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Description of Statistical Computation

Author's title

Author*Unverified author*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationTue, 13 Dec 2011 11:22:12 -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/t1323793433cw4uei4pa1yd213.htm/, Retrieved Thu, 02 May 2024 22:21:39 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=154490, Retrieved Thu, 02 May 2024 22:21:39 +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)
-     [Kendall tau Correlation Matrix] [] [2010-12-05 18:04:16] [b98453cac15ba1066b407e146608df68]
- RMPD  [Multiple Regression] [Multiple Regression] [2011-12-12 20:14:09] [f2faabc3a2466a29562900bc59f67898]
- R P       [Multiple Regression] [Multiple Regression] [2011-12-13 16:22:12] [d41d8cd98f00b204e9800998ecf8427e] [Current]
-   P         [Multiple Regression] [Multiple Regression] [2011-12-13 16:24:33] [74be16979710d4c4e7c6647856088456]
-   P           [Multiple Regression] [Multiple Regression] [2011-12-13 16:26:06] [74be16979710d4c4e7c6647856088456]
-   P             [Multiple Regression] [Multiple Regression] [2011-12-13 16:28:51] [74be16979710d4c4e7c6647856088456]
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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 time7 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 & 7 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154490&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]7 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154490&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154490&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 time7 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net






Multiple Linear Regression - Estimated Regression Equation
Connected[t] = + 22.7909294318289 -0.21454833129549Gender[t] + 0.343580744918266Separate[t] + 0.0891602788754077Happiness[t] -0.0607029551704905Depression[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Connected[t] =  +  22.7909294318289 -0.21454833129549Gender[t] +  0.343580744918266Separate[t] +  0.0891602788754077Happiness[t] -0.0607029551704905Depression[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154490&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Connected[t] =  +  22.7909294318289 -0.21454833129549Gender[t] +  0.343580744918266Separate[t] +  0.0891602788754077Happiness[t] -0.0607029551704905Depression[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154490&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154490&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
Connected[t] = + 22.7909294318289 -0.21454833129549Gender[t] + 0.343580744918266Separate[t] + 0.0891602788754077Happiness[t] -0.0607029551704905Depression[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)22.79092943182893.4409336.623500
Gender-0.214548331295490.542014-0.39580.6927640.346382
Separate0.3435807449182660.072674.72795e-063e-06
Happiness0.08916027887540770.131290.67910.4980690.249034
Depression-0.06070295517049050.095179-0.63780.5245480.262274

\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) & 22.7909294318289 & 3.440933 & 6.6235 & 0 & 0 \tabularnewline
Gender & -0.21454833129549 & 0.542014 & -0.3958 & 0.692764 & 0.346382 \tabularnewline
Separate & 0.343580744918266 & 0.07267 & 4.7279 & 5e-06 & 3e-06 \tabularnewline
Happiness & 0.0891602788754077 & 0.13129 & 0.6791 & 0.498069 & 0.249034 \tabularnewline
Depression & -0.0607029551704905 & 0.095179 & -0.6378 & 0.524548 & 0.262274 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154490&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]22.7909294318289[/C][C]3.440933[/C][C]6.6235[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Gender[/C][C]-0.21454833129549[/C][C]0.542014[/C][C]-0.3958[/C][C]0.692764[/C][C]0.346382[/C][/ROW]
[ROW][C]Separate[/C][C]0.343580744918266[/C][C]0.07267[/C][C]4.7279[/C][C]5e-06[/C][C]3e-06[/C][/ROW]
[ROW][C]Happiness[/C][C]0.0891602788754077[/C][C]0.13129[/C][C]0.6791[/C][C]0.498069[/C][C]0.249034[/C][/ROW]
[ROW][C]Depression[/C][C]-0.0607029551704905[/C][C]0.095179[/C][C]-0.6378[/C][C]0.524548[/C][C]0.262274[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154490&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154490&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)22.79092943182893.4409336.623500
Gender-0.214548331295490.542014-0.39580.6927640.346382
Separate0.3435807449182660.072674.72795e-063e-06
Happiness0.08916027887540770.131290.67910.4980690.249034
Depression-0.06070295517049050.095179-0.63780.5245480.262274






Multiple Linear Regression - Regression Statistics
Multiple R0.383141760036383
R-squared0.146797608283777
Adjusted R-squared0.125059967730498
F-TEST (value)6.75315280533654
F-TEST (DF numerator)4
F-TEST (DF denominator)157
p-value4.84525515964673e-05
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.15703593935619
Sum Squared Residuals1564.7995198147

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.383141760036383 \tabularnewline
R-squared & 0.146797608283777 \tabularnewline
Adjusted R-squared & 0.125059967730498 \tabularnewline
F-TEST (value) & 6.75315280533654 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 157 \tabularnewline
p-value & 4.84525515964673e-05 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 3.15703593935619 \tabularnewline
Sum Squared Residuals & 1564.7995198147 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154490&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.383141760036383[/C][/ROW]
[ROW][C]R-squared[/C][C]0.146797608283777[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.125059967730498[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]6.75315280533654[/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]4.84525515964673e-05[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]3.15703593935619[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1564.7995198147[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154490&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154490&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 R0.383141760036383
R-squared0.146797608283777
Adjusted R-squared0.125059967730498
F-TEST (value)6.75315280533654
F-TEST (DF numerator)4
F-TEST (DF denominator)157
p-value4.84525515964673e-05
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.15703593935619
Sum Squared Residuals1564.7995198147






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
14135.9377095183425.06229048165801
23934.29356911950444.70643088049557
33034.5180805366199-4.5180805366199
43134.2560335672952-3.25603356729525
53435.22612273464-1.22612273464004
63533.20212392957911.79787607042086
73932.92561475220916.07438524779086
83435.3112509836759-1.31125098367586
93635.11753347280350.882466527196508
103735.96616684204681.03383315795318
113834.13607938217673.86392061782327
123635.25199975372780.748000246272162
133834.58276563386953.41723436613051
143935.99462416575173.00537583424826
153336.0721757992662-3.07217579926625
163234.3129482147051-2.31294821470508
173633.96936746978682.03063253021319
183836.26589331013861.73410668986139
193936.27366375997842.72633624002163
203233.9938426514126-1.99384265141263
213235.0945100164001-3.09451001640014
223133.1437575569468-2.14375755694684
233935.93770951834193.0622904816581
243735.98156379516841.01843620483163
253934.41895717192454.58104282807549
264133.61253251996677.38746748003326
273635.36053912780390.639460872196101
283335.4120199079122-2.41201990791216
293334.187560162285-1.187560162285
303434.1061703332493-0.106170333249348
313132.8623314924215-1.86233149242153
322733.8195042357409-6.81950423574092
333733.92153105088123.07846894911876
343435.7439920074695-1.74399200746953
353433.34290893512080.657091064879227
363234.8554865034788-2.85548650347884
372932.8157675994686-3.81576759946857
383634.52351440392151.47648559607853
392933.9215310508812-4.92153105088124
403534.46659975651160.533400243488365
413734.36204252451472.63795747548533
423434.2767204411604-0.276720441160406
433834.51190575856063.48809424143943
443534.13462765695430.865372343045735
453832.38428446657895.61571553342108
463734.57878349179042.42121650820961
473836.5525594076471.44744059235295
483334.7132497727147-1.71324977271474
493636.0230814894567-0.0230814894566533
503834.06231605642293.93768394357712
513236.2374359864337-4.2374359864337
523233.2822059799503-1.28220597995028
533233.8026555573969-1.8026555573969
543435.6379830502501-1.6379830502501
553233.9731557775475-1.97315577754747
563733.96538532770773.03461467229229
573934.68100414124924.31899585875084
582934.8346556830557-5.83465568305572
593735.4081817123911.59181828760898
603534.20295711540650.797042884593449
613031.6958650858384-1.69586508583836
623834.68877459108893.31122540891108
633434.6394864469609-0.639486446960881
643134.7855613732461-3.78556137324612
653433.5611016276190.438898372381043
663535.512738944388-0.512738944387985
673634.33597167110841.66402832889157
683032.1945490977857-2.19454909778565
693936.65332833188342.34667166811665
703535.7764314732536-0.776431473253552
713835.40439340463042.59560659536963
723135.9339212105812-4.93392121058125
733436.3097475869651-2.30974758696508
743837.39356627360860.606433726391422
753432.34441233183151.65558766816845
763932.58582231505156.41417768494847
773735.80090665487941.19909334512063
783433.13996924918620.86003075081382
792833.7756499589144-5.77564995891445
803732.46581824217254.53418175782747
813335.6149595938468-2.61495959384676
823736.45198431772920.548015682270812
833535.8653979178105-0.865397917810514
843734.61267468279692.38732531720312
853234.6525468175442-2.65254681754424
863333.4719413487435-0.471941348743549
873836.52429591826061.47570408173943
883334.5633865386688-1.56338653866884
892933.6049559044454-4.60495590444543
903332.98865428991780.011345710082187
913135.4573259099611-4.4573259099611
923633.60495590444542.39504409555457
933536.9969090768016-1.99690907680162
943232.3197932036478-0.319793203647771
952933.2213091904613-4.22130919046135
963935.48957154142673.51042845857332
973734.48583490515432.51416509484567
983533.59334725908451.40665274091547
993735.1061186617611.89388133823896
1003235.6756625490172-3.67566254901725
1013835.63943477547262.36056522452743
1023734.70038323644982.29961676355018
1033635.77244933117440.227550668825549
1043232.509672518999-0.509672518998999
1053336.1276387214536-3.12763872145361
1064033.22005129955736.77994870044268
1073835.14599079650842.85400920349159
1084136.34218705274914.6578129472509
1093634.58276563386951.41723436613051
1104336.22695592046756.77304407953255
1113034.5311409072033-4.53114090720326
1123133.7702160916129-2.77021609161288
1133236.9399944293918-4.93999442939178
1143234.2522452595346-2.25224525953459
1153734.92002765417052.07997234582953
1163734.39049984821962.60950015178041
1173336.5034650978375-3.50346509783745
1183436.5641680530079-2.56416805300794
1193335.0890761490986-2.08907614909857
1203835.06843916299392.9315608370061
1213335.2336993501613-2.23369935016135
1223132.0369154139-1.03691541389999
1233836.43115349730611.56884650269393
1243736.26968161789930.730318382100729
1253333.6825075379599-0.682507537959938
1263134.1913484700457-3.19134847004565
1273934.6010660374364.39893396256402
1284436.67017701022747.32982298977263
1293335.7570523780529-2.7570523780529
1303534.24826311745550.751736882544511
1313234.4058968013411-2.40589680134114
1322833.1593483443868-5.15934834438683
1334035.804694962644.19530503735997
1342733.3106633036552-6.3106633036552
1353736.24141812851280.758581871487201
1363232.9023974614873-0.902397461487342
1372829.7197525874435-1.71975258744352
1383435.0001097045416-1.00010970454161
1393034.3966746262789-4.39667462627892
1403534.71324977271470.286750227285264
1413135.0323553360072-4.03235533600719
1423234.7739527278852-2.77395272788523
1433034.8348495173742-4.83484951737416
1443034.9069672835871-4.9069672835871
1453131.0877069547388-0.0877069547388094
1464033.77166781683536.22833218316465
1473232.3366418819918-0.336641881991791
1483635.13691256800410.863087431995855
1493233.6295750326292-1.6295750326292
1503533.22005129955731.77994870044268
1513836.4596108210111.54038917898902
1524235.20145371869586.79854628130422
1533436.5126872728997-2.51268727289968
1543536.1029697055094-1.10296970550935
1553533.69266445809841.30733554190163
1563333.0792662940157-0.0792662940156892
1573633.60495590444542.39504409555457
1583235.4727228630827-3.47272286308265
1593335.7570523780529-2.7570523780529
1603433.96917363546840.0308263645316305
1613234.3644289948133-2.36442899481334
1623434.918575928948-0.918575928948

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 41 & 35.937709518342 & 5.06229048165801 \tabularnewline
2 & 39 & 34.2935691195044 & 4.70643088049557 \tabularnewline
3 & 30 & 34.5180805366199 & -4.5180805366199 \tabularnewline
4 & 31 & 34.2560335672952 & -3.25603356729525 \tabularnewline
5 & 34 & 35.22612273464 & -1.22612273464004 \tabularnewline
6 & 35 & 33.2021239295791 & 1.79787607042086 \tabularnewline
7 & 39 & 32.9256147522091 & 6.07438524779086 \tabularnewline
8 & 34 & 35.3112509836759 & -1.31125098367586 \tabularnewline
9 & 36 & 35.1175334728035 & 0.882466527196508 \tabularnewline
10 & 37 & 35.9661668420468 & 1.03383315795318 \tabularnewline
11 & 38 & 34.1360793821767 & 3.86392061782327 \tabularnewline
12 & 36 & 35.2519997537278 & 0.748000246272162 \tabularnewline
13 & 38 & 34.5827656338695 & 3.41723436613051 \tabularnewline
14 & 39 & 35.9946241657517 & 3.00537583424826 \tabularnewline
15 & 33 & 36.0721757992662 & -3.07217579926625 \tabularnewline
16 & 32 & 34.3129482147051 & -2.31294821470508 \tabularnewline
17 & 36 & 33.9693674697868 & 2.03063253021319 \tabularnewline
18 & 38 & 36.2658933101386 & 1.73410668986139 \tabularnewline
19 & 39 & 36.2736637599784 & 2.72633624002163 \tabularnewline
20 & 32 & 33.9938426514126 & -1.99384265141263 \tabularnewline
21 & 32 & 35.0945100164001 & -3.09451001640014 \tabularnewline
22 & 31 & 33.1437575569468 & -2.14375755694684 \tabularnewline
23 & 39 & 35.9377095183419 & 3.0622904816581 \tabularnewline
24 & 37 & 35.9815637951684 & 1.01843620483163 \tabularnewline
25 & 39 & 34.4189571719245 & 4.58104282807549 \tabularnewline
26 & 41 & 33.6125325199667 & 7.38746748003326 \tabularnewline
27 & 36 & 35.3605391278039 & 0.639460872196101 \tabularnewline
28 & 33 & 35.4120199079122 & -2.41201990791216 \tabularnewline
29 & 33 & 34.187560162285 & -1.187560162285 \tabularnewline
30 & 34 & 34.1061703332493 & -0.106170333249348 \tabularnewline
31 & 31 & 32.8623314924215 & -1.86233149242153 \tabularnewline
32 & 27 & 33.8195042357409 & -6.81950423574092 \tabularnewline
33 & 37 & 33.9215310508812 & 3.07846894911876 \tabularnewline
34 & 34 & 35.7439920074695 & -1.74399200746953 \tabularnewline
35 & 34 & 33.3429089351208 & 0.657091064879227 \tabularnewline
36 & 32 & 34.8554865034788 & -2.85548650347884 \tabularnewline
37 & 29 & 32.8157675994686 & -3.81576759946857 \tabularnewline
38 & 36 & 34.5235144039215 & 1.47648559607853 \tabularnewline
39 & 29 & 33.9215310508812 & -4.92153105088124 \tabularnewline
40 & 35 & 34.4665997565116 & 0.533400243488365 \tabularnewline
41 & 37 & 34.3620425245147 & 2.63795747548533 \tabularnewline
42 & 34 & 34.2767204411604 & -0.276720441160406 \tabularnewline
43 & 38 & 34.5119057585606 & 3.48809424143943 \tabularnewline
44 & 35 & 34.1346276569543 & 0.865372343045735 \tabularnewline
45 & 38 & 32.3842844665789 & 5.61571553342108 \tabularnewline
46 & 37 & 34.5787834917904 & 2.42121650820961 \tabularnewline
47 & 38 & 36.552559407647 & 1.44744059235295 \tabularnewline
48 & 33 & 34.7132497727147 & -1.71324977271474 \tabularnewline
49 & 36 & 36.0230814894567 & -0.0230814894566533 \tabularnewline
50 & 38 & 34.0623160564229 & 3.93768394357712 \tabularnewline
51 & 32 & 36.2374359864337 & -4.2374359864337 \tabularnewline
52 & 32 & 33.2822059799503 & -1.28220597995028 \tabularnewline
53 & 32 & 33.8026555573969 & -1.8026555573969 \tabularnewline
54 & 34 & 35.6379830502501 & -1.6379830502501 \tabularnewline
55 & 32 & 33.9731557775475 & -1.97315577754747 \tabularnewline
56 & 37 & 33.9653853277077 & 3.03461467229229 \tabularnewline
57 & 39 & 34.6810041412492 & 4.31899585875084 \tabularnewline
58 & 29 & 34.8346556830557 & -5.83465568305572 \tabularnewline
59 & 37 & 35.408181712391 & 1.59181828760898 \tabularnewline
60 & 35 & 34.2029571154065 & 0.797042884593449 \tabularnewline
61 & 30 & 31.6958650858384 & -1.69586508583836 \tabularnewline
62 & 38 & 34.6887745910889 & 3.31122540891108 \tabularnewline
63 & 34 & 34.6394864469609 & -0.639486446960881 \tabularnewline
64 & 31 & 34.7855613732461 & -3.78556137324612 \tabularnewline
65 & 34 & 33.561101627619 & 0.438898372381043 \tabularnewline
66 & 35 & 35.512738944388 & -0.512738944387985 \tabularnewline
67 & 36 & 34.3359716711084 & 1.66402832889157 \tabularnewline
68 & 30 & 32.1945490977857 & -2.19454909778565 \tabularnewline
69 & 39 & 36.6533283318834 & 2.34667166811665 \tabularnewline
70 & 35 & 35.7764314732536 & -0.776431473253552 \tabularnewline
71 & 38 & 35.4043934046304 & 2.59560659536963 \tabularnewline
72 & 31 & 35.9339212105812 & -4.93392121058125 \tabularnewline
73 & 34 & 36.3097475869651 & -2.30974758696508 \tabularnewline
74 & 38 & 37.3935662736086 & 0.606433726391422 \tabularnewline
75 & 34 & 32.3444123318315 & 1.65558766816845 \tabularnewline
76 & 39 & 32.5858223150515 & 6.41417768494847 \tabularnewline
77 & 37 & 35.8009066548794 & 1.19909334512063 \tabularnewline
78 & 34 & 33.1399692491862 & 0.86003075081382 \tabularnewline
79 & 28 & 33.7756499589144 & -5.77564995891445 \tabularnewline
80 & 37 & 32.4658182421725 & 4.53418175782747 \tabularnewline
81 & 33 & 35.6149595938468 & -2.61495959384676 \tabularnewline
82 & 37 & 36.4519843177292 & 0.548015682270812 \tabularnewline
83 & 35 & 35.8653979178105 & -0.865397917810514 \tabularnewline
84 & 37 & 34.6126746827969 & 2.38732531720312 \tabularnewline
85 & 32 & 34.6525468175442 & -2.65254681754424 \tabularnewline
86 & 33 & 33.4719413487435 & -0.471941348743549 \tabularnewline
87 & 38 & 36.5242959182606 & 1.47570408173943 \tabularnewline
88 & 33 & 34.5633865386688 & -1.56338653866884 \tabularnewline
89 & 29 & 33.6049559044454 & -4.60495590444543 \tabularnewline
90 & 33 & 32.9886542899178 & 0.011345710082187 \tabularnewline
91 & 31 & 35.4573259099611 & -4.4573259099611 \tabularnewline
92 & 36 & 33.6049559044454 & 2.39504409555457 \tabularnewline
93 & 35 & 36.9969090768016 & -1.99690907680162 \tabularnewline
94 & 32 & 32.3197932036478 & -0.319793203647771 \tabularnewline
95 & 29 & 33.2213091904613 & -4.22130919046135 \tabularnewline
96 & 39 & 35.4895715414267 & 3.51042845857332 \tabularnewline
97 & 37 & 34.4858349051543 & 2.51416509484567 \tabularnewline
98 & 35 & 33.5933472590845 & 1.40665274091547 \tabularnewline
99 & 37 & 35.106118661761 & 1.89388133823896 \tabularnewline
100 & 32 & 35.6756625490172 & -3.67566254901725 \tabularnewline
101 & 38 & 35.6394347754726 & 2.36056522452743 \tabularnewline
102 & 37 & 34.7003832364498 & 2.29961676355018 \tabularnewline
103 & 36 & 35.7724493311744 & 0.227550668825549 \tabularnewline
104 & 32 & 32.509672518999 & -0.509672518998999 \tabularnewline
105 & 33 & 36.1276387214536 & -3.12763872145361 \tabularnewline
106 & 40 & 33.2200512995573 & 6.77994870044268 \tabularnewline
107 & 38 & 35.1459907965084 & 2.85400920349159 \tabularnewline
108 & 41 & 36.3421870527491 & 4.6578129472509 \tabularnewline
109 & 36 & 34.5827656338695 & 1.41723436613051 \tabularnewline
110 & 43 & 36.2269559204675 & 6.77304407953255 \tabularnewline
111 & 30 & 34.5311409072033 & -4.53114090720326 \tabularnewline
112 & 31 & 33.7702160916129 & -2.77021609161288 \tabularnewline
113 & 32 & 36.9399944293918 & -4.93999442939178 \tabularnewline
114 & 32 & 34.2522452595346 & -2.25224525953459 \tabularnewline
115 & 37 & 34.9200276541705 & 2.07997234582953 \tabularnewline
116 & 37 & 34.3904998482196 & 2.60950015178041 \tabularnewline
117 & 33 & 36.5034650978375 & -3.50346509783745 \tabularnewline
118 & 34 & 36.5641680530079 & -2.56416805300794 \tabularnewline
119 & 33 & 35.0890761490986 & -2.08907614909857 \tabularnewline
120 & 38 & 35.0684391629939 & 2.9315608370061 \tabularnewline
121 & 33 & 35.2336993501613 & -2.23369935016135 \tabularnewline
122 & 31 & 32.0369154139 & -1.03691541389999 \tabularnewline
123 & 38 & 36.4311534973061 & 1.56884650269393 \tabularnewline
124 & 37 & 36.2696816178993 & 0.730318382100729 \tabularnewline
125 & 33 & 33.6825075379599 & -0.682507537959938 \tabularnewline
126 & 31 & 34.1913484700457 & -3.19134847004565 \tabularnewline
127 & 39 & 34.601066037436 & 4.39893396256402 \tabularnewline
128 & 44 & 36.6701770102274 & 7.32982298977263 \tabularnewline
129 & 33 & 35.7570523780529 & -2.7570523780529 \tabularnewline
130 & 35 & 34.2482631174555 & 0.751736882544511 \tabularnewline
131 & 32 & 34.4058968013411 & -2.40589680134114 \tabularnewline
132 & 28 & 33.1593483443868 & -5.15934834438683 \tabularnewline
133 & 40 & 35.80469496264 & 4.19530503735997 \tabularnewline
134 & 27 & 33.3106633036552 & -6.3106633036552 \tabularnewline
135 & 37 & 36.2414181285128 & 0.758581871487201 \tabularnewline
136 & 32 & 32.9023974614873 & -0.902397461487342 \tabularnewline
137 & 28 & 29.7197525874435 & -1.71975258744352 \tabularnewline
138 & 34 & 35.0001097045416 & -1.00010970454161 \tabularnewline
139 & 30 & 34.3966746262789 & -4.39667462627892 \tabularnewline
140 & 35 & 34.7132497727147 & 0.286750227285264 \tabularnewline
141 & 31 & 35.0323553360072 & -4.03235533600719 \tabularnewline
142 & 32 & 34.7739527278852 & -2.77395272788523 \tabularnewline
143 & 30 & 34.8348495173742 & -4.83484951737416 \tabularnewline
144 & 30 & 34.9069672835871 & -4.9069672835871 \tabularnewline
145 & 31 & 31.0877069547388 & -0.0877069547388094 \tabularnewline
146 & 40 & 33.7716678168353 & 6.22833218316465 \tabularnewline
147 & 32 & 32.3366418819918 & -0.336641881991791 \tabularnewline
148 & 36 & 35.1369125680041 & 0.863087431995855 \tabularnewline
149 & 32 & 33.6295750326292 & -1.6295750326292 \tabularnewline
150 & 35 & 33.2200512995573 & 1.77994870044268 \tabularnewline
151 & 38 & 36.459610821011 & 1.54038917898902 \tabularnewline
152 & 42 & 35.2014537186958 & 6.79854628130422 \tabularnewline
153 & 34 & 36.5126872728997 & -2.51268727289968 \tabularnewline
154 & 35 & 36.1029697055094 & -1.10296970550935 \tabularnewline
155 & 35 & 33.6926644580984 & 1.30733554190163 \tabularnewline
156 & 33 & 33.0792662940157 & -0.0792662940156892 \tabularnewline
157 & 36 & 33.6049559044454 & 2.39504409555457 \tabularnewline
158 & 32 & 35.4727228630827 & -3.47272286308265 \tabularnewline
159 & 33 & 35.7570523780529 & -2.7570523780529 \tabularnewline
160 & 34 & 33.9691736354684 & 0.0308263645316305 \tabularnewline
161 & 32 & 34.3644289948133 & -2.36442899481334 \tabularnewline
162 & 34 & 34.918575928948 & -0.918575928948 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154490&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]41[/C][C]35.937709518342[/C][C]5.06229048165801[/C][/ROW]
[ROW][C]2[/C][C]39[/C][C]34.2935691195044[/C][C]4.70643088049557[/C][/ROW]
[ROW][C]3[/C][C]30[/C][C]34.5180805366199[/C][C]-4.5180805366199[/C][/ROW]
[ROW][C]4[/C][C]31[/C][C]34.2560335672952[/C][C]-3.25603356729525[/C][/ROW]
[ROW][C]5[/C][C]34[/C][C]35.22612273464[/C][C]-1.22612273464004[/C][/ROW]
[ROW][C]6[/C][C]35[/C][C]33.2021239295791[/C][C]1.79787607042086[/C][/ROW]
[ROW][C]7[/C][C]39[/C][C]32.9256147522091[/C][C]6.07438524779086[/C][/ROW]
[ROW][C]8[/C][C]34[/C][C]35.3112509836759[/C][C]-1.31125098367586[/C][/ROW]
[ROW][C]9[/C][C]36[/C][C]35.1175334728035[/C][C]0.882466527196508[/C][/ROW]
[ROW][C]10[/C][C]37[/C][C]35.9661668420468[/C][C]1.03383315795318[/C][/ROW]
[ROW][C]11[/C][C]38[/C][C]34.1360793821767[/C][C]3.86392061782327[/C][/ROW]
[ROW][C]12[/C][C]36[/C][C]35.2519997537278[/C][C]0.748000246272162[/C][/ROW]
[ROW][C]13[/C][C]38[/C][C]34.5827656338695[/C][C]3.41723436613051[/C][/ROW]
[ROW][C]14[/C][C]39[/C][C]35.9946241657517[/C][C]3.00537583424826[/C][/ROW]
[ROW][C]15[/C][C]33[/C][C]36.0721757992662[/C][C]-3.07217579926625[/C][/ROW]
[ROW][C]16[/C][C]32[/C][C]34.3129482147051[/C][C]-2.31294821470508[/C][/ROW]
[ROW][C]17[/C][C]36[/C][C]33.9693674697868[/C][C]2.03063253021319[/C][/ROW]
[ROW][C]18[/C][C]38[/C][C]36.2658933101386[/C][C]1.73410668986139[/C][/ROW]
[ROW][C]19[/C][C]39[/C][C]36.2736637599784[/C][C]2.72633624002163[/C][/ROW]
[ROW][C]20[/C][C]32[/C][C]33.9938426514126[/C][C]-1.99384265141263[/C][/ROW]
[ROW][C]21[/C][C]32[/C][C]35.0945100164001[/C][C]-3.09451001640014[/C][/ROW]
[ROW][C]22[/C][C]31[/C][C]33.1437575569468[/C][C]-2.14375755694684[/C][/ROW]
[ROW][C]23[/C][C]39[/C][C]35.9377095183419[/C][C]3.0622904816581[/C][/ROW]
[ROW][C]24[/C][C]37[/C][C]35.9815637951684[/C][C]1.01843620483163[/C][/ROW]
[ROW][C]25[/C][C]39[/C][C]34.4189571719245[/C][C]4.58104282807549[/C][/ROW]
[ROW][C]26[/C][C]41[/C][C]33.6125325199667[/C][C]7.38746748003326[/C][/ROW]
[ROW][C]27[/C][C]36[/C][C]35.3605391278039[/C][C]0.639460872196101[/C][/ROW]
[ROW][C]28[/C][C]33[/C][C]35.4120199079122[/C][C]-2.41201990791216[/C][/ROW]
[ROW][C]29[/C][C]33[/C][C]34.187560162285[/C][C]-1.187560162285[/C][/ROW]
[ROW][C]30[/C][C]34[/C][C]34.1061703332493[/C][C]-0.106170333249348[/C][/ROW]
[ROW][C]31[/C][C]31[/C][C]32.8623314924215[/C][C]-1.86233149242153[/C][/ROW]
[ROW][C]32[/C][C]27[/C][C]33.8195042357409[/C][C]-6.81950423574092[/C][/ROW]
[ROW][C]33[/C][C]37[/C][C]33.9215310508812[/C][C]3.07846894911876[/C][/ROW]
[ROW][C]34[/C][C]34[/C][C]35.7439920074695[/C][C]-1.74399200746953[/C][/ROW]
[ROW][C]35[/C][C]34[/C][C]33.3429089351208[/C][C]0.657091064879227[/C][/ROW]
[ROW][C]36[/C][C]32[/C][C]34.8554865034788[/C][C]-2.85548650347884[/C][/ROW]
[ROW][C]37[/C][C]29[/C][C]32.8157675994686[/C][C]-3.81576759946857[/C][/ROW]
[ROW][C]38[/C][C]36[/C][C]34.5235144039215[/C][C]1.47648559607853[/C][/ROW]
[ROW][C]39[/C][C]29[/C][C]33.9215310508812[/C][C]-4.92153105088124[/C][/ROW]
[ROW][C]40[/C][C]35[/C][C]34.4665997565116[/C][C]0.533400243488365[/C][/ROW]
[ROW][C]41[/C][C]37[/C][C]34.3620425245147[/C][C]2.63795747548533[/C][/ROW]
[ROW][C]42[/C][C]34[/C][C]34.2767204411604[/C][C]-0.276720441160406[/C][/ROW]
[ROW][C]43[/C][C]38[/C][C]34.5119057585606[/C][C]3.48809424143943[/C][/ROW]
[ROW][C]44[/C][C]35[/C][C]34.1346276569543[/C][C]0.865372343045735[/C][/ROW]
[ROW][C]45[/C][C]38[/C][C]32.3842844665789[/C][C]5.61571553342108[/C][/ROW]
[ROW][C]46[/C][C]37[/C][C]34.5787834917904[/C][C]2.42121650820961[/C][/ROW]
[ROW][C]47[/C][C]38[/C][C]36.552559407647[/C][C]1.44744059235295[/C][/ROW]
[ROW][C]48[/C][C]33[/C][C]34.7132497727147[/C][C]-1.71324977271474[/C][/ROW]
[ROW][C]49[/C][C]36[/C][C]36.0230814894567[/C][C]-0.0230814894566533[/C][/ROW]
[ROW][C]50[/C][C]38[/C][C]34.0623160564229[/C][C]3.93768394357712[/C][/ROW]
[ROW][C]51[/C][C]32[/C][C]36.2374359864337[/C][C]-4.2374359864337[/C][/ROW]
[ROW][C]52[/C][C]32[/C][C]33.2822059799503[/C][C]-1.28220597995028[/C][/ROW]
[ROW][C]53[/C][C]32[/C][C]33.8026555573969[/C][C]-1.8026555573969[/C][/ROW]
[ROW][C]54[/C][C]34[/C][C]35.6379830502501[/C][C]-1.6379830502501[/C][/ROW]
[ROW][C]55[/C][C]32[/C][C]33.9731557775475[/C][C]-1.97315577754747[/C][/ROW]
[ROW][C]56[/C][C]37[/C][C]33.9653853277077[/C][C]3.03461467229229[/C][/ROW]
[ROW][C]57[/C][C]39[/C][C]34.6810041412492[/C][C]4.31899585875084[/C][/ROW]
[ROW][C]58[/C][C]29[/C][C]34.8346556830557[/C][C]-5.83465568305572[/C][/ROW]
[ROW][C]59[/C][C]37[/C][C]35.408181712391[/C][C]1.59181828760898[/C][/ROW]
[ROW][C]60[/C][C]35[/C][C]34.2029571154065[/C][C]0.797042884593449[/C][/ROW]
[ROW][C]61[/C][C]30[/C][C]31.6958650858384[/C][C]-1.69586508583836[/C][/ROW]
[ROW][C]62[/C][C]38[/C][C]34.6887745910889[/C][C]3.31122540891108[/C][/ROW]
[ROW][C]63[/C][C]34[/C][C]34.6394864469609[/C][C]-0.639486446960881[/C][/ROW]
[ROW][C]64[/C][C]31[/C][C]34.7855613732461[/C][C]-3.78556137324612[/C][/ROW]
[ROW][C]65[/C][C]34[/C][C]33.561101627619[/C][C]0.438898372381043[/C][/ROW]
[ROW][C]66[/C][C]35[/C][C]35.512738944388[/C][C]-0.512738944387985[/C][/ROW]
[ROW][C]67[/C][C]36[/C][C]34.3359716711084[/C][C]1.66402832889157[/C][/ROW]
[ROW][C]68[/C][C]30[/C][C]32.1945490977857[/C][C]-2.19454909778565[/C][/ROW]
[ROW][C]69[/C][C]39[/C][C]36.6533283318834[/C][C]2.34667166811665[/C][/ROW]
[ROW][C]70[/C][C]35[/C][C]35.7764314732536[/C][C]-0.776431473253552[/C][/ROW]
[ROW][C]71[/C][C]38[/C][C]35.4043934046304[/C][C]2.59560659536963[/C][/ROW]
[ROW][C]72[/C][C]31[/C][C]35.9339212105812[/C][C]-4.93392121058125[/C][/ROW]
[ROW][C]73[/C][C]34[/C][C]36.3097475869651[/C][C]-2.30974758696508[/C][/ROW]
[ROW][C]74[/C][C]38[/C][C]37.3935662736086[/C][C]0.606433726391422[/C][/ROW]
[ROW][C]75[/C][C]34[/C][C]32.3444123318315[/C][C]1.65558766816845[/C][/ROW]
[ROW][C]76[/C][C]39[/C][C]32.5858223150515[/C][C]6.41417768494847[/C][/ROW]
[ROW][C]77[/C][C]37[/C][C]35.8009066548794[/C][C]1.19909334512063[/C][/ROW]
[ROW][C]78[/C][C]34[/C][C]33.1399692491862[/C][C]0.86003075081382[/C][/ROW]
[ROW][C]79[/C][C]28[/C][C]33.7756499589144[/C][C]-5.77564995891445[/C][/ROW]
[ROW][C]80[/C][C]37[/C][C]32.4658182421725[/C][C]4.53418175782747[/C][/ROW]
[ROW][C]81[/C][C]33[/C][C]35.6149595938468[/C][C]-2.61495959384676[/C][/ROW]
[ROW][C]82[/C][C]37[/C][C]36.4519843177292[/C][C]0.548015682270812[/C][/ROW]
[ROW][C]83[/C][C]35[/C][C]35.8653979178105[/C][C]-0.865397917810514[/C][/ROW]
[ROW][C]84[/C][C]37[/C][C]34.6126746827969[/C][C]2.38732531720312[/C][/ROW]
[ROW][C]85[/C][C]32[/C][C]34.6525468175442[/C][C]-2.65254681754424[/C][/ROW]
[ROW][C]86[/C][C]33[/C][C]33.4719413487435[/C][C]-0.471941348743549[/C][/ROW]
[ROW][C]87[/C][C]38[/C][C]36.5242959182606[/C][C]1.47570408173943[/C][/ROW]
[ROW][C]88[/C][C]33[/C][C]34.5633865386688[/C][C]-1.56338653866884[/C][/ROW]
[ROW][C]89[/C][C]29[/C][C]33.6049559044454[/C][C]-4.60495590444543[/C][/ROW]
[ROW][C]90[/C][C]33[/C][C]32.9886542899178[/C][C]0.011345710082187[/C][/ROW]
[ROW][C]91[/C][C]31[/C][C]35.4573259099611[/C][C]-4.4573259099611[/C][/ROW]
[ROW][C]92[/C][C]36[/C][C]33.6049559044454[/C][C]2.39504409555457[/C][/ROW]
[ROW][C]93[/C][C]35[/C][C]36.9969090768016[/C][C]-1.99690907680162[/C][/ROW]
[ROW][C]94[/C][C]32[/C][C]32.3197932036478[/C][C]-0.319793203647771[/C][/ROW]
[ROW][C]95[/C][C]29[/C][C]33.2213091904613[/C][C]-4.22130919046135[/C][/ROW]
[ROW][C]96[/C][C]39[/C][C]35.4895715414267[/C][C]3.51042845857332[/C][/ROW]
[ROW][C]97[/C][C]37[/C][C]34.4858349051543[/C][C]2.51416509484567[/C][/ROW]
[ROW][C]98[/C][C]35[/C][C]33.5933472590845[/C][C]1.40665274091547[/C][/ROW]
[ROW][C]99[/C][C]37[/C][C]35.106118661761[/C][C]1.89388133823896[/C][/ROW]
[ROW][C]100[/C][C]32[/C][C]35.6756625490172[/C][C]-3.67566254901725[/C][/ROW]
[ROW][C]101[/C][C]38[/C][C]35.6394347754726[/C][C]2.36056522452743[/C][/ROW]
[ROW][C]102[/C][C]37[/C][C]34.7003832364498[/C][C]2.29961676355018[/C][/ROW]
[ROW][C]103[/C][C]36[/C][C]35.7724493311744[/C][C]0.227550668825549[/C][/ROW]
[ROW][C]104[/C][C]32[/C][C]32.509672518999[/C][C]-0.509672518998999[/C][/ROW]
[ROW][C]105[/C][C]33[/C][C]36.1276387214536[/C][C]-3.12763872145361[/C][/ROW]
[ROW][C]106[/C][C]40[/C][C]33.2200512995573[/C][C]6.77994870044268[/C][/ROW]
[ROW][C]107[/C][C]38[/C][C]35.1459907965084[/C][C]2.85400920349159[/C][/ROW]
[ROW][C]108[/C][C]41[/C][C]36.3421870527491[/C][C]4.6578129472509[/C][/ROW]
[ROW][C]109[/C][C]36[/C][C]34.5827656338695[/C][C]1.41723436613051[/C][/ROW]
[ROW][C]110[/C][C]43[/C][C]36.2269559204675[/C][C]6.77304407953255[/C][/ROW]
[ROW][C]111[/C][C]30[/C][C]34.5311409072033[/C][C]-4.53114090720326[/C][/ROW]
[ROW][C]112[/C][C]31[/C][C]33.7702160916129[/C][C]-2.77021609161288[/C][/ROW]
[ROW][C]113[/C][C]32[/C][C]36.9399944293918[/C][C]-4.93999442939178[/C][/ROW]
[ROW][C]114[/C][C]32[/C][C]34.2522452595346[/C][C]-2.25224525953459[/C][/ROW]
[ROW][C]115[/C][C]37[/C][C]34.9200276541705[/C][C]2.07997234582953[/C][/ROW]
[ROW][C]116[/C][C]37[/C][C]34.3904998482196[/C][C]2.60950015178041[/C][/ROW]
[ROW][C]117[/C][C]33[/C][C]36.5034650978375[/C][C]-3.50346509783745[/C][/ROW]
[ROW][C]118[/C][C]34[/C][C]36.5641680530079[/C][C]-2.56416805300794[/C][/ROW]
[ROW][C]119[/C][C]33[/C][C]35.0890761490986[/C][C]-2.08907614909857[/C][/ROW]
[ROW][C]120[/C][C]38[/C][C]35.0684391629939[/C][C]2.9315608370061[/C][/ROW]
[ROW][C]121[/C][C]33[/C][C]35.2336993501613[/C][C]-2.23369935016135[/C][/ROW]
[ROW][C]122[/C][C]31[/C][C]32.0369154139[/C][C]-1.03691541389999[/C][/ROW]
[ROW][C]123[/C][C]38[/C][C]36.4311534973061[/C][C]1.56884650269393[/C][/ROW]
[ROW][C]124[/C][C]37[/C][C]36.2696816178993[/C][C]0.730318382100729[/C][/ROW]
[ROW][C]125[/C][C]33[/C][C]33.6825075379599[/C][C]-0.682507537959938[/C][/ROW]
[ROW][C]126[/C][C]31[/C][C]34.1913484700457[/C][C]-3.19134847004565[/C][/ROW]
[ROW][C]127[/C][C]39[/C][C]34.601066037436[/C][C]4.39893396256402[/C][/ROW]
[ROW][C]128[/C][C]44[/C][C]36.6701770102274[/C][C]7.32982298977263[/C][/ROW]
[ROW][C]129[/C][C]33[/C][C]35.7570523780529[/C][C]-2.7570523780529[/C][/ROW]
[ROW][C]130[/C][C]35[/C][C]34.2482631174555[/C][C]0.751736882544511[/C][/ROW]
[ROW][C]131[/C][C]32[/C][C]34.4058968013411[/C][C]-2.40589680134114[/C][/ROW]
[ROW][C]132[/C][C]28[/C][C]33.1593483443868[/C][C]-5.15934834438683[/C][/ROW]
[ROW][C]133[/C][C]40[/C][C]35.80469496264[/C][C]4.19530503735997[/C][/ROW]
[ROW][C]134[/C][C]27[/C][C]33.3106633036552[/C][C]-6.3106633036552[/C][/ROW]
[ROW][C]135[/C][C]37[/C][C]36.2414181285128[/C][C]0.758581871487201[/C][/ROW]
[ROW][C]136[/C][C]32[/C][C]32.9023974614873[/C][C]-0.902397461487342[/C][/ROW]
[ROW][C]137[/C][C]28[/C][C]29.7197525874435[/C][C]-1.71975258744352[/C][/ROW]
[ROW][C]138[/C][C]34[/C][C]35.0001097045416[/C][C]-1.00010970454161[/C][/ROW]
[ROW][C]139[/C][C]30[/C][C]34.3966746262789[/C][C]-4.39667462627892[/C][/ROW]
[ROW][C]140[/C][C]35[/C][C]34.7132497727147[/C][C]0.286750227285264[/C][/ROW]
[ROW][C]141[/C][C]31[/C][C]35.0323553360072[/C][C]-4.03235533600719[/C][/ROW]
[ROW][C]142[/C][C]32[/C][C]34.7739527278852[/C][C]-2.77395272788523[/C][/ROW]
[ROW][C]143[/C][C]30[/C][C]34.8348495173742[/C][C]-4.83484951737416[/C][/ROW]
[ROW][C]144[/C][C]30[/C][C]34.9069672835871[/C][C]-4.9069672835871[/C][/ROW]
[ROW][C]145[/C][C]31[/C][C]31.0877069547388[/C][C]-0.0877069547388094[/C][/ROW]
[ROW][C]146[/C][C]40[/C][C]33.7716678168353[/C][C]6.22833218316465[/C][/ROW]
[ROW][C]147[/C][C]32[/C][C]32.3366418819918[/C][C]-0.336641881991791[/C][/ROW]
[ROW][C]148[/C][C]36[/C][C]35.1369125680041[/C][C]0.863087431995855[/C][/ROW]
[ROW][C]149[/C][C]32[/C][C]33.6295750326292[/C][C]-1.6295750326292[/C][/ROW]
[ROW][C]150[/C][C]35[/C][C]33.2200512995573[/C][C]1.77994870044268[/C][/ROW]
[ROW][C]151[/C][C]38[/C][C]36.459610821011[/C][C]1.54038917898902[/C][/ROW]
[ROW][C]152[/C][C]42[/C][C]35.2014537186958[/C][C]6.79854628130422[/C][/ROW]
[ROW][C]153[/C][C]34[/C][C]36.5126872728997[/C][C]-2.51268727289968[/C][/ROW]
[ROW][C]154[/C][C]35[/C][C]36.1029697055094[/C][C]-1.10296970550935[/C][/ROW]
[ROW][C]155[/C][C]35[/C][C]33.6926644580984[/C][C]1.30733554190163[/C][/ROW]
[ROW][C]156[/C][C]33[/C][C]33.0792662940157[/C][C]-0.0792662940156892[/C][/ROW]
[ROW][C]157[/C][C]36[/C][C]33.6049559044454[/C][C]2.39504409555457[/C][/ROW]
[ROW][C]158[/C][C]32[/C][C]35.4727228630827[/C][C]-3.47272286308265[/C][/ROW]
[ROW][C]159[/C][C]33[/C][C]35.7570523780529[/C][C]-2.7570523780529[/C][/ROW]
[ROW][C]160[/C][C]34[/C][C]33.9691736354684[/C][C]0.0308263645316305[/C][/ROW]
[ROW][C]161[/C][C]32[/C][C]34.3644289948133[/C][C]-2.36442899481334[/C][/ROW]
[ROW][C]162[/C][C]34[/C][C]34.918575928948[/C][C]-0.918575928948[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154490&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154490&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
14135.9377095183425.06229048165801
23934.29356911950444.70643088049557
33034.5180805366199-4.5180805366199
43134.2560335672952-3.25603356729525
53435.22612273464-1.22612273464004
63533.20212392957911.79787607042086
73932.92561475220916.07438524779086
83435.3112509836759-1.31125098367586
93635.11753347280350.882466527196508
103735.96616684204681.03383315795318
113834.13607938217673.86392061782327
123635.25199975372780.748000246272162
133834.58276563386953.41723436613051
143935.99462416575173.00537583424826
153336.0721757992662-3.07217579926625
163234.3129482147051-2.31294821470508
173633.96936746978682.03063253021319
183836.26589331013861.73410668986139
193936.27366375997842.72633624002163
203233.9938426514126-1.99384265141263
213235.0945100164001-3.09451001640014
223133.1437575569468-2.14375755694684
233935.93770951834193.0622904816581
243735.98156379516841.01843620483163
253934.41895717192454.58104282807549
264133.61253251996677.38746748003326
273635.36053912780390.639460872196101
283335.4120199079122-2.41201990791216
293334.187560162285-1.187560162285
303434.1061703332493-0.106170333249348
313132.8623314924215-1.86233149242153
322733.8195042357409-6.81950423574092
333733.92153105088123.07846894911876
343435.7439920074695-1.74399200746953
353433.34290893512080.657091064879227
363234.8554865034788-2.85548650347884
372932.8157675994686-3.81576759946857
383634.52351440392151.47648559607853
392933.9215310508812-4.92153105088124
403534.46659975651160.533400243488365
413734.36204252451472.63795747548533
423434.2767204411604-0.276720441160406
433834.51190575856063.48809424143943
443534.13462765695430.865372343045735
453832.38428446657895.61571553342108
463734.57878349179042.42121650820961
473836.5525594076471.44744059235295
483334.7132497727147-1.71324977271474
493636.0230814894567-0.0230814894566533
503834.06231605642293.93768394357712
513236.2374359864337-4.2374359864337
523233.2822059799503-1.28220597995028
533233.8026555573969-1.8026555573969
543435.6379830502501-1.6379830502501
553233.9731557775475-1.97315577754747
563733.96538532770773.03461467229229
573934.68100414124924.31899585875084
582934.8346556830557-5.83465568305572
593735.4081817123911.59181828760898
603534.20295711540650.797042884593449
613031.6958650858384-1.69586508583836
623834.68877459108893.31122540891108
633434.6394864469609-0.639486446960881
643134.7855613732461-3.78556137324612
653433.5611016276190.438898372381043
663535.512738944388-0.512738944387985
673634.33597167110841.66402832889157
683032.1945490977857-2.19454909778565
693936.65332833188342.34667166811665
703535.7764314732536-0.776431473253552
713835.40439340463042.59560659536963
723135.9339212105812-4.93392121058125
733436.3097475869651-2.30974758696508
743837.39356627360860.606433726391422
753432.34441233183151.65558766816845
763932.58582231505156.41417768494847
773735.80090665487941.19909334512063
783433.13996924918620.86003075081382
792833.7756499589144-5.77564995891445
803732.46581824217254.53418175782747
813335.6149595938468-2.61495959384676
823736.45198431772920.548015682270812
833535.8653979178105-0.865397917810514
843734.61267468279692.38732531720312
853234.6525468175442-2.65254681754424
863333.4719413487435-0.471941348743549
873836.52429591826061.47570408173943
883334.5633865386688-1.56338653866884
892933.6049559044454-4.60495590444543
903332.98865428991780.011345710082187
913135.4573259099611-4.4573259099611
923633.60495590444542.39504409555457
933536.9969090768016-1.99690907680162
943232.3197932036478-0.319793203647771
952933.2213091904613-4.22130919046135
963935.48957154142673.51042845857332
973734.48583490515432.51416509484567
983533.59334725908451.40665274091547
993735.1061186617611.89388133823896
1003235.6756625490172-3.67566254901725
1013835.63943477547262.36056522452743
1023734.70038323644982.29961676355018
1033635.77244933117440.227550668825549
1043232.509672518999-0.509672518998999
1053336.1276387214536-3.12763872145361
1064033.22005129955736.77994870044268
1073835.14599079650842.85400920349159
1084136.34218705274914.6578129472509
1093634.58276563386951.41723436613051
1104336.22695592046756.77304407953255
1113034.5311409072033-4.53114090720326
1123133.7702160916129-2.77021609161288
1133236.9399944293918-4.93999442939178
1143234.2522452595346-2.25224525953459
1153734.92002765417052.07997234582953
1163734.39049984821962.60950015178041
1173336.5034650978375-3.50346509783745
1183436.5641680530079-2.56416805300794
1193335.0890761490986-2.08907614909857
1203835.06843916299392.9315608370061
1213335.2336993501613-2.23369935016135
1223132.0369154139-1.03691541389999
1233836.43115349730611.56884650269393
1243736.26968161789930.730318382100729
1253333.6825075379599-0.682507537959938
1263134.1913484700457-3.19134847004565
1273934.6010660374364.39893396256402
1284436.67017701022747.32982298977263
1293335.7570523780529-2.7570523780529
1303534.24826311745550.751736882544511
1313234.4058968013411-2.40589680134114
1322833.1593483443868-5.15934834438683
1334035.804694962644.19530503735997
1342733.3106633036552-6.3106633036552
1353736.24141812851280.758581871487201
1363232.9023974614873-0.902397461487342
1372829.7197525874435-1.71975258744352
1383435.0001097045416-1.00010970454161
1393034.3966746262789-4.39667462627892
1403534.71324977271470.286750227285264
1413135.0323553360072-4.03235533600719
1423234.7739527278852-2.77395272788523
1433034.8348495173742-4.83484951737416
1443034.9069672835871-4.9069672835871
1453131.0877069547388-0.0877069547388094
1464033.77166781683536.22833218316465
1473232.3366418819918-0.336641881991791
1483635.13691256800410.863087431995855
1493233.6295750326292-1.6295750326292
1503533.22005129955731.77994870044268
1513836.4596108210111.54038917898902
1524235.20145371869586.79854628130422
1533436.5126872728997-2.51268727289968
1543536.1029697055094-1.10296970550935
1553533.69266445809841.30733554190163
1563333.0792662940157-0.0792662940156892
1573633.60495590444542.39504409555457
1583235.4727228630827-3.47272286308265
1593335.7570523780529-2.7570523780529
1603433.96917363546840.0308263645316305
1613234.3644289948133-2.36442899481334
1623434.918575928948-0.918575928948






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.9417641580479290.1164716839041410.0582358419520706
90.8861935003654810.2276129992690380.113806499634519
100.8103953864643380.3792092270713240.189604613535662
110.7665377838289660.4669244323420680.233462216171034
120.7299916409207120.5400167181585750.270008359079288
130.7783799014278880.4432401971442240.221620098572112
140.7184036336502050.563192732699590.281596366349795
150.7662111330813270.4675777338373450.233788866918673
160.7763593307611390.4472813384777220.223640669238861
170.7140498318128680.5719003363742640.285950168187132
180.6518099401409570.6963801197180860.348190059859043
190.6292759506609370.7414480986781250.370724049339063
200.6218640839104650.7562718321790710.378135916089535
210.6535175615019680.6929648769960640.346482438498032
220.6041318765202510.7917362469594980.395868123479749
230.5863152582919150.8273694834161710.413684741708085
240.5183219871774320.9633560256451350.481678012822568
250.5342671910477470.9314656179045050.465732808952253
260.7867164146864030.4265671706271940.213283585313597
270.7370122858025550.5259754283948910.262987714197445
280.7346096645312110.5307806709375790.265390335468789
290.7055025790063830.5889948419872340.294497420993617
300.6564901215250570.6870197569498860.343509878474943
310.6430294455770850.713941108845830.356970554422915
320.8233499538215860.3533000923568280.176650046178414
330.8060429210257350.3879141579485310.193957078974265
340.7875028390266570.4249943219466870.212497160973344
350.7447036470415580.5105927059168840.255296352958442
360.7363220646749270.5273558706501460.263677935325073
370.7413545484620390.5172909030759230.258645451537962
380.7062497228051220.5875005543897560.293750277194878
390.7823062069830230.4353875860339540.217693793016977
400.7416328133290340.5167343733419330.258367186670967
410.7254206095890730.5491587808218540.274579390410927
420.6803947581176030.6392104837647950.319605241882397
430.680893554401150.63821289119770.31910644559885
440.6360140440059070.7279719119881870.363985955994094
450.7080205488097940.5839589023804120.291979451190206
460.6788450845411710.6423098309176580.321154915458829
470.6372354297464960.7255291405070080.362764570253504
480.6142528122756620.7714943754486760.385747187724338
490.5647896098193790.8704207803612420.435210390180621
500.5808194558730370.8383610882539260.419180544126963
510.6303248449314280.7393503101371450.369675155068572
520.5924740461630510.8150519076738980.407525953836949
530.5581037167578550.883792566484290.441896283242145
540.5247057279933380.9505885440133240.475294272006662
550.5006476376069740.9987047247860520.499352362393026
560.4871847197218320.9743694394436640.512815280278168
570.5194866089505280.9610267820989440.480513391049472
580.6520308292274940.6959383415450110.347969170772506
590.6182247959322490.7635504081355030.381775204067751
600.5738982540532190.8522034918935610.426101745946781
610.540874245784140.918251508431720.45912575421586
620.543758259636920.912483480726160.45624174036308
630.5007805553045250.9984388893909510.499219444695475
640.522215257054930.9555694858901390.47778474294507
650.4761522125730750.952304425146150.523847787426925
660.4307826792941360.8615653585882710.569217320705865
670.3980278763029550.7960557526059110.601972123697045
680.3778027067584830.7556054135169660.622197293241517
690.3563157905339080.7126315810678160.643684209466092
700.3160353959335040.6320707918670090.683964604066496
710.3022962950387530.6045925900775050.697703704961247
720.3634427079973440.7268854159946870.636557292002656
730.3428762403318060.6857524806636110.657123759668194
740.3032159721089440.6064319442178870.696784027891056
750.2753938143497680.5507876286995360.724606185650232
760.3989467403277530.7978934806555060.601053259672247
770.3607327877882650.721465575576530.639267212211735
780.3211815980834490.6423631961668970.678818401916551
790.420402299122420.840804598244840.57959770087758
800.4749645747185290.9499291494370590.525035425281471
810.4564905661196570.9129811322393140.543509433880343
820.4137156033968190.8274312067936380.586284396603181
830.3735373090481540.7470746180963080.626462690951846
840.3561430769119060.7122861538238120.643856923088094
850.3442628489672660.6885256979345330.655737151032734
860.3055544066382480.6111088132764950.694445593361752
870.2762970775362880.5525941550725750.723702922463712
880.2478972685901540.4957945371803090.752102731409846
890.288865881020650.5777317620412990.711134118979351
900.2506609687921370.5013219375842740.749339031207863
910.290439500455790.580879000911580.70956049954421
920.2738472163792380.5476944327584750.726152783620762
930.2524149102550740.5048298205101480.747585089744926
940.2177650243935950.4355300487871910.782234975606405
950.2379734316073910.4759468632147830.762026568392609
960.2451778936532810.4903557873065620.754822106346719
970.2317560048180110.4635120096360230.768243995181989
980.2075866907491270.4151733814982540.792413309250873
990.1867983960976760.3735967921953510.813201603902324
1000.1927689932256380.3855379864512760.807231006774362
1010.1789575337389790.3579150674779580.821042466261021
1020.1648502023861230.3297004047722450.835149797613877
1030.1371693447480310.2743386894960620.862830655251969
1040.1151876645905960.2303753291811920.884812335409404
1050.1186041213514610.2372082427029210.881395878648539
1060.2433912252102670.4867824504205340.756608774789733
1070.2359078268568020.4718156537136030.764092173143198
1080.2736741612948880.5473483225897770.726325838705112
1090.2584611156247750.516922231249550.741538884375225
1100.4407249079722140.8814498159444280.559275092027786
1110.4885291578265980.9770583156531970.511470842173402
1120.4655149443262660.9310298886525310.534485055673734
1130.5228908797040690.9542182405918620.477109120295931
1140.4867062767822540.9734125535645080.513293723217746
1150.4530506259583690.9061012519167380.546949374041631
1160.4539505769382980.9079011538765960.546049423061702
1170.46000668961050.9200133792210.5399933103895
1180.4431300680770560.8862601361541110.556869931922944
1190.4173328327349560.8346656654699130.582667167265044
1200.41212536551720.8242507310343990.5878746344828
1210.3829909363101090.7659818726202170.617009063689891
1220.3367760881055830.6735521762111650.663223911894417
1230.297334820763840.5946696415276810.70266517923616
1240.2531449274674240.5062898549348480.746855072532576
1250.2135927567862710.4271855135725430.786407243213729
1260.2214163469686870.4428326939373730.778583653031313
1270.3019555931348420.6039111862696850.698044406865158
1280.5599081568762720.8801836862474560.440091843123728
1290.5276320045384550.944735990923090.472367995461545
1300.4717393585975350.9434787171950690.528260641402465
1310.4224986497600170.8449972995200340.577501350239983
1320.4616109098347010.9232218196694010.538389090165299
1330.5253500346119510.9492999307760980.474649965388049
1340.6220475994874430.7559048010251130.377952400512557
1350.6045747588136790.7908504823726410.395425241186321
1360.5428765266048580.9142469467902840.457123473395142
1370.5152977601584620.9694044796830760.484702239841538
1380.4507041883036470.9014083766072940.549295811696353
1390.5437809956006010.9124380087987990.456219004399399
1400.475086237119120.950172474238240.52491376288088
1410.4481939525316640.8963879050633280.551806047468336
1420.4120738338920260.8241476677840510.587926166107974
1430.4336737971820640.8673475943641270.566326202817936
1440.5406608495890960.9186783008218080.459339150410904
1450.457622522762490.9152450455249810.54237747723751
1460.7399471668888690.5201056662222610.260052833111131
1470.6615933025980550.676813394803890.338406697401945
1480.5774694843290980.8450610313418040.422530515670902
1490.4778682249917620.9557364499835240.522131775008238
1500.3814592743163110.7629185486326220.618540725683689
1510.3362500216859730.6725000433719460.663749978314027
1520.9541049087424330.09179018251513360.0458950912575668
1530.894479975236980.211040049526040.10552002476302
1540.814383293807270.371233412385460.18561670619273

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 0.941764158047929 & 0.116471683904141 & 0.0582358419520706 \tabularnewline
9 & 0.886193500365481 & 0.227612999269038 & 0.113806499634519 \tabularnewline
10 & 0.810395386464338 & 0.379209227071324 & 0.189604613535662 \tabularnewline
11 & 0.766537783828966 & 0.466924432342068 & 0.233462216171034 \tabularnewline
12 & 0.729991640920712 & 0.540016718158575 & 0.270008359079288 \tabularnewline
13 & 0.778379901427888 & 0.443240197144224 & 0.221620098572112 \tabularnewline
14 & 0.718403633650205 & 0.56319273269959 & 0.281596366349795 \tabularnewline
15 & 0.766211133081327 & 0.467577733837345 & 0.233788866918673 \tabularnewline
16 & 0.776359330761139 & 0.447281338477722 & 0.223640669238861 \tabularnewline
17 & 0.714049831812868 & 0.571900336374264 & 0.285950168187132 \tabularnewline
18 & 0.651809940140957 & 0.696380119718086 & 0.348190059859043 \tabularnewline
19 & 0.629275950660937 & 0.741448098678125 & 0.370724049339063 \tabularnewline
20 & 0.621864083910465 & 0.756271832179071 & 0.378135916089535 \tabularnewline
21 & 0.653517561501968 & 0.692964876996064 & 0.346482438498032 \tabularnewline
22 & 0.604131876520251 & 0.791736246959498 & 0.395868123479749 \tabularnewline
23 & 0.586315258291915 & 0.827369483416171 & 0.413684741708085 \tabularnewline
24 & 0.518321987177432 & 0.963356025645135 & 0.481678012822568 \tabularnewline
25 & 0.534267191047747 & 0.931465617904505 & 0.465732808952253 \tabularnewline
26 & 0.786716414686403 & 0.426567170627194 & 0.213283585313597 \tabularnewline
27 & 0.737012285802555 & 0.525975428394891 & 0.262987714197445 \tabularnewline
28 & 0.734609664531211 & 0.530780670937579 & 0.265390335468789 \tabularnewline
29 & 0.705502579006383 & 0.588994841987234 & 0.294497420993617 \tabularnewline
30 & 0.656490121525057 & 0.687019756949886 & 0.343509878474943 \tabularnewline
31 & 0.643029445577085 & 0.71394110884583 & 0.356970554422915 \tabularnewline
32 & 0.823349953821586 & 0.353300092356828 & 0.176650046178414 \tabularnewline
33 & 0.806042921025735 & 0.387914157948531 & 0.193957078974265 \tabularnewline
34 & 0.787502839026657 & 0.424994321946687 & 0.212497160973344 \tabularnewline
35 & 0.744703647041558 & 0.510592705916884 & 0.255296352958442 \tabularnewline
36 & 0.736322064674927 & 0.527355870650146 & 0.263677935325073 \tabularnewline
37 & 0.741354548462039 & 0.517290903075923 & 0.258645451537962 \tabularnewline
38 & 0.706249722805122 & 0.587500554389756 & 0.293750277194878 \tabularnewline
39 & 0.782306206983023 & 0.435387586033954 & 0.217693793016977 \tabularnewline
40 & 0.741632813329034 & 0.516734373341933 & 0.258367186670967 \tabularnewline
41 & 0.725420609589073 & 0.549158780821854 & 0.274579390410927 \tabularnewline
42 & 0.680394758117603 & 0.639210483764795 & 0.319605241882397 \tabularnewline
43 & 0.68089355440115 & 0.6382128911977 & 0.31910644559885 \tabularnewline
44 & 0.636014044005907 & 0.727971911988187 & 0.363985955994094 \tabularnewline
45 & 0.708020548809794 & 0.583958902380412 & 0.291979451190206 \tabularnewline
46 & 0.678845084541171 & 0.642309830917658 & 0.321154915458829 \tabularnewline
47 & 0.637235429746496 & 0.725529140507008 & 0.362764570253504 \tabularnewline
48 & 0.614252812275662 & 0.771494375448676 & 0.385747187724338 \tabularnewline
49 & 0.564789609819379 & 0.870420780361242 & 0.435210390180621 \tabularnewline
50 & 0.580819455873037 & 0.838361088253926 & 0.419180544126963 \tabularnewline
51 & 0.630324844931428 & 0.739350310137145 & 0.369675155068572 \tabularnewline
52 & 0.592474046163051 & 0.815051907673898 & 0.407525953836949 \tabularnewline
53 & 0.558103716757855 & 0.88379256648429 & 0.441896283242145 \tabularnewline
54 & 0.524705727993338 & 0.950588544013324 & 0.475294272006662 \tabularnewline
55 & 0.500647637606974 & 0.998704724786052 & 0.499352362393026 \tabularnewline
56 & 0.487184719721832 & 0.974369439443664 & 0.512815280278168 \tabularnewline
57 & 0.519486608950528 & 0.961026782098944 & 0.480513391049472 \tabularnewline
58 & 0.652030829227494 & 0.695938341545011 & 0.347969170772506 \tabularnewline
59 & 0.618224795932249 & 0.763550408135503 & 0.381775204067751 \tabularnewline
60 & 0.573898254053219 & 0.852203491893561 & 0.426101745946781 \tabularnewline
61 & 0.54087424578414 & 0.91825150843172 & 0.45912575421586 \tabularnewline
62 & 0.54375825963692 & 0.91248348072616 & 0.45624174036308 \tabularnewline
63 & 0.500780555304525 & 0.998438889390951 & 0.499219444695475 \tabularnewline
64 & 0.52221525705493 & 0.955569485890139 & 0.47778474294507 \tabularnewline
65 & 0.476152212573075 & 0.95230442514615 & 0.523847787426925 \tabularnewline
66 & 0.430782679294136 & 0.861565358588271 & 0.569217320705865 \tabularnewline
67 & 0.398027876302955 & 0.796055752605911 & 0.601972123697045 \tabularnewline
68 & 0.377802706758483 & 0.755605413516966 & 0.622197293241517 \tabularnewline
69 & 0.356315790533908 & 0.712631581067816 & 0.643684209466092 \tabularnewline
70 & 0.316035395933504 & 0.632070791867009 & 0.683964604066496 \tabularnewline
71 & 0.302296295038753 & 0.604592590077505 & 0.697703704961247 \tabularnewline
72 & 0.363442707997344 & 0.726885415994687 & 0.636557292002656 \tabularnewline
73 & 0.342876240331806 & 0.685752480663611 & 0.657123759668194 \tabularnewline
74 & 0.303215972108944 & 0.606431944217887 & 0.696784027891056 \tabularnewline
75 & 0.275393814349768 & 0.550787628699536 & 0.724606185650232 \tabularnewline
76 & 0.398946740327753 & 0.797893480655506 & 0.601053259672247 \tabularnewline
77 & 0.360732787788265 & 0.72146557557653 & 0.639267212211735 \tabularnewline
78 & 0.321181598083449 & 0.642363196166897 & 0.678818401916551 \tabularnewline
79 & 0.42040229912242 & 0.84080459824484 & 0.57959770087758 \tabularnewline
80 & 0.474964574718529 & 0.949929149437059 & 0.525035425281471 \tabularnewline
81 & 0.456490566119657 & 0.912981132239314 & 0.543509433880343 \tabularnewline
82 & 0.413715603396819 & 0.827431206793638 & 0.586284396603181 \tabularnewline
83 & 0.373537309048154 & 0.747074618096308 & 0.626462690951846 \tabularnewline
84 & 0.356143076911906 & 0.712286153823812 & 0.643856923088094 \tabularnewline
85 & 0.344262848967266 & 0.688525697934533 & 0.655737151032734 \tabularnewline
86 & 0.305554406638248 & 0.611108813276495 & 0.694445593361752 \tabularnewline
87 & 0.276297077536288 & 0.552594155072575 & 0.723702922463712 \tabularnewline
88 & 0.247897268590154 & 0.495794537180309 & 0.752102731409846 \tabularnewline
89 & 0.28886588102065 & 0.577731762041299 & 0.711134118979351 \tabularnewline
90 & 0.250660968792137 & 0.501321937584274 & 0.749339031207863 \tabularnewline
91 & 0.29043950045579 & 0.58087900091158 & 0.70956049954421 \tabularnewline
92 & 0.273847216379238 & 0.547694432758475 & 0.726152783620762 \tabularnewline
93 & 0.252414910255074 & 0.504829820510148 & 0.747585089744926 \tabularnewline
94 & 0.217765024393595 & 0.435530048787191 & 0.782234975606405 \tabularnewline
95 & 0.237973431607391 & 0.475946863214783 & 0.762026568392609 \tabularnewline
96 & 0.245177893653281 & 0.490355787306562 & 0.754822106346719 \tabularnewline
97 & 0.231756004818011 & 0.463512009636023 & 0.768243995181989 \tabularnewline
98 & 0.207586690749127 & 0.415173381498254 & 0.792413309250873 \tabularnewline
99 & 0.186798396097676 & 0.373596792195351 & 0.813201603902324 \tabularnewline
100 & 0.192768993225638 & 0.385537986451276 & 0.807231006774362 \tabularnewline
101 & 0.178957533738979 & 0.357915067477958 & 0.821042466261021 \tabularnewline
102 & 0.164850202386123 & 0.329700404772245 & 0.835149797613877 \tabularnewline
103 & 0.137169344748031 & 0.274338689496062 & 0.862830655251969 \tabularnewline
104 & 0.115187664590596 & 0.230375329181192 & 0.884812335409404 \tabularnewline
105 & 0.118604121351461 & 0.237208242702921 & 0.881395878648539 \tabularnewline
106 & 0.243391225210267 & 0.486782450420534 & 0.756608774789733 \tabularnewline
107 & 0.235907826856802 & 0.471815653713603 & 0.764092173143198 \tabularnewline
108 & 0.273674161294888 & 0.547348322589777 & 0.726325838705112 \tabularnewline
109 & 0.258461115624775 & 0.51692223124955 & 0.741538884375225 \tabularnewline
110 & 0.440724907972214 & 0.881449815944428 & 0.559275092027786 \tabularnewline
111 & 0.488529157826598 & 0.977058315653197 & 0.511470842173402 \tabularnewline
112 & 0.465514944326266 & 0.931029888652531 & 0.534485055673734 \tabularnewline
113 & 0.522890879704069 & 0.954218240591862 & 0.477109120295931 \tabularnewline
114 & 0.486706276782254 & 0.973412553564508 & 0.513293723217746 \tabularnewline
115 & 0.453050625958369 & 0.906101251916738 & 0.546949374041631 \tabularnewline
116 & 0.453950576938298 & 0.907901153876596 & 0.546049423061702 \tabularnewline
117 & 0.4600066896105 & 0.920013379221 & 0.5399933103895 \tabularnewline
118 & 0.443130068077056 & 0.886260136154111 & 0.556869931922944 \tabularnewline
119 & 0.417332832734956 & 0.834665665469913 & 0.582667167265044 \tabularnewline
120 & 0.4121253655172 & 0.824250731034399 & 0.5878746344828 \tabularnewline
121 & 0.382990936310109 & 0.765981872620217 & 0.617009063689891 \tabularnewline
122 & 0.336776088105583 & 0.673552176211165 & 0.663223911894417 \tabularnewline
123 & 0.29733482076384 & 0.594669641527681 & 0.70266517923616 \tabularnewline
124 & 0.253144927467424 & 0.506289854934848 & 0.746855072532576 \tabularnewline
125 & 0.213592756786271 & 0.427185513572543 & 0.786407243213729 \tabularnewline
126 & 0.221416346968687 & 0.442832693937373 & 0.778583653031313 \tabularnewline
127 & 0.301955593134842 & 0.603911186269685 & 0.698044406865158 \tabularnewline
128 & 0.559908156876272 & 0.880183686247456 & 0.440091843123728 \tabularnewline
129 & 0.527632004538455 & 0.94473599092309 & 0.472367995461545 \tabularnewline
130 & 0.471739358597535 & 0.943478717195069 & 0.528260641402465 \tabularnewline
131 & 0.422498649760017 & 0.844997299520034 & 0.577501350239983 \tabularnewline
132 & 0.461610909834701 & 0.923221819669401 & 0.538389090165299 \tabularnewline
133 & 0.525350034611951 & 0.949299930776098 & 0.474649965388049 \tabularnewline
134 & 0.622047599487443 & 0.755904801025113 & 0.377952400512557 \tabularnewline
135 & 0.604574758813679 & 0.790850482372641 & 0.395425241186321 \tabularnewline
136 & 0.542876526604858 & 0.914246946790284 & 0.457123473395142 \tabularnewline
137 & 0.515297760158462 & 0.969404479683076 & 0.484702239841538 \tabularnewline
138 & 0.450704188303647 & 0.901408376607294 & 0.549295811696353 \tabularnewline
139 & 0.543780995600601 & 0.912438008798799 & 0.456219004399399 \tabularnewline
140 & 0.47508623711912 & 0.95017247423824 & 0.52491376288088 \tabularnewline
141 & 0.448193952531664 & 0.896387905063328 & 0.551806047468336 \tabularnewline
142 & 0.412073833892026 & 0.824147667784051 & 0.587926166107974 \tabularnewline
143 & 0.433673797182064 & 0.867347594364127 & 0.566326202817936 \tabularnewline
144 & 0.540660849589096 & 0.918678300821808 & 0.459339150410904 \tabularnewline
145 & 0.45762252276249 & 0.915245045524981 & 0.54237747723751 \tabularnewline
146 & 0.739947166888869 & 0.520105666222261 & 0.260052833111131 \tabularnewline
147 & 0.661593302598055 & 0.67681339480389 & 0.338406697401945 \tabularnewline
148 & 0.577469484329098 & 0.845061031341804 & 0.422530515670902 \tabularnewline
149 & 0.477868224991762 & 0.955736449983524 & 0.522131775008238 \tabularnewline
150 & 0.381459274316311 & 0.762918548632622 & 0.618540725683689 \tabularnewline
151 & 0.336250021685973 & 0.672500043371946 & 0.663749978314027 \tabularnewline
152 & 0.954104908742433 & 0.0917901825151336 & 0.0458950912575668 \tabularnewline
153 & 0.89447997523698 & 0.21104004952604 & 0.10552002476302 \tabularnewline
154 & 0.81438329380727 & 0.37123341238546 & 0.18561670619273 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154490&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]0.941764158047929[/C][C]0.116471683904141[/C][C]0.0582358419520706[/C][/ROW]
[ROW][C]9[/C][C]0.886193500365481[/C][C]0.227612999269038[/C][C]0.113806499634519[/C][/ROW]
[ROW][C]10[/C][C]0.810395386464338[/C][C]0.379209227071324[/C][C]0.189604613535662[/C][/ROW]
[ROW][C]11[/C][C]0.766537783828966[/C][C]0.466924432342068[/C][C]0.233462216171034[/C][/ROW]
[ROW][C]12[/C][C]0.729991640920712[/C][C]0.540016718158575[/C][C]0.270008359079288[/C][/ROW]
[ROW][C]13[/C][C]0.778379901427888[/C][C]0.443240197144224[/C][C]0.221620098572112[/C][/ROW]
[ROW][C]14[/C][C]0.718403633650205[/C][C]0.56319273269959[/C][C]0.281596366349795[/C][/ROW]
[ROW][C]15[/C][C]0.766211133081327[/C][C]0.467577733837345[/C][C]0.233788866918673[/C][/ROW]
[ROW][C]16[/C][C]0.776359330761139[/C][C]0.447281338477722[/C][C]0.223640669238861[/C][/ROW]
[ROW][C]17[/C][C]0.714049831812868[/C][C]0.571900336374264[/C][C]0.285950168187132[/C][/ROW]
[ROW][C]18[/C][C]0.651809940140957[/C][C]0.696380119718086[/C][C]0.348190059859043[/C][/ROW]
[ROW][C]19[/C][C]0.629275950660937[/C][C]0.741448098678125[/C][C]0.370724049339063[/C][/ROW]
[ROW][C]20[/C][C]0.621864083910465[/C][C]0.756271832179071[/C][C]0.378135916089535[/C][/ROW]
[ROW][C]21[/C][C]0.653517561501968[/C][C]0.692964876996064[/C][C]0.346482438498032[/C][/ROW]
[ROW][C]22[/C][C]0.604131876520251[/C][C]0.791736246959498[/C][C]0.395868123479749[/C][/ROW]
[ROW][C]23[/C][C]0.586315258291915[/C][C]0.827369483416171[/C][C]0.413684741708085[/C][/ROW]
[ROW][C]24[/C][C]0.518321987177432[/C][C]0.963356025645135[/C][C]0.481678012822568[/C][/ROW]
[ROW][C]25[/C][C]0.534267191047747[/C][C]0.931465617904505[/C][C]0.465732808952253[/C][/ROW]
[ROW][C]26[/C][C]0.786716414686403[/C][C]0.426567170627194[/C][C]0.213283585313597[/C][/ROW]
[ROW][C]27[/C][C]0.737012285802555[/C][C]0.525975428394891[/C][C]0.262987714197445[/C][/ROW]
[ROW][C]28[/C][C]0.734609664531211[/C][C]0.530780670937579[/C][C]0.265390335468789[/C][/ROW]
[ROW][C]29[/C][C]0.705502579006383[/C][C]0.588994841987234[/C][C]0.294497420993617[/C][/ROW]
[ROW][C]30[/C][C]0.656490121525057[/C][C]0.687019756949886[/C][C]0.343509878474943[/C][/ROW]
[ROW][C]31[/C][C]0.643029445577085[/C][C]0.71394110884583[/C][C]0.356970554422915[/C][/ROW]
[ROW][C]32[/C][C]0.823349953821586[/C][C]0.353300092356828[/C][C]0.176650046178414[/C][/ROW]
[ROW][C]33[/C][C]0.806042921025735[/C][C]0.387914157948531[/C][C]0.193957078974265[/C][/ROW]
[ROW][C]34[/C][C]0.787502839026657[/C][C]0.424994321946687[/C][C]0.212497160973344[/C][/ROW]
[ROW][C]35[/C][C]0.744703647041558[/C][C]0.510592705916884[/C][C]0.255296352958442[/C][/ROW]
[ROW][C]36[/C][C]0.736322064674927[/C][C]0.527355870650146[/C][C]0.263677935325073[/C][/ROW]
[ROW][C]37[/C][C]0.741354548462039[/C][C]0.517290903075923[/C][C]0.258645451537962[/C][/ROW]
[ROW][C]38[/C][C]0.706249722805122[/C][C]0.587500554389756[/C][C]0.293750277194878[/C][/ROW]
[ROW][C]39[/C][C]0.782306206983023[/C][C]0.435387586033954[/C][C]0.217693793016977[/C][/ROW]
[ROW][C]40[/C][C]0.741632813329034[/C][C]0.516734373341933[/C][C]0.258367186670967[/C][/ROW]
[ROW][C]41[/C][C]0.725420609589073[/C][C]0.549158780821854[/C][C]0.274579390410927[/C][/ROW]
[ROW][C]42[/C][C]0.680394758117603[/C][C]0.639210483764795[/C][C]0.319605241882397[/C][/ROW]
[ROW][C]43[/C][C]0.68089355440115[/C][C]0.6382128911977[/C][C]0.31910644559885[/C][/ROW]
[ROW][C]44[/C][C]0.636014044005907[/C][C]0.727971911988187[/C][C]0.363985955994094[/C][/ROW]
[ROW][C]45[/C][C]0.708020548809794[/C][C]0.583958902380412[/C][C]0.291979451190206[/C][/ROW]
[ROW][C]46[/C][C]0.678845084541171[/C][C]0.642309830917658[/C][C]0.321154915458829[/C][/ROW]
[ROW][C]47[/C][C]0.637235429746496[/C][C]0.725529140507008[/C][C]0.362764570253504[/C][/ROW]
[ROW][C]48[/C][C]0.614252812275662[/C][C]0.771494375448676[/C][C]0.385747187724338[/C][/ROW]
[ROW][C]49[/C][C]0.564789609819379[/C][C]0.870420780361242[/C][C]0.435210390180621[/C][/ROW]
[ROW][C]50[/C][C]0.580819455873037[/C][C]0.838361088253926[/C][C]0.419180544126963[/C][/ROW]
[ROW][C]51[/C][C]0.630324844931428[/C][C]0.739350310137145[/C][C]0.369675155068572[/C][/ROW]
[ROW][C]52[/C][C]0.592474046163051[/C][C]0.815051907673898[/C][C]0.407525953836949[/C][/ROW]
[ROW][C]53[/C][C]0.558103716757855[/C][C]0.88379256648429[/C][C]0.441896283242145[/C][/ROW]
[ROW][C]54[/C][C]0.524705727993338[/C][C]0.950588544013324[/C][C]0.475294272006662[/C][/ROW]
[ROW][C]55[/C][C]0.500647637606974[/C][C]0.998704724786052[/C][C]0.499352362393026[/C][/ROW]
[ROW][C]56[/C][C]0.487184719721832[/C][C]0.974369439443664[/C][C]0.512815280278168[/C][/ROW]
[ROW][C]57[/C][C]0.519486608950528[/C][C]0.961026782098944[/C][C]0.480513391049472[/C][/ROW]
[ROW][C]58[/C][C]0.652030829227494[/C][C]0.695938341545011[/C][C]0.347969170772506[/C][/ROW]
[ROW][C]59[/C][C]0.618224795932249[/C][C]0.763550408135503[/C][C]0.381775204067751[/C][/ROW]
[ROW][C]60[/C][C]0.573898254053219[/C][C]0.852203491893561[/C][C]0.426101745946781[/C][/ROW]
[ROW][C]61[/C][C]0.54087424578414[/C][C]0.91825150843172[/C][C]0.45912575421586[/C][/ROW]
[ROW][C]62[/C][C]0.54375825963692[/C][C]0.91248348072616[/C][C]0.45624174036308[/C][/ROW]
[ROW][C]63[/C][C]0.500780555304525[/C][C]0.998438889390951[/C][C]0.499219444695475[/C][/ROW]
[ROW][C]64[/C][C]0.52221525705493[/C][C]0.955569485890139[/C][C]0.47778474294507[/C][/ROW]
[ROW][C]65[/C][C]0.476152212573075[/C][C]0.95230442514615[/C][C]0.523847787426925[/C][/ROW]
[ROW][C]66[/C][C]0.430782679294136[/C][C]0.861565358588271[/C][C]0.569217320705865[/C][/ROW]
[ROW][C]67[/C][C]0.398027876302955[/C][C]0.796055752605911[/C][C]0.601972123697045[/C][/ROW]
[ROW][C]68[/C][C]0.377802706758483[/C][C]0.755605413516966[/C][C]0.622197293241517[/C][/ROW]
[ROW][C]69[/C][C]0.356315790533908[/C][C]0.712631581067816[/C][C]0.643684209466092[/C][/ROW]
[ROW][C]70[/C][C]0.316035395933504[/C][C]0.632070791867009[/C][C]0.683964604066496[/C][/ROW]
[ROW][C]71[/C][C]0.302296295038753[/C][C]0.604592590077505[/C][C]0.697703704961247[/C][/ROW]
[ROW][C]72[/C][C]0.363442707997344[/C][C]0.726885415994687[/C][C]0.636557292002656[/C][/ROW]
[ROW][C]73[/C][C]0.342876240331806[/C][C]0.685752480663611[/C][C]0.657123759668194[/C][/ROW]
[ROW][C]74[/C][C]0.303215972108944[/C][C]0.606431944217887[/C][C]0.696784027891056[/C][/ROW]
[ROW][C]75[/C][C]0.275393814349768[/C][C]0.550787628699536[/C][C]0.724606185650232[/C][/ROW]
[ROW][C]76[/C][C]0.398946740327753[/C][C]0.797893480655506[/C][C]0.601053259672247[/C][/ROW]
[ROW][C]77[/C][C]0.360732787788265[/C][C]0.72146557557653[/C][C]0.639267212211735[/C][/ROW]
[ROW][C]78[/C][C]0.321181598083449[/C][C]0.642363196166897[/C][C]0.678818401916551[/C][/ROW]
[ROW][C]79[/C][C]0.42040229912242[/C][C]0.84080459824484[/C][C]0.57959770087758[/C][/ROW]
[ROW][C]80[/C][C]0.474964574718529[/C][C]0.949929149437059[/C][C]0.525035425281471[/C][/ROW]
[ROW][C]81[/C][C]0.456490566119657[/C][C]0.912981132239314[/C][C]0.543509433880343[/C][/ROW]
[ROW][C]82[/C][C]0.413715603396819[/C][C]0.827431206793638[/C][C]0.586284396603181[/C][/ROW]
[ROW][C]83[/C][C]0.373537309048154[/C][C]0.747074618096308[/C][C]0.626462690951846[/C][/ROW]
[ROW][C]84[/C][C]0.356143076911906[/C][C]0.712286153823812[/C][C]0.643856923088094[/C][/ROW]
[ROW][C]85[/C][C]0.344262848967266[/C][C]0.688525697934533[/C][C]0.655737151032734[/C][/ROW]
[ROW][C]86[/C][C]0.305554406638248[/C][C]0.611108813276495[/C][C]0.694445593361752[/C][/ROW]
[ROW][C]87[/C][C]0.276297077536288[/C][C]0.552594155072575[/C][C]0.723702922463712[/C][/ROW]
[ROW][C]88[/C][C]0.247897268590154[/C][C]0.495794537180309[/C][C]0.752102731409846[/C][/ROW]
[ROW][C]89[/C][C]0.28886588102065[/C][C]0.577731762041299[/C][C]0.711134118979351[/C][/ROW]
[ROW][C]90[/C][C]0.250660968792137[/C][C]0.501321937584274[/C][C]0.749339031207863[/C][/ROW]
[ROW][C]91[/C][C]0.29043950045579[/C][C]0.58087900091158[/C][C]0.70956049954421[/C][/ROW]
[ROW][C]92[/C][C]0.273847216379238[/C][C]0.547694432758475[/C][C]0.726152783620762[/C][/ROW]
[ROW][C]93[/C][C]0.252414910255074[/C][C]0.504829820510148[/C][C]0.747585089744926[/C][/ROW]
[ROW][C]94[/C][C]0.217765024393595[/C][C]0.435530048787191[/C][C]0.782234975606405[/C][/ROW]
[ROW][C]95[/C][C]0.237973431607391[/C][C]0.475946863214783[/C][C]0.762026568392609[/C][/ROW]
[ROW][C]96[/C][C]0.245177893653281[/C][C]0.490355787306562[/C][C]0.754822106346719[/C][/ROW]
[ROW][C]97[/C][C]0.231756004818011[/C][C]0.463512009636023[/C][C]0.768243995181989[/C][/ROW]
[ROW][C]98[/C][C]0.207586690749127[/C][C]0.415173381498254[/C][C]0.792413309250873[/C][/ROW]
[ROW][C]99[/C][C]0.186798396097676[/C][C]0.373596792195351[/C][C]0.813201603902324[/C][/ROW]
[ROW][C]100[/C][C]0.192768993225638[/C][C]0.385537986451276[/C][C]0.807231006774362[/C][/ROW]
[ROW][C]101[/C][C]0.178957533738979[/C][C]0.357915067477958[/C][C]0.821042466261021[/C][/ROW]
[ROW][C]102[/C][C]0.164850202386123[/C][C]0.329700404772245[/C][C]0.835149797613877[/C][/ROW]
[ROW][C]103[/C][C]0.137169344748031[/C][C]0.274338689496062[/C][C]0.862830655251969[/C][/ROW]
[ROW][C]104[/C][C]0.115187664590596[/C][C]0.230375329181192[/C][C]0.884812335409404[/C][/ROW]
[ROW][C]105[/C][C]0.118604121351461[/C][C]0.237208242702921[/C][C]0.881395878648539[/C][/ROW]
[ROW][C]106[/C][C]0.243391225210267[/C][C]0.486782450420534[/C][C]0.756608774789733[/C][/ROW]
[ROW][C]107[/C][C]0.235907826856802[/C][C]0.471815653713603[/C][C]0.764092173143198[/C][/ROW]
[ROW][C]108[/C][C]0.273674161294888[/C][C]0.547348322589777[/C][C]0.726325838705112[/C][/ROW]
[ROW][C]109[/C][C]0.258461115624775[/C][C]0.51692223124955[/C][C]0.741538884375225[/C][/ROW]
[ROW][C]110[/C][C]0.440724907972214[/C][C]0.881449815944428[/C][C]0.559275092027786[/C][/ROW]
[ROW][C]111[/C][C]0.488529157826598[/C][C]0.977058315653197[/C][C]0.511470842173402[/C][/ROW]
[ROW][C]112[/C][C]0.465514944326266[/C][C]0.931029888652531[/C][C]0.534485055673734[/C][/ROW]
[ROW][C]113[/C][C]0.522890879704069[/C][C]0.954218240591862[/C][C]0.477109120295931[/C][/ROW]
[ROW][C]114[/C][C]0.486706276782254[/C][C]0.973412553564508[/C][C]0.513293723217746[/C][/ROW]
[ROW][C]115[/C][C]0.453050625958369[/C][C]0.906101251916738[/C][C]0.546949374041631[/C][/ROW]
[ROW][C]116[/C][C]0.453950576938298[/C][C]0.907901153876596[/C][C]0.546049423061702[/C][/ROW]
[ROW][C]117[/C][C]0.4600066896105[/C][C]0.920013379221[/C][C]0.5399933103895[/C][/ROW]
[ROW][C]118[/C][C]0.443130068077056[/C][C]0.886260136154111[/C][C]0.556869931922944[/C][/ROW]
[ROW][C]119[/C][C]0.417332832734956[/C][C]0.834665665469913[/C][C]0.582667167265044[/C][/ROW]
[ROW][C]120[/C][C]0.4121253655172[/C][C]0.824250731034399[/C][C]0.5878746344828[/C][/ROW]
[ROW][C]121[/C][C]0.382990936310109[/C][C]0.765981872620217[/C][C]0.617009063689891[/C][/ROW]
[ROW][C]122[/C][C]0.336776088105583[/C][C]0.673552176211165[/C][C]0.663223911894417[/C][/ROW]
[ROW][C]123[/C][C]0.29733482076384[/C][C]0.594669641527681[/C][C]0.70266517923616[/C][/ROW]
[ROW][C]124[/C][C]0.253144927467424[/C][C]0.506289854934848[/C][C]0.746855072532576[/C][/ROW]
[ROW][C]125[/C][C]0.213592756786271[/C][C]0.427185513572543[/C][C]0.786407243213729[/C][/ROW]
[ROW][C]126[/C][C]0.221416346968687[/C][C]0.442832693937373[/C][C]0.778583653031313[/C][/ROW]
[ROW][C]127[/C][C]0.301955593134842[/C][C]0.603911186269685[/C][C]0.698044406865158[/C][/ROW]
[ROW][C]128[/C][C]0.559908156876272[/C][C]0.880183686247456[/C][C]0.440091843123728[/C][/ROW]
[ROW][C]129[/C][C]0.527632004538455[/C][C]0.94473599092309[/C][C]0.472367995461545[/C][/ROW]
[ROW][C]130[/C][C]0.471739358597535[/C][C]0.943478717195069[/C][C]0.528260641402465[/C][/ROW]
[ROW][C]131[/C][C]0.422498649760017[/C][C]0.844997299520034[/C][C]0.577501350239983[/C][/ROW]
[ROW][C]132[/C][C]0.461610909834701[/C][C]0.923221819669401[/C][C]0.538389090165299[/C][/ROW]
[ROW][C]133[/C][C]0.525350034611951[/C][C]0.949299930776098[/C][C]0.474649965388049[/C][/ROW]
[ROW][C]134[/C][C]0.622047599487443[/C][C]0.755904801025113[/C][C]0.377952400512557[/C][/ROW]
[ROW][C]135[/C][C]0.604574758813679[/C][C]0.790850482372641[/C][C]0.395425241186321[/C][/ROW]
[ROW][C]136[/C][C]0.542876526604858[/C][C]0.914246946790284[/C][C]0.457123473395142[/C][/ROW]
[ROW][C]137[/C][C]0.515297760158462[/C][C]0.969404479683076[/C][C]0.484702239841538[/C][/ROW]
[ROW][C]138[/C][C]0.450704188303647[/C][C]0.901408376607294[/C][C]0.549295811696353[/C][/ROW]
[ROW][C]139[/C][C]0.543780995600601[/C][C]0.912438008798799[/C][C]0.456219004399399[/C][/ROW]
[ROW][C]140[/C][C]0.47508623711912[/C][C]0.95017247423824[/C][C]0.52491376288088[/C][/ROW]
[ROW][C]141[/C][C]0.448193952531664[/C][C]0.896387905063328[/C][C]0.551806047468336[/C][/ROW]
[ROW][C]142[/C][C]0.412073833892026[/C][C]0.824147667784051[/C][C]0.587926166107974[/C][/ROW]
[ROW][C]143[/C][C]0.433673797182064[/C][C]0.867347594364127[/C][C]0.566326202817936[/C][/ROW]
[ROW][C]144[/C][C]0.540660849589096[/C][C]0.918678300821808[/C][C]0.459339150410904[/C][/ROW]
[ROW][C]145[/C][C]0.45762252276249[/C][C]0.915245045524981[/C][C]0.54237747723751[/C][/ROW]
[ROW][C]146[/C][C]0.739947166888869[/C][C]0.520105666222261[/C][C]0.260052833111131[/C][/ROW]
[ROW][C]147[/C][C]0.661593302598055[/C][C]0.67681339480389[/C][C]0.338406697401945[/C][/ROW]
[ROW][C]148[/C][C]0.577469484329098[/C][C]0.845061031341804[/C][C]0.422530515670902[/C][/ROW]
[ROW][C]149[/C][C]0.477868224991762[/C][C]0.955736449983524[/C][C]0.522131775008238[/C][/ROW]
[ROW][C]150[/C][C]0.381459274316311[/C][C]0.762918548632622[/C][C]0.618540725683689[/C][/ROW]
[ROW][C]151[/C][C]0.336250021685973[/C][C]0.672500043371946[/C][C]0.663749978314027[/C][/ROW]
[ROW][C]152[/C][C]0.954104908742433[/C][C]0.0917901825151336[/C][C]0.0458950912575668[/C][/ROW]
[ROW][C]153[/C][C]0.89447997523698[/C][C]0.21104004952604[/C][C]0.10552002476302[/C][/ROW]
[ROW][C]154[/C][C]0.81438329380727[/C][C]0.37123341238546[/C][C]0.18561670619273[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154490&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154490&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
80.9417641580479290.1164716839041410.0582358419520706
90.8861935003654810.2276129992690380.113806499634519
100.8103953864643380.3792092270713240.189604613535662
110.7665377838289660.4669244323420680.233462216171034
120.7299916409207120.5400167181585750.270008359079288
130.7783799014278880.4432401971442240.221620098572112
140.7184036336502050.563192732699590.281596366349795
150.7662111330813270.4675777338373450.233788866918673
160.7763593307611390.4472813384777220.223640669238861
170.7140498318128680.5719003363742640.285950168187132
180.6518099401409570.6963801197180860.348190059859043
190.6292759506609370.7414480986781250.370724049339063
200.6218640839104650.7562718321790710.378135916089535
210.6535175615019680.6929648769960640.346482438498032
220.6041318765202510.7917362469594980.395868123479749
230.5863152582919150.8273694834161710.413684741708085
240.5183219871774320.9633560256451350.481678012822568
250.5342671910477470.9314656179045050.465732808952253
260.7867164146864030.4265671706271940.213283585313597
270.7370122858025550.5259754283948910.262987714197445
280.7346096645312110.5307806709375790.265390335468789
290.7055025790063830.5889948419872340.294497420993617
300.6564901215250570.6870197569498860.343509878474943
310.6430294455770850.713941108845830.356970554422915
320.8233499538215860.3533000923568280.176650046178414
330.8060429210257350.3879141579485310.193957078974265
340.7875028390266570.4249943219466870.212497160973344
350.7447036470415580.5105927059168840.255296352958442
360.7363220646749270.5273558706501460.263677935325073
370.7413545484620390.5172909030759230.258645451537962
380.7062497228051220.5875005543897560.293750277194878
390.7823062069830230.4353875860339540.217693793016977
400.7416328133290340.5167343733419330.258367186670967
410.7254206095890730.5491587808218540.274579390410927
420.6803947581176030.6392104837647950.319605241882397
430.680893554401150.63821289119770.31910644559885
440.6360140440059070.7279719119881870.363985955994094
450.7080205488097940.5839589023804120.291979451190206
460.6788450845411710.6423098309176580.321154915458829
470.6372354297464960.7255291405070080.362764570253504
480.6142528122756620.7714943754486760.385747187724338
490.5647896098193790.8704207803612420.435210390180621
500.5808194558730370.8383610882539260.419180544126963
510.6303248449314280.7393503101371450.369675155068572
520.5924740461630510.8150519076738980.407525953836949
530.5581037167578550.883792566484290.441896283242145
540.5247057279933380.9505885440133240.475294272006662
550.5006476376069740.9987047247860520.499352362393026
560.4871847197218320.9743694394436640.512815280278168
570.5194866089505280.9610267820989440.480513391049472
580.6520308292274940.6959383415450110.347969170772506
590.6182247959322490.7635504081355030.381775204067751
600.5738982540532190.8522034918935610.426101745946781
610.540874245784140.918251508431720.45912575421586
620.543758259636920.912483480726160.45624174036308
630.5007805553045250.9984388893909510.499219444695475
640.522215257054930.9555694858901390.47778474294507
650.4761522125730750.952304425146150.523847787426925
660.4307826792941360.8615653585882710.569217320705865
670.3980278763029550.7960557526059110.601972123697045
680.3778027067584830.7556054135169660.622197293241517
690.3563157905339080.7126315810678160.643684209466092
700.3160353959335040.6320707918670090.683964604066496
710.3022962950387530.6045925900775050.697703704961247
720.3634427079973440.7268854159946870.636557292002656
730.3428762403318060.6857524806636110.657123759668194
740.3032159721089440.6064319442178870.696784027891056
750.2753938143497680.5507876286995360.724606185650232
760.3989467403277530.7978934806555060.601053259672247
770.3607327877882650.721465575576530.639267212211735
780.3211815980834490.6423631961668970.678818401916551
790.420402299122420.840804598244840.57959770087758
800.4749645747185290.9499291494370590.525035425281471
810.4564905661196570.9129811322393140.543509433880343
820.4137156033968190.8274312067936380.586284396603181
830.3735373090481540.7470746180963080.626462690951846
840.3561430769119060.7122861538238120.643856923088094
850.3442628489672660.6885256979345330.655737151032734
860.3055544066382480.6111088132764950.694445593361752
870.2762970775362880.5525941550725750.723702922463712
880.2478972685901540.4957945371803090.752102731409846
890.288865881020650.5777317620412990.711134118979351
900.2506609687921370.5013219375842740.749339031207863
910.290439500455790.580879000911580.70956049954421
920.2738472163792380.5476944327584750.726152783620762
930.2524149102550740.5048298205101480.747585089744926
940.2177650243935950.4355300487871910.782234975606405
950.2379734316073910.4759468632147830.762026568392609
960.2451778936532810.4903557873065620.754822106346719
970.2317560048180110.4635120096360230.768243995181989
980.2075866907491270.4151733814982540.792413309250873
990.1867983960976760.3735967921953510.813201603902324
1000.1927689932256380.3855379864512760.807231006774362
1010.1789575337389790.3579150674779580.821042466261021
1020.1648502023861230.3297004047722450.835149797613877
1030.1371693447480310.2743386894960620.862830655251969
1040.1151876645905960.2303753291811920.884812335409404
1050.1186041213514610.2372082427029210.881395878648539
1060.2433912252102670.4867824504205340.756608774789733
1070.2359078268568020.4718156537136030.764092173143198
1080.2736741612948880.5473483225897770.726325838705112
1090.2584611156247750.516922231249550.741538884375225
1100.4407249079722140.8814498159444280.559275092027786
1110.4885291578265980.9770583156531970.511470842173402
1120.4655149443262660.9310298886525310.534485055673734
1130.5228908797040690.9542182405918620.477109120295931
1140.4867062767822540.9734125535645080.513293723217746
1150.4530506259583690.9061012519167380.546949374041631
1160.4539505769382980.9079011538765960.546049423061702
1170.46000668961050.9200133792210.5399933103895
1180.4431300680770560.8862601361541110.556869931922944
1190.4173328327349560.8346656654699130.582667167265044
1200.41212536551720.8242507310343990.5878746344828
1210.3829909363101090.7659818726202170.617009063689891
1220.3367760881055830.6735521762111650.663223911894417
1230.297334820763840.5946696415276810.70266517923616
1240.2531449274674240.5062898549348480.746855072532576
1250.2135927567862710.4271855135725430.786407243213729
1260.2214163469686870.4428326939373730.778583653031313
1270.3019555931348420.6039111862696850.698044406865158
1280.5599081568762720.8801836862474560.440091843123728
1290.5276320045384550.944735990923090.472367995461545
1300.4717393585975350.9434787171950690.528260641402465
1310.4224986497600170.8449972995200340.577501350239983
1320.4616109098347010.9232218196694010.538389090165299
1330.5253500346119510.9492999307760980.474649965388049
1340.6220475994874430.7559048010251130.377952400512557
1350.6045747588136790.7908504823726410.395425241186321
1360.5428765266048580.9142469467902840.457123473395142
1370.5152977601584620.9694044796830760.484702239841538
1380.4507041883036470.9014083766072940.549295811696353
1390.5437809956006010.9124380087987990.456219004399399
1400.475086237119120.950172474238240.52491376288088
1410.4481939525316640.8963879050633280.551806047468336
1420.4120738338920260.8241476677840510.587926166107974
1430.4336737971820640.8673475943641270.566326202817936
1440.5406608495890960.9186783008218080.459339150410904
1450.457622522762490.9152450455249810.54237747723751
1460.7399471668888690.5201056662222610.260052833111131
1470.6615933025980550.676813394803890.338406697401945
1480.5774694843290980.8450610313418040.422530515670902
1490.4778682249917620.9557364499835240.522131775008238
1500.3814592743163110.7629185486326220.618540725683689
1510.3362500216859730.6725000433719460.663749978314027
1520.9541049087424330.09179018251513360.0458950912575668
1530.894479975236980.211040049526040.10552002476302
1540.814383293807270.371233412385460.18561670619273






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

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 1 & 0.00680272108843537 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154490&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]1[/C][C]0.00680272108843537[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154490&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154490&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 level00OK
10% type I error level10.00680272108843537OK


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 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 2 ; 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')
}