Free Statistics

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Author's title

Author*Unverified author*
R Software Module--
Title produced by softwareMultiple Regression
Date of computationTue, 22 Nov 2011 16:52:28 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Nov/22/t13219988002xjb9yo0kd9npq7.htm/, Retrieved Thu, 28 Mar 2024 10:21:08 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=146430, Retrieved Thu, 28 Mar 2024 10:21:08 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact118
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2010-11-17 09:14:55] [b98453cac15ba1066b407e146608df68]
- R PD  [Multiple Regression] [ws7 Tutorial Popu...] [2010-11-22 11:00:33] [afe9379cca749d06b3d6872e02cc47ed]
- RM        [Multiple Regression] [WS 7 - 4 ] [2011-11-22 21:52:28] [d41d8cd98f00b204e9800998ecf8427e] [Current]
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Dataseries X:
9	13	13	14	13	3	1	1	0
9	12	12	8	13	5	1	0	0
9	15	10	12	16	6	0	0	0
9	12	9	7	12	6	2	0	1
9	10	10	10	11	5	0	1	2
9	12	12	7	12	3	0	0	1
9	15	13	16	18	8	1	1	1
9	9	12	11	11	4	1	0	0
9	12	12	14	14	4	4	0	0
9	11	6	6	9	4	0	0	0
9	11	5	16	14	6	0	2	1
9	11	12	11	12	6	2	0	0
9	15	11	16	11	5	0	2	2
9	7	14	12	12	4	1	1	1
9	11	14	7	13	6	0	1	0
9	11	12	13	11	4	0	0	1
9	10	12	11	12	6	1	1	0
9	14	11	15	16	6	2	0	1
9	10	11	7	9	4	1	0	0
9	6	7	9	11	4	1	0	0
9	11	9	7	13	2	0	1	1
9	15	11	14	15	7	1	2	0
9	11	11	15	10	5	1	2	1
9	12	12	7	11	4	2	0	0
9	14	12	15	13	6	1	0	0
9	15	11	17	16	6	1	1	0
9	9	11	15	15	7	1	1	0
9	13	8	14	14	5	2	2	0
9	13	9	14	14	6	0	0	2
9	16	12	8	14	4	1	1	1
9	13	10	8	8	4	0	1	2
9	12	10	14	13	7	1	1	1
9	14	12	14	15	7	1	2	1
9	11	8	8	13	4	0	2	0
9	9	12	11	11	4	1	1	0
9	16	11	16	15	6	2	2	0
9	12	12	10	15	6	1	1	1
9	10	7	8	9	5	1	1	2
9	13	11	14	13	6	1	0	1
9	16	11	16	16	7	1	3	1
9	14	12	13	13	6	0	1	2
9	15	9	5	11	3	1	0	0
9	5	15	8	12	3	1	0	0
9	8	11	10	12	4	1	0	0
9	11	11	8	12	6	0	1	1
9	16	11	13	14	7	2	0	1
9	17	11	15	14	5	1	4	4
9	9	15	6	8	4	0	0	0
9	9	11	12	13	5	0	0	0
9	13	12	16	16	6	1	0	1
9	10	12	5	13	6	1	1	0
9	6	9	15	11	6	0	2	1
9	12	12	12	14	5	0	1	0
9	8	12	8	13	4	0	1	1
9	14	13	13	13	5	0	0	0
9	12	11	14	13	5	1	2	2
10	11	9	12	12	4	0	0	2
10	16	9	16	16	6	0	3	1
10	8	11	10	15	2	1	2	0
10	15	11	15	15	8	0	0	0
10	7	12	8	12	3	0	0	0
10	16	12	16	14	6	2	2	0
10	14	9	19	12	6	0	1	0
10	16	11	14	15	6	0	0	1
10	9	9	6	12	5	1	2	1
10	14	12	13	13	5	2	0	0
10	11	12	15	12	6	3	1	0
10	13	12	7	12	5	1	0	0
10	15	12	13	13	6	1	2	1
10	5	14	4	5	2	2	0	0
10	15	11	14	13	5	1	2	2
10	13	12	13	13	5	1	3	0
10	11	11	11	14	5	2	0	2
10	11	6	14	17	6	1	2	1
10	12	10	12	13	6	0	3	1
10	12	12	15	13	6	1	1	1
10	12	13	14	12	5	1	0	2
10	12	8	13	13	5	0	1	2
10	14	12	8	14	4	2	0	0
10	6	12	6	11	2	1	0	0
10	7	12	7	12	4	0	1	0
10	14	6	13	12	6	3	1	1
10	14	11	13	16	6	1	2	1
10	10	10	11	12	5	1	1	0
10	13	12	5	12	3	3	0	0
10	12	13	12	12	6	2	0	0
10	9	11	8	10	4	1	1	0
10	12	7	11	15	5	0	0	2
10	16	11	14	15	8	1	0	1
10	10	11	9	12	4	2	0	1
10	14	11	10	16	6	1	1	0
10	10	11	13	15	6	1	1	1
10	16	12	16	16	7	0	3	1
10	15	10	16	13	6	2	1	0
10	12	11	11	12	5	1	1	1
10	10	12	8	11	4	0	0	0
10	8	7	4	13	6	0	0	1
10	8	13	7	10	3	1	1	0
10	11	8	14	15	5	1	1	0
10	13	12	11	13	6	1	0	2
10	16	11	17	16	7	1	1	2
10	16	12	15	15	7	1	1	2
10	14	14	17	18	6	0	0	1
10	11	10	5	13	3	0	1	1
10	4	10	4	10	2	1	0	1
10	14	13	10	16	8	2	1	0
10	9	10	11	13	3	1	1	1
10	14	11	15	15	8	1	1	1
10	8	10	10	14	3	0	1	0
10	8	7	9	15	4	0	1	0
10	11	10	12	14	5	1	0	0
10	12	8	15	13	7	1	0	0
10	11	12	7	13	6	0	0	0
10	14	12	13	15	6	0	1	0
10	15	12	12	16	7	2	1	0
10	16	11	14	14	6	2	1	0
10	16	12	14	14	6	0	0	0
10	11	12	8	16	6	1	1	0
10	14	12	15	14	6	0	4	1
10	14	11	12	12	4	2	0	0
10	12	12	12	13	4	1	1	1
10	14	11	16	12	5	0	0	3
10	8	11	9	12	4	1	2	2
10	13	13	15	14	6	1	1	2
10	16	12	15	14	6	2	0	2
10	12	12	6	14	5	0	0	0
10	16	12	14	16	8	2	0	1
10	12	12	15	13	6	0	0	0
10	11	8	10	14	5	1	1	0
10	4	8	6	4	4	0	0	0
10	16	12	14	16	8	3	2	1
10	15	11	12	13	6	1	0	2
10	10	12	8	16	4	0	1	0
10	13	13	11	15	6	0	2	4
10	15	12	13	14	6	0	2	0
10	12	12	9	13	4	0	1	0
10	14	11	15	14	6	0	3	0
10	7	12	13	12	3	1	0	0
10	19	12	15	15	6	1	1	0
10	12	10	14	14	5	2	1	1
10	12	11	16	13	4	1	0	0
10	13	12	14	14	6	0	1	1
10	15	12	14	16	4	0	0	0
10	8	10	10	6	4	2	1	2
10	12	12	10	13	4	1	0	1
10	10	13	4	13	6	0	1	0
10	8	12	8	14	5	1	0	0
10	10	15	15	15	6	2	2	0
10	15	11	16	14	6	2	0	1
10	16	12	12	15	8	0	0	0
10	13	11	12	13	7	1	1	1
10	16	12	15	16	7	2	1	0
10	9	11	9	12	4	0	0	0
10	14	10	12	15	6	1	0	1
10	14	11	14	12	6	2	1	2
10	12	11	11	14	2	1	1	0




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'AstonUniversity' @ aston.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 7 seconds \tabularnewline
R Server & 'AstonUniversity' @ aston.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146430&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]7 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'AstonUniversity' @ aston.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146430&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146430&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'AstonUniversity' @ aston.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Popularity[t] = -0.983357375578938 + 0.089381370655549month[t] + 0.104378264723487FindingFriends[t] + 0.211622867223575KnowingPeople[t] + 0.385193850989471Liked[t] + 0.59441013592851Celebrity[t] + 0.307649945898061bestfriend[t] -0.0324002150208811secondbestfriend[t] + 0.411552931191424thirdbestfriend[t] -0.00181007322403911t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Popularity[t] =  -0.983357375578938 +  0.089381370655549month[t] +  0.104378264723487FindingFriends[t] +  0.211622867223575KnowingPeople[t] +  0.385193850989471Liked[t] +  0.59441013592851Celebrity[t] +  0.307649945898061bestfriend[t] -0.0324002150208811secondbestfriend[t] +  0.411552931191424thirdbestfriend[t] -0.00181007322403911t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146430&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Popularity[t] =  -0.983357375578938 +  0.089381370655549month[t] +  0.104378264723487FindingFriends[t] +  0.211622867223575KnowingPeople[t] +  0.385193850989471Liked[t] +  0.59441013592851Celebrity[t] +  0.307649945898061bestfriend[t] -0.0324002150208811secondbestfriend[t] +  0.411552931191424thirdbestfriend[t] -0.00181007322403911t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146430&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146430&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
Popularity[t] = -0.983357375578938 + 0.089381370655549month[t] + 0.104378264723487FindingFriends[t] + 0.211622867223575KnowingPeople[t] + 0.385193850989471Liked[t] + 0.59441013592851Celebrity[t] + 0.307649945898061bestfriend[t] -0.0324002150208811secondbestfriend[t] + 0.411552931191424thirdbestfriend[t] -0.00181007322403911t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-0.9833573755789386.011657-0.16360.8702920.435146
month0.0893813706555490.6408420.13950.8892670.444634
FindingFriends0.1043782647234870.0985591.0590.2913270.145664
KnowingPeople0.2116228672235750.064053.3040.0011996e-04
Liked0.3851938509894710.0990383.88940.0001527.6e-05
Celebrity0.594410135928510.1569613.7870.0002220.000111
bestfriend0.3076499458980610.2119911.45120.1488590.074429
secondbestfriend-0.03240021502088110.20236-0.16010.8730140.436507
thirdbestfriend0.4115529311914240.2146121.91770.0571070.028553
t-0.001810073224039110.006864-0.26370.7923890.396194

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & -0.983357375578938 & 6.011657 & -0.1636 & 0.870292 & 0.435146 \tabularnewline
month & 0.089381370655549 & 0.640842 & 0.1395 & 0.889267 & 0.444634 \tabularnewline
FindingFriends & 0.104378264723487 & 0.098559 & 1.059 & 0.291327 & 0.145664 \tabularnewline
KnowingPeople & 0.211622867223575 & 0.06405 & 3.304 & 0.001199 & 6e-04 \tabularnewline
Liked & 0.385193850989471 & 0.099038 & 3.8894 & 0.000152 & 7.6e-05 \tabularnewline
Celebrity & 0.59441013592851 & 0.156961 & 3.787 & 0.000222 & 0.000111 \tabularnewline
bestfriend & 0.307649945898061 & 0.211991 & 1.4512 & 0.148859 & 0.074429 \tabularnewline
secondbestfriend & -0.0324002150208811 & 0.20236 & -0.1601 & 0.873014 & 0.436507 \tabularnewline
thirdbestfriend & 0.411552931191424 & 0.214612 & 1.9177 & 0.057107 & 0.028553 \tabularnewline
t & -0.00181007322403911 & 0.006864 & -0.2637 & 0.792389 & 0.396194 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146430&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]-0.983357375578938[/C][C]6.011657[/C][C]-0.1636[/C][C]0.870292[/C][C]0.435146[/C][/ROW]
[ROW][C]month[/C][C]0.089381370655549[/C][C]0.640842[/C][C]0.1395[/C][C]0.889267[/C][C]0.444634[/C][/ROW]
[ROW][C]FindingFriends[/C][C]0.104378264723487[/C][C]0.098559[/C][C]1.059[/C][C]0.291327[/C][C]0.145664[/C][/ROW]
[ROW][C]KnowingPeople[/C][C]0.211622867223575[/C][C]0.06405[/C][C]3.304[/C][C]0.001199[/C][C]6e-04[/C][/ROW]
[ROW][C]Liked[/C][C]0.385193850989471[/C][C]0.099038[/C][C]3.8894[/C][C]0.000152[/C][C]7.6e-05[/C][/ROW]
[ROW][C]Celebrity[/C][C]0.59441013592851[/C][C]0.156961[/C][C]3.787[/C][C]0.000222[/C][C]0.000111[/C][/ROW]
[ROW][C]bestfriend[/C][C]0.307649945898061[/C][C]0.211991[/C][C]1.4512[/C][C]0.148859[/C][C]0.074429[/C][/ROW]
[ROW][C]secondbestfriend[/C][C]-0.0324002150208811[/C][C]0.20236[/C][C]-0.1601[/C][C]0.873014[/C][C]0.436507[/C][/ROW]
[ROW][C]thirdbestfriend[/C][C]0.411552931191424[/C][C]0.214612[/C][C]1.9177[/C][C]0.057107[/C][C]0.028553[/C][/ROW]
[ROW][C]t[/C][C]-0.00181007322403911[/C][C]0.006864[/C][C]-0.2637[/C][C]0.792389[/C][C]0.396194[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146430&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146430&T=2

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-0.9833573755789386.011657-0.16360.8702920.435146
month0.0893813706555490.6408420.13950.8892670.444634
FindingFriends0.1043782647234870.0985591.0590.2913270.145664
KnowingPeople0.2116228672235750.064053.3040.0011996e-04
Liked0.3851938509894710.0990383.88940.0001527.6e-05
Celebrity0.594410135928510.1569613.7870.0002220.000111
bestfriend0.3076499458980610.2119911.45120.1488590.074429
secondbestfriend-0.03240021502088110.20236-0.16010.8730140.436507
thirdbestfriend0.4115529311914240.2146121.91770.0571070.028553
t-0.001810073224039110.006864-0.26370.7923890.396194







Multiple Linear Regression - Regression Statistics
Multiple R0.719101919830674
R-squared0.51710757110416
Adjusted R-squared0.487340229596883
F-TEST (value)17.3716410307495
F-TEST (DF numerator)9
F-TEST (DF denominator)146
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.1026328891003
Sum Squared Residuals645.475499683636

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.719101919830674 \tabularnewline
R-squared & 0.51710757110416 \tabularnewline
Adjusted R-squared & 0.487340229596883 \tabularnewline
F-TEST (value) & 17.3716410307495 \tabularnewline
F-TEST (DF numerator) & 9 \tabularnewline
F-TEST (DF denominator) & 146 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.1026328891003 \tabularnewline
Sum Squared Residuals & 645.475499683636 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146430&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.719101919830674[/C][/ROW]
[ROW][C]R-squared[/C][C]0.51710757110416[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.487340229596883[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]17.3716410307495[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]9[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]146[/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]2.1026328891003[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]645.475499683636[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146430&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146430&T=3

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Regression Statistics
Multiple R0.719101919830674
R-squared0.51710757110416
Adjusted R-squared0.487340229596883
F-TEST (value)17.3716410307495
F-TEST (DF numerator)9
F-TEST (DF denominator)146
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.1026328891003
Sum Squared Residuals645.475499683636







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11311.20490267115821.79509732884181
21211.05019761674710.949802383252893
31513.12846422596931.87153577403075
41211.45023897093350.549761029066483
51010.9719246015601-0.97192460156013
6129.361223319074252.63877668092575
71516.9268608320424-1.92686083204242
8910.3094079411661-1.30940794116614
91213.0209978602754-1.02099786027543
10117.543366222382263.45663377761774
111113.0149485846245-2.01494858462452
121112.1838317170145-1.18383171701454
131512.29915926881192.70084073118813
14711.4832734656525-4.48327346565248
151111.2781603020676-0.278160302067566
161110.82207607511430.177923924885657
171011.8347311899754-1.8347311899754
181414.8674128169904-0.867412816990424
19108.568239700104991.43176029989501
2069.34255000441309-3.34255000441309
21118.779320926583282.22067907341672
221514.07376263467850.926237365321516
231111.5803388330651-0.580338833065069
24129.741605246585282.25839475341472
251413.08433613908770.91566386091226
261514.52457487353490.475425126465107
27914.3087353508027-5.30873535080274
281312.48340322421540.516596775784642
291313.4529879522718-0.452987952271803
301611.16945199770554.83054800229448
31138.751625274391044.24837472560896
321213.624849081948-1.62484908194797
331414.569783025129-0.569783025128966
34119.607901702815571.39209829718443
35910.2281357490962-1.22813574909621
361614.18490715395861.81509284604136
371213.1540413424309-1.15404134243088
38109.713073900468340.286926099531656
391313.1545469131956-0.154546913195554
401615.22877361825290.771226381747057
411413.11518493451990.88481506548013
42158.07058331810846.92941668189161
4359.71510528588547-4.71510528588547
44810.3134380241431-2.31343802414315
451111.1487052586015-0.148705258601458
461614.21750746621971.78249253378025
471714.24753084317852.75246915682148
4898.02879397139070.971206028609305
49911.39958743349-2.39958743349002
501314.8178416598702-1.81784165987017
511010.8886453480061-0.888645348006095
52611.9910442211409-5.99104422114088
531211.84951904128590.150480958714061
54810.433166443441-2.43316644344104
551411.80910639081632.19089360918367
561212.876118033608-0.87611803360804
571111.1092335643711-0.109233564371146
581614.17475705960221.82524294039776
59810.2776291475077-2.27762914750774
601514.65754471011630.342455289883668
6179.1511205984398-2.1511205984398
621613.94641103452322.05358896547681
631412.91304739004521.08695260995478
641613.6614142093312.33858579066899
65910.2507224958308-1.25072249583077
661412.38949858308011.61050141691991
671113.2954002601194-2.29540026011942
681310.4232974364032.57670256359697
691513.01758105458811.98241894541192
7055.82162779891744-0.821627798917437
711512.9383483059032.061651694097
721311.97378755277511.02621244722485
731113.0575037847135-2.0575037847135
741114.1346593713084-3.13465937130842
751212.2462910576544-0.246291057654353
761213.4605564914878-1.46055649148784
771212.815850975058-0.815850975058034
781212.1256704010635-0.125670401063511
791411.09863701011072.90136298988933
8068.02152943171598-2.02152943171598
8179.46530618764307-2.46530618764307
821412.63028677016221.36971322983782
831414.0434433176965-0.0434433176964578
841010.9996709892448-0.999670989244845
85139.395760077086323.60423992291368
861212.4552688010383-0.455268801038257
8799.09895259471804-0.0989525947180393
881212.3827335866523-0.382733586652307
891615.11263259648510.887367403514892
901010.8271360363588-0.827136036358805
911413.01494141406290.985058585937122
921013.6743590227115-3.67435902271152
931615.01894942685980.981050573140155
941513.32693852593841.67306147406163
951211.49569137969530.504308620304673
96109.296984320537470.703015679462534
97810.2975523598291-2.29755235982908
9888.4817653155485-0.4817653155485
991112.5542135160764-1.55421351607642
1001313.0145764114289-0.0145764114289043
1011615.89571675069890.104283249301134
1021615.18984535676170.810154643238305
1031415.6944063024031-1.69440630240308
104118.994008885848432.00599111415157
10547.37063441742284-3.37063441742284
1061414.6930170629043-0.693017062904348
107910.5659658154158-1.56596581541582
1081415.2574638574311-1.25746385743106
109810.0167137756442-2.01671377564415
110810.4697500279441-2.46975002794406
1111111.9652097964192-0.965209796419187
1121213.1931382162864-1.19313821628645
1131111.0137981823412-0.0137981823411882
1141413.01971279941670.980287200583342
1151514.40118373768310.598816262316853
1161613.35344329627532.64655670372468
1171612.87311181099953.12688818900048
1181112.6472019672902-1.64720196729016
1191413.36306660288290.636933397117079
1201410.99614951011693.00385048988307
1211211.55541432287830.444585677121667
1221413.05278987026980.94721012973019
123810.8065061752171-2.80650617521712
1241314.2747980236383-1.27479802363834
1251614.50865984660981.49134015339024
1261210.56942807826611.43057192173393
1271615.84107187558260.158928124417358
1281212.6796300217692-0.679630021769198
1291111.2682259994715-0.268225999471477
13045.69832608065274-1.69832608065274
1311616.0766810985428-0.0766810985427848
1321513.06389867075971.93610132924026
1331011.1235806511745-1.12358065117449
1341314.278455374957-1.27845537495703
1351512.56410719570152.43589280429852
1361210.17419174575751.82580825424246
1371412.84695430395621.15304569604381
138710.3775092422047-3.37750924220472
1391913.70535664916095.29464335083911
1401213.0227660694378-1.02276606943781
1411211.88217334639780.117826653602182
1421313.2070126965691-0.20701269656909
1431512.40761733729642.59238266270357
14488.9046262949945-0.904626294994498
1451211.12112704607510.878872953924874
1461010.3911752139798-0.39117521397978
147811.2623122209065-3.26231222090646
1481014.2774505151922-4.27745051519218
1491514.16090976054150.839090239458519
1501613.96414778300562.03585221699442
1511313.1799642692192-0.179964269219201
1521614.96907963006441.03092036993558
15399.6862486002568-0.686248600256794
1541413.27853356589490.72146643410509
1551413.33556860094170.664431399058301
156129.960881276030932.03911872396907

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 13 & 11.2049026711582 & 1.79509732884181 \tabularnewline
2 & 12 & 11.0501976167471 & 0.949802383252893 \tabularnewline
3 & 15 & 13.1284642259693 & 1.87153577403075 \tabularnewline
4 & 12 & 11.4502389709335 & 0.549761029066483 \tabularnewline
5 & 10 & 10.9719246015601 & -0.97192460156013 \tabularnewline
6 & 12 & 9.36122331907425 & 2.63877668092575 \tabularnewline
7 & 15 & 16.9268608320424 & -1.92686083204242 \tabularnewline
8 & 9 & 10.3094079411661 & -1.30940794116614 \tabularnewline
9 & 12 & 13.0209978602754 & -1.02099786027543 \tabularnewline
10 & 11 & 7.54336622238226 & 3.45663377761774 \tabularnewline
11 & 11 & 13.0149485846245 & -2.01494858462452 \tabularnewline
12 & 11 & 12.1838317170145 & -1.18383171701454 \tabularnewline
13 & 15 & 12.2991592688119 & 2.70084073118813 \tabularnewline
14 & 7 & 11.4832734656525 & -4.48327346565248 \tabularnewline
15 & 11 & 11.2781603020676 & -0.278160302067566 \tabularnewline
16 & 11 & 10.8220760751143 & 0.177923924885657 \tabularnewline
17 & 10 & 11.8347311899754 & -1.8347311899754 \tabularnewline
18 & 14 & 14.8674128169904 & -0.867412816990424 \tabularnewline
19 & 10 & 8.56823970010499 & 1.43176029989501 \tabularnewline
20 & 6 & 9.34255000441309 & -3.34255000441309 \tabularnewline
21 & 11 & 8.77932092658328 & 2.22067907341672 \tabularnewline
22 & 15 & 14.0737626346785 & 0.926237365321516 \tabularnewline
23 & 11 & 11.5803388330651 & -0.580338833065069 \tabularnewline
24 & 12 & 9.74160524658528 & 2.25839475341472 \tabularnewline
25 & 14 & 13.0843361390877 & 0.91566386091226 \tabularnewline
26 & 15 & 14.5245748735349 & 0.475425126465107 \tabularnewline
27 & 9 & 14.3087353508027 & -5.30873535080274 \tabularnewline
28 & 13 & 12.4834032242154 & 0.516596775784642 \tabularnewline
29 & 13 & 13.4529879522718 & -0.452987952271803 \tabularnewline
30 & 16 & 11.1694519977055 & 4.83054800229448 \tabularnewline
31 & 13 & 8.75162527439104 & 4.24837472560896 \tabularnewline
32 & 12 & 13.624849081948 & -1.62484908194797 \tabularnewline
33 & 14 & 14.569783025129 & -0.569783025128966 \tabularnewline
34 & 11 & 9.60790170281557 & 1.39209829718443 \tabularnewline
35 & 9 & 10.2281357490962 & -1.22813574909621 \tabularnewline
36 & 16 & 14.1849071539586 & 1.81509284604136 \tabularnewline
37 & 12 & 13.1540413424309 & -1.15404134243088 \tabularnewline
38 & 10 & 9.71307390046834 & 0.286926099531656 \tabularnewline
39 & 13 & 13.1545469131956 & -0.154546913195554 \tabularnewline
40 & 16 & 15.2287736182529 & 0.771226381747057 \tabularnewline
41 & 14 & 13.1151849345199 & 0.88481506548013 \tabularnewline
42 & 15 & 8.0705833181084 & 6.92941668189161 \tabularnewline
43 & 5 & 9.71510528588547 & -4.71510528588547 \tabularnewline
44 & 8 & 10.3134380241431 & -2.31343802414315 \tabularnewline
45 & 11 & 11.1487052586015 & -0.148705258601458 \tabularnewline
46 & 16 & 14.2175074662197 & 1.78249253378025 \tabularnewline
47 & 17 & 14.2475308431785 & 2.75246915682148 \tabularnewline
48 & 9 & 8.0287939713907 & 0.971206028609305 \tabularnewline
49 & 9 & 11.39958743349 & -2.39958743349002 \tabularnewline
50 & 13 & 14.8178416598702 & -1.81784165987017 \tabularnewline
51 & 10 & 10.8886453480061 & -0.888645348006095 \tabularnewline
52 & 6 & 11.9910442211409 & -5.99104422114088 \tabularnewline
53 & 12 & 11.8495190412859 & 0.150480958714061 \tabularnewline
54 & 8 & 10.433166443441 & -2.43316644344104 \tabularnewline
55 & 14 & 11.8091063908163 & 2.19089360918367 \tabularnewline
56 & 12 & 12.876118033608 & -0.87611803360804 \tabularnewline
57 & 11 & 11.1092335643711 & -0.109233564371146 \tabularnewline
58 & 16 & 14.1747570596022 & 1.82524294039776 \tabularnewline
59 & 8 & 10.2776291475077 & -2.27762914750774 \tabularnewline
60 & 15 & 14.6575447101163 & 0.342455289883668 \tabularnewline
61 & 7 & 9.1511205984398 & -2.1511205984398 \tabularnewline
62 & 16 & 13.9464110345232 & 2.05358896547681 \tabularnewline
63 & 14 & 12.9130473900452 & 1.08695260995478 \tabularnewline
64 & 16 & 13.661414209331 & 2.33858579066899 \tabularnewline
65 & 9 & 10.2507224958308 & -1.25072249583077 \tabularnewline
66 & 14 & 12.3894985830801 & 1.61050141691991 \tabularnewline
67 & 11 & 13.2954002601194 & -2.29540026011942 \tabularnewline
68 & 13 & 10.423297436403 & 2.57670256359697 \tabularnewline
69 & 15 & 13.0175810545881 & 1.98241894541192 \tabularnewline
70 & 5 & 5.82162779891744 & -0.821627798917437 \tabularnewline
71 & 15 & 12.938348305903 & 2.061651694097 \tabularnewline
72 & 13 & 11.9737875527751 & 1.02621244722485 \tabularnewline
73 & 11 & 13.0575037847135 & -2.0575037847135 \tabularnewline
74 & 11 & 14.1346593713084 & -3.13465937130842 \tabularnewline
75 & 12 & 12.2462910576544 & -0.246291057654353 \tabularnewline
76 & 12 & 13.4605564914878 & -1.46055649148784 \tabularnewline
77 & 12 & 12.815850975058 & -0.815850975058034 \tabularnewline
78 & 12 & 12.1256704010635 & -0.125670401063511 \tabularnewline
79 & 14 & 11.0986370101107 & 2.90136298988933 \tabularnewline
80 & 6 & 8.02152943171598 & -2.02152943171598 \tabularnewline
81 & 7 & 9.46530618764307 & -2.46530618764307 \tabularnewline
82 & 14 & 12.6302867701622 & 1.36971322983782 \tabularnewline
83 & 14 & 14.0434433176965 & -0.0434433176964578 \tabularnewline
84 & 10 & 10.9996709892448 & -0.999670989244845 \tabularnewline
85 & 13 & 9.39576007708632 & 3.60423992291368 \tabularnewline
86 & 12 & 12.4552688010383 & -0.455268801038257 \tabularnewline
87 & 9 & 9.09895259471804 & -0.0989525947180393 \tabularnewline
88 & 12 & 12.3827335866523 & -0.382733586652307 \tabularnewline
89 & 16 & 15.1126325964851 & 0.887367403514892 \tabularnewline
90 & 10 & 10.8271360363588 & -0.827136036358805 \tabularnewline
91 & 14 & 13.0149414140629 & 0.985058585937122 \tabularnewline
92 & 10 & 13.6743590227115 & -3.67435902271152 \tabularnewline
93 & 16 & 15.0189494268598 & 0.981050573140155 \tabularnewline
94 & 15 & 13.3269385259384 & 1.67306147406163 \tabularnewline
95 & 12 & 11.4956913796953 & 0.504308620304673 \tabularnewline
96 & 10 & 9.29698432053747 & 0.703015679462534 \tabularnewline
97 & 8 & 10.2975523598291 & -2.29755235982908 \tabularnewline
98 & 8 & 8.4817653155485 & -0.4817653155485 \tabularnewline
99 & 11 & 12.5542135160764 & -1.55421351607642 \tabularnewline
100 & 13 & 13.0145764114289 & -0.0145764114289043 \tabularnewline
101 & 16 & 15.8957167506989 & 0.104283249301134 \tabularnewline
102 & 16 & 15.1898453567617 & 0.810154643238305 \tabularnewline
103 & 14 & 15.6944063024031 & -1.69440630240308 \tabularnewline
104 & 11 & 8.99400888584843 & 2.00599111415157 \tabularnewline
105 & 4 & 7.37063441742284 & -3.37063441742284 \tabularnewline
106 & 14 & 14.6930170629043 & -0.693017062904348 \tabularnewline
107 & 9 & 10.5659658154158 & -1.56596581541582 \tabularnewline
108 & 14 & 15.2574638574311 & -1.25746385743106 \tabularnewline
109 & 8 & 10.0167137756442 & -2.01671377564415 \tabularnewline
110 & 8 & 10.4697500279441 & -2.46975002794406 \tabularnewline
111 & 11 & 11.9652097964192 & -0.965209796419187 \tabularnewline
112 & 12 & 13.1931382162864 & -1.19313821628645 \tabularnewline
113 & 11 & 11.0137981823412 & -0.0137981823411882 \tabularnewline
114 & 14 & 13.0197127994167 & 0.980287200583342 \tabularnewline
115 & 15 & 14.4011837376831 & 0.598816262316853 \tabularnewline
116 & 16 & 13.3534432962753 & 2.64655670372468 \tabularnewline
117 & 16 & 12.8731118109995 & 3.12688818900048 \tabularnewline
118 & 11 & 12.6472019672902 & -1.64720196729016 \tabularnewline
119 & 14 & 13.3630666028829 & 0.636933397117079 \tabularnewline
120 & 14 & 10.9961495101169 & 3.00385048988307 \tabularnewline
121 & 12 & 11.5554143228783 & 0.444585677121667 \tabularnewline
122 & 14 & 13.0527898702698 & 0.94721012973019 \tabularnewline
123 & 8 & 10.8065061752171 & -2.80650617521712 \tabularnewline
124 & 13 & 14.2747980236383 & -1.27479802363834 \tabularnewline
125 & 16 & 14.5086598466098 & 1.49134015339024 \tabularnewline
126 & 12 & 10.5694280782661 & 1.43057192173393 \tabularnewline
127 & 16 & 15.8410718755826 & 0.158928124417358 \tabularnewline
128 & 12 & 12.6796300217692 & -0.679630021769198 \tabularnewline
129 & 11 & 11.2682259994715 & -0.268225999471477 \tabularnewline
130 & 4 & 5.69832608065274 & -1.69832608065274 \tabularnewline
131 & 16 & 16.0766810985428 & -0.0766810985427848 \tabularnewline
132 & 15 & 13.0638986707597 & 1.93610132924026 \tabularnewline
133 & 10 & 11.1235806511745 & -1.12358065117449 \tabularnewline
134 & 13 & 14.278455374957 & -1.27845537495703 \tabularnewline
135 & 15 & 12.5641071957015 & 2.43589280429852 \tabularnewline
136 & 12 & 10.1741917457575 & 1.82580825424246 \tabularnewline
137 & 14 & 12.8469543039562 & 1.15304569604381 \tabularnewline
138 & 7 & 10.3775092422047 & -3.37750924220472 \tabularnewline
139 & 19 & 13.7053566491609 & 5.29464335083911 \tabularnewline
140 & 12 & 13.0227660694378 & -1.02276606943781 \tabularnewline
141 & 12 & 11.8821733463978 & 0.117826653602182 \tabularnewline
142 & 13 & 13.2070126965691 & -0.20701269656909 \tabularnewline
143 & 15 & 12.4076173372964 & 2.59238266270357 \tabularnewline
144 & 8 & 8.9046262949945 & -0.904626294994498 \tabularnewline
145 & 12 & 11.1211270460751 & 0.878872953924874 \tabularnewline
146 & 10 & 10.3911752139798 & -0.39117521397978 \tabularnewline
147 & 8 & 11.2623122209065 & -3.26231222090646 \tabularnewline
148 & 10 & 14.2774505151922 & -4.27745051519218 \tabularnewline
149 & 15 & 14.1609097605415 & 0.839090239458519 \tabularnewline
150 & 16 & 13.9641477830056 & 2.03585221699442 \tabularnewline
151 & 13 & 13.1799642692192 & -0.179964269219201 \tabularnewline
152 & 16 & 14.9690796300644 & 1.03092036993558 \tabularnewline
153 & 9 & 9.6862486002568 & -0.686248600256794 \tabularnewline
154 & 14 & 13.2785335658949 & 0.72146643410509 \tabularnewline
155 & 14 & 13.3355686009417 & 0.664431399058301 \tabularnewline
156 & 12 & 9.96088127603093 & 2.03911872396907 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146430&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]13[/C][C]11.2049026711582[/C][C]1.79509732884181[/C][/ROW]
[ROW][C]2[/C][C]12[/C][C]11.0501976167471[/C][C]0.949802383252893[/C][/ROW]
[ROW][C]3[/C][C]15[/C][C]13.1284642259693[/C][C]1.87153577403075[/C][/ROW]
[ROW][C]4[/C][C]12[/C][C]11.4502389709335[/C][C]0.549761029066483[/C][/ROW]
[ROW][C]5[/C][C]10[/C][C]10.9719246015601[/C][C]-0.97192460156013[/C][/ROW]
[ROW][C]6[/C][C]12[/C][C]9.36122331907425[/C][C]2.63877668092575[/C][/ROW]
[ROW][C]7[/C][C]15[/C][C]16.9268608320424[/C][C]-1.92686083204242[/C][/ROW]
[ROW][C]8[/C][C]9[/C][C]10.3094079411661[/C][C]-1.30940794116614[/C][/ROW]
[ROW][C]9[/C][C]12[/C][C]13.0209978602754[/C][C]-1.02099786027543[/C][/ROW]
[ROW][C]10[/C][C]11[/C][C]7.54336622238226[/C][C]3.45663377761774[/C][/ROW]
[ROW][C]11[/C][C]11[/C][C]13.0149485846245[/C][C]-2.01494858462452[/C][/ROW]
[ROW][C]12[/C][C]11[/C][C]12.1838317170145[/C][C]-1.18383171701454[/C][/ROW]
[ROW][C]13[/C][C]15[/C][C]12.2991592688119[/C][C]2.70084073118813[/C][/ROW]
[ROW][C]14[/C][C]7[/C][C]11.4832734656525[/C][C]-4.48327346565248[/C][/ROW]
[ROW][C]15[/C][C]11[/C][C]11.2781603020676[/C][C]-0.278160302067566[/C][/ROW]
[ROW][C]16[/C][C]11[/C][C]10.8220760751143[/C][C]0.177923924885657[/C][/ROW]
[ROW][C]17[/C][C]10[/C][C]11.8347311899754[/C][C]-1.8347311899754[/C][/ROW]
[ROW][C]18[/C][C]14[/C][C]14.8674128169904[/C][C]-0.867412816990424[/C][/ROW]
[ROW][C]19[/C][C]10[/C][C]8.56823970010499[/C][C]1.43176029989501[/C][/ROW]
[ROW][C]20[/C][C]6[/C][C]9.34255000441309[/C][C]-3.34255000441309[/C][/ROW]
[ROW][C]21[/C][C]11[/C][C]8.77932092658328[/C][C]2.22067907341672[/C][/ROW]
[ROW][C]22[/C][C]15[/C][C]14.0737626346785[/C][C]0.926237365321516[/C][/ROW]
[ROW][C]23[/C][C]11[/C][C]11.5803388330651[/C][C]-0.580338833065069[/C][/ROW]
[ROW][C]24[/C][C]12[/C][C]9.74160524658528[/C][C]2.25839475341472[/C][/ROW]
[ROW][C]25[/C][C]14[/C][C]13.0843361390877[/C][C]0.91566386091226[/C][/ROW]
[ROW][C]26[/C][C]15[/C][C]14.5245748735349[/C][C]0.475425126465107[/C][/ROW]
[ROW][C]27[/C][C]9[/C][C]14.3087353508027[/C][C]-5.30873535080274[/C][/ROW]
[ROW][C]28[/C][C]13[/C][C]12.4834032242154[/C][C]0.516596775784642[/C][/ROW]
[ROW][C]29[/C][C]13[/C][C]13.4529879522718[/C][C]-0.452987952271803[/C][/ROW]
[ROW][C]30[/C][C]16[/C][C]11.1694519977055[/C][C]4.83054800229448[/C][/ROW]
[ROW][C]31[/C][C]13[/C][C]8.75162527439104[/C][C]4.24837472560896[/C][/ROW]
[ROW][C]32[/C][C]12[/C][C]13.624849081948[/C][C]-1.62484908194797[/C][/ROW]
[ROW][C]33[/C][C]14[/C][C]14.569783025129[/C][C]-0.569783025128966[/C][/ROW]
[ROW][C]34[/C][C]11[/C][C]9.60790170281557[/C][C]1.39209829718443[/C][/ROW]
[ROW][C]35[/C][C]9[/C][C]10.2281357490962[/C][C]-1.22813574909621[/C][/ROW]
[ROW][C]36[/C][C]16[/C][C]14.1849071539586[/C][C]1.81509284604136[/C][/ROW]
[ROW][C]37[/C][C]12[/C][C]13.1540413424309[/C][C]-1.15404134243088[/C][/ROW]
[ROW][C]38[/C][C]10[/C][C]9.71307390046834[/C][C]0.286926099531656[/C][/ROW]
[ROW][C]39[/C][C]13[/C][C]13.1545469131956[/C][C]-0.154546913195554[/C][/ROW]
[ROW][C]40[/C][C]16[/C][C]15.2287736182529[/C][C]0.771226381747057[/C][/ROW]
[ROW][C]41[/C][C]14[/C][C]13.1151849345199[/C][C]0.88481506548013[/C][/ROW]
[ROW][C]42[/C][C]15[/C][C]8.0705833181084[/C][C]6.92941668189161[/C][/ROW]
[ROW][C]43[/C][C]5[/C][C]9.71510528588547[/C][C]-4.71510528588547[/C][/ROW]
[ROW][C]44[/C][C]8[/C][C]10.3134380241431[/C][C]-2.31343802414315[/C][/ROW]
[ROW][C]45[/C][C]11[/C][C]11.1487052586015[/C][C]-0.148705258601458[/C][/ROW]
[ROW][C]46[/C][C]16[/C][C]14.2175074662197[/C][C]1.78249253378025[/C][/ROW]
[ROW][C]47[/C][C]17[/C][C]14.2475308431785[/C][C]2.75246915682148[/C][/ROW]
[ROW][C]48[/C][C]9[/C][C]8.0287939713907[/C][C]0.971206028609305[/C][/ROW]
[ROW][C]49[/C][C]9[/C][C]11.39958743349[/C][C]-2.39958743349002[/C][/ROW]
[ROW][C]50[/C][C]13[/C][C]14.8178416598702[/C][C]-1.81784165987017[/C][/ROW]
[ROW][C]51[/C][C]10[/C][C]10.8886453480061[/C][C]-0.888645348006095[/C][/ROW]
[ROW][C]52[/C][C]6[/C][C]11.9910442211409[/C][C]-5.99104422114088[/C][/ROW]
[ROW][C]53[/C][C]12[/C][C]11.8495190412859[/C][C]0.150480958714061[/C][/ROW]
[ROW][C]54[/C][C]8[/C][C]10.433166443441[/C][C]-2.43316644344104[/C][/ROW]
[ROW][C]55[/C][C]14[/C][C]11.8091063908163[/C][C]2.19089360918367[/C][/ROW]
[ROW][C]56[/C][C]12[/C][C]12.876118033608[/C][C]-0.87611803360804[/C][/ROW]
[ROW][C]57[/C][C]11[/C][C]11.1092335643711[/C][C]-0.109233564371146[/C][/ROW]
[ROW][C]58[/C][C]16[/C][C]14.1747570596022[/C][C]1.82524294039776[/C][/ROW]
[ROW][C]59[/C][C]8[/C][C]10.2776291475077[/C][C]-2.27762914750774[/C][/ROW]
[ROW][C]60[/C][C]15[/C][C]14.6575447101163[/C][C]0.342455289883668[/C][/ROW]
[ROW][C]61[/C][C]7[/C][C]9.1511205984398[/C][C]-2.1511205984398[/C][/ROW]
[ROW][C]62[/C][C]16[/C][C]13.9464110345232[/C][C]2.05358896547681[/C][/ROW]
[ROW][C]63[/C][C]14[/C][C]12.9130473900452[/C][C]1.08695260995478[/C][/ROW]
[ROW][C]64[/C][C]16[/C][C]13.661414209331[/C][C]2.33858579066899[/C][/ROW]
[ROW][C]65[/C][C]9[/C][C]10.2507224958308[/C][C]-1.25072249583077[/C][/ROW]
[ROW][C]66[/C][C]14[/C][C]12.3894985830801[/C][C]1.61050141691991[/C][/ROW]
[ROW][C]67[/C][C]11[/C][C]13.2954002601194[/C][C]-2.29540026011942[/C][/ROW]
[ROW][C]68[/C][C]13[/C][C]10.423297436403[/C][C]2.57670256359697[/C][/ROW]
[ROW][C]69[/C][C]15[/C][C]13.0175810545881[/C][C]1.98241894541192[/C][/ROW]
[ROW][C]70[/C][C]5[/C][C]5.82162779891744[/C][C]-0.821627798917437[/C][/ROW]
[ROW][C]71[/C][C]15[/C][C]12.938348305903[/C][C]2.061651694097[/C][/ROW]
[ROW][C]72[/C][C]13[/C][C]11.9737875527751[/C][C]1.02621244722485[/C][/ROW]
[ROW][C]73[/C][C]11[/C][C]13.0575037847135[/C][C]-2.0575037847135[/C][/ROW]
[ROW][C]74[/C][C]11[/C][C]14.1346593713084[/C][C]-3.13465937130842[/C][/ROW]
[ROW][C]75[/C][C]12[/C][C]12.2462910576544[/C][C]-0.246291057654353[/C][/ROW]
[ROW][C]76[/C][C]12[/C][C]13.4605564914878[/C][C]-1.46055649148784[/C][/ROW]
[ROW][C]77[/C][C]12[/C][C]12.815850975058[/C][C]-0.815850975058034[/C][/ROW]
[ROW][C]78[/C][C]12[/C][C]12.1256704010635[/C][C]-0.125670401063511[/C][/ROW]
[ROW][C]79[/C][C]14[/C][C]11.0986370101107[/C][C]2.90136298988933[/C][/ROW]
[ROW][C]80[/C][C]6[/C][C]8.02152943171598[/C][C]-2.02152943171598[/C][/ROW]
[ROW][C]81[/C][C]7[/C][C]9.46530618764307[/C][C]-2.46530618764307[/C][/ROW]
[ROW][C]82[/C][C]14[/C][C]12.6302867701622[/C][C]1.36971322983782[/C][/ROW]
[ROW][C]83[/C][C]14[/C][C]14.0434433176965[/C][C]-0.0434433176964578[/C][/ROW]
[ROW][C]84[/C][C]10[/C][C]10.9996709892448[/C][C]-0.999670989244845[/C][/ROW]
[ROW][C]85[/C][C]13[/C][C]9.39576007708632[/C][C]3.60423992291368[/C][/ROW]
[ROW][C]86[/C][C]12[/C][C]12.4552688010383[/C][C]-0.455268801038257[/C][/ROW]
[ROW][C]87[/C][C]9[/C][C]9.09895259471804[/C][C]-0.0989525947180393[/C][/ROW]
[ROW][C]88[/C][C]12[/C][C]12.3827335866523[/C][C]-0.382733586652307[/C][/ROW]
[ROW][C]89[/C][C]16[/C][C]15.1126325964851[/C][C]0.887367403514892[/C][/ROW]
[ROW][C]90[/C][C]10[/C][C]10.8271360363588[/C][C]-0.827136036358805[/C][/ROW]
[ROW][C]91[/C][C]14[/C][C]13.0149414140629[/C][C]0.985058585937122[/C][/ROW]
[ROW][C]92[/C][C]10[/C][C]13.6743590227115[/C][C]-3.67435902271152[/C][/ROW]
[ROW][C]93[/C][C]16[/C][C]15.0189494268598[/C][C]0.981050573140155[/C][/ROW]
[ROW][C]94[/C][C]15[/C][C]13.3269385259384[/C][C]1.67306147406163[/C][/ROW]
[ROW][C]95[/C][C]12[/C][C]11.4956913796953[/C][C]0.504308620304673[/C][/ROW]
[ROW][C]96[/C][C]10[/C][C]9.29698432053747[/C][C]0.703015679462534[/C][/ROW]
[ROW][C]97[/C][C]8[/C][C]10.2975523598291[/C][C]-2.29755235982908[/C][/ROW]
[ROW][C]98[/C][C]8[/C][C]8.4817653155485[/C][C]-0.4817653155485[/C][/ROW]
[ROW][C]99[/C][C]11[/C][C]12.5542135160764[/C][C]-1.55421351607642[/C][/ROW]
[ROW][C]100[/C][C]13[/C][C]13.0145764114289[/C][C]-0.0145764114289043[/C][/ROW]
[ROW][C]101[/C][C]16[/C][C]15.8957167506989[/C][C]0.104283249301134[/C][/ROW]
[ROW][C]102[/C][C]16[/C][C]15.1898453567617[/C][C]0.810154643238305[/C][/ROW]
[ROW][C]103[/C][C]14[/C][C]15.6944063024031[/C][C]-1.69440630240308[/C][/ROW]
[ROW][C]104[/C][C]11[/C][C]8.99400888584843[/C][C]2.00599111415157[/C][/ROW]
[ROW][C]105[/C][C]4[/C][C]7.37063441742284[/C][C]-3.37063441742284[/C][/ROW]
[ROW][C]106[/C][C]14[/C][C]14.6930170629043[/C][C]-0.693017062904348[/C][/ROW]
[ROW][C]107[/C][C]9[/C][C]10.5659658154158[/C][C]-1.56596581541582[/C][/ROW]
[ROW][C]108[/C][C]14[/C][C]15.2574638574311[/C][C]-1.25746385743106[/C][/ROW]
[ROW][C]109[/C][C]8[/C][C]10.0167137756442[/C][C]-2.01671377564415[/C][/ROW]
[ROW][C]110[/C][C]8[/C][C]10.4697500279441[/C][C]-2.46975002794406[/C][/ROW]
[ROW][C]111[/C][C]11[/C][C]11.9652097964192[/C][C]-0.965209796419187[/C][/ROW]
[ROW][C]112[/C][C]12[/C][C]13.1931382162864[/C][C]-1.19313821628645[/C][/ROW]
[ROW][C]113[/C][C]11[/C][C]11.0137981823412[/C][C]-0.0137981823411882[/C][/ROW]
[ROW][C]114[/C][C]14[/C][C]13.0197127994167[/C][C]0.980287200583342[/C][/ROW]
[ROW][C]115[/C][C]15[/C][C]14.4011837376831[/C][C]0.598816262316853[/C][/ROW]
[ROW][C]116[/C][C]16[/C][C]13.3534432962753[/C][C]2.64655670372468[/C][/ROW]
[ROW][C]117[/C][C]16[/C][C]12.8731118109995[/C][C]3.12688818900048[/C][/ROW]
[ROW][C]118[/C][C]11[/C][C]12.6472019672902[/C][C]-1.64720196729016[/C][/ROW]
[ROW][C]119[/C][C]14[/C][C]13.3630666028829[/C][C]0.636933397117079[/C][/ROW]
[ROW][C]120[/C][C]14[/C][C]10.9961495101169[/C][C]3.00385048988307[/C][/ROW]
[ROW][C]121[/C][C]12[/C][C]11.5554143228783[/C][C]0.444585677121667[/C][/ROW]
[ROW][C]122[/C][C]14[/C][C]13.0527898702698[/C][C]0.94721012973019[/C][/ROW]
[ROW][C]123[/C][C]8[/C][C]10.8065061752171[/C][C]-2.80650617521712[/C][/ROW]
[ROW][C]124[/C][C]13[/C][C]14.2747980236383[/C][C]-1.27479802363834[/C][/ROW]
[ROW][C]125[/C][C]16[/C][C]14.5086598466098[/C][C]1.49134015339024[/C][/ROW]
[ROW][C]126[/C][C]12[/C][C]10.5694280782661[/C][C]1.43057192173393[/C][/ROW]
[ROW][C]127[/C][C]16[/C][C]15.8410718755826[/C][C]0.158928124417358[/C][/ROW]
[ROW][C]128[/C][C]12[/C][C]12.6796300217692[/C][C]-0.679630021769198[/C][/ROW]
[ROW][C]129[/C][C]11[/C][C]11.2682259994715[/C][C]-0.268225999471477[/C][/ROW]
[ROW][C]130[/C][C]4[/C][C]5.69832608065274[/C][C]-1.69832608065274[/C][/ROW]
[ROW][C]131[/C][C]16[/C][C]16.0766810985428[/C][C]-0.0766810985427848[/C][/ROW]
[ROW][C]132[/C][C]15[/C][C]13.0638986707597[/C][C]1.93610132924026[/C][/ROW]
[ROW][C]133[/C][C]10[/C][C]11.1235806511745[/C][C]-1.12358065117449[/C][/ROW]
[ROW][C]134[/C][C]13[/C][C]14.278455374957[/C][C]-1.27845537495703[/C][/ROW]
[ROW][C]135[/C][C]15[/C][C]12.5641071957015[/C][C]2.43589280429852[/C][/ROW]
[ROW][C]136[/C][C]12[/C][C]10.1741917457575[/C][C]1.82580825424246[/C][/ROW]
[ROW][C]137[/C][C]14[/C][C]12.8469543039562[/C][C]1.15304569604381[/C][/ROW]
[ROW][C]138[/C][C]7[/C][C]10.3775092422047[/C][C]-3.37750924220472[/C][/ROW]
[ROW][C]139[/C][C]19[/C][C]13.7053566491609[/C][C]5.29464335083911[/C][/ROW]
[ROW][C]140[/C][C]12[/C][C]13.0227660694378[/C][C]-1.02276606943781[/C][/ROW]
[ROW][C]141[/C][C]12[/C][C]11.8821733463978[/C][C]0.117826653602182[/C][/ROW]
[ROW][C]142[/C][C]13[/C][C]13.2070126965691[/C][C]-0.20701269656909[/C][/ROW]
[ROW][C]143[/C][C]15[/C][C]12.4076173372964[/C][C]2.59238266270357[/C][/ROW]
[ROW][C]144[/C][C]8[/C][C]8.9046262949945[/C][C]-0.904626294994498[/C][/ROW]
[ROW][C]145[/C][C]12[/C][C]11.1211270460751[/C][C]0.878872953924874[/C][/ROW]
[ROW][C]146[/C][C]10[/C][C]10.3911752139798[/C][C]-0.39117521397978[/C][/ROW]
[ROW][C]147[/C][C]8[/C][C]11.2623122209065[/C][C]-3.26231222090646[/C][/ROW]
[ROW][C]148[/C][C]10[/C][C]14.2774505151922[/C][C]-4.27745051519218[/C][/ROW]
[ROW][C]149[/C][C]15[/C][C]14.1609097605415[/C][C]0.839090239458519[/C][/ROW]
[ROW][C]150[/C][C]16[/C][C]13.9641477830056[/C][C]2.03585221699442[/C][/ROW]
[ROW][C]151[/C][C]13[/C][C]13.1799642692192[/C][C]-0.179964269219201[/C][/ROW]
[ROW][C]152[/C][C]16[/C][C]14.9690796300644[/C][C]1.03092036993558[/C][/ROW]
[ROW][C]153[/C][C]9[/C][C]9.6862486002568[/C][C]-0.686248600256794[/C][/ROW]
[ROW][C]154[/C][C]14[/C][C]13.2785335658949[/C][C]0.72146643410509[/C][/ROW]
[ROW][C]155[/C][C]14[/C][C]13.3355686009417[/C][C]0.664431399058301[/C][/ROW]
[ROW][C]156[/C][C]12[/C][C]9.96088127603093[/C][C]2.03911872396907[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146430&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146430&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11311.20490267115821.79509732884181
21211.05019761674710.949802383252893
31513.12846422596931.87153577403075
41211.45023897093350.549761029066483
51010.9719246015601-0.97192460156013
6129.361223319074252.63877668092575
71516.9268608320424-1.92686083204242
8910.3094079411661-1.30940794116614
91213.0209978602754-1.02099786027543
10117.543366222382263.45663377761774
111113.0149485846245-2.01494858462452
121112.1838317170145-1.18383171701454
131512.29915926881192.70084073118813
14711.4832734656525-4.48327346565248
151111.2781603020676-0.278160302067566
161110.82207607511430.177923924885657
171011.8347311899754-1.8347311899754
181414.8674128169904-0.867412816990424
19108.568239700104991.43176029989501
2069.34255000441309-3.34255000441309
21118.779320926583282.22067907341672
221514.07376263467850.926237365321516
231111.5803388330651-0.580338833065069
24129.741605246585282.25839475341472
251413.08433613908770.91566386091226
261514.52457487353490.475425126465107
27914.3087353508027-5.30873535080274
281312.48340322421540.516596775784642
291313.4529879522718-0.452987952271803
301611.16945199770554.83054800229448
31138.751625274391044.24837472560896
321213.624849081948-1.62484908194797
331414.569783025129-0.569783025128966
34119.607901702815571.39209829718443
35910.2281357490962-1.22813574909621
361614.18490715395861.81509284604136
371213.1540413424309-1.15404134243088
38109.713073900468340.286926099531656
391313.1545469131956-0.154546913195554
401615.22877361825290.771226381747057
411413.11518493451990.88481506548013
42158.07058331810846.92941668189161
4359.71510528588547-4.71510528588547
44810.3134380241431-2.31343802414315
451111.1487052586015-0.148705258601458
461614.21750746621971.78249253378025
471714.24753084317852.75246915682148
4898.02879397139070.971206028609305
49911.39958743349-2.39958743349002
501314.8178416598702-1.81784165987017
511010.8886453480061-0.888645348006095
52611.9910442211409-5.99104422114088
531211.84951904128590.150480958714061
54810.433166443441-2.43316644344104
551411.80910639081632.19089360918367
561212.876118033608-0.87611803360804
571111.1092335643711-0.109233564371146
581614.17475705960221.82524294039776
59810.2776291475077-2.27762914750774
601514.65754471011630.342455289883668
6179.1511205984398-2.1511205984398
621613.94641103452322.05358896547681
631412.91304739004521.08695260995478
641613.6614142093312.33858579066899
65910.2507224958308-1.25072249583077
661412.38949858308011.61050141691991
671113.2954002601194-2.29540026011942
681310.4232974364032.57670256359697
691513.01758105458811.98241894541192
7055.82162779891744-0.821627798917437
711512.9383483059032.061651694097
721311.97378755277511.02621244722485
731113.0575037847135-2.0575037847135
741114.1346593713084-3.13465937130842
751212.2462910576544-0.246291057654353
761213.4605564914878-1.46055649148784
771212.815850975058-0.815850975058034
781212.1256704010635-0.125670401063511
791411.09863701011072.90136298988933
8068.02152943171598-2.02152943171598
8179.46530618764307-2.46530618764307
821412.63028677016221.36971322983782
831414.0434433176965-0.0434433176964578
841010.9996709892448-0.999670989244845
85139.395760077086323.60423992291368
861212.4552688010383-0.455268801038257
8799.09895259471804-0.0989525947180393
881212.3827335866523-0.382733586652307
891615.11263259648510.887367403514892
901010.8271360363588-0.827136036358805
911413.01494141406290.985058585937122
921013.6743590227115-3.67435902271152
931615.01894942685980.981050573140155
941513.32693852593841.67306147406163
951211.49569137969530.504308620304673
96109.296984320537470.703015679462534
97810.2975523598291-2.29755235982908
9888.4817653155485-0.4817653155485
991112.5542135160764-1.55421351607642
1001313.0145764114289-0.0145764114289043
1011615.89571675069890.104283249301134
1021615.18984535676170.810154643238305
1031415.6944063024031-1.69440630240308
104118.994008885848432.00599111415157
10547.37063441742284-3.37063441742284
1061414.6930170629043-0.693017062904348
107910.5659658154158-1.56596581541582
1081415.2574638574311-1.25746385743106
109810.0167137756442-2.01671377564415
110810.4697500279441-2.46975002794406
1111111.9652097964192-0.965209796419187
1121213.1931382162864-1.19313821628645
1131111.0137981823412-0.0137981823411882
1141413.01971279941670.980287200583342
1151514.40118373768310.598816262316853
1161613.35344329627532.64655670372468
1171612.87311181099953.12688818900048
1181112.6472019672902-1.64720196729016
1191413.36306660288290.636933397117079
1201410.99614951011693.00385048988307
1211211.55541432287830.444585677121667
1221413.05278987026980.94721012973019
123810.8065061752171-2.80650617521712
1241314.2747980236383-1.27479802363834
1251614.50865984660981.49134015339024
1261210.56942807826611.43057192173393
1271615.84107187558260.158928124417358
1281212.6796300217692-0.679630021769198
1291111.2682259994715-0.268225999471477
13045.69832608065274-1.69832608065274
1311616.0766810985428-0.0766810985427848
1321513.06389867075971.93610132924026
1331011.1235806511745-1.12358065117449
1341314.278455374957-1.27845537495703
1351512.56410719570152.43589280429852
1361210.17419174575751.82580825424246
1371412.84695430395621.15304569604381
138710.3775092422047-3.37750924220472
1391913.70535664916095.29464335083911
1401213.0227660694378-1.02276606943781
1411211.88217334639780.117826653602182
1421313.2070126965691-0.20701269656909
1431512.40761733729642.59238266270357
14488.9046262949945-0.904626294994498
1451211.12112704607510.878872953924874
1461010.3911752139798-0.39117521397978
147811.2623122209065-3.26231222090646
1481014.2774505151922-4.27745051519218
1491514.16090976054150.839090239458519
1501613.96414778300562.03585221699442
1511313.1799642692192-0.179964269219201
1521614.96907963006441.03092036993558
15399.6862486002568-0.686248600256794
1541413.27853356589490.72146643410509
1551413.33556860094170.664431399058301
156129.960881276030932.03911872396907







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
130.8360017920639080.3279964158721840.163998207936092
140.901958394078540.1960832118429210.0980416059214603
150.8792405576445190.2415188847109610.120759442355481
160.8086167936731950.382766412653610.191383206326805
170.7265851617577060.5468296764845880.273414838242294
180.6667970381310560.6664059237378890.333202961868944
190.6079103465340510.7841793069318980.392089653465949
200.7021283567818560.5957432864362870.297871643218144
210.694192286731520.6116154265369590.305807713268479
220.7453161201656740.5093677596686510.254683879834326
230.6834184238435530.6331631523128940.316581576156447
240.707777132038690.5844457359226210.29222286796131
250.6845357751627280.6309284496745440.315464224837272
260.6260797639906070.7478404720187870.373920236009393
270.8061671630168050.3876656739663910.193832836983195
280.7709756533203480.4580486933593050.229024346679652
290.7170351166570260.5659297666859490.282964883342974
300.8186239096800450.3627521806399110.181376090319955
310.8601566196548290.2796867606903430.139843380345171
320.8296662109924670.3406675780150670.170333789007533
330.7873125932527150.4253748134945710.212687406747286
340.7549054105990880.4901891788018230.245094589400912
350.736412067999250.5271758640015020.263587932000751
360.7567194634653790.4865610730692420.243280536534621
370.7400780364288230.5198439271423530.259921963571177
380.696462188811620.6070756223767590.303537811188379
390.6463023439943410.7073953120113190.353697656005659
400.6025816866541110.7948366266917780.397418313345889
410.5526456395378250.894708720924350.447354360462175
420.8431763007365380.3136473985269240.156823699263462
430.9708149181968720.05837016360625620.0291850818031281
440.9717544762045280.05649104759094410.0282455237954721
450.963116069055830.0737678618883390.0368839309441695
460.9686958461038150.06260830779237010.0313041538961851
470.9750191368960980.04996172620780410.024980863103902
480.9715922006109850.05681559877802910.0284077993890146
490.968059130521680.06388173895663850.0319408694783192
500.9594327589301220.08113448213975610.0405672410698781
510.9527291428159770.09454171436804560.0472708571840228
520.989272727210280.02145454557943960.0107272727897198
530.98580342590630.02839314818739870.0141965740936994
540.9891549151957050.02169016960858970.0108450848042948
550.9927678910020530.01446421799589430.00723210899794715
560.9902117190816380.01957656183672420.00978828091836208
570.9865131544645910.02697369107081790.013486845535409
580.9858478311767560.0283043376464890.0141521688232445
590.9890595788110890.0218808423778230.0109404211889115
600.9878590335761450.02428193284771070.0121409664238553
610.9877983317135950.02440333657281050.0122016682864053
620.989447218573090.02110556285381870.0105527814269094
630.9878855753545760.02422884929084840.0121144246454242
640.9882874413655620.02342511726887590.011712558634438
650.987659898327960.02468020334407930.0123401016720396
660.9856703082117070.02865938357658690.0143296917882935
670.9873147163489450.02537056730210930.0126852836510546
680.988869906293760.02226018741247730.0111300937062387
690.988298091441910.0234038171161810.0117019085580905
700.9850823550256860.0298352899486290.0149176449743145
710.9855569406129760.0288861187740470.0144430593870235
720.9821643168462680.03567136630746360.0178356831537318
730.9824987923777140.0350024152445720.017501207622286
740.9874030103962650.02519397920747040.0125969896037352
750.9833495355852780.03330092882944390.016650464414722
760.9801562422371860.03968751552562760.0198437577628138
770.9742931165743890.05141376685122290.0257068834256115
780.966661732462630.06667653507474120.0333382675373706
790.9756401282116540.04871974357669240.0243598717883462
800.97399429858950.05201140282099960.0260057014104998
810.9749669441903630.05006611161927370.0250330558096368
820.9729834829159950.05403303416800970.0270165170840049
830.9644489453169550.07110210936609090.0355510546830455
840.9552105811569720.08957883768605670.0447894188430283
850.9819980737612210.03600385247755770.0180019262387789
860.9760140750782780.04797184984344370.0239859249217219
870.9687225114776150.06255497704477050.0312774885223853
880.9597489503999020.0805020992001950.0402510496000975
890.951008179153530.09798364169293980.0489918208464699
900.9384758045615820.1230483908768360.0615241954384179
910.9306311776964690.1387376446070620.069368822303531
920.9547720784722720.0904558430554560.045227921527728
930.945151260319080.109697479361840.0548487396809202
940.9434307560432380.1131384879135250.0565692439567624
950.9320031673230590.1359936653538820.067996832676941
960.9197028008542980.1605943982914040.080297199145702
970.9127613963768930.1744772072462150.0872386036231074
980.8944599622838120.2110800754323760.105540037716188
990.8789282253723880.2421435492552230.121071774627612
1000.8530227067171310.2939545865657380.146977293282869
1010.8222658893142340.3554682213715320.177734110685766
1020.795713023220720.408573953558560.20428697677928
1030.8081798667721190.3836402664557620.191820133227881
1040.8644358748211280.2711282503577450.135564125178872
1050.8618593918989080.2762812162021830.138140608101092
1060.8298872560317030.3402254879365940.170112743968297
1070.7974678520633540.4050642958732920.202532147936646
1080.7836563727571270.4326872544857460.216343627242873
1090.7648546255359220.4702907489281570.235145374464078
1100.7841433090689440.4317133818621120.215856690931056
1110.7690453858734060.4619092282531870.230954614126594
1120.8343242561006170.3313514877987660.165675743899383
1130.7972502037923040.4054995924153920.202749796207696
1140.7663321627394740.4673356745210520.233667837260526
1150.7229761302440680.5540477395118640.277023869755932
1160.7321701317578510.5356597364842970.267829868242149
1170.744374822732680.5112503545346410.25562517726732
1180.7256229710673910.5487540578652170.274377028932609
1190.6753799183396880.6492401633206240.324620081660312
1200.7713185474106790.4573629051786420.228681452589321
1210.7409583544455080.5180832911089840.259041645554492
1220.6882524185663680.6234951628672640.311747581433632
1230.6743283621109530.6513432757780940.325671637889047
1240.6314835368724560.7370329262550890.368516463127544
1250.6121768779478880.7756462441042250.387823122052112
1260.6125528192807940.7748943614384120.387447180719206
1270.546035670759180.907928658481640.45396432924082
1280.5048787291707880.9902425416584240.495121270829212
1290.4829941377381170.9659882754762330.517005862261883
1300.4650092500288740.9300185000577480.534990749971126
1310.3929231015754660.7858462031509310.607076898424534
1320.3714622525341480.7429245050682950.628537747465852
1330.3256673343316130.6513346686632270.674332665668387
1340.2813238243717260.5626476487434520.718676175628274
1350.2375461515696070.4750923031392130.762453848430393
1360.2190691513409930.4381383026819860.780930848659007
1370.184306688933710.368613377867420.81569331106629
1380.197580908202360.3951618164047210.80241909179764
1390.6160697450689150.767860509862170.383930254931085
1400.5261505179104370.9476989641791260.473849482089563
1410.4055698626061730.8111397252123460.594430137393827
1420.4008516816240120.8017033632480240.599148318375988
1430.2780890056171930.5561780112343860.721910994382807

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
13 & 0.836001792063908 & 0.327996415872184 & 0.163998207936092 \tabularnewline
14 & 0.90195839407854 & 0.196083211842921 & 0.0980416059214603 \tabularnewline
15 & 0.879240557644519 & 0.241518884710961 & 0.120759442355481 \tabularnewline
16 & 0.808616793673195 & 0.38276641265361 & 0.191383206326805 \tabularnewline
17 & 0.726585161757706 & 0.546829676484588 & 0.273414838242294 \tabularnewline
18 & 0.666797038131056 & 0.666405923737889 & 0.333202961868944 \tabularnewline
19 & 0.607910346534051 & 0.784179306931898 & 0.392089653465949 \tabularnewline
20 & 0.702128356781856 & 0.595743286436287 & 0.297871643218144 \tabularnewline
21 & 0.69419228673152 & 0.611615426536959 & 0.305807713268479 \tabularnewline
22 & 0.745316120165674 & 0.509367759668651 & 0.254683879834326 \tabularnewline
23 & 0.683418423843553 & 0.633163152312894 & 0.316581576156447 \tabularnewline
24 & 0.70777713203869 & 0.584445735922621 & 0.29222286796131 \tabularnewline
25 & 0.684535775162728 & 0.630928449674544 & 0.315464224837272 \tabularnewline
26 & 0.626079763990607 & 0.747840472018787 & 0.373920236009393 \tabularnewline
27 & 0.806167163016805 & 0.387665673966391 & 0.193832836983195 \tabularnewline
28 & 0.770975653320348 & 0.458048693359305 & 0.229024346679652 \tabularnewline
29 & 0.717035116657026 & 0.565929766685949 & 0.282964883342974 \tabularnewline
30 & 0.818623909680045 & 0.362752180639911 & 0.181376090319955 \tabularnewline
31 & 0.860156619654829 & 0.279686760690343 & 0.139843380345171 \tabularnewline
32 & 0.829666210992467 & 0.340667578015067 & 0.170333789007533 \tabularnewline
33 & 0.787312593252715 & 0.425374813494571 & 0.212687406747286 \tabularnewline
34 & 0.754905410599088 & 0.490189178801823 & 0.245094589400912 \tabularnewline
35 & 0.73641206799925 & 0.527175864001502 & 0.263587932000751 \tabularnewline
36 & 0.756719463465379 & 0.486561073069242 & 0.243280536534621 \tabularnewline
37 & 0.740078036428823 & 0.519843927142353 & 0.259921963571177 \tabularnewline
38 & 0.69646218881162 & 0.607075622376759 & 0.303537811188379 \tabularnewline
39 & 0.646302343994341 & 0.707395312011319 & 0.353697656005659 \tabularnewline
40 & 0.602581686654111 & 0.794836626691778 & 0.397418313345889 \tabularnewline
41 & 0.552645639537825 & 0.89470872092435 & 0.447354360462175 \tabularnewline
42 & 0.843176300736538 & 0.313647398526924 & 0.156823699263462 \tabularnewline
43 & 0.970814918196872 & 0.0583701636062562 & 0.0291850818031281 \tabularnewline
44 & 0.971754476204528 & 0.0564910475909441 & 0.0282455237954721 \tabularnewline
45 & 0.96311606905583 & 0.073767861888339 & 0.0368839309441695 \tabularnewline
46 & 0.968695846103815 & 0.0626083077923701 & 0.0313041538961851 \tabularnewline
47 & 0.975019136896098 & 0.0499617262078041 & 0.024980863103902 \tabularnewline
48 & 0.971592200610985 & 0.0568155987780291 & 0.0284077993890146 \tabularnewline
49 & 0.96805913052168 & 0.0638817389566385 & 0.0319408694783192 \tabularnewline
50 & 0.959432758930122 & 0.0811344821397561 & 0.0405672410698781 \tabularnewline
51 & 0.952729142815977 & 0.0945417143680456 & 0.0472708571840228 \tabularnewline
52 & 0.98927272721028 & 0.0214545455794396 & 0.0107272727897198 \tabularnewline
53 & 0.9858034259063 & 0.0283931481873987 & 0.0141965740936994 \tabularnewline
54 & 0.989154915195705 & 0.0216901696085897 & 0.0108450848042948 \tabularnewline
55 & 0.992767891002053 & 0.0144642179958943 & 0.00723210899794715 \tabularnewline
56 & 0.990211719081638 & 0.0195765618367242 & 0.00978828091836208 \tabularnewline
57 & 0.986513154464591 & 0.0269736910708179 & 0.013486845535409 \tabularnewline
58 & 0.985847831176756 & 0.028304337646489 & 0.0141521688232445 \tabularnewline
59 & 0.989059578811089 & 0.021880842377823 & 0.0109404211889115 \tabularnewline
60 & 0.987859033576145 & 0.0242819328477107 & 0.0121409664238553 \tabularnewline
61 & 0.987798331713595 & 0.0244033365728105 & 0.0122016682864053 \tabularnewline
62 & 0.98944721857309 & 0.0211055628538187 & 0.0105527814269094 \tabularnewline
63 & 0.987885575354576 & 0.0242288492908484 & 0.0121144246454242 \tabularnewline
64 & 0.988287441365562 & 0.0234251172688759 & 0.011712558634438 \tabularnewline
65 & 0.98765989832796 & 0.0246802033440793 & 0.0123401016720396 \tabularnewline
66 & 0.985670308211707 & 0.0286593835765869 & 0.0143296917882935 \tabularnewline
67 & 0.987314716348945 & 0.0253705673021093 & 0.0126852836510546 \tabularnewline
68 & 0.98886990629376 & 0.0222601874124773 & 0.0111300937062387 \tabularnewline
69 & 0.98829809144191 & 0.023403817116181 & 0.0117019085580905 \tabularnewline
70 & 0.985082355025686 & 0.029835289948629 & 0.0149176449743145 \tabularnewline
71 & 0.985556940612976 & 0.028886118774047 & 0.0144430593870235 \tabularnewline
72 & 0.982164316846268 & 0.0356713663074636 & 0.0178356831537318 \tabularnewline
73 & 0.982498792377714 & 0.035002415244572 & 0.017501207622286 \tabularnewline
74 & 0.987403010396265 & 0.0251939792074704 & 0.0125969896037352 \tabularnewline
75 & 0.983349535585278 & 0.0333009288294439 & 0.016650464414722 \tabularnewline
76 & 0.980156242237186 & 0.0396875155256276 & 0.0198437577628138 \tabularnewline
77 & 0.974293116574389 & 0.0514137668512229 & 0.0257068834256115 \tabularnewline
78 & 0.96666173246263 & 0.0666765350747412 & 0.0333382675373706 \tabularnewline
79 & 0.975640128211654 & 0.0487197435766924 & 0.0243598717883462 \tabularnewline
80 & 0.9739942985895 & 0.0520114028209996 & 0.0260057014104998 \tabularnewline
81 & 0.974966944190363 & 0.0500661116192737 & 0.0250330558096368 \tabularnewline
82 & 0.972983482915995 & 0.0540330341680097 & 0.0270165170840049 \tabularnewline
83 & 0.964448945316955 & 0.0711021093660909 & 0.0355510546830455 \tabularnewline
84 & 0.955210581156972 & 0.0895788376860567 & 0.0447894188430283 \tabularnewline
85 & 0.981998073761221 & 0.0360038524775577 & 0.0180019262387789 \tabularnewline
86 & 0.976014075078278 & 0.0479718498434437 & 0.0239859249217219 \tabularnewline
87 & 0.968722511477615 & 0.0625549770447705 & 0.0312774885223853 \tabularnewline
88 & 0.959748950399902 & 0.080502099200195 & 0.0402510496000975 \tabularnewline
89 & 0.95100817915353 & 0.0979836416929398 & 0.0489918208464699 \tabularnewline
90 & 0.938475804561582 & 0.123048390876836 & 0.0615241954384179 \tabularnewline
91 & 0.930631177696469 & 0.138737644607062 & 0.069368822303531 \tabularnewline
92 & 0.954772078472272 & 0.090455843055456 & 0.045227921527728 \tabularnewline
93 & 0.94515126031908 & 0.10969747936184 & 0.0548487396809202 \tabularnewline
94 & 0.943430756043238 & 0.113138487913525 & 0.0565692439567624 \tabularnewline
95 & 0.932003167323059 & 0.135993665353882 & 0.067996832676941 \tabularnewline
96 & 0.919702800854298 & 0.160594398291404 & 0.080297199145702 \tabularnewline
97 & 0.912761396376893 & 0.174477207246215 & 0.0872386036231074 \tabularnewline
98 & 0.894459962283812 & 0.211080075432376 & 0.105540037716188 \tabularnewline
99 & 0.878928225372388 & 0.242143549255223 & 0.121071774627612 \tabularnewline
100 & 0.853022706717131 & 0.293954586565738 & 0.146977293282869 \tabularnewline
101 & 0.822265889314234 & 0.355468221371532 & 0.177734110685766 \tabularnewline
102 & 0.79571302322072 & 0.40857395355856 & 0.20428697677928 \tabularnewline
103 & 0.808179866772119 & 0.383640266455762 & 0.191820133227881 \tabularnewline
104 & 0.864435874821128 & 0.271128250357745 & 0.135564125178872 \tabularnewline
105 & 0.861859391898908 & 0.276281216202183 & 0.138140608101092 \tabularnewline
106 & 0.829887256031703 & 0.340225487936594 & 0.170112743968297 \tabularnewline
107 & 0.797467852063354 & 0.405064295873292 & 0.202532147936646 \tabularnewline
108 & 0.783656372757127 & 0.432687254485746 & 0.216343627242873 \tabularnewline
109 & 0.764854625535922 & 0.470290748928157 & 0.235145374464078 \tabularnewline
110 & 0.784143309068944 & 0.431713381862112 & 0.215856690931056 \tabularnewline
111 & 0.769045385873406 & 0.461909228253187 & 0.230954614126594 \tabularnewline
112 & 0.834324256100617 & 0.331351487798766 & 0.165675743899383 \tabularnewline
113 & 0.797250203792304 & 0.405499592415392 & 0.202749796207696 \tabularnewline
114 & 0.766332162739474 & 0.467335674521052 & 0.233667837260526 \tabularnewline
115 & 0.722976130244068 & 0.554047739511864 & 0.277023869755932 \tabularnewline
116 & 0.732170131757851 & 0.535659736484297 & 0.267829868242149 \tabularnewline
117 & 0.74437482273268 & 0.511250354534641 & 0.25562517726732 \tabularnewline
118 & 0.725622971067391 & 0.548754057865217 & 0.274377028932609 \tabularnewline
119 & 0.675379918339688 & 0.649240163320624 & 0.324620081660312 \tabularnewline
120 & 0.771318547410679 & 0.457362905178642 & 0.228681452589321 \tabularnewline
121 & 0.740958354445508 & 0.518083291108984 & 0.259041645554492 \tabularnewline
122 & 0.688252418566368 & 0.623495162867264 & 0.311747581433632 \tabularnewline
123 & 0.674328362110953 & 0.651343275778094 & 0.325671637889047 \tabularnewline
124 & 0.631483536872456 & 0.737032926255089 & 0.368516463127544 \tabularnewline
125 & 0.612176877947888 & 0.775646244104225 & 0.387823122052112 \tabularnewline
126 & 0.612552819280794 & 0.774894361438412 & 0.387447180719206 \tabularnewline
127 & 0.54603567075918 & 0.90792865848164 & 0.45396432924082 \tabularnewline
128 & 0.504878729170788 & 0.990242541658424 & 0.495121270829212 \tabularnewline
129 & 0.482994137738117 & 0.965988275476233 & 0.517005862261883 \tabularnewline
130 & 0.465009250028874 & 0.930018500057748 & 0.534990749971126 \tabularnewline
131 & 0.392923101575466 & 0.785846203150931 & 0.607076898424534 \tabularnewline
132 & 0.371462252534148 & 0.742924505068295 & 0.628537747465852 \tabularnewline
133 & 0.325667334331613 & 0.651334668663227 & 0.674332665668387 \tabularnewline
134 & 0.281323824371726 & 0.562647648743452 & 0.718676175628274 \tabularnewline
135 & 0.237546151569607 & 0.475092303139213 & 0.762453848430393 \tabularnewline
136 & 0.219069151340993 & 0.438138302681986 & 0.780930848659007 \tabularnewline
137 & 0.18430668893371 & 0.36861337786742 & 0.81569331106629 \tabularnewline
138 & 0.19758090820236 & 0.395161816404721 & 0.80241909179764 \tabularnewline
139 & 0.616069745068915 & 0.76786050986217 & 0.383930254931085 \tabularnewline
140 & 0.526150517910437 & 0.947698964179126 & 0.473849482089563 \tabularnewline
141 & 0.405569862606173 & 0.811139725212346 & 0.594430137393827 \tabularnewline
142 & 0.400851681624012 & 0.801703363248024 & 0.599148318375988 \tabularnewline
143 & 0.278089005617193 & 0.556178011234386 & 0.721910994382807 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146430&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]13[/C][C]0.836001792063908[/C][C]0.327996415872184[/C][C]0.163998207936092[/C][/ROW]
[ROW][C]14[/C][C]0.90195839407854[/C][C]0.196083211842921[/C][C]0.0980416059214603[/C][/ROW]
[ROW][C]15[/C][C]0.879240557644519[/C][C]0.241518884710961[/C][C]0.120759442355481[/C][/ROW]
[ROW][C]16[/C][C]0.808616793673195[/C][C]0.38276641265361[/C][C]0.191383206326805[/C][/ROW]
[ROW][C]17[/C][C]0.726585161757706[/C][C]0.546829676484588[/C][C]0.273414838242294[/C][/ROW]
[ROW][C]18[/C][C]0.666797038131056[/C][C]0.666405923737889[/C][C]0.333202961868944[/C][/ROW]
[ROW][C]19[/C][C]0.607910346534051[/C][C]0.784179306931898[/C][C]0.392089653465949[/C][/ROW]
[ROW][C]20[/C][C]0.702128356781856[/C][C]0.595743286436287[/C][C]0.297871643218144[/C][/ROW]
[ROW][C]21[/C][C]0.69419228673152[/C][C]0.611615426536959[/C][C]0.305807713268479[/C][/ROW]
[ROW][C]22[/C][C]0.745316120165674[/C][C]0.509367759668651[/C][C]0.254683879834326[/C][/ROW]
[ROW][C]23[/C][C]0.683418423843553[/C][C]0.633163152312894[/C][C]0.316581576156447[/C][/ROW]
[ROW][C]24[/C][C]0.70777713203869[/C][C]0.584445735922621[/C][C]0.29222286796131[/C][/ROW]
[ROW][C]25[/C][C]0.684535775162728[/C][C]0.630928449674544[/C][C]0.315464224837272[/C][/ROW]
[ROW][C]26[/C][C]0.626079763990607[/C][C]0.747840472018787[/C][C]0.373920236009393[/C][/ROW]
[ROW][C]27[/C][C]0.806167163016805[/C][C]0.387665673966391[/C][C]0.193832836983195[/C][/ROW]
[ROW][C]28[/C][C]0.770975653320348[/C][C]0.458048693359305[/C][C]0.229024346679652[/C][/ROW]
[ROW][C]29[/C][C]0.717035116657026[/C][C]0.565929766685949[/C][C]0.282964883342974[/C][/ROW]
[ROW][C]30[/C][C]0.818623909680045[/C][C]0.362752180639911[/C][C]0.181376090319955[/C][/ROW]
[ROW][C]31[/C][C]0.860156619654829[/C][C]0.279686760690343[/C][C]0.139843380345171[/C][/ROW]
[ROW][C]32[/C][C]0.829666210992467[/C][C]0.340667578015067[/C][C]0.170333789007533[/C][/ROW]
[ROW][C]33[/C][C]0.787312593252715[/C][C]0.425374813494571[/C][C]0.212687406747286[/C][/ROW]
[ROW][C]34[/C][C]0.754905410599088[/C][C]0.490189178801823[/C][C]0.245094589400912[/C][/ROW]
[ROW][C]35[/C][C]0.73641206799925[/C][C]0.527175864001502[/C][C]0.263587932000751[/C][/ROW]
[ROW][C]36[/C][C]0.756719463465379[/C][C]0.486561073069242[/C][C]0.243280536534621[/C][/ROW]
[ROW][C]37[/C][C]0.740078036428823[/C][C]0.519843927142353[/C][C]0.259921963571177[/C][/ROW]
[ROW][C]38[/C][C]0.69646218881162[/C][C]0.607075622376759[/C][C]0.303537811188379[/C][/ROW]
[ROW][C]39[/C][C]0.646302343994341[/C][C]0.707395312011319[/C][C]0.353697656005659[/C][/ROW]
[ROW][C]40[/C][C]0.602581686654111[/C][C]0.794836626691778[/C][C]0.397418313345889[/C][/ROW]
[ROW][C]41[/C][C]0.552645639537825[/C][C]0.89470872092435[/C][C]0.447354360462175[/C][/ROW]
[ROW][C]42[/C][C]0.843176300736538[/C][C]0.313647398526924[/C][C]0.156823699263462[/C][/ROW]
[ROW][C]43[/C][C]0.970814918196872[/C][C]0.0583701636062562[/C][C]0.0291850818031281[/C][/ROW]
[ROW][C]44[/C][C]0.971754476204528[/C][C]0.0564910475909441[/C][C]0.0282455237954721[/C][/ROW]
[ROW][C]45[/C][C]0.96311606905583[/C][C]0.073767861888339[/C][C]0.0368839309441695[/C][/ROW]
[ROW][C]46[/C][C]0.968695846103815[/C][C]0.0626083077923701[/C][C]0.0313041538961851[/C][/ROW]
[ROW][C]47[/C][C]0.975019136896098[/C][C]0.0499617262078041[/C][C]0.024980863103902[/C][/ROW]
[ROW][C]48[/C][C]0.971592200610985[/C][C]0.0568155987780291[/C][C]0.0284077993890146[/C][/ROW]
[ROW][C]49[/C][C]0.96805913052168[/C][C]0.0638817389566385[/C][C]0.0319408694783192[/C][/ROW]
[ROW][C]50[/C][C]0.959432758930122[/C][C]0.0811344821397561[/C][C]0.0405672410698781[/C][/ROW]
[ROW][C]51[/C][C]0.952729142815977[/C][C]0.0945417143680456[/C][C]0.0472708571840228[/C][/ROW]
[ROW][C]52[/C][C]0.98927272721028[/C][C]0.0214545455794396[/C][C]0.0107272727897198[/C][/ROW]
[ROW][C]53[/C][C]0.9858034259063[/C][C]0.0283931481873987[/C][C]0.0141965740936994[/C][/ROW]
[ROW][C]54[/C][C]0.989154915195705[/C][C]0.0216901696085897[/C][C]0.0108450848042948[/C][/ROW]
[ROW][C]55[/C][C]0.992767891002053[/C][C]0.0144642179958943[/C][C]0.00723210899794715[/C][/ROW]
[ROW][C]56[/C][C]0.990211719081638[/C][C]0.0195765618367242[/C][C]0.00978828091836208[/C][/ROW]
[ROW][C]57[/C][C]0.986513154464591[/C][C]0.0269736910708179[/C][C]0.013486845535409[/C][/ROW]
[ROW][C]58[/C][C]0.985847831176756[/C][C]0.028304337646489[/C][C]0.0141521688232445[/C][/ROW]
[ROW][C]59[/C][C]0.989059578811089[/C][C]0.021880842377823[/C][C]0.0109404211889115[/C][/ROW]
[ROW][C]60[/C][C]0.987859033576145[/C][C]0.0242819328477107[/C][C]0.0121409664238553[/C][/ROW]
[ROW][C]61[/C][C]0.987798331713595[/C][C]0.0244033365728105[/C][C]0.0122016682864053[/C][/ROW]
[ROW][C]62[/C][C]0.98944721857309[/C][C]0.0211055628538187[/C][C]0.0105527814269094[/C][/ROW]
[ROW][C]63[/C][C]0.987885575354576[/C][C]0.0242288492908484[/C][C]0.0121144246454242[/C][/ROW]
[ROW][C]64[/C][C]0.988287441365562[/C][C]0.0234251172688759[/C][C]0.011712558634438[/C][/ROW]
[ROW][C]65[/C][C]0.98765989832796[/C][C]0.0246802033440793[/C][C]0.0123401016720396[/C][/ROW]
[ROW][C]66[/C][C]0.985670308211707[/C][C]0.0286593835765869[/C][C]0.0143296917882935[/C][/ROW]
[ROW][C]67[/C][C]0.987314716348945[/C][C]0.0253705673021093[/C][C]0.0126852836510546[/C][/ROW]
[ROW][C]68[/C][C]0.98886990629376[/C][C]0.0222601874124773[/C][C]0.0111300937062387[/C][/ROW]
[ROW][C]69[/C][C]0.98829809144191[/C][C]0.023403817116181[/C][C]0.0117019085580905[/C][/ROW]
[ROW][C]70[/C][C]0.985082355025686[/C][C]0.029835289948629[/C][C]0.0149176449743145[/C][/ROW]
[ROW][C]71[/C][C]0.985556940612976[/C][C]0.028886118774047[/C][C]0.0144430593870235[/C][/ROW]
[ROW][C]72[/C][C]0.982164316846268[/C][C]0.0356713663074636[/C][C]0.0178356831537318[/C][/ROW]
[ROW][C]73[/C][C]0.982498792377714[/C][C]0.035002415244572[/C][C]0.017501207622286[/C][/ROW]
[ROW][C]74[/C][C]0.987403010396265[/C][C]0.0251939792074704[/C][C]0.0125969896037352[/C][/ROW]
[ROW][C]75[/C][C]0.983349535585278[/C][C]0.0333009288294439[/C][C]0.016650464414722[/C][/ROW]
[ROW][C]76[/C][C]0.980156242237186[/C][C]0.0396875155256276[/C][C]0.0198437577628138[/C][/ROW]
[ROW][C]77[/C][C]0.974293116574389[/C][C]0.0514137668512229[/C][C]0.0257068834256115[/C][/ROW]
[ROW][C]78[/C][C]0.96666173246263[/C][C]0.0666765350747412[/C][C]0.0333382675373706[/C][/ROW]
[ROW][C]79[/C][C]0.975640128211654[/C][C]0.0487197435766924[/C][C]0.0243598717883462[/C][/ROW]
[ROW][C]80[/C][C]0.9739942985895[/C][C]0.0520114028209996[/C][C]0.0260057014104998[/C][/ROW]
[ROW][C]81[/C][C]0.974966944190363[/C][C]0.0500661116192737[/C][C]0.0250330558096368[/C][/ROW]
[ROW][C]82[/C][C]0.972983482915995[/C][C]0.0540330341680097[/C][C]0.0270165170840049[/C][/ROW]
[ROW][C]83[/C][C]0.964448945316955[/C][C]0.0711021093660909[/C][C]0.0355510546830455[/C][/ROW]
[ROW][C]84[/C][C]0.955210581156972[/C][C]0.0895788376860567[/C][C]0.0447894188430283[/C][/ROW]
[ROW][C]85[/C][C]0.981998073761221[/C][C]0.0360038524775577[/C][C]0.0180019262387789[/C][/ROW]
[ROW][C]86[/C][C]0.976014075078278[/C][C]0.0479718498434437[/C][C]0.0239859249217219[/C][/ROW]
[ROW][C]87[/C][C]0.968722511477615[/C][C]0.0625549770447705[/C][C]0.0312774885223853[/C][/ROW]
[ROW][C]88[/C][C]0.959748950399902[/C][C]0.080502099200195[/C][C]0.0402510496000975[/C][/ROW]
[ROW][C]89[/C][C]0.95100817915353[/C][C]0.0979836416929398[/C][C]0.0489918208464699[/C][/ROW]
[ROW][C]90[/C][C]0.938475804561582[/C][C]0.123048390876836[/C][C]0.0615241954384179[/C][/ROW]
[ROW][C]91[/C][C]0.930631177696469[/C][C]0.138737644607062[/C][C]0.069368822303531[/C][/ROW]
[ROW][C]92[/C][C]0.954772078472272[/C][C]0.090455843055456[/C][C]0.045227921527728[/C][/ROW]
[ROW][C]93[/C][C]0.94515126031908[/C][C]0.10969747936184[/C][C]0.0548487396809202[/C][/ROW]
[ROW][C]94[/C][C]0.943430756043238[/C][C]0.113138487913525[/C][C]0.0565692439567624[/C][/ROW]
[ROW][C]95[/C][C]0.932003167323059[/C][C]0.135993665353882[/C][C]0.067996832676941[/C][/ROW]
[ROW][C]96[/C][C]0.919702800854298[/C][C]0.160594398291404[/C][C]0.080297199145702[/C][/ROW]
[ROW][C]97[/C][C]0.912761396376893[/C][C]0.174477207246215[/C][C]0.0872386036231074[/C][/ROW]
[ROW][C]98[/C][C]0.894459962283812[/C][C]0.211080075432376[/C][C]0.105540037716188[/C][/ROW]
[ROW][C]99[/C][C]0.878928225372388[/C][C]0.242143549255223[/C][C]0.121071774627612[/C][/ROW]
[ROW][C]100[/C][C]0.853022706717131[/C][C]0.293954586565738[/C][C]0.146977293282869[/C][/ROW]
[ROW][C]101[/C][C]0.822265889314234[/C][C]0.355468221371532[/C][C]0.177734110685766[/C][/ROW]
[ROW][C]102[/C][C]0.79571302322072[/C][C]0.40857395355856[/C][C]0.20428697677928[/C][/ROW]
[ROW][C]103[/C][C]0.808179866772119[/C][C]0.383640266455762[/C][C]0.191820133227881[/C][/ROW]
[ROW][C]104[/C][C]0.864435874821128[/C][C]0.271128250357745[/C][C]0.135564125178872[/C][/ROW]
[ROW][C]105[/C][C]0.861859391898908[/C][C]0.276281216202183[/C][C]0.138140608101092[/C][/ROW]
[ROW][C]106[/C][C]0.829887256031703[/C][C]0.340225487936594[/C][C]0.170112743968297[/C][/ROW]
[ROW][C]107[/C][C]0.797467852063354[/C][C]0.405064295873292[/C][C]0.202532147936646[/C][/ROW]
[ROW][C]108[/C][C]0.783656372757127[/C][C]0.432687254485746[/C][C]0.216343627242873[/C][/ROW]
[ROW][C]109[/C][C]0.764854625535922[/C][C]0.470290748928157[/C][C]0.235145374464078[/C][/ROW]
[ROW][C]110[/C][C]0.784143309068944[/C][C]0.431713381862112[/C][C]0.215856690931056[/C][/ROW]
[ROW][C]111[/C][C]0.769045385873406[/C][C]0.461909228253187[/C][C]0.230954614126594[/C][/ROW]
[ROW][C]112[/C][C]0.834324256100617[/C][C]0.331351487798766[/C][C]0.165675743899383[/C][/ROW]
[ROW][C]113[/C][C]0.797250203792304[/C][C]0.405499592415392[/C][C]0.202749796207696[/C][/ROW]
[ROW][C]114[/C][C]0.766332162739474[/C][C]0.467335674521052[/C][C]0.233667837260526[/C][/ROW]
[ROW][C]115[/C][C]0.722976130244068[/C][C]0.554047739511864[/C][C]0.277023869755932[/C][/ROW]
[ROW][C]116[/C][C]0.732170131757851[/C][C]0.535659736484297[/C][C]0.267829868242149[/C][/ROW]
[ROW][C]117[/C][C]0.74437482273268[/C][C]0.511250354534641[/C][C]0.25562517726732[/C][/ROW]
[ROW][C]118[/C][C]0.725622971067391[/C][C]0.548754057865217[/C][C]0.274377028932609[/C][/ROW]
[ROW][C]119[/C][C]0.675379918339688[/C][C]0.649240163320624[/C][C]0.324620081660312[/C][/ROW]
[ROW][C]120[/C][C]0.771318547410679[/C][C]0.457362905178642[/C][C]0.228681452589321[/C][/ROW]
[ROW][C]121[/C][C]0.740958354445508[/C][C]0.518083291108984[/C][C]0.259041645554492[/C][/ROW]
[ROW][C]122[/C][C]0.688252418566368[/C][C]0.623495162867264[/C][C]0.311747581433632[/C][/ROW]
[ROW][C]123[/C][C]0.674328362110953[/C][C]0.651343275778094[/C][C]0.325671637889047[/C][/ROW]
[ROW][C]124[/C][C]0.631483536872456[/C][C]0.737032926255089[/C][C]0.368516463127544[/C][/ROW]
[ROW][C]125[/C][C]0.612176877947888[/C][C]0.775646244104225[/C][C]0.387823122052112[/C][/ROW]
[ROW][C]126[/C][C]0.612552819280794[/C][C]0.774894361438412[/C][C]0.387447180719206[/C][/ROW]
[ROW][C]127[/C][C]0.54603567075918[/C][C]0.90792865848164[/C][C]0.45396432924082[/C][/ROW]
[ROW][C]128[/C][C]0.504878729170788[/C][C]0.990242541658424[/C][C]0.495121270829212[/C][/ROW]
[ROW][C]129[/C][C]0.482994137738117[/C][C]0.965988275476233[/C][C]0.517005862261883[/C][/ROW]
[ROW][C]130[/C][C]0.465009250028874[/C][C]0.930018500057748[/C][C]0.534990749971126[/C][/ROW]
[ROW][C]131[/C][C]0.392923101575466[/C][C]0.785846203150931[/C][C]0.607076898424534[/C][/ROW]
[ROW][C]132[/C][C]0.371462252534148[/C][C]0.742924505068295[/C][C]0.628537747465852[/C][/ROW]
[ROW][C]133[/C][C]0.325667334331613[/C][C]0.651334668663227[/C][C]0.674332665668387[/C][/ROW]
[ROW][C]134[/C][C]0.281323824371726[/C][C]0.562647648743452[/C][C]0.718676175628274[/C][/ROW]
[ROW][C]135[/C][C]0.237546151569607[/C][C]0.475092303139213[/C][C]0.762453848430393[/C][/ROW]
[ROW][C]136[/C][C]0.219069151340993[/C][C]0.438138302681986[/C][C]0.780930848659007[/C][/ROW]
[ROW][C]137[/C][C]0.18430668893371[/C][C]0.36861337786742[/C][C]0.81569331106629[/C][/ROW]
[ROW][C]138[/C][C]0.19758090820236[/C][C]0.395161816404721[/C][C]0.80241909179764[/C][/ROW]
[ROW][C]139[/C][C]0.616069745068915[/C][C]0.76786050986217[/C][C]0.383930254931085[/C][/ROW]
[ROW][C]140[/C][C]0.526150517910437[/C][C]0.947698964179126[/C][C]0.473849482089563[/C][/ROW]
[ROW][C]141[/C][C]0.405569862606173[/C][C]0.811139725212346[/C][C]0.594430137393827[/C][/ROW]
[ROW][C]142[/C][C]0.400851681624012[/C][C]0.801703363248024[/C][C]0.599148318375988[/C][/ROW]
[ROW][C]143[/C][C]0.278089005617193[/C][C]0.556178011234386[/C][C]0.721910994382807[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146430&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146430&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
130.8360017920639080.3279964158721840.163998207936092
140.901958394078540.1960832118429210.0980416059214603
150.8792405576445190.2415188847109610.120759442355481
160.8086167936731950.382766412653610.191383206326805
170.7265851617577060.5468296764845880.273414838242294
180.6667970381310560.6664059237378890.333202961868944
190.6079103465340510.7841793069318980.392089653465949
200.7021283567818560.5957432864362870.297871643218144
210.694192286731520.6116154265369590.305807713268479
220.7453161201656740.5093677596686510.254683879834326
230.6834184238435530.6331631523128940.316581576156447
240.707777132038690.5844457359226210.29222286796131
250.6845357751627280.6309284496745440.315464224837272
260.6260797639906070.7478404720187870.373920236009393
270.8061671630168050.3876656739663910.193832836983195
280.7709756533203480.4580486933593050.229024346679652
290.7170351166570260.5659297666859490.282964883342974
300.8186239096800450.3627521806399110.181376090319955
310.8601566196548290.2796867606903430.139843380345171
320.8296662109924670.3406675780150670.170333789007533
330.7873125932527150.4253748134945710.212687406747286
340.7549054105990880.4901891788018230.245094589400912
350.736412067999250.5271758640015020.263587932000751
360.7567194634653790.4865610730692420.243280536534621
370.7400780364288230.5198439271423530.259921963571177
380.696462188811620.6070756223767590.303537811188379
390.6463023439943410.7073953120113190.353697656005659
400.6025816866541110.7948366266917780.397418313345889
410.5526456395378250.894708720924350.447354360462175
420.8431763007365380.3136473985269240.156823699263462
430.9708149181968720.05837016360625620.0291850818031281
440.9717544762045280.05649104759094410.0282455237954721
450.963116069055830.0737678618883390.0368839309441695
460.9686958461038150.06260830779237010.0313041538961851
470.9750191368960980.04996172620780410.024980863103902
480.9715922006109850.05681559877802910.0284077993890146
490.968059130521680.06388173895663850.0319408694783192
500.9594327589301220.08113448213975610.0405672410698781
510.9527291428159770.09454171436804560.0472708571840228
520.989272727210280.02145454557943960.0107272727897198
530.98580342590630.02839314818739870.0141965740936994
540.9891549151957050.02169016960858970.0108450848042948
550.9927678910020530.01446421799589430.00723210899794715
560.9902117190816380.01957656183672420.00978828091836208
570.9865131544645910.02697369107081790.013486845535409
580.9858478311767560.0283043376464890.0141521688232445
590.9890595788110890.0218808423778230.0109404211889115
600.9878590335761450.02428193284771070.0121409664238553
610.9877983317135950.02440333657281050.0122016682864053
620.989447218573090.02110556285381870.0105527814269094
630.9878855753545760.02422884929084840.0121144246454242
640.9882874413655620.02342511726887590.011712558634438
650.987659898327960.02468020334407930.0123401016720396
660.9856703082117070.02865938357658690.0143296917882935
670.9873147163489450.02537056730210930.0126852836510546
680.988869906293760.02226018741247730.0111300937062387
690.988298091441910.0234038171161810.0117019085580905
700.9850823550256860.0298352899486290.0149176449743145
710.9855569406129760.0288861187740470.0144430593870235
720.9821643168462680.03567136630746360.0178356831537318
730.9824987923777140.0350024152445720.017501207622286
740.9874030103962650.02519397920747040.0125969896037352
750.9833495355852780.03330092882944390.016650464414722
760.9801562422371860.03968751552562760.0198437577628138
770.9742931165743890.05141376685122290.0257068834256115
780.966661732462630.06667653507474120.0333382675373706
790.9756401282116540.04871974357669240.0243598717883462
800.97399429858950.05201140282099960.0260057014104998
810.9749669441903630.05006611161927370.0250330558096368
820.9729834829159950.05403303416800970.0270165170840049
830.9644489453169550.07110210936609090.0355510546830455
840.9552105811569720.08957883768605670.0447894188430283
850.9819980737612210.03600385247755770.0180019262387789
860.9760140750782780.04797184984344370.0239859249217219
870.9687225114776150.06255497704477050.0312774885223853
880.9597489503999020.0805020992001950.0402510496000975
890.951008179153530.09798364169293980.0489918208464699
900.9384758045615820.1230483908768360.0615241954384179
910.9306311776964690.1387376446070620.069368822303531
920.9547720784722720.0904558430554560.045227921527728
930.945151260319080.109697479361840.0548487396809202
940.9434307560432380.1131384879135250.0565692439567624
950.9320031673230590.1359936653538820.067996832676941
960.9197028008542980.1605943982914040.080297199145702
970.9127613963768930.1744772072462150.0872386036231074
980.8944599622838120.2110800754323760.105540037716188
990.8789282253723880.2421435492552230.121071774627612
1000.8530227067171310.2939545865657380.146977293282869
1010.8222658893142340.3554682213715320.177734110685766
1020.795713023220720.408573953558560.20428697677928
1030.8081798667721190.3836402664557620.191820133227881
1040.8644358748211280.2711282503577450.135564125178872
1050.8618593918989080.2762812162021830.138140608101092
1060.8298872560317030.3402254879365940.170112743968297
1070.7974678520633540.4050642958732920.202532147936646
1080.7836563727571270.4326872544857460.216343627242873
1090.7648546255359220.4702907489281570.235145374464078
1100.7841433090689440.4317133818621120.215856690931056
1110.7690453858734060.4619092282531870.230954614126594
1120.8343242561006170.3313514877987660.165675743899383
1130.7972502037923040.4054995924153920.202749796207696
1140.7663321627394740.4673356745210520.233667837260526
1150.7229761302440680.5540477395118640.277023869755932
1160.7321701317578510.5356597364842970.267829868242149
1170.744374822732680.5112503545346410.25562517726732
1180.7256229710673910.5487540578652170.274377028932609
1190.6753799183396880.6492401633206240.324620081660312
1200.7713185474106790.4573629051786420.228681452589321
1210.7409583544455080.5180832911089840.259041645554492
1220.6882524185663680.6234951628672640.311747581433632
1230.6743283621109530.6513432757780940.325671637889047
1240.6314835368724560.7370329262550890.368516463127544
1250.6121768779478880.7756462441042250.387823122052112
1260.6125528192807940.7748943614384120.387447180719206
1270.546035670759180.907928658481640.45396432924082
1280.5048787291707880.9902425416584240.495121270829212
1290.4829941377381170.9659882754762330.517005862261883
1300.4650092500288740.9300185000577480.534990749971126
1310.3929231015754660.7858462031509310.607076898424534
1320.3714622525341480.7429245050682950.628537747465852
1330.3256673343316130.6513346686632270.674332665668387
1340.2813238243717260.5626476487434520.718676175628274
1350.2375461515696070.4750923031392130.762453848430393
1360.2190691513409930.4381383026819860.780930848659007
1370.184306688933710.368613377867420.81569331106629
1380.197580908202360.3951618164047210.80241909179764
1390.6160697450689150.767860509862170.383930254931085
1400.5261505179104370.9476989641791260.473849482089563
1410.4055698626061730.8111397252123460.594430137393827
1420.4008516816240120.8017033632480240.599148318375988
1430.2780890056171930.5561780112343860.721910994382807







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level290.221374045801527NOK
10% type I error level480.366412213740458NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 29 & 0.221374045801527 & NOK \tabularnewline
10% type I error level & 48 & 0.366412213740458 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146430&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]29[/C][C]0.221374045801527[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]48[/C][C]0.366412213740458[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146430&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146430&T=6

As an alternative you can also use a QR Code:  

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level290.221374045801527NOK
10% type I error level480.366412213740458NOK



Parameters (Session):
par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ; par4 = ; par5 = ; par6 = ; par7 = ; par8 = ; par9 = ; par10 = ; par11 = ; par12 = ; par13 = ; par14 = ; par15 = ; par16 = ; par17 = ; par18 = ; par19 = ; par20 = ;
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')
}