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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationThu, 02 Dec 2010 14:07:40 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/02/t12912987910cc1rnwmb8ol49j.htm/, Retrieved Thu, 28 Mar 2024 17:06:03 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=104299, Retrieved Thu, 28 Mar 2024 17:06:03 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact194
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [Concern, tut] [2010-12-02 14:07:40] [92762195fa34a5ca20b359b4f0975e64] [Current]
-    D    [Multiple Regression] [WS7 - Eerste Regr...] [2011-11-21 15:36:32] [b8fde34a99ee6a7d49500940cae4da2a]
-    D    [Multiple Regression] [WS7 - Eerste Regr...] [2011-11-21 15:46:36] [b8fde34a99ee6a7d49500940cae4da2a]
- RM        [Multiple Regression] [] [2011-11-23 15:05:33] [d0c153a232569da05656a074c1bdec10]
-    D      [Multiple Regression] [multiple regression] [2012-11-04 15:20:41] [456f9f31a5baae2eb9a0b13ee35c0d42]
-    D        [Multiple Regression] [multiple regression] [2012-11-04 17:07:32] [456f9f31a5baae2eb9a0b13ee35c0d42]
-               [Multiple Regression] [] [2012-11-05 14:05:58] [754b40392725f52c97d5cd2ee03f2d3f]
- R             [Multiple Regression] [] [2012-12-16 13:14:37] [c5439e3d68865f12e8f66507a7e773a8]
- R  D      [Multiple Regression] [Workshop 7 ] [2012-11-05 14:37:18] [8cc09667e588c76c8c3bfb3e2ed3290a]
-    D      [Multiple Regression] [ws7] [2012-11-05 15:55:01] [8cc09667e588c76c8c3bfb3e2ed3290a]
-   P     [Multiple Regression] [Concern] [2011-11-24 21:53:37] [1ed874da5cc4aa1cd1ced057f766d90b]
-   P     [Multiple Regression] [concern] [2011-11-24 22:25:52] [1ed874da5cc4aa1cd1ced057f766d90b]
- R  D    [Multiple Regression] [] [2012-11-05 13:17:27] [5bd5238c609be1efcd3f9af130e08c87]
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Dataseries X:
24	14	11	12	24	26
25	11	7	8	25	23
17	6	17	8	30	25
18	12	10	8	19	23
18	8	12	9	22	19
16	10	12	7	22	29
20	10	11	4	25	25
16	11	11	11	23	21
18	16	12	7	17	22
17	11	13	7	21	25
23	13	14	12	19	24
30	12	16	10	19	18
23	8	11	10	15	22
18	12	10	8	16	15
15	11	11	8	23	22
12	4	15	4	27	28
21	9	9	9	22	20
15	8	11	8	14	12
20	8	17	7	22	24
31	14	17	11	23	20
27	15	11	9	23	21
34	16	18	11	21	20
21	9	14	13	19	21
31	14	10	8	18	23
19	11	11	8	20	28
16	8	15	9	23	24
20	9	15	6	25	24
21	9	13	9	19	24
22	9	16	9	24	23
17	9	13	6	22	23
24	10	9	6	25	29
25	16	18	16	26	24
26	11	18	5	29	18
25	8	12	7	32	25
17	9	17	9	25	21
32	16	9	6	29	26
33	11	9	6	28	22
13	16	12	5	17	22
32	12	18	12	28	22
25	12	12	7	29	23
29	14	18	10	26	30
22	9	14	9	25	23
18	10	15	8	14	17
17	9	16	5	25	23
20	10	10	8	26	23
15	12	11	8	20	25
20	14	14	10	18	24
33	14	9	6	32	24
29	10	12	8	25	23
23	14	17	7	25	21
26	16	5	4	23	24
18	9	12	8	21	24
20	10	12	8	20	28
11	6	6	4	15	16
28	8	24	20	30	20
26	13	12	8	24	29
22	10	12	8	26	27
17	8	14	6	24	22
12	7	7	4	22	28
14	15	13	8	14	16
17	9	12	9	24	25
21	10	13	6	24	24
19	12	14	7	24	28
18	13	8	9	24	24
10	10	11	5	19	23
29	11	9	5	31	30
31	8	11	8	22	24
19	9	13	8	27	21
9	13	10	6	19	25
20	11	11	8	25	25
28	8	12	7	20	22
19	9	9	7	21	23
30	9	15	9	27	26
29	15	18	11	23	23
26	9	15	6	25	25
23	10	12	8	20	21
13	14	13	6	21	25
21	12	14	9	22	24
19	12	10	8	23	29
28	11	13	6	25	22
23	14	13	10	25	27
18	6	11	8	17	26
21	12	13	8	19	22
20	8	16	10	25	24
23	14	8	5	19	27
21	11	16	7	20	24
21	10	11	5	26	24
15	14	9	8	23	29
28	12	16	14	27	22
19	10	12	7	17	21
26	14	14	8	17	24
10	5	8	6	19	24
16	11	9	5	17	23
22	10	15	6	22	20
19	9	11	10	21	27
31	10	21	12	32	26
31	16	14	9	21	25
29	13	18	12	21	21
19	9	12	7	18	21
22	10	13	8	18	19
23	10	15	10	23	21
15	7	12	6	19	21
20	9	19	10	20	16
18	8	15	10	21	22
23	14	11	10	20	29
25	14	11	5	17	15
21	8	10	7	18	17
24	9	13	10	19	15
25	14	15	11	22	21
17	14	12	6	15	21
13	8	12	7	14	19
28	8	16	12	18	24
21	8	9	11	24	20
25	7	18	11	35	17
9	6	8	11	29	23
16	8	13	5	21	24
19	6	17	8	25	14
17	11	9	6	20	19
25	14	15	9	22	24
20	11	8	4	13	13
29	11	7	4	26	22
14	11	12	7	17	16
22	14	14	11	25	19
15	8	6	6	20	25
19	20	8	7	19	25
20	11	17	8	21	23
15	8	10	4	22	24
20	11	11	8	24	26
18	10	14	9	21	26
33	14	11	8	26	25
22	11	13	11	24	18
16	9	12	8	16	21
17	9	11	5	23	26
16	8	9	4	18	23
21	10	12	8	16	23
26	13	20	10	26	22
18	13	12	6	19	20
18	12	13	9	21	13
17	8	12	9	21	24
22	13	12	13	22	15
30	14	9	9	23	14
30	12	15	10	29	22
24	14	24	20	21	10
21	15	7	5	21	24
21	13	17	11	23	22
29	16	11	6	27	24
31	9	17	9	25	19
20	9	11	7	21	20
16	9	12	9	10	13
22	8	14	10	20	20
20	7	11	9	26	22
28	16	16	8	24	24
38	11	21	7	29	29
22	9	14	6	19	12
20	11	20	13	24	20
17	9	13	6	19	21
28	14	11	8	24	24
22	13	15	10	22	22
31	16	19	16	17	20




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time10 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\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 & 10 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104299&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]10 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104299&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104299&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 time10 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Multiple Linear Regression - Estimated Regression Equation
concern[t] = -1.97155708164816 + 0.810124516285501doubts[t] + 0.251253601241108expectations[t] + 0.188518804715735criticism[t] + 0.566064813317674standard[t] -0.115718741599823organisation[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
concern[t] =  -1.97155708164816 +  0.810124516285501doubts[t] +  0.251253601241108expectations[t] +  0.188518804715735criticism[t] +  0.566064813317674standard[t] -0.115718741599823organisation[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104299&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]concern[t] =  -1.97155708164816 +  0.810124516285501doubts[t] +  0.251253601241108expectations[t] +  0.188518804715735criticism[t] +  0.566064813317674standard[t] -0.115718741599823organisation[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104299&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104299&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
concern[t] = -1.97155708164816 + 0.810124516285501doubts[t] + 0.251253601241108expectations[t] + 0.188518804715735criticism[t] + 0.566064813317674standard[t] -0.115718741599823organisation[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-1.971557081648163.052906-0.64580.5193780.259689
doubts0.8101245162855010.1303356.215700
expectations0.2512536012411080.132761.89250.0603070.030154
criticism0.1885188047157350.1682591.12040.2642940.132147
standard0.5660648133176740.0958135.90800
organisation-0.1157187415998230.103024-1.12320.2631030.131551

\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) & -1.97155708164816 & 3.052906 & -0.6458 & 0.519378 & 0.259689 \tabularnewline
doubts & 0.810124516285501 & 0.130335 & 6.2157 & 0 & 0 \tabularnewline
expectations & 0.251253601241108 & 0.13276 & 1.8925 & 0.060307 & 0.030154 \tabularnewline
criticism & 0.188518804715735 & 0.168259 & 1.1204 & 0.264294 & 0.132147 \tabularnewline
standard & 0.566064813317674 & 0.095813 & 5.908 & 0 & 0 \tabularnewline
organisation & -0.115718741599823 & 0.103024 & -1.1232 & 0.263103 & 0.131551 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104299&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]-1.97155708164816[/C][C]3.052906[/C][C]-0.6458[/C][C]0.519378[/C][C]0.259689[/C][/ROW]
[ROW][C]doubts[/C][C]0.810124516285501[/C][C]0.130335[/C][C]6.2157[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]expectations[/C][C]0.251253601241108[/C][C]0.13276[/C][C]1.8925[/C][C]0.060307[/C][C]0.030154[/C][/ROW]
[ROW][C]criticism[/C][C]0.188518804715735[/C][C]0.168259[/C][C]1.1204[/C][C]0.264294[/C][C]0.132147[/C][/ROW]
[ROW][C]standard[/C][C]0.566064813317674[/C][C]0.095813[/C][C]5.908[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]organisation[/C][C]-0.115718741599823[/C][C]0.103024[/C][C]-1.1232[/C][C]0.263103[/C][C]0.131551[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104299&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104299&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)-1.971557081648163.052906-0.64580.5193780.259689
doubts0.8101245162855010.1303356.215700
expectations0.2512536012411080.132761.89250.0603070.030154
criticism0.1885188047157350.1682591.12040.2642940.132147
standard0.5660648133176740.0958135.90800
organisation-0.1157187415998230.103024-1.12320.2631030.131551







Multiple Linear Regression - Regression Statistics
Multiple R0.638102890589065
R-squared0.40717529897812
Adjusted R-squared0.387801942735575
F-TEST (value)21.0172823893021
F-TEST (DF numerator)5
F-TEST (DF denominator)153
p-value6.66133814775094e-16
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation4.47771018087769
Sum Squared Residuals3067.63293498216

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.638102890589065 \tabularnewline
R-squared & 0.40717529897812 \tabularnewline
Adjusted R-squared & 0.387801942735575 \tabularnewline
F-TEST (value) & 21.0172823893021 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 153 \tabularnewline
p-value & 6.66133814775094e-16 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 4.47771018087769 \tabularnewline
Sum Squared Residuals & 3067.63293498216 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104299&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.638102890589065[/C][/ROW]
[ROW][C]R-squared[/C][C]0.40717529897812[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.387801942735575[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]21.0172823893021[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]153[/C][/ROW]
[ROW][C]p-value[/C][C]6.66133814775094e-16[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]4.47771018087769[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]3067.63293498216[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104299&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104299&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 R0.638102890589065
R-squared0.40717529897812
Adjusted R-squared0.387801942735575
F-TEST (value)21.0172823893021
F-TEST (DF numerator)5
F-TEST (DF denominator)153
p-value6.66133814775094e-16
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation4.47771018087769
Sum Squared Residuals3067.63293498216







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
12424.973069654619-0.973069654619013
22521.69682752005193.30317247994805
31722.7576275344242-5.75762753442423
41819.8643239601547-1.86432396015471
51819.475921308563-1.47592130856301
61619.5619453157043-3.56194531570427
72020.9062047066683-0.90620470666828
81622.3667061957279-6.36670619572787
91822.4023995380277-4.40239953802768
101720.5201335863125-3.52013358631251
112322.31781935866780.68218064133223
123022.32747688503207.67252311496804
132315.10357659401447.89642340598558
141819.0918794530003-1.09187945300029
151521.6854310399808-6.68543103998084
161217.8354454157556-5.83544541575558
172119.41656627952531.58343372047467
181515.3176615872635-0.317661587263513
192019.77655799733790.223442002662066
203126.42032009363084.57967990636915
212725.23016665143841.76983334856159
223427.15969310080766.84030689919239
232119.61299632304101.38700367695903
243120.918508179408110.0814918205919
251919.2929241504289-0.292924150428885
261620.2171532176049-4.21715321760486
272021.5938509463785-1.59385094637850
282118.26051127813752.73948872186255
292221.96031489004900.0396851099510343
301719.5088680455431-2.50886804554309
312420.31786014721823.68213985278176
322530.4697362245754-5.46973622457536
332626.7379136808267-0.737913680826726
342524.06521938270930.9347806172907
351723.0090707878074-6.0090707878074
363227.79002272300144.20997727699859
373323.63621029465559.36378970534448
381322.0253619285962-9.02536192859621
393227.83873005040544.16126994959459
402525.8389604910979-0.838960491097932
412927.0240619141111.97593808588899
422222.0238725008844-0.0238725008844226
431817.36433131679980.63566868320017
441721.7723044845037-4.7723044845037
452022.2065286208074-2.20652862080743
461520.4502048915139-5.45020489151386
472022.1848414522041-2.18484145220413
483328.09940561358314.90059438641693
492922.14297100997206.85702899002803
502326.6826557598034-3.68265575980343
512623.24301931189912.75698068810089
521818.9528684988160-0.952868498815956
532018.73405323538451.26594676461551
541111.7902591765424-0.79025917654241
552828.9774711402709-0.977471140270921
562623.31296729591192.68703270408814
572222.2461608568904-0.246160856890353
581720.1978454987339-3.19784549873387
591215.4254660880949-3.42546608809486
601421.0281654373449-7.02816543734494
611720.7238630018849-3.72386300188489
622121.3354034468641-0.335403446864115
631922.9325499189927-3.93254991899267
641823.0750654036623-5.07506540366228
651017.9297721146776-7.92977211467762
662924.22013599709424.77986400290578
673118.457555194607012.5424448053930
681922.9476672047626-3.94766720476257
69920.0659733838091-11.0659733838091
702022.4704044418167-2.47040444181672
712817.619597847696710.3804021523033
721918.12630763197670.87369236802328
733023.06009950396146.93990049603859
742927.1345419863581.86545801364201
752621.47813220477874.52186779522132
762319.54408442658333.45591557341675
771322.7619883304533-9.76198833045328
782122.6403328681881-1.64033286818808
791921.4342707638265-2.43427076382647
802822.94303025966695.05696974033307
812325.5488853193873-2.54888531938726
821813.7755446122484.22445538775199
832120.73380350547790.266196494522133
842021.7890552501971-1.78905525019705
852319.9536344096973.04636559030299
862120.8235483183180.176451681682017
872121.7765070663015-0.77650706630151
881522.8032661951564-7.80326619515637
892827.1471956440370.852804355963014
901917.65737118191451.3426288180855
912621.2417390294554.75826097054501
921013.1981887926427-3.1981887926427
931617.1052598018456-1.10525980184556
942221.16865598911030.831344010889723
951918.73149628220680.268503717793152
963128.77362610842912.22637389157087
973125.19904737841265.80095262158741
982924.8021196150674.19788038493298
991917.41331147894671.58668852105333
1002218.89464588438873.10535411561134
1012322.37307727969110.626922720308933
1021516.1706084549776-1.17060845497761
1032021.4483664364161-1.44836643641609
1041819.5049798788849-1.50497987888490
1052321.98461656711701.01538343288297
1062520.96389048598294.03610951401713
1072116.56355472657824.43644527342175
1082419.49049875725164.50950124274840
1092525.2360293362311-0.236029336231133
1101719.5772208157054-2.57722081570542
1111314.5703651925901-1.57036519259012
1122818.20363916640489.7963608335952
1132120.11560899930660.884391000693354
1142528.140636065485-3.14063606548499
115920.7272742072834-11.7272742072834
1161617.8284411696244-1.82844116962436
1171921.2002096254339-2.20020962543392
1181719.4548480129136-2.45484801291361
1192524.51183550200020.488164497999806
1202015.55841555861634.44158444138374
1212921.62453585610657.37546414389351
1221419.0460894061991-5.04608940619912
1232226.9144076581427-4.91440765814269
1241515.5764012107348-0.57640121073484
1251925.4228566000411-6.42285660004113
1262021.9451042791923-1.94510427919232
1271517.4522263745030-2.45222637450297
1282021.7886208868992-1.78862088689922
1291820.2225815390998-2.22258153909976
1303325.46684280399097.5331571960091
1312223.7824344363272-1.78243443632723
1321616.4697006570271-0.46970065702706
1331719.0367506268633-2.03675062686334
1341615.0524322615910.947567738409005
1352117.04838769011293.95161230988709
1362627.6421945331063-1.64219453310631
1371821.1470742942904-3.14707429429044
1381823.0959206112274-5.09592061122736
1391718.3312627872462-1.33126278724619
1402224.7434940752527-2.74349407525272
1413024.72756612386955.27243387613055
1423027.27399645056832.72600354943172
1432429.9008223341231-5.9008223341231
1442121.9917911761762-0.991791176176217
1452125.3787580941457-4.3787580941457
1462927.39183778204791.60816221795208
1473123.24050827100707.75949172899296
1482018.97597105925841.02402894074160
1491614.18758051463531.81241948536466
1502218.91909894752583.08090105247424
1512020.3316462195076-0.331646219507592
1522827.32694895773190.673051042268084
1533826.595805936383511.4041940636165
1542219.33483336442922.66516663557077
1552025.6868097712468-5.68680977124681
1561718.0421110887897-1.04211108878972
1572824.45043191895543.54956808104463
1582224.1216672736301-2.12166727363007
1593126.08928147235674.9107185276433

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 24 & 24.973069654619 & -0.973069654619013 \tabularnewline
2 & 25 & 21.6968275200519 & 3.30317247994805 \tabularnewline
3 & 17 & 22.7576275344242 & -5.75762753442423 \tabularnewline
4 & 18 & 19.8643239601547 & -1.86432396015471 \tabularnewline
5 & 18 & 19.475921308563 & -1.47592130856301 \tabularnewline
6 & 16 & 19.5619453157043 & -3.56194531570427 \tabularnewline
7 & 20 & 20.9062047066683 & -0.90620470666828 \tabularnewline
8 & 16 & 22.3667061957279 & -6.36670619572787 \tabularnewline
9 & 18 & 22.4023995380277 & -4.40239953802768 \tabularnewline
10 & 17 & 20.5201335863125 & -3.52013358631251 \tabularnewline
11 & 23 & 22.3178193586678 & 0.68218064133223 \tabularnewline
12 & 30 & 22.3274768850320 & 7.67252311496804 \tabularnewline
13 & 23 & 15.1035765940144 & 7.89642340598558 \tabularnewline
14 & 18 & 19.0918794530003 & -1.09187945300029 \tabularnewline
15 & 15 & 21.6854310399808 & -6.68543103998084 \tabularnewline
16 & 12 & 17.8354454157556 & -5.83544541575558 \tabularnewline
17 & 21 & 19.4165662795253 & 1.58343372047467 \tabularnewline
18 & 15 & 15.3176615872635 & -0.317661587263513 \tabularnewline
19 & 20 & 19.7765579973379 & 0.223442002662066 \tabularnewline
20 & 31 & 26.4203200936308 & 4.57967990636915 \tabularnewline
21 & 27 & 25.2301666514384 & 1.76983334856159 \tabularnewline
22 & 34 & 27.1596931008076 & 6.84030689919239 \tabularnewline
23 & 21 & 19.6129963230410 & 1.38700367695903 \tabularnewline
24 & 31 & 20.9185081794081 & 10.0814918205919 \tabularnewline
25 & 19 & 19.2929241504289 & -0.292924150428885 \tabularnewline
26 & 16 & 20.2171532176049 & -4.21715321760486 \tabularnewline
27 & 20 & 21.5938509463785 & -1.59385094637850 \tabularnewline
28 & 21 & 18.2605112781375 & 2.73948872186255 \tabularnewline
29 & 22 & 21.9603148900490 & 0.0396851099510343 \tabularnewline
30 & 17 & 19.5088680455431 & -2.50886804554309 \tabularnewline
31 & 24 & 20.3178601472182 & 3.68213985278176 \tabularnewline
32 & 25 & 30.4697362245754 & -5.46973622457536 \tabularnewline
33 & 26 & 26.7379136808267 & -0.737913680826726 \tabularnewline
34 & 25 & 24.0652193827093 & 0.9347806172907 \tabularnewline
35 & 17 & 23.0090707878074 & -6.0090707878074 \tabularnewline
36 & 32 & 27.7900227230014 & 4.20997727699859 \tabularnewline
37 & 33 & 23.6362102946555 & 9.36378970534448 \tabularnewline
38 & 13 & 22.0253619285962 & -9.02536192859621 \tabularnewline
39 & 32 & 27.8387300504054 & 4.16126994959459 \tabularnewline
40 & 25 & 25.8389604910979 & -0.838960491097932 \tabularnewline
41 & 29 & 27.024061914111 & 1.97593808588899 \tabularnewline
42 & 22 & 22.0238725008844 & -0.0238725008844226 \tabularnewline
43 & 18 & 17.3643313167998 & 0.63566868320017 \tabularnewline
44 & 17 & 21.7723044845037 & -4.7723044845037 \tabularnewline
45 & 20 & 22.2065286208074 & -2.20652862080743 \tabularnewline
46 & 15 & 20.4502048915139 & -5.45020489151386 \tabularnewline
47 & 20 & 22.1848414522041 & -2.18484145220413 \tabularnewline
48 & 33 & 28.0994056135831 & 4.90059438641693 \tabularnewline
49 & 29 & 22.1429710099720 & 6.85702899002803 \tabularnewline
50 & 23 & 26.6826557598034 & -3.68265575980343 \tabularnewline
51 & 26 & 23.2430193118991 & 2.75698068810089 \tabularnewline
52 & 18 & 18.9528684988160 & -0.952868498815956 \tabularnewline
53 & 20 & 18.7340532353845 & 1.26594676461551 \tabularnewline
54 & 11 & 11.7902591765424 & -0.79025917654241 \tabularnewline
55 & 28 & 28.9774711402709 & -0.977471140270921 \tabularnewline
56 & 26 & 23.3129672959119 & 2.68703270408814 \tabularnewline
57 & 22 & 22.2461608568904 & -0.246160856890353 \tabularnewline
58 & 17 & 20.1978454987339 & -3.19784549873387 \tabularnewline
59 & 12 & 15.4254660880949 & -3.42546608809486 \tabularnewline
60 & 14 & 21.0281654373449 & -7.02816543734494 \tabularnewline
61 & 17 & 20.7238630018849 & -3.72386300188489 \tabularnewline
62 & 21 & 21.3354034468641 & -0.335403446864115 \tabularnewline
63 & 19 & 22.9325499189927 & -3.93254991899267 \tabularnewline
64 & 18 & 23.0750654036623 & -5.07506540366228 \tabularnewline
65 & 10 & 17.9297721146776 & -7.92977211467762 \tabularnewline
66 & 29 & 24.2201359970942 & 4.77986400290578 \tabularnewline
67 & 31 & 18.4575551946070 & 12.5424448053930 \tabularnewline
68 & 19 & 22.9476672047626 & -3.94766720476257 \tabularnewline
69 & 9 & 20.0659733838091 & -11.0659733838091 \tabularnewline
70 & 20 & 22.4704044418167 & -2.47040444181672 \tabularnewline
71 & 28 & 17.6195978476967 & 10.3804021523033 \tabularnewline
72 & 19 & 18.1263076319767 & 0.87369236802328 \tabularnewline
73 & 30 & 23.0600995039614 & 6.93990049603859 \tabularnewline
74 & 29 & 27.134541986358 & 1.86545801364201 \tabularnewline
75 & 26 & 21.4781322047787 & 4.52186779522132 \tabularnewline
76 & 23 & 19.5440844265833 & 3.45591557341675 \tabularnewline
77 & 13 & 22.7619883304533 & -9.76198833045328 \tabularnewline
78 & 21 & 22.6403328681881 & -1.64033286818808 \tabularnewline
79 & 19 & 21.4342707638265 & -2.43427076382647 \tabularnewline
80 & 28 & 22.9430302596669 & 5.05696974033307 \tabularnewline
81 & 23 & 25.5488853193873 & -2.54888531938726 \tabularnewline
82 & 18 & 13.775544612248 & 4.22445538775199 \tabularnewline
83 & 21 & 20.7338035054779 & 0.266196494522133 \tabularnewline
84 & 20 & 21.7890552501971 & -1.78905525019705 \tabularnewline
85 & 23 & 19.953634409697 & 3.04636559030299 \tabularnewline
86 & 21 & 20.823548318318 & 0.176451681682017 \tabularnewline
87 & 21 & 21.7765070663015 & -0.77650706630151 \tabularnewline
88 & 15 & 22.8032661951564 & -7.80326619515637 \tabularnewline
89 & 28 & 27.147195644037 & 0.852804355963014 \tabularnewline
90 & 19 & 17.6573711819145 & 1.3426288180855 \tabularnewline
91 & 26 & 21.241739029455 & 4.75826097054501 \tabularnewline
92 & 10 & 13.1981887926427 & -3.1981887926427 \tabularnewline
93 & 16 & 17.1052598018456 & -1.10525980184556 \tabularnewline
94 & 22 & 21.1686559891103 & 0.831344010889723 \tabularnewline
95 & 19 & 18.7314962822068 & 0.268503717793152 \tabularnewline
96 & 31 & 28.7736261084291 & 2.22637389157087 \tabularnewline
97 & 31 & 25.1990473784126 & 5.80095262158741 \tabularnewline
98 & 29 & 24.802119615067 & 4.19788038493298 \tabularnewline
99 & 19 & 17.4133114789467 & 1.58668852105333 \tabularnewline
100 & 22 & 18.8946458843887 & 3.10535411561134 \tabularnewline
101 & 23 & 22.3730772796911 & 0.626922720308933 \tabularnewline
102 & 15 & 16.1706084549776 & -1.17060845497761 \tabularnewline
103 & 20 & 21.4483664364161 & -1.44836643641609 \tabularnewline
104 & 18 & 19.5049798788849 & -1.50497987888490 \tabularnewline
105 & 23 & 21.9846165671170 & 1.01538343288297 \tabularnewline
106 & 25 & 20.9638904859829 & 4.03610951401713 \tabularnewline
107 & 21 & 16.5635547265782 & 4.43644527342175 \tabularnewline
108 & 24 & 19.4904987572516 & 4.50950124274840 \tabularnewline
109 & 25 & 25.2360293362311 & -0.236029336231133 \tabularnewline
110 & 17 & 19.5772208157054 & -2.57722081570542 \tabularnewline
111 & 13 & 14.5703651925901 & -1.57036519259012 \tabularnewline
112 & 28 & 18.2036391664048 & 9.7963608335952 \tabularnewline
113 & 21 & 20.1156089993066 & 0.884391000693354 \tabularnewline
114 & 25 & 28.140636065485 & -3.14063606548499 \tabularnewline
115 & 9 & 20.7272742072834 & -11.7272742072834 \tabularnewline
116 & 16 & 17.8284411696244 & -1.82844116962436 \tabularnewline
117 & 19 & 21.2002096254339 & -2.20020962543392 \tabularnewline
118 & 17 & 19.4548480129136 & -2.45484801291361 \tabularnewline
119 & 25 & 24.5118355020002 & 0.488164497999806 \tabularnewline
120 & 20 & 15.5584155586163 & 4.44158444138374 \tabularnewline
121 & 29 & 21.6245358561065 & 7.37546414389351 \tabularnewline
122 & 14 & 19.0460894061991 & -5.04608940619912 \tabularnewline
123 & 22 & 26.9144076581427 & -4.91440765814269 \tabularnewline
124 & 15 & 15.5764012107348 & -0.57640121073484 \tabularnewline
125 & 19 & 25.4228566000411 & -6.42285660004113 \tabularnewline
126 & 20 & 21.9451042791923 & -1.94510427919232 \tabularnewline
127 & 15 & 17.4522263745030 & -2.45222637450297 \tabularnewline
128 & 20 & 21.7886208868992 & -1.78862088689922 \tabularnewline
129 & 18 & 20.2225815390998 & -2.22258153909976 \tabularnewline
130 & 33 & 25.4668428039909 & 7.5331571960091 \tabularnewline
131 & 22 & 23.7824344363272 & -1.78243443632723 \tabularnewline
132 & 16 & 16.4697006570271 & -0.46970065702706 \tabularnewline
133 & 17 & 19.0367506268633 & -2.03675062686334 \tabularnewline
134 & 16 & 15.052432261591 & 0.947567738409005 \tabularnewline
135 & 21 & 17.0483876901129 & 3.95161230988709 \tabularnewline
136 & 26 & 27.6421945331063 & -1.64219453310631 \tabularnewline
137 & 18 & 21.1470742942904 & -3.14707429429044 \tabularnewline
138 & 18 & 23.0959206112274 & -5.09592061122736 \tabularnewline
139 & 17 & 18.3312627872462 & -1.33126278724619 \tabularnewline
140 & 22 & 24.7434940752527 & -2.74349407525272 \tabularnewline
141 & 30 & 24.7275661238695 & 5.27243387613055 \tabularnewline
142 & 30 & 27.2739964505683 & 2.72600354943172 \tabularnewline
143 & 24 & 29.9008223341231 & -5.9008223341231 \tabularnewline
144 & 21 & 21.9917911761762 & -0.991791176176217 \tabularnewline
145 & 21 & 25.3787580941457 & -4.3787580941457 \tabularnewline
146 & 29 & 27.3918377820479 & 1.60816221795208 \tabularnewline
147 & 31 & 23.2405082710070 & 7.75949172899296 \tabularnewline
148 & 20 & 18.9759710592584 & 1.02402894074160 \tabularnewline
149 & 16 & 14.1875805146353 & 1.81241948536466 \tabularnewline
150 & 22 & 18.9190989475258 & 3.08090105247424 \tabularnewline
151 & 20 & 20.3316462195076 & -0.331646219507592 \tabularnewline
152 & 28 & 27.3269489577319 & 0.673051042268084 \tabularnewline
153 & 38 & 26.5958059363835 & 11.4041940636165 \tabularnewline
154 & 22 & 19.3348333644292 & 2.66516663557077 \tabularnewline
155 & 20 & 25.6868097712468 & -5.68680977124681 \tabularnewline
156 & 17 & 18.0421110887897 & -1.04211108878972 \tabularnewline
157 & 28 & 24.4504319189554 & 3.54956808104463 \tabularnewline
158 & 22 & 24.1216672736301 & -2.12166727363007 \tabularnewline
159 & 31 & 26.0892814723567 & 4.9107185276433 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104299&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]24[/C][C]24.973069654619[/C][C]-0.973069654619013[/C][/ROW]
[ROW][C]2[/C][C]25[/C][C]21.6968275200519[/C][C]3.30317247994805[/C][/ROW]
[ROW][C]3[/C][C]17[/C][C]22.7576275344242[/C][C]-5.75762753442423[/C][/ROW]
[ROW][C]4[/C][C]18[/C][C]19.8643239601547[/C][C]-1.86432396015471[/C][/ROW]
[ROW][C]5[/C][C]18[/C][C]19.475921308563[/C][C]-1.47592130856301[/C][/ROW]
[ROW][C]6[/C][C]16[/C][C]19.5619453157043[/C][C]-3.56194531570427[/C][/ROW]
[ROW][C]7[/C][C]20[/C][C]20.9062047066683[/C][C]-0.90620470666828[/C][/ROW]
[ROW][C]8[/C][C]16[/C][C]22.3667061957279[/C][C]-6.36670619572787[/C][/ROW]
[ROW][C]9[/C][C]18[/C][C]22.4023995380277[/C][C]-4.40239953802768[/C][/ROW]
[ROW][C]10[/C][C]17[/C][C]20.5201335863125[/C][C]-3.52013358631251[/C][/ROW]
[ROW][C]11[/C][C]23[/C][C]22.3178193586678[/C][C]0.68218064133223[/C][/ROW]
[ROW][C]12[/C][C]30[/C][C]22.3274768850320[/C][C]7.67252311496804[/C][/ROW]
[ROW][C]13[/C][C]23[/C][C]15.1035765940144[/C][C]7.89642340598558[/C][/ROW]
[ROW][C]14[/C][C]18[/C][C]19.0918794530003[/C][C]-1.09187945300029[/C][/ROW]
[ROW][C]15[/C][C]15[/C][C]21.6854310399808[/C][C]-6.68543103998084[/C][/ROW]
[ROW][C]16[/C][C]12[/C][C]17.8354454157556[/C][C]-5.83544541575558[/C][/ROW]
[ROW][C]17[/C][C]21[/C][C]19.4165662795253[/C][C]1.58343372047467[/C][/ROW]
[ROW][C]18[/C][C]15[/C][C]15.3176615872635[/C][C]-0.317661587263513[/C][/ROW]
[ROW][C]19[/C][C]20[/C][C]19.7765579973379[/C][C]0.223442002662066[/C][/ROW]
[ROW][C]20[/C][C]31[/C][C]26.4203200936308[/C][C]4.57967990636915[/C][/ROW]
[ROW][C]21[/C][C]27[/C][C]25.2301666514384[/C][C]1.76983334856159[/C][/ROW]
[ROW][C]22[/C][C]34[/C][C]27.1596931008076[/C][C]6.84030689919239[/C][/ROW]
[ROW][C]23[/C][C]21[/C][C]19.6129963230410[/C][C]1.38700367695903[/C][/ROW]
[ROW][C]24[/C][C]31[/C][C]20.9185081794081[/C][C]10.0814918205919[/C][/ROW]
[ROW][C]25[/C][C]19[/C][C]19.2929241504289[/C][C]-0.292924150428885[/C][/ROW]
[ROW][C]26[/C][C]16[/C][C]20.2171532176049[/C][C]-4.21715321760486[/C][/ROW]
[ROW][C]27[/C][C]20[/C][C]21.5938509463785[/C][C]-1.59385094637850[/C][/ROW]
[ROW][C]28[/C][C]21[/C][C]18.2605112781375[/C][C]2.73948872186255[/C][/ROW]
[ROW][C]29[/C][C]22[/C][C]21.9603148900490[/C][C]0.0396851099510343[/C][/ROW]
[ROW][C]30[/C][C]17[/C][C]19.5088680455431[/C][C]-2.50886804554309[/C][/ROW]
[ROW][C]31[/C][C]24[/C][C]20.3178601472182[/C][C]3.68213985278176[/C][/ROW]
[ROW][C]32[/C][C]25[/C][C]30.4697362245754[/C][C]-5.46973622457536[/C][/ROW]
[ROW][C]33[/C][C]26[/C][C]26.7379136808267[/C][C]-0.737913680826726[/C][/ROW]
[ROW][C]34[/C][C]25[/C][C]24.0652193827093[/C][C]0.9347806172907[/C][/ROW]
[ROW][C]35[/C][C]17[/C][C]23.0090707878074[/C][C]-6.0090707878074[/C][/ROW]
[ROW][C]36[/C][C]32[/C][C]27.7900227230014[/C][C]4.20997727699859[/C][/ROW]
[ROW][C]37[/C][C]33[/C][C]23.6362102946555[/C][C]9.36378970534448[/C][/ROW]
[ROW][C]38[/C][C]13[/C][C]22.0253619285962[/C][C]-9.02536192859621[/C][/ROW]
[ROW][C]39[/C][C]32[/C][C]27.8387300504054[/C][C]4.16126994959459[/C][/ROW]
[ROW][C]40[/C][C]25[/C][C]25.8389604910979[/C][C]-0.838960491097932[/C][/ROW]
[ROW][C]41[/C][C]29[/C][C]27.024061914111[/C][C]1.97593808588899[/C][/ROW]
[ROW][C]42[/C][C]22[/C][C]22.0238725008844[/C][C]-0.0238725008844226[/C][/ROW]
[ROW][C]43[/C][C]18[/C][C]17.3643313167998[/C][C]0.63566868320017[/C][/ROW]
[ROW][C]44[/C][C]17[/C][C]21.7723044845037[/C][C]-4.7723044845037[/C][/ROW]
[ROW][C]45[/C][C]20[/C][C]22.2065286208074[/C][C]-2.20652862080743[/C][/ROW]
[ROW][C]46[/C][C]15[/C][C]20.4502048915139[/C][C]-5.45020489151386[/C][/ROW]
[ROW][C]47[/C][C]20[/C][C]22.1848414522041[/C][C]-2.18484145220413[/C][/ROW]
[ROW][C]48[/C][C]33[/C][C]28.0994056135831[/C][C]4.90059438641693[/C][/ROW]
[ROW][C]49[/C][C]29[/C][C]22.1429710099720[/C][C]6.85702899002803[/C][/ROW]
[ROW][C]50[/C][C]23[/C][C]26.6826557598034[/C][C]-3.68265575980343[/C][/ROW]
[ROW][C]51[/C][C]26[/C][C]23.2430193118991[/C][C]2.75698068810089[/C][/ROW]
[ROW][C]52[/C][C]18[/C][C]18.9528684988160[/C][C]-0.952868498815956[/C][/ROW]
[ROW][C]53[/C][C]20[/C][C]18.7340532353845[/C][C]1.26594676461551[/C][/ROW]
[ROW][C]54[/C][C]11[/C][C]11.7902591765424[/C][C]-0.79025917654241[/C][/ROW]
[ROW][C]55[/C][C]28[/C][C]28.9774711402709[/C][C]-0.977471140270921[/C][/ROW]
[ROW][C]56[/C][C]26[/C][C]23.3129672959119[/C][C]2.68703270408814[/C][/ROW]
[ROW][C]57[/C][C]22[/C][C]22.2461608568904[/C][C]-0.246160856890353[/C][/ROW]
[ROW][C]58[/C][C]17[/C][C]20.1978454987339[/C][C]-3.19784549873387[/C][/ROW]
[ROW][C]59[/C][C]12[/C][C]15.4254660880949[/C][C]-3.42546608809486[/C][/ROW]
[ROW][C]60[/C][C]14[/C][C]21.0281654373449[/C][C]-7.02816543734494[/C][/ROW]
[ROW][C]61[/C][C]17[/C][C]20.7238630018849[/C][C]-3.72386300188489[/C][/ROW]
[ROW][C]62[/C][C]21[/C][C]21.3354034468641[/C][C]-0.335403446864115[/C][/ROW]
[ROW][C]63[/C][C]19[/C][C]22.9325499189927[/C][C]-3.93254991899267[/C][/ROW]
[ROW][C]64[/C][C]18[/C][C]23.0750654036623[/C][C]-5.07506540366228[/C][/ROW]
[ROW][C]65[/C][C]10[/C][C]17.9297721146776[/C][C]-7.92977211467762[/C][/ROW]
[ROW][C]66[/C][C]29[/C][C]24.2201359970942[/C][C]4.77986400290578[/C][/ROW]
[ROW][C]67[/C][C]31[/C][C]18.4575551946070[/C][C]12.5424448053930[/C][/ROW]
[ROW][C]68[/C][C]19[/C][C]22.9476672047626[/C][C]-3.94766720476257[/C][/ROW]
[ROW][C]69[/C][C]9[/C][C]20.0659733838091[/C][C]-11.0659733838091[/C][/ROW]
[ROW][C]70[/C][C]20[/C][C]22.4704044418167[/C][C]-2.47040444181672[/C][/ROW]
[ROW][C]71[/C][C]28[/C][C]17.6195978476967[/C][C]10.3804021523033[/C][/ROW]
[ROW][C]72[/C][C]19[/C][C]18.1263076319767[/C][C]0.87369236802328[/C][/ROW]
[ROW][C]73[/C][C]30[/C][C]23.0600995039614[/C][C]6.93990049603859[/C][/ROW]
[ROW][C]74[/C][C]29[/C][C]27.134541986358[/C][C]1.86545801364201[/C][/ROW]
[ROW][C]75[/C][C]26[/C][C]21.4781322047787[/C][C]4.52186779522132[/C][/ROW]
[ROW][C]76[/C][C]23[/C][C]19.5440844265833[/C][C]3.45591557341675[/C][/ROW]
[ROW][C]77[/C][C]13[/C][C]22.7619883304533[/C][C]-9.76198833045328[/C][/ROW]
[ROW][C]78[/C][C]21[/C][C]22.6403328681881[/C][C]-1.64033286818808[/C][/ROW]
[ROW][C]79[/C][C]19[/C][C]21.4342707638265[/C][C]-2.43427076382647[/C][/ROW]
[ROW][C]80[/C][C]28[/C][C]22.9430302596669[/C][C]5.05696974033307[/C][/ROW]
[ROW][C]81[/C][C]23[/C][C]25.5488853193873[/C][C]-2.54888531938726[/C][/ROW]
[ROW][C]82[/C][C]18[/C][C]13.775544612248[/C][C]4.22445538775199[/C][/ROW]
[ROW][C]83[/C][C]21[/C][C]20.7338035054779[/C][C]0.266196494522133[/C][/ROW]
[ROW][C]84[/C][C]20[/C][C]21.7890552501971[/C][C]-1.78905525019705[/C][/ROW]
[ROW][C]85[/C][C]23[/C][C]19.953634409697[/C][C]3.04636559030299[/C][/ROW]
[ROW][C]86[/C][C]21[/C][C]20.823548318318[/C][C]0.176451681682017[/C][/ROW]
[ROW][C]87[/C][C]21[/C][C]21.7765070663015[/C][C]-0.77650706630151[/C][/ROW]
[ROW][C]88[/C][C]15[/C][C]22.8032661951564[/C][C]-7.80326619515637[/C][/ROW]
[ROW][C]89[/C][C]28[/C][C]27.147195644037[/C][C]0.852804355963014[/C][/ROW]
[ROW][C]90[/C][C]19[/C][C]17.6573711819145[/C][C]1.3426288180855[/C][/ROW]
[ROW][C]91[/C][C]26[/C][C]21.241739029455[/C][C]4.75826097054501[/C][/ROW]
[ROW][C]92[/C][C]10[/C][C]13.1981887926427[/C][C]-3.1981887926427[/C][/ROW]
[ROW][C]93[/C][C]16[/C][C]17.1052598018456[/C][C]-1.10525980184556[/C][/ROW]
[ROW][C]94[/C][C]22[/C][C]21.1686559891103[/C][C]0.831344010889723[/C][/ROW]
[ROW][C]95[/C][C]19[/C][C]18.7314962822068[/C][C]0.268503717793152[/C][/ROW]
[ROW][C]96[/C][C]31[/C][C]28.7736261084291[/C][C]2.22637389157087[/C][/ROW]
[ROW][C]97[/C][C]31[/C][C]25.1990473784126[/C][C]5.80095262158741[/C][/ROW]
[ROW][C]98[/C][C]29[/C][C]24.802119615067[/C][C]4.19788038493298[/C][/ROW]
[ROW][C]99[/C][C]19[/C][C]17.4133114789467[/C][C]1.58668852105333[/C][/ROW]
[ROW][C]100[/C][C]22[/C][C]18.8946458843887[/C][C]3.10535411561134[/C][/ROW]
[ROW][C]101[/C][C]23[/C][C]22.3730772796911[/C][C]0.626922720308933[/C][/ROW]
[ROW][C]102[/C][C]15[/C][C]16.1706084549776[/C][C]-1.17060845497761[/C][/ROW]
[ROW][C]103[/C][C]20[/C][C]21.4483664364161[/C][C]-1.44836643641609[/C][/ROW]
[ROW][C]104[/C][C]18[/C][C]19.5049798788849[/C][C]-1.50497987888490[/C][/ROW]
[ROW][C]105[/C][C]23[/C][C]21.9846165671170[/C][C]1.01538343288297[/C][/ROW]
[ROW][C]106[/C][C]25[/C][C]20.9638904859829[/C][C]4.03610951401713[/C][/ROW]
[ROW][C]107[/C][C]21[/C][C]16.5635547265782[/C][C]4.43644527342175[/C][/ROW]
[ROW][C]108[/C][C]24[/C][C]19.4904987572516[/C][C]4.50950124274840[/C][/ROW]
[ROW][C]109[/C][C]25[/C][C]25.2360293362311[/C][C]-0.236029336231133[/C][/ROW]
[ROW][C]110[/C][C]17[/C][C]19.5772208157054[/C][C]-2.57722081570542[/C][/ROW]
[ROW][C]111[/C][C]13[/C][C]14.5703651925901[/C][C]-1.57036519259012[/C][/ROW]
[ROW][C]112[/C][C]28[/C][C]18.2036391664048[/C][C]9.7963608335952[/C][/ROW]
[ROW][C]113[/C][C]21[/C][C]20.1156089993066[/C][C]0.884391000693354[/C][/ROW]
[ROW][C]114[/C][C]25[/C][C]28.140636065485[/C][C]-3.14063606548499[/C][/ROW]
[ROW][C]115[/C][C]9[/C][C]20.7272742072834[/C][C]-11.7272742072834[/C][/ROW]
[ROW][C]116[/C][C]16[/C][C]17.8284411696244[/C][C]-1.82844116962436[/C][/ROW]
[ROW][C]117[/C][C]19[/C][C]21.2002096254339[/C][C]-2.20020962543392[/C][/ROW]
[ROW][C]118[/C][C]17[/C][C]19.4548480129136[/C][C]-2.45484801291361[/C][/ROW]
[ROW][C]119[/C][C]25[/C][C]24.5118355020002[/C][C]0.488164497999806[/C][/ROW]
[ROW][C]120[/C][C]20[/C][C]15.5584155586163[/C][C]4.44158444138374[/C][/ROW]
[ROW][C]121[/C][C]29[/C][C]21.6245358561065[/C][C]7.37546414389351[/C][/ROW]
[ROW][C]122[/C][C]14[/C][C]19.0460894061991[/C][C]-5.04608940619912[/C][/ROW]
[ROW][C]123[/C][C]22[/C][C]26.9144076581427[/C][C]-4.91440765814269[/C][/ROW]
[ROW][C]124[/C][C]15[/C][C]15.5764012107348[/C][C]-0.57640121073484[/C][/ROW]
[ROW][C]125[/C][C]19[/C][C]25.4228566000411[/C][C]-6.42285660004113[/C][/ROW]
[ROW][C]126[/C][C]20[/C][C]21.9451042791923[/C][C]-1.94510427919232[/C][/ROW]
[ROW][C]127[/C][C]15[/C][C]17.4522263745030[/C][C]-2.45222637450297[/C][/ROW]
[ROW][C]128[/C][C]20[/C][C]21.7886208868992[/C][C]-1.78862088689922[/C][/ROW]
[ROW][C]129[/C][C]18[/C][C]20.2225815390998[/C][C]-2.22258153909976[/C][/ROW]
[ROW][C]130[/C][C]33[/C][C]25.4668428039909[/C][C]7.5331571960091[/C][/ROW]
[ROW][C]131[/C][C]22[/C][C]23.7824344363272[/C][C]-1.78243443632723[/C][/ROW]
[ROW][C]132[/C][C]16[/C][C]16.4697006570271[/C][C]-0.46970065702706[/C][/ROW]
[ROW][C]133[/C][C]17[/C][C]19.0367506268633[/C][C]-2.03675062686334[/C][/ROW]
[ROW][C]134[/C][C]16[/C][C]15.052432261591[/C][C]0.947567738409005[/C][/ROW]
[ROW][C]135[/C][C]21[/C][C]17.0483876901129[/C][C]3.95161230988709[/C][/ROW]
[ROW][C]136[/C][C]26[/C][C]27.6421945331063[/C][C]-1.64219453310631[/C][/ROW]
[ROW][C]137[/C][C]18[/C][C]21.1470742942904[/C][C]-3.14707429429044[/C][/ROW]
[ROW][C]138[/C][C]18[/C][C]23.0959206112274[/C][C]-5.09592061122736[/C][/ROW]
[ROW][C]139[/C][C]17[/C][C]18.3312627872462[/C][C]-1.33126278724619[/C][/ROW]
[ROW][C]140[/C][C]22[/C][C]24.7434940752527[/C][C]-2.74349407525272[/C][/ROW]
[ROW][C]141[/C][C]30[/C][C]24.7275661238695[/C][C]5.27243387613055[/C][/ROW]
[ROW][C]142[/C][C]30[/C][C]27.2739964505683[/C][C]2.72600354943172[/C][/ROW]
[ROW][C]143[/C][C]24[/C][C]29.9008223341231[/C][C]-5.9008223341231[/C][/ROW]
[ROW][C]144[/C][C]21[/C][C]21.9917911761762[/C][C]-0.991791176176217[/C][/ROW]
[ROW][C]145[/C][C]21[/C][C]25.3787580941457[/C][C]-4.3787580941457[/C][/ROW]
[ROW][C]146[/C][C]29[/C][C]27.3918377820479[/C][C]1.60816221795208[/C][/ROW]
[ROW][C]147[/C][C]31[/C][C]23.2405082710070[/C][C]7.75949172899296[/C][/ROW]
[ROW][C]148[/C][C]20[/C][C]18.9759710592584[/C][C]1.02402894074160[/C][/ROW]
[ROW][C]149[/C][C]16[/C][C]14.1875805146353[/C][C]1.81241948536466[/C][/ROW]
[ROW][C]150[/C][C]22[/C][C]18.9190989475258[/C][C]3.08090105247424[/C][/ROW]
[ROW][C]151[/C][C]20[/C][C]20.3316462195076[/C][C]-0.331646219507592[/C][/ROW]
[ROW][C]152[/C][C]28[/C][C]27.3269489577319[/C][C]0.673051042268084[/C][/ROW]
[ROW][C]153[/C][C]38[/C][C]26.5958059363835[/C][C]11.4041940636165[/C][/ROW]
[ROW][C]154[/C][C]22[/C][C]19.3348333644292[/C][C]2.66516663557077[/C][/ROW]
[ROW][C]155[/C][C]20[/C][C]25.6868097712468[/C][C]-5.68680977124681[/C][/ROW]
[ROW][C]156[/C][C]17[/C][C]18.0421110887897[/C][C]-1.04211108878972[/C][/ROW]
[ROW][C]157[/C][C]28[/C][C]24.4504319189554[/C][C]3.54956808104463[/C][/ROW]
[ROW][C]158[/C][C]22[/C][C]24.1216672736301[/C][C]-2.12166727363007[/C][/ROW]
[ROW][C]159[/C][C]31[/C][C]26.0892814723567[/C][C]4.9107185276433[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104299&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104299&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
12424.973069654619-0.973069654619013
22521.69682752005193.30317247994805
31722.7576275344242-5.75762753442423
41819.8643239601547-1.86432396015471
51819.475921308563-1.47592130856301
61619.5619453157043-3.56194531570427
72020.9062047066683-0.90620470666828
81622.3667061957279-6.36670619572787
91822.4023995380277-4.40239953802768
101720.5201335863125-3.52013358631251
112322.31781935866780.68218064133223
123022.32747688503207.67252311496804
132315.10357659401447.89642340598558
141819.0918794530003-1.09187945300029
151521.6854310399808-6.68543103998084
161217.8354454157556-5.83544541575558
172119.41656627952531.58343372047467
181515.3176615872635-0.317661587263513
192019.77655799733790.223442002662066
203126.42032009363084.57967990636915
212725.23016665143841.76983334856159
223427.15969310080766.84030689919239
232119.61299632304101.38700367695903
243120.918508179408110.0814918205919
251919.2929241504289-0.292924150428885
261620.2171532176049-4.21715321760486
272021.5938509463785-1.59385094637850
282118.26051127813752.73948872186255
292221.96031489004900.0396851099510343
301719.5088680455431-2.50886804554309
312420.31786014721823.68213985278176
322530.4697362245754-5.46973622457536
332626.7379136808267-0.737913680826726
342524.06521938270930.9347806172907
351723.0090707878074-6.0090707878074
363227.79002272300144.20997727699859
373323.63621029465559.36378970534448
381322.0253619285962-9.02536192859621
393227.83873005040544.16126994959459
402525.8389604910979-0.838960491097932
412927.0240619141111.97593808588899
422222.0238725008844-0.0238725008844226
431817.36433131679980.63566868320017
441721.7723044845037-4.7723044845037
452022.2065286208074-2.20652862080743
461520.4502048915139-5.45020489151386
472022.1848414522041-2.18484145220413
483328.09940561358314.90059438641693
492922.14297100997206.85702899002803
502326.6826557598034-3.68265575980343
512623.24301931189912.75698068810089
521818.9528684988160-0.952868498815956
532018.73405323538451.26594676461551
541111.7902591765424-0.79025917654241
552828.9774711402709-0.977471140270921
562623.31296729591192.68703270408814
572222.2461608568904-0.246160856890353
581720.1978454987339-3.19784549873387
591215.4254660880949-3.42546608809486
601421.0281654373449-7.02816543734494
611720.7238630018849-3.72386300188489
622121.3354034468641-0.335403446864115
631922.9325499189927-3.93254991899267
641823.0750654036623-5.07506540366228
651017.9297721146776-7.92977211467762
662924.22013599709424.77986400290578
673118.457555194607012.5424448053930
681922.9476672047626-3.94766720476257
69920.0659733838091-11.0659733838091
702022.4704044418167-2.47040444181672
712817.619597847696710.3804021523033
721918.12630763197670.87369236802328
733023.06009950396146.93990049603859
742927.1345419863581.86545801364201
752621.47813220477874.52186779522132
762319.54408442658333.45591557341675
771322.7619883304533-9.76198833045328
782122.6403328681881-1.64033286818808
791921.4342707638265-2.43427076382647
802822.94303025966695.05696974033307
812325.5488853193873-2.54888531938726
821813.7755446122484.22445538775199
832120.73380350547790.266196494522133
842021.7890552501971-1.78905525019705
852319.9536344096973.04636559030299
862120.8235483183180.176451681682017
872121.7765070663015-0.77650706630151
881522.8032661951564-7.80326619515637
892827.1471956440370.852804355963014
901917.65737118191451.3426288180855
912621.2417390294554.75826097054501
921013.1981887926427-3.1981887926427
931617.1052598018456-1.10525980184556
942221.16865598911030.831344010889723
951918.73149628220680.268503717793152
963128.77362610842912.22637389157087
973125.19904737841265.80095262158741
982924.8021196150674.19788038493298
991917.41331147894671.58668852105333
1002218.89464588438873.10535411561134
1012322.37307727969110.626922720308933
1021516.1706084549776-1.17060845497761
1032021.4483664364161-1.44836643641609
1041819.5049798788849-1.50497987888490
1052321.98461656711701.01538343288297
1062520.96389048598294.03610951401713
1072116.56355472657824.43644527342175
1082419.49049875725164.50950124274840
1092525.2360293362311-0.236029336231133
1101719.5772208157054-2.57722081570542
1111314.5703651925901-1.57036519259012
1122818.20363916640489.7963608335952
1132120.11560899930660.884391000693354
1142528.140636065485-3.14063606548499
115920.7272742072834-11.7272742072834
1161617.8284411696244-1.82844116962436
1171921.2002096254339-2.20020962543392
1181719.4548480129136-2.45484801291361
1192524.51183550200020.488164497999806
1202015.55841555861634.44158444138374
1212921.62453585610657.37546414389351
1221419.0460894061991-5.04608940619912
1232226.9144076581427-4.91440765814269
1241515.5764012107348-0.57640121073484
1251925.4228566000411-6.42285660004113
1262021.9451042791923-1.94510427919232
1271517.4522263745030-2.45222637450297
1282021.7886208868992-1.78862088689922
1291820.2225815390998-2.22258153909976
1303325.46684280399097.5331571960091
1312223.7824344363272-1.78243443632723
1321616.4697006570271-0.46970065702706
1331719.0367506268633-2.03675062686334
1341615.0524322615910.947567738409005
1352117.04838769011293.95161230988709
1362627.6421945331063-1.64219453310631
1371821.1470742942904-3.14707429429044
1381823.0959206112274-5.09592061122736
1391718.3312627872462-1.33126278724619
1402224.7434940752527-2.74349407525272
1413024.72756612386955.27243387613055
1423027.27399645056832.72600354943172
1432429.9008223341231-5.9008223341231
1442121.9917911761762-0.991791176176217
1452125.3787580941457-4.3787580941457
1462927.39183778204791.60816221795208
1473123.24050827100707.75949172899296
1482018.97597105925841.02402894074160
1491614.18758051463531.81241948536466
1502218.91909894752583.08090105247424
1512020.3316462195076-0.331646219507592
1522827.32694895773190.673051042268084
1533826.595805936383511.4041940636165
1542219.33483336442922.66516663557077
1552025.6868097712468-5.68680977124681
1561718.0421110887897-1.04211108878972
1572824.45043191895543.54956808104463
1582224.1216672736301-2.12166727363007
1593126.08928147235674.9107185276433







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.2113960233023270.4227920466046540.788603976697673
100.1020763753425870.2041527506851750.897923624657412
110.2272380381920760.4544760763841510.772761961807924
120.6516882361919510.6966235276160980.348311763808049
130.6673072325423310.6653855349153380.332692767457669
140.6420154149810760.7159691700378480.357984585018924
150.6474213299422290.7051573401155420.352578670057771
160.5820071701802100.8359856596395810.417992829819790
170.4974416798337950.994883359667590.502558320166205
180.4668537175174820.9337074350349650.533146282482518
190.4031851594547610.8063703189095220.596814840545239
200.4376092172551570.8752184345103130.562390782744844
210.3969015250977720.7938030501955440.603098474902228
220.4007779696537920.8015559393075840.599222030346208
230.3387699516316920.6775399032633840.661230048368308
240.6033415677282870.7933168645434260.396658432271713
250.5335512010448590.9328975979102820.466448798955141
260.5035440241504260.9929119516991490.496455975849574
270.4417309983698710.8834619967397410.558269001630129
280.3951831675487840.7903663350975680.604816832451216
290.3349775204123830.6699550408247670.665022479587617
300.2829189677787480.5658379355574970.717081032221252
310.3429018151285180.6858036302570360.657098184871482
320.4042320700720380.8084641401440760.595767929927962
330.3603988805327850.7207977610655690.639601119467215
340.3706313728358480.7412627456716960.629368627164152
350.3853071543426160.7706143086852310.614692845657384
360.3759496983599770.7518993967199550.624050301640023
370.5549897033900580.8900205932198830.445010296609942
380.7584657462238270.4830685075523450.241534253776173
390.7545179416165120.4909641167669760.245482058383488
400.7126200474291350.574759905141730.287379952570865
410.6876137233218280.6247725533563440.312386276678172
420.6381735776537380.7236528446925250.361826422346262
430.5895753437708560.8208493124582890.410424656229145
440.5761872667996290.8476254664007420.423812733200371
450.5417375471659990.9165249056680020.458262452834001
460.563830488846730.872339022306540.43616951115327
470.522810428163520.954379143672960.47718957183648
480.513226336040150.973547327919700.48677366395985
490.5781190982572910.8437618034854180.421880901742709
500.5572045651713530.8855908696572950.442795434828647
510.5218326102529970.9563347794940060.478167389747003
520.4725186986921830.9450373973843670.527481301307817
530.4343880243849050.868776048769810.565611975615095
540.3875974418534480.7751948837068960.612402558146552
550.3447994330060130.6895988660120260.655200566993987
560.317679472653810.635358945307620.68232052734619
570.2747521586034920.5495043172069840.725247841396508
580.2511059244539740.5022118489079480.748894075546026
590.2314343197213690.4628686394427370.768565680278631
600.2901440782193610.5802881564387220.709855921780639
610.2784971865247180.5569943730494350.721502813475282
620.2412489614241520.4824979228483050.758751038575848
630.2286751236501970.4573502473003940.771324876349803
640.2625362638972040.5250725277944070.737463736102796
650.3388384144525330.6776768289050670.661161585547467
660.3415592785387660.6831185570775330.658440721461234
670.6745262176738520.6509475646522970.325473782326148
680.6672981476756080.6654037046487850.332701852324392
690.8442068400413780.3115863199172430.155793159958622
700.8244781891795270.3510436216409470.175521810820473
710.9305284986215070.1389430027569860.0694715013784930
720.9142375319136360.1715249361727290.0857624680863643
730.9374225765849640.1251548468300730.0625774234150364
740.925574321233510.1488513575329790.0744256787664895
750.9273220425530020.1453559148939970.0726779574469983
760.9210024044478750.157995191104250.078997595552125
770.970096122990050.05980775401989940.0299038770099497
780.9627371234349930.0745257531300140.037262876565007
790.9553221127204190.08935577455916220.0446778872795811
800.9580010692352870.08399786152942540.0419989307647127
810.9503272401933870.09934551961322680.0496727598066134
820.9500097695588780.09998046088224460.0499902304411223
830.9371893773116470.1256212453767070.0628106226883533
840.9244295166649790.1511409666700420.075570483335021
850.9159516728922380.1680966542155230.0840483271077617
860.9006531811342380.1986936377315230.0993468188657616
870.8797100421322960.2405799157354080.120289957867704
880.9237274000228140.1525451999543730.0762725999771865
890.9077949479771050.1844101040457910.0922050520228953
900.8885210283355090.2229579433289810.111478971664491
910.8903485081722570.2193029836554860.109651491827743
920.879723294041730.2405534119165390.120276705958270
930.8584863885676740.2830272228646530.141513611432326
940.831802333240560.336395333518880.16819766675944
950.7998917411512730.4002165176974540.200108258848727
960.7713635890097160.4572728219805690.228636410990284
970.7904457067976120.4191085864047750.209554293202388
980.7852534165610850.4294931668778290.214746583438914
990.7513156992978260.4973686014043480.248684300702174
1000.7277723553648080.5444552892703840.272227644635192
1010.6864158629623080.6271682740753840.313584137037692
1020.6486695842340220.7026608315319570.351330415765979
1030.6106887359428070.7786225281143860.389311264057193
1040.5689125131836810.8621749736326380.431087486816319
1050.5233482611138840.9533034777722320.476651738886116
1060.5037448028681890.9925103942636210.496255197131811
1070.500601496808820.998797006382360.49939850319118
1080.5120668888361960.9758662223276080.487933111163804
1090.4619706071906030.9239412143812070.538029392809397
1100.4388719527451310.8777439054902610.56112804725487
1110.3992451119491240.7984902238982470.600754888050876
1120.6224640031835920.7550719936328170.377535996816408
1130.61435043167660.77129913664680.3856495683234
1140.5860122340607290.8279755318785410.413987765939271
1150.7879950573792440.4240098852415120.212004942620756
1160.7660028407470520.4679943185058960.233997159252948
1170.7550532828315690.4898934343368610.244946717168431
1180.7283100276160570.5433799447678850.271689972383943
1190.6805217232468620.6389565535062760.319478276753138
1200.689821686952420.6203566260951610.310178313047580
1210.7412504567360660.5174990865278670.258749543263934
1220.7463110920562030.5073778158875940.253688907943797
1230.7520664555156740.4958670889686520.247933544484326
1240.7027228659240850.594554268151830.297277134075915
1250.7466719360720120.5066561278559760.253328063927988
1260.7197101758597930.5605796482804140.280289824140207
1270.7097206993921580.5805586012156850.290279300607842
1280.6760296791451590.6479406417096830.323970320854841
1290.6508809551557080.6982380896885840.349119044844292
1300.720462230266050.5590755394679010.279537769733951
1310.6669860570993350.666027885801330.333013942900665
1320.6062302143605460.7875395712789090.393769785639454
1330.6058951725096340.7882096549807330.394104827490366
1340.5469647779203120.9060704441593770.453035222079688
1350.5062124791455980.9875750417088040.493787520854402
1360.4788697788589940.9577395577179870.521130221141006
1370.4851433508719940.9702867017439870.514856649128006
1380.5287334868851480.9425330262297030.471266513114852
1390.461648300276760.923296600553520.53835169972324
1400.3875089209721590.7750178419443170.612491079027841
1410.5003989923338270.9992020153323460.499601007666173
1420.4477314301060220.8954628602120430.552268569893978
1430.3779897607247860.7559795214495730.622010239275214
1440.2980098463753720.5960196927507440.701990153624628
1450.3552632752933760.7105265505867530.644736724706624
1460.2629508766098580.5259017532197160.737049123390142
1470.340573335736030.681146671472060.65942666426397
1480.2349212715407720.4698425430815440.765078728459228
1490.1460779735049850.2921559470099700.853922026495015
1500.09191941347048950.1838388269409790.90808058652951

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.211396023302327 & 0.422792046604654 & 0.788603976697673 \tabularnewline
10 & 0.102076375342587 & 0.204152750685175 & 0.897923624657412 \tabularnewline
11 & 0.227238038192076 & 0.454476076384151 & 0.772761961807924 \tabularnewline
12 & 0.651688236191951 & 0.696623527616098 & 0.348311763808049 \tabularnewline
13 & 0.667307232542331 & 0.665385534915338 & 0.332692767457669 \tabularnewline
14 & 0.642015414981076 & 0.715969170037848 & 0.357984585018924 \tabularnewline
15 & 0.647421329942229 & 0.705157340115542 & 0.352578670057771 \tabularnewline
16 & 0.582007170180210 & 0.835985659639581 & 0.417992829819790 \tabularnewline
17 & 0.497441679833795 & 0.99488335966759 & 0.502558320166205 \tabularnewline
18 & 0.466853717517482 & 0.933707435034965 & 0.533146282482518 \tabularnewline
19 & 0.403185159454761 & 0.806370318909522 & 0.596814840545239 \tabularnewline
20 & 0.437609217255157 & 0.875218434510313 & 0.562390782744844 \tabularnewline
21 & 0.396901525097772 & 0.793803050195544 & 0.603098474902228 \tabularnewline
22 & 0.400777969653792 & 0.801555939307584 & 0.599222030346208 \tabularnewline
23 & 0.338769951631692 & 0.677539903263384 & 0.661230048368308 \tabularnewline
24 & 0.603341567728287 & 0.793316864543426 & 0.396658432271713 \tabularnewline
25 & 0.533551201044859 & 0.932897597910282 & 0.466448798955141 \tabularnewline
26 & 0.503544024150426 & 0.992911951699149 & 0.496455975849574 \tabularnewline
27 & 0.441730998369871 & 0.883461996739741 & 0.558269001630129 \tabularnewline
28 & 0.395183167548784 & 0.790366335097568 & 0.604816832451216 \tabularnewline
29 & 0.334977520412383 & 0.669955040824767 & 0.665022479587617 \tabularnewline
30 & 0.282918967778748 & 0.565837935557497 & 0.717081032221252 \tabularnewline
31 & 0.342901815128518 & 0.685803630257036 & 0.657098184871482 \tabularnewline
32 & 0.404232070072038 & 0.808464140144076 & 0.595767929927962 \tabularnewline
33 & 0.360398880532785 & 0.720797761065569 & 0.639601119467215 \tabularnewline
34 & 0.370631372835848 & 0.741262745671696 & 0.629368627164152 \tabularnewline
35 & 0.385307154342616 & 0.770614308685231 & 0.614692845657384 \tabularnewline
36 & 0.375949698359977 & 0.751899396719955 & 0.624050301640023 \tabularnewline
37 & 0.554989703390058 & 0.890020593219883 & 0.445010296609942 \tabularnewline
38 & 0.758465746223827 & 0.483068507552345 & 0.241534253776173 \tabularnewline
39 & 0.754517941616512 & 0.490964116766976 & 0.245482058383488 \tabularnewline
40 & 0.712620047429135 & 0.57475990514173 & 0.287379952570865 \tabularnewline
41 & 0.687613723321828 & 0.624772553356344 & 0.312386276678172 \tabularnewline
42 & 0.638173577653738 & 0.723652844692525 & 0.361826422346262 \tabularnewline
43 & 0.589575343770856 & 0.820849312458289 & 0.410424656229145 \tabularnewline
44 & 0.576187266799629 & 0.847625466400742 & 0.423812733200371 \tabularnewline
45 & 0.541737547165999 & 0.916524905668002 & 0.458262452834001 \tabularnewline
46 & 0.56383048884673 & 0.87233902230654 & 0.43616951115327 \tabularnewline
47 & 0.52281042816352 & 0.95437914367296 & 0.47718957183648 \tabularnewline
48 & 0.51322633604015 & 0.97354732791970 & 0.48677366395985 \tabularnewline
49 & 0.578119098257291 & 0.843761803485418 & 0.421880901742709 \tabularnewline
50 & 0.557204565171353 & 0.885590869657295 & 0.442795434828647 \tabularnewline
51 & 0.521832610252997 & 0.956334779494006 & 0.478167389747003 \tabularnewline
52 & 0.472518698692183 & 0.945037397384367 & 0.527481301307817 \tabularnewline
53 & 0.434388024384905 & 0.86877604876981 & 0.565611975615095 \tabularnewline
54 & 0.387597441853448 & 0.775194883706896 & 0.612402558146552 \tabularnewline
55 & 0.344799433006013 & 0.689598866012026 & 0.655200566993987 \tabularnewline
56 & 0.31767947265381 & 0.63535894530762 & 0.68232052734619 \tabularnewline
57 & 0.274752158603492 & 0.549504317206984 & 0.725247841396508 \tabularnewline
58 & 0.251105924453974 & 0.502211848907948 & 0.748894075546026 \tabularnewline
59 & 0.231434319721369 & 0.462868639442737 & 0.768565680278631 \tabularnewline
60 & 0.290144078219361 & 0.580288156438722 & 0.709855921780639 \tabularnewline
61 & 0.278497186524718 & 0.556994373049435 & 0.721502813475282 \tabularnewline
62 & 0.241248961424152 & 0.482497922848305 & 0.758751038575848 \tabularnewline
63 & 0.228675123650197 & 0.457350247300394 & 0.771324876349803 \tabularnewline
64 & 0.262536263897204 & 0.525072527794407 & 0.737463736102796 \tabularnewline
65 & 0.338838414452533 & 0.677676828905067 & 0.661161585547467 \tabularnewline
66 & 0.341559278538766 & 0.683118557077533 & 0.658440721461234 \tabularnewline
67 & 0.674526217673852 & 0.650947564652297 & 0.325473782326148 \tabularnewline
68 & 0.667298147675608 & 0.665403704648785 & 0.332701852324392 \tabularnewline
69 & 0.844206840041378 & 0.311586319917243 & 0.155793159958622 \tabularnewline
70 & 0.824478189179527 & 0.351043621640947 & 0.175521810820473 \tabularnewline
71 & 0.930528498621507 & 0.138943002756986 & 0.0694715013784930 \tabularnewline
72 & 0.914237531913636 & 0.171524936172729 & 0.0857624680863643 \tabularnewline
73 & 0.937422576584964 & 0.125154846830073 & 0.0625774234150364 \tabularnewline
74 & 0.92557432123351 & 0.148851357532979 & 0.0744256787664895 \tabularnewline
75 & 0.927322042553002 & 0.145355914893997 & 0.0726779574469983 \tabularnewline
76 & 0.921002404447875 & 0.15799519110425 & 0.078997595552125 \tabularnewline
77 & 0.97009612299005 & 0.0598077540198994 & 0.0299038770099497 \tabularnewline
78 & 0.962737123434993 & 0.074525753130014 & 0.037262876565007 \tabularnewline
79 & 0.955322112720419 & 0.0893557745591622 & 0.0446778872795811 \tabularnewline
80 & 0.958001069235287 & 0.0839978615294254 & 0.0419989307647127 \tabularnewline
81 & 0.950327240193387 & 0.0993455196132268 & 0.0496727598066134 \tabularnewline
82 & 0.950009769558878 & 0.0999804608822446 & 0.0499902304411223 \tabularnewline
83 & 0.937189377311647 & 0.125621245376707 & 0.0628106226883533 \tabularnewline
84 & 0.924429516664979 & 0.151140966670042 & 0.075570483335021 \tabularnewline
85 & 0.915951672892238 & 0.168096654215523 & 0.0840483271077617 \tabularnewline
86 & 0.900653181134238 & 0.198693637731523 & 0.0993468188657616 \tabularnewline
87 & 0.879710042132296 & 0.240579915735408 & 0.120289957867704 \tabularnewline
88 & 0.923727400022814 & 0.152545199954373 & 0.0762725999771865 \tabularnewline
89 & 0.907794947977105 & 0.184410104045791 & 0.0922050520228953 \tabularnewline
90 & 0.888521028335509 & 0.222957943328981 & 0.111478971664491 \tabularnewline
91 & 0.890348508172257 & 0.219302983655486 & 0.109651491827743 \tabularnewline
92 & 0.87972329404173 & 0.240553411916539 & 0.120276705958270 \tabularnewline
93 & 0.858486388567674 & 0.283027222864653 & 0.141513611432326 \tabularnewline
94 & 0.83180233324056 & 0.33639533351888 & 0.16819766675944 \tabularnewline
95 & 0.799891741151273 & 0.400216517697454 & 0.200108258848727 \tabularnewline
96 & 0.771363589009716 & 0.457272821980569 & 0.228636410990284 \tabularnewline
97 & 0.790445706797612 & 0.419108586404775 & 0.209554293202388 \tabularnewline
98 & 0.785253416561085 & 0.429493166877829 & 0.214746583438914 \tabularnewline
99 & 0.751315699297826 & 0.497368601404348 & 0.248684300702174 \tabularnewline
100 & 0.727772355364808 & 0.544455289270384 & 0.272227644635192 \tabularnewline
101 & 0.686415862962308 & 0.627168274075384 & 0.313584137037692 \tabularnewline
102 & 0.648669584234022 & 0.702660831531957 & 0.351330415765979 \tabularnewline
103 & 0.610688735942807 & 0.778622528114386 & 0.389311264057193 \tabularnewline
104 & 0.568912513183681 & 0.862174973632638 & 0.431087486816319 \tabularnewline
105 & 0.523348261113884 & 0.953303477772232 & 0.476651738886116 \tabularnewline
106 & 0.503744802868189 & 0.992510394263621 & 0.496255197131811 \tabularnewline
107 & 0.50060149680882 & 0.99879700638236 & 0.49939850319118 \tabularnewline
108 & 0.512066888836196 & 0.975866222327608 & 0.487933111163804 \tabularnewline
109 & 0.461970607190603 & 0.923941214381207 & 0.538029392809397 \tabularnewline
110 & 0.438871952745131 & 0.877743905490261 & 0.56112804725487 \tabularnewline
111 & 0.399245111949124 & 0.798490223898247 & 0.600754888050876 \tabularnewline
112 & 0.622464003183592 & 0.755071993632817 & 0.377535996816408 \tabularnewline
113 & 0.6143504316766 & 0.7712991366468 & 0.3856495683234 \tabularnewline
114 & 0.586012234060729 & 0.827975531878541 & 0.413987765939271 \tabularnewline
115 & 0.787995057379244 & 0.424009885241512 & 0.212004942620756 \tabularnewline
116 & 0.766002840747052 & 0.467994318505896 & 0.233997159252948 \tabularnewline
117 & 0.755053282831569 & 0.489893434336861 & 0.244946717168431 \tabularnewline
118 & 0.728310027616057 & 0.543379944767885 & 0.271689972383943 \tabularnewline
119 & 0.680521723246862 & 0.638956553506276 & 0.319478276753138 \tabularnewline
120 & 0.68982168695242 & 0.620356626095161 & 0.310178313047580 \tabularnewline
121 & 0.741250456736066 & 0.517499086527867 & 0.258749543263934 \tabularnewline
122 & 0.746311092056203 & 0.507377815887594 & 0.253688907943797 \tabularnewline
123 & 0.752066455515674 & 0.495867088968652 & 0.247933544484326 \tabularnewline
124 & 0.702722865924085 & 0.59455426815183 & 0.297277134075915 \tabularnewline
125 & 0.746671936072012 & 0.506656127855976 & 0.253328063927988 \tabularnewline
126 & 0.719710175859793 & 0.560579648280414 & 0.280289824140207 \tabularnewline
127 & 0.709720699392158 & 0.580558601215685 & 0.290279300607842 \tabularnewline
128 & 0.676029679145159 & 0.647940641709683 & 0.323970320854841 \tabularnewline
129 & 0.650880955155708 & 0.698238089688584 & 0.349119044844292 \tabularnewline
130 & 0.72046223026605 & 0.559075539467901 & 0.279537769733951 \tabularnewline
131 & 0.666986057099335 & 0.66602788580133 & 0.333013942900665 \tabularnewline
132 & 0.606230214360546 & 0.787539571278909 & 0.393769785639454 \tabularnewline
133 & 0.605895172509634 & 0.788209654980733 & 0.394104827490366 \tabularnewline
134 & 0.546964777920312 & 0.906070444159377 & 0.453035222079688 \tabularnewline
135 & 0.506212479145598 & 0.987575041708804 & 0.493787520854402 \tabularnewline
136 & 0.478869778858994 & 0.957739557717987 & 0.521130221141006 \tabularnewline
137 & 0.485143350871994 & 0.970286701743987 & 0.514856649128006 \tabularnewline
138 & 0.528733486885148 & 0.942533026229703 & 0.471266513114852 \tabularnewline
139 & 0.46164830027676 & 0.92329660055352 & 0.53835169972324 \tabularnewline
140 & 0.387508920972159 & 0.775017841944317 & 0.612491079027841 \tabularnewline
141 & 0.500398992333827 & 0.999202015332346 & 0.499601007666173 \tabularnewline
142 & 0.447731430106022 & 0.895462860212043 & 0.552268569893978 \tabularnewline
143 & 0.377989760724786 & 0.755979521449573 & 0.622010239275214 \tabularnewline
144 & 0.298009846375372 & 0.596019692750744 & 0.701990153624628 \tabularnewline
145 & 0.355263275293376 & 0.710526550586753 & 0.644736724706624 \tabularnewline
146 & 0.262950876609858 & 0.525901753219716 & 0.737049123390142 \tabularnewline
147 & 0.34057333573603 & 0.68114667147206 & 0.65942666426397 \tabularnewline
148 & 0.234921271540772 & 0.469842543081544 & 0.765078728459228 \tabularnewline
149 & 0.146077973504985 & 0.292155947009970 & 0.853922026495015 \tabularnewline
150 & 0.0919194134704895 & 0.183838826940979 & 0.90808058652951 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104299&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]9[/C][C]0.211396023302327[/C][C]0.422792046604654[/C][C]0.788603976697673[/C][/ROW]
[ROW][C]10[/C][C]0.102076375342587[/C][C]0.204152750685175[/C][C]0.897923624657412[/C][/ROW]
[ROW][C]11[/C][C]0.227238038192076[/C][C]0.454476076384151[/C][C]0.772761961807924[/C][/ROW]
[ROW][C]12[/C][C]0.651688236191951[/C][C]0.696623527616098[/C][C]0.348311763808049[/C][/ROW]
[ROW][C]13[/C][C]0.667307232542331[/C][C]0.665385534915338[/C][C]0.332692767457669[/C][/ROW]
[ROW][C]14[/C][C]0.642015414981076[/C][C]0.715969170037848[/C][C]0.357984585018924[/C][/ROW]
[ROW][C]15[/C][C]0.647421329942229[/C][C]0.705157340115542[/C][C]0.352578670057771[/C][/ROW]
[ROW][C]16[/C][C]0.582007170180210[/C][C]0.835985659639581[/C][C]0.417992829819790[/C][/ROW]
[ROW][C]17[/C][C]0.497441679833795[/C][C]0.99488335966759[/C][C]0.502558320166205[/C][/ROW]
[ROW][C]18[/C][C]0.466853717517482[/C][C]0.933707435034965[/C][C]0.533146282482518[/C][/ROW]
[ROW][C]19[/C][C]0.403185159454761[/C][C]0.806370318909522[/C][C]0.596814840545239[/C][/ROW]
[ROW][C]20[/C][C]0.437609217255157[/C][C]0.875218434510313[/C][C]0.562390782744844[/C][/ROW]
[ROW][C]21[/C][C]0.396901525097772[/C][C]0.793803050195544[/C][C]0.603098474902228[/C][/ROW]
[ROW][C]22[/C][C]0.400777969653792[/C][C]0.801555939307584[/C][C]0.599222030346208[/C][/ROW]
[ROW][C]23[/C][C]0.338769951631692[/C][C]0.677539903263384[/C][C]0.661230048368308[/C][/ROW]
[ROW][C]24[/C][C]0.603341567728287[/C][C]0.793316864543426[/C][C]0.396658432271713[/C][/ROW]
[ROW][C]25[/C][C]0.533551201044859[/C][C]0.932897597910282[/C][C]0.466448798955141[/C][/ROW]
[ROW][C]26[/C][C]0.503544024150426[/C][C]0.992911951699149[/C][C]0.496455975849574[/C][/ROW]
[ROW][C]27[/C][C]0.441730998369871[/C][C]0.883461996739741[/C][C]0.558269001630129[/C][/ROW]
[ROW][C]28[/C][C]0.395183167548784[/C][C]0.790366335097568[/C][C]0.604816832451216[/C][/ROW]
[ROW][C]29[/C][C]0.334977520412383[/C][C]0.669955040824767[/C][C]0.665022479587617[/C][/ROW]
[ROW][C]30[/C][C]0.282918967778748[/C][C]0.565837935557497[/C][C]0.717081032221252[/C][/ROW]
[ROW][C]31[/C][C]0.342901815128518[/C][C]0.685803630257036[/C][C]0.657098184871482[/C][/ROW]
[ROW][C]32[/C][C]0.404232070072038[/C][C]0.808464140144076[/C][C]0.595767929927962[/C][/ROW]
[ROW][C]33[/C][C]0.360398880532785[/C][C]0.720797761065569[/C][C]0.639601119467215[/C][/ROW]
[ROW][C]34[/C][C]0.370631372835848[/C][C]0.741262745671696[/C][C]0.629368627164152[/C][/ROW]
[ROW][C]35[/C][C]0.385307154342616[/C][C]0.770614308685231[/C][C]0.614692845657384[/C][/ROW]
[ROW][C]36[/C][C]0.375949698359977[/C][C]0.751899396719955[/C][C]0.624050301640023[/C][/ROW]
[ROW][C]37[/C][C]0.554989703390058[/C][C]0.890020593219883[/C][C]0.445010296609942[/C][/ROW]
[ROW][C]38[/C][C]0.758465746223827[/C][C]0.483068507552345[/C][C]0.241534253776173[/C][/ROW]
[ROW][C]39[/C][C]0.754517941616512[/C][C]0.490964116766976[/C][C]0.245482058383488[/C][/ROW]
[ROW][C]40[/C][C]0.712620047429135[/C][C]0.57475990514173[/C][C]0.287379952570865[/C][/ROW]
[ROW][C]41[/C][C]0.687613723321828[/C][C]0.624772553356344[/C][C]0.312386276678172[/C][/ROW]
[ROW][C]42[/C][C]0.638173577653738[/C][C]0.723652844692525[/C][C]0.361826422346262[/C][/ROW]
[ROW][C]43[/C][C]0.589575343770856[/C][C]0.820849312458289[/C][C]0.410424656229145[/C][/ROW]
[ROW][C]44[/C][C]0.576187266799629[/C][C]0.847625466400742[/C][C]0.423812733200371[/C][/ROW]
[ROW][C]45[/C][C]0.541737547165999[/C][C]0.916524905668002[/C][C]0.458262452834001[/C][/ROW]
[ROW][C]46[/C][C]0.56383048884673[/C][C]0.87233902230654[/C][C]0.43616951115327[/C][/ROW]
[ROW][C]47[/C][C]0.52281042816352[/C][C]0.95437914367296[/C][C]0.47718957183648[/C][/ROW]
[ROW][C]48[/C][C]0.51322633604015[/C][C]0.97354732791970[/C][C]0.48677366395985[/C][/ROW]
[ROW][C]49[/C][C]0.578119098257291[/C][C]0.843761803485418[/C][C]0.421880901742709[/C][/ROW]
[ROW][C]50[/C][C]0.557204565171353[/C][C]0.885590869657295[/C][C]0.442795434828647[/C][/ROW]
[ROW][C]51[/C][C]0.521832610252997[/C][C]0.956334779494006[/C][C]0.478167389747003[/C][/ROW]
[ROW][C]52[/C][C]0.472518698692183[/C][C]0.945037397384367[/C][C]0.527481301307817[/C][/ROW]
[ROW][C]53[/C][C]0.434388024384905[/C][C]0.86877604876981[/C][C]0.565611975615095[/C][/ROW]
[ROW][C]54[/C][C]0.387597441853448[/C][C]0.775194883706896[/C][C]0.612402558146552[/C][/ROW]
[ROW][C]55[/C][C]0.344799433006013[/C][C]0.689598866012026[/C][C]0.655200566993987[/C][/ROW]
[ROW][C]56[/C][C]0.31767947265381[/C][C]0.63535894530762[/C][C]0.68232052734619[/C][/ROW]
[ROW][C]57[/C][C]0.274752158603492[/C][C]0.549504317206984[/C][C]0.725247841396508[/C][/ROW]
[ROW][C]58[/C][C]0.251105924453974[/C][C]0.502211848907948[/C][C]0.748894075546026[/C][/ROW]
[ROW][C]59[/C][C]0.231434319721369[/C][C]0.462868639442737[/C][C]0.768565680278631[/C][/ROW]
[ROW][C]60[/C][C]0.290144078219361[/C][C]0.580288156438722[/C][C]0.709855921780639[/C][/ROW]
[ROW][C]61[/C][C]0.278497186524718[/C][C]0.556994373049435[/C][C]0.721502813475282[/C][/ROW]
[ROW][C]62[/C][C]0.241248961424152[/C][C]0.482497922848305[/C][C]0.758751038575848[/C][/ROW]
[ROW][C]63[/C][C]0.228675123650197[/C][C]0.457350247300394[/C][C]0.771324876349803[/C][/ROW]
[ROW][C]64[/C][C]0.262536263897204[/C][C]0.525072527794407[/C][C]0.737463736102796[/C][/ROW]
[ROW][C]65[/C][C]0.338838414452533[/C][C]0.677676828905067[/C][C]0.661161585547467[/C][/ROW]
[ROW][C]66[/C][C]0.341559278538766[/C][C]0.683118557077533[/C][C]0.658440721461234[/C][/ROW]
[ROW][C]67[/C][C]0.674526217673852[/C][C]0.650947564652297[/C][C]0.325473782326148[/C][/ROW]
[ROW][C]68[/C][C]0.667298147675608[/C][C]0.665403704648785[/C][C]0.332701852324392[/C][/ROW]
[ROW][C]69[/C][C]0.844206840041378[/C][C]0.311586319917243[/C][C]0.155793159958622[/C][/ROW]
[ROW][C]70[/C][C]0.824478189179527[/C][C]0.351043621640947[/C][C]0.175521810820473[/C][/ROW]
[ROW][C]71[/C][C]0.930528498621507[/C][C]0.138943002756986[/C][C]0.0694715013784930[/C][/ROW]
[ROW][C]72[/C][C]0.914237531913636[/C][C]0.171524936172729[/C][C]0.0857624680863643[/C][/ROW]
[ROW][C]73[/C][C]0.937422576584964[/C][C]0.125154846830073[/C][C]0.0625774234150364[/C][/ROW]
[ROW][C]74[/C][C]0.92557432123351[/C][C]0.148851357532979[/C][C]0.0744256787664895[/C][/ROW]
[ROW][C]75[/C][C]0.927322042553002[/C][C]0.145355914893997[/C][C]0.0726779574469983[/C][/ROW]
[ROW][C]76[/C][C]0.921002404447875[/C][C]0.15799519110425[/C][C]0.078997595552125[/C][/ROW]
[ROW][C]77[/C][C]0.97009612299005[/C][C]0.0598077540198994[/C][C]0.0299038770099497[/C][/ROW]
[ROW][C]78[/C][C]0.962737123434993[/C][C]0.074525753130014[/C][C]0.037262876565007[/C][/ROW]
[ROW][C]79[/C][C]0.955322112720419[/C][C]0.0893557745591622[/C][C]0.0446778872795811[/C][/ROW]
[ROW][C]80[/C][C]0.958001069235287[/C][C]0.0839978615294254[/C][C]0.0419989307647127[/C][/ROW]
[ROW][C]81[/C][C]0.950327240193387[/C][C]0.0993455196132268[/C][C]0.0496727598066134[/C][/ROW]
[ROW][C]82[/C][C]0.950009769558878[/C][C]0.0999804608822446[/C][C]0.0499902304411223[/C][/ROW]
[ROW][C]83[/C][C]0.937189377311647[/C][C]0.125621245376707[/C][C]0.0628106226883533[/C][/ROW]
[ROW][C]84[/C][C]0.924429516664979[/C][C]0.151140966670042[/C][C]0.075570483335021[/C][/ROW]
[ROW][C]85[/C][C]0.915951672892238[/C][C]0.168096654215523[/C][C]0.0840483271077617[/C][/ROW]
[ROW][C]86[/C][C]0.900653181134238[/C][C]0.198693637731523[/C][C]0.0993468188657616[/C][/ROW]
[ROW][C]87[/C][C]0.879710042132296[/C][C]0.240579915735408[/C][C]0.120289957867704[/C][/ROW]
[ROW][C]88[/C][C]0.923727400022814[/C][C]0.152545199954373[/C][C]0.0762725999771865[/C][/ROW]
[ROW][C]89[/C][C]0.907794947977105[/C][C]0.184410104045791[/C][C]0.0922050520228953[/C][/ROW]
[ROW][C]90[/C][C]0.888521028335509[/C][C]0.222957943328981[/C][C]0.111478971664491[/C][/ROW]
[ROW][C]91[/C][C]0.890348508172257[/C][C]0.219302983655486[/C][C]0.109651491827743[/C][/ROW]
[ROW][C]92[/C][C]0.87972329404173[/C][C]0.240553411916539[/C][C]0.120276705958270[/C][/ROW]
[ROW][C]93[/C][C]0.858486388567674[/C][C]0.283027222864653[/C][C]0.141513611432326[/C][/ROW]
[ROW][C]94[/C][C]0.83180233324056[/C][C]0.33639533351888[/C][C]0.16819766675944[/C][/ROW]
[ROW][C]95[/C][C]0.799891741151273[/C][C]0.400216517697454[/C][C]0.200108258848727[/C][/ROW]
[ROW][C]96[/C][C]0.771363589009716[/C][C]0.457272821980569[/C][C]0.228636410990284[/C][/ROW]
[ROW][C]97[/C][C]0.790445706797612[/C][C]0.419108586404775[/C][C]0.209554293202388[/C][/ROW]
[ROW][C]98[/C][C]0.785253416561085[/C][C]0.429493166877829[/C][C]0.214746583438914[/C][/ROW]
[ROW][C]99[/C][C]0.751315699297826[/C][C]0.497368601404348[/C][C]0.248684300702174[/C][/ROW]
[ROW][C]100[/C][C]0.727772355364808[/C][C]0.544455289270384[/C][C]0.272227644635192[/C][/ROW]
[ROW][C]101[/C][C]0.686415862962308[/C][C]0.627168274075384[/C][C]0.313584137037692[/C][/ROW]
[ROW][C]102[/C][C]0.648669584234022[/C][C]0.702660831531957[/C][C]0.351330415765979[/C][/ROW]
[ROW][C]103[/C][C]0.610688735942807[/C][C]0.778622528114386[/C][C]0.389311264057193[/C][/ROW]
[ROW][C]104[/C][C]0.568912513183681[/C][C]0.862174973632638[/C][C]0.431087486816319[/C][/ROW]
[ROW][C]105[/C][C]0.523348261113884[/C][C]0.953303477772232[/C][C]0.476651738886116[/C][/ROW]
[ROW][C]106[/C][C]0.503744802868189[/C][C]0.992510394263621[/C][C]0.496255197131811[/C][/ROW]
[ROW][C]107[/C][C]0.50060149680882[/C][C]0.99879700638236[/C][C]0.49939850319118[/C][/ROW]
[ROW][C]108[/C][C]0.512066888836196[/C][C]0.975866222327608[/C][C]0.487933111163804[/C][/ROW]
[ROW][C]109[/C][C]0.461970607190603[/C][C]0.923941214381207[/C][C]0.538029392809397[/C][/ROW]
[ROW][C]110[/C][C]0.438871952745131[/C][C]0.877743905490261[/C][C]0.56112804725487[/C][/ROW]
[ROW][C]111[/C][C]0.399245111949124[/C][C]0.798490223898247[/C][C]0.600754888050876[/C][/ROW]
[ROW][C]112[/C][C]0.622464003183592[/C][C]0.755071993632817[/C][C]0.377535996816408[/C][/ROW]
[ROW][C]113[/C][C]0.6143504316766[/C][C]0.7712991366468[/C][C]0.3856495683234[/C][/ROW]
[ROW][C]114[/C][C]0.586012234060729[/C][C]0.827975531878541[/C][C]0.413987765939271[/C][/ROW]
[ROW][C]115[/C][C]0.787995057379244[/C][C]0.424009885241512[/C][C]0.212004942620756[/C][/ROW]
[ROW][C]116[/C][C]0.766002840747052[/C][C]0.467994318505896[/C][C]0.233997159252948[/C][/ROW]
[ROW][C]117[/C][C]0.755053282831569[/C][C]0.489893434336861[/C][C]0.244946717168431[/C][/ROW]
[ROW][C]118[/C][C]0.728310027616057[/C][C]0.543379944767885[/C][C]0.271689972383943[/C][/ROW]
[ROW][C]119[/C][C]0.680521723246862[/C][C]0.638956553506276[/C][C]0.319478276753138[/C][/ROW]
[ROW][C]120[/C][C]0.68982168695242[/C][C]0.620356626095161[/C][C]0.310178313047580[/C][/ROW]
[ROW][C]121[/C][C]0.741250456736066[/C][C]0.517499086527867[/C][C]0.258749543263934[/C][/ROW]
[ROW][C]122[/C][C]0.746311092056203[/C][C]0.507377815887594[/C][C]0.253688907943797[/C][/ROW]
[ROW][C]123[/C][C]0.752066455515674[/C][C]0.495867088968652[/C][C]0.247933544484326[/C][/ROW]
[ROW][C]124[/C][C]0.702722865924085[/C][C]0.59455426815183[/C][C]0.297277134075915[/C][/ROW]
[ROW][C]125[/C][C]0.746671936072012[/C][C]0.506656127855976[/C][C]0.253328063927988[/C][/ROW]
[ROW][C]126[/C][C]0.719710175859793[/C][C]0.560579648280414[/C][C]0.280289824140207[/C][/ROW]
[ROW][C]127[/C][C]0.709720699392158[/C][C]0.580558601215685[/C][C]0.290279300607842[/C][/ROW]
[ROW][C]128[/C][C]0.676029679145159[/C][C]0.647940641709683[/C][C]0.323970320854841[/C][/ROW]
[ROW][C]129[/C][C]0.650880955155708[/C][C]0.698238089688584[/C][C]0.349119044844292[/C][/ROW]
[ROW][C]130[/C][C]0.72046223026605[/C][C]0.559075539467901[/C][C]0.279537769733951[/C][/ROW]
[ROW][C]131[/C][C]0.666986057099335[/C][C]0.66602788580133[/C][C]0.333013942900665[/C][/ROW]
[ROW][C]132[/C][C]0.606230214360546[/C][C]0.787539571278909[/C][C]0.393769785639454[/C][/ROW]
[ROW][C]133[/C][C]0.605895172509634[/C][C]0.788209654980733[/C][C]0.394104827490366[/C][/ROW]
[ROW][C]134[/C][C]0.546964777920312[/C][C]0.906070444159377[/C][C]0.453035222079688[/C][/ROW]
[ROW][C]135[/C][C]0.506212479145598[/C][C]0.987575041708804[/C][C]0.493787520854402[/C][/ROW]
[ROW][C]136[/C][C]0.478869778858994[/C][C]0.957739557717987[/C][C]0.521130221141006[/C][/ROW]
[ROW][C]137[/C][C]0.485143350871994[/C][C]0.970286701743987[/C][C]0.514856649128006[/C][/ROW]
[ROW][C]138[/C][C]0.528733486885148[/C][C]0.942533026229703[/C][C]0.471266513114852[/C][/ROW]
[ROW][C]139[/C][C]0.46164830027676[/C][C]0.92329660055352[/C][C]0.53835169972324[/C][/ROW]
[ROW][C]140[/C][C]0.387508920972159[/C][C]0.775017841944317[/C][C]0.612491079027841[/C][/ROW]
[ROW][C]141[/C][C]0.500398992333827[/C][C]0.999202015332346[/C][C]0.499601007666173[/C][/ROW]
[ROW][C]142[/C][C]0.447731430106022[/C][C]0.895462860212043[/C][C]0.552268569893978[/C][/ROW]
[ROW][C]143[/C][C]0.377989760724786[/C][C]0.755979521449573[/C][C]0.622010239275214[/C][/ROW]
[ROW][C]144[/C][C]0.298009846375372[/C][C]0.596019692750744[/C][C]0.701990153624628[/C][/ROW]
[ROW][C]145[/C][C]0.355263275293376[/C][C]0.710526550586753[/C][C]0.644736724706624[/C][/ROW]
[ROW][C]146[/C][C]0.262950876609858[/C][C]0.525901753219716[/C][C]0.737049123390142[/C][/ROW]
[ROW][C]147[/C][C]0.34057333573603[/C][C]0.68114667147206[/C][C]0.65942666426397[/C][/ROW]
[ROW][C]148[/C][C]0.234921271540772[/C][C]0.469842543081544[/C][C]0.765078728459228[/C][/ROW]
[ROW][C]149[/C][C]0.146077973504985[/C][C]0.292155947009970[/C][C]0.853922026495015[/C][/ROW]
[ROW][C]150[/C][C]0.0919194134704895[/C][C]0.183838826940979[/C][C]0.90808058652951[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104299&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104299&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
90.2113960233023270.4227920466046540.788603976697673
100.1020763753425870.2041527506851750.897923624657412
110.2272380381920760.4544760763841510.772761961807924
120.6516882361919510.6966235276160980.348311763808049
130.6673072325423310.6653855349153380.332692767457669
140.6420154149810760.7159691700378480.357984585018924
150.6474213299422290.7051573401155420.352578670057771
160.5820071701802100.8359856596395810.417992829819790
170.4974416798337950.994883359667590.502558320166205
180.4668537175174820.9337074350349650.533146282482518
190.4031851594547610.8063703189095220.596814840545239
200.4376092172551570.8752184345103130.562390782744844
210.3969015250977720.7938030501955440.603098474902228
220.4007779696537920.8015559393075840.599222030346208
230.3387699516316920.6775399032633840.661230048368308
240.6033415677282870.7933168645434260.396658432271713
250.5335512010448590.9328975979102820.466448798955141
260.5035440241504260.9929119516991490.496455975849574
270.4417309983698710.8834619967397410.558269001630129
280.3951831675487840.7903663350975680.604816832451216
290.3349775204123830.6699550408247670.665022479587617
300.2829189677787480.5658379355574970.717081032221252
310.3429018151285180.6858036302570360.657098184871482
320.4042320700720380.8084641401440760.595767929927962
330.3603988805327850.7207977610655690.639601119467215
340.3706313728358480.7412627456716960.629368627164152
350.3853071543426160.7706143086852310.614692845657384
360.3759496983599770.7518993967199550.624050301640023
370.5549897033900580.8900205932198830.445010296609942
380.7584657462238270.4830685075523450.241534253776173
390.7545179416165120.4909641167669760.245482058383488
400.7126200474291350.574759905141730.287379952570865
410.6876137233218280.6247725533563440.312386276678172
420.6381735776537380.7236528446925250.361826422346262
430.5895753437708560.8208493124582890.410424656229145
440.5761872667996290.8476254664007420.423812733200371
450.5417375471659990.9165249056680020.458262452834001
460.563830488846730.872339022306540.43616951115327
470.522810428163520.954379143672960.47718957183648
480.513226336040150.973547327919700.48677366395985
490.5781190982572910.8437618034854180.421880901742709
500.5572045651713530.8855908696572950.442795434828647
510.5218326102529970.9563347794940060.478167389747003
520.4725186986921830.9450373973843670.527481301307817
530.4343880243849050.868776048769810.565611975615095
540.3875974418534480.7751948837068960.612402558146552
550.3447994330060130.6895988660120260.655200566993987
560.317679472653810.635358945307620.68232052734619
570.2747521586034920.5495043172069840.725247841396508
580.2511059244539740.5022118489079480.748894075546026
590.2314343197213690.4628686394427370.768565680278631
600.2901440782193610.5802881564387220.709855921780639
610.2784971865247180.5569943730494350.721502813475282
620.2412489614241520.4824979228483050.758751038575848
630.2286751236501970.4573502473003940.771324876349803
640.2625362638972040.5250725277944070.737463736102796
650.3388384144525330.6776768289050670.661161585547467
660.3415592785387660.6831185570775330.658440721461234
670.6745262176738520.6509475646522970.325473782326148
680.6672981476756080.6654037046487850.332701852324392
690.8442068400413780.3115863199172430.155793159958622
700.8244781891795270.3510436216409470.175521810820473
710.9305284986215070.1389430027569860.0694715013784930
720.9142375319136360.1715249361727290.0857624680863643
730.9374225765849640.1251548468300730.0625774234150364
740.925574321233510.1488513575329790.0744256787664895
750.9273220425530020.1453559148939970.0726779574469983
760.9210024044478750.157995191104250.078997595552125
770.970096122990050.05980775401989940.0299038770099497
780.9627371234349930.0745257531300140.037262876565007
790.9553221127204190.08935577455916220.0446778872795811
800.9580010692352870.08399786152942540.0419989307647127
810.9503272401933870.09934551961322680.0496727598066134
820.9500097695588780.09998046088224460.0499902304411223
830.9371893773116470.1256212453767070.0628106226883533
840.9244295166649790.1511409666700420.075570483335021
850.9159516728922380.1680966542155230.0840483271077617
860.9006531811342380.1986936377315230.0993468188657616
870.8797100421322960.2405799157354080.120289957867704
880.9237274000228140.1525451999543730.0762725999771865
890.9077949479771050.1844101040457910.0922050520228953
900.8885210283355090.2229579433289810.111478971664491
910.8903485081722570.2193029836554860.109651491827743
920.879723294041730.2405534119165390.120276705958270
930.8584863885676740.2830272228646530.141513611432326
940.831802333240560.336395333518880.16819766675944
950.7998917411512730.4002165176974540.200108258848727
960.7713635890097160.4572728219805690.228636410990284
970.7904457067976120.4191085864047750.209554293202388
980.7852534165610850.4294931668778290.214746583438914
990.7513156992978260.4973686014043480.248684300702174
1000.7277723553648080.5444552892703840.272227644635192
1010.6864158629623080.6271682740753840.313584137037692
1020.6486695842340220.7026608315319570.351330415765979
1030.6106887359428070.7786225281143860.389311264057193
1040.5689125131836810.8621749736326380.431087486816319
1050.5233482611138840.9533034777722320.476651738886116
1060.5037448028681890.9925103942636210.496255197131811
1070.500601496808820.998797006382360.49939850319118
1080.5120668888361960.9758662223276080.487933111163804
1090.4619706071906030.9239412143812070.538029392809397
1100.4388719527451310.8777439054902610.56112804725487
1110.3992451119491240.7984902238982470.600754888050876
1120.6224640031835920.7550719936328170.377535996816408
1130.61435043167660.77129913664680.3856495683234
1140.5860122340607290.8279755318785410.413987765939271
1150.7879950573792440.4240098852415120.212004942620756
1160.7660028407470520.4679943185058960.233997159252948
1170.7550532828315690.4898934343368610.244946717168431
1180.7283100276160570.5433799447678850.271689972383943
1190.6805217232468620.6389565535062760.319478276753138
1200.689821686952420.6203566260951610.310178313047580
1210.7412504567360660.5174990865278670.258749543263934
1220.7463110920562030.5073778158875940.253688907943797
1230.7520664555156740.4958670889686520.247933544484326
1240.7027228659240850.594554268151830.297277134075915
1250.7466719360720120.5066561278559760.253328063927988
1260.7197101758597930.5605796482804140.280289824140207
1270.7097206993921580.5805586012156850.290279300607842
1280.6760296791451590.6479406417096830.323970320854841
1290.6508809551557080.6982380896885840.349119044844292
1300.720462230266050.5590755394679010.279537769733951
1310.6669860570993350.666027885801330.333013942900665
1320.6062302143605460.7875395712789090.393769785639454
1330.6058951725096340.7882096549807330.394104827490366
1340.5469647779203120.9060704441593770.453035222079688
1350.5062124791455980.9875750417088040.493787520854402
1360.4788697788589940.9577395577179870.521130221141006
1370.4851433508719940.9702867017439870.514856649128006
1380.5287334868851480.9425330262297030.471266513114852
1390.461648300276760.923296600553520.53835169972324
1400.3875089209721590.7750178419443170.612491079027841
1410.5003989923338270.9992020153323460.499601007666173
1420.4477314301060220.8954628602120430.552268569893978
1430.3779897607247860.7559795214495730.622010239275214
1440.2980098463753720.5960196927507440.701990153624628
1450.3552632752933760.7105265505867530.644736724706624
1460.2629508766098580.5259017532197160.737049123390142
1470.340573335736030.681146671472060.65942666426397
1480.2349212715407720.4698425430815440.765078728459228
1490.1460779735049850.2921559470099700.853922026495015
1500.09191941347048950.1838388269409790.90808058652951







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level60.0422535211267606OK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 6 & 0.0422535211267606 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104299&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]6[/C][C]0.0422535211267606[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104299&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104299&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 level00OK
5% type I error level00OK
10% type I error level60.0422535211267606OK



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