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Author's title

Multiple lineair regression aantal werklozen(onder 25jaar) - Rente op basis...

Author*Unverified author*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationFri, 20 Nov 2009 03:11:45 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Nov/20/t1258711969i8ouxqg1ryszveq.htm/, Retrieved Thu, 28 Mar 2024 12:22:47 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58005, Retrieved Thu, 28 Mar 2024 12:22:47 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact167
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Data Series] [Rente op basis-he...] [2009-11-03 09:33:19] [1ff3eeaee490dfcff07aa4917fec66b8]
- RMPD    [Multiple Regression] [Multiple lineair ...] [2009-11-20 10:11:45] [6df9bd2792d60592b4a24994398a86db] [Current]
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Dataseries X:
127			2.75
123	127		2.75
118	123	127	2.55
114	118	123	2.5
108	114	118	2.5
111	108	114	2.1
151	111	108	2
159	151	111	2
158	159	151	2
148	158	159	2
138	148	158	2
137	138	148	2
136	137	138	2
133	136	137	2
126	133	136	2
120	126	133	2
114	120	126	2
116	114	120	2
153	116	114	2
162	153	116	2
161	162	153	2
149	161	162	2
139	149	161	2
135	139	149	2
130	135	139	2
127	130	135	2
122	127	130	2
117	122	127	2
112	117	122	2
113	112	117	2
149	113	112	2
157	149	113	2
157	157	149	2
147	157	157	2
137	147	157	2
132	137	147	2.21
125	132	137	2.25
123	125	132	2.25
117	123	125	2.45
114	117	123	2.5
111	114	117	2.5
112	111	114	2.64
144	112	111	2.75
150	144	112	2.93
149	150	144	3
134	149	150	3.17
123	134	149	3.25
116	123	134	3.39
117	116	123	3.5
111	117	116	3.5
105	111	117	3.65
102	105	111	3.75
95	102	105	3.75
93	95	102	3.9
124	93	95	4
130	124	93	4
124	130	124	4
115	124	130	4
106	115	124	4
105	106	115	4
105	105	106	4
101	105	105	4
95	101	105	4
93	95	101	4
84	93	95	4
87	84	93	4
116	87	84	4.18
120	116	87	4.25
117	120	116	4.25
109	117	120	3.97
105	109	117	3.42
107	105	109	2.75








Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135
R Framework error message
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.

\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 & 3 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
R Framework error message & 
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.
\tabularnewline \hline \end{tabular} %Source: https://freestatistics.org/blog/index.php?pk=58005&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]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.
[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=58005&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58005&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135
R Framework error message
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.







Multiple Linear Regression - Estimated Regression Equation
Werkloosheid[t] = + 158.519300076387 -0.299884858226074Rente[t] -100.778317979711M1[t] + 16.5659692799141M2[t] -116.562586977264M3[t] + 24.9440899381617M4[t] -119.081511013485M5[t] + 22.3906427702763M6[t] -121.575435049707M7[t] + 13.4711814057765M8[t] -121.911850935308M9[t] + 3.15225736955490M10[t] -117.081929278324M11[t] + 0.0682463233919803t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Werkloosheid[t] =  +  158.519300076387 -0.299884858226074Rente[t] -100.778317979711M1[t] +  16.5659692799141M2[t] -116.562586977264M3[t] +  24.9440899381617M4[t] -119.081511013485M5[t] +  22.3906427702763M6[t] -121.575435049707M7[t] +  13.4711814057765M8[t] -121.911850935308M9[t] +  3.15225736955490M10[t] -117.081929278324M11[t] +  0.0682463233919803t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58005&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Werkloosheid[t] =  +  158.519300076387 -0.299884858226074Rente[t] -100.778317979711M1[t] +  16.5659692799141M2[t] -116.562586977264M3[t] +  24.9440899381617M4[t] -119.081511013485M5[t] +  22.3906427702763M6[t] -121.575435049707M7[t] +  13.4711814057765M8[t] -121.911850935308M9[t] +  3.15225736955490M10[t] -117.081929278324M11[t] +  0.0682463233919803t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58005&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58005&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
Werkloosheid[t] = + 158.519300076387 -0.299884858226074Rente[t] -100.778317979711M1[t] + 16.5659692799141M2[t] -116.562586977264M3[t] + 24.9440899381617M4[t] -119.081511013485M5[t] + 22.3906427702763M6[t] -121.575435049707M7[t] + 13.4711814057765M8[t] -121.911850935308M9[t] + 3.15225736955490M10[t] -117.081929278324M11[t] + 0.0682463233919803t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)158.51930007638714.25391111.121100
Rente-0.2998848582260740.102336-2.93040.0048350.002418
M1-100.77831797971110.235135-9.846300
M216.565969279914110.2236561.62040.1105810.05529
M3-116.56258697726410.381712-11.227700
M424.944089938161710.3804482.4030.0194810.009741
M5-119.08151101348510.246385-11.621800
M622.390642770276310.4047792.1520.0355750.017788
M7-121.57543504970710.18606-11.935500
M813.471181405776510.3245471.30480.1971240.098562
M9-121.91185093530810.176989-11.979200
M103.1522573695549010.2106730.30870.7586390.37932
M11-117.08192927832410.308487-11.357800
t0.06824632339198030.1053440.64780.5196430.259821

\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) & 158.519300076387 & 14.253911 & 11.1211 & 0 & 0 \tabularnewline
Rente & -0.299884858226074 & 0.102336 & -2.9304 & 0.004835 & 0.002418 \tabularnewline
M1 & -100.778317979711 & 10.235135 & -9.8463 & 0 & 0 \tabularnewline
M2 & 16.5659692799141 & 10.223656 & 1.6204 & 0.110581 & 0.05529 \tabularnewline
M3 & -116.562586977264 & 10.381712 & -11.2277 & 0 & 0 \tabularnewline
M4 & 24.9440899381617 & 10.380448 & 2.403 & 0.019481 & 0.009741 \tabularnewline
M5 & -119.081511013485 & 10.246385 & -11.6218 & 0 & 0 \tabularnewline
M6 & 22.3906427702763 & 10.404779 & 2.152 & 0.035575 & 0.017788 \tabularnewline
M7 & -121.575435049707 & 10.18606 & -11.9355 & 0 & 0 \tabularnewline
M8 & 13.4711814057765 & 10.324547 & 1.3048 & 0.197124 & 0.098562 \tabularnewline
M9 & -121.911850935308 & 10.176989 & -11.9792 & 0 & 0 \tabularnewline
M10 & 3.15225736955490 & 10.210673 & 0.3087 & 0.758639 & 0.37932 \tabularnewline
M11 & -117.081929278324 & 10.308487 & -11.3578 & 0 & 0 \tabularnewline
t & 0.0682463233919803 & 0.105344 & 0.6478 & 0.519643 & 0.259821 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58005&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]158.519300076387[/C][C]14.253911[/C][C]11.1211[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Rente[/C][C]-0.299884858226074[/C][C]0.102336[/C][C]-2.9304[/C][C]0.004835[/C][C]0.002418[/C][/ROW]
[ROW][C]M1[/C][C]-100.778317979711[/C][C]10.235135[/C][C]-9.8463[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M2[/C][C]16.5659692799141[/C][C]10.223656[/C][C]1.6204[/C][C]0.110581[/C][C]0.05529[/C][/ROW]
[ROW][C]M3[/C][C]-116.562586977264[/C][C]10.381712[/C][C]-11.2277[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M4[/C][C]24.9440899381617[/C][C]10.380448[/C][C]2.403[/C][C]0.019481[/C][C]0.009741[/C][/ROW]
[ROW][C]M5[/C][C]-119.081511013485[/C][C]10.246385[/C][C]-11.6218[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M6[/C][C]22.3906427702763[/C][C]10.404779[/C][C]2.152[/C][C]0.035575[/C][C]0.017788[/C][/ROW]
[ROW][C]M7[/C][C]-121.575435049707[/C][C]10.18606[/C][C]-11.9355[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M8[/C][C]13.4711814057765[/C][C]10.324547[/C][C]1.3048[/C][C]0.197124[/C][C]0.098562[/C][/ROW]
[ROW][C]M9[/C][C]-121.911850935308[/C][C]10.176989[/C][C]-11.9792[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M10[/C][C]3.15225736955490[/C][C]10.210673[/C][C]0.3087[/C][C]0.758639[/C][C]0.37932[/C][/ROW]
[ROW][C]M11[/C][C]-117.081929278324[/C][C]10.308487[/C][C]-11.3578[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]t[/C][C]0.0682463233919803[/C][C]0.105344[/C][C]0.6478[/C][C]0.519643[/C][C]0.259821[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58005&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58005&T=2

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)158.51930007638714.25391111.121100
Rente-0.2998848582260740.102336-2.93040.0048350.002418
M1-100.77831797971110.235135-9.846300
M216.565969279914110.2236561.62040.1105810.05529
M3-116.56258697726410.381712-11.227700
M424.944089938161710.3804482.4030.0194810.009741
M5-119.08151101348510.246385-11.621800
M622.390642770276310.4047792.1520.0355750.017788
M7-121.57543504970710.18606-11.935500
M813.471181405776510.3245471.30480.1971240.098562
M9-121.91185093530810.176989-11.979200
M103.1522573695549010.2106730.30870.7586390.37932
M11-117.08192927832410.308487-11.357800
t0.06824632339198030.1053440.64780.5196430.259821







Multiple Linear Regression - Regression Statistics
Multiple R0.971731181323792
R-squared0.944261488756932
Adjusted R-squared0.931768374167969
F-TEST (value)75.5825524558227
F-TEST (DF numerator)13
F-TEST (DF denominator)58
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation17.6191696745276
Sum Squared Residuals18005.2381211480

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.971731181323792 \tabularnewline
R-squared & 0.944261488756932 \tabularnewline
Adjusted R-squared & 0.931768374167969 \tabularnewline
F-TEST (value) & 75.5825524558227 \tabularnewline
F-TEST (DF numerator) & 13 \tabularnewline
F-TEST (DF denominator) & 58 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 17.6191696745276 \tabularnewline
Sum Squared Residuals & 18005.2381211480 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58005&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.971731181323792[/C][/ROW]
[ROW][C]R-squared[/C][C]0.944261488756932[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.931768374167969[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]75.5825524558227[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]13[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]58[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]17.6191696745276[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]18005.2381211480[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58005&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58005&T=3

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Regression Statistics
Multiple R0.971731181323792
R-squared0.944261488756932
Adjusted R-squared0.931768374167969
F-TEST (value)75.5825524558227
F-TEST (DF numerator)13
F-TEST (DF denominator)58
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation17.6191696745276
Sum Squared Residuals18005.2381211480







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
112756.98454505994670.015454940054
2123137.136385008374-14.1363850083740
32.756.77503879862289-4.02503879862289
4123145.650998313406-22.6509983134057
52.555.59214684208969-3.04214684208969
6118144.433583225208-26.4335832252084
72.55.03402460200857-2.53402460200857
8114137.150038798623-23.1500387986229
92.53.93444678851288-1.43444678851288
10108128.167146842090-20.1671468420897
112.1-3.094533236762215.19453323676221
12111126.950691268675-15.9506912686751
13210.9464918428261-8.9464918428261
14151142.7534986206958.24650137930508
152-4.401399649716296.40139964971629
16159139.27271759668419.7272824033164
172-3.784982456893165.78498245689316
18158134.45668420977423.5433157902265
192-3.143565264075.14356526407
20148125.97360035028422.0263996497163
212-3.043603644661285.04360364466128
22138118.79001754310719.2099824568931
2322.22269551733269-0.222695517332693
24137118.77310140259718.2268985974033
25219.5624540374077-17.5624540374077
26136135.7754481875210.224551812479244
2726.01387169422184-4.01387169422184
28133144.589946350778-11.5899463507785
2925.43074945414071-3.43074945414071
30126143.072646404355-17.0726464043552
3124.87262721405956-2.87262721405956
32120136.388871694222-16.3888716942218
3324.07293425879006-2.07293425879006
34114128.005749454141-14.0057494541407
352-2.056391191806784.05639119180678
36116126.789293880726-10.7892938807262
37211.6847490295553-9.68474902955532
38153142.89198609097210.1080139090279
392-3.663142462986955.66314246298695
40162140.31085964163921.689140358361
412-2.446955553711684.44695555371168
42161135.19494139650325.8050586034971
432-1.805538360888543.80553836088854
44149126.71185753701322.288142462987
452-0.8059221668015992.8059221668016
46139120.12804444628818.8719555537117
4725.6599164280967-3.6599164280967
48135120.11112830577814.8888716942218
49222.9996749481717-20.9996749481717
50130138.013129665380-8.01312966538044
5128.85132288853368-6.85132288853368
52127148.027167261542-21.0271672615424
5327.96831579022646-5.96831579022646
54122146.509867315119-24.5098673151192
5527.11030869191927-5.11030869191927
56117139.226322888534-22.2263228885337
5726.61050059487576-4.61050059487576
58112130.543315790226-18.5433157902265
5920.7810600025050291.21893999749497
60113129.026975358586-16.0269753585859
61214.8220850820932-12.8220850820932
62149145.4295524270583.57044757294218
632-0.825691268675152.82569126867515
64157143.14831083595113.8516891640492
652-0.2092740758520032.20927407585200
66157138.33227744904118.6677225509592
6720.4321431169711441.56785688302886
68147129.54930873132517.4506912686751
6921.731644169284190.268355830715807
70137122.36572592414814.634274075852
712.218.79725248063456-6.58725248063456
72132122.3488097836389.65119021636217

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 127 & 56.984545059946 & 70.015454940054 \tabularnewline
2 & 123 & 137.136385008374 & -14.1363850083740 \tabularnewline
3 & 2.75 & 6.77503879862289 & -4.02503879862289 \tabularnewline
4 & 123 & 145.650998313406 & -22.6509983134057 \tabularnewline
5 & 2.55 & 5.59214684208969 & -3.04214684208969 \tabularnewline
6 & 118 & 144.433583225208 & -26.4335832252084 \tabularnewline
7 & 2.5 & 5.03402460200857 & -2.53402460200857 \tabularnewline
8 & 114 & 137.150038798623 & -23.1500387986229 \tabularnewline
9 & 2.5 & 3.93444678851288 & -1.43444678851288 \tabularnewline
10 & 108 & 128.167146842090 & -20.1671468420897 \tabularnewline
11 & 2.1 & -3.09453323676221 & 5.19453323676221 \tabularnewline
12 & 111 & 126.950691268675 & -15.9506912686751 \tabularnewline
13 & 2 & 10.9464918428261 & -8.9464918428261 \tabularnewline
14 & 151 & 142.753498620695 & 8.24650137930508 \tabularnewline
15 & 2 & -4.40139964971629 & 6.40139964971629 \tabularnewline
16 & 159 & 139.272717596684 & 19.7272824033164 \tabularnewline
17 & 2 & -3.78498245689316 & 5.78498245689316 \tabularnewline
18 & 158 & 134.456684209774 & 23.5433157902265 \tabularnewline
19 & 2 & -3.14356526407 & 5.14356526407 \tabularnewline
20 & 148 & 125.973600350284 & 22.0263996497163 \tabularnewline
21 & 2 & -3.04360364466128 & 5.04360364466128 \tabularnewline
22 & 138 & 118.790017543107 & 19.2099824568931 \tabularnewline
23 & 2 & 2.22269551733269 & -0.222695517332693 \tabularnewline
24 & 137 & 118.773101402597 & 18.2268985974033 \tabularnewline
25 & 2 & 19.5624540374077 & -17.5624540374077 \tabularnewline
26 & 136 & 135.775448187521 & 0.224551812479244 \tabularnewline
27 & 2 & 6.01387169422184 & -4.01387169422184 \tabularnewline
28 & 133 & 144.589946350778 & -11.5899463507785 \tabularnewline
29 & 2 & 5.43074945414071 & -3.43074945414071 \tabularnewline
30 & 126 & 143.072646404355 & -17.0726464043552 \tabularnewline
31 & 2 & 4.87262721405956 & -2.87262721405956 \tabularnewline
32 & 120 & 136.388871694222 & -16.3888716942218 \tabularnewline
33 & 2 & 4.07293425879006 & -2.07293425879006 \tabularnewline
34 & 114 & 128.005749454141 & -14.0057494541407 \tabularnewline
35 & 2 & -2.05639119180678 & 4.05639119180678 \tabularnewline
36 & 116 & 126.789293880726 & -10.7892938807262 \tabularnewline
37 & 2 & 11.6847490295553 & -9.68474902955532 \tabularnewline
38 & 153 & 142.891986090972 & 10.1080139090279 \tabularnewline
39 & 2 & -3.66314246298695 & 5.66314246298695 \tabularnewline
40 & 162 & 140.310859641639 & 21.689140358361 \tabularnewline
41 & 2 & -2.44695555371168 & 4.44695555371168 \tabularnewline
42 & 161 & 135.194941396503 & 25.8050586034971 \tabularnewline
43 & 2 & -1.80553836088854 & 3.80553836088854 \tabularnewline
44 & 149 & 126.711857537013 & 22.288142462987 \tabularnewline
45 & 2 & -0.805922166801599 & 2.8059221668016 \tabularnewline
46 & 139 & 120.128044446288 & 18.8719555537117 \tabularnewline
47 & 2 & 5.6599164280967 & -3.6599164280967 \tabularnewline
48 & 135 & 120.111128305778 & 14.8888716942218 \tabularnewline
49 & 2 & 22.9996749481717 & -20.9996749481717 \tabularnewline
50 & 130 & 138.013129665380 & -8.01312966538044 \tabularnewline
51 & 2 & 8.85132288853368 & -6.85132288853368 \tabularnewline
52 & 127 & 148.027167261542 & -21.0271672615424 \tabularnewline
53 & 2 & 7.96831579022646 & -5.96831579022646 \tabularnewline
54 & 122 & 146.509867315119 & -24.5098673151192 \tabularnewline
55 & 2 & 7.11030869191927 & -5.11030869191927 \tabularnewline
56 & 117 & 139.226322888534 & -22.2263228885337 \tabularnewline
57 & 2 & 6.61050059487576 & -4.61050059487576 \tabularnewline
58 & 112 & 130.543315790226 & -18.5433157902265 \tabularnewline
59 & 2 & 0.781060002505029 & 1.21893999749497 \tabularnewline
60 & 113 & 129.026975358586 & -16.0269753585859 \tabularnewline
61 & 2 & 14.8220850820932 & -12.8220850820932 \tabularnewline
62 & 149 & 145.429552427058 & 3.57044757294218 \tabularnewline
63 & 2 & -0.82569126867515 & 2.82569126867515 \tabularnewline
64 & 157 & 143.148310835951 & 13.8516891640492 \tabularnewline
65 & 2 & -0.209274075852003 & 2.20927407585200 \tabularnewline
66 & 157 & 138.332277449041 & 18.6677225509592 \tabularnewline
67 & 2 & 0.432143116971144 & 1.56785688302886 \tabularnewline
68 & 147 & 129.549308731325 & 17.4506912686751 \tabularnewline
69 & 2 & 1.73164416928419 & 0.268355830715807 \tabularnewline
70 & 137 & 122.365725924148 & 14.634274075852 \tabularnewline
71 & 2.21 & 8.79725248063456 & -6.58725248063456 \tabularnewline
72 & 132 & 122.348809783638 & 9.65119021636217 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58005&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]127[/C][C]56.984545059946[/C][C]70.015454940054[/C][/ROW]
[ROW][C]2[/C][C]123[/C][C]137.136385008374[/C][C]-14.1363850083740[/C][/ROW]
[ROW][C]3[/C][C]2.75[/C][C]6.77503879862289[/C][C]-4.02503879862289[/C][/ROW]
[ROW][C]4[/C][C]123[/C][C]145.650998313406[/C][C]-22.6509983134057[/C][/ROW]
[ROW][C]5[/C][C]2.55[/C][C]5.59214684208969[/C][C]-3.04214684208969[/C][/ROW]
[ROW][C]6[/C][C]118[/C][C]144.433583225208[/C][C]-26.4335832252084[/C][/ROW]
[ROW][C]7[/C][C]2.5[/C][C]5.03402460200857[/C][C]-2.53402460200857[/C][/ROW]
[ROW][C]8[/C][C]114[/C][C]137.150038798623[/C][C]-23.1500387986229[/C][/ROW]
[ROW][C]9[/C][C]2.5[/C][C]3.93444678851288[/C][C]-1.43444678851288[/C][/ROW]
[ROW][C]10[/C][C]108[/C][C]128.167146842090[/C][C]-20.1671468420897[/C][/ROW]
[ROW][C]11[/C][C]2.1[/C][C]-3.09453323676221[/C][C]5.19453323676221[/C][/ROW]
[ROW][C]12[/C][C]111[/C][C]126.950691268675[/C][C]-15.9506912686751[/C][/ROW]
[ROW][C]13[/C][C]2[/C][C]10.9464918428261[/C][C]-8.9464918428261[/C][/ROW]
[ROW][C]14[/C][C]151[/C][C]142.753498620695[/C][C]8.24650137930508[/C][/ROW]
[ROW][C]15[/C][C]2[/C][C]-4.40139964971629[/C][C]6.40139964971629[/C][/ROW]
[ROW][C]16[/C][C]159[/C][C]139.272717596684[/C][C]19.7272824033164[/C][/ROW]
[ROW][C]17[/C][C]2[/C][C]-3.78498245689316[/C][C]5.78498245689316[/C][/ROW]
[ROW][C]18[/C][C]158[/C][C]134.456684209774[/C][C]23.5433157902265[/C][/ROW]
[ROW][C]19[/C][C]2[/C][C]-3.14356526407[/C][C]5.14356526407[/C][/ROW]
[ROW][C]20[/C][C]148[/C][C]125.973600350284[/C][C]22.0263996497163[/C][/ROW]
[ROW][C]21[/C][C]2[/C][C]-3.04360364466128[/C][C]5.04360364466128[/C][/ROW]
[ROW][C]22[/C][C]138[/C][C]118.790017543107[/C][C]19.2099824568931[/C][/ROW]
[ROW][C]23[/C][C]2[/C][C]2.22269551733269[/C][C]-0.222695517332693[/C][/ROW]
[ROW][C]24[/C][C]137[/C][C]118.773101402597[/C][C]18.2268985974033[/C][/ROW]
[ROW][C]25[/C][C]2[/C][C]19.5624540374077[/C][C]-17.5624540374077[/C][/ROW]
[ROW][C]26[/C][C]136[/C][C]135.775448187521[/C][C]0.224551812479244[/C][/ROW]
[ROW][C]27[/C][C]2[/C][C]6.01387169422184[/C][C]-4.01387169422184[/C][/ROW]
[ROW][C]28[/C][C]133[/C][C]144.589946350778[/C][C]-11.5899463507785[/C][/ROW]
[ROW][C]29[/C][C]2[/C][C]5.43074945414071[/C][C]-3.43074945414071[/C][/ROW]
[ROW][C]30[/C][C]126[/C][C]143.072646404355[/C][C]-17.0726464043552[/C][/ROW]
[ROW][C]31[/C][C]2[/C][C]4.87262721405956[/C][C]-2.87262721405956[/C][/ROW]
[ROW][C]32[/C][C]120[/C][C]136.388871694222[/C][C]-16.3888716942218[/C][/ROW]
[ROW][C]33[/C][C]2[/C][C]4.07293425879006[/C][C]-2.07293425879006[/C][/ROW]
[ROW][C]34[/C][C]114[/C][C]128.005749454141[/C][C]-14.0057494541407[/C][/ROW]
[ROW][C]35[/C][C]2[/C][C]-2.05639119180678[/C][C]4.05639119180678[/C][/ROW]
[ROW][C]36[/C][C]116[/C][C]126.789293880726[/C][C]-10.7892938807262[/C][/ROW]
[ROW][C]37[/C][C]2[/C][C]11.6847490295553[/C][C]-9.68474902955532[/C][/ROW]
[ROW][C]38[/C][C]153[/C][C]142.891986090972[/C][C]10.1080139090279[/C][/ROW]
[ROW][C]39[/C][C]2[/C][C]-3.66314246298695[/C][C]5.66314246298695[/C][/ROW]
[ROW][C]40[/C][C]162[/C][C]140.310859641639[/C][C]21.689140358361[/C][/ROW]
[ROW][C]41[/C][C]2[/C][C]-2.44695555371168[/C][C]4.44695555371168[/C][/ROW]
[ROW][C]42[/C][C]161[/C][C]135.194941396503[/C][C]25.8050586034971[/C][/ROW]
[ROW][C]43[/C][C]2[/C][C]-1.80553836088854[/C][C]3.80553836088854[/C][/ROW]
[ROW][C]44[/C][C]149[/C][C]126.711857537013[/C][C]22.288142462987[/C][/ROW]
[ROW][C]45[/C][C]2[/C][C]-0.805922166801599[/C][C]2.8059221668016[/C][/ROW]
[ROW][C]46[/C][C]139[/C][C]120.128044446288[/C][C]18.8719555537117[/C][/ROW]
[ROW][C]47[/C][C]2[/C][C]5.6599164280967[/C][C]-3.6599164280967[/C][/ROW]
[ROW][C]48[/C][C]135[/C][C]120.111128305778[/C][C]14.8888716942218[/C][/ROW]
[ROW][C]49[/C][C]2[/C][C]22.9996749481717[/C][C]-20.9996749481717[/C][/ROW]
[ROW][C]50[/C][C]130[/C][C]138.013129665380[/C][C]-8.01312966538044[/C][/ROW]
[ROW][C]51[/C][C]2[/C][C]8.85132288853368[/C][C]-6.85132288853368[/C][/ROW]
[ROW][C]52[/C][C]127[/C][C]148.027167261542[/C][C]-21.0271672615424[/C][/ROW]
[ROW][C]53[/C][C]2[/C][C]7.96831579022646[/C][C]-5.96831579022646[/C][/ROW]
[ROW][C]54[/C][C]122[/C][C]146.509867315119[/C][C]-24.5098673151192[/C][/ROW]
[ROW][C]55[/C][C]2[/C][C]7.11030869191927[/C][C]-5.11030869191927[/C][/ROW]
[ROW][C]56[/C][C]117[/C][C]139.226322888534[/C][C]-22.2263228885337[/C][/ROW]
[ROW][C]57[/C][C]2[/C][C]6.61050059487576[/C][C]-4.61050059487576[/C][/ROW]
[ROW][C]58[/C][C]112[/C][C]130.543315790226[/C][C]-18.5433157902265[/C][/ROW]
[ROW][C]59[/C][C]2[/C][C]0.781060002505029[/C][C]1.21893999749497[/C][/ROW]
[ROW][C]60[/C][C]113[/C][C]129.026975358586[/C][C]-16.0269753585859[/C][/ROW]
[ROW][C]61[/C][C]2[/C][C]14.8220850820932[/C][C]-12.8220850820932[/C][/ROW]
[ROW][C]62[/C][C]149[/C][C]145.429552427058[/C][C]3.57044757294218[/C][/ROW]
[ROW][C]63[/C][C]2[/C][C]-0.82569126867515[/C][C]2.82569126867515[/C][/ROW]
[ROW][C]64[/C][C]157[/C][C]143.148310835951[/C][C]13.8516891640492[/C][/ROW]
[ROW][C]65[/C][C]2[/C][C]-0.209274075852003[/C][C]2.20927407585200[/C][/ROW]
[ROW][C]66[/C][C]157[/C][C]138.332277449041[/C][C]18.6677225509592[/C][/ROW]
[ROW][C]67[/C][C]2[/C][C]0.432143116971144[/C][C]1.56785688302886[/C][/ROW]
[ROW][C]68[/C][C]147[/C][C]129.549308731325[/C][C]17.4506912686751[/C][/ROW]
[ROW][C]69[/C][C]2[/C][C]1.73164416928419[/C][C]0.268355830715807[/C][/ROW]
[ROW][C]70[/C][C]137[/C][C]122.365725924148[/C][C]14.634274075852[/C][/ROW]
[ROW][C]71[/C][C]2.21[/C][C]8.79725248063456[/C][C]-6.58725248063456[/C][/ROW]
[ROW][C]72[/C][C]132[/C][C]122.348809783638[/C][C]9.65119021636217[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58005&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58005&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
112756.98454505994670.015454940054
2123137.136385008374-14.1363850083740
32.756.77503879862289-4.02503879862289
4123145.650998313406-22.6509983134057
52.555.59214684208969-3.04214684208969
6118144.433583225208-26.4335832252084
72.55.03402460200857-2.53402460200857
8114137.150038798623-23.1500387986229
92.53.93444678851288-1.43444678851288
10108128.167146842090-20.1671468420897
112.1-3.094533236762215.19453323676221
12111126.950691268675-15.9506912686751
13210.9464918428261-8.9464918428261
14151142.7534986206958.24650137930508
152-4.401399649716296.40139964971629
16159139.27271759668419.7272824033164
172-3.784982456893165.78498245689316
18158134.45668420977423.5433157902265
192-3.143565264075.14356526407
20148125.97360035028422.0263996497163
212-3.043603644661285.04360364466128
22138118.79001754310719.2099824568931
2322.22269551733269-0.222695517332693
24137118.77310140259718.2268985974033
25219.5624540374077-17.5624540374077
26136135.7754481875210.224551812479244
2726.01387169422184-4.01387169422184
28133144.589946350778-11.5899463507785
2925.43074945414071-3.43074945414071
30126143.072646404355-17.0726464043552
3124.87262721405956-2.87262721405956
32120136.388871694222-16.3888716942218
3324.07293425879006-2.07293425879006
34114128.005749454141-14.0057494541407
352-2.056391191806784.05639119180678
36116126.789293880726-10.7892938807262
37211.6847490295553-9.68474902955532
38153142.89198609097210.1080139090279
392-3.663142462986955.66314246298695
40162140.31085964163921.689140358361
412-2.446955553711684.44695555371168
42161135.19494139650325.8050586034971
432-1.805538360888543.80553836088854
44149126.71185753701322.288142462987
452-0.8059221668015992.8059221668016
46139120.12804444628818.8719555537117
4725.6599164280967-3.6599164280967
48135120.11112830577814.8888716942218
49222.9996749481717-20.9996749481717
50130138.013129665380-8.01312966538044
5128.85132288853368-6.85132288853368
52127148.027167261542-21.0271672615424
5327.96831579022646-5.96831579022646
54122146.509867315119-24.5098673151192
5527.11030869191927-5.11030869191927
56117139.226322888534-22.2263228885337
5726.61050059487576-4.61050059487576
58112130.543315790226-18.5433157902265
5920.7810600025050291.21893999749497
60113129.026975358586-16.0269753585859
61214.8220850820932-12.8220850820932
62149145.4295524270583.57044757294218
632-0.825691268675152.82569126867515
64157143.14831083595113.8516891640492
652-0.2092740758520032.20927407585200
66157138.33227744904118.6677225509592
6720.4321431169711441.56785688302886
68147129.54930873132517.4506912686751
6921.731644169284190.268355830715807
70137122.36572592414814.634274075852
712.218.79725248063456-6.58725248063456
72132122.3488097836389.65119021636217







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
170.922342229364130.1553155412717410.0776577706358706
180.9923620330976530.01527593380469450.00763796690234727
190.9865428569892430.02691428602151410.0134571430107571
200.9952073502398740.009585299520251810.00479264976012591
210.993697611572150.01260477685569910.00630238842784955
220.9934975337828440.01300493243431290.00650246621715644
230.999499992469660.001000015060680920.000500007530340459
240.9993234302385740.001353139522851440.000676569761425719
250.9999891488759192.17022481622116e-051.08511240811058e-05
260.9999871836586842.56326826329696e-051.28163413164848e-05
270.999992057162681.58856746413996e-057.94283732069981e-06
280.9999921023634471.57952731067676e-057.89763655338382e-06
290.9999880677133332.38645733349535e-051.19322866674768e-05
300.9999880671223892.38657552225055e-051.19328776112528e-05
310.999976415444484.7169111039779e-052.35845555198895e-05
320.9999635539121187.28921757635726e-053.64460878817863e-05
330.9999202539813120.0001594920373759857.97460186879927e-05
340.9998569424758610.0002861150482775020.000143057524138751
350.9997046436791870.0005907126416270760.000295356320813538
360.9994260018345940.001147996330811470.000573998165405733
370.9990092010726070.001981597854786950.000990798927393476
380.9988932234938830.002213553012234560.00110677650611728
390.9981585909620110.003682818075977620.00184140903798881
400.9982203355053060.003559328989388360.00177966449469418
410.9966519285704730.006696142859054310.00334807142952716
420.9969175492983910.00616490140321730.00308245070160865
430.9938666020604970.01226679587900620.00613339793950308
440.9920155473921110.01596890521577810.00798445260788904
450.9843636617895460.03127267642090780.0156363382104539
460.9795030873613460.04099382527730870.0204969126386543
470.968166298228870.0636674035422610.0318337017711305
480.9746797681906890.05064046361862240.0253202318093112
490.9728208295868020.05435834082639630.0271791704131981
500.9596212186399560.08075756272008870.0403787813600444
510.9568136560984380.08637268780312320.0431863439015616
520.9489841629190450.1020316741619110.0510158370809555
530.9454378397111520.1091243205776960.0545621602888481
540.9367269761526250.1265460476947500.0632730238473752
550.9298628010298470.1402743979403070.0701371989701533

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
17 & 0.92234222936413 & 0.155315541271741 & 0.0776577706358706 \tabularnewline
18 & 0.992362033097653 & 0.0152759338046945 & 0.00763796690234727 \tabularnewline
19 & 0.986542856989243 & 0.0269142860215141 & 0.0134571430107571 \tabularnewline
20 & 0.995207350239874 & 0.00958529952025181 & 0.00479264976012591 \tabularnewline
21 & 0.99369761157215 & 0.0126047768556991 & 0.00630238842784955 \tabularnewline
22 & 0.993497533782844 & 0.0130049324343129 & 0.00650246621715644 \tabularnewline
23 & 0.99949999246966 & 0.00100001506068092 & 0.000500007530340459 \tabularnewline
24 & 0.999323430238574 & 0.00135313952285144 & 0.000676569761425719 \tabularnewline
25 & 0.999989148875919 & 2.17022481622116e-05 & 1.08511240811058e-05 \tabularnewline
26 & 0.999987183658684 & 2.56326826329696e-05 & 1.28163413164848e-05 \tabularnewline
27 & 0.99999205716268 & 1.58856746413996e-05 & 7.94283732069981e-06 \tabularnewline
28 & 0.999992102363447 & 1.57952731067676e-05 & 7.89763655338382e-06 \tabularnewline
29 & 0.999988067713333 & 2.38645733349535e-05 & 1.19322866674768e-05 \tabularnewline
30 & 0.999988067122389 & 2.38657552225055e-05 & 1.19328776112528e-05 \tabularnewline
31 & 0.99997641544448 & 4.7169111039779e-05 & 2.35845555198895e-05 \tabularnewline
32 & 0.999963553912118 & 7.28921757635726e-05 & 3.64460878817863e-05 \tabularnewline
33 & 0.999920253981312 & 0.000159492037375985 & 7.97460186879927e-05 \tabularnewline
34 & 0.999856942475861 & 0.000286115048277502 & 0.000143057524138751 \tabularnewline
35 & 0.999704643679187 & 0.000590712641627076 & 0.000295356320813538 \tabularnewline
36 & 0.999426001834594 & 0.00114799633081147 & 0.000573998165405733 \tabularnewline
37 & 0.999009201072607 & 0.00198159785478695 & 0.000990798927393476 \tabularnewline
38 & 0.998893223493883 & 0.00221355301223456 & 0.00110677650611728 \tabularnewline
39 & 0.998158590962011 & 0.00368281807597762 & 0.00184140903798881 \tabularnewline
40 & 0.998220335505306 & 0.00355932898938836 & 0.00177966449469418 \tabularnewline
41 & 0.996651928570473 & 0.00669614285905431 & 0.00334807142952716 \tabularnewline
42 & 0.996917549298391 & 0.0061649014032173 & 0.00308245070160865 \tabularnewline
43 & 0.993866602060497 & 0.0122667958790062 & 0.00613339793950308 \tabularnewline
44 & 0.992015547392111 & 0.0159689052157781 & 0.00798445260788904 \tabularnewline
45 & 0.984363661789546 & 0.0312726764209078 & 0.0156363382104539 \tabularnewline
46 & 0.979503087361346 & 0.0409938252773087 & 0.0204969126386543 \tabularnewline
47 & 0.96816629822887 & 0.063667403542261 & 0.0318337017711305 \tabularnewline
48 & 0.974679768190689 & 0.0506404636186224 & 0.0253202318093112 \tabularnewline
49 & 0.972820829586802 & 0.0543583408263963 & 0.0271791704131981 \tabularnewline
50 & 0.959621218639956 & 0.0807575627200887 & 0.0403787813600444 \tabularnewline
51 & 0.956813656098438 & 0.0863726878031232 & 0.0431863439015616 \tabularnewline
52 & 0.948984162919045 & 0.102031674161911 & 0.0510158370809555 \tabularnewline
53 & 0.945437839711152 & 0.109124320577696 & 0.0545621602888481 \tabularnewline
54 & 0.936726976152625 & 0.126546047694750 & 0.0632730238473752 \tabularnewline
55 & 0.929862801029847 & 0.140274397940307 & 0.0701371989701533 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58005&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]17[/C][C]0.92234222936413[/C][C]0.155315541271741[/C][C]0.0776577706358706[/C][/ROW]
[ROW][C]18[/C][C]0.992362033097653[/C][C]0.0152759338046945[/C][C]0.00763796690234727[/C][/ROW]
[ROW][C]19[/C][C]0.986542856989243[/C][C]0.0269142860215141[/C][C]0.0134571430107571[/C][/ROW]
[ROW][C]20[/C][C]0.995207350239874[/C][C]0.00958529952025181[/C][C]0.00479264976012591[/C][/ROW]
[ROW][C]21[/C][C]0.99369761157215[/C][C]0.0126047768556991[/C][C]0.00630238842784955[/C][/ROW]
[ROW][C]22[/C][C]0.993497533782844[/C][C]0.0130049324343129[/C][C]0.00650246621715644[/C][/ROW]
[ROW][C]23[/C][C]0.99949999246966[/C][C]0.00100001506068092[/C][C]0.000500007530340459[/C][/ROW]
[ROW][C]24[/C][C]0.999323430238574[/C][C]0.00135313952285144[/C][C]0.000676569761425719[/C][/ROW]
[ROW][C]25[/C][C]0.999989148875919[/C][C]2.17022481622116e-05[/C][C]1.08511240811058e-05[/C][/ROW]
[ROW][C]26[/C][C]0.999987183658684[/C][C]2.56326826329696e-05[/C][C]1.28163413164848e-05[/C][/ROW]
[ROW][C]27[/C][C]0.99999205716268[/C][C]1.58856746413996e-05[/C][C]7.94283732069981e-06[/C][/ROW]
[ROW][C]28[/C][C]0.999992102363447[/C][C]1.57952731067676e-05[/C][C]7.89763655338382e-06[/C][/ROW]
[ROW][C]29[/C][C]0.999988067713333[/C][C]2.38645733349535e-05[/C][C]1.19322866674768e-05[/C][/ROW]
[ROW][C]30[/C][C]0.999988067122389[/C][C]2.38657552225055e-05[/C][C]1.19328776112528e-05[/C][/ROW]
[ROW][C]31[/C][C]0.99997641544448[/C][C]4.7169111039779e-05[/C][C]2.35845555198895e-05[/C][/ROW]
[ROW][C]32[/C][C]0.999963553912118[/C][C]7.28921757635726e-05[/C][C]3.64460878817863e-05[/C][/ROW]
[ROW][C]33[/C][C]0.999920253981312[/C][C]0.000159492037375985[/C][C]7.97460186879927e-05[/C][/ROW]
[ROW][C]34[/C][C]0.999856942475861[/C][C]0.000286115048277502[/C][C]0.000143057524138751[/C][/ROW]
[ROW][C]35[/C][C]0.999704643679187[/C][C]0.000590712641627076[/C][C]0.000295356320813538[/C][/ROW]
[ROW][C]36[/C][C]0.999426001834594[/C][C]0.00114799633081147[/C][C]0.000573998165405733[/C][/ROW]
[ROW][C]37[/C][C]0.999009201072607[/C][C]0.00198159785478695[/C][C]0.000990798927393476[/C][/ROW]
[ROW][C]38[/C][C]0.998893223493883[/C][C]0.00221355301223456[/C][C]0.00110677650611728[/C][/ROW]
[ROW][C]39[/C][C]0.998158590962011[/C][C]0.00368281807597762[/C][C]0.00184140903798881[/C][/ROW]
[ROW][C]40[/C][C]0.998220335505306[/C][C]0.00355932898938836[/C][C]0.00177966449469418[/C][/ROW]
[ROW][C]41[/C][C]0.996651928570473[/C][C]0.00669614285905431[/C][C]0.00334807142952716[/C][/ROW]
[ROW][C]42[/C][C]0.996917549298391[/C][C]0.0061649014032173[/C][C]0.00308245070160865[/C][/ROW]
[ROW][C]43[/C][C]0.993866602060497[/C][C]0.0122667958790062[/C][C]0.00613339793950308[/C][/ROW]
[ROW][C]44[/C][C]0.992015547392111[/C][C]0.0159689052157781[/C][C]0.00798445260788904[/C][/ROW]
[ROW][C]45[/C][C]0.984363661789546[/C][C]0.0312726764209078[/C][C]0.0156363382104539[/C][/ROW]
[ROW][C]46[/C][C]0.979503087361346[/C][C]0.0409938252773087[/C][C]0.0204969126386543[/C][/ROW]
[ROW][C]47[/C][C]0.96816629822887[/C][C]0.063667403542261[/C][C]0.0318337017711305[/C][/ROW]
[ROW][C]48[/C][C]0.974679768190689[/C][C]0.0506404636186224[/C][C]0.0253202318093112[/C][/ROW]
[ROW][C]49[/C][C]0.972820829586802[/C][C]0.0543583408263963[/C][C]0.0271791704131981[/C][/ROW]
[ROW][C]50[/C][C]0.959621218639956[/C][C]0.0807575627200887[/C][C]0.0403787813600444[/C][/ROW]
[ROW][C]51[/C][C]0.956813656098438[/C][C]0.0863726878031232[/C][C]0.0431863439015616[/C][/ROW]
[ROW][C]52[/C][C]0.948984162919045[/C][C]0.102031674161911[/C][C]0.0510158370809555[/C][/ROW]
[ROW][C]53[/C][C]0.945437839711152[/C][C]0.109124320577696[/C][C]0.0545621602888481[/C][/ROW]
[ROW][C]54[/C][C]0.936726976152625[/C][C]0.126546047694750[/C][C]0.0632730238473752[/C][/ROW]
[ROW][C]55[/C][C]0.929862801029847[/C][C]0.140274397940307[/C][C]0.0701371989701533[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58005&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58005&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
170.922342229364130.1553155412717410.0776577706358706
180.9923620330976530.01527593380469450.00763796690234727
190.9865428569892430.02691428602151410.0134571430107571
200.9952073502398740.009585299520251810.00479264976012591
210.993697611572150.01260477685569910.00630238842784955
220.9934975337828440.01300493243431290.00650246621715644
230.999499992469660.001000015060680920.000500007530340459
240.9993234302385740.001353139522851440.000676569761425719
250.9999891488759192.17022481622116e-051.08511240811058e-05
260.9999871836586842.56326826329696e-051.28163413164848e-05
270.999992057162681.58856746413996e-057.94283732069981e-06
280.9999921023634471.57952731067676e-057.89763655338382e-06
290.9999880677133332.38645733349535e-051.19322866674768e-05
300.9999880671223892.38657552225055e-051.19328776112528e-05
310.999976415444484.7169111039779e-052.35845555198895e-05
320.9999635539121187.28921757635726e-053.64460878817863e-05
330.9999202539813120.0001594920373759857.97460186879927e-05
340.9998569424758610.0002861150482775020.000143057524138751
350.9997046436791870.0005907126416270760.000295356320813538
360.9994260018345940.001147996330811470.000573998165405733
370.9990092010726070.001981597854786950.000990798927393476
380.9988932234938830.002213553012234560.00110677650611728
390.9981585909620110.003682818075977620.00184140903798881
400.9982203355053060.003559328989388360.00177966449469418
410.9966519285704730.006696142859054310.00334807142952716
420.9969175492983910.00616490140321730.00308245070160865
430.9938666020604970.01226679587900620.00613339793950308
440.9920155473921110.01596890521577810.00798445260788904
450.9843636617895460.03127267642090780.0156363382104539
460.9795030873613460.04099382527730870.0204969126386543
470.968166298228870.0636674035422610.0318337017711305
480.9746797681906890.05064046361862240.0253202318093112
490.9728208295868020.05435834082639630.0271791704131981
500.9596212186399560.08075756272008870.0403787813600444
510.9568136560984380.08637268780312320.0431863439015616
520.9489841629190450.1020316741619110.0510158370809555
530.9454378397111520.1091243205776960.0545621602888481
540.9367269761526250.1265460476947500.0632730238473752
550.9298628010298470.1402743979403070.0701371989701533







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level210.538461538461538NOK
5% type I error level290.743589743589744NOK
10% type I error level340.871794871794872NOK

\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 & 21 & 0.538461538461538 & NOK \tabularnewline
5% type I error level & 29 & 0.743589743589744 & NOK \tabularnewline
10% type I error level & 34 & 0.871794871794872 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58005&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]21[/C][C]0.538461538461538[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]29[/C][C]0.743589743589744[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]34[/C][C]0.871794871794872[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58005&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58005&T=6

As an alternative you can also use a QR Code:  

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level210.538461538461538NOK
5% type I error level290.743589743589744NOK
10% type I error level340.871794871794872NOK



Parameters (Session):
Parameters (R input):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = 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')
}