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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationThu, 01 Nov 2012 12:16:51 -0400
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Nov/01/t1351786741va6h7h9o9hc48yw.htm/, Retrieved Fri, 29 Mar 2024 10:01:03 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=185553, Retrieved Fri, 29 Mar 2024 10:01:03 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact71
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [WS 7 5] [2012-11-01 16:16:51] [b65d2c16eb5ddb762daa9974bc4b1f13] [Current]
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Dataseries X:
32	33	16	11	18	7	66
31	31	16	12	11	14	68
39	38	19	13	14	12	54
37	39	16	11	12	14	56
39	32	17	9	17	11	86
41	32	17	13	9	9	80
36	35	16	10	16	11	76
33	37	15	14	14	15	69
33	33	16	12	15	14	78
34	33	14	10	11	13	67
31	28	15	12	16	9	80
27	32	12	8	13	15	54
37	31	14	10	17	10	71
34	37	16	12	15	11	84
34	30	14	12	14	13	74
32	33	7	7	16	8	71
29	31	10	6	9	20	63
36	33	14	12	15	12	71
29	31	16	10	17	10	76
35	33	16	10	13	10	69
37	32	16	10	15	9	74
34	33	14	12	16	14	75
38	32	20	15	16	8	54
35	33	14	10	12	14	52
38	28	14	10	12	11	69
37	35	11	12	11	13	68
38	39	14	13	15	9	65
33	34	15	11	15	11	75
36	38	16	11	17	15	74
38	32	14	12	13	11	75
32	38	16	14	16	10	72
32	30	14	10	14	14	67
32	33	12	12	11	18	63
34	38	16	13	12	14	62
32	32	9	5	12	11	63
37	32	14	6	15	12	76
39	34	16	12	16	13	74
29	34	16	12	15	9	67
37	36	15	11	12	10	73
35	34	16	10	12	15	70
30	28	12	7	8	20	53
38	34	16	12	13	12	77
34	35	16	14	11	12	77
31	35	14	11	14	14	52
34	31	16	12	15	13	54
35	37	17	13	10	11	80
36	35	18	14	11	17	66
30	27	18	11	12	12	73
39	40	12	12	15	13	63
35	37	16	12	15	14	69
38	36	10	8	14	13	67
31	38	14	11	16	15	54
34	39	18	14	15	13	81
38	41	18	14	15	10	69
34	27	16	12	13	11	84
39	30	17	9	12	19	80
37	37	16	13	17	13	70
34	31	16	11	13	17	69
28	31	13	12	15	13	77
37	27	16	12	13	9	54
33	36	16	12	15	11	79
37	38	20	12	16	10	30
35	37	16	12	15	9	71
37	33	15	12	16	12	73
32	34	15	11	15	12	72
33	31	16	10	14	13	77
38	39	14	9	15	13	75
33	34	16	12	14	12	69
29	32	16	12	13	15	54
33	33	15	12	7	22	70
31	36	12	9	17	13	73
36	32	17	15	13	15	54
35	41	16	12	15	13	77
32	28	15	12	14	15	82
29	30	13	12	13	10	80
39	36	16	10	16	11	80
37	35	16	13	12	16	69
35	31	16	9	14	11	78
37	34	16	12	17	11	81
32	36	14	10	15	10	76
38	36	16	14	17	10	76
37	35	16	11	12	16	73
36	37	20	15	16	12	85
32	28	15	11	11	11	66
33	39	16	11	15	16	79
40	32	13	12	9	19	68
38	35	17	12	16	11	76
41	39	16	12	15	16	71
36	35	16	11	10	15	54
43	42	12	7	10	24	46
30	34	16	12	15	14	82
31	33	16	14	11	15	74
32	41	17	11	13	11	88
32	33	13	11	14	15	38
37	34	12	10	18	12	76
37	32	18	13	16	10	86
33	40	14	13	14	14	54
34	40	14	8	14	13	70
33	35	13	11	14	9	69
38	36	16	12	14	15	90
33	37	13	11	12	15	54
31	27	16	13	14	14	76
38	39	13	12	15	11	89
37	38	16	14	15	8	76
33	31	15	13	15	11	73
31	33	16	15	13	11	79
39	32	15	10	17	8	90
44	39	17	11	17	10	74
33	36	15	9	19	11	81
35	33	12	11	15	13	72
32	33	16	10	13	11	71
28	32	10	11	9	20	66
40	37	16	8	15	10	77
27	30	12	11	15	15	65
37	38	14	12	15	12	74
32	29	15	12	16	14	82
28	22	13	9	11	23	54
34	35	15	11	14	14	63
30	35	11	10	11	16	54
35	34	12	8	15	11	64
31	35	8	9	13	12	69
32	34	16	8	15	10	54
30	34	15	9	16	14	84
30	35	17	15	14	12	86
31	23	16	11	15	12	77
40	31	10	8	16	11	89
32	27	18	13	16	12	76
36	36	13	12	11	13	60
32	31	16	12	12	11	75
35	32	13	9	9	19	73
38	39	10	7	16	12	85
42	37	15	13	13	17	79
34	38	16	9	16	9	71
35	39	16	6	12	12	72
35	34	14	8	9	19	69
33	31	10	8	13	18	78
36	32	17	15	13	15	54
32	37	13	6	14	14	69
33	36	15	9	19	11	81
34	32	16	11	13	9	84
32	35	12	8	12	18	84
34	36	13	8	13	16	69




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time10 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.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 & 10 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ fisher.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185553&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]10 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ fisher.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185553&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185553&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 time10 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Learning[t] = + 5.76026176710084 + 0.107709501362639Connected[t] -0.0281633972582685Separate[t] + 0.580005731309102Software[t] + 0.0686845771588498Happiness[t] -0.0885767852871986Depression[t] + 0.00157183286835583Belonging[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Learning[t] =  +  5.76026176710084 +  0.107709501362639Connected[t] -0.0281633972582685Separate[t] +  0.580005731309102Software[t] +  0.0686845771588498Happiness[t] -0.0885767852871986Depression[t] +  0.00157183286835583Belonging[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185553&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Learning[t] =  +  5.76026176710084 +  0.107709501362639Connected[t] -0.0281633972582685Separate[t] +  0.580005731309102Software[t] +  0.0686845771588498Happiness[t] -0.0885767852871986Depression[t] +  0.00157183286835583Belonging[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185553&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185553&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
Learning[t] = + 5.76026176710084 + 0.107709501362639Connected[t] -0.0281633972582685Separate[t] + 0.580005731309102Software[t] + 0.0686845771588498Happiness[t] -0.0885767852871986Depression[t] + 0.00157183286835583Belonging[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)5.760261767100842.7577912.08870.0386110.019305
Connected0.1077095013626390.0494822.17670.0312380.015619
Separate-0.02816339725826850.046263-0.60880.5437060.271853
Software0.5800057313091020.0733997.902100
Happiness0.06868457715884980.0845530.81230.4180350.209017
Depression-0.08857678528719860.061553-1.4390.1524590.076229
Belonging0.001571832868355830.0153870.10220.9187850.459393

\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) & 5.76026176710084 & 2.757791 & 2.0887 & 0.038611 & 0.019305 \tabularnewline
Connected & 0.107709501362639 & 0.049482 & 2.1767 & 0.031238 & 0.015619 \tabularnewline
Separate & -0.0281633972582685 & 0.046263 & -0.6088 & 0.543706 & 0.271853 \tabularnewline
Software & 0.580005731309102 & 0.073399 & 7.9021 & 0 & 0 \tabularnewline
Happiness & 0.0686845771588498 & 0.084553 & 0.8123 & 0.418035 & 0.209017 \tabularnewline
Depression & -0.0885767852871986 & 0.061553 & -1.439 & 0.152459 & 0.076229 \tabularnewline
Belonging & 0.00157183286835583 & 0.015387 & 0.1022 & 0.918785 & 0.459393 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185553&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]5.76026176710084[/C][C]2.757791[/C][C]2.0887[/C][C]0.038611[/C][C]0.019305[/C][/ROW]
[ROW][C]Connected[/C][C]0.107709501362639[/C][C]0.049482[/C][C]2.1767[/C][C]0.031238[/C][C]0.015619[/C][/ROW]
[ROW][C]Separate[/C][C]-0.0281633972582685[/C][C]0.046263[/C][C]-0.6088[/C][C]0.543706[/C][C]0.271853[/C][/ROW]
[ROW][C]Software[/C][C]0.580005731309102[/C][C]0.073399[/C][C]7.9021[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Happiness[/C][C]0.0686845771588498[/C][C]0.084553[/C][C]0.8123[/C][C]0.418035[/C][C]0.209017[/C][/ROW]
[ROW][C]Depression[/C][C]-0.0885767852871986[/C][C]0.061553[/C][C]-1.439[/C][C]0.152459[/C][C]0.076229[/C][/ROW]
[ROW][C]Belonging[/C][C]0.00157183286835583[/C][C]0.015387[/C][C]0.1022[/C][C]0.918785[/C][C]0.459393[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185553&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185553&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)5.760261767100842.7577912.08870.0386110.019305
Connected0.1077095013626390.0494822.17670.0312380.015619
Separate-0.02816339725826850.046263-0.60880.5437060.271853
Software0.5800057313091020.0733997.902100
Happiness0.06868457715884980.0845530.81230.4180350.209017
Depression-0.08857678528719860.061553-1.4390.1524590.076229
Belonging0.001571832868355830.0153870.10220.9187850.459393







Multiple Linear Regression - Regression Statistics
Multiple R0.62502737778305
R-squared0.390659222978356
Adjusted R-squared0.363577410666283
F-TEST (value)14.4251506685245
F-TEST (DF numerator)6
F-TEST (DF denominator)135
p-value1.14308562615406e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.81425492296871
Sum Squared Residuals444.355324944685

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.62502737778305 \tabularnewline
R-squared & 0.390659222978356 \tabularnewline
Adjusted R-squared & 0.363577410666283 \tabularnewline
F-TEST (value) & 14.4251506685245 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 135 \tabularnewline
p-value & 1.14308562615406e-12 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.81425492296871 \tabularnewline
Sum Squared Residuals & 444.355324944685 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185553&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.62502737778305[/C][/ROW]
[ROW][C]R-squared[/C][C]0.390659222978356[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.363577410666283[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]14.4251506685245[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]135[/C][/ROW]
[ROW][C]p-value[/C][C]1.14308562615406e-12[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.81425492296871[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]444.355324944685[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185553&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185553&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 R0.62502737778305
R-squared0.390659222978356
Adjusted R-squared0.363577410666283
F-TEST (value)14.4251506685245
F-TEST (DF numerator)6
F-TEST (DF denominator)135
p-value1.14308562615406e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.81425492296871
Sum Squared Residuals444.355324944685







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11615.37766260674290.622337393257072
21614.80859975982031.19140024017969
31916.41433936311662.58566063688339
41614.69936644135951.30063355864052
51714.60822598998092.39177401001905
61716.76191387403610.238086125963903
71614.69621011958491.30378988041508
81516.1340986206719-1.13409862067189
91615.2581486053480.741851394651991
101414.0023949591923-0.00239495919232862
111515.6982587582456-0.698258758245628
121212.0563621407486-0.056362140748597
131415.0659754080849-1.0659754080849
141615.52836587074930.471634129250696
151415.4639531751404-1.46395317514038
16712.8395529062434-5.83955290624341
171010.4364573388577-0.436457338857749
181415.7474278499318-1.74742784993183
191614.21215856152561.78784143847443
201614.5163476364711.48365236352903
211614.99373514040121.0062648595988
221415.4298271852644-1.42982718526443
232018.12729800338831.87270199661172
241414.0666347594013-0.0666347594012815
251414.8230317644042-0.823031764404186
261115.4307799642503-4.43077996425027
271416.6301715590681-2.63017155906806
281514.91099433403720.0890056659628334
291614.90295942939261.09704057060744
301415.9485052123583-1.9485052123583
311616.5831943014097-0.583194301409741
321413.98894309443120.0110569055687925
331214.4978161611758-2.49781616117584
341615.57384379435820.426156205641835
35911.1546615134396-2.15466151343963
361412.41112552503991.5888744749601
371616.0272162472382-0.0272162472381986
381615.22474096752330.775259032476736
391515.1648849330451-0.164884933045123
401613.97818756848622.02181243151378
411211.12425985746190.875740142538112
421615.80674529829130.193254701708723
431616.370386203883-0.370386203882957
441414.296844845061-0.296844845060991
451615.47303769767380.526962302326156
461715.86637088615341.13362911384663
471816.12563111862031.87436888137967
481814.48723542825673.51276457174333
491215.7722611249778-3.77226112497782
501615.3467675232250.65323247677499
511013.3947850417264-3.39478504172642
521414.2642906880534-0.264290688053398
531816.45018146967151.54981853032849
541817.07156104204690.928438957953148
551615.67263068901430.32736931098571
561713.60308461919553.39691538080449
571616.3697100297326-0.369710029732644
581614.42493316392361.57506683607642
591314.8629328454702-1.86293284547019
601616.1257577776259-0.125757777625929
611615.44096060230320.559039397696845
622015.89571336513384.10428663486621
631615.79279511539770.207204884602285
641515.92696559419-0.926965594190032
651514.70999254878230.290007451217738
661614.17278431250631.82721568749367
671413.97155982136640.0284401786335832
681615.32430770569010.675692294309915
691614.59180406871031.40819593128972
701513.98748304283281.01251695716723
711213.4363089921837-1.43630899218374
721717.0857877721761-0.0857877721760632
731615.3352653824260.664734617574019
741515.1402820593041-0.140282059304088
751315.1318824442401-2.13188244424007
761614.9974625578881.00253744211201
771615.8153117497250.184688250275019
781613.98692298736532.01307701263474
791616.0686382223247-0.0686382223246587
801414.2571009250044-0.257100925004443
811617.3607500127344-1.36075001273438
821614.66158761858021.3384123814198
832017.46548169214192.5345183078581
841514.68338041177350.316619588226463
851614.33358075278331.66641924721675
861315.1695687940721-2.1695687940721
871716.07164058492840.9283594150716
881615.76268783204660.237312167953379
891614.47522092368831.5247790763117
901211.90225499665080.0977450033491526
911614.91314403547521.08685596452475
921615.83313863984490.166861360155085
931714.48920572483762.51079427516243
941314.350298695496-1.35029869549599
951214.8808753872363-2.88087538723633
961816.73272212062041.26727787937957
971415.5346019898498-1.53460198984984
981412.85600894584791.14399105415214
991314.9818689329843-1.98186893298431
1001615.57380655236060.426193447639381
1011314.2331347794015-1.23313477940154
1021615.71988747458590.280112525414117
1031315.9107362460251-2.9107362460251
1041617.2364981331119-1.23649813311191
1051516.1523523226935-1.15235232269346
1061616.9126798309623-0.91267983096229
1071515.4602494086251-0.460249408625067
1081716.17935596947140.820644030528606
1091513.9788253827481.02117461725204
1101214.9727076648412-2.97270766484125
1111614.10778601283261.89221398716743
1121013.2053285953874-3.20532859538742
1131613.93217390889742.06782609110256
1141214.0073654450621-2.00736544506206
1151415.7190358636082-1.7190358636082
1161515.3380646016507-0.338064601650717
1171312.17972790938770.820272090612265
1181514.63726351070080.362736489299179
1191113.229065976075-2.22906597607501
1201213.3691059812832-1.36910598128322
121813.2720235346204-5.27202353462038
1221613.11883593379892.88116406620105
1231513.24495508444351.7550449155565
1241716.73975415703330.260245842966744
1251614.91993958160241.08006041839764
1261014.100124078739-4.10012407873897
1271816.14211970084061.85788029915938
1281315.2823324026825-2.28233240268251
1291615.26172702428190.738272975718114
1301312.89886325767340.101136742326622
1311012.9845280498778-2.98452804987782
1321516.2933585825768-1.29335858257685
1331613.98558960000832.01441039999165
1341611.78622167855684.21377832144323
1351412.25624340037431.74375659962568
1361012.5027761791616-2.50277617916164
1371717.0857877721761-0.0857877721760632
1381311.47492005412361.52507994587637
1391513.9788253827481.02117461725204
1401615.12896154198820.871038458011757
1411212.2231595088172-0.223159508817216
1421312.63267576899210.367324231007866

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 16 & 15.3776626067429 & 0.622337393257072 \tabularnewline
2 & 16 & 14.8085997598203 & 1.19140024017969 \tabularnewline
3 & 19 & 16.4143393631166 & 2.58566063688339 \tabularnewline
4 & 16 & 14.6993664413595 & 1.30063355864052 \tabularnewline
5 & 17 & 14.6082259899809 & 2.39177401001905 \tabularnewline
6 & 17 & 16.7619138740361 & 0.238086125963903 \tabularnewline
7 & 16 & 14.6962101195849 & 1.30378988041508 \tabularnewline
8 & 15 & 16.1340986206719 & -1.13409862067189 \tabularnewline
9 & 16 & 15.258148605348 & 0.741851394651991 \tabularnewline
10 & 14 & 14.0023949591923 & -0.00239495919232862 \tabularnewline
11 & 15 & 15.6982587582456 & -0.698258758245628 \tabularnewline
12 & 12 & 12.0563621407486 & -0.056362140748597 \tabularnewline
13 & 14 & 15.0659754080849 & -1.0659754080849 \tabularnewline
14 & 16 & 15.5283658707493 & 0.471634129250696 \tabularnewline
15 & 14 & 15.4639531751404 & -1.46395317514038 \tabularnewline
16 & 7 & 12.8395529062434 & -5.83955290624341 \tabularnewline
17 & 10 & 10.4364573388577 & -0.436457338857749 \tabularnewline
18 & 14 & 15.7474278499318 & -1.74742784993183 \tabularnewline
19 & 16 & 14.2121585615256 & 1.78784143847443 \tabularnewline
20 & 16 & 14.516347636471 & 1.48365236352903 \tabularnewline
21 & 16 & 14.9937351404012 & 1.0062648595988 \tabularnewline
22 & 14 & 15.4298271852644 & -1.42982718526443 \tabularnewline
23 & 20 & 18.1272980033883 & 1.87270199661172 \tabularnewline
24 & 14 & 14.0666347594013 & -0.0666347594012815 \tabularnewline
25 & 14 & 14.8230317644042 & -0.823031764404186 \tabularnewline
26 & 11 & 15.4307799642503 & -4.43077996425027 \tabularnewline
27 & 14 & 16.6301715590681 & -2.63017155906806 \tabularnewline
28 & 15 & 14.9109943340372 & 0.0890056659628334 \tabularnewline
29 & 16 & 14.9029594293926 & 1.09704057060744 \tabularnewline
30 & 14 & 15.9485052123583 & -1.9485052123583 \tabularnewline
31 & 16 & 16.5831943014097 & -0.583194301409741 \tabularnewline
32 & 14 & 13.9889430944312 & 0.0110569055687925 \tabularnewline
33 & 12 & 14.4978161611758 & -2.49781616117584 \tabularnewline
34 & 16 & 15.5738437943582 & 0.426156205641835 \tabularnewline
35 & 9 & 11.1546615134396 & -2.15466151343963 \tabularnewline
36 & 14 & 12.4111255250399 & 1.5888744749601 \tabularnewline
37 & 16 & 16.0272162472382 & -0.0272162472381986 \tabularnewline
38 & 16 & 15.2247409675233 & 0.775259032476736 \tabularnewline
39 & 15 & 15.1648849330451 & -0.164884933045123 \tabularnewline
40 & 16 & 13.9781875684862 & 2.02181243151378 \tabularnewline
41 & 12 & 11.1242598574619 & 0.875740142538112 \tabularnewline
42 & 16 & 15.8067452982913 & 0.193254701708723 \tabularnewline
43 & 16 & 16.370386203883 & -0.370386203882957 \tabularnewline
44 & 14 & 14.296844845061 & -0.296844845060991 \tabularnewline
45 & 16 & 15.4730376976738 & 0.526962302326156 \tabularnewline
46 & 17 & 15.8663708861534 & 1.13362911384663 \tabularnewline
47 & 18 & 16.1256311186203 & 1.87436888137967 \tabularnewline
48 & 18 & 14.4872354282567 & 3.51276457174333 \tabularnewline
49 & 12 & 15.7722611249778 & -3.77226112497782 \tabularnewline
50 & 16 & 15.346767523225 & 0.65323247677499 \tabularnewline
51 & 10 & 13.3947850417264 & -3.39478504172642 \tabularnewline
52 & 14 & 14.2642906880534 & -0.264290688053398 \tabularnewline
53 & 18 & 16.4501814696715 & 1.54981853032849 \tabularnewline
54 & 18 & 17.0715610420469 & 0.928438957953148 \tabularnewline
55 & 16 & 15.6726306890143 & 0.32736931098571 \tabularnewline
56 & 17 & 13.6030846191955 & 3.39691538080449 \tabularnewline
57 & 16 & 16.3697100297326 & -0.369710029732644 \tabularnewline
58 & 16 & 14.4249331639236 & 1.57506683607642 \tabularnewline
59 & 13 & 14.8629328454702 & -1.86293284547019 \tabularnewline
60 & 16 & 16.1257577776259 & -0.125757777625929 \tabularnewline
61 & 16 & 15.4409606023032 & 0.559039397696845 \tabularnewline
62 & 20 & 15.8957133651338 & 4.10428663486621 \tabularnewline
63 & 16 & 15.7927951153977 & 0.207204884602285 \tabularnewline
64 & 15 & 15.92696559419 & -0.926965594190032 \tabularnewline
65 & 15 & 14.7099925487823 & 0.290007451217738 \tabularnewline
66 & 16 & 14.1727843125063 & 1.82721568749367 \tabularnewline
67 & 14 & 13.9715598213664 & 0.0284401786335832 \tabularnewline
68 & 16 & 15.3243077056901 & 0.675692294309915 \tabularnewline
69 & 16 & 14.5918040687103 & 1.40819593128972 \tabularnewline
70 & 15 & 13.9874830428328 & 1.01251695716723 \tabularnewline
71 & 12 & 13.4363089921837 & -1.43630899218374 \tabularnewline
72 & 17 & 17.0857877721761 & -0.0857877721760632 \tabularnewline
73 & 16 & 15.335265382426 & 0.664734617574019 \tabularnewline
74 & 15 & 15.1402820593041 & -0.140282059304088 \tabularnewline
75 & 13 & 15.1318824442401 & -2.13188244424007 \tabularnewline
76 & 16 & 14.997462557888 & 1.00253744211201 \tabularnewline
77 & 16 & 15.815311749725 & 0.184688250275019 \tabularnewline
78 & 16 & 13.9869229873653 & 2.01307701263474 \tabularnewline
79 & 16 & 16.0686382223247 & -0.0686382223246587 \tabularnewline
80 & 14 & 14.2571009250044 & -0.257100925004443 \tabularnewline
81 & 16 & 17.3607500127344 & -1.36075001273438 \tabularnewline
82 & 16 & 14.6615876185802 & 1.3384123814198 \tabularnewline
83 & 20 & 17.4654816921419 & 2.5345183078581 \tabularnewline
84 & 15 & 14.6833804117735 & 0.316619588226463 \tabularnewline
85 & 16 & 14.3335807527833 & 1.66641924721675 \tabularnewline
86 & 13 & 15.1695687940721 & -2.1695687940721 \tabularnewline
87 & 17 & 16.0716405849284 & 0.9283594150716 \tabularnewline
88 & 16 & 15.7626878320466 & 0.237312167953379 \tabularnewline
89 & 16 & 14.4752209236883 & 1.5247790763117 \tabularnewline
90 & 12 & 11.9022549966508 & 0.0977450033491526 \tabularnewline
91 & 16 & 14.9131440354752 & 1.08685596452475 \tabularnewline
92 & 16 & 15.8331386398449 & 0.166861360155085 \tabularnewline
93 & 17 & 14.4892057248376 & 2.51079427516243 \tabularnewline
94 & 13 & 14.350298695496 & -1.35029869549599 \tabularnewline
95 & 12 & 14.8808753872363 & -2.88087538723633 \tabularnewline
96 & 18 & 16.7327221206204 & 1.26727787937957 \tabularnewline
97 & 14 & 15.5346019898498 & -1.53460198984984 \tabularnewline
98 & 14 & 12.8560089458479 & 1.14399105415214 \tabularnewline
99 & 13 & 14.9818689329843 & -1.98186893298431 \tabularnewline
100 & 16 & 15.5738065523606 & 0.426193447639381 \tabularnewline
101 & 13 & 14.2331347794015 & -1.23313477940154 \tabularnewline
102 & 16 & 15.7198874745859 & 0.280112525414117 \tabularnewline
103 & 13 & 15.9107362460251 & -2.9107362460251 \tabularnewline
104 & 16 & 17.2364981331119 & -1.23649813311191 \tabularnewline
105 & 15 & 16.1523523226935 & -1.15235232269346 \tabularnewline
106 & 16 & 16.9126798309623 & -0.91267983096229 \tabularnewline
107 & 15 & 15.4602494086251 & -0.460249408625067 \tabularnewline
108 & 17 & 16.1793559694714 & 0.820644030528606 \tabularnewline
109 & 15 & 13.978825382748 & 1.02117461725204 \tabularnewline
110 & 12 & 14.9727076648412 & -2.97270766484125 \tabularnewline
111 & 16 & 14.1077860128326 & 1.89221398716743 \tabularnewline
112 & 10 & 13.2053285953874 & -3.20532859538742 \tabularnewline
113 & 16 & 13.9321739088974 & 2.06782609110256 \tabularnewline
114 & 12 & 14.0073654450621 & -2.00736544506206 \tabularnewline
115 & 14 & 15.7190358636082 & -1.7190358636082 \tabularnewline
116 & 15 & 15.3380646016507 & -0.338064601650717 \tabularnewline
117 & 13 & 12.1797279093877 & 0.820272090612265 \tabularnewline
118 & 15 & 14.6372635107008 & 0.362736489299179 \tabularnewline
119 & 11 & 13.229065976075 & -2.22906597607501 \tabularnewline
120 & 12 & 13.3691059812832 & -1.36910598128322 \tabularnewline
121 & 8 & 13.2720235346204 & -5.27202353462038 \tabularnewline
122 & 16 & 13.1188359337989 & 2.88116406620105 \tabularnewline
123 & 15 & 13.2449550844435 & 1.7550449155565 \tabularnewline
124 & 17 & 16.7397541570333 & 0.260245842966744 \tabularnewline
125 & 16 & 14.9199395816024 & 1.08006041839764 \tabularnewline
126 & 10 & 14.100124078739 & -4.10012407873897 \tabularnewline
127 & 18 & 16.1421197008406 & 1.85788029915938 \tabularnewline
128 & 13 & 15.2823324026825 & -2.28233240268251 \tabularnewline
129 & 16 & 15.2617270242819 & 0.738272975718114 \tabularnewline
130 & 13 & 12.8988632576734 & 0.101136742326622 \tabularnewline
131 & 10 & 12.9845280498778 & -2.98452804987782 \tabularnewline
132 & 15 & 16.2933585825768 & -1.29335858257685 \tabularnewline
133 & 16 & 13.9855896000083 & 2.01441039999165 \tabularnewline
134 & 16 & 11.7862216785568 & 4.21377832144323 \tabularnewline
135 & 14 & 12.2562434003743 & 1.74375659962568 \tabularnewline
136 & 10 & 12.5027761791616 & -2.50277617916164 \tabularnewline
137 & 17 & 17.0857877721761 & -0.0857877721760632 \tabularnewline
138 & 13 & 11.4749200541236 & 1.52507994587637 \tabularnewline
139 & 15 & 13.978825382748 & 1.02117461725204 \tabularnewline
140 & 16 & 15.1289615419882 & 0.871038458011757 \tabularnewline
141 & 12 & 12.2231595088172 & -0.223159508817216 \tabularnewline
142 & 13 & 12.6326757689921 & 0.367324231007866 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185553&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]16[/C][C]15.3776626067429[/C][C]0.622337393257072[/C][/ROW]
[ROW][C]2[/C][C]16[/C][C]14.8085997598203[/C][C]1.19140024017969[/C][/ROW]
[ROW][C]3[/C][C]19[/C][C]16.4143393631166[/C][C]2.58566063688339[/C][/ROW]
[ROW][C]4[/C][C]16[/C][C]14.6993664413595[/C][C]1.30063355864052[/C][/ROW]
[ROW][C]5[/C][C]17[/C][C]14.6082259899809[/C][C]2.39177401001905[/C][/ROW]
[ROW][C]6[/C][C]17[/C][C]16.7619138740361[/C][C]0.238086125963903[/C][/ROW]
[ROW][C]7[/C][C]16[/C][C]14.6962101195849[/C][C]1.30378988041508[/C][/ROW]
[ROW][C]8[/C][C]15[/C][C]16.1340986206719[/C][C]-1.13409862067189[/C][/ROW]
[ROW][C]9[/C][C]16[/C][C]15.258148605348[/C][C]0.741851394651991[/C][/ROW]
[ROW][C]10[/C][C]14[/C][C]14.0023949591923[/C][C]-0.00239495919232862[/C][/ROW]
[ROW][C]11[/C][C]15[/C][C]15.6982587582456[/C][C]-0.698258758245628[/C][/ROW]
[ROW][C]12[/C][C]12[/C][C]12.0563621407486[/C][C]-0.056362140748597[/C][/ROW]
[ROW][C]13[/C][C]14[/C][C]15.0659754080849[/C][C]-1.0659754080849[/C][/ROW]
[ROW][C]14[/C][C]16[/C][C]15.5283658707493[/C][C]0.471634129250696[/C][/ROW]
[ROW][C]15[/C][C]14[/C][C]15.4639531751404[/C][C]-1.46395317514038[/C][/ROW]
[ROW][C]16[/C][C]7[/C][C]12.8395529062434[/C][C]-5.83955290624341[/C][/ROW]
[ROW][C]17[/C][C]10[/C][C]10.4364573388577[/C][C]-0.436457338857749[/C][/ROW]
[ROW][C]18[/C][C]14[/C][C]15.7474278499318[/C][C]-1.74742784993183[/C][/ROW]
[ROW][C]19[/C][C]16[/C][C]14.2121585615256[/C][C]1.78784143847443[/C][/ROW]
[ROW][C]20[/C][C]16[/C][C]14.516347636471[/C][C]1.48365236352903[/C][/ROW]
[ROW][C]21[/C][C]16[/C][C]14.9937351404012[/C][C]1.0062648595988[/C][/ROW]
[ROW][C]22[/C][C]14[/C][C]15.4298271852644[/C][C]-1.42982718526443[/C][/ROW]
[ROW][C]23[/C][C]20[/C][C]18.1272980033883[/C][C]1.87270199661172[/C][/ROW]
[ROW][C]24[/C][C]14[/C][C]14.0666347594013[/C][C]-0.0666347594012815[/C][/ROW]
[ROW][C]25[/C][C]14[/C][C]14.8230317644042[/C][C]-0.823031764404186[/C][/ROW]
[ROW][C]26[/C][C]11[/C][C]15.4307799642503[/C][C]-4.43077996425027[/C][/ROW]
[ROW][C]27[/C][C]14[/C][C]16.6301715590681[/C][C]-2.63017155906806[/C][/ROW]
[ROW][C]28[/C][C]15[/C][C]14.9109943340372[/C][C]0.0890056659628334[/C][/ROW]
[ROW][C]29[/C][C]16[/C][C]14.9029594293926[/C][C]1.09704057060744[/C][/ROW]
[ROW][C]30[/C][C]14[/C][C]15.9485052123583[/C][C]-1.9485052123583[/C][/ROW]
[ROW][C]31[/C][C]16[/C][C]16.5831943014097[/C][C]-0.583194301409741[/C][/ROW]
[ROW][C]32[/C][C]14[/C][C]13.9889430944312[/C][C]0.0110569055687925[/C][/ROW]
[ROW][C]33[/C][C]12[/C][C]14.4978161611758[/C][C]-2.49781616117584[/C][/ROW]
[ROW][C]34[/C][C]16[/C][C]15.5738437943582[/C][C]0.426156205641835[/C][/ROW]
[ROW][C]35[/C][C]9[/C][C]11.1546615134396[/C][C]-2.15466151343963[/C][/ROW]
[ROW][C]36[/C][C]14[/C][C]12.4111255250399[/C][C]1.5888744749601[/C][/ROW]
[ROW][C]37[/C][C]16[/C][C]16.0272162472382[/C][C]-0.0272162472381986[/C][/ROW]
[ROW][C]38[/C][C]16[/C][C]15.2247409675233[/C][C]0.775259032476736[/C][/ROW]
[ROW][C]39[/C][C]15[/C][C]15.1648849330451[/C][C]-0.164884933045123[/C][/ROW]
[ROW][C]40[/C][C]16[/C][C]13.9781875684862[/C][C]2.02181243151378[/C][/ROW]
[ROW][C]41[/C][C]12[/C][C]11.1242598574619[/C][C]0.875740142538112[/C][/ROW]
[ROW][C]42[/C][C]16[/C][C]15.8067452982913[/C][C]0.193254701708723[/C][/ROW]
[ROW][C]43[/C][C]16[/C][C]16.370386203883[/C][C]-0.370386203882957[/C][/ROW]
[ROW][C]44[/C][C]14[/C][C]14.296844845061[/C][C]-0.296844845060991[/C][/ROW]
[ROW][C]45[/C][C]16[/C][C]15.4730376976738[/C][C]0.526962302326156[/C][/ROW]
[ROW][C]46[/C][C]17[/C][C]15.8663708861534[/C][C]1.13362911384663[/C][/ROW]
[ROW][C]47[/C][C]18[/C][C]16.1256311186203[/C][C]1.87436888137967[/C][/ROW]
[ROW][C]48[/C][C]18[/C][C]14.4872354282567[/C][C]3.51276457174333[/C][/ROW]
[ROW][C]49[/C][C]12[/C][C]15.7722611249778[/C][C]-3.77226112497782[/C][/ROW]
[ROW][C]50[/C][C]16[/C][C]15.346767523225[/C][C]0.65323247677499[/C][/ROW]
[ROW][C]51[/C][C]10[/C][C]13.3947850417264[/C][C]-3.39478504172642[/C][/ROW]
[ROW][C]52[/C][C]14[/C][C]14.2642906880534[/C][C]-0.264290688053398[/C][/ROW]
[ROW][C]53[/C][C]18[/C][C]16.4501814696715[/C][C]1.54981853032849[/C][/ROW]
[ROW][C]54[/C][C]18[/C][C]17.0715610420469[/C][C]0.928438957953148[/C][/ROW]
[ROW][C]55[/C][C]16[/C][C]15.6726306890143[/C][C]0.32736931098571[/C][/ROW]
[ROW][C]56[/C][C]17[/C][C]13.6030846191955[/C][C]3.39691538080449[/C][/ROW]
[ROW][C]57[/C][C]16[/C][C]16.3697100297326[/C][C]-0.369710029732644[/C][/ROW]
[ROW][C]58[/C][C]16[/C][C]14.4249331639236[/C][C]1.57506683607642[/C][/ROW]
[ROW][C]59[/C][C]13[/C][C]14.8629328454702[/C][C]-1.86293284547019[/C][/ROW]
[ROW][C]60[/C][C]16[/C][C]16.1257577776259[/C][C]-0.125757777625929[/C][/ROW]
[ROW][C]61[/C][C]16[/C][C]15.4409606023032[/C][C]0.559039397696845[/C][/ROW]
[ROW][C]62[/C][C]20[/C][C]15.8957133651338[/C][C]4.10428663486621[/C][/ROW]
[ROW][C]63[/C][C]16[/C][C]15.7927951153977[/C][C]0.207204884602285[/C][/ROW]
[ROW][C]64[/C][C]15[/C][C]15.92696559419[/C][C]-0.926965594190032[/C][/ROW]
[ROW][C]65[/C][C]15[/C][C]14.7099925487823[/C][C]0.290007451217738[/C][/ROW]
[ROW][C]66[/C][C]16[/C][C]14.1727843125063[/C][C]1.82721568749367[/C][/ROW]
[ROW][C]67[/C][C]14[/C][C]13.9715598213664[/C][C]0.0284401786335832[/C][/ROW]
[ROW][C]68[/C][C]16[/C][C]15.3243077056901[/C][C]0.675692294309915[/C][/ROW]
[ROW][C]69[/C][C]16[/C][C]14.5918040687103[/C][C]1.40819593128972[/C][/ROW]
[ROW][C]70[/C][C]15[/C][C]13.9874830428328[/C][C]1.01251695716723[/C][/ROW]
[ROW][C]71[/C][C]12[/C][C]13.4363089921837[/C][C]-1.43630899218374[/C][/ROW]
[ROW][C]72[/C][C]17[/C][C]17.0857877721761[/C][C]-0.0857877721760632[/C][/ROW]
[ROW][C]73[/C][C]16[/C][C]15.335265382426[/C][C]0.664734617574019[/C][/ROW]
[ROW][C]74[/C][C]15[/C][C]15.1402820593041[/C][C]-0.140282059304088[/C][/ROW]
[ROW][C]75[/C][C]13[/C][C]15.1318824442401[/C][C]-2.13188244424007[/C][/ROW]
[ROW][C]76[/C][C]16[/C][C]14.997462557888[/C][C]1.00253744211201[/C][/ROW]
[ROW][C]77[/C][C]16[/C][C]15.815311749725[/C][C]0.184688250275019[/C][/ROW]
[ROW][C]78[/C][C]16[/C][C]13.9869229873653[/C][C]2.01307701263474[/C][/ROW]
[ROW][C]79[/C][C]16[/C][C]16.0686382223247[/C][C]-0.0686382223246587[/C][/ROW]
[ROW][C]80[/C][C]14[/C][C]14.2571009250044[/C][C]-0.257100925004443[/C][/ROW]
[ROW][C]81[/C][C]16[/C][C]17.3607500127344[/C][C]-1.36075001273438[/C][/ROW]
[ROW][C]82[/C][C]16[/C][C]14.6615876185802[/C][C]1.3384123814198[/C][/ROW]
[ROW][C]83[/C][C]20[/C][C]17.4654816921419[/C][C]2.5345183078581[/C][/ROW]
[ROW][C]84[/C][C]15[/C][C]14.6833804117735[/C][C]0.316619588226463[/C][/ROW]
[ROW][C]85[/C][C]16[/C][C]14.3335807527833[/C][C]1.66641924721675[/C][/ROW]
[ROW][C]86[/C][C]13[/C][C]15.1695687940721[/C][C]-2.1695687940721[/C][/ROW]
[ROW][C]87[/C][C]17[/C][C]16.0716405849284[/C][C]0.9283594150716[/C][/ROW]
[ROW][C]88[/C][C]16[/C][C]15.7626878320466[/C][C]0.237312167953379[/C][/ROW]
[ROW][C]89[/C][C]16[/C][C]14.4752209236883[/C][C]1.5247790763117[/C][/ROW]
[ROW][C]90[/C][C]12[/C][C]11.9022549966508[/C][C]0.0977450033491526[/C][/ROW]
[ROW][C]91[/C][C]16[/C][C]14.9131440354752[/C][C]1.08685596452475[/C][/ROW]
[ROW][C]92[/C][C]16[/C][C]15.8331386398449[/C][C]0.166861360155085[/C][/ROW]
[ROW][C]93[/C][C]17[/C][C]14.4892057248376[/C][C]2.51079427516243[/C][/ROW]
[ROW][C]94[/C][C]13[/C][C]14.350298695496[/C][C]-1.35029869549599[/C][/ROW]
[ROW][C]95[/C][C]12[/C][C]14.8808753872363[/C][C]-2.88087538723633[/C][/ROW]
[ROW][C]96[/C][C]18[/C][C]16.7327221206204[/C][C]1.26727787937957[/C][/ROW]
[ROW][C]97[/C][C]14[/C][C]15.5346019898498[/C][C]-1.53460198984984[/C][/ROW]
[ROW][C]98[/C][C]14[/C][C]12.8560089458479[/C][C]1.14399105415214[/C][/ROW]
[ROW][C]99[/C][C]13[/C][C]14.9818689329843[/C][C]-1.98186893298431[/C][/ROW]
[ROW][C]100[/C][C]16[/C][C]15.5738065523606[/C][C]0.426193447639381[/C][/ROW]
[ROW][C]101[/C][C]13[/C][C]14.2331347794015[/C][C]-1.23313477940154[/C][/ROW]
[ROW][C]102[/C][C]16[/C][C]15.7198874745859[/C][C]0.280112525414117[/C][/ROW]
[ROW][C]103[/C][C]13[/C][C]15.9107362460251[/C][C]-2.9107362460251[/C][/ROW]
[ROW][C]104[/C][C]16[/C][C]17.2364981331119[/C][C]-1.23649813311191[/C][/ROW]
[ROW][C]105[/C][C]15[/C][C]16.1523523226935[/C][C]-1.15235232269346[/C][/ROW]
[ROW][C]106[/C][C]16[/C][C]16.9126798309623[/C][C]-0.91267983096229[/C][/ROW]
[ROW][C]107[/C][C]15[/C][C]15.4602494086251[/C][C]-0.460249408625067[/C][/ROW]
[ROW][C]108[/C][C]17[/C][C]16.1793559694714[/C][C]0.820644030528606[/C][/ROW]
[ROW][C]109[/C][C]15[/C][C]13.978825382748[/C][C]1.02117461725204[/C][/ROW]
[ROW][C]110[/C][C]12[/C][C]14.9727076648412[/C][C]-2.97270766484125[/C][/ROW]
[ROW][C]111[/C][C]16[/C][C]14.1077860128326[/C][C]1.89221398716743[/C][/ROW]
[ROW][C]112[/C][C]10[/C][C]13.2053285953874[/C][C]-3.20532859538742[/C][/ROW]
[ROW][C]113[/C][C]16[/C][C]13.9321739088974[/C][C]2.06782609110256[/C][/ROW]
[ROW][C]114[/C][C]12[/C][C]14.0073654450621[/C][C]-2.00736544506206[/C][/ROW]
[ROW][C]115[/C][C]14[/C][C]15.7190358636082[/C][C]-1.7190358636082[/C][/ROW]
[ROW][C]116[/C][C]15[/C][C]15.3380646016507[/C][C]-0.338064601650717[/C][/ROW]
[ROW][C]117[/C][C]13[/C][C]12.1797279093877[/C][C]0.820272090612265[/C][/ROW]
[ROW][C]118[/C][C]15[/C][C]14.6372635107008[/C][C]0.362736489299179[/C][/ROW]
[ROW][C]119[/C][C]11[/C][C]13.229065976075[/C][C]-2.22906597607501[/C][/ROW]
[ROW][C]120[/C][C]12[/C][C]13.3691059812832[/C][C]-1.36910598128322[/C][/ROW]
[ROW][C]121[/C][C]8[/C][C]13.2720235346204[/C][C]-5.27202353462038[/C][/ROW]
[ROW][C]122[/C][C]16[/C][C]13.1188359337989[/C][C]2.88116406620105[/C][/ROW]
[ROW][C]123[/C][C]15[/C][C]13.2449550844435[/C][C]1.7550449155565[/C][/ROW]
[ROW][C]124[/C][C]17[/C][C]16.7397541570333[/C][C]0.260245842966744[/C][/ROW]
[ROW][C]125[/C][C]16[/C][C]14.9199395816024[/C][C]1.08006041839764[/C][/ROW]
[ROW][C]126[/C][C]10[/C][C]14.100124078739[/C][C]-4.10012407873897[/C][/ROW]
[ROW][C]127[/C][C]18[/C][C]16.1421197008406[/C][C]1.85788029915938[/C][/ROW]
[ROW][C]128[/C][C]13[/C][C]15.2823324026825[/C][C]-2.28233240268251[/C][/ROW]
[ROW][C]129[/C][C]16[/C][C]15.2617270242819[/C][C]0.738272975718114[/C][/ROW]
[ROW][C]130[/C][C]13[/C][C]12.8988632576734[/C][C]0.101136742326622[/C][/ROW]
[ROW][C]131[/C][C]10[/C][C]12.9845280498778[/C][C]-2.98452804987782[/C][/ROW]
[ROW][C]132[/C][C]15[/C][C]16.2933585825768[/C][C]-1.29335858257685[/C][/ROW]
[ROW][C]133[/C][C]16[/C][C]13.9855896000083[/C][C]2.01441039999165[/C][/ROW]
[ROW][C]134[/C][C]16[/C][C]11.7862216785568[/C][C]4.21377832144323[/C][/ROW]
[ROW][C]135[/C][C]14[/C][C]12.2562434003743[/C][C]1.74375659962568[/C][/ROW]
[ROW][C]136[/C][C]10[/C][C]12.5027761791616[/C][C]-2.50277617916164[/C][/ROW]
[ROW][C]137[/C][C]17[/C][C]17.0857877721761[/C][C]-0.0857877721760632[/C][/ROW]
[ROW][C]138[/C][C]13[/C][C]11.4749200541236[/C][C]1.52507994587637[/C][/ROW]
[ROW][C]139[/C][C]15[/C][C]13.978825382748[/C][C]1.02117461725204[/C][/ROW]
[ROW][C]140[/C][C]16[/C][C]15.1289615419882[/C][C]0.871038458011757[/C][/ROW]
[ROW][C]141[/C][C]12[/C][C]12.2231595088172[/C][C]-0.223159508817216[/C][/ROW]
[ROW][C]142[/C][C]13[/C][C]12.6326757689921[/C][C]0.367324231007866[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185553&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185553&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
11615.37766260674290.622337393257072
21614.80859975982031.19140024017969
31916.41433936311662.58566063688339
41614.69936644135951.30063355864052
51714.60822598998092.39177401001905
61716.76191387403610.238086125963903
71614.69621011958491.30378988041508
81516.1340986206719-1.13409862067189
91615.2581486053480.741851394651991
101414.0023949591923-0.00239495919232862
111515.6982587582456-0.698258758245628
121212.0563621407486-0.056362140748597
131415.0659754080849-1.0659754080849
141615.52836587074930.471634129250696
151415.4639531751404-1.46395317514038
16712.8395529062434-5.83955290624341
171010.4364573388577-0.436457338857749
181415.7474278499318-1.74742784993183
191614.21215856152561.78784143847443
201614.5163476364711.48365236352903
211614.99373514040121.0062648595988
221415.4298271852644-1.42982718526443
232018.12729800338831.87270199661172
241414.0666347594013-0.0666347594012815
251414.8230317644042-0.823031764404186
261115.4307799642503-4.43077996425027
271416.6301715590681-2.63017155906806
281514.91099433403720.0890056659628334
291614.90295942939261.09704057060744
301415.9485052123583-1.9485052123583
311616.5831943014097-0.583194301409741
321413.98894309443120.0110569055687925
331214.4978161611758-2.49781616117584
341615.57384379435820.426156205641835
35911.1546615134396-2.15466151343963
361412.41112552503991.5888744749601
371616.0272162472382-0.0272162472381986
381615.22474096752330.775259032476736
391515.1648849330451-0.164884933045123
401613.97818756848622.02181243151378
411211.12425985746190.875740142538112
421615.80674529829130.193254701708723
431616.370386203883-0.370386203882957
441414.296844845061-0.296844845060991
451615.47303769767380.526962302326156
461715.86637088615341.13362911384663
471816.12563111862031.87436888137967
481814.48723542825673.51276457174333
491215.7722611249778-3.77226112497782
501615.3467675232250.65323247677499
511013.3947850417264-3.39478504172642
521414.2642906880534-0.264290688053398
531816.45018146967151.54981853032849
541817.07156104204690.928438957953148
551615.67263068901430.32736931098571
561713.60308461919553.39691538080449
571616.3697100297326-0.369710029732644
581614.42493316392361.57506683607642
591314.8629328454702-1.86293284547019
601616.1257577776259-0.125757777625929
611615.44096060230320.559039397696845
622015.89571336513384.10428663486621
631615.79279511539770.207204884602285
641515.92696559419-0.926965594190032
651514.70999254878230.290007451217738
661614.17278431250631.82721568749367
671413.97155982136640.0284401786335832
681615.32430770569010.675692294309915
691614.59180406871031.40819593128972
701513.98748304283281.01251695716723
711213.4363089921837-1.43630899218374
721717.0857877721761-0.0857877721760632
731615.3352653824260.664734617574019
741515.1402820593041-0.140282059304088
751315.1318824442401-2.13188244424007
761614.9974625578881.00253744211201
771615.8153117497250.184688250275019
781613.98692298736532.01307701263474
791616.0686382223247-0.0686382223246587
801414.2571009250044-0.257100925004443
811617.3607500127344-1.36075001273438
821614.66158761858021.3384123814198
832017.46548169214192.5345183078581
841514.68338041177350.316619588226463
851614.33358075278331.66641924721675
861315.1695687940721-2.1695687940721
871716.07164058492840.9283594150716
881615.76268783204660.237312167953379
891614.47522092368831.5247790763117
901211.90225499665080.0977450033491526
911614.91314403547521.08685596452475
921615.83313863984490.166861360155085
931714.48920572483762.51079427516243
941314.350298695496-1.35029869549599
951214.8808753872363-2.88087538723633
961816.73272212062041.26727787937957
971415.5346019898498-1.53460198984984
981412.85600894584791.14399105415214
991314.9818689329843-1.98186893298431
1001615.57380655236060.426193447639381
1011314.2331347794015-1.23313477940154
1021615.71988747458590.280112525414117
1031315.9107362460251-2.9107362460251
1041617.2364981331119-1.23649813311191
1051516.1523523226935-1.15235232269346
1061616.9126798309623-0.91267983096229
1071515.4602494086251-0.460249408625067
1081716.17935596947140.820644030528606
1091513.9788253827481.02117461725204
1101214.9727076648412-2.97270766484125
1111614.10778601283261.89221398716743
1121013.2053285953874-3.20532859538742
1131613.93217390889742.06782609110256
1141214.0073654450621-2.00736544506206
1151415.7190358636082-1.7190358636082
1161515.3380646016507-0.338064601650717
1171312.17972790938770.820272090612265
1181514.63726351070080.362736489299179
1191113.229065976075-2.22906597607501
1201213.3691059812832-1.36910598128322
121813.2720235346204-5.27202353462038
1221613.11883593379892.88116406620105
1231513.24495508444351.7550449155565
1241716.73975415703330.260245842966744
1251614.91993958160241.08006041839764
1261014.100124078739-4.10012407873897
1271816.14211970084061.85788029915938
1281315.2823324026825-2.28233240268251
1291615.26172702428190.738272975718114
1301312.89886325767340.101136742326622
1311012.9845280498778-2.98452804987782
1321516.2933585825768-1.29335858257685
1331613.98558960000832.01441039999165
1341611.78622167855684.21377832144323
1351412.25624340037431.74375659962568
1361012.5027761791616-2.50277617916164
1371717.0857877721761-0.0857877721760632
1381311.47492005412361.52507994587637
1391513.9788253827481.02117461725204
1401615.12896154198820.871038458011757
1411212.2231595088172-0.223159508817216
1421312.63267576899210.367324231007866







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.09720326758105430.1944065351621090.902796732418946
110.07672699360475250.1534539872095050.923273006395247
120.04980353346078640.09960706692157290.950196466539214
130.2438529210372410.4877058420744820.756147078962759
140.1547741748615960.3095483497231920.845225825138404
150.12955501008580.2591100201716010.8704449899142
160.7519284422525730.4961431154948530.248071557747427
170.6717468836989170.6565062326021670.328253116301083
180.7228675748587810.5542648502824390.27713242514122
190.8046192335610070.3907615328779870.195380766438993
200.7985153002366140.4029693995267720.201484699763386
210.7560259682853970.4879480634292070.243974031714603
220.7521859760450440.4956280479099120.247814023954956
230.7067800354810820.5864399290378350.293219964518918
240.6458676494023240.7082647011953530.354132350597676
250.5908792542809060.8182414914381870.409120745719094
260.864165447446980.2716691051060390.13583455255302
270.8948272356934040.2103455286131930.105172764306596
280.8635565175877020.2728869648245950.136443482412298
290.8311702115807650.3376595768384690.168829788419235
300.8291556993585590.3416886012828820.170844300641441
310.7871954941070320.4256090117859370.212804505892968
320.7387034597571150.522593080485770.261296540242885
330.7688390542128080.4623218915743850.231160945787192
340.7281111196051280.5437777607897440.271888880394872
350.7103160754031850.5793678491936290.289683924596815
360.6985308806031050.602938238793790.301469119396895
370.6524689108110140.6950621783779720.347531089188986
380.6256835608467330.7486328783065330.374316439153267
390.5735805357014940.8528389285970120.426419464298506
400.5968453962573920.8063092074852160.403154603742608
410.5620697736538360.8758604526923280.437930226346164
420.5068244191232020.9863511617535960.493175580876798
430.453594055475310.9071881109506210.54640594452469
440.4004344545389370.8008689090778750.599565545461063
450.3491627371071480.6983254742142960.650837262892852
460.3385966885998150.677193377199630.661403311400185
470.3276784893151270.6553569786302540.672321510684873
480.4692110988493730.9384221976987470.530788901150627
490.609240247555810.781519504888380.39075975244419
500.5657643142855360.8684713714289280.434235685714464
510.6472950847789630.7054098304420750.352704915221037
520.5984315279467880.8031369441064250.401568472053212
530.5838380506414740.8323238987170520.416161949358526
540.5557088294573590.8885823410852820.444291170542641
550.5077105717899160.9845788564201680.492289428210084
560.5979748489582250.8040503020835510.402025151041775
570.5515359845693610.8969280308612780.448464015430639
580.526444390444710.947111219110580.47355560955529
590.5476965437479750.904606912504050.452303456252025
600.4979651225137770.9959302450275550.502034877486223
610.4530813711872790.9061627423745570.546918628812721
620.6697521274108380.6604957451783230.330247872589161
630.6257290853361280.7485418293277440.374270914663872
640.5937147183102790.8125705633794430.406285281689721
650.5460919611006590.9078160777986830.453908038899341
660.5448757903607860.9102484192784280.455124209639214
670.4968289270372790.9936578540745580.503171072962721
680.4537237461957340.9074474923914690.546276253804266
690.4311900579621930.8623801159243860.568809942037807
700.3946713361729590.7893426723459170.605328663827041
710.3715396308295660.7430792616591330.628460369170434
720.3437691739940050.6875383479880110.656230826005995
730.3079125768554430.6158251537108860.692087423144557
740.2690031701489560.5380063402979120.730996829851044
750.2815683118325620.5631366236651240.718431688167438
760.2544758075968020.5089516151936040.745524192403198
770.2202000368770780.4404000737541550.779799963122922
780.229655061546710.4593101230934210.77034493845329
790.1938570264741790.3877140529483580.806142973525821
800.1630738809921370.3261477619842740.836926119007863
810.1480578797596880.2961157595193760.851942120240312
820.1364170518803950.2728341037607910.863582948119604
830.1710273602091130.3420547204182260.828972639790887
840.1416357891436160.2832715782872320.858364210856384
850.1398962076658970.2797924153317930.860103792334103
860.1537841434350370.3075682868700740.846215856564963
870.13600932749320.2720186549864010.8639906725068
880.1181065799199730.2362131598399460.881893420080027
890.1148037634460730.2296075268921460.885196236553927
900.1041252104164030.2082504208328050.895874789583597
910.09062192231836870.1812438446367370.909378077681631
920.07330835035731280.1466167007146260.926691649642687
930.09343428164990630.1868685632998130.906565718350094
940.07999553096299280.1599910619259860.920004469037007
950.1062665576989620.2125331153979240.893733442301038
960.09892495391934460.1978499078386890.901075046080655
970.08507729059537010.170154581190740.91492270940463
980.07393592092804620.1478718418560920.926064079071954
990.07666384572556920.1533276914511380.923336154274431
1000.0707934560263810.1415869120527620.929206543973619
1010.05829531382180330.1165906276436070.941704686178197
1020.04557761222781010.09115522445562010.95442238777219
1030.05317915824387110.1063583164877420.946820841756129
1040.04251004397985530.08502008795971060.957489956020145
1050.03433024654670480.06866049309340950.965669753453295
1060.02588264520131510.05176529040263030.974117354798685
1070.01899333410076090.03798666820152190.981006665899239
1080.01544287983091020.03088575966182030.98455712016909
1090.01186065783416640.02372131566833270.988139342165834
1100.01710531179546170.03421062359092340.982894688204538
1110.01514627480061020.03029254960122040.98485372519939
1120.02355730627474910.04711461254949820.976442693725251
1130.02739511983742650.0547902396748530.972604880162574
1140.03564025850914560.07128051701829110.964359741490854
1150.02837038469621320.05674076939242640.971629615303787
1160.01942329409731510.03884658819463020.980576705902685
1170.01308390136681190.02616780273362380.986916098633188
1180.008530005897219820.01706001179443960.99146999410278
1190.01596560742308470.03193121484616940.984034392576915
1200.01360663830343230.02721327660686470.986393361696568
1210.4764542707110160.9529085414220320.523545729288984
1220.4251208281012840.8502416562025680.574879171898716
1230.3659559901481360.7319119802962730.634044009851864
1240.3105299037619070.6210598075238140.689470096238093
1250.2568314675184380.5136629350368770.743168532481562
1260.2615804226969560.5231608453939120.738419577303044
1270.2893691360679820.5787382721359640.710630863932018
1280.7703101232301130.4593797535397730.229689876769886
1290.7105309995517220.5789380008965560.289469000448278
1300.5886540050842290.8226919898315430.411345994915771
1310.8340595999834140.3318808000331720.165940400016586
1320.8372104304675090.3255791390649830.162789569532491

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.0972032675810543 & 0.194406535162109 & 0.902796732418946 \tabularnewline
11 & 0.0767269936047525 & 0.153453987209505 & 0.923273006395247 \tabularnewline
12 & 0.0498035334607864 & 0.0996070669215729 & 0.950196466539214 \tabularnewline
13 & 0.243852921037241 & 0.487705842074482 & 0.756147078962759 \tabularnewline
14 & 0.154774174861596 & 0.309548349723192 & 0.845225825138404 \tabularnewline
15 & 0.1295550100858 & 0.259110020171601 & 0.8704449899142 \tabularnewline
16 & 0.751928442252573 & 0.496143115494853 & 0.248071557747427 \tabularnewline
17 & 0.671746883698917 & 0.656506232602167 & 0.328253116301083 \tabularnewline
18 & 0.722867574858781 & 0.554264850282439 & 0.27713242514122 \tabularnewline
19 & 0.804619233561007 & 0.390761532877987 & 0.195380766438993 \tabularnewline
20 & 0.798515300236614 & 0.402969399526772 & 0.201484699763386 \tabularnewline
21 & 0.756025968285397 & 0.487948063429207 & 0.243974031714603 \tabularnewline
22 & 0.752185976045044 & 0.495628047909912 & 0.247814023954956 \tabularnewline
23 & 0.706780035481082 & 0.586439929037835 & 0.293219964518918 \tabularnewline
24 & 0.645867649402324 & 0.708264701195353 & 0.354132350597676 \tabularnewline
25 & 0.590879254280906 & 0.818241491438187 & 0.409120745719094 \tabularnewline
26 & 0.86416544744698 & 0.271669105106039 & 0.13583455255302 \tabularnewline
27 & 0.894827235693404 & 0.210345528613193 & 0.105172764306596 \tabularnewline
28 & 0.863556517587702 & 0.272886964824595 & 0.136443482412298 \tabularnewline
29 & 0.831170211580765 & 0.337659576838469 & 0.168829788419235 \tabularnewline
30 & 0.829155699358559 & 0.341688601282882 & 0.170844300641441 \tabularnewline
31 & 0.787195494107032 & 0.425609011785937 & 0.212804505892968 \tabularnewline
32 & 0.738703459757115 & 0.52259308048577 & 0.261296540242885 \tabularnewline
33 & 0.768839054212808 & 0.462321891574385 & 0.231160945787192 \tabularnewline
34 & 0.728111119605128 & 0.543777760789744 & 0.271888880394872 \tabularnewline
35 & 0.710316075403185 & 0.579367849193629 & 0.289683924596815 \tabularnewline
36 & 0.698530880603105 & 0.60293823879379 & 0.301469119396895 \tabularnewline
37 & 0.652468910811014 & 0.695062178377972 & 0.347531089188986 \tabularnewline
38 & 0.625683560846733 & 0.748632878306533 & 0.374316439153267 \tabularnewline
39 & 0.573580535701494 & 0.852838928597012 & 0.426419464298506 \tabularnewline
40 & 0.596845396257392 & 0.806309207485216 & 0.403154603742608 \tabularnewline
41 & 0.562069773653836 & 0.875860452692328 & 0.437930226346164 \tabularnewline
42 & 0.506824419123202 & 0.986351161753596 & 0.493175580876798 \tabularnewline
43 & 0.45359405547531 & 0.907188110950621 & 0.54640594452469 \tabularnewline
44 & 0.400434454538937 & 0.800868909077875 & 0.599565545461063 \tabularnewline
45 & 0.349162737107148 & 0.698325474214296 & 0.650837262892852 \tabularnewline
46 & 0.338596688599815 & 0.67719337719963 & 0.661403311400185 \tabularnewline
47 & 0.327678489315127 & 0.655356978630254 & 0.672321510684873 \tabularnewline
48 & 0.469211098849373 & 0.938422197698747 & 0.530788901150627 \tabularnewline
49 & 0.60924024755581 & 0.78151950488838 & 0.39075975244419 \tabularnewline
50 & 0.565764314285536 & 0.868471371428928 & 0.434235685714464 \tabularnewline
51 & 0.647295084778963 & 0.705409830442075 & 0.352704915221037 \tabularnewline
52 & 0.598431527946788 & 0.803136944106425 & 0.401568472053212 \tabularnewline
53 & 0.583838050641474 & 0.832323898717052 & 0.416161949358526 \tabularnewline
54 & 0.555708829457359 & 0.888582341085282 & 0.444291170542641 \tabularnewline
55 & 0.507710571789916 & 0.984578856420168 & 0.492289428210084 \tabularnewline
56 & 0.597974848958225 & 0.804050302083551 & 0.402025151041775 \tabularnewline
57 & 0.551535984569361 & 0.896928030861278 & 0.448464015430639 \tabularnewline
58 & 0.52644439044471 & 0.94711121911058 & 0.47355560955529 \tabularnewline
59 & 0.547696543747975 & 0.90460691250405 & 0.452303456252025 \tabularnewline
60 & 0.497965122513777 & 0.995930245027555 & 0.502034877486223 \tabularnewline
61 & 0.453081371187279 & 0.906162742374557 & 0.546918628812721 \tabularnewline
62 & 0.669752127410838 & 0.660495745178323 & 0.330247872589161 \tabularnewline
63 & 0.625729085336128 & 0.748541829327744 & 0.374270914663872 \tabularnewline
64 & 0.593714718310279 & 0.812570563379443 & 0.406285281689721 \tabularnewline
65 & 0.546091961100659 & 0.907816077798683 & 0.453908038899341 \tabularnewline
66 & 0.544875790360786 & 0.910248419278428 & 0.455124209639214 \tabularnewline
67 & 0.496828927037279 & 0.993657854074558 & 0.503171072962721 \tabularnewline
68 & 0.453723746195734 & 0.907447492391469 & 0.546276253804266 \tabularnewline
69 & 0.431190057962193 & 0.862380115924386 & 0.568809942037807 \tabularnewline
70 & 0.394671336172959 & 0.789342672345917 & 0.605328663827041 \tabularnewline
71 & 0.371539630829566 & 0.743079261659133 & 0.628460369170434 \tabularnewline
72 & 0.343769173994005 & 0.687538347988011 & 0.656230826005995 \tabularnewline
73 & 0.307912576855443 & 0.615825153710886 & 0.692087423144557 \tabularnewline
74 & 0.269003170148956 & 0.538006340297912 & 0.730996829851044 \tabularnewline
75 & 0.281568311832562 & 0.563136623665124 & 0.718431688167438 \tabularnewline
76 & 0.254475807596802 & 0.508951615193604 & 0.745524192403198 \tabularnewline
77 & 0.220200036877078 & 0.440400073754155 & 0.779799963122922 \tabularnewline
78 & 0.22965506154671 & 0.459310123093421 & 0.77034493845329 \tabularnewline
79 & 0.193857026474179 & 0.387714052948358 & 0.806142973525821 \tabularnewline
80 & 0.163073880992137 & 0.326147761984274 & 0.836926119007863 \tabularnewline
81 & 0.148057879759688 & 0.296115759519376 & 0.851942120240312 \tabularnewline
82 & 0.136417051880395 & 0.272834103760791 & 0.863582948119604 \tabularnewline
83 & 0.171027360209113 & 0.342054720418226 & 0.828972639790887 \tabularnewline
84 & 0.141635789143616 & 0.283271578287232 & 0.858364210856384 \tabularnewline
85 & 0.139896207665897 & 0.279792415331793 & 0.860103792334103 \tabularnewline
86 & 0.153784143435037 & 0.307568286870074 & 0.846215856564963 \tabularnewline
87 & 0.1360093274932 & 0.272018654986401 & 0.8639906725068 \tabularnewline
88 & 0.118106579919973 & 0.236213159839946 & 0.881893420080027 \tabularnewline
89 & 0.114803763446073 & 0.229607526892146 & 0.885196236553927 \tabularnewline
90 & 0.104125210416403 & 0.208250420832805 & 0.895874789583597 \tabularnewline
91 & 0.0906219223183687 & 0.181243844636737 & 0.909378077681631 \tabularnewline
92 & 0.0733083503573128 & 0.146616700714626 & 0.926691649642687 \tabularnewline
93 & 0.0934342816499063 & 0.186868563299813 & 0.906565718350094 \tabularnewline
94 & 0.0799955309629928 & 0.159991061925986 & 0.920004469037007 \tabularnewline
95 & 0.106266557698962 & 0.212533115397924 & 0.893733442301038 \tabularnewline
96 & 0.0989249539193446 & 0.197849907838689 & 0.901075046080655 \tabularnewline
97 & 0.0850772905953701 & 0.17015458119074 & 0.91492270940463 \tabularnewline
98 & 0.0739359209280462 & 0.147871841856092 & 0.926064079071954 \tabularnewline
99 & 0.0766638457255692 & 0.153327691451138 & 0.923336154274431 \tabularnewline
100 & 0.070793456026381 & 0.141586912052762 & 0.929206543973619 \tabularnewline
101 & 0.0582953138218033 & 0.116590627643607 & 0.941704686178197 \tabularnewline
102 & 0.0455776122278101 & 0.0911552244556201 & 0.95442238777219 \tabularnewline
103 & 0.0531791582438711 & 0.106358316487742 & 0.946820841756129 \tabularnewline
104 & 0.0425100439798553 & 0.0850200879597106 & 0.957489956020145 \tabularnewline
105 & 0.0343302465467048 & 0.0686604930934095 & 0.965669753453295 \tabularnewline
106 & 0.0258826452013151 & 0.0517652904026303 & 0.974117354798685 \tabularnewline
107 & 0.0189933341007609 & 0.0379866682015219 & 0.981006665899239 \tabularnewline
108 & 0.0154428798309102 & 0.0308857596618203 & 0.98455712016909 \tabularnewline
109 & 0.0118606578341664 & 0.0237213156683327 & 0.988139342165834 \tabularnewline
110 & 0.0171053117954617 & 0.0342106235909234 & 0.982894688204538 \tabularnewline
111 & 0.0151462748006102 & 0.0302925496012204 & 0.98485372519939 \tabularnewline
112 & 0.0235573062747491 & 0.0471146125494982 & 0.976442693725251 \tabularnewline
113 & 0.0273951198374265 & 0.054790239674853 & 0.972604880162574 \tabularnewline
114 & 0.0356402585091456 & 0.0712805170182911 & 0.964359741490854 \tabularnewline
115 & 0.0283703846962132 & 0.0567407693924264 & 0.971629615303787 \tabularnewline
116 & 0.0194232940973151 & 0.0388465881946302 & 0.980576705902685 \tabularnewline
117 & 0.0130839013668119 & 0.0261678027336238 & 0.986916098633188 \tabularnewline
118 & 0.00853000589721982 & 0.0170600117944396 & 0.99146999410278 \tabularnewline
119 & 0.0159656074230847 & 0.0319312148461694 & 0.984034392576915 \tabularnewline
120 & 0.0136066383034323 & 0.0272132766068647 & 0.986393361696568 \tabularnewline
121 & 0.476454270711016 & 0.952908541422032 & 0.523545729288984 \tabularnewline
122 & 0.425120828101284 & 0.850241656202568 & 0.574879171898716 \tabularnewline
123 & 0.365955990148136 & 0.731911980296273 & 0.634044009851864 \tabularnewline
124 & 0.310529903761907 & 0.621059807523814 & 0.689470096238093 \tabularnewline
125 & 0.256831467518438 & 0.513662935036877 & 0.743168532481562 \tabularnewline
126 & 0.261580422696956 & 0.523160845393912 & 0.738419577303044 \tabularnewline
127 & 0.289369136067982 & 0.578738272135964 & 0.710630863932018 \tabularnewline
128 & 0.770310123230113 & 0.459379753539773 & 0.229689876769886 \tabularnewline
129 & 0.710530999551722 & 0.578938000896556 & 0.289469000448278 \tabularnewline
130 & 0.588654005084229 & 0.822691989831543 & 0.411345994915771 \tabularnewline
131 & 0.834059599983414 & 0.331880800033172 & 0.165940400016586 \tabularnewline
132 & 0.837210430467509 & 0.325579139064983 & 0.162789569532491 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185553&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]10[/C][C]0.0972032675810543[/C][C]0.194406535162109[/C][C]0.902796732418946[/C][/ROW]
[ROW][C]11[/C][C]0.0767269936047525[/C][C]0.153453987209505[/C][C]0.923273006395247[/C][/ROW]
[ROW][C]12[/C][C]0.0498035334607864[/C][C]0.0996070669215729[/C][C]0.950196466539214[/C][/ROW]
[ROW][C]13[/C][C]0.243852921037241[/C][C]0.487705842074482[/C][C]0.756147078962759[/C][/ROW]
[ROW][C]14[/C][C]0.154774174861596[/C][C]0.309548349723192[/C][C]0.845225825138404[/C][/ROW]
[ROW][C]15[/C][C]0.1295550100858[/C][C]0.259110020171601[/C][C]0.8704449899142[/C][/ROW]
[ROW][C]16[/C][C]0.751928442252573[/C][C]0.496143115494853[/C][C]0.248071557747427[/C][/ROW]
[ROW][C]17[/C][C]0.671746883698917[/C][C]0.656506232602167[/C][C]0.328253116301083[/C][/ROW]
[ROW][C]18[/C][C]0.722867574858781[/C][C]0.554264850282439[/C][C]0.27713242514122[/C][/ROW]
[ROW][C]19[/C][C]0.804619233561007[/C][C]0.390761532877987[/C][C]0.195380766438993[/C][/ROW]
[ROW][C]20[/C][C]0.798515300236614[/C][C]0.402969399526772[/C][C]0.201484699763386[/C][/ROW]
[ROW][C]21[/C][C]0.756025968285397[/C][C]0.487948063429207[/C][C]0.243974031714603[/C][/ROW]
[ROW][C]22[/C][C]0.752185976045044[/C][C]0.495628047909912[/C][C]0.247814023954956[/C][/ROW]
[ROW][C]23[/C][C]0.706780035481082[/C][C]0.586439929037835[/C][C]0.293219964518918[/C][/ROW]
[ROW][C]24[/C][C]0.645867649402324[/C][C]0.708264701195353[/C][C]0.354132350597676[/C][/ROW]
[ROW][C]25[/C][C]0.590879254280906[/C][C]0.818241491438187[/C][C]0.409120745719094[/C][/ROW]
[ROW][C]26[/C][C]0.86416544744698[/C][C]0.271669105106039[/C][C]0.13583455255302[/C][/ROW]
[ROW][C]27[/C][C]0.894827235693404[/C][C]0.210345528613193[/C][C]0.105172764306596[/C][/ROW]
[ROW][C]28[/C][C]0.863556517587702[/C][C]0.272886964824595[/C][C]0.136443482412298[/C][/ROW]
[ROW][C]29[/C][C]0.831170211580765[/C][C]0.337659576838469[/C][C]0.168829788419235[/C][/ROW]
[ROW][C]30[/C][C]0.829155699358559[/C][C]0.341688601282882[/C][C]0.170844300641441[/C][/ROW]
[ROW][C]31[/C][C]0.787195494107032[/C][C]0.425609011785937[/C][C]0.212804505892968[/C][/ROW]
[ROW][C]32[/C][C]0.738703459757115[/C][C]0.52259308048577[/C][C]0.261296540242885[/C][/ROW]
[ROW][C]33[/C][C]0.768839054212808[/C][C]0.462321891574385[/C][C]0.231160945787192[/C][/ROW]
[ROW][C]34[/C][C]0.728111119605128[/C][C]0.543777760789744[/C][C]0.271888880394872[/C][/ROW]
[ROW][C]35[/C][C]0.710316075403185[/C][C]0.579367849193629[/C][C]0.289683924596815[/C][/ROW]
[ROW][C]36[/C][C]0.698530880603105[/C][C]0.60293823879379[/C][C]0.301469119396895[/C][/ROW]
[ROW][C]37[/C][C]0.652468910811014[/C][C]0.695062178377972[/C][C]0.347531089188986[/C][/ROW]
[ROW][C]38[/C][C]0.625683560846733[/C][C]0.748632878306533[/C][C]0.374316439153267[/C][/ROW]
[ROW][C]39[/C][C]0.573580535701494[/C][C]0.852838928597012[/C][C]0.426419464298506[/C][/ROW]
[ROW][C]40[/C][C]0.596845396257392[/C][C]0.806309207485216[/C][C]0.403154603742608[/C][/ROW]
[ROW][C]41[/C][C]0.562069773653836[/C][C]0.875860452692328[/C][C]0.437930226346164[/C][/ROW]
[ROW][C]42[/C][C]0.506824419123202[/C][C]0.986351161753596[/C][C]0.493175580876798[/C][/ROW]
[ROW][C]43[/C][C]0.45359405547531[/C][C]0.907188110950621[/C][C]0.54640594452469[/C][/ROW]
[ROW][C]44[/C][C]0.400434454538937[/C][C]0.800868909077875[/C][C]0.599565545461063[/C][/ROW]
[ROW][C]45[/C][C]0.349162737107148[/C][C]0.698325474214296[/C][C]0.650837262892852[/C][/ROW]
[ROW][C]46[/C][C]0.338596688599815[/C][C]0.67719337719963[/C][C]0.661403311400185[/C][/ROW]
[ROW][C]47[/C][C]0.327678489315127[/C][C]0.655356978630254[/C][C]0.672321510684873[/C][/ROW]
[ROW][C]48[/C][C]0.469211098849373[/C][C]0.938422197698747[/C][C]0.530788901150627[/C][/ROW]
[ROW][C]49[/C][C]0.60924024755581[/C][C]0.78151950488838[/C][C]0.39075975244419[/C][/ROW]
[ROW][C]50[/C][C]0.565764314285536[/C][C]0.868471371428928[/C][C]0.434235685714464[/C][/ROW]
[ROW][C]51[/C][C]0.647295084778963[/C][C]0.705409830442075[/C][C]0.352704915221037[/C][/ROW]
[ROW][C]52[/C][C]0.598431527946788[/C][C]0.803136944106425[/C][C]0.401568472053212[/C][/ROW]
[ROW][C]53[/C][C]0.583838050641474[/C][C]0.832323898717052[/C][C]0.416161949358526[/C][/ROW]
[ROW][C]54[/C][C]0.555708829457359[/C][C]0.888582341085282[/C][C]0.444291170542641[/C][/ROW]
[ROW][C]55[/C][C]0.507710571789916[/C][C]0.984578856420168[/C][C]0.492289428210084[/C][/ROW]
[ROW][C]56[/C][C]0.597974848958225[/C][C]0.804050302083551[/C][C]0.402025151041775[/C][/ROW]
[ROW][C]57[/C][C]0.551535984569361[/C][C]0.896928030861278[/C][C]0.448464015430639[/C][/ROW]
[ROW][C]58[/C][C]0.52644439044471[/C][C]0.94711121911058[/C][C]0.47355560955529[/C][/ROW]
[ROW][C]59[/C][C]0.547696543747975[/C][C]0.90460691250405[/C][C]0.452303456252025[/C][/ROW]
[ROW][C]60[/C][C]0.497965122513777[/C][C]0.995930245027555[/C][C]0.502034877486223[/C][/ROW]
[ROW][C]61[/C][C]0.453081371187279[/C][C]0.906162742374557[/C][C]0.546918628812721[/C][/ROW]
[ROW][C]62[/C][C]0.669752127410838[/C][C]0.660495745178323[/C][C]0.330247872589161[/C][/ROW]
[ROW][C]63[/C][C]0.625729085336128[/C][C]0.748541829327744[/C][C]0.374270914663872[/C][/ROW]
[ROW][C]64[/C][C]0.593714718310279[/C][C]0.812570563379443[/C][C]0.406285281689721[/C][/ROW]
[ROW][C]65[/C][C]0.546091961100659[/C][C]0.907816077798683[/C][C]0.453908038899341[/C][/ROW]
[ROW][C]66[/C][C]0.544875790360786[/C][C]0.910248419278428[/C][C]0.455124209639214[/C][/ROW]
[ROW][C]67[/C][C]0.496828927037279[/C][C]0.993657854074558[/C][C]0.503171072962721[/C][/ROW]
[ROW][C]68[/C][C]0.453723746195734[/C][C]0.907447492391469[/C][C]0.546276253804266[/C][/ROW]
[ROW][C]69[/C][C]0.431190057962193[/C][C]0.862380115924386[/C][C]0.568809942037807[/C][/ROW]
[ROW][C]70[/C][C]0.394671336172959[/C][C]0.789342672345917[/C][C]0.605328663827041[/C][/ROW]
[ROW][C]71[/C][C]0.371539630829566[/C][C]0.743079261659133[/C][C]0.628460369170434[/C][/ROW]
[ROW][C]72[/C][C]0.343769173994005[/C][C]0.687538347988011[/C][C]0.656230826005995[/C][/ROW]
[ROW][C]73[/C][C]0.307912576855443[/C][C]0.615825153710886[/C][C]0.692087423144557[/C][/ROW]
[ROW][C]74[/C][C]0.269003170148956[/C][C]0.538006340297912[/C][C]0.730996829851044[/C][/ROW]
[ROW][C]75[/C][C]0.281568311832562[/C][C]0.563136623665124[/C][C]0.718431688167438[/C][/ROW]
[ROW][C]76[/C][C]0.254475807596802[/C][C]0.508951615193604[/C][C]0.745524192403198[/C][/ROW]
[ROW][C]77[/C][C]0.220200036877078[/C][C]0.440400073754155[/C][C]0.779799963122922[/C][/ROW]
[ROW][C]78[/C][C]0.22965506154671[/C][C]0.459310123093421[/C][C]0.77034493845329[/C][/ROW]
[ROW][C]79[/C][C]0.193857026474179[/C][C]0.387714052948358[/C][C]0.806142973525821[/C][/ROW]
[ROW][C]80[/C][C]0.163073880992137[/C][C]0.326147761984274[/C][C]0.836926119007863[/C][/ROW]
[ROW][C]81[/C][C]0.148057879759688[/C][C]0.296115759519376[/C][C]0.851942120240312[/C][/ROW]
[ROW][C]82[/C][C]0.136417051880395[/C][C]0.272834103760791[/C][C]0.863582948119604[/C][/ROW]
[ROW][C]83[/C][C]0.171027360209113[/C][C]0.342054720418226[/C][C]0.828972639790887[/C][/ROW]
[ROW][C]84[/C][C]0.141635789143616[/C][C]0.283271578287232[/C][C]0.858364210856384[/C][/ROW]
[ROW][C]85[/C][C]0.139896207665897[/C][C]0.279792415331793[/C][C]0.860103792334103[/C][/ROW]
[ROW][C]86[/C][C]0.153784143435037[/C][C]0.307568286870074[/C][C]0.846215856564963[/C][/ROW]
[ROW][C]87[/C][C]0.1360093274932[/C][C]0.272018654986401[/C][C]0.8639906725068[/C][/ROW]
[ROW][C]88[/C][C]0.118106579919973[/C][C]0.236213159839946[/C][C]0.881893420080027[/C][/ROW]
[ROW][C]89[/C][C]0.114803763446073[/C][C]0.229607526892146[/C][C]0.885196236553927[/C][/ROW]
[ROW][C]90[/C][C]0.104125210416403[/C][C]0.208250420832805[/C][C]0.895874789583597[/C][/ROW]
[ROW][C]91[/C][C]0.0906219223183687[/C][C]0.181243844636737[/C][C]0.909378077681631[/C][/ROW]
[ROW][C]92[/C][C]0.0733083503573128[/C][C]0.146616700714626[/C][C]0.926691649642687[/C][/ROW]
[ROW][C]93[/C][C]0.0934342816499063[/C][C]0.186868563299813[/C][C]0.906565718350094[/C][/ROW]
[ROW][C]94[/C][C]0.0799955309629928[/C][C]0.159991061925986[/C][C]0.920004469037007[/C][/ROW]
[ROW][C]95[/C][C]0.106266557698962[/C][C]0.212533115397924[/C][C]0.893733442301038[/C][/ROW]
[ROW][C]96[/C][C]0.0989249539193446[/C][C]0.197849907838689[/C][C]0.901075046080655[/C][/ROW]
[ROW][C]97[/C][C]0.0850772905953701[/C][C]0.17015458119074[/C][C]0.91492270940463[/C][/ROW]
[ROW][C]98[/C][C]0.0739359209280462[/C][C]0.147871841856092[/C][C]0.926064079071954[/C][/ROW]
[ROW][C]99[/C][C]0.0766638457255692[/C][C]0.153327691451138[/C][C]0.923336154274431[/C][/ROW]
[ROW][C]100[/C][C]0.070793456026381[/C][C]0.141586912052762[/C][C]0.929206543973619[/C][/ROW]
[ROW][C]101[/C][C]0.0582953138218033[/C][C]0.116590627643607[/C][C]0.941704686178197[/C][/ROW]
[ROW][C]102[/C][C]0.0455776122278101[/C][C]0.0911552244556201[/C][C]0.95442238777219[/C][/ROW]
[ROW][C]103[/C][C]0.0531791582438711[/C][C]0.106358316487742[/C][C]0.946820841756129[/C][/ROW]
[ROW][C]104[/C][C]0.0425100439798553[/C][C]0.0850200879597106[/C][C]0.957489956020145[/C][/ROW]
[ROW][C]105[/C][C]0.0343302465467048[/C][C]0.0686604930934095[/C][C]0.965669753453295[/C][/ROW]
[ROW][C]106[/C][C]0.0258826452013151[/C][C]0.0517652904026303[/C][C]0.974117354798685[/C][/ROW]
[ROW][C]107[/C][C]0.0189933341007609[/C][C]0.0379866682015219[/C][C]0.981006665899239[/C][/ROW]
[ROW][C]108[/C][C]0.0154428798309102[/C][C]0.0308857596618203[/C][C]0.98455712016909[/C][/ROW]
[ROW][C]109[/C][C]0.0118606578341664[/C][C]0.0237213156683327[/C][C]0.988139342165834[/C][/ROW]
[ROW][C]110[/C][C]0.0171053117954617[/C][C]0.0342106235909234[/C][C]0.982894688204538[/C][/ROW]
[ROW][C]111[/C][C]0.0151462748006102[/C][C]0.0302925496012204[/C][C]0.98485372519939[/C][/ROW]
[ROW][C]112[/C][C]0.0235573062747491[/C][C]0.0471146125494982[/C][C]0.976442693725251[/C][/ROW]
[ROW][C]113[/C][C]0.0273951198374265[/C][C]0.054790239674853[/C][C]0.972604880162574[/C][/ROW]
[ROW][C]114[/C][C]0.0356402585091456[/C][C]0.0712805170182911[/C][C]0.964359741490854[/C][/ROW]
[ROW][C]115[/C][C]0.0283703846962132[/C][C]0.0567407693924264[/C][C]0.971629615303787[/C][/ROW]
[ROW][C]116[/C][C]0.0194232940973151[/C][C]0.0388465881946302[/C][C]0.980576705902685[/C][/ROW]
[ROW][C]117[/C][C]0.0130839013668119[/C][C]0.0261678027336238[/C][C]0.986916098633188[/C][/ROW]
[ROW][C]118[/C][C]0.00853000589721982[/C][C]0.0170600117944396[/C][C]0.99146999410278[/C][/ROW]
[ROW][C]119[/C][C]0.0159656074230847[/C][C]0.0319312148461694[/C][C]0.984034392576915[/C][/ROW]
[ROW][C]120[/C][C]0.0136066383034323[/C][C]0.0272132766068647[/C][C]0.986393361696568[/C][/ROW]
[ROW][C]121[/C][C]0.476454270711016[/C][C]0.952908541422032[/C][C]0.523545729288984[/C][/ROW]
[ROW][C]122[/C][C]0.425120828101284[/C][C]0.850241656202568[/C][C]0.574879171898716[/C][/ROW]
[ROW][C]123[/C][C]0.365955990148136[/C][C]0.731911980296273[/C][C]0.634044009851864[/C][/ROW]
[ROW][C]124[/C][C]0.310529903761907[/C][C]0.621059807523814[/C][C]0.689470096238093[/C][/ROW]
[ROW][C]125[/C][C]0.256831467518438[/C][C]0.513662935036877[/C][C]0.743168532481562[/C][/ROW]
[ROW][C]126[/C][C]0.261580422696956[/C][C]0.523160845393912[/C][C]0.738419577303044[/C][/ROW]
[ROW][C]127[/C][C]0.289369136067982[/C][C]0.578738272135964[/C][C]0.710630863932018[/C][/ROW]
[ROW][C]128[/C][C]0.770310123230113[/C][C]0.459379753539773[/C][C]0.229689876769886[/C][/ROW]
[ROW][C]129[/C][C]0.710530999551722[/C][C]0.578938000896556[/C][C]0.289469000448278[/C][/ROW]
[ROW][C]130[/C][C]0.588654005084229[/C][C]0.822691989831543[/C][C]0.411345994915771[/C][/ROW]
[ROW][C]131[/C][C]0.834059599983414[/C][C]0.331880800033172[/C][C]0.165940400016586[/C][/ROW]
[ROW][C]132[/C][C]0.837210430467509[/C][C]0.325579139064983[/C][C]0.162789569532491[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185553&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185553&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
100.09720326758105430.1944065351621090.902796732418946
110.07672699360475250.1534539872095050.923273006395247
120.04980353346078640.09960706692157290.950196466539214
130.2438529210372410.4877058420744820.756147078962759
140.1547741748615960.3095483497231920.845225825138404
150.12955501008580.2591100201716010.8704449899142
160.7519284422525730.4961431154948530.248071557747427
170.6717468836989170.6565062326021670.328253116301083
180.7228675748587810.5542648502824390.27713242514122
190.8046192335610070.3907615328779870.195380766438993
200.7985153002366140.4029693995267720.201484699763386
210.7560259682853970.4879480634292070.243974031714603
220.7521859760450440.4956280479099120.247814023954956
230.7067800354810820.5864399290378350.293219964518918
240.6458676494023240.7082647011953530.354132350597676
250.5908792542809060.8182414914381870.409120745719094
260.864165447446980.2716691051060390.13583455255302
270.8948272356934040.2103455286131930.105172764306596
280.8635565175877020.2728869648245950.136443482412298
290.8311702115807650.3376595768384690.168829788419235
300.8291556993585590.3416886012828820.170844300641441
310.7871954941070320.4256090117859370.212804505892968
320.7387034597571150.522593080485770.261296540242885
330.7688390542128080.4623218915743850.231160945787192
340.7281111196051280.5437777607897440.271888880394872
350.7103160754031850.5793678491936290.289683924596815
360.6985308806031050.602938238793790.301469119396895
370.6524689108110140.6950621783779720.347531089188986
380.6256835608467330.7486328783065330.374316439153267
390.5735805357014940.8528389285970120.426419464298506
400.5968453962573920.8063092074852160.403154603742608
410.5620697736538360.8758604526923280.437930226346164
420.5068244191232020.9863511617535960.493175580876798
430.453594055475310.9071881109506210.54640594452469
440.4004344545389370.8008689090778750.599565545461063
450.3491627371071480.6983254742142960.650837262892852
460.3385966885998150.677193377199630.661403311400185
470.3276784893151270.6553569786302540.672321510684873
480.4692110988493730.9384221976987470.530788901150627
490.609240247555810.781519504888380.39075975244419
500.5657643142855360.8684713714289280.434235685714464
510.6472950847789630.7054098304420750.352704915221037
520.5984315279467880.8031369441064250.401568472053212
530.5838380506414740.8323238987170520.416161949358526
540.5557088294573590.8885823410852820.444291170542641
550.5077105717899160.9845788564201680.492289428210084
560.5979748489582250.8040503020835510.402025151041775
570.5515359845693610.8969280308612780.448464015430639
580.526444390444710.947111219110580.47355560955529
590.5476965437479750.904606912504050.452303456252025
600.4979651225137770.9959302450275550.502034877486223
610.4530813711872790.9061627423745570.546918628812721
620.6697521274108380.6604957451783230.330247872589161
630.6257290853361280.7485418293277440.374270914663872
640.5937147183102790.8125705633794430.406285281689721
650.5460919611006590.9078160777986830.453908038899341
660.5448757903607860.9102484192784280.455124209639214
670.4968289270372790.9936578540745580.503171072962721
680.4537237461957340.9074474923914690.546276253804266
690.4311900579621930.8623801159243860.568809942037807
700.3946713361729590.7893426723459170.605328663827041
710.3715396308295660.7430792616591330.628460369170434
720.3437691739940050.6875383479880110.656230826005995
730.3079125768554430.6158251537108860.692087423144557
740.2690031701489560.5380063402979120.730996829851044
750.2815683118325620.5631366236651240.718431688167438
760.2544758075968020.5089516151936040.745524192403198
770.2202000368770780.4404000737541550.779799963122922
780.229655061546710.4593101230934210.77034493845329
790.1938570264741790.3877140529483580.806142973525821
800.1630738809921370.3261477619842740.836926119007863
810.1480578797596880.2961157595193760.851942120240312
820.1364170518803950.2728341037607910.863582948119604
830.1710273602091130.3420547204182260.828972639790887
840.1416357891436160.2832715782872320.858364210856384
850.1398962076658970.2797924153317930.860103792334103
860.1537841434350370.3075682868700740.846215856564963
870.13600932749320.2720186549864010.8639906725068
880.1181065799199730.2362131598399460.881893420080027
890.1148037634460730.2296075268921460.885196236553927
900.1041252104164030.2082504208328050.895874789583597
910.09062192231836870.1812438446367370.909378077681631
920.07330835035731280.1466167007146260.926691649642687
930.09343428164990630.1868685632998130.906565718350094
940.07999553096299280.1599910619259860.920004469037007
950.1062665576989620.2125331153979240.893733442301038
960.09892495391934460.1978499078386890.901075046080655
970.08507729059537010.170154581190740.91492270940463
980.07393592092804620.1478718418560920.926064079071954
990.07666384572556920.1533276914511380.923336154274431
1000.0707934560263810.1415869120527620.929206543973619
1010.05829531382180330.1165906276436070.941704686178197
1020.04557761222781010.09115522445562010.95442238777219
1030.05317915824387110.1063583164877420.946820841756129
1040.04251004397985530.08502008795971060.957489956020145
1050.03433024654670480.06866049309340950.965669753453295
1060.02588264520131510.05176529040263030.974117354798685
1070.01899333410076090.03798666820152190.981006665899239
1080.01544287983091020.03088575966182030.98455712016909
1090.01186065783416640.02372131566833270.988139342165834
1100.01710531179546170.03421062359092340.982894688204538
1110.01514627480061020.03029254960122040.98485372519939
1120.02355730627474910.04711461254949820.976442693725251
1130.02739511983742650.0547902396748530.972604880162574
1140.03564025850914560.07128051701829110.964359741490854
1150.02837038469621320.05674076939242640.971629615303787
1160.01942329409731510.03884658819463020.980576705902685
1170.01308390136681190.02616780273362380.986916098633188
1180.008530005897219820.01706001179443960.99146999410278
1190.01596560742308470.03193121484616940.984034392576915
1200.01360663830343230.02721327660686470.986393361696568
1210.4764542707110160.9529085414220320.523545729288984
1220.4251208281012840.8502416562025680.574879171898716
1230.3659559901481360.7319119802962730.634044009851864
1240.3105299037619070.6210598075238140.689470096238093
1250.2568314675184380.5136629350368770.743168532481562
1260.2615804226969560.5231608453939120.738419577303044
1270.2893691360679820.5787382721359640.710630863932018
1280.7703101232301130.4593797535397730.229689876769886
1290.7105309995517220.5789380008965560.289469000448278
1300.5886540050842290.8226919898315430.411345994915771
1310.8340595999834140.3318808000331720.165940400016586
1320.8372104304675090.3255791390649830.162789569532491







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level110.0894308943089431NOK
10% type I error level190.154471544715447NOK

\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 & 11 & 0.0894308943089431 & NOK \tabularnewline
10% type I error level & 19 & 0.154471544715447 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185553&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]11[/C][C]0.0894308943089431[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]19[/C][C]0.154471544715447[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185553&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185553&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 level110.0894308943089431NOK
10% type I error level190.154471544715447NOK



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')
}