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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationFri, 18 Nov 2011 04:07:17 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Nov/18/t1321607254zstz3f9ludhvzmq.htm/, Retrieved Tue, 30 Apr 2024 13:16:13 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=145408, Retrieved Tue, 30 Apr 2024 13:16:13 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact125

Tree of Dependent Computations

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]
- R PD    [Multiple Regression] [time effect? ] [2011-11-18 09:07:17] [e1aba6efa0fba8dc2a9839c208d0186e] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
9	68	63	4	12
9	51	61	4	11
9	56	60	6	14
9	48	62	8	12
9	44	68	8	21
9	67	77	4	12
9	46	70	4	22
9	54	69	8	11
9	61	65	5	10
9	52	64	4	13
9	46	76	4	10
9	55	71	4	8
9	46	63	4	15
9	52	63	4	14
9	76	79	4	10
9	49	65	8	14
9	30	74	4	14
9	75	78	4	11
9	51	75	4	10
9	50	73	8	13
9	38	52	4	7
9	55	76	7	14
9	18	55	4	12
9	52	69	4	14
9	42	76	5	11
9	66	61	4	9
9	66	61	4	11
9	33	55	4	15
9	48	53	4	14
9	57	68	4	13
9	64	72	4	9
9	58	65	4	15
9	59	54	15	10
10	42	55	10	11
10	39	66	4	13
10	59	64	8	8
10	37	76	4	20
10	49	64	4	12
10	80	83	4	10
10	62	71	4	10
10	52	74	7	9
10	53	70	4	14
10	58	70	6	8
10	69	67	5	14
10	63	61	4	11
10	36	62	16	13
10	38	53	5	9
10	46	71	12	11
10	56	64	6	15
10	37	72	9	11
10	51	58	9	10
10	44	59	4	14
10	58	79	5	18
10	37	49	4	14
10	65	71	4	11
10	48	64	5	12
10	53	65	4	13
10	51	63	4	9
10	39	70	4	10
10	64	62	5	15
10	51	62	4	20
10	47	65	6	12
10	64	64	4	12
10	59	65	4	14
10	54	55	18	13
10	55	75	4	11
10	72	72	6	17
10	58	64	4	12
10	59	73	4	13
10	36	67	5	14
10	62	75	4	13
10	63	71	4	15
10	50	58	5	13
10	67	67	10	10
10	70	77	5	11
10	46	58	8	19
10	46	55	8	13
10	59	75	5	17
10	73	81	4	13
10	38	54	4	9
10	62	67	4	11
10	41	56	5	10
10	56	64	4	9
10	52	69	4	12
10	54	66	8	12
10	73	75	4	13
10	60	75	5	13
10	40	61	14	12
10	41	59	8	15
10	54	68	8	22
10	42	43	4	13
10	70	61	4	15
10	51	70	6	13
10	60	67	4	15
10	49	73	7	10
10	52	72	7	11
10	57	64	4	16
10	50	59	6	11
10	47	65	4	11
11	74	72	7	10
11	47	70	4	10
11	47	54	4	16
11	59	66	8	12
11	64	73	4	11
11	55	64	4	16
11	52	61	10	19
11	44	59	8	11
11	60	63	6	16
11	51	66	4	15
11	63	68	4	24
11	49	81	4	14
11	52	72	5	15
11	48	53	4	11
11	50	61	6	15
11	67	77	4	12
11	42	54	5	10
11	44	75	7	14
11	51	70	8	13
11	47	60	5	9
11	37	63	8	15
11	51	57	10	15
11	60	70	8	14
11	38	67	5	11
11	52	44	12	8
11	65	81	4	11
11	60	69	5	11
11	70	71	4	8
11	44	67	6	10
11	50	60	4	11
11	63	66	4	13
11	50	61	7	11
11	68	69	7	20
11	32	57	10	10
11	47	65	4	15
11	67	74	5	12
11	50	56	8	14
11	57	74	11	23
11	46	69	7	14
11	67	76	4	16
11	63	68	8	11
11	36	60	6	12
11	54	72	7	10
11	36	74	5	14
11	57	57	4	12
11	70	73	8	12
11	47	58	4	11
11	51	71	8	12
11	62	62	6	13
11	60	64	4	11
11	59	58	9	19
11	52	67	5	12
11	52	76	6	17
11	69	67	4	9
11	56	78	4	12
11	62	72	4	19
11	55	62	5	18
11	52	68	6	15
11	48	71	16	14
11	51	70	6	11
11	53	61	6	9
11	48	50	4	18
11	55	54	4	16

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'Gertrude Mary Cox' @ cox.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 & 5 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145408&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]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145408&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145408&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net






Multiple Linear Regression - Estimated Regression Equation
depression[t] = + 6.58797994876194 + 0.488652503217881month[t] -0.0166948282146893extrinsic[t] + 0.0320052694405513intrinsic[t] + 0.0230697145628817amotivation[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
depression[t] =  +  6.58797994876194 +  0.488652503217881month[t] -0.0166948282146893extrinsic[t] +  0.0320052694405513intrinsic[t] +  0.0230697145628817amotivation[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145408&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]depression[t] =  +  6.58797994876194 +  0.488652503217881month[t] -0.0166948282146893extrinsic[t] +  0.0320052694405513intrinsic[t] +  0.0230697145628817amotivation[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145408&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145408&T=1

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


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

Multiple Linear Regression - Estimated Regression Equation
depression[t] = + 6.58797994876194 + 0.488652503217881month[t] -0.0166948282146893extrinsic[t] + 0.0320052694405513intrinsic[t] + 0.0230697145628817amotivation[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)6.587979948761944.1208921.59870.1119030.055951
month0.4886525032178810.3374651.4480.1496080.074804
extrinsic-0.01669482821468930.02658-0.62810.5308480.265424
intrinsic0.03200526944055130.036050.88780.3759990.188
amotivation0.02306971456288170.0981060.23520.8143980.407199

\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) & 6.58797994876194 & 4.120892 & 1.5987 & 0.111903 & 0.055951 \tabularnewline
month & 0.488652503217881 & 0.337465 & 1.448 & 0.149608 & 0.074804 \tabularnewline
extrinsic & -0.0166948282146893 & 0.02658 & -0.6281 & 0.530848 & 0.265424 \tabularnewline
intrinsic & 0.0320052694405513 & 0.03605 & 0.8878 & 0.375999 & 0.188 \tabularnewline
amotivation & 0.0230697145628817 & 0.098106 & 0.2352 & 0.814398 & 0.407199 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145408&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]6.58797994876194[/C][C]4.120892[/C][C]1.5987[/C][C]0.111903[/C][C]0.055951[/C][/ROW]
[ROW][C]month[/C][C]0.488652503217881[/C][C]0.337465[/C][C]1.448[/C][C]0.149608[/C][C]0.074804[/C][/ROW]
[ROW][C]extrinsic[/C][C]-0.0166948282146893[/C][C]0.02658[/C][C]-0.6281[/C][C]0.530848[/C][C]0.265424[/C][/ROW]
[ROW][C]intrinsic[/C][C]0.0320052694405513[/C][C]0.03605[/C][C]0.8878[/C][C]0.375999[/C][C]0.188[/C][/ROW]
[ROW][C]amotivation[/C][C]0.0230697145628817[/C][C]0.098106[/C][C]0.2352[/C][C]0.814398[/C][C]0.407199[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145408&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145408&T=2

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


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)6.587979948761944.1208921.59870.1119030.055951
month0.4886525032178810.3374651.4480.1496080.074804
extrinsic-0.01669482821468930.02658-0.62810.5308480.265424
intrinsic0.03200526944055130.036050.88780.3759990.188
amotivation0.02306971456288170.0981060.23520.8143980.407199






Multiple Linear Regression - Regression Statistics
Multiple R0.136456050689232
R-squared0.0186202537697023
Adjusted R-squared-0.00638305186673827
F-TEST (value)0.74471168094528
F-TEST (DF numerator)4
F-TEST (DF denominator)157
p-value0.562917785991773
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.17624994716511
Sum Squared Residuals1583.90450511802

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.136456050689232 \tabularnewline
R-squared & 0.0186202537697023 \tabularnewline
Adjusted R-squared & -0.00638305186673827 \tabularnewline
F-TEST (value) & 0.74471168094528 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 157 \tabularnewline
p-value & 0.562917785991773 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 3.17624994716511 \tabularnewline
Sum Squared Residuals & 1583.90450511802 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145408&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.136456050689232[/C][/ROW]
[ROW][C]R-squared[/C][C]0.0186202537697023[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]-0.00638305186673827[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]0.74471168094528[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]157[/C][/ROW]
[ROW][C]p-value[/C][C]0.562917785991773[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]3.17624994716511[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1583.90450511802[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145408&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145408&T=3

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


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

Multiple Linear Regression - Regression Statistics
Multiple R0.136456050689232
R-squared0.0186202537697023
Adjusted R-squared-0.00638305186673827
F-TEST (value)0.74471168094528
F-TEST (DF numerator)4
F-TEST (DF denominator)157
p-value0.562917785991773
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.17624994716511
Sum Squared Residuals1583.90450511802






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11211.95921499213030.0407850078697264
21112.1790165328989-1.17901653289887
31412.10967655151061.89032344848936
41212.353385145235-0.35338514523502
52112.61219607473718.38780392526291
61212.4239835925127-0.423983592512665
72212.55053809893739.44946190106272
81112.4772530620307-1.47725306203074
91012.1631590430771-2.16315904307707
101312.25833751300580.741662486994162
111012.7425697155806-2.74256971558059
12812.4322899144456-4.43228991444563
131512.32650121285342.67349878714658
141412.22633224356531.77366775643471
151012.3377406774616-2.33774067746156
161412.4327061253421.56729387465802
171412.94567642813451.05432357186548
181112.3224302362357-1.3224302362357
191012.6270903050666-2.62709030506659
201312.67205345265170.327946547348294
21712.1080018747249-5.10800187472487
221412.6615254053371.33847459466297
231212.5379142473403-0.537914247340314
241412.41836386020861.5816361397914
251112.8324187430022-1.83241874300223
26911.9285941096785-2.92859410967853
271111.9285941096785-0.928594109678534
281512.287491824122.71250817588003
291411.97305886201852.02694113798147
301312.30288444969460.697115550305403
31912.314041729954-3.31404172995398
321512.19017381315832.80982618684175
331012.0751878812892-2.0751878812892
341112.7643091607829-1.76430916078294
351313.0280333218958-0.028033321895784
36812.7224050769724-4.72240507697242
372013.38147567273076.61852432726932
381212.7970745008678-0.797074500867788
391012.8876349455829-2.88763494558289
401012.8040786201607-2.80407862016069
41913.1362518543179-4.13625185431788
421412.92232680465231.07767319534766
43812.8849920927047-4.88499209270466
441412.58226345945851.41773654054146
451112.4673310975405-1.46733109754048
461313.2269333035322-0.226933303532228
47912.6517293619462-3.65172936194619
481113.2557535880988-2.25575358809877
491512.72635013249072.27364986750927
501113.3688031677829-2.36880316778288
511012.6870018006095-2.68700180060951
521412.72052229473851.27947770526152
531813.14996980310674.85003019689326
541412.51733339783581.48266660216421
551112.7539941355166-1.75399413551662
561212.8368390436454-0.836839043645359
571312.76230045744960.237699542550418
58912.7316795749979-3.73167957499786
591013.156054399658-3.15605439965799
601512.50571125332922.49428874667077
612012.69967430555737.30032569444269
621212.9086088558635-0.908608855863481
631212.5466520776474-0.546652077647447
641412.66213148816141.33786851183855
651312.74852893870970.251471061290277
661113.0489634954257-2.04896349542572
671712.71527503658014.28472496341989
681212.6468210469356-0.646821046935584
691312.91817364368590.0818263563141436
701413.13319279054330.866807209456715
711312.93209969792290.067900302077109
721512.7873837919462.212616208054
731312.61141777057270.388582229427328
741012.7310016887023-2.73100168870232
751112.8856213256494-1.88562132564936
761912.74740622712016.25259377287993
771312.65139041879840.348609581201579
781713.00525389712983.99474610287016
791312.94048820420460.0595117957953838
80912.6606649168239-3.66066491682386
811112.6760575423985-1.67605754239848
821012.6976606856238-2.69766068562377
83912.680210703365-3.68021070336496
841212.9070163634265-0.907016363426477
851212.869889756927-0.869889756926971
861312.74845658756130.251543412438692
871312.98855906891520.0114409310848485
881213.0820092921072-1.08200929210716
891512.86288563763412.13711436236593
902212.93390029580819.06609970419193
911312.2418276401190.758172359880965
921512.35046730003772.64953269996234
931313.0018558902075-0.00185589020748068
941512.70944719882792.29055280117214
951013.1543310695214-3.15433106952139
961113.0722413154368-2.07224131543678
971612.66351587515033.33648412484973
981112.6664927545761-1.66649275457611
991112.8624694267377-1.86246942673772
1001013.1936075979315-3.19360759793149
1011013.5111482771584-3.51114827715836
1021612.99906396610953.00093603389047
1031213.2750681190714-1.27506811907141
1041113.3233520058303-2.32335200583029
1051613.18555803479752.81444196520247
1061913.27804499849725.72195500150276
1071113.3014536562079-2.30145365620789
1081613.11621805340932.8837819465907
1091513.31634788653741.68365211346261
1102413.180020486842210.8199795131578
1111413.8298165845750.170183415424958
1121513.51475438952891.48524561047111
1131112.9503638684543-1.95036386845429
1141513.21915579667511.78084420332491
1151213.4012885989484-1.40128859894843
1161013.1056078217459-3.10560782174586
1171413.79046825269380.209531747306174
1181313.5366478225511-0.536647822551126
119913.2141652973157-4.21416529731573
1201513.54633853147291.45366146852708
1211513.16671874894971.83328125105028
1221413.38639436861890.613605631381079
1231113.5884556373318-2.58845563733179
124812.7800948471336-4.78009484713363
1251113.56269933314-2.56269933314001
1261113.2851799554897-2.28517995548972
127813.1591724976611-5.15917249766105
1281013.5113563826065-3.51135638260653
1291113.1410110981088-2.14101109810877
1301313.1160099479611-0.116009947961121
1311113.242225511238-2.24222551123797
1322013.1977607588986.80223924110203
1331013.4839204850288-3.48392048502882
1341513.35112192995561.6488780700444
1351213.3283425051897-1.32834250518966
1361413.10526887859810.894731121401904
1372313.63370907471389.36629092528616
1381413.56504697962110.434953020378861
1391613.36928332950792.63071667049212
1401113.2722993450937-2.27229934509375
1411213.4208781222402-1.42087812224019
1421013.5275041622253-3.52750416222528
1431413.8458821798450.154117820154974
1441212.9281314922843-0.928131492284295
1451213.3154618947937-1.31546189479368
1461113.1270850438717-2.12708504387174
1471213.5686530919917-1.56865309199168
1481313.0508231275394-0.0508231275393683
1491113.1020838937241-2.10208389372409
1501913.04209567810995.95790432189012
1511213.3547280423261-1.35472804232614
1521713.6658451818543.33415481814602
153913.0478462481135-4.04784624811354
1541213.6169369787506-1.61693697875056
1551913.32473639281915.67526360718088
1561813.14461721047934.85538278952069
1571513.40980302632961.59019697367043
1581413.80329529313880.196704706861201
1591113.4905083934254-2.49050839342536
160913.169071312031-4.16907131203102
1611812.85434806013265.14565193986736
1621612.8655053403923.13449465960798

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 12 & 11.9592149921303 & 0.0407850078697264 \tabularnewline
2 & 11 & 12.1790165328989 & -1.17901653289887 \tabularnewline
3 & 14 & 12.1096765515106 & 1.89032344848936 \tabularnewline
4 & 12 & 12.353385145235 & -0.35338514523502 \tabularnewline
5 & 21 & 12.6121960747371 & 8.38780392526291 \tabularnewline
6 & 12 & 12.4239835925127 & -0.423983592512665 \tabularnewline
7 & 22 & 12.5505380989373 & 9.44946190106272 \tabularnewline
8 & 11 & 12.4772530620307 & -1.47725306203074 \tabularnewline
9 & 10 & 12.1631590430771 & -2.16315904307707 \tabularnewline
10 & 13 & 12.2583375130058 & 0.741662486994162 \tabularnewline
11 & 10 & 12.7425697155806 & -2.74256971558059 \tabularnewline
12 & 8 & 12.4322899144456 & -4.43228991444563 \tabularnewline
13 & 15 & 12.3265012128534 & 2.67349878714658 \tabularnewline
14 & 14 & 12.2263322435653 & 1.77366775643471 \tabularnewline
15 & 10 & 12.3377406774616 & -2.33774067746156 \tabularnewline
16 & 14 & 12.432706125342 & 1.56729387465802 \tabularnewline
17 & 14 & 12.9456764281345 & 1.05432357186548 \tabularnewline
18 & 11 & 12.3224302362357 & -1.3224302362357 \tabularnewline
19 & 10 & 12.6270903050666 & -2.62709030506659 \tabularnewline
20 & 13 & 12.6720534526517 & 0.327946547348294 \tabularnewline
21 & 7 & 12.1080018747249 & -5.10800187472487 \tabularnewline
22 & 14 & 12.661525405337 & 1.33847459466297 \tabularnewline
23 & 12 & 12.5379142473403 & -0.537914247340314 \tabularnewline
24 & 14 & 12.4183638602086 & 1.5816361397914 \tabularnewline
25 & 11 & 12.8324187430022 & -1.83241874300223 \tabularnewline
26 & 9 & 11.9285941096785 & -2.92859410967853 \tabularnewline
27 & 11 & 11.9285941096785 & -0.928594109678534 \tabularnewline
28 & 15 & 12.28749182412 & 2.71250817588003 \tabularnewline
29 & 14 & 11.9730588620185 & 2.02694113798147 \tabularnewline
30 & 13 & 12.3028844496946 & 0.697115550305403 \tabularnewline
31 & 9 & 12.314041729954 & -3.31404172995398 \tabularnewline
32 & 15 & 12.1901738131583 & 2.80982618684175 \tabularnewline
33 & 10 & 12.0751878812892 & -2.0751878812892 \tabularnewline
34 & 11 & 12.7643091607829 & -1.76430916078294 \tabularnewline
35 & 13 & 13.0280333218958 & -0.028033321895784 \tabularnewline
36 & 8 & 12.7224050769724 & -4.72240507697242 \tabularnewline
37 & 20 & 13.3814756727307 & 6.61852432726932 \tabularnewline
38 & 12 & 12.7970745008678 & -0.797074500867788 \tabularnewline
39 & 10 & 12.8876349455829 & -2.88763494558289 \tabularnewline
40 & 10 & 12.8040786201607 & -2.80407862016069 \tabularnewline
41 & 9 & 13.1362518543179 & -4.13625185431788 \tabularnewline
42 & 14 & 12.9223268046523 & 1.07767319534766 \tabularnewline
43 & 8 & 12.8849920927047 & -4.88499209270466 \tabularnewline
44 & 14 & 12.5822634594585 & 1.41773654054146 \tabularnewline
45 & 11 & 12.4673310975405 & -1.46733109754048 \tabularnewline
46 & 13 & 13.2269333035322 & -0.226933303532228 \tabularnewline
47 & 9 & 12.6517293619462 & -3.65172936194619 \tabularnewline
48 & 11 & 13.2557535880988 & -2.25575358809877 \tabularnewline
49 & 15 & 12.7263501324907 & 2.27364986750927 \tabularnewline
50 & 11 & 13.3688031677829 & -2.36880316778288 \tabularnewline
51 & 10 & 12.6870018006095 & -2.68700180060951 \tabularnewline
52 & 14 & 12.7205222947385 & 1.27947770526152 \tabularnewline
53 & 18 & 13.1499698031067 & 4.85003019689326 \tabularnewline
54 & 14 & 12.5173333978358 & 1.48266660216421 \tabularnewline
55 & 11 & 12.7539941355166 & -1.75399413551662 \tabularnewline
56 & 12 & 12.8368390436454 & -0.836839043645359 \tabularnewline
57 & 13 & 12.7623004574496 & 0.237699542550418 \tabularnewline
58 & 9 & 12.7316795749979 & -3.73167957499786 \tabularnewline
59 & 10 & 13.156054399658 & -3.15605439965799 \tabularnewline
60 & 15 & 12.5057112533292 & 2.49428874667077 \tabularnewline
61 & 20 & 12.6996743055573 & 7.30032569444269 \tabularnewline
62 & 12 & 12.9086088558635 & -0.908608855863481 \tabularnewline
63 & 12 & 12.5466520776474 & -0.546652077647447 \tabularnewline
64 & 14 & 12.6621314881614 & 1.33786851183855 \tabularnewline
65 & 13 & 12.7485289387097 & 0.251471061290277 \tabularnewline
66 & 11 & 13.0489634954257 & -2.04896349542572 \tabularnewline
67 & 17 & 12.7152750365801 & 4.28472496341989 \tabularnewline
68 & 12 & 12.6468210469356 & -0.646821046935584 \tabularnewline
69 & 13 & 12.9181736436859 & 0.0818263563141436 \tabularnewline
70 & 14 & 13.1331927905433 & 0.866807209456715 \tabularnewline
71 & 13 & 12.9320996979229 & 0.067900302077109 \tabularnewline
72 & 15 & 12.787383791946 & 2.212616208054 \tabularnewline
73 & 13 & 12.6114177705727 & 0.388582229427328 \tabularnewline
74 & 10 & 12.7310016887023 & -2.73100168870232 \tabularnewline
75 & 11 & 12.8856213256494 & -1.88562132564936 \tabularnewline
76 & 19 & 12.7474062271201 & 6.25259377287993 \tabularnewline
77 & 13 & 12.6513904187984 & 0.348609581201579 \tabularnewline
78 & 17 & 13.0052538971298 & 3.99474610287016 \tabularnewline
79 & 13 & 12.9404882042046 & 0.0595117957953838 \tabularnewline
80 & 9 & 12.6606649168239 & -3.66066491682386 \tabularnewline
81 & 11 & 12.6760575423985 & -1.67605754239848 \tabularnewline
82 & 10 & 12.6976606856238 & -2.69766068562377 \tabularnewline
83 & 9 & 12.680210703365 & -3.68021070336496 \tabularnewline
84 & 12 & 12.9070163634265 & -0.907016363426477 \tabularnewline
85 & 12 & 12.869889756927 & -0.869889756926971 \tabularnewline
86 & 13 & 12.7484565875613 & 0.251543412438692 \tabularnewline
87 & 13 & 12.9885590689152 & 0.0114409310848485 \tabularnewline
88 & 12 & 13.0820092921072 & -1.08200929210716 \tabularnewline
89 & 15 & 12.8628856376341 & 2.13711436236593 \tabularnewline
90 & 22 & 12.9339002958081 & 9.06609970419193 \tabularnewline
91 & 13 & 12.241827640119 & 0.758172359880965 \tabularnewline
92 & 15 & 12.3504673000377 & 2.64953269996234 \tabularnewline
93 & 13 & 13.0018558902075 & -0.00185589020748068 \tabularnewline
94 & 15 & 12.7094471988279 & 2.29055280117214 \tabularnewline
95 & 10 & 13.1543310695214 & -3.15433106952139 \tabularnewline
96 & 11 & 13.0722413154368 & -2.07224131543678 \tabularnewline
97 & 16 & 12.6635158751503 & 3.33648412484973 \tabularnewline
98 & 11 & 12.6664927545761 & -1.66649275457611 \tabularnewline
99 & 11 & 12.8624694267377 & -1.86246942673772 \tabularnewline
100 & 10 & 13.1936075979315 & -3.19360759793149 \tabularnewline
101 & 10 & 13.5111482771584 & -3.51114827715836 \tabularnewline
102 & 16 & 12.9990639661095 & 3.00093603389047 \tabularnewline
103 & 12 & 13.2750681190714 & -1.27506811907141 \tabularnewline
104 & 11 & 13.3233520058303 & -2.32335200583029 \tabularnewline
105 & 16 & 13.1855580347975 & 2.81444196520247 \tabularnewline
106 & 19 & 13.2780449984972 & 5.72195500150276 \tabularnewline
107 & 11 & 13.3014536562079 & -2.30145365620789 \tabularnewline
108 & 16 & 13.1162180534093 & 2.8837819465907 \tabularnewline
109 & 15 & 13.3163478865374 & 1.68365211346261 \tabularnewline
110 & 24 & 13.1800204868422 & 10.8199795131578 \tabularnewline
111 & 14 & 13.829816584575 & 0.170183415424958 \tabularnewline
112 & 15 & 13.5147543895289 & 1.48524561047111 \tabularnewline
113 & 11 & 12.9503638684543 & -1.95036386845429 \tabularnewline
114 & 15 & 13.2191557966751 & 1.78084420332491 \tabularnewline
115 & 12 & 13.4012885989484 & -1.40128859894843 \tabularnewline
116 & 10 & 13.1056078217459 & -3.10560782174586 \tabularnewline
117 & 14 & 13.7904682526938 & 0.209531747306174 \tabularnewline
118 & 13 & 13.5366478225511 & -0.536647822551126 \tabularnewline
119 & 9 & 13.2141652973157 & -4.21416529731573 \tabularnewline
120 & 15 & 13.5463385314729 & 1.45366146852708 \tabularnewline
121 & 15 & 13.1667187489497 & 1.83328125105028 \tabularnewline
122 & 14 & 13.3863943686189 & 0.613605631381079 \tabularnewline
123 & 11 & 13.5884556373318 & -2.58845563733179 \tabularnewline
124 & 8 & 12.7800948471336 & -4.78009484713363 \tabularnewline
125 & 11 & 13.56269933314 & -2.56269933314001 \tabularnewline
126 & 11 & 13.2851799554897 & -2.28517995548972 \tabularnewline
127 & 8 & 13.1591724976611 & -5.15917249766105 \tabularnewline
128 & 10 & 13.5113563826065 & -3.51135638260653 \tabularnewline
129 & 11 & 13.1410110981088 & -2.14101109810877 \tabularnewline
130 & 13 & 13.1160099479611 & -0.116009947961121 \tabularnewline
131 & 11 & 13.242225511238 & -2.24222551123797 \tabularnewline
132 & 20 & 13.197760758898 & 6.80223924110203 \tabularnewline
133 & 10 & 13.4839204850288 & -3.48392048502882 \tabularnewline
134 & 15 & 13.3511219299556 & 1.6488780700444 \tabularnewline
135 & 12 & 13.3283425051897 & -1.32834250518966 \tabularnewline
136 & 14 & 13.1052688785981 & 0.894731121401904 \tabularnewline
137 & 23 & 13.6337090747138 & 9.36629092528616 \tabularnewline
138 & 14 & 13.5650469796211 & 0.434953020378861 \tabularnewline
139 & 16 & 13.3692833295079 & 2.63071667049212 \tabularnewline
140 & 11 & 13.2722993450937 & -2.27229934509375 \tabularnewline
141 & 12 & 13.4208781222402 & -1.42087812224019 \tabularnewline
142 & 10 & 13.5275041622253 & -3.52750416222528 \tabularnewline
143 & 14 & 13.845882179845 & 0.154117820154974 \tabularnewline
144 & 12 & 12.9281314922843 & -0.928131492284295 \tabularnewline
145 & 12 & 13.3154618947937 & -1.31546189479368 \tabularnewline
146 & 11 & 13.1270850438717 & -2.12708504387174 \tabularnewline
147 & 12 & 13.5686530919917 & -1.56865309199168 \tabularnewline
148 & 13 & 13.0508231275394 & -0.0508231275393683 \tabularnewline
149 & 11 & 13.1020838937241 & -2.10208389372409 \tabularnewline
150 & 19 & 13.0420956781099 & 5.95790432189012 \tabularnewline
151 & 12 & 13.3547280423261 & -1.35472804232614 \tabularnewline
152 & 17 & 13.665845181854 & 3.33415481814602 \tabularnewline
153 & 9 & 13.0478462481135 & -4.04784624811354 \tabularnewline
154 & 12 & 13.6169369787506 & -1.61693697875056 \tabularnewline
155 & 19 & 13.3247363928191 & 5.67526360718088 \tabularnewline
156 & 18 & 13.1446172104793 & 4.85538278952069 \tabularnewline
157 & 15 & 13.4098030263296 & 1.59019697367043 \tabularnewline
158 & 14 & 13.8032952931388 & 0.196704706861201 \tabularnewline
159 & 11 & 13.4905083934254 & -2.49050839342536 \tabularnewline
160 & 9 & 13.169071312031 & -4.16907131203102 \tabularnewline
161 & 18 & 12.8543480601326 & 5.14565193986736 \tabularnewline
162 & 16 & 12.865505340392 & 3.13449465960798 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145408&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]12[/C][C]11.9592149921303[/C][C]0.0407850078697264[/C][/ROW]
[ROW][C]2[/C][C]11[/C][C]12.1790165328989[/C][C]-1.17901653289887[/C][/ROW]
[ROW][C]3[/C][C]14[/C][C]12.1096765515106[/C][C]1.89032344848936[/C][/ROW]
[ROW][C]4[/C][C]12[/C][C]12.353385145235[/C][C]-0.35338514523502[/C][/ROW]
[ROW][C]5[/C][C]21[/C][C]12.6121960747371[/C][C]8.38780392526291[/C][/ROW]
[ROW][C]6[/C][C]12[/C][C]12.4239835925127[/C][C]-0.423983592512665[/C][/ROW]
[ROW][C]7[/C][C]22[/C][C]12.5505380989373[/C][C]9.44946190106272[/C][/ROW]
[ROW][C]8[/C][C]11[/C][C]12.4772530620307[/C][C]-1.47725306203074[/C][/ROW]
[ROW][C]9[/C][C]10[/C][C]12.1631590430771[/C][C]-2.16315904307707[/C][/ROW]
[ROW][C]10[/C][C]13[/C][C]12.2583375130058[/C][C]0.741662486994162[/C][/ROW]
[ROW][C]11[/C][C]10[/C][C]12.7425697155806[/C][C]-2.74256971558059[/C][/ROW]
[ROW][C]12[/C][C]8[/C][C]12.4322899144456[/C][C]-4.43228991444563[/C][/ROW]
[ROW][C]13[/C][C]15[/C][C]12.3265012128534[/C][C]2.67349878714658[/C][/ROW]
[ROW][C]14[/C][C]14[/C][C]12.2263322435653[/C][C]1.77366775643471[/C][/ROW]
[ROW][C]15[/C][C]10[/C][C]12.3377406774616[/C][C]-2.33774067746156[/C][/ROW]
[ROW][C]16[/C][C]14[/C][C]12.432706125342[/C][C]1.56729387465802[/C][/ROW]
[ROW][C]17[/C][C]14[/C][C]12.9456764281345[/C][C]1.05432357186548[/C][/ROW]
[ROW][C]18[/C][C]11[/C][C]12.3224302362357[/C][C]-1.3224302362357[/C][/ROW]
[ROW][C]19[/C][C]10[/C][C]12.6270903050666[/C][C]-2.62709030506659[/C][/ROW]
[ROW][C]20[/C][C]13[/C][C]12.6720534526517[/C][C]0.327946547348294[/C][/ROW]
[ROW][C]21[/C][C]7[/C][C]12.1080018747249[/C][C]-5.10800187472487[/C][/ROW]
[ROW][C]22[/C][C]14[/C][C]12.661525405337[/C][C]1.33847459466297[/C][/ROW]
[ROW][C]23[/C][C]12[/C][C]12.5379142473403[/C][C]-0.537914247340314[/C][/ROW]
[ROW][C]24[/C][C]14[/C][C]12.4183638602086[/C][C]1.5816361397914[/C][/ROW]
[ROW][C]25[/C][C]11[/C][C]12.8324187430022[/C][C]-1.83241874300223[/C][/ROW]
[ROW][C]26[/C][C]9[/C][C]11.9285941096785[/C][C]-2.92859410967853[/C][/ROW]
[ROW][C]27[/C][C]11[/C][C]11.9285941096785[/C][C]-0.928594109678534[/C][/ROW]
[ROW][C]28[/C][C]15[/C][C]12.28749182412[/C][C]2.71250817588003[/C][/ROW]
[ROW][C]29[/C][C]14[/C][C]11.9730588620185[/C][C]2.02694113798147[/C][/ROW]
[ROW][C]30[/C][C]13[/C][C]12.3028844496946[/C][C]0.697115550305403[/C][/ROW]
[ROW][C]31[/C][C]9[/C][C]12.314041729954[/C][C]-3.31404172995398[/C][/ROW]
[ROW][C]32[/C][C]15[/C][C]12.1901738131583[/C][C]2.80982618684175[/C][/ROW]
[ROW][C]33[/C][C]10[/C][C]12.0751878812892[/C][C]-2.0751878812892[/C][/ROW]
[ROW][C]34[/C][C]11[/C][C]12.7643091607829[/C][C]-1.76430916078294[/C][/ROW]
[ROW][C]35[/C][C]13[/C][C]13.0280333218958[/C][C]-0.028033321895784[/C][/ROW]
[ROW][C]36[/C][C]8[/C][C]12.7224050769724[/C][C]-4.72240507697242[/C][/ROW]
[ROW][C]37[/C][C]20[/C][C]13.3814756727307[/C][C]6.61852432726932[/C][/ROW]
[ROW][C]38[/C][C]12[/C][C]12.7970745008678[/C][C]-0.797074500867788[/C][/ROW]
[ROW][C]39[/C][C]10[/C][C]12.8876349455829[/C][C]-2.88763494558289[/C][/ROW]
[ROW][C]40[/C][C]10[/C][C]12.8040786201607[/C][C]-2.80407862016069[/C][/ROW]
[ROW][C]41[/C][C]9[/C][C]13.1362518543179[/C][C]-4.13625185431788[/C][/ROW]
[ROW][C]42[/C][C]14[/C][C]12.9223268046523[/C][C]1.07767319534766[/C][/ROW]
[ROW][C]43[/C][C]8[/C][C]12.8849920927047[/C][C]-4.88499209270466[/C][/ROW]
[ROW][C]44[/C][C]14[/C][C]12.5822634594585[/C][C]1.41773654054146[/C][/ROW]
[ROW][C]45[/C][C]11[/C][C]12.4673310975405[/C][C]-1.46733109754048[/C][/ROW]
[ROW][C]46[/C][C]13[/C][C]13.2269333035322[/C][C]-0.226933303532228[/C][/ROW]
[ROW][C]47[/C][C]9[/C][C]12.6517293619462[/C][C]-3.65172936194619[/C][/ROW]
[ROW][C]48[/C][C]11[/C][C]13.2557535880988[/C][C]-2.25575358809877[/C][/ROW]
[ROW][C]49[/C][C]15[/C][C]12.7263501324907[/C][C]2.27364986750927[/C][/ROW]
[ROW][C]50[/C][C]11[/C][C]13.3688031677829[/C][C]-2.36880316778288[/C][/ROW]
[ROW][C]51[/C][C]10[/C][C]12.6870018006095[/C][C]-2.68700180060951[/C][/ROW]
[ROW][C]52[/C][C]14[/C][C]12.7205222947385[/C][C]1.27947770526152[/C][/ROW]
[ROW][C]53[/C][C]18[/C][C]13.1499698031067[/C][C]4.85003019689326[/C][/ROW]
[ROW][C]54[/C][C]14[/C][C]12.5173333978358[/C][C]1.48266660216421[/C][/ROW]
[ROW][C]55[/C][C]11[/C][C]12.7539941355166[/C][C]-1.75399413551662[/C][/ROW]
[ROW][C]56[/C][C]12[/C][C]12.8368390436454[/C][C]-0.836839043645359[/C][/ROW]
[ROW][C]57[/C][C]13[/C][C]12.7623004574496[/C][C]0.237699542550418[/C][/ROW]
[ROW][C]58[/C][C]9[/C][C]12.7316795749979[/C][C]-3.73167957499786[/C][/ROW]
[ROW][C]59[/C][C]10[/C][C]13.156054399658[/C][C]-3.15605439965799[/C][/ROW]
[ROW][C]60[/C][C]15[/C][C]12.5057112533292[/C][C]2.49428874667077[/C][/ROW]
[ROW][C]61[/C][C]20[/C][C]12.6996743055573[/C][C]7.30032569444269[/C][/ROW]
[ROW][C]62[/C][C]12[/C][C]12.9086088558635[/C][C]-0.908608855863481[/C][/ROW]
[ROW][C]63[/C][C]12[/C][C]12.5466520776474[/C][C]-0.546652077647447[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]12.6621314881614[/C][C]1.33786851183855[/C][/ROW]
[ROW][C]65[/C][C]13[/C][C]12.7485289387097[/C][C]0.251471061290277[/C][/ROW]
[ROW][C]66[/C][C]11[/C][C]13.0489634954257[/C][C]-2.04896349542572[/C][/ROW]
[ROW][C]67[/C][C]17[/C][C]12.7152750365801[/C][C]4.28472496341989[/C][/ROW]
[ROW][C]68[/C][C]12[/C][C]12.6468210469356[/C][C]-0.646821046935584[/C][/ROW]
[ROW][C]69[/C][C]13[/C][C]12.9181736436859[/C][C]0.0818263563141436[/C][/ROW]
[ROW][C]70[/C][C]14[/C][C]13.1331927905433[/C][C]0.866807209456715[/C][/ROW]
[ROW][C]71[/C][C]13[/C][C]12.9320996979229[/C][C]0.067900302077109[/C][/ROW]
[ROW][C]72[/C][C]15[/C][C]12.787383791946[/C][C]2.212616208054[/C][/ROW]
[ROW][C]73[/C][C]13[/C][C]12.6114177705727[/C][C]0.388582229427328[/C][/ROW]
[ROW][C]74[/C][C]10[/C][C]12.7310016887023[/C][C]-2.73100168870232[/C][/ROW]
[ROW][C]75[/C][C]11[/C][C]12.8856213256494[/C][C]-1.88562132564936[/C][/ROW]
[ROW][C]76[/C][C]19[/C][C]12.7474062271201[/C][C]6.25259377287993[/C][/ROW]
[ROW][C]77[/C][C]13[/C][C]12.6513904187984[/C][C]0.348609581201579[/C][/ROW]
[ROW][C]78[/C][C]17[/C][C]13.0052538971298[/C][C]3.99474610287016[/C][/ROW]
[ROW][C]79[/C][C]13[/C][C]12.9404882042046[/C][C]0.0595117957953838[/C][/ROW]
[ROW][C]80[/C][C]9[/C][C]12.6606649168239[/C][C]-3.66066491682386[/C][/ROW]
[ROW][C]81[/C][C]11[/C][C]12.6760575423985[/C][C]-1.67605754239848[/C][/ROW]
[ROW][C]82[/C][C]10[/C][C]12.6976606856238[/C][C]-2.69766068562377[/C][/ROW]
[ROW][C]83[/C][C]9[/C][C]12.680210703365[/C][C]-3.68021070336496[/C][/ROW]
[ROW][C]84[/C][C]12[/C][C]12.9070163634265[/C][C]-0.907016363426477[/C][/ROW]
[ROW][C]85[/C][C]12[/C][C]12.869889756927[/C][C]-0.869889756926971[/C][/ROW]
[ROW][C]86[/C][C]13[/C][C]12.7484565875613[/C][C]0.251543412438692[/C][/ROW]
[ROW][C]87[/C][C]13[/C][C]12.9885590689152[/C][C]0.0114409310848485[/C][/ROW]
[ROW][C]88[/C][C]12[/C][C]13.0820092921072[/C][C]-1.08200929210716[/C][/ROW]
[ROW][C]89[/C][C]15[/C][C]12.8628856376341[/C][C]2.13711436236593[/C][/ROW]
[ROW][C]90[/C][C]22[/C][C]12.9339002958081[/C][C]9.06609970419193[/C][/ROW]
[ROW][C]91[/C][C]13[/C][C]12.241827640119[/C][C]0.758172359880965[/C][/ROW]
[ROW][C]92[/C][C]15[/C][C]12.3504673000377[/C][C]2.64953269996234[/C][/ROW]
[ROW][C]93[/C][C]13[/C][C]13.0018558902075[/C][C]-0.00185589020748068[/C][/ROW]
[ROW][C]94[/C][C]15[/C][C]12.7094471988279[/C][C]2.29055280117214[/C][/ROW]
[ROW][C]95[/C][C]10[/C][C]13.1543310695214[/C][C]-3.15433106952139[/C][/ROW]
[ROW][C]96[/C][C]11[/C][C]13.0722413154368[/C][C]-2.07224131543678[/C][/ROW]
[ROW][C]97[/C][C]16[/C][C]12.6635158751503[/C][C]3.33648412484973[/C][/ROW]
[ROW][C]98[/C][C]11[/C][C]12.6664927545761[/C][C]-1.66649275457611[/C][/ROW]
[ROW][C]99[/C][C]11[/C][C]12.8624694267377[/C][C]-1.86246942673772[/C][/ROW]
[ROW][C]100[/C][C]10[/C][C]13.1936075979315[/C][C]-3.19360759793149[/C][/ROW]
[ROW][C]101[/C][C]10[/C][C]13.5111482771584[/C][C]-3.51114827715836[/C][/ROW]
[ROW][C]102[/C][C]16[/C][C]12.9990639661095[/C][C]3.00093603389047[/C][/ROW]
[ROW][C]103[/C][C]12[/C][C]13.2750681190714[/C][C]-1.27506811907141[/C][/ROW]
[ROW][C]104[/C][C]11[/C][C]13.3233520058303[/C][C]-2.32335200583029[/C][/ROW]
[ROW][C]105[/C][C]16[/C][C]13.1855580347975[/C][C]2.81444196520247[/C][/ROW]
[ROW][C]106[/C][C]19[/C][C]13.2780449984972[/C][C]5.72195500150276[/C][/ROW]
[ROW][C]107[/C][C]11[/C][C]13.3014536562079[/C][C]-2.30145365620789[/C][/ROW]
[ROW][C]108[/C][C]16[/C][C]13.1162180534093[/C][C]2.8837819465907[/C][/ROW]
[ROW][C]109[/C][C]15[/C][C]13.3163478865374[/C][C]1.68365211346261[/C][/ROW]
[ROW][C]110[/C][C]24[/C][C]13.1800204868422[/C][C]10.8199795131578[/C][/ROW]
[ROW][C]111[/C][C]14[/C][C]13.829816584575[/C][C]0.170183415424958[/C][/ROW]
[ROW][C]112[/C][C]15[/C][C]13.5147543895289[/C][C]1.48524561047111[/C][/ROW]
[ROW][C]113[/C][C]11[/C][C]12.9503638684543[/C][C]-1.95036386845429[/C][/ROW]
[ROW][C]114[/C][C]15[/C][C]13.2191557966751[/C][C]1.78084420332491[/C][/ROW]
[ROW][C]115[/C][C]12[/C][C]13.4012885989484[/C][C]-1.40128859894843[/C][/ROW]
[ROW][C]116[/C][C]10[/C][C]13.1056078217459[/C][C]-3.10560782174586[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]13.7904682526938[/C][C]0.209531747306174[/C][/ROW]
[ROW][C]118[/C][C]13[/C][C]13.5366478225511[/C][C]-0.536647822551126[/C][/ROW]
[ROW][C]119[/C][C]9[/C][C]13.2141652973157[/C][C]-4.21416529731573[/C][/ROW]
[ROW][C]120[/C][C]15[/C][C]13.5463385314729[/C][C]1.45366146852708[/C][/ROW]
[ROW][C]121[/C][C]15[/C][C]13.1667187489497[/C][C]1.83328125105028[/C][/ROW]
[ROW][C]122[/C][C]14[/C][C]13.3863943686189[/C][C]0.613605631381079[/C][/ROW]
[ROW][C]123[/C][C]11[/C][C]13.5884556373318[/C][C]-2.58845563733179[/C][/ROW]
[ROW][C]124[/C][C]8[/C][C]12.7800948471336[/C][C]-4.78009484713363[/C][/ROW]
[ROW][C]125[/C][C]11[/C][C]13.56269933314[/C][C]-2.56269933314001[/C][/ROW]
[ROW][C]126[/C][C]11[/C][C]13.2851799554897[/C][C]-2.28517995548972[/C][/ROW]
[ROW][C]127[/C][C]8[/C][C]13.1591724976611[/C][C]-5.15917249766105[/C][/ROW]
[ROW][C]128[/C][C]10[/C][C]13.5113563826065[/C][C]-3.51135638260653[/C][/ROW]
[ROW][C]129[/C][C]11[/C][C]13.1410110981088[/C][C]-2.14101109810877[/C][/ROW]
[ROW][C]130[/C][C]13[/C][C]13.1160099479611[/C][C]-0.116009947961121[/C][/ROW]
[ROW][C]131[/C][C]11[/C][C]13.242225511238[/C][C]-2.24222551123797[/C][/ROW]
[ROW][C]132[/C][C]20[/C][C]13.197760758898[/C][C]6.80223924110203[/C][/ROW]
[ROW][C]133[/C][C]10[/C][C]13.4839204850288[/C][C]-3.48392048502882[/C][/ROW]
[ROW][C]134[/C][C]15[/C][C]13.3511219299556[/C][C]1.6488780700444[/C][/ROW]
[ROW][C]135[/C][C]12[/C][C]13.3283425051897[/C][C]-1.32834250518966[/C][/ROW]
[ROW][C]136[/C][C]14[/C][C]13.1052688785981[/C][C]0.894731121401904[/C][/ROW]
[ROW][C]137[/C][C]23[/C][C]13.6337090747138[/C][C]9.36629092528616[/C][/ROW]
[ROW][C]138[/C][C]14[/C][C]13.5650469796211[/C][C]0.434953020378861[/C][/ROW]
[ROW][C]139[/C][C]16[/C][C]13.3692833295079[/C][C]2.63071667049212[/C][/ROW]
[ROW][C]140[/C][C]11[/C][C]13.2722993450937[/C][C]-2.27229934509375[/C][/ROW]
[ROW][C]141[/C][C]12[/C][C]13.4208781222402[/C][C]-1.42087812224019[/C][/ROW]
[ROW][C]142[/C][C]10[/C][C]13.5275041622253[/C][C]-3.52750416222528[/C][/ROW]
[ROW][C]143[/C][C]14[/C][C]13.845882179845[/C][C]0.154117820154974[/C][/ROW]
[ROW][C]144[/C][C]12[/C][C]12.9281314922843[/C][C]-0.928131492284295[/C][/ROW]
[ROW][C]145[/C][C]12[/C][C]13.3154618947937[/C][C]-1.31546189479368[/C][/ROW]
[ROW][C]146[/C][C]11[/C][C]13.1270850438717[/C][C]-2.12708504387174[/C][/ROW]
[ROW][C]147[/C][C]12[/C][C]13.5686530919917[/C][C]-1.56865309199168[/C][/ROW]
[ROW][C]148[/C][C]13[/C][C]13.0508231275394[/C][C]-0.0508231275393683[/C][/ROW]
[ROW][C]149[/C][C]11[/C][C]13.1020838937241[/C][C]-2.10208389372409[/C][/ROW]
[ROW][C]150[/C][C]19[/C][C]13.0420956781099[/C][C]5.95790432189012[/C][/ROW]
[ROW][C]151[/C][C]12[/C][C]13.3547280423261[/C][C]-1.35472804232614[/C][/ROW]
[ROW][C]152[/C][C]17[/C][C]13.665845181854[/C][C]3.33415481814602[/C][/ROW]
[ROW][C]153[/C][C]9[/C][C]13.0478462481135[/C][C]-4.04784624811354[/C][/ROW]
[ROW][C]154[/C][C]12[/C][C]13.6169369787506[/C][C]-1.61693697875056[/C][/ROW]
[ROW][C]155[/C][C]19[/C][C]13.3247363928191[/C][C]5.67526360718088[/C][/ROW]
[ROW][C]156[/C][C]18[/C][C]13.1446172104793[/C][C]4.85538278952069[/C][/ROW]
[ROW][C]157[/C][C]15[/C][C]13.4098030263296[/C][C]1.59019697367043[/C][/ROW]
[ROW][C]158[/C][C]14[/C][C]13.8032952931388[/C][C]0.196704706861201[/C][/ROW]
[ROW][C]159[/C][C]11[/C][C]13.4905083934254[/C][C]-2.49050839342536[/C][/ROW]
[ROW][C]160[/C][C]9[/C][C]13.169071312031[/C][C]-4.16907131203102[/C][/ROW]
[ROW][C]161[/C][C]18[/C][C]12.8543480601326[/C][C]5.14565193986736[/C][/ROW]
[ROW][C]162[/C][C]16[/C][C]12.865505340392[/C][C]3.13449465960798[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145408&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145408&T=4

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


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11211.95921499213030.0407850078697264
21112.1790165328989-1.17901653289887
31412.10967655151061.89032344848936
41212.353385145235-0.35338514523502
52112.61219607473718.38780392526291
61212.4239835925127-0.423983592512665
72212.55053809893739.44946190106272
81112.4772530620307-1.47725306203074
91012.1631590430771-2.16315904307707
101312.25833751300580.741662486994162
111012.7425697155806-2.74256971558059
12812.4322899144456-4.43228991444563
131512.32650121285342.67349878714658
141412.22633224356531.77366775643471
151012.3377406774616-2.33774067746156
161412.4327061253421.56729387465802
171412.94567642813451.05432357186548
181112.3224302362357-1.3224302362357
191012.6270903050666-2.62709030506659
201312.67205345265170.327946547348294
21712.1080018747249-5.10800187472487
221412.6615254053371.33847459466297
231212.5379142473403-0.537914247340314
241412.41836386020861.5816361397914
251112.8324187430022-1.83241874300223
26911.9285941096785-2.92859410967853
271111.9285941096785-0.928594109678534
281512.287491824122.71250817588003
291411.97305886201852.02694113798147
301312.30288444969460.697115550305403
31912.314041729954-3.31404172995398
321512.19017381315832.80982618684175
331012.0751878812892-2.0751878812892
341112.7643091607829-1.76430916078294
351313.0280333218958-0.028033321895784
36812.7224050769724-4.72240507697242
372013.38147567273076.61852432726932
381212.7970745008678-0.797074500867788
391012.8876349455829-2.88763494558289
401012.8040786201607-2.80407862016069
41913.1362518543179-4.13625185431788
421412.92232680465231.07767319534766
43812.8849920927047-4.88499209270466
441412.58226345945851.41773654054146
451112.4673310975405-1.46733109754048
461313.2269333035322-0.226933303532228
47912.6517293619462-3.65172936194619
481113.2557535880988-2.25575358809877
491512.72635013249072.27364986750927
501113.3688031677829-2.36880316778288
511012.6870018006095-2.68700180060951
521412.72052229473851.27947770526152
531813.14996980310674.85003019689326
541412.51733339783581.48266660216421
551112.7539941355166-1.75399413551662
561212.8368390436454-0.836839043645359
571312.76230045744960.237699542550418
58912.7316795749979-3.73167957499786
591013.156054399658-3.15605439965799
601512.50571125332922.49428874667077
612012.69967430555737.30032569444269
621212.9086088558635-0.908608855863481
631212.5466520776474-0.546652077647447
641412.66213148816141.33786851183855
651312.74852893870970.251471061290277
661113.0489634954257-2.04896349542572
671712.71527503658014.28472496341989
681212.6468210469356-0.646821046935584
691312.91817364368590.0818263563141436
701413.13319279054330.866807209456715
711312.93209969792290.067900302077109
721512.7873837919462.212616208054
731312.61141777057270.388582229427328
741012.7310016887023-2.73100168870232
751112.8856213256494-1.88562132564936
761912.74740622712016.25259377287993
771312.65139041879840.348609581201579
781713.00525389712983.99474610287016
791312.94048820420460.0595117957953838
80912.6606649168239-3.66066491682386
811112.6760575423985-1.67605754239848
821012.6976606856238-2.69766068562377
83912.680210703365-3.68021070336496
841212.9070163634265-0.907016363426477
851212.869889756927-0.869889756926971
861312.74845658756130.251543412438692
871312.98855906891520.0114409310848485
881213.0820092921072-1.08200929210716
891512.86288563763412.13711436236593
902212.93390029580819.06609970419193
911312.2418276401190.758172359880965
921512.35046730003772.64953269996234
931313.0018558902075-0.00185589020748068
941512.70944719882792.29055280117214
951013.1543310695214-3.15433106952139
961113.0722413154368-2.07224131543678
971612.66351587515033.33648412484973
981112.6664927545761-1.66649275457611
991112.8624694267377-1.86246942673772
1001013.1936075979315-3.19360759793149
1011013.5111482771584-3.51114827715836
1021612.99906396610953.00093603389047
1031213.2750681190714-1.27506811907141
1041113.3233520058303-2.32335200583029
1051613.18555803479752.81444196520247
1061913.27804499849725.72195500150276
1071113.3014536562079-2.30145365620789
1081613.11621805340932.8837819465907
1091513.31634788653741.68365211346261
1102413.180020486842210.8199795131578
1111413.8298165845750.170183415424958
1121513.51475438952891.48524561047111
1131112.9503638684543-1.95036386845429
1141513.21915579667511.78084420332491
1151213.4012885989484-1.40128859894843
1161013.1056078217459-3.10560782174586
1171413.79046825269380.209531747306174
1181313.5366478225511-0.536647822551126
119913.2141652973157-4.21416529731573
1201513.54633853147291.45366146852708
1211513.16671874894971.83328125105028
1221413.38639436861890.613605631381079
1231113.5884556373318-2.58845563733179
124812.7800948471336-4.78009484713363
1251113.56269933314-2.56269933314001
1261113.2851799554897-2.28517995548972
127813.1591724976611-5.15917249766105
1281013.5113563826065-3.51135638260653
1291113.1410110981088-2.14101109810877
1301313.1160099479611-0.116009947961121
1311113.242225511238-2.24222551123797
1322013.1977607588986.80223924110203
1331013.4839204850288-3.48392048502882
1341513.35112192995561.6488780700444
1351213.3283425051897-1.32834250518966
1361413.10526887859810.894731121401904
1372313.63370907471389.36629092528616
1381413.56504697962110.434953020378861
1391613.36928332950792.63071667049212
1401113.2722993450937-2.27229934509375
1411213.4208781222402-1.42087812224019
1421013.5275041622253-3.52750416222528
1431413.8458821798450.154117820154974
1441212.9281314922843-0.928131492284295
1451213.3154618947937-1.31546189479368
1461113.1270850438717-2.12708504387174
1471213.5686530919917-1.56865309199168
1481313.0508231275394-0.0508231275393683
1491113.1020838937241-2.10208389372409
1501913.04209567810995.95790432189012
1511213.3547280423261-1.35472804232614
1521713.6658451818543.33415481814602
153913.0478462481135-4.04784624811354
1541213.6169369787506-1.61693697875056
1551913.32473639281915.67526360718088
1561813.14461721047934.85538278952069
1571513.40980302632961.59019697367043
1581413.80329529313880.196704706861201
1591113.4905083934254-2.49050839342536
160913.169071312031-4.16907131203102
1611812.85434806013265.14565193986736
1621612.8655053403923.13449465960798






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.9146086588793130.1707826822413730.0853913411206867
90.8576720307091670.2846559385816670.142327969290833
100.8094516749980280.3810966500039450.190548325001972
110.9655332155267440.06893356894651250.0344667844732563
120.975176164935590.04964767012881980.0248238350644099
130.9588706125300430.08225877493991470.0411293874699573
140.935619677022140.128760645955720.06438032297786
150.9109842808517810.1780314382964390.0890157191482195
160.8732638887368160.2534722225263680.126736111263184
170.8537148194100340.2925703611799320.146285180589966
180.8102515015353080.3794969969293850.189748498464692
190.7921824188370990.4156351623258030.207817581162902
200.7368861005509420.5262277988981160.263113899449058
210.8669256278943170.2661487442113670.133074372105683
220.825975721700510.3480485565989790.17402427829949
230.7896850937117380.4206298125765240.210314906288262
240.7534317072025130.4931365855949740.246568292797487
250.7317456062035830.5365087875928340.268254393796417
260.6938629895990750.612274020801850.306137010400925
270.6364181341025020.7271637317949970.363581865897498
280.6122730360882710.7754539278234580.387726963911729
290.5795306193579320.8409387612841350.420469380642068
300.525425841549210.949148316901580.47457415845079
310.5023703120893810.9952593758212380.497629687910619
320.5060743503580730.9878512992838540.493925649641927
330.5196048916440470.9607902167119070.480395108355954
340.4627749992183030.9255499984366060.537225000781697
350.4116768181341090.8233536362682180.588323181865891
360.4019632073009470.8039264146018940.598036792699053
370.594909354495940.8101812910081210.40509064550406
380.5401715334273530.9196569331452940.459828466572647
390.5015803223364740.9968393553270520.498419677663526
400.464233656904390.928467313808780.53576634309561
410.4771507954790950.954301590958190.522849204520905
420.4456246037575650.891249207515130.554375396242435
430.4679113460726940.9358226921453870.532088653927306
440.4860170173657170.9720340347314340.513982982634283
450.4413793085279180.8827586170558360.558620691472082
460.390612032881630.781224065763260.60938796711837
470.3907070903683770.7814141807367550.609292909631623
480.3552160325143430.7104320650286860.644783967485657
490.3675921401318350.7351842802636690.632407859868165
500.3429167977263750.685833595452750.657083202273625
510.3140805146190830.6281610292381650.685919485380918
520.2857091001228510.5714182002457020.714290899877149
530.3768472514158740.7536945028317480.623152748584126
540.3459279580651820.6918559161303650.654072041934818
550.3090598247053750.618119649410750.690940175294625
560.2679389123706040.5358778247412070.732061087629396
570.2311852420611880.4623704841223770.768814757938812
580.2363352073415960.4726704146831910.763664792658404
590.2389181025551510.4778362051103020.761081897444849
600.2519052570836940.5038105141673880.748094742916306
610.4667592252723310.9335184505446620.533240774727669
620.4215106966982090.8430213933964180.578489303301791
630.3782061442179220.7564122884358440.621793855782078
640.3466228215558240.6932456431116490.653377178444176
650.3249270315277610.6498540630555210.675072968472239
660.2964252269037990.5928504538075970.703574773096202
670.3508650611659630.7017301223319270.649134938834037
680.3096222052865590.6192444105731180.690377794713441
690.2699785231977650.539957046395530.730021476802235
700.2370208739003420.4740417478006840.762979126099658
710.202943621891630.4058872437832590.79705637810837
720.1893047865401170.3786095730802330.810695213459883
730.1600740586951580.3201481173903170.839925941304842
740.155657763958960.3113155279179190.84434223604104
750.1392873335219680.2785746670439360.860712666478032
760.2294932307142070.4589864614284140.770506769285793
770.1962811439601850.392562287920370.803718856039815
780.2191565176058650.4383130352117310.780843482394135
790.1877172275006710.3754344550013430.812282772499329
800.1957325614672070.3914651229344140.804267438532793
810.1736084146868130.3472168293736270.826391585313187
820.1634457184291930.3268914368583870.836554281570807
830.1737061809967940.3474123619935880.826293819003206
840.1480857410740150.2961714821480310.851914258925984
850.1266818858423310.2533637716846620.873318114157669
860.1069738987058050.213947797411610.893026101294195
870.08803600257749450.1760720051549890.911963997422505
880.07539485862777160.1507897172555430.924605141372228
890.06564476441126770.1312895288225350.934355235588732
900.2294849824816920.4589699649633830.770515017518308
910.1990908846896930.3981817693793850.800909115310307
920.1887516392077210.3775032784154420.811248360792279
930.1594649135916820.3189298271833650.840535086408318
940.1496534542341310.2993069084682630.850346545765869
950.1424957629362880.2849915258725760.857504237063712
960.1263661867716440.2527323735432870.873633813228356
970.1337373214373740.2674746428747480.866262678562626
980.1127068585790430.2254137171580870.887293141420957
990.09528881126296370.1905776225259270.904711188737036
1000.1011504761665560.2023009523331120.898849523833444
1010.09870865164454350.1974173032890870.901291348355456
1020.1015545499117610.2031090998235220.898445450088239
1030.08644589856135120.1728917971227020.913554101438649
1040.07859952147710520.157199042954210.921400478522895
1050.07645338902633740.1529067780526750.923546610973663
1060.1163267252966480.2326534505932950.883673274703352
1070.1027529229507120.2055058459014240.897247077049288
1080.09644912628737420.1928982525747480.903550873712626
1090.08330723515217850.1666144703043570.916692764847821
1100.4294934074098910.8589868148197820.570506592590109
1110.3827089258915860.7654178517831720.617291074108414
1120.3482063481097290.6964126962194590.651793651890271
1130.3151721883313660.6303443766627320.684827811668634
1140.2882483408913690.5764966817827380.711751659108631
1150.2559148377768090.5118296755536180.744085162223191
1160.2414434878788090.4828869757576180.758556512121191
1170.2040009535905560.4080019071811120.795999046409444
1180.1704472172641770.3408944345283550.829552782735823
1190.1824805558751270.3649611117502540.817519444124873
1200.1596198760705190.3192397521410380.840380123929481
1210.1368502644308580.2737005288617160.863149735569142
1220.1100483083524710.2200966167049410.889951691647529
1230.0950976668174540.1901953336349080.904902333182546
1240.1497851478706770.2995702957413540.850214852129323
1250.1314010018421340.2628020036842670.868598998157866
1260.116401875993150.23280375198630.88359812400685
1270.1778625048471430.3557250096942850.822137495152857
1280.1711050012682740.3422100025365490.828894998731726
1290.1481910302122920.2963820604245830.851808969787708
1300.1187015898376090.2374031796752180.881298410162391
1310.1069845074091140.2139690148182290.893015492590886
1320.1738698985012890.3477397970025780.826130101498711
1330.1935798400006290.3871596800012580.806420159999371
1340.1638047061816860.3276094123633720.836195293818314
1350.1332826183743180.2665652367486350.866717381625682
1360.1030746616458350.206149323291670.896925338354165
1370.4032642584896240.8065285169792470.596735741510376
1380.3404569116309980.6809138232619960.659543088369002
1390.3429744070861340.6859488141722690.657025592913866
1400.3084593785487150.6169187570974310.691540621451285
1410.2754287853329290.5508575706658590.724571214667071
1420.2723146391613760.5446292783227510.727685360838624
1430.2126560232677080.4253120465354150.787343976732292
1440.1780186502800390.3560373005600780.821981349719961
1450.1346356518182960.2692713036365910.865364348181704
1460.1391422291277990.2782844582555970.860857770872201
1470.1082525689538410.2165051379076830.891747431046159
1480.07444479322783380.1488895864556680.925555206772166
1490.06512636016128620.1302527203225720.934873639838714
1500.08734917608542230.1746983521708450.912650823914578
1510.06971378768946150.1394275753789230.930286212310539
1520.05489520534198840.1097904106839770.945104794658012
1530.1513506832052920.3027013664105840.848649316794708
1540.08326790798649350.1665358159729870.916732092013506

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 0.914608658879313 & 0.170782682241373 & 0.0853913411206867 \tabularnewline
9 & 0.857672030709167 & 0.284655938581667 & 0.142327969290833 \tabularnewline
10 & 0.809451674998028 & 0.381096650003945 & 0.190548325001972 \tabularnewline
11 & 0.965533215526744 & 0.0689335689465125 & 0.0344667844732563 \tabularnewline
12 & 0.97517616493559 & 0.0496476701288198 & 0.0248238350644099 \tabularnewline
13 & 0.958870612530043 & 0.0822587749399147 & 0.0411293874699573 \tabularnewline
14 & 0.93561967702214 & 0.12876064595572 & 0.06438032297786 \tabularnewline
15 & 0.910984280851781 & 0.178031438296439 & 0.0890157191482195 \tabularnewline
16 & 0.873263888736816 & 0.253472222526368 & 0.126736111263184 \tabularnewline
17 & 0.853714819410034 & 0.292570361179932 & 0.146285180589966 \tabularnewline
18 & 0.810251501535308 & 0.379496996929385 & 0.189748498464692 \tabularnewline
19 & 0.792182418837099 & 0.415635162325803 & 0.207817581162902 \tabularnewline
20 & 0.736886100550942 & 0.526227798898116 & 0.263113899449058 \tabularnewline
21 & 0.866925627894317 & 0.266148744211367 & 0.133074372105683 \tabularnewline
22 & 0.82597572170051 & 0.348048556598979 & 0.17402427829949 \tabularnewline
23 & 0.789685093711738 & 0.420629812576524 & 0.210314906288262 \tabularnewline
24 & 0.753431707202513 & 0.493136585594974 & 0.246568292797487 \tabularnewline
25 & 0.731745606203583 & 0.536508787592834 & 0.268254393796417 \tabularnewline
26 & 0.693862989599075 & 0.61227402080185 & 0.306137010400925 \tabularnewline
27 & 0.636418134102502 & 0.727163731794997 & 0.363581865897498 \tabularnewline
28 & 0.612273036088271 & 0.775453927823458 & 0.387726963911729 \tabularnewline
29 & 0.579530619357932 & 0.840938761284135 & 0.420469380642068 \tabularnewline
30 & 0.52542584154921 & 0.94914831690158 & 0.47457415845079 \tabularnewline
31 & 0.502370312089381 & 0.995259375821238 & 0.497629687910619 \tabularnewline
32 & 0.506074350358073 & 0.987851299283854 & 0.493925649641927 \tabularnewline
33 & 0.519604891644047 & 0.960790216711907 & 0.480395108355954 \tabularnewline
34 & 0.462774999218303 & 0.925549998436606 & 0.537225000781697 \tabularnewline
35 & 0.411676818134109 & 0.823353636268218 & 0.588323181865891 \tabularnewline
36 & 0.401963207300947 & 0.803926414601894 & 0.598036792699053 \tabularnewline
37 & 0.59490935449594 & 0.810181291008121 & 0.40509064550406 \tabularnewline
38 & 0.540171533427353 & 0.919656933145294 & 0.459828466572647 \tabularnewline
39 & 0.501580322336474 & 0.996839355327052 & 0.498419677663526 \tabularnewline
40 & 0.46423365690439 & 0.92846731380878 & 0.53576634309561 \tabularnewline
41 & 0.477150795479095 & 0.95430159095819 & 0.522849204520905 \tabularnewline
42 & 0.445624603757565 & 0.89124920751513 & 0.554375396242435 \tabularnewline
43 & 0.467911346072694 & 0.935822692145387 & 0.532088653927306 \tabularnewline
44 & 0.486017017365717 & 0.972034034731434 & 0.513982982634283 \tabularnewline
45 & 0.441379308527918 & 0.882758617055836 & 0.558620691472082 \tabularnewline
46 & 0.39061203288163 & 0.78122406576326 & 0.60938796711837 \tabularnewline
47 & 0.390707090368377 & 0.781414180736755 & 0.609292909631623 \tabularnewline
48 & 0.355216032514343 & 0.710432065028686 & 0.644783967485657 \tabularnewline
49 & 0.367592140131835 & 0.735184280263669 & 0.632407859868165 \tabularnewline
50 & 0.342916797726375 & 0.68583359545275 & 0.657083202273625 \tabularnewline
51 & 0.314080514619083 & 0.628161029238165 & 0.685919485380918 \tabularnewline
52 & 0.285709100122851 & 0.571418200245702 & 0.714290899877149 \tabularnewline
53 & 0.376847251415874 & 0.753694502831748 & 0.623152748584126 \tabularnewline
54 & 0.345927958065182 & 0.691855916130365 & 0.654072041934818 \tabularnewline
55 & 0.309059824705375 & 0.61811964941075 & 0.690940175294625 \tabularnewline
56 & 0.267938912370604 & 0.535877824741207 & 0.732061087629396 \tabularnewline
57 & 0.231185242061188 & 0.462370484122377 & 0.768814757938812 \tabularnewline
58 & 0.236335207341596 & 0.472670414683191 & 0.763664792658404 \tabularnewline
59 & 0.238918102555151 & 0.477836205110302 & 0.761081897444849 \tabularnewline
60 & 0.251905257083694 & 0.503810514167388 & 0.748094742916306 \tabularnewline
61 & 0.466759225272331 & 0.933518450544662 & 0.533240774727669 \tabularnewline
62 & 0.421510696698209 & 0.843021393396418 & 0.578489303301791 \tabularnewline
63 & 0.378206144217922 & 0.756412288435844 & 0.621793855782078 \tabularnewline
64 & 0.346622821555824 & 0.693245643111649 & 0.653377178444176 \tabularnewline
65 & 0.324927031527761 & 0.649854063055521 & 0.675072968472239 \tabularnewline
66 & 0.296425226903799 & 0.592850453807597 & 0.703574773096202 \tabularnewline
67 & 0.350865061165963 & 0.701730122331927 & 0.649134938834037 \tabularnewline
68 & 0.309622205286559 & 0.619244410573118 & 0.690377794713441 \tabularnewline
69 & 0.269978523197765 & 0.53995704639553 & 0.730021476802235 \tabularnewline
70 & 0.237020873900342 & 0.474041747800684 & 0.762979126099658 \tabularnewline
71 & 0.20294362189163 & 0.405887243783259 & 0.79705637810837 \tabularnewline
72 & 0.189304786540117 & 0.378609573080233 & 0.810695213459883 \tabularnewline
73 & 0.160074058695158 & 0.320148117390317 & 0.839925941304842 \tabularnewline
74 & 0.15565776395896 & 0.311315527917919 & 0.84434223604104 \tabularnewline
75 & 0.139287333521968 & 0.278574667043936 & 0.860712666478032 \tabularnewline
76 & 0.229493230714207 & 0.458986461428414 & 0.770506769285793 \tabularnewline
77 & 0.196281143960185 & 0.39256228792037 & 0.803718856039815 \tabularnewline
78 & 0.219156517605865 & 0.438313035211731 & 0.780843482394135 \tabularnewline
79 & 0.187717227500671 & 0.375434455001343 & 0.812282772499329 \tabularnewline
80 & 0.195732561467207 & 0.391465122934414 & 0.804267438532793 \tabularnewline
81 & 0.173608414686813 & 0.347216829373627 & 0.826391585313187 \tabularnewline
82 & 0.163445718429193 & 0.326891436858387 & 0.836554281570807 \tabularnewline
83 & 0.173706180996794 & 0.347412361993588 & 0.826293819003206 \tabularnewline
84 & 0.148085741074015 & 0.296171482148031 & 0.851914258925984 \tabularnewline
85 & 0.126681885842331 & 0.253363771684662 & 0.873318114157669 \tabularnewline
86 & 0.106973898705805 & 0.21394779741161 & 0.893026101294195 \tabularnewline
87 & 0.0880360025774945 & 0.176072005154989 & 0.911963997422505 \tabularnewline
88 & 0.0753948586277716 & 0.150789717255543 & 0.924605141372228 \tabularnewline
89 & 0.0656447644112677 & 0.131289528822535 & 0.934355235588732 \tabularnewline
90 & 0.229484982481692 & 0.458969964963383 & 0.770515017518308 \tabularnewline
91 & 0.199090884689693 & 0.398181769379385 & 0.800909115310307 \tabularnewline
92 & 0.188751639207721 & 0.377503278415442 & 0.811248360792279 \tabularnewline
93 & 0.159464913591682 & 0.318929827183365 & 0.840535086408318 \tabularnewline
94 & 0.149653454234131 & 0.299306908468263 & 0.850346545765869 \tabularnewline
95 & 0.142495762936288 & 0.284991525872576 & 0.857504237063712 \tabularnewline
96 & 0.126366186771644 & 0.252732373543287 & 0.873633813228356 \tabularnewline
97 & 0.133737321437374 & 0.267474642874748 & 0.866262678562626 \tabularnewline
98 & 0.112706858579043 & 0.225413717158087 & 0.887293141420957 \tabularnewline
99 & 0.0952888112629637 & 0.190577622525927 & 0.904711188737036 \tabularnewline
100 & 0.101150476166556 & 0.202300952333112 & 0.898849523833444 \tabularnewline
101 & 0.0987086516445435 & 0.197417303289087 & 0.901291348355456 \tabularnewline
102 & 0.101554549911761 & 0.203109099823522 & 0.898445450088239 \tabularnewline
103 & 0.0864458985613512 & 0.172891797122702 & 0.913554101438649 \tabularnewline
104 & 0.0785995214771052 & 0.15719904295421 & 0.921400478522895 \tabularnewline
105 & 0.0764533890263374 & 0.152906778052675 & 0.923546610973663 \tabularnewline
106 & 0.116326725296648 & 0.232653450593295 & 0.883673274703352 \tabularnewline
107 & 0.102752922950712 & 0.205505845901424 & 0.897247077049288 \tabularnewline
108 & 0.0964491262873742 & 0.192898252574748 & 0.903550873712626 \tabularnewline
109 & 0.0833072351521785 & 0.166614470304357 & 0.916692764847821 \tabularnewline
110 & 0.429493407409891 & 0.858986814819782 & 0.570506592590109 \tabularnewline
111 & 0.382708925891586 & 0.765417851783172 & 0.617291074108414 \tabularnewline
112 & 0.348206348109729 & 0.696412696219459 & 0.651793651890271 \tabularnewline
113 & 0.315172188331366 & 0.630344376662732 & 0.684827811668634 \tabularnewline
114 & 0.288248340891369 & 0.576496681782738 & 0.711751659108631 \tabularnewline
115 & 0.255914837776809 & 0.511829675553618 & 0.744085162223191 \tabularnewline
116 & 0.241443487878809 & 0.482886975757618 & 0.758556512121191 \tabularnewline
117 & 0.204000953590556 & 0.408001907181112 & 0.795999046409444 \tabularnewline
118 & 0.170447217264177 & 0.340894434528355 & 0.829552782735823 \tabularnewline
119 & 0.182480555875127 & 0.364961111750254 & 0.817519444124873 \tabularnewline
120 & 0.159619876070519 & 0.319239752141038 & 0.840380123929481 \tabularnewline
121 & 0.136850264430858 & 0.273700528861716 & 0.863149735569142 \tabularnewline
122 & 0.110048308352471 & 0.220096616704941 & 0.889951691647529 \tabularnewline
123 & 0.095097666817454 & 0.190195333634908 & 0.904902333182546 \tabularnewline
124 & 0.149785147870677 & 0.299570295741354 & 0.850214852129323 \tabularnewline
125 & 0.131401001842134 & 0.262802003684267 & 0.868598998157866 \tabularnewline
126 & 0.11640187599315 & 0.2328037519863 & 0.88359812400685 \tabularnewline
127 & 0.177862504847143 & 0.355725009694285 & 0.822137495152857 \tabularnewline
128 & 0.171105001268274 & 0.342210002536549 & 0.828894998731726 \tabularnewline
129 & 0.148191030212292 & 0.296382060424583 & 0.851808969787708 \tabularnewline
130 & 0.118701589837609 & 0.237403179675218 & 0.881298410162391 \tabularnewline
131 & 0.106984507409114 & 0.213969014818229 & 0.893015492590886 \tabularnewline
132 & 0.173869898501289 & 0.347739797002578 & 0.826130101498711 \tabularnewline
133 & 0.193579840000629 & 0.387159680001258 & 0.806420159999371 \tabularnewline
134 & 0.163804706181686 & 0.327609412363372 & 0.836195293818314 \tabularnewline
135 & 0.133282618374318 & 0.266565236748635 & 0.866717381625682 \tabularnewline
136 & 0.103074661645835 & 0.20614932329167 & 0.896925338354165 \tabularnewline
137 & 0.403264258489624 & 0.806528516979247 & 0.596735741510376 \tabularnewline
138 & 0.340456911630998 & 0.680913823261996 & 0.659543088369002 \tabularnewline
139 & 0.342974407086134 & 0.685948814172269 & 0.657025592913866 \tabularnewline
140 & 0.308459378548715 & 0.616918757097431 & 0.691540621451285 \tabularnewline
141 & 0.275428785332929 & 0.550857570665859 & 0.724571214667071 \tabularnewline
142 & 0.272314639161376 & 0.544629278322751 & 0.727685360838624 \tabularnewline
143 & 0.212656023267708 & 0.425312046535415 & 0.787343976732292 \tabularnewline
144 & 0.178018650280039 & 0.356037300560078 & 0.821981349719961 \tabularnewline
145 & 0.134635651818296 & 0.269271303636591 & 0.865364348181704 \tabularnewline
146 & 0.139142229127799 & 0.278284458255597 & 0.860857770872201 \tabularnewline
147 & 0.108252568953841 & 0.216505137907683 & 0.891747431046159 \tabularnewline
148 & 0.0744447932278338 & 0.148889586455668 & 0.925555206772166 \tabularnewline
149 & 0.0651263601612862 & 0.130252720322572 & 0.934873639838714 \tabularnewline
150 & 0.0873491760854223 & 0.174698352170845 & 0.912650823914578 \tabularnewline
151 & 0.0697137876894615 & 0.139427575378923 & 0.930286212310539 \tabularnewline
152 & 0.0548952053419884 & 0.109790410683977 & 0.945104794658012 \tabularnewline
153 & 0.151350683205292 & 0.302701366410584 & 0.848649316794708 \tabularnewline
154 & 0.0832679079864935 & 0.166535815972987 & 0.916732092013506 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145408&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]8[/C][C]0.914608658879313[/C][C]0.170782682241373[/C][C]0.0853913411206867[/C][/ROW]
[ROW][C]9[/C][C]0.857672030709167[/C][C]0.284655938581667[/C][C]0.142327969290833[/C][/ROW]
[ROW][C]10[/C][C]0.809451674998028[/C][C]0.381096650003945[/C][C]0.190548325001972[/C][/ROW]
[ROW][C]11[/C][C]0.965533215526744[/C][C]0.0689335689465125[/C][C]0.0344667844732563[/C][/ROW]
[ROW][C]12[/C][C]0.97517616493559[/C][C]0.0496476701288198[/C][C]0.0248238350644099[/C][/ROW]
[ROW][C]13[/C][C]0.958870612530043[/C][C]0.0822587749399147[/C][C]0.0411293874699573[/C][/ROW]
[ROW][C]14[/C][C]0.93561967702214[/C][C]0.12876064595572[/C][C]0.06438032297786[/C][/ROW]
[ROW][C]15[/C][C]0.910984280851781[/C][C]0.178031438296439[/C][C]0.0890157191482195[/C][/ROW]
[ROW][C]16[/C][C]0.873263888736816[/C][C]0.253472222526368[/C][C]0.126736111263184[/C][/ROW]
[ROW][C]17[/C][C]0.853714819410034[/C][C]0.292570361179932[/C][C]0.146285180589966[/C][/ROW]
[ROW][C]18[/C][C]0.810251501535308[/C][C]0.379496996929385[/C][C]0.189748498464692[/C][/ROW]
[ROW][C]19[/C][C]0.792182418837099[/C][C]0.415635162325803[/C][C]0.207817581162902[/C][/ROW]
[ROW][C]20[/C][C]0.736886100550942[/C][C]0.526227798898116[/C][C]0.263113899449058[/C][/ROW]
[ROW][C]21[/C][C]0.866925627894317[/C][C]0.266148744211367[/C][C]0.133074372105683[/C][/ROW]
[ROW][C]22[/C][C]0.82597572170051[/C][C]0.348048556598979[/C][C]0.17402427829949[/C][/ROW]
[ROW][C]23[/C][C]0.789685093711738[/C][C]0.420629812576524[/C][C]0.210314906288262[/C][/ROW]
[ROW][C]24[/C][C]0.753431707202513[/C][C]0.493136585594974[/C][C]0.246568292797487[/C][/ROW]
[ROW][C]25[/C][C]0.731745606203583[/C][C]0.536508787592834[/C][C]0.268254393796417[/C][/ROW]
[ROW][C]26[/C][C]0.693862989599075[/C][C]0.61227402080185[/C][C]0.306137010400925[/C][/ROW]
[ROW][C]27[/C][C]0.636418134102502[/C][C]0.727163731794997[/C][C]0.363581865897498[/C][/ROW]
[ROW][C]28[/C][C]0.612273036088271[/C][C]0.775453927823458[/C][C]0.387726963911729[/C][/ROW]
[ROW][C]29[/C][C]0.579530619357932[/C][C]0.840938761284135[/C][C]0.420469380642068[/C][/ROW]
[ROW][C]30[/C][C]0.52542584154921[/C][C]0.94914831690158[/C][C]0.47457415845079[/C][/ROW]
[ROW][C]31[/C][C]0.502370312089381[/C][C]0.995259375821238[/C][C]0.497629687910619[/C][/ROW]
[ROW][C]32[/C][C]0.506074350358073[/C][C]0.987851299283854[/C][C]0.493925649641927[/C][/ROW]
[ROW][C]33[/C][C]0.519604891644047[/C][C]0.960790216711907[/C][C]0.480395108355954[/C][/ROW]
[ROW][C]34[/C][C]0.462774999218303[/C][C]0.925549998436606[/C][C]0.537225000781697[/C][/ROW]
[ROW][C]35[/C][C]0.411676818134109[/C][C]0.823353636268218[/C][C]0.588323181865891[/C][/ROW]
[ROW][C]36[/C][C]0.401963207300947[/C][C]0.803926414601894[/C][C]0.598036792699053[/C][/ROW]
[ROW][C]37[/C][C]0.59490935449594[/C][C]0.810181291008121[/C][C]0.40509064550406[/C][/ROW]
[ROW][C]38[/C][C]0.540171533427353[/C][C]0.919656933145294[/C][C]0.459828466572647[/C][/ROW]
[ROW][C]39[/C][C]0.501580322336474[/C][C]0.996839355327052[/C][C]0.498419677663526[/C][/ROW]
[ROW][C]40[/C][C]0.46423365690439[/C][C]0.92846731380878[/C][C]0.53576634309561[/C][/ROW]
[ROW][C]41[/C][C]0.477150795479095[/C][C]0.95430159095819[/C][C]0.522849204520905[/C][/ROW]
[ROW][C]42[/C][C]0.445624603757565[/C][C]0.89124920751513[/C][C]0.554375396242435[/C][/ROW]
[ROW][C]43[/C][C]0.467911346072694[/C][C]0.935822692145387[/C][C]0.532088653927306[/C][/ROW]
[ROW][C]44[/C][C]0.486017017365717[/C][C]0.972034034731434[/C][C]0.513982982634283[/C][/ROW]
[ROW][C]45[/C][C]0.441379308527918[/C][C]0.882758617055836[/C][C]0.558620691472082[/C][/ROW]
[ROW][C]46[/C][C]0.39061203288163[/C][C]0.78122406576326[/C][C]0.60938796711837[/C][/ROW]
[ROW][C]47[/C][C]0.390707090368377[/C][C]0.781414180736755[/C][C]0.609292909631623[/C][/ROW]
[ROW][C]48[/C][C]0.355216032514343[/C][C]0.710432065028686[/C][C]0.644783967485657[/C][/ROW]
[ROW][C]49[/C][C]0.367592140131835[/C][C]0.735184280263669[/C][C]0.632407859868165[/C][/ROW]
[ROW][C]50[/C][C]0.342916797726375[/C][C]0.68583359545275[/C][C]0.657083202273625[/C][/ROW]
[ROW][C]51[/C][C]0.314080514619083[/C][C]0.628161029238165[/C][C]0.685919485380918[/C][/ROW]
[ROW][C]52[/C][C]0.285709100122851[/C][C]0.571418200245702[/C][C]0.714290899877149[/C][/ROW]
[ROW][C]53[/C][C]0.376847251415874[/C][C]0.753694502831748[/C][C]0.623152748584126[/C][/ROW]
[ROW][C]54[/C][C]0.345927958065182[/C][C]0.691855916130365[/C][C]0.654072041934818[/C][/ROW]
[ROW][C]55[/C][C]0.309059824705375[/C][C]0.61811964941075[/C][C]0.690940175294625[/C][/ROW]
[ROW][C]56[/C][C]0.267938912370604[/C][C]0.535877824741207[/C][C]0.732061087629396[/C][/ROW]
[ROW][C]57[/C][C]0.231185242061188[/C][C]0.462370484122377[/C][C]0.768814757938812[/C][/ROW]
[ROW][C]58[/C][C]0.236335207341596[/C][C]0.472670414683191[/C][C]0.763664792658404[/C][/ROW]
[ROW][C]59[/C][C]0.238918102555151[/C][C]0.477836205110302[/C][C]0.761081897444849[/C][/ROW]
[ROW][C]60[/C][C]0.251905257083694[/C][C]0.503810514167388[/C][C]0.748094742916306[/C][/ROW]
[ROW][C]61[/C][C]0.466759225272331[/C][C]0.933518450544662[/C][C]0.533240774727669[/C][/ROW]
[ROW][C]62[/C][C]0.421510696698209[/C][C]0.843021393396418[/C][C]0.578489303301791[/C][/ROW]
[ROW][C]63[/C][C]0.378206144217922[/C][C]0.756412288435844[/C][C]0.621793855782078[/C][/ROW]
[ROW][C]64[/C][C]0.346622821555824[/C][C]0.693245643111649[/C][C]0.653377178444176[/C][/ROW]
[ROW][C]65[/C][C]0.324927031527761[/C][C]0.649854063055521[/C][C]0.675072968472239[/C][/ROW]
[ROW][C]66[/C][C]0.296425226903799[/C][C]0.592850453807597[/C][C]0.703574773096202[/C][/ROW]
[ROW][C]67[/C][C]0.350865061165963[/C][C]0.701730122331927[/C][C]0.649134938834037[/C][/ROW]
[ROW][C]68[/C][C]0.309622205286559[/C][C]0.619244410573118[/C][C]0.690377794713441[/C][/ROW]
[ROW][C]69[/C][C]0.269978523197765[/C][C]0.53995704639553[/C][C]0.730021476802235[/C][/ROW]
[ROW][C]70[/C][C]0.237020873900342[/C][C]0.474041747800684[/C][C]0.762979126099658[/C][/ROW]
[ROW][C]71[/C][C]0.20294362189163[/C][C]0.405887243783259[/C][C]0.79705637810837[/C][/ROW]
[ROW][C]72[/C][C]0.189304786540117[/C][C]0.378609573080233[/C][C]0.810695213459883[/C][/ROW]
[ROW][C]73[/C][C]0.160074058695158[/C][C]0.320148117390317[/C][C]0.839925941304842[/C][/ROW]
[ROW][C]74[/C][C]0.15565776395896[/C][C]0.311315527917919[/C][C]0.84434223604104[/C][/ROW]
[ROW][C]75[/C][C]0.139287333521968[/C][C]0.278574667043936[/C][C]0.860712666478032[/C][/ROW]
[ROW][C]76[/C][C]0.229493230714207[/C][C]0.458986461428414[/C][C]0.770506769285793[/C][/ROW]
[ROW][C]77[/C][C]0.196281143960185[/C][C]0.39256228792037[/C][C]0.803718856039815[/C][/ROW]
[ROW][C]78[/C][C]0.219156517605865[/C][C]0.438313035211731[/C][C]0.780843482394135[/C][/ROW]
[ROW][C]79[/C][C]0.187717227500671[/C][C]0.375434455001343[/C][C]0.812282772499329[/C][/ROW]
[ROW][C]80[/C][C]0.195732561467207[/C][C]0.391465122934414[/C][C]0.804267438532793[/C][/ROW]
[ROW][C]81[/C][C]0.173608414686813[/C][C]0.347216829373627[/C][C]0.826391585313187[/C][/ROW]
[ROW][C]82[/C][C]0.163445718429193[/C][C]0.326891436858387[/C][C]0.836554281570807[/C][/ROW]
[ROW][C]83[/C][C]0.173706180996794[/C][C]0.347412361993588[/C][C]0.826293819003206[/C][/ROW]
[ROW][C]84[/C][C]0.148085741074015[/C][C]0.296171482148031[/C][C]0.851914258925984[/C][/ROW]
[ROW][C]85[/C][C]0.126681885842331[/C][C]0.253363771684662[/C][C]0.873318114157669[/C][/ROW]
[ROW][C]86[/C][C]0.106973898705805[/C][C]0.21394779741161[/C][C]0.893026101294195[/C][/ROW]
[ROW][C]87[/C][C]0.0880360025774945[/C][C]0.176072005154989[/C][C]0.911963997422505[/C][/ROW]
[ROW][C]88[/C][C]0.0753948586277716[/C][C]0.150789717255543[/C][C]0.924605141372228[/C][/ROW]
[ROW][C]89[/C][C]0.0656447644112677[/C][C]0.131289528822535[/C][C]0.934355235588732[/C][/ROW]
[ROW][C]90[/C][C]0.229484982481692[/C][C]0.458969964963383[/C][C]0.770515017518308[/C][/ROW]
[ROW][C]91[/C][C]0.199090884689693[/C][C]0.398181769379385[/C][C]0.800909115310307[/C][/ROW]
[ROW][C]92[/C][C]0.188751639207721[/C][C]0.377503278415442[/C][C]0.811248360792279[/C][/ROW]
[ROW][C]93[/C][C]0.159464913591682[/C][C]0.318929827183365[/C][C]0.840535086408318[/C][/ROW]
[ROW][C]94[/C][C]0.149653454234131[/C][C]0.299306908468263[/C][C]0.850346545765869[/C][/ROW]
[ROW][C]95[/C][C]0.142495762936288[/C][C]0.284991525872576[/C][C]0.857504237063712[/C][/ROW]
[ROW][C]96[/C][C]0.126366186771644[/C][C]0.252732373543287[/C][C]0.873633813228356[/C][/ROW]
[ROW][C]97[/C][C]0.133737321437374[/C][C]0.267474642874748[/C][C]0.866262678562626[/C][/ROW]
[ROW][C]98[/C][C]0.112706858579043[/C][C]0.225413717158087[/C][C]0.887293141420957[/C][/ROW]
[ROW][C]99[/C][C]0.0952888112629637[/C][C]0.190577622525927[/C][C]0.904711188737036[/C][/ROW]
[ROW][C]100[/C][C]0.101150476166556[/C][C]0.202300952333112[/C][C]0.898849523833444[/C][/ROW]
[ROW][C]101[/C][C]0.0987086516445435[/C][C]0.197417303289087[/C][C]0.901291348355456[/C][/ROW]
[ROW][C]102[/C][C]0.101554549911761[/C][C]0.203109099823522[/C][C]0.898445450088239[/C][/ROW]
[ROW][C]103[/C][C]0.0864458985613512[/C][C]0.172891797122702[/C][C]0.913554101438649[/C][/ROW]
[ROW][C]104[/C][C]0.0785995214771052[/C][C]0.15719904295421[/C][C]0.921400478522895[/C][/ROW]
[ROW][C]105[/C][C]0.0764533890263374[/C][C]0.152906778052675[/C][C]0.923546610973663[/C][/ROW]
[ROW][C]106[/C][C]0.116326725296648[/C][C]0.232653450593295[/C][C]0.883673274703352[/C][/ROW]
[ROW][C]107[/C][C]0.102752922950712[/C][C]0.205505845901424[/C][C]0.897247077049288[/C][/ROW]
[ROW][C]108[/C][C]0.0964491262873742[/C][C]0.192898252574748[/C][C]0.903550873712626[/C][/ROW]
[ROW][C]109[/C][C]0.0833072351521785[/C][C]0.166614470304357[/C][C]0.916692764847821[/C][/ROW]
[ROW][C]110[/C][C]0.429493407409891[/C][C]0.858986814819782[/C][C]0.570506592590109[/C][/ROW]
[ROW][C]111[/C][C]0.382708925891586[/C][C]0.765417851783172[/C][C]0.617291074108414[/C][/ROW]
[ROW][C]112[/C][C]0.348206348109729[/C][C]0.696412696219459[/C][C]0.651793651890271[/C][/ROW]
[ROW][C]113[/C][C]0.315172188331366[/C][C]0.630344376662732[/C][C]0.684827811668634[/C][/ROW]
[ROW][C]114[/C][C]0.288248340891369[/C][C]0.576496681782738[/C][C]0.711751659108631[/C][/ROW]
[ROW][C]115[/C][C]0.255914837776809[/C][C]0.511829675553618[/C][C]0.744085162223191[/C][/ROW]
[ROW][C]116[/C][C]0.241443487878809[/C][C]0.482886975757618[/C][C]0.758556512121191[/C][/ROW]
[ROW][C]117[/C][C]0.204000953590556[/C][C]0.408001907181112[/C][C]0.795999046409444[/C][/ROW]
[ROW][C]118[/C][C]0.170447217264177[/C][C]0.340894434528355[/C][C]0.829552782735823[/C][/ROW]
[ROW][C]119[/C][C]0.182480555875127[/C][C]0.364961111750254[/C][C]0.817519444124873[/C][/ROW]
[ROW][C]120[/C][C]0.159619876070519[/C][C]0.319239752141038[/C][C]0.840380123929481[/C][/ROW]
[ROW][C]121[/C][C]0.136850264430858[/C][C]0.273700528861716[/C][C]0.863149735569142[/C][/ROW]
[ROW][C]122[/C][C]0.110048308352471[/C][C]0.220096616704941[/C][C]0.889951691647529[/C][/ROW]
[ROW][C]123[/C][C]0.095097666817454[/C][C]0.190195333634908[/C][C]0.904902333182546[/C][/ROW]
[ROW][C]124[/C][C]0.149785147870677[/C][C]0.299570295741354[/C][C]0.850214852129323[/C][/ROW]
[ROW][C]125[/C][C]0.131401001842134[/C][C]0.262802003684267[/C][C]0.868598998157866[/C][/ROW]
[ROW][C]126[/C][C]0.11640187599315[/C][C]0.2328037519863[/C][C]0.88359812400685[/C][/ROW]
[ROW][C]127[/C][C]0.177862504847143[/C][C]0.355725009694285[/C][C]0.822137495152857[/C][/ROW]
[ROW][C]128[/C][C]0.171105001268274[/C][C]0.342210002536549[/C][C]0.828894998731726[/C][/ROW]
[ROW][C]129[/C][C]0.148191030212292[/C][C]0.296382060424583[/C][C]0.851808969787708[/C][/ROW]
[ROW][C]130[/C][C]0.118701589837609[/C][C]0.237403179675218[/C][C]0.881298410162391[/C][/ROW]
[ROW][C]131[/C][C]0.106984507409114[/C][C]0.213969014818229[/C][C]0.893015492590886[/C][/ROW]
[ROW][C]132[/C][C]0.173869898501289[/C][C]0.347739797002578[/C][C]0.826130101498711[/C][/ROW]
[ROW][C]133[/C][C]0.193579840000629[/C][C]0.387159680001258[/C][C]0.806420159999371[/C][/ROW]
[ROW][C]134[/C][C]0.163804706181686[/C][C]0.327609412363372[/C][C]0.836195293818314[/C][/ROW]
[ROW][C]135[/C][C]0.133282618374318[/C][C]0.266565236748635[/C][C]0.866717381625682[/C][/ROW]
[ROW][C]136[/C][C]0.103074661645835[/C][C]0.20614932329167[/C][C]0.896925338354165[/C][/ROW]
[ROW][C]137[/C][C]0.403264258489624[/C][C]0.806528516979247[/C][C]0.596735741510376[/C][/ROW]
[ROW][C]138[/C][C]0.340456911630998[/C][C]0.680913823261996[/C][C]0.659543088369002[/C][/ROW]
[ROW][C]139[/C][C]0.342974407086134[/C][C]0.685948814172269[/C][C]0.657025592913866[/C][/ROW]
[ROW][C]140[/C][C]0.308459378548715[/C][C]0.616918757097431[/C][C]0.691540621451285[/C][/ROW]
[ROW][C]141[/C][C]0.275428785332929[/C][C]0.550857570665859[/C][C]0.724571214667071[/C][/ROW]
[ROW][C]142[/C][C]0.272314639161376[/C][C]0.544629278322751[/C][C]0.727685360838624[/C][/ROW]
[ROW][C]143[/C][C]0.212656023267708[/C][C]0.425312046535415[/C][C]0.787343976732292[/C][/ROW]
[ROW][C]144[/C][C]0.178018650280039[/C][C]0.356037300560078[/C][C]0.821981349719961[/C][/ROW]
[ROW][C]145[/C][C]0.134635651818296[/C][C]0.269271303636591[/C][C]0.865364348181704[/C][/ROW]
[ROW][C]146[/C][C]0.139142229127799[/C][C]0.278284458255597[/C][C]0.860857770872201[/C][/ROW]
[ROW][C]147[/C][C]0.108252568953841[/C][C]0.216505137907683[/C][C]0.891747431046159[/C][/ROW]
[ROW][C]148[/C][C]0.0744447932278338[/C][C]0.148889586455668[/C][C]0.925555206772166[/C][/ROW]
[ROW][C]149[/C][C]0.0651263601612862[/C][C]0.130252720322572[/C][C]0.934873639838714[/C][/ROW]
[ROW][C]150[/C][C]0.0873491760854223[/C][C]0.174698352170845[/C][C]0.912650823914578[/C][/ROW]
[ROW][C]151[/C][C]0.0697137876894615[/C][C]0.139427575378923[/C][C]0.930286212310539[/C][/ROW]
[ROW][C]152[/C][C]0.0548952053419884[/C][C]0.109790410683977[/C][C]0.945104794658012[/C][/ROW]
[ROW][C]153[/C][C]0.151350683205292[/C][C]0.302701366410584[/C][C]0.848649316794708[/C][/ROW]
[ROW][C]154[/C][C]0.0832679079864935[/C][C]0.166535815972987[/C][C]0.916732092013506[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145408&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145408&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.9146086588793130.1707826822413730.0853913411206867
90.8576720307091670.2846559385816670.142327969290833
100.8094516749980280.3810966500039450.190548325001972
110.9655332155267440.06893356894651250.0344667844732563
120.975176164935590.04964767012881980.0248238350644099
130.9588706125300430.08225877493991470.0411293874699573
140.935619677022140.128760645955720.06438032297786
150.9109842808517810.1780314382964390.0890157191482195
160.8732638887368160.2534722225263680.126736111263184
170.8537148194100340.2925703611799320.146285180589966
180.8102515015353080.3794969969293850.189748498464692
190.7921824188370990.4156351623258030.207817581162902
200.7368861005509420.5262277988981160.263113899449058
210.8669256278943170.2661487442113670.133074372105683
220.825975721700510.3480485565989790.17402427829949
230.7896850937117380.4206298125765240.210314906288262
240.7534317072025130.4931365855949740.246568292797487
250.7317456062035830.5365087875928340.268254393796417
260.6938629895990750.612274020801850.306137010400925
270.6364181341025020.7271637317949970.363581865897498
280.6122730360882710.7754539278234580.387726963911729
290.5795306193579320.8409387612841350.420469380642068
300.525425841549210.949148316901580.47457415845079
310.5023703120893810.9952593758212380.497629687910619
320.5060743503580730.9878512992838540.493925649641927
330.5196048916440470.9607902167119070.480395108355954
340.4627749992183030.9255499984366060.537225000781697
350.4116768181341090.8233536362682180.588323181865891
360.4019632073009470.8039264146018940.598036792699053
370.594909354495940.8101812910081210.40509064550406
380.5401715334273530.9196569331452940.459828466572647
390.5015803223364740.9968393553270520.498419677663526
400.464233656904390.928467313808780.53576634309561
410.4771507954790950.954301590958190.522849204520905
420.4456246037575650.891249207515130.554375396242435
430.4679113460726940.9358226921453870.532088653927306
440.4860170173657170.9720340347314340.513982982634283
450.4413793085279180.8827586170558360.558620691472082
460.390612032881630.781224065763260.60938796711837
470.3907070903683770.7814141807367550.609292909631623
480.3552160325143430.7104320650286860.644783967485657
490.3675921401318350.7351842802636690.632407859868165
500.3429167977263750.685833595452750.657083202273625
510.3140805146190830.6281610292381650.685919485380918
520.2857091001228510.5714182002457020.714290899877149
530.3768472514158740.7536945028317480.623152748584126
540.3459279580651820.6918559161303650.654072041934818
550.3090598247053750.618119649410750.690940175294625
560.2679389123706040.5358778247412070.732061087629396
570.2311852420611880.4623704841223770.768814757938812
580.2363352073415960.4726704146831910.763664792658404
590.2389181025551510.4778362051103020.761081897444849
600.2519052570836940.5038105141673880.748094742916306
610.4667592252723310.9335184505446620.533240774727669
620.4215106966982090.8430213933964180.578489303301791
630.3782061442179220.7564122884358440.621793855782078
640.3466228215558240.6932456431116490.653377178444176
650.3249270315277610.6498540630555210.675072968472239
660.2964252269037990.5928504538075970.703574773096202
670.3508650611659630.7017301223319270.649134938834037
680.3096222052865590.6192444105731180.690377794713441
690.2699785231977650.539957046395530.730021476802235
700.2370208739003420.4740417478006840.762979126099658
710.202943621891630.4058872437832590.79705637810837
720.1893047865401170.3786095730802330.810695213459883
730.1600740586951580.3201481173903170.839925941304842
740.155657763958960.3113155279179190.84434223604104
750.1392873335219680.2785746670439360.860712666478032
760.2294932307142070.4589864614284140.770506769285793
770.1962811439601850.392562287920370.803718856039815
780.2191565176058650.4383130352117310.780843482394135
790.1877172275006710.3754344550013430.812282772499329
800.1957325614672070.3914651229344140.804267438532793
810.1736084146868130.3472168293736270.826391585313187
820.1634457184291930.3268914368583870.836554281570807
830.1737061809967940.3474123619935880.826293819003206
840.1480857410740150.2961714821480310.851914258925984
850.1266818858423310.2533637716846620.873318114157669
860.1069738987058050.213947797411610.893026101294195
870.08803600257749450.1760720051549890.911963997422505
880.07539485862777160.1507897172555430.924605141372228
890.06564476441126770.1312895288225350.934355235588732
900.2294849824816920.4589699649633830.770515017518308
910.1990908846896930.3981817693793850.800909115310307
920.1887516392077210.3775032784154420.811248360792279
930.1594649135916820.3189298271833650.840535086408318
940.1496534542341310.2993069084682630.850346545765869
950.1424957629362880.2849915258725760.857504237063712
960.1263661867716440.2527323735432870.873633813228356
970.1337373214373740.2674746428747480.866262678562626
980.1127068585790430.2254137171580870.887293141420957
990.09528881126296370.1905776225259270.904711188737036
1000.1011504761665560.2023009523331120.898849523833444
1010.09870865164454350.1974173032890870.901291348355456
1020.1015545499117610.2031090998235220.898445450088239
1030.08644589856135120.1728917971227020.913554101438649
1040.07859952147710520.157199042954210.921400478522895
1050.07645338902633740.1529067780526750.923546610973663
1060.1163267252966480.2326534505932950.883673274703352
1070.1027529229507120.2055058459014240.897247077049288
1080.09644912628737420.1928982525747480.903550873712626
1090.08330723515217850.1666144703043570.916692764847821
1100.4294934074098910.8589868148197820.570506592590109
1110.3827089258915860.7654178517831720.617291074108414
1120.3482063481097290.6964126962194590.651793651890271
1130.3151721883313660.6303443766627320.684827811668634
1140.2882483408913690.5764966817827380.711751659108631
1150.2559148377768090.5118296755536180.744085162223191
1160.2414434878788090.4828869757576180.758556512121191
1170.2040009535905560.4080019071811120.795999046409444
1180.1704472172641770.3408944345283550.829552782735823
1190.1824805558751270.3649611117502540.817519444124873
1200.1596198760705190.3192397521410380.840380123929481
1210.1368502644308580.2737005288617160.863149735569142
1220.1100483083524710.2200966167049410.889951691647529
1230.0950976668174540.1901953336349080.904902333182546
1240.1497851478706770.2995702957413540.850214852129323
1250.1314010018421340.2628020036842670.868598998157866
1260.116401875993150.23280375198630.88359812400685
1270.1778625048471430.3557250096942850.822137495152857
1280.1711050012682740.3422100025365490.828894998731726
1290.1481910302122920.2963820604245830.851808969787708
1300.1187015898376090.2374031796752180.881298410162391
1310.1069845074091140.2139690148182290.893015492590886
1320.1738698985012890.3477397970025780.826130101498711
1330.1935798400006290.3871596800012580.806420159999371
1340.1638047061816860.3276094123633720.836195293818314
1350.1332826183743180.2665652367486350.866717381625682
1360.1030746616458350.206149323291670.896925338354165
1370.4032642584896240.8065285169792470.596735741510376
1380.3404569116309980.6809138232619960.659543088369002
1390.3429744070861340.6859488141722690.657025592913866
1400.3084593785487150.6169187570974310.691540621451285
1410.2754287853329290.5508575706658590.724571214667071
1420.2723146391613760.5446292783227510.727685360838624
1430.2126560232677080.4253120465354150.787343976732292
1440.1780186502800390.3560373005600780.821981349719961
1450.1346356518182960.2692713036365910.865364348181704
1460.1391422291277990.2782844582555970.860857770872201
1470.1082525689538410.2165051379076830.891747431046159
1480.07444479322783380.1488895864556680.925555206772166
1490.06512636016128620.1302527203225720.934873639838714
1500.08734917608542230.1746983521708450.912650823914578
1510.06971378768946150.1394275753789230.930286212310539
1520.05489520534198840.1097904106839770.945104794658012
1530.1513506832052920.3027013664105840.848649316794708
1540.08326790798649350.1665358159729870.916732092013506






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level10.00680272108843537OK
10% type I error level30.0204081632653061OK

\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 & 1 & 0.00680272108843537 & OK \tabularnewline
10% type I error level & 3 & 0.0204081632653061 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145408&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]1[/C][C]0.00680272108843537[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]3[/C][C]0.0204081632653061[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145408&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145408&T=6

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


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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level10.00680272108843537OK
10% type I error level30.0204081632653061OK


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 4
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Figure 5
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Figure 6
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Figure 7
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Figure 8
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Figure 9
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Figure 10
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Input Parameters & R Code

Parameters (Session):
par1 = 0 ; par2 = 36 ;
Parameters (R input):
par1 = 5 ; 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')
}