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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationWed, 19 Dec 2012 17:47:18 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Dec/19/t1355957279gw7hk2qlfmlnspb.htm/, Retrieved Thu, 31 Oct 2024 23:43:00 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=202459, Retrieved Thu, 31 Oct 2024 23:43:00 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact169
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
-   PD  [Multiple Regression] [Workshop 7 mini-t...] [2010-11-20 16:10:06] [87d60b8864dc39f7ed759c345edfb471]
-   PD    [Multiple Regression] [Workshop 7 mini-t...] [2010-11-21 12:07:24] [87d60b8864dc39f7ed759c345edfb471]
- R PD      [Multiple Regression] [W7-model4] [2010-11-21 20:43:25] [48146708a479232c43a8f6e52fbf83b4]
-   PD        [Multiple Regression] [W7 - model 4] [2010-11-22 19:23:10] [48146708a479232c43a8f6e52fbf83b4]
- R             [Multiple Regression] [Multiple Linear R...] [2011-11-22 18:07:13] [74be16979710d4c4e7c6647856088456]
-                 [Multiple Regression] [WS 7 - Deel 4] [2011-11-22 20:10:30] [95a4a8598e82ac3272c4dca488d0ba38]
-   P               [Multiple Regression] [WS7 taak 4] [2012-11-19 15:10:18] [d31c851fa7fbee45412c0a7bcdad10e5]
-   P                   [Multiple Regression] [Paper regression ...] [2012-12-19 22:47:18] [885fe6c051c4f145d5c497ce1b2b5522] [Current]
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Dataseries X:
24	14	11	12	24	26
25	11	7	8	25	23
17	6	17	8	30	25
18	12	10	8	19	23
18	8	12	9	22	19
16	10	12	7	22	29
20	10	11	4	25	25
16	11	11	11	23	21
18	16	12	7	17	22
17	11	13	7	21	25
23	13	14	12	19	24
30	12	16	10	19	18
23	8	11	10	15	22
18	12	10	8	16	15
15	11	11	8	23	22
12	4	15	4	27	28
21	9	9	9	22	20
15	8	11	8	14	12
20	8	17	7	22	24
31	14	17	11	23	20
27	15	11	9	23	21
34	16	18	11	21	20
21	9	14	13	19	21
31	14	10	8	18	23
19	11	11	8	20	28
16	8	15	9	23	24
20	9	15	6	25	24
21	9	13	9	19	24
22	9	16	9	24	23
17	9	13	6	22	23
24	10	9	6	25	29
25	16	18	16	26	24
26	11	18	5	29	18
25	8	12	7	32	25
17	9	17	9	25	21
32	16	9	6	29	26
33	11	9	6	28	22
13	16	12	5	17	22
32	12	18	12	28	22
25	12	12	7	29	23
29	14	18	10	26	30
22	9	14	9	25	23
18	10	15	8	14	17
17	9	16	5	25	23
20	10	10	8	26	23
15	12	11	8	20	25
20	14	14	10	18	24
33	14	9	6	32	24
29	10	12	8	25	23
23	14	17	7	25	21
26	16	5	4	23	24
18	9	12	8	21	24
20	10	12	8	20	28
11	6	6	4	15	16
28	8	24	20	30	20
26	13	12	8	24	29
22	10	12	8	26	27
17	8	14	6	24	22
12	7	7	4	22	28
14	15	13	8	14	16
17	9	12	9	24	25
21	10	13	6	24	24
19	12	14	7	24	28
18	13	8	9	24	24
10	10	11	5	19	23
29	11	9	5	31	30
31	8	11	8	22	24
19	9	13	8	27	21
9	13	10	6	19	25
20	11	11	8	25	25
28	8	12	7	20	22
19	9	9	7	21	23
30	9	15	9	27	26
29	15	18	11	23	23
26	9	15	6	25	25
23	10	12	8	20	21
13	14	13	6	21	25
21	12	14	9	22	24
19	12	10	8	23	29
28	11	13	6	25	22
23	14	13	10	25	27
18	6	11	8	17	26
21	12	13	8	19	22
20	8	16	10	25	24
23	14	8	5	19	27
21	11	16	7	20	24
21	10	11	5	26	24
15	14	9	8	23	29
28	12	16	14	27	22
19	10	12	7	17	21
26	14	14	8	17	24
10	5	8	6	19	24
16	11	9	5	17	23
22	10	15	6	22	20
19	9	11	10	21	27
31	10	21	12	32	26
31	16	14	9	21	25
29	13	18	12	21	21
19	9	12	7	18	21
22	10	13	8	18	19
23	10	15	10	23	21
15	7	12	6	19	21
20	9	19	10	20	16
18	8	15	10	21	22
23	14	11	10	20	29
25	14	11	5	17	15
21	8	10	7	18	17
24	9	13	10	19	15
25	14	15	11	22	21
17	14	12	6	15	21
13	8	12	7	14	19
28	8	16	12	18	24
21	8	9	11	24	20
25	7	18	11	35	17
9	6	8	11	29	23
16	8	13	5	21	24
19	6	17	8	25	14
17	11	9	6	20	19
25	14	15	9	22	24
20	11	8	4	13	13
29	11	7	4	26	22
14	11	12	7	17	16
22	14	14	11	25	19
15	8	6	6	20	25
19	20	8	7	19	25
20	11	17	8	21	23
15	8	10	4	22	24
20	11	11	8	24	26
18	10	14	9	21	26
33	14	11	8	26	25
22	11	13	11	24	18
16	9	12	8	16	21
17	9	11	5	23	26
16	8	9	4	18	23
21	10	12	8	16	23
26	13	20	10	26	22
18	13	12	6	19	20
18	12	13	9	21	13
17	8	12	9	21	24
22	13	12	13	22	15
30	14	9	9	23	14
30	12	15	10	29	22
24	14	24	20	21	10
21	15	7	5	21	24
21	13	17	11	23	22
29	16	11	6	27	24
31	9	17	9	25	19
20	9	11	7	21	20
16	9	12	9	10	13
22	8	14	10	20	20
20	7	11	9	26	22
28	16	16	8	24	24
38	11	21	7	29	29
22	9	14	6	19	12
20	11	20	13	24	20
17	9	13	6	19	21
28	14	11	8	24	24
22	13	15	10	22	22
31	16	19	16	17	20




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time10 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 & 10 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202459&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]10 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202459&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202459&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time10 seconds
R Server'George Udny Yule' @ yule.wessa.net







Multiple Linear Regression - Estimated Regression Equation
YT[t] = -2.70676782746938 + 0.804825696596371X1[t] + 0.246618734551963X2[t] + 0.190858166207396X3[t] + 0.5682129740418X4[t] -0.100756792404192X5[t] + 0.00564436253658327t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
YT[t] =  -2.70676782746938 +  0.804825696596371X1[t] +  0.246618734551963X2[t] +  0.190858166207396X3[t] +  0.5682129740418X4[t] -0.100756792404192X5[t] +  0.00564436253658327t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202459&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]YT[t] =  -2.70676782746938 +  0.804825696596371X1[t] +  0.246618734551963X2[t] +  0.190858166207396X3[t] +  0.5682129740418X4[t] -0.100756792404192X5[t] +  0.00564436253658327t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202459&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202459&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
YT[t] = -2.70676782746938 + 0.804825696596371X1[t] + 0.246618734551963X2[t] + 0.190858166207396X3[t] + 0.5682129740418X4[t] -0.100756792404192X5[t] + 0.00564436253658327t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-2.706767827469383.230753-0.83780.4034510.201726
X10.8048256965963710.1307656.154700
X20.2466187345519630.1331411.85230.065920.03296
X30.1908581662073960.1685681.13220.259320.12966
X40.56821297404180.0960195.917700
X5-0.1007567924041920.105352-0.95640.3403970.170199
t0.005644362536583270.0080040.70520.4817420.240871

\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) & -2.70676782746938 & 3.230753 & -0.8378 & 0.403451 & 0.201726 \tabularnewline
X1 & 0.804825696596371 & 0.130765 & 6.1547 & 0 & 0 \tabularnewline
X2 & 0.246618734551963 & 0.133141 & 1.8523 & 0.06592 & 0.03296 \tabularnewline
X3 & 0.190858166207396 & 0.168568 & 1.1322 & 0.25932 & 0.12966 \tabularnewline
X4 & 0.5682129740418 & 0.096019 & 5.9177 & 0 & 0 \tabularnewline
X5 & -0.100756792404192 & 0.105352 & -0.9564 & 0.340397 & 0.170199 \tabularnewline
t & 0.00564436253658327 & 0.008004 & 0.7052 & 0.481742 & 0.240871 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202459&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]-2.70676782746938[/C][C]3.230753[/C][C]-0.8378[/C][C]0.403451[/C][C]0.201726[/C][/ROW]
[ROW][C]X1[/C][C]0.804825696596371[/C][C]0.130765[/C][C]6.1547[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]X2[/C][C]0.246618734551963[/C][C]0.133141[/C][C]1.8523[/C][C]0.06592[/C][C]0.03296[/C][/ROW]
[ROW][C]X3[/C][C]0.190858166207396[/C][C]0.168568[/C][C]1.1322[/C][C]0.25932[/C][C]0.12966[/C][/ROW]
[ROW][C]X4[/C][C]0.5682129740418[/C][C]0.096019[/C][C]5.9177[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]X5[/C][C]-0.100756792404192[/C][C]0.105352[/C][C]-0.9564[/C][C]0.340397[/C][C]0.170199[/C][/ROW]
[ROW][C]t[/C][C]0.00564436253658327[/C][C]0.008004[/C][C]0.7052[/C][C]0.481742[/C][C]0.240871[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202459&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202459&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)-2.706767827469383.230753-0.83780.4034510.201726
X10.8048256965963710.1307656.154700
X20.2466187345519630.1331411.85230.065920.03296
X30.1908581662073960.1685681.13220.259320.12966
X40.56821297404180.0960195.917700
X5-0.1007567924041920.105352-0.95640.3403970.170199
t0.005644362536583270.0080040.70520.4817420.240871







Multiple Linear Regression - Regression Statistics
Multiple R0.639616097712746
R-squared0.40910875245328
Adjusted R-squared0.385784097944857
F-TEST (value)17.5397561539675
F-TEST (DF numerator)6
F-TEST (DF denominator)152
p-value2.22044604925031e-15
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation4.48508352276095
Sum Squared Residuals3057.62807933355

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.639616097712746 \tabularnewline
R-squared & 0.40910875245328 \tabularnewline
Adjusted R-squared & 0.385784097944857 \tabularnewline
F-TEST (value) & 17.5397561539675 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 152 \tabularnewline
p-value & 2.22044604925031e-15 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 4.48508352276095 \tabularnewline
Sum Squared Residuals & 3057.62807933355 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202459&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.639616097712746[/C][/ROW]
[ROW][C]R-squared[/C][C]0.40910875245328[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.385784097944857[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]17.5397561539675[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]152[/C][/ROW]
[ROW][C]p-value[/C][C]2.22044604925031e-15[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]4.48508352276095[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]3057.62807933355[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202459&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202459&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 R0.639616097712746
R-squared0.40910875245328
Adjusted R-squared0.385784097944857
F-TEST (value)17.5397561539675
F-TEST (DF numerator)6
F-TEST (DF denominator)152
p-value2.22044604925031e-15
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation4.48508352276095
Sum Squared Residuals3057.62807933355







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
12424.5869751364709-0.586975136470904
22521.29871815743543.70128184256464
31722.3859726679103-5.38597266791034
41819.44541093851-1.44541093850998
51819.0235142417146-1.02351424171457
61619.2495257409872-3.24952574098718
72020.5436429620918-0.543642962091785
81621.9567214062097-5.95672140620968
91821.9596456847955-3.9596456847955
101720.1583618178568-3.15836181785682
112321.93889798349571.06110201650432
123021.85577854055028.14422145944982
132314.73314737815758.2668526218425
141818.603269980984-0.603269980983953
151521.3229006529394-6.32290065293938
161217.5861185544217-5.58611855442168
172119.05545929268991.94454070731014
181514.81900780842570.180992191574259
192019.45012869555080.549871304449199
203126.01940004615384.98059995384624
212724.8676845731562.13231542684405
223426.7505329508887.24946704911197
232119.28045609096921.71954390903083
243120.599736608392610.4002633916074
251919.0701646017547-0.0701646017546602
261619.9463310706596-3.94633107065955
272021.3206525792539-1.32065257925392
282117.9963561270583.00364387294204
292221.68367835586360.316321644136373
301719.2404660680385-2.24046606803853
312420.16455935666393.83544064333612
322530.1993051078831-5.19930510788308
332626.390560835707-0.390560835707007
342523.88307340885351.11692659114645
351722.7338898244853-5.73388982448528
363227.59685762230484.4031423776952
373323.41318769743459.5868123025655
381321.7416158659416-8.74161586594163
393227.59401972731614.40598027268392
402525.6331170331415-0.633117033141512
412926.89076322585012.10923677414995
422221.83203057377710.167969426222917
431817.052459241220.947540758780044
441721.5731241031246-4.57312410312459
452022.0446692276098-2.04466922760976
461520.2957922888319-5.29579228883186
472021.9969914249525-1.99699142495246
483327.96109108648485.03890891351515
492921.99227117281827.00772882718179
502326.4609674131011-3.46096741310109
512623.10556753028852.89443246971151
521818.8307698752602-0.8307698752602
532018.66999979073461.33000020926542
541111.5812129333856-0.58121293338563
552828.8095440113789-0.809544011378862
562623.27350507189652.72649492810354
572222.2026118775359-0.202611877535911
581720.0774839975062-3.07748399750625
591215.4292884866592-3.42928848665917
601420.780061210624-6.78006121062399
611720.6763094340181-3.67630943401805
622121.261580521485-0.261580521484976
631922.9113260083569-3.91132600835689
641823.0268271622096-5.02682716220963
651017.8541098959786-7.85410989597859
662924.28460062767994.71539937232012
673118.432203856202412.5677961437976
681922.8792466318609-3.87924663186088
69920.0338902827611-11.0338902827611
702022.4674961633225-2.46749616332249
712817.575629511418110.4243704885819
721918.11369954853280.886300451467227
733023.08778011783416.91221988216585
742927.0733696770651.92663032293498
752621.49082518860574.50917481139428
762319.50511767590533.49488232409465
771322.7601530313896-9.76015303138962
782122.6443090003536-1.6443090003536
791921.5370492704958-2.53704927049577
802822.937731302595.06226869740997
812325.6175014577243-2.61750145772435
821813.8646394460414.13536055395896
832120.73192857496010.268071425039858
842021.8476069466243-1.84760694662434
852320.04341655982312.95658344017692
862120.85973339265540.140266607344571
872121.8550198976718-0.855019897671836
881522.9508811919658-7.9508811919658
892827.19650374341430.803496256585691
901917.68864166308471.31135833691528
912621.29541407010554.70458592989447
921013.3326243716318-3.3326243716318
931617.1873143264118-1.18731432641177
942221.20203881329270.797961186707265
951918.90630468498350.0936953150164672
963128.91577792891492.0842220710851
973125.30188490848825.69811509151183
982924.85512878768254.14487121231755
991917.50282820335941.49717179664061
1002218.95228874806013.04771125193991
1012322.4724381975160.52756180248399
1021516.2874647056108-1.28746470561081
1032021.4645212040962-1.46452120409622
1041819.6425371514452-1.64253715144523
1052322.2171502344810.782849765518965
1062520.97445993751394.02554006248607
1072116.65294710756854.34705289243147
1082419.54557442782974.45442557217025
1092525.3595410763598-0.359541076359756
1101719.6935475859109-2.69354758591087
1111314.6943965458432-1.69439654584321
1122818.40987461177099.59012538822914
1132120.31063468010390.689365319896121
1142528.2836350486841-3.28363504868414
115921.0044477704288-12.0044477704288
1161618.0612276169349-2.06122761693493
1171921.2966898433179-2.29668984331794
1181719.6269476477759-2.62694764777592
1192524.73199799209820.268002007901781
1202015.63695124201494.36304875798512
1212921.87593440090527.12406559909483
1221419.1778709228727-5.17787092287271
1232227.0980959242537-5.09809592425374
1241515.9019397751253-0.901939775125261
1251925.6813751580878-6.68137515808782
1262022.1919545613241-2.19195456132411
1271517.7608142090159-2.76081420901586
1282022.1258994239983-2.12589942399832
1291820.5527935376764-2.55279353767642
1303325.78884797934847.2111520206516
1312224.0146988185677-2.01469881856773
1321616.7435243851904-0.743524385190438
1331719.4036823708245-2.40368237082451
1341615.3816109084570.618389091543024
1352117.36376958458823.63623041541183
1362627.9214437785666-1.92144377856656
1371821.4147283663736-3.41472836637364
1381823.276463760401-5.27646376040095
1391718.707861885554-1.70786188555397
1402224.9760915015815-2.97609150158152
1413024.9522424586755.047757541325
1423027.62202950655532.37797049344472
1432430.0288532518421-6.02885325184214
1442122.3733372368221-1.37333723682208
1452125.7186060818219-4.71860608182192
1462927.77606260715771.22393739284233
1473123.5675720133917.43242798660902
1482019.33817894762960.661821052370395
1491614.42711320950251.57288679049751
1502219.28885970434272.71114029565732
1512020.766728259862-0.766728259862021
1522827.72009986542640.279900134573624
1533827.081132159721610.9188678402784
1542219.59067155144752.40932844855246
1552026.0566974089159-6.05669740891587
1561718.448530410331-1.44853041033101
1572824.90557661215673.09442338784326
1582224.5396741854444-2.53967418544438
1593126.45186828782174.54813171217832

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 24 & 24.5869751364709 & -0.586975136470904 \tabularnewline
2 & 25 & 21.2987181574354 & 3.70128184256464 \tabularnewline
3 & 17 & 22.3859726679103 & -5.38597266791034 \tabularnewline
4 & 18 & 19.44541093851 & -1.44541093850998 \tabularnewline
5 & 18 & 19.0235142417146 & -1.02351424171457 \tabularnewline
6 & 16 & 19.2495257409872 & -3.24952574098718 \tabularnewline
7 & 20 & 20.5436429620918 & -0.543642962091785 \tabularnewline
8 & 16 & 21.9567214062097 & -5.95672140620968 \tabularnewline
9 & 18 & 21.9596456847955 & -3.9596456847955 \tabularnewline
10 & 17 & 20.1583618178568 & -3.15836181785682 \tabularnewline
11 & 23 & 21.9388979834957 & 1.06110201650432 \tabularnewline
12 & 30 & 21.8557785405502 & 8.14422145944982 \tabularnewline
13 & 23 & 14.7331473781575 & 8.2668526218425 \tabularnewline
14 & 18 & 18.603269980984 & -0.603269980983953 \tabularnewline
15 & 15 & 21.3229006529394 & -6.32290065293938 \tabularnewline
16 & 12 & 17.5861185544217 & -5.58611855442168 \tabularnewline
17 & 21 & 19.0554592926899 & 1.94454070731014 \tabularnewline
18 & 15 & 14.8190078084257 & 0.180992191574259 \tabularnewline
19 & 20 & 19.4501286955508 & 0.549871304449199 \tabularnewline
20 & 31 & 26.0194000461538 & 4.98059995384624 \tabularnewline
21 & 27 & 24.867684573156 & 2.13231542684405 \tabularnewline
22 & 34 & 26.750532950888 & 7.24946704911197 \tabularnewline
23 & 21 & 19.2804560909692 & 1.71954390903083 \tabularnewline
24 & 31 & 20.5997366083926 & 10.4002633916074 \tabularnewline
25 & 19 & 19.0701646017547 & -0.0701646017546602 \tabularnewline
26 & 16 & 19.9463310706596 & -3.94633107065955 \tabularnewline
27 & 20 & 21.3206525792539 & -1.32065257925392 \tabularnewline
28 & 21 & 17.996356127058 & 3.00364387294204 \tabularnewline
29 & 22 & 21.6836783558636 & 0.316321644136373 \tabularnewline
30 & 17 & 19.2404660680385 & -2.24046606803853 \tabularnewline
31 & 24 & 20.1645593566639 & 3.83544064333612 \tabularnewline
32 & 25 & 30.1993051078831 & -5.19930510788308 \tabularnewline
33 & 26 & 26.390560835707 & -0.390560835707007 \tabularnewline
34 & 25 & 23.8830734088535 & 1.11692659114645 \tabularnewline
35 & 17 & 22.7338898244853 & -5.73388982448528 \tabularnewline
36 & 32 & 27.5968576223048 & 4.4031423776952 \tabularnewline
37 & 33 & 23.4131876974345 & 9.5868123025655 \tabularnewline
38 & 13 & 21.7416158659416 & -8.74161586594163 \tabularnewline
39 & 32 & 27.5940197273161 & 4.40598027268392 \tabularnewline
40 & 25 & 25.6331170331415 & -0.633117033141512 \tabularnewline
41 & 29 & 26.8907632258501 & 2.10923677414995 \tabularnewline
42 & 22 & 21.8320305737771 & 0.167969426222917 \tabularnewline
43 & 18 & 17.05245924122 & 0.947540758780044 \tabularnewline
44 & 17 & 21.5731241031246 & -4.57312410312459 \tabularnewline
45 & 20 & 22.0446692276098 & -2.04466922760976 \tabularnewline
46 & 15 & 20.2957922888319 & -5.29579228883186 \tabularnewline
47 & 20 & 21.9969914249525 & -1.99699142495246 \tabularnewline
48 & 33 & 27.9610910864848 & 5.03890891351515 \tabularnewline
49 & 29 & 21.9922711728182 & 7.00772882718179 \tabularnewline
50 & 23 & 26.4609674131011 & -3.46096741310109 \tabularnewline
51 & 26 & 23.1055675302885 & 2.89443246971151 \tabularnewline
52 & 18 & 18.8307698752602 & -0.8307698752602 \tabularnewline
53 & 20 & 18.6699997907346 & 1.33000020926542 \tabularnewline
54 & 11 & 11.5812129333856 & -0.58121293338563 \tabularnewline
55 & 28 & 28.8095440113789 & -0.809544011378862 \tabularnewline
56 & 26 & 23.2735050718965 & 2.72649492810354 \tabularnewline
57 & 22 & 22.2026118775359 & -0.202611877535911 \tabularnewline
58 & 17 & 20.0774839975062 & -3.07748399750625 \tabularnewline
59 & 12 & 15.4292884866592 & -3.42928848665917 \tabularnewline
60 & 14 & 20.780061210624 & -6.78006121062399 \tabularnewline
61 & 17 & 20.6763094340181 & -3.67630943401805 \tabularnewline
62 & 21 & 21.261580521485 & -0.261580521484976 \tabularnewline
63 & 19 & 22.9113260083569 & -3.91132600835689 \tabularnewline
64 & 18 & 23.0268271622096 & -5.02682716220963 \tabularnewline
65 & 10 & 17.8541098959786 & -7.85410989597859 \tabularnewline
66 & 29 & 24.2846006276799 & 4.71539937232012 \tabularnewline
67 & 31 & 18.4322038562024 & 12.5677961437976 \tabularnewline
68 & 19 & 22.8792466318609 & -3.87924663186088 \tabularnewline
69 & 9 & 20.0338902827611 & -11.0338902827611 \tabularnewline
70 & 20 & 22.4674961633225 & -2.46749616332249 \tabularnewline
71 & 28 & 17.5756295114181 & 10.4243704885819 \tabularnewline
72 & 19 & 18.1136995485328 & 0.886300451467227 \tabularnewline
73 & 30 & 23.0877801178341 & 6.91221988216585 \tabularnewline
74 & 29 & 27.073369677065 & 1.92663032293498 \tabularnewline
75 & 26 & 21.4908251886057 & 4.50917481139428 \tabularnewline
76 & 23 & 19.5051176759053 & 3.49488232409465 \tabularnewline
77 & 13 & 22.7601530313896 & -9.76015303138962 \tabularnewline
78 & 21 & 22.6443090003536 & -1.6443090003536 \tabularnewline
79 & 19 & 21.5370492704958 & -2.53704927049577 \tabularnewline
80 & 28 & 22.93773130259 & 5.06226869740997 \tabularnewline
81 & 23 & 25.6175014577243 & -2.61750145772435 \tabularnewline
82 & 18 & 13.864639446041 & 4.13536055395896 \tabularnewline
83 & 21 & 20.7319285749601 & 0.268071425039858 \tabularnewline
84 & 20 & 21.8476069466243 & -1.84760694662434 \tabularnewline
85 & 23 & 20.0434165598231 & 2.95658344017692 \tabularnewline
86 & 21 & 20.8597333926554 & 0.140266607344571 \tabularnewline
87 & 21 & 21.8550198976718 & -0.855019897671836 \tabularnewline
88 & 15 & 22.9508811919658 & -7.9508811919658 \tabularnewline
89 & 28 & 27.1965037434143 & 0.803496256585691 \tabularnewline
90 & 19 & 17.6886416630847 & 1.31135833691528 \tabularnewline
91 & 26 & 21.2954140701055 & 4.70458592989447 \tabularnewline
92 & 10 & 13.3326243716318 & -3.3326243716318 \tabularnewline
93 & 16 & 17.1873143264118 & -1.18731432641177 \tabularnewline
94 & 22 & 21.2020388132927 & 0.797961186707265 \tabularnewline
95 & 19 & 18.9063046849835 & 0.0936953150164672 \tabularnewline
96 & 31 & 28.9157779289149 & 2.0842220710851 \tabularnewline
97 & 31 & 25.3018849084882 & 5.69811509151183 \tabularnewline
98 & 29 & 24.8551287876825 & 4.14487121231755 \tabularnewline
99 & 19 & 17.5028282033594 & 1.49717179664061 \tabularnewline
100 & 22 & 18.9522887480601 & 3.04771125193991 \tabularnewline
101 & 23 & 22.472438197516 & 0.52756180248399 \tabularnewline
102 & 15 & 16.2874647056108 & -1.28746470561081 \tabularnewline
103 & 20 & 21.4645212040962 & -1.46452120409622 \tabularnewline
104 & 18 & 19.6425371514452 & -1.64253715144523 \tabularnewline
105 & 23 & 22.217150234481 & 0.782849765518965 \tabularnewline
106 & 25 & 20.9744599375139 & 4.02554006248607 \tabularnewline
107 & 21 & 16.6529471075685 & 4.34705289243147 \tabularnewline
108 & 24 & 19.5455744278297 & 4.45442557217025 \tabularnewline
109 & 25 & 25.3595410763598 & -0.359541076359756 \tabularnewline
110 & 17 & 19.6935475859109 & -2.69354758591087 \tabularnewline
111 & 13 & 14.6943965458432 & -1.69439654584321 \tabularnewline
112 & 28 & 18.4098746117709 & 9.59012538822914 \tabularnewline
113 & 21 & 20.3106346801039 & 0.689365319896121 \tabularnewline
114 & 25 & 28.2836350486841 & -3.28363504868414 \tabularnewline
115 & 9 & 21.0044477704288 & -12.0044477704288 \tabularnewline
116 & 16 & 18.0612276169349 & -2.06122761693493 \tabularnewline
117 & 19 & 21.2966898433179 & -2.29668984331794 \tabularnewline
118 & 17 & 19.6269476477759 & -2.62694764777592 \tabularnewline
119 & 25 & 24.7319979920982 & 0.268002007901781 \tabularnewline
120 & 20 & 15.6369512420149 & 4.36304875798512 \tabularnewline
121 & 29 & 21.8759344009052 & 7.12406559909483 \tabularnewline
122 & 14 & 19.1778709228727 & -5.17787092287271 \tabularnewline
123 & 22 & 27.0980959242537 & -5.09809592425374 \tabularnewline
124 & 15 & 15.9019397751253 & -0.901939775125261 \tabularnewline
125 & 19 & 25.6813751580878 & -6.68137515808782 \tabularnewline
126 & 20 & 22.1919545613241 & -2.19195456132411 \tabularnewline
127 & 15 & 17.7608142090159 & -2.76081420901586 \tabularnewline
128 & 20 & 22.1258994239983 & -2.12589942399832 \tabularnewline
129 & 18 & 20.5527935376764 & -2.55279353767642 \tabularnewline
130 & 33 & 25.7888479793484 & 7.2111520206516 \tabularnewline
131 & 22 & 24.0146988185677 & -2.01469881856773 \tabularnewline
132 & 16 & 16.7435243851904 & -0.743524385190438 \tabularnewline
133 & 17 & 19.4036823708245 & -2.40368237082451 \tabularnewline
134 & 16 & 15.381610908457 & 0.618389091543024 \tabularnewline
135 & 21 & 17.3637695845882 & 3.63623041541183 \tabularnewline
136 & 26 & 27.9214437785666 & -1.92144377856656 \tabularnewline
137 & 18 & 21.4147283663736 & -3.41472836637364 \tabularnewline
138 & 18 & 23.276463760401 & -5.27646376040095 \tabularnewline
139 & 17 & 18.707861885554 & -1.70786188555397 \tabularnewline
140 & 22 & 24.9760915015815 & -2.97609150158152 \tabularnewline
141 & 30 & 24.952242458675 & 5.047757541325 \tabularnewline
142 & 30 & 27.6220295065553 & 2.37797049344472 \tabularnewline
143 & 24 & 30.0288532518421 & -6.02885325184214 \tabularnewline
144 & 21 & 22.3733372368221 & -1.37333723682208 \tabularnewline
145 & 21 & 25.7186060818219 & -4.71860608182192 \tabularnewline
146 & 29 & 27.7760626071577 & 1.22393739284233 \tabularnewline
147 & 31 & 23.567572013391 & 7.43242798660902 \tabularnewline
148 & 20 & 19.3381789476296 & 0.661821052370395 \tabularnewline
149 & 16 & 14.4271132095025 & 1.57288679049751 \tabularnewline
150 & 22 & 19.2888597043427 & 2.71114029565732 \tabularnewline
151 & 20 & 20.766728259862 & -0.766728259862021 \tabularnewline
152 & 28 & 27.7200998654264 & 0.279900134573624 \tabularnewline
153 & 38 & 27.0811321597216 & 10.9188678402784 \tabularnewline
154 & 22 & 19.5906715514475 & 2.40932844855246 \tabularnewline
155 & 20 & 26.0566974089159 & -6.05669740891587 \tabularnewline
156 & 17 & 18.448530410331 & -1.44853041033101 \tabularnewline
157 & 28 & 24.9055766121567 & 3.09442338784326 \tabularnewline
158 & 22 & 24.5396741854444 & -2.53967418544438 \tabularnewline
159 & 31 & 26.4518682878217 & 4.54813171217832 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202459&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]24[/C][C]24.5869751364709[/C][C]-0.586975136470904[/C][/ROW]
[ROW][C]2[/C][C]25[/C][C]21.2987181574354[/C][C]3.70128184256464[/C][/ROW]
[ROW][C]3[/C][C]17[/C][C]22.3859726679103[/C][C]-5.38597266791034[/C][/ROW]
[ROW][C]4[/C][C]18[/C][C]19.44541093851[/C][C]-1.44541093850998[/C][/ROW]
[ROW][C]5[/C][C]18[/C][C]19.0235142417146[/C][C]-1.02351424171457[/C][/ROW]
[ROW][C]6[/C][C]16[/C][C]19.2495257409872[/C][C]-3.24952574098718[/C][/ROW]
[ROW][C]7[/C][C]20[/C][C]20.5436429620918[/C][C]-0.543642962091785[/C][/ROW]
[ROW][C]8[/C][C]16[/C][C]21.9567214062097[/C][C]-5.95672140620968[/C][/ROW]
[ROW][C]9[/C][C]18[/C][C]21.9596456847955[/C][C]-3.9596456847955[/C][/ROW]
[ROW][C]10[/C][C]17[/C][C]20.1583618178568[/C][C]-3.15836181785682[/C][/ROW]
[ROW][C]11[/C][C]23[/C][C]21.9388979834957[/C][C]1.06110201650432[/C][/ROW]
[ROW][C]12[/C][C]30[/C][C]21.8557785405502[/C][C]8.14422145944982[/C][/ROW]
[ROW][C]13[/C][C]23[/C][C]14.7331473781575[/C][C]8.2668526218425[/C][/ROW]
[ROW][C]14[/C][C]18[/C][C]18.603269980984[/C][C]-0.603269980983953[/C][/ROW]
[ROW][C]15[/C][C]15[/C][C]21.3229006529394[/C][C]-6.32290065293938[/C][/ROW]
[ROW][C]16[/C][C]12[/C][C]17.5861185544217[/C][C]-5.58611855442168[/C][/ROW]
[ROW][C]17[/C][C]21[/C][C]19.0554592926899[/C][C]1.94454070731014[/C][/ROW]
[ROW][C]18[/C][C]15[/C][C]14.8190078084257[/C][C]0.180992191574259[/C][/ROW]
[ROW][C]19[/C][C]20[/C][C]19.4501286955508[/C][C]0.549871304449199[/C][/ROW]
[ROW][C]20[/C][C]31[/C][C]26.0194000461538[/C][C]4.98059995384624[/C][/ROW]
[ROW][C]21[/C][C]27[/C][C]24.867684573156[/C][C]2.13231542684405[/C][/ROW]
[ROW][C]22[/C][C]34[/C][C]26.750532950888[/C][C]7.24946704911197[/C][/ROW]
[ROW][C]23[/C][C]21[/C][C]19.2804560909692[/C][C]1.71954390903083[/C][/ROW]
[ROW][C]24[/C][C]31[/C][C]20.5997366083926[/C][C]10.4002633916074[/C][/ROW]
[ROW][C]25[/C][C]19[/C][C]19.0701646017547[/C][C]-0.0701646017546602[/C][/ROW]
[ROW][C]26[/C][C]16[/C][C]19.9463310706596[/C][C]-3.94633107065955[/C][/ROW]
[ROW][C]27[/C][C]20[/C][C]21.3206525792539[/C][C]-1.32065257925392[/C][/ROW]
[ROW][C]28[/C][C]21[/C][C]17.996356127058[/C][C]3.00364387294204[/C][/ROW]
[ROW][C]29[/C][C]22[/C][C]21.6836783558636[/C][C]0.316321644136373[/C][/ROW]
[ROW][C]30[/C][C]17[/C][C]19.2404660680385[/C][C]-2.24046606803853[/C][/ROW]
[ROW][C]31[/C][C]24[/C][C]20.1645593566639[/C][C]3.83544064333612[/C][/ROW]
[ROW][C]32[/C][C]25[/C][C]30.1993051078831[/C][C]-5.19930510788308[/C][/ROW]
[ROW][C]33[/C][C]26[/C][C]26.390560835707[/C][C]-0.390560835707007[/C][/ROW]
[ROW][C]34[/C][C]25[/C][C]23.8830734088535[/C][C]1.11692659114645[/C][/ROW]
[ROW][C]35[/C][C]17[/C][C]22.7338898244853[/C][C]-5.73388982448528[/C][/ROW]
[ROW][C]36[/C][C]32[/C][C]27.5968576223048[/C][C]4.4031423776952[/C][/ROW]
[ROW][C]37[/C][C]33[/C][C]23.4131876974345[/C][C]9.5868123025655[/C][/ROW]
[ROW][C]38[/C][C]13[/C][C]21.7416158659416[/C][C]-8.74161586594163[/C][/ROW]
[ROW][C]39[/C][C]32[/C][C]27.5940197273161[/C][C]4.40598027268392[/C][/ROW]
[ROW][C]40[/C][C]25[/C][C]25.6331170331415[/C][C]-0.633117033141512[/C][/ROW]
[ROW][C]41[/C][C]29[/C][C]26.8907632258501[/C][C]2.10923677414995[/C][/ROW]
[ROW][C]42[/C][C]22[/C][C]21.8320305737771[/C][C]0.167969426222917[/C][/ROW]
[ROW][C]43[/C][C]18[/C][C]17.05245924122[/C][C]0.947540758780044[/C][/ROW]
[ROW][C]44[/C][C]17[/C][C]21.5731241031246[/C][C]-4.57312410312459[/C][/ROW]
[ROW][C]45[/C][C]20[/C][C]22.0446692276098[/C][C]-2.04466922760976[/C][/ROW]
[ROW][C]46[/C][C]15[/C][C]20.2957922888319[/C][C]-5.29579228883186[/C][/ROW]
[ROW][C]47[/C][C]20[/C][C]21.9969914249525[/C][C]-1.99699142495246[/C][/ROW]
[ROW][C]48[/C][C]33[/C][C]27.9610910864848[/C][C]5.03890891351515[/C][/ROW]
[ROW][C]49[/C][C]29[/C][C]21.9922711728182[/C][C]7.00772882718179[/C][/ROW]
[ROW][C]50[/C][C]23[/C][C]26.4609674131011[/C][C]-3.46096741310109[/C][/ROW]
[ROW][C]51[/C][C]26[/C][C]23.1055675302885[/C][C]2.89443246971151[/C][/ROW]
[ROW][C]52[/C][C]18[/C][C]18.8307698752602[/C][C]-0.8307698752602[/C][/ROW]
[ROW][C]53[/C][C]20[/C][C]18.6699997907346[/C][C]1.33000020926542[/C][/ROW]
[ROW][C]54[/C][C]11[/C][C]11.5812129333856[/C][C]-0.58121293338563[/C][/ROW]
[ROW][C]55[/C][C]28[/C][C]28.8095440113789[/C][C]-0.809544011378862[/C][/ROW]
[ROW][C]56[/C][C]26[/C][C]23.2735050718965[/C][C]2.72649492810354[/C][/ROW]
[ROW][C]57[/C][C]22[/C][C]22.2026118775359[/C][C]-0.202611877535911[/C][/ROW]
[ROW][C]58[/C][C]17[/C][C]20.0774839975062[/C][C]-3.07748399750625[/C][/ROW]
[ROW][C]59[/C][C]12[/C][C]15.4292884866592[/C][C]-3.42928848665917[/C][/ROW]
[ROW][C]60[/C][C]14[/C][C]20.780061210624[/C][C]-6.78006121062399[/C][/ROW]
[ROW][C]61[/C][C]17[/C][C]20.6763094340181[/C][C]-3.67630943401805[/C][/ROW]
[ROW][C]62[/C][C]21[/C][C]21.261580521485[/C][C]-0.261580521484976[/C][/ROW]
[ROW][C]63[/C][C]19[/C][C]22.9113260083569[/C][C]-3.91132600835689[/C][/ROW]
[ROW][C]64[/C][C]18[/C][C]23.0268271622096[/C][C]-5.02682716220963[/C][/ROW]
[ROW][C]65[/C][C]10[/C][C]17.8541098959786[/C][C]-7.85410989597859[/C][/ROW]
[ROW][C]66[/C][C]29[/C][C]24.2846006276799[/C][C]4.71539937232012[/C][/ROW]
[ROW][C]67[/C][C]31[/C][C]18.4322038562024[/C][C]12.5677961437976[/C][/ROW]
[ROW][C]68[/C][C]19[/C][C]22.8792466318609[/C][C]-3.87924663186088[/C][/ROW]
[ROW][C]69[/C][C]9[/C][C]20.0338902827611[/C][C]-11.0338902827611[/C][/ROW]
[ROW][C]70[/C][C]20[/C][C]22.4674961633225[/C][C]-2.46749616332249[/C][/ROW]
[ROW][C]71[/C][C]28[/C][C]17.5756295114181[/C][C]10.4243704885819[/C][/ROW]
[ROW][C]72[/C][C]19[/C][C]18.1136995485328[/C][C]0.886300451467227[/C][/ROW]
[ROW][C]73[/C][C]30[/C][C]23.0877801178341[/C][C]6.91221988216585[/C][/ROW]
[ROW][C]74[/C][C]29[/C][C]27.073369677065[/C][C]1.92663032293498[/C][/ROW]
[ROW][C]75[/C][C]26[/C][C]21.4908251886057[/C][C]4.50917481139428[/C][/ROW]
[ROW][C]76[/C][C]23[/C][C]19.5051176759053[/C][C]3.49488232409465[/C][/ROW]
[ROW][C]77[/C][C]13[/C][C]22.7601530313896[/C][C]-9.76015303138962[/C][/ROW]
[ROW][C]78[/C][C]21[/C][C]22.6443090003536[/C][C]-1.6443090003536[/C][/ROW]
[ROW][C]79[/C][C]19[/C][C]21.5370492704958[/C][C]-2.53704927049577[/C][/ROW]
[ROW][C]80[/C][C]28[/C][C]22.93773130259[/C][C]5.06226869740997[/C][/ROW]
[ROW][C]81[/C][C]23[/C][C]25.6175014577243[/C][C]-2.61750145772435[/C][/ROW]
[ROW][C]82[/C][C]18[/C][C]13.864639446041[/C][C]4.13536055395896[/C][/ROW]
[ROW][C]83[/C][C]21[/C][C]20.7319285749601[/C][C]0.268071425039858[/C][/ROW]
[ROW][C]84[/C][C]20[/C][C]21.8476069466243[/C][C]-1.84760694662434[/C][/ROW]
[ROW][C]85[/C][C]23[/C][C]20.0434165598231[/C][C]2.95658344017692[/C][/ROW]
[ROW][C]86[/C][C]21[/C][C]20.8597333926554[/C][C]0.140266607344571[/C][/ROW]
[ROW][C]87[/C][C]21[/C][C]21.8550198976718[/C][C]-0.855019897671836[/C][/ROW]
[ROW][C]88[/C][C]15[/C][C]22.9508811919658[/C][C]-7.9508811919658[/C][/ROW]
[ROW][C]89[/C][C]28[/C][C]27.1965037434143[/C][C]0.803496256585691[/C][/ROW]
[ROW][C]90[/C][C]19[/C][C]17.6886416630847[/C][C]1.31135833691528[/C][/ROW]
[ROW][C]91[/C][C]26[/C][C]21.2954140701055[/C][C]4.70458592989447[/C][/ROW]
[ROW][C]92[/C][C]10[/C][C]13.3326243716318[/C][C]-3.3326243716318[/C][/ROW]
[ROW][C]93[/C][C]16[/C][C]17.1873143264118[/C][C]-1.18731432641177[/C][/ROW]
[ROW][C]94[/C][C]22[/C][C]21.2020388132927[/C][C]0.797961186707265[/C][/ROW]
[ROW][C]95[/C][C]19[/C][C]18.9063046849835[/C][C]0.0936953150164672[/C][/ROW]
[ROW][C]96[/C][C]31[/C][C]28.9157779289149[/C][C]2.0842220710851[/C][/ROW]
[ROW][C]97[/C][C]31[/C][C]25.3018849084882[/C][C]5.69811509151183[/C][/ROW]
[ROW][C]98[/C][C]29[/C][C]24.8551287876825[/C][C]4.14487121231755[/C][/ROW]
[ROW][C]99[/C][C]19[/C][C]17.5028282033594[/C][C]1.49717179664061[/C][/ROW]
[ROW][C]100[/C][C]22[/C][C]18.9522887480601[/C][C]3.04771125193991[/C][/ROW]
[ROW][C]101[/C][C]23[/C][C]22.472438197516[/C][C]0.52756180248399[/C][/ROW]
[ROW][C]102[/C][C]15[/C][C]16.2874647056108[/C][C]-1.28746470561081[/C][/ROW]
[ROW][C]103[/C][C]20[/C][C]21.4645212040962[/C][C]-1.46452120409622[/C][/ROW]
[ROW][C]104[/C][C]18[/C][C]19.6425371514452[/C][C]-1.64253715144523[/C][/ROW]
[ROW][C]105[/C][C]23[/C][C]22.217150234481[/C][C]0.782849765518965[/C][/ROW]
[ROW][C]106[/C][C]25[/C][C]20.9744599375139[/C][C]4.02554006248607[/C][/ROW]
[ROW][C]107[/C][C]21[/C][C]16.6529471075685[/C][C]4.34705289243147[/C][/ROW]
[ROW][C]108[/C][C]24[/C][C]19.5455744278297[/C][C]4.45442557217025[/C][/ROW]
[ROW][C]109[/C][C]25[/C][C]25.3595410763598[/C][C]-0.359541076359756[/C][/ROW]
[ROW][C]110[/C][C]17[/C][C]19.6935475859109[/C][C]-2.69354758591087[/C][/ROW]
[ROW][C]111[/C][C]13[/C][C]14.6943965458432[/C][C]-1.69439654584321[/C][/ROW]
[ROW][C]112[/C][C]28[/C][C]18.4098746117709[/C][C]9.59012538822914[/C][/ROW]
[ROW][C]113[/C][C]21[/C][C]20.3106346801039[/C][C]0.689365319896121[/C][/ROW]
[ROW][C]114[/C][C]25[/C][C]28.2836350486841[/C][C]-3.28363504868414[/C][/ROW]
[ROW][C]115[/C][C]9[/C][C]21.0044477704288[/C][C]-12.0044477704288[/C][/ROW]
[ROW][C]116[/C][C]16[/C][C]18.0612276169349[/C][C]-2.06122761693493[/C][/ROW]
[ROW][C]117[/C][C]19[/C][C]21.2966898433179[/C][C]-2.29668984331794[/C][/ROW]
[ROW][C]118[/C][C]17[/C][C]19.6269476477759[/C][C]-2.62694764777592[/C][/ROW]
[ROW][C]119[/C][C]25[/C][C]24.7319979920982[/C][C]0.268002007901781[/C][/ROW]
[ROW][C]120[/C][C]20[/C][C]15.6369512420149[/C][C]4.36304875798512[/C][/ROW]
[ROW][C]121[/C][C]29[/C][C]21.8759344009052[/C][C]7.12406559909483[/C][/ROW]
[ROW][C]122[/C][C]14[/C][C]19.1778709228727[/C][C]-5.17787092287271[/C][/ROW]
[ROW][C]123[/C][C]22[/C][C]27.0980959242537[/C][C]-5.09809592425374[/C][/ROW]
[ROW][C]124[/C][C]15[/C][C]15.9019397751253[/C][C]-0.901939775125261[/C][/ROW]
[ROW][C]125[/C][C]19[/C][C]25.6813751580878[/C][C]-6.68137515808782[/C][/ROW]
[ROW][C]126[/C][C]20[/C][C]22.1919545613241[/C][C]-2.19195456132411[/C][/ROW]
[ROW][C]127[/C][C]15[/C][C]17.7608142090159[/C][C]-2.76081420901586[/C][/ROW]
[ROW][C]128[/C][C]20[/C][C]22.1258994239983[/C][C]-2.12589942399832[/C][/ROW]
[ROW][C]129[/C][C]18[/C][C]20.5527935376764[/C][C]-2.55279353767642[/C][/ROW]
[ROW][C]130[/C][C]33[/C][C]25.7888479793484[/C][C]7.2111520206516[/C][/ROW]
[ROW][C]131[/C][C]22[/C][C]24.0146988185677[/C][C]-2.01469881856773[/C][/ROW]
[ROW][C]132[/C][C]16[/C][C]16.7435243851904[/C][C]-0.743524385190438[/C][/ROW]
[ROW][C]133[/C][C]17[/C][C]19.4036823708245[/C][C]-2.40368237082451[/C][/ROW]
[ROW][C]134[/C][C]16[/C][C]15.381610908457[/C][C]0.618389091543024[/C][/ROW]
[ROW][C]135[/C][C]21[/C][C]17.3637695845882[/C][C]3.63623041541183[/C][/ROW]
[ROW][C]136[/C][C]26[/C][C]27.9214437785666[/C][C]-1.92144377856656[/C][/ROW]
[ROW][C]137[/C][C]18[/C][C]21.4147283663736[/C][C]-3.41472836637364[/C][/ROW]
[ROW][C]138[/C][C]18[/C][C]23.276463760401[/C][C]-5.27646376040095[/C][/ROW]
[ROW][C]139[/C][C]17[/C][C]18.707861885554[/C][C]-1.70786188555397[/C][/ROW]
[ROW][C]140[/C][C]22[/C][C]24.9760915015815[/C][C]-2.97609150158152[/C][/ROW]
[ROW][C]141[/C][C]30[/C][C]24.952242458675[/C][C]5.047757541325[/C][/ROW]
[ROW][C]142[/C][C]30[/C][C]27.6220295065553[/C][C]2.37797049344472[/C][/ROW]
[ROW][C]143[/C][C]24[/C][C]30.0288532518421[/C][C]-6.02885325184214[/C][/ROW]
[ROW][C]144[/C][C]21[/C][C]22.3733372368221[/C][C]-1.37333723682208[/C][/ROW]
[ROW][C]145[/C][C]21[/C][C]25.7186060818219[/C][C]-4.71860608182192[/C][/ROW]
[ROW][C]146[/C][C]29[/C][C]27.7760626071577[/C][C]1.22393739284233[/C][/ROW]
[ROW][C]147[/C][C]31[/C][C]23.567572013391[/C][C]7.43242798660902[/C][/ROW]
[ROW][C]148[/C][C]20[/C][C]19.3381789476296[/C][C]0.661821052370395[/C][/ROW]
[ROW][C]149[/C][C]16[/C][C]14.4271132095025[/C][C]1.57288679049751[/C][/ROW]
[ROW][C]150[/C][C]22[/C][C]19.2888597043427[/C][C]2.71114029565732[/C][/ROW]
[ROW][C]151[/C][C]20[/C][C]20.766728259862[/C][C]-0.766728259862021[/C][/ROW]
[ROW][C]152[/C][C]28[/C][C]27.7200998654264[/C][C]0.279900134573624[/C][/ROW]
[ROW][C]153[/C][C]38[/C][C]27.0811321597216[/C][C]10.9188678402784[/C][/ROW]
[ROW][C]154[/C][C]22[/C][C]19.5906715514475[/C][C]2.40932844855246[/C][/ROW]
[ROW][C]155[/C][C]20[/C][C]26.0566974089159[/C][C]-6.05669740891587[/C][/ROW]
[ROW][C]156[/C][C]17[/C][C]18.448530410331[/C][C]-1.44853041033101[/C][/ROW]
[ROW][C]157[/C][C]28[/C][C]24.9055766121567[/C][C]3.09442338784326[/C][/ROW]
[ROW][C]158[/C][C]22[/C][C]24.5396741854444[/C][C]-2.53967418544438[/C][/ROW]
[ROW][C]159[/C][C]31[/C][C]26.4518682878217[/C][C]4.54813171217832[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202459&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202459&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
12424.5869751364709-0.586975136470904
22521.29871815743543.70128184256464
31722.3859726679103-5.38597266791034
41819.44541093851-1.44541093850998
51819.0235142417146-1.02351424171457
61619.2495257409872-3.24952574098718
72020.5436429620918-0.543642962091785
81621.9567214062097-5.95672140620968
91821.9596456847955-3.9596456847955
101720.1583618178568-3.15836181785682
112321.93889798349571.06110201650432
123021.85577854055028.14422145944982
132314.73314737815758.2668526218425
141818.603269980984-0.603269980983953
151521.3229006529394-6.32290065293938
161217.5861185544217-5.58611855442168
172119.05545929268991.94454070731014
181514.81900780842570.180992191574259
192019.45012869555080.549871304449199
203126.01940004615384.98059995384624
212724.8676845731562.13231542684405
223426.7505329508887.24946704911197
232119.28045609096921.71954390903083
243120.599736608392610.4002633916074
251919.0701646017547-0.0701646017546602
261619.9463310706596-3.94633107065955
272021.3206525792539-1.32065257925392
282117.9963561270583.00364387294204
292221.68367835586360.316321644136373
301719.2404660680385-2.24046606803853
312420.16455935666393.83544064333612
322530.1993051078831-5.19930510788308
332626.390560835707-0.390560835707007
342523.88307340885351.11692659114645
351722.7338898244853-5.73388982448528
363227.59685762230484.4031423776952
373323.41318769743459.5868123025655
381321.7416158659416-8.74161586594163
393227.59401972731614.40598027268392
402525.6331170331415-0.633117033141512
412926.89076322585012.10923677414995
422221.83203057377710.167969426222917
431817.052459241220.947540758780044
441721.5731241031246-4.57312410312459
452022.0446692276098-2.04466922760976
461520.2957922888319-5.29579228883186
472021.9969914249525-1.99699142495246
483327.96109108648485.03890891351515
492921.99227117281827.00772882718179
502326.4609674131011-3.46096741310109
512623.10556753028852.89443246971151
521818.8307698752602-0.8307698752602
532018.66999979073461.33000020926542
541111.5812129333856-0.58121293338563
552828.8095440113789-0.809544011378862
562623.27350507189652.72649492810354
572222.2026118775359-0.202611877535911
581720.0774839975062-3.07748399750625
591215.4292884866592-3.42928848665917
601420.780061210624-6.78006121062399
611720.6763094340181-3.67630943401805
622121.261580521485-0.261580521484976
631922.9113260083569-3.91132600835689
641823.0268271622096-5.02682716220963
651017.8541098959786-7.85410989597859
662924.28460062767994.71539937232012
673118.432203856202412.5677961437976
681922.8792466318609-3.87924663186088
69920.0338902827611-11.0338902827611
702022.4674961633225-2.46749616332249
712817.575629511418110.4243704885819
721918.11369954853280.886300451467227
733023.08778011783416.91221988216585
742927.0733696770651.92663032293498
752621.49082518860574.50917481139428
762319.50511767590533.49488232409465
771322.7601530313896-9.76015303138962
782122.6443090003536-1.6443090003536
791921.5370492704958-2.53704927049577
802822.937731302595.06226869740997
812325.6175014577243-2.61750145772435
821813.8646394460414.13536055395896
832120.73192857496010.268071425039858
842021.8476069466243-1.84760694662434
852320.04341655982312.95658344017692
862120.85973339265540.140266607344571
872121.8550198976718-0.855019897671836
881522.9508811919658-7.9508811919658
892827.19650374341430.803496256585691
901917.68864166308471.31135833691528
912621.29541407010554.70458592989447
921013.3326243716318-3.3326243716318
931617.1873143264118-1.18731432641177
942221.20203881329270.797961186707265
951918.90630468498350.0936953150164672
963128.91577792891492.0842220710851
973125.30188490848825.69811509151183
982924.85512878768254.14487121231755
991917.50282820335941.49717179664061
1002218.95228874806013.04771125193991
1012322.4724381975160.52756180248399
1021516.2874647056108-1.28746470561081
1032021.4645212040962-1.46452120409622
1041819.6425371514452-1.64253715144523
1052322.2171502344810.782849765518965
1062520.97445993751394.02554006248607
1072116.65294710756854.34705289243147
1082419.54557442782974.45442557217025
1092525.3595410763598-0.359541076359756
1101719.6935475859109-2.69354758591087
1111314.6943965458432-1.69439654584321
1122818.40987461177099.59012538822914
1132120.31063468010390.689365319896121
1142528.2836350486841-3.28363504868414
115921.0044477704288-12.0044477704288
1161618.0612276169349-2.06122761693493
1171921.2966898433179-2.29668984331794
1181719.6269476477759-2.62694764777592
1192524.73199799209820.268002007901781
1202015.63695124201494.36304875798512
1212921.87593440090527.12406559909483
1221419.1778709228727-5.17787092287271
1232227.0980959242537-5.09809592425374
1241515.9019397751253-0.901939775125261
1251925.6813751580878-6.68137515808782
1262022.1919545613241-2.19195456132411
1271517.7608142090159-2.76081420901586
1282022.1258994239983-2.12589942399832
1291820.5527935376764-2.55279353767642
1303325.78884797934847.2111520206516
1312224.0146988185677-2.01469881856773
1321616.7435243851904-0.743524385190438
1331719.4036823708245-2.40368237082451
1341615.3816109084570.618389091543024
1352117.36376958458823.63623041541183
1362627.9214437785666-1.92144377856656
1371821.4147283663736-3.41472836637364
1381823.276463760401-5.27646376040095
1391718.707861885554-1.70786188555397
1402224.9760915015815-2.97609150158152
1413024.9522424586755.047757541325
1423027.62202950655532.37797049344472
1432430.0288532518421-6.02885325184214
1442122.3733372368221-1.37333723682208
1452125.7186060818219-4.71860608182192
1462927.77606260715771.22393739284233
1473123.5675720133917.43242798660902
1482019.33817894762960.661821052370395
1491614.42711320950251.57288679049751
1502219.28885970434272.71114029565732
1512020.766728259862-0.766728259862021
1522827.72009986542640.279900134573624
1533827.081132159721610.9188678402784
1542219.59067155144752.40932844855246
1552026.0566974089159-6.05669740891587
1561718.448530410331-1.44853041033101
1572824.90557661215673.09442338784326
1582224.5396741854444-2.53967418544438
1593126.45186828782174.54813171217832







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.09861376610248370.1972275322049670.901386233897516
110.3549755718872730.7099511437745460.645024428112727
120.7492056729243740.5015886541512520.250794327075626
130.7439942746184630.5120114507630750.256005725381537
140.7271423515756790.5457152968486410.272857648424321
150.6898060323229990.6203879353540030.310193967677001
160.6075205230409110.7849589539181780.392479476959089
170.5529639770054730.8940720459890530.447036022994527
180.5244892972136230.9510214055727530.475510702786377
190.4697051197851470.9394102395702930.530294880214853
200.4986893216137580.9973786432275160.501310678386242
210.4311761806996540.8623523613993070.568823819300347
220.4194510925287390.8389021850574780.580548907471261
230.3769374835137520.7538749670275030.623062516486248
240.5309343490541270.9381313018917470.469065650945873
250.4887782716235470.9775565432470950.511221728376453
260.5057243044372710.9885513911254570.494275695562729
270.4383934621962860.8767869243925720.561606537803714
280.3793593171190680.7587186342381360.620640682880932
290.3178232475813530.6356464951627070.682176752418647
300.2780751083641660.5561502167283310.721924891635834
310.2696041099587240.5392082199174480.730395890041276
320.3990065914215260.7980131828430510.600993408578474
330.3432220705289270.6864441410578540.656777929471073
340.3136417154916680.6272834309833350.686358284508332
350.3567734065587750.713546813117550.643226593441225
360.3239982756858650.647996551371730.676001724314135
370.4385551926157250.8771103852314510.561444807384274
380.7456244379030730.5087511241938550.254375562096927
390.72998173480890.5400365303822010.2700182651911
400.6939340840255180.6121318319489640.306065915974482
410.6567229917388230.6865540165223550.343277008261177
420.609635486056110.780729027887780.39036451394389
430.5594323516939840.8811352966120330.440567648306016
440.552798911731340.8944021765373210.44720108826866
450.5401932485095580.9196135029808840.459806751490442
460.5841750100225670.8316499799548670.415824989977433
470.5471175912121230.9057648175757550.452882408787877
480.5370116247742410.9259767504515170.462988375225759
490.5896696370225970.8206607259548070.410330362977403
500.5684220332217540.8631559335564930.431577966778246
510.5381782265344370.9236435469311260.461821773465563
520.4910579843459140.9821159686918290.508942015654086
530.4455369726952110.8910739453904210.554463027304789
540.4049352117232140.8098704234464290.595064788276786
550.3665446293371980.7330892586743960.633455370662802
560.3357626061080490.6715252122160990.664237393891951
570.292733820431060.5854676408621210.70726617956894
580.2673715418270750.5347430836541510.732628458172925
590.2495105008442370.4990210016884730.750489499155763
600.3086708782982860.6173417565965720.691329121701714
610.2940977137318820.5881954274637640.705902286268118
620.255099579665820.510199159331640.74490042033418
630.2394300655797740.4788601311595470.760569934420226
640.2656442274263840.5312884548527690.734355772573616
650.3303643276374650.6607286552749290.669635672362535
660.3389668445756480.6779336891512960.661033155424352
670.6838004982063620.6323990035872760.316199501793638
680.6733154810450890.6533690379098210.326684518954911
690.8411901611838280.3176196776323440.158809838816172
700.8191383275213770.3617233449572470.180861672478623
710.9308517380612750.1382965238774510.0691482619387253
720.9146569017311750.1706861965376490.0853430982688246
730.9382829337850670.1234341324298660.0617170662149329
740.9263861039557440.1472277920885110.0736138960442556
750.9276281033889790.1447437932220420.072371896611021
760.9215329032608190.1569341934783620.0784670967391811
770.9693287817645060.06134243647098840.0306712182354942
780.9616673404392930.07666531912141340.0383326595607067
790.9541074868971410.09178502620571860.0458925131028593
800.9566019515869030.08679609682619440.0433980484130972
810.948666401436410.102667197127180.0513335985635901
820.9476745572405540.1046508855188910.0523254427594455
830.9338768639516510.1322462720966980.0661231360483489
840.9207784649871140.1584430700257720.079221535012886
850.9112558434902320.1774883130195360.088744156509768
860.8936859551560780.2126280896878440.106314044843922
870.8716472922200260.2567054155599480.128352707779974
880.9193797757638680.1612404484722630.0806202242361317
890.9026115625421930.1947768749156140.0973884374578069
900.8817652436334710.2364695127330590.118234756366529
910.881397413989920.2372051720201590.11860258601008
920.8713980396681860.2572039206636290.128601960331815
930.8491324834995320.3017350330009370.150867516500468
940.8204223519995740.3591552960008520.179577648000426
950.7870177432280470.4259645135439060.212982256771953
960.7557157175194140.4885685649611720.244284282480586
970.7732214987018410.4535570025963180.226778501298159
980.7699916228250970.4600167543498060.230008377174903
990.7343600261062770.5312799477874460.265639973893723
1000.7122853425320180.5754293149359640.287714657467982
1010.6738041808869690.6523916382260630.326195819113031
1020.6328806957375110.7342386085249770.367119304262489
1030.5917954923026490.8164090153947030.408204507697351
1040.5486312193378190.9027375613243610.451368780662181
1050.5063404886872420.9873190226255160.493659511312758
1060.4926916343885430.9853832687770870.507308365611457
1070.5000020890271860.9999958219456290.499997910972814
1080.5393036213969180.9213927572061650.460696378603082
1090.5018653437622560.9962693124754880.498134656237744
1100.4607042898028230.9214085796056450.539295710197177
1110.4137439132316110.8274878264632220.586256086768389
1120.74061884066950.5187623186610.2593811593305
1130.7755791622930010.4488416754139970.224420837706999
1140.7511924937027930.4976150125944150.248807506297207
1150.8754598640234440.2490802719531110.124540135976556
1160.8470931873958180.3058136252083630.152906812604182
1170.8176305257582430.3647389484835140.182369474241757
1180.7841697785052680.4316604429894630.215830221494732
1190.756239619419450.48752076116110.24376038058055
1200.8049705765881360.3900588468237270.195029423411864
1210.8840595190699330.2318809618601330.115940480930067
1220.8652497380235420.2695005239529170.134750261976458
1230.8449103247330520.3101793505338970.155089675266948
1240.8069001298555630.3861997402888730.193099870144437
1250.8115641803558520.3768716392882960.188435819644148
1260.7682122314611830.4635755370776340.231787768538817
1270.7368674094556410.5262651810887190.263132590544359
1280.690330756079320.619338487841360.30966924392068
1290.6450159225602040.7099681548795910.354984077439796
1300.7658939350196660.4682121299606680.234106064980334
1310.7135802827241450.572839434551710.286419717275855
1320.6534074306808310.6931851386383380.346592569319169
1330.6153801898032810.7692396203934370.384619810196719
1340.545690341410030.908619317179940.45430965858997
1350.5710007073765480.8579985852469040.428999292623452
1360.5005357463897370.9989285072205270.499464253610263
1370.4534084939071970.9068169878143940.546591506092803
1380.4701866307889550.9403732615779110.529813369211045
1390.3962812666779530.7925625333559060.603718733322047
1400.3228377971210870.6456755942421740.677162202878913
1410.4259405330405820.8518810660811630.574059466959418
1420.37594692793440.7518938558687990.6240530720656
1430.3033504922152390.6067009844304780.696649507784761
1440.2271858956990650.4543717913981290.772814104300935
1450.3335255447779450.6670510895558890.666474455222055
1460.2541505742180360.5083011484360710.745849425781964
1470.2528706232855870.5057412465711750.747129376714413
1480.1596123012853570.3192246025707130.840387698714643
1490.08634341410370020.17268682820740.9136565858963

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.0986137661024837 & 0.197227532204967 & 0.901386233897516 \tabularnewline
11 & 0.354975571887273 & 0.709951143774546 & 0.645024428112727 \tabularnewline
12 & 0.749205672924374 & 0.501588654151252 & 0.250794327075626 \tabularnewline
13 & 0.743994274618463 & 0.512011450763075 & 0.256005725381537 \tabularnewline
14 & 0.727142351575679 & 0.545715296848641 & 0.272857648424321 \tabularnewline
15 & 0.689806032322999 & 0.620387935354003 & 0.310193967677001 \tabularnewline
16 & 0.607520523040911 & 0.784958953918178 & 0.392479476959089 \tabularnewline
17 & 0.552963977005473 & 0.894072045989053 & 0.447036022994527 \tabularnewline
18 & 0.524489297213623 & 0.951021405572753 & 0.475510702786377 \tabularnewline
19 & 0.469705119785147 & 0.939410239570293 & 0.530294880214853 \tabularnewline
20 & 0.498689321613758 & 0.997378643227516 & 0.501310678386242 \tabularnewline
21 & 0.431176180699654 & 0.862352361399307 & 0.568823819300347 \tabularnewline
22 & 0.419451092528739 & 0.838902185057478 & 0.580548907471261 \tabularnewline
23 & 0.376937483513752 & 0.753874967027503 & 0.623062516486248 \tabularnewline
24 & 0.530934349054127 & 0.938131301891747 & 0.469065650945873 \tabularnewline
25 & 0.488778271623547 & 0.977556543247095 & 0.511221728376453 \tabularnewline
26 & 0.505724304437271 & 0.988551391125457 & 0.494275695562729 \tabularnewline
27 & 0.438393462196286 & 0.876786924392572 & 0.561606537803714 \tabularnewline
28 & 0.379359317119068 & 0.758718634238136 & 0.620640682880932 \tabularnewline
29 & 0.317823247581353 & 0.635646495162707 & 0.682176752418647 \tabularnewline
30 & 0.278075108364166 & 0.556150216728331 & 0.721924891635834 \tabularnewline
31 & 0.269604109958724 & 0.539208219917448 & 0.730395890041276 \tabularnewline
32 & 0.399006591421526 & 0.798013182843051 & 0.600993408578474 \tabularnewline
33 & 0.343222070528927 & 0.686444141057854 & 0.656777929471073 \tabularnewline
34 & 0.313641715491668 & 0.627283430983335 & 0.686358284508332 \tabularnewline
35 & 0.356773406558775 & 0.71354681311755 & 0.643226593441225 \tabularnewline
36 & 0.323998275685865 & 0.64799655137173 & 0.676001724314135 \tabularnewline
37 & 0.438555192615725 & 0.877110385231451 & 0.561444807384274 \tabularnewline
38 & 0.745624437903073 & 0.508751124193855 & 0.254375562096927 \tabularnewline
39 & 0.7299817348089 & 0.540036530382201 & 0.2700182651911 \tabularnewline
40 & 0.693934084025518 & 0.612131831948964 & 0.306065915974482 \tabularnewline
41 & 0.656722991738823 & 0.686554016522355 & 0.343277008261177 \tabularnewline
42 & 0.60963548605611 & 0.78072902788778 & 0.39036451394389 \tabularnewline
43 & 0.559432351693984 & 0.881135296612033 & 0.440567648306016 \tabularnewline
44 & 0.55279891173134 & 0.894402176537321 & 0.44720108826866 \tabularnewline
45 & 0.540193248509558 & 0.919613502980884 & 0.459806751490442 \tabularnewline
46 & 0.584175010022567 & 0.831649979954867 & 0.415824989977433 \tabularnewline
47 & 0.547117591212123 & 0.905764817575755 & 0.452882408787877 \tabularnewline
48 & 0.537011624774241 & 0.925976750451517 & 0.462988375225759 \tabularnewline
49 & 0.589669637022597 & 0.820660725954807 & 0.410330362977403 \tabularnewline
50 & 0.568422033221754 & 0.863155933556493 & 0.431577966778246 \tabularnewline
51 & 0.538178226534437 & 0.923643546931126 & 0.461821773465563 \tabularnewline
52 & 0.491057984345914 & 0.982115968691829 & 0.508942015654086 \tabularnewline
53 & 0.445536972695211 & 0.891073945390421 & 0.554463027304789 \tabularnewline
54 & 0.404935211723214 & 0.809870423446429 & 0.595064788276786 \tabularnewline
55 & 0.366544629337198 & 0.733089258674396 & 0.633455370662802 \tabularnewline
56 & 0.335762606108049 & 0.671525212216099 & 0.664237393891951 \tabularnewline
57 & 0.29273382043106 & 0.585467640862121 & 0.70726617956894 \tabularnewline
58 & 0.267371541827075 & 0.534743083654151 & 0.732628458172925 \tabularnewline
59 & 0.249510500844237 & 0.499021001688473 & 0.750489499155763 \tabularnewline
60 & 0.308670878298286 & 0.617341756596572 & 0.691329121701714 \tabularnewline
61 & 0.294097713731882 & 0.588195427463764 & 0.705902286268118 \tabularnewline
62 & 0.25509957966582 & 0.51019915933164 & 0.74490042033418 \tabularnewline
63 & 0.239430065579774 & 0.478860131159547 & 0.760569934420226 \tabularnewline
64 & 0.265644227426384 & 0.531288454852769 & 0.734355772573616 \tabularnewline
65 & 0.330364327637465 & 0.660728655274929 & 0.669635672362535 \tabularnewline
66 & 0.338966844575648 & 0.677933689151296 & 0.661033155424352 \tabularnewline
67 & 0.683800498206362 & 0.632399003587276 & 0.316199501793638 \tabularnewline
68 & 0.673315481045089 & 0.653369037909821 & 0.326684518954911 \tabularnewline
69 & 0.841190161183828 & 0.317619677632344 & 0.158809838816172 \tabularnewline
70 & 0.819138327521377 & 0.361723344957247 & 0.180861672478623 \tabularnewline
71 & 0.930851738061275 & 0.138296523877451 & 0.0691482619387253 \tabularnewline
72 & 0.914656901731175 & 0.170686196537649 & 0.0853430982688246 \tabularnewline
73 & 0.938282933785067 & 0.123434132429866 & 0.0617170662149329 \tabularnewline
74 & 0.926386103955744 & 0.147227792088511 & 0.0736138960442556 \tabularnewline
75 & 0.927628103388979 & 0.144743793222042 & 0.072371896611021 \tabularnewline
76 & 0.921532903260819 & 0.156934193478362 & 0.0784670967391811 \tabularnewline
77 & 0.969328781764506 & 0.0613424364709884 & 0.0306712182354942 \tabularnewline
78 & 0.961667340439293 & 0.0766653191214134 & 0.0383326595607067 \tabularnewline
79 & 0.954107486897141 & 0.0917850262057186 & 0.0458925131028593 \tabularnewline
80 & 0.956601951586903 & 0.0867960968261944 & 0.0433980484130972 \tabularnewline
81 & 0.94866640143641 & 0.10266719712718 & 0.0513335985635901 \tabularnewline
82 & 0.947674557240554 & 0.104650885518891 & 0.0523254427594455 \tabularnewline
83 & 0.933876863951651 & 0.132246272096698 & 0.0661231360483489 \tabularnewline
84 & 0.920778464987114 & 0.158443070025772 & 0.079221535012886 \tabularnewline
85 & 0.911255843490232 & 0.177488313019536 & 0.088744156509768 \tabularnewline
86 & 0.893685955156078 & 0.212628089687844 & 0.106314044843922 \tabularnewline
87 & 0.871647292220026 & 0.256705415559948 & 0.128352707779974 \tabularnewline
88 & 0.919379775763868 & 0.161240448472263 & 0.0806202242361317 \tabularnewline
89 & 0.902611562542193 & 0.194776874915614 & 0.0973884374578069 \tabularnewline
90 & 0.881765243633471 & 0.236469512733059 & 0.118234756366529 \tabularnewline
91 & 0.88139741398992 & 0.237205172020159 & 0.11860258601008 \tabularnewline
92 & 0.871398039668186 & 0.257203920663629 & 0.128601960331815 \tabularnewline
93 & 0.849132483499532 & 0.301735033000937 & 0.150867516500468 \tabularnewline
94 & 0.820422351999574 & 0.359155296000852 & 0.179577648000426 \tabularnewline
95 & 0.787017743228047 & 0.425964513543906 & 0.212982256771953 \tabularnewline
96 & 0.755715717519414 & 0.488568564961172 & 0.244284282480586 \tabularnewline
97 & 0.773221498701841 & 0.453557002596318 & 0.226778501298159 \tabularnewline
98 & 0.769991622825097 & 0.460016754349806 & 0.230008377174903 \tabularnewline
99 & 0.734360026106277 & 0.531279947787446 & 0.265639973893723 \tabularnewline
100 & 0.712285342532018 & 0.575429314935964 & 0.287714657467982 \tabularnewline
101 & 0.673804180886969 & 0.652391638226063 & 0.326195819113031 \tabularnewline
102 & 0.632880695737511 & 0.734238608524977 & 0.367119304262489 \tabularnewline
103 & 0.591795492302649 & 0.816409015394703 & 0.408204507697351 \tabularnewline
104 & 0.548631219337819 & 0.902737561324361 & 0.451368780662181 \tabularnewline
105 & 0.506340488687242 & 0.987319022625516 & 0.493659511312758 \tabularnewline
106 & 0.492691634388543 & 0.985383268777087 & 0.507308365611457 \tabularnewline
107 & 0.500002089027186 & 0.999995821945629 & 0.499997910972814 \tabularnewline
108 & 0.539303621396918 & 0.921392757206165 & 0.460696378603082 \tabularnewline
109 & 0.501865343762256 & 0.996269312475488 & 0.498134656237744 \tabularnewline
110 & 0.460704289802823 & 0.921408579605645 & 0.539295710197177 \tabularnewline
111 & 0.413743913231611 & 0.827487826463222 & 0.586256086768389 \tabularnewline
112 & 0.7406188406695 & 0.518762318661 & 0.2593811593305 \tabularnewline
113 & 0.775579162293001 & 0.448841675413997 & 0.224420837706999 \tabularnewline
114 & 0.751192493702793 & 0.497615012594415 & 0.248807506297207 \tabularnewline
115 & 0.875459864023444 & 0.249080271953111 & 0.124540135976556 \tabularnewline
116 & 0.847093187395818 & 0.305813625208363 & 0.152906812604182 \tabularnewline
117 & 0.817630525758243 & 0.364738948483514 & 0.182369474241757 \tabularnewline
118 & 0.784169778505268 & 0.431660442989463 & 0.215830221494732 \tabularnewline
119 & 0.75623961941945 & 0.4875207611611 & 0.24376038058055 \tabularnewline
120 & 0.804970576588136 & 0.390058846823727 & 0.195029423411864 \tabularnewline
121 & 0.884059519069933 & 0.231880961860133 & 0.115940480930067 \tabularnewline
122 & 0.865249738023542 & 0.269500523952917 & 0.134750261976458 \tabularnewline
123 & 0.844910324733052 & 0.310179350533897 & 0.155089675266948 \tabularnewline
124 & 0.806900129855563 & 0.386199740288873 & 0.193099870144437 \tabularnewline
125 & 0.811564180355852 & 0.376871639288296 & 0.188435819644148 \tabularnewline
126 & 0.768212231461183 & 0.463575537077634 & 0.231787768538817 \tabularnewline
127 & 0.736867409455641 & 0.526265181088719 & 0.263132590544359 \tabularnewline
128 & 0.69033075607932 & 0.61933848784136 & 0.30966924392068 \tabularnewline
129 & 0.645015922560204 & 0.709968154879591 & 0.354984077439796 \tabularnewline
130 & 0.765893935019666 & 0.468212129960668 & 0.234106064980334 \tabularnewline
131 & 0.713580282724145 & 0.57283943455171 & 0.286419717275855 \tabularnewline
132 & 0.653407430680831 & 0.693185138638338 & 0.346592569319169 \tabularnewline
133 & 0.615380189803281 & 0.769239620393437 & 0.384619810196719 \tabularnewline
134 & 0.54569034141003 & 0.90861931717994 & 0.45430965858997 \tabularnewline
135 & 0.571000707376548 & 0.857998585246904 & 0.428999292623452 \tabularnewline
136 & 0.500535746389737 & 0.998928507220527 & 0.499464253610263 \tabularnewline
137 & 0.453408493907197 & 0.906816987814394 & 0.546591506092803 \tabularnewline
138 & 0.470186630788955 & 0.940373261577911 & 0.529813369211045 \tabularnewline
139 & 0.396281266677953 & 0.792562533355906 & 0.603718733322047 \tabularnewline
140 & 0.322837797121087 & 0.645675594242174 & 0.677162202878913 \tabularnewline
141 & 0.425940533040582 & 0.851881066081163 & 0.574059466959418 \tabularnewline
142 & 0.3759469279344 & 0.751893855868799 & 0.6240530720656 \tabularnewline
143 & 0.303350492215239 & 0.606700984430478 & 0.696649507784761 \tabularnewline
144 & 0.227185895699065 & 0.454371791398129 & 0.772814104300935 \tabularnewline
145 & 0.333525544777945 & 0.667051089555889 & 0.666474455222055 \tabularnewline
146 & 0.254150574218036 & 0.508301148436071 & 0.745849425781964 \tabularnewline
147 & 0.252870623285587 & 0.505741246571175 & 0.747129376714413 \tabularnewline
148 & 0.159612301285357 & 0.319224602570713 & 0.840387698714643 \tabularnewline
149 & 0.0863434141037002 & 0.1726868282074 & 0.9136565858963 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202459&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]10[/C][C]0.0986137661024837[/C][C]0.197227532204967[/C][C]0.901386233897516[/C][/ROW]
[ROW][C]11[/C][C]0.354975571887273[/C][C]0.709951143774546[/C][C]0.645024428112727[/C][/ROW]
[ROW][C]12[/C][C]0.749205672924374[/C][C]0.501588654151252[/C][C]0.250794327075626[/C][/ROW]
[ROW][C]13[/C][C]0.743994274618463[/C][C]0.512011450763075[/C][C]0.256005725381537[/C][/ROW]
[ROW][C]14[/C][C]0.727142351575679[/C][C]0.545715296848641[/C][C]0.272857648424321[/C][/ROW]
[ROW][C]15[/C][C]0.689806032322999[/C][C]0.620387935354003[/C][C]0.310193967677001[/C][/ROW]
[ROW][C]16[/C][C]0.607520523040911[/C][C]0.784958953918178[/C][C]0.392479476959089[/C][/ROW]
[ROW][C]17[/C][C]0.552963977005473[/C][C]0.894072045989053[/C][C]0.447036022994527[/C][/ROW]
[ROW][C]18[/C][C]0.524489297213623[/C][C]0.951021405572753[/C][C]0.475510702786377[/C][/ROW]
[ROW][C]19[/C][C]0.469705119785147[/C][C]0.939410239570293[/C][C]0.530294880214853[/C][/ROW]
[ROW][C]20[/C][C]0.498689321613758[/C][C]0.997378643227516[/C][C]0.501310678386242[/C][/ROW]
[ROW][C]21[/C][C]0.431176180699654[/C][C]0.862352361399307[/C][C]0.568823819300347[/C][/ROW]
[ROW][C]22[/C][C]0.419451092528739[/C][C]0.838902185057478[/C][C]0.580548907471261[/C][/ROW]
[ROW][C]23[/C][C]0.376937483513752[/C][C]0.753874967027503[/C][C]0.623062516486248[/C][/ROW]
[ROW][C]24[/C][C]0.530934349054127[/C][C]0.938131301891747[/C][C]0.469065650945873[/C][/ROW]
[ROW][C]25[/C][C]0.488778271623547[/C][C]0.977556543247095[/C][C]0.511221728376453[/C][/ROW]
[ROW][C]26[/C][C]0.505724304437271[/C][C]0.988551391125457[/C][C]0.494275695562729[/C][/ROW]
[ROW][C]27[/C][C]0.438393462196286[/C][C]0.876786924392572[/C][C]0.561606537803714[/C][/ROW]
[ROW][C]28[/C][C]0.379359317119068[/C][C]0.758718634238136[/C][C]0.620640682880932[/C][/ROW]
[ROW][C]29[/C][C]0.317823247581353[/C][C]0.635646495162707[/C][C]0.682176752418647[/C][/ROW]
[ROW][C]30[/C][C]0.278075108364166[/C][C]0.556150216728331[/C][C]0.721924891635834[/C][/ROW]
[ROW][C]31[/C][C]0.269604109958724[/C][C]0.539208219917448[/C][C]0.730395890041276[/C][/ROW]
[ROW][C]32[/C][C]0.399006591421526[/C][C]0.798013182843051[/C][C]0.600993408578474[/C][/ROW]
[ROW][C]33[/C][C]0.343222070528927[/C][C]0.686444141057854[/C][C]0.656777929471073[/C][/ROW]
[ROW][C]34[/C][C]0.313641715491668[/C][C]0.627283430983335[/C][C]0.686358284508332[/C][/ROW]
[ROW][C]35[/C][C]0.356773406558775[/C][C]0.71354681311755[/C][C]0.643226593441225[/C][/ROW]
[ROW][C]36[/C][C]0.323998275685865[/C][C]0.64799655137173[/C][C]0.676001724314135[/C][/ROW]
[ROW][C]37[/C][C]0.438555192615725[/C][C]0.877110385231451[/C][C]0.561444807384274[/C][/ROW]
[ROW][C]38[/C][C]0.745624437903073[/C][C]0.508751124193855[/C][C]0.254375562096927[/C][/ROW]
[ROW][C]39[/C][C]0.7299817348089[/C][C]0.540036530382201[/C][C]0.2700182651911[/C][/ROW]
[ROW][C]40[/C][C]0.693934084025518[/C][C]0.612131831948964[/C][C]0.306065915974482[/C][/ROW]
[ROW][C]41[/C][C]0.656722991738823[/C][C]0.686554016522355[/C][C]0.343277008261177[/C][/ROW]
[ROW][C]42[/C][C]0.60963548605611[/C][C]0.78072902788778[/C][C]0.39036451394389[/C][/ROW]
[ROW][C]43[/C][C]0.559432351693984[/C][C]0.881135296612033[/C][C]0.440567648306016[/C][/ROW]
[ROW][C]44[/C][C]0.55279891173134[/C][C]0.894402176537321[/C][C]0.44720108826866[/C][/ROW]
[ROW][C]45[/C][C]0.540193248509558[/C][C]0.919613502980884[/C][C]0.459806751490442[/C][/ROW]
[ROW][C]46[/C][C]0.584175010022567[/C][C]0.831649979954867[/C][C]0.415824989977433[/C][/ROW]
[ROW][C]47[/C][C]0.547117591212123[/C][C]0.905764817575755[/C][C]0.452882408787877[/C][/ROW]
[ROW][C]48[/C][C]0.537011624774241[/C][C]0.925976750451517[/C][C]0.462988375225759[/C][/ROW]
[ROW][C]49[/C][C]0.589669637022597[/C][C]0.820660725954807[/C][C]0.410330362977403[/C][/ROW]
[ROW][C]50[/C][C]0.568422033221754[/C][C]0.863155933556493[/C][C]0.431577966778246[/C][/ROW]
[ROW][C]51[/C][C]0.538178226534437[/C][C]0.923643546931126[/C][C]0.461821773465563[/C][/ROW]
[ROW][C]52[/C][C]0.491057984345914[/C][C]0.982115968691829[/C][C]0.508942015654086[/C][/ROW]
[ROW][C]53[/C][C]0.445536972695211[/C][C]0.891073945390421[/C][C]0.554463027304789[/C][/ROW]
[ROW][C]54[/C][C]0.404935211723214[/C][C]0.809870423446429[/C][C]0.595064788276786[/C][/ROW]
[ROW][C]55[/C][C]0.366544629337198[/C][C]0.733089258674396[/C][C]0.633455370662802[/C][/ROW]
[ROW][C]56[/C][C]0.335762606108049[/C][C]0.671525212216099[/C][C]0.664237393891951[/C][/ROW]
[ROW][C]57[/C][C]0.29273382043106[/C][C]0.585467640862121[/C][C]0.70726617956894[/C][/ROW]
[ROW][C]58[/C][C]0.267371541827075[/C][C]0.534743083654151[/C][C]0.732628458172925[/C][/ROW]
[ROW][C]59[/C][C]0.249510500844237[/C][C]0.499021001688473[/C][C]0.750489499155763[/C][/ROW]
[ROW][C]60[/C][C]0.308670878298286[/C][C]0.617341756596572[/C][C]0.691329121701714[/C][/ROW]
[ROW][C]61[/C][C]0.294097713731882[/C][C]0.588195427463764[/C][C]0.705902286268118[/C][/ROW]
[ROW][C]62[/C][C]0.25509957966582[/C][C]0.51019915933164[/C][C]0.74490042033418[/C][/ROW]
[ROW][C]63[/C][C]0.239430065579774[/C][C]0.478860131159547[/C][C]0.760569934420226[/C][/ROW]
[ROW][C]64[/C][C]0.265644227426384[/C][C]0.531288454852769[/C][C]0.734355772573616[/C][/ROW]
[ROW][C]65[/C][C]0.330364327637465[/C][C]0.660728655274929[/C][C]0.669635672362535[/C][/ROW]
[ROW][C]66[/C][C]0.338966844575648[/C][C]0.677933689151296[/C][C]0.661033155424352[/C][/ROW]
[ROW][C]67[/C][C]0.683800498206362[/C][C]0.632399003587276[/C][C]0.316199501793638[/C][/ROW]
[ROW][C]68[/C][C]0.673315481045089[/C][C]0.653369037909821[/C][C]0.326684518954911[/C][/ROW]
[ROW][C]69[/C][C]0.841190161183828[/C][C]0.317619677632344[/C][C]0.158809838816172[/C][/ROW]
[ROW][C]70[/C][C]0.819138327521377[/C][C]0.361723344957247[/C][C]0.180861672478623[/C][/ROW]
[ROW][C]71[/C][C]0.930851738061275[/C][C]0.138296523877451[/C][C]0.0691482619387253[/C][/ROW]
[ROW][C]72[/C][C]0.914656901731175[/C][C]0.170686196537649[/C][C]0.0853430982688246[/C][/ROW]
[ROW][C]73[/C][C]0.938282933785067[/C][C]0.123434132429866[/C][C]0.0617170662149329[/C][/ROW]
[ROW][C]74[/C][C]0.926386103955744[/C][C]0.147227792088511[/C][C]0.0736138960442556[/C][/ROW]
[ROW][C]75[/C][C]0.927628103388979[/C][C]0.144743793222042[/C][C]0.072371896611021[/C][/ROW]
[ROW][C]76[/C][C]0.921532903260819[/C][C]0.156934193478362[/C][C]0.0784670967391811[/C][/ROW]
[ROW][C]77[/C][C]0.969328781764506[/C][C]0.0613424364709884[/C][C]0.0306712182354942[/C][/ROW]
[ROW][C]78[/C][C]0.961667340439293[/C][C]0.0766653191214134[/C][C]0.0383326595607067[/C][/ROW]
[ROW][C]79[/C][C]0.954107486897141[/C][C]0.0917850262057186[/C][C]0.0458925131028593[/C][/ROW]
[ROW][C]80[/C][C]0.956601951586903[/C][C]0.0867960968261944[/C][C]0.0433980484130972[/C][/ROW]
[ROW][C]81[/C][C]0.94866640143641[/C][C]0.10266719712718[/C][C]0.0513335985635901[/C][/ROW]
[ROW][C]82[/C][C]0.947674557240554[/C][C]0.104650885518891[/C][C]0.0523254427594455[/C][/ROW]
[ROW][C]83[/C][C]0.933876863951651[/C][C]0.132246272096698[/C][C]0.0661231360483489[/C][/ROW]
[ROW][C]84[/C][C]0.920778464987114[/C][C]0.158443070025772[/C][C]0.079221535012886[/C][/ROW]
[ROW][C]85[/C][C]0.911255843490232[/C][C]0.177488313019536[/C][C]0.088744156509768[/C][/ROW]
[ROW][C]86[/C][C]0.893685955156078[/C][C]0.212628089687844[/C][C]0.106314044843922[/C][/ROW]
[ROW][C]87[/C][C]0.871647292220026[/C][C]0.256705415559948[/C][C]0.128352707779974[/C][/ROW]
[ROW][C]88[/C][C]0.919379775763868[/C][C]0.161240448472263[/C][C]0.0806202242361317[/C][/ROW]
[ROW][C]89[/C][C]0.902611562542193[/C][C]0.194776874915614[/C][C]0.0973884374578069[/C][/ROW]
[ROW][C]90[/C][C]0.881765243633471[/C][C]0.236469512733059[/C][C]0.118234756366529[/C][/ROW]
[ROW][C]91[/C][C]0.88139741398992[/C][C]0.237205172020159[/C][C]0.11860258601008[/C][/ROW]
[ROW][C]92[/C][C]0.871398039668186[/C][C]0.257203920663629[/C][C]0.128601960331815[/C][/ROW]
[ROW][C]93[/C][C]0.849132483499532[/C][C]0.301735033000937[/C][C]0.150867516500468[/C][/ROW]
[ROW][C]94[/C][C]0.820422351999574[/C][C]0.359155296000852[/C][C]0.179577648000426[/C][/ROW]
[ROW][C]95[/C][C]0.787017743228047[/C][C]0.425964513543906[/C][C]0.212982256771953[/C][/ROW]
[ROW][C]96[/C][C]0.755715717519414[/C][C]0.488568564961172[/C][C]0.244284282480586[/C][/ROW]
[ROW][C]97[/C][C]0.773221498701841[/C][C]0.453557002596318[/C][C]0.226778501298159[/C][/ROW]
[ROW][C]98[/C][C]0.769991622825097[/C][C]0.460016754349806[/C][C]0.230008377174903[/C][/ROW]
[ROW][C]99[/C][C]0.734360026106277[/C][C]0.531279947787446[/C][C]0.265639973893723[/C][/ROW]
[ROW][C]100[/C][C]0.712285342532018[/C][C]0.575429314935964[/C][C]0.287714657467982[/C][/ROW]
[ROW][C]101[/C][C]0.673804180886969[/C][C]0.652391638226063[/C][C]0.326195819113031[/C][/ROW]
[ROW][C]102[/C][C]0.632880695737511[/C][C]0.734238608524977[/C][C]0.367119304262489[/C][/ROW]
[ROW][C]103[/C][C]0.591795492302649[/C][C]0.816409015394703[/C][C]0.408204507697351[/C][/ROW]
[ROW][C]104[/C][C]0.548631219337819[/C][C]0.902737561324361[/C][C]0.451368780662181[/C][/ROW]
[ROW][C]105[/C][C]0.506340488687242[/C][C]0.987319022625516[/C][C]0.493659511312758[/C][/ROW]
[ROW][C]106[/C][C]0.492691634388543[/C][C]0.985383268777087[/C][C]0.507308365611457[/C][/ROW]
[ROW][C]107[/C][C]0.500002089027186[/C][C]0.999995821945629[/C][C]0.499997910972814[/C][/ROW]
[ROW][C]108[/C][C]0.539303621396918[/C][C]0.921392757206165[/C][C]0.460696378603082[/C][/ROW]
[ROW][C]109[/C][C]0.501865343762256[/C][C]0.996269312475488[/C][C]0.498134656237744[/C][/ROW]
[ROW][C]110[/C][C]0.460704289802823[/C][C]0.921408579605645[/C][C]0.539295710197177[/C][/ROW]
[ROW][C]111[/C][C]0.413743913231611[/C][C]0.827487826463222[/C][C]0.586256086768389[/C][/ROW]
[ROW][C]112[/C][C]0.7406188406695[/C][C]0.518762318661[/C][C]0.2593811593305[/C][/ROW]
[ROW][C]113[/C][C]0.775579162293001[/C][C]0.448841675413997[/C][C]0.224420837706999[/C][/ROW]
[ROW][C]114[/C][C]0.751192493702793[/C][C]0.497615012594415[/C][C]0.248807506297207[/C][/ROW]
[ROW][C]115[/C][C]0.875459864023444[/C][C]0.249080271953111[/C][C]0.124540135976556[/C][/ROW]
[ROW][C]116[/C][C]0.847093187395818[/C][C]0.305813625208363[/C][C]0.152906812604182[/C][/ROW]
[ROW][C]117[/C][C]0.817630525758243[/C][C]0.364738948483514[/C][C]0.182369474241757[/C][/ROW]
[ROW][C]118[/C][C]0.784169778505268[/C][C]0.431660442989463[/C][C]0.215830221494732[/C][/ROW]
[ROW][C]119[/C][C]0.75623961941945[/C][C]0.4875207611611[/C][C]0.24376038058055[/C][/ROW]
[ROW][C]120[/C][C]0.804970576588136[/C][C]0.390058846823727[/C][C]0.195029423411864[/C][/ROW]
[ROW][C]121[/C][C]0.884059519069933[/C][C]0.231880961860133[/C][C]0.115940480930067[/C][/ROW]
[ROW][C]122[/C][C]0.865249738023542[/C][C]0.269500523952917[/C][C]0.134750261976458[/C][/ROW]
[ROW][C]123[/C][C]0.844910324733052[/C][C]0.310179350533897[/C][C]0.155089675266948[/C][/ROW]
[ROW][C]124[/C][C]0.806900129855563[/C][C]0.386199740288873[/C][C]0.193099870144437[/C][/ROW]
[ROW][C]125[/C][C]0.811564180355852[/C][C]0.376871639288296[/C][C]0.188435819644148[/C][/ROW]
[ROW][C]126[/C][C]0.768212231461183[/C][C]0.463575537077634[/C][C]0.231787768538817[/C][/ROW]
[ROW][C]127[/C][C]0.736867409455641[/C][C]0.526265181088719[/C][C]0.263132590544359[/C][/ROW]
[ROW][C]128[/C][C]0.69033075607932[/C][C]0.61933848784136[/C][C]0.30966924392068[/C][/ROW]
[ROW][C]129[/C][C]0.645015922560204[/C][C]0.709968154879591[/C][C]0.354984077439796[/C][/ROW]
[ROW][C]130[/C][C]0.765893935019666[/C][C]0.468212129960668[/C][C]0.234106064980334[/C][/ROW]
[ROW][C]131[/C][C]0.713580282724145[/C][C]0.57283943455171[/C][C]0.286419717275855[/C][/ROW]
[ROW][C]132[/C][C]0.653407430680831[/C][C]0.693185138638338[/C][C]0.346592569319169[/C][/ROW]
[ROW][C]133[/C][C]0.615380189803281[/C][C]0.769239620393437[/C][C]0.384619810196719[/C][/ROW]
[ROW][C]134[/C][C]0.54569034141003[/C][C]0.90861931717994[/C][C]0.45430965858997[/C][/ROW]
[ROW][C]135[/C][C]0.571000707376548[/C][C]0.857998585246904[/C][C]0.428999292623452[/C][/ROW]
[ROW][C]136[/C][C]0.500535746389737[/C][C]0.998928507220527[/C][C]0.499464253610263[/C][/ROW]
[ROW][C]137[/C][C]0.453408493907197[/C][C]0.906816987814394[/C][C]0.546591506092803[/C][/ROW]
[ROW][C]138[/C][C]0.470186630788955[/C][C]0.940373261577911[/C][C]0.529813369211045[/C][/ROW]
[ROW][C]139[/C][C]0.396281266677953[/C][C]0.792562533355906[/C][C]0.603718733322047[/C][/ROW]
[ROW][C]140[/C][C]0.322837797121087[/C][C]0.645675594242174[/C][C]0.677162202878913[/C][/ROW]
[ROW][C]141[/C][C]0.425940533040582[/C][C]0.851881066081163[/C][C]0.574059466959418[/C][/ROW]
[ROW][C]142[/C][C]0.3759469279344[/C][C]0.751893855868799[/C][C]0.6240530720656[/C][/ROW]
[ROW][C]143[/C][C]0.303350492215239[/C][C]0.606700984430478[/C][C]0.696649507784761[/C][/ROW]
[ROW][C]144[/C][C]0.227185895699065[/C][C]0.454371791398129[/C][C]0.772814104300935[/C][/ROW]
[ROW][C]145[/C][C]0.333525544777945[/C][C]0.667051089555889[/C][C]0.666474455222055[/C][/ROW]
[ROW][C]146[/C][C]0.254150574218036[/C][C]0.508301148436071[/C][C]0.745849425781964[/C][/ROW]
[ROW][C]147[/C][C]0.252870623285587[/C][C]0.505741246571175[/C][C]0.747129376714413[/C][/ROW]
[ROW][C]148[/C][C]0.159612301285357[/C][C]0.319224602570713[/C][C]0.840387698714643[/C][/ROW]
[ROW][C]149[/C][C]0.0863434141037002[/C][C]0.1726868282074[/C][C]0.9136565858963[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202459&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202459&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.09861376610248370.1972275322049670.901386233897516
110.3549755718872730.7099511437745460.645024428112727
120.7492056729243740.5015886541512520.250794327075626
130.7439942746184630.5120114507630750.256005725381537
140.7271423515756790.5457152968486410.272857648424321
150.6898060323229990.6203879353540030.310193967677001
160.6075205230409110.7849589539181780.392479476959089
170.5529639770054730.8940720459890530.447036022994527
180.5244892972136230.9510214055727530.475510702786377
190.4697051197851470.9394102395702930.530294880214853
200.4986893216137580.9973786432275160.501310678386242
210.4311761806996540.8623523613993070.568823819300347
220.4194510925287390.8389021850574780.580548907471261
230.3769374835137520.7538749670275030.623062516486248
240.5309343490541270.9381313018917470.469065650945873
250.4887782716235470.9775565432470950.511221728376453
260.5057243044372710.9885513911254570.494275695562729
270.4383934621962860.8767869243925720.561606537803714
280.3793593171190680.7587186342381360.620640682880932
290.3178232475813530.6356464951627070.682176752418647
300.2780751083641660.5561502167283310.721924891635834
310.2696041099587240.5392082199174480.730395890041276
320.3990065914215260.7980131828430510.600993408578474
330.3432220705289270.6864441410578540.656777929471073
340.3136417154916680.6272834309833350.686358284508332
350.3567734065587750.713546813117550.643226593441225
360.3239982756858650.647996551371730.676001724314135
370.4385551926157250.8771103852314510.561444807384274
380.7456244379030730.5087511241938550.254375562096927
390.72998173480890.5400365303822010.2700182651911
400.6939340840255180.6121318319489640.306065915974482
410.6567229917388230.6865540165223550.343277008261177
420.609635486056110.780729027887780.39036451394389
430.5594323516939840.8811352966120330.440567648306016
440.552798911731340.8944021765373210.44720108826866
450.5401932485095580.9196135029808840.459806751490442
460.5841750100225670.8316499799548670.415824989977433
470.5471175912121230.9057648175757550.452882408787877
480.5370116247742410.9259767504515170.462988375225759
490.5896696370225970.8206607259548070.410330362977403
500.5684220332217540.8631559335564930.431577966778246
510.5381782265344370.9236435469311260.461821773465563
520.4910579843459140.9821159686918290.508942015654086
530.4455369726952110.8910739453904210.554463027304789
540.4049352117232140.8098704234464290.595064788276786
550.3665446293371980.7330892586743960.633455370662802
560.3357626061080490.6715252122160990.664237393891951
570.292733820431060.5854676408621210.70726617956894
580.2673715418270750.5347430836541510.732628458172925
590.2495105008442370.4990210016884730.750489499155763
600.3086708782982860.6173417565965720.691329121701714
610.2940977137318820.5881954274637640.705902286268118
620.255099579665820.510199159331640.74490042033418
630.2394300655797740.4788601311595470.760569934420226
640.2656442274263840.5312884548527690.734355772573616
650.3303643276374650.6607286552749290.669635672362535
660.3389668445756480.6779336891512960.661033155424352
670.6838004982063620.6323990035872760.316199501793638
680.6733154810450890.6533690379098210.326684518954911
690.8411901611838280.3176196776323440.158809838816172
700.8191383275213770.3617233449572470.180861672478623
710.9308517380612750.1382965238774510.0691482619387253
720.9146569017311750.1706861965376490.0853430982688246
730.9382829337850670.1234341324298660.0617170662149329
740.9263861039557440.1472277920885110.0736138960442556
750.9276281033889790.1447437932220420.072371896611021
760.9215329032608190.1569341934783620.0784670967391811
770.9693287817645060.06134243647098840.0306712182354942
780.9616673404392930.07666531912141340.0383326595607067
790.9541074868971410.09178502620571860.0458925131028593
800.9566019515869030.08679609682619440.0433980484130972
810.948666401436410.102667197127180.0513335985635901
820.9476745572405540.1046508855188910.0523254427594455
830.9338768639516510.1322462720966980.0661231360483489
840.9207784649871140.1584430700257720.079221535012886
850.9112558434902320.1774883130195360.088744156509768
860.8936859551560780.2126280896878440.106314044843922
870.8716472922200260.2567054155599480.128352707779974
880.9193797757638680.1612404484722630.0806202242361317
890.9026115625421930.1947768749156140.0973884374578069
900.8817652436334710.2364695127330590.118234756366529
910.881397413989920.2372051720201590.11860258601008
920.8713980396681860.2572039206636290.128601960331815
930.8491324834995320.3017350330009370.150867516500468
940.8204223519995740.3591552960008520.179577648000426
950.7870177432280470.4259645135439060.212982256771953
960.7557157175194140.4885685649611720.244284282480586
970.7732214987018410.4535570025963180.226778501298159
980.7699916228250970.4600167543498060.230008377174903
990.7343600261062770.5312799477874460.265639973893723
1000.7122853425320180.5754293149359640.287714657467982
1010.6738041808869690.6523916382260630.326195819113031
1020.6328806957375110.7342386085249770.367119304262489
1030.5917954923026490.8164090153947030.408204507697351
1040.5486312193378190.9027375613243610.451368780662181
1050.5063404886872420.9873190226255160.493659511312758
1060.4926916343885430.9853832687770870.507308365611457
1070.5000020890271860.9999958219456290.499997910972814
1080.5393036213969180.9213927572061650.460696378603082
1090.5018653437622560.9962693124754880.498134656237744
1100.4607042898028230.9214085796056450.539295710197177
1110.4137439132316110.8274878264632220.586256086768389
1120.74061884066950.5187623186610.2593811593305
1130.7755791622930010.4488416754139970.224420837706999
1140.7511924937027930.4976150125944150.248807506297207
1150.8754598640234440.2490802719531110.124540135976556
1160.8470931873958180.3058136252083630.152906812604182
1170.8176305257582430.3647389484835140.182369474241757
1180.7841697785052680.4316604429894630.215830221494732
1190.756239619419450.48752076116110.24376038058055
1200.8049705765881360.3900588468237270.195029423411864
1210.8840595190699330.2318809618601330.115940480930067
1220.8652497380235420.2695005239529170.134750261976458
1230.8449103247330520.3101793505338970.155089675266948
1240.8069001298555630.3861997402888730.193099870144437
1250.8115641803558520.3768716392882960.188435819644148
1260.7682122314611830.4635755370776340.231787768538817
1270.7368674094556410.5262651810887190.263132590544359
1280.690330756079320.619338487841360.30966924392068
1290.6450159225602040.7099681548795910.354984077439796
1300.7658939350196660.4682121299606680.234106064980334
1310.7135802827241450.572839434551710.286419717275855
1320.6534074306808310.6931851386383380.346592569319169
1330.6153801898032810.7692396203934370.384619810196719
1340.545690341410030.908619317179940.45430965858997
1350.5710007073765480.8579985852469040.428999292623452
1360.5005357463897370.9989285072205270.499464253610263
1370.4534084939071970.9068169878143940.546591506092803
1380.4701866307889550.9403732615779110.529813369211045
1390.3962812666779530.7925625333559060.603718733322047
1400.3228377971210870.6456755942421740.677162202878913
1410.4259405330405820.8518810660811630.574059466959418
1420.37594692793440.7518938558687990.6240530720656
1430.3033504922152390.6067009844304780.696649507784761
1440.2271858956990650.4543717913981290.772814104300935
1450.3335255447779450.6670510895558890.666474455222055
1460.2541505742180360.5083011484360710.745849425781964
1470.2528706232855870.5057412465711750.747129376714413
1480.1596123012853570.3192246025707130.840387698714643
1490.08634341410370020.17268682820740.9136565858963







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level40.0285714285714286OK

\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 & 0 & 0 & OK \tabularnewline
10% type I error level & 4 & 0.0285714285714286 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202459&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]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]4[/C][C]0.0285714285714286[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202459&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202459&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 level00OK
10% type I error level40.0285714285714286OK



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