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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationThu, 19 Nov 2009 11:03:37 -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/19/t1258654898uv0fh3no628ackk.htm/, Retrieved Mon, 10 Nov 2025 03:41:10 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=57873, Retrieved Mon, 10 Nov 2025 03:41:10 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact461

Tree of Dependent Computations

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] [ed603017d2bee8fbd82b6d5ec04e12c3]
-    D        [Multiple Regression] [miltiple regression] [2009-11-19 18:03:37] [87085ce7f5378f281469a8b1f0969170] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
5.7	97.33
6.1	97.89
6	98.69
5.9	99.01
5.8	99.18
5.7	98.45
5.6	98.13
5.4	98.29
5.4	99.1
5.5	99.26
5.6	98.85
5.7	98.05
5.9	98.53
6.1	99.34
6	100.14
5.8	100.3
5.8	100.22
5.7	99.9
5.5	99.58
5.3	99.9
5.2	100.78
5.2	100.78
5	100.46
5.1	100.06
5.1	100.28
5.2	100.78
4.9	101.58
4.8	102.06
4.5	102.02
4.5	101.68
4.4	101.32
4.4	101.81
4.2	102.3
4.1	102.12
3.9	102.1
3.8	101.75
3.9	101.5
4.2	102.16
4.1	103.47
3.8	104.05
3.6	104.09
3.7	103.55
3.5	102.77
3.4	102.89
3.1	103.6
3.1	103.76
3.1	103.92
3.2	103.35
3.3	103.32
3.5	104.2
3.6	105.44
3.5	105.81
3.3	106.25
3.2	105.94
3.1	105.82
3.2	105.96
3	106.49
3	106.32
3.1	105.88
3.4	105.07

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 3 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57873&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57873&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57873&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 time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Multiple Linear Regression - Estimated Regression Equation
manwerk[t] = + 43.1129876094284 -0.379210799699017infl[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
manwerk[t] =  +  43.1129876094284 -0.379210799699017infl[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57873&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]manwerk[t] =  +  43.1129876094284 -0.379210799699017infl[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57873&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57873&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
manwerk[t] = + 43.1129876094284 -0.379210799699017infl[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)43.11298760942842.10717920.4600
infl-0.3792107996990170.020694-18.324700

\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) & 43.1129876094284 & 2.107179 & 20.46 & 0 & 0 \tabularnewline
infl & -0.379210799699017 & 0.020694 & -18.3247 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57873&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]43.1129876094284[/C][C]2.107179[/C][C]20.46[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]infl[/C][C]-0.379210799699017[/C][C]0.020694[/C][C]-18.3247[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57873&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57873&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)43.11298760942842.10717920.4600
infl-0.3792107996990170.020694-18.324700






Multiple Linear Regression - Regression Statistics
Multiple R0.923425815020273
R-squared0.852715235845855
Adjusted R-squared0.850175843360438
F-TEST (value)335.794974878042
F-TEST (DF numerator)1
F-TEST (DF denominator)58
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.409023021958564
Sum Squared Residuals9.70339028454272

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.923425815020273 \tabularnewline
R-squared & 0.852715235845855 \tabularnewline
Adjusted R-squared & 0.850175843360438 \tabularnewline
F-TEST (value) & 335.794974878042 \tabularnewline
F-TEST (DF numerator) & 1 \tabularnewline
F-TEST (DF denominator) & 58 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.409023021958564 \tabularnewline
Sum Squared Residuals & 9.70339028454272 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57873&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.923425815020273[/C][/ROW]
[ROW][C]R-squared[/C][C]0.852715235845855[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.850175843360438[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]335.794974878042[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]1[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]58[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.409023021958564[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]9.70339028454272[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57873&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57873&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 R0.923425815020273
R-squared0.852715235845855
Adjusted R-squared0.850175843360438
F-TEST (value)335.794974878042
F-TEST (DF numerator)1
F-TEST (DF denominator)58
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.409023021958564
Sum Squared Residuals9.70339028454272






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
15.76.20440047472313-0.50440047472313
26.15.992042426891680.107957573108319
365.688673787132470.311326212867535
45.95.567326331228780.332673668771223
55.85.502860495279940.297139504720056
65.75.77968437906023-0.0796843790602275
75.65.90103183496392-0.301031834963916
85.45.84035810701207-0.440358107012069
95.45.53319735925587-0.133197359255870
105.55.472523631304020.0274763686959769
115.65.62800005918062-0.0280000591806245
125.75.93136869893984-0.231368698939836
135.95.749347515084310.150652484915694
146.15.44218676732810.657813232671897
1565.138818127568890.86118187243111
165.85.078144399617050.721855600382951
175.85.108481263592970.69151873640703
185.75.229828719496650.470171280503348
195.55.351176175400340.148823824599660
205.35.229828719496650.0701712805033476
215.24.896123215761520.303876784238481
225.24.896123215761520.303876784238481
2355.01747067166521-0.0174706716652073
245.15.16915499154481-0.0691549915448111
255.15.085728615611030.0142713843889721
265.24.896123215761520.303876784238481
274.94.592754576002310.307245423997694
284.84.410733392146780.389266607853223
294.54.425901824134740.0740981758652598
304.54.5548334960324-0.0548334960324018
314.44.69134938392405-0.291349383924053
324.44.50553609207153-0.105536092071531
334.24.31972280021902-0.119722800219015
344.14.38798074416484-0.287980744164836
353.94.39556496015882-0.495564960158820
363.84.52828874005347-0.728288740053473
373.94.62309143997823-0.723091439978228
384.24.37281231217688-0.172812312176877
394.13.876046164571160.223953835428835
403.83.656103900745740.143896099254264
413.63.64093546875777-0.0409354687577725
423.73.84570930059524-0.145709300595244
433.54.14149372436048-0.641493724360478
443.44.09598842839659-0.695988428396594
453.13.82674876061029-0.726748760610294
463.13.76607503265845-0.666075032658447
473.13.70540130470661-0.605401304706606
483.23.92155146053505-0.721551460535048
493.33.93292778452602-0.63292778452602
503.53.59922228079088-0.099222280790881
513.63.12900088916410.470999110835898
523.52.988692893275460.511307106724536
533.32.82184014140790.478159858592102
543.22.939395489314590.260604510685407
553.12.984900785278480.115099214721523
563.22.931811273320610.268188726679385
5732.730829549480140.269170450519864
5832.795295385428970.204704614571031
593.12.962148137296540.137851862703465
603.43.269308885052740.13069111494726

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 5.7 & 6.20440047472313 & -0.50440047472313 \tabularnewline
2 & 6.1 & 5.99204242689168 & 0.107957573108319 \tabularnewline
3 & 6 & 5.68867378713247 & 0.311326212867535 \tabularnewline
4 & 5.9 & 5.56732633122878 & 0.332673668771223 \tabularnewline
5 & 5.8 & 5.50286049527994 & 0.297139504720056 \tabularnewline
6 & 5.7 & 5.77968437906023 & -0.0796843790602275 \tabularnewline
7 & 5.6 & 5.90103183496392 & -0.301031834963916 \tabularnewline
8 & 5.4 & 5.84035810701207 & -0.440358107012069 \tabularnewline
9 & 5.4 & 5.53319735925587 & -0.133197359255870 \tabularnewline
10 & 5.5 & 5.47252363130402 & 0.0274763686959769 \tabularnewline
11 & 5.6 & 5.62800005918062 & -0.0280000591806245 \tabularnewline
12 & 5.7 & 5.93136869893984 & -0.231368698939836 \tabularnewline
13 & 5.9 & 5.74934751508431 & 0.150652484915694 \tabularnewline
14 & 6.1 & 5.4421867673281 & 0.657813232671897 \tabularnewline
15 & 6 & 5.13881812756889 & 0.86118187243111 \tabularnewline
16 & 5.8 & 5.07814439961705 & 0.721855600382951 \tabularnewline
17 & 5.8 & 5.10848126359297 & 0.69151873640703 \tabularnewline
18 & 5.7 & 5.22982871949665 & 0.470171280503348 \tabularnewline
19 & 5.5 & 5.35117617540034 & 0.148823824599660 \tabularnewline
20 & 5.3 & 5.22982871949665 & 0.0701712805033476 \tabularnewline
21 & 5.2 & 4.89612321576152 & 0.303876784238481 \tabularnewline
22 & 5.2 & 4.89612321576152 & 0.303876784238481 \tabularnewline
23 & 5 & 5.01747067166521 & -0.0174706716652073 \tabularnewline
24 & 5.1 & 5.16915499154481 & -0.0691549915448111 \tabularnewline
25 & 5.1 & 5.08572861561103 & 0.0142713843889721 \tabularnewline
26 & 5.2 & 4.89612321576152 & 0.303876784238481 \tabularnewline
27 & 4.9 & 4.59275457600231 & 0.307245423997694 \tabularnewline
28 & 4.8 & 4.41073339214678 & 0.389266607853223 \tabularnewline
29 & 4.5 & 4.42590182413474 & 0.0740981758652598 \tabularnewline
30 & 4.5 & 4.5548334960324 & -0.0548334960324018 \tabularnewline
31 & 4.4 & 4.69134938392405 & -0.291349383924053 \tabularnewline
32 & 4.4 & 4.50553609207153 & -0.105536092071531 \tabularnewline
33 & 4.2 & 4.31972280021902 & -0.119722800219015 \tabularnewline
34 & 4.1 & 4.38798074416484 & -0.287980744164836 \tabularnewline
35 & 3.9 & 4.39556496015882 & -0.495564960158820 \tabularnewline
36 & 3.8 & 4.52828874005347 & -0.728288740053473 \tabularnewline
37 & 3.9 & 4.62309143997823 & -0.723091439978228 \tabularnewline
38 & 4.2 & 4.37281231217688 & -0.172812312176877 \tabularnewline
39 & 4.1 & 3.87604616457116 & 0.223953835428835 \tabularnewline
40 & 3.8 & 3.65610390074574 & 0.143896099254264 \tabularnewline
41 & 3.6 & 3.64093546875777 & -0.0409354687577725 \tabularnewline
42 & 3.7 & 3.84570930059524 & -0.145709300595244 \tabularnewline
43 & 3.5 & 4.14149372436048 & -0.641493724360478 \tabularnewline
44 & 3.4 & 4.09598842839659 & -0.695988428396594 \tabularnewline
45 & 3.1 & 3.82674876061029 & -0.726748760610294 \tabularnewline
46 & 3.1 & 3.76607503265845 & -0.666075032658447 \tabularnewline
47 & 3.1 & 3.70540130470661 & -0.605401304706606 \tabularnewline
48 & 3.2 & 3.92155146053505 & -0.721551460535048 \tabularnewline
49 & 3.3 & 3.93292778452602 & -0.63292778452602 \tabularnewline
50 & 3.5 & 3.59922228079088 & -0.099222280790881 \tabularnewline
51 & 3.6 & 3.1290008891641 & 0.470999110835898 \tabularnewline
52 & 3.5 & 2.98869289327546 & 0.511307106724536 \tabularnewline
53 & 3.3 & 2.8218401414079 & 0.478159858592102 \tabularnewline
54 & 3.2 & 2.93939548931459 & 0.260604510685407 \tabularnewline
55 & 3.1 & 2.98490078527848 & 0.115099214721523 \tabularnewline
56 & 3.2 & 2.93181127332061 & 0.268188726679385 \tabularnewline
57 & 3 & 2.73082954948014 & 0.269170450519864 \tabularnewline
58 & 3 & 2.79529538542897 & 0.204704614571031 \tabularnewline
59 & 3.1 & 2.96214813729654 & 0.137851862703465 \tabularnewline
60 & 3.4 & 3.26930888505274 & 0.13069111494726 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57873&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]6.20440047472313[/C][C]-0.50440047472313[/C][/ROW]
[ROW][C]2[/C][C]6.1[/C][C]5.99204242689168[/C][C]0.107957573108319[/C][/ROW]
[ROW][C]3[/C][C]6[/C][C]5.68867378713247[/C][C]0.311326212867535[/C][/ROW]
[ROW][C]4[/C][C]5.9[/C][C]5.56732633122878[/C][C]0.332673668771223[/C][/ROW]
[ROW][C]5[/C][C]5.8[/C][C]5.50286049527994[/C][C]0.297139504720056[/C][/ROW]
[ROW][C]6[/C][C]5.7[/C][C]5.77968437906023[/C][C]-0.0796843790602275[/C][/ROW]
[ROW][C]7[/C][C]5.6[/C][C]5.90103183496392[/C][C]-0.301031834963916[/C][/ROW]
[ROW][C]8[/C][C]5.4[/C][C]5.84035810701207[/C][C]-0.440358107012069[/C][/ROW]
[ROW][C]9[/C][C]5.4[/C][C]5.53319735925587[/C][C]-0.133197359255870[/C][/ROW]
[ROW][C]10[/C][C]5.5[/C][C]5.47252363130402[/C][C]0.0274763686959769[/C][/ROW]
[ROW][C]11[/C][C]5.6[/C][C]5.62800005918062[/C][C]-0.0280000591806245[/C][/ROW]
[ROW][C]12[/C][C]5.7[/C][C]5.93136869893984[/C][C]-0.231368698939836[/C][/ROW]
[ROW][C]13[/C][C]5.9[/C][C]5.74934751508431[/C][C]0.150652484915694[/C][/ROW]
[ROW][C]14[/C][C]6.1[/C][C]5.4421867673281[/C][C]0.657813232671897[/C][/ROW]
[ROW][C]15[/C][C]6[/C][C]5.13881812756889[/C][C]0.86118187243111[/C][/ROW]
[ROW][C]16[/C][C]5.8[/C][C]5.07814439961705[/C][C]0.721855600382951[/C][/ROW]
[ROW][C]17[/C][C]5.8[/C][C]5.10848126359297[/C][C]0.69151873640703[/C][/ROW]
[ROW][C]18[/C][C]5.7[/C][C]5.22982871949665[/C][C]0.470171280503348[/C][/ROW]
[ROW][C]19[/C][C]5.5[/C][C]5.35117617540034[/C][C]0.148823824599660[/C][/ROW]
[ROW][C]20[/C][C]5.3[/C][C]5.22982871949665[/C][C]0.0701712805033476[/C][/ROW]
[ROW][C]21[/C][C]5.2[/C][C]4.89612321576152[/C][C]0.303876784238481[/C][/ROW]
[ROW][C]22[/C][C]5.2[/C][C]4.89612321576152[/C][C]0.303876784238481[/C][/ROW]
[ROW][C]23[/C][C]5[/C][C]5.01747067166521[/C][C]-0.0174706716652073[/C][/ROW]
[ROW][C]24[/C][C]5.1[/C][C]5.16915499154481[/C][C]-0.0691549915448111[/C][/ROW]
[ROW][C]25[/C][C]5.1[/C][C]5.08572861561103[/C][C]0.0142713843889721[/C][/ROW]
[ROW][C]26[/C][C]5.2[/C][C]4.89612321576152[/C][C]0.303876784238481[/C][/ROW]
[ROW][C]27[/C][C]4.9[/C][C]4.59275457600231[/C][C]0.307245423997694[/C][/ROW]
[ROW][C]28[/C][C]4.8[/C][C]4.41073339214678[/C][C]0.389266607853223[/C][/ROW]
[ROW][C]29[/C][C]4.5[/C][C]4.42590182413474[/C][C]0.0740981758652598[/C][/ROW]
[ROW][C]30[/C][C]4.5[/C][C]4.5548334960324[/C][C]-0.0548334960324018[/C][/ROW]
[ROW][C]31[/C][C]4.4[/C][C]4.69134938392405[/C][C]-0.291349383924053[/C][/ROW]
[ROW][C]32[/C][C]4.4[/C][C]4.50553609207153[/C][C]-0.105536092071531[/C][/ROW]
[ROW][C]33[/C][C]4.2[/C][C]4.31972280021902[/C][C]-0.119722800219015[/C][/ROW]
[ROW][C]34[/C][C]4.1[/C][C]4.38798074416484[/C][C]-0.287980744164836[/C][/ROW]
[ROW][C]35[/C][C]3.9[/C][C]4.39556496015882[/C][C]-0.495564960158820[/C][/ROW]
[ROW][C]36[/C][C]3.8[/C][C]4.52828874005347[/C][C]-0.728288740053473[/C][/ROW]
[ROW][C]37[/C][C]3.9[/C][C]4.62309143997823[/C][C]-0.723091439978228[/C][/ROW]
[ROW][C]38[/C][C]4.2[/C][C]4.37281231217688[/C][C]-0.172812312176877[/C][/ROW]
[ROW][C]39[/C][C]4.1[/C][C]3.87604616457116[/C][C]0.223953835428835[/C][/ROW]
[ROW][C]40[/C][C]3.8[/C][C]3.65610390074574[/C][C]0.143896099254264[/C][/ROW]
[ROW][C]41[/C][C]3.6[/C][C]3.64093546875777[/C][C]-0.0409354687577725[/C][/ROW]
[ROW][C]42[/C][C]3.7[/C][C]3.84570930059524[/C][C]-0.145709300595244[/C][/ROW]
[ROW][C]43[/C][C]3.5[/C][C]4.14149372436048[/C][C]-0.641493724360478[/C][/ROW]
[ROW][C]44[/C][C]3.4[/C][C]4.09598842839659[/C][C]-0.695988428396594[/C][/ROW]
[ROW][C]45[/C][C]3.1[/C][C]3.82674876061029[/C][C]-0.726748760610294[/C][/ROW]
[ROW][C]46[/C][C]3.1[/C][C]3.76607503265845[/C][C]-0.666075032658447[/C][/ROW]
[ROW][C]47[/C][C]3.1[/C][C]3.70540130470661[/C][C]-0.605401304706606[/C][/ROW]
[ROW][C]48[/C][C]3.2[/C][C]3.92155146053505[/C][C]-0.721551460535048[/C][/ROW]
[ROW][C]49[/C][C]3.3[/C][C]3.93292778452602[/C][C]-0.63292778452602[/C][/ROW]
[ROW][C]50[/C][C]3.5[/C][C]3.59922228079088[/C][C]-0.099222280790881[/C][/ROW]
[ROW][C]51[/C][C]3.6[/C][C]3.1290008891641[/C][C]0.470999110835898[/C][/ROW]
[ROW][C]52[/C][C]3.5[/C][C]2.98869289327546[/C][C]0.511307106724536[/C][/ROW]
[ROW][C]53[/C][C]3.3[/C][C]2.8218401414079[/C][C]0.478159858592102[/C][/ROW]
[ROW][C]54[/C][C]3.2[/C][C]2.93939548931459[/C][C]0.260604510685407[/C][/ROW]
[ROW][C]55[/C][C]3.1[/C][C]2.98490078527848[/C][C]0.115099214721523[/C][/ROW]
[ROW][C]56[/C][C]3.2[/C][C]2.93181127332061[/C][C]0.268188726679385[/C][/ROW]
[ROW][C]57[/C][C]3[/C][C]2.73082954948014[/C][C]0.269170450519864[/C][/ROW]
[ROW][C]58[/C][C]3[/C][C]2.79529538542897[/C][C]0.204704614571031[/C][/ROW]
[ROW][C]59[/C][C]3.1[/C][C]2.96214813729654[/C][C]0.137851862703465[/C][/ROW]
[ROW][C]60[/C][C]3.4[/C][C]3.26930888505274[/C][C]0.13069111494726[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57873&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57873&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
15.76.20440047472313-0.50440047472313
26.15.992042426891680.107957573108319
365.688673787132470.311326212867535
45.95.567326331228780.332673668771223
55.85.502860495279940.297139504720056
65.75.77968437906023-0.0796843790602275
75.65.90103183496392-0.301031834963916
85.45.84035810701207-0.440358107012069
95.45.53319735925587-0.133197359255870
105.55.472523631304020.0274763686959769
115.65.62800005918062-0.0280000591806245
125.75.93136869893984-0.231368698939836
135.95.749347515084310.150652484915694
146.15.44218676732810.657813232671897
1565.138818127568890.86118187243111
165.85.078144399617050.721855600382951
175.85.108481263592970.69151873640703
185.75.229828719496650.470171280503348
195.55.351176175400340.148823824599660
205.35.229828719496650.0701712805033476
215.24.896123215761520.303876784238481
225.24.896123215761520.303876784238481
2355.01747067166521-0.0174706716652073
245.15.16915499154481-0.0691549915448111
255.15.085728615611030.0142713843889721
265.24.896123215761520.303876784238481
274.94.592754576002310.307245423997694
284.84.410733392146780.389266607853223
294.54.425901824134740.0740981758652598
304.54.5548334960324-0.0548334960324018
314.44.69134938392405-0.291349383924053
324.44.50553609207153-0.105536092071531
334.24.31972280021902-0.119722800219015
344.14.38798074416484-0.287980744164836
353.94.39556496015882-0.495564960158820
363.84.52828874005347-0.728288740053473
373.94.62309143997823-0.723091439978228
384.24.37281231217688-0.172812312176877
394.13.876046164571160.223953835428835
403.83.656103900745740.143896099254264
413.63.64093546875777-0.0409354687577725
423.73.84570930059524-0.145709300595244
433.54.14149372436048-0.641493724360478
443.44.09598842839659-0.695988428396594
453.13.82674876061029-0.726748760610294
463.13.76607503265845-0.666075032658447
473.13.70540130470661-0.605401304706606
483.23.92155146053505-0.721551460535048
493.33.93292778452602-0.63292778452602
503.53.59922228079088-0.099222280790881
513.63.12900088916410.470999110835898
523.52.988692893275460.511307106724536
533.32.82184014140790.478159858592102
543.22.939395489314590.260604510685407
553.12.984900785278480.115099214721523
563.22.931811273320610.268188726679385
5732.730829549480140.269170450519864
5832.795295385428970.204704614571031
593.12.962148137296540.137851862703465
603.43.269308885052740.13069111494726






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
50.09747004907024770.1949400981404950.902529950929752
60.05633828911302370.1126765782260470.943661710886976
70.0441761191910870.0883522383821740.955823880808913
80.07948259892284370.1589651978456870.920517401077156
90.0902927766517150.180585553303430.909707223348285
100.05811639182361220.1162327836472240.941883608176388
110.03134944901275870.06269889802551750.968650550987241
120.01616932696747290.03233865393494590.983830673032527
130.01013703291662750.02027406583325500.989862967083373
140.01701571912285540.03403143824571070.982984280877145
150.01704741105828150.03409482211656310.982952588941719
160.01343824820330070.02687649640660130.9865617517967
170.01148145472900390.02296290945800780.988518545270996
180.009258935458944720.01851787091788940.990741064541055
190.009241816518104850.01848363303620970.990758183481895
200.01721896159190800.03443792318381610.982781038408092
210.03326159254089830.06652318508179670.966738407459102
220.04645047312319950.0929009462463990.9535495268768
230.07812547304416410.1562509460883280.921874526955836
240.0940086461497190.1880172922994380.905991353850281
250.1063248593548150.2126497187096310.893675140645185
260.1369209640745020.2738419281490050.863079035925498
270.1978099173722590.3956198347445190.80219008262774
280.3119404807343220.6238809614686450.688059519265678
290.4287138674982480.8574277349964970.571286132501752
300.5445214960209520.9109570079580950.455478503979048
310.6631611294299690.6736777411400610.336838870570031
320.7512143016219830.4975713967560350.248785698378017
330.8063945788080720.3872108423838570.193605421191928
340.8476883025532290.3046233948935420.152311697446771
350.8770088796722680.2459822406554650.122991120327732
360.911804765962210.176390468075580.08819523403779
370.9267650886279060.1464698227441870.0732349113720936
380.9567680844089350.08646383118213040.0432319155910652
390.9904962966842150.01900740663156950.00950370331578473
400.9954815067948280.00903698641034350.00451849320517175
410.9951969352961170.009606129407766170.00480306470388309
420.9973731692450760.005253661509847630.00262683075492381
430.9974013458306240.005197308338751260.00259865416937563
440.9967271843825690.006545631234862410.00327281561743121
450.9959493311816180.008101337636763810.00405066881838191
460.9948802144623630.01023957107527340.00511978553763672
470.9940143209438560.01197135811228810.00598567905614403
480.9937530659642970.01249386807140620.00624693403570309
490.9964676937121790.007064612575642730.00353230628782136
500.9947376790028520.01052464199429610.00526232099714807
510.995882748065020.008234503869960510.00411725193498025
520.998789632956950.002420734086099250.00121036704304963
530.9998243471121190.0003513057757621880.000175652887881094
540.9992624271284830.001475145743033070.000737572871516535
550.9969906068911340.00601878621773230.00300939310886615

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
5 & 0.0974700490702477 & 0.194940098140495 & 0.902529950929752 \tabularnewline
6 & 0.0563382891130237 & 0.112676578226047 & 0.943661710886976 \tabularnewline
7 & 0.044176119191087 & 0.088352238382174 & 0.955823880808913 \tabularnewline
8 & 0.0794825989228437 & 0.158965197845687 & 0.920517401077156 \tabularnewline
9 & 0.090292776651715 & 0.18058555330343 & 0.909707223348285 \tabularnewline
10 & 0.0581163918236122 & 0.116232783647224 & 0.941883608176388 \tabularnewline
11 & 0.0313494490127587 & 0.0626988980255175 & 0.968650550987241 \tabularnewline
12 & 0.0161693269674729 & 0.0323386539349459 & 0.983830673032527 \tabularnewline
13 & 0.0101370329166275 & 0.0202740658332550 & 0.989862967083373 \tabularnewline
14 & 0.0170157191228554 & 0.0340314382457107 & 0.982984280877145 \tabularnewline
15 & 0.0170474110582815 & 0.0340948221165631 & 0.982952588941719 \tabularnewline
16 & 0.0134382482033007 & 0.0268764964066013 & 0.9865617517967 \tabularnewline
17 & 0.0114814547290039 & 0.0229629094580078 & 0.988518545270996 \tabularnewline
18 & 0.00925893545894472 & 0.0185178709178894 & 0.990741064541055 \tabularnewline
19 & 0.00924181651810485 & 0.0184836330362097 & 0.990758183481895 \tabularnewline
20 & 0.0172189615919080 & 0.0344379231838161 & 0.982781038408092 \tabularnewline
21 & 0.0332615925408983 & 0.0665231850817967 & 0.966738407459102 \tabularnewline
22 & 0.0464504731231995 & 0.092900946246399 & 0.9535495268768 \tabularnewline
23 & 0.0781254730441641 & 0.156250946088328 & 0.921874526955836 \tabularnewline
24 & 0.094008646149719 & 0.188017292299438 & 0.905991353850281 \tabularnewline
25 & 0.106324859354815 & 0.212649718709631 & 0.893675140645185 \tabularnewline
26 & 0.136920964074502 & 0.273841928149005 & 0.863079035925498 \tabularnewline
27 & 0.197809917372259 & 0.395619834744519 & 0.80219008262774 \tabularnewline
28 & 0.311940480734322 & 0.623880961468645 & 0.688059519265678 \tabularnewline
29 & 0.428713867498248 & 0.857427734996497 & 0.571286132501752 \tabularnewline
30 & 0.544521496020952 & 0.910957007958095 & 0.455478503979048 \tabularnewline
31 & 0.663161129429969 & 0.673677741140061 & 0.336838870570031 \tabularnewline
32 & 0.751214301621983 & 0.497571396756035 & 0.248785698378017 \tabularnewline
33 & 0.806394578808072 & 0.387210842383857 & 0.193605421191928 \tabularnewline
34 & 0.847688302553229 & 0.304623394893542 & 0.152311697446771 \tabularnewline
35 & 0.877008879672268 & 0.245982240655465 & 0.122991120327732 \tabularnewline
36 & 0.91180476596221 & 0.17639046807558 & 0.08819523403779 \tabularnewline
37 & 0.926765088627906 & 0.146469822744187 & 0.0732349113720936 \tabularnewline
38 & 0.956768084408935 & 0.0864638311821304 & 0.0432319155910652 \tabularnewline
39 & 0.990496296684215 & 0.0190074066315695 & 0.00950370331578473 \tabularnewline
40 & 0.995481506794828 & 0.0090369864103435 & 0.00451849320517175 \tabularnewline
41 & 0.995196935296117 & 0.00960612940776617 & 0.00480306470388309 \tabularnewline
42 & 0.997373169245076 & 0.00525366150984763 & 0.00262683075492381 \tabularnewline
43 & 0.997401345830624 & 0.00519730833875126 & 0.00259865416937563 \tabularnewline
44 & 0.996727184382569 & 0.00654563123486241 & 0.00327281561743121 \tabularnewline
45 & 0.995949331181618 & 0.00810133763676381 & 0.00405066881838191 \tabularnewline
46 & 0.994880214462363 & 0.0102395710752734 & 0.00511978553763672 \tabularnewline
47 & 0.994014320943856 & 0.0119713581122881 & 0.00598567905614403 \tabularnewline
48 & 0.993753065964297 & 0.0124938680714062 & 0.00624693403570309 \tabularnewline
49 & 0.996467693712179 & 0.00706461257564273 & 0.00353230628782136 \tabularnewline
50 & 0.994737679002852 & 0.0105246419942961 & 0.00526232099714807 \tabularnewline
51 & 0.99588274806502 & 0.00823450386996051 & 0.00411725193498025 \tabularnewline
52 & 0.99878963295695 & 0.00242073408609925 & 0.00121036704304963 \tabularnewline
53 & 0.999824347112119 & 0.000351305775762188 & 0.000175652887881094 \tabularnewline
54 & 0.999262427128483 & 0.00147514574303307 & 0.000737572871516535 \tabularnewline
55 & 0.996990606891134 & 0.0060187862177323 & 0.00300939310886615 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57873&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]5[/C][C]0.0974700490702477[/C][C]0.194940098140495[/C][C]0.902529950929752[/C][/ROW]
[ROW][C]6[/C][C]0.0563382891130237[/C][C]0.112676578226047[/C][C]0.943661710886976[/C][/ROW]
[ROW][C]7[/C][C]0.044176119191087[/C][C]0.088352238382174[/C][C]0.955823880808913[/C][/ROW]
[ROW][C]8[/C][C]0.0794825989228437[/C][C]0.158965197845687[/C][C]0.920517401077156[/C][/ROW]
[ROW][C]9[/C][C]0.090292776651715[/C][C]0.18058555330343[/C][C]0.909707223348285[/C][/ROW]
[ROW][C]10[/C][C]0.0581163918236122[/C][C]0.116232783647224[/C][C]0.941883608176388[/C][/ROW]
[ROW][C]11[/C][C]0.0313494490127587[/C][C]0.0626988980255175[/C][C]0.968650550987241[/C][/ROW]
[ROW][C]12[/C][C]0.0161693269674729[/C][C]0.0323386539349459[/C][C]0.983830673032527[/C][/ROW]
[ROW][C]13[/C][C]0.0101370329166275[/C][C]0.0202740658332550[/C][C]0.989862967083373[/C][/ROW]
[ROW][C]14[/C][C]0.0170157191228554[/C][C]0.0340314382457107[/C][C]0.982984280877145[/C][/ROW]
[ROW][C]15[/C][C]0.0170474110582815[/C][C]0.0340948221165631[/C][C]0.982952588941719[/C][/ROW]
[ROW][C]16[/C][C]0.0134382482033007[/C][C]0.0268764964066013[/C][C]0.9865617517967[/C][/ROW]
[ROW][C]17[/C][C]0.0114814547290039[/C][C]0.0229629094580078[/C][C]0.988518545270996[/C][/ROW]
[ROW][C]18[/C][C]0.00925893545894472[/C][C]0.0185178709178894[/C][C]0.990741064541055[/C][/ROW]
[ROW][C]19[/C][C]0.00924181651810485[/C][C]0.0184836330362097[/C][C]0.990758183481895[/C][/ROW]
[ROW][C]20[/C][C]0.0172189615919080[/C][C]0.0344379231838161[/C][C]0.982781038408092[/C][/ROW]
[ROW][C]21[/C][C]0.0332615925408983[/C][C]0.0665231850817967[/C][C]0.966738407459102[/C][/ROW]
[ROW][C]22[/C][C]0.0464504731231995[/C][C]0.092900946246399[/C][C]0.9535495268768[/C][/ROW]
[ROW][C]23[/C][C]0.0781254730441641[/C][C]0.156250946088328[/C][C]0.921874526955836[/C][/ROW]
[ROW][C]24[/C][C]0.094008646149719[/C][C]0.188017292299438[/C][C]0.905991353850281[/C][/ROW]
[ROW][C]25[/C][C]0.106324859354815[/C][C]0.212649718709631[/C][C]0.893675140645185[/C][/ROW]
[ROW][C]26[/C][C]0.136920964074502[/C][C]0.273841928149005[/C][C]0.863079035925498[/C][/ROW]
[ROW][C]27[/C][C]0.197809917372259[/C][C]0.395619834744519[/C][C]0.80219008262774[/C][/ROW]
[ROW][C]28[/C][C]0.311940480734322[/C][C]0.623880961468645[/C][C]0.688059519265678[/C][/ROW]
[ROW][C]29[/C][C]0.428713867498248[/C][C]0.857427734996497[/C][C]0.571286132501752[/C][/ROW]
[ROW][C]30[/C][C]0.544521496020952[/C][C]0.910957007958095[/C][C]0.455478503979048[/C][/ROW]
[ROW][C]31[/C][C]0.663161129429969[/C][C]0.673677741140061[/C][C]0.336838870570031[/C][/ROW]
[ROW][C]32[/C][C]0.751214301621983[/C][C]0.497571396756035[/C][C]0.248785698378017[/C][/ROW]
[ROW][C]33[/C][C]0.806394578808072[/C][C]0.387210842383857[/C][C]0.193605421191928[/C][/ROW]
[ROW][C]34[/C][C]0.847688302553229[/C][C]0.304623394893542[/C][C]0.152311697446771[/C][/ROW]
[ROW][C]35[/C][C]0.877008879672268[/C][C]0.245982240655465[/C][C]0.122991120327732[/C][/ROW]
[ROW][C]36[/C][C]0.91180476596221[/C][C]0.17639046807558[/C][C]0.08819523403779[/C][/ROW]
[ROW][C]37[/C][C]0.926765088627906[/C][C]0.146469822744187[/C][C]0.0732349113720936[/C][/ROW]
[ROW][C]38[/C][C]0.956768084408935[/C][C]0.0864638311821304[/C][C]0.0432319155910652[/C][/ROW]
[ROW][C]39[/C][C]0.990496296684215[/C][C]0.0190074066315695[/C][C]0.00950370331578473[/C][/ROW]
[ROW][C]40[/C][C]0.995481506794828[/C][C]0.0090369864103435[/C][C]0.00451849320517175[/C][/ROW]
[ROW][C]41[/C][C]0.995196935296117[/C][C]0.00960612940776617[/C][C]0.00480306470388309[/C][/ROW]
[ROW][C]42[/C][C]0.997373169245076[/C][C]0.00525366150984763[/C][C]0.00262683075492381[/C][/ROW]
[ROW][C]43[/C][C]0.997401345830624[/C][C]0.00519730833875126[/C][C]0.00259865416937563[/C][/ROW]
[ROW][C]44[/C][C]0.996727184382569[/C][C]0.00654563123486241[/C][C]0.00327281561743121[/C][/ROW]
[ROW][C]45[/C][C]0.995949331181618[/C][C]0.00810133763676381[/C][C]0.00405066881838191[/C][/ROW]
[ROW][C]46[/C][C]0.994880214462363[/C][C]0.0102395710752734[/C][C]0.00511978553763672[/C][/ROW]
[ROW][C]47[/C][C]0.994014320943856[/C][C]0.0119713581122881[/C][C]0.00598567905614403[/C][/ROW]
[ROW][C]48[/C][C]0.993753065964297[/C][C]0.0124938680714062[/C][C]0.00624693403570309[/C][/ROW]
[ROW][C]49[/C][C]0.996467693712179[/C][C]0.00706461257564273[/C][C]0.00353230628782136[/C][/ROW]
[ROW][C]50[/C][C]0.994737679002852[/C][C]0.0105246419942961[/C][C]0.00526232099714807[/C][/ROW]
[ROW][C]51[/C][C]0.99588274806502[/C][C]0.00823450386996051[/C][C]0.00411725193498025[/C][/ROW]
[ROW][C]52[/C][C]0.99878963295695[/C][C]0.00242073408609925[/C][C]0.00121036704304963[/C][/ROW]
[ROW][C]53[/C][C]0.999824347112119[/C][C]0.000351305775762188[/C][C]0.000175652887881094[/C][/ROW]
[ROW][C]54[/C][C]0.999262427128483[/C][C]0.00147514574303307[/C][C]0.000737572871516535[/C][/ROW]
[ROW][C]55[/C][C]0.996990606891134[/C][C]0.0060187862177323[/C][C]0.00300939310886615[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57873&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57873&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
50.09747004907024770.1949400981404950.902529950929752
60.05633828911302370.1126765782260470.943661710886976
70.0441761191910870.0883522383821740.955823880808913
80.07948259892284370.1589651978456870.920517401077156
90.0902927766517150.180585553303430.909707223348285
100.05811639182361220.1162327836472240.941883608176388
110.03134944901275870.06269889802551750.968650550987241
120.01616932696747290.03233865393494590.983830673032527
130.01013703291662750.02027406583325500.989862967083373
140.01701571912285540.03403143824571070.982984280877145
150.01704741105828150.03409482211656310.982952588941719
160.01343824820330070.02687649640660130.9865617517967
170.01148145472900390.02296290945800780.988518545270996
180.009258935458944720.01851787091788940.990741064541055
190.009241816518104850.01848363303620970.990758183481895
200.01721896159190800.03443792318381610.982781038408092
210.03326159254089830.06652318508179670.966738407459102
220.04645047312319950.0929009462463990.9535495268768
230.07812547304416410.1562509460883280.921874526955836
240.0940086461497190.1880172922994380.905991353850281
250.1063248593548150.2126497187096310.893675140645185
260.1369209640745020.2738419281490050.863079035925498
270.1978099173722590.3956198347445190.80219008262774
280.3119404807343220.6238809614686450.688059519265678
290.4287138674982480.8574277349964970.571286132501752
300.5445214960209520.9109570079580950.455478503979048
310.6631611294299690.6736777411400610.336838870570031
320.7512143016219830.4975713967560350.248785698378017
330.8063945788080720.3872108423838570.193605421191928
340.8476883025532290.3046233948935420.152311697446771
350.8770088796722680.2459822406554650.122991120327732
360.911804765962210.176390468075580.08819523403779
370.9267650886279060.1464698227441870.0732349113720936
380.9567680844089350.08646383118213040.0432319155910652
390.9904962966842150.01900740663156950.00950370331578473
400.9954815067948280.00903698641034350.00451849320517175
410.9951969352961170.009606129407766170.00480306470388309
420.9973731692450760.005253661509847630.00262683075492381
430.9974013458306240.005197308338751260.00259865416937563
440.9967271843825690.006545631234862410.00327281561743121
450.9959493311816180.008101337636763810.00405066881838191
460.9948802144623630.01023957107527340.00511978553763672
470.9940143209438560.01197135811228810.00598567905614403
480.9937530659642970.01249386807140620.00624693403570309
490.9964676937121790.007064612575642730.00353230628782136
500.9947376790028520.01052464199429610.00526232099714807
510.995882748065020.008234503869960510.00411725193498025
520.998789632956950.002420734086099250.00121036704304963
530.9998243471121190.0003513057757621880.000175652887881094
540.9992624271284830.001475145743033070.000737572871516535
550.9969906068911340.00601878621773230.00300939310886615






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level120.235294117647059NOK
5% type I error level260.509803921568627NOK
10% type I error level310.607843137254902NOK

\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 & 12 & 0.235294117647059 & NOK \tabularnewline
5% type I error level & 26 & 0.509803921568627 & NOK \tabularnewline
10% type I error level & 31 & 0.607843137254902 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57873&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]12[/C][C]0.235294117647059[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]26[/C][C]0.509803921568627[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]31[/C][C]0.607843137254902[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57873&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57873&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 level120.235294117647059NOK
5% type I error level260.509803921568627NOK
10% type I error level310.607843137254902NOK


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')
}