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Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationTue, 22 Nov 2011 11:42:44 -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/Nov/22/t1321980329igflm2033axlnca.htm/, Retrieved Thu, 28 Mar 2024 20:09:57 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=146302, Retrieved Thu, 28 Mar 2024 20:09:57 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact55
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [] [2011-11-22 16:42:44] [f04206f511735117c791d4a2bb2fa643] [Current]
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Dataseries X:
26	21	21	23	17	23	4
20	16	15	24	17	20	4
19	19	18	22	18	20	6
19	18	11	20	21	21	8
20	16	8	24	20	24	8
25	23	19	27	28	22	4
25	17	4	28	19	23	4
22	12	20	27	22	20	8
26	19	16	24	16	25	5
22	16	14	23	18	23	4
17	19	10	24	25	27	4
22	20	13	27	17	27	4
19	13	14	27	14	22	4
24	20	8	28	11	24	4
26	27	23	27	27	25	4
21	17	11	23	20	22	8
13	8	9	24	22	28	4
26	25	24	28	22	28	4
20	26	5	27	21	27	4
22	13	15	25	23	25	8
14	19	5	19	17	16	4
21	15	19	24	24	28	7
7	5	6	20	14	21	4
23	16	13	28	17	24	4
17	14	11	26	23	27	5
25	24	17	23	24	14	4
25	24	17	23	24	14	4
19	9	5	20	8	27	4
20	19	9	11	22	20	4
23	19	15	24	23	21	4
22	25	17	25	25	22	4
22	19	17	23	21	21	4
21	18	20	18	24	12	15
15	15	12	20	15	20	10
20	12	7	20	22	24	4
22	21	16	24	21	19	8
18	12	7	23	25	28	4
20	15	14	25	16	23	4
28	28	24	28	28	27	4
22	25	15	26	23	22	4
18	19	15	26	21	27	7
23	20	10	23	21	26	4
20	24	14	22	26	22	6
25	26	18	24	22	21	5
26	25	12	21	21	19	4
15	12	9	20	18	24	16
17	12	9	22	12	19	5
23	15	8	20	25	26	12
21	17	18	25	17	22	6
13	14	10	20	24	28	9
18	16	17	22	15	21	9
19	11	14	23	13	23	4
22	20	16	25	26	28	5
16	11	10	23	16	10	4
24	22	19	23	24	24	4
18	20	10	22	21	21	5
20	19	14	24	20	21	4
24	17	10	25	14	24	4
14	21	4	21	25	24	4
22	23	19	12	25	25	5
24	18	9	17	20	25	4
18	17	12	20	22	23	6
21	27	16	23	20	21	4
23	25	11	23	26	16	4
17	19	18	20	18	17	18
22	22	11	28	22	25	4
24	24	24	24	24	24	6
21	20	17	24	17	23	4
22	19	18	24	24	25	4
16	11	9	24	20	23	5
21	22	19	28	19	28	4
23	22	18	25	20	26	4
22	16	12	21	15	22	5
24	20	23	25	23	19	10
24	24	22	25	26	26	5
16	16	14	18	22	18	8
16	16	14	17	20	18	8
21	22	16	26	24	25	5
26	24	23	28	26	27	4
15	16	7	21	21	12	4
25	27	10	27	25	15	4
18	11	12	22	13	21	5
23	21	12	21	20	23	4
20	20	12	25	22	22	4
17	20	17	22	23	21	8
25	27	21	23	28	24	4
24	20	16	26	22	27	5
17	12	11	19	20	22	14
19	8	14	25	6	28	8
20	21	13	21	21	26	8
15	18	9	13	20	10	4
27	24	19	24	18	19	4
22	16	13	25	23	22	6
23	18	19	26	20	21	4
16	20	13	25	24	24	7
19	20	13	25	22	25	7
25	19	13	22	21	21	4
19	17	14	21	18	20	6
19	16	12	23	21	21	4
26	26	22	25	23	24	7
21	15	11	24	23	23	4
20	22	5	21	15	18	4
24	17	18	21	21	24	8
22	23	19	25	24	24	4
20	21	14	22	23	19	4
18	19	15	20	21	20	10
18	14	12	20	21	18	8
24	17	19	23	20	20	6
24	12	15	28	11	27	4
22	24	17	23	22	23	4
23	18	8	28	27	26	4
22	20	10	24	25	23	5
20	16	12	18	18	17	4
18	20	12	20	20	21	6
25	22	20	28	24	25	4
18	12	12	21	10	23	5
16	16	12	21	27	27	7
20	17	14	25	21	24	8
19	22	6	19	21	20	5
15	12	10	18	18	27	8
19	14	18	21	15	21	10
19	23	18	22	24	24	8
16	15	7	24	22	21	5
17	17	18	15	14	15	12
28	28	9	28	28	25	4
23	20	17	26	18	25	5
25	23	22	23	26	22	4
20	13	11	26	17	24	6
17	18	15	20	19	21	4
23	23	17	22	22	22	4
16	19	15	20	18	23	7
23	23	22	23	24	22	7
11	12	9	22	15	20	10
18	16	13	24	18	23	4
24	23	20	23	26	25	5
23	13	14	22	11	23	8
21	22	14	26	26	22	11
16	18	12	23	21	25	7
24	23	20	27	23	26	4
23	20	20	23	23	22	8
18	10	8	21	15	24	6
20	17	17	26	22	24	7
9	18	9	23	26	25	5
24	15	18	21	16	20	4
25	23	22	27	20	26	8
20	17	10	19	18	21	4
21	17	13	23	22	26	8
25	22	15	25	16	21	6
22	20	18	23	19	22	4
21	20	18	22	20	16	9
21	19	12	22	19	26	5
22	18	12	25	23	28	6
27	22	20	25	24	18	4
24	20	12	28	25	25	4
24	22	16	28	21	23	4
21	18	16	20	21	21	5
18	16	18	25	23	20	6
16	16	16	19	27	25	16
22	16	13	25	23	22	6
20	16	17	22	18	21	6
18	17	13	18	16	16	4
20	18	17	20	16	18	4




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'AstonUniversity' @ aston.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 & 5 seconds \tabularnewline
R Server & 'AstonUniversity' @ aston.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146302&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]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'AstonUniversity' @ aston.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146302&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146302&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'AstonUniversity' @ aston.wessa.net







Multiple Linear Regression - Estimated Regression Equation
E3[t] = + 10.3288057716668 + 0.0662314177181074I1[t] -0.218622347582736I2[t] -0.0256580678377272I3[t] + 0.489389408006227E1[t] + 0.186887233303046E2[t] -0.000139057351789513A[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
E3[t] =  +  10.3288057716668 +  0.0662314177181074I1[t] -0.218622347582736I2[t] -0.0256580678377272I3[t] +  0.489389408006227E1[t] +  0.186887233303046E2[t] -0.000139057351789513A[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146302&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]E3[t] =  +  10.3288057716668 +  0.0662314177181074I1[t] -0.218622347582736I2[t] -0.0256580678377272I3[t] +  0.489389408006227E1[t] +  0.186887233303046E2[t] -0.000139057351789513A[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146302&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146302&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
E3[t] = + 10.3288057716668 + 0.0662314177181074I1[t] -0.218622347582736I2[t] -0.0256580678377272I3[t] + 0.489389408006227E1[t] + 0.186887233303046E2[t] -0.000139057351789513A[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)10.32880577166682.5742614.01239.3e-054.7e-05
I10.06623141771810740.1051990.62960.5298950.264947
I2-0.2186223475827360.089089-2.4540.0152360.007618
I3-0.02565806783772720.071005-0.36140.7183260.359163
E10.4893894080062270.0940835.20171e-060
E20.1868872333030460.0765972.43990.0158190.00791
A-0.0001390573517895130.112408-0.00120.9990150.499507

\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) & 10.3288057716668 & 2.574261 & 4.0123 & 9.3e-05 & 4.7e-05 \tabularnewline
I1 & 0.0662314177181074 & 0.105199 & 0.6296 & 0.529895 & 0.264947 \tabularnewline
I2 & -0.218622347582736 & 0.089089 & -2.454 & 0.015236 & 0.007618 \tabularnewline
I3 & -0.0256580678377272 & 0.071005 & -0.3614 & 0.718326 & 0.359163 \tabularnewline
E1 & 0.489389408006227 & 0.094083 & 5.2017 & 1e-06 & 0 \tabularnewline
E2 & 0.186887233303046 & 0.076597 & 2.4399 & 0.015819 & 0.00791 \tabularnewline
A & -0.000139057351789513 & 0.112408 & -0.0012 & 0.999015 & 0.499507 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146302&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]10.3288057716668[/C][C]2.574261[/C][C]4.0123[/C][C]9.3e-05[/C][C]4.7e-05[/C][/ROW]
[ROW][C]I1[/C][C]0.0662314177181074[/C][C]0.105199[/C][C]0.6296[/C][C]0.529895[/C][C]0.264947[/C][/ROW]
[ROW][C]I2[/C][C]-0.218622347582736[/C][C]0.089089[/C][C]-2.454[/C][C]0.015236[/C][C]0.007618[/C][/ROW]
[ROW][C]I3[/C][C]-0.0256580678377272[/C][C]0.071005[/C][C]-0.3614[/C][C]0.718326[/C][C]0.359163[/C][/ROW]
[ROW][C]E1[/C][C]0.489389408006227[/C][C]0.094083[/C][C]5.2017[/C][C]1e-06[/C][C]0[/C][/ROW]
[ROW][C]E2[/C][C]0.186887233303046[/C][C]0.076597[/C][C]2.4399[/C][C]0.015819[/C][C]0.00791[/C][/ROW]
[ROW][C]A[/C][C]-0.000139057351789513[/C][C]0.112408[/C][C]-0.0012[/C][C]0.999015[/C][C]0.499507[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146302&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146302&T=2

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)10.32880577166682.5742614.01239.3e-054.7e-05
I10.06623141771810740.1051990.62960.5298950.264947
I2-0.2186223475827360.089089-2.4540.0152360.007618
I3-0.02565806783772720.071005-0.36140.7183260.359163
E10.4893894080062270.0940835.20171e-060
E20.1868872333030460.0765972.43990.0158190.00791
A-0.0001390573517895130.112408-0.00120.9990150.499507







Multiple Linear Regression - Regression Statistics
Multiple R0.467778321315379
R-squared0.218816557892634
Adjusted R-squared0.188577198843316
F-TEST (value)7.2361506583445
F-TEST (DF numerator)6
F-TEST (DF denominator)155
p-value7.98283715686132e-07
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.27851506954815
Sum Squared Residuals1666.04246449441

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.467778321315379 \tabularnewline
R-squared & 0.218816557892634 \tabularnewline
Adjusted R-squared & 0.188577198843316 \tabularnewline
F-TEST (value) & 7.2361506583445 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 155 \tabularnewline
p-value & 7.98283715686132e-07 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 3.27851506954815 \tabularnewline
Sum Squared Residuals & 1666.04246449441 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146302&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.467778321315379[/C][/ROW]
[ROW][C]R-squared[/C][C]0.218816557892634[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.188577198843316[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]7.2361506583445[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]155[/C][/ROW]
[ROW][C]p-value[/C][C]7.98283715686132e-07[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]3.27851506954815[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1666.04246449441[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146302&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146302&T=3

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Regression Statistics
Multiple R0.467778321315379
R-squared0.218816557892634
Adjusted R-squared0.188577198843316
F-TEST (value)7.2361506583445
F-TEST (DF numerator)6
F-TEST (DF denominator)155
p-value7.98283715686132e-07
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.27851506954815
Sum Squared Residuals1666.04246449441







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
12321.35341702939581.64658297060425
22022.6924780760333-2.69247807603334
32021.1012357146409-1.10123571464086
42121.0810693062808-0.0810693062807893
52423.43219002139940.567809978600593
62224.914574250546-2.91457425054601
72325.4185836618871-2.41858366188714
82025.9731881237386-5.97318812373862
92522.22131518110122.77868481889878
102322.54809680460410.451903195395899
112723.46130498574383.53869501425618
122723.46993588083273.53006411916725
132224.2152782930107-2.21527829301071
142423.09875506364550.901244936354452
152523.81679677327921.18320322672078
162222.7134354800154-0.713435480015374
172825.06622150621012.93377849378993
182823.7833366420984.21666335790204
192722.97855243581414.02144756418588
202525.0909645326351-0.0909645326351083
211619.4488561653226-3.44885616532263
222824.18249303104923.81750696895077
232121.9490187477135-0.949018747713532
242424.900046096888-0.900046096888024
252725.13362394787431.86637605212566
261422.0421614734016-8.04216147340164
271422.0421614734016-8.04216147340164
282720.77364103801936.22635896198067
292016.76293330274583.23706669725422
302123.3566286862577-2.35662868625773
312222.7905109219801-0.790510921980078
322122.3759172582519-1.37591725825185
331220.5635190136116-8.56351901361161
342020.324751095797-0.324751095796982
352422.74911054355641.25088945644358
361922.4531638095232-3.45316380952318
372824.6454776320483.35452236795197
382323.239260666157-0.239260666156983
392724.38125583460422.61874416539575
402222.9574419990557-0.957441999055666
412723.63005877501823.36994122498181
422622.40313280325133.59686719674869
432221.67208553222060.327914467779434
442121.6947345944468-0.694734594446782
451920.4786203668041-1.47862036680405
462421.61741969785682.38258030214323
471921.6088675803568-2.60886758035685
482622.82582892721963.17417107278036
492222.9522242359582-0.952224235958178
502822.14435090069825.85564909930184
512121.1554505355442-0.155450535544176
522322.50807812284820.491921877151775
532824.09602890368273.90397109631726
541022.9726778409539-12.9726778409539
552422.36185861517351.63814138482646
562121.5824472493028-0.58244724930276
572122.622930801032-1.622930801032
582422.79579944660881.20420055339124
592421.51114622043172.48885377956829
602516.81423812003748.18576187996265
612519.80904330263225.19095669736779
622321.39496751631431.60503248368569
632120.39947789440650.60052210559346
641622.2187991640151-6.21879916401514
651719.9883253749707-2.98832537497071
662524.50783289586420.49216710413581
672422.28543487412211.71456512587792
682321.83290396774511.16709603225495
692523.40031029832951.59968970167051
702324.2351351926583-1.2351351926583
712823.67567523553514.32432476446487
722622.55251514809343.44748485190657
732221.05983336700620.940166632993827
741923.4875282775868-4.48752827758678
752623.20005394176162.79994605823835
761820.4507539620693-2.45075396206935
771819.587590087457-1.58759008745703
782523.70816773219931.2918322678007
792724.77516599073062.22483400926939
801221.8459662393381-9.84596623933812
811523.7103457708455-8.71034577084547
822122.0036343754476-1.00363437544755
832320.96752827067762.03247172932238
842223.318788463737-1.31878846373704
852121.7099666512713-0.709966651271286
862422.03121109251471.9687889074853
872723.9703322139133.029667786087
882221.5832295709210.416770429079023
892822.83395711908045.1660428809196
902620.92950695358155.07049304641854
911017.2554029111443-7.25540291114434
921921.4913741813503-2.49137418135035
932224.4866917402659-2.48669174026594
942123.8907358785929-2.89073587859287
952423.40156201957760.598437980422395
962523.22648180612581.77351819387417
972122.1878543747509-1.18785437475086
982021.151723273151-1.15172327315101
992122.9613803870344-1.96138038703437
1002422.33433226741971.66566773258033
1012324.2012875125034-1.20128751250337
1021819.7953819782894-1.79538197828942
1032421.94063167559622.0593683244038
1042422.9895522481671.01044775183295
1051921.7675689897632-2.76756898976321
1062020.6933051549255-0.693305154925458
1071821.8636692110559-3.8636692110559
1082022.7071433051715-2.70714330517146
1092724.66812736944332.33187263055666
1102321.46969275364121.53030724635878
1112626.4599640739417-0.459964073941652
1122323.5737006693998-0.57370066939983
1131720.0200030648122-3.0200030648122
1142120.365326006960.634673993039984
1152524.84937900508510.15062099491494
1162320.73496091994952.26503908005055
1172722.90481354563054.0951864543695
1182423.73589590809960.264104091900414
1192019.84589801918760.154101980812375
1202720.61409527282096.3859047271791
1212121.143740115232-0.14374011523201
1222421.34779160942462.65220839057539
1232123.7857364046089-2.78573640460891
1241517.2319084410036-2.2319084410036
1252524.76612685217020.233873147829846
1262523.1308937951451.86910620485502
1272222.5062679484018-0.506267948401827
1282424.4294780914413-0.42947809144133
1292120.47275596229470.527244037705269
1302221.26515711093580.734842889064164
1312320.00059779163552.99940220836453
1322221.99961347430420.000386525695848698
1332021.7714454871984-1.77144548719839
1342322.79821860957560.201781390424375
1352522.49121360900742.50878639099261
1362321.47203899453571.52796100546429
1372224.1324239903701-2.13242399037011
1382522.32502426665922.67497573334079
1392623.88824859847492.11175140152506
1402222.519770362073-0.519770362072977
1412422.20913499562931.79086500437072
1422424.3353374032475-0.335337403247467
1432522.87309282736452.12690717263555
1442021.4439964336536-1.4439964336536
1452623.34194595120132.6580540487987
1462120.34208626091120.657913739088849
1472623.0358938109462.96410618905399
1482122.0141251391271-1.01412513912707
1492221.75786237622530.242137623774704
1501621.3884334970451-5.38843349704506
1512621.57467324775834.42532675224173
1522824.07510511293823.92489488706181
1531823.5136736165026-5.51367361650259
1542525.6125440585373-0.612544058537288
1552324.3251181588087-1.32511815880872
1562121.0856589745837-0.0856589745837365
1572024.0934757302049-4.09347573020487
1582521.8221509421013.17784905789896
1592224.4866917402659-2.48669174026594
1602121.8489922429449-0.848992242944898
1611619.2694853473494-3.26948534734944
1621820.0594723798645-2.05947237986446

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 23 & 21.3534170293958 & 1.64658297060425 \tabularnewline
2 & 20 & 22.6924780760333 & -2.69247807603334 \tabularnewline
3 & 20 & 21.1012357146409 & -1.10123571464086 \tabularnewline
4 & 21 & 21.0810693062808 & -0.0810693062807893 \tabularnewline
5 & 24 & 23.4321900213994 & 0.567809978600593 \tabularnewline
6 & 22 & 24.914574250546 & -2.91457425054601 \tabularnewline
7 & 23 & 25.4185836618871 & -2.41858366188714 \tabularnewline
8 & 20 & 25.9731881237386 & -5.97318812373862 \tabularnewline
9 & 25 & 22.2213151811012 & 2.77868481889878 \tabularnewline
10 & 23 & 22.5480968046041 & 0.451903195395899 \tabularnewline
11 & 27 & 23.4613049857438 & 3.53869501425618 \tabularnewline
12 & 27 & 23.4699358808327 & 3.53006411916725 \tabularnewline
13 & 22 & 24.2152782930107 & -2.21527829301071 \tabularnewline
14 & 24 & 23.0987550636455 & 0.901244936354452 \tabularnewline
15 & 25 & 23.8167967732792 & 1.18320322672078 \tabularnewline
16 & 22 & 22.7134354800154 & -0.713435480015374 \tabularnewline
17 & 28 & 25.0662215062101 & 2.93377849378993 \tabularnewline
18 & 28 & 23.783336642098 & 4.21666335790204 \tabularnewline
19 & 27 & 22.9785524358141 & 4.02144756418588 \tabularnewline
20 & 25 & 25.0909645326351 & -0.0909645326351083 \tabularnewline
21 & 16 & 19.4488561653226 & -3.44885616532263 \tabularnewline
22 & 28 & 24.1824930310492 & 3.81750696895077 \tabularnewline
23 & 21 & 21.9490187477135 & -0.949018747713532 \tabularnewline
24 & 24 & 24.900046096888 & -0.900046096888024 \tabularnewline
25 & 27 & 25.1336239478743 & 1.86637605212566 \tabularnewline
26 & 14 & 22.0421614734016 & -8.04216147340164 \tabularnewline
27 & 14 & 22.0421614734016 & -8.04216147340164 \tabularnewline
28 & 27 & 20.7736410380193 & 6.22635896198067 \tabularnewline
29 & 20 & 16.7629333027458 & 3.23706669725422 \tabularnewline
30 & 21 & 23.3566286862577 & -2.35662868625773 \tabularnewline
31 & 22 & 22.7905109219801 & -0.790510921980078 \tabularnewline
32 & 21 & 22.3759172582519 & -1.37591725825185 \tabularnewline
33 & 12 & 20.5635190136116 & -8.56351901361161 \tabularnewline
34 & 20 & 20.324751095797 & -0.324751095796982 \tabularnewline
35 & 24 & 22.7491105435564 & 1.25088945644358 \tabularnewline
36 & 19 & 22.4531638095232 & -3.45316380952318 \tabularnewline
37 & 28 & 24.645477632048 & 3.35452236795197 \tabularnewline
38 & 23 & 23.239260666157 & -0.239260666156983 \tabularnewline
39 & 27 & 24.3812558346042 & 2.61874416539575 \tabularnewline
40 & 22 & 22.9574419990557 & -0.957441999055666 \tabularnewline
41 & 27 & 23.6300587750182 & 3.36994122498181 \tabularnewline
42 & 26 & 22.4031328032513 & 3.59686719674869 \tabularnewline
43 & 22 & 21.6720855322206 & 0.327914467779434 \tabularnewline
44 & 21 & 21.6947345944468 & -0.694734594446782 \tabularnewline
45 & 19 & 20.4786203668041 & -1.47862036680405 \tabularnewline
46 & 24 & 21.6174196978568 & 2.38258030214323 \tabularnewline
47 & 19 & 21.6088675803568 & -2.60886758035685 \tabularnewline
48 & 26 & 22.8258289272196 & 3.17417107278036 \tabularnewline
49 & 22 & 22.9522242359582 & -0.952224235958178 \tabularnewline
50 & 28 & 22.1443509006982 & 5.85564909930184 \tabularnewline
51 & 21 & 21.1554505355442 & -0.155450535544176 \tabularnewline
52 & 23 & 22.5080781228482 & 0.491921877151775 \tabularnewline
53 & 28 & 24.0960289036827 & 3.90397109631726 \tabularnewline
54 & 10 & 22.9726778409539 & -12.9726778409539 \tabularnewline
55 & 24 & 22.3618586151735 & 1.63814138482646 \tabularnewline
56 & 21 & 21.5824472493028 & -0.58244724930276 \tabularnewline
57 & 21 & 22.622930801032 & -1.622930801032 \tabularnewline
58 & 24 & 22.7957994466088 & 1.20420055339124 \tabularnewline
59 & 24 & 21.5111462204317 & 2.48885377956829 \tabularnewline
60 & 25 & 16.8142381200374 & 8.18576187996265 \tabularnewline
61 & 25 & 19.8090433026322 & 5.19095669736779 \tabularnewline
62 & 23 & 21.3949675163143 & 1.60503248368569 \tabularnewline
63 & 21 & 20.3994778944065 & 0.60052210559346 \tabularnewline
64 & 16 & 22.2187991640151 & -6.21879916401514 \tabularnewline
65 & 17 & 19.9883253749707 & -2.98832537497071 \tabularnewline
66 & 25 & 24.5078328958642 & 0.49216710413581 \tabularnewline
67 & 24 & 22.2854348741221 & 1.71456512587792 \tabularnewline
68 & 23 & 21.8329039677451 & 1.16709603225495 \tabularnewline
69 & 25 & 23.4003102983295 & 1.59968970167051 \tabularnewline
70 & 23 & 24.2351351926583 & -1.2351351926583 \tabularnewline
71 & 28 & 23.6756752355351 & 4.32432476446487 \tabularnewline
72 & 26 & 22.5525151480934 & 3.44748485190657 \tabularnewline
73 & 22 & 21.0598333670062 & 0.940166632993827 \tabularnewline
74 & 19 & 23.4875282775868 & -4.48752827758678 \tabularnewline
75 & 26 & 23.2000539417616 & 2.79994605823835 \tabularnewline
76 & 18 & 20.4507539620693 & -2.45075396206935 \tabularnewline
77 & 18 & 19.587590087457 & -1.58759008745703 \tabularnewline
78 & 25 & 23.7081677321993 & 1.2918322678007 \tabularnewline
79 & 27 & 24.7751659907306 & 2.22483400926939 \tabularnewline
80 & 12 & 21.8459662393381 & -9.84596623933812 \tabularnewline
81 & 15 & 23.7103457708455 & -8.71034577084547 \tabularnewline
82 & 21 & 22.0036343754476 & -1.00363437544755 \tabularnewline
83 & 23 & 20.9675282706776 & 2.03247172932238 \tabularnewline
84 & 22 & 23.318788463737 & -1.31878846373704 \tabularnewline
85 & 21 & 21.7099666512713 & -0.709966651271286 \tabularnewline
86 & 24 & 22.0312110925147 & 1.9687889074853 \tabularnewline
87 & 27 & 23.970332213913 & 3.029667786087 \tabularnewline
88 & 22 & 21.583229570921 & 0.416770429079023 \tabularnewline
89 & 28 & 22.8339571190804 & 5.1660428809196 \tabularnewline
90 & 26 & 20.9295069535815 & 5.07049304641854 \tabularnewline
91 & 10 & 17.2554029111443 & -7.25540291114434 \tabularnewline
92 & 19 & 21.4913741813503 & -2.49137418135035 \tabularnewline
93 & 22 & 24.4866917402659 & -2.48669174026594 \tabularnewline
94 & 21 & 23.8907358785929 & -2.89073587859287 \tabularnewline
95 & 24 & 23.4015620195776 & 0.598437980422395 \tabularnewline
96 & 25 & 23.2264818061258 & 1.77351819387417 \tabularnewline
97 & 21 & 22.1878543747509 & -1.18785437475086 \tabularnewline
98 & 20 & 21.151723273151 & -1.15172327315101 \tabularnewline
99 & 21 & 22.9613803870344 & -1.96138038703437 \tabularnewline
100 & 24 & 22.3343322674197 & 1.66566773258033 \tabularnewline
101 & 23 & 24.2012875125034 & -1.20128751250337 \tabularnewline
102 & 18 & 19.7953819782894 & -1.79538197828942 \tabularnewline
103 & 24 & 21.9406316755962 & 2.0593683244038 \tabularnewline
104 & 24 & 22.989552248167 & 1.01044775183295 \tabularnewline
105 & 19 & 21.7675689897632 & -2.76756898976321 \tabularnewline
106 & 20 & 20.6933051549255 & -0.693305154925458 \tabularnewline
107 & 18 & 21.8636692110559 & -3.8636692110559 \tabularnewline
108 & 20 & 22.7071433051715 & -2.70714330517146 \tabularnewline
109 & 27 & 24.6681273694433 & 2.33187263055666 \tabularnewline
110 & 23 & 21.4696927536412 & 1.53030724635878 \tabularnewline
111 & 26 & 26.4599640739417 & -0.459964073941652 \tabularnewline
112 & 23 & 23.5737006693998 & -0.57370066939983 \tabularnewline
113 & 17 & 20.0200030648122 & -3.0200030648122 \tabularnewline
114 & 21 & 20.36532600696 & 0.634673993039984 \tabularnewline
115 & 25 & 24.8493790050851 & 0.15062099491494 \tabularnewline
116 & 23 & 20.7349609199495 & 2.26503908005055 \tabularnewline
117 & 27 & 22.9048135456305 & 4.0951864543695 \tabularnewline
118 & 24 & 23.7358959080996 & 0.264104091900414 \tabularnewline
119 & 20 & 19.8458980191876 & 0.154101980812375 \tabularnewline
120 & 27 & 20.6140952728209 & 6.3859047271791 \tabularnewline
121 & 21 & 21.143740115232 & -0.14374011523201 \tabularnewline
122 & 24 & 21.3477916094246 & 2.65220839057539 \tabularnewline
123 & 21 & 23.7857364046089 & -2.78573640460891 \tabularnewline
124 & 15 & 17.2319084410036 & -2.2319084410036 \tabularnewline
125 & 25 & 24.7661268521702 & 0.233873147829846 \tabularnewline
126 & 25 & 23.130893795145 & 1.86910620485502 \tabularnewline
127 & 22 & 22.5062679484018 & -0.506267948401827 \tabularnewline
128 & 24 & 24.4294780914413 & -0.42947809144133 \tabularnewline
129 & 21 & 20.4727559622947 & 0.527244037705269 \tabularnewline
130 & 22 & 21.2651571109358 & 0.734842889064164 \tabularnewline
131 & 23 & 20.0005977916355 & 2.99940220836453 \tabularnewline
132 & 22 & 21.9996134743042 & 0.000386525695848698 \tabularnewline
133 & 20 & 21.7714454871984 & -1.77144548719839 \tabularnewline
134 & 23 & 22.7982186095756 & 0.201781390424375 \tabularnewline
135 & 25 & 22.4912136090074 & 2.50878639099261 \tabularnewline
136 & 23 & 21.4720389945357 & 1.52796100546429 \tabularnewline
137 & 22 & 24.1324239903701 & -2.13242399037011 \tabularnewline
138 & 25 & 22.3250242666592 & 2.67497573334079 \tabularnewline
139 & 26 & 23.8882485984749 & 2.11175140152506 \tabularnewline
140 & 22 & 22.519770362073 & -0.519770362072977 \tabularnewline
141 & 24 & 22.2091349956293 & 1.79086500437072 \tabularnewline
142 & 24 & 24.3353374032475 & -0.335337403247467 \tabularnewline
143 & 25 & 22.8730928273645 & 2.12690717263555 \tabularnewline
144 & 20 & 21.4439964336536 & -1.4439964336536 \tabularnewline
145 & 26 & 23.3419459512013 & 2.6580540487987 \tabularnewline
146 & 21 & 20.3420862609112 & 0.657913739088849 \tabularnewline
147 & 26 & 23.035893810946 & 2.96410618905399 \tabularnewline
148 & 21 & 22.0141251391271 & -1.01412513912707 \tabularnewline
149 & 22 & 21.7578623762253 & 0.242137623774704 \tabularnewline
150 & 16 & 21.3884334970451 & -5.38843349704506 \tabularnewline
151 & 26 & 21.5746732477583 & 4.42532675224173 \tabularnewline
152 & 28 & 24.0751051129382 & 3.92489488706181 \tabularnewline
153 & 18 & 23.5136736165026 & -5.51367361650259 \tabularnewline
154 & 25 & 25.6125440585373 & -0.612544058537288 \tabularnewline
155 & 23 & 24.3251181588087 & -1.32511815880872 \tabularnewline
156 & 21 & 21.0856589745837 & -0.0856589745837365 \tabularnewline
157 & 20 & 24.0934757302049 & -4.09347573020487 \tabularnewline
158 & 25 & 21.822150942101 & 3.17784905789896 \tabularnewline
159 & 22 & 24.4866917402659 & -2.48669174026594 \tabularnewline
160 & 21 & 21.8489922429449 & -0.848992242944898 \tabularnewline
161 & 16 & 19.2694853473494 & -3.26948534734944 \tabularnewline
162 & 18 & 20.0594723798645 & -2.05947237986446 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146302&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]23[/C][C]21.3534170293958[/C][C]1.64658297060425[/C][/ROW]
[ROW][C]2[/C][C]20[/C][C]22.6924780760333[/C][C]-2.69247807603334[/C][/ROW]
[ROW][C]3[/C][C]20[/C][C]21.1012357146409[/C][C]-1.10123571464086[/C][/ROW]
[ROW][C]4[/C][C]21[/C][C]21.0810693062808[/C][C]-0.0810693062807893[/C][/ROW]
[ROW][C]5[/C][C]24[/C][C]23.4321900213994[/C][C]0.567809978600593[/C][/ROW]
[ROW][C]6[/C][C]22[/C][C]24.914574250546[/C][C]-2.91457425054601[/C][/ROW]
[ROW][C]7[/C][C]23[/C][C]25.4185836618871[/C][C]-2.41858366188714[/C][/ROW]
[ROW][C]8[/C][C]20[/C][C]25.9731881237386[/C][C]-5.97318812373862[/C][/ROW]
[ROW][C]9[/C][C]25[/C][C]22.2213151811012[/C][C]2.77868481889878[/C][/ROW]
[ROW][C]10[/C][C]23[/C][C]22.5480968046041[/C][C]0.451903195395899[/C][/ROW]
[ROW][C]11[/C][C]27[/C][C]23.4613049857438[/C][C]3.53869501425618[/C][/ROW]
[ROW][C]12[/C][C]27[/C][C]23.4699358808327[/C][C]3.53006411916725[/C][/ROW]
[ROW][C]13[/C][C]22[/C][C]24.2152782930107[/C][C]-2.21527829301071[/C][/ROW]
[ROW][C]14[/C][C]24[/C][C]23.0987550636455[/C][C]0.901244936354452[/C][/ROW]
[ROW][C]15[/C][C]25[/C][C]23.8167967732792[/C][C]1.18320322672078[/C][/ROW]
[ROW][C]16[/C][C]22[/C][C]22.7134354800154[/C][C]-0.713435480015374[/C][/ROW]
[ROW][C]17[/C][C]28[/C][C]25.0662215062101[/C][C]2.93377849378993[/C][/ROW]
[ROW][C]18[/C][C]28[/C][C]23.783336642098[/C][C]4.21666335790204[/C][/ROW]
[ROW][C]19[/C][C]27[/C][C]22.9785524358141[/C][C]4.02144756418588[/C][/ROW]
[ROW][C]20[/C][C]25[/C][C]25.0909645326351[/C][C]-0.0909645326351083[/C][/ROW]
[ROW][C]21[/C][C]16[/C][C]19.4488561653226[/C][C]-3.44885616532263[/C][/ROW]
[ROW][C]22[/C][C]28[/C][C]24.1824930310492[/C][C]3.81750696895077[/C][/ROW]
[ROW][C]23[/C][C]21[/C][C]21.9490187477135[/C][C]-0.949018747713532[/C][/ROW]
[ROW][C]24[/C][C]24[/C][C]24.900046096888[/C][C]-0.900046096888024[/C][/ROW]
[ROW][C]25[/C][C]27[/C][C]25.1336239478743[/C][C]1.86637605212566[/C][/ROW]
[ROW][C]26[/C][C]14[/C][C]22.0421614734016[/C][C]-8.04216147340164[/C][/ROW]
[ROW][C]27[/C][C]14[/C][C]22.0421614734016[/C][C]-8.04216147340164[/C][/ROW]
[ROW][C]28[/C][C]27[/C][C]20.7736410380193[/C][C]6.22635896198067[/C][/ROW]
[ROW][C]29[/C][C]20[/C][C]16.7629333027458[/C][C]3.23706669725422[/C][/ROW]
[ROW][C]30[/C][C]21[/C][C]23.3566286862577[/C][C]-2.35662868625773[/C][/ROW]
[ROW][C]31[/C][C]22[/C][C]22.7905109219801[/C][C]-0.790510921980078[/C][/ROW]
[ROW][C]32[/C][C]21[/C][C]22.3759172582519[/C][C]-1.37591725825185[/C][/ROW]
[ROW][C]33[/C][C]12[/C][C]20.5635190136116[/C][C]-8.56351901361161[/C][/ROW]
[ROW][C]34[/C][C]20[/C][C]20.324751095797[/C][C]-0.324751095796982[/C][/ROW]
[ROW][C]35[/C][C]24[/C][C]22.7491105435564[/C][C]1.25088945644358[/C][/ROW]
[ROW][C]36[/C][C]19[/C][C]22.4531638095232[/C][C]-3.45316380952318[/C][/ROW]
[ROW][C]37[/C][C]28[/C][C]24.645477632048[/C][C]3.35452236795197[/C][/ROW]
[ROW][C]38[/C][C]23[/C][C]23.239260666157[/C][C]-0.239260666156983[/C][/ROW]
[ROW][C]39[/C][C]27[/C][C]24.3812558346042[/C][C]2.61874416539575[/C][/ROW]
[ROW][C]40[/C][C]22[/C][C]22.9574419990557[/C][C]-0.957441999055666[/C][/ROW]
[ROW][C]41[/C][C]27[/C][C]23.6300587750182[/C][C]3.36994122498181[/C][/ROW]
[ROW][C]42[/C][C]26[/C][C]22.4031328032513[/C][C]3.59686719674869[/C][/ROW]
[ROW][C]43[/C][C]22[/C][C]21.6720855322206[/C][C]0.327914467779434[/C][/ROW]
[ROW][C]44[/C][C]21[/C][C]21.6947345944468[/C][C]-0.694734594446782[/C][/ROW]
[ROW][C]45[/C][C]19[/C][C]20.4786203668041[/C][C]-1.47862036680405[/C][/ROW]
[ROW][C]46[/C][C]24[/C][C]21.6174196978568[/C][C]2.38258030214323[/C][/ROW]
[ROW][C]47[/C][C]19[/C][C]21.6088675803568[/C][C]-2.60886758035685[/C][/ROW]
[ROW][C]48[/C][C]26[/C][C]22.8258289272196[/C][C]3.17417107278036[/C][/ROW]
[ROW][C]49[/C][C]22[/C][C]22.9522242359582[/C][C]-0.952224235958178[/C][/ROW]
[ROW][C]50[/C][C]28[/C][C]22.1443509006982[/C][C]5.85564909930184[/C][/ROW]
[ROW][C]51[/C][C]21[/C][C]21.1554505355442[/C][C]-0.155450535544176[/C][/ROW]
[ROW][C]52[/C][C]23[/C][C]22.5080781228482[/C][C]0.491921877151775[/C][/ROW]
[ROW][C]53[/C][C]28[/C][C]24.0960289036827[/C][C]3.90397109631726[/C][/ROW]
[ROW][C]54[/C][C]10[/C][C]22.9726778409539[/C][C]-12.9726778409539[/C][/ROW]
[ROW][C]55[/C][C]24[/C][C]22.3618586151735[/C][C]1.63814138482646[/C][/ROW]
[ROW][C]56[/C][C]21[/C][C]21.5824472493028[/C][C]-0.58244724930276[/C][/ROW]
[ROW][C]57[/C][C]21[/C][C]22.622930801032[/C][C]-1.622930801032[/C][/ROW]
[ROW][C]58[/C][C]24[/C][C]22.7957994466088[/C][C]1.20420055339124[/C][/ROW]
[ROW][C]59[/C][C]24[/C][C]21.5111462204317[/C][C]2.48885377956829[/C][/ROW]
[ROW][C]60[/C][C]25[/C][C]16.8142381200374[/C][C]8.18576187996265[/C][/ROW]
[ROW][C]61[/C][C]25[/C][C]19.8090433026322[/C][C]5.19095669736779[/C][/ROW]
[ROW][C]62[/C][C]23[/C][C]21.3949675163143[/C][C]1.60503248368569[/C][/ROW]
[ROW][C]63[/C][C]21[/C][C]20.3994778944065[/C][C]0.60052210559346[/C][/ROW]
[ROW][C]64[/C][C]16[/C][C]22.2187991640151[/C][C]-6.21879916401514[/C][/ROW]
[ROW][C]65[/C][C]17[/C][C]19.9883253749707[/C][C]-2.98832537497071[/C][/ROW]
[ROW][C]66[/C][C]25[/C][C]24.5078328958642[/C][C]0.49216710413581[/C][/ROW]
[ROW][C]67[/C][C]24[/C][C]22.2854348741221[/C][C]1.71456512587792[/C][/ROW]
[ROW][C]68[/C][C]23[/C][C]21.8329039677451[/C][C]1.16709603225495[/C][/ROW]
[ROW][C]69[/C][C]25[/C][C]23.4003102983295[/C][C]1.59968970167051[/C][/ROW]
[ROW][C]70[/C][C]23[/C][C]24.2351351926583[/C][C]-1.2351351926583[/C][/ROW]
[ROW][C]71[/C][C]28[/C][C]23.6756752355351[/C][C]4.32432476446487[/C][/ROW]
[ROW][C]72[/C][C]26[/C][C]22.5525151480934[/C][C]3.44748485190657[/C][/ROW]
[ROW][C]73[/C][C]22[/C][C]21.0598333670062[/C][C]0.940166632993827[/C][/ROW]
[ROW][C]74[/C][C]19[/C][C]23.4875282775868[/C][C]-4.48752827758678[/C][/ROW]
[ROW][C]75[/C][C]26[/C][C]23.2000539417616[/C][C]2.79994605823835[/C][/ROW]
[ROW][C]76[/C][C]18[/C][C]20.4507539620693[/C][C]-2.45075396206935[/C][/ROW]
[ROW][C]77[/C][C]18[/C][C]19.587590087457[/C][C]-1.58759008745703[/C][/ROW]
[ROW][C]78[/C][C]25[/C][C]23.7081677321993[/C][C]1.2918322678007[/C][/ROW]
[ROW][C]79[/C][C]27[/C][C]24.7751659907306[/C][C]2.22483400926939[/C][/ROW]
[ROW][C]80[/C][C]12[/C][C]21.8459662393381[/C][C]-9.84596623933812[/C][/ROW]
[ROW][C]81[/C][C]15[/C][C]23.7103457708455[/C][C]-8.71034577084547[/C][/ROW]
[ROW][C]82[/C][C]21[/C][C]22.0036343754476[/C][C]-1.00363437544755[/C][/ROW]
[ROW][C]83[/C][C]23[/C][C]20.9675282706776[/C][C]2.03247172932238[/C][/ROW]
[ROW][C]84[/C][C]22[/C][C]23.318788463737[/C][C]-1.31878846373704[/C][/ROW]
[ROW][C]85[/C][C]21[/C][C]21.7099666512713[/C][C]-0.709966651271286[/C][/ROW]
[ROW][C]86[/C][C]24[/C][C]22.0312110925147[/C][C]1.9687889074853[/C][/ROW]
[ROW][C]87[/C][C]27[/C][C]23.970332213913[/C][C]3.029667786087[/C][/ROW]
[ROW][C]88[/C][C]22[/C][C]21.583229570921[/C][C]0.416770429079023[/C][/ROW]
[ROW][C]89[/C][C]28[/C][C]22.8339571190804[/C][C]5.1660428809196[/C][/ROW]
[ROW][C]90[/C][C]26[/C][C]20.9295069535815[/C][C]5.07049304641854[/C][/ROW]
[ROW][C]91[/C][C]10[/C][C]17.2554029111443[/C][C]-7.25540291114434[/C][/ROW]
[ROW][C]92[/C][C]19[/C][C]21.4913741813503[/C][C]-2.49137418135035[/C][/ROW]
[ROW][C]93[/C][C]22[/C][C]24.4866917402659[/C][C]-2.48669174026594[/C][/ROW]
[ROW][C]94[/C][C]21[/C][C]23.8907358785929[/C][C]-2.89073587859287[/C][/ROW]
[ROW][C]95[/C][C]24[/C][C]23.4015620195776[/C][C]0.598437980422395[/C][/ROW]
[ROW][C]96[/C][C]25[/C][C]23.2264818061258[/C][C]1.77351819387417[/C][/ROW]
[ROW][C]97[/C][C]21[/C][C]22.1878543747509[/C][C]-1.18785437475086[/C][/ROW]
[ROW][C]98[/C][C]20[/C][C]21.151723273151[/C][C]-1.15172327315101[/C][/ROW]
[ROW][C]99[/C][C]21[/C][C]22.9613803870344[/C][C]-1.96138038703437[/C][/ROW]
[ROW][C]100[/C][C]24[/C][C]22.3343322674197[/C][C]1.66566773258033[/C][/ROW]
[ROW][C]101[/C][C]23[/C][C]24.2012875125034[/C][C]-1.20128751250337[/C][/ROW]
[ROW][C]102[/C][C]18[/C][C]19.7953819782894[/C][C]-1.79538197828942[/C][/ROW]
[ROW][C]103[/C][C]24[/C][C]21.9406316755962[/C][C]2.0593683244038[/C][/ROW]
[ROW][C]104[/C][C]24[/C][C]22.989552248167[/C][C]1.01044775183295[/C][/ROW]
[ROW][C]105[/C][C]19[/C][C]21.7675689897632[/C][C]-2.76756898976321[/C][/ROW]
[ROW][C]106[/C][C]20[/C][C]20.6933051549255[/C][C]-0.693305154925458[/C][/ROW]
[ROW][C]107[/C][C]18[/C][C]21.8636692110559[/C][C]-3.8636692110559[/C][/ROW]
[ROW][C]108[/C][C]20[/C][C]22.7071433051715[/C][C]-2.70714330517146[/C][/ROW]
[ROW][C]109[/C][C]27[/C][C]24.6681273694433[/C][C]2.33187263055666[/C][/ROW]
[ROW][C]110[/C][C]23[/C][C]21.4696927536412[/C][C]1.53030724635878[/C][/ROW]
[ROW][C]111[/C][C]26[/C][C]26.4599640739417[/C][C]-0.459964073941652[/C][/ROW]
[ROW][C]112[/C][C]23[/C][C]23.5737006693998[/C][C]-0.57370066939983[/C][/ROW]
[ROW][C]113[/C][C]17[/C][C]20.0200030648122[/C][C]-3.0200030648122[/C][/ROW]
[ROW][C]114[/C][C]21[/C][C]20.36532600696[/C][C]0.634673993039984[/C][/ROW]
[ROW][C]115[/C][C]25[/C][C]24.8493790050851[/C][C]0.15062099491494[/C][/ROW]
[ROW][C]116[/C][C]23[/C][C]20.7349609199495[/C][C]2.26503908005055[/C][/ROW]
[ROW][C]117[/C][C]27[/C][C]22.9048135456305[/C][C]4.0951864543695[/C][/ROW]
[ROW][C]118[/C][C]24[/C][C]23.7358959080996[/C][C]0.264104091900414[/C][/ROW]
[ROW][C]119[/C][C]20[/C][C]19.8458980191876[/C][C]0.154101980812375[/C][/ROW]
[ROW][C]120[/C][C]27[/C][C]20.6140952728209[/C][C]6.3859047271791[/C][/ROW]
[ROW][C]121[/C][C]21[/C][C]21.143740115232[/C][C]-0.14374011523201[/C][/ROW]
[ROW][C]122[/C][C]24[/C][C]21.3477916094246[/C][C]2.65220839057539[/C][/ROW]
[ROW][C]123[/C][C]21[/C][C]23.7857364046089[/C][C]-2.78573640460891[/C][/ROW]
[ROW][C]124[/C][C]15[/C][C]17.2319084410036[/C][C]-2.2319084410036[/C][/ROW]
[ROW][C]125[/C][C]25[/C][C]24.7661268521702[/C][C]0.233873147829846[/C][/ROW]
[ROW][C]126[/C][C]25[/C][C]23.130893795145[/C][C]1.86910620485502[/C][/ROW]
[ROW][C]127[/C][C]22[/C][C]22.5062679484018[/C][C]-0.506267948401827[/C][/ROW]
[ROW][C]128[/C][C]24[/C][C]24.4294780914413[/C][C]-0.42947809144133[/C][/ROW]
[ROW][C]129[/C][C]21[/C][C]20.4727559622947[/C][C]0.527244037705269[/C][/ROW]
[ROW][C]130[/C][C]22[/C][C]21.2651571109358[/C][C]0.734842889064164[/C][/ROW]
[ROW][C]131[/C][C]23[/C][C]20.0005977916355[/C][C]2.99940220836453[/C][/ROW]
[ROW][C]132[/C][C]22[/C][C]21.9996134743042[/C][C]0.000386525695848698[/C][/ROW]
[ROW][C]133[/C][C]20[/C][C]21.7714454871984[/C][C]-1.77144548719839[/C][/ROW]
[ROW][C]134[/C][C]23[/C][C]22.7982186095756[/C][C]0.201781390424375[/C][/ROW]
[ROW][C]135[/C][C]25[/C][C]22.4912136090074[/C][C]2.50878639099261[/C][/ROW]
[ROW][C]136[/C][C]23[/C][C]21.4720389945357[/C][C]1.52796100546429[/C][/ROW]
[ROW][C]137[/C][C]22[/C][C]24.1324239903701[/C][C]-2.13242399037011[/C][/ROW]
[ROW][C]138[/C][C]25[/C][C]22.3250242666592[/C][C]2.67497573334079[/C][/ROW]
[ROW][C]139[/C][C]26[/C][C]23.8882485984749[/C][C]2.11175140152506[/C][/ROW]
[ROW][C]140[/C][C]22[/C][C]22.519770362073[/C][C]-0.519770362072977[/C][/ROW]
[ROW][C]141[/C][C]24[/C][C]22.2091349956293[/C][C]1.79086500437072[/C][/ROW]
[ROW][C]142[/C][C]24[/C][C]24.3353374032475[/C][C]-0.335337403247467[/C][/ROW]
[ROW][C]143[/C][C]25[/C][C]22.8730928273645[/C][C]2.12690717263555[/C][/ROW]
[ROW][C]144[/C][C]20[/C][C]21.4439964336536[/C][C]-1.4439964336536[/C][/ROW]
[ROW][C]145[/C][C]26[/C][C]23.3419459512013[/C][C]2.6580540487987[/C][/ROW]
[ROW][C]146[/C][C]21[/C][C]20.3420862609112[/C][C]0.657913739088849[/C][/ROW]
[ROW][C]147[/C][C]26[/C][C]23.035893810946[/C][C]2.96410618905399[/C][/ROW]
[ROW][C]148[/C][C]21[/C][C]22.0141251391271[/C][C]-1.01412513912707[/C][/ROW]
[ROW][C]149[/C][C]22[/C][C]21.7578623762253[/C][C]0.242137623774704[/C][/ROW]
[ROW][C]150[/C][C]16[/C][C]21.3884334970451[/C][C]-5.38843349704506[/C][/ROW]
[ROW][C]151[/C][C]26[/C][C]21.5746732477583[/C][C]4.42532675224173[/C][/ROW]
[ROW][C]152[/C][C]28[/C][C]24.0751051129382[/C][C]3.92489488706181[/C][/ROW]
[ROW][C]153[/C][C]18[/C][C]23.5136736165026[/C][C]-5.51367361650259[/C][/ROW]
[ROW][C]154[/C][C]25[/C][C]25.6125440585373[/C][C]-0.612544058537288[/C][/ROW]
[ROW][C]155[/C][C]23[/C][C]24.3251181588087[/C][C]-1.32511815880872[/C][/ROW]
[ROW][C]156[/C][C]21[/C][C]21.0856589745837[/C][C]-0.0856589745837365[/C][/ROW]
[ROW][C]157[/C][C]20[/C][C]24.0934757302049[/C][C]-4.09347573020487[/C][/ROW]
[ROW][C]158[/C][C]25[/C][C]21.822150942101[/C][C]3.17784905789896[/C][/ROW]
[ROW][C]159[/C][C]22[/C][C]24.4866917402659[/C][C]-2.48669174026594[/C][/ROW]
[ROW][C]160[/C][C]21[/C][C]21.8489922429449[/C][C]-0.848992242944898[/C][/ROW]
[ROW][C]161[/C][C]16[/C][C]19.2694853473494[/C][C]-3.26948534734944[/C][/ROW]
[ROW][C]162[/C][C]18[/C][C]20.0594723798645[/C][C]-2.05947237986446[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146302&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146302&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
12321.35341702939581.64658297060425
22022.6924780760333-2.69247807603334
32021.1012357146409-1.10123571464086
42121.0810693062808-0.0810693062807893
52423.43219002139940.567809978600593
62224.914574250546-2.91457425054601
72325.4185836618871-2.41858366188714
82025.9731881237386-5.97318812373862
92522.22131518110122.77868481889878
102322.54809680460410.451903195395899
112723.46130498574383.53869501425618
122723.46993588083273.53006411916725
132224.2152782930107-2.21527829301071
142423.09875506364550.901244936354452
152523.81679677327921.18320322672078
162222.7134354800154-0.713435480015374
172825.06622150621012.93377849378993
182823.7833366420984.21666335790204
192722.97855243581414.02144756418588
202525.0909645326351-0.0909645326351083
211619.4488561653226-3.44885616532263
222824.18249303104923.81750696895077
232121.9490187477135-0.949018747713532
242424.900046096888-0.900046096888024
252725.13362394787431.86637605212566
261422.0421614734016-8.04216147340164
271422.0421614734016-8.04216147340164
282720.77364103801936.22635896198067
292016.76293330274583.23706669725422
302123.3566286862577-2.35662868625773
312222.7905109219801-0.790510921980078
322122.3759172582519-1.37591725825185
331220.5635190136116-8.56351901361161
342020.324751095797-0.324751095796982
352422.74911054355641.25088945644358
361922.4531638095232-3.45316380952318
372824.6454776320483.35452236795197
382323.239260666157-0.239260666156983
392724.38125583460422.61874416539575
402222.9574419990557-0.957441999055666
412723.63005877501823.36994122498181
422622.40313280325133.59686719674869
432221.67208553222060.327914467779434
442121.6947345944468-0.694734594446782
451920.4786203668041-1.47862036680405
462421.61741969785682.38258030214323
471921.6088675803568-2.60886758035685
482622.82582892721963.17417107278036
492222.9522242359582-0.952224235958178
502822.14435090069825.85564909930184
512121.1554505355442-0.155450535544176
522322.50807812284820.491921877151775
532824.09602890368273.90397109631726
541022.9726778409539-12.9726778409539
552422.36185861517351.63814138482646
562121.5824472493028-0.58244724930276
572122.622930801032-1.622930801032
582422.79579944660881.20420055339124
592421.51114622043172.48885377956829
602516.81423812003748.18576187996265
612519.80904330263225.19095669736779
622321.39496751631431.60503248368569
632120.39947789440650.60052210559346
641622.2187991640151-6.21879916401514
651719.9883253749707-2.98832537497071
662524.50783289586420.49216710413581
672422.28543487412211.71456512587792
682321.83290396774511.16709603225495
692523.40031029832951.59968970167051
702324.2351351926583-1.2351351926583
712823.67567523553514.32432476446487
722622.55251514809343.44748485190657
732221.05983336700620.940166632993827
741923.4875282775868-4.48752827758678
752623.20005394176162.79994605823835
761820.4507539620693-2.45075396206935
771819.587590087457-1.58759008745703
782523.70816773219931.2918322678007
792724.77516599073062.22483400926939
801221.8459662393381-9.84596623933812
811523.7103457708455-8.71034577084547
822122.0036343754476-1.00363437544755
832320.96752827067762.03247172932238
842223.318788463737-1.31878846373704
852121.7099666512713-0.709966651271286
862422.03121109251471.9687889074853
872723.9703322139133.029667786087
882221.5832295709210.416770429079023
892822.83395711908045.1660428809196
902620.92950695358155.07049304641854
911017.2554029111443-7.25540291114434
921921.4913741813503-2.49137418135035
932224.4866917402659-2.48669174026594
942123.8907358785929-2.89073587859287
952423.40156201957760.598437980422395
962523.22648180612581.77351819387417
972122.1878543747509-1.18785437475086
982021.151723273151-1.15172327315101
992122.9613803870344-1.96138038703437
1002422.33433226741971.66566773258033
1012324.2012875125034-1.20128751250337
1021819.7953819782894-1.79538197828942
1032421.94063167559622.0593683244038
1042422.9895522481671.01044775183295
1051921.7675689897632-2.76756898976321
1062020.6933051549255-0.693305154925458
1071821.8636692110559-3.8636692110559
1082022.7071433051715-2.70714330517146
1092724.66812736944332.33187263055666
1102321.46969275364121.53030724635878
1112626.4599640739417-0.459964073941652
1122323.5737006693998-0.57370066939983
1131720.0200030648122-3.0200030648122
1142120.365326006960.634673993039984
1152524.84937900508510.15062099491494
1162320.73496091994952.26503908005055
1172722.90481354563054.0951864543695
1182423.73589590809960.264104091900414
1192019.84589801918760.154101980812375
1202720.61409527282096.3859047271791
1212121.143740115232-0.14374011523201
1222421.34779160942462.65220839057539
1232123.7857364046089-2.78573640460891
1241517.2319084410036-2.2319084410036
1252524.76612685217020.233873147829846
1262523.1308937951451.86910620485502
1272222.5062679484018-0.506267948401827
1282424.4294780914413-0.42947809144133
1292120.47275596229470.527244037705269
1302221.26515711093580.734842889064164
1312320.00059779163552.99940220836453
1322221.99961347430420.000386525695848698
1332021.7714454871984-1.77144548719839
1342322.79821860957560.201781390424375
1352522.49121360900742.50878639099261
1362321.47203899453571.52796100546429
1372224.1324239903701-2.13242399037011
1382522.32502426665922.67497573334079
1392623.88824859847492.11175140152506
1402222.519770362073-0.519770362072977
1412422.20913499562931.79086500437072
1422424.3353374032475-0.335337403247467
1432522.87309282736452.12690717263555
1442021.4439964336536-1.4439964336536
1452623.34194595120132.6580540487987
1462120.34208626091120.657913739088849
1472623.0358938109462.96410618905399
1482122.0141251391271-1.01412513912707
1492221.75786237622530.242137623774704
1501621.3884334970451-5.38843349704506
1512621.57467324775834.42532675224173
1522824.07510511293823.92489488706181
1531823.5136736165026-5.51367361650259
1542525.6125440585373-0.612544058537288
1552324.3251181588087-1.32511815880872
1562121.0856589745837-0.0856589745837365
1572024.0934757302049-4.09347573020487
1582521.8221509421013.17784905789896
1592224.4866917402659-2.48669174026594
1602121.8489922429449-0.848992242944898
1611619.2694853473494-3.26948534734944
1621820.0594723798645-2.05947237986446







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.1373702010730790.2747404021461570.862629798926921
110.4315744085033660.8631488170067330.568425591496634
120.3953931065721830.7907862131443660.604606893427817
130.2800744335063770.5601488670127550.719925566493622
140.2151573389033220.4303146778066430.784842661096678
150.1396420092309720.2792840184619440.860357990769028
160.08565400336160870.1713080067232170.914345996638391
170.1391739486408530.2783478972817070.860826051359147
180.2051647919016590.4103295838033180.794835208098341
190.1531946394919190.3063892789838370.846805360508081
200.1609923921547670.3219847843095340.839007607845233
210.3414607535822770.6829215071645530.658539246417723
220.4111523834626880.8223047669253770.588847616537312
230.3374314707325460.6748629414650930.662568529267453
240.2750724431665460.5501448863330920.724927556833454
250.2288534583022280.4577069166044560.771146541697772
260.5688986794323390.8622026411353220.431101320567661
270.7365578323705950.5268843352588090.263442167629405
280.8847649861615840.2304700276768310.115235013838416
290.904754696392640.190490607214720.0952453036073598
300.8849840649675370.2300318700649260.115015935032463
310.8525332538876060.2949334922247880.147466746112394
320.8183616233638630.3632767532722740.181638376636137
330.8866753053532220.2266493892935570.113324694646778
340.8591181389757680.2817637220484630.140881861024232
350.8263773075700590.3472453848598820.173622692429941
360.8050331243733650.3899337512532690.194966875626635
370.7942781604745130.4114436790509730.205721839525487
380.7540681066152470.4918637867695070.245931893384753
390.7647924863190470.4704150273619060.235207513680953
400.7227152929624550.5545694140750890.277284707037545
410.746635420213780.5067291595724410.253364579786221
420.7465935308886730.5068129382226540.253406469111327
430.7052502104527030.5894995790945940.294749789547297
440.658392909474390.6832141810512190.341607090525609
450.6185680295665750.762863940866850.381431970433425
460.6567595174150170.6864809651699650.343240482584983
470.6502302229165040.6995395541669910.349769777083495
480.6550121313936260.6899757372127470.344987868606374
490.6077853861300830.7844292277398330.392214613869917
500.7007125460423380.5985749079153230.299287453957662
510.658119813937780.6837603721244410.341880186062221
520.6126703934399290.7746592131201420.387329606560071
530.629250579681930.741498840636140.37074942031807
540.98127482375840.0374503524831980.018725176241599
550.9771886409195260.04562271816094910.0228113590804745
560.970311700115330.05937659976933940.0296882998846697
570.963276850999920.07344629800016140.0367231490000807
580.9541483165139750.09170336697205030.0458516834860252
590.94875602127620.10248795744760.0512439787237999
600.987927648921740.0241447021565210.0120723510782605
610.9933846095253580.01323078094928370.00661539047464185
620.991764055808860.01647188838228220.0082359441911411
630.9887838585525280.02243228289494320.0112161414474716
640.995265839085040.00946832182991770.00473416091495885
650.9954331579575210.00913368408495840.0045668420424792
660.9936390313850520.01272193722989620.00636096861494812
670.9920865307733450.01582693845330960.0079134692266548
680.9895654700297620.02086905994047550.0104345299702378
690.9870357768951720.02592844620965680.0129642231048284
700.9832298669050120.03354026618997580.0167701330949879
710.9859554828036550.0280890343926910.0140445171963455
720.986275227809760.02744954438047990.0137247721902399
730.9823382078520380.03532358429592420.0176617921479621
740.9882565682808320.02348686343833690.0117434317191685
750.9871875382636570.02562492347268690.0128124617363435
760.9853140098104820.02937198037903610.0146859901895181
770.981517613693840.03696477261232190.018482386306161
780.97648473086440.04703053827120.0235152691356
790.9725673052031880.05486538959362380.0274326947968119
800.997754137908820.004491724182361080.00224586209118054
810.9998557005299990.0002885989400020590.00014429947000103
820.9997826858705440.0004346282589116280.000217314129455814
830.9997437593135660.0005124813728679570.000256240686433979
840.9996358312937550.0007283374124909910.000364168706245496
850.999471218967050.001057562065901730.000528781032950864
860.9994070415472940.001185916905411990.000592958452705997
870.9993978475646140.001204304870772570.000602152435386286
880.9991318482808570.001736303438286760.000868151719143378
890.9994207430593890.001158513881221830.000579256940610916
900.9997068046536420.0005863906927151470.000293195346357574
910.9999503416096129.93167807755292e-054.96583903877646e-05
920.999937548204610.0001249035907799576.24517953899786e-05
930.9999244959575470.0001510080849052137.55040424526067e-05
940.999916752920660.0001664941586785258.32470793392627e-05
950.9998676633693540.0002646732612917080.000132336630645854
960.9998082966782840.0003834066434326760.000191703321716338
970.9997054206920430.0005891586159139680.000294579307956984
980.9995656347921770.0008687304156467210.000434365207823361
990.999431516839340.001136966321319430.000568483160659714
1000.999234892870340.001530214259319480.00076510712965974
1010.9988965940000250.002206811999950080.00110340599997504
1020.998620822828760.00275835434247980.0013791771712399
1030.998421296702190.00315740659562080.0015787032978104
1040.9977460372909470.004507925418105570.00225396270905279
1050.9976260004101350.004747999179730440.00237399958986522
1060.9966469779545760.006706044090847040.00335302204542352
1070.9975962047259040.004807590548192650.00240379527409632
1080.9972041042676730.005591791464653220.00279589573232661
1090.9971079160774820.005784167845036840.00289208392251842
1100.996097376953060.00780524609387980.0039026230469399
1110.9943963363761920.01120732724761620.0056036636238081
1120.9922378209118570.01552435817628660.00776217908814328
1130.99295661632020.01408676735960170.00704338367980087
1140.989850741174950.02029851765009880.0101492588250494
1150.9858450701898950.028309859620210.014154929810105
1160.9840878646831950.03182427063360970.0159121353168049
1170.984388254662940.03122349067411920.0156117453370596
1180.9779218632314350.04415627353712950.0220781367685648
1190.971950887459480.05609822508103910.0280491125405196
1200.9895881671696330.0208236656607340.010411832830367
1210.9848434685506980.0303130628986030.0151565314493015
1220.981982690158410.03603461968317950.0180173098415897
1230.9844008197808670.03119836043826620.0155991802191331
1240.9826924938729380.03461501225412350.0173075061270617
1250.9781542475063310.04369150498733720.0218457524936686
1260.974686471787180.05062705642563890.0253135282128194
1270.9639257648061070.07214847038778630.0360742351938931
1280.9492880144450490.1014239711099020.0507119855549512
1290.9306285064196240.1387429871607520.0693714935803758
1300.906419635196170.1871607296076590.0935803648038294
1310.9009741309586620.1980517380826750.0990258690413377
1320.8705097418539740.2589805162920530.129490258146026
1330.8641131474942350.2717737050115290.135886852505765
1340.8225229745816780.3549540508366440.177477025418322
1350.8540322990733690.2919354018532620.145967700926631
1360.811040451178310.3779190976433810.18895954882169
1370.8620813631478520.2758372737042950.137918636852148
1380.8230029452993480.3539941094013030.176997054700652
1390.8592882567289050.2814234865421910.140711743271095
1400.8150263044924940.3699473910150120.184973695507506
1410.7575536504463830.4848926991072340.242446349553617
1420.6864271719768270.6271456560463460.313572828023173
1430.6229675996155610.7540648007688780.377032400384439
1440.5499877200463340.9000245599073310.450012279953666
1450.6948644203815740.6102711592368510.305135579618426
1460.6323009567987740.7353980864024520.367699043201226
1470.5617396613897710.8765206772204570.438260338610229
1480.4528992704133940.9057985408267870.547100729586606
1490.4775095659349720.9550191318699440.522490434065028
1500.6428224780210560.7143550439578880.357177521978944
1510.6173664979276590.7652670041446820.382633502072341
1520.6893578471217860.6212843057564270.310642152878214

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.137370201073079 & 0.274740402146157 & 0.862629798926921 \tabularnewline
11 & 0.431574408503366 & 0.863148817006733 & 0.568425591496634 \tabularnewline
12 & 0.395393106572183 & 0.790786213144366 & 0.604606893427817 \tabularnewline
13 & 0.280074433506377 & 0.560148867012755 & 0.719925566493622 \tabularnewline
14 & 0.215157338903322 & 0.430314677806643 & 0.784842661096678 \tabularnewline
15 & 0.139642009230972 & 0.279284018461944 & 0.860357990769028 \tabularnewline
16 & 0.0856540033616087 & 0.171308006723217 & 0.914345996638391 \tabularnewline
17 & 0.139173948640853 & 0.278347897281707 & 0.860826051359147 \tabularnewline
18 & 0.205164791901659 & 0.410329583803318 & 0.794835208098341 \tabularnewline
19 & 0.153194639491919 & 0.306389278983837 & 0.846805360508081 \tabularnewline
20 & 0.160992392154767 & 0.321984784309534 & 0.839007607845233 \tabularnewline
21 & 0.341460753582277 & 0.682921507164553 & 0.658539246417723 \tabularnewline
22 & 0.411152383462688 & 0.822304766925377 & 0.588847616537312 \tabularnewline
23 & 0.337431470732546 & 0.674862941465093 & 0.662568529267453 \tabularnewline
24 & 0.275072443166546 & 0.550144886333092 & 0.724927556833454 \tabularnewline
25 & 0.228853458302228 & 0.457706916604456 & 0.771146541697772 \tabularnewline
26 & 0.568898679432339 & 0.862202641135322 & 0.431101320567661 \tabularnewline
27 & 0.736557832370595 & 0.526884335258809 & 0.263442167629405 \tabularnewline
28 & 0.884764986161584 & 0.230470027676831 & 0.115235013838416 \tabularnewline
29 & 0.90475469639264 & 0.19049060721472 & 0.0952453036073598 \tabularnewline
30 & 0.884984064967537 & 0.230031870064926 & 0.115015935032463 \tabularnewline
31 & 0.852533253887606 & 0.294933492224788 & 0.147466746112394 \tabularnewline
32 & 0.818361623363863 & 0.363276753272274 & 0.181638376636137 \tabularnewline
33 & 0.886675305353222 & 0.226649389293557 & 0.113324694646778 \tabularnewline
34 & 0.859118138975768 & 0.281763722048463 & 0.140881861024232 \tabularnewline
35 & 0.826377307570059 & 0.347245384859882 & 0.173622692429941 \tabularnewline
36 & 0.805033124373365 & 0.389933751253269 & 0.194966875626635 \tabularnewline
37 & 0.794278160474513 & 0.411443679050973 & 0.205721839525487 \tabularnewline
38 & 0.754068106615247 & 0.491863786769507 & 0.245931893384753 \tabularnewline
39 & 0.764792486319047 & 0.470415027361906 & 0.235207513680953 \tabularnewline
40 & 0.722715292962455 & 0.554569414075089 & 0.277284707037545 \tabularnewline
41 & 0.74663542021378 & 0.506729159572441 & 0.253364579786221 \tabularnewline
42 & 0.746593530888673 & 0.506812938222654 & 0.253406469111327 \tabularnewline
43 & 0.705250210452703 & 0.589499579094594 & 0.294749789547297 \tabularnewline
44 & 0.65839290947439 & 0.683214181051219 & 0.341607090525609 \tabularnewline
45 & 0.618568029566575 & 0.76286394086685 & 0.381431970433425 \tabularnewline
46 & 0.656759517415017 & 0.686480965169965 & 0.343240482584983 \tabularnewline
47 & 0.650230222916504 & 0.699539554166991 & 0.349769777083495 \tabularnewline
48 & 0.655012131393626 & 0.689975737212747 & 0.344987868606374 \tabularnewline
49 & 0.607785386130083 & 0.784429227739833 & 0.392214613869917 \tabularnewline
50 & 0.700712546042338 & 0.598574907915323 & 0.299287453957662 \tabularnewline
51 & 0.65811981393778 & 0.683760372124441 & 0.341880186062221 \tabularnewline
52 & 0.612670393439929 & 0.774659213120142 & 0.387329606560071 \tabularnewline
53 & 0.62925057968193 & 0.74149884063614 & 0.37074942031807 \tabularnewline
54 & 0.9812748237584 & 0.037450352483198 & 0.018725176241599 \tabularnewline
55 & 0.977188640919526 & 0.0456227181609491 & 0.0228113590804745 \tabularnewline
56 & 0.97031170011533 & 0.0593765997693394 & 0.0296882998846697 \tabularnewline
57 & 0.96327685099992 & 0.0734462980001614 & 0.0367231490000807 \tabularnewline
58 & 0.954148316513975 & 0.0917033669720503 & 0.0458516834860252 \tabularnewline
59 & 0.9487560212762 & 0.1024879574476 & 0.0512439787237999 \tabularnewline
60 & 0.98792764892174 & 0.024144702156521 & 0.0120723510782605 \tabularnewline
61 & 0.993384609525358 & 0.0132307809492837 & 0.00661539047464185 \tabularnewline
62 & 0.99176405580886 & 0.0164718883822822 & 0.0082359441911411 \tabularnewline
63 & 0.988783858552528 & 0.0224322828949432 & 0.0112161414474716 \tabularnewline
64 & 0.99526583908504 & 0.0094683218299177 & 0.00473416091495885 \tabularnewline
65 & 0.995433157957521 & 0.0091336840849584 & 0.0045668420424792 \tabularnewline
66 & 0.993639031385052 & 0.0127219372298962 & 0.00636096861494812 \tabularnewline
67 & 0.992086530773345 & 0.0158269384533096 & 0.0079134692266548 \tabularnewline
68 & 0.989565470029762 & 0.0208690599404755 & 0.0104345299702378 \tabularnewline
69 & 0.987035776895172 & 0.0259284462096568 & 0.0129642231048284 \tabularnewline
70 & 0.983229866905012 & 0.0335402661899758 & 0.0167701330949879 \tabularnewline
71 & 0.985955482803655 & 0.028089034392691 & 0.0140445171963455 \tabularnewline
72 & 0.98627522780976 & 0.0274495443804799 & 0.0137247721902399 \tabularnewline
73 & 0.982338207852038 & 0.0353235842959242 & 0.0176617921479621 \tabularnewline
74 & 0.988256568280832 & 0.0234868634383369 & 0.0117434317191685 \tabularnewline
75 & 0.987187538263657 & 0.0256249234726869 & 0.0128124617363435 \tabularnewline
76 & 0.985314009810482 & 0.0293719803790361 & 0.0146859901895181 \tabularnewline
77 & 0.98151761369384 & 0.0369647726123219 & 0.018482386306161 \tabularnewline
78 & 0.9764847308644 & 0.0470305382712 & 0.0235152691356 \tabularnewline
79 & 0.972567305203188 & 0.0548653895936238 & 0.0274326947968119 \tabularnewline
80 & 0.99775413790882 & 0.00449172418236108 & 0.00224586209118054 \tabularnewline
81 & 0.999855700529999 & 0.000288598940002059 & 0.00014429947000103 \tabularnewline
82 & 0.999782685870544 & 0.000434628258911628 & 0.000217314129455814 \tabularnewline
83 & 0.999743759313566 & 0.000512481372867957 & 0.000256240686433979 \tabularnewline
84 & 0.999635831293755 & 0.000728337412490991 & 0.000364168706245496 \tabularnewline
85 & 0.99947121896705 & 0.00105756206590173 & 0.000528781032950864 \tabularnewline
86 & 0.999407041547294 & 0.00118591690541199 & 0.000592958452705997 \tabularnewline
87 & 0.999397847564614 & 0.00120430487077257 & 0.000602152435386286 \tabularnewline
88 & 0.999131848280857 & 0.00173630343828676 & 0.000868151719143378 \tabularnewline
89 & 0.999420743059389 & 0.00115851388122183 & 0.000579256940610916 \tabularnewline
90 & 0.999706804653642 & 0.000586390692715147 & 0.000293195346357574 \tabularnewline
91 & 0.999950341609612 & 9.93167807755292e-05 & 4.96583903877646e-05 \tabularnewline
92 & 0.99993754820461 & 0.000124903590779957 & 6.24517953899786e-05 \tabularnewline
93 & 0.999924495957547 & 0.000151008084905213 & 7.55040424526067e-05 \tabularnewline
94 & 0.99991675292066 & 0.000166494158678525 & 8.32470793392627e-05 \tabularnewline
95 & 0.999867663369354 & 0.000264673261291708 & 0.000132336630645854 \tabularnewline
96 & 0.999808296678284 & 0.000383406643432676 & 0.000191703321716338 \tabularnewline
97 & 0.999705420692043 & 0.000589158615913968 & 0.000294579307956984 \tabularnewline
98 & 0.999565634792177 & 0.000868730415646721 & 0.000434365207823361 \tabularnewline
99 & 0.99943151683934 & 0.00113696632131943 & 0.000568483160659714 \tabularnewline
100 & 0.99923489287034 & 0.00153021425931948 & 0.00076510712965974 \tabularnewline
101 & 0.998896594000025 & 0.00220681199995008 & 0.00110340599997504 \tabularnewline
102 & 0.99862082282876 & 0.0027583543424798 & 0.0013791771712399 \tabularnewline
103 & 0.99842129670219 & 0.0031574065956208 & 0.0015787032978104 \tabularnewline
104 & 0.997746037290947 & 0.00450792541810557 & 0.00225396270905279 \tabularnewline
105 & 0.997626000410135 & 0.00474799917973044 & 0.00237399958986522 \tabularnewline
106 & 0.996646977954576 & 0.00670604409084704 & 0.00335302204542352 \tabularnewline
107 & 0.997596204725904 & 0.00480759054819265 & 0.00240379527409632 \tabularnewline
108 & 0.997204104267673 & 0.00559179146465322 & 0.00279589573232661 \tabularnewline
109 & 0.997107916077482 & 0.00578416784503684 & 0.00289208392251842 \tabularnewline
110 & 0.99609737695306 & 0.0078052460938798 & 0.0039026230469399 \tabularnewline
111 & 0.994396336376192 & 0.0112073272476162 & 0.0056036636238081 \tabularnewline
112 & 0.992237820911857 & 0.0155243581762866 & 0.00776217908814328 \tabularnewline
113 & 0.9929566163202 & 0.0140867673596017 & 0.00704338367980087 \tabularnewline
114 & 0.98985074117495 & 0.0202985176500988 & 0.0101492588250494 \tabularnewline
115 & 0.985845070189895 & 0.02830985962021 & 0.014154929810105 \tabularnewline
116 & 0.984087864683195 & 0.0318242706336097 & 0.0159121353168049 \tabularnewline
117 & 0.98438825466294 & 0.0312234906741192 & 0.0156117453370596 \tabularnewline
118 & 0.977921863231435 & 0.0441562735371295 & 0.0220781367685648 \tabularnewline
119 & 0.97195088745948 & 0.0560982250810391 & 0.0280491125405196 \tabularnewline
120 & 0.989588167169633 & 0.020823665660734 & 0.010411832830367 \tabularnewline
121 & 0.984843468550698 & 0.030313062898603 & 0.0151565314493015 \tabularnewline
122 & 0.98198269015841 & 0.0360346196831795 & 0.0180173098415897 \tabularnewline
123 & 0.984400819780867 & 0.0311983604382662 & 0.0155991802191331 \tabularnewline
124 & 0.982692493872938 & 0.0346150122541235 & 0.0173075061270617 \tabularnewline
125 & 0.978154247506331 & 0.0436915049873372 & 0.0218457524936686 \tabularnewline
126 & 0.97468647178718 & 0.0506270564256389 & 0.0253135282128194 \tabularnewline
127 & 0.963925764806107 & 0.0721484703877863 & 0.0360742351938931 \tabularnewline
128 & 0.949288014445049 & 0.101423971109902 & 0.0507119855549512 \tabularnewline
129 & 0.930628506419624 & 0.138742987160752 & 0.0693714935803758 \tabularnewline
130 & 0.90641963519617 & 0.187160729607659 & 0.0935803648038294 \tabularnewline
131 & 0.900974130958662 & 0.198051738082675 & 0.0990258690413377 \tabularnewline
132 & 0.870509741853974 & 0.258980516292053 & 0.129490258146026 \tabularnewline
133 & 0.864113147494235 & 0.271773705011529 & 0.135886852505765 \tabularnewline
134 & 0.822522974581678 & 0.354954050836644 & 0.177477025418322 \tabularnewline
135 & 0.854032299073369 & 0.291935401853262 & 0.145967700926631 \tabularnewline
136 & 0.81104045117831 & 0.377919097643381 & 0.18895954882169 \tabularnewline
137 & 0.862081363147852 & 0.275837273704295 & 0.137918636852148 \tabularnewline
138 & 0.823002945299348 & 0.353994109401303 & 0.176997054700652 \tabularnewline
139 & 0.859288256728905 & 0.281423486542191 & 0.140711743271095 \tabularnewline
140 & 0.815026304492494 & 0.369947391015012 & 0.184973695507506 \tabularnewline
141 & 0.757553650446383 & 0.484892699107234 & 0.242446349553617 \tabularnewline
142 & 0.686427171976827 & 0.627145656046346 & 0.313572828023173 \tabularnewline
143 & 0.622967599615561 & 0.754064800768878 & 0.377032400384439 \tabularnewline
144 & 0.549987720046334 & 0.900024559907331 & 0.450012279953666 \tabularnewline
145 & 0.694864420381574 & 0.610271159236851 & 0.305135579618426 \tabularnewline
146 & 0.632300956798774 & 0.735398086402452 & 0.367699043201226 \tabularnewline
147 & 0.561739661389771 & 0.876520677220457 & 0.438260338610229 \tabularnewline
148 & 0.452899270413394 & 0.905798540826787 & 0.547100729586606 \tabularnewline
149 & 0.477509565934972 & 0.955019131869944 & 0.522490434065028 \tabularnewline
150 & 0.642822478021056 & 0.714355043957888 & 0.357177521978944 \tabularnewline
151 & 0.617366497927659 & 0.765267004144682 & 0.382633502072341 \tabularnewline
152 & 0.689357847121786 & 0.621284305756427 & 0.310642152878214 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146302&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]10[/C][C]0.137370201073079[/C][C]0.274740402146157[/C][C]0.862629798926921[/C][/ROW]
[ROW][C]11[/C][C]0.431574408503366[/C][C]0.863148817006733[/C][C]0.568425591496634[/C][/ROW]
[ROW][C]12[/C][C]0.395393106572183[/C][C]0.790786213144366[/C][C]0.604606893427817[/C][/ROW]
[ROW][C]13[/C][C]0.280074433506377[/C][C]0.560148867012755[/C][C]0.719925566493622[/C][/ROW]
[ROW][C]14[/C][C]0.215157338903322[/C][C]0.430314677806643[/C][C]0.784842661096678[/C][/ROW]
[ROW][C]15[/C][C]0.139642009230972[/C][C]0.279284018461944[/C][C]0.860357990769028[/C][/ROW]
[ROW][C]16[/C][C]0.0856540033616087[/C][C]0.171308006723217[/C][C]0.914345996638391[/C][/ROW]
[ROW][C]17[/C][C]0.139173948640853[/C][C]0.278347897281707[/C][C]0.860826051359147[/C][/ROW]
[ROW][C]18[/C][C]0.205164791901659[/C][C]0.410329583803318[/C][C]0.794835208098341[/C][/ROW]
[ROW][C]19[/C][C]0.153194639491919[/C][C]0.306389278983837[/C][C]0.846805360508081[/C][/ROW]
[ROW][C]20[/C][C]0.160992392154767[/C][C]0.321984784309534[/C][C]0.839007607845233[/C][/ROW]
[ROW][C]21[/C][C]0.341460753582277[/C][C]0.682921507164553[/C][C]0.658539246417723[/C][/ROW]
[ROW][C]22[/C][C]0.411152383462688[/C][C]0.822304766925377[/C][C]0.588847616537312[/C][/ROW]
[ROW][C]23[/C][C]0.337431470732546[/C][C]0.674862941465093[/C][C]0.662568529267453[/C][/ROW]
[ROW][C]24[/C][C]0.275072443166546[/C][C]0.550144886333092[/C][C]0.724927556833454[/C][/ROW]
[ROW][C]25[/C][C]0.228853458302228[/C][C]0.457706916604456[/C][C]0.771146541697772[/C][/ROW]
[ROW][C]26[/C][C]0.568898679432339[/C][C]0.862202641135322[/C][C]0.431101320567661[/C][/ROW]
[ROW][C]27[/C][C]0.736557832370595[/C][C]0.526884335258809[/C][C]0.263442167629405[/C][/ROW]
[ROW][C]28[/C][C]0.884764986161584[/C][C]0.230470027676831[/C][C]0.115235013838416[/C][/ROW]
[ROW][C]29[/C][C]0.90475469639264[/C][C]0.19049060721472[/C][C]0.0952453036073598[/C][/ROW]
[ROW][C]30[/C][C]0.884984064967537[/C][C]0.230031870064926[/C][C]0.115015935032463[/C][/ROW]
[ROW][C]31[/C][C]0.852533253887606[/C][C]0.294933492224788[/C][C]0.147466746112394[/C][/ROW]
[ROW][C]32[/C][C]0.818361623363863[/C][C]0.363276753272274[/C][C]0.181638376636137[/C][/ROW]
[ROW][C]33[/C][C]0.886675305353222[/C][C]0.226649389293557[/C][C]0.113324694646778[/C][/ROW]
[ROW][C]34[/C][C]0.859118138975768[/C][C]0.281763722048463[/C][C]0.140881861024232[/C][/ROW]
[ROW][C]35[/C][C]0.826377307570059[/C][C]0.347245384859882[/C][C]0.173622692429941[/C][/ROW]
[ROW][C]36[/C][C]0.805033124373365[/C][C]0.389933751253269[/C][C]0.194966875626635[/C][/ROW]
[ROW][C]37[/C][C]0.794278160474513[/C][C]0.411443679050973[/C][C]0.205721839525487[/C][/ROW]
[ROW][C]38[/C][C]0.754068106615247[/C][C]0.491863786769507[/C][C]0.245931893384753[/C][/ROW]
[ROW][C]39[/C][C]0.764792486319047[/C][C]0.470415027361906[/C][C]0.235207513680953[/C][/ROW]
[ROW][C]40[/C][C]0.722715292962455[/C][C]0.554569414075089[/C][C]0.277284707037545[/C][/ROW]
[ROW][C]41[/C][C]0.74663542021378[/C][C]0.506729159572441[/C][C]0.253364579786221[/C][/ROW]
[ROW][C]42[/C][C]0.746593530888673[/C][C]0.506812938222654[/C][C]0.253406469111327[/C][/ROW]
[ROW][C]43[/C][C]0.705250210452703[/C][C]0.589499579094594[/C][C]0.294749789547297[/C][/ROW]
[ROW][C]44[/C][C]0.65839290947439[/C][C]0.683214181051219[/C][C]0.341607090525609[/C][/ROW]
[ROW][C]45[/C][C]0.618568029566575[/C][C]0.76286394086685[/C][C]0.381431970433425[/C][/ROW]
[ROW][C]46[/C][C]0.656759517415017[/C][C]0.686480965169965[/C][C]0.343240482584983[/C][/ROW]
[ROW][C]47[/C][C]0.650230222916504[/C][C]0.699539554166991[/C][C]0.349769777083495[/C][/ROW]
[ROW][C]48[/C][C]0.655012131393626[/C][C]0.689975737212747[/C][C]0.344987868606374[/C][/ROW]
[ROW][C]49[/C][C]0.607785386130083[/C][C]0.784429227739833[/C][C]0.392214613869917[/C][/ROW]
[ROW][C]50[/C][C]0.700712546042338[/C][C]0.598574907915323[/C][C]0.299287453957662[/C][/ROW]
[ROW][C]51[/C][C]0.65811981393778[/C][C]0.683760372124441[/C][C]0.341880186062221[/C][/ROW]
[ROW][C]52[/C][C]0.612670393439929[/C][C]0.774659213120142[/C][C]0.387329606560071[/C][/ROW]
[ROW][C]53[/C][C]0.62925057968193[/C][C]0.74149884063614[/C][C]0.37074942031807[/C][/ROW]
[ROW][C]54[/C][C]0.9812748237584[/C][C]0.037450352483198[/C][C]0.018725176241599[/C][/ROW]
[ROW][C]55[/C][C]0.977188640919526[/C][C]0.0456227181609491[/C][C]0.0228113590804745[/C][/ROW]
[ROW][C]56[/C][C]0.97031170011533[/C][C]0.0593765997693394[/C][C]0.0296882998846697[/C][/ROW]
[ROW][C]57[/C][C]0.96327685099992[/C][C]0.0734462980001614[/C][C]0.0367231490000807[/C][/ROW]
[ROW][C]58[/C][C]0.954148316513975[/C][C]0.0917033669720503[/C][C]0.0458516834860252[/C][/ROW]
[ROW][C]59[/C][C]0.9487560212762[/C][C]0.1024879574476[/C][C]0.0512439787237999[/C][/ROW]
[ROW][C]60[/C][C]0.98792764892174[/C][C]0.024144702156521[/C][C]0.0120723510782605[/C][/ROW]
[ROW][C]61[/C][C]0.993384609525358[/C][C]0.0132307809492837[/C][C]0.00661539047464185[/C][/ROW]
[ROW][C]62[/C][C]0.99176405580886[/C][C]0.0164718883822822[/C][C]0.0082359441911411[/C][/ROW]
[ROW][C]63[/C][C]0.988783858552528[/C][C]0.0224322828949432[/C][C]0.0112161414474716[/C][/ROW]
[ROW][C]64[/C][C]0.99526583908504[/C][C]0.0094683218299177[/C][C]0.00473416091495885[/C][/ROW]
[ROW][C]65[/C][C]0.995433157957521[/C][C]0.0091336840849584[/C][C]0.0045668420424792[/C][/ROW]
[ROW][C]66[/C][C]0.993639031385052[/C][C]0.0127219372298962[/C][C]0.00636096861494812[/C][/ROW]
[ROW][C]67[/C][C]0.992086530773345[/C][C]0.0158269384533096[/C][C]0.0079134692266548[/C][/ROW]
[ROW][C]68[/C][C]0.989565470029762[/C][C]0.0208690599404755[/C][C]0.0104345299702378[/C][/ROW]
[ROW][C]69[/C][C]0.987035776895172[/C][C]0.0259284462096568[/C][C]0.0129642231048284[/C][/ROW]
[ROW][C]70[/C][C]0.983229866905012[/C][C]0.0335402661899758[/C][C]0.0167701330949879[/C][/ROW]
[ROW][C]71[/C][C]0.985955482803655[/C][C]0.028089034392691[/C][C]0.0140445171963455[/C][/ROW]
[ROW][C]72[/C][C]0.98627522780976[/C][C]0.0274495443804799[/C][C]0.0137247721902399[/C][/ROW]
[ROW][C]73[/C][C]0.982338207852038[/C][C]0.0353235842959242[/C][C]0.0176617921479621[/C][/ROW]
[ROW][C]74[/C][C]0.988256568280832[/C][C]0.0234868634383369[/C][C]0.0117434317191685[/C][/ROW]
[ROW][C]75[/C][C]0.987187538263657[/C][C]0.0256249234726869[/C][C]0.0128124617363435[/C][/ROW]
[ROW][C]76[/C][C]0.985314009810482[/C][C]0.0293719803790361[/C][C]0.0146859901895181[/C][/ROW]
[ROW][C]77[/C][C]0.98151761369384[/C][C]0.0369647726123219[/C][C]0.018482386306161[/C][/ROW]
[ROW][C]78[/C][C]0.9764847308644[/C][C]0.0470305382712[/C][C]0.0235152691356[/C][/ROW]
[ROW][C]79[/C][C]0.972567305203188[/C][C]0.0548653895936238[/C][C]0.0274326947968119[/C][/ROW]
[ROW][C]80[/C][C]0.99775413790882[/C][C]0.00449172418236108[/C][C]0.00224586209118054[/C][/ROW]
[ROW][C]81[/C][C]0.999855700529999[/C][C]0.000288598940002059[/C][C]0.00014429947000103[/C][/ROW]
[ROW][C]82[/C][C]0.999782685870544[/C][C]0.000434628258911628[/C][C]0.000217314129455814[/C][/ROW]
[ROW][C]83[/C][C]0.999743759313566[/C][C]0.000512481372867957[/C][C]0.000256240686433979[/C][/ROW]
[ROW][C]84[/C][C]0.999635831293755[/C][C]0.000728337412490991[/C][C]0.000364168706245496[/C][/ROW]
[ROW][C]85[/C][C]0.99947121896705[/C][C]0.00105756206590173[/C][C]0.000528781032950864[/C][/ROW]
[ROW][C]86[/C][C]0.999407041547294[/C][C]0.00118591690541199[/C][C]0.000592958452705997[/C][/ROW]
[ROW][C]87[/C][C]0.999397847564614[/C][C]0.00120430487077257[/C][C]0.000602152435386286[/C][/ROW]
[ROW][C]88[/C][C]0.999131848280857[/C][C]0.00173630343828676[/C][C]0.000868151719143378[/C][/ROW]
[ROW][C]89[/C][C]0.999420743059389[/C][C]0.00115851388122183[/C][C]0.000579256940610916[/C][/ROW]
[ROW][C]90[/C][C]0.999706804653642[/C][C]0.000586390692715147[/C][C]0.000293195346357574[/C][/ROW]
[ROW][C]91[/C][C]0.999950341609612[/C][C]9.93167807755292e-05[/C][C]4.96583903877646e-05[/C][/ROW]
[ROW][C]92[/C][C]0.99993754820461[/C][C]0.000124903590779957[/C][C]6.24517953899786e-05[/C][/ROW]
[ROW][C]93[/C][C]0.999924495957547[/C][C]0.000151008084905213[/C][C]7.55040424526067e-05[/C][/ROW]
[ROW][C]94[/C][C]0.99991675292066[/C][C]0.000166494158678525[/C][C]8.32470793392627e-05[/C][/ROW]
[ROW][C]95[/C][C]0.999867663369354[/C][C]0.000264673261291708[/C][C]0.000132336630645854[/C][/ROW]
[ROW][C]96[/C][C]0.999808296678284[/C][C]0.000383406643432676[/C][C]0.000191703321716338[/C][/ROW]
[ROW][C]97[/C][C]0.999705420692043[/C][C]0.000589158615913968[/C][C]0.000294579307956984[/C][/ROW]
[ROW][C]98[/C][C]0.999565634792177[/C][C]0.000868730415646721[/C][C]0.000434365207823361[/C][/ROW]
[ROW][C]99[/C][C]0.99943151683934[/C][C]0.00113696632131943[/C][C]0.000568483160659714[/C][/ROW]
[ROW][C]100[/C][C]0.99923489287034[/C][C]0.00153021425931948[/C][C]0.00076510712965974[/C][/ROW]
[ROW][C]101[/C][C]0.998896594000025[/C][C]0.00220681199995008[/C][C]0.00110340599997504[/C][/ROW]
[ROW][C]102[/C][C]0.99862082282876[/C][C]0.0027583543424798[/C][C]0.0013791771712399[/C][/ROW]
[ROW][C]103[/C][C]0.99842129670219[/C][C]0.0031574065956208[/C][C]0.0015787032978104[/C][/ROW]
[ROW][C]104[/C][C]0.997746037290947[/C][C]0.00450792541810557[/C][C]0.00225396270905279[/C][/ROW]
[ROW][C]105[/C][C]0.997626000410135[/C][C]0.00474799917973044[/C][C]0.00237399958986522[/C][/ROW]
[ROW][C]106[/C][C]0.996646977954576[/C][C]0.00670604409084704[/C][C]0.00335302204542352[/C][/ROW]
[ROW][C]107[/C][C]0.997596204725904[/C][C]0.00480759054819265[/C][C]0.00240379527409632[/C][/ROW]
[ROW][C]108[/C][C]0.997204104267673[/C][C]0.00559179146465322[/C][C]0.00279589573232661[/C][/ROW]
[ROW][C]109[/C][C]0.997107916077482[/C][C]0.00578416784503684[/C][C]0.00289208392251842[/C][/ROW]
[ROW][C]110[/C][C]0.99609737695306[/C][C]0.0078052460938798[/C][C]0.0039026230469399[/C][/ROW]
[ROW][C]111[/C][C]0.994396336376192[/C][C]0.0112073272476162[/C][C]0.0056036636238081[/C][/ROW]
[ROW][C]112[/C][C]0.992237820911857[/C][C]0.0155243581762866[/C][C]0.00776217908814328[/C][/ROW]
[ROW][C]113[/C][C]0.9929566163202[/C][C]0.0140867673596017[/C][C]0.00704338367980087[/C][/ROW]
[ROW][C]114[/C][C]0.98985074117495[/C][C]0.0202985176500988[/C][C]0.0101492588250494[/C][/ROW]
[ROW][C]115[/C][C]0.985845070189895[/C][C]0.02830985962021[/C][C]0.014154929810105[/C][/ROW]
[ROW][C]116[/C][C]0.984087864683195[/C][C]0.0318242706336097[/C][C]0.0159121353168049[/C][/ROW]
[ROW][C]117[/C][C]0.98438825466294[/C][C]0.0312234906741192[/C][C]0.0156117453370596[/C][/ROW]
[ROW][C]118[/C][C]0.977921863231435[/C][C]0.0441562735371295[/C][C]0.0220781367685648[/C][/ROW]
[ROW][C]119[/C][C]0.97195088745948[/C][C]0.0560982250810391[/C][C]0.0280491125405196[/C][/ROW]
[ROW][C]120[/C][C]0.989588167169633[/C][C]0.020823665660734[/C][C]0.010411832830367[/C][/ROW]
[ROW][C]121[/C][C]0.984843468550698[/C][C]0.030313062898603[/C][C]0.0151565314493015[/C][/ROW]
[ROW][C]122[/C][C]0.98198269015841[/C][C]0.0360346196831795[/C][C]0.0180173098415897[/C][/ROW]
[ROW][C]123[/C][C]0.984400819780867[/C][C]0.0311983604382662[/C][C]0.0155991802191331[/C][/ROW]
[ROW][C]124[/C][C]0.982692493872938[/C][C]0.0346150122541235[/C][C]0.0173075061270617[/C][/ROW]
[ROW][C]125[/C][C]0.978154247506331[/C][C]0.0436915049873372[/C][C]0.0218457524936686[/C][/ROW]
[ROW][C]126[/C][C]0.97468647178718[/C][C]0.0506270564256389[/C][C]0.0253135282128194[/C][/ROW]
[ROW][C]127[/C][C]0.963925764806107[/C][C]0.0721484703877863[/C][C]0.0360742351938931[/C][/ROW]
[ROW][C]128[/C][C]0.949288014445049[/C][C]0.101423971109902[/C][C]0.0507119855549512[/C][/ROW]
[ROW][C]129[/C][C]0.930628506419624[/C][C]0.138742987160752[/C][C]0.0693714935803758[/C][/ROW]
[ROW][C]130[/C][C]0.90641963519617[/C][C]0.187160729607659[/C][C]0.0935803648038294[/C][/ROW]
[ROW][C]131[/C][C]0.900974130958662[/C][C]0.198051738082675[/C][C]0.0990258690413377[/C][/ROW]
[ROW][C]132[/C][C]0.870509741853974[/C][C]0.258980516292053[/C][C]0.129490258146026[/C][/ROW]
[ROW][C]133[/C][C]0.864113147494235[/C][C]0.271773705011529[/C][C]0.135886852505765[/C][/ROW]
[ROW][C]134[/C][C]0.822522974581678[/C][C]0.354954050836644[/C][C]0.177477025418322[/C][/ROW]
[ROW][C]135[/C][C]0.854032299073369[/C][C]0.291935401853262[/C][C]0.145967700926631[/C][/ROW]
[ROW][C]136[/C][C]0.81104045117831[/C][C]0.377919097643381[/C][C]0.18895954882169[/C][/ROW]
[ROW][C]137[/C][C]0.862081363147852[/C][C]0.275837273704295[/C][C]0.137918636852148[/C][/ROW]
[ROW][C]138[/C][C]0.823002945299348[/C][C]0.353994109401303[/C][C]0.176997054700652[/C][/ROW]
[ROW][C]139[/C][C]0.859288256728905[/C][C]0.281423486542191[/C][C]0.140711743271095[/C][/ROW]
[ROW][C]140[/C][C]0.815026304492494[/C][C]0.369947391015012[/C][C]0.184973695507506[/C][/ROW]
[ROW][C]141[/C][C]0.757553650446383[/C][C]0.484892699107234[/C][C]0.242446349553617[/C][/ROW]
[ROW][C]142[/C][C]0.686427171976827[/C][C]0.627145656046346[/C][C]0.313572828023173[/C][/ROW]
[ROW][C]143[/C][C]0.622967599615561[/C][C]0.754064800768878[/C][C]0.377032400384439[/C][/ROW]
[ROW][C]144[/C][C]0.549987720046334[/C][C]0.900024559907331[/C][C]0.450012279953666[/C][/ROW]
[ROW][C]145[/C][C]0.694864420381574[/C][C]0.610271159236851[/C][C]0.305135579618426[/C][/ROW]
[ROW][C]146[/C][C]0.632300956798774[/C][C]0.735398086402452[/C][C]0.367699043201226[/C][/ROW]
[ROW][C]147[/C][C]0.561739661389771[/C][C]0.876520677220457[/C][C]0.438260338610229[/C][/ROW]
[ROW][C]148[/C][C]0.452899270413394[/C][C]0.905798540826787[/C][C]0.547100729586606[/C][/ROW]
[ROW][C]149[/C][C]0.477509565934972[/C][C]0.955019131869944[/C][C]0.522490434065028[/C][/ROW]
[ROW][C]150[/C][C]0.642822478021056[/C][C]0.714355043957888[/C][C]0.357177521978944[/C][/ROW]
[ROW][C]151[/C][C]0.617366497927659[/C][C]0.765267004144682[/C][C]0.382633502072341[/C][/ROW]
[ROW][C]152[/C][C]0.689357847121786[/C][C]0.621284305756427[/C][C]0.310642152878214[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146302&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146302&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.1373702010730790.2747404021461570.862629798926921
110.4315744085033660.8631488170067330.568425591496634
120.3953931065721830.7907862131443660.604606893427817
130.2800744335063770.5601488670127550.719925566493622
140.2151573389033220.4303146778066430.784842661096678
150.1396420092309720.2792840184619440.860357990769028
160.08565400336160870.1713080067232170.914345996638391
170.1391739486408530.2783478972817070.860826051359147
180.2051647919016590.4103295838033180.794835208098341
190.1531946394919190.3063892789838370.846805360508081
200.1609923921547670.3219847843095340.839007607845233
210.3414607535822770.6829215071645530.658539246417723
220.4111523834626880.8223047669253770.588847616537312
230.3374314707325460.6748629414650930.662568529267453
240.2750724431665460.5501448863330920.724927556833454
250.2288534583022280.4577069166044560.771146541697772
260.5688986794323390.8622026411353220.431101320567661
270.7365578323705950.5268843352588090.263442167629405
280.8847649861615840.2304700276768310.115235013838416
290.904754696392640.190490607214720.0952453036073598
300.8849840649675370.2300318700649260.115015935032463
310.8525332538876060.2949334922247880.147466746112394
320.8183616233638630.3632767532722740.181638376636137
330.8866753053532220.2266493892935570.113324694646778
340.8591181389757680.2817637220484630.140881861024232
350.8263773075700590.3472453848598820.173622692429941
360.8050331243733650.3899337512532690.194966875626635
370.7942781604745130.4114436790509730.205721839525487
380.7540681066152470.4918637867695070.245931893384753
390.7647924863190470.4704150273619060.235207513680953
400.7227152929624550.5545694140750890.277284707037545
410.746635420213780.5067291595724410.253364579786221
420.7465935308886730.5068129382226540.253406469111327
430.7052502104527030.5894995790945940.294749789547297
440.658392909474390.6832141810512190.341607090525609
450.6185680295665750.762863940866850.381431970433425
460.6567595174150170.6864809651699650.343240482584983
470.6502302229165040.6995395541669910.349769777083495
480.6550121313936260.6899757372127470.344987868606374
490.6077853861300830.7844292277398330.392214613869917
500.7007125460423380.5985749079153230.299287453957662
510.658119813937780.6837603721244410.341880186062221
520.6126703934399290.7746592131201420.387329606560071
530.629250579681930.741498840636140.37074942031807
540.98127482375840.0374503524831980.018725176241599
550.9771886409195260.04562271816094910.0228113590804745
560.970311700115330.05937659976933940.0296882998846697
570.963276850999920.07344629800016140.0367231490000807
580.9541483165139750.09170336697205030.0458516834860252
590.94875602127620.10248795744760.0512439787237999
600.987927648921740.0241447021565210.0120723510782605
610.9933846095253580.01323078094928370.00661539047464185
620.991764055808860.01647188838228220.0082359441911411
630.9887838585525280.02243228289494320.0112161414474716
640.995265839085040.00946832182991770.00473416091495885
650.9954331579575210.00913368408495840.0045668420424792
660.9936390313850520.01272193722989620.00636096861494812
670.9920865307733450.01582693845330960.0079134692266548
680.9895654700297620.02086905994047550.0104345299702378
690.9870357768951720.02592844620965680.0129642231048284
700.9832298669050120.03354026618997580.0167701330949879
710.9859554828036550.0280890343926910.0140445171963455
720.986275227809760.02744954438047990.0137247721902399
730.9823382078520380.03532358429592420.0176617921479621
740.9882565682808320.02348686343833690.0117434317191685
750.9871875382636570.02562492347268690.0128124617363435
760.9853140098104820.02937198037903610.0146859901895181
770.981517613693840.03696477261232190.018482386306161
780.97648473086440.04703053827120.0235152691356
790.9725673052031880.05486538959362380.0274326947968119
800.997754137908820.004491724182361080.00224586209118054
810.9998557005299990.0002885989400020590.00014429947000103
820.9997826858705440.0004346282589116280.000217314129455814
830.9997437593135660.0005124813728679570.000256240686433979
840.9996358312937550.0007283374124909910.000364168706245496
850.999471218967050.001057562065901730.000528781032950864
860.9994070415472940.001185916905411990.000592958452705997
870.9993978475646140.001204304870772570.000602152435386286
880.9991318482808570.001736303438286760.000868151719143378
890.9994207430593890.001158513881221830.000579256940610916
900.9997068046536420.0005863906927151470.000293195346357574
910.9999503416096129.93167807755292e-054.96583903877646e-05
920.999937548204610.0001249035907799576.24517953899786e-05
930.9999244959575470.0001510080849052137.55040424526067e-05
940.999916752920660.0001664941586785258.32470793392627e-05
950.9998676633693540.0002646732612917080.000132336630645854
960.9998082966782840.0003834066434326760.000191703321716338
970.9997054206920430.0005891586159139680.000294579307956984
980.9995656347921770.0008687304156467210.000434365207823361
990.999431516839340.001136966321319430.000568483160659714
1000.999234892870340.001530214259319480.00076510712965974
1010.9988965940000250.002206811999950080.00110340599997504
1020.998620822828760.00275835434247980.0013791771712399
1030.998421296702190.00315740659562080.0015787032978104
1040.9977460372909470.004507925418105570.00225396270905279
1050.9976260004101350.004747999179730440.00237399958986522
1060.9966469779545760.006706044090847040.00335302204542352
1070.9975962047259040.004807590548192650.00240379527409632
1080.9972041042676730.005591791464653220.00279589573232661
1090.9971079160774820.005784167845036840.00289208392251842
1100.996097376953060.00780524609387980.0039026230469399
1110.9943963363761920.01120732724761620.0056036636238081
1120.9922378209118570.01552435817628660.00776217908814328
1130.99295661632020.01408676735960170.00704338367980087
1140.989850741174950.02029851765009880.0101492588250494
1150.9858450701898950.028309859620210.014154929810105
1160.9840878646831950.03182427063360970.0159121353168049
1170.984388254662940.03122349067411920.0156117453370596
1180.9779218632314350.04415627353712950.0220781367685648
1190.971950887459480.05609822508103910.0280491125405196
1200.9895881671696330.0208236656607340.010411832830367
1210.9848434685506980.0303130628986030.0151565314493015
1220.981982690158410.03603461968317950.0180173098415897
1230.9844008197808670.03119836043826620.0155991802191331
1240.9826924938729380.03461501225412350.0173075061270617
1250.9781542475063310.04369150498733720.0218457524936686
1260.974686471787180.05062705642563890.0253135282128194
1270.9639257648061070.07214847038778630.0360742351938931
1280.9492880144450490.1014239711099020.0507119855549512
1290.9306285064196240.1387429871607520.0693714935803758
1300.906419635196170.1871607296076590.0935803648038294
1310.9009741309586620.1980517380826750.0990258690413377
1320.8705097418539740.2589805162920530.129490258146026
1330.8641131474942350.2717737050115290.135886852505765
1340.8225229745816780.3549540508366440.177477025418322
1350.8540322990733690.2919354018532620.145967700926631
1360.811040451178310.3779190976433810.18895954882169
1370.8620813631478520.2758372737042950.137918636852148
1380.8230029452993480.3539941094013030.176997054700652
1390.8592882567289050.2814234865421910.140711743271095
1400.8150263044924940.3699473910150120.184973695507506
1410.7575536504463830.4848926991072340.242446349553617
1420.6864271719768270.6271456560463460.313572828023173
1430.6229675996155610.7540648007688780.377032400384439
1440.5499877200463340.9000245599073310.450012279953666
1450.6948644203815740.6102711592368510.305135579618426
1460.6323009567987740.7353980864024520.367699043201226
1470.5617396613897710.8765206772204570.438260338610229
1480.4528992704133940.9057985408267870.547100729586606
1490.4775095659349720.9550191318699440.522490434065028
1500.6428224780210560.7143550439578880.357177521978944
1510.6173664979276590.7652670041446820.382633502072341
1520.6893578471217860.6212843057564270.310642152878214







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level330.230769230769231NOK
5% type I error level660.461538461538462NOK
10% type I error level730.51048951048951NOK

\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 & 33 & 0.230769230769231 & NOK \tabularnewline
5% type I error level & 66 & 0.461538461538462 & NOK \tabularnewline
10% type I error level & 73 & 0.51048951048951 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146302&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]33[/C][C]0.230769230769231[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]66[/C][C]0.461538461538462[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]73[/C][C]0.51048951048951[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146302&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146302&T=6

As an alternative you can also use a QR Code:  

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level330.230769230769231NOK
5% type I error level660.461538461538462NOK
10% type I error level730.51048951048951NOK



Parameters (Session):
par1 = 6 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 6 ; 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')
}