Free Statistics

of Irreproducible Research!

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 05:39:28 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Nov/24/t1322131191whxh2wadtipsb6j.htm/, Retrieved Fri, 29 Mar 2024 06:46:46 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=146617, Retrieved Fri, 29 Mar 2024 06:46:46 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact154
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2010-11-17 09:55:05] [b98453cac15ba1066b407e146608df68]
-   PD  [Multiple Regression] [Two way anova FMPS] [2010-11-22 18:59:20] [95e8426e0df851c9330605aa1e892ab5]
-         [Multiple Regression] [] [2010-12-01 15:05:12] [42a441ca3193af442aa2201743dfb347]
- R PD        [Multiple Regression] [multiple regressi...] [2011-11-24 10:39:28] [d519577d845e738b812f706f10c86f64] [Current]
Feedback Forum

Post a new message
Dataseries X:
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




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 4 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146617&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146617&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146617&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net







Multiple Linear Regression - Estimated Regression Equation
last[t] = + 3.42663860924614 + 0.352334244344399position[t] + 0.0449403567869172starters[t] -0.00140864859833935since[t] -0.204183757308926number[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
last[t] =  +  3.42663860924614 +  0.352334244344399position[t] +  0.0449403567869172starters[t] -0.00140864859833935since[t] -0.204183757308926number[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146617&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]last[t] =  +  3.42663860924614 +  0.352334244344399position[t] +  0.0449403567869172starters[t] -0.00140864859833935since[t] -0.204183757308926number[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146617&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146617&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
last[t] = + 3.42663860924614 + 0.352334244344399position[t] + 0.0449403567869172starters[t] -0.00140864859833935since[t] -0.204183757308926number[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)3.426638609246141.6396682.08980.0392490.019624
position0.3523342443443990.0999333.52570.0006470.000323
starters0.04494035678691720.1303710.34470.7310570.365529
since-0.001408648598339350.007271-0.19370.8467870.423394
number-0.2041837573089260.093056-2.19420.0306090.015305

\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) & 3.42663860924614 & 1.639668 & 2.0898 & 0.039249 & 0.019624 \tabularnewline
position & 0.352334244344399 & 0.099933 & 3.5257 & 0.000647 & 0.000323 \tabularnewline
starters & 0.0449403567869172 & 0.130371 & 0.3447 & 0.731057 & 0.365529 \tabularnewline
since & -0.00140864859833935 & 0.007271 & -0.1937 & 0.846787 & 0.423394 \tabularnewline
number & -0.204183757308926 & 0.093056 & -2.1942 & 0.030609 & 0.015305 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146617&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]3.42663860924614[/C][C]1.639668[/C][C]2.0898[/C][C]0.039249[/C][C]0.019624[/C][/ROW]
[ROW][C]position[/C][C]0.352334244344399[/C][C]0.099933[/C][C]3.5257[/C][C]0.000647[/C][C]0.000323[/C][/ROW]
[ROW][C]starters[/C][C]0.0449403567869172[/C][C]0.130371[/C][C]0.3447[/C][C]0.731057[/C][C]0.365529[/C][/ROW]
[ROW][C]since[/C][C]-0.00140864859833935[/C][C]0.007271[/C][C]-0.1937[/C][C]0.846787[/C][C]0.423394[/C][/ROW]
[ROW][C]number[/C][C]-0.204183757308926[/C][C]0.093056[/C][C]-2.1942[/C][C]0.030609[/C][C]0.015305[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146617&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146617&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)3.426638609246141.6396682.08980.0392490.019624
position0.3523342443443990.0999333.52570.0006470.000323
starters0.04494035678691720.1303710.34470.7310570.365529
since-0.001408648598339350.007271-0.19370.8467870.423394
number-0.2041837573089260.093056-2.19420.0306090.015305







Multiple Linear Regression - Regression Statistics
Multiple R0.359603331503972
R-squared0.129314556028756
Adjusted R-squared0.093410001638189
F-TEST (value)3.60161985641384
F-TEST (DF numerator)4
F-TEST (DF denominator)97
p-value0.00880869497153691
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.74917071343406
Sum Squared Residuals1363.45926073175

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.359603331503972 \tabularnewline
R-squared & 0.129314556028756 \tabularnewline
Adjusted R-squared & 0.093410001638189 \tabularnewline
F-TEST (value) & 3.60161985641384 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 97 \tabularnewline
p-value & 0.00880869497153691 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 3.74917071343406 \tabularnewline
Sum Squared Residuals & 1363.45926073175 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146617&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.359603331503972[/C][/ROW]
[ROW][C]R-squared[/C][C]0.129314556028756[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.093410001638189[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]3.60161985641384[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]97[/C][/ROW]
[ROW][C]p-value[/C][C]0.00880869497153691[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]3.74917071343406[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1363.45926073175[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146617&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146617&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 R0.359603331503972
R-squared0.129314556028756
Adjusted R-squared0.093410001638189
F-TEST (value)3.60161985641384
F-TEST (DF numerator)4
F-TEST (DF denominator)97
p-value0.00880869497153691
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.74917071343406
Sum Squared Residuals1363.45926073175







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
113.30203959827342-2.30203959827342
234.17113700985753-1.17113700985753
324.21089184427115-2.21089184427115
414.15204127680102-3.15204127680102
553.921335580333491.07866441966651
685.248103207477322.75189679252268
734.60635327809582-1.60635327809582
885.361420442664712.63857955733529
9123.477575079626088.52242492037392
1032.810399186024190.189600813975814
1183.564057701994744.43594229800526
1234.94717127307214-1.94717127307214
1333.25625929572895-0.256259295728946
1434.43237181230075-1.43237181230075
1536.20835776341427-3.20835776341427
1615.53695592401736-4.53695592401736
1725.07537243632273-3.07537243632273
18206.852767036034613.1472329639654
1926.80659430594952-4.80659430594952
2015.91551357466195-4.91551357466195
2113.74548692948982-2.74548692948982
2263.699314199404742.30068580059526
2383.439097171822364.56090282817764
2452.974696386931062.02530361306894
2514.54931587793234-3.54931587793234
2675.721202448709121.27879755129088
2774.440066634582112.55993336541789
2856.02736396296844-1.02736396296844
2984.936325420166963.06367457983304
3023.77586549630386-1.77586549630386
3153.914155443150951.08584455684905
3223.64971246977356-1.64971246977356
3354.230177497598650.769822502401349
3413.12082652301876-2.12082652301876
3522.77186463374101-0.771864633741008
3664.66476841851671.3352315814833
3736.6505727213325-3.6505727213325
3865.034248830251010.965751169748991
3966.23282943494596-0.232829434945959
4016.98078306707288-5.98078306707288
4126.01807212562084-4.01807212562084
42105.736725369063374.26327463093663
4315.90318828787726-4.90318828787726
4422.31149937854283-0.311499378542833
4514.07339884367296-3.07339884367296
4614.63977738551466-3.63977738551466
4712.74186435366586-1.74186435366586
4864.936078359585611.06392164041439
4945.89110333566841-1.89110333566841
5094.618419413131444.38158058686856
51105.794531929703244.20546807029676
5265.527271659129160.472728340870837
5316.0950588495692-5.0950588495692
5465.419431064377280.580568935622723
55186.895445153494811.1045548465052
5636.3212400673833-3.3212400673833
5743.796383522929510.203616477070485
5813.13343357512264-2.13343357512264
5932.250804735425110.749195264574888
6052.703082740801322.29691725919868
6142.54147111510271.4585288848973
6245.75237796177206-1.75237796177206
6315.29502041987246-4.29502041987246
64176.8456928847019610.154307115298
6524.16062644418258-2.16062644418258
6615.12832925765044-4.12832925765044
6767.11554220906458-1.11554220906458
68104.793766528016195.20623347198381
6997.616026940444451.38397305955555
7057.15162615555315-2.15162615555315
7117.08714099368966-6.08714099368966
72135.784875450587567.21512454941244
73118.597275322827452.40272467717255
7498.35255342982680.647446570173197
7547.07564356149482-3.07564356149482
7643.532375976601370.46762402339863
7754.497261492872550.502738507127454
7824.43559362820574-2.43559362820574
7913.57268586888496-2.57268586888496
8023.50679205842313-1.50679205842313
8143.65494254545860.345057454541398
82126.054748957285625.94525104271438
83145.386164415085398.61383558491461
8425.92718728215698-3.92718728215698
8576.499200418392040.500799581607963
8646.22912285062128-2.22912285062128
8716.17308958034783-5.17308958034783
8865.903012012577080.0969879874229223
8923.9772319564161-1.9772319564161
9012.83407341547607-1.83407341547607
9144.27212428188871-0.27212428188871
9265.231375203766530.768624796233471
9372.949041683471644.05095831652836
9494.671498987848274.32850101215173
9514.47044520139615-3.47044520139615
9635.62965393478787-2.62965393478787
9764.684974267313131.31502573268687
9886.508924560220471.49107543977953
9987.299207939747850.700792060252152
10045.58998353062624-1.58998353062624
10186.107059371526061.89294062847394
10275.882058558902171.11794144109783

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 1 & 3.30203959827342 & -2.30203959827342 \tabularnewline
2 & 3 & 4.17113700985753 & -1.17113700985753 \tabularnewline
3 & 2 & 4.21089184427115 & -2.21089184427115 \tabularnewline
4 & 1 & 4.15204127680102 & -3.15204127680102 \tabularnewline
5 & 5 & 3.92133558033349 & 1.07866441966651 \tabularnewline
6 & 8 & 5.24810320747732 & 2.75189679252268 \tabularnewline
7 & 3 & 4.60635327809582 & -1.60635327809582 \tabularnewline
8 & 8 & 5.36142044266471 & 2.63857955733529 \tabularnewline
9 & 12 & 3.47757507962608 & 8.52242492037392 \tabularnewline
10 & 3 & 2.81039918602419 & 0.189600813975814 \tabularnewline
11 & 8 & 3.56405770199474 & 4.43594229800526 \tabularnewline
12 & 3 & 4.94717127307214 & -1.94717127307214 \tabularnewline
13 & 3 & 3.25625929572895 & -0.256259295728946 \tabularnewline
14 & 3 & 4.43237181230075 & -1.43237181230075 \tabularnewline
15 & 3 & 6.20835776341427 & -3.20835776341427 \tabularnewline
16 & 1 & 5.53695592401736 & -4.53695592401736 \tabularnewline
17 & 2 & 5.07537243632273 & -3.07537243632273 \tabularnewline
18 & 20 & 6.8527670360346 & 13.1472329639654 \tabularnewline
19 & 2 & 6.80659430594952 & -4.80659430594952 \tabularnewline
20 & 1 & 5.91551357466195 & -4.91551357466195 \tabularnewline
21 & 1 & 3.74548692948982 & -2.74548692948982 \tabularnewline
22 & 6 & 3.69931419940474 & 2.30068580059526 \tabularnewline
23 & 8 & 3.43909717182236 & 4.56090282817764 \tabularnewline
24 & 5 & 2.97469638693106 & 2.02530361306894 \tabularnewline
25 & 1 & 4.54931587793234 & -3.54931587793234 \tabularnewline
26 & 7 & 5.72120244870912 & 1.27879755129088 \tabularnewline
27 & 7 & 4.44006663458211 & 2.55993336541789 \tabularnewline
28 & 5 & 6.02736396296844 & -1.02736396296844 \tabularnewline
29 & 8 & 4.93632542016696 & 3.06367457983304 \tabularnewline
30 & 2 & 3.77586549630386 & -1.77586549630386 \tabularnewline
31 & 5 & 3.91415544315095 & 1.08584455684905 \tabularnewline
32 & 2 & 3.64971246977356 & -1.64971246977356 \tabularnewline
33 & 5 & 4.23017749759865 & 0.769822502401349 \tabularnewline
34 & 1 & 3.12082652301876 & -2.12082652301876 \tabularnewline
35 & 2 & 2.77186463374101 & -0.771864633741008 \tabularnewline
36 & 6 & 4.6647684185167 & 1.3352315814833 \tabularnewline
37 & 3 & 6.6505727213325 & -3.6505727213325 \tabularnewline
38 & 6 & 5.03424883025101 & 0.965751169748991 \tabularnewline
39 & 6 & 6.23282943494596 & -0.232829434945959 \tabularnewline
40 & 1 & 6.98078306707288 & -5.98078306707288 \tabularnewline
41 & 2 & 6.01807212562084 & -4.01807212562084 \tabularnewline
42 & 10 & 5.73672536906337 & 4.26327463093663 \tabularnewline
43 & 1 & 5.90318828787726 & -4.90318828787726 \tabularnewline
44 & 2 & 2.31149937854283 & -0.311499378542833 \tabularnewline
45 & 1 & 4.07339884367296 & -3.07339884367296 \tabularnewline
46 & 1 & 4.63977738551466 & -3.63977738551466 \tabularnewline
47 & 1 & 2.74186435366586 & -1.74186435366586 \tabularnewline
48 & 6 & 4.93607835958561 & 1.06392164041439 \tabularnewline
49 & 4 & 5.89110333566841 & -1.89110333566841 \tabularnewline
50 & 9 & 4.61841941313144 & 4.38158058686856 \tabularnewline
51 & 10 & 5.79453192970324 & 4.20546807029676 \tabularnewline
52 & 6 & 5.52727165912916 & 0.472728340870837 \tabularnewline
53 & 1 & 6.0950588495692 & -5.0950588495692 \tabularnewline
54 & 6 & 5.41943106437728 & 0.580568935622723 \tabularnewline
55 & 18 & 6.8954451534948 & 11.1045548465052 \tabularnewline
56 & 3 & 6.3212400673833 & -3.3212400673833 \tabularnewline
57 & 4 & 3.79638352292951 & 0.203616477070485 \tabularnewline
58 & 1 & 3.13343357512264 & -2.13343357512264 \tabularnewline
59 & 3 & 2.25080473542511 & 0.749195264574888 \tabularnewline
60 & 5 & 2.70308274080132 & 2.29691725919868 \tabularnewline
61 & 4 & 2.5414711151027 & 1.4585288848973 \tabularnewline
62 & 4 & 5.75237796177206 & -1.75237796177206 \tabularnewline
63 & 1 & 5.29502041987246 & -4.29502041987246 \tabularnewline
64 & 17 & 6.84569288470196 & 10.154307115298 \tabularnewline
65 & 2 & 4.16062644418258 & -2.16062644418258 \tabularnewline
66 & 1 & 5.12832925765044 & -4.12832925765044 \tabularnewline
67 & 6 & 7.11554220906458 & -1.11554220906458 \tabularnewline
68 & 10 & 4.79376652801619 & 5.20623347198381 \tabularnewline
69 & 9 & 7.61602694044445 & 1.38397305955555 \tabularnewline
70 & 5 & 7.15162615555315 & -2.15162615555315 \tabularnewline
71 & 1 & 7.08714099368966 & -6.08714099368966 \tabularnewline
72 & 13 & 5.78487545058756 & 7.21512454941244 \tabularnewline
73 & 11 & 8.59727532282745 & 2.40272467717255 \tabularnewline
74 & 9 & 8.3525534298268 & 0.647446570173197 \tabularnewline
75 & 4 & 7.07564356149482 & -3.07564356149482 \tabularnewline
76 & 4 & 3.53237597660137 & 0.46762402339863 \tabularnewline
77 & 5 & 4.49726149287255 & 0.502738507127454 \tabularnewline
78 & 2 & 4.43559362820574 & -2.43559362820574 \tabularnewline
79 & 1 & 3.57268586888496 & -2.57268586888496 \tabularnewline
80 & 2 & 3.50679205842313 & -1.50679205842313 \tabularnewline
81 & 4 & 3.6549425454586 & 0.345057454541398 \tabularnewline
82 & 12 & 6.05474895728562 & 5.94525104271438 \tabularnewline
83 & 14 & 5.38616441508539 & 8.61383558491461 \tabularnewline
84 & 2 & 5.92718728215698 & -3.92718728215698 \tabularnewline
85 & 7 & 6.49920041839204 & 0.500799581607963 \tabularnewline
86 & 4 & 6.22912285062128 & -2.22912285062128 \tabularnewline
87 & 1 & 6.17308958034783 & -5.17308958034783 \tabularnewline
88 & 6 & 5.90301201257708 & 0.0969879874229223 \tabularnewline
89 & 2 & 3.9772319564161 & -1.9772319564161 \tabularnewline
90 & 1 & 2.83407341547607 & -1.83407341547607 \tabularnewline
91 & 4 & 4.27212428188871 & -0.27212428188871 \tabularnewline
92 & 6 & 5.23137520376653 & 0.768624796233471 \tabularnewline
93 & 7 & 2.94904168347164 & 4.05095831652836 \tabularnewline
94 & 9 & 4.67149898784827 & 4.32850101215173 \tabularnewline
95 & 1 & 4.47044520139615 & -3.47044520139615 \tabularnewline
96 & 3 & 5.62965393478787 & -2.62965393478787 \tabularnewline
97 & 6 & 4.68497426731313 & 1.31502573268687 \tabularnewline
98 & 8 & 6.50892456022047 & 1.49107543977953 \tabularnewline
99 & 8 & 7.29920793974785 & 0.700792060252152 \tabularnewline
100 & 4 & 5.58998353062624 & -1.58998353062624 \tabularnewline
101 & 8 & 6.10705937152606 & 1.89294062847394 \tabularnewline
102 & 7 & 5.88205855890217 & 1.11794144109783 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146617&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]3.30203959827342[/C][C]-2.30203959827342[/C][/ROW]
[ROW][C]2[/C][C]3[/C][C]4.17113700985753[/C][C]-1.17113700985753[/C][/ROW]
[ROW][C]3[/C][C]2[/C][C]4.21089184427115[/C][C]-2.21089184427115[/C][/ROW]
[ROW][C]4[/C][C]1[/C][C]4.15204127680102[/C][C]-3.15204127680102[/C][/ROW]
[ROW][C]5[/C][C]5[/C][C]3.92133558033349[/C][C]1.07866441966651[/C][/ROW]
[ROW][C]6[/C][C]8[/C][C]5.24810320747732[/C][C]2.75189679252268[/C][/ROW]
[ROW][C]7[/C][C]3[/C][C]4.60635327809582[/C][C]-1.60635327809582[/C][/ROW]
[ROW][C]8[/C][C]8[/C][C]5.36142044266471[/C][C]2.63857955733529[/C][/ROW]
[ROW][C]9[/C][C]12[/C][C]3.47757507962608[/C][C]8.52242492037392[/C][/ROW]
[ROW][C]10[/C][C]3[/C][C]2.81039918602419[/C][C]0.189600813975814[/C][/ROW]
[ROW][C]11[/C][C]8[/C][C]3.56405770199474[/C][C]4.43594229800526[/C][/ROW]
[ROW][C]12[/C][C]3[/C][C]4.94717127307214[/C][C]-1.94717127307214[/C][/ROW]
[ROW][C]13[/C][C]3[/C][C]3.25625929572895[/C][C]-0.256259295728946[/C][/ROW]
[ROW][C]14[/C][C]3[/C][C]4.43237181230075[/C][C]-1.43237181230075[/C][/ROW]
[ROW][C]15[/C][C]3[/C][C]6.20835776341427[/C][C]-3.20835776341427[/C][/ROW]
[ROW][C]16[/C][C]1[/C][C]5.53695592401736[/C][C]-4.53695592401736[/C][/ROW]
[ROW][C]17[/C][C]2[/C][C]5.07537243632273[/C][C]-3.07537243632273[/C][/ROW]
[ROW][C]18[/C][C]20[/C][C]6.8527670360346[/C][C]13.1472329639654[/C][/ROW]
[ROW][C]19[/C][C]2[/C][C]6.80659430594952[/C][C]-4.80659430594952[/C][/ROW]
[ROW][C]20[/C][C]1[/C][C]5.91551357466195[/C][C]-4.91551357466195[/C][/ROW]
[ROW][C]21[/C][C]1[/C][C]3.74548692948982[/C][C]-2.74548692948982[/C][/ROW]
[ROW][C]22[/C][C]6[/C][C]3.69931419940474[/C][C]2.30068580059526[/C][/ROW]
[ROW][C]23[/C][C]8[/C][C]3.43909717182236[/C][C]4.56090282817764[/C][/ROW]
[ROW][C]24[/C][C]5[/C][C]2.97469638693106[/C][C]2.02530361306894[/C][/ROW]
[ROW][C]25[/C][C]1[/C][C]4.54931587793234[/C][C]-3.54931587793234[/C][/ROW]
[ROW][C]26[/C][C]7[/C][C]5.72120244870912[/C][C]1.27879755129088[/C][/ROW]
[ROW][C]27[/C][C]7[/C][C]4.44006663458211[/C][C]2.55993336541789[/C][/ROW]
[ROW][C]28[/C][C]5[/C][C]6.02736396296844[/C][C]-1.02736396296844[/C][/ROW]
[ROW][C]29[/C][C]8[/C][C]4.93632542016696[/C][C]3.06367457983304[/C][/ROW]
[ROW][C]30[/C][C]2[/C][C]3.77586549630386[/C][C]-1.77586549630386[/C][/ROW]
[ROW][C]31[/C][C]5[/C][C]3.91415544315095[/C][C]1.08584455684905[/C][/ROW]
[ROW][C]32[/C][C]2[/C][C]3.64971246977356[/C][C]-1.64971246977356[/C][/ROW]
[ROW][C]33[/C][C]5[/C][C]4.23017749759865[/C][C]0.769822502401349[/C][/ROW]
[ROW][C]34[/C][C]1[/C][C]3.12082652301876[/C][C]-2.12082652301876[/C][/ROW]
[ROW][C]35[/C][C]2[/C][C]2.77186463374101[/C][C]-0.771864633741008[/C][/ROW]
[ROW][C]36[/C][C]6[/C][C]4.6647684185167[/C][C]1.3352315814833[/C][/ROW]
[ROW][C]37[/C][C]3[/C][C]6.6505727213325[/C][C]-3.6505727213325[/C][/ROW]
[ROW][C]38[/C][C]6[/C][C]5.03424883025101[/C][C]0.965751169748991[/C][/ROW]
[ROW][C]39[/C][C]6[/C][C]6.23282943494596[/C][C]-0.232829434945959[/C][/ROW]
[ROW][C]40[/C][C]1[/C][C]6.98078306707288[/C][C]-5.98078306707288[/C][/ROW]
[ROW][C]41[/C][C]2[/C][C]6.01807212562084[/C][C]-4.01807212562084[/C][/ROW]
[ROW][C]42[/C][C]10[/C][C]5.73672536906337[/C][C]4.26327463093663[/C][/ROW]
[ROW][C]43[/C][C]1[/C][C]5.90318828787726[/C][C]-4.90318828787726[/C][/ROW]
[ROW][C]44[/C][C]2[/C][C]2.31149937854283[/C][C]-0.311499378542833[/C][/ROW]
[ROW][C]45[/C][C]1[/C][C]4.07339884367296[/C][C]-3.07339884367296[/C][/ROW]
[ROW][C]46[/C][C]1[/C][C]4.63977738551466[/C][C]-3.63977738551466[/C][/ROW]
[ROW][C]47[/C][C]1[/C][C]2.74186435366586[/C][C]-1.74186435366586[/C][/ROW]
[ROW][C]48[/C][C]6[/C][C]4.93607835958561[/C][C]1.06392164041439[/C][/ROW]
[ROW][C]49[/C][C]4[/C][C]5.89110333566841[/C][C]-1.89110333566841[/C][/ROW]
[ROW][C]50[/C][C]9[/C][C]4.61841941313144[/C][C]4.38158058686856[/C][/ROW]
[ROW][C]51[/C][C]10[/C][C]5.79453192970324[/C][C]4.20546807029676[/C][/ROW]
[ROW][C]52[/C][C]6[/C][C]5.52727165912916[/C][C]0.472728340870837[/C][/ROW]
[ROW][C]53[/C][C]1[/C][C]6.0950588495692[/C][C]-5.0950588495692[/C][/ROW]
[ROW][C]54[/C][C]6[/C][C]5.41943106437728[/C][C]0.580568935622723[/C][/ROW]
[ROW][C]55[/C][C]18[/C][C]6.8954451534948[/C][C]11.1045548465052[/C][/ROW]
[ROW][C]56[/C][C]3[/C][C]6.3212400673833[/C][C]-3.3212400673833[/C][/ROW]
[ROW][C]57[/C][C]4[/C][C]3.79638352292951[/C][C]0.203616477070485[/C][/ROW]
[ROW][C]58[/C][C]1[/C][C]3.13343357512264[/C][C]-2.13343357512264[/C][/ROW]
[ROW][C]59[/C][C]3[/C][C]2.25080473542511[/C][C]0.749195264574888[/C][/ROW]
[ROW][C]60[/C][C]5[/C][C]2.70308274080132[/C][C]2.29691725919868[/C][/ROW]
[ROW][C]61[/C][C]4[/C][C]2.5414711151027[/C][C]1.4585288848973[/C][/ROW]
[ROW][C]62[/C][C]4[/C][C]5.75237796177206[/C][C]-1.75237796177206[/C][/ROW]
[ROW][C]63[/C][C]1[/C][C]5.29502041987246[/C][C]-4.29502041987246[/C][/ROW]
[ROW][C]64[/C][C]17[/C][C]6.84569288470196[/C][C]10.154307115298[/C][/ROW]
[ROW][C]65[/C][C]2[/C][C]4.16062644418258[/C][C]-2.16062644418258[/C][/ROW]
[ROW][C]66[/C][C]1[/C][C]5.12832925765044[/C][C]-4.12832925765044[/C][/ROW]
[ROW][C]67[/C][C]6[/C][C]7.11554220906458[/C][C]-1.11554220906458[/C][/ROW]
[ROW][C]68[/C][C]10[/C][C]4.79376652801619[/C][C]5.20623347198381[/C][/ROW]
[ROW][C]69[/C][C]9[/C][C]7.61602694044445[/C][C]1.38397305955555[/C][/ROW]
[ROW][C]70[/C][C]5[/C][C]7.15162615555315[/C][C]-2.15162615555315[/C][/ROW]
[ROW][C]71[/C][C]1[/C][C]7.08714099368966[/C][C]-6.08714099368966[/C][/ROW]
[ROW][C]72[/C][C]13[/C][C]5.78487545058756[/C][C]7.21512454941244[/C][/ROW]
[ROW][C]73[/C][C]11[/C][C]8.59727532282745[/C][C]2.40272467717255[/C][/ROW]
[ROW][C]74[/C][C]9[/C][C]8.3525534298268[/C][C]0.647446570173197[/C][/ROW]
[ROW][C]75[/C][C]4[/C][C]7.07564356149482[/C][C]-3.07564356149482[/C][/ROW]
[ROW][C]76[/C][C]4[/C][C]3.53237597660137[/C][C]0.46762402339863[/C][/ROW]
[ROW][C]77[/C][C]5[/C][C]4.49726149287255[/C][C]0.502738507127454[/C][/ROW]
[ROW][C]78[/C][C]2[/C][C]4.43559362820574[/C][C]-2.43559362820574[/C][/ROW]
[ROW][C]79[/C][C]1[/C][C]3.57268586888496[/C][C]-2.57268586888496[/C][/ROW]
[ROW][C]80[/C][C]2[/C][C]3.50679205842313[/C][C]-1.50679205842313[/C][/ROW]
[ROW][C]81[/C][C]4[/C][C]3.6549425454586[/C][C]0.345057454541398[/C][/ROW]
[ROW][C]82[/C][C]12[/C][C]6.05474895728562[/C][C]5.94525104271438[/C][/ROW]
[ROW][C]83[/C][C]14[/C][C]5.38616441508539[/C][C]8.61383558491461[/C][/ROW]
[ROW][C]84[/C][C]2[/C][C]5.92718728215698[/C][C]-3.92718728215698[/C][/ROW]
[ROW][C]85[/C][C]7[/C][C]6.49920041839204[/C][C]0.500799581607963[/C][/ROW]
[ROW][C]86[/C][C]4[/C][C]6.22912285062128[/C][C]-2.22912285062128[/C][/ROW]
[ROW][C]87[/C][C]1[/C][C]6.17308958034783[/C][C]-5.17308958034783[/C][/ROW]
[ROW][C]88[/C][C]6[/C][C]5.90301201257708[/C][C]0.0969879874229223[/C][/ROW]
[ROW][C]89[/C][C]2[/C][C]3.9772319564161[/C][C]-1.9772319564161[/C][/ROW]
[ROW][C]90[/C][C]1[/C][C]2.83407341547607[/C][C]-1.83407341547607[/C][/ROW]
[ROW][C]91[/C][C]4[/C][C]4.27212428188871[/C][C]-0.27212428188871[/C][/ROW]
[ROW][C]92[/C][C]6[/C][C]5.23137520376653[/C][C]0.768624796233471[/C][/ROW]
[ROW][C]93[/C][C]7[/C][C]2.94904168347164[/C][C]4.05095831652836[/C][/ROW]
[ROW][C]94[/C][C]9[/C][C]4.67149898784827[/C][C]4.32850101215173[/C][/ROW]
[ROW][C]95[/C][C]1[/C][C]4.47044520139615[/C][C]-3.47044520139615[/C][/ROW]
[ROW][C]96[/C][C]3[/C][C]5.62965393478787[/C][C]-2.62965393478787[/C][/ROW]
[ROW][C]97[/C][C]6[/C][C]4.68497426731313[/C][C]1.31502573268687[/C][/ROW]
[ROW][C]98[/C][C]8[/C][C]6.50892456022047[/C][C]1.49107543977953[/C][/ROW]
[ROW][C]99[/C][C]8[/C][C]7.29920793974785[/C][C]0.700792060252152[/C][/ROW]
[ROW][C]100[/C][C]4[/C][C]5.58998353062624[/C][C]-1.58998353062624[/C][/ROW]
[ROW][C]101[/C][C]8[/C][C]6.10705937152606[/C][C]1.89294062847394[/C][/ROW]
[ROW][C]102[/C][C]7[/C][C]5.88205855890217[/C][C]1.11794144109783[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146617&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146617&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
113.30203959827342-2.30203959827342
234.17113700985753-1.17113700985753
324.21089184427115-2.21089184427115
414.15204127680102-3.15204127680102
553.921335580333491.07866441966651
685.248103207477322.75189679252268
734.60635327809582-1.60635327809582
885.361420442664712.63857955733529
9123.477575079626088.52242492037392
1032.810399186024190.189600813975814
1183.564057701994744.43594229800526
1234.94717127307214-1.94717127307214
1333.25625929572895-0.256259295728946
1434.43237181230075-1.43237181230075
1536.20835776341427-3.20835776341427
1615.53695592401736-4.53695592401736
1725.07537243632273-3.07537243632273
18206.852767036034613.1472329639654
1926.80659430594952-4.80659430594952
2015.91551357466195-4.91551357466195
2113.74548692948982-2.74548692948982
2263.699314199404742.30068580059526
2383.439097171822364.56090282817764
2452.974696386931062.02530361306894
2514.54931587793234-3.54931587793234
2675.721202448709121.27879755129088
2774.440066634582112.55993336541789
2856.02736396296844-1.02736396296844
2984.936325420166963.06367457983304
3023.77586549630386-1.77586549630386
3153.914155443150951.08584455684905
3223.64971246977356-1.64971246977356
3354.230177497598650.769822502401349
3413.12082652301876-2.12082652301876
3522.77186463374101-0.771864633741008
3664.66476841851671.3352315814833
3736.6505727213325-3.6505727213325
3865.034248830251010.965751169748991
3966.23282943494596-0.232829434945959
4016.98078306707288-5.98078306707288
4126.01807212562084-4.01807212562084
42105.736725369063374.26327463093663
4315.90318828787726-4.90318828787726
4422.31149937854283-0.311499378542833
4514.07339884367296-3.07339884367296
4614.63977738551466-3.63977738551466
4712.74186435366586-1.74186435366586
4864.936078359585611.06392164041439
4945.89110333566841-1.89110333566841
5094.618419413131444.38158058686856
51105.794531929703244.20546807029676
5265.527271659129160.472728340870837
5316.0950588495692-5.0950588495692
5465.419431064377280.580568935622723
55186.895445153494811.1045548465052
5636.3212400673833-3.3212400673833
5743.796383522929510.203616477070485
5813.13343357512264-2.13343357512264
5932.250804735425110.749195264574888
6052.703082740801322.29691725919868
6142.54147111510271.4585288848973
6245.75237796177206-1.75237796177206
6315.29502041987246-4.29502041987246
64176.8456928847019610.154307115298
6524.16062644418258-2.16062644418258
6615.12832925765044-4.12832925765044
6767.11554220906458-1.11554220906458
68104.793766528016195.20623347198381
6997.616026940444451.38397305955555
7057.15162615555315-2.15162615555315
7117.08714099368966-6.08714099368966
72135.784875450587567.21512454941244
73118.597275322827452.40272467717255
7498.35255342982680.647446570173197
7547.07564356149482-3.07564356149482
7643.532375976601370.46762402339863
7754.497261492872550.502738507127454
7824.43559362820574-2.43559362820574
7913.57268586888496-2.57268586888496
8023.50679205842313-1.50679205842313
8143.65494254545860.345057454541398
82126.054748957285625.94525104271438
83145.386164415085398.61383558491461
8425.92718728215698-3.92718728215698
8576.499200418392040.500799581607963
8646.22912285062128-2.22912285062128
8716.17308958034783-5.17308958034783
8865.903012012577080.0969879874229223
8923.9772319564161-1.9772319564161
9012.83407341547607-1.83407341547607
9144.27212428188871-0.27212428188871
9265.231375203766530.768624796233471
9372.949041683471644.05095831652836
9494.671498987848274.32850101215173
9514.47044520139615-3.47044520139615
9635.62965393478787-2.62965393478787
9764.684974267313131.31502573268687
9886.508924560220471.49107543977953
9987.299207939747850.700792060252152
10045.58998353062624-1.58998353062624
10186.107059371526061.89294062847394
10275.882058558902171.11794144109783







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.1034299475445640.2068598950891270.896570052455436
90.04035020352190530.08070040704381060.959649796478095
100.1659998670600030.3319997341200050.834000132939997
110.09737379734580120.1947475946916020.902626202654199
120.4408826632254130.8817653264508260.559117336774587
130.3946057106573690.7892114213147380.605394289342631
140.3646713501433350.7293427002866690.635328649856665
150.3498836463426430.6997672926852860.650116353657357
160.3484179101987310.6968358203974610.651582089801269
170.2832322995059710.5664645990119410.716767700494029
180.9506448306266520.09871033874669630.0493551693733482
190.9635614759301840.07287704813963110.0364385240698155
200.9617795579901840.07644088401963130.0382204420098156
210.9545487682520430.0909024634959140.045451231747957
220.9427867768571950.114426446285610.0572132231428052
230.9536477779322940.09270444413541130.0463522220677057
240.9435457989173490.1129084021653020.0564542010826512
250.9351450120819880.1297099758360230.0648549879180115
260.9151608143211370.1696783713577270.0848391856788634
270.9080374771144750.1839250457710510.0919625228855253
280.8777206578484550.244558684303090.122279342151545
290.8755904791934720.2488190416130570.124409520806528
300.8546822638766420.2906354722467160.145317736123358
310.8161264642649290.3677470714701430.183873535735071
320.7821534683856220.4356930632287560.217846531614378
330.7339775823075750.532044835384850.266022417692425
340.6957722375099870.6084555249800260.304227762490013
350.6404028370657930.7191943258684140.359597162934207
360.59067380735730.81865238528540.4093261926427
370.5769334495622310.8461331008755390.423066550437769
380.5195943413351540.9608113173296920.480405658664846
390.4608120335496280.9216240670992560.539187966450372
400.6079722879048530.7840554241902940.392027712095147
410.5921587122207740.8156825755584530.407841287779227
420.6311814155112680.7376371689774630.368818584488732
430.6464773279451650.7070453441096710.353522672054835
440.5921493230432420.8157013539135170.407850676956758
450.5705966022055820.8588067955888350.429403397794418
460.5595342061767050.880931587646590.440465793823295
470.5101609908031860.9796780183936280.489839009196814
480.4598054846498790.9196109692997570.540194515350121
490.4160787992557170.8321575985114340.583921200744283
500.4514339403646850.9028678807293710.548566059635315
510.480978288206220.9619565764124390.51902171179378
520.4249405917220460.8498811834440910.575059408277954
530.4566389183750780.9132778367501570.543361081624922
540.4028348178640220.8056696357280440.597165182135978
550.7745729044916150.4508541910167690.225427095508385
560.7588587303617970.4822825392764050.241141269638203
570.7115463168818180.5769073662363640.288453683118182
580.6712350080250680.6575299839498640.328764991974932
590.6200280105453940.7599439789092130.379971989454606
600.5771492026312450.845701594737510.422850797368755
610.5309933255028950.938013348994210.469006674497105
620.4858662461736150.9717324923472290.514133753826385
630.5089351707304290.9821296585391410.491064829269571
640.8136033692964840.3727932614070320.186396630703516
650.7818156297483290.4363687405033410.218184370251671
660.7955560496557480.4088879006885050.204443950344252
670.753295320198060.493409359603880.24670467980194
680.7879790865597130.4240418268805750.212020913440287
690.7451546453716250.5096907092567490.254845354628375
700.7071180240310030.5857639519379950.292881975968997
710.8218864536226450.356227092754710.178113546377355
720.9142990883943460.1714018232113090.0857009116056543
730.8949921459328110.2100157081343780.105007854067189
740.8661112282009890.2677775435980220.133888771799011
750.8368862484968490.3262275030063020.163113751503151
760.7897859679471070.4204280641057870.210214032052893
770.7340794732094860.5318410535810290.265920526790515
780.6983766461479890.6032467077040210.301623353852011
790.6611944083211660.6776111833576680.338805591678834
800.6048689201102520.7902621597794960.395131079889748
810.5276092522524890.9447814954950220.472390747747511
820.6299163094688710.7401673810622590.370083690531129
830.9664636828045250.067072634390950.033536317195475
840.9543870246533670.09122595069326680.0456129753466334
850.9489336381994860.1021327236010280.0510663618005142
860.9187509720868210.1624980558263580.0812490279131792
870.9279334152457820.1441331695084370.0720665847542184
880.8818202294270390.2363595411459220.118179770572961
890.8326308906893830.3347382186212340.167369109310617
900.8075127119309030.3849745761381940.192487288069097
910.719595990762820.5608080184743610.28040400923718
920.5931104766236310.8137790467527380.406889523376369
930.6316283697348740.7367432605302520.368371630265126
940.9611701092506750.07765978149864950.0388298907493247

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 0.103429947544564 & 0.206859895089127 & 0.896570052455436 \tabularnewline
9 & 0.0403502035219053 & 0.0807004070438106 & 0.959649796478095 \tabularnewline
10 & 0.165999867060003 & 0.331999734120005 & 0.834000132939997 \tabularnewline
11 & 0.0973737973458012 & 0.194747594691602 & 0.902626202654199 \tabularnewline
12 & 0.440882663225413 & 0.881765326450826 & 0.559117336774587 \tabularnewline
13 & 0.394605710657369 & 0.789211421314738 & 0.605394289342631 \tabularnewline
14 & 0.364671350143335 & 0.729342700286669 & 0.635328649856665 \tabularnewline
15 & 0.349883646342643 & 0.699767292685286 & 0.650116353657357 \tabularnewline
16 & 0.348417910198731 & 0.696835820397461 & 0.651582089801269 \tabularnewline
17 & 0.283232299505971 & 0.566464599011941 & 0.716767700494029 \tabularnewline
18 & 0.950644830626652 & 0.0987103387466963 & 0.0493551693733482 \tabularnewline
19 & 0.963561475930184 & 0.0728770481396311 & 0.0364385240698155 \tabularnewline
20 & 0.961779557990184 & 0.0764408840196313 & 0.0382204420098156 \tabularnewline
21 & 0.954548768252043 & 0.090902463495914 & 0.045451231747957 \tabularnewline
22 & 0.942786776857195 & 0.11442644628561 & 0.0572132231428052 \tabularnewline
23 & 0.953647777932294 & 0.0927044441354113 & 0.0463522220677057 \tabularnewline
24 & 0.943545798917349 & 0.112908402165302 & 0.0564542010826512 \tabularnewline
25 & 0.935145012081988 & 0.129709975836023 & 0.0648549879180115 \tabularnewline
26 & 0.915160814321137 & 0.169678371357727 & 0.0848391856788634 \tabularnewline
27 & 0.908037477114475 & 0.183925045771051 & 0.0919625228855253 \tabularnewline
28 & 0.877720657848455 & 0.24455868430309 & 0.122279342151545 \tabularnewline
29 & 0.875590479193472 & 0.248819041613057 & 0.124409520806528 \tabularnewline
30 & 0.854682263876642 & 0.290635472246716 & 0.145317736123358 \tabularnewline
31 & 0.816126464264929 & 0.367747071470143 & 0.183873535735071 \tabularnewline
32 & 0.782153468385622 & 0.435693063228756 & 0.217846531614378 \tabularnewline
33 & 0.733977582307575 & 0.53204483538485 & 0.266022417692425 \tabularnewline
34 & 0.695772237509987 & 0.608455524980026 & 0.304227762490013 \tabularnewline
35 & 0.640402837065793 & 0.719194325868414 & 0.359597162934207 \tabularnewline
36 & 0.5906738073573 & 0.8186523852854 & 0.4093261926427 \tabularnewline
37 & 0.576933449562231 & 0.846133100875539 & 0.423066550437769 \tabularnewline
38 & 0.519594341335154 & 0.960811317329692 & 0.480405658664846 \tabularnewline
39 & 0.460812033549628 & 0.921624067099256 & 0.539187966450372 \tabularnewline
40 & 0.607972287904853 & 0.784055424190294 & 0.392027712095147 \tabularnewline
41 & 0.592158712220774 & 0.815682575558453 & 0.407841287779227 \tabularnewline
42 & 0.631181415511268 & 0.737637168977463 & 0.368818584488732 \tabularnewline
43 & 0.646477327945165 & 0.707045344109671 & 0.353522672054835 \tabularnewline
44 & 0.592149323043242 & 0.815701353913517 & 0.407850676956758 \tabularnewline
45 & 0.570596602205582 & 0.858806795588835 & 0.429403397794418 \tabularnewline
46 & 0.559534206176705 & 0.88093158764659 & 0.440465793823295 \tabularnewline
47 & 0.510160990803186 & 0.979678018393628 & 0.489839009196814 \tabularnewline
48 & 0.459805484649879 & 0.919610969299757 & 0.540194515350121 \tabularnewline
49 & 0.416078799255717 & 0.832157598511434 & 0.583921200744283 \tabularnewline
50 & 0.451433940364685 & 0.902867880729371 & 0.548566059635315 \tabularnewline
51 & 0.48097828820622 & 0.961956576412439 & 0.51902171179378 \tabularnewline
52 & 0.424940591722046 & 0.849881183444091 & 0.575059408277954 \tabularnewline
53 & 0.456638918375078 & 0.913277836750157 & 0.543361081624922 \tabularnewline
54 & 0.402834817864022 & 0.805669635728044 & 0.597165182135978 \tabularnewline
55 & 0.774572904491615 & 0.450854191016769 & 0.225427095508385 \tabularnewline
56 & 0.758858730361797 & 0.482282539276405 & 0.241141269638203 \tabularnewline
57 & 0.711546316881818 & 0.576907366236364 & 0.288453683118182 \tabularnewline
58 & 0.671235008025068 & 0.657529983949864 & 0.328764991974932 \tabularnewline
59 & 0.620028010545394 & 0.759943978909213 & 0.379971989454606 \tabularnewline
60 & 0.577149202631245 & 0.84570159473751 & 0.422850797368755 \tabularnewline
61 & 0.530993325502895 & 0.93801334899421 & 0.469006674497105 \tabularnewline
62 & 0.485866246173615 & 0.971732492347229 & 0.514133753826385 \tabularnewline
63 & 0.508935170730429 & 0.982129658539141 & 0.491064829269571 \tabularnewline
64 & 0.813603369296484 & 0.372793261407032 & 0.186396630703516 \tabularnewline
65 & 0.781815629748329 & 0.436368740503341 & 0.218184370251671 \tabularnewline
66 & 0.795556049655748 & 0.408887900688505 & 0.204443950344252 \tabularnewline
67 & 0.75329532019806 & 0.49340935960388 & 0.24670467980194 \tabularnewline
68 & 0.787979086559713 & 0.424041826880575 & 0.212020913440287 \tabularnewline
69 & 0.745154645371625 & 0.509690709256749 & 0.254845354628375 \tabularnewline
70 & 0.707118024031003 & 0.585763951937995 & 0.292881975968997 \tabularnewline
71 & 0.821886453622645 & 0.35622709275471 & 0.178113546377355 \tabularnewline
72 & 0.914299088394346 & 0.171401823211309 & 0.0857009116056543 \tabularnewline
73 & 0.894992145932811 & 0.210015708134378 & 0.105007854067189 \tabularnewline
74 & 0.866111228200989 & 0.267777543598022 & 0.133888771799011 \tabularnewline
75 & 0.836886248496849 & 0.326227503006302 & 0.163113751503151 \tabularnewline
76 & 0.789785967947107 & 0.420428064105787 & 0.210214032052893 \tabularnewline
77 & 0.734079473209486 & 0.531841053581029 & 0.265920526790515 \tabularnewline
78 & 0.698376646147989 & 0.603246707704021 & 0.301623353852011 \tabularnewline
79 & 0.661194408321166 & 0.677611183357668 & 0.338805591678834 \tabularnewline
80 & 0.604868920110252 & 0.790262159779496 & 0.395131079889748 \tabularnewline
81 & 0.527609252252489 & 0.944781495495022 & 0.472390747747511 \tabularnewline
82 & 0.629916309468871 & 0.740167381062259 & 0.370083690531129 \tabularnewline
83 & 0.966463682804525 & 0.06707263439095 & 0.033536317195475 \tabularnewline
84 & 0.954387024653367 & 0.0912259506932668 & 0.0456129753466334 \tabularnewline
85 & 0.948933638199486 & 0.102132723601028 & 0.0510663618005142 \tabularnewline
86 & 0.918750972086821 & 0.162498055826358 & 0.0812490279131792 \tabularnewline
87 & 0.927933415245782 & 0.144133169508437 & 0.0720665847542184 \tabularnewline
88 & 0.881820229427039 & 0.236359541145922 & 0.118179770572961 \tabularnewline
89 & 0.832630890689383 & 0.334738218621234 & 0.167369109310617 \tabularnewline
90 & 0.807512711930903 & 0.384974576138194 & 0.192487288069097 \tabularnewline
91 & 0.71959599076282 & 0.560808018474361 & 0.28040400923718 \tabularnewline
92 & 0.593110476623631 & 0.813779046752738 & 0.406889523376369 \tabularnewline
93 & 0.631628369734874 & 0.736743260530252 & 0.368371630265126 \tabularnewline
94 & 0.961170109250675 & 0.0776597814986495 & 0.0388298907493247 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146617&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]8[/C][C]0.103429947544564[/C][C]0.206859895089127[/C][C]0.896570052455436[/C][/ROW]
[ROW][C]9[/C][C]0.0403502035219053[/C][C]0.0807004070438106[/C][C]0.959649796478095[/C][/ROW]
[ROW][C]10[/C][C]0.165999867060003[/C][C]0.331999734120005[/C][C]0.834000132939997[/C][/ROW]
[ROW][C]11[/C][C]0.0973737973458012[/C][C]0.194747594691602[/C][C]0.902626202654199[/C][/ROW]
[ROW][C]12[/C][C]0.440882663225413[/C][C]0.881765326450826[/C][C]0.559117336774587[/C][/ROW]
[ROW][C]13[/C][C]0.394605710657369[/C][C]0.789211421314738[/C][C]0.605394289342631[/C][/ROW]
[ROW][C]14[/C][C]0.364671350143335[/C][C]0.729342700286669[/C][C]0.635328649856665[/C][/ROW]
[ROW][C]15[/C][C]0.349883646342643[/C][C]0.699767292685286[/C][C]0.650116353657357[/C][/ROW]
[ROW][C]16[/C][C]0.348417910198731[/C][C]0.696835820397461[/C][C]0.651582089801269[/C][/ROW]
[ROW][C]17[/C][C]0.283232299505971[/C][C]0.566464599011941[/C][C]0.716767700494029[/C][/ROW]
[ROW][C]18[/C][C]0.950644830626652[/C][C]0.0987103387466963[/C][C]0.0493551693733482[/C][/ROW]
[ROW][C]19[/C][C]0.963561475930184[/C][C]0.0728770481396311[/C][C]0.0364385240698155[/C][/ROW]
[ROW][C]20[/C][C]0.961779557990184[/C][C]0.0764408840196313[/C][C]0.0382204420098156[/C][/ROW]
[ROW][C]21[/C][C]0.954548768252043[/C][C]0.090902463495914[/C][C]0.045451231747957[/C][/ROW]
[ROW][C]22[/C][C]0.942786776857195[/C][C]0.11442644628561[/C][C]0.0572132231428052[/C][/ROW]
[ROW][C]23[/C][C]0.953647777932294[/C][C]0.0927044441354113[/C][C]0.0463522220677057[/C][/ROW]
[ROW][C]24[/C][C]0.943545798917349[/C][C]0.112908402165302[/C][C]0.0564542010826512[/C][/ROW]
[ROW][C]25[/C][C]0.935145012081988[/C][C]0.129709975836023[/C][C]0.0648549879180115[/C][/ROW]
[ROW][C]26[/C][C]0.915160814321137[/C][C]0.169678371357727[/C][C]0.0848391856788634[/C][/ROW]
[ROW][C]27[/C][C]0.908037477114475[/C][C]0.183925045771051[/C][C]0.0919625228855253[/C][/ROW]
[ROW][C]28[/C][C]0.877720657848455[/C][C]0.24455868430309[/C][C]0.122279342151545[/C][/ROW]
[ROW][C]29[/C][C]0.875590479193472[/C][C]0.248819041613057[/C][C]0.124409520806528[/C][/ROW]
[ROW][C]30[/C][C]0.854682263876642[/C][C]0.290635472246716[/C][C]0.145317736123358[/C][/ROW]
[ROW][C]31[/C][C]0.816126464264929[/C][C]0.367747071470143[/C][C]0.183873535735071[/C][/ROW]
[ROW][C]32[/C][C]0.782153468385622[/C][C]0.435693063228756[/C][C]0.217846531614378[/C][/ROW]
[ROW][C]33[/C][C]0.733977582307575[/C][C]0.53204483538485[/C][C]0.266022417692425[/C][/ROW]
[ROW][C]34[/C][C]0.695772237509987[/C][C]0.608455524980026[/C][C]0.304227762490013[/C][/ROW]
[ROW][C]35[/C][C]0.640402837065793[/C][C]0.719194325868414[/C][C]0.359597162934207[/C][/ROW]
[ROW][C]36[/C][C]0.5906738073573[/C][C]0.8186523852854[/C][C]0.4093261926427[/C][/ROW]
[ROW][C]37[/C][C]0.576933449562231[/C][C]0.846133100875539[/C][C]0.423066550437769[/C][/ROW]
[ROW][C]38[/C][C]0.519594341335154[/C][C]0.960811317329692[/C][C]0.480405658664846[/C][/ROW]
[ROW][C]39[/C][C]0.460812033549628[/C][C]0.921624067099256[/C][C]0.539187966450372[/C][/ROW]
[ROW][C]40[/C][C]0.607972287904853[/C][C]0.784055424190294[/C][C]0.392027712095147[/C][/ROW]
[ROW][C]41[/C][C]0.592158712220774[/C][C]0.815682575558453[/C][C]0.407841287779227[/C][/ROW]
[ROW][C]42[/C][C]0.631181415511268[/C][C]0.737637168977463[/C][C]0.368818584488732[/C][/ROW]
[ROW][C]43[/C][C]0.646477327945165[/C][C]0.707045344109671[/C][C]0.353522672054835[/C][/ROW]
[ROW][C]44[/C][C]0.592149323043242[/C][C]0.815701353913517[/C][C]0.407850676956758[/C][/ROW]
[ROW][C]45[/C][C]0.570596602205582[/C][C]0.858806795588835[/C][C]0.429403397794418[/C][/ROW]
[ROW][C]46[/C][C]0.559534206176705[/C][C]0.88093158764659[/C][C]0.440465793823295[/C][/ROW]
[ROW][C]47[/C][C]0.510160990803186[/C][C]0.979678018393628[/C][C]0.489839009196814[/C][/ROW]
[ROW][C]48[/C][C]0.459805484649879[/C][C]0.919610969299757[/C][C]0.540194515350121[/C][/ROW]
[ROW][C]49[/C][C]0.416078799255717[/C][C]0.832157598511434[/C][C]0.583921200744283[/C][/ROW]
[ROW][C]50[/C][C]0.451433940364685[/C][C]0.902867880729371[/C][C]0.548566059635315[/C][/ROW]
[ROW][C]51[/C][C]0.48097828820622[/C][C]0.961956576412439[/C][C]0.51902171179378[/C][/ROW]
[ROW][C]52[/C][C]0.424940591722046[/C][C]0.849881183444091[/C][C]0.575059408277954[/C][/ROW]
[ROW][C]53[/C][C]0.456638918375078[/C][C]0.913277836750157[/C][C]0.543361081624922[/C][/ROW]
[ROW][C]54[/C][C]0.402834817864022[/C][C]0.805669635728044[/C][C]0.597165182135978[/C][/ROW]
[ROW][C]55[/C][C]0.774572904491615[/C][C]0.450854191016769[/C][C]0.225427095508385[/C][/ROW]
[ROW][C]56[/C][C]0.758858730361797[/C][C]0.482282539276405[/C][C]0.241141269638203[/C][/ROW]
[ROW][C]57[/C][C]0.711546316881818[/C][C]0.576907366236364[/C][C]0.288453683118182[/C][/ROW]
[ROW][C]58[/C][C]0.671235008025068[/C][C]0.657529983949864[/C][C]0.328764991974932[/C][/ROW]
[ROW][C]59[/C][C]0.620028010545394[/C][C]0.759943978909213[/C][C]0.379971989454606[/C][/ROW]
[ROW][C]60[/C][C]0.577149202631245[/C][C]0.84570159473751[/C][C]0.422850797368755[/C][/ROW]
[ROW][C]61[/C][C]0.530993325502895[/C][C]0.93801334899421[/C][C]0.469006674497105[/C][/ROW]
[ROW][C]62[/C][C]0.485866246173615[/C][C]0.971732492347229[/C][C]0.514133753826385[/C][/ROW]
[ROW][C]63[/C][C]0.508935170730429[/C][C]0.982129658539141[/C][C]0.491064829269571[/C][/ROW]
[ROW][C]64[/C][C]0.813603369296484[/C][C]0.372793261407032[/C][C]0.186396630703516[/C][/ROW]
[ROW][C]65[/C][C]0.781815629748329[/C][C]0.436368740503341[/C][C]0.218184370251671[/C][/ROW]
[ROW][C]66[/C][C]0.795556049655748[/C][C]0.408887900688505[/C][C]0.204443950344252[/C][/ROW]
[ROW][C]67[/C][C]0.75329532019806[/C][C]0.49340935960388[/C][C]0.24670467980194[/C][/ROW]
[ROW][C]68[/C][C]0.787979086559713[/C][C]0.424041826880575[/C][C]0.212020913440287[/C][/ROW]
[ROW][C]69[/C][C]0.745154645371625[/C][C]0.509690709256749[/C][C]0.254845354628375[/C][/ROW]
[ROW][C]70[/C][C]0.707118024031003[/C][C]0.585763951937995[/C][C]0.292881975968997[/C][/ROW]
[ROW][C]71[/C][C]0.821886453622645[/C][C]0.35622709275471[/C][C]0.178113546377355[/C][/ROW]
[ROW][C]72[/C][C]0.914299088394346[/C][C]0.171401823211309[/C][C]0.0857009116056543[/C][/ROW]
[ROW][C]73[/C][C]0.894992145932811[/C][C]0.210015708134378[/C][C]0.105007854067189[/C][/ROW]
[ROW][C]74[/C][C]0.866111228200989[/C][C]0.267777543598022[/C][C]0.133888771799011[/C][/ROW]
[ROW][C]75[/C][C]0.836886248496849[/C][C]0.326227503006302[/C][C]0.163113751503151[/C][/ROW]
[ROW][C]76[/C][C]0.789785967947107[/C][C]0.420428064105787[/C][C]0.210214032052893[/C][/ROW]
[ROW][C]77[/C][C]0.734079473209486[/C][C]0.531841053581029[/C][C]0.265920526790515[/C][/ROW]
[ROW][C]78[/C][C]0.698376646147989[/C][C]0.603246707704021[/C][C]0.301623353852011[/C][/ROW]
[ROW][C]79[/C][C]0.661194408321166[/C][C]0.677611183357668[/C][C]0.338805591678834[/C][/ROW]
[ROW][C]80[/C][C]0.604868920110252[/C][C]0.790262159779496[/C][C]0.395131079889748[/C][/ROW]
[ROW][C]81[/C][C]0.527609252252489[/C][C]0.944781495495022[/C][C]0.472390747747511[/C][/ROW]
[ROW][C]82[/C][C]0.629916309468871[/C][C]0.740167381062259[/C][C]0.370083690531129[/C][/ROW]
[ROW][C]83[/C][C]0.966463682804525[/C][C]0.06707263439095[/C][C]0.033536317195475[/C][/ROW]
[ROW][C]84[/C][C]0.954387024653367[/C][C]0.0912259506932668[/C][C]0.0456129753466334[/C][/ROW]
[ROW][C]85[/C][C]0.948933638199486[/C][C]0.102132723601028[/C][C]0.0510663618005142[/C][/ROW]
[ROW][C]86[/C][C]0.918750972086821[/C][C]0.162498055826358[/C][C]0.0812490279131792[/C][/ROW]
[ROW][C]87[/C][C]0.927933415245782[/C][C]0.144133169508437[/C][C]0.0720665847542184[/C][/ROW]
[ROW][C]88[/C][C]0.881820229427039[/C][C]0.236359541145922[/C][C]0.118179770572961[/C][/ROW]
[ROW][C]89[/C][C]0.832630890689383[/C][C]0.334738218621234[/C][C]0.167369109310617[/C][/ROW]
[ROW][C]90[/C][C]0.807512711930903[/C][C]0.384974576138194[/C][C]0.192487288069097[/C][/ROW]
[ROW][C]91[/C][C]0.71959599076282[/C][C]0.560808018474361[/C][C]0.28040400923718[/C][/ROW]
[ROW][C]92[/C][C]0.593110476623631[/C][C]0.813779046752738[/C][C]0.406889523376369[/C][/ROW]
[ROW][C]93[/C][C]0.631628369734874[/C][C]0.736743260530252[/C][C]0.368371630265126[/C][/ROW]
[ROW][C]94[/C][C]0.961170109250675[/C][C]0.0776597814986495[/C][C]0.0388298907493247[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146617&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146617&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.1034299475445640.2068598950891270.896570052455436
90.04035020352190530.08070040704381060.959649796478095
100.1659998670600030.3319997341200050.834000132939997
110.09737379734580120.1947475946916020.902626202654199
120.4408826632254130.8817653264508260.559117336774587
130.3946057106573690.7892114213147380.605394289342631
140.3646713501433350.7293427002866690.635328649856665
150.3498836463426430.6997672926852860.650116353657357
160.3484179101987310.6968358203974610.651582089801269
170.2832322995059710.5664645990119410.716767700494029
180.9506448306266520.09871033874669630.0493551693733482
190.9635614759301840.07287704813963110.0364385240698155
200.9617795579901840.07644088401963130.0382204420098156
210.9545487682520430.0909024634959140.045451231747957
220.9427867768571950.114426446285610.0572132231428052
230.9536477779322940.09270444413541130.0463522220677057
240.9435457989173490.1129084021653020.0564542010826512
250.9351450120819880.1297099758360230.0648549879180115
260.9151608143211370.1696783713577270.0848391856788634
270.9080374771144750.1839250457710510.0919625228855253
280.8777206578484550.244558684303090.122279342151545
290.8755904791934720.2488190416130570.124409520806528
300.8546822638766420.2906354722467160.145317736123358
310.8161264642649290.3677470714701430.183873535735071
320.7821534683856220.4356930632287560.217846531614378
330.7339775823075750.532044835384850.266022417692425
340.6957722375099870.6084555249800260.304227762490013
350.6404028370657930.7191943258684140.359597162934207
360.59067380735730.81865238528540.4093261926427
370.5769334495622310.8461331008755390.423066550437769
380.5195943413351540.9608113173296920.480405658664846
390.4608120335496280.9216240670992560.539187966450372
400.6079722879048530.7840554241902940.392027712095147
410.5921587122207740.8156825755584530.407841287779227
420.6311814155112680.7376371689774630.368818584488732
430.6464773279451650.7070453441096710.353522672054835
440.5921493230432420.8157013539135170.407850676956758
450.5705966022055820.8588067955888350.429403397794418
460.5595342061767050.880931587646590.440465793823295
470.5101609908031860.9796780183936280.489839009196814
480.4598054846498790.9196109692997570.540194515350121
490.4160787992557170.8321575985114340.583921200744283
500.4514339403646850.9028678807293710.548566059635315
510.480978288206220.9619565764124390.51902171179378
520.4249405917220460.8498811834440910.575059408277954
530.4566389183750780.9132778367501570.543361081624922
540.4028348178640220.8056696357280440.597165182135978
550.7745729044916150.4508541910167690.225427095508385
560.7588587303617970.4822825392764050.241141269638203
570.7115463168818180.5769073662363640.288453683118182
580.6712350080250680.6575299839498640.328764991974932
590.6200280105453940.7599439789092130.379971989454606
600.5771492026312450.845701594737510.422850797368755
610.5309933255028950.938013348994210.469006674497105
620.4858662461736150.9717324923472290.514133753826385
630.5089351707304290.9821296585391410.491064829269571
640.8136033692964840.3727932614070320.186396630703516
650.7818156297483290.4363687405033410.218184370251671
660.7955560496557480.4088879006885050.204443950344252
670.753295320198060.493409359603880.24670467980194
680.7879790865597130.4240418268805750.212020913440287
690.7451546453716250.5096907092567490.254845354628375
700.7071180240310030.5857639519379950.292881975968997
710.8218864536226450.356227092754710.178113546377355
720.9142990883943460.1714018232113090.0857009116056543
730.8949921459328110.2100157081343780.105007854067189
740.8661112282009890.2677775435980220.133888771799011
750.8368862484968490.3262275030063020.163113751503151
760.7897859679471070.4204280641057870.210214032052893
770.7340794732094860.5318410535810290.265920526790515
780.6983766461479890.6032467077040210.301623353852011
790.6611944083211660.6776111833576680.338805591678834
800.6048689201102520.7902621597794960.395131079889748
810.5276092522524890.9447814954950220.472390747747511
820.6299163094688710.7401673810622590.370083690531129
830.9664636828045250.067072634390950.033536317195475
840.9543870246533670.09122595069326680.0456129753466334
850.9489336381994860.1021327236010280.0510663618005142
860.9187509720868210.1624980558263580.0812490279131792
870.9279334152457820.1441331695084370.0720665847542184
880.8818202294270390.2363595411459220.118179770572961
890.8326308906893830.3347382186212340.167369109310617
900.8075127119309030.3849745761381940.192487288069097
910.719595990762820.5608080184743610.28040400923718
920.5931104766236310.8137790467527380.406889523376369
930.6316283697348740.7367432605302520.368371630265126
940.9611701092506750.07765978149864950.0388298907493247







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level90.103448275862069NOK

\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 & 0 & 0 & OK \tabularnewline
10% type I error level & 9 & 0.103448275862069 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146617&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]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]9[/C][C]0.103448275862069[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146617&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146617&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 level00OK
10% type I error level90.103448275862069NOK



Parameters (Session):
par1 = 3 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 3 ; 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')
}