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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 computationMon, 21 Nov 2011 11:47:29 -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/21/t13218940785t5qhvr3lontgce.htm/, Retrieved Thu, 31 Oct 2024 23:43:45 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=145817, Retrieved Thu, 31 Oct 2024 23:43:45 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact167
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [] [2011-11-21 16:47:29] [44862125718ce971d51d71f1796e585f] [Current]
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Dataseries X:
130	280	70	68.402973	3
15	135	120	33.983679	1
260	320	70	59.425505	3
200	158	110	34.384843	1
125	30	110	33.174094	2
200	125	90	49.120253	1
210	190	90	53.313813	1
220	35	120	18.042851	2
290	105	110	50.765	1
210	45	120	19.823573	2
140	105	110	40.400208	3
180	55	110	22.736446	2
280	25	110	41.445019	1
290	35	100	45.863324	1
90	20	110	35.782791	2
180	65	110	22.396513	1
80	25	100	64.533816	1
220	30	110	46.895644	3
190	80	100	44.330856	3
125	30	110	32.207582	2
200	25	110	31.435973	1
240	190	120	41.015492	3
135	25	110	28.025765	1
45	40	100	35.252444	1
280	45	110	23.804043	2
140	85	100	52.076897	3
170	90	110	53.371007	1
220	45	120	21.871292	2
250	90	110	31.072217	1
170	60	110	36.523683	1
260	40	110	39.241114	1
150	95	100	45.328074	2
180	55	110	26.734515	1
0	95	100	54.850917	1
220	90	100	40.105965	1
190	40	120	29.924285	1
170	120	130	30.450843	3
0	15	50	60.756112	3
0	50	50	63.005645	3
135	110	100	49.511874	3
0	110	100	50.828392	1
210	240	120	39.259197	2
140	140	100	3.7034	1
0	110	90	55.333142	3
240	30	110	41.998933	1
290	35	110	40.560159	1
0	95	80	68.235885	1
0	140	90	74.472949	1
0	120	90	72.801787	1
70	40	110	31.230054	2
230	55	110	53.131324	1
15	90	90	59.363993	2
200	35	110	38.839746	3
190	230	140	28.592785	3
200	110	100	46.658844	3
250	60	110	39.106174	1
140	25	110	27.753301	2
230	115	100	49.787445	1
200	110	100	51.592193	1
200	60	110	36.187559	1




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=145817&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=145817&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145817&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
rating[t] = + 99.7465175437067 -0.0259260786851935fat[t] + 0.0490031953731906sugars[t] -0.533827627418403calories[t] -1.4235803508721shelf[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
rating[t] =  +  99.7465175437067 -0.0259260786851935fat[t] +  0.0490031953731906sugars[t] -0.533827627418403calories[t] -1.4235803508721shelf[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145817&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]rating[t] =  +  99.7465175437067 -0.0259260786851935fat[t] +  0.0490031953731906sugars[t] -0.533827627418403calories[t] -1.4235803508721shelf[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145817&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145817&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
rating[t] = + 99.7465175437067 -0.0259260786851935fat[t] + 0.0490031953731906sugars[t] -0.533827627418403calories[t] -1.4235803508721shelf[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)99.746517543706710.5315649.471200
fat-0.02592607868519350.017501-1.48140.1442120.072106
sugars0.04900319537319060.021992.22840.0299620.014981
calories-0.5338276274184030.099197-5.38152e-061e-06
shelf-1.42358035087211.690679-0.8420.4034250.201713

\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) & 99.7465175437067 & 10.531564 & 9.4712 & 0 & 0 \tabularnewline
fat & -0.0259260786851935 & 0.017501 & -1.4814 & 0.144212 & 0.072106 \tabularnewline
sugars & 0.0490031953731906 & 0.02199 & 2.2284 & 0.029962 & 0.014981 \tabularnewline
calories & -0.533827627418403 & 0.099197 & -5.3815 & 2e-06 & 1e-06 \tabularnewline
shelf & -1.4235803508721 & 1.690679 & -0.842 & 0.403425 & 0.201713 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145817&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]99.7465175437067[/C][C]10.531564[/C][C]9.4712[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]fat[/C][C]-0.0259260786851935[/C][C]0.017501[/C][C]-1.4814[/C][C]0.144212[/C][C]0.072106[/C][/ROW]
[ROW][C]sugars[/C][C]0.0490031953731906[/C][C]0.02199[/C][C]2.2284[/C][C]0.029962[/C][C]0.014981[/C][/ROW]
[ROW][C]calories[/C][C]-0.533827627418403[/C][C]0.099197[/C][C]-5.3815[/C][C]2e-06[/C][C]1e-06[/C][/ROW]
[ROW][C]shelf[/C][C]-1.4235803508721[/C][C]1.690679[/C][C]-0.842[/C][C]0.403425[/C][C]0.201713[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145817&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145817&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)99.746517543706710.5315649.471200
fat-0.02592607868519350.017501-1.48140.1442120.072106
sugars0.04900319537319060.021992.22840.0299620.014981
calories-0.5338276274184030.099197-5.38152e-061e-06
shelf-1.42358035087211.690679-0.8420.4034250.201713







Multiple Linear Regression - Regression Statistics
Multiple R0.714665117106891
R-squared0.510746229609406
Adjusted R-squared0.475164137217363
F-TEST (value)14.3540246026571
F-TEST (DF numerator)4
F-TEST (DF denominator)55
p-value4.36111182722243e-08
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation10.570214921725
Sum Squared Residuals6145.11939203018

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.714665117106891 \tabularnewline
R-squared & 0.510746229609406 \tabularnewline
Adjusted R-squared & 0.475164137217363 \tabularnewline
F-TEST (value) & 14.3540246026571 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 55 \tabularnewline
p-value & 4.36111182722243e-08 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 10.570214921725 \tabularnewline
Sum Squared Residuals & 6145.11939203018 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145817&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.714665117106891[/C][/ROW]
[ROW][C]R-squared[/C][C]0.510746229609406[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.475164137217363[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]14.3540246026571[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]55[/C][/ROW]
[ROW][C]p-value[/C][C]4.36111182722243e-08[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]10.570214921725[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]6145.11939203018[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145817&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145817&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 R0.714665117106891
R-squared0.510746229609406
Adjusted R-squared0.475164137217363
F-TEST (value)14.3540246026571
F-TEST (DF numerator)4
F-TEST (DF denominator)55
p-value4.36111182722243e-08
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation10.570214921725
Sum Squared Residuals6145.11939203018







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
168.40297368.4583470472202-0.0553740472201881
233.98367940.4901620977291-6.50648309772911
359.42550567.0480846330729-7.62257963307288
434.38484342.1591873087357-7.77434430873573
533.17409436.4076538514847-3.23355985148475
649.12025351.2186344097885-2.0983814097885
753.31381354.1445813221939-0.830768322193942
818.04285128.8514160790733-10.8085650790733
950.76537.228670872289213.5363291277108
1019.82357329.6007088196571-9.77713581965713
1140.40020838.2704219733242.12978602667597
1222.73644636.2067994081289-13.4703534081289
1341.44501933.56767602928597.87734297071409
1445.86332439.13672347034996.72660052965009
1535.78279136.8250346517346-1.04224365173461
1622.39651338.1204117127329-15.7238987127329
1764.53381644.091168040508620.4426479594914
1846.89564432.521096025519314.3745479744807
1944.33085641.08731442891863.24354157108138
2032.20758236.4076538514848-4.20007185148475
2131.43597335.6417623241014-4.20578932410139
2241.01549234.50480943734196.51068256265815
2328.02576537.326957438639-9.30119243863896
2435.25244445.7336287250883-10.4811847250883
2523.80404333.1241595858776-9.32011658587762
2652.07689742.62863434004439.44826265995575
2753.37100739.604752383914613.7662546160854
2821.87129229.3414480328052-7.4701560328052
2931.07221737.5306660890991-6.4584490890991
3036.52368338.1346565227189-1.61097352271886
3139.24111434.82124553358764.41986846641237
3245.32807444.28298585779631.04508814220368
3326.73451537.630379759001-10.895864759001
3454.85091749.59547801144755.25543898855255
3540.10596543.6467247238389-3.54075972383893
3629.92428531.2977947673672-1.37350976736715
3730.45084327.5511349949982.89970800500197
3860.75611269.5194430507681-8.76333105076812
3963.00564571.2345548888298-8.22890988882979
4049.51187443.98334461785.52852938220002
4150.82839250.33052594204530.497866057954691
4239.25919739.15633191742930.102865082570706
433.703448.1709707873139-44.4675707873139
4455.33314252.82164151448512.51150048551487
4541.99893334.84973515355967.1491978464404
4640.56015933.79844719616596.76171180383412
4768.23588560.27203055981557.96385444018449
4874.47294957.138898077425117.3340509225749
4972.80178756.158834169961216.6429528300388
5031.23005438.3236201329023-7.0935661329023
5153.13132436.334075824741316.7972481752587
5259.36399352.87626677761556.48772622238448
5338.83974633.28463357608915.55511242391091
5428.59278527.08468863816111.5080963618389
5546.65884442.29814950326244.3606944967376
5639.10617436.06057022790343.04560377209662
5727.75330135.7737466943409-8.02044569434089
5849.78744544.61254382131685.17490117868324
5951.59219345.14531020500666.44688279499339
6036.18755937.3568741621631-1.16931516216306

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 68.402973 & 68.4583470472202 & -0.0553740472201881 \tabularnewline
2 & 33.983679 & 40.4901620977291 & -6.50648309772911 \tabularnewline
3 & 59.425505 & 67.0480846330729 & -7.62257963307288 \tabularnewline
4 & 34.384843 & 42.1591873087357 & -7.77434430873573 \tabularnewline
5 & 33.174094 & 36.4076538514847 & -3.23355985148475 \tabularnewline
6 & 49.120253 & 51.2186344097885 & -2.0983814097885 \tabularnewline
7 & 53.313813 & 54.1445813221939 & -0.830768322193942 \tabularnewline
8 & 18.042851 & 28.8514160790733 & -10.8085650790733 \tabularnewline
9 & 50.765 & 37.2286708722892 & 13.5363291277108 \tabularnewline
10 & 19.823573 & 29.6007088196571 & -9.77713581965713 \tabularnewline
11 & 40.400208 & 38.270421973324 & 2.12978602667597 \tabularnewline
12 & 22.736446 & 36.2067994081289 & -13.4703534081289 \tabularnewline
13 & 41.445019 & 33.5676760292859 & 7.87734297071409 \tabularnewline
14 & 45.863324 & 39.1367234703499 & 6.72660052965009 \tabularnewline
15 & 35.782791 & 36.8250346517346 & -1.04224365173461 \tabularnewline
16 & 22.396513 & 38.1204117127329 & -15.7238987127329 \tabularnewline
17 & 64.533816 & 44.0911680405086 & 20.4426479594914 \tabularnewline
18 & 46.895644 & 32.5210960255193 & 14.3745479744807 \tabularnewline
19 & 44.330856 & 41.0873144289186 & 3.24354157108138 \tabularnewline
20 & 32.207582 & 36.4076538514848 & -4.20007185148475 \tabularnewline
21 & 31.435973 & 35.6417623241014 & -4.20578932410139 \tabularnewline
22 & 41.015492 & 34.5048094373419 & 6.51068256265815 \tabularnewline
23 & 28.025765 & 37.326957438639 & -9.30119243863896 \tabularnewline
24 & 35.252444 & 45.7336287250883 & -10.4811847250883 \tabularnewline
25 & 23.804043 & 33.1241595858776 & -9.32011658587762 \tabularnewline
26 & 52.076897 & 42.6286343400443 & 9.44826265995575 \tabularnewline
27 & 53.371007 & 39.6047523839146 & 13.7662546160854 \tabularnewline
28 & 21.871292 & 29.3414480328052 & -7.4701560328052 \tabularnewline
29 & 31.072217 & 37.5306660890991 & -6.4584490890991 \tabularnewline
30 & 36.523683 & 38.1346565227189 & -1.61097352271886 \tabularnewline
31 & 39.241114 & 34.8212455335876 & 4.41986846641237 \tabularnewline
32 & 45.328074 & 44.2829858577963 & 1.04508814220368 \tabularnewline
33 & 26.734515 & 37.630379759001 & -10.895864759001 \tabularnewline
34 & 54.850917 & 49.5954780114475 & 5.25543898855255 \tabularnewline
35 & 40.105965 & 43.6467247238389 & -3.54075972383893 \tabularnewline
36 & 29.924285 & 31.2977947673672 & -1.37350976736715 \tabularnewline
37 & 30.450843 & 27.551134994998 & 2.89970800500197 \tabularnewline
38 & 60.756112 & 69.5194430507681 & -8.76333105076812 \tabularnewline
39 & 63.005645 & 71.2345548888298 & -8.22890988882979 \tabularnewline
40 & 49.511874 & 43.9833446178 & 5.52852938220002 \tabularnewline
41 & 50.828392 & 50.3305259420453 & 0.497866057954691 \tabularnewline
42 & 39.259197 & 39.1563319174293 & 0.102865082570706 \tabularnewline
43 & 3.7034 & 48.1709707873139 & -44.4675707873139 \tabularnewline
44 & 55.333142 & 52.8216415144851 & 2.51150048551487 \tabularnewline
45 & 41.998933 & 34.8497351535596 & 7.1491978464404 \tabularnewline
46 & 40.560159 & 33.7984471961659 & 6.76171180383412 \tabularnewline
47 & 68.235885 & 60.2720305598155 & 7.96385444018449 \tabularnewline
48 & 74.472949 & 57.1388980774251 & 17.3340509225749 \tabularnewline
49 & 72.801787 & 56.1588341699612 & 16.6429528300388 \tabularnewline
50 & 31.230054 & 38.3236201329023 & -7.0935661329023 \tabularnewline
51 & 53.131324 & 36.3340758247413 & 16.7972481752587 \tabularnewline
52 & 59.363993 & 52.8762667776155 & 6.48772622238448 \tabularnewline
53 & 38.839746 & 33.2846335760891 & 5.55511242391091 \tabularnewline
54 & 28.592785 & 27.0846886381611 & 1.5080963618389 \tabularnewline
55 & 46.658844 & 42.2981495032624 & 4.3606944967376 \tabularnewline
56 & 39.106174 & 36.0605702279034 & 3.04560377209662 \tabularnewline
57 & 27.753301 & 35.7737466943409 & -8.02044569434089 \tabularnewline
58 & 49.787445 & 44.6125438213168 & 5.17490117868324 \tabularnewline
59 & 51.592193 & 45.1453102050066 & 6.44688279499339 \tabularnewline
60 & 36.187559 & 37.3568741621631 & -1.16931516216306 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145817&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]68.402973[/C][C]68.4583470472202[/C][C]-0.0553740472201881[/C][/ROW]
[ROW][C]2[/C][C]33.983679[/C][C]40.4901620977291[/C][C]-6.50648309772911[/C][/ROW]
[ROW][C]3[/C][C]59.425505[/C][C]67.0480846330729[/C][C]-7.62257963307288[/C][/ROW]
[ROW][C]4[/C][C]34.384843[/C][C]42.1591873087357[/C][C]-7.77434430873573[/C][/ROW]
[ROW][C]5[/C][C]33.174094[/C][C]36.4076538514847[/C][C]-3.23355985148475[/C][/ROW]
[ROW][C]6[/C][C]49.120253[/C][C]51.2186344097885[/C][C]-2.0983814097885[/C][/ROW]
[ROW][C]7[/C][C]53.313813[/C][C]54.1445813221939[/C][C]-0.830768322193942[/C][/ROW]
[ROW][C]8[/C][C]18.042851[/C][C]28.8514160790733[/C][C]-10.8085650790733[/C][/ROW]
[ROW][C]9[/C][C]50.765[/C][C]37.2286708722892[/C][C]13.5363291277108[/C][/ROW]
[ROW][C]10[/C][C]19.823573[/C][C]29.6007088196571[/C][C]-9.77713581965713[/C][/ROW]
[ROW][C]11[/C][C]40.400208[/C][C]38.270421973324[/C][C]2.12978602667597[/C][/ROW]
[ROW][C]12[/C][C]22.736446[/C][C]36.2067994081289[/C][C]-13.4703534081289[/C][/ROW]
[ROW][C]13[/C][C]41.445019[/C][C]33.5676760292859[/C][C]7.87734297071409[/C][/ROW]
[ROW][C]14[/C][C]45.863324[/C][C]39.1367234703499[/C][C]6.72660052965009[/C][/ROW]
[ROW][C]15[/C][C]35.782791[/C][C]36.8250346517346[/C][C]-1.04224365173461[/C][/ROW]
[ROW][C]16[/C][C]22.396513[/C][C]38.1204117127329[/C][C]-15.7238987127329[/C][/ROW]
[ROW][C]17[/C][C]64.533816[/C][C]44.0911680405086[/C][C]20.4426479594914[/C][/ROW]
[ROW][C]18[/C][C]46.895644[/C][C]32.5210960255193[/C][C]14.3745479744807[/C][/ROW]
[ROW][C]19[/C][C]44.330856[/C][C]41.0873144289186[/C][C]3.24354157108138[/C][/ROW]
[ROW][C]20[/C][C]32.207582[/C][C]36.4076538514848[/C][C]-4.20007185148475[/C][/ROW]
[ROW][C]21[/C][C]31.435973[/C][C]35.6417623241014[/C][C]-4.20578932410139[/C][/ROW]
[ROW][C]22[/C][C]41.015492[/C][C]34.5048094373419[/C][C]6.51068256265815[/C][/ROW]
[ROW][C]23[/C][C]28.025765[/C][C]37.326957438639[/C][C]-9.30119243863896[/C][/ROW]
[ROW][C]24[/C][C]35.252444[/C][C]45.7336287250883[/C][C]-10.4811847250883[/C][/ROW]
[ROW][C]25[/C][C]23.804043[/C][C]33.1241595858776[/C][C]-9.32011658587762[/C][/ROW]
[ROW][C]26[/C][C]52.076897[/C][C]42.6286343400443[/C][C]9.44826265995575[/C][/ROW]
[ROW][C]27[/C][C]53.371007[/C][C]39.6047523839146[/C][C]13.7662546160854[/C][/ROW]
[ROW][C]28[/C][C]21.871292[/C][C]29.3414480328052[/C][C]-7.4701560328052[/C][/ROW]
[ROW][C]29[/C][C]31.072217[/C][C]37.5306660890991[/C][C]-6.4584490890991[/C][/ROW]
[ROW][C]30[/C][C]36.523683[/C][C]38.1346565227189[/C][C]-1.61097352271886[/C][/ROW]
[ROW][C]31[/C][C]39.241114[/C][C]34.8212455335876[/C][C]4.41986846641237[/C][/ROW]
[ROW][C]32[/C][C]45.328074[/C][C]44.2829858577963[/C][C]1.04508814220368[/C][/ROW]
[ROW][C]33[/C][C]26.734515[/C][C]37.630379759001[/C][C]-10.895864759001[/C][/ROW]
[ROW][C]34[/C][C]54.850917[/C][C]49.5954780114475[/C][C]5.25543898855255[/C][/ROW]
[ROW][C]35[/C][C]40.105965[/C][C]43.6467247238389[/C][C]-3.54075972383893[/C][/ROW]
[ROW][C]36[/C][C]29.924285[/C][C]31.2977947673672[/C][C]-1.37350976736715[/C][/ROW]
[ROW][C]37[/C][C]30.450843[/C][C]27.551134994998[/C][C]2.89970800500197[/C][/ROW]
[ROW][C]38[/C][C]60.756112[/C][C]69.5194430507681[/C][C]-8.76333105076812[/C][/ROW]
[ROW][C]39[/C][C]63.005645[/C][C]71.2345548888298[/C][C]-8.22890988882979[/C][/ROW]
[ROW][C]40[/C][C]49.511874[/C][C]43.9833446178[/C][C]5.52852938220002[/C][/ROW]
[ROW][C]41[/C][C]50.828392[/C][C]50.3305259420453[/C][C]0.497866057954691[/C][/ROW]
[ROW][C]42[/C][C]39.259197[/C][C]39.1563319174293[/C][C]0.102865082570706[/C][/ROW]
[ROW][C]43[/C][C]3.7034[/C][C]48.1709707873139[/C][C]-44.4675707873139[/C][/ROW]
[ROW][C]44[/C][C]55.333142[/C][C]52.8216415144851[/C][C]2.51150048551487[/C][/ROW]
[ROW][C]45[/C][C]41.998933[/C][C]34.8497351535596[/C][C]7.1491978464404[/C][/ROW]
[ROW][C]46[/C][C]40.560159[/C][C]33.7984471961659[/C][C]6.76171180383412[/C][/ROW]
[ROW][C]47[/C][C]68.235885[/C][C]60.2720305598155[/C][C]7.96385444018449[/C][/ROW]
[ROW][C]48[/C][C]74.472949[/C][C]57.1388980774251[/C][C]17.3340509225749[/C][/ROW]
[ROW][C]49[/C][C]72.801787[/C][C]56.1588341699612[/C][C]16.6429528300388[/C][/ROW]
[ROW][C]50[/C][C]31.230054[/C][C]38.3236201329023[/C][C]-7.0935661329023[/C][/ROW]
[ROW][C]51[/C][C]53.131324[/C][C]36.3340758247413[/C][C]16.7972481752587[/C][/ROW]
[ROW][C]52[/C][C]59.363993[/C][C]52.8762667776155[/C][C]6.48772622238448[/C][/ROW]
[ROW][C]53[/C][C]38.839746[/C][C]33.2846335760891[/C][C]5.55511242391091[/C][/ROW]
[ROW][C]54[/C][C]28.592785[/C][C]27.0846886381611[/C][C]1.5080963618389[/C][/ROW]
[ROW][C]55[/C][C]46.658844[/C][C]42.2981495032624[/C][C]4.3606944967376[/C][/ROW]
[ROW][C]56[/C][C]39.106174[/C][C]36.0605702279034[/C][C]3.04560377209662[/C][/ROW]
[ROW][C]57[/C][C]27.753301[/C][C]35.7737466943409[/C][C]-8.02044569434089[/C][/ROW]
[ROW][C]58[/C][C]49.787445[/C][C]44.6125438213168[/C][C]5.17490117868324[/C][/ROW]
[ROW][C]59[/C][C]51.592193[/C][C]45.1453102050066[/C][C]6.44688279499339[/C][/ROW]
[ROW][C]60[/C][C]36.187559[/C][C]37.3568741621631[/C][C]-1.16931516216306[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145817&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145817&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
168.40297368.4583470472202-0.0553740472201881
233.98367940.4901620977291-6.50648309772911
359.42550567.0480846330729-7.62257963307288
434.38484342.1591873087357-7.77434430873573
533.17409436.4076538514847-3.23355985148475
649.12025351.2186344097885-2.0983814097885
753.31381354.1445813221939-0.830768322193942
818.04285128.8514160790733-10.8085650790733
950.76537.228670872289213.5363291277108
1019.82357329.6007088196571-9.77713581965713
1140.40020838.2704219733242.12978602667597
1222.73644636.2067994081289-13.4703534081289
1341.44501933.56767602928597.87734297071409
1445.86332439.13672347034996.72660052965009
1535.78279136.8250346517346-1.04224365173461
1622.39651338.1204117127329-15.7238987127329
1764.53381644.091168040508620.4426479594914
1846.89564432.521096025519314.3745479744807
1944.33085641.08731442891863.24354157108138
2032.20758236.4076538514848-4.20007185148475
2131.43597335.6417623241014-4.20578932410139
2241.01549234.50480943734196.51068256265815
2328.02576537.326957438639-9.30119243863896
2435.25244445.7336287250883-10.4811847250883
2523.80404333.1241595858776-9.32011658587762
2652.07689742.62863434004439.44826265995575
2753.37100739.604752383914613.7662546160854
2821.87129229.3414480328052-7.4701560328052
2931.07221737.5306660890991-6.4584490890991
3036.52368338.1346565227189-1.61097352271886
3139.24111434.82124553358764.41986846641237
3245.32807444.28298585779631.04508814220368
3326.73451537.630379759001-10.895864759001
3454.85091749.59547801144755.25543898855255
3540.10596543.6467247238389-3.54075972383893
3629.92428531.2977947673672-1.37350976736715
3730.45084327.5511349949982.89970800500197
3860.75611269.5194430507681-8.76333105076812
3963.00564571.2345548888298-8.22890988882979
4049.51187443.98334461785.52852938220002
4150.82839250.33052594204530.497866057954691
4239.25919739.15633191742930.102865082570706
433.703448.1709707873139-44.4675707873139
4455.33314252.82164151448512.51150048551487
4541.99893334.84973515355967.1491978464404
4640.56015933.79844719616596.76171180383412
4768.23588560.27203055981557.96385444018449
4874.47294957.138898077425117.3340509225749
4972.80178756.158834169961216.6429528300388
5031.23005438.3236201329023-7.0935661329023
5153.13132436.334075824741316.7972481752587
5259.36399352.87626677761556.48772622238448
5338.83974633.28463357608915.55511242391091
5428.59278527.08468863816111.5080963618389
5546.65884442.29814950326244.3606944967376
5639.10617436.06057022790343.04560377209662
5727.75330135.7737466943409-8.02044569434089
5849.78744544.61254382131685.17490117868324
5951.59219345.14531020500666.44688279499339
6036.18755937.3568741621631-1.16931516216306







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.007866795263716120.01573359052743220.992133204736284
90.3503236512845010.7006473025690020.649676348715499
100.2367583790723960.4735167581447920.763241620927604
110.2474805238930160.4949610477860320.752519476106984
120.2451734776387950.490346955277590.754826522361205
130.2068229013691290.4136458027382590.793177098630871
140.1412953867294020.2825907734588030.858704613270598
150.09163665605690730.1832733121138150.908363343943093
160.1612406426232290.3224812852464580.838759357376771
170.3081532923146270.6163065846292550.691846707685373
180.4163352668667580.8326705337335160.583664733133242
190.3312547615311010.6625095230622020.668745238468899
200.2713571339599520.5427142679199040.728642866040048
210.2230361232427680.4460722464855360.776963876757232
220.2727912914326510.5455825828653020.727208708567349
230.2479809257644420.4959618515288850.752019074235558
240.2304875359292620.4609750718585240.769512464070738
250.2291157868394830.4582315736789650.770884213160517
260.2115402188915820.4230804377831650.788459781108418
270.2792592715781820.5585185431563650.720740728421818
280.2417301897323070.4834603794646130.758269810267693
290.1980155145323580.3960310290647160.801984485467642
300.1484014040646690.2968028081293370.851598595935331
310.1119539893494140.2239079786988280.888046010650586
320.07814956075233880.1562991215046780.921850439247661
330.07874972750954150.1574994550190830.921250272490459
340.06117416673214480.122348333464290.938825833267855
350.04227900028411960.08455800056823920.95772099971588
360.02864441985940120.05728883971880240.971355580140599
370.01925320753048740.03850641506097480.980746792469513
380.0163736604475640.0327473208951280.983626339552436
390.0140752668644880.02815053372897610.985924733135512
400.008938406810851230.01787681362170250.991061593189149
410.005144263334521740.01028852666904350.994855736665478
420.002738408346067520.005476816692135050.997261591653932
430.974600394236410.0507992115271810.0253996057635905
440.9558586149116620.08828277017667660.0441413850883383
450.9348217877795970.1303564244408060.0651782122204029
460.9004546421879780.1990907156240430.0995453578120216
470.8716858621529960.2566282756940090.128314137847004
480.8466626900994810.3066746198010370.153337309900519
490.8681301474978360.2637397050043280.131869852502164
500.7957962250246970.4084075499506060.204203774975303
510.9598227095962150.08035458080756910.0401772904037846
520.9633034789089470.07339304218210650.0366965210910532

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 0.00786679526371612 & 0.0157335905274322 & 0.992133204736284 \tabularnewline
9 & 0.350323651284501 & 0.700647302569002 & 0.649676348715499 \tabularnewline
10 & 0.236758379072396 & 0.473516758144792 & 0.763241620927604 \tabularnewline
11 & 0.247480523893016 & 0.494961047786032 & 0.752519476106984 \tabularnewline
12 & 0.245173477638795 & 0.49034695527759 & 0.754826522361205 \tabularnewline
13 & 0.206822901369129 & 0.413645802738259 & 0.793177098630871 \tabularnewline
14 & 0.141295386729402 & 0.282590773458803 & 0.858704613270598 \tabularnewline
15 & 0.0916366560569073 & 0.183273312113815 & 0.908363343943093 \tabularnewline
16 & 0.161240642623229 & 0.322481285246458 & 0.838759357376771 \tabularnewline
17 & 0.308153292314627 & 0.616306584629255 & 0.691846707685373 \tabularnewline
18 & 0.416335266866758 & 0.832670533733516 & 0.583664733133242 \tabularnewline
19 & 0.331254761531101 & 0.662509523062202 & 0.668745238468899 \tabularnewline
20 & 0.271357133959952 & 0.542714267919904 & 0.728642866040048 \tabularnewline
21 & 0.223036123242768 & 0.446072246485536 & 0.776963876757232 \tabularnewline
22 & 0.272791291432651 & 0.545582582865302 & 0.727208708567349 \tabularnewline
23 & 0.247980925764442 & 0.495961851528885 & 0.752019074235558 \tabularnewline
24 & 0.230487535929262 & 0.460975071858524 & 0.769512464070738 \tabularnewline
25 & 0.229115786839483 & 0.458231573678965 & 0.770884213160517 \tabularnewline
26 & 0.211540218891582 & 0.423080437783165 & 0.788459781108418 \tabularnewline
27 & 0.279259271578182 & 0.558518543156365 & 0.720740728421818 \tabularnewline
28 & 0.241730189732307 & 0.483460379464613 & 0.758269810267693 \tabularnewline
29 & 0.198015514532358 & 0.396031029064716 & 0.801984485467642 \tabularnewline
30 & 0.148401404064669 & 0.296802808129337 & 0.851598595935331 \tabularnewline
31 & 0.111953989349414 & 0.223907978698828 & 0.888046010650586 \tabularnewline
32 & 0.0781495607523388 & 0.156299121504678 & 0.921850439247661 \tabularnewline
33 & 0.0787497275095415 & 0.157499455019083 & 0.921250272490459 \tabularnewline
34 & 0.0611741667321448 & 0.12234833346429 & 0.938825833267855 \tabularnewline
35 & 0.0422790002841196 & 0.0845580005682392 & 0.95772099971588 \tabularnewline
36 & 0.0286444198594012 & 0.0572888397188024 & 0.971355580140599 \tabularnewline
37 & 0.0192532075304874 & 0.0385064150609748 & 0.980746792469513 \tabularnewline
38 & 0.016373660447564 & 0.032747320895128 & 0.983626339552436 \tabularnewline
39 & 0.014075266864488 & 0.0281505337289761 & 0.985924733135512 \tabularnewline
40 & 0.00893840681085123 & 0.0178768136217025 & 0.991061593189149 \tabularnewline
41 & 0.00514426333452174 & 0.0102885266690435 & 0.994855736665478 \tabularnewline
42 & 0.00273840834606752 & 0.00547681669213505 & 0.997261591653932 \tabularnewline
43 & 0.97460039423641 & 0.050799211527181 & 0.0253996057635905 \tabularnewline
44 & 0.955858614911662 & 0.0882827701766766 & 0.0441413850883383 \tabularnewline
45 & 0.934821787779597 & 0.130356424440806 & 0.0651782122204029 \tabularnewline
46 & 0.900454642187978 & 0.199090715624043 & 0.0995453578120216 \tabularnewline
47 & 0.871685862152996 & 0.256628275694009 & 0.128314137847004 \tabularnewline
48 & 0.846662690099481 & 0.306674619801037 & 0.153337309900519 \tabularnewline
49 & 0.868130147497836 & 0.263739705004328 & 0.131869852502164 \tabularnewline
50 & 0.795796225024697 & 0.408407549950606 & 0.204203774975303 \tabularnewline
51 & 0.959822709596215 & 0.0803545808075691 & 0.0401772904037846 \tabularnewline
52 & 0.963303478908947 & 0.0733930421821065 & 0.0366965210910532 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145817&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]8[/C][C]0.00786679526371612[/C][C]0.0157335905274322[/C][C]0.992133204736284[/C][/ROW]
[ROW][C]9[/C][C]0.350323651284501[/C][C]0.700647302569002[/C][C]0.649676348715499[/C][/ROW]
[ROW][C]10[/C][C]0.236758379072396[/C][C]0.473516758144792[/C][C]0.763241620927604[/C][/ROW]
[ROW][C]11[/C][C]0.247480523893016[/C][C]0.494961047786032[/C][C]0.752519476106984[/C][/ROW]
[ROW][C]12[/C][C]0.245173477638795[/C][C]0.49034695527759[/C][C]0.754826522361205[/C][/ROW]
[ROW][C]13[/C][C]0.206822901369129[/C][C]0.413645802738259[/C][C]0.793177098630871[/C][/ROW]
[ROW][C]14[/C][C]0.141295386729402[/C][C]0.282590773458803[/C][C]0.858704613270598[/C][/ROW]
[ROW][C]15[/C][C]0.0916366560569073[/C][C]0.183273312113815[/C][C]0.908363343943093[/C][/ROW]
[ROW][C]16[/C][C]0.161240642623229[/C][C]0.322481285246458[/C][C]0.838759357376771[/C][/ROW]
[ROW][C]17[/C][C]0.308153292314627[/C][C]0.616306584629255[/C][C]0.691846707685373[/C][/ROW]
[ROW][C]18[/C][C]0.416335266866758[/C][C]0.832670533733516[/C][C]0.583664733133242[/C][/ROW]
[ROW][C]19[/C][C]0.331254761531101[/C][C]0.662509523062202[/C][C]0.668745238468899[/C][/ROW]
[ROW][C]20[/C][C]0.271357133959952[/C][C]0.542714267919904[/C][C]0.728642866040048[/C][/ROW]
[ROW][C]21[/C][C]0.223036123242768[/C][C]0.446072246485536[/C][C]0.776963876757232[/C][/ROW]
[ROW][C]22[/C][C]0.272791291432651[/C][C]0.545582582865302[/C][C]0.727208708567349[/C][/ROW]
[ROW][C]23[/C][C]0.247980925764442[/C][C]0.495961851528885[/C][C]0.752019074235558[/C][/ROW]
[ROW][C]24[/C][C]0.230487535929262[/C][C]0.460975071858524[/C][C]0.769512464070738[/C][/ROW]
[ROW][C]25[/C][C]0.229115786839483[/C][C]0.458231573678965[/C][C]0.770884213160517[/C][/ROW]
[ROW][C]26[/C][C]0.211540218891582[/C][C]0.423080437783165[/C][C]0.788459781108418[/C][/ROW]
[ROW][C]27[/C][C]0.279259271578182[/C][C]0.558518543156365[/C][C]0.720740728421818[/C][/ROW]
[ROW][C]28[/C][C]0.241730189732307[/C][C]0.483460379464613[/C][C]0.758269810267693[/C][/ROW]
[ROW][C]29[/C][C]0.198015514532358[/C][C]0.396031029064716[/C][C]0.801984485467642[/C][/ROW]
[ROW][C]30[/C][C]0.148401404064669[/C][C]0.296802808129337[/C][C]0.851598595935331[/C][/ROW]
[ROW][C]31[/C][C]0.111953989349414[/C][C]0.223907978698828[/C][C]0.888046010650586[/C][/ROW]
[ROW][C]32[/C][C]0.0781495607523388[/C][C]0.156299121504678[/C][C]0.921850439247661[/C][/ROW]
[ROW][C]33[/C][C]0.0787497275095415[/C][C]0.157499455019083[/C][C]0.921250272490459[/C][/ROW]
[ROW][C]34[/C][C]0.0611741667321448[/C][C]0.12234833346429[/C][C]0.938825833267855[/C][/ROW]
[ROW][C]35[/C][C]0.0422790002841196[/C][C]0.0845580005682392[/C][C]0.95772099971588[/C][/ROW]
[ROW][C]36[/C][C]0.0286444198594012[/C][C]0.0572888397188024[/C][C]0.971355580140599[/C][/ROW]
[ROW][C]37[/C][C]0.0192532075304874[/C][C]0.0385064150609748[/C][C]0.980746792469513[/C][/ROW]
[ROW][C]38[/C][C]0.016373660447564[/C][C]0.032747320895128[/C][C]0.983626339552436[/C][/ROW]
[ROW][C]39[/C][C]0.014075266864488[/C][C]0.0281505337289761[/C][C]0.985924733135512[/C][/ROW]
[ROW][C]40[/C][C]0.00893840681085123[/C][C]0.0178768136217025[/C][C]0.991061593189149[/C][/ROW]
[ROW][C]41[/C][C]0.00514426333452174[/C][C]0.0102885266690435[/C][C]0.994855736665478[/C][/ROW]
[ROW][C]42[/C][C]0.00273840834606752[/C][C]0.00547681669213505[/C][C]0.997261591653932[/C][/ROW]
[ROW][C]43[/C][C]0.97460039423641[/C][C]0.050799211527181[/C][C]0.0253996057635905[/C][/ROW]
[ROW][C]44[/C][C]0.955858614911662[/C][C]0.0882827701766766[/C][C]0.0441413850883383[/C][/ROW]
[ROW][C]45[/C][C]0.934821787779597[/C][C]0.130356424440806[/C][C]0.0651782122204029[/C][/ROW]
[ROW][C]46[/C][C]0.900454642187978[/C][C]0.199090715624043[/C][C]0.0995453578120216[/C][/ROW]
[ROW][C]47[/C][C]0.871685862152996[/C][C]0.256628275694009[/C][C]0.128314137847004[/C][/ROW]
[ROW][C]48[/C][C]0.846662690099481[/C][C]0.306674619801037[/C][C]0.153337309900519[/C][/ROW]
[ROW][C]49[/C][C]0.868130147497836[/C][C]0.263739705004328[/C][C]0.131869852502164[/C][/ROW]
[ROW][C]50[/C][C]0.795796225024697[/C][C]0.408407549950606[/C][C]0.204203774975303[/C][/ROW]
[ROW][C]51[/C][C]0.959822709596215[/C][C]0.0803545808075691[/C][C]0.0401772904037846[/C][/ROW]
[ROW][C]52[/C][C]0.963303478908947[/C][C]0.0733930421821065[/C][C]0.0366965210910532[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145817&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145817&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
80.007866795263716120.01573359052743220.992133204736284
90.3503236512845010.7006473025690020.649676348715499
100.2367583790723960.4735167581447920.763241620927604
110.2474805238930160.4949610477860320.752519476106984
120.2451734776387950.490346955277590.754826522361205
130.2068229013691290.4136458027382590.793177098630871
140.1412953867294020.2825907734588030.858704613270598
150.09163665605690730.1832733121138150.908363343943093
160.1612406426232290.3224812852464580.838759357376771
170.3081532923146270.6163065846292550.691846707685373
180.4163352668667580.8326705337335160.583664733133242
190.3312547615311010.6625095230622020.668745238468899
200.2713571339599520.5427142679199040.728642866040048
210.2230361232427680.4460722464855360.776963876757232
220.2727912914326510.5455825828653020.727208708567349
230.2479809257644420.4959618515288850.752019074235558
240.2304875359292620.4609750718585240.769512464070738
250.2291157868394830.4582315736789650.770884213160517
260.2115402188915820.4230804377831650.788459781108418
270.2792592715781820.5585185431563650.720740728421818
280.2417301897323070.4834603794646130.758269810267693
290.1980155145323580.3960310290647160.801984485467642
300.1484014040646690.2968028081293370.851598595935331
310.1119539893494140.2239079786988280.888046010650586
320.07814956075233880.1562991215046780.921850439247661
330.07874972750954150.1574994550190830.921250272490459
340.06117416673214480.122348333464290.938825833267855
350.04227900028411960.08455800056823920.95772099971588
360.02864441985940120.05728883971880240.971355580140599
370.01925320753048740.03850641506097480.980746792469513
380.0163736604475640.0327473208951280.983626339552436
390.0140752668644880.02815053372897610.985924733135512
400.008938406810851230.01787681362170250.991061593189149
410.005144263334521740.01028852666904350.994855736665478
420.002738408346067520.005476816692135050.997261591653932
430.974600394236410.0507992115271810.0253996057635905
440.9558586149116620.08828277017667660.0441413850883383
450.9348217877795970.1303564244408060.0651782122204029
460.9004546421879780.1990907156240430.0995453578120216
470.8716858621529960.2566282756940090.128314137847004
480.8466626900994810.3066746198010370.153337309900519
490.8681301474978360.2637397050043280.131869852502164
500.7957962250246970.4084075499506060.204203774975303
510.9598227095962150.08035458080756910.0401772904037846
520.9633034789089470.07339304218210650.0366965210910532







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level10.0222222222222222NOK
5% type I error level70.155555555555556NOK
10% type I error level130.288888888888889NOK

\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 & 1 & 0.0222222222222222 & NOK \tabularnewline
5% type I error level & 7 & 0.155555555555556 & NOK \tabularnewline
10% type I error level & 13 & 0.288888888888889 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145817&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]1[/C][C]0.0222222222222222[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]7[/C][C]0.155555555555556[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]13[/C][C]0.288888888888889[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145817&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145817&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 level10.0222222222222222NOK
5% type I error level70.155555555555556NOK
10% type I error level130.288888888888889NOK



Parameters (Session):
par1 = 4 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 4 ; 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')
}