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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationWed, 14 Dec 2011 08:13:10 -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/14/t1323868443fbpgnd38zuragvz.htm/, Retrieved Wed, 01 May 2024 19:29:50 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=154927, Retrieved Wed, 01 May 2024 19:29:50 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact72

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2010-12-05 18:56:24] [b98453cac15ba1066b407e146608df68]
-   PD    [Multiple Regression] [multiple regression] [2011-12-14 13:13:10] [3ce5305e82abe5b27e1176cc99946857] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
2	41	38	13	12	14
2	39	32	16	11	18
2	30	35	19	15	11
1	31	33	15	6	12
2	34	37	14	13	16
2	35	29	13	10	18
2	39	31	19	12	14
2	34	36	15	14	14
2	36	35	14	12	15
2	37	38	15	6	15
1	38	31	16	10	17
2	36	34	16	12	19
1	38	35	16	12	10
2	39	38	16	11	16
2	33	37	17	15	18
1	32	33	15	12	14
1	36	32	15	10	14
2	38	38	20	12	17
1	39	38	18	11	14
2	32	32	16	12	16
1	32	33	16	11	18
2	31	31	16	12	11
2	39	38	19	13	14
2	37	39	16	11	12
1	39	32	17	9	17
2	41	32	17	13	9
1	36	35	16	10	16
2	33	37	15	14	14
2	33	33	16	12	15
1	34	33	14	10	11
2	31	28	15	12	16
1	27	32	12	8	13
2	37	31	14	10	17
2	34	37	16	12	15
1	34	30	14	12	14
1	32	33	7	7	16
1	29	31	10	6	9
1	36	33	14	12	15
2	29	31	16	10	17
1	35	33	16	10	13
1	37	32	16	10	15
2	34	33	14	12	16
1	38	32	20	15	16
1	35	33	14	10	12
2	38	28	14	10	12
2	37	35	11	12	11
2	38	39	14	13	15
2	33	34	15	11	15
2	36	38	16	11	17
1	38	32	14	12	13
2	32	38	16	14	16
1	32	30	14	10	14
1	32	33	12	12	11
2	34	38	16	13	12
1	32	32	9	5	12
2	37	32	14	6	15
2	39	34	16	12	16
2	29	34	16	12	15
1	37	36	15	11	12
2	35	34	16	10	12
1	30	28	12	7	8
1	38	34	16	12	13
2	34	35	16	14	11
2	31	35	14	11	14
2	34	31	16	12	15
1	35	37	17	13	10
2	36	35	18	14	11
1	30	27	18	11	12
2	39	40	12	12	15
1	35	37	16	12	15
1	38	36	10	8	14
2	31	38	14	11	16
2	34	39	18	14	15
1	38	41	18	14	15
1	34	27	16	12	13
2	39	30	17	9	12
2	37	37	16	13	17
2	34	31	16	11	13
1	28	31	13	12	15
1	37	27	16	12	13
1	33	36	16	12	15
1	37	38	20	12	16
2	35	37	16	12	15
1	37	33	15	12	16
2	32	34	15	11	15
2	33	31	16	10	14
1	38	39	14	9	15
2	33	34	16	12	14
2	29	32	16	12	13
2	33	33	15	12	7
2	31	36	12	9	17
2	36	32	17	15	13
2	35	41	16	12	15
2	32	28	15	12	14
2	29	30	13	12	13
2	39	36	16	10	16
2	37	35	16	13	12
2	35	31	16	9	14
1	37	34	16	12	17
1	32	36	14	10	15
2	38	36	16	14	17
1	37	35	16	11	12
2	36	37	20	15	16
1	32	28	15	11	11
2	33	39	16	11	15
1	40	32	13	12	9
2	38	35	17	12	16
1	41	39	16	12	15
1	36	35	16	11	10
2	43	42	12	7	10
2	30	34	16	12	15
2	31	33	16	14	11
2	32	41	17	11	13
1	32	33	13	11	14
2	37	34	12	10	18
1	37	32	18	13	16
2	33	40	14	13	14
2	34	40	14	8	14
2	33	35	13	11	14
2	38	36	16	12	14
2	33	37	13	11	12
2	31	27	16	13	14
2	38	39	13	12	15
2	37	38	16	14	15
2	33	31	15	13	15
2	31	33	16	15	13
1	39	32	15	10	17
2	44	39	17	11	17
2	33	36	15	9	19
2	35	33	12	11	15
1	32	33	16	10	13
1	28	32	10	11	9
2	40	37	16	8	15
1	27	30	12	11	15
1	37	38	14	12	15
2	32	29	15	12	16
1	28	22	13	9	11
1	34	35	15	11	14
2	30	35	11	10	11
2	35	34	12	8	15
1	31	35	8	9	13
2	32	34	16	8	15
1	30	34	15	9	16
2	30	35	17	15	14
1	31	23	16	11	15
2	40	31	10	8	16
2	32	27	18	13	16
1	36	36	13	12	11
1	32	31	16	12	12
1	35	32	13	9	9
2	38	39	10	7	16
2	42	37	15	13	13
1	34	38	16	9	16
2	35	39	16	6	12
2	35	34	14	8	9
2	33	31	10	8	13
2	36	32	17	15	13
2	32	37	13	6	14
2	33	36	15	9	19
2	34	32	16	11	13
2	32	35	12	8	12
2	34	36	13	8	13

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 6 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154927&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154927&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154927&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net






Multiple Linear Regression - Estimated Regression Equation
software[t] = + 3.86486110005002 + 0.450020067915905gender[t] -0.0431725199068259connected[t] + 0.0184995462606891separate[t] + 0.524883753581253learning[t] -0.0372689345137121happiness[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
software[t] =  +  3.86486110005002 +  0.450020067915905gender[t] -0.0431725199068259connected[t] +  0.0184995462606891separate[t] +  0.524883753581253learning[t] -0.0372689345137121happiness[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154927&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]software[t] =  +  3.86486110005002 +  0.450020067915905gender[t] -0.0431725199068259connected[t] +  0.0184995462606891separate[t] +  0.524883753581253learning[t] -0.0372689345137121happiness[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154927&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154927&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
software[t] = + 3.86486110005002 + 0.450020067915905gender[t] -0.0431725199068259connected[t] + 0.0184995462606891separate[t] + 0.524883753581253learning[t] -0.0372689345137121happiness[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)3.864861100050021.8884932.04650.0423830.021191
gender0.4500200679159050.3061281.470.1435650.071782
connected-0.04317251990682590.046371-0.9310.3532820.176641
separate0.01849954626068910.0443860.41680.6774090.338705
learning0.5248837535812530.0655548.00700
happiness-0.03726893451371210.063202-0.58970.5562580.278129

\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) & 3.86486110005002 & 1.888493 & 2.0465 & 0.042383 & 0.021191 \tabularnewline
gender & 0.450020067915905 & 0.306128 & 1.47 & 0.143565 & 0.071782 \tabularnewline
connected & -0.0431725199068259 & 0.046371 & -0.931 & 0.353282 & 0.176641 \tabularnewline
separate & 0.0184995462606891 & 0.044386 & 0.4168 & 0.677409 & 0.338705 \tabularnewline
learning & 0.524883753581253 & 0.065554 & 8.007 & 0 & 0 \tabularnewline
happiness & -0.0372689345137121 & 0.063202 & -0.5897 & 0.556258 & 0.278129 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154927&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]3.86486110005002[/C][C]1.888493[/C][C]2.0465[/C][C]0.042383[/C][C]0.021191[/C][/ROW]
[ROW][C]gender[/C][C]0.450020067915905[/C][C]0.306128[/C][C]1.47[/C][C]0.143565[/C][C]0.071782[/C][/ROW]
[ROW][C]connected[/C][C]-0.0431725199068259[/C][C]0.046371[/C][C]-0.931[/C][C]0.353282[/C][C]0.176641[/C][/ROW]
[ROW][C]separate[/C][C]0.0184995462606891[/C][C]0.044386[/C][C]0.4168[/C][C]0.677409[/C][C]0.338705[/C][/ROW]
[ROW][C]learning[/C][C]0.524883753581253[/C][C]0.065554[/C][C]8.007[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]happiness[/C][C]-0.0372689345137121[/C][C]0.063202[/C][C]-0.5897[/C][C]0.556258[/C][C]0.278129[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154927&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154927&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)3.864861100050021.8884932.04650.0423830.021191
gender0.4500200679159050.3061281.470.1435650.071782
connected-0.04317251990682590.046371-0.9310.3532820.176641
separate0.01849954626068910.0443860.41680.6774090.338705
learning0.5248837535812530.0655548.00700
happiness-0.03726893451371210.063202-0.58970.5562580.278129






Multiple Linear Regression - Regression Statistics
Multiple R0.559643910123
R-squared0.31320130613776
Adjusted R-squared0.291188527488329
F-TEST (value)14.2281586130363
F-TEST (DF numerator)5
F-TEST (DF denominator)156
p-value1.81652470843119e-11
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.80313266470355
Sum Squared Residuals507.200835417264

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.559643910123 \tabularnewline
R-squared & 0.31320130613776 \tabularnewline
Adjusted R-squared & 0.291188527488329 \tabularnewline
F-TEST (value) & 14.2281586130363 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 156 \tabularnewline
p-value & 1.81652470843119e-11 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.80313266470355 \tabularnewline
Sum Squared Residuals & 507.200835417264 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154927&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.559643910123[/C][/ROW]
[ROW][C]R-squared[/C][C]0.31320130613776[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.291188527488329[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]14.2281586130363[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]156[/C][/ROW]
[ROW][C]p-value[/C][C]1.81652470843119e-11[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.80313266470355[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]507.200835417264[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154927&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154927&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 R0.559643910123
R-squared0.31320130613776
Adjusted R-squared0.291188527488329
F-TEST (value)14.2281586130363
F-TEST (DF numerator)5
F-TEST (DF denominator)156
p-value1.81652470843119e-11
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.80313266470355
Sum Squared Residuals507.200835417264






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1129.999534390972482.00046560902752
21111.4004576759109-0.400457675910907
31513.68004279619421.31995720380585
4611.0130471670113-5.01304716701132
51310.73358836861342.2664116313866
6109.942997856012380.0570021439876165
71213.1056851284488-1.10568512844883
81411.31451044496142.68548955503861
91210.64751317079211.35248682920792
10611.1847230432486-5.18472304324858
111011.0123795161549-1.01237951615485
121211.52970539363910.470294606360949
131211.34726024279360.652739757206408
141111.5859928225025-0.585992822502466
151512.27687428023662.72312571976344
161210.89533677807711.10466322192293
171010.7041471521891-0.704147152189075
181213.6914314222206-1.69143142222059
191112.2602781307765-1.26027813077649
201211.77720318428610.222796815713887
211111.2711447936035-0.271144793603472
221211.98822083050080.0117791694991905
231313.2351819522736-0.23518195227365
241111.8399131466317-0.839913146631655
25911.51259029609-2.51259029608997
261312.17441680030190.825583199698083
271011.209991675525-1.20999167552497
281411.37618251112892.6238174888711
291211.78979914515370.210200854846312
301010.3959147882233-0.395914788223299
311211.22149376556890.778506234431071
3289.55531750512046-1.55531750512046
331010.4558045968151-0.455804596815076
341211.82062481028960.179375189710382
351210.22860934590011.7713906540999
3676.621728880399620.378271119600382
3768.54978114993846-2.54978114993846
381210.16049401035481.8395059896452
391011.8509522632322-1.85095226323219
401011.3279719064516-1.32797190645155
411011.1485894513498-1.14858945134979
421210.65959018357061.34040981642936
431513.16768301125431.83231698874574
441010.3154733338028-0.315473333802761
451010.5434781106947-0.543478110694743
46129.178765128196342.82123487180366
471310.63516631602122.36483368397881
481111.2834149378331-0.283414937833124
491111.6782414477092-0.678241447709231
501210.13018729330791.86981270669212
511411.88820046185022.11179953814975
521010.3149543857137-0.314954385713747
53129.432492320874452.56750767912556
541311.95093116009141.04906883990856
5557.80207257935628-2.80207257935628
56610.5488420121032-4.54884201210319
571211.51199463745970.48800536254029
581211.98098877104170.0190112289583197
591110.80951068635240.190489313647571
601011.8337604551419-1.83376045514186
6179.53814643292579-2.53814643292579
621211.21695389299180.783046107008234
631411.93270145582312.06729854417691
641110.90064470483990.0993552951600766
651211.70962753272550.290372467274516
661312.03866064861670.961339351383299
671412.89612392317191.10387607682806
681112.5198736700978-1.51987367009777
69129.560725835212542.43927416478746
701211.32743222246690.672567777533113
7188.06738152951191-0.0673815295119132
721110.88160547459460.118394525405433
731412.90739140997351.0926085900265
741412.32168035495171.67831964504833
751211.26014714879420.739852851205754
76912.1119559440531-3.11195594405305
771311.61656938154171.38343061845828
781111.7841654017529-0.784165401752908
79129.943991323506772.05600867649323
801211.13062958907380.869370410926231
811211.39527771601980.60472228398015
821213.3218528087252-1.32185280872523
831211.77745229038280.222547709617208
841210.60493630951551.39506369048449
851111.3265874577399-0.32658745773995
861011.790068987146-1.79006898714602
87910.1851462481053-1.18514624810528
881211.84556762592810.154432374071911
891212.0185275475477-0.0185275475477263
901211.56306686768210.436933132317869
9199.75756994039697-0.75756994039697
921512.24120366178122.7587963382188
931211.85145047542550.148549524574451
941211.25285911468950.747140885310473
951210.40687719428261.59312280571741
961011.5489937299811-1.54899372998109
971311.76591496158891.2340850384111
98911.7037239473324-2.70372394733237
991211.11105067484370.888949325156256
1001010.3886827287642-0.38868272876417
1011411.55489731537422.4451026846258
1021111.315894893673-0.315894893672994
1031513.79654585028731.20345414971273
1041110.91464585031480.0853541496852418
1051111.9007964227178-0.900796422717822
106129.668034237967832.33196576203217
1071212.0985504572085-0.0985504572084777
1081211.10539619554730.89460380445269
1091111.4336052826072-0.433605282607244
11079.61137952067518-2.61137952067518
1111211.93781625113490.0621837488651456
1121412.02521992302221.97478007697781
1131112.5803896577547-1.5803896577547
114119.845569270914561.15443072908544
115109.424266793920920.575733206079076
1161312.16108802399860.838911976001416
1171310.90679739632972.09320260367028
118810.8636248764229-2.86362487642289
1191110.2894159114450.710584088554982
1201211.66670411891530.333295881084662
1211110.40095287299380.599047127006179
1221311.80241584191691.19758415808308
1231210.11028256243991.88971743756007
1241411.70960679682982.29039320317017
1251311.22791629905111.77208370094894
1261511.95068205399483.04931794600524
1271010.4628227889275-0.462822788927461
1281111.8762445882966-0.876244588296567
129911.1713382922997-2.17133829229965
130119.603919091015021.39608090898498
1311011.457489466172-1.45748946617203
132118.611453216105982.38854678389402
133811.5615896908487-3.56158969084866
134119.443780543571661.55621945642834
1351210.20981922175141.79018077824858
1361211.19682079192280.803179208077208
13799.92657114521542-0.92657114521542
1381110.84599083078480.154009169215206
139109.480972767544130.519027232455874
14089.62241863727571-1.62241863727571
14197.338591049950211.66140895004979
142811.8514712113212-3.8514712113212
143910.925643495124-1.92564349512398
1441512.51846848549052.48153151450949
1451111.2411286544445-0.241128654444544
14688.2640209572833-0.264020957283298
1471312.73447296014520.265527039854827
148129.840184633610462.15981536638954
1491211.45775930816440.542240691835634
15099.88389683750195-0.883896837501954
15178.49836236718246-1.49836236718246
1521311.02489876648121.97510123351882
153911.3518353541207-2.35183535412069
154611.9262581864453-5.92625818644531
155810.8957997515205-2.89579975152049
15688.67803540017221-0.678035400172215
1571512.24120366178122.7587963382188
158610.3695875238732-4.36958752387322
159911.1713382922997-2.17133829229965
1601111.8026649480136-0.802664948013597
16189.88224254679801-1.88224254679802
162810.3020118723126-2.30201187231259

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 12 & 9.99953439097248 & 2.00046560902752 \tabularnewline
2 & 11 & 11.4004576759109 & -0.400457675910907 \tabularnewline
3 & 15 & 13.6800427961942 & 1.31995720380585 \tabularnewline
4 & 6 & 11.0130471670113 & -5.01304716701132 \tabularnewline
5 & 13 & 10.7335883686134 & 2.2664116313866 \tabularnewline
6 & 10 & 9.94299785601238 & 0.0570021439876165 \tabularnewline
7 & 12 & 13.1056851284488 & -1.10568512844883 \tabularnewline
8 & 14 & 11.3145104449614 & 2.68548955503861 \tabularnewline
9 & 12 & 10.6475131707921 & 1.35248682920792 \tabularnewline
10 & 6 & 11.1847230432486 & -5.18472304324858 \tabularnewline
11 & 10 & 11.0123795161549 & -1.01237951615485 \tabularnewline
12 & 12 & 11.5297053936391 & 0.470294606360949 \tabularnewline
13 & 12 & 11.3472602427936 & 0.652739757206408 \tabularnewline
14 & 11 & 11.5859928225025 & -0.585992822502466 \tabularnewline
15 & 15 & 12.2768742802366 & 2.72312571976344 \tabularnewline
16 & 12 & 10.8953367780771 & 1.10466322192293 \tabularnewline
17 & 10 & 10.7041471521891 & -0.704147152189075 \tabularnewline
18 & 12 & 13.6914314222206 & -1.69143142222059 \tabularnewline
19 & 11 & 12.2602781307765 & -1.26027813077649 \tabularnewline
20 & 12 & 11.7772031842861 & 0.222796815713887 \tabularnewline
21 & 11 & 11.2711447936035 & -0.271144793603472 \tabularnewline
22 & 12 & 11.9882208305008 & 0.0117791694991905 \tabularnewline
23 & 13 & 13.2351819522736 & -0.23518195227365 \tabularnewline
24 & 11 & 11.8399131466317 & -0.839913146631655 \tabularnewline
25 & 9 & 11.51259029609 & -2.51259029608997 \tabularnewline
26 & 13 & 12.1744168003019 & 0.825583199698083 \tabularnewline
27 & 10 & 11.209991675525 & -1.20999167552497 \tabularnewline
28 & 14 & 11.3761825111289 & 2.6238174888711 \tabularnewline
29 & 12 & 11.7897991451537 & 0.210200854846312 \tabularnewline
30 & 10 & 10.3959147882233 & -0.395914788223299 \tabularnewline
31 & 12 & 11.2214937655689 & 0.778506234431071 \tabularnewline
32 & 8 & 9.55531750512046 & -1.55531750512046 \tabularnewline
33 & 10 & 10.4558045968151 & -0.455804596815076 \tabularnewline
34 & 12 & 11.8206248102896 & 0.179375189710382 \tabularnewline
35 & 12 & 10.2286093459001 & 1.7713906540999 \tabularnewline
36 & 7 & 6.62172888039962 & 0.378271119600382 \tabularnewline
37 & 6 & 8.54978114993846 & -2.54978114993846 \tabularnewline
38 & 12 & 10.1604940103548 & 1.8395059896452 \tabularnewline
39 & 10 & 11.8509522632322 & -1.85095226323219 \tabularnewline
40 & 10 & 11.3279719064516 & -1.32797190645155 \tabularnewline
41 & 10 & 11.1485894513498 & -1.14858945134979 \tabularnewline
42 & 12 & 10.6595901835706 & 1.34040981642936 \tabularnewline
43 & 15 & 13.1676830112543 & 1.83231698874574 \tabularnewline
44 & 10 & 10.3154733338028 & -0.315473333802761 \tabularnewline
45 & 10 & 10.5434781106947 & -0.543478110694743 \tabularnewline
46 & 12 & 9.17876512819634 & 2.82123487180366 \tabularnewline
47 & 13 & 10.6351663160212 & 2.36483368397881 \tabularnewline
48 & 11 & 11.2834149378331 & -0.283414937833124 \tabularnewline
49 & 11 & 11.6782414477092 & -0.678241447709231 \tabularnewline
50 & 12 & 10.1301872933079 & 1.86981270669212 \tabularnewline
51 & 14 & 11.8882004618502 & 2.11179953814975 \tabularnewline
52 & 10 & 10.3149543857137 & -0.314954385713747 \tabularnewline
53 & 12 & 9.43249232087445 & 2.56750767912556 \tabularnewline
54 & 13 & 11.9509311600914 & 1.04906883990856 \tabularnewline
55 & 5 & 7.80207257935628 & -2.80207257935628 \tabularnewline
56 & 6 & 10.5488420121032 & -4.54884201210319 \tabularnewline
57 & 12 & 11.5119946374597 & 0.48800536254029 \tabularnewline
58 & 12 & 11.9809887710417 & 0.0190112289583197 \tabularnewline
59 & 11 & 10.8095106863524 & 0.190489313647571 \tabularnewline
60 & 10 & 11.8337604551419 & -1.83376045514186 \tabularnewline
61 & 7 & 9.53814643292579 & -2.53814643292579 \tabularnewline
62 & 12 & 11.2169538929918 & 0.783046107008234 \tabularnewline
63 & 14 & 11.9327014558231 & 2.06729854417691 \tabularnewline
64 & 11 & 10.9006447048399 & 0.0993552951600766 \tabularnewline
65 & 12 & 11.7096275327255 & 0.290372467274516 \tabularnewline
66 & 13 & 12.0386606486167 & 0.961339351383299 \tabularnewline
67 & 14 & 12.8961239231719 & 1.10387607682806 \tabularnewline
68 & 11 & 12.5198736700978 & -1.51987367009777 \tabularnewline
69 & 12 & 9.56072583521254 & 2.43927416478746 \tabularnewline
70 & 12 & 11.3274322224669 & 0.672567777533113 \tabularnewline
71 & 8 & 8.06738152951191 & -0.0673815295119132 \tabularnewline
72 & 11 & 10.8816054745946 & 0.118394525405433 \tabularnewline
73 & 14 & 12.9073914099735 & 1.0926085900265 \tabularnewline
74 & 14 & 12.3216803549517 & 1.67831964504833 \tabularnewline
75 & 12 & 11.2601471487942 & 0.739852851205754 \tabularnewline
76 & 9 & 12.1119559440531 & -3.11195594405305 \tabularnewline
77 & 13 & 11.6165693815417 & 1.38343061845828 \tabularnewline
78 & 11 & 11.7841654017529 & -0.784165401752908 \tabularnewline
79 & 12 & 9.94399132350677 & 2.05600867649323 \tabularnewline
80 & 12 & 11.1306295890738 & 0.869370410926231 \tabularnewline
81 & 12 & 11.3952777160198 & 0.60472228398015 \tabularnewline
82 & 12 & 13.3218528087252 & -1.32185280872523 \tabularnewline
83 & 12 & 11.7774522903828 & 0.222547709617208 \tabularnewline
84 & 12 & 10.6049363095155 & 1.39506369048449 \tabularnewline
85 & 11 & 11.3265874577399 & -0.32658745773995 \tabularnewline
86 & 10 & 11.790068987146 & -1.79006898714602 \tabularnewline
87 & 9 & 10.1851462481053 & -1.18514624810528 \tabularnewline
88 & 12 & 11.8455676259281 & 0.154432374071911 \tabularnewline
89 & 12 & 12.0185275475477 & -0.0185275475477263 \tabularnewline
90 & 12 & 11.5630668676821 & 0.436933132317869 \tabularnewline
91 & 9 & 9.75756994039697 & -0.75756994039697 \tabularnewline
92 & 15 & 12.2412036617812 & 2.7587963382188 \tabularnewline
93 & 12 & 11.8514504754255 & 0.148549524574451 \tabularnewline
94 & 12 & 11.2528591146895 & 0.747140885310473 \tabularnewline
95 & 12 & 10.4068771942826 & 1.59312280571741 \tabularnewline
96 & 10 & 11.5489937299811 & -1.54899372998109 \tabularnewline
97 & 13 & 11.7659149615889 & 1.2340850384111 \tabularnewline
98 & 9 & 11.7037239473324 & -2.70372394733237 \tabularnewline
99 & 12 & 11.1110506748437 & 0.888949325156256 \tabularnewline
100 & 10 & 10.3886827287642 & -0.38868272876417 \tabularnewline
101 & 14 & 11.5548973153742 & 2.4451026846258 \tabularnewline
102 & 11 & 11.315894893673 & -0.315894893672994 \tabularnewline
103 & 15 & 13.7965458502873 & 1.20345414971273 \tabularnewline
104 & 11 & 10.9146458503148 & 0.0853541496852418 \tabularnewline
105 & 11 & 11.9007964227178 & -0.900796422717822 \tabularnewline
106 & 12 & 9.66803423796783 & 2.33196576203217 \tabularnewline
107 & 12 & 12.0985504572085 & -0.0985504572084777 \tabularnewline
108 & 12 & 11.1053961955473 & 0.89460380445269 \tabularnewline
109 & 11 & 11.4336052826072 & -0.433605282607244 \tabularnewline
110 & 7 & 9.61137952067518 & -2.61137952067518 \tabularnewline
111 & 12 & 11.9378162511349 & 0.0621837488651456 \tabularnewline
112 & 14 & 12.0252199230222 & 1.97478007697781 \tabularnewline
113 & 11 & 12.5803896577547 & -1.5803896577547 \tabularnewline
114 & 11 & 9.84556927091456 & 1.15443072908544 \tabularnewline
115 & 10 & 9.42426679392092 & 0.575733206079076 \tabularnewline
116 & 13 & 12.1610880239986 & 0.838911976001416 \tabularnewline
117 & 13 & 10.9067973963297 & 2.09320260367028 \tabularnewline
118 & 8 & 10.8636248764229 & -2.86362487642289 \tabularnewline
119 & 11 & 10.289415911445 & 0.710584088554982 \tabularnewline
120 & 12 & 11.6667041189153 & 0.333295881084662 \tabularnewline
121 & 11 & 10.4009528729938 & 0.599047127006179 \tabularnewline
122 & 13 & 11.8024158419169 & 1.19758415808308 \tabularnewline
123 & 12 & 10.1102825624399 & 1.88971743756007 \tabularnewline
124 & 14 & 11.7096067968298 & 2.29039320317017 \tabularnewline
125 & 13 & 11.2279162990511 & 1.77208370094894 \tabularnewline
126 & 15 & 11.9506820539948 & 3.04931794600524 \tabularnewline
127 & 10 & 10.4628227889275 & -0.462822788927461 \tabularnewline
128 & 11 & 11.8762445882966 & -0.876244588296567 \tabularnewline
129 & 9 & 11.1713382922997 & -2.17133829229965 \tabularnewline
130 & 11 & 9.60391909101502 & 1.39608090898498 \tabularnewline
131 & 10 & 11.457489466172 & -1.45748946617203 \tabularnewline
132 & 11 & 8.61145321610598 & 2.38854678389402 \tabularnewline
133 & 8 & 11.5615896908487 & -3.56158969084866 \tabularnewline
134 & 11 & 9.44378054357166 & 1.55621945642834 \tabularnewline
135 & 12 & 10.2098192217514 & 1.79018077824858 \tabularnewline
136 & 12 & 11.1968207919228 & 0.803179208077208 \tabularnewline
137 & 9 & 9.92657114521542 & -0.92657114521542 \tabularnewline
138 & 11 & 10.8459908307848 & 0.154009169215206 \tabularnewline
139 & 10 & 9.48097276754413 & 0.519027232455874 \tabularnewline
140 & 8 & 9.62241863727571 & -1.62241863727571 \tabularnewline
141 & 9 & 7.33859104995021 & 1.66140895004979 \tabularnewline
142 & 8 & 11.8514712113212 & -3.8514712113212 \tabularnewline
143 & 9 & 10.925643495124 & -1.92564349512398 \tabularnewline
144 & 15 & 12.5184684854905 & 2.48153151450949 \tabularnewline
145 & 11 & 11.2411286544445 & -0.241128654444544 \tabularnewline
146 & 8 & 8.2640209572833 & -0.264020957283298 \tabularnewline
147 & 13 & 12.7344729601452 & 0.265527039854827 \tabularnewline
148 & 12 & 9.84018463361046 & 2.15981536638954 \tabularnewline
149 & 12 & 11.4577593081644 & 0.542240691835634 \tabularnewline
150 & 9 & 9.88389683750195 & -0.883896837501954 \tabularnewline
151 & 7 & 8.49836236718246 & -1.49836236718246 \tabularnewline
152 & 13 & 11.0248987664812 & 1.97510123351882 \tabularnewline
153 & 9 & 11.3518353541207 & -2.35183535412069 \tabularnewline
154 & 6 & 11.9262581864453 & -5.92625818644531 \tabularnewline
155 & 8 & 10.8957997515205 & -2.89579975152049 \tabularnewline
156 & 8 & 8.67803540017221 & -0.678035400172215 \tabularnewline
157 & 15 & 12.2412036617812 & 2.7587963382188 \tabularnewline
158 & 6 & 10.3695875238732 & -4.36958752387322 \tabularnewline
159 & 9 & 11.1713382922997 & -2.17133829229965 \tabularnewline
160 & 11 & 11.8026649480136 & -0.802664948013597 \tabularnewline
161 & 8 & 9.88224254679801 & -1.88224254679802 \tabularnewline
162 & 8 & 10.3020118723126 & -2.30201187231259 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154927&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]12[/C][C]9.99953439097248[/C][C]2.00046560902752[/C][/ROW]
[ROW][C]2[/C][C]11[/C][C]11.4004576759109[/C][C]-0.400457675910907[/C][/ROW]
[ROW][C]3[/C][C]15[/C][C]13.6800427961942[/C][C]1.31995720380585[/C][/ROW]
[ROW][C]4[/C][C]6[/C][C]11.0130471670113[/C][C]-5.01304716701132[/C][/ROW]
[ROW][C]5[/C][C]13[/C][C]10.7335883686134[/C][C]2.2664116313866[/C][/ROW]
[ROW][C]6[/C][C]10[/C][C]9.94299785601238[/C][C]0.0570021439876165[/C][/ROW]
[ROW][C]7[/C][C]12[/C][C]13.1056851284488[/C][C]-1.10568512844883[/C][/ROW]
[ROW][C]8[/C][C]14[/C][C]11.3145104449614[/C][C]2.68548955503861[/C][/ROW]
[ROW][C]9[/C][C]12[/C][C]10.6475131707921[/C][C]1.35248682920792[/C][/ROW]
[ROW][C]10[/C][C]6[/C][C]11.1847230432486[/C][C]-5.18472304324858[/C][/ROW]
[ROW][C]11[/C][C]10[/C][C]11.0123795161549[/C][C]-1.01237951615485[/C][/ROW]
[ROW][C]12[/C][C]12[/C][C]11.5297053936391[/C][C]0.470294606360949[/C][/ROW]
[ROW][C]13[/C][C]12[/C][C]11.3472602427936[/C][C]0.652739757206408[/C][/ROW]
[ROW][C]14[/C][C]11[/C][C]11.5859928225025[/C][C]-0.585992822502466[/C][/ROW]
[ROW][C]15[/C][C]15[/C][C]12.2768742802366[/C][C]2.72312571976344[/C][/ROW]
[ROW][C]16[/C][C]12[/C][C]10.8953367780771[/C][C]1.10466322192293[/C][/ROW]
[ROW][C]17[/C][C]10[/C][C]10.7041471521891[/C][C]-0.704147152189075[/C][/ROW]
[ROW][C]18[/C][C]12[/C][C]13.6914314222206[/C][C]-1.69143142222059[/C][/ROW]
[ROW][C]19[/C][C]11[/C][C]12.2602781307765[/C][C]-1.26027813077649[/C][/ROW]
[ROW][C]20[/C][C]12[/C][C]11.7772031842861[/C][C]0.222796815713887[/C][/ROW]
[ROW][C]21[/C][C]11[/C][C]11.2711447936035[/C][C]-0.271144793603472[/C][/ROW]
[ROW][C]22[/C][C]12[/C][C]11.9882208305008[/C][C]0.0117791694991905[/C][/ROW]
[ROW][C]23[/C][C]13[/C][C]13.2351819522736[/C][C]-0.23518195227365[/C][/ROW]
[ROW][C]24[/C][C]11[/C][C]11.8399131466317[/C][C]-0.839913146631655[/C][/ROW]
[ROW][C]25[/C][C]9[/C][C]11.51259029609[/C][C]-2.51259029608997[/C][/ROW]
[ROW][C]26[/C][C]13[/C][C]12.1744168003019[/C][C]0.825583199698083[/C][/ROW]
[ROW][C]27[/C][C]10[/C][C]11.209991675525[/C][C]-1.20999167552497[/C][/ROW]
[ROW][C]28[/C][C]14[/C][C]11.3761825111289[/C][C]2.6238174888711[/C][/ROW]
[ROW][C]29[/C][C]12[/C][C]11.7897991451537[/C][C]0.210200854846312[/C][/ROW]
[ROW][C]30[/C][C]10[/C][C]10.3959147882233[/C][C]-0.395914788223299[/C][/ROW]
[ROW][C]31[/C][C]12[/C][C]11.2214937655689[/C][C]0.778506234431071[/C][/ROW]
[ROW][C]32[/C][C]8[/C][C]9.55531750512046[/C][C]-1.55531750512046[/C][/ROW]
[ROW][C]33[/C][C]10[/C][C]10.4558045968151[/C][C]-0.455804596815076[/C][/ROW]
[ROW][C]34[/C][C]12[/C][C]11.8206248102896[/C][C]0.179375189710382[/C][/ROW]
[ROW][C]35[/C][C]12[/C][C]10.2286093459001[/C][C]1.7713906540999[/C][/ROW]
[ROW][C]36[/C][C]7[/C][C]6.62172888039962[/C][C]0.378271119600382[/C][/ROW]
[ROW][C]37[/C][C]6[/C][C]8.54978114993846[/C][C]-2.54978114993846[/C][/ROW]
[ROW][C]38[/C][C]12[/C][C]10.1604940103548[/C][C]1.8395059896452[/C][/ROW]
[ROW][C]39[/C][C]10[/C][C]11.8509522632322[/C][C]-1.85095226323219[/C][/ROW]
[ROW][C]40[/C][C]10[/C][C]11.3279719064516[/C][C]-1.32797190645155[/C][/ROW]
[ROW][C]41[/C][C]10[/C][C]11.1485894513498[/C][C]-1.14858945134979[/C][/ROW]
[ROW][C]42[/C][C]12[/C][C]10.6595901835706[/C][C]1.34040981642936[/C][/ROW]
[ROW][C]43[/C][C]15[/C][C]13.1676830112543[/C][C]1.83231698874574[/C][/ROW]
[ROW][C]44[/C][C]10[/C][C]10.3154733338028[/C][C]-0.315473333802761[/C][/ROW]
[ROW][C]45[/C][C]10[/C][C]10.5434781106947[/C][C]-0.543478110694743[/C][/ROW]
[ROW][C]46[/C][C]12[/C][C]9.17876512819634[/C][C]2.82123487180366[/C][/ROW]
[ROW][C]47[/C][C]13[/C][C]10.6351663160212[/C][C]2.36483368397881[/C][/ROW]
[ROW][C]48[/C][C]11[/C][C]11.2834149378331[/C][C]-0.283414937833124[/C][/ROW]
[ROW][C]49[/C][C]11[/C][C]11.6782414477092[/C][C]-0.678241447709231[/C][/ROW]
[ROW][C]50[/C][C]12[/C][C]10.1301872933079[/C][C]1.86981270669212[/C][/ROW]
[ROW][C]51[/C][C]14[/C][C]11.8882004618502[/C][C]2.11179953814975[/C][/ROW]
[ROW][C]52[/C][C]10[/C][C]10.3149543857137[/C][C]-0.314954385713747[/C][/ROW]
[ROW][C]53[/C][C]12[/C][C]9.43249232087445[/C][C]2.56750767912556[/C][/ROW]
[ROW][C]54[/C][C]13[/C][C]11.9509311600914[/C][C]1.04906883990856[/C][/ROW]
[ROW][C]55[/C][C]5[/C][C]7.80207257935628[/C][C]-2.80207257935628[/C][/ROW]
[ROW][C]56[/C][C]6[/C][C]10.5488420121032[/C][C]-4.54884201210319[/C][/ROW]
[ROW][C]57[/C][C]12[/C][C]11.5119946374597[/C][C]0.48800536254029[/C][/ROW]
[ROW][C]58[/C][C]12[/C][C]11.9809887710417[/C][C]0.0190112289583197[/C][/ROW]
[ROW][C]59[/C][C]11[/C][C]10.8095106863524[/C][C]0.190489313647571[/C][/ROW]
[ROW][C]60[/C][C]10[/C][C]11.8337604551419[/C][C]-1.83376045514186[/C][/ROW]
[ROW][C]61[/C][C]7[/C][C]9.53814643292579[/C][C]-2.53814643292579[/C][/ROW]
[ROW][C]62[/C][C]12[/C][C]11.2169538929918[/C][C]0.783046107008234[/C][/ROW]
[ROW][C]63[/C][C]14[/C][C]11.9327014558231[/C][C]2.06729854417691[/C][/ROW]
[ROW][C]64[/C][C]11[/C][C]10.9006447048399[/C][C]0.0993552951600766[/C][/ROW]
[ROW][C]65[/C][C]12[/C][C]11.7096275327255[/C][C]0.290372467274516[/C][/ROW]
[ROW][C]66[/C][C]13[/C][C]12.0386606486167[/C][C]0.961339351383299[/C][/ROW]
[ROW][C]67[/C][C]14[/C][C]12.8961239231719[/C][C]1.10387607682806[/C][/ROW]
[ROW][C]68[/C][C]11[/C][C]12.5198736700978[/C][C]-1.51987367009777[/C][/ROW]
[ROW][C]69[/C][C]12[/C][C]9.56072583521254[/C][C]2.43927416478746[/C][/ROW]
[ROW][C]70[/C][C]12[/C][C]11.3274322224669[/C][C]0.672567777533113[/C][/ROW]
[ROW][C]71[/C][C]8[/C][C]8.06738152951191[/C][C]-0.0673815295119132[/C][/ROW]
[ROW][C]72[/C][C]11[/C][C]10.8816054745946[/C][C]0.118394525405433[/C][/ROW]
[ROW][C]73[/C][C]14[/C][C]12.9073914099735[/C][C]1.0926085900265[/C][/ROW]
[ROW][C]74[/C][C]14[/C][C]12.3216803549517[/C][C]1.67831964504833[/C][/ROW]
[ROW][C]75[/C][C]12[/C][C]11.2601471487942[/C][C]0.739852851205754[/C][/ROW]
[ROW][C]76[/C][C]9[/C][C]12.1119559440531[/C][C]-3.11195594405305[/C][/ROW]
[ROW][C]77[/C][C]13[/C][C]11.6165693815417[/C][C]1.38343061845828[/C][/ROW]
[ROW][C]78[/C][C]11[/C][C]11.7841654017529[/C][C]-0.784165401752908[/C][/ROW]
[ROW][C]79[/C][C]12[/C][C]9.94399132350677[/C][C]2.05600867649323[/C][/ROW]
[ROW][C]80[/C][C]12[/C][C]11.1306295890738[/C][C]0.869370410926231[/C][/ROW]
[ROW][C]81[/C][C]12[/C][C]11.3952777160198[/C][C]0.60472228398015[/C][/ROW]
[ROW][C]82[/C][C]12[/C][C]13.3218528087252[/C][C]-1.32185280872523[/C][/ROW]
[ROW][C]83[/C][C]12[/C][C]11.7774522903828[/C][C]0.222547709617208[/C][/ROW]
[ROW][C]84[/C][C]12[/C][C]10.6049363095155[/C][C]1.39506369048449[/C][/ROW]
[ROW][C]85[/C][C]11[/C][C]11.3265874577399[/C][C]-0.32658745773995[/C][/ROW]
[ROW][C]86[/C][C]10[/C][C]11.790068987146[/C][C]-1.79006898714602[/C][/ROW]
[ROW][C]87[/C][C]9[/C][C]10.1851462481053[/C][C]-1.18514624810528[/C][/ROW]
[ROW][C]88[/C][C]12[/C][C]11.8455676259281[/C][C]0.154432374071911[/C][/ROW]
[ROW][C]89[/C][C]12[/C][C]12.0185275475477[/C][C]-0.0185275475477263[/C][/ROW]
[ROW][C]90[/C][C]12[/C][C]11.5630668676821[/C][C]0.436933132317869[/C][/ROW]
[ROW][C]91[/C][C]9[/C][C]9.75756994039697[/C][C]-0.75756994039697[/C][/ROW]
[ROW][C]92[/C][C]15[/C][C]12.2412036617812[/C][C]2.7587963382188[/C][/ROW]
[ROW][C]93[/C][C]12[/C][C]11.8514504754255[/C][C]0.148549524574451[/C][/ROW]
[ROW][C]94[/C][C]12[/C][C]11.2528591146895[/C][C]0.747140885310473[/C][/ROW]
[ROW][C]95[/C][C]12[/C][C]10.4068771942826[/C][C]1.59312280571741[/C][/ROW]
[ROW][C]96[/C][C]10[/C][C]11.5489937299811[/C][C]-1.54899372998109[/C][/ROW]
[ROW][C]97[/C][C]13[/C][C]11.7659149615889[/C][C]1.2340850384111[/C][/ROW]
[ROW][C]98[/C][C]9[/C][C]11.7037239473324[/C][C]-2.70372394733237[/C][/ROW]
[ROW][C]99[/C][C]12[/C][C]11.1110506748437[/C][C]0.888949325156256[/C][/ROW]
[ROW][C]100[/C][C]10[/C][C]10.3886827287642[/C][C]-0.38868272876417[/C][/ROW]
[ROW][C]101[/C][C]14[/C][C]11.5548973153742[/C][C]2.4451026846258[/C][/ROW]
[ROW][C]102[/C][C]11[/C][C]11.315894893673[/C][C]-0.315894893672994[/C][/ROW]
[ROW][C]103[/C][C]15[/C][C]13.7965458502873[/C][C]1.20345414971273[/C][/ROW]
[ROW][C]104[/C][C]11[/C][C]10.9146458503148[/C][C]0.0853541496852418[/C][/ROW]
[ROW][C]105[/C][C]11[/C][C]11.9007964227178[/C][C]-0.900796422717822[/C][/ROW]
[ROW][C]106[/C][C]12[/C][C]9.66803423796783[/C][C]2.33196576203217[/C][/ROW]
[ROW][C]107[/C][C]12[/C][C]12.0985504572085[/C][C]-0.0985504572084777[/C][/ROW]
[ROW][C]108[/C][C]12[/C][C]11.1053961955473[/C][C]0.89460380445269[/C][/ROW]
[ROW][C]109[/C][C]11[/C][C]11.4336052826072[/C][C]-0.433605282607244[/C][/ROW]
[ROW][C]110[/C][C]7[/C][C]9.61137952067518[/C][C]-2.61137952067518[/C][/ROW]
[ROW][C]111[/C][C]12[/C][C]11.9378162511349[/C][C]0.0621837488651456[/C][/ROW]
[ROW][C]112[/C][C]14[/C][C]12.0252199230222[/C][C]1.97478007697781[/C][/ROW]
[ROW][C]113[/C][C]11[/C][C]12.5803896577547[/C][C]-1.5803896577547[/C][/ROW]
[ROW][C]114[/C][C]11[/C][C]9.84556927091456[/C][C]1.15443072908544[/C][/ROW]
[ROW][C]115[/C][C]10[/C][C]9.42426679392092[/C][C]0.575733206079076[/C][/ROW]
[ROW][C]116[/C][C]13[/C][C]12.1610880239986[/C][C]0.838911976001416[/C][/ROW]
[ROW][C]117[/C][C]13[/C][C]10.9067973963297[/C][C]2.09320260367028[/C][/ROW]
[ROW][C]118[/C][C]8[/C][C]10.8636248764229[/C][C]-2.86362487642289[/C][/ROW]
[ROW][C]119[/C][C]11[/C][C]10.289415911445[/C][C]0.710584088554982[/C][/ROW]
[ROW][C]120[/C][C]12[/C][C]11.6667041189153[/C][C]0.333295881084662[/C][/ROW]
[ROW][C]121[/C][C]11[/C][C]10.4009528729938[/C][C]0.599047127006179[/C][/ROW]
[ROW][C]122[/C][C]13[/C][C]11.8024158419169[/C][C]1.19758415808308[/C][/ROW]
[ROW][C]123[/C][C]12[/C][C]10.1102825624399[/C][C]1.88971743756007[/C][/ROW]
[ROW][C]124[/C][C]14[/C][C]11.7096067968298[/C][C]2.29039320317017[/C][/ROW]
[ROW][C]125[/C][C]13[/C][C]11.2279162990511[/C][C]1.77208370094894[/C][/ROW]
[ROW][C]126[/C][C]15[/C][C]11.9506820539948[/C][C]3.04931794600524[/C][/ROW]
[ROW][C]127[/C][C]10[/C][C]10.4628227889275[/C][C]-0.462822788927461[/C][/ROW]
[ROW][C]128[/C][C]11[/C][C]11.8762445882966[/C][C]-0.876244588296567[/C][/ROW]
[ROW][C]129[/C][C]9[/C][C]11.1713382922997[/C][C]-2.17133829229965[/C][/ROW]
[ROW][C]130[/C][C]11[/C][C]9.60391909101502[/C][C]1.39608090898498[/C][/ROW]
[ROW][C]131[/C][C]10[/C][C]11.457489466172[/C][C]-1.45748946617203[/C][/ROW]
[ROW][C]132[/C][C]11[/C][C]8.61145321610598[/C][C]2.38854678389402[/C][/ROW]
[ROW][C]133[/C][C]8[/C][C]11.5615896908487[/C][C]-3.56158969084866[/C][/ROW]
[ROW][C]134[/C][C]11[/C][C]9.44378054357166[/C][C]1.55621945642834[/C][/ROW]
[ROW][C]135[/C][C]12[/C][C]10.2098192217514[/C][C]1.79018077824858[/C][/ROW]
[ROW][C]136[/C][C]12[/C][C]11.1968207919228[/C][C]0.803179208077208[/C][/ROW]
[ROW][C]137[/C][C]9[/C][C]9.92657114521542[/C][C]-0.92657114521542[/C][/ROW]
[ROW][C]138[/C][C]11[/C][C]10.8459908307848[/C][C]0.154009169215206[/C][/ROW]
[ROW][C]139[/C][C]10[/C][C]9.48097276754413[/C][C]0.519027232455874[/C][/ROW]
[ROW][C]140[/C][C]8[/C][C]9.62241863727571[/C][C]-1.62241863727571[/C][/ROW]
[ROW][C]141[/C][C]9[/C][C]7.33859104995021[/C][C]1.66140895004979[/C][/ROW]
[ROW][C]142[/C][C]8[/C][C]11.8514712113212[/C][C]-3.8514712113212[/C][/ROW]
[ROW][C]143[/C][C]9[/C][C]10.925643495124[/C][C]-1.92564349512398[/C][/ROW]
[ROW][C]144[/C][C]15[/C][C]12.5184684854905[/C][C]2.48153151450949[/C][/ROW]
[ROW][C]145[/C][C]11[/C][C]11.2411286544445[/C][C]-0.241128654444544[/C][/ROW]
[ROW][C]146[/C][C]8[/C][C]8.2640209572833[/C][C]-0.264020957283298[/C][/ROW]
[ROW][C]147[/C][C]13[/C][C]12.7344729601452[/C][C]0.265527039854827[/C][/ROW]
[ROW][C]148[/C][C]12[/C][C]9.84018463361046[/C][C]2.15981536638954[/C][/ROW]
[ROW][C]149[/C][C]12[/C][C]11.4577593081644[/C][C]0.542240691835634[/C][/ROW]
[ROW][C]150[/C][C]9[/C][C]9.88389683750195[/C][C]-0.883896837501954[/C][/ROW]
[ROW][C]151[/C][C]7[/C][C]8.49836236718246[/C][C]-1.49836236718246[/C][/ROW]
[ROW][C]152[/C][C]13[/C][C]11.0248987664812[/C][C]1.97510123351882[/C][/ROW]
[ROW][C]153[/C][C]9[/C][C]11.3518353541207[/C][C]-2.35183535412069[/C][/ROW]
[ROW][C]154[/C][C]6[/C][C]11.9262581864453[/C][C]-5.92625818644531[/C][/ROW]
[ROW][C]155[/C][C]8[/C][C]10.8957997515205[/C][C]-2.89579975152049[/C][/ROW]
[ROW][C]156[/C][C]8[/C][C]8.67803540017221[/C][C]-0.678035400172215[/C][/ROW]
[ROW][C]157[/C][C]15[/C][C]12.2412036617812[/C][C]2.7587963382188[/C][/ROW]
[ROW][C]158[/C][C]6[/C][C]10.3695875238732[/C][C]-4.36958752387322[/C][/ROW]
[ROW][C]159[/C][C]9[/C][C]11.1713382922997[/C][C]-2.17133829229965[/C][/ROW]
[ROW][C]160[/C][C]11[/C][C]11.8026649480136[/C][C]-0.802664948013597[/C][/ROW]
[ROW][C]161[/C][C]8[/C][C]9.88224254679801[/C][C]-1.88224254679802[/C][/ROW]
[ROW][C]162[/C][C]8[/C][C]10.3020118723126[/C][C]-2.30201187231259[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154927&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154927&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
1129.999534390972482.00046560902752
21111.4004576759109-0.400457675910907
31513.68004279619421.31995720380585
4611.0130471670113-5.01304716701132
51310.73358836861342.2664116313866
6109.942997856012380.0570021439876165
71213.1056851284488-1.10568512844883
81411.31451044496142.68548955503861
91210.64751317079211.35248682920792
10611.1847230432486-5.18472304324858
111011.0123795161549-1.01237951615485
121211.52970539363910.470294606360949
131211.34726024279360.652739757206408
141111.5859928225025-0.585992822502466
151512.27687428023662.72312571976344
161210.89533677807711.10466322192293
171010.7041471521891-0.704147152189075
181213.6914314222206-1.69143142222059
191112.2602781307765-1.26027813077649
201211.77720318428610.222796815713887
211111.2711447936035-0.271144793603472
221211.98822083050080.0117791694991905
231313.2351819522736-0.23518195227365
241111.8399131466317-0.839913146631655
25911.51259029609-2.51259029608997
261312.17441680030190.825583199698083
271011.209991675525-1.20999167552497
281411.37618251112892.6238174888711
291211.78979914515370.210200854846312
301010.3959147882233-0.395914788223299
311211.22149376556890.778506234431071
3289.55531750512046-1.55531750512046
331010.4558045968151-0.455804596815076
341211.82062481028960.179375189710382
351210.22860934590011.7713906540999
3676.621728880399620.378271119600382
3768.54978114993846-2.54978114993846
381210.16049401035481.8395059896452
391011.8509522632322-1.85095226323219
401011.3279719064516-1.32797190645155
411011.1485894513498-1.14858945134979
421210.65959018357061.34040981642936
431513.16768301125431.83231698874574
441010.3154733338028-0.315473333802761
451010.5434781106947-0.543478110694743
46129.178765128196342.82123487180366
471310.63516631602122.36483368397881
481111.2834149378331-0.283414937833124
491111.6782414477092-0.678241447709231
501210.13018729330791.86981270669212
511411.88820046185022.11179953814975
521010.3149543857137-0.314954385713747
53129.432492320874452.56750767912556
541311.95093116009141.04906883990856
5557.80207257935628-2.80207257935628
56610.5488420121032-4.54884201210319
571211.51199463745970.48800536254029
581211.98098877104170.0190112289583197
591110.80951068635240.190489313647571
601011.8337604551419-1.83376045514186
6179.53814643292579-2.53814643292579
621211.21695389299180.783046107008234
631411.93270145582312.06729854417691
641110.90064470483990.0993552951600766
651211.70962753272550.290372467274516
661312.03866064861670.961339351383299
671412.89612392317191.10387607682806
681112.5198736700978-1.51987367009777
69129.560725835212542.43927416478746
701211.32743222246690.672567777533113
7188.06738152951191-0.0673815295119132
721110.88160547459460.118394525405433
731412.90739140997351.0926085900265
741412.32168035495171.67831964504833
751211.26014714879420.739852851205754
76912.1119559440531-3.11195594405305
771311.61656938154171.38343061845828
781111.7841654017529-0.784165401752908
79129.943991323506772.05600867649323
801211.13062958907380.869370410926231
811211.39527771601980.60472228398015
821213.3218528087252-1.32185280872523
831211.77745229038280.222547709617208
841210.60493630951551.39506369048449
851111.3265874577399-0.32658745773995
861011.790068987146-1.79006898714602
87910.1851462481053-1.18514624810528
881211.84556762592810.154432374071911
891212.0185275475477-0.0185275475477263
901211.56306686768210.436933132317869
9199.75756994039697-0.75756994039697
921512.24120366178122.7587963382188
931211.85145047542550.148549524574451
941211.25285911468950.747140885310473
951210.40687719428261.59312280571741
961011.5489937299811-1.54899372998109
971311.76591496158891.2340850384111
98911.7037239473324-2.70372394733237
991211.11105067484370.888949325156256
1001010.3886827287642-0.38868272876417
1011411.55489731537422.4451026846258
1021111.315894893673-0.315894893672994
1031513.79654585028731.20345414971273
1041110.91464585031480.0853541496852418
1051111.9007964227178-0.900796422717822
106129.668034237967832.33196576203217
1071212.0985504572085-0.0985504572084777
1081211.10539619554730.89460380445269
1091111.4336052826072-0.433605282607244
11079.61137952067518-2.61137952067518
1111211.93781625113490.0621837488651456
1121412.02521992302221.97478007697781
1131112.5803896577547-1.5803896577547
114119.845569270914561.15443072908544
115109.424266793920920.575733206079076
1161312.16108802399860.838911976001416
1171310.90679739632972.09320260367028
118810.8636248764229-2.86362487642289
1191110.2894159114450.710584088554982
1201211.66670411891530.333295881084662
1211110.40095287299380.599047127006179
1221311.80241584191691.19758415808308
1231210.11028256243991.88971743756007
1241411.70960679682982.29039320317017
1251311.22791629905111.77208370094894
1261511.95068205399483.04931794600524
1271010.4628227889275-0.462822788927461
1281111.8762445882966-0.876244588296567
129911.1713382922997-2.17133829229965
130119.603919091015021.39608090898498
1311011.457489466172-1.45748946617203
132118.611453216105982.38854678389402
133811.5615896908487-3.56158969084866
134119.443780543571661.55621945642834
1351210.20981922175141.79018077824858
1361211.19682079192280.803179208077208
13799.92657114521542-0.92657114521542
1381110.84599083078480.154009169215206
139109.480972767544130.519027232455874
14089.62241863727571-1.62241863727571
14197.338591049950211.66140895004979
142811.8514712113212-3.8514712113212
143910.925643495124-1.92564349512398
1441512.51846848549052.48153151450949
1451111.2411286544445-0.241128654444544
14688.2640209572833-0.264020957283298
1471312.73447296014520.265527039854827
148129.840184633610462.15981536638954
1491211.45775930816440.542240691835634
15099.88389683750195-0.883896837501954
15178.49836236718246-1.49836236718246
1521311.02489876648121.97510123351882
153911.3518353541207-2.35183535412069
154611.9262581864453-5.92625818644531
155810.8957997515205-2.89579975152049
15688.67803540017221-0.678035400172215
1571512.24120366178122.7587963382188
158610.3695875238732-4.36958752387322
159911.1713382922997-2.17133829229965
1601111.8026649480136-0.802664948013597
16189.88224254679801-1.88224254679802
162810.3020118723126-2.30201187231259






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.02059484132892260.04118968265784520.979405158671077
100.9747762052319450.05044758953611060.0252237947680553
110.9959033057513990.008193388497202010.00409669424860101
120.9913461326869360.01730773462612780.00865386731306389
130.9930377069397660.01392458612046740.00696229306023369
140.9875547224473610.0248905551052780.012445277552639
150.9908664457952670.01826710840946550.00913355420473277
160.9904283370685980.01914332586280410.00957166293140203
170.9842156434084090.03156871318318290.0157843565915914
180.9796075008875510.04078499822489730.0203924991124487
190.9695137580122270.06097248397554540.0304862419877727
200.9543782397735150.09124352045296960.0456217602264848
210.9346531084053170.1306937831893660.0653468915946828
220.9093317815512540.1813364368974930.0906682184487464
230.877189467084880.2456210658302390.12281053291512
240.8500568514351740.2998862971296520.149943148564826
250.8303886574326770.3392226851346450.169611342567323
260.8069184462911190.3861631074177630.193081553708881
270.7637032301302130.4725935397395740.236296769869787
280.7683949897205060.4632100205589890.231605010279494
290.7184213095209940.5631573809580120.281578690479006
300.6636478044123920.6727043911752160.336352195587608
310.6074598204389860.7850803591220290.392540179561014
320.6000081048902780.7999837902194440.399991895109722
330.5501141869924660.8997716260150690.449885813007534
340.4921875744414240.9843751488828470.507812425558576
350.5395263637128840.9209472725742320.460473636287116
360.4820116380455650.964023276091130.517988361954435
370.5402570678360260.9194858643279490.459742932163974
380.5868693782633570.8262612434732860.413130621736643
390.6014183870872770.7971632258254460.398581612912723
400.5584546233921750.883090753215650.441545376607825
410.5122756402391580.9754487195216840.487724359760842
420.4754069071324140.9508138142648280.524593092867586
430.5447189209654670.9105621580690650.455281079034533
440.4937822064416560.9875644128833120.506217793558344
450.4472884952670360.8945769905340710.552711504732964
460.4886407072740610.9772814145481220.511359292725939
470.4921389700531750.9842779401063510.507861029946825
480.4455805556789250.8911611113578490.554419444321075
490.4125100615607990.8250201231215980.587489938439201
500.4296871284737030.8593742569474060.570312871526297
510.4360094932425030.8720189864850070.563990506757497
520.3891310531565920.7782621063131830.610868946843409
530.4406788310800450.881357662160090.559321168919955
540.4010027945985980.8020055891971960.598997205401402
550.4855224941528480.9710449883056950.514477505847152
560.7454400725662150.509119854867570.254559927433785
570.706866173609260.586267652781480.29313382639074
580.6641186570031130.6717626859937740.335881342996887
590.6198115629028750.7603768741942490.380188437097125
600.6241055896843680.7517888206312650.375894410315632
610.6484434565651860.7031130868696280.351556543434814
620.6164998196464290.7670003607071420.383500180353571
630.6264127924831230.7471744150337540.373587207516877
640.5815225971666130.8369548056667730.418477402833387
650.5371044589546250.9257910820907490.462895541045375
660.5028297388627770.9943405222744470.497170261137223
670.4726820543299920.9453641086599830.527317945670008
680.451174580004480.902349160008960.54882541999552
690.468489314115540.9369786282310790.53151068588446
700.4265938991126760.8531877982253520.573406100887324
710.3827475758361890.7654951516723780.617252424163811
720.3438392108824610.6876784217649210.656160789117539
730.3165943696543440.6331887393086880.683405630345656
740.3042770144435080.6085540288870160.695722985556492
750.2917811029804260.5835622059608530.708218897019574
760.3717457977571440.7434915955142870.628254202242856
770.3535849698485470.7071699396970950.646415030151453
780.3184275539134710.6368551078269410.681572446086529
790.3386962791516760.6773925583033520.661303720848324
800.3264131440668690.6528262881337380.673586855933131
810.2898041763068150.579608352613630.710195823693185
820.2745153682497290.5490307364994580.725484631750271
830.2403086624812160.4806173249624310.759691337518784
840.2253911818749240.4507823637498480.774608818125076
850.1937381053144820.3874762106289650.806261894685518
860.1924454913709420.3848909827418850.807554508629058
870.1818624798250230.3637249596500460.818137520174977
880.1531270447636930.3062540895273870.846872955236307
890.1272911709550690.2545823419101390.872708829044931
900.1056275605551550.211255121110310.894372439444845
910.0914085203726750.182817040745350.908591479627325
920.1199302011381360.2398604022762730.880069798861864
930.1035204091611350.2070408183222690.896479590838865
940.08809820301595330.1761964060319070.911901796984047
950.08441427282196830.1688285456439370.915585727178032
960.08041468261215110.1608293652243020.919585317387849
970.07152233358375930.1430446671675190.928477666416241
980.09369758113534260.1873951622706850.906302418864657
990.07927529691824150.1585505938364830.920724703081758
1000.06396113093152920.1279222618630580.936038869068471
1010.07874888604673780.1574977720934760.921251113953262
1020.06350141722539310.1270028344507860.936498582774607
1030.05931126458443140.1186225291688630.940688735415569
1040.04864630333917170.09729260667834340.951353696660828
1050.04099844220567890.08199688441135770.959001557794321
1060.04295635969043140.08591271938086270.957043640309569
1070.03332802902774050.06665605805548110.966671970972259
1080.02795089466859310.05590178933718630.972049105331407
1090.02137376589363130.04274753178726270.978626234106369
1100.0295653731321190.05913074626423810.970434626867881
1110.02262574593053710.04525149186107430.977374254069463
1120.02441824641281340.04883649282562670.975581753587187
1130.0213148628080860.04262972561617210.978685137191914
1140.01740242701064090.03480485402128190.982597572989359
1150.01327522913880350.0265504582776070.986724770861196
1160.01031412932281070.02062825864562140.989685870677189
1170.01458348306279090.02916696612558180.985416516937209
1180.0181101662062780.0362203324125560.981889833793722
1190.01438977909334580.02877955818669160.985610220906654
1200.01079316112231360.02158632224462710.989206838877686
1210.008481309935038030.01696261987007610.991518690064962
1220.006870818899115460.01374163779823090.993129181100885
1230.008740840568639340.01748168113727870.991259159431361
1240.01617265739408520.03234531478817050.983827342605915
1250.01791024700906070.03582049401812140.982089752990939
1260.05139276664178450.1027855332835690.948607233358216
1270.04104072843784180.08208145687568360.958959271562158
1280.03178245881184690.06356491762369380.968217541188153
1290.02730761167315520.05461522334631030.972692388326845
1300.02707255133063820.05414510266127650.972927448669362
1310.02278621348959690.04557242697919370.977213786510403
1320.02524446371053450.05048892742106890.974755536289466
1330.04063566437158560.08127132874317110.959364335628414
1340.04114878578334640.08229757156669280.958851214216654
1350.0410372489070040.0820744978140080.958962751092996
1360.03562897925848840.07125795851697690.964371020741512
1370.03343388126067590.06686776252135170.966566118739324
1380.02365819156805790.04731638313611570.976341808431942
1390.02733039181652240.05466078363304480.972669608183478
1400.02002697189389810.04005394378779620.979973028106102
1410.04212830950922870.08425661901845730.957871690490771
1420.05923363402733410.1184672680546680.940766365972666
1430.04288959004499330.08577918008998660.957110409955007
1440.2926440037082840.5852880074165670.707355996291716
1450.3429224705703450.685844941140690.657077529429655
1460.6122602677974880.7754794644050240.387739732202512
1470.5952369985620420.8095260028759170.404763001437958
1480.8565491112524940.2869017774950110.143450888747506
1490.8044493460685570.3911013078628860.195550653931443
1500.7417400348497850.516519930300430.258259965150215
1510.6249653211471840.7500693577056330.375034678852816
1520.5206002425646550.958799514870690.479399757435345
1530.3630412476624470.7260824953248940.636958752337553

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.0205948413289226 & 0.0411896826578452 & 0.979405158671077 \tabularnewline
10 & 0.974776205231945 & 0.0504475895361106 & 0.0252237947680553 \tabularnewline
11 & 0.995903305751399 & 0.00819338849720201 & 0.00409669424860101 \tabularnewline
12 & 0.991346132686936 & 0.0173077346261278 & 0.00865386731306389 \tabularnewline
13 & 0.993037706939766 & 0.0139245861204674 & 0.00696229306023369 \tabularnewline
14 & 0.987554722447361 & 0.024890555105278 & 0.012445277552639 \tabularnewline
15 & 0.990866445795267 & 0.0182671084094655 & 0.00913355420473277 \tabularnewline
16 & 0.990428337068598 & 0.0191433258628041 & 0.00957166293140203 \tabularnewline
17 & 0.984215643408409 & 0.0315687131831829 & 0.0157843565915914 \tabularnewline
18 & 0.979607500887551 & 0.0407849982248973 & 0.0203924991124487 \tabularnewline
19 & 0.969513758012227 & 0.0609724839755454 & 0.0304862419877727 \tabularnewline
20 & 0.954378239773515 & 0.0912435204529696 & 0.0456217602264848 \tabularnewline
21 & 0.934653108405317 & 0.130693783189366 & 0.0653468915946828 \tabularnewline
22 & 0.909331781551254 & 0.181336436897493 & 0.0906682184487464 \tabularnewline
23 & 0.87718946708488 & 0.245621065830239 & 0.12281053291512 \tabularnewline
24 & 0.850056851435174 & 0.299886297129652 & 0.149943148564826 \tabularnewline
25 & 0.830388657432677 & 0.339222685134645 & 0.169611342567323 \tabularnewline
26 & 0.806918446291119 & 0.386163107417763 & 0.193081553708881 \tabularnewline
27 & 0.763703230130213 & 0.472593539739574 & 0.236296769869787 \tabularnewline
28 & 0.768394989720506 & 0.463210020558989 & 0.231605010279494 \tabularnewline
29 & 0.718421309520994 & 0.563157380958012 & 0.281578690479006 \tabularnewline
30 & 0.663647804412392 & 0.672704391175216 & 0.336352195587608 \tabularnewline
31 & 0.607459820438986 & 0.785080359122029 & 0.392540179561014 \tabularnewline
32 & 0.600008104890278 & 0.799983790219444 & 0.399991895109722 \tabularnewline
33 & 0.550114186992466 & 0.899771626015069 & 0.449885813007534 \tabularnewline
34 & 0.492187574441424 & 0.984375148882847 & 0.507812425558576 \tabularnewline
35 & 0.539526363712884 & 0.920947272574232 & 0.460473636287116 \tabularnewline
36 & 0.482011638045565 & 0.96402327609113 & 0.517988361954435 \tabularnewline
37 & 0.540257067836026 & 0.919485864327949 & 0.459742932163974 \tabularnewline
38 & 0.586869378263357 & 0.826261243473286 & 0.413130621736643 \tabularnewline
39 & 0.601418387087277 & 0.797163225825446 & 0.398581612912723 \tabularnewline
40 & 0.558454623392175 & 0.88309075321565 & 0.441545376607825 \tabularnewline
41 & 0.512275640239158 & 0.975448719521684 & 0.487724359760842 \tabularnewline
42 & 0.475406907132414 & 0.950813814264828 & 0.524593092867586 \tabularnewline
43 & 0.544718920965467 & 0.910562158069065 & 0.455281079034533 \tabularnewline
44 & 0.493782206441656 & 0.987564412883312 & 0.506217793558344 \tabularnewline
45 & 0.447288495267036 & 0.894576990534071 & 0.552711504732964 \tabularnewline
46 & 0.488640707274061 & 0.977281414548122 & 0.511359292725939 \tabularnewline
47 & 0.492138970053175 & 0.984277940106351 & 0.507861029946825 \tabularnewline
48 & 0.445580555678925 & 0.891161111357849 & 0.554419444321075 \tabularnewline
49 & 0.412510061560799 & 0.825020123121598 & 0.587489938439201 \tabularnewline
50 & 0.429687128473703 & 0.859374256947406 & 0.570312871526297 \tabularnewline
51 & 0.436009493242503 & 0.872018986485007 & 0.563990506757497 \tabularnewline
52 & 0.389131053156592 & 0.778262106313183 & 0.610868946843409 \tabularnewline
53 & 0.440678831080045 & 0.88135766216009 & 0.559321168919955 \tabularnewline
54 & 0.401002794598598 & 0.802005589197196 & 0.598997205401402 \tabularnewline
55 & 0.485522494152848 & 0.971044988305695 & 0.514477505847152 \tabularnewline
56 & 0.745440072566215 & 0.50911985486757 & 0.254559927433785 \tabularnewline
57 & 0.70686617360926 & 0.58626765278148 & 0.29313382639074 \tabularnewline
58 & 0.664118657003113 & 0.671762685993774 & 0.335881342996887 \tabularnewline
59 & 0.619811562902875 & 0.760376874194249 & 0.380188437097125 \tabularnewline
60 & 0.624105589684368 & 0.751788820631265 & 0.375894410315632 \tabularnewline
61 & 0.648443456565186 & 0.703113086869628 & 0.351556543434814 \tabularnewline
62 & 0.616499819646429 & 0.767000360707142 & 0.383500180353571 \tabularnewline
63 & 0.626412792483123 & 0.747174415033754 & 0.373587207516877 \tabularnewline
64 & 0.581522597166613 & 0.836954805666773 & 0.418477402833387 \tabularnewline
65 & 0.537104458954625 & 0.925791082090749 & 0.462895541045375 \tabularnewline
66 & 0.502829738862777 & 0.994340522274447 & 0.497170261137223 \tabularnewline
67 & 0.472682054329992 & 0.945364108659983 & 0.527317945670008 \tabularnewline
68 & 0.45117458000448 & 0.90234916000896 & 0.54882541999552 \tabularnewline
69 & 0.46848931411554 & 0.936978628231079 & 0.53151068588446 \tabularnewline
70 & 0.426593899112676 & 0.853187798225352 & 0.573406100887324 \tabularnewline
71 & 0.382747575836189 & 0.765495151672378 & 0.617252424163811 \tabularnewline
72 & 0.343839210882461 & 0.687678421764921 & 0.656160789117539 \tabularnewline
73 & 0.316594369654344 & 0.633188739308688 & 0.683405630345656 \tabularnewline
74 & 0.304277014443508 & 0.608554028887016 & 0.695722985556492 \tabularnewline
75 & 0.291781102980426 & 0.583562205960853 & 0.708218897019574 \tabularnewline
76 & 0.371745797757144 & 0.743491595514287 & 0.628254202242856 \tabularnewline
77 & 0.353584969848547 & 0.707169939697095 & 0.646415030151453 \tabularnewline
78 & 0.318427553913471 & 0.636855107826941 & 0.681572446086529 \tabularnewline
79 & 0.338696279151676 & 0.677392558303352 & 0.661303720848324 \tabularnewline
80 & 0.326413144066869 & 0.652826288133738 & 0.673586855933131 \tabularnewline
81 & 0.289804176306815 & 0.57960835261363 & 0.710195823693185 \tabularnewline
82 & 0.274515368249729 & 0.549030736499458 & 0.725484631750271 \tabularnewline
83 & 0.240308662481216 & 0.480617324962431 & 0.759691337518784 \tabularnewline
84 & 0.225391181874924 & 0.450782363749848 & 0.774608818125076 \tabularnewline
85 & 0.193738105314482 & 0.387476210628965 & 0.806261894685518 \tabularnewline
86 & 0.192445491370942 & 0.384890982741885 & 0.807554508629058 \tabularnewline
87 & 0.181862479825023 & 0.363724959650046 & 0.818137520174977 \tabularnewline
88 & 0.153127044763693 & 0.306254089527387 & 0.846872955236307 \tabularnewline
89 & 0.127291170955069 & 0.254582341910139 & 0.872708829044931 \tabularnewline
90 & 0.105627560555155 & 0.21125512111031 & 0.894372439444845 \tabularnewline
91 & 0.091408520372675 & 0.18281704074535 & 0.908591479627325 \tabularnewline
92 & 0.119930201138136 & 0.239860402276273 & 0.880069798861864 \tabularnewline
93 & 0.103520409161135 & 0.207040818322269 & 0.896479590838865 \tabularnewline
94 & 0.0880982030159533 & 0.176196406031907 & 0.911901796984047 \tabularnewline
95 & 0.0844142728219683 & 0.168828545643937 & 0.915585727178032 \tabularnewline
96 & 0.0804146826121511 & 0.160829365224302 & 0.919585317387849 \tabularnewline
97 & 0.0715223335837593 & 0.143044667167519 & 0.928477666416241 \tabularnewline
98 & 0.0936975811353426 & 0.187395162270685 & 0.906302418864657 \tabularnewline
99 & 0.0792752969182415 & 0.158550593836483 & 0.920724703081758 \tabularnewline
100 & 0.0639611309315292 & 0.127922261863058 & 0.936038869068471 \tabularnewline
101 & 0.0787488860467378 & 0.157497772093476 & 0.921251113953262 \tabularnewline
102 & 0.0635014172253931 & 0.127002834450786 & 0.936498582774607 \tabularnewline
103 & 0.0593112645844314 & 0.118622529168863 & 0.940688735415569 \tabularnewline
104 & 0.0486463033391717 & 0.0972926066783434 & 0.951353696660828 \tabularnewline
105 & 0.0409984422056789 & 0.0819968844113577 & 0.959001557794321 \tabularnewline
106 & 0.0429563596904314 & 0.0859127193808627 & 0.957043640309569 \tabularnewline
107 & 0.0333280290277405 & 0.0666560580554811 & 0.966671970972259 \tabularnewline
108 & 0.0279508946685931 & 0.0559017893371863 & 0.972049105331407 \tabularnewline
109 & 0.0213737658936313 & 0.0427475317872627 & 0.978626234106369 \tabularnewline
110 & 0.029565373132119 & 0.0591307462642381 & 0.970434626867881 \tabularnewline
111 & 0.0226257459305371 & 0.0452514918610743 & 0.977374254069463 \tabularnewline
112 & 0.0244182464128134 & 0.0488364928256267 & 0.975581753587187 \tabularnewline
113 & 0.021314862808086 & 0.0426297256161721 & 0.978685137191914 \tabularnewline
114 & 0.0174024270106409 & 0.0348048540212819 & 0.982597572989359 \tabularnewline
115 & 0.0132752291388035 & 0.026550458277607 & 0.986724770861196 \tabularnewline
116 & 0.0103141293228107 & 0.0206282586456214 & 0.989685870677189 \tabularnewline
117 & 0.0145834830627909 & 0.0291669661255818 & 0.985416516937209 \tabularnewline
118 & 0.018110166206278 & 0.036220332412556 & 0.981889833793722 \tabularnewline
119 & 0.0143897790933458 & 0.0287795581866916 & 0.985610220906654 \tabularnewline
120 & 0.0107931611223136 & 0.0215863222446271 & 0.989206838877686 \tabularnewline
121 & 0.00848130993503803 & 0.0169626198700761 & 0.991518690064962 \tabularnewline
122 & 0.00687081889911546 & 0.0137416377982309 & 0.993129181100885 \tabularnewline
123 & 0.00874084056863934 & 0.0174816811372787 & 0.991259159431361 \tabularnewline
124 & 0.0161726573940852 & 0.0323453147881705 & 0.983827342605915 \tabularnewline
125 & 0.0179102470090607 & 0.0358204940181214 & 0.982089752990939 \tabularnewline
126 & 0.0513927666417845 & 0.102785533283569 & 0.948607233358216 \tabularnewline
127 & 0.0410407284378418 & 0.0820814568756836 & 0.958959271562158 \tabularnewline
128 & 0.0317824588118469 & 0.0635649176236938 & 0.968217541188153 \tabularnewline
129 & 0.0273076116731552 & 0.0546152233463103 & 0.972692388326845 \tabularnewline
130 & 0.0270725513306382 & 0.0541451026612765 & 0.972927448669362 \tabularnewline
131 & 0.0227862134895969 & 0.0455724269791937 & 0.977213786510403 \tabularnewline
132 & 0.0252444637105345 & 0.0504889274210689 & 0.974755536289466 \tabularnewline
133 & 0.0406356643715856 & 0.0812713287431711 & 0.959364335628414 \tabularnewline
134 & 0.0411487857833464 & 0.0822975715666928 & 0.958851214216654 \tabularnewline
135 & 0.041037248907004 & 0.082074497814008 & 0.958962751092996 \tabularnewline
136 & 0.0356289792584884 & 0.0712579585169769 & 0.964371020741512 \tabularnewline
137 & 0.0334338812606759 & 0.0668677625213517 & 0.966566118739324 \tabularnewline
138 & 0.0236581915680579 & 0.0473163831361157 & 0.976341808431942 \tabularnewline
139 & 0.0273303918165224 & 0.0546607836330448 & 0.972669608183478 \tabularnewline
140 & 0.0200269718938981 & 0.0400539437877962 & 0.979973028106102 \tabularnewline
141 & 0.0421283095092287 & 0.0842566190184573 & 0.957871690490771 \tabularnewline
142 & 0.0592336340273341 & 0.118467268054668 & 0.940766365972666 \tabularnewline
143 & 0.0428895900449933 & 0.0857791800899866 & 0.957110409955007 \tabularnewline
144 & 0.292644003708284 & 0.585288007416567 & 0.707355996291716 \tabularnewline
145 & 0.342922470570345 & 0.68584494114069 & 0.657077529429655 \tabularnewline
146 & 0.612260267797488 & 0.775479464405024 & 0.387739732202512 \tabularnewline
147 & 0.595236998562042 & 0.809526002875917 & 0.404763001437958 \tabularnewline
148 & 0.856549111252494 & 0.286901777495011 & 0.143450888747506 \tabularnewline
149 & 0.804449346068557 & 0.391101307862886 & 0.195550653931443 \tabularnewline
150 & 0.741740034849785 & 0.51651993030043 & 0.258259965150215 \tabularnewline
151 & 0.624965321147184 & 0.750069357705633 & 0.375034678852816 \tabularnewline
152 & 0.520600242564655 & 0.95879951487069 & 0.479399757435345 \tabularnewline
153 & 0.363041247662447 & 0.726082495324894 & 0.636958752337553 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154927&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]9[/C][C]0.0205948413289226[/C][C]0.0411896826578452[/C][C]0.979405158671077[/C][/ROW]
[ROW][C]10[/C][C]0.974776205231945[/C][C]0.0504475895361106[/C][C]0.0252237947680553[/C][/ROW]
[ROW][C]11[/C][C]0.995903305751399[/C][C]0.00819338849720201[/C][C]0.00409669424860101[/C][/ROW]
[ROW][C]12[/C][C]0.991346132686936[/C][C]0.0173077346261278[/C][C]0.00865386731306389[/C][/ROW]
[ROW][C]13[/C][C]0.993037706939766[/C][C]0.0139245861204674[/C][C]0.00696229306023369[/C][/ROW]
[ROW][C]14[/C][C]0.987554722447361[/C][C]0.024890555105278[/C][C]0.012445277552639[/C][/ROW]
[ROW][C]15[/C][C]0.990866445795267[/C][C]0.0182671084094655[/C][C]0.00913355420473277[/C][/ROW]
[ROW][C]16[/C][C]0.990428337068598[/C][C]0.0191433258628041[/C][C]0.00957166293140203[/C][/ROW]
[ROW][C]17[/C][C]0.984215643408409[/C][C]0.0315687131831829[/C][C]0.0157843565915914[/C][/ROW]
[ROW][C]18[/C][C]0.979607500887551[/C][C]0.0407849982248973[/C][C]0.0203924991124487[/C][/ROW]
[ROW][C]19[/C][C]0.969513758012227[/C][C]0.0609724839755454[/C][C]0.0304862419877727[/C][/ROW]
[ROW][C]20[/C][C]0.954378239773515[/C][C]0.0912435204529696[/C][C]0.0456217602264848[/C][/ROW]
[ROW][C]21[/C][C]0.934653108405317[/C][C]0.130693783189366[/C][C]0.0653468915946828[/C][/ROW]
[ROW][C]22[/C][C]0.909331781551254[/C][C]0.181336436897493[/C][C]0.0906682184487464[/C][/ROW]
[ROW][C]23[/C][C]0.87718946708488[/C][C]0.245621065830239[/C][C]0.12281053291512[/C][/ROW]
[ROW][C]24[/C][C]0.850056851435174[/C][C]0.299886297129652[/C][C]0.149943148564826[/C][/ROW]
[ROW][C]25[/C][C]0.830388657432677[/C][C]0.339222685134645[/C][C]0.169611342567323[/C][/ROW]
[ROW][C]26[/C][C]0.806918446291119[/C][C]0.386163107417763[/C][C]0.193081553708881[/C][/ROW]
[ROW][C]27[/C][C]0.763703230130213[/C][C]0.472593539739574[/C][C]0.236296769869787[/C][/ROW]
[ROW][C]28[/C][C]0.768394989720506[/C][C]0.463210020558989[/C][C]0.231605010279494[/C][/ROW]
[ROW][C]29[/C][C]0.718421309520994[/C][C]0.563157380958012[/C][C]0.281578690479006[/C][/ROW]
[ROW][C]30[/C][C]0.663647804412392[/C][C]0.672704391175216[/C][C]0.336352195587608[/C][/ROW]
[ROW][C]31[/C][C]0.607459820438986[/C][C]0.785080359122029[/C][C]0.392540179561014[/C][/ROW]
[ROW][C]32[/C][C]0.600008104890278[/C][C]0.799983790219444[/C][C]0.399991895109722[/C][/ROW]
[ROW][C]33[/C][C]0.550114186992466[/C][C]0.899771626015069[/C][C]0.449885813007534[/C][/ROW]
[ROW][C]34[/C][C]0.492187574441424[/C][C]0.984375148882847[/C][C]0.507812425558576[/C][/ROW]
[ROW][C]35[/C][C]0.539526363712884[/C][C]0.920947272574232[/C][C]0.460473636287116[/C][/ROW]
[ROW][C]36[/C][C]0.482011638045565[/C][C]0.96402327609113[/C][C]0.517988361954435[/C][/ROW]
[ROW][C]37[/C][C]0.540257067836026[/C][C]0.919485864327949[/C][C]0.459742932163974[/C][/ROW]
[ROW][C]38[/C][C]0.586869378263357[/C][C]0.826261243473286[/C][C]0.413130621736643[/C][/ROW]
[ROW][C]39[/C][C]0.601418387087277[/C][C]0.797163225825446[/C][C]0.398581612912723[/C][/ROW]
[ROW][C]40[/C][C]0.558454623392175[/C][C]0.88309075321565[/C][C]0.441545376607825[/C][/ROW]
[ROW][C]41[/C][C]0.512275640239158[/C][C]0.975448719521684[/C][C]0.487724359760842[/C][/ROW]
[ROW][C]42[/C][C]0.475406907132414[/C][C]0.950813814264828[/C][C]0.524593092867586[/C][/ROW]
[ROW][C]43[/C][C]0.544718920965467[/C][C]0.910562158069065[/C][C]0.455281079034533[/C][/ROW]
[ROW][C]44[/C][C]0.493782206441656[/C][C]0.987564412883312[/C][C]0.506217793558344[/C][/ROW]
[ROW][C]45[/C][C]0.447288495267036[/C][C]0.894576990534071[/C][C]0.552711504732964[/C][/ROW]
[ROW][C]46[/C][C]0.488640707274061[/C][C]0.977281414548122[/C][C]0.511359292725939[/C][/ROW]
[ROW][C]47[/C][C]0.492138970053175[/C][C]0.984277940106351[/C][C]0.507861029946825[/C][/ROW]
[ROW][C]48[/C][C]0.445580555678925[/C][C]0.891161111357849[/C][C]0.554419444321075[/C][/ROW]
[ROW][C]49[/C][C]0.412510061560799[/C][C]0.825020123121598[/C][C]0.587489938439201[/C][/ROW]
[ROW][C]50[/C][C]0.429687128473703[/C][C]0.859374256947406[/C][C]0.570312871526297[/C][/ROW]
[ROW][C]51[/C][C]0.436009493242503[/C][C]0.872018986485007[/C][C]0.563990506757497[/C][/ROW]
[ROW][C]52[/C][C]0.389131053156592[/C][C]0.778262106313183[/C][C]0.610868946843409[/C][/ROW]
[ROW][C]53[/C][C]0.440678831080045[/C][C]0.88135766216009[/C][C]0.559321168919955[/C][/ROW]
[ROW][C]54[/C][C]0.401002794598598[/C][C]0.802005589197196[/C][C]0.598997205401402[/C][/ROW]
[ROW][C]55[/C][C]0.485522494152848[/C][C]0.971044988305695[/C][C]0.514477505847152[/C][/ROW]
[ROW][C]56[/C][C]0.745440072566215[/C][C]0.50911985486757[/C][C]0.254559927433785[/C][/ROW]
[ROW][C]57[/C][C]0.70686617360926[/C][C]0.58626765278148[/C][C]0.29313382639074[/C][/ROW]
[ROW][C]58[/C][C]0.664118657003113[/C][C]0.671762685993774[/C][C]0.335881342996887[/C][/ROW]
[ROW][C]59[/C][C]0.619811562902875[/C][C]0.760376874194249[/C][C]0.380188437097125[/C][/ROW]
[ROW][C]60[/C][C]0.624105589684368[/C][C]0.751788820631265[/C][C]0.375894410315632[/C][/ROW]
[ROW][C]61[/C][C]0.648443456565186[/C][C]0.703113086869628[/C][C]0.351556543434814[/C][/ROW]
[ROW][C]62[/C][C]0.616499819646429[/C][C]0.767000360707142[/C][C]0.383500180353571[/C][/ROW]
[ROW][C]63[/C][C]0.626412792483123[/C][C]0.747174415033754[/C][C]0.373587207516877[/C][/ROW]
[ROW][C]64[/C][C]0.581522597166613[/C][C]0.836954805666773[/C][C]0.418477402833387[/C][/ROW]
[ROW][C]65[/C][C]0.537104458954625[/C][C]0.925791082090749[/C][C]0.462895541045375[/C][/ROW]
[ROW][C]66[/C][C]0.502829738862777[/C][C]0.994340522274447[/C][C]0.497170261137223[/C][/ROW]
[ROW][C]67[/C][C]0.472682054329992[/C][C]0.945364108659983[/C][C]0.527317945670008[/C][/ROW]
[ROW][C]68[/C][C]0.45117458000448[/C][C]0.90234916000896[/C][C]0.54882541999552[/C][/ROW]
[ROW][C]69[/C][C]0.46848931411554[/C][C]0.936978628231079[/C][C]0.53151068588446[/C][/ROW]
[ROW][C]70[/C][C]0.426593899112676[/C][C]0.853187798225352[/C][C]0.573406100887324[/C][/ROW]
[ROW][C]71[/C][C]0.382747575836189[/C][C]0.765495151672378[/C][C]0.617252424163811[/C][/ROW]
[ROW][C]72[/C][C]0.343839210882461[/C][C]0.687678421764921[/C][C]0.656160789117539[/C][/ROW]
[ROW][C]73[/C][C]0.316594369654344[/C][C]0.633188739308688[/C][C]0.683405630345656[/C][/ROW]
[ROW][C]74[/C][C]0.304277014443508[/C][C]0.608554028887016[/C][C]0.695722985556492[/C][/ROW]
[ROW][C]75[/C][C]0.291781102980426[/C][C]0.583562205960853[/C][C]0.708218897019574[/C][/ROW]
[ROW][C]76[/C][C]0.371745797757144[/C][C]0.743491595514287[/C][C]0.628254202242856[/C][/ROW]
[ROW][C]77[/C][C]0.353584969848547[/C][C]0.707169939697095[/C][C]0.646415030151453[/C][/ROW]
[ROW][C]78[/C][C]0.318427553913471[/C][C]0.636855107826941[/C][C]0.681572446086529[/C][/ROW]
[ROW][C]79[/C][C]0.338696279151676[/C][C]0.677392558303352[/C][C]0.661303720848324[/C][/ROW]
[ROW][C]80[/C][C]0.326413144066869[/C][C]0.652826288133738[/C][C]0.673586855933131[/C][/ROW]
[ROW][C]81[/C][C]0.289804176306815[/C][C]0.57960835261363[/C][C]0.710195823693185[/C][/ROW]
[ROW][C]82[/C][C]0.274515368249729[/C][C]0.549030736499458[/C][C]0.725484631750271[/C][/ROW]
[ROW][C]83[/C][C]0.240308662481216[/C][C]0.480617324962431[/C][C]0.759691337518784[/C][/ROW]
[ROW][C]84[/C][C]0.225391181874924[/C][C]0.450782363749848[/C][C]0.774608818125076[/C][/ROW]
[ROW][C]85[/C][C]0.193738105314482[/C][C]0.387476210628965[/C][C]0.806261894685518[/C][/ROW]
[ROW][C]86[/C][C]0.192445491370942[/C][C]0.384890982741885[/C][C]0.807554508629058[/C][/ROW]
[ROW][C]87[/C][C]0.181862479825023[/C][C]0.363724959650046[/C][C]0.818137520174977[/C][/ROW]
[ROW][C]88[/C][C]0.153127044763693[/C][C]0.306254089527387[/C][C]0.846872955236307[/C][/ROW]
[ROW][C]89[/C][C]0.127291170955069[/C][C]0.254582341910139[/C][C]0.872708829044931[/C][/ROW]
[ROW][C]90[/C][C]0.105627560555155[/C][C]0.21125512111031[/C][C]0.894372439444845[/C][/ROW]
[ROW][C]91[/C][C]0.091408520372675[/C][C]0.18281704074535[/C][C]0.908591479627325[/C][/ROW]
[ROW][C]92[/C][C]0.119930201138136[/C][C]0.239860402276273[/C][C]0.880069798861864[/C][/ROW]
[ROW][C]93[/C][C]0.103520409161135[/C][C]0.207040818322269[/C][C]0.896479590838865[/C][/ROW]
[ROW][C]94[/C][C]0.0880982030159533[/C][C]0.176196406031907[/C][C]0.911901796984047[/C][/ROW]
[ROW][C]95[/C][C]0.0844142728219683[/C][C]0.168828545643937[/C][C]0.915585727178032[/C][/ROW]
[ROW][C]96[/C][C]0.0804146826121511[/C][C]0.160829365224302[/C][C]0.919585317387849[/C][/ROW]
[ROW][C]97[/C][C]0.0715223335837593[/C][C]0.143044667167519[/C][C]0.928477666416241[/C][/ROW]
[ROW][C]98[/C][C]0.0936975811353426[/C][C]0.187395162270685[/C][C]0.906302418864657[/C][/ROW]
[ROW][C]99[/C][C]0.0792752969182415[/C][C]0.158550593836483[/C][C]0.920724703081758[/C][/ROW]
[ROW][C]100[/C][C]0.0639611309315292[/C][C]0.127922261863058[/C][C]0.936038869068471[/C][/ROW]
[ROW][C]101[/C][C]0.0787488860467378[/C][C]0.157497772093476[/C][C]0.921251113953262[/C][/ROW]
[ROW][C]102[/C][C]0.0635014172253931[/C][C]0.127002834450786[/C][C]0.936498582774607[/C][/ROW]
[ROW][C]103[/C][C]0.0593112645844314[/C][C]0.118622529168863[/C][C]0.940688735415569[/C][/ROW]
[ROW][C]104[/C][C]0.0486463033391717[/C][C]0.0972926066783434[/C][C]0.951353696660828[/C][/ROW]
[ROW][C]105[/C][C]0.0409984422056789[/C][C]0.0819968844113577[/C][C]0.959001557794321[/C][/ROW]
[ROW][C]106[/C][C]0.0429563596904314[/C][C]0.0859127193808627[/C][C]0.957043640309569[/C][/ROW]
[ROW][C]107[/C][C]0.0333280290277405[/C][C]0.0666560580554811[/C][C]0.966671970972259[/C][/ROW]
[ROW][C]108[/C][C]0.0279508946685931[/C][C]0.0559017893371863[/C][C]0.972049105331407[/C][/ROW]
[ROW][C]109[/C][C]0.0213737658936313[/C][C]0.0427475317872627[/C][C]0.978626234106369[/C][/ROW]
[ROW][C]110[/C][C]0.029565373132119[/C][C]0.0591307462642381[/C][C]0.970434626867881[/C][/ROW]
[ROW][C]111[/C][C]0.0226257459305371[/C][C]0.0452514918610743[/C][C]0.977374254069463[/C][/ROW]
[ROW][C]112[/C][C]0.0244182464128134[/C][C]0.0488364928256267[/C][C]0.975581753587187[/C][/ROW]
[ROW][C]113[/C][C]0.021314862808086[/C][C]0.0426297256161721[/C][C]0.978685137191914[/C][/ROW]
[ROW][C]114[/C][C]0.0174024270106409[/C][C]0.0348048540212819[/C][C]0.982597572989359[/C][/ROW]
[ROW][C]115[/C][C]0.0132752291388035[/C][C]0.026550458277607[/C][C]0.986724770861196[/C][/ROW]
[ROW][C]116[/C][C]0.0103141293228107[/C][C]0.0206282586456214[/C][C]0.989685870677189[/C][/ROW]
[ROW][C]117[/C][C]0.0145834830627909[/C][C]0.0291669661255818[/C][C]0.985416516937209[/C][/ROW]
[ROW][C]118[/C][C]0.018110166206278[/C][C]0.036220332412556[/C][C]0.981889833793722[/C][/ROW]
[ROW][C]119[/C][C]0.0143897790933458[/C][C]0.0287795581866916[/C][C]0.985610220906654[/C][/ROW]
[ROW][C]120[/C][C]0.0107931611223136[/C][C]0.0215863222446271[/C][C]0.989206838877686[/C][/ROW]
[ROW][C]121[/C][C]0.00848130993503803[/C][C]0.0169626198700761[/C][C]0.991518690064962[/C][/ROW]
[ROW][C]122[/C][C]0.00687081889911546[/C][C]0.0137416377982309[/C][C]0.993129181100885[/C][/ROW]
[ROW][C]123[/C][C]0.00874084056863934[/C][C]0.0174816811372787[/C][C]0.991259159431361[/C][/ROW]
[ROW][C]124[/C][C]0.0161726573940852[/C][C]0.0323453147881705[/C][C]0.983827342605915[/C][/ROW]
[ROW][C]125[/C][C]0.0179102470090607[/C][C]0.0358204940181214[/C][C]0.982089752990939[/C][/ROW]
[ROW][C]126[/C][C]0.0513927666417845[/C][C]0.102785533283569[/C][C]0.948607233358216[/C][/ROW]
[ROW][C]127[/C][C]0.0410407284378418[/C][C]0.0820814568756836[/C][C]0.958959271562158[/C][/ROW]
[ROW][C]128[/C][C]0.0317824588118469[/C][C]0.0635649176236938[/C][C]0.968217541188153[/C][/ROW]
[ROW][C]129[/C][C]0.0273076116731552[/C][C]0.0546152233463103[/C][C]0.972692388326845[/C][/ROW]
[ROW][C]130[/C][C]0.0270725513306382[/C][C]0.0541451026612765[/C][C]0.972927448669362[/C][/ROW]
[ROW][C]131[/C][C]0.0227862134895969[/C][C]0.0455724269791937[/C][C]0.977213786510403[/C][/ROW]
[ROW][C]132[/C][C]0.0252444637105345[/C][C]0.0504889274210689[/C][C]0.974755536289466[/C][/ROW]
[ROW][C]133[/C][C]0.0406356643715856[/C][C]0.0812713287431711[/C][C]0.959364335628414[/C][/ROW]
[ROW][C]134[/C][C]0.0411487857833464[/C][C]0.0822975715666928[/C][C]0.958851214216654[/C][/ROW]
[ROW][C]135[/C][C]0.041037248907004[/C][C]0.082074497814008[/C][C]0.958962751092996[/C][/ROW]
[ROW][C]136[/C][C]0.0356289792584884[/C][C]0.0712579585169769[/C][C]0.964371020741512[/C][/ROW]
[ROW][C]137[/C][C]0.0334338812606759[/C][C]0.0668677625213517[/C][C]0.966566118739324[/C][/ROW]
[ROW][C]138[/C][C]0.0236581915680579[/C][C]0.0473163831361157[/C][C]0.976341808431942[/C][/ROW]
[ROW][C]139[/C][C]0.0273303918165224[/C][C]0.0546607836330448[/C][C]0.972669608183478[/C][/ROW]
[ROW][C]140[/C][C]0.0200269718938981[/C][C]0.0400539437877962[/C][C]0.979973028106102[/C][/ROW]
[ROW][C]141[/C][C]0.0421283095092287[/C][C]0.0842566190184573[/C][C]0.957871690490771[/C][/ROW]
[ROW][C]142[/C][C]0.0592336340273341[/C][C]0.118467268054668[/C][C]0.940766365972666[/C][/ROW]
[ROW][C]143[/C][C]0.0428895900449933[/C][C]0.0857791800899866[/C][C]0.957110409955007[/C][/ROW]
[ROW][C]144[/C][C]0.292644003708284[/C][C]0.585288007416567[/C][C]0.707355996291716[/C][/ROW]
[ROW][C]145[/C][C]0.342922470570345[/C][C]0.68584494114069[/C][C]0.657077529429655[/C][/ROW]
[ROW][C]146[/C][C]0.612260267797488[/C][C]0.775479464405024[/C][C]0.387739732202512[/C][/ROW]
[ROW][C]147[/C][C]0.595236998562042[/C][C]0.809526002875917[/C][C]0.404763001437958[/C][/ROW]
[ROW][C]148[/C][C]0.856549111252494[/C][C]0.286901777495011[/C][C]0.143450888747506[/C][/ROW]
[ROW][C]149[/C][C]0.804449346068557[/C][C]0.391101307862886[/C][C]0.195550653931443[/C][/ROW]
[ROW][C]150[/C][C]0.741740034849785[/C][C]0.51651993030043[/C][C]0.258259965150215[/C][/ROW]
[ROW][C]151[/C][C]0.624965321147184[/C][C]0.750069357705633[/C][C]0.375034678852816[/C][/ROW]
[ROW][C]152[/C][C]0.520600242564655[/C][C]0.95879951487069[/C][C]0.479399757435345[/C][/ROW]
[ROW][C]153[/C][C]0.363041247662447[/C][C]0.726082495324894[/C][C]0.636958752337553[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154927&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154927&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
90.02059484132892260.04118968265784520.979405158671077
100.9747762052319450.05044758953611060.0252237947680553
110.9959033057513990.008193388497202010.00409669424860101
120.9913461326869360.01730773462612780.00865386731306389
130.9930377069397660.01392458612046740.00696229306023369
140.9875547224473610.0248905551052780.012445277552639
150.9908664457952670.01826710840946550.00913355420473277
160.9904283370685980.01914332586280410.00957166293140203
170.9842156434084090.03156871318318290.0157843565915914
180.9796075008875510.04078499822489730.0203924991124487
190.9695137580122270.06097248397554540.0304862419877727
200.9543782397735150.09124352045296960.0456217602264848
210.9346531084053170.1306937831893660.0653468915946828
220.9093317815512540.1813364368974930.0906682184487464
230.877189467084880.2456210658302390.12281053291512
240.8500568514351740.2998862971296520.149943148564826
250.8303886574326770.3392226851346450.169611342567323
260.8069184462911190.3861631074177630.193081553708881
270.7637032301302130.4725935397395740.236296769869787
280.7683949897205060.4632100205589890.231605010279494
290.7184213095209940.5631573809580120.281578690479006
300.6636478044123920.6727043911752160.336352195587608
310.6074598204389860.7850803591220290.392540179561014
320.6000081048902780.7999837902194440.399991895109722
330.5501141869924660.8997716260150690.449885813007534
340.4921875744414240.9843751488828470.507812425558576
350.5395263637128840.9209472725742320.460473636287116
360.4820116380455650.964023276091130.517988361954435
370.5402570678360260.9194858643279490.459742932163974
380.5868693782633570.8262612434732860.413130621736643
390.6014183870872770.7971632258254460.398581612912723
400.5584546233921750.883090753215650.441545376607825
410.5122756402391580.9754487195216840.487724359760842
420.4754069071324140.9508138142648280.524593092867586
430.5447189209654670.9105621580690650.455281079034533
440.4937822064416560.9875644128833120.506217793558344
450.4472884952670360.8945769905340710.552711504732964
460.4886407072740610.9772814145481220.511359292725939
470.4921389700531750.9842779401063510.507861029946825
480.4455805556789250.8911611113578490.554419444321075
490.4125100615607990.8250201231215980.587489938439201
500.4296871284737030.8593742569474060.570312871526297
510.4360094932425030.8720189864850070.563990506757497
520.3891310531565920.7782621063131830.610868946843409
530.4406788310800450.881357662160090.559321168919955
540.4010027945985980.8020055891971960.598997205401402
550.4855224941528480.9710449883056950.514477505847152
560.7454400725662150.509119854867570.254559927433785
570.706866173609260.586267652781480.29313382639074
580.6641186570031130.6717626859937740.335881342996887
590.6198115629028750.7603768741942490.380188437097125
600.6241055896843680.7517888206312650.375894410315632
610.6484434565651860.7031130868696280.351556543434814
620.6164998196464290.7670003607071420.383500180353571
630.6264127924831230.7471744150337540.373587207516877
640.5815225971666130.8369548056667730.418477402833387
650.5371044589546250.9257910820907490.462895541045375
660.5028297388627770.9943405222744470.497170261137223
670.4726820543299920.9453641086599830.527317945670008
680.451174580004480.902349160008960.54882541999552
690.468489314115540.9369786282310790.53151068588446
700.4265938991126760.8531877982253520.573406100887324
710.3827475758361890.7654951516723780.617252424163811
720.3438392108824610.6876784217649210.656160789117539
730.3165943696543440.6331887393086880.683405630345656
740.3042770144435080.6085540288870160.695722985556492
750.2917811029804260.5835622059608530.708218897019574
760.3717457977571440.7434915955142870.628254202242856
770.3535849698485470.7071699396970950.646415030151453
780.3184275539134710.6368551078269410.681572446086529
790.3386962791516760.6773925583033520.661303720848324
800.3264131440668690.6528262881337380.673586855933131
810.2898041763068150.579608352613630.710195823693185
820.2745153682497290.5490307364994580.725484631750271
830.2403086624812160.4806173249624310.759691337518784
840.2253911818749240.4507823637498480.774608818125076
850.1937381053144820.3874762106289650.806261894685518
860.1924454913709420.3848909827418850.807554508629058
870.1818624798250230.3637249596500460.818137520174977
880.1531270447636930.3062540895273870.846872955236307
890.1272911709550690.2545823419101390.872708829044931
900.1056275605551550.211255121110310.894372439444845
910.0914085203726750.182817040745350.908591479627325
920.1199302011381360.2398604022762730.880069798861864
930.1035204091611350.2070408183222690.896479590838865
940.08809820301595330.1761964060319070.911901796984047
950.08441427282196830.1688285456439370.915585727178032
960.08041468261215110.1608293652243020.919585317387849
970.07152233358375930.1430446671675190.928477666416241
980.09369758113534260.1873951622706850.906302418864657
990.07927529691824150.1585505938364830.920724703081758
1000.06396113093152920.1279222618630580.936038869068471
1010.07874888604673780.1574977720934760.921251113953262
1020.06350141722539310.1270028344507860.936498582774607
1030.05931126458443140.1186225291688630.940688735415569
1040.04864630333917170.09729260667834340.951353696660828
1050.04099844220567890.08199688441135770.959001557794321
1060.04295635969043140.08591271938086270.957043640309569
1070.03332802902774050.06665605805548110.966671970972259
1080.02795089466859310.05590178933718630.972049105331407
1090.02137376589363130.04274753178726270.978626234106369
1100.0295653731321190.05913074626423810.970434626867881
1110.02262574593053710.04525149186107430.977374254069463
1120.02441824641281340.04883649282562670.975581753587187
1130.0213148628080860.04262972561617210.978685137191914
1140.01740242701064090.03480485402128190.982597572989359
1150.01327522913880350.0265504582776070.986724770861196
1160.01031412932281070.02062825864562140.989685870677189
1170.01458348306279090.02916696612558180.985416516937209
1180.0181101662062780.0362203324125560.981889833793722
1190.01438977909334580.02877955818669160.985610220906654
1200.01079316112231360.02158632224462710.989206838877686
1210.008481309935038030.01696261987007610.991518690064962
1220.006870818899115460.01374163779823090.993129181100885
1230.008740840568639340.01748168113727870.991259159431361
1240.01617265739408520.03234531478817050.983827342605915
1250.01791024700906070.03582049401812140.982089752990939
1260.05139276664178450.1027855332835690.948607233358216
1270.04104072843784180.08208145687568360.958959271562158
1280.03178245881184690.06356491762369380.968217541188153
1290.02730761167315520.05461522334631030.972692388326845
1300.02707255133063820.05414510266127650.972927448669362
1310.02278621348959690.04557242697919370.977213786510403
1320.02524446371053450.05048892742106890.974755536289466
1330.04063566437158560.08127132874317110.959364335628414
1340.04114878578334640.08229757156669280.958851214216654
1350.0410372489070040.0820744978140080.958962751092996
1360.03562897925848840.07125795851697690.964371020741512
1370.03343388126067590.06686776252135170.966566118739324
1380.02365819156805790.04731638313611570.976341808431942
1390.02733039181652240.05466078363304480.972669608183478
1400.02002697189389810.04005394378779620.979973028106102
1410.04212830950922870.08425661901845730.957871690490771
1420.05923363402733410.1184672680546680.940766365972666
1430.04288959004499330.08577918008998660.957110409955007
1440.2926440037082840.5852880074165670.707355996291716
1450.3429224705703450.685844941140690.657077529429655
1460.6122602677974880.7754794644050240.387739732202512
1470.5952369985620420.8095260028759170.404763001437958
1480.8565491112524940.2869017774950110.143450888747506
1490.8044493460685570.3911013078628860.195550653931443
1500.7417400348497850.516519930300430.258259965150215
1510.6249653211471840.7500693577056330.375034678852816
1520.5206002425646550.958799514870690.479399757435345
1530.3630412476624470.7260824953248940.636958752337553






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level10.00689655172413793OK
5% type I error level280.193103448275862NOK
10% type I error level500.344827586206897NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 1 & 0.00689655172413793 & OK \tabularnewline
5% type I error level & 28 & 0.193103448275862 & NOK \tabularnewline
10% type I error level & 50 & 0.344827586206897 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154927&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]1[/C][C]0.00689655172413793[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]28[/C][C]0.193103448275862[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]50[/C][C]0.344827586206897[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154927&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154927&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 level10.00689655172413793OK
5% type I error level280.193103448275862NOK
10% type I error level500.344827586206897NOK


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 4
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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 = 5 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 5 ; 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')
}