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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 computationTue, 23 Nov 2010 22:02:00 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Nov/23/t1290549609kvdspg8il57ojul.htm/, Retrieved Thu, 28 Mar 2024 19:00:24 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=99676, Retrieved Thu, 28 Mar 2024 19:00:24 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact144
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Decreasing Compet...] [2010-11-17 09:04:39] [b98453cac15ba1066b407e146608df68]
-   PD  [Multiple Regression] [run sequence plot...] [2010-11-19 12:01:57] [74deae64b71f9d77c839af86f7c687b5]
-   PD      [Multiple Regression] [] [2010-11-23 22:02:00] [e26438ba7029caa0090c95690001dbf5] [Current]
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Dataseries X:
41	12	14	12
39	11	18	11
30	15	11	14
31	6	12	12
34	13	16	21
35	10	18	12
39	12	14	22
34	14	14	11
36	12	15	10
37	6	15	13
38	10	17	10
36	12	19	8
38	12	10	15
39	11	16	14
33	15	18	10
32	12	14	14
36	10	14	14
38	12	17	11
39	11	14	10
32	12	16	13
32	11	18	7
31	12	11	14
39	13	14	12
37	11	12	14
39	9	17	11
41	13	9	9
36	10	16	11
33	14	14	15
33	12	15	14
34	10	11	13
31	12	16	9
27	8	13	15
37	10	17	10
34	12	15	11
34	12	14	13
32	7	16	8
29	6	9	20
36	12	15	12
29	10	17	10
35	10	13	10
37	10	15	9
34	12	16	14
38	15	16	8
35	10	12	14
38	10	12	11
37	12	11	13
38	13	15	9
33	11	15	11
36	11	17	15
38	12	13	11
32	14	16	10
32	10	14	14
32	12	11	18
34	13	12	14
32	5	12	11
37	6	15	12
39	12	16	13
29	12	15	9
37	11	12	10
35	10	12	15
30	7	8	20
38	12	13	12
34	14	11	12
31	11	14	14
34	12	15	13
35	13	10	11
36	14	11	17
30	11	12	12
39	12	15	13
35	12	15	14
38	8	14	13
31	11	16	15
34	14	15	13
38	14	15	10
34	12	13	11
39	9	12	19
37	13	17	13
34	11	13	17
28	12	15	13
37	12	13	9
33	12	15	11
37	12	16	10
35	12	15	9
37	12	16	12
32	11	15	12
33	10	14	13
38	9	15	13
33	12	14	12
29	12	13	15
33	12	7	22
31	9	17	13
36	15	13	15
35	12	15	13
32	12	14	15
29	12	13	10
39	10	16	11
37	13	12	16
35	9	14	11
37	12	17	11
32	10	15	10
38	14	17	10
37	11	12	16
36	15	16	12
32	11	11	11
33	11	15	16
40	12	9	19
38	12	16	11
41	12	15	16
36	11	10	15
43	7	10	24
30	12	15	14
31	14	11	15
32	11	13	11
32	11	14	15
37	10	18	12
37	13	16	10
33	13	14	14
34	8	14	13
33	11	14	9
38	12	14	15
33	11	12	15
31	13	14	14
38	12	15	11
37	14	15	8
33	13	15	11
31	15	13	11
39	10	17	8
44	11	17	10
33	9	19	11
35	11	15	13
32	10	13	11
28	11	9	20
40	8	15	10
27	11	15	15
37	12	15	12
32	12	16	14
28	9	11	23
34	11	14	14
30	10	11	16
35	8	15	11
31	9	13	12
32	8	15	10
30	9	16	14
30	15	14	12
31	11	15	12
40	8	16	11
32	13	16	12
36	12	11	13
32	12	12	11
35	9	9	19
38	7	16	12
42	13	13	17
34	9	16	9
35	6	12	12
35	8	9	19
33	8	13	18
36	15	13	15
32	6	14	14
33	9	19	11
34	11	13	9
32	8	12	18
34	8	13	16




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 7 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99676&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]7 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99676&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99676&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24







Multiple Linear Regression - Estimated Regression Equation
Connected[t] = + 32.3319878002113 + 0.073693050794334Software[t] + 0.158414769830047Happiness[t] -0.0578124530341171Depression[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Connected[t] =  +  32.3319878002113 +  0.073693050794334Software[t] +  0.158414769830047Happiness[t] -0.0578124530341171Depression[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99676&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Connected[t] =  +  32.3319878002113 +  0.073693050794334Software[t] +  0.158414769830047Happiness[t] -0.0578124530341171Depression[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99676&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99676&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
Connected[t] = + 32.3319878002113 + 0.073693050794334Software[t] + 0.158414769830047Happiness[t] -0.0578124530341171Depression[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)32.33198780021133.21373510.060600
Software0.0736930507943340.1249310.58990.5561210.27806
Happiness0.1584147698300470.135191.17180.2430450.121523
Depression-0.05781245303411710.100457-0.57550.5657760.282888

\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) & 32.3319878002113 & 3.213735 & 10.0606 & 0 & 0 \tabularnewline
Software & 0.073693050794334 & 0.124931 & 0.5899 & 0.556121 & 0.27806 \tabularnewline
Happiness & 0.158414769830047 & 0.13519 & 1.1718 & 0.243045 & 0.121523 \tabularnewline
Depression & -0.0578124530341171 & 0.100457 & -0.5755 & 0.565776 & 0.282888 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99676&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]32.3319878002113[/C][C]3.213735[/C][C]10.0606[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Software[/C][C]0.073693050794334[/C][C]0.124931[/C][C]0.5899[/C][C]0.556121[/C][C]0.27806[/C][/ROW]
[ROW][C]Happiness[/C][C]0.158414769830047[/C][C]0.13519[/C][C]1.1718[/C][C]0.243045[/C][C]0.121523[/C][/ROW]
[ROW][C]Depression[/C][C]-0.0578124530341171[/C][C]0.100457[/C][C]-0.5755[/C][C]0.565776[/C][C]0.282888[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99676&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99676&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)32.33198780021133.21373510.060600
Software0.0736930507943340.1249310.58990.5561210.27806
Happiness0.1584147698300470.135191.17180.2430450.121523
Depression-0.05781245303411710.100457-0.57550.5657760.282888







Multiple Linear Regression - Regression Statistics
Multiple R0.158387868304105
R-squared0.0250867168259186
Adjusted R-squared0.00657570512008154
F-TEST (value)1.35523207615974
F-TEST (DF numerator)3
F-TEST (DF denominator)158
p-value0.258572965130434
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.36401381269592
Sum Squared Residuals1788.02105125741

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.158387868304105 \tabularnewline
R-squared & 0.0250867168259186 \tabularnewline
Adjusted R-squared & 0.00657570512008154 \tabularnewline
F-TEST (value) & 1.35523207615974 \tabularnewline
F-TEST (DF numerator) & 3 \tabularnewline
F-TEST (DF denominator) & 158 \tabularnewline
p-value & 0.258572965130434 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 3.36401381269592 \tabularnewline
Sum Squared Residuals & 1788.02105125741 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99676&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.158387868304105[/C][/ROW]
[ROW][C]R-squared[/C][C]0.0250867168259186[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.00657570512008154[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]1.35523207615974[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]3[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]158[/C][/ROW]
[ROW][C]p-value[/C][C]0.258572965130434[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]3.36401381269592[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1788.02105125741[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99676&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99676&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 R0.158387868304105
R-squared0.0250867168259186
Adjusted R-squared0.00657570512008154
F-TEST (value)1.35523207615974
F-TEST (DF numerator)3
F-TEST (DF denominator)158
p-value0.258572965130434
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.36401381269592
Sum Squared Residuals1788.02105125741







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
14134.74036175095476.25963824904534
23935.35814023251453.64185976748549
33034.3705716877792-4.37057168777917
43133.9813739065284-2.98137390652845
53434.6105722641019-0.610572264101918
63535.2266347286861-0.226634728686063
73934.16223722061344.83776277938663
83434.9455603055773-0.94556030557733
93635.01440142685280.985598573147175
103734.39880576298452.60119423701553
113835.18384486492432.81615513507575
123635.76368541224120.236314587758754
133833.9332653125324.06673468746799
143934.86787333375214.13212666624793
153335.710724888726-2.71072488872597
163234.6247368448863-2.62473684488631
173634.47735074329761.52264925670236
183835.27341851347882.7265814865212
193934.78229360622844.21770639377156
203234.9993788375805-2.99937883758052
213235.589390044651-3.58939004465098
223134.1494925353962-3.14949253539617
233934.81405480174894.18594519825112
243734.23421425443192.76578574556812
253935.05233936109583.9476606389042
264134.1954183117016.804581688299
273634.96761764206011.03238235793991
283334.7143104934409-1.71431049344086
293334.7831516147164-1.78315161471636
303434.0599188868416-0.0599188868416191
313135.230628649717-4.23062864971699
322734.1137374188448-7.11373741884481
333735.18384486492431.81615513507575
343434.9565889738187-0.956588973818708
353434.6825492979204-0.682549297920427
363234.9199758487794-2.91997584877944
372933.0436299727654-4.04362997276537
383634.89877652078461.10122347921541
392935.1838448649243-6.18384486492425
403534.55018578560410.449814214395936
413734.92482777829832.07517222170173
423434.9415663845464-0.941566384546404
433835.50952025513412.49047974486589
443534.16052120363760.839478796362451
453834.33395856273993.6660414372601
463734.20730498843032.79269501156971
473835.14590693068132.85409306931872
483334.8828959230244-1.88289592302437
493634.9684756505481.031524349452
503834.63975943415863.36024056584138
513235.3202022982715-3.32020229827154
523234.4773507432976-2.47735074329764
533233.9182427232597-1.9182427232597
543434.3816003560205-0.381600356020551
553233.9654933087682-1.96549330876823
563734.45661821601862.54338178398141
573934.99937883758054.00062116241948
582935.0722138798869-6.07221387988694
593734.46546406656842.53453593343165
603534.10270875060340.897291249396568
613032.9589082537297-2.95890825372966
623834.58194698112453.4180530188755
633434.4125035430531-0.412503543053072
643134.551043794092-3.55104379409198
653434.8409640677505-0.840964067750474
663534.23820817546280.761791824537192
673634.12344127788251.87655872211751
683034.3498391605001-4.34983916050012
693934.84096406775054.15903593224953
703534.78315161471640.216848385283643
713834.38777709474313.61222290525691
723134.8100608807180-3.81006088071795
733434.9883501693391-0.988350169339142
743835.16178752844152.83821247155851
753434.6397594341586-0.639759434158615
763933.79776588767265.20223411232737
773735.23148665820491.7685133417951
783434.2191916651596-0.219191665159578
792834.8409640677505-6.84096406775047
803734.75538434022682.24461565977315
813334.9565889738187-1.95658897381871
823735.17281619668291.82718380331713
833535.0722138798869-0.0722138798869423
843735.05719129061461.94280870938536
853234.8250834699903-2.82508346999026
863334.5351631963318-1.53516319633176
873834.61988491536753.38011508463253
883334.7403617509545-1.74036175095454
892934.4085096220221-5.40850962202215
903333.0533338318030-0.053333831803046
913134.9367144550276-3.93671445502757
923634.62958877440511.37041122559485
933534.84096406775050.159035932249526
943234.5669243918522-2.56692439185219
952934.6975718871927-5.69757188719273
963934.96761764206014.03238235793991
973734.26597544995232.73402455004768
983534.57709505160570.422904948394340
993735.27341851347881.7265814865212
1003234.8670153252642-2.86701532526416
1013835.47861706810162.52138293189841
1023734.11858934836362.88141065163635
1033635.27827044299760.72172955700236
1043234.2492368437042-2.24923684370419
1053334.5938336578538-1.59383365785379
1064033.54360073056556.45639926943451
1073835.11500374364882.88499625635124
1084134.66752670864816.33247329135188
1093633.85957226173772.14042773826233
1104333.04448798125339.95551201874672
1113034.7831516147164-4.78315161471636
1123134.2390661839507-3.23906618395072
1133234.5660663833643-2.56606638336428
1143234.4932313410579-2.49323134105786
1153735.22663472868611.77336527131394
1163735.24650924747721.75349075252279
1173334.6984298956806-1.69842989568064
1183434.3877770947431-0.387777094743091
1193334.8401060592626-1.84010605926256
1203834.56692439185223.43307560814781
1213334.1764018013978-1.17640180139777
1223134.6984298956806-3.69842989568064
1233834.95658897381873.04341102618129
1243735.27741243450971.72258756549027
1253335.0302820246130-2.03028202461304
1263134.8608385865416-3.86083858654162
1273935.29946977099253.70053022900751
1284435.25753791571868.74246208428142
1293335.3691689007559-2.36916890075589
1303534.76727101695610.23272898304386
1313234.4923733325699-2.49237333256995
1322833.4120952267370-5.41209522673704
1334034.71962922367555.28037077632451
1342734.6516461108879-7.6516461108879
1353734.89877652078462.10122347921541
1363234.9415663845464-2.94156638454640
1372833.4081013057061-5.40810130570611
1383434.551043794092-0.551043794091976
1393033.8864815277393-3.88648152773927
1403534.66181677064140.338183229358628
1413134.3608678287415-3.36086782874150
1423234.7196292236755-2.71962922367549
1433034.7204872321634-4.7204872321634
1443034.9614409033375-4.96144090333755
1453134.8250834699903-3.82508346999026
1464034.82023154047145.17976845952858
1473235.130884341409-3.13088434140897
1483634.20730498843031.79269501156971
1493234.4813446643286-2.48134466432857
1503533.32252157818251.67747842181751
1513834.6887260366433.31127396335703
1524234.36657776674827.63342223325176
1533435.009549497334-1.00954949733399
1543533.98137390652841.01862609347155
1553533.24882852738821.75117147261185
1563333.9403000597425-0.940300059742459
1573634.62958877440511.37041122559485
1583234.1825785401203-2.18257854012031
1593335.3691689007559-2.36916890075589
1603434.6816912894325-0.681691289432515
1613233.7818852899124-1.78188528991241
1623434.0559249658107-0.0559249658106935

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 41 & 34.7403617509547 & 6.25963824904534 \tabularnewline
2 & 39 & 35.3581402325145 & 3.64185976748549 \tabularnewline
3 & 30 & 34.3705716877792 & -4.37057168777917 \tabularnewline
4 & 31 & 33.9813739065284 & -2.98137390652845 \tabularnewline
5 & 34 & 34.6105722641019 & -0.610572264101918 \tabularnewline
6 & 35 & 35.2266347286861 & -0.226634728686063 \tabularnewline
7 & 39 & 34.1622372206134 & 4.83776277938663 \tabularnewline
8 & 34 & 34.9455603055773 & -0.94556030557733 \tabularnewline
9 & 36 & 35.0144014268528 & 0.985598573147175 \tabularnewline
10 & 37 & 34.3988057629845 & 2.60119423701553 \tabularnewline
11 & 38 & 35.1838448649243 & 2.81615513507575 \tabularnewline
12 & 36 & 35.7636854122412 & 0.236314587758754 \tabularnewline
13 & 38 & 33.933265312532 & 4.06673468746799 \tabularnewline
14 & 39 & 34.8678733337521 & 4.13212666624793 \tabularnewline
15 & 33 & 35.710724888726 & -2.71072488872597 \tabularnewline
16 & 32 & 34.6247368448863 & -2.62473684488631 \tabularnewline
17 & 36 & 34.4773507432976 & 1.52264925670236 \tabularnewline
18 & 38 & 35.2734185134788 & 2.7265814865212 \tabularnewline
19 & 39 & 34.7822936062284 & 4.21770639377156 \tabularnewline
20 & 32 & 34.9993788375805 & -2.99937883758052 \tabularnewline
21 & 32 & 35.589390044651 & -3.58939004465098 \tabularnewline
22 & 31 & 34.1494925353962 & -3.14949253539617 \tabularnewline
23 & 39 & 34.8140548017489 & 4.18594519825112 \tabularnewline
24 & 37 & 34.2342142544319 & 2.76578574556812 \tabularnewline
25 & 39 & 35.0523393610958 & 3.9476606389042 \tabularnewline
26 & 41 & 34.195418311701 & 6.804581688299 \tabularnewline
27 & 36 & 34.9676176420601 & 1.03238235793991 \tabularnewline
28 & 33 & 34.7143104934409 & -1.71431049344086 \tabularnewline
29 & 33 & 34.7831516147164 & -1.78315161471636 \tabularnewline
30 & 34 & 34.0599188868416 & -0.0599188868416191 \tabularnewline
31 & 31 & 35.230628649717 & -4.23062864971699 \tabularnewline
32 & 27 & 34.1137374188448 & -7.11373741884481 \tabularnewline
33 & 37 & 35.1838448649243 & 1.81615513507575 \tabularnewline
34 & 34 & 34.9565889738187 & -0.956588973818708 \tabularnewline
35 & 34 & 34.6825492979204 & -0.682549297920427 \tabularnewline
36 & 32 & 34.9199758487794 & -2.91997584877944 \tabularnewline
37 & 29 & 33.0436299727654 & -4.04362997276537 \tabularnewline
38 & 36 & 34.8987765207846 & 1.10122347921541 \tabularnewline
39 & 29 & 35.1838448649243 & -6.18384486492425 \tabularnewline
40 & 35 & 34.5501857856041 & 0.449814214395936 \tabularnewline
41 & 37 & 34.9248277782983 & 2.07517222170173 \tabularnewline
42 & 34 & 34.9415663845464 & -0.941566384546404 \tabularnewline
43 & 38 & 35.5095202551341 & 2.49047974486589 \tabularnewline
44 & 35 & 34.1605212036376 & 0.839478796362451 \tabularnewline
45 & 38 & 34.3339585627399 & 3.6660414372601 \tabularnewline
46 & 37 & 34.2073049884303 & 2.79269501156971 \tabularnewline
47 & 38 & 35.1459069306813 & 2.85409306931872 \tabularnewline
48 & 33 & 34.8828959230244 & -1.88289592302437 \tabularnewline
49 & 36 & 34.968475650548 & 1.031524349452 \tabularnewline
50 & 38 & 34.6397594341586 & 3.36024056584138 \tabularnewline
51 & 32 & 35.3202022982715 & -3.32020229827154 \tabularnewline
52 & 32 & 34.4773507432976 & -2.47735074329764 \tabularnewline
53 & 32 & 33.9182427232597 & -1.9182427232597 \tabularnewline
54 & 34 & 34.3816003560205 & -0.381600356020551 \tabularnewline
55 & 32 & 33.9654933087682 & -1.96549330876823 \tabularnewline
56 & 37 & 34.4566182160186 & 2.54338178398141 \tabularnewline
57 & 39 & 34.9993788375805 & 4.00062116241948 \tabularnewline
58 & 29 & 35.0722138798869 & -6.07221387988694 \tabularnewline
59 & 37 & 34.4654640665684 & 2.53453593343165 \tabularnewline
60 & 35 & 34.1027087506034 & 0.897291249396568 \tabularnewline
61 & 30 & 32.9589082537297 & -2.95890825372966 \tabularnewline
62 & 38 & 34.5819469811245 & 3.4180530188755 \tabularnewline
63 & 34 & 34.4125035430531 & -0.412503543053072 \tabularnewline
64 & 31 & 34.551043794092 & -3.55104379409198 \tabularnewline
65 & 34 & 34.8409640677505 & -0.840964067750474 \tabularnewline
66 & 35 & 34.2382081754628 & 0.761791824537192 \tabularnewline
67 & 36 & 34.1234412778825 & 1.87655872211751 \tabularnewline
68 & 30 & 34.3498391605001 & -4.34983916050012 \tabularnewline
69 & 39 & 34.8409640677505 & 4.15903593224953 \tabularnewline
70 & 35 & 34.7831516147164 & 0.216848385283643 \tabularnewline
71 & 38 & 34.3877770947431 & 3.61222290525691 \tabularnewline
72 & 31 & 34.8100608807180 & -3.81006088071795 \tabularnewline
73 & 34 & 34.9883501693391 & -0.988350169339142 \tabularnewline
74 & 38 & 35.1617875284415 & 2.83821247155851 \tabularnewline
75 & 34 & 34.6397594341586 & -0.639759434158615 \tabularnewline
76 & 39 & 33.7977658876726 & 5.20223411232737 \tabularnewline
77 & 37 & 35.2314866582049 & 1.7685133417951 \tabularnewline
78 & 34 & 34.2191916651596 & -0.219191665159578 \tabularnewline
79 & 28 & 34.8409640677505 & -6.84096406775047 \tabularnewline
80 & 37 & 34.7553843402268 & 2.24461565977315 \tabularnewline
81 & 33 & 34.9565889738187 & -1.95658897381871 \tabularnewline
82 & 37 & 35.1728161966829 & 1.82718380331713 \tabularnewline
83 & 35 & 35.0722138798869 & -0.0722138798869423 \tabularnewline
84 & 37 & 35.0571912906146 & 1.94280870938536 \tabularnewline
85 & 32 & 34.8250834699903 & -2.82508346999026 \tabularnewline
86 & 33 & 34.5351631963318 & -1.53516319633176 \tabularnewline
87 & 38 & 34.6198849153675 & 3.38011508463253 \tabularnewline
88 & 33 & 34.7403617509545 & -1.74036175095454 \tabularnewline
89 & 29 & 34.4085096220221 & -5.40850962202215 \tabularnewline
90 & 33 & 33.0533338318030 & -0.053333831803046 \tabularnewline
91 & 31 & 34.9367144550276 & -3.93671445502757 \tabularnewline
92 & 36 & 34.6295887744051 & 1.37041122559485 \tabularnewline
93 & 35 & 34.8409640677505 & 0.159035932249526 \tabularnewline
94 & 32 & 34.5669243918522 & -2.56692439185219 \tabularnewline
95 & 29 & 34.6975718871927 & -5.69757188719273 \tabularnewline
96 & 39 & 34.9676176420601 & 4.03238235793991 \tabularnewline
97 & 37 & 34.2659754499523 & 2.73402455004768 \tabularnewline
98 & 35 & 34.5770950516057 & 0.422904948394340 \tabularnewline
99 & 37 & 35.2734185134788 & 1.7265814865212 \tabularnewline
100 & 32 & 34.8670153252642 & -2.86701532526416 \tabularnewline
101 & 38 & 35.4786170681016 & 2.52138293189841 \tabularnewline
102 & 37 & 34.1185893483636 & 2.88141065163635 \tabularnewline
103 & 36 & 35.2782704429976 & 0.72172955700236 \tabularnewline
104 & 32 & 34.2492368437042 & -2.24923684370419 \tabularnewline
105 & 33 & 34.5938336578538 & -1.59383365785379 \tabularnewline
106 & 40 & 33.5436007305655 & 6.45639926943451 \tabularnewline
107 & 38 & 35.1150037436488 & 2.88499625635124 \tabularnewline
108 & 41 & 34.6675267086481 & 6.33247329135188 \tabularnewline
109 & 36 & 33.8595722617377 & 2.14042773826233 \tabularnewline
110 & 43 & 33.0444879812533 & 9.95551201874672 \tabularnewline
111 & 30 & 34.7831516147164 & -4.78315161471636 \tabularnewline
112 & 31 & 34.2390661839507 & -3.23906618395072 \tabularnewline
113 & 32 & 34.5660663833643 & -2.56606638336428 \tabularnewline
114 & 32 & 34.4932313410579 & -2.49323134105786 \tabularnewline
115 & 37 & 35.2266347286861 & 1.77336527131394 \tabularnewline
116 & 37 & 35.2465092474772 & 1.75349075252279 \tabularnewline
117 & 33 & 34.6984298956806 & -1.69842989568064 \tabularnewline
118 & 34 & 34.3877770947431 & -0.387777094743091 \tabularnewline
119 & 33 & 34.8401060592626 & -1.84010605926256 \tabularnewline
120 & 38 & 34.5669243918522 & 3.43307560814781 \tabularnewline
121 & 33 & 34.1764018013978 & -1.17640180139777 \tabularnewline
122 & 31 & 34.6984298956806 & -3.69842989568064 \tabularnewline
123 & 38 & 34.9565889738187 & 3.04341102618129 \tabularnewline
124 & 37 & 35.2774124345097 & 1.72258756549027 \tabularnewline
125 & 33 & 35.0302820246130 & -2.03028202461304 \tabularnewline
126 & 31 & 34.8608385865416 & -3.86083858654162 \tabularnewline
127 & 39 & 35.2994697709925 & 3.70053022900751 \tabularnewline
128 & 44 & 35.2575379157186 & 8.74246208428142 \tabularnewline
129 & 33 & 35.3691689007559 & -2.36916890075589 \tabularnewline
130 & 35 & 34.7672710169561 & 0.23272898304386 \tabularnewline
131 & 32 & 34.4923733325699 & -2.49237333256995 \tabularnewline
132 & 28 & 33.4120952267370 & -5.41209522673704 \tabularnewline
133 & 40 & 34.7196292236755 & 5.28037077632451 \tabularnewline
134 & 27 & 34.6516461108879 & -7.6516461108879 \tabularnewline
135 & 37 & 34.8987765207846 & 2.10122347921541 \tabularnewline
136 & 32 & 34.9415663845464 & -2.94156638454640 \tabularnewline
137 & 28 & 33.4081013057061 & -5.40810130570611 \tabularnewline
138 & 34 & 34.551043794092 & -0.551043794091976 \tabularnewline
139 & 30 & 33.8864815277393 & -3.88648152773927 \tabularnewline
140 & 35 & 34.6618167706414 & 0.338183229358628 \tabularnewline
141 & 31 & 34.3608678287415 & -3.36086782874150 \tabularnewline
142 & 32 & 34.7196292236755 & -2.71962922367549 \tabularnewline
143 & 30 & 34.7204872321634 & -4.7204872321634 \tabularnewline
144 & 30 & 34.9614409033375 & -4.96144090333755 \tabularnewline
145 & 31 & 34.8250834699903 & -3.82508346999026 \tabularnewline
146 & 40 & 34.8202315404714 & 5.17976845952858 \tabularnewline
147 & 32 & 35.130884341409 & -3.13088434140897 \tabularnewline
148 & 36 & 34.2073049884303 & 1.79269501156971 \tabularnewline
149 & 32 & 34.4813446643286 & -2.48134466432857 \tabularnewline
150 & 35 & 33.3225215781825 & 1.67747842181751 \tabularnewline
151 & 38 & 34.688726036643 & 3.31127396335703 \tabularnewline
152 & 42 & 34.3665777667482 & 7.63342223325176 \tabularnewline
153 & 34 & 35.009549497334 & -1.00954949733399 \tabularnewline
154 & 35 & 33.9813739065284 & 1.01862609347155 \tabularnewline
155 & 35 & 33.2488285273882 & 1.75117147261185 \tabularnewline
156 & 33 & 33.9403000597425 & -0.940300059742459 \tabularnewline
157 & 36 & 34.6295887744051 & 1.37041122559485 \tabularnewline
158 & 32 & 34.1825785401203 & -2.18257854012031 \tabularnewline
159 & 33 & 35.3691689007559 & -2.36916890075589 \tabularnewline
160 & 34 & 34.6816912894325 & -0.681691289432515 \tabularnewline
161 & 32 & 33.7818852899124 & -1.78188528991241 \tabularnewline
162 & 34 & 34.0559249658107 & -0.0559249658106935 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99676&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]41[/C][C]34.7403617509547[/C][C]6.25963824904534[/C][/ROW]
[ROW][C]2[/C][C]39[/C][C]35.3581402325145[/C][C]3.64185976748549[/C][/ROW]
[ROW][C]3[/C][C]30[/C][C]34.3705716877792[/C][C]-4.37057168777917[/C][/ROW]
[ROW][C]4[/C][C]31[/C][C]33.9813739065284[/C][C]-2.98137390652845[/C][/ROW]
[ROW][C]5[/C][C]34[/C][C]34.6105722641019[/C][C]-0.610572264101918[/C][/ROW]
[ROW][C]6[/C][C]35[/C][C]35.2266347286861[/C][C]-0.226634728686063[/C][/ROW]
[ROW][C]7[/C][C]39[/C][C]34.1622372206134[/C][C]4.83776277938663[/C][/ROW]
[ROW][C]8[/C][C]34[/C][C]34.9455603055773[/C][C]-0.94556030557733[/C][/ROW]
[ROW][C]9[/C][C]36[/C][C]35.0144014268528[/C][C]0.985598573147175[/C][/ROW]
[ROW][C]10[/C][C]37[/C][C]34.3988057629845[/C][C]2.60119423701553[/C][/ROW]
[ROW][C]11[/C][C]38[/C][C]35.1838448649243[/C][C]2.81615513507575[/C][/ROW]
[ROW][C]12[/C][C]36[/C][C]35.7636854122412[/C][C]0.236314587758754[/C][/ROW]
[ROW][C]13[/C][C]38[/C][C]33.933265312532[/C][C]4.06673468746799[/C][/ROW]
[ROW][C]14[/C][C]39[/C][C]34.8678733337521[/C][C]4.13212666624793[/C][/ROW]
[ROW][C]15[/C][C]33[/C][C]35.710724888726[/C][C]-2.71072488872597[/C][/ROW]
[ROW][C]16[/C][C]32[/C][C]34.6247368448863[/C][C]-2.62473684488631[/C][/ROW]
[ROW][C]17[/C][C]36[/C][C]34.4773507432976[/C][C]1.52264925670236[/C][/ROW]
[ROW][C]18[/C][C]38[/C][C]35.2734185134788[/C][C]2.7265814865212[/C][/ROW]
[ROW][C]19[/C][C]39[/C][C]34.7822936062284[/C][C]4.21770639377156[/C][/ROW]
[ROW][C]20[/C][C]32[/C][C]34.9993788375805[/C][C]-2.99937883758052[/C][/ROW]
[ROW][C]21[/C][C]32[/C][C]35.589390044651[/C][C]-3.58939004465098[/C][/ROW]
[ROW][C]22[/C][C]31[/C][C]34.1494925353962[/C][C]-3.14949253539617[/C][/ROW]
[ROW][C]23[/C][C]39[/C][C]34.8140548017489[/C][C]4.18594519825112[/C][/ROW]
[ROW][C]24[/C][C]37[/C][C]34.2342142544319[/C][C]2.76578574556812[/C][/ROW]
[ROW][C]25[/C][C]39[/C][C]35.0523393610958[/C][C]3.9476606389042[/C][/ROW]
[ROW][C]26[/C][C]41[/C][C]34.195418311701[/C][C]6.804581688299[/C][/ROW]
[ROW][C]27[/C][C]36[/C][C]34.9676176420601[/C][C]1.03238235793991[/C][/ROW]
[ROW][C]28[/C][C]33[/C][C]34.7143104934409[/C][C]-1.71431049344086[/C][/ROW]
[ROW][C]29[/C][C]33[/C][C]34.7831516147164[/C][C]-1.78315161471636[/C][/ROW]
[ROW][C]30[/C][C]34[/C][C]34.0599188868416[/C][C]-0.0599188868416191[/C][/ROW]
[ROW][C]31[/C][C]31[/C][C]35.230628649717[/C][C]-4.23062864971699[/C][/ROW]
[ROW][C]32[/C][C]27[/C][C]34.1137374188448[/C][C]-7.11373741884481[/C][/ROW]
[ROW][C]33[/C][C]37[/C][C]35.1838448649243[/C][C]1.81615513507575[/C][/ROW]
[ROW][C]34[/C][C]34[/C][C]34.9565889738187[/C][C]-0.956588973818708[/C][/ROW]
[ROW][C]35[/C][C]34[/C][C]34.6825492979204[/C][C]-0.682549297920427[/C][/ROW]
[ROW][C]36[/C][C]32[/C][C]34.9199758487794[/C][C]-2.91997584877944[/C][/ROW]
[ROW][C]37[/C][C]29[/C][C]33.0436299727654[/C][C]-4.04362997276537[/C][/ROW]
[ROW][C]38[/C][C]36[/C][C]34.8987765207846[/C][C]1.10122347921541[/C][/ROW]
[ROW][C]39[/C][C]29[/C][C]35.1838448649243[/C][C]-6.18384486492425[/C][/ROW]
[ROW][C]40[/C][C]35[/C][C]34.5501857856041[/C][C]0.449814214395936[/C][/ROW]
[ROW][C]41[/C][C]37[/C][C]34.9248277782983[/C][C]2.07517222170173[/C][/ROW]
[ROW][C]42[/C][C]34[/C][C]34.9415663845464[/C][C]-0.941566384546404[/C][/ROW]
[ROW][C]43[/C][C]38[/C][C]35.5095202551341[/C][C]2.49047974486589[/C][/ROW]
[ROW][C]44[/C][C]35[/C][C]34.1605212036376[/C][C]0.839478796362451[/C][/ROW]
[ROW][C]45[/C][C]38[/C][C]34.3339585627399[/C][C]3.6660414372601[/C][/ROW]
[ROW][C]46[/C][C]37[/C][C]34.2073049884303[/C][C]2.79269501156971[/C][/ROW]
[ROW][C]47[/C][C]38[/C][C]35.1459069306813[/C][C]2.85409306931872[/C][/ROW]
[ROW][C]48[/C][C]33[/C][C]34.8828959230244[/C][C]-1.88289592302437[/C][/ROW]
[ROW][C]49[/C][C]36[/C][C]34.968475650548[/C][C]1.031524349452[/C][/ROW]
[ROW][C]50[/C][C]38[/C][C]34.6397594341586[/C][C]3.36024056584138[/C][/ROW]
[ROW][C]51[/C][C]32[/C][C]35.3202022982715[/C][C]-3.32020229827154[/C][/ROW]
[ROW][C]52[/C][C]32[/C][C]34.4773507432976[/C][C]-2.47735074329764[/C][/ROW]
[ROW][C]53[/C][C]32[/C][C]33.9182427232597[/C][C]-1.9182427232597[/C][/ROW]
[ROW][C]54[/C][C]34[/C][C]34.3816003560205[/C][C]-0.381600356020551[/C][/ROW]
[ROW][C]55[/C][C]32[/C][C]33.9654933087682[/C][C]-1.96549330876823[/C][/ROW]
[ROW][C]56[/C][C]37[/C][C]34.4566182160186[/C][C]2.54338178398141[/C][/ROW]
[ROW][C]57[/C][C]39[/C][C]34.9993788375805[/C][C]4.00062116241948[/C][/ROW]
[ROW][C]58[/C][C]29[/C][C]35.0722138798869[/C][C]-6.07221387988694[/C][/ROW]
[ROW][C]59[/C][C]37[/C][C]34.4654640665684[/C][C]2.53453593343165[/C][/ROW]
[ROW][C]60[/C][C]35[/C][C]34.1027087506034[/C][C]0.897291249396568[/C][/ROW]
[ROW][C]61[/C][C]30[/C][C]32.9589082537297[/C][C]-2.95890825372966[/C][/ROW]
[ROW][C]62[/C][C]38[/C][C]34.5819469811245[/C][C]3.4180530188755[/C][/ROW]
[ROW][C]63[/C][C]34[/C][C]34.4125035430531[/C][C]-0.412503543053072[/C][/ROW]
[ROW][C]64[/C][C]31[/C][C]34.551043794092[/C][C]-3.55104379409198[/C][/ROW]
[ROW][C]65[/C][C]34[/C][C]34.8409640677505[/C][C]-0.840964067750474[/C][/ROW]
[ROW][C]66[/C][C]35[/C][C]34.2382081754628[/C][C]0.761791824537192[/C][/ROW]
[ROW][C]67[/C][C]36[/C][C]34.1234412778825[/C][C]1.87655872211751[/C][/ROW]
[ROW][C]68[/C][C]30[/C][C]34.3498391605001[/C][C]-4.34983916050012[/C][/ROW]
[ROW][C]69[/C][C]39[/C][C]34.8409640677505[/C][C]4.15903593224953[/C][/ROW]
[ROW][C]70[/C][C]35[/C][C]34.7831516147164[/C][C]0.216848385283643[/C][/ROW]
[ROW][C]71[/C][C]38[/C][C]34.3877770947431[/C][C]3.61222290525691[/C][/ROW]
[ROW][C]72[/C][C]31[/C][C]34.8100608807180[/C][C]-3.81006088071795[/C][/ROW]
[ROW][C]73[/C][C]34[/C][C]34.9883501693391[/C][C]-0.988350169339142[/C][/ROW]
[ROW][C]74[/C][C]38[/C][C]35.1617875284415[/C][C]2.83821247155851[/C][/ROW]
[ROW][C]75[/C][C]34[/C][C]34.6397594341586[/C][C]-0.639759434158615[/C][/ROW]
[ROW][C]76[/C][C]39[/C][C]33.7977658876726[/C][C]5.20223411232737[/C][/ROW]
[ROW][C]77[/C][C]37[/C][C]35.2314866582049[/C][C]1.7685133417951[/C][/ROW]
[ROW][C]78[/C][C]34[/C][C]34.2191916651596[/C][C]-0.219191665159578[/C][/ROW]
[ROW][C]79[/C][C]28[/C][C]34.8409640677505[/C][C]-6.84096406775047[/C][/ROW]
[ROW][C]80[/C][C]37[/C][C]34.7553843402268[/C][C]2.24461565977315[/C][/ROW]
[ROW][C]81[/C][C]33[/C][C]34.9565889738187[/C][C]-1.95658897381871[/C][/ROW]
[ROW][C]82[/C][C]37[/C][C]35.1728161966829[/C][C]1.82718380331713[/C][/ROW]
[ROW][C]83[/C][C]35[/C][C]35.0722138798869[/C][C]-0.0722138798869423[/C][/ROW]
[ROW][C]84[/C][C]37[/C][C]35.0571912906146[/C][C]1.94280870938536[/C][/ROW]
[ROW][C]85[/C][C]32[/C][C]34.8250834699903[/C][C]-2.82508346999026[/C][/ROW]
[ROW][C]86[/C][C]33[/C][C]34.5351631963318[/C][C]-1.53516319633176[/C][/ROW]
[ROW][C]87[/C][C]38[/C][C]34.6198849153675[/C][C]3.38011508463253[/C][/ROW]
[ROW][C]88[/C][C]33[/C][C]34.7403617509545[/C][C]-1.74036175095454[/C][/ROW]
[ROW][C]89[/C][C]29[/C][C]34.4085096220221[/C][C]-5.40850962202215[/C][/ROW]
[ROW][C]90[/C][C]33[/C][C]33.0533338318030[/C][C]-0.053333831803046[/C][/ROW]
[ROW][C]91[/C][C]31[/C][C]34.9367144550276[/C][C]-3.93671445502757[/C][/ROW]
[ROW][C]92[/C][C]36[/C][C]34.6295887744051[/C][C]1.37041122559485[/C][/ROW]
[ROW][C]93[/C][C]35[/C][C]34.8409640677505[/C][C]0.159035932249526[/C][/ROW]
[ROW][C]94[/C][C]32[/C][C]34.5669243918522[/C][C]-2.56692439185219[/C][/ROW]
[ROW][C]95[/C][C]29[/C][C]34.6975718871927[/C][C]-5.69757188719273[/C][/ROW]
[ROW][C]96[/C][C]39[/C][C]34.9676176420601[/C][C]4.03238235793991[/C][/ROW]
[ROW][C]97[/C][C]37[/C][C]34.2659754499523[/C][C]2.73402455004768[/C][/ROW]
[ROW][C]98[/C][C]35[/C][C]34.5770950516057[/C][C]0.422904948394340[/C][/ROW]
[ROW][C]99[/C][C]37[/C][C]35.2734185134788[/C][C]1.7265814865212[/C][/ROW]
[ROW][C]100[/C][C]32[/C][C]34.8670153252642[/C][C]-2.86701532526416[/C][/ROW]
[ROW][C]101[/C][C]38[/C][C]35.4786170681016[/C][C]2.52138293189841[/C][/ROW]
[ROW][C]102[/C][C]37[/C][C]34.1185893483636[/C][C]2.88141065163635[/C][/ROW]
[ROW][C]103[/C][C]36[/C][C]35.2782704429976[/C][C]0.72172955700236[/C][/ROW]
[ROW][C]104[/C][C]32[/C][C]34.2492368437042[/C][C]-2.24923684370419[/C][/ROW]
[ROW][C]105[/C][C]33[/C][C]34.5938336578538[/C][C]-1.59383365785379[/C][/ROW]
[ROW][C]106[/C][C]40[/C][C]33.5436007305655[/C][C]6.45639926943451[/C][/ROW]
[ROW][C]107[/C][C]38[/C][C]35.1150037436488[/C][C]2.88499625635124[/C][/ROW]
[ROW][C]108[/C][C]41[/C][C]34.6675267086481[/C][C]6.33247329135188[/C][/ROW]
[ROW][C]109[/C][C]36[/C][C]33.8595722617377[/C][C]2.14042773826233[/C][/ROW]
[ROW][C]110[/C][C]43[/C][C]33.0444879812533[/C][C]9.95551201874672[/C][/ROW]
[ROW][C]111[/C][C]30[/C][C]34.7831516147164[/C][C]-4.78315161471636[/C][/ROW]
[ROW][C]112[/C][C]31[/C][C]34.2390661839507[/C][C]-3.23906618395072[/C][/ROW]
[ROW][C]113[/C][C]32[/C][C]34.5660663833643[/C][C]-2.56606638336428[/C][/ROW]
[ROW][C]114[/C][C]32[/C][C]34.4932313410579[/C][C]-2.49323134105786[/C][/ROW]
[ROW][C]115[/C][C]37[/C][C]35.2266347286861[/C][C]1.77336527131394[/C][/ROW]
[ROW][C]116[/C][C]37[/C][C]35.2465092474772[/C][C]1.75349075252279[/C][/ROW]
[ROW][C]117[/C][C]33[/C][C]34.6984298956806[/C][C]-1.69842989568064[/C][/ROW]
[ROW][C]118[/C][C]34[/C][C]34.3877770947431[/C][C]-0.387777094743091[/C][/ROW]
[ROW][C]119[/C][C]33[/C][C]34.8401060592626[/C][C]-1.84010605926256[/C][/ROW]
[ROW][C]120[/C][C]38[/C][C]34.5669243918522[/C][C]3.43307560814781[/C][/ROW]
[ROW][C]121[/C][C]33[/C][C]34.1764018013978[/C][C]-1.17640180139777[/C][/ROW]
[ROW][C]122[/C][C]31[/C][C]34.6984298956806[/C][C]-3.69842989568064[/C][/ROW]
[ROW][C]123[/C][C]38[/C][C]34.9565889738187[/C][C]3.04341102618129[/C][/ROW]
[ROW][C]124[/C][C]37[/C][C]35.2774124345097[/C][C]1.72258756549027[/C][/ROW]
[ROW][C]125[/C][C]33[/C][C]35.0302820246130[/C][C]-2.03028202461304[/C][/ROW]
[ROW][C]126[/C][C]31[/C][C]34.8608385865416[/C][C]-3.86083858654162[/C][/ROW]
[ROW][C]127[/C][C]39[/C][C]35.2994697709925[/C][C]3.70053022900751[/C][/ROW]
[ROW][C]128[/C][C]44[/C][C]35.2575379157186[/C][C]8.74246208428142[/C][/ROW]
[ROW][C]129[/C][C]33[/C][C]35.3691689007559[/C][C]-2.36916890075589[/C][/ROW]
[ROW][C]130[/C][C]35[/C][C]34.7672710169561[/C][C]0.23272898304386[/C][/ROW]
[ROW][C]131[/C][C]32[/C][C]34.4923733325699[/C][C]-2.49237333256995[/C][/ROW]
[ROW][C]132[/C][C]28[/C][C]33.4120952267370[/C][C]-5.41209522673704[/C][/ROW]
[ROW][C]133[/C][C]40[/C][C]34.7196292236755[/C][C]5.28037077632451[/C][/ROW]
[ROW][C]134[/C][C]27[/C][C]34.6516461108879[/C][C]-7.6516461108879[/C][/ROW]
[ROW][C]135[/C][C]37[/C][C]34.8987765207846[/C][C]2.10122347921541[/C][/ROW]
[ROW][C]136[/C][C]32[/C][C]34.9415663845464[/C][C]-2.94156638454640[/C][/ROW]
[ROW][C]137[/C][C]28[/C][C]33.4081013057061[/C][C]-5.40810130570611[/C][/ROW]
[ROW][C]138[/C][C]34[/C][C]34.551043794092[/C][C]-0.551043794091976[/C][/ROW]
[ROW][C]139[/C][C]30[/C][C]33.8864815277393[/C][C]-3.88648152773927[/C][/ROW]
[ROW][C]140[/C][C]35[/C][C]34.6618167706414[/C][C]0.338183229358628[/C][/ROW]
[ROW][C]141[/C][C]31[/C][C]34.3608678287415[/C][C]-3.36086782874150[/C][/ROW]
[ROW][C]142[/C][C]32[/C][C]34.7196292236755[/C][C]-2.71962922367549[/C][/ROW]
[ROW][C]143[/C][C]30[/C][C]34.7204872321634[/C][C]-4.7204872321634[/C][/ROW]
[ROW][C]144[/C][C]30[/C][C]34.9614409033375[/C][C]-4.96144090333755[/C][/ROW]
[ROW][C]145[/C][C]31[/C][C]34.8250834699903[/C][C]-3.82508346999026[/C][/ROW]
[ROW][C]146[/C][C]40[/C][C]34.8202315404714[/C][C]5.17976845952858[/C][/ROW]
[ROW][C]147[/C][C]32[/C][C]35.130884341409[/C][C]-3.13088434140897[/C][/ROW]
[ROW][C]148[/C][C]36[/C][C]34.2073049884303[/C][C]1.79269501156971[/C][/ROW]
[ROW][C]149[/C][C]32[/C][C]34.4813446643286[/C][C]-2.48134466432857[/C][/ROW]
[ROW][C]150[/C][C]35[/C][C]33.3225215781825[/C][C]1.67747842181751[/C][/ROW]
[ROW][C]151[/C][C]38[/C][C]34.688726036643[/C][C]3.31127396335703[/C][/ROW]
[ROW][C]152[/C][C]42[/C][C]34.3665777667482[/C][C]7.63342223325176[/C][/ROW]
[ROW][C]153[/C][C]34[/C][C]35.009549497334[/C][C]-1.00954949733399[/C][/ROW]
[ROW][C]154[/C][C]35[/C][C]33.9813739065284[/C][C]1.01862609347155[/C][/ROW]
[ROW][C]155[/C][C]35[/C][C]33.2488285273882[/C][C]1.75117147261185[/C][/ROW]
[ROW][C]156[/C][C]33[/C][C]33.9403000597425[/C][C]-0.940300059742459[/C][/ROW]
[ROW][C]157[/C][C]36[/C][C]34.6295887744051[/C][C]1.37041122559485[/C][/ROW]
[ROW][C]158[/C][C]32[/C][C]34.1825785401203[/C][C]-2.18257854012031[/C][/ROW]
[ROW][C]159[/C][C]33[/C][C]35.3691689007559[/C][C]-2.36916890075589[/C][/ROW]
[ROW][C]160[/C][C]34[/C][C]34.6816912894325[/C][C]-0.681691289432515[/C][/ROW]
[ROW][C]161[/C][C]32[/C][C]33.7818852899124[/C][C]-1.78188528991241[/C][/ROW]
[ROW][C]162[/C][C]34[/C][C]34.0559249658107[/C][C]-0.0559249658106935[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99676&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99676&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
14134.74036175095476.25963824904534
23935.35814023251453.64185976748549
33034.3705716877792-4.37057168777917
43133.9813739065284-2.98137390652845
53434.6105722641019-0.610572264101918
63535.2266347286861-0.226634728686063
73934.16223722061344.83776277938663
83434.9455603055773-0.94556030557733
93635.01440142685280.985598573147175
103734.39880576298452.60119423701553
113835.18384486492432.81615513507575
123635.76368541224120.236314587758754
133833.9332653125324.06673468746799
143934.86787333375214.13212666624793
153335.710724888726-2.71072488872597
163234.6247368448863-2.62473684488631
173634.47735074329761.52264925670236
183835.27341851347882.7265814865212
193934.78229360622844.21770639377156
203234.9993788375805-2.99937883758052
213235.589390044651-3.58939004465098
223134.1494925353962-3.14949253539617
233934.81405480174894.18594519825112
243734.23421425443192.76578574556812
253935.05233936109583.9476606389042
264134.1954183117016.804581688299
273634.96761764206011.03238235793991
283334.7143104934409-1.71431049344086
293334.7831516147164-1.78315161471636
303434.0599188868416-0.0599188868416191
313135.230628649717-4.23062864971699
322734.1137374188448-7.11373741884481
333735.18384486492431.81615513507575
343434.9565889738187-0.956588973818708
353434.6825492979204-0.682549297920427
363234.9199758487794-2.91997584877944
372933.0436299727654-4.04362997276537
383634.89877652078461.10122347921541
392935.1838448649243-6.18384486492425
403534.55018578560410.449814214395936
413734.92482777829832.07517222170173
423434.9415663845464-0.941566384546404
433835.50952025513412.49047974486589
443534.16052120363760.839478796362451
453834.33395856273993.6660414372601
463734.20730498843032.79269501156971
473835.14590693068132.85409306931872
483334.8828959230244-1.88289592302437
493634.9684756505481.031524349452
503834.63975943415863.36024056584138
513235.3202022982715-3.32020229827154
523234.4773507432976-2.47735074329764
533233.9182427232597-1.9182427232597
543434.3816003560205-0.381600356020551
553233.9654933087682-1.96549330876823
563734.45661821601862.54338178398141
573934.99937883758054.00062116241948
582935.0722138798869-6.07221387988694
593734.46546406656842.53453593343165
603534.10270875060340.897291249396568
613032.9589082537297-2.95890825372966
623834.58194698112453.4180530188755
633434.4125035430531-0.412503543053072
643134.551043794092-3.55104379409198
653434.8409640677505-0.840964067750474
663534.23820817546280.761791824537192
673634.12344127788251.87655872211751
683034.3498391605001-4.34983916050012
693934.84096406775054.15903593224953
703534.78315161471640.216848385283643
713834.38777709474313.61222290525691
723134.8100608807180-3.81006088071795
733434.9883501693391-0.988350169339142
743835.16178752844152.83821247155851
753434.6397594341586-0.639759434158615
763933.79776588767265.20223411232737
773735.23148665820491.7685133417951
783434.2191916651596-0.219191665159578
792834.8409640677505-6.84096406775047
803734.75538434022682.24461565977315
813334.9565889738187-1.95658897381871
823735.17281619668291.82718380331713
833535.0722138798869-0.0722138798869423
843735.05719129061461.94280870938536
853234.8250834699903-2.82508346999026
863334.5351631963318-1.53516319633176
873834.61988491536753.38011508463253
883334.7403617509545-1.74036175095454
892934.4085096220221-5.40850962202215
903333.0533338318030-0.053333831803046
913134.9367144550276-3.93671445502757
923634.62958877440511.37041122559485
933534.84096406775050.159035932249526
943234.5669243918522-2.56692439185219
952934.6975718871927-5.69757188719273
963934.96761764206014.03238235793991
973734.26597544995232.73402455004768
983534.57709505160570.422904948394340
993735.27341851347881.7265814865212
1003234.8670153252642-2.86701532526416
1013835.47861706810162.52138293189841
1023734.11858934836362.88141065163635
1033635.27827044299760.72172955700236
1043234.2492368437042-2.24923684370419
1053334.5938336578538-1.59383365785379
1064033.54360073056556.45639926943451
1073835.11500374364882.88499625635124
1084134.66752670864816.33247329135188
1093633.85957226173772.14042773826233
1104333.04448798125339.95551201874672
1113034.7831516147164-4.78315161471636
1123134.2390661839507-3.23906618395072
1133234.5660663833643-2.56606638336428
1143234.4932313410579-2.49323134105786
1153735.22663472868611.77336527131394
1163735.24650924747721.75349075252279
1173334.6984298956806-1.69842989568064
1183434.3877770947431-0.387777094743091
1193334.8401060592626-1.84010605926256
1203834.56692439185223.43307560814781
1213334.1764018013978-1.17640180139777
1223134.6984298956806-3.69842989568064
1233834.95658897381873.04341102618129
1243735.27741243450971.72258756549027
1253335.0302820246130-2.03028202461304
1263134.8608385865416-3.86083858654162
1273935.29946977099253.70053022900751
1284435.25753791571868.74246208428142
1293335.3691689007559-2.36916890075589
1303534.76727101695610.23272898304386
1313234.4923733325699-2.49237333256995
1322833.4120952267370-5.41209522673704
1334034.71962922367555.28037077632451
1342734.6516461108879-7.6516461108879
1353734.89877652078462.10122347921541
1363234.9415663845464-2.94156638454640
1372833.4081013057061-5.40810130570611
1383434.551043794092-0.551043794091976
1393033.8864815277393-3.88648152773927
1403534.66181677064140.338183229358628
1413134.3608678287415-3.36086782874150
1423234.7196292236755-2.71962922367549
1433034.7204872321634-4.7204872321634
1443034.9614409033375-4.96144090333755
1453134.8250834699903-3.82508346999026
1464034.82023154047145.17976845952858
1473235.130884341409-3.13088434140897
1483634.20730498843031.79269501156971
1493234.4813446643286-2.48134466432857
1503533.32252157818251.67747842181751
1513834.6887260366433.31127396335703
1524234.36657776674827.63342223325176
1533435.009549497334-1.00954949733399
1543533.98137390652841.01862609347155
1553533.24882852738821.75117147261185
1563333.9403000597425-0.940300059742459
1573634.62958877440511.37041122559485
1583234.1825785401203-2.18257854012031
1593335.3691689007559-2.36916890075589
1603434.6816912894325-0.681691289432515
1613233.7818852899124-1.78188528991241
1623434.0559249658107-0.0559249658106935







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
70.9296590753041760.1406818493916480.0703409246958238
80.8706590962029350.2586818075941310.129340903797065
90.7892478009905260.4215043980189480.210752199009474
100.7092574445009830.5814851109980340.290742555499017
110.6117162750158850.7765674499682310.388283724984115
120.5464141320867280.9071717358265440.453585867913272
130.6155024177310210.7689951645379580.384497582268979
140.5722835498920690.8554329002158630.427716450107931
150.5626431202044380.8747137595911250.437356879795562
160.5636751511478170.8726496977043660.436324848852183
170.4804530281700670.9609060563401350.519546971829933
180.4270444180698580.8540888361397160.572955581930142
190.4455416987152460.8910833974304930.554458301284754
200.4777760928273490.9555521856546980.522223907172651
210.5031822295591390.9936355408817210.496817770440861
220.5040260722195580.9919478555608840.495973927780442
230.542388502915750.91522299416850.45761149708425
240.49833185964010.99666371928020.5016681403599
250.474111122542170.948222245084340.52588887745783
260.637117361040830.7257652779183410.362882638959170
270.5765018858454660.8469962283090680.423498114154534
280.5416755686396710.9166488627206590.458324431360329
290.5133477895176140.9733044209647720.486652210482386
300.4695639609075130.9391279218150270.530436039092487
310.5215794324489970.9568411351020070.478420567551003
320.7755783287634670.4488433424730660.224421671236533
330.7380966717007760.5238066565984490.261903328299224
340.6969286261178370.6061427477643250.303071373882163
350.6506155777258340.6987688445483320.349384422274166
360.6454135989208360.7091728021583290.354586401079164
370.6641392199252070.6717215601495870.335860780074794
380.615426309200370.7691473815992610.384573690799631
390.7250947170095480.5498105659809040.274905282990452
400.6789228289138160.6421543421723680.321077171086184
410.6464422919194720.7071154161610560.353557708080528
420.6007303563587040.7985392872825920.399269643641296
430.5649067326293710.8701865347412570.435093267370629
440.5154815709878280.9690368580243450.484518429012172
450.5152237649557280.9695524700885430.484776235044272
460.4870671729794620.9741343459589240.512932827020538
470.460218901921890.920437803843780.53978109807811
480.4301024921033420.8602049842066840.569897507896658
490.3871174133640160.7742348267280310.612882586635984
500.3744995927139220.7489991854278440.625500407286078
510.3911668927162880.7823337854325760.608833107283712
520.3701828596116750.740365719223350.629817140388325
530.3429615289249190.6859230578498370.657038471075081
540.3019769396770990.6039538793541980.698023060322901
550.2710779937482640.5421559874965280.728922006251736
560.2618197972585970.5236395945171930.738180202741404
570.2760496235172920.5520992470345830.723950376482708
580.3887706829900810.7775413659801630.611229317009919
590.3647204682202050.7294409364404110.635279531779795
600.3232172187686440.6464344375372890.676782781231356
610.3104080847575660.6208161695151320.689591915242434
620.306850083403670.613700166807340.69314991659633
630.2712854998361340.5425709996722690.728714500163866
640.2764722411499800.5529444822999590.72352775885002
650.2412264030618070.4824528061236150.758773596938193
660.2090279014116110.4180558028232230.790972098588389
670.1857092490042590.3714184980085170.814290750995741
680.2100128825427590.4200257650855180.789987117457241
690.2267434136062260.4534868272124510.773256586393774
700.1933765796178760.3867531592357520.806623420382124
710.2006373991546440.4012747983092880.799362600845356
720.2098693536708910.4197387073417830.790130646329109
730.1818334992786240.3636669985572480.818166500721376
740.1722263756469860.3444527512939730.827773624353013
750.1463713041174120.2927426082348240.853628695882588
760.1884977823310740.3769955646621480.811502217668926
770.1663093197119340.3326186394238680.833690680288066
780.1393894943610570.2787789887221150.860610505638943
790.2348782643988750.4697565287977490.765121735601125
800.2176289634009690.4352579268019380.782371036599031
810.1960347432372420.3920694864744830.803965256762758
820.1748422121219070.3496844242438130.825157787878093
830.1476457341398640.2952914682797280.852354265860136
840.1307800993487050.2615601986974090.869219900651295
850.1231755566725340.2463511133450670.876824443327466
860.1053262985477180.2106525970954350.894673701452282
870.1056090789962820.2112181579925650.894390921003718
880.09089039951575030.1817807990315010.90910960048425
890.1223071459433840.2446142918867680.877692854056616
900.100554126810490.201108253620980.89944587318951
910.1083334998799530.2166669997599060.891666500120047
920.09199909277967440.1839981855593490.908000907220326
930.0744652627744620.1489305255489240.925534737225538
940.0675550661999520.1351101323999040.932444933800048
950.0958948339105580.1917896678211160.904105166089442
960.1038697432715330.2077394865430660.896130256728467
970.0972699394593450.194539878918690.902730060540655
980.07912688602806280.1582537720561260.920873113971937
990.06725960264008810.1345192052801760.932740397359912
1000.0621152911203750.124230582240750.937884708879625
1010.05687968369699330.1137593673939870.943120316303007
1020.05350740553767780.1070148110753560.946492594462322
1030.04319802492498690.08639604984997370.956801975075013
1040.0368119873853290.0736239747706580.963188012614671
1050.02973714723963130.05947429447926260.970262852760369
1060.06000664830598990.1200132966119800.93999335169401
1070.05731937056719350.1146387411343870.942680629432807
1080.1064554678116310.2129109356232620.89354453218837
1090.09691342888486620.1938268577697320.903086571115134
1100.4094883824114010.8189767648228030.590511617588599
1110.4381803690806290.8763607381612580.561819630919371
1120.4168645435139180.8337290870278370.583135456486082
1130.3944803849665230.7889607699330460.605519615033477
1140.3633325146449520.7266650292899040.636667485355048
1150.3315519491209120.6631038982418240.668448050879088
1160.3011195906979970.6022391813959930.698880409302003
1170.2632762917857730.5265525835715450.736723708214227
1180.2234477292860980.4468954585721960.776552270713902
1190.2005770485513430.4011540971026850.799422951448657
1200.2260116087697660.4520232175395320.773988391230234
1210.1907333000421440.3814666000842880.809266699957856
1220.1804009054136660.3608018108273330.819599094586334
1230.1775940131703320.3551880263406640.822405986829668
1240.1534186528370380.3068373056740770.846581347162962
1250.1289287569257190.2578575138514380.87107124307428
1260.1272340344801650.254468068960330.872765965519835
1270.1245280619042020.2490561238084030.875471938095798
1280.3905164665142420.7810329330284840.609483533485758
1290.3462451668355640.6924903336711280.653754833164436
1300.3023603826873140.6047207653746290.697639617312686
1310.2780057164731080.5560114329462170.721994283526892
1320.3180292185139930.6360584370279860.681970781486007
1330.4061989160838220.8123978321676440.593801083916178
1340.5693614662602250.861277067479550.430638533739775
1350.5600539038704380.8798921922591250.439946096129562
1360.5109547301223790.9780905397552420.489045269877621
1370.6282671780537290.7434656438925420.371732821946271
1380.5618639549420120.8762720901159750.438136045057988
1390.6000279694863160.7999440610273680.399972030513684
1400.5406492152301440.9187015695397130.459350784769856
1410.5225568012832050.954886397433590.477443198716795
1420.4707770559657810.9415541119315610.529222944034219
1430.5207704454640450.958459109071910.479229554535955
1440.5894584162662280.8210831674675440.410541583733772
1450.621032739351690.757934521296620.37896726064831
1460.7930372187845830.4139255624308340.206962781215417
1470.818356068896330.363287862207340.18164393110367
1480.7527837785440370.4944324429119250.247216221455963
1490.7659919409789360.4680161180421280.234008059021064
1500.6760569365494150.647886126901170.323943063450585
1510.8118522704316410.3762954591367180.188147729568359
1520.9959656149387960.008068770122408940.00403438506120447
1530.9870785691229820.02584286175403600.0129214308770180
1540.9859117227628960.02817655447420770.0140882772371038
1550.9801478807381940.03970423852361220.0198521192618061

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
7 & 0.929659075304176 & 0.140681849391648 & 0.0703409246958238 \tabularnewline
8 & 0.870659096202935 & 0.258681807594131 & 0.129340903797065 \tabularnewline
9 & 0.789247800990526 & 0.421504398018948 & 0.210752199009474 \tabularnewline
10 & 0.709257444500983 & 0.581485110998034 & 0.290742555499017 \tabularnewline
11 & 0.611716275015885 & 0.776567449968231 & 0.388283724984115 \tabularnewline
12 & 0.546414132086728 & 0.907171735826544 & 0.453585867913272 \tabularnewline
13 & 0.615502417731021 & 0.768995164537958 & 0.384497582268979 \tabularnewline
14 & 0.572283549892069 & 0.855432900215863 & 0.427716450107931 \tabularnewline
15 & 0.562643120204438 & 0.874713759591125 & 0.437356879795562 \tabularnewline
16 & 0.563675151147817 & 0.872649697704366 & 0.436324848852183 \tabularnewline
17 & 0.480453028170067 & 0.960906056340135 & 0.519546971829933 \tabularnewline
18 & 0.427044418069858 & 0.854088836139716 & 0.572955581930142 \tabularnewline
19 & 0.445541698715246 & 0.891083397430493 & 0.554458301284754 \tabularnewline
20 & 0.477776092827349 & 0.955552185654698 & 0.522223907172651 \tabularnewline
21 & 0.503182229559139 & 0.993635540881721 & 0.496817770440861 \tabularnewline
22 & 0.504026072219558 & 0.991947855560884 & 0.495973927780442 \tabularnewline
23 & 0.54238850291575 & 0.9152229941685 & 0.45761149708425 \tabularnewline
24 & 0.4983318596401 & 0.9966637192802 & 0.5016681403599 \tabularnewline
25 & 0.47411112254217 & 0.94822224508434 & 0.52588887745783 \tabularnewline
26 & 0.63711736104083 & 0.725765277918341 & 0.362882638959170 \tabularnewline
27 & 0.576501885845466 & 0.846996228309068 & 0.423498114154534 \tabularnewline
28 & 0.541675568639671 & 0.916648862720659 & 0.458324431360329 \tabularnewline
29 & 0.513347789517614 & 0.973304420964772 & 0.486652210482386 \tabularnewline
30 & 0.469563960907513 & 0.939127921815027 & 0.530436039092487 \tabularnewline
31 & 0.521579432448997 & 0.956841135102007 & 0.478420567551003 \tabularnewline
32 & 0.775578328763467 & 0.448843342473066 & 0.224421671236533 \tabularnewline
33 & 0.738096671700776 & 0.523806656598449 & 0.261903328299224 \tabularnewline
34 & 0.696928626117837 & 0.606142747764325 & 0.303071373882163 \tabularnewline
35 & 0.650615577725834 & 0.698768844548332 & 0.349384422274166 \tabularnewline
36 & 0.645413598920836 & 0.709172802158329 & 0.354586401079164 \tabularnewline
37 & 0.664139219925207 & 0.671721560149587 & 0.335860780074794 \tabularnewline
38 & 0.61542630920037 & 0.769147381599261 & 0.384573690799631 \tabularnewline
39 & 0.725094717009548 & 0.549810565980904 & 0.274905282990452 \tabularnewline
40 & 0.678922828913816 & 0.642154342172368 & 0.321077171086184 \tabularnewline
41 & 0.646442291919472 & 0.707115416161056 & 0.353557708080528 \tabularnewline
42 & 0.600730356358704 & 0.798539287282592 & 0.399269643641296 \tabularnewline
43 & 0.564906732629371 & 0.870186534741257 & 0.435093267370629 \tabularnewline
44 & 0.515481570987828 & 0.969036858024345 & 0.484518429012172 \tabularnewline
45 & 0.515223764955728 & 0.969552470088543 & 0.484776235044272 \tabularnewline
46 & 0.487067172979462 & 0.974134345958924 & 0.512932827020538 \tabularnewline
47 & 0.46021890192189 & 0.92043780384378 & 0.53978109807811 \tabularnewline
48 & 0.430102492103342 & 0.860204984206684 & 0.569897507896658 \tabularnewline
49 & 0.387117413364016 & 0.774234826728031 & 0.612882586635984 \tabularnewline
50 & 0.374499592713922 & 0.748999185427844 & 0.625500407286078 \tabularnewline
51 & 0.391166892716288 & 0.782333785432576 & 0.608833107283712 \tabularnewline
52 & 0.370182859611675 & 0.74036571922335 & 0.629817140388325 \tabularnewline
53 & 0.342961528924919 & 0.685923057849837 & 0.657038471075081 \tabularnewline
54 & 0.301976939677099 & 0.603953879354198 & 0.698023060322901 \tabularnewline
55 & 0.271077993748264 & 0.542155987496528 & 0.728922006251736 \tabularnewline
56 & 0.261819797258597 & 0.523639594517193 & 0.738180202741404 \tabularnewline
57 & 0.276049623517292 & 0.552099247034583 & 0.723950376482708 \tabularnewline
58 & 0.388770682990081 & 0.777541365980163 & 0.611229317009919 \tabularnewline
59 & 0.364720468220205 & 0.729440936440411 & 0.635279531779795 \tabularnewline
60 & 0.323217218768644 & 0.646434437537289 & 0.676782781231356 \tabularnewline
61 & 0.310408084757566 & 0.620816169515132 & 0.689591915242434 \tabularnewline
62 & 0.30685008340367 & 0.61370016680734 & 0.69314991659633 \tabularnewline
63 & 0.271285499836134 & 0.542570999672269 & 0.728714500163866 \tabularnewline
64 & 0.276472241149980 & 0.552944482299959 & 0.72352775885002 \tabularnewline
65 & 0.241226403061807 & 0.482452806123615 & 0.758773596938193 \tabularnewline
66 & 0.209027901411611 & 0.418055802823223 & 0.790972098588389 \tabularnewline
67 & 0.185709249004259 & 0.371418498008517 & 0.814290750995741 \tabularnewline
68 & 0.210012882542759 & 0.420025765085518 & 0.789987117457241 \tabularnewline
69 & 0.226743413606226 & 0.453486827212451 & 0.773256586393774 \tabularnewline
70 & 0.193376579617876 & 0.386753159235752 & 0.806623420382124 \tabularnewline
71 & 0.200637399154644 & 0.401274798309288 & 0.799362600845356 \tabularnewline
72 & 0.209869353670891 & 0.419738707341783 & 0.790130646329109 \tabularnewline
73 & 0.181833499278624 & 0.363666998557248 & 0.818166500721376 \tabularnewline
74 & 0.172226375646986 & 0.344452751293973 & 0.827773624353013 \tabularnewline
75 & 0.146371304117412 & 0.292742608234824 & 0.853628695882588 \tabularnewline
76 & 0.188497782331074 & 0.376995564662148 & 0.811502217668926 \tabularnewline
77 & 0.166309319711934 & 0.332618639423868 & 0.833690680288066 \tabularnewline
78 & 0.139389494361057 & 0.278778988722115 & 0.860610505638943 \tabularnewline
79 & 0.234878264398875 & 0.469756528797749 & 0.765121735601125 \tabularnewline
80 & 0.217628963400969 & 0.435257926801938 & 0.782371036599031 \tabularnewline
81 & 0.196034743237242 & 0.392069486474483 & 0.803965256762758 \tabularnewline
82 & 0.174842212121907 & 0.349684424243813 & 0.825157787878093 \tabularnewline
83 & 0.147645734139864 & 0.295291468279728 & 0.852354265860136 \tabularnewline
84 & 0.130780099348705 & 0.261560198697409 & 0.869219900651295 \tabularnewline
85 & 0.123175556672534 & 0.246351113345067 & 0.876824443327466 \tabularnewline
86 & 0.105326298547718 & 0.210652597095435 & 0.894673701452282 \tabularnewline
87 & 0.105609078996282 & 0.211218157992565 & 0.894390921003718 \tabularnewline
88 & 0.0908903995157503 & 0.181780799031501 & 0.90910960048425 \tabularnewline
89 & 0.122307145943384 & 0.244614291886768 & 0.877692854056616 \tabularnewline
90 & 0.10055412681049 & 0.20110825362098 & 0.89944587318951 \tabularnewline
91 & 0.108333499879953 & 0.216666999759906 & 0.891666500120047 \tabularnewline
92 & 0.0919990927796744 & 0.183998185559349 & 0.908000907220326 \tabularnewline
93 & 0.074465262774462 & 0.148930525548924 & 0.925534737225538 \tabularnewline
94 & 0.067555066199952 & 0.135110132399904 & 0.932444933800048 \tabularnewline
95 & 0.095894833910558 & 0.191789667821116 & 0.904105166089442 \tabularnewline
96 & 0.103869743271533 & 0.207739486543066 & 0.896130256728467 \tabularnewline
97 & 0.097269939459345 & 0.19453987891869 & 0.902730060540655 \tabularnewline
98 & 0.0791268860280628 & 0.158253772056126 & 0.920873113971937 \tabularnewline
99 & 0.0672596026400881 & 0.134519205280176 & 0.932740397359912 \tabularnewline
100 & 0.062115291120375 & 0.12423058224075 & 0.937884708879625 \tabularnewline
101 & 0.0568796836969933 & 0.113759367393987 & 0.943120316303007 \tabularnewline
102 & 0.0535074055376778 & 0.107014811075356 & 0.946492594462322 \tabularnewline
103 & 0.0431980249249869 & 0.0863960498499737 & 0.956801975075013 \tabularnewline
104 & 0.036811987385329 & 0.073623974770658 & 0.963188012614671 \tabularnewline
105 & 0.0297371472396313 & 0.0594742944792626 & 0.970262852760369 \tabularnewline
106 & 0.0600066483059899 & 0.120013296611980 & 0.93999335169401 \tabularnewline
107 & 0.0573193705671935 & 0.114638741134387 & 0.942680629432807 \tabularnewline
108 & 0.106455467811631 & 0.212910935623262 & 0.89354453218837 \tabularnewline
109 & 0.0969134288848662 & 0.193826857769732 & 0.903086571115134 \tabularnewline
110 & 0.409488382411401 & 0.818976764822803 & 0.590511617588599 \tabularnewline
111 & 0.438180369080629 & 0.876360738161258 & 0.561819630919371 \tabularnewline
112 & 0.416864543513918 & 0.833729087027837 & 0.583135456486082 \tabularnewline
113 & 0.394480384966523 & 0.788960769933046 & 0.605519615033477 \tabularnewline
114 & 0.363332514644952 & 0.726665029289904 & 0.636667485355048 \tabularnewline
115 & 0.331551949120912 & 0.663103898241824 & 0.668448050879088 \tabularnewline
116 & 0.301119590697997 & 0.602239181395993 & 0.698880409302003 \tabularnewline
117 & 0.263276291785773 & 0.526552583571545 & 0.736723708214227 \tabularnewline
118 & 0.223447729286098 & 0.446895458572196 & 0.776552270713902 \tabularnewline
119 & 0.200577048551343 & 0.401154097102685 & 0.799422951448657 \tabularnewline
120 & 0.226011608769766 & 0.452023217539532 & 0.773988391230234 \tabularnewline
121 & 0.190733300042144 & 0.381466600084288 & 0.809266699957856 \tabularnewline
122 & 0.180400905413666 & 0.360801810827333 & 0.819599094586334 \tabularnewline
123 & 0.177594013170332 & 0.355188026340664 & 0.822405986829668 \tabularnewline
124 & 0.153418652837038 & 0.306837305674077 & 0.846581347162962 \tabularnewline
125 & 0.128928756925719 & 0.257857513851438 & 0.87107124307428 \tabularnewline
126 & 0.127234034480165 & 0.25446806896033 & 0.872765965519835 \tabularnewline
127 & 0.124528061904202 & 0.249056123808403 & 0.875471938095798 \tabularnewline
128 & 0.390516466514242 & 0.781032933028484 & 0.609483533485758 \tabularnewline
129 & 0.346245166835564 & 0.692490333671128 & 0.653754833164436 \tabularnewline
130 & 0.302360382687314 & 0.604720765374629 & 0.697639617312686 \tabularnewline
131 & 0.278005716473108 & 0.556011432946217 & 0.721994283526892 \tabularnewline
132 & 0.318029218513993 & 0.636058437027986 & 0.681970781486007 \tabularnewline
133 & 0.406198916083822 & 0.812397832167644 & 0.593801083916178 \tabularnewline
134 & 0.569361466260225 & 0.86127706747955 & 0.430638533739775 \tabularnewline
135 & 0.560053903870438 & 0.879892192259125 & 0.439946096129562 \tabularnewline
136 & 0.510954730122379 & 0.978090539755242 & 0.489045269877621 \tabularnewline
137 & 0.628267178053729 & 0.743465643892542 & 0.371732821946271 \tabularnewline
138 & 0.561863954942012 & 0.876272090115975 & 0.438136045057988 \tabularnewline
139 & 0.600027969486316 & 0.799944061027368 & 0.399972030513684 \tabularnewline
140 & 0.540649215230144 & 0.918701569539713 & 0.459350784769856 \tabularnewline
141 & 0.522556801283205 & 0.95488639743359 & 0.477443198716795 \tabularnewline
142 & 0.470777055965781 & 0.941554111931561 & 0.529222944034219 \tabularnewline
143 & 0.520770445464045 & 0.95845910907191 & 0.479229554535955 \tabularnewline
144 & 0.589458416266228 & 0.821083167467544 & 0.410541583733772 \tabularnewline
145 & 0.62103273935169 & 0.75793452129662 & 0.37896726064831 \tabularnewline
146 & 0.793037218784583 & 0.413925562430834 & 0.206962781215417 \tabularnewline
147 & 0.81835606889633 & 0.36328786220734 & 0.18164393110367 \tabularnewline
148 & 0.752783778544037 & 0.494432442911925 & 0.247216221455963 \tabularnewline
149 & 0.765991940978936 & 0.468016118042128 & 0.234008059021064 \tabularnewline
150 & 0.676056936549415 & 0.64788612690117 & 0.323943063450585 \tabularnewline
151 & 0.811852270431641 & 0.376295459136718 & 0.188147729568359 \tabularnewline
152 & 0.995965614938796 & 0.00806877012240894 & 0.00403438506120447 \tabularnewline
153 & 0.987078569122982 & 0.0258428617540360 & 0.0129214308770180 \tabularnewline
154 & 0.985911722762896 & 0.0281765544742077 & 0.0140882772371038 \tabularnewline
155 & 0.980147880738194 & 0.0397042385236122 & 0.0198521192618061 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99676&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]7[/C][C]0.929659075304176[/C][C]0.140681849391648[/C][C]0.0703409246958238[/C][/ROW]
[ROW][C]8[/C][C]0.870659096202935[/C][C]0.258681807594131[/C][C]0.129340903797065[/C][/ROW]
[ROW][C]9[/C][C]0.789247800990526[/C][C]0.421504398018948[/C][C]0.210752199009474[/C][/ROW]
[ROW][C]10[/C][C]0.709257444500983[/C][C]0.581485110998034[/C][C]0.290742555499017[/C][/ROW]
[ROW][C]11[/C][C]0.611716275015885[/C][C]0.776567449968231[/C][C]0.388283724984115[/C][/ROW]
[ROW][C]12[/C][C]0.546414132086728[/C][C]0.907171735826544[/C][C]0.453585867913272[/C][/ROW]
[ROW][C]13[/C][C]0.615502417731021[/C][C]0.768995164537958[/C][C]0.384497582268979[/C][/ROW]
[ROW][C]14[/C][C]0.572283549892069[/C][C]0.855432900215863[/C][C]0.427716450107931[/C][/ROW]
[ROW][C]15[/C][C]0.562643120204438[/C][C]0.874713759591125[/C][C]0.437356879795562[/C][/ROW]
[ROW][C]16[/C][C]0.563675151147817[/C][C]0.872649697704366[/C][C]0.436324848852183[/C][/ROW]
[ROW][C]17[/C][C]0.480453028170067[/C][C]0.960906056340135[/C][C]0.519546971829933[/C][/ROW]
[ROW][C]18[/C][C]0.427044418069858[/C][C]0.854088836139716[/C][C]0.572955581930142[/C][/ROW]
[ROW][C]19[/C][C]0.445541698715246[/C][C]0.891083397430493[/C][C]0.554458301284754[/C][/ROW]
[ROW][C]20[/C][C]0.477776092827349[/C][C]0.955552185654698[/C][C]0.522223907172651[/C][/ROW]
[ROW][C]21[/C][C]0.503182229559139[/C][C]0.993635540881721[/C][C]0.496817770440861[/C][/ROW]
[ROW][C]22[/C][C]0.504026072219558[/C][C]0.991947855560884[/C][C]0.495973927780442[/C][/ROW]
[ROW][C]23[/C][C]0.54238850291575[/C][C]0.9152229941685[/C][C]0.45761149708425[/C][/ROW]
[ROW][C]24[/C][C]0.4983318596401[/C][C]0.9966637192802[/C][C]0.5016681403599[/C][/ROW]
[ROW][C]25[/C][C]0.47411112254217[/C][C]0.94822224508434[/C][C]0.52588887745783[/C][/ROW]
[ROW][C]26[/C][C]0.63711736104083[/C][C]0.725765277918341[/C][C]0.362882638959170[/C][/ROW]
[ROW][C]27[/C][C]0.576501885845466[/C][C]0.846996228309068[/C][C]0.423498114154534[/C][/ROW]
[ROW][C]28[/C][C]0.541675568639671[/C][C]0.916648862720659[/C][C]0.458324431360329[/C][/ROW]
[ROW][C]29[/C][C]0.513347789517614[/C][C]0.973304420964772[/C][C]0.486652210482386[/C][/ROW]
[ROW][C]30[/C][C]0.469563960907513[/C][C]0.939127921815027[/C][C]0.530436039092487[/C][/ROW]
[ROW][C]31[/C][C]0.521579432448997[/C][C]0.956841135102007[/C][C]0.478420567551003[/C][/ROW]
[ROW][C]32[/C][C]0.775578328763467[/C][C]0.448843342473066[/C][C]0.224421671236533[/C][/ROW]
[ROW][C]33[/C][C]0.738096671700776[/C][C]0.523806656598449[/C][C]0.261903328299224[/C][/ROW]
[ROW][C]34[/C][C]0.696928626117837[/C][C]0.606142747764325[/C][C]0.303071373882163[/C][/ROW]
[ROW][C]35[/C][C]0.650615577725834[/C][C]0.698768844548332[/C][C]0.349384422274166[/C][/ROW]
[ROW][C]36[/C][C]0.645413598920836[/C][C]0.709172802158329[/C][C]0.354586401079164[/C][/ROW]
[ROW][C]37[/C][C]0.664139219925207[/C][C]0.671721560149587[/C][C]0.335860780074794[/C][/ROW]
[ROW][C]38[/C][C]0.61542630920037[/C][C]0.769147381599261[/C][C]0.384573690799631[/C][/ROW]
[ROW][C]39[/C][C]0.725094717009548[/C][C]0.549810565980904[/C][C]0.274905282990452[/C][/ROW]
[ROW][C]40[/C][C]0.678922828913816[/C][C]0.642154342172368[/C][C]0.321077171086184[/C][/ROW]
[ROW][C]41[/C][C]0.646442291919472[/C][C]0.707115416161056[/C][C]0.353557708080528[/C][/ROW]
[ROW][C]42[/C][C]0.600730356358704[/C][C]0.798539287282592[/C][C]0.399269643641296[/C][/ROW]
[ROW][C]43[/C][C]0.564906732629371[/C][C]0.870186534741257[/C][C]0.435093267370629[/C][/ROW]
[ROW][C]44[/C][C]0.515481570987828[/C][C]0.969036858024345[/C][C]0.484518429012172[/C][/ROW]
[ROW][C]45[/C][C]0.515223764955728[/C][C]0.969552470088543[/C][C]0.484776235044272[/C][/ROW]
[ROW][C]46[/C][C]0.487067172979462[/C][C]0.974134345958924[/C][C]0.512932827020538[/C][/ROW]
[ROW][C]47[/C][C]0.46021890192189[/C][C]0.92043780384378[/C][C]0.53978109807811[/C][/ROW]
[ROW][C]48[/C][C]0.430102492103342[/C][C]0.860204984206684[/C][C]0.569897507896658[/C][/ROW]
[ROW][C]49[/C][C]0.387117413364016[/C][C]0.774234826728031[/C][C]0.612882586635984[/C][/ROW]
[ROW][C]50[/C][C]0.374499592713922[/C][C]0.748999185427844[/C][C]0.625500407286078[/C][/ROW]
[ROW][C]51[/C][C]0.391166892716288[/C][C]0.782333785432576[/C][C]0.608833107283712[/C][/ROW]
[ROW][C]52[/C][C]0.370182859611675[/C][C]0.74036571922335[/C][C]0.629817140388325[/C][/ROW]
[ROW][C]53[/C][C]0.342961528924919[/C][C]0.685923057849837[/C][C]0.657038471075081[/C][/ROW]
[ROW][C]54[/C][C]0.301976939677099[/C][C]0.603953879354198[/C][C]0.698023060322901[/C][/ROW]
[ROW][C]55[/C][C]0.271077993748264[/C][C]0.542155987496528[/C][C]0.728922006251736[/C][/ROW]
[ROW][C]56[/C][C]0.261819797258597[/C][C]0.523639594517193[/C][C]0.738180202741404[/C][/ROW]
[ROW][C]57[/C][C]0.276049623517292[/C][C]0.552099247034583[/C][C]0.723950376482708[/C][/ROW]
[ROW][C]58[/C][C]0.388770682990081[/C][C]0.777541365980163[/C][C]0.611229317009919[/C][/ROW]
[ROW][C]59[/C][C]0.364720468220205[/C][C]0.729440936440411[/C][C]0.635279531779795[/C][/ROW]
[ROW][C]60[/C][C]0.323217218768644[/C][C]0.646434437537289[/C][C]0.676782781231356[/C][/ROW]
[ROW][C]61[/C][C]0.310408084757566[/C][C]0.620816169515132[/C][C]0.689591915242434[/C][/ROW]
[ROW][C]62[/C][C]0.30685008340367[/C][C]0.61370016680734[/C][C]0.69314991659633[/C][/ROW]
[ROW][C]63[/C][C]0.271285499836134[/C][C]0.542570999672269[/C][C]0.728714500163866[/C][/ROW]
[ROW][C]64[/C][C]0.276472241149980[/C][C]0.552944482299959[/C][C]0.72352775885002[/C][/ROW]
[ROW][C]65[/C][C]0.241226403061807[/C][C]0.482452806123615[/C][C]0.758773596938193[/C][/ROW]
[ROW][C]66[/C][C]0.209027901411611[/C][C]0.418055802823223[/C][C]0.790972098588389[/C][/ROW]
[ROW][C]67[/C][C]0.185709249004259[/C][C]0.371418498008517[/C][C]0.814290750995741[/C][/ROW]
[ROW][C]68[/C][C]0.210012882542759[/C][C]0.420025765085518[/C][C]0.789987117457241[/C][/ROW]
[ROW][C]69[/C][C]0.226743413606226[/C][C]0.453486827212451[/C][C]0.773256586393774[/C][/ROW]
[ROW][C]70[/C][C]0.193376579617876[/C][C]0.386753159235752[/C][C]0.806623420382124[/C][/ROW]
[ROW][C]71[/C][C]0.200637399154644[/C][C]0.401274798309288[/C][C]0.799362600845356[/C][/ROW]
[ROW][C]72[/C][C]0.209869353670891[/C][C]0.419738707341783[/C][C]0.790130646329109[/C][/ROW]
[ROW][C]73[/C][C]0.181833499278624[/C][C]0.363666998557248[/C][C]0.818166500721376[/C][/ROW]
[ROW][C]74[/C][C]0.172226375646986[/C][C]0.344452751293973[/C][C]0.827773624353013[/C][/ROW]
[ROW][C]75[/C][C]0.146371304117412[/C][C]0.292742608234824[/C][C]0.853628695882588[/C][/ROW]
[ROW][C]76[/C][C]0.188497782331074[/C][C]0.376995564662148[/C][C]0.811502217668926[/C][/ROW]
[ROW][C]77[/C][C]0.166309319711934[/C][C]0.332618639423868[/C][C]0.833690680288066[/C][/ROW]
[ROW][C]78[/C][C]0.139389494361057[/C][C]0.278778988722115[/C][C]0.860610505638943[/C][/ROW]
[ROW][C]79[/C][C]0.234878264398875[/C][C]0.469756528797749[/C][C]0.765121735601125[/C][/ROW]
[ROW][C]80[/C][C]0.217628963400969[/C][C]0.435257926801938[/C][C]0.782371036599031[/C][/ROW]
[ROW][C]81[/C][C]0.196034743237242[/C][C]0.392069486474483[/C][C]0.803965256762758[/C][/ROW]
[ROW][C]82[/C][C]0.174842212121907[/C][C]0.349684424243813[/C][C]0.825157787878093[/C][/ROW]
[ROW][C]83[/C][C]0.147645734139864[/C][C]0.295291468279728[/C][C]0.852354265860136[/C][/ROW]
[ROW][C]84[/C][C]0.130780099348705[/C][C]0.261560198697409[/C][C]0.869219900651295[/C][/ROW]
[ROW][C]85[/C][C]0.123175556672534[/C][C]0.246351113345067[/C][C]0.876824443327466[/C][/ROW]
[ROW][C]86[/C][C]0.105326298547718[/C][C]0.210652597095435[/C][C]0.894673701452282[/C][/ROW]
[ROW][C]87[/C][C]0.105609078996282[/C][C]0.211218157992565[/C][C]0.894390921003718[/C][/ROW]
[ROW][C]88[/C][C]0.0908903995157503[/C][C]0.181780799031501[/C][C]0.90910960048425[/C][/ROW]
[ROW][C]89[/C][C]0.122307145943384[/C][C]0.244614291886768[/C][C]0.877692854056616[/C][/ROW]
[ROW][C]90[/C][C]0.10055412681049[/C][C]0.20110825362098[/C][C]0.89944587318951[/C][/ROW]
[ROW][C]91[/C][C]0.108333499879953[/C][C]0.216666999759906[/C][C]0.891666500120047[/C][/ROW]
[ROW][C]92[/C][C]0.0919990927796744[/C][C]0.183998185559349[/C][C]0.908000907220326[/C][/ROW]
[ROW][C]93[/C][C]0.074465262774462[/C][C]0.148930525548924[/C][C]0.925534737225538[/C][/ROW]
[ROW][C]94[/C][C]0.067555066199952[/C][C]0.135110132399904[/C][C]0.932444933800048[/C][/ROW]
[ROW][C]95[/C][C]0.095894833910558[/C][C]0.191789667821116[/C][C]0.904105166089442[/C][/ROW]
[ROW][C]96[/C][C]0.103869743271533[/C][C]0.207739486543066[/C][C]0.896130256728467[/C][/ROW]
[ROW][C]97[/C][C]0.097269939459345[/C][C]0.19453987891869[/C][C]0.902730060540655[/C][/ROW]
[ROW][C]98[/C][C]0.0791268860280628[/C][C]0.158253772056126[/C][C]0.920873113971937[/C][/ROW]
[ROW][C]99[/C][C]0.0672596026400881[/C][C]0.134519205280176[/C][C]0.932740397359912[/C][/ROW]
[ROW][C]100[/C][C]0.062115291120375[/C][C]0.12423058224075[/C][C]0.937884708879625[/C][/ROW]
[ROW][C]101[/C][C]0.0568796836969933[/C][C]0.113759367393987[/C][C]0.943120316303007[/C][/ROW]
[ROW][C]102[/C][C]0.0535074055376778[/C][C]0.107014811075356[/C][C]0.946492594462322[/C][/ROW]
[ROW][C]103[/C][C]0.0431980249249869[/C][C]0.0863960498499737[/C][C]0.956801975075013[/C][/ROW]
[ROW][C]104[/C][C]0.036811987385329[/C][C]0.073623974770658[/C][C]0.963188012614671[/C][/ROW]
[ROW][C]105[/C][C]0.0297371472396313[/C][C]0.0594742944792626[/C][C]0.970262852760369[/C][/ROW]
[ROW][C]106[/C][C]0.0600066483059899[/C][C]0.120013296611980[/C][C]0.93999335169401[/C][/ROW]
[ROW][C]107[/C][C]0.0573193705671935[/C][C]0.114638741134387[/C][C]0.942680629432807[/C][/ROW]
[ROW][C]108[/C][C]0.106455467811631[/C][C]0.212910935623262[/C][C]0.89354453218837[/C][/ROW]
[ROW][C]109[/C][C]0.0969134288848662[/C][C]0.193826857769732[/C][C]0.903086571115134[/C][/ROW]
[ROW][C]110[/C][C]0.409488382411401[/C][C]0.818976764822803[/C][C]0.590511617588599[/C][/ROW]
[ROW][C]111[/C][C]0.438180369080629[/C][C]0.876360738161258[/C][C]0.561819630919371[/C][/ROW]
[ROW][C]112[/C][C]0.416864543513918[/C][C]0.833729087027837[/C][C]0.583135456486082[/C][/ROW]
[ROW][C]113[/C][C]0.394480384966523[/C][C]0.788960769933046[/C][C]0.605519615033477[/C][/ROW]
[ROW][C]114[/C][C]0.363332514644952[/C][C]0.726665029289904[/C][C]0.636667485355048[/C][/ROW]
[ROW][C]115[/C][C]0.331551949120912[/C][C]0.663103898241824[/C][C]0.668448050879088[/C][/ROW]
[ROW][C]116[/C][C]0.301119590697997[/C][C]0.602239181395993[/C][C]0.698880409302003[/C][/ROW]
[ROW][C]117[/C][C]0.263276291785773[/C][C]0.526552583571545[/C][C]0.736723708214227[/C][/ROW]
[ROW][C]118[/C][C]0.223447729286098[/C][C]0.446895458572196[/C][C]0.776552270713902[/C][/ROW]
[ROW][C]119[/C][C]0.200577048551343[/C][C]0.401154097102685[/C][C]0.799422951448657[/C][/ROW]
[ROW][C]120[/C][C]0.226011608769766[/C][C]0.452023217539532[/C][C]0.773988391230234[/C][/ROW]
[ROW][C]121[/C][C]0.190733300042144[/C][C]0.381466600084288[/C][C]0.809266699957856[/C][/ROW]
[ROW][C]122[/C][C]0.180400905413666[/C][C]0.360801810827333[/C][C]0.819599094586334[/C][/ROW]
[ROW][C]123[/C][C]0.177594013170332[/C][C]0.355188026340664[/C][C]0.822405986829668[/C][/ROW]
[ROW][C]124[/C][C]0.153418652837038[/C][C]0.306837305674077[/C][C]0.846581347162962[/C][/ROW]
[ROW][C]125[/C][C]0.128928756925719[/C][C]0.257857513851438[/C][C]0.87107124307428[/C][/ROW]
[ROW][C]126[/C][C]0.127234034480165[/C][C]0.25446806896033[/C][C]0.872765965519835[/C][/ROW]
[ROW][C]127[/C][C]0.124528061904202[/C][C]0.249056123808403[/C][C]0.875471938095798[/C][/ROW]
[ROW][C]128[/C][C]0.390516466514242[/C][C]0.781032933028484[/C][C]0.609483533485758[/C][/ROW]
[ROW][C]129[/C][C]0.346245166835564[/C][C]0.692490333671128[/C][C]0.653754833164436[/C][/ROW]
[ROW][C]130[/C][C]0.302360382687314[/C][C]0.604720765374629[/C][C]0.697639617312686[/C][/ROW]
[ROW][C]131[/C][C]0.278005716473108[/C][C]0.556011432946217[/C][C]0.721994283526892[/C][/ROW]
[ROW][C]132[/C][C]0.318029218513993[/C][C]0.636058437027986[/C][C]0.681970781486007[/C][/ROW]
[ROW][C]133[/C][C]0.406198916083822[/C][C]0.812397832167644[/C][C]0.593801083916178[/C][/ROW]
[ROW][C]134[/C][C]0.569361466260225[/C][C]0.86127706747955[/C][C]0.430638533739775[/C][/ROW]
[ROW][C]135[/C][C]0.560053903870438[/C][C]0.879892192259125[/C][C]0.439946096129562[/C][/ROW]
[ROW][C]136[/C][C]0.510954730122379[/C][C]0.978090539755242[/C][C]0.489045269877621[/C][/ROW]
[ROW][C]137[/C][C]0.628267178053729[/C][C]0.743465643892542[/C][C]0.371732821946271[/C][/ROW]
[ROW][C]138[/C][C]0.561863954942012[/C][C]0.876272090115975[/C][C]0.438136045057988[/C][/ROW]
[ROW][C]139[/C][C]0.600027969486316[/C][C]0.799944061027368[/C][C]0.399972030513684[/C][/ROW]
[ROW][C]140[/C][C]0.540649215230144[/C][C]0.918701569539713[/C][C]0.459350784769856[/C][/ROW]
[ROW][C]141[/C][C]0.522556801283205[/C][C]0.95488639743359[/C][C]0.477443198716795[/C][/ROW]
[ROW][C]142[/C][C]0.470777055965781[/C][C]0.941554111931561[/C][C]0.529222944034219[/C][/ROW]
[ROW][C]143[/C][C]0.520770445464045[/C][C]0.95845910907191[/C][C]0.479229554535955[/C][/ROW]
[ROW][C]144[/C][C]0.589458416266228[/C][C]0.821083167467544[/C][C]0.410541583733772[/C][/ROW]
[ROW][C]145[/C][C]0.62103273935169[/C][C]0.75793452129662[/C][C]0.37896726064831[/C][/ROW]
[ROW][C]146[/C][C]0.793037218784583[/C][C]0.413925562430834[/C][C]0.206962781215417[/C][/ROW]
[ROW][C]147[/C][C]0.81835606889633[/C][C]0.36328786220734[/C][C]0.18164393110367[/C][/ROW]
[ROW][C]148[/C][C]0.752783778544037[/C][C]0.494432442911925[/C][C]0.247216221455963[/C][/ROW]
[ROW][C]149[/C][C]0.765991940978936[/C][C]0.468016118042128[/C][C]0.234008059021064[/C][/ROW]
[ROW][C]150[/C][C]0.676056936549415[/C][C]0.64788612690117[/C][C]0.323943063450585[/C][/ROW]
[ROW][C]151[/C][C]0.811852270431641[/C][C]0.376295459136718[/C][C]0.188147729568359[/C][/ROW]
[ROW][C]152[/C][C]0.995965614938796[/C][C]0.00806877012240894[/C][C]0.00403438506120447[/C][/ROW]
[ROW][C]153[/C][C]0.987078569122982[/C][C]0.0258428617540360[/C][C]0.0129214308770180[/C][/ROW]
[ROW][C]154[/C][C]0.985911722762896[/C][C]0.0281765544742077[/C][C]0.0140882772371038[/C][/ROW]
[ROW][C]155[/C][C]0.980147880738194[/C][C]0.0397042385236122[/C][C]0.0198521192618061[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99676&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99676&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
70.9296590753041760.1406818493916480.0703409246958238
80.8706590962029350.2586818075941310.129340903797065
90.7892478009905260.4215043980189480.210752199009474
100.7092574445009830.5814851109980340.290742555499017
110.6117162750158850.7765674499682310.388283724984115
120.5464141320867280.9071717358265440.453585867913272
130.6155024177310210.7689951645379580.384497582268979
140.5722835498920690.8554329002158630.427716450107931
150.5626431202044380.8747137595911250.437356879795562
160.5636751511478170.8726496977043660.436324848852183
170.4804530281700670.9609060563401350.519546971829933
180.4270444180698580.8540888361397160.572955581930142
190.4455416987152460.8910833974304930.554458301284754
200.4777760928273490.9555521856546980.522223907172651
210.5031822295591390.9936355408817210.496817770440861
220.5040260722195580.9919478555608840.495973927780442
230.542388502915750.91522299416850.45761149708425
240.49833185964010.99666371928020.5016681403599
250.474111122542170.948222245084340.52588887745783
260.637117361040830.7257652779183410.362882638959170
270.5765018858454660.8469962283090680.423498114154534
280.5416755686396710.9166488627206590.458324431360329
290.5133477895176140.9733044209647720.486652210482386
300.4695639609075130.9391279218150270.530436039092487
310.5215794324489970.9568411351020070.478420567551003
320.7755783287634670.4488433424730660.224421671236533
330.7380966717007760.5238066565984490.261903328299224
340.6969286261178370.6061427477643250.303071373882163
350.6506155777258340.6987688445483320.349384422274166
360.6454135989208360.7091728021583290.354586401079164
370.6641392199252070.6717215601495870.335860780074794
380.615426309200370.7691473815992610.384573690799631
390.7250947170095480.5498105659809040.274905282990452
400.6789228289138160.6421543421723680.321077171086184
410.6464422919194720.7071154161610560.353557708080528
420.6007303563587040.7985392872825920.399269643641296
430.5649067326293710.8701865347412570.435093267370629
440.5154815709878280.9690368580243450.484518429012172
450.5152237649557280.9695524700885430.484776235044272
460.4870671729794620.9741343459589240.512932827020538
470.460218901921890.920437803843780.53978109807811
480.4301024921033420.8602049842066840.569897507896658
490.3871174133640160.7742348267280310.612882586635984
500.3744995927139220.7489991854278440.625500407286078
510.3911668927162880.7823337854325760.608833107283712
520.3701828596116750.740365719223350.629817140388325
530.3429615289249190.6859230578498370.657038471075081
540.3019769396770990.6039538793541980.698023060322901
550.2710779937482640.5421559874965280.728922006251736
560.2618197972585970.5236395945171930.738180202741404
570.2760496235172920.5520992470345830.723950376482708
580.3887706829900810.7775413659801630.611229317009919
590.3647204682202050.7294409364404110.635279531779795
600.3232172187686440.6464344375372890.676782781231356
610.3104080847575660.6208161695151320.689591915242434
620.306850083403670.613700166807340.69314991659633
630.2712854998361340.5425709996722690.728714500163866
640.2764722411499800.5529444822999590.72352775885002
650.2412264030618070.4824528061236150.758773596938193
660.2090279014116110.4180558028232230.790972098588389
670.1857092490042590.3714184980085170.814290750995741
680.2100128825427590.4200257650855180.789987117457241
690.2267434136062260.4534868272124510.773256586393774
700.1933765796178760.3867531592357520.806623420382124
710.2006373991546440.4012747983092880.799362600845356
720.2098693536708910.4197387073417830.790130646329109
730.1818334992786240.3636669985572480.818166500721376
740.1722263756469860.3444527512939730.827773624353013
750.1463713041174120.2927426082348240.853628695882588
760.1884977823310740.3769955646621480.811502217668926
770.1663093197119340.3326186394238680.833690680288066
780.1393894943610570.2787789887221150.860610505638943
790.2348782643988750.4697565287977490.765121735601125
800.2176289634009690.4352579268019380.782371036599031
810.1960347432372420.3920694864744830.803965256762758
820.1748422121219070.3496844242438130.825157787878093
830.1476457341398640.2952914682797280.852354265860136
840.1307800993487050.2615601986974090.869219900651295
850.1231755566725340.2463511133450670.876824443327466
860.1053262985477180.2106525970954350.894673701452282
870.1056090789962820.2112181579925650.894390921003718
880.09089039951575030.1817807990315010.90910960048425
890.1223071459433840.2446142918867680.877692854056616
900.100554126810490.201108253620980.89944587318951
910.1083334998799530.2166669997599060.891666500120047
920.09199909277967440.1839981855593490.908000907220326
930.0744652627744620.1489305255489240.925534737225538
940.0675550661999520.1351101323999040.932444933800048
950.0958948339105580.1917896678211160.904105166089442
960.1038697432715330.2077394865430660.896130256728467
970.0972699394593450.194539878918690.902730060540655
980.07912688602806280.1582537720561260.920873113971937
990.06725960264008810.1345192052801760.932740397359912
1000.0621152911203750.124230582240750.937884708879625
1010.05687968369699330.1137593673939870.943120316303007
1020.05350740553767780.1070148110753560.946492594462322
1030.04319802492498690.08639604984997370.956801975075013
1040.0368119873853290.0736239747706580.963188012614671
1050.02973714723963130.05947429447926260.970262852760369
1060.06000664830598990.1200132966119800.93999335169401
1070.05731937056719350.1146387411343870.942680629432807
1080.1064554678116310.2129109356232620.89354453218837
1090.09691342888486620.1938268577697320.903086571115134
1100.4094883824114010.8189767648228030.590511617588599
1110.4381803690806290.8763607381612580.561819630919371
1120.4168645435139180.8337290870278370.583135456486082
1130.3944803849665230.7889607699330460.605519615033477
1140.3633325146449520.7266650292899040.636667485355048
1150.3315519491209120.6631038982418240.668448050879088
1160.3011195906979970.6022391813959930.698880409302003
1170.2632762917857730.5265525835715450.736723708214227
1180.2234477292860980.4468954585721960.776552270713902
1190.2005770485513430.4011540971026850.799422951448657
1200.2260116087697660.4520232175395320.773988391230234
1210.1907333000421440.3814666000842880.809266699957856
1220.1804009054136660.3608018108273330.819599094586334
1230.1775940131703320.3551880263406640.822405986829668
1240.1534186528370380.3068373056740770.846581347162962
1250.1289287569257190.2578575138514380.87107124307428
1260.1272340344801650.254468068960330.872765965519835
1270.1245280619042020.2490561238084030.875471938095798
1280.3905164665142420.7810329330284840.609483533485758
1290.3462451668355640.6924903336711280.653754833164436
1300.3023603826873140.6047207653746290.697639617312686
1310.2780057164731080.5560114329462170.721994283526892
1320.3180292185139930.6360584370279860.681970781486007
1330.4061989160838220.8123978321676440.593801083916178
1340.5693614662602250.861277067479550.430638533739775
1350.5600539038704380.8798921922591250.439946096129562
1360.5109547301223790.9780905397552420.489045269877621
1370.6282671780537290.7434656438925420.371732821946271
1380.5618639549420120.8762720901159750.438136045057988
1390.6000279694863160.7999440610273680.399972030513684
1400.5406492152301440.9187015695397130.459350784769856
1410.5225568012832050.954886397433590.477443198716795
1420.4707770559657810.9415541119315610.529222944034219
1430.5207704454640450.958459109071910.479229554535955
1440.5894584162662280.8210831674675440.410541583733772
1450.621032739351690.757934521296620.37896726064831
1460.7930372187845830.4139255624308340.206962781215417
1470.818356068896330.363287862207340.18164393110367
1480.7527837785440370.4944324429119250.247216221455963
1490.7659919409789360.4680161180421280.234008059021064
1500.6760569365494150.647886126901170.323943063450585
1510.8118522704316410.3762954591367180.188147729568359
1520.9959656149387960.008068770122408940.00403438506120447
1530.9870785691229820.02584286175403600.0129214308770180
1540.9859117227628960.02817655447420770.0140882772371038
1550.9801478807381940.03970423852361220.0198521192618061







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level10.00671140939597315OK
5% type I error level40.0268456375838926OK
10% type I error level70.0469798657718121OK

\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 & 1 & 0.00671140939597315 & OK \tabularnewline
5% type I error level & 4 & 0.0268456375838926 & OK \tabularnewline
10% type I error level & 7 & 0.0469798657718121 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99676&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]1[/C][C]0.00671140939597315[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]4[/C][C]0.0268456375838926[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]7[/C][C]0.0469798657718121[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99676&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99676&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 level10.00671140939597315OK
5% type I error level40.0268456375838926OK
10% type I error level70.0469798657718121OK



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