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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 computationFri, 20 Nov 2009 08:14:28 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Nov/20/t1258730888gt147eu3qn0uodm.htm/, Retrieved Fri, 29 Mar 2024 11:49:00 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58267, Retrieved Fri, 29 Mar 2024 11:49:00 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsShwWs7.1
Estimated Impact235
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RMPD  [Multiple Regression] [Seatbelt] [2009-11-12 13:54:52] [b98453cac15ba1066b407e146608df68]
-    D      [Multiple Regression] [Ws7.1 multiple re...] [2009-11-20 15:14:28] [51108381f3361ca8af49c4f74052c840] [Current]
-    D        [Multiple Regression] [WS 7 MULTIPLE REG...] [2010-11-23 08:05:19] [814f53995537cd15c528d8efbf1cf544]
-    D          [Multiple Regression] [Multiple regression] [2011-11-21 18:18:36] [c505444e07acba7694d29053ca5d114e]
- RM            [Multiple Regression] [] [2011-11-21 20:34:28] [74be16979710d4c4e7c6647856088456]
- R             [Multiple Regression] [Workshop 7 - 2] [2011-11-22 22:17:33] [ec29c78521a0445a37e4526edb78f709]
- R             [Multiple Regression] [Workshop 7 - 2] [2011-11-22 22:22:55] [ec29c78521a0445a37e4526edb78f709]
-    D          [Multiple Regression] [workshop 7 task 1] [2012-11-18 14:55:44] [053e1951668d36d4a346500be2403869]
- R             [Multiple Regression] [] [2013-11-20 14:59:32] [74be16979710d4c4e7c6647856088456]
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Dataseries X:
151,7	105,2
121,3	105,2
133,0	105,6
119,6	105,6
122,2	106,2
117,4	106,3
106,7	106,4
87,5	106,9
81,0	107,2
110,3	107,3
87,0	107,3
55,7	107,4
146,0	107,55
137,5	107,87
138,5	108,37
135,6	108,38
107,3	107,92
99,0	108,03
91,4	108,14
68,4	108,3
82,6	108,64
98,4	108,66
71,3	109,04
47,6	109,03
130,8	109,03
113,6	109,54
125,7	109,75
113,6	109,83
97,1	109,65
104,4	109,82
91,8	109,95
75,1	110,12
89,2	110,15
110,2	110,2
78,4	109,99
68,4	110,14
122,8	110,14
129,7	110,81
159,1	110,97
139,0	110,99
102,2	109,73
113,6	109,81
81,5	110,02
77,4	110,18
87,6	110,21
101,2	110,25
87,2	110,36
64,9	110,51
133,1	110,64
118,0	110,95
135,9	111,18
125,7	111,19
108,0	111,69
128,3	111,7
84,7	111,83
86,4	111,77
92,2	111,73
95,8	112,01
92,3	111,86
54,3	112,04




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

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 3 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58267&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58267&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58267&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 time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Multiple Linear Regression - Estimated Regression Equation
Yt[t] = + 365.825961771422 -2.39632617023324Xt[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Yt[t] =  +  365.825961771422 -2.39632617023324Xt[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58267&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Yt[t] =  +  365.825961771422 -2.39632617023324Xt[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58267&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58267&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
Yt[t] = + 365.825961771422 -2.39632617023324Xt[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)365.825961771422194.0271181.88540.0643830.032191
Xt-2.396326170233241.775122-1.350.1822770.091139

\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) & 365.825961771422 & 194.027118 & 1.8854 & 0.064383 & 0.032191 \tabularnewline
Xt & -2.39632617023324 & 1.775122 & -1.35 & 0.182277 & 0.091139 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58267&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]365.825961771422[/C][C]194.027118[/C][C]1.8854[/C][C]0.064383[/C][C]0.032191[/C][/ROW]
[ROW][C]Xt[/C][C]-2.39632617023324[/C][C]1.775122[/C][C]-1.35[/C][C]0.182277[/C][C]0.091139[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58267&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58267&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)365.825961771422194.0271181.88540.0643830.032191
Xt-2.396326170233241.775122-1.350.1822770.091139







Multiple Linear Regression - Regression Statistics
Multiple R0.174536352936846
R-squared0.0304629384964952
Adjusted R-squared0.0137467822636762
F-TEST (value)1.82236502651769
F-TEST (DF numerator)1
F-TEST (DF denominator)58
p-value0.182277044430929
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation25.4697443187088
Sum Squared Residuals37625.0567883032

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.174536352936846 \tabularnewline
R-squared & 0.0304629384964952 \tabularnewline
Adjusted R-squared & 0.0137467822636762 \tabularnewline
F-TEST (value) & 1.82236502651769 \tabularnewline
F-TEST (DF numerator) & 1 \tabularnewline
F-TEST (DF denominator) & 58 \tabularnewline
p-value & 0.182277044430929 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 25.4697443187088 \tabularnewline
Sum Squared Residuals & 37625.0567883032 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58267&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.174536352936846[/C][/ROW]
[ROW][C]R-squared[/C][C]0.0304629384964952[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.0137467822636762[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]1.82236502651769[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]1[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]58[/C][/ROW]
[ROW][C]p-value[/C][C]0.182277044430929[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]25.4697443187088[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]37625.0567883032[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58267&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58267&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 R0.174536352936846
R-squared0.0304629384964952
Adjusted R-squared0.0137467822636762
F-TEST (value)1.82236502651769
F-TEST (DF numerator)1
F-TEST (DF denominator)58
p-value0.182277044430929
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation25.4697443187088
Sum Squared Residuals37625.0567883032







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1151.7113.73244866288537.9675513371147
2121.3113.7324486628857.56755133711488
3133112.77391819479220.2260818052082
4119.6112.7739181947926.82608180520817
5122.2111.33612249265210.8638775073481
6117.4111.0964898756296.30351012437146
7106.7110.856857258605-4.1568572586052
887.5109.658694173489-22.1586941734886
981108.939796322419-27.9397963224186
10110.3108.7001637053951.59983629460468
1187108.700163705395-21.7001637053953
1255.7108.460531088372-52.760531088372
13146108.10108216283737.898917837163
14137.5107.33425778836230.1657422116376
15138.5106.13609470324632.3639052967543
16135.6106.11213144154329.4878685584566
17107.3107.2144414798510.085558520149303
1899106.950845601125-7.95084560112504
1991.4106.687249722399-15.2872497223994
2068.4106.303837535162-37.9038375351621
2182.6105.489086637283-22.8890866372828
2298.4105.441160113878-7.0411601138781
2371.3104.530556169189-33.2305561691895
2447.6104.554519430892-56.9545194308918
25130.8104.55451943089226.2454805691082
26113.6103.33239308407310.2676069159272
27125.7102.82916458832422.8708354116761
28113.6102.63745849470510.9625415052948
2997.1103.068797205347-5.96879720534719
30104.4102.6614217564081.73857824359244
3191.8102.349899354277-10.5498993542772
3275.1101.942523905338-26.8425239053376
3389.2101.870634120231-12.6706341202306
34110.2101.7508178117198.4491821882811
3578.4102.254046307468-23.8540463074679
3668.4101.894597381933-33.4945973819329
37122.8101.89459738193320.9054026180671
38129.7100.28905884787729.4109411521234
39159.199.905646660639359.1943533393607
4013999.857720137234739.1422798627653
41102.2102.877091111729-0.677091111728526
42113.6102.68538501811010.9146149818901
4381.5102.182156522361-20.6821565223609
4477.4101.798744335124-24.3987443351236
4587.6101.726854550017-14.1268545500166
46101.2101.631001503207-0.431001503207252
4787.2101.367405624482-14.1674056244816
4864.9101.007956698947-36.1079566989466
49133.1100.69643429681632.4035657031837
5011899.95357318404418.0464268159560
51135.999.402418164890336.4975818351097
52125.799.37845490318826.321545096812
5310898.18029181807149.8197081819286
54128.398.15632855636930.1436714436310
5584.797.8448061542387-13.1448061542387
5686.497.9885857244527-11.5885857244527
5792.298.084438771262-5.88443877126205
5895.897.4134674435967-1.61346744359675
5992.397.7729163691317-5.47291636913175
6054.397.3415776584897-43.0415776584898

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 151.7 & 113.732448662885 & 37.9675513371147 \tabularnewline
2 & 121.3 & 113.732448662885 & 7.56755133711488 \tabularnewline
3 & 133 & 112.773918194792 & 20.2260818052082 \tabularnewline
4 & 119.6 & 112.773918194792 & 6.82608180520817 \tabularnewline
5 & 122.2 & 111.336122492652 & 10.8638775073481 \tabularnewline
6 & 117.4 & 111.096489875629 & 6.30351012437146 \tabularnewline
7 & 106.7 & 110.856857258605 & -4.1568572586052 \tabularnewline
8 & 87.5 & 109.658694173489 & -22.1586941734886 \tabularnewline
9 & 81 & 108.939796322419 & -27.9397963224186 \tabularnewline
10 & 110.3 & 108.700163705395 & 1.59983629460468 \tabularnewline
11 & 87 & 108.700163705395 & -21.7001637053953 \tabularnewline
12 & 55.7 & 108.460531088372 & -52.760531088372 \tabularnewline
13 & 146 & 108.101082162837 & 37.898917837163 \tabularnewline
14 & 137.5 & 107.334257788362 & 30.1657422116376 \tabularnewline
15 & 138.5 & 106.136094703246 & 32.3639052967543 \tabularnewline
16 & 135.6 & 106.112131441543 & 29.4878685584566 \tabularnewline
17 & 107.3 & 107.214441479851 & 0.085558520149303 \tabularnewline
18 & 99 & 106.950845601125 & -7.95084560112504 \tabularnewline
19 & 91.4 & 106.687249722399 & -15.2872497223994 \tabularnewline
20 & 68.4 & 106.303837535162 & -37.9038375351621 \tabularnewline
21 & 82.6 & 105.489086637283 & -22.8890866372828 \tabularnewline
22 & 98.4 & 105.441160113878 & -7.0411601138781 \tabularnewline
23 & 71.3 & 104.530556169189 & -33.2305561691895 \tabularnewline
24 & 47.6 & 104.554519430892 & -56.9545194308918 \tabularnewline
25 & 130.8 & 104.554519430892 & 26.2454805691082 \tabularnewline
26 & 113.6 & 103.332393084073 & 10.2676069159272 \tabularnewline
27 & 125.7 & 102.829164588324 & 22.8708354116761 \tabularnewline
28 & 113.6 & 102.637458494705 & 10.9625415052948 \tabularnewline
29 & 97.1 & 103.068797205347 & -5.96879720534719 \tabularnewline
30 & 104.4 & 102.661421756408 & 1.73857824359244 \tabularnewline
31 & 91.8 & 102.349899354277 & -10.5498993542772 \tabularnewline
32 & 75.1 & 101.942523905338 & -26.8425239053376 \tabularnewline
33 & 89.2 & 101.870634120231 & -12.6706341202306 \tabularnewline
34 & 110.2 & 101.750817811719 & 8.4491821882811 \tabularnewline
35 & 78.4 & 102.254046307468 & -23.8540463074679 \tabularnewline
36 & 68.4 & 101.894597381933 & -33.4945973819329 \tabularnewline
37 & 122.8 & 101.894597381933 & 20.9054026180671 \tabularnewline
38 & 129.7 & 100.289058847877 & 29.4109411521234 \tabularnewline
39 & 159.1 & 99.9056466606393 & 59.1943533393607 \tabularnewline
40 & 139 & 99.8577201372347 & 39.1422798627653 \tabularnewline
41 & 102.2 & 102.877091111729 & -0.677091111728526 \tabularnewline
42 & 113.6 & 102.685385018110 & 10.9146149818901 \tabularnewline
43 & 81.5 & 102.182156522361 & -20.6821565223609 \tabularnewline
44 & 77.4 & 101.798744335124 & -24.3987443351236 \tabularnewline
45 & 87.6 & 101.726854550017 & -14.1268545500166 \tabularnewline
46 & 101.2 & 101.631001503207 & -0.431001503207252 \tabularnewline
47 & 87.2 & 101.367405624482 & -14.1674056244816 \tabularnewline
48 & 64.9 & 101.007956698947 & -36.1079566989466 \tabularnewline
49 & 133.1 & 100.696434296816 & 32.4035657031837 \tabularnewline
50 & 118 & 99.953573184044 & 18.0464268159560 \tabularnewline
51 & 135.9 & 99.4024181648903 & 36.4975818351097 \tabularnewline
52 & 125.7 & 99.378454903188 & 26.321545096812 \tabularnewline
53 & 108 & 98.1802918180714 & 9.8197081819286 \tabularnewline
54 & 128.3 & 98.156328556369 & 30.1436714436310 \tabularnewline
55 & 84.7 & 97.8448061542387 & -13.1448061542387 \tabularnewline
56 & 86.4 & 97.9885857244527 & -11.5885857244527 \tabularnewline
57 & 92.2 & 98.084438771262 & -5.88443877126205 \tabularnewline
58 & 95.8 & 97.4134674435967 & -1.61346744359675 \tabularnewline
59 & 92.3 & 97.7729163691317 & -5.47291636913175 \tabularnewline
60 & 54.3 & 97.3415776584897 & -43.0415776584898 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58267&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]151.7[/C][C]113.732448662885[/C][C]37.9675513371147[/C][/ROW]
[ROW][C]2[/C][C]121.3[/C][C]113.732448662885[/C][C]7.56755133711488[/C][/ROW]
[ROW][C]3[/C][C]133[/C][C]112.773918194792[/C][C]20.2260818052082[/C][/ROW]
[ROW][C]4[/C][C]119.6[/C][C]112.773918194792[/C][C]6.82608180520817[/C][/ROW]
[ROW][C]5[/C][C]122.2[/C][C]111.336122492652[/C][C]10.8638775073481[/C][/ROW]
[ROW][C]6[/C][C]117.4[/C][C]111.096489875629[/C][C]6.30351012437146[/C][/ROW]
[ROW][C]7[/C][C]106.7[/C][C]110.856857258605[/C][C]-4.1568572586052[/C][/ROW]
[ROW][C]8[/C][C]87.5[/C][C]109.658694173489[/C][C]-22.1586941734886[/C][/ROW]
[ROW][C]9[/C][C]81[/C][C]108.939796322419[/C][C]-27.9397963224186[/C][/ROW]
[ROW][C]10[/C][C]110.3[/C][C]108.700163705395[/C][C]1.59983629460468[/C][/ROW]
[ROW][C]11[/C][C]87[/C][C]108.700163705395[/C][C]-21.7001637053953[/C][/ROW]
[ROW][C]12[/C][C]55.7[/C][C]108.460531088372[/C][C]-52.760531088372[/C][/ROW]
[ROW][C]13[/C][C]146[/C][C]108.101082162837[/C][C]37.898917837163[/C][/ROW]
[ROW][C]14[/C][C]137.5[/C][C]107.334257788362[/C][C]30.1657422116376[/C][/ROW]
[ROW][C]15[/C][C]138.5[/C][C]106.136094703246[/C][C]32.3639052967543[/C][/ROW]
[ROW][C]16[/C][C]135.6[/C][C]106.112131441543[/C][C]29.4878685584566[/C][/ROW]
[ROW][C]17[/C][C]107.3[/C][C]107.214441479851[/C][C]0.085558520149303[/C][/ROW]
[ROW][C]18[/C][C]99[/C][C]106.950845601125[/C][C]-7.95084560112504[/C][/ROW]
[ROW][C]19[/C][C]91.4[/C][C]106.687249722399[/C][C]-15.2872497223994[/C][/ROW]
[ROW][C]20[/C][C]68.4[/C][C]106.303837535162[/C][C]-37.9038375351621[/C][/ROW]
[ROW][C]21[/C][C]82.6[/C][C]105.489086637283[/C][C]-22.8890866372828[/C][/ROW]
[ROW][C]22[/C][C]98.4[/C][C]105.441160113878[/C][C]-7.0411601138781[/C][/ROW]
[ROW][C]23[/C][C]71.3[/C][C]104.530556169189[/C][C]-33.2305561691895[/C][/ROW]
[ROW][C]24[/C][C]47.6[/C][C]104.554519430892[/C][C]-56.9545194308918[/C][/ROW]
[ROW][C]25[/C][C]130.8[/C][C]104.554519430892[/C][C]26.2454805691082[/C][/ROW]
[ROW][C]26[/C][C]113.6[/C][C]103.332393084073[/C][C]10.2676069159272[/C][/ROW]
[ROW][C]27[/C][C]125.7[/C][C]102.829164588324[/C][C]22.8708354116761[/C][/ROW]
[ROW][C]28[/C][C]113.6[/C][C]102.637458494705[/C][C]10.9625415052948[/C][/ROW]
[ROW][C]29[/C][C]97.1[/C][C]103.068797205347[/C][C]-5.96879720534719[/C][/ROW]
[ROW][C]30[/C][C]104.4[/C][C]102.661421756408[/C][C]1.73857824359244[/C][/ROW]
[ROW][C]31[/C][C]91.8[/C][C]102.349899354277[/C][C]-10.5498993542772[/C][/ROW]
[ROW][C]32[/C][C]75.1[/C][C]101.942523905338[/C][C]-26.8425239053376[/C][/ROW]
[ROW][C]33[/C][C]89.2[/C][C]101.870634120231[/C][C]-12.6706341202306[/C][/ROW]
[ROW][C]34[/C][C]110.2[/C][C]101.750817811719[/C][C]8.4491821882811[/C][/ROW]
[ROW][C]35[/C][C]78.4[/C][C]102.254046307468[/C][C]-23.8540463074679[/C][/ROW]
[ROW][C]36[/C][C]68.4[/C][C]101.894597381933[/C][C]-33.4945973819329[/C][/ROW]
[ROW][C]37[/C][C]122.8[/C][C]101.894597381933[/C][C]20.9054026180671[/C][/ROW]
[ROW][C]38[/C][C]129.7[/C][C]100.289058847877[/C][C]29.4109411521234[/C][/ROW]
[ROW][C]39[/C][C]159.1[/C][C]99.9056466606393[/C][C]59.1943533393607[/C][/ROW]
[ROW][C]40[/C][C]139[/C][C]99.8577201372347[/C][C]39.1422798627653[/C][/ROW]
[ROW][C]41[/C][C]102.2[/C][C]102.877091111729[/C][C]-0.677091111728526[/C][/ROW]
[ROW][C]42[/C][C]113.6[/C][C]102.685385018110[/C][C]10.9146149818901[/C][/ROW]
[ROW][C]43[/C][C]81.5[/C][C]102.182156522361[/C][C]-20.6821565223609[/C][/ROW]
[ROW][C]44[/C][C]77.4[/C][C]101.798744335124[/C][C]-24.3987443351236[/C][/ROW]
[ROW][C]45[/C][C]87.6[/C][C]101.726854550017[/C][C]-14.1268545500166[/C][/ROW]
[ROW][C]46[/C][C]101.2[/C][C]101.631001503207[/C][C]-0.431001503207252[/C][/ROW]
[ROW][C]47[/C][C]87.2[/C][C]101.367405624482[/C][C]-14.1674056244816[/C][/ROW]
[ROW][C]48[/C][C]64.9[/C][C]101.007956698947[/C][C]-36.1079566989466[/C][/ROW]
[ROW][C]49[/C][C]133.1[/C][C]100.696434296816[/C][C]32.4035657031837[/C][/ROW]
[ROW][C]50[/C][C]118[/C][C]99.953573184044[/C][C]18.0464268159560[/C][/ROW]
[ROW][C]51[/C][C]135.9[/C][C]99.4024181648903[/C][C]36.4975818351097[/C][/ROW]
[ROW][C]52[/C][C]125.7[/C][C]99.378454903188[/C][C]26.321545096812[/C][/ROW]
[ROW][C]53[/C][C]108[/C][C]98.1802918180714[/C][C]9.8197081819286[/C][/ROW]
[ROW][C]54[/C][C]128.3[/C][C]98.156328556369[/C][C]30.1436714436310[/C][/ROW]
[ROW][C]55[/C][C]84.7[/C][C]97.8448061542387[/C][C]-13.1448061542387[/C][/ROW]
[ROW][C]56[/C][C]86.4[/C][C]97.9885857244527[/C][C]-11.5885857244527[/C][/ROW]
[ROW][C]57[/C][C]92.2[/C][C]98.084438771262[/C][C]-5.88443877126205[/C][/ROW]
[ROW][C]58[/C][C]95.8[/C][C]97.4134674435967[/C][C]-1.61346744359675[/C][/ROW]
[ROW][C]59[/C][C]92.3[/C][C]97.7729163691317[/C][C]-5.47291636913175[/C][/ROW]
[ROW][C]60[/C][C]54.3[/C][C]97.3415776584897[/C][C]-43.0415776584898[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58267&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58267&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
1151.7113.73244866288537.9675513371147
2121.3113.7324486628857.56755133711488
3133112.77391819479220.2260818052082
4119.6112.7739181947926.82608180520817
5122.2111.33612249265210.8638775073481
6117.4111.0964898756296.30351012437146
7106.7110.856857258605-4.1568572586052
887.5109.658694173489-22.1586941734886
981108.939796322419-27.9397963224186
10110.3108.7001637053951.59983629460468
1187108.700163705395-21.7001637053953
1255.7108.460531088372-52.760531088372
13146108.10108216283737.898917837163
14137.5107.33425778836230.1657422116376
15138.5106.13609470324632.3639052967543
16135.6106.11213144154329.4878685584566
17107.3107.2144414798510.085558520149303
1899106.950845601125-7.95084560112504
1991.4106.687249722399-15.2872497223994
2068.4106.303837535162-37.9038375351621
2182.6105.489086637283-22.8890866372828
2298.4105.441160113878-7.0411601138781
2371.3104.530556169189-33.2305561691895
2447.6104.554519430892-56.9545194308918
25130.8104.55451943089226.2454805691082
26113.6103.33239308407310.2676069159272
27125.7102.82916458832422.8708354116761
28113.6102.63745849470510.9625415052948
2997.1103.068797205347-5.96879720534719
30104.4102.6614217564081.73857824359244
3191.8102.349899354277-10.5498993542772
3275.1101.942523905338-26.8425239053376
3389.2101.870634120231-12.6706341202306
34110.2101.7508178117198.4491821882811
3578.4102.254046307468-23.8540463074679
3668.4101.894597381933-33.4945973819329
37122.8101.89459738193320.9054026180671
38129.7100.28905884787729.4109411521234
39159.199.905646660639359.1943533393607
4013999.857720137234739.1422798627653
41102.2102.877091111729-0.677091111728526
42113.6102.68538501811010.9146149818901
4381.5102.182156522361-20.6821565223609
4477.4101.798744335124-24.3987443351236
4587.6101.726854550017-14.1268545500166
46101.2101.631001503207-0.431001503207252
4787.2101.367405624482-14.1674056244816
4864.9101.007956698947-36.1079566989466
49133.1100.69643429681632.4035657031837
5011899.95357318404418.0464268159560
51135.999.402418164890336.4975818351097
52125.799.37845490318826.321545096812
5310898.18029181807149.8197081819286
54128.398.15632855636930.1436714436310
5584.797.8448061542387-13.1448061542387
5686.497.9885857244527-11.5885857244527
5792.298.084438771262-5.88443877126205
5895.897.4134674435967-1.61346744359675
5992.397.7729163691317-5.47291636913175
6054.397.3415776584897-43.0415776584898







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
50.1711005885754010.3422011771508020.8288994114246
60.07347696374087450.1469539274817490.926523036259125
70.03489359940226380.06978719880452770.965106400597736
80.02070432549363540.04140865098727080.979295674506365
90.008932837420565540.01786567484113110.991067162579435
100.01782566473004550.03565132946009100.982174335269955
110.008141554886893970.01628310977378790.991858445113106
120.0284646369456160.0569292738912320.971535363054384
130.3868089800711870.7736179601423730.613191019928813
140.5782226354849760.8435547290300480.421777364515024
150.6953828269390920.6092343461218170.304617173060908
160.7327887658732040.5344224682535920.267211234126796
170.6688574616974940.6622850766050110.331142538302506
180.6037622858964970.7924754282070050.396237714103503
190.5468857693789230.9062284612421530.453114230621077
200.593274398282070.8134512034358610.406725601717931
210.5386595956032530.9226808087934940.461340404396747
220.4587867331576140.9175734663152280.541213266842386
230.444344895422460.888689790844920.55565510457754
240.6285960834188150.742807833162370.371403916581185
250.7056382633946620.5887234732106750.294361736605338
260.6818845119352820.6362309761294360.318115488064718
270.7100558938333420.5798882123333170.289944106166658
280.6720363409159840.6559273181680320.327963659084016
290.6001940820043760.7996118359912480.399805917995624
300.5306717886023660.9386564227952680.469328211397634
310.4579958788589210.9159917577178420.542004121141079
320.4383877271908300.8767754543816610.56161227280917
330.3736454226749040.7472908453498070.626354577325096
340.3218759193674590.6437518387349180.678124080632541
350.2969914499898870.5939828999797750.703008550010113
360.3320408472163160.6640816944326320.667959152783684
370.3216470367673940.6432940735347880.678352963232606
380.3560664649083640.7121329298167290.643933535091636
390.6883765094194760.6232469811610480.311623490580524
400.7823216334743130.4353567330513750.217678366525687
410.7157543553838720.5684912892322560.284245644616128
420.6654565236280250.669086952743950.334543476371975
430.6224541161642370.7550917676715250.377545883835763
440.6128266862311730.7743466275376530.387173313768827
450.5656193199343840.8687613601312330.434380680065616
460.4804808041182310.9609616082364630.519519195881769
470.4754080622745290.9508161245490580.524591937725471
480.9404283829752150.1191432340495690.0595716170247847
490.9259515929882130.1480968140235740.0740484070117868
500.9408423980524340.1183152038951330.0591576019475664
510.901813145008770.1963737099824580.098186854991229
520.8850042279954720.2299915440090570.114995772004528
530.7960741747909230.4078516504181540.203925825209077
540.8627964894937820.2744070210124360.137203510506218
550.7332582602947720.5334834794104560.266741739705228

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
5 & 0.171100588575401 & 0.342201177150802 & 0.8288994114246 \tabularnewline
6 & 0.0734769637408745 & 0.146953927481749 & 0.926523036259125 \tabularnewline
7 & 0.0348935994022638 & 0.0697871988045277 & 0.965106400597736 \tabularnewline
8 & 0.0207043254936354 & 0.0414086509872708 & 0.979295674506365 \tabularnewline
9 & 0.00893283742056554 & 0.0178656748411311 & 0.991067162579435 \tabularnewline
10 & 0.0178256647300455 & 0.0356513294600910 & 0.982174335269955 \tabularnewline
11 & 0.00814155488689397 & 0.0162831097737879 & 0.991858445113106 \tabularnewline
12 & 0.028464636945616 & 0.056929273891232 & 0.971535363054384 \tabularnewline
13 & 0.386808980071187 & 0.773617960142373 & 0.613191019928813 \tabularnewline
14 & 0.578222635484976 & 0.843554729030048 & 0.421777364515024 \tabularnewline
15 & 0.695382826939092 & 0.609234346121817 & 0.304617173060908 \tabularnewline
16 & 0.732788765873204 & 0.534422468253592 & 0.267211234126796 \tabularnewline
17 & 0.668857461697494 & 0.662285076605011 & 0.331142538302506 \tabularnewline
18 & 0.603762285896497 & 0.792475428207005 & 0.396237714103503 \tabularnewline
19 & 0.546885769378923 & 0.906228461242153 & 0.453114230621077 \tabularnewline
20 & 0.59327439828207 & 0.813451203435861 & 0.406725601717931 \tabularnewline
21 & 0.538659595603253 & 0.922680808793494 & 0.461340404396747 \tabularnewline
22 & 0.458786733157614 & 0.917573466315228 & 0.541213266842386 \tabularnewline
23 & 0.44434489542246 & 0.88868979084492 & 0.55565510457754 \tabularnewline
24 & 0.628596083418815 & 0.74280783316237 & 0.371403916581185 \tabularnewline
25 & 0.705638263394662 & 0.588723473210675 & 0.294361736605338 \tabularnewline
26 & 0.681884511935282 & 0.636230976129436 & 0.318115488064718 \tabularnewline
27 & 0.710055893833342 & 0.579888212333317 & 0.289944106166658 \tabularnewline
28 & 0.672036340915984 & 0.655927318168032 & 0.327963659084016 \tabularnewline
29 & 0.600194082004376 & 0.799611835991248 & 0.399805917995624 \tabularnewline
30 & 0.530671788602366 & 0.938656422795268 & 0.469328211397634 \tabularnewline
31 & 0.457995878858921 & 0.915991757717842 & 0.542004121141079 \tabularnewline
32 & 0.438387727190830 & 0.876775454381661 & 0.56161227280917 \tabularnewline
33 & 0.373645422674904 & 0.747290845349807 & 0.626354577325096 \tabularnewline
34 & 0.321875919367459 & 0.643751838734918 & 0.678124080632541 \tabularnewline
35 & 0.296991449989887 & 0.593982899979775 & 0.703008550010113 \tabularnewline
36 & 0.332040847216316 & 0.664081694432632 & 0.667959152783684 \tabularnewline
37 & 0.321647036767394 & 0.643294073534788 & 0.678352963232606 \tabularnewline
38 & 0.356066464908364 & 0.712132929816729 & 0.643933535091636 \tabularnewline
39 & 0.688376509419476 & 0.623246981161048 & 0.311623490580524 \tabularnewline
40 & 0.782321633474313 & 0.435356733051375 & 0.217678366525687 \tabularnewline
41 & 0.715754355383872 & 0.568491289232256 & 0.284245644616128 \tabularnewline
42 & 0.665456523628025 & 0.66908695274395 & 0.334543476371975 \tabularnewline
43 & 0.622454116164237 & 0.755091767671525 & 0.377545883835763 \tabularnewline
44 & 0.612826686231173 & 0.774346627537653 & 0.387173313768827 \tabularnewline
45 & 0.565619319934384 & 0.868761360131233 & 0.434380680065616 \tabularnewline
46 & 0.480480804118231 & 0.960961608236463 & 0.519519195881769 \tabularnewline
47 & 0.475408062274529 & 0.950816124549058 & 0.524591937725471 \tabularnewline
48 & 0.940428382975215 & 0.119143234049569 & 0.0595716170247847 \tabularnewline
49 & 0.925951592988213 & 0.148096814023574 & 0.0740484070117868 \tabularnewline
50 & 0.940842398052434 & 0.118315203895133 & 0.0591576019475664 \tabularnewline
51 & 0.90181314500877 & 0.196373709982458 & 0.098186854991229 \tabularnewline
52 & 0.885004227995472 & 0.229991544009057 & 0.114995772004528 \tabularnewline
53 & 0.796074174790923 & 0.407851650418154 & 0.203925825209077 \tabularnewline
54 & 0.862796489493782 & 0.274407021012436 & 0.137203510506218 \tabularnewline
55 & 0.733258260294772 & 0.533483479410456 & 0.266741739705228 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58267&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]5[/C][C]0.171100588575401[/C][C]0.342201177150802[/C][C]0.8288994114246[/C][/ROW]
[ROW][C]6[/C][C]0.0734769637408745[/C][C]0.146953927481749[/C][C]0.926523036259125[/C][/ROW]
[ROW][C]7[/C][C]0.0348935994022638[/C][C]0.0697871988045277[/C][C]0.965106400597736[/C][/ROW]
[ROW][C]8[/C][C]0.0207043254936354[/C][C]0.0414086509872708[/C][C]0.979295674506365[/C][/ROW]
[ROW][C]9[/C][C]0.00893283742056554[/C][C]0.0178656748411311[/C][C]0.991067162579435[/C][/ROW]
[ROW][C]10[/C][C]0.0178256647300455[/C][C]0.0356513294600910[/C][C]0.982174335269955[/C][/ROW]
[ROW][C]11[/C][C]0.00814155488689397[/C][C]0.0162831097737879[/C][C]0.991858445113106[/C][/ROW]
[ROW][C]12[/C][C]0.028464636945616[/C][C]0.056929273891232[/C][C]0.971535363054384[/C][/ROW]
[ROW][C]13[/C][C]0.386808980071187[/C][C]0.773617960142373[/C][C]0.613191019928813[/C][/ROW]
[ROW][C]14[/C][C]0.578222635484976[/C][C]0.843554729030048[/C][C]0.421777364515024[/C][/ROW]
[ROW][C]15[/C][C]0.695382826939092[/C][C]0.609234346121817[/C][C]0.304617173060908[/C][/ROW]
[ROW][C]16[/C][C]0.732788765873204[/C][C]0.534422468253592[/C][C]0.267211234126796[/C][/ROW]
[ROW][C]17[/C][C]0.668857461697494[/C][C]0.662285076605011[/C][C]0.331142538302506[/C][/ROW]
[ROW][C]18[/C][C]0.603762285896497[/C][C]0.792475428207005[/C][C]0.396237714103503[/C][/ROW]
[ROW][C]19[/C][C]0.546885769378923[/C][C]0.906228461242153[/C][C]0.453114230621077[/C][/ROW]
[ROW][C]20[/C][C]0.59327439828207[/C][C]0.813451203435861[/C][C]0.406725601717931[/C][/ROW]
[ROW][C]21[/C][C]0.538659595603253[/C][C]0.922680808793494[/C][C]0.461340404396747[/C][/ROW]
[ROW][C]22[/C][C]0.458786733157614[/C][C]0.917573466315228[/C][C]0.541213266842386[/C][/ROW]
[ROW][C]23[/C][C]0.44434489542246[/C][C]0.88868979084492[/C][C]0.55565510457754[/C][/ROW]
[ROW][C]24[/C][C]0.628596083418815[/C][C]0.74280783316237[/C][C]0.371403916581185[/C][/ROW]
[ROW][C]25[/C][C]0.705638263394662[/C][C]0.588723473210675[/C][C]0.294361736605338[/C][/ROW]
[ROW][C]26[/C][C]0.681884511935282[/C][C]0.636230976129436[/C][C]0.318115488064718[/C][/ROW]
[ROW][C]27[/C][C]0.710055893833342[/C][C]0.579888212333317[/C][C]0.289944106166658[/C][/ROW]
[ROW][C]28[/C][C]0.672036340915984[/C][C]0.655927318168032[/C][C]0.327963659084016[/C][/ROW]
[ROW][C]29[/C][C]0.600194082004376[/C][C]0.799611835991248[/C][C]0.399805917995624[/C][/ROW]
[ROW][C]30[/C][C]0.530671788602366[/C][C]0.938656422795268[/C][C]0.469328211397634[/C][/ROW]
[ROW][C]31[/C][C]0.457995878858921[/C][C]0.915991757717842[/C][C]0.542004121141079[/C][/ROW]
[ROW][C]32[/C][C]0.438387727190830[/C][C]0.876775454381661[/C][C]0.56161227280917[/C][/ROW]
[ROW][C]33[/C][C]0.373645422674904[/C][C]0.747290845349807[/C][C]0.626354577325096[/C][/ROW]
[ROW][C]34[/C][C]0.321875919367459[/C][C]0.643751838734918[/C][C]0.678124080632541[/C][/ROW]
[ROW][C]35[/C][C]0.296991449989887[/C][C]0.593982899979775[/C][C]0.703008550010113[/C][/ROW]
[ROW][C]36[/C][C]0.332040847216316[/C][C]0.664081694432632[/C][C]0.667959152783684[/C][/ROW]
[ROW][C]37[/C][C]0.321647036767394[/C][C]0.643294073534788[/C][C]0.678352963232606[/C][/ROW]
[ROW][C]38[/C][C]0.356066464908364[/C][C]0.712132929816729[/C][C]0.643933535091636[/C][/ROW]
[ROW][C]39[/C][C]0.688376509419476[/C][C]0.623246981161048[/C][C]0.311623490580524[/C][/ROW]
[ROW][C]40[/C][C]0.782321633474313[/C][C]0.435356733051375[/C][C]0.217678366525687[/C][/ROW]
[ROW][C]41[/C][C]0.715754355383872[/C][C]0.568491289232256[/C][C]0.284245644616128[/C][/ROW]
[ROW][C]42[/C][C]0.665456523628025[/C][C]0.66908695274395[/C][C]0.334543476371975[/C][/ROW]
[ROW][C]43[/C][C]0.622454116164237[/C][C]0.755091767671525[/C][C]0.377545883835763[/C][/ROW]
[ROW][C]44[/C][C]0.612826686231173[/C][C]0.774346627537653[/C][C]0.387173313768827[/C][/ROW]
[ROW][C]45[/C][C]0.565619319934384[/C][C]0.868761360131233[/C][C]0.434380680065616[/C][/ROW]
[ROW][C]46[/C][C]0.480480804118231[/C][C]0.960961608236463[/C][C]0.519519195881769[/C][/ROW]
[ROW][C]47[/C][C]0.475408062274529[/C][C]0.950816124549058[/C][C]0.524591937725471[/C][/ROW]
[ROW][C]48[/C][C]0.940428382975215[/C][C]0.119143234049569[/C][C]0.0595716170247847[/C][/ROW]
[ROW][C]49[/C][C]0.925951592988213[/C][C]0.148096814023574[/C][C]0.0740484070117868[/C][/ROW]
[ROW][C]50[/C][C]0.940842398052434[/C][C]0.118315203895133[/C][C]0.0591576019475664[/C][/ROW]
[ROW][C]51[/C][C]0.90181314500877[/C][C]0.196373709982458[/C][C]0.098186854991229[/C][/ROW]
[ROW][C]52[/C][C]0.885004227995472[/C][C]0.229991544009057[/C][C]0.114995772004528[/C][/ROW]
[ROW][C]53[/C][C]0.796074174790923[/C][C]0.407851650418154[/C][C]0.203925825209077[/C][/ROW]
[ROW][C]54[/C][C]0.862796489493782[/C][C]0.274407021012436[/C][C]0.137203510506218[/C][/ROW]
[ROW][C]55[/C][C]0.733258260294772[/C][C]0.533483479410456[/C][C]0.266741739705228[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58267&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58267&T=5

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
50.1711005885754010.3422011771508020.8288994114246
60.07347696374087450.1469539274817490.926523036259125
70.03489359940226380.06978719880452770.965106400597736
80.02070432549363540.04140865098727080.979295674506365
90.008932837420565540.01786567484113110.991067162579435
100.01782566473004550.03565132946009100.982174335269955
110.008141554886893970.01628310977378790.991858445113106
120.0284646369456160.0569292738912320.971535363054384
130.3868089800711870.7736179601423730.613191019928813
140.5782226354849760.8435547290300480.421777364515024
150.6953828269390920.6092343461218170.304617173060908
160.7327887658732040.5344224682535920.267211234126796
170.6688574616974940.6622850766050110.331142538302506
180.6037622858964970.7924754282070050.396237714103503
190.5468857693789230.9062284612421530.453114230621077
200.593274398282070.8134512034358610.406725601717931
210.5386595956032530.9226808087934940.461340404396747
220.4587867331576140.9175734663152280.541213266842386
230.444344895422460.888689790844920.55565510457754
240.6285960834188150.742807833162370.371403916581185
250.7056382633946620.5887234732106750.294361736605338
260.6818845119352820.6362309761294360.318115488064718
270.7100558938333420.5798882123333170.289944106166658
280.6720363409159840.6559273181680320.327963659084016
290.6001940820043760.7996118359912480.399805917995624
300.5306717886023660.9386564227952680.469328211397634
310.4579958788589210.9159917577178420.542004121141079
320.4383877271908300.8767754543816610.56161227280917
330.3736454226749040.7472908453498070.626354577325096
340.3218759193674590.6437518387349180.678124080632541
350.2969914499898870.5939828999797750.703008550010113
360.3320408472163160.6640816944326320.667959152783684
370.3216470367673940.6432940735347880.678352963232606
380.3560664649083640.7121329298167290.643933535091636
390.6883765094194760.6232469811610480.311623490580524
400.7823216334743130.4353567330513750.217678366525687
410.7157543553838720.5684912892322560.284245644616128
420.6654565236280250.669086952743950.334543476371975
430.6224541161642370.7550917676715250.377545883835763
440.6128266862311730.7743466275376530.387173313768827
450.5656193199343840.8687613601312330.434380680065616
460.4804808041182310.9609616082364630.519519195881769
470.4754080622745290.9508161245490580.524591937725471
480.9404283829752150.1191432340495690.0595716170247847
490.9259515929882130.1480968140235740.0740484070117868
500.9408423980524340.1183152038951330.0591576019475664
510.901813145008770.1963737099824580.098186854991229
520.8850042279954720.2299915440090570.114995772004528
530.7960741747909230.4078516504181540.203925825209077
540.8627964894937820.2744070210124360.137203510506218
550.7332582602947720.5334834794104560.266741739705228







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level40.0784313725490196NOK
10% type I error level60.117647058823529NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 4 & 0.0784313725490196 & NOK \tabularnewline
10% type I error level & 6 & 0.117647058823529 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58267&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]4[/C][C]0.0784313725490196[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]6[/C][C]0.117647058823529[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58267&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58267&T=6

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level40.0784313725490196NOK
10% type I error level60.117647058823529NOK



Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}