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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationThu, 01 Nov 2012 12:25:02 -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/t135178723760oqpakxfp5ebbc.htm/, Retrieved Thu, 25 Apr 2024 20:11:43 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=185556, Retrieved Thu, 25 Apr 2024 20:11:43 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact78

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [WS7 6] [2012-11-01 16:25:02] [b65d2c16eb5ddb762daa9974bc4b1f13] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
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

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 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 & 9 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ fisher.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185556&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]9 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=185556&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185556&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.wessa.net






Multiple Linear Regression - Estimated Regression Equation
Learning[t] = + 5.70898691934613 + 0.105959670525903Connected[t] -0.0260018017531869Separate[t] + 0.575965070361028Software[t] + 0.0708931995534126Happiness[t] -0.0829610688988634Depression[t] + 0.00303304347695284Belonging[t] -0.00173898699833552t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Learning[t] =  +  5.70898691934613 +  0.105959670525903Connected[t] -0.0260018017531869Separate[t] +  0.575965070361028Software[t] +  0.0708931995534126Happiness[t] -0.0829610688988634Depression[t] +  0.00303304347695284Belonging[t] -0.00173898699833552t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185556&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Learning[t] =  +  5.70898691934613 +  0.105959670525903Connected[t] -0.0260018017531869Separate[t] +  0.575965070361028Software[t] +  0.0708931995534126Happiness[t] -0.0829610688988634Depression[t] +  0.00303304347695284Belonging[t] -0.00173898699833552t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185556&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185556&T=1

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


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

Multiple Linear Regression - Estimated Regression Equation
Learning[t] = + 5.70898691934613 + 0.105959670525903Connected[t] -0.0260018017531869Separate[t] + 0.575965070361028Software[t] + 0.0708931995534126Happiness[t] -0.0829610688988634Depression[t] + 0.00303304347695284Belonging[t] -0.00173898699833552t + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)5.708986919346132.7684722.06210.0411260.020563
Connected0.1059596705259030.0497872.12820.0351470.017574
Separate-0.02600180175318690.046659-0.55730.5782680.289134
Software0.5759650703610280.0741837.764100
Happiness0.07089319955341260.0849530.83450.4054850.202742
Depression-0.08296106889886340.06303-1.31620.1903480.095174
Belonging0.003033043476952840.0157820.19220.8478940.423947
t-0.001738986998335520.003932-0.44230.6590070.329503

\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) & 5.70898691934613 & 2.768472 & 2.0621 & 0.041126 & 0.020563 \tabularnewline
Connected & 0.105959670525903 & 0.049787 & 2.1282 & 0.035147 & 0.017574 \tabularnewline
Separate & -0.0260018017531869 & 0.046659 & -0.5573 & 0.578268 & 0.289134 \tabularnewline
Software & 0.575965070361028 & 0.074183 & 7.7641 & 0 & 0 \tabularnewline
Happiness & 0.0708931995534126 & 0.084953 & 0.8345 & 0.405485 & 0.202742 \tabularnewline
Depression & -0.0829610688988634 & 0.06303 & -1.3162 & 0.190348 & 0.095174 \tabularnewline
Belonging & 0.00303304347695284 & 0.015782 & 0.1922 & 0.847894 & 0.423947 \tabularnewline
t & -0.00173898699833552 & 0.003932 & -0.4423 & 0.659007 & 0.329503 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185556&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]5.70898691934613[/C][C]2.768472[/C][C]2.0621[/C][C]0.041126[/C][C]0.020563[/C][/ROW]
[ROW][C]Connected[/C][C]0.105959670525903[/C][C]0.049787[/C][C]2.1282[/C][C]0.035147[/C][C]0.017574[/C][/ROW]
[ROW][C]Separate[/C][C]-0.0260018017531869[/C][C]0.046659[/C][C]-0.5573[/C][C]0.578268[/C][C]0.289134[/C][/ROW]
[ROW][C]Software[/C][C]0.575965070361028[/C][C]0.074183[/C][C]7.7641[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Happiness[/C][C]0.0708931995534126[/C][C]0.084953[/C][C]0.8345[/C][C]0.405485[/C][C]0.202742[/C][/ROW]
[ROW][C]Depression[/C][C]-0.0829610688988634[/C][C]0.06303[/C][C]-1.3162[/C][C]0.190348[/C][C]0.095174[/C][/ROW]
[ROW][C]Belonging[/C][C]0.00303304347695284[/C][C]0.015782[/C][C]0.1922[/C][C]0.847894[/C][C]0.423947[/C][/ROW]
[ROW][C]t[/C][C]-0.00173898699833552[/C][C]0.003932[/C][C]-0.4423[/C][C]0.659007[/C][C]0.329503[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185556&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185556&T=2

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


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)5.708986919346132.7684722.06210.0411260.020563
Connected0.1059596705259030.0497872.12820.0351470.017574
Separate-0.02600180175318690.046659-0.55730.5782680.289134
Software0.5759650703610280.0741837.764100
Happiness0.07089319955341260.0849530.83450.4054850.202742
Depression-0.08296106889886340.06303-1.31620.1903480.095174
Belonging0.003033043476952840.0157820.19220.8478940.423947
t-0.001738986998335520.003932-0.44230.6590070.329503






Multiple Linear Regression - Regression Statistics
Multiple R0.625737480297672
R-squared0.391547394249279
Adjusted R-squared0.359762556635436
F-TEST (value)12.3186847454191
F-TEST (DF numerator)7
F-TEST (DF denominator)134
p-value4.18443057981222e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.8196843162078
Sum Squared Residuals443.707635427455

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.625737480297672 \tabularnewline
R-squared & 0.391547394249279 \tabularnewline
Adjusted R-squared & 0.359762556635436 \tabularnewline
F-TEST (value) & 12.3186847454191 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 134 \tabularnewline
p-value & 4.18443057981222e-12 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.8196843162078 \tabularnewline
Sum Squared Residuals & 443.707635427455 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185556&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.625737480297672[/C][/ROW]
[ROW][C]R-squared[/C][C]0.391547394249279[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.359762556635436[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]12.3186847454191[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]134[/C][/ROW]
[ROW][C]p-value[/C][C]4.18443057981222e-12[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.8196843162078[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]443.707635427455[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185556&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185556&T=3

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


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

Multiple Linear Regression - Regression Statistics
Multiple R0.625737480297672
R-squared0.391547394249279
Adjusted R-squared0.359762556635436
F-TEST (value)12.3186847454191
F-TEST (DF numerator)7
F-TEST (DF denominator)134
p-value4.18443057981222e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.8196843162078
Sum Squared Residuals443.707635427455






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11615.47104468444110.528955315558916
21614.92040090857221.07959909142777
31916.49643087165052.50356912834954
41614.80319815117441.19680184882556
51714.73780148555042.26219851444961
61716.83242040155670.167579598443314
71614.81318053075451.18681946924546
81516.2505572310753-1.25055723107528
91615.38204697011250.617953029887508
101414.1003623053567-0.100362305356736
111515.7884232948316-0.788423294831586
121212.1656729948186-0.165672994818587
131415.1514025373707-1.15140253737069
141615.64238596619230.357614033807723
151415.5555138193456-1.55551381934558
16712.931517347401-5.93151734740102
171010.5718883104945-0.571888310494455
181415.8289659321642-1.82896593216424
191614.30845646855821.69154353144178
201614.58566779865661.41433220134339
211615.06176263985370.938237360146285
221415.5271938787826-1.52719387878259
232018.13726308710131.86273691289869
241414.1244126364062-0.124412636406199
251414.8710066155563-0.871006615556295
261115.4933771056537-4.49337710565371
271416.6758735959078-2.6758735959078
281514.98682342129560.0131765787043652
291615.00586531889670.994134681103321
301415.9993260746891-1.99932607468906
311616.6442899318665-0.644289931866478
321414.0579091853625-0.0579091853624841
331214.5734388856631-2.57343888566314
341615.62927973298360.370720267016383
35911.2158279027379-2.21582790273792
361412.48900043369191.51099956630811
371616.0848336501058-0.0848336501057932
381615.26321772955180.736782270448201
391515.2037450261959-0.203745026195902
401614.04222075636591.95777924363407
411211.1888591343580.811140865642047
421615.84955964525770.150440354742303
431616.4081239160178-0.408123916017793
441414.3315420802974-0.331542080297355
451615.48757473765670.512425262343314
461715.90206495145761.0979350485424
471816.16491848633551.83508151366454
481814.51447052751053.48552947248948
491215.8036983178068-3.80369831780675
501615.39136324592720.608636754072782
511013.4356465732072-3.43564657320721
521414.304516196213-0.304516196212977
531816.49947074224431.50052925775571
541817.08205351881630.917946481183653
551615.68931911768560.310680882314435
561713.6647639423223.33523605767803
571616.3948552598343-0.394855259834345
581614.46086781376931.53913218623069
591314.8972308964945-1.89723089649449
601616.0734340277607-0.0734340277607383
611615.4655304911130.534469508886973
622015.84086172079354.15913827920648
631615.78962784639710.21037215360292
641515.931891487274-0.931891487274026
651514.72446103250160.275538967498405
661614.19203299986021.80796700013982
671414.0009399937043-0.000939993704347349
681615.32117648240920.678823517590752
691614.58233035840941.41766964159062
701514.02087026778020.979129732219805
711213.4659920694422-1.46599206944216
721717.0467263021788-0.0467263021787771
731615.35458475466720.64541524533276
741515.1513400589163-0.151340058916251
751315.1175645148208-2.11756451482083
761614.99719981160171.00280018839828
771615.80569687543340.194303124566597
781613.97607460784562.02392539215438
791616.0579234968137-0.0579234968137048
801414.2484618653647-0.248461865364699
811617.3281275820727-1.32812758207272
821614.65720397362751.34279602637252
832017.45317558957352.54682441042648
841514.62862109987580.371378900124161
851614.35501898303811.64498101696193
861315.1453694900908-2.14536949009084
871716.03791105266160.96208894733844
881615.74918010879570.250819891204308
891614.42261823784321.57738176215684
901211.90581008290430.0941899170956652
911614.90770139682591.0922986031741
921615.79905580790060.200944192099431
931714.48346014969922.51653985030082
941314.2771323268367-1.27713232683665
951214.8509364773881-2.85093647738807
961816.68356247843961.31643752156038
971415.4792823293474-1.4792823293474
981412.83516742559991.16483257440006
991314.9141842200432-1.91418422004322
1001615.55813435390510.441865646094928
1011314.1736541778859-1.17365417788588
1021615.66341843258830.336581567411686
1031315.8746164193224-2.87461641932242
1041617.1543033457696-1.15430334576963
1051516.0767908814515-1.07679088145151
1061616.8394709523719-0.839470952371942
1071515.3974052626856-0.397405262685601
1081716.10496625297650.895033747023474
1091513.94380278927741.0561972107226
1101214.9071263620085-2.90712636200853
1111614.03264598828541.96735401171459
1121013.1636475556095-3.16364755560949
1131613.86385375962862.13614624037144
1141213.9433450129311-1.94334501293113
1151415.6453339855165-1.64533398551652
1161515.2770482712387-0.27704827123866
1171312.11954716811440.880452831885575
1181514.55409953224470.445900467755283
1191113.1466576650312-2.1466576650312
1201213.277497269171-1.27749726917101
121813.192300618056-5.19230061805598
1221613.0087709177262.99122908227404
1231513.20111788830341.79888211169661
1241716.65936934736280.340630652637155
1251614.81534717874541.18465282125462
1261014.0215863915473-4.0215863915473
1271816.03361196506041.96638803493962
1281315.1597746117288-2.15977461172878
1291615.14651694089820.853483059101805
1301312.82632571583620.173674284163753
1311012.9218993883106-2.92189938831062
1321516.2061099050722-1.20610990507217
1331613.87893527270482.12106472729517
1341611.69983598196284.30016401803716
1351412.17752993306941.82247006693064
1361012.4357082686839-2.43570826868387
1371716.9336921472870.0663078527130319
1381311.39376975677641.6062302432236
1391513.89163317932731.10836682067267
1401615.00145328149780.998546718502189
1411212.1643515174618-0.164351517461842
1421312.5398497549590.460150245041028

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 16 & 15.4710446844411 & 0.528955315558916 \tabularnewline
2 & 16 & 14.9204009085722 & 1.07959909142777 \tabularnewline
3 & 19 & 16.4964308716505 & 2.50356912834954 \tabularnewline
4 & 16 & 14.8031981511744 & 1.19680184882556 \tabularnewline
5 & 17 & 14.7378014855504 & 2.26219851444961 \tabularnewline
6 & 17 & 16.8324204015567 & 0.167579598443314 \tabularnewline
7 & 16 & 14.8131805307545 & 1.18681946924546 \tabularnewline
8 & 15 & 16.2505572310753 & -1.25055723107528 \tabularnewline
9 & 16 & 15.3820469701125 & 0.617953029887508 \tabularnewline
10 & 14 & 14.1003623053567 & -0.100362305356736 \tabularnewline
11 & 15 & 15.7884232948316 & -0.788423294831586 \tabularnewline
12 & 12 & 12.1656729948186 & -0.165672994818587 \tabularnewline
13 & 14 & 15.1514025373707 & -1.15140253737069 \tabularnewline
14 & 16 & 15.6423859661923 & 0.357614033807723 \tabularnewline
15 & 14 & 15.5555138193456 & -1.55551381934558 \tabularnewline
16 & 7 & 12.931517347401 & -5.93151734740102 \tabularnewline
17 & 10 & 10.5718883104945 & -0.571888310494455 \tabularnewline
18 & 14 & 15.8289659321642 & -1.82896593216424 \tabularnewline
19 & 16 & 14.3084564685582 & 1.69154353144178 \tabularnewline
20 & 16 & 14.5856677986566 & 1.41433220134339 \tabularnewline
21 & 16 & 15.0617626398537 & 0.938237360146285 \tabularnewline
22 & 14 & 15.5271938787826 & -1.52719387878259 \tabularnewline
23 & 20 & 18.1372630871013 & 1.86273691289869 \tabularnewline
24 & 14 & 14.1244126364062 & -0.124412636406199 \tabularnewline
25 & 14 & 14.8710066155563 & -0.871006615556295 \tabularnewline
26 & 11 & 15.4933771056537 & -4.49337710565371 \tabularnewline
27 & 14 & 16.6758735959078 & -2.6758735959078 \tabularnewline
28 & 15 & 14.9868234212956 & 0.0131765787043652 \tabularnewline
29 & 16 & 15.0058653188967 & 0.994134681103321 \tabularnewline
30 & 14 & 15.9993260746891 & -1.99932607468906 \tabularnewline
31 & 16 & 16.6442899318665 & -0.644289931866478 \tabularnewline
32 & 14 & 14.0579091853625 & -0.0579091853624841 \tabularnewline
33 & 12 & 14.5734388856631 & -2.57343888566314 \tabularnewline
34 & 16 & 15.6292797329836 & 0.370720267016383 \tabularnewline
35 & 9 & 11.2158279027379 & -2.21582790273792 \tabularnewline
36 & 14 & 12.4890004336919 & 1.51099956630811 \tabularnewline
37 & 16 & 16.0848336501058 & -0.0848336501057932 \tabularnewline
38 & 16 & 15.2632177295518 & 0.736782270448201 \tabularnewline
39 & 15 & 15.2037450261959 & -0.203745026195902 \tabularnewline
40 & 16 & 14.0422207563659 & 1.95777924363407 \tabularnewline
41 & 12 & 11.188859134358 & 0.811140865642047 \tabularnewline
42 & 16 & 15.8495596452577 & 0.150440354742303 \tabularnewline
43 & 16 & 16.4081239160178 & -0.408123916017793 \tabularnewline
44 & 14 & 14.3315420802974 & -0.331542080297355 \tabularnewline
45 & 16 & 15.4875747376567 & 0.512425262343314 \tabularnewline
46 & 17 & 15.9020649514576 & 1.0979350485424 \tabularnewline
47 & 18 & 16.1649184863355 & 1.83508151366454 \tabularnewline
48 & 18 & 14.5144705275105 & 3.48552947248948 \tabularnewline
49 & 12 & 15.8036983178068 & -3.80369831780675 \tabularnewline
50 & 16 & 15.3913632459272 & 0.608636754072782 \tabularnewline
51 & 10 & 13.4356465732072 & -3.43564657320721 \tabularnewline
52 & 14 & 14.304516196213 & -0.304516196212977 \tabularnewline
53 & 18 & 16.4994707422443 & 1.50052925775571 \tabularnewline
54 & 18 & 17.0820535188163 & 0.917946481183653 \tabularnewline
55 & 16 & 15.6893191176856 & 0.310680882314435 \tabularnewline
56 & 17 & 13.664763942322 & 3.33523605767803 \tabularnewline
57 & 16 & 16.3948552598343 & -0.394855259834345 \tabularnewline
58 & 16 & 14.4608678137693 & 1.53913218623069 \tabularnewline
59 & 13 & 14.8972308964945 & -1.89723089649449 \tabularnewline
60 & 16 & 16.0734340277607 & -0.0734340277607383 \tabularnewline
61 & 16 & 15.465530491113 & 0.534469508886973 \tabularnewline
62 & 20 & 15.8408617207935 & 4.15913827920648 \tabularnewline
63 & 16 & 15.7896278463971 & 0.21037215360292 \tabularnewline
64 & 15 & 15.931891487274 & -0.931891487274026 \tabularnewline
65 & 15 & 14.7244610325016 & 0.275538967498405 \tabularnewline
66 & 16 & 14.1920329998602 & 1.80796700013982 \tabularnewline
67 & 14 & 14.0009399937043 & -0.000939993704347349 \tabularnewline
68 & 16 & 15.3211764824092 & 0.678823517590752 \tabularnewline
69 & 16 & 14.5823303584094 & 1.41766964159062 \tabularnewline
70 & 15 & 14.0208702677802 & 0.979129732219805 \tabularnewline
71 & 12 & 13.4659920694422 & -1.46599206944216 \tabularnewline
72 & 17 & 17.0467263021788 & -0.0467263021787771 \tabularnewline
73 & 16 & 15.3545847546672 & 0.64541524533276 \tabularnewline
74 & 15 & 15.1513400589163 & -0.151340058916251 \tabularnewline
75 & 13 & 15.1175645148208 & -2.11756451482083 \tabularnewline
76 & 16 & 14.9971998116017 & 1.00280018839828 \tabularnewline
77 & 16 & 15.8056968754334 & 0.194303124566597 \tabularnewline
78 & 16 & 13.9760746078456 & 2.02392539215438 \tabularnewline
79 & 16 & 16.0579234968137 & -0.0579234968137048 \tabularnewline
80 & 14 & 14.2484618653647 & -0.248461865364699 \tabularnewline
81 & 16 & 17.3281275820727 & -1.32812758207272 \tabularnewline
82 & 16 & 14.6572039736275 & 1.34279602637252 \tabularnewline
83 & 20 & 17.4531755895735 & 2.54682441042648 \tabularnewline
84 & 15 & 14.6286210998758 & 0.371378900124161 \tabularnewline
85 & 16 & 14.3550189830381 & 1.64498101696193 \tabularnewline
86 & 13 & 15.1453694900908 & -2.14536949009084 \tabularnewline
87 & 17 & 16.0379110526616 & 0.96208894733844 \tabularnewline
88 & 16 & 15.7491801087957 & 0.250819891204308 \tabularnewline
89 & 16 & 14.4226182378432 & 1.57738176215684 \tabularnewline
90 & 12 & 11.9058100829043 & 0.0941899170956652 \tabularnewline
91 & 16 & 14.9077013968259 & 1.0922986031741 \tabularnewline
92 & 16 & 15.7990558079006 & 0.200944192099431 \tabularnewline
93 & 17 & 14.4834601496992 & 2.51653985030082 \tabularnewline
94 & 13 & 14.2771323268367 & -1.27713232683665 \tabularnewline
95 & 12 & 14.8509364773881 & -2.85093647738807 \tabularnewline
96 & 18 & 16.6835624784396 & 1.31643752156038 \tabularnewline
97 & 14 & 15.4792823293474 & -1.4792823293474 \tabularnewline
98 & 14 & 12.8351674255999 & 1.16483257440006 \tabularnewline
99 & 13 & 14.9141842200432 & -1.91418422004322 \tabularnewline
100 & 16 & 15.5581343539051 & 0.441865646094928 \tabularnewline
101 & 13 & 14.1736541778859 & -1.17365417788588 \tabularnewline
102 & 16 & 15.6634184325883 & 0.336581567411686 \tabularnewline
103 & 13 & 15.8746164193224 & -2.87461641932242 \tabularnewline
104 & 16 & 17.1543033457696 & -1.15430334576963 \tabularnewline
105 & 15 & 16.0767908814515 & -1.07679088145151 \tabularnewline
106 & 16 & 16.8394709523719 & -0.839470952371942 \tabularnewline
107 & 15 & 15.3974052626856 & -0.397405262685601 \tabularnewline
108 & 17 & 16.1049662529765 & 0.895033747023474 \tabularnewline
109 & 15 & 13.9438027892774 & 1.0561972107226 \tabularnewline
110 & 12 & 14.9071263620085 & -2.90712636200853 \tabularnewline
111 & 16 & 14.0326459882854 & 1.96735401171459 \tabularnewline
112 & 10 & 13.1636475556095 & -3.16364755560949 \tabularnewline
113 & 16 & 13.8638537596286 & 2.13614624037144 \tabularnewline
114 & 12 & 13.9433450129311 & -1.94334501293113 \tabularnewline
115 & 14 & 15.6453339855165 & -1.64533398551652 \tabularnewline
116 & 15 & 15.2770482712387 & -0.27704827123866 \tabularnewline
117 & 13 & 12.1195471681144 & 0.880452831885575 \tabularnewline
118 & 15 & 14.5540995322447 & 0.445900467755283 \tabularnewline
119 & 11 & 13.1466576650312 & -2.1466576650312 \tabularnewline
120 & 12 & 13.277497269171 & -1.27749726917101 \tabularnewline
121 & 8 & 13.192300618056 & -5.19230061805598 \tabularnewline
122 & 16 & 13.008770917726 & 2.99122908227404 \tabularnewline
123 & 15 & 13.2011178883034 & 1.79888211169661 \tabularnewline
124 & 17 & 16.6593693473628 & 0.340630652637155 \tabularnewline
125 & 16 & 14.8153471787454 & 1.18465282125462 \tabularnewline
126 & 10 & 14.0215863915473 & -4.0215863915473 \tabularnewline
127 & 18 & 16.0336119650604 & 1.96638803493962 \tabularnewline
128 & 13 & 15.1597746117288 & -2.15977461172878 \tabularnewline
129 & 16 & 15.1465169408982 & 0.853483059101805 \tabularnewline
130 & 13 & 12.8263257158362 & 0.173674284163753 \tabularnewline
131 & 10 & 12.9218993883106 & -2.92189938831062 \tabularnewline
132 & 15 & 16.2061099050722 & -1.20610990507217 \tabularnewline
133 & 16 & 13.8789352727048 & 2.12106472729517 \tabularnewline
134 & 16 & 11.6998359819628 & 4.30016401803716 \tabularnewline
135 & 14 & 12.1775299330694 & 1.82247006693064 \tabularnewline
136 & 10 & 12.4357082686839 & -2.43570826868387 \tabularnewline
137 & 17 & 16.933692147287 & 0.0663078527130319 \tabularnewline
138 & 13 & 11.3937697567764 & 1.6062302432236 \tabularnewline
139 & 15 & 13.8916331793273 & 1.10836682067267 \tabularnewline
140 & 16 & 15.0014532814978 & 0.998546718502189 \tabularnewline
141 & 12 & 12.1643515174618 & -0.164351517461842 \tabularnewline
142 & 13 & 12.539849754959 & 0.460150245041028 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185556&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]15.4710446844411[/C][C]0.528955315558916[/C][/ROW]
[ROW][C]2[/C][C]16[/C][C]14.9204009085722[/C][C]1.07959909142777[/C][/ROW]
[ROW][C]3[/C][C]19[/C][C]16.4964308716505[/C][C]2.50356912834954[/C][/ROW]
[ROW][C]4[/C][C]16[/C][C]14.8031981511744[/C][C]1.19680184882556[/C][/ROW]
[ROW][C]5[/C][C]17[/C][C]14.7378014855504[/C][C]2.26219851444961[/C][/ROW]
[ROW][C]6[/C][C]17[/C][C]16.8324204015567[/C][C]0.167579598443314[/C][/ROW]
[ROW][C]7[/C][C]16[/C][C]14.8131805307545[/C][C]1.18681946924546[/C][/ROW]
[ROW][C]8[/C][C]15[/C][C]16.2505572310753[/C][C]-1.25055723107528[/C][/ROW]
[ROW][C]9[/C][C]16[/C][C]15.3820469701125[/C][C]0.617953029887508[/C][/ROW]
[ROW][C]10[/C][C]14[/C][C]14.1003623053567[/C][C]-0.100362305356736[/C][/ROW]
[ROW][C]11[/C][C]15[/C][C]15.7884232948316[/C][C]-0.788423294831586[/C][/ROW]
[ROW][C]12[/C][C]12[/C][C]12.1656729948186[/C][C]-0.165672994818587[/C][/ROW]
[ROW][C]13[/C][C]14[/C][C]15.1514025373707[/C][C]-1.15140253737069[/C][/ROW]
[ROW][C]14[/C][C]16[/C][C]15.6423859661923[/C][C]0.357614033807723[/C][/ROW]
[ROW][C]15[/C][C]14[/C][C]15.5555138193456[/C][C]-1.55551381934558[/C][/ROW]
[ROW][C]16[/C][C]7[/C][C]12.931517347401[/C][C]-5.93151734740102[/C][/ROW]
[ROW][C]17[/C][C]10[/C][C]10.5718883104945[/C][C]-0.571888310494455[/C][/ROW]
[ROW][C]18[/C][C]14[/C][C]15.8289659321642[/C][C]-1.82896593216424[/C][/ROW]
[ROW][C]19[/C][C]16[/C][C]14.3084564685582[/C][C]1.69154353144178[/C][/ROW]
[ROW][C]20[/C][C]16[/C][C]14.5856677986566[/C][C]1.41433220134339[/C][/ROW]
[ROW][C]21[/C][C]16[/C][C]15.0617626398537[/C][C]0.938237360146285[/C][/ROW]
[ROW][C]22[/C][C]14[/C][C]15.5271938787826[/C][C]-1.52719387878259[/C][/ROW]
[ROW][C]23[/C][C]20[/C][C]18.1372630871013[/C][C]1.86273691289869[/C][/ROW]
[ROW][C]24[/C][C]14[/C][C]14.1244126364062[/C][C]-0.124412636406199[/C][/ROW]
[ROW][C]25[/C][C]14[/C][C]14.8710066155563[/C][C]-0.871006615556295[/C][/ROW]
[ROW][C]26[/C][C]11[/C][C]15.4933771056537[/C][C]-4.49337710565371[/C][/ROW]
[ROW][C]27[/C][C]14[/C][C]16.6758735959078[/C][C]-2.6758735959078[/C][/ROW]
[ROW][C]28[/C][C]15[/C][C]14.9868234212956[/C][C]0.0131765787043652[/C][/ROW]
[ROW][C]29[/C][C]16[/C][C]15.0058653188967[/C][C]0.994134681103321[/C][/ROW]
[ROW][C]30[/C][C]14[/C][C]15.9993260746891[/C][C]-1.99932607468906[/C][/ROW]
[ROW][C]31[/C][C]16[/C][C]16.6442899318665[/C][C]-0.644289931866478[/C][/ROW]
[ROW][C]32[/C][C]14[/C][C]14.0579091853625[/C][C]-0.0579091853624841[/C][/ROW]
[ROW][C]33[/C][C]12[/C][C]14.5734388856631[/C][C]-2.57343888566314[/C][/ROW]
[ROW][C]34[/C][C]16[/C][C]15.6292797329836[/C][C]0.370720267016383[/C][/ROW]
[ROW][C]35[/C][C]9[/C][C]11.2158279027379[/C][C]-2.21582790273792[/C][/ROW]
[ROW][C]36[/C][C]14[/C][C]12.4890004336919[/C][C]1.51099956630811[/C][/ROW]
[ROW][C]37[/C][C]16[/C][C]16.0848336501058[/C][C]-0.0848336501057932[/C][/ROW]
[ROW][C]38[/C][C]16[/C][C]15.2632177295518[/C][C]0.736782270448201[/C][/ROW]
[ROW][C]39[/C][C]15[/C][C]15.2037450261959[/C][C]-0.203745026195902[/C][/ROW]
[ROW][C]40[/C][C]16[/C][C]14.0422207563659[/C][C]1.95777924363407[/C][/ROW]
[ROW][C]41[/C][C]12[/C][C]11.188859134358[/C][C]0.811140865642047[/C][/ROW]
[ROW][C]42[/C][C]16[/C][C]15.8495596452577[/C][C]0.150440354742303[/C][/ROW]
[ROW][C]43[/C][C]16[/C][C]16.4081239160178[/C][C]-0.408123916017793[/C][/ROW]
[ROW][C]44[/C][C]14[/C][C]14.3315420802974[/C][C]-0.331542080297355[/C][/ROW]
[ROW][C]45[/C][C]16[/C][C]15.4875747376567[/C][C]0.512425262343314[/C][/ROW]
[ROW][C]46[/C][C]17[/C][C]15.9020649514576[/C][C]1.0979350485424[/C][/ROW]
[ROW][C]47[/C][C]18[/C][C]16.1649184863355[/C][C]1.83508151366454[/C][/ROW]
[ROW][C]48[/C][C]18[/C][C]14.5144705275105[/C][C]3.48552947248948[/C][/ROW]
[ROW][C]49[/C][C]12[/C][C]15.8036983178068[/C][C]-3.80369831780675[/C][/ROW]
[ROW][C]50[/C][C]16[/C][C]15.3913632459272[/C][C]0.608636754072782[/C][/ROW]
[ROW][C]51[/C][C]10[/C][C]13.4356465732072[/C][C]-3.43564657320721[/C][/ROW]
[ROW][C]52[/C][C]14[/C][C]14.304516196213[/C][C]-0.304516196212977[/C][/ROW]
[ROW][C]53[/C][C]18[/C][C]16.4994707422443[/C][C]1.50052925775571[/C][/ROW]
[ROW][C]54[/C][C]18[/C][C]17.0820535188163[/C][C]0.917946481183653[/C][/ROW]
[ROW][C]55[/C][C]16[/C][C]15.6893191176856[/C][C]0.310680882314435[/C][/ROW]
[ROW][C]56[/C][C]17[/C][C]13.664763942322[/C][C]3.33523605767803[/C][/ROW]
[ROW][C]57[/C][C]16[/C][C]16.3948552598343[/C][C]-0.394855259834345[/C][/ROW]
[ROW][C]58[/C][C]16[/C][C]14.4608678137693[/C][C]1.53913218623069[/C][/ROW]
[ROW][C]59[/C][C]13[/C][C]14.8972308964945[/C][C]-1.89723089649449[/C][/ROW]
[ROW][C]60[/C][C]16[/C][C]16.0734340277607[/C][C]-0.0734340277607383[/C][/ROW]
[ROW][C]61[/C][C]16[/C][C]15.465530491113[/C][C]0.534469508886973[/C][/ROW]
[ROW][C]62[/C][C]20[/C][C]15.8408617207935[/C][C]4.15913827920648[/C][/ROW]
[ROW][C]63[/C][C]16[/C][C]15.7896278463971[/C][C]0.21037215360292[/C][/ROW]
[ROW][C]64[/C][C]15[/C][C]15.931891487274[/C][C]-0.931891487274026[/C][/ROW]
[ROW][C]65[/C][C]15[/C][C]14.7244610325016[/C][C]0.275538967498405[/C][/ROW]
[ROW][C]66[/C][C]16[/C][C]14.1920329998602[/C][C]1.80796700013982[/C][/ROW]
[ROW][C]67[/C][C]14[/C][C]14.0009399937043[/C][C]-0.000939993704347349[/C][/ROW]
[ROW][C]68[/C][C]16[/C][C]15.3211764824092[/C][C]0.678823517590752[/C][/ROW]
[ROW][C]69[/C][C]16[/C][C]14.5823303584094[/C][C]1.41766964159062[/C][/ROW]
[ROW][C]70[/C][C]15[/C][C]14.0208702677802[/C][C]0.979129732219805[/C][/ROW]
[ROW][C]71[/C][C]12[/C][C]13.4659920694422[/C][C]-1.46599206944216[/C][/ROW]
[ROW][C]72[/C][C]17[/C][C]17.0467263021788[/C][C]-0.0467263021787771[/C][/ROW]
[ROW][C]73[/C][C]16[/C][C]15.3545847546672[/C][C]0.64541524533276[/C][/ROW]
[ROW][C]74[/C][C]15[/C][C]15.1513400589163[/C][C]-0.151340058916251[/C][/ROW]
[ROW][C]75[/C][C]13[/C][C]15.1175645148208[/C][C]-2.11756451482083[/C][/ROW]
[ROW][C]76[/C][C]16[/C][C]14.9971998116017[/C][C]1.00280018839828[/C][/ROW]
[ROW][C]77[/C][C]16[/C][C]15.8056968754334[/C][C]0.194303124566597[/C][/ROW]
[ROW][C]78[/C][C]16[/C][C]13.9760746078456[/C][C]2.02392539215438[/C][/ROW]
[ROW][C]79[/C][C]16[/C][C]16.0579234968137[/C][C]-0.0579234968137048[/C][/ROW]
[ROW][C]80[/C][C]14[/C][C]14.2484618653647[/C][C]-0.248461865364699[/C][/ROW]
[ROW][C]81[/C][C]16[/C][C]17.3281275820727[/C][C]-1.32812758207272[/C][/ROW]
[ROW][C]82[/C][C]16[/C][C]14.6572039736275[/C][C]1.34279602637252[/C][/ROW]
[ROW][C]83[/C][C]20[/C][C]17.4531755895735[/C][C]2.54682441042648[/C][/ROW]
[ROW][C]84[/C][C]15[/C][C]14.6286210998758[/C][C]0.371378900124161[/C][/ROW]
[ROW][C]85[/C][C]16[/C][C]14.3550189830381[/C][C]1.64498101696193[/C][/ROW]
[ROW][C]86[/C][C]13[/C][C]15.1453694900908[/C][C]-2.14536949009084[/C][/ROW]
[ROW][C]87[/C][C]17[/C][C]16.0379110526616[/C][C]0.96208894733844[/C][/ROW]
[ROW][C]88[/C][C]16[/C][C]15.7491801087957[/C][C]0.250819891204308[/C][/ROW]
[ROW][C]89[/C][C]16[/C][C]14.4226182378432[/C][C]1.57738176215684[/C][/ROW]
[ROW][C]90[/C][C]12[/C][C]11.9058100829043[/C][C]0.0941899170956652[/C][/ROW]
[ROW][C]91[/C][C]16[/C][C]14.9077013968259[/C][C]1.0922986031741[/C][/ROW]
[ROW][C]92[/C][C]16[/C][C]15.7990558079006[/C][C]0.200944192099431[/C][/ROW]
[ROW][C]93[/C][C]17[/C][C]14.4834601496992[/C][C]2.51653985030082[/C][/ROW]
[ROW][C]94[/C][C]13[/C][C]14.2771323268367[/C][C]-1.27713232683665[/C][/ROW]
[ROW][C]95[/C][C]12[/C][C]14.8509364773881[/C][C]-2.85093647738807[/C][/ROW]
[ROW][C]96[/C][C]18[/C][C]16.6835624784396[/C][C]1.31643752156038[/C][/ROW]
[ROW][C]97[/C][C]14[/C][C]15.4792823293474[/C][C]-1.4792823293474[/C][/ROW]
[ROW][C]98[/C][C]14[/C][C]12.8351674255999[/C][C]1.16483257440006[/C][/ROW]
[ROW][C]99[/C][C]13[/C][C]14.9141842200432[/C][C]-1.91418422004322[/C][/ROW]
[ROW][C]100[/C][C]16[/C][C]15.5581343539051[/C][C]0.441865646094928[/C][/ROW]
[ROW][C]101[/C][C]13[/C][C]14.1736541778859[/C][C]-1.17365417788588[/C][/ROW]
[ROW][C]102[/C][C]16[/C][C]15.6634184325883[/C][C]0.336581567411686[/C][/ROW]
[ROW][C]103[/C][C]13[/C][C]15.8746164193224[/C][C]-2.87461641932242[/C][/ROW]
[ROW][C]104[/C][C]16[/C][C]17.1543033457696[/C][C]-1.15430334576963[/C][/ROW]
[ROW][C]105[/C][C]15[/C][C]16.0767908814515[/C][C]-1.07679088145151[/C][/ROW]
[ROW][C]106[/C][C]16[/C][C]16.8394709523719[/C][C]-0.839470952371942[/C][/ROW]
[ROW][C]107[/C][C]15[/C][C]15.3974052626856[/C][C]-0.397405262685601[/C][/ROW]
[ROW][C]108[/C][C]17[/C][C]16.1049662529765[/C][C]0.895033747023474[/C][/ROW]
[ROW][C]109[/C][C]15[/C][C]13.9438027892774[/C][C]1.0561972107226[/C][/ROW]
[ROW][C]110[/C][C]12[/C][C]14.9071263620085[/C][C]-2.90712636200853[/C][/ROW]
[ROW][C]111[/C][C]16[/C][C]14.0326459882854[/C][C]1.96735401171459[/C][/ROW]
[ROW][C]112[/C][C]10[/C][C]13.1636475556095[/C][C]-3.16364755560949[/C][/ROW]
[ROW][C]113[/C][C]16[/C][C]13.8638537596286[/C][C]2.13614624037144[/C][/ROW]
[ROW][C]114[/C][C]12[/C][C]13.9433450129311[/C][C]-1.94334501293113[/C][/ROW]
[ROW][C]115[/C][C]14[/C][C]15.6453339855165[/C][C]-1.64533398551652[/C][/ROW]
[ROW][C]116[/C][C]15[/C][C]15.2770482712387[/C][C]-0.27704827123866[/C][/ROW]
[ROW][C]117[/C][C]13[/C][C]12.1195471681144[/C][C]0.880452831885575[/C][/ROW]
[ROW][C]118[/C][C]15[/C][C]14.5540995322447[/C][C]0.445900467755283[/C][/ROW]
[ROW][C]119[/C][C]11[/C][C]13.1466576650312[/C][C]-2.1466576650312[/C][/ROW]
[ROW][C]120[/C][C]12[/C][C]13.277497269171[/C][C]-1.27749726917101[/C][/ROW]
[ROW][C]121[/C][C]8[/C][C]13.192300618056[/C][C]-5.19230061805598[/C][/ROW]
[ROW][C]122[/C][C]16[/C][C]13.008770917726[/C][C]2.99122908227404[/C][/ROW]
[ROW][C]123[/C][C]15[/C][C]13.2011178883034[/C][C]1.79888211169661[/C][/ROW]
[ROW][C]124[/C][C]17[/C][C]16.6593693473628[/C][C]0.340630652637155[/C][/ROW]
[ROW][C]125[/C][C]16[/C][C]14.8153471787454[/C][C]1.18465282125462[/C][/ROW]
[ROW][C]126[/C][C]10[/C][C]14.0215863915473[/C][C]-4.0215863915473[/C][/ROW]
[ROW][C]127[/C][C]18[/C][C]16.0336119650604[/C][C]1.96638803493962[/C][/ROW]
[ROW][C]128[/C][C]13[/C][C]15.1597746117288[/C][C]-2.15977461172878[/C][/ROW]
[ROW][C]129[/C][C]16[/C][C]15.1465169408982[/C][C]0.853483059101805[/C][/ROW]
[ROW][C]130[/C][C]13[/C][C]12.8263257158362[/C][C]0.173674284163753[/C][/ROW]
[ROW][C]131[/C][C]10[/C][C]12.9218993883106[/C][C]-2.92189938831062[/C][/ROW]
[ROW][C]132[/C][C]15[/C][C]16.2061099050722[/C][C]-1.20610990507217[/C][/ROW]
[ROW][C]133[/C][C]16[/C][C]13.8789352727048[/C][C]2.12106472729517[/C][/ROW]
[ROW][C]134[/C][C]16[/C][C]11.6998359819628[/C][C]4.30016401803716[/C][/ROW]
[ROW][C]135[/C][C]14[/C][C]12.1775299330694[/C][C]1.82247006693064[/C][/ROW]
[ROW][C]136[/C][C]10[/C][C]12.4357082686839[/C][C]-2.43570826868387[/C][/ROW]
[ROW][C]137[/C][C]17[/C][C]16.933692147287[/C][C]0.0663078527130319[/C][/ROW]
[ROW][C]138[/C][C]13[/C][C]11.3937697567764[/C][C]1.6062302432236[/C][/ROW]
[ROW][C]139[/C][C]15[/C][C]13.8916331793273[/C][C]1.10836682067267[/C][/ROW]
[ROW][C]140[/C][C]16[/C][C]15.0014532814978[/C][C]0.998546718502189[/C][/ROW]
[ROW][C]141[/C][C]12[/C][C]12.1643515174618[/C][C]-0.164351517461842[/C][/ROW]
[ROW][C]142[/C][C]13[/C][C]12.539849754959[/C][C]0.460150245041028[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185556&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185556&T=4

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


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11615.47104468444110.528955315558916
21614.92040090857221.07959909142777
31916.49643087165052.50356912834954
41614.80319815117441.19680184882556
51714.73780148555042.26219851444961
61716.83242040155670.167579598443314
71614.81318053075451.18681946924546
81516.2505572310753-1.25055723107528
91615.38204697011250.617953029887508
101414.1003623053567-0.100362305356736
111515.7884232948316-0.788423294831586
121212.1656729948186-0.165672994818587
131415.1514025373707-1.15140253737069
141615.64238596619230.357614033807723
151415.5555138193456-1.55551381934558
16712.931517347401-5.93151734740102
171010.5718883104945-0.571888310494455
181415.8289659321642-1.82896593216424
191614.30845646855821.69154353144178
201614.58566779865661.41433220134339
211615.06176263985370.938237360146285
221415.5271938787826-1.52719387878259
232018.13726308710131.86273691289869
241414.1244126364062-0.124412636406199
251414.8710066155563-0.871006615556295
261115.4933771056537-4.49337710565371
271416.6758735959078-2.6758735959078
281514.98682342129560.0131765787043652
291615.00586531889670.994134681103321
301415.9993260746891-1.99932607468906
311616.6442899318665-0.644289931866478
321414.0579091853625-0.0579091853624841
331214.5734388856631-2.57343888566314
341615.62927973298360.370720267016383
35911.2158279027379-2.21582790273792
361412.48900043369191.51099956630811
371616.0848336501058-0.0848336501057932
381615.26321772955180.736782270448201
391515.2037450261959-0.203745026195902
401614.04222075636591.95777924363407
411211.1888591343580.811140865642047
421615.84955964525770.150440354742303
431616.4081239160178-0.408123916017793
441414.3315420802974-0.331542080297355
451615.48757473765670.512425262343314
461715.90206495145761.0979350485424
471816.16491848633551.83508151366454
481814.51447052751053.48552947248948
491215.8036983178068-3.80369831780675
501615.39136324592720.608636754072782
511013.4356465732072-3.43564657320721
521414.304516196213-0.304516196212977
531816.49947074224431.50052925775571
541817.08205351881630.917946481183653
551615.68931911768560.310680882314435
561713.6647639423223.33523605767803
571616.3948552598343-0.394855259834345
581614.46086781376931.53913218623069
591314.8972308964945-1.89723089649449
601616.0734340277607-0.0734340277607383
611615.4655304911130.534469508886973
622015.84086172079354.15913827920648
631615.78962784639710.21037215360292
641515.931891487274-0.931891487274026
651514.72446103250160.275538967498405
661614.19203299986021.80796700013982
671414.0009399937043-0.000939993704347349
681615.32117648240920.678823517590752
691614.58233035840941.41766964159062
701514.02087026778020.979129732219805
711213.4659920694422-1.46599206944216
721717.0467263021788-0.0467263021787771
731615.35458475466720.64541524533276
741515.1513400589163-0.151340058916251
751315.1175645148208-2.11756451482083
761614.99719981160171.00280018839828
771615.80569687543340.194303124566597
781613.97607460784562.02392539215438
791616.0579234968137-0.0579234968137048
801414.2484618653647-0.248461865364699
811617.3281275820727-1.32812758207272
821614.65720397362751.34279602637252
832017.45317558957352.54682441042648
841514.62862109987580.371378900124161
851614.35501898303811.64498101696193
861315.1453694900908-2.14536949009084
871716.03791105266160.96208894733844
881615.74918010879570.250819891204308
891614.42261823784321.57738176215684
901211.90581008290430.0941899170956652
911614.90770139682591.0922986031741
921615.79905580790060.200944192099431
931714.48346014969922.51653985030082
941314.2771323268367-1.27713232683665
951214.8509364773881-2.85093647738807
961816.68356247843961.31643752156038
971415.4792823293474-1.4792823293474
981412.83516742559991.16483257440006
991314.9141842200432-1.91418422004322
1001615.55813435390510.441865646094928
1011314.1736541778859-1.17365417788588
1021615.66341843258830.336581567411686
1031315.8746164193224-2.87461641932242
1041617.1543033457696-1.15430334576963
1051516.0767908814515-1.07679088145151
1061616.8394709523719-0.839470952371942
1071515.3974052626856-0.397405262685601
1081716.10496625297650.895033747023474
1091513.94380278927741.0561972107226
1101214.9071263620085-2.90712636200853
1111614.03264598828541.96735401171459
1121013.1636475556095-3.16364755560949
1131613.86385375962862.13614624037144
1141213.9433450129311-1.94334501293113
1151415.6453339855165-1.64533398551652
1161515.2770482712387-0.27704827123866
1171312.11954716811440.880452831885575
1181514.55409953224470.445900467755283
1191113.1466576650312-2.1466576650312
1201213.277497269171-1.27749726917101
121813.192300618056-5.19230061805598
1221613.0087709177262.99122908227404
1231513.20111788830341.79888211169661
1241716.65936934736280.340630652637155
1251614.81534717874541.18465282125462
1261014.0215863915473-4.0215863915473
1271816.03361196506041.96638803493962
1281315.1597746117288-2.15977461172878
1291615.14651694089820.853483059101805
1301312.82632571583620.173674284163753
1311012.9218993883106-2.92189938831062
1321516.2061099050722-1.20610990507217
1331613.87893527270482.12106472729517
1341611.69983598196284.30016401803716
1351412.17752993306941.82247006693064
1361012.4357082686839-2.43570826868387
1371716.9336921472870.0663078527130319
1381311.39376975677641.6062302432236
1391513.89163317932731.10836682067267
1401615.00145328149780.998546718502189
1411212.1643515174618-0.164351517461842
1421312.5398497549590.460150245041028






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.05300643585926840.1060128717185370.946993564140732
120.01583511919824960.03167023839649910.98416488080175
130.04646792085429130.09293584170858250.953532079145709
140.05096335876825860.1019267175365170.949036641231741
150.02341275096777930.04682550193555860.976587249032221
160.2978399972157920.5956799944315840.702160002784208
170.2145413673158420.4290827346316840.785458632684158
180.1518915917134630.3037831834269250.848108408286537
190.6037886583074890.7924226833850230.396211341692511
200.7612090166057310.4775819667885370.238790983394269
210.7561793185420390.4876413629159220.243820681457961
220.7115536985691760.5768926028616480.288446301430824
230.6996116693511390.6007766612977220.300388330648861
240.6296939781837810.7406120436324370.370306021816219
250.5627834365240690.8744331269518620.437216563475931
260.7330423597250.533915280550.266957640275
270.7025084092739710.5949831814520590.297491590726029
280.7153703020626470.5692593958747060.284629697937353
290.7108041169093670.5783917661812660.289195883090633
300.6737542191424880.6524915617150240.326245780857512
310.6353678960196730.7292642079606540.364632103980327
320.5942575153156960.8114849693686080.405742484684304
330.5882998537054940.8234002925890120.411700146294506
340.5857487752136260.8285024495727480.414251224786374
350.5722958753473090.8554082493053820.427704124652691
360.6189061029514620.7621877940970770.381093897048538
370.5607483117996570.8785033764006860.439251688200343
380.5900800157907030.8198399684185950.409919984209297
390.556547432388590.886905135222820.44345256761141
400.6112629224942510.7774741550114970.388737077505749
410.5886573259341290.8226853481317410.41134267406587
420.5390084082812110.9219831834375780.460991591718789
430.4924215784354760.9848431568709520.507578421564524
440.4394122010508790.8788244021017580.560587798949121
450.3872945885807370.7745891771614730.612705411419263
460.3850708411500540.7701416823001070.614929158849946
470.3721856974479250.7443713948958490.627814302552075
480.5030137036314820.9939725927370350.496986296368518
490.649386008240850.70122798351830.35061399175915
500.6067343328025290.7865313343949420.393265667197471
510.7048262377781380.5903475244437230.295173762221862
520.6632355080019990.6735289839960020.336764491998001
530.6462298268598390.7075403462803220.353770173140161
540.6180520137111370.7638959725777260.381947986288863
550.5690389450096670.8619221099806660.430961054990333
560.6345020721288050.7309958557423910.365497927871195
570.5899232097162110.8201535805675770.410076790283788
580.5563174725141760.8873650549716470.443682527485824
590.5846509348173890.8306981303652210.415349065182611
600.5364819743811810.9270360512376370.463518025618819
610.4911960327950560.9823920655901120.508803967204944
620.6929337699428750.614132460114250.307066230057125
630.6482684154542040.7034631690915930.351731584545796
640.6226341895968570.7547316208062870.377365810403143
650.5743017101883540.8513965796232920.425698289811646
660.5582436134274620.8835127731450770.441756386572538
670.5097425041814990.9805149916370030.490257495818501
680.4617780439976510.9235560879953010.538221956002349
690.4303573116925560.8607146233851130.569642688307444
700.3894512549733660.7789025099467320.610548745026634
710.3769360754938810.7538721509877620.623063924506119
720.3501387780979840.7002775561959680.649861221902016
730.3101825261832470.6203650523664940.689817473816753
740.2723852930259890.5447705860519770.727614706974011
750.3006044059380840.6012088118761680.699395594061916
760.2667704734336490.5335409468672980.733229526566351
770.2298408425903960.4596816851807920.770159157409604
780.2266725802497240.4533451604994490.773327419750276
790.1913462132907580.3826924265815170.808653786709242
800.1623114581240130.3246229162480260.837688541875987
810.1505845782540240.3011691565080470.849415421745976
820.1341044642871570.2682089285743130.865895535712844
830.1626245800086490.3252491600172980.837375419991351
840.1332224357342710.2664448714685430.866777564265729
850.1284790491747220.2569580983494440.871520950825278
860.1460634947345770.2921269894691540.853936505265423
870.1272944774177210.2545889548354420.872705522582279
880.1102540384644050.2205080769288110.889745961535595
890.1064099539880770.2128199079761540.893590046011923
900.1005400546926860.2010801093853720.899459945307314
910.08953529268043110.1790705853608620.910464707319569
920.07493833974812580.1498766794962520.925061660251874
930.1068161285143430.2136322570286860.893183871485657
940.09272547317590050.1854509463518010.907274526824099
950.1138070250217020.2276140500434050.886192974978298
960.1141421785290880.2282843570581760.885857821470912
970.09843863047264950.1968772609452990.90156136952735
980.09707297353812810.1941459470762560.902927026461872
990.09186069662880580.1837213932576120.908139303371194
1000.1028182484899390.2056364969798770.897181751510061
1010.0834629408439490.1669258816878980.916537059156051
1020.07281814041451660.1456362808290330.927181859585483
1030.07371805206624680.1474361041324940.926281947933753
1040.05760477134158660.1152095426831730.942395228658413
1050.04470767118045330.08941534236090660.955292328819547
1060.03374715049831280.06749430099662550.966252849501687
1070.02412967410890850.0482593482178170.975870325891091
1080.02449912392643970.04899824785287940.97550087607356
1090.02373296398050070.04746592796100140.976267036019499
1100.02481328138503950.0496265627700790.97518671861496
1110.02934011382061190.05868022764122370.970659886179388
1120.03157134241049490.06314268482098970.968428657589505
1130.07492237522620670.1498447504524130.925077624773793
1140.07646278017320380.1529255603464080.923537219826796
1150.06004385261317310.1200877052263460.939956147386827
1160.04536899432279240.09073798864558480.954631005677208
1170.03465714968329330.06931429936658650.965342850316707
1180.0314564842920680.0629129685841360.968543515707932
1190.03071020638188130.06142041276376260.969289793618119
1200.02093864262868020.04187728525736040.97906135737132
1210.3983983392551410.7967966785102820.601601660744859
1220.3557223133367970.7114446266735940.644277686663203
1230.3151625402165780.6303250804331560.684837459783422
1240.246737550290610.4934751005812190.75326244970939
1250.2010245577924280.4020491155848560.798975442207572
1260.2015471509504640.4030943019009280.798452849049536
1270.3070125446781220.6140250893562440.692987455321878
1280.6841822267756310.6316355464487380.315817773224369
1290.5879212500464560.8241574999070880.412078749953544
1300.4460129174012050.892025834802410.553987082598795
1310.6919352697950430.6161294604099130.308064730204957

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 & 0.0530064358592684 & 0.106012871718537 & 0.946993564140732 \tabularnewline
12 & 0.0158351191982496 & 0.0316702383964991 & 0.98416488080175 \tabularnewline
13 & 0.0464679208542913 & 0.0929358417085825 & 0.953532079145709 \tabularnewline
14 & 0.0509633587682586 & 0.101926717536517 & 0.949036641231741 \tabularnewline
15 & 0.0234127509677793 & 0.0468255019355586 & 0.976587249032221 \tabularnewline
16 & 0.297839997215792 & 0.595679994431584 & 0.702160002784208 \tabularnewline
17 & 0.214541367315842 & 0.429082734631684 & 0.785458632684158 \tabularnewline
18 & 0.151891591713463 & 0.303783183426925 & 0.848108408286537 \tabularnewline
19 & 0.603788658307489 & 0.792422683385023 & 0.396211341692511 \tabularnewline
20 & 0.761209016605731 & 0.477581966788537 & 0.238790983394269 \tabularnewline
21 & 0.756179318542039 & 0.487641362915922 & 0.243820681457961 \tabularnewline
22 & 0.711553698569176 & 0.576892602861648 & 0.288446301430824 \tabularnewline
23 & 0.699611669351139 & 0.600776661297722 & 0.300388330648861 \tabularnewline
24 & 0.629693978183781 & 0.740612043632437 & 0.370306021816219 \tabularnewline
25 & 0.562783436524069 & 0.874433126951862 & 0.437216563475931 \tabularnewline
26 & 0.733042359725 & 0.53391528055 & 0.266957640275 \tabularnewline
27 & 0.702508409273971 & 0.594983181452059 & 0.297491590726029 \tabularnewline
28 & 0.715370302062647 & 0.569259395874706 & 0.284629697937353 \tabularnewline
29 & 0.710804116909367 & 0.578391766181266 & 0.289195883090633 \tabularnewline
30 & 0.673754219142488 & 0.652491561715024 & 0.326245780857512 \tabularnewline
31 & 0.635367896019673 & 0.729264207960654 & 0.364632103980327 \tabularnewline
32 & 0.594257515315696 & 0.811484969368608 & 0.405742484684304 \tabularnewline
33 & 0.588299853705494 & 0.823400292589012 & 0.411700146294506 \tabularnewline
34 & 0.585748775213626 & 0.828502449572748 & 0.414251224786374 \tabularnewline
35 & 0.572295875347309 & 0.855408249305382 & 0.427704124652691 \tabularnewline
36 & 0.618906102951462 & 0.762187794097077 & 0.381093897048538 \tabularnewline
37 & 0.560748311799657 & 0.878503376400686 & 0.439251688200343 \tabularnewline
38 & 0.590080015790703 & 0.819839968418595 & 0.409919984209297 \tabularnewline
39 & 0.55654743238859 & 0.88690513522282 & 0.44345256761141 \tabularnewline
40 & 0.611262922494251 & 0.777474155011497 & 0.388737077505749 \tabularnewline
41 & 0.588657325934129 & 0.822685348131741 & 0.41134267406587 \tabularnewline
42 & 0.539008408281211 & 0.921983183437578 & 0.460991591718789 \tabularnewline
43 & 0.492421578435476 & 0.984843156870952 & 0.507578421564524 \tabularnewline
44 & 0.439412201050879 & 0.878824402101758 & 0.560587798949121 \tabularnewline
45 & 0.387294588580737 & 0.774589177161473 & 0.612705411419263 \tabularnewline
46 & 0.385070841150054 & 0.770141682300107 & 0.614929158849946 \tabularnewline
47 & 0.372185697447925 & 0.744371394895849 & 0.627814302552075 \tabularnewline
48 & 0.503013703631482 & 0.993972592737035 & 0.496986296368518 \tabularnewline
49 & 0.64938600824085 & 0.7012279835183 & 0.35061399175915 \tabularnewline
50 & 0.606734332802529 & 0.786531334394942 & 0.393265667197471 \tabularnewline
51 & 0.704826237778138 & 0.590347524443723 & 0.295173762221862 \tabularnewline
52 & 0.663235508001999 & 0.673528983996002 & 0.336764491998001 \tabularnewline
53 & 0.646229826859839 & 0.707540346280322 & 0.353770173140161 \tabularnewline
54 & 0.618052013711137 & 0.763895972577726 & 0.381947986288863 \tabularnewline
55 & 0.569038945009667 & 0.861922109980666 & 0.430961054990333 \tabularnewline
56 & 0.634502072128805 & 0.730995855742391 & 0.365497927871195 \tabularnewline
57 & 0.589923209716211 & 0.820153580567577 & 0.410076790283788 \tabularnewline
58 & 0.556317472514176 & 0.887365054971647 & 0.443682527485824 \tabularnewline
59 & 0.584650934817389 & 0.830698130365221 & 0.415349065182611 \tabularnewline
60 & 0.536481974381181 & 0.927036051237637 & 0.463518025618819 \tabularnewline
61 & 0.491196032795056 & 0.982392065590112 & 0.508803967204944 \tabularnewline
62 & 0.692933769942875 & 0.61413246011425 & 0.307066230057125 \tabularnewline
63 & 0.648268415454204 & 0.703463169091593 & 0.351731584545796 \tabularnewline
64 & 0.622634189596857 & 0.754731620806287 & 0.377365810403143 \tabularnewline
65 & 0.574301710188354 & 0.851396579623292 & 0.425698289811646 \tabularnewline
66 & 0.558243613427462 & 0.883512773145077 & 0.441756386572538 \tabularnewline
67 & 0.509742504181499 & 0.980514991637003 & 0.490257495818501 \tabularnewline
68 & 0.461778043997651 & 0.923556087995301 & 0.538221956002349 \tabularnewline
69 & 0.430357311692556 & 0.860714623385113 & 0.569642688307444 \tabularnewline
70 & 0.389451254973366 & 0.778902509946732 & 0.610548745026634 \tabularnewline
71 & 0.376936075493881 & 0.753872150987762 & 0.623063924506119 \tabularnewline
72 & 0.350138778097984 & 0.700277556195968 & 0.649861221902016 \tabularnewline
73 & 0.310182526183247 & 0.620365052366494 & 0.689817473816753 \tabularnewline
74 & 0.272385293025989 & 0.544770586051977 & 0.727614706974011 \tabularnewline
75 & 0.300604405938084 & 0.601208811876168 & 0.699395594061916 \tabularnewline
76 & 0.266770473433649 & 0.533540946867298 & 0.733229526566351 \tabularnewline
77 & 0.229840842590396 & 0.459681685180792 & 0.770159157409604 \tabularnewline
78 & 0.226672580249724 & 0.453345160499449 & 0.773327419750276 \tabularnewline
79 & 0.191346213290758 & 0.382692426581517 & 0.808653786709242 \tabularnewline
80 & 0.162311458124013 & 0.324622916248026 & 0.837688541875987 \tabularnewline
81 & 0.150584578254024 & 0.301169156508047 & 0.849415421745976 \tabularnewline
82 & 0.134104464287157 & 0.268208928574313 & 0.865895535712844 \tabularnewline
83 & 0.162624580008649 & 0.325249160017298 & 0.837375419991351 \tabularnewline
84 & 0.133222435734271 & 0.266444871468543 & 0.866777564265729 \tabularnewline
85 & 0.128479049174722 & 0.256958098349444 & 0.871520950825278 \tabularnewline
86 & 0.146063494734577 & 0.292126989469154 & 0.853936505265423 \tabularnewline
87 & 0.127294477417721 & 0.254588954835442 & 0.872705522582279 \tabularnewline
88 & 0.110254038464405 & 0.220508076928811 & 0.889745961535595 \tabularnewline
89 & 0.106409953988077 & 0.212819907976154 & 0.893590046011923 \tabularnewline
90 & 0.100540054692686 & 0.201080109385372 & 0.899459945307314 \tabularnewline
91 & 0.0895352926804311 & 0.179070585360862 & 0.910464707319569 \tabularnewline
92 & 0.0749383397481258 & 0.149876679496252 & 0.925061660251874 \tabularnewline
93 & 0.106816128514343 & 0.213632257028686 & 0.893183871485657 \tabularnewline
94 & 0.0927254731759005 & 0.185450946351801 & 0.907274526824099 \tabularnewline
95 & 0.113807025021702 & 0.227614050043405 & 0.886192974978298 \tabularnewline
96 & 0.114142178529088 & 0.228284357058176 & 0.885857821470912 \tabularnewline
97 & 0.0984386304726495 & 0.196877260945299 & 0.90156136952735 \tabularnewline
98 & 0.0970729735381281 & 0.194145947076256 & 0.902927026461872 \tabularnewline
99 & 0.0918606966288058 & 0.183721393257612 & 0.908139303371194 \tabularnewline
100 & 0.102818248489939 & 0.205636496979877 & 0.897181751510061 \tabularnewline
101 & 0.083462940843949 & 0.166925881687898 & 0.916537059156051 \tabularnewline
102 & 0.0728181404145166 & 0.145636280829033 & 0.927181859585483 \tabularnewline
103 & 0.0737180520662468 & 0.147436104132494 & 0.926281947933753 \tabularnewline
104 & 0.0576047713415866 & 0.115209542683173 & 0.942395228658413 \tabularnewline
105 & 0.0447076711804533 & 0.0894153423609066 & 0.955292328819547 \tabularnewline
106 & 0.0337471504983128 & 0.0674943009966255 & 0.966252849501687 \tabularnewline
107 & 0.0241296741089085 & 0.048259348217817 & 0.975870325891091 \tabularnewline
108 & 0.0244991239264397 & 0.0489982478528794 & 0.97550087607356 \tabularnewline
109 & 0.0237329639805007 & 0.0474659279610014 & 0.976267036019499 \tabularnewline
110 & 0.0248132813850395 & 0.049626562770079 & 0.97518671861496 \tabularnewline
111 & 0.0293401138206119 & 0.0586802276412237 & 0.970659886179388 \tabularnewline
112 & 0.0315713424104949 & 0.0631426848209897 & 0.968428657589505 \tabularnewline
113 & 0.0749223752262067 & 0.149844750452413 & 0.925077624773793 \tabularnewline
114 & 0.0764627801732038 & 0.152925560346408 & 0.923537219826796 \tabularnewline
115 & 0.0600438526131731 & 0.120087705226346 & 0.939956147386827 \tabularnewline
116 & 0.0453689943227924 & 0.0907379886455848 & 0.954631005677208 \tabularnewline
117 & 0.0346571496832933 & 0.0693142993665865 & 0.965342850316707 \tabularnewline
118 & 0.031456484292068 & 0.062912968584136 & 0.968543515707932 \tabularnewline
119 & 0.0307102063818813 & 0.0614204127637626 & 0.969289793618119 \tabularnewline
120 & 0.0209386426286802 & 0.0418772852573604 & 0.97906135737132 \tabularnewline
121 & 0.398398339255141 & 0.796796678510282 & 0.601601660744859 \tabularnewline
122 & 0.355722313336797 & 0.711444626673594 & 0.644277686663203 \tabularnewline
123 & 0.315162540216578 & 0.630325080433156 & 0.684837459783422 \tabularnewline
124 & 0.24673755029061 & 0.493475100581219 & 0.75326244970939 \tabularnewline
125 & 0.201024557792428 & 0.402049115584856 & 0.798975442207572 \tabularnewline
126 & 0.201547150950464 & 0.403094301900928 & 0.798452849049536 \tabularnewline
127 & 0.307012544678122 & 0.614025089356244 & 0.692987455321878 \tabularnewline
128 & 0.684182226775631 & 0.631635546448738 & 0.315817773224369 \tabularnewline
129 & 0.587921250046456 & 0.824157499907088 & 0.412078749953544 \tabularnewline
130 & 0.446012917401205 & 0.89202583480241 & 0.553987082598795 \tabularnewline
131 & 0.691935269795043 & 0.616129460409913 & 0.308064730204957 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185556&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]11[/C][C]0.0530064358592684[/C][C]0.106012871718537[/C][C]0.946993564140732[/C][/ROW]
[ROW][C]12[/C][C]0.0158351191982496[/C][C]0.0316702383964991[/C][C]0.98416488080175[/C][/ROW]
[ROW][C]13[/C][C]0.0464679208542913[/C][C]0.0929358417085825[/C][C]0.953532079145709[/C][/ROW]
[ROW][C]14[/C][C]0.0509633587682586[/C][C]0.101926717536517[/C][C]0.949036641231741[/C][/ROW]
[ROW][C]15[/C][C]0.0234127509677793[/C][C]0.0468255019355586[/C][C]0.976587249032221[/C][/ROW]
[ROW][C]16[/C][C]0.297839997215792[/C][C]0.595679994431584[/C][C]0.702160002784208[/C][/ROW]
[ROW][C]17[/C][C]0.214541367315842[/C][C]0.429082734631684[/C][C]0.785458632684158[/C][/ROW]
[ROW][C]18[/C][C]0.151891591713463[/C][C]0.303783183426925[/C][C]0.848108408286537[/C][/ROW]
[ROW][C]19[/C][C]0.603788658307489[/C][C]0.792422683385023[/C][C]0.396211341692511[/C][/ROW]
[ROW][C]20[/C][C]0.761209016605731[/C][C]0.477581966788537[/C][C]0.238790983394269[/C][/ROW]
[ROW][C]21[/C][C]0.756179318542039[/C][C]0.487641362915922[/C][C]0.243820681457961[/C][/ROW]
[ROW][C]22[/C][C]0.711553698569176[/C][C]0.576892602861648[/C][C]0.288446301430824[/C][/ROW]
[ROW][C]23[/C][C]0.699611669351139[/C][C]0.600776661297722[/C][C]0.300388330648861[/C][/ROW]
[ROW][C]24[/C][C]0.629693978183781[/C][C]0.740612043632437[/C][C]0.370306021816219[/C][/ROW]
[ROW][C]25[/C][C]0.562783436524069[/C][C]0.874433126951862[/C][C]0.437216563475931[/C][/ROW]
[ROW][C]26[/C][C]0.733042359725[/C][C]0.53391528055[/C][C]0.266957640275[/C][/ROW]
[ROW][C]27[/C][C]0.702508409273971[/C][C]0.594983181452059[/C][C]0.297491590726029[/C][/ROW]
[ROW][C]28[/C][C]0.715370302062647[/C][C]0.569259395874706[/C][C]0.284629697937353[/C][/ROW]
[ROW][C]29[/C][C]0.710804116909367[/C][C]0.578391766181266[/C][C]0.289195883090633[/C][/ROW]
[ROW][C]30[/C][C]0.673754219142488[/C][C]0.652491561715024[/C][C]0.326245780857512[/C][/ROW]
[ROW][C]31[/C][C]0.635367896019673[/C][C]0.729264207960654[/C][C]0.364632103980327[/C][/ROW]
[ROW][C]32[/C][C]0.594257515315696[/C][C]0.811484969368608[/C][C]0.405742484684304[/C][/ROW]
[ROW][C]33[/C][C]0.588299853705494[/C][C]0.823400292589012[/C][C]0.411700146294506[/C][/ROW]
[ROW][C]34[/C][C]0.585748775213626[/C][C]0.828502449572748[/C][C]0.414251224786374[/C][/ROW]
[ROW][C]35[/C][C]0.572295875347309[/C][C]0.855408249305382[/C][C]0.427704124652691[/C][/ROW]
[ROW][C]36[/C][C]0.618906102951462[/C][C]0.762187794097077[/C][C]0.381093897048538[/C][/ROW]
[ROW][C]37[/C][C]0.560748311799657[/C][C]0.878503376400686[/C][C]0.439251688200343[/C][/ROW]
[ROW][C]38[/C][C]0.590080015790703[/C][C]0.819839968418595[/C][C]0.409919984209297[/C][/ROW]
[ROW][C]39[/C][C]0.55654743238859[/C][C]0.88690513522282[/C][C]0.44345256761141[/C][/ROW]
[ROW][C]40[/C][C]0.611262922494251[/C][C]0.777474155011497[/C][C]0.388737077505749[/C][/ROW]
[ROW][C]41[/C][C]0.588657325934129[/C][C]0.822685348131741[/C][C]0.41134267406587[/C][/ROW]
[ROW][C]42[/C][C]0.539008408281211[/C][C]0.921983183437578[/C][C]0.460991591718789[/C][/ROW]
[ROW][C]43[/C][C]0.492421578435476[/C][C]0.984843156870952[/C][C]0.507578421564524[/C][/ROW]
[ROW][C]44[/C][C]0.439412201050879[/C][C]0.878824402101758[/C][C]0.560587798949121[/C][/ROW]
[ROW][C]45[/C][C]0.387294588580737[/C][C]0.774589177161473[/C][C]0.612705411419263[/C][/ROW]
[ROW][C]46[/C][C]0.385070841150054[/C][C]0.770141682300107[/C][C]0.614929158849946[/C][/ROW]
[ROW][C]47[/C][C]0.372185697447925[/C][C]0.744371394895849[/C][C]0.627814302552075[/C][/ROW]
[ROW][C]48[/C][C]0.503013703631482[/C][C]0.993972592737035[/C][C]0.496986296368518[/C][/ROW]
[ROW][C]49[/C][C]0.64938600824085[/C][C]0.7012279835183[/C][C]0.35061399175915[/C][/ROW]
[ROW][C]50[/C][C]0.606734332802529[/C][C]0.786531334394942[/C][C]0.393265667197471[/C][/ROW]
[ROW][C]51[/C][C]0.704826237778138[/C][C]0.590347524443723[/C][C]0.295173762221862[/C][/ROW]
[ROW][C]52[/C][C]0.663235508001999[/C][C]0.673528983996002[/C][C]0.336764491998001[/C][/ROW]
[ROW][C]53[/C][C]0.646229826859839[/C][C]0.707540346280322[/C][C]0.353770173140161[/C][/ROW]
[ROW][C]54[/C][C]0.618052013711137[/C][C]0.763895972577726[/C][C]0.381947986288863[/C][/ROW]
[ROW][C]55[/C][C]0.569038945009667[/C][C]0.861922109980666[/C][C]0.430961054990333[/C][/ROW]
[ROW][C]56[/C][C]0.634502072128805[/C][C]0.730995855742391[/C][C]0.365497927871195[/C][/ROW]
[ROW][C]57[/C][C]0.589923209716211[/C][C]0.820153580567577[/C][C]0.410076790283788[/C][/ROW]
[ROW][C]58[/C][C]0.556317472514176[/C][C]0.887365054971647[/C][C]0.443682527485824[/C][/ROW]
[ROW][C]59[/C][C]0.584650934817389[/C][C]0.830698130365221[/C][C]0.415349065182611[/C][/ROW]
[ROW][C]60[/C][C]0.536481974381181[/C][C]0.927036051237637[/C][C]0.463518025618819[/C][/ROW]
[ROW][C]61[/C][C]0.491196032795056[/C][C]0.982392065590112[/C][C]0.508803967204944[/C][/ROW]
[ROW][C]62[/C][C]0.692933769942875[/C][C]0.61413246011425[/C][C]0.307066230057125[/C][/ROW]
[ROW][C]63[/C][C]0.648268415454204[/C][C]0.703463169091593[/C][C]0.351731584545796[/C][/ROW]
[ROW][C]64[/C][C]0.622634189596857[/C][C]0.754731620806287[/C][C]0.377365810403143[/C][/ROW]
[ROW][C]65[/C][C]0.574301710188354[/C][C]0.851396579623292[/C][C]0.425698289811646[/C][/ROW]
[ROW][C]66[/C][C]0.558243613427462[/C][C]0.883512773145077[/C][C]0.441756386572538[/C][/ROW]
[ROW][C]67[/C][C]0.509742504181499[/C][C]0.980514991637003[/C][C]0.490257495818501[/C][/ROW]
[ROW][C]68[/C][C]0.461778043997651[/C][C]0.923556087995301[/C][C]0.538221956002349[/C][/ROW]
[ROW][C]69[/C][C]0.430357311692556[/C][C]0.860714623385113[/C][C]0.569642688307444[/C][/ROW]
[ROW][C]70[/C][C]0.389451254973366[/C][C]0.778902509946732[/C][C]0.610548745026634[/C][/ROW]
[ROW][C]71[/C][C]0.376936075493881[/C][C]0.753872150987762[/C][C]0.623063924506119[/C][/ROW]
[ROW][C]72[/C][C]0.350138778097984[/C][C]0.700277556195968[/C][C]0.649861221902016[/C][/ROW]
[ROW][C]73[/C][C]0.310182526183247[/C][C]0.620365052366494[/C][C]0.689817473816753[/C][/ROW]
[ROW][C]74[/C][C]0.272385293025989[/C][C]0.544770586051977[/C][C]0.727614706974011[/C][/ROW]
[ROW][C]75[/C][C]0.300604405938084[/C][C]0.601208811876168[/C][C]0.699395594061916[/C][/ROW]
[ROW][C]76[/C][C]0.266770473433649[/C][C]0.533540946867298[/C][C]0.733229526566351[/C][/ROW]
[ROW][C]77[/C][C]0.229840842590396[/C][C]0.459681685180792[/C][C]0.770159157409604[/C][/ROW]
[ROW][C]78[/C][C]0.226672580249724[/C][C]0.453345160499449[/C][C]0.773327419750276[/C][/ROW]
[ROW][C]79[/C][C]0.191346213290758[/C][C]0.382692426581517[/C][C]0.808653786709242[/C][/ROW]
[ROW][C]80[/C][C]0.162311458124013[/C][C]0.324622916248026[/C][C]0.837688541875987[/C][/ROW]
[ROW][C]81[/C][C]0.150584578254024[/C][C]0.301169156508047[/C][C]0.849415421745976[/C][/ROW]
[ROW][C]82[/C][C]0.134104464287157[/C][C]0.268208928574313[/C][C]0.865895535712844[/C][/ROW]
[ROW][C]83[/C][C]0.162624580008649[/C][C]0.325249160017298[/C][C]0.837375419991351[/C][/ROW]
[ROW][C]84[/C][C]0.133222435734271[/C][C]0.266444871468543[/C][C]0.866777564265729[/C][/ROW]
[ROW][C]85[/C][C]0.128479049174722[/C][C]0.256958098349444[/C][C]0.871520950825278[/C][/ROW]
[ROW][C]86[/C][C]0.146063494734577[/C][C]0.292126989469154[/C][C]0.853936505265423[/C][/ROW]
[ROW][C]87[/C][C]0.127294477417721[/C][C]0.254588954835442[/C][C]0.872705522582279[/C][/ROW]
[ROW][C]88[/C][C]0.110254038464405[/C][C]0.220508076928811[/C][C]0.889745961535595[/C][/ROW]
[ROW][C]89[/C][C]0.106409953988077[/C][C]0.212819907976154[/C][C]0.893590046011923[/C][/ROW]
[ROW][C]90[/C][C]0.100540054692686[/C][C]0.201080109385372[/C][C]0.899459945307314[/C][/ROW]
[ROW][C]91[/C][C]0.0895352926804311[/C][C]0.179070585360862[/C][C]0.910464707319569[/C][/ROW]
[ROW][C]92[/C][C]0.0749383397481258[/C][C]0.149876679496252[/C][C]0.925061660251874[/C][/ROW]
[ROW][C]93[/C][C]0.106816128514343[/C][C]0.213632257028686[/C][C]0.893183871485657[/C][/ROW]
[ROW][C]94[/C][C]0.0927254731759005[/C][C]0.185450946351801[/C][C]0.907274526824099[/C][/ROW]
[ROW][C]95[/C][C]0.113807025021702[/C][C]0.227614050043405[/C][C]0.886192974978298[/C][/ROW]
[ROW][C]96[/C][C]0.114142178529088[/C][C]0.228284357058176[/C][C]0.885857821470912[/C][/ROW]
[ROW][C]97[/C][C]0.0984386304726495[/C][C]0.196877260945299[/C][C]0.90156136952735[/C][/ROW]
[ROW][C]98[/C][C]0.0970729735381281[/C][C]0.194145947076256[/C][C]0.902927026461872[/C][/ROW]
[ROW][C]99[/C][C]0.0918606966288058[/C][C]0.183721393257612[/C][C]0.908139303371194[/C][/ROW]
[ROW][C]100[/C][C]0.102818248489939[/C][C]0.205636496979877[/C][C]0.897181751510061[/C][/ROW]
[ROW][C]101[/C][C]0.083462940843949[/C][C]0.166925881687898[/C][C]0.916537059156051[/C][/ROW]
[ROW][C]102[/C][C]0.0728181404145166[/C][C]0.145636280829033[/C][C]0.927181859585483[/C][/ROW]
[ROW][C]103[/C][C]0.0737180520662468[/C][C]0.147436104132494[/C][C]0.926281947933753[/C][/ROW]
[ROW][C]104[/C][C]0.0576047713415866[/C][C]0.115209542683173[/C][C]0.942395228658413[/C][/ROW]
[ROW][C]105[/C][C]0.0447076711804533[/C][C]0.0894153423609066[/C][C]0.955292328819547[/C][/ROW]
[ROW][C]106[/C][C]0.0337471504983128[/C][C]0.0674943009966255[/C][C]0.966252849501687[/C][/ROW]
[ROW][C]107[/C][C]0.0241296741089085[/C][C]0.048259348217817[/C][C]0.975870325891091[/C][/ROW]
[ROW][C]108[/C][C]0.0244991239264397[/C][C]0.0489982478528794[/C][C]0.97550087607356[/C][/ROW]
[ROW][C]109[/C][C]0.0237329639805007[/C][C]0.0474659279610014[/C][C]0.976267036019499[/C][/ROW]
[ROW][C]110[/C][C]0.0248132813850395[/C][C]0.049626562770079[/C][C]0.97518671861496[/C][/ROW]
[ROW][C]111[/C][C]0.0293401138206119[/C][C]0.0586802276412237[/C][C]0.970659886179388[/C][/ROW]
[ROW][C]112[/C][C]0.0315713424104949[/C][C]0.0631426848209897[/C][C]0.968428657589505[/C][/ROW]
[ROW][C]113[/C][C]0.0749223752262067[/C][C]0.149844750452413[/C][C]0.925077624773793[/C][/ROW]
[ROW][C]114[/C][C]0.0764627801732038[/C][C]0.152925560346408[/C][C]0.923537219826796[/C][/ROW]
[ROW][C]115[/C][C]0.0600438526131731[/C][C]0.120087705226346[/C][C]0.939956147386827[/C][/ROW]
[ROW][C]116[/C][C]0.0453689943227924[/C][C]0.0907379886455848[/C][C]0.954631005677208[/C][/ROW]
[ROW][C]117[/C][C]0.0346571496832933[/C][C]0.0693142993665865[/C][C]0.965342850316707[/C][/ROW]
[ROW][C]118[/C][C]0.031456484292068[/C][C]0.062912968584136[/C][C]0.968543515707932[/C][/ROW]
[ROW][C]119[/C][C]0.0307102063818813[/C][C]0.0614204127637626[/C][C]0.969289793618119[/C][/ROW]
[ROW][C]120[/C][C]0.0209386426286802[/C][C]0.0418772852573604[/C][C]0.97906135737132[/C][/ROW]
[ROW][C]121[/C][C]0.398398339255141[/C][C]0.796796678510282[/C][C]0.601601660744859[/C][/ROW]
[ROW][C]122[/C][C]0.355722313336797[/C][C]0.711444626673594[/C][C]0.644277686663203[/C][/ROW]
[ROW][C]123[/C][C]0.315162540216578[/C][C]0.630325080433156[/C][C]0.684837459783422[/C][/ROW]
[ROW][C]124[/C][C]0.24673755029061[/C][C]0.493475100581219[/C][C]0.75326244970939[/C][/ROW]
[ROW][C]125[/C][C]0.201024557792428[/C][C]0.402049115584856[/C][C]0.798975442207572[/C][/ROW]
[ROW][C]126[/C][C]0.201547150950464[/C][C]0.403094301900928[/C][C]0.798452849049536[/C][/ROW]
[ROW][C]127[/C][C]0.307012544678122[/C][C]0.614025089356244[/C][C]0.692987455321878[/C][/ROW]
[ROW][C]128[/C][C]0.684182226775631[/C][C]0.631635546448738[/C][C]0.315817773224369[/C][/ROW]
[ROW][C]129[/C][C]0.587921250046456[/C][C]0.824157499907088[/C][C]0.412078749953544[/C][/ROW]
[ROW][C]130[/C][C]0.446012917401205[/C][C]0.89202583480241[/C][C]0.553987082598795[/C][/ROW]
[ROW][C]131[/C][C]0.691935269795043[/C][C]0.616129460409913[/C][C]0.308064730204957[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185556&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185556&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.05300643585926840.1060128717185370.946993564140732
120.01583511919824960.03167023839649910.98416488080175
130.04646792085429130.09293584170858250.953532079145709
140.05096335876825860.1019267175365170.949036641231741
150.02341275096777930.04682550193555860.976587249032221
160.2978399972157920.5956799944315840.702160002784208
170.2145413673158420.4290827346316840.785458632684158
180.1518915917134630.3037831834269250.848108408286537
190.6037886583074890.7924226833850230.396211341692511
200.7612090166057310.4775819667885370.238790983394269
210.7561793185420390.4876413629159220.243820681457961
220.7115536985691760.5768926028616480.288446301430824
230.6996116693511390.6007766612977220.300388330648861
240.6296939781837810.7406120436324370.370306021816219
250.5627834365240690.8744331269518620.437216563475931
260.7330423597250.533915280550.266957640275
270.7025084092739710.5949831814520590.297491590726029
280.7153703020626470.5692593958747060.284629697937353
290.7108041169093670.5783917661812660.289195883090633
300.6737542191424880.6524915617150240.326245780857512
310.6353678960196730.7292642079606540.364632103980327
320.5942575153156960.8114849693686080.405742484684304
330.5882998537054940.8234002925890120.411700146294506
340.5857487752136260.8285024495727480.414251224786374
350.5722958753473090.8554082493053820.427704124652691
360.6189061029514620.7621877940970770.381093897048538
370.5607483117996570.8785033764006860.439251688200343
380.5900800157907030.8198399684185950.409919984209297
390.556547432388590.886905135222820.44345256761141
400.6112629224942510.7774741550114970.388737077505749
410.5886573259341290.8226853481317410.41134267406587
420.5390084082812110.9219831834375780.460991591718789
430.4924215784354760.9848431568709520.507578421564524
440.4394122010508790.8788244021017580.560587798949121
450.3872945885807370.7745891771614730.612705411419263
460.3850708411500540.7701416823001070.614929158849946
470.3721856974479250.7443713948958490.627814302552075
480.5030137036314820.9939725927370350.496986296368518
490.649386008240850.70122798351830.35061399175915
500.6067343328025290.7865313343949420.393265667197471
510.7048262377781380.5903475244437230.295173762221862
520.6632355080019990.6735289839960020.336764491998001
530.6462298268598390.7075403462803220.353770173140161
540.6180520137111370.7638959725777260.381947986288863
550.5690389450096670.8619221099806660.430961054990333
560.6345020721288050.7309958557423910.365497927871195
570.5899232097162110.8201535805675770.410076790283788
580.5563174725141760.8873650549716470.443682527485824
590.5846509348173890.8306981303652210.415349065182611
600.5364819743811810.9270360512376370.463518025618819
610.4911960327950560.9823920655901120.508803967204944
620.6929337699428750.614132460114250.307066230057125
630.6482684154542040.7034631690915930.351731584545796
640.6226341895968570.7547316208062870.377365810403143
650.5743017101883540.8513965796232920.425698289811646
660.5582436134274620.8835127731450770.441756386572538
670.5097425041814990.9805149916370030.490257495818501
680.4617780439976510.9235560879953010.538221956002349
690.4303573116925560.8607146233851130.569642688307444
700.3894512549733660.7789025099467320.610548745026634
710.3769360754938810.7538721509877620.623063924506119
720.3501387780979840.7002775561959680.649861221902016
730.3101825261832470.6203650523664940.689817473816753
740.2723852930259890.5447705860519770.727614706974011
750.3006044059380840.6012088118761680.699395594061916
760.2667704734336490.5335409468672980.733229526566351
770.2298408425903960.4596816851807920.770159157409604
780.2266725802497240.4533451604994490.773327419750276
790.1913462132907580.3826924265815170.808653786709242
800.1623114581240130.3246229162480260.837688541875987
810.1505845782540240.3011691565080470.849415421745976
820.1341044642871570.2682089285743130.865895535712844
830.1626245800086490.3252491600172980.837375419991351
840.1332224357342710.2664448714685430.866777564265729
850.1284790491747220.2569580983494440.871520950825278
860.1460634947345770.2921269894691540.853936505265423
870.1272944774177210.2545889548354420.872705522582279
880.1102540384644050.2205080769288110.889745961535595
890.1064099539880770.2128199079761540.893590046011923
900.1005400546926860.2010801093853720.899459945307314
910.08953529268043110.1790705853608620.910464707319569
920.07493833974812580.1498766794962520.925061660251874
930.1068161285143430.2136322570286860.893183871485657
940.09272547317590050.1854509463518010.907274526824099
950.1138070250217020.2276140500434050.886192974978298
960.1141421785290880.2282843570581760.885857821470912
970.09843863047264950.1968772609452990.90156136952735
980.09707297353812810.1941459470762560.902927026461872
990.09186069662880580.1837213932576120.908139303371194
1000.1028182484899390.2056364969798770.897181751510061
1010.0834629408439490.1669258816878980.916537059156051
1020.07281814041451660.1456362808290330.927181859585483
1030.07371805206624680.1474361041324940.926281947933753
1040.05760477134158660.1152095426831730.942395228658413
1050.04470767118045330.08941534236090660.955292328819547
1060.03374715049831280.06749430099662550.966252849501687
1070.02412967410890850.0482593482178170.975870325891091
1080.02449912392643970.04899824785287940.97550087607356
1090.02373296398050070.04746592796100140.976267036019499
1100.02481328138503950.0496265627700790.97518671861496
1110.02934011382061190.05868022764122370.970659886179388
1120.03157134241049490.06314268482098970.968428657589505
1130.07492237522620670.1498447504524130.925077624773793
1140.07646278017320380.1529255603464080.923537219826796
1150.06004385261317310.1200877052263460.939956147386827
1160.04536899432279240.09073798864558480.954631005677208
1170.03465714968329330.06931429936658650.965342850316707
1180.0314564842920680.0629129685841360.968543515707932
1190.03071020638188130.06142041276376260.969289793618119
1200.02093864262868020.04187728525736040.97906135737132
1210.3983983392551410.7967966785102820.601601660744859
1220.3557223133367970.7114446266735940.644277686663203
1230.3151625402165780.6303250804331560.684837459783422
1240.246737550290610.4934751005812190.75326244970939
1250.2010245577924280.4020491155848560.798975442207572
1260.2015471509504640.4030943019009280.798452849049536
1270.3070125446781220.6140250893562440.692987455321878
1280.6841822267756310.6316355464487380.315817773224369
1290.5879212500464560.8241574999070880.412078749953544
1300.4460129174012050.892025834802410.553987082598795
1310.6919352697950430.6161294604099130.308064730204957






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level70.0578512396694215NOK
10% type I error level160.132231404958678NOK

\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 & 7 & 0.0578512396694215 & NOK \tabularnewline
10% type I error level & 16 & 0.132231404958678 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185556&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]7[/C][C]0.0578512396694215[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]16[/C][C]0.132231404958678[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185556&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185556&T=6

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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 level70.0578512396694215NOK
10% type I error level160.132231404958678NOK


Figures (Output of Computation)

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Input Parameters & R Code

Parameters (Session):
par1 = 3 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 3 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}