]0.00104268430708515[/C][/ROW]
[ROW][C]68[/C][C]0.999060368971588[/C][C]0.00187926205682379[/C][C]0.000939631028411894[/C][/ROW]
[ROW][C]69[/C][C]0.999035234907788[/C][C]0.00192953018442359[/C][C]0.000964765092211796[/C][/ROW]
[ROW][C]70[/C][C]0.998915973637327[/C][C]0.00216805272534543[/C][C]0.00108402636267272[/C][/ROW]
[ROW][C]71[/C][C]0.998771823584617[/C][C]0.00245635283076509[/C][C]0.00122817641538255[/C][/ROW]
[ROW][C]72[/C][C]0.998721436592671[/C][C]0.00255712681465780[/C][C]0.00127856340732890[/C][/ROW]
[ROW][C]73[/C][C]0.998468406748182[/C][C]0.00306318650363502[/C][C]0.00153159325181751[/C][/ROW]
[ROW][C]74[/C][C]0.998122421921552[/C][C]0.00375515615689553[/C][C]0.00187757807844777[/C][/ROW]
[ROW][C]75[/C][C]0.997553120177472[/C][C]0.00489375964505651[/C][C]0.00244687982252825[/C][/ROW]
[ROW][C]76[/C][C]0.996796461937967[/C][C]0.00640707612406659[/C][C]0.00320353806203329[/C][/ROW]
[ROW][C]77[/C][C]0.99575663162761[/C][C]0.00848673674477976[/C][C]0.00424336837238988[/C][/ROW]
[ROW][C]78[/C][C]0.994701910567166[/C][C]0.0105961788656685[/C][C]0.00529808943283426[/C][/ROW]
[ROW][C]79[/C][C]0.993323775705369[/C][C]0.0133524485892626[/C][C]0.00667622429463132[/C][/ROW]
[ROW][C]80[/C][C]0.991938066609506[/C][C]0.0161238667809889[/C][C]0.00806193339049445[/C][/ROW]
[ROW][C]81[/C][C]0.990316761249846[/C][C]0.0193664775003075[/C][C]0.00968323875015375[/C][/ROW]
[ROW][C]82[/C][C]0.989000812142191[/C][C]0.0219983757156172[/C][C]0.0109991878578086[/C][/ROW]
[ROW][C]83[/C][C]0.98842637182737[/C][C]0.0231472563452595[/C][C]0.0115736281726298[/C][/ROW]
[ROW][C]84[/C][C]0.987710013473071[/C][C]0.0245799730538583[/C][C]0.0122899865269291[/C][/ROW]
[ROW][C]85[/C][C]0.987422983283562[/C][C]0.0251540334328763[/C][C]0.0125770167164381[/C][/ROW]
[ROW][C]86[/C][C]0.987202504783559[/C][C]0.0255949904328824[/C][C]0.0127974952164412[/C][/ROW]
[ROW][C]87[/C][C]0.987380116180003[/C][C]0.0252397676399943[/C][C]0.0126198838199971[/C][/ROW]
[ROW][C]88[/C][C]0.987883273937457[/C][C]0.0242334521250859[/C][C]0.0121167260625429[/C][/ROW]
[ROW][C]89[/C][C]0.988719352382597[/C][C]0.0225612952348060[/C][C]0.0112806476174030[/C][/ROW]
[ROW][C]90[/C][C]0.990846139358769[/C][C]0.0183077212824628[/C][C]0.00915386064123138[/C][/ROW]
[ROW][C]91[/C][C]0.994171846651562[/C][C]0.0116563066968761[/C][C]0.00582815334843803[/C][/ROW]
[ROW][C]92[/C][C]0.996572837397685[/C][C]0.00685432520463089[/C][C]0.00342716260231545[/C][/ROW]
[ROW][C]93[/C][C]0.996853100444787[/C][C]0.00629379911042673[/C][C]0.00314689955521336[/C][/ROW]
[ROW][C]94[/C][C]0.996959448451295[/C][C]0.00608110309741047[/C][C]0.00304055154870524[/C][/ROW]
[ROW][C]95[/C][C]0.997619633688807[/C][C]0.00476073262238515[/C][C]0.00238036631119258[/C][/ROW]
[ROW][C]96[/C][C]0.997775851068017[/C][C]0.00444829786396683[/C][C]0.00222414893198341[/C][/ROW]
[ROW][C]97[/C][C]0.99807375608026[/C][C]0.00385248783948217[/C][C]0.00192624391974109[/C][/ROW]
[ROW][C]98[/C][C]0.998231567774051[/C][C]0.00353686445189765[/C][C]0.00176843222594882[/C][/ROW]
[ROW][C]99[/C][C]0.997950965955145[/C][C]0.00409806808971032[/C][C]0.00204903404485516[/C][/ROW]
[ROW][C]100[/C][C]0.997634697014862[/C][C]0.00473060597027667[/C][C]0.00236530298513833[/C][/ROW]
[ROW][C]101[/C][C]0.997299118827654[/C][C]0.00540176234469261[/C][C]0.00270088117234631[/C][/ROW]
[ROW][C]102[/C][C]0.996779666088247[/C][C]0.00644066782350664[/C][C]0.00322033391175332[/C][/ROW]
[ROW][C]103[/C][C]0.99581600798613[/C][C]0.00836798402774238[/C][C]0.00418399201387119[/C][/ROW]
[ROW][C]104[/C][C]0.99477665279039[/C][C]0.0104466944192181[/C][C]0.00522334720960907[/C][/ROW]
[ROW][C]105[/C][C]0.993082319312398[/C][C]0.0138353613752042[/C][C]0.00691768068760212[/C][/ROW]
[ROW][C]106[/C][C]0.991144521992295[/C][C]0.0177109560154091[/C][C]0.00885547800770456[/C][/ROW]
[ROW][C]107[/C][C]0.989549969924612[/C][C]0.0209000601507757[/C][C]0.0104500300753878[/C][/ROW]
[ROW][C]108[/C][C]0.986395757477453[/C][C]0.027208485045094[/C][C]0.013604242522547[/C][/ROW]
[ROW][C]109[/C][C]0.982446373262624[/C][C]0.0351072534747528[/C][C]0.0175536267373764[/C][/ROW]
[ROW][C]110[/C][C]0.976808693286236[/C][C]0.0463826134275281[/C][C]0.0231913067137641[/C][/ROW]
[ROW][C]111[/C][C]0.971785823174737[/C][C]0.0564283536505258[/C][C]0.0282141768252629[/C][/ROW]
[ROW][C]112[/C][C]0.964421728751552[/C][C]0.0711565424968953[/C][C]0.0355782712484477[/C][/ROW]
[ROW][C]113[/C][C]0.953185912000697[/C][C]0.0936281759986054[/C][C]0.0468140879993027[/C][/ROW]
[ROW][C]114[/C][C]0.942872944868137[/C][C]0.114254110263726[/C][C]0.0571270551318631[/C][/ROW]
[ROW][C]115[/C][C]0.939563092562403[/C][C]0.120873814875194[/C][C]0.060436907437597[/C][/ROW]
[ROW][C]116[/C][C]0.944865105865242[/C][C]0.110269788269516[/C][C]0.0551348941347578[/C][/ROW]
[ROW][C]117[/C][C]0.962528003337122[/C][C]0.074943993325756[/C][C]0.037471996662878[/C][/ROW]
[ROW][C]118[/C][C]0.968356250990519[/C][C]0.0632874980189618[/C][C]0.0316437490094809[/C][/ROW]
[ROW][C]119[/C][C]0.966731389843521[/C][C]0.0665372203129576[/C][C]0.0332686101564788[/C][/ROW]
[ROW][C]120[/C][C]0.976673444592531[/C][C]0.0466531108149373[/C][C]0.0233265554074687[/C][/ROW]
[ROW][C]121[/C][C]0.986470700187803[/C][C]0.0270585996243936[/C][C]0.0135292998121968[/C][/ROW]
[ROW][C]122[/C][C]0.993266193470603[/C][C]0.0134676130587944[/C][C]0.00673380652939718[/C][/ROW]
[ROW][C]123[/C][C]0.998018238552218[/C][C]0.00396352289556364[/C][C]0.00198176144778182[/C][/ROW]
[ROW][C]124[/C][C]0.998009465604993[/C][C]0.0039810687900133[/C][C]0.00199053439500665[/C][/ROW]
[ROW][C]125[/C][C]0.99756868519775[/C][C]0.00486262960450201[/C][C]0.00243131480225101[/C][/ROW]
[ROW][C]126[/C][C]0.996697764168193[/C][C]0.0066044716636146[/C][C]0.0033022358318073[/C][/ROW]
[ROW][C]127[/C][C]0.99757314463477[/C][C]0.00485371073046069[/C][C]0.00242685536523034[/C][/ROW]
[ROW][C]128[/C][C]0.998729601111119[/C][C]0.00254079777776206[/C][C]0.00127039888888103[/C][/ROW]
[ROW][C]129[/C][C]0.998871907594588[/C][C]0.00225618481082306[/C][C]0.00112809240541153[/C][/ROW]
[ROW][C]130[/C][C]0.999057645184[/C][C]0.00188470963199957[/C][C]0.000942354815999787[/C][/ROW]
[ROW][C]131[/C][C]0.999790907700383[/C][C]0.000418184599233913[/C][C]0.000209092299616957[/C][/ROW]
[ROW][C]132[/C][C]0.999963173478877[/C][C]7.3653042245963e-05[/C][C]3.68265211229815e-05[/C][/ROW]
[ROW][C]133[/C][C]0.999984338999673[/C][C]3.13220006532921e-05[/C][C]1.56610003266460e-05[/C][/ROW]
[ROW][C]134[/C][C]0.999992436194005[/C][C]1.51276119891310e-05[/C][C]7.56380599456552e-06[/C][/ROW]
[ROW][C]135[/C][C]0.999997203443047[/C][C]5.59311390682643e-06[/C][C]2.79655695341322e-06[/C][/ROW]
[ROW][C]136[/C][C]0.999993376158204[/C][C]1.32476835912680e-05[/C][C]6.62384179563402e-06[/C][/ROW]
[ROW][C]137[/C][C]0.999958389802188[/C][C]8.32203956239369e-05[/C][C]4.16101978119684e-05[/C][/ROW]
[ROW][C]138[/C][C]0.999811725498136[/C][C]0.000376549003728766[/C][C]0.000188274501864383[/C][/ROW]
[ROW][C]139[/C][C]0.998756440558705[/C][C]0.00248711888258927[/C][C]0.00124355944129464[/C][/ROW]
[ROW][C]140[/C][C]0.991726866379096[/C][C]0.016546267241808[/C][C]0.008273133620904[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=67220&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=67220&T=5

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
5	0.0543336217108625	0.108667243421725	0.945666378289137
6	0.0481150990737843	0.0962301981475685	0.951884900926216
7	0.0206389496865986	0.0412778993731972	0.979361050313401
8	0.00799378093007457	0.0159875618601491	0.992006219069925
9	0.00273576178032791	0.00547152356065582	0.997264238219672
10	0.00140573783459866	0.00281147566919732	0.998594262165401
11	0.000595578619164258	0.00119115723832852	0.999404421380836
12	0.00186585911491275	0.00373171822982551	0.998134140885087
13	0.00118440319504998	0.00236880639009996	0.99881559680495
14	0.000537723878008446	0.00107544775601689	0.999462276121992
15	0.000263010128489922	0.000526020256979843	0.99973698987151
16	0.000103358197089751	0.000206716394179502	0.99989664180291
17	3.73003912277326e-05	7.46007824554652e-05	0.999962699608772
18	1.33461383485114e-05	2.66922766970228e-05	0.999986653861652
19	5.93858773318676e-06	1.18771754663735e-05	0.999994061412267
20	2.04075595374329e-06	4.08151190748657e-06	0.999997959244046
21	6.6504338572235e-07	1.3300867714447e-06	0.999999334956614
22	2.37136201376090e-07	4.74272402752179e-07	0.999999762863799
23	7.42122166915262e-08	1.48424433383052e-07	0.999999925787783
24	2.41498551227396e-08	4.82997102454793e-08	0.999999975850145
25	9.29196852313395e-09	1.85839370462679e-08	0.999999990708031
26	1.13489341433591e-08	2.26978682867182e-08	0.999999988651066
27	1.01878887898270e-07	2.03757775796540e-07	0.999999898121112
28	6.01899630032177e-08	1.20379926006435e-07	0.999999939810037
29	2.02048673220298e-07	4.04097346440596e-07	0.999999797951327
30	8.70470962682326e-07	1.74094192536465e-06	0.999999129529037
31	1.27733751278629e-06	2.55467502557257e-06	0.999998722662487
32	1.22309891374483e-06	2.44619782748966e-06	0.999998776901086
33	8.0817365147768e-07	1.61634730295536e-06	0.999999191826348
34	6.12885517880207e-07	1.22577103576041e-06	0.999999387114482
35	1.07257126921125e-06	2.14514253842250e-06	0.99999892742873
36	8.2359946401838e-07	1.64719892803676e-06	0.999999176400536
37	1.02103442185417e-06	2.04206884370833e-06	0.999998978965578
38	8.76865649238785e-07	1.75373129847757e-06	0.99999912313435
39	1.70977582206830e-06	3.41955164413659e-06	0.999998290224178
40	3.63299791760851e-06	7.26599583521701e-06	0.999996367002082
41	3.15095786303396e-05	6.30191572606792e-05	0.99996849042137
42	0.000227058925574136	0.000454117851148271	0.999772941074426
43	0.000691531958936874	0.00138306391787375	0.999308468041063
44	0.00219679790088322	0.00439359580176643	0.997803202099117
45	0.0102322677908677	0.0204645355817354	0.989767732209132
46	0.0293131396797708	0.0586262793595416	0.97068686032023
47	0.0716507837019145	0.143301567403829	0.928349216298086
48	0.222995354377805	0.445990708755611	0.777004645622195
49	0.364080575790869	0.728161151581739	0.63591942420913
50	0.49637064025509	0.99274128051018	0.50362935974491
51	0.629249935711324	0.741500128577352	0.370750064288676
52	0.715747591003395	0.568504817993211	0.284252408996605
53	0.777301713384288	0.445396573231424	0.222698286615712
54	0.81823161353911	0.363536772921782	0.181768386460891
55	0.858407445367312	0.283185109265376	0.141592554632688
56	0.891205847357055	0.217588305285890	0.108794152642945
57	0.926312932663523	0.147374134672954	0.0736870673364772
58	0.955741725607158	0.0885165487856848	0.0442582743928424
59	0.970394207742918	0.0592115845141645	0.0296057922570823
60	0.986712207412366	0.0265755851752688	0.0132877925876344
61	0.990663141078824	0.0186737178423512	0.00933685892117559
62	0.992242198846722	0.0155156023065567	0.00775780115327834
63	0.994636541165487	0.0107269176690251	0.00536345883451255
64	0.9966628802517	0.00667423949660182	0.00333711974830091
65	0.997959082275746	0.00408183544850744	0.00204091772425372
66	0.998794491529132	0.0024110169417349	0.00120550847086745
67	0.998957315692915	0.00208536861417031	0.00104268430708515
68	0.999060368971588	0.00187926205682379	0.000939631028411894
69	0.999035234907788	0.00192953018442359	0.000964765092211796
70	0.998915973637327	0.00216805272534543	0.00108402636267272
71	0.998771823584617	0.00245635283076509	0.00122817641538255
72	0.998721436592671	0.00255712681465780	0.00127856340732890
73	0.998468406748182	0.00306318650363502	0.00153159325181751
74	0.998122421921552	0.00375515615689553	0.00187757807844777
75	0.997553120177472	0.00489375964505651	0.00244687982252825
76	0.996796461937967	0.00640707612406659	0.00320353806203329
77	0.99575663162761	0.00848673674477976	0.00424336837238988
78	0.994701910567166	0.0105961788656685	0.00529808943283426
79	0.993323775705369	0.0133524485892626	0.00667622429463132
80	0.991938066609506	0.0161238667809889	0.00806193339049445
81	0.990316761249846	0.0193664775003075	0.00968323875015375
82	0.989000812142191	0.0219983757156172	0.0109991878578086
83	0.98842637182737	0.0231472563452595	0.0115736281726298
84	0.987710013473071	0.0245799730538583	0.0122899865269291
85	0.987422983283562	0.0251540334328763	0.0125770167164381
86	0.987202504783559	0.0255949904328824	0.0127974952164412
87	0.987380116180003	0.0252397676399943	0.0126198838199971
88	0.987883273937457	0.0242334521250859	0.0121167260625429
89	0.988719352382597	0.0225612952348060	0.0112806476174030
90	0.990846139358769	0.0183077212824628	0.00915386064123138
91	0.994171846651562	0.0116563066968761	0.00582815334843803
92	0.996572837397685	0.00685432520463089	0.00342716260231545
93	0.996853100444787	0.00629379911042673	0.00314689955521336
94	0.996959448451295	0.00608110309741047	0.00304055154870524
95	0.997619633688807	0.00476073262238515	0.00238036631119258
96	0.997775851068017	0.00444829786396683	0.00222414893198341
97	0.99807375608026	0.00385248783948217	0.00192624391974109
98	0.998231567774051	0.00353686445189765	0.00176843222594882
99	0.997950965955145	0.00409806808971032	0.00204903404485516
100	0.997634697014862	0.00473060597027667	0.00236530298513833
101	0.997299118827654	0.00540176234469261	0.00270088117234631
102	0.996779666088247	0.00644066782350664	0.00322033391175332
103	0.99581600798613	0.00836798402774238	0.00418399201387119
104	0.99477665279039	0.0104466944192181	0.00522334720960907
105	0.993082319312398	0.0138353613752042	0.00691768068760212
106	0.991144521992295	0.0177109560154091	0.00885547800770456
107	0.989549969924612	0.0209000601507757	0.0104500300753878
108	0.986395757477453	0.027208485045094	0.013604242522547
109	0.982446373262624	0.0351072534747528	0.0175536267373764
110	0.976808693286236	0.0463826134275281	0.0231913067137641
111	0.971785823174737	0.0564283536505258	0.0282141768252629
112	0.964421728751552	0.0711565424968953	0.0355782712484477
113	0.953185912000697	0.0936281759986054	0.0468140879993027
114	0.942872944868137	0.114254110263726	0.0571270551318631
115	0.939563092562403	0.120873814875194	0.060436907437597
116	0.944865105865242	0.110269788269516	0.0551348941347578
117	0.962528003337122	0.074943993325756	0.037471996662878
118	0.968356250990519	0.0632874980189618	0.0316437490094809
119	0.966731389843521	0.0665372203129576	0.0332686101564788
120	0.976673444592531	0.0466531108149373	0.0233265554074687
121	0.986470700187803	0.0270585996243936	0.0135292998121968
122	0.993266193470603	0.0134676130587944	0.00673380652939718
123	0.998018238552218	0.00396352289556364	0.00198176144778182
124	0.998009465604993	0.0039810687900133	0.00199053439500665
125	0.99756868519775	0.00486262960450201	0.00243131480225101
126	0.996697764168193	0.0066044716636146	0.0033022358318073
127	0.99757314463477	0.00485371073046069	0.00242685536523034
128	0.998729601111119	0.00254079777776206	0.00127039888888103
129	0.998871907594588	0.00225618481082306	0.00112809240541153
130	0.999057645184	0.00188470963199957	0.000942354815999787
131	0.999790907700383	0.000418184599233913	0.000209092299616957
132	0.999963173478877	7.3653042245963e-05	3.68265211229815e-05
133	0.999984338999673	3.13220006532921e-05	1.56610003266460e-05
134	0.999992436194005	1.51276119891310e-05	7.56380599456552e-06
135	0.999997203443047	5.59311390682643e-06	2.79655695341322e-06
136	0.999993376158204	1.32476835912680e-05	6.62384179563402e-06
137	0.999958389802188	8.32203956239369e-05	4.16101978119684e-05
138	0.999811725498136	0.000376549003728766	0.000188274501864383
139	0.998756440558705	0.00248711888258927	0.00124355944129464
140	0.991726866379096	0.016546267241808	0.008273133620904

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	79	0.580882352941177	NOK
5% type I error level	111	0.816176470588235	NOK
10% type I error level	121	0.889705882352941	NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 79 & 0.580882352941177 & NOK \tabularnewline
5% type I error level & 111 & 0.816176470588235 & NOK \tabularnewline
10% type I error level & 121 & 0.889705882352941 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=67220&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]79[/C][C]0.580882352941177[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]111[/C][C]0.816176470588235[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]121[/C][C]0.889705882352941[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=67220&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=67220&T=6

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	79	0.580882352941177	NOK
5% type I error level	111	0.816176470588235	NOK
10% type I error level	121	0.889705882352941	NOK

Figure 1

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Figure 5

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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Figure 10

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Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

R code (references can be found in the software module):

library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- 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'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
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[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
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$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
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, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
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,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
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,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
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,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
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,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
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')
}

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code