Free Statistics

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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationTue, 17 Nov 2009 07:36:29 -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/17/t1258468648hnp3qef5ppr2hlt.htm/, Retrieved Thu, 31 Oct 2024 22:49:26 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=57369, Retrieved Thu, 31 Oct 2024 22:49:26 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact312
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RMPD  [Multiple Regression] [Seatbelt] [2009-11-12 13:54:52] [b98453cac15ba1066b407e146608df68]
-    D      [Multiple Regression] [model 1] [2009-11-17 14:36:29] [87085ce7f5378f281469a8b1f0969170] [Current]
-    D        [Multiple Regression] [miltiple regression] [2009-11-19 18:03:37] [ed603017d2bee8fbd82b6d5ec04e12c3]
-    D        [Multiple Regression] [multiple regression] [2009-11-19 21:38:11] [ed603017d2bee8fbd82b6d5ec04e12c3]
-   P           [Multiple Regression] [monthly dummies] [2009-11-19 22:00:07] [ed603017d2bee8fbd82b6d5ec04e12c3]
-   P             [Multiple Regression] [model3] [2009-11-20 08:47:44] [ed603017d2bee8fbd82b6d5ec04e12c3]
-    D              [Multiple Regression] [model 4] [2009-11-20 08:59:37] [ed603017d2bee8fbd82b6d5ec04e12c3]
-    D                [Multiple Regression] [W7: Model 4] [2009-11-22 13:34:45] [03d5b865e91ca35b5a5d21b8d6da5aba]
-    D                  [Multiple Regression] [review 7] [2009-11-24 21:51:11] [309ee52d0058ff0a6f7eec15e07b2d9f]
-    D                [Multiple Regression] [] [2009-11-22 15:02:10] [9f35ad889e41dd0c9322ca60d75b9f47]
-    D                [Multiple Regression] [Beste model] [2009-12-05 15:17:52] [34b80aeb109c116fd63bf2eb7493a276]
-    D              [Multiple Regression] [Workshop7] [2009-11-20 13:14:04] [34b80aeb109c116fd63bf2eb7493a276]
-    D                [Multiple Regression] [workshop7] [2009-11-20 13:34:45] [34b80aeb109c116fd63bf2eb7493a276]
-   P                   [Multiple Regression] [Workshop 7: verbe...] [2009-11-27 14:49:24] [7c2a5b25a196bd646844b8f5223c9b3e]
-   PD                [Multiple Regression] [Workshop 7] [2009-11-20 16:57:31] [78762f311bef5a0e45c439762ada383c]
-   P                   [Multiple Regression] [verb ws 7] [2009-11-21 09:45:52] [134dc66689e3d457a82860db6471d419]
-    D                [Multiple Regression] [model 3] [2009-12-05 14:58:14] [34b80aeb109c116fd63bf2eb7493a276]
-    D              [Multiple Regression] [W7: Linear Trend] [2009-11-21 14:22:35] [03d5b865e91ca35b5a5d21b8d6da5aba]
-    D              [Multiple Regression] [] [2009-11-22 14:13:11] [9f35ad889e41dd0c9322ca60d75b9f47]
-    D            [Multiple Regression] [W7: Monthly Dummies] [2009-11-21 14:07:55] [03d5b865e91ca35b5a5d21b8d6da5aba]
-    D            [Multiple Regression] [WS7] [2009-11-21 15:04:45] [9f35ad889e41dd0c9322ca60d75b9f47]
-    D          [Multiple Regression] [WS7] [2009-11-20 22:38:21] [9f35ad889e41dd0c9322ca60d75b9f47]
-    D          [Multiple Regression] [W7: Multiple regr...] [2009-11-21 13:40:01] [03d5b865e91ca35b5a5d21b8d6da5aba]
-    D          [Multiple Regression] [model 1 multiple ...] [2009-12-06 11:43:28] [ed603017d2bee8fbd82b6d5ec04e12c3]
-   PD          [Multiple Regression] [multiple regressi...] [2009-12-06 12:47:31] [ed603017d2bee8fbd82b6d5ec04e12c3]
-   PD            [Multiple Regression] [multiple regressi...] [2009-12-08 20:02:18] [ed603017d2bee8fbd82b6d5ec04e12c3]
-   PD          [Multiple Regression] [multiple regressi...] [2009-12-06 13:06:41] [ed603017d2bee8fbd82b6d5ec04e12c3]
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Dataseries X:
5.7	97.33	91.4
6.1	97.89	91.1
6	98.69	104.4
5.9	99.01	97.6
5.8	99.18	93.7
5.7	98.45	104.5
5.6	98.13	95.4
5.4	98.29	86.5
5.4	99.1	102.9
5.5	99.26	101.9
5.6	98.85	103.7
5.7	98.05	100.7
5.9	98.53	94.2
6.1	99.34	93.6
6	100.14	104.7
5.8	100.3	101
5.8	100.22	97.6
5.7	99.9	105.8
5.5	99.58	93.7
5.3	99.9	91.2
5.2	100.78	106.3
5.2	100.78	103.4
5	100.46	107.4
5.1	100.06	101.2
5.1	100.28	96.9
5.2	100.78	96.3
4.9	101.58	109.8
4.8	102.06	97.9
4.5	102.02	105.1
4.5	101.68	107.9
4.4	101.32	95
4.4	101.81	95.2
4.2	102.3	105.8
4.1	102.12	110.1
3.9	102.1	112.2
3.8	101.75	102.5
3.9	101.5	103.7
4.2	102.16	102
4.1	103.47	112.3
3.8	104.05	103.3
3.6	104.09	106.9
3.7	103.55	104.6
3.5	102.77	100.7
3.4	102.89	99
3.1	103.6	106.5
3.1	103.76	114.9
3.1	103.92	114.1
3.2	103.35	102.2
3.3	103.32	107
3.5	104.2	107.4
3.6	105.44	107.4
3.5	105.81	110.1
3.3	106.25	105.6
3.2	105.94	110.9
3.1	105.82	101.9
3.2	105.96	93.2
3	106.49	110.5
3	106.32	113.1
3.1	105.88	101.7
3.4	105.07	96.7




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\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 & 4 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57369&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]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57369&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57369&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 time4 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Multiple Linear Regression - Estimated Regression Equation
manwerk[t] = + 42.9053953972478 -0.371174663740103infl[t] -0.00596100651594128indprod[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
manwerk[t] =  +  42.9053953972478 -0.371174663740103infl[t] -0.00596100651594128indprod[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57369&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]manwerk[t] =  +  42.9053953972478 -0.371174663740103infl[t] -0.00596100651594128indprod[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57369&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57369&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
manwerk[t] = + 42.9053953972478 -0.371174663740103infl[t] -0.00596100651594128indprod[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)42.90539539724782.14533319.999400
infl-0.3711746637401030.024574-15.104600
indprod-0.005961006515941280.009699-0.61460.5412760.270638

\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) & 42.9053953972478 & 2.145333 & 19.9994 & 0 & 0 \tabularnewline
infl & -0.371174663740103 & 0.024574 & -15.1046 & 0 & 0 \tabularnewline
indprod & -0.00596100651594128 & 0.009699 & -0.6146 & 0.541276 & 0.270638 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57369&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]42.9053953972478[/C][C]2.145333[/C][C]19.9994[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]infl[/C][C]-0.371174663740103[/C][C]0.024574[/C][C]-15.1046[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]indprod[/C][C]-0.00596100651594128[/C][C]0.009699[/C][C]-0.6146[/C][C]0.541276[/C][C]0.270638[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57369&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57369&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)42.90539539724782.14533319.999400
infl-0.3711746637401030.024574-15.104600
indprod-0.005961006515941280.009699-0.61460.5412760.270638







Multiple Linear Regression - Regression Statistics
Multiple R0.923950647602837
R-squared0.853684799205702
Adjusted R-squared0.848550932511165
F-TEST (value)166.284956349597
F-TEST (DF numerator)2
F-TEST (DF denominator)57
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.41123506047334
Sum Squared Residuals9.63951367286317

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.923950647602837 \tabularnewline
R-squared & 0.853684799205702 \tabularnewline
Adjusted R-squared & 0.848550932511165 \tabularnewline
F-TEST (value) & 166.284956349597 \tabularnewline
F-TEST (DF numerator) & 2 \tabularnewline
F-TEST (DF denominator) & 57 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.41123506047334 \tabularnewline
Sum Squared Residuals & 9.63951367286317 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57369&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.923950647602837[/C][/ROW]
[ROW][C]R-squared[/C][C]0.853684799205702[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.848550932511165[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]166.284956349597[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]2[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]57[/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]0.41123506047334[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]9.63951367286317[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57369&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57369&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 R0.923950647602837
R-squared0.853684799205702
Adjusted R-squared0.848550932511165
F-TEST (value)166.284956349597
F-TEST (DF numerator)2
F-TEST (DF denominator)57
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.41123506047334
Sum Squared Residuals9.63951367286317







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
15.76.23412937986647-0.53412937986647
26.16.028059870126790.0719401298732074
365.651838752472690.348161247527311
45.95.573597704384250.326402295615746
55.85.533745936960610.266254063039393
65.75.74032457111872-0.0403245711187177
75.65.91334562281062-0.313345622810620
85.45.90701063460408-0.507010634604076
95.45.50859865011316-0.108598650113159
105.55.455171710430680.0448282895693196
115.65.596623510835430.00337648916456707
125.75.91144626137534-0.211446261375338
135.95.77202896513370.127971034866295
146.15.474954091413790.625045908586214
1565.111847188094750.888152811905245
165.85.074514966005320.725485033994677
175.85.124476361258730.675523638741269
185.75.194372000224840.505627999775157
195.55.385276071464570.114723928535432
205.35.281402695357590.0185973046424141
215.24.864757792875580.335242207124417
225.24.882044711771810.317955288228188
2354.976976578104880.0230234218951166
245.15.16240468399976-0.0624046839997579
255.15.10637858599548-0.00637858599548303
265.24.9243678580350.275632141965004
274.94.546954539077710.353045460922294
284.84.439726678022160.360273321977843
294.54.411654417656990.088345582343014
304.54.52116298508398-0.0211629850839815
314.44.73168284808607-0.331682848086066
324.44.54861506155022-0.148615061550224
334.24.3035528072486-0.103552807248597
344.14.34473191870327-0.244731918703266
353.94.33963729829459-0.439637298294595
363.84.52737019380826-0.72737019380826
373.94.61301065192416-0.713010651924156
384.24.37816908493279-0.178169084932789
394.13.830531908319060.269468091680942
403.83.668899661993270.131100338006730
413.63.63259305198627-0.0325930519862744
423.73.8467376853926-0.146737685392598
433.54.15950184852205-0.65950184852205
443.44.12509459995034-0.725094599950336
453.13.81685303982531-0.716853039825305
463.13.70739263889298-0.607392638892978
473.13.65277349790732-0.552773497907315
483.23.93527903377888-0.735279033778878
493.33.91780144241456-0.617801442414564
503.53.58878333571689-0.0887833357168928
513.63.128526752679170.471473247320834
523.52.975097409502280.524902590497715
533.32.838605086778380.461394913221624
543.22.922075898003320.27792410199668
553.13.020265916295610.0797340837043944
563.23.020162220060680.17983777993932
5732.720314235552640.279685764447359
5832.767915311447010.232084688552988
593.12.999187637774390.100812362225613
603.43.329644147983580.0703558520164221

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 5.7 & 6.23412937986647 & -0.53412937986647 \tabularnewline
2 & 6.1 & 6.02805987012679 & 0.0719401298732074 \tabularnewline
3 & 6 & 5.65183875247269 & 0.348161247527311 \tabularnewline
4 & 5.9 & 5.57359770438425 & 0.326402295615746 \tabularnewline
5 & 5.8 & 5.53374593696061 & 0.266254063039393 \tabularnewline
6 & 5.7 & 5.74032457111872 & -0.0403245711187177 \tabularnewline
7 & 5.6 & 5.91334562281062 & -0.313345622810620 \tabularnewline
8 & 5.4 & 5.90701063460408 & -0.507010634604076 \tabularnewline
9 & 5.4 & 5.50859865011316 & -0.108598650113159 \tabularnewline
10 & 5.5 & 5.45517171043068 & 0.0448282895693196 \tabularnewline
11 & 5.6 & 5.59662351083543 & 0.00337648916456707 \tabularnewline
12 & 5.7 & 5.91144626137534 & -0.211446261375338 \tabularnewline
13 & 5.9 & 5.7720289651337 & 0.127971034866295 \tabularnewline
14 & 6.1 & 5.47495409141379 & 0.625045908586214 \tabularnewline
15 & 6 & 5.11184718809475 & 0.888152811905245 \tabularnewline
16 & 5.8 & 5.07451496600532 & 0.725485033994677 \tabularnewline
17 & 5.8 & 5.12447636125873 & 0.675523638741269 \tabularnewline
18 & 5.7 & 5.19437200022484 & 0.505627999775157 \tabularnewline
19 & 5.5 & 5.38527607146457 & 0.114723928535432 \tabularnewline
20 & 5.3 & 5.28140269535759 & 0.0185973046424141 \tabularnewline
21 & 5.2 & 4.86475779287558 & 0.335242207124417 \tabularnewline
22 & 5.2 & 4.88204471177181 & 0.317955288228188 \tabularnewline
23 & 5 & 4.97697657810488 & 0.0230234218951166 \tabularnewline
24 & 5.1 & 5.16240468399976 & -0.0624046839997579 \tabularnewline
25 & 5.1 & 5.10637858599548 & -0.00637858599548303 \tabularnewline
26 & 5.2 & 4.924367858035 & 0.275632141965004 \tabularnewline
27 & 4.9 & 4.54695453907771 & 0.353045460922294 \tabularnewline
28 & 4.8 & 4.43972667802216 & 0.360273321977843 \tabularnewline
29 & 4.5 & 4.41165441765699 & 0.088345582343014 \tabularnewline
30 & 4.5 & 4.52116298508398 & -0.0211629850839815 \tabularnewline
31 & 4.4 & 4.73168284808607 & -0.331682848086066 \tabularnewline
32 & 4.4 & 4.54861506155022 & -0.148615061550224 \tabularnewline
33 & 4.2 & 4.3035528072486 & -0.103552807248597 \tabularnewline
34 & 4.1 & 4.34473191870327 & -0.244731918703266 \tabularnewline
35 & 3.9 & 4.33963729829459 & -0.439637298294595 \tabularnewline
36 & 3.8 & 4.52737019380826 & -0.72737019380826 \tabularnewline
37 & 3.9 & 4.61301065192416 & -0.713010651924156 \tabularnewline
38 & 4.2 & 4.37816908493279 & -0.178169084932789 \tabularnewline
39 & 4.1 & 3.83053190831906 & 0.269468091680942 \tabularnewline
40 & 3.8 & 3.66889966199327 & 0.131100338006730 \tabularnewline
41 & 3.6 & 3.63259305198627 & -0.0325930519862744 \tabularnewline
42 & 3.7 & 3.8467376853926 & -0.146737685392598 \tabularnewline
43 & 3.5 & 4.15950184852205 & -0.65950184852205 \tabularnewline
44 & 3.4 & 4.12509459995034 & -0.725094599950336 \tabularnewline
45 & 3.1 & 3.81685303982531 & -0.716853039825305 \tabularnewline
46 & 3.1 & 3.70739263889298 & -0.607392638892978 \tabularnewline
47 & 3.1 & 3.65277349790732 & -0.552773497907315 \tabularnewline
48 & 3.2 & 3.93527903377888 & -0.735279033778878 \tabularnewline
49 & 3.3 & 3.91780144241456 & -0.617801442414564 \tabularnewline
50 & 3.5 & 3.58878333571689 & -0.0887833357168928 \tabularnewline
51 & 3.6 & 3.12852675267917 & 0.471473247320834 \tabularnewline
52 & 3.5 & 2.97509740950228 & 0.524902590497715 \tabularnewline
53 & 3.3 & 2.83860508677838 & 0.461394913221624 \tabularnewline
54 & 3.2 & 2.92207589800332 & 0.27792410199668 \tabularnewline
55 & 3.1 & 3.02026591629561 & 0.0797340837043944 \tabularnewline
56 & 3.2 & 3.02016222006068 & 0.17983777993932 \tabularnewline
57 & 3 & 2.72031423555264 & 0.279685764447359 \tabularnewline
58 & 3 & 2.76791531144701 & 0.232084688552988 \tabularnewline
59 & 3.1 & 2.99918763777439 & 0.100812362225613 \tabularnewline
60 & 3.4 & 3.32964414798358 & 0.0703558520164221 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57369&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]5.7[/C][C]6.23412937986647[/C][C]-0.53412937986647[/C][/ROW]
[ROW][C]2[/C][C]6.1[/C][C]6.02805987012679[/C][C]0.0719401298732074[/C][/ROW]
[ROW][C]3[/C][C]6[/C][C]5.65183875247269[/C][C]0.348161247527311[/C][/ROW]
[ROW][C]4[/C][C]5.9[/C][C]5.57359770438425[/C][C]0.326402295615746[/C][/ROW]
[ROW][C]5[/C][C]5.8[/C][C]5.53374593696061[/C][C]0.266254063039393[/C][/ROW]
[ROW][C]6[/C][C]5.7[/C][C]5.74032457111872[/C][C]-0.0403245711187177[/C][/ROW]
[ROW][C]7[/C][C]5.6[/C][C]5.91334562281062[/C][C]-0.313345622810620[/C][/ROW]
[ROW][C]8[/C][C]5.4[/C][C]5.90701063460408[/C][C]-0.507010634604076[/C][/ROW]
[ROW][C]9[/C][C]5.4[/C][C]5.50859865011316[/C][C]-0.108598650113159[/C][/ROW]
[ROW][C]10[/C][C]5.5[/C][C]5.45517171043068[/C][C]0.0448282895693196[/C][/ROW]
[ROW][C]11[/C][C]5.6[/C][C]5.59662351083543[/C][C]0.00337648916456707[/C][/ROW]
[ROW][C]12[/C][C]5.7[/C][C]5.91144626137534[/C][C]-0.211446261375338[/C][/ROW]
[ROW][C]13[/C][C]5.9[/C][C]5.7720289651337[/C][C]0.127971034866295[/C][/ROW]
[ROW][C]14[/C][C]6.1[/C][C]5.47495409141379[/C][C]0.625045908586214[/C][/ROW]
[ROW][C]15[/C][C]6[/C][C]5.11184718809475[/C][C]0.888152811905245[/C][/ROW]
[ROW][C]16[/C][C]5.8[/C][C]5.07451496600532[/C][C]0.725485033994677[/C][/ROW]
[ROW][C]17[/C][C]5.8[/C][C]5.12447636125873[/C][C]0.675523638741269[/C][/ROW]
[ROW][C]18[/C][C]5.7[/C][C]5.19437200022484[/C][C]0.505627999775157[/C][/ROW]
[ROW][C]19[/C][C]5.5[/C][C]5.38527607146457[/C][C]0.114723928535432[/C][/ROW]
[ROW][C]20[/C][C]5.3[/C][C]5.28140269535759[/C][C]0.0185973046424141[/C][/ROW]
[ROW][C]21[/C][C]5.2[/C][C]4.86475779287558[/C][C]0.335242207124417[/C][/ROW]
[ROW][C]22[/C][C]5.2[/C][C]4.88204471177181[/C][C]0.317955288228188[/C][/ROW]
[ROW][C]23[/C][C]5[/C][C]4.97697657810488[/C][C]0.0230234218951166[/C][/ROW]
[ROW][C]24[/C][C]5.1[/C][C]5.16240468399976[/C][C]-0.0624046839997579[/C][/ROW]
[ROW][C]25[/C][C]5.1[/C][C]5.10637858599548[/C][C]-0.00637858599548303[/C][/ROW]
[ROW][C]26[/C][C]5.2[/C][C]4.924367858035[/C][C]0.275632141965004[/C][/ROW]
[ROW][C]27[/C][C]4.9[/C][C]4.54695453907771[/C][C]0.353045460922294[/C][/ROW]
[ROW][C]28[/C][C]4.8[/C][C]4.43972667802216[/C][C]0.360273321977843[/C][/ROW]
[ROW][C]29[/C][C]4.5[/C][C]4.41165441765699[/C][C]0.088345582343014[/C][/ROW]
[ROW][C]30[/C][C]4.5[/C][C]4.52116298508398[/C][C]-0.0211629850839815[/C][/ROW]
[ROW][C]31[/C][C]4.4[/C][C]4.73168284808607[/C][C]-0.331682848086066[/C][/ROW]
[ROW][C]32[/C][C]4.4[/C][C]4.54861506155022[/C][C]-0.148615061550224[/C][/ROW]
[ROW][C]33[/C][C]4.2[/C][C]4.3035528072486[/C][C]-0.103552807248597[/C][/ROW]
[ROW][C]34[/C][C]4.1[/C][C]4.34473191870327[/C][C]-0.244731918703266[/C][/ROW]
[ROW][C]35[/C][C]3.9[/C][C]4.33963729829459[/C][C]-0.439637298294595[/C][/ROW]
[ROW][C]36[/C][C]3.8[/C][C]4.52737019380826[/C][C]-0.72737019380826[/C][/ROW]
[ROW][C]37[/C][C]3.9[/C][C]4.61301065192416[/C][C]-0.713010651924156[/C][/ROW]
[ROW][C]38[/C][C]4.2[/C][C]4.37816908493279[/C][C]-0.178169084932789[/C][/ROW]
[ROW][C]39[/C][C]4.1[/C][C]3.83053190831906[/C][C]0.269468091680942[/C][/ROW]
[ROW][C]40[/C][C]3.8[/C][C]3.66889966199327[/C][C]0.131100338006730[/C][/ROW]
[ROW][C]41[/C][C]3.6[/C][C]3.63259305198627[/C][C]-0.0325930519862744[/C][/ROW]
[ROW][C]42[/C][C]3.7[/C][C]3.8467376853926[/C][C]-0.146737685392598[/C][/ROW]
[ROW][C]43[/C][C]3.5[/C][C]4.15950184852205[/C][C]-0.65950184852205[/C][/ROW]
[ROW][C]44[/C][C]3.4[/C][C]4.12509459995034[/C][C]-0.725094599950336[/C][/ROW]
[ROW][C]45[/C][C]3.1[/C][C]3.81685303982531[/C][C]-0.716853039825305[/C][/ROW]
[ROW][C]46[/C][C]3.1[/C][C]3.70739263889298[/C][C]-0.607392638892978[/C][/ROW]
[ROW][C]47[/C][C]3.1[/C][C]3.65277349790732[/C][C]-0.552773497907315[/C][/ROW]
[ROW][C]48[/C][C]3.2[/C][C]3.93527903377888[/C][C]-0.735279033778878[/C][/ROW]
[ROW][C]49[/C][C]3.3[/C][C]3.91780144241456[/C][C]-0.617801442414564[/C][/ROW]
[ROW][C]50[/C][C]3.5[/C][C]3.58878333571689[/C][C]-0.0887833357168928[/C][/ROW]
[ROW][C]51[/C][C]3.6[/C][C]3.12852675267917[/C][C]0.471473247320834[/C][/ROW]
[ROW][C]52[/C][C]3.5[/C][C]2.97509740950228[/C][C]0.524902590497715[/C][/ROW]
[ROW][C]53[/C][C]3.3[/C][C]2.83860508677838[/C][C]0.461394913221624[/C][/ROW]
[ROW][C]54[/C][C]3.2[/C][C]2.92207589800332[/C][C]0.27792410199668[/C][/ROW]
[ROW][C]55[/C][C]3.1[/C][C]3.02026591629561[/C][C]0.0797340837043944[/C][/ROW]
[ROW][C]56[/C][C]3.2[/C][C]3.02016222006068[/C][C]0.17983777993932[/C][/ROW]
[ROW][C]57[/C][C]3[/C][C]2.72031423555264[/C][C]0.279685764447359[/C][/ROW]
[ROW][C]58[/C][C]3[/C][C]2.76791531144701[/C][C]0.232084688552988[/C][/ROW]
[ROW][C]59[/C][C]3.1[/C][C]2.99918763777439[/C][C]0.100812362225613[/C][/ROW]
[ROW][C]60[/C][C]3.4[/C][C]3.32964414798358[/C][C]0.0703558520164221[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57369&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57369&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
15.76.23412937986647-0.53412937986647
26.16.028059870126790.0719401298732074
365.651838752472690.348161247527311
45.95.573597704384250.326402295615746
55.85.533745936960610.266254063039393
65.75.74032457111872-0.0403245711187177
75.65.91334562281062-0.313345622810620
85.45.90701063460408-0.507010634604076
95.45.50859865011316-0.108598650113159
105.55.455171710430680.0448282895693196
115.65.596623510835430.00337648916456707
125.75.91144626137534-0.211446261375338
135.95.77202896513370.127971034866295
146.15.474954091413790.625045908586214
1565.111847188094750.888152811905245
165.85.074514966005320.725485033994677
175.85.124476361258730.675523638741269
185.75.194372000224840.505627999775157
195.55.385276071464570.114723928535432
205.35.281402695357590.0185973046424141
215.24.864757792875580.335242207124417
225.24.882044711771810.317955288228188
2354.976976578104880.0230234218951166
245.15.16240468399976-0.0624046839997579
255.15.10637858599548-0.00637858599548303
265.24.9243678580350.275632141965004
274.94.546954539077710.353045460922294
284.84.439726678022160.360273321977843
294.54.411654417656990.088345582343014
304.54.52116298508398-0.0211629850839815
314.44.73168284808607-0.331682848086066
324.44.54861506155022-0.148615061550224
334.24.3035528072486-0.103552807248597
344.14.34473191870327-0.244731918703266
353.94.33963729829459-0.439637298294595
363.84.52737019380826-0.72737019380826
373.94.61301065192416-0.713010651924156
384.24.37816908493279-0.178169084932789
394.13.830531908319060.269468091680942
403.83.668899661993270.131100338006730
413.63.63259305198627-0.0325930519862744
423.73.8467376853926-0.146737685392598
433.54.15950184852205-0.65950184852205
443.44.12509459995034-0.725094599950336
453.13.81685303982531-0.716853039825305
463.13.70739263889298-0.607392638892978
473.13.65277349790732-0.552773497907315
483.23.93527903377888-0.735279033778878
493.33.91780144241456-0.617801442414564
503.53.58878333571689-0.0887833357168928
513.63.128526752679170.471473247320834
523.52.975097409502280.524902590497715
533.32.838605086778380.461394913221624
543.22.922075898003320.27792410199668
553.13.020265916295610.0797340837043944
563.23.020162220060680.17983777993932
5732.720314235552640.279685764447359
5832.767915311447010.232084688552988
593.12.999187637774390.100812362225613
603.43.329644147983580.0703558520164221







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
60.1336546547292250.2673093094584490.866345345270775
70.09790293248284190.1958058649656840.902097067517158
80.1176752594721000.2353505189442010.8823247405279
90.1470826535425420.2941653070850840.852917346457458
100.0979124710932270.1958249421864540.902087528906773
110.05610450268386780.1122090053677360.943895497316132
120.0301626228798870.0603252457597740.969837377120113
130.01938789703204630.03877579406409250.980612102967954
140.02615845525371440.05231691050742870.973841544746286
150.02689622032714670.05379244065429330.973103779672853
160.02129957453054140.04259914906108270.978700425469459
170.01755923796352520.03511847592705040.982440762036475
180.01451435553666440.02902871107332880.985485644463336
190.01480915649339660.02961831298679320.985190843506603
200.02261637611699210.04523275223398410.977383623883008
210.04571911018409130.09143822036818270.954280889815909
220.06248319991651490.1249663998330300.937516800083485
230.1035693975119210.2071387950238430.896430602488079
240.1216149301479840.2432298602959670.878385069852016
250.1305459268122660.2610918536245330.869454073187734
260.1524279245006320.3048558490012650.847572075499368
270.2322615311372930.4645230622745850.767738468862707
280.3291008844880030.6582017689760050.670899115511997
290.4466353302514460.8932706605028920.553364669748554
300.5743274289307530.8513451421384950.425672571069247
310.66001799709860.67996400580280.3399820029014
320.7202444713850010.5595110572299980.279755528614999
330.7806936059162760.4386127881674490.219306394083724
340.8333837833871740.3332324332256520.166616216612826
350.8684381365680650.263123726863870.131561863431935
360.9028182023148450.194363595370310.097181797685155
370.9172671822069420.1654656355861160.0827328177930578
380.9476195439102880.1047609121794230.0523804560897117
390.9903420974251970.01931580514960580.0096579025748029
400.9949547057163540.01009058856729200.00504529428364601
410.994883997156490.01023200568701920.0051160028435096
420.997147061965250.005705876069499530.00285293803474976
430.9966542093465260.006691581306948230.00334579065347411
440.995007330422560.009985339154881110.00499266957744056
450.9938201531768770.01235969364624630.00617984682312316
460.9911957723993310.01760845520133810.00880422760066904
470.9888026319301840.02239473613963260.0111973680698163
480.9875716726182720.02485665476345660.0124283273817283
490.9923898975256420.01522020494871560.0076101024743578
500.9919951086097360.01600978278052770.00800489139026383
510.9902811559303470.01943768813930680.0097188440696534
520.9957915081239410.008416983752117310.00420849187605866
530.9988324929257880.002335014148423080.00116750707421154
540.9966914817704870.006617036459026150.00330851822951308

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
6 & 0.133654654729225 & 0.267309309458449 & 0.866345345270775 \tabularnewline
7 & 0.0979029324828419 & 0.195805864965684 & 0.902097067517158 \tabularnewline
8 & 0.117675259472100 & 0.235350518944201 & 0.8823247405279 \tabularnewline
9 & 0.147082653542542 & 0.294165307085084 & 0.852917346457458 \tabularnewline
10 & 0.097912471093227 & 0.195824942186454 & 0.902087528906773 \tabularnewline
11 & 0.0561045026838678 & 0.112209005367736 & 0.943895497316132 \tabularnewline
12 & 0.030162622879887 & 0.060325245759774 & 0.969837377120113 \tabularnewline
13 & 0.0193878970320463 & 0.0387757940640925 & 0.980612102967954 \tabularnewline
14 & 0.0261584552537144 & 0.0523169105074287 & 0.973841544746286 \tabularnewline
15 & 0.0268962203271467 & 0.0537924406542933 & 0.973103779672853 \tabularnewline
16 & 0.0212995745305414 & 0.0425991490610827 & 0.978700425469459 \tabularnewline
17 & 0.0175592379635252 & 0.0351184759270504 & 0.982440762036475 \tabularnewline
18 & 0.0145143555366644 & 0.0290287110733288 & 0.985485644463336 \tabularnewline
19 & 0.0148091564933966 & 0.0296183129867932 & 0.985190843506603 \tabularnewline
20 & 0.0226163761169921 & 0.0452327522339841 & 0.977383623883008 \tabularnewline
21 & 0.0457191101840913 & 0.0914382203681827 & 0.954280889815909 \tabularnewline
22 & 0.0624831999165149 & 0.124966399833030 & 0.937516800083485 \tabularnewline
23 & 0.103569397511921 & 0.207138795023843 & 0.896430602488079 \tabularnewline
24 & 0.121614930147984 & 0.243229860295967 & 0.878385069852016 \tabularnewline
25 & 0.130545926812266 & 0.261091853624533 & 0.869454073187734 \tabularnewline
26 & 0.152427924500632 & 0.304855849001265 & 0.847572075499368 \tabularnewline
27 & 0.232261531137293 & 0.464523062274585 & 0.767738468862707 \tabularnewline
28 & 0.329100884488003 & 0.658201768976005 & 0.670899115511997 \tabularnewline
29 & 0.446635330251446 & 0.893270660502892 & 0.553364669748554 \tabularnewline
30 & 0.574327428930753 & 0.851345142138495 & 0.425672571069247 \tabularnewline
31 & 0.6600179970986 & 0.6799640058028 & 0.3399820029014 \tabularnewline
32 & 0.720244471385001 & 0.559511057229998 & 0.279755528614999 \tabularnewline
33 & 0.780693605916276 & 0.438612788167449 & 0.219306394083724 \tabularnewline
34 & 0.833383783387174 & 0.333232433225652 & 0.166616216612826 \tabularnewline
35 & 0.868438136568065 & 0.26312372686387 & 0.131561863431935 \tabularnewline
36 & 0.902818202314845 & 0.19436359537031 & 0.097181797685155 \tabularnewline
37 & 0.917267182206942 & 0.165465635586116 & 0.0827328177930578 \tabularnewline
38 & 0.947619543910288 & 0.104760912179423 & 0.0523804560897117 \tabularnewline
39 & 0.990342097425197 & 0.0193158051496058 & 0.0096579025748029 \tabularnewline
40 & 0.994954705716354 & 0.0100905885672920 & 0.00504529428364601 \tabularnewline
41 & 0.99488399715649 & 0.0102320056870192 & 0.0051160028435096 \tabularnewline
42 & 0.99714706196525 & 0.00570587606949953 & 0.00285293803474976 \tabularnewline
43 & 0.996654209346526 & 0.00669158130694823 & 0.00334579065347411 \tabularnewline
44 & 0.99500733042256 & 0.00998533915488111 & 0.00499266957744056 \tabularnewline
45 & 0.993820153176877 & 0.0123596936462463 & 0.00617984682312316 \tabularnewline
46 & 0.991195772399331 & 0.0176084552013381 & 0.00880422760066904 \tabularnewline
47 & 0.988802631930184 & 0.0223947361396326 & 0.0111973680698163 \tabularnewline
48 & 0.987571672618272 & 0.0248566547634566 & 0.0124283273817283 \tabularnewline
49 & 0.992389897525642 & 0.0152202049487156 & 0.0076101024743578 \tabularnewline
50 & 0.991995108609736 & 0.0160097827805277 & 0.00800489139026383 \tabularnewline
51 & 0.990281155930347 & 0.0194376881393068 & 0.0097188440696534 \tabularnewline
52 & 0.995791508123941 & 0.00841698375211731 & 0.00420849187605866 \tabularnewline
53 & 0.998832492925788 & 0.00233501414842308 & 0.00116750707421154 \tabularnewline
54 & 0.996691481770487 & 0.00661703645902615 & 0.00330851822951308 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57369&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]6[/C][C]0.133654654729225[/C][C]0.267309309458449[/C][C]0.866345345270775[/C][/ROW]
[ROW][C]7[/C][C]0.0979029324828419[/C][C]0.195805864965684[/C][C]0.902097067517158[/C][/ROW]
[ROW][C]8[/C][C]0.117675259472100[/C][C]0.235350518944201[/C][C]0.8823247405279[/C][/ROW]
[ROW][C]9[/C][C]0.147082653542542[/C][C]0.294165307085084[/C][C]0.852917346457458[/C][/ROW]
[ROW][C]10[/C][C]0.097912471093227[/C][C]0.195824942186454[/C][C]0.902087528906773[/C][/ROW]
[ROW][C]11[/C][C]0.0561045026838678[/C][C]0.112209005367736[/C][C]0.943895497316132[/C][/ROW]
[ROW][C]12[/C][C]0.030162622879887[/C][C]0.060325245759774[/C][C]0.969837377120113[/C][/ROW]
[ROW][C]13[/C][C]0.0193878970320463[/C][C]0.0387757940640925[/C][C]0.980612102967954[/C][/ROW]
[ROW][C]14[/C][C]0.0261584552537144[/C][C]0.0523169105074287[/C][C]0.973841544746286[/C][/ROW]
[ROW][C]15[/C][C]0.0268962203271467[/C][C]0.0537924406542933[/C][C]0.973103779672853[/C][/ROW]
[ROW][C]16[/C][C]0.0212995745305414[/C][C]0.0425991490610827[/C][C]0.978700425469459[/C][/ROW]
[ROW][C]17[/C][C]0.0175592379635252[/C][C]0.0351184759270504[/C][C]0.982440762036475[/C][/ROW]
[ROW][C]18[/C][C]0.0145143555366644[/C][C]0.0290287110733288[/C][C]0.985485644463336[/C][/ROW]
[ROW][C]19[/C][C]0.0148091564933966[/C][C]0.0296183129867932[/C][C]0.985190843506603[/C][/ROW]
[ROW][C]20[/C][C]0.0226163761169921[/C][C]0.0452327522339841[/C][C]0.977383623883008[/C][/ROW]
[ROW][C]21[/C][C]0.0457191101840913[/C][C]0.0914382203681827[/C][C]0.954280889815909[/C][/ROW]
[ROW][C]22[/C][C]0.0624831999165149[/C][C]0.124966399833030[/C][C]0.937516800083485[/C][/ROW]
[ROW][C]23[/C][C]0.103569397511921[/C][C]0.207138795023843[/C][C]0.896430602488079[/C][/ROW]
[ROW][C]24[/C][C]0.121614930147984[/C][C]0.243229860295967[/C][C]0.878385069852016[/C][/ROW]
[ROW][C]25[/C][C]0.130545926812266[/C][C]0.261091853624533[/C][C]0.869454073187734[/C][/ROW]
[ROW][C]26[/C][C]0.152427924500632[/C][C]0.304855849001265[/C][C]0.847572075499368[/C][/ROW]
[ROW][C]27[/C][C]0.232261531137293[/C][C]0.464523062274585[/C][C]0.767738468862707[/C][/ROW]
[ROW][C]28[/C][C]0.329100884488003[/C][C]0.658201768976005[/C][C]0.670899115511997[/C][/ROW]
[ROW][C]29[/C][C]0.446635330251446[/C][C]0.893270660502892[/C][C]0.553364669748554[/C][/ROW]
[ROW][C]30[/C][C]0.574327428930753[/C][C]0.851345142138495[/C][C]0.425672571069247[/C][/ROW]
[ROW][C]31[/C][C]0.6600179970986[/C][C]0.6799640058028[/C][C]0.3399820029014[/C][/ROW]
[ROW][C]32[/C][C]0.720244471385001[/C][C]0.559511057229998[/C][C]0.279755528614999[/C][/ROW]
[ROW][C]33[/C][C]0.780693605916276[/C][C]0.438612788167449[/C][C]0.219306394083724[/C][/ROW]
[ROW][C]34[/C][C]0.833383783387174[/C][C]0.333232433225652[/C][C]0.166616216612826[/C][/ROW]
[ROW][C]35[/C][C]0.868438136568065[/C][C]0.26312372686387[/C][C]0.131561863431935[/C][/ROW]
[ROW][C]36[/C][C]0.902818202314845[/C][C]0.19436359537031[/C][C]0.097181797685155[/C][/ROW]
[ROW][C]37[/C][C]0.917267182206942[/C][C]0.165465635586116[/C][C]0.0827328177930578[/C][/ROW]
[ROW][C]38[/C][C]0.947619543910288[/C][C]0.104760912179423[/C][C]0.0523804560897117[/C][/ROW]
[ROW][C]39[/C][C]0.990342097425197[/C][C]0.0193158051496058[/C][C]0.0096579025748029[/C][/ROW]
[ROW][C]40[/C][C]0.994954705716354[/C][C]0.0100905885672920[/C][C]0.00504529428364601[/C][/ROW]
[ROW][C]41[/C][C]0.99488399715649[/C][C]0.0102320056870192[/C][C]0.0051160028435096[/C][/ROW]
[ROW][C]42[/C][C]0.99714706196525[/C][C]0.00570587606949953[/C][C]0.00285293803474976[/C][/ROW]
[ROW][C]43[/C][C]0.996654209346526[/C][C]0.00669158130694823[/C][C]0.00334579065347411[/C][/ROW]
[ROW][C]44[/C][C]0.99500733042256[/C][C]0.00998533915488111[/C][C]0.00499266957744056[/C][/ROW]
[ROW][C]45[/C][C]0.993820153176877[/C][C]0.0123596936462463[/C][C]0.00617984682312316[/C][/ROW]
[ROW][C]46[/C][C]0.991195772399331[/C][C]0.0176084552013381[/C][C]0.00880422760066904[/C][/ROW]
[ROW][C]47[/C][C]0.988802631930184[/C][C]0.0223947361396326[/C][C]0.0111973680698163[/C][/ROW]
[ROW][C]48[/C][C]0.987571672618272[/C][C]0.0248566547634566[/C][C]0.0124283273817283[/C][/ROW]
[ROW][C]49[/C][C]0.992389897525642[/C][C]0.0152202049487156[/C][C]0.0076101024743578[/C][/ROW]
[ROW][C]50[/C][C]0.991995108609736[/C][C]0.0160097827805277[/C][C]0.00800489139026383[/C][/ROW]
[ROW][C]51[/C][C]0.990281155930347[/C][C]0.0194376881393068[/C][C]0.0097188440696534[/C][/ROW]
[ROW][C]52[/C][C]0.995791508123941[/C][C]0.00841698375211731[/C][C]0.00420849187605866[/C][/ROW]
[ROW][C]53[/C][C]0.998832492925788[/C][C]0.00233501414842308[/C][C]0.00116750707421154[/C][/ROW]
[ROW][C]54[/C][C]0.996691481770487[/C][C]0.00661703645902615[/C][C]0.00330851822951308[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57369&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57369&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
60.1336546547292250.2673093094584490.866345345270775
70.09790293248284190.1958058649656840.902097067517158
80.1176752594721000.2353505189442010.8823247405279
90.1470826535425420.2941653070850840.852917346457458
100.0979124710932270.1958249421864540.902087528906773
110.05610450268386780.1122090053677360.943895497316132
120.0301626228798870.0603252457597740.969837377120113
130.01938789703204630.03877579406409250.980612102967954
140.02615845525371440.05231691050742870.973841544746286
150.02689622032714670.05379244065429330.973103779672853
160.02129957453054140.04259914906108270.978700425469459
170.01755923796352520.03511847592705040.982440762036475
180.01451435553666440.02902871107332880.985485644463336
190.01480915649339660.02961831298679320.985190843506603
200.02261637611699210.04523275223398410.977383623883008
210.04571911018409130.09143822036818270.954280889815909
220.06248319991651490.1249663998330300.937516800083485
230.1035693975119210.2071387950238430.896430602488079
240.1216149301479840.2432298602959670.878385069852016
250.1305459268122660.2610918536245330.869454073187734
260.1524279245006320.3048558490012650.847572075499368
270.2322615311372930.4645230622745850.767738468862707
280.3291008844880030.6582017689760050.670899115511997
290.4466353302514460.8932706605028920.553364669748554
300.5743274289307530.8513451421384950.425672571069247
310.66001799709860.67996400580280.3399820029014
320.7202444713850010.5595110572299980.279755528614999
330.7806936059162760.4386127881674490.219306394083724
340.8333837833871740.3332324332256520.166616216612826
350.8684381365680650.263123726863870.131561863431935
360.9028182023148450.194363595370310.097181797685155
370.9172671822069420.1654656355861160.0827328177930578
380.9476195439102880.1047609121794230.0523804560897117
390.9903420974251970.01931580514960580.0096579025748029
400.9949547057163540.01009058856729200.00504529428364601
410.994883997156490.01023200568701920.0051160028435096
420.997147061965250.005705876069499530.00285293803474976
430.9966542093465260.006691581306948230.00334579065347411
440.995007330422560.009985339154881110.00499266957744056
450.9938201531768770.01235969364624630.00617984682312316
460.9911957723993310.01760845520133810.00880422760066904
470.9888026319301840.02239473613963260.0111973680698163
480.9875716726182720.02485665476345660.0124283273817283
490.9923898975256420.01522020494871560.0076101024743578
500.9919951086097360.01600978278052770.00800489139026383
510.9902811559303470.01943768813930680.0097188440696534
520.9957915081239410.008416983752117310.00420849187605866
530.9988324929257880.002335014148423080.00116750707421154
540.9966914817704870.006617036459026150.00330851822951308







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level60.122448979591837NOK
5% type I error level220.448979591836735NOK
10% type I error level260.530612244897959NOK

\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 & 6 & 0.122448979591837 & NOK \tabularnewline
5% type I error level & 22 & 0.448979591836735 & NOK \tabularnewline
10% type I error level & 26 & 0.530612244897959 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57369&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]6[/C][C]0.122448979591837[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]22[/C][C]0.448979591836735[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]26[/C][C]0.530612244897959[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57369&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57369&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 level60.122448979591837NOK
5% type I error level220.448979591836735NOK
10% type I error level260.530612244897959NOK



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')
}