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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationThu, 24 Nov 2011 05:14:29 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Nov/24/t13221296975b21kmpzh08p3jf.htm/, Retrieved Fri, 06 Jun 2025 09:18:15 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=146608, Retrieved Fri, 06 Jun 2025 09:18:15 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact355

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Explorative Data Analysis] [time effect in su...] [2010-11-17 08:55:33] [b98453cac15ba1066b407e146608df68]
-    D  [Univariate Explorative Data Analysis] [Run sequency plot...] [2010-11-20 14:46:56] [95e8426e0df851c9330605aa1e892ab5]
-         [Univariate Explorative Data Analysis] [] [2010-12-01 15:00:34] [42a441ca3193af442aa2201743dfb347]
- RMPD        [Multiple Regression] [tre] [2011-11-24 10:14:29] [d519577d845e738b812f706f10c86f64] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
1	8	1	14	4
2	8	3	82	1
3	8	2	14	3
4	8	1	16	5
5	8	5	140	7
6	8	8	173	2
7	8	3	9	8
8	8	8	13	6
1	12	12	17	4
2	12	3	16	9
3	12	8	21	7
4	12	3	14	2
5	12	3	15	12
6	12	3	10	8
7	12	3	14	1
8	12	1	16	6
9	12	2	14	10
10	12	20	17	3
11	12	2	10	5
12	12	1	23	11
1	9	1	21	2
2	9	6	14	4
3	9	8	14	7
4	9	5	14	11
5	9	1	16	5
6	9	7	14	1
7	9	7	14	9
8	9	5	7	3
9	9	8	17	10
1	14	2	14	3
2	14	5	21	4
3	14	2	24	7
4	14	5	7	6
5	14	1	30	13
6	14	2	93	16
7	14	6	14	9
8	14	3	14	1
9	14	6	107	10
10	14	6	231	5
11	14	1	385	2
12	14	2	14	11
13	14	10	29	14
14	14	1	16	15
1	13	2	7	10
2	13	1	21	3
3	13	1	14	2
4	13	1	17	13
5	13	6	14	4
6	13	4	21	1
7	13	9	15	9
8	13	10	10	5
9	13	6	15	8
10	13	1	7	7
11	13	6	12	12
12	13	18	84	6
13	13	3	17	11
1	19	4	14	4
2	19	1	10	9
3	19	3	17	15
4	19	5	91	14
5	19	4	21	17
6	19	4	21	3
7	19	1	16	7
8	19	17	35	1
9	19	2	17	16
10	19	1	15	13
11	19	6	14	5
12	19	10	28	18
13	19	9	14	6
14	19	5	14	10
15	19	1	20	12
16	19	13	35	20
17	19	11	28	8
18	19	9	17	11
19	19	4	14	19
1	13	4	10	4
2	13	5	10	1
3	13	2	14	3
4	13	1	7	9
5	13	2	14	11
6	13	4	14	12
7	13	12	10	2
8	13	14	10	7
9	13	2	21	6
10	13	7	10	5
11	13	4	17	8
12	13	1	17	10
13	13	6	24	13
1	14	2	16	2
2	14	1	63	9
3	14	4	17	4
4	14	6	21	1
5	14	7	7	14
6	14	9	49	7
7	14	1	7	10
8	14	3	14	6
9	14	6	210	11
10	14	8	35	5
11	14	8	14	3
12	14	4	28	13
13	14	8	56	12
14	14	7	31	15

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'George Udny Yule' @ yule.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 & 6 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146608&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]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146608&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146608&T=0

As an alternative you can also use a QR Code:  
QR Code


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'George Udny Yule' @ yule.wessa.net






Multiple Linear Regression - Estimated Regression Equation
Postition[t] = -0.603703743713651 + 0.162678684314451starters[t] + 0.310458030411594last[t] + 0.0103730047865599since[t] + 0.352921408894487number[t] + 0.0209951325099079t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Postition[t] =  -0.603703743713651 +  0.162678684314451starters[t] +  0.310458030411594last[t] +  0.0103730047865599since[t] +  0.352921408894487number[t] +  0.0209951325099079t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146608&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Postition[t] =  -0.603703743713651 +  0.162678684314451starters[t] +  0.310458030411594last[t] +  0.0103730047865599since[t] +  0.352921408894487number[t] +  0.0209951325099079t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146608&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146608&T=1

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Estimated Regression Equation
Postition[t] = -0.603703743713651 + 0.162678684314451starters[t] + 0.310458030411594last[t] + 0.0103730047865599since[t] + 0.352921408894487number[t] + 0.0209951325099079t + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-0.6037037437136511.61545-0.37370.7094470.354723
starters0.1626786843144510.140571.15730.250030.125015
last0.3104580304115940.0913083.40010.0009830.000491
since0.01037300478655990.0068511.51410.1332910.066646
number0.3529214088944870.0832414.23985.1e-052.6e-05
t0.02099513250990790.0145211.44590.1514690.075735

\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) & -0.603703743713651 & 1.61545 & -0.3737 & 0.709447 & 0.354723 \tabularnewline
starters & 0.162678684314451 & 0.14057 & 1.1573 & 0.25003 & 0.125015 \tabularnewline
last & 0.310458030411594 & 0.091308 & 3.4001 & 0.000983 & 0.000491 \tabularnewline
since & 0.0103730047865599 & 0.006851 & 1.5141 & 0.133291 & 0.066646 \tabularnewline
number & 0.352921408894487 & 0.083241 & 4.2398 & 5.1e-05 & 2.6e-05 \tabularnewline
t & 0.0209951325099079 & 0.014521 & 1.4459 & 0.151469 & 0.075735 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146608&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]-0.603703743713651[/C][C]1.61545[/C][C]-0.3737[/C][C]0.709447[/C][C]0.354723[/C][/ROW]
[ROW][C]starters[/C][C]0.162678684314451[/C][C]0.14057[/C][C]1.1573[/C][C]0.25003[/C][C]0.125015[/C][/ROW]
[ROW][C]last[/C][C]0.310458030411594[/C][C]0.091308[/C][C]3.4001[/C][C]0.000983[/C][C]0.000491[/C][/ROW]
[ROW][C]since[/C][C]0.0103730047865599[/C][C]0.006851[/C][C]1.5141[/C][C]0.133291[/C][C]0.066646[/C][/ROW]
[ROW][C]number[/C][C]0.352921408894487[/C][C]0.083241[/C][C]4.2398[/C][C]5.1e-05[/C][C]2.6e-05[/C][/ROW]
[ROW][C]t[/C][C]0.0209951325099079[/C][C]0.014521[/C][C]1.4459[/C][C]0.151469[/C][C]0.075735[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146608&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146608&T=2

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-0.6037037437136511.61545-0.37370.7094470.354723
starters0.1626786843144510.140571.15730.250030.125015
last0.3104580304115940.0913083.40010.0009830.000491
since0.01037300478655990.0068511.51410.1332910.066646
number0.3529214088944870.0832414.23985.1e-052.6e-05
t0.02099513250990790.0145211.44590.1514690.075735






Multiple Linear Regression - Regression Statistics
Multiple R0.596037812000243
R-squared0.355261073334037
Adjusted R-squared0.321680920903518
F-TEST (value)10.5794955537832
F-TEST (DF numerator)5
F-TEST (DF denominator)96
p-value4.12188936316227e-08
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.5663951111926
Sum Squared Residuals1221.04071255729

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.596037812000243 \tabularnewline
R-squared & 0.355261073334037 \tabularnewline
Adjusted R-squared & 0.321680920903518 \tabularnewline
F-TEST (value) & 10.5794955537832 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 96 \tabularnewline
p-value & 4.12188936316227e-08 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 3.5663951111926 \tabularnewline
Sum Squared Residuals & 1221.04071255729 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146608&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.596037812000243[/C][/ROW]
[ROW][C]R-squared[/C][C]0.355261073334037[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.321680920903518[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]10.5794955537832[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]96[/C][/ROW]
[ROW][C]p-value[/C][C]4.12188936316227e-08[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]3.5663951111926[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1221.04071255729[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146608&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146608&T=3

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Regression Statistics
Multiple R0.596037812000243
R-squared0.355261073334037
Adjusted R-squared0.321680920903518
F-TEST (value)10.5794955537832
F-TEST (DF numerator)5
F-TEST (DF denominator)96
p-value4.12188936316227e-08
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.5663951111926
Sum Squared Residuals1221.04071255729






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
112.58608659631325-1.58608659631325
222.87459788844895-0.874597888448954
332.585613482850170.414386517149833
443.022739412310580.977260587689424
556.27766207778926-1.27766207778926
665.807733415017990.19226658498201
774.692794063841032.30720593615897
885.601728549766172.39827145023383
916.85091974253752-5.85091974253752
1025.83202664102896-3.83202664102896
1136.75133413174066-3.75133413174066
1243.382821034214250.617178965785751
1356.94340326045559-1.94340326045559
1465.500847733454750.499152266545252
1573.092885022849483.90711497715052
1684.278317148581763.72168285141824
1796.000709937508092.99929006249191
18109.170618769524960.829381230475045
19114.236601138909236.76339886109077
20126.199515756599755.80048424340025
2112.5354361465428-1.5354361465428
2224.74195321539373-2.74195321539373
2336.44262863541029-3.44262863541029
2446.94393531226336-2.94393531226336
2553.626315879333091.37368412066691
2664.077627549161491.92237245083851
2776.92199395282730.0780060471727008
2884.131933537641183.86806646235882
2997.658482671512881.34151732848712
3014.12855416650439-3.12855416650439
3125.50645583264948-3.50645583264948
3235.68596011496775-2.68596011496775
3346.10906684844643-2.10906684844643
3457.59725883166225-2.59725883166225
3569.6409755228205-3.64097552282049
3677.61388553657713-0.613885536577132
3783.880135306696364.11986469330364
3898.97348665564150.0265133443584941
39108.51612733721241.48387266278759
40117.523510828111113.47648917188889
41127.182871895269274.81712810473073
421310.90189056955382.09810943044621
43148.346835775028565.65316422497144
4416.65764616608414-5.65764616608414
4524.04295547293288-2.04295547293288
4633.63841816304238-0.638418163042382
4747.57266780775133-3.57266780775133
4855.93854139790914-0.93854139790914
4964.352467276418321.64753272358168
5078.68688580342273-1.68688580342273
5187.554788306833490.445211693166514
5297.444580568313281.55541943168672
53105.477380101578254.52261989842175
54118.867137454551372.13286254544864
551211.24295684326580.757043156734213
56137.676697243374715.32330275662529
5716.48265363556183-5.48265363556183
5827.29538970216315-5.29538970216315
59310.1274403823691-7.12744038236909
60411.1840325210131-7.18403252101313
61511.2272235147357-6.22722351473571
6266.3073189227228-0.307318922722801
6376.756760575643080.243239424356923
6489.8246428323162-1.8246428323162
65910.2958745559114-1.29587455591143
66108.926901421753161.07309857824684
67117.666442430378583.33355756962142
681213.662470067175-1.66247006717504
69138.992728195527674.00727180447233
70149.183576841969154.81642315803085
71158.730820699341026.26917930065898
721615.45627853974430.543721460255657
731710.54868967119136.4513103288087
741810.89342991690937.10657008309068
751912.15438715415756.84561284584252
7615.86399702821714-4.86399702821714
7725.13668596445518-3.13668596445517
7834.97364184266552-1.97364184266552
7946.72909636462483-2.72909636462483
8057.83900337884123-2.83900337884123
8168.83383598106881-2.83383598106881
8277.76778924878036-0.767789248780358
83810.1743074865859-2.17430748658589
8496.230987897914342.76901210208566
85107.337248720935582.66275127906442
86117.558245022400083.44175497759992
87127.353708881464184.64629111853582
881310.05836942622142.94163057377856
8915.03509158526759-4.03509158526759
9027.70360977459563-5.70360977459563
9136.41421373368612-3.41421373368612
9246.038852719482-2.038852719482
93510.81306213102-5.81306213101999
9469.4201896631272-3.42018966312719
9577.5806185779923-0.580618577992299
9686.883455169253361.11654483074664
97911.6335403756362-2.63354037563623
98108.342647277954421.65735272204558
99117.43996649215763.5600335078424
100129.893565658977842.10643434102216
1011311.09391563826331.90608436173669
1021411.60389184738112.39610815261891

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 1 & 2.58608659631325 & -1.58608659631325 \tabularnewline
2 & 2 & 2.87459788844895 & -0.874597888448954 \tabularnewline
3 & 3 & 2.58561348285017 & 0.414386517149833 \tabularnewline
4 & 4 & 3.02273941231058 & 0.977260587689424 \tabularnewline
5 & 5 & 6.27766207778926 & -1.27766207778926 \tabularnewline
6 & 6 & 5.80773341501799 & 0.19226658498201 \tabularnewline
7 & 7 & 4.69279406384103 & 2.30720593615897 \tabularnewline
8 & 8 & 5.60172854976617 & 2.39827145023383 \tabularnewline
9 & 1 & 6.85091974253752 & -5.85091974253752 \tabularnewline
10 & 2 & 5.83202664102896 & -3.83202664102896 \tabularnewline
11 & 3 & 6.75133413174066 & -3.75133413174066 \tabularnewline
12 & 4 & 3.38282103421425 & 0.617178965785751 \tabularnewline
13 & 5 & 6.94340326045559 & -1.94340326045559 \tabularnewline
14 & 6 & 5.50084773345475 & 0.499152266545252 \tabularnewline
15 & 7 & 3.09288502284948 & 3.90711497715052 \tabularnewline
16 & 8 & 4.27831714858176 & 3.72168285141824 \tabularnewline
17 & 9 & 6.00070993750809 & 2.99929006249191 \tabularnewline
18 & 10 & 9.17061876952496 & 0.829381230475045 \tabularnewline
19 & 11 & 4.23660113890923 & 6.76339886109077 \tabularnewline
20 & 12 & 6.19951575659975 & 5.80048424340025 \tabularnewline
21 & 1 & 2.5354361465428 & -1.5354361465428 \tabularnewline
22 & 2 & 4.74195321539373 & -2.74195321539373 \tabularnewline
23 & 3 & 6.44262863541029 & -3.44262863541029 \tabularnewline
24 & 4 & 6.94393531226336 & -2.94393531226336 \tabularnewline
25 & 5 & 3.62631587933309 & 1.37368412066691 \tabularnewline
26 & 6 & 4.07762754916149 & 1.92237245083851 \tabularnewline
27 & 7 & 6.9219939528273 & 0.0780060471727008 \tabularnewline
28 & 8 & 4.13193353764118 & 3.86806646235882 \tabularnewline
29 & 9 & 7.65848267151288 & 1.34151732848712 \tabularnewline
30 & 1 & 4.12855416650439 & -3.12855416650439 \tabularnewline
31 & 2 & 5.50645583264948 & -3.50645583264948 \tabularnewline
32 & 3 & 5.68596011496775 & -2.68596011496775 \tabularnewline
33 & 4 & 6.10906684844643 & -2.10906684844643 \tabularnewline
34 & 5 & 7.59725883166225 & -2.59725883166225 \tabularnewline
35 & 6 & 9.6409755228205 & -3.64097552282049 \tabularnewline
36 & 7 & 7.61388553657713 & -0.613885536577132 \tabularnewline
37 & 8 & 3.88013530669636 & 4.11986469330364 \tabularnewline
38 & 9 & 8.9734866556415 & 0.0265133443584941 \tabularnewline
39 & 10 & 8.5161273372124 & 1.48387266278759 \tabularnewline
40 & 11 & 7.52351082811111 & 3.47648917188889 \tabularnewline
41 & 12 & 7.18287189526927 & 4.81712810473073 \tabularnewline
42 & 13 & 10.9018905695538 & 2.09810943044621 \tabularnewline
43 & 14 & 8.34683577502856 & 5.65316422497144 \tabularnewline
44 & 1 & 6.65764616608414 & -5.65764616608414 \tabularnewline
45 & 2 & 4.04295547293288 & -2.04295547293288 \tabularnewline
46 & 3 & 3.63841816304238 & -0.638418163042382 \tabularnewline
47 & 4 & 7.57266780775133 & -3.57266780775133 \tabularnewline
48 & 5 & 5.93854139790914 & -0.93854139790914 \tabularnewline
49 & 6 & 4.35246727641832 & 1.64753272358168 \tabularnewline
50 & 7 & 8.68688580342273 & -1.68688580342273 \tabularnewline
51 & 8 & 7.55478830683349 & 0.445211693166514 \tabularnewline
52 & 9 & 7.44458056831328 & 1.55541943168672 \tabularnewline
53 & 10 & 5.47738010157825 & 4.52261989842175 \tabularnewline
54 & 11 & 8.86713745455137 & 2.13286254544864 \tabularnewline
55 & 12 & 11.2429568432658 & 0.757043156734213 \tabularnewline
56 & 13 & 7.67669724337471 & 5.32330275662529 \tabularnewline
57 & 1 & 6.48265363556183 & -5.48265363556183 \tabularnewline
58 & 2 & 7.29538970216315 & -5.29538970216315 \tabularnewline
59 & 3 & 10.1274403823691 & -7.12744038236909 \tabularnewline
60 & 4 & 11.1840325210131 & -7.18403252101313 \tabularnewline
61 & 5 & 11.2272235147357 & -6.22722351473571 \tabularnewline
62 & 6 & 6.3073189227228 & -0.307318922722801 \tabularnewline
63 & 7 & 6.75676057564308 & 0.243239424356923 \tabularnewline
64 & 8 & 9.8246428323162 & -1.8246428323162 \tabularnewline
65 & 9 & 10.2958745559114 & -1.29587455591143 \tabularnewline
66 & 10 & 8.92690142175316 & 1.07309857824684 \tabularnewline
67 & 11 & 7.66644243037858 & 3.33355756962142 \tabularnewline
68 & 12 & 13.662470067175 & -1.66247006717504 \tabularnewline
69 & 13 & 8.99272819552767 & 4.00727180447233 \tabularnewline
70 & 14 & 9.18357684196915 & 4.81642315803085 \tabularnewline
71 & 15 & 8.73082069934102 & 6.26917930065898 \tabularnewline
72 & 16 & 15.4562785397443 & 0.543721460255657 \tabularnewline
73 & 17 & 10.5486896711913 & 6.4513103288087 \tabularnewline
74 & 18 & 10.8934299169093 & 7.10657008309068 \tabularnewline
75 & 19 & 12.1543871541575 & 6.84561284584252 \tabularnewline
76 & 1 & 5.86399702821714 & -4.86399702821714 \tabularnewline
77 & 2 & 5.13668596445518 & -3.13668596445517 \tabularnewline
78 & 3 & 4.97364184266552 & -1.97364184266552 \tabularnewline
79 & 4 & 6.72909636462483 & -2.72909636462483 \tabularnewline
80 & 5 & 7.83900337884123 & -2.83900337884123 \tabularnewline
81 & 6 & 8.83383598106881 & -2.83383598106881 \tabularnewline
82 & 7 & 7.76778924878036 & -0.767789248780358 \tabularnewline
83 & 8 & 10.1743074865859 & -2.17430748658589 \tabularnewline
84 & 9 & 6.23098789791434 & 2.76901210208566 \tabularnewline
85 & 10 & 7.33724872093558 & 2.66275127906442 \tabularnewline
86 & 11 & 7.55824502240008 & 3.44175497759992 \tabularnewline
87 & 12 & 7.35370888146418 & 4.64629111853582 \tabularnewline
88 & 13 & 10.0583694262214 & 2.94163057377856 \tabularnewline
89 & 1 & 5.03509158526759 & -4.03509158526759 \tabularnewline
90 & 2 & 7.70360977459563 & -5.70360977459563 \tabularnewline
91 & 3 & 6.41421373368612 & -3.41421373368612 \tabularnewline
92 & 4 & 6.038852719482 & -2.038852719482 \tabularnewline
93 & 5 & 10.81306213102 & -5.81306213101999 \tabularnewline
94 & 6 & 9.4201896631272 & -3.42018966312719 \tabularnewline
95 & 7 & 7.5806185779923 & -0.580618577992299 \tabularnewline
96 & 8 & 6.88345516925336 & 1.11654483074664 \tabularnewline
97 & 9 & 11.6335403756362 & -2.63354037563623 \tabularnewline
98 & 10 & 8.34264727795442 & 1.65735272204558 \tabularnewline
99 & 11 & 7.4399664921576 & 3.5600335078424 \tabularnewline
100 & 12 & 9.89356565897784 & 2.10643434102216 \tabularnewline
101 & 13 & 11.0939156382633 & 1.90608436173669 \tabularnewline
102 & 14 & 11.6038918473811 & 2.39610815261891 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146608&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]1[/C][C]2.58608659631325[/C][C]-1.58608659631325[/C][/ROW]
[ROW][C]2[/C][C]2[/C][C]2.87459788844895[/C][C]-0.874597888448954[/C][/ROW]
[ROW][C]3[/C][C]3[/C][C]2.58561348285017[/C][C]0.414386517149833[/C][/ROW]
[ROW][C]4[/C][C]4[/C][C]3.02273941231058[/C][C]0.977260587689424[/C][/ROW]
[ROW][C]5[/C][C]5[/C][C]6.27766207778926[/C][C]-1.27766207778926[/C][/ROW]
[ROW][C]6[/C][C]6[/C][C]5.80773341501799[/C][C]0.19226658498201[/C][/ROW]
[ROW][C]7[/C][C]7[/C][C]4.69279406384103[/C][C]2.30720593615897[/C][/ROW]
[ROW][C]8[/C][C]8[/C][C]5.60172854976617[/C][C]2.39827145023383[/C][/ROW]
[ROW][C]9[/C][C]1[/C][C]6.85091974253752[/C][C]-5.85091974253752[/C][/ROW]
[ROW][C]10[/C][C]2[/C][C]5.83202664102896[/C][C]-3.83202664102896[/C][/ROW]
[ROW][C]11[/C][C]3[/C][C]6.75133413174066[/C][C]-3.75133413174066[/C][/ROW]
[ROW][C]12[/C][C]4[/C][C]3.38282103421425[/C][C]0.617178965785751[/C][/ROW]
[ROW][C]13[/C][C]5[/C][C]6.94340326045559[/C][C]-1.94340326045559[/C][/ROW]
[ROW][C]14[/C][C]6[/C][C]5.50084773345475[/C][C]0.499152266545252[/C][/ROW]
[ROW][C]15[/C][C]7[/C][C]3.09288502284948[/C][C]3.90711497715052[/C][/ROW]
[ROW][C]16[/C][C]8[/C][C]4.27831714858176[/C][C]3.72168285141824[/C][/ROW]
[ROW][C]17[/C][C]9[/C][C]6.00070993750809[/C][C]2.99929006249191[/C][/ROW]
[ROW][C]18[/C][C]10[/C][C]9.17061876952496[/C][C]0.829381230475045[/C][/ROW]
[ROW][C]19[/C][C]11[/C][C]4.23660113890923[/C][C]6.76339886109077[/C][/ROW]
[ROW][C]20[/C][C]12[/C][C]6.19951575659975[/C][C]5.80048424340025[/C][/ROW]
[ROW][C]21[/C][C]1[/C][C]2.5354361465428[/C][C]-1.5354361465428[/C][/ROW]
[ROW][C]22[/C][C]2[/C][C]4.74195321539373[/C][C]-2.74195321539373[/C][/ROW]
[ROW][C]23[/C][C]3[/C][C]6.44262863541029[/C][C]-3.44262863541029[/C][/ROW]
[ROW][C]24[/C][C]4[/C][C]6.94393531226336[/C][C]-2.94393531226336[/C][/ROW]
[ROW][C]25[/C][C]5[/C][C]3.62631587933309[/C][C]1.37368412066691[/C][/ROW]
[ROW][C]26[/C][C]6[/C][C]4.07762754916149[/C][C]1.92237245083851[/C][/ROW]
[ROW][C]27[/C][C]7[/C][C]6.9219939528273[/C][C]0.0780060471727008[/C][/ROW]
[ROW][C]28[/C][C]8[/C][C]4.13193353764118[/C][C]3.86806646235882[/C][/ROW]
[ROW][C]29[/C][C]9[/C][C]7.65848267151288[/C][C]1.34151732848712[/C][/ROW]
[ROW][C]30[/C][C]1[/C][C]4.12855416650439[/C][C]-3.12855416650439[/C][/ROW]
[ROW][C]31[/C][C]2[/C][C]5.50645583264948[/C][C]-3.50645583264948[/C][/ROW]
[ROW][C]32[/C][C]3[/C][C]5.68596011496775[/C][C]-2.68596011496775[/C][/ROW]
[ROW][C]33[/C][C]4[/C][C]6.10906684844643[/C][C]-2.10906684844643[/C][/ROW]
[ROW][C]34[/C][C]5[/C][C]7.59725883166225[/C][C]-2.59725883166225[/C][/ROW]
[ROW][C]35[/C][C]6[/C][C]9.6409755228205[/C][C]-3.64097552282049[/C][/ROW]
[ROW][C]36[/C][C]7[/C][C]7.61388553657713[/C][C]-0.613885536577132[/C][/ROW]
[ROW][C]37[/C][C]8[/C][C]3.88013530669636[/C][C]4.11986469330364[/C][/ROW]
[ROW][C]38[/C][C]9[/C][C]8.9734866556415[/C][C]0.0265133443584941[/C][/ROW]
[ROW][C]39[/C][C]10[/C][C]8.5161273372124[/C][C]1.48387266278759[/C][/ROW]
[ROW][C]40[/C][C]11[/C][C]7.52351082811111[/C][C]3.47648917188889[/C][/ROW]
[ROW][C]41[/C][C]12[/C][C]7.18287189526927[/C][C]4.81712810473073[/C][/ROW]
[ROW][C]42[/C][C]13[/C][C]10.9018905695538[/C][C]2.09810943044621[/C][/ROW]
[ROW][C]43[/C][C]14[/C][C]8.34683577502856[/C][C]5.65316422497144[/C][/ROW]
[ROW][C]44[/C][C]1[/C][C]6.65764616608414[/C][C]-5.65764616608414[/C][/ROW]
[ROW][C]45[/C][C]2[/C][C]4.04295547293288[/C][C]-2.04295547293288[/C][/ROW]
[ROW][C]46[/C][C]3[/C][C]3.63841816304238[/C][C]-0.638418163042382[/C][/ROW]
[ROW][C]47[/C][C]4[/C][C]7.57266780775133[/C][C]-3.57266780775133[/C][/ROW]
[ROW][C]48[/C][C]5[/C][C]5.93854139790914[/C][C]-0.93854139790914[/C][/ROW]
[ROW][C]49[/C][C]6[/C][C]4.35246727641832[/C][C]1.64753272358168[/C][/ROW]
[ROW][C]50[/C][C]7[/C][C]8.68688580342273[/C][C]-1.68688580342273[/C][/ROW]
[ROW][C]51[/C][C]8[/C][C]7.55478830683349[/C][C]0.445211693166514[/C][/ROW]
[ROW][C]52[/C][C]9[/C][C]7.44458056831328[/C][C]1.55541943168672[/C][/ROW]
[ROW][C]53[/C][C]10[/C][C]5.47738010157825[/C][C]4.52261989842175[/C][/ROW]
[ROW][C]54[/C][C]11[/C][C]8.86713745455137[/C][C]2.13286254544864[/C][/ROW]
[ROW][C]55[/C][C]12[/C][C]11.2429568432658[/C][C]0.757043156734213[/C][/ROW]
[ROW][C]56[/C][C]13[/C][C]7.67669724337471[/C][C]5.32330275662529[/C][/ROW]
[ROW][C]57[/C][C]1[/C][C]6.48265363556183[/C][C]-5.48265363556183[/C][/ROW]
[ROW][C]58[/C][C]2[/C][C]7.29538970216315[/C][C]-5.29538970216315[/C][/ROW]
[ROW][C]59[/C][C]3[/C][C]10.1274403823691[/C][C]-7.12744038236909[/C][/ROW]
[ROW][C]60[/C][C]4[/C][C]11.1840325210131[/C][C]-7.18403252101313[/C][/ROW]
[ROW][C]61[/C][C]5[/C][C]11.2272235147357[/C][C]-6.22722351473571[/C][/ROW]
[ROW][C]62[/C][C]6[/C][C]6.3073189227228[/C][C]-0.307318922722801[/C][/ROW]
[ROW][C]63[/C][C]7[/C][C]6.75676057564308[/C][C]0.243239424356923[/C][/ROW]
[ROW][C]64[/C][C]8[/C][C]9.8246428323162[/C][C]-1.8246428323162[/C][/ROW]
[ROW][C]65[/C][C]9[/C][C]10.2958745559114[/C][C]-1.29587455591143[/C][/ROW]
[ROW][C]66[/C][C]10[/C][C]8.92690142175316[/C][C]1.07309857824684[/C][/ROW]
[ROW][C]67[/C][C]11[/C][C]7.66644243037858[/C][C]3.33355756962142[/C][/ROW]
[ROW][C]68[/C][C]12[/C][C]13.662470067175[/C][C]-1.66247006717504[/C][/ROW]
[ROW][C]69[/C][C]13[/C][C]8.99272819552767[/C][C]4.00727180447233[/C][/ROW]
[ROW][C]70[/C][C]14[/C][C]9.18357684196915[/C][C]4.81642315803085[/C][/ROW]
[ROW][C]71[/C][C]15[/C][C]8.73082069934102[/C][C]6.26917930065898[/C][/ROW]
[ROW][C]72[/C][C]16[/C][C]15.4562785397443[/C][C]0.543721460255657[/C][/ROW]
[ROW][C]73[/C][C]17[/C][C]10.5486896711913[/C][C]6.4513103288087[/C][/ROW]
[ROW][C]74[/C][C]18[/C][C]10.8934299169093[/C][C]7.10657008309068[/C][/ROW]
[ROW][C]75[/C][C]19[/C][C]12.1543871541575[/C][C]6.84561284584252[/C][/ROW]
[ROW][C]76[/C][C]1[/C][C]5.86399702821714[/C][C]-4.86399702821714[/C][/ROW]
[ROW][C]77[/C][C]2[/C][C]5.13668596445518[/C][C]-3.13668596445517[/C][/ROW]
[ROW][C]78[/C][C]3[/C][C]4.97364184266552[/C][C]-1.97364184266552[/C][/ROW]
[ROW][C]79[/C][C]4[/C][C]6.72909636462483[/C][C]-2.72909636462483[/C][/ROW]
[ROW][C]80[/C][C]5[/C][C]7.83900337884123[/C][C]-2.83900337884123[/C][/ROW]
[ROW][C]81[/C][C]6[/C][C]8.83383598106881[/C][C]-2.83383598106881[/C][/ROW]
[ROW][C]82[/C][C]7[/C][C]7.76778924878036[/C][C]-0.767789248780358[/C][/ROW]
[ROW][C]83[/C][C]8[/C][C]10.1743074865859[/C][C]-2.17430748658589[/C][/ROW]
[ROW][C]84[/C][C]9[/C][C]6.23098789791434[/C][C]2.76901210208566[/C][/ROW]
[ROW][C]85[/C][C]10[/C][C]7.33724872093558[/C][C]2.66275127906442[/C][/ROW]
[ROW][C]86[/C][C]11[/C][C]7.55824502240008[/C][C]3.44175497759992[/C][/ROW]
[ROW][C]87[/C][C]12[/C][C]7.35370888146418[/C][C]4.64629111853582[/C][/ROW]
[ROW][C]88[/C][C]13[/C][C]10.0583694262214[/C][C]2.94163057377856[/C][/ROW]
[ROW][C]89[/C][C]1[/C][C]5.03509158526759[/C][C]-4.03509158526759[/C][/ROW]
[ROW][C]90[/C][C]2[/C][C]7.70360977459563[/C][C]-5.70360977459563[/C][/ROW]
[ROW][C]91[/C][C]3[/C][C]6.41421373368612[/C][C]-3.41421373368612[/C][/ROW]
[ROW][C]92[/C][C]4[/C][C]6.038852719482[/C][C]-2.038852719482[/C][/ROW]
[ROW][C]93[/C][C]5[/C][C]10.81306213102[/C][C]-5.81306213101999[/C][/ROW]
[ROW][C]94[/C][C]6[/C][C]9.4201896631272[/C][C]-3.42018966312719[/C][/ROW]
[ROW][C]95[/C][C]7[/C][C]7.5806185779923[/C][C]-0.580618577992299[/C][/ROW]
[ROW][C]96[/C][C]8[/C][C]6.88345516925336[/C][C]1.11654483074664[/C][/ROW]
[ROW][C]97[/C][C]9[/C][C]11.6335403756362[/C][C]-2.63354037563623[/C][/ROW]
[ROW][C]98[/C][C]10[/C][C]8.34264727795442[/C][C]1.65735272204558[/C][/ROW]
[ROW][C]99[/C][C]11[/C][C]7.4399664921576[/C][C]3.5600335078424[/C][/ROW]
[ROW][C]100[/C][C]12[/C][C]9.89356565897784[/C][C]2.10643434102216[/C][/ROW]
[ROW][C]101[/C][C]13[/C][C]11.0939156382633[/C][C]1.90608436173669[/C][/ROW]
[ROW][C]102[/C][C]14[/C][C]11.6038918473811[/C][C]2.39610815261891[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146608&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146608&T=4

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
112.58608659631325-1.58608659631325
222.87459788844895-0.874597888448954
332.585613482850170.414386517149833
443.022739412310580.977260587689424
556.27766207778926-1.27766207778926
665.807733415017990.19226658498201
774.692794063841032.30720593615897
885.601728549766172.39827145023383
916.85091974253752-5.85091974253752
1025.83202664102896-3.83202664102896
1136.75133413174066-3.75133413174066
1243.382821034214250.617178965785751
1356.94340326045559-1.94340326045559
1465.500847733454750.499152266545252
1573.092885022849483.90711497715052
1684.278317148581763.72168285141824
1796.000709937508092.99929006249191
18109.170618769524960.829381230475045
19114.236601138909236.76339886109077
20126.199515756599755.80048424340025
2112.5354361465428-1.5354361465428
2224.74195321539373-2.74195321539373
2336.44262863541029-3.44262863541029
2446.94393531226336-2.94393531226336
2553.626315879333091.37368412066691
2664.077627549161491.92237245083851
2776.92199395282730.0780060471727008
2884.131933537641183.86806646235882
2997.658482671512881.34151732848712
3014.12855416650439-3.12855416650439
3125.50645583264948-3.50645583264948
3235.68596011496775-2.68596011496775
3346.10906684844643-2.10906684844643
3457.59725883166225-2.59725883166225
3569.6409755228205-3.64097552282049
3677.61388553657713-0.613885536577132
3783.880135306696364.11986469330364
3898.97348665564150.0265133443584941
39108.51612733721241.48387266278759
40117.523510828111113.47648917188889
41127.182871895269274.81712810473073
421310.90189056955382.09810943044621
43148.346835775028565.65316422497144
4416.65764616608414-5.65764616608414
4524.04295547293288-2.04295547293288
4633.63841816304238-0.638418163042382
4747.57266780775133-3.57266780775133
4855.93854139790914-0.93854139790914
4964.352467276418321.64753272358168
5078.68688580342273-1.68688580342273
5187.554788306833490.445211693166514
5297.444580568313281.55541943168672
53105.477380101578254.52261989842175
54118.867137454551372.13286254544864
551211.24295684326580.757043156734213
56137.676697243374715.32330275662529
5716.48265363556183-5.48265363556183
5827.29538970216315-5.29538970216315
59310.1274403823691-7.12744038236909
60411.1840325210131-7.18403252101313
61511.2272235147357-6.22722351473571
6266.3073189227228-0.307318922722801
6376.756760575643080.243239424356923
6489.8246428323162-1.8246428323162
65910.2958745559114-1.29587455591143
66108.926901421753161.07309857824684
67117.666442430378583.33355756962142
681213.662470067175-1.66247006717504
69138.992728195527674.00727180447233
70149.183576841969154.81642315803085
71158.730820699341026.26917930065898
721615.45627853974430.543721460255657
731710.54868967119136.4513103288087
741810.89342991690937.10657008309068
751912.15438715415756.84561284584252
7615.86399702821714-4.86399702821714
7725.13668596445518-3.13668596445517
7834.97364184266552-1.97364184266552
7946.72909636462483-2.72909636462483
8057.83900337884123-2.83900337884123
8168.83383598106881-2.83383598106881
8277.76778924878036-0.767789248780358
83810.1743074865859-2.17430748658589
8496.230987897914342.76901210208566
85107.337248720935582.66275127906442
86117.558245022400083.44175497759992
87127.353708881464184.64629111853582
881310.05836942622142.94163057377856
8915.03509158526759-4.03509158526759
9027.70360977459563-5.70360977459563
9136.41421373368612-3.41421373368612
9246.038852719482-2.038852719482
93510.81306213102-5.81306213101999
9469.4201896631272-3.42018966312719
9577.5806185779923-0.580618577992299
9686.883455169253361.11654483074664
97911.6335403756362-2.63354037563623
98108.342647277954421.65735272204558
99117.43996649215763.5600335078424
100129.893565658977842.10643434102216
1011311.09391563826331.90608436173669
1021411.60389184738112.39610815261891






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
91.57180728852578e-473.14361457705156e-471
103.93870366397687e-637.87740732795375e-631
111.14380453574025e-782.28760907148051e-781
123.98484071634166e-957.96968143268332e-951
131.8843187198294e-1073.7686374396588e-1071
146.89424775519183e-1211.37884955103837e-1201
151.49761744202907e-1382.99523488405813e-1381
162.43609504946008e-1554.87219009892016e-1551
172.76359697851623e-1645.52719395703245e-1641
186.088111807342e-1831.2176223614684e-1821
194.38423432809635e-2018.7684686561927e-2011
209.87002194058952e-2171.9740043881179e-2161
210.1748690828926520.3497381657853050.825130917107348
220.2879957833199530.5759915666399060.712004216680047
230.3065390797434960.6130781594869930.693460920256504
240.2806677168496430.5613354336992860.719332283150357
250.2217281971300220.4434563942600440.778271802869978
260.1779943356809610.3559886713619220.822005664319039
270.1345850971192840.2691701942385670.865414902880716
280.12270325959940.2454065191988010.8772967404006
290.09651804572408180.1930360914481640.903481954275918
300.1572618066482170.3145236132964340.842738193351783
310.1681878135990710.3363756271981420.83181218640093
320.152158456481830.304316912963660.84784154351817
330.1232477469364770.2464954938729540.876752253063523
340.1011217618799260.2022435237598530.898878238120074
350.0840776949875670.1681553899751340.915922305012433
360.06296069133439090.1259213826687820.93703930866561
370.06949827916845380.1389965583369080.930501720831546
380.05552945662265440.1110589132453090.944470543377346
390.04570176382317320.09140352764634630.954298236176827
400.049359328307570.098718656615140.95064067169243
410.06917937258372960.1383587451674590.93082062741627
420.06470120061543450.1294024012308690.935298799384566
430.0989568643872950.197913728774590.901043135612705
440.1773301615103360.3546603230206710.822669838489664
450.1638298379871420.3276596759742840.836170162012858
460.1345108442199370.2690216884398740.865489155780063
470.1332693964134410.2665387928268810.86673060358656
480.1043688193672020.2087376387344050.895631180632798
490.08762093806220890.1752418761244180.912379061937791
500.06873673239833120.1374734647966620.931263267601669
510.05173469965754930.1034693993150990.94826530034245
520.04096529073060160.08193058146120320.959034709269398
530.05547249529388510.110944990587770.944527504706115
540.05074585045857620.1014917009171520.949254149541424
550.04730959417034880.09461918834069750.95269040582965
560.1914619680158550.382923936031710.808538031984145
570.2290520365787880.4581040731575750.770947963421212
580.25756417566020.5151283513203990.7424358243398
590.3639453523782960.7278907047565920.636054647621704
600.4210590686629650.842118137325930.578940931337035
610.5505994455035280.8988011089929440.449400554496472
620.5064108940483080.9871782119033850.493589105951692
630.4667109016807130.9334218033614270.533289098319287
640.4511024201482150.902204840296430.548897579851785
650.4555680054323820.9111360108647630.544431994567618
660.4369551244245030.8739102488490070.563044875575497
670.4196356338513680.8392712677027350.580364366148632
680.4827147423964440.9654294847928890.517285257603556
690.4699485255711840.9398970511423680.530051474428816
700.4624432151531120.9248864303062240.537556784846888
710.4808544742600060.9617089485200120.519145525739994
720.5375018473493350.924996305301330.462498152650665
730.5417755102862850.9164489794274310.458224489713715
740.5708881631489550.858223673702090.429111836851045
7511.22027020086253e-3016.10135100431266e-302
7613.21008248997008e-2801.60504124498504e-280
7718.01781461923734e-2684.00890730961867e-268
7815.71301031733854e-2732.85650515866927e-273
7912.83413109987659e-2591.41706554993829e-259
8012.98652411702093e-2321.49326205851047e-232
8113.22908405731299e-2171.6145420286565e-217
8218.06074562615027e-2074.03037281307513e-207
8314.40941413512038e-1862.20470706756019e-186
8411.87549703198455e-1779.37748515992274e-178
8513.0669965342982e-1551.5334982671491e-155
8611.62951554010327e-1458.14757770051634e-146
8714.4441042194752e-1392.2220521097376e-139
8811.25965073606402e-1176.29825368032009e-118
8913.94837735300578e-1051.97418867650289e-105
9011.29366600790676e-876.4683300395338e-88
9115.33125328368102e-732.66562664184051e-73
9213.07142248092887e-611.53571124046443e-61
9311.79752503440215e-468.98762517201076e-47

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 1.57180728852578e-47 & 3.14361457705156e-47 & 1 \tabularnewline
10 & 3.93870366397687e-63 & 7.87740732795375e-63 & 1 \tabularnewline
11 & 1.14380453574025e-78 & 2.28760907148051e-78 & 1 \tabularnewline
12 & 3.98484071634166e-95 & 7.96968143268332e-95 & 1 \tabularnewline
13 & 1.8843187198294e-107 & 3.7686374396588e-107 & 1 \tabularnewline
14 & 6.89424775519183e-121 & 1.37884955103837e-120 & 1 \tabularnewline
15 & 1.49761744202907e-138 & 2.99523488405813e-138 & 1 \tabularnewline
16 & 2.43609504946008e-155 & 4.87219009892016e-155 & 1 \tabularnewline
17 & 2.76359697851623e-164 & 5.52719395703245e-164 & 1 \tabularnewline
18 & 6.088111807342e-183 & 1.2176223614684e-182 & 1 \tabularnewline
19 & 4.38423432809635e-201 & 8.7684686561927e-201 & 1 \tabularnewline
20 & 9.87002194058952e-217 & 1.9740043881179e-216 & 1 \tabularnewline
21 & 0.174869082892652 & 0.349738165785305 & 0.825130917107348 \tabularnewline
22 & 0.287995783319953 & 0.575991566639906 & 0.712004216680047 \tabularnewline
23 & 0.306539079743496 & 0.613078159486993 & 0.693460920256504 \tabularnewline
24 & 0.280667716849643 & 0.561335433699286 & 0.719332283150357 \tabularnewline
25 & 0.221728197130022 & 0.443456394260044 & 0.778271802869978 \tabularnewline
26 & 0.177994335680961 & 0.355988671361922 & 0.822005664319039 \tabularnewline
27 & 0.134585097119284 & 0.269170194238567 & 0.865414902880716 \tabularnewline
28 & 0.1227032595994 & 0.245406519198801 & 0.8772967404006 \tabularnewline
29 & 0.0965180457240818 & 0.193036091448164 & 0.903481954275918 \tabularnewline
30 & 0.157261806648217 & 0.314523613296434 & 0.842738193351783 \tabularnewline
31 & 0.168187813599071 & 0.336375627198142 & 0.83181218640093 \tabularnewline
32 & 0.15215845648183 & 0.30431691296366 & 0.84784154351817 \tabularnewline
33 & 0.123247746936477 & 0.246495493872954 & 0.876752253063523 \tabularnewline
34 & 0.101121761879926 & 0.202243523759853 & 0.898878238120074 \tabularnewline
35 & 0.084077694987567 & 0.168155389975134 & 0.915922305012433 \tabularnewline
36 & 0.0629606913343909 & 0.125921382668782 & 0.93703930866561 \tabularnewline
37 & 0.0694982791684538 & 0.138996558336908 & 0.930501720831546 \tabularnewline
38 & 0.0555294566226544 & 0.111058913245309 & 0.944470543377346 \tabularnewline
39 & 0.0457017638231732 & 0.0914035276463463 & 0.954298236176827 \tabularnewline
40 & 0.04935932830757 & 0.09871865661514 & 0.95064067169243 \tabularnewline
41 & 0.0691793725837296 & 0.138358745167459 & 0.93082062741627 \tabularnewline
42 & 0.0647012006154345 & 0.129402401230869 & 0.935298799384566 \tabularnewline
43 & 0.098956864387295 & 0.19791372877459 & 0.901043135612705 \tabularnewline
44 & 0.177330161510336 & 0.354660323020671 & 0.822669838489664 \tabularnewline
45 & 0.163829837987142 & 0.327659675974284 & 0.836170162012858 \tabularnewline
46 & 0.134510844219937 & 0.269021688439874 & 0.865489155780063 \tabularnewline
47 & 0.133269396413441 & 0.266538792826881 & 0.86673060358656 \tabularnewline
48 & 0.104368819367202 & 0.208737638734405 & 0.895631180632798 \tabularnewline
49 & 0.0876209380622089 & 0.175241876124418 & 0.912379061937791 \tabularnewline
50 & 0.0687367323983312 & 0.137473464796662 & 0.931263267601669 \tabularnewline
51 & 0.0517346996575493 & 0.103469399315099 & 0.94826530034245 \tabularnewline
52 & 0.0409652907306016 & 0.0819305814612032 & 0.959034709269398 \tabularnewline
53 & 0.0554724952938851 & 0.11094499058777 & 0.944527504706115 \tabularnewline
54 & 0.0507458504585762 & 0.101491700917152 & 0.949254149541424 \tabularnewline
55 & 0.0473095941703488 & 0.0946191883406975 & 0.95269040582965 \tabularnewline
56 & 0.191461968015855 & 0.38292393603171 & 0.808538031984145 \tabularnewline
57 & 0.229052036578788 & 0.458104073157575 & 0.770947963421212 \tabularnewline
58 & 0.2575641756602 & 0.515128351320399 & 0.7424358243398 \tabularnewline
59 & 0.363945352378296 & 0.727890704756592 & 0.636054647621704 \tabularnewline
60 & 0.421059068662965 & 0.84211813732593 & 0.578940931337035 \tabularnewline
61 & 0.550599445503528 & 0.898801108992944 & 0.449400554496472 \tabularnewline
62 & 0.506410894048308 & 0.987178211903385 & 0.493589105951692 \tabularnewline
63 & 0.466710901680713 & 0.933421803361427 & 0.533289098319287 \tabularnewline
64 & 0.451102420148215 & 0.90220484029643 & 0.548897579851785 \tabularnewline
65 & 0.455568005432382 & 0.911136010864763 & 0.544431994567618 \tabularnewline
66 & 0.436955124424503 & 0.873910248849007 & 0.563044875575497 \tabularnewline
67 & 0.419635633851368 & 0.839271267702735 & 0.580364366148632 \tabularnewline
68 & 0.482714742396444 & 0.965429484792889 & 0.517285257603556 \tabularnewline
69 & 0.469948525571184 & 0.939897051142368 & 0.530051474428816 \tabularnewline
70 & 0.462443215153112 & 0.924886430306224 & 0.537556784846888 \tabularnewline
71 & 0.480854474260006 & 0.961708948520012 & 0.519145525739994 \tabularnewline
72 & 0.537501847349335 & 0.92499630530133 & 0.462498152650665 \tabularnewline
73 & 0.541775510286285 & 0.916448979427431 & 0.458224489713715 \tabularnewline
74 & 0.570888163148955 & 0.85822367370209 & 0.429111836851045 \tabularnewline
75 & 1 & 1.22027020086253e-301 & 6.10135100431266e-302 \tabularnewline
76 & 1 & 3.21008248997008e-280 & 1.60504124498504e-280 \tabularnewline
77 & 1 & 8.01781461923734e-268 & 4.00890730961867e-268 \tabularnewline
78 & 1 & 5.71301031733854e-273 & 2.85650515866927e-273 \tabularnewline
79 & 1 & 2.83413109987659e-259 & 1.41706554993829e-259 \tabularnewline
80 & 1 & 2.98652411702093e-232 & 1.49326205851047e-232 \tabularnewline
81 & 1 & 3.22908405731299e-217 & 1.6145420286565e-217 \tabularnewline
82 & 1 & 8.06074562615027e-207 & 4.03037281307513e-207 \tabularnewline
83 & 1 & 4.40941413512038e-186 & 2.20470706756019e-186 \tabularnewline
84 & 1 & 1.87549703198455e-177 & 9.37748515992274e-178 \tabularnewline
85 & 1 & 3.0669965342982e-155 & 1.5334982671491e-155 \tabularnewline
86 & 1 & 1.62951554010327e-145 & 8.14757770051634e-146 \tabularnewline
87 & 1 & 4.4441042194752e-139 & 2.2220521097376e-139 \tabularnewline
88 & 1 & 1.25965073606402e-117 & 6.29825368032009e-118 \tabularnewline
89 & 1 & 3.94837735300578e-105 & 1.97418867650289e-105 \tabularnewline
90 & 1 & 1.29366600790676e-87 & 6.4683300395338e-88 \tabularnewline
91 & 1 & 5.33125328368102e-73 & 2.66562664184051e-73 \tabularnewline
92 & 1 & 3.07142248092887e-61 & 1.53571124046443e-61 \tabularnewline
93 & 1 & 1.79752503440215e-46 & 8.98762517201076e-47 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146608&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]9[/C][C]1.57180728852578e-47[/C][C]3.14361457705156e-47[/C][C]1[/C][/ROW]
[ROW][C]10[/C][C]3.93870366397687e-63[/C][C]7.87740732795375e-63[/C][C]1[/C][/ROW]
[ROW][C]11[/C][C]1.14380453574025e-78[/C][C]2.28760907148051e-78[/C][C]1[/C][/ROW]
[ROW][C]12[/C][C]3.98484071634166e-95[/C][C]7.96968143268332e-95[/C][C]1[/C][/ROW]
[ROW][C]13[/C][C]1.8843187198294e-107[/C][C]3.7686374396588e-107[/C][C]1[/C][/ROW]
[ROW][C]14[/C][C]6.89424775519183e-121[/C][C]1.37884955103837e-120[/C][C]1[/C][/ROW]
[ROW][C]15[/C][C]1.49761744202907e-138[/C][C]2.99523488405813e-138[/C][C]1[/C][/ROW]
[ROW][C]16[/C][C]2.43609504946008e-155[/C][C]4.87219009892016e-155[/C][C]1[/C][/ROW]
[ROW][C]17[/C][C]2.76359697851623e-164[/C][C]5.52719395703245e-164[/C][C]1[/C][/ROW]
[ROW][C]18[/C][C]6.088111807342e-183[/C][C]1.2176223614684e-182[/C][C]1[/C][/ROW]
[ROW][C]19[/C][C]4.38423432809635e-201[/C][C]8.7684686561927e-201[/C][C]1[/C][/ROW]
[ROW][C]20[/C][C]9.87002194058952e-217[/C][C]1.9740043881179e-216[/C][C]1[/C][/ROW]
[ROW][C]21[/C][C]0.174869082892652[/C][C]0.349738165785305[/C][C]0.825130917107348[/C][/ROW]
[ROW][C]22[/C][C]0.287995783319953[/C][C]0.575991566639906[/C][C]0.712004216680047[/C][/ROW]
[ROW][C]23[/C][C]0.306539079743496[/C][C]0.613078159486993[/C][C]0.693460920256504[/C][/ROW]
[ROW][C]24[/C][C]0.280667716849643[/C][C]0.561335433699286[/C][C]0.719332283150357[/C][/ROW]
[ROW][C]25[/C][C]0.221728197130022[/C][C]0.443456394260044[/C][C]0.778271802869978[/C][/ROW]
[ROW][C]26[/C][C]0.177994335680961[/C][C]0.355988671361922[/C][C]0.822005664319039[/C][/ROW]
[ROW][C]27[/C][C]0.134585097119284[/C][C]0.269170194238567[/C][C]0.865414902880716[/C][/ROW]
[ROW][C]28[/C][C]0.1227032595994[/C][C]0.245406519198801[/C][C]0.8772967404006[/C][/ROW]
[ROW][C]29[/C][C]0.0965180457240818[/C][C]0.193036091448164[/C][C]0.903481954275918[/C][/ROW]
[ROW][C]30[/C][C]0.157261806648217[/C][C]0.314523613296434[/C][C]0.842738193351783[/C][/ROW]
[ROW][C]31[/C][C]0.168187813599071[/C][C]0.336375627198142[/C][C]0.83181218640093[/C][/ROW]
[ROW][C]32[/C][C]0.15215845648183[/C][C]0.30431691296366[/C][C]0.84784154351817[/C][/ROW]
[ROW][C]33[/C][C]0.123247746936477[/C][C]0.246495493872954[/C][C]0.876752253063523[/C][/ROW]
[ROW][C]34[/C][C]0.101121761879926[/C][C]0.202243523759853[/C][C]0.898878238120074[/C][/ROW]
[ROW][C]35[/C][C]0.084077694987567[/C][C]0.168155389975134[/C][C]0.915922305012433[/C][/ROW]
[ROW][C]36[/C][C]0.0629606913343909[/C][C]0.125921382668782[/C][C]0.93703930866561[/C][/ROW]
[ROW][C]37[/C][C]0.0694982791684538[/C][C]0.138996558336908[/C][C]0.930501720831546[/C][/ROW]
[ROW][C]38[/C][C]0.0555294566226544[/C][C]0.111058913245309[/C][C]0.944470543377346[/C][/ROW]
[ROW][C]39[/C][C]0.0457017638231732[/C][C]0.0914035276463463[/C][C]0.954298236176827[/C][/ROW]
[ROW][C]40[/C][C]0.04935932830757[/C][C]0.09871865661514[/C][C]0.95064067169243[/C][/ROW]
[ROW][C]41[/C][C]0.0691793725837296[/C][C]0.138358745167459[/C][C]0.93082062741627[/C][/ROW]
[ROW][C]42[/C][C]0.0647012006154345[/C][C]0.129402401230869[/C][C]0.935298799384566[/C][/ROW]
[ROW][C]43[/C][C]0.098956864387295[/C][C]0.19791372877459[/C][C]0.901043135612705[/C][/ROW]
[ROW][C]44[/C][C]0.177330161510336[/C][C]0.354660323020671[/C][C]0.822669838489664[/C][/ROW]
[ROW][C]45[/C][C]0.163829837987142[/C][C]0.327659675974284[/C][C]0.836170162012858[/C][/ROW]
[ROW][C]46[/C][C]0.134510844219937[/C][C]0.269021688439874[/C][C]0.865489155780063[/C][/ROW]
[ROW][C]47[/C][C]0.133269396413441[/C][C]0.266538792826881[/C][C]0.86673060358656[/C][/ROW]
[ROW][C]48[/C][C]0.104368819367202[/C][C]0.208737638734405[/C][C]0.895631180632798[/C][/ROW]
[ROW][C]49[/C][C]0.0876209380622089[/C][C]0.175241876124418[/C][C]0.912379061937791[/C][/ROW]
[ROW][C]50[/C][C]0.0687367323983312[/C][C]0.137473464796662[/C][C]0.931263267601669[/C][/ROW]
[ROW][C]51[/C][C]0.0517346996575493[/C][C]0.103469399315099[/C][C]0.94826530034245[/C][/ROW]
[ROW][C]52[/C][C]0.0409652907306016[/C][C]0.0819305814612032[/C][C]0.959034709269398[/C][/ROW]
[ROW][C]53[/C][C]0.0554724952938851[/C][C]0.11094499058777[/C][C]0.944527504706115[/C][/ROW]
[ROW][C]54[/C][C]0.0507458504585762[/C][C]0.101491700917152[/C][C]0.949254149541424[/C][/ROW]
[ROW][C]55[/C][C]0.0473095941703488[/C][C]0.0946191883406975[/C][C]0.95269040582965[/C][/ROW]
[ROW][C]56[/C][C]0.191461968015855[/C][C]0.38292393603171[/C][C]0.808538031984145[/C][/ROW]
[ROW][C]57[/C][C]0.229052036578788[/C][C]0.458104073157575[/C][C]0.770947963421212[/C][/ROW]
[ROW][C]58[/C][C]0.2575641756602[/C][C]0.515128351320399[/C][C]0.7424358243398[/C][/ROW]
[ROW][C]59[/C][C]0.363945352378296[/C][C]0.727890704756592[/C][C]0.636054647621704[/C][/ROW]
[ROW][C]60[/C][C]0.421059068662965[/C][C]0.84211813732593[/C][C]0.578940931337035[/C][/ROW]
[ROW][C]61[/C][C]0.550599445503528[/C][C]0.898801108992944[/C][C]0.449400554496472[/C][/ROW]
[ROW][C]62[/C][C]0.506410894048308[/C][C]0.987178211903385[/C][C]0.493589105951692[/C][/ROW]
[ROW][C]63[/C][C]0.466710901680713[/C][C]0.933421803361427[/C][C]0.533289098319287[/C][/ROW]
[ROW][C]64[/C][C]0.451102420148215[/C][C]0.90220484029643[/C][C]0.548897579851785[/C][/ROW]
[ROW][C]65[/C][C]0.455568005432382[/C][C]0.911136010864763[/C][C]0.544431994567618[/C][/ROW]
[ROW][C]66[/C][C]0.436955124424503[/C][C]0.873910248849007[/C][C]0.563044875575497[/C][/ROW]
[ROW][C]67[/C][C]0.419635633851368[/C][C]0.839271267702735[/C][C]0.580364366148632[/C][/ROW]
[ROW][C]68[/C][C]0.482714742396444[/C][C]0.965429484792889[/C][C]0.517285257603556[/C][/ROW]
[ROW][C]69[/C][C]0.469948525571184[/C][C]0.939897051142368[/C][C]0.530051474428816[/C][/ROW]
[ROW][C]70[/C][C]0.462443215153112[/C][C]0.924886430306224[/C][C]0.537556784846888[/C][/ROW]
[ROW][C]71[/C][C]0.480854474260006[/C][C]0.961708948520012[/C][C]0.519145525739994[/C][/ROW]
[ROW][C]72[/C][C]0.537501847349335[/C][C]0.92499630530133[/C][C]0.462498152650665[/C][/ROW]
[ROW][C]73[/C][C]0.541775510286285[/C][C]0.916448979427431[/C][C]0.458224489713715[/C][/ROW]
[ROW][C]74[/C][C]0.570888163148955[/C][C]0.85822367370209[/C][C]0.429111836851045[/C][/ROW]
[ROW][C]75[/C][C]1[/C][C]1.22027020086253e-301[/C][C]6.10135100431266e-302[/C][/ROW]
[ROW][C]76[/C][C]1[/C][C]3.21008248997008e-280[/C][C]1.60504124498504e-280[/C][/ROW]
[ROW][C]77[/C][C]1[/C][C]8.01781461923734e-268[/C][C]4.00890730961867e-268[/C][/ROW]
[ROW][C]78[/C][C]1[/C][C]5.71301031733854e-273[/C][C]2.85650515866927e-273[/C][/ROW]
[ROW][C]79[/C][C]1[/C][C]2.83413109987659e-259[/C][C]1.41706554993829e-259[/C][/ROW]
[ROW][C]80[/C][C]1[/C][C]2.98652411702093e-232[/C][C]1.49326205851047e-232[/C][/ROW]
[ROW][C]81[/C][C]1[/C][C]3.22908405731299e-217[/C][C]1.6145420286565e-217[/C][/ROW]
[ROW][C]82[/C][C]1[/C][C]8.06074562615027e-207[/C][C]4.03037281307513e-207[/C][/ROW]
[ROW][C]83[/C][C]1[/C][C]4.40941413512038e-186[/C][C]2.20470706756019e-186[/C][/ROW]
[ROW][C]84[/C][C]1[/C][C]1.87549703198455e-177[/C][C]9.37748515992274e-178[/C][/ROW]
[ROW][C]85[/C][C]1[/C][C]3.0669965342982e-155[/C][C]1.5334982671491e-155[/C][/ROW]
[ROW][C]86[/C][C]1[/C][C]1.62951554010327e-145[/C][C]8.14757770051634e-146[/C][/ROW]
[ROW][C]87[/C][C]1[/C][C]4.4441042194752e-139[/C][C]2.2220521097376e-139[/C][/ROW]
[ROW][C]88[/C][C]1[/C][C]1.25965073606402e-117[/C][C]6.29825368032009e-118[/C][/ROW]
[ROW][C]89[/C][C]1[/C][C]3.94837735300578e-105[/C][C]1.97418867650289e-105[/C][/ROW]
[ROW][C]90[/C][C]1[/C][C]1.29366600790676e-87[/C][C]6.4683300395338e-88[/C][/ROW]
[ROW][C]91[/C][C]1[/C][C]5.33125328368102e-73[/C][C]2.66562664184051e-73[/C][/ROW]
[ROW][C]92[/C][C]1[/C][C]3.07142248092887e-61[/C][C]1.53571124046443e-61[/C][/ROW]
[ROW][C]93[/C][C]1[/C][C]1.79752503440215e-46[/C][C]8.98762517201076e-47[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146608&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146608&T=5

As an alternative you can also use a QR Code:  
QR Code


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
91.57180728852578e-473.14361457705156e-471
103.93870366397687e-637.87740732795375e-631
111.14380453574025e-782.28760907148051e-781
123.98484071634166e-957.96968143268332e-951
131.8843187198294e-1073.7686374396588e-1071
146.89424775519183e-1211.37884955103837e-1201
151.49761744202907e-1382.99523488405813e-1381
162.43609504946008e-1554.87219009892016e-1551
172.76359697851623e-1645.52719395703245e-1641
186.088111807342e-1831.2176223614684e-1821
194.38423432809635e-2018.7684686561927e-2011
209.87002194058952e-2171.9740043881179e-2161
210.1748690828926520.3497381657853050.825130917107348
220.2879957833199530.5759915666399060.712004216680047
230.3065390797434960.6130781594869930.693460920256504
240.2806677168496430.5613354336992860.719332283150357
250.2217281971300220.4434563942600440.778271802869978
260.1779943356809610.3559886713619220.822005664319039
270.1345850971192840.2691701942385670.865414902880716
280.12270325959940.2454065191988010.8772967404006
290.09651804572408180.1930360914481640.903481954275918
300.1572618066482170.3145236132964340.842738193351783
310.1681878135990710.3363756271981420.83181218640093
320.152158456481830.304316912963660.84784154351817
330.1232477469364770.2464954938729540.876752253063523
340.1011217618799260.2022435237598530.898878238120074
350.0840776949875670.1681553899751340.915922305012433
360.06296069133439090.1259213826687820.93703930866561
370.06949827916845380.1389965583369080.930501720831546
380.05552945662265440.1110589132453090.944470543377346
390.04570176382317320.09140352764634630.954298236176827
400.049359328307570.098718656615140.95064067169243
410.06917937258372960.1383587451674590.93082062741627
420.06470120061543450.1294024012308690.935298799384566
430.0989568643872950.197913728774590.901043135612705
440.1773301615103360.3546603230206710.822669838489664
450.1638298379871420.3276596759742840.836170162012858
460.1345108442199370.2690216884398740.865489155780063
470.1332693964134410.2665387928268810.86673060358656
480.1043688193672020.2087376387344050.895631180632798
490.08762093806220890.1752418761244180.912379061937791
500.06873673239833120.1374734647966620.931263267601669
510.05173469965754930.1034693993150990.94826530034245
520.04096529073060160.08193058146120320.959034709269398
530.05547249529388510.110944990587770.944527504706115
540.05074585045857620.1014917009171520.949254149541424
550.04730959417034880.09461918834069750.95269040582965
560.1914619680158550.382923936031710.808538031984145
570.2290520365787880.4581040731575750.770947963421212
580.25756417566020.5151283513203990.7424358243398
590.3639453523782960.7278907047565920.636054647621704
600.4210590686629650.842118137325930.578940931337035
610.5505994455035280.8988011089929440.449400554496472
620.5064108940483080.9871782119033850.493589105951692
630.4667109016807130.9334218033614270.533289098319287
640.4511024201482150.902204840296430.548897579851785
650.4555680054323820.9111360108647630.544431994567618
660.4369551244245030.8739102488490070.563044875575497
670.4196356338513680.8392712677027350.580364366148632
680.4827147423964440.9654294847928890.517285257603556
690.4699485255711840.9398970511423680.530051474428816
700.4624432151531120.9248864303062240.537556784846888
710.4808544742600060.9617089485200120.519145525739994
720.5375018473493350.924996305301330.462498152650665
730.5417755102862850.9164489794274310.458224489713715
740.5708881631489550.858223673702090.429111836851045
7511.22027020086253e-3016.10135100431266e-302
7613.21008248997008e-2801.60504124498504e-280
7718.01781461923734e-2684.00890730961867e-268
7815.71301031733854e-2732.85650515866927e-273
7912.83413109987659e-2591.41706554993829e-259
8012.98652411702093e-2321.49326205851047e-232
8113.22908405731299e-2171.6145420286565e-217
8218.06074562615027e-2074.03037281307513e-207
8314.40941413512038e-1862.20470706756019e-186
8411.87549703198455e-1779.37748515992274e-178
8513.0669965342982e-1551.5334982671491e-155
8611.62951554010327e-1458.14757770051634e-146
8714.4441042194752e-1392.2220521097376e-139
8811.25965073606402e-1176.29825368032009e-118
8913.94837735300578e-1051.97418867650289e-105
9011.29366600790676e-876.4683300395338e-88
9115.33125328368102e-732.66562664184051e-73
9213.07142248092887e-611.53571124046443e-61
9311.79752503440215e-468.98762517201076e-47






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level310.364705882352941NOK
5% type I error level310.364705882352941NOK
10% type I error level350.411764705882353NOK

\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 & 31 & 0.364705882352941 & NOK \tabularnewline
5% type I error level & 31 & 0.364705882352941 & NOK \tabularnewline
10% type I error level & 35 & 0.411764705882353 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146608&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]31[/C][C]0.364705882352941[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]31[/C][C]0.364705882352941[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]35[/C][C]0.411764705882353[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146608&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146608&T=6

As an alternative you can also use a QR Code:  
QR Code


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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level310.364705882352941NOK
5% type I error level310.364705882352941NOK
10% type I error level350.411764705882353NOK


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 6
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Figure 7
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Figure 8
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Figure 9
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Figure 10
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Input Parameters & R Code

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