Free Statistics

of Irreproducible Research!

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationMon, 21 Nov 2011 15:16:12 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Nov/21/t1321906636f5qg1a2uhlh5fhk.htm/, Retrieved Fri, 29 Mar 2024 07:24:18 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=145963, Retrieved Fri, 29 Mar 2024 07:24:18 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact148
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
- R  D  [Multiple Regression] [WS7] [2011-11-21 20:04:04] [8501ca4b76170905b8a207a77f626994]
-    D    [Multiple Regression] [WS7 (2)] [2011-11-21 20:07:43] [8501ca4b76170905b8a207a77f626994]
-             [Multiple Regression] [Workshop7_Tutorial] [2011-11-21 20:16:12] [3e64eea457df40fcb7af8f28e1ee6256] [Current]
-    D          [Multiple Regression] [Workshop7_Tutorial] [2011-11-21 20:27:04] [f722e8e78b9e5c5ebaa2263f273aa636]
- R P             [Multiple Regression] [Paper: Multiple R...] [2011-12-21 16:51:41] [f722e8e78b9e5c5ebaa2263f273aa636]
- R PD              [Multiple Regression] [Paper: Multiple R...] [2011-12-22 21:45:13] [f722e8e78b9e5c5ebaa2263f273aa636]
- R PD              [Multiple Regression] [Paper: Multiple R...] [2011-12-22 23:01:32] [f722e8e78b9e5c5ebaa2263f273aa636]
Feedback Forum

Post a new message
Dataseries X:
47	46	26	99
24	48	20	77
31	37	24	90
42	75	25	96
24	31	15	41
10	18	16	64
85	79	20	76
9	16	18	67
32	38	19	72
36	24	20	75
45	65	30	113
36	74	37	139
28	43	23	76
54	42	36	123
39	55	29	110
70	121	35	133
50	42	24	92
55	102	22	83
32	36	19	72
44	50	30	115
46	48	27	99
80	56	26	92
25	19	15	56
30	32	30	120
41	77	28	107
40	90	24	90
45	81	21	78
45	55	27	103
30	34	21	81
52	38	30	114
53	53	30	115
36	48	33	118
57	63	30	113
17	25	20	75
68	56	27	103
46	37	25	93
73	83	30	114
34	50	20	76
22	26	8	27
58	108	24	92
62	55	25	96
32	41	25	92
38	49	21	76
23	31	21	79
26	49	21	57
85	96	26	99
22	42	26	82
44	55	30	113
62	70	34	129
36	39	30	110
36	53	18	78
7	24	4	12
72	209	31	114
18	17	18	67
27	58	14	52
48	27	20	76
50	58	36	138
55	114	24	92
59	75	26	93
39	51	22	83
68	86	31	118
57	77	21	77
40	62	31	122
47	60	26	99
39	39	24	92
32	35	15	58
32	86	19	73
40	102	28	103
42	49	24	92
26	35	18	69
33	33	25	95
19	28	20	76
35	44	25	95
41	37	24	92
27	33	23	88
53	45	25	95
55	57	20	76
29	58	23	87
25	36	22	84
33	42	25	95
27	30	18	69
76	67	30	115
37	53	22	83
38	59	25	47
22	25	8	28
30	39	21	79
27	36	22	83
63	114	24	92
48	54	30	98
33	70	27	103
37	51	24	89
42	49	25	95
31	42	21	78
47	51	24	92
52	51	24	92
36	27	20	76
40	29	20	67
53	54	24	92
56	92	40	151
69	72	22	83
43	63	31	118
51	41	26	98
30	111	20	76
12	14	19	71
35	45	15	57
36	91	21	79
41	29	22	83
52	64	24	92
21	32	19	75
26	65	24	95
49	42	23	88
39	55	27	99
6	10	1	0
35	53	24	91
17	25	11	32
25	33	27	101
71	66	22	84
6	16	0	0
47	35	17	60
9	19	8	25
52	76	24	90
38	35	31	115
21	46	24	92
21	29	20	71
11	34	8	27
25	25	22	83
54	48	33	126
38	38	33	125
68	50	31	119
56	65	33	127
71	72	35	133
39	23	21	79
21	29	20	76
53	194	24	92
78	114	29	109
14	15	20	76
70	86	27	100
29	50	24	87
47	33	26	97
36	50	26	95
21	72	12	48
69	81	21	80
42	54	24	91
48	63	21	79
55	69	30	114
19	39	32	120
39	49	24	89
51	67	29	111
0	0	0	0
4	10	0	0
0	1	0	0
0	2	0	0
0	0	0	0
0	0	0	0
38	58	20	74
51	72	27	107
0	0	0	0
0	4	0	0
2	5	0	0
13	20	5	15
5	5	1	4
20	27	23	82
0	2	0	0
29	33	16	54




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net

\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 & 5 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145963&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]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145963&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145963&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 Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net







Multiple Linear Regression - Estimated Regression Equation
X2[t] = + 0.843098364063296 + 0.00580484590595102Y[t] + 0.000767014506162262X1[t] + 0.254588050490673X3[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
X2[t] =  +  0.843098364063296 +  0.00580484590595102Y[t] +  0.000767014506162262X1[t] +  0.254588050490673X3[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145963&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]X2[t] =  +  0.843098364063296 +  0.00580484590595102Y[t] +  0.000767014506162262X1[t] +  0.254588050490673X3[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145963&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145963&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
X2[t] = + 0.843098364063296 + 0.00580484590595102Y[t] + 0.000767014506162262X1[t] + 0.254588050490673X3[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.8430983640632960.28722.93560.003820.00191
Y0.005804845905951020.0094570.61380.5402070.270103
X10.0007670145061622620.0049460.15510.8769480.438474
X30.2545880504906730.00470254.142700

\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) & 0.843098364063296 & 0.2872 & 2.9356 & 0.00382 & 0.00191 \tabularnewline
Y & 0.00580484590595102 & 0.009457 & 0.6138 & 0.540207 & 0.270103 \tabularnewline
X1 & 0.000767014506162262 & 0.004946 & 0.1551 & 0.876948 & 0.438474 \tabularnewline
X3 & 0.254588050490673 & 0.004702 & 54.1427 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145963&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]0.843098364063296[/C][C]0.2872[/C][C]2.9356[/C][C]0.00382[/C][C]0.00191[/C][/ROW]
[ROW][C]Y[/C][C]0.00580484590595102[/C][C]0.009457[/C][C]0.6138[/C][C]0.540207[/C][C]0.270103[/C][/ROW]
[ROW][C]X1[/C][C]0.000767014506162262[/C][C]0.004946[/C][C]0.1551[/C][C]0.876948[/C][C]0.438474[/C][/ROW]
[ROW][C]X3[/C][C]0.254588050490673[/C][C]0.004702[/C][C]54.1427[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145963&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145963&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
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.8430983640632960.28722.93560.003820.00191
Y0.005804845905951020.0094570.61380.5402070.270103
X10.0007670145061622620.0049460.15510.8769480.438474
X30.2545880504906730.00470254.142700







Multiple Linear Regression - Regression Statistics
Multiple R0.987567424780638
R-squared0.975289418487861
Adjusted R-squared0.974826095084509
F-TEST (value)2104.98630423302
F-TEST (DF numerator)3
F-TEST (DF denominator)160
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.3826061804566
Sum Squared Residuals305.855976037886

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.987567424780638 \tabularnewline
R-squared & 0.975289418487861 \tabularnewline
Adjusted R-squared & 0.974826095084509 \tabularnewline
F-TEST (value) & 2104.98630423302 \tabularnewline
F-TEST (DF numerator) & 3 \tabularnewline
F-TEST (DF denominator) & 160 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.3826061804566 \tabularnewline
Sum Squared Residuals & 305.855976037886 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145963&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.987567424780638[/C][/ROW]
[ROW][C]R-squared[/C][C]0.975289418487861[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.974826095084509[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]2104.98630423302[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]3[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]160[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.3826061804566[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]305.855976037886[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145963&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145963&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 R0.987567424780638
R-squared0.975289418487861
Adjusted R-squared0.974826095084509
F-TEST (value)2104.98630423302
F-TEST (DF numerator)3
F-TEST (DF denominator)160
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.3826061804566
Sum Squared Residuals305.855976037886







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
12626.355425787503-0.355425787503012
22020.6225112498837-0.622511249883693
32423.96435266803630.0356473319636946
42525.58488082718-0.584880827179963
51511.44430218561473.55569781438528
61617.2085883156368-1.20858831563676
72020.7457962493471-0.745796249347058
81817.96501359219050.0349864078094928
91919.3883396196163-0.388339619616313
102020.1645849516259-0.164584951625863
113029.92262207817760.0773779218223699
123736.4965709083370.503429091662981
132320.3873075104862.61269248951399
143632.50310486259623.49689513740382
152929.1163587062083-0.116358706208284
163535.2024570479849-0.202457047984944
172424.5876559137615-0.587655913761531
182222.371408559245-0.371408559244969
191919.386805590604-0.386805590603989
203030.4144881156606-0.41448811566059
212726.35115497060940.648845029390592
222624.77253949402631.22746050597367
231515.2597236148068-0.259723614806813
243031.5923542643197-1.59235426431972
252828.3810785656837-0.381078565683739
262424.0572480500165-0.0572480500164639
272121.0243125431027-0.0243125431026892
282727.3690714282093-0.369071428209282
292121.6649543241958-0.664954324195815
303030.1971346583436-0.197134658343579
313030.4690327723326-0.469032772332636
323331.13027947087271.86972052912732
333029.99074620003670.00925379996328201
342020.055059893919-0.0550598939189551
352727.5033498985523-0.503349898552318
362524.81518950809760.184810491902412
373030.3535520751459-0.353552075145852
382020.4275056874649-0.427505687464853
3987.864624714402590.135375285597405
402424.6847176384158-0.684717638415849
412525.6856374551757-0.685637455175742
422524.48240167294820.51759832705175
432120.44995805658250.550041943417505
442121.1128432583543-0.112843258354326
452115.54312694638835.4568730536117
462626.6143606572373-0.614360657237286
472621.87923972348824.12076027651182
483029.90914708721010.0908529127899446
493434.0985483389604-0.0985483389603693
503029.08667193639180.913328063608167
511820.9505925237766-2.95059252377659
5243.957197239440920.0428027605590836
533130.44439105701630.555608942983653
541818.0180242198502-0.0180242198502292
551414.2828946703964-0.282894670396355
562020.4911321965064-0.491132196506435
573636.3109784684311-0.310978468431063
582424.671905187735-0.671905187734969
592624.91979905610911.08020094389088
602222.2394132849355-0.239413284935477
613131.3451810910973-0.345181091097275
622120.83631458545880.163685414541222
633132.1825892595454-1.18258925954544
642626.3661639905893-0.366163990589306
652424.5215015652776-0.521501565277583
661515.8218058692284-0.82180586922841
671919.6797443664028-0.679744366402774
682827.37609688046920.623903119530847
692424.5465862480571-0.546586248057059
701818.5874453491901-0.587445349190103
712525.2458345542769-0.24583455427692
722020.32355867974-0.323558679740017
732525.2658814056566-0.265881405656609
742424.5315772280772-0.531577228077161
752323.4288891254065-0.428889125406507
762525.3711356464699-0.371135646469888
772020.554776553033-0.554776553032959
782323.2050861293818-0.205086129381793
792222.4012282751504-0.401228275150401
802525.2527376848324-0.252737684832381
811818.5894151225652-0.589415122565242
823030.6132824312558-0.613282431255781
832222.2293376221359-0.2293376221359
842513.074574737414611.9254252625854
8588.1184457503871-0.118445750387105
862121.1596132957453-0.159613295745281
872222.1582499164716-0.158249916471632
882424.7183439549826-0.718343954982578
893026.11277869896763.88722130103239
902727.3109184949303-0.310918494930305
912423.75533189606760.244668103932389
922525.3103503995291-0.310350399529076
932120.9131311346790.0868688653209536
942424.5771445065991-0.577144506599138
952424.6061687361289-0.606168736128894
962020.421474045635-0.421474045635022
972018.15493500385511.8450649961449
982424.6142746255533-0.614274625553331
994039.6815306934550.318469306544972
1002222.4296659667434-0.429665966743414
1013131.1824186098068-0.182418609806767
1022626.1202220481054-0.120222048105355
1032020.4510741887169-0.451074188716946
1041918.99924630285870.00075369714127343
1051515.5923025015172-0.592302501517216
1062121.2343271255014-0.234327125501425
1072222.2341486576118-0.23414865761181
1082424.616139924709-0.616139924709003
1091920.0836483790859-1.0836483790859
1102425.2297450971325-1.22974509713246
1112323.5634988658929-0.56349886589289
1122726.31589015081090.684109849189114
11310.8855975845606230.114402415439377
1142424.2544323342494-0.254432334249378
115119.107773722820041.89222627717996
1162726.72692408997330.273075910026651
1172222.691261622009-0.691261622009018
11800.890199671597596-0.890199671597596
1191716.4180546587990.581945341200978
12087.274616515100750.72538348489925
1212424.1161679978016-0.116167997801605
1223130.36815382263250.631846177367549
1232424.4223834405136-0.4223834405136
1242019.06299513360470.93700486639528
12587.806907525486430.193092474513569
1262222.1382030650919-0.138203065091944
1273333.2714711011052-0.271471101105175
1283332.91633537105770.0836646289423359
1293131.5721566193661-0.572156619366106
1303333.5507080900125-0.550708090012506
1313535.1706781830889-0.170678183088943
1322121.1995846768002-0.199584676800244
1332020.3359353860581-0.335935386058082
1342424.721656656416-0.721656656416047
1352929.1334135019133-0.133413501913275
1362020.2845632616302-0.284563261630154
1372726.77420587407710.225794125922929
1382423.19895001333250.801049986667505
1392625.83627849794160.16372150205842
1402625.27628833859950.723711661400468
1411213.2404515960842-1.24045159608423
1422121.6728049458269-0.672804945826858
1432424.2958332700972-0.295833270097198
1442121.2825088702003-0.282508870200294
1453030.2383266457525-0.238326645752462
1463231.53387006089740.466129939102609
1472423.76540755886720.234592441132812
1482929.4498090816443-0.449809081644315
14900.843098364063292-0.843098364063292
15000.873987892748717-0.873987892748717
15100.843865378569455-0.843865378569455
15200.844632393075618-0.844632393075618
15300.843098364063292-0.843098364063292
15400.843098364063292-0.843098364063292
1552019.94768508615660.0523149138433904
1562728.4352919522124-1.43529195221244
15700.843098364063292-0.843098364063292
15800.846166422087942-0.846166422087942
15900.858543128406006-0.858543128406006
16054.752722408323990.247277591676011
16111.89430986808655-0.894309868086547
1622321.85612481408381.14387518591616
16300.844632393075618-0.844632393075618
1641614.78450510053551.21549489946446

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 26 & 26.355425787503 & -0.355425787503012 \tabularnewline
2 & 20 & 20.6225112498837 & -0.622511249883693 \tabularnewline
3 & 24 & 23.9643526680363 & 0.0356473319636946 \tabularnewline
4 & 25 & 25.58488082718 & -0.584880827179963 \tabularnewline
5 & 15 & 11.4443021856147 & 3.55569781438528 \tabularnewline
6 & 16 & 17.2085883156368 & -1.20858831563676 \tabularnewline
7 & 20 & 20.7457962493471 & -0.745796249347058 \tabularnewline
8 & 18 & 17.9650135921905 & 0.0349864078094928 \tabularnewline
9 & 19 & 19.3883396196163 & -0.388339619616313 \tabularnewline
10 & 20 & 20.1645849516259 & -0.164584951625863 \tabularnewline
11 & 30 & 29.9226220781776 & 0.0773779218223699 \tabularnewline
12 & 37 & 36.496570908337 & 0.503429091662981 \tabularnewline
13 & 23 & 20.387307510486 & 2.61269248951399 \tabularnewline
14 & 36 & 32.5031048625962 & 3.49689513740382 \tabularnewline
15 & 29 & 29.1163587062083 & -0.116358706208284 \tabularnewline
16 & 35 & 35.2024570479849 & -0.202457047984944 \tabularnewline
17 & 24 & 24.5876559137615 & -0.587655913761531 \tabularnewline
18 & 22 & 22.371408559245 & -0.371408559244969 \tabularnewline
19 & 19 & 19.386805590604 & -0.386805590603989 \tabularnewline
20 & 30 & 30.4144881156606 & -0.41448811566059 \tabularnewline
21 & 27 & 26.3511549706094 & 0.648845029390592 \tabularnewline
22 & 26 & 24.7725394940263 & 1.22746050597367 \tabularnewline
23 & 15 & 15.2597236148068 & -0.259723614806813 \tabularnewline
24 & 30 & 31.5923542643197 & -1.59235426431972 \tabularnewline
25 & 28 & 28.3810785656837 & -0.381078565683739 \tabularnewline
26 & 24 & 24.0572480500165 & -0.0572480500164639 \tabularnewline
27 & 21 & 21.0243125431027 & -0.0243125431026892 \tabularnewline
28 & 27 & 27.3690714282093 & -0.369071428209282 \tabularnewline
29 & 21 & 21.6649543241958 & -0.664954324195815 \tabularnewline
30 & 30 & 30.1971346583436 & -0.197134658343579 \tabularnewline
31 & 30 & 30.4690327723326 & -0.469032772332636 \tabularnewline
32 & 33 & 31.1302794708727 & 1.86972052912732 \tabularnewline
33 & 30 & 29.9907462000367 & 0.00925379996328201 \tabularnewline
34 & 20 & 20.055059893919 & -0.0550598939189551 \tabularnewline
35 & 27 & 27.5033498985523 & -0.503349898552318 \tabularnewline
36 & 25 & 24.8151895080976 & 0.184810491902412 \tabularnewline
37 & 30 & 30.3535520751459 & -0.353552075145852 \tabularnewline
38 & 20 & 20.4275056874649 & -0.427505687464853 \tabularnewline
39 & 8 & 7.86462471440259 & 0.135375285597405 \tabularnewline
40 & 24 & 24.6847176384158 & -0.684717638415849 \tabularnewline
41 & 25 & 25.6856374551757 & -0.685637455175742 \tabularnewline
42 & 25 & 24.4824016729482 & 0.51759832705175 \tabularnewline
43 & 21 & 20.4499580565825 & 0.550041943417505 \tabularnewline
44 & 21 & 21.1128432583543 & -0.112843258354326 \tabularnewline
45 & 21 & 15.5431269463883 & 5.4568730536117 \tabularnewline
46 & 26 & 26.6143606572373 & -0.614360657237286 \tabularnewline
47 & 26 & 21.8792397234882 & 4.12076027651182 \tabularnewline
48 & 30 & 29.9091470872101 & 0.0908529127899446 \tabularnewline
49 & 34 & 34.0985483389604 & -0.0985483389603693 \tabularnewline
50 & 30 & 29.0866719363918 & 0.913328063608167 \tabularnewline
51 & 18 & 20.9505925237766 & -2.95059252377659 \tabularnewline
52 & 4 & 3.95719723944092 & 0.0428027605590836 \tabularnewline
53 & 31 & 30.4443910570163 & 0.555608942983653 \tabularnewline
54 & 18 & 18.0180242198502 & -0.0180242198502292 \tabularnewline
55 & 14 & 14.2828946703964 & -0.282894670396355 \tabularnewline
56 & 20 & 20.4911321965064 & -0.491132196506435 \tabularnewline
57 & 36 & 36.3109784684311 & -0.310978468431063 \tabularnewline
58 & 24 & 24.671905187735 & -0.671905187734969 \tabularnewline
59 & 26 & 24.9197990561091 & 1.08020094389088 \tabularnewline
60 & 22 & 22.2394132849355 & -0.239413284935477 \tabularnewline
61 & 31 & 31.3451810910973 & -0.345181091097275 \tabularnewline
62 & 21 & 20.8363145854588 & 0.163685414541222 \tabularnewline
63 & 31 & 32.1825892595454 & -1.18258925954544 \tabularnewline
64 & 26 & 26.3661639905893 & -0.366163990589306 \tabularnewline
65 & 24 & 24.5215015652776 & -0.521501565277583 \tabularnewline
66 & 15 & 15.8218058692284 & -0.82180586922841 \tabularnewline
67 & 19 & 19.6797443664028 & -0.679744366402774 \tabularnewline
68 & 28 & 27.3760968804692 & 0.623903119530847 \tabularnewline
69 & 24 & 24.5465862480571 & -0.546586248057059 \tabularnewline
70 & 18 & 18.5874453491901 & -0.587445349190103 \tabularnewline
71 & 25 & 25.2458345542769 & -0.24583455427692 \tabularnewline
72 & 20 & 20.32355867974 & -0.323558679740017 \tabularnewline
73 & 25 & 25.2658814056566 & -0.265881405656609 \tabularnewline
74 & 24 & 24.5315772280772 & -0.531577228077161 \tabularnewline
75 & 23 & 23.4288891254065 & -0.428889125406507 \tabularnewline
76 & 25 & 25.3711356464699 & -0.371135646469888 \tabularnewline
77 & 20 & 20.554776553033 & -0.554776553032959 \tabularnewline
78 & 23 & 23.2050861293818 & -0.205086129381793 \tabularnewline
79 & 22 & 22.4012282751504 & -0.401228275150401 \tabularnewline
80 & 25 & 25.2527376848324 & -0.252737684832381 \tabularnewline
81 & 18 & 18.5894151225652 & -0.589415122565242 \tabularnewline
82 & 30 & 30.6132824312558 & -0.613282431255781 \tabularnewline
83 & 22 & 22.2293376221359 & -0.2293376221359 \tabularnewline
84 & 25 & 13.0745747374146 & 11.9254252625854 \tabularnewline
85 & 8 & 8.1184457503871 & -0.118445750387105 \tabularnewline
86 & 21 & 21.1596132957453 & -0.159613295745281 \tabularnewline
87 & 22 & 22.1582499164716 & -0.158249916471632 \tabularnewline
88 & 24 & 24.7183439549826 & -0.718343954982578 \tabularnewline
89 & 30 & 26.1127786989676 & 3.88722130103239 \tabularnewline
90 & 27 & 27.3109184949303 & -0.310918494930305 \tabularnewline
91 & 24 & 23.7553318960676 & 0.244668103932389 \tabularnewline
92 & 25 & 25.3103503995291 & -0.310350399529076 \tabularnewline
93 & 21 & 20.913131134679 & 0.0868688653209536 \tabularnewline
94 & 24 & 24.5771445065991 & -0.577144506599138 \tabularnewline
95 & 24 & 24.6061687361289 & -0.606168736128894 \tabularnewline
96 & 20 & 20.421474045635 & -0.421474045635022 \tabularnewline
97 & 20 & 18.1549350038551 & 1.8450649961449 \tabularnewline
98 & 24 & 24.6142746255533 & -0.614274625553331 \tabularnewline
99 & 40 & 39.681530693455 & 0.318469306544972 \tabularnewline
100 & 22 & 22.4296659667434 & -0.429665966743414 \tabularnewline
101 & 31 & 31.1824186098068 & -0.182418609806767 \tabularnewline
102 & 26 & 26.1202220481054 & -0.120222048105355 \tabularnewline
103 & 20 & 20.4510741887169 & -0.451074188716946 \tabularnewline
104 & 19 & 18.9992463028587 & 0.00075369714127343 \tabularnewline
105 & 15 & 15.5923025015172 & -0.592302501517216 \tabularnewline
106 & 21 & 21.2343271255014 & -0.234327125501425 \tabularnewline
107 & 22 & 22.2341486576118 & -0.23414865761181 \tabularnewline
108 & 24 & 24.616139924709 & -0.616139924709003 \tabularnewline
109 & 19 & 20.0836483790859 & -1.0836483790859 \tabularnewline
110 & 24 & 25.2297450971325 & -1.22974509713246 \tabularnewline
111 & 23 & 23.5634988658929 & -0.56349886589289 \tabularnewline
112 & 27 & 26.3158901508109 & 0.684109849189114 \tabularnewline
113 & 1 & 0.885597584560623 & 0.114402415439377 \tabularnewline
114 & 24 & 24.2544323342494 & -0.254432334249378 \tabularnewline
115 & 11 & 9.10777372282004 & 1.89222627717996 \tabularnewline
116 & 27 & 26.7269240899733 & 0.273075910026651 \tabularnewline
117 & 22 & 22.691261622009 & -0.691261622009018 \tabularnewline
118 & 0 & 0.890199671597596 & -0.890199671597596 \tabularnewline
119 & 17 & 16.418054658799 & 0.581945341200978 \tabularnewline
120 & 8 & 7.27461651510075 & 0.72538348489925 \tabularnewline
121 & 24 & 24.1161679978016 & -0.116167997801605 \tabularnewline
122 & 31 & 30.3681538226325 & 0.631846177367549 \tabularnewline
123 & 24 & 24.4223834405136 & -0.4223834405136 \tabularnewline
124 & 20 & 19.0629951336047 & 0.93700486639528 \tabularnewline
125 & 8 & 7.80690752548643 & 0.193092474513569 \tabularnewline
126 & 22 & 22.1382030650919 & -0.138203065091944 \tabularnewline
127 & 33 & 33.2714711011052 & -0.271471101105175 \tabularnewline
128 & 33 & 32.9163353710577 & 0.0836646289423359 \tabularnewline
129 & 31 & 31.5721566193661 & -0.572156619366106 \tabularnewline
130 & 33 & 33.5507080900125 & -0.550708090012506 \tabularnewline
131 & 35 & 35.1706781830889 & -0.170678183088943 \tabularnewline
132 & 21 & 21.1995846768002 & -0.199584676800244 \tabularnewline
133 & 20 & 20.3359353860581 & -0.335935386058082 \tabularnewline
134 & 24 & 24.721656656416 & -0.721656656416047 \tabularnewline
135 & 29 & 29.1334135019133 & -0.133413501913275 \tabularnewline
136 & 20 & 20.2845632616302 & -0.284563261630154 \tabularnewline
137 & 27 & 26.7742058740771 & 0.225794125922929 \tabularnewline
138 & 24 & 23.1989500133325 & 0.801049986667505 \tabularnewline
139 & 26 & 25.8362784979416 & 0.16372150205842 \tabularnewline
140 & 26 & 25.2762883385995 & 0.723711661400468 \tabularnewline
141 & 12 & 13.2404515960842 & -1.24045159608423 \tabularnewline
142 & 21 & 21.6728049458269 & -0.672804945826858 \tabularnewline
143 & 24 & 24.2958332700972 & -0.295833270097198 \tabularnewline
144 & 21 & 21.2825088702003 & -0.282508870200294 \tabularnewline
145 & 30 & 30.2383266457525 & -0.238326645752462 \tabularnewline
146 & 32 & 31.5338700608974 & 0.466129939102609 \tabularnewline
147 & 24 & 23.7654075588672 & 0.234592441132812 \tabularnewline
148 & 29 & 29.4498090816443 & -0.449809081644315 \tabularnewline
149 & 0 & 0.843098364063292 & -0.843098364063292 \tabularnewline
150 & 0 & 0.873987892748717 & -0.873987892748717 \tabularnewline
151 & 0 & 0.843865378569455 & -0.843865378569455 \tabularnewline
152 & 0 & 0.844632393075618 & -0.844632393075618 \tabularnewline
153 & 0 & 0.843098364063292 & -0.843098364063292 \tabularnewline
154 & 0 & 0.843098364063292 & -0.843098364063292 \tabularnewline
155 & 20 & 19.9476850861566 & 0.0523149138433904 \tabularnewline
156 & 27 & 28.4352919522124 & -1.43529195221244 \tabularnewline
157 & 0 & 0.843098364063292 & -0.843098364063292 \tabularnewline
158 & 0 & 0.846166422087942 & -0.846166422087942 \tabularnewline
159 & 0 & 0.858543128406006 & -0.858543128406006 \tabularnewline
160 & 5 & 4.75272240832399 & 0.247277591676011 \tabularnewline
161 & 1 & 1.89430986808655 & -0.894309868086547 \tabularnewline
162 & 23 & 21.8561248140838 & 1.14387518591616 \tabularnewline
163 & 0 & 0.844632393075618 & -0.844632393075618 \tabularnewline
164 & 16 & 14.7845051005355 & 1.21549489946446 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145963&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]26[/C][C]26.355425787503[/C][C]-0.355425787503012[/C][/ROW]
[ROW][C]2[/C][C]20[/C][C]20.6225112498837[/C][C]-0.622511249883693[/C][/ROW]
[ROW][C]3[/C][C]24[/C][C]23.9643526680363[/C][C]0.0356473319636946[/C][/ROW]
[ROW][C]4[/C][C]25[/C][C]25.58488082718[/C][C]-0.584880827179963[/C][/ROW]
[ROW][C]5[/C][C]15[/C][C]11.4443021856147[/C][C]3.55569781438528[/C][/ROW]
[ROW][C]6[/C][C]16[/C][C]17.2085883156368[/C][C]-1.20858831563676[/C][/ROW]
[ROW][C]7[/C][C]20[/C][C]20.7457962493471[/C][C]-0.745796249347058[/C][/ROW]
[ROW][C]8[/C][C]18[/C][C]17.9650135921905[/C][C]0.0349864078094928[/C][/ROW]
[ROW][C]9[/C][C]19[/C][C]19.3883396196163[/C][C]-0.388339619616313[/C][/ROW]
[ROW][C]10[/C][C]20[/C][C]20.1645849516259[/C][C]-0.164584951625863[/C][/ROW]
[ROW][C]11[/C][C]30[/C][C]29.9226220781776[/C][C]0.0773779218223699[/C][/ROW]
[ROW][C]12[/C][C]37[/C][C]36.496570908337[/C][C]0.503429091662981[/C][/ROW]
[ROW][C]13[/C][C]23[/C][C]20.387307510486[/C][C]2.61269248951399[/C][/ROW]
[ROW][C]14[/C][C]36[/C][C]32.5031048625962[/C][C]3.49689513740382[/C][/ROW]
[ROW][C]15[/C][C]29[/C][C]29.1163587062083[/C][C]-0.116358706208284[/C][/ROW]
[ROW][C]16[/C][C]35[/C][C]35.2024570479849[/C][C]-0.202457047984944[/C][/ROW]
[ROW][C]17[/C][C]24[/C][C]24.5876559137615[/C][C]-0.587655913761531[/C][/ROW]
[ROW][C]18[/C][C]22[/C][C]22.371408559245[/C][C]-0.371408559244969[/C][/ROW]
[ROW][C]19[/C][C]19[/C][C]19.386805590604[/C][C]-0.386805590603989[/C][/ROW]
[ROW][C]20[/C][C]30[/C][C]30.4144881156606[/C][C]-0.41448811566059[/C][/ROW]
[ROW][C]21[/C][C]27[/C][C]26.3511549706094[/C][C]0.648845029390592[/C][/ROW]
[ROW][C]22[/C][C]26[/C][C]24.7725394940263[/C][C]1.22746050597367[/C][/ROW]
[ROW][C]23[/C][C]15[/C][C]15.2597236148068[/C][C]-0.259723614806813[/C][/ROW]
[ROW][C]24[/C][C]30[/C][C]31.5923542643197[/C][C]-1.59235426431972[/C][/ROW]
[ROW][C]25[/C][C]28[/C][C]28.3810785656837[/C][C]-0.381078565683739[/C][/ROW]
[ROW][C]26[/C][C]24[/C][C]24.0572480500165[/C][C]-0.0572480500164639[/C][/ROW]
[ROW][C]27[/C][C]21[/C][C]21.0243125431027[/C][C]-0.0243125431026892[/C][/ROW]
[ROW][C]28[/C][C]27[/C][C]27.3690714282093[/C][C]-0.369071428209282[/C][/ROW]
[ROW][C]29[/C][C]21[/C][C]21.6649543241958[/C][C]-0.664954324195815[/C][/ROW]
[ROW][C]30[/C][C]30[/C][C]30.1971346583436[/C][C]-0.197134658343579[/C][/ROW]
[ROW][C]31[/C][C]30[/C][C]30.4690327723326[/C][C]-0.469032772332636[/C][/ROW]
[ROW][C]32[/C][C]33[/C][C]31.1302794708727[/C][C]1.86972052912732[/C][/ROW]
[ROW][C]33[/C][C]30[/C][C]29.9907462000367[/C][C]0.00925379996328201[/C][/ROW]
[ROW][C]34[/C][C]20[/C][C]20.055059893919[/C][C]-0.0550598939189551[/C][/ROW]
[ROW][C]35[/C][C]27[/C][C]27.5033498985523[/C][C]-0.503349898552318[/C][/ROW]
[ROW][C]36[/C][C]25[/C][C]24.8151895080976[/C][C]0.184810491902412[/C][/ROW]
[ROW][C]37[/C][C]30[/C][C]30.3535520751459[/C][C]-0.353552075145852[/C][/ROW]
[ROW][C]38[/C][C]20[/C][C]20.4275056874649[/C][C]-0.427505687464853[/C][/ROW]
[ROW][C]39[/C][C]8[/C][C]7.86462471440259[/C][C]0.135375285597405[/C][/ROW]
[ROW][C]40[/C][C]24[/C][C]24.6847176384158[/C][C]-0.684717638415849[/C][/ROW]
[ROW][C]41[/C][C]25[/C][C]25.6856374551757[/C][C]-0.685637455175742[/C][/ROW]
[ROW][C]42[/C][C]25[/C][C]24.4824016729482[/C][C]0.51759832705175[/C][/ROW]
[ROW][C]43[/C][C]21[/C][C]20.4499580565825[/C][C]0.550041943417505[/C][/ROW]
[ROW][C]44[/C][C]21[/C][C]21.1128432583543[/C][C]-0.112843258354326[/C][/ROW]
[ROW][C]45[/C][C]21[/C][C]15.5431269463883[/C][C]5.4568730536117[/C][/ROW]
[ROW][C]46[/C][C]26[/C][C]26.6143606572373[/C][C]-0.614360657237286[/C][/ROW]
[ROW][C]47[/C][C]26[/C][C]21.8792397234882[/C][C]4.12076027651182[/C][/ROW]
[ROW][C]48[/C][C]30[/C][C]29.9091470872101[/C][C]0.0908529127899446[/C][/ROW]
[ROW][C]49[/C][C]34[/C][C]34.0985483389604[/C][C]-0.0985483389603693[/C][/ROW]
[ROW][C]50[/C][C]30[/C][C]29.0866719363918[/C][C]0.913328063608167[/C][/ROW]
[ROW][C]51[/C][C]18[/C][C]20.9505925237766[/C][C]-2.95059252377659[/C][/ROW]
[ROW][C]52[/C][C]4[/C][C]3.95719723944092[/C][C]0.0428027605590836[/C][/ROW]
[ROW][C]53[/C][C]31[/C][C]30.4443910570163[/C][C]0.555608942983653[/C][/ROW]
[ROW][C]54[/C][C]18[/C][C]18.0180242198502[/C][C]-0.0180242198502292[/C][/ROW]
[ROW][C]55[/C][C]14[/C][C]14.2828946703964[/C][C]-0.282894670396355[/C][/ROW]
[ROW][C]56[/C][C]20[/C][C]20.4911321965064[/C][C]-0.491132196506435[/C][/ROW]
[ROW][C]57[/C][C]36[/C][C]36.3109784684311[/C][C]-0.310978468431063[/C][/ROW]
[ROW][C]58[/C][C]24[/C][C]24.671905187735[/C][C]-0.671905187734969[/C][/ROW]
[ROW][C]59[/C][C]26[/C][C]24.9197990561091[/C][C]1.08020094389088[/C][/ROW]
[ROW][C]60[/C][C]22[/C][C]22.2394132849355[/C][C]-0.239413284935477[/C][/ROW]
[ROW][C]61[/C][C]31[/C][C]31.3451810910973[/C][C]-0.345181091097275[/C][/ROW]
[ROW][C]62[/C][C]21[/C][C]20.8363145854588[/C][C]0.163685414541222[/C][/ROW]
[ROW][C]63[/C][C]31[/C][C]32.1825892595454[/C][C]-1.18258925954544[/C][/ROW]
[ROW][C]64[/C][C]26[/C][C]26.3661639905893[/C][C]-0.366163990589306[/C][/ROW]
[ROW][C]65[/C][C]24[/C][C]24.5215015652776[/C][C]-0.521501565277583[/C][/ROW]
[ROW][C]66[/C][C]15[/C][C]15.8218058692284[/C][C]-0.82180586922841[/C][/ROW]
[ROW][C]67[/C][C]19[/C][C]19.6797443664028[/C][C]-0.679744366402774[/C][/ROW]
[ROW][C]68[/C][C]28[/C][C]27.3760968804692[/C][C]0.623903119530847[/C][/ROW]
[ROW][C]69[/C][C]24[/C][C]24.5465862480571[/C][C]-0.546586248057059[/C][/ROW]
[ROW][C]70[/C][C]18[/C][C]18.5874453491901[/C][C]-0.587445349190103[/C][/ROW]
[ROW][C]71[/C][C]25[/C][C]25.2458345542769[/C][C]-0.24583455427692[/C][/ROW]
[ROW][C]72[/C][C]20[/C][C]20.32355867974[/C][C]-0.323558679740017[/C][/ROW]
[ROW][C]73[/C][C]25[/C][C]25.2658814056566[/C][C]-0.265881405656609[/C][/ROW]
[ROW][C]74[/C][C]24[/C][C]24.5315772280772[/C][C]-0.531577228077161[/C][/ROW]
[ROW][C]75[/C][C]23[/C][C]23.4288891254065[/C][C]-0.428889125406507[/C][/ROW]
[ROW][C]76[/C][C]25[/C][C]25.3711356464699[/C][C]-0.371135646469888[/C][/ROW]
[ROW][C]77[/C][C]20[/C][C]20.554776553033[/C][C]-0.554776553032959[/C][/ROW]
[ROW][C]78[/C][C]23[/C][C]23.2050861293818[/C][C]-0.205086129381793[/C][/ROW]
[ROW][C]79[/C][C]22[/C][C]22.4012282751504[/C][C]-0.401228275150401[/C][/ROW]
[ROW][C]80[/C][C]25[/C][C]25.2527376848324[/C][C]-0.252737684832381[/C][/ROW]
[ROW][C]81[/C][C]18[/C][C]18.5894151225652[/C][C]-0.589415122565242[/C][/ROW]
[ROW][C]82[/C][C]30[/C][C]30.6132824312558[/C][C]-0.613282431255781[/C][/ROW]
[ROW][C]83[/C][C]22[/C][C]22.2293376221359[/C][C]-0.2293376221359[/C][/ROW]
[ROW][C]84[/C][C]25[/C][C]13.0745747374146[/C][C]11.9254252625854[/C][/ROW]
[ROW][C]85[/C][C]8[/C][C]8.1184457503871[/C][C]-0.118445750387105[/C][/ROW]
[ROW][C]86[/C][C]21[/C][C]21.1596132957453[/C][C]-0.159613295745281[/C][/ROW]
[ROW][C]87[/C][C]22[/C][C]22.1582499164716[/C][C]-0.158249916471632[/C][/ROW]
[ROW][C]88[/C][C]24[/C][C]24.7183439549826[/C][C]-0.718343954982578[/C][/ROW]
[ROW][C]89[/C][C]30[/C][C]26.1127786989676[/C][C]3.88722130103239[/C][/ROW]
[ROW][C]90[/C][C]27[/C][C]27.3109184949303[/C][C]-0.310918494930305[/C][/ROW]
[ROW][C]91[/C][C]24[/C][C]23.7553318960676[/C][C]0.244668103932389[/C][/ROW]
[ROW][C]92[/C][C]25[/C][C]25.3103503995291[/C][C]-0.310350399529076[/C][/ROW]
[ROW][C]93[/C][C]21[/C][C]20.913131134679[/C][C]0.0868688653209536[/C][/ROW]
[ROW][C]94[/C][C]24[/C][C]24.5771445065991[/C][C]-0.577144506599138[/C][/ROW]
[ROW][C]95[/C][C]24[/C][C]24.6061687361289[/C][C]-0.606168736128894[/C][/ROW]
[ROW][C]96[/C][C]20[/C][C]20.421474045635[/C][C]-0.421474045635022[/C][/ROW]
[ROW][C]97[/C][C]20[/C][C]18.1549350038551[/C][C]1.8450649961449[/C][/ROW]
[ROW][C]98[/C][C]24[/C][C]24.6142746255533[/C][C]-0.614274625553331[/C][/ROW]
[ROW][C]99[/C][C]40[/C][C]39.681530693455[/C][C]0.318469306544972[/C][/ROW]
[ROW][C]100[/C][C]22[/C][C]22.4296659667434[/C][C]-0.429665966743414[/C][/ROW]
[ROW][C]101[/C][C]31[/C][C]31.1824186098068[/C][C]-0.182418609806767[/C][/ROW]
[ROW][C]102[/C][C]26[/C][C]26.1202220481054[/C][C]-0.120222048105355[/C][/ROW]
[ROW][C]103[/C][C]20[/C][C]20.4510741887169[/C][C]-0.451074188716946[/C][/ROW]
[ROW][C]104[/C][C]19[/C][C]18.9992463028587[/C][C]0.00075369714127343[/C][/ROW]
[ROW][C]105[/C][C]15[/C][C]15.5923025015172[/C][C]-0.592302501517216[/C][/ROW]
[ROW][C]106[/C][C]21[/C][C]21.2343271255014[/C][C]-0.234327125501425[/C][/ROW]
[ROW][C]107[/C][C]22[/C][C]22.2341486576118[/C][C]-0.23414865761181[/C][/ROW]
[ROW][C]108[/C][C]24[/C][C]24.616139924709[/C][C]-0.616139924709003[/C][/ROW]
[ROW][C]109[/C][C]19[/C][C]20.0836483790859[/C][C]-1.0836483790859[/C][/ROW]
[ROW][C]110[/C][C]24[/C][C]25.2297450971325[/C][C]-1.22974509713246[/C][/ROW]
[ROW][C]111[/C][C]23[/C][C]23.5634988658929[/C][C]-0.56349886589289[/C][/ROW]
[ROW][C]112[/C][C]27[/C][C]26.3158901508109[/C][C]0.684109849189114[/C][/ROW]
[ROW][C]113[/C][C]1[/C][C]0.885597584560623[/C][C]0.114402415439377[/C][/ROW]
[ROW][C]114[/C][C]24[/C][C]24.2544323342494[/C][C]-0.254432334249378[/C][/ROW]
[ROW][C]115[/C][C]11[/C][C]9.10777372282004[/C][C]1.89222627717996[/C][/ROW]
[ROW][C]116[/C][C]27[/C][C]26.7269240899733[/C][C]0.273075910026651[/C][/ROW]
[ROW][C]117[/C][C]22[/C][C]22.691261622009[/C][C]-0.691261622009018[/C][/ROW]
[ROW][C]118[/C][C]0[/C][C]0.890199671597596[/C][C]-0.890199671597596[/C][/ROW]
[ROW][C]119[/C][C]17[/C][C]16.418054658799[/C][C]0.581945341200978[/C][/ROW]
[ROW][C]120[/C][C]8[/C][C]7.27461651510075[/C][C]0.72538348489925[/C][/ROW]
[ROW][C]121[/C][C]24[/C][C]24.1161679978016[/C][C]-0.116167997801605[/C][/ROW]
[ROW][C]122[/C][C]31[/C][C]30.3681538226325[/C][C]0.631846177367549[/C][/ROW]
[ROW][C]123[/C][C]24[/C][C]24.4223834405136[/C][C]-0.4223834405136[/C][/ROW]
[ROW][C]124[/C][C]20[/C][C]19.0629951336047[/C][C]0.93700486639528[/C][/ROW]
[ROW][C]125[/C][C]8[/C][C]7.80690752548643[/C][C]0.193092474513569[/C][/ROW]
[ROW][C]126[/C][C]22[/C][C]22.1382030650919[/C][C]-0.138203065091944[/C][/ROW]
[ROW][C]127[/C][C]33[/C][C]33.2714711011052[/C][C]-0.271471101105175[/C][/ROW]
[ROW][C]128[/C][C]33[/C][C]32.9163353710577[/C][C]0.0836646289423359[/C][/ROW]
[ROW][C]129[/C][C]31[/C][C]31.5721566193661[/C][C]-0.572156619366106[/C][/ROW]
[ROW][C]130[/C][C]33[/C][C]33.5507080900125[/C][C]-0.550708090012506[/C][/ROW]
[ROW][C]131[/C][C]35[/C][C]35.1706781830889[/C][C]-0.170678183088943[/C][/ROW]
[ROW][C]132[/C][C]21[/C][C]21.1995846768002[/C][C]-0.199584676800244[/C][/ROW]
[ROW][C]133[/C][C]20[/C][C]20.3359353860581[/C][C]-0.335935386058082[/C][/ROW]
[ROW][C]134[/C][C]24[/C][C]24.721656656416[/C][C]-0.721656656416047[/C][/ROW]
[ROW][C]135[/C][C]29[/C][C]29.1334135019133[/C][C]-0.133413501913275[/C][/ROW]
[ROW][C]136[/C][C]20[/C][C]20.2845632616302[/C][C]-0.284563261630154[/C][/ROW]
[ROW][C]137[/C][C]27[/C][C]26.7742058740771[/C][C]0.225794125922929[/C][/ROW]
[ROW][C]138[/C][C]24[/C][C]23.1989500133325[/C][C]0.801049986667505[/C][/ROW]
[ROW][C]139[/C][C]26[/C][C]25.8362784979416[/C][C]0.16372150205842[/C][/ROW]
[ROW][C]140[/C][C]26[/C][C]25.2762883385995[/C][C]0.723711661400468[/C][/ROW]
[ROW][C]141[/C][C]12[/C][C]13.2404515960842[/C][C]-1.24045159608423[/C][/ROW]
[ROW][C]142[/C][C]21[/C][C]21.6728049458269[/C][C]-0.672804945826858[/C][/ROW]
[ROW][C]143[/C][C]24[/C][C]24.2958332700972[/C][C]-0.295833270097198[/C][/ROW]
[ROW][C]144[/C][C]21[/C][C]21.2825088702003[/C][C]-0.282508870200294[/C][/ROW]
[ROW][C]145[/C][C]30[/C][C]30.2383266457525[/C][C]-0.238326645752462[/C][/ROW]
[ROW][C]146[/C][C]32[/C][C]31.5338700608974[/C][C]0.466129939102609[/C][/ROW]
[ROW][C]147[/C][C]24[/C][C]23.7654075588672[/C][C]0.234592441132812[/C][/ROW]
[ROW][C]148[/C][C]29[/C][C]29.4498090816443[/C][C]-0.449809081644315[/C][/ROW]
[ROW][C]149[/C][C]0[/C][C]0.843098364063292[/C][C]-0.843098364063292[/C][/ROW]
[ROW][C]150[/C][C]0[/C][C]0.873987892748717[/C][C]-0.873987892748717[/C][/ROW]
[ROW][C]151[/C][C]0[/C][C]0.843865378569455[/C][C]-0.843865378569455[/C][/ROW]
[ROW][C]152[/C][C]0[/C][C]0.844632393075618[/C][C]-0.844632393075618[/C][/ROW]
[ROW][C]153[/C][C]0[/C][C]0.843098364063292[/C][C]-0.843098364063292[/C][/ROW]
[ROW][C]154[/C][C]0[/C][C]0.843098364063292[/C][C]-0.843098364063292[/C][/ROW]
[ROW][C]155[/C][C]20[/C][C]19.9476850861566[/C][C]0.0523149138433904[/C][/ROW]
[ROW][C]156[/C][C]27[/C][C]28.4352919522124[/C][C]-1.43529195221244[/C][/ROW]
[ROW][C]157[/C][C]0[/C][C]0.843098364063292[/C][C]-0.843098364063292[/C][/ROW]
[ROW][C]158[/C][C]0[/C][C]0.846166422087942[/C][C]-0.846166422087942[/C][/ROW]
[ROW][C]159[/C][C]0[/C][C]0.858543128406006[/C][C]-0.858543128406006[/C][/ROW]
[ROW][C]160[/C][C]5[/C][C]4.75272240832399[/C][C]0.247277591676011[/C][/ROW]
[ROW][C]161[/C][C]1[/C][C]1.89430986808655[/C][C]-0.894309868086547[/C][/ROW]
[ROW][C]162[/C][C]23[/C][C]21.8561248140838[/C][C]1.14387518591616[/C][/ROW]
[ROW][C]163[/C][C]0[/C][C]0.844632393075618[/C][C]-0.844632393075618[/C][/ROW]
[ROW][C]164[/C][C]16[/C][C]14.7845051005355[/C][C]1.21549489946446[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145963&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145963&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 IndexActualsInterpolationForecastResidualsPrediction Error
12626.355425787503-0.355425787503012
22020.6225112498837-0.622511249883693
32423.96435266803630.0356473319636946
42525.58488082718-0.584880827179963
51511.44430218561473.55569781438528
61617.2085883156368-1.20858831563676
72020.7457962493471-0.745796249347058
81817.96501359219050.0349864078094928
91919.3883396196163-0.388339619616313
102020.1645849516259-0.164584951625863
113029.92262207817760.0773779218223699
123736.4965709083370.503429091662981
132320.3873075104862.61269248951399
143632.50310486259623.49689513740382
152929.1163587062083-0.116358706208284
163535.2024570479849-0.202457047984944
172424.5876559137615-0.587655913761531
182222.371408559245-0.371408559244969
191919.386805590604-0.386805590603989
203030.4144881156606-0.41448811566059
212726.35115497060940.648845029390592
222624.77253949402631.22746050597367
231515.2597236148068-0.259723614806813
243031.5923542643197-1.59235426431972
252828.3810785656837-0.381078565683739
262424.0572480500165-0.0572480500164639
272121.0243125431027-0.0243125431026892
282727.3690714282093-0.369071428209282
292121.6649543241958-0.664954324195815
303030.1971346583436-0.197134658343579
313030.4690327723326-0.469032772332636
323331.13027947087271.86972052912732
333029.99074620003670.00925379996328201
342020.055059893919-0.0550598939189551
352727.5033498985523-0.503349898552318
362524.81518950809760.184810491902412
373030.3535520751459-0.353552075145852
382020.4275056874649-0.427505687464853
3987.864624714402590.135375285597405
402424.6847176384158-0.684717638415849
412525.6856374551757-0.685637455175742
422524.48240167294820.51759832705175
432120.44995805658250.550041943417505
442121.1128432583543-0.112843258354326
452115.54312694638835.4568730536117
462626.6143606572373-0.614360657237286
472621.87923972348824.12076027651182
483029.90914708721010.0908529127899446
493434.0985483389604-0.0985483389603693
503029.08667193639180.913328063608167
511820.9505925237766-2.95059252377659
5243.957197239440920.0428027605590836
533130.44439105701630.555608942983653
541818.0180242198502-0.0180242198502292
551414.2828946703964-0.282894670396355
562020.4911321965064-0.491132196506435
573636.3109784684311-0.310978468431063
582424.671905187735-0.671905187734969
592624.91979905610911.08020094389088
602222.2394132849355-0.239413284935477
613131.3451810910973-0.345181091097275
622120.83631458545880.163685414541222
633132.1825892595454-1.18258925954544
642626.3661639905893-0.366163990589306
652424.5215015652776-0.521501565277583
661515.8218058692284-0.82180586922841
671919.6797443664028-0.679744366402774
682827.37609688046920.623903119530847
692424.5465862480571-0.546586248057059
701818.5874453491901-0.587445349190103
712525.2458345542769-0.24583455427692
722020.32355867974-0.323558679740017
732525.2658814056566-0.265881405656609
742424.5315772280772-0.531577228077161
752323.4288891254065-0.428889125406507
762525.3711356464699-0.371135646469888
772020.554776553033-0.554776553032959
782323.2050861293818-0.205086129381793
792222.4012282751504-0.401228275150401
802525.2527376848324-0.252737684832381
811818.5894151225652-0.589415122565242
823030.6132824312558-0.613282431255781
832222.2293376221359-0.2293376221359
842513.074574737414611.9254252625854
8588.1184457503871-0.118445750387105
862121.1596132957453-0.159613295745281
872222.1582499164716-0.158249916471632
882424.7183439549826-0.718343954982578
893026.11277869896763.88722130103239
902727.3109184949303-0.310918494930305
912423.75533189606760.244668103932389
922525.3103503995291-0.310350399529076
932120.9131311346790.0868688653209536
942424.5771445065991-0.577144506599138
952424.6061687361289-0.606168736128894
962020.421474045635-0.421474045635022
972018.15493500385511.8450649961449
982424.6142746255533-0.614274625553331
994039.6815306934550.318469306544972
1002222.4296659667434-0.429665966743414
1013131.1824186098068-0.182418609806767
1022626.1202220481054-0.120222048105355
1032020.4510741887169-0.451074188716946
1041918.99924630285870.00075369714127343
1051515.5923025015172-0.592302501517216
1062121.2343271255014-0.234327125501425
1072222.2341486576118-0.23414865761181
1082424.616139924709-0.616139924709003
1091920.0836483790859-1.0836483790859
1102425.2297450971325-1.22974509713246
1112323.5634988658929-0.56349886589289
1122726.31589015081090.684109849189114
11310.8855975845606230.114402415439377
1142424.2544323342494-0.254432334249378
115119.107773722820041.89222627717996
1162726.72692408997330.273075910026651
1172222.691261622009-0.691261622009018
11800.890199671597596-0.890199671597596
1191716.4180546587990.581945341200978
12087.274616515100750.72538348489925
1212424.1161679978016-0.116167997801605
1223130.36815382263250.631846177367549
1232424.4223834405136-0.4223834405136
1242019.06299513360470.93700486639528
12587.806907525486430.193092474513569
1262222.1382030650919-0.138203065091944
1273333.2714711011052-0.271471101105175
1283332.91633537105770.0836646289423359
1293131.5721566193661-0.572156619366106
1303333.5507080900125-0.550708090012506
1313535.1706781830889-0.170678183088943
1322121.1995846768002-0.199584676800244
1332020.3359353860581-0.335935386058082
1342424.721656656416-0.721656656416047
1352929.1334135019133-0.133413501913275
1362020.2845632616302-0.284563261630154
1372726.77420587407710.225794125922929
1382423.19895001333250.801049986667505
1392625.83627849794160.16372150205842
1402625.27628833859950.723711661400468
1411213.2404515960842-1.24045159608423
1422121.6728049458269-0.672804945826858
1432424.2958332700972-0.295833270097198
1442121.2825088702003-0.282508870200294
1453030.2383266457525-0.238326645752462
1463231.53387006089740.466129939102609
1472423.76540755886720.234592441132812
1482929.4498090816443-0.449809081644315
14900.843098364063292-0.843098364063292
15000.873987892748717-0.873987892748717
15100.843865378569455-0.843865378569455
15200.844632393075618-0.844632393075618
15300.843098364063292-0.843098364063292
15400.843098364063292-0.843098364063292
1552019.94768508615660.0523149138433904
1562728.4352919522124-1.43529195221244
15700.843098364063292-0.843098364063292
15800.846166422087942-0.846166422087942
15900.858543128406006-0.858543128406006
16054.752722408323990.247277591676011
16111.89430986808655-0.894309868086547
1622321.85612481408381.14387518591616
16300.844632393075618-0.844632393075618
1641614.78450510053551.21549489946446







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
70.78962416003330.42075167993340.2103758399667
80.6671728406998080.6656543186003850.332827159300192
90.5502013098011320.8995973803977350.449798690198868
100.4190428338666320.8380856677332630.580957166133368
110.4423654345089540.8847308690179080.557634565491046
120.4697326533537240.9394653067074490.530267346646276
130.6219192587540610.7561614824918770.378080741245939
140.9028722821023610.1942554357952790.0971277178976394
150.8650715690068730.2698568619862550.134928430993127
160.8170810106820170.3658379786359650.182918989317983
170.7949493081267560.4101013837464870.205050691873244
180.7346014923405740.5307970153188520.265398507659426
190.6843928080220580.6312143839558830.315607191977942
200.6348031302347320.7303937395305360.365196869765268
210.5681648930419330.8636702139161330.431835106958067
220.5167824306913950.9664351386172110.483217569308605
230.4618705206784140.9237410413568270.538129479321586
240.516036198865560.967927602268880.48396380113444
250.4502414543803080.9004829087606170.549758545619692
260.385658202963340.7713164059266790.61434179703666
270.3239177770138750.647835554027750.676082222986125
280.2741272419253460.5482544838506920.725872758074654
290.2411385209434840.4822770418869680.758861479056516
300.1987038063555610.3974076127111220.801296193644439
310.1641599999506910.3283199999013820.835840000049309
320.206074713712570.412149427425140.79392528628743
330.1656761691685950.3313523383371910.834323830831405
340.1313513446620230.2627026893240460.868648655337977
350.1105402673669410.2210805347338810.889459732633059
360.08517342420689880.1703468484137980.914826575793101
370.06605448368500180.1321089673700040.933945516314998
380.05148179074741190.1029635814948240.948518209252588
390.03800534113000670.07601068226001340.961994658869993
400.02885345957555410.05770691915110820.971146540424446
410.02350470324428690.04700940648857380.976495296755713
420.0174249453596690.0348498907193380.982575054640331
430.01291126642215960.02582253284431930.98708873357784
440.009076570266816640.01815314053363330.990923429733183
450.3486489678964410.6972979357928820.651351032103559
460.3086574830338130.6173149660676260.691342516966187
470.6413837994969310.7172324010061380.358616200503069
480.5934579780090970.8130840439818060.406542021990903
490.5444563366302750.9110873267394490.455543663369724
500.5129497740345570.9741004519308860.487050225965443
510.7088153523330770.5823692953338450.291184647666923
520.6750651979722250.649869604055550.324934802027775
530.641053316093460.717893367813080.35894668390654
540.5984727890695980.8030544218608030.401527210930402
550.5595350881052860.8809298237894280.440464911894714
560.5188997984910760.9622004030178470.481100201508924
570.4726092761614930.9452185523229860.527390723838507
580.4379592196815020.8759184393630040.562040780318498
590.4192784476415250.8385568952830510.580721552358475
600.3764764271336020.7529528542672040.623523572866398
610.3340320564225580.6680641128451160.665967943577442
620.2922920334862750.5845840669725490.707707966513725
630.2820210096253020.5640420192506040.717978990374698
640.2465992128738880.4931984257477760.753400787126112
650.2172364160421440.4344728320842870.782763583957856
660.2011605909732570.4023211819465130.798839409026743
670.1807363201771210.3614726403542420.819263679822879
680.1568251309211060.3136502618422120.843174869078894
690.1350019294758860.2700038589517720.864998070524114
700.1176166491567020.2352332983134050.882383350843298
710.09721652311620010.19443304623240.9027834768838
720.08075289037410620.1615057807482120.919247109625894
730.06550372347362250.1310074469472450.934496276526377
740.05397063082525320.1079412616505060.946029369174747
750.04379054668072320.08758109336144630.956209453319277
760.03470022154371720.06940044308743440.965299778456283
770.02802771314552740.05605542629105480.971972286854473
780.02166185837270830.04332371674541670.978338141627292
790.01691207797942060.03382415595884110.983087922020579
800.01279082710946340.02558165421892670.987209172890537
810.01015387368832770.02030774737665540.989846126311672
820.007891680173234030.01578336034646810.992108319826766
830.005798302651905150.01159660530381030.994201697348095
840.9999999999913091.73817820906367e-118.69089104531835e-12
850.9999999999833513.32972309553239e-111.66486154776619e-11
860.9999999999633437.33141460471425e-113.66570730235712e-11
870.9999999999203681.59264371788958e-107.9632185894479e-11
880.9999999998500032.99993319569164e-101.49996659784582e-10
890.9999999999999992.62657745543445e-151.31328872771723e-15
900.9999999999999976.77642575436534e-153.38821287718267e-15
910.9999999999999921.62033101862969e-148.10165509314846e-15
920.999999999999984.06105516553412e-142.03052758276706e-14
930.9999999999999491.02099301598087e-135.10496507990433e-14
940.9999999999998942.1275364413092e-131.0637682206546e-13
950.9999999999997854.30235459171509e-132.15117729585754e-13
960.9999999999995249.50930699262956e-134.75465349631478e-13
970.9999999999999774.63777403113906e-142.31888701556953e-14
980.999999999999959.93944480562705e-144.96972240281352e-14
990.9999999999998832.33290391114942e-131.16645195557471e-13
1000.9999999999997035.9340468110546e-132.9670234055273e-13
1010.9999999999992521.49563189440086e-127.47815947200431e-13
1020.9999999999980713.85713819439278e-121.92856909719639e-12
1030.9999999999954699.06293562221245e-124.53146781110623e-12
1040.9999999999886312.27384118091068e-111.13692059045534e-11
1050.9999999999759324.81353188385125e-112.40676594192563e-11
1060.9999999999430591.13881723506335e-105.69408617531673e-11
1070.9999999998658262.68348287492355e-101.34174143746177e-10
1080.9999999997312565.37487228938729e-102.68743614469364e-10
1090.9999999997639114.72177513226845e-102.36088756613423e-10
1100.9999999998617162.76568098426163e-101.38284049213082e-10
1110.9999999997264835.47033818896186e-102.73516909448093e-10
1120.9999999995631968.73608581877023e-104.36804290938512e-10
1130.9999999992680841.46383190621491e-097.31915953107457e-10
1140.9999999983189973.36200526345345e-091.68100263172672e-09
1150.9999999999793244.1351555640186e-112.0675777820093e-11
1160.9999999999447181.10563657077623e-105.52818285388115e-11
1170.9999999998717492.56502011506416e-101.28251005753208e-10
1180.9999999997475285.0494494491589e-102.52472472457945e-10
1190.9999999997722494.55501744111557e-102.27750872055778e-10
1200.9999999998802732.39453981073688e-101.19726990536844e-10
1210.9999999996846446.30712018617705e-103.15356009308853e-10
1220.9999999993370921.32581644720254e-096.62908223601271e-10
1230.999999998727582.54483949090176e-091.27241974545088e-09
1240.999999999137131.72573997206316e-098.62869986031582e-10
1250.9999999988207562.3584875533684e-091.1792437766842e-09
1260.9999999967969836.40603410409961e-093.2030170520498e-09
1270.9999999930964551.38070903872374e-086.90354519361868e-09
1280.999999983046353.39073010624799e-081.69536505312399e-08
1290.9999999720558355.58883301862608e-082.79441650931304e-08
1300.9999999657608676.84782665995282e-083.42391332997641e-08
1310.9999999278419241.44316152867493e-077.21580764337465e-08
1320.9999998223793943.55241212690539e-071.77620606345269e-07
1330.9999996155609227.68878155755779e-073.84439077877889e-07
1340.9999992774047141.44519057146572e-067.22595285732859e-07
1350.9999984027194653.19456107075556e-061.59728053537778e-06
1360.9999974967273575.0065452860835e-062.50327264304175e-06
1370.9999960440317977.91193640543638e-063.95596820271819e-06
1380.9999957509546198.49809076242395e-064.24904538121197e-06
1390.9999911503294591.76993410825578e-058.84967054127889e-06
1400.9999866615086662.66769826671e-051.333849133355e-05
1410.9999789278758774.21442482448973e-052.10721241224487e-05
1420.9999475981412660.0001048037174683495.24018587341747e-05
1430.9998838095456480.0002323809087038650.000116190454351932
1440.9997360974655570.0005278050688850080.000263902534442504
1450.9995126668441770.0009746663116468550.000487333155823427
1460.9988951873330170.002209625333966940.00110481266698347
1470.9975343596056940.00493128078861220.0024656403943061
1480.9969841623235720.006031675352856270.00301583767642813
1490.9940657579544620.01186848409107590.00593424204553793
1500.988251970420450.02349605915909980.0117480295795499
1510.9769702280731790.04605954385364290.0230297719268215
1520.955593450041340.08881309991732020.0444065499586601
1530.9229436932707960.1541126134584080.0770563067292039
1540.8731950127213280.2536099745573430.126804987278672
1550.8831819598824290.2336360802351420.116818040117571
1560.9997762460977640.0004475078044711690.000223753902235585
1570.9995469553550130.0009060892899744920.000453044644987246

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
7 & 0.7896241600333 & 0.4207516799334 & 0.2103758399667 \tabularnewline
8 & 0.667172840699808 & 0.665654318600385 & 0.332827159300192 \tabularnewline
9 & 0.550201309801132 & 0.899597380397735 & 0.449798690198868 \tabularnewline
10 & 0.419042833866632 & 0.838085667733263 & 0.580957166133368 \tabularnewline
11 & 0.442365434508954 & 0.884730869017908 & 0.557634565491046 \tabularnewline
12 & 0.469732653353724 & 0.939465306707449 & 0.530267346646276 \tabularnewline
13 & 0.621919258754061 & 0.756161482491877 & 0.378080741245939 \tabularnewline
14 & 0.902872282102361 & 0.194255435795279 & 0.0971277178976394 \tabularnewline
15 & 0.865071569006873 & 0.269856861986255 & 0.134928430993127 \tabularnewline
16 & 0.817081010682017 & 0.365837978635965 & 0.182918989317983 \tabularnewline
17 & 0.794949308126756 & 0.410101383746487 & 0.205050691873244 \tabularnewline
18 & 0.734601492340574 & 0.530797015318852 & 0.265398507659426 \tabularnewline
19 & 0.684392808022058 & 0.631214383955883 & 0.315607191977942 \tabularnewline
20 & 0.634803130234732 & 0.730393739530536 & 0.365196869765268 \tabularnewline
21 & 0.568164893041933 & 0.863670213916133 & 0.431835106958067 \tabularnewline
22 & 0.516782430691395 & 0.966435138617211 & 0.483217569308605 \tabularnewline
23 & 0.461870520678414 & 0.923741041356827 & 0.538129479321586 \tabularnewline
24 & 0.51603619886556 & 0.96792760226888 & 0.48396380113444 \tabularnewline
25 & 0.450241454380308 & 0.900482908760617 & 0.549758545619692 \tabularnewline
26 & 0.38565820296334 & 0.771316405926679 & 0.61434179703666 \tabularnewline
27 & 0.323917777013875 & 0.64783555402775 & 0.676082222986125 \tabularnewline
28 & 0.274127241925346 & 0.548254483850692 & 0.725872758074654 \tabularnewline
29 & 0.241138520943484 & 0.482277041886968 & 0.758861479056516 \tabularnewline
30 & 0.198703806355561 & 0.397407612711122 & 0.801296193644439 \tabularnewline
31 & 0.164159999950691 & 0.328319999901382 & 0.835840000049309 \tabularnewline
32 & 0.20607471371257 & 0.41214942742514 & 0.79392528628743 \tabularnewline
33 & 0.165676169168595 & 0.331352338337191 & 0.834323830831405 \tabularnewline
34 & 0.131351344662023 & 0.262702689324046 & 0.868648655337977 \tabularnewline
35 & 0.110540267366941 & 0.221080534733881 & 0.889459732633059 \tabularnewline
36 & 0.0851734242068988 & 0.170346848413798 & 0.914826575793101 \tabularnewline
37 & 0.0660544836850018 & 0.132108967370004 & 0.933945516314998 \tabularnewline
38 & 0.0514817907474119 & 0.102963581494824 & 0.948518209252588 \tabularnewline
39 & 0.0380053411300067 & 0.0760106822600134 & 0.961994658869993 \tabularnewline
40 & 0.0288534595755541 & 0.0577069191511082 & 0.971146540424446 \tabularnewline
41 & 0.0235047032442869 & 0.0470094064885738 & 0.976495296755713 \tabularnewline
42 & 0.017424945359669 & 0.034849890719338 & 0.982575054640331 \tabularnewline
43 & 0.0129112664221596 & 0.0258225328443193 & 0.98708873357784 \tabularnewline
44 & 0.00907657026681664 & 0.0181531405336333 & 0.990923429733183 \tabularnewline
45 & 0.348648967896441 & 0.697297935792882 & 0.651351032103559 \tabularnewline
46 & 0.308657483033813 & 0.617314966067626 & 0.691342516966187 \tabularnewline
47 & 0.641383799496931 & 0.717232401006138 & 0.358616200503069 \tabularnewline
48 & 0.593457978009097 & 0.813084043981806 & 0.406542021990903 \tabularnewline
49 & 0.544456336630275 & 0.911087326739449 & 0.455543663369724 \tabularnewline
50 & 0.512949774034557 & 0.974100451930886 & 0.487050225965443 \tabularnewline
51 & 0.708815352333077 & 0.582369295333845 & 0.291184647666923 \tabularnewline
52 & 0.675065197972225 & 0.64986960405555 & 0.324934802027775 \tabularnewline
53 & 0.64105331609346 & 0.71789336781308 & 0.35894668390654 \tabularnewline
54 & 0.598472789069598 & 0.803054421860803 & 0.401527210930402 \tabularnewline
55 & 0.559535088105286 & 0.880929823789428 & 0.440464911894714 \tabularnewline
56 & 0.518899798491076 & 0.962200403017847 & 0.481100201508924 \tabularnewline
57 & 0.472609276161493 & 0.945218552322986 & 0.527390723838507 \tabularnewline
58 & 0.437959219681502 & 0.875918439363004 & 0.562040780318498 \tabularnewline
59 & 0.419278447641525 & 0.838556895283051 & 0.580721552358475 \tabularnewline
60 & 0.376476427133602 & 0.752952854267204 & 0.623523572866398 \tabularnewline
61 & 0.334032056422558 & 0.668064112845116 & 0.665967943577442 \tabularnewline
62 & 0.292292033486275 & 0.584584066972549 & 0.707707966513725 \tabularnewline
63 & 0.282021009625302 & 0.564042019250604 & 0.717978990374698 \tabularnewline
64 & 0.246599212873888 & 0.493198425747776 & 0.753400787126112 \tabularnewline
65 & 0.217236416042144 & 0.434472832084287 & 0.782763583957856 \tabularnewline
66 & 0.201160590973257 & 0.402321181946513 & 0.798839409026743 \tabularnewline
67 & 0.180736320177121 & 0.361472640354242 & 0.819263679822879 \tabularnewline
68 & 0.156825130921106 & 0.313650261842212 & 0.843174869078894 \tabularnewline
69 & 0.135001929475886 & 0.270003858951772 & 0.864998070524114 \tabularnewline
70 & 0.117616649156702 & 0.235233298313405 & 0.882383350843298 \tabularnewline
71 & 0.0972165231162001 & 0.1944330462324 & 0.9027834768838 \tabularnewline
72 & 0.0807528903741062 & 0.161505780748212 & 0.919247109625894 \tabularnewline
73 & 0.0655037234736225 & 0.131007446947245 & 0.934496276526377 \tabularnewline
74 & 0.0539706308252532 & 0.107941261650506 & 0.946029369174747 \tabularnewline
75 & 0.0437905466807232 & 0.0875810933614463 & 0.956209453319277 \tabularnewline
76 & 0.0347002215437172 & 0.0694004430874344 & 0.965299778456283 \tabularnewline
77 & 0.0280277131455274 & 0.0560554262910548 & 0.971972286854473 \tabularnewline
78 & 0.0216618583727083 & 0.0433237167454167 & 0.978338141627292 \tabularnewline
79 & 0.0169120779794206 & 0.0338241559588411 & 0.983087922020579 \tabularnewline
80 & 0.0127908271094634 & 0.0255816542189267 & 0.987209172890537 \tabularnewline
81 & 0.0101538736883277 & 0.0203077473766554 & 0.989846126311672 \tabularnewline
82 & 0.00789168017323403 & 0.0157833603464681 & 0.992108319826766 \tabularnewline
83 & 0.00579830265190515 & 0.0115966053038103 & 0.994201697348095 \tabularnewline
84 & 0.999999999991309 & 1.73817820906367e-11 & 8.69089104531835e-12 \tabularnewline
85 & 0.999999999983351 & 3.32972309553239e-11 & 1.66486154776619e-11 \tabularnewline
86 & 0.999999999963343 & 7.33141460471425e-11 & 3.66570730235712e-11 \tabularnewline
87 & 0.999999999920368 & 1.59264371788958e-10 & 7.9632185894479e-11 \tabularnewline
88 & 0.999999999850003 & 2.99993319569164e-10 & 1.49996659784582e-10 \tabularnewline
89 & 0.999999999999999 & 2.62657745543445e-15 & 1.31328872771723e-15 \tabularnewline
90 & 0.999999999999997 & 6.77642575436534e-15 & 3.38821287718267e-15 \tabularnewline
91 & 0.999999999999992 & 1.62033101862969e-14 & 8.10165509314846e-15 \tabularnewline
92 & 0.99999999999998 & 4.06105516553412e-14 & 2.03052758276706e-14 \tabularnewline
93 & 0.999999999999949 & 1.02099301598087e-13 & 5.10496507990433e-14 \tabularnewline
94 & 0.999999999999894 & 2.1275364413092e-13 & 1.0637682206546e-13 \tabularnewline
95 & 0.999999999999785 & 4.30235459171509e-13 & 2.15117729585754e-13 \tabularnewline
96 & 0.999999999999524 & 9.50930699262956e-13 & 4.75465349631478e-13 \tabularnewline
97 & 0.999999999999977 & 4.63777403113906e-14 & 2.31888701556953e-14 \tabularnewline
98 & 0.99999999999995 & 9.93944480562705e-14 & 4.96972240281352e-14 \tabularnewline
99 & 0.999999999999883 & 2.33290391114942e-13 & 1.16645195557471e-13 \tabularnewline
100 & 0.999999999999703 & 5.9340468110546e-13 & 2.9670234055273e-13 \tabularnewline
101 & 0.999999999999252 & 1.49563189440086e-12 & 7.47815947200431e-13 \tabularnewline
102 & 0.999999999998071 & 3.85713819439278e-12 & 1.92856909719639e-12 \tabularnewline
103 & 0.999999999995469 & 9.06293562221245e-12 & 4.53146781110623e-12 \tabularnewline
104 & 0.999999999988631 & 2.27384118091068e-11 & 1.13692059045534e-11 \tabularnewline
105 & 0.999999999975932 & 4.81353188385125e-11 & 2.40676594192563e-11 \tabularnewline
106 & 0.999999999943059 & 1.13881723506335e-10 & 5.69408617531673e-11 \tabularnewline
107 & 0.999999999865826 & 2.68348287492355e-10 & 1.34174143746177e-10 \tabularnewline
108 & 0.999999999731256 & 5.37487228938729e-10 & 2.68743614469364e-10 \tabularnewline
109 & 0.999999999763911 & 4.72177513226845e-10 & 2.36088756613423e-10 \tabularnewline
110 & 0.999999999861716 & 2.76568098426163e-10 & 1.38284049213082e-10 \tabularnewline
111 & 0.999999999726483 & 5.47033818896186e-10 & 2.73516909448093e-10 \tabularnewline
112 & 0.999999999563196 & 8.73608581877023e-10 & 4.36804290938512e-10 \tabularnewline
113 & 0.999999999268084 & 1.46383190621491e-09 & 7.31915953107457e-10 \tabularnewline
114 & 0.999999998318997 & 3.36200526345345e-09 & 1.68100263172672e-09 \tabularnewline
115 & 0.999999999979324 & 4.1351555640186e-11 & 2.0675777820093e-11 \tabularnewline
116 & 0.999999999944718 & 1.10563657077623e-10 & 5.52818285388115e-11 \tabularnewline
117 & 0.999999999871749 & 2.56502011506416e-10 & 1.28251005753208e-10 \tabularnewline
118 & 0.999999999747528 & 5.0494494491589e-10 & 2.52472472457945e-10 \tabularnewline
119 & 0.999999999772249 & 4.55501744111557e-10 & 2.27750872055778e-10 \tabularnewline
120 & 0.999999999880273 & 2.39453981073688e-10 & 1.19726990536844e-10 \tabularnewline
121 & 0.999999999684644 & 6.30712018617705e-10 & 3.15356009308853e-10 \tabularnewline
122 & 0.999999999337092 & 1.32581644720254e-09 & 6.62908223601271e-10 \tabularnewline
123 & 0.99999999872758 & 2.54483949090176e-09 & 1.27241974545088e-09 \tabularnewline
124 & 0.99999999913713 & 1.72573997206316e-09 & 8.62869986031582e-10 \tabularnewline
125 & 0.999999998820756 & 2.3584875533684e-09 & 1.1792437766842e-09 \tabularnewline
126 & 0.999999996796983 & 6.40603410409961e-09 & 3.2030170520498e-09 \tabularnewline
127 & 0.999999993096455 & 1.38070903872374e-08 & 6.90354519361868e-09 \tabularnewline
128 & 0.99999998304635 & 3.39073010624799e-08 & 1.69536505312399e-08 \tabularnewline
129 & 0.999999972055835 & 5.58883301862608e-08 & 2.79441650931304e-08 \tabularnewline
130 & 0.999999965760867 & 6.84782665995282e-08 & 3.42391332997641e-08 \tabularnewline
131 & 0.999999927841924 & 1.44316152867493e-07 & 7.21580764337465e-08 \tabularnewline
132 & 0.999999822379394 & 3.55241212690539e-07 & 1.77620606345269e-07 \tabularnewline
133 & 0.999999615560922 & 7.68878155755779e-07 & 3.84439077877889e-07 \tabularnewline
134 & 0.999999277404714 & 1.44519057146572e-06 & 7.22595285732859e-07 \tabularnewline
135 & 0.999998402719465 & 3.19456107075556e-06 & 1.59728053537778e-06 \tabularnewline
136 & 0.999997496727357 & 5.0065452860835e-06 & 2.50327264304175e-06 \tabularnewline
137 & 0.999996044031797 & 7.91193640543638e-06 & 3.95596820271819e-06 \tabularnewline
138 & 0.999995750954619 & 8.49809076242395e-06 & 4.24904538121197e-06 \tabularnewline
139 & 0.999991150329459 & 1.76993410825578e-05 & 8.84967054127889e-06 \tabularnewline
140 & 0.999986661508666 & 2.66769826671e-05 & 1.333849133355e-05 \tabularnewline
141 & 0.999978927875877 & 4.21442482448973e-05 & 2.10721241224487e-05 \tabularnewline
142 & 0.999947598141266 & 0.000104803717468349 & 5.24018587341747e-05 \tabularnewline
143 & 0.999883809545648 & 0.000232380908703865 & 0.000116190454351932 \tabularnewline
144 & 0.999736097465557 & 0.000527805068885008 & 0.000263902534442504 \tabularnewline
145 & 0.999512666844177 & 0.000974666311646855 & 0.000487333155823427 \tabularnewline
146 & 0.998895187333017 & 0.00220962533396694 & 0.00110481266698347 \tabularnewline
147 & 0.997534359605694 & 0.0049312807886122 & 0.0024656403943061 \tabularnewline
148 & 0.996984162323572 & 0.00603167535285627 & 0.00301583767642813 \tabularnewline
149 & 0.994065757954462 & 0.0118684840910759 & 0.00593424204553793 \tabularnewline
150 & 0.98825197042045 & 0.0234960591590998 & 0.0117480295795499 \tabularnewline
151 & 0.976970228073179 & 0.0460595438536429 & 0.0230297719268215 \tabularnewline
152 & 0.95559345004134 & 0.0888130999173202 & 0.0444065499586601 \tabularnewline
153 & 0.922943693270796 & 0.154112613458408 & 0.0770563067292039 \tabularnewline
154 & 0.873195012721328 & 0.253609974557343 & 0.126804987278672 \tabularnewline
155 & 0.883181959882429 & 0.233636080235142 & 0.116818040117571 \tabularnewline
156 & 0.999776246097764 & 0.000447507804471169 & 0.000223753902235585 \tabularnewline
157 & 0.999546955355013 & 0.000906089289974492 & 0.000453044644987246 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145963&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]7[/C][C]0.7896241600333[/C][C]0.4207516799334[/C][C]0.2103758399667[/C][/ROW]
[ROW][C]8[/C][C]0.667172840699808[/C][C]0.665654318600385[/C][C]0.332827159300192[/C][/ROW]
[ROW][C]9[/C][C]0.550201309801132[/C][C]0.899597380397735[/C][C]0.449798690198868[/C][/ROW]
[ROW][C]10[/C][C]0.419042833866632[/C][C]0.838085667733263[/C][C]0.580957166133368[/C][/ROW]
[ROW][C]11[/C][C]0.442365434508954[/C][C]0.884730869017908[/C][C]0.557634565491046[/C][/ROW]
[ROW][C]12[/C][C]0.469732653353724[/C][C]0.939465306707449[/C][C]0.530267346646276[/C][/ROW]
[ROW][C]13[/C][C]0.621919258754061[/C][C]0.756161482491877[/C][C]0.378080741245939[/C][/ROW]
[ROW][C]14[/C][C]0.902872282102361[/C][C]0.194255435795279[/C][C]0.0971277178976394[/C][/ROW]
[ROW][C]15[/C][C]0.865071569006873[/C][C]0.269856861986255[/C][C]0.134928430993127[/C][/ROW]
[ROW][C]16[/C][C]0.817081010682017[/C][C]0.365837978635965[/C][C]0.182918989317983[/C][/ROW]
[ROW][C]17[/C][C]0.794949308126756[/C][C]0.410101383746487[/C][C]0.205050691873244[/C][/ROW]
[ROW][C]18[/C][C]0.734601492340574[/C][C]0.530797015318852[/C][C]0.265398507659426[/C][/ROW]
[ROW][C]19[/C][C]0.684392808022058[/C][C]0.631214383955883[/C][C]0.315607191977942[/C][/ROW]
[ROW][C]20[/C][C]0.634803130234732[/C][C]0.730393739530536[/C][C]0.365196869765268[/C][/ROW]
[ROW][C]21[/C][C]0.568164893041933[/C][C]0.863670213916133[/C][C]0.431835106958067[/C][/ROW]
[ROW][C]22[/C][C]0.516782430691395[/C][C]0.966435138617211[/C][C]0.483217569308605[/C][/ROW]
[ROW][C]23[/C][C]0.461870520678414[/C][C]0.923741041356827[/C][C]0.538129479321586[/C][/ROW]
[ROW][C]24[/C][C]0.51603619886556[/C][C]0.96792760226888[/C][C]0.48396380113444[/C][/ROW]
[ROW][C]25[/C][C]0.450241454380308[/C][C]0.900482908760617[/C][C]0.549758545619692[/C][/ROW]
[ROW][C]26[/C][C]0.38565820296334[/C][C]0.771316405926679[/C][C]0.61434179703666[/C][/ROW]
[ROW][C]27[/C][C]0.323917777013875[/C][C]0.64783555402775[/C][C]0.676082222986125[/C][/ROW]
[ROW][C]28[/C][C]0.274127241925346[/C][C]0.548254483850692[/C][C]0.725872758074654[/C][/ROW]
[ROW][C]29[/C][C]0.241138520943484[/C][C]0.482277041886968[/C][C]0.758861479056516[/C][/ROW]
[ROW][C]30[/C][C]0.198703806355561[/C][C]0.397407612711122[/C][C]0.801296193644439[/C][/ROW]
[ROW][C]31[/C][C]0.164159999950691[/C][C]0.328319999901382[/C][C]0.835840000049309[/C][/ROW]
[ROW][C]32[/C][C]0.20607471371257[/C][C]0.41214942742514[/C][C]0.79392528628743[/C][/ROW]
[ROW][C]33[/C][C]0.165676169168595[/C][C]0.331352338337191[/C][C]0.834323830831405[/C][/ROW]
[ROW][C]34[/C][C]0.131351344662023[/C][C]0.262702689324046[/C][C]0.868648655337977[/C][/ROW]
[ROW][C]35[/C][C]0.110540267366941[/C][C]0.221080534733881[/C][C]0.889459732633059[/C][/ROW]
[ROW][C]36[/C][C]0.0851734242068988[/C][C]0.170346848413798[/C][C]0.914826575793101[/C][/ROW]
[ROW][C]37[/C][C]0.0660544836850018[/C][C]0.132108967370004[/C][C]0.933945516314998[/C][/ROW]
[ROW][C]38[/C][C]0.0514817907474119[/C][C]0.102963581494824[/C][C]0.948518209252588[/C][/ROW]
[ROW][C]39[/C][C]0.0380053411300067[/C][C]0.0760106822600134[/C][C]0.961994658869993[/C][/ROW]
[ROW][C]40[/C][C]0.0288534595755541[/C][C]0.0577069191511082[/C][C]0.971146540424446[/C][/ROW]
[ROW][C]41[/C][C]0.0235047032442869[/C][C]0.0470094064885738[/C][C]0.976495296755713[/C][/ROW]
[ROW][C]42[/C][C]0.017424945359669[/C][C]0.034849890719338[/C][C]0.982575054640331[/C][/ROW]
[ROW][C]43[/C][C]0.0129112664221596[/C][C]0.0258225328443193[/C][C]0.98708873357784[/C][/ROW]
[ROW][C]44[/C][C]0.00907657026681664[/C][C]0.0181531405336333[/C][C]0.990923429733183[/C][/ROW]
[ROW][C]45[/C][C]0.348648967896441[/C][C]0.697297935792882[/C][C]0.651351032103559[/C][/ROW]
[ROW][C]46[/C][C]0.308657483033813[/C][C]0.617314966067626[/C][C]0.691342516966187[/C][/ROW]
[ROW][C]47[/C][C]0.641383799496931[/C][C]0.717232401006138[/C][C]0.358616200503069[/C][/ROW]
[ROW][C]48[/C][C]0.593457978009097[/C][C]0.813084043981806[/C][C]0.406542021990903[/C][/ROW]
[ROW][C]49[/C][C]0.544456336630275[/C][C]0.911087326739449[/C][C]0.455543663369724[/C][/ROW]
[ROW][C]50[/C][C]0.512949774034557[/C][C]0.974100451930886[/C][C]0.487050225965443[/C][/ROW]
[ROW][C]51[/C][C]0.708815352333077[/C][C]0.582369295333845[/C][C]0.291184647666923[/C][/ROW]
[ROW][C]52[/C][C]0.675065197972225[/C][C]0.64986960405555[/C][C]0.324934802027775[/C][/ROW]
[ROW][C]53[/C][C]0.64105331609346[/C][C]0.71789336781308[/C][C]0.35894668390654[/C][/ROW]
[ROW][C]54[/C][C]0.598472789069598[/C][C]0.803054421860803[/C][C]0.401527210930402[/C][/ROW]
[ROW][C]55[/C][C]0.559535088105286[/C][C]0.880929823789428[/C][C]0.440464911894714[/C][/ROW]
[ROW][C]56[/C][C]0.518899798491076[/C][C]0.962200403017847[/C][C]0.481100201508924[/C][/ROW]
[ROW][C]57[/C][C]0.472609276161493[/C][C]0.945218552322986[/C][C]0.527390723838507[/C][/ROW]
[ROW][C]58[/C][C]0.437959219681502[/C][C]0.875918439363004[/C][C]0.562040780318498[/C][/ROW]
[ROW][C]59[/C][C]0.419278447641525[/C][C]0.838556895283051[/C][C]0.580721552358475[/C][/ROW]
[ROW][C]60[/C][C]0.376476427133602[/C][C]0.752952854267204[/C][C]0.623523572866398[/C][/ROW]
[ROW][C]61[/C][C]0.334032056422558[/C][C]0.668064112845116[/C][C]0.665967943577442[/C][/ROW]
[ROW][C]62[/C][C]0.292292033486275[/C][C]0.584584066972549[/C][C]0.707707966513725[/C][/ROW]
[ROW][C]63[/C][C]0.282021009625302[/C][C]0.564042019250604[/C][C]0.717978990374698[/C][/ROW]
[ROW][C]64[/C][C]0.246599212873888[/C][C]0.493198425747776[/C][C]0.753400787126112[/C][/ROW]
[ROW][C]65[/C][C]0.217236416042144[/C][C]0.434472832084287[/C][C]0.782763583957856[/C][/ROW]
[ROW][C]66[/C][C]0.201160590973257[/C][C]0.402321181946513[/C][C]0.798839409026743[/C][/ROW]
[ROW][C]67[/C][C]0.180736320177121[/C][C]0.361472640354242[/C][C]0.819263679822879[/C][/ROW]
[ROW][C]68[/C][C]0.156825130921106[/C][C]0.313650261842212[/C][C]0.843174869078894[/C][/ROW]
[ROW][C]69[/C][C]0.135001929475886[/C][C]0.270003858951772[/C][C]0.864998070524114[/C][/ROW]
[ROW][C]70[/C][C]0.117616649156702[/C][C]0.235233298313405[/C][C]0.882383350843298[/C][/ROW]
[ROW][C]71[/C][C]0.0972165231162001[/C][C]0.1944330462324[/C][C]0.9027834768838[/C][/ROW]
[ROW][C]72[/C][C]0.0807528903741062[/C][C]0.161505780748212[/C][C]0.919247109625894[/C][/ROW]
[ROW][C]73[/C][C]0.0655037234736225[/C][C]0.131007446947245[/C][C]0.934496276526377[/C][/ROW]
[ROW][C]74[/C][C]0.0539706308252532[/C][C]0.107941261650506[/C][C]0.946029369174747[/C][/ROW]
[ROW][C]75[/C][C]0.0437905466807232[/C][C]0.0875810933614463[/C][C]0.956209453319277[/C][/ROW]
[ROW][C]76[/C][C]0.0347002215437172[/C][C]0.0694004430874344[/C][C]0.965299778456283[/C][/ROW]
[ROW][C]77[/C][C]0.0280277131455274[/C][C]0.0560554262910548[/C][C]0.971972286854473[/C][/ROW]
[ROW][C]78[/C][C]0.0216618583727083[/C][C]0.0433237167454167[/C][C]0.978338141627292[/C][/ROW]
[ROW][C]79[/C][C]0.0169120779794206[/C][C]0.0338241559588411[/C][C]0.983087922020579[/C][/ROW]
[ROW][C]80[/C][C]0.0127908271094634[/C][C]0.0255816542189267[/C][C]0.987209172890537[/C][/ROW]
[ROW][C]81[/C][C]0.0101538736883277[/C][C]0.0203077473766554[/C][C]0.989846126311672[/C][/ROW]
[ROW][C]82[/C][C]0.00789168017323403[/C][C]0.0157833603464681[/C][C]0.992108319826766[/C][/ROW]
[ROW][C]83[/C][C]0.00579830265190515[/C][C]0.0115966053038103[/C][C]0.994201697348095[/C][/ROW]
[ROW][C]84[/C][C]0.999999999991309[/C][C]1.73817820906367e-11[/C][C]8.69089104531835e-12[/C][/ROW]
[ROW][C]85[/C][C]0.999999999983351[/C][C]3.32972309553239e-11[/C][C]1.66486154776619e-11[/C][/ROW]
[ROW][C]86[/C][C]0.999999999963343[/C][C]7.33141460471425e-11[/C][C]3.66570730235712e-11[/C][/ROW]
[ROW][C]87[/C][C]0.999999999920368[/C][C]1.59264371788958e-10[/C][C]7.9632185894479e-11[/C][/ROW]
[ROW][C]88[/C][C]0.999999999850003[/C][C]2.99993319569164e-10[/C][C]1.49996659784582e-10[/C][/ROW]
[ROW][C]89[/C][C]0.999999999999999[/C][C]2.62657745543445e-15[/C][C]1.31328872771723e-15[/C][/ROW]
[ROW][C]90[/C][C]0.999999999999997[/C][C]6.77642575436534e-15[/C][C]3.38821287718267e-15[/C][/ROW]
[ROW][C]91[/C][C]0.999999999999992[/C][C]1.62033101862969e-14[/C][C]8.10165509314846e-15[/C][/ROW]
[ROW][C]92[/C][C]0.99999999999998[/C][C]4.06105516553412e-14[/C][C]2.03052758276706e-14[/C][/ROW]
[ROW][C]93[/C][C]0.999999999999949[/C][C]1.02099301598087e-13[/C][C]5.10496507990433e-14[/C][/ROW]
[ROW][C]94[/C][C]0.999999999999894[/C][C]2.1275364413092e-13[/C][C]1.0637682206546e-13[/C][/ROW]
[ROW][C]95[/C][C]0.999999999999785[/C][C]4.30235459171509e-13[/C][C]2.15117729585754e-13[/C][/ROW]
[ROW][C]96[/C][C]0.999999999999524[/C][C]9.50930699262956e-13[/C][C]4.75465349631478e-13[/C][/ROW]
[ROW][C]97[/C][C]0.999999999999977[/C][C]4.63777403113906e-14[/C][C]2.31888701556953e-14[/C][/ROW]
[ROW][C]98[/C][C]0.99999999999995[/C][C]9.93944480562705e-14[/C][C]4.96972240281352e-14[/C][/ROW]
[ROW][C]99[/C][C]0.999999999999883[/C][C]2.33290391114942e-13[/C][C]1.16645195557471e-13[/C][/ROW]
[ROW][C]100[/C][C]0.999999999999703[/C][C]5.9340468110546e-13[/C][C]2.9670234055273e-13[/C][/ROW]
[ROW][C]101[/C][C]0.999999999999252[/C][C]1.49563189440086e-12[/C][C]7.47815947200431e-13[/C][/ROW]
[ROW][C]102[/C][C]0.999999999998071[/C][C]3.85713819439278e-12[/C][C]1.92856909719639e-12[/C][/ROW]
[ROW][C]103[/C][C]0.999999999995469[/C][C]9.06293562221245e-12[/C][C]4.53146781110623e-12[/C][/ROW]
[ROW][C]104[/C][C]0.999999999988631[/C][C]2.27384118091068e-11[/C][C]1.13692059045534e-11[/C][/ROW]
[ROW][C]105[/C][C]0.999999999975932[/C][C]4.81353188385125e-11[/C][C]2.40676594192563e-11[/C][/ROW]
[ROW][C]106[/C][C]0.999999999943059[/C][C]1.13881723506335e-10[/C][C]5.69408617531673e-11[/C][/ROW]
[ROW][C]107[/C][C]0.999999999865826[/C][C]2.68348287492355e-10[/C][C]1.34174143746177e-10[/C][/ROW]
[ROW][C]108[/C][C]0.999999999731256[/C][C]5.37487228938729e-10[/C][C]2.68743614469364e-10[/C][/ROW]
[ROW][C]109[/C][C]0.999999999763911[/C][C]4.72177513226845e-10[/C][C]2.36088756613423e-10[/C][/ROW]
[ROW][C]110[/C][C]0.999999999861716[/C][C]2.76568098426163e-10[/C][C]1.38284049213082e-10[/C][/ROW]
[ROW][C]111[/C][C]0.999999999726483[/C][C]5.47033818896186e-10[/C][C]2.73516909448093e-10[/C][/ROW]
[ROW][C]112[/C][C]0.999999999563196[/C][C]8.73608581877023e-10[/C][C]4.36804290938512e-10[/C][/ROW]
[ROW][C]113[/C][C]0.999999999268084[/C][C]1.46383190621491e-09[/C][C]7.31915953107457e-10[/C][/ROW]
[ROW][C]114[/C][C]0.999999998318997[/C][C]3.36200526345345e-09[/C][C]1.68100263172672e-09[/C][/ROW]
[ROW][C]115[/C][C]0.999999999979324[/C][C]4.1351555640186e-11[/C][C]2.0675777820093e-11[/C][/ROW]
[ROW][C]116[/C][C]0.999999999944718[/C][C]1.10563657077623e-10[/C][C]5.52818285388115e-11[/C][/ROW]
[ROW][C]117[/C][C]0.999999999871749[/C][C]2.56502011506416e-10[/C][C]1.28251005753208e-10[/C][/ROW]
[ROW][C]118[/C][C]0.999999999747528[/C][C]5.0494494491589e-10[/C][C]2.52472472457945e-10[/C][/ROW]
[ROW][C]119[/C][C]0.999999999772249[/C][C]4.55501744111557e-10[/C][C]2.27750872055778e-10[/C][/ROW]
[ROW][C]120[/C][C]0.999999999880273[/C][C]2.39453981073688e-10[/C][C]1.19726990536844e-10[/C][/ROW]
[ROW][C]121[/C][C]0.999999999684644[/C][C]6.30712018617705e-10[/C][C]3.15356009308853e-10[/C][/ROW]
[ROW][C]122[/C][C]0.999999999337092[/C][C]1.32581644720254e-09[/C][C]6.62908223601271e-10[/C][/ROW]
[ROW][C]123[/C][C]0.99999999872758[/C][C]2.54483949090176e-09[/C][C]1.27241974545088e-09[/C][/ROW]
[ROW][C]124[/C][C]0.99999999913713[/C][C]1.72573997206316e-09[/C][C]8.62869986031582e-10[/C][/ROW]
[ROW][C]125[/C][C]0.999999998820756[/C][C]2.3584875533684e-09[/C][C]1.1792437766842e-09[/C][/ROW]
[ROW][C]126[/C][C]0.999999996796983[/C][C]6.40603410409961e-09[/C][C]3.2030170520498e-09[/C][/ROW]
[ROW][C]127[/C][C]0.999999993096455[/C][C]1.38070903872374e-08[/C][C]6.90354519361868e-09[/C][/ROW]
[ROW][C]128[/C][C]0.99999998304635[/C][C]3.39073010624799e-08[/C][C]1.69536505312399e-08[/C][/ROW]
[ROW][C]129[/C][C]0.999999972055835[/C][C]5.58883301862608e-08[/C][C]2.79441650931304e-08[/C][/ROW]
[ROW][C]130[/C][C]0.999999965760867[/C][C]6.84782665995282e-08[/C][C]3.42391332997641e-08[/C][/ROW]
[ROW][C]131[/C][C]0.999999927841924[/C][C]1.44316152867493e-07[/C][C]7.21580764337465e-08[/C][/ROW]
[ROW][C]132[/C][C]0.999999822379394[/C][C]3.55241212690539e-07[/C][C]1.77620606345269e-07[/C][/ROW]
[ROW][C]133[/C][C]0.999999615560922[/C][C]7.68878155755779e-07[/C][C]3.84439077877889e-07[/C][/ROW]
[ROW][C]134[/C][C]0.999999277404714[/C][C]1.44519057146572e-06[/C][C]7.22595285732859e-07[/C][/ROW]
[ROW][C]135[/C][C]0.999998402719465[/C][C]3.19456107075556e-06[/C][C]1.59728053537778e-06[/C][/ROW]
[ROW][C]136[/C][C]0.999997496727357[/C][C]5.0065452860835e-06[/C][C]2.50327264304175e-06[/C][/ROW]
[ROW][C]137[/C][C]0.999996044031797[/C][C]7.91193640543638e-06[/C][C]3.95596820271819e-06[/C][/ROW]
[ROW][C]138[/C][C]0.999995750954619[/C][C]8.49809076242395e-06[/C][C]4.24904538121197e-06[/C][/ROW]
[ROW][C]139[/C][C]0.999991150329459[/C][C]1.76993410825578e-05[/C][C]8.84967054127889e-06[/C][/ROW]
[ROW][C]140[/C][C]0.999986661508666[/C][C]2.66769826671e-05[/C][C]1.333849133355e-05[/C][/ROW]
[ROW][C]141[/C][C]0.999978927875877[/C][C]4.21442482448973e-05[/C][C]2.10721241224487e-05[/C][/ROW]
[ROW][C]142[/C][C]0.999947598141266[/C][C]0.000104803717468349[/C][C]5.24018587341747e-05[/C][/ROW]
[ROW][C]143[/C][C]0.999883809545648[/C][C]0.000232380908703865[/C][C]0.000116190454351932[/C][/ROW]
[ROW][C]144[/C][C]0.999736097465557[/C][C]0.000527805068885008[/C][C]0.000263902534442504[/C][/ROW]
[ROW][C]145[/C][C]0.999512666844177[/C][C]0.000974666311646855[/C][C]0.000487333155823427[/C][/ROW]
[ROW][C]146[/C][C]0.998895187333017[/C][C]0.00220962533396694[/C][C]0.00110481266698347[/C][/ROW]
[ROW][C]147[/C][C]0.997534359605694[/C][C]0.0049312807886122[/C][C]0.0024656403943061[/C][/ROW]
[ROW][C]148[/C][C]0.996984162323572[/C][C]0.00603167535285627[/C][C]0.00301583767642813[/C][/ROW]
[ROW][C]149[/C][C]0.994065757954462[/C][C]0.0118684840910759[/C][C]0.00593424204553793[/C][/ROW]
[ROW][C]150[/C][C]0.98825197042045[/C][C]0.0234960591590998[/C][C]0.0117480295795499[/C][/ROW]
[ROW][C]151[/C][C]0.976970228073179[/C][C]0.0460595438536429[/C][C]0.0230297719268215[/C][/ROW]
[ROW][C]152[/C][C]0.95559345004134[/C][C]0.0888130999173202[/C][C]0.0444065499586601[/C][/ROW]
[ROW][C]153[/C][C]0.922943693270796[/C][C]0.154112613458408[/C][C]0.0770563067292039[/C][/ROW]
[ROW][C]154[/C][C]0.873195012721328[/C][C]0.253609974557343[/C][C]0.126804987278672[/C][/ROW]
[ROW][C]155[/C][C]0.883181959882429[/C][C]0.233636080235142[/C][C]0.116818040117571[/C][/ROW]
[ROW][C]156[/C][C]0.999776246097764[/C][C]0.000447507804471169[/C][C]0.000223753902235585[/C][/ROW]
[ROW][C]157[/C][C]0.999546955355013[/C][C]0.000906089289974492[/C][C]0.000453044644987246[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145963&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145963&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-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
70.78962416003330.42075167993340.2103758399667
80.6671728406998080.6656543186003850.332827159300192
90.5502013098011320.8995973803977350.449798690198868
100.4190428338666320.8380856677332630.580957166133368
110.4423654345089540.8847308690179080.557634565491046
120.4697326533537240.9394653067074490.530267346646276
130.6219192587540610.7561614824918770.378080741245939
140.9028722821023610.1942554357952790.0971277178976394
150.8650715690068730.2698568619862550.134928430993127
160.8170810106820170.3658379786359650.182918989317983
170.7949493081267560.4101013837464870.205050691873244
180.7346014923405740.5307970153188520.265398507659426
190.6843928080220580.6312143839558830.315607191977942
200.6348031302347320.7303937395305360.365196869765268
210.5681648930419330.8636702139161330.431835106958067
220.5167824306913950.9664351386172110.483217569308605
230.4618705206784140.9237410413568270.538129479321586
240.516036198865560.967927602268880.48396380113444
250.4502414543803080.9004829087606170.549758545619692
260.385658202963340.7713164059266790.61434179703666
270.3239177770138750.647835554027750.676082222986125
280.2741272419253460.5482544838506920.725872758074654
290.2411385209434840.4822770418869680.758861479056516
300.1987038063555610.3974076127111220.801296193644439
310.1641599999506910.3283199999013820.835840000049309
320.206074713712570.412149427425140.79392528628743
330.1656761691685950.3313523383371910.834323830831405
340.1313513446620230.2627026893240460.868648655337977
350.1105402673669410.2210805347338810.889459732633059
360.08517342420689880.1703468484137980.914826575793101
370.06605448368500180.1321089673700040.933945516314998
380.05148179074741190.1029635814948240.948518209252588
390.03800534113000670.07601068226001340.961994658869993
400.02885345957555410.05770691915110820.971146540424446
410.02350470324428690.04700940648857380.976495296755713
420.0174249453596690.0348498907193380.982575054640331
430.01291126642215960.02582253284431930.98708873357784
440.009076570266816640.01815314053363330.990923429733183
450.3486489678964410.6972979357928820.651351032103559
460.3086574830338130.6173149660676260.691342516966187
470.6413837994969310.7172324010061380.358616200503069
480.5934579780090970.8130840439818060.406542021990903
490.5444563366302750.9110873267394490.455543663369724
500.5129497740345570.9741004519308860.487050225965443
510.7088153523330770.5823692953338450.291184647666923
520.6750651979722250.649869604055550.324934802027775
530.641053316093460.717893367813080.35894668390654
540.5984727890695980.8030544218608030.401527210930402
550.5595350881052860.8809298237894280.440464911894714
560.5188997984910760.9622004030178470.481100201508924
570.4726092761614930.9452185523229860.527390723838507
580.4379592196815020.8759184393630040.562040780318498
590.4192784476415250.8385568952830510.580721552358475
600.3764764271336020.7529528542672040.623523572866398
610.3340320564225580.6680641128451160.665967943577442
620.2922920334862750.5845840669725490.707707966513725
630.2820210096253020.5640420192506040.717978990374698
640.2465992128738880.4931984257477760.753400787126112
650.2172364160421440.4344728320842870.782763583957856
660.2011605909732570.4023211819465130.798839409026743
670.1807363201771210.3614726403542420.819263679822879
680.1568251309211060.3136502618422120.843174869078894
690.1350019294758860.2700038589517720.864998070524114
700.1176166491567020.2352332983134050.882383350843298
710.09721652311620010.19443304623240.9027834768838
720.08075289037410620.1615057807482120.919247109625894
730.06550372347362250.1310074469472450.934496276526377
740.05397063082525320.1079412616505060.946029369174747
750.04379054668072320.08758109336144630.956209453319277
760.03470022154371720.06940044308743440.965299778456283
770.02802771314552740.05605542629105480.971972286854473
780.02166185837270830.04332371674541670.978338141627292
790.01691207797942060.03382415595884110.983087922020579
800.01279082710946340.02558165421892670.987209172890537
810.01015387368832770.02030774737665540.989846126311672
820.007891680173234030.01578336034646810.992108319826766
830.005798302651905150.01159660530381030.994201697348095
840.9999999999913091.73817820906367e-118.69089104531835e-12
850.9999999999833513.32972309553239e-111.66486154776619e-11
860.9999999999633437.33141460471425e-113.66570730235712e-11
870.9999999999203681.59264371788958e-107.9632185894479e-11
880.9999999998500032.99993319569164e-101.49996659784582e-10
890.9999999999999992.62657745543445e-151.31328872771723e-15
900.9999999999999976.77642575436534e-153.38821287718267e-15
910.9999999999999921.62033101862969e-148.10165509314846e-15
920.999999999999984.06105516553412e-142.03052758276706e-14
930.9999999999999491.02099301598087e-135.10496507990433e-14
940.9999999999998942.1275364413092e-131.0637682206546e-13
950.9999999999997854.30235459171509e-132.15117729585754e-13
960.9999999999995249.50930699262956e-134.75465349631478e-13
970.9999999999999774.63777403113906e-142.31888701556953e-14
980.999999999999959.93944480562705e-144.96972240281352e-14
990.9999999999998832.33290391114942e-131.16645195557471e-13
1000.9999999999997035.9340468110546e-132.9670234055273e-13
1010.9999999999992521.49563189440086e-127.47815947200431e-13
1020.9999999999980713.85713819439278e-121.92856909719639e-12
1030.9999999999954699.06293562221245e-124.53146781110623e-12
1040.9999999999886312.27384118091068e-111.13692059045534e-11
1050.9999999999759324.81353188385125e-112.40676594192563e-11
1060.9999999999430591.13881723506335e-105.69408617531673e-11
1070.9999999998658262.68348287492355e-101.34174143746177e-10
1080.9999999997312565.37487228938729e-102.68743614469364e-10
1090.9999999997639114.72177513226845e-102.36088756613423e-10
1100.9999999998617162.76568098426163e-101.38284049213082e-10
1110.9999999997264835.47033818896186e-102.73516909448093e-10
1120.9999999995631968.73608581877023e-104.36804290938512e-10
1130.9999999992680841.46383190621491e-097.31915953107457e-10
1140.9999999983189973.36200526345345e-091.68100263172672e-09
1150.9999999999793244.1351555640186e-112.0675777820093e-11
1160.9999999999447181.10563657077623e-105.52818285388115e-11
1170.9999999998717492.56502011506416e-101.28251005753208e-10
1180.9999999997475285.0494494491589e-102.52472472457945e-10
1190.9999999997722494.55501744111557e-102.27750872055778e-10
1200.9999999998802732.39453981073688e-101.19726990536844e-10
1210.9999999996846446.30712018617705e-103.15356009308853e-10
1220.9999999993370921.32581644720254e-096.62908223601271e-10
1230.999999998727582.54483949090176e-091.27241974545088e-09
1240.999999999137131.72573997206316e-098.62869986031582e-10
1250.9999999988207562.3584875533684e-091.1792437766842e-09
1260.9999999967969836.40603410409961e-093.2030170520498e-09
1270.9999999930964551.38070903872374e-086.90354519361868e-09
1280.999999983046353.39073010624799e-081.69536505312399e-08
1290.9999999720558355.58883301862608e-082.79441650931304e-08
1300.9999999657608676.84782665995282e-083.42391332997641e-08
1310.9999999278419241.44316152867493e-077.21580764337465e-08
1320.9999998223793943.55241212690539e-071.77620606345269e-07
1330.9999996155609227.68878155755779e-073.84439077877889e-07
1340.9999992774047141.44519057146572e-067.22595285732859e-07
1350.9999984027194653.19456107075556e-061.59728053537778e-06
1360.9999974967273575.0065452860835e-062.50327264304175e-06
1370.9999960440317977.91193640543638e-063.95596820271819e-06
1380.9999957509546198.49809076242395e-064.24904538121197e-06
1390.9999911503294591.76993410825578e-058.84967054127889e-06
1400.9999866615086662.66769826671e-051.333849133355e-05
1410.9999789278758774.21442482448973e-052.10721241224487e-05
1420.9999475981412660.0001048037174683495.24018587341747e-05
1430.9998838095456480.0002323809087038650.000116190454351932
1440.9997360974655570.0005278050688850080.000263902534442504
1450.9995126668441770.0009746663116468550.000487333155823427
1460.9988951873330170.002209625333966940.00110481266698347
1470.9975343596056940.00493128078861220.0024656403943061
1480.9969841623235720.006031675352856270.00301583767642813
1490.9940657579544620.01186848409107590.00593424204553793
1500.988251970420450.02349605915909980.0117480295795499
1510.9769702280731790.04605954385364290.0230297719268215
1520.955593450041340.08881309991732020.0444065499586601
1530.9229436932707960.1541126134584080.0770563067292039
1540.8731950127213280.2536099745573430.126804987278672
1550.8831819598824290.2336360802351420.116818040117571
1560.9997762460977640.0004475078044711690.000223753902235585
1570.9995469553550130.0009060892899744920.000453044644987246







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level670.443708609271523NOK
5% type I error level800.529801324503311NOK
10% type I error level860.56953642384106NOK

\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 & 67 & 0.443708609271523 & NOK \tabularnewline
5% type I error level & 80 & 0.529801324503311 & NOK \tabularnewline
10% type I error level & 86 & 0.56953642384106 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145963&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]67[/C][C]0.443708609271523[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]80[/C][C]0.529801324503311[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]86[/C][C]0.56953642384106[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145963&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145963&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 testsOK/NOK
1% type I error level670.443708609271523NOK
5% type I error level800.529801324503311NOK
10% type I error level860.56953642384106NOK



Parameters (Session):
par1 = 3 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 3 ; 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')
}