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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, 15 Nov 2011 16:59:28 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Nov/15/t1321394387uvp7xq3kat0ce83.htm/, Retrieved Thu, 31 Oct 2024 23:54:09 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=143601, Retrieved Thu, 31 Oct 2024 23:54:09 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact150
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Linear Regression Graphical Model Validation] [Colombia Coffee -...] [2008-02-26 10:22:06] [74be16979710d4c4e7c6647856088456]
- RMPD    [Multiple Regression] [ws2] [2011-11-15 21:59:28] [13dfa60174f50d862e8699db2153bfc5] [Current]
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Dataseries X:
108	392,5
19	46,2
13	15,7
124	422,2
40	119,4
57	170,9
23	56,9
14	77,5
45	214
10	65,3
5	20,9
48	248,1
11	23,5
23	39,6
7	48,8
2	6,6
24	134,9
6	50,9
3	4,4
23	113
6	14,8
9	48,7
9	52,1
3	13,2
29	103,9
7	77,5
4	11,8
20	98,1
7	27,9
4	38,1
0	0
25	69,2
6	14,6
5	40,3
22	161,5
11	57,2
61	217,6
12	58,1
4	12,6
16	59,6
13	89,9
60	202,4
41	181,3
37	152,8
55	162,8
41	73,4
11	21,3
27	92,6
8	76,1
3	39,9
17	142,1
13	93
13	31,9
15	32,1
8	55,6
29	133,3
30	194,5
24	137,9
9	87,4
31	209,8
14	95,5
53	244,6
26	187,5




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

\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' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=143601&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' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=143601&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=143601&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' @ jenkins.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Claims[t] = -1.0636885932567 + 0.24410947353215Payments[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Claims[t] =  -1.0636885932567 +  0.24410947353215Payments[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=143601&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Claims[t] =  -1.0636885932567 +  0.24410947353215Payments[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=143601&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=143601&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
Claims[t] = -1.0636885932567 + 0.24410947353215Payments[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-1.06368859325671.830175-0.58120.5632510.281625
Payments0.244109473532150.01397717.465100

\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) & -1.0636885932567 & 1.830175 & -0.5812 & 0.563251 & 0.281625 \tabularnewline
Payments & 0.24410947353215 & 0.013977 & 17.4651 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=143601&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]-1.0636885932567[/C][C]1.830175[/C][C]-0.5812[/C][C]0.563251[/C][C]0.281625[/C][/ROW]
[ROW][C]Payments[/C][C]0.24410947353215[/C][C]0.013977[/C][C]17.4651[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=143601&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=143601&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)-1.06368859325671.830175-0.58120.5632510.281625
Payments0.244109473532150.01397717.465100







Multiple Linear Regression - Regression Statistics
Multiple R0.912878235023407
R-squared0.83334667197945
Adjusted R-squared0.830614650208621
F-TEST (value)305.029294011327
F-TEST (DF numerator)1
F-TEST (DF denominator)61
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation9.61083216565664
Sum Squared Residuals5634.45378990163

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.912878235023407 \tabularnewline
R-squared & 0.83334667197945 \tabularnewline
Adjusted R-squared & 0.830614650208621 \tabularnewline
F-TEST (value) & 305.029294011327 \tabularnewline
F-TEST (DF numerator) & 1 \tabularnewline
F-TEST (DF denominator) & 61 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 9.61083216565664 \tabularnewline
Sum Squared Residuals & 5634.45378990163 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=143601&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.912878235023407[/C][/ROW]
[ROW][C]R-squared[/C][C]0.83334667197945[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.830614650208621[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]305.029294011327[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]1[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]61[/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]9.61083216565664[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]5634.45378990163[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=143601&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=143601&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 R0.912878235023407
R-squared0.83334667197945
Adjusted R-squared0.830614650208621
F-TEST (value)305.029294011327
F-TEST (DF numerator)1
F-TEST (DF denominator)61
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation9.61083216565664
Sum Squared Residuals5634.45378990163







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
110894.749279768112113.2507202318879
21910.21416908392868.78583091607138
3132.7688301411980410.231169858802
4124101.99933113201722.000668867983
54028.08298254648211.917017453518
65740.654620433387716.3453795666123
72312.826140450722610.1738595492774
81417.8547956054849-3.85479560548491
94551.1757387426234-6.17573874262336
101014.8766600283927-4.87666002839268
1154.038199403565220.961800596434777
124859.4998717900697-11.4998717900697
13114.672884034748816.32711596525119
14238.6030465586164314.3969534413836
15710.8488537151122-3.8488537151122
1620.5474339320554851.45256606794451
172431.8666793862303-7.86667938623031
18611.3614836095297-5.36148360952972
1930.01039309028475532.98960690971524
202326.5206819158762-3.52068191587623
2162.549131615019113.45086838498089
22910.824442767759-1.82444276775899
23911.6544149777683-2.6544149777683
2432.158556457367670.841443542632329
252924.29928570673374.70071429326634
26717.8547956054849-10.8547956054849
2741.816803194422672.18319680557733
282022.8834507602472-2.88345076024719
2975.746965718290271.25303428170973
3048.2368823483182-4.2368823483182
310-1.063688593256711.06368859325671
322515.82868697516819.17131302483194
3362.500309720312683.49969027968732
3458.77392319008893-3.77392319008893
352238.3599913821855-16.3599913821855
361112.8993732927823-1.89937329278226
376152.05453284733918.94546715266091
381213.1190718189612-1.1190718189612
3942.012090773248381.98790922675162
401613.48523602925942.51476397074058
411320.8817530772836-7.88175307728357
426048.344068849650411.6559311503496
434143.1933589581221-2.19335895812206
443736.23623896245580.763761037544208
455538.677333697777316.3226663022227
464116.853946764003124.1460532359969
47114.135843192978096.86415680702191
482721.54084865582045.45915134417963
49817.5130423425399-9.5130423425399
5038.67627940067607-5.67627940067607
511733.6242675956618-16.6242675956618
521321.6384924452332-8.63849244523323
53136.723403612418876.27659638758113
54156.77222550712538.2277744928747
55812.5087981351308-4.50879813513082
562931.4761042285789-2.47610422857887
573046.4156040087464-16.4156040087464
582432.5990078068268-8.59900780682676
59920.2714793934532-11.2714793934532
603150.1504789537883-19.1504789537883
611422.2487661290636-8.2487661290636
625358.6454886327071-5.64548863270714
632644.7068376940214-18.7068376940214

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 108 & 94.7492797681121 & 13.2507202318879 \tabularnewline
2 & 19 & 10.2141690839286 & 8.78583091607138 \tabularnewline
3 & 13 & 2.76883014119804 & 10.231169858802 \tabularnewline
4 & 124 & 101.999331132017 & 22.000668867983 \tabularnewline
5 & 40 & 28.082982546482 & 11.917017453518 \tabularnewline
6 & 57 & 40.6546204333877 & 16.3453795666123 \tabularnewline
7 & 23 & 12.8261404507226 & 10.1738595492774 \tabularnewline
8 & 14 & 17.8547956054849 & -3.85479560548491 \tabularnewline
9 & 45 & 51.1757387426234 & -6.17573874262336 \tabularnewline
10 & 10 & 14.8766600283927 & -4.87666002839268 \tabularnewline
11 & 5 & 4.03819940356522 & 0.961800596434777 \tabularnewline
12 & 48 & 59.4998717900697 & -11.4998717900697 \tabularnewline
13 & 11 & 4.67288403474881 & 6.32711596525119 \tabularnewline
14 & 23 & 8.60304655861643 & 14.3969534413836 \tabularnewline
15 & 7 & 10.8488537151122 & -3.8488537151122 \tabularnewline
16 & 2 & 0.547433932055485 & 1.45256606794451 \tabularnewline
17 & 24 & 31.8666793862303 & -7.86667938623031 \tabularnewline
18 & 6 & 11.3614836095297 & -5.36148360952972 \tabularnewline
19 & 3 & 0.0103930902847553 & 2.98960690971524 \tabularnewline
20 & 23 & 26.5206819158762 & -3.52068191587623 \tabularnewline
21 & 6 & 2.54913161501911 & 3.45086838498089 \tabularnewline
22 & 9 & 10.824442767759 & -1.82444276775899 \tabularnewline
23 & 9 & 11.6544149777683 & -2.6544149777683 \tabularnewline
24 & 3 & 2.15855645736767 & 0.841443542632329 \tabularnewline
25 & 29 & 24.2992857067337 & 4.70071429326634 \tabularnewline
26 & 7 & 17.8547956054849 & -10.8547956054849 \tabularnewline
27 & 4 & 1.81680319442267 & 2.18319680557733 \tabularnewline
28 & 20 & 22.8834507602472 & -2.88345076024719 \tabularnewline
29 & 7 & 5.74696571829027 & 1.25303428170973 \tabularnewline
30 & 4 & 8.2368823483182 & -4.2368823483182 \tabularnewline
31 & 0 & -1.06368859325671 & 1.06368859325671 \tabularnewline
32 & 25 & 15.8286869751681 & 9.17131302483194 \tabularnewline
33 & 6 & 2.50030972031268 & 3.49969027968732 \tabularnewline
34 & 5 & 8.77392319008893 & -3.77392319008893 \tabularnewline
35 & 22 & 38.3599913821855 & -16.3599913821855 \tabularnewline
36 & 11 & 12.8993732927823 & -1.89937329278226 \tabularnewline
37 & 61 & 52.0545328473391 & 8.94546715266091 \tabularnewline
38 & 12 & 13.1190718189612 & -1.1190718189612 \tabularnewline
39 & 4 & 2.01209077324838 & 1.98790922675162 \tabularnewline
40 & 16 & 13.4852360292594 & 2.51476397074058 \tabularnewline
41 & 13 & 20.8817530772836 & -7.88175307728357 \tabularnewline
42 & 60 & 48.3440688496504 & 11.6559311503496 \tabularnewline
43 & 41 & 43.1933589581221 & -2.19335895812206 \tabularnewline
44 & 37 & 36.2362389624558 & 0.763761037544208 \tabularnewline
45 & 55 & 38.6773336977773 & 16.3226663022227 \tabularnewline
46 & 41 & 16.8539467640031 & 24.1460532359969 \tabularnewline
47 & 11 & 4.13584319297809 & 6.86415680702191 \tabularnewline
48 & 27 & 21.5408486558204 & 5.45915134417963 \tabularnewline
49 & 8 & 17.5130423425399 & -9.5130423425399 \tabularnewline
50 & 3 & 8.67627940067607 & -5.67627940067607 \tabularnewline
51 & 17 & 33.6242675956618 & -16.6242675956618 \tabularnewline
52 & 13 & 21.6384924452332 & -8.63849244523323 \tabularnewline
53 & 13 & 6.72340361241887 & 6.27659638758113 \tabularnewline
54 & 15 & 6.7722255071253 & 8.2277744928747 \tabularnewline
55 & 8 & 12.5087981351308 & -4.50879813513082 \tabularnewline
56 & 29 & 31.4761042285789 & -2.47610422857887 \tabularnewline
57 & 30 & 46.4156040087464 & -16.4156040087464 \tabularnewline
58 & 24 & 32.5990078068268 & -8.59900780682676 \tabularnewline
59 & 9 & 20.2714793934532 & -11.2714793934532 \tabularnewline
60 & 31 & 50.1504789537883 & -19.1504789537883 \tabularnewline
61 & 14 & 22.2487661290636 & -8.2487661290636 \tabularnewline
62 & 53 & 58.6454886327071 & -5.64548863270714 \tabularnewline
63 & 26 & 44.7068376940214 & -18.7068376940214 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=143601&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]108[/C][C]94.7492797681121[/C][C]13.2507202318879[/C][/ROW]
[ROW][C]2[/C][C]19[/C][C]10.2141690839286[/C][C]8.78583091607138[/C][/ROW]
[ROW][C]3[/C][C]13[/C][C]2.76883014119804[/C][C]10.231169858802[/C][/ROW]
[ROW][C]4[/C][C]124[/C][C]101.999331132017[/C][C]22.000668867983[/C][/ROW]
[ROW][C]5[/C][C]40[/C][C]28.082982546482[/C][C]11.917017453518[/C][/ROW]
[ROW][C]6[/C][C]57[/C][C]40.6546204333877[/C][C]16.3453795666123[/C][/ROW]
[ROW][C]7[/C][C]23[/C][C]12.8261404507226[/C][C]10.1738595492774[/C][/ROW]
[ROW][C]8[/C][C]14[/C][C]17.8547956054849[/C][C]-3.85479560548491[/C][/ROW]
[ROW][C]9[/C][C]45[/C][C]51.1757387426234[/C][C]-6.17573874262336[/C][/ROW]
[ROW][C]10[/C][C]10[/C][C]14.8766600283927[/C][C]-4.87666002839268[/C][/ROW]
[ROW][C]11[/C][C]5[/C][C]4.03819940356522[/C][C]0.961800596434777[/C][/ROW]
[ROW][C]12[/C][C]48[/C][C]59.4998717900697[/C][C]-11.4998717900697[/C][/ROW]
[ROW][C]13[/C][C]11[/C][C]4.67288403474881[/C][C]6.32711596525119[/C][/ROW]
[ROW][C]14[/C][C]23[/C][C]8.60304655861643[/C][C]14.3969534413836[/C][/ROW]
[ROW][C]15[/C][C]7[/C][C]10.8488537151122[/C][C]-3.8488537151122[/C][/ROW]
[ROW][C]16[/C][C]2[/C][C]0.547433932055485[/C][C]1.45256606794451[/C][/ROW]
[ROW][C]17[/C][C]24[/C][C]31.8666793862303[/C][C]-7.86667938623031[/C][/ROW]
[ROW][C]18[/C][C]6[/C][C]11.3614836095297[/C][C]-5.36148360952972[/C][/ROW]
[ROW][C]19[/C][C]3[/C][C]0.0103930902847553[/C][C]2.98960690971524[/C][/ROW]
[ROW][C]20[/C][C]23[/C][C]26.5206819158762[/C][C]-3.52068191587623[/C][/ROW]
[ROW][C]21[/C][C]6[/C][C]2.54913161501911[/C][C]3.45086838498089[/C][/ROW]
[ROW][C]22[/C][C]9[/C][C]10.824442767759[/C][C]-1.82444276775899[/C][/ROW]
[ROW][C]23[/C][C]9[/C][C]11.6544149777683[/C][C]-2.6544149777683[/C][/ROW]
[ROW][C]24[/C][C]3[/C][C]2.15855645736767[/C][C]0.841443542632329[/C][/ROW]
[ROW][C]25[/C][C]29[/C][C]24.2992857067337[/C][C]4.70071429326634[/C][/ROW]
[ROW][C]26[/C][C]7[/C][C]17.8547956054849[/C][C]-10.8547956054849[/C][/ROW]
[ROW][C]27[/C][C]4[/C][C]1.81680319442267[/C][C]2.18319680557733[/C][/ROW]
[ROW][C]28[/C][C]20[/C][C]22.8834507602472[/C][C]-2.88345076024719[/C][/ROW]
[ROW][C]29[/C][C]7[/C][C]5.74696571829027[/C][C]1.25303428170973[/C][/ROW]
[ROW][C]30[/C][C]4[/C][C]8.2368823483182[/C][C]-4.2368823483182[/C][/ROW]
[ROW][C]31[/C][C]0[/C][C]-1.06368859325671[/C][C]1.06368859325671[/C][/ROW]
[ROW][C]32[/C][C]25[/C][C]15.8286869751681[/C][C]9.17131302483194[/C][/ROW]
[ROW][C]33[/C][C]6[/C][C]2.50030972031268[/C][C]3.49969027968732[/C][/ROW]
[ROW][C]34[/C][C]5[/C][C]8.77392319008893[/C][C]-3.77392319008893[/C][/ROW]
[ROW][C]35[/C][C]22[/C][C]38.3599913821855[/C][C]-16.3599913821855[/C][/ROW]
[ROW][C]36[/C][C]11[/C][C]12.8993732927823[/C][C]-1.89937329278226[/C][/ROW]
[ROW][C]37[/C][C]61[/C][C]52.0545328473391[/C][C]8.94546715266091[/C][/ROW]
[ROW][C]38[/C][C]12[/C][C]13.1190718189612[/C][C]-1.1190718189612[/C][/ROW]
[ROW][C]39[/C][C]4[/C][C]2.01209077324838[/C][C]1.98790922675162[/C][/ROW]
[ROW][C]40[/C][C]16[/C][C]13.4852360292594[/C][C]2.51476397074058[/C][/ROW]
[ROW][C]41[/C][C]13[/C][C]20.8817530772836[/C][C]-7.88175307728357[/C][/ROW]
[ROW][C]42[/C][C]60[/C][C]48.3440688496504[/C][C]11.6559311503496[/C][/ROW]
[ROW][C]43[/C][C]41[/C][C]43.1933589581221[/C][C]-2.19335895812206[/C][/ROW]
[ROW][C]44[/C][C]37[/C][C]36.2362389624558[/C][C]0.763761037544208[/C][/ROW]
[ROW][C]45[/C][C]55[/C][C]38.6773336977773[/C][C]16.3226663022227[/C][/ROW]
[ROW][C]46[/C][C]41[/C][C]16.8539467640031[/C][C]24.1460532359969[/C][/ROW]
[ROW][C]47[/C][C]11[/C][C]4.13584319297809[/C][C]6.86415680702191[/C][/ROW]
[ROW][C]48[/C][C]27[/C][C]21.5408486558204[/C][C]5.45915134417963[/C][/ROW]
[ROW][C]49[/C][C]8[/C][C]17.5130423425399[/C][C]-9.5130423425399[/C][/ROW]
[ROW][C]50[/C][C]3[/C][C]8.67627940067607[/C][C]-5.67627940067607[/C][/ROW]
[ROW][C]51[/C][C]17[/C][C]33.6242675956618[/C][C]-16.6242675956618[/C][/ROW]
[ROW][C]52[/C][C]13[/C][C]21.6384924452332[/C][C]-8.63849244523323[/C][/ROW]
[ROW][C]53[/C][C]13[/C][C]6.72340361241887[/C][C]6.27659638758113[/C][/ROW]
[ROW][C]54[/C][C]15[/C][C]6.7722255071253[/C][C]8.2277744928747[/C][/ROW]
[ROW][C]55[/C][C]8[/C][C]12.5087981351308[/C][C]-4.50879813513082[/C][/ROW]
[ROW][C]56[/C][C]29[/C][C]31.4761042285789[/C][C]-2.47610422857887[/C][/ROW]
[ROW][C]57[/C][C]30[/C][C]46.4156040087464[/C][C]-16.4156040087464[/C][/ROW]
[ROW][C]58[/C][C]24[/C][C]32.5990078068268[/C][C]-8.59900780682676[/C][/ROW]
[ROW][C]59[/C][C]9[/C][C]20.2714793934532[/C][C]-11.2714793934532[/C][/ROW]
[ROW][C]60[/C][C]31[/C][C]50.1504789537883[/C][C]-19.1504789537883[/C][/ROW]
[ROW][C]61[/C][C]14[/C][C]22.2487661290636[/C][C]-8.2487661290636[/C][/ROW]
[ROW][C]62[/C][C]53[/C][C]58.6454886327071[/C][C]-5.64548863270714[/C][/ROW]
[ROW][C]63[/C][C]26[/C][C]44.7068376940214[/C][C]-18.7068376940214[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=143601&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=143601&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
110894.749279768112113.2507202318879
21910.21416908392868.78583091607138
3132.7688301411980410.231169858802
4124101.99933113201722.000668867983
54028.08298254648211.917017453518
65740.654620433387716.3453795666123
72312.826140450722610.1738595492774
81417.8547956054849-3.85479560548491
94551.1757387426234-6.17573874262336
101014.8766600283927-4.87666002839268
1154.038199403565220.961800596434777
124859.4998717900697-11.4998717900697
13114.672884034748816.32711596525119
14238.6030465586164314.3969534413836
15710.8488537151122-3.8488537151122
1620.5474339320554851.45256606794451
172431.8666793862303-7.86667938623031
18611.3614836095297-5.36148360952972
1930.01039309028475532.98960690971524
202326.5206819158762-3.52068191587623
2162.549131615019113.45086838498089
22910.824442767759-1.82444276775899
23911.6544149777683-2.6544149777683
2432.158556457367670.841443542632329
252924.29928570673374.70071429326634
26717.8547956054849-10.8547956054849
2741.816803194422672.18319680557733
282022.8834507602472-2.88345076024719
2975.746965718290271.25303428170973
3048.2368823483182-4.2368823483182
310-1.063688593256711.06368859325671
322515.82868697516819.17131302483194
3362.500309720312683.49969027968732
3458.77392319008893-3.77392319008893
352238.3599913821855-16.3599913821855
361112.8993732927823-1.89937329278226
376152.05453284733918.94546715266091
381213.1190718189612-1.1190718189612
3942.012090773248381.98790922675162
401613.48523602925942.51476397074058
411320.8817530772836-7.88175307728357
426048.344068849650411.6559311503496
434143.1933589581221-2.19335895812206
443736.23623896245580.763761037544208
455538.677333697777316.3226663022227
464116.853946764003124.1460532359969
47114.135843192978096.86415680702191
482721.54084865582045.45915134417963
49817.5130423425399-9.5130423425399
5038.67627940067607-5.67627940067607
511733.6242675956618-16.6242675956618
521321.6384924452332-8.63849244523323
53136.723403612418876.27659638758113
54156.77222550712538.2277744928747
55812.5087981351308-4.50879813513082
562931.4761042285789-2.47610422857887
573046.4156040087464-16.4156040087464
582432.5990078068268-8.59900780682676
59920.2714793934532-11.2714793934532
603150.1504789537883-19.1504789537883
611422.2487661290636-8.2487661290636
625358.6454886327071-5.64548863270714
632644.7068376940214-18.7068376940214







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
50.07615876674035840.1523175334807170.923841233259642
60.0489109897338920.0978219794677840.951089010266108
70.01819643565686660.03639287131373330.981803564343133
80.2517335583399320.5034671166798640.748266441660068
90.6365526061672890.7268947876654210.36344739383271
100.6660404312922650.6679191374154710.333959568707735
110.5709748385879170.8580503228241660.429025161412083
120.8274790931007530.3450418137984950.172520906899247
130.7715088893110750.4569822213778510.228491110688925
140.8022344278306890.3955311443386220.197765572169311
150.7780765865621040.4438468268757920.221923413437896
160.709574282997070.580851434005860.29042571700293
170.7429420570574060.5141158858851870.257057942942594
180.7176075008642030.5647849982715940.282392499135797
190.6455235488346090.7089529023307820.354476451165391
200.6011018407838590.7977963184322810.398898159216141
210.5254065293086640.9491869413826720.474593470691336
220.4576438867223240.9152877734446480.542356113277676
230.3962897699759780.7925795399519560.603710230024022
240.3233786906353950.646757381270790.676621309364605
250.2706425582986050.541285116597210.729357441701395
260.3223188003824880.6446376007649770.677681199617512
270.2584001416896490.5168002833792980.741599858310351
280.213070209074490.426140418148980.78692979092551
290.1623626376002050.3247252752004090.837637362399795
300.132832120680810.265664241361620.86716787931919
310.09727573759945570.1945514751989110.902724262400544
320.09264655988906160.1852931197781230.907353440110938
330.06716113388330990.134322267766620.93283886611669
340.05116889638264570.1023377927652910.948831103617354
350.1166439192956580.2332878385913160.883356080704342
360.08605529911529790.1721105982305960.913944700884702
370.1018903617631390.2037807235262780.898109638236861
380.07330568086059560.1466113617211910.926694319139404
390.05094426558955130.1018885311791030.949055734410449
400.03443338956533660.06886677913067310.965566610434663
410.03127588418397370.06255176836794730.968724115816026
420.06386121710397730.1277224342079550.936138782896023
430.05118724190884210.1023744838176840.948812758091158
440.03984856899277450.07969713798554890.960151431007226
450.2132780175165070.4265560350330130.786721982483493
460.8453509415138430.3092981169723140.154649058486157
470.8207128286299760.3585743427400480.179287171370024
480.8701083062000320.2597833875999360.129891693799968
490.854118084903120.291763830193760.14588191509688
500.8197011119190250.360597776161950.180298888080975
510.8565801308232710.2868397383534570.143419869176729
520.8184233972068290.3631532055863410.181576602793171
530.7830444151759060.4339111696481890.216955584824094
540.8621414882374350.2757170235251310.137858511762565
550.7871626539405720.4256746921188550.212837346059428
560.8158661853449750.3682676293100490.184133814655025
570.7501403288346780.4997193423306440.249859671165322
580.6218953457912070.7562093084175860.378104654208793

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
5 & 0.0761587667403584 & 0.152317533480717 & 0.923841233259642 \tabularnewline
6 & 0.048910989733892 & 0.097821979467784 & 0.951089010266108 \tabularnewline
7 & 0.0181964356568666 & 0.0363928713137333 & 0.981803564343133 \tabularnewline
8 & 0.251733558339932 & 0.503467116679864 & 0.748266441660068 \tabularnewline
9 & 0.636552606167289 & 0.726894787665421 & 0.36344739383271 \tabularnewline
10 & 0.666040431292265 & 0.667919137415471 & 0.333959568707735 \tabularnewline
11 & 0.570974838587917 & 0.858050322824166 & 0.429025161412083 \tabularnewline
12 & 0.827479093100753 & 0.345041813798495 & 0.172520906899247 \tabularnewline
13 & 0.771508889311075 & 0.456982221377851 & 0.228491110688925 \tabularnewline
14 & 0.802234427830689 & 0.395531144338622 & 0.197765572169311 \tabularnewline
15 & 0.778076586562104 & 0.443846826875792 & 0.221923413437896 \tabularnewline
16 & 0.70957428299707 & 0.58085143400586 & 0.29042571700293 \tabularnewline
17 & 0.742942057057406 & 0.514115885885187 & 0.257057942942594 \tabularnewline
18 & 0.717607500864203 & 0.564784998271594 & 0.282392499135797 \tabularnewline
19 & 0.645523548834609 & 0.708952902330782 & 0.354476451165391 \tabularnewline
20 & 0.601101840783859 & 0.797796318432281 & 0.398898159216141 \tabularnewline
21 & 0.525406529308664 & 0.949186941382672 & 0.474593470691336 \tabularnewline
22 & 0.457643886722324 & 0.915287773444648 & 0.542356113277676 \tabularnewline
23 & 0.396289769975978 & 0.792579539951956 & 0.603710230024022 \tabularnewline
24 & 0.323378690635395 & 0.64675738127079 & 0.676621309364605 \tabularnewline
25 & 0.270642558298605 & 0.54128511659721 & 0.729357441701395 \tabularnewline
26 & 0.322318800382488 & 0.644637600764977 & 0.677681199617512 \tabularnewline
27 & 0.258400141689649 & 0.516800283379298 & 0.741599858310351 \tabularnewline
28 & 0.21307020907449 & 0.42614041814898 & 0.78692979092551 \tabularnewline
29 & 0.162362637600205 & 0.324725275200409 & 0.837637362399795 \tabularnewline
30 & 0.13283212068081 & 0.26566424136162 & 0.86716787931919 \tabularnewline
31 & 0.0972757375994557 & 0.194551475198911 & 0.902724262400544 \tabularnewline
32 & 0.0926465598890616 & 0.185293119778123 & 0.907353440110938 \tabularnewline
33 & 0.0671611338833099 & 0.13432226776662 & 0.93283886611669 \tabularnewline
34 & 0.0511688963826457 & 0.102337792765291 & 0.948831103617354 \tabularnewline
35 & 0.116643919295658 & 0.233287838591316 & 0.883356080704342 \tabularnewline
36 & 0.0860552991152979 & 0.172110598230596 & 0.913944700884702 \tabularnewline
37 & 0.101890361763139 & 0.203780723526278 & 0.898109638236861 \tabularnewline
38 & 0.0733056808605956 & 0.146611361721191 & 0.926694319139404 \tabularnewline
39 & 0.0509442655895513 & 0.101888531179103 & 0.949055734410449 \tabularnewline
40 & 0.0344333895653366 & 0.0688667791306731 & 0.965566610434663 \tabularnewline
41 & 0.0312758841839737 & 0.0625517683679473 & 0.968724115816026 \tabularnewline
42 & 0.0638612171039773 & 0.127722434207955 & 0.936138782896023 \tabularnewline
43 & 0.0511872419088421 & 0.102374483817684 & 0.948812758091158 \tabularnewline
44 & 0.0398485689927745 & 0.0796971379855489 & 0.960151431007226 \tabularnewline
45 & 0.213278017516507 & 0.426556035033013 & 0.786721982483493 \tabularnewline
46 & 0.845350941513843 & 0.309298116972314 & 0.154649058486157 \tabularnewline
47 & 0.820712828629976 & 0.358574342740048 & 0.179287171370024 \tabularnewline
48 & 0.870108306200032 & 0.259783387599936 & 0.129891693799968 \tabularnewline
49 & 0.85411808490312 & 0.29176383019376 & 0.14588191509688 \tabularnewline
50 & 0.819701111919025 & 0.36059777616195 & 0.180298888080975 \tabularnewline
51 & 0.856580130823271 & 0.286839738353457 & 0.143419869176729 \tabularnewline
52 & 0.818423397206829 & 0.363153205586341 & 0.181576602793171 \tabularnewline
53 & 0.783044415175906 & 0.433911169648189 & 0.216955584824094 \tabularnewline
54 & 0.862141488237435 & 0.275717023525131 & 0.137858511762565 \tabularnewline
55 & 0.787162653940572 & 0.425674692118855 & 0.212837346059428 \tabularnewline
56 & 0.815866185344975 & 0.368267629310049 & 0.184133814655025 \tabularnewline
57 & 0.750140328834678 & 0.499719342330644 & 0.249859671165322 \tabularnewline
58 & 0.621895345791207 & 0.756209308417586 & 0.378104654208793 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=143601&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]0.0761587667403584[/C][C]0.152317533480717[/C][C]0.923841233259642[/C][/ROW]
[ROW][C]6[/C][C]0.048910989733892[/C][C]0.097821979467784[/C][C]0.951089010266108[/C][/ROW]
[ROW][C]7[/C][C]0.0181964356568666[/C][C]0.0363928713137333[/C][C]0.981803564343133[/C][/ROW]
[ROW][C]8[/C][C]0.251733558339932[/C][C]0.503467116679864[/C][C]0.748266441660068[/C][/ROW]
[ROW][C]9[/C][C]0.636552606167289[/C][C]0.726894787665421[/C][C]0.36344739383271[/C][/ROW]
[ROW][C]10[/C][C]0.666040431292265[/C][C]0.667919137415471[/C][C]0.333959568707735[/C][/ROW]
[ROW][C]11[/C][C]0.570974838587917[/C][C]0.858050322824166[/C][C]0.429025161412083[/C][/ROW]
[ROW][C]12[/C][C]0.827479093100753[/C][C]0.345041813798495[/C][C]0.172520906899247[/C][/ROW]
[ROW][C]13[/C][C]0.771508889311075[/C][C]0.456982221377851[/C][C]0.228491110688925[/C][/ROW]
[ROW][C]14[/C][C]0.802234427830689[/C][C]0.395531144338622[/C][C]0.197765572169311[/C][/ROW]
[ROW][C]15[/C][C]0.778076586562104[/C][C]0.443846826875792[/C][C]0.221923413437896[/C][/ROW]
[ROW][C]16[/C][C]0.70957428299707[/C][C]0.58085143400586[/C][C]0.29042571700293[/C][/ROW]
[ROW][C]17[/C][C]0.742942057057406[/C][C]0.514115885885187[/C][C]0.257057942942594[/C][/ROW]
[ROW][C]18[/C][C]0.717607500864203[/C][C]0.564784998271594[/C][C]0.282392499135797[/C][/ROW]
[ROW][C]19[/C][C]0.645523548834609[/C][C]0.708952902330782[/C][C]0.354476451165391[/C][/ROW]
[ROW][C]20[/C][C]0.601101840783859[/C][C]0.797796318432281[/C][C]0.398898159216141[/C][/ROW]
[ROW][C]21[/C][C]0.525406529308664[/C][C]0.949186941382672[/C][C]0.474593470691336[/C][/ROW]
[ROW][C]22[/C][C]0.457643886722324[/C][C]0.915287773444648[/C][C]0.542356113277676[/C][/ROW]
[ROW][C]23[/C][C]0.396289769975978[/C][C]0.792579539951956[/C][C]0.603710230024022[/C][/ROW]
[ROW][C]24[/C][C]0.323378690635395[/C][C]0.64675738127079[/C][C]0.676621309364605[/C][/ROW]
[ROW][C]25[/C][C]0.270642558298605[/C][C]0.54128511659721[/C][C]0.729357441701395[/C][/ROW]
[ROW][C]26[/C][C]0.322318800382488[/C][C]0.644637600764977[/C][C]0.677681199617512[/C][/ROW]
[ROW][C]27[/C][C]0.258400141689649[/C][C]0.516800283379298[/C][C]0.741599858310351[/C][/ROW]
[ROW][C]28[/C][C]0.21307020907449[/C][C]0.42614041814898[/C][C]0.78692979092551[/C][/ROW]
[ROW][C]29[/C][C]0.162362637600205[/C][C]0.324725275200409[/C][C]0.837637362399795[/C][/ROW]
[ROW][C]30[/C][C]0.13283212068081[/C][C]0.26566424136162[/C][C]0.86716787931919[/C][/ROW]
[ROW][C]31[/C][C]0.0972757375994557[/C][C]0.194551475198911[/C][C]0.902724262400544[/C][/ROW]
[ROW][C]32[/C][C]0.0926465598890616[/C][C]0.185293119778123[/C][C]0.907353440110938[/C][/ROW]
[ROW][C]33[/C][C]0.0671611338833099[/C][C]0.13432226776662[/C][C]0.93283886611669[/C][/ROW]
[ROW][C]34[/C][C]0.0511688963826457[/C][C]0.102337792765291[/C][C]0.948831103617354[/C][/ROW]
[ROW][C]35[/C][C]0.116643919295658[/C][C]0.233287838591316[/C][C]0.883356080704342[/C][/ROW]
[ROW][C]36[/C][C]0.0860552991152979[/C][C]0.172110598230596[/C][C]0.913944700884702[/C][/ROW]
[ROW][C]37[/C][C]0.101890361763139[/C][C]0.203780723526278[/C][C]0.898109638236861[/C][/ROW]
[ROW][C]38[/C][C]0.0733056808605956[/C][C]0.146611361721191[/C][C]0.926694319139404[/C][/ROW]
[ROW][C]39[/C][C]0.0509442655895513[/C][C]0.101888531179103[/C][C]0.949055734410449[/C][/ROW]
[ROW][C]40[/C][C]0.0344333895653366[/C][C]0.0688667791306731[/C][C]0.965566610434663[/C][/ROW]
[ROW][C]41[/C][C]0.0312758841839737[/C][C]0.0625517683679473[/C][C]0.968724115816026[/C][/ROW]
[ROW][C]42[/C][C]0.0638612171039773[/C][C]0.127722434207955[/C][C]0.936138782896023[/C][/ROW]
[ROW][C]43[/C][C]0.0511872419088421[/C][C]0.102374483817684[/C][C]0.948812758091158[/C][/ROW]
[ROW][C]44[/C][C]0.0398485689927745[/C][C]0.0796971379855489[/C][C]0.960151431007226[/C][/ROW]
[ROW][C]45[/C][C]0.213278017516507[/C][C]0.426556035033013[/C][C]0.786721982483493[/C][/ROW]
[ROW][C]46[/C][C]0.845350941513843[/C][C]0.309298116972314[/C][C]0.154649058486157[/C][/ROW]
[ROW][C]47[/C][C]0.820712828629976[/C][C]0.358574342740048[/C][C]0.179287171370024[/C][/ROW]
[ROW][C]48[/C][C]0.870108306200032[/C][C]0.259783387599936[/C][C]0.129891693799968[/C][/ROW]
[ROW][C]49[/C][C]0.85411808490312[/C][C]0.29176383019376[/C][C]0.14588191509688[/C][/ROW]
[ROW][C]50[/C][C]0.819701111919025[/C][C]0.36059777616195[/C][C]0.180298888080975[/C][/ROW]
[ROW][C]51[/C][C]0.856580130823271[/C][C]0.286839738353457[/C][C]0.143419869176729[/C][/ROW]
[ROW][C]52[/C][C]0.818423397206829[/C][C]0.363153205586341[/C][C]0.181576602793171[/C][/ROW]
[ROW][C]53[/C][C]0.783044415175906[/C][C]0.433911169648189[/C][C]0.216955584824094[/C][/ROW]
[ROW][C]54[/C][C]0.862141488237435[/C][C]0.275717023525131[/C][C]0.137858511762565[/C][/ROW]
[ROW][C]55[/C][C]0.787162653940572[/C][C]0.425674692118855[/C][C]0.212837346059428[/C][/ROW]
[ROW][C]56[/C][C]0.815866185344975[/C][C]0.368267629310049[/C][C]0.184133814655025[/C][/ROW]
[ROW][C]57[/C][C]0.750140328834678[/C][C]0.499719342330644[/C][C]0.249859671165322[/C][/ROW]
[ROW][C]58[/C][C]0.621895345791207[/C][C]0.756209308417586[/C][C]0.378104654208793[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=143601&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=143601&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
50.07615876674035840.1523175334807170.923841233259642
60.0489109897338920.0978219794677840.951089010266108
70.01819643565686660.03639287131373330.981803564343133
80.2517335583399320.5034671166798640.748266441660068
90.6365526061672890.7268947876654210.36344739383271
100.6660404312922650.6679191374154710.333959568707735
110.5709748385879170.8580503228241660.429025161412083
120.8274790931007530.3450418137984950.172520906899247
130.7715088893110750.4569822213778510.228491110688925
140.8022344278306890.3955311443386220.197765572169311
150.7780765865621040.4438468268757920.221923413437896
160.709574282997070.580851434005860.29042571700293
170.7429420570574060.5141158858851870.257057942942594
180.7176075008642030.5647849982715940.282392499135797
190.6455235488346090.7089529023307820.354476451165391
200.6011018407838590.7977963184322810.398898159216141
210.5254065293086640.9491869413826720.474593470691336
220.4576438867223240.9152877734446480.542356113277676
230.3962897699759780.7925795399519560.603710230024022
240.3233786906353950.646757381270790.676621309364605
250.2706425582986050.541285116597210.729357441701395
260.3223188003824880.6446376007649770.677681199617512
270.2584001416896490.5168002833792980.741599858310351
280.213070209074490.426140418148980.78692979092551
290.1623626376002050.3247252752004090.837637362399795
300.132832120680810.265664241361620.86716787931919
310.09727573759945570.1945514751989110.902724262400544
320.09264655988906160.1852931197781230.907353440110938
330.06716113388330990.134322267766620.93283886611669
340.05116889638264570.1023377927652910.948831103617354
350.1166439192956580.2332878385913160.883356080704342
360.08605529911529790.1721105982305960.913944700884702
370.1018903617631390.2037807235262780.898109638236861
380.07330568086059560.1466113617211910.926694319139404
390.05094426558955130.1018885311791030.949055734410449
400.03443338956533660.06886677913067310.965566610434663
410.03127588418397370.06255176836794730.968724115816026
420.06386121710397730.1277224342079550.936138782896023
430.05118724190884210.1023744838176840.948812758091158
440.03984856899277450.07969713798554890.960151431007226
450.2132780175165070.4265560350330130.786721982483493
460.8453509415138430.3092981169723140.154649058486157
470.8207128286299760.3585743427400480.179287171370024
480.8701083062000320.2597833875999360.129891693799968
490.854118084903120.291763830193760.14588191509688
500.8197011119190250.360597776161950.180298888080975
510.8565801308232710.2868397383534570.143419869176729
520.8184233972068290.3631532055863410.181576602793171
530.7830444151759060.4339111696481890.216955584824094
540.8621414882374350.2757170235251310.137858511762565
550.7871626539405720.4256746921188550.212837346059428
560.8158661853449750.3682676293100490.184133814655025
570.7501403288346780.4997193423306440.249859671165322
580.6218953457912070.7562093084175860.378104654208793







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level10.0185185185185185OK
10% type I error level50.0925925925925926OK

\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 & 0 & 0 & OK \tabularnewline
5% type I error level & 1 & 0.0185185185185185 & OK \tabularnewline
10% type I error level & 5 & 0.0925925925925926 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=143601&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]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]1[/C][C]0.0185185185185185[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]5[/C][C]0.0925925925925926[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=143601&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=143601&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 level00OK
5% type I error level10.0185185185185185OK
10% type I error level50.0925925925925926OK



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')
}