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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationTue, 20 Nov 2012 11:41:17 -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/2012/Nov/20/t1353429691hyfvpf0mmdjq8sr.htm/, Retrieved Mon, 29 Apr 2024 22:07:38 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=191168, Retrieved Mon, 29 Apr 2024 22:07:38 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact58

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2010-11-17 09:55:05] [b98453cac15ba1066b407e146608df68]
- R PD    [Multiple Regression] [] [2012-11-20 16:41:17] [cff8eb8c83aab4f7b97680faed5ecb28] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
100	100	100	100
102,815	101,542	100,254	102
104,301	102,179	102,839	103,65
104,964	105,494	104,726	104,974
104,83	106,14	103,387	104,641
105,878	106,371	101,746	104,902
107,542	107,249	100,371	105,695
107,954	109,481	101,337	106,489
108,09	111,951	102,307	107,146
109,19	111,972	101,794	107,695
110,115	110,661	100,294	107,711
110,439	113,149	100,578	108,313
111,054	113,853	97,9592	108,124
112,319	115,143	100,107	109,615
113,607	116,923	102,865	111,34
112,716	116,638	102,719	110,717
113,126	116,227	103,921	111,217
112,818	115,942	105,751	111,452
112,565	116,42	106,746	111,611
112,698	113,365	108,454	111,717
113,701	112,709	107,724	112,062
113,844	115,609	108,936	112,842
114,151	115,626	109,764	113,241
114,069	116,697	108,502	113,015
114,798	119,368	109,211	113,998
114,537	120,264	113,097	114,936
114,118	118,74	112,18	114,245
113,814	116,522	114,855	114,437
115,232	116,967	114,53	115,286
115,945	118,061	115,328	116,071
117,543	118,711	117,973	117,807
118,205	119,223	117,863	118,255
119,899	119,196	116,582	118,969
121,35	120,729	117,645	120,333
122,563	121,828	120,711	121,998
124,143	122,603	121,37	123,239
126,574	123,803	120,473	124,666
128,069	127,692	122,204	126,54
128,101	128,336	124,943	127,336
128,752	128,718	125,276	127,871
129,991	130,539	130,192	130,115
133,236	132,864	131,595	132,773
134,689	134,529	133,091	134,265
135,058	135,166	133,167	134,596
135,615	133,458	131,858	134,38
136,088	135,621	132,5	135,121
136,114	137,409	131,551	135,136
136,177	138,866	131,422	135,336
136,883	135,802	131,112	135,284
139,095	139,408	131,193	137,144
141,551	142,191	136,448	140,349
144,647	146,027	138,433	143,264
147,403	145,695	136,323	144,381
148,778	148,469	137,453	145,881
149,123	152,221	137,072	146,497
150,925	157,061	139,485	148,857
152,195	160,782	142,049	150,78
155,762	164,581	141,315	153,293
159,863	171,274	145,023	157,641
164,488	177,848	148,287	162,182
172,288	185,538	147,732	167,86
181,098	193,704	151,23	175,245
186,026	203,366	150,278	179,32
191,144	213,692	154,789	184,979
196,021	220,819	153,029	188,482
200,338	225,005	157,658	192,86
202,319	229,096	161,039	195,475
204,148	233,982	165,599	198,4
205,288	234,529	171,248	200,598
206,439	238,753	172,249	202,121
210,638	238,258	177,164	205,875
212,831	241,42	174,947	207,085
214,227	242,44	179,407	209,204
216,573	248,809	181,625	212,246
217,504	254,991	188,871	215,466
219,151	255,458	189,866	216,693
220,494	261,125	192,114	219,019
220,484	258,58	189,665	217,924
220,269	257,981	191,006	217,978
222,524	257,756	186,398	218,186
221,905	257,984	189,577	218,54
222,286	252,604	190,244	217,886
219,929	251,688	190,269	216,347
222,144	255,734	196,606	219,825
224,73	257,646	197,796	221,956
228,912	263,016	205,874	227,184
231,613	265,367	206,229	229,247
235,936	271,406	208,473	233,33
239,005	278,478	211,102	236,987
242,293	284,415	211,503	240,027
248,077	287,685	218,055	245,433
248,956	287,97	221,076	246,641
252,358	290,44	226,743	250,328
254,122	292,298	223,179	250,849
255,015	296,637	219,996	251,435
253,493	299,882	223,847	252,091
255,976	292,588	227,227	252,946
255,878	292,523	226,757	252,773
254,149	290,063	223,928	250,677
252,408	296,831	220,682	250,105
252,503	296,742	227,654	251,788
253,733	296,479	218,398	250,212
252,299	295,557	213,639	248,073
248,838	288,037	212,71	244,468
247,559	287,377	217,355	244,727
245,331	290,101	217,786	244,034
242,351	296,679	218,186	243,588
238,172	285,712	206,917	236,447
226,723	270,085	197,833	224,906
225,84	261,006	194,438	221,934
225,751	266,44	202,508	224,903
226,192	267,075	196,651	223,798
220,037	263,672	191,446	218,529
220,406	259,121	190,056	217,521
223,551	262,711	190,322	219,971
223,373	265,838	203,701	223,841
224,678	265,766	200,524	223,764
223,629	269,162	200,524	223,664
220,855	256,573	191,582	217,678
220,127	257,917	195,727	218,478
215,471	253,316	194,766	214,815
214,691	257,496	194,576	215,143
216,2	264,861	198,563	218,381
219,85	257,795	201,679	219,962
220,182	251,318	201,506	218,933
220,283	243,526	204,453	218,36
216,675	247,503	206,552	217,72
217,808	256,9	205,642	219,934
217,66	261,806	205,679	220,842
217,951	260,758	204,583	220,584
215,9	244,361	204,484	216,346
217,141	255,116	208,73	220,221
219,459	256,46	210,264	222,182
222,898	258,249	214,211	225,455
225,478	256,327	214,169	226,42
228,098	259,192	213,656	228,287
230,729	260,776	219,028	231,349
230,535	261,166	217,602	231,015
229,735	265,351	220,635	232,241
233,148	261,627	222,011	233,688
235,221	266,932	224,948	236,667
237,46	268,695	225,566	238,439
239,951	274,37	219,318	239,488
240,436	275,671	213,558	238,741
241,588	270,831	214,026	238,5
241,512	275,141	225,59	242,116
243,05	277,59	227,637	243,923
246,469	276,357	229	245,813
248,64	279,389	226,841	247,143
251,147	274,787	221,488	246,381

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time10 seconds
R Server'Gwilym Jenkins' @ jenkins.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 & 10 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191168&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]10 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191168&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191168&T=0

As an alternative you can also use a QR Code:  
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 time10 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net






Multiple Linear Regression - Estimated Regression Equation
TOT[t] = + 0.554470836380811 + 0.603776119890983SMF[t] + 0.140659978436731SSF[t] + 0.250930273088723NS[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TOT[t] =  +  0.554470836380811 +  0.603776119890983SMF[t] +  0.140659978436731SSF[t] +  0.250930273088723NS[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191168&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TOT[t] =  +  0.554470836380811 +  0.603776119890983SMF[t] +  0.140659978436731SSF[t] +  0.250930273088723NS[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191168&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191168&T=1

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Estimated Regression Equation
TOT[t] = + 0.554470836380811 + 0.603776119890983SMF[t] + 0.140659978436731SSF[t] + 0.250930273088723NS[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.5544708363808110.1203684.60659e-064e-06
SMF0.6037761198909830.004448135.755900
SSF0.1406599784367310.00292748.058800
NS0.2509302730887230.002144117.013700

\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) & 0.554470836380811 & 0.120368 & 4.6065 & 9e-06 & 4e-06 \tabularnewline
SMF & 0.603776119890983 & 0.004448 & 135.7559 & 0 & 0 \tabularnewline
SSF & 0.140659978436731 & 0.002927 & 48.0588 & 0 & 0 \tabularnewline
NS & 0.250930273088723 & 0.002144 & 117.0137 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191168&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]0.554470836380811[/C][C]0.120368[/C][C]4.6065[/C][C]9e-06[/C][C]4e-06[/C][/ROW]
[ROW][C]SMF[/C][C]0.603776119890983[/C][C]0.004448[/C][C]135.7559[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]SSF[/C][C]0.140659978436731[/C][C]0.002927[/C][C]48.0588[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]NS[/C][C]0.250930273088723[/C][C]0.002144[/C][C]117.0137[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191168&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191168&T=2

As an alternative you can also use a QR Code:  
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)0.5544708363808110.1203684.60659e-064e-06
SMF0.6037761198909830.004448135.755900
SSF0.1406599784367310.00292748.058800
NS0.2509302730887230.002144117.013700






Multiple Linear Regression - Regression Statistics
Multiple R0.999992807831922
R-squared0.999985615715572
Adjusted R-squared0.999985320148083
F-TEST (value)3383273.38241475
F-TEST (DF numerator)3
F-TEST (DF denominator)146
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.204753324074159
Sum Squared Residuals6.12089286303498

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.999992807831922 \tabularnewline
R-squared & 0.999985615715572 \tabularnewline
Adjusted R-squared & 0.999985320148083 \tabularnewline
F-TEST (value) & 3383273.38241475 \tabularnewline
F-TEST (DF numerator) & 3 \tabularnewline
F-TEST (DF denominator) & 146 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.204753324074159 \tabularnewline
Sum Squared Residuals & 6.12089286303498 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191168&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.999992807831922[/C][/ROW]
[ROW][C]R-squared[/C][C]0.999985615715572[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.999985320148083[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]3383273.38241475[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]3[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]146[/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]0.204753324074159[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]6.12089286303498[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191168&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191168&T=3

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Regression Statistics
Multiple R0.999992807831922
R-squared0.999985615715572
Adjusted R-squared0.999985320148083
F-TEST (value)3383273.38241475
F-TEST (DF numerator)3
F-TEST (DF denominator)146
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.204753324074159
Sum Squared Residuals6.12089286303498






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1100100.091107978024-0.0911079780243972
2102102.071371731632-0.0713717316316115
3103.65103.706838207988-0.0568382079881307
4104.974105.046935029312-0.0729350293120359
5104.641104.720899739651-0.0798997396509742
6104.902104.974372990177-0.0723729901770181
7105.695105.757526789246-0.0625267892460743
8106.489106.562634266316-0.0736342663156355
9107.146107.235580330256-0.0895803302556037
10107.695107.773960691588-0.0789606915883473
11107.711107.771652961124-0.0606529611238597
12108.313108.388502647876-0.0755026478763158
13108.124108.201713387264-0.0777133872639922
14109.615109.685889591649-0.0708895916494314
15111.34111.405993688865-0.0659936888650848
16110.717110.791305252317-0.0743052523167984
17111.217111.282660398587-0.0656603985872617
18111.452111.515811659559-0.0638116595587272
19111.611111.679967392642-0.0689673926423379
20111.717111.759142288899-0.0421422888991677
21112.062112.089277691941-0.0272776919405687
22112.842112.887659105535-0.0456591055350281
23113.241113.283179860092-0.0421798600924466
24113.015113.067643050529-0.0526430505291557
25113.998114.061408207954-0.0634082079540899
26114.936115.004969022565-0.0689690225646308
27114.245114.30751796077-0.0625179607703714
28114.437114.483224668663-0.0462246686631883
29115.286115.320420558319-0.0344205583191092
30116.071116.105037306136-0.0340373061359615
31117.807117.825011104025-0.0180111040253013
32118.255118.269126474313-0.0141264743129846
33118.969118.9666837221640.00231627783613826
34120.333120.3251334993620.00786650063750944
35121.998121.9814514663820.0165485336177504
36123.239123.2097922690640.0292077309360662
37124.666124.6212795356820.0447204643175857
38126.54126.5053117937760.034688206223558
39127.336127.3025156737160.0334843262837629
40127.871127.8328658204670.0381341795333484
41130.115130.0706594762490.0443405237509795
42132.773132.7090026083040.0639973916958607
43134.265134.1958798631440.0691201368563661
44134.596134.5273443584020.0686556415976705
45134.38134.2949326865390.0850673134614486
46135.121135.0458635599290.0751364400714272
47135.136135.0749289513290.0610710486705723
48135.336135.2855384302360.0504615697635877
49135.284135.2030338122920.0809661877081743
50137.144137.0661318238540.0778681761462983
51140.349140.2591012793770.0898987206233708
52143.264143.1660604159240.0979395840764952
53144.381144.2539054132850.127094586715131
54145.881145.7578395669090.123160433091286
55146.497146.3982941333190.0987058666810998
56148.857148.7725877459590.0844122540406527
57150.78150.7061644181830.0738355818165579
58153.293153.2100182754690.0829817245314101
59157.641157.5579908314320.0830091685684487
60162.182162.0941904955320.087809504467982
61167.86167.7460531632960.113946836704119
62175.245175.0917042587140.153295741285854
63179.32179.1872840692120.132715930787851
64184.979184.8618116500550.117188349944914
65188.482188.3672741724460.114725827554161
66192.86192.7241345858790.135865414120945
67195.475195.3440503044810.130949695519251
68198.4198.2798635276880.120136472312206
69200.598200.4626144252470.135385574753396
70202.121202.002889691520.118110308480319
71205.875205.7018412218470.17315877815317
72207.085206.9143766891470.17062331085302
73209.204209.0198703484960.184129651504021
74212.246211.8887558741350.35724412586545
75215.466215.138672187250.327327812750191
76216.693216.4484552883640.244544711636501
77219.019218.6205379690810.39846203091851
78217.924217.6419923239670.282007676033185
79217.978217.7644226273190.21357737268137
80218.186217.9380025841320.247997415868313
81218.54218.3940429791520.145957020848188
82217.886218.034701488991-0.148701488990848
83216.347216.489029890987-0.14202989098696
84219.825219.985649409864-0.160649409863758
85221.956222.114563359648-0.15856335964844
86227.184227.421913923248-0.237913923248485
87229.247229.472485079325-0.225485079325266
88233.33233.495142388205-0.165142388204502
89236.987237.002574355605-0.0155743556047465
90240.027239.9235115692940.10348843070624
91245.433245.519805925509-0.0868059255086306
92246.641246.848673583748-0.207673583748301
93250.328250.67217194795-0.344171947949936
94250.849251.104263770085-0.255263770084889
95251.435251.455048432343-0.0200484323430785
96252.091251.9588752895610.132124710439137
97252.946253.280221835573-0.334221835572564
98252.773253.093971648873-0.320971648873151
99250.677250.994137448059-0.317137448059293
100250.105250.0804302909430.0245697090571143
101251.788251.874756148226-0.0867561482262184
102250.212250.257796593654-0.0457965936540713
103248.073248.0681159679820.0048840320175077
104244.468244.687569555496-0.219569555496165
105244.727244.988075430884-0.261075430884467
106244.034244.13417096473-0.100170964730257
107243.588243.3605515748470.227448425152572
108236.447236.467019938871-0.0200199388705524
109224.906225.07684305847-0.17084305846994
110221.934222.414748523243-0.480748523242911
111224.903225.150366075224-0.247366075223814
112223.798224.036251820922-0.238251820922401
113218.529218.535251824946-0.00625182494640918
114217.521217.769108571727-0.248108571727302
115219.971220.239701244014-0.26870124401388
116223.841223.929268970899-0.0882689708989691
117223.764223.909863811306-0.145863811306387
118223.664223.754183948312-0.0901839483118927
119217.678218.064722021235-0.386722021234932
120218.478218.854325998926-0.376325998926023
121214.815215.154823831488-0.339823831487951
122215.143215.224160415952-0.0811604159516569
123218.381218.1716783208580.209321679141601
124219.962220.163456481771-0.201456481771034
125218.933219.409444535996-0.476444535995782
126218.36219.113894886918-0.753894886918212
127217.72218.021578023808-0.301578023807676
128219.934219.7990916365030.134908363496632
129220.842220.4090950450740.432904954925626
130220.584220.1623626592560.421637340744278
131216.346216.592774073896-0.246774073896474
132220.221219.9203082463030.300691753697073
133222.182221.8938353421470.28816465785268
134225.455225.2122839077570.242716092243112
135226.42226.489138747051-0.0691387470505351
136228.287228.345295789292-0.0582957892916089
137231.349231.504633593601-0.155633593601202
138231.015231.084531848508-0.0695318485081521
139232.241231.9512444806310.289755519368842
140233.688233.833394673891-0.145394673890803
141236.667236.5682059680930.0987940319067558
142238.439238.3231191512820.115880848718051
143239.488239.05755849730.430441502699525
144238.741238.0880301744030.652969825597271
145238.5238.2202213366890.279778663311093
146242.116241.6823365366380.433663463362516
147243.923243.4690747652340.453925234766004
148245.813245.7019695279490.111030472051273
149247.143246.8974900792540.245509920746357
150246.381246.420609839211-0.0396098392105676

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 100 & 100.091107978024 & -0.0911079780243972 \tabularnewline
2 & 102 & 102.071371731632 & -0.0713717316316115 \tabularnewline
3 & 103.65 & 103.706838207988 & -0.0568382079881307 \tabularnewline
4 & 104.974 & 105.046935029312 & -0.0729350293120359 \tabularnewline
5 & 104.641 & 104.720899739651 & -0.0798997396509742 \tabularnewline
6 & 104.902 & 104.974372990177 & -0.0723729901770181 \tabularnewline
7 & 105.695 & 105.757526789246 & -0.0625267892460743 \tabularnewline
8 & 106.489 & 106.562634266316 & -0.0736342663156355 \tabularnewline
9 & 107.146 & 107.235580330256 & -0.0895803302556037 \tabularnewline
10 & 107.695 & 107.773960691588 & -0.0789606915883473 \tabularnewline
11 & 107.711 & 107.771652961124 & -0.0606529611238597 \tabularnewline
12 & 108.313 & 108.388502647876 & -0.0755026478763158 \tabularnewline
13 & 108.124 & 108.201713387264 & -0.0777133872639922 \tabularnewline
14 & 109.615 & 109.685889591649 & -0.0708895916494314 \tabularnewline
15 & 111.34 & 111.405993688865 & -0.0659936888650848 \tabularnewline
16 & 110.717 & 110.791305252317 & -0.0743052523167984 \tabularnewline
17 & 111.217 & 111.282660398587 & -0.0656603985872617 \tabularnewline
18 & 111.452 & 111.515811659559 & -0.0638116595587272 \tabularnewline
19 & 111.611 & 111.679967392642 & -0.0689673926423379 \tabularnewline
20 & 111.717 & 111.759142288899 & -0.0421422888991677 \tabularnewline
21 & 112.062 & 112.089277691941 & -0.0272776919405687 \tabularnewline
22 & 112.842 & 112.887659105535 & -0.0456591055350281 \tabularnewline
23 & 113.241 & 113.283179860092 & -0.0421798600924466 \tabularnewline
24 & 113.015 & 113.067643050529 & -0.0526430505291557 \tabularnewline
25 & 113.998 & 114.061408207954 & -0.0634082079540899 \tabularnewline
26 & 114.936 & 115.004969022565 & -0.0689690225646308 \tabularnewline
27 & 114.245 & 114.30751796077 & -0.0625179607703714 \tabularnewline
28 & 114.437 & 114.483224668663 & -0.0462246686631883 \tabularnewline
29 & 115.286 & 115.320420558319 & -0.0344205583191092 \tabularnewline
30 & 116.071 & 116.105037306136 & -0.0340373061359615 \tabularnewline
31 & 117.807 & 117.825011104025 & -0.0180111040253013 \tabularnewline
32 & 118.255 & 118.269126474313 & -0.0141264743129846 \tabularnewline
33 & 118.969 & 118.966683722164 & 0.00231627783613826 \tabularnewline
34 & 120.333 & 120.325133499362 & 0.00786650063750944 \tabularnewline
35 & 121.998 & 121.981451466382 & 0.0165485336177504 \tabularnewline
36 & 123.239 & 123.209792269064 & 0.0292077309360662 \tabularnewline
37 & 124.666 & 124.621279535682 & 0.0447204643175857 \tabularnewline
38 & 126.54 & 126.505311793776 & 0.034688206223558 \tabularnewline
39 & 127.336 & 127.302515673716 & 0.0334843262837629 \tabularnewline
40 & 127.871 & 127.832865820467 & 0.0381341795333484 \tabularnewline
41 & 130.115 & 130.070659476249 & 0.0443405237509795 \tabularnewline
42 & 132.773 & 132.709002608304 & 0.0639973916958607 \tabularnewline
43 & 134.265 & 134.195879863144 & 0.0691201368563661 \tabularnewline
44 & 134.596 & 134.527344358402 & 0.0686556415976705 \tabularnewline
45 & 134.38 & 134.294932686539 & 0.0850673134614486 \tabularnewline
46 & 135.121 & 135.045863559929 & 0.0751364400714272 \tabularnewline
47 & 135.136 & 135.074928951329 & 0.0610710486705723 \tabularnewline
48 & 135.336 & 135.285538430236 & 0.0504615697635877 \tabularnewline
49 & 135.284 & 135.203033812292 & 0.0809661877081743 \tabularnewline
50 & 137.144 & 137.066131823854 & 0.0778681761462983 \tabularnewline
51 & 140.349 & 140.259101279377 & 0.0898987206233708 \tabularnewline
52 & 143.264 & 143.166060415924 & 0.0979395840764952 \tabularnewline
53 & 144.381 & 144.253905413285 & 0.127094586715131 \tabularnewline
54 & 145.881 & 145.757839566909 & 0.123160433091286 \tabularnewline
55 & 146.497 & 146.398294133319 & 0.0987058666810998 \tabularnewline
56 & 148.857 & 148.772587745959 & 0.0844122540406527 \tabularnewline
57 & 150.78 & 150.706164418183 & 0.0738355818165579 \tabularnewline
58 & 153.293 & 153.210018275469 & 0.0829817245314101 \tabularnewline
59 & 157.641 & 157.557990831432 & 0.0830091685684487 \tabularnewline
60 & 162.182 & 162.094190495532 & 0.087809504467982 \tabularnewline
61 & 167.86 & 167.746053163296 & 0.113946836704119 \tabularnewline
62 & 175.245 & 175.091704258714 & 0.153295741285854 \tabularnewline
63 & 179.32 & 179.187284069212 & 0.132715930787851 \tabularnewline
64 & 184.979 & 184.861811650055 & 0.117188349944914 \tabularnewline
65 & 188.482 & 188.367274172446 & 0.114725827554161 \tabularnewline
66 & 192.86 & 192.724134585879 & 0.135865414120945 \tabularnewline
67 & 195.475 & 195.344050304481 & 0.130949695519251 \tabularnewline
68 & 198.4 & 198.279863527688 & 0.120136472312206 \tabularnewline
69 & 200.598 & 200.462614425247 & 0.135385574753396 \tabularnewline
70 & 202.121 & 202.00288969152 & 0.118110308480319 \tabularnewline
71 & 205.875 & 205.701841221847 & 0.17315877815317 \tabularnewline
72 & 207.085 & 206.914376689147 & 0.17062331085302 \tabularnewline
73 & 209.204 & 209.019870348496 & 0.184129651504021 \tabularnewline
74 & 212.246 & 211.888755874135 & 0.35724412586545 \tabularnewline
75 & 215.466 & 215.13867218725 & 0.327327812750191 \tabularnewline
76 & 216.693 & 216.448455288364 & 0.244544711636501 \tabularnewline
77 & 219.019 & 218.620537969081 & 0.39846203091851 \tabularnewline
78 & 217.924 & 217.641992323967 & 0.282007676033185 \tabularnewline
79 & 217.978 & 217.764422627319 & 0.21357737268137 \tabularnewline
80 & 218.186 & 217.938002584132 & 0.247997415868313 \tabularnewline
81 & 218.54 & 218.394042979152 & 0.145957020848188 \tabularnewline
82 & 217.886 & 218.034701488991 & -0.148701488990848 \tabularnewline
83 & 216.347 & 216.489029890987 & -0.14202989098696 \tabularnewline
84 & 219.825 & 219.985649409864 & -0.160649409863758 \tabularnewline
85 & 221.956 & 222.114563359648 & -0.15856335964844 \tabularnewline
86 & 227.184 & 227.421913923248 & -0.237913923248485 \tabularnewline
87 & 229.247 & 229.472485079325 & -0.225485079325266 \tabularnewline
88 & 233.33 & 233.495142388205 & -0.165142388204502 \tabularnewline
89 & 236.987 & 237.002574355605 & -0.0155743556047465 \tabularnewline
90 & 240.027 & 239.923511569294 & 0.10348843070624 \tabularnewline
91 & 245.433 & 245.519805925509 & -0.0868059255086306 \tabularnewline
92 & 246.641 & 246.848673583748 & -0.207673583748301 \tabularnewline
93 & 250.328 & 250.67217194795 & -0.344171947949936 \tabularnewline
94 & 250.849 & 251.104263770085 & -0.255263770084889 \tabularnewline
95 & 251.435 & 251.455048432343 & -0.0200484323430785 \tabularnewline
96 & 252.091 & 251.958875289561 & 0.132124710439137 \tabularnewline
97 & 252.946 & 253.280221835573 & -0.334221835572564 \tabularnewline
98 & 252.773 & 253.093971648873 & -0.320971648873151 \tabularnewline
99 & 250.677 & 250.994137448059 & -0.317137448059293 \tabularnewline
100 & 250.105 & 250.080430290943 & 0.0245697090571143 \tabularnewline
101 & 251.788 & 251.874756148226 & -0.0867561482262184 \tabularnewline
102 & 250.212 & 250.257796593654 & -0.0457965936540713 \tabularnewline
103 & 248.073 & 248.068115967982 & 0.0048840320175077 \tabularnewline
104 & 244.468 & 244.687569555496 & -0.219569555496165 \tabularnewline
105 & 244.727 & 244.988075430884 & -0.261075430884467 \tabularnewline
106 & 244.034 & 244.13417096473 & -0.100170964730257 \tabularnewline
107 & 243.588 & 243.360551574847 & 0.227448425152572 \tabularnewline
108 & 236.447 & 236.467019938871 & -0.0200199388705524 \tabularnewline
109 & 224.906 & 225.07684305847 & -0.17084305846994 \tabularnewline
110 & 221.934 & 222.414748523243 & -0.480748523242911 \tabularnewline
111 & 224.903 & 225.150366075224 & -0.247366075223814 \tabularnewline
112 & 223.798 & 224.036251820922 & -0.238251820922401 \tabularnewline
113 & 218.529 & 218.535251824946 & -0.00625182494640918 \tabularnewline
114 & 217.521 & 217.769108571727 & -0.248108571727302 \tabularnewline
115 & 219.971 & 220.239701244014 & -0.26870124401388 \tabularnewline
116 & 223.841 & 223.929268970899 & -0.0882689708989691 \tabularnewline
117 & 223.764 & 223.909863811306 & -0.145863811306387 \tabularnewline
118 & 223.664 & 223.754183948312 & -0.0901839483118927 \tabularnewline
119 & 217.678 & 218.064722021235 & -0.386722021234932 \tabularnewline
120 & 218.478 & 218.854325998926 & -0.376325998926023 \tabularnewline
121 & 214.815 & 215.154823831488 & -0.339823831487951 \tabularnewline
122 & 215.143 & 215.224160415952 & -0.0811604159516569 \tabularnewline
123 & 218.381 & 218.171678320858 & 0.209321679141601 \tabularnewline
124 & 219.962 & 220.163456481771 & -0.201456481771034 \tabularnewline
125 & 218.933 & 219.409444535996 & -0.476444535995782 \tabularnewline
126 & 218.36 & 219.113894886918 & -0.753894886918212 \tabularnewline
127 & 217.72 & 218.021578023808 & -0.301578023807676 \tabularnewline
128 & 219.934 & 219.799091636503 & 0.134908363496632 \tabularnewline
129 & 220.842 & 220.409095045074 & 0.432904954925626 \tabularnewline
130 & 220.584 & 220.162362659256 & 0.421637340744278 \tabularnewline
131 & 216.346 & 216.592774073896 & -0.246774073896474 \tabularnewline
132 & 220.221 & 219.920308246303 & 0.300691753697073 \tabularnewline
133 & 222.182 & 221.893835342147 & 0.28816465785268 \tabularnewline
134 & 225.455 & 225.212283907757 & 0.242716092243112 \tabularnewline
135 & 226.42 & 226.489138747051 & -0.0691387470505351 \tabularnewline
136 & 228.287 & 228.345295789292 & -0.0582957892916089 \tabularnewline
137 & 231.349 & 231.504633593601 & -0.155633593601202 \tabularnewline
138 & 231.015 & 231.084531848508 & -0.0695318485081521 \tabularnewline
139 & 232.241 & 231.951244480631 & 0.289755519368842 \tabularnewline
140 & 233.688 & 233.833394673891 & -0.145394673890803 \tabularnewline
141 & 236.667 & 236.568205968093 & 0.0987940319067558 \tabularnewline
142 & 238.439 & 238.323119151282 & 0.115880848718051 \tabularnewline
143 & 239.488 & 239.0575584973 & 0.430441502699525 \tabularnewline
144 & 238.741 & 238.088030174403 & 0.652969825597271 \tabularnewline
145 & 238.5 & 238.220221336689 & 0.279778663311093 \tabularnewline
146 & 242.116 & 241.682336536638 & 0.433663463362516 \tabularnewline
147 & 243.923 & 243.469074765234 & 0.453925234766004 \tabularnewline
148 & 245.813 & 245.701969527949 & 0.111030472051273 \tabularnewline
149 & 247.143 & 246.897490079254 & 0.245509920746357 \tabularnewline
150 & 246.381 & 246.420609839211 & -0.0396098392105676 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191168&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]100[/C][C]100.091107978024[/C][C]-0.0911079780243972[/C][/ROW]
[ROW][C]2[/C][C]102[/C][C]102.071371731632[/C][C]-0.0713717316316115[/C][/ROW]
[ROW][C]3[/C][C]103.65[/C][C]103.706838207988[/C][C]-0.0568382079881307[/C][/ROW]
[ROW][C]4[/C][C]104.974[/C][C]105.046935029312[/C][C]-0.0729350293120359[/C][/ROW]
[ROW][C]5[/C][C]104.641[/C][C]104.720899739651[/C][C]-0.0798997396509742[/C][/ROW]
[ROW][C]6[/C][C]104.902[/C][C]104.974372990177[/C][C]-0.0723729901770181[/C][/ROW]
[ROW][C]7[/C][C]105.695[/C][C]105.757526789246[/C][C]-0.0625267892460743[/C][/ROW]
[ROW][C]8[/C][C]106.489[/C][C]106.562634266316[/C][C]-0.0736342663156355[/C][/ROW]
[ROW][C]9[/C][C]107.146[/C][C]107.235580330256[/C][C]-0.0895803302556037[/C][/ROW]
[ROW][C]10[/C][C]107.695[/C][C]107.773960691588[/C][C]-0.0789606915883473[/C][/ROW]
[ROW][C]11[/C][C]107.711[/C][C]107.771652961124[/C][C]-0.0606529611238597[/C][/ROW]
[ROW][C]12[/C][C]108.313[/C][C]108.388502647876[/C][C]-0.0755026478763158[/C][/ROW]
[ROW][C]13[/C][C]108.124[/C][C]108.201713387264[/C][C]-0.0777133872639922[/C][/ROW]
[ROW][C]14[/C][C]109.615[/C][C]109.685889591649[/C][C]-0.0708895916494314[/C][/ROW]
[ROW][C]15[/C][C]111.34[/C][C]111.405993688865[/C][C]-0.0659936888650848[/C][/ROW]
[ROW][C]16[/C][C]110.717[/C][C]110.791305252317[/C][C]-0.0743052523167984[/C][/ROW]
[ROW][C]17[/C][C]111.217[/C][C]111.282660398587[/C][C]-0.0656603985872617[/C][/ROW]
[ROW][C]18[/C][C]111.452[/C][C]111.515811659559[/C][C]-0.0638116595587272[/C][/ROW]
[ROW][C]19[/C][C]111.611[/C][C]111.679967392642[/C][C]-0.0689673926423379[/C][/ROW]
[ROW][C]20[/C][C]111.717[/C][C]111.759142288899[/C][C]-0.0421422888991677[/C][/ROW]
[ROW][C]21[/C][C]112.062[/C][C]112.089277691941[/C][C]-0.0272776919405687[/C][/ROW]
[ROW][C]22[/C][C]112.842[/C][C]112.887659105535[/C][C]-0.0456591055350281[/C][/ROW]
[ROW][C]23[/C][C]113.241[/C][C]113.283179860092[/C][C]-0.0421798600924466[/C][/ROW]
[ROW][C]24[/C][C]113.015[/C][C]113.067643050529[/C][C]-0.0526430505291557[/C][/ROW]
[ROW][C]25[/C][C]113.998[/C][C]114.061408207954[/C][C]-0.0634082079540899[/C][/ROW]
[ROW][C]26[/C][C]114.936[/C][C]115.004969022565[/C][C]-0.0689690225646308[/C][/ROW]
[ROW][C]27[/C][C]114.245[/C][C]114.30751796077[/C][C]-0.0625179607703714[/C][/ROW]
[ROW][C]28[/C][C]114.437[/C][C]114.483224668663[/C][C]-0.0462246686631883[/C][/ROW]
[ROW][C]29[/C][C]115.286[/C][C]115.320420558319[/C][C]-0.0344205583191092[/C][/ROW]
[ROW][C]30[/C][C]116.071[/C][C]116.105037306136[/C][C]-0.0340373061359615[/C][/ROW]
[ROW][C]31[/C][C]117.807[/C][C]117.825011104025[/C][C]-0.0180111040253013[/C][/ROW]
[ROW][C]32[/C][C]118.255[/C][C]118.269126474313[/C][C]-0.0141264743129846[/C][/ROW]
[ROW][C]33[/C][C]118.969[/C][C]118.966683722164[/C][C]0.00231627783613826[/C][/ROW]
[ROW][C]34[/C][C]120.333[/C][C]120.325133499362[/C][C]0.00786650063750944[/C][/ROW]
[ROW][C]35[/C][C]121.998[/C][C]121.981451466382[/C][C]0.0165485336177504[/C][/ROW]
[ROW][C]36[/C][C]123.239[/C][C]123.209792269064[/C][C]0.0292077309360662[/C][/ROW]
[ROW][C]37[/C][C]124.666[/C][C]124.621279535682[/C][C]0.0447204643175857[/C][/ROW]
[ROW][C]38[/C][C]126.54[/C][C]126.505311793776[/C][C]0.034688206223558[/C][/ROW]
[ROW][C]39[/C][C]127.336[/C][C]127.302515673716[/C][C]0.0334843262837629[/C][/ROW]
[ROW][C]40[/C][C]127.871[/C][C]127.832865820467[/C][C]0.0381341795333484[/C][/ROW]
[ROW][C]41[/C][C]130.115[/C][C]130.070659476249[/C][C]0.0443405237509795[/C][/ROW]
[ROW][C]42[/C][C]132.773[/C][C]132.709002608304[/C][C]0.0639973916958607[/C][/ROW]
[ROW][C]43[/C][C]134.265[/C][C]134.195879863144[/C][C]0.0691201368563661[/C][/ROW]
[ROW][C]44[/C][C]134.596[/C][C]134.527344358402[/C][C]0.0686556415976705[/C][/ROW]
[ROW][C]45[/C][C]134.38[/C][C]134.294932686539[/C][C]0.0850673134614486[/C][/ROW]
[ROW][C]46[/C][C]135.121[/C][C]135.045863559929[/C][C]0.0751364400714272[/C][/ROW]
[ROW][C]47[/C][C]135.136[/C][C]135.074928951329[/C][C]0.0610710486705723[/C][/ROW]
[ROW][C]48[/C][C]135.336[/C][C]135.285538430236[/C][C]0.0504615697635877[/C][/ROW]
[ROW][C]49[/C][C]135.284[/C][C]135.203033812292[/C][C]0.0809661877081743[/C][/ROW]
[ROW][C]50[/C][C]137.144[/C][C]137.066131823854[/C][C]0.0778681761462983[/C][/ROW]
[ROW][C]51[/C][C]140.349[/C][C]140.259101279377[/C][C]0.0898987206233708[/C][/ROW]
[ROW][C]52[/C][C]143.264[/C][C]143.166060415924[/C][C]0.0979395840764952[/C][/ROW]
[ROW][C]53[/C][C]144.381[/C][C]144.253905413285[/C][C]0.127094586715131[/C][/ROW]
[ROW][C]54[/C][C]145.881[/C][C]145.757839566909[/C][C]0.123160433091286[/C][/ROW]
[ROW][C]55[/C][C]146.497[/C][C]146.398294133319[/C][C]0.0987058666810998[/C][/ROW]
[ROW][C]56[/C][C]148.857[/C][C]148.772587745959[/C][C]0.0844122540406527[/C][/ROW]
[ROW][C]57[/C][C]150.78[/C][C]150.706164418183[/C][C]0.0738355818165579[/C][/ROW]
[ROW][C]58[/C][C]153.293[/C][C]153.210018275469[/C][C]0.0829817245314101[/C][/ROW]
[ROW][C]59[/C][C]157.641[/C][C]157.557990831432[/C][C]0.0830091685684487[/C][/ROW]
[ROW][C]60[/C][C]162.182[/C][C]162.094190495532[/C][C]0.087809504467982[/C][/ROW]
[ROW][C]61[/C][C]167.86[/C][C]167.746053163296[/C][C]0.113946836704119[/C][/ROW]
[ROW][C]62[/C][C]175.245[/C][C]175.091704258714[/C][C]0.153295741285854[/C][/ROW]
[ROW][C]63[/C][C]179.32[/C][C]179.187284069212[/C][C]0.132715930787851[/C][/ROW]
[ROW][C]64[/C][C]184.979[/C][C]184.861811650055[/C][C]0.117188349944914[/C][/ROW]
[ROW][C]65[/C][C]188.482[/C][C]188.367274172446[/C][C]0.114725827554161[/C][/ROW]
[ROW][C]66[/C][C]192.86[/C][C]192.724134585879[/C][C]0.135865414120945[/C][/ROW]
[ROW][C]67[/C][C]195.475[/C][C]195.344050304481[/C][C]0.130949695519251[/C][/ROW]
[ROW][C]68[/C][C]198.4[/C][C]198.279863527688[/C][C]0.120136472312206[/C][/ROW]
[ROW][C]69[/C][C]200.598[/C][C]200.462614425247[/C][C]0.135385574753396[/C][/ROW]
[ROW][C]70[/C][C]202.121[/C][C]202.00288969152[/C][C]0.118110308480319[/C][/ROW]
[ROW][C]71[/C][C]205.875[/C][C]205.701841221847[/C][C]0.17315877815317[/C][/ROW]
[ROW][C]72[/C][C]207.085[/C][C]206.914376689147[/C][C]0.17062331085302[/C][/ROW]
[ROW][C]73[/C][C]209.204[/C][C]209.019870348496[/C][C]0.184129651504021[/C][/ROW]
[ROW][C]74[/C][C]212.246[/C][C]211.888755874135[/C][C]0.35724412586545[/C][/ROW]
[ROW][C]75[/C][C]215.466[/C][C]215.13867218725[/C][C]0.327327812750191[/C][/ROW]
[ROW][C]76[/C][C]216.693[/C][C]216.448455288364[/C][C]0.244544711636501[/C][/ROW]
[ROW][C]77[/C][C]219.019[/C][C]218.620537969081[/C][C]0.39846203091851[/C][/ROW]
[ROW][C]78[/C][C]217.924[/C][C]217.641992323967[/C][C]0.282007676033185[/C][/ROW]
[ROW][C]79[/C][C]217.978[/C][C]217.764422627319[/C][C]0.21357737268137[/C][/ROW]
[ROW][C]80[/C][C]218.186[/C][C]217.938002584132[/C][C]0.247997415868313[/C][/ROW]
[ROW][C]81[/C][C]218.54[/C][C]218.394042979152[/C][C]0.145957020848188[/C][/ROW]
[ROW][C]82[/C][C]217.886[/C][C]218.034701488991[/C][C]-0.148701488990848[/C][/ROW]
[ROW][C]83[/C][C]216.347[/C][C]216.489029890987[/C][C]-0.14202989098696[/C][/ROW]
[ROW][C]84[/C][C]219.825[/C][C]219.985649409864[/C][C]-0.160649409863758[/C][/ROW]
[ROW][C]85[/C][C]221.956[/C][C]222.114563359648[/C][C]-0.15856335964844[/C][/ROW]
[ROW][C]86[/C][C]227.184[/C][C]227.421913923248[/C][C]-0.237913923248485[/C][/ROW]
[ROW][C]87[/C][C]229.247[/C][C]229.472485079325[/C][C]-0.225485079325266[/C][/ROW]
[ROW][C]88[/C][C]233.33[/C][C]233.495142388205[/C][C]-0.165142388204502[/C][/ROW]
[ROW][C]89[/C][C]236.987[/C][C]237.002574355605[/C][C]-0.0155743556047465[/C][/ROW]
[ROW][C]90[/C][C]240.027[/C][C]239.923511569294[/C][C]0.10348843070624[/C][/ROW]
[ROW][C]91[/C][C]245.433[/C][C]245.519805925509[/C][C]-0.0868059255086306[/C][/ROW]
[ROW][C]92[/C][C]246.641[/C][C]246.848673583748[/C][C]-0.207673583748301[/C][/ROW]
[ROW][C]93[/C][C]250.328[/C][C]250.67217194795[/C][C]-0.344171947949936[/C][/ROW]
[ROW][C]94[/C][C]250.849[/C][C]251.104263770085[/C][C]-0.255263770084889[/C][/ROW]
[ROW][C]95[/C][C]251.435[/C][C]251.455048432343[/C][C]-0.0200484323430785[/C][/ROW]
[ROW][C]96[/C][C]252.091[/C][C]251.958875289561[/C][C]0.132124710439137[/C][/ROW]
[ROW][C]97[/C][C]252.946[/C][C]253.280221835573[/C][C]-0.334221835572564[/C][/ROW]
[ROW][C]98[/C][C]252.773[/C][C]253.093971648873[/C][C]-0.320971648873151[/C][/ROW]
[ROW][C]99[/C][C]250.677[/C][C]250.994137448059[/C][C]-0.317137448059293[/C][/ROW]
[ROW][C]100[/C][C]250.105[/C][C]250.080430290943[/C][C]0.0245697090571143[/C][/ROW]
[ROW][C]101[/C][C]251.788[/C][C]251.874756148226[/C][C]-0.0867561482262184[/C][/ROW]
[ROW][C]102[/C][C]250.212[/C][C]250.257796593654[/C][C]-0.0457965936540713[/C][/ROW]
[ROW][C]103[/C][C]248.073[/C][C]248.068115967982[/C][C]0.0048840320175077[/C][/ROW]
[ROW][C]104[/C][C]244.468[/C][C]244.687569555496[/C][C]-0.219569555496165[/C][/ROW]
[ROW][C]105[/C][C]244.727[/C][C]244.988075430884[/C][C]-0.261075430884467[/C][/ROW]
[ROW][C]106[/C][C]244.034[/C][C]244.13417096473[/C][C]-0.100170964730257[/C][/ROW]
[ROW][C]107[/C][C]243.588[/C][C]243.360551574847[/C][C]0.227448425152572[/C][/ROW]
[ROW][C]108[/C][C]236.447[/C][C]236.467019938871[/C][C]-0.0200199388705524[/C][/ROW]
[ROW][C]109[/C][C]224.906[/C][C]225.07684305847[/C][C]-0.17084305846994[/C][/ROW]
[ROW][C]110[/C][C]221.934[/C][C]222.414748523243[/C][C]-0.480748523242911[/C][/ROW]
[ROW][C]111[/C][C]224.903[/C][C]225.150366075224[/C][C]-0.247366075223814[/C][/ROW]
[ROW][C]112[/C][C]223.798[/C][C]224.036251820922[/C][C]-0.238251820922401[/C][/ROW]
[ROW][C]113[/C][C]218.529[/C][C]218.535251824946[/C][C]-0.00625182494640918[/C][/ROW]
[ROW][C]114[/C][C]217.521[/C][C]217.769108571727[/C][C]-0.248108571727302[/C][/ROW]
[ROW][C]115[/C][C]219.971[/C][C]220.239701244014[/C][C]-0.26870124401388[/C][/ROW]
[ROW][C]116[/C][C]223.841[/C][C]223.929268970899[/C][C]-0.0882689708989691[/C][/ROW]
[ROW][C]117[/C][C]223.764[/C][C]223.909863811306[/C][C]-0.145863811306387[/C][/ROW]
[ROW][C]118[/C][C]223.664[/C][C]223.754183948312[/C][C]-0.0901839483118927[/C][/ROW]
[ROW][C]119[/C][C]217.678[/C][C]218.064722021235[/C][C]-0.386722021234932[/C][/ROW]
[ROW][C]120[/C][C]218.478[/C][C]218.854325998926[/C][C]-0.376325998926023[/C][/ROW]
[ROW][C]121[/C][C]214.815[/C][C]215.154823831488[/C][C]-0.339823831487951[/C][/ROW]
[ROW][C]122[/C][C]215.143[/C][C]215.224160415952[/C][C]-0.0811604159516569[/C][/ROW]
[ROW][C]123[/C][C]218.381[/C][C]218.171678320858[/C][C]0.209321679141601[/C][/ROW]
[ROW][C]124[/C][C]219.962[/C][C]220.163456481771[/C][C]-0.201456481771034[/C][/ROW]
[ROW][C]125[/C][C]218.933[/C][C]219.409444535996[/C][C]-0.476444535995782[/C][/ROW]
[ROW][C]126[/C][C]218.36[/C][C]219.113894886918[/C][C]-0.753894886918212[/C][/ROW]
[ROW][C]127[/C][C]217.72[/C][C]218.021578023808[/C][C]-0.301578023807676[/C][/ROW]
[ROW][C]128[/C][C]219.934[/C][C]219.799091636503[/C][C]0.134908363496632[/C][/ROW]
[ROW][C]129[/C][C]220.842[/C][C]220.409095045074[/C][C]0.432904954925626[/C][/ROW]
[ROW][C]130[/C][C]220.584[/C][C]220.162362659256[/C][C]0.421637340744278[/C][/ROW]
[ROW][C]131[/C][C]216.346[/C][C]216.592774073896[/C][C]-0.246774073896474[/C][/ROW]
[ROW][C]132[/C][C]220.221[/C][C]219.920308246303[/C][C]0.300691753697073[/C][/ROW]
[ROW][C]133[/C][C]222.182[/C][C]221.893835342147[/C][C]0.28816465785268[/C][/ROW]
[ROW][C]134[/C][C]225.455[/C][C]225.212283907757[/C][C]0.242716092243112[/C][/ROW]
[ROW][C]135[/C][C]226.42[/C][C]226.489138747051[/C][C]-0.0691387470505351[/C][/ROW]
[ROW][C]136[/C][C]228.287[/C][C]228.345295789292[/C][C]-0.0582957892916089[/C][/ROW]
[ROW][C]137[/C][C]231.349[/C][C]231.504633593601[/C][C]-0.155633593601202[/C][/ROW]
[ROW][C]138[/C][C]231.015[/C][C]231.084531848508[/C][C]-0.0695318485081521[/C][/ROW]
[ROW][C]139[/C][C]232.241[/C][C]231.951244480631[/C][C]0.289755519368842[/C][/ROW]
[ROW][C]140[/C][C]233.688[/C][C]233.833394673891[/C][C]-0.145394673890803[/C][/ROW]
[ROW][C]141[/C][C]236.667[/C][C]236.568205968093[/C][C]0.0987940319067558[/C][/ROW]
[ROW][C]142[/C][C]238.439[/C][C]238.323119151282[/C][C]0.115880848718051[/C][/ROW]
[ROW][C]143[/C][C]239.488[/C][C]239.0575584973[/C][C]0.430441502699525[/C][/ROW]
[ROW][C]144[/C][C]238.741[/C][C]238.088030174403[/C][C]0.652969825597271[/C][/ROW]
[ROW][C]145[/C][C]238.5[/C][C]238.220221336689[/C][C]0.279778663311093[/C][/ROW]
[ROW][C]146[/C][C]242.116[/C][C]241.682336536638[/C][C]0.433663463362516[/C][/ROW]
[ROW][C]147[/C][C]243.923[/C][C]243.469074765234[/C][C]0.453925234766004[/C][/ROW]
[ROW][C]148[/C][C]245.813[/C][C]245.701969527949[/C][C]0.111030472051273[/C][/ROW]
[ROW][C]149[/C][C]247.143[/C][C]246.897490079254[/C][C]0.245509920746357[/C][/ROW]
[ROW][C]150[/C][C]246.381[/C][C]246.420609839211[/C][C]-0.0396098392105676[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191168&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191168&T=4

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1100100.091107978024-0.0911079780243972
2102102.071371731632-0.0713717316316115
3103.65103.706838207988-0.0568382079881307
4104.974105.046935029312-0.0729350293120359
5104.641104.720899739651-0.0798997396509742
6104.902104.974372990177-0.0723729901770181
7105.695105.757526789246-0.0625267892460743
8106.489106.562634266316-0.0736342663156355
9107.146107.235580330256-0.0895803302556037
10107.695107.773960691588-0.0789606915883473
11107.711107.771652961124-0.0606529611238597
12108.313108.388502647876-0.0755026478763158
13108.124108.201713387264-0.0777133872639922
14109.615109.685889591649-0.0708895916494314
15111.34111.405993688865-0.0659936888650848
16110.717110.791305252317-0.0743052523167984
17111.217111.282660398587-0.0656603985872617
18111.452111.515811659559-0.0638116595587272
19111.611111.679967392642-0.0689673926423379
20111.717111.759142288899-0.0421422888991677
21112.062112.089277691941-0.0272776919405687
22112.842112.887659105535-0.0456591055350281
23113.241113.283179860092-0.0421798600924466
24113.015113.067643050529-0.0526430505291557
25113.998114.061408207954-0.0634082079540899
26114.936115.004969022565-0.0689690225646308
27114.245114.30751796077-0.0625179607703714
28114.437114.483224668663-0.0462246686631883
29115.286115.320420558319-0.0344205583191092
30116.071116.105037306136-0.0340373061359615
31117.807117.825011104025-0.0180111040253013
32118.255118.269126474313-0.0141264743129846
33118.969118.9666837221640.00231627783613826
34120.333120.3251334993620.00786650063750944
35121.998121.9814514663820.0165485336177504
36123.239123.2097922690640.0292077309360662
37124.666124.6212795356820.0447204643175857
38126.54126.5053117937760.034688206223558
39127.336127.3025156737160.0334843262837629
40127.871127.8328658204670.0381341795333484
41130.115130.0706594762490.0443405237509795
42132.773132.7090026083040.0639973916958607
43134.265134.1958798631440.0691201368563661
44134.596134.5273443584020.0686556415976705
45134.38134.2949326865390.0850673134614486
46135.121135.0458635599290.0751364400714272
47135.136135.0749289513290.0610710486705723
48135.336135.2855384302360.0504615697635877
49135.284135.2030338122920.0809661877081743
50137.144137.0661318238540.0778681761462983
51140.349140.2591012793770.0898987206233708
52143.264143.1660604159240.0979395840764952
53144.381144.2539054132850.127094586715131
54145.881145.7578395669090.123160433091286
55146.497146.3982941333190.0987058666810998
56148.857148.7725877459590.0844122540406527
57150.78150.7061644181830.0738355818165579
58153.293153.2100182754690.0829817245314101
59157.641157.5579908314320.0830091685684487
60162.182162.0941904955320.087809504467982
61167.86167.7460531632960.113946836704119
62175.245175.0917042587140.153295741285854
63179.32179.1872840692120.132715930787851
64184.979184.8618116500550.117188349944914
65188.482188.3672741724460.114725827554161
66192.86192.7241345858790.135865414120945
67195.475195.3440503044810.130949695519251
68198.4198.2798635276880.120136472312206
69200.598200.4626144252470.135385574753396
70202.121202.002889691520.118110308480319
71205.875205.7018412218470.17315877815317
72207.085206.9143766891470.17062331085302
73209.204209.0198703484960.184129651504021
74212.246211.8887558741350.35724412586545
75215.466215.138672187250.327327812750191
76216.693216.4484552883640.244544711636501
77219.019218.6205379690810.39846203091851
78217.924217.6419923239670.282007676033185
79217.978217.7644226273190.21357737268137
80218.186217.9380025841320.247997415868313
81218.54218.3940429791520.145957020848188
82217.886218.034701488991-0.148701488990848
83216.347216.489029890987-0.14202989098696
84219.825219.985649409864-0.160649409863758
85221.956222.114563359648-0.15856335964844
86227.184227.421913923248-0.237913923248485
87229.247229.472485079325-0.225485079325266
88233.33233.495142388205-0.165142388204502
89236.987237.002574355605-0.0155743556047465
90240.027239.9235115692940.10348843070624
91245.433245.519805925509-0.0868059255086306
92246.641246.848673583748-0.207673583748301
93250.328250.67217194795-0.344171947949936
94250.849251.104263770085-0.255263770084889
95251.435251.455048432343-0.0200484323430785
96252.091251.9588752895610.132124710439137
97252.946253.280221835573-0.334221835572564
98252.773253.093971648873-0.320971648873151
99250.677250.994137448059-0.317137448059293
100250.105250.0804302909430.0245697090571143
101251.788251.874756148226-0.0867561482262184
102250.212250.257796593654-0.0457965936540713
103248.073248.0681159679820.0048840320175077
104244.468244.687569555496-0.219569555496165
105244.727244.988075430884-0.261075430884467
106244.034244.13417096473-0.100170964730257
107243.588243.3605515748470.227448425152572
108236.447236.467019938871-0.0200199388705524
109224.906225.07684305847-0.17084305846994
110221.934222.414748523243-0.480748523242911
111224.903225.150366075224-0.247366075223814
112223.798224.036251820922-0.238251820922401
113218.529218.535251824946-0.00625182494640918
114217.521217.769108571727-0.248108571727302
115219.971220.239701244014-0.26870124401388
116223.841223.929268970899-0.0882689708989691
117223.764223.909863811306-0.145863811306387
118223.664223.754183948312-0.0901839483118927
119217.678218.064722021235-0.386722021234932
120218.478218.854325998926-0.376325998926023
121214.815215.154823831488-0.339823831487951
122215.143215.224160415952-0.0811604159516569
123218.381218.1716783208580.209321679141601
124219.962220.163456481771-0.201456481771034
125218.933219.409444535996-0.476444535995782
126218.36219.113894886918-0.753894886918212
127217.72218.021578023808-0.301578023807676
128219.934219.7990916365030.134908363496632
129220.842220.4090950450740.432904954925626
130220.584220.1623626592560.421637340744278
131216.346216.592774073896-0.246774073896474
132220.221219.9203082463030.300691753697073
133222.182221.8938353421470.28816465785268
134225.455225.2122839077570.242716092243112
135226.42226.489138747051-0.0691387470505351
136228.287228.345295789292-0.0582957892916089
137231.349231.504633593601-0.155633593601202
138231.015231.084531848508-0.0695318485081521
139232.241231.9512444806310.289755519368842
140233.688233.833394673891-0.145394673890803
141236.667236.5682059680930.0987940319067558
142238.439238.3231191512820.115880848718051
143239.488239.05755849730.430441502699525
144238.741238.0880301744030.652969825597271
145238.5238.2202213366890.279778663311093
146242.116241.6823365366380.433663463362516
147243.923243.4690747652340.453925234766004
148245.813245.7019695279490.111030472051273
149247.143246.8974900792540.245509920746357
150246.381246.420609839211-0.0396098392105676






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
79.08317216186449e-091.8166344323729e-080.999999990916828
81.42207730361256e-112.84415460722513e-110.999999999985779
91.96960444102696e-143.93920888205393e-140.99999999999998
101.28603927790831e-162.57207855581663e-161
113.34991441967755e-196.6998288393551e-191
126.4318196606254e-221.28636393212508e-211
132.66022818158182e-245.32045636316363e-241
145.56976856150379e-271.11395371230076e-261
152.21602696562763e-294.43205393125525e-291
163.88215020925935e-327.76430041851871e-321
171.68211651303936e-343.36423302607872e-341
183.69992066974484e-377.39984133948968e-371
196.5816593099045e-401.3163318619809e-391
201.17555246415895e-422.3511049283179e-421
211.80425384517561e-453.60850769035122e-451
225.10172826281285e-481.02034565256257e-471
231.81171809984101e-503.62343619968201e-501
243.38381709627757e-536.76763419255515e-531
258.26562043122762e-561.65312408624552e-551
264.31677873665809e-588.63355747331618e-581
278.43656615724694e-611.68731323144939e-601
282.30737256464957e-634.61474512929914e-631
294.64398686669465e-669.2879737333893e-661
301.17313129545941e-682.34626259091883e-681
312.0240703127449e-714.0481406254898e-711
327.63987284438889e-741.52797456887778e-731
331.56291064315687e-763.12582128631373e-761
343.11997760254169e-796.23995520508338e-791
357.22473839442161e-821.44494767888432e-811
361.14949276515891e-842.29898553031782e-841
378.47555449082476e-871.69511089816495e-861
381.60670034521531e-893.21340069043062e-891
392.3845481549263e-924.7690963098526e-921
403.48325020012655e-956.9665004002531e-951
416.16549190118053e-981.23309838023611e-971
421.0687049958242e-1002.1374099916484e-1001
431.56995763633584e-1033.13991527267168e-1031
443.30765861344811e-1066.61531722689622e-1061
451.65663378875891e-1083.31326757751781e-1081
462.37679862196312e-1114.75359724392625e-1111
473.85523830926257e-1147.71047661852513e-1141
485.23741275383765e-1171.04748255076753e-1161
497.9875377185933e-1201.59750754371866e-1191
501.03420455359062e-1222.06840910718123e-1221
513.20745593987762e-1256.41491187975523e-1251
525.80641647611855e-1281.16128329522371e-1271
531.42291396102137e-1302.84582792204273e-1301
542.11665945897152e-1334.23331891794304e-1331
551.3925946761349e-1352.7851893522698e-1351
561.83430534665646e-1383.66861069331292e-1381
572.76443875077809e-1415.52887750155617e-1411
583.44895690915686e-1446.89791381831372e-1441
599.60857345775374e-1461.92171469155075e-1451
601.83044172746187e-1483.66088345492375e-1481
612.29584555861353e-1514.59169111722705e-1511
625.25115678854616e-1541.05023135770923e-1531
639.19066929687892e-1571.83813385937578e-1561
641.43646679353994e-1592.87293358707988e-1591
652.66097989146383e-1625.32195978292766e-1621
663.66781292258752e-1657.33562584517504e-1651
675.37824120636256e-1681.07564824127251e-1671
687.23671220055103e-1711.44734244011021e-1701
691.1356549353138e-1732.27130987062761e-1731
703.0822541279165e-1766.16450825583301e-1761
715.80132800616972e-1791.16026560123394e-1781
721.31250558631973e-1812.62501117263946e-1811
737.40429097849086e-1841.48085819569817e-1831
742.92131541743151e-575.84263083486301e-571
757.03639794246443e-521.40727958849289e-511
762.345313422001e-524.690626844002e-521
771.18538851769208e-462.37077703538416e-461
781.04180114596997e-462.08360229193993e-461
792.78898768990298e-465.57797537980596e-461
801.26636412149165e-452.5327282429833e-451
817.00072322768813e-431.40014464553763e-421
822.4969451165419e-264.99389023308381e-261
832.94577375515921e-205.89154751031842e-201
841.55342845891431e-163.10685691782862e-161
853.16753207148315e-146.3350641429663e-140.999999999999968
865.13548742194822e-121.02709748438964e-110.999999999994865
871.0067277895695e-102.01345557913899e-100.999999999899327
883.21574911104828e-106.43149822209655e-100.999999999678425
891.91883130370157e-103.83766260740315e-100.999999999808117
901.22362280415675e-102.4472456083135e-100.999999999877638
911.22587875434513e-102.45175750869026e-100.999999999877412
924.41777641561159e-108.83555283122319e-100.999999999558222
931.49179105717415e-082.98358211434831e-080.999999985082089
945.29955545306423e-081.05991109061285e-070.999999947004445
952.8956144709448e-085.79122894188959e-080.999999971043855
962.2683176240142e-084.5366352480284e-080.999999977316824
972.37471030362519e-074.74942060725039e-070.99999976252897
981.54518855621134e-063.09037711242268e-060.999998454811444
996.68782406123056e-061.33756481224611e-050.999993312175939
1004.54266164676134e-069.08532329352267e-060.999995457338353
1011.36690222109063e-052.73380444218125e-050.999986330977789
1021.02469141323505e-052.04938282647009e-050.999989753085868
1035.9557794681194e-061.19115589362388e-050.999994044220532
1048.79573362320771e-061.75914672464154e-050.999991204266377
1053.70936170067502e-057.41872340135005e-050.999962906382993
1060.0001359702961208540.0002719405922417080.999864029703879
1070.002320462053515840.004640924107031670.997679537946484
1080.006905443040916860.01381088608183370.993094556959083
1090.008174897393422220.01634979478684440.991825102606578
1100.02152024236593060.04304048473186120.978479757634069
1110.03709585254497590.07419170508995180.962904147455024
1120.04346397620857470.08692795241714950.956536023791425
1130.03343213421271450.06686426842542890.966567865787286
1140.03128765503004110.06257531006008210.968712344969959
1150.02978382832617120.05956765665234240.970216171673829
1160.03781726130321510.07563452260643020.962182738696785
1170.04137027028273360.08274054056546720.958629729717266
1180.11398370964550.2279674192910.8860162903545
1190.1262239334995410.2524478669990820.873776066500459
1200.1943209749955840.3886419499911670.805679025004416
1210.226569705750580.453139411501160.77343029424942
1220.2596076024502250.519215204900450.740392397549775
1230.6123334408139260.7753331183721470.387666559186074
1240.8912659190176440.2174681619647110.108734080982356
1250.9836828281472260.03263434370554860.0163171718527743
1260.994266501922810.01146699615437980.00573349807718992
1270.9926682934939220.01466341301215610.00733170650607803
1280.9962566993489540.00748660130209140.0037433006510457
1290.9994452481403570.001109503719286040.000554751859643021
1300.9999983055222283.38895554423927e-061.69447777211964e-06
1310.9999984274456723.14510865644035e-061.57255432822018e-06
1320.9999960957359887.80852802316088e-063.90426401158044e-06
1330.9999889571611052.20856777892397e-051.10428388946198e-05
1340.9999815048340823.69903318364012e-051.84951659182006e-05
1350.999943580963610.0001128380727795435.64190363897713e-05
1360.99988286431310.000234271373800460.00011713568690023
1370.9998616587030870.0002766825938256930.000138341296912847
1380.9998790734929690.0002418530140621490.000120926507031075
1390.9996205125329470.0007589749341067370.000379487467053368
1400.9987449707555860.002510058488827870.00125502924441394
1410.995547016434190.008905967131620120.00445298356581006
1420.9861199607169460.02776007856610850.0138800392830543
1430.9832517550193780.03349648996124350.0167482449806218

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
7 & 9.08317216186449e-09 & 1.8166344323729e-08 & 0.999999990916828 \tabularnewline
8 & 1.42207730361256e-11 & 2.84415460722513e-11 & 0.999999999985779 \tabularnewline
9 & 1.96960444102696e-14 & 3.93920888205393e-14 & 0.99999999999998 \tabularnewline
10 & 1.28603927790831e-16 & 2.57207855581663e-16 & 1 \tabularnewline
11 & 3.34991441967755e-19 & 6.6998288393551e-19 & 1 \tabularnewline
12 & 6.4318196606254e-22 & 1.28636393212508e-21 & 1 \tabularnewline
13 & 2.66022818158182e-24 & 5.32045636316363e-24 & 1 \tabularnewline
14 & 5.56976856150379e-27 & 1.11395371230076e-26 & 1 \tabularnewline
15 & 2.21602696562763e-29 & 4.43205393125525e-29 & 1 \tabularnewline
16 & 3.88215020925935e-32 & 7.76430041851871e-32 & 1 \tabularnewline
17 & 1.68211651303936e-34 & 3.36423302607872e-34 & 1 \tabularnewline
18 & 3.69992066974484e-37 & 7.39984133948968e-37 & 1 \tabularnewline
19 & 6.5816593099045e-40 & 1.3163318619809e-39 & 1 \tabularnewline
20 & 1.17555246415895e-42 & 2.3511049283179e-42 & 1 \tabularnewline
21 & 1.80425384517561e-45 & 3.60850769035122e-45 & 1 \tabularnewline
22 & 5.10172826281285e-48 & 1.02034565256257e-47 & 1 \tabularnewline
23 & 1.81171809984101e-50 & 3.62343619968201e-50 & 1 \tabularnewline
24 & 3.38381709627757e-53 & 6.76763419255515e-53 & 1 \tabularnewline
25 & 8.26562043122762e-56 & 1.65312408624552e-55 & 1 \tabularnewline
26 & 4.31677873665809e-58 & 8.63355747331618e-58 & 1 \tabularnewline
27 & 8.43656615724694e-61 & 1.68731323144939e-60 & 1 \tabularnewline
28 & 2.30737256464957e-63 & 4.61474512929914e-63 & 1 \tabularnewline
29 & 4.64398686669465e-66 & 9.2879737333893e-66 & 1 \tabularnewline
30 & 1.17313129545941e-68 & 2.34626259091883e-68 & 1 \tabularnewline
31 & 2.0240703127449e-71 & 4.0481406254898e-71 & 1 \tabularnewline
32 & 7.63987284438889e-74 & 1.52797456887778e-73 & 1 \tabularnewline
33 & 1.56291064315687e-76 & 3.12582128631373e-76 & 1 \tabularnewline
34 & 3.11997760254169e-79 & 6.23995520508338e-79 & 1 \tabularnewline
35 & 7.22473839442161e-82 & 1.44494767888432e-81 & 1 \tabularnewline
36 & 1.14949276515891e-84 & 2.29898553031782e-84 & 1 \tabularnewline
37 & 8.47555449082476e-87 & 1.69511089816495e-86 & 1 \tabularnewline
38 & 1.60670034521531e-89 & 3.21340069043062e-89 & 1 \tabularnewline
39 & 2.3845481549263e-92 & 4.7690963098526e-92 & 1 \tabularnewline
40 & 3.48325020012655e-95 & 6.9665004002531e-95 & 1 \tabularnewline
41 & 6.16549190118053e-98 & 1.23309838023611e-97 & 1 \tabularnewline
42 & 1.0687049958242e-100 & 2.1374099916484e-100 & 1 \tabularnewline
43 & 1.56995763633584e-103 & 3.13991527267168e-103 & 1 \tabularnewline
44 & 3.30765861344811e-106 & 6.61531722689622e-106 & 1 \tabularnewline
45 & 1.65663378875891e-108 & 3.31326757751781e-108 & 1 \tabularnewline
46 & 2.37679862196312e-111 & 4.75359724392625e-111 & 1 \tabularnewline
47 & 3.85523830926257e-114 & 7.71047661852513e-114 & 1 \tabularnewline
48 & 5.23741275383765e-117 & 1.04748255076753e-116 & 1 \tabularnewline
49 & 7.9875377185933e-120 & 1.59750754371866e-119 & 1 \tabularnewline
50 & 1.03420455359062e-122 & 2.06840910718123e-122 & 1 \tabularnewline
51 & 3.20745593987762e-125 & 6.41491187975523e-125 & 1 \tabularnewline
52 & 5.80641647611855e-128 & 1.16128329522371e-127 & 1 \tabularnewline
53 & 1.42291396102137e-130 & 2.84582792204273e-130 & 1 \tabularnewline
54 & 2.11665945897152e-133 & 4.23331891794304e-133 & 1 \tabularnewline
55 & 1.3925946761349e-135 & 2.7851893522698e-135 & 1 \tabularnewline
56 & 1.83430534665646e-138 & 3.66861069331292e-138 & 1 \tabularnewline
57 & 2.76443875077809e-141 & 5.52887750155617e-141 & 1 \tabularnewline
58 & 3.44895690915686e-144 & 6.89791381831372e-144 & 1 \tabularnewline
59 & 9.60857345775374e-146 & 1.92171469155075e-145 & 1 \tabularnewline
60 & 1.83044172746187e-148 & 3.66088345492375e-148 & 1 \tabularnewline
61 & 2.29584555861353e-151 & 4.59169111722705e-151 & 1 \tabularnewline
62 & 5.25115678854616e-154 & 1.05023135770923e-153 & 1 \tabularnewline
63 & 9.19066929687892e-157 & 1.83813385937578e-156 & 1 \tabularnewline
64 & 1.43646679353994e-159 & 2.87293358707988e-159 & 1 \tabularnewline
65 & 2.66097989146383e-162 & 5.32195978292766e-162 & 1 \tabularnewline
66 & 3.66781292258752e-165 & 7.33562584517504e-165 & 1 \tabularnewline
67 & 5.37824120636256e-168 & 1.07564824127251e-167 & 1 \tabularnewline
68 & 7.23671220055103e-171 & 1.44734244011021e-170 & 1 \tabularnewline
69 & 1.1356549353138e-173 & 2.27130987062761e-173 & 1 \tabularnewline
70 & 3.0822541279165e-176 & 6.16450825583301e-176 & 1 \tabularnewline
71 & 5.80132800616972e-179 & 1.16026560123394e-178 & 1 \tabularnewline
72 & 1.31250558631973e-181 & 2.62501117263946e-181 & 1 \tabularnewline
73 & 7.40429097849086e-184 & 1.48085819569817e-183 & 1 \tabularnewline
74 & 2.92131541743151e-57 & 5.84263083486301e-57 & 1 \tabularnewline
75 & 7.03639794246443e-52 & 1.40727958849289e-51 & 1 \tabularnewline
76 & 2.345313422001e-52 & 4.690626844002e-52 & 1 \tabularnewline
77 & 1.18538851769208e-46 & 2.37077703538416e-46 & 1 \tabularnewline
78 & 1.04180114596997e-46 & 2.08360229193993e-46 & 1 \tabularnewline
79 & 2.78898768990298e-46 & 5.57797537980596e-46 & 1 \tabularnewline
80 & 1.26636412149165e-45 & 2.5327282429833e-45 & 1 \tabularnewline
81 & 7.00072322768813e-43 & 1.40014464553763e-42 & 1 \tabularnewline
82 & 2.4969451165419e-26 & 4.99389023308381e-26 & 1 \tabularnewline
83 & 2.94577375515921e-20 & 5.89154751031842e-20 & 1 \tabularnewline
84 & 1.55342845891431e-16 & 3.10685691782862e-16 & 1 \tabularnewline
85 & 3.16753207148315e-14 & 6.3350641429663e-14 & 0.999999999999968 \tabularnewline
86 & 5.13548742194822e-12 & 1.02709748438964e-11 & 0.999999999994865 \tabularnewline
87 & 1.0067277895695e-10 & 2.01345557913899e-10 & 0.999999999899327 \tabularnewline
88 & 3.21574911104828e-10 & 6.43149822209655e-10 & 0.999999999678425 \tabularnewline
89 & 1.91883130370157e-10 & 3.83766260740315e-10 & 0.999999999808117 \tabularnewline
90 & 1.22362280415675e-10 & 2.4472456083135e-10 & 0.999999999877638 \tabularnewline
91 & 1.22587875434513e-10 & 2.45175750869026e-10 & 0.999999999877412 \tabularnewline
92 & 4.41777641561159e-10 & 8.83555283122319e-10 & 0.999999999558222 \tabularnewline
93 & 1.49179105717415e-08 & 2.98358211434831e-08 & 0.999999985082089 \tabularnewline
94 & 5.29955545306423e-08 & 1.05991109061285e-07 & 0.999999947004445 \tabularnewline
95 & 2.8956144709448e-08 & 5.79122894188959e-08 & 0.999999971043855 \tabularnewline
96 & 2.2683176240142e-08 & 4.5366352480284e-08 & 0.999999977316824 \tabularnewline
97 & 2.37471030362519e-07 & 4.74942060725039e-07 & 0.99999976252897 \tabularnewline
98 & 1.54518855621134e-06 & 3.09037711242268e-06 & 0.999998454811444 \tabularnewline
99 & 6.68782406123056e-06 & 1.33756481224611e-05 & 0.999993312175939 \tabularnewline
100 & 4.54266164676134e-06 & 9.08532329352267e-06 & 0.999995457338353 \tabularnewline
101 & 1.36690222109063e-05 & 2.73380444218125e-05 & 0.999986330977789 \tabularnewline
102 & 1.02469141323505e-05 & 2.04938282647009e-05 & 0.999989753085868 \tabularnewline
103 & 5.9557794681194e-06 & 1.19115589362388e-05 & 0.999994044220532 \tabularnewline
104 & 8.79573362320771e-06 & 1.75914672464154e-05 & 0.999991204266377 \tabularnewline
105 & 3.70936170067502e-05 & 7.41872340135005e-05 & 0.999962906382993 \tabularnewline
106 & 0.000135970296120854 & 0.000271940592241708 & 0.999864029703879 \tabularnewline
107 & 0.00232046205351584 & 0.00464092410703167 & 0.997679537946484 \tabularnewline
108 & 0.00690544304091686 & 0.0138108860818337 & 0.993094556959083 \tabularnewline
109 & 0.00817489739342222 & 0.0163497947868444 & 0.991825102606578 \tabularnewline
110 & 0.0215202423659306 & 0.0430404847318612 & 0.978479757634069 \tabularnewline
111 & 0.0370958525449759 & 0.0741917050899518 & 0.962904147455024 \tabularnewline
112 & 0.0434639762085747 & 0.0869279524171495 & 0.956536023791425 \tabularnewline
113 & 0.0334321342127145 & 0.0668642684254289 & 0.966567865787286 \tabularnewline
114 & 0.0312876550300411 & 0.0625753100600821 & 0.968712344969959 \tabularnewline
115 & 0.0297838283261712 & 0.0595676566523424 & 0.970216171673829 \tabularnewline
116 & 0.0378172613032151 & 0.0756345226064302 & 0.962182738696785 \tabularnewline
117 & 0.0413702702827336 & 0.0827405405654672 & 0.958629729717266 \tabularnewline
118 & 0.1139837096455 & 0.227967419291 & 0.8860162903545 \tabularnewline
119 & 0.126223933499541 & 0.252447866999082 & 0.873776066500459 \tabularnewline
120 & 0.194320974995584 & 0.388641949991167 & 0.805679025004416 \tabularnewline
121 & 0.22656970575058 & 0.45313941150116 & 0.77343029424942 \tabularnewline
122 & 0.259607602450225 & 0.51921520490045 & 0.740392397549775 \tabularnewline
123 & 0.612333440813926 & 0.775333118372147 & 0.387666559186074 \tabularnewline
124 & 0.891265919017644 & 0.217468161964711 & 0.108734080982356 \tabularnewline
125 & 0.983682828147226 & 0.0326343437055486 & 0.0163171718527743 \tabularnewline
126 & 0.99426650192281 & 0.0114669961543798 & 0.00573349807718992 \tabularnewline
127 & 0.992668293493922 & 0.0146634130121561 & 0.00733170650607803 \tabularnewline
128 & 0.996256699348954 & 0.0074866013020914 & 0.0037433006510457 \tabularnewline
129 & 0.999445248140357 & 0.00110950371928604 & 0.000554751859643021 \tabularnewline
130 & 0.999998305522228 & 3.38895554423927e-06 & 1.69447777211964e-06 \tabularnewline
131 & 0.999998427445672 & 3.14510865644035e-06 & 1.57255432822018e-06 \tabularnewline
132 & 0.999996095735988 & 7.80852802316088e-06 & 3.90426401158044e-06 \tabularnewline
133 & 0.999988957161105 & 2.20856777892397e-05 & 1.10428388946198e-05 \tabularnewline
134 & 0.999981504834082 & 3.69903318364012e-05 & 1.84951659182006e-05 \tabularnewline
135 & 0.99994358096361 & 0.000112838072779543 & 5.64190363897713e-05 \tabularnewline
136 & 0.9998828643131 & 0.00023427137380046 & 0.00011713568690023 \tabularnewline
137 & 0.999861658703087 & 0.000276682593825693 & 0.000138341296912847 \tabularnewline
138 & 0.999879073492969 & 0.000241853014062149 & 0.000120926507031075 \tabularnewline
139 & 0.999620512532947 & 0.000758974934106737 & 0.000379487467053368 \tabularnewline
140 & 0.998744970755586 & 0.00251005848882787 & 0.00125502924441394 \tabularnewline
141 & 0.99554701643419 & 0.00890596713162012 & 0.00445298356581006 \tabularnewline
142 & 0.986119960716946 & 0.0277600785661085 & 0.0138800392830543 \tabularnewline
143 & 0.983251755019378 & 0.0334964899612435 & 0.0167482449806218 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191168&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]9.08317216186449e-09[/C][C]1.8166344323729e-08[/C][C]0.999999990916828[/C][/ROW]
[ROW][C]8[/C][C]1.42207730361256e-11[/C][C]2.84415460722513e-11[/C][C]0.999999999985779[/C][/ROW]
[ROW][C]9[/C][C]1.96960444102696e-14[/C][C]3.93920888205393e-14[/C][C]0.99999999999998[/C][/ROW]
[ROW][C]10[/C][C]1.28603927790831e-16[/C][C]2.57207855581663e-16[/C][C]1[/C][/ROW]
[ROW][C]11[/C][C]3.34991441967755e-19[/C][C]6.6998288393551e-19[/C][C]1[/C][/ROW]
[ROW][C]12[/C][C]6.4318196606254e-22[/C][C]1.28636393212508e-21[/C][C]1[/C][/ROW]
[ROW][C]13[/C][C]2.66022818158182e-24[/C][C]5.32045636316363e-24[/C][C]1[/C][/ROW]
[ROW][C]14[/C][C]5.56976856150379e-27[/C][C]1.11395371230076e-26[/C][C]1[/C][/ROW]
[ROW][C]15[/C][C]2.21602696562763e-29[/C][C]4.43205393125525e-29[/C][C]1[/C][/ROW]
[ROW][C]16[/C][C]3.88215020925935e-32[/C][C]7.76430041851871e-32[/C][C]1[/C][/ROW]
[ROW][C]17[/C][C]1.68211651303936e-34[/C][C]3.36423302607872e-34[/C][C]1[/C][/ROW]
[ROW][C]18[/C][C]3.69992066974484e-37[/C][C]7.39984133948968e-37[/C][C]1[/C][/ROW]
[ROW][C]19[/C][C]6.5816593099045e-40[/C][C]1.3163318619809e-39[/C][C]1[/C][/ROW]
[ROW][C]20[/C][C]1.17555246415895e-42[/C][C]2.3511049283179e-42[/C][C]1[/C][/ROW]
[ROW][C]21[/C][C]1.80425384517561e-45[/C][C]3.60850769035122e-45[/C][C]1[/C][/ROW]
[ROW][C]22[/C][C]5.10172826281285e-48[/C][C]1.02034565256257e-47[/C][C]1[/C][/ROW]
[ROW][C]23[/C][C]1.81171809984101e-50[/C][C]3.62343619968201e-50[/C][C]1[/C][/ROW]
[ROW][C]24[/C][C]3.38381709627757e-53[/C][C]6.76763419255515e-53[/C][C]1[/C][/ROW]
[ROW][C]25[/C][C]8.26562043122762e-56[/C][C]1.65312408624552e-55[/C][C]1[/C][/ROW]
[ROW][C]26[/C][C]4.31677873665809e-58[/C][C]8.63355747331618e-58[/C][C]1[/C][/ROW]
[ROW][C]27[/C][C]8.43656615724694e-61[/C][C]1.68731323144939e-60[/C][C]1[/C][/ROW]
[ROW][C]28[/C][C]2.30737256464957e-63[/C][C]4.61474512929914e-63[/C][C]1[/C][/ROW]
[ROW][C]29[/C][C]4.64398686669465e-66[/C][C]9.2879737333893e-66[/C][C]1[/C][/ROW]
[ROW][C]30[/C][C]1.17313129545941e-68[/C][C]2.34626259091883e-68[/C][C]1[/C][/ROW]
[ROW][C]31[/C][C]2.0240703127449e-71[/C][C]4.0481406254898e-71[/C][C]1[/C][/ROW]
[ROW][C]32[/C][C]7.63987284438889e-74[/C][C]1.52797456887778e-73[/C][C]1[/C][/ROW]
[ROW][C]33[/C][C]1.56291064315687e-76[/C][C]3.12582128631373e-76[/C][C]1[/C][/ROW]
[ROW][C]34[/C][C]3.11997760254169e-79[/C][C]6.23995520508338e-79[/C][C]1[/C][/ROW]
[ROW][C]35[/C][C]7.22473839442161e-82[/C][C]1.44494767888432e-81[/C][C]1[/C][/ROW]
[ROW][C]36[/C][C]1.14949276515891e-84[/C][C]2.29898553031782e-84[/C][C]1[/C][/ROW]
[ROW][C]37[/C][C]8.47555449082476e-87[/C][C]1.69511089816495e-86[/C][C]1[/C][/ROW]
[ROW][C]38[/C][C]1.60670034521531e-89[/C][C]3.21340069043062e-89[/C][C]1[/C][/ROW]
[ROW][C]39[/C][C]2.3845481549263e-92[/C][C]4.7690963098526e-92[/C][C]1[/C][/ROW]
[ROW][C]40[/C][C]3.48325020012655e-95[/C][C]6.9665004002531e-95[/C][C]1[/C][/ROW]
[ROW][C]41[/C][C]6.16549190118053e-98[/C][C]1.23309838023611e-97[/C][C]1[/C][/ROW]
[ROW][C]42[/C][C]1.0687049958242e-100[/C][C]2.1374099916484e-100[/C][C]1[/C][/ROW]
[ROW][C]43[/C][C]1.56995763633584e-103[/C][C]3.13991527267168e-103[/C][C]1[/C][/ROW]
[ROW][C]44[/C][C]3.30765861344811e-106[/C][C]6.61531722689622e-106[/C][C]1[/C][/ROW]
[ROW][C]45[/C][C]1.65663378875891e-108[/C][C]3.31326757751781e-108[/C][C]1[/C][/ROW]
[ROW][C]46[/C][C]2.37679862196312e-111[/C][C]4.75359724392625e-111[/C][C]1[/C][/ROW]
[ROW][C]47[/C][C]3.85523830926257e-114[/C][C]7.71047661852513e-114[/C][C]1[/C][/ROW]
[ROW][C]48[/C][C]5.23741275383765e-117[/C][C]1.04748255076753e-116[/C][C]1[/C][/ROW]
[ROW][C]49[/C][C]7.9875377185933e-120[/C][C]1.59750754371866e-119[/C][C]1[/C][/ROW]
[ROW][C]50[/C][C]1.03420455359062e-122[/C][C]2.06840910718123e-122[/C][C]1[/C][/ROW]
[ROW][C]51[/C][C]3.20745593987762e-125[/C][C]6.41491187975523e-125[/C][C]1[/C][/ROW]
[ROW][C]52[/C][C]5.80641647611855e-128[/C][C]1.16128329522371e-127[/C][C]1[/C][/ROW]
[ROW][C]53[/C][C]1.42291396102137e-130[/C][C]2.84582792204273e-130[/C][C]1[/C][/ROW]
[ROW][C]54[/C][C]2.11665945897152e-133[/C][C]4.23331891794304e-133[/C][C]1[/C][/ROW]
[ROW][C]55[/C][C]1.3925946761349e-135[/C][C]2.7851893522698e-135[/C][C]1[/C][/ROW]
[ROW][C]56[/C][C]1.83430534665646e-138[/C][C]3.66861069331292e-138[/C][C]1[/C][/ROW]
[ROW][C]57[/C][C]2.76443875077809e-141[/C][C]5.52887750155617e-141[/C][C]1[/C][/ROW]
[ROW][C]58[/C][C]3.44895690915686e-144[/C][C]6.89791381831372e-144[/C][C]1[/C][/ROW]
[ROW][C]59[/C][C]9.60857345775374e-146[/C][C]1.92171469155075e-145[/C][C]1[/C][/ROW]
[ROW][C]60[/C][C]1.83044172746187e-148[/C][C]3.66088345492375e-148[/C][C]1[/C][/ROW]
[ROW][C]61[/C][C]2.29584555861353e-151[/C][C]4.59169111722705e-151[/C][C]1[/C][/ROW]
[ROW][C]62[/C][C]5.25115678854616e-154[/C][C]1.05023135770923e-153[/C][C]1[/C][/ROW]
[ROW][C]63[/C][C]9.19066929687892e-157[/C][C]1.83813385937578e-156[/C][C]1[/C][/ROW]
[ROW][C]64[/C][C]1.43646679353994e-159[/C][C]2.87293358707988e-159[/C][C]1[/C][/ROW]
[ROW][C]65[/C][C]2.66097989146383e-162[/C][C]5.32195978292766e-162[/C][C]1[/C][/ROW]
[ROW][C]66[/C][C]3.66781292258752e-165[/C][C]7.33562584517504e-165[/C][C]1[/C][/ROW]
[ROW][C]67[/C][C]5.37824120636256e-168[/C][C]1.07564824127251e-167[/C][C]1[/C][/ROW]
[ROW][C]68[/C][C]7.23671220055103e-171[/C][C]1.44734244011021e-170[/C][C]1[/C][/ROW]
[ROW][C]69[/C][C]1.1356549353138e-173[/C][C]2.27130987062761e-173[/C][C]1[/C][/ROW]
[ROW][C]70[/C][C]3.0822541279165e-176[/C][C]6.16450825583301e-176[/C][C]1[/C][/ROW]
[ROW][C]71[/C][C]5.80132800616972e-179[/C][C]1.16026560123394e-178[/C][C]1[/C][/ROW]
[ROW][C]72[/C][C]1.31250558631973e-181[/C][C]2.62501117263946e-181[/C][C]1[/C][/ROW]
[ROW][C]73[/C][C]7.40429097849086e-184[/C][C]1.48085819569817e-183[/C][C]1[/C][/ROW]
[ROW][C]74[/C][C]2.92131541743151e-57[/C][C]5.84263083486301e-57[/C][C]1[/C][/ROW]
[ROW][C]75[/C][C]7.03639794246443e-52[/C][C]1.40727958849289e-51[/C][C]1[/C][/ROW]
[ROW][C]76[/C][C]2.345313422001e-52[/C][C]4.690626844002e-52[/C][C]1[/C][/ROW]
[ROW][C]77[/C][C]1.18538851769208e-46[/C][C]2.37077703538416e-46[/C][C]1[/C][/ROW]
[ROW][C]78[/C][C]1.04180114596997e-46[/C][C]2.08360229193993e-46[/C][C]1[/C][/ROW]
[ROW][C]79[/C][C]2.78898768990298e-46[/C][C]5.57797537980596e-46[/C][C]1[/C][/ROW]
[ROW][C]80[/C][C]1.26636412149165e-45[/C][C]2.5327282429833e-45[/C][C]1[/C][/ROW]
[ROW][C]81[/C][C]7.00072322768813e-43[/C][C]1.40014464553763e-42[/C][C]1[/C][/ROW]
[ROW][C]82[/C][C]2.4969451165419e-26[/C][C]4.99389023308381e-26[/C][C]1[/C][/ROW]
[ROW][C]83[/C][C]2.94577375515921e-20[/C][C]5.89154751031842e-20[/C][C]1[/C][/ROW]
[ROW][C]84[/C][C]1.55342845891431e-16[/C][C]3.10685691782862e-16[/C][C]1[/C][/ROW]
[ROW][C]85[/C][C]3.16753207148315e-14[/C][C]6.3350641429663e-14[/C][C]0.999999999999968[/C][/ROW]
[ROW][C]86[/C][C]5.13548742194822e-12[/C][C]1.02709748438964e-11[/C][C]0.999999999994865[/C][/ROW]
[ROW][C]87[/C][C]1.0067277895695e-10[/C][C]2.01345557913899e-10[/C][C]0.999999999899327[/C][/ROW]
[ROW][C]88[/C][C]3.21574911104828e-10[/C][C]6.43149822209655e-10[/C][C]0.999999999678425[/C][/ROW]
[ROW][C]89[/C][C]1.91883130370157e-10[/C][C]3.83766260740315e-10[/C][C]0.999999999808117[/C][/ROW]
[ROW][C]90[/C][C]1.22362280415675e-10[/C][C]2.4472456083135e-10[/C][C]0.999999999877638[/C][/ROW]
[ROW][C]91[/C][C]1.22587875434513e-10[/C][C]2.45175750869026e-10[/C][C]0.999999999877412[/C][/ROW]
[ROW][C]92[/C][C]4.41777641561159e-10[/C][C]8.83555283122319e-10[/C][C]0.999999999558222[/C][/ROW]
[ROW][C]93[/C][C]1.49179105717415e-08[/C][C]2.98358211434831e-08[/C][C]0.999999985082089[/C][/ROW]
[ROW][C]94[/C][C]5.29955545306423e-08[/C][C]1.05991109061285e-07[/C][C]0.999999947004445[/C][/ROW]
[ROW][C]95[/C][C]2.8956144709448e-08[/C][C]5.79122894188959e-08[/C][C]0.999999971043855[/C][/ROW]
[ROW][C]96[/C][C]2.2683176240142e-08[/C][C]4.5366352480284e-08[/C][C]0.999999977316824[/C][/ROW]
[ROW][C]97[/C][C]2.37471030362519e-07[/C][C]4.74942060725039e-07[/C][C]0.99999976252897[/C][/ROW]
[ROW][C]98[/C][C]1.54518855621134e-06[/C][C]3.09037711242268e-06[/C][C]0.999998454811444[/C][/ROW]
[ROW][C]99[/C][C]6.68782406123056e-06[/C][C]1.33756481224611e-05[/C][C]0.999993312175939[/C][/ROW]
[ROW][C]100[/C][C]4.54266164676134e-06[/C][C]9.08532329352267e-06[/C][C]0.999995457338353[/C][/ROW]
[ROW][C]101[/C][C]1.36690222109063e-05[/C][C]2.73380444218125e-05[/C][C]0.999986330977789[/C][/ROW]
[ROW][C]102[/C][C]1.02469141323505e-05[/C][C]2.04938282647009e-05[/C][C]0.999989753085868[/C][/ROW]
[ROW][C]103[/C][C]5.9557794681194e-06[/C][C]1.19115589362388e-05[/C][C]0.999994044220532[/C][/ROW]
[ROW][C]104[/C][C]8.79573362320771e-06[/C][C]1.75914672464154e-05[/C][C]0.999991204266377[/C][/ROW]
[ROW][C]105[/C][C]3.70936170067502e-05[/C][C]7.41872340135005e-05[/C][C]0.999962906382993[/C][/ROW]
[ROW][C]106[/C][C]0.000135970296120854[/C][C]0.000271940592241708[/C][C]0.999864029703879[/C][/ROW]
[ROW][C]107[/C][C]0.00232046205351584[/C][C]0.00464092410703167[/C][C]0.997679537946484[/C][/ROW]
[ROW][C]108[/C][C]0.00690544304091686[/C][C]0.0138108860818337[/C][C]0.993094556959083[/C][/ROW]
[ROW][C]109[/C][C]0.00817489739342222[/C][C]0.0163497947868444[/C][C]0.991825102606578[/C][/ROW]
[ROW][C]110[/C][C]0.0215202423659306[/C][C]0.0430404847318612[/C][C]0.978479757634069[/C][/ROW]
[ROW][C]111[/C][C]0.0370958525449759[/C][C]0.0741917050899518[/C][C]0.962904147455024[/C][/ROW]
[ROW][C]112[/C][C]0.0434639762085747[/C][C]0.0869279524171495[/C][C]0.956536023791425[/C][/ROW]
[ROW][C]113[/C][C]0.0334321342127145[/C][C]0.0668642684254289[/C][C]0.966567865787286[/C][/ROW]
[ROW][C]114[/C][C]0.0312876550300411[/C][C]0.0625753100600821[/C][C]0.968712344969959[/C][/ROW]
[ROW][C]115[/C][C]0.0297838283261712[/C][C]0.0595676566523424[/C][C]0.970216171673829[/C][/ROW]
[ROW][C]116[/C][C]0.0378172613032151[/C][C]0.0756345226064302[/C][C]0.962182738696785[/C][/ROW]
[ROW][C]117[/C][C]0.0413702702827336[/C][C]0.0827405405654672[/C][C]0.958629729717266[/C][/ROW]
[ROW][C]118[/C][C]0.1139837096455[/C][C]0.227967419291[/C][C]0.8860162903545[/C][/ROW]
[ROW][C]119[/C][C]0.126223933499541[/C][C]0.252447866999082[/C][C]0.873776066500459[/C][/ROW]
[ROW][C]120[/C][C]0.194320974995584[/C][C]0.388641949991167[/C][C]0.805679025004416[/C][/ROW]
[ROW][C]121[/C][C]0.22656970575058[/C][C]0.45313941150116[/C][C]0.77343029424942[/C][/ROW]
[ROW][C]122[/C][C]0.259607602450225[/C][C]0.51921520490045[/C][C]0.740392397549775[/C][/ROW]
[ROW][C]123[/C][C]0.612333440813926[/C][C]0.775333118372147[/C][C]0.387666559186074[/C][/ROW]
[ROW][C]124[/C][C]0.891265919017644[/C][C]0.217468161964711[/C][C]0.108734080982356[/C][/ROW]
[ROW][C]125[/C][C]0.983682828147226[/C][C]0.0326343437055486[/C][C]0.0163171718527743[/C][/ROW]
[ROW][C]126[/C][C]0.99426650192281[/C][C]0.0114669961543798[/C][C]0.00573349807718992[/C][/ROW]
[ROW][C]127[/C][C]0.992668293493922[/C][C]0.0146634130121561[/C][C]0.00733170650607803[/C][/ROW]
[ROW][C]128[/C][C]0.996256699348954[/C][C]0.0074866013020914[/C][C]0.0037433006510457[/C][/ROW]
[ROW][C]129[/C][C]0.999445248140357[/C][C]0.00110950371928604[/C][C]0.000554751859643021[/C][/ROW]
[ROW][C]130[/C][C]0.999998305522228[/C][C]3.38895554423927e-06[/C][C]1.69447777211964e-06[/C][/ROW]
[ROW][C]131[/C][C]0.999998427445672[/C][C]3.14510865644035e-06[/C][C]1.57255432822018e-06[/C][/ROW]
[ROW][C]132[/C][C]0.999996095735988[/C][C]7.80852802316088e-06[/C][C]3.90426401158044e-06[/C][/ROW]
[ROW][C]133[/C][C]0.999988957161105[/C][C]2.20856777892397e-05[/C][C]1.10428388946198e-05[/C][/ROW]
[ROW][C]134[/C][C]0.999981504834082[/C][C]3.69903318364012e-05[/C][C]1.84951659182006e-05[/C][/ROW]
[ROW][C]135[/C][C]0.99994358096361[/C][C]0.000112838072779543[/C][C]5.64190363897713e-05[/C][/ROW]
[ROW][C]136[/C][C]0.9998828643131[/C][C]0.00023427137380046[/C][C]0.00011713568690023[/C][/ROW]
[ROW][C]137[/C][C]0.999861658703087[/C][C]0.000276682593825693[/C][C]0.000138341296912847[/C][/ROW]
[ROW][C]138[/C][C]0.999879073492969[/C][C]0.000241853014062149[/C][C]0.000120926507031075[/C][/ROW]
[ROW][C]139[/C][C]0.999620512532947[/C][C]0.000758974934106737[/C][C]0.000379487467053368[/C][/ROW]
[ROW][C]140[/C][C]0.998744970755586[/C][C]0.00251005848882787[/C][C]0.00125502924441394[/C][/ROW]
[ROW][C]141[/C][C]0.99554701643419[/C][C]0.00890596713162012[/C][C]0.00445298356581006[/C][/ROW]
[ROW][C]142[/C][C]0.986119960716946[/C][C]0.0277600785661085[/C][C]0.0138800392830543[/C][/ROW]
[ROW][C]143[/C][C]0.983251755019378[/C][C]0.0334964899612435[/C][C]0.0167482449806218[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191168&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191168&T=5

As an alternative you can also use a QR Code:  
QR Code


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
79.08317216186449e-091.8166344323729e-080.999999990916828
81.42207730361256e-112.84415460722513e-110.999999999985779
91.96960444102696e-143.93920888205393e-140.99999999999998
101.28603927790831e-162.57207855581663e-161
113.34991441967755e-196.6998288393551e-191
126.4318196606254e-221.28636393212508e-211
132.66022818158182e-245.32045636316363e-241
145.56976856150379e-271.11395371230076e-261
152.21602696562763e-294.43205393125525e-291
163.88215020925935e-327.76430041851871e-321
171.68211651303936e-343.36423302607872e-341
183.69992066974484e-377.39984133948968e-371
196.5816593099045e-401.3163318619809e-391
201.17555246415895e-422.3511049283179e-421
211.80425384517561e-453.60850769035122e-451
225.10172826281285e-481.02034565256257e-471
231.81171809984101e-503.62343619968201e-501
243.38381709627757e-536.76763419255515e-531
258.26562043122762e-561.65312408624552e-551
264.31677873665809e-588.63355747331618e-581
278.43656615724694e-611.68731323144939e-601
282.30737256464957e-634.61474512929914e-631
294.64398686669465e-669.2879737333893e-661
301.17313129545941e-682.34626259091883e-681
312.0240703127449e-714.0481406254898e-711
327.63987284438889e-741.52797456887778e-731
331.56291064315687e-763.12582128631373e-761
343.11997760254169e-796.23995520508338e-791
357.22473839442161e-821.44494767888432e-811
361.14949276515891e-842.29898553031782e-841
378.47555449082476e-871.69511089816495e-861
381.60670034521531e-893.21340069043062e-891
392.3845481549263e-924.7690963098526e-921
403.48325020012655e-956.9665004002531e-951
416.16549190118053e-981.23309838023611e-971
421.0687049958242e-1002.1374099916484e-1001
431.56995763633584e-1033.13991527267168e-1031
443.30765861344811e-1066.61531722689622e-1061
451.65663378875891e-1083.31326757751781e-1081
462.37679862196312e-1114.75359724392625e-1111
473.85523830926257e-1147.71047661852513e-1141
485.23741275383765e-1171.04748255076753e-1161
497.9875377185933e-1201.59750754371866e-1191
501.03420455359062e-1222.06840910718123e-1221
513.20745593987762e-1256.41491187975523e-1251
525.80641647611855e-1281.16128329522371e-1271
531.42291396102137e-1302.84582792204273e-1301
542.11665945897152e-1334.23331891794304e-1331
551.3925946761349e-1352.7851893522698e-1351
561.83430534665646e-1383.66861069331292e-1381
572.76443875077809e-1415.52887750155617e-1411
583.44895690915686e-1446.89791381831372e-1441
599.60857345775374e-1461.92171469155075e-1451
601.83044172746187e-1483.66088345492375e-1481
612.29584555861353e-1514.59169111722705e-1511
625.25115678854616e-1541.05023135770923e-1531
639.19066929687892e-1571.83813385937578e-1561
641.43646679353994e-1592.87293358707988e-1591
652.66097989146383e-1625.32195978292766e-1621
663.66781292258752e-1657.33562584517504e-1651
675.37824120636256e-1681.07564824127251e-1671
687.23671220055103e-1711.44734244011021e-1701
691.1356549353138e-1732.27130987062761e-1731
703.0822541279165e-1766.16450825583301e-1761
715.80132800616972e-1791.16026560123394e-1781
721.31250558631973e-1812.62501117263946e-1811
737.40429097849086e-1841.48085819569817e-1831
742.92131541743151e-575.84263083486301e-571
757.03639794246443e-521.40727958849289e-511
762.345313422001e-524.690626844002e-521
771.18538851769208e-462.37077703538416e-461
781.04180114596997e-462.08360229193993e-461
792.78898768990298e-465.57797537980596e-461
801.26636412149165e-452.5327282429833e-451
817.00072322768813e-431.40014464553763e-421
822.4969451165419e-264.99389023308381e-261
832.94577375515921e-205.89154751031842e-201
841.55342845891431e-163.10685691782862e-161
853.16753207148315e-146.3350641429663e-140.999999999999968
865.13548742194822e-121.02709748438964e-110.999999999994865
871.0067277895695e-102.01345557913899e-100.999999999899327
883.21574911104828e-106.43149822209655e-100.999999999678425
891.91883130370157e-103.83766260740315e-100.999999999808117
901.22362280415675e-102.4472456083135e-100.999999999877638
911.22587875434513e-102.45175750869026e-100.999999999877412
924.41777641561159e-108.83555283122319e-100.999999999558222
931.49179105717415e-082.98358211434831e-080.999999985082089
945.29955545306423e-081.05991109061285e-070.999999947004445
952.8956144709448e-085.79122894188959e-080.999999971043855
962.2683176240142e-084.5366352480284e-080.999999977316824
972.37471030362519e-074.74942060725039e-070.99999976252897
981.54518855621134e-063.09037711242268e-060.999998454811444
996.68782406123056e-061.33756481224611e-050.999993312175939
1004.54266164676134e-069.08532329352267e-060.999995457338353
1011.36690222109063e-052.73380444218125e-050.999986330977789
1021.02469141323505e-052.04938282647009e-050.999989753085868
1035.9557794681194e-061.19115589362388e-050.999994044220532
1048.79573362320771e-061.75914672464154e-050.999991204266377
1053.70936170067502e-057.41872340135005e-050.999962906382993
1060.0001359702961208540.0002719405922417080.999864029703879
1070.002320462053515840.004640924107031670.997679537946484
1080.006905443040916860.01381088608183370.993094556959083
1090.008174897393422220.01634979478684440.991825102606578
1100.02152024236593060.04304048473186120.978479757634069
1110.03709585254497590.07419170508995180.962904147455024
1120.04346397620857470.08692795241714950.956536023791425
1130.03343213421271450.06686426842542890.966567865787286
1140.03128765503004110.06257531006008210.968712344969959
1150.02978382832617120.05956765665234240.970216171673829
1160.03781726130321510.07563452260643020.962182738696785
1170.04137027028273360.08274054056546720.958629729717266
1180.11398370964550.2279674192910.8860162903545
1190.1262239334995410.2524478669990820.873776066500459
1200.1943209749955840.3886419499911670.805679025004416
1210.226569705750580.453139411501160.77343029424942
1220.2596076024502250.519215204900450.740392397549775
1230.6123334408139260.7753331183721470.387666559186074
1240.8912659190176440.2174681619647110.108734080982356
1250.9836828281472260.03263434370554860.0163171718527743
1260.994266501922810.01146699615437980.00573349807718992
1270.9926682934939220.01466341301215610.00733170650607803
1280.9962566993489540.00748660130209140.0037433006510457
1290.9994452481403570.001109503719286040.000554751859643021
1300.9999983055222283.38895554423927e-061.69447777211964e-06
1310.9999984274456723.14510865644035e-061.57255432822018e-06
1320.9999960957359887.80852802316088e-063.90426401158044e-06
1330.9999889571611052.20856777892397e-051.10428388946198e-05
1340.9999815048340823.69903318364012e-051.84951659182006e-05
1350.999943580963610.0001128380727795435.64190363897713e-05
1360.99988286431310.000234271373800460.00011713568690023
1370.9998616587030870.0002766825938256930.000138341296912847
1380.9998790734929690.0002418530140621490.000120926507031075
1390.9996205125329470.0007589749341067370.000379487467053368
1400.9987449707555860.002510058488827870.00125502924441394
1410.995547016434190.008905967131620120.00445298356581006
1420.9861199607169460.02776007856610850.0138800392830543
1430.9832517550193780.03349648996124350.0167482449806218






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level1150.839416058394161NOK
5% type I error level1230.897810218978102NOK
10% type I error level1300.948905109489051NOK

\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 & 115 & 0.839416058394161 & NOK \tabularnewline
5% type I error level & 123 & 0.897810218978102 & NOK \tabularnewline
10% type I error level & 130 & 0.948905109489051 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191168&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]115[/C][C]0.839416058394161[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]123[/C][C]0.897810218978102[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]130[/C][C]0.948905109489051[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191168&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191168&T=6

As an alternative you can also use a QR Code:  
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 level1150.839416058394161NOK
5% type I error level1230.897810218978102NOK
10% type I error level1300.948905109489051NOK


Figures (Output of Computation)

Figure 1
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Figure 6
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Figure 7
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Figure 8
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Figure 9
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Figure 10
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Input Parameters & R Code

Parameters (Session):
par1 = 4 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 4 ; 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')
}