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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationThu, 12 Nov 2009 15:06:21 +0100
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Nov/12/t1258034831gyl7a6ypo68uqsr.htm/, Retrieved Fri, 09 May 2025 16:11:13 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=55999, Retrieved Fri, 09 May 2025 16:11:13 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact741

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RM D    [Multiple Regression] [Seatbelt] [2009-11-12 14:06:21] [d76b387543b13b5e3afd8ff9e5fdc89f] [Current]
-    D      [Multiple Regression] [multiple regressi...] [2009-11-14 12:58:47] [d46757a0a8c9b00540ab7e7e0c34bfc4]
-   PD        [Multiple Regression] [Lineaire trend] [2009-11-21 00:10:00] [3dd791303389e75e672968b227170a72]
-    D        [Multiple Regression] [Multiple Regressi...] [2009-12-20 12:23:34] [73863f7f907331e734eff34b7de6fc83]
-    D      [Multiple Regression] [WS 7 3] [2009-11-14 13:31:17] [6e4e01d7eb22a9f33d58ebb35753a195]
-   PD        [Multiple Regression] [WS7 3] [2009-11-18 20:27:09] [6e4e01d7eb22a9f33d58ebb35753a195]
-    D      [Multiple Regression] [Regressiemodel me...] [2009-11-17 21:35:13] [e2a6b1b31bd881219e1879835b4c60d0]
-    D      [Multiple Regression] [] [2009-11-18 12:11:35] [6ba840d2473f9a55d7b3e13093db69b8]
-    D        [Multiple Regression] [] [2009-12-15 15:01:33] [6ba840d2473f9a55d7b3e13093db69b8]
-    D          [Multiple Regression] [] [2009-12-21 09:25:37] [6ba840d2473f9a55d7b3e13093db69b8]
-    D      [Multiple Regression] [] [2009-11-18 14:31:41] [ee35698a38947a6c6c039b1e3deafc05]
-    D      [Multiple Regression] [SHW WS7 - Lineair...] [2009-11-18 14:41:08] [253127ae8da904b75450fbd69fe4eb21]
-    D      [Multiple Regression] [] [2009-11-18 14:51:34] [94b62ad0aa784646217b93aa983cee13]
-    D      [Multiple Regression] [] [2009-11-18 14:52:22] [7e8bf94ac9834384fa22d029eca19fa6]
-    D      [Multiple Regression] [WS7 Lineair trend] [2009-11-18 15:53:19] [445b292c553470d9fed8bc2796fd3a00]
-    D      [Multiple Regression] [] [2009-11-18 16:38:41] [90f6d58d515a4caed6fb4b8be4e11eaa]
-   PD      [Multiple Regression] [] [2009-11-18 17:09:25] [96d96f181930b548ce74f8c3116c4873]
-   P         [Multiple Regression] [] [2009-11-19 18:19:09] [96d96f181930b548ce74f8c3116c4873]
-   PD      [Multiple Regression] [] [2009-11-18 17:18:34] [7369a9baefff1ba9d2171738b4c9faa6]
-    D      [Multiple Regression] [] [2009-11-18 17:34:19] [e48499dbc39d7bbb032694d069ad98a5]
-    D      [Multiple Regression] [SHw WS7] [2009-11-18 18:21:38] [af2352cd9a951bedd08ebe247d0de1a2]
-    D        [Multiple Regression] [REVIEW 7] [2009-11-24 20:27:08] [309ee52d0058ff0a6f7eec15e07b2d9f]
-    D      [Multiple Regression] [WS7_3] [2009-11-18 18:28:36] [8b1aef4e7013bd33fbc2a5833375c5f5]
-             [Multiple Regression] [] [2009-11-19 14:03:28] [08fc5c07292c885b941f0cb515ce13f3]
-    D          [Multiple Regression] [] [2009-11-20 17:16:53] [4d62210f0915d3a20cbf115865da7cd4]
-    D        [Multiple Regression] [Paper Multiple re...] [2010-12-04 13:53:29] [814f53995537cd15c528d8efbf1cf544]
-   PD      [Multiple Regression] [] [2009-11-18 19:04:16] [7369a9baefff1ba9d2171738b4c9faa6]
-    D      [Multiple Regression] [] [2009-11-18 19:45:07] [539cd8be0bf6326526ff2d448281a204]
-   PD      [Multiple Regression] [Grondstofprijsind...] [2009-11-18 19:47:38] [016baa4dcb32aa0a4ae1d7f97a4b0730]
-    D      [Multiple Regression] [] [2009-11-18 20:49:00] [ca7a691f2b8ebdc7b81799394c1aa70d]
-    D        [Multiple Regression] [] [2009-11-20 13:54:15] [74be16979710d4c4e7c6647856088456]
-   PD      [Multiple Regression] [multiple regressi...] [2009-11-18 21:54:04] [74be16979710d4c4e7c6647856088456]
-    D      [Multiple Regression] [] [2009-11-18 22:32:40] [7e8bf94ac9834384fa22d029eca19fa6]
- R PD      [Multiple Regression] [] [2009-11-19 08:06:13] [639dd97b6eeebe46a3c92d62cb04fb95]
-   PD        [Multiple Regression] [] [2009-11-19 08:07:59] [639dd97b6eeebe46a3c92d62cb04fb95]
-   P           [Multiple Regression] [] [2009-11-19 08:09:26] [639dd97b6eeebe46a3c92d62cb04fb95]
-    D            [Multiple Regression] [] [2009-11-19 08:16:57] [639dd97b6eeebe46a3c92d62cb04fb95]
- R  D              [Multiple Regression] [model 4] [2010-12-28 21:11:46] [82643889efeee0b265cd2ff213e5137b]
- R  D              [Multiple Regression] [Model 5] [2010-12-28 21:19:23] [82643889efeee0b265cd2ff213e5137b]
-    D            [Multiple Regression] [] [2009-11-19 08:27:40] [639dd97b6eeebe46a3c92d62cb04fb95]
-    D              [Multiple Regression] [] [2009-11-19 18:16:13] [639dd97b6eeebe46a3c92d62cb04fb95]
- RMPD        [ARIMA Forecasting] [] [2009-12-14 08:41:55] [639dd97b6eeebe46a3c92d62cb04fb95]
-   PD          [ARIMA Forecasting] [] [2009-12-19 10:06:44] [ea26ab7ea3bba830cfeb08d06278d52c]
- R PD            [ARIMA Forecasting] [Arima forecast 6 ...] [2009-12-21 17:12:24] [9dbb467a28ad600d808a4e47d5e0774e]
-   P               [ARIMA Forecasting] [Arima forecast 12...] [2009-12-21 17:19:37] [9dbb467a28ad600d808a4e47d5e0774e]
-                     [ARIMA Forecasting] [paper] [2010-12-28 17:28:42] [654616a560d52fe6eb611aa3bbf6b3c7]
-   P               [ARIMA Forecasting] [Arima forecast 24...] [2009-12-21 17:25:53] [9dbb467a28ad600d808a4e47d5e0774e]
-                     [ARIMA Forecasting] [paper] [2010-12-28 17:32:12] [654616a560d52fe6eb611aa3bbf6b3c7]
-                   [ARIMA Forecasting] [paper] [2010-12-28 17:26:19] [654616a560d52fe6eb611aa3bbf6b3c7]

[Truncated]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
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1508	0
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1632	0
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2242	0
2478	0
2030	0
1655	0
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1992	0
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1976	0
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1963	0
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1941	0
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2012	0
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2004	0
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1185	1
1222	1
1284	1
1444	1
1575	1
1737	1
1763	1

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time23 seconds
R Server'George Udny Yule' @ 72.249.76.132

\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 & 23 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=55999&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]23 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=55999&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=55999&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time23 seconds
R Server'George Udny Yule' @ 72.249.76.132






Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 2324.06337310277 -226.385033602657X[t] -451.374973256309M1[t] -635.461053323771M2[t] -583.133697991392M3[t] -694.556342659014M4[t] -555.478987326639M5[t] -609.464131994259M6[t] -532.074276661885M7[t] -515.434421329508M8[t] -460.85706599713M9[t] -319.717210664754M10[t] -118.389855332377M11[t] -1.76485533237686t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  2324.06337310277 -226.385033602657X[t] -451.374973256309M1[t] -635.461053323771M2[t] -583.133697991392M3[t] -694.556342659014M4[t] -555.478987326639M5[t] -609.464131994259M6[t] -532.074276661885M7[t] -515.434421329508M8[t] -460.85706599713M9[t] -319.717210664754M10[t] -118.389855332377M11[t] -1.76485533237686t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=55999&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  2324.06337310277 -226.385033602657X[t] -451.374973256309M1[t] -635.461053323771M2[t] -583.133697991392M3[t] -694.556342659014M4[t] -555.478987326639M5[t] -609.464131994259M6[t] -532.074276661885M7[t] -515.434421329508M8[t] -460.85706599713M9[t] -319.717210664754M10[t] -118.389855332377M11[t] -1.76485533237686t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=55999&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=55999&T=1

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


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

Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 2324.06337310277 -226.385033602657X[t] -451.374973256309M1[t] -635.461053323771M2[t] -583.133697991392M3[t] -694.556342659014M4[t] -555.478987326639M5[t] -609.464131994259M6[t] -532.074276661885M7[t] -515.434421329508M8[t] -460.85706599713M9[t] -319.717210664754M10[t] -118.389855332377M11[t] -1.76485533237686t + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)2324.0633731027744.02993952.783700
X-226.38503360265741.037226-5.516600
M1-451.37497325630953.942919-8.367600
M2-635.46105332377153.941479-11.780600
M3-583.13369799139253.931287-10.812500
M4-694.55634265901453.922166-12.880700
M5-555.47898732663953.914117-10.30300
M6-609.46413199425953.907141-11.305800
M7-532.07427666188553.901237-9.871300
M8-515.43442132950853.896405-9.563400
M9-460.8570659971353.892648-8.551400
M10-319.71721066475453.889963-5.932800
M11-118.38985533237753.888353-2.19690.0293160.014658
t-1.764855332376860.240551-7.336700

\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) & 2324.06337310277 & 44.029939 & 52.7837 & 0 & 0 \tabularnewline
X & -226.385033602657 & 41.037226 & -5.5166 & 0 & 0 \tabularnewline
M1 & -451.374973256309 & 53.942919 & -8.3676 & 0 & 0 \tabularnewline
M2 & -635.461053323771 & 53.941479 & -11.7806 & 0 & 0 \tabularnewline
M3 & -583.133697991392 & 53.931287 & -10.8125 & 0 & 0 \tabularnewline
M4 & -694.556342659014 & 53.922166 & -12.8807 & 0 & 0 \tabularnewline
M5 & -555.478987326639 & 53.914117 & -10.303 & 0 & 0 \tabularnewline
M6 & -609.464131994259 & 53.907141 & -11.3058 & 0 & 0 \tabularnewline
M7 & -532.074276661885 & 53.901237 & -9.8713 & 0 & 0 \tabularnewline
M8 & -515.434421329508 & 53.896405 & -9.5634 & 0 & 0 \tabularnewline
M9 & -460.85706599713 & 53.892648 & -8.5514 & 0 & 0 \tabularnewline
M10 & -319.717210664754 & 53.889963 & -5.9328 & 0 & 0 \tabularnewline
M11 & -118.389855332377 & 53.888353 & -2.1969 & 0.029316 & 0.014658 \tabularnewline
t & -1.76485533237686 & 0.240551 & -7.3367 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=55999&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]2324.06337310277[/C][C]44.029939[/C][C]52.7837[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]X[/C][C]-226.385033602657[/C][C]41.037226[/C][C]-5.5166[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M1[/C][C]-451.374973256309[/C][C]53.942919[/C][C]-8.3676[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M2[/C][C]-635.461053323771[/C][C]53.941479[/C][C]-11.7806[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M3[/C][C]-583.133697991392[/C][C]53.931287[/C][C]-10.8125[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M4[/C][C]-694.556342659014[/C][C]53.922166[/C][C]-12.8807[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M5[/C][C]-555.478987326639[/C][C]53.914117[/C][C]-10.303[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M6[/C][C]-609.464131994259[/C][C]53.907141[/C][C]-11.3058[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M7[/C][C]-532.074276661885[/C][C]53.901237[/C][C]-9.8713[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M8[/C][C]-515.434421329508[/C][C]53.896405[/C][C]-9.5634[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M9[/C][C]-460.85706599713[/C][C]53.892648[/C][C]-8.5514[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M10[/C][C]-319.717210664754[/C][C]53.889963[/C][C]-5.9328[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M11[/C][C]-118.389855332377[/C][C]53.888353[/C][C]-2.1969[/C][C]0.029316[/C][C]0.014658[/C][/ROW]
[ROW][C]t[/C][C]-1.76485533237686[/C][C]0.240551[/C][C]-7.3367[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=55999&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=55999&T=2

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


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)2324.0633731027744.02993952.783700
X-226.38503360265741.037226-5.516600
M1-451.37497325630953.942919-8.367600
M2-635.46105332377153.941479-11.780600
M3-583.13369799139253.931287-10.812500
M4-694.55634265901453.922166-12.880700
M5-555.47898732663953.914117-10.30300
M6-609.46413199425953.907141-11.305800
M7-532.07427666188553.901237-9.871300
M8-515.43442132950853.896405-9.563400
M9-460.8570659971353.892648-8.551400
M10-319.71721066475453.889963-5.932800
M11-118.38985533237753.888353-2.19690.0293160.014658
t-1.764855332376860.240551-7.336700






Multiple Linear Regression - Regression Statistics
Multiple R0.861322441473346
R-squared0.741876348185605
Adjusted R-squared0.723024620805902
F-TEST (value)39.3532291891914
F-TEST (DF numerator)13
F-TEST (DF denominator)178
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation152.417759557721
Sum Squared Residuals4135148.87028996

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.861322441473346 \tabularnewline
R-squared & 0.741876348185605 \tabularnewline
Adjusted R-squared & 0.723024620805902 \tabularnewline
F-TEST (value) & 39.3532291891914 \tabularnewline
F-TEST (DF numerator) & 13 \tabularnewline
F-TEST (DF denominator) & 178 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 152.417759557721 \tabularnewline
Sum Squared Residuals & 4135148.87028996 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=55999&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.861322441473346[/C][/ROW]
[ROW][C]R-squared[/C][C]0.741876348185605[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.723024620805902[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]39.3532291891914[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]13[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]178[/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]152.417759557721[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]4135148.87028996[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=55999&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=55999&T=3

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


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

Multiple Linear Regression - Regression Statistics
Multiple R0.861322441473346
R-squared0.741876348185605
Adjusted R-squared0.723024620805902
F-TEST (value)39.3532291891914
F-TEST (DF numerator)13
F-TEST (DF denominator)178
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation152.417759557721
Sum Squared Residuals4135148.87028996






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
116871870.92354451406-183.923544514060
215081685.07260911425-177.072609114252
315071735.63510911425-228.635109114254
413851622.44760911424-237.447609114242
516321759.76010911425-127.760109114251
615111704.01010911425-193.010109114250
715591779.63510911425-220.635109114248
816301794.51010911426-164.510109114256
915791847.32260911425-268.322609114246
1016531986.69760911425-333.697609114249
1121522186.26010911425-34.2601091142503
1221482302.88510911425-154.885109114249
1317521849.74528052556-97.7452805255622
1417651663.89434512573101.105654874272
1517171714.456845125732.54315487427317
1615581601.26934512573-43.2693451257275
1715751738.58184512573-163.581845125727
1815201682.83184512573-162.831845125727
1918051758.4568451257346.5431548742728
2018001773.3318451257326.6681548742733
2117191826.14434512573-107.144345125727
2220081965.5193451257342.4806548742729
2322422165.0818451257376.918154874273
2424782281.70684512573196.293154874273
2520301828.56701653704201.43298346296
2616551642.7160811372012.2839188627955
2716931693.27858113720-0.278581137204542
2816231580.0910811372142.9089188627948
2918051717.4035811372087.5964188627952
3017461661.6535811372084.3464188627952
3117951737.2785811372157.721418862795
3219261752.15358113720173.846418862796
3316191804.96608113721-185.966081137205
3419921944.3410811372047.6589188627952
3522332143.9035811372089.0964188627953
3621922260.52858113721-68.5285811372049
3720801807.38875254852272.611247451482
3817681621.53781714868146.462182851318
3918351672.10031714868162.899682851318
4015691558.9128171486810.0871828513171
4119761696.22531714868279.774682851318
4218531640.47531714868212.524682851317
4319651716.10031714868248.899682851317
4416891730.97531714868-41.9753171486821
4517781783.78781714868-5.78781714868267
4619761923.1628171486852.8371828513174
4723972122.72531714868274.274682851318
4826542239.35031714868414.649682851317
4920971786.21048856000310.789511440004
5019631600.35955316016362.64044683984
5116771650.9220531601626.0779468398400
5219411537.73455316016403.265446839839
5320031675.04705316016327.95294683984
5418131619.29705316016193.702946839840
5520121694.92205316016317.07794683984
5619121709.79705316016202.20294683984
5720841762.60955316016321.39044683984
5820801901.98455316016178.015446839840
5921182101.5470531601616.4529468398398
6021502218.17205316016-68.1720531601603
6116081765.03222457147-157.032224571473
6215031579.18128917164-76.1812891716376
6315481629.74378917164-81.7437891716377
6413821516.55628917164-134.556289171638
6517311653.8687891716477.1312108283621
6617981598.11878917164199.881210828362
6717791673.74378917164105.256210828362
6818871688.61878917164198.381210828362
6920041741.43128917164262.568710828362
7020771880.80628917164196.193710828362
7120922080.3687891716411.6312108283621
7220512196.99378917164-145.993789171638
7315771743.85396058295-166.853960582951
7413561558.00302518312-202.003025183115
7516521608.5655251831243.4344748168846
7613821495.37802518312-113.378025183116
7715191632.69052518312-113.690525183116
7814211576.94052518312-155.940525183116
7914421652.56552518312-210.565525183116
8015431667.44052518312-124.440525183115
8116561720.25302518312-64.2530251831158
8215611859.62802518312-298.628025183116
8319052059.19052518312-154.190525183116
8421992175.8155251831223.1844748168843
8514731722.67569659443-249.675696594429
8616551536.82476119459118.175238805407
8714071587.38726119459-180.387261194593
8813951474.19976119459-79.1997611945937
8915301611.51226119459-81.5122611945933
9013091555.76226119459-246.762261194593
9115261631.38726119459-105.387261194593
9213271646.26226119459-319.262261194593
9316271699.07476119459-72.0747611945935
9417481838.44976119459-90.4497611945934
9519582038.01226119459-80.0122611945933
9622742154.63726119459119.362738805407
9716481701.49743260591-53.4974326059064
9814011515.64649720607-114.646497206071
9914111566.20899720607-155.208997206071
10014031453.02149720607-50.0214972060714
10113941590.33399720607-196.333997206071
10215201534.58399720607-14.5839972060711
10315281610.20899720607-82.2089972060712
10416431625.0839972060717.9160027939294
10515151677.89649720607-162.896497206071
10616851817.27149720607-132.271497206071
10720002016.83399720607-16.8339972060710
10822152133.4589972060781.5410027939288
10919561680.31916861738275.680831382616
11014621494.46823321755-32.4682332175485
11115631545.0307332175517.9692667824515
11214591431.8432332175527.1567667824508
11314461569.15573321755-123.155733217549
11416221513.40573321755108.594266782451
11516571589.0307332175567.9692667824511
11616381603.9057332175534.0942667824517
11716431656.71823321755-13.7182332175489
11816831796.09323321755-113.093233217549
11920501995.6557332175554.3442667824512
12022622112.28073321755149.719266782451
12118131659.14090462886153.859095371138
12214451473.28996922903-28.2899692290262
12317621523.85246922903238.147530770974
12414611410.6649692290350.3350307709731
12515561547.977469229038.02253077097358
12614311492.22746922903-61.2274692290265
12714271567.85246922903-140.852469229027
12815541582.72746922903-28.7274692290261
12916451635.539969229039.46003077097336
13016531774.91496922903-121.914969229026
13120161974.4774692290341.5225307709736
13222072091.10246922903115.897530770973
13316651637.9626406403427.0373593596605
13413611452.11170524050-91.111705240504
13515061502.674205240503.32579475949605
13613601389.48670524050-29.4867052405046
13714531526.79920524050-73.7992052405041
13815221471.0492052405050.9507947594957
13914601546.67420524050-86.6742052405044
14015521561.54920524050-9.54920524050376
14115481614.36170524050-66.3617052405043
14218271753.7367052405073.2632947594957
14317371953.29920524050-216.299205240504
14419412069.92420524050-128.924205240504
14514741616.78437665182-142.784376651817
14614581430.9334412519827.0665587480184
14715421481.4959412519860.5040587480183
14814041368.3084412519835.6915587480177
14915221505.6209412519816.3790587480182
15013851449.87094125198-64.870941251982
15116411525.49594125198115.504058748018
15215101540.37094125198-30.3709412519815
15316811593.1834412519887.816558748018
15419381732.55844125198205.441558748018
15518681932.12094125198-64.1209412519819
15617262048.74594125198-322.745941251982
15714561595.60611266329-139.606112663295
15814451409.7551772634635.2448227365406
15914561460.31767726346-4.31767726345942
16013651347.1301772634617.8698227365400
16114871484.442677263462.55732273654045
16215581428.69267726346129.307322736540
16314881504.31767726346-16.3176772634598
16416841519.19267726346164.807322736541
16515941572.0051772634621.9948227365402
16618501711.38017726346138.619822736540
16719981910.9426772634687.0573227365404
16820792027.5676772634651.4323227365403
16914941574.42784867477-80.4278486747727
17010571162.19187967228-105.191879672279
17112181212.754379672285.24562032772056
17211681099.5668796722868.4331203277199
17312361236.87937967228-0.879379672279542
17410761181.12937967228-105.129379672280
17511741256.75437967228-82.7543796722797
17611391271.62937967228-132.629379672279
17714271324.44187967228102.558120327720
17814871463.8168796722823.1831203277203
17914831663.37937967228-180.379379672280
18015131780.00437967228-267.00437967228
18113571326.8645510835930.1354489164073
18211651141.0136156837623.986384316243
18312821191.5761156837690.4238843162428
18411101078.3886156837631.6113843162422
18512971215.7011156837681.2988843162427
18611851159.9511156837625.0488843162426
18712221235.57611568376-13.5761156837575
18812841250.4511156837633.548884316243
18914441303.26361568376140.736384316242
19015751442.63861568376132.361384316243
19117371642.2011156837694.7988843162427
19217631758.826115683764.17388431624253

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 1687 & 1870.92354451406 & -183.923544514060 \tabularnewline
2 & 1508 & 1685.07260911425 & -177.072609114252 \tabularnewline
3 & 1507 & 1735.63510911425 & -228.635109114254 \tabularnewline
4 & 1385 & 1622.44760911424 & -237.447609114242 \tabularnewline
5 & 1632 & 1759.76010911425 & -127.760109114251 \tabularnewline
6 & 1511 & 1704.01010911425 & -193.010109114250 \tabularnewline
7 & 1559 & 1779.63510911425 & -220.635109114248 \tabularnewline
8 & 1630 & 1794.51010911426 & -164.510109114256 \tabularnewline
9 & 1579 & 1847.32260911425 & -268.322609114246 \tabularnewline
10 & 1653 & 1986.69760911425 & -333.697609114249 \tabularnewline
11 & 2152 & 2186.26010911425 & -34.2601091142503 \tabularnewline
12 & 2148 & 2302.88510911425 & -154.885109114249 \tabularnewline
13 & 1752 & 1849.74528052556 & -97.7452805255622 \tabularnewline
14 & 1765 & 1663.89434512573 & 101.105654874272 \tabularnewline
15 & 1717 & 1714.45684512573 & 2.54315487427317 \tabularnewline
16 & 1558 & 1601.26934512573 & -43.2693451257275 \tabularnewline
17 & 1575 & 1738.58184512573 & -163.581845125727 \tabularnewline
18 & 1520 & 1682.83184512573 & -162.831845125727 \tabularnewline
19 & 1805 & 1758.45684512573 & 46.5431548742728 \tabularnewline
20 & 1800 & 1773.33184512573 & 26.6681548742733 \tabularnewline
21 & 1719 & 1826.14434512573 & -107.144345125727 \tabularnewline
22 & 2008 & 1965.51934512573 & 42.4806548742729 \tabularnewline
23 & 2242 & 2165.08184512573 & 76.918154874273 \tabularnewline
24 & 2478 & 2281.70684512573 & 196.293154874273 \tabularnewline
25 & 2030 & 1828.56701653704 & 201.43298346296 \tabularnewline
26 & 1655 & 1642.71608113720 & 12.2839188627955 \tabularnewline
27 & 1693 & 1693.27858113720 & -0.278581137204542 \tabularnewline
28 & 1623 & 1580.09108113721 & 42.9089188627948 \tabularnewline
29 & 1805 & 1717.40358113720 & 87.5964188627952 \tabularnewline
30 & 1746 & 1661.65358113720 & 84.3464188627952 \tabularnewline
31 & 1795 & 1737.27858113721 & 57.721418862795 \tabularnewline
32 & 1926 & 1752.15358113720 & 173.846418862796 \tabularnewline
33 & 1619 & 1804.96608113721 & -185.966081137205 \tabularnewline
34 & 1992 & 1944.34108113720 & 47.6589188627952 \tabularnewline
35 & 2233 & 2143.90358113720 & 89.0964188627953 \tabularnewline
36 & 2192 & 2260.52858113721 & -68.5285811372049 \tabularnewline
37 & 2080 & 1807.38875254852 & 272.611247451482 \tabularnewline
38 & 1768 & 1621.53781714868 & 146.462182851318 \tabularnewline
39 & 1835 & 1672.10031714868 & 162.899682851318 \tabularnewline
40 & 1569 & 1558.91281714868 & 10.0871828513171 \tabularnewline
41 & 1976 & 1696.22531714868 & 279.774682851318 \tabularnewline
42 & 1853 & 1640.47531714868 & 212.524682851317 \tabularnewline
43 & 1965 & 1716.10031714868 & 248.899682851317 \tabularnewline
44 & 1689 & 1730.97531714868 & -41.9753171486821 \tabularnewline
45 & 1778 & 1783.78781714868 & -5.78781714868267 \tabularnewline
46 & 1976 & 1923.16281714868 & 52.8371828513174 \tabularnewline
47 & 2397 & 2122.72531714868 & 274.274682851318 \tabularnewline
48 & 2654 & 2239.35031714868 & 414.649682851317 \tabularnewline
49 & 2097 & 1786.21048856000 & 310.789511440004 \tabularnewline
50 & 1963 & 1600.35955316016 & 362.64044683984 \tabularnewline
51 & 1677 & 1650.92205316016 & 26.0779468398400 \tabularnewline
52 & 1941 & 1537.73455316016 & 403.265446839839 \tabularnewline
53 & 2003 & 1675.04705316016 & 327.95294683984 \tabularnewline
54 & 1813 & 1619.29705316016 & 193.702946839840 \tabularnewline
55 & 2012 & 1694.92205316016 & 317.07794683984 \tabularnewline
56 & 1912 & 1709.79705316016 & 202.20294683984 \tabularnewline
57 & 2084 & 1762.60955316016 & 321.39044683984 \tabularnewline
58 & 2080 & 1901.98455316016 & 178.015446839840 \tabularnewline
59 & 2118 & 2101.54705316016 & 16.4529468398398 \tabularnewline
60 & 2150 & 2218.17205316016 & -68.1720531601603 \tabularnewline
61 & 1608 & 1765.03222457147 & -157.032224571473 \tabularnewline
62 & 1503 & 1579.18128917164 & -76.1812891716376 \tabularnewline
63 & 1548 & 1629.74378917164 & -81.7437891716377 \tabularnewline
64 & 1382 & 1516.55628917164 & -134.556289171638 \tabularnewline
65 & 1731 & 1653.86878917164 & 77.1312108283621 \tabularnewline
66 & 1798 & 1598.11878917164 & 199.881210828362 \tabularnewline
67 & 1779 & 1673.74378917164 & 105.256210828362 \tabularnewline
68 & 1887 & 1688.61878917164 & 198.381210828362 \tabularnewline
69 & 2004 & 1741.43128917164 & 262.568710828362 \tabularnewline
70 & 2077 & 1880.80628917164 & 196.193710828362 \tabularnewline
71 & 2092 & 2080.36878917164 & 11.6312108283621 \tabularnewline
72 & 2051 & 2196.99378917164 & -145.993789171638 \tabularnewline
73 & 1577 & 1743.85396058295 & -166.853960582951 \tabularnewline
74 & 1356 & 1558.00302518312 & -202.003025183115 \tabularnewline
75 & 1652 & 1608.56552518312 & 43.4344748168846 \tabularnewline
76 & 1382 & 1495.37802518312 & -113.378025183116 \tabularnewline
77 & 1519 & 1632.69052518312 & -113.690525183116 \tabularnewline
78 & 1421 & 1576.94052518312 & -155.940525183116 \tabularnewline
79 & 1442 & 1652.56552518312 & -210.565525183116 \tabularnewline
80 & 1543 & 1667.44052518312 & -124.440525183115 \tabularnewline
81 & 1656 & 1720.25302518312 & -64.2530251831158 \tabularnewline
82 & 1561 & 1859.62802518312 & -298.628025183116 \tabularnewline
83 & 1905 & 2059.19052518312 & -154.190525183116 \tabularnewline
84 & 2199 & 2175.81552518312 & 23.1844748168843 \tabularnewline
85 & 1473 & 1722.67569659443 & -249.675696594429 \tabularnewline
86 & 1655 & 1536.82476119459 & 118.175238805407 \tabularnewline
87 & 1407 & 1587.38726119459 & -180.387261194593 \tabularnewline
88 & 1395 & 1474.19976119459 & -79.1997611945937 \tabularnewline
89 & 1530 & 1611.51226119459 & -81.5122611945933 \tabularnewline
90 & 1309 & 1555.76226119459 & -246.762261194593 \tabularnewline
91 & 1526 & 1631.38726119459 & -105.387261194593 \tabularnewline
92 & 1327 & 1646.26226119459 & -319.262261194593 \tabularnewline
93 & 1627 & 1699.07476119459 & -72.0747611945935 \tabularnewline
94 & 1748 & 1838.44976119459 & -90.4497611945934 \tabularnewline
95 & 1958 & 2038.01226119459 & -80.0122611945933 \tabularnewline
96 & 2274 & 2154.63726119459 & 119.362738805407 \tabularnewline
97 & 1648 & 1701.49743260591 & -53.4974326059064 \tabularnewline
98 & 1401 & 1515.64649720607 & -114.646497206071 \tabularnewline
99 & 1411 & 1566.20899720607 & -155.208997206071 \tabularnewline
100 & 1403 & 1453.02149720607 & -50.0214972060714 \tabularnewline
101 & 1394 & 1590.33399720607 & -196.333997206071 \tabularnewline
102 & 1520 & 1534.58399720607 & -14.5839972060711 \tabularnewline
103 & 1528 & 1610.20899720607 & -82.2089972060712 \tabularnewline
104 & 1643 & 1625.08399720607 & 17.9160027939294 \tabularnewline
105 & 1515 & 1677.89649720607 & -162.896497206071 \tabularnewline
106 & 1685 & 1817.27149720607 & -132.271497206071 \tabularnewline
107 & 2000 & 2016.83399720607 & -16.8339972060710 \tabularnewline
108 & 2215 & 2133.45899720607 & 81.5410027939288 \tabularnewline
109 & 1956 & 1680.31916861738 & 275.680831382616 \tabularnewline
110 & 1462 & 1494.46823321755 & -32.4682332175485 \tabularnewline
111 & 1563 & 1545.03073321755 & 17.9692667824515 \tabularnewline
112 & 1459 & 1431.84323321755 & 27.1567667824508 \tabularnewline
113 & 1446 & 1569.15573321755 & -123.155733217549 \tabularnewline
114 & 1622 & 1513.40573321755 & 108.594266782451 \tabularnewline
115 & 1657 & 1589.03073321755 & 67.9692667824511 \tabularnewline
116 & 1638 & 1603.90573321755 & 34.0942667824517 \tabularnewline
117 & 1643 & 1656.71823321755 & -13.7182332175489 \tabularnewline
118 & 1683 & 1796.09323321755 & -113.093233217549 \tabularnewline
119 & 2050 & 1995.65573321755 & 54.3442667824512 \tabularnewline
120 & 2262 & 2112.28073321755 & 149.719266782451 \tabularnewline
121 & 1813 & 1659.14090462886 & 153.859095371138 \tabularnewline
122 & 1445 & 1473.28996922903 & -28.2899692290262 \tabularnewline
123 & 1762 & 1523.85246922903 & 238.147530770974 \tabularnewline
124 & 1461 & 1410.66496922903 & 50.3350307709731 \tabularnewline
125 & 1556 & 1547.97746922903 & 8.02253077097358 \tabularnewline
126 & 1431 & 1492.22746922903 & -61.2274692290265 \tabularnewline
127 & 1427 & 1567.85246922903 & -140.852469229027 \tabularnewline
128 & 1554 & 1582.72746922903 & -28.7274692290261 \tabularnewline
129 & 1645 & 1635.53996922903 & 9.46003077097336 \tabularnewline
130 & 1653 & 1774.91496922903 & -121.914969229026 \tabularnewline
131 & 2016 & 1974.47746922903 & 41.5225307709736 \tabularnewline
132 & 2207 & 2091.10246922903 & 115.897530770973 \tabularnewline
133 & 1665 & 1637.96264064034 & 27.0373593596605 \tabularnewline
134 & 1361 & 1452.11170524050 & -91.111705240504 \tabularnewline
135 & 1506 & 1502.67420524050 & 3.32579475949605 \tabularnewline
136 & 1360 & 1389.48670524050 & -29.4867052405046 \tabularnewline
137 & 1453 & 1526.79920524050 & -73.7992052405041 \tabularnewline
138 & 1522 & 1471.04920524050 & 50.9507947594957 \tabularnewline
139 & 1460 & 1546.67420524050 & -86.6742052405044 \tabularnewline
140 & 1552 & 1561.54920524050 & -9.54920524050376 \tabularnewline
141 & 1548 & 1614.36170524050 & -66.3617052405043 \tabularnewline
142 & 1827 & 1753.73670524050 & 73.2632947594957 \tabularnewline
143 & 1737 & 1953.29920524050 & -216.299205240504 \tabularnewline
144 & 1941 & 2069.92420524050 & -128.924205240504 \tabularnewline
145 & 1474 & 1616.78437665182 & -142.784376651817 \tabularnewline
146 & 1458 & 1430.93344125198 & 27.0665587480184 \tabularnewline
147 & 1542 & 1481.49594125198 & 60.5040587480183 \tabularnewline
148 & 1404 & 1368.30844125198 & 35.6915587480177 \tabularnewline
149 & 1522 & 1505.62094125198 & 16.3790587480182 \tabularnewline
150 & 1385 & 1449.87094125198 & -64.870941251982 \tabularnewline
151 & 1641 & 1525.49594125198 & 115.504058748018 \tabularnewline
152 & 1510 & 1540.37094125198 & -30.3709412519815 \tabularnewline
153 & 1681 & 1593.18344125198 & 87.816558748018 \tabularnewline
154 & 1938 & 1732.55844125198 & 205.441558748018 \tabularnewline
155 & 1868 & 1932.12094125198 & -64.1209412519819 \tabularnewline
156 & 1726 & 2048.74594125198 & -322.745941251982 \tabularnewline
157 & 1456 & 1595.60611266329 & -139.606112663295 \tabularnewline
158 & 1445 & 1409.75517726346 & 35.2448227365406 \tabularnewline
159 & 1456 & 1460.31767726346 & -4.31767726345942 \tabularnewline
160 & 1365 & 1347.13017726346 & 17.8698227365400 \tabularnewline
161 & 1487 & 1484.44267726346 & 2.55732273654045 \tabularnewline
162 & 1558 & 1428.69267726346 & 129.307322736540 \tabularnewline
163 & 1488 & 1504.31767726346 & -16.3176772634598 \tabularnewline
164 & 1684 & 1519.19267726346 & 164.807322736541 \tabularnewline
165 & 1594 & 1572.00517726346 & 21.9948227365402 \tabularnewline
166 & 1850 & 1711.38017726346 & 138.619822736540 \tabularnewline
167 & 1998 & 1910.94267726346 & 87.0573227365404 \tabularnewline
168 & 2079 & 2027.56767726346 & 51.4323227365403 \tabularnewline
169 & 1494 & 1574.42784867477 & -80.4278486747727 \tabularnewline
170 & 1057 & 1162.19187967228 & -105.191879672279 \tabularnewline
171 & 1218 & 1212.75437967228 & 5.24562032772056 \tabularnewline
172 & 1168 & 1099.56687967228 & 68.4331203277199 \tabularnewline
173 & 1236 & 1236.87937967228 & -0.879379672279542 \tabularnewline
174 & 1076 & 1181.12937967228 & -105.129379672280 \tabularnewline
175 & 1174 & 1256.75437967228 & -82.7543796722797 \tabularnewline
176 & 1139 & 1271.62937967228 & -132.629379672279 \tabularnewline
177 & 1427 & 1324.44187967228 & 102.558120327720 \tabularnewline
178 & 1487 & 1463.81687967228 & 23.1831203277203 \tabularnewline
179 & 1483 & 1663.37937967228 & -180.379379672280 \tabularnewline
180 & 1513 & 1780.00437967228 & -267.00437967228 \tabularnewline
181 & 1357 & 1326.86455108359 & 30.1354489164073 \tabularnewline
182 & 1165 & 1141.01361568376 & 23.986384316243 \tabularnewline
183 & 1282 & 1191.57611568376 & 90.4238843162428 \tabularnewline
184 & 1110 & 1078.38861568376 & 31.6113843162422 \tabularnewline
185 & 1297 & 1215.70111568376 & 81.2988843162427 \tabularnewline
186 & 1185 & 1159.95111568376 & 25.0488843162426 \tabularnewline
187 & 1222 & 1235.57611568376 & -13.5761156837575 \tabularnewline
188 & 1284 & 1250.45111568376 & 33.548884316243 \tabularnewline
189 & 1444 & 1303.26361568376 & 140.736384316242 \tabularnewline
190 & 1575 & 1442.63861568376 & 132.361384316243 \tabularnewline
191 & 1737 & 1642.20111568376 & 94.7988843162427 \tabularnewline
192 & 1763 & 1758.82611568376 & 4.17388431624253 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=55999&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]1687[/C][C]1870.92354451406[/C][C]-183.923544514060[/C][/ROW]
[ROW][C]2[/C][C]1508[/C][C]1685.07260911425[/C][C]-177.072609114252[/C][/ROW]
[ROW][C]3[/C][C]1507[/C][C]1735.63510911425[/C][C]-228.635109114254[/C][/ROW]
[ROW][C]4[/C][C]1385[/C][C]1622.44760911424[/C][C]-237.447609114242[/C][/ROW]
[ROW][C]5[/C][C]1632[/C][C]1759.76010911425[/C][C]-127.760109114251[/C][/ROW]
[ROW][C]6[/C][C]1511[/C][C]1704.01010911425[/C][C]-193.010109114250[/C][/ROW]
[ROW][C]7[/C][C]1559[/C][C]1779.63510911425[/C][C]-220.635109114248[/C][/ROW]
[ROW][C]8[/C][C]1630[/C][C]1794.51010911426[/C][C]-164.510109114256[/C][/ROW]
[ROW][C]9[/C][C]1579[/C][C]1847.32260911425[/C][C]-268.322609114246[/C][/ROW]
[ROW][C]10[/C][C]1653[/C][C]1986.69760911425[/C][C]-333.697609114249[/C][/ROW]
[ROW][C]11[/C][C]2152[/C][C]2186.26010911425[/C][C]-34.2601091142503[/C][/ROW]
[ROW][C]12[/C][C]2148[/C][C]2302.88510911425[/C][C]-154.885109114249[/C][/ROW]
[ROW][C]13[/C][C]1752[/C][C]1849.74528052556[/C][C]-97.7452805255622[/C][/ROW]
[ROW][C]14[/C][C]1765[/C][C]1663.89434512573[/C][C]101.105654874272[/C][/ROW]
[ROW][C]15[/C][C]1717[/C][C]1714.45684512573[/C][C]2.54315487427317[/C][/ROW]
[ROW][C]16[/C][C]1558[/C][C]1601.26934512573[/C][C]-43.2693451257275[/C][/ROW]
[ROW][C]17[/C][C]1575[/C][C]1738.58184512573[/C][C]-163.581845125727[/C][/ROW]
[ROW][C]18[/C][C]1520[/C][C]1682.83184512573[/C][C]-162.831845125727[/C][/ROW]
[ROW][C]19[/C][C]1805[/C][C]1758.45684512573[/C][C]46.5431548742728[/C][/ROW]
[ROW][C]20[/C][C]1800[/C][C]1773.33184512573[/C][C]26.6681548742733[/C][/ROW]
[ROW][C]21[/C][C]1719[/C][C]1826.14434512573[/C][C]-107.144345125727[/C][/ROW]
[ROW][C]22[/C][C]2008[/C][C]1965.51934512573[/C][C]42.4806548742729[/C][/ROW]
[ROW][C]23[/C][C]2242[/C][C]2165.08184512573[/C][C]76.918154874273[/C][/ROW]
[ROW][C]24[/C][C]2478[/C][C]2281.70684512573[/C][C]196.293154874273[/C][/ROW]
[ROW][C]25[/C][C]2030[/C][C]1828.56701653704[/C][C]201.43298346296[/C][/ROW]
[ROW][C]26[/C][C]1655[/C][C]1642.71608113720[/C][C]12.2839188627955[/C][/ROW]
[ROW][C]27[/C][C]1693[/C][C]1693.27858113720[/C][C]-0.278581137204542[/C][/ROW]
[ROW][C]28[/C][C]1623[/C][C]1580.09108113721[/C][C]42.9089188627948[/C][/ROW]
[ROW][C]29[/C][C]1805[/C][C]1717.40358113720[/C][C]87.5964188627952[/C][/ROW]
[ROW][C]30[/C][C]1746[/C][C]1661.65358113720[/C][C]84.3464188627952[/C][/ROW]
[ROW][C]31[/C][C]1795[/C][C]1737.27858113721[/C][C]57.721418862795[/C][/ROW]
[ROW][C]32[/C][C]1926[/C][C]1752.15358113720[/C][C]173.846418862796[/C][/ROW]
[ROW][C]33[/C][C]1619[/C][C]1804.96608113721[/C][C]-185.966081137205[/C][/ROW]
[ROW][C]34[/C][C]1992[/C][C]1944.34108113720[/C][C]47.6589188627952[/C][/ROW]
[ROW][C]35[/C][C]2233[/C][C]2143.90358113720[/C][C]89.0964188627953[/C][/ROW]
[ROW][C]36[/C][C]2192[/C][C]2260.52858113721[/C][C]-68.5285811372049[/C][/ROW]
[ROW][C]37[/C][C]2080[/C][C]1807.38875254852[/C][C]272.611247451482[/C][/ROW]
[ROW][C]38[/C][C]1768[/C][C]1621.53781714868[/C][C]146.462182851318[/C][/ROW]
[ROW][C]39[/C][C]1835[/C][C]1672.10031714868[/C][C]162.899682851318[/C][/ROW]
[ROW][C]40[/C][C]1569[/C][C]1558.91281714868[/C][C]10.0871828513171[/C][/ROW]
[ROW][C]41[/C][C]1976[/C][C]1696.22531714868[/C][C]279.774682851318[/C][/ROW]
[ROW][C]42[/C][C]1853[/C][C]1640.47531714868[/C][C]212.524682851317[/C][/ROW]
[ROW][C]43[/C][C]1965[/C][C]1716.10031714868[/C][C]248.899682851317[/C][/ROW]
[ROW][C]44[/C][C]1689[/C][C]1730.97531714868[/C][C]-41.9753171486821[/C][/ROW]
[ROW][C]45[/C][C]1778[/C][C]1783.78781714868[/C][C]-5.78781714868267[/C][/ROW]
[ROW][C]46[/C][C]1976[/C][C]1923.16281714868[/C][C]52.8371828513174[/C][/ROW]
[ROW][C]47[/C][C]2397[/C][C]2122.72531714868[/C][C]274.274682851318[/C][/ROW]
[ROW][C]48[/C][C]2654[/C][C]2239.35031714868[/C][C]414.649682851317[/C][/ROW]
[ROW][C]49[/C][C]2097[/C][C]1786.21048856000[/C][C]310.789511440004[/C][/ROW]
[ROW][C]50[/C][C]1963[/C][C]1600.35955316016[/C][C]362.64044683984[/C][/ROW]
[ROW][C]51[/C][C]1677[/C][C]1650.92205316016[/C][C]26.0779468398400[/C][/ROW]
[ROW][C]52[/C][C]1941[/C][C]1537.73455316016[/C][C]403.265446839839[/C][/ROW]
[ROW][C]53[/C][C]2003[/C][C]1675.04705316016[/C][C]327.95294683984[/C][/ROW]
[ROW][C]54[/C][C]1813[/C][C]1619.29705316016[/C][C]193.702946839840[/C][/ROW]
[ROW][C]55[/C][C]2012[/C][C]1694.92205316016[/C][C]317.07794683984[/C][/ROW]
[ROW][C]56[/C][C]1912[/C][C]1709.79705316016[/C][C]202.20294683984[/C][/ROW]
[ROW][C]57[/C][C]2084[/C][C]1762.60955316016[/C][C]321.39044683984[/C][/ROW]
[ROW][C]58[/C][C]2080[/C][C]1901.98455316016[/C][C]178.015446839840[/C][/ROW]
[ROW][C]59[/C][C]2118[/C][C]2101.54705316016[/C][C]16.4529468398398[/C][/ROW]
[ROW][C]60[/C][C]2150[/C][C]2218.17205316016[/C][C]-68.1720531601603[/C][/ROW]
[ROW][C]61[/C][C]1608[/C][C]1765.03222457147[/C][C]-157.032224571473[/C][/ROW]
[ROW][C]62[/C][C]1503[/C][C]1579.18128917164[/C][C]-76.1812891716376[/C][/ROW]
[ROW][C]63[/C][C]1548[/C][C]1629.74378917164[/C][C]-81.7437891716377[/C][/ROW]
[ROW][C]64[/C][C]1382[/C][C]1516.55628917164[/C][C]-134.556289171638[/C][/ROW]
[ROW][C]65[/C][C]1731[/C][C]1653.86878917164[/C][C]77.1312108283621[/C][/ROW]
[ROW][C]66[/C][C]1798[/C][C]1598.11878917164[/C][C]199.881210828362[/C][/ROW]
[ROW][C]67[/C][C]1779[/C][C]1673.74378917164[/C][C]105.256210828362[/C][/ROW]
[ROW][C]68[/C][C]1887[/C][C]1688.61878917164[/C][C]198.381210828362[/C][/ROW]
[ROW][C]69[/C][C]2004[/C][C]1741.43128917164[/C][C]262.568710828362[/C][/ROW]
[ROW][C]70[/C][C]2077[/C][C]1880.80628917164[/C][C]196.193710828362[/C][/ROW]
[ROW][C]71[/C][C]2092[/C][C]2080.36878917164[/C][C]11.6312108283621[/C][/ROW]
[ROW][C]72[/C][C]2051[/C][C]2196.99378917164[/C][C]-145.993789171638[/C][/ROW]
[ROW][C]73[/C][C]1577[/C][C]1743.85396058295[/C][C]-166.853960582951[/C][/ROW]
[ROW][C]74[/C][C]1356[/C][C]1558.00302518312[/C][C]-202.003025183115[/C][/ROW]
[ROW][C]75[/C][C]1652[/C][C]1608.56552518312[/C][C]43.4344748168846[/C][/ROW]
[ROW][C]76[/C][C]1382[/C][C]1495.37802518312[/C][C]-113.378025183116[/C][/ROW]
[ROW][C]77[/C][C]1519[/C][C]1632.69052518312[/C][C]-113.690525183116[/C][/ROW]
[ROW][C]78[/C][C]1421[/C][C]1576.94052518312[/C][C]-155.940525183116[/C][/ROW]
[ROW][C]79[/C][C]1442[/C][C]1652.56552518312[/C][C]-210.565525183116[/C][/ROW]
[ROW][C]80[/C][C]1543[/C][C]1667.44052518312[/C][C]-124.440525183115[/C][/ROW]
[ROW][C]81[/C][C]1656[/C][C]1720.25302518312[/C][C]-64.2530251831158[/C][/ROW]
[ROW][C]82[/C][C]1561[/C][C]1859.62802518312[/C][C]-298.628025183116[/C][/ROW]
[ROW][C]83[/C][C]1905[/C][C]2059.19052518312[/C][C]-154.190525183116[/C][/ROW]
[ROW][C]84[/C][C]2199[/C][C]2175.81552518312[/C][C]23.1844748168843[/C][/ROW]
[ROW][C]85[/C][C]1473[/C][C]1722.67569659443[/C][C]-249.675696594429[/C][/ROW]
[ROW][C]86[/C][C]1655[/C][C]1536.82476119459[/C][C]118.175238805407[/C][/ROW]
[ROW][C]87[/C][C]1407[/C][C]1587.38726119459[/C][C]-180.387261194593[/C][/ROW]
[ROW][C]88[/C][C]1395[/C][C]1474.19976119459[/C][C]-79.1997611945937[/C][/ROW]
[ROW][C]89[/C][C]1530[/C][C]1611.51226119459[/C][C]-81.5122611945933[/C][/ROW]
[ROW][C]90[/C][C]1309[/C][C]1555.76226119459[/C][C]-246.762261194593[/C][/ROW]
[ROW][C]91[/C][C]1526[/C][C]1631.38726119459[/C][C]-105.387261194593[/C][/ROW]
[ROW][C]92[/C][C]1327[/C][C]1646.26226119459[/C][C]-319.262261194593[/C][/ROW]
[ROW][C]93[/C][C]1627[/C][C]1699.07476119459[/C][C]-72.0747611945935[/C][/ROW]
[ROW][C]94[/C][C]1748[/C][C]1838.44976119459[/C][C]-90.4497611945934[/C][/ROW]
[ROW][C]95[/C][C]1958[/C][C]2038.01226119459[/C][C]-80.0122611945933[/C][/ROW]
[ROW][C]96[/C][C]2274[/C][C]2154.63726119459[/C][C]119.362738805407[/C][/ROW]
[ROW][C]97[/C][C]1648[/C][C]1701.49743260591[/C][C]-53.4974326059064[/C][/ROW]
[ROW][C]98[/C][C]1401[/C][C]1515.64649720607[/C][C]-114.646497206071[/C][/ROW]
[ROW][C]99[/C][C]1411[/C][C]1566.20899720607[/C][C]-155.208997206071[/C][/ROW]
[ROW][C]100[/C][C]1403[/C][C]1453.02149720607[/C][C]-50.0214972060714[/C][/ROW]
[ROW][C]101[/C][C]1394[/C][C]1590.33399720607[/C][C]-196.333997206071[/C][/ROW]
[ROW][C]102[/C][C]1520[/C][C]1534.58399720607[/C][C]-14.5839972060711[/C][/ROW]
[ROW][C]103[/C][C]1528[/C][C]1610.20899720607[/C][C]-82.2089972060712[/C][/ROW]
[ROW][C]104[/C][C]1643[/C][C]1625.08399720607[/C][C]17.9160027939294[/C][/ROW]
[ROW][C]105[/C][C]1515[/C][C]1677.89649720607[/C][C]-162.896497206071[/C][/ROW]
[ROW][C]106[/C][C]1685[/C][C]1817.27149720607[/C][C]-132.271497206071[/C][/ROW]
[ROW][C]107[/C][C]2000[/C][C]2016.83399720607[/C][C]-16.8339972060710[/C][/ROW]
[ROW][C]108[/C][C]2215[/C][C]2133.45899720607[/C][C]81.5410027939288[/C][/ROW]
[ROW][C]109[/C][C]1956[/C][C]1680.31916861738[/C][C]275.680831382616[/C][/ROW]
[ROW][C]110[/C][C]1462[/C][C]1494.46823321755[/C][C]-32.4682332175485[/C][/ROW]
[ROW][C]111[/C][C]1563[/C][C]1545.03073321755[/C][C]17.9692667824515[/C][/ROW]
[ROW][C]112[/C][C]1459[/C][C]1431.84323321755[/C][C]27.1567667824508[/C][/ROW]
[ROW][C]113[/C][C]1446[/C][C]1569.15573321755[/C][C]-123.155733217549[/C][/ROW]
[ROW][C]114[/C][C]1622[/C][C]1513.40573321755[/C][C]108.594266782451[/C][/ROW]
[ROW][C]115[/C][C]1657[/C][C]1589.03073321755[/C][C]67.9692667824511[/C][/ROW]
[ROW][C]116[/C][C]1638[/C][C]1603.90573321755[/C][C]34.0942667824517[/C][/ROW]
[ROW][C]117[/C][C]1643[/C][C]1656.71823321755[/C][C]-13.7182332175489[/C][/ROW]
[ROW][C]118[/C][C]1683[/C][C]1796.09323321755[/C][C]-113.093233217549[/C][/ROW]
[ROW][C]119[/C][C]2050[/C][C]1995.65573321755[/C][C]54.3442667824512[/C][/ROW]
[ROW][C]120[/C][C]2262[/C][C]2112.28073321755[/C][C]149.719266782451[/C][/ROW]
[ROW][C]121[/C][C]1813[/C][C]1659.14090462886[/C][C]153.859095371138[/C][/ROW]
[ROW][C]122[/C][C]1445[/C][C]1473.28996922903[/C][C]-28.2899692290262[/C][/ROW]
[ROW][C]123[/C][C]1762[/C][C]1523.85246922903[/C][C]238.147530770974[/C][/ROW]
[ROW][C]124[/C][C]1461[/C][C]1410.66496922903[/C][C]50.3350307709731[/C][/ROW]
[ROW][C]125[/C][C]1556[/C][C]1547.97746922903[/C][C]8.02253077097358[/C][/ROW]
[ROW][C]126[/C][C]1431[/C][C]1492.22746922903[/C][C]-61.2274692290265[/C][/ROW]
[ROW][C]127[/C][C]1427[/C][C]1567.85246922903[/C][C]-140.852469229027[/C][/ROW]
[ROW][C]128[/C][C]1554[/C][C]1582.72746922903[/C][C]-28.7274692290261[/C][/ROW]
[ROW][C]129[/C][C]1645[/C][C]1635.53996922903[/C][C]9.46003077097336[/C][/ROW]
[ROW][C]130[/C][C]1653[/C][C]1774.91496922903[/C][C]-121.914969229026[/C][/ROW]
[ROW][C]131[/C][C]2016[/C][C]1974.47746922903[/C][C]41.5225307709736[/C][/ROW]
[ROW][C]132[/C][C]2207[/C][C]2091.10246922903[/C][C]115.897530770973[/C][/ROW]
[ROW][C]133[/C][C]1665[/C][C]1637.96264064034[/C][C]27.0373593596605[/C][/ROW]
[ROW][C]134[/C][C]1361[/C][C]1452.11170524050[/C][C]-91.111705240504[/C][/ROW]
[ROW][C]135[/C][C]1506[/C][C]1502.67420524050[/C][C]3.32579475949605[/C][/ROW]
[ROW][C]136[/C][C]1360[/C][C]1389.48670524050[/C][C]-29.4867052405046[/C][/ROW]
[ROW][C]137[/C][C]1453[/C][C]1526.79920524050[/C][C]-73.7992052405041[/C][/ROW]
[ROW][C]138[/C][C]1522[/C][C]1471.04920524050[/C][C]50.9507947594957[/C][/ROW]
[ROW][C]139[/C][C]1460[/C][C]1546.67420524050[/C][C]-86.6742052405044[/C][/ROW]
[ROW][C]140[/C][C]1552[/C][C]1561.54920524050[/C][C]-9.54920524050376[/C][/ROW]
[ROW][C]141[/C][C]1548[/C][C]1614.36170524050[/C][C]-66.3617052405043[/C][/ROW]
[ROW][C]142[/C][C]1827[/C][C]1753.73670524050[/C][C]73.2632947594957[/C][/ROW]
[ROW][C]143[/C][C]1737[/C][C]1953.29920524050[/C][C]-216.299205240504[/C][/ROW]
[ROW][C]144[/C][C]1941[/C][C]2069.92420524050[/C][C]-128.924205240504[/C][/ROW]
[ROW][C]145[/C][C]1474[/C][C]1616.78437665182[/C][C]-142.784376651817[/C][/ROW]
[ROW][C]146[/C][C]1458[/C][C]1430.93344125198[/C][C]27.0665587480184[/C][/ROW]
[ROW][C]147[/C][C]1542[/C][C]1481.49594125198[/C][C]60.5040587480183[/C][/ROW]
[ROW][C]148[/C][C]1404[/C][C]1368.30844125198[/C][C]35.6915587480177[/C][/ROW]
[ROW][C]149[/C][C]1522[/C][C]1505.62094125198[/C][C]16.3790587480182[/C][/ROW]
[ROW][C]150[/C][C]1385[/C][C]1449.87094125198[/C][C]-64.870941251982[/C][/ROW]
[ROW][C]151[/C][C]1641[/C][C]1525.49594125198[/C][C]115.504058748018[/C][/ROW]
[ROW][C]152[/C][C]1510[/C][C]1540.37094125198[/C][C]-30.3709412519815[/C][/ROW]
[ROW][C]153[/C][C]1681[/C][C]1593.18344125198[/C][C]87.816558748018[/C][/ROW]
[ROW][C]154[/C][C]1938[/C][C]1732.55844125198[/C][C]205.441558748018[/C][/ROW]
[ROW][C]155[/C][C]1868[/C][C]1932.12094125198[/C][C]-64.1209412519819[/C][/ROW]
[ROW][C]156[/C][C]1726[/C][C]2048.74594125198[/C][C]-322.745941251982[/C][/ROW]
[ROW][C]157[/C][C]1456[/C][C]1595.60611266329[/C][C]-139.606112663295[/C][/ROW]
[ROW][C]158[/C][C]1445[/C][C]1409.75517726346[/C][C]35.2448227365406[/C][/ROW]
[ROW][C]159[/C][C]1456[/C][C]1460.31767726346[/C][C]-4.31767726345942[/C][/ROW]
[ROW][C]160[/C][C]1365[/C][C]1347.13017726346[/C][C]17.8698227365400[/C][/ROW]
[ROW][C]161[/C][C]1487[/C][C]1484.44267726346[/C][C]2.55732273654045[/C][/ROW]
[ROW][C]162[/C][C]1558[/C][C]1428.69267726346[/C][C]129.307322736540[/C][/ROW]
[ROW][C]163[/C][C]1488[/C][C]1504.31767726346[/C][C]-16.3176772634598[/C][/ROW]
[ROW][C]164[/C][C]1684[/C][C]1519.19267726346[/C][C]164.807322736541[/C][/ROW]
[ROW][C]165[/C][C]1594[/C][C]1572.00517726346[/C][C]21.9948227365402[/C][/ROW]
[ROW][C]166[/C][C]1850[/C][C]1711.38017726346[/C][C]138.619822736540[/C][/ROW]
[ROW][C]167[/C][C]1998[/C][C]1910.94267726346[/C][C]87.0573227365404[/C][/ROW]
[ROW][C]168[/C][C]2079[/C][C]2027.56767726346[/C][C]51.4323227365403[/C][/ROW]
[ROW][C]169[/C][C]1494[/C][C]1574.42784867477[/C][C]-80.4278486747727[/C][/ROW]
[ROW][C]170[/C][C]1057[/C][C]1162.19187967228[/C][C]-105.191879672279[/C][/ROW]
[ROW][C]171[/C][C]1218[/C][C]1212.75437967228[/C][C]5.24562032772056[/C][/ROW]
[ROW][C]172[/C][C]1168[/C][C]1099.56687967228[/C][C]68.4331203277199[/C][/ROW]
[ROW][C]173[/C][C]1236[/C][C]1236.87937967228[/C][C]-0.879379672279542[/C][/ROW]
[ROW][C]174[/C][C]1076[/C][C]1181.12937967228[/C][C]-105.129379672280[/C][/ROW]
[ROW][C]175[/C][C]1174[/C][C]1256.75437967228[/C][C]-82.7543796722797[/C][/ROW]
[ROW][C]176[/C][C]1139[/C][C]1271.62937967228[/C][C]-132.629379672279[/C][/ROW]
[ROW][C]177[/C][C]1427[/C][C]1324.44187967228[/C][C]102.558120327720[/C][/ROW]
[ROW][C]178[/C][C]1487[/C][C]1463.81687967228[/C][C]23.1831203277203[/C][/ROW]
[ROW][C]179[/C][C]1483[/C][C]1663.37937967228[/C][C]-180.379379672280[/C][/ROW]
[ROW][C]180[/C][C]1513[/C][C]1780.00437967228[/C][C]-267.00437967228[/C][/ROW]
[ROW][C]181[/C][C]1357[/C][C]1326.86455108359[/C][C]30.1354489164073[/C][/ROW]
[ROW][C]182[/C][C]1165[/C][C]1141.01361568376[/C][C]23.986384316243[/C][/ROW]
[ROW][C]183[/C][C]1282[/C][C]1191.57611568376[/C][C]90.4238843162428[/C][/ROW]
[ROW][C]184[/C][C]1110[/C][C]1078.38861568376[/C][C]31.6113843162422[/C][/ROW]
[ROW][C]185[/C][C]1297[/C][C]1215.70111568376[/C][C]81.2988843162427[/C][/ROW]
[ROW][C]186[/C][C]1185[/C][C]1159.95111568376[/C][C]25.0488843162426[/C][/ROW]
[ROW][C]187[/C][C]1222[/C][C]1235.57611568376[/C][C]-13.5761156837575[/C][/ROW]
[ROW][C]188[/C][C]1284[/C][C]1250.45111568376[/C][C]33.548884316243[/C][/ROW]
[ROW][C]189[/C][C]1444[/C][C]1303.26361568376[/C][C]140.736384316242[/C][/ROW]
[ROW][C]190[/C][C]1575[/C][C]1442.63861568376[/C][C]132.361384316243[/C][/ROW]
[ROW][C]191[/C][C]1737[/C][C]1642.20111568376[/C][C]94.7988843162427[/C][/ROW]
[ROW][C]192[/C][C]1763[/C][C]1758.82611568376[/C][C]4.17388431624253[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=55999&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=55999&T=4

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


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
116871870.92354451406-183.923544514060
215081685.07260911425-177.072609114252
315071735.63510911425-228.635109114254
413851622.44760911424-237.447609114242
516321759.76010911425-127.760109114251
615111704.01010911425-193.010109114250
715591779.63510911425-220.635109114248
816301794.51010911426-164.510109114256
915791847.32260911425-268.322609114246
1016531986.69760911425-333.697609114249
1121522186.26010911425-34.2601091142503
1221482302.88510911425-154.885109114249
1317521849.74528052556-97.7452805255622
1417651663.89434512573101.105654874272
1517171714.456845125732.54315487427317
1615581601.26934512573-43.2693451257275
1715751738.58184512573-163.581845125727
1815201682.83184512573-162.831845125727
1918051758.4568451257346.5431548742728
2018001773.3318451257326.6681548742733
2117191826.14434512573-107.144345125727
2220081965.5193451257342.4806548742729
2322422165.0818451257376.918154874273
2424782281.70684512573196.293154874273
2520301828.56701653704201.43298346296
2616551642.7160811372012.2839188627955
2716931693.27858113720-0.278581137204542
2816231580.0910811372142.9089188627948
2918051717.4035811372087.5964188627952
3017461661.6535811372084.3464188627952
3117951737.2785811372157.721418862795
3219261752.15358113720173.846418862796
3316191804.96608113721-185.966081137205
3419921944.3410811372047.6589188627952
3522332143.9035811372089.0964188627953
3621922260.52858113721-68.5285811372049
3720801807.38875254852272.611247451482
3817681621.53781714868146.462182851318
3918351672.10031714868162.899682851318
4015691558.9128171486810.0871828513171
4119761696.22531714868279.774682851318
4218531640.47531714868212.524682851317
4319651716.10031714868248.899682851317
4416891730.97531714868-41.9753171486821
4517781783.78781714868-5.78781714868267
4619761923.1628171486852.8371828513174
4723972122.72531714868274.274682851318
4826542239.35031714868414.649682851317
4920971786.21048856000310.789511440004
5019631600.35955316016362.64044683984
5116771650.9220531601626.0779468398400
5219411537.73455316016403.265446839839
5320031675.04705316016327.95294683984
5418131619.29705316016193.702946839840
5520121694.92205316016317.07794683984
5619121709.79705316016202.20294683984
5720841762.60955316016321.39044683984
5820801901.98455316016178.015446839840
5921182101.5470531601616.4529468398398
6021502218.17205316016-68.1720531601603
6116081765.03222457147-157.032224571473
6215031579.18128917164-76.1812891716376
6315481629.74378917164-81.7437891716377
6413821516.55628917164-134.556289171638
6517311653.8687891716477.1312108283621
6617981598.11878917164199.881210828362
6717791673.74378917164105.256210828362
6818871688.61878917164198.381210828362
6920041741.43128917164262.568710828362
7020771880.80628917164196.193710828362
7120922080.3687891716411.6312108283621
7220512196.99378917164-145.993789171638
7315771743.85396058295-166.853960582951
7413561558.00302518312-202.003025183115
7516521608.5655251831243.4344748168846
7613821495.37802518312-113.378025183116
7715191632.69052518312-113.690525183116
7814211576.94052518312-155.940525183116
7914421652.56552518312-210.565525183116
8015431667.44052518312-124.440525183115
8116561720.25302518312-64.2530251831158
8215611859.62802518312-298.628025183116
8319052059.19052518312-154.190525183116
8421992175.8155251831223.1844748168843
8514731722.67569659443-249.675696594429
8616551536.82476119459118.175238805407
8714071587.38726119459-180.387261194593
8813951474.19976119459-79.1997611945937
8915301611.51226119459-81.5122611945933
9013091555.76226119459-246.762261194593
9115261631.38726119459-105.387261194593
9213271646.26226119459-319.262261194593
9316271699.07476119459-72.0747611945935
9417481838.44976119459-90.4497611945934
9519582038.01226119459-80.0122611945933
9622742154.63726119459119.362738805407
9716481701.49743260591-53.4974326059064
9814011515.64649720607-114.646497206071
9914111566.20899720607-155.208997206071
10014031453.02149720607-50.0214972060714
10113941590.33399720607-196.333997206071
10215201534.58399720607-14.5839972060711
10315281610.20899720607-82.2089972060712
10416431625.0839972060717.9160027939294
10515151677.89649720607-162.896497206071
10616851817.27149720607-132.271497206071
10720002016.83399720607-16.8339972060710
10822152133.4589972060781.5410027939288
10919561680.31916861738275.680831382616
11014621494.46823321755-32.4682332175485
11115631545.0307332175517.9692667824515
11214591431.8432332175527.1567667824508
11314461569.15573321755-123.155733217549
11416221513.40573321755108.594266782451
11516571589.0307332175567.9692667824511
11616381603.9057332175534.0942667824517
11716431656.71823321755-13.7182332175489
11816831796.09323321755-113.093233217549
11920501995.6557332175554.3442667824512
12022622112.28073321755149.719266782451
12118131659.14090462886153.859095371138
12214451473.28996922903-28.2899692290262
12317621523.85246922903238.147530770974
12414611410.6649692290350.3350307709731
12515561547.977469229038.02253077097358
12614311492.22746922903-61.2274692290265
12714271567.85246922903-140.852469229027
12815541582.72746922903-28.7274692290261
12916451635.539969229039.46003077097336
13016531774.91496922903-121.914969229026
13120161974.4774692290341.5225307709736
13222072091.10246922903115.897530770973
13316651637.9626406403427.0373593596605
13413611452.11170524050-91.111705240504
13515061502.674205240503.32579475949605
13613601389.48670524050-29.4867052405046
13714531526.79920524050-73.7992052405041
13815221471.0492052405050.9507947594957
13914601546.67420524050-86.6742052405044
14015521561.54920524050-9.54920524050376
14115481614.36170524050-66.3617052405043
14218271753.7367052405073.2632947594957
14317371953.29920524050-216.299205240504
14419412069.92420524050-128.924205240504
14514741616.78437665182-142.784376651817
14614581430.9334412519827.0665587480184
14715421481.4959412519860.5040587480183
14814041368.3084412519835.6915587480177
14915221505.6209412519816.3790587480182
15013851449.87094125198-64.870941251982
15116411525.49594125198115.504058748018
15215101540.37094125198-30.3709412519815
15316811593.1834412519887.816558748018
15419381732.55844125198205.441558748018
15518681932.12094125198-64.1209412519819
15617262048.74594125198-322.745941251982
15714561595.60611266329-139.606112663295
15814451409.7551772634635.2448227365406
15914561460.31767726346-4.31767726345942
16013651347.1301772634617.8698227365400
16114871484.442677263462.55732273654045
16215581428.69267726346129.307322736540
16314881504.31767726346-16.3176772634598
16416841519.19267726346164.807322736541
16515941572.0051772634621.9948227365402
16618501711.38017726346138.619822736540
16719981910.9426772634687.0573227365404
16820792027.5676772634651.4323227365403
16914941574.42784867477-80.4278486747727
17010571162.19187967228-105.191879672279
17112181212.754379672285.24562032772056
17211681099.5668796722868.4331203277199
17312361236.87937967228-0.879379672279542
17410761181.12937967228-105.129379672280
17511741256.75437967228-82.7543796722797
17611391271.62937967228-132.629379672279
17714271324.44187967228102.558120327720
17814871463.8168796722823.1831203277203
17914831663.37937967228-180.379379672280
18015131780.00437967228-267.00437967228
18113571326.8645510835930.1354489164073
18211651141.0136156837623.986384316243
18312821191.5761156837690.4238843162428
18411101078.3886156837631.6113843162422
18512971215.7011156837681.2988843162427
18611851159.9511156837625.0488843162426
18712221235.57611568376-13.5761156837575
18812841250.4511156837633.548884316243
18914441303.26361568376140.736384316242
19015751442.63861568376132.361384316243
19117371642.2011156837694.7988843162427
19217631758.826115683764.17388431624253






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
170.3220302286924620.6440604573849240.677969771307538
180.2335210890959760.4670421781919510.766478910904024
190.1806855784785960.3613711569571920.819314421521404
200.1036689106671450.207337821334290.896331089332855
210.05594253793034310.1118850758606860.944057462069657
220.08584219237076970.1716843847415390.91415780762923
230.05298521090866150.1059704218173230.947014789091339
240.05685007762106940.1137001552421390.94314992237893
250.03611929194377110.07223858388754210.963880708056229
260.07056197359914530.1411239471982910.929438026400855
270.06276451543735430.1255290308747090.937235484562646
280.04110667206251370.08221334412502740.958893327937486
290.02500352156246460.05000704312492910.974996478437535
300.01517277417268810.03034554834537620.984827225827312
310.01048625145647990.02097250291295990.98951374854352
320.006139053853430690.01227810770686140.99386094614657
330.01374482965265010.02748965930530020.98625517034735
340.008287862483525150.01657572496705030.991712137516475
350.00766107975814670.01532215951629340.992338920241853
360.02407759818054770.04815519636109540.975922401819452
370.01833419852318040.03666839704636070.98166580147682
380.01347733582748220.02695467165496440.986522664172518
390.008735154192975910.01747030838595180.991264845807024
400.008726024882234820.01745204976446960.991273975117765
410.00844524385165650.0168904877033130.991554756148344
420.006249497549605690.01249899509921140.993750502450394
430.004631340671584860.009262681343169710.995368659328415
440.01585979725056940.03171959450113880.98414020274943
450.01127350949369190.02254701898738380.988726490506308
460.008556565238717450.01711313047743490.991443434761283
470.00682271056520.01364542113040.9931772894348
480.01502338854254600.03004677708509210.984976611457454
490.0142690968509970.0285381937019940.985730903149003
500.01653371529091900.03306743058183810.983466284709081
510.02939038045950.0587807609190.9706096195405
520.05664310793481260.1132862158696250.943356892065187
530.06467971957555930.1293594391511190.93532028042444
540.06142328323536130.1228465664707230.938576716764639
550.07444424133630210.1488884826726040.925555758663698
560.0761684088326550.152336817665310.923831591167345
570.1269548837736270.2539097675472540.873045116226373
580.1248078889635040.2496157779270090.875192111036496
590.3083768837683830.6167537675367650.691623116231618
600.6002611986436950.799477602712610.399738801356305
610.9103345897471480.1793308205057040.0896654102528519
620.9659594625522480.0680810748955050.0340405374477525
630.9776006118954070.04479877620918560.0223993881045928
640.990218318611570.01956336277685840.00978168138842921
650.991795631225380.01640873754924230.00820436877462113
660.9934237772990940.01315244540181200.00657622270090601
670.9948866097014060.01022678059718860.0051133902985943
680.996511940239780.006976119520440160.00348805976022008
690.9984146526847670.00317069463046640.0015853473152332
700.9990711342116390.001857731576722740.00092886578836137
710.9994169625900620.001166074819875860.000583037409937929
720.9997688126384240.0004623747231514140.000231187361575707
730.9999247209671450.0001505580657090187.52790328545092e-05
740.9999794983808364.100323832797e-052.0501619163985e-05
750.9999745073462125.09853075752223e-052.54926537876112e-05
760.9999779256191944.41487616115049e-052.20743808057524e-05
770.999985408929072.91821418598767e-051.45910709299383e-05
780.9999905935973161.88128053673771e-059.40640268368857e-06
790.9999963065959457.38680811026777e-063.69340405513389e-06
800.9999966579799026.68404019588085e-063.34202009794043e-06
810.999995281059169.43788167924907e-064.71894083962453e-06
820.9999989772538852.04549223090433e-061.02274611545216e-06
830.9999990850055361.82998892833481e-069.14994464167405e-07
840.9999987655233922.46895321580061e-061.23447660790030e-06
850.9999994673938241.06521235236867e-065.32606176184337e-07
860.9999995786832998.42633402778393e-074.21316701389197e-07
870.9999996427861077.14427786983761e-073.57213893491880e-07
880.999999435762871.12847426032615e-065.64237130163076e-07
890.9999991947837951.61043241071844e-068.05216205359219e-07
900.9999996239060027.52187994909272e-073.76093997454636e-07
910.9999994640397831.07192043430264e-065.3596021715132e-07
920.9999999136772361.72645528621443e-078.63227643107215e-08
930.9999998541724842.91655031674888e-071.45827515837444e-07
940.9999997763282894.47343422178674e-072.23671711089337e-07
950.9999996365908177.26818366422688e-073.63409183211344e-07
960.999999706932795.86134421328664e-072.93067210664332e-07
970.9999994912858121.01742837608735e-065.08714188043675e-07
980.999999260080281.47983943967688e-067.3991971983844e-07
990.9999993825245751.23495085093429e-066.17475425467145e-07
1000.9999989743435162.05131296810265e-061.02565648405133e-06
1010.9999992037447381.59251052292049e-067.96255261460247e-07
1020.9999985989037052.8021925908099e-061.40109629540495e-06
1030.9999977726557034.45468859316504e-062.22734429658252e-06
1040.9999962326317497.5347365024918e-063.7673682512459e-06
1050.999997127611635.74477673973872e-062.87238836986936e-06
1060.9999975174215974.96515680639093e-062.48257840319547e-06
1070.999995703814228.59237155936023e-064.29618577968012e-06
1080.9999949403718731.01192562541705e-055.05962812708527e-06
1090.9999993059416451.38811671063981e-066.94058355319906e-07
1100.9999987588257772.48234844669864e-061.24117422334932e-06
1110.9999979106727564.17865448729335e-062.08932724364668e-06
1120.9999964024012087.19519758351085e-063.59759879175543e-06
1130.9999956317270948.73654581208899e-064.36827290604449e-06
1140.9999949641256581.00717486844338e-055.03587434221692e-06
1150.9999937901985781.24196028448702e-056.20980142243508e-06
1160.999990094324761.98113504793609e-059.90567523968047e-06
1170.9999836016579353.27966841308944e-051.63983420654472e-05
1180.999985072519452.98549610993064e-051.49274805496532e-05
1190.9999803172748653.93654502706745e-051.96827251353372e-05
1200.9999938155445031.23689109940820e-056.18445549704102e-06
1210.999998228566013.54286798047977e-061.77143399023989e-06
1220.9999969061658546.1876682928923e-063.09383414644615e-06
1230.999999405207661.18958468107428e-065.94792340537142e-07
1240.999999119634381.76073123977003e-068.80365619885017e-07
1250.9999985613479422.87730411688845e-061.43865205844423e-06
1260.9999973602112265.27957754712046e-062.63978877356023e-06
1270.9999960290415557.94191688977771e-063.97095844488886e-06
1280.9999929488397151.41023205709067e-057.05116028545335e-06
1290.9999874501413132.50997173744961e-051.25498586872481e-05
1300.9999903301517571.93396964858222e-059.66984824291112e-06
1310.9999914662150911.70675698175420e-058.53378490877098e-06
1320.9999996705869476.58826104949665e-073.29413052474832e-07
1330.9999999076940071.84611986309583e-079.23059931547913e-08
1340.9999998112876873.77424626322019e-071.88712313161010e-07
1350.9999996398243437.20351314320861e-073.60175657160431e-07
1360.9999992594027521.48119449536726e-067.4059724768363e-07
1370.9999985198656582.9602686848403e-061.48013434242015e-06
1380.999998728249742.54350051936023e-061.27175025968011e-06
1390.9999974785201025.04295979493376e-062.52147989746688e-06
1400.9999960837587457.83248251027385e-063.91624125513693e-06
1410.9999930195564361.39608871282583e-056.98044356412913e-06
1420.999988065454692.38690906208563e-051.19345453104281e-05
1430.9999869864624722.60270750557366e-051.30135375278683e-05
1440.9999844276001253.11447997500722e-051.55723998750361e-05
1450.9999717042434945.65915130113381e-052.82957565056690e-05
1460.999956782458658.6435082698033e-054.32175413490165e-05
1470.9999326787413040.0001346425173916206.73212586958098e-05
1480.9998808921350520.0002382157298960120.000119107864948006
1490.9997878403267420.0004243193465165220.000212159673258261
1500.9996175144837440.0007649710325122770.000382485516256139
1510.99984934763780.0003013047243998440.000150652362199922
1520.9997124556533830.0005750886932337120.000287544346616856
1530.9995665141748410.0008669716503181530.000433485825159076
1540.999832416347410.0003351673051782930.000167583652589146
1550.9996890386372530.0006219227254937540.000310961362746877
1560.9998140077319250.0003719845361496290.000185992268074815
1570.9996637509021310.0006724981957374210.000336249097868710
1580.9993434560423880.001313087915223170.000656543957611584
1590.9990544363992570.001891127201485520.00094556360074276
1600.9985508548805760.002898290238848430.00144914511942421
1610.9980072674174620.003985465165076780.00199273258253839
1620.9973269090925480.005346181814904110.00267309090745206
1630.9949010093092420.01019798138151600.00509899069075801
1640.9961929795188690.007614040962262740.00380702048113137
1650.9965947475689370.006810504862126180.00340525243106309
1660.9931032326308020.01379353473839590.00689676736919795
1670.9887368045970050.02252639080598990.0112631954029950
1680.9979813145644070.004037370871185560.00201868543559278
1690.994934053556240.01013189288751970.00506594644375987
1700.9878668994044940.02426620119101230.0121331005955062
1710.9736500401250430.05269991974991410.0263499598749570
1720.979706390025280.04058721994944160.0202936099747208
1730.9576677757183660.08466444856326860.0423322242816343
1740.902218059854980.1955638802900400.0977819401450201
1750.8323476540212660.3353046919574680.167652345978734

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
17 & 0.322030228692462 & 0.644060457384924 & 0.677969771307538 \tabularnewline
18 & 0.233521089095976 & 0.467042178191951 & 0.766478910904024 \tabularnewline
19 & 0.180685578478596 & 0.361371156957192 & 0.819314421521404 \tabularnewline
20 & 0.103668910667145 & 0.20733782133429 & 0.896331089332855 \tabularnewline
21 & 0.0559425379303431 & 0.111885075860686 & 0.944057462069657 \tabularnewline
22 & 0.0858421923707697 & 0.171684384741539 & 0.91415780762923 \tabularnewline
23 & 0.0529852109086615 & 0.105970421817323 & 0.947014789091339 \tabularnewline
24 & 0.0568500776210694 & 0.113700155242139 & 0.94314992237893 \tabularnewline
25 & 0.0361192919437711 & 0.0722385838875421 & 0.963880708056229 \tabularnewline
26 & 0.0705619735991453 & 0.141123947198291 & 0.929438026400855 \tabularnewline
27 & 0.0627645154373543 & 0.125529030874709 & 0.937235484562646 \tabularnewline
28 & 0.0411066720625137 & 0.0822133441250274 & 0.958893327937486 \tabularnewline
29 & 0.0250035215624646 & 0.0500070431249291 & 0.974996478437535 \tabularnewline
30 & 0.0151727741726881 & 0.0303455483453762 & 0.984827225827312 \tabularnewline
31 & 0.0104862514564799 & 0.0209725029129599 & 0.98951374854352 \tabularnewline
32 & 0.00613905385343069 & 0.0122781077068614 & 0.99386094614657 \tabularnewline
33 & 0.0137448296526501 & 0.0274896593053002 & 0.98625517034735 \tabularnewline
34 & 0.00828786248352515 & 0.0165757249670503 & 0.991712137516475 \tabularnewline
35 & 0.0076610797581467 & 0.0153221595162934 & 0.992338920241853 \tabularnewline
36 & 0.0240775981805477 & 0.0481551963610954 & 0.975922401819452 \tabularnewline
37 & 0.0183341985231804 & 0.0366683970463607 & 0.98166580147682 \tabularnewline
38 & 0.0134773358274822 & 0.0269546716549644 & 0.986522664172518 \tabularnewline
39 & 0.00873515419297591 & 0.0174703083859518 & 0.991264845807024 \tabularnewline
40 & 0.00872602488223482 & 0.0174520497644696 & 0.991273975117765 \tabularnewline
41 & 0.0084452438516565 & 0.016890487703313 & 0.991554756148344 \tabularnewline
42 & 0.00624949754960569 & 0.0124989950992114 & 0.993750502450394 \tabularnewline
43 & 0.00463134067158486 & 0.00926268134316971 & 0.995368659328415 \tabularnewline
44 & 0.0158597972505694 & 0.0317195945011388 & 0.98414020274943 \tabularnewline
45 & 0.0112735094936919 & 0.0225470189873838 & 0.988726490506308 \tabularnewline
46 & 0.00855656523871745 & 0.0171131304774349 & 0.991443434761283 \tabularnewline
47 & 0.0068227105652 & 0.0136454211304 & 0.9931772894348 \tabularnewline
48 & 0.0150233885425460 & 0.0300467770850921 & 0.984976611457454 \tabularnewline
49 & 0.014269096850997 & 0.028538193701994 & 0.985730903149003 \tabularnewline
50 & 0.0165337152909190 & 0.0330674305818381 & 0.983466284709081 \tabularnewline
51 & 0.0293903804595 & 0.058780760919 & 0.9706096195405 \tabularnewline
52 & 0.0566431079348126 & 0.113286215869625 & 0.943356892065187 \tabularnewline
53 & 0.0646797195755593 & 0.129359439151119 & 0.93532028042444 \tabularnewline
54 & 0.0614232832353613 & 0.122846566470723 & 0.938576716764639 \tabularnewline
55 & 0.0744442413363021 & 0.148888482672604 & 0.925555758663698 \tabularnewline
56 & 0.076168408832655 & 0.15233681766531 & 0.923831591167345 \tabularnewline
57 & 0.126954883773627 & 0.253909767547254 & 0.873045116226373 \tabularnewline
58 & 0.124807888963504 & 0.249615777927009 & 0.875192111036496 \tabularnewline
59 & 0.308376883768383 & 0.616753767536765 & 0.691623116231618 \tabularnewline
60 & 0.600261198643695 & 0.79947760271261 & 0.399738801356305 \tabularnewline
61 & 0.910334589747148 & 0.179330820505704 & 0.0896654102528519 \tabularnewline
62 & 0.965959462552248 & 0.068081074895505 & 0.0340405374477525 \tabularnewline
63 & 0.977600611895407 & 0.0447987762091856 & 0.0223993881045928 \tabularnewline
64 & 0.99021831861157 & 0.0195633627768584 & 0.00978168138842921 \tabularnewline
65 & 0.99179563122538 & 0.0164087375492423 & 0.00820436877462113 \tabularnewline
66 & 0.993423777299094 & 0.0131524454018120 & 0.00657622270090601 \tabularnewline
67 & 0.994886609701406 & 0.0102267805971886 & 0.0051133902985943 \tabularnewline
68 & 0.99651194023978 & 0.00697611952044016 & 0.00348805976022008 \tabularnewline
69 & 0.998414652684767 & 0.0031706946304664 & 0.0015853473152332 \tabularnewline
70 & 0.999071134211639 & 0.00185773157672274 & 0.00092886578836137 \tabularnewline
71 & 0.999416962590062 & 0.00116607481987586 & 0.000583037409937929 \tabularnewline
72 & 0.999768812638424 & 0.000462374723151414 & 0.000231187361575707 \tabularnewline
73 & 0.999924720967145 & 0.000150558065709018 & 7.52790328545092e-05 \tabularnewline
74 & 0.999979498380836 & 4.100323832797e-05 & 2.0501619163985e-05 \tabularnewline
75 & 0.999974507346212 & 5.09853075752223e-05 & 2.54926537876112e-05 \tabularnewline
76 & 0.999977925619194 & 4.41487616115049e-05 & 2.20743808057524e-05 \tabularnewline
77 & 0.99998540892907 & 2.91821418598767e-05 & 1.45910709299383e-05 \tabularnewline
78 & 0.999990593597316 & 1.88128053673771e-05 & 9.40640268368857e-06 \tabularnewline
79 & 0.999996306595945 & 7.38680811026777e-06 & 3.69340405513389e-06 \tabularnewline
80 & 0.999996657979902 & 6.68404019588085e-06 & 3.34202009794043e-06 \tabularnewline
81 & 0.99999528105916 & 9.43788167924907e-06 & 4.71894083962453e-06 \tabularnewline
82 & 0.999998977253885 & 2.04549223090433e-06 & 1.02274611545216e-06 \tabularnewline
83 & 0.999999085005536 & 1.82998892833481e-06 & 9.14994464167405e-07 \tabularnewline
84 & 0.999998765523392 & 2.46895321580061e-06 & 1.23447660790030e-06 \tabularnewline
85 & 0.999999467393824 & 1.06521235236867e-06 & 5.32606176184337e-07 \tabularnewline
86 & 0.999999578683299 & 8.42633402778393e-07 & 4.21316701389197e-07 \tabularnewline
87 & 0.999999642786107 & 7.14427786983761e-07 & 3.57213893491880e-07 \tabularnewline
88 & 0.99999943576287 & 1.12847426032615e-06 & 5.64237130163076e-07 \tabularnewline
89 & 0.999999194783795 & 1.61043241071844e-06 & 8.05216205359219e-07 \tabularnewline
90 & 0.999999623906002 & 7.52187994909272e-07 & 3.76093997454636e-07 \tabularnewline
91 & 0.999999464039783 & 1.07192043430264e-06 & 5.3596021715132e-07 \tabularnewline
92 & 0.999999913677236 & 1.72645528621443e-07 & 8.63227643107215e-08 \tabularnewline
93 & 0.999999854172484 & 2.91655031674888e-07 & 1.45827515837444e-07 \tabularnewline
94 & 0.999999776328289 & 4.47343422178674e-07 & 2.23671711089337e-07 \tabularnewline
95 & 0.999999636590817 & 7.26818366422688e-07 & 3.63409183211344e-07 \tabularnewline
96 & 0.99999970693279 & 5.86134421328664e-07 & 2.93067210664332e-07 \tabularnewline
97 & 0.999999491285812 & 1.01742837608735e-06 & 5.08714188043675e-07 \tabularnewline
98 & 0.99999926008028 & 1.47983943967688e-06 & 7.3991971983844e-07 \tabularnewline
99 & 0.999999382524575 & 1.23495085093429e-06 & 6.17475425467145e-07 \tabularnewline
100 & 0.999998974343516 & 2.05131296810265e-06 & 1.02565648405133e-06 \tabularnewline
101 & 0.999999203744738 & 1.59251052292049e-06 & 7.96255261460247e-07 \tabularnewline
102 & 0.999998598903705 & 2.8021925908099e-06 & 1.40109629540495e-06 \tabularnewline
103 & 0.999997772655703 & 4.45468859316504e-06 & 2.22734429658252e-06 \tabularnewline
104 & 0.999996232631749 & 7.5347365024918e-06 & 3.7673682512459e-06 \tabularnewline
105 & 0.99999712761163 & 5.74477673973872e-06 & 2.87238836986936e-06 \tabularnewline
106 & 0.999997517421597 & 4.96515680639093e-06 & 2.48257840319547e-06 \tabularnewline
107 & 0.99999570381422 & 8.59237155936023e-06 & 4.29618577968012e-06 \tabularnewline
108 & 0.999994940371873 & 1.01192562541705e-05 & 5.05962812708527e-06 \tabularnewline
109 & 0.999999305941645 & 1.38811671063981e-06 & 6.94058355319906e-07 \tabularnewline
110 & 0.999998758825777 & 2.48234844669864e-06 & 1.24117422334932e-06 \tabularnewline
111 & 0.999997910672756 & 4.17865448729335e-06 & 2.08932724364668e-06 \tabularnewline
112 & 0.999996402401208 & 7.19519758351085e-06 & 3.59759879175543e-06 \tabularnewline
113 & 0.999995631727094 & 8.73654581208899e-06 & 4.36827290604449e-06 \tabularnewline
114 & 0.999994964125658 & 1.00717486844338e-05 & 5.03587434221692e-06 \tabularnewline
115 & 0.999993790198578 & 1.24196028448702e-05 & 6.20980142243508e-06 \tabularnewline
116 & 0.99999009432476 & 1.98113504793609e-05 & 9.90567523968047e-06 \tabularnewline
117 & 0.999983601657935 & 3.27966841308944e-05 & 1.63983420654472e-05 \tabularnewline
118 & 0.99998507251945 & 2.98549610993064e-05 & 1.49274805496532e-05 \tabularnewline
119 & 0.999980317274865 & 3.93654502706745e-05 & 1.96827251353372e-05 \tabularnewline
120 & 0.999993815544503 & 1.23689109940820e-05 & 6.18445549704102e-06 \tabularnewline
121 & 0.99999822856601 & 3.54286798047977e-06 & 1.77143399023989e-06 \tabularnewline
122 & 0.999996906165854 & 6.1876682928923e-06 & 3.09383414644615e-06 \tabularnewline
123 & 0.99999940520766 & 1.18958468107428e-06 & 5.94792340537142e-07 \tabularnewline
124 & 0.99999911963438 & 1.76073123977003e-06 & 8.80365619885017e-07 \tabularnewline
125 & 0.999998561347942 & 2.87730411688845e-06 & 1.43865205844423e-06 \tabularnewline
126 & 0.999997360211226 & 5.27957754712046e-06 & 2.63978877356023e-06 \tabularnewline
127 & 0.999996029041555 & 7.94191688977771e-06 & 3.97095844488886e-06 \tabularnewline
128 & 0.999992948839715 & 1.41023205709067e-05 & 7.05116028545335e-06 \tabularnewline
129 & 0.999987450141313 & 2.50997173744961e-05 & 1.25498586872481e-05 \tabularnewline
130 & 0.999990330151757 & 1.93396964858222e-05 & 9.66984824291112e-06 \tabularnewline
131 & 0.999991466215091 & 1.70675698175420e-05 & 8.53378490877098e-06 \tabularnewline
132 & 0.999999670586947 & 6.58826104949665e-07 & 3.29413052474832e-07 \tabularnewline
133 & 0.999999907694007 & 1.84611986309583e-07 & 9.23059931547913e-08 \tabularnewline
134 & 0.999999811287687 & 3.77424626322019e-07 & 1.88712313161010e-07 \tabularnewline
135 & 0.999999639824343 & 7.20351314320861e-07 & 3.60175657160431e-07 \tabularnewline
136 & 0.999999259402752 & 1.48119449536726e-06 & 7.4059724768363e-07 \tabularnewline
137 & 0.999998519865658 & 2.9602686848403e-06 & 1.48013434242015e-06 \tabularnewline
138 & 0.99999872824974 & 2.54350051936023e-06 & 1.27175025968011e-06 \tabularnewline
139 & 0.999997478520102 & 5.04295979493376e-06 & 2.52147989746688e-06 \tabularnewline
140 & 0.999996083758745 & 7.83248251027385e-06 & 3.91624125513693e-06 \tabularnewline
141 & 0.999993019556436 & 1.39608871282583e-05 & 6.98044356412913e-06 \tabularnewline
142 & 0.99998806545469 & 2.38690906208563e-05 & 1.19345453104281e-05 \tabularnewline
143 & 0.999986986462472 & 2.60270750557366e-05 & 1.30135375278683e-05 \tabularnewline
144 & 0.999984427600125 & 3.11447997500722e-05 & 1.55723998750361e-05 \tabularnewline
145 & 0.999971704243494 & 5.65915130113381e-05 & 2.82957565056690e-05 \tabularnewline
146 & 0.99995678245865 & 8.6435082698033e-05 & 4.32175413490165e-05 \tabularnewline
147 & 0.999932678741304 & 0.000134642517391620 & 6.73212586958098e-05 \tabularnewline
148 & 0.999880892135052 & 0.000238215729896012 & 0.000119107864948006 \tabularnewline
149 & 0.999787840326742 & 0.000424319346516522 & 0.000212159673258261 \tabularnewline
150 & 0.999617514483744 & 0.000764971032512277 & 0.000382485516256139 \tabularnewline
151 & 0.9998493476378 & 0.000301304724399844 & 0.000150652362199922 \tabularnewline
152 & 0.999712455653383 & 0.000575088693233712 & 0.000287544346616856 \tabularnewline
153 & 0.999566514174841 & 0.000866971650318153 & 0.000433485825159076 \tabularnewline
154 & 0.99983241634741 & 0.000335167305178293 & 0.000167583652589146 \tabularnewline
155 & 0.999689038637253 & 0.000621922725493754 & 0.000310961362746877 \tabularnewline
156 & 0.999814007731925 & 0.000371984536149629 & 0.000185992268074815 \tabularnewline
157 & 0.999663750902131 & 0.000672498195737421 & 0.000336249097868710 \tabularnewline
158 & 0.999343456042388 & 0.00131308791522317 & 0.000656543957611584 \tabularnewline
159 & 0.999054436399257 & 0.00189112720148552 & 0.00094556360074276 \tabularnewline
160 & 0.998550854880576 & 0.00289829023884843 & 0.00144914511942421 \tabularnewline
161 & 0.998007267417462 & 0.00398546516507678 & 0.00199273258253839 \tabularnewline
162 & 0.997326909092548 & 0.00534618181490411 & 0.00267309090745206 \tabularnewline
163 & 0.994901009309242 & 0.0101979813815160 & 0.00509899069075801 \tabularnewline
164 & 0.996192979518869 & 0.00761404096226274 & 0.00380702048113137 \tabularnewline
165 & 0.996594747568937 & 0.00681050486212618 & 0.00340525243106309 \tabularnewline
166 & 0.993103232630802 & 0.0137935347383959 & 0.00689676736919795 \tabularnewline
167 & 0.988736804597005 & 0.0225263908059899 & 0.0112631954029950 \tabularnewline
168 & 0.997981314564407 & 0.00403737087118556 & 0.00201868543559278 \tabularnewline
169 & 0.99493405355624 & 0.0101318928875197 & 0.00506594644375987 \tabularnewline
170 & 0.987866899404494 & 0.0242662011910123 & 0.0121331005955062 \tabularnewline
171 & 0.973650040125043 & 0.0526999197499141 & 0.0263499598749570 \tabularnewline
172 & 0.97970639002528 & 0.0405872199494416 & 0.0202936099747208 \tabularnewline
173 & 0.957667775718366 & 0.0846644485632686 & 0.0423322242816343 \tabularnewline
174 & 0.90221805985498 & 0.195563880290040 & 0.0977819401450201 \tabularnewline
175 & 0.832347654021266 & 0.335304691957468 & 0.167652345978734 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=55999&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]17[/C][C]0.322030228692462[/C][C]0.644060457384924[/C][C]0.677969771307538[/C][/ROW]
[ROW][C]18[/C][C]0.233521089095976[/C][C]0.467042178191951[/C][C]0.766478910904024[/C][/ROW]
[ROW][C]19[/C][C]0.180685578478596[/C][C]0.361371156957192[/C][C]0.819314421521404[/C][/ROW]
[ROW][C]20[/C][C]0.103668910667145[/C][C]0.20733782133429[/C][C]0.896331089332855[/C][/ROW]
[ROW][C]21[/C][C]0.0559425379303431[/C][C]0.111885075860686[/C][C]0.944057462069657[/C][/ROW]
[ROW][C]22[/C][C]0.0858421923707697[/C][C]0.171684384741539[/C][C]0.91415780762923[/C][/ROW]
[ROW][C]23[/C][C]0.0529852109086615[/C][C]0.105970421817323[/C][C]0.947014789091339[/C][/ROW]
[ROW][C]24[/C][C]0.0568500776210694[/C][C]0.113700155242139[/C][C]0.94314992237893[/C][/ROW]
[ROW][C]25[/C][C]0.0361192919437711[/C][C]0.0722385838875421[/C][C]0.963880708056229[/C][/ROW]
[ROW][C]26[/C][C]0.0705619735991453[/C][C]0.141123947198291[/C][C]0.929438026400855[/C][/ROW]
[ROW][C]27[/C][C]0.0627645154373543[/C][C]0.125529030874709[/C][C]0.937235484562646[/C][/ROW]
[ROW][C]28[/C][C]0.0411066720625137[/C][C]0.0822133441250274[/C][C]0.958893327937486[/C][/ROW]
[ROW][C]29[/C][C]0.0250035215624646[/C][C]0.0500070431249291[/C][C]0.974996478437535[/C][/ROW]
[ROW][C]30[/C][C]0.0151727741726881[/C][C]0.0303455483453762[/C][C]0.984827225827312[/C][/ROW]
[ROW][C]31[/C][C]0.0104862514564799[/C][C]0.0209725029129599[/C][C]0.98951374854352[/C][/ROW]
[ROW][C]32[/C][C]0.00613905385343069[/C][C]0.0122781077068614[/C][C]0.99386094614657[/C][/ROW]
[ROW][C]33[/C][C]0.0137448296526501[/C][C]0.0274896593053002[/C][C]0.98625517034735[/C][/ROW]
[ROW][C]34[/C][C]0.00828786248352515[/C][C]0.0165757249670503[/C][C]0.991712137516475[/C][/ROW]
[ROW][C]35[/C][C]0.0076610797581467[/C][C]0.0153221595162934[/C][C]0.992338920241853[/C][/ROW]
[ROW][C]36[/C][C]0.0240775981805477[/C][C]0.0481551963610954[/C][C]0.975922401819452[/C][/ROW]
[ROW][C]37[/C][C]0.0183341985231804[/C][C]0.0366683970463607[/C][C]0.98166580147682[/C][/ROW]
[ROW][C]38[/C][C]0.0134773358274822[/C][C]0.0269546716549644[/C][C]0.986522664172518[/C][/ROW]
[ROW][C]39[/C][C]0.00873515419297591[/C][C]0.0174703083859518[/C][C]0.991264845807024[/C][/ROW]
[ROW][C]40[/C][C]0.00872602488223482[/C][C]0.0174520497644696[/C][C]0.991273975117765[/C][/ROW]
[ROW][C]41[/C][C]0.0084452438516565[/C][C]0.016890487703313[/C][C]0.991554756148344[/C][/ROW]
[ROW][C]42[/C][C]0.00624949754960569[/C][C]0.0124989950992114[/C][C]0.993750502450394[/C][/ROW]
[ROW][C]43[/C][C]0.00463134067158486[/C][C]0.00926268134316971[/C][C]0.995368659328415[/C][/ROW]
[ROW][C]44[/C][C]0.0158597972505694[/C][C]0.0317195945011388[/C][C]0.98414020274943[/C][/ROW]
[ROW][C]45[/C][C]0.0112735094936919[/C][C]0.0225470189873838[/C][C]0.988726490506308[/C][/ROW]
[ROW][C]46[/C][C]0.00855656523871745[/C][C]0.0171131304774349[/C][C]0.991443434761283[/C][/ROW]
[ROW][C]47[/C][C]0.0068227105652[/C][C]0.0136454211304[/C][C]0.9931772894348[/C][/ROW]
[ROW][C]48[/C][C]0.0150233885425460[/C][C]0.0300467770850921[/C][C]0.984976611457454[/C][/ROW]
[ROW][C]49[/C][C]0.014269096850997[/C][C]0.028538193701994[/C][C]0.985730903149003[/C][/ROW]
[ROW][C]50[/C][C]0.0165337152909190[/C][C]0.0330674305818381[/C][C]0.983466284709081[/C][/ROW]
[ROW][C]51[/C][C]0.0293903804595[/C][C]0.058780760919[/C][C]0.9706096195405[/C][/ROW]
[ROW][C]52[/C][C]0.0566431079348126[/C][C]0.113286215869625[/C][C]0.943356892065187[/C][/ROW]
[ROW][C]53[/C][C]0.0646797195755593[/C][C]0.129359439151119[/C][C]0.93532028042444[/C][/ROW]
[ROW][C]54[/C][C]0.0614232832353613[/C][C]0.122846566470723[/C][C]0.938576716764639[/C][/ROW]
[ROW][C]55[/C][C]0.0744442413363021[/C][C]0.148888482672604[/C][C]0.925555758663698[/C][/ROW]
[ROW][C]56[/C][C]0.076168408832655[/C][C]0.15233681766531[/C][C]0.923831591167345[/C][/ROW]
[ROW][C]57[/C][C]0.126954883773627[/C][C]0.253909767547254[/C][C]0.873045116226373[/C][/ROW]
[ROW][C]58[/C][C]0.124807888963504[/C][C]0.249615777927009[/C][C]0.875192111036496[/C][/ROW]
[ROW][C]59[/C][C]0.308376883768383[/C][C]0.616753767536765[/C][C]0.691623116231618[/C][/ROW]
[ROW][C]60[/C][C]0.600261198643695[/C][C]0.79947760271261[/C][C]0.399738801356305[/C][/ROW]
[ROW][C]61[/C][C]0.910334589747148[/C][C]0.179330820505704[/C][C]0.0896654102528519[/C][/ROW]
[ROW][C]62[/C][C]0.965959462552248[/C][C]0.068081074895505[/C][C]0.0340405374477525[/C][/ROW]
[ROW][C]63[/C][C]0.977600611895407[/C][C]0.0447987762091856[/C][C]0.0223993881045928[/C][/ROW]
[ROW][C]64[/C][C]0.99021831861157[/C][C]0.0195633627768584[/C][C]0.00978168138842921[/C][/ROW]
[ROW][C]65[/C][C]0.99179563122538[/C][C]0.0164087375492423[/C][C]0.00820436877462113[/C][/ROW]
[ROW][C]66[/C][C]0.993423777299094[/C][C]0.0131524454018120[/C][C]0.00657622270090601[/C][/ROW]
[ROW][C]67[/C][C]0.994886609701406[/C][C]0.0102267805971886[/C][C]0.0051133902985943[/C][/ROW]
[ROW][C]68[/C][C]0.99651194023978[/C][C]0.00697611952044016[/C][C]0.00348805976022008[/C][/ROW]
[ROW][C]69[/C][C]0.998414652684767[/C][C]0.0031706946304664[/C][C]0.0015853473152332[/C][/ROW]
[ROW][C]70[/C][C]0.999071134211639[/C][C]0.00185773157672274[/C][C]0.00092886578836137[/C][/ROW]
[ROW][C]71[/C][C]0.999416962590062[/C][C]0.00116607481987586[/C][C]0.000583037409937929[/C][/ROW]
[ROW][C]72[/C][C]0.999768812638424[/C][C]0.000462374723151414[/C][C]0.000231187361575707[/C][/ROW]
[ROW][C]73[/C][C]0.999924720967145[/C][C]0.000150558065709018[/C][C]7.52790328545092e-05[/C][/ROW]
[ROW][C]74[/C][C]0.999979498380836[/C][C]4.100323832797e-05[/C][C]2.0501619163985e-05[/C][/ROW]
[ROW][C]75[/C][C]0.999974507346212[/C][C]5.09853075752223e-05[/C][C]2.54926537876112e-05[/C][/ROW]
[ROW][C]76[/C][C]0.999977925619194[/C][C]4.41487616115049e-05[/C][C]2.20743808057524e-05[/C][/ROW]
[ROW][C]77[/C][C]0.99998540892907[/C][C]2.91821418598767e-05[/C][C]1.45910709299383e-05[/C][/ROW]
[ROW][C]78[/C][C]0.999990593597316[/C][C]1.88128053673771e-05[/C][C]9.40640268368857e-06[/C][/ROW]
[ROW][C]79[/C][C]0.999996306595945[/C][C]7.38680811026777e-06[/C][C]3.69340405513389e-06[/C][/ROW]
[ROW][C]80[/C][C]0.999996657979902[/C][C]6.68404019588085e-06[/C][C]3.34202009794043e-06[/C][/ROW]
[ROW][C]81[/C][C]0.99999528105916[/C][C]9.43788167924907e-06[/C][C]4.71894083962453e-06[/C][/ROW]
[ROW][C]82[/C][C]0.999998977253885[/C][C]2.04549223090433e-06[/C][C]1.02274611545216e-06[/C][/ROW]
[ROW][C]83[/C][C]0.999999085005536[/C][C]1.82998892833481e-06[/C][C]9.14994464167405e-07[/C][/ROW]
[ROW][C]84[/C][C]0.999998765523392[/C][C]2.46895321580061e-06[/C][C]1.23447660790030e-06[/C][/ROW]
[ROW][C]85[/C][C]0.999999467393824[/C][C]1.06521235236867e-06[/C][C]5.32606176184337e-07[/C][/ROW]
[ROW][C]86[/C][C]0.999999578683299[/C][C]8.42633402778393e-07[/C][C]4.21316701389197e-07[/C][/ROW]
[ROW][C]87[/C][C]0.999999642786107[/C][C]7.14427786983761e-07[/C][C]3.57213893491880e-07[/C][/ROW]
[ROW][C]88[/C][C]0.99999943576287[/C][C]1.12847426032615e-06[/C][C]5.64237130163076e-07[/C][/ROW]
[ROW][C]89[/C][C]0.999999194783795[/C][C]1.61043241071844e-06[/C][C]8.05216205359219e-07[/C][/ROW]
[ROW][C]90[/C][C]0.999999623906002[/C][C]7.52187994909272e-07[/C][C]3.76093997454636e-07[/C][/ROW]
[ROW][C]91[/C][C]0.999999464039783[/C][C]1.07192043430264e-06[/C][C]5.3596021715132e-07[/C][/ROW]
[ROW][C]92[/C][C]0.999999913677236[/C][C]1.72645528621443e-07[/C][C]8.63227643107215e-08[/C][/ROW]
[ROW][C]93[/C][C]0.999999854172484[/C][C]2.91655031674888e-07[/C][C]1.45827515837444e-07[/C][/ROW]
[ROW][C]94[/C][C]0.999999776328289[/C][C]4.47343422178674e-07[/C][C]2.23671711089337e-07[/C][/ROW]
[ROW][C]95[/C][C]0.999999636590817[/C][C]7.26818366422688e-07[/C][C]3.63409183211344e-07[/C][/ROW]
[ROW][C]96[/C][C]0.99999970693279[/C][C]5.86134421328664e-07[/C][C]2.93067210664332e-07[/C][/ROW]
[ROW][C]97[/C][C]0.999999491285812[/C][C]1.01742837608735e-06[/C][C]5.08714188043675e-07[/C][/ROW]
[ROW][C]98[/C][C]0.99999926008028[/C][C]1.47983943967688e-06[/C][C]7.3991971983844e-07[/C][/ROW]
[ROW][C]99[/C][C]0.999999382524575[/C][C]1.23495085093429e-06[/C][C]6.17475425467145e-07[/C][/ROW]
[ROW][C]100[/C][C]0.999998974343516[/C][C]2.05131296810265e-06[/C][C]1.02565648405133e-06[/C][/ROW]
[ROW][C]101[/C][C]0.999999203744738[/C][C]1.59251052292049e-06[/C][C]7.96255261460247e-07[/C][/ROW]
[ROW][C]102[/C][C]0.999998598903705[/C][C]2.8021925908099e-06[/C][C]1.40109629540495e-06[/C][/ROW]
[ROW][C]103[/C][C]0.999997772655703[/C][C]4.45468859316504e-06[/C][C]2.22734429658252e-06[/C][/ROW]
[ROW][C]104[/C][C]0.999996232631749[/C][C]7.5347365024918e-06[/C][C]3.7673682512459e-06[/C][/ROW]
[ROW][C]105[/C][C]0.99999712761163[/C][C]5.74477673973872e-06[/C][C]2.87238836986936e-06[/C][/ROW]
[ROW][C]106[/C][C]0.999997517421597[/C][C]4.96515680639093e-06[/C][C]2.48257840319547e-06[/C][/ROW]
[ROW][C]107[/C][C]0.99999570381422[/C][C]8.59237155936023e-06[/C][C]4.29618577968012e-06[/C][/ROW]
[ROW][C]108[/C][C]0.999994940371873[/C][C]1.01192562541705e-05[/C][C]5.05962812708527e-06[/C][/ROW]
[ROW][C]109[/C][C]0.999999305941645[/C][C]1.38811671063981e-06[/C][C]6.94058355319906e-07[/C][/ROW]
[ROW][C]110[/C][C]0.999998758825777[/C][C]2.48234844669864e-06[/C][C]1.24117422334932e-06[/C][/ROW]
[ROW][C]111[/C][C]0.999997910672756[/C][C]4.17865448729335e-06[/C][C]2.08932724364668e-06[/C][/ROW]
[ROW][C]112[/C][C]0.999996402401208[/C][C]7.19519758351085e-06[/C][C]3.59759879175543e-06[/C][/ROW]
[ROW][C]113[/C][C]0.999995631727094[/C][C]8.73654581208899e-06[/C][C]4.36827290604449e-06[/C][/ROW]
[ROW][C]114[/C][C]0.999994964125658[/C][C]1.00717486844338e-05[/C][C]5.03587434221692e-06[/C][/ROW]
[ROW][C]115[/C][C]0.999993790198578[/C][C]1.24196028448702e-05[/C][C]6.20980142243508e-06[/C][/ROW]
[ROW][C]116[/C][C]0.99999009432476[/C][C]1.98113504793609e-05[/C][C]9.90567523968047e-06[/C][/ROW]
[ROW][C]117[/C][C]0.999983601657935[/C][C]3.27966841308944e-05[/C][C]1.63983420654472e-05[/C][/ROW]
[ROW][C]118[/C][C]0.99998507251945[/C][C]2.98549610993064e-05[/C][C]1.49274805496532e-05[/C][/ROW]
[ROW][C]119[/C][C]0.999980317274865[/C][C]3.93654502706745e-05[/C][C]1.96827251353372e-05[/C][/ROW]
[ROW][C]120[/C][C]0.999993815544503[/C][C]1.23689109940820e-05[/C][C]6.18445549704102e-06[/C][/ROW]
[ROW][C]121[/C][C]0.99999822856601[/C][C]3.54286798047977e-06[/C][C]1.77143399023989e-06[/C][/ROW]
[ROW][C]122[/C][C]0.999996906165854[/C][C]6.1876682928923e-06[/C][C]3.09383414644615e-06[/C][/ROW]
[ROW][C]123[/C][C]0.99999940520766[/C][C]1.18958468107428e-06[/C][C]5.94792340537142e-07[/C][/ROW]
[ROW][C]124[/C][C]0.99999911963438[/C][C]1.76073123977003e-06[/C][C]8.80365619885017e-07[/C][/ROW]
[ROW][C]125[/C][C]0.999998561347942[/C][C]2.87730411688845e-06[/C][C]1.43865205844423e-06[/C][/ROW]
[ROW][C]126[/C][C]0.999997360211226[/C][C]5.27957754712046e-06[/C][C]2.63978877356023e-06[/C][/ROW]
[ROW][C]127[/C][C]0.999996029041555[/C][C]7.94191688977771e-06[/C][C]3.97095844488886e-06[/C][/ROW]
[ROW][C]128[/C][C]0.999992948839715[/C][C]1.41023205709067e-05[/C][C]7.05116028545335e-06[/C][/ROW]
[ROW][C]129[/C][C]0.999987450141313[/C][C]2.50997173744961e-05[/C][C]1.25498586872481e-05[/C][/ROW]
[ROW][C]130[/C][C]0.999990330151757[/C][C]1.93396964858222e-05[/C][C]9.66984824291112e-06[/C][/ROW]
[ROW][C]131[/C][C]0.999991466215091[/C][C]1.70675698175420e-05[/C][C]8.53378490877098e-06[/C][/ROW]
[ROW][C]132[/C][C]0.999999670586947[/C][C]6.58826104949665e-07[/C][C]3.29413052474832e-07[/C][/ROW]
[ROW][C]133[/C][C]0.999999907694007[/C][C]1.84611986309583e-07[/C][C]9.23059931547913e-08[/C][/ROW]
[ROW][C]134[/C][C]0.999999811287687[/C][C]3.77424626322019e-07[/C][C]1.88712313161010e-07[/C][/ROW]
[ROW][C]135[/C][C]0.999999639824343[/C][C]7.20351314320861e-07[/C][C]3.60175657160431e-07[/C][/ROW]
[ROW][C]136[/C][C]0.999999259402752[/C][C]1.48119449536726e-06[/C][C]7.4059724768363e-07[/C][/ROW]
[ROW][C]137[/C][C]0.999998519865658[/C][C]2.9602686848403e-06[/C][C]1.48013434242015e-06[/C][/ROW]
[ROW][C]138[/C][C]0.99999872824974[/C][C]2.54350051936023e-06[/C][C]1.27175025968011e-06[/C][/ROW]
[ROW][C]139[/C][C]0.999997478520102[/C][C]5.04295979493376e-06[/C][C]2.52147989746688e-06[/C][/ROW]
[ROW][C]140[/C][C]0.999996083758745[/C][C]7.83248251027385e-06[/C][C]3.91624125513693e-06[/C][/ROW]
[ROW][C]141[/C][C]0.999993019556436[/C][C]1.39608871282583e-05[/C][C]6.98044356412913e-06[/C][/ROW]
[ROW][C]142[/C][C]0.99998806545469[/C][C]2.38690906208563e-05[/C][C]1.19345453104281e-05[/C][/ROW]
[ROW][C]143[/C][C]0.999986986462472[/C][C]2.60270750557366e-05[/C][C]1.30135375278683e-05[/C][/ROW]
[ROW][C]144[/C][C]0.999984427600125[/C][C]3.11447997500722e-05[/C][C]1.55723998750361e-05[/C][/ROW]
[ROW][C]145[/C][C]0.999971704243494[/C][C]5.65915130113381e-05[/C][C]2.82957565056690e-05[/C][/ROW]
[ROW][C]146[/C][C]0.99995678245865[/C][C]8.6435082698033e-05[/C][C]4.32175413490165e-05[/C][/ROW]
[ROW][C]147[/C][C]0.999932678741304[/C][C]0.000134642517391620[/C][C]6.73212586958098e-05[/C][/ROW]
[ROW][C]148[/C][C]0.999880892135052[/C][C]0.000238215729896012[/C][C]0.000119107864948006[/C][/ROW]
[ROW][C]149[/C][C]0.999787840326742[/C][C]0.000424319346516522[/C][C]0.000212159673258261[/C][/ROW]
[ROW][C]150[/C][C]0.999617514483744[/C][C]0.000764971032512277[/C][C]0.000382485516256139[/C][/ROW]
[ROW][C]151[/C][C]0.9998493476378[/C][C]0.000301304724399844[/C][C]0.000150652362199922[/C][/ROW]
[ROW][C]152[/C][C]0.999712455653383[/C][C]0.000575088693233712[/C][C]0.000287544346616856[/C][/ROW]
[ROW][C]153[/C][C]0.999566514174841[/C][C]0.000866971650318153[/C][C]0.000433485825159076[/C][/ROW]
[ROW][C]154[/C][C]0.99983241634741[/C][C]0.000335167305178293[/C][C]0.000167583652589146[/C][/ROW]
[ROW][C]155[/C][C]0.999689038637253[/C][C]0.000621922725493754[/C][C]0.000310961362746877[/C][/ROW]
[ROW][C]156[/C][C]0.999814007731925[/C][C]0.000371984536149629[/C][C]0.000185992268074815[/C][/ROW]
[ROW][C]157[/C][C]0.999663750902131[/C][C]0.000672498195737421[/C][C]0.000336249097868710[/C][/ROW]
[ROW][C]158[/C][C]0.999343456042388[/C][C]0.00131308791522317[/C][C]0.000656543957611584[/C][/ROW]
[ROW][C]159[/C][C]0.999054436399257[/C][C]0.00189112720148552[/C][C]0.00094556360074276[/C][/ROW]
[ROW][C]160[/C][C]0.998550854880576[/C][C]0.00289829023884843[/C][C]0.00144914511942421[/C][/ROW]
[ROW][C]161[/C][C]0.998007267417462[/C][C]0.00398546516507678[/C][C]0.00199273258253839[/C][/ROW]
[ROW][C]162[/C][C]0.997326909092548[/C][C]0.00534618181490411[/C][C]0.00267309090745206[/C][/ROW]
[ROW][C]163[/C][C]0.994901009309242[/C][C]0.0101979813815160[/C][C]0.00509899069075801[/C][/ROW]
[ROW][C]164[/C][C]0.996192979518869[/C][C]0.00761404096226274[/C][C]0.00380702048113137[/C][/ROW]
[ROW][C]165[/C][C]0.996594747568937[/C][C]0.00681050486212618[/C][C]0.00340525243106309[/C][/ROW]
[ROW][C]166[/C][C]0.993103232630802[/C][C]0.0137935347383959[/C][C]0.00689676736919795[/C][/ROW]
[ROW][C]167[/C][C]0.988736804597005[/C][C]0.0225263908059899[/C][C]0.0112631954029950[/C][/ROW]
[ROW][C]168[/C][C]0.997981314564407[/C][C]0.00403737087118556[/C][C]0.00201868543559278[/C][/ROW]
[ROW][C]169[/C][C]0.99493405355624[/C][C]0.0101318928875197[/C][C]0.00506594644375987[/C][/ROW]
[ROW][C]170[/C][C]0.987866899404494[/C][C]0.0242662011910123[/C][C]0.0121331005955062[/C][/ROW]
[ROW][C]171[/C][C]0.973650040125043[/C][C]0.0526999197499141[/C][C]0.0263499598749570[/C][/ROW]
[ROW][C]172[/C][C]0.97970639002528[/C][C]0.0405872199494416[/C][C]0.0202936099747208[/C][/ROW]
[ROW][C]173[/C][C]0.957667775718366[/C][C]0.0846644485632686[/C][C]0.0423322242816343[/C][/ROW]
[ROW][C]174[/C][C]0.90221805985498[/C][C]0.195563880290040[/C][C]0.0977819401450201[/C][/ROW]
[ROW][C]175[/C][C]0.832347654021266[/C][C]0.335304691957468[/C][C]0.167652345978734[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=55999&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=55999&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
170.3220302286924620.6440604573849240.677969771307538
180.2335210890959760.4670421781919510.766478910904024
190.1806855784785960.3613711569571920.819314421521404
200.1036689106671450.207337821334290.896331089332855
210.05594253793034310.1118850758606860.944057462069657
220.08584219237076970.1716843847415390.91415780762923
230.05298521090866150.1059704218173230.947014789091339
240.05685007762106940.1137001552421390.94314992237893
250.03611929194377110.07223858388754210.963880708056229
260.07056197359914530.1411239471982910.929438026400855
270.06276451543735430.1255290308747090.937235484562646
280.04110667206251370.08221334412502740.958893327937486
290.02500352156246460.05000704312492910.974996478437535
300.01517277417268810.03034554834537620.984827225827312
310.01048625145647990.02097250291295990.98951374854352
320.006139053853430690.01227810770686140.99386094614657
330.01374482965265010.02748965930530020.98625517034735
340.008287862483525150.01657572496705030.991712137516475
350.00766107975814670.01532215951629340.992338920241853
360.02407759818054770.04815519636109540.975922401819452
370.01833419852318040.03666839704636070.98166580147682
380.01347733582748220.02695467165496440.986522664172518
390.008735154192975910.01747030838595180.991264845807024
400.008726024882234820.01745204976446960.991273975117765
410.00844524385165650.0168904877033130.991554756148344
420.006249497549605690.01249899509921140.993750502450394
430.004631340671584860.009262681343169710.995368659328415
440.01585979725056940.03171959450113880.98414020274943
450.01127350949369190.02254701898738380.988726490506308
460.008556565238717450.01711313047743490.991443434761283
470.00682271056520.01364542113040.9931772894348
480.01502338854254600.03004677708509210.984976611457454
490.0142690968509970.0285381937019940.985730903149003
500.01653371529091900.03306743058183810.983466284709081
510.02939038045950.0587807609190.9706096195405
520.05664310793481260.1132862158696250.943356892065187
530.06467971957555930.1293594391511190.93532028042444
540.06142328323536130.1228465664707230.938576716764639
550.07444424133630210.1488884826726040.925555758663698
560.0761684088326550.152336817665310.923831591167345
570.1269548837736270.2539097675472540.873045116226373
580.1248078889635040.2496157779270090.875192111036496
590.3083768837683830.6167537675367650.691623116231618
600.6002611986436950.799477602712610.399738801356305
610.9103345897471480.1793308205057040.0896654102528519
620.9659594625522480.0680810748955050.0340405374477525
630.9776006118954070.04479877620918560.0223993881045928
640.990218318611570.01956336277685840.00978168138842921
650.991795631225380.01640873754924230.00820436877462113
660.9934237772990940.01315244540181200.00657622270090601
670.9948866097014060.01022678059718860.0051133902985943
680.996511940239780.006976119520440160.00348805976022008
690.9984146526847670.00317069463046640.0015853473152332
700.9990711342116390.001857731576722740.00092886578836137
710.9994169625900620.001166074819875860.000583037409937929
720.9997688126384240.0004623747231514140.000231187361575707
730.9999247209671450.0001505580657090187.52790328545092e-05
740.9999794983808364.100323832797e-052.0501619163985e-05
750.9999745073462125.09853075752223e-052.54926537876112e-05
760.9999779256191944.41487616115049e-052.20743808057524e-05
770.999985408929072.91821418598767e-051.45910709299383e-05
780.9999905935973161.88128053673771e-059.40640268368857e-06
790.9999963065959457.38680811026777e-063.69340405513389e-06
800.9999966579799026.68404019588085e-063.34202009794043e-06
810.999995281059169.43788167924907e-064.71894083962453e-06
820.9999989772538852.04549223090433e-061.02274611545216e-06
830.9999990850055361.82998892833481e-069.14994464167405e-07
840.9999987655233922.46895321580061e-061.23447660790030e-06
850.9999994673938241.06521235236867e-065.32606176184337e-07
860.9999995786832998.42633402778393e-074.21316701389197e-07
870.9999996427861077.14427786983761e-073.57213893491880e-07
880.999999435762871.12847426032615e-065.64237130163076e-07
890.9999991947837951.61043241071844e-068.05216205359219e-07
900.9999996239060027.52187994909272e-073.76093997454636e-07
910.9999994640397831.07192043430264e-065.3596021715132e-07
920.9999999136772361.72645528621443e-078.63227643107215e-08
930.9999998541724842.91655031674888e-071.45827515837444e-07
940.9999997763282894.47343422178674e-072.23671711089337e-07
950.9999996365908177.26818366422688e-073.63409183211344e-07
960.999999706932795.86134421328664e-072.93067210664332e-07
970.9999994912858121.01742837608735e-065.08714188043675e-07
980.999999260080281.47983943967688e-067.3991971983844e-07
990.9999993825245751.23495085093429e-066.17475425467145e-07
1000.9999989743435162.05131296810265e-061.02565648405133e-06
1010.9999992037447381.59251052292049e-067.96255261460247e-07
1020.9999985989037052.8021925908099e-061.40109629540495e-06
1030.9999977726557034.45468859316504e-062.22734429658252e-06
1040.9999962326317497.5347365024918e-063.7673682512459e-06
1050.999997127611635.74477673973872e-062.87238836986936e-06
1060.9999975174215974.96515680639093e-062.48257840319547e-06
1070.999995703814228.59237155936023e-064.29618577968012e-06
1080.9999949403718731.01192562541705e-055.05962812708527e-06
1090.9999993059416451.38811671063981e-066.94058355319906e-07
1100.9999987588257772.48234844669864e-061.24117422334932e-06
1110.9999979106727564.17865448729335e-062.08932724364668e-06
1120.9999964024012087.19519758351085e-063.59759879175543e-06
1130.9999956317270948.73654581208899e-064.36827290604449e-06
1140.9999949641256581.00717486844338e-055.03587434221692e-06
1150.9999937901985781.24196028448702e-056.20980142243508e-06
1160.999990094324761.98113504793609e-059.90567523968047e-06
1170.9999836016579353.27966841308944e-051.63983420654472e-05
1180.999985072519452.98549610993064e-051.49274805496532e-05
1190.9999803172748653.93654502706745e-051.96827251353372e-05
1200.9999938155445031.23689109940820e-056.18445549704102e-06
1210.999998228566013.54286798047977e-061.77143399023989e-06
1220.9999969061658546.1876682928923e-063.09383414644615e-06
1230.999999405207661.18958468107428e-065.94792340537142e-07
1240.999999119634381.76073123977003e-068.80365619885017e-07
1250.9999985613479422.87730411688845e-061.43865205844423e-06
1260.9999973602112265.27957754712046e-062.63978877356023e-06
1270.9999960290415557.94191688977771e-063.97095844488886e-06
1280.9999929488397151.41023205709067e-057.05116028545335e-06
1290.9999874501413132.50997173744961e-051.25498586872481e-05
1300.9999903301517571.93396964858222e-059.66984824291112e-06
1310.9999914662150911.70675698175420e-058.53378490877098e-06
1320.9999996705869476.58826104949665e-073.29413052474832e-07
1330.9999999076940071.84611986309583e-079.23059931547913e-08
1340.9999998112876873.77424626322019e-071.88712313161010e-07
1350.9999996398243437.20351314320861e-073.60175657160431e-07
1360.9999992594027521.48119449536726e-067.4059724768363e-07
1370.9999985198656582.9602686848403e-061.48013434242015e-06
1380.999998728249742.54350051936023e-061.27175025968011e-06
1390.9999974785201025.04295979493376e-062.52147989746688e-06
1400.9999960837587457.83248251027385e-063.91624125513693e-06
1410.9999930195564361.39608871282583e-056.98044356412913e-06
1420.999988065454692.38690906208563e-051.19345453104281e-05
1430.9999869864624722.60270750557366e-051.30135375278683e-05
1440.9999844276001253.11447997500722e-051.55723998750361e-05
1450.9999717042434945.65915130113381e-052.82957565056690e-05
1460.999956782458658.6435082698033e-054.32175413490165e-05
1470.9999326787413040.0001346425173916206.73212586958098e-05
1480.9998808921350520.0002382157298960120.000119107864948006
1490.9997878403267420.0004243193465165220.000212159673258261
1500.9996175144837440.0007649710325122770.000382485516256139
1510.99984934763780.0003013047243998440.000150652362199922
1520.9997124556533830.0005750886932337120.000287544346616856
1530.9995665141748410.0008669716503181530.000433485825159076
1540.999832416347410.0003351673051782930.000167583652589146
1550.9996890386372530.0006219227254937540.000310961362746877
1560.9998140077319250.0003719845361496290.000185992268074815
1570.9996637509021310.0006724981957374210.000336249097868710
1580.9993434560423880.001313087915223170.000656543957611584
1590.9990544363992570.001891127201485520.00094556360074276
1600.9985508548805760.002898290238848430.00144914511942421
1610.9980072674174620.003985465165076780.00199273258253839
1620.9973269090925480.005346181814904110.00267309090745206
1630.9949010093092420.01019798138151600.00509899069075801
1640.9961929795188690.007614040962262740.00380702048113137
1650.9965947475689370.006810504862126180.00340525243106309
1660.9931032326308020.01379353473839590.00689676736919795
1670.9887368045970050.02252639080598990.0112631954029950
1680.9979813145644070.004037370871185560.00201868543559278
1690.994934053556240.01013189288751970.00506594644375987
1700.9878668994044940.02426620119101230.0121331005955062
1710.9736500401250430.05269991974991410.0263499598749570
1720.979706390025280.04058721994944160.0202936099747208
1730.9576677757183660.08466444856326860.0423322242816343
1740.902218059854980.1955638802900400.0977819401450201
1750.8323476540212660.3353046919574680.167652345978734






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level990.622641509433962NOK
5% type I error level1300.817610062893082NOK
10% type I error level1370.861635220125786NOK

\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 & 99 & 0.622641509433962 & NOK \tabularnewline
5% type I error level & 130 & 0.817610062893082 & NOK \tabularnewline
10% type I error level & 137 & 0.861635220125786 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=55999&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]99[/C][C]0.622641509433962[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]130[/C][C]0.817610062893082[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]137[/C][C]0.861635220125786[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=55999&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=55999&T=6

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


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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level990.622641509433962NOK
5% type I error level1300.817610062893082NOK
10% type I error level1370.861635220125786NOK


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 5
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Figure 6
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Figure 7
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Figure 8
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Figure 9
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Figure 10
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Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}