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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 computationThu, 24 Nov 2011 05:36:05 -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/24/t1322131056t0wyxwb52yaiy7f.htm/, Retrieved Thu, 28 Mar 2024 23:21:50 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=146616, Retrieved Thu, 28 Mar 2024 23:21:50 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact155
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [Multiple regression] [2011-11-24 10:36:05] [2279ad719e09a38a1d31a45bdb1b1d06] [Current]
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Dataseries X:
122,7	119,4	104	126.6
126,2	129,8	113.1	128.5
125,9	131,9	117.3	127.1
133,2	129,8	120.5	135.9
131	131,6	119.9	133.2
133,8	134,3	120.5	126.4
142,4	136,7	124.7	146.2
134,2	134,7	118.9	137.1
124,1	138,1	120.2	123.9
118,7	132,4	116.5	118.2
114,3	125	110.9	114.2
107,8	117,7	108.6	106.9
104,4	112	108.6	103.1
98,1	106,3	104.9	96.2
97,1	100,5	102.1	95.9
95,3	95,6	98	94.8
94,3	89,5	93.1	94.9
95,6	87,7	96.4	96
105	88,2	103.7	106.4
98,3	88,7	93.5	99.9
93,1	91,4	87.9	94.2
96,6	95,7	91.8	97.6
93,1	96,8	89.3	93.6
94,9	93,8	85.1	96.8
90,8	91	82.6	92.4
84,6	86,8	80.4	85.3
88,4	91,5	80.6	89.6
80,4	89,3	72.3	81.3
84,6	97,9	69.4	86.7
73,2	95,7	65.1	73.3
64,6	86,9	62.6	63.4
60,5	82	59.2	59.3
56,4	83,2	58.2	54.2
58,5	85,7	60.1	56.3
56,7	77,8	63.1	54
69,6	79,4	69.4	69
89,1	83,4	79.2	91.5
121,3	102,8	96.7	127.3
137,2	108,7	105	145.5
157,5	120,3	113.2	168.7
155,4	121,9	112.3	166
146,2	112,7	112.9	155
131,5	113,1	113.2	136.4
125,8	115,7	112.6	129
116,7	113,5	106.9	118.8
111,2	103,1	101.3	113.7
107,9	95,5	92.7	111.7
110	88,5	96.2	114.2
100,9	86,2	98.5	102.4
94,8	83,8	96.2	95.3
88,5	76,4	97.3	87.7
92,4	76	103	91.5
87,2	75,7	102.6	85
84,4	71,5	108.1	80.7
84,4	69,7	107.7	80.9
79,2	72,1	101.6	75.4
75,8	72,6	98.3	71.7
71,4	70,2	96.6	66.6
78,7	69,4	96.8	75.8
75,3	68	94.5	72.1




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Herman Ole Andreas Wold' @ wold.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 & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146616&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]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146616&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146616&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'Herman Ole Andreas Wold' @ wold.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Algemeen[t] = -1.80649806320193 + 0.0759183108153765Levensmiddelen[t] + 0.165414060441962Industrie[t] + 0.778151663668414Energie[t] + 0.00240269305281314t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Algemeen[t] =  -1.80649806320193 +  0.0759183108153765Levensmiddelen[t] +  0.165414060441962Industrie[t] +  0.778151663668414Energie[t] +  0.00240269305281314t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146616&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Algemeen[t] =  -1.80649806320193 +  0.0759183108153765Levensmiddelen[t] +  0.165414060441962Industrie[t] +  0.778151663668414Energie[t] +  0.00240269305281314t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146616&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146616&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
Algemeen[t] = -1.80649806320193 + 0.0759183108153765Levensmiddelen[t] + 0.165414060441962Industrie[t] + 0.778151663668414Energie[t] + 0.00240269305281314t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-1.806498063201931.42379-1.26880.2098580.104929
Levensmiddelen0.07591831081537650.0174124.36025.7e-052.9e-05
Industrie0.1654140604419620.01173114.100500
Energie0.7781516636684140.01090571.358100
t0.002402693052813140.0130290.18440.8543710.427185

\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) & -1.80649806320193 & 1.42379 & -1.2688 & 0.209858 & 0.104929 \tabularnewline
Levensmiddelen & 0.0759183108153765 & 0.017412 & 4.3602 & 5.7e-05 & 2.9e-05 \tabularnewline
Industrie & 0.165414060441962 & 0.011731 & 14.1005 & 0 & 0 \tabularnewline
Energie & 0.778151663668414 & 0.010905 & 71.3581 & 0 & 0 \tabularnewline
t & 0.00240269305281314 & 0.013029 & 0.1844 & 0.854371 & 0.427185 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146616&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]-1.80649806320193[/C][C]1.42379[/C][C]-1.2688[/C][C]0.209858[/C][C]0.104929[/C][/ROW]
[ROW][C]Levensmiddelen[/C][C]0.0759183108153765[/C][C]0.017412[/C][C]4.3602[/C][C]5.7e-05[/C][C]2.9e-05[/C][/ROW]
[ROW][C]Industrie[/C][C]0.165414060441962[/C][C]0.011731[/C][C]14.1005[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Energie[/C][C]0.778151663668414[/C][C]0.010905[/C][C]71.3581[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]t[/C][C]0.00240269305281314[/C][C]0.013029[/C][C]0.1844[/C][C]0.854371[/C][C]0.427185[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146616&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146616&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)-1.806498063201931.42379-1.26880.2098580.104929
Levensmiddelen0.07591831081537650.0174124.36025.7e-052.9e-05
Industrie0.1654140604419620.01173114.100500
Energie0.7781516636684140.01090571.358100
t0.002402693052813140.0130290.18440.8543710.427185







Multiple Linear Regression - Regression Statistics
Multiple R0.999241466065092
R-squared0.998483507503915
Adjusted R-squared0.998373217140563
F-TEST (value)9053.22529694157
F-TEST (DF numerator)4
F-TEST (DF denominator)55
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.00886538300042
Sum Squared Residuals55.9795148559126

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.999241466065092 \tabularnewline
R-squared & 0.998483507503915 \tabularnewline
Adjusted R-squared & 0.998373217140563 \tabularnewline
F-TEST (value) & 9053.22529694157 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 55 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.00886538300042 \tabularnewline
Sum Squared Residuals & 55.9795148559126 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146616&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.999241466065092[/C][/ROW]
[ROW][C]R-squared[/C][C]0.998483507503915[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.998373217140563[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]9053.22529694157[/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]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.00886538300042[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]55.9795148559126[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146616&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146616&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 R0.999241466065092
R-squared0.998483507503915
Adjusted R-squared0.998373217140563
F-TEST (value)9053.22529694157
F-TEST (DF numerator)4
F-TEST (DF denominator)55
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.00886538300042
Sum Squared Residuals55.9795148559126







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1122.7122.977613847592-0.277613847592179
2126.2126.753323084117-0.553323084116746
3125.9126.520480954602-0.620480954602289
4133.2133.740514828639-0.54051482863916
5131131.67931255299-0.679312552989735
6133.8126.6945118085647.10548819143597
7142.4142.981260442065-0.581260442064586
8134.2134.791244823541-0.591244823540715
9124.1124.995206091517-0.895206091517286
10118.7119.517377906377-0.817377906377216
11114.3114.919059706248-0.619059706247607
12107.8108.306299246552-0.506299246552233
13104.4104.918991246017-0.518991246017407
1498.198.5073810644753-0.407381064475276
1597.197.3728526864609-0.272852686460887
1695.395.469091178671-0.169091178671045
1794.394.27567844595130.0243215540487033
1895.695.54326140903020.0567385909698362
19105104.8839232008680.1160767991315
2098.398.17907581897630.120924181023706
2193.193.02467472984570.075325270154325
2296.696.6443566516009-0.0443566516008603
2393.193.204127680772-0.104127680772023
2494.994.77412171126140.125878288738616
2590.890.72655066278520.0734493372147714
2684.684.52130870539540.0786912946045999
2788.488.2596624251430.140337574856959
2880.480.26344932428590.136550675714095
2984.684.6410676988787-0.0410676988787217
3073.273.3379373550805-0.137937355080496
3164.664.55502229153580.044977708464197
3260.560.43259563505010.067404364949904
3356.456.39211275593050.00788724406951095
3458.558.5327164345651-0.0327164345651369
3556.756.6418578270650.0581421729349944
3669.669.4801133532330.119886646766988
3789.188.91565951441790.184340485582118
38121.3121.1434530543530.156546945647427
39137.2137.129070761650.0709292383504482
40157.5157.4216397528920.0783602471079794
41155.4155.2956295969470.10437040305305
42146.2146.1391639664110.0608360335890668
43131.5131.74793725769-0.247937257689965
44125.8126.090156811451-0.29015681145131
45116.7117.045532106773-0.345532106773274
46111.2111.363492144162-0.163492144162274
47107.9107.8100514278810.0899485721194798
48110109.8053543159440.194645684056389
49100.9100.831407601850.0685923981497268
5094.894.74627719788390.053722802116072
5188.588.45488721350920.0451127864908354
5292.492.3267590486950.0732409513050239
5387.287.18223481048170.0177651895182994
5484.484.4295057767665-0.0295057767665403
5584.484.38472021890860.0152797810914225
5679.279.2804669390461-0.0804669390460463
5775.875.8958012324749-0.0958012324749462
5871.471.4662225921106-0.0662225921105912
5978.778.59996875434890.100031245651087
6075.375.23647231767060.063527682329442

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 122.7 & 122.977613847592 & -0.277613847592179 \tabularnewline
2 & 126.2 & 126.753323084117 & -0.553323084116746 \tabularnewline
3 & 125.9 & 126.520480954602 & -0.620480954602289 \tabularnewline
4 & 133.2 & 133.740514828639 & -0.54051482863916 \tabularnewline
5 & 131 & 131.67931255299 & -0.679312552989735 \tabularnewline
6 & 133.8 & 126.694511808564 & 7.10548819143597 \tabularnewline
7 & 142.4 & 142.981260442065 & -0.581260442064586 \tabularnewline
8 & 134.2 & 134.791244823541 & -0.591244823540715 \tabularnewline
9 & 124.1 & 124.995206091517 & -0.895206091517286 \tabularnewline
10 & 118.7 & 119.517377906377 & -0.817377906377216 \tabularnewline
11 & 114.3 & 114.919059706248 & -0.619059706247607 \tabularnewline
12 & 107.8 & 108.306299246552 & -0.506299246552233 \tabularnewline
13 & 104.4 & 104.918991246017 & -0.518991246017407 \tabularnewline
14 & 98.1 & 98.5073810644753 & -0.407381064475276 \tabularnewline
15 & 97.1 & 97.3728526864609 & -0.272852686460887 \tabularnewline
16 & 95.3 & 95.469091178671 & -0.169091178671045 \tabularnewline
17 & 94.3 & 94.2756784459513 & 0.0243215540487033 \tabularnewline
18 & 95.6 & 95.5432614090302 & 0.0567385909698362 \tabularnewline
19 & 105 & 104.883923200868 & 0.1160767991315 \tabularnewline
20 & 98.3 & 98.1790758189763 & 0.120924181023706 \tabularnewline
21 & 93.1 & 93.0246747298457 & 0.075325270154325 \tabularnewline
22 & 96.6 & 96.6443566516009 & -0.0443566516008603 \tabularnewline
23 & 93.1 & 93.204127680772 & -0.104127680772023 \tabularnewline
24 & 94.9 & 94.7741217112614 & 0.125878288738616 \tabularnewline
25 & 90.8 & 90.7265506627852 & 0.0734493372147714 \tabularnewline
26 & 84.6 & 84.5213087053954 & 0.0786912946045999 \tabularnewline
27 & 88.4 & 88.259662425143 & 0.140337574856959 \tabularnewline
28 & 80.4 & 80.2634493242859 & 0.136550675714095 \tabularnewline
29 & 84.6 & 84.6410676988787 & -0.0410676988787217 \tabularnewline
30 & 73.2 & 73.3379373550805 & -0.137937355080496 \tabularnewline
31 & 64.6 & 64.5550222915358 & 0.044977708464197 \tabularnewline
32 & 60.5 & 60.4325956350501 & 0.067404364949904 \tabularnewline
33 & 56.4 & 56.3921127559305 & 0.00788724406951095 \tabularnewline
34 & 58.5 & 58.5327164345651 & -0.0327164345651369 \tabularnewline
35 & 56.7 & 56.641857827065 & 0.0581421729349944 \tabularnewline
36 & 69.6 & 69.480113353233 & 0.119886646766988 \tabularnewline
37 & 89.1 & 88.9156595144179 & 0.184340485582118 \tabularnewline
38 & 121.3 & 121.143453054353 & 0.156546945647427 \tabularnewline
39 & 137.2 & 137.12907076165 & 0.0709292383504482 \tabularnewline
40 & 157.5 & 157.421639752892 & 0.0783602471079794 \tabularnewline
41 & 155.4 & 155.295629596947 & 0.10437040305305 \tabularnewline
42 & 146.2 & 146.139163966411 & 0.0608360335890668 \tabularnewline
43 & 131.5 & 131.74793725769 & -0.247937257689965 \tabularnewline
44 & 125.8 & 126.090156811451 & -0.29015681145131 \tabularnewline
45 & 116.7 & 117.045532106773 & -0.345532106773274 \tabularnewline
46 & 111.2 & 111.363492144162 & -0.163492144162274 \tabularnewline
47 & 107.9 & 107.810051427881 & 0.0899485721194798 \tabularnewline
48 & 110 & 109.805354315944 & 0.194645684056389 \tabularnewline
49 & 100.9 & 100.83140760185 & 0.0685923981497268 \tabularnewline
50 & 94.8 & 94.7462771978839 & 0.053722802116072 \tabularnewline
51 & 88.5 & 88.4548872135092 & 0.0451127864908354 \tabularnewline
52 & 92.4 & 92.326759048695 & 0.0732409513050239 \tabularnewline
53 & 87.2 & 87.1822348104817 & 0.0177651895182994 \tabularnewline
54 & 84.4 & 84.4295057767665 & -0.0295057767665403 \tabularnewline
55 & 84.4 & 84.3847202189086 & 0.0152797810914225 \tabularnewline
56 & 79.2 & 79.2804669390461 & -0.0804669390460463 \tabularnewline
57 & 75.8 & 75.8958012324749 & -0.0958012324749462 \tabularnewline
58 & 71.4 & 71.4662225921106 & -0.0662225921105912 \tabularnewline
59 & 78.7 & 78.5999687543489 & 0.100031245651087 \tabularnewline
60 & 75.3 & 75.2364723176706 & 0.063527682329442 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146616&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]122.7[/C][C]122.977613847592[/C][C]-0.277613847592179[/C][/ROW]
[ROW][C]2[/C][C]126.2[/C][C]126.753323084117[/C][C]-0.553323084116746[/C][/ROW]
[ROW][C]3[/C][C]125.9[/C][C]126.520480954602[/C][C]-0.620480954602289[/C][/ROW]
[ROW][C]4[/C][C]133.2[/C][C]133.740514828639[/C][C]-0.54051482863916[/C][/ROW]
[ROW][C]5[/C][C]131[/C][C]131.67931255299[/C][C]-0.679312552989735[/C][/ROW]
[ROW][C]6[/C][C]133.8[/C][C]126.694511808564[/C][C]7.10548819143597[/C][/ROW]
[ROW][C]7[/C][C]142.4[/C][C]142.981260442065[/C][C]-0.581260442064586[/C][/ROW]
[ROW][C]8[/C][C]134.2[/C][C]134.791244823541[/C][C]-0.591244823540715[/C][/ROW]
[ROW][C]9[/C][C]124.1[/C][C]124.995206091517[/C][C]-0.895206091517286[/C][/ROW]
[ROW][C]10[/C][C]118.7[/C][C]119.517377906377[/C][C]-0.817377906377216[/C][/ROW]
[ROW][C]11[/C][C]114.3[/C][C]114.919059706248[/C][C]-0.619059706247607[/C][/ROW]
[ROW][C]12[/C][C]107.8[/C][C]108.306299246552[/C][C]-0.506299246552233[/C][/ROW]
[ROW][C]13[/C][C]104.4[/C][C]104.918991246017[/C][C]-0.518991246017407[/C][/ROW]
[ROW][C]14[/C][C]98.1[/C][C]98.5073810644753[/C][C]-0.407381064475276[/C][/ROW]
[ROW][C]15[/C][C]97.1[/C][C]97.3728526864609[/C][C]-0.272852686460887[/C][/ROW]
[ROW][C]16[/C][C]95.3[/C][C]95.469091178671[/C][C]-0.169091178671045[/C][/ROW]
[ROW][C]17[/C][C]94.3[/C][C]94.2756784459513[/C][C]0.0243215540487033[/C][/ROW]
[ROW][C]18[/C][C]95.6[/C][C]95.5432614090302[/C][C]0.0567385909698362[/C][/ROW]
[ROW][C]19[/C][C]105[/C][C]104.883923200868[/C][C]0.1160767991315[/C][/ROW]
[ROW][C]20[/C][C]98.3[/C][C]98.1790758189763[/C][C]0.120924181023706[/C][/ROW]
[ROW][C]21[/C][C]93.1[/C][C]93.0246747298457[/C][C]0.075325270154325[/C][/ROW]
[ROW][C]22[/C][C]96.6[/C][C]96.6443566516009[/C][C]-0.0443566516008603[/C][/ROW]
[ROW][C]23[/C][C]93.1[/C][C]93.204127680772[/C][C]-0.104127680772023[/C][/ROW]
[ROW][C]24[/C][C]94.9[/C][C]94.7741217112614[/C][C]0.125878288738616[/C][/ROW]
[ROW][C]25[/C][C]90.8[/C][C]90.7265506627852[/C][C]0.0734493372147714[/C][/ROW]
[ROW][C]26[/C][C]84.6[/C][C]84.5213087053954[/C][C]0.0786912946045999[/C][/ROW]
[ROW][C]27[/C][C]88.4[/C][C]88.259662425143[/C][C]0.140337574856959[/C][/ROW]
[ROW][C]28[/C][C]80.4[/C][C]80.2634493242859[/C][C]0.136550675714095[/C][/ROW]
[ROW][C]29[/C][C]84.6[/C][C]84.6410676988787[/C][C]-0.0410676988787217[/C][/ROW]
[ROW][C]30[/C][C]73.2[/C][C]73.3379373550805[/C][C]-0.137937355080496[/C][/ROW]
[ROW][C]31[/C][C]64.6[/C][C]64.5550222915358[/C][C]0.044977708464197[/C][/ROW]
[ROW][C]32[/C][C]60.5[/C][C]60.4325956350501[/C][C]0.067404364949904[/C][/ROW]
[ROW][C]33[/C][C]56.4[/C][C]56.3921127559305[/C][C]0.00788724406951095[/C][/ROW]
[ROW][C]34[/C][C]58.5[/C][C]58.5327164345651[/C][C]-0.0327164345651369[/C][/ROW]
[ROW][C]35[/C][C]56.7[/C][C]56.641857827065[/C][C]0.0581421729349944[/C][/ROW]
[ROW][C]36[/C][C]69.6[/C][C]69.480113353233[/C][C]0.119886646766988[/C][/ROW]
[ROW][C]37[/C][C]89.1[/C][C]88.9156595144179[/C][C]0.184340485582118[/C][/ROW]
[ROW][C]38[/C][C]121.3[/C][C]121.143453054353[/C][C]0.156546945647427[/C][/ROW]
[ROW][C]39[/C][C]137.2[/C][C]137.12907076165[/C][C]0.0709292383504482[/C][/ROW]
[ROW][C]40[/C][C]157.5[/C][C]157.421639752892[/C][C]0.0783602471079794[/C][/ROW]
[ROW][C]41[/C][C]155.4[/C][C]155.295629596947[/C][C]0.10437040305305[/C][/ROW]
[ROW][C]42[/C][C]146.2[/C][C]146.139163966411[/C][C]0.0608360335890668[/C][/ROW]
[ROW][C]43[/C][C]131.5[/C][C]131.74793725769[/C][C]-0.247937257689965[/C][/ROW]
[ROW][C]44[/C][C]125.8[/C][C]126.090156811451[/C][C]-0.29015681145131[/C][/ROW]
[ROW][C]45[/C][C]116.7[/C][C]117.045532106773[/C][C]-0.345532106773274[/C][/ROW]
[ROW][C]46[/C][C]111.2[/C][C]111.363492144162[/C][C]-0.163492144162274[/C][/ROW]
[ROW][C]47[/C][C]107.9[/C][C]107.810051427881[/C][C]0.0899485721194798[/C][/ROW]
[ROW][C]48[/C][C]110[/C][C]109.805354315944[/C][C]0.194645684056389[/C][/ROW]
[ROW][C]49[/C][C]100.9[/C][C]100.83140760185[/C][C]0.0685923981497268[/C][/ROW]
[ROW][C]50[/C][C]94.8[/C][C]94.7462771978839[/C][C]0.053722802116072[/C][/ROW]
[ROW][C]51[/C][C]88.5[/C][C]88.4548872135092[/C][C]0.0451127864908354[/C][/ROW]
[ROW][C]52[/C][C]92.4[/C][C]92.326759048695[/C][C]0.0732409513050239[/C][/ROW]
[ROW][C]53[/C][C]87.2[/C][C]87.1822348104817[/C][C]0.0177651895182994[/C][/ROW]
[ROW][C]54[/C][C]84.4[/C][C]84.4295057767665[/C][C]-0.0295057767665403[/C][/ROW]
[ROW][C]55[/C][C]84.4[/C][C]84.3847202189086[/C][C]0.0152797810914225[/C][/ROW]
[ROW][C]56[/C][C]79.2[/C][C]79.2804669390461[/C][C]-0.0804669390460463[/C][/ROW]
[ROW][C]57[/C][C]75.8[/C][C]75.8958012324749[/C][C]-0.0958012324749462[/C][/ROW]
[ROW][C]58[/C][C]71.4[/C][C]71.4662225921106[/C][C]-0.0662225921105912[/C][/ROW]
[ROW][C]59[/C][C]78.7[/C][C]78.5999687543489[/C][C]0.100031245651087[/C][/ROW]
[ROW][C]60[/C][C]75.3[/C][C]75.2364723176706[/C][C]0.063527682329442[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146616&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146616&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
1122.7122.977613847592-0.277613847592179
2126.2126.753323084117-0.553323084116746
3125.9126.520480954602-0.620480954602289
4133.2133.740514828639-0.54051482863916
5131131.67931255299-0.679312552989735
6133.8126.6945118085647.10548819143597
7142.4142.981260442065-0.581260442064586
8134.2134.791244823541-0.591244823540715
9124.1124.995206091517-0.895206091517286
10118.7119.517377906377-0.817377906377216
11114.3114.919059706248-0.619059706247607
12107.8108.306299246552-0.506299246552233
13104.4104.918991246017-0.518991246017407
1498.198.5073810644753-0.407381064475276
1597.197.3728526864609-0.272852686460887
1695.395.469091178671-0.169091178671045
1794.394.27567844595130.0243215540487033
1895.695.54326140903020.0567385909698362
19105104.8839232008680.1160767991315
2098.398.17907581897630.120924181023706
2193.193.02467472984570.075325270154325
2296.696.6443566516009-0.0443566516008603
2393.193.204127680772-0.104127680772023
2494.994.77412171126140.125878288738616
2590.890.72655066278520.0734493372147714
2684.684.52130870539540.0786912946045999
2788.488.2596624251430.140337574856959
2880.480.26344932428590.136550675714095
2984.684.6410676988787-0.0410676988787217
3073.273.3379373550805-0.137937355080496
3164.664.55502229153580.044977708464197
3260.560.43259563505010.067404364949904
3356.456.39211275593050.00788724406951095
3458.558.5327164345651-0.0327164345651369
3556.756.6418578270650.0581421729349944
3669.669.4801133532330.119886646766988
3789.188.91565951441790.184340485582118
38121.3121.1434530543530.156546945647427
39137.2137.129070761650.0709292383504482
40157.5157.4216397528920.0783602471079794
41155.4155.2956295969470.10437040305305
42146.2146.1391639664110.0608360335890668
43131.5131.74793725769-0.247937257689965
44125.8126.090156811451-0.29015681145131
45116.7117.045532106773-0.345532106773274
46111.2111.363492144162-0.163492144162274
47107.9107.8100514278810.0899485721194798
48110109.8053543159440.194645684056389
49100.9100.831407601850.0685923981497268
5094.894.74627719788390.053722802116072
5188.588.45488721350920.0451127864908354
5292.492.3267590486950.0732409513050239
5387.287.18223481048170.0177651895182994
5484.484.4295057767665-0.0295057767665403
5584.484.38472021890860.0152797810914225
5679.279.2804669390461-0.0804669390460463
5775.875.8958012324749-0.0958012324749462
5871.471.4662225921106-0.0662225921105912
5978.778.59996875434890.100031245651087
6075.375.23647231767060.063527682329442







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
815.32967464818893e-602.66483732409447e-60
911.05199961523236e-645.2599980761618e-65
1014.74749984838876e-642.37374992419438e-64
1114.28048280181643e-622.14024140090822e-62
1211.89243427636682e-609.46217138183408e-61
1317.19681993883397e-593.59840996941699e-59
1415.04176250292366e-572.52088125146183e-57
1512.39899861900735e-551.19949930950367e-55
1613.60288132515945e-541.80144066257972e-54
1715.09601019236104e-532.54800509618052e-53
1812.36086720398852e-511.18043360199426e-51
1911.3177428347607e-496.58871417380352e-50
2013.80627942908779e-481.90313971454389e-48
2111.22432147597755e-466.12160737988777e-47
2215.05401425879738e-452.52700712939869e-45
2311.51085035547248e-437.55425177736242e-44
2412.43005658151591e-421.21502829075795e-42
2519.67811926195525e-414.83905963097762e-41
2612.66987894727893e-391.33493947363947e-39
2711.22790919464601e-386.13954597323007e-39
2816.884896341372e-383.442448170686e-38
2911.6706634015905e-368.35331700795251e-37
3011.06368900812002e-355.31844504060012e-36
3111.85646563143066e-349.28232815715331e-35
3218.33157034454978e-334.16578517227489e-33
3313.77373895017128e-311.88686947508564e-31
3411.56832715119593e-297.84163575597966e-30
3514.55923943536233e-282.27961971768116e-28
3611.64699083312493e-268.23495416562466e-27
3715.33188074852433e-252.66594037426216e-25
3811.1487548046428e-245.74377402321399e-25
3913.32734302623084e-231.66367151311542e-23
4017.29736620461758e-223.64868310230879e-22
4116.96079467913329e-213.48039733956665e-21
4213.19169934447429e-191.59584967223714e-19
4311.11955033197218e-195.59775165986092e-20
4416.22803831885414e-183.11401915942707e-18
4513.62477498971912e-161.81238749485956e-16
460.9999999999999951.00401742479343e-145.02008712396716e-15
470.9999999999997634.74663028817393e-132.37331514408696e-13
480.9999999999926891.46220509754458e-117.31102548772291e-12
490.9999999996667956.66409570227306e-103.33204785113653e-10
500.9999999841417673.17164662632611e-081.58582331316305e-08
510.999999378177271.2436454595663e-066.21822729783152e-07
520.9999822499473553.55001052904732e-051.77500526452366e-05

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 1 & 5.32967464818893e-60 & 2.66483732409447e-60 \tabularnewline
9 & 1 & 1.05199961523236e-64 & 5.2599980761618e-65 \tabularnewline
10 & 1 & 4.74749984838876e-64 & 2.37374992419438e-64 \tabularnewline
11 & 1 & 4.28048280181643e-62 & 2.14024140090822e-62 \tabularnewline
12 & 1 & 1.89243427636682e-60 & 9.46217138183408e-61 \tabularnewline
13 & 1 & 7.19681993883397e-59 & 3.59840996941699e-59 \tabularnewline
14 & 1 & 5.04176250292366e-57 & 2.52088125146183e-57 \tabularnewline
15 & 1 & 2.39899861900735e-55 & 1.19949930950367e-55 \tabularnewline
16 & 1 & 3.60288132515945e-54 & 1.80144066257972e-54 \tabularnewline
17 & 1 & 5.09601019236104e-53 & 2.54800509618052e-53 \tabularnewline
18 & 1 & 2.36086720398852e-51 & 1.18043360199426e-51 \tabularnewline
19 & 1 & 1.3177428347607e-49 & 6.58871417380352e-50 \tabularnewline
20 & 1 & 3.80627942908779e-48 & 1.90313971454389e-48 \tabularnewline
21 & 1 & 1.22432147597755e-46 & 6.12160737988777e-47 \tabularnewline
22 & 1 & 5.05401425879738e-45 & 2.52700712939869e-45 \tabularnewline
23 & 1 & 1.51085035547248e-43 & 7.55425177736242e-44 \tabularnewline
24 & 1 & 2.43005658151591e-42 & 1.21502829075795e-42 \tabularnewline
25 & 1 & 9.67811926195525e-41 & 4.83905963097762e-41 \tabularnewline
26 & 1 & 2.66987894727893e-39 & 1.33493947363947e-39 \tabularnewline
27 & 1 & 1.22790919464601e-38 & 6.13954597323007e-39 \tabularnewline
28 & 1 & 6.884896341372e-38 & 3.442448170686e-38 \tabularnewline
29 & 1 & 1.6706634015905e-36 & 8.35331700795251e-37 \tabularnewline
30 & 1 & 1.06368900812002e-35 & 5.31844504060012e-36 \tabularnewline
31 & 1 & 1.85646563143066e-34 & 9.28232815715331e-35 \tabularnewline
32 & 1 & 8.33157034454978e-33 & 4.16578517227489e-33 \tabularnewline
33 & 1 & 3.77373895017128e-31 & 1.88686947508564e-31 \tabularnewline
34 & 1 & 1.56832715119593e-29 & 7.84163575597966e-30 \tabularnewline
35 & 1 & 4.55923943536233e-28 & 2.27961971768116e-28 \tabularnewline
36 & 1 & 1.64699083312493e-26 & 8.23495416562466e-27 \tabularnewline
37 & 1 & 5.33188074852433e-25 & 2.66594037426216e-25 \tabularnewline
38 & 1 & 1.1487548046428e-24 & 5.74377402321399e-25 \tabularnewline
39 & 1 & 3.32734302623084e-23 & 1.66367151311542e-23 \tabularnewline
40 & 1 & 7.29736620461758e-22 & 3.64868310230879e-22 \tabularnewline
41 & 1 & 6.96079467913329e-21 & 3.48039733956665e-21 \tabularnewline
42 & 1 & 3.19169934447429e-19 & 1.59584967223714e-19 \tabularnewline
43 & 1 & 1.11955033197218e-19 & 5.59775165986092e-20 \tabularnewline
44 & 1 & 6.22803831885414e-18 & 3.11401915942707e-18 \tabularnewline
45 & 1 & 3.62477498971912e-16 & 1.81238749485956e-16 \tabularnewline
46 & 0.999999999999995 & 1.00401742479343e-14 & 5.02008712396716e-15 \tabularnewline
47 & 0.999999999999763 & 4.74663028817393e-13 & 2.37331514408696e-13 \tabularnewline
48 & 0.999999999992689 & 1.46220509754458e-11 & 7.31102548772291e-12 \tabularnewline
49 & 0.999999999666795 & 6.66409570227306e-10 & 3.33204785113653e-10 \tabularnewline
50 & 0.999999984141767 & 3.17164662632611e-08 & 1.58582331316305e-08 \tabularnewline
51 & 0.99999937817727 & 1.2436454595663e-06 & 6.21822729783152e-07 \tabularnewline
52 & 0.999982249947355 & 3.55001052904732e-05 & 1.77500526452366e-05 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146616&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]1[/C][C]5.32967464818893e-60[/C][C]2.66483732409447e-60[/C][/ROW]
[ROW][C]9[/C][C]1[/C][C]1.05199961523236e-64[/C][C]5.2599980761618e-65[/C][/ROW]
[ROW][C]10[/C][C]1[/C][C]4.74749984838876e-64[/C][C]2.37374992419438e-64[/C][/ROW]
[ROW][C]11[/C][C]1[/C][C]4.28048280181643e-62[/C][C]2.14024140090822e-62[/C][/ROW]
[ROW][C]12[/C][C]1[/C][C]1.89243427636682e-60[/C][C]9.46217138183408e-61[/C][/ROW]
[ROW][C]13[/C][C]1[/C][C]7.19681993883397e-59[/C][C]3.59840996941699e-59[/C][/ROW]
[ROW][C]14[/C][C]1[/C][C]5.04176250292366e-57[/C][C]2.52088125146183e-57[/C][/ROW]
[ROW][C]15[/C][C]1[/C][C]2.39899861900735e-55[/C][C]1.19949930950367e-55[/C][/ROW]
[ROW][C]16[/C][C]1[/C][C]3.60288132515945e-54[/C][C]1.80144066257972e-54[/C][/ROW]
[ROW][C]17[/C][C]1[/C][C]5.09601019236104e-53[/C][C]2.54800509618052e-53[/C][/ROW]
[ROW][C]18[/C][C]1[/C][C]2.36086720398852e-51[/C][C]1.18043360199426e-51[/C][/ROW]
[ROW][C]19[/C][C]1[/C][C]1.3177428347607e-49[/C][C]6.58871417380352e-50[/C][/ROW]
[ROW][C]20[/C][C]1[/C][C]3.80627942908779e-48[/C][C]1.90313971454389e-48[/C][/ROW]
[ROW][C]21[/C][C]1[/C][C]1.22432147597755e-46[/C][C]6.12160737988777e-47[/C][/ROW]
[ROW][C]22[/C][C]1[/C][C]5.05401425879738e-45[/C][C]2.52700712939869e-45[/C][/ROW]
[ROW][C]23[/C][C]1[/C][C]1.51085035547248e-43[/C][C]7.55425177736242e-44[/C][/ROW]
[ROW][C]24[/C][C]1[/C][C]2.43005658151591e-42[/C][C]1.21502829075795e-42[/C][/ROW]
[ROW][C]25[/C][C]1[/C][C]9.67811926195525e-41[/C][C]4.83905963097762e-41[/C][/ROW]
[ROW][C]26[/C][C]1[/C][C]2.66987894727893e-39[/C][C]1.33493947363947e-39[/C][/ROW]
[ROW][C]27[/C][C]1[/C][C]1.22790919464601e-38[/C][C]6.13954597323007e-39[/C][/ROW]
[ROW][C]28[/C][C]1[/C][C]6.884896341372e-38[/C][C]3.442448170686e-38[/C][/ROW]
[ROW][C]29[/C][C]1[/C][C]1.6706634015905e-36[/C][C]8.35331700795251e-37[/C][/ROW]
[ROW][C]30[/C][C]1[/C][C]1.06368900812002e-35[/C][C]5.31844504060012e-36[/C][/ROW]
[ROW][C]31[/C][C]1[/C][C]1.85646563143066e-34[/C][C]9.28232815715331e-35[/C][/ROW]
[ROW][C]32[/C][C]1[/C][C]8.33157034454978e-33[/C][C]4.16578517227489e-33[/C][/ROW]
[ROW][C]33[/C][C]1[/C][C]3.77373895017128e-31[/C][C]1.88686947508564e-31[/C][/ROW]
[ROW][C]34[/C][C]1[/C][C]1.56832715119593e-29[/C][C]7.84163575597966e-30[/C][/ROW]
[ROW][C]35[/C][C]1[/C][C]4.55923943536233e-28[/C][C]2.27961971768116e-28[/C][/ROW]
[ROW][C]36[/C][C]1[/C][C]1.64699083312493e-26[/C][C]8.23495416562466e-27[/C][/ROW]
[ROW][C]37[/C][C]1[/C][C]5.33188074852433e-25[/C][C]2.66594037426216e-25[/C][/ROW]
[ROW][C]38[/C][C]1[/C][C]1.1487548046428e-24[/C][C]5.74377402321399e-25[/C][/ROW]
[ROW][C]39[/C][C]1[/C][C]3.32734302623084e-23[/C][C]1.66367151311542e-23[/C][/ROW]
[ROW][C]40[/C][C]1[/C][C]7.29736620461758e-22[/C][C]3.64868310230879e-22[/C][/ROW]
[ROW][C]41[/C][C]1[/C][C]6.96079467913329e-21[/C][C]3.48039733956665e-21[/C][/ROW]
[ROW][C]42[/C][C]1[/C][C]3.19169934447429e-19[/C][C]1.59584967223714e-19[/C][/ROW]
[ROW][C]43[/C][C]1[/C][C]1.11955033197218e-19[/C][C]5.59775165986092e-20[/C][/ROW]
[ROW][C]44[/C][C]1[/C][C]6.22803831885414e-18[/C][C]3.11401915942707e-18[/C][/ROW]
[ROW][C]45[/C][C]1[/C][C]3.62477498971912e-16[/C][C]1.81238749485956e-16[/C][/ROW]
[ROW][C]46[/C][C]0.999999999999995[/C][C]1.00401742479343e-14[/C][C]5.02008712396716e-15[/C][/ROW]
[ROW][C]47[/C][C]0.999999999999763[/C][C]4.74663028817393e-13[/C][C]2.37331514408696e-13[/C][/ROW]
[ROW][C]48[/C][C]0.999999999992689[/C][C]1.46220509754458e-11[/C][C]7.31102548772291e-12[/C][/ROW]
[ROW][C]49[/C][C]0.999999999666795[/C][C]6.66409570227306e-10[/C][C]3.33204785113653e-10[/C][/ROW]
[ROW][C]50[/C][C]0.999999984141767[/C][C]3.17164662632611e-08[/C][C]1.58582331316305e-08[/C][/ROW]
[ROW][C]51[/C][C]0.99999937817727[/C][C]1.2436454595663e-06[/C][C]6.21822729783152e-07[/C][/ROW]
[ROW][C]52[/C][C]0.999982249947355[/C][C]3.55001052904732e-05[/C][C]1.77500526452366e-05[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146616&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146616&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
815.32967464818893e-602.66483732409447e-60
911.05199961523236e-645.2599980761618e-65
1014.74749984838876e-642.37374992419438e-64
1114.28048280181643e-622.14024140090822e-62
1211.89243427636682e-609.46217138183408e-61
1317.19681993883397e-593.59840996941699e-59
1415.04176250292366e-572.52088125146183e-57
1512.39899861900735e-551.19949930950367e-55
1613.60288132515945e-541.80144066257972e-54
1715.09601019236104e-532.54800509618052e-53
1812.36086720398852e-511.18043360199426e-51
1911.3177428347607e-496.58871417380352e-50
2013.80627942908779e-481.90313971454389e-48
2111.22432147597755e-466.12160737988777e-47
2215.05401425879738e-452.52700712939869e-45
2311.51085035547248e-437.55425177736242e-44
2412.43005658151591e-421.21502829075795e-42
2519.67811926195525e-414.83905963097762e-41
2612.66987894727893e-391.33493947363947e-39
2711.22790919464601e-386.13954597323007e-39
2816.884896341372e-383.442448170686e-38
2911.6706634015905e-368.35331700795251e-37
3011.06368900812002e-355.31844504060012e-36
3111.85646563143066e-349.28232815715331e-35
3218.33157034454978e-334.16578517227489e-33
3313.77373895017128e-311.88686947508564e-31
3411.56832715119593e-297.84163575597966e-30
3514.55923943536233e-282.27961971768116e-28
3611.64699083312493e-268.23495416562466e-27
3715.33188074852433e-252.66594037426216e-25
3811.1487548046428e-245.74377402321399e-25
3913.32734302623084e-231.66367151311542e-23
4017.29736620461758e-223.64868310230879e-22
4116.96079467913329e-213.48039733956665e-21
4213.19169934447429e-191.59584967223714e-19
4311.11955033197218e-195.59775165986092e-20
4416.22803831885414e-183.11401915942707e-18
4513.62477498971912e-161.81238749485956e-16
460.9999999999999951.00401742479343e-145.02008712396716e-15
470.9999999999997634.74663028817393e-132.37331514408696e-13
480.9999999999926891.46220509754458e-117.31102548772291e-12
490.9999999996667956.66409570227306e-103.33204785113653e-10
500.9999999841417673.17164662632611e-081.58582331316305e-08
510.999999378177271.2436454595663e-066.21822729783152e-07
520.9999822499473553.55001052904732e-051.77500526452366e-05







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level451NOK
5% type I error level451NOK
10% type I error level451NOK

\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 & 45 & 1 & NOK \tabularnewline
5% type I error level & 45 & 1 & NOK \tabularnewline
10% type I error level & 45 & 1 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146616&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]45[/C][C]1[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]45[/C][C]1[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]45[/C][C]1[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146616&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146616&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 level451NOK
5% type I error level451NOK
10% type I error level451NOK



Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = 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')
}