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Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationMon, 05 Nov 2012 08:07:18 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Nov/05/t1352120882gerjzhlvx816av6.htm/, Retrieved Mon, 06 Feb 2023 00:09:46 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=186030, Retrieved Mon, 06 Feb 2023 00:09:46 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact74
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [] [2012-11-05 13:07:18] [13972f3e090f04e91ee8432db4988af4] [Current]
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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' @ jenkins.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 10 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186030&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' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186030&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186030&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' @ jenkins.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Bezorgdheid_om_fouten[t] = -1.97155708164814 + 0.810124516285502Twijfels_over_acties[t] + 0.251253601241107Verwachtingen_ouders[t] + 0.188518804715737Kritiek_ouders[t] + 0.566064813317673Persoonlijke_normen[t] -0.115718741599822Organisatie_student[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Bezorgdheid_om_fouten[t] =  -1.97155708164814 +  0.810124516285502Twijfels_over_acties[t] +  0.251253601241107Verwachtingen_ouders[t] +  0.188518804715737Kritiek_ouders[t] +  0.566064813317673Persoonlijke_normen[t] -0.115718741599822Organisatie_student[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186030&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Bezorgdheid_om_fouten[t] =  -1.97155708164814 +  0.810124516285502Twijfels_over_acties[t] +  0.251253601241107Verwachtingen_ouders[t] +  0.188518804715737Kritiek_ouders[t] +  0.566064813317673Persoonlijke_normen[t] -0.115718741599822Organisatie_student[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186030&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186030&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
Bezorgdheid_om_fouten[t] = -1.97155708164814 + 0.810124516285502Twijfels_over_acties[t] + 0.251253601241107Verwachtingen_ouders[t] + 0.188518804715737Kritiek_ouders[t] + 0.566064813317673Persoonlijke_normen[t] -0.115718741599822Organisatie_student[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-1.971557081648143.052906-0.64580.5193780.259689
Twijfels_over_acties0.8101245162855020.1303356.215700
Verwachtingen_ouders0.2512536012411070.132761.89250.0603070.030154
Kritiek_ouders0.1885188047157370.1682591.12040.2642940.132147
Persoonlijke_normen0.5660648133176730.0958135.90800
Organisatie_student-0.1157187415998220.103024-1.12320.2631030.131551

\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) & -1.97155708164814 & 3.052906 & -0.6458 & 0.519378 & 0.259689 \tabularnewline
Twijfels_over_acties & 0.810124516285502 & 0.130335 & 6.2157 & 0 & 0 \tabularnewline
Verwachtingen_ouders & 0.251253601241107 & 0.13276 & 1.8925 & 0.060307 & 0.030154 \tabularnewline
Kritiek_ouders & 0.188518804715737 & 0.168259 & 1.1204 & 0.264294 & 0.132147 \tabularnewline
Persoonlijke_normen & 0.566064813317673 & 0.095813 & 5.908 & 0 & 0 \tabularnewline
Organisatie_student & -0.115718741599822 & 0.103024 & -1.1232 & 0.263103 & 0.131551 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186030&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]-1.97155708164814[/C][C]3.052906[/C][C]-0.6458[/C][C]0.519378[/C][C]0.259689[/C][/ROW]
[ROW][C]Twijfels_over_acties[/C][C]0.810124516285502[/C][C]0.130335[/C][C]6.2157[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Verwachtingen_ouders[/C][C]0.251253601241107[/C][C]0.13276[/C][C]1.8925[/C][C]0.060307[/C][C]0.030154[/C][/ROW]
[ROW][C]Kritiek_ouders[/C][C]0.188518804715737[/C][C]0.168259[/C][C]1.1204[/C][C]0.264294[/C][C]0.132147[/C][/ROW]
[ROW][C]Persoonlijke_normen[/C][C]0.566064813317673[/C][C]0.095813[/C][C]5.908[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Organisatie_student[/C][C]-0.115718741599822[/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=186030&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186030&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)-1.971557081648143.052906-0.64580.5193780.259689
Twijfels_over_acties0.8101245162855020.1303356.215700
Verwachtingen_ouders0.2512536012411070.132761.89250.0603070.030154
Kritiek_ouders0.1885188047157370.1682591.12040.2642940.132147
Persoonlijke_normen0.5660648133176730.0958135.90800
Organisatie_student-0.1157187415998220.103024-1.12320.2631030.131551







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-value5.55111512312578e-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 & 5.55111512312578e-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=186030&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]5.55111512312578e-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=186030&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186030&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-value5.55111512312578e-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
12424.9730696546186-0.973069654618644
22521.69682752005193.30317247994805
31722.7576275344242-5.75762753442423
41819.8643239601547-1.86432396015473
51819.475921308563-1.47592130856298
61619.5619453157043-3.56194531570428
72020.9062047066683-0.906204706668275
81622.3667061957279-6.36670619572788
91822.4023995380277-4.40239953802769
101720.5201335863125-3.52013358631251
112322.31781935866780.682180641332218
123022.3274768850327.67252311496805
132315.10357659401447.89642340598557
141819.0918794530003-1.09187945300029
151521.6854310399808-6.68543103998085
161217.8354454157556-5.83544541575557
172119.41656627952531.58343372047466
181515.3176615872635-0.317661587263506
192019.77655799733790.223442002662073
203126.42032009363084.57967990636915
212725.23016665143841.76983334856159
223427.15969310080766.84030689919239
232119.6129963230411.38700367695902
243120.918508179408110.0814918205919
251919.2929241504289-0.292924150428892
261620.2171532176049-4.21715321760486
272021.5938509463785-1.5938509463785
282118.26051127813752.73948872186254
292221.9603148900490.0396851099510344
301719.5088680455431-2.50886804554309
312420.31786014721823.68213985278175
322530.4697362245754-5.46973622457538
332626.7379136808267-0.737913680826712
342524.06521938270930.934780617290695
351723.0090707878074-6.00907078780739
363227.79002272300144.20997727699858
373323.63621029465559.36378970534447
381322.0253619285962-9.02536192859621
393227.83873005040544.16126994959459
402525.8389604910979-0.838960491097935
412927.0240619141111.97593808588899
422222.0238725008844-0.0238725008844256
431817.36433131679980.635668683200175
441721.7723044845037-4.77230448450369
452022.2065286208074-2.20652862080744
461520.4502048915139-5.45020489151386
472022.1848414522041-2.18484145220414
483328.09940561358314.90059438641692
492922.1429710099726.85702899002803
502326.6826557598034-3.68265575980342
512623.24301931189912.75698068810088
521818.952868498816-0.952868498815959
532018.73405323538451.2659467646155
541111.7902591765424-0.790259176542405
552828.9774711402709-0.977471140270937
562623.31296729591192.68703270408813
572222.2461608568904-0.24616085689036
581720.1978454987339-3.19784549873386
591215.4254660880949-3.42546608809486
601421.0281654373449-7.02816543734494
611720.7238630018849-3.72386300188489
622121.3354034468641-0.335403446864113
631922.9325499189927-3.93254991899267
641823.0750654036623-5.0750654036623
651017.9297721146776-7.92977211467762
662924.22013599709424.77986400290577
673118.45755519460712.542444805393
681922.9476672047626-3.94766720476257
69920.0659733838091-11.0659733838091
702022.4704044418167-2.47040444181673
712817.619597847696710.3804021523033
721918.12630763197670.873692368023276
733023.06009950396146.93990049603859
742927.1345419863581.86545801364201
752621.47813220477874.52186779522132
762319.54408442658333.45591557341674
771322.7619883304533-9.76198833045328
782122.6403328681881-1.64033286818809
791921.4342707638265-2.43427076382648
802822.94303025966695.05696974033307
812325.5488853193873-2.54888531938728
821813.7755446122484.22445538775199
832120.73380350547790.266196494522131
842021.7890552501971-1.78905525019705
852319.9536344096973.04636559030299
862120.8235483183180.176451681682022
872121.7765070663015-0.776507066301509
881522.8032661951564-7.80326619515638
892827.1471956440370.852804355963
901917.65737118191451.3426288180855
912621.2417390294554.75826097054501
921013.1981887926427-3.1981887926427
931617.1052598018456-1.10525980184556
942221.16865598911030.831344010889731
951918.73149628220690.26850371779314
963128.77362610842912.22637389157087
973125.19904737841265.8009526215874
982924.8021196150674.19788038493298
991917.41331147894671.58668852105333
1002218.89464588438873.10535411561134
1012322.37307727969110.62692272030893
1021516.1706084549776-1.1706084549776
1032021.4483664364161-1.44836643641609
1041819.5049798788849-1.5049798788849
1052321.98461656711711.01538343288295
1062520.96389048598294.03610951401714
1072116.56355472657824.43644527342176
1082419.49049875725164.5095012427484
1092525.2360293362311-0.23602933623114
1101719.5772208157054-2.57722081570542
1111314.5703651925901-1.57036519259012
1122818.20363916640489.79636083359519
1132120.11560899930670.88439100069334
1142528.140636065485-3.14063606548499
115920.7272742072835-11.7272742072834
1161617.8284411696244-1.82844116962435
1171921.2002096254339-2.20020962543391
1181719.4548480129136-2.45484801291361
1192524.51183550200020.488164497999802
1202015.55841555861624.44158444138375
1212921.62453585610657.37546414389351
1221419.0460894061991-5.04608940619911
1232226.9144076581427-4.9144076581427
1241515.5764012107348-0.576401210734847
1251925.4228566000411-6.42285660004114
1262021.9451042791923-1.94510427919232
1271517.452226374503-2.45222637450297
1282021.7886208868992-1.78862088689923
1291820.2225815390998-2.22258153909977
1303325.46684280399097.5331571960091
1312223.7824344363272-1.78243443632724
1321616.4697006570271-0.46970065702706
1331719.0367506268633-2.03675062686334
1341615.0524322615910.947567738409009
1352117.04838769011293.95161230988708
1362627.6421945331063-1.64219453310631
1371821.1470742942904-3.14707429429044
1381823.0959206112274-5.09592061122736
1391718.3312627872462-1.3312627872462
1402224.7434940752527-2.74349407525273
1413024.72756612386955.27243387613054
1423027.27399645056832.72600354943171
1432429.9008223341231-5.90082233412311
1442121.9917911761762-0.991791176176224
1452125.3787580941457-4.3787580941457
1462927.39183778204791.60816221795207
1473123.2405082710077.75949172899297
1482018.97597105925841.0240289407416
1491614.18758051463531.81241948536466
1502218.91909894752583.08090105247424
1512020.3316462195076-0.331646219507597
1522827.32694895773190.673051042268083
1533826.595805936383511.4041940636165
1542219.33483336442922.66516663557078
1552025.6868097712468-5.68680977124681
1561718.0421110887897-1.04211108878971
1572824.45043191895543.54956808104462
1582224.1216672736301-2.12166727363008
1593126.08928147235674.91071852764329

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 24 & 24.9730696546186 & -0.973069654618644 \tabularnewline
2 & 25 & 21.6968275200519 & 3.30317247994805 \tabularnewline
3 & 17 & 22.7576275344242 & -5.75762753442423 \tabularnewline
4 & 18 & 19.8643239601547 & -1.86432396015473 \tabularnewline
5 & 18 & 19.475921308563 & -1.47592130856298 \tabularnewline
6 & 16 & 19.5619453157043 & -3.56194531570428 \tabularnewline
7 & 20 & 20.9062047066683 & -0.906204706668275 \tabularnewline
8 & 16 & 22.3667061957279 & -6.36670619572788 \tabularnewline
9 & 18 & 22.4023995380277 & -4.40239953802769 \tabularnewline
10 & 17 & 20.5201335863125 & -3.52013358631251 \tabularnewline
11 & 23 & 22.3178193586678 & 0.682180641332218 \tabularnewline
12 & 30 & 22.327476885032 & 7.67252311496805 \tabularnewline
13 & 23 & 15.1035765940144 & 7.89642340598557 \tabularnewline
14 & 18 & 19.0918794530003 & -1.09187945300029 \tabularnewline
15 & 15 & 21.6854310399808 & -6.68543103998085 \tabularnewline
16 & 12 & 17.8354454157556 & -5.83544541575557 \tabularnewline
17 & 21 & 19.4165662795253 & 1.58343372047466 \tabularnewline
18 & 15 & 15.3176615872635 & -0.317661587263506 \tabularnewline
19 & 20 & 19.7765579973379 & 0.223442002662073 \tabularnewline
20 & 31 & 26.4203200936308 & 4.57967990636915 \tabularnewline
21 & 27 & 25.2301666514384 & 1.76983334856159 \tabularnewline
22 & 34 & 27.1596931008076 & 6.84030689919239 \tabularnewline
23 & 21 & 19.612996323041 & 1.38700367695902 \tabularnewline
24 & 31 & 20.9185081794081 & 10.0814918205919 \tabularnewline
25 & 19 & 19.2929241504289 & -0.292924150428892 \tabularnewline
26 & 16 & 20.2171532176049 & -4.21715321760486 \tabularnewline
27 & 20 & 21.5938509463785 & -1.5938509463785 \tabularnewline
28 & 21 & 18.2605112781375 & 2.73948872186254 \tabularnewline
29 & 22 & 21.960314890049 & 0.0396851099510344 \tabularnewline
30 & 17 & 19.5088680455431 & -2.50886804554309 \tabularnewline
31 & 24 & 20.3178601472182 & 3.68213985278175 \tabularnewline
32 & 25 & 30.4697362245754 & -5.46973622457538 \tabularnewline
33 & 26 & 26.7379136808267 & -0.737913680826712 \tabularnewline
34 & 25 & 24.0652193827093 & 0.934780617290695 \tabularnewline
35 & 17 & 23.0090707878074 & -6.00907078780739 \tabularnewline
36 & 32 & 27.7900227230014 & 4.20997727699858 \tabularnewline
37 & 33 & 23.6362102946555 & 9.36378970534447 \tabularnewline
38 & 13 & 22.0253619285962 & -9.02536192859621 \tabularnewline
39 & 32 & 27.8387300504054 & 4.16126994959459 \tabularnewline
40 & 25 & 25.8389604910979 & -0.838960491097935 \tabularnewline
41 & 29 & 27.024061914111 & 1.97593808588899 \tabularnewline
42 & 22 & 22.0238725008844 & -0.0238725008844256 \tabularnewline
43 & 18 & 17.3643313167998 & 0.635668683200175 \tabularnewline
44 & 17 & 21.7723044845037 & -4.77230448450369 \tabularnewline
45 & 20 & 22.2065286208074 & -2.20652862080744 \tabularnewline
46 & 15 & 20.4502048915139 & -5.45020489151386 \tabularnewline
47 & 20 & 22.1848414522041 & -2.18484145220414 \tabularnewline
48 & 33 & 28.0994056135831 & 4.90059438641692 \tabularnewline
49 & 29 & 22.142971009972 & 6.85702899002803 \tabularnewline
50 & 23 & 26.6826557598034 & -3.68265575980342 \tabularnewline
51 & 26 & 23.2430193118991 & 2.75698068810088 \tabularnewline
52 & 18 & 18.952868498816 & -0.952868498815959 \tabularnewline
53 & 20 & 18.7340532353845 & 1.2659467646155 \tabularnewline
54 & 11 & 11.7902591765424 & -0.790259176542405 \tabularnewline
55 & 28 & 28.9774711402709 & -0.977471140270937 \tabularnewline
56 & 26 & 23.3129672959119 & 2.68703270408813 \tabularnewline
57 & 22 & 22.2461608568904 & -0.24616085689036 \tabularnewline
58 & 17 & 20.1978454987339 & -3.19784549873386 \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 & -0.335403446864113 \tabularnewline
63 & 19 & 22.9325499189927 & -3.93254991899267 \tabularnewline
64 & 18 & 23.0750654036623 & -5.0750654036623 \tabularnewline
65 & 10 & 17.9297721146776 & -7.92977211467762 \tabularnewline
66 & 29 & 24.2201359970942 & 4.77986400290577 \tabularnewline
67 & 31 & 18.457555194607 & 12.542444805393 \tabularnewline
68 & 19 & 22.9476672047626 & -3.94766720476257 \tabularnewline
69 & 9 & 20.0659733838091 & -11.0659733838091 \tabularnewline
70 & 20 & 22.4704044418167 & -2.47040444181673 \tabularnewline
71 & 28 & 17.6195978476967 & 10.3804021523033 \tabularnewline
72 & 19 & 18.1263076319767 & 0.873692368023276 \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 & 3.45591557341674 \tabularnewline
77 & 13 & 22.7619883304533 & -9.76198833045328 \tabularnewline
78 & 21 & 22.6403328681881 & -1.64033286818809 \tabularnewline
79 & 19 & 21.4342707638265 & -2.43427076382648 \tabularnewline
80 & 28 & 22.9430302596669 & 5.05696974033307 \tabularnewline
81 & 23 & 25.5488853193873 & -2.54888531938728 \tabularnewline
82 & 18 & 13.775544612248 & 4.22445538775199 \tabularnewline
83 & 21 & 20.7338035054779 & 0.266196494522131 \tabularnewline
84 & 20 & 21.7890552501971 & -1.78905525019705 \tabularnewline
85 & 23 & 19.953634409697 & 3.04636559030299 \tabularnewline
86 & 21 & 20.823548318318 & 0.176451681682022 \tabularnewline
87 & 21 & 21.7765070663015 & -0.776507066301509 \tabularnewline
88 & 15 & 22.8032661951564 & -7.80326619515638 \tabularnewline
89 & 28 & 27.147195644037 & 0.852804355963 \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 & 0.831344010889731 \tabularnewline
95 & 19 & 18.7314962822069 & 0.26850371779314 \tabularnewline
96 & 31 & 28.7736261084291 & 2.22637389157087 \tabularnewline
97 & 31 & 25.1990473784126 & 5.8009526215874 \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 & 0.62692272030893 \tabularnewline
102 & 15 & 16.1706084549776 & -1.1706084549776 \tabularnewline
103 & 20 & 21.4483664364161 & -1.44836643641609 \tabularnewline
104 & 18 & 19.5049798788849 & -1.5049798788849 \tabularnewline
105 & 23 & 21.9846165671171 & 1.01538343288295 \tabularnewline
106 & 25 & 20.9638904859829 & 4.03610951401714 \tabularnewline
107 & 21 & 16.5635547265782 & 4.43644527342176 \tabularnewline
108 & 24 & 19.4904987572516 & 4.5095012427484 \tabularnewline
109 & 25 & 25.2360293362311 & -0.23602933623114 \tabularnewline
110 & 17 & 19.5772208157054 & -2.57722081570542 \tabularnewline
111 & 13 & 14.5703651925901 & -1.57036519259012 \tabularnewline
112 & 28 & 18.2036391664048 & 9.79636083359519 \tabularnewline
113 & 21 & 20.1156089993067 & 0.88439100069334 \tabularnewline
114 & 25 & 28.140636065485 & -3.14063606548499 \tabularnewline
115 & 9 & 20.7272742072835 & -11.7272742072834 \tabularnewline
116 & 16 & 17.8284411696244 & -1.82844116962435 \tabularnewline
117 & 19 & 21.2002096254339 & -2.20020962543391 \tabularnewline
118 & 17 & 19.4548480129136 & -2.45484801291361 \tabularnewline
119 & 25 & 24.5118355020002 & 0.488164497999802 \tabularnewline
120 & 20 & 15.5584155586162 & 4.44158444138375 \tabularnewline
121 & 29 & 21.6245358561065 & 7.37546414389351 \tabularnewline
122 & 14 & 19.0460894061991 & -5.04608940619911 \tabularnewline
123 & 22 & 26.9144076581427 & -4.9144076581427 \tabularnewline
124 & 15 & 15.5764012107348 & -0.576401210734847 \tabularnewline
125 & 19 & 25.4228566000411 & -6.42285660004114 \tabularnewline
126 & 20 & 21.9451042791923 & -1.94510427919232 \tabularnewline
127 & 15 & 17.452226374503 & -2.45222637450297 \tabularnewline
128 & 20 & 21.7886208868992 & -1.78862088689923 \tabularnewline
129 & 18 & 20.2225815390998 & -2.22258153909977 \tabularnewline
130 & 33 & 25.4668428039909 & 7.5331571960091 \tabularnewline
131 & 22 & 23.7824344363272 & -1.78243443632724 \tabularnewline
132 & 16 & 16.4697006570271 & -0.46970065702706 \tabularnewline
133 & 17 & 19.0367506268633 & -2.03675062686334 \tabularnewline
134 & 16 & 15.052432261591 & 0.947567738409009 \tabularnewline
135 & 21 & 17.0483876901129 & 3.95161230988708 \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 & -1.3312627872462 \tabularnewline
140 & 22 & 24.7434940752527 & -2.74349407525273 \tabularnewline
141 & 30 & 24.7275661238695 & 5.27243387613054 \tabularnewline
142 & 30 & 27.2739964505683 & 2.72600354943171 \tabularnewline
143 & 24 & 29.9008223341231 & -5.90082233412311 \tabularnewline
144 & 21 & 21.9917911761762 & -0.991791176176224 \tabularnewline
145 & 21 & 25.3787580941457 & -4.3787580941457 \tabularnewline
146 & 29 & 27.3918377820479 & 1.60816221795207 \tabularnewline
147 & 31 & 23.240508271007 & 7.75949172899297 \tabularnewline
148 & 20 & 18.9759710592584 & 1.0240289407416 \tabularnewline
149 & 16 & 14.1875805146353 & 1.81241948536466 \tabularnewline
150 & 22 & 18.9190989475258 & 3.08090105247424 \tabularnewline
151 & 20 & 20.3316462195076 & -0.331646219507597 \tabularnewline
152 & 28 & 27.3269489577319 & 0.673051042268083 \tabularnewline
153 & 38 & 26.5958059363835 & 11.4041940636165 \tabularnewline
154 & 22 & 19.3348333644292 & 2.66516663557078 \tabularnewline
155 & 20 & 25.6868097712468 & -5.68680977124681 \tabularnewline
156 & 17 & 18.0421110887897 & -1.04211108878971 \tabularnewline
157 & 28 & 24.4504319189554 & 3.54956808104462 \tabularnewline
158 & 22 & 24.1216672736301 & -2.12166727363008 \tabularnewline
159 & 31 & 26.0892814723567 & 4.91071852764329 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186030&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]24.9730696546186[/C][C]-0.973069654618644[/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]-1.86432396015473[/C][/ROW]
[ROW][C]5[/C][C]18[/C][C]19.475921308563[/C][C]-1.47592130856298[/C][/ROW]
[ROW][C]6[/C][C]16[/C][C]19.5619453157043[/C][C]-3.56194531570428[/C][/ROW]
[ROW][C]7[/C][C]20[/C][C]20.9062047066683[/C][C]-0.906204706668275[/C][/ROW]
[ROW][C]8[/C][C]16[/C][C]22.3667061957279[/C][C]-6.36670619572788[/C][/ROW]
[ROW][C]9[/C][C]18[/C][C]22.4023995380277[/C][C]-4.40239953802769[/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]0.682180641332218[/C][/ROW]
[ROW][C]12[/C][C]30[/C][C]22.327476885032[/C][C]7.67252311496805[/C][/ROW]
[ROW][C]13[/C][C]23[/C][C]15.1035765940144[/C][C]7.89642340598557[/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]-6.68543103998085[/C][/ROW]
[ROW][C]16[/C][C]12[/C][C]17.8354454157556[/C][C]-5.83544541575557[/C][/ROW]
[ROW][C]17[/C][C]21[/C][C]19.4165662795253[/C][C]1.58343372047466[/C][/ROW]
[ROW][C]18[/C][C]15[/C][C]15.3176615872635[/C][C]-0.317661587263506[/C][/ROW]
[ROW][C]19[/C][C]20[/C][C]19.7765579973379[/C][C]0.223442002662073[/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]19.612996323041[/C][C]1.38700367695902[/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]-0.292924150428892[/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]-1.5938509463785[/C][/ROW]
[ROW][C]28[/C][C]21[/C][C]18.2605112781375[/C][C]2.73948872186254[/C][/ROW]
[ROW][C]29[/C][C]22[/C][C]21.960314890049[/C][C]0.0396851099510344[/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]3.68213985278175[/C][/ROW]
[ROW][C]32[/C][C]25[/C][C]30.4697362245754[/C][C]-5.46973622457538[/C][/ROW]
[ROW][C]33[/C][C]26[/C][C]26.7379136808267[/C][C]-0.737913680826712[/C][/ROW]
[ROW][C]34[/C][C]25[/C][C]24.0652193827093[/C][C]0.934780617290695[/C][/ROW]
[ROW][C]35[/C][C]17[/C][C]23.0090707878074[/C][C]-6.00907078780739[/C][/ROW]
[ROW][C]36[/C][C]32[/C][C]27.7900227230014[/C][C]4.20997727699858[/C][/ROW]
[ROW][C]37[/C][C]33[/C][C]23.6362102946555[/C][C]9.36378970534447[/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]-0.838960491097935[/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]-0.0238725008844256[/C][/ROW]
[ROW][C]43[/C][C]18[/C][C]17.3643313167998[/C][C]0.635668683200175[/C][/ROW]
[ROW][C]44[/C][C]17[/C][C]21.7723044845037[/C][C]-4.77230448450369[/C][/ROW]
[ROW][C]45[/C][C]20[/C][C]22.2065286208074[/C][C]-2.20652862080744[/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]-2.18484145220414[/C][/ROW]
[ROW][C]48[/C][C]33[/C][C]28.0994056135831[/C][C]4.90059438641692[/C][/ROW]
[ROW][C]49[/C][C]29[/C][C]22.142971009972[/C][C]6.85702899002803[/C][/ROW]
[ROW][C]50[/C][C]23[/C][C]26.6826557598034[/C][C]-3.68265575980342[/C][/ROW]
[ROW][C]51[/C][C]26[/C][C]23.2430193118991[/C][C]2.75698068810088[/C][/ROW]
[ROW][C]52[/C][C]18[/C][C]18.952868498816[/C][C]-0.952868498815959[/C][/ROW]
[ROW][C]53[/C][C]20[/C][C]18.7340532353845[/C][C]1.2659467646155[/C][/ROW]
[ROW][C]54[/C][C]11[/C][C]11.7902591765424[/C][C]-0.790259176542405[/C][/ROW]
[ROW][C]55[/C][C]28[/C][C]28.9774711402709[/C][C]-0.977471140270937[/C][/ROW]
[ROW][C]56[/C][C]26[/C][C]23.3129672959119[/C][C]2.68703270408813[/C][/ROW]
[ROW][C]57[/C][C]22[/C][C]22.2461608568904[/C][C]-0.24616085689036[/C][/ROW]
[ROW][C]58[/C][C]17[/C][C]20.1978454987339[/C][C]-3.19784549873386[/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]-0.335403446864113[/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]-5.0750654036623[/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]4.77986400290577[/C][/ROW]
[ROW][C]67[/C][C]31[/C][C]18.457555194607[/C][C]12.542444805393[/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]-2.47040444181673[/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]0.873692368023276[/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]3.45591557341674[/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]-1.64033286818809[/C][/ROW]
[ROW][C]79[/C][C]19[/C][C]21.4342707638265[/C][C]-2.43427076382648[/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]-2.54888531938728[/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]0.266196494522131[/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]0.176451681682022[/C][/ROW]
[ROW][C]87[/C][C]21[/C][C]21.7765070663015[/C][C]-0.776507066301509[/C][/ROW]
[ROW][C]88[/C][C]15[/C][C]22.8032661951564[/C][C]-7.80326619515638[/C][/ROW]
[ROW][C]89[/C][C]28[/C][C]27.147195644037[/C][C]0.852804355963[/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]0.831344010889731[/C][/ROW]
[ROW][C]95[/C][C]19[/C][C]18.7314962822069[/C][C]0.26850371779314[/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]5.8009526215874[/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]0.62692272030893[/C][/ROW]
[ROW][C]102[/C][C]15[/C][C]16.1706084549776[/C][C]-1.1706084549776[/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]-1.5049798788849[/C][/ROW]
[ROW][C]105[/C][C]23[/C][C]21.9846165671171[/C][C]1.01538343288295[/C][/ROW]
[ROW][C]106[/C][C]25[/C][C]20.9638904859829[/C][C]4.03610951401714[/C][/ROW]
[ROW][C]107[/C][C]21[/C][C]16.5635547265782[/C][C]4.43644527342176[/C][/ROW]
[ROW][C]108[/C][C]24[/C][C]19.4904987572516[/C][C]4.5095012427484[/C][/ROW]
[ROW][C]109[/C][C]25[/C][C]25.2360293362311[/C][C]-0.23602933623114[/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]9.79636083359519[/C][/ROW]
[ROW][C]113[/C][C]21[/C][C]20.1156089993067[/C][C]0.88439100069334[/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]20.7272742072835[/C][C]-11.7272742072834[/C][/ROW]
[ROW][C]116[/C][C]16[/C][C]17.8284411696244[/C][C]-1.82844116962435[/C][/ROW]
[ROW][C]117[/C][C]19[/C][C]21.2002096254339[/C][C]-2.20020962543391[/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]0.488164497999802[/C][/ROW]
[ROW][C]120[/C][C]20[/C][C]15.5584155586162[/C][C]4.44158444138375[/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]-5.04608940619911[/C][/ROW]
[ROW][C]123[/C][C]22[/C][C]26.9144076581427[/C][C]-4.9144076581427[/C][/ROW]
[ROW][C]124[/C][C]15[/C][C]15.5764012107348[/C][C]-0.576401210734847[/C][/ROW]
[ROW][C]125[/C][C]19[/C][C]25.4228566000411[/C][C]-6.42285660004114[/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]17.452226374503[/C][C]-2.45222637450297[/C][/ROW]
[ROW][C]128[/C][C]20[/C][C]21.7886208868992[/C][C]-1.78862088689923[/C][/ROW]
[ROW][C]129[/C][C]18[/C][C]20.2225815390998[/C][C]-2.22258153909977[/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]-1.78243443632724[/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]0.947567738409009[/C][/ROW]
[ROW][C]135[/C][C]21[/C][C]17.0483876901129[/C][C]3.95161230988708[/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]-1.3312627872462[/C][/ROW]
[ROW][C]140[/C][C]22[/C][C]24.7434940752527[/C][C]-2.74349407525273[/C][/ROW]
[ROW][C]141[/C][C]30[/C][C]24.7275661238695[/C][C]5.27243387613054[/C][/ROW]
[ROW][C]142[/C][C]30[/C][C]27.2739964505683[/C][C]2.72600354943171[/C][/ROW]
[ROW][C]143[/C][C]24[/C][C]29.9008223341231[/C][C]-5.90082233412311[/C][/ROW]
[ROW][C]144[/C][C]21[/C][C]21.9917911761762[/C][C]-0.991791176176224[/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]1.60816221795207[/C][/ROW]
[ROW][C]147[/C][C]31[/C][C]23.240508271007[/C][C]7.75949172899297[/C][/ROW]
[ROW][C]148[/C][C]20[/C][C]18.9759710592584[/C][C]1.0240289407416[/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]-0.331646219507597[/C][/ROW]
[ROW][C]152[/C][C]28[/C][C]27.3269489577319[/C][C]0.673051042268083[/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]2.66516663557078[/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]-1.04211108878971[/C][/ROW]
[ROW][C]157[/C][C]28[/C][C]24.4504319189554[/C][C]3.54956808104462[/C][/ROW]
[ROW][C]158[/C][C]22[/C][C]24.1216672736301[/C][C]-2.12166727363008[/C][/ROW]
[ROW][C]159[/C][C]31[/C][C]26.0892814723567[/C][C]4.91071852764329[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186030&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186030&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
12424.9730696546186-0.973069654618644
22521.69682752005193.30317247994805
31722.7576275344242-5.75762753442423
41819.8643239601547-1.86432396015473
51819.475921308563-1.47592130856298
61619.5619453157043-3.56194531570428
72020.9062047066683-0.906204706668275
81622.3667061957279-6.36670619572788
91822.4023995380277-4.40239953802769
101720.5201335863125-3.52013358631251
112322.31781935866780.682180641332218
123022.3274768850327.67252311496805
132315.10357659401447.89642340598557
141819.0918794530003-1.09187945300029
151521.6854310399808-6.68543103998085
161217.8354454157556-5.83544541575557
172119.41656627952531.58343372047466
181515.3176615872635-0.317661587263506
192019.77655799733790.223442002662073
203126.42032009363084.57967990636915
212725.23016665143841.76983334856159
223427.15969310080766.84030689919239
232119.6129963230411.38700367695902
243120.918508179408110.0814918205919
251919.2929241504289-0.292924150428892
261620.2171532176049-4.21715321760486
272021.5938509463785-1.5938509463785
282118.26051127813752.73948872186254
292221.9603148900490.0396851099510344
301719.5088680455431-2.50886804554309
312420.31786014721823.68213985278175
322530.4697362245754-5.46973622457538
332626.7379136808267-0.737913680826712
342524.06521938270930.934780617290695
351723.0090707878074-6.00907078780739
363227.79002272300144.20997727699858
373323.63621029465559.36378970534447
381322.0253619285962-9.02536192859621
393227.83873005040544.16126994959459
402525.8389604910979-0.838960491097935
412927.0240619141111.97593808588899
422222.0238725008844-0.0238725008844256
431817.36433131679980.635668683200175
441721.7723044845037-4.77230448450369
452022.2065286208074-2.20652862080744
461520.4502048915139-5.45020489151386
472022.1848414522041-2.18484145220414
483328.09940561358314.90059438641692
492922.1429710099726.85702899002803
502326.6826557598034-3.68265575980342
512623.24301931189912.75698068810088
521818.952868498816-0.952868498815959
532018.73405323538451.2659467646155
541111.7902591765424-0.790259176542405
552828.9774711402709-0.977471140270937
562623.31296729591192.68703270408813
572222.2461608568904-0.24616085689036
581720.1978454987339-3.19784549873386
591215.4254660880949-3.42546608809486
601421.0281654373449-7.02816543734494
611720.7238630018849-3.72386300188489
622121.3354034468641-0.335403446864113
631922.9325499189927-3.93254991899267
641823.0750654036623-5.0750654036623
651017.9297721146776-7.92977211467762
662924.22013599709424.77986400290577
673118.45755519460712.542444805393
681922.9476672047626-3.94766720476257
69920.0659733838091-11.0659733838091
702022.4704044418167-2.47040444181673
712817.619597847696710.3804021523033
721918.12630763197670.873692368023276
733023.06009950396146.93990049603859
742927.1345419863581.86545801364201
752621.47813220477874.52186779522132
762319.54408442658333.45591557341674
771322.7619883304533-9.76198833045328
782122.6403328681881-1.64033286818809
791921.4342707638265-2.43427076382648
802822.94303025966695.05696974033307
812325.5488853193873-2.54888531938728
821813.7755446122484.22445538775199
832120.73380350547790.266196494522131
842021.7890552501971-1.78905525019705
852319.9536344096973.04636559030299
862120.8235483183180.176451681682022
872121.7765070663015-0.776507066301509
881522.8032661951564-7.80326619515638
892827.1471956440370.852804355963
901917.65737118191451.3426288180855
912621.2417390294554.75826097054501
921013.1981887926427-3.1981887926427
931617.1052598018456-1.10525980184556
942221.16865598911030.831344010889731
951918.73149628220690.26850371779314
963128.77362610842912.22637389157087
973125.19904737841265.8009526215874
982924.8021196150674.19788038493298
991917.41331147894671.58668852105333
1002218.89464588438873.10535411561134
1012322.37307727969110.62692272030893
1021516.1706084549776-1.1706084549776
1032021.4483664364161-1.44836643641609
1041819.5049798788849-1.5049798788849
1052321.98461656711711.01538343288295
1062520.96389048598294.03610951401714
1072116.56355472657824.43644527342176
1082419.49049875725164.5095012427484
1092525.2360293362311-0.23602933623114
1101719.5772208157054-2.57722081570542
1111314.5703651925901-1.57036519259012
1122818.20363916640489.79636083359519
1132120.11560899930670.88439100069334
1142528.140636065485-3.14063606548499
115920.7272742072835-11.7272742072834
1161617.8284411696244-1.82844116962435
1171921.2002096254339-2.20020962543391
1181719.4548480129136-2.45484801291361
1192524.51183550200020.488164497999802
1202015.55841555861624.44158444138375
1212921.62453585610657.37546414389351
1221419.0460894061991-5.04608940619911
1232226.9144076581427-4.9144076581427
1241515.5764012107348-0.576401210734847
1251925.4228566000411-6.42285660004114
1262021.9451042791923-1.94510427919232
1271517.452226374503-2.45222637450297
1282021.7886208868992-1.78862088689923
1291820.2225815390998-2.22258153909977
1303325.46684280399097.5331571960091
1312223.7824344363272-1.78243443632724
1321616.4697006570271-0.46970065702706
1331719.0367506268633-2.03675062686334
1341615.0524322615910.947567738409009
1352117.04838769011293.95161230988708
1362627.6421945331063-1.64219453310631
1371821.1470742942904-3.14707429429044
1381823.0959206112274-5.09592061122736
1391718.3312627872462-1.3312627872462
1402224.7434940752527-2.74349407525273
1413024.72756612386955.27243387613054
1423027.27399645056832.72600354943171
1432429.9008223341231-5.90082233412311
1442121.9917911761762-0.991791176176224
1452125.3787580941457-4.3787580941457
1462927.39183778204791.60816221795207
1473123.2405082710077.75949172899297
1482018.97597105925841.0240289407416
1491614.18758051463531.81241948536466
1502218.91909894752583.08090105247424
1512020.3316462195076-0.331646219507597
1522827.32694895773190.673051042268083
1533826.595805936383511.4041940636165
1542219.33483336442922.66516663557078
1552025.6868097712468-5.68680977124681
1561718.0421110887897-1.04211108878971
1572824.45043191895543.54956808104462
1582224.1216672736301-2.12166727363008
1593126.08928147235674.91071852764329







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.2113960233023270.4227920466046540.788603976697673
100.1020763753425880.2041527506851750.897923624657412
110.2272380381920740.4544760763841480.772761961807926
120.6516882361919510.6966235276160980.348311763808049
130.6673072325423310.6653855349153370.332692767457669
140.6420154149810760.7159691700378490.357984585018924
150.6474213299422280.7051573401155430.352578670057772
160.582007170180210.835985659639580.41799282981979
170.4974416798337970.9948833596675930.502558320166203
180.4668537175174820.9337074350349640.533146282482518
190.403185159454760.8063703189095210.59681484054524
200.4376092172551570.8752184345103150.562390782744843
210.3969015250977710.7938030501955420.603098474902229
220.4007779696537920.8015559393075840.599222030346208
230.3387699516316920.6775399032633840.661230048368308
240.6033415677282860.7933168645434280.396658432271714
250.533551201044860.9328975979102810.46644879895514
260.5035440241504270.9929119516991470.496455975849573
270.4417309983698710.8834619967397410.558269001630129
280.3951831675487850.7903663350975690.604816832451215
290.3349775204123840.6699550408247670.665022479587617
300.2829189677787490.5658379355574970.717081032221251
310.3429018151285170.6858036302570330.657098184871483
320.4042320700720380.8084641401440770.595767929927962
330.3603988805327850.720797761065570.639601119467215
340.3706313728358480.7412627456716970.629368627164152
350.3853071543426160.7706143086852320.614692845657384
360.3759496983599780.7518993967199550.624050301640022
370.5549897033900570.8900205932198850.445010296609943
380.7584657462238270.4830685075523460.241534253776173
390.7545179416165120.4909641167669760.245482058383488
400.7126200474291360.5747599051417280.287379952570864
410.687613723321830.6247725533563410.31238627667817
420.6381735776537390.7236528446925230.361826422346261
430.5895753437708540.8208493124582930.410424656229147
440.576187266799630.847625466400740.42381273320037
450.5417375471660010.9165249056679980.458262452833999
460.563830488846730.872339022306540.43616951115327
470.5228104281635160.9543791436729670.477189571836484
480.5132263360401520.9735473279196960.486773663959848
490.5781190982572910.8437618034854180.421880901742709
500.5572045651713530.8855908696572940.442795434828647
510.5218326102529950.956334779494010.478167389747005
520.4725186986921830.9450373973843660.527481301307817
530.4343880243849040.8687760487698090.565611975615096
540.3875974418534470.7751948837068950.612402558146553
550.3447994330060130.6895988660120250.655200566993987
560.3176794726538090.6353589453076190.682320527346191
570.2747521586034930.5495043172069850.725247841396508
580.2511059244539740.5022118489079470.748894075546026
590.2314343197213690.4628686394427370.768565680278631
600.2901440782193620.5802881564387230.709855921780638
610.2784971865247180.5569943730494370.721502813475282
620.2412489614241530.4824979228483050.758751038575847
630.2286751236501970.4573502473003930.771324876349803
640.2625362638972050.5250725277944090.737463736102795
650.3388384144525320.6776768289050650.661161585547468
660.3415592785387620.6831185570775240.658440721461238
670.674526217673850.6509475646522990.32547378232615
680.667298147675610.665403704648780.33270185232439
690.8442068400413780.3115863199172430.155793159958622
700.8244781891795310.3510436216409390.175521810820469
710.9305284986215070.1389430027569870.0694715013784933
720.9142375319136360.1715249361727280.0857624680863641
730.9374225765849640.1251548468300720.0625774234150359
740.9255743212335110.1488513575329790.0744256787664894
750.9273220425530020.1453559148939970.0726779574469984
760.9210024044478750.1579951911042490.0789975955521247
770.970096122990050.0598077540198990.0299038770099495
780.9627371234349920.07452575313001510.0372628765650076
790.9553221127204190.08935577455916280.0446778872795814
800.9580010692352870.0839978615294250.0419989307647125
810.9503272401933870.09934551961322680.0496727598066134
820.9500097695588780.09998046088224390.0499902304411219
830.9371893773116470.1256212453767060.0628106226883528
840.9244295166649780.1511409666700430.0755704833350216
850.9159516728922380.1680966542155240.0840483271077622
860.9006531811342390.1986936377315230.0993468188657613
870.8797100421322970.2405799157354070.120289957867703
880.9237274000228140.1525451999543730.0762725999771865
890.9077949479771050.1844101040457910.0922050520228953
900.888521028335510.2229579433289790.11147897166449
910.8903485081722570.2193029836554870.109651491827743
920.879723294041730.2405534119165390.12027670595827
930.8584863885676740.2830272228646520.141513611432326
940.8318023332405590.3363953335188810.168197666759441
950.7998917411512730.4002165176974540.200108258848727
960.7713635890097150.457272821980570.228636410990285
970.7904457067976120.4191085864047760.209554293202388
980.7852534165610850.4294931668778290.214746583438915
990.7513156992978250.497368601404350.248684300702175
1000.7277723553648090.5444552892703830.272227644635191
1010.6864158629623090.6271682740753820.313584137037691
1020.6486695842340220.7026608315319570.351330415765978
1030.6106887359428070.7786225281143870.389311264057193
1040.5689125131836820.8621749736326370.431087486816318
1050.5233482611138840.9533034777722320.476651738886116
1060.503744802868190.992510394263620.49625519713181
1070.5006014968088180.9987970063823630.499398503191182
1080.5120668888361970.9758662223276060.487933111163803
1090.4619706071906060.9239412143812120.538029392809394
1100.4388719527451310.8777439054902620.561128047254869
1110.3992451119491250.7984902238982490.600754888050876
1120.6224640031835920.7550719936328160.377535996816408
1130.61435043167660.77129913664680.3856495683234
1140.5860122340607280.8279755318785440.413987765939272
1150.7879950573792450.4240098852415110.212004942620755
1160.7660028407470510.4679943185058980.233997159252949
1170.7550532828315690.4898934343368620.244946717168431
1180.7283100276160570.5433799447678850.271689972383943
1190.6805217232468620.6389565535062760.319478276753138
1200.6898216869524180.6203566260951640.310178313047582
1210.7412504567360660.5174990865278680.258749543263934
1220.7463110920562030.5073778158875940.253688907943797
1230.7520664555156740.4958670889686520.247933544484326
1240.7027228659240830.5945542681518340.297277134075917
1250.7466719360720110.5066561278559780.253328063927989
1260.7197101758597920.5605796482804150.280289824140208
1270.7097206993921570.5805586012156850.290279300607843
1280.6760296791451580.6479406417096840.323970320854842
1290.6508809551557080.6982380896885830.349119044844292
1300.7204622302660490.5590755394679010.279537769733951
1310.6669860570993350.666027885801330.333013942900665
1320.6062302143605450.7875395712789090.393769785639455
1330.6058951725096330.7882096549807340.394104827490367
1340.5469647779203110.9060704441593780.453035222079689
1350.5062124791455980.9875750417088040.493787520854402
1360.4788697788589940.9577395577179880.521130221141006
1370.4851433508719940.9702867017439890.514856649128006
1380.528733486885150.9425330262297010.47126651311485
1390.4616483002767590.9232966005535180.538351699723241
1400.3875089209721580.7750178419443160.612491079027842
1410.5003989923338280.9992020153323450.499601007666172
1420.4477314301060210.8954628602120410.552268569893979
1430.3779897607247870.7559795214495730.622010239275213
1440.2980098463753710.5960196927507420.701990153624629
1450.3552632752933760.7105265505867530.644736724706624
1460.2629508766098580.5259017532197160.737049123390142
1470.3405733357360290.6811466714720590.659426664263971
1480.2349212715407720.4698425430815450.765078728459228
1490.1460779735049850.2921559470099690.853922026495015
1500.09191941347048930.1838388269409790.908080586529511

\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 & 0.102076375342588 & 0.204152750685175 & 0.897923624657412 \tabularnewline
11 & 0.227238038192074 & 0.454476076384148 & 0.772761961807926 \tabularnewline
12 & 0.651688236191951 & 0.696623527616098 & 0.348311763808049 \tabularnewline
13 & 0.667307232542331 & 0.665385534915337 & 0.332692767457669 \tabularnewline
14 & 0.642015414981076 & 0.715969170037849 & 0.357984585018924 \tabularnewline
15 & 0.647421329942228 & 0.705157340115543 & 0.352578670057772 \tabularnewline
16 & 0.58200717018021 & 0.83598565963958 & 0.41799282981979 \tabularnewline
17 & 0.497441679833797 & 0.994883359667593 & 0.502558320166203 \tabularnewline
18 & 0.466853717517482 & 0.933707435034964 & 0.533146282482518 \tabularnewline
19 & 0.40318515945476 & 0.806370318909521 & 0.59681484054524 \tabularnewline
20 & 0.437609217255157 & 0.875218434510315 & 0.562390782744843 \tabularnewline
21 & 0.396901525097771 & 0.793803050195542 & 0.603098474902229 \tabularnewline
22 & 0.400777969653792 & 0.801555939307584 & 0.599222030346208 \tabularnewline
23 & 0.338769951631692 & 0.677539903263384 & 0.661230048368308 \tabularnewline
24 & 0.603341567728286 & 0.793316864543428 & 0.396658432271714 \tabularnewline
25 & 0.53355120104486 & 0.932897597910281 & 0.46644879895514 \tabularnewline
26 & 0.503544024150427 & 0.992911951699147 & 0.496455975849573 \tabularnewline
27 & 0.441730998369871 & 0.883461996739741 & 0.558269001630129 \tabularnewline
28 & 0.395183167548785 & 0.790366335097569 & 0.604816832451215 \tabularnewline
29 & 0.334977520412384 & 0.669955040824767 & 0.665022479587617 \tabularnewline
30 & 0.282918967778749 & 0.565837935557497 & 0.717081032221251 \tabularnewline
31 & 0.342901815128517 & 0.685803630257033 & 0.657098184871483 \tabularnewline
32 & 0.404232070072038 & 0.808464140144077 & 0.595767929927962 \tabularnewline
33 & 0.360398880532785 & 0.72079776106557 & 0.639601119467215 \tabularnewline
34 & 0.370631372835848 & 0.741262745671697 & 0.629368627164152 \tabularnewline
35 & 0.385307154342616 & 0.770614308685232 & 0.614692845657384 \tabularnewline
36 & 0.375949698359978 & 0.751899396719955 & 0.624050301640022 \tabularnewline
37 & 0.554989703390057 & 0.890020593219885 & 0.445010296609943 \tabularnewline
38 & 0.758465746223827 & 0.483068507552346 & 0.241534253776173 \tabularnewline
39 & 0.754517941616512 & 0.490964116766976 & 0.245482058383488 \tabularnewline
40 & 0.712620047429136 & 0.574759905141728 & 0.287379952570864 \tabularnewline
41 & 0.68761372332183 & 0.624772553356341 & 0.31238627667817 \tabularnewline
42 & 0.638173577653739 & 0.723652844692523 & 0.361826422346261 \tabularnewline
43 & 0.589575343770854 & 0.820849312458293 & 0.410424656229147 \tabularnewline
44 & 0.57618726679963 & 0.84762546640074 & 0.42381273320037 \tabularnewline
45 & 0.541737547166001 & 0.916524905667998 & 0.458262452833999 \tabularnewline
46 & 0.56383048884673 & 0.87233902230654 & 0.43616951115327 \tabularnewline
47 & 0.522810428163516 & 0.954379143672967 & 0.477189571836484 \tabularnewline
48 & 0.513226336040152 & 0.973547327919696 & 0.486773663959848 \tabularnewline
49 & 0.578119098257291 & 0.843761803485418 & 0.421880901742709 \tabularnewline
50 & 0.557204565171353 & 0.885590869657294 & 0.442795434828647 \tabularnewline
51 & 0.521832610252995 & 0.95633477949401 & 0.478167389747005 \tabularnewline
52 & 0.472518698692183 & 0.945037397384366 & 0.527481301307817 \tabularnewline
53 & 0.434388024384904 & 0.868776048769809 & 0.565611975615096 \tabularnewline
54 & 0.387597441853447 & 0.775194883706895 & 0.612402558146553 \tabularnewline
55 & 0.344799433006013 & 0.689598866012025 & 0.655200566993987 \tabularnewline
56 & 0.317679472653809 & 0.635358945307619 & 0.682320527346191 \tabularnewline
57 & 0.274752158603493 & 0.549504317206985 & 0.725247841396508 \tabularnewline
58 & 0.251105924453974 & 0.502211848907947 & 0.748894075546026 \tabularnewline
59 & 0.231434319721369 & 0.462868639442737 & 0.768565680278631 \tabularnewline
60 & 0.290144078219362 & 0.580288156438723 & 0.709855921780638 \tabularnewline
61 & 0.278497186524718 & 0.556994373049437 & 0.721502813475282 \tabularnewline
62 & 0.241248961424153 & 0.482497922848305 & 0.758751038575847 \tabularnewline
63 & 0.228675123650197 & 0.457350247300393 & 0.771324876349803 \tabularnewline
64 & 0.262536263897205 & 0.525072527794409 & 0.737463736102795 \tabularnewline
65 & 0.338838414452532 & 0.677676828905065 & 0.661161585547468 \tabularnewline
66 & 0.341559278538762 & 0.683118557077524 & 0.658440721461238 \tabularnewline
67 & 0.67452621767385 & 0.650947564652299 & 0.32547378232615 \tabularnewline
68 & 0.66729814767561 & 0.66540370464878 & 0.33270185232439 \tabularnewline
69 & 0.844206840041378 & 0.311586319917243 & 0.155793159958622 \tabularnewline
70 & 0.824478189179531 & 0.351043621640939 & 0.175521810820469 \tabularnewline
71 & 0.930528498621507 & 0.138943002756987 & 0.0694715013784933 \tabularnewline
72 & 0.914237531913636 & 0.171524936172728 & 0.0857624680863641 \tabularnewline
73 & 0.937422576584964 & 0.125154846830072 & 0.0625774234150359 \tabularnewline
74 & 0.925574321233511 & 0.148851357532979 & 0.0744256787664894 \tabularnewline
75 & 0.927322042553002 & 0.145355914893997 & 0.0726779574469984 \tabularnewline
76 & 0.921002404447875 & 0.157995191104249 & 0.0789975955521247 \tabularnewline
77 & 0.97009612299005 & 0.059807754019899 & 0.0299038770099495 \tabularnewline
78 & 0.962737123434992 & 0.0745257531300151 & 0.0372628765650076 \tabularnewline
79 & 0.955322112720419 & 0.0893557745591628 & 0.0446778872795814 \tabularnewline
80 & 0.958001069235287 & 0.083997861529425 & 0.0419989307647125 \tabularnewline
81 & 0.950327240193387 & 0.0993455196132268 & 0.0496727598066134 \tabularnewline
82 & 0.950009769558878 & 0.0999804608822439 & 0.0499902304411219 \tabularnewline
83 & 0.937189377311647 & 0.125621245376706 & 0.0628106226883528 \tabularnewline
84 & 0.924429516664978 & 0.151140966670043 & 0.0755704833350216 \tabularnewline
85 & 0.915951672892238 & 0.168096654215524 & 0.0840483271077622 \tabularnewline
86 & 0.900653181134239 & 0.198693637731523 & 0.0993468188657613 \tabularnewline
87 & 0.879710042132297 & 0.240579915735407 & 0.120289957867703 \tabularnewline
88 & 0.923727400022814 & 0.152545199954373 & 0.0762725999771865 \tabularnewline
89 & 0.907794947977105 & 0.184410104045791 & 0.0922050520228953 \tabularnewline
90 & 0.88852102833551 & 0.222957943328979 & 0.11147897166449 \tabularnewline
91 & 0.890348508172257 & 0.219302983655487 & 0.109651491827743 \tabularnewline
92 & 0.87972329404173 & 0.240553411916539 & 0.12027670595827 \tabularnewline
93 & 0.858486388567674 & 0.283027222864652 & 0.141513611432326 \tabularnewline
94 & 0.831802333240559 & 0.336395333518881 & 0.168197666759441 \tabularnewline
95 & 0.799891741151273 & 0.400216517697454 & 0.200108258848727 \tabularnewline
96 & 0.771363589009715 & 0.45727282198057 & 0.228636410990285 \tabularnewline
97 & 0.790445706797612 & 0.419108586404776 & 0.209554293202388 \tabularnewline
98 & 0.785253416561085 & 0.429493166877829 & 0.214746583438915 \tabularnewline
99 & 0.751315699297825 & 0.49736860140435 & 0.248684300702175 \tabularnewline
100 & 0.727772355364809 & 0.544455289270383 & 0.272227644635191 \tabularnewline
101 & 0.686415862962309 & 0.627168274075382 & 0.313584137037691 \tabularnewline
102 & 0.648669584234022 & 0.702660831531957 & 0.351330415765978 \tabularnewline
103 & 0.610688735942807 & 0.778622528114387 & 0.389311264057193 \tabularnewline
104 & 0.568912513183682 & 0.862174973632637 & 0.431087486816318 \tabularnewline
105 & 0.523348261113884 & 0.953303477772232 & 0.476651738886116 \tabularnewline
106 & 0.50374480286819 & 0.99251039426362 & 0.49625519713181 \tabularnewline
107 & 0.500601496808818 & 0.998797006382363 & 0.499398503191182 \tabularnewline
108 & 0.512066888836197 & 0.975866222327606 & 0.487933111163803 \tabularnewline
109 & 0.461970607190606 & 0.923941214381212 & 0.538029392809394 \tabularnewline
110 & 0.438871952745131 & 0.877743905490262 & 0.561128047254869 \tabularnewline
111 & 0.399245111949125 & 0.798490223898249 & 0.600754888050876 \tabularnewline
112 & 0.622464003183592 & 0.755071993632816 & 0.377535996816408 \tabularnewline
113 & 0.6143504316766 & 0.7712991366468 & 0.3856495683234 \tabularnewline
114 & 0.586012234060728 & 0.827975531878544 & 0.413987765939272 \tabularnewline
115 & 0.787995057379245 & 0.424009885241511 & 0.212004942620755 \tabularnewline
116 & 0.766002840747051 & 0.467994318505898 & 0.233997159252949 \tabularnewline
117 & 0.755053282831569 & 0.489893434336862 & 0.244946717168431 \tabularnewline
118 & 0.728310027616057 & 0.543379944767885 & 0.271689972383943 \tabularnewline
119 & 0.680521723246862 & 0.638956553506276 & 0.319478276753138 \tabularnewline
120 & 0.689821686952418 & 0.620356626095164 & 0.310178313047582 \tabularnewline
121 & 0.741250456736066 & 0.517499086527868 & 0.258749543263934 \tabularnewline
122 & 0.746311092056203 & 0.507377815887594 & 0.253688907943797 \tabularnewline
123 & 0.752066455515674 & 0.495867088968652 & 0.247933544484326 \tabularnewline
124 & 0.702722865924083 & 0.594554268151834 & 0.297277134075917 \tabularnewline
125 & 0.746671936072011 & 0.506656127855978 & 0.253328063927989 \tabularnewline
126 & 0.719710175859792 & 0.560579648280415 & 0.280289824140208 \tabularnewline
127 & 0.709720699392157 & 0.580558601215685 & 0.290279300607843 \tabularnewline
128 & 0.676029679145158 & 0.647940641709684 & 0.323970320854842 \tabularnewline
129 & 0.650880955155708 & 0.698238089688583 & 0.349119044844292 \tabularnewline
130 & 0.720462230266049 & 0.559075539467901 & 0.279537769733951 \tabularnewline
131 & 0.666986057099335 & 0.66602788580133 & 0.333013942900665 \tabularnewline
132 & 0.606230214360545 & 0.787539571278909 & 0.393769785639455 \tabularnewline
133 & 0.605895172509633 & 0.788209654980734 & 0.394104827490367 \tabularnewline
134 & 0.546964777920311 & 0.906070444159378 & 0.453035222079689 \tabularnewline
135 & 0.506212479145598 & 0.987575041708804 & 0.493787520854402 \tabularnewline
136 & 0.478869778858994 & 0.957739557717988 & 0.521130221141006 \tabularnewline
137 & 0.485143350871994 & 0.970286701743989 & 0.514856649128006 \tabularnewline
138 & 0.52873348688515 & 0.942533026229701 & 0.47126651311485 \tabularnewline
139 & 0.461648300276759 & 0.923296600553518 & 0.538351699723241 \tabularnewline
140 & 0.387508920972158 & 0.775017841944316 & 0.612491079027842 \tabularnewline
141 & 0.500398992333828 & 0.999202015332345 & 0.499601007666172 \tabularnewline
142 & 0.447731430106021 & 0.895462860212041 & 0.552268569893979 \tabularnewline
143 & 0.377989760724787 & 0.755979521449573 & 0.622010239275213 \tabularnewline
144 & 0.298009846375371 & 0.596019692750742 & 0.701990153624629 \tabularnewline
145 & 0.355263275293376 & 0.710526550586753 & 0.644736724706624 \tabularnewline
146 & 0.262950876609858 & 0.525901753219716 & 0.737049123390142 \tabularnewline
147 & 0.340573335736029 & 0.681146671472059 & 0.659426664263971 \tabularnewline
148 & 0.234921271540772 & 0.469842543081545 & 0.765078728459228 \tabularnewline
149 & 0.146077973504985 & 0.292155947009969 & 0.853922026495015 \tabularnewline
150 & 0.0919194134704893 & 0.183838826940979 & 0.908080586529511 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186030&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]0.102076375342588[/C][C]0.204152750685175[/C][C]0.897923624657412[/C][/ROW]
[ROW][C]11[/C][C]0.227238038192074[/C][C]0.454476076384148[/C][C]0.772761961807926[/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]0.665385534915337[/C][C]0.332692767457669[/C][/ROW]
[ROW][C]14[/C][C]0.642015414981076[/C][C]0.715969170037849[/C][C]0.357984585018924[/C][/ROW]
[ROW][C]15[/C][C]0.647421329942228[/C][C]0.705157340115543[/C][C]0.352578670057772[/C][/ROW]
[ROW][C]16[/C][C]0.58200717018021[/C][C]0.83598565963958[/C][C]0.41799282981979[/C][/ROW]
[ROW][C]17[/C][C]0.497441679833797[/C][C]0.994883359667593[/C][C]0.502558320166203[/C][/ROW]
[ROW][C]18[/C][C]0.466853717517482[/C][C]0.933707435034964[/C][C]0.533146282482518[/C][/ROW]
[ROW][C]19[/C][C]0.40318515945476[/C][C]0.806370318909521[/C][C]0.59681484054524[/C][/ROW]
[ROW][C]20[/C][C]0.437609217255157[/C][C]0.875218434510315[/C][C]0.562390782744843[/C][/ROW]
[ROW][C]21[/C][C]0.396901525097771[/C][C]0.793803050195542[/C][C]0.603098474902229[/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]0.603341567728286[/C][C]0.793316864543428[/C][C]0.396658432271714[/C][/ROW]
[ROW][C]25[/C][C]0.53355120104486[/C][C]0.932897597910281[/C][C]0.46644879895514[/C][/ROW]
[ROW][C]26[/C][C]0.503544024150427[/C][C]0.992911951699147[/C][C]0.496455975849573[/C][/ROW]
[ROW][C]27[/C][C]0.441730998369871[/C][C]0.883461996739741[/C][C]0.558269001630129[/C][/ROW]
[ROW][C]28[/C][C]0.395183167548785[/C][C]0.790366335097569[/C][C]0.604816832451215[/C][/ROW]
[ROW][C]29[/C][C]0.334977520412384[/C][C]0.669955040824767[/C][C]0.665022479587617[/C][/ROW]
[ROW][C]30[/C][C]0.282918967778749[/C][C]0.565837935557497[/C][C]0.717081032221251[/C][/ROW]
[ROW][C]31[/C][C]0.342901815128517[/C][C]0.685803630257033[/C][C]0.657098184871483[/C][/ROW]
[ROW][C]32[/C][C]0.404232070072038[/C][C]0.808464140144077[/C][C]0.595767929927962[/C][/ROW]
[ROW][C]33[/C][C]0.360398880532785[/C][C]0.72079776106557[/C][C]0.639601119467215[/C][/ROW]
[ROW][C]34[/C][C]0.370631372835848[/C][C]0.741262745671697[/C][C]0.629368627164152[/C][/ROW]
[ROW][C]35[/C][C]0.385307154342616[/C][C]0.770614308685232[/C][C]0.614692845657384[/C][/ROW]
[ROW][C]36[/C][C]0.375949698359978[/C][C]0.751899396719955[/C][C]0.624050301640022[/C][/ROW]
[ROW][C]37[/C][C]0.554989703390057[/C][C]0.890020593219885[/C][C]0.445010296609943[/C][/ROW]
[ROW][C]38[/C][C]0.758465746223827[/C][C]0.483068507552346[/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]0.712620047429136[/C][C]0.574759905141728[/C][C]0.287379952570864[/C][/ROW]
[ROW][C]41[/C][C]0.68761372332183[/C][C]0.624772553356341[/C][C]0.31238627667817[/C][/ROW]
[ROW][C]42[/C][C]0.638173577653739[/C][C]0.723652844692523[/C][C]0.361826422346261[/C][/ROW]
[ROW][C]43[/C][C]0.589575343770854[/C][C]0.820849312458293[/C][C]0.410424656229147[/C][/ROW]
[ROW][C]44[/C][C]0.57618726679963[/C][C]0.84762546640074[/C][C]0.42381273320037[/C][/ROW]
[ROW][C]45[/C][C]0.541737547166001[/C][C]0.916524905667998[/C][C]0.458262452833999[/C][/ROW]
[ROW][C]46[/C][C]0.56383048884673[/C][C]0.87233902230654[/C][C]0.43616951115327[/C][/ROW]
[ROW][C]47[/C][C]0.522810428163516[/C][C]0.954379143672967[/C][C]0.477189571836484[/C][/ROW]
[ROW][C]48[/C][C]0.513226336040152[/C][C]0.973547327919696[/C][C]0.486773663959848[/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]0.885590869657294[/C][C]0.442795434828647[/C][/ROW]
[ROW][C]51[/C][C]0.521832610252995[/C][C]0.95633477949401[/C][C]0.478167389747005[/C][/ROW]
[ROW][C]52[/C][C]0.472518698692183[/C][C]0.945037397384366[/C][C]0.527481301307817[/C][/ROW]
[ROW][C]53[/C][C]0.434388024384904[/C][C]0.868776048769809[/C][C]0.565611975615096[/C][/ROW]
[ROW][C]54[/C][C]0.387597441853447[/C][C]0.775194883706895[/C][C]0.612402558146553[/C][/ROW]
[ROW][C]55[/C][C]0.344799433006013[/C][C]0.689598866012025[/C][C]0.655200566993987[/C][/ROW]
[ROW][C]56[/C][C]0.317679472653809[/C][C]0.635358945307619[/C][C]0.682320527346191[/C][/ROW]
[ROW][C]57[/C][C]0.274752158603493[/C][C]0.549504317206985[/C][C]0.725247841396508[/C][/ROW]
[ROW][C]58[/C][C]0.251105924453974[/C][C]0.502211848907947[/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]0.290144078219362[/C][C]0.580288156438723[/C][C]0.709855921780638[/C][/ROW]
[ROW][C]61[/C][C]0.278497186524718[/C][C]0.556994373049437[/C][C]0.721502813475282[/C][/ROW]
[ROW][C]62[/C][C]0.241248961424153[/C][C]0.482497922848305[/C][C]0.758751038575847[/C][/ROW]
[ROW][C]63[/C][C]0.228675123650197[/C][C]0.457350247300393[/C][C]0.771324876349803[/C][/ROW]
[ROW][C]64[/C][C]0.262536263897205[/C][C]0.525072527794409[/C][C]0.737463736102795[/C][/ROW]
[ROW][C]65[/C][C]0.338838414452532[/C][C]0.677676828905065[/C][C]0.661161585547468[/C][/ROW]
[ROW][C]66[/C][C]0.341559278538762[/C][C]0.683118557077524[/C][C]0.658440721461238[/C][/ROW]
[ROW][C]67[/C][C]0.67452621767385[/C][C]0.650947564652299[/C][C]0.32547378232615[/C][/ROW]
[ROW][C]68[/C][C]0.66729814767561[/C][C]0.66540370464878[/C][C]0.33270185232439[/C][/ROW]
[ROW][C]69[/C][C]0.844206840041378[/C][C]0.311586319917243[/C][C]0.155793159958622[/C][/ROW]
[ROW][C]70[/C][C]0.824478189179531[/C][C]0.351043621640939[/C][C]0.175521810820469[/C][/ROW]
[ROW][C]71[/C][C]0.930528498621507[/C][C]0.138943002756987[/C][C]0.0694715013784933[/C][/ROW]
[ROW][C]72[/C][C]0.914237531913636[/C][C]0.171524936172728[/C][C]0.0857624680863641[/C][/ROW]
[ROW][C]73[/C][C]0.937422576584964[/C][C]0.125154846830072[/C][C]0.0625774234150359[/C][/ROW]
[ROW][C]74[/C][C]0.925574321233511[/C][C]0.148851357532979[/C][C]0.0744256787664894[/C][/ROW]
[ROW][C]75[/C][C]0.927322042553002[/C][C]0.145355914893997[/C][C]0.0726779574469984[/C][/ROW]
[ROW][C]76[/C][C]0.921002404447875[/C][C]0.157995191104249[/C][C]0.0789975955521247[/C][/ROW]
[ROW][C]77[/C][C]0.97009612299005[/C][C]0.059807754019899[/C][C]0.0299038770099495[/C][/ROW]
[ROW][C]78[/C][C]0.962737123434992[/C][C]0.0745257531300151[/C][C]0.0372628765650076[/C][/ROW]
[ROW][C]79[/C][C]0.955322112720419[/C][C]0.0893557745591628[/C][C]0.0446778872795814[/C][/ROW]
[ROW][C]80[/C][C]0.958001069235287[/C][C]0.083997861529425[/C][C]0.0419989307647125[/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]0.0999804608822439[/C][C]0.0499902304411219[/C][/ROW]
[ROW][C]83[/C][C]0.937189377311647[/C][C]0.125621245376706[/C][C]0.0628106226883528[/C][/ROW]
[ROW][C]84[/C][C]0.924429516664978[/C][C]0.151140966670043[/C][C]0.0755704833350216[/C][/ROW]
[ROW][C]85[/C][C]0.915951672892238[/C][C]0.168096654215524[/C][C]0.0840483271077622[/C][/ROW]
[ROW][C]86[/C][C]0.900653181134239[/C][C]0.198693637731523[/C][C]0.0993468188657613[/C][/ROW]
[ROW][C]87[/C][C]0.879710042132297[/C][C]0.240579915735407[/C][C]0.120289957867703[/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]0.88852102833551[/C][C]0.222957943328979[/C][C]0.11147897166449[/C][/ROW]
[ROW][C]91[/C][C]0.890348508172257[/C][C]0.219302983655487[/C][C]0.109651491827743[/C][/ROW]
[ROW][C]92[/C][C]0.87972329404173[/C][C]0.240553411916539[/C][C]0.12027670595827[/C][/ROW]
[ROW][C]93[/C][C]0.858486388567674[/C][C]0.283027222864652[/C][C]0.141513611432326[/C][/ROW]
[ROW][C]94[/C][C]0.831802333240559[/C][C]0.336395333518881[/C][C]0.168197666759441[/C][/ROW]
[ROW][C]95[/C][C]0.799891741151273[/C][C]0.400216517697454[/C][C]0.200108258848727[/C][/ROW]
[ROW][C]96[/C][C]0.771363589009715[/C][C]0.45727282198057[/C][C]0.228636410990285[/C][/ROW]
[ROW][C]97[/C][C]0.790445706797612[/C][C]0.419108586404776[/C][C]0.209554293202388[/C][/ROW]
[ROW][C]98[/C][C]0.785253416561085[/C][C]0.429493166877829[/C][C]0.214746583438915[/C][/ROW]
[ROW][C]99[/C][C]0.751315699297825[/C][C]0.49736860140435[/C][C]0.248684300702175[/C][/ROW]
[ROW][C]100[/C][C]0.727772355364809[/C][C]0.544455289270383[/C][C]0.272227644635191[/C][/ROW]
[ROW][C]101[/C][C]0.686415862962309[/C][C]0.627168274075382[/C][C]0.313584137037691[/C][/ROW]
[ROW][C]102[/C][C]0.648669584234022[/C][C]0.702660831531957[/C][C]0.351330415765978[/C][/ROW]
[ROW][C]103[/C][C]0.610688735942807[/C][C]0.778622528114387[/C][C]0.389311264057193[/C][/ROW]
[ROW][C]104[/C][C]0.568912513183682[/C][C]0.862174973632637[/C][C]0.431087486816318[/C][/ROW]
[ROW][C]105[/C][C]0.523348261113884[/C][C]0.953303477772232[/C][C]0.476651738886116[/C][/ROW]
[ROW][C]106[/C][C]0.50374480286819[/C][C]0.99251039426362[/C][C]0.49625519713181[/C][/ROW]
[ROW][C]107[/C][C]0.500601496808818[/C][C]0.998797006382363[/C][C]0.499398503191182[/C][/ROW]
[ROW][C]108[/C][C]0.512066888836197[/C][C]0.975866222327606[/C][C]0.487933111163803[/C][/ROW]
[ROW][C]109[/C][C]0.461970607190606[/C][C]0.923941214381212[/C][C]0.538029392809394[/C][/ROW]
[ROW][C]110[/C][C]0.438871952745131[/C][C]0.877743905490262[/C][C]0.561128047254869[/C][/ROW]
[ROW][C]111[/C][C]0.399245111949125[/C][C]0.798490223898249[/C][C]0.600754888050876[/C][/ROW]
[ROW][C]112[/C][C]0.622464003183592[/C][C]0.755071993632816[/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]0.586012234060728[/C][C]0.827975531878544[/C][C]0.413987765939272[/C][/ROW]
[ROW][C]115[/C][C]0.787995057379245[/C][C]0.424009885241511[/C][C]0.212004942620755[/C][/ROW]
[ROW][C]116[/C][C]0.766002840747051[/C][C]0.467994318505898[/C][C]0.233997159252949[/C][/ROW]
[ROW][C]117[/C][C]0.755053282831569[/C][C]0.489893434336862[/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]0.689821686952418[/C][C]0.620356626095164[/C][C]0.310178313047582[/C][/ROW]
[ROW][C]121[/C][C]0.741250456736066[/C][C]0.517499086527868[/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]0.702722865924083[/C][C]0.594554268151834[/C][C]0.297277134075917[/C][/ROW]
[ROW][C]125[/C][C]0.746671936072011[/C][C]0.506656127855978[/C][C]0.253328063927989[/C][/ROW]
[ROW][C]126[/C][C]0.719710175859792[/C][C]0.560579648280415[/C][C]0.280289824140208[/C][/ROW]
[ROW][C]127[/C][C]0.709720699392157[/C][C]0.580558601215685[/C][C]0.290279300607843[/C][/ROW]
[ROW][C]128[/C][C]0.676029679145158[/C][C]0.647940641709684[/C][C]0.323970320854842[/C][/ROW]
[ROW][C]129[/C][C]0.650880955155708[/C][C]0.698238089688583[/C][C]0.349119044844292[/C][/ROW]
[ROW][C]130[/C][C]0.720462230266049[/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]0.606230214360545[/C][C]0.787539571278909[/C][C]0.393769785639455[/C][/ROW]
[ROW][C]133[/C][C]0.605895172509633[/C][C]0.788209654980734[/C][C]0.394104827490367[/C][/ROW]
[ROW][C]134[/C][C]0.546964777920311[/C][C]0.906070444159378[/C][C]0.453035222079689[/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]0.957739557717988[/C][C]0.521130221141006[/C][/ROW]
[ROW][C]137[/C][C]0.485143350871994[/C][C]0.970286701743989[/C][C]0.514856649128006[/C][/ROW]
[ROW][C]138[/C][C]0.52873348688515[/C][C]0.942533026229701[/C][C]0.47126651311485[/C][/ROW]
[ROW][C]139[/C][C]0.461648300276759[/C][C]0.923296600553518[/C][C]0.538351699723241[/C][/ROW]
[ROW][C]140[/C][C]0.387508920972158[/C][C]0.775017841944316[/C][C]0.612491079027842[/C][/ROW]
[ROW][C]141[/C][C]0.500398992333828[/C][C]0.999202015332345[/C][C]0.499601007666172[/C][/ROW]
[ROW][C]142[/C][C]0.447731430106021[/C][C]0.895462860212041[/C][C]0.552268569893979[/C][/ROW]
[ROW][C]143[/C][C]0.377989760724787[/C][C]0.755979521449573[/C][C]0.622010239275213[/C][/ROW]
[ROW][C]144[/C][C]0.298009846375371[/C][C]0.596019692750742[/C][C]0.701990153624629[/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]0.340573335736029[/C][C]0.681146671472059[/C][C]0.659426664263971[/C][/ROW]
[ROW][C]148[/C][C]0.234921271540772[/C][C]0.469842543081545[/C][C]0.765078728459228[/C][/ROW]
[ROW][C]149[/C][C]0.146077973504985[/C][C]0.292155947009969[/C][C]0.853922026495015[/C][/ROW]
[ROW][C]150[/C][C]0.0919194134704893[/C][C]0.183838826940979[/C][C]0.908080586529511[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186030&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186030&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
100.1020763753425880.2041527506851750.897923624657412
110.2272380381920740.4544760763841480.772761961807926
120.6516882361919510.6966235276160980.348311763808049
130.6673072325423310.6653855349153370.332692767457669
140.6420154149810760.7159691700378490.357984585018924
150.6474213299422280.7051573401155430.352578670057772
160.582007170180210.835985659639580.41799282981979
170.4974416798337970.9948833596675930.502558320166203
180.4668537175174820.9337074350349640.533146282482518
190.403185159454760.8063703189095210.59681484054524
200.4376092172551570.8752184345103150.562390782744843
210.3969015250977710.7938030501955420.603098474902229
220.4007779696537920.8015559393075840.599222030346208
230.3387699516316920.6775399032633840.661230048368308
240.6033415677282860.7933168645434280.396658432271714
250.533551201044860.9328975979102810.46644879895514
260.5035440241504270.9929119516991470.496455975849573
270.4417309983698710.8834619967397410.558269001630129
280.3951831675487850.7903663350975690.604816832451215
290.3349775204123840.6699550408247670.665022479587617
300.2829189677787490.5658379355574970.717081032221251
310.3429018151285170.6858036302570330.657098184871483
320.4042320700720380.8084641401440770.595767929927962
330.3603988805327850.720797761065570.639601119467215
340.3706313728358480.7412627456716970.629368627164152
350.3853071543426160.7706143086852320.614692845657384
360.3759496983599780.7518993967199550.624050301640022
370.5549897033900570.8900205932198850.445010296609943
380.7584657462238270.4830685075523460.241534253776173
390.7545179416165120.4909641167669760.245482058383488
400.7126200474291360.5747599051417280.287379952570864
410.687613723321830.6247725533563410.31238627667817
420.6381735776537390.7236528446925230.361826422346261
430.5895753437708540.8208493124582930.410424656229147
440.576187266799630.847625466400740.42381273320037
450.5417375471660010.9165249056679980.458262452833999
460.563830488846730.872339022306540.43616951115327
470.5228104281635160.9543791436729670.477189571836484
480.5132263360401520.9735473279196960.486773663959848
490.5781190982572910.8437618034854180.421880901742709
500.5572045651713530.8855908696572940.442795434828647
510.5218326102529950.956334779494010.478167389747005
520.4725186986921830.9450373973843660.527481301307817
530.4343880243849040.8687760487698090.565611975615096
540.3875974418534470.7751948837068950.612402558146553
550.3447994330060130.6895988660120250.655200566993987
560.3176794726538090.6353589453076190.682320527346191
570.2747521586034930.5495043172069850.725247841396508
580.2511059244539740.5022118489079470.748894075546026
590.2314343197213690.4628686394427370.768565680278631
600.2901440782193620.5802881564387230.709855921780638
610.2784971865247180.5569943730494370.721502813475282
620.2412489614241530.4824979228483050.758751038575847
630.2286751236501970.4573502473003930.771324876349803
640.2625362638972050.5250725277944090.737463736102795
650.3388384144525320.6776768289050650.661161585547468
660.3415592785387620.6831185570775240.658440721461238
670.674526217673850.6509475646522990.32547378232615
680.667298147675610.665403704648780.33270185232439
690.8442068400413780.3115863199172430.155793159958622
700.8244781891795310.3510436216409390.175521810820469
710.9305284986215070.1389430027569870.0694715013784933
720.9142375319136360.1715249361727280.0857624680863641
730.9374225765849640.1251548468300720.0625774234150359
740.9255743212335110.1488513575329790.0744256787664894
750.9273220425530020.1453559148939970.0726779574469984
760.9210024044478750.1579951911042490.0789975955521247
770.970096122990050.0598077540198990.0299038770099495
780.9627371234349920.07452575313001510.0372628765650076
790.9553221127204190.08935577455916280.0446778872795814
800.9580010692352870.0839978615294250.0419989307647125
810.9503272401933870.09934551961322680.0496727598066134
820.9500097695588780.09998046088224390.0499902304411219
830.9371893773116470.1256212453767060.0628106226883528
840.9244295166649780.1511409666700430.0755704833350216
850.9159516728922380.1680966542155240.0840483271077622
860.9006531811342390.1986936377315230.0993468188657613
870.8797100421322970.2405799157354070.120289957867703
880.9237274000228140.1525451999543730.0762725999771865
890.9077949479771050.1844101040457910.0922050520228953
900.888521028335510.2229579433289790.11147897166449
910.8903485081722570.2193029836554870.109651491827743
920.879723294041730.2405534119165390.12027670595827
930.8584863885676740.2830272228646520.141513611432326
940.8318023332405590.3363953335188810.168197666759441
950.7998917411512730.4002165176974540.200108258848727
960.7713635890097150.457272821980570.228636410990285
970.7904457067976120.4191085864047760.209554293202388
980.7852534165610850.4294931668778290.214746583438915
990.7513156992978250.497368601404350.248684300702175
1000.7277723553648090.5444552892703830.272227644635191
1010.6864158629623090.6271682740753820.313584137037691
1020.6486695842340220.7026608315319570.351330415765978
1030.6106887359428070.7786225281143870.389311264057193
1040.5689125131836820.8621749736326370.431087486816318
1050.5233482611138840.9533034777722320.476651738886116
1060.503744802868190.992510394263620.49625519713181
1070.5006014968088180.9987970063823630.499398503191182
1080.5120668888361970.9758662223276060.487933111163803
1090.4619706071906060.9239412143812120.538029392809394
1100.4388719527451310.8777439054902620.561128047254869
1110.3992451119491250.7984902238982490.600754888050876
1120.6224640031835920.7550719936328160.377535996816408
1130.61435043167660.77129913664680.3856495683234
1140.5860122340607280.8279755318785440.413987765939272
1150.7879950573792450.4240098852415110.212004942620755
1160.7660028407470510.4679943185058980.233997159252949
1170.7550532828315690.4898934343368620.244946717168431
1180.7283100276160570.5433799447678850.271689972383943
1190.6805217232468620.6389565535062760.319478276753138
1200.6898216869524180.6203566260951640.310178313047582
1210.7412504567360660.5174990865278680.258749543263934
1220.7463110920562030.5073778158875940.253688907943797
1230.7520664555156740.4958670889686520.247933544484326
1240.7027228659240830.5945542681518340.297277134075917
1250.7466719360720110.5066561278559780.253328063927989
1260.7197101758597920.5605796482804150.280289824140208
1270.7097206993921570.5805586012156850.290279300607843
1280.6760296791451580.6479406417096840.323970320854842
1290.6508809551557080.6982380896885830.349119044844292
1300.7204622302660490.5590755394679010.279537769733951
1310.6669860570993350.666027885801330.333013942900665
1320.6062302143605450.7875395712789090.393769785639455
1330.6058951725096330.7882096549807340.394104827490367
1340.5469647779203110.9060704441593780.453035222079689
1350.5062124791455980.9875750417088040.493787520854402
1360.4788697788589940.9577395577179880.521130221141006
1370.4851433508719940.9702867017439890.514856649128006
1380.528733486885150.9425330262297010.47126651311485
1390.4616483002767590.9232966005535180.538351699723241
1400.3875089209721580.7750178419443160.612491079027842
1410.5003989923338280.9992020153323450.499601007666172
1420.4477314301060210.8954628602120410.552268569893979
1430.3779897607247870.7559795214495730.622010239275213
1440.2980098463753710.5960196927507420.701990153624629
1450.3552632752933760.7105265505867530.644736724706624
1460.2629508766098580.5259017532197160.737049123390142
1470.3405733357360290.6811466714720590.659426664263971
1480.2349212715407720.4698425430815450.765078728459228
1490.1460779735049850.2921559470099690.853922026495015
1500.09191941347048930.1838388269409790.908080586529511







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=186030&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=186030&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186030&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')
}