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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 computationThu, 02 Dec 2010 10:24:01 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/02/t1291286743ag9ms3vecwc8112.htm/, Retrieved Sun, 05 May 2024 19:14:29 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=104211, Retrieved Sun, 05 May 2024 19:14:29 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact135

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [workshop 7] [2010-12-02 10:24:01] [fdda052f11cae2ac9ab9683c59d96811] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
13	14	3	25	55	147
12	8	5	158	7	71
10	12	6	0	0	0
9	7	6	143	10	0
10	10	5	67	74	43
12	7	3	0	0	0
13	16	8	148	138	8
12	11	4	28	0	0
12	14	4	114	113	34
6	6	4	0	0	0
5	16	6	123	115	103
12	11	6	145	9	0
11	16	5	113	114	73
14	12	4	152	59	159
14	7	6	0	0	0
12	13	4	36	114	113
12	11	6	0	0	0
11	15	6	8	102	44
11	7	4	108	0	0
7	9	4	112	86	0
9	7	2	51	17	41
11	14	7	43	45	74
11	15	5	120	123	0
12	7	4	13	24	0
12	15	6	55	5	0
11	17	6	103	123	32
11	15	7	127	136	126
8	14	5	14	4	154
9	14	6	135	76	129
12	8	4	38	99	98
10	8	4	11	98	82
10	14	7	43	67	45
12	14	7	141	92	8
8	8	4	62	13	0
12	11	4	62	24	129
11	16	6	135	129	31
12	10	6	117	117	117
7	8	5	82	11	99
11	14	6	145	20	55
11	16	7	87	91	132
12	13	6	76	111	58
9	5	3	124	0	0
15	8	3	151	58	0
11	10	4	131	0	0
11	8	6	127	146	101
11	13	7	76	129	31
11	15	5	25	48	147
15	6	4	0	0	0
11	12	5	58	111	132
12	16	6	115	32	123
12	5	6	130	112	39
9	15	6	17	51	136
12	12	5	102	53	141
12	8	4	21	131	0
13	13	5	0	0	0
11	14	5	14	76	135
9	12	4	110	106	118
9	16	6	133	26	154
11	10	2	83	44	
11	15	8	56	63	116
12	8	3	0	0	0
12	16	6	44	116	88
9	19	6	70	119	25
11	14	6	36	18	113
9	6	5	5	134	157
12	13	5	118	138	26
12	15	6	17	41	38
12	7	5	79	0	0
12	13	6	122	57	53
14	4	2	119	101	0
11	14	5	36	114	106
12	13	5	36	113	106
11	11	5	141	122	102
6	14	6	0	14	138
10	12	6	37	10	142
12	15	6	110	27	73
13	14	5	10	39	130
8	13	5	14	133	86
12	8	4	157	42	78
12	6	2	59	0	0
12	7	4	77	58	0
6	13	6	129	133	4
11	13	6	125	151	91
10	11	5	87	111	132
12	5	3	61	139	0
13	12	6	146	126	0
11	8	4	96	139	0
7	11	5	133	138	14
11	14	8	47	52	97
11	9	4	74	67	45
11	10	6	109	97	0
11	13	6	30	137	149
12	16	7	116	56	57
10	16	6	149	3	105
11	11	5	19	78	0
12	8	4	96	0	0
7	4	6	0	0	0
13	7	3	21	0	0
8	14	5	26	118	128
12	11	6	156	39	29
11	17	7	53	63	148
12	15	7	72	78	93
14	17	6	27	26	4
10	5	3	66	50	0
10	4	2	71	104	158
13	10	8	66	54	144
10	11	3	40	104	0
11	15	8	57	148	122
10	10	3	3	30	149
7	9	4	12	38	17
10	12	5	107	132	91
8	15	7	80	132	111
12	7	6	98	84	99
12	13	6	155	71	40
12	12	7	111	125	132
11	14	6	81	25	123
12	14	6	50	66	54
12	8	6	49	86	90
12	15	6	96	61	86
11	12	4	2	60	152
12	12	4	1	144	152
11	16	5	22	120	123
11	9	4	64	139	100
13	15	6	56	131	116
12	15	6	144	159	59
12	6	5	0	0	0
12	14	8	94	18	5
12	15	6	25	123	147
8	10	5	93	18	139
8	6	4	0	0	0
12	14	8	48	123	81
11	12	6	30	105	3
12	8	4	19	0	0
13	11	6	0	0	0
12	13	6	10	68	37
12	9	4	78	157	5
11	15	6	93	94	69
12	13	3	0	0	0
12	15	6	95	87	0
10	14	5	50	156	142
11	16	4	86	139	17
12	14	6	33	145	100
12	14	4	152	55	70
10	10	4	51	41	0
12	10	4	48	25	123
13	4	6	97	47	109
12	8	5	77	0	0
15	15	6	130	143	37
11	16	6	8	102	44
12	12	8	84	148	98
11	12	7	51	153	11
12	15	7	33	32	9
11	9	4	6	106	0
10	12	6	116	63	57
11	14	6	88	56	63
11	11	2	142	39	66

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time11 seconds
R Server'George Udny Yule' @ 72.249.76.132

\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 & 11 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104211&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]11 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104211&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104211&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 time11 seconds
R Server'George Udny Yule' @ 72.249.76.132






Multiple Linear Regression - Estimated Regression Equation
FindingFriends[t] = + 9.01508211087726 + 0.173745393657251KnowingPeople[t] + 0.00935140411484961Celebrity[t] + 0.0055986399334022firstbestfriend[t] + 0.0106393108702864secondbestfriend[t] -0.0156929343329173thirdbestfriend[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
FindingFriends[t] =  +  9.01508211087726 +  0.173745393657251KnowingPeople[t] +  0.00935140411484961Celebrity[t] +  0.0055986399334022firstbestfriend[t] +  0.0106393108702864secondbestfriend[t] -0.0156929343329173thirdbestfriend[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104211&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]FindingFriends[t] =  +  9.01508211087726 +  0.173745393657251KnowingPeople[t] +  0.00935140411484961Celebrity[t] +  0.0055986399334022firstbestfriend[t] +  0.0106393108702864secondbestfriend[t] -0.0156929343329173thirdbestfriend[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104211&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104211&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
FindingFriends[t] = + 9.01508211087726 + 0.173745393657251KnowingPeople[t] + 0.00935140411484961Celebrity[t] + 0.0055986399334022firstbestfriend[t] + 0.0106393108702864secondbestfriend[t] -0.0156929343329173thirdbestfriend[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)9.015082110877260.76289911.816900
KnowingPeople0.1737453936572510.0829662.09420.0379250.018963
Celebrity0.009351404114849610.0057161.63590.1039630.051982
firstbestfriend0.00559863993340220.0047051.18990.2359770.117988
secondbestfriend0.01063931087028640.004582.3230.0215240.010762
thirdbestfriend-0.01569293433291730.007084-2.21520.0282550.014127

\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) & 9.01508211087726 & 0.762899 & 11.8169 & 0 & 0 \tabularnewline
KnowingPeople & 0.173745393657251 & 0.082966 & 2.0942 & 0.037925 & 0.018963 \tabularnewline
Celebrity & 0.00935140411484961 & 0.005716 & 1.6359 & 0.103963 & 0.051982 \tabularnewline
firstbestfriend & 0.0055986399334022 & 0.004705 & 1.1899 & 0.235977 & 0.117988 \tabularnewline
secondbestfriend & 0.0106393108702864 & 0.00458 & 2.323 & 0.021524 & 0.010762 \tabularnewline
thirdbestfriend & -0.0156929343329173 & 0.007084 & -2.2152 & 0.028255 & 0.014127 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104211&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]9.01508211087726[/C][C]0.762899[/C][C]11.8169[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]KnowingPeople[/C][C]0.173745393657251[/C][C]0.082966[/C][C]2.0942[/C][C]0.037925[/C][C]0.018963[/C][/ROW]
[ROW][C]Celebrity[/C][C]0.00935140411484961[/C][C]0.005716[/C][C]1.6359[/C][C]0.103963[/C][C]0.051982[/C][/ROW]
[ROW][C]firstbestfriend[/C][C]0.0055986399334022[/C][C]0.004705[/C][C]1.1899[/C][C]0.235977[/C][C]0.117988[/C][/ROW]
[ROW][C]secondbestfriend[/C][C]0.0106393108702864[/C][C]0.00458[/C][C]2.323[/C][C]0.021524[/C][C]0.010762[/C][/ROW]
[ROW][C]thirdbestfriend[/C][C]-0.0156929343329173[/C][C]0.007084[/C][C]-2.2152[/C][C]0.028255[/C][C]0.014127[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104211&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104211&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)9.015082110877260.76289911.816900
KnowingPeople0.1737453936572510.0829662.09420.0379250.018963
Celebrity0.009351404114849610.0057161.63590.1039630.051982
firstbestfriend0.00559863993340220.0047051.18990.2359770.117988
secondbestfriend0.01063931087028640.004582.3230.0215240.010762
thirdbestfriend-0.01569293433291730.007084-2.21520.0282550.014127






Multiple Linear Regression - Regression Statistics
Multiple R0.332773506741778
R-squared0.110738206789220
Adjusted R-squared0.0810961470155275
F-TEST (value)3.73584722636247
F-TEST (DF numerator)5
F-TEST (DF denominator)150
p-value0.00323763782245301
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.88804771231853
Sum Squared Residuals1251.12293829424

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.332773506741778 \tabularnewline
R-squared & 0.110738206789220 \tabularnewline
Adjusted R-squared & 0.0810961470155275 \tabularnewline
F-TEST (value) & 3.73584722636247 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 150 \tabularnewline
p-value & 0.00323763782245301 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.88804771231853 \tabularnewline
Sum Squared Residuals & 1251.12293829424 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104211&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.332773506741778[/C][/ROW]
[ROW][C]R-squared[/C][C]0.110738206789220[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.0810961470155275[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]3.73584722636247[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]150[/C][/ROW]
[ROW][C]p-value[/C][C]0.00323763782245301[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.88804771231853[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1251.12293829424[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104211&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104211&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 R0.332773506741778
R-squared0.110738206789220
Adjusted R-squared0.0810961470155275
F-TEST (value)3.73584722636247
F-TEST (DF numerator)5
F-TEST (DF denominator)150
p-value0.00323763782245301
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.88804771231853
Sum Squared Residuals1251.12293829424






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1139.893838583685283.10616141631472
21210.29666422864191.70333577135806
31011.1561352594534-1.15613525945336
4911.1944069103465-2.19440691034649
51011.2869147716477-1.28691477164771
61210.25935407882261.74064592117744
71314.0410997778918-1.04109977789177
81211.12044897570170.879551024298322
91212.7918505519692-0.79185055196919
10610.0949600892802-4.09496008928016
11512.1468980596833-7.14689805968329
121211.88994645397200.110053546027988
131112.5417089753517-1.54170897535165
141410.12096850351393.87903149648615
151410.28740829116713.71259170883289
161210.95230874207641.04769125792361
171210.98238986579611.01761013420388
181112.1168811580132-1.11688115801318
191110.87335859574490.126641404255147
20712.1582246776376-5.15822467763759
21910.0729912884565-1.07299128845649
221111.0712108165460-0.07121081654602
231113.6484920653638-2.64849206536375
241210.59683126295851.40316873704148
251212.0384931911137-0.0384931911136705
261113.4079834792719-2.40798347927191
271111.8673866684934-0.867386668493416
2889.19850095793254-1.19850095793254
29911.0436415349726-2.0436415349726
301210.17058340559641.82941659440359
311010.2598677658509-0.259867765850937
321011.7603707513469-1.76037075134692
331213.1556588068954-1.15565880689543
34810.9278775937793-2.92787759377932
35129.54175766537792.4582423346221
361113.4929233630382-2.49292336303817
371210.87241139921911.12758860078089
3879.47432267586284-2.47432267586284
391111.6651036662065-0.665103666206464
401111.2442598696542-0.244259869654189
411212.0261506033418-0.0261506033417682
42910.6060946432499-1.60609464324994
431511.89557413290023.10442586709984
441111.5233634951849-0.523363495184854
451111.1405339758036-0.140533975803608
461112.6507188301105-1.65071883011054
471110.01181160948020.98818839051977
481510.09496008928024.90503991071984
491110.58100114613260.418998853867449
501210.90518745132401.09481254867604
511211.24731907368320.752680926316802
52910.1809141044008-1.18091410440081
531210.06902486372941.93097513627062
541211.95377203920360.0462279607963763
551311.32052924899581.67947075100423
561110.26269709291860.737302907081419
57911.0292835448640-2.02928354486403
58910.4556461405830-1.45564614058305
591111.5314333707824-0.531433370782359
601512.32728305532942.67271694467061
6189.34800307985401-1.34800307985401
621611.91348143373774.08651856626225
631911.47175140703027.52824859296983
641411.55598625910272.44401374089731
65612.1628404454536-6.1628404454536
661311.84819394615771.15180605384229
671510.66205118111864.33794881888143
68710.4342547922416-3.43425479224163
691311.86173064650071.13826935349932
70410.8682303434704-6.8682303434704
711411.79815631996132.2018436800387
721311.80825061436081.19174938563919
731112.8764432340041-1.87644323400408
741411.44723098865872.55276901134126
751211.78200975598990.217990244010121
761511.81003375085913.18996624914094
771411.45323701618862.54676298381141
781311.48601337076351.51398662923648
79812.0549280446279-4.05492804462788
8069.72599052897288-3.72599052897288
81710.6606853124895-3.66068531248951
821311.87843968059791.12156031940209
831312.88312256298760.116877437012414
841112.5349040926459-1.53490409264587
85510.6809567472698-5.68095674726982
861211.95566582753540.0443341724646023
87811.2761588909443-3.27615889094431
881111.8764862117699-0.876486211769938
891411.99508140682582.0049185931742
90911.0833231770439-2.08332317704388
911011.4473033172173-1.44730331721730
921312.50205237482000.497947625179985
931612.07909795634823.92090204365176
941612.41221496945153.58778503054846
951110.30986446015600.690135539843977
96810.4979479402014-2.49794794020141
9749.85354632649284-5.85354632649284
9879.60715430359752-2.60715430359752
991411.96110167769272.03889832230729
1001111.8706382097162-0.870638209716206
1011712.46594139717684.53405860282324
1021512.11104970782842.88895029217165
1031710.34123492234216.65876507765786
104510.2765136167700-5.27651361677003
105412.0857841145972-8.08578411459724
1061012.6996959101111-2.69969591011112
1071110.32001073185470.679989268145262
1081512.90774058767102.09225941232902
1091011.2077384815379-1.20773848153788
110910.0589677938194-1.05896779381944
1111212.4660636051942-0.466063605194235
1121512.71108096148192.28891903851815
113712.3092543946452-5.30925439464515
1141312.14178276871050.85821723128954
1151213.2009024721173-1.20090247211731
1161412.07530422950891.92469577049115
1171411.28084248916822.71915751083175
118811.7664790750518-3.76647907505175
1191512.03916476096642.9608352390336
1201211.49354493002860.506455069971386
1211211.97017221465250.0298277853475329
1221611.89738972108164.1026102789184
123911.9466874403003-2.94668744030026
1241512.36049978348622.63950021651376
1251512.73374454412192.26625545587805
12669.6954938671685-3.69549386716851
1271411.24973410808882.75026589191121
1281512.41840751076922.58159248923077
1291012.2075859169522-2.20758591695223
13069.52174847351126-3.52174847351126
1311412.23170727228761.76829272771238
1321210.76955650988931.23044349011066
13389.68373221736048-1.68373221736048
134119.869239260825761.13076073917424
1351310.73711531964622.2628846803538
136911.1990339526980-2.19903395269802
1371511.99930444729633.00069555270366
138139.348003079854013.65199692014599
1391511.27609019460833.7239098053917
1401412.56292698043531.43707301956469
1411611.28504846292614.7149515370739
1421412.05356947398781.94643052601225
1431412.02722472489141.9727752751086
1441010.2282143206381-0.228214320638078
1451011.4035241720714-1.4035241720714
146412.1991464216973-8.19914642169729
147810.3684731810132-2.36847318101318
1481512.29487474276622.70512525723376
1491610.98324144524425.01675855475582
1501212.8891921035521-0.889192103552134
1511211.49353059372400.506469406275974
1521510.64218420030744.35781579969259
153910.2026985998068-1.20269859980682
1541211.92885010789190.0711498921081077
1551411.6916561783642.30834382163599
1561111.4070056110141-0.40700561101407

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 13 & 9.89383858368528 & 3.10616141631472 \tabularnewline
2 & 12 & 10.2966642286419 & 1.70333577135806 \tabularnewline
3 & 10 & 11.1561352594534 & -1.15613525945336 \tabularnewline
4 & 9 & 11.1944069103465 & -2.19440691034649 \tabularnewline
5 & 10 & 11.2869147716477 & -1.28691477164771 \tabularnewline
6 & 12 & 10.2593540788226 & 1.74064592117744 \tabularnewline
7 & 13 & 14.0410997778918 & -1.04109977789177 \tabularnewline
8 & 12 & 11.1204489757017 & 0.879551024298322 \tabularnewline
9 & 12 & 12.7918505519692 & -0.79185055196919 \tabularnewline
10 & 6 & 10.0949600892802 & -4.09496008928016 \tabularnewline
11 & 5 & 12.1468980596833 & -7.14689805968329 \tabularnewline
12 & 12 & 11.8899464539720 & 0.110053546027988 \tabularnewline
13 & 11 & 12.5417089753517 & -1.54170897535165 \tabularnewline
14 & 14 & 10.1209685035139 & 3.87903149648615 \tabularnewline
15 & 14 & 10.2874082911671 & 3.71259170883289 \tabularnewline
16 & 12 & 10.9523087420764 & 1.04769125792361 \tabularnewline
17 & 12 & 10.9823898657961 & 1.01761013420388 \tabularnewline
18 & 11 & 12.1168811580132 & -1.11688115801318 \tabularnewline
19 & 11 & 10.8733585957449 & 0.126641404255147 \tabularnewline
20 & 7 & 12.1582246776376 & -5.15822467763759 \tabularnewline
21 & 9 & 10.0729912884565 & -1.07299128845649 \tabularnewline
22 & 11 & 11.0712108165460 & -0.07121081654602 \tabularnewline
23 & 11 & 13.6484920653638 & -2.64849206536375 \tabularnewline
24 & 12 & 10.5968312629585 & 1.40316873704148 \tabularnewline
25 & 12 & 12.0384931911137 & -0.0384931911136705 \tabularnewline
26 & 11 & 13.4079834792719 & -2.40798347927191 \tabularnewline
27 & 11 & 11.8673866684934 & -0.867386668493416 \tabularnewline
28 & 8 & 9.19850095793254 & -1.19850095793254 \tabularnewline
29 & 9 & 11.0436415349726 & -2.0436415349726 \tabularnewline
30 & 12 & 10.1705834055964 & 1.82941659440359 \tabularnewline
31 & 10 & 10.2598677658509 & -0.259867765850937 \tabularnewline
32 & 10 & 11.7603707513469 & -1.76037075134692 \tabularnewline
33 & 12 & 13.1556588068954 & -1.15565880689543 \tabularnewline
34 & 8 & 10.9278775937793 & -2.92787759377932 \tabularnewline
35 & 12 & 9.5417576653779 & 2.4582423346221 \tabularnewline
36 & 11 & 13.4929233630382 & -2.49292336303817 \tabularnewline
37 & 12 & 10.8724113992191 & 1.12758860078089 \tabularnewline
38 & 7 & 9.47432267586284 & -2.47432267586284 \tabularnewline
39 & 11 & 11.6651036662065 & -0.665103666206464 \tabularnewline
40 & 11 & 11.2442598696542 & -0.244259869654189 \tabularnewline
41 & 12 & 12.0261506033418 & -0.0261506033417682 \tabularnewline
42 & 9 & 10.6060946432499 & -1.60609464324994 \tabularnewline
43 & 15 & 11.8955741329002 & 3.10442586709984 \tabularnewline
44 & 11 & 11.5233634951849 & -0.523363495184854 \tabularnewline
45 & 11 & 11.1405339758036 & -0.140533975803608 \tabularnewline
46 & 11 & 12.6507188301105 & -1.65071883011054 \tabularnewline
47 & 11 & 10.0118116094802 & 0.98818839051977 \tabularnewline
48 & 15 & 10.0949600892802 & 4.90503991071984 \tabularnewline
49 & 11 & 10.5810011461326 & 0.418998853867449 \tabularnewline
50 & 12 & 10.9051874513240 & 1.09481254867604 \tabularnewline
51 & 12 & 11.2473190736832 & 0.752680926316802 \tabularnewline
52 & 9 & 10.1809141044008 & -1.18091410440081 \tabularnewline
53 & 12 & 10.0690248637294 & 1.93097513627062 \tabularnewline
54 & 12 & 11.9537720392036 & 0.0462279607963763 \tabularnewline
55 & 13 & 11.3205292489958 & 1.67947075100423 \tabularnewline
56 & 11 & 10.2626970929186 & 0.737302907081419 \tabularnewline
57 & 9 & 11.0292835448640 & -2.02928354486403 \tabularnewline
58 & 9 & 10.4556461405830 & -1.45564614058305 \tabularnewline
59 & 11 & 11.5314333707824 & -0.531433370782359 \tabularnewline
60 & 15 & 12.3272830553294 & 2.67271694467061 \tabularnewline
61 & 8 & 9.34800307985401 & -1.34800307985401 \tabularnewline
62 & 16 & 11.9134814337377 & 4.08651856626225 \tabularnewline
63 & 19 & 11.4717514070302 & 7.52824859296983 \tabularnewline
64 & 14 & 11.5559862591027 & 2.44401374089731 \tabularnewline
65 & 6 & 12.1628404454536 & -6.1628404454536 \tabularnewline
66 & 13 & 11.8481939461577 & 1.15180605384229 \tabularnewline
67 & 15 & 10.6620511811186 & 4.33794881888143 \tabularnewline
68 & 7 & 10.4342547922416 & -3.43425479224163 \tabularnewline
69 & 13 & 11.8617306465007 & 1.13826935349932 \tabularnewline
70 & 4 & 10.8682303434704 & -6.8682303434704 \tabularnewline
71 & 14 & 11.7981563199613 & 2.2018436800387 \tabularnewline
72 & 13 & 11.8082506143608 & 1.19174938563919 \tabularnewline
73 & 11 & 12.8764432340041 & -1.87644323400408 \tabularnewline
74 & 14 & 11.4472309886587 & 2.55276901134126 \tabularnewline
75 & 12 & 11.7820097559899 & 0.217990244010121 \tabularnewline
76 & 15 & 11.8100337508591 & 3.18996624914094 \tabularnewline
77 & 14 & 11.4532370161886 & 2.54676298381141 \tabularnewline
78 & 13 & 11.4860133707635 & 1.51398662923648 \tabularnewline
79 & 8 & 12.0549280446279 & -4.05492804462788 \tabularnewline
80 & 6 & 9.72599052897288 & -3.72599052897288 \tabularnewline
81 & 7 & 10.6606853124895 & -3.66068531248951 \tabularnewline
82 & 13 & 11.8784396805979 & 1.12156031940209 \tabularnewline
83 & 13 & 12.8831225629876 & 0.116877437012414 \tabularnewline
84 & 11 & 12.5349040926459 & -1.53490409264587 \tabularnewline
85 & 5 & 10.6809567472698 & -5.68095674726982 \tabularnewline
86 & 12 & 11.9556658275354 & 0.0443341724646023 \tabularnewline
87 & 8 & 11.2761588909443 & -3.27615889094431 \tabularnewline
88 & 11 & 11.8764862117699 & -0.876486211769938 \tabularnewline
89 & 14 & 11.9950814068258 & 2.0049185931742 \tabularnewline
90 & 9 & 11.0833231770439 & -2.08332317704388 \tabularnewline
91 & 10 & 11.4473033172173 & -1.44730331721730 \tabularnewline
92 & 13 & 12.5020523748200 & 0.497947625179985 \tabularnewline
93 & 16 & 12.0790979563482 & 3.92090204365176 \tabularnewline
94 & 16 & 12.4122149694515 & 3.58778503054846 \tabularnewline
95 & 11 & 10.3098644601560 & 0.690135539843977 \tabularnewline
96 & 8 & 10.4979479402014 & -2.49794794020141 \tabularnewline
97 & 4 & 9.85354632649284 & -5.85354632649284 \tabularnewline
98 & 7 & 9.60715430359752 & -2.60715430359752 \tabularnewline
99 & 14 & 11.9611016776927 & 2.03889832230729 \tabularnewline
100 & 11 & 11.8706382097162 & -0.870638209716206 \tabularnewline
101 & 17 & 12.4659413971768 & 4.53405860282324 \tabularnewline
102 & 15 & 12.1110497078284 & 2.88895029217165 \tabularnewline
103 & 17 & 10.3412349223421 & 6.65876507765786 \tabularnewline
104 & 5 & 10.2765136167700 & -5.27651361677003 \tabularnewline
105 & 4 & 12.0857841145972 & -8.08578411459724 \tabularnewline
106 & 10 & 12.6996959101111 & -2.69969591011112 \tabularnewline
107 & 11 & 10.3200107318547 & 0.679989268145262 \tabularnewline
108 & 15 & 12.9077405876710 & 2.09225941232902 \tabularnewline
109 & 10 & 11.2077384815379 & -1.20773848153788 \tabularnewline
110 & 9 & 10.0589677938194 & -1.05896779381944 \tabularnewline
111 & 12 & 12.4660636051942 & -0.466063605194235 \tabularnewline
112 & 15 & 12.7110809614819 & 2.28891903851815 \tabularnewline
113 & 7 & 12.3092543946452 & -5.30925439464515 \tabularnewline
114 & 13 & 12.1417827687105 & 0.85821723128954 \tabularnewline
115 & 12 & 13.2009024721173 & -1.20090247211731 \tabularnewline
116 & 14 & 12.0753042295089 & 1.92469577049115 \tabularnewline
117 & 14 & 11.2808424891682 & 2.71915751083175 \tabularnewline
118 & 8 & 11.7664790750518 & -3.76647907505175 \tabularnewline
119 & 15 & 12.0391647609664 & 2.9608352390336 \tabularnewline
120 & 12 & 11.4935449300286 & 0.506455069971386 \tabularnewline
121 & 12 & 11.9701722146525 & 0.0298277853475329 \tabularnewline
122 & 16 & 11.8973897210816 & 4.1026102789184 \tabularnewline
123 & 9 & 11.9466874403003 & -2.94668744030026 \tabularnewline
124 & 15 & 12.3604997834862 & 2.63950021651376 \tabularnewline
125 & 15 & 12.7337445441219 & 2.26625545587805 \tabularnewline
126 & 6 & 9.6954938671685 & -3.69549386716851 \tabularnewline
127 & 14 & 11.2497341080888 & 2.75026589191121 \tabularnewline
128 & 15 & 12.4184075107692 & 2.58159248923077 \tabularnewline
129 & 10 & 12.2075859169522 & -2.20758591695223 \tabularnewline
130 & 6 & 9.52174847351126 & -3.52174847351126 \tabularnewline
131 & 14 & 12.2317072722876 & 1.76829272771238 \tabularnewline
132 & 12 & 10.7695565098893 & 1.23044349011066 \tabularnewline
133 & 8 & 9.68373221736048 & -1.68373221736048 \tabularnewline
134 & 11 & 9.86923926082576 & 1.13076073917424 \tabularnewline
135 & 13 & 10.7371153196462 & 2.2628846803538 \tabularnewline
136 & 9 & 11.1990339526980 & -2.19903395269802 \tabularnewline
137 & 15 & 11.9993044472963 & 3.00069555270366 \tabularnewline
138 & 13 & 9.34800307985401 & 3.65199692014599 \tabularnewline
139 & 15 & 11.2760901946083 & 3.7239098053917 \tabularnewline
140 & 14 & 12.5629269804353 & 1.43707301956469 \tabularnewline
141 & 16 & 11.2850484629261 & 4.7149515370739 \tabularnewline
142 & 14 & 12.0535694739878 & 1.94643052601225 \tabularnewline
143 & 14 & 12.0272247248914 & 1.9727752751086 \tabularnewline
144 & 10 & 10.2282143206381 & -0.228214320638078 \tabularnewline
145 & 10 & 11.4035241720714 & -1.4035241720714 \tabularnewline
146 & 4 & 12.1991464216973 & -8.19914642169729 \tabularnewline
147 & 8 & 10.3684731810132 & -2.36847318101318 \tabularnewline
148 & 15 & 12.2948747427662 & 2.70512525723376 \tabularnewline
149 & 16 & 10.9832414452442 & 5.01675855475582 \tabularnewline
150 & 12 & 12.8891921035521 & -0.889192103552134 \tabularnewline
151 & 12 & 11.4935305937240 & 0.506469406275974 \tabularnewline
152 & 15 & 10.6421842003074 & 4.35781579969259 \tabularnewline
153 & 9 & 10.2026985998068 & -1.20269859980682 \tabularnewline
154 & 12 & 11.9288501078919 & 0.0711498921081077 \tabularnewline
155 & 14 & 11.691656178364 & 2.30834382163599 \tabularnewline
156 & 11 & 11.4070056110141 & -0.40700561101407 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104211&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]13[/C][C]9.89383858368528[/C][C]3.10616141631472[/C][/ROW]
[ROW][C]2[/C][C]12[/C][C]10.2966642286419[/C][C]1.70333577135806[/C][/ROW]
[ROW][C]3[/C][C]10[/C][C]11.1561352594534[/C][C]-1.15613525945336[/C][/ROW]
[ROW][C]4[/C][C]9[/C][C]11.1944069103465[/C][C]-2.19440691034649[/C][/ROW]
[ROW][C]5[/C][C]10[/C][C]11.2869147716477[/C][C]-1.28691477164771[/C][/ROW]
[ROW][C]6[/C][C]12[/C][C]10.2593540788226[/C][C]1.74064592117744[/C][/ROW]
[ROW][C]7[/C][C]13[/C][C]14.0410997778918[/C][C]-1.04109977789177[/C][/ROW]
[ROW][C]8[/C][C]12[/C][C]11.1204489757017[/C][C]0.879551024298322[/C][/ROW]
[ROW][C]9[/C][C]12[/C][C]12.7918505519692[/C][C]-0.79185055196919[/C][/ROW]
[ROW][C]10[/C][C]6[/C][C]10.0949600892802[/C][C]-4.09496008928016[/C][/ROW]
[ROW][C]11[/C][C]5[/C][C]12.1468980596833[/C][C]-7.14689805968329[/C][/ROW]
[ROW][C]12[/C][C]12[/C][C]11.8899464539720[/C][C]0.110053546027988[/C][/ROW]
[ROW][C]13[/C][C]11[/C][C]12.5417089753517[/C][C]-1.54170897535165[/C][/ROW]
[ROW][C]14[/C][C]14[/C][C]10.1209685035139[/C][C]3.87903149648615[/C][/ROW]
[ROW][C]15[/C][C]14[/C][C]10.2874082911671[/C][C]3.71259170883289[/C][/ROW]
[ROW][C]16[/C][C]12[/C][C]10.9523087420764[/C][C]1.04769125792361[/C][/ROW]
[ROW][C]17[/C][C]12[/C][C]10.9823898657961[/C][C]1.01761013420388[/C][/ROW]
[ROW][C]18[/C][C]11[/C][C]12.1168811580132[/C][C]-1.11688115801318[/C][/ROW]
[ROW][C]19[/C][C]11[/C][C]10.8733585957449[/C][C]0.126641404255147[/C][/ROW]
[ROW][C]20[/C][C]7[/C][C]12.1582246776376[/C][C]-5.15822467763759[/C][/ROW]
[ROW][C]21[/C][C]9[/C][C]10.0729912884565[/C][C]-1.07299128845649[/C][/ROW]
[ROW][C]22[/C][C]11[/C][C]11.0712108165460[/C][C]-0.07121081654602[/C][/ROW]
[ROW][C]23[/C][C]11[/C][C]13.6484920653638[/C][C]-2.64849206536375[/C][/ROW]
[ROW][C]24[/C][C]12[/C][C]10.5968312629585[/C][C]1.40316873704148[/C][/ROW]
[ROW][C]25[/C][C]12[/C][C]12.0384931911137[/C][C]-0.0384931911136705[/C][/ROW]
[ROW][C]26[/C][C]11[/C][C]13.4079834792719[/C][C]-2.40798347927191[/C][/ROW]
[ROW][C]27[/C][C]11[/C][C]11.8673866684934[/C][C]-0.867386668493416[/C][/ROW]
[ROW][C]28[/C][C]8[/C][C]9.19850095793254[/C][C]-1.19850095793254[/C][/ROW]
[ROW][C]29[/C][C]9[/C][C]11.0436415349726[/C][C]-2.0436415349726[/C][/ROW]
[ROW][C]30[/C][C]12[/C][C]10.1705834055964[/C][C]1.82941659440359[/C][/ROW]
[ROW][C]31[/C][C]10[/C][C]10.2598677658509[/C][C]-0.259867765850937[/C][/ROW]
[ROW][C]32[/C][C]10[/C][C]11.7603707513469[/C][C]-1.76037075134692[/C][/ROW]
[ROW][C]33[/C][C]12[/C][C]13.1556588068954[/C][C]-1.15565880689543[/C][/ROW]
[ROW][C]34[/C][C]8[/C][C]10.9278775937793[/C][C]-2.92787759377932[/C][/ROW]
[ROW][C]35[/C][C]12[/C][C]9.5417576653779[/C][C]2.4582423346221[/C][/ROW]
[ROW][C]36[/C][C]11[/C][C]13.4929233630382[/C][C]-2.49292336303817[/C][/ROW]
[ROW][C]37[/C][C]12[/C][C]10.8724113992191[/C][C]1.12758860078089[/C][/ROW]
[ROW][C]38[/C][C]7[/C][C]9.47432267586284[/C][C]-2.47432267586284[/C][/ROW]
[ROW][C]39[/C][C]11[/C][C]11.6651036662065[/C][C]-0.665103666206464[/C][/ROW]
[ROW][C]40[/C][C]11[/C][C]11.2442598696542[/C][C]-0.244259869654189[/C][/ROW]
[ROW][C]41[/C][C]12[/C][C]12.0261506033418[/C][C]-0.0261506033417682[/C][/ROW]
[ROW][C]42[/C][C]9[/C][C]10.6060946432499[/C][C]-1.60609464324994[/C][/ROW]
[ROW][C]43[/C][C]15[/C][C]11.8955741329002[/C][C]3.10442586709984[/C][/ROW]
[ROW][C]44[/C][C]11[/C][C]11.5233634951849[/C][C]-0.523363495184854[/C][/ROW]
[ROW][C]45[/C][C]11[/C][C]11.1405339758036[/C][C]-0.140533975803608[/C][/ROW]
[ROW][C]46[/C][C]11[/C][C]12.6507188301105[/C][C]-1.65071883011054[/C][/ROW]
[ROW][C]47[/C][C]11[/C][C]10.0118116094802[/C][C]0.98818839051977[/C][/ROW]
[ROW][C]48[/C][C]15[/C][C]10.0949600892802[/C][C]4.90503991071984[/C][/ROW]
[ROW][C]49[/C][C]11[/C][C]10.5810011461326[/C][C]0.418998853867449[/C][/ROW]
[ROW][C]50[/C][C]12[/C][C]10.9051874513240[/C][C]1.09481254867604[/C][/ROW]
[ROW][C]51[/C][C]12[/C][C]11.2473190736832[/C][C]0.752680926316802[/C][/ROW]
[ROW][C]52[/C][C]9[/C][C]10.1809141044008[/C][C]-1.18091410440081[/C][/ROW]
[ROW][C]53[/C][C]12[/C][C]10.0690248637294[/C][C]1.93097513627062[/C][/ROW]
[ROW][C]54[/C][C]12[/C][C]11.9537720392036[/C][C]0.0462279607963763[/C][/ROW]
[ROW][C]55[/C][C]13[/C][C]11.3205292489958[/C][C]1.67947075100423[/C][/ROW]
[ROW][C]56[/C][C]11[/C][C]10.2626970929186[/C][C]0.737302907081419[/C][/ROW]
[ROW][C]57[/C][C]9[/C][C]11.0292835448640[/C][C]-2.02928354486403[/C][/ROW]
[ROW][C]58[/C][C]9[/C][C]10.4556461405830[/C][C]-1.45564614058305[/C][/ROW]
[ROW][C]59[/C][C]11[/C][C]11.5314333707824[/C][C]-0.531433370782359[/C][/ROW]
[ROW][C]60[/C][C]15[/C][C]12.3272830553294[/C][C]2.67271694467061[/C][/ROW]
[ROW][C]61[/C][C]8[/C][C]9.34800307985401[/C][C]-1.34800307985401[/C][/ROW]
[ROW][C]62[/C][C]16[/C][C]11.9134814337377[/C][C]4.08651856626225[/C][/ROW]
[ROW][C]63[/C][C]19[/C][C]11.4717514070302[/C][C]7.52824859296983[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]11.5559862591027[/C][C]2.44401374089731[/C][/ROW]
[ROW][C]65[/C][C]6[/C][C]12.1628404454536[/C][C]-6.1628404454536[/C][/ROW]
[ROW][C]66[/C][C]13[/C][C]11.8481939461577[/C][C]1.15180605384229[/C][/ROW]
[ROW][C]67[/C][C]15[/C][C]10.6620511811186[/C][C]4.33794881888143[/C][/ROW]
[ROW][C]68[/C][C]7[/C][C]10.4342547922416[/C][C]-3.43425479224163[/C][/ROW]
[ROW][C]69[/C][C]13[/C][C]11.8617306465007[/C][C]1.13826935349932[/C][/ROW]
[ROW][C]70[/C][C]4[/C][C]10.8682303434704[/C][C]-6.8682303434704[/C][/ROW]
[ROW][C]71[/C][C]14[/C][C]11.7981563199613[/C][C]2.2018436800387[/C][/ROW]
[ROW][C]72[/C][C]13[/C][C]11.8082506143608[/C][C]1.19174938563919[/C][/ROW]
[ROW][C]73[/C][C]11[/C][C]12.8764432340041[/C][C]-1.87644323400408[/C][/ROW]
[ROW][C]74[/C][C]14[/C][C]11.4472309886587[/C][C]2.55276901134126[/C][/ROW]
[ROW][C]75[/C][C]12[/C][C]11.7820097559899[/C][C]0.217990244010121[/C][/ROW]
[ROW][C]76[/C][C]15[/C][C]11.8100337508591[/C][C]3.18996624914094[/C][/ROW]
[ROW][C]77[/C][C]14[/C][C]11.4532370161886[/C][C]2.54676298381141[/C][/ROW]
[ROW][C]78[/C][C]13[/C][C]11.4860133707635[/C][C]1.51398662923648[/C][/ROW]
[ROW][C]79[/C][C]8[/C][C]12.0549280446279[/C][C]-4.05492804462788[/C][/ROW]
[ROW][C]80[/C][C]6[/C][C]9.72599052897288[/C][C]-3.72599052897288[/C][/ROW]
[ROW][C]81[/C][C]7[/C][C]10.6606853124895[/C][C]-3.66068531248951[/C][/ROW]
[ROW][C]82[/C][C]13[/C][C]11.8784396805979[/C][C]1.12156031940209[/C][/ROW]
[ROW][C]83[/C][C]13[/C][C]12.8831225629876[/C][C]0.116877437012414[/C][/ROW]
[ROW][C]84[/C][C]11[/C][C]12.5349040926459[/C][C]-1.53490409264587[/C][/ROW]
[ROW][C]85[/C][C]5[/C][C]10.6809567472698[/C][C]-5.68095674726982[/C][/ROW]
[ROW][C]86[/C][C]12[/C][C]11.9556658275354[/C][C]0.0443341724646023[/C][/ROW]
[ROW][C]87[/C][C]8[/C][C]11.2761588909443[/C][C]-3.27615889094431[/C][/ROW]
[ROW][C]88[/C][C]11[/C][C]11.8764862117699[/C][C]-0.876486211769938[/C][/ROW]
[ROW][C]89[/C][C]14[/C][C]11.9950814068258[/C][C]2.0049185931742[/C][/ROW]
[ROW][C]90[/C][C]9[/C][C]11.0833231770439[/C][C]-2.08332317704388[/C][/ROW]
[ROW][C]91[/C][C]10[/C][C]11.4473033172173[/C][C]-1.44730331721730[/C][/ROW]
[ROW][C]92[/C][C]13[/C][C]12.5020523748200[/C][C]0.497947625179985[/C][/ROW]
[ROW][C]93[/C][C]16[/C][C]12.0790979563482[/C][C]3.92090204365176[/C][/ROW]
[ROW][C]94[/C][C]16[/C][C]12.4122149694515[/C][C]3.58778503054846[/C][/ROW]
[ROW][C]95[/C][C]11[/C][C]10.3098644601560[/C][C]0.690135539843977[/C][/ROW]
[ROW][C]96[/C][C]8[/C][C]10.4979479402014[/C][C]-2.49794794020141[/C][/ROW]
[ROW][C]97[/C][C]4[/C][C]9.85354632649284[/C][C]-5.85354632649284[/C][/ROW]
[ROW][C]98[/C][C]7[/C][C]9.60715430359752[/C][C]-2.60715430359752[/C][/ROW]
[ROW][C]99[/C][C]14[/C][C]11.9611016776927[/C][C]2.03889832230729[/C][/ROW]
[ROW][C]100[/C][C]11[/C][C]11.8706382097162[/C][C]-0.870638209716206[/C][/ROW]
[ROW][C]101[/C][C]17[/C][C]12.4659413971768[/C][C]4.53405860282324[/C][/ROW]
[ROW][C]102[/C][C]15[/C][C]12.1110497078284[/C][C]2.88895029217165[/C][/ROW]
[ROW][C]103[/C][C]17[/C][C]10.3412349223421[/C][C]6.65876507765786[/C][/ROW]
[ROW][C]104[/C][C]5[/C][C]10.2765136167700[/C][C]-5.27651361677003[/C][/ROW]
[ROW][C]105[/C][C]4[/C][C]12.0857841145972[/C][C]-8.08578411459724[/C][/ROW]
[ROW][C]106[/C][C]10[/C][C]12.6996959101111[/C][C]-2.69969591011112[/C][/ROW]
[ROW][C]107[/C][C]11[/C][C]10.3200107318547[/C][C]0.679989268145262[/C][/ROW]
[ROW][C]108[/C][C]15[/C][C]12.9077405876710[/C][C]2.09225941232902[/C][/ROW]
[ROW][C]109[/C][C]10[/C][C]11.2077384815379[/C][C]-1.20773848153788[/C][/ROW]
[ROW][C]110[/C][C]9[/C][C]10.0589677938194[/C][C]-1.05896779381944[/C][/ROW]
[ROW][C]111[/C][C]12[/C][C]12.4660636051942[/C][C]-0.466063605194235[/C][/ROW]
[ROW][C]112[/C][C]15[/C][C]12.7110809614819[/C][C]2.28891903851815[/C][/ROW]
[ROW][C]113[/C][C]7[/C][C]12.3092543946452[/C][C]-5.30925439464515[/C][/ROW]
[ROW][C]114[/C][C]13[/C][C]12.1417827687105[/C][C]0.85821723128954[/C][/ROW]
[ROW][C]115[/C][C]12[/C][C]13.2009024721173[/C][C]-1.20090247211731[/C][/ROW]
[ROW][C]116[/C][C]14[/C][C]12.0753042295089[/C][C]1.92469577049115[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]11.2808424891682[/C][C]2.71915751083175[/C][/ROW]
[ROW][C]118[/C][C]8[/C][C]11.7664790750518[/C][C]-3.76647907505175[/C][/ROW]
[ROW][C]119[/C][C]15[/C][C]12.0391647609664[/C][C]2.9608352390336[/C][/ROW]
[ROW][C]120[/C][C]12[/C][C]11.4935449300286[/C][C]0.506455069971386[/C][/ROW]
[ROW][C]121[/C][C]12[/C][C]11.9701722146525[/C][C]0.0298277853475329[/C][/ROW]
[ROW][C]122[/C][C]16[/C][C]11.8973897210816[/C][C]4.1026102789184[/C][/ROW]
[ROW][C]123[/C][C]9[/C][C]11.9466874403003[/C][C]-2.94668744030026[/C][/ROW]
[ROW][C]124[/C][C]15[/C][C]12.3604997834862[/C][C]2.63950021651376[/C][/ROW]
[ROW][C]125[/C][C]15[/C][C]12.7337445441219[/C][C]2.26625545587805[/C][/ROW]
[ROW][C]126[/C][C]6[/C][C]9.6954938671685[/C][C]-3.69549386716851[/C][/ROW]
[ROW][C]127[/C][C]14[/C][C]11.2497341080888[/C][C]2.75026589191121[/C][/ROW]
[ROW][C]128[/C][C]15[/C][C]12.4184075107692[/C][C]2.58159248923077[/C][/ROW]
[ROW][C]129[/C][C]10[/C][C]12.2075859169522[/C][C]-2.20758591695223[/C][/ROW]
[ROW][C]130[/C][C]6[/C][C]9.52174847351126[/C][C]-3.52174847351126[/C][/ROW]
[ROW][C]131[/C][C]14[/C][C]12.2317072722876[/C][C]1.76829272771238[/C][/ROW]
[ROW][C]132[/C][C]12[/C][C]10.7695565098893[/C][C]1.23044349011066[/C][/ROW]
[ROW][C]133[/C][C]8[/C][C]9.68373221736048[/C][C]-1.68373221736048[/C][/ROW]
[ROW][C]134[/C][C]11[/C][C]9.86923926082576[/C][C]1.13076073917424[/C][/ROW]
[ROW][C]135[/C][C]13[/C][C]10.7371153196462[/C][C]2.2628846803538[/C][/ROW]
[ROW][C]136[/C][C]9[/C][C]11.1990339526980[/C][C]-2.19903395269802[/C][/ROW]
[ROW][C]137[/C][C]15[/C][C]11.9993044472963[/C][C]3.00069555270366[/C][/ROW]
[ROW][C]138[/C][C]13[/C][C]9.34800307985401[/C][C]3.65199692014599[/C][/ROW]
[ROW][C]139[/C][C]15[/C][C]11.2760901946083[/C][C]3.7239098053917[/C][/ROW]
[ROW][C]140[/C][C]14[/C][C]12.5629269804353[/C][C]1.43707301956469[/C][/ROW]
[ROW][C]141[/C][C]16[/C][C]11.2850484629261[/C][C]4.7149515370739[/C][/ROW]
[ROW][C]142[/C][C]14[/C][C]12.0535694739878[/C][C]1.94643052601225[/C][/ROW]
[ROW][C]143[/C][C]14[/C][C]12.0272247248914[/C][C]1.9727752751086[/C][/ROW]
[ROW][C]144[/C][C]10[/C][C]10.2282143206381[/C][C]-0.228214320638078[/C][/ROW]
[ROW][C]145[/C][C]10[/C][C]11.4035241720714[/C][C]-1.4035241720714[/C][/ROW]
[ROW][C]146[/C][C]4[/C][C]12.1991464216973[/C][C]-8.19914642169729[/C][/ROW]
[ROW][C]147[/C][C]8[/C][C]10.3684731810132[/C][C]-2.36847318101318[/C][/ROW]
[ROW][C]148[/C][C]15[/C][C]12.2948747427662[/C][C]2.70512525723376[/C][/ROW]
[ROW][C]149[/C][C]16[/C][C]10.9832414452442[/C][C]5.01675855475582[/C][/ROW]
[ROW][C]150[/C][C]12[/C][C]12.8891921035521[/C][C]-0.889192103552134[/C][/ROW]
[ROW][C]151[/C][C]12[/C][C]11.4935305937240[/C][C]0.506469406275974[/C][/ROW]
[ROW][C]152[/C][C]15[/C][C]10.6421842003074[/C][C]4.35781579969259[/C][/ROW]
[ROW][C]153[/C][C]9[/C][C]10.2026985998068[/C][C]-1.20269859980682[/C][/ROW]
[ROW][C]154[/C][C]12[/C][C]11.9288501078919[/C][C]0.0711498921081077[/C][/ROW]
[ROW][C]155[/C][C]14[/C][C]11.691656178364[/C][C]2.30834382163599[/C][/ROW]
[ROW][C]156[/C][C]11[/C][C]11.4070056110141[/C][C]-0.40700561101407[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104211&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104211&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
1139.893838583685283.10616141631472
21210.29666422864191.70333577135806
31011.1561352594534-1.15613525945336
4911.1944069103465-2.19440691034649
51011.2869147716477-1.28691477164771
61210.25935407882261.74064592117744
71314.0410997778918-1.04109977789177
81211.12044897570170.879551024298322
91212.7918505519692-0.79185055196919
10610.0949600892802-4.09496008928016
11512.1468980596833-7.14689805968329
121211.88994645397200.110053546027988
131112.5417089753517-1.54170897535165
141410.12096850351393.87903149648615
151410.28740829116713.71259170883289
161210.95230874207641.04769125792361
171210.98238986579611.01761013420388
181112.1168811580132-1.11688115801318
191110.87335859574490.126641404255147
20712.1582246776376-5.15822467763759
21910.0729912884565-1.07299128845649
221111.0712108165460-0.07121081654602
231113.6484920653638-2.64849206536375
241210.59683126295851.40316873704148
251212.0384931911137-0.0384931911136705
261113.4079834792719-2.40798347927191
271111.8673866684934-0.867386668493416
2889.19850095793254-1.19850095793254
29911.0436415349726-2.0436415349726
301210.17058340559641.82941659440359
311010.2598677658509-0.259867765850937
321011.7603707513469-1.76037075134692
331213.1556588068954-1.15565880689543
34810.9278775937793-2.92787759377932
35129.54175766537792.4582423346221
361113.4929233630382-2.49292336303817
371210.87241139921911.12758860078089
3879.47432267586284-2.47432267586284
391111.6651036662065-0.665103666206464
401111.2442598696542-0.244259869654189
411212.0261506033418-0.0261506033417682
42910.6060946432499-1.60609464324994
431511.89557413290023.10442586709984
441111.5233634951849-0.523363495184854
451111.1405339758036-0.140533975803608
461112.6507188301105-1.65071883011054
471110.01181160948020.98818839051977
481510.09496008928024.90503991071984
491110.58100114613260.418998853867449
501210.90518745132401.09481254867604
511211.24731907368320.752680926316802
52910.1809141044008-1.18091410440081
531210.06902486372941.93097513627062
541211.95377203920360.0462279607963763
551311.32052924899581.67947075100423
561110.26269709291860.737302907081419
57911.0292835448640-2.02928354486403
58910.4556461405830-1.45564614058305
591111.5314333707824-0.531433370782359
601512.32728305532942.67271694467061
6189.34800307985401-1.34800307985401
621611.91348143373774.08651856626225
631911.47175140703027.52824859296983
641411.55598625910272.44401374089731
65612.1628404454536-6.1628404454536
661311.84819394615771.15180605384229
671510.66205118111864.33794881888143
68710.4342547922416-3.43425479224163
691311.86173064650071.13826935349932
70410.8682303434704-6.8682303434704
711411.79815631996132.2018436800387
721311.80825061436081.19174938563919
731112.8764432340041-1.87644323400408
741411.44723098865872.55276901134126
751211.78200975598990.217990244010121
761511.81003375085913.18996624914094
771411.45323701618862.54676298381141
781311.48601337076351.51398662923648
79812.0549280446279-4.05492804462788
8069.72599052897288-3.72599052897288
81710.6606853124895-3.66068531248951
821311.87843968059791.12156031940209
831312.88312256298760.116877437012414
841112.5349040926459-1.53490409264587
85510.6809567472698-5.68095674726982
861211.95566582753540.0443341724646023
87811.2761588909443-3.27615889094431
881111.8764862117699-0.876486211769938
891411.99508140682582.0049185931742
90911.0833231770439-2.08332317704388
911011.4473033172173-1.44730331721730
921312.50205237482000.497947625179985
931612.07909795634823.92090204365176
941612.41221496945153.58778503054846
951110.30986446015600.690135539843977
96810.4979479402014-2.49794794020141
9749.85354632649284-5.85354632649284
9879.60715430359752-2.60715430359752
991411.96110167769272.03889832230729
1001111.8706382097162-0.870638209716206
1011712.46594139717684.53405860282324
1021512.11104970782842.88895029217165
1031710.34123492234216.65876507765786
104510.2765136167700-5.27651361677003
105412.0857841145972-8.08578411459724
1061012.6996959101111-2.69969591011112
1071110.32001073185470.679989268145262
1081512.90774058767102.09225941232902
1091011.2077384815379-1.20773848153788
110910.0589677938194-1.05896779381944
1111212.4660636051942-0.466063605194235
1121512.71108096148192.28891903851815
113712.3092543946452-5.30925439464515
1141312.14178276871050.85821723128954
1151213.2009024721173-1.20090247211731
1161412.07530422950891.92469577049115
1171411.28084248916822.71915751083175
118811.7664790750518-3.76647907505175
1191512.03916476096642.9608352390336
1201211.49354493002860.506455069971386
1211211.97017221465250.0298277853475329
1221611.89738972108164.1026102789184
123911.9466874403003-2.94668744030026
1241512.36049978348622.63950021651376
1251512.73374454412192.26625545587805
12669.6954938671685-3.69549386716851
1271411.24973410808882.75026589191121
1281512.41840751076922.58159248923077
1291012.2075859169522-2.20758591695223
13069.52174847351126-3.52174847351126
1311412.23170727228761.76829272771238
1321210.76955650988931.23044349011066
13389.68373221736048-1.68373221736048
134119.869239260825761.13076073917424
1351310.73711531964622.2628846803538
136911.1990339526980-2.19903395269802
1371511.99930444729633.00069555270366
138139.348003079854013.65199692014599
1391511.27609019460833.7239098053917
1401412.56292698043531.43707301956469
1411611.28504846292614.7149515370739
1421412.05356947398781.94643052601225
1431412.02722472489141.9727752751086
1441010.2282143206381-0.228214320638078
1451011.4035241720714-1.4035241720714
146412.1991464216973-8.19914642169729
147810.3684731810132-2.36847318101318
1481512.29487474276622.70512525723376
1491610.98324144524425.01675855475582
1501212.8891921035521-0.889192103552134
1511211.49353059372400.506469406275974
1521510.64218420030744.35781579969259
153910.2026985998068-1.20269859980682
1541211.92885010789190.0711498921081077
1551411.6916561783642.30834382163599
1561111.4070056110141-0.40700561101407






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.1767016883628580.3534033767257170.823298311637142
100.2162144339863860.4324288679727720.783785566013614
110.7504969256500710.4990061486998580.249503074349929
120.6439707934911360.7120584130177270.356029206508864
130.5379394528093040.9241210943813920.462060547190696
140.5175032494054940.9649935011890130.482496750594506
150.7223968896533680.5552062206932630.277603110346632
160.6560734259913340.6878531480173310.343926574008666
170.5771607491136090.8456785017727820.422839250886391
180.4911648949131540.9823297898263070.508835105086846
190.405436519912730.810873039825460.59456348008727
200.4132620405932720.8265240811865450.586737959406728
210.350108159728420.700216319456840.64989184027158
220.291390172935950.58278034587190.70860982706405
230.2452013500968030.4904027001936060.754798649903197
240.2177769172740230.4355538345480450.782223082725977
250.1687707526947260.3375415053894510.831229247305274
260.1361629870844680.2723259741689360.863837012915532
270.1014000544507850.202800108901570.898599945549215
280.1429600080849880.2859200161699750.857039991915012
290.1238447894448480.2476895788896960.876155210555152
300.1090019258248090.2180038516496190.89099807417519
310.08147158425154240.1629431685030850.918528415748458
320.06482208659265260.1296441731853050.935177913407347
330.05365593568659130.1073118713731830.946344064313409
340.05802841988756190.1160568397751240.941971580112438
350.04828709519020440.09657419038040870.951712904809796
360.04531939149695260.09063878299390520.954680608503047
370.0368564572334360.0737129144668720.963143542766564
380.04599303412379430.09198606824758850.954006965876206
390.03639424195202230.07278848390404460.963605758047978
400.02643728739740290.05287457479480570.973562712602597
410.02089998258922160.04179996517844320.979100017410778
420.01608453153669220.03216906307338430.983915468463308
430.02752821946053230.05505643892106460.972471780539468
440.02265346187714120.04530692375428240.97734653812286
450.01656549847760310.03313099695520620.983434501522397
460.01620500002019640.03241000004039280.983794999979804
470.01153835769217450.0230767153843490.988461642307826
480.02288622851940340.04577245703880680.977113771480597
490.01695532559193760.03391065118387520.983044674408062
500.01307146253683360.02614292507366710.986928537463166
510.01018049425289430.02036098850578850.989819505747106
520.008292191568695070.01658438313739010.991707808431305
530.007366941813399180.01473388362679840.9926330581866
540.005683742895981420.01136748579196280.994316257104018
550.007169164433069490.01433832886613900.99283083556693
560.00533224667210820.01066449334421640.994667753327892
570.004388783705688680.008777567411377360.995611216294311
580.003305620399204140.006611240798408280.996694379600796
590.003878848471379870.007757696942759740.99612115152862
600.002771888337690170.005543776675380330.99722811166231
610.002664062610710170.005328125221420340.99733593738929
620.00232989331848910.00465978663697820.99767010668151
630.003487818107093360.006975636214186730.996512181892907
640.002507908865490880.005015817730981770.99749209113451
650.009982985477040880.01996597095408180.99001701452296
660.0389253900694570.0778507801389140.961074609930543
670.04720978822120880.09441957644241760.952790211778791
680.1410341725108590.2820683450217190.85896582748914
690.1246046850765660.2492093701531320.875395314923434
700.3422745843181850.684549168636370.657725415681815
710.3297965942099150.6595931884198310.670203405790085
720.2959191210743150.5918382421486310.704080878925685
730.2678309193989940.5356618387979870.732169080601006
740.2493389866971300.4986779733942590.75066101330287
750.2133905242429330.4267810484858660.786609475757067
760.2205415840707490.4410831681414990.77945841592925
770.2091993562054700.4183987124109410.79080064379453
780.1843162234383940.3686324468767880.815683776561606
790.2027018724665680.4054037449331350.797298127533432
800.2264185242372150.452837048474430.773581475762785
810.2424230197500630.4848460395001260.757576980249937
820.2197659097106000.4395318194212010.7802340902894
830.1901515793907610.3803031587815220.80984842060924
840.1641057455095600.3282114910191200.83589425449044
850.234310599363360.468621198726720.76568940063664
860.2047908415961580.4095816831923170.795209158403842
870.2116762705198840.4233525410397690.788323729480116
880.1840284371740010.3680568743480020.815971562825999
890.1625481536063880.3250963072127770.837451846393612
900.1441642940366620.2883285880733240.855835705963338
910.1324091706924580.2648183413849160.867590829307542
920.1096279440157200.2192558880314410.89037205598428
930.1241953160723150.2483906321446290.875804683927685
940.1520969936816250.304193987363250.847903006318375
950.1264650407328930.2529300814657860.873534959267107
960.1189677563631220.2379355127262440.881032243636878
970.2500983411608010.5001966823216020.749901658839199
980.2386402850916860.4772805701833720.761359714908314
990.2217393756133550.443478751226710.778260624386645
1000.1904746160316440.3809492320632880.809525383968356
1010.2379426032092980.4758852064185960.762057396790702
1020.2373289647583190.4746579295166380.762671035241681
1030.3724840860307340.7449681720614680.627515913969266
1040.4925502133536360.9851004267072720.507449786646364
1050.7016564885356360.5966870229287280.298343511464364
1060.7064325129484590.5871349741030820.293567487051541
1070.6630660371452030.6738679257095950.336933962854797
1080.6267589276606880.7464821446786230.373241072339312
1090.5814985331507390.8370029336985220.418501466849261
1100.5451778520024750.909644295995050.454822147997525
1110.5175865506077510.9648268987844980.482413449392249
1120.4886515855818180.9773031711636360.511348414418182
1130.6203367492013090.7593265015973820.379663250798691
1140.5703536832759660.8592926334480670.429646316724034
1150.5457812782843080.9084374434313840.454218721715692
1160.5294156144409870.9411687711180270.470584385559013
1170.5124496679768110.9751006640463770.487550332023189
1180.572549389686620.8549012206267590.427450610313379
1190.5646850373670170.8706299252659660.435314962632983
1200.5228491884595670.9543016230808650.477150811540433
1210.4650630247394820.9301260494789630.534936975260518
1220.5135365843657590.9729268312684820.486463415634241
1230.5099947498531800.9800105002936390.490005250146820
1240.4868090026382920.9736180052765850.513190997361708
1250.4383530042751650.876706008550330.561646995724835
1260.4887725380745900.9775450761491790.511227461925410
1270.4562806651150920.9125613302301840.543719334884908
1280.4123799737303820.8247599474607650.587620026269618
1290.3805768768557390.7611537537114790.619423123144261
1300.4472836538697210.8945673077394420.552716346130279
1310.3850749559879000.7701499119758010.6149250440121
1320.3248173601812430.6496347203624860.675182639818757
1330.2963600659089940.5927201318179890.703639934091006
1340.2406355943708020.4812711887416050.759364405629198
1350.1939957440952100.3879914881904210.80600425590479
1360.2942443099893390.5884886199786780.705755690010661
1370.2911187192087010.5822374384174010.7088812807913
1380.2651049724752570.5302099449505130.734895027524743
1390.2171037485575820.4342074971151640.782896251442418
1400.1640341169028030.3280682338056060.835965883097197
1410.1586224090575660.3172448181151320.841377590942434
1420.1343281259341560.2686562518683130.865671874065844
1430.1002970390670230.2005940781340460.899702960932977
1440.06550063838207610.1310012767641520.934499361617924
1450.05008156457391860.1001631291478370.949918435426081
1460.2915581518974030.5831163037948070.708441848102596
1470.4239156105874160.8478312211748320.576084389412584

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.176701688362858 & 0.353403376725717 & 0.823298311637142 \tabularnewline
10 & 0.216214433986386 & 0.432428867972772 & 0.783785566013614 \tabularnewline
11 & 0.750496925650071 & 0.499006148699858 & 0.249503074349929 \tabularnewline
12 & 0.643970793491136 & 0.712058413017727 & 0.356029206508864 \tabularnewline
13 & 0.537939452809304 & 0.924121094381392 & 0.462060547190696 \tabularnewline
14 & 0.517503249405494 & 0.964993501189013 & 0.482496750594506 \tabularnewline
15 & 0.722396889653368 & 0.555206220693263 & 0.277603110346632 \tabularnewline
16 & 0.656073425991334 & 0.687853148017331 & 0.343926574008666 \tabularnewline
17 & 0.577160749113609 & 0.845678501772782 & 0.422839250886391 \tabularnewline
18 & 0.491164894913154 & 0.982329789826307 & 0.508835105086846 \tabularnewline
19 & 0.40543651991273 & 0.81087303982546 & 0.59456348008727 \tabularnewline
20 & 0.413262040593272 & 0.826524081186545 & 0.586737959406728 \tabularnewline
21 & 0.35010815972842 & 0.70021631945684 & 0.64989184027158 \tabularnewline
22 & 0.29139017293595 & 0.5827803458719 & 0.70860982706405 \tabularnewline
23 & 0.245201350096803 & 0.490402700193606 & 0.754798649903197 \tabularnewline
24 & 0.217776917274023 & 0.435553834548045 & 0.782223082725977 \tabularnewline
25 & 0.168770752694726 & 0.337541505389451 & 0.831229247305274 \tabularnewline
26 & 0.136162987084468 & 0.272325974168936 & 0.863837012915532 \tabularnewline
27 & 0.101400054450785 & 0.20280010890157 & 0.898599945549215 \tabularnewline
28 & 0.142960008084988 & 0.285920016169975 & 0.857039991915012 \tabularnewline
29 & 0.123844789444848 & 0.247689578889696 & 0.876155210555152 \tabularnewline
30 & 0.109001925824809 & 0.218003851649619 & 0.89099807417519 \tabularnewline
31 & 0.0814715842515424 & 0.162943168503085 & 0.918528415748458 \tabularnewline
32 & 0.0648220865926526 & 0.129644173185305 & 0.935177913407347 \tabularnewline
33 & 0.0536559356865913 & 0.107311871373183 & 0.946344064313409 \tabularnewline
34 & 0.0580284198875619 & 0.116056839775124 & 0.941971580112438 \tabularnewline
35 & 0.0482870951902044 & 0.0965741903804087 & 0.951712904809796 \tabularnewline
36 & 0.0453193914969526 & 0.0906387829939052 & 0.954680608503047 \tabularnewline
37 & 0.036856457233436 & 0.073712914466872 & 0.963143542766564 \tabularnewline
38 & 0.0459930341237943 & 0.0919860682475885 & 0.954006965876206 \tabularnewline
39 & 0.0363942419520223 & 0.0727884839040446 & 0.963605758047978 \tabularnewline
40 & 0.0264372873974029 & 0.0528745747948057 & 0.973562712602597 \tabularnewline
41 & 0.0208999825892216 & 0.0417999651784432 & 0.979100017410778 \tabularnewline
42 & 0.0160845315366922 & 0.0321690630733843 & 0.983915468463308 \tabularnewline
43 & 0.0275282194605323 & 0.0550564389210646 & 0.972471780539468 \tabularnewline
44 & 0.0226534618771412 & 0.0453069237542824 & 0.97734653812286 \tabularnewline
45 & 0.0165654984776031 & 0.0331309969552062 & 0.983434501522397 \tabularnewline
46 & 0.0162050000201964 & 0.0324100000403928 & 0.983794999979804 \tabularnewline
47 & 0.0115383576921745 & 0.023076715384349 & 0.988461642307826 \tabularnewline
48 & 0.0228862285194034 & 0.0457724570388068 & 0.977113771480597 \tabularnewline
49 & 0.0169553255919376 & 0.0339106511838752 & 0.983044674408062 \tabularnewline
50 & 0.0130714625368336 & 0.0261429250736671 & 0.986928537463166 \tabularnewline
51 & 0.0101804942528943 & 0.0203609885057885 & 0.989819505747106 \tabularnewline
52 & 0.00829219156869507 & 0.0165843831373901 & 0.991707808431305 \tabularnewline
53 & 0.00736694181339918 & 0.0147338836267984 & 0.9926330581866 \tabularnewline
54 & 0.00568374289598142 & 0.0113674857919628 & 0.994316257104018 \tabularnewline
55 & 0.00716916443306949 & 0.0143383288661390 & 0.99283083556693 \tabularnewline
56 & 0.0053322466721082 & 0.0106644933442164 & 0.994667753327892 \tabularnewline
57 & 0.00438878370568868 & 0.00877756741137736 & 0.995611216294311 \tabularnewline
58 & 0.00330562039920414 & 0.00661124079840828 & 0.996694379600796 \tabularnewline
59 & 0.00387884847137987 & 0.00775769694275974 & 0.99612115152862 \tabularnewline
60 & 0.00277188833769017 & 0.00554377667538033 & 0.99722811166231 \tabularnewline
61 & 0.00266406261071017 & 0.00532812522142034 & 0.99733593738929 \tabularnewline
62 & 0.0023298933184891 & 0.0046597866369782 & 0.99767010668151 \tabularnewline
63 & 0.00348781810709336 & 0.00697563621418673 & 0.996512181892907 \tabularnewline
64 & 0.00250790886549088 & 0.00501581773098177 & 0.99749209113451 \tabularnewline
65 & 0.00998298547704088 & 0.0199659709540818 & 0.99001701452296 \tabularnewline
66 & 0.038925390069457 & 0.077850780138914 & 0.961074609930543 \tabularnewline
67 & 0.0472097882212088 & 0.0944195764424176 & 0.952790211778791 \tabularnewline
68 & 0.141034172510859 & 0.282068345021719 & 0.85896582748914 \tabularnewline
69 & 0.124604685076566 & 0.249209370153132 & 0.875395314923434 \tabularnewline
70 & 0.342274584318185 & 0.68454916863637 & 0.657725415681815 \tabularnewline
71 & 0.329796594209915 & 0.659593188419831 & 0.670203405790085 \tabularnewline
72 & 0.295919121074315 & 0.591838242148631 & 0.704080878925685 \tabularnewline
73 & 0.267830919398994 & 0.535661838797987 & 0.732169080601006 \tabularnewline
74 & 0.249338986697130 & 0.498677973394259 & 0.75066101330287 \tabularnewline
75 & 0.213390524242933 & 0.426781048485866 & 0.786609475757067 \tabularnewline
76 & 0.220541584070749 & 0.441083168141499 & 0.77945841592925 \tabularnewline
77 & 0.209199356205470 & 0.418398712410941 & 0.79080064379453 \tabularnewline
78 & 0.184316223438394 & 0.368632446876788 & 0.815683776561606 \tabularnewline
79 & 0.202701872466568 & 0.405403744933135 & 0.797298127533432 \tabularnewline
80 & 0.226418524237215 & 0.45283704847443 & 0.773581475762785 \tabularnewline
81 & 0.242423019750063 & 0.484846039500126 & 0.757576980249937 \tabularnewline
82 & 0.219765909710600 & 0.439531819421201 & 0.7802340902894 \tabularnewline
83 & 0.190151579390761 & 0.380303158781522 & 0.80984842060924 \tabularnewline
84 & 0.164105745509560 & 0.328211491019120 & 0.83589425449044 \tabularnewline
85 & 0.23431059936336 & 0.46862119872672 & 0.76568940063664 \tabularnewline
86 & 0.204790841596158 & 0.409581683192317 & 0.795209158403842 \tabularnewline
87 & 0.211676270519884 & 0.423352541039769 & 0.788323729480116 \tabularnewline
88 & 0.184028437174001 & 0.368056874348002 & 0.815971562825999 \tabularnewline
89 & 0.162548153606388 & 0.325096307212777 & 0.837451846393612 \tabularnewline
90 & 0.144164294036662 & 0.288328588073324 & 0.855835705963338 \tabularnewline
91 & 0.132409170692458 & 0.264818341384916 & 0.867590829307542 \tabularnewline
92 & 0.109627944015720 & 0.219255888031441 & 0.89037205598428 \tabularnewline
93 & 0.124195316072315 & 0.248390632144629 & 0.875804683927685 \tabularnewline
94 & 0.152096993681625 & 0.30419398736325 & 0.847903006318375 \tabularnewline
95 & 0.126465040732893 & 0.252930081465786 & 0.873534959267107 \tabularnewline
96 & 0.118967756363122 & 0.237935512726244 & 0.881032243636878 \tabularnewline
97 & 0.250098341160801 & 0.500196682321602 & 0.749901658839199 \tabularnewline
98 & 0.238640285091686 & 0.477280570183372 & 0.761359714908314 \tabularnewline
99 & 0.221739375613355 & 0.44347875122671 & 0.778260624386645 \tabularnewline
100 & 0.190474616031644 & 0.380949232063288 & 0.809525383968356 \tabularnewline
101 & 0.237942603209298 & 0.475885206418596 & 0.762057396790702 \tabularnewline
102 & 0.237328964758319 & 0.474657929516638 & 0.762671035241681 \tabularnewline
103 & 0.372484086030734 & 0.744968172061468 & 0.627515913969266 \tabularnewline
104 & 0.492550213353636 & 0.985100426707272 & 0.507449786646364 \tabularnewline
105 & 0.701656488535636 & 0.596687022928728 & 0.298343511464364 \tabularnewline
106 & 0.706432512948459 & 0.587134974103082 & 0.293567487051541 \tabularnewline
107 & 0.663066037145203 & 0.673867925709595 & 0.336933962854797 \tabularnewline
108 & 0.626758927660688 & 0.746482144678623 & 0.373241072339312 \tabularnewline
109 & 0.581498533150739 & 0.837002933698522 & 0.418501466849261 \tabularnewline
110 & 0.545177852002475 & 0.90964429599505 & 0.454822147997525 \tabularnewline
111 & 0.517586550607751 & 0.964826898784498 & 0.482413449392249 \tabularnewline
112 & 0.488651585581818 & 0.977303171163636 & 0.511348414418182 \tabularnewline
113 & 0.620336749201309 & 0.759326501597382 & 0.379663250798691 \tabularnewline
114 & 0.570353683275966 & 0.859292633448067 & 0.429646316724034 \tabularnewline
115 & 0.545781278284308 & 0.908437443431384 & 0.454218721715692 \tabularnewline
116 & 0.529415614440987 & 0.941168771118027 & 0.470584385559013 \tabularnewline
117 & 0.512449667976811 & 0.975100664046377 & 0.487550332023189 \tabularnewline
118 & 0.57254938968662 & 0.854901220626759 & 0.427450610313379 \tabularnewline
119 & 0.564685037367017 & 0.870629925265966 & 0.435314962632983 \tabularnewline
120 & 0.522849188459567 & 0.954301623080865 & 0.477150811540433 \tabularnewline
121 & 0.465063024739482 & 0.930126049478963 & 0.534936975260518 \tabularnewline
122 & 0.513536584365759 & 0.972926831268482 & 0.486463415634241 \tabularnewline
123 & 0.509994749853180 & 0.980010500293639 & 0.490005250146820 \tabularnewline
124 & 0.486809002638292 & 0.973618005276585 & 0.513190997361708 \tabularnewline
125 & 0.438353004275165 & 0.87670600855033 & 0.561646995724835 \tabularnewline
126 & 0.488772538074590 & 0.977545076149179 & 0.511227461925410 \tabularnewline
127 & 0.456280665115092 & 0.912561330230184 & 0.543719334884908 \tabularnewline
128 & 0.412379973730382 & 0.824759947460765 & 0.587620026269618 \tabularnewline
129 & 0.380576876855739 & 0.761153753711479 & 0.619423123144261 \tabularnewline
130 & 0.447283653869721 & 0.894567307739442 & 0.552716346130279 \tabularnewline
131 & 0.385074955987900 & 0.770149911975801 & 0.6149250440121 \tabularnewline
132 & 0.324817360181243 & 0.649634720362486 & 0.675182639818757 \tabularnewline
133 & 0.296360065908994 & 0.592720131817989 & 0.703639934091006 \tabularnewline
134 & 0.240635594370802 & 0.481271188741605 & 0.759364405629198 \tabularnewline
135 & 0.193995744095210 & 0.387991488190421 & 0.80600425590479 \tabularnewline
136 & 0.294244309989339 & 0.588488619978678 & 0.705755690010661 \tabularnewline
137 & 0.291118719208701 & 0.582237438417401 & 0.7088812807913 \tabularnewline
138 & 0.265104972475257 & 0.530209944950513 & 0.734895027524743 \tabularnewline
139 & 0.217103748557582 & 0.434207497115164 & 0.782896251442418 \tabularnewline
140 & 0.164034116902803 & 0.328068233805606 & 0.835965883097197 \tabularnewline
141 & 0.158622409057566 & 0.317244818115132 & 0.841377590942434 \tabularnewline
142 & 0.134328125934156 & 0.268656251868313 & 0.865671874065844 \tabularnewline
143 & 0.100297039067023 & 0.200594078134046 & 0.899702960932977 \tabularnewline
144 & 0.0655006383820761 & 0.131001276764152 & 0.934499361617924 \tabularnewline
145 & 0.0500815645739186 & 0.100163129147837 & 0.949918435426081 \tabularnewline
146 & 0.291558151897403 & 0.583116303794807 & 0.708441848102596 \tabularnewline
147 & 0.423915610587416 & 0.847831221174832 & 0.576084389412584 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104211&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]9[/C][C]0.176701688362858[/C][C]0.353403376725717[/C][C]0.823298311637142[/C][/ROW]
[ROW][C]10[/C][C]0.216214433986386[/C][C]0.432428867972772[/C][C]0.783785566013614[/C][/ROW]
[ROW][C]11[/C][C]0.750496925650071[/C][C]0.499006148699858[/C][C]0.249503074349929[/C][/ROW]
[ROW][C]12[/C][C]0.643970793491136[/C][C]0.712058413017727[/C][C]0.356029206508864[/C][/ROW]
[ROW][C]13[/C][C]0.537939452809304[/C][C]0.924121094381392[/C][C]0.462060547190696[/C][/ROW]
[ROW][C]14[/C][C]0.517503249405494[/C][C]0.964993501189013[/C][C]0.482496750594506[/C][/ROW]
[ROW][C]15[/C][C]0.722396889653368[/C][C]0.555206220693263[/C][C]0.277603110346632[/C][/ROW]
[ROW][C]16[/C][C]0.656073425991334[/C][C]0.687853148017331[/C][C]0.343926574008666[/C][/ROW]
[ROW][C]17[/C][C]0.577160749113609[/C][C]0.845678501772782[/C][C]0.422839250886391[/C][/ROW]
[ROW][C]18[/C][C]0.491164894913154[/C][C]0.982329789826307[/C][C]0.508835105086846[/C][/ROW]
[ROW][C]19[/C][C]0.40543651991273[/C][C]0.81087303982546[/C][C]0.59456348008727[/C][/ROW]
[ROW][C]20[/C][C]0.413262040593272[/C][C]0.826524081186545[/C][C]0.586737959406728[/C][/ROW]
[ROW][C]21[/C][C]0.35010815972842[/C][C]0.70021631945684[/C][C]0.64989184027158[/C][/ROW]
[ROW][C]22[/C][C]0.29139017293595[/C][C]0.5827803458719[/C][C]0.70860982706405[/C][/ROW]
[ROW][C]23[/C][C]0.245201350096803[/C][C]0.490402700193606[/C][C]0.754798649903197[/C][/ROW]
[ROW][C]24[/C][C]0.217776917274023[/C][C]0.435553834548045[/C][C]0.782223082725977[/C][/ROW]
[ROW][C]25[/C][C]0.168770752694726[/C][C]0.337541505389451[/C][C]0.831229247305274[/C][/ROW]
[ROW][C]26[/C][C]0.136162987084468[/C][C]0.272325974168936[/C][C]0.863837012915532[/C][/ROW]
[ROW][C]27[/C][C]0.101400054450785[/C][C]0.20280010890157[/C][C]0.898599945549215[/C][/ROW]
[ROW][C]28[/C][C]0.142960008084988[/C][C]0.285920016169975[/C][C]0.857039991915012[/C][/ROW]
[ROW][C]29[/C][C]0.123844789444848[/C][C]0.247689578889696[/C][C]0.876155210555152[/C][/ROW]
[ROW][C]30[/C][C]0.109001925824809[/C][C]0.218003851649619[/C][C]0.89099807417519[/C][/ROW]
[ROW][C]31[/C][C]0.0814715842515424[/C][C]0.162943168503085[/C][C]0.918528415748458[/C][/ROW]
[ROW][C]32[/C][C]0.0648220865926526[/C][C]0.129644173185305[/C][C]0.935177913407347[/C][/ROW]
[ROW][C]33[/C][C]0.0536559356865913[/C][C]0.107311871373183[/C][C]0.946344064313409[/C][/ROW]
[ROW][C]34[/C][C]0.0580284198875619[/C][C]0.116056839775124[/C][C]0.941971580112438[/C][/ROW]
[ROW][C]35[/C][C]0.0482870951902044[/C][C]0.0965741903804087[/C][C]0.951712904809796[/C][/ROW]
[ROW][C]36[/C][C]0.0453193914969526[/C][C]0.0906387829939052[/C][C]0.954680608503047[/C][/ROW]
[ROW][C]37[/C][C]0.036856457233436[/C][C]0.073712914466872[/C][C]0.963143542766564[/C][/ROW]
[ROW][C]38[/C][C]0.0459930341237943[/C][C]0.0919860682475885[/C][C]0.954006965876206[/C][/ROW]
[ROW][C]39[/C][C]0.0363942419520223[/C][C]0.0727884839040446[/C][C]0.963605758047978[/C][/ROW]
[ROW][C]40[/C][C]0.0264372873974029[/C][C]0.0528745747948057[/C][C]0.973562712602597[/C][/ROW]
[ROW][C]41[/C][C]0.0208999825892216[/C][C]0.0417999651784432[/C][C]0.979100017410778[/C][/ROW]
[ROW][C]42[/C][C]0.0160845315366922[/C][C]0.0321690630733843[/C][C]0.983915468463308[/C][/ROW]
[ROW][C]43[/C][C]0.0275282194605323[/C][C]0.0550564389210646[/C][C]0.972471780539468[/C][/ROW]
[ROW][C]44[/C][C]0.0226534618771412[/C][C]0.0453069237542824[/C][C]0.97734653812286[/C][/ROW]
[ROW][C]45[/C][C]0.0165654984776031[/C][C]0.0331309969552062[/C][C]0.983434501522397[/C][/ROW]
[ROW][C]46[/C][C]0.0162050000201964[/C][C]0.0324100000403928[/C][C]0.983794999979804[/C][/ROW]
[ROW][C]47[/C][C]0.0115383576921745[/C][C]0.023076715384349[/C][C]0.988461642307826[/C][/ROW]
[ROW][C]48[/C][C]0.0228862285194034[/C][C]0.0457724570388068[/C][C]0.977113771480597[/C][/ROW]
[ROW][C]49[/C][C]0.0169553255919376[/C][C]0.0339106511838752[/C][C]0.983044674408062[/C][/ROW]
[ROW][C]50[/C][C]0.0130714625368336[/C][C]0.0261429250736671[/C][C]0.986928537463166[/C][/ROW]
[ROW][C]51[/C][C]0.0101804942528943[/C][C]0.0203609885057885[/C][C]0.989819505747106[/C][/ROW]
[ROW][C]52[/C][C]0.00829219156869507[/C][C]0.0165843831373901[/C][C]0.991707808431305[/C][/ROW]
[ROW][C]53[/C][C]0.00736694181339918[/C][C]0.0147338836267984[/C][C]0.9926330581866[/C][/ROW]
[ROW][C]54[/C][C]0.00568374289598142[/C][C]0.0113674857919628[/C][C]0.994316257104018[/C][/ROW]
[ROW][C]55[/C][C]0.00716916443306949[/C][C]0.0143383288661390[/C][C]0.99283083556693[/C][/ROW]
[ROW][C]56[/C][C]0.0053322466721082[/C][C]0.0106644933442164[/C][C]0.994667753327892[/C][/ROW]
[ROW][C]57[/C][C]0.00438878370568868[/C][C]0.00877756741137736[/C][C]0.995611216294311[/C][/ROW]
[ROW][C]58[/C][C]0.00330562039920414[/C][C]0.00661124079840828[/C][C]0.996694379600796[/C][/ROW]
[ROW][C]59[/C][C]0.00387884847137987[/C][C]0.00775769694275974[/C][C]0.99612115152862[/C][/ROW]
[ROW][C]60[/C][C]0.00277188833769017[/C][C]0.00554377667538033[/C][C]0.99722811166231[/C][/ROW]
[ROW][C]61[/C][C]0.00266406261071017[/C][C]0.00532812522142034[/C][C]0.99733593738929[/C][/ROW]
[ROW][C]62[/C][C]0.0023298933184891[/C][C]0.0046597866369782[/C][C]0.99767010668151[/C][/ROW]
[ROW][C]63[/C][C]0.00348781810709336[/C][C]0.00697563621418673[/C][C]0.996512181892907[/C][/ROW]
[ROW][C]64[/C][C]0.00250790886549088[/C][C]0.00501581773098177[/C][C]0.99749209113451[/C][/ROW]
[ROW][C]65[/C][C]0.00998298547704088[/C][C]0.0199659709540818[/C][C]0.99001701452296[/C][/ROW]
[ROW][C]66[/C][C]0.038925390069457[/C][C]0.077850780138914[/C][C]0.961074609930543[/C][/ROW]
[ROW][C]67[/C][C]0.0472097882212088[/C][C]0.0944195764424176[/C][C]0.952790211778791[/C][/ROW]
[ROW][C]68[/C][C]0.141034172510859[/C][C]0.282068345021719[/C][C]0.85896582748914[/C][/ROW]
[ROW][C]69[/C][C]0.124604685076566[/C][C]0.249209370153132[/C][C]0.875395314923434[/C][/ROW]
[ROW][C]70[/C][C]0.342274584318185[/C][C]0.68454916863637[/C][C]0.657725415681815[/C][/ROW]
[ROW][C]71[/C][C]0.329796594209915[/C][C]0.659593188419831[/C][C]0.670203405790085[/C][/ROW]
[ROW][C]72[/C][C]0.295919121074315[/C][C]0.591838242148631[/C][C]0.704080878925685[/C][/ROW]
[ROW][C]73[/C][C]0.267830919398994[/C][C]0.535661838797987[/C][C]0.732169080601006[/C][/ROW]
[ROW][C]74[/C][C]0.249338986697130[/C][C]0.498677973394259[/C][C]0.75066101330287[/C][/ROW]
[ROW][C]75[/C][C]0.213390524242933[/C][C]0.426781048485866[/C][C]0.786609475757067[/C][/ROW]
[ROW][C]76[/C][C]0.220541584070749[/C][C]0.441083168141499[/C][C]0.77945841592925[/C][/ROW]
[ROW][C]77[/C][C]0.209199356205470[/C][C]0.418398712410941[/C][C]0.79080064379453[/C][/ROW]
[ROW][C]78[/C][C]0.184316223438394[/C][C]0.368632446876788[/C][C]0.815683776561606[/C][/ROW]
[ROW][C]79[/C][C]0.202701872466568[/C][C]0.405403744933135[/C][C]0.797298127533432[/C][/ROW]
[ROW][C]80[/C][C]0.226418524237215[/C][C]0.45283704847443[/C][C]0.773581475762785[/C][/ROW]
[ROW][C]81[/C][C]0.242423019750063[/C][C]0.484846039500126[/C][C]0.757576980249937[/C][/ROW]
[ROW][C]82[/C][C]0.219765909710600[/C][C]0.439531819421201[/C][C]0.7802340902894[/C][/ROW]
[ROW][C]83[/C][C]0.190151579390761[/C][C]0.380303158781522[/C][C]0.80984842060924[/C][/ROW]
[ROW][C]84[/C][C]0.164105745509560[/C][C]0.328211491019120[/C][C]0.83589425449044[/C][/ROW]
[ROW][C]85[/C][C]0.23431059936336[/C][C]0.46862119872672[/C][C]0.76568940063664[/C][/ROW]
[ROW][C]86[/C][C]0.204790841596158[/C][C]0.409581683192317[/C][C]0.795209158403842[/C][/ROW]
[ROW][C]87[/C][C]0.211676270519884[/C][C]0.423352541039769[/C][C]0.788323729480116[/C][/ROW]
[ROW][C]88[/C][C]0.184028437174001[/C][C]0.368056874348002[/C][C]0.815971562825999[/C][/ROW]
[ROW][C]89[/C][C]0.162548153606388[/C][C]0.325096307212777[/C][C]0.837451846393612[/C][/ROW]
[ROW][C]90[/C][C]0.144164294036662[/C][C]0.288328588073324[/C][C]0.855835705963338[/C][/ROW]
[ROW][C]91[/C][C]0.132409170692458[/C][C]0.264818341384916[/C][C]0.867590829307542[/C][/ROW]
[ROW][C]92[/C][C]0.109627944015720[/C][C]0.219255888031441[/C][C]0.89037205598428[/C][/ROW]
[ROW][C]93[/C][C]0.124195316072315[/C][C]0.248390632144629[/C][C]0.875804683927685[/C][/ROW]
[ROW][C]94[/C][C]0.152096993681625[/C][C]0.30419398736325[/C][C]0.847903006318375[/C][/ROW]
[ROW][C]95[/C][C]0.126465040732893[/C][C]0.252930081465786[/C][C]0.873534959267107[/C][/ROW]
[ROW][C]96[/C][C]0.118967756363122[/C][C]0.237935512726244[/C][C]0.881032243636878[/C][/ROW]
[ROW][C]97[/C][C]0.250098341160801[/C][C]0.500196682321602[/C][C]0.749901658839199[/C][/ROW]
[ROW][C]98[/C][C]0.238640285091686[/C][C]0.477280570183372[/C][C]0.761359714908314[/C][/ROW]
[ROW][C]99[/C][C]0.221739375613355[/C][C]0.44347875122671[/C][C]0.778260624386645[/C][/ROW]
[ROW][C]100[/C][C]0.190474616031644[/C][C]0.380949232063288[/C][C]0.809525383968356[/C][/ROW]
[ROW][C]101[/C][C]0.237942603209298[/C][C]0.475885206418596[/C][C]0.762057396790702[/C][/ROW]
[ROW][C]102[/C][C]0.237328964758319[/C][C]0.474657929516638[/C][C]0.762671035241681[/C][/ROW]
[ROW][C]103[/C][C]0.372484086030734[/C][C]0.744968172061468[/C][C]0.627515913969266[/C][/ROW]
[ROW][C]104[/C][C]0.492550213353636[/C][C]0.985100426707272[/C][C]0.507449786646364[/C][/ROW]
[ROW][C]105[/C][C]0.701656488535636[/C][C]0.596687022928728[/C][C]0.298343511464364[/C][/ROW]
[ROW][C]106[/C][C]0.706432512948459[/C][C]0.587134974103082[/C][C]0.293567487051541[/C][/ROW]
[ROW][C]107[/C][C]0.663066037145203[/C][C]0.673867925709595[/C][C]0.336933962854797[/C][/ROW]
[ROW][C]108[/C][C]0.626758927660688[/C][C]0.746482144678623[/C][C]0.373241072339312[/C][/ROW]
[ROW][C]109[/C][C]0.581498533150739[/C][C]0.837002933698522[/C][C]0.418501466849261[/C][/ROW]
[ROW][C]110[/C][C]0.545177852002475[/C][C]0.90964429599505[/C][C]0.454822147997525[/C][/ROW]
[ROW][C]111[/C][C]0.517586550607751[/C][C]0.964826898784498[/C][C]0.482413449392249[/C][/ROW]
[ROW][C]112[/C][C]0.488651585581818[/C][C]0.977303171163636[/C][C]0.511348414418182[/C][/ROW]
[ROW][C]113[/C][C]0.620336749201309[/C][C]0.759326501597382[/C][C]0.379663250798691[/C][/ROW]
[ROW][C]114[/C][C]0.570353683275966[/C][C]0.859292633448067[/C][C]0.429646316724034[/C][/ROW]
[ROW][C]115[/C][C]0.545781278284308[/C][C]0.908437443431384[/C][C]0.454218721715692[/C][/ROW]
[ROW][C]116[/C][C]0.529415614440987[/C][C]0.941168771118027[/C][C]0.470584385559013[/C][/ROW]
[ROW][C]117[/C][C]0.512449667976811[/C][C]0.975100664046377[/C][C]0.487550332023189[/C][/ROW]
[ROW][C]118[/C][C]0.57254938968662[/C][C]0.854901220626759[/C][C]0.427450610313379[/C][/ROW]
[ROW][C]119[/C][C]0.564685037367017[/C][C]0.870629925265966[/C][C]0.435314962632983[/C][/ROW]
[ROW][C]120[/C][C]0.522849188459567[/C][C]0.954301623080865[/C][C]0.477150811540433[/C][/ROW]
[ROW][C]121[/C][C]0.465063024739482[/C][C]0.930126049478963[/C][C]0.534936975260518[/C][/ROW]
[ROW][C]122[/C][C]0.513536584365759[/C][C]0.972926831268482[/C][C]0.486463415634241[/C][/ROW]
[ROW][C]123[/C][C]0.509994749853180[/C][C]0.980010500293639[/C][C]0.490005250146820[/C][/ROW]
[ROW][C]124[/C][C]0.486809002638292[/C][C]0.973618005276585[/C][C]0.513190997361708[/C][/ROW]
[ROW][C]125[/C][C]0.438353004275165[/C][C]0.87670600855033[/C][C]0.561646995724835[/C][/ROW]
[ROW][C]126[/C][C]0.488772538074590[/C][C]0.977545076149179[/C][C]0.511227461925410[/C][/ROW]
[ROW][C]127[/C][C]0.456280665115092[/C][C]0.912561330230184[/C][C]0.543719334884908[/C][/ROW]
[ROW][C]128[/C][C]0.412379973730382[/C][C]0.824759947460765[/C][C]0.587620026269618[/C][/ROW]
[ROW][C]129[/C][C]0.380576876855739[/C][C]0.761153753711479[/C][C]0.619423123144261[/C][/ROW]
[ROW][C]130[/C][C]0.447283653869721[/C][C]0.894567307739442[/C][C]0.552716346130279[/C][/ROW]
[ROW][C]131[/C][C]0.385074955987900[/C][C]0.770149911975801[/C][C]0.6149250440121[/C][/ROW]
[ROW][C]132[/C][C]0.324817360181243[/C][C]0.649634720362486[/C][C]0.675182639818757[/C][/ROW]
[ROW][C]133[/C][C]0.296360065908994[/C][C]0.592720131817989[/C][C]0.703639934091006[/C][/ROW]
[ROW][C]134[/C][C]0.240635594370802[/C][C]0.481271188741605[/C][C]0.759364405629198[/C][/ROW]
[ROW][C]135[/C][C]0.193995744095210[/C][C]0.387991488190421[/C][C]0.80600425590479[/C][/ROW]
[ROW][C]136[/C][C]0.294244309989339[/C][C]0.588488619978678[/C][C]0.705755690010661[/C][/ROW]
[ROW][C]137[/C][C]0.291118719208701[/C][C]0.582237438417401[/C][C]0.7088812807913[/C][/ROW]
[ROW][C]138[/C][C]0.265104972475257[/C][C]0.530209944950513[/C][C]0.734895027524743[/C][/ROW]
[ROW][C]139[/C][C]0.217103748557582[/C][C]0.434207497115164[/C][C]0.782896251442418[/C][/ROW]
[ROW][C]140[/C][C]0.164034116902803[/C][C]0.328068233805606[/C][C]0.835965883097197[/C][/ROW]
[ROW][C]141[/C][C]0.158622409057566[/C][C]0.317244818115132[/C][C]0.841377590942434[/C][/ROW]
[ROW][C]142[/C][C]0.134328125934156[/C][C]0.268656251868313[/C][C]0.865671874065844[/C][/ROW]
[ROW][C]143[/C][C]0.100297039067023[/C][C]0.200594078134046[/C][C]0.899702960932977[/C][/ROW]
[ROW][C]144[/C][C]0.0655006383820761[/C][C]0.131001276764152[/C][C]0.934499361617924[/C][/ROW]
[ROW][C]145[/C][C]0.0500815645739186[/C][C]0.100163129147837[/C][C]0.949918435426081[/C][/ROW]
[ROW][C]146[/C][C]0.291558151897403[/C][C]0.583116303794807[/C][C]0.708441848102596[/C][/ROW]
[ROW][C]147[/C][C]0.423915610587416[/C][C]0.847831221174832[/C][C]0.576084389412584[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104211&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104211&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
90.1767016883628580.3534033767257170.823298311637142
100.2162144339863860.4324288679727720.783785566013614
110.7504969256500710.4990061486998580.249503074349929
120.6439707934911360.7120584130177270.356029206508864
130.5379394528093040.9241210943813920.462060547190696
140.5175032494054940.9649935011890130.482496750594506
150.7223968896533680.5552062206932630.277603110346632
160.6560734259913340.6878531480173310.343926574008666
170.5771607491136090.8456785017727820.422839250886391
180.4911648949131540.9823297898263070.508835105086846
190.405436519912730.810873039825460.59456348008727
200.4132620405932720.8265240811865450.586737959406728
210.350108159728420.700216319456840.64989184027158
220.291390172935950.58278034587190.70860982706405
230.2452013500968030.4904027001936060.754798649903197
240.2177769172740230.4355538345480450.782223082725977
250.1687707526947260.3375415053894510.831229247305274
260.1361629870844680.2723259741689360.863837012915532
270.1014000544507850.202800108901570.898599945549215
280.1429600080849880.2859200161699750.857039991915012
290.1238447894448480.2476895788896960.876155210555152
300.1090019258248090.2180038516496190.89099807417519
310.08147158425154240.1629431685030850.918528415748458
320.06482208659265260.1296441731853050.935177913407347
330.05365593568659130.1073118713731830.946344064313409
340.05802841988756190.1160568397751240.941971580112438
350.04828709519020440.09657419038040870.951712904809796
360.04531939149695260.09063878299390520.954680608503047
370.0368564572334360.0737129144668720.963143542766564
380.04599303412379430.09198606824758850.954006965876206
390.03639424195202230.07278848390404460.963605758047978
400.02643728739740290.05287457479480570.973562712602597
410.02089998258922160.04179996517844320.979100017410778
420.01608453153669220.03216906307338430.983915468463308
430.02752821946053230.05505643892106460.972471780539468
440.02265346187714120.04530692375428240.97734653812286
450.01656549847760310.03313099695520620.983434501522397
460.01620500002019640.03241000004039280.983794999979804
470.01153835769217450.0230767153843490.988461642307826
480.02288622851940340.04577245703880680.977113771480597
490.01695532559193760.03391065118387520.983044674408062
500.01307146253683360.02614292507366710.986928537463166
510.01018049425289430.02036098850578850.989819505747106
520.008292191568695070.01658438313739010.991707808431305
530.007366941813399180.01473388362679840.9926330581866
540.005683742895981420.01136748579196280.994316257104018
550.007169164433069490.01433832886613900.99283083556693
560.00533224667210820.01066449334421640.994667753327892
570.004388783705688680.008777567411377360.995611216294311
580.003305620399204140.006611240798408280.996694379600796
590.003878848471379870.007757696942759740.99612115152862
600.002771888337690170.005543776675380330.99722811166231
610.002664062610710170.005328125221420340.99733593738929
620.00232989331848910.00465978663697820.99767010668151
630.003487818107093360.006975636214186730.996512181892907
640.002507908865490880.005015817730981770.99749209113451
650.009982985477040880.01996597095408180.99001701452296
660.0389253900694570.0778507801389140.961074609930543
670.04720978822120880.09441957644241760.952790211778791
680.1410341725108590.2820683450217190.85896582748914
690.1246046850765660.2492093701531320.875395314923434
700.3422745843181850.684549168636370.657725415681815
710.3297965942099150.6595931884198310.670203405790085
720.2959191210743150.5918382421486310.704080878925685
730.2678309193989940.5356618387979870.732169080601006
740.2493389866971300.4986779733942590.75066101330287
750.2133905242429330.4267810484858660.786609475757067
760.2205415840707490.4410831681414990.77945841592925
770.2091993562054700.4183987124109410.79080064379453
780.1843162234383940.3686324468767880.815683776561606
790.2027018724665680.4054037449331350.797298127533432
800.2264185242372150.452837048474430.773581475762785
810.2424230197500630.4848460395001260.757576980249937
820.2197659097106000.4395318194212010.7802340902894
830.1901515793907610.3803031587815220.80984842060924
840.1641057455095600.3282114910191200.83589425449044
850.234310599363360.468621198726720.76568940063664
860.2047908415961580.4095816831923170.795209158403842
870.2116762705198840.4233525410397690.788323729480116
880.1840284371740010.3680568743480020.815971562825999
890.1625481536063880.3250963072127770.837451846393612
900.1441642940366620.2883285880733240.855835705963338
910.1324091706924580.2648183413849160.867590829307542
920.1096279440157200.2192558880314410.89037205598428
930.1241953160723150.2483906321446290.875804683927685
940.1520969936816250.304193987363250.847903006318375
950.1264650407328930.2529300814657860.873534959267107
960.1189677563631220.2379355127262440.881032243636878
970.2500983411608010.5001966823216020.749901658839199
980.2386402850916860.4772805701833720.761359714908314
990.2217393756133550.443478751226710.778260624386645
1000.1904746160316440.3809492320632880.809525383968356
1010.2379426032092980.4758852064185960.762057396790702
1020.2373289647583190.4746579295166380.762671035241681
1030.3724840860307340.7449681720614680.627515913969266
1040.4925502133536360.9851004267072720.507449786646364
1050.7016564885356360.5966870229287280.298343511464364
1060.7064325129484590.5871349741030820.293567487051541
1070.6630660371452030.6738679257095950.336933962854797
1080.6267589276606880.7464821446786230.373241072339312
1090.5814985331507390.8370029336985220.418501466849261
1100.5451778520024750.909644295995050.454822147997525
1110.5175865506077510.9648268987844980.482413449392249
1120.4886515855818180.9773031711636360.511348414418182
1130.6203367492013090.7593265015973820.379663250798691
1140.5703536832759660.8592926334480670.429646316724034
1150.5457812782843080.9084374434313840.454218721715692
1160.5294156144409870.9411687711180270.470584385559013
1170.5124496679768110.9751006640463770.487550332023189
1180.572549389686620.8549012206267590.427450610313379
1190.5646850373670170.8706299252659660.435314962632983
1200.5228491884595670.9543016230808650.477150811540433
1210.4650630247394820.9301260494789630.534936975260518
1220.5135365843657590.9729268312684820.486463415634241
1230.5099947498531800.9800105002936390.490005250146820
1240.4868090026382920.9736180052765850.513190997361708
1250.4383530042751650.876706008550330.561646995724835
1260.4887725380745900.9775450761491790.511227461925410
1270.4562806651150920.9125613302301840.543719334884908
1280.4123799737303820.8247599474607650.587620026269618
1290.3805768768557390.7611537537114790.619423123144261
1300.4472836538697210.8945673077394420.552716346130279
1310.3850749559879000.7701499119758010.6149250440121
1320.3248173601812430.6496347203624860.675182639818757
1330.2963600659089940.5927201318179890.703639934091006
1340.2406355943708020.4812711887416050.759364405629198
1350.1939957440952100.3879914881904210.80600425590479
1360.2942443099893390.5884886199786780.705755690010661
1370.2911187192087010.5822374384174010.7088812807913
1380.2651049724752570.5302099449505130.734895027524743
1390.2171037485575820.4342074971151640.782896251442418
1400.1640341169028030.3280682338056060.835965883097197
1410.1586224090575660.3172448181151320.841377590942434
1420.1343281259341560.2686562518683130.865671874065844
1430.1002970390670230.2005940781340460.899702960932977
1440.06550063838207610.1310012767641520.934499361617924
1450.05008156457391860.1001631291478370.949918435426081
1460.2915581518974030.5831163037948070.708441848102596
1470.4239156105874160.8478312211748320.576084389412584






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level80.0575539568345324NOK
5% type I error level240.172661870503597NOK
10% type I error level330.237410071942446NOK

\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 & 8 & 0.0575539568345324 & NOK \tabularnewline
5% type I error level & 24 & 0.172661870503597 & NOK \tabularnewline
10% type I error level & 33 & 0.237410071942446 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104211&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]8[/C][C]0.0575539568345324[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]24[/C][C]0.172661870503597[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]33[/C][C]0.237410071942446[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104211&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104211&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 level80.0575539568345324NOK
5% type I error level240.172661870503597NOK
10% type I error level330.237410071942446NOK


Figures (Output of Computation)

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