Free Statistics

of Irreproducible Research!

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationThu, 24 Nov 2011 13:20:05 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Nov/24/t1322159020ryy0kkecet2x3qb.htm/, Retrieved Fri, 29 Mar 2024 06:16:07 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=147129, Retrieved Fri, 29 Mar 2024 06:16:07 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact70
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [workshop 7: multi...] [2011-11-24 18:20:05] [d7127d50f40450f0f3837a0965e389eb] [Current]
Feedback Forum

Post a new message
Dataseries X:
41	38	13	12	14	12
39	32	16	11	18	11
30	35	19	15	11	14
31	33	15	6	12	12
34	37	14	13	16	21
35	29	13	10	18	12
39	31	19	12	14	22
34	36	15	14	14	11
36	35	14	12	15	10
37	38	15	6	15	13
38	31	16	10	17	10
36	34	16	12	19	8
38	35	16	12	10	15
39	38	16	11	16	14
33	37	17	15	18	10
32	33	15	12	14	14
36	32	15	10	14	14
38	38	20	12	17	11
39	38	18	11	14	10
32	32	16	12	16	13
32	33	16	11	18	7
31	31	16	12	11	14
39	38	19	13	14	12
37	39	16	11	12	14
39	32	17	9	17	11
41	32	17	13	9	9
36	35	16	10	16	11
33	37	15	14	14	15
33	33	16	12	15	14
34	33	14	10	11	13
31	28	15	12	16	9
27	32	12	8	13	15
37	31	14	10	17	10
34	37	16	12	15	11
34	30	14	12	14	13
32	33	7	7	16	8
29	31	10	6	9	20
36	33	14	12	15	12
29	31	16	10	17	10
35	33	16	10	13	10
37	32	16	10	15	9
34	33	14	12	16	14
38	32	20	15	16	8
35	33	14	10	12	14
38	28	14	10	12	11
37	35	11	12	11	13
38	39	14	13	15	9
33	34	15	11	15	11
36	38	16	11	17	15
38	32	14	12	13	11
32	38	16	14	16	10
32	30	14	10	14	14
32	33	12	12	11	18
34	38	16	13	12	14
32	32	9	5	12	11
37	32	14	6	15	12
39	34	16	12	16	13
29	34	16	12	15	9
37	36	15	11	12	10
35	34	16	10	12	15
30	28	12	7	8	20
38	34	16	12	13	12
34	35	16	14	11	12
31	35	14	11	14	14
34	31	16	12	15	13
35	37	17	13	10	11
36	35	18	14	11	17
30	27	18	11	12	12
39	40	12	12	15	13
35	37	16	12	15	14
38	36	10	8	14	13
31	38	14	11	16	15
34	39	18	14	15	13
38	41	18	14	15	10
34	27	16	12	13	11
39	30	17	9	12	19
37	37	16	13	17	13
34	31	16	11	13	17
28	31	13	12	15	13
37	27	16	12	13	9
33	36	16	12	15	11
37	38	20	12	16	10
35	37	16	12	15	9
37	33	15	12	16	12
32	34	15	11	15	12
33	31	16	10	14	13
38	39	14	9	15	13
33	34	16	12	14	12
29	32	16	12	13	15
33	33	15	12	7	22
31	36	12	9	17	13
36	32	17	15	13	15
35	41	16	12	15	13
32	28	15	12	14	15
29	30	13	12	13	10
39	36	16	10	16	11
37	35	16	13	12	16
35	31	16	9	14	11
37	34	16	12	17	11
32	36	14	10	15	10
38	36	16	14	17	10
37	35	16	11	12	16
36	37	20	15	16	12
32	28	15	11	11	11
33	39	16	11	15	16
40	32	13	12	9	19
38	35	17	12	16	11
41	39	16	12	15	16
36	35	16	11	10	15
43	42	12	7	10	24
30	34	16	12	15	14
31	33	16	14	11	15
32	41	17	11	13	11
32	33	13	11	14	15
37	34	12	10	18	12
37	32	18	13	16	10
33	40	14	13	14	14
34	40	14	8	14	13
33	35	13	11	14	9
38	36	16	12	14	15
33	37	13	11	12	15
31	27	16	13	14	14
38	39	13	12	15	11
37	38	16	14	15	8
33	31	15	13	15	11
31	33	16	15	13	11
39	32	15	10	17	8
44	39	17	11	17	10
33	36	15	9	19	11
35	33	12	11	15	13
32	33	16	10	13	11
28	32	10	11	9	20
40	37	16	8	15	10
27	30	12	11	15	15
37	38	14	12	15	12
32	29	15	12	16	14
28	22	13	9	11	23
34	35	15	11	14	14
30	35	11	10	11	16
35	34	12	8	15	11
31	35	8	9	13	12
32	34	16	8	15	10
30	34	15	9	16	14
30	35	17	15	14	12
31	23	16	11	15	12
40	31	10	8	16	11
32	27	18	13	16	12
36	36	13	12	11	13
32	31	16	12	12	11
35	32	13	9	9	19
38	39	10	7	16	12
42	37	15	13	13	17
34	38	16	9	16	9
35	39	16	6	12	12
35	34	14	8	9	19
33	31	10	8	13	18
36	32	17	15	13	15
32	37	13	6	14	14
33	36	15	9	19	11
34	32	16	11	13	9
32	35	12	8	12	18
34	36	13	8	13	16




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'George Udny Yule' @ yule.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 & 7 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147129&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]7 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147129&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147129&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'George Udny Yule' @ yule.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Learning[t] = + 5.84193985565273 + 0.115557843089715Connected[t] -0.0240820190849224Separate[t] + 0.545468871332887Software[t] + 0.0641530463219263Happiness[t] -0.077578587192158Depression[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Learning[t] =  +  5.84193985565273 +  0.115557843089715Connected[t] -0.0240820190849224Separate[t] +  0.545468871332887Software[t] +  0.0641530463219263Happiness[t] -0.077578587192158Depression[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147129&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Learning[t] =  +  5.84193985565273 +  0.115557843089715Connected[t] -0.0240820190849224Separate[t] +  0.545468871332887Software[t] +  0.0641530463219263Happiness[t] -0.077578587192158Depression[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147129&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147129&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
Learning[t] = + 5.84193985565273 + 0.115557843089715Connected[t] -0.0240820190849224Separate[t] + 0.545468871332887Software[t] + 0.0641530463219263Happiness[t] -0.077578587192158Depression[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)5.841939855652732.3830462.45150.0153310.007666
Connected0.1155578430897150.0465712.48130.0141510.007075
Separate-0.02408201908492240.044292-0.54370.5874180.293709
Software0.5454688713328870.0685687.955200
Happiness0.06415304632192630.0747940.85770.3923580.196179
Depression-0.0775785871921580.055104-1.40790.1611630.080581

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & 5.84193985565273 & 2.383046 & 2.4515 & 0.015331 & 0.007666 \tabularnewline
Connected & 0.115557843089715 & 0.046571 & 2.4813 & 0.014151 & 0.007075 \tabularnewline
Separate & -0.0240820190849224 & 0.044292 & -0.5437 & 0.587418 & 0.293709 \tabularnewline
Software & 0.545468871332887 & 0.068568 & 7.9552 & 0 & 0 \tabularnewline
Happiness & 0.0641530463219263 & 0.074794 & 0.8577 & 0.392358 & 0.196179 \tabularnewline
Depression & -0.077578587192158 & 0.055104 & -1.4079 & 0.161163 & 0.080581 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147129&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]5.84193985565273[/C][C]2.383046[/C][C]2.4515[/C][C]0.015331[/C][C]0.007666[/C][/ROW]
[ROW][C]Connected[/C][C]0.115557843089715[/C][C]0.046571[/C][C]2.4813[/C][C]0.014151[/C][C]0.007075[/C][/ROW]
[ROW][C]Separate[/C][C]-0.0240820190849224[/C][C]0.044292[/C][C]-0.5437[/C][C]0.587418[/C][C]0.293709[/C][/ROW]
[ROW][C]Software[/C][C]0.545468871332887[/C][C]0.068568[/C][C]7.9552[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Happiness[/C][C]0.0641530463219263[/C][C]0.074794[/C][C]0.8577[/C][C]0.392358[/C][C]0.196179[/C][/ROW]
[ROW][C]Depression[/C][C]-0.077578587192158[/C][C]0.055104[/C][C]-1.4079[/C][C]0.161163[/C][C]0.080581[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147129&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147129&T=2

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)5.841939855652732.3830462.45150.0153310.007666
Connected0.1155578430897150.0465712.48130.0141510.007075
Separate-0.02408201908492240.044292-0.54370.5874180.293709
Software0.5454688713328870.0685687.955200
Happiness0.06415304632192630.0747940.85770.3923580.196179
Depression-0.0775785871921580.055104-1.40790.1611630.080581







Multiple Linear Regression - Regression Statistics
Multiple R0.594884268267061
R-squared0.353887292631636
Adjusted R-squared0.333178552010855
F-TEST (value)17.0887886961371
F-TEST (DF numerator)5
F-TEST (DF denominator)156
p-value1.84963155902551e-13
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.84244327338335
Sum Squared Residuals529.557165639147

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.594884268267061 \tabularnewline
R-squared & 0.353887292631636 \tabularnewline
Adjusted R-squared & 0.333178552010855 \tabularnewline
F-TEST (value) & 17.0887886961371 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 156 \tabularnewline
p-value & 1.84963155902551e-13 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.84244327338335 \tabularnewline
Sum Squared Residuals & 529.557165639147 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147129&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.594884268267061[/C][/ROW]
[ROW][C]R-squared[/C][C]0.353887292631636[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.333178552010855[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]17.0887886961371[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]156[/C][/ROW]
[ROW][C]p-value[/C][C]1.84963155902551e-13[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.84244327338335[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]529.557165639147[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147129&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147129&T=3

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Regression Statistics
Multiple R0.594884268267061
R-squared0.353887292631636
Adjusted R-squared0.333178552010855
F-TEST (value)17.0887886961371
F-TEST (DF numerator)5
F-TEST (DF denominator)156
p-value1.84963155902551e-13
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.84244327338335
Sum Squared Residuals529.557165639147







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11316.1775207552997-3.17752075529972
21615.87961908477680.12038091522322
31916.26742083921622.73257916078383
41511.7412330991863.25876690081401
51415.3682655520039-1.36826555200394
61314.8665863111476-1.86658631114764
71915.33919333079313.66080666920685
81516.5852962216995-1.58529622169947
91415.8912878178121-1.89128781781213
101512.42905061407332.57094938592672
111615.25609993030930.743900069690669
121616.3271391965691-0.327139196569078
131615.41374533642110.586254663578858
141615.37408511604690.625914883953083
151717.3253160033376-0.325316003337587
161515.1027530885326-0.102753088532562
171514.49812873731060.501871262689432
182016.10088495218853.89911504781151
191815.55609337217172.4439066278283
201615.33271978745350.667280212546505
211615.35694651283250.643053487167514
221614.84290014464691.15709985535309
231916.49187394045322.50812605954684
241614.86227522549491.13772477450514
251714.72452829578912.27547170421092
261716.7794522711090.220547728891038
271614.78692453427611.21307546572387
281516.1353420107562-1.1353420107562
291615.28246397794420.717536022055797
301414.1280504802726-0.128050480272596
311515.6238043694721-0.623804369472103
321212.2254387733233-0.225438773323278
331415.1405420872196-1.14054208721962
341615.53442950627070.465570493729298
351415.4836934191589-1.48369341915892
36712.9691863476649-5.96918634766493
371010.7451936146734-0.74519361467336
381415.7842946815977-1.78429468159766
391614.21607934250191.7839206574981
401614.60465017758261.39534982241736
411615.0657325626830.934267437317001
421415.4621748673558-1.46217486735584
432018.05036639595121.94963360404877
441414.2301827824921-0.230182782492079
451414.9300021687623-0.930002168762309
461115.5174977140377-4.51749771403767
471416.6491228861769-2.64912288617692
481514.94564884910290.0543511508971324
491615.01398604590750.986013954092458
501415.9887648814103-1.98876488141032
511616.5119011771862-0.51190117718621
521414.0840614031216-0.0840614031215548
531214.5999796007982-2.59997960079815
541615.63062145797640.369378542023586
55911.4129826772199-2.41298267721989
561412.6511213157751.34887868422502
571616.0934606509117-0.0934606509116523
581615.18404352246120.815956477538787
591515.2448356315183-0.24483563151826
601614.1285221762151.871477823785
611211.41431334002880.585686659971197
621615.86302225604830.136977743951683
631616.3393405146265-0.339340514626459
641414.3935623359401-0.393562335940116
651615.52376444639590.47623555360408
661715.87469098908371.12530901091633
671816.18256326484511.8174367351549
681814.49751172727023.50248827272976
691215.8848154900802-3.88481549008019
701615.41725158778390.582748412216057
711013.6195571916767-3.61955719167669
721414.372043784137-0.372043784137043
731816.42204603638231.57795396361768
741817.06884913214780.931150867852196
751615.64694360447610.353056395523927
761713.8312984048123.16870159518797
771616.3997208251323-0.39972082513227
781614.53967513365051.46032486634945
791314.8304173878576-1.83041738785763
801616.1487743081295-0.148774308129534
811615.44295368226590.55704631773409
822015.9987526499694.00124735003099
831615.80514452374470.194855476255267
841515.9640055710093-0.964005571009304
851514.7525124188210.247487581179005
861614.25311581431851.74688418568149
871414.1569330520767-0.156933052076737
881615.34938608692170.65061391307833
891614.63842994483431.36157005516574
901514.14861090983150.851389090168471
911213.5485803003474-1.54858030034736
921717.0837414604609-0.0837414604609213
931615.39850209863640.601497901363589
941515.145584596765-0.145584596765016
951315.0744869189649-2.07448691896489
961615.10951604446030.89048395553965
971615.8943838701160.105616129883991
981614.09391980354941.90608019645064
991616.0816551854385-0.0816551854384656
1001414.3140366837026-0.314036683702579
1011617.3175653202163-1.31756532021627
1021614.80344612745021.19655387254977
1032017.38852626557862.61147373442144
1041514.7179709352350.282029064765018
1051614.43734581771751.56265418228253
1061315.3426396847648-2.34263968476478
1071716.10897796312130.891022036878669
1081615.90727743376810.092722566231931
1091614.63716077890881.36283922109117
1101212.3974087768814-0.397408776881397
1111614.91170842959011.08829157040986
1121615.80809526195070.191904738049314
1131714.53321077977482.46678922022516
1141314.4797056300075-1.47970563000752
1151214.9772919019025-2.97729190190246
1161816.68871363581141.31128636418857
1171415.5952056693607-1.59520566936071
1181413.06099774297810.939002257021857
1191315.0125709580803-2.01257095808033
1201615.64627550262390.353724497376076
1211314.3706293041137-1.37062930411369
1221615.67715623128530.32284376871473
1231315.9484968404597-2.94849684045972
1241617.1806945206972-1.18069452069717
1251516.1088326490234-1.10883264902341
1261616.7921845746961-0.792184574696058
1271515.5027329286984-0.502732928698439
1281716.30225970750110.697740292498874
1291514.0631592535550.936840746445047
1301215.0456893799829-3.0456893799829
1311614.18039806112131.81960193887866
1321013.3328981091632-3.33289810916316
1331614.12347966666961.8765203333304
1341214.0383155181356-2.03831551813564
1351415.7794424292628-1.77944242926277
1361515.3273872575161-0.327387257516104
1371312.3783508884140.621649111586012
1381514.74023586520930.259764134790741
1391113.3849193081674-2.38491930816742
1401213.5403579212836-1.54035792128363
141813.3936287213367-5.39362872133673
1421613.27126297920662.72873702079335
1431513.33945486191341.6605451380866
1441716.61503715256630.384962847433733
1451614.90185678566541.09814321433457
1461014.2545462403089-4.2545462403089
1471816.07617734140321.92382265859685
1481315.377857851863-2.37785785186303
1491615.2553467956350.744653204364971
1501313.1284438553175-0.128443855317545
1511013.207726942925-3.20772694292505
1521516.4105835065245-1.4105835065245
1531614.09325109389341.90674890610664
1541612.05897235703533.94102764296469
1551412.53481094581481.46518905418519
1561012.71013208937-2.71013208937001
1571717.0837414604609-0.0837414604609213
1581311.73361178419551.26638821580445
1591514.0631592535550.936840746445047
1601615.13622181210290.863778187897109
1611212.4340931236187-0.434093123618684
1621312.86043701141940.139562988580568

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 13 & 16.1775207552997 & -3.17752075529972 \tabularnewline
2 & 16 & 15.8796190847768 & 0.12038091522322 \tabularnewline
3 & 19 & 16.2674208392162 & 2.73257916078383 \tabularnewline
4 & 15 & 11.741233099186 & 3.25876690081401 \tabularnewline
5 & 14 & 15.3682655520039 & -1.36826555200394 \tabularnewline
6 & 13 & 14.8665863111476 & -1.86658631114764 \tabularnewline
7 & 19 & 15.3391933307931 & 3.66080666920685 \tabularnewline
8 & 15 & 16.5852962216995 & -1.58529622169947 \tabularnewline
9 & 14 & 15.8912878178121 & -1.89128781781213 \tabularnewline
10 & 15 & 12.4290506140733 & 2.57094938592672 \tabularnewline
11 & 16 & 15.2560999303093 & 0.743900069690669 \tabularnewline
12 & 16 & 16.3271391965691 & -0.327139196569078 \tabularnewline
13 & 16 & 15.4137453364211 & 0.586254663578858 \tabularnewline
14 & 16 & 15.3740851160469 & 0.625914883953083 \tabularnewline
15 & 17 & 17.3253160033376 & -0.325316003337587 \tabularnewline
16 & 15 & 15.1027530885326 & -0.102753088532562 \tabularnewline
17 & 15 & 14.4981287373106 & 0.501871262689432 \tabularnewline
18 & 20 & 16.1008849521885 & 3.89911504781151 \tabularnewline
19 & 18 & 15.5560933721717 & 2.4439066278283 \tabularnewline
20 & 16 & 15.3327197874535 & 0.667280212546505 \tabularnewline
21 & 16 & 15.3569465128325 & 0.643053487167514 \tabularnewline
22 & 16 & 14.8429001446469 & 1.15709985535309 \tabularnewline
23 & 19 & 16.4918739404532 & 2.50812605954684 \tabularnewline
24 & 16 & 14.8622752254949 & 1.13772477450514 \tabularnewline
25 & 17 & 14.7245282957891 & 2.27547170421092 \tabularnewline
26 & 17 & 16.779452271109 & 0.220547728891038 \tabularnewline
27 & 16 & 14.7869245342761 & 1.21307546572387 \tabularnewline
28 & 15 & 16.1353420107562 & -1.1353420107562 \tabularnewline
29 & 16 & 15.2824639779442 & 0.717536022055797 \tabularnewline
30 & 14 & 14.1280504802726 & -0.128050480272596 \tabularnewline
31 & 15 & 15.6238043694721 & -0.623804369472103 \tabularnewline
32 & 12 & 12.2254387733233 & -0.225438773323278 \tabularnewline
33 & 14 & 15.1405420872196 & -1.14054208721962 \tabularnewline
34 & 16 & 15.5344295062707 & 0.465570493729298 \tabularnewline
35 & 14 & 15.4836934191589 & -1.48369341915892 \tabularnewline
36 & 7 & 12.9691863476649 & -5.96918634766493 \tabularnewline
37 & 10 & 10.7451936146734 & -0.74519361467336 \tabularnewline
38 & 14 & 15.7842946815977 & -1.78429468159766 \tabularnewline
39 & 16 & 14.2160793425019 & 1.7839206574981 \tabularnewline
40 & 16 & 14.6046501775826 & 1.39534982241736 \tabularnewline
41 & 16 & 15.065732562683 & 0.934267437317001 \tabularnewline
42 & 14 & 15.4621748673558 & -1.46217486735584 \tabularnewline
43 & 20 & 18.0503663959512 & 1.94963360404877 \tabularnewline
44 & 14 & 14.2301827824921 & -0.230182782492079 \tabularnewline
45 & 14 & 14.9300021687623 & -0.930002168762309 \tabularnewline
46 & 11 & 15.5174977140377 & -4.51749771403767 \tabularnewline
47 & 14 & 16.6491228861769 & -2.64912288617692 \tabularnewline
48 & 15 & 14.9456488491029 & 0.0543511508971324 \tabularnewline
49 & 16 & 15.0139860459075 & 0.986013954092458 \tabularnewline
50 & 14 & 15.9887648814103 & -1.98876488141032 \tabularnewline
51 & 16 & 16.5119011771862 & -0.51190117718621 \tabularnewline
52 & 14 & 14.0840614031216 & -0.0840614031215548 \tabularnewline
53 & 12 & 14.5999796007982 & -2.59997960079815 \tabularnewline
54 & 16 & 15.6306214579764 & 0.369378542023586 \tabularnewline
55 & 9 & 11.4129826772199 & -2.41298267721989 \tabularnewline
56 & 14 & 12.651121315775 & 1.34887868422502 \tabularnewline
57 & 16 & 16.0934606509117 & -0.0934606509116523 \tabularnewline
58 & 16 & 15.1840435224612 & 0.815956477538787 \tabularnewline
59 & 15 & 15.2448356315183 & -0.24483563151826 \tabularnewline
60 & 16 & 14.128522176215 & 1.871477823785 \tabularnewline
61 & 12 & 11.4143133400288 & 0.585686659971197 \tabularnewline
62 & 16 & 15.8630222560483 & 0.136977743951683 \tabularnewline
63 & 16 & 16.3393405146265 & -0.339340514626459 \tabularnewline
64 & 14 & 14.3935623359401 & -0.393562335940116 \tabularnewline
65 & 16 & 15.5237644463959 & 0.47623555360408 \tabularnewline
66 & 17 & 15.8746909890837 & 1.12530901091633 \tabularnewline
67 & 18 & 16.1825632648451 & 1.8174367351549 \tabularnewline
68 & 18 & 14.4975117272702 & 3.50248827272976 \tabularnewline
69 & 12 & 15.8848154900802 & -3.88481549008019 \tabularnewline
70 & 16 & 15.4172515877839 & 0.582748412216057 \tabularnewline
71 & 10 & 13.6195571916767 & -3.61955719167669 \tabularnewline
72 & 14 & 14.372043784137 & -0.372043784137043 \tabularnewline
73 & 18 & 16.4220460363823 & 1.57795396361768 \tabularnewline
74 & 18 & 17.0688491321478 & 0.931150867852196 \tabularnewline
75 & 16 & 15.6469436044761 & 0.353056395523927 \tabularnewline
76 & 17 & 13.831298404812 & 3.16870159518797 \tabularnewline
77 & 16 & 16.3997208251323 & -0.39972082513227 \tabularnewline
78 & 16 & 14.5396751336505 & 1.46032486634945 \tabularnewline
79 & 13 & 14.8304173878576 & -1.83041738785763 \tabularnewline
80 & 16 & 16.1487743081295 & -0.148774308129534 \tabularnewline
81 & 16 & 15.4429536822659 & 0.55704631773409 \tabularnewline
82 & 20 & 15.998752649969 & 4.00124735003099 \tabularnewline
83 & 16 & 15.8051445237447 & 0.194855476255267 \tabularnewline
84 & 15 & 15.9640055710093 & -0.964005571009304 \tabularnewline
85 & 15 & 14.752512418821 & 0.247487581179005 \tabularnewline
86 & 16 & 14.2531158143185 & 1.74688418568149 \tabularnewline
87 & 14 & 14.1569330520767 & -0.156933052076737 \tabularnewline
88 & 16 & 15.3493860869217 & 0.65061391307833 \tabularnewline
89 & 16 & 14.6384299448343 & 1.36157005516574 \tabularnewline
90 & 15 & 14.1486109098315 & 0.851389090168471 \tabularnewline
91 & 12 & 13.5485803003474 & -1.54858030034736 \tabularnewline
92 & 17 & 17.0837414604609 & -0.0837414604609213 \tabularnewline
93 & 16 & 15.3985020986364 & 0.601497901363589 \tabularnewline
94 & 15 & 15.145584596765 & -0.145584596765016 \tabularnewline
95 & 13 & 15.0744869189649 & -2.07448691896489 \tabularnewline
96 & 16 & 15.1095160444603 & 0.89048395553965 \tabularnewline
97 & 16 & 15.894383870116 & 0.105616129883991 \tabularnewline
98 & 16 & 14.0939198035494 & 1.90608019645064 \tabularnewline
99 & 16 & 16.0816551854385 & -0.0816551854384656 \tabularnewline
100 & 14 & 14.3140366837026 & -0.314036683702579 \tabularnewline
101 & 16 & 17.3175653202163 & -1.31756532021627 \tabularnewline
102 & 16 & 14.8034461274502 & 1.19655387254977 \tabularnewline
103 & 20 & 17.3885262655786 & 2.61147373442144 \tabularnewline
104 & 15 & 14.717970935235 & 0.282029064765018 \tabularnewline
105 & 16 & 14.4373458177175 & 1.56265418228253 \tabularnewline
106 & 13 & 15.3426396847648 & -2.34263968476478 \tabularnewline
107 & 17 & 16.1089779631213 & 0.891022036878669 \tabularnewline
108 & 16 & 15.9072774337681 & 0.092722566231931 \tabularnewline
109 & 16 & 14.6371607789088 & 1.36283922109117 \tabularnewline
110 & 12 & 12.3974087768814 & -0.397408776881397 \tabularnewline
111 & 16 & 14.9117084295901 & 1.08829157040986 \tabularnewline
112 & 16 & 15.8080952619507 & 0.191904738049314 \tabularnewline
113 & 17 & 14.5332107797748 & 2.46678922022516 \tabularnewline
114 & 13 & 14.4797056300075 & -1.47970563000752 \tabularnewline
115 & 12 & 14.9772919019025 & -2.97729190190246 \tabularnewline
116 & 18 & 16.6887136358114 & 1.31128636418857 \tabularnewline
117 & 14 & 15.5952056693607 & -1.59520566936071 \tabularnewline
118 & 14 & 13.0609977429781 & 0.939002257021857 \tabularnewline
119 & 13 & 15.0125709580803 & -2.01257095808033 \tabularnewline
120 & 16 & 15.6462755026239 & 0.353724497376076 \tabularnewline
121 & 13 & 14.3706293041137 & -1.37062930411369 \tabularnewline
122 & 16 & 15.6771562312853 & 0.32284376871473 \tabularnewline
123 & 13 & 15.9484968404597 & -2.94849684045972 \tabularnewline
124 & 16 & 17.1806945206972 & -1.18069452069717 \tabularnewline
125 & 15 & 16.1088326490234 & -1.10883264902341 \tabularnewline
126 & 16 & 16.7921845746961 & -0.792184574696058 \tabularnewline
127 & 15 & 15.5027329286984 & -0.502732928698439 \tabularnewline
128 & 17 & 16.3022597075011 & 0.697740292498874 \tabularnewline
129 & 15 & 14.063159253555 & 0.936840746445047 \tabularnewline
130 & 12 & 15.0456893799829 & -3.0456893799829 \tabularnewline
131 & 16 & 14.1803980611213 & 1.81960193887866 \tabularnewline
132 & 10 & 13.3328981091632 & -3.33289810916316 \tabularnewline
133 & 16 & 14.1234796666696 & 1.8765203333304 \tabularnewline
134 & 12 & 14.0383155181356 & -2.03831551813564 \tabularnewline
135 & 14 & 15.7794424292628 & -1.77944242926277 \tabularnewline
136 & 15 & 15.3273872575161 & -0.327387257516104 \tabularnewline
137 & 13 & 12.378350888414 & 0.621649111586012 \tabularnewline
138 & 15 & 14.7402358652093 & 0.259764134790741 \tabularnewline
139 & 11 & 13.3849193081674 & -2.38491930816742 \tabularnewline
140 & 12 & 13.5403579212836 & -1.54035792128363 \tabularnewline
141 & 8 & 13.3936287213367 & -5.39362872133673 \tabularnewline
142 & 16 & 13.2712629792066 & 2.72873702079335 \tabularnewline
143 & 15 & 13.3394548619134 & 1.6605451380866 \tabularnewline
144 & 17 & 16.6150371525663 & 0.384962847433733 \tabularnewline
145 & 16 & 14.9018567856654 & 1.09814321433457 \tabularnewline
146 & 10 & 14.2545462403089 & -4.2545462403089 \tabularnewline
147 & 18 & 16.0761773414032 & 1.92382265859685 \tabularnewline
148 & 13 & 15.377857851863 & -2.37785785186303 \tabularnewline
149 & 16 & 15.255346795635 & 0.744653204364971 \tabularnewline
150 & 13 & 13.1284438553175 & -0.128443855317545 \tabularnewline
151 & 10 & 13.207726942925 & -3.20772694292505 \tabularnewline
152 & 15 & 16.4105835065245 & -1.4105835065245 \tabularnewline
153 & 16 & 14.0932510938934 & 1.90674890610664 \tabularnewline
154 & 16 & 12.0589723570353 & 3.94102764296469 \tabularnewline
155 & 14 & 12.5348109458148 & 1.46518905418519 \tabularnewline
156 & 10 & 12.71013208937 & -2.71013208937001 \tabularnewline
157 & 17 & 17.0837414604609 & -0.0837414604609213 \tabularnewline
158 & 13 & 11.7336117841955 & 1.26638821580445 \tabularnewline
159 & 15 & 14.063159253555 & 0.936840746445047 \tabularnewline
160 & 16 & 15.1362218121029 & 0.863778187897109 \tabularnewline
161 & 12 & 12.4340931236187 & -0.434093123618684 \tabularnewline
162 & 13 & 12.8604370114194 & 0.139562988580568 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147129&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]13[/C][C]16.1775207552997[/C][C]-3.17752075529972[/C][/ROW]
[ROW][C]2[/C][C]16[/C][C]15.8796190847768[/C][C]0.12038091522322[/C][/ROW]
[ROW][C]3[/C][C]19[/C][C]16.2674208392162[/C][C]2.73257916078383[/C][/ROW]
[ROW][C]4[/C][C]15[/C][C]11.741233099186[/C][C]3.25876690081401[/C][/ROW]
[ROW][C]5[/C][C]14[/C][C]15.3682655520039[/C][C]-1.36826555200394[/C][/ROW]
[ROW][C]6[/C][C]13[/C][C]14.8665863111476[/C][C]-1.86658631114764[/C][/ROW]
[ROW][C]7[/C][C]19[/C][C]15.3391933307931[/C][C]3.66080666920685[/C][/ROW]
[ROW][C]8[/C][C]15[/C][C]16.5852962216995[/C][C]-1.58529622169947[/C][/ROW]
[ROW][C]9[/C][C]14[/C][C]15.8912878178121[/C][C]-1.89128781781213[/C][/ROW]
[ROW][C]10[/C][C]15[/C][C]12.4290506140733[/C][C]2.57094938592672[/C][/ROW]
[ROW][C]11[/C][C]16[/C][C]15.2560999303093[/C][C]0.743900069690669[/C][/ROW]
[ROW][C]12[/C][C]16[/C][C]16.3271391965691[/C][C]-0.327139196569078[/C][/ROW]
[ROW][C]13[/C][C]16[/C][C]15.4137453364211[/C][C]0.586254663578858[/C][/ROW]
[ROW][C]14[/C][C]16[/C][C]15.3740851160469[/C][C]0.625914883953083[/C][/ROW]
[ROW][C]15[/C][C]17[/C][C]17.3253160033376[/C][C]-0.325316003337587[/C][/ROW]
[ROW][C]16[/C][C]15[/C][C]15.1027530885326[/C][C]-0.102753088532562[/C][/ROW]
[ROW][C]17[/C][C]15[/C][C]14.4981287373106[/C][C]0.501871262689432[/C][/ROW]
[ROW][C]18[/C][C]20[/C][C]16.1008849521885[/C][C]3.89911504781151[/C][/ROW]
[ROW][C]19[/C][C]18[/C][C]15.5560933721717[/C][C]2.4439066278283[/C][/ROW]
[ROW][C]20[/C][C]16[/C][C]15.3327197874535[/C][C]0.667280212546505[/C][/ROW]
[ROW][C]21[/C][C]16[/C][C]15.3569465128325[/C][C]0.643053487167514[/C][/ROW]
[ROW][C]22[/C][C]16[/C][C]14.8429001446469[/C][C]1.15709985535309[/C][/ROW]
[ROW][C]23[/C][C]19[/C][C]16.4918739404532[/C][C]2.50812605954684[/C][/ROW]
[ROW][C]24[/C][C]16[/C][C]14.8622752254949[/C][C]1.13772477450514[/C][/ROW]
[ROW][C]25[/C][C]17[/C][C]14.7245282957891[/C][C]2.27547170421092[/C][/ROW]
[ROW][C]26[/C][C]17[/C][C]16.779452271109[/C][C]0.220547728891038[/C][/ROW]
[ROW][C]27[/C][C]16[/C][C]14.7869245342761[/C][C]1.21307546572387[/C][/ROW]
[ROW][C]28[/C][C]15[/C][C]16.1353420107562[/C][C]-1.1353420107562[/C][/ROW]
[ROW][C]29[/C][C]16[/C][C]15.2824639779442[/C][C]0.717536022055797[/C][/ROW]
[ROW][C]30[/C][C]14[/C][C]14.1280504802726[/C][C]-0.128050480272596[/C][/ROW]
[ROW][C]31[/C][C]15[/C][C]15.6238043694721[/C][C]-0.623804369472103[/C][/ROW]
[ROW][C]32[/C][C]12[/C][C]12.2254387733233[/C][C]-0.225438773323278[/C][/ROW]
[ROW][C]33[/C][C]14[/C][C]15.1405420872196[/C][C]-1.14054208721962[/C][/ROW]
[ROW][C]34[/C][C]16[/C][C]15.5344295062707[/C][C]0.465570493729298[/C][/ROW]
[ROW][C]35[/C][C]14[/C][C]15.4836934191589[/C][C]-1.48369341915892[/C][/ROW]
[ROW][C]36[/C][C]7[/C][C]12.9691863476649[/C][C]-5.96918634766493[/C][/ROW]
[ROW][C]37[/C][C]10[/C][C]10.7451936146734[/C][C]-0.74519361467336[/C][/ROW]
[ROW][C]38[/C][C]14[/C][C]15.7842946815977[/C][C]-1.78429468159766[/C][/ROW]
[ROW][C]39[/C][C]16[/C][C]14.2160793425019[/C][C]1.7839206574981[/C][/ROW]
[ROW][C]40[/C][C]16[/C][C]14.6046501775826[/C][C]1.39534982241736[/C][/ROW]
[ROW][C]41[/C][C]16[/C][C]15.065732562683[/C][C]0.934267437317001[/C][/ROW]
[ROW][C]42[/C][C]14[/C][C]15.4621748673558[/C][C]-1.46217486735584[/C][/ROW]
[ROW][C]43[/C][C]20[/C][C]18.0503663959512[/C][C]1.94963360404877[/C][/ROW]
[ROW][C]44[/C][C]14[/C][C]14.2301827824921[/C][C]-0.230182782492079[/C][/ROW]
[ROW][C]45[/C][C]14[/C][C]14.9300021687623[/C][C]-0.930002168762309[/C][/ROW]
[ROW][C]46[/C][C]11[/C][C]15.5174977140377[/C][C]-4.51749771403767[/C][/ROW]
[ROW][C]47[/C][C]14[/C][C]16.6491228861769[/C][C]-2.64912288617692[/C][/ROW]
[ROW][C]48[/C][C]15[/C][C]14.9456488491029[/C][C]0.0543511508971324[/C][/ROW]
[ROW][C]49[/C][C]16[/C][C]15.0139860459075[/C][C]0.986013954092458[/C][/ROW]
[ROW][C]50[/C][C]14[/C][C]15.9887648814103[/C][C]-1.98876488141032[/C][/ROW]
[ROW][C]51[/C][C]16[/C][C]16.5119011771862[/C][C]-0.51190117718621[/C][/ROW]
[ROW][C]52[/C][C]14[/C][C]14.0840614031216[/C][C]-0.0840614031215548[/C][/ROW]
[ROW][C]53[/C][C]12[/C][C]14.5999796007982[/C][C]-2.59997960079815[/C][/ROW]
[ROW][C]54[/C][C]16[/C][C]15.6306214579764[/C][C]0.369378542023586[/C][/ROW]
[ROW][C]55[/C][C]9[/C][C]11.4129826772199[/C][C]-2.41298267721989[/C][/ROW]
[ROW][C]56[/C][C]14[/C][C]12.651121315775[/C][C]1.34887868422502[/C][/ROW]
[ROW][C]57[/C][C]16[/C][C]16.0934606509117[/C][C]-0.0934606509116523[/C][/ROW]
[ROW][C]58[/C][C]16[/C][C]15.1840435224612[/C][C]0.815956477538787[/C][/ROW]
[ROW][C]59[/C][C]15[/C][C]15.2448356315183[/C][C]-0.24483563151826[/C][/ROW]
[ROW][C]60[/C][C]16[/C][C]14.128522176215[/C][C]1.871477823785[/C][/ROW]
[ROW][C]61[/C][C]12[/C][C]11.4143133400288[/C][C]0.585686659971197[/C][/ROW]
[ROW][C]62[/C][C]16[/C][C]15.8630222560483[/C][C]0.136977743951683[/C][/ROW]
[ROW][C]63[/C][C]16[/C][C]16.3393405146265[/C][C]-0.339340514626459[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]14.3935623359401[/C][C]-0.393562335940116[/C][/ROW]
[ROW][C]65[/C][C]16[/C][C]15.5237644463959[/C][C]0.47623555360408[/C][/ROW]
[ROW][C]66[/C][C]17[/C][C]15.8746909890837[/C][C]1.12530901091633[/C][/ROW]
[ROW][C]67[/C][C]18[/C][C]16.1825632648451[/C][C]1.8174367351549[/C][/ROW]
[ROW][C]68[/C][C]18[/C][C]14.4975117272702[/C][C]3.50248827272976[/C][/ROW]
[ROW][C]69[/C][C]12[/C][C]15.8848154900802[/C][C]-3.88481549008019[/C][/ROW]
[ROW][C]70[/C][C]16[/C][C]15.4172515877839[/C][C]0.582748412216057[/C][/ROW]
[ROW][C]71[/C][C]10[/C][C]13.6195571916767[/C][C]-3.61955719167669[/C][/ROW]
[ROW][C]72[/C][C]14[/C][C]14.372043784137[/C][C]-0.372043784137043[/C][/ROW]
[ROW][C]73[/C][C]18[/C][C]16.4220460363823[/C][C]1.57795396361768[/C][/ROW]
[ROW][C]74[/C][C]18[/C][C]17.0688491321478[/C][C]0.931150867852196[/C][/ROW]
[ROW][C]75[/C][C]16[/C][C]15.6469436044761[/C][C]0.353056395523927[/C][/ROW]
[ROW][C]76[/C][C]17[/C][C]13.831298404812[/C][C]3.16870159518797[/C][/ROW]
[ROW][C]77[/C][C]16[/C][C]16.3997208251323[/C][C]-0.39972082513227[/C][/ROW]
[ROW][C]78[/C][C]16[/C][C]14.5396751336505[/C][C]1.46032486634945[/C][/ROW]
[ROW][C]79[/C][C]13[/C][C]14.8304173878576[/C][C]-1.83041738785763[/C][/ROW]
[ROW][C]80[/C][C]16[/C][C]16.1487743081295[/C][C]-0.148774308129534[/C][/ROW]
[ROW][C]81[/C][C]16[/C][C]15.4429536822659[/C][C]0.55704631773409[/C][/ROW]
[ROW][C]82[/C][C]20[/C][C]15.998752649969[/C][C]4.00124735003099[/C][/ROW]
[ROW][C]83[/C][C]16[/C][C]15.8051445237447[/C][C]0.194855476255267[/C][/ROW]
[ROW][C]84[/C][C]15[/C][C]15.9640055710093[/C][C]-0.964005571009304[/C][/ROW]
[ROW][C]85[/C][C]15[/C][C]14.752512418821[/C][C]0.247487581179005[/C][/ROW]
[ROW][C]86[/C][C]16[/C][C]14.2531158143185[/C][C]1.74688418568149[/C][/ROW]
[ROW][C]87[/C][C]14[/C][C]14.1569330520767[/C][C]-0.156933052076737[/C][/ROW]
[ROW][C]88[/C][C]16[/C][C]15.3493860869217[/C][C]0.65061391307833[/C][/ROW]
[ROW][C]89[/C][C]16[/C][C]14.6384299448343[/C][C]1.36157005516574[/C][/ROW]
[ROW][C]90[/C][C]15[/C][C]14.1486109098315[/C][C]0.851389090168471[/C][/ROW]
[ROW][C]91[/C][C]12[/C][C]13.5485803003474[/C][C]-1.54858030034736[/C][/ROW]
[ROW][C]92[/C][C]17[/C][C]17.0837414604609[/C][C]-0.0837414604609213[/C][/ROW]
[ROW][C]93[/C][C]16[/C][C]15.3985020986364[/C][C]0.601497901363589[/C][/ROW]
[ROW][C]94[/C][C]15[/C][C]15.145584596765[/C][C]-0.145584596765016[/C][/ROW]
[ROW][C]95[/C][C]13[/C][C]15.0744869189649[/C][C]-2.07448691896489[/C][/ROW]
[ROW][C]96[/C][C]16[/C][C]15.1095160444603[/C][C]0.89048395553965[/C][/ROW]
[ROW][C]97[/C][C]16[/C][C]15.894383870116[/C][C]0.105616129883991[/C][/ROW]
[ROW][C]98[/C][C]16[/C][C]14.0939198035494[/C][C]1.90608019645064[/C][/ROW]
[ROW][C]99[/C][C]16[/C][C]16.0816551854385[/C][C]-0.0816551854384656[/C][/ROW]
[ROW][C]100[/C][C]14[/C][C]14.3140366837026[/C][C]-0.314036683702579[/C][/ROW]
[ROW][C]101[/C][C]16[/C][C]17.3175653202163[/C][C]-1.31756532021627[/C][/ROW]
[ROW][C]102[/C][C]16[/C][C]14.8034461274502[/C][C]1.19655387254977[/C][/ROW]
[ROW][C]103[/C][C]20[/C][C]17.3885262655786[/C][C]2.61147373442144[/C][/ROW]
[ROW][C]104[/C][C]15[/C][C]14.717970935235[/C][C]0.282029064765018[/C][/ROW]
[ROW][C]105[/C][C]16[/C][C]14.4373458177175[/C][C]1.56265418228253[/C][/ROW]
[ROW][C]106[/C][C]13[/C][C]15.3426396847648[/C][C]-2.34263968476478[/C][/ROW]
[ROW][C]107[/C][C]17[/C][C]16.1089779631213[/C][C]0.891022036878669[/C][/ROW]
[ROW][C]108[/C][C]16[/C][C]15.9072774337681[/C][C]0.092722566231931[/C][/ROW]
[ROW][C]109[/C][C]16[/C][C]14.6371607789088[/C][C]1.36283922109117[/C][/ROW]
[ROW][C]110[/C][C]12[/C][C]12.3974087768814[/C][C]-0.397408776881397[/C][/ROW]
[ROW][C]111[/C][C]16[/C][C]14.9117084295901[/C][C]1.08829157040986[/C][/ROW]
[ROW][C]112[/C][C]16[/C][C]15.8080952619507[/C][C]0.191904738049314[/C][/ROW]
[ROW][C]113[/C][C]17[/C][C]14.5332107797748[/C][C]2.46678922022516[/C][/ROW]
[ROW][C]114[/C][C]13[/C][C]14.4797056300075[/C][C]-1.47970563000752[/C][/ROW]
[ROW][C]115[/C][C]12[/C][C]14.9772919019025[/C][C]-2.97729190190246[/C][/ROW]
[ROW][C]116[/C][C]18[/C][C]16.6887136358114[/C][C]1.31128636418857[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]15.5952056693607[/C][C]-1.59520566936071[/C][/ROW]
[ROW][C]118[/C][C]14[/C][C]13.0609977429781[/C][C]0.939002257021857[/C][/ROW]
[ROW][C]119[/C][C]13[/C][C]15.0125709580803[/C][C]-2.01257095808033[/C][/ROW]
[ROW][C]120[/C][C]16[/C][C]15.6462755026239[/C][C]0.353724497376076[/C][/ROW]
[ROW][C]121[/C][C]13[/C][C]14.3706293041137[/C][C]-1.37062930411369[/C][/ROW]
[ROW][C]122[/C][C]16[/C][C]15.6771562312853[/C][C]0.32284376871473[/C][/ROW]
[ROW][C]123[/C][C]13[/C][C]15.9484968404597[/C][C]-2.94849684045972[/C][/ROW]
[ROW][C]124[/C][C]16[/C][C]17.1806945206972[/C][C]-1.18069452069717[/C][/ROW]
[ROW][C]125[/C][C]15[/C][C]16.1088326490234[/C][C]-1.10883264902341[/C][/ROW]
[ROW][C]126[/C][C]16[/C][C]16.7921845746961[/C][C]-0.792184574696058[/C][/ROW]
[ROW][C]127[/C][C]15[/C][C]15.5027329286984[/C][C]-0.502732928698439[/C][/ROW]
[ROW][C]128[/C][C]17[/C][C]16.3022597075011[/C][C]0.697740292498874[/C][/ROW]
[ROW][C]129[/C][C]15[/C][C]14.063159253555[/C][C]0.936840746445047[/C][/ROW]
[ROW][C]130[/C][C]12[/C][C]15.0456893799829[/C][C]-3.0456893799829[/C][/ROW]
[ROW][C]131[/C][C]16[/C][C]14.1803980611213[/C][C]1.81960193887866[/C][/ROW]
[ROW][C]132[/C][C]10[/C][C]13.3328981091632[/C][C]-3.33289810916316[/C][/ROW]
[ROW][C]133[/C][C]16[/C][C]14.1234796666696[/C][C]1.8765203333304[/C][/ROW]
[ROW][C]134[/C][C]12[/C][C]14.0383155181356[/C][C]-2.03831551813564[/C][/ROW]
[ROW][C]135[/C][C]14[/C][C]15.7794424292628[/C][C]-1.77944242926277[/C][/ROW]
[ROW][C]136[/C][C]15[/C][C]15.3273872575161[/C][C]-0.327387257516104[/C][/ROW]
[ROW][C]137[/C][C]13[/C][C]12.378350888414[/C][C]0.621649111586012[/C][/ROW]
[ROW][C]138[/C][C]15[/C][C]14.7402358652093[/C][C]0.259764134790741[/C][/ROW]
[ROW][C]139[/C][C]11[/C][C]13.3849193081674[/C][C]-2.38491930816742[/C][/ROW]
[ROW][C]140[/C][C]12[/C][C]13.5403579212836[/C][C]-1.54035792128363[/C][/ROW]
[ROW][C]141[/C][C]8[/C][C]13.3936287213367[/C][C]-5.39362872133673[/C][/ROW]
[ROW][C]142[/C][C]16[/C][C]13.2712629792066[/C][C]2.72873702079335[/C][/ROW]
[ROW][C]143[/C][C]15[/C][C]13.3394548619134[/C][C]1.6605451380866[/C][/ROW]
[ROW][C]144[/C][C]17[/C][C]16.6150371525663[/C][C]0.384962847433733[/C][/ROW]
[ROW][C]145[/C][C]16[/C][C]14.9018567856654[/C][C]1.09814321433457[/C][/ROW]
[ROW][C]146[/C][C]10[/C][C]14.2545462403089[/C][C]-4.2545462403089[/C][/ROW]
[ROW][C]147[/C][C]18[/C][C]16.0761773414032[/C][C]1.92382265859685[/C][/ROW]
[ROW][C]148[/C][C]13[/C][C]15.377857851863[/C][C]-2.37785785186303[/C][/ROW]
[ROW][C]149[/C][C]16[/C][C]15.255346795635[/C][C]0.744653204364971[/C][/ROW]
[ROW][C]150[/C][C]13[/C][C]13.1284438553175[/C][C]-0.128443855317545[/C][/ROW]
[ROW][C]151[/C][C]10[/C][C]13.207726942925[/C][C]-3.20772694292505[/C][/ROW]
[ROW][C]152[/C][C]15[/C][C]16.4105835065245[/C][C]-1.4105835065245[/C][/ROW]
[ROW][C]153[/C][C]16[/C][C]14.0932510938934[/C][C]1.90674890610664[/C][/ROW]
[ROW][C]154[/C][C]16[/C][C]12.0589723570353[/C][C]3.94102764296469[/C][/ROW]
[ROW][C]155[/C][C]14[/C][C]12.5348109458148[/C][C]1.46518905418519[/C][/ROW]
[ROW][C]156[/C][C]10[/C][C]12.71013208937[/C][C]-2.71013208937001[/C][/ROW]
[ROW][C]157[/C][C]17[/C][C]17.0837414604609[/C][C]-0.0837414604609213[/C][/ROW]
[ROW][C]158[/C][C]13[/C][C]11.7336117841955[/C][C]1.26638821580445[/C][/ROW]
[ROW][C]159[/C][C]15[/C][C]14.063159253555[/C][C]0.936840746445047[/C][/ROW]
[ROW][C]160[/C][C]16[/C][C]15.1362218121029[/C][C]0.863778187897109[/C][/ROW]
[ROW][C]161[/C][C]12[/C][C]12.4340931236187[/C][C]-0.434093123618684[/C][/ROW]
[ROW][C]162[/C][C]13[/C][C]12.8604370114194[/C][C]0.139562988580568[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147129&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147129&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11316.1775207552997-3.17752075529972
21615.87961908477680.12038091522322
31916.26742083921622.73257916078383
41511.7412330991863.25876690081401
51415.3682655520039-1.36826555200394
61314.8665863111476-1.86658631114764
71915.33919333079313.66080666920685
81516.5852962216995-1.58529622169947
91415.8912878178121-1.89128781781213
101512.42905061407332.57094938592672
111615.25609993030930.743900069690669
121616.3271391965691-0.327139196569078
131615.41374533642110.586254663578858
141615.37408511604690.625914883953083
151717.3253160033376-0.325316003337587
161515.1027530885326-0.102753088532562
171514.49812873731060.501871262689432
182016.10088495218853.89911504781151
191815.55609337217172.4439066278283
201615.33271978745350.667280212546505
211615.35694651283250.643053487167514
221614.84290014464691.15709985535309
231916.49187394045322.50812605954684
241614.86227522549491.13772477450514
251714.72452829578912.27547170421092
261716.7794522711090.220547728891038
271614.78692453427611.21307546572387
281516.1353420107562-1.1353420107562
291615.28246397794420.717536022055797
301414.1280504802726-0.128050480272596
311515.6238043694721-0.623804369472103
321212.2254387733233-0.225438773323278
331415.1405420872196-1.14054208721962
341615.53442950627070.465570493729298
351415.4836934191589-1.48369341915892
36712.9691863476649-5.96918634766493
371010.7451936146734-0.74519361467336
381415.7842946815977-1.78429468159766
391614.21607934250191.7839206574981
401614.60465017758261.39534982241736
411615.0657325626830.934267437317001
421415.4621748673558-1.46217486735584
432018.05036639595121.94963360404877
441414.2301827824921-0.230182782492079
451414.9300021687623-0.930002168762309
461115.5174977140377-4.51749771403767
471416.6491228861769-2.64912288617692
481514.94564884910290.0543511508971324
491615.01398604590750.986013954092458
501415.9887648814103-1.98876488141032
511616.5119011771862-0.51190117718621
521414.0840614031216-0.0840614031215548
531214.5999796007982-2.59997960079815
541615.63062145797640.369378542023586
55911.4129826772199-2.41298267721989
561412.6511213157751.34887868422502
571616.0934606509117-0.0934606509116523
581615.18404352246120.815956477538787
591515.2448356315183-0.24483563151826
601614.1285221762151.871477823785
611211.41431334002880.585686659971197
621615.86302225604830.136977743951683
631616.3393405146265-0.339340514626459
641414.3935623359401-0.393562335940116
651615.52376444639590.47623555360408
661715.87469098908371.12530901091633
671816.18256326484511.8174367351549
681814.49751172727023.50248827272976
691215.8848154900802-3.88481549008019
701615.41725158778390.582748412216057
711013.6195571916767-3.61955719167669
721414.372043784137-0.372043784137043
731816.42204603638231.57795396361768
741817.06884913214780.931150867852196
751615.64694360447610.353056395523927
761713.8312984048123.16870159518797
771616.3997208251323-0.39972082513227
781614.53967513365051.46032486634945
791314.8304173878576-1.83041738785763
801616.1487743081295-0.148774308129534
811615.44295368226590.55704631773409
822015.9987526499694.00124735003099
831615.80514452374470.194855476255267
841515.9640055710093-0.964005571009304
851514.7525124188210.247487581179005
861614.25311581431851.74688418568149
871414.1569330520767-0.156933052076737
881615.34938608692170.65061391307833
891614.63842994483431.36157005516574
901514.14861090983150.851389090168471
911213.5485803003474-1.54858030034736
921717.0837414604609-0.0837414604609213
931615.39850209863640.601497901363589
941515.145584596765-0.145584596765016
951315.0744869189649-2.07448691896489
961615.10951604446030.89048395553965
971615.8943838701160.105616129883991
981614.09391980354941.90608019645064
991616.0816551854385-0.0816551854384656
1001414.3140366837026-0.314036683702579
1011617.3175653202163-1.31756532021627
1021614.80344612745021.19655387254977
1032017.38852626557862.61147373442144
1041514.7179709352350.282029064765018
1051614.43734581771751.56265418228253
1061315.3426396847648-2.34263968476478
1071716.10897796312130.891022036878669
1081615.90727743376810.092722566231931
1091614.63716077890881.36283922109117
1101212.3974087768814-0.397408776881397
1111614.91170842959011.08829157040986
1121615.80809526195070.191904738049314
1131714.53321077977482.46678922022516
1141314.4797056300075-1.47970563000752
1151214.9772919019025-2.97729190190246
1161816.68871363581141.31128636418857
1171415.5952056693607-1.59520566936071
1181413.06099774297810.939002257021857
1191315.0125709580803-2.01257095808033
1201615.64627550262390.353724497376076
1211314.3706293041137-1.37062930411369
1221615.67715623128530.32284376871473
1231315.9484968404597-2.94849684045972
1241617.1806945206972-1.18069452069717
1251516.1088326490234-1.10883264902341
1261616.7921845746961-0.792184574696058
1271515.5027329286984-0.502732928698439
1281716.30225970750110.697740292498874
1291514.0631592535550.936840746445047
1301215.0456893799829-3.0456893799829
1311614.18039806112131.81960193887866
1321013.3328981091632-3.33289810916316
1331614.12347966666961.8765203333304
1341214.0383155181356-2.03831551813564
1351415.7794424292628-1.77944242926277
1361515.3273872575161-0.327387257516104
1371312.3783508884140.621649111586012
1381514.74023586520930.259764134790741
1391113.3849193081674-2.38491930816742
1401213.5403579212836-1.54035792128363
141813.3936287213367-5.39362872133673
1421613.27126297920662.72873702079335
1431513.33945486191341.6605451380866
1441716.61503715256630.384962847433733
1451614.90185678566541.09814321433457
1461014.2545462403089-4.2545462403089
1471816.07617734140321.92382265859685
1481315.377857851863-2.37785785186303
1491615.2553467956350.744653204364971
1501313.1284438553175-0.128443855317545
1511013.207726942925-3.20772694292505
1521516.4105835065245-1.4105835065245
1531614.09325109389341.90674890610664
1541612.05897235703533.94102764296469
1551412.53481094581481.46518905418519
1561012.71013208937-2.71013208937001
1571717.0837414604609-0.0837414604609213
1581311.73361178419551.26638821580445
1591514.0631592535550.936840746445047
1601615.13622181210290.863778187897109
1611212.4340931236187-0.434093123618684
1621312.86043701141940.139562988580568







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.6043351589890190.7913296820219630.395664841010981
100.7667049416651420.4665901166697160.233295058334858
110.7047764781382410.5904470437235180.295223521861759
120.7842458493023120.4315083013953760.215754150697688
130.7152803318142010.5694393363715980.284719668185799
140.6837928089967170.6324143820065660.316207191003283
150.6647582807803730.6704834384392540.335241719219627
160.6132895877536190.7734208244927630.386710412246381
170.5349916116908640.9300167766182720.465008388309136
180.858423919821550.28315216035690.14157608017845
190.8631054877713570.2737890244572860.136894512228643
200.8176824775621260.3646350448757480.182317522437874
210.7694462796505090.4611074406989810.230553720349491
220.711622614097180.576754771805640.28837738590282
230.7317117717517930.5365764564964130.268288228248207
240.6783215206528650.643356958694270.321678479347135
250.6672996546523960.6654006906952080.332700345347604
260.6051728728141840.7896542543716320.394827127185816
270.5454187478441670.9091625043116650.454581252155833
280.5202324458518410.9595351082963180.479767554148159
290.4570647757124270.9141295514248550.542935224287573
300.4362629998132960.8725259996265920.563737000186704
310.3779613318839210.7559226637678410.62203866811608
320.3641534339382560.7283068678765120.635846566061744
330.3444999439855510.6889998879711030.655500056014448
340.2907085648838230.5814171297676460.709291435116177
350.2755651383503460.5511302767006930.724434861649654
360.8123178660601290.3753642678797430.187682133939871
370.8001394646879510.3997210706240980.199860535312049
380.7993340148080290.4013319703839410.200665985191971
390.8202908360529350.3594183278941310.179709163947065
400.8001752683864950.399649463227010.199824731613505
410.7688509292124790.4622981415750430.231149070787521
420.7543727528714170.4912544942571650.245627247128582
430.7601762951671540.4796474096656920.239823704832846
440.722545174948020.554909650103960.27745482505198
450.6920980285767760.6158039428464490.307901971423224
460.8854076702502870.2291846594994260.114592329749713
470.9101124594342310.1797750811315370.0898875405657687
480.887768652856910.2244626942861810.112231347143091
490.866366935685630.2672661286287410.133633064314371
500.8679150886798220.2641698226403550.132084911320178
510.8406867107433390.3186265785133230.159313289256661
520.8085908971848020.3828182056303950.191409102815197
530.8370873114595450.3258253770809110.162912688540456
540.8061397303182720.3877205393634570.193860269681728
550.825612742618640.3487745147627190.174387257381359
560.8056295746533750.388740850693250.194370425346625
570.7721872463079240.4556255073841530.227812753692076
580.7485301479754430.5029397040491140.251469852024557
590.709665167863380.580669664273240.29033483213662
600.7061064588802060.5877870822395870.293893541119794
610.6677927099327040.6644145801345920.332207290067296
620.6233499166751520.7533001666496960.376650083324848
630.5790631856474270.8418736287051470.420936814352574
640.5341852726772450.931629454645510.465814727322755
650.4906833416064520.9813666832129040.509316658393548
660.462037979508680.924075959017360.53796202049132
670.4544400201248170.9088800402496340.545559979875183
680.5844065975906580.8311868048186830.415593402409342
690.7274053569844230.5451892860311540.272594643015577
700.6910638665295170.6178722669409670.308936133470483
710.7899935387770050.420012922445990.210006461222995
720.7556911310170760.4886177379658480.244308868982924
730.746673536932010.5066529261359790.253326463067989
740.7200143325070780.5599713349858450.279985667492922
750.6803698029488170.6392603941023660.319630197051183
760.7475263344195480.5049473311609040.252473665580452
770.7109087417660880.5781825164678230.289091258233912
780.6957781133023710.6084437733952570.304221886697628
790.6958618455299930.6082763089400130.304138154470007
800.6550657861506890.6898684276986230.344934213849311
810.6160701882154520.7678596235690960.383929811784548
820.7676266080855430.4647467838289150.232373391914458
830.7314067479080480.5371865041839030.268593252091952
840.7022855970707580.5954288058584840.297714402929242
850.6616340844890310.6767318310219380.338365915510969
860.6570267974992720.6859464050014570.342973202500728
870.6135034143440510.7729931713118990.386496585655949
880.5741481309093710.8517037381812580.425851869090629
890.5529802354883810.8940395290232370.447019764511619
900.5240096500424830.9519806999150350.475990349957517
910.5094517854185370.9810964291629260.490548214581463
920.4685265952955120.9370531905910240.531473404704488
930.4279425172302670.8558850344605330.572057482769733
940.3850172092225770.7700344184451540.614982790777423
950.395959290727350.79191858145470.60404070927265
960.3626577124547820.7253154249095640.637342287545218
970.3244318152840.6488636305679990.675568184716
980.3270939001819150.654187800363830.672906099818085
990.286025545781040.572051091562080.71397445421896
1000.249035357100080.498070714200160.75096464289992
1010.226960010043710.453920020087420.77303998995629
1020.2129786434298180.4259572868596360.787021356570182
1030.2638157330057780.5276314660115560.736184266994222
1040.226830736297560.453661472595120.77316926370244
1050.2243701423251680.4487402846503370.775629857674832
1060.2360132718709690.4720265437419390.76398672812903
1070.2142412329510170.4284824659020350.785758767048983
1080.1921262956186270.3842525912372530.807873704381373
1090.1826923834870630.3653847669741260.817307616512937
1100.1681840732475550.3363681464951090.831815926752445
1110.1534113573203240.3068227146406490.846588642679676
1120.1300073788694290.2600147577388580.869992621130571
1130.1575592316845570.3151184633691140.842440768315443
1140.1403053960179810.2806107920359610.85969460398202
1150.1751453477696930.3502906955393850.824854652230307
1160.166428370263910.3328567405278210.83357162973609
1170.1459695991367930.2919391982735860.854030400863207
1180.1277734335473360.2555468670946720.872226566452664
1190.1315714231754790.2631428463509590.86842857682452
1200.1195832021480510.2391664042961030.880416797851949
1210.1006785511571970.2013571023143950.899321448842803
1220.0812615304287890.1625230608575780.918738469571211
1230.093030372462620.186060744925240.90696962753738
1240.0761995287204860.1523990574409720.923800471279514
1250.06253041933865530.1250608386773110.937469580661345
1260.04860763774489020.09721527548978050.95139236225511
1270.03778137122555370.07556274245110750.962218628774446
1280.03209875040210650.0641975008042130.967901249597893
1290.02529829535108450.0505965907021690.974701704648915
1300.03374542804424690.06749085608849380.966254571955753
1310.02944556321427820.05889112642855640.970554436785722
1320.04390327680127660.08780655360255310.956096723198723
1330.04957516526227460.09915033052454920.950424834737725
1340.06244509014814480.124890180296290.937554909851855
1350.05064527646204240.1012905529240850.949354723537958
1360.03642918343402420.07285836686804850.963570816565976
1370.02575956702827860.05151913405655710.974240432971721
1380.01776504375685120.03553008751370250.982234956243149
1390.02788027196209790.05576054392419570.972119728037902
1400.02203559598242150.0440711919648430.977964404017578
1410.4840507659828040.9681015319656080.515949234017196
1420.4602009444720.9204018889440.539799055528
1430.3925066519165630.7850133038331260.607493348083437
1440.4051954896889580.8103909793779160.594804510311042
1450.3560776740661780.7121553481323570.643922325933822
1460.3440998867509860.6881997735019730.655900113249014
1470.3961277698092350.792255539618470.603872230190765
1480.8147776472951350.370444705409730.185222352704865
1490.7889644945437460.4220710109125080.211035505456254
1500.6905743183403730.6188513633192530.309425681659627
1510.8836830018228920.2326339963542150.116316998177107
1520.9336259163644630.1327481672710740.0663740836355371
1530.900694983710770.1986100325784610.0993050162892306

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.604335158989019 & 0.791329682021963 & 0.395664841010981 \tabularnewline
10 & 0.766704941665142 & 0.466590116669716 & 0.233295058334858 \tabularnewline
11 & 0.704776478138241 & 0.590447043723518 & 0.295223521861759 \tabularnewline
12 & 0.784245849302312 & 0.431508301395376 & 0.215754150697688 \tabularnewline
13 & 0.715280331814201 & 0.569439336371598 & 0.284719668185799 \tabularnewline
14 & 0.683792808996717 & 0.632414382006566 & 0.316207191003283 \tabularnewline
15 & 0.664758280780373 & 0.670483438439254 & 0.335241719219627 \tabularnewline
16 & 0.613289587753619 & 0.773420824492763 & 0.386710412246381 \tabularnewline
17 & 0.534991611690864 & 0.930016776618272 & 0.465008388309136 \tabularnewline
18 & 0.85842391982155 & 0.2831521603569 & 0.14157608017845 \tabularnewline
19 & 0.863105487771357 & 0.273789024457286 & 0.136894512228643 \tabularnewline
20 & 0.817682477562126 & 0.364635044875748 & 0.182317522437874 \tabularnewline
21 & 0.769446279650509 & 0.461107440698981 & 0.230553720349491 \tabularnewline
22 & 0.71162261409718 & 0.57675477180564 & 0.28837738590282 \tabularnewline
23 & 0.731711771751793 & 0.536576456496413 & 0.268288228248207 \tabularnewline
24 & 0.678321520652865 & 0.64335695869427 & 0.321678479347135 \tabularnewline
25 & 0.667299654652396 & 0.665400690695208 & 0.332700345347604 \tabularnewline
26 & 0.605172872814184 & 0.789654254371632 & 0.394827127185816 \tabularnewline
27 & 0.545418747844167 & 0.909162504311665 & 0.454581252155833 \tabularnewline
28 & 0.520232445851841 & 0.959535108296318 & 0.479767554148159 \tabularnewline
29 & 0.457064775712427 & 0.914129551424855 & 0.542935224287573 \tabularnewline
30 & 0.436262999813296 & 0.872525999626592 & 0.563737000186704 \tabularnewline
31 & 0.377961331883921 & 0.755922663767841 & 0.62203866811608 \tabularnewline
32 & 0.364153433938256 & 0.728306867876512 & 0.635846566061744 \tabularnewline
33 & 0.344499943985551 & 0.688999887971103 & 0.655500056014448 \tabularnewline
34 & 0.290708564883823 & 0.581417129767646 & 0.709291435116177 \tabularnewline
35 & 0.275565138350346 & 0.551130276700693 & 0.724434861649654 \tabularnewline
36 & 0.812317866060129 & 0.375364267879743 & 0.187682133939871 \tabularnewline
37 & 0.800139464687951 & 0.399721070624098 & 0.199860535312049 \tabularnewline
38 & 0.799334014808029 & 0.401331970383941 & 0.200665985191971 \tabularnewline
39 & 0.820290836052935 & 0.359418327894131 & 0.179709163947065 \tabularnewline
40 & 0.800175268386495 & 0.39964946322701 & 0.199824731613505 \tabularnewline
41 & 0.768850929212479 & 0.462298141575043 & 0.231149070787521 \tabularnewline
42 & 0.754372752871417 & 0.491254494257165 & 0.245627247128582 \tabularnewline
43 & 0.760176295167154 & 0.479647409665692 & 0.239823704832846 \tabularnewline
44 & 0.72254517494802 & 0.55490965010396 & 0.27745482505198 \tabularnewline
45 & 0.692098028576776 & 0.615803942846449 & 0.307901971423224 \tabularnewline
46 & 0.885407670250287 & 0.229184659499426 & 0.114592329749713 \tabularnewline
47 & 0.910112459434231 & 0.179775081131537 & 0.0898875405657687 \tabularnewline
48 & 0.88776865285691 & 0.224462694286181 & 0.112231347143091 \tabularnewline
49 & 0.86636693568563 & 0.267266128628741 & 0.133633064314371 \tabularnewline
50 & 0.867915088679822 & 0.264169822640355 & 0.132084911320178 \tabularnewline
51 & 0.840686710743339 & 0.318626578513323 & 0.159313289256661 \tabularnewline
52 & 0.808590897184802 & 0.382818205630395 & 0.191409102815197 \tabularnewline
53 & 0.837087311459545 & 0.325825377080911 & 0.162912688540456 \tabularnewline
54 & 0.806139730318272 & 0.387720539363457 & 0.193860269681728 \tabularnewline
55 & 0.82561274261864 & 0.348774514762719 & 0.174387257381359 \tabularnewline
56 & 0.805629574653375 & 0.38874085069325 & 0.194370425346625 \tabularnewline
57 & 0.772187246307924 & 0.455625507384153 & 0.227812753692076 \tabularnewline
58 & 0.748530147975443 & 0.502939704049114 & 0.251469852024557 \tabularnewline
59 & 0.70966516786338 & 0.58066966427324 & 0.29033483213662 \tabularnewline
60 & 0.706106458880206 & 0.587787082239587 & 0.293893541119794 \tabularnewline
61 & 0.667792709932704 & 0.664414580134592 & 0.332207290067296 \tabularnewline
62 & 0.623349916675152 & 0.753300166649696 & 0.376650083324848 \tabularnewline
63 & 0.579063185647427 & 0.841873628705147 & 0.420936814352574 \tabularnewline
64 & 0.534185272677245 & 0.93162945464551 & 0.465814727322755 \tabularnewline
65 & 0.490683341606452 & 0.981366683212904 & 0.509316658393548 \tabularnewline
66 & 0.46203797950868 & 0.92407595901736 & 0.53796202049132 \tabularnewline
67 & 0.454440020124817 & 0.908880040249634 & 0.545559979875183 \tabularnewline
68 & 0.584406597590658 & 0.831186804818683 & 0.415593402409342 \tabularnewline
69 & 0.727405356984423 & 0.545189286031154 & 0.272594643015577 \tabularnewline
70 & 0.691063866529517 & 0.617872266940967 & 0.308936133470483 \tabularnewline
71 & 0.789993538777005 & 0.42001292244599 & 0.210006461222995 \tabularnewline
72 & 0.755691131017076 & 0.488617737965848 & 0.244308868982924 \tabularnewline
73 & 0.74667353693201 & 0.506652926135979 & 0.253326463067989 \tabularnewline
74 & 0.720014332507078 & 0.559971334985845 & 0.279985667492922 \tabularnewline
75 & 0.680369802948817 & 0.639260394102366 & 0.319630197051183 \tabularnewline
76 & 0.747526334419548 & 0.504947331160904 & 0.252473665580452 \tabularnewline
77 & 0.710908741766088 & 0.578182516467823 & 0.289091258233912 \tabularnewline
78 & 0.695778113302371 & 0.608443773395257 & 0.304221886697628 \tabularnewline
79 & 0.695861845529993 & 0.608276308940013 & 0.304138154470007 \tabularnewline
80 & 0.655065786150689 & 0.689868427698623 & 0.344934213849311 \tabularnewline
81 & 0.616070188215452 & 0.767859623569096 & 0.383929811784548 \tabularnewline
82 & 0.767626608085543 & 0.464746783828915 & 0.232373391914458 \tabularnewline
83 & 0.731406747908048 & 0.537186504183903 & 0.268593252091952 \tabularnewline
84 & 0.702285597070758 & 0.595428805858484 & 0.297714402929242 \tabularnewline
85 & 0.661634084489031 & 0.676731831021938 & 0.338365915510969 \tabularnewline
86 & 0.657026797499272 & 0.685946405001457 & 0.342973202500728 \tabularnewline
87 & 0.613503414344051 & 0.772993171311899 & 0.386496585655949 \tabularnewline
88 & 0.574148130909371 & 0.851703738181258 & 0.425851869090629 \tabularnewline
89 & 0.552980235488381 & 0.894039529023237 & 0.447019764511619 \tabularnewline
90 & 0.524009650042483 & 0.951980699915035 & 0.475990349957517 \tabularnewline
91 & 0.509451785418537 & 0.981096429162926 & 0.490548214581463 \tabularnewline
92 & 0.468526595295512 & 0.937053190591024 & 0.531473404704488 \tabularnewline
93 & 0.427942517230267 & 0.855885034460533 & 0.572057482769733 \tabularnewline
94 & 0.385017209222577 & 0.770034418445154 & 0.614982790777423 \tabularnewline
95 & 0.39595929072735 & 0.7919185814547 & 0.60404070927265 \tabularnewline
96 & 0.362657712454782 & 0.725315424909564 & 0.637342287545218 \tabularnewline
97 & 0.324431815284 & 0.648863630567999 & 0.675568184716 \tabularnewline
98 & 0.327093900181915 & 0.65418780036383 & 0.672906099818085 \tabularnewline
99 & 0.28602554578104 & 0.57205109156208 & 0.71397445421896 \tabularnewline
100 & 0.24903535710008 & 0.49807071420016 & 0.75096464289992 \tabularnewline
101 & 0.22696001004371 & 0.45392002008742 & 0.77303998995629 \tabularnewline
102 & 0.212978643429818 & 0.425957286859636 & 0.787021356570182 \tabularnewline
103 & 0.263815733005778 & 0.527631466011556 & 0.736184266994222 \tabularnewline
104 & 0.22683073629756 & 0.45366147259512 & 0.77316926370244 \tabularnewline
105 & 0.224370142325168 & 0.448740284650337 & 0.775629857674832 \tabularnewline
106 & 0.236013271870969 & 0.472026543741939 & 0.76398672812903 \tabularnewline
107 & 0.214241232951017 & 0.428482465902035 & 0.785758767048983 \tabularnewline
108 & 0.192126295618627 & 0.384252591237253 & 0.807873704381373 \tabularnewline
109 & 0.182692383487063 & 0.365384766974126 & 0.817307616512937 \tabularnewline
110 & 0.168184073247555 & 0.336368146495109 & 0.831815926752445 \tabularnewline
111 & 0.153411357320324 & 0.306822714640649 & 0.846588642679676 \tabularnewline
112 & 0.130007378869429 & 0.260014757738858 & 0.869992621130571 \tabularnewline
113 & 0.157559231684557 & 0.315118463369114 & 0.842440768315443 \tabularnewline
114 & 0.140305396017981 & 0.280610792035961 & 0.85969460398202 \tabularnewline
115 & 0.175145347769693 & 0.350290695539385 & 0.824854652230307 \tabularnewline
116 & 0.16642837026391 & 0.332856740527821 & 0.83357162973609 \tabularnewline
117 & 0.145969599136793 & 0.291939198273586 & 0.854030400863207 \tabularnewline
118 & 0.127773433547336 & 0.255546867094672 & 0.872226566452664 \tabularnewline
119 & 0.131571423175479 & 0.263142846350959 & 0.86842857682452 \tabularnewline
120 & 0.119583202148051 & 0.239166404296103 & 0.880416797851949 \tabularnewline
121 & 0.100678551157197 & 0.201357102314395 & 0.899321448842803 \tabularnewline
122 & 0.081261530428789 & 0.162523060857578 & 0.918738469571211 \tabularnewline
123 & 0.09303037246262 & 0.18606074492524 & 0.90696962753738 \tabularnewline
124 & 0.076199528720486 & 0.152399057440972 & 0.923800471279514 \tabularnewline
125 & 0.0625304193386553 & 0.125060838677311 & 0.937469580661345 \tabularnewline
126 & 0.0486076377448902 & 0.0972152754897805 & 0.95139236225511 \tabularnewline
127 & 0.0377813712255537 & 0.0755627424511075 & 0.962218628774446 \tabularnewline
128 & 0.0320987504021065 & 0.064197500804213 & 0.967901249597893 \tabularnewline
129 & 0.0252982953510845 & 0.050596590702169 & 0.974701704648915 \tabularnewline
130 & 0.0337454280442469 & 0.0674908560884938 & 0.966254571955753 \tabularnewline
131 & 0.0294455632142782 & 0.0588911264285564 & 0.970554436785722 \tabularnewline
132 & 0.0439032768012766 & 0.0878065536025531 & 0.956096723198723 \tabularnewline
133 & 0.0495751652622746 & 0.0991503305245492 & 0.950424834737725 \tabularnewline
134 & 0.0624450901481448 & 0.12489018029629 & 0.937554909851855 \tabularnewline
135 & 0.0506452764620424 & 0.101290552924085 & 0.949354723537958 \tabularnewline
136 & 0.0364291834340242 & 0.0728583668680485 & 0.963570816565976 \tabularnewline
137 & 0.0257595670282786 & 0.0515191340565571 & 0.974240432971721 \tabularnewline
138 & 0.0177650437568512 & 0.0355300875137025 & 0.982234956243149 \tabularnewline
139 & 0.0278802719620979 & 0.0557605439241957 & 0.972119728037902 \tabularnewline
140 & 0.0220355959824215 & 0.044071191964843 & 0.977964404017578 \tabularnewline
141 & 0.484050765982804 & 0.968101531965608 & 0.515949234017196 \tabularnewline
142 & 0.460200944472 & 0.920401888944 & 0.539799055528 \tabularnewline
143 & 0.392506651916563 & 0.785013303833126 & 0.607493348083437 \tabularnewline
144 & 0.405195489688958 & 0.810390979377916 & 0.594804510311042 \tabularnewline
145 & 0.356077674066178 & 0.712155348132357 & 0.643922325933822 \tabularnewline
146 & 0.344099886750986 & 0.688199773501973 & 0.655900113249014 \tabularnewline
147 & 0.396127769809235 & 0.79225553961847 & 0.603872230190765 \tabularnewline
148 & 0.814777647295135 & 0.37044470540973 & 0.185222352704865 \tabularnewline
149 & 0.788964494543746 & 0.422071010912508 & 0.211035505456254 \tabularnewline
150 & 0.690574318340373 & 0.618851363319253 & 0.309425681659627 \tabularnewline
151 & 0.883683001822892 & 0.232633996354215 & 0.116316998177107 \tabularnewline
152 & 0.933625916364463 & 0.132748167271074 & 0.0663740836355371 \tabularnewline
153 & 0.90069498371077 & 0.198610032578461 & 0.0993050162892306 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147129&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]9[/C][C]0.604335158989019[/C][C]0.791329682021963[/C][C]0.395664841010981[/C][/ROW]
[ROW][C]10[/C][C]0.766704941665142[/C][C]0.466590116669716[/C][C]0.233295058334858[/C][/ROW]
[ROW][C]11[/C][C]0.704776478138241[/C][C]0.590447043723518[/C][C]0.295223521861759[/C][/ROW]
[ROW][C]12[/C][C]0.784245849302312[/C][C]0.431508301395376[/C][C]0.215754150697688[/C][/ROW]
[ROW][C]13[/C][C]0.715280331814201[/C][C]0.569439336371598[/C][C]0.284719668185799[/C][/ROW]
[ROW][C]14[/C][C]0.683792808996717[/C][C]0.632414382006566[/C][C]0.316207191003283[/C][/ROW]
[ROW][C]15[/C][C]0.664758280780373[/C][C]0.670483438439254[/C][C]0.335241719219627[/C][/ROW]
[ROW][C]16[/C][C]0.613289587753619[/C][C]0.773420824492763[/C][C]0.386710412246381[/C][/ROW]
[ROW][C]17[/C][C]0.534991611690864[/C][C]0.930016776618272[/C][C]0.465008388309136[/C][/ROW]
[ROW][C]18[/C][C]0.85842391982155[/C][C]0.2831521603569[/C][C]0.14157608017845[/C][/ROW]
[ROW][C]19[/C][C]0.863105487771357[/C][C]0.273789024457286[/C][C]0.136894512228643[/C][/ROW]
[ROW][C]20[/C][C]0.817682477562126[/C][C]0.364635044875748[/C][C]0.182317522437874[/C][/ROW]
[ROW][C]21[/C][C]0.769446279650509[/C][C]0.461107440698981[/C][C]0.230553720349491[/C][/ROW]
[ROW][C]22[/C][C]0.71162261409718[/C][C]0.57675477180564[/C][C]0.28837738590282[/C][/ROW]
[ROW][C]23[/C][C]0.731711771751793[/C][C]0.536576456496413[/C][C]0.268288228248207[/C][/ROW]
[ROW][C]24[/C][C]0.678321520652865[/C][C]0.64335695869427[/C][C]0.321678479347135[/C][/ROW]
[ROW][C]25[/C][C]0.667299654652396[/C][C]0.665400690695208[/C][C]0.332700345347604[/C][/ROW]
[ROW][C]26[/C][C]0.605172872814184[/C][C]0.789654254371632[/C][C]0.394827127185816[/C][/ROW]
[ROW][C]27[/C][C]0.545418747844167[/C][C]0.909162504311665[/C][C]0.454581252155833[/C][/ROW]
[ROW][C]28[/C][C]0.520232445851841[/C][C]0.959535108296318[/C][C]0.479767554148159[/C][/ROW]
[ROW][C]29[/C][C]0.457064775712427[/C][C]0.914129551424855[/C][C]0.542935224287573[/C][/ROW]
[ROW][C]30[/C][C]0.436262999813296[/C][C]0.872525999626592[/C][C]0.563737000186704[/C][/ROW]
[ROW][C]31[/C][C]0.377961331883921[/C][C]0.755922663767841[/C][C]0.62203866811608[/C][/ROW]
[ROW][C]32[/C][C]0.364153433938256[/C][C]0.728306867876512[/C][C]0.635846566061744[/C][/ROW]
[ROW][C]33[/C][C]0.344499943985551[/C][C]0.688999887971103[/C][C]0.655500056014448[/C][/ROW]
[ROW][C]34[/C][C]0.290708564883823[/C][C]0.581417129767646[/C][C]0.709291435116177[/C][/ROW]
[ROW][C]35[/C][C]0.275565138350346[/C][C]0.551130276700693[/C][C]0.724434861649654[/C][/ROW]
[ROW][C]36[/C][C]0.812317866060129[/C][C]0.375364267879743[/C][C]0.187682133939871[/C][/ROW]
[ROW][C]37[/C][C]0.800139464687951[/C][C]0.399721070624098[/C][C]0.199860535312049[/C][/ROW]
[ROW][C]38[/C][C]0.799334014808029[/C][C]0.401331970383941[/C][C]0.200665985191971[/C][/ROW]
[ROW][C]39[/C][C]0.820290836052935[/C][C]0.359418327894131[/C][C]0.179709163947065[/C][/ROW]
[ROW][C]40[/C][C]0.800175268386495[/C][C]0.39964946322701[/C][C]0.199824731613505[/C][/ROW]
[ROW][C]41[/C][C]0.768850929212479[/C][C]0.462298141575043[/C][C]0.231149070787521[/C][/ROW]
[ROW][C]42[/C][C]0.754372752871417[/C][C]0.491254494257165[/C][C]0.245627247128582[/C][/ROW]
[ROW][C]43[/C][C]0.760176295167154[/C][C]0.479647409665692[/C][C]0.239823704832846[/C][/ROW]
[ROW][C]44[/C][C]0.72254517494802[/C][C]0.55490965010396[/C][C]0.27745482505198[/C][/ROW]
[ROW][C]45[/C][C]0.692098028576776[/C][C]0.615803942846449[/C][C]0.307901971423224[/C][/ROW]
[ROW][C]46[/C][C]0.885407670250287[/C][C]0.229184659499426[/C][C]0.114592329749713[/C][/ROW]
[ROW][C]47[/C][C]0.910112459434231[/C][C]0.179775081131537[/C][C]0.0898875405657687[/C][/ROW]
[ROW][C]48[/C][C]0.88776865285691[/C][C]0.224462694286181[/C][C]0.112231347143091[/C][/ROW]
[ROW][C]49[/C][C]0.86636693568563[/C][C]0.267266128628741[/C][C]0.133633064314371[/C][/ROW]
[ROW][C]50[/C][C]0.867915088679822[/C][C]0.264169822640355[/C][C]0.132084911320178[/C][/ROW]
[ROW][C]51[/C][C]0.840686710743339[/C][C]0.318626578513323[/C][C]0.159313289256661[/C][/ROW]
[ROW][C]52[/C][C]0.808590897184802[/C][C]0.382818205630395[/C][C]0.191409102815197[/C][/ROW]
[ROW][C]53[/C][C]0.837087311459545[/C][C]0.325825377080911[/C][C]0.162912688540456[/C][/ROW]
[ROW][C]54[/C][C]0.806139730318272[/C][C]0.387720539363457[/C][C]0.193860269681728[/C][/ROW]
[ROW][C]55[/C][C]0.82561274261864[/C][C]0.348774514762719[/C][C]0.174387257381359[/C][/ROW]
[ROW][C]56[/C][C]0.805629574653375[/C][C]0.38874085069325[/C][C]0.194370425346625[/C][/ROW]
[ROW][C]57[/C][C]0.772187246307924[/C][C]0.455625507384153[/C][C]0.227812753692076[/C][/ROW]
[ROW][C]58[/C][C]0.748530147975443[/C][C]0.502939704049114[/C][C]0.251469852024557[/C][/ROW]
[ROW][C]59[/C][C]0.70966516786338[/C][C]0.58066966427324[/C][C]0.29033483213662[/C][/ROW]
[ROW][C]60[/C][C]0.706106458880206[/C][C]0.587787082239587[/C][C]0.293893541119794[/C][/ROW]
[ROW][C]61[/C][C]0.667792709932704[/C][C]0.664414580134592[/C][C]0.332207290067296[/C][/ROW]
[ROW][C]62[/C][C]0.623349916675152[/C][C]0.753300166649696[/C][C]0.376650083324848[/C][/ROW]
[ROW][C]63[/C][C]0.579063185647427[/C][C]0.841873628705147[/C][C]0.420936814352574[/C][/ROW]
[ROW][C]64[/C][C]0.534185272677245[/C][C]0.93162945464551[/C][C]0.465814727322755[/C][/ROW]
[ROW][C]65[/C][C]0.490683341606452[/C][C]0.981366683212904[/C][C]0.509316658393548[/C][/ROW]
[ROW][C]66[/C][C]0.46203797950868[/C][C]0.92407595901736[/C][C]0.53796202049132[/C][/ROW]
[ROW][C]67[/C][C]0.454440020124817[/C][C]0.908880040249634[/C][C]0.545559979875183[/C][/ROW]
[ROW][C]68[/C][C]0.584406597590658[/C][C]0.831186804818683[/C][C]0.415593402409342[/C][/ROW]
[ROW][C]69[/C][C]0.727405356984423[/C][C]0.545189286031154[/C][C]0.272594643015577[/C][/ROW]
[ROW][C]70[/C][C]0.691063866529517[/C][C]0.617872266940967[/C][C]0.308936133470483[/C][/ROW]
[ROW][C]71[/C][C]0.789993538777005[/C][C]0.42001292244599[/C][C]0.210006461222995[/C][/ROW]
[ROW][C]72[/C][C]0.755691131017076[/C][C]0.488617737965848[/C][C]0.244308868982924[/C][/ROW]
[ROW][C]73[/C][C]0.74667353693201[/C][C]0.506652926135979[/C][C]0.253326463067989[/C][/ROW]
[ROW][C]74[/C][C]0.720014332507078[/C][C]0.559971334985845[/C][C]0.279985667492922[/C][/ROW]
[ROW][C]75[/C][C]0.680369802948817[/C][C]0.639260394102366[/C][C]0.319630197051183[/C][/ROW]
[ROW][C]76[/C][C]0.747526334419548[/C][C]0.504947331160904[/C][C]0.252473665580452[/C][/ROW]
[ROW][C]77[/C][C]0.710908741766088[/C][C]0.578182516467823[/C][C]0.289091258233912[/C][/ROW]
[ROW][C]78[/C][C]0.695778113302371[/C][C]0.608443773395257[/C][C]0.304221886697628[/C][/ROW]
[ROW][C]79[/C][C]0.695861845529993[/C][C]0.608276308940013[/C][C]0.304138154470007[/C][/ROW]
[ROW][C]80[/C][C]0.655065786150689[/C][C]0.689868427698623[/C][C]0.344934213849311[/C][/ROW]
[ROW][C]81[/C][C]0.616070188215452[/C][C]0.767859623569096[/C][C]0.383929811784548[/C][/ROW]
[ROW][C]82[/C][C]0.767626608085543[/C][C]0.464746783828915[/C][C]0.232373391914458[/C][/ROW]
[ROW][C]83[/C][C]0.731406747908048[/C][C]0.537186504183903[/C][C]0.268593252091952[/C][/ROW]
[ROW][C]84[/C][C]0.702285597070758[/C][C]0.595428805858484[/C][C]0.297714402929242[/C][/ROW]
[ROW][C]85[/C][C]0.661634084489031[/C][C]0.676731831021938[/C][C]0.338365915510969[/C][/ROW]
[ROW][C]86[/C][C]0.657026797499272[/C][C]0.685946405001457[/C][C]0.342973202500728[/C][/ROW]
[ROW][C]87[/C][C]0.613503414344051[/C][C]0.772993171311899[/C][C]0.386496585655949[/C][/ROW]
[ROW][C]88[/C][C]0.574148130909371[/C][C]0.851703738181258[/C][C]0.425851869090629[/C][/ROW]
[ROW][C]89[/C][C]0.552980235488381[/C][C]0.894039529023237[/C][C]0.447019764511619[/C][/ROW]
[ROW][C]90[/C][C]0.524009650042483[/C][C]0.951980699915035[/C][C]0.475990349957517[/C][/ROW]
[ROW][C]91[/C][C]0.509451785418537[/C][C]0.981096429162926[/C][C]0.490548214581463[/C][/ROW]
[ROW][C]92[/C][C]0.468526595295512[/C][C]0.937053190591024[/C][C]0.531473404704488[/C][/ROW]
[ROW][C]93[/C][C]0.427942517230267[/C][C]0.855885034460533[/C][C]0.572057482769733[/C][/ROW]
[ROW][C]94[/C][C]0.385017209222577[/C][C]0.770034418445154[/C][C]0.614982790777423[/C][/ROW]
[ROW][C]95[/C][C]0.39595929072735[/C][C]0.7919185814547[/C][C]0.60404070927265[/C][/ROW]
[ROW][C]96[/C][C]0.362657712454782[/C][C]0.725315424909564[/C][C]0.637342287545218[/C][/ROW]
[ROW][C]97[/C][C]0.324431815284[/C][C]0.648863630567999[/C][C]0.675568184716[/C][/ROW]
[ROW][C]98[/C][C]0.327093900181915[/C][C]0.65418780036383[/C][C]0.672906099818085[/C][/ROW]
[ROW][C]99[/C][C]0.28602554578104[/C][C]0.57205109156208[/C][C]0.71397445421896[/C][/ROW]
[ROW][C]100[/C][C]0.24903535710008[/C][C]0.49807071420016[/C][C]0.75096464289992[/C][/ROW]
[ROW][C]101[/C][C]0.22696001004371[/C][C]0.45392002008742[/C][C]0.77303998995629[/C][/ROW]
[ROW][C]102[/C][C]0.212978643429818[/C][C]0.425957286859636[/C][C]0.787021356570182[/C][/ROW]
[ROW][C]103[/C][C]0.263815733005778[/C][C]0.527631466011556[/C][C]0.736184266994222[/C][/ROW]
[ROW][C]104[/C][C]0.22683073629756[/C][C]0.45366147259512[/C][C]0.77316926370244[/C][/ROW]
[ROW][C]105[/C][C]0.224370142325168[/C][C]0.448740284650337[/C][C]0.775629857674832[/C][/ROW]
[ROW][C]106[/C][C]0.236013271870969[/C][C]0.472026543741939[/C][C]0.76398672812903[/C][/ROW]
[ROW][C]107[/C][C]0.214241232951017[/C][C]0.428482465902035[/C][C]0.785758767048983[/C][/ROW]
[ROW][C]108[/C][C]0.192126295618627[/C][C]0.384252591237253[/C][C]0.807873704381373[/C][/ROW]
[ROW][C]109[/C][C]0.182692383487063[/C][C]0.365384766974126[/C][C]0.817307616512937[/C][/ROW]
[ROW][C]110[/C][C]0.168184073247555[/C][C]0.336368146495109[/C][C]0.831815926752445[/C][/ROW]
[ROW][C]111[/C][C]0.153411357320324[/C][C]0.306822714640649[/C][C]0.846588642679676[/C][/ROW]
[ROW][C]112[/C][C]0.130007378869429[/C][C]0.260014757738858[/C][C]0.869992621130571[/C][/ROW]
[ROW][C]113[/C][C]0.157559231684557[/C][C]0.315118463369114[/C][C]0.842440768315443[/C][/ROW]
[ROW][C]114[/C][C]0.140305396017981[/C][C]0.280610792035961[/C][C]0.85969460398202[/C][/ROW]
[ROW][C]115[/C][C]0.175145347769693[/C][C]0.350290695539385[/C][C]0.824854652230307[/C][/ROW]
[ROW][C]116[/C][C]0.16642837026391[/C][C]0.332856740527821[/C][C]0.83357162973609[/C][/ROW]
[ROW][C]117[/C][C]0.145969599136793[/C][C]0.291939198273586[/C][C]0.854030400863207[/C][/ROW]
[ROW][C]118[/C][C]0.127773433547336[/C][C]0.255546867094672[/C][C]0.872226566452664[/C][/ROW]
[ROW][C]119[/C][C]0.131571423175479[/C][C]0.263142846350959[/C][C]0.86842857682452[/C][/ROW]
[ROW][C]120[/C][C]0.119583202148051[/C][C]0.239166404296103[/C][C]0.880416797851949[/C][/ROW]
[ROW][C]121[/C][C]0.100678551157197[/C][C]0.201357102314395[/C][C]0.899321448842803[/C][/ROW]
[ROW][C]122[/C][C]0.081261530428789[/C][C]0.162523060857578[/C][C]0.918738469571211[/C][/ROW]
[ROW][C]123[/C][C]0.09303037246262[/C][C]0.18606074492524[/C][C]0.90696962753738[/C][/ROW]
[ROW][C]124[/C][C]0.076199528720486[/C][C]0.152399057440972[/C][C]0.923800471279514[/C][/ROW]
[ROW][C]125[/C][C]0.0625304193386553[/C][C]0.125060838677311[/C][C]0.937469580661345[/C][/ROW]
[ROW][C]126[/C][C]0.0486076377448902[/C][C]0.0972152754897805[/C][C]0.95139236225511[/C][/ROW]
[ROW][C]127[/C][C]0.0377813712255537[/C][C]0.0755627424511075[/C][C]0.962218628774446[/C][/ROW]
[ROW][C]128[/C][C]0.0320987504021065[/C][C]0.064197500804213[/C][C]0.967901249597893[/C][/ROW]
[ROW][C]129[/C][C]0.0252982953510845[/C][C]0.050596590702169[/C][C]0.974701704648915[/C][/ROW]
[ROW][C]130[/C][C]0.0337454280442469[/C][C]0.0674908560884938[/C][C]0.966254571955753[/C][/ROW]
[ROW][C]131[/C][C]0.0294455632142782[/C][C]0.0588911264285564[/C][C]0.970554436785722[/C][/ROW]
[ROW][C]132[/C][C]0.0439032768012766[/C][C]0.0878065536025531[/C][C]0.956096723198723[/C][/ROW]
[ROW][C]133[/C][C]0.0495751652622746[/C][C]0.0991503305245492[/C][C]0.950424834737725[/C][/ROW]
[ROW][C]134[/C][C]0.0624450901481448[/C][C]0.12489018029629[/C][C]0.937554909851855[/C][/ROW]
[ROW][C]135[/C][C]0.0506452764620424[/C][C]0.101290552924085[/C][C]0.949354723537958[/C][/ROW]
[ROW][C]136[/C][C]0.0364291834340242[/C][C]0.0728583668680485[/C][C]0.963570816565976[/C][/ROW]
[ROW][C]137[/C][C]0.0257595670282786[/C][C]0.0515191340565571[/C][C]0.974240432971721[/C][/ROW]
[ROW][C]138[/C][C]0.0177650437568512[/C][C]0.0355300875137025[/C][C]0.982234956243149[/C][/ROW]
[ROW][C]139[/C][C]0.0278802719620979[/C][C]0.0557605439241957[/C][C]0.972119728037902[/C][/ROW]
[ROW][C]140[/C][C]0.0220355959824215[/C][C]0.044071191964843[/C][C]0.977964404017578[/C][/ROW]
[ROW][C]141[/C][C]0.484050765982804[/C][C]0.968101531965608[/C][C]0.515949234017196[/C][/ROW]
[ROW][C]142[/C][C]0.460200944472[/C][C]0.920401888944[/C][C]0.539799055528[/C][/ROW]
[ROW][C]143[/C][C]0.392506651916563[/C][C]0.785013303833126[/C][C]0.607493348083437[/C][/ROW]
[ROW][C]144[/C][C]0.405195489688958[/C][C]0.810390979377916[/C][C]0.594804510311042[/C][/ROW]
[ROW][C]145[/C][C]0.356077674066178[/C][C]0.712155348132357[/C][C]0.643922325933822[/C][/ROW]
[ROW][C]146[/C][C]0.344099886750986[/C][C]0.688199773501973[/C][C]0.655900113249014[/C][/ROW]
[ROW][C]147[/C][C]0.396127769809235[/C][C]0.79225553961847[/C][C]0.603872230190765[/C][/ROW]
[ROW][C]148[/C][C]0.814777647295135[/C][C]0.37044470540973[/C][C]0.185222352704865[/C][/ROW]
[ROW][C]149[/C][C]0.788964494543746[/C][C]0.422071010912508[/C][C]0.211035505456254[/C][/ROW]
[ROW][C]150[/C][C]0.690574318340373[/C][C]0.618851363319253[/C][C]0.309425681659627[/C][/ROW]
[ROW][C]151[/C][C]0.883683001822892[/C][C]0.232633996354215[/C][C]0.116316998177107[/C][/ROW]
[ROW][C]152[/C][C]0.933625916364463[/C][C]0.132748167271074[/C][C]0.0663740836355371[/C][/ROW]
[ROW][C]153[/C][C]0.90069498371077[/C][C]0.198610032578461[/C][C]0.0993050162892306[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147129&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147129&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.6043351589890190.7913296820219630.395664841010981
100.7667049416651420.4665901166697160.233295058334858
110.7047764781382410.5904470437235180.295223521861759
120.7842458493023120.4315083013953760.215754150697688
130.7152803318142010.5694393363715980.284719668185799
140.6837928089967170.6324143820065660.316207191003283
150.6647582807803730.6704834384392540.335241719219627
160.6132895877536190.7734208244927630.386710412246381
170.5349916116908640.9300167766182720.465008388309136
180.858423919821550.28315216035690.14157608017845
190.8631054877713570.2737890244572860.136894512228643
200.8176824775621260.3646350448757480.182317522437874
210.7694462796505090.4611074406989810.230553720349491
220.711622614097180.576754771805640.28837738590282
230.7317117717517930.5365764564964130.268288228248207
240.6783215206528650.643356958694270.321678479347135
250.6672996546523960.6654006906952080.332700345347604
260.6051728728141840.7896542543716320.394827127185816
270.5454187478441670.9091625043116650.454581252155833
280.5202324458518410.9595351082963180.479767554148159
290.4570647757124270.9141295514248550.542935224287573
300.4362629998132960.8725259996265920.563737000186704
310.3779613318839210.7559226637678410.62203866811608
320.3641534339382560.7283068678765120.635846566061744
330.3444999439855510.6889998879711030.655500056014448
340.2907085648838230.5814171297676460.709291435116177
350.2755651383503460.5511302767006930.724434861649654
360.8123178660601290.3753642678797430.187682133939871
370.8001394646879510.3997210706240980.199860535312049
380.7993340148080290.4013319703839410.200665985191971
390.8202908360529350.3594183278941310.179709163947065
400.8001752683864950.399649463227010.199824731613505
410.7688509292124790.4622981415750430.231149070787521
420.7543727528714170.4912544942571650.245627247128582
430.7601762951671540.4796474096656920.239823704832846
440.722545174948020.554909650103960.27745482505198
450.6920980285767760.6158039428464490.307901971423224
460.8854076702502870.2291846594994260.114592329749713
470.9101124594342310.1797750811315370.0898875405657687
480.887768652856910.2244626942861810.112231347143091
490.866366935685630.2672661286287410.133633064314371
500.8679150886798220.2641698226403550.132084911320178
510.8406867107433390.3186265785133230.159313289256661
520.8085908971848020.3828182056303950.191409102815197
530.8370873114595450.3258253770809110.162912688540456
540.8061397303182720.3877205393634570.193860269681728
550.825612742618640.3487745147627190.174387257381359
560.8056295746533750.388740850693250.194370425346625
570.7721872463079240.4556255073841530.227812753692076
580.7485301479754430.5029397040491140.251469852024557
590.709665167863380.580669664273240.29033483213662
600.7061064588802060.5877870822395870.293893541119794
610.6677927099327040.6644145801345920.332207290067296
620.6233499166751520.7533001666496960.376650083324848
630.5790631856474270.8418736287051470.420936814352574
640.5341852726772450.931629454645510.465814727322755
650.4906833416064520.9813666832129040.509316658393548
660.462037979508680.924075959017360.53796202049132
670.4544400201248170.9088800402496340.545559979875183
680.5844065975906580.8311868048186830.415593402409342
690.7274053569844230.5451892860311540.272594643015577
700.6910638665295170.6178722669409670.308936133470483
710.7899935387770050.420012922445990.210006461222995
720.7556911310170760.4886177379658480.244308868982924
730.746673536932010.5066529261359790.253326463067989
740.7200143325070780.5599713349858450.279985667492922
750.6803698029488170.6392603941023660.319630197051183
760.7475263344195480.5049473311609040.252473665580452
770.7109087417660880.5781825164678230.289091258233912
780.6957781133023710.6084437733952570.304221886697628
790.6958618455299930.6082763089400130.304138154470007
800.6550657861506890.6898684276986230.344934213849311
810.6160701882154520.7678596235690960.383929811784548
820.7676266080855430.4647467838289150.232373391914458
830.7314067479080480.5371865041839030.268593252091952
840.7022855970707580.5954288058584840.297714402929242
850.6616340844890310.6767318310219380.338365915510969
860.6570267974992720.6859464050014570.342973202500728
870.6135034143440510.7729931713118990.386496585655949
880.5741481309093710.8517037381812580.425851869090629
890.5529802354883810.8940395290232370.447019764511619
900.5240096500424830.9519806999150350.475990349957517
910.5094517854185370.9810964291629260.490548214581463
920.4685265952955120.9370531905910240.531473404704488
930.4279425172302670.8558850344605330.572057482769733
940.3850172092225770.7700344184451540.614982790777423
950.395959290727350.79191858145470.60404070927265
960.3626577124547820.7253154249095640.637342287545218
970.3244318152840.6488636305679990.675568184716
980.3270939001819150.654187800363830.672906099818085
990.286025545781040.572051091562080.71397445421896
1000.249035357100080.498070714200160.75096464289992
1010.226960010043710.453920020087420.77303998995629
1020.2129786434298180.4259572868596360.787021356570182
1030.2638157330057780.5276314660115560.736184266994222
1040.226830736297560.453661472595120.77316926370244
1050.2243701423251680.4487402846503370.775629857674832
1060.2360132718709690.4720265437419390.76398672812903
1070.2142412329510170.4284824659020350.785758767048983
1080.1921262956186270.3842525912372530.807873704381373
1090.1826923834870630.3653847669741260.817307616512937
1100.1681840732475550.3363681464951090.831815926752445
1110.1534113573203240.3068227146406490.846588642679676
1120.1300073788694290.2600147577388580.869992621130571
1130.1575592316845570.3151184633691140.842440768315443
1140.1403053960179810.2806107920359610.85969460398202
1150.1751453477696930.3502906955393850.824854652230307
1160.166428370263910.3328567405278210.83357162973609
1170.1459695991367930.2919391982735860.854030400863207
1180.1277734335473360.2555468670946720.872226566452664
1190.1315714231754790.2631428463509590.86842857682452
1200.1195832021480510.2391664042961030.880416797851949
1210.1006785511571970.2013571023143950.899321448842803
1220.0812615304287890.1625230608575780.918738469571211
1230.093030372462620.186060744925240.90696962753738
1240.0761995287204860.1523990574409720.923800471279514
1250.06253041933865530.1250608386773110.937469580661345
1260.04860763774489020.09721527548978050.95139236225511
1270.03778137122555370.07556274245110750.962218628774446
1280.03209875040210650.0641975008042130.967901249597893
1290.02529829535108450.0505965907021690.974701704648915
1300.03374542804424690.06749085608849380.966254571955753
1310.02944556321427820.05889112642855640.970554436785722
1320.04390327680127660.08780655360255310.956096723198723
1330.04957516526227460.09915033052454920.950424834737725
1340.06244509014814480.124890180296290.937554909851855
1350.05064527646204240.1012905529240850.949354723537958
1360.03642918343402420.07285836686804850.963570816565976
1370.02575956702827860.05151913405655710.974240432971721
1380.01776504375685120.03553008751370250.982234956243149
1390.02788027196209790.05576054392419570.972119728037902
1400.02203559598242150.0440711919648430.977964404017578
1410.4840507659828040.9681015319656080.515949234017196
1420.4602009444720.9204018889440.539799055528
1430.3925066519165630.7850133038331260.607493348083437
1440.4051954896889580.8103909793779160.594804510311042
1450.3560776740661780.7121553481323570.643922325933822
1460.3440998867509860.6881997735019730.655900113249014
1470.3961277698092350.792255539618470.603872230190765
1480.8147776472951350.370444705409730.185222352704865
1490.7889644945437460.4220710109125080.211035505456254
1500.6905743183403730.6188513633192530.309425681659627
1510.8836830018228920.2326339963542150.116316998177107
1520.9336259163644630.1327481672710740.0663740836355371
1530.900694983710770.1986100325784610.0993050162892306







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level20.0137931034482759OK
10% type I error level130.0896551724137931OK

\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 & 2 & 0.0137931034482759 & OK \tabularnewline
10% type I error level & 13 & 0.0896551724137931 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147129&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]2[/C][C]0.0137931034482759[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]13[/C][C]0.0896551724137931[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147129&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147129&T=6

As an alternative you can also use a QR Code:  

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level20.0137931034482759OK
10% type I error level130.0896551724137931OK



Parameters (Session):
par1 = 3 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 3 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}