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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 computationMon, 05 Nov 2012 13:18:59 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Nov/05/t1352139594hbmq6q7782g5ika.htm/, Retrieved Wed, 01 Feb 2023 16:54:40 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=186194, Retrieved Wed, 01 Feb 2023 16:54:40 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact116
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Decreasing Compet...] [2010-11-17 09:04:39] [b98453cac15ba1066b407e146608df68]
-   PD    [Multiple Regression] [Without belonging...] [2012-11-05 18:18:59] [b7b610b08ce09537f4b16b68ce5f31b7] [Current]
-           [Multiple Regression] [Without belonging...] [2012-11-05 22:03:47] [0dc867bfbaab36a894719867823e3cb9]
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Dataseries X:
41	38	13	12	14	12	53
39	32	16	11	18	11	86
30	35	19	15	11	14	66
31	33	15	6	12	12	67
34	37	14	13	16	21	76
35	29	13	10	18	12	78
39	31	19	12	14	22	53
34	36	15	14	14	11	80
36	35	14	12	15	10	74
37	38	15	6	15	13	76
38	31	16	10	17	10	79
36	34	16	12	19	8	54
38	35	16	12	10	15	67
39	38	16	11	16	14	54
33	37	17	15	18	10	87
32	33	15	12	14	14	58
36	32	15	10	14	14	75
38	38	20	12	17	11	88
39	38	18	11	14	10	64
32	32	16	12	16	13	57
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=186194&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=186194&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186194&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] = + 5.75162899505497 + 0.115277436449707Connected[t] -0.0237127470059973Separate[t] + 0.545447305595596Software[t] + 0.0629073129849922Happiness[t] -0.0768312591878517Depression[t] + 0.00135058357917087Belonging[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Learning[t] =  +  5.75162899505497 +  0.115277436449707Connected[t] -0.0237127470059973Separate[t] +  0.545447305595596Software[t] +  0.0629073129849922Happiness[t] -0.0768312591878517Depression[t] +  0.00135058357917087Belonging[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186194&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Learning[t] =  +  5.75162899505497 +  0.115277436449707Connected[t] -0.0237127470059973Separate[t] +  0.545447305595596Software[t] +  0.0629073129849922Happiness[t] -0.0768312591878517Depression[t] +  0.00135058357917087Belonging[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186194&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186194&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] = + 5.75162899505497 + 0.115277436449707Connected[t] -0.0237127470059973Separate[t] + 0.545447305595596Software[t] + 0.0629073129849922Happiness[t] -0.0768312591878517Depression[t] + 0.00135058357917087Belonging[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)5.751628995054972.5771922.23170.0270660.013533
Connected0.1152774364497070.0468152.46240.0148970.007448
Separate-0.02371274700599730.044607-0.53160.5957730.297887
Software0.5454473055955960.0687877.929500
Happiness0.06290731298499220.0761990.82560.4103170.205159
Depression-0.07683125918785170.055851-1.37560.1709150.085457
Belonging0.001350583579170870.0143960.09380.9253770.462689

\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) & 5.75162899505497 & 2.577192 & 2.2317 & 0.027066 & 0.013533 \tabularnewline
Connected & 0.115277436449707 & 0.046815 & 2.4624 & 0.014897 & 0.007448 \tabularnewline
Separate & -0.0237127470059973 & 0.044607 & -0.5316 & 0.595773 & 0.297887 \tabularnewline
Software & 0.545447305595596 & 0.068787 & 7.9295 & 0 & 0 \tabularnewline
Happiness & 0.0629073129849922 & 0.076199 & 0.8256 & 0.410317 & 0.205159 \tabularnewline
Depression & -0.0768312591878517 & 0.055851 & -1.3756 & 0.170915 & 0.085457 \tabularnewline
Belonging & 0.00135058357917087 & 0.014396 & 0.0938 & 0.925377 & 0.462689 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186194&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]5.75162899505497[/C][C]2.577192[/C][C]2.2317[/C][C]0.027066[/C][C]0.013533[/C][/ROW]
[ROW][C]Connected[/C][C]0.115277436449707[/C][C]0.046815[/C][C]2.4624[/C][C]0.014897[/C][C]0.007448[/C][/ROW]
[ROW][C]Separate[/C][C]-0.0237127470059973[/C][C]0.044607[/C][C]-0.5316[/C][C]0.595773[/C][C]0.297887[/C][/ROW]
[ROW][C]Software[/C][C]0.545447305595596[/C][C]0.068787[/C][C]7.9295[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Happiness[/C][C]0.0629073129849922[/C][C]0.076199[/C][C]0.8256[/C][C]0.410317[/C][C]0.205159[/C][/ROW]
[ROW][C]Depression[/C][C]-0.0768312591878517[/C][C]0.055851[/C][C]-1.3756[/C][C]0.170915[/C][C]0.085457[/C][/ROW]
[ROW][C]Belonging[/C][C]0.00135058357917087[/C][C]0.014396[/C][C]0.0938[/C][C]0.925377[/C][C]0.462689[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186194&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186194&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)5.751628995054972.5771922.23170.0270660.013533
Connected0.1152774364497070.0468152.46240.0148970.007448
Separate-0.02371274700599730.044607-0.53160.5957730.297887
Software0.5454473055955960.0687877.929500
Happiness0.06290731298499220.0761990.82560.4103170.205159
Depression-0.07683125918785170.055851-1.37560.1709150.085457
Belonging0.001350583579170870.0143960.09380.9253770.462689







Multiple Linear Regression - Regression Statistics
Multiple R0.594915101616055
R-squared0.353923978130841
Adjusted R-squared0.328914583735905
F-TEST (value)14.1516412809466
F-TEST (DF numerator)6
F-TEST (DF denominator)155
p-value8.11350986396064e-13
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.84832460845271
Sum Squared Residuals529.527098022837

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.594915101616055 \tabularnewline
R-squared & 0.353923978130841 \tabularnewline
Adjusted R-squared & 0.328914583735905 \tabularnewline
F-TEST (value) & 14.1516412809466 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 155 \tabularnewline
p-value & 8.11350986396064e-13 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.84832460845271 \tabularnewline
Sum Squared Residuals & 529.527098022837 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186194&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.594915101616055[/C][/ROW]
[ROW][C]R-squared[/C][C]0.353923978130841[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.328914583735905[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]14.1516412809466[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]155[/C][/ROW]
[ROW][C]p-value[/C][C]8.11350986396064e-13[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.84832460845271[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]529.527098022837[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186194&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186194&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 R0.594915101616055
R-squared0.353923978130841
Adjusted R-squared0.328914583735905
F-TEST (value)14.1516412809466
F-TEST (DF numerator)6
F-TEST (DF denominator)155
p-value8.11350986396064e-13
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.84832460845271
Sum Squared Residuals529.527098022837







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11316.1525953716439-3.15259537164395
21615.89189944442540.108100555574641
31916.26719685770052.73280314229953
41511.73879445274173.26120554725832
51415.3802100846978-1.38021008469782
61314.8688447062277-1.86884470622771
71915.3197171359083.68028286409202
81516.5972704375246-1.59727043752463
91415.8922785169367-1.89227851693667
101512.43594126838962.56405873161041
111615.25935731053470.740642689465284
121616.2942713626749-0.294271362674917
131615.41468644391770.585313556082336
141615.35009588432240.649904115677633
151717.3416421558465-0.341642155846535
161515.0867525781467-0.0867525781466997
171514.50364038060620.496359619393762
182016.11958668570863.88041331429139
191815.54511213089552.4548878691045
201615.31176062673140.688239373268638
211615.34155800743940.658441992560599
221614.84368453254571.15631546745428
231916.46883838791932.53116161208072
241614.84690017963531.15309982036467
251714.73809752024922.26190247975083
261716.79274212855170.207257871448289
271614.7901611267011.20983887329904
281516.1360987979466-1.13609879794664
291615.29194899916480.708051000835184
301414.1266774123003-0.126677412300335
311515.629722637378-0.629722637377981
321212.2071480140323-0.207148014032253
331415.1332752054516-1.13327520545164
341615.55097272662910.449027273370886
351415.4868862885187-1.48688628851869
36712.963875817795-5.96387581779504
371010.7468907270798-0.746890727079784
381415.7819897418354-1.78198974183544
391614.21780863174981.78219136825016
401614.60096441944191.39903558055808
411615.0646308424010.935369157598975
421415.466081997862-1.466081997862
432018.01987170741811.98012829258186
441414.2077721488596-0.20777214885962
451414.9256218916482-0.925621891648186
461115.5173294224078-4.51732942240782
471416.638105714383-2.638105714383
481514.94923097338930.0507690266107342
491615.01735130035380.982648699646196
501415.9926763292754-1.99267632927541
511616.5111312871377-0.511131287137688
521414.079151460186-0.0791514601860379
531214.5974585203362-2.59745852033617
541615.62377872995840.376221270041578
55911.403766255473-2.40376625547295
561412.65504900961341.34495099038657
571616.0942371087132-0.0942371087132206
581615.17642638292840.82357361707163
591515.2483233782506-0.248323378250632
601614.13153864709081.86846135290915
611211.40234056136640.597659438633623
621615.87112074323390.128879256766098
631616.3513782356503-0.351378235650284
641414.3704988406144-0.370498840614378
651615.49906918291430.500930817085732
661715.89175856943281.10824143056723
671816.18292039323961.81707960676044
681814.5011335277813.49886647221904
691215.8741968943214-3.87419689432137
701615.41549763182770.584502368172297
711013.614476244845-3.61447624484496
721414.3510451335369-0.35104513353686
731816.43672757469511.5632724253049
741817.06469860109540.935301398904567
751615.66228557071910.337714429280898
761713.84823287435843.15176712564163
771616.3954962790597-0.395496279059719
781614.54074096828481.45925903171516
791314.838467986537-1.83846798653696
801616.1212628910688-0.1212628910688
811615.45265511928960.54734488071045
822015.93989934786994.06010065213015
831615.80235509492530.197644905074697
841515.9628756584285-0.962875658428485
851514.75307052701420.246929472985806
861614.26105324460931.73894675539069
871414.1624972910409-0.162497291040919
881615.3468362053370.653163794663008
891614.6194921093141.38050789068595
901514.16323575314870.836764246851301
911213.5498069357226-1.54980693572261
921717.0627760812488-0.0627760812487845
931615.40828257162490.591717428375067
941515.1608990598889-0.160899059888935
951315.0861890723237-2.08618907232374
961615.11768302336080.882316976639236
971615.8965408470040.103459152995973
981614.1011739138681.898826086132
991616.0897061522287-0.0897061522286984
1001414.319262580099-0.319262580098991
1011617.3185310471496-1.3185310471496
1021614.81104857012951.18895142987048
1032017.40529615369162.59470384630837
1041514.7124455148230.287554485176965
1051614.45191327673671.5480867232633
1061315.3474977916778-2.34749779167784
1071716.11161061079160.888389389208438
1081615.90877540529660.0912245947034114
1091614.62112667889341.37887332110657
1101212.378004281293-0.378004281292986
1111614.92780627712641.07219372287362
1121615.81842589201210.18157410798791
1131714.55970726845682.44029273154318
1141314.4374623417798-1.43746234177984
1151214.9781346769388-2.97813467693879
1161816.7032558159351.296744184065
1171415.5760857568333-1.57608575683334
1181413.06256726175970.937432738240347
1191315.008169930299-2.00816993029895
1201615.67366637117260.326333628827435
1211314.3536835015023-1.3536835015023
1221615.68350943375360.316490566246352
1231315.9714098963118-2.9714098963118
1241617.1836760090936-1.18367600909362
1251516.1085626584401-1.10856265844011
1261616.8037657782249-0.803765778224931
1271515.519440937725-0.519440937725008
1281716.30001434088470.699985659115279
1291514.07064362160110.929356378398927
1301215.0457843241815-3.04578432418146
1311614.18100201806331.81899798193671
1321013.3391888223396-3.33918882233957
1331614.12822529707861.87177470292137
1341214.0315864701719-2.0315864701719
1351415.7827551939927-1.78275519399268
1361515.3398321980408-0.339832198040774
1371312.36453552666470.63546447333527
1381514.73118756933440.268812430665622
1391113.3700908083967-2.37009080839674
1401213.528587510131-1.52858751013101
141813.3933193556598-5.3933193556598
1421613.2460806241782.75391937582196
1431513.35707284048291.64292715951707
1441716.63659298661460.363407013385434
1451614.90538622552631.09461377447369
1461014.2727848358618-4.2727848358618
1471816.0782640145491.92173598545096
1481315.3675345703188-2.36753457031875
1491615.26181714459820.73818285540183
1501313.1415216105384-0.141521610538375
1511013.2248470878143-3.22484708781428
1521516.4250844248294-1.42508442482942
1531614.08993030766781.9100696923322
1541612.06438063440043.93561936559963
1551412.54324647661411.4567535233859
1561012.724445608073-2.72444560807304
1571717.0627760812488-0.0627760812487845
1581311.734074175921.26592582407998
1591514.07064362160110.929356378398927
1601615.15193704846920.848062951530776
1611212.4595133720894-0.459513372089375
1621312.86266657565590.137333424344077

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 13 & 16.1525953716439 & -3.15259537164395 \tabularnewline
2 & 16 & 15.8918994444254 & 0.108100555574641 \tabularnewline
3 & 19 & 16.2671968577005 & 2.73280314229953 \tabularnewline
4 & 15 & 11.7387944527417 & 3.26120554725832 \tabularnewline
5 & 14 & 15.3802100846978 & -1.38021008469782 \tabularnewline
6 & 13 & 14.8688447062277 & -1.86884470622771 \tabularnewline
7 & 19 & 15.319717135908 & 3.68028286409202 \tabularnewline
8 & 15 & 16.5972704375246 & -1.59727043752463 \tabularnewline
9 & 14 & 15.8922785169367 & -1.89227851693667 \tabularnewline
10 & 15 & 12.4359412683896 & 2.56405873161041 \tabularnewline
11 & 16 & 15.2593573105347 & 0.740642689465284 \tabularnewline
12 & 16 & 16.2942713626749 & -0.294271362674917 \tabularnewline
13 & 16 & 15.4146864439177 & 0.585313556082336 \tabularnewline
14 & 16 & 15.3500958843224 & 0.649904115677633 \tabularnewline
15 & 17 & 17.3416421558465 & -0.341642155846535 \tabularnewline
16 & 15 & 15.0867525781467 & -0.0867525781466997 \tabularnewline
17 & 15 & 14.5036403806062 & 0.496359619393762 \tabularnewline
18 & 20 & 16.1195866857086 & 3.88041331429139 \tabularnewline
19 & 18 & 15.5451121308955 & 2.4548878691045 \tabularnewline
20 & 16 & 15.3117606267314 & 0.688239373268638 \tabularnewline
21 & 16 & 15.3415580074394 & 0.658441992560599 \tabularnewline
22 & 16 & 14.8436845325457 & 1.15631546745428 \tabularnewline
23 & 19 & 16.4688383879193 & 2.53116161208072 \tabularnewline
24 & 16 & 14.8469001796353 & 1.15309982036467 \tabularnewline
25 & 17 & 14.7380975202492 & 2.26190247975083 \tabularnewline
26 & 17 & 16.7927421285517 & 0.207257871448289 \tabularnewline
27 & 16 & 14.790161126701 & 1.20983887329904 \tabularnewline
28 & 15 & 16.1360987979466 & -1.13609879794664 \tabularnewline
29 & 16 & 15.2919489991648 & 0.708051000835184 \tabularnewline
30 & 14 & 14.1266774123003 & -0.126677412300335 \tabularnewline
31 & 15 & 15.629722637378 & -0.629722637377981 \tabularnewline
32 & 12 & 12.2071480140323 & -0.207148014032253 \tabularnewline
33 & 14 & 15.1332752054516 & -1.13327520545164 \tabularnewline
34 & 16 & 15.5509727266291 & 0.449027273370886 \tabularnewline
35 & 14 & 15.4868862885187 & -1.48688628851869 \tabularnewline
36 & 7 & 12.963875817795 & -5.96387581779504 \tabularnewline
37 & 10 & 10.7468907270798 & -0.746890727079784 \tabularnewline
38 & 14 & 15.7819897418354 & -1.78198974183544 \tabularnewline
39 & 16 & 14.2178086317498 & 1.78219136825016 \tabularnewline
40 & 16 & 14.6009644194419 & 1.39903558055808 \tabularnewline
41 & 16 & 15.064630842401 & 0.935369157598975 \tabularnewline
42 & 14 & 15.466081997862 & -1.466081997862 \tabularnewline
43 & 20 & 18.0198717074181 & 1.98012829258186 \tabularnewline
44 & 14 & 14.2077721488596 & -0.20777214885962 \tabularnewline
45 & 14 & 14.9256218916482 & -0.925621891648186 \tabularnewline
46 & 11 & 15.5173294224078 & -4.51732942240782 \tabularnewline
47 & 14 & 16.638105714383 & -2.638105714383 \tabularnewline
48 & 15 & 14.9492309733893 & 0.0507690266107342 \tabularnewline
49 & 16 & 15.0173513003538 & 0.982648699646196 \tabularnewline
50 & 14 & 15.9926763292754 & -1.99267632927541 \tabularnewline
51 & 16 & 16.5111312871377 & -0.511131287137688 \tabularnewline
52 & 14 & 14.079151460186 & -0.0791514601860379 \tabularnewline
53 & 12 & 14.5974585203362 & -2.59745852033617 \tabularnewline
54 & 16 & 15.6237787299584 & 0.376221270041578 \tabularnewline
55 & 9 & 11.403766255473 & -2.40376625547295 \tabularnewline
56 & 14 & 12.6550490096134 & 1.34495099038657 \tabularnewline
57 & 16 & 16.0942371087132 & -0.0942371087132206 \tabularnewline
58 & 16 & 15.1764263829284 & 0.82357361707163 \tabularnewline
59 & 15 & 15.2483233782506 & -0.248323378250632 \tabularnewline
60 & 16 & 14.1315386470908 & 1.86846135290915 \tabularnewline
61 & 12 & 11.4023405613664 & 0.597659438633623 \tabularnewline
62 & 16 & 15.8711207432339 & 0.128879256766098 \tabularnewline
63 & 16 & 16.3513782356503 & -0.351378235650284 \tabularnewline
64 & 14 & 14.3704988406144 & -0.370498840614378 \tabularnewline
65 & 16 & 15.4990691829143 & 0.500930817085732 \tabularnewline
66 & 17 & 15.8917585694328 & 1.10824143056723 \tabularnewline
67 & 18 & 16.1829203932396 & 1.81707960676044 \tabularnewline
68 & 18 & 14.501133527781 & 3.49886647221904 \tabularnewline
69 & 12 & 15.8741968943214 & -3.87419689432137 \tabularnewline
70 & 16 & 15.4154976318277 & 0.584502368172297 \tabularnewline
71 & 10 & 13.614476244845 & -3.61447624484496 \tabularnewline
72 & 14 & 14.3510451335369 & -0.35104513353686 \tabularnewline
73 & 18 & 16.4367275746951 & 1.5632724253049 \tabularnewline
74 & 18 & 17.0646986010954 & 0.935301398904567 \tabularnewline
75 & 16 & 15.6622855707191 & 0.337714429280898 \tabularnewline
76 & 17 & 13.8482328743584 & 3.15176712564163 \tabularnewline
77 & 16 & 16.3954962790597 & -0.395496279059719 \tabularnewline
78 & 16 & 14.5407409682848 & 1.45925903171516 \tabularnewline
79 & 13 & 14.838467986537 & -1.83846798653696 \tabularnewline
80 & 16 & 16.1212628910688 & -0.1212628910688 \tabularnewline
81 & 16 & 15.4526551192896 & 0.54734488071045 \tabularnewline
82 & 20 & 15.9398993478699 & 4.06010065213015 \tabularnewline
83 & 16 & 15.8023550949253 & 0.197644905074697 \tabularnewline
84 & 15 & 15.9628756584285 & -0.962875658428485 \tabularnewline
85 & 15 & 14.7530705270142 & 0.246929472985806 \tabularnewline
86 & 16 & 14.2610532446093 & 1.73894675539069 \tabularnewline
87 & 14 & 14.1624972910409 & -0.162497291040919 \tabularnewline
88 & 16 & 15.346836205337 & 0.653163794663008 \tabularnewline
89 & 16 & 14.619492109314 & 1.38050789068595 \tabularnewline
90 & 15 & 14.1632357531487 & 0.836764246851301 \tabularnewline
91 & 12 & 13.5498069357226 & -1.54980693572261 \tabularnewline
92 & 17 & 17.0627760812488 & -0.0627760812487845 \tabularnewline
93 & 16 & 15.4082825716249 & 0.591717428375067 \tabularnewline
94 & 15 & 15.1608990598889 & -0.160899059888935 \tabularnewline
95 & 13 & 15.0861890723237 & -2.08618907232374 \tabularnewline
96 & 16 & 15.1176830233608 & 0.882316976639236 \tabularnewline
97 & 16 & 15.896540847004 & 0.103459152995973 \tabularnewline
98 & 16 & 14.101173913868 & 1.898826086132 \tabularnewline
99 & 16 & 16.0897061522287 & -0.0897061522286984 \tabularnewline
100 & 14 & 14.319262580099 & -0.319262580098991 \tabularnewline
101 & 16 & 17.3185310471496 & -1.3185310471496 \tabularnewline
102 & 16 & 14.8110485701295 & 1.18895142987048 \tabularnewline
103 & 20 & 17.4052961536916 & 2.59470384630837 \tabularnewline
104 & 15 & 14.712445514823 & 0.287554485176965 \tabularnewline
105 & 16 & 14.4519132767367 & 1.5480867232633 \tabularnewline
106 & 13 & 15.3474977916778 & -2.34749779167784 \tabularnewline
107 & 17 & 16.1116106107916 & 0.888389389208438 \tabularnewline
108 & 16 & 15.9087754052966 & 0.0912245947034114 \tabularnewline
109 & 16 & 14.6211266788934 & 1.37887332110657 \tabularnewline
110 & 12 & 12.378004281293 & -0.378004281292986 \tabularnewline
111 & 16 & 14.9278062771264 & 1.07219372287362 \tabularnewline
112 & 16 & 15.8184258920121 & 0.18157410798791 \tabularnewline
113 & 17 & 14.5597072684568 & 2.44029273154318 \tabularnewline
114 & 13 & 14.4374623417798 & -1.43746234177984 \tabularnewline
115 & 12 & 14.9781346769388 & -2.97813467693879 \tabularnewline
116 & 18 & 16.703255815935 & 1.296744184065 \tabularnewline
117 & 14 & 15.5760857568333 & -1.57608575683334 \tabularnewline
118 & 14 & 13.0625672617597 & 0.937432738240347 \tabularnewline
119 & 13 & 15.008169930299 & -2.00816993029895 \tabularnewline
120 & 16 & 15.6736663711726 & 0.326333628827435 \tabularnewline
121 & 13 & 14.3536835015023 & -1.3536835015023 \tabularnewline
122 & 16 & 15.6835094337536 & 0.316490566246352 \tabularnewline
123 & 13 & 15.9714098963118 & -2.9714098963118 \tabularnewline
124 & 16 & 17.1836760090936 & -1.18367600909362 \tabularnewline
125 & 15 & 16.1085626584401 & -1.10856265844011 \tabularnewline
126 & 16 & 16.8037657782249 & -0.803765778224931 \tabularnewline
127 & 15 & 15.519440937725 & -0.519440937725008 \tabularnewline
128 & 17 & 16.3000143408847 & 0.699985659115279 \tabularnewline
129 & 15 & 14.0706436216011 & 0.929356378398927 \tabularnewline
130 & 12 & 15.0457843241815 & -3.04578432418146 \tabularnewline
131 & 16 & 14.1810020180633 & 1.81899798193671 \tabularnewline
132 & 10 & 13.3391888223396 & -3.33918882233957 \tabularnewline
133 & 16 & 14.1282252970786 & 1.87177470292137 \tabularnewline
134 & 12 & 14.0315864701719 & -2.0315864701719 \tabularnewline
135 & 14 & 15.7827551939927 & -1.78275519399268 \tabularnewline
136 & 15 & 15.3398321980408 & -0.339832198040774 \tabularnewline
137 & 13 & 12.3645355266647 & 0.63546447333527 \tabularnewline
138 & 15 & 14.7311875693344 & 0.268812430665622 \tabularnewline
139 & 11 & 13.3700908083967 & -2.37009080839674 \tabularnewline
140 & 12 & 13.528587510131 & -1.52858751013101 \tabularnewline
141 & 8 & 13.3933193556598 & -5.3933193556598 \tabularnewline
142 & 16 & 13.246080624178 & 2.75391937582196 \tabularnewline
143 & 15 & 13.3570728404829 & 1.64292715951707 \tabularnewline
144 & 17 & 16.6365929866146 & 0.363407013385434 \tabularnewline
145 & 16 & 14.9053862255263 & 1.09461377447369 \tabularnewline
146 & 10 & 14.2727848358618 & -4.2727848358618 \tabularnewline
147 & 18 & 16.078264014549 & 1.92173598545096 \tabularnewline
148 & 13 & 15.3675345703188 & -2.36753457031875 \tabularnewline
149 & 16 & 15.2618171445982 & 0.73818285540183 \tabularnewline
150 & 13 & 13.1415216105384 & -0.141521610538375 \tabularnewline
151 & 10 & 13.2248470878143 & -3.22484708781428 \tabularnewline
152 & 15 & 16.4250844248294 & -1.42508442482942 \tabularnewline
153 & 16 & 14.0899303076678 & 1.9100696923322 \tabularnewline
154 & 16 & 12.0643806344004 & 3.93561936559963 \tabularnewline
155 & 14 & 12.5432464766141 & 1.4567535233859 \tabularnewline
156 & 10 & 12.724445608073 & -2.72444560807304 \tabularnewline
157 & 17 & 17.0627760812488 & -0.0627760812487845 \tabularnewline
158 & 13 & 11.73407417592 & 1.26592582407998 \tabularnewline
159 & 15 & 14.0706436216011 & 0.929356378398927 \tabularnewline
160 & 16 & 15.1519370484692 & 0.848062951530776 \tabularnewline
161 & 12 & 12.4595133720894 & -0.459513372089375 \tabularnewline
162 & 13 & 12.8626665756559 & 0.137333424344077 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186194&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]13[/C][C]16.1525953716439[/C][C]-3.15259537164395[/C][/ROW]
[ROW][C]2[/C][C]16[/C][C]15.8918994444254[/C][C]0.108100555574641[/C][/ROW]
[ROW][C]3[/C][C]19[/C][C]16.2671968577005[/C][C]2.73280314229953[/C][/ROW]
[ROW][C]4[/C][C]15[/C][C]11.7387944527417[/C][C]3.26120554725832[/C][/ROW]
[ROW][C]5[/C][C]14[/C][C]15.3802100846978[/C][C]-1.38021008469782[/C][/ROW]
[ROW][C]6[/C][C]13[/C][C]14.8688447062277[/C][C]-1.86884470622771[/C][/ROW]
[ROW][C]7[/C][C]19[/C][C]15.319717135908[/C][C]3.68028286409202[/C][/ROW]
[ROW][C]8[/C][C]15[/C][C]16.5972704375246[/C][C]-1.59727043752463[/C][/ROW]
[ROW][C]9[/C][C]14[/C][C]15.8922785169367[/C][C]-1.89227851693667[/C][/ROW]
[ROW][C]10[/C][C]15[/C][C]12.4359412683896[/C][C]2.56405873161041[/C][/ROW]
[ROW][C]11[/C][C]16[/C][C]15.2593573105347[/C][C]0.740642689465284[/C][/ROW]
[ROW][C]12[/C][C]16[/C][C]16.2942713626749[/C][C]-0.294271362674917[/C][/ROW]
[ROW][C]13[/C][C]16[/C][C]15.4146864439177[/C][C]0.585313556082336[/C][/ROW]
[ROW][C]14[/C][C]16[/C][C]15.3500958843224[/C][C]0.649904115677633[/C][/ROW]
[ROW][C]15[/C][C]17[/C][C]17.3416421558465[/C][C]-0.341642155846535[/C][/ROW]
[ROW][C]16[/C][C]15[/C][C]15.0867525781467[/C][C]-0.0867525781466997[/C][/ROW]
[ROW][C]17[/C][C]15[/C][C]14.5036403806062[/C][C]0.496359619393762[/C][/ROW]
[ROW][C]18[/C][C]20[/C][C]16.1195866857086[/C][C]3.88041331429139[/C][/ROW]
[ROW][C]19[/C][C]18[/C][C]15.5451121308955[/C][C]2.4548878691045[/C][/ROW]
[ROW][C]20[/C][C]16[/C][C]15.3117606267314[/C][C]0.688239373268638[/C][/ROW]
[ROW][C]21[/C][C]16[/C][C]15.3415580074394[/C][C]0.658441992560599[/C][/ROW]
[ROW][C]22[/C][C]16[/C][C]14.8436845325457[/C][C]1.15631546745428[/C][/ROW]
[ROW][C]23[/C][C]19[/C][C]16.4688383879193[/C][C]2.53116161208072[/C][/ROW]
[ROW][C]24[/C][C]16[/C][C]14.8469001796353[/C][C]1.15309982036467[/C][/ROW]
[ROW][C]25[/C][C]17[/C][C]14.7380975202492[/C][C]2.26190247975083[/C][/ROW]
[ROW][C]26[/C][C]17[/C][C]16.7927421285517[/C][C]0.207257871448289[/C][/ROW]
[ROW][C]27[/C][C]16[/C][C]14.790161126701[/C][C]1.20983887329904[/C][/ROW]
[ROW][C]28[/C][C]15[/C][C]16.1360987979466[/C][C]-1.13609879794664[/C][/ROW]
[ROW][C]29[/C][C]16[/C][C]15.2919489991648[/C][C]0.708051000835184[/C][/ROW]
[ROW][C]30[/C][C]14[/C][C]14.1266774123003[/C][C]-0.126677412300335[/C][/ROW]
[ROW][C]31[/C][C]15[/C][C]15.629722637378[/C][C]-0.629722637377981[/C][/ROW]
[ROW][C]32[/C][C]12[/C][C]12.2071480140323[/C][C]-0.207148014032253[/C][/ROW]
[ROW][C]33[/C][C]14[/C][C]15.1332752054516[/C][C]-1.13327520545164[/C][/ROW]
[ROW][C]34[/C][C]16[/C][C]15.5509727266291[/C][C]0.449027273370886[/C][/ROW]
[ROW][C]35[/C][C]14[/C][C]15.4868862885187[/C][C]-1.48688628851869[/C][/ROW]
[ROW][C]36[/C][C]7[/C][C]12.963875817795[/C][C]-5.96387581779504[/C][/ROW]
[ROW][C]37[/C][C]10[/C][C]10.7468907270798[/C][C]-0.746890727079784[/C][/ROW]
[ROW][C]38[/C][C]14[/C][C]15.7819897418354[/C][C]-1.78198974183544[/C][/ROW]
[ROW][C]39[/C][C]16[/C][C]14.2178086317498[/C][C]1.78219136825016[/C][/ROW]
[ROW][C]40[/C][C]16[/C][C]14.6009644194419[/C][C]1.39903558055808[/C][/ROW]
[ROW][C]41[/C][C]16[/C][C]15.064630842401[/C][C]0.935369157598975[/C][/ROW]
[ROW][C]42[/C][C]14[/C][C]15.466081997862[/C][C]-1.466081997862[/C][/ROW]
[ROW][C]43[/C][C]20[/C][C]18.0198717074181[/C][C]1.98012829258186[/C][/ROW]
[ROW][C]44[/C][C]14[/C][C]14.2077721488596[/C][C]-0.20777214885962[/C][/ROW]
[ROW][C]45[/C][C]14[/C][C]14.9256218916482[/C][C]-0.925621891648186[/C][/ROW]
[ROW][C]46[/C][C]11[/C][C]15.5173294224078[/C][C]-4.51732942240782[/C][/ROW]
[ROW][C]47[/C][C]14[/C][C]16.638105714383[/C][C]-2.638105714383[/C][/ROW]
[ROW][C]48[/C][C]15[/C][C]14.9492309733893[/C][C]0.0507690266107342[/C][/ROW]
[ROW][C]49[/C][C]16[/C][C]15.0173513003538[/C][C]0.982648699646196[/C][/ROW]
[ROW][C]50[/C][C]14[/C][C]15.9926763292754[/C][C]-1.99267632927541[/C][/ROW]
[ROW][C]51[/C][C]16[/C][C]16.5111312871377[/C][C]-0.511131287137688[/C][/ROW]
[ROW][C]52[/C][C]14[/C][C]14.079151460186[/C][C]-0.0791514601860379[/C][/ROW]
[ROW][C]53[/C][C]12[/C][C]14.5974585203362[/C][C]-2.59745852033617[/C][/ROW]
[ROW][C]54[/C][C]16[/C][C]15.6237787299584[/C][C]0.376221270041578[/C][/ROW]
[ROW][C]55[/C][C]9[/C][C]11.403766255473[/C][C]-2.40376625547295[/C][/ROW]
[ROW][C]56[/C][C]14[/C][C]12.6550490096134[/C][C]1.34495099038657[/C][/ROW]
[ROW][C]57[/C][C]16[/C][C]16.0942371087132[/C][C]-0.0942371087132206[/C][/ROW]
[ROW][C]58[/C][C]16[/C][C]15.1764263829284[/C][C]0.82357361707163[/C][/ROW]
[ROW][C]59[/C][C]15[/C][C]15.2483233782506[/C][C]-0.248323378250632[/C][/ROW]
[ROW][C]60[/C][C]16[/C][C]14.1315386470908[/C][C]1.86846135290915[/C][/ROW]
[ROW][C]61[/C][C]12[/C][C]11.4023405613664[/C][C]0.597659438633623[/C][/ROW]
[ROW][C]62[/C][C]16[/C][C]15.8711207432339[/C][C]0.128879256766098[/C][/ROW]
[ROW][C]63[/C][C]16[/C][C]16.3513782356503[/C][C]-0.351378235650284[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]14.3704988406144[/C][C]-0.370498840614378[/C][/ROW]
[ROW][C]65[/C][C]16[/C][C]15.4990691829143[/C][C]0.500930817085732[/C][/ROW]
[ROW][C]66[/C][C]17[/C][C]15.8917585694328[/C][C]1.10824143056723[/C][/ROW]
[ROW][C]67[/C][C]18[/C][C]16.1829203932396[/C][C]1.81707960676044[/C][/ROW]
[ROW][C]68[/C][C]18[/C][C]14.501133527781[/C][C]3.49886647221904[/C][/ROW]
[ROW][C]69[/C][C]12[/C][C]15.8741968943214[/C][C]-3.87419689432137[/C][/ROW]
[ROW][C]70[/C][C]16[/C][C]15.4154976318277[/C][C]0.584502368172297[/C][/ROW]
[ROW][C]71[/C][C]10[/C][C]13.614476244845[/C][C]-3.61447624484496[/C][/ROW]
[ROW][C]72[/C][C]14[/C][C]14.3510451335369[/C][C]-0.35104513353686[/C][/ROW]
[ROW][C]73[/C][C]18[/C][C]16.4367275746951[/C][C]1.5632724253049[/C][/ROW]
[ROW][C]74[/C][C]18[/C][C]17.0646986010954[/C][C]0.935301398904567[/C][/ROW]
[ROW][C]75[/C][C]16[/C][C]15.6622855707191[/C][C]0.337714429280898[/C][/ROW]
[ROW][C]76[/C][C]17[/C][C]13.8482328743584[/C][C]3.15176712564163[/C][/ROW]
[ROW][C]77[/C][C]16[/C][C]16.3954962790597[/C][C]-0.395496279059719[/C][/ROW]
[ROW][C]78[/C][C]16[/C][C]14.5407409682848[/C][C]1.45925903171516[/C][/ROW]
[ROW][C]79[/C][C]13[/C][C]14.838467986537[/C][C]-1.83846798653696[/C][/ROW]
[ROW][C]80[/C][C]16[/C][C]16.1212628910688[/C][C]-0.1212628910688[/C][/ROW]
[ROW][C]81[/C][C]16[/C][C]15.4526551192896[/C][C]0.54734488071045[/C][/ROW]
[ROW][C]82[/C][C]20[/C][C]15.9398993478699[/C][C]4.06010065213015[/C][/ROW]
[ROW][C]83[/C][C]16[/C][C]15.8023550949253[/C][C]0.197644905074697[/C][/ROW]
[ROW][C]84[/C][C]15[/C][C]15.9628756584285[/C][C]-0.962875658428485[/C][/ROW]
[ROW][C]85[/C][C]15[/C][C]14.7530705270142[/C][C]0.246929472985806[/C][/ROW]
[ROW][C]86[/C][C]16[/C][C]14.2610532446093[/C][C]1.73894675539069[/C][/ROW]
[ROW][C]87[/C][C]14[/C][C]14.1624972910409[/C][C]-0.162497291040919[/C][/ROW]
[ROW][C]88[/C][C]16[/C][C]15.346836205337[/C][C]0.653163794663008[/C][/ROW]
[ROW][C]89[/C][C]16[/C][C]14.619492109314[/C][C]1.38050789068595[/C][/ROW]
[ROW][C]90[/C][C]15[/C][C]14.1632357531487[/C][C]0.836764246851301[/C][/ROW]
[ROW][C]91[/C][C]12[/C][C]13.5498069357226[/C][C]-1.54980693572261[/C][/ROW]
[ROW][C]92[/C][C]17[/C][C]17.0627760812488[/C][C]-0.0627760812487845[/C][/ROW]
[ROW][C]93[/C][C]16[/C][C]15.4082825716249[/C][C]0.591717428375067[/C][/ROW]
[ROW][C]94[/C][C]15[/C][C]15.1608990598889[/C][C]-0.160899059888935[/C][/ROW]
[ROW][C]95[/C][C]13[/C][C]15.0861890723237[/C][C]-2.08618907232374[/C][/ROW]
[ROW][C]96[/C][C]16[/C][C]15.1176830233608[/C][C]0.882316976639236[/C][/ROW]
[ROW][C]97[/C][C]16[/C][C]15.896540847004[/C][C]0.103459152995973[/C][/ROW]
[ROW][C]98[/C][C]16[/C][C]14.101173913868[/C][C]1.898826086132[/C][/ROW]
[ROW][C]99[/C][C]16[/C][C]16.0897061522287[/C][C]-0.0897061522286984[/C][/ROW]
[ROW][C]100[/C][C]14[/C][C]14.319262580099[/C][C]-0.319262580098991[/C][/ROW]
[ROW][C]101[/C][C]16[/C][C]17.3185310471496[/C][C]-1.3185310471496[/C][/ROW]
[ROW][C]102[/C][C]16[/C][C]14.8110485701295[/C][C]1.18895142987048[/C][/ROW]
[ROW][C]103[/C][C]20[/C][C]17.4052961536916[/C][C]2.59470384630837[/C][/ROW]
[ROW][C]104[/C][C]15[/C][C]14.712445514823[/C][C]0.287554485176965[/C][/ROW]
[ROW][C]105[/C][C]16[/C][C]14.4519132767367[/C][C]1.5480867232633[/C][/ROW]
[ROW][C]106[/C][C]13[/C][C]15.3474977916778[/C][C]-2.34749779167784[/C][/ROW]
[ROW][C]107[/C][C]17[/C][C]16.1116106107916[/C][C]0.888389389208438[/C][/ROW]
[ROW][C]108[/C][C]16[/C][C]15.9087754052966[/C][C]0.0912245947034114[/C][/ROW]
[ROW][C]109[/C][C]16[/C][C]14.6211266788934[/C][C]1.37887332110657[/C][/ROW]
[ROW][C]110[/C][C]12[/C][C]12.378004281293[/C][C]-0.378004281292986[/C][/ROW]
[ROW][C]111[/C][C]16[/C][C]14.9278062771264[/C][C]1.07219372287362[/C][/ROW]
[ROW][C]112[/C][C]16[/C][C]15.8184258920121[/C][C]0.18157410798791[/C][/ROW]
[ROW][C]113[/C][C]17[/C][C]14.5597072684568[/C][C]2.44029273154318[/C][/ROW]
[ROW][C]114[/C][C]13[/C][C]14.4374623417798[/C][C]-1.43746234177984[/C][/ROW]
[ROW][C]115[/C][C]12[/C][C]14.9781346769388[/C][C]-2.97813467693879[/C][/ROW]
[ROW][C]116[/C][C]18[/C][C]16.703255815935[/C][C]1.296744184065[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]15.5760857568333[/C][C]-1.57608575683334[/C][/ROW]
[ROW][C]118[/C][C]14[/C][C]13.0625672617597[/C][C]0.937432738240347[/C][/ROW]
[ROW][C]119[/C][C]13[/C][C]15.008169930299[/C][C]-2.00816993029895[/C][/ROW]
[ROW][C]120[/C][C]16[/C][C]15.6736663711726[/C][C]0.326333628827435[/C][/ROW]
[ROW][C]121[/C][C]13[/C][C]14.3536835015023[/C][C]-1.3536835015023[/C][/ROW]
[ROW][C]122[/C][C]16[/C][C]15.6835094337536[/C][C]0.316490566246352[/C][/ROW]
[ROW][C]123[/C][C]13[/C][C]15.9714098963118[/C][C]-2.9714098963118[/C][/ROW]
[ROW][C]124[/C][C]16[/C][C]17.1836760090936[/C][C]-1.18367600909362[/C][/ROW]
[ROW][C]125[/C][C]15[/C][C]16.1085626584401[/C][C]-1.10856265844011[/C][/ROW]
[ROW][C]126[/C][C]16[/C][C]16.8037657782249[/C][C]-0.803765778224931[/C][/ROW]
[ROW][C]127[/C][C]15[/C][C]15.519440937725[/C][C]-0.519440937725008[/C][/ROW]
[ROW][C]128[/C][C]17[/C][C]16.3000143408847[/C][C]0.699985659115279[/C][/ROW]
[ROW][C]129[/C][C]15[/C][C]14.0706436216011[/C][C]0.929356378398927[/C][/ROW]
[ROW][C]130[/C][C]12[/C][C]15.0457843241815[/C][C]-3.04578432418146[/C][/ROW]
[ROW][C]131[/C][C]16[/C][C]14.1810020180633[/C][C]1.81899798193671[/C][/ROW]
[ROW][C]132[/C][C]10[/C][C]13.3391888223396[/C][C]-3.33918882233957[/C][/ROW]
[ROW][C]133[/C][C]16[/C][C]14.1282252970786[/C][C]1.87177470292137[/C][/ROW]
[ROW][C]134[/C][C]12[/C][C]14.0315864701719[/C][C]-2.0315864701719[/C][/ROW]
[ROW][C]135[/C][C]14[/C][C]15.7827551939927[/C][C]-1.78275519399268[/C][/ROW]
[ROW][C]136[/C][C]15[/C][C]15.3398321980408[/C][C]-0.339832198040774[/C][/ROW]
[ROW][C]137[/C][C]13[/C][C]12.3645355266647[/C][C]0.63546447333527[/C][/ROW]
[ROW][C]138[/C][C]15[/C][C]14.7311875693344[/C][C]0.268812430665622[/C][/ROW]
[ROW][C]139[/C][C]11[/C][C]13.3700908083967[/C][C]-2.37009080839674[/C][/ROW]
[ROW][C]140[/C][C]12[/C][C]13.528587510131[/C][C]-1.52858751013101[/C][/ROW]
[ROW][C]141[/C][C]8[/C][C]13.3933193556598[/C][C]-5.3933193556598[/C][/ROW]
[ROW][C]142[/C][C]16[/C][C]13.246080624178[/C][C]2.75391937582196[/C][/ROW]
[ROW][C]143[/C][C]15[/C][C]13.3570728404829[/C][C]1.64292715951707[/C][/ROW]
[ROW][C]144[/C][C]17[/C][C]16.6365929866146[/C][C]0.363407013385434[/C][/ROW]
[ROW][C]145[/C][C]16[/C][C]14.9053862255263[/C][C]1.09461377447369[/C][/ROW]
[ROW][C]146[/C][C]10[/C][C]14.2727848358618[/C][C]-4.2727848358618[/C][/ROW]
[ROW][C]147[/C][C]18[/C][C]16.078264014549[/C][C]1.92173598545096[/C][/ROW]
[ROW][C]148[/C][C]13[/C][C]15.3675345703188[/C][C]-2.36753457031875[/C][/ROW]
[ROW][C]149[/C][C]16[/C][C]15.2618171445982[/C][C]0.73818285540183[/C][/ROW]
[ROW][C]150[/C][C]13[/C][C]13.1415216105384[/C][C]-0.141521610538375[/C][/ROW]
[ROW][C]151[/C][C]10[/C][C]13.2248470878143[/C][C]-3.22484708781428[/C][/ROW]
[ROW][C]152[/C][C]15[/C][C]16.4250844248294[/C][C]-1.42508442482942[/C][/ROW]
[ROW][C]153[/C][C]16[/C][C]14.0899303076678[/C][C]1.9100696923322[/C][/ROW]
[ROW][C]154[/C][C]16[/C][C]12.0643806344004[/C][C]3.93561936559963[/C][/ROW]
[ROW][C]155[/C][C]14[/C][C]12.5432464766141[/C][C]1.4567535233859[/C][/ROW]
[ROW][C]156[/C][C]10[/C][C]12.724445608073[/C][C]-2.72444560807304[/C][/ROW]
[ROW][C]157[/C][C]17[/C][C]17.0627760812488[/C][C]-0.0627760812487845[/C][/ROW]
[ROW][C]158[/C][C]13[/C][C]11.73407417592[/C][C]1.26592582407998[/C][/ROW]
[ROW][C]159[/C][C]15[/C][C]14.0706436216011[/C][C]0.929356378398927[/C][/ROW]
[ROW][C]160[/C][C]16[/C][C]15.1519370484692[/C][C]0.848062951530776[/C][/ROW]
[ROW][C]161[/C][C]12[/C][C]12.4595133720894[/C][C]-0.459513372089375[/C][/ROW]
[ROW][C]162[/C][C]13[/C][C]12.8626665756559[/C][C]0.137333424344077[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186194&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186194&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
11316.1525953716439-3.15259537164395
21615.89189944442540.108100555574641
31916.26719685770052.73280314229953
41511.73879445274173.26120554725832
51415.3802100846978-1.38021008469782
61314.8688447062277-1.86884470622771
71915.3197171359083.68028286409202
81516.5972704375246-1.59727043752463
91415.8922785169367-1.89227851693667
101512.43594126838962.56405873161041
111615.25935731053470.740642689465284
121616.2942713626749-0.294271362674917
131615.41468644391770.585313556082336
141615.35009588432240.649904115677633
151717.3416421558465-0.341642155846535
161515.0867525781467-0.0867525781466997
171514.50364038060620.496359619393762
182016.11958668570863.88041331429139
191815.54511213089552.4548878691045
201615.31176062673140.688239373268638
211615.34155800743940.658441992560599
221614.84368453254571.15631546745428
231916.46883838791932.53116161208072
241614.84690017963531.15309982036467
251714.73809752024922.26190247975083
261716.79274212855170.207257871448289
271614.7901611267011.20983887329904
281516.1360987979466-1.13609879794664
291615.29194899916480.708051000835184
301414.1266774123003-0.126677412300335
311515.629722637378-0.629722637377981
321212.2071480140323-0.207148014032253
331415.1332752054516-1.13327520545164
341615.55097272662910.449027273370886
351415.4868862885187-1.48688628851869
36712.963875817795-5.96387581779504
371010.7468907270798-0.746890727079784
381415.7819897418354-1.78198974183544
391614.21780863174981.78219136825016
401614.60096441944191.39903558055808
411615.0646308424010.935369157598975
421415.466081997862-1.466081997862
432018.01987170741811.98012829258186
441414.2077721488596-0.20777214885962
451414.9256218916482-0.925621891648186
461115.5173294224078-4.51732942240782
471416.638105714383-2.638105714383
481514.94923097338930.0507690266107342
491615.01735130035380.982648699646196
501415.9926763292754-1.99267632927541
511616.5111312871377-0.511131287137688
521414.079151460186-0.0791514601860379
531214.5974585203362-2.59745852033617
541615.62377872995840.376221270041578
55911.403766255473-2.40376625547295
561412.65504900961341.34495099038657
571616.0942371087132-0.0942371087132206
581615.17642638292840.82357361707163
591515.2483233782506-0.248323378250632
601614.13153864709081.86846135290915
611211.40234056136640.597659438633623
621615.87112074323390.128879256766098
631616.3513782356503-0.351378235650284
641414.3704988406144-0.370498840614378
651615.49906918291430.500930817085732
661715.89175856943281.10824143056723
671816.18292039323961.81707960676044
681814.5011335277813.49886647221904
691215.8741968943214-3.87419689432137
701615.41549763182770.584502368172297
711013.614476244845-3.61447624484496
721414.3510451335369-0.35104513353686
731816.43672757469511.5632724253049
741817.06469860109540.935301398904567
751615.66228557071910.337714429280898
761713.84823287435843.15176712564163
771616.3954962790597-0.395496279059719
781614.54074096828481.45925903171516
791314.838467986537-1.83846798653696
801616.1212628910688-0.1212628910688
811615.45265511928960.54734488071045
822015.93989934786994.06010065213015
831615.80235509492530.197644905074697
841515.9628756584285-0.962875658428485
851514.75307052701420.246929472985806
861614.26105324460931.73894675539069
871414.1624972910409-0.162497291040919
881615.3468362053370.653163794663008
891614.6194921093141.38050789068595
901514.16323575314870.836764246851301
911213.5498069357226-1.54980693572261
921717.0627760812488-0.0627760812487845
931615.40828257162490.591717428375067
941515.1608990598889-0.160899059888935
951315.0861890723237-2.08618907232374
961615.11768302336080.882316976639236
971615.8965408470040.103459152995973
981614.1011739138681.898826086132
991616.0897061522287-0.0897061522286984
1001414.319262580099-0.319262580098991
1011617.3185310471496-1.3185310471496
1021614.81104857012951.18895142987048
1032017.40529615369162.59470384630837
1041514.7124455148230.287554485176965
1051614.45191327673671.5480867232633
1061315.3474977916778-2.34749779167784
1071716.11161061079160.888389389208438
1081615.90877540529660.0912245947034114
1091614.62112667889341.37887332110657
1101212.378004281293-0.378004281292986
1111614.92780627712641.07219372287362
1121615.81842589201210.18157410798791
1131714.55970726845682.44029273154318
1141314.4374623417798-1.43746234177984
1151214.9781346769388-2.97813467693879
1161816.7032558159351.296744184065
1171415.5760857568333-1.57608575683334
1181413.06256726175970.937432738240347
1191315.008169930299-2.00816993029895
1201615.67366637117260.326333628827435
1211314.3536835015023-1.3536835015023
1221615.68350943375360.316490566246352
1231315.9714098963118-2.9714098963118
1241617.1836760090936-1.18367600909362
1251516.1085626584401-1.10856265844011
1261616.8037657782249-0.803765778224931
1271515.519440937725-0.519440937725008
1281716.30001434088470.699985659115279
1291514.07064362160110.929356378398927
1301215.0457843241815-3.04578432418146
1311614.18100201806331.81899798193671
1321013.3391888223396-3.33918882233957
1331614.12822529707861.87177470292137
1341214.0315864701719-2.0315864701719
1351415.7827551939927-1.78275519399268
1361515.3398321980408-0.339832198040774
1371312.36453552666470.63546447333527
1381514.73118756933440.268812430665622
1391113.3700908083967-2.37009080839674
1401213.528587510131-1.52858751013101
141813.3933193556598-5.3933193556598
1421613.2460806241782.75391937582196
1431513.35707284048291.64292715951707
1441716.63659298661460.363407013385434
1451614.90538622552631.09461377447369
1461014.2727848358618-4.2727848358618
1471816.0782640145491.92173598545096
1481315.3675345703188-2.36753457031875
1491615.26181714459820.73818285540183
1501313.1415216105384-0.141521610538375
1511013.2248470878143-3.22484708781428
1521516.4250844248294-1.42508442482942
1531614.08993030766781.9100696923322
1541612.06438063440043.93561936559963
1551412.54324647661411.4567535233859
1561012.724445608073-2.72444560807304
1571717.0627760812488-0.0627760812487845
1581311.734074175921.26592582407998
1591514.07064362160110.929356378398927
1601615.15193704846920.848062951530776
1611212.4595133720894-0.459513372089375
1621312.86266657565590.137333424344077







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.4117989383137390.8235978766274780.588201061686261
110.2842826481475140.5685652962950290.715717351852486
120.8394576071028950.321084785794210.160542392897105
130.7661402161979380.4677195676041250.233859783802062
140.7024440145619460.5951119708761090.297555985438054
150.7161001019130870.5677997961738250.283899898086912
160.6833947687732950.633210462453410.316605231226705
170.602299687783520.795400624432960.39770031221648
180.8843994831153770.2312010337692450.115600516884623
190.8898310802948480.2203378394103050.110168919705152
200.851742174824980.2965156503500410.14825782517502
210.8099506971857670.3800986056284660.190049302814233
220.7562316916008950.487536616798210.243768308399105
230.7761828082647810.4476343834704380.223817191735219
240.7263882245136450.5472235509727110.273611775486355
250.711129228286890.577741543426220.28887077171311
260.6519987327962070.6960025344075860.348001267203793
270.592516278930260.814967442139480.40748372106974
280.5672476113577530.8655047772844930.432752388642247
290.5028747172809010.9942505654381970.497125282719099
300.4802960051928260.9605920103856510.519703994807174
310.4200387949517030.8400775899034070.579961205048297
320.3996093423491120.7992186846982240.600390657650888
330.375524581823390.7510491636467790.62447541817661
340.3191248714029990.6382497428059990.680875128597001
350.3036969783444440.6073939566888880.696303021655556
360.8282056741882830.3435886516234330.171794325811717
370.8179241954247970.3641516091504060.182075804575203
380.8161861200545970.3676277598908060.183813879945403
390.8345178790237490.3309642419525010.165482120976251
400.8153517827849160.3692964344301680.184648217215084
410.7849295067865890.4301409864268210.215070493213411
420.7716997132797410.4566005734405190.228300286720259
430.7809470396685670.4381059206628650.219052960331433
440.7445676900258050.510864619948390.255432309974195
450.714569541526080.5708609169478390.28543045847392
460.8960130151602980.2079739696794040.103986984839702
470.9181297500077110.1637404999845790.0818702499922895
480.8970134844795730.2059730310408530.102986515520427
490.8765232065103790.2469535869792410.123476793489621
500.8776497940785460.2447004118429090.122350205921454
510.8514883161949840.2970233676100310.148511683805016
520.8204824429245430.3590351141509140.179517557075457
530.8470778157087560.3058443685824890.152922184291244
540.8170682212228660.3658635575542680.182931778777134
550.8349076930697170.3301846138605660.165092306930283
560.8150740277075510.3698519445848980.184925972292449
570.7823347873956760.4353304252086490.217665212604324
580.7590433176135470.4819133647729060.240956682386453
590.7206734796101660.5586530407796690.279326520389834
600.7163692090889320.5672615818221360.283630790911068
610.6784981269107390.6430037461785210.321501873089261
620.6341979654174130.7316040691651740.365802034582587
630.5899820064597390.8200359870805220.410017993540261
640.5445720093710050.9108559812579890.455427990628995
650.5012952685445070.9974094629109860.498704731455493
660.4710740531533970.9421481063067940.528925946846603
670.4626635807593350.9253271615186710.537336419240665
680.5892642484559070.8214715030881850.410735751544093
690.7275859977091130.5448280045817740.272414002290887
700.6908095009078250.618380998184350.309190499092175
710.7890583356323140.4218833287353710.210941664367686
720.7542715186971540.4914569626056920.245728481302846
730.7424847394189420.5150305211621160.257515260581058
740.7155339142297690.5689321715404620.284466085770231
750.6756655803993360.6486688392013270.324334419600664
760.7393819875209760.5212360249580490.260618012479024
770.7020370210189730.5959259579620540.297962978981027
780.6858212171029170.6283575657941650.314178782897083
790.6885250407219890.6229499185560230.311474959278011
800.6464206626588910.7071586746822190.353579337341109
810.6057211921175560.7885576157648880.394278807882444
820.771470499092080.457059001815840.22852950090792
830.7354472023649970.5291055952700070.264552797635003
840.7056447776653170.5887104446693660.294355222334683
850.6650225922724730.6699548154550540.334977407727527
860.6592959786776190.6814080426447620.340704021322381
870.6155096398023850.768980720395230.384490360197615
880.5764516485748830.8470967028502340.423548351425117
890.5596926660186590.8806146679626820.440307333981341
900.5262770852715040.9474458294569910.473722914728496
910.5099358243539810.9801283512920380.490064175646019
920.4721333146793210.9442666293586420.527866685320679
930.4308511543152290.8617023086304580.569148845684771
940.3867251713534570.7734503427069150.613274828646543
950.3997811253189010.7995622506378010.600218874681099
960.366063369906940.7321267398138790.63393663009306
970.3273952887992460.6547905775984910.672604711200754
980.3287808122126560.6575616244253130.671219187787344
990.2871906570341820.5743813140683640.712809342965818
1000.2499385408861130.4998770817722260.750061459113887
1010.2263156589155870.4526313178311750.773684341084413
1020.2109405087160880.4218810174321760.789059491283912
1030.2578036557136660.5156073114273320.742196344286334
1040.2212439934455630.4424879868911260.778756006554437
1050.2161624382345920.4323248764691850.783837561765408
1060.2274301687101140.4548603374202280.772569831289886
1070.2061070954374250.412214190874850.793892904562575
1080.1843964847385660.3687929694771320.815603515261434
1090.1779848497046430.3559696994092870.822015150295357
1100.1684850172684120.3369700345368240.831514982731588
1110.150239580593380.300479161186760.84976041940662
1120.1259658819378240.2519317638756480.874034118062176
1130.1457364263799490.2914728527598990.854263573620051
1140.1266929949137280.2533859898274570.873307005086271
1150.1599157359682130.3198314719364260.840084264031787
1160.1514010279841680.3028020559683370.848598972015832
1170.1313979207115350.2627958414230690.868602079288465
1180.1138506177543120.2277012355086240.886149382245688
1190.1176009141922980.2352018283845950.882399085807702
1200.1106393509032840.2212787018065680.889360649096716
1210.09365159437632410.1873031887526480.906348405623676
1220.07560006288143240.1512001257628650.924399937118568
1230.08417026820967070.1683405364193410.915829731790329
1240.06847620243932780.1369524048786560.931523797560672
1250.05592659533400860.1118531906680170.944073404665991
1260.04280570319530490.08561140639060980.957194296804695
1270.03251415997340040.06502831994680080.9674858400266
1280.02680762499264080.05361524998528160.973192375007359
1290.02083675794866030.04167351589732060.97916324205134
1300.02822872772562310.05645745545124610.971771272274377
1310.02442982875864230.04885965751728470.975570171241358
1320.03590857158858170.07181714317716340.964091428411418
1330.04001263727462890.08002527454925770.959987362725371
1340.05109564972760940.1021912994552190.948904350272391
1350.04103907664256230.08207815328512460.958960923357438
1360.02884803658692180.05769607317384370.971151963413078
1370.01980623411614030.03961246823228060.98019376588386
1380.01324879346829930.02649758693659870.986751206531701
1390.0241352826221850.048270565244370.975864717377815
1400.02094691629299830.04189383258599650.979053083707002
1410.5345635383124470.9308729233751070.465436461687553
1420.4734716873548140.9469433747096290.526528312645186
1430.4095938355858230.8191876711716460.590406164414177
1440.3517879570871390.7035759141742770.648212042912861
1450.2945808092671120.5891616185342240.705419190732888
1460.2950195224801510.5900390449603020.704980477519849
1470.3231902027568990.6463804055137990.676809797243101
1480.7930089530006570.4139820939986860.206991046999343
1490.7350377002221170.5299245995557650.264962299777883
1500.6161461711313410.7677076577373170.383853828868659
1510.8474374792064430.3051250415871140.152562520793557
1520.8480305265628350.3039389468743310.151969473437165

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.411798938313739 & 0.823597876627478 & 0.588201061686261 \tabularnewline
11 & 0.284282648147514 & 0.568565296295029 & 0.715717351852486 \tabularnewline
12 & 0.839457607102895 & 0.32108478579421 & 0.160542392897105 \tabularnewline
13 & 0.766140216197938 & 0.467719567604125 & 0.233859783802062 \tabularnewline
14 & 0.702444014561946 & 0.595111970876109 & 0.297555985438054 \tabularnewline
15 & 0.716100101913087 & 0.567799796173825 & 0.283899898086912 \tabularnewline
16 & 0.683394768773295 & 0.63321046245341 & 0.316605231226705 \tabularnewline
17 & 0.60229968778352 & 0.79540062443296 & 0.39770031221648 \tabularnewline
18 & 0.884399483115377 & 0.231201033769245 & 0.115600516884623 \tabularnewline
19 & 0.889831080294848 & 0.220337839410305 & 0.110168919705152 \tabularnewline
20 & 0.85174217482498 & 0.296515650350041 & 0.14825782517502 \tabularnewline
21 & 0.809950697185767 & 0.380098605628466 & 0.190049302814233 \tabularnewline
22 & 0.756231691600895 & 0.48753661679821 & 0.243768308399105 \tabularnewline
23 & 0.776182808264781 & 0.447634383470438 & 0.223817191735219 \tabularnewline
24 & 0.726388224513645 & 0.547223550972711 & 0.273611775486355 \tabularnewline
25 & 0.71112922828689 & 0.57774154342622 & 0.28887077171311 \tabularnewline
26 & 0.651998732796207 & 0.696002534407586 & 0.348001267203793 \tabularnewline
27 & 0.59251627893026 & 0.81496744213948 & 0.40748372106974 \tabularnewline
28 & 0.567247611357753 & 0.865504777284493 & 0.432752388642247 \tabularnewline
29 & 0.502874717280901 & 0.994250565438197 & 0.497125282719099 \tabularnewline
30 & 0.480296005192826 & 0.960592010385651 & 0.519703994807174 \tabularnewline
31 & 0.420038794951703 & 0.840077589903407 & 0.579961205048297 \tabularnewline
32 & 0.399609342349112 & 0.799218684698224 & 0.600390657650888 \tabularnewline
33 & 0.37552458182339 & 0.751049163646779 & 0.62447541817661 \tabularnewline
34 & 0.319124871402999 & 0.638249742805999 & 0.680875128597001 \tabularnewline
35 & 0.303696978344444 & 0.607393956688888 & 0.696303021655556 \tabularnewline
36 & 0.828205674188283 & 0.343588651623433 & 0.171794325811717 \tabularnewline
37 & 0.817924195424797 & 0.364151609150406 & 0.182075804575203 \tabularnewline
38 & 0.816186120054597 & 0.367627759890806 & 0.183813879945403 \tabularnewline
39 & 0.834517879023749 & 0.330964241952501 & 0.165482120976251 \tabularnewline
40 & 0.815351782784916 & 0.369296434430168 & 0.184648217215084 \tabularnewline
41 & 0.784929506786589 & 0.430140986426821 & 0.215070493213411 \tabularnewline
42 & 0.771699713279741 & 0.456600573440519 & 0.228300286720259 \tabularnewline
43 & 0.780947039668567 & 0.438105920662865 & 0.219052960331433 \tabularnewline
44 & 0.744567690025805 & 0.51086461994839 & 0.255432309974195 \tabularnewline
45 & 0.71456954152608 & 0.570860916947839 & 0.28543045847392 \tabularnewline
46 & 0.896013015160298 & 0.207973969679404 & 0.103986984839702 \tabularnewline
47 & 0.918129750007711 & 0.163740499984579 & 0.0818702499922895 \tabularnewline
48 & 0.897013484479573 & 0.205973031040853 & 0.102986515520427 \tabularnewline
49 & 0.876523206510379 & 0.246953586979241 & 0.123476793489621 \tabularnewline
50 & 0.877649794078546 & 0.244700411842909 & 0.122350205921454 \tabularnewline
51 & 0.851488316194984 & 0.297023367610031 & 0.148511683805016 \tabularnewline
52 & 0.820482442924543 & 0.359035114150914 & 0.179517557075457 \tabularnewline
53 & 0.847077815708756 & 0.305844368582489 & 0.152922184291244 \tabularnewline
54 & 0.817068221222866 & 0.365863557554268 & 0.182931778777134 \tabularnewline
55 & 0.834907693069717 & 0.330184613860566 & 0.165092306930283 \tabularnewline
56 & 0.815074027707551 & 0.369851944584898 & 0.184925972292449 \tabularnewline
57 & 0.782334787395676 & 0.435330425208649 & 0.217665212604324 \tabularnewline
58 & 0.759043317613547 & 0.481913364772906 & 0.240956682386453 \tabularnewline
59 & 0.720673479610166 & 0.558653040779669 & 0.279326520389834 \tabularnewline
60 & 0.716369209088932 & 0.567261581822136 & 0.283630790911068 \tabularnewline
61 & 0.678498126910739 & 0.643003746178521 & 0.321501873089261 \tabularnewline
62 & 0.634197965417413 & 0.731604069165174 & 0.365802034582587 \tabularnewline
63 & 0.589982006459739 & 0.820035987080522 & 0.410017993540261 \tabularnewline
64 & 0.544572009371005 & 0.910855981257989 & 0.455427990628995 \tabularnewline
65 & 0.501295268544507 & 0.997409462910986 & 0.498704731455493 \tabularnewline
66 & 0.471074053153397 & 0.942148106306794 & 0.528925946846603 \tabularnewline
67 & 0.462663580759335 & 0.925327161518671 & 0.537336419240665 \tabularnewline
68 & 0.589264248455907 & 0.821471503088185 & 0.410735751544093 \tabularnewline
69 & 0.727585997709113 & 0.544828004581774 & 0.272414002290887 \tabularnewline
70 & 0.690809500907825 & 0.61838099818435 & 0.309190499092175 \tabularnewline
71 & 0.789058335632314 & 0.421883328735371 & 0.210941664367686 \tabularnewline
72 & 0.754271518697154 & 0.491456962605692 & 0.245728481302846 \tabularnewline
73 & 0.742484739418942 & 0.515030521162116 & 0.257515260581058 \tabularnewline
74 & 0.715533914229769 & 0.568932171540462 & 0.284466085770231 \tabularnewline
75 & 0.675665580399336 & 0.648668839201327 & 0.324334419600664 \tabularnewline
76 & 0.739381987520976 & 0.521236024958049 & 0.260618012479024 \tabularnewline
77 & 0.702037021018973 & 0.595925957962054 & 0.297962978981027 \tabularnewline
78 & 0.685821217102917 & 0.628357565794165 & 0.314178782897083 \tabularnewline
79 & 0.688525040721989 & 0.622949918556023 & 0.311474959278011 \tabularnewline
80 & 0.646420662658891 & 0.707158674682219 & 0.353579337341109 \tabularnewline
81 & 0.605721192117556 & 0.788557615764888 & 0.394278807882444 \tabularnewline
82 & 0.77147049909208 & 0.45705900181584 & 0.22852950090792 \tabularnewline
83 & 0.735447202364997 & 0.529105595270007 & 0.264552797635003 \tabularnewline
84 & 0.705644777665317 & 0.588710444669366 & 0.294355222334683 \tabularnewline
85 & 0.665022592272473 & 0.669954815455054 & 0.334977407727527 \tabularnewline
86 & 0.659295978677619 & 0.681408042644762 & 0.340704021322381 \tabularnewline
87 & 0.615509639802385 & 0.76898072039523 & 0.384490360197615 \tabularnewline
88 & 0.576451648574883 & 0.847096702850234 & 0.423548351425117 \tabularnewline
89 & 0.559692666018659 & 0.880614667962682 & 0.440307333981341 \tabularnewline
90 & 0.526277085271504 & 0.947445829456991 & 0.473722914728496 \tabularnewline
91 & 0.509935824353981 & 0.980128351292038 & 0.490064175646019 \tabularnewline
92 & 0.472133314679321 & 0.944266629358642 & 0.527866685320679 \tabularnewline
93 & 0.430851154315229 & 0.861702308630458 & 0.569148845684771 \tabularnewline
94 & 0.386725171353457 & 0.773450342706915 & 0.613274828646543 \tabularnewline
95 & 0.399781125318901 & 0.799562250637801 & 0.600218874681099 \tabularnewline
96 & 0.36606336990694 & 0.732126739813879 & 0.63393663009306 \tabularnewline
97 & 0.327395288799246 & 0.654790577598491 & 0.672604711200754 \tabularnewline
98 & 0.328780812212656 & 0.657561624425313 & 0.671219187787344 \tabularnewline
99 & 0.287190657034182 & 0.574381314068364 & 0.712809342965818 \tabularnewline
100 & 0.249938540886113 & 0.499877081772226 & 0.750061459113887 \tabularnewline
101 & 0.226315658915587 & 0.452631317831175 & 0.773684341084413 \tabularnewline
102 & 0.210940508716088 & 0.421881017432176 & 0.789059491283912 \tabularnewline
103 & 0.257803655713666 & 0.515607311427332 & 0.742196344286334 \tabularnewline
104 & 0.221243993445563 & 0.442487986891126 & 0.778756006554437 \tabularnewline
105 & 0.216162438234592 & 0.432324876469185 & 0.783837561765408 \tabularnewline
106 & 0.227430168710114 & 0.454860337420228 & 0.772569831289886 \tabularnewline
107 & 0.206107095437425 & 0.41221419087485 & 0.793892904562575 \tabularnewline
108 & 0.184396484738566 & 0.368792969477132 & 0.815603515261434 \tabularnewline
109 & 0.177984849704643 & 0.355969699409287 & 0.822015150295357 \tabularnewline
110 & 0.168485017268412 & 0.336970034536824 & 0.831514982731588 \tabularnewline
111 & 0.15023958059338 & 0.30047916118676 & 0.84976041940662 \tabularnewline
112 & 0.125965881937824 & 0.251931763875648 & 0.874034118062176 \tabularnewline
113 & 0.145736426379949 & 0.291472852759899 & 0.854263573620051 \tabularnewline
114 & 0.126692994913728 & 0.253385989827457 & 0.873307005086271 \tabularnewline
115 & 0.159915735968213 & 0.319831471936426 & 0.840084264031787 \tabularnewline
116 & 0.151401027984168 & 0.302802055968337 & 0.848598972015832 \tabularnewline
117 & 0.131397920711535 & 0.262795841423069 & 0.868602079288465 \tabularnewline
118 & 0.113850617754312 & 0.227701235508624 & 0.886149382245688 \tabularnewline
119 & 0.117600914192298 & 0.235201828384595 & 0.882399085807702 \tabularnewline
120 & 0.110639350903284 & 0.221278701806568 & 0.889360649096716 \tabularnewline
121 & 0.0936515943763241 & 0.187303188752648 & 0.906348405623676 \tabularnewline
122 & 0.0756000628814324 & 0.151200125762865 & 0.924399937118568 \tabularnewline
123 & 0.0841702682096707 & 0.168340536419341 & 0.915829731790329 \tabularnewline
124 & 0.0684762024393278 & 0.136952404878656 & 0.931523797560672 \tabularnewline
125 & 0.0559265953340086 & 0.111853190668017 & 0.944073404665991 \tabularnewline
126 & 0.0428057031953049 & 0.0856114063906098 & 0.957194296804695 \tabularnewline
127 & 0.0325141599734004 & 0.0650283199468008 & 0.9674858400266 \tabularnewline
128 & 0.0268076249926408 & 0.0536152499852816 & 0.973192375007359 \tabularnewline
129 & 0.0208367579486603 & 0.0416735158973206 & 0.97916324205134 \tabularnewline
130 & 0.0282287277256231 & 0.0564574554512461 & 0.971771272274377 \tabularnewline
131 & 0.0244298287586423 & 0.0488596575172847 & 0.975570171241358 \tabularnewline
132 & 0.0359085715885817 & 0.0718171431771634 & 0.964091428411418 \tabularnewline
133 & 0.0400126372746289 & 0.0800252745492577 & 0.959987362725371 \tabularnewline
134 & 0.0510956497276094 & 0.102191299455219 & 0.948904350272391 \tabularnewline
135 & 0.0410390766425623 & 0.0820781532851246 & 0.958960923357438 \tabularnewline
136 & 0.0288480365869218 & 0.0576960731738437 & 0.971151963413078 \tabularnewline
137 & 0.0198062341161403 & 0.0396124682322806 & 0.98019376588386 \tabularnewline
138 & 0.0132487934682993 & 0.0264975869365987 & 0.986751206531701 \tabularnewline
139 & 0.024135282622185 & 0.04827056524437 & 0.975864717377815 \tabularnewline
140 & 0.0209469162929983 & 0.0418938325859965 & 0.979053083707002 \tabularnewline
141 & 0.534563538312447 & 0.930872923375107 & 0.465436461687553 \tabularnewline
142 & 0.473471687354814 & 0.946943374709629 & 0.526528312645186 \tabularnewline
143 & 0.409593835585823 & 0.819187671171646 & 0.590406164414177 \tabularnewline
144 & 0.351787957087139 & 0.703575914174277 & 0.648212042912861 \tabularnewline
145 & 0.294580809267112 & 0.589161618534224 & 0.705419190732888 \tabularnewline
146 & 0.295019522480151 & 0.590039044960302 & 0.704980477519849 \tabularnewline
147 & 0.323190202756899 & 0.646380405513799 & 0.676809797243101 \tabularnewline
148 & 0.793008953000657 & 0.413982093998686 & 0.206991046999343 \tabularnewline
149 & 0.735037700222117 & 0.529924599555765 & 0.264962299777883 \tabularnewline
150 & 0.616146171131341 & 0.767707657737317 & 0.383853828868659 \tabularnewline
151 & 0.847437479206443 & 0.305125041587114 & 0.152562520793557 \tabularnewline
152 & 0.848030526562835 & 0.303938946874331 & 0.151969473437165 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186194&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]0.411798938313739[/C][C]0.823597876627478[/C][C]0.588201061686261[/C][/ROW]
[ROW][C]11[/C][C]0.284282648147514[/C][C]0.568565296295029[/C][C]0.715717351852486[/C][/ROW]
[ROW][C]12[/C][C]0.839457607102895[/C][C]0.32108478579421[/C][C]0.160542392897105[/C][/ROW]
[ROW][C]13[/C][C]0.766140216197938[/C][C]0.467719567604125[/C][C]0.233859783802062[/C][/ROW]
[ROW][C]14[/C][C]0.702444014561946[/C][C]0.595111970876109[/C][C]0.297555985438054[/C][/ROW]
[ROW][C]15[/C][C]0.716100101913087[/C][C]0.567799796173825[/C][C]0.283899898086912[/C][/ROW]
[ROW][C]16[/C][C]0.683394768773295[/C][C]0.63321046245341[/C][C]0.316605231226705[/C][/ROW]
[ROW][C]17[/C][C]0.60229968778352[/C][C]0.79540062443296[/C][C]0.39770031221648[/C][/ROW]
[ROW][C]18[/C][C]0.884399483115377[/C][C]0.231201033769245[/C][C]0.115600516884623[/C][/ROW]
[ROW][C]19[/C][C]0.889831080294848[/C][C]0.220337839410305[/C][C]0.110168919705152[/C][/ROW]
[ROW][C]20[/C][C]0.85174217482498[/C][C]0.296515650350041[/C][C]0.14825782517502[/C][/ROW]
[ROW][C]21[/C][C]0.809950697185767[/C][C]0.380098605628466[/C][C]0.190049302814233[/C][/ROW]
[ROW][C]22[/C][C]0.756231691600895[/C][C]0.48753661679821[/C][C]0.243768308399105[/C][/ROW]
[ROW][C]23[/C][C]0.776182808264781[/C][C]0.447634383470438[/C][C]0.223817191735219[/C][/ROW]
[ROW][C]24[/C][C]0.726388224513645[/C][C]0.547223550972711[/C][C]0.273611775486355[/C][/ROW]
[ROW][C]25[/C][C]0.71112922828689[/C][C]0.57774154342622[/C][C]0.28887077171311[/C][/ROW]
[ROW][C]26[/C][C]0.651998732796207[/C][C]0.696002534407586[/C][C]0.348001267203793[/C][/ROW]
[ROW][C]27[/C][C]0.59251627893026[/C][C]0.81496744213948[/C][C]0.40748372106974[/C][/ROW]
[ROW][C]28[/C][C]0.567247611357753[/C][C]0.865504777284493[/C][C]0.432752388642247[/C][/ROW]
[ROW][C]29[/C][C]0.502874717280901[/C][C]0.994250565438197[/C][C]0.497125282719099[/C][/ROW]
[ROW][C]30[/C][C]0.480296005192826[/C][C]0.960592010385651[/C][C]0.519703994807174[/C][/ROW]
[ROW][C]31[/C][C]0.420038794951703[/C][C]0.840077589903407[/C][C]0.579961205048297[/C][/ROW]
[ROW][C]32[/C][C]0.399609342349112[/C][C]0.799218684698224[/C][C]0.600390657650888[/C][/ROW]
[ROW][C]33[/C][C]0.37552458182339[/C][C]0.751049163646779[/C][C]0.62447541817661[/C][/ROW]
[ROW][C]34[/C][C]0.319124871402999[/C][C]0.638249742805999[/C][C]0.680875128597001[/C][/ROW]
[ROW][C]35[/C][C]0.303696978344444[/C][C]0.607393956688888[/C][C]0.696303021655556[/C][/ROW]
[ROW][C]36[/C][C]0.828205674188283[/C][C]0.343588651623433[/C][C]0.171794325811717[/C][/ROW]
[ROW][C]37[/C][C]0.817924195424797[/C][C]0.364151609150406[/C][C]0.182075804575203[/C][/ROW]
[ROW][C]38[/C][C]0.816186120054597[/C][C]0.367627759890806[/C][C]0.183813879945403[/C][/ROW]
[ROW][C]39[/C][C]0.834517879023749[/C][C]0.330964241952501[/C][C]0.165482120976251[/C][/ROW]
[ROW][C]40[/C][C]0.815351782784916[/C][C]0.369296434430168[/C][C]0.184648217215084[/C][/ROW]
[ROW][C]41[/C][C]0.784929506786589[/C][C]0.430140986426821[/C][C]0.215070493213411[/C][/ROW]
[ROW][C]42[/C][C]0.771699713279741[/C][C]0.456600573440519[/C][C]0.228300286720259[/C][/ROW]
[ROW][C]43[/C][C]0.780947039668567[/C][C]0.438105920662865[/C][C]0.219052960331433[/C][/ROW]
[ROW][C]44[/C][C]0.744567690025805[/C][C]0.51086461994839[/C][C]0.255432309974195[/C][/ROW]
[ROW][C]45[/C][C]0.71456954152608[/C][C]0.570860916947839[/C][C]0.28543045847392[/C][/ROW]
[ROW][C]46[/C][C]0.896013015160298[/C][C]0.207973969679404[/C][C]0.103986984839702[/C][/ROW]
[ROW][C]47[/C][C]0.918129750007711[/C][C]0.163740499984579[/C][C]0.0818702499922895[/C][/ROW]
[ROW][C]48[/C][C]0.897013484479573[/C][C]0.205973031040853[/C][C]0.102986515520427[/C][/ROW]
[ROW][C]49[/C][C]0.876523206510379[/C][C]0.246953586979241[/C][C]0.123476793489621[/C][/ROW]
[ROW][C]50[/C][C]0.877649794078546[/C][C]0.244700411842909[/C][C]0.122350205921454[/C][/ROW]
[ROW][C]51[/C][C]0.851488316194984[/C][C]0.297023367610031[/C][C]0.148511683805016[/C][/ROW]
[ROW][C]52[/C][C]0.820482442924543[/C][C]0.359035114150914[/C][C]0.179517557075457[/C][/ROW]
[ROW][C]53[/C][C]0.847077815708756[/C][C]0.305844368582489[/C][C]0.152922184291244[/C][/ROW]
[ROW][C]54[/C][C]0.817068221222866[/C][C]0.365863557554268[/C][C]0.182931778777134[/C][/ROW]
[ROW][C]55[/C][C]0.834907693069717[/C][C]0.330184613860566[/C][C]0.165092306930283[/C][/ROW]
[ROW][C]56[/C][C]0.815074027707551[/C][C]0.369851944584898[/C][C]0.184925972292449[/C][/ROW]
[ROW][C]57[/C][C]0.782334787395676[/C][C]0.435330425208649[/C][C]0.217665212604324[/C][/ROW]
[ROW][C]58[/C][C]0.759043317613547[/C][C]0.481913364772906[/C][C]0.240956682386453[/C][/ROW]
[ROW][C]59[/C][C]0.720673479610166[/C][C]0.558653040779669[/C][C]0.279326520389834[/C][/ROW]
[ROW][C]60[/C][C]0.716369209088932[/C][C]0.567261581822136[/C][C]0.283630790911068[/C][/ROW]
[ROW][C]61[/C][C]0.678498126910739[/C][C]0.643003746178521[/C][C]0.321501873089261[/C][/ROW]
[ROW][C]62[/C][C]0.634197965417413[/C][C]0.731604069165174[/C][C]0.365802034582587[/C][/ROW]
[ROW][C]63[/C][C]0.589982006459739[/C][C]0.820035987080522[/C][C]0.410017993540261[/C][/ROW]
[ROW][C]64[/C][C]0.544572009371005[/C][C]0.910855981257989[/C][C]0.455427990628995[/C][/ROW]
[ROW][C]65[/C][C]0.501295268544507[/C][C]0.997409462910986[/C][C]0.498704731455493[/C][/ROW]
[ROW][C]66[/C][C]0.471074053153397[/C][C]0.942148106306794[/C][C]0.528925946846603[/C][/ROW]
[ROW][C]67[/C][C]0.462663580759335[/C][C]0.925327161518671[/C][C]0.537336419240665[/C][/ROW]
[ROW][C]68[/C][C]0.589264248455907[/C][C]0.821471503088185[/C][C]0.410735751544093[/C][/ROW]
[ROW][C]69[/C][C]0.727585997709113[/C][C]0.544828004581774[/C][C]0.272414002290887[/C][/ROW]
[ROW][C]70[/C][C]0.690809500907825[/C][C]0.61838099818435[/C][C]0.309190499092175[/C][/ROW]
[ROW][C]71[/C][C]0.789058335632314[/C][C]0.421883328735371[/C][C]0.210941664367686[/C][/ROW]
[ROW][C]72[/C][C]0.754271518697154[/C][C]0.491456962605692[/C][C]0.245728481302846[/C][/ROW]
[ROW][C]73[/C][C]0.742484739418942[/C][C]0.515030521162116[/C][C]0.257515260581058[/C][/ROW]
[ROW][C]74[/C][C]0.715533914229769[/C][C]0.568932171540462[/C][C]0.284466085770231[/C][/ROW]
[ROW][C]75[/C][C]0.675665580399336[/C][C]0.648668839201327[/C][C]0.324334419600664[/C][/ROW]
[ROW][C]76[/C][C]0.739381987520976[/C][C]0.521236024958049[/C][C]0.260618012479024[/C][/ROW]
[ROW][C]77[/C][C]0.702037021018973[/C][C]0.595925957962054[/C][C]0.297962978981027[/C][/ROW]
[ROW][C]78[/C][C]0.685821217102917[/C][C]0.628357565794165[/C][C]0.314178782897083[/C][/ROW]
[ROW][C]79[/C][C]0.688525040721989[/C][C]0.622949918556023[/C][C]0.311474959278011[/C][/ROW]
[ROW][C]80[/C][C]0.646420662658891[/C][C]0.707158674682219[/C][C]0.353579337341109[/C][/ROW]
[ROW][C]81[/C][C]0.605721192117556[/C][C]0.788557615764888[/C][C]0.394278807882444[/C][/ROW]
[ROW][C]82[/C][C]0.77147049909208[/C][C]0.45705900181584[/C][C]0.22852950090792[/C][/ROW]
[ROW][C]83[/C][C]0.735447202364997[/C][C]0.529105595270007[/C][C]0.264552797635003[/C][/ROW]
[ROW][C]84[/C][C]0.705644777665317[/C][C]0.588710444669366[/C][C]0.294355222334683[/C][/ROW]
[ROW][C]85[/C][C]0.665022592272473[/C][C]0.669954815455054[/C][C]0.334977407727527[/C][/ROW]
[ROW][C]86[/C][C]0.659295978677619[/C][C]0.681408042644762[/C][C]0.340704021322381[/C][/ROW]
[ROW][C]87[/C][C]0.615509639802385[/C][C]0.76898072039523[/C][C]0.384490360197615[/C][/ROW]
[ROW][C]88[/C][C]0.576451648574883[/C][C]0.847096702850234[/C][C]0.423548351425117[/C][/ROW]
[ROW][C]89[/C][C]0.559692666018659[/C][C]0.880614667962682[/C][C]0.440307333981341[/C][/ROW]
[ROW][C]90[/C][C]0.526277085271504[/C][C]0.947445829456991[/C][C]0.473722914728496[/C][/ROW]
[ROW][C]91[/C][C]0.509935824353981[/C][C]0.980128351292038[/C][C]0.490064175646019[/C][/ROW]
[ROW][C]92[/C][C]0.472133314679321[/C][C]0.944266629358642[/C][C]0.527866685320679[/C][/ROW]
[ROW][C]93[/C][C]0.430851154315229[/C][C]0.861702308630458[/C][C]0.569148845684771[/C][/ROW]
[ROW][C]94[/C][C]0.386725171353457[/C][C]0.773450342706915[/C][C]0.613274828646543[/C][/ROW]
[ROW][C]95[/C][C]0.399781125318901[/C][C]0.799562250637801[/C][C]0.600218874681099[/C][/ROW]
[ROW][C]96[/C][C]0.36606336990694[/C][C]0.732126739813879[/C][C]0.63393663009306[/C][/ROW]
[ROW][C]97[/C][C]0.327395288799246[/C][C]0.654790577598491[/C][C]0.672604711200754[/C][/ROW]
[ROW][C]98[/C][C]0.328780812212656[/C][C]0.657561624425313[/C][C]0.671219187787344[/C][/ROW]
[ROW][C]99[/C][C]0.287190657034182[/C][C]0.574381314068364[/C][C]0.712809342965818[/C][/ROW]
[ROW][C]100[/C][C]0.249938540886113[/C][C]0.499877081772226[/C][C]0.750061459113887[/C][/ROW]
[ROW][C]101[/C][C]0.226315658915587[/C][C]0.452631317831175[/C][C]0.773684341084413[/C][/ROW]
[ROW][C]102[/C][C]0.210940508716088[/C][C]0.421881017432176[/C][C]0.789059491283912[/C][/ROW]
[ROW][C]103[/C][C]0.257803655713666[/C][C]0.515607311427332[/C][C]0.742196344286334[/C][/ROW]
[ROW][C]104[/C][C]0.221243993445563[/C][C]0.442487986891126[/C][C]0.778756006554437[/C][/ROW]
[ROW][C]105[/C][C]0.216162438234592[/C][C]0.432324876469185[/C][C]0.783837561765408[/C][/ROW]
[ROW][C]106[/C][C]0.227430168710114[/C][C]0.454860337420228[/C][C]0.772569831289886[/C][/ROW]
[ROW][C]107[/C][C]0.206107095437425[/C][C]0.41221419087485[/C][C]0.793892904562575[/C][/ROW]
[ROW][C]108[/C][C]0.184396484738566[/C][C]0.368792969477132[/C][C]0.815603515261434[/C][/ROW]
[ROW][C]109[/C][C]0.177984849704643[/C][C]0.355969699409287[/C][C]0.822015150295357[/C][/ROW]
[ROW][C]110[/C][C]0.168485017268412[/C][C]0.336970034536824[/C][C]0.831514982731588[/C][/ROW]
[ROW][C]111[/C][C]0.15023958059338[/C][C]0.30047916118676[/C][C]0.84976041940662[/C][/ROW]
[ROW][C]112[/C][C]0.125965881937824[/C][C]0.251931763875648[/C][C]0.874034118062176[/C][/ROW]
[ROW][C]113[/C][C]0.145736426379949[/C][C]0.291472852759899[/C][C]0.854263573620051[/C][/ROW]
[ROW][C]114[/C][C]0.126692994913728[/C][C]0.253385989827457[/C][C]0.873307005086271[/C][/ROW]
[ROW][C]115[/C][C]0.159915735968213[/C][C]0.319831471936426[/C][C]0.840084264031787[/C][/ROW]
[ROW][C]116[/C][C]0.151401027984168[/C][C]0.302802055968337[/C][C]0.848598972015832[/C][/ROW]
[ROW][C]117[/C][C]0.131397920711535[/C][C]0.262795841423069[/C][C]0.868602079288465[/C][/ROW]
[ROW][C]118[/C][C]0.113850617754312[/C][C]0.227701235508624[/C][C]0.886149382245688[/C][/ROW]
[ROW][C]119[/C][C]0.117600914192298[/C][C]0.235201828384595[/C][C]0.882399085807702[/C][/ROW]
[ROW][C]120[/C][C]0.110639350903284[/C][C]0.221278701806568[/C][C]0.889360649096716[/C][/ROW]
[ROW][C]121[/C][C]0.0936515943763241[/C][C]0.187303188752648[/C][C]0.906348405623676[/C][/ROW]
[ROW][C]122[/C][C]0.0756000628814324[/C][C]0.151200125762865[/C][C]0.924399937118568[/C][/ROW]
[ROW][C]123[/C][C]0.0841702682096707[/C][C]0.168340536419341[/C][C]0.915829731790329[/C][/ROW]
[ROW][C]124[/C][C]0.0684762024393278[/C][C]0.136952404878656[/C][C]0.931523797560672[/C][/ROW]
[ROW][C]125[/C][C]0.0559265953340086[/C][C]0.111853190668017[/C][C]0.944073404665991[/C][/ROW]
[ROW][C]126[/C][C]0.0428057031953049[/C][C]0.0856114063906098[/C][C]0.957194296804695[/C][/ROW]
[ROW][C]127[/C][C]0.0325141599734004[/C][C]0.0650283199468008[/C][C]0.9674858400266[/C][/ROW]
[ROW][C]128[/C][C]0.0268076249926408[/C][C]0.0536152499852816[/C][C]0.973192375007359[/C][/ROW]
[ROW][C]129[/C][C]0.0208367579486603[/C][C]0.0416735158973206[/C][C]0.97916324205134[/C][/ROW]
[ROW][C]130[/C][C]0.0282287277256231[/C][C]0.0564574554512461[/C][C]0.971771272274377[/C][/ROW]
[ROW][C]131[/C][C]0.0244298287586423[/C][C]0.0488596575172847[/C][C]0.975570171241358[/C][/ROW]
[ROW][C]132[/C][C]0.0359085715885817[/C][C]0.0718171431771634[/C][C]0.964091428411418[/C][/ROW]
[ROW][C]133[/C][C]0.0400126372746289[/C][C]0.0800252745492577[/C][C]0.959987362725371[/C][/ROW]
[ROW][C]134[/C][C]0.0510956497276094[/C][C]0.102191299455219[/C][C]0.948904350272391[/C][/ROW]
[ROW][C]135[/C][C]0.0410390766425623[/C][C]0.0820781532851246[/C][C]0.958960923357438[/C][/ROW]
[ROW][C]136[/C][C]0.0288480365869218[/C][C]0.0576960731738437[/C][C]0.971151963413078[/C][/ROW]
[ROW][C]137[/C][C]0.0198062341161403[/C][C]0.0396124682322806[/C][C]0.98019376588386[/C][/ROW]
[ROW][C]138[/C][C]0.0132487934682993[/C][C]0.0264975869365987[/C][C]0.986751206531701[/C][/ROW]
[ROW][C]139[/C][C]0.024135282622185[/C][C]0.04827056524437[/C][C]0.975864717377815[/C][/ROW]
[ROW][C]140[/C][C]0.0209469162929983[/C][C]0.0418938325859965[/C][C]0.979053083707002[/C][/ROW]
[ROW][C]141[/C][C]0.534563538312447[/C][C]0.930872923375107[/C][C]0.465436461687553[/C][/ROW]
[ROW][C]142[/C][C]0.473471687354814[/C][C]0.946943374709629[/C][C]0.526528312645186[/C][/ROW]
[ROW][C]143[/C][C]0.409593835585823[/C][C]0.819187671171646[/C][C]0.590406164414177[/C][/ROW]
[ROW][C]144[/C][C]0.351787957087139[/C][C]0.703575914174277[/C][C]0.648212042912861[/C][/ROW]
[ROW][C]145[/C][C]0.294580809267112[/C][C]0.589161618534224[/C][C]0.705419190732888[/C][/ROW]
[ROW][C]146[/C][C]0.295019522480151[/C][C]0.590039044960302[/C][C]0.704980477519849[/C][/ROW]
[ROW][C]147[/C][C]0.323190202756899[/C][C]0.646380405513799[/C][C]0.676809797243101[/C][/ROW]
[ROW][C]148[/C][C]0.793008953000657[/C][C]0.413982093998686[/C][C]0.206991046999343[/C][/ROW]
[ROW][C]149[/C][C]0.735037700222117[/C][C]0.529924599555765[/C][C]0.264962299777883[/C][/ROW]
[ROW][C]150[/C][C]0.616146171131341[/C][C]0.767707657737317[/C][C]0.383853828868659[/C][/ROW]
[ROW][C]151[/C][C]0.847437479206443[/C][C]0.305125041587114[/C][C]0.152562520793557[/C][/ROW]
[ROW][C]152[/C][C]0.848030526562835[/C][C]0.303938946874331[/C][C]0.151969473437165[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186194&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186194&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
100.4117989383137390.8235978766274780.588201061686261
110.2842826481475140.5685652962950290.715717351852486
120.8394576071028950.321084785794210.160542392897105
130.7661402161979380.4677195676041250.233859783802062
140.7024440145619460.5951119708761090.297555985438054
150.7161001019130870.5677997961738250.283899898086912
160.6833947687732950.633210462453410.316605231226705
170.602299687783520.795400624432960.39770031221648
180.8843994831153770.2312010337692450.115600516884623
190.8898310802948480.2203378394103050.110168919705152
200.851742174824980.2965156503500410.14825782517502
210.8099506971857670.3800986056284660.190049302814233
220.7562316916008950.487536616798210.243768308399105
230.7761828082647810.4476343834704380.223817191735219
240.7263882245136450.5472235509727110.273611775486355
250.711129228286890.577741543426220.28887077171311
260.6519987327962070.6960025344075860.348001267203793
270.592516278930260.814967442139480.40748372106974
280.5672476113577530.8655047772844930.432752388642247
290.5028747172809010.9942505654381970.497125282719099
300.4802960051928260.9605920103856510.519703994807174
310.4200387949517030.8400775899034070.579961205048297
320.3996093423491120.7992186846982240.600390657650888
330.375524581823390.7510491636467790.62447541817661
340.3191248714029990.6382497428059990.680875128597001
350.3036969783444440.6073939566888880.696303021655556
360.8282056741882830.3435886516234330.171794325811717
370.8179241954247970.3641516091504060.182075804575203
380.8161861200545970.3676277598908060.183813879945403
390.8345178790237490.3309642419525010.165482120976251
400.8153517827849160.3692964344301680.184648217215084
410.7849295067865890.4301409864268210.215070493213411
420.7716997132797410.4566005734405190.228300286720259
430.7809470396685670.4381059206628650.219052960331433
440.7445676900258050.510864619948390.255432309974195
450.714569541526080.5708609169478390.28543045847392
460.8960130151602980.2079739696794040.103986984839702
470.9181297500077110.1637404999845790.0818702499922895
480.8970134844795730.2059730310408530.102986515520427
490.8765232065103790.2469535869792410.123476793489621
500.8776497940785460.2447004118429090.122350205921454
510.8514883161949840.2970233676100310.148511683805016
520.8204824429245430.3590351141509140.179517557075457
530.8470778157087560.3058443685824890.152922184291244
540.8170682212228660.3658635575542680.182931778777134
550.8349076930697170.3301846138605660.165092306930283
560.8150740277075510.3698519445848980.184925972292449
570.7823347873956760.4353304252086490.217665212604324
580.7590433176135470.4819133647729060.240956682386453
590.7206734796101660.5586530407796690.279326520389834
600.7163692090889320.5672615818221360.283630790911068
610.6784981269107390.6430037461785210.321501873089261
620.6341979654174130.7316040691651740.365802034582587
630.5899820064597390.8200359870805220.410017993540261
640.5445720093710050.9108559812579890.455427990628995
650.5012952685445070.9974094629109860.498704731455493
660.4710740531533970.9421481063067940.528925946846603
670.4626635807593350.9253271615186710.537336419240665
680.5892642484559070.8214715030881850.410735751544093
690.7275859977091130.5448280045817740.272414002290887
700.6908095009078250.618380998184350.309190499092175
710.7890583356323140.4218833287353710.210941664367686
720.7542715186971540.4914569626056920.245728481302846
730.7424847394189420.5150305211621160.257515260581058
740.7155339142297690.5689321715404620.284466085770231
750.6756655803993360.6486688392013270.324334419600664
760.7393819875209760.5212360249580490.260618012479024
770.7020370210189730.5959259579620540.297962978981027
780.6858212171029170.6283575657941650.314178782897083
790.6885250407219890.6229499185560230.311474959278011
800.6464206626588910.7071586746822190.353579337341109
810.6057211921175560.7885576157648880.394278807882444
820.771470499092080.457059001815840.22852950090792
830.7354472023649970.5291055952700070.264552797635003
840.7056447776653170.5887104446693660.294355222334683
850.6650225922724730.6699548154550540.334977407727527
860.6592959786776190.6814080426447620.340704021322381
870.6155096398023850.768980720395230.384490360197615
880.5764516485748830.8470967028502340.423548351425117
890.5596926660186590.8806146679626820.440307333981341
900.5262770852715040.9474458294569910.473722914728496
910.5099358243539810.9801283512920380.490064175646019
920.4721333146793210.9442666293586420.527866685320679
930.4308511543152290.8617023086304580.569148845684771
940.3867251713534570.7734503427069150.613274828646543
950.3997811253189010.7995622506378010.600218874681099
960.366063369906940.7321267398138790.63393663009306
970.3273952887992460.6547905775984910.672604711200754
980.3287808122126560.6575616244253130.671219187787344
990.2871906570341820.5743813140683640.712809342965818
1000.2499385408861130.4998770817722260.750061459113887
1010.2263156589155870.4526313178311750.773684341084413
1020.2109405087160880.4218810174321760.789059491283912
1030.2578036557136660.5156073114273320.742196344286334
1040.2212439934455630.4424879868911260.778756006554437
1050.2161624382345920.4323248764691850.783837561765408
1060.2274301687101140.4548603374202280.772569831289886
1070.2061070954374250.412214190874850.793892904562575
1080.1843964847385660.3687929694771320.815603515261434
1090.1779848497046430.3559696994092870.822015150295357
1100.1684850172684120.3369700345368240.831514982731588
1110.150239580593380.300479161186760.84976041940662
1120.1259658819378240.2519317638756480.874034118062176
1130.1457364263799490.2914728527598990.854263573620051
1140.1266929949137280.2533859898274570.873307005086271
1150.1599157359682130.3198314719364260.840084264031787
1160.1514010279841680.3028020559683370.848598972015832
1170.1313979207115350.2627958414230690.868602079288465
1180.1138506177543120.2277012355086240.886149382245688
1190.1176009141922980.2352018283845950.882399085807702
1200.1106393509032840.2212787018065680.889360649096716
1210.09365159437632410.1873031887526480.906348405623676
1220.07560006288143240.1512001257628650.924399937118568
1230.08417026820967070.1683405364193410.915829731790329
1240.06847620243932780.1369524048786560.931523797560672
1250.05592659533400860.1118531906680170.944073404665991
1260.04280570319530490.08561140639060980.957194296804695
1270.03251415997340040.06502831994680080.9674858400266
1280.02680762499264080.05361524998528160.973192375007359
1290.02083675794866030.04167351589732060.97916324205134
1300.02822872772562310.05645745545124610.971771272274377
1310.02442982875864230.04885965751728470.975570171241358
1320.03590857158858170.07181714317716340.964091428411418
1330.04001263727462890.08002527454925770.959987362725371
1340.05109564972760940.1021912994552190.948904350272391
1350.04103907664256230.08207815328512460.958960923357438
1360.02884803658692180.05769607317384370.971151963413078
1370.01980623411614030.03961246823228060.98019376588386
1380.01324879346829930.02649758693659870.986751206531701
1390.0241352826221850.048270565244370.975864717377815
1400.02094691629299830.04189383258599650.979053083707002
1410.5345635383124470.9308729233751070.465436461687553
1420.4734716873548140.9469433747096290.526528312645186
1430.4095938355858230.8191876711716460.590406164414177
1440.3517879570871390.7035759141742770.648212042912861
1450.2945808092671120.5891616185342240.705419190732888
1460.2950195224801510.5900390449603020.704980477519849
1470.3231902027568990.6463804055137990.676809797243101
1480.7930089530006570.4139820939986860.206991046999343
1490.7350377002221170.5299245995557650.264962299777883
1500.6161461711313410.7677076577373170.383853828868659
1510.8474374792064430.3051250415871140.152562520793557
1520.8480305265628350.3039389468743310.151969473437165







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level60.041958041958042OK
10% type I error level140.0979020979020979OK

\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 & 6 & 0.041958041958042 & OK \tabularnewline
10% type I error level & 14 & 0.0979020979020979 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186194&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]6[/C][C]0.041958041958042[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]14[/C][C]0.0979020979020979[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186194&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186194&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 level60.041958041958042OK
10% type I error level140.0979020979020979OK



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')
}