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Description of Statistical Computation

Author's title

Author*Unverified author*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationSun, 19 Dec 2010 16:21:48 +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/19/t1292775622ufnr5bj0mr3msr9.htm/, Retrieved Tue, 03 Jun 2025 19:51:15 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=112566, Retrieved Tue, 03 Jun 2025 19:51:15 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact261

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Decreasing Compet...] [2010-11-17 09:04:39] [b98453cac15ba1066b407e146608df68]
-   PD  [Multiple Regression] [Ws 7 - Daginvloed] [2010-11-21 15:59:47] [603e2f5305d3a2a4e062624458fa1155]
-    D    [Multiple Regression] [PAPER - maandinvloed] [2010-12-19 15:48:17] [603e2f5305d3a2a4e062624458fa1155]
-    D        [Multiple Regression] [bbb] [2010-12-19 16:21:48] [d41d8cd98f00b204e9800998ecf8427e] [Current]
-               [Multiple Regression] [Multiple Regressi...] [2010-12-19 16:38:07] [8ef49741e164ec6343c90c7935194465]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
104,37	1	1	167.16	101,56	100,93
104,89	2	2	179.84	102,13	101,18
105,15	3	3	174.44	102,39	101,11
105,72	4	4	180.35	102,42	102,42
106,38	5	5	193.17	103,87	102,37
106,40	6	6	195.16	104,44	101,95
106,47	7	7	202.43	104,97	102,20
106,59	8	8	189.91	105,17	103,35
106,76	9	9	195.98	105,35	103,65
107,35	10	10	212.09	104,65	102,06
107,81	11	11	205.81	106,62	102,66
108,03	12	12	204.31	107,05	102,32
109,08	1	13	196.07	112,30	102,21
109,86	2	14	199.98	114,70	102,33
110,29	3	15	199.1	115,40	104,41
110,34	4	16	198.31	115,64	104,33
110,59	5	17	195.72	115,66	105,27
110,64	6	18	223.04	114,50	105,34
110,83	7	19	238.41	115,14	104,88
111,51	8	20	259.73	115,41	105,49
113,32	9	21	326.54	119,32	105,90
115,89	10	22	335.15	124,77	105,39
116,51	11	23	321.81	130,96	104,40
117,44	12	24	368.62	141,02	106,19
118,25	1	25	369.59	150,60	106,54
118,65	2	26	425	151,10	108,26
118,52	3	27	439.72	157,19	106,95
119,07	4	28	362.23	157,28	108,32
119,12	5	29	328.76	156,54	108,35
119,28	6	30	348.55	159,62	109,29
119,30	7	31	328.18	163,77	109,46
119,44	8	32	329.34	165,08	109,50
119,57	9	33	295.55	164,75	109,84
119,93	10	34	237.38	163,93	108,73
120,03	11	35	226.85	157,51	109,38
119,66	12	36	220.14	153,36	109,97
119,46	1	37	239.36	156,83	111,10
119,48	2	38	224.69	154,98	110,53
119,56	3	39	230.98	155,02	110,23
119,43	4	40	233.47	153,34	109,41
119,57	5	41	256.7	153,19	108,94
119,59	6	42	253.41	152,80	109,81
119,50	7	43	224.95	152,97	109,20
119,54	8	44	210.37	152,96	109,45
119,56	9	45	191.09	152,35	110,61
119,61	10	46	198.85	151,88	109,44
119,64	11	47	211.04	150,27	109,77
119,60	12	48	206.25	148,80	108,04
119,71	1	49	201.19	149,28	109,65
119,72	2	50	194.37	148,64	111,69
119,66	3	51	191.08	150,36	111,65
119,76	4	52	192.87	149,69	112,04
119,80	5	53	181.61	152,94	111,42
119,88	6	54	157.67	155,18	112,25
119,78	7	55	196.14	156,32	111,46
120,08	8	56	246.35	156,25	111,62
120,22	9	57	271.9 	155,52	111,77

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 5 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112566&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112566&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112566&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 time5 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24






Multiple Linear Regression - Estimated Regression Equation
Brood[t] = + 86.2737357314676 + 0.0469329787978492Maand[t] + 0.140300419345309Trend[t] + 0.0119520172881480Tarwe[t] + 0.136581920664192Meel[t] + 0.0278949529470116Water[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Brood[t] =  +  86.2737357314676 +  0.0469329787978492Maand[t] +  0.140300419345309Trend[t] +  0.0119520172881480Tarwe[t] +  0.136581920664192Meel[t] +  0.0278949529470116Water[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112566&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Brood[t] =  +  86.2737357314676 +  0.0469329787978492Maand[t] +  0.140300419345309Trend[t] +  0.0119520172881480Tarwe[t] +  0.136581920664192Meel[t] +  0.0278949529470116Water[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112566&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112566&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
Brood[t] = + 86.2737357314676 + 0.0469329787978492Maand[t] + 0.140300419345309Trend[t] + 0.0119520172881480Tarwe[t] + 0.136581920664192Meel[t] + 0.0278949529470116Water[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)86.273735731467613.3309126.471700
Maand0.04693297879784920.0336171.39610.1687280.084364
Trend0.1403004193453090.0241515.809300
Tarwe0.01195201728814800.0023675.04876e-063e-06
Meel0.1365819206641920.0163358.361100
Water0.02789495294701160.1399720.19930.8428280.421414

\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) & 86.2737357314676 & 13.330912 & 6.4717 & 0 & 0 \tabularnewline
Maand & 0.0469329787978492 & 0.033617 & 1.3961 & 0.168728 & 0.084364 \tabularnewline
Trend & 0.140300419345309 & 0.024151 & 5.8093 & 0 & 0 \tabularnewline
Tarwe & 0.0119520172881480 & 0.002367 & 5.0487 & 6e-06 & 3e-06 \tabularnewline
Meel & 0.136581920664192 & 0.016335 & 8.3611 & 0 & 0 \tabularnewline
Water & 0.0278949529470116 & 0.139972 & 0.1993 & 0.842828 & 0.421414 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112566&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]86.2737357314676[/C][C]13.330912[/C][C]6.4717[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Maand[/C][C]0.0469329787978492[/C][C]0.033617[/C][C]1.3961[/C][C]0.168728[/C][C]0.084364[/C][/ROW]
[ROW][C]Trend[/C][C]0.140300419345309[/C][C]0.024151[/C][C]5.8093[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Tarwe[/C][C]0.0119520172881480[/C][C]0.002367[/C][C]5.0487[/C][C]6e-06[/C][C]3e-06[/C][/ROW]
[ROW][C]Meel[/C][C]0.136581920664192[/C][C]0.016335[/C][C]8.3611[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Water[/C][C]0.0278949529470116[/C][C]0.139972[/C][C]0.1993[/C][C]0.842828[/C][C]0.421414[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112566&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112566&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)86.273735731467613.3309126.471700
Maand0.04693297879784920.0336171.39610.1687280.084364
Trend0.1403004193453090.0241515.809300
Tarwe0.01195201728814800.0023675.04876e-063e-06
Meel0.1365819206641920.0163358.361100
Water0.02789495294701160.1399720.19930.8428280.421414






Multiple Linear Regression - Regression Statistics
Multiple R0.990026897365883
R-squared0.980153257507916
Adjusted R-squared0.978207498440064
F-TEST (value)503.738244730502
F-TEST (DF numerator)5
F-TEST (DF denominator)51
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.831791597301993
Sum Squared Residuals35.2857403284523

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.990026897365883 \tabularnewline
R-squared & 0.980153257507916 \tabularnewline
Adjusted R-squared & 0.978207498440064 \tabularnewline
F-TEST (value) & 503.738244730502 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 51 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.831791597301993 \tabularnewline
Sum Squared Residuals & 35.2857403284523 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112566&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.990026897365883[/C][/ROW]
[ROW][C]R-squared[/C][C]0.980153257507916[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.978207498440064[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]503.738244730502[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]51[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.831791597301993[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]35.2857403284523[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112566&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112566&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 R0.990026897365883
R-squared0.980153257507916
Adjusted R-squared0.978207498440064
F-TEST (value)503.738244730502
F-TEST (DF numerator)5
F-TEST (DF denominator)51
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.831791597301993
Sum Squared Residuals35.2857403284523






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1104.37105.145565803095-0.775565803094795
2104.89105.569176213467-0.679176213466997
3105.15105.725427370921-0.575427370920542
4105.72106.023937037217-0.303937037217179
5106.38106.561044334310-0.181044334310126
6106.4106.838198061398-0.438198061397531
7106.47107.191684781414-0.721684781414306
8106.59107.288674503132-0.698674503131749
9106.76107.581409877818-0.82140987781762
10107.35107.821229954822-0.471229954822171
11107.81108.219208039872-0.409208039872416
12108.03108.437759353967-0.407759353966970
13109.08108.6772990227440.402700977255563
14109.86109.2424088124320.617591187568045
15110.29109.5727532819560.717246718043745
16110.34109.7810926511650.558907348834577
17110.59109.9663232187160.623676781284248
18110.64110.3236033479070.316396652093056
19110.83110.7691200026380.0608799973616042
20111.51111.2650634492420.244936550758131
21113.32112.7962833629110.523716637088532
22115.89113.8165686715222.07343132847756
23116.51114.6621882445361.84781175546449
24117.44116.8328416595940.6071583404062
25118.25117.7866908024270.463309197573323
26118.65118.752455757907-0.102455757907064
27118.52119.910864359016-1.39086435901611
28119.07119.222444395898-0.152444395897871
29119.12118.9094100027040.210589997296388
30119.28119.780067394395-0.500067394395126
31119.3120.295395313136-0.995395313136102
32119.44120.676531165521-1.23653116552148
33119.57120.424318149681-0.854318149680924
34119.93119.7733421294570.156657870543317
35120.03118.9759965743071.05400342569291
36119.66118.5326749879291.12732501207087
37119.46118.8918909743110.568109025688831
38119.48118.6352116024290.84478839757137
39119.56118.8947179802570.665282019743291
40119.43118.8593804133150.570619586685042
41119.57119.2906612570770.279338742922919
42119.59119.4095741783470.180425821652910
43119.5119.2628561696850.237143830315206
44119.54119.2814370747970.258562925203140
45119.56119.1872787534380.372721246562096
46119.61119.3704292080770.239570791923080
47119.64119.4926661391660.147333860834233
48119.6119.3736156825240.226384317475989
49119.71119.0476463237780.66235367622155
50119.72119.1228602388030.597139761196748
51119.66119.5045766054930.155423394507061
52119.76119.6325732593860.127426740613803
53119.8120.111823314196-0.311823314196292
54119.88120.342021731695-0.462021731695
55119.78121.122715611642-1.34271561164224
56120.08121.904962255848-1.82496225584834
57120.22122.302049136561-2.08204913656087

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 104.37 & 105.145565803095 & -0.775565803094795 \tabularnewline
2 & 104.89 & 105.569176213467 & -0.679176213466997 \tabularnewline
3 & 105.15 & 105.725427370921 & -0.575427370920542 \tabularnewline
4 & 105.72 & 106.023937037217 & -0.303937037217179 \tabularnewline
5 & 106.38 & 106.561044334310 & -0.181044334310126 \tabularnewline
6 & 106.4 & 106.838198061398 & -0.438198061397531 \tabularnewline
7 & 106.47 & 107.191684781414 & -0.721684781414306 \tabularnewline
8 & 106.59 & 107.288674503132 & -0.698674503131749 \tabularnewline
9 & 106.76 & 107.581409877818 & -0.82140987781762 \tabularnewline
10 & 107.35 & 107.821229954822 & -0.471229954822171 \tabularnewline
11 & 107.81 & 108.219208039872 & -0.409208039872416 \tabularnewline
12 & 108.03 & 108.437759353967 & -0.407759353966970 \tabularnewline
13 & 109.08 & 108.677299022744 & 0.402700977255563 \tabularnewline
14 & 109.86 & 109.242408812432 & 0.617591187568045 \tabularnewline
15 & 110.29 & 109.572753281956 & 0.717246718043745 \tabularnewline
16 & 110.34 & 109.781092651165 & 0.558907348834577 \tabularnewline
17 & 110.59 & 109.966323218716 & 0.623676781284248 \tabularnewline
18 & 110.64 & 110.323603347907 & 0.316396652093056 \tabularnewline
19 & 110.83 & 110.769120002638 & 0.0608799973616042 \tabularnewline
20 & 111.51 & 111.265063449242 & 0.244936550758131 \tabularnewline
21 & 113.32 & 112.796283362911 & 0.523716637088532 \tabularnewline
22 & 115.89 & 113.816568671522 & 2.07343132847756 \tabularnewline
23 & 116.51 & 114.662188244536 & 1.84781175546449 \tabularnewline
24 & 117.44 & 116.832841659594 & 0.6071583404062 \tabularnewline
25 & 118.25 & 117.786690802427 & 0.463309197573323 \tabularnewline
26 & 118.65 & 118.752455757907 & -0.102455757907064 \tabularnewline
27 & 118.52 & 119.910864359016 & -1.39086435901611 \tabularnewline
28 & 119.07 & 119.222444395898 & -0.152444395897871 \tabularnewline
29 & 119.12 & 118.909410002704 & 0.210589997296388 \tabularnewline
30 & 119.28 & 119.780067394395 & -0.500067394395126 \tabularnewline
31 & 119.3 & 120.295395313136 & -0.995395313136102 \tabularnewline
32 & 119.44 & 120.676531165521 & -1.23653116552148 \tabularnewline
33 & 119.57 & 120.424318149681 & -0.854318149680924 \tabularnewline
34 & 119.93 & 119.773342129457 & 0.156657870543317 \tabularnewline
35 & 120.03 & 118.975996574307 & 1.05400342569291 \tabularnewline
36 & 119.66 & 118.532674987929 & 1.12732501207087 \tabularnewline
37 & 119.46 & 118.891890974311 & 0.568109025688831 \tabularnewline
38 & 119.48 & 118.635211602429 & 0.84478839757137 \tabularnewline
39 & 119.56 & 118.894717980257 & 0.665282019743291 \tabularnewline
40 & 119.43 & 118.859380413315 & 0.570619586685042 \tabularnewline
41 & 119.57 & 119.290661257077 & 0.279338742922919 \tabularnewline
42 & 119.59 & 119.409574178347 & 0.180425821652910 \tabularnewline
43 & 119.5 & 119.262856169685 & 0.237143830315206 \tabularnewline
44 & 119.54 & 119.281437074797 & 0.258562925203140 \tabularnewline
45 & 119.56 & 119.187278753438 & 0.372721246562096 \tabularnewline
46 & 119.61 & 119.370429208077 & 0.239570791923080 \tabularnewline
47 & 119.64 & 119.492666139166 & 0.147333860834233 \tabularnewline
48 & 119.6 & 119.373615682524 & 0.226384317475989 \tabularnewline
49 & 119.71 & 119.047646323778 & 0.66235367622155 \tabularnewline
50 & 119.72 & 119.122860238803 & 0.597139761196748 \tabularnewline
51 & 119.66 & 119.504576605493 & 0.155423394507061 \tabularnewline
52 & 119.76 & 119.632573259386 & 0.127426740613803 \tabularnewline
53 & 119.8 & 120.111823314196 & -0.311823314196292 \tabularnewline
54 & 119.88 & 120.342021731695 & -0.462021731695 \tabularnewline
55 & 119.78 & 121.122715611642 & -1.34271561164224 \tabularnewline
56 & 120.08 & 121.904962255848 & -1.82496225584834 \tabularnewline
57 & 120.22 & 122.302049136561 & -2.08204913656087 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112566&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]104.37[/C][C]105.145565803095[/C][C]-0.775565803094795[/C][/ROW]
[ROW][C]2[/C][C]104.89[/C][C]105.569176213467[/C][C]-0.679176213466997[/C][/ROW]
[ROW][C]3[/C][C]105.15[/C][C]105.725427370921[/C][C]-0.575427370920542[/C][/ROW]
[ROW][C]4[/C][C]105.72[/C][C]106.023937037217[/C][C]-0.303937037217179[/C][/ROW]
[ROW][C]5[/C][C]106.38[/C][C]106.561044334310[/C][C]-0.181044334310126[/C][/ROW]
[ROW][C]6[/C][C]106.4[/C][C]106.838198061398[/C][C]-0.438198061397531[/C][/ROW]
[ROW][C]7[/C][C]106.47[/C][C]107.191684781414[/C][C]-0.721684781414306[/C][/ROW]
[ROW][C]8[/C][C]106.59[/C][C]107.288674503132[/C][C]-0.698674503131749[/C][/ROW]
[ROW][C]9[/C][C]106.76[/C][C]107.581409877818[/C][C]-0.82140987781762[/C][/ROW]
[ROW][C]10[/C][C]107.35[/C][C]107.821229954822[/C][C]-0.471229954822171[/C][/ROW]
[ROW][C]11[/C][C]107.81[/C][C]108.219208039872[/C][C]-0.409208039872416[/C][/ROW]
[ROW][C]12[/C][C]108.03[/C][C]108.437759353967[/C][C]-0.407759353966970[/C][/ROW]
[ROW][C]13[/C][C]109.08[/C][C]108.677299022744[/C][C]0.402700977255563[/C][/ROW]
[ROW][C]14[/C][C]109.86[/C][C]109.242408812432[/C][C]0.617591187568045[/C][/ROW]
[ROW][C]15[/C][C]110.29[/C][C]109.572753281956[/C][C]0.717246718043745[/C][/ROW]
[ROW][C]16[/C][C]110.34[/C][C]109.781092651165[/C][C]0.558907348834577[/C][/ROW]
[ROW][C]17[/C][C]110.59[/C][C]109.966323218716[/C][C]0.623676781284248[/C][/ROW]
[ROW][C]18[/C][C]110.64[/C][C]110.323603347907[/C][C]0.316396652093056[/C][/ROW]
[ROW][C]19[/C][C]110.83[/C][C]110.769120002638[/C][C]0.0608799973616042[/C][/ROW]
[ROW][C]20[/C][C]111.51[/C][C]111.265063449242[/C][C]0.244936550758131[/C][/ROW]
[ROW][C]21[/C][C]113.32[/C][C]112.796283362911[/C][C]0.523716637088532[/C][/ROW]
[ROW][C]22[/C][C]115.89[/C][C]113.816568671522[/C][C]2.07343132847756[/C][/ROW]
[ROW][C]23[/C][C]116.51[/C][C]114.662188244536[/C][C]1.84781175546449[/C][/ROW]
[ROW][C]24[/C][C]117.44[/C][C]116.832841659594[/C][C]0.6071583404062[/C][/ROW]
[ROW][C]25[/C][C]118.25[/C][C]117.786690802427[/C][C]0.463309197573323[/C][/ROW]
[ROW][C]26[/C][C]118.65[/C][C]118.752455757907[/C][C]-0.102455757907064[/C][/ROW]
[ROW][C]27[/C][C]118.52[/C][C]119.910864359016[/C][C]-1.39086435901611[/C][/ROW]
[ROW][C]28[/C][C]119.07[/C][C]119.222444395898[/C][C]-0.152444395897871[/C][/ROW]
[ROW][C]29[/C][C]119.12[/C][C]118.909410002704[/C][C]0.210589997296388[/C][/ROW]
[ROW][C]30[/C][C]119.28[/C][C]119.780067394395[/C][C]-0.500067394395126[/C][/ROW]
[ROW][C]31[/C][C]119.3[/C][C]120.295395313136[/C][C]-0.995395313136102[/C][/ROW]
[ROW][C]32[/C][C]119.44[/C][C]120.676531165521[/C][C]-1.23653116552148[/C][/ROW]
[ROW][C]33[/C][C]119.57[/C][C]120.424318149681[/C][C]-0.854318149680924[/C][/ROW]
[ROW][C]34[/C][C]119.93[/C][C]119.773342129457[/C][C]0.156657870543317[/C][/ROW]
[ROW][C]35[/C][C]120.03[/C][C]118.975996574307[/C][C]1.05400342569291[/C][/ROW]
[ROW][C]36[/C][C]119.66[/C][C]118.532674987929[/C][C]1.12732501207087[/C][/ROW]
[ROW][C]37[/C][C]119.46[/C][C]118.891890974311[/C][C]0.568109025688831[/C][/ROW]
[ROW][C]38[/C][C]119.48[/C][C]118.635211602429[/C][C]0.84478839757137[/C][/ROW]
[ROW][C]39[/C][C]119.56[/C][C]118.894717980257[/C][C]0.665282019743291[/C][/ROW]
[ROW][C]40[/C][C]119.43[/C][C]118.859380413315[/C][C]0.570619586685042[/C][/ROW]
[ROW][C]41[/C][C]119.57[/C][C]119.290661257077[/C][C]0.279338742922919[/C][/ROW]
[ROW][C]42[/C][C]119.59[/C][C]119.409574178347[/C][C]0.180425821652910[/C][/ROW]
[ROW][C]43[/C][C]119.5[/C][C]119.262856169685[/C][C]0.237143830315206[/C][/ROW]
[ROW][C]44[/C][C]119.54[/C][C]119.281437074797[/C][C]0.258562925203140[/C][/ROW]
[ROW][C]45[/C][C]119.56[/C][C]119.187278753438[/C][C]0.372721246562096[/C][/ROW]
[ROW][C]46[/C][C]119.61[/C][C]119.370429208077[/C][C]0.239570791923080[/C][/ROW]
[ROW][C]47[/C][C]119.64[/C][C]119.492666139166[/C][C]0.147333860834233[/C][/ROW]
[ROW][C]48[/C][C]119.6[/C][C]119.373615682524[/C][C]0.226384317475989[/C][/ROW]
[ROW][C]49[/C][C]119.71[/C][C]119.047646323778[/C][C]0.66235367622155[/C][/ROW]
[ROW][C]50[/C][C]119.72[/C][C]119.122860238803[/C][C]0.597139761196748[/C][/ROW]
[ROW][C]51[/C][C]119.66[/C][C]119.504576605493[/C][C]0.155423394507061[/C][/ROW]
[ROW][C]52[/C][C]119.76[/C][C]119.632573259386[/C][C]0.127426740613803[/C][/ROW]
[ROW][C]53[/C][C]119.8[/C][C]120.111823314196[/C][C]-0.311823314196292[/C][/ROW]
[ROW][C]54[/C][C]119.88[/C][C]120.342021731695[/C][C]-0.462021731695[/C][/ROW]
[ROW][C]55[/C][C]119.78[/C][C]121.122715611642[/C][C]-1.34271561164224[/C][/ROW]
[ROW][C]56[/C][C]120.08[/C][C]121.904962255848[/C][C]-1.82496225584834[/C][/ROW]
[ROW][C]57[/C][C]120.22[/C][C]122.302049136561[/C][C]-2.08204913656087[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112566&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112566&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
1104.37105.145565803095-0.775565803094795
2104.89105.569176213467-0.679176213466997
3105.15105.725427370921-0.575427370920542
4105.72106.023937037217-0.303937037217179
5106.38106.561044334310-0.181044334310126
6106.4106.838198061398-0.438198061397531
7106.47107.191684781414-0.721684781414306
8106.59107.288674503132-0.698674503131749
9106.76107.581409877818-0.82140987781762
10107.35107.821229954822-0.471229954822171
11107.81108.219208039872-0.409208039872416
12108.03108.437759353967-0.407759353966970
13109.08108.6772990227440.402700977255563
14109.86109.2424088124320.617591187568045
15110.29109.5727532819560.717246718043745
16110.34109.7810926511650.558907348834577
17110.59109.9663232187160.623676781284248
18110.64110.3236033479070.316396652093056
19110.83110.7691200026380.0608799973616042
20111.51111.2650634492420.244936550758131
21113.32112.7962833629110.523716637088532
22115.89113.8165686715222.07343132847756
23116.51114.6621882445361.84781175546449
24117.44116.8328416595940.6071583404062
25118.25117.7866908024270.463309197573323
26118.65118.752455757907-0.102455757907064
27118.52119.910864359016-1.39086435901611
28119.07119.222444395898-0.152444395897871
29119.12118.9094100027040.210589997296388
30119.28119.780067394395-0.500067394395126
31119.3120.295395313136-0.995395313136102
32119.44120.676531165521-1.23653116552148
33119.57120.424318149681-0.854318149680924
34119.93119.7733421294570.156657870543317
35120.03118.9759965743071.05400342569291
36119.66118.5326749879291.12732501207087
37119.46118.8918909743110.568109025688831
38119.48118.6352116024290.84478839757137
39119.56118.8947179802570.665282019743291
40119.43118.8593804133150.570619586685042
41119.57119.2906612570770.279338742922919
42119.59119.4095741783470.180425821652910
43119.5119.2628561696850.237143830315206
44119.54119.2814370747970.258562925203140
45119.56119.1872787534380.372721246562096
46119.61119.3704292080770.239570791923080
47119.64119.4926661391660.147333860834233
48119.6119.3736156825240.226384317475989
49119.71119.0476463237780.66235367622155
50119.72119.1228602388030.597139761196748
51119.66119.5045766054930.155423394507061
52119.76119.6325732593860.127426740613803
53119.8120.111823314196-0.311823314196292
54119.88120.342021731695-0.462021731695
55119.78121.122715611642-1.34271561164224
56120.08121.904962255848-1.82496225584834
57120.22122.302049136561-2.08204913656087






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.05505755867827990.1101151173565600.94494244132172
100.01697016777908440.03394033555816870.983029832220916
110.01255367317038300.02510734634076610.987446326829617
120.006257984215481520.01251596843096300.993742015784518
130.00203348536412350.0040669707282470.997966514635876
140.0007559689303556490.001511937860711300.999244031069644
150.0002843851731879040.0005687703463758070.999715614826812
169.8821501049854e-050.0001976430020997080.99990117849895
173.78605267089099e-057.57210534178198e-050.999962139473291
183.25076415995319e-056.50152831990637e-050.9999674923584
190.0001820385340427690.0003640770680855370.999817961465957
200.003900349744312610.007800699488625210.996099650255687
210.1831936246626470.3663872493252940.816806375337353
220.391870594600270.783741189200540.60812940539973
230.860361299983490.2792774000330210.139638700016511
240.9999884216010172.31567979667903e-051.15783989833952e-05
250.9999997565887954.86822409450413e-072.43411204725206e-07
260.9999998230144633.53971074895282e-071.76985537447641e-07
270.9999999991822961.63540728488650e-098.17703642443248e-10
280.9999999967088316.58233727334197e-093.29116863667099e-09
290.999999988736392.25272199547836e-081.12636099773918e-08
300.9999999644305457.11389105865679e-083.55694552932840e-08
310.999999964191127.16177603627023e-083.58088801813511e-08
320.9999999744268285.11463434041318e-082.55731717020659e-08
330.9999999845825143.08349728975206e-081.54174864487603e-08
340.999999967662516.46749810209135e-083.23374905104567e-08
350.9999999999882722.34553392907448e-111.17276696453724e-11
360.9999999999988362.32867266970921e-121.16433633485460e-12
370.999999999989862.02790167012056e-111.01395083506028e-11
380.9999999999159551.68089901524581e-108.40449507622904e-11
390.9999999998028533.94294645621236e-101.97147322810618e-10
400.9999999989461432.10771356126984e-091.05385678063492e-09
410.9999999947021951.05956107182679e-085.29780535913394e-09
420.9999999635981687.28036631610388e-083.64018315805194e-08
430.9999997698292844.60341432793175e-072.30170716396587e-07
440.999998173307923.65338415838868e-061.82669207919434e-06
450.9999841976393383.16047213247703e-051.58023606623851e-05
460.9998771679202070.0002456641595865750.000122832079793288
470.9991131769506570.001773646098686120.000886823049343058
480.9936495681490250.01270086370194980.00635043185097492

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.0550575586782799 & 0.110115117356560 & 0.94494244132172 \tabularnewline
10 & 0.0169701677790844 & 0.0339403355581687 & 0.983029832220916 \tabularnewline
11 & 0.0125536731703830 & 0.0251073463407661 & 0.987446326829617 \tabularnewline
12 & 0.00625798421548152 & 0.0125159684309630 & 0.993742015784518 \tabularnewline
13 & 0.0020334853641235 & 0.004066970728247 & 0.997966514635876 \tabularnewline
14 & 0.000755968930355649 & 0.00151193786071130 & 0.999244031069644 \tabularnewline
15 & 0.000284385173187904 & 0.000568770346375807 & 0.999715614826812 \tabularnewline
16 & 9.8821501049854e-05 & 0.000197643002099708 & 0.99990117849895 \tabularnewline
17 & 3.78605267089099e-05 & 7.57210534178198e-05 & 0.999962139473291 \tabularnewline
18 & 3.25076415995319e-05 & 6.50152831990637e-05 & 0.9999674923584 \tabularnewline
19 & 0.000182038534042769 & 0.000364077068085537 & 0.999817961465957 \tabularnewline
20 & 0.00390034974431261 & 0.00780069948862521 & 0.996099650255687 \tabularnewline
21 & 0.183193624662647 & 0.366387249325294 & 0.816806375337353 \tabularnewline
22 & 0.39187059460027 & 0.78374118920054 & 0.60812940539973 \tabularnewline
23 & 0.86036129998349 & 0.279277400033021 & 0.139638700016511 \tabularnewline
24 & 0.999988421601017 & 2.31567979667903e-05 & 1.15783989833952e-05 \tabularnewline
25 & 0.999999756588795 & 4.86822409450413e-07 & 2.43411204725206e-07 \tabularnewline
26 & 0.999999823014463 & 3.53971074895282e-07 & 1.76985537447641e-07 \tabularnewline
27 & 0.999999999182296 & 1.63540728488650e-09 & 8.17703642443248e-10 \tabularnewline
28 & 0.999999996708831 & 6.58233727334197e-09 & 3.29116863667099e-09 \tabularnewline
29 & 0.99999998873639 & 2.25272199547836e-08 & 1.12636099773918e-08 \tabularnewline
30 & 0.999999964430545 & 7.11389105865679e-08 & 3.55694552932840e-08 \tabularnewline
31 & 0.99999996419112 & 7.16177603627023e-08 & 3.58088801813511e-08 \tabularnewline
32 & 0.999999974426828 & 5.11463434041318e-08 & 2.55731717020659e-08 \tabularnewline
33 & 0.999999984582514 & 3.08349728975206e-08 & 1.54174864487603e-08 \tabularnewline
34 & 0.99999996766251 & 6.46749810209135e-08 & 3.23374905104567e-08 \tabularnewline
35 & 0.999999999988272 & 2.34553392907448e-11 & 1.17276696453724e-11 \tabularnewline
36 & 0.999999999998836 & 2.32867266970921e-12 & 1.16433633485460e-12 \tabularnewline
37 & 0.99999999998986 & 2.02790167012056e-11 & 1.01395083506028e-11 \tabularnewline
38 & 0.999999999915955 & 1.68089901524581e-10 & 8.40449507622904e-11 \tabularnewline
39 & 0.999999999802853 & 3.94294645621236e-10 & 1.97147322810618e-10 \tabularnewline
40 & 0.999999998946143 & 2.10771356126984e-09 & 1.05385678063492e-09 \tabularnewline
41 & 0.999999994702195 & 1.05956107182679e-08 & 5.29780535913394e-09 \tabularnewline
42 & 0.999999963598168 & 7.28036631610388e-08 & 3.64018315805194e-08 \tabularnewline
43 & 0.999999769829284 & 4.60341432793175e-07 & 2.30170716396587e-07 \tabularnewline
44 & 0.99999817330792 & 3.65338415838868e-06 & 1.82669207919434e-06 \tabularnewline
45 & 0.999984197639338 & 3.16047213247703e-05 & 1.58023606623851e-05 \tabularnewline
46 & 0.999877167920207 & 0.000245664159586575 & 0.000122832079793288 \tabularnewline
47 & 0.999113176950657 & 0.00177364609868612 & 0.000886823049343058 \tabularnewline
48 & 0.993649568149025 & 0.0127008637019498 & 0.00635043185097492 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112566&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]0.0550575586782799[/C][C]0.110115117356560[/C][C]0.94494244132172[/C][/ROW]
[ROW][C]10[/C][C]0.0169701677790844[/C][C]0.0339403355581687[/C][C]0.983029832220916[/C][/ROW]
[ROW][C]11[/C][C]0.0125536731703830[/C][C]0.0251073463407661[/C][C]0.987446326829617[/C][/ROW]
[ROW][C]12[/C][C]0.00625798421548152[/C][C]0.0125159684309630[/C][C]0.993742015784518[/C][/ROW]
[ROW][C]13[/C][C]0.0020334853641235[/C][C]0.004066970728247[/C][C]0.997966514635876[/C][/ROW]
[ROW][C]14[/C][C]0.000755968930355649[/C][C]0.00151193786071130[/C][C]0.999244031069644[/C][/ROW]
[ROW][C]15[/C][C]0.000284385173187904[/C][C]0.000568770346375807[/C][C]0.999715614826812[/C][/ROW]
[ROW][C]16[/C][C]9.8821501049854e-05[/C][C]0.000197643002099708[/C][C]0.99990117849895[/C][/ROW]
[ROW][C]17[/C][C]3.78605267089099e-05[/C][C]7.57210534178198e-05[/C][C]0.999962139473291[/C][/ROW]
[ROW][C]18[/C][C]3.25076415995319e-05[/C][C]6.50152831990637e-05[/C][C]0.9999674923584[/C][/ROW]
[ROW][C]19[/C][C]0.000182038534042769[/C][C]0.000364077068085537[/C][C]0.999817961465957[/C][/ROW]
[ROW][C]20[/C][C]0.00390034974431261[/C][C]0.00780069948862521[/C][C]0.996099650255687[/C][/ROW]
[ROW][C]21[/C][C]0.183193624662647[/C][C]0.366387249325294[/C][C]0.816806375337353[/C][/ROW]
[ROW][C]22[/C][C]0.39187059460027[/C][C]0.78374118920054[/C][C]0.60812940539973[/C][/ROW]
[ROW][C]23[/C][C]0.86036129998349[/C][C]0.279277400033021[/C][C]0.139638700016511[/C][/ROW]
[ROW][C]24[/C][C]0.999988421601017[/C][C]2.31567979667903e-05[/C][C]1.15783989833952e-05[/C][/ROW]
[ROW][C]25[/C][C]0.999999756588795[/C][C]4.86822409450413e-07[/C][C]2.43411204725206e-07[/C][/ROW]
[ROW][C]26[/C][C]0.999999823014463[/C][C]3.53971074895282e-07[/C][C]1.76985537447641e-07[/C][/ROW]
[ROW][C]27[/C][C]0.999999999182296[/C][C]1.63540728488650e-09[/C][C]8.17703642443248e-10[/C][/ROW]
[ROW][C]28[/C][C]0.999999996708831[/C][C]6.58233727334197e-09[/C][C]3.29116863667099e-09[/C][/ROW]
[ROW][C]29[/C][C]0.99999998873639[/C][C]2.25272199547836e-08[/C][C]1.12636099773918e-08[/C][/ROW]
[ROW][C]30[/C][C]0.999999964430545[/C][C]7.11389105865679e-08[/C][C]3.55694552932840e-08[/C][/ROW]
[ROW][C]31[/C][C]0.99999996419112[/C][C]7.16177603627023e-08[/C][C]3.58088801813511e-08[/C][/ROW]
[ROW][C]32[/C][C]0.999999974426828[/C][C]5.11463434041318e-08[/C][C]2.55731717020659e-08[/C][/ROW]
[ROW][C]33[/C][C]0.999999984582514[/C][C]3.08349728975206e-08[/C][C]1.54174864487603e-08[/C][/ROW]
[ROW][C]34[/C][C]0.99999996766251[/C][C]6.46749810209135e-08[/C][C]3.23374905104567e-08[/C][/ROW]
[ROW][C]35[/C][C]0.999999999988272[/C][C]2.34553392907448e-11[/C][C]1.17276696453724e-11[/C][/ROW]
[ROW][C]36[/C][C]0.999999999998836[/C][C]2.32867266970921e-12[/C][C]1.16433633485460e-12[/C][/ROW]
[ROW][C]37[/C][C]0.99999999998986[/C][C]2.02790167012056e-11[/C][C]1.01395083506028e-11[/C][/ROW]
[ROW][C]38[/C][C]0.999999999915955[/C][C]1.68089901524581e-10[/C][C]8.40449507622904e-11[/C][/ROW]
[ROW][C]39[/C][C]0.999999999802853[/C][C]3.94294645621236e-10[/C][C]1.97147322810618e-10[/C][/ROW]
[ROW][C]40[/C][C]0.999999998946143[/C][C]2.10771356126984e-09[/C][C]1.05385678063492e-09[/C][/ROW]
[ROW][C]41[/C][C]0.999999994702195[/C][C]1.05956107182679e-08[/C][C]5.29780535913394e-09[/C][/ROW]
[ROW][C]42[/C][C]0.999999963598168[/C][C]7.28036631610388e-08[/C][C]3.64018315805194e-08[/C][/ROW]
[ROW][C]43[/C][C]0.999999769829284[/C][C]4.60341432793175e-07[/C][C]2.30170716396587e-07[/C][/ROW]
[ROW][C]44[/C][C]0.99999817330792[/C][C]3.65338415838868e-06[/C][C]1.82669207919434e-06[/C][/ROW]
[ROW][C]45[/C][C]0.999984197639338[/C][C]3.16047213247703e-05[/C][C]1.58023606623851e-05[/C][/ROW]
[ROW][C]46[/C][C]0.999877167920207[/C][C]0.000245664159586575[/C][C]0.000122832079793288[/C][/ROW]
[ROW][C]47[/C][C]0.999113176950657[/C][C]0.00177364609868612[/C][C]0.000886823049343058[/C][/ROW]
[ROW][C]48[/C][C]0.993649568149025[/C][C]0.0127008637019498[/C][C]0.00635043185097492[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112566&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112566&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
90.05505755867827990.1101151173565600.94494244132172
100.01697016777908440.03394033555816870.983029832220916
110.01255367317038300.02510734634076610.987446326829617
120.006257984215481520.01251596843096300.993742015784518
130.00203348536412350.0040669707282470.997966514635876
140.0007559689303556490.001511937860711300.999244031069644
150.0002843851731879040.0005687703463758070.999715614826812
169.8821501049854e-050.0001976430020997080.99990117849895
173.78605267089099e-057.57210534178198e-050.999962139473291
183.25076415995319e-056.50152831990637e-050.9999674923584
190.0001820385340427690.0003640770680855370.999817961465957
200.003900349744312610.007800699488625210.996099650255687
210.1831936246626470.3663872493252940.816806375337353
220.391870594600270.783741189200540.60812940539973
230.860361299983490.2792774000330210.139638700016511
240.9999884216010172.31567979667903e-051.15783989833952e-05
250.9999997565887954.86822409450413e-072.43411204725206e-07
260.9999998230144633.53971074895282e-071.76985537447641e-07
270.9999999991822961.63540728488650e-098.17703642443248e-10
280.9999999967088316.58233727334197e-093.29116863667099e-09
290.999999988736392.25272199547836e-081.12636099773918e-08
300.9999999644305457.11389105865679e-083.55694552932840e-08
310.999999964191127.16177603627023e-083.58088801813511e-08
320.9999999744268285.11463434041318e-082.55731717020659e-08
330.9999999845825143.08349728975206e-081.54174864487603e-08
340.999999967662516.46749810209135e-083.23374905104567e-08
350.9999999999882722.34553392907448e-111.17276696453724e-11
360.9999999999988362.32867266970921e-121.16433633485460e-12
370.999999999989862.02790167012056e-111.01395083506028e-11
380.9999999999159551.68089901524581e-108.40449507622904e-11
390.9999999998028533.94294645621236e-101.97147322810618e-10
400.9999999989461432.10771356126984e-091.05385678063492e-09
410.9999999947021951.05956107182679e-085.29780535913394e-09
420.9999999635981687.28036631610388e-083.64018315805194e-08
430.9999997698292844.60341432793175e-072.30170716396587e-07
440.999998173307923.65338415838868e-061.82669207919434e-06
450.9999841976393383.16047213247703e-051.58023606623851e-05
460.9998771679202070.0002456641595865750.000122832079793288
470.9991131769506570.001773646098686120.000886823049343058
480.9936495681490250.01270086370194980.00635043185097492






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level320.8NOK
5% type I error level360.9NOK
10% type I error level360.9NOK

\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 & 32 & 0.8 & NOK \tabularnewline
5% type I error level & 36 & 0.9 & NOK \tabularnewline
10% type I error level & 36 & 0.9 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112566&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]32[/C][C]0.8[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]36[/C][C]0.9[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]36[/C][C]0.9[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112566&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112566&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 level320.8NOK
5% type I error level360.9NOK
10% type I error level360.9NOK


Figures (Output of Computation)

Figure 1
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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')
}