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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationTue, 13 Dec 2011 13:48:13 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Dec/13/t1323802131xiqpwwkrt8pdayb.htm/, Retrieved Thu, 02 May 2024 15:02:19 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=154628, Retrieved Thu, 02 May 2024 15:02:19 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact84

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2010-12-05 18:56:24] [b98453cac15ba1066b407e146608df68]
-   PD    [Multiple Regression] [WS 10 endogene va...] [2011-12-13 18:48:13] [cb05b01fd3da20a46af540a30bcf4c06] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
2	41	38	14	12
2	39	32	18	11
2	30	35	11	14
1	31	33	12	12
2	34	37	16	21
2	35	29	18	12
2	39	31	14	22
2	34	36	14	11
2	36	35	15	10
2	37	38	15	13
1	38	31	17	10
2	36	34	19	8
1	38	35	10	15
2	39	38	16	14
2	33	37	18	10
1	32	33	14	14
1	36	32	14	14
2	38	38	17	11
1	39	38	14	10
2	32	32	16	13
1	32	33	18	7
2	31	31	11	14
2	39	38	14	12
2	37	39	12	14
1	39	32	17	11
2	41	32	9	9
1	36	35	16	11
2	33	37	14	15
2	33	33	15	14
1	34	33	11	13
2	31	28	16	9
1	27	32	13	15
2	37	31	17	10
2	34	37	15	11
1	34	30	14	13
1	32	33	16	8
1	29	31	9	20
1	36	33	15	12
2	29	31	17	10
1	35	33	13	10
1	37	32	15	9
2	34	33	16	14
1	38	32	16	8
1	35	33	12	14
2	38	28	12	11
2	37	35	11	13
2	38	39	15	9
2	33	34	15	11
2	36	38	17	15
1	38	32	13	11
2	32	38	16	10
1	32	30	14	14
1	32	33	11	18
2	34	38	12	14
1	32	32	12	11
2	37	32	15	12
2	39	34	16	13
2	29	34	15	9
1	37	36	12	10
2	35	34	12	15
1	30	28	8	20
1	38	34	13	12
2	34	35	11	12
2	31	35	14	14
2	34	31	15	13
1	35	37	10	11
2	36	35	11	17
1	30	27	12	12
2	39	40	15	13
1	35	37	15	14
1	38	36	14	13
2	31	38	16	15
2	34	39	15	13
1	38	41	15	10
1	34	27	13	11
2	39	30	12	19
2	37	37	17	13
2	34	31	13	17
1	28	31	15	13
1	37	27	13	9
1	33	36	15	11
1	37	38	16	10
2	35	37	15	9
1	37	33	16	12
2	32	34	15	12
2	33	31	14	13
1	38	39	15	13
2	33	34	14	12
2	29	32	13	15
2	33	33	7	22
2	31	36	17	13
2	36	32	13	15
2	35	41	15	13
2	32	28	14	15
2	29	30	13	10
2	39	36	16	11
2	37	35	12	16
2	35	31	14	11
1	37	34	17	11
1	32	36	15	10
2	38	36	17	10
1	37	35	12	16
2	36	37	16	12
1	32	28	11	11
2	33	39	15	16
1	40	32	9	19
2	38	35	16	11
1	41	39	15	16
1	36	35	10	15
2	43	42	10	24
2	30	34	15	14
2	31	33	11	15
2	32	41	13	11
1	32	33	14	15
2	37	34	18	12
1	37	32	16	10
2	33	40	14	14
2	34	40	14	13
2	33	35	14	9
2	38	36	14	15
2	33	37	12	15
2	31	27	14	14
2	38	39	15	11
2	37	38	15	8
2	33	31	15	11
2	31	33	13	11
1	39	32	17	8
2	44	39	17	10
2	33	36	19	11
2	35	33	15	13
1	32	33	13	11
1	28	32	9	20
2	40	37	15	10
1	27	30	15	15
1	37	38	15	12
2	32	29	16	14
1	28	22	11	23
1	34	35	14	14
2	30	35	11	16
2	35	34	15	11
1	31	35	13	12
2	32	34	15	10
1	30	34	16	14
2	30	35	14	12
1	31	23	15	12
2	40	31	16	11
2	32	27	16	12
1	36	36	11	13
1	32	31	12	11
1	35	32	9	19
2	38	39	16	12
2	42	37	13	17
1	34	38	16	9
2	35	39	12	12
2	35	34	9	19
2	33	31	13	18
2	36	32	13	15
2	32	37	14	14
2	33	36	19	11
2	34	32	13	9
2	32	35	12	18
2	34	36	13	16

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 6 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154628&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]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154628&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154628&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net






Multiple Linear Regression - Estimated Regression Equation
Separate[t] = + 18.0115979112142 + 1.51594405760465Gender[t] + 0.362747856836213Connected[t] + 0.0899396874387374Happiness[t] -0.0156463100796588Depression[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Separate[t] =  +  18.0115979112142 +  1.51594405760465Gender[t] +  0.362747856836213Connected[t] +  0.0899396874387374Happiness[t] -0.0156463100796588Depression[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154628&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Separate[t] =  +  18.0115979112142 +  1.51594405760465Gender[t] +  0.362747856836213Connected[t] +  0.0899396874387374Happiness[t] -0.0156463100796588Depression[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154628&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154628&T=1

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


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

Multiple Linear Regression - Estimated Regression Equation
Separate[t] = + 18.0115979112142 + 1.51594405760465Gender[t] + 0.362747856836213Connected[t] + 0.0899396874387374Happiness[t] -0.0156463100796588Depression[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)18.01159791121423.7319154.82643e-062e-06
Gender1.515944057604650.5439112.78710.0059750.002987
Connected0.3627478568362130.0767244.72795e-063e-06
Happiness0.08993968743873740.134910.66670.5059640.252982
Depression-0.01564631007965880.097916-0.15980.873250.436625

\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) & 18.0115979112142 & 3.731915 & 4.8264 & 3e-06 & 2e-06 \tabularnewline
Gender & 1.51594405760465 & 0.543911 & 2.7871 & 0.005975 & 0.002987 \tabularnewline
Connected & 0.362747856836213 & 0.076724 & 4.7279 & 5e-06 & 3e-06 \tabularnewline
Happiness & 0.0899396874387374 & 0.13491 & 0.6667 & 0.505964 & 0.252982 \tabularnewline
Depression & -0.0156463100796588 & 0.097916 & -0.1598 & 0.87325 & 0.436625 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154628&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]18.0115979112142[/C][C]3.731915[/C][C]4.8264[/C][C]3e-06[/C][C]2e-06[/C][/ROW]
[ROW][C]Gender[/C][C]1.51594405760465[/C][C]0.543911[/C][C]2.7871[/C][C]0.005975[/C][C]0.002987[/C][/ROW]
[ROW][C]Connected[/C][C]0.362747856836213[/C][C]0.076724[/C][C]4.7279[/C][C]5e-06[/C][C]3e-06[/C][/ROW]
[ROW][C]Happiness[/C][C]0.0899396874387374[/C][C]0.13491[/C][C]0.6667[/C][C]0.505964[/C][C]0.252982[/C][/ROW]
[ROW][C]Depression[/C][C]-0.0156463100796588[/C][C]0.097916[/C][C]-0.1598[/C][C]0.87325[/C][C]0.436625[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154628&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154628&T=2

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


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)18.01159791121423.7319154.82643e-062e-06
Gender1.515944057604650.5439112.78710.0059750.002987
Connected0.3627478568362130.0767244.72795e-063e-06
Happiness0.08993968743873740.134910.66670.5059640.252982
Depression-0.01564631007965880.097916-0.15980.873250.436625






Multiple Linear Regression - Regression Statistics
Multiple R0.432210602258505
R-squared0.18680600470466
Adjusted R-squared0.166087686353186
F-TEST (value)9.01646559993963
F-TEST (DF numerator)4
F-TEST (DF denominator)157
p-value1.39685717204241e-06
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.24390065223918
Sum Squared Residuals1652.09395633085

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.432210602258505 \tabularnewline
R-squared & 0.18680600470466 \tabularnewline
Adjusted R-squared & 0.166087686353186 \tabularnewline
F-TEST (value) & 9.01646559993963 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 157 \tabularnewline
p-value & 1.39685717204241e-06 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 3.24390065223918 \tabularnewline
Sum Squared Residuals & 1652.09395633085 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154628&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.432210602258505[/C][/ROW]
[ROW][C]R-squared[/C][C]0.18680600470466[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.166087686353186[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]9.01646559993963[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]157[/C][/ROW]
[ROW][C]p-value[/C][C]1.39685717204241e-06[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]3.24390065223918[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1652.09395633085[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154628&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154628&T=3

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


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

Multiple Linear Regression - Regression Statistics
Multiple R0.432210602258505
R-squared0.18680600470466
Adjusted R-squared0.166087686353186
F-TEST (value)9.01646559993963
F-TEST (DF numerator)4
F-TEST (DF denominator)157
p-value1.39685717204241e-06
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.24390065223918
Sum Squared Residuals1652.09395633085






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
13836.98754805989471.01245194010533
23236.6374574060568-4.6374574060568
33532.69620995222072.30379004777925
43331.66424605905041.33575394094964
53734.48737564620172.51262435379832
62935.1708196686323-6.17081966863229
73136.1055892454256-5.1055892454256
83634.46395937212081.53604062787921
93535.2950410833116-0.295041083311611
103835.61085000990882.38914999009115
113134.6844721142569-3.68447211425686
123435.6860924532259-1.68609245322588
133533.97666275178741.0233372482126
143836.41063910094041.58936089905965
153734.47661657511922.52338342488081
163332.17558067060470.824419329395267
173233.6265720979496-1.62657209794958
183836.18476986178191.81523013821815
193834.77740090877693.22259909122314
203233.8870504131665-1.88705041316652
213332.64486359091730.355136409082706
223133.058957809057-2.05895780905696
233836.26205234622221.73794765377781
243935.3253846375133.67461536248702
253235.0315736610134-3.03157366101341
263236.5847885529399-4.58478855293991
273533.8533904030661.14660959693396
283734.03862627496592.96137372503406
293334.1442122724843-1.14421227248434
303332.64690363204060.353096367959395
312833.5868877966489-5.58688779664894
323230.25625538890531.74374461109473
333135.8376683150253-4.8376683150253
343734.55389905955952.44610094044047
353032.9167226943568-2.91672269435682
363332.44933790596020.550662094039839
373130.54376080242450.456239197575545
383333.7478044055476-0.747804405547639
393132.9356854603356-1.9356854603356
403333.2364697939933-0.236469793993269
413234.1574911926228-2.15749119262283
423334.5968998167593-1.59689981675929
433234.6258250469774-2.62582504697744
443333.0839448662359-0.0839448662358964
452835.7350714245882-7.73507142458816
463535.2510912601539-0.251091260153898
473936.03618310706372.96381689293631
483434.1911512027233-0.191151202723314
493835.39668890779082.60331109220921
503234.3090670544223-2.30906705442225
513833.93398934340554.0660106565945
523032.1755806706047-2.17558067060473
533331.84317636796991.15682363203011
543834.23714106700433.76285893299566
553232.0426402259662-0.0426402259662346
563235.6264963199885-3.62649631998851
573436.42628541102-2.42628541102001
583432.77145239553781.22854760446222
593633.8720258202272.12797417977304
603434.5842426137609-0.584242613760892
612830.8165689718219-2.81656897182193
623434.2934207443426-0.29342074434259
633534.17849399972490.821506000275082
643533.32877687137321.67122312862683
653134.5226064394002-3.52260643940021
663732.95100442159744.0489955784026
673534.82575816299910.17424183700095
682731.3014982022141-4.30149820221415
694036.33634572358133.66365427641873
703733.35376392855213.64623607144789
713634.36771412170171.63228587829833
723833.4930099361714.50699006382901
733934.52260643940024.47739356059979
744134.50459273937946.49540726062062
752732.8580756270774-5.8580756270774
763035.9726488007871-5.97264880078711
773735.79072938478631.20927061521368
783134.2801418242041-3.2801418242041
793130.83017524077830.169824759221722
802733.9776118177454-6.97761181774535
813632.67520714511873.32479285488134
823834.23178456998193.76821543001809
833734.94793953655512.05206046344494
843334.2004919498226-1.20049194982259
853433.81275703580740.187242964192558
863134.0699188951253-3.06991889512526
873934.45765380914044.54234619085959
883434.0855652052049-0.0855652052049178
893232.4976951601824-0.497695160182353
903333.2995242923372-0.299524292337168
913633.6142422437692.38575775623095
923235.0369301580358-3.03693015803584
934134.88535429623646.11464570376358
942833.6758784181297-5.67587841812973
953032.5759267105806-2.57592671058065
963636.4575780311793-0.457578031179328
973535.2940920173537-0.294092017353659
983134.826707228957-3.826707228957
993434.306077947341-0.306077947340985
1003632.32810559836213.67189440163789
1013636.2004161718615-0.200416171861511
1023533.7781479597491.221852040251
1033735.3536881505911.64631184940897
1042831.9527005385275-3.9527005385275
1053934.1129196523254.88708034767498
1063234.5496335377025-2.54963353770246
1073536.0948301743431-1.09483017434312
1083935.49895844941013.50104155058993
1093533.2511670381151.74883296188502
1104237.16552930285624.83447069714381
1113433.05596870197570.944031298024301
1123333.0433114989773-0.0433114989773037
1134133.64852397100967.35147602899037
1143332.15993436052510.840065639474926
1153435.8963153823047-1.89631538230472
1163234.2317845699819-2.23178456998191
1174034.05427258504565.9457274149544
1184034.43266675196155.56733324803853
1193534.13250413544390.867495864556106
1203635.8523655591470.147634440852995
1213733.85874690008853.14125309991153
1222733.3287768713732-6.32877687137317
1233936.00489048690442.99510951309562
1243835.68908156030712.31091843969286
1253134.1911512027233-3.19115120272331
1263333.2857761141734-0.285776114173414
1273235.0785125912524-3.07851259125239
1283938.37690331287880.623096687121213
1293634.55090995247831.44909004752174
1303334.8853542962364-1.88535429623642
1313332.1325799134050.867420086595028
1323230.18101294558821.81898705441176
1333736.74603251065650.253967489343538
1343030.4361347637827-0.436134763782747
1353834.11055226238393.88944773761615
1362933.8714041030869-4.87140410308686
1372230.3139533902267-8.31395339022674
1383532.90107638427722.09892361572284
1393532.66491733206142.33508266793857
1403434.9166469163957-0.91664691639574
1413531.75418574648913.2458142535109
1423433.84404965596680.15595034403324
1433431.62996433180982.37003566819022
1443532.99732163469632.00267836530372
1452331.9340651213666-8.93406512136658
1463136.8203258880155-5.82032588801554
1472733.9026967232462-6.90269672324618
1483633.3723993457132.62760065428697
1493132.0426402259662-1.04264022596623
1503232.7358942535214-0.735894253521391
1513936.07918386426352.92081613573654
1523737.1821246788938-0.182124678893801
1533833.15918730955294.84081269044707
1543934.63118154399994.36881845600013
1553434.251838311126-0.251838311126045
1563133.9017476572882-2.90174765728823
1573235.0369301580358-3.03693015803584
1583733.69152472820943.30847527179061
1593634.55090995247831.44909004752174
1603234.4053123048414-2.40531230484137
1613533.44906011301331.55093988698672
1623634.29578813428381.70421186571624

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 38 & 36.9875480598947 & 1.01245194010533 \tabularnewline
2 & 32 & 36.6374574060568 & -4.6374574060568 \tabularnewline
3 & 35 & 32.6962099522207 & 2.30379004777925 \tabularnewline
4 & 33 & 31.6642460590504 & 1.33575394094964 \tabularnewline
5 & 37 & 34.4873756462017 & 2.51262435379832 \tabularnewline
6 & 29 & 35.1708196686323 & -6.17081966863229 \tabularnewline
7 & 31 & 36.1055892454256 & -5.1055892454256 \tabularnewline
8 & 36 & 34.4639593721208 & 1.53604062787921 \tabularnewline
9 & 35 & 35.2950410833116 & -0.295041083311611 \tabularnewline
10 & 38 & 35.6108500099088 & 2.38914999009115 \tabularnewline
11 & 31 & 34.6844721142569 & -3.68447211425686 \tabularnewline
12 & 34 & 35.6860924532259 & -1.68609245322588 \tabularnewline
13 & 35 & 33.9766627517874 & 1.0233372482126 \tabularnewline
14 & 38 & 36.4106391009404 & 1.58936089905965 \tabularnewline
15 & 37 & 34.4766165751192 & 2.52338342488081 \tabularnewline
16 & 33 & 32.1755806706047 & 0.824419329395267 \tabularnewline
17 & 32 & 33.6265720979496 & -1.62657209794958 \tabularnewline
18 & 38 & 36.1847698617819 & 1.81523013821815 \tabularnewline
19 & 38 & 34.7774009087769 & 3.22259909122314 \tabularnewline
20 & 32 & 33.8870504131665 & -1.88705041316652 \tabularnewline
21 & 33 & 32.6448635909173 & 0.355136409082706 \tabularnewline
22 & 31 & 33.058957809057 & -2.05895780905696 \tabularnewline
23 & 38 & 36.2620523462222 & 1.73794765377781 \tabularnewline
24 & 39 & 35.325384637513 & 3.67461536248702 \tabularnewline
25 & 32 & 35.0315736610134 & -3.03157366101341 \tabularnewline
26 & 32 & 36.5847885529399 & -4.58478855293991 \tabularnewline
27 & 35 & 33.853390403066 & 1.14660959693396 \tabularnewline
28 & 37 & 34.0386262749659 & 2.96137372503406 \tabularnewline
29 & 33 & 34.1442122724843 & -1.14421227248434 \tabularnewline
30 & 33 & 32.6469036320406 & 0.353096367959395 \tabularnewline
31 & 28 & 33.5868877966489 & -5.58688779664894 \tabularnewline
32 & 32 & 30.2562553889053 & 1.74374461109473 \tabularnewline
33 & 31 & 35.8376683150253 & -4.8376683150253 \tabularnewline
34 & 37 & 34.5538990595595 & 2.44610094044047 \tabularnewline
35 & 30 & 32.9167226943568 & -2.91672269435682 \tabularnewline
36 & 33 & 32.4493379059602 & 0.550662094039839 \tabularnewline
37 & 31 & 30.5437608024245 & 0.456239197575545 \tabularnewline
38 & 33 & 33.7478044055476 & -0.747804405547639 \tabularnewline
39 & 31 & 32.9356854603356 & -1.9356854603356 \tabularnewline
40 & 33 & 33.2364697939933 & -0.236469793993269 \tabularnewline
41 & 32 & 34.1574911926228 & -2.15749119262283 \tabularnewline
42 & 33 & 34.5968998167593 & -1.59689981675929 \tabularnewline
43 & 32 & 34.6258250469774 & -2.62582504697744 \tabularnewline
44 & 33 & 33.0839448662359 & -0.0839448662358964 \tabularnewline
45 & 28 & 35.7350714245882 & -7.73507142458816 \tabularnewline
46 & 35 & 35.2510912601539 & -0.251091260153898 \tabularnewline
47 & 39 & 36.0361831070637 & 2.96381689293631 \tabularnewline
48 & 34 & 34.1911512027233 & -0.191151202723314 \tabularnewline
49 & 38 & 35.3966889077908 & 2.60331109220921 \tabularnewline
50 & 32 & 34.3090670544223 & -2.30906705442225 \tabularnewline
51 & 38 & 33.9339893434055 & 4.0660106565945 \tabularnewline
52 & 30 & 32.1755806706047 & -2.17558067060473 \tabularnewline
53 & 33 & 31.8431763679699 & 1.15682363203011 \tabularnewline
54 & 38 & 34.2371410670043 & 3.76285893299566 \tabularnewline
55 & 32 & 32.0426402259662 & -0.0426402259662346 \tabularnewline
56 & 32 & 35.6264963199885 & -3.62649631998851 \tabularnewline
57 & 34 & 36.42628541102 & -2.42628541102001 \tabularnewline
58 & 34 & 32.7714523955378 & 1.22854760446222 \tabularnewline
59 & 36 & 33.872025820227 & 2.12797417977304 \tabularnewline
60 & 34 & 34.5842426137609 & -0.584242613760892 \tabularnewline
61 & 28 & 30.8165689718219 & -2.81656897182193 \tabularnewline
62 & 34 & 34.2934207443426 & -0.29342074434259 \tabularnewline
63 & 35 & 34.1784939997249 & 0.821506000275082 \tabularnewline
64 & 35 & 33.3287768713732 & 1.67122312862683 \tabularnewline
65 & 31 & 34.5226064394002 & -3.52260643940021 \tabularnewline
66 & 37 & 32.9510044215974 & 4.0489955784026 \tabularnewline
67 & 35 & 34.8257581629991 & 0.17424183700095 \tabularnewline
68 & 27 & 31.3014982022141 & -4.30149820221415 \tabularnewline
69 & 40 & 36.3363457235813 & 3.66365427641873 \tabularnewline
70 & 37 & 33.3537639285521 & 3.64623607144789 \tabularnewline
71 & 36 & 34.3677141217017 & 1.63228587829833 \tabularnewline
72 & 38 & 33.493009936171 & 4.50699006382901 \tabularnewline
73 & 39 & 34.5226064394002 & 4.47739356059979 \tabularnewline
74 & 41 & 34.5045927393794 & 6.49540726062062 \tabularnewline
75 & 27 & 32.8580756270774 & -5.8580756270774 \tabularnewline
76 & 30 & 35.9726488007871 & -5.97264880078711 \tabularnewline
77 & 37 & 35.7907293847863 & 1.20927061521368 \tabularnewline
78 & 31 & 34.2801418242041 & -3.2801418242041 \tabularnewline
79 & 31 & 30.8301752407783 & 0.169824759221722 \tabularnewline
80 & 27 & 33.9776118177454 & -6.97761181774535 \tabularnewline
81 & 36 & 32.6752071451187 & 3.32479285488134 \tabularnewline
82 & 38 & 34.2317845699819 & 3.76821543001809 \tabularnewline
83 & 37 & 34.9479395365551 & 2.05206046344494 \tabularnewline
84 & 33 & 34.2004919498226 & -1.20049194982259 \tabularnewline
85 & 34 & 33.8127570358074 & 0.187242964192558 \tabularnewline
86 & 31 & 34.0699188951253 & -3.06991889512526 \tabularnewline
87 & 39 & 34.4576538091404 & 4.54234619085959 \tabularnewline
88 & 34 & 34.0855652052049 & -0.0855652052049178 \tabularnewline
89 & 32 & 32.4976951601824 & -0.497695160182353 \tabularnewline
90 & 33 & 33.2995242923372 & -0.299524292337168 \tabularnewline
91 & 36 & 33.614242243769 & 2.38575775623095 \tabularnewline
92 & 32 & 35.0369301580358 & -3.03693015803584 \tabularnewline
93 & 41 & 34.8853542962364 & 6.11464570376358 \tabularnewline
94 & 28 & 33.6758784181297 & -5.67587841812973 \tabularnewline
95 & 30 & 32.5759267105806 & -2.57592671058065 \tabularnewline
96 & 36 & 36.4575780311793 & -0.457578031179328 \tabularnewline
97 & 35 & 35.2940920173537 & -0.294092017353659 \tabularnewline
98 & 31 & 34.826707228957 & -3.826707228957 \tabularnewline
99 & 34 & 34.306077947341 & -0.306077947340985 \tabularnewline
100 & 36 & 32.3281055983621 & 3.67189440163789 \tabularnewline
101 & 36 & 36.2004161718615 & -0.200416171861511 \tabularnewline
102 & 35 & 33.778147959749 & 1.221852040251 \tabularnewline
103 & 37 & 35.353688150591 & 1.64631184940897 \tabularnewline
104 & 28 & 31.9527005385275 & -3.9527005385275 \tabularnewline
105 & 39 & 34.112919652325 & 4.88708034767498 \tabularnewline
106 & 32 & 34.5496335377025 & -2.54963353770246 \tabularnewline
107 & 35 & 36.0948301743431 & -1.09483017434312 \tabularnewline
108 & 39 & 35.4989584494101 & 3.50104155058993 \tabularnewline
109 & 35 & 33.251167038115 & 1.74883296188502 \tabularnewline
110 & 42 & 37.1655293028562 & 4.83447069714381 \tabularnewline
111 & 34 & 33.0559687019757 & 0.944031298024301 \tabularnewline
112 & 33 & 33.0433114989773 & -0.0433114989773037 \tabularnewline
113 & 41 & 33.6485239710096 & 7.35147602899037 \tabularnewline
114 & 33 & 32.1599343605251 & 0.840065639474926 \tabularnewline
115 & 34 & 35.8963153823047 & -1.89631538230472 \tabularnewline
116 & 32 & 34.2317845699819 & -2.23178456998191 \tabularnewline
117 & 40 & 34.0542725850456 & 5.9457274149544 \tabularnewline
118 & 40 & 34.4326667519615 & 5.56733324803853 \tabularnewline
119 & 35 & 34.1325041354439 & 0.867495864556106 \tabularnewline
120 & 36 & 35.852365559147 & 0.147634440852995 \tabularnewline
121 & 37 & 33.8587469000885 & 3.14125309991153 \tabularnewline
122 & 27 & 33.3287768713732 & -6.32877687137317 \tabularnewline
123 & 39 & 36.0048904869044 & 2.99510951309562 \tabularnewline
124 & 38 & 35.6890815603071 & 2.31091843969286 \tabularnewline
125 & 31 & 34.1911512027233 & -3.19115120272331 \tabularnewline
126 & 33 & 33.2857761141734 & -0.285776114173414 \tabularnewline
127 & 32 & 35.0785125912524 & -3.07851259125239 \tabularnewline
128 & 39 & 38.3769033128788 & 0.623096687121213 \tabularnewline
129 & 36 & 34.5509099524783 & 1.44909004752174 \tabularnewline
130 & 33 & 34.8853542962364 & -1.88535429623642 \tabularnewline
131 & 33 & 32.132579913405 & 0.867420086595028 \tabularnewline
132 & 32 & 30.1810129455882 & 1.81898705441176 \tabularnewline
133 & 37 & 36.7460325106565 & 0.253967489343538 \tabularnewline
134 & 30 & 30.4361347637827 & -0.436134763782747 \tabularnewline
135 & 38 & 34.1105522623839 & 3.88944773761615 \tabularnewline
136 & 29 & 33.8714041030869 & -4.87140410308686 \tabularnewline
137 & 22 & 30.3139533902267 & -8.31395339022674 \tabularnewline
138 & 35 & 32.9010763842772 & 2.09892361572284 \tabularnewline
139 & 35 & 32.6649173320614 & 2.33508266793857 \tabularnewline
140 & 34 & 34.9166469163957 & -0.91664691639574 \tabularnewline
141 & 35 & 31.7541857464891 & 3.2458142535109 \tabularnewline
142 & 34 & 33.8440496559668 & 0.15595034403324 \tabularnewline
143 & 34 & 31.6299643318098 & 2.37003566819022 \tabularnewline
144 & 35 & 32.9973216346963 & 2.00267836530372 \tabularnewline
145 & 23 & 31.9340651213666 & -8.93406512136658 \tabularnewline
146 & 31 & 36.8203258880155 & -5.82032588801554 \tabularnewline
147 & 27 & 33.9026967232462 & -6.90269672324618 \tabularnewline
148 & 36 & 33.372399345713 & 2.62760065428697 \tabularnewline
149 & 31 & 32.0426402259662 & -1.04264022596623 \tabularnewline
150 & 32 & 32.7358942535214 & -0.735894253521391 \tabularnewline
151 & 39 & 36.0791838642635 & 2.92081613573654 \tabularnewline
152 & 37 & 37.1821246788938 & -0.182124678893801 \tabularnewline
153 & 38 & 33.1591873095529 & 4.84081269044707 \tabularnewline
154 & 39 & 34.6311815439999 & 4.36881845600013 \tabularnewline
155 & 34 & 34.251838311126 & -0.251838311126045 \tabularnewline
156 & 31 & 33.9017476572882 & -2.90174765728823 \tabularnewline
157 & 32 & 35.0369301580358 & -3.03693015803584 \tabularnewline
158 & 37 & 33.6915247282094 & 3.30847527179061 \tabularnewline
159 & 36 & 34.5509099524783 & 1.44909004752174 \tabularnewline
160 & 32 & 34.4053123048414 & -2.40531230484137 \tabularnewline
161 & 35 & 33.4490601130133 & 1.55093988698672 \tabularnewline
162 & 36 & 34.2957881342838 & 1.70421186571624 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154628&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]38[/C][C]36.9875480598947[/C][C]1.01245194010533[/C][/ROW]
[ROW][C]2[/C][C]32[/C][C]36.6374574060568[/C][C]-4.6374574060568[/C][/ROW]
[ROW][C]3[/C][C]35[/C][C]32.6962099522207[/C][C]2.30379004777925[/C][/ROW]
[ROW][C]4[/C][C]33[/C][C]31.6642460590504[/C][C]1.33575394094964[/C][/ROW]
[ROW][C]5[/C][C]37[/C][C]34.4873756462017[/C][C]2.51262435379832[/C][/ROW]
[ROW][C]6[/C][C]29[/C][C]35.1708196686323[/C][C]-6.17081966863229[/C][/ROW]
[ROW][C]7[/C][C]31[/C][C]36.1055892454256[/C][C]-5.1055892454256[/C][/ROW]
[ROW][C]8[/C][C]36[/C][C]34.4639593721208[/C][C]1.53604062787921[/C][/ROW]
[ROW][C]9[/C][C]35[/C][C]35.2950410833116[/C][C]-0.295041083311611[/C][/ROW]
[ROW][C]10[/C][C]38[/C][C]35.6108500099088[/C][C]2.38914999009115[/C][/ROW]
[ROW][C]11[/C][C]31[/C][C]34.6844721142569[/C][C]-3.68447211425686[/C][/ROW]
[ROW][C]12[/C][C]34[/C][C]35.6860924532259[/C][C]-1.68609245322588[/C][/ROW]
[ROW][C]13[/C][C]35[/C][C]33.9766627517874[/C][C]1.0233372482126[/C][/ROW]
[ROW][C]14[/C][C]38[/C][C]36.4106391009404[/C][C]1.58936089905965[/C][/ROW]
[ROW][C]15[/C][C]37[/C][C]34.4766165751192[/C][C]2.52338342488081[/C][/ROW]
[ROW][C]16[/C][C]33[/C][C]32.1755806706047[/C][C]0.824419329395267[/C][/ROW]
[ROW][C]17[/C][C]32[/C][C]33.6265720979496[/C][C]-1.62657209794958[/C][/ROW]
[ROW][C]18[/C][C]38[/C][C]36.1847698617819[/C][C]1.81523013821815[/C][/ROW]
[ROW][C]19[/C][C]38[/C][C]34.7774009087769[/C][C]3.22259909122314[/C][/ROW]
[ROW][C]20[/C][C]32[/C][C]33.8870504131665[/C][C]-1.88705041316652[/C][/ROW]
[ROW][C]21[/C][C]33[/C][C]32.6448635909173[/C][C]0.355136409082706[/C][/ROW]
[ROW][C]22[/C][C]31[/C][C]33.058957809057[/C][C]-2.05895780905696[/C][/ROW]
[ROW][C]23[/C][C]38[/C][C]36.2620523462222[/C][C]1.73794765377781[/C][/ROW]
[ROW][C]24[/C][C]39[/C][C]35.325384637513[/C][C]3.67461536248702[/C][/ROW]
[ROW][C]25[/C][C]32[/C][C]35.0315736610134[/C][C]-3.03157366101341[/C][/ROW]
[ROW][C]26[/C][C]32[/C][C]36.5847885529399[/C][C]-4.58478855293991[/C][/ROW]
[ROW][C]27[/C][C]35[/C][C]33.853390403066[/C][C]1.14660959693396[/C][/ROW]
[ROW][C]28[/C][C]37[/C][C]34.0386262749659[/C][C]2.96137372503406[/C][/ROW]
[ROW][C]29[/C][C]33[/C][C]34.1442122724843[/C][C]-1.14421227248434[/C][/ROW]
[ROW][C]30[/C][C]33[/C][C]32.6469036320406[/C][C]0.353096367959395[/C][/ROW]
[ROW][C]31[/C][C]28[/C][C]33.5868877966489[/C][C]-5.58688779664894[/C][/ROW]
[ROW][C]32[/C][C]32[/C][C]30.2562553889053[/C][C]1.74374461109473[/C][/ROW]
[ROW][C]33[/C][C]31[/C][C]35.8376683150253[/C][C]-4.8376683150253[/C][/ROW]
[ROW][C]34[/C][C]37[/C][C]34.5538990595595[/C][C]2.44610094044047[/C][/ROW]
[ROW][C]35[/C][C]30[/C][C]32.9167226943568[/C][C]-2.91672269435682[/C][/ROW]
[ROW][C]36[/C][C]33[/C][C]32.4493379059602[/C][C]0.550662094039839[/C][/ROW]
[ROW][C]37[/C][C]31[/C][C]30.5437608024245[/C][C]0.456239197575545[/C][/ROW]
[ROW][C]38[/C][C]33[/C][C]33.7478044055476[/C][C]-0.747804405547639[/C][/ROW]
[ROW][C]39[/C][C]31[/C][C]32.9356854603356[/C][C]-1.9356854603356[/C][/ROW]
[ROW][C]40[/C][C]33[/C][C]33.2364697939933[/C][C]-0.236469793993269[/C][/ROW]
[ROW][C]41[/C][C]32[/C][C]34.1574911926228[/C][C]-2.15749119262283[/C][/ROW]
[ROW][C]42[/C][C]33[/C][C]34.5968998167593[/C][C]-1.59689981675929[/C][/ROW]
[ROW][C]43[/C][C]32[/C][C]34.6258250469774[/C][C]-2.62582504697744[/C][/ROW]
[ROW][C]44[/C][C]33[/C][C]33.0839448662359[/C][C]-0.0839448662358964[/C][/ROW]
[ROW][C]45[/C][C]28[/C][C]35.7350714245882[/C][C]-7.73507142458816[/C][/ROW]
[ROW][C]46[/C][C]35[/C][C]35.2510912601539[/C][C]-0.251091260153898[/C][/ROW]
[ROW][C]47[/C][C]39[/C][C]36.0361831070637[/C][C]2.96381689293631[/C][/ROW]
[ROW][C]48[/C][C]34[/C][C]34.1911512027233[/C][C]-0.191151202723314[/C][/ROW]
[ROW][C]49[/C][C]38[/C][C]35.3966889077908[/C][C]2.60331109220921[/C][/ROW]
[ROW][C]50[/C][C]32[/C][C]34.3090670544223[/C][C]-2.30906705442225[/C][/ROW]
[ROW][C]51[/C][C]38[/C][C]33.9339893434055[/C][C]4.0660106565945[/C][/ROW]
[ROW][C]52[/C][C]30[/C][C]32.1755806706047[/C][C]-2.17558067060473[/C][/ROW]
[ROW][C]53[/C][C]33[/C][C]31.8431763679699[/C][C]1.15682363203011[/C][/ROW]
[ROW][C]54[/C][C]38[/C][C]34.2371410670043[/C][C]3.76285893299566[/C][/ROW]
[ROW][C]55[/C][C]32[/C][C]32.0426402259662[/C][C]-0.0426402259662346[/C][/ROW]
[ROW][C]56[/C][C]32[/C][C]35.6264963199885[/C][C]-3.62649631998851[/C][/ROW]
[ROW][C]57[/C][C]34[/C][C]36.42628541102[/C][C]-2.42628541102001[/C][/ROW]
[ROW][C]58[/C][C]34[/C][C]32.7714523955378[/C][C]1.22854760446222[/C][/ROW]
[ROW][C]59[/C][C]36[/C][C]33.872025820227[/C][C]2.12797417977304[/C][/ROW]
[ROW][C]60[/C][C]34[/C][C]34.5842426137609[/C][C]-0.584242613760892[/C][/ROW]
[ROW][C]61[/C][C]28[/C][C]30.8165689718219[/C][C]-2.81656897182193[/C][/ROW]
[ROW][C]62[/C][C]34[/C][C]34.2934207443426[/C][C]-0.29342074434259[/C][/ROW]
[ROW][C]63[/C][C]35[/C][C]34.1784939997249[/C][C]0.821506000275082[/C][/ROW]
[ROW][C]64[/C][C]35[/C][C]33.3287768713732[/C][C]1.67122312862683[/C][/ROW]
[ROW][C]65[/C][C]31[/C][C]34.5226064394002[/C][C]-3.52260643940021[/C][/ROW]
[ROW][C]66[/C][C]37[/C][C]32.9510044215974[/C][C]4.0489955784026[/C][/ROW]
[ROW][C]67[/C][C]35[/C][C]34.8257581629991[/C][C]0.17424183700095[/C][/ROW]
[ROW][C]68[/C][C]27[/C][C]31.3014982022141[/C][C]-4.30149820221415[/C][/ROW]
[ROW][C]69[/C][C]40[/C][C]36.3363457235813[/C][C]3.66365427641873[/C][/ROW]
[ROW][C]70[/C][C]37[/C][C]33.3537639285521[/C][C]3.64623607144789[/C][/ROW]
[ROW][C]71[/C][C]36[/C][C]34.3677141217017[/C][C]1.63228587829833[/C][/ROW]
[ROW][C]72[/C][C]38[/C][C]33.493009936171[/C][C]4.50699006382901[/C][/ROW]
[ROW][C]73[/C][C]39[/C][C]34.5226064394002[/C][C]4.47739356059979[/C][/ROW]
[ROW][C]74[/C][C]41[/C][C]34.5045927393794[/C][C]6.49540726062062[/C][/ROW]
[ROW][C]75[/C][C]27[/C][C]32.8580756270774[/C][C]-5.8580756270774[/C][/ROW]
[ROW][C]76[/C][C]30[/C][C]35.9726488007871[/C][C]-5.97264880078711[/C][/ROW]
[ROW][C]77[/C][C]37[/C][C]35.7907293847863[/C][C]1.20927061521368[/C][/ROW]
[ROW][C]78[/C][C]31[/C][C]34.2801418242041[/C][C]-3.2801418242041[/C][/ROW]
[ROW][C]79[/C][C]31[/C][C]30.8301752407783[/C][C]0.169824759221722[/C][/ROW]
[ROW][C]80[/C][C]27[/C][C]33.9776118177454[/C][C]-6.97761181774535[/C][/ROW]
[ROW][C]81[/C][C]36[/C][C]32.6752071451187[/C][C]3.32479285488134[/C][/ROW]
[ROW][C]82[/C][C]38[/C][C]34.2317845699819[/C][C]3.76821543001809[/C][/ROW]
[ROW][C]83[/C][C]37[/C][C]34.9479395365551[/C][C]2.05206046344494[/C][/ROW]
[ROW][C]84[/C][C]33[/C][C]34.2004919498226[/C][C]-1.20049194982259[/C][/ROW]
[ROW][C]85[/C][C]34[/C][C]33.8127570358074[/C][C]0.187242964192558[/C][/ROW]
[ROW][C]86[/C][C]31[/C][C]34.0699188951253[/C][C]-3.06991889512526[/C][/ROW]
[ROW][C]87[/C][C]39[/C][C]34.4576538091404[/C][C]4.54234619085959[/C][/ROW]
[ROW][C]88[/C][C]34[/C][C]34.0855652052049[/C][C]-0.0855652052049178[/C][/ROW]
[ROW][C]89[/C][C]32[/C][C]32.4976951601824[/C][C]-0.497695160182353[/C][/ROW]
[ROW][C]90[/C][C]33[/C][C]33.2995242923372[/C][C]-0.299524292337168[/C][/ROW]
[ROW][C]91[/C][C]36[/C][C]33.614242243769[/C][C]2.38575775623095[/C][/ROW]
[ROW][C]92[/C][C]32[/C][C]35.0369301580358[/C][C]-3.03693015803584[/C][/ROW]
[ROW][C]93[/C][C]41[/C][C]34.8853542962364[/C][C]6.11464570376358[/C][/ROW]
[ROW][C]94[/C][C]28[/C][C]33.6758784181297[/C][C]-5.67587841812973[/C][/ROW]
[ROW][C]95[/C][C]30[/C][C]32.5759267105806[/C][C]-2.57592671058065[/C][/ROW]
[ROW][C]96[/C][C]36[/C][C]36.4575780311793[/C][C]-0.457578031179328[/C][/ROW]
[ROW][C]97[/C][C]35[/C][C]35.2940920173537[/C][C]-0.294092017353659[/C][/ROW]
[ROW][C]98[/C][C]31[/C][C]34.826707228957[/C][C]-3.826707228957[/C][/ROW]
[ROW][C]99[/C][C]34[/C][C]34.306077947341[/C][C]-0.306077947340985[/C][/ROW]
[ROW][C]100[/C][C]36[/C][C]32.3281055983621[/C][C]3.67189440163789[/C][/ROW]
[ROW][C]101[/C][C]36[/C][C]36.2004161718615[/C][C]-0.200416171861511[/C][/ROW]
[ROW][C]102[/C][C]35[/C][C]33.778147959749[/C][C]1.221852040251[/C][/ROW]
[ROW][C]103[/C][C]37[/C][C]35.353688150591[/C][C]1.64631184940897[/C][/ROW]
[ROW][C]104[/C][C]28[/C][C]31.9527005385275[/C][C]-3.9527005385275[/C][/ROW]
[ROW][C]105[/C][C]39[/C][C]34.112919652325[/C][C]4.88708034767498[/C][/ROW]
[ROW][C]106[/C][C]32[/C][C]34.5496335377025[/C][C]-2.54963353770246[/C][/ROW]
[ROW][C]107[/C][C]35[/C][C]36.0948301743431[/C][C]-1.09483017434312[/C][/ROW]
[ROW][C]108[/C][C]39[/C][C]35.4989584494101[/C][C]3.50104155058993[/C][/ROW]
[ROW][C]109[/C][C]35[/C][C]33.251167038115[/C][C]1.74883296188502[/C][/ROW]
[ROW][C]110[/C][C]42[/C][C]37.1655293028562[/C][C]4.83447069714381[/C][/ROW]
[ROW][C]111[/C][C]34[/C][C]33.0559687019757[/C][C]0.944031298024301[/C][/ROW]
[ROW][C]112[/C][C]33[/C][C]33.0433114989773[/C][C]-0.0433114989773037[/C][/ROW]
[ROW][C]113[/C][C]41[/C][C]33.6485239710096[/C][C]7.35147602899037[/C][/ROW]
[ROW][C]114[/C][C]33[/C][C]32.1599343605251[/C][C]0.840065639474926[/C][/ROW]
[ROW][C]115[/C][C]34[/C][C]35.8963153823047[/C][C]-1.89631538230472[/C][/ROW]
[ROW][C]116[/C][C]32[/C][C]34.2317845699819[/C][C]-2.23178456998191[/C][/ROW]
[ROW][C]117[/C][C]40[/C][C]34.0542725850456[/C][C]5.9457274149544[/C][/ROW]
[ROW][C]118[/C][C]40[/C][C]34.4326667519615[/C][C]5.56733324803853[/C][/ROW]
[ROW][C]119[/C][C]35[/C][C]34.1325041354439[/C][C]0.867495864556106[/C][/ROW]
[ROW][C]120[/C][C]36[/C][C]35.852365559147[/C][C]0.147634440852995[/C][/ROW]
[ROW][C]121[/C][C]37[/C][C]33.8587469000885[/C][C]3.14125309991153[/C][/ROW]
[ROW][C]122[/C][C]27[/C][C]33.3287768713732[/C][C]-6.32877687137317[/C][/ROW]
[ROW][C]123[/C][C]39[/C][C]36.0048904869044[/C][C]2.99510951309562[/C][/ROW]
[ROW][C]124[/C][C]38[/C][C]35.6890815603071[/C][C]2.31091843969286[/C][/ROW]
[ROW][C]125[/C][C]31[/C][C]34.1911512027233[/C][C]-3.19115120272331[/C][/ROW]
[ROW][C]126[/C][C]33[/C][C]33.2857761141734[/C][C]-0.285776114173414[/C][/ROW]
[ROW][C]127[/C][C]32[/C][C]35.0785125912524[/C][C]-3.07851259125239[/C][/ROW]
[ROW][C]128[/C][C]39[/C][C]38.3769033128788[/C][C]0.623096687121213[/C][/ROW]
[ROW][C]129[/C][C]36[/C][C]34.5509099524783[/C][C]1.44909004752174[/C][/ROW]
[ROW][C]130[/C][C]33[/C][C]34.8853542962364[/C][C]-1.88535429623642[/C][/ROW]
[ROW][C]131[/C][C]33[/C][C]32.132579913405[/C][C]0.867420086595028[/C][/ROW]
[ROW][C]132[/C][C]32[/C][C]30.1810129455882[/C][C]1.81898705441176[/C][/ROW]
[ROW][C]133[/C][C]37[/C][C]36.7460325106565[/C][C]0.253967489343538[/C][/ROW]
[ROW][C]134[/C][C]30[/C][C]30.4361347637827[/C][C]-0.436134763782747[/C][/ROW]
[ROW][C]135[/C][C]38[/C][C]34.1105522623839[/C][C]3.88944773761615[/C][/ROW]
[ROW][C]136[/C][C]29[/C][C]33.8714041030869[/C][C]-4.87140410308686[/C][/ROW]
[ROW][C]137[/C][C]22[/C][C]30.3139533902267[/C][C]-8.31395339022674[/C][/ROW]
[ROW][C]138[/C][C]35[/C][C]32.9010763842772[/C][C]2.09892361572284[/C][/ROW]
[ROW][C]139[/C][C]35[/C][C]32.6649173320614[/C][C]2.33508266793857[/C][/ROW]
[ROW][C]140[/C][C]34[/C][C]34.9166469163957[/C][C]-0.91664691639574[/C][/ROW]
[ROW][C]141[/C][C]35[/C][C]31.7541857464891[/C][C]3.2458142535109[/C][/ROW]
[ROW][C]142[/C][C]34[/C][C]33.8440496559668[/C][C]0.15595034403324[/C][/ROW]
[ROW][C]143[/C][C]34[/C][C]31.6299643318098[/C][C]2.37003566819022[/C][/ROW]
[ROW][C]144[/C][C]35[/C][C]32.9973216346963[/C][C]2.00267836530372[/C][/ROW]
[ROW][C]145[/C][C]23[/C][C]31.9340651213666[/C][C]-8.93406512136658[/C][/ROW]
[ROW][C]146[/C][C]31[/C][C]36.8203258880155[/C][C]-5.82032588801554[/C][/ROW]
[ROW][C]147[/C][C]27[/C][C]33.9026967232462[/C][C]-6.90269672324618[/C][/ROW]
[ROW][C]148[/C][C]36[/C][C]33.372399345713[/C][C]2.62760065428697[/C][/ROW]
[ROW][C]149[/C][C]31[/C][C]32.0426402259662[/C][C]-1.04264022596623[/C][/ROW]
[ROW][C]150[/C][C]32[/C][C]32.7358942535214[/C][C]-0.735894253521391[/C][/ROW]
[ROW][C]151[/C][C]39[/C][C]36.0791838642635[/C][C]2.92081613573654[/C][/ROW]
[ROW][C]152[/C][C]37[/C][C]37.1821246788938[/C][C]-0.182124678893801[/C][/ROW]
[ROW][C]153[/C][C]38[/C][C]33.1591873095529[/C][C]4.84081269044707[/C][/ROW]
[ROW][C]154[/C][C]39[/C][C]34.6311815439999[/C][C]4.36881845600013[/C][/ROW]
[ROW][C]155[/C][C]34[/C][C]34.251838311126[/C][C]-0.251838311126045[/C][/ROW]
[ROW][C]156[/C][C]31[/C][C]33.9017476572882[/C][C]-2.90174765728823[/C][/ROW]
[ROW][C]157[/C][C]32[/C][C]35.0369301580358[/C][C]-3.03693015803584[/C][/ROW]
[ROW][C]158[/C][C]37[/C][C]33.6915247282094[/C][C]3.30847527179061[/C][/ROW]
[ROW][C]159[/C][C]36[/C][C]34.5509099524783[/C][C]1.44909004752174[/C][/ROW]
[ROW][C]160[/C][C]32[/C][C]34.4053123048414[/C][C]-2.40531230484137[/C][/ROW]
[ROW][C]161[/C][C]35[/C][C]33.4490601130133[/C][C]1.55093988698672[/C][/ROW]
[ROW][C]162[/C][C]36[/C][C]34.2957881342838[/C][C]1.70421186571624[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154628&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154628&T=4

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


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
13836.98754805989471.01245194010533
23236.6374574060568-4.6374574060568
33532.69620995222072.30379004777925
43331.66424605905041.33575394094964
53734.48737564620172.51262435379832
62935.1708196686323-6.17081966863229
73136.1055892454256-5.1055892454256
83634.46395937212081.53604062787921
93535.2950410833116-0.295041083311611
103835.61085000990882.38914999009115
113134.6844721142569-3.68447211425686
123435.6860924532259-1.68609245322588
133533.97666275178741.0233372482126
143836.41063910094041.58936089905965
153734.47661657511922.52338342488081
163332.17558067060470.824419329395267
173233.6265720979496-1.62657209794958
183836.18476986178191.81523013821815
193834.77740090877693.22259909122314
203233.8870504131665-1.88705041316652
213332.64486359091730.355136409082706
223133.058957809057-2.05895780905696
233836.26205234622221.73794765377781
243935.3253846375133.67461536248702
253235.0315736610134-3.03157366101341
263236.5847885529399-4.58478855293991
273533.8533904030661.14660959693396
283734.03862627496592.96137372503406
293334.1442122724843-1.14421227248434
303332.64690363204060.353096367959395
312833.5868877966489-5.58688779664894
323230.25625538890531.74374461109473
333135.8376683150253-4.8376683150253
343734.55389905955952.44610094044047
353032.9167226943568-2.91672269435682
363332.44933790596020.550662094039839
373130.54376080242450.456239197575545
383333.7478044055476-0.747804405547639
393132.9356854603356-1.9356854603356
403333.2364697939933-0.236469793993269
413234.1574911926228-2.15749119262283
423334.5968998167593-1.59689981675929
433234.6258250469774-2.62582504697744
443333.0839448662359-0.0839448662358964
452835.7350714245882-7.73507142458816
463535.2510912601539-0.251091260153898
473936.03618310706372.96381689293631
483434.1911512027233-0.191151202723314
493835.39668890779082.60331109220921
503234.3090670544223-2.30906705442225
513833.93398934340554.0660106565945
523032.1755806706047-2.17558067060473
533331.84317636796991.15682363203011
543834.23714106700433.76285893299566
553232.0426402259662-0.0426402259662346
563235.6264963199885-3.62649631998851
573436.42628541102-2.42628541102001
583432.77145239553781.22854760446222
593633.8720258202272.12797417977304
603434.5842426137609-0.584242613760892
612830.8165689718219-2.81656897182193
623434.2934207443426-0.29342074434259
633534.17849399972490.821506000275082
643533.32877687137321.67122312862683
653134.5226064394002-3.52260643940021
663732.95100442159744.0489955784026
673534.82575816299910.17424183700095
682731.3014982022141-4.30149820221415
694036.33634572358133.66365427641873
703733.35376392855213.64623607144789
713634.36771412170171.63228587829833
723833.4930099361714.50699006382901
733934.52260643940024.47739356059979
744134.50459273937946.49540726062062
752732.8580756270774-5.8580756270774
763035.9726488007871-5.97264880078711
773735.79072938478631.20927061521368
783134.2801418242041-3.2801418242041
793130.83017524077830.169824759221722
802733.9776118177454-6.97761181774535
813632.67520714511873.32479285488134
823834.23178456998193.76821543001809
833734.94793953655512.05206046344494
843334.2004919498226-1.20049194982259
853433.81275703580740.187242964192558
863134.0699188951253-3.06991889512526
873934.45765380914044.54234619085959
883434.0855652052049-0.0855652052049178
893232.4976951601824-0.497695160182353
903333.2995242923372-0.299524292337168
913633.6142422437692.38575775623095
923235.0369301580358-3.03693015803584
934134.88535429623646.11464570376358
942833.6758784181297-5.67587841812973
953032.5759267105806-2.57592671058065
963636.4575780311793-0.457578031179328
973535.2940920173537-0.294092017353659
983134.826707228957-3.826707228957
993434.306077947341-0.306077947340985
1003632.32810559836213.67189440163789
1013636.2004161718615-0.200416171861511
1023533.7781479597491.221852040251
1033735.3536881505911.64631184940897
1042831.9527005385275-3.9527005385275
1053934.1129196523254.88708034767498
1063234.5496335377025-2.54963353770246
1073536.0948301743431-1.09483017434312
1083935.49895844941013.50104155058993
1093533.2511670381151.74883296188502
1104237.16552930285624.83447069714381
1113433.05596870197570.944031298024301
1123333.0433114989773-0.0433114989773037
1134133.64852397100967.35147602899037
1143332.15993436052510.840065639474926
1153435.8963153823047-1.89631538230472
1163234.2317845699819-2.23178456998191
1174034.05427258504565.9457274149544
1184034.43266675196155.56733324803853
1193534.13250413544390.867495864556106
1203635.8523655591470.147634440852995
1213733.85874690008853.14125309991153
1222733.3287768713732-6.32877687137317
1233936.00489048690442.99510951309562
1243835.68908156030712.31091843969286
1253134.1911512027233-3.19115120272331
1263333.2857761141734-0.285776114173414
1273235.0785125912524-3.07851259125239
1283938.37690331287880.623096687121213
1293634.55090995247831.44909004752174
1303334.8853542962364-1.88535429623642
1313332.1325799134050.867420086595028
1323230.18101294558821.81898705441176
1333736.74603251065650.253967489343538
1343030.4361347637827-0.436134763782747
1353834.11055226238393.88944773761615
1362933.8714041030869-4.87140410308686
1372230.3139533902267-8.31395339022674
1383532.90107638427722.09892361572284
1393532.66491733206142.33508266793857
1403434.9166469163957-0.91664691639574
1413531.75418574648913.2458142535109
1423433.84404965596680.15595034403324
1433431.62996433180982.37003566819022
1443532.99732163469632.00267836530372
1452331.9340651213666-8.93406512136658
1463136.8203258880155-5.82032588801554
1472733.9026967232462-6.90269672324618
1483633.3723993457132.62760065428697
1493132.0426402259662-1.04264022596623
1503232.7358942535214-0.735894253521391
1513936.07918386426352.92081613573654
1523737.1821246788938-0.182124678893801
1533833.15918730955294.84081269044707
1543934.63118154399994.36881845600013
1553434.251838311126-0.251838311126045
1563133.9017476572882-2.90174765728823
1573235.0369301580358-3.03693015803584
1583733.69152472820943.30847527179061
1593634.55090995247831.44909004752174
1603234.4053123048414-2.40531230484137
1613533.44906011301331.55093988698672
1623634.29578813428381.70421186571624






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.8106835395413290.3786329209173420.189316460458671
90.69037207700590.6192558459881990.3096279229941
100.6814127370434920.6371745259130160.318587262956508
110.5684667039104230.8630665921791550.431533296089577
120.4732248322225720.9464496644451440.526775167777428
130.3636548494770710.7273096989541420.636345150522929
140.3730681672950220.7461363345900440.626931832704978
150.395369113886430.790738227772860.60463088611357
160.3140217720012850.628043544002570.685978227998715
170.2396974258550210.4793948517100420.760302574144979
180.2367198394311580.4734396788623150.763280160568842
190.2775588186613440.5551176373226880.722441181338656
200.2404949180207780.4809898360415550.759505081979222
210.1898477761343230.3796955522686470.810152223865677
220.235380120054980.470760240109960.76461987994502
230.1920668506328560.3841337012657120.807933149367144
240.1789881583970850.3579763167941710.821011841602915
250.1515508010819410.3031016021638830.848449198918059
260.3058367683280440.6116735366560880.694163231671956
270.2612933257737790.5225866515475580.738706674226221
280.2421527911614790.4843055823229590.757847208838521
290.2037893744931050.407578748986210.796210625506895
300.1619029612280320.3238059224560640.838097038771968
310.2638620521060730.5277241042121460.736137947893927
320.2184585856197760.4369171712395530.781541414380224
330.2408202666620.4816405333240010.759179733337999
340.2324447656005270.4648895312010530.767555234399473
350.2309130670673040.4618261341346070.769086932932696
360.1912679311278760.3825358622557520.808732068872124
370.1614361519189360.3228723038378730.838563848081064
380.1290524758569410.2581049517138820.870947524143059
390.1088666476018510.2177332952037020.891133352398149
400.08455432211460620.1691086442292120.915445677885394
410.06998494756492940.1399698951298590.930015052435071
420.05545000453718250.1109000090743650.944549995462818
430.04630974280174090.09261948560348190.953690257198259
440.03441610689581260.06883221379162520.965583893104187
450.116642473866110.233284947732220.88335752613389
460.09276116737492030.1855223347498410.90723883262508
470.1092450529224220.2184901058448450.890754947077578
480.08677445502462450.1735489100492490.913225544975376
490.08664060117217340.1732812023443470.913359398827827
500.07446560748618060.1489312149723610.925534392513819
510.09165075771293160.1833015154258630.908349242287068
520.08207537927141640.1641507585428330.917924620728584
530.06560479601733450.1312095920346690.934395203982666
540.07107057795962860.1421411559192570.928929422040371
550.05547247504156520.110944950083130.944527524958435
560.05708366353825680.1141673270765140.942916336461743
570.04891347900632640.09782695801265290.951086520993674
580.03843640583017570.07687281166035140.961563594169824
590.03531234381855250.07062468763710490.964687656181448
600.02713057853713090.05426115707426180.972869421462869
610.03001413945196230.06002827890392450.969985860548038
620.02293624115909090.04587248231818190.977063758840909
630.01732600918242250.03465201836484490.982673990817578
640.01366512591104350.0273302518220870.986334874088956
650.01465786286127250.02931572572254490.985342137138728
660.01789062734603840.03578125469207690.982109372653962
670.01324682212083480.02649364424166950.986753177879165
680.01871257359449490.03742514718898990.981287426405505
690.022842131340370.04568426268074010.97715786865963
700.02655238824228670.05310477648457330.973447611757713
710.02228132310501650.0445626462100330.977718676894983
720.02894974748907380.05789949497814770.971050252510926
730.03715944863123260.07431889726246510.962840551368767
740.08178982550009920.1635796510001980.918210174499901
750.1295540426962440.2591080853924880.870445957303756
760.1943054087332660.3886108174665330.805694591266734
770.1690831111568460.3381662223136920.830916888843154
780.1691290048828940.3382580097657890.830870995117106
790.1420357868179740.2840715736359480.857964213182026
800.2590303155486440.5180606310972890.740969684451356
810.2586746855181150.5173493710362290.741325314481886
820.2707081174684180.5414162349368360.729291882531582
830.2471712192020260.4943424384040520.752828780797974
840.2172456263440960.4344912526881920.782754373655904
850.1850071304803020.3700142609606040.814992869519698
860.1817567182187760.3635134364375510.818243281781224
870.21044132327120.4208826465424010.7895586767288
880.1787850445260690.3575700890521390.821214955473931
890.1509229076971820.3018458153943650.849077092302818
900.1259474486069990.2518948972139980.874052551393001
910.1161751222372730.2323502444745450.883824877762727
920.1134366251618240.2268732503236490.886563374838176
930.1781838922371860.3563677844743730.821816107762814
940.2419980195498780.4839960390997560.758001980450122
950.2306143852650080.4612287705300150.769385614734992
960.1982522276619910.3965044553239830.801747772338009
970.1690865914277550.338173182855510.830913408572245
980.1853493216323580.3706986432647150.814650678367642
990.1558051604555410.3116103209110830.844194839544459
1000.1629698523891520.3259397047783030.837030147610848
1010.1361877994721030.2723755989442060.863812200527897
1020.1148578906000220.2297157812000430.885142109399978
1030.09811829573604950.1962365914720990.90188170426395
1040.1135906949142340.2271813898284680.886409305085766
1050.1499518194579260.2999036389158520.850048180542074
1060.1527686255042950.3055372510085890.847231374495705
1070.1299636551121470.2599273102242940.870036344887853
1080.1349491344887060.2698982689774120.865050865511294
1090.1143145586186130.2286291172372270.885685441381387
1100.1486021524118810.2972043048237620.851397847588119
1110.1257068138276250.2514136276552490.874293186172376
1120.1037411117999190.2074822235998390.896258888200081
1130.1898215131367570.3796430262735130.810178486863243
1140.1631026315875070.3262052631750150.836897368412493
1150.1384269285576280.2768538571152570.861573071442372
1160.1242460239115130.2484920478230270.875753976088487
1170.1980611184284890.3961222368569780.801938881571511
1180.274065720777270.548131441554540.72593427922273
1190.2335826550772870.4671653101545740.766417344922713
1200.1963158185661380.3926316371322770.803684181433862
1210.1948077532274640.3896155064549290.805192246772536
1220.2806064059133040.5612128118266080.719393594086696
1230.2693993768072650.5387987536145310.730600623192735
1240.2377935913625970.4755871827251930.762206408637404
1250.2333062434925780.4666124869851560.766693756507422
1260.196223134182630.392446268365260.80377686581737
1270.2049702422793350.4099404845586710.795029757720665
1280.167549698353270.335099396706540.83245030164673
1290.1549144182771430.3098288365542860.845085581722857
1300.1281268619556630.2562537239113250.871873138044337
1310.1009133299199120.2018266598398240.899086670080088
1320.08705900099751650.1741180019950330.912940999002483
1330.06676972786308260.1335394557261650.933230272136917
1340.05152772725079060.1030554545015810.948472272749209
1350.05370195145850810.1074039029170160.946298048541492
1360.05676547866099150.1135309573219830.943234521339009
1370.1414988188722610.2829976377445230.858501181127739
1380.1186422149716770.2372844299433540.881357785028323
1390.09562756142314090.1912551228462820.904372438576859
1400.07037664031233090.1407532806246620.929623359687669
1410.06304913632108340.1260982726421670.936950863678917
1420.04376290303365330.08752580606730660.956237096966347
1430.04317261670660150.0863452334132030.956827383293398
1440.03366105338029230.06732210676058450.966338946619708
1450.1917112367761770.3834224735523530.808288763223823
1460.3376457506927830.6752915013855660.662354249307217
1470.7217471541613540.5565056916772920.278252845838646
1480.6606643065156610.6786713869686780.339335693484339
1490.6380934482231950.7238131035536110.361906551776805
1500.5721744901357830.8556510197284350.427825509864217
1510.5143383211739170.9713233576521660.485661678826083
1520.4410151916977080.8820303833954160.558984808302292
1530.3148004920926130.6296009841852250.685199507907387
1540.5032757163148020.9934485673703970.496724283685198

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 0.810683539541329 & 0.378632920917342 & 0.189316460458671 \tabularnewline
9 & 0.6903720770059 & 0.619255845988199 & 0.3096279229941 \tabularnewline
10 & 0.681412737043492 & 0.637174525913016 & 0.318587262956508 \tabularnewline
11 & 0.568466703910423 & 0.863066592179155 & 0.431533296089577 \tabularnewline
12 & 0.473224832222572 & 0.946449664445144 & 0.526775167777428 \tabularnewline
13 & 0.363654849477071 & 0.727309698954142 & 0.636345150522929 \tabularnewline
14 & 0.373068167295022 & 0.746136334590044 & 0.626931832704978 \tabularnewline
15 & 0.39536911388643 & 0.79073822777286 & 0.60463088611357 \tabularnewline
16 & 0.314021772001285 & 0.62804354400257 & 0.685978227998715 \tabularnewline
17 & 0.239697425855021 & 0.479394851710042 & 0.760302574144979 \tabularnewline
18 & 0.236719839431158 & 0.473439678862315 & 0.763280160568842 \tabularnewline
19 & 0.277558818661344 & 0.555117637322688 & 0.722441181338656 \tabularnewline
20 & 0.240494918020778 & 0.480989836041555 & 0.759505081979222 \tabularnewline
21 & 0.189847776134323 & 0.379695552268647 & 0.810152223865677 \tabularnewline
22 & 0.23538012005498 & 0.47076024010996 & 0.76461987994502 \tabularnewline
23 & 0.192066850632856 & 0.384133701265712 & 0.807933149367144 \tabularnewline
24 & 0.178988158397085 & 0.357976316794171 & 0.821011841602915 \tabularnewline
25 & 0.151550801081941 & 0.303101602163883 & 0.848449198918059 \tabularnewline
26 & 0.305836768328044 & 0.611673536656088 & 0.694163231671956 \tabularnewline
27 & 0.261293325773779 & 0.522586651547558 & 0.738706674226221 \tabularnewline
28 & 0.242152791161479 & 0.484305582322959 & 0.757847208838521 \tabularnewline
29 & 0.203789374493105 & 0.40757874898621 & 0.796210625506895 \tabularnewline
30 & 0.161902961228032 & 0.323805922456064 & 0.838097038771968 \tabularnewline
31 & 0.263862052106073 & 0.527724104212146 & 0.736137947893927 \tabularnewline
32 & 0.218458585619776 & 0.436917171239553 & 0.781541414380224 \tabularnewline
33 & 0.240820266662 & 0.481640533324001 & 0.759179733337999 \tabularnewline
34 & 0.232444765600527 & 0.464889531201053 & 0.767555234399473 \tabularnewline
35 & 0.230913067067304 & 0.461826134134607 & 0.769086932932696 \tabularnewline
36 & 0.191267931127876 & 0.382535862255752 & 0.808732068872124 \tabularnewline
37 & 0.161436151918936 & 0.322872303837873 & 0.838563848081064 \tabularnewline
38 & 0.129052475856941 & 0.258104951713882 & 0.870947524143059 \tabularnewline
39 & 0.108866647601851 & 0.217733295203702 & 0.891133352398149 \tabularnewline
40 & 0.0845543221146062 & 0.169108644229212 & 0.915445677885394 \tabularnewline
41 & 0.0699849475649294 & 0.139969895129859 & 0.930015052435071 \tabularnewline
42 & 0.0554500045371825 & 0.110900009074365 & 0.944549995462818 \tabularnewline
43 & 0.0463097428017409 & 0.0926194856034819 & 0.953690257198259 \tabularnewline
44 & 0.0344161068958126 & 0.0688322137916252 & 0.965583893104187 \tabularnewline
45 & 0.11664247386611 & 0.23328494773222 & 0.88335752613389 \tabularnewline
46 & 0.0927611673749203 & 0.185522334749841 & 0.90723883262508 \tabularnewline
47 & 0.109245052922422 & 0.218490105844845 & 0.890754947077578 \tabularnewline
48 & 0.0867744550246245 & 0.173548910049249 & 0.913225544975376 \tabularnewline
49 & 0.0866406011721734 & 0.173281202344347 & 0.913359398827827 \tabularnewline
50 & 0.0744656074861806 & 0.148931214972361 & 0.925534392513819 \tabularnewline
51 & 0.0916507577129316 & 0.183301515425863 & 0.908349242287068 \tabularnewline
52 & 0.0820753792714164 & 0.164150758542833 & 0.917924620728584 \tabularnewline
53 & 0.0656047960173345 & 0.131209592034669 & 0.934395203982666 \tabularnewline
54 & 0.0710705779596286 & 0.142141155919257 & 0.928929422040371 \tabularnewline
55 & 0.0554724750415652 & 0.11094495008313 & 0.944527524958435 \tabularnewline
56 & 0.0570836635382568 & 0.114167327076514 & 0.942916336461743 \tabularnewline
57 & 0.0489134790063264 & 0.0978269580126529 & 0.951086520993674 \tabularnewline
58 & 0.0384364058301757 & 0.0768728116603514 & 0.961563594169824 \tabularnewline
59 & 0.0353123438185525 & 0.0706246876371049 & 0.964687656181448 \tabularnewline
60 & 0.0271305785371309 & 0.0542611570742618 & 0.972869421462869 \tabularnewline
61 & 0.0300141394519623 & 0.0600282789039245 & 0.969985860548038 \tabularnewline
62 & 0.0229362411590909 & 0.0458724823181819 & 0.977063758840909 \tabularnewline
63 & 0.0173260091824225 & 0.0346520183648449 & 0.982673990817578 \tabularnewline
64 & 0.0136651259110435 & 0.027330251822087 & 0.986334874088956 \tabularnewline
65 & 0.0146578628612725 & 0.0293157257225449 & 0.985342137138728 \tabularnewline
66 & 0.0178906273460384 & 0.0357812546920769 & 0.982109372653962 \tabularnewline
67 & 0.0132468221208348 & 0.0264936442416695 & 0.986753177879165 \tabularnewline
68 & 0.0187125735944949 & 0.0374251471889899 & 0.981287426405505 \tabularnewline
69 & 0.02284213134037 & 0.0456842626807401 & 0.97715786865963 \tabularnewline
70 & 0.0265523882422867 & 0.0531047764845733 & 0.973447611757713 \tabularnewline
71 & 0.0222813231050165 & 0.044562646210033 & 0.977718676894983 \tabularnewline
72 & 0.0289497474890738 & 0.0578994949781477 & 0.971050252510926 \tabularnewline
73 & 0.0371594486312326 & 0.0743188972624651 & 0.962840551368767 \tabularnewline
74 & 0.0817898255000992 & 0.163579651000198 & 0.918210174499901 \tabularnewline
75 & 0.129554042696244 & 0.259108085392488 & 0.870445957303756 \tabularnewline
76 & 0.194305408733266 & 0.388610817466533 & 0.805694591266734 \tabularnewline
77 & 0.169083111156846 & 0.338166222313692 & 0.830916888843154 \tabularnewline
78 & 0.169129004882894 & 0.338258009765789 & 0.830870995117106 \tabularnewline
79 & 0.142035786817974 & 0.284071573635948 & 0.857964213182026 \tabularnewline
80 & 0.259030315548644 & 0.518060631097289 & 0.740969684451356 \tabularnewline
81 & 0.258674685518115 & 0.517349371036229 & 0.741325314481886 \tabularnewline
82 & 0.270708117468418 & 0.541416234936836 & 0.729291882531582 \tabularnewline
83 & 0.247171219202026 & 0.494342438404052 & 0.752828780797974 \tabularnewline
84 & 0.217245626344096 & 0.434491252688192 & 0.782754373655904 \tabularnewline
85 & 0.185007130480302 & 0.370014260960604 & 0.814992869519698 \tabularnewline
86 & 0.181756718218776 & 0.363513436437551 & 0.818243281781224 \tabularnewline
87 & 0.2104413232712 & 0.420882646542401 & 0.7895586767288 \tabularnewline
88 & 0.178785044526069 & 0.357570089052139 & 0.821214955473931 \tabularnewline
89 & 0.150922907697182 & 0.301845815394365 & 0.849077092302818 \tabularnewline
90 & 0.125947448606999 & 0.251894897213998 & 0.874052551393001 \tabularnewline
91 & 0.116175122237273 & 0.232350244474545 & 0.883824877762727 \tabularnewline
92 & 0.113436625161824 & 0.226873250323649 & 0.886563374838176 \tabularnewline
93 & 0.178183892237186 & 0.356367784474373 & 0.821816107762814 \tabularnewline
94 & 0.241998019549878 & 0.483996039099756 & 0.758001980450122 \tabularnewline
95 & 0.230614385265008 & 0.461228770530015 & 0.769385614734992 \tabularnewline
96 & 0.198252227661991 & 0.396504455323983 & 0.801747772338009 \tabularnewline
97 & 0.169086591427755 & 0.33817318285551 & 0.830913408572245 \tabularnewline
98 & 0.185349321632358 & 0.370698643264715 & 0.814650678367642 \tabularnewline
99 & 0.155805160455541 & 0.311610320911083 & 0.844194839544459 \tabularnewline
100 & 0.162969852389152 & 0.325939704778303 & 0.837030147610848 \tabularnewline
101 & 0.136187799472103 & 0.272375598944206 & 0.863812200527897 \tabularnewline
102 & 0.114857890600022 & 0.229715781200043 & 0.885142109399978 \tabularnewline
103 & 0.0981182957360495 & 0.196236591472099 & 0.90188170426395 \tabularnewline
104 & 0.113590694914234 & 0.227181389828468 & 0.886409305085766 \tabularnewline
105 & 0.149951819457926 & 0.299903638915852 & 0.850048180542074 \tabularnewline
106 & 0.152768625504295 & 0.305537251008589 & 0.847231374495705 \tabularnewline
107 & 0.129963655112147 & 0.259927310224294 & 0.870036344887853 \tabularnewline
108 & 0.134949134488706 & 0.269898268977412 & 0.865050865511294 \tabularnewline
109 & 0.114314558618613 & 0.228629117237227 & 0.885685441381387 \tabularnewline
110 & 0.148602152411881 & 0.297204304823762 & 0.851397847588119 \tabularnewline
111 & 0.125706813827625 & 0.251413627655249 & 0.874293186172376 \tabularnewline
112 & 0.103741111799919 & 0.207482223599839 & 0.896258888200081 \tabularnewline
113 & 0.189821513136757 & 0.379643026273513 & 0.810178486863243 \tabularnewline
114 & 0.163102631587507 & 0.326205263175015 & 0.836897368412493 \tabularnewline
115 & 0.138426928557628 & 0.276853857115257 & 0.861573071442372 \tabularnewline
116 & 0.124246023911513 & 0.248492047823027 & 0.875753976088487 \tabularnewline
117 & 0.198061118428489 & 0.396122236856978 & 0.801938881571511 \tabularnewline
118 & 0.27406572077727 & 0.54813144155454 & 0.72593427922273 \tabularnewline
119 & 0.233582655077287 & 0.467165310154574 & 0.766417344922713 \tabularnewline
120 & 0.196315818566138 & 0.392631637132277 & 0.803684181433862 \tabularnewline
121 & 0.194807753227464 & 0.389615506454929 & 0.805192246772536 \tabularnewline
122 & 0.280606405913304 & 0.561212811826608 & 0.719393594086696 \tabularnewline
123 & 0.269399376807265 & 0.538798753614531 & 0.730600623192735 \tabularnewline
124 & 0.237793591362597 & 0.475587182725193 & 0.762206408637404 \tabularnewline
125 & 0.233306243492578 & 0.466612486985156 & 0.766693756507422 \tabularnewline
126 & 0.19622313418263 & 0.39244626836526 & 0.80377686581737 \tabularnewline
127 & 0.204970242279335 & 0.409940484558671 & 0.795029757720665 \tabularnewline
128 & 0.16754969835327 & 0.33509939670654 & 0.83245030164673 \tabularnewline
129 & 0.154914418277143 & 0.309828836554286 & 0.845085581722857 \tabularnewline
130 & 0.128126861955663 & 0.256253723911325 & 0.871873138044337 \tabularnewline
131 & 0.100913329919912 & 0.201826659839824 & 0.899086670080088 \tabularnewline
132 & 0.0870590009975165 & 0.174118001995033 & 0.912940999002483 \tabularnewline
133 & 0.0667697278630826 & 0.133539455726165 & 0.933230272136917 \tabularnewline
134 & 0.0515277272507906 & 0.103055454501581 & 0.948472272749209 \tabularnewline
135 & 0.0537019514585081 & 0.107403902917016 & 0.946298048541492 \tabularnewline
136 & 0.0567654786609915 & 0.113530957321983 & 0.943234521339009 \tabularnewline
137 & 0.141498818872261 & 0.282997637744523 & 0.858501181127739 \tabularnewline
138 & 0.118642214971677 & 0.237284429943354 & 0.881357785028323 \tabularnewline
139 & 0.0956275614231409 & 0.191255122846282 & 0.904372438576859 \tabularnewline
140 & 0.0703766403123309 & 0.140753280624662 & 0.929623359687669 \tabularnewline
141 & 0.0630491363210834 & 0.126098272642167 & 0.936950863678917 \tabularnewline
142 & 0.0437629030336533 & 0.0875258060673066 & 0.956237096966347 \tabularnewline
143 & 0.0431726167066015 & 0.086345233413203 & 0.956827383293398 \tabularnewline
144 & 0.0336610533802923 & 0.0673221067605845 & 0.966338946619708 \tabularnewline
145 & 0.191711236776177 & 0.383422473552353 & 0.808288763223823 \tabularnewline
146 & 0.337645750692783 & 0.675291501385566 & 0.662354249307217 \tabularnewline
147 & 0.721747154161354 & 0.556505691677292 & 0.278252845838646 \tabularnewline
148 & 0.660664306515661 & 0.678671386968678 & 0.339335693484339 \tabularnewline
149 & 0.638093448223195 & 0.723813103553611 & 0.361906551776805 \tabularnewline
150 & 0.572174490135783 & 0.855651019728435 & 0.427825509864217 \tabularnewline
151 & 0.514338321173917 & 0.971323357652166 & 0.485661678826083 \tabularnewline
152 & 0.441015191697708 & 0.882030383395416 & 0.558984808302292 \tabularnewline
153 & 0.314800492092613 & 0.629600984185225 & 0.685199507907387 \tabularnewline
154 & 0.503275716314802 & 0.993448567370397 & 0.496724283685198 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154628&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]8[/C][C]0.810683539541329[/C][C]0.378632920917342[/C][C]0.189316460458671[/C][/ROW]
[ROW][C]9[/C][C]0.6903720770059[/C][C]0.619255845988199[/C][C]0.3096279229941[/C][/ROW]
[ROW][C]10[/C][C]0.681412737043492[/C][C]0.637174525913016[/C][C]0.318587262956508[/C][/ROW]
[ROW][C]11[/C][C]0.568466703910423[/C][C]0.863066592179155[/C][C]0.431533296089577[/C][/ROW]
[ROW][C]12[/C][C]0.473224832222572[/C][C]0.946449664445144[/C][C]0.526775167777428[/C][/ROW]
[ROW][C]13[/C][C]0.363654849477071[/C][C]0.727309698954142[/C][C]0.636345150522929[/C][/ROW]
[ROW][C]14[/C][C]0.373068167295022[/C][C]0.746136334590044[/C][C]0.626931832704978[/C][/ROW]
[ROW][C]15[/C][C]0.39536911388643[/C][C]0.79073822777286[/C][C]0.60463088611357[/C][/ROW]
[ROW][C]16[/C][C]0.314021772001285[/C][C]0.62804354400257[/C][C]0.685978227998715[/C][/ROW]
[ROW][C]17[/C][C]0.239697425855021[/C][C]0.479394851710042[/C][C]0.760302574144979[/C][/ROW]
[ROW][C]18[/C][C]0.236719839431158[/C][C]0.473439678862315[/C][C]0.763280160568842[/C][/ROW]
[ROW][C]19[/C][C]0.277558818661344[/C][C]0.555117637322688[/C][C]0.722441181338656[/C][/ROW]
[ROW][C]20[/C][C]0.240494918020778[/C][C]0.480989836041555[/C][C]0.759505081979222[/C][/ROW]
[ROW][C]21[/C][C]0.189847776134323[/C][C]0.379695552268647[/C][C]0.810152223865677[/C][/ROW]
[ROW][C]22[/C][C]0.23538012005498[/C][C]0.47076024010996[/C][C]0.76461987994502[/C][/ROW]
[ROW][C]23[/C][C]0.192066850632856[/C][C]0.384133701265712[/C][C]0.807933149367144[/C][/ROW]
[ROW][C]24[/C][C]0.178988158397085[/C][C]0.357976316794171[/C][C]0.821011841602915[/C][/ROW]
[ROW][C]25[/C][C]0.151550801081941[/C][C]0.303101602163883[/C][C]0.848449198918059[/C][/ROW]
[ROW][C]26[/C][C]0.305836768328044[/C][C]0.611673536656088[/C][C]0.694163231671956[/C][/ROW]
[ROW][C]27[/C][C]0.261293325773779[/C][C]0.522586651547558[/C][C]0.738706674226221[/C][/ROW]
[ROW][C]28[/C][C]0.242152791161479[/C][C]0.484305582322959[/C][C]0.757847208838521[/C][/ROW]
[ROW][C]29[/C][C]0.203789374493105[/C][C]0.40757874898621[/C][C]0.796210625506895[/C][/ROW]
[ROW][C]30[/C][C]0.161902961228032[/C][C]0.323805922456064[/C][C]0.838097038771968[/C][/ROW]
[ROW][C]31[/C][C]0.263862052106073[/C][C]0.527724104212146[/C][C]0.736137947893927[/C][/ROW]
[ROW][C]32[/C][C]0.218458585619776[/C][C]0.436917171239553[/C][C]0.781541414380224[/C][/ROW]
[ROW][C]33[/C][C]0.240820266662[/C][C]0.481640533324001[/C][C]0.759179733337999[/C][/ROW]
[ROW][C]34[/C][C]0.232444765600527[/C][C]0.464889531201053[/C][C]0.767555234399473[/C][/ROW]
[ROW][C]35[/C][C]0.230913067067304[/C][C]0.461826134134607[/C][C]0.769086932932696[/C][/ROW]
[ROW][C]36[/C][C]0.191267931127876[/C][C]0.382535862255752[/C][C]0.808732068872124[/C][/ROW]
[ROW][C]37[/C][C]0.161436151918936[/C][C]0.322872303837873[/C][C]0.838563848081064[/C][/ROW]
[ROW][C]38[/C][C]0.129052475856941[/C][C]0.258104951713882[/C][C]0.870947524143059[/C][/ROW]
[ROW][C]39[/C][C]0.108866647601851[/C][C]0.217733295203702[/C][C]0.891133352398149[/C][/ROW]
[ROW][C]40[/C][C]0.0845543221146062[/C][C]0.169108644229212[/C][C]0.915445677885394[/C][/ROW]
[ROW][C]41[/C][C]0.0699849475649294[/C][C]0.139969895129859[/C][C]0.930015052435071[/C][/ROW]
[ROW][C]42[/C][C]0.0554500045371825[/C][C]0.110900009074365[/C][C]0.944549995462818[/C][/ROW]
[ROW][C]43[/C][C]0.0463097428017409[/C][C]0.0926194856034819[/C][C]0.953690257198259[/C][/ROW]
[ROW][C]44[/C][C]0.0344161068958126[/C][C]0.0688322137916252[/C][C]0.965583893104187[/C][/ROW]
[ROW][C]45[/C][C]0.11664247386611[/C][C]0.23328494773222[/C][C]0.88335752613389[/C][/ROW]
[ROW][C]46[/C][C]0.0927611673749203[/C][C]0.185522334749841[/C][C]0.90723883262508[/C][/ROW]
[ROW][C]47[/C][C]0.109245052922422[/C][C]0.218490105844845[/C][C]0.890754947077578[/C][/ROW]
[ROW][C]48[/C][C]0.0867744550246245[/C][C]0.173548910049249[/C][C]0.913225544975376[/C][/ROW]
[ROW][C]49[/C][C]0.0866406011721734[/C][C]0.173281202344347[/C][C]0.913359398827827[/C][/ROW]
[ROW][C]50[/C][C]0.0744656074861806[/C][C]0.148931214972361[/C][C]0.925534392513819[/C][/ROW]
[ROW][C]51[/C][C]0.0916507577129316[/C][C]0.183301515425863[/C][C]0.908349242287068[/C][/ROW]
[ROW][C]52[/C][C]0.0820753792714164[/C][C]0.164150758542833[/C][C]0.917924620728584[/C][/ROW]
[ROW][C]53[/C][C]0.0656047960173345[/C][C]0.131209592034669[/C][C]0.934395203982666[/C][/ROW]
[ROW][C]54[/C][C]0.0710705779596286[/C][C]0.142141155919257[/C][C]0.928929422040371[/C][/ROW]
[ROW][C]55[/C][C]0.0554724750415652[/C][C]0.11094495008313[/C][C]0.944527524958435[/C][/ROW]
[ROW][C]56[/C][C]0.0570836635382568[/C][C]0.114167327076514[/C][C]0.942916336461743[/C][/ROW]
[ROW][C]57[/C][C]0.0489134790063264[/C][C]0.0978269580126529[/C][C]0.951086520993674[/C][/ROW]
[ROW][C]58[/C][C]0.0384364058301757[/C][C]0.0768728116603514[/C][C]0.961563594169824[/C][/ROW]
[ROW][C]59[/C][C]0.0353123438185525[/C][C]0.0706246876371049[/C][C]0.964687656181448[/C][/ROW]
[ROW][C]60[/C][C]0.0271305785371309[/C][C]0.0542611570742618[/C][C]0.972869421462869[/C][/ROW]
[ROW][C]61[/C][C]0.0300141394519623[/C][C]0.0600282789039245[/C][C]0.969985860548038[/C][/ROW]
[ROW][C]62[/C][C]0.0229362411590909[/C][C]0.0458724823181819[/C][C]0.977063758840909[/C][/ROW]
[ROW][C]63[/C][C]0.0173260091824225[/C][C]0.0346520183648449[/C][C]0.982673990817578[/C][/ROW]
[ROW][C]64[/C][C]0.0136651259110435[/C][C]0.027330251822087[/C][C]0.986334874088956[/C][/ROW]
[ROW][C]65[/C][C]0.0146578628612725[/C][C]0.0293157257225449[/C][C]0.985342137138728[/C][/ROW]
[ROW][C]66[/C][C]0.0178906273460384[/C][C]0.0357812546920769[/C][C]0.982109372653962[/C][/ROW]
[ROW][C]67[/C][C]0.0132468221208348[/C][C]0.0264936442416695[/C][C]0.986753177879165[/C][/ROW]
[ROW][C]68[/C][C]0.0187125735944949[/C][C]0.0374251471889899[/C][C]0.981287426405505[/C][/ROW]
[ROW][C]69[/C][C]0.02284213134037[/C][C]0.0456842626807401[/C][C]0.97715786865963[/C][/ROW]
[ROW][C]70[/C][C]0.0265523882422867[/C][C]0.0531047764845733[/C][C]0.973447611757713[/C][/ROW]
[ROW][C]71[/C][C]0.0222813231050165[/C][C]0.044562646210033[/C][C]0.977718676894983[/C][/ROW]
[ROW][C]72[/C][C]0.0289497474890738[/C][C]0.0578994949781477[/C][C]0.971050252510926[/C][/ROW]
[ROW][C]73[/C][C]0.0371594486312326[/C][C]0.0743188972624651[/C][C]0.962840551368767[/C][/ROW]
[ROW][C]74[/C][C]0.0817898255000992[/C][C]0.163579651000198[/C][C]0.918210174499901[/C][/ROW]
[ROW][C]75[/C][C]0.129554042696244[/C][C]0.259108085392488[/C][C]0.870445957303756[/C][/ROW]
[ROW][C]76[/C][C]0.194305408733266[/C][C]0.388610817466533[/C][C]0.805694591266734[/C][/ROW]
[ROW][C]77[/C][C]0.169083111156846[/C][C]0.338166222313692[/C][C]0.830916888843154[/C][/ROW]
[ROW][C]78[/C][C]0.169129004882894[/C][C]0.338258009765789[/C][C]0.830870995117106[/C][/ROW]
[ROW][C]79[/C][C]0.142035786817974[/C][C]0.284071573635948[/C][C]0.857964213182026[/C][/ROW]
[ROW][C]80[/C][C]0.259030315548644[/C][C]0.518060631097289[/C][C]0.740969684451356[/C][/ROW]
[ROW][C]81[/C][C]0.258674685518115[/C][C]0.517349371036229[/C][C]0.741325314481886[/C][/ROW]
[ROW][C]82[/C][C]0.270708117468418[/C][C]0.541416234936836[/C][C]0.729291882531582[/C][/ROW]
[ROW][C]83[/C][C]0.247171219202026[/C][C]0.494342438404052[/C][C]0.752828780797974[/C][/ROW]
[ROW][C]84[/C][C]0.217245626344096[/C][C]0.434491252688192[/C][C]0.782754373655904[/C][/ROW]
[ROW][C]85[/C][C]0.185007130480302[/C][C]0.370014260960604[/C][C]0.814992869519698[/C][/ROW]
[ROW][C]86[/C][C]0.181756718218776[/C][C]0.363513436437551[/C][C]0.818243281781224[/C][/ROW]
[ROW][C]87[/C][C]0.2104413232712[/C][C]0.420882646542401[/C][C]0.7895586767288[/C][/ROW]
[ROW][C]88[/C][C]0.178785044526069[/C][C]0.357570089052139[/C][C]0.821214955473931[/C][/ROW]
[ROW][C]89[/C][C]0.150922907697182[/C][C]0.301845815394365[/C][C]0.849077092302818[/C][/ROW]
[ROW][C]90[/C][C]0.125947448606999[/C][C]0.251894897213998[/C][C]0.874052551393001[/C][/ROW]
[ROW][C]91[/C][C]0.116175122237273[/C][C]0.232350244474545[/C][C]0.883824877762727[/C][/ROW]
[ROW][C]92[/C][C]0.113436625161824[/C][C]0.226873250323649[/C][C]0.886563374838176[/C][/ROW]
[ROW][C]93[/C][C]0.178183892237186[/C][C]0.356367784474373[/C][C]0.821816107762814[/C][/ROW]
[ROW][C]94[/C][C]0.241998019549878[/C][C]0.483996039099756[/C][C]0.758001980450122[/C][/ROW]
[ROW][C]95[/C][C]0.230614385265008[/C][C]0.461228770530015[/C][C]0.769385614734992[/C][/ROW]
[ROW][C]96[/C][C]0.198252227661991[/C][C]0.396504455323983[/C][C]0.801747772338009[/C][/ROW]
[ROW][C]97[/C][C]0.169086591427755[/C][C]0.33817318285551[/C][C]0.830913408572245[/C][/ROW]
[ROW][C]98[/C][C]0.185349321632358[/C][C]0.370698643264715[/C][C]0.814650678367642[/C][/ROW]
[ROW][C]99[/C][C]0.155805160455541[/C][C]0.311610320911083[/C][C]0.844194839544459[/C][/ROW]
[ROW][C]100[/C][C]0.162969852389152[/C][C]0.325939704778303[/C][C]0.837030147610848[/C][/ROW]
[ROW][C]101[/C][C]0.136187799472103[/C][C]0.272375598944206[/C][C]0.863812200527897[/C][/ROW]
[ROW][C]102[/C][C]0.114857890600022[/C][C]0.229715781200043[/C][C]0.885142109399978[/C][/ROW]
[ROW][C]103[/C][C]0.0981182957360495[/C][C]0.196236591472099[/C][C]0.90188170426395[/C][/ROW]
[ROW][C]104[/C][C]0.113590694914234[/C][C]0.227181389828468[/C][C]0.886409305085766[/C][/ROW]
[ROW][C]105[/C][C]0.149951819457926[/C][C]0.299903638915852[/C][C]0.850048180542074[/C][/ROW]
[ROW][C]106[/C][C]0.152768625504295[/C][C]0.305537251008589[/C][C]0.847231374495705[/C][/ROW]
[ROW][C]107[/C][C]0.129963655112147[/C][C]0.259927310224294[/C][C]0.870036344887853[/C][/ROW]
[ROW][C]108[/C][C]0.134949134488706[/C][C]0.269898268977412[/C][C]0.865050865511294[/C][/ROW]
[ROW][C]109[/C][C]0.114314558618613[/C][C]0.228629117237227[/C][C]0.885685441381387[/C][/ROW]
[ROW][C]110[/C][C]0.148602152411881[/C][C]0.297204304823762[/C][C]0.851397847588119[/C][/ROW]
[ROW][C]111[/C][C]0.125706813827625[/C][C]0.251413627655249[/C][C]0.874293186172376[/C][/ROW]
[ROW][C]112[/C][C]0.103741111799919[/C][C]0.207482223599839[/C][C]0.896258888200081[/C][/ROW]
[ROW][C]113[/C][C]0.189821513136757[/C][C]0.379643026273513[/C][C]0.810178486863243[/C][/ROW]
[ROW][C]114[/C][C]0.163102631587507[/C][C]0.326205263175015[/C][C]0.836897368412493[/C][/ROW]
[ROW][C]115[/C][C]0.138426928557628[/C][C]0.276853857115257[/C][C]0.861573071442372[/C][/ROW]
[ROW][C]116[/C][C]0.124246023911513[/C][C]0.248492047823027[/C][C]0.875753976088487[/C][/ROW]
[ROW][C]117[/C][C]0.198061118428489[/C][C]0.396122236856978[/C][C]0.801938881571511[/C][/ROW]
[ROW][C]118[/C][C]0.27406572077727[/C][C]0.54813144155454[/C][C]0.72593427922273[/C][/ROW]
[ROW][C]119[/C][C]0.233582655077287[/C][C]0.467165310154574[/C][C]0.766417344922713[/C][/ROW]
[ROW][C]120[/C][C]0.196315818566138[/C][C]0.392631637132277[/C][C]0.803684181433862[/C][/ROW]
[ROW][C]121[/C][C]0.194807753227464[/C][C]0.389615506454929[/C][C]0.805192246772536[/C][/ROW]
[ROW][C]122[/C][C]0.280606405913304[/C][C]0.561212811826608[/C][C]0.719393594086696[/C][/ROW]
[ROW][C]123[/C][C]0.269399376807265[/C][C]0.538798753614531[/C][C]0.730600623192735[/C][/ROW]
[ROW][C]124[/C][C]0.237793591362597[/C][C]0.475587182725193[/C][C]0.762206408637404[/C][/ROW]
[ROW][C]125[/C][C]0.233306243492578[/C][C]0.466612486985156[/C][C]0.766693756507422[/C][/ROW]
[ROW][C]126[/C][C]0.19622313418263[/C][C]0.39244626836526[/C][C]0.80377686581737[/C][/ROW]
[ROW][C]127[/C][C]0.204970242279335[/C][C]0.409940484558671[/C][C]0.795029757720665[/C][/ROW]
[ROW][C]128[/C][C]0.16754969835327[/C][C]0.33509939670654[/C][C]0.83245030164673[/C][/ROW]
[ROW][C]129[/C][C]0.154914418277143[/C][C]0.309828836554286[/C][C]0.845085581722857[/C][/ROW]
[ROW][C]130[/C][C]0.128126861955663[/C][C]0.256253723911325[/C][C]0.871873138044337[/C][/ROW]
[ROW][C]131[/C][C]0.100913329919912[/C][C]0.201826659839824[/C][C]0.899086670080088[/C][/ROW]
[ROW][C]132[/C][C]0.0870590009975165[/C][C]0.174118001995033[/C][C]0.912940999002483[/C][/ROW]
[ROW][C]133[/C][C]0.0667697278630826[/C][C]0.133539455726165[/C][C]0.933230272136917[/C][/ROW]
[ROW][C]134[/C][C]0.0515277272507906[/C][C]0.103055454501581[/C][C]0.948472272749209[/C][/ROW]
[ROW][C]135[/C][C]0.0537019514585081[/C][C]0.107403902917016[/C][C]0.946298048541492[/C][/ROW]
[ROW][C]136[/C][C]0.0567654786609915[/C][C]0.113530957321983[/C][C]0.943234521339009[/C][/ROW]
[ROW][C]137[/C][C]0.141498818872261[/C][C]0.282997637744523[/C][C]0.858501181127739[/C][/ROW]
[ROW][C]138[/C][C]0.118642214971677[/C][C]0.237284429943354[/C][C]0.881357785028323[/C][/ROW]
[ROW][C]139[/C][C]0.0956275614231409[/C][C]0.191255122846282[/C][C]0.904372438576859[/C][/ROW]
[ROW][C]140[/C][C]0.0703766403123309[/C][C]0.140753280624662[/C][C]0.929623359687669[/C][/ROW]
[ROW][C]141[/C][C]0.0630491363210834[/C][C]0.126098272642167[/C][C]0.936950863678917[/C][/ROW]
[ROW][C]142[/C][C]0.0437629030336533[/C][C]0.0875258060673066[/C][C]0.956237096966347[/C][/ROW]
[ROW][C]143[/C][C]0.0431726167066015[/C][C]0.086345233413203[/C][C]0.956827383293398[/C][/ROW]
[ROW][C]144[/C][C]0.0336610533802923[/C][C]0.0673221067605845[/C][C]0.966338946619708[/C][/ROW]
[ROW][C]145[/C][C]0.191711236776177[/C][C]0.383422473552353[/C][C]0.808288763223823[/C][/ROW]
[ROW][C]146[/C][C]0.337645750692783[/C][C]0.675291501385566[/C][C]0.662354249307217[/C][/ROW]
[ROW][C]147[/C][C]0.721747154161354[/C][C]0.556505691677292[/C][C]0.278252845838646[/C][/ROW]
[ROW][C]148[/C][C]0.660664306515661[/C][C]0.678671386968678[/C][C]0.339335693484339[/C][/ROW]
[ROW][C]149[/C][C]0.638093448223195[/C][C]0.723813103553611[/C][C]0.361906551776805[/C][/ROW]
[ROW][C]150[/C][C]0.572174490135783[/C][C]0.855651019728435[/C][C]0.427825509864217[/C][/ROW]
[ROW][C]151[/C][C]0.514338321173917[/C][C]0.971323357652166[/C][C]0.485661678826083[/C][/ROW]
[ROW][C]152[/C][C]0.441015191697708[/C][C]0.882030383395416[/C][C]0.558984808302292[/C][/ROW]
[ROW][C]153[/C][C]0.314800492092613[/C][C]0.629600984185225[/C][C]0.685199507907387[/C][/ROW]
[ROW][C]154[/C][C]0.503275716314802[/C][C]0.993448567370397[/C][C]0.496724283685198[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154628&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154628&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.8106835395413290.3786329209173420.189316460458671
90.69037207700590.6192558459881990.3096279229941
100.6814127370434920.6371745259130160.318587262956508
110.5684667039104230.8630665921791550.431533296089577
120.4732248322225720.9464496644451440.526775167777428
130.3636548494770710.7273096989541420.636345150522929
140.3730681672950220.7461363345900440.626931832704978
150.395369113886430.790738227772860.60463088611357
160.3140217720012850.628043544002570.685978227998715
170.2396974258550210.4793948517100420.760302574144979
180.2367198394311580.4734396788623150.763280160568842
190.2775588186613440.5551176373226880.722441181338656
200.2404949180207780.4809898360415550.759505081979222
210.1898477761343230.3796955522686470.810152223865677
220.235380120054980.470760240109960.76461987994502
230.1920668506328560.3841337012657120.807933149367144
240.1789881583970850.3579763167941710.821011841602915
250.1515508010819410.3031016021638830.848449198918059
260.3058367683280440.6116735366560880.694163231671956
270.2612933257737790.5225866515475580.738706674226221
280.2421527911614790.4843055823229590.757847208838521
290.2037893744931050.407578748986210.796210625506895
300.1619029612280320.3238059224560640.838097038771968
310.2638620521060730.5277241042121460.736137947893927
320.2184585856197760.4369171712395530.781541414380224
330.2408202666620.4816405333240010.759179733337999
340.2324447656005270.4648895312010530.767555234399473
350.2309130670673040.4618261341346070.769086932932696
360.1912679311278760.3825358622557520.808732068872124
370.1614361519189360.3228723038378730.838563848081064
380.1290524758569410.2581049517138820.870947524143059
390.1088666476018510.2177332952037020.891133352398149
400.08455432211460620.1691086442292120.915445677885394
410.06998494756492940.1399698951298590.930015052435071
420.05545000453718250.1109000090743650.944549995462818
430.04630974280174090.09261948560348190.953690257198259
440.03441610689581260.06883221379162520.965583893104187
450.116642473866110.233284947732220.88335752613389
460.09276116737492030.1855223347498410.90723883262508
470.1092450529224220.2184901058448450.890754947077578
480.08677445502462450.1735489100492490.913225544975376
490.08664060117217340.1732812023443470.913359398827827
500.07446560748618060.1489312149723610.925534392513819
510.09165075771293160.1833015154258630.908349242287068
520.08207537927141640.1641507585428330.917924620728584
530.06560479601733450.1312095920346690.934395203982666
540.07107057795962860.1421411559192570.928929422040371
550.05547247504156520.110944950083130.944527524958435
560.05708366353825680.1141673270765140.942916336461743
570.04891347900632640.09782695801265290.951086520993674
580.03843640583017570.07687281166035140.961563594169824
590.03531234381855250.07062468763710490.964687656181448
600.02713057853713090.05426115707426180.972869421462869
610.03001413945196230.06002827890392450.969985860548038
620.02293624115909090.04587248231818190.977063758840909
630.01732600918242250.03465201836484490.982673990817578
640.01366512591104350.0273302518220870.986334874088956
650.01465786286127250.02931572572254490.985342137138728
660.01789062734603840.03578125469207690.982109372653962
670.01324682212083480.02649364424166950.986753177879165
680.01871257359449490.03742514718898990.981287426405505
690.022842131340370.04568426268074010.97715786865963
700.02655238824228670.05310477648457330.973447611757713
710.02228132310501650.0445626462100330.977718676894983
720.02894974748907380.05789949497814770.971050252510926
730.03715944863123260.07431889726246510.962840551368767
740.08178982550009920.1635796510001980.918210174499901
750.1295540426962440.2591080853924880.870445957303756
760.1943054087332660.3886108174665330.805694591266734
770.1690831111568460.3381662223136920.830916888843154
780.1691290048828940.3382580097657890.830870995117106
790.1420357868179740.2840715736359480.857964213182026
800.2590303155486440.5180606310972890.740969684451356
810.2586746855181150.5173493710362290.741325314481886
820.2707081174684180.5414162349368360.729291882531582
830.2471712192020260.4943424384040520.752828780797974
840.2172456263440960.4344912526881920.782754373655904
850.1850071304803020.3700142609606040.814992869519698
860.1817567182187760.3635134364375510.818243281781224
870.21044132327120.4208826465424010.7895586767288
880.1787850445260690.3575700890521390.821214955473931
890.1509229076971820.3018458153943650.849077092302818
900.1259474486069990.2518948972139980.874052551393001
910.1161751222372730.2323502444745450.883824877762727
920.1134366251618240.2268732503236490.886563374838176
930.1781838922371860.3563677844743730.821816107762814
940.2419980195498780.4839960390997560.758001980450122
950.2306143852650080.4612287705300150.769385614734992
960.1982522276619910.3965044553239830.801747772338009
970.1690865914277550.338173182855510.830913408572245
980.1853493216323580.3706986432647150.814650678367642
990.1558051604555410.3116103209110830.844194839544459
1000.1629698523891520.3259397047783030.837030147610848
1010.1361877994721030.2723755989442060.863812200527897
1020.1148578906000220.2297157812000430.885142109399978
1030.09811829573604950.1962365914720990.90188170426395
1040.1135906949142340.2271813898284680.886409305085766
1050.1499518194579260.2999036389158520.850048180542074
1060.1527686255042950.3055372510085890.847231374495705
1070.1299636551121470.2599273102242940.870036344887853
1080.1349491344887060.2698982689774120.865050865511294
1090.1143145586186130.2286291172372270.885685441381387
1100.1486021524118810.2972043048237620.851397847588119
1110.1257068138276250.2514136276552490.874293186172376
1120.1037411117999190.2074822235998390.896258888200081
1130.1898215131367570.3796430262735130.810178486863243
1140.1631026315875070.3262052631750150.836897368412493
1150.1384269285576280.2768538571152570.861573071442372
1160.1242460239115130.2484920478230270.875753976088487
1170.1980611184284890.3961222368569780.801938881571511
1180.274065720777270.548131441554540.72593427922273
1190.2335826550772870.4671653101545740.766417344922713
1200.1963158185661380.3926316371322770.803684181433862
1210.1948077532274640.3896155064549290.805192246772536
1220.2806064059133040.5612128118266080.719393594086696
1230.2693993768072650.5387987536145310.730600623192735
1240.2377935913625970.4755871827251930.762206408637404
1250.2333062434925780.4666124869851560.766693756507422
1260.196223134182630.392446268365260.80377686581737
1270.2049702422793350.4099404845586710.795029757720665
1280.167549698353270.335099396706540.83245030164673
1290.1549144182771430.3098288365542860.845085581722857
1300.1281268619556630.2562537239113250.871873138044337
1310.1009133299199120.2018266598398240.899086670080088
1320.08705900099751650.1741180019950330.912940999002483
1330.06676972786308260.1335394557261650.933230272136917
1340.05152772725079060.1030554545015810.948472272749209
1350.05370195145850810.1074039029170160.946298048541492
1360.05676547866099150.1135309573219830.943234521339009
1370.1414988188722610.2829976377445230.858501181127739
1380.1186422149716770.2372844299433540.881357785028323
1390.09562756142314090.1912551228462820.904372438576859
1400.07037664031233090.1407532806246620.929623359687669
1410.06304913632108340.1260982726421670.936950863678917
1420.04376290303365330.08752580606730660.956237096966347
1430.04317261670660150.0863452334132030.956827383293398
1440.03366105338029230.06732210676058450.966338946619708
1450.1917112367761770.3834224735523530.808288763223823
1460.3376457506927830.6752915013855660.662354249307217
1470.7217471541613540.5565056916772920.278252845838646
1480.6606643065156610.6786713869686780.339335693484339
1490.6380934482231950.7238131035536110.361906551776805
1500.5721744901357830.8556510197284350.427825509864217
1510.5143383211739170.9713233576521660.485661678826083
1520.4410151916977080.8820303833954160.558984808302292
1530.3148004920926130.6296009841852250.685199507907387
1540.5032757163148020.9934485673703970.496724283685198






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level90.0612244897959184NOK
10% type I error level220.149659863945578NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 9 & 0.0612244897959184 & NOK \tabularnewline
10% type I error level & 22 & 0.149659863945578 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154628&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]9[/C][C]0.0612244897959184[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]22[/C][C]0.149659863945578[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154628&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154628&T=6

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


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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level90.0612244897959184NOK
10% type I error level220.149659863945578NOK


Figures (Output of Computation)

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

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