## Free Statistics

of Irreproducible Research!

Author's title
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 07:23:24 -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/t1352118234ovtvnqovbmm1wsn.htm/, Retrieved Wed, 01 Feb 2023 16:06:51 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=186009, Retrieved Wed, 01 Feb 2023 16:06:51 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact123
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [] [2012-11-05 12:23:24] [44856cc66eb94b5de7ab259e2cb08a95] [Current]
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Dataseries X:
9	5	-1	6	24
11	5	-4	6	29
13	9	-6	8	29
12	10	-9	4	25
13	14	-13	8	16
15	19	-13	10	18
13	18	-10	9	13
16	16	-12	12	22
10	8	-9	9	15
14	10	-15	11	20
14	12	-14	11	19
15	13	-18	11	18
13	15	-13	11	13
8	3	-2	11	17
7	2	-1	9	17
3	-2	5	8	13
3	1	8	6	14
4	1	6	7	13
4	-1	7	8	17
0	-6	15	6	17
-4	-13	23	5	15
-14	-25	43	2	9
-18	-26	60	3	10
-8	-9	36	3	9
-1	1	28	7	14
1	3	23	8	18
2	6	23	7	18
0	2	22	7	12
1	5	22	6	16
0	5	24	6	12
-1	0	32	7	19
-3	-5	27	5	13
-3	-4	27	5	12
-3	-2	27	5	13
-4	-1	29	4	11
-8	-8	38	4	10
-9	-16	40	4	16
-13	-19	45	1	12
-18	-28	50	-1	6
-11	-11	43	3	8
-9	-4	44	4	6
-10	-9	44	3	8
-13	-12	49	2	8
-11	-10	42	1	9
-5	-2	36	4	13
-15	-13	57	3	8
-6	0	42	5	11
-6	0	39	6	8
-3	4	33	6	10
-1	7	32	6	15
-3	5	34	6	12
-4	2	37	6	13
-6	-2	38	5	12
0	6	28	6	15
-4	-3	31	5	13
-2	1	28	6	13
-2	0	30	5	16
-6	-7	39	7	14
-7	-6	38	4	12
-6	-4	39	5	15
-6	-4	38	6	14
-3	-2	37	6	19
-2	2	32	5	16
-5	-5	32	3	16
-11	-15	44	2	11
-11	-16	43	3	13
-11	-18	42	3	12
-10	-13	38	2	11
-14	-23	37	0	6
-8	-10	35	4	9
-9	-10	37	4	6
-5	-6	33	5	15
-1	-3	24	6	17
-2	-4	24	6	13
-5	-7	31	5	12
-4	-7	25	5	13
-6	-7	28	3	10
-2	-3	24	5	14
-2	0	25	5	13
-2	-5	16	5	10
-2	-3	17	3	11
2	3	11	6	12
1	2	12	6	7
-8	-7	39	4	11
-1	-1	19	6	9
1	0	14	5	13
-1	-3	15	4	12
2	4	7	5	5
2	2	12	5	13
1	3	12	4	11
-1	0	14	3	8
-2	-10	9	2	8
-2	-10	8	3	8
-1	-9	4	2	8
-8	-22	7	-1	0
-4	-16	3	0	3
-6	-18	5	-2	0
-3	-14	0	1	-1
-3	-12	-2	-2	-1
-7	-17	6	-2	-4
-9	-23	11	-2	1
-11	-28	9	-6	-1
-13	-31	17	-4	0
-11	-21	21	-2	-1
-9	-19	21	0	6
-17	-22	41	-5	0
-22	-22	57	-4	-3
-25	-25	65	-5	-3
-20	-16	68	-1	4
-24	-22	73	-2	1
-24	-21	71	-4	0
-22	-10	71	-1	-4
-19	-7	70	1	-2
-18	-5	69	1	3
-17	-4	65	-2	2
-11	7	57	1	5
-11	6	57	1	6
-12	3	57	3	6
-10	10	55	3	3
-15	0	65	1	4
-15	-2	65	1	7
-15	-1	64	0	5
-13	2	60	2	6
-8	8	43	2	1
-13	-6	47	-1	3
-9	-4	40	1	6
-7	4	31	0	0
-4	7	27	1	3
-4	3	24	1	4
-2	3	23	3	7
0	8	17	2	6
-2	3	16	0	6
-3	-3	15	0	6
1	4	8	3	6
-2	-5	5	-2	2
-1	-1	6	0	2
1	5	5	1	2
-3	0	12	-1	3
-4	-6	8	-2	-1
-9	-13	17	-1	-4
-9	-15	22	-1	4
-7	-8	24	1	5
-14	-20	36	-2	3
-12	-10	31	-5	-1
-16	-22	34	-5	-4
-20	-25	47	-6	0
-12	-10	33	-4	-1
-12	-8	35	-3	-1
-10	-9	31	-3	3
-10	-5	35	-1	2
-13	-7	39	-2	-4
-16	-11	46	-3	-3
-14	-11	40	-3	-1
-17	-16	50	-3	3



 Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 8 seconds R Server 'Gwilym Jenkins' @ jenkins.wessa.net R Framework error message Warning: there are blank lines in the 'Data X' field. Please, use NA for missing data - blank lines are simply deleted and are NOT treated as missing values.

\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 & 8 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
R Framework error message & Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
deleted and are NOT treated as missing values. \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186009&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]8 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[ROW][C]R Framework error message[/C][C]Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
deleted and are NOT treated as missing values.[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186009&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186009&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 Input view raw input (R code) Raw Output view raw output of R engine Computing time 8 seconds R Server 'Gwilym Jenkins' @ jenkins.wessa.net R Framework error message Warning: there are blank lines in the 'Data X' field. Please, use NA for missing data - blank lines are simply deleted and are NOT treated as missing values.

 Multiple Linear Regression - Estimated Regression Equation consumentenvert[t] = -0.0126014706029706 + 0.250036433976062Economie[t] -0.250696282667499Whl[t] + 0.275162218178276Financ[t] + 0.240262823809418Spaarverm\r\r\r[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
consumentenvert[t] =  -0.0126014706029706 +  0.250036433976062Economie[t] -0.250696282667499Whl[t] +  0.275162218178276Financ[t] +  0.240262823809418Spaarverm\r\r\r[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186009&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]consumentenvert[t] =  -0.0126014706029706 +  0.250036433976062Economie[t] -0.250696282667499Whl[t] +  0.275162218178276Financ[t] +  0.240262823809418Spaarverm\r\r\r[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186009&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186009&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 consumentenvert[t] = -0.0126014706029706 + 0.250036433976062Economie[t] -0.250696282667499Whl[t] + 0.275162218178276Financ[t] + 0.240262823809418Spaarverm\r\r\r[t] + e[t]

 Multiple Linear Regression - Ordinary Least Squares Variable Parameter S.D. T-STATH0: parameter = 0 2-tail p-value 1-tail p-value (Intercept) -0.0126014706029706 0.067967 -0.1854 0.853163 0.426581 Economie 0.250036433976062 0.003565 70.1444 0 0 Whl -0.250696282667499 0.001367 -183.3619 0 0 Financ 0.275162218178276 0.014895 18.4735 0 0 Spaarverm\r\r\r 0.240262823809418 0.007054 34.0623 0 0

\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) & -0.0126014706029706 & 0.067967 & -0.1854 & 0.853163 & 0.426581 \tabularnewline
Economie & 0.250036433976062 & 0.003565 & 70.1444 & 0 & 0 \tabularnewline
Whl & -0.250696282667499 & 0.001367 & -183.3619 & 0 & 0 \tabularnewline
Financ & 0.275162218178276 & 0.014895 & 18.4735 & 0 & 0 \tabularnewline
Spaarverm\r\r\r & 0.240262823809418 & 0.007054 & 34.0623 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186009&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]-0.0126014706029706[/C][C]0.067967[/C][C]-0.1854[/C][C]0.853163[/C][C]0.426581[/C][/ROW]
[ROW][C]Economie[/C][C]0.250036433976062[/C][C]0.003565[/C][C]70.1444[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Whl[/C][C]-0.250696282667499[/C][C]0.001367[/C][C]-183.3619[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Financ[/C][C]0.275162218178276[/C][C]0.014895[/C][C]18.4735[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Spaarverm\r\r\r[/C][C]0.240262823809418[/C][C]0.007054[/C][C]34.0623[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186009&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186009&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 Variable Parameter S.D. T-STATH0: parameter = 0 2-tail p-value 1-tail p-value (Intercept) -0.0126014706029706 0.067967 -0.1854 0.853163 0.426581 Economie 0.250036433976062 0.003565 70.1444 0 0 Whl -0.250696282667499 0.001367 -183.3619 0 0 Financ 0.275162218178276 0.014895 18.4735 0 0 Spaarverm\r\r\r 0.240262823809418 0.007054 34.0623 0 0

 Multiple Linear Regression - Regression Statistics Multiple R 0.999346975958035 R-squared 0.99869437835647 Adjusted R-squared 0.998659328111006 F-TEST (value) 28493.2206647462 F-TEST (DF numerator) 4 F-TEST (DF denominator) 149 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.313094003609779 Sum Squared Residuals 14.6061504093637

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.999346975958035 \tabularnewline
R-squared & 0.99869437835647 \tabularnewline
F-TEST (value) & 28493.2206647462 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 149 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.313094003609779 \tabularnewline
Sum Squared Residuals & 14.6061504093637 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186009&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.999346975958035[/C][/ROW]
[ROW][C]R-squared[/C][C]0.99869437835647[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]28493.2206647462[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]149[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.313094003609779[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]14.6061504093637[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186009&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186009&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 R 0.999346975958035 R-squared 0.99869437835647 Adjusted R-squared 0.998659328111006 F-TEST (value) 28493.2206647462 F-TEST (DF numerator) 4 F-TEST (DF denominator) 149 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.313094003609779 Sum Squared Residuals 14.6061504093637

 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals InterpolationForecast ResidualsPrediction Error 1 9 8.90555806244052 0.0944419375594796 2 11 10.8589610294901 0.141038970509895 3 13 12.9108237670859 0.0891762329140841 4 12 11.8512488811137 0.1487511188863 5 13 12.7924632061163 0.20753679388372 6 15 15.073495459972 -0.073495459971976 7 13 12.594893840768 0.40510615923195 8 16 15.5840656069705 0.415934393029485 9 10 10.3243588659588 -0.32435886595877 10 14 14.0802479853195 -0.0802479853195346 11 14 14.0893617467947 -0.08936174679474 12 15 15.1019204876314 -0.10192048763138 13 13 13.1471978231989 -0.147197823198915 14 8 8.35015280138135 -0.350152801381353 15 7 7.29909564838124 -0.29909564838124 16 3 3.55855870305605 -0.558558703056048 17 3 3.2465175444346 -0.246517544434602 18 4 3.78280950413846 0.217190495861542 19 4 4.26825386693478 -0.268253866934783 20 0 0.462176999357929 -0.462176999357929 21 -4 -4.04933616561161 0.0493361656116098 22 -14 -14.3307626240657 0.330762624065675 23 -18 -18.3272108214015 0.327210821401532 24 -8 -8.30014348359792 0.300143483597917 25 -1 -1.49224589073711 0.492245890737113 26 1 1.49752190396846 -0.497521903968456 27 2 1.97246898771836 0.0275310122816356 28 0 -0.218557408374891 0.218557408374891 29 1 1.21744097061269 -0.217440970612691 30 0 -0.245002889959981 0.245002889959981 31 -1 -1.54375333633608 0.543753336336081 32 -3 -3.53235547209195 0.532355472091952 33 -3 -3.52258186192531 0.522581861925308 34 -3 -2.78224617016377 -0.217753829836233 35 -4 -3.78929016731982 -0.210709832680183 36 -8 -8.03607457296916 0.0360745729691612 37 -9 -9.09618166725614 0.0961816672561438 38 -13 -12.8863103322943 -0.113689667705675 39 -18 -18.3820210306294 0.382021030629437 40 -11 -10.795353154032 -0.204646845968044 41 -9 -9.50115782830759 0.501157828307585 42 -10 -10.5459765687473 0.545976568747332 43 -13 -12.8247295021913 -0.17527049780871 44 -11 -10.6046820499355 -0.395317950064472 45 -5 -5.31367493234954 0.313674932349539 46 -15 -14.8051739793291 -0.194826020670929 47 -6 -6.52314318984297 0.523143189842974 48 -6 -6.21668059509045 0.216680595090454 49 -3 -3.23183151556237 0.231831515562374 50 -1 -1.0297118119196 0.0297118119195983 51 -3 -2.75196571663497 -0.248034283365025 52 -4 -4.01390104275624 0.0139010427562404 53 -6 -5.78016810331568 -0.21983189668432 54 0 -0.276963115225663 0.276963115225663 55 -4 -4.03506773480983 0.0350677348098269 56 -2 -2.00767093272481 0.00767093272480671 57 -2 -2.31347367878589 0.313473678785888 58 -6 -6.2501964718881 0.250196471888099 59 -7 -7.0554760573982 0.0554760573982013 60 -6 -5.81014878250705 -0.189851217492952 61 -6 -5.52455310547069 -0.475446894529309 62 -3 -3.57246983580398 0.572469835803978 63 -2 -2.31479337616876 0.314793376168764 64 -5 -4.61537285035775 -0.384627149642254 65 -11 -11.6005689193537 0.600568919353722 66 -11 -10.8442212048652 -0.155778795134828 67 -11 -11.3338606139592 0.333860613959214 68 -10 -9.5963183553966 -0.403681644603398 69 -14 -13.5976249678934 -0.40237503210664 70 -8 -8.0243214167282 0.0243214167282036 71 -9 -9.24650245349146 0.246502453491457 72 -5 -4.80604395445417 -0.193956045545826 73 -1 -1.04398024272138 0.0439802427213818 74 -2 -2.25506797193512 0.255067971935116 75 -5 -5.27547629452349 0.275476294523491 76 -4 -3.53103577470908 -0.468964225290924 77 -6 -5.55423753049638 -0.44576246950362 78 -2 -2.03993093232791 0.0399309323279119 79 -2 -1.78078073687664 -0.219219263123355 80 -2 -1.49548483417771 -0.504515165822289 81 -2 -1.55616986144022 -0.443830138559779 82 2 2.51397591676539 -0.51397591676539 83 1 0.811929081074738 0.188070918925262 84 -8 -7.79647159785118 -0.203528402148819 85 -1 -1.21252855190711 0.212528551907106 86 1 0.976878372465849 0.023121627534151 87 -1 -0.539352254117528 -0.460647745882472 88 2 1.80979549656725 0.190204503432753 89 2 1.97834380575297 0.021656194247029 90 1 1.47269237393192 -0.472692373931921 91 -1 -0.774760182937792 -0.225239817062208 92 -2 -2.29680532753919 0.296805327539186 93 -2 -1.77094682669341 -0.229053173306589 94 -1 -0.793287480225627 -0.206712519774373 95 -8 -7.5434392149271 -0.4565607850729 96 -4 -4.0444847907942 0.0444847907942006 97 -6 -6.31706313186613 0.317063131866129 98 -3 -3.47821215189898 0.478212151898977 99 -3 -3.30223337314668 0.30223337314668 100 -7 -7.27877427579524 0.278774275795238 101 -9 -8.83116017394202 -0.168839826057984 102 -11 -11.1611242988193 0.161124298819264 103 -13 -13.1262166019215 0.126216601921474 104 -11 -11.3185757802837 0.318575780283723 105 -9 -8.58633870930912 -0.413661290690878 106 -17 -17.1677616983352 0.167761698335184 107 -22 -21.6245284742652 -0.375471525734846 108 -25 -24.6553702557116 -0.344629744288391 109 -20 -20.3746425585505 0.374642558550524 110 -24 -24.1242932653509 0.124293265350921 111 -24 -24.1634515262058 0.163451526205829 112 -22 -21.548615393172 -0.451384606828001 113 -19 -19.5169597246009 0.51695972460093 114 -18 -17.5648764549342 -0.435123545065785 115 -17 -17.3778043686324 0.3778043686324 116 -11 -11.0755582075926 0.0755582075926458 117 -11 -11.0853318177593 0.0853318177592905 118 -12 -11.2851166833309 -0.714883316669077 119 -10 -9.75425755159175 -0.245742448408252 120 -15 -15.0716463305745 0.0716463305744911 121 -15 -14.8509307270984 -0.149069272901641 122 -15 -15.1058858762519 0.105885876251911 123 -13 -12.5624041834878 -0.437595816512244 124 -8 -8.00166289333099 0.00166289333098973 125 -13 -12.8499191065818 -0.150080893418161 126 -9 -9.32385935217241 0.323859352172414 127 -7 -6.78404049739121 -0.215959502608789 128 -4 -4.0351953751865 0.0351953751864984 129 -4 -4.04298943927883 0.0429894392788264 130 -2 -2.52118024882652 0.521180248826522 131 0 -0.282245424928911 0.282245424928911 132 -2 -1.83205574849827 -0.16794425150173 133 -3 -3.08157806968714 0.0815780696871399 134 1 1.24903760135261 -0.249037601352614 135 -2 -2.58606384255849 0.586063842558492 136 -1 -1.28628995296519 0.286289952965193 137 1 0.73978715173695 0.26021284826305 138 -3 -2.57533060936299 -0.424669390637013 139 -4 -4.30897759596531 0.308977595965307 140 -9 -8.76112543105521 -0.238874568944787 141 -9 -8.59257712186949 -0.407422878130513 142 -7 -6.55312738920609 -0.446872610793914 143 -14 -13.8679322910825 -0.132067708917519 144 -12 -11.9006244877569 -0.0993755122431316 145 -16 -16.3739390149004 0.37393901490036 146 -20 -19.6972109144466 -0.302789085553358 147 -12 -12.1268548349136 0.126854834913592 148 -12 -11.8530123141182 -0.146987685881808 149 -10 -10.1392123221866 0.139212322186583 150 -10 -9.8317901044052 -0.168209895594798 151 -13 -13.0513872640621 0.051387264062108 152 -16 -15.8413063730077 -0.158693626992292 153 -14 -13.8566030293839 -0.143396970616126 154 -17 -16.6526967307015 -0.347303269298495

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 9 & 8.90555806244052 & 0.0944419375594796 \tabularnewline
2 & 11 & 10.8589610294901 & 0.141038970509895 \tabularnewline
3 & 13 & 12.9108237670859 & 0.0891762329140841 \tabularnewline
4 & 12 & 11.8512488811137 & 0.1487511188863 \tabularnewline
5 & 13 & 12.7924632061163 & 0.20753679388372 \tabularnewline
6 & 15 & 15.073495459972 & -0.073495459971976 \tabularnewline
7 & 13 & 12.594893840768 & 0.40510615923195 \tabularnewline
8 & 16 & 15.5840656069705 & 0.415934393029485 \tabularnewline
9 & 10 & 10.3243588659588 & -0.32435886595877 \tabularnewline
10 & 14 & 14.0802479853195 & -0.0802479853195346 \tabularnewline
11 & 14 & 14.0893617467947 & -0.08936174679474 \tabularnewline
12 & 15 & 15.1019204876314 & -0.10192048763138 \tabularnewline
13 & 13 & 13.1471978231989 & -0.147197823198915 \tabularnewline
14 & 8 & 8.35015280138135 & -0.350152801381353 \tabularnewline
15 & 7 & 7.29909564838124 & -0.29909564838124 \tabularnewline
16 & 3 & 3.55855870305605 & -0.558558703056048 \tabularnewline
17 & 3 & 3.2465175444346 & -0.246517544434602 \tabularnewline
18 & 4 & 3.78280950413846 & 0.217190495861542 \tabularnewline
19 & 4 & 4.26825386693478 & -0.268253866934783 \tabularnewline
20 & 0 & 0.462176999357929 & -0.462176999357929 \tabularnewline
21 & -4 & -4.04933616561161 & 0.0493361656116098 \tabularnewline
22 & -14 & -14.3307626240657 & 0.330762624065675 \tabularnewline
23 & -18 & -18.3272108214015 & 0.327210821401532 \tabularnewline
24 & -8 & -8.30014348359792 & 0.300143483597917 \tabularnewline
25 & -1 & -1.49224589073711 & 0.492245890737113 \tabularnewline
26 & 1 & 1.49752190396846 & -0.497521903968456 \tabularnewline
27 & 2 & 1.97246898771836 & 0.0275310122816356 \tabularnewline
28 & 0 & -0.218557408374891 & 0.218557408374891 \tabularnewline
29 & 1 & 1.21744097061269 & -0.217440970612691 \tabularnewline
30 & 0 & -0.245002889959981 & 0.245002889959981 \tabularnewline
31 & -1 & -1.54375333633608 & 0.543753336336081 \tabularnewline
32 & -3 & -3.53235547209195 & 0.532355472091952 \tabularnewline
33 & -3 & -3.52258186192531 & 0.522581861925308 \tabularnewline
34 & -3 & -2.78224617016377 & -0.217753829836233 \tabularnewline
35 & -4 & -3.78929016731982 & -0.210709832680183 \tabularnewline
36 & -8 & -8.03607457296916 & 0.0360745729691612 \tabularnewline
37 & -9 & -9.09618166725614 & 0.0961816672561438 \tabularnewline
38 & -13 & -12.8863103322943 & -0.113689667705675 \tabularnewline
39 & -18 & -18.3820210306294 & 0.382021030629437 \tabularnewline
40 & -11 & -10.795353154032 & -0.204646845968044 \tabularnewline
41 & -9 & -9.50115782830759 & 0.501157828307585 \tabularnewline
42 & -10 & -10.5459765687473 & 0.545976568747332 \tabularnewline
43 & -13 & -12.8247295021913 & -0.17527049780871 \tabularnewline
44 & -11 & -10.6046820499355 & -0.395317950064472 \tabularnewline
45 & -5 & -5.31367493234954 & 0.313674932349539 \tabularnewline
46 & -15 & -14.8051739793291 & -0.194826020670929 \tabularnewline
47 & -6 & -6.52314318984297 & 0.523143189842974 \tabularnewline
48 & -6 & -6.21668059509045 & 0.216680595090454 \tabularnewline
49 & -3 & -3.23183151556237 & 0.231831515562374 \tabularnewline
50 & -1 & -1.0297118119196 & 0.0297118119195983 \tabularnewline
51 & -3 & -2.75196571663497 & -0.248034283365025 \tabularnewline
52 & -4 & -4.01390104275624 & 0.0139010427562404 \tabularnewline
53 & -6 & -5.78016810331568 & -0.21983189668432 \tabularnewline
54 & 0 & -0.276963115225663 & 0.276963115225663 \tabularnewline
55 & -4 & -4.03506773480983 & 0.0350677348098269 \tabularnewline
56 & -2 & -2.00767093272481 & 0.00767093272480671 \tabularnewline
57 & -2 & -2.31347367878589 & 0.313473678785888 \tabularnewline
58 & -6 & -6.2501964718881 & 0.250196471888099 \tabularnewline
59 & -7 & -7.0554760573982 & 0.0554760573982013 \tabularnewline
60 & -6 & -5.81014878250705 & -0.189851217492952 \tabularnewline
61 & -6 & -5.52455310547069 & -0.475446894529309 \tabularnewline
62 & -3 & -3.57246983580398 & 0.572469835803978 \tabularnewline
63 & -2 & -2.31479337616876 & 0.314793376168764 \tabularnewline
64 & -5 & -4.61537285035775 & -0.384627149642254 \tabularnewline
65 & -11 & -11.6005689193537 & 0.600568919353722 \tabularnewline
66 & -11 & -10.8442212048652 & -0.155778795134828 \tabularnewline
67 & -11 & -11.3338606139592 & 0.333860613959214 \tabularnewline
68 & -10 & -9.5963183553966 & -0.403681644603398 \tabularnewline
69 & -14 & -13.5976249678934 & -0.40237503210664 \tabularnewline
70 & -8 & -8.0243214167282 & 0.0243214167282036 \tabularnewline
71 & -9 & -9.24650245349146 & 0.246502453491457 \tabularnewline
72 & -5 & -4.80604395445417 & -0.193956045545826 \tabularnewline
73 & -1 & -1.04398024272138 & 0.0439802427213818 \tabularnewline
74 & -2 & -2.25506797193512 & 0.255067971935116 \tabularnewline
75 & -5 & -5.27547629452349 & 0.275476294523491 \tabularnewline
76 & -4 & -3.53103577470908 & -0.468964225290924 \tabularnewline
77 & -6 & -5.55423753049638 & -0.44576246950362 \tabularnewline
78 & -2 & -2.03993093232791 & 0.0399309323279119 \tabularnewline
79 & -2 & -1.78078073687664 & -0.219219263123355 \tabularnewline
80 & -2 & -1.49548483417771 & -0.504515165822289 \tabularnewline
81 & -2 & -1.55616986144022 & -0.443830138559779 \tabularnewline
82 & 2 & 2.51397591676539 & -0.51397591676539 \tabularnewline
83 & 1 & 0.811929081074738 & 0.188070918925262 \tabularnewline
84 & -8 & -7.79647159785118 & -0.203528402148819 \tabularnewline
85 & -1 & -1.21252855190711 & 0.212528551907106 \tabularnewline
86 & 1 & 0.976878372465849 & 0.023121627534151 \tabularnewline
87 & -1 & -0.539352254117528 & -0.460647745882472 \tabularnewline
88 & 2 & 1.80979549656725 & 0.190204503432753 \tabularnewline
89 & 2 & 1.97834380575297 & 0.021656194247029 \tabularnewline
90 & 1 & 1.47269237393192 & -0.472692373931921 \tabularnewline
91 & -1 & -0.774760182937792 & -0.225239817062208 \tabularnewline
92 & -2 & -2.29680532753919 & 0.296805327539186 \tabularnewline
93 & -2 & -1.77094682669341 & -0.229053173306589 \tabularnewline
94 & -1 & -0.793287480225627 & -0.206712519774373 \tabularnewline
95 & -8 & -7.5434392149271 & -0.4565607850729 \tabularnewline
96 & -4 & -4.0444847907942 & 0.0444847907942006 \tabularnewline
97 & -6 & -6.31706313186613 & 0.317063131866129 \tabularnewline
98 & -3 & -3.47821215189898 & 0.478212151898977 \tabularnewline
99 & -3 & -3.30223337314668 & 0.30223337314668 \tabularnewline
100 & -7 & -7.27877427579524 & 0.278774275795238 \tabularnewline
101 & -9 & -8.83116017394202 & -0.168839826057984 \tabularnewline
102 & -11 & -11.1611242988193 & 0.161124298819264 \tabularnewline
103 & -13 & -13.1262166019215 & 0.126216601921474 \tabularnewline
104 & -11 & -11.3185757802837 & 0.318575780283723 \tabularnewline
105 & -9 & -8.58633870930912 & -0.413661290690878 \tabularnewline
106 & -17 & -17.1677616983352 & 0.167761698335184 \tabularnewline
107 & -22 & -21.6245284742652 & -0.375471525734846 \tabularnewline
108 & -25 & -24.6553702557116 & -0.344629744288391 \tabularnewline
109 & -20 & -20.3746425585505 & 0.374642558550524 \tabularnewline
110 & -24 & -24.1242932653509 & 0.124293265350921 \tabularnewline
111 & -24 & -24.1634515262058 & 0.163451526205829 \tabularnewline
112 & -22 & -21.548615393172 & -0.451384606828001 \tabularnewline
113 & -19 & -19.5169597246009 & 0.51695972460093 \tabularnewline
114 & -18 & -17.5648764549342 & -0.435123545065785 \tabularnewline
115 & -17 & -17.3778043686324 & 0.3778043686324 \tabularnewline
116 & -11 & -11.0755582075926 & 0.0755582075926458 \tabularnewline
117 & -11 & -11.0853318177593 & 0.0853318177592905 \tabularnewline
118 & -12 & -11.2851166833309 & -0.714883316669077 \tabularnewline
119 & -10 & -9.75425755159175 & -0.245742448408252 \tabularnewline
120 & -15 & -15.0716463305745 & 0.0716463305744911 \tabularnewline
121 & -15 & -14.8509307270984 & -0.149069272901641 \tabularnewline
122 & -15 & -15.1058858762519 & 0.105885876251911 \tabularnewline
123 & -13 & -12.5624041834878 & -0.437595816512244 \tabularnewline
124 & -8 & -8.00166289333099 & 0.00166289333098973 \tabularnewline
125 & -13 & -12.8499191065818 & -0.150080893418161 \tabularnewline
126 & -9 & -9.32385935217241 & 0.323859352172414 \tabularnewline
127 & -7 & -6.78404049739121 & -0.215959502608789 \tabularnewline
128 & -4 & -4.0351953751865 & 0.0351953751864984 \tabularnewline
129 & -4 & -4.04298943927883 & 0.0429894392788264 \tabularnewline
130 & -2 & -2.52118024882652 & 0.521180248826522 \tabularnewline
131 & 0 & -0.282245424928911 & 0.282245424928911 \tabularnewline
132 & -2 & -1.83205574849827 & -0.16794425150173 \tabularnewline
133 & -3 & -3.08157806968714 & 0.0815780696871399 \tabularnewline
134 & 1 & 1.24903760135261 & -0.249037601352614 \tabularnewline
135 & -2 & -2.58606384255849 & 0.586063842558492 \tabularnewline
136 & -1 & -1.28628995296519 & 0.286289952965193 \tabularnewline
137 & 1 & 0.73978715173695 & 0.26021284826305 \tabularnewline
138 & -3 & -2.57533060936299 & -0.424669390637013 \tabularnewline
139 & -4 & -4.30897759596531 & 0.308977595965307 \tabularnewline
140 & -9 & -8.76112543105521 & -0.238874568944787 \tabularnewline
141 & -9 & -8.59257712186949 & -0.407422878130513 \tabularnewline
142 & -7 & -6.55312738920609 & -0.446872610793914 \tabularnewline
143 & -14 & -13.8679322910825 & -0.132067708917519 \tabularnewline
144 & -12 & -11.9006244877569 & -0.0993755122431316 \tabularnewline
145 & -16 & -16.3739390149004 & 0.37393901490036 \tabularnewline
146 & -20 & -19.6972109144466 & -0.302789085553358 \tabularnewline
147 & -12 & -12.1268548349136 & 0.126854834913592 \tabularnewline
148 & -12 & -11.8530123141182 & -0.146987685881808 \tabularnewline
149 & -10 & -10.1392123221866 & 0.139212322186583 \tabularnewline
150 & -10 & -9.8317901044052 & -0.168209895594798 \tabularnewline
151 & -13 & -13.0513872640621 & 0.051387264062108 \tabularnewline
152 & -16 & -15.8413063730077 & -0.158693626992292 \tabularnewline
153 & -14 & -13.8566030293839 & -0.143396970616126 \tabularnewline
154 & -17 & -16.6526967307015 & -0.347303269298495 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186009&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]9[/C][C]8.90555806244052[/C][C]0.0944419375594796[/C][/ROW]
[ROW][C]2[/C][C]11[/C][C]10.8589610294901[/C][C]0.141038970509895[/C][/ROW]
[ROW][C]3[/C][C]13[/C][C]12.9108237670859[/C][C]0.0891762329140841[/C][/ROW]
[ROW][C]4[/C][C]12[/C][C]11.8512488811137[/C][C]0.1487511188863[/C][/ROW]
[ROW][C]5[/C][C]13[/C][C]12.7924632061163[/C][C]0.20753679388372[/C][/ROW]
[ROW][C]6[/C][C]15[/C][C]15.073495459972[/C][C]-0.073495459971976[/C][/ROW]
[ROW][C]7[/C][C]13[/C][C]12.594893840768[/C][C]0.40510615923195[/C][/ROW]
[ROW][C]8[/C][C]16[/C][C]15.5840656069705[/C][C]0.415934393029485[/C][/ROW]
[ROW][C]9[/C][C]10[/C][C]10.3243588659588[/C][C]-0.32435886595877[/C][/ROW]
[ROW][C]10[/C][C]14[/C][C]14.0802479853195[/C][C]-0.0802479853195346[/C][/ROW]
[ROW][C]11[/C][C]14[/C][C]14.0893617467947[/C][C]-0.08936174679474[/C][/ROW]
[ROW][C]12[/C][C]15[/C][C]15.1019204876314[/C][C]-0.10192048763138[/C][/ROW]
[ROW][C]13[/C][C]13[/C][C]13.1471978231989[/C][C]-0.147197823198915[/C][/ROW]
[ROW][C]14[/C][C]8[/C][C]8.35015280138135[/C][C]-0.350152801381353[/C][/ROW]
[ROW][C]15[/C][C]7[/C][C]7.29909564838124[/C][C]-0.29909564838124[/C][/ROW]
[ROW][C]16[/C][C]3[/C][C]3.55855870305605[/C][C]-0.558558703056048[/C][/ROW]
[ROW][C]17[/C][C]3[/C][C]3.2465175444346[/C][C]-0.246517544434602[/C][/ROW]
[ROW][C]18[/C][C]4[/C][C]3.78280950413846[/C][C]0.217190495861542[/C][/ROW]
[ROW][C]19[/C][C]4[/C][C]4.26825386693478[/C][C]-0.268253866934783[/C][/ROW]
[ROW][C]20[/C][C]0[/C][C]0.462176999357929[/C][C]-0.462176999357929[/C][/ROW]
[ROW][C]21[/C][C]-4[/C][C]-4.04933616561161[/C][C]0.0493361656116098[/C][/ROW]
[ROW][C]22[/C][C]-14[/C][C]-14.3307626240657[/C][C]0.330762624065675[/C][/ROW]
[ROW][C]23[/C][C]-18[/C][C]-18.3272108214015[/C][C]0.327210821401532[/C][/ROW]
[ROW][C]24[/C][C]-8[/C][C]-8.30014348359792[/C][C]0.300143483597917[/C][/ROW]
[ROW][C]25[/C][C]-1[/C][C]-1.49224589073711[/C][C]0.492245890737113[/C][/ROW]
[ROW][C]26[/C][C]1[/C][C]1.49752190396846[/C][C]-0.497521903968456[/C][/ROW]
[ROW][C]27[/C][C]2[/C][C]1.97246898771836[/C][C]0.0275310122816356[/C][/ROW]
[ROW][C]28[/C][C]0[/C][C]-0.218557408374891[/C][C]0.218557408374891[/C][/ROW]
[ROW][C]29[/C][C]1[/C][C]1.21744097061269[/C][C]-0.217440970612691[/C][/ROW]
[ROW][C]30[/C][C]0[/C][C]-0.245002889959981[/C][C]0.245002889959981[/C][/ROW]
[ROW][C]31[/C][C]-1[/C][C]-1.54375333633608[/C][C]0.543753336336081[/C][/ROW]
[ROW][C]32[/C][C]-3[/C][C]-3.53235547209195[/C][C]0.532355472091952[/C][/ROW]
[ROW][C]33[/C][C]-3[/C][C]-3.52258186192531[/C][C]0.522581861925308[/C][/ROW]
[ROW][C]34[/C][C]-3[/C][C]-2.78224617016377[/C][C]-0.217753829836233[/C][/ROW]
[ROW][C]35[/C][C]-4[/C][C]-3.78929016731982[/C][C]-0.210709832680183[/C][/ROW]
[ROW][C]36[/C][C]-8[/C][C]-8.03607457296916[/C][C]0.0360745729691612[/C][/ROW]
[ROW][C]37[/C][C]-9[/C][C]-9.09618166725614[/C][C]0.0961816672561438[/C][/ROW]
[ROW][C]38[/C][C]-13[/C][C]-12.8863103322943[/C][C]-0.113689667705675[/C][/ROW]
[ROW][C]39[/C][C]-18[/C][C]-18.3820210306294[/C][C]0.382021030629437[/C][/ROW]
[ROW][C]40[/C][C]-11[/C][C]-10.795353154032[/C][C]-0.204646845968044[/C][/ROW]
[ROW][C]41[/C][C]-9[/C][C]-9.50115782830759[/C][C]0.501157828307585[/C][/ROW]
[ROW][C]42[/C][C]-10[/C][C]-10.5459765687473[/C][C]0.545976568747332[/C][/ROW]
[ROW][C]43[/C][C]-13[/C][C]-12.8247295021913[/C][C]-0.17527049780871[/C][/ROW]
[ROW][C]44[/C][C]-11[/C][C]-10.6046820499355[/C][C]-0.395317950064472[/C][/ROW]
[ROW][C]45[/C][C]-5[/C][C]-5.31367493234954[/C][C]0.313674932349539[/C][/ROW]
[ROW][C]46[/C][C]-15[/C][C]-14.8051739793291[/C][C]-0.194826020670929[/C][/ROW]
[ROW][C]47[/C][C]-6[/C][C]-6.52314318984297[/C][C]0.523143189842974[/C][/ROW]
[ROW][C]48[/C][C]-6[/C][C]-6.21668059509045[/C][C]0.216680595090454[/C][/ROW]
[ROW][C]49[/C][C]-3[/C][C]-3.23183151556237[/C][C]0.231831515562374[/C][/ROW]
[ROW][C]50[/C][C]-1[/C][C]-1.0297118119196[/C][C]0.0297118119195983[/C][/ROW]
[ROW][C]51[/C][C]-3[/C][C]-2.75196571663497[/C][C]-0.248034283365025[/C][/ROW]
[ROW][C]52[/C][C]-4[/C][C]-4.01390104275624[/C][C]0.0139010427562404[/C][/ROW]
[ROW][C]53[/C][C]-6[/C][C]-5.78016810331568[/C][C]-0.21983189668432[/C][/ROW]
[ROW][C]54[/C][C]0[/C][C]-0.276963115225663[/C][C]0.276963115225663[/C][/ROW]
[ROW][C]55[/C][C]-4[/C][C]-4.03506773480983[/C][C]0.0350677348098269[/C][/ROW]
[ROW][C]56[/C][C]-2[/C][C]-2.00767093272481[/C][C]0.00767093272480671[/C][/ROW]
[ROW][C]57[/C][C]-2[/C][C]-2.31347367878589[/C][C]0.313473678785888[/C][/ROW]
[ROW][C]58[/C][C]-6[/C][C]-6.2501964718881[/C][C]0.250196471888099[/C][/ROW]
[ROW][C]59[/C][C]-7[/C][C]-7.0554760573982[/C][C]0.0554760573982013[/C][/ROW]
[ROW][C]60[/C][C]-6[/C][C]-5.81014878250705[/C][C]-0.189851217492952[/C][/ROW]
[ROW][C]61[/C][C]-6[/C][C]-5.52455310547069[/C][C]-0.475446894529309[/C][/ROW]
[ROW][C]62[/C][C]-3[/C][C]-3.57246983580398[/C][C]0.572469835803978[/C][/ROW]
[ROW][C]63[/C][C]-2[/C][C]-2.31479337616876[/C][C]0.314793376168764[/C][/ROW]
[ROW][C]64[/C][C]-5[/C][C]-4.61537285035775[/C][C]-0.384627149642254[/C][/ROW]
[ROW][C]65[/C][C]-11[/C][C]-11.6005689193537[/C][C]0.600568919353722[/C][/ROW]
[ROW][C]66[/C][C]-11[/C][C]-10.8442212048652[/C][C]-0.155778795134828[/C][/ROW]
[ROW][C]67[/C][C]-11[/C][C]-11.3338606139592[/C][C]0.333860613959214[/C][/ROW]
[ROW][C]68[/C][C]-10[/C][C]-9.5963183553966[/C][C]-0.403681644603398[/C][/ROW]
[ROW][C]69[/C][C]-14[/C][C]-13.5976249678934[/C][C]-0.40237503210664[/C][/ROW]
[ROW][C]70[/C][C]-8[/C][C]-8.0243214167282[/C][C]0.0243214167282036[/C][/ROW]
[ROW][C]71[/C][C]-9[/C][C]-9.24650245349146[/C][C]0.246502453491457[/C][/ROW]
[ROW][C]72[/C][C]-5[/C][C]-4.80604395445417[/C][C]-0.193956045545826[/C][/ROW]
[ROW][C]73[/C][C]-1[/C][C]-1.04398024272138[/C][C]0.0439802427213818[/C][/ROW]
[ROW][C]74[/C][C]-2[/C][C]-2.25506797193512[/C][C]0.255067971935116[/C][/ROW]
[ROW][C]75[/C][C]-5[/C][C]-5.27547629452349[/C][C]0.275476294523491[/C][/ROW]
[ROW][C]76[/C][C]-4[/C][C]-3.53103577470908[/C][C]-0.468964225290924[/C][/ROW]
[ROW][C]77[/C][C]-6[/C][C]-5.55423753049638[/C][C]-0.44576246950362[/C][/ROW]
[ROW][C]78[/C][C]-2[/C][C]-2.03993093232791[/C][C]0.0399309323279119[/C][/ROW]
[ROW][C]79[/C][C]-2[/C][C]-1.78078073687664[/C][C]-0.219219263123355[/C][/ROW]
[ROW][C]80[/C][C]-2[/C][C]-1.49548483417771[/C][C]-0.504515165822289[/C][/ROW]
[ROW][C]81[/C][C]-2[/C][C]-1.55616986144022[/C][C]-0.443830138559779[/C][/ROW]
[ROW][C]82[/C][C]2[/C][C]2.51397591676539[/C][C]-0.51397591676539[/C][/ROW]
[ROW][C]83[/C][C]1[/C][C]0.811929081074738[/C][C]0.188070918925262[/C][/ROW]
[ROW][C]84[/C][C]-8[/C][C]-7.79647159785118[/C][C]-0.203528402148819[/C][/ROW]
[ROW][C]85[/C][C]-1[/C][C]-1.21252855190711[/C][C]0.212528551907106[/C][/ROW]
[ROW][C]86[/C][C]1[/C][C]0.976878372465849[/C][C]0.023121627534151[/C][/ROW]
[ROW][C]87[/C][C]-1[/C][C]-0.539352254117528[/C][C]-0.460647745882472[/C][/ROW]
[ROW][C]88[/C][C]2[/C][C]1.80979549656725[/C][C]0.190204503432753[/C][/ROW]
[ROW][C]89[/C][C]2[/C][C]1.97834380575297[/C][C]0.021656194247029[/C][/ROW]
[ROW][C]90[/C][C]1[/C][C]1.47269237393192[/C][C]-0.472692373931921[/C][/ROW]
[ROW][C]91[/C][C]-1[/C][C]-0.774760182937792[/C][C]-0.225239817062208[/C][/ROW]
[ROW][C]92[/C][C]-2[/C][C]-2.29680532753919[/C][C]0.296805327539186[/C][/ROW]
[ROW][C]93[/C][C]-2[/C][C]-1.77094682669341[/C][C]-0.229053173306589[/C][/ROW]
[ROW][C]94[/C][C]-1[/C][C]-0.793287480225627[/C][C]-0.206712519774373[/C][/ROW]
[ROW][C]95[/C][C]-8[/C][C]-7.5434392149271[/C][C]-0.4565607850729[/C][/ROW]
[ROW][C]96[/C][C]-4[/C][C]-4.0444847907942[/C][C]0.0444847907942006[/C][/ROW]
[ROW][C]97[/C][C]-6[/C][C]-6.31706313186613[/C][C]0.317063131866129[/C][/ROW]
[ROW][C]98[/C][C]-3[/C][C]-3.47821215189898[/C][C]0.478212151898977[/C][/ROW]
[ROW][C]99[/C][C]-3[/C][C]-3.30223337314668[/C][C]0.30223337314668[/C][/ROW]
[ROW][C]100[/C][C]-7[/C][C]-7.27877427579524[/C][C]0.278774275795238[/C][/ROW]
[ROW][C]101[/C][C]-9[/C][C]-8.83116017394202[/C][C]-0.168839826057984[/C][/ROW]
[ROW][C]102[/C][C]-11[/C][C]-11.1611242988193[/C][C]0.161124298819264[/C][/ROW]
[ROW][C]103[/C][C]-13[/C][C]-13.1262166019215[/C][C]0.126216601921474[/C][/ROW]
[ROW][C]104[/C][C]-11[/C][C]-11.3185757802837[/C][C]0.318575780283723[/C][/ROW]
[ROW][C]105[/C][C]-9[/C][C]-8.58633870930912[/C][C]-0.413661290690878[/C][/ROW]
[ROW][C]106[/C][C]-17[/C][C]-17.1677616983352[/C][C]0.167761698335184[/C][/ROW]
[ROW][C]107[/C][C]-22[/C][C]-21.6245284742652[/C][C]-0.375471525734846[/C][/ROW]
[ROW][C]108[/C][C]-25[/C][C]-24.6553702557116[/C][C]-0.344629744288391[/C][/ROW]
[ROW][C]109[/C][C]-20[/C][C]-20.3746425585505[/C][C]0.374642558550524[/C][/ROW]
[ROW][C]110[/C][C]-24[/C][C]-24.1242932653509[/C][C]0.124293265350921[/C][/ROW]
[ROW][C]111[/C][C]-24[/C][C]-24.1634515262058[/C][C]0.163451526205829[/C][/ROW]
[ROW][C]112[/C][C]-22[/C][C]-21.548615393172[/C][C]-0.451384606828001[/C][/ROW]
[ROW][C]113[/C][C]-19[/C][C]-19.5169597246009[/C][C]0.51695972460093[/C][/ROW]
[ROW][C]114[/C][C]-18[/C][C]-17.5648764549342[/C][C]-0.435123545065785[/C][/ROW]
[ROW][C]115[/C][C]-17[/C][C]-17.3778043686324[/C][C]0.3778043686324[/C][/ROW]
[ROW][C]116[/C][C]-11[/C][C]-11.0755582075926[/C][C]0.0755582075926458[/C][/ROW]
[ROW][C]117[/C][C]-11[/C][C]-11.0853318177593[/C][C]0.0853318177592905[/C][/ROW]
[ROW][C]118[/C][C]-12[/C][C]-11.2851166833309[/C][C]-0.714883316669077[/C][/ROW]
[ROW][C]119[/C][C]-10[/C][C]-9.75425755159175[/C][C]-0.245742448408252[/C][/ROW]
[ROW][C]120[/C][C]-15[/C][C]-15.0716463305745[/C][C]0.0716463305744911[/C][/ROW]
[ROW][C]121[/C][C]-15[/C][C]-14.8509307270984[/C][C]-0.149069272901641[/C][/ROW]
[ROW][C]122[/C][C]-15[/C][C]-15.1058858762519[/C][C]0.105885876251911[/C][/ROW]
[ROW][C]123[/C][C]-13[/C][C]-12.5624041834878[/C][C]-0.437595816512244[/C][/ROW]
[ROW][C]124[/C][C]-8[/C][C]-8.00166289333099[/C][C]0.00166289333098973[/C][/ROW]
[ROW][C]125[/C][C]-13[/C][C]-12.8499191065818[/C][C]-0.150080893418161[/C][/ROW]
[ROW][C]126[/C][C]-9[/C][C]-9.32385935217241[/C][C]0.323859352172414[/C][/ROW]
[ROW][C]127[/C][C]-7[/C][C]-6.78404049739121[/C][C]-0.215959502608789[/C][/ROW]
[ROW][C]128[/C][C]-4[/C][C]-4.0351953751865[/C][C]0.0351953751864984[/C][/ROW]
[ROW][C]129[/C][C]-4[/C][C]-4.04298943927883[/C][C]0.0429894392788264[/C][/ROW]
[ROW][C]130[/C][C]-2[/C][C]-2.52118024882652[/C][C]0.521180248826522[/C][/ROW]
[ROW][C]131[/C][C]0[/C][C]-0.282245424928911[/C][C]0.282245424928911[/C][/ROW]
[ROW][C]132[/C][C]-2[/C][C]-1.83205574849827[/C][C]-0.16794425150173[/C][/ROW]
[ROW][C]133[/C][C]-3[/C][C]-3.08157806968714[/C][C]0.0815780696871399[/C][/ROW]
[ROW][C]134[/C][C]1[/C][C]1.24903760135261[/C][C]-0.249037601352614[/C][/ROW]
[ROW][C]135[/C][C]-2[/C][C]-2.58606384255849[/C][C]0.586063842558492[/C][/ROW]
[ROW][C]136[/C][C]-1[/C][C]-1.28628995296519[/C][C]0.286289952965193[/C][/ROW]
[ROW][C]137[/C][C]1[/C][C]0.73978715173695[/C][C]0.26021284826305[/C][/ROW]
[ROW][C]138[/C][C]-3[/C][C]-2.57533060936299[/C][C]-0.424669390637013[/C][/ROW]
[ROW][C]139[/C][C]-4[/C][C]-4.30897759596531[/C][C]0.308977595965307[/C][/ROW]
[ROW][C]140[/C][C]-9[/C][C]-8.76112543105521[/C][C]-0.238874568944787[/C][/ROW]
[ROW][C]141[/C][C]-9[/C][C]-8.59257712186949[/C][C]-0.407422878130513[/C][/ROW]
[ROW][C]142[/C][C]-7[/C][C]-6.55312738920609[/C][C]-0.446872610793914[/C][/ROW]
[ROW][C]143[/C][C]-14[/C][C]-13.8679322910825[/C][C]-0.132067708917519[/C][/ROW]
[ROW][C]144[/C][C]-12[/C][C]-11.9006244877569[/C][C]-0.0993755122431316[/C][/ROW]
[ROW][C]145[/C][C]-16[/C][C]-16.3739390149004[/C][C]0.37393901490036[/C][/ROW]
[ROW][C]146[/C][C]-20[/C][C]-19.6972109144466[/C][C]-0.302789085553358[/C][/ROW]
[ROW][C]147[/C][C]-12[/C][C]-12.1268548349136[/C][C]0.126854834913592[/C][/ROW]
[ROW][C]148[/C][C]-12[/C][C]-11.8530123141182[/C][C]-0.146987685881808[/C][/ROW]
[ROW][C]149[/C][C]-10[/C][C]-10.1392123221866[/C][C]0.139212322186583[/C][/ROW]
[ROW][C]150[/C][C]-10[/C][C]-9.8317901044052[/C][C]-0.168209895594798[/C][/ROW]
[ROW][C]151[/C][C]-13[/C][C]-13.0513872640621[/C][C]0.051387264062108[/C][/ROW]
[ROW][C]152[/C][C]-16[/C][C]-15.8413063730077[/C][C]-0.158693626992292[/C][/ROW]
[ROW][C]153[/C][C]-14[/C][C]-13.8566030293839[/C][C]-0.143396970616126[/C][/ROW]
[ROW][C]154[/C][C]-17[/C][C]-16.6526967307015[/C][C]-0.347303269298495[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186009&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186009&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 Index Actuals InterpolationForecast ResidualsPrediction Error 1 9 8.90555806244052 0.0944419375594796 2 11 10.8589610294901 0.141038970509895 3 13 12.9108237670859 0.0891762329140841 4 12 11.8512488811137 0.1487511188863 5 13 12.7924632061163 0.20753679388372 6 15 15.073495459972 -0.073495459971976 7 13 12.594893840768 0.40510615923195 8 16 15.5840656069705 0.415934393029485 9 10 10.3243588659588 -0.32435886595877 10 14 14.0802479853195 -0.0802479853195346 11 14 14.0893617467947 -0.08936174679474 12 15 15.1019204876314 -0.10192048763138 13 13 13.1471978231989 -0.147197823198915 14 8 8.35015280138135 -0.350152801381353 15 7 7.29909564838124 -0.29909564838124 16 3 3.55855870305605 -0.558558703056048 17 3 3.2465175444346 -0.246517544434602 18 4 3.78280950413846 0.217190495861542 19 4 4.26825386693478 -0.268253866934783 20 0 0.462176999357929 -0.462176999357929 21 -4 -4.04933616561161 0.0493361656116098 22 -14 -14.3307626240657 0.330762624065675 23 -18 -18.3272108214015 0.327210821401532 24 -8 -8.30014348359792 0.300143483597917 25 -1 -1.49224589073711 0.492245890737113 26 1 1.49752190396846 -0.497521903968456 27 2 1.97246898771836 0.0275310122816356 28 0 -0.218557408374891 0.218557408374891 29 1 1.21744097061269 -0.217440970612691 30 0 -0.245002889959981 0.245002889959981 31 -1 -1.54375333633608 0.543753336336081 32 -3 -3.53235547209195 0.532355472091952 33 -3 -3.52258186192531 0.522581861925308 34 -3 -2.78224617016377 -0.217753829836233 35 -4 -3.78929016731982 -0.210709832680183 36 -8 -8.03607457296916 0.0360745729691612 37 -9 -9.09618166725614 0.0961816672561438 38 -13 -12.8863103322943 -0.113689667705675 39 -18 -18.3820210306294 0.382021030629437 40 -11 -10.795353154032 -0.204646845968044 41 -9 -9.50115782830759 0.501157828307585 42 -10 -10.5459765687473 0.545976568747332 43 -13 -12.8247295021913 -0.17527049780871 44 -11 -10.6046820499355 -0.395317950064472 45 -5 -5.31367493234954 0.313674932349539 46 -15 -14.8051739793291 -0.194826020670929 47 -6 -6.52314318984297 0.523143189842974 48 -6 -6.21668059509045 0.216680595090454 49 -3 -3.23183151556237 0.231831515562374 50 -1 -1.0297118119196 0.0297118119195983 51 -3 -2.75196571663497 -0.248034283365025 52 -4 -4.01390104275624 0.0139010427562404 53 -6 -5.78016810331568 -0.21983189668432 54 0 -0.276963115225663 0.276963115225663 55 -4 -4.03506773480983 0.0350677348098269 56 -2 -2.00767093272481 0.00767093272480671 57 -2 -2.31347367878589 0.313473678785888 58 -6 -6.2501964718881 0.250196471888099 59 -7 -7.0554760573982 0.0554760573982013 60 -6 -5.81014878250705 -0.189851217492952 61 -6 -5.52455310547069 -0.475446894529309 62 -3 -3.57246983580398 0.572469835803978 63 -2 -2.31479337616876 0.314793376168764 64 -5 -4.61537285035775 -0.384627149642254 65 -11 -11.6005689193537 0.600568919353722 66 -11 -10.8442212048652 -0.155778795134828 67 -11 -11.3338606139592 0.333860613959214 68 -10 -9.5963183553966 -0.403681644603398 69 -14 -13.5976249678934 -0.40237503210664 70 -8 -8.0243214167282 0.0243214167282036 71 -9 -9.24650245349146 0.246502453491457 72 -5 -4.80604395445417 -0.193956045545826 73 -1 -1.04398024272138 0.0439802427213818 74 -2 -2.25506797193512 0.255067971935116 75 -5 -5.27547629452349 0.275476294523491 76 -4 -3.53103577470908 -0.468964225290924 77 -6 -5.55423753049638 -0.44576246950362 78 -2 -2.03993093232791 0.0399309323279119 79 -2 -1.78078073687664 -0.219219263123355 80 -2 -1.49548483417771 -0.504515165822289 81 -2 -1.55616986144022 -0.443830138559779 82 2 2.51397591676539 -0.51397591676539 83 1 0.811929081074738 0.188070918925262 84 -8 -7.79647159785118 -0.203528402148819 85 -1 -1.21252855190711 0.212528551907106 86 1 0.976878372465849 0.023121627534151 87 -1 -0.539352254117528 -0.460647745882472 88 2 1.80979549656725 0.190204503432753 89 2 1.97834380575297 0.021656194247029 90 1 1.47269237393192 -0.472692373931921 91 -1 -0.774760182937792 -0.225239817062208 92 -2 -2.29680532753919 0.296805327539186 93 -2 -1.77094682669341 -0.229053173306589 94 -1 -0.793287480225627 -0.206712519774373 95 -8 -7.5434392149271 -0.4565607850729 96 -4 -4.0444847907942 0.0444847907942006 97 -6 -6.31706313186613 0.317063131866129 98 -3 -3.47821215189898 0.478212151898977 99 -3 -3.30223337314668 0.30223337314668 100 -7 -7.27877427579524 0.278774275795238 101 -9 -8.83116017394202 -0.168839826057984 102 -11 -11.1611242988193 0.161124298819264 103 -13 -13.1262166019215 0.126216601921474 104 -11 -11.3185757802837 0.318575780283723 105 -9 -8.58633870930912 -0.413661290690878 106 -17 -17.1677616983352 0.167761698335184 107 -22 -21.6245284742652 -0.375471525734846 108 -25 -24.6553702557116 -0.344629744288391 109 -20 -20.3746425585505 0.374642558550524 110 -24 -24.1242932653509 0.124293265350921 111 -24 -24.1634515262058 0.163451526205829 112 -22 -21.548615393172 -0.451384606828001 113 -19 -19.5169597246009 0.51695972460093 114 -18 -17.5648764549342 -0.435123545065785 115 -17 -17.3778043686324 0.3778043686324 116 -11 -11.0755582075926 0.0755582075926458 117 -11 -11.0853318177593 0.0853318177592905 118 -12 -11.2851166833309 -0.714883316669077 119 -10 -9.75425755159175 -0.245742448408252 120 -15 -15.0716463305745 0.0716463305744911 121 -15 -14.8509307270984 -0.149069272901641 122 -15 -15.1058858762519 0.105885876251911 123 -13 -12.5624041834878 -0.437595816512244 124 -8 -8.00166289333099 0.00166289333098973 125 -13 -12.8499191065818 -0.150080893418161 126 -9 -9.32385935217241 0.323859352172414 127 -7 -6.78404049739121 -0.215959502608789 128 -4 -4.0351953751865 0.0351953751864984 129 -4 -4.04298943927883 0.0429894392788264 130 -2 -2.52118024882652 0.521180248826522 131 0 -0.282245424928911 0.282245424928911 132 -2 -1.83205574849827 -0.16794425150173 133 -3 -3.08157806968714 0.0815780696871399 134 1 1.24903760135261 -0.249037601352614 135 -2 -2.58606384255849 0.586063842558492 136 -1 -1.28628995296519 0.286289952965193 137 1 0.73978715173695 0.26021284826305 138 -3 -2.57533060936299 -0.424669390637013 139 -4 -4.30897759596531 0.308977595965307 140 -9 -8.76112543105521 -0.238874568944787 141 -9 -8.59257712186949 -0.407422878130513 142 -7 -6.55312738920609 -0.446872610793914 143 -14 -13.8679322910825 -0.132067708917519 144 -12 -11.9006244877569 -0.0993755122431316 145 -16 -16.3739390149004 0.37393901490036 146 -20 -19.6972109144466 -0.302789085553358 147 -12 -12.1268548349136 0.126854834913592 148 -12 -11.8530123141182 -0.146987685881808 149 -10 -10.1392123221866 0.139212322186583 150 -10 -9.8317901044052 -0.168209895594798 151 -13 -13.0513872640621 0.051387264062108 152 -16 -15.8413063730077 -0.158693626992292 153 -14 -13.8566030293839 -0.143396970616126 154 -17 -16.6526967307015 -0.347303269298495

 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 8 0.312502804888174 0.625005609776347 0.687497195111826 9 0.379313659613926 0.758627319227852 0.620686340386074 10 0.250053719595139 0.500107439190278 0.749946280404861 11 0.153443129707264 0.306886259414528 0.846556870292736 12 0.0872036706017757 0.174407341203551 0.912796329398224 13 0.0582192419809876 0.116438483961975 0.941780758019012 14 0.0329659766448537 0.0659319532897074 0.967034023355146 15 0.0171758589823543 0.0343517179647086 0.982824141017646 16 0.010387727365412 0.020775454730824 0.989612272634588 17 0.00508431349489226 0.0101686269897845 0.994915686505108 18 0.0308313333519913 0.0616626667039827 0.969168666648009 19 0.0187234526186492 0.0374469052372985 0.981276547381351 20 0.0155036982573819 0.0310073965147639 0.984496301742618 21 0.0427870989947805 0.0855741979895611 0.957212901005219 22 0.118482268274294 0.236964536548588 0.881517731725706 23 0.0873890904086093 0.174778180817219 0.912610909591391 24 0.0664141683617499 0.1328283367235 0.93358583163825 25 0.0499480409256597 0.0998960818513193 0.95005195907434 26 0.321733499278601 0.643466998557201 0.678266500721399 27 0.294015627833033 0.588031255666066 0.705984372166967 28 0.243142688889617 0.486285377779233 0.756857311110383 29 0.295656135483938 0.591312270967876 0.704343864516062 30 0.246249357040226 0.492498714080451 0.753750642959774 31 0.295184626352806 0.590369252705612 0.704815373647194 32 0.342124910243429 0.684249820486858 0.657875089756571 33 0.369952853623658 0.739905707247315 0.630047146376342 34 0.418650233436095 0.837300466872189 0.581349766563905 35 0.473413556503143 0.946827113006286 0.526586443496857 36 0.425998372189008 0.851996744378017 0.574001627810992 37 0.372818980997273 0.745637961994546 0.627181019002727 38 0.351645717585975 0.70329143517195 0.648354282414025 39 0.354664684142947 0.709329368285895 0.645335315857053 40 0.370472610498758 0.740945220997516 0.629527389501242 41 0.377419444903477 0.754838889806954 0.622580555096523 42 0.408975679102274 0.817951358204548 0.591024320897726 43 0.439186450018487 0.878372900036974 0.560813549981513 44 0.553134997257937 0.893730005484126 0.446865002742063 45 0.524512925284711 0.950974149430578 0.475487074715289 46 0.536263977733772 0.927472044532456 0.463736022266228 47 0.565228156944112 0.869543686111777 0.434771843055888 48 0.522671378029993 0.954657243940013 0.477328621970007 49 0.482422667615759 0.964845335231518 0.517577332384241 50 0.451837721314238 0.903675442628477 0.548162278685762 51 0.480629686354399 0.961259372708798 0.519370313645601 52 0.438982874918642 0.877965749837284 0.561017125081358 53 0.437601545203211 0.875203090406422 0.562398454796789 54 0.412792328285646 0.825584656571292 0.587207671714354 55 0.367343513083158 0.734687026166316 0.632656486916842 56 0.324207572378903 0.648415144757806 0.675792427621097 57 0.315991966954205 0.63198393390841 0.684008033045795 58 0.301460684705517 0.602921369411034 0.698539315294483 59 0.264571190670928 0.529142381341855 0.735428809329072 60 0.254727148357493 0.509454296714987 0.745272851642507 61 0.323362631808308 0.646725263616617 0.676637368191692 62 0.438499555268616 0.876999110537232 0.561500444731384 63 0.444938505525659 0.889877011051317 0.555061494474341 64 0.483910774281511 0.967821548563022 0.516089225718489 65 0.633377224564036 0.733245550871927 0.366622775435964 66 0.6032652652625 0.793469469475 0.3967347347375 67 0.645334983548307 0.709330032903385 0.354665016451693 68 0.670845480927383 0.658309038145233 0.329154519072617 69 0.67514908909527 0.649701821809461 0.32485091090473 70 0.635922859480199 0.728154281039603 0.364077140519801 71 0.629065495065964 0.741869009868071 0.370934504934036 72 0.602650177432517 0.794699645134967 0.397349822567483 73 0.57735012688295 0.8452997462341 0.42264987311705 74 0.597903054278662 0.804193891442677 0.402096945721338 75 0.635760164173669 0.728479671652662 0.364239835826331 76 0.655486041806269 0.689027916387462 0.344513958193731 77 0.674944667182956 0.650110665634088 0.325055332817044 78 0.653759382202312 0.692481235595375 0.346240617797687 79 0.626333699022848 0.747332601954305 0.373666300977152 80 0.651615372963367 0.696769254073267 0.348384627036633 81 0.657666296349373 0.684667407301254 0.342333703650627 82 0.696806671707683 0.606386656584635 0.303193328292317 83 0.684894169745098 0.630211660509803 0.315105830254902 84 0.656336833682377 0.687326332635246 0.343663166317623 85 0.655639451523052 0.688721096953895 0.344360548476948 86 0.623829812761055 0.75234037447789 0.376170187238945 87 0.629515030783786 0.740969938432428 0.370484969216214 88 0.618289491229358 0.763421017541284 0.381710508770642 89 0.583266630559604 0.833466738880792 0.416733369440396 90 0.607990071523243 0.784019856953513 0.392009928476757 91 0.572611959249763 0.854776081500473 0.427388040750237 92 0.624577377686413 0.750845244627175 0.375422622313587 93 0.582632818287882 0.834734363424236 0.417367181712118 94 0.543015925741453 0.913968148517094 0.456984074258547 95 0.580588981388392 0.838822037223215 0.419411018611608 96 0.557755069638444 0.884489860723112 0.442244930361556 97 0.585131281516819 0.829737436966361 0.41486871848318 98 0.659325623586663 0.681348752826674 0.340674376413337 99 0.648107127537677 0.703785744924646 0.351892872462323 100 0.632557548274412 0.734884903451176 0.367442451725588 101 0.593591774655283 0.812816450689434 0.406408225344717 102 0.552840442538796 0.894319114922408 0.447159557461204 103 0.517005882720352 0.965988234559297 0.482994117279648 104 0.538283226387117 0.923433547225767 0.461716773612883 105 0.541228516823785 0.917542966352429 0.458771483176214 106 0.504706017128897 0.990587965742207 0.495293982871104 107 0.52651496774551 0.946970064508979 0.47348503225449 108 0.535658442808301 0.928683114383397 0.464341557191699 109 0.597412992007695 0.805174015984609 0.402587007992305 110 0.594346715711497 0.811306568577005 0.405653284288503 111 0.59178389893659 0.816432202126819 0.40821610106341 112 0.631137272623167 0.737725454753667 0.368862727376833 113 0.82066907031427 0.358661859371459 0.17933092968573 114 0.814308169356745 0.37138366128651 0.185691830643255 115 0.850488409034045 0.299023181931911 0.149511590965955 116 0.821216729327828 0.357566541344344 0.178783270672172 117 0.793738195459362 0.412523609081276 0.206261804540638 118 0.884039406645545 0.23192118670891 0.115960593354455 119 0.867170182829591 0.265659634340818 0.132829817170409 120 0.851229957265061 0.297540085469879 0.148770042734939 121 0.817131116230592 0.365737767538816 0.182868883769408 122 0.819146567224771 0.361706865550458 0.180853432775229 123 0.805247776997019 0.389504446005962 0.194752223002981 124 0.758530398828595 0.482939202342809 0.241469601171405 125 0.707064262962457 0.585871474075086 0.292935737037543 126 0.794977595247912 0.410044809504177 0.205022404752088 127 0.779650254230221 0.440699491539558 0.220349745769779 128 0.725895512069541 0.548208975860917 0.274104487930459 129 0.665572667594606 0.668854664810787 0.334427332405394 130 0.905064080587328 0.189871838825345 0.0949359194126724 131 0.941476596196425 0.11704680760715 0.058523403803575 132 0.919374295537953 0.161251408924095 0.0806257044620473 133 0.902183542078996 0.195632915842008 0.0978164579210038 134 0.864061128149052 0.271877743701896 0.135938871850948 135 0.908182975366005 0.18363404926799 0.0918170246339949 136 0.908282286779689 0.183435426440621 0.0917177132203107 137 0.932351955808468 0.135296088383063 0.0676480441915316 138 0.957760961319805 0.0844780773603897 0.0422390386801949 139 0.947865489357178 0.104269021285644 0.0521345106428222 140 0.954187933618789 0.0916241327624217 0.0458120663812109 141 0.957535051228936 0.0849298975421286 0.0424649487710643 142 0.986098553842573 0.0278028923148546 0.0139014461574273 143 0.988214231596306 0.0235715368073886 0.0117857684036943 144 0.976352896948374 0.0472942061032517 0.0236471030516259 145 0.957075698910428 0.0858486021791443 0.0429243010895721 146 0.929869019172508 0.140261961654985 0.0701309808274924

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 0.312502804888174 & 0.625005609776347 & 0.687497195111826 \tabularnewline
9 & 0.379313659613926 & 0.758627319227852 & 0.620686340386074 \tabularnewline
10 & 0.250053719595139 & 0.500107439190278 & 0.749946280404861 \tabularnewline
11 & 0.153443129707264 & 0.306886259414528 & 0.846556870292736 \tabularnewline
12 & 0.0872036706017757 & 0.174407341203551 & 0.912796329398224 \tabularnewline
13 & 0.0582192419809876 & 0.116438483961975 & 0.941780758019012 \tabularnewline
14 & 0.0329659766448537 & 0.0659319532897074 & 0.967034023355146 \tabularnewline
15 & 0.0171758589823543 & 0.0343517179647086 & 0.982824141017646 \tabularnewline
16 & 0.010387727365412 & 0.020775454730824 & 0.989612272634588 \tabularnewline
17 & 0.00508431349489226 & 0.0101686269897845 & 0.994915686505108 \tabularnewline
18 & 0.0308313333519913 & 0.0616626667039827 & 0.969168666648009 \tabularnewline
19 & 0.0187234526186492 & 0.0374469052372985 & 0.981276547381351 \tabularnewline
20 & 0.0155036982573819 & 0.0310073965147639 & 0.984496301742618 \tabularnewline
21 & 0.0427870989947805 & 0.0855741979895611 & 0.957212901005219 \tabularnewline
22 & 0.118482268274294 & 0.236964536548588 & 0.881517731725706 \tabularnewline
23 & 0.0873890904086093 & 0.174778180817219 & 0.912610909591391 \tabularnewline
24 & 0.0664141683617499 & 0.1328283367235 & 0.93358583163825 \tabularnewline
25 & 0.0499480409256597 & 0.0998960818513193 & 0.95005195907434 \tabularnewline
26 & 0.321733499278601 & 0.643466998557201 & 0.678266500721399 \tabularnewline
27 & 0.294015627833033 & 0.588031255666066 & 0.705984372166967 \tabularnewline
28 & 0.243142688889617 & 0.486285377779233 & 0.756857311110383 \tabularnewline
29 & 0.295656135483938 & 0.591312270967876 & 0.704343864516062 \tabularnewline
30 & 0.246249357040226 & 0.492498714080451 & 0.753750642959774 \tabularnewline
31 & 0.295184626352806 & 0.590369252705612 & 0.704815373647194 \tabularnewline
32 & 0.342124910243429 & 0.684249820486858 & 0.657875089756571 \tabularnewline
33 & 0.369952853623658 & 0.739905707247315 & 0.630047146376342 \tabularnewline
34 & 0.418650233436095 & 0.837300466872189 & 0.581349766563905 \tabularnewline
35 & 0.473413556503143 & 0.946827113006286 & 0.526586443496857 \tabularnewline
36 & 0.425998372189008 & 0.851996744378017 & 0.574001627810992 \tabularnewline
37 & 0.372818980997273 & 0.745637961994546 & 0.627181019002727 \tabularnewline
38 & 0.351645717585975 & 0.70329143517195 & 0.648354282414025 \tabularnewline
39 & 0.354664684142947 & 0.709329368285895 & 0.645335315857053 \tabularnewline
40 & 0.370472610498758 & 0.740945220997516 & 0.629527389501242 \tabularnewline
41 & 0.377419444903477 & 0.754838889806954 & 0.622580555096523 \tabularnewline
42 & 0.408975679102274 & 0.817951358204548 & 0.591024320897726 \tabularnewline
43 & 0.439186450018487 & 0.878372900036974 & 0.560813549981513 \tabularnewline
44 & 0.553134997257937 & 0.893730005484126 & 0.446865002742063 \tabularnewline
45 & 0.524512925284711 & 0.950974149430578 & 0.475487074715289 \tabularnewline
46 & 0.536263977733772 & 0.927472044532456 & 0.463736022266228 \tabularnewline
47 & 0.565228156944112 & 0.869543686111777 & 0.434771843055888 \tabularnewline
48 & 0.522671378029993 & 0.954657243940013 & 0.477328621970007 \tabularnewline
49 & 0.482422667615759 & 0.964845335231518 & 0.517577332384241 \tabularnewline
50 & 0.451837721314238 & 0.903675442628477 & 0.548162278685762 \tabularnewline
51 & 0.480629686354399 & 0.961259372708798 & 0.519370313645601 \tabularnewline
52 & 0.438982874918642 & 0.877965749837284 & 0.561017125081358 \tabularnewline
53 & 0.437601545203211 & 0.875203090406422 & 0.562398454796789 \tabularnewline
54 & 0.412792328285646 & 0.825584656571292 & 0.587207671714354 \tabularnewline
55 & 0.367343513083158 & 0.734687026166316 & 0.632656486916842 \tabularnewline
56 & 0.324207572378903 & 0.648415144757806 & 0.675792427621097 \tabularnewline
57 & 0.315991966954205 & 0.63198393390841 & 0.684008033045795 \tabularnewline
58 & 0.301460684705517 & 0.602921369411034 & 0.698539315294483 \tabularnewline
59 & 0.264571190670928 & 0.529142381341855 & 0.735428809329072 \tabularnewline
60 & 0.254727148357493 & 0.509454296714987 & 0.745272851642507 \tabularnewline
61 & 0.323362631808308 & 0.646725263616617 & 0.676637368191692 \tabularnewline
62 & 0.438499555268616 & 0.876999110537232 & 0.561500444731384 \tabularnewline
63 & 0.444938505525659 & 0.889877011051317 & 0.555061494474341 \tabularnewline
64 & 0.483910774281511 & 0.967821548563022 & 0.516089225718489 \tabularnewline
65 & 0.633377224564036 & 0.733245550871927 & 0.366622775435964 \tabularnewline
66 & 0.6032652652625 & 0.793469469475 & 0.3967347347375 \tabularnewline
67 & 0.645334983548307 & 0.709330032903385 & 0.354665016451693 \tabularnewline
68 & 0.670845480927383 & 0.658309038145233 & 0.329154519072617 \tabularnewline
69 & 0.67514908909527 & 0.649701821809461 & 0.32485091090473 \tabularnewline
70 & 0.635922859480199 & 0.728154281039603 & 0.364077140519801 \tabularnewline
71 & 0.629065495065964 & 0.741869009868071 & 0.370934504934036 \tabularnewline
72 & 0.602650177432517 & 0.794699645134967 & 0.397349822567483 \tabularnewline
73 & 0.57735012688295 & 0.8452997462341 & 0.42264987311705 \tabularnewline
74 & 0.597903054278662 & 0.804193891442677 & 0.402096945721338 \tabularnewline
75 & 0.635760164173669 & 0.728479671652662 & 0.364239835826331 \tabularnewline
76 & 0.655486041806269 & 0.689027916387462 & 0.344513958193731 \tabularnewline
77 & 0.674944667182956 & 0.650110665634088 & 0.325055332817044 \tabularnewline
78 & 0.653759382202312 & 0.692481235595375 & 0.346240617797687 \tabularnewline
79 & 0.626333699022848 & 0.747332601954305 & 0.373666300977152 \tabularnewline
80 & 0.651615372963367 & 0.696769254073267 & 0.348384627036633 \tabularnewline
81 & 0.657666296349373 & 0.684667407301254 & 0.342333703650627 \tabularnewline
82 & 0.696806671707683 & 0.606386656584635 & 0.303193328292317 \tabularnewline
83 & 0.684894169745098 & 0.630211660509803 & 0.315105830254902 \tabularnewline
84 & 0.656336833682377 & 0.687326332635246 & 0.343663166317623 \tabularnewline
85 & 0.655639451523052 & 0.688721096953895 & 0.344360548476948 \tabularnewline
86 & 0.623829812761055 & 0.75234037447789 & 0.376170187238945 \tabularnewline
87 & 0.629515030783786 & 0.740969938432428 & 0.370484969216214 \tabularnewline
88 & 0.618289491229358 & 0.763421017541284 & 0.381710508770642 \tabularnewline
89 & 0.583266630559604 & 0.833466738880792 & 0.416733369440396 \tabularnewline
90 & 0.607990071523243 & 0.784019856953513 & 0.392009928476757 \tabularnewline
91 & 0.572611959249763 & 0.854776081500473 & 0.427388040750237 \tabularnewline
92 & 0.624577377686413 & 0.750845244627175 & 0.375422622313587 \tabularnewline
93 & 0.582632818287882 & 0.834734363424236 & 0.417367181712118 \tabularnewline
94 & 0.543015925741453 & 0.913968148517094 & 0.456984074258547 \tabularnewline
95 & 0.580588981388392 & 0.838822037223215 & 0.419411018611608 \tabularnewline
96 & 0.557755069638444 & 0.884489860723112 & 0.442244930361556 \tabularnewline
97 & 0.585131281516819 & 0.829737436966361 & 0.41486871848318 \tabularnewline
98 & 0.659325623586663 & 0.681348752826674 & 0.340674376413337 \tabularnewline
99 & 0.648107127537677 & 0.703785744924646 & 0.351892872462323 \tabularnewline
100 & 0.632557548274412 & 0.734884903451176 & 0.367442451725588 \tabularnewline
101 & 0.593591774655283 & 0.812816450689434 & 0.406408225344717 \tabularnewline
102 & 0.552840442538796 & 0.894319114922408 & 0.447159557461204 \tabularnewline
103 & 0.517005882720352 & 0.965988234559297 & 0.482994117279648 \tabularnewline
104 & 0.538283226387117 & 0.923433547225767 & 0.461716773612883 \tabularnewline
105 & 0.541228516823785 & 0.917542966352429 & 0.458771483176214 \tabularnewline
106 & 0.504706017128897 & 0.990587965742207 & 0.495293982871104 \tabularnewline
107 & 0.52651496774551 & 0.946970064508979 & 0.47348503225449 \tabularnewline
108 & 0.535658442808301 & 0.928683114383397 & 0.464341557191699 \tabularnewline
109 & 0.597412992007695 & 0.805174015984609 & 0.402587007992305 \tabularnewline
110 & 0.594346715711497 & 0.811306568577005 & 0.405653284288503 \tabularnewline
111 & 0.59178389893659 & 0.816432202126819 & 0.40821610106341 \tabularnewline
112 & 0.631137272623167 & 0.737725454753667 & 0.368862727376833 \tabularnewline
113 & 0.82066907031427 & 0.358661859371459 & 0.17933092968573 \tabularnewline
114 & 0.814308169356745 & 0.37138366128651 & 0.185691830643255 \tabularnewline
115 & 0.850488409034045 & 0.299023181931911 & 0.149511590965955 \tabularnewline
116 & 0.821216729327828 & 0.357566541344344 & 0.178783270672172 \tabularnewline
117 & 0.793738195459362 & 0.412523609081276 & 0.206261804540638 \tabularnewline
118 & 0.884039406645545 & 0.23192118670891 & 0.115960593354455 \tabularnewline
119 & 0.867170182829591 & 0.265659634340818 & 0.132829817170409 \tabularnewline
120 & 0.851229957265061 & 0.297540085469879 & 0.148770042734939 \tabularnewline
121 & 0.817131116230592 & 0.365737767538816 & 0.182868883769408 \tabularnewline
122 & 0.819146567224771 & 0.361706865550458 & 0.180853432775229 \tabularnewline
123 & 0.805247776997019 & 0.389504446005962 & 0.194752223002981 \tabularnewline
124 & 0.758530398828595 & 0.482939202342809 & 0.241469601171405 \tabularnewline
125 & 0.707064262962457 & 0.585871474075086 & 0.292935737037543 \tabularnewline
126 & 0.794977595247912 & 0.410044809504177 & 0.205022404752088 \tabularnewline
127 & 0.779650254230221 & 0.440699491539558 & 0.220349745769779 \tabularnewline
128 & 0.725895512069541 & 0.548208975860917 & 0.274104487930459 \tabularnewline
129 & 0.665572667594606 & 0.668854664810787 & 0.334427332405394 \tabularnewline
130 & 0.905064080587328 & 0.189871838825345 & 0.0949359194126724 \tabularnewline
131 & 0.941476596196425 & 0.11704680760715 & 0.058523403803575 \tabularnewline
132 & 0.919374295537953 & 0.161251408924095 & 0.0806257044620473 \tabularnewline
133 & 0.902183542078996 & 0.195632915842008 & 0.0978164579210038 \tabularnewline
134 & 0.864061128149052 & 0.271877743701896 & 0.135938871850948 \tabularnewline
135 & 0.908182975366005 & 0.18363404926799 & 0.0918170246339949 \tabularnewline
136 & 0.908282286779689 & 0.183435426440621 & 0.0917177132203107 \tabularnewline
137 & 0.932351955808468 & 0.135296088383063 & 0.0676480441915316 \tabularnewline
138 & 0.957760961319805 & 0.0844780773603897 & 0.0422390386801949 \tabularnewline
139 & 0.947865489357178 & 0.104269021285644 & 0.0521345106428222 \tabularnewline
140 & 0.954187933618789 & 0.0916241327624217 & 0.0458120663812109 \tabularnewline
141 & 0.957535051228936 & 0.0849298975421286 & 0.0424649487710643 \tabularnewline
142 & 0.986098553842573 & 0.0278028923148546 & 0.0139014461574273 \tabularnewline
143 & 0.988214231596306 & 0.0235715368073886 & 0.0117857684036943 \tabularnewline
144 & 0.976352896948374 & 0.0472942061032517 & 0.0236471030516259 \tabularnewline
145 & 0.957075698910428 & 0.0858486021791443 & 0.0429243010895721 \tabularnewline
146 & 0.929869019172508 & 0.140261961654985 & 0.0701309808274924 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186009&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]8[/C][C]0.312502804888174[/C][C]0.625005609776347[/C][C]0.687497195111826[/C][/ROW]
[ROW][C]9[/C][C]0.379313659613926[/C][C]0.758627319227852[/C][C]0.620686340386074[/C][/ROW]
[ROW][C]10[/C][C]0.250053719595139[/C][C]0.500107439190278[/C][C]0.749946280404861[/C][/ROW]
[ROW][C]11[/C][C]0.153443129707264[/C][C]0.306886259414528[/C][C]0.846556870292736[/C][/ROW]
[ROW][C]12[/C][C]0.0872036706017757[/C][C]0.174407341203551[/C][C]0.912796329398224[/C][/ROW]
[ROW][C]13[/C][C]0.0582192419809876[/C][C]0.116438483961975[/C][C]0.941780758019012[/C][/ROW]
[ROW][C]14[/C][C]0.0329659766448537[/C][C]0.0659319532897074[/C][C]0.967034023355146[/C][/ROW]
[ROW][C]15[/C][C]0.0171758589823543[/C][C]0.0343517179647086[/C][C]0.982824141017646[/C][/ROW]
[ROW][C]16[/C][C]0.010387727365412[/C][C]0.020775454730824[/C][C]0.989612272634588[/C][/ROW]
[ROW][C]17[/C][C]0.00508431349489226[/C][C]0.0101686269897845[/C][C]0.994915686505108[/C][/ROW]
[ROW][C]18[/C][C]0.0308313333519913[/C][C]0.0616626667039827[/C][C]0.969168666648009[/C][/ROW]
[ROW][C]19[/C][C]0.0187234526186492[/C][C]0.0374469052372985[/C][C]0.981276547381351[/C][/ROW]
[ROW][C]20[/C][C]0.0155036982573819[/C][C]0.0310073965147639[/C][C]0.984496301742618[/C][/ROW]
[ROW][C]21[/C][C]0.0427870989947805[/C][C]0.0855741979895611[/C][C]0.957212901005219[/C][/ROW]
[ROW][C]22[/C][C]0.118482268274294[/C][C]0.236964536548588[/C][C]0.881517731725706[/C][/ROW]
[ROW][C]23[/C][C]0.0873890904086093[/C][C]0.174778180817219[/C][C]0.912610909591391[/C][/ROW]
[ROW][C]24[/C][C]0.0664141683617499[/C][C]0.1328283367235[/C][C]0.93358583163825[/C][/ROW]
[ROW][C]25[/C][C]0.0499480409256597[/C][C]0.0998960818513193[/C][C]0.95005195907434[/C][/ROW]
[ROW][C]26[/C][C]0.321733499278601[/C][C]0.643466998557201[/C][C]0.678266500721399[/C][/ROW]
[ROW][C]27[/C][C]0.294015627833033[/C][C]0.588031255666066[/C][C]0.705984372166967[/C][/ROW]
[ROW][C]28[/C][C]0.243142688889617[/C][C]0.486285377779233[/C][C]0.756857311110383[/C][/ROW]
[ROW][C]29[/C][C]0.295656135483938[/C][C]0.591312270967876[/C][C]0.704343864516062[/C][/ROW]
[ROW][C]30[/C][C]0.246249357040226[/C][C]0.492498714080451[/C][C]0.753750642959774[/C][/ROW]
[ROW][C]31[/C][C]0.295184626352806[/C][C]0.590369252705612[/C][C]0.704815373647194[/C][/ROW]
[ROW][C]32[/C][C]0.342124910243429[/C][C]0.684249820486858[/C][C]0.657875089756571[/C][/ROW]
[ROW][C]33[/C][C]0.369952853623658[/C][C]0.739905707247315[/C][C]0.630047146376342[/C][/ROW]
[ROW][C]34[/C][C]0.418650233436095[/C][C]0.837300466872189[/C][C]0.581349766563905[/C][/ROW]
[ROW][C]35[/C][C]0.473413556503143[/C][C]0.946827113006286[/C][C]0.526586443496857[/C][/ROW]
[ROW][C]36[/C][C]0.425998372189008[/C][C]0.851996744378017[/C][C]0.574001627810992[/C][/ROW]
[ROW][C]37[/C][C]0.372818980997273[/C][C]0.745637961994546[/C][C]0.627181019002727[/C][/ROW]
[ROW][C]38[/C][C]0.351645717585975[/C][C]0.70329143517195[/C][C]0.648354282414025[/C][/ROW]
[ROW][C]39[/C][C]0.354664684142947[/C][C]0.709329368285895[/C][C]0.645335315857053[/C][/ROW]
[ROW][C]40[/C][C]0.370472610498758[/C][C]0.740945220997516[/C][C]0.629527389501242[/C][/ROW]
[ROW][C]41[/C][C]0.377419444903477[/C][C]0.754838889806954[/C][C]0.622580555096523[/C][/ROW]
[ROW][C]42[/C][C]0.408975679102274[/C][C]0.817951358204548[/C][C]0.591024320897726[/C][/ROW]
[ROW][C]43[/C][C]0.439186450018487[/C][C]0.878372900036974[/C][C]0.560813549981513[/C][/ROW]
[ROW][C]44[/C][C]0.553134997257937[/C][C]0.893730005484126[/C][C]0.446865002742063[/C][/ROW]
[ROW][C]45[/C][C]0.524512925284711[/C][C]0.950974149430578[/C][C]0.475487074715289[/C][/ROW]
[ROW][C]46[/C][C]0.536263977733772[/C][C]0.927472044532456[/C][C]0.463736022266228[/C][/ROW]
[ROW][C]47[/C][C]0.565228156944112[/C][C]0.869543686111777[/C][C]0.434771843055888[/C][/ROW]
[ROW][C]48[/C][C]0.522671378029993[/C][C]0.954657243940013[/C][C]0.477328621970007[/C][/ROW]
[ROW][C]49[/C][C]0.482422667615759[/C][C]0.964845335231518[/C][C]0.517577332384241[/C][/ROW]
[ROW][C]50[/C][C]0.451837721314238[/C][C]0.903675442628477[/C][C]0.548162278685762[/C][/ROW]
[ROW][C]51[/C][C]0.480629686354399[/C][C]0.961259372708798[/C][C]0.519370313645601[/C][/ROW]
[ROW][C]52[/C][C]0.438982874918642[/C][C]0.877965749837284[/C][C]0.561017125081358[/C][/ROW]
[ROW][C]53[/C][C]0.437601545203211[/C][C]0.875203090406422[/C][C]0.562398454796789[/C][/ROW]
[ROW][C]54[/C][C]0.412792328285646[/C][C]0.825584656571292[/C][C]0.587207671714354[/C][/ROW]
[ROW][C]55[/C][C]0.367343513083158[/C][C]0.734687026166316[/C][C]0.632656486916842[/C][/ROW]
[ROW][C]56[/C][C]0.324207572378903[/C][C]0.648415144757806[/C][C]0.675792427621097[/C][/ROW]
[ROW][C]57[/C][C]0.315991966954205[/C][C]0.63198393390841[/C][C]0.684008033045795[/C][/ROW]
[ROW][C]58[/C][C]0.301460684705517[/C][C]0.602921369411034[/C][C]0.698539315294483[/C][/ROW]
[ROW][C]59[/C][C]0.264571190670928[/C][C]0.529142381341855[/C][C]0.735428809329072[/C][/ROW]
[ROW][C]60[/C][C]0.254727148357493[/C][C]0.509454296714987[/C][C]0.745272851642507[/C][/ROW]
[ROW][C]61[/C][C]0.323362631808308[/C][C]0.646725263616617[/C][C]0.676637368191692[/C][/ROW]
[ROW][C]62[/C][C]0.438499555268616[/C][C]0.876999110537232[/C][C]0.561500444731384[/C][/ROW]
[ROW][C]63[/C][C]0.444938505525659[/C][C]0.889877011051317[/C][C]0.555061494474341[/C][/ROW]
[ROW][C]64[/C][C]0.483910774281511[/C][C]0.967821548563022[/C][C]0.516089225718489[/C][/ROW]
[ROW][C]65[/C][C]0.633377224564036[/C][C]0.733245550871927[/C][C]0.366622775435964[/C][/ROW]
[ROW][C]66[/C][C]0.6032652652625[/C][C]0.793469469475[/C][C]0.3967347347375[/C][/ROW]
[ROW][C]67[/C][C]0.645334983548307[/C][C]0.709330032903385[/C][C]0.354665016451693[/C][/ROW]
[ROW][C]68[/C][C]0.670845480927383[/C][C]0.658309038145233[/C][C]0.329154519072617[/C][/ROW]
[ROW][C]69[/C][C]0.67514908909527[/C][C]0.649701821809461[/C][C]0.32485091090473[/C][/ROW]
[ROW][C]70[/C][C]0.635922859480199[/C][C]0.728154281039603[/C][C]0.364077140519801[/C][/ROW]
[ROW][C]71[/C][C]0.629065495065964[/C][C]0.741869009868071[/C][C]0.370934504934036[/C][/ROW]
[ROW][C]72[/C][C]0.602650177432517[/C][C]0.794699645134967[/C][C]0.397349822567483[/C][/ROW]
[ROW][C]73[/C][C]0.57735012688295[/C][C]0.8452997462341[/C][C]0.42264987311705[/C][/ROW]
[ROW][C]74[/C][C]0.597903054278662[/C][C]0.804193891442677[/C][C]0.402096945721338[/C][/ROW]
[ROW][C]75[/C][C]0.635760164173669[/C][C]0.728479671652662[/C][C]0.364239835826331[/C][/ROW]
[ROW][C]76[/C][C]0.655486041806269[/C][C]0.689027916387462[/C][C]0.344513958193731[/C][/ROW]
[ROW][C]77[/C][C]0.674944667182956[/C][C]0.650110665634088[/C][C]0.325055332817044[/C][/ROW]
[ROW][C]78[/C][C]0.653759382202312[/C][C]0.692481235595375[/C][C]0.346240617797687[/C][/ROW]
[ROW][C]79[/C][C]0.626333699022848[/C][C]0.747332601954305[/C][C]0.373666300977152[/C][/ROW]
[ROW][C]80[/C][C]0.651615372963367[/C][C]0.696769254073267[/C][C]0.348384627036633[/C][/ROW]
[ROW][C]81[/C][C]0.657666296349373[/C][C]0.684667407301254[/C][C]0.342333703650627[/C][/ROW]
[ROW][C]82[/C][C]0.696806671707683[/C][C]0.606386656584635[/C][C]0.303193328292317[/C][/ROW]
[ROW][C]83[/C][C]0.684894169745098[/C][C]0.630211660509803[/C][C]0.315105830254902[/C][/ROW]
[ROW][C]84[/C][C]0.656336833682377[/C][C]0.687326332635246[/C][C]0.343663166317623[/C][/ROW]
[ROW][C]85[/C][C]0.655639451523052[/C][C]0.688721096953895[/C][C]0.344360548476948[/C][/ROW]
[ROW][C]86[/C][C]0.623829812761055[/C][C]0.75234037447789[/C][C]0.376170187238945[/C][/ROW]
[ROW][C]87[/C][C]0.629515030783786[/C][C]0.740969938432428[/C][C]0.370484969216214[/C][/ROW]
[ROW][C]88[/C][C]0.618289491229358[/C][C]0.763421017541284[/C][C]0.381710508770642[/C][/ROW]
[ROW][C]89[/C][C]0.583266630559604[/C][C]0.833466738880792[/C][C]0.416733369440396[/C][/ROW]
[ROW][C]90[/C][C]0.607990071523243[/C][C]0.784019856953513[/C][C]0.392009928476757[/C][/ROW]
[ROW][C]91[/C][C]0.572611959249763[/C][C]0.854776081500473[/C][C]0.427388040750237[/C][/ROW]
[ROW][C]92[/C][C]0.624577377686413[/C][C]0.750845244627175[/C][C]0.375422622313587[/C][/ROW]
[ROW][C]93[/C][C]0.582632818287882[/C][C]0.834734363424236[/C][C]0.417367181712118[/C][/ROW]
[ROW][C]94[/C][C]0.543015925741453[/C][C]0.913968148517094[/C][C]0.456984074258547[/C][/ROW]
[ROW][C]95[/C][C]0.580588981388392[/C][C]0.838822037223215[/C][C]0.419411018611608[/C][/ROW]
[ROW][C]96[/C][C]0.557755069638444[/C][C]0.884489860723112[/C][C]0.442244930361556[/C][/ROW]
[ROW][C]97[/C][C]0.585131281516819[/C][C]0.829737436966361[/C][C]0.41486871848318[/C][/ROW]
[ROW][C]98[/C][C]0.659325623586663[/C][C]0.681348752826674[/C][C]0.340674376413337[/C][/ROW]
[ROW][C]99[/C][C]0.648107127537677[/C][C]0.703785744924646[/C][C]0.351892872462323[/C][/ROW]
[ROW][C]100[/C][C]0.632557548274412[/C][C]0.734884903451176[/C][C]0.367442451725588[/C][/ROW]
[ROW][C]101[/C][C]0.593591774655283[/C][C]0.812816450689434[/C][C]0.406408225344717[/C][/ROW]
[ROW][C]102[/C][C]0.552840442538796[/C][C]0.894319114922408[/C][C]0.447159557461204[/C][/ROW]
[ROW][C]103[/C][C]0.517005882720352[/C][C]0.965988234559297[/C][C]0.482994117279648[/C][/ROW]
[ROW][C]104[/C][C]0.538283226387117[/C][C]0.923433547225767[/C][C]0.461716773612883[/C][/ROW]
[ROW][C]105[/C][C]0.541228516823785[/C][C]0.917542966352429[/C][C]0.458771483176214[/C][/ROW]
[ROW][C]106[/C][C]0.504706017128897[/C][C]0.990587965742207[/C][C]0.495293982871104[/C][/ROW]
[ROW][C]107[/C][C]0.52651496774551[/C][C]0.946970064508979[/C][C]0.47348503225449[/C][/ROW]
[ROW][C]108[/C][C]0.535658442808301[/C][C]0.928683114383397[/C][C]0.464341557191699[/C][/ROW]
[ROW][C]109[/C][C]0.597412992007695[/C][C]0.805174015984609[/C][C]0.402587007992305[/C][/ROW]
[ROW][C]110[/C][C]0.594346715711497[/C][C]0.811306568577005[/C][C]0.405653284288503[/C][/ROW]
[ROW][C]111[/C][C]0.59178389893659[/C][C]0.816432202126819[/C][C]0.40821610106341[/C][/ROW]
[ROW][C]112[/C][C]0.631137272623167[/C][C]0.737725454753667[/C][C]0.368862727376833[/C][/ROW]
[ROW][C]113[/C][C]0.82066907031427[/C][C]0.358661859371459[/C][C]0.17933092968573[/C][/ROW]
[ROW][C]114[/C][C]0.814308169356745[/C][C]0.37138366128651[/C][C]0.185691830643255[/C][/ROW]
[ROW][C]115[/C][C]0.850488409034045[/C][C]0.299023181931911[/C][C]0.149511590965955[/C][/ROW]
[ROW][C]116[/C][C]0.821216729327828[/C][C]0.357566541344344[/C][C]0.178783270672172[/C][/ROW]
[ROW][C]117[/C][C]0.793738195459362[/C][C]0.412523609081276[/C][C]0.206261804540638[/C][/ROW]
[ROW][C]118[/C][C]0.884039406645545[/C][C]0.23192118670891[/C][C]0.115960593354455[/C][/ROW]
[ROW][C]119[/C][C]0.867170182829591[/C][C]0.265659634340818[/C][C]0.132829817170409[/C][/ROW]
[ROW][C]120[/C][C]0.851229957265061[/C][C]0.297540085469879[/C][C]0.148770042734939[/C][/ROW]
[ROW][C]121[/C][C]0.817131116230592[/C][C]0.365737767538816[/C][C]0.182868883769408[/C][/ROW]
[ROW][C]122[/C][C]0.819146567224771[/C][C]0.361706865550458[/C][C]0.180853432775229[/C][/ROW]
[ROW][C]123[/C][C]0.805247776997019[/C][C]0.389504446005962[/C][C]0.194752223002981[/C][/ROW]
[ROW][C]124[/C][C]0.758530398828595[/C][C]0.482939202342809[/C][C]0.241469601171405[/C][/ROW]
[ROW][C]125[/C][C]0.707064262962457[/C][C]0.585871474075086[/C][C]0.292935737037543[/C][/ROW]
[ROW][C]126[/C][C]0.794977595247912[/C][C]0.410044809504177[/C][C]0.205022404752088[/C][/ROW]
[ROW][C]127[/C][C]0.779650254230221[/C][C]0.440699491539558[/C][C]0.220349745769779[/C][/ROW]
[ROW][C]128[/C][C]0.725895512069541[/C][C]0.548208975860917[/C][C]0.274104487930459[/C][/ROW]
[ROW][C]129[/C][C]0.665572667594606[/C][C]0.668854664810787[/C][C]0.334427332405394[/C][/ROW]
[ROW][C]130[/C][C]0.905064080587328[/C][C]0.189871838825345[/C][C]0.0949359194126724[/C][/ROW]
[ROW][C]131[/C][C]0.941476596196425[/C][C]0.11704680760715[/C][C]0.058523403803575[/C][/ROW]
[ROW][C]132[/C][C]0.919374295537953[/C][C]0.161251408924095[/C][C]0.0806257044620473[/C][/ROW]
[ROW][C]133[/C][C]0.902183542078996[/C][C]0.195632915842008[/C][C]0.0978164579210038[/C][/ROW]
[ROW][C]134[/C][C]0.864061128149052[/C][C]0.271877743701896[/C][C]0.135938871850948[/C][/ROW]
[ROW][C]135[/C][C]0.908182975366005[/C][C]0.18363404926799[/C][C]0.0918170246339949[/C][/ROW]
[ROW][C]136[/C][C]0.908282286779689[/C][C]0.183435426440621[/C][C]0.0917177132203107[/C][/ROW]
[ROW][C]137[/C][C]0.932351955808468[/C][C]0.135296088383063[/C][C]0.0676480441915316[/C][/ROW]
[ROW][C]138[/C][C]0.957760961319805[/C][C]0.0844780773603897[/C][C]0.0422390386801949[/C][/ROW]
[ROW][C]139[/C][C]0.947865489357178[/C][C]0.104269021285644[/C][C]0.0521345106428222[/C][/ROW]
[ROW][C]140[/C][C]0.954187933618789[/C][C]0.0916241327624217[/C][C]0.0458120663812109[/C][/ROW]
[ROW][C]141[/C][C]0.957535051228936[/C][C]0.0849298975421286[/C][C]0.0424649487710643[/C][/ROW]
[ROW][C]142[/C][C]0.986098553842573[/C][C]0.0278028923148546[/C][C]0.0139014461574273[/C][/ROW]
[ROW][C]143[/C][C]0.988214231596306[/C][C]0.0235715368073886[/C][C]0.0117857684036943[/C][/ROW]
[ROW][C]144[/C][C]0.976352896948374[/C][C]0.0472942061032517[/C][C]0.0236471030516259[/C][/ROW]
[ROW][C]145[/C][C]0.957075698910428[/C][C]0.0858486021791443[/C][C]0.0429243010895721[/C][/ROW]
[ROW][C]146[/C][C]0.929869019172508[/C][C]0.140261961654985[/C][C]0.0701309808274924[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186009&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186009&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-values Alternative Hypothesis breakpoint index greater 2-sided less 8 0.312502804888174 0.625005609776347 0.687497195111826 9 0.379313659613926 0.758627319227852 0.620686340386074 10 0.250053719595139 0.500107439190278 0.749946280404861 11 0.153443129707264 0.306886259414528 0.846556870292736 12 0.0872036706017757 0.174407341203551 0.912796329398224 13 0.0582192419809876 0.116438483961975 0.941780758019012 14 0.0329659766448537 0.0659319532897074 0.967034023355146 15 0.0171758589823543 0.0343517179647086 0.982824141017646 16 0.010387727365412 0.020775454730824 0.989612272634588 17 0.00508431349489226 0.0101686269897845 0.994915686505108 18 0.0308313333519913 0.0616626667039827 0.969168666648009 19 0.0187234526186492 0.0374469052372985 0.981276547381351 20 0.0155036982573819 0.0310073965147639 0.984496301742618 21 0.0427870989947805 0.0855741979895611 0.957212901005219 22 0.118482268274294 0.236964536548588 0.881517731725706 23 0.0873890904086093 0.174778180817219 0.912610909591391 24 0.0664141683617499 0.1328283367235 0.93358583163825 25 0.0499480409256597 0.0998960818513193 0.95005195907434 26 0.321733499278601 0.643466998557201 0.678266500721399 27 0.294015627833033 0.588031255666066 0.705984372166967 28 0.243142688889617 0.486285377779233 0.756857311110383 29 0.295656135483938 0.591312270967876 0.704343864516062 30 0.246249357040226 0.492498714080451 0.753750642959774 31 0.295184626352806 0.590369252705612 0.704815373647194 32 0.342124910243429 0.684249820486858 0.657875089756571 33 0.369952853623658 0.739905707247315 0.630047146376342 34 0.418650233436095 0.837300466872189 0.581349766563905 35 0.473413556503143 0.946827113006286 0.526586443496857 36 0.425998372189008 0.851996744378017 0.574001627810992 37 0.372818980997273 0.745637961994546 0.627181019002727 38 0.351645717585975 0.70329143517195 0.648354282414025 39 0.354664684142947 0.709329368285895 0.645335315857053 40 0.370472610498758 0.740945220997516 0.629527389501242 41 0.377419444903477 0.754838889806954 0.622580555096523 42 0.408975679102274 0.817951358204548 0.591024320897726 43 0.439186450018487 0.878372900036974 0.560813549981513 44 0.553134997257937 0.893730005484126 0.446865002742063 45 0.524512925284711 0.950974149430578 0.475487074715289 46 0.536263977733772 0.927472044532456 0.463736022266228 47 0.565228156944112 0.869543686111777 0.434771843055888 48 0.522671378029993 0.954657243940013 0.477328621970007 49 0.482422667615759 0.964845335231518 0.517577332384241 50 0.451837721314238 0.903675442628477 0.548162278685762 51 0.480629686354399 0.961259372708798 0.519370313645601 52 0.438982874918642 0.877965749837284 0.561017125081358 53 0.437601545203211 0.875203090406422 0.562398454796789 54 0.412792328285646 0.825584656571292 0.587207671714354 55 0.367343513083158 0.734687026166316 0.632656486916842 56 0.324207572378903 0.648415144757806 0.675792427621097 57 0.315991966954205 0.63198393390841 0.684008033045795 58 0.301460684705517 0.602921369411034 0.698539315294483 59 0.264571190670928 0.529142381341855 0.735428809329072 60 0.254727148357493 0.509454296714987 0.745272851642507 61 0.323362631808308 0.646725263616617 0.676637368191692 62 0.438499555268616 0.876999110537232 0.561500444731384 63 0.444938505525659 0.889877011051317 0.555061494474341 64 0.483910774281511 0.967821548563022 0.516089225718489 65 0.633377224564036 0.733245550871927 0.366622775435964 66 0.6032652652625 0.793469469475 0.3967347347375 67 0.645334983548307 0.709330032903385 0.354665016451693 68 0.670845480927383 0.658309038145233 0.329154519072617 69 0.67514908909527 0.649701821809461 0.32485091090473 70 0.635922859480199 0.728154281039603 0.364077140519801 71 0.629065495065964 0.741869009868071 0.370934504934036 72 0.602650177432517 0.794699645134967 0.397349822567483 73 0.57735012688295 0.8452997462341 0.42264987311705 74 0.597903054278662 0.804193891442677 0.402096945721338 75 0.635760164173669 0.728479671652662 0.364239835826331 76 0.655486041806269 0.689027916387462 0.344513958193731 77 0.674944667182956 0.650110665634088 0.325055332817044 78 0.653759382202312 0.692481235595375 0.346240617797687 79 0.626333699022848 0.747332601954305 0.373666300977152 80 0.651615372963367 0.696769254073267 0.348384627036633 81 0.657666296349373 0.684667407301254 0.342333703650627 82 0.696806671707683 0.606386656584635 0.303193328292317 83 0.684894169745098 0.630211660509803 0.315105830254902 84 0.656336833682377 0.687326332635246 0.343663166317623 85 0.655639451523052 0.688721096953895 0.344360548476948 86 0.623829812761055 0.75234037447789 0.376170187238945 87 0.629515030783786 0.740969938432428 0.370484969216214 88 0.618289491229358 0.763421017541284 0.381710508770642 89 0.583266630559604 0.833466738880792 0.416733369440396 90 0.607990071523243 0.784019856953513 0.392009928476757 91 0.572611959249763 0.854776081500473 0.427388040750237 92 0.624577377686413 0.750845244627175 0.375422622313587 93 0.582632818287882 0.834734363424236 0.417367181712118 94 0.543015925741453 0.913968148517094 0.456984074258547 95 0.580588981388392 0.838822037223215 0.419411018611608 96 0.557755069638444 0.884489860723112 0.442244930361556 97 0.585131281516819 0.829737436966361 0.41486871848318 98 0.659325623586663 0.681348752826674 0.340674376413337 99 0.648107127537677 0.703785744924646 0.351892872462323 100 0.632557548274412 0.734884903451176 0.367442451725588 101 0.593591774655283 0.812816450689434 0.406408225344717 102 0.552840442538796 0.894319114922408 0.447159557461204 103 0.517005882720352 0.965988234559297 0.482994117279648 104 0.538283226387117 0.923433547225767 0.461716773612883 105 0.541228516823785 0.917542966352429 0.458771483176214 106 0.504706017128897 0.990587965742207 0.495293982871104 107 0.52651496774551 0.946970064508979 0.47348503225449 108 0.535658442808301 0.928683114383397 0.464341557191699 109 0.597412992007695 0.805174015984609 0.402587007992305 110 0.594346715711497 0.811306568577005 0.405653284288503 111 0.59178389893659 0.816432202126819 0.40821610106341 112 0.631137272623167 0.737725454753667 0.368862727376833 113 0.82066907031427 0.358661859371459 0.17933092968573 114 0.814308169356745 0.37138366128651 0.185691830643255 115 0.850488409034045 0.299023181931911 0.149511590965955 116 0.821216729327828 0.357566541344344 0.178783270672172 117 0.793738195459362 0.412523609081276 0.206261804540638 118 0.884039406645545 0.23192118670891 0.115960593354455 119 0.867170182829591 0.265659634340818 0.132829817170409 120 0.851229957265061 0.297540085469879 0.148770042734939 121 0.817131116230592 0.365737767538816 0.182868883769408 122 0.819146567224771 0.361706865550458 0.180853432775229 123 0.805247776997019 0.389504446005962 0.194752223002981 124 0.758530398828595 0.482939202342809 0.241469601171405 125 0.707064262962457 0.585871474075086 0.292935737037543 126 0.794977595247912 0.410044809504177 0.205022404752088 127 0.779650254230221 0.440699491539558 0.220349745769779 128 0.725895512069541 0.548208975860917 0.274104487930459 129 0.665572667594606 0.668854664810787 0.334427332405394 130 0.905064080587328 0.189871838825345 0.0949359194126724 131 0.941476596196425 0.11704680760715 0.058523403803575 132 0.919374295537953 0.161251408924095 0.0806257044620473 133 0.902183542078996 0.195632915842008 0.0978164579210038 134 0.864061128149052 0.271877743701896 0.135938871850948 135 0.908182975366005 0.18363404926799 0.0918170246339949 136 0.908282286779689 0.183435426440621 0.0917177132203107 137 0.932351955808468 0.135296088383063 0.0676480441915316 138 0.957760961319805 0.0844780773603897 0.0422390386801949 139 0.947865489357178 0.104269021285644 0.0521345106428222 140 0.954187933618789 0.0916241327624217 0.0458120663812109 141 0.957535051228936 0.0849298975421286 0.0424649487710643 142 0.986098553842573 0.0278028923148546 0.0139014461574273 143 0.988214231596306 0.0235715368073886 0.0117857684036943 144 0.976352896948374 0.0472942061032517 0.0236471030516259 145 0.957075698910428 0.0858486021791443 0.0429243010895721 146 0.929869019172508 0.140261961654985 0.0701309808274924

 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 0 0 OK 5% type I error level 8 0.0575539568345324 NOK 10% type I error level 16 0.115107913669065 NOK

\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 & 8 & 0.0575539568345324 & NOK \tabularnewline
10% type I error level & 16 & 0.115107913669065 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186009&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]8[/C][C]0.0575539568345324[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]16[/C][C]0.115107913669065[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186009&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186009&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 tests OK/NOK 1% type I error level 0 0 OK 5% type I error level 8 0.0575539568345324 NOK 10% type I error level 16 0.115107913669065 NOK

library(lattice)library(lmtest)n25 <- 25 #minimum number of obs. for Goldfeld-Quandt testpar1 <- 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 <- x1if (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'}xk <- length(x[1,])df <- as.data.frame(x)(mylm <- lm(df))(mysum <- summary(mylm))if (n > n25) {kp3 <- k + 3nmkm3 <- n - k - 3gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))numgqtests <- 0numsignificant1 <- 0numsignificant5 <- 0numsignificant10 <- 0for (mypoint in kp3:nmkm3) {j <- 0numgqtests <- numgqtests + 1for (myalt in c('greater', 'two.sided', 'less')) {j <- j + 1gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value}if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1}gqarr}bitmap(file='test0.png')plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')points(x[,1]-mysum$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)dumdum1 <- dum[2:length(myerror),]dum1z <- as.data.frame(dum1)zplot(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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')}}myeq <- paste(myeq, ' + e[t]')a<-table.row.start(a)a<-table.element(a, myeq)a<-table.row.end(a)a<-table.end(a)table.save(a,file='mytable1.tab')a<-table.start()a<-table.row.start(a)a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a,'Variable',header=TRUE)a<-table.element(a,'Parameter',header=TRUE)a<-table.element(a,'S.D.',header=TRUE)a<-table.element(a,'T-STATH0: parameter = 0',header=TRUE)a<-table.element(a,'2-tail p-value',header=TRUE)a<-table.element(a,'1-tail p-value',header=TRUE)a<-table.row.end(a)for (i in 1:k){a<-table.row.start(a)a<-table.element(a,rownames(mysum$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$sigma)a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a, 'Sum Squared Residuals',1,TRUE)a<-table.element(a, sum(myerror*myerror))a<-table.row.end(a)a<-table.end(a)table.save(a,file='mytable3.tab')a<-table.start()a<-table.row.start(a)a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a, 'Time or Index', 1, TRUE)a<-table.element(a, 'Actuals', 1, TRUE)a<-table.element(a, 'InterpolationForecast', 1, TRUE)a<-table.element(a, 'ResidualsPrediction Error', 1, TRUE)a<-table.row.end(a)for (i in 1:n) {a<-table.row.start(a)a<-table.element(a,i, 1, TRUE)a<-table.element(a,x[i])a<-table.element(a,x[i]-mysum$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')}