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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationFri, 17 Dec 2010 13:46:43 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/17/t1292593565r4n81vc9fe4m8pv.htm/, Retrieved Tue, 07 May 2024 03:09:32 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=111464, Retrieved Tue, 07 May 2024 03:09:32 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact220

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [Multiple Regressi...] [2010-12-17 13:46:43] [1638ccfec791c539017705f3e680eb33] [Current]
-   PD    [Multiple Regression] [Multiple Regressi...] [2010-12-17 15:41:32] [1251ac2db27b84d4a3ba43449388906b]
-    D      [Multiple Regression] [MR Paper (extra)] [2010-12-17 15:58:09] [1251ac2db27b84d4a3ba43449388906b]
-             [Multiple Regression] [] [2010-12-24 15:14:51] [dc30d19c3bc2be07fe595ad36c2cf923]
-             [Multiple Regression] [] [2010-12-24 15:52:22] [dc30d19c3bc2be07fe595ad36c2cf923]
-   P       [Multiple Regression] [MR Paper (monthly...] [2010-12-17 16:35:58] [1251ac2db27b84d4a3ba43449388906b]
-   PD        [Multiple Regression] [MR Paper (month)] [2010-12-17 16:45:18] [1251ac2db27b84d4a3ba43449388906b]
-   P           [Multiple Regression] [MR Paper (trend)b] [2010-12-18 12:41:58] [1251ac2db27b84d4a3ba43449388906b]
-   PD            [Multiple Regression] [] [2010-12-18 14:02:17] [1251ac2db27b84d4a3ba43449388906b]
-                   [Multiple Regression] [] [2010-12-24 15:25:05] [dc30d19c3bc2be07fe595ad36c2cf923]
-                   [Multiple Regression] [] [2010-12-24 15:58:02] [dc30d19c3bc2be07fe595ad36c2cf923]
- RMPD            [Classical Decomposition] [CD] [2010-12-18 14:47:46] [1251ac2db27b84d4a3ba43449388906b]
-                   [Classical Decomposition] [CD] [2010-12-18 15:39:32] [1251ac2db27b84d4a3ba43449388906b]
-   P                 [Classical Decomposition] [] [2010-12-24 15:28:04] [dc30d19c3bc2be07fe595ad36c2cf923]
-   P                 [Classical Decomposition] [] [2010-12-24 16:01:09] [dc30d19c3bc2be07fe595ad36c2cf923]
- RM                [Variance Reduction Matrix] [VRM] [2010-12-18 15:58:17] [1251ac2db27b84d4a3ba43449388906b]
-   P                 [Variance Reduction Matrix] [] [2010-12-24 16:05:37] [dc30d19c3bc2be07fe595ad36c2cf923]
- RM                [Spectral Analysis] [Spectral Analysis...] [2010-12-18 18:17:41] [1251ac2db27b84d4a3ba43449388906b]
-   P                 [Spectral Analysis] [] [2010-12-24 15:30:25] [dc30d19c3bc2be07fe595ad36c2cf923]
-   P                 [Spectral Analysis] [] [2010-12-24 16:03:03] [dc30d19c3bc2be07fe595ad36c2cf923]
- RM                [Standard Deviation-Mean Plot] [Mean vs Median Paper] [2010-12-18 18:40:22] [1251ac2db27b84d4a3ba43449388906b]
-   P                 [Standard Deviation-Mean Plot] [] [2010-12-24 16:06:39] [dc30d19c3bc2be07fe595ad36c2cf923]
- RM                [Exponential Smoothing] [Exponential Smoot...] [2010-12-18 18:46:08] [1251ac2db27b84d4a3ba43449388906b]
-   P                 [Exponential Smoothing] [] [2010-12-24 15:31:52] [dc30d19c3bc2be07fe595ad36c2cf923]
-   P                 [Exponential Smoothing] [] [2010-12-24 16:04:11] [dc30d19c3bc2be07fe595ad36c2cf923]
-                 [Multiple Regression] [] [2010-12-24 15:21:29] [dc30d19c3bc2be07fe595ad36c2cf923]
-                 [Multiple Regression] [] [2010-12-24 15:56:42] [dc30d19c3bc2be07fe595ad36c2cf923]
-               [Multiple Regression] [] [2010-12-24 15:18:27] [dc30d19c3bc2be07fe595ad36c2cf923]
-               [Multiple Regression] [] [2010-12-24 15:55:06] [dc30d19c3bc2be07fe595ad36c2cf923]
-           [Multiple Regression] [] [2010-12-24 15:12:46] [dc30d19c3bc2be07fe595ad36c2cf923]
-           [Multiple Regression] [] [2010-12-24 15:50:20] [dc30d19c3bc2be07fe595ad36c2cf923]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
15	10	12	16	6
12	9	7	12	6
9	12	11	11	4
10	12	11	12	6
13	9	14	14	6
16	11	16	16	7
14	12	13	13	6
16	11	13	14	7
10	12	5	13	6
8	12	8	13	4
12	11	14	13	5
15	11	15	15	8
14	12	8	14	4
14	6	13	12	6
12	13	12	12	6
12	11	11	12	5
10	12	8	11	4
4	10	4	10	2
14	11	15	15	8
15	12	12	16	7
16	12	14	14	6
12	12	9	13	4
12	11	16	13	4
12	12	10	13	4
12	12	8	13	5
12	12	14	14	4
11	6	6	9	4
11	5	16	14	6
11	12	11	12	6
11	14	7	13	6
11	12	13	11	4
11	9	7	13	2
15	11	14	15	7
15	11	17	16	6
9	11	15	15	7
16	12	8	14	4
13	10	8	8	4
9	12	11	11	4
16	11	16	15	6
12	12	10	15	6
15	9	5	11	3
5	15	8	12	3
11	11	8	12	6
17	11	15	14	5
9	15	6	8	4
13	12	16	16	6
16	9	16	16	6
16	12	16	14	6
14	9	19	12	6
16	11	14	15	6
11	12	15	12	6
11	11	11	14	5
11	6	14	17	6
12	10	12	13	6
12	12	15	13	6
12	13	14	12	5
14	11	13	16	6
10	10	11	12	5
9	11	8	10	4
12	7	11	15	5
10	11	9	12	4
14	11	10	16	6
8	7	4	13	6
16	12	15	15	7
14	14	17	18	6
14	11	12	12	4
12	12	12	13	4
14	11	15	14	6
7	12	13	12	3
19	12	15	15	6
15	12	14	16	4
8	12	8	14	5
10	15	15	15	6
13	11	12	13	7
13	13	14	13	3
10	10	10	11	5
12	12	7	12	3
15	13	16	18	8
7	14	12	12	4
14	11	15	16	6
10	11	7	9	4
6	7	9	11	4
11	11	15	10	5
12	12	7	11	4
14	12	15	13	6
12	10	14	13	7
14	12	14	15	7
11	8	8	13	4
10	7	8	9	5
13	11	14	13	6
8	11	10	12	4
9	11	12	13	5
6	9	15	11	6
12	12	12	14	5
14	13	13	13	5
11	9	12	12	4
8	11	10	15	2
7	12	8	12	3
9	9	6	12	5
14	12	13	13	5
13	12	7	12	5
15	12	13	13	6
5	14	4	5	2
15	11	14	13	5
13	12	13	13	5
12	8	13	13	5
6	12	6	11	2
7	12	7	12	4
13	12	5	12	3
16	11	14	15	8
10	11	13	15	6
16	12	16	16	7
15	10	16	13	6
8	13	7	10	3
11	8	14	15	5
13	12	11	13	6
16	11	17	16	7
11	10	5	13	3
14	13	10	16	8
9	10	11	13	3
8	10	10	14	3
8	7	9	15	4
11	10	12	14	5
12	8	15	13	7
11	12	7	13	6
14	12	13	15	6
11	12	8	16	6
14	11	16	12	5
13	13	15	14	6
12	12	6	14	5
4	8	6	4	4
15	11	12	13	6
10	12	8	16	4
13	13	11	15	6
15	12	13	14	6
12	10	14	14	5
13	12	14	14	6
8	10	10	6	4
10	13	4	13	6
15	11	16	14	6
16	12	12	15	8
16	12	15	16	7
14	10	12	15	6
14	11	14	12	6
12	11	11	14	2
15	11	16	11	5
13	8	14	14	5
16	11	14	14	6
14	12	15	14	6
8	11	9	12	4
16	12	15	14	6
16	12	14	16	8
12	12	15	13	6
11	8	10	14	5
16	12	14	16	8
9	11	9	12	4

Tables (Output of Computation)





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

\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 & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=111464&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]'RServer@AstonUniversity' @ vre.aston.ac.uk[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=111464&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=111464&T=0

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


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

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






Multiple Linear Regression - Estimated Regression Equation
KnowingPeople[t] = + 0.704773125332251 + 0.388112646909159Popularity[t] -0.072086130850391FindingFriends[t] + 0.267672517283153Liked[t] + 0.6695223593835Celebrity[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
KnowingPeople[t] =  +  0.704773125332251 +  0.388112646909159Popularity[t] -0.072086130850391FindingFriends[t] +  0.267672517283153Liked[t] +  0.6695223593835Celebrity[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=111464&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]KnowingPeople[t] =  +  0.704773125332251 +  0.388112646909159Popularity[t] -0.072086130850391FindingFriends[t] +  0.267672517283153Liked[t] +  0.6695223593835Celebrity[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=111464&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=111464&T=1

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


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

Multiple Linear Regression - Estimated Regression Equation
KnowingPeople[t] = + 0.704773125332251 + 0.388112646909159Popularity[t] -0.072086130850391FindingFriends[t] + 0.267672517283153Liked[t] + 0.6695223593835Celebrity[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.7047731253322511.7973720.39210.6955280.347764
Popularity0.3881126469091590.0976943.97270.000115.5e-05
FindingFriends-0.0720861308503910.121313-0.59420.5532570.276628
Liked0.2676725172831530.1250022.14130.0338510.016926
Celebrity0.66952235938350.1998273.35050.0010190.00051

\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) & 0.704773125332251 & 1.797372 & 0.3921 & 0.695528 & 0.347764 \tabularnewline
Popularity & 0.388112646909159 & 0.097694 & 3.9727 & 0.00011 & 5.5e-05 \tabularnewline
FindingFriends & -0.072086130850391 & 0.121313 & -0.5942 & 0.553257 & 0.276628 \tabularnewline
Liked & 0.267672517283153 & 0.125002 & 2.1413 & 0.033851 & 0.016926 \tabularnewline
Celebrity & 0.6695223593835 & 0.199827 & 3.3505 & 0.001019 & 0.00051 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=111464&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]0.704773125332251[/C][C]1.797372[/C][C]0.3921[/C][C]0.695528[/C][C]0.347764[/C][/ROW]
[ROW][C]Popularity[/C][C]0.388112646909159[/C][C]0.097694[/C][C]3.9727[/C][C]0.00011[/C][C]5.5e-05[/C][/ROW]
[ROW][C]FindingFriends[/C][C]-0.072086130850391[/C][C]0.121313[/C][C]-0.5942[/C][C]0.553257[/C][C]0.276628[/C][/ROW]
[ROW][C]Liked[/C][C]0.267672517283153[/C][C]0.125002[/C][C]2.1413[/C][C]0.033851[/C][C]0.016926[/C][/ROW]
[ROW][C]Celebrity[/C][C]0.6695223593835[/C][C]0.199827[/C][C]3.3505[/C][C]0.001019[/C][C]0.00051[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=111464&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=111464&T=2

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


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.7047731253322511.7973720.39210.6955280.347764
Popularity0.3881126469091590.0976943.97270.000115.5e-05
FindingFriends-0.0720861308503910.121313-0.59420.5532570.276628
Liked0.2676725172831530.1250022.14130.0338510.016926
Celebrity0.66952235938350.1998273.35050.0010190.00051






Multiple Linear Regression - Regression Statistics
Multiple R0.652918056368862
R-squared0.426301988332492
Adjusted R-squared0.411104690010174
F-TEST (value)28.0511693125379
F-TEST (DF numerator)4
F-TEST (DF denominator)151
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.65644680094332
Sum Squared Residuals1065.56315054255

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.652918056368862 \tabularnewline
R-squared & 0.426301988332492 \tabularnewline
Adjusted R-squared & 0.411104690010174 \tabularnewline
F-TEST (value) & 28.0511693125379 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 151 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.65644680094332 \tabularnewline
Sum Squared Residuals & 1065.56315054255 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=111464&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.652918056368862[/C][/ROW]
[ROW][C]R-squared[/C][C]0.426301988332492[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.411104690010174[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]28.0511693125379[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]151[/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]2.65644680094332[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1065.56315054255[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=111464&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=111464&T=3

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


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

Multiple Linear Regression - Regression Statistics
Multiple R0.652918056368862
R-squared0.426301988332492
Adjusted R-squared0.411104690010174
F-TEST (value)28.0511693125379
F-TEST (DF numerator)4
F-TEST (DF denominator)151
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.65644680094332
Sum Squared Residuals1065.56315054255






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11214.1054959532972-2.10549595329718
2711.9425540742875-4.94255407428748
3118.955240504958682.04475949504132
41110.9500703879180.0499296120820082
51412.86601175576291.13398824423705
61615.09104482873950.908955171260549
71312.77019349283780.229806507162218
81314.5556997941731-1.55569979417314
9511.2177429052011-6.21774290520114
1089.10247289261583-1.10247289261583
111411.39653197048642.60346802951365
121515.1047820239306-0.104782023930638
13811.6988212913539-3.69882129135394
141312.9350377606570.0649622393430255
151211.65420955088590.345790449114081
161111.1288594532032-0.128859453203201
1789.34335315186784-1.34335315186784
1845.55213229606351-1.55213229606351
191514.71666937702150.283330622978522
201214.6308460509799-2.6308460509799
211413.81409130393930.185908696060746
22910.6549234802525-1.65492348025246
231610.72700961110295.27299038889714
241010.6549234802525-0.654923480252464
25811.324445839636-3.32444583963596
261410.92259599753563.07740400246438
2769.62863754931304-3.62863754931304
281612.37813098534623.62186901465381
291111.3381830348271-0.33818303482715
30711.4616832904095-4.46168329040952
31139.7314657987773.268534201223
3279.14402450712748-2.14402450712748
331414.4352596645471-0.435259664547138
341714.03340982244682.96659017755321
351512.10658378309222.89341621690782
36812.4750465851723-4.47504658517226
3789.84884580244664-1.84884580244664
38118.955240504958682.04475949504132
391614.15384995207281.8461500479272
401012.5293132335858-2.52931323358577
41510.8306524195813-5.83065241958131
4286.784681682670521.21531831732948
43811.4102691656775-3.41026916567754
441513.60476772231531.3952322776847
4567.93596456055805-1.93596456055805
461613.18509839777812.81490160222192
471614.56569473105671.43430526894327
481613.81409130393932.18590869606075
491912.71877936810586.2812206318942
501414.1538499520728-0.153849952072798
511511.33818303482713.66181696517285
521111.2760918408603-0.276091840860348
531413.10906240634530.890937593654738
541212.1381404607202-0.138140460720245
551511.99396819901953.00603180098054
561410.98468719150243.01531280849758
571313.6452971755376-0.645297175537633
581110.42472029023530.575279709764726
5988.75965411852592-0.759654118525917
601112.2202215284542-1.22022152845423
6199.68311180000138-0.683111800001383
621013.6452971755376-3.64529717553763
63410.8019482656348-6.80194826563478
641514.75128618060590.248713819394093
651713.96438381755283.03561618244723
661211.2355623876380.76443761236198
671210.65492348025251.34507651974754
681513.10995214097131.89004785902867
69137.777165369040015.22283463095999
701515.2461017619499-0.246101761949886
711412.62227897282941.3777210271706
72810.0396677692825-2.03966776928248
731511.53682954721633.46317045278372
741213.1236893361625-1.12368933616251
751410.30142763692773.69857236307227
761010.1570477729521-0.15704777295212
7779.71772860358581-2.71772860358581
781615.76362731407930.236372685920685
79128.302515466722733.69748453327727
801513.64529717553761.35470282446237
8178.88009424815192-1.88009424815192
8298.151333218483160.848666781516844
831510.20540177172774.79459822827226
84710.1195784456862-3.11957844568616
851512.77019349283782.22980650716222
861412.80766282010371.19233717989626
871413.97506088678760.0249391132124122
88810.5551553567449-2.55515535674487
8989.83796113093699-1.83796113093699
901412.4541669767791.54583302322099
91108.906886506183071.09311349381694
921210.23219402975891.76780597024112
93159.346205675549375.65379432445063
941211.59211835691910.407881643080883
951312.02858500260390.971414997396109
961210.21539670861131.78460329138868
97108.370859339265531.62914066073447
9887.777165369040010.222834630959986
99610.1086937741765-4.1086937741765
1001312.10067113345430.899328866545718
101711.444885969262-4.44488596926197
1021313.1583061397469-0.158306139746941
10344.31353783315534-0.31353783315534
1041412.56086991121381.43913008878617
1051311.71255848654511.28744151345488
1061311.61279036303751.38720963696247
10766.4518578454642-0.451857845464202
10878.44668772842351-1.44668772842351
109510.105841250495-5.10584125049497
1101415.4928946708398-1.4928946708398
1111311.82517407061781.17482592938216
1121615.01895869788910.98104130211094
1131613.30247840144772.69752159855228
11477.55784685053248-0.557846850532476
1151411.76002275069472.23997724930533
1161112.3820808459286-1.38208084592862
1171715.09104482873951.90895517126055
11859.74146073566059-4.74146073566059
1191014.8401696326038-4.84016963260385
120118.965235441842272.03476455815773
121108.844795312216261.15520468778374
12299.9982485814341-0.998248581434088
1231211.34817797171070.651822028289261
1241512.95183508180452.04816491819547
125711.6058555521103-4.6058555521103
1261313.3055385274041-0.305538527404088
127812.4088731039598-4.40887310395976
1281611.90508474702154.09491525297848
1291512.57766723236142.42233276763862
130611.5921183569191-5.59211835691912
13165.429314172832370.570685827167627
1321213.2303922705973-1.23039227059733
133810.6817157382836-2.6817157382836
1341112.8453397496445-1.84533974964454
1351313.4259786570301-0.425978657030095
1361411.73629061861992.2637093813801
1371412.64975336321181.35024663678822
138107.372937533334542.62706246666546
139411.1456567743508-7.14565677435075
1401613.49806478788052.50193521211951
1411215.4208085399894-3.42080853998941
1421515.0189586978891-0.01895869788906
1431213.4497107891049-1.44971078910487
1441412.5746071064051.42539289359498
145119.655637409619011.34436259038099
1461612.02552487664753.97447512335247
1471412.26857552722981.73142447277016
1481413.88617743478960.113822565210355
1491513.03786601012091.96213398987907
15098.906886506183060.093113493816936
1511513.81409130393931.18590869606075
1521415.6884810572726-1.68848105727256
1531511.99396819901953.00603180098054
1541011.4923502334115-1.49235023341152
1551415.6884810572726-1.68848105727256
15699.29499915309222-0.294999153092224

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 12 & 14.1054959532972 & -2.10549595329718 \tabularnewline
2 & 7 & 11.9425540742875 & -4.94255407428748 \tabularnewline
3 & 11 & 8.95524050495868 & 2.04475949504132 \tabularnewline
4 & 11 & 10.950070387918 & 0.0499296120820082 \tabularnewline
5 & 14 & 12.8660117557629 & 1.13398824423705 \tabularnewline
6 & 16 & 15.0910448287395 & 0.908955171260549 \tabularnewline
7 & 13 & 12.7701934928378 & 0.229806507162218 \tabularnewline
8 & 13 & 14.5556997941731 & -1.55569979417314 \tabularnewline
9 & 5 & 11.2177429052011 & -6.21774290520114 \tabularnewline
10 & 8 & 9.10247289261583 & -1.10247289261583 \tabularnewline
11 & 14 & 11.3965319704864 & 2.60346802951365 \tabularnewline
12 & 15 & 15.1047820239306 & -0.104782023930638 \tabularnewline
13 & 8 & 11.6988212913539 & -3.69882129135394 \tabularnewline
14 & 13 & 12.935037760657 & 0.0649622393430255 \tabularnewline
15 & 12 & 11.6542095508859 & 0.345790449114081 \tabularnewline
16 & 11 & 11.1288594532032 & -0.128859453203201 \tabularnewline
17 & 8 & 9.34335315186784 & -1.34335315186784 \tabularnewline
18 & 4 & 5.55213229606351 & -1.55213229606351 \tabularnewline
19 & 15 & 14.7166693770215 & 0.283330622978522 \tabularnewline
20 & 12 & 14.6308460509799 & -2.6308460509799 \tabularnewline
21 & 14 & 13.8140913039393 & 0.185908696060746 \tabularnewline
22 & 9 & 10.6549234802525 & -1.65492348025246 \tabularnewline
23 & 16 & 10.7270096111029 & 5.27299038889714 \tabularnewline
24 & 10 & 10.6549234802525 & -0.654923480252464 \tabularnewline
25 & 8 & 11.324445839636 & -3.32444583963596 \tabularnewline
26 & 14 & 10.9225959975356 & 3.07740400246438 \tabularnewline
27 & 6 & 9.62863754931304 & -3.62863754931304 \tabularnewline
28 & 16 & 12.3781309853462 & 3.62186901465381 \tabularnewline
29 & 11 & 11.3381830348271 & -0.33818303482715 \tabularnewline
30 & 7 & 11.4616832904095 & -4.46168329040952 \tabularnewline
31 & 13 & 9.731465798777 & 3.268534201223 \tabularnewline
32 & 7 & 9.14402450712748 & -2.14402450712748 \tabularnewline
33 & 14 & 14.4352596645471 & -0.435259664547138 \tabularnewline
34 & 17 & 14.0334098224468 & 2.96659017755321 \tabularnewline
35 & 15 & 12.1065837830922 & 2.89341621690782 \tabularnewline
36 & 8 & 12.4750465851723 & -4.47504658517226 \tabularnewline
37 & 8 & 9.84884580244664 & -1.84884580244664 \tabularnewline
38 & 11 & 8.95524050495868 & 2.04475949504132 \tabularnewline
39 & 16 & 14.1538499520728 & 1.8461500479272 \tabularnewline
40 & 10 & 12.5293132335858 & -2.52931323358577 \tabularnewline
41 & 5 & 10.8306524195813 & -5.83065241958131 \tabularnewline
42 & 8 & 6.78468168267052 & 1.21531831732948 \tabularnewline
43 & 8 & 11.4102691656775 & -3.41026916567754 \tabularnewline
44 & 15 & 13.6047677223153 & 1.3952322776847 \tabularnewline
45 & 6 & 7.93596456055805 & -1.93596456055805 \tabularnewline
46 & 16 & 13.1850983977781 & 2.81490160222192 \tabularnewline
47 & 16 & 14.5656947310567 & 1.43430526894327 \tabularnewline
48 & 16 & 13.8140913039393 & 2.18590869606075 \tabularnewline
49 & 19 & 12.7187793681058 & 6.2812206318942 \tabularnewline
50 & 14 & 14.1538499520728 & -0.153849952072798 \tabularnewline
51 & 15 & 11.3381830348271 & 3.66181696517285 \tabularnewline
52 & 11 & 11.2760918408603 & -0.276091840860348 \tabularnewline
53 & 14 & 13.1090624063453 & 0.890937593654738 \tabularnewline
54 & 12 & 12.1381404607202 & -0.138140460720245 \tabularnewline
55 & 15 & 11.9939681990195 & 3.00603180098054 \tabularnewline
56 & 14 & 10.9846871915024 & 3.01531280849758 \tabularnewline
57 & 13 & 13.6452971755376 & -0.645297175537633 \tabularnewline
58 & 11 & 10.4247202902353 & 0.575279709764726 \tabularnewline
59 & 8 & 8.75965411852592 & -0.759654118525917 \tabularnewline
60 & 11 & 12.2202215284542 & -1.22022152845423 \tabularnewline
61 & 9 & 9.68311180000138 & -0.683111800001383 \tabularnewline
62 & 10 & 13.6452971755376 & -3.64529717553763 \tabularnewline
63 & 4 & 10.8019482656348 & -6.80194826563478 \tabularnewline
64 & 15 & 14.7512861806059 & 0.248713819394093 \tabularnewline
65 & 17 & 13.9643838175528 & 3.03561618244723 \tabularnewline
66 & 12 & 11.235562387638 & 0.76443761236198 \tabularnewline
67 & 12 & 10.6549234802525 & 1.34507651974754 \tabularnewline
68 & 15 & 13.1099521409713 & 1.89004785902867 \tabularnewline
69 & 13 & 7.77716536904001 & 5.22283463095999 \tabularnewline
70 & 15 & 15.2461017619499 & -0.246101761949886 \tabularnewline
71 & 14 & 12.6222789728294 & 1.3777210271706 \tabularnewline
72 & 8 & 10.0396677692825 & -2.03966776928248 \tabularnewline
73 & 15 & 11.5368295472163 & 3.46317045278372 \tabularnewline
74 & 12 & 13.1236893361625 & -1.12368933616251 \tabularnewline
75 & 14 & 10.3014276369277 & 3.69857236307227 \tabularnewline
76 & 10 & 10.1570477729521 & -0.15704777295212 \tabularnewline
77 & 7 & 9.71772860358581 & -2.71772860358581 \tabularnewline
78 & 16 & 15.7636273140793 & 0.236372685920685 \tabularnewline
79 & 12 & 8.30251546672273 & 3.69748453327727 \tabularnewline
80 & 15 & 13.6452971755376 & 1.35470282446237 \tabularnewline
81 & 7 & 8.88009424815192 & -1.88009424815192 \tabularnewline
82 & 9 & 8.15133321848316 & 0.848666781516844 \tabularnewline
83 & 15 & 10.2054017717277 & 4.79459822827226 \tabularnewline
84 & 7 & 10.1195784456862 & -3.11957844568616 \tabularnewline
85 & 15 & 12.7701934928378 & 2.22980650716222 \tabularnewline
86 & 14 & 12.8076628201037 & 1.19233717989626 \tabularnewline
87 & 14 & 13.9750608867876 & 0.0249391132124122 \tabularnewline
88 & 8 & 10.5551553567449 & -2.55515535674487 \tabularnewline
89 & 8 & 9.83796113093699 & -1.83796113093699 \tabularnewline
90 & 14 & 12.454166976779 & 1.54583302322099 \tabularnewline
91 & 10 & 8.90688650618307 & 1.09311349381694 \tabularnewline
92 & 12 & 10.2321940297589 & 1.76780597024112 \tabularnewline
93 & 15 & 9.34620567554937 & 5.65379432445063 \tabularnewline
94 & 12 & 11.5921183569191 & 0.407881643080883 \tabularnewline
95 & 13 & 12.0285850026039 & 0.971414997396109 \tabularnewline
96 & 12 & 10.2153967086113 & 1.78460329138868 \tabularnewline
97 & 10 & 8.37085933926553 & 1.62914066073447 \tabularnewline
98 & 8 & 7.77716536904001 & 0.222834630959986 \tabularnewline
99 & 6 & 10.1086937741765 & -4.1086937741765 \tabularnewline
100 & 13 & 12.1006711334543 & 0.899328866545718 \tabularnewline
101 & 7 & 11.444885969262 & -4.44488596926197 \tabularnewline
102 & 13 & 13.1583061397469 & -0.158306139746941 \tabularnewline
103 & 4 & 4.31353783315534 & -0.31353783315534 \tabularnewline
104 & 14 & 12.5608699112138 & 1.43913008878617 \tabularnewline
105 & 13 & 11.7125584865451 & 1.28744151345488 \tabularnewline
106 & 13 & 11.6127903630375 & 1.38720963696247 \tabularnewline
107 & 6 & 6.4518578454642 & -0.451857845464202 \tabularnewline
108 & 7 & 8.44668772842351 & -1.44668772842351 \tabularnewline
109 & 5 & 10.105841250495 & -5.10584125049497 \tabularnewline
110 & 14 & 15.4928946708398 & -1.4928946708398 \tabularnewline
111 & 13 & 11.8251740706178 & 1.17482592938216 \tabularnewline
112 & 16 & 15.0189586978891 & 0.98104130211094 \tabularnewline
113 & 16 & 13.3024784014477 & 2.69752159855228 \tabularnewline
114 & 7 & 7.55784685053248 & -0.557846850532476 \tabularnewline
115 & 14 & 11.7600227506947 & 2.23997724930533 \tabularnewline
116 & 11 & 12.3820808459286 & -1.38208084592862 \tabularnewline
117 & 17 & 15.0910448287395 & 1.90895517126055 \tabularnewline
118 & 5 & 9.74146073566059 & -4.74146073566059 \tabularnewline
119 & 10 & 14.8401696326038 & -4.84016963260385 \tabularnewline
120 & 11 & 8.96523544184227 & 2.03476455815773 \tabularnewline
121 & 10 & 8.84479531221626 & 1.15520468778374 \tabularnewline
122 & 9 & 9.9982485814341 & -0.998248581434088 \tabularnewline
123 & 12 & 11.3481779717107 & 0.651822028289261 \tabularnewline
124 & 15 & 12.9518350818045 & 2.04816491819547 \tabularnewline
125 & 7 & 11.6058555521103 & -4.6058555521103 \tabularnewline
126 & 13 & 13.3055385274041 & -0.305538527404088 \tabularnewline
127 & 8 & 12.4088731039598 & -4.40887310395976 \tabularnewline
128 & 16 & 11.9050847470215 & 4.09491525297848 \tabularnewline
129 & 15 & 12.5776672323614 & 2.42233276763862 \tabularnewline
130 & 6 & 11.5921183569191 & -5.59211835691912 \tabularnewline
131 & 6 & 5.42931417283237 & 0.570685827167627 \tabularnewline
132 & 12 & 13.2303922705973 & -1.23039227059733 \tabularnewline
133 & 8 & 10.6817157382836 & -2.6817157382836 \tabularnewline
134 & 11 & 12.8453397496445 & -1.84533974964454 \tabularnewline
135 & 13 & 13.4259786570301 & -0.425978657030095 \tabularnewline
136 & 14 & 11.7362906186199 & 2.2637093813801 \tabularnewline
137 & 14 & 12.6497533632118 & 1.35024663678822 \tabularnewline
138 & 10 & 7.37293753333454 & 2.62706246666546 \tabularnewline
139 & 4 & 11.1456567743508 & -7.14565677435075 \tabularnewline
140 & 16 & 13.4980647878805 & 2.50193521211951 \tabularnewline
141 & 12 & 15.4208085399894 & -3.42080853998941 \tabularnewline
142 & 15 & 15.0189586978891 & -0.01895869788906 \tabularnewline
143 & 12 & 13.4497107891049 & -1.44971078910487 \tabularnewline
144 & 14 & 12.574607106405 & 1.42539289359498 \tabularnewline
145 & 11 & 9.65563740961901 & 1.34436259038099 \tabularnewline
146 & 16 & 12.0255248766475 & 3.97447512335247 \tabularnewline
147 & 14 & 12.2685755272298 & 1.73142447277016 \tabularnewline
148 & 14 & 13.8861774347896 & 0.113822565210355 \tabularnewline
149 & 15 & 13.0378660101209 & 1.96213398987907 \tabularnewline
150 & 9 & 8.90688650618306 & 0.093113493816936 \tabularnewline
151 & 15 & 13.8140913039393 & 1.18590869606075 \tabularnewline
152 & 14 & 15.6884810572726 & -1.68848105727256 \tabularnewline
153 & 15 & 11.9939681990195 & 3.00603180098054 \tabularnewline
154 & 10 & 11.4923502334115 & -1.49235023341152 \tabularnewline
155 & 14 & 15.6884810572726 & -1.68848105727256 \tabularnewline
156 & 9 & 9.29499915309222 & -0.294999153092224 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=111464&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]12[/C][C]14.1054959532972[/C][C]-2.10549595329718[/C][/ROW]
[ROW][C]2[/C][C]7[/C][C]11.9425540742875[/C][C]-4.94255407428748[/C][/ROW]
[ROW][C]3[/C][C]11[/C][C]8.95524050495868[/C][C]2.04475949504132[/C][/ROW]
[ROW][C]4[/C][C]11[/C][C]10.950070387918[/C][C]0.0499296120820082[/C][/ROW]
[ROW][C]5[/C][C]14[/C][C]12.8660117557629[/C][C]1.13398824423705[/C][/ROW]
[ROW][C]6[/C][C]16[/C][C]15.0910448287395[/C][C]0.908955171260549[/C][/ROW]
[ROW][C]7[/C][C]13[/C][C]12.7701934928378[/C][C]0.229806507162218[/C][/ROW]
[ROW][C]8[/C][C]13[/C][C]14.5556997941731[/C][C]-1.55569979417314[/C][/ROW]
[ROW][C]9[/C][C]5[/C][C]11.2177429052011[/C][C]-6.21774290520114[/C][/ROW]
[ROW][C]10[/C][C]8[/C][C]9.10247289261583[/C][C]-1.10247289261583[/C][/ROW]
[ROW][C]11[/C][C]14[/C][C]11.3965319704864[/C][C]2.60346802951365[/C][/ROW]
[ROW][C]12[/C][C]15[/C][C]15.1047820239306[/C][C]-0.104782023930638[/C][/ROW]
[ROW][C]13[/C][C]8[/C][C]11.6988212913539[/C][C]-3.69882129135394[/C][/ROW]
[ROW][C]14[/C][C]13[/C][C]12.935037760657[/C][C]0.0649622393430255[/C][/ROW]
[ROW][C]15[/C][C]12[/C][C]11.6542095508859[/C][C]0.345790449114081[/C][/ROW]
[ROW][C]16[/C][C]11[/C][C]11.1288594532032[/C][C]-0.128859453203201[/C][/ROW]
[ROW][C]17[/C][C]8[/C][C]9.34335315186784[/C][C]-1.34335315186784[/C][/ROW]
[ROW][C]18[/C][C]4[/C][C]5.55213229606351[/C][C]-1.55213229606351[/C][/ROW]
[ROW][C]19[/C][C]15[/C][C]14.7166693770215[/C][C]0.283330622978522[/C][/ROW]
[ROW][C]20[/C][C]12[/C][C]14.6308460509799[/C][C]-2.6308460509799[/C][/ROW]
[ROW][C]21[/C][C]14[/C][C]13.8140913039393[/C][C]0.185908696060746[/C][/ROW]
[ROW][C]22[/C][C]9[/C][C]10.6549234802525[/C][C]-1.65492348025246[/C][/ROW]
[ROW][C]23[/C][C]16[/C][C]10.7270096111029[/C][C]5.27299038889714[/C][/ROW]
[ROW][C]24[/C][C]10[/C][C]10.6549234802525[/C][C]-0.654923480252464[/C][/ROW]
[ROW][C]25[/C][C]8[/C][C]11.324445839636[/C][C]-3.32444583963596[/C][/ROW]
[ROW][C]26[/C][C]14[/C][C]10.9225959975356[/C][C]3.07740400246438[/C][/ROW]
[ROW][C]27[/C][C]6[/C][C]9.62863754931304[/C][C]-3.62863754931304[/C][/ROW]
[ROW][C]28[/C][C]16[/C][C]12.3781309853462[/C][C]3.62186901465381[/C][/ROW]
[ROW][C]29[/C][C]11[/C][C]11.3381830348271[/C][C]-0.33818303482715[/C][/ROW]
[ROW][C]30[/C][C]7[/C][C]11.4616832904095[/C][C]-4.46168329040952[/C][/ROW]
[ROW][C]31[/C][C]13[/C][C]9.731465798777[/C][C]3.268534201223[/C][/ROW]
[ROW][C]32[/C][C]7[/C][C]9.14402450712748[/C][C]-2.14402450712748[/C][/ROW]
[ROW][C]33[/C][C]14[/C][C]14.4352596645471[/C][C]-0.435259664547138[/C][/ROW]
[ROW][C]34[/C][C]17[/C][C]14.0334098224468[/C][C]2.96659017755321[/C][/ROW]
[ROW][C]35[/C][C]15[/C][C]12.1065837830922[/C][C]2.89341621690782[/C][/ROW]
[ROW][C]36[/C][C]8[/C][C]12.4750465851723[/C][C]-4.47504658517226[/C][/ROW]
[ROW][C]37[/C][C]8[/C][C]9.84884580244664[/C][C]-1.84884580244664[/C][/ROW]
[ROW][C]38[/C][C]11[/C][C]8.95524050495868[/C][C]2.04475949504132[/C][/ROW]
[ROW][C]39[/C][C]16[/C][C]14.1538499520728[/C][C]1.8461500479272[/C][/ROW]
[ROW][C]40[/C][C]10[/C][C]12.5293132335858[/C][C]-2.52931323358577[/C][/ROW]
[ROW][C]41[/C][C]5[/C][C]10.8306524195813[/C][C]-5.83065241958131[/C][/ROW]
[ROW][C]42[/C][C]8[/C][C]6.78468168267052[/C][C]1.21531831732948[/C][/ROW]
[ROW][C]43[/C][C]8[/C][C]11.4102691656775[/C][C]-3.41026916567754[/C][/ROW]
[ROW][C]44[/C][C]15[/C][C]13.6047677223153[/C][C]1.3952322776847[/C][/ROW]
[ROW][C]45[/C][C]6[/C][C]7.93596456055805[/C][C]-1.93596456055805[/C][/ROW]
[ROW][C]46[/C][C]16[/C][C]13.1850983977781[/C][C]2.81490160222192[/C][/ROW]
[ROW][C]47[/C][C]16[/C][C]14.5656947310567[/C][C]1.43430526894327[/C][/ROW]
[ROW][C]48[/C][C]16[/C][C]13.8140913039393[/C][C]2.18590869606075[/C][/ROW]
[ROW][C]49[/C][C]19[/C][C]12.7187793681058[/C][C]6.2812206318942[/C][/ROW]
[ROW][C]50[/C][C]14[/C][C]14.1538499520728[/C][C]-0.153849952072798[/C][/ROW]
[ROW][C]51[/C][C]15[/C][C]11.3381830348271[/C][C]3.66181696517285[/C][/ROW]
[ROW][C]52[/C][C]11[/C][C]11.2760918408603[/C][C]-0.276091840860348[/C][/ROW]
[ROW][C]53[/C][C]14[/C][C]13.1090624063453[/C][C]0.890937593654738[/C][/ROW]
[ROW][C]54[/C][C]12[/C][C]12.1381404607202[/C][C]-0.138140460720245[/C][/ROW]
[ROW][C]55[/C][C]15[/C][C]11.9939681990195[/C][C]3.00603180098054[/C][/ROW]
[ROW][C]56[/C][C]14[/C][C]10.9846871915024[/C][C]3.01531280849758[/C][/ROW]
[ROW][C]57[/C][C]13[/C][C]13.6452971755376[/C][C]-0.645297175537633[/C][/ROW]
[ROW][C]58[/C][C]11[/C][C]10.4247202902353[/C][C]0.575279709764726[/C][/ROW]
[ROW][C]59[/C][C]8[/C][C]8.75965411852592[/C][C]-0.759654118525917[/C][/ROW]
[ROW][C]60[/C][C]11[/C][C]12.2202215284542[/C][C]-1.22022152845423[/C][/ROW]
[ROW][C]61[/C][C]9[/C][C]9.68311180000138[/C][C]-0.683111800001383[/C][/ROW]
[ROW][C]62[/C][C]10[/C][C]13.6452971755376[/C][C]-3.64529717553763[/C][/ROW]
[ROW][C]63[/C][C]4[/C][C]10.8019482656348[/C][C]-6.80194826563478[/C][/ROW]
[ROW][C]64[/C][C]15[/C][C]14.7512861806059[/C][C]0.248713819394093[/C][/ROW]
[ROW][C]65[/C][C]17[/C][C]13.9643838175528[/C][C]3.03561618244723[/C][/ROW]
[ROW][C]66[/C][C]12[/C][C]11.235562387638[/C][C]0.76443761236198[/C][/ROW]
[ROW][C]67[/C][C]12[/C][C]10.6549234802525[/C][C]1.34507651974754[/C][/ROW]
[ROW][C]68[/C][C]15[/C][C]13.1099521409713[/C][C]1.89004785902867[/C][/ROW]
[ROW][C]69[/C][C]13[/C][C]7.77716536904001[/C][C]5.22283463095999[/C][/ROW]
[ROW][C]70[/C][C]15[/C][C]15.2461017619499[/C][C]-0.246101761949886[/C][/ROW]
[ROW][C]71[/C][C]14[/C][C]12.6222789728294[/C][C]1.3777210271706[/C][/ROW]
[ROW][C]72[/C][C]8[/C][C]10.0396677692825[/C][C]-2.03966776928248[/C][/ROW]
[ROW][C]73[/C][C]15[/C][C]11.5368295472163[/C][C]3.46317045278372[/C][/ROW]
[ROW][C]74[/C][C]12[/C][C]13.1236893361625[/C][C]-1.12368933616251[/C][/ROW]
[ROW][C]75[/C][C]14[/C][C]10.3014276369277[/C][C]3.69857236307227[/C][/ROW]
[ROW][C]76[/C][C]10[/C][C]10.1570477729521[/C][C]-0.15704777295212[/C][/ROW]
[ROW][C]77[/C][C]7[/C][C]9.71772860358581[/C][C]-2.71772860358581[/C][/ROW]
[ROW][C]78[/C][C]16[/C][C]15.7636273140793[/C][C]0.236372685920685[/C][/ROW]
[ROW][C]79[/C][C]12[/C][C]8.30251546672273[/C][C]3.69748453327727[/C][/ROW]
[ROW][C]80[/C][C]15[/C][C]13.6452971755376[/C][C]1.35470282446237[/C][/ROW]
[ROW][C]81[/C][C]7[/C][C]8.88009424815192[/C][C]-1.88009424815192[/C][/ROW]
[ROW][C]82[/C][C]9[/C][C]8.15133321848316[/C][C]0.848666781516844[/C][/ROW]
[ROW][C]83[/C][C]15[/C][C]10.2054017717277[/C][C]4.79459822827226[/C][/ROW]
[ROW][C]84[/C][C]7[/C][C]10.1195784456862[/C][C]-3.11957844568616[/C][/ROW]
[ROW][C]85[/C][C]15[/C][C]12.7701934928378[/C][C]2.22980650716222[/C][/ROW]
[ROW][C]86[/C][C]14[/C][C]12.8076628201037[/C][C]1.19233717989626[/C][/ROW]
[ROW][C]87[/C][C]14[/C][C]13.9750608867876[/C][C]0.0249391132124122[/C][/ROW]
[ROW][C]88[/C][C]8[/C][C]10.5551553567449[/C][C]-2.55515535674487[/C][/ROW]
[ROW][C]89[/C][C]8[/C][C]9.83796113093699[/C][C]-1.83796113093699[/C][/ROW]
[ROW][C]90[/C][C]14[/C][C]12.454166976779[/C][C]1.54583302322099[/C][/ROW]
[ROW][C]91[/C][C]10[/C][C]8.90688650618307[/C][C]1.09311349381694[/C][/ROW]
[ROW][C]92[/C][C]12[/C][C]10.2321940297589[/C][C]1.76780597024112[/C][/ROW]
[ROW][C]93[/C][C]15[/C][C]9.34620567554937[/C][C]5.65379432445063[/C][/ROW]
[ROW][C]94[/C][C]12[/C][C]11.5921183569191[/C][C]0.407881643080883[/C][/ROW]
[ROW][C]95[/C][C]13[/C][C]12.0285850026039[/C][C]0.971414997396109[/C][/ROW]
[ROW][C]96[/C][C]12[/C][C]10.2153967086113[/C][C]1.78460329138868[/C][/ROW]
[ROW][C]97[/C][C]10[/C][C]8.37085933926553[/C][C]1.62914066073447[/C][/ROW]
[ROW][C]98[/C][C]8[/C][C]7.77716536904001[/C][C]0.222834630959986[/C][/ROW]
[ROW][C]99[/C][C]6[/C][C]10.1086937741765[/C][C]-4.1086937741765[/C][/ROW]
[ROW][C]100[/C][C]13[/C][C]12.1006711334543[/C][C]0.899328866545718[/C][/ROW]
[ROW][C]101[/C][C]7[/C][C]11.444885969262[/C][C]-4.44488596926197[/C][/ROW]
[ROW][C]102[/C][C]13[/C][C]13.1583061397469[/C][C]-0.158306139746941[/C][/ROW]
[ROW][C]103[/C][C]4[/C][C]4.31353783315534[/C][C]-0.31353783315534[/C][/ROW]
[ROW][C]104[/C][C]14[/C][C]12.5608699112138[/C][C]1.43913008878617[/C][/ROW]
[ROW][C]105[/C][C]13[/C][C]11.7125584865451[/C][C]1.28744151345488[/C][/ROW]
[ROW][C]106[/C][C]13[/C][C]11.6127903630375[/C][C]1.38720963696247[/C][/ROW]
[ROW][C]107[/C][C]6[/C][C]6.4518578454642[/C][C]-0.451857845464202[/C][/ROW]
[ROW][C]108[/C][C]7[/C][C]8.44668772842351[/C][C]-1.44668772842351[/C][/ROW]
[ROW][C]109[/C][C]5[/C][C]10.105841250495[/C][C]-5.10584125049497[/C][/ROW]
[ROW][C]110[/C][C]14[/C][C]15.4928946708398[/C][C]-1.4928946708398[/C][/ROW]
[ROW][C]111[/C][C]13[/C][C]11.8251740706178[/C][C]1.17482592938216[/C][/ROW]
[ROW][C]112[/C][C]16[/C][C]15.0189586978891[/C][C]0.98104130211094[/C][/ROW]
[ROW][C]113[/C][C]16[/C][C]13.3024784014477[/C][C]2.69752159855228[/C][/ROW]
[ROW][C]114[/C][C]7[/C][C]7.55784685053248[/C][C]-0.557846850532476[/C][/ROW]
[ROW][C]115[/C][C]14[/C][C]11.7600227506947[/C][C]2.23997724930533[/C][/ROW]
[ROW][C]116[/C][C]11[/C][C]12.3820808459286[/C][C]-1.38208084592862[/C][/ROW]
[ROW][C]117[/C][C]17[/C][C]15.0910448287395[/C][C]1.90895517126055[/C][/ROW]
[ROW][C]118[/C][C]5[/C][C]9.74146073566059[/C][C]-4.74146073566059[/C][/ROW]
[ROW][C]119[/C][C]10[/C][C]14.8401696326038[/C][C]-4.84016963260385[/C][/ROW]
[ROW][C]120[/C][C]11[/C][C]8.96523544184227[/C][C]2.03476455815773[/C][/ROW]
[ROW][C]121[/C][C]10[/C][C]8.84479531221626[/C][C]1.15520468778374[/C][/ROW]
[ROW][C]122[/C][C]9[/C][C]9.9982485814341[/C][C]-0.998248581434088[/C][/ROW]
[ROW][C]123[/C][C]12[/C][C]11.3481779717107[/C][C]0.651822028289261[/C][/ROW]
[ROW][C]124[/C][C]15[/C][C]12.9518350818045[/C][C]2.04816491819547[/C][/ROW]
[ROW][C]125[/C][C]7[/C][C]11.6058555521103[/C][C]-4.6058555521103[/C][/ROW]
[ROW][C]126[/C][C]13[/C][C]13.3055385274041[/C][C]-0.305538527404088[/C][/ROW]
[ROW][C]127[/C][C]8[/C][C]12.4088731039598[/C][C]-4.40887310395976[/C][/ROW]
[ROW][C]128[/C][C]16[/C][C]11.9050847470215[/C][C]4.09491525297848[/C][/ROW]
[ROW][C]129[/C][C]15[/C][C]12.5776672323614[/C][C]2.42233276763862[/C][/ROW]
[ROW][C]130[/C][C]6[/C][C]11.5921183569191[/C][C]-5.59211835691912[/C][/ROW]
[ROW][C]131[/C][C]6[/C][C]5.42931417283237[/C][C]0.570685827167627[/C][/ROW]
[ROW][C]132[/C][C]12[/C][C]13.2303922705973[/C][C]-1.23039227059733[/C][/ROW]
[ROW][C]133[/C][C]8[/C][C]10.6817157382836[/C][C]-2.6817157382836[/C][/ROW]
[ROW][C]134[/C][C]11[/C][C]12.8453397496445[/C][C]-1.84533974964454[/C][/ROW]
[ROW][C]135[/C][C]13[/C][C]13.4259786570301[/C][C]-0.425978657030095[/C][/ROW]
[ROW][C]136[/C][C]14[/C][C]11.7362906186199[/C][C]2.2637093813801[/C][/ROW]
[ROW][C]137[/C][C]14[/C][C]12.6497533632118[/C][C]1.35024663678822[/C][/ROW]
[ROW][C]138[/C][C]10[/C][C]7.37293753333454[/C][C]2.62706246666546[/C][/ROW]
[ROW][C]139[/C][C]4[/C][C]11.1456567743508[/C][C]-7.14565677435075[/C][/ROW]
[ROW][C]140[/C][C]16[/C][C]13.4980647878805[/C][C]2.50193521211951[/C][/ROW]
[ROW][C]141[/C][C]12[/C][C]15.4208085399894[/C][C]-3.42080853998941[/C][/ROW]
[ROW][C]142[/C][C]15[/C][C]15.0189586978891[/C][C]-0.01895869788906[/C][/ROW]
[ROW][C]143[/C][C]12[/C][C]13.4497107891049[/C][C]-1.44971078910487[/C][/ROW]
[ROW][C]144[/C][C]14[/C][C]12.574607106405[/C][C]1.42539289359498[/C][/ROW]
[ROW][C]145[/C][C]11[/C][C]9.65563740961901[/C][C]1.34436259038099[/C][/ROW]
[ROW][C]146[/C][C]16[/C][C]12.0255248766475[/C][C]3.97447512335247[/C][/ROW]
[ROW][C]147[/C][C]14[/C][C]12.2685755272298[/C][C]1.73142447277016[/C][/ROW]
[ROW][C]148[/C][C]14[/C][C]13.8861774347896[/C][C]0.113822565210355[/C][/ROW]
[ROW][C]149[/C][C]15[/C][C]13.0378660101209[/C][C]1.96213398987907[/C][/ROW]
[ROW][C]150[/C][C]9[/C][C]8.90688650618306[/C][C]0.093113493816936[/C][/ROW]
[ROW][C]151[/C][C]15[/C][C]13.8140913039393[/C][C]1.18590869606075[/C][/ROW]
[ROW][C]152[/C][C]14[/C][C]15.6884810572726[/C][C]-1.68848105727256[/C][/ROW]
[ROW][C]153[/C][C]15[/C][C]11.9939681990195[/C][C]3.00603180098054[/C][/ROW]
[ROW][C]154[/C][C]10[/C][C]11.4923502334115[/C][C]-1.49235023341152[/C][/ROW]
[ROW][C]155[/C][C]14[/C][C]15.6884810572726[/C][C]-1.68848105727256[/C][/ROW]
[ROW][C]156[/C][C]9[/C][C]9.29499915309222[/C][C]-0.294999153092224[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=111464&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=111464&T=4

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


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11214.1054959532972-2.10549595329718
2711.9425540742875-4.94255407428748
3118.955240504958682.04475949504132
41110.9500703879180.0499296120820082
51412.86601175576291.13398824423705
61615.09104482873950.908955171260549
71312.77019349283780.229806507162218
81314.5556997941731-1.55569979417314
9511.2177429052011-6.21774290520114
1089.10247289261583-1.10247289261583
111411.39653197048642.60346802951365
121515.1047820239306-0.104782023930638
13811.6988212913539-3.69882129135394
141312.9350377606570.0649622393430255
151211.65420955088590.345790449114081
161111.1288594532032-0.128859453203201
1789.34335315186784-1.34335315186784
1845.55213229606351-1.55213229606351
191514.71666937702150.283330622978522
201214.6308460509799-2.6308460509799
211413.81409130393930.185908696060746
22910.6549234802525-1.65492348025246
231610.72700961110295.27299038889714
241010.6549234802525-0.654923480252464
25811.324445839636-3.32444583963596
261410.92259599753563.07740400246438
2769.62863754931304-3.62863754931304
281612.37813098534623.62186901465381
291111.3381830348271-0.33818303482715
30711.4616832904095-4.46168329040952
31139.7314657987773.268534201223
3279.14402450712748-2.14402450712748
331414.4352596645471-0.435259664547138
341714.03340982244682.96659017755321
351512.10658378309222.89341621690782
36812.4750465851723-4.47504658517226
3789.84884580244664-1.84884580244664
38118.955240504958682.04475949504132
391614.15384995207281.8461500479272
401012.5293132335858-2.52931323358577
41510.8306524195813-5.83065241958131
4286.784681682670521.21531831732948
43811.4102691656775-3.41026916567754
441513.60476772231531.3952322776847
4567.93596456055805-1.93596456055805
461613.18509839777812.81490160222192
471614.56569473105671.43430526894327
481613.81409130393932.18590869606075
491912.71877936810586.2812206318942
501414.1538499520728-0.153849952072798
511511.33818303482713.66181696517285
521111.2760918408603-0.276091840860348
531413.10906240634530.890937593654738
541212.1381404607202-0.138140460720245
551511.99396819901953.00603180098054
561410.98468719150243.01531280849758
571313.6452971755376-0.645297175537633
581110.42472029023530.575279709764726
5988.75965411852592-0.759654118525917
601112.2202215284542-1.22022152845423
6199.68311180000138-0.683111800001383
621013.6452971755376-3.64529717553763
63410.8019482656348-6.80194826563478
641514.75128618060590.248713819394093
651713.96438381755283.03561618244723
661211.2355623876380.76443761236198
671210.65492348025251.34507651974754
681513.10995214097131.89004785902867
69137.777165369040015.22283463095999
701515.2461017619499-0.246101761949886
711412.62227897282941.3777210271706
72810.0396677692825-2.03966776928248
731511.53682954721633.46317045278372
741213.1236893361625-1.12368933616251
751410.30142763692773.69857236307227
761010.1570477729521-0.15704777295212
7779.71772860358581-2.71772860358581
781615.76362731407930.236372685920685
79128.302515466722733.69748453327727
801513.64529717553761.35470282446237
8178.88009424815192-1.88009424815192
8298.151333218483160.848666781516844
831510.20540177172774.79459822827226
84710.1195784456862-3.11957844568616
851512.77019349283782.22980650716222
861412.80766282010371.19233717989626
871413.97506088678760.0249391132124122
88810.5551553567449-2.55515535674487
8989.83796113093699-1.83796113093699
901412.4541669767791.54583302322099
91108.906886506183071.09311349381694
921210.23219402975891.76780597024112
93159.346205675549375.65379432445063
941211.59211835691910.407881643080883
951312.02858500260390.971414997396109
961210.21539670861131.78460329138868
97108.370859339265531.62914066073447
9887.777165369040010.222834630959986
99610.1086937741765-4.1086937741765
1001312.10067113345430.899328866545718
101711.444885969262-4.44488596926197
1021313.1583061397469-0.158306139746941
10344.31353783315534-0.31353783315534
1041412.56086991121381.43913008878617
1051311.71255848654511.28744151345488
1061311.61279036303751.38720963696247
10766.4518578454642-0.451857845464202
10878.44668772842351-1.44668772842351
109510.105841250495-5.10584125049497
1101415.4928946708398-1.4928946708398
1111311.82517407061781.17482592938216
1121615.01895869788910.98104130211094
1131613.30247840144772.69752159855228
11477.55784685053248-0.557846850532476
1151411.76002275069472.23997724930533
1161112.3820808459286-1.38208084592862
1171715.09104482873951.90895517126055
11859.74146073566059-4.74146073566059
1191014.8401696326038-4.84016963260385
120118.965235441842272.03476455815773
121108.844795312216261.15520468778374
12299.9982485814341-0.998248581434088
1231211.34817797171070.651822028289261
1241512.95183508180452.04816491819547
125711.6058555521103-4.6058555521103
1261313.3055385274041-0.305538527404088
127812.4088731039598-4.40887310395976
1281611.90508474702154.09491525297848
1291512.57766723236142.42233276763862
130611.5921183569191-5.59211835691912
13165.429314172832370.570685827167627
1321213.2303922705973-1.23039227059733
133810.6817157382836-2.6817157382836
1341112.8453397496445-1.84533974964454
1351313.4259786570301-0.425978657030095
1361411.73629061861992.2637093813801
1371412.64975336321181.35024663678822
138107.372937533334542.62706246666546
139411.1456567743508-7.14565677435075
1401613.49806478788052.50193521211951
1411215.4208085399894-3.42080853998941
1421515.0189586978891-0.01895869788906
1431213.4497107891049-1.44971078910487
1441412.5746071064051.42539289359498
145119.655637409619011.34436259038099
1461612.02552487664753.97447512335247
1471412.26857552722981.73142447277016
1481413.88617743478960.113822565210355
1491513.03786601012091.96213398987907
15098.906886506183060.093113493816936
1511513.81409130393931.18590869606075
1521415.6884810572726-1.68848105727256
1531511.99396819901953.00603180098054
1541011.4923502334115-1.49235023341152
1551415.6884810572726-1.68848105727256
15699.29499915309222-0.294999153092224






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.5315176023147180.9369647953705650.468482397685282
90.8139124393705410.3721751212589170.186087560629459
100.7202824420225330.5594351159549340.279717557977467
110.6579046309006640.6841907381986720.342095369099336
120.6820461984110080.6359076031779840.317953801588992
130.8396970073549280.3206059852901440.160302992645072
140.7776532517119270.4446934965761460.222346748288073
150.7086308527158970.5827382945682050.291369147284103
160.6267098517047330.7465802965905340.373290148295267
170.5514771622086860.8970456755826280.448522837791314
180.4685317784266220.9370635568532450.531468221573378
190.4033634869576460.8067269739152930.596636513042354
200.3581300583189990.7162601166379980.641869941681001
210.289663820442650.5793276408853010.71033617955735
220.2336596222511390.4673192445022790.76634037774886
230.4998969045902340.9997938091804680.500103095409766
240.4309986384187120.8619972768374250.569001361581288
250.4396816557945390.8793633115890770.560318344205461
260.4817293278124430.9634586556248870.518270672187557
270.4986788046798550.997357609359710.501321195320146
280.5698679224152260.8602641551695480.430132077584774
290.5153707536262730.9692584927474530.484629246373727
300.5482344008544440.9035311982911110.451765599145556
310.6248741592013820.7502516815972370.375125840798618
320.6317079683508810.7365840632982380.368292031649119
330.5743976004744950.851204799051010.425602399525505
340.5802947967360320.8394104065279360.419705203263968
350.5867811634013020.8264376731973960.413218836598698
360.6440655513578840.7118688972842320.355934448642116
370.6037170523939960.7925658952120080.396282947606004
380.6019845169062670.7960309661874650.398015483093733
390.5832037066769270.8335925866461460.416796293323073
400.5800668703650030.8398662592699950.419933129634997
410.6956763591349820.6086472817300370.304323640865018
420.656190427991850.68761914401630.34380957200815
430.6704737321132470.6590525357735060.329526267886753
440.6602851235959490.6794297528081030.339714876404051
450.626174174773560.747651650452880.37382582522644
460.619987988380260.7600240232394790.380012011619739
470.5798487540689950.840302491862010.420151245931005
480.5819429072407750.836114185518450.418057092759225
490.810423788880930.3791524222381390.189576211119069
500.7744664822591610.4510670354816780.225533517740839
510.8118140229228360.3763719541543290.188185977077164
520.7773125966845770.4453748066308460.222687403315423
530.747795868203260.5044082635934810.25220413179674
540.7067534563883610.5864930872232780.293246543611639
550.7182775483778230.5634449032443530.281722451622177
560.7364600495041340.5270799009917320.263539950495866
570.7003078380922540.5993843238154910.299692161907746
580.6587987413447570.6824025173104870.341201258655243
590.6162908958372430.7674182083255130.383709104162757
600.5820546940672270.8358906118655460.417945305932773
610.5368918574567610.9262162850864780.463108142543239
620.5841500919511620.8316998160976760.415849908048838
630.8039974884039130.3920050231921740.196002511596087
640.7694633581160710.4610732837678580.230536641883929
650.7744961646971850.451007670605630.225503835302815
660.7429369158286950.514126168342610.257063084171305
670.7142029704454630.5715940591090730.285797029554537
680.6923965932958330.6152068134083340.307603406704167
690.8025772362501450.3948455274997110.197422763749855
700.7689881895687340.4620236208625330.231011810431266
710.7393777566228040.5212444867543910.260622243377195
720.7240882930074180.5518234139851630.275911706992582
730.7606445665366280.4787108669267430.239355433463372
740.7288307103164290.5423385793671430.271169289683571
750.76437047943030.47125904113940.2356295205697
760.7286923448381090.5426153103237830.271307655161891
770.7301771651491840.5396456697016330.269822834850816
780.6993176706533940.6013646586932120.300682329346606
790.7654787294998770.4690425410002450.234521270500123
800.7390006218627280.5219987562745450.260999378137272
810.7234688404645860.5530623190708280.276531159535414
820.6905903630257620.6188192739484760.309409636974238
830.7821307465547530.4357385068904930.217869253445247
840.7977442862727760.4045114274544470.202255713727224
850.7898901240736590.4202197518526820.210109875926341
860.7621859646283670.4756280707432660.237814035371633
870.7268709278540690.5462581442918630.273129072145931
880.7477373932042460.5045252135915090.252262606795754
890.7803363765726140.4393272468547720.219663623427386
900.755832027870520.4883359442589590.24416797212948
910.7276070120400020.5447859759199970.272392987959998
920.7167402859731810.5665194280536380.283259714026819
930.8776258546301270.2447482907397450.122374145369873
940.8557872157481430.2884255685037130.144212784251857
950.8322429685780950.335514062843810.167757031421905
960.808881187923660.382237624152680.19111881207634
970.8026560150062720.3946879699874560.197343984993728
980.783198604699080.4336027906018390.216801395300919
990.8428328839172360.3143342321655290.157167116082764
1000.814556684888760.3708866302224790.185443315111239
1010.8815302496232830.2369395007534340.118469750376717
1020.8555509171602460.2888981656795080.144449082839754
1030.8257680407657120.3484639184685760.174231959234288
1040.7964329340377160.4071341319245680.203567065962284
1050.7709514863157430.4580970273685140.229048513684257
1060.737544946377030.5249101072459390.26245505362297
1070.7033760280250470.5932479439499060.296623971974953
1080.6705825107127490.6588349785745030.329417489287251
1090.8601212468949540.2797575062100930.139878753105046
1100.8403819215316120.3192361569367770.159618078468388
1110.8731243615243160.2537512769513680.126875638475684
1120.853925905970440.292148188059120.14607409402956
1130.8339723250865820.3320553498268370.166027674913418
1140.7987578185682790.4024843628634420.201242181431721
1150.787682390084740.4246352198305180.212317609915259
1160.7529080171853120.4941839656293750.247091982814688
1170.7382311062526230.5235377874947550.261768893747377
1180.9306930904328110.1386138191343770.0693069095671885
1190.9326985137608330.1346029724783350.0673014862391673
1200.9207882909128930.1584234181742140.0792117090871072
1210.9147960760202150.170407847959570.0852039239797852
1220.8899046028827230.2201907942345550.110095397117277
1230.8683648774367040.2632702451265920.131635122563296
1240.8772558137655860.2454883724688290.122744186234414
1250.8954690211218690.2090619577562630.104530978878132
1260.8642699454314380.2714601091371230.135730054568562
1270.8497554095704970.3004891808590060.150244590429503
1280.8448254288461480.3103491423077030.155174571153852
1290.8918895460963210.2162209078073580.108110453903679
1300.9669168369636120.0661663260727760.033083163036388
1310.9535894148230930.09282117035381360.0464105851769068
1320.9601976647151520.07960467056969530.0398023352848476
1330.9434043068786090.1131913862427830.0565956931213915
1340.918792014353090.1624159712938180.081207985646909
1350.8930002015858370.2139995968283250.106999798414163
1360.892528530529650.2149429389406980.107471469470349
1370.8895202074815310.2209595850369380.110479792518469
1380.8524268257903290.2951463484193410.147573174209671
1390.986561788596730.02687642280654060.0134382114032703
1400.9848939494333260.03021210113334850.0151060505666742
1410.9940319286642840.01193614267143180.00596807133571591
1420.9882554367419530.02348912651609430.0117445632580471
1430.9781534096626980.04369318067460390.021846590337302
1440.960587505775670.07882498844866110.0394124942243306
1450.930492432340450.1390151353190980.0695075676595491
1460.9186516114802950.162696777039410.0813483885197051
1470.9298171996738930.1403656006522140.070182800326107
1480.908698685482330.1826026290353380.0913013145176692

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 0.531517602314718 & 0.936964795370565 & 0.468482397685282 \tabularnewline
9 & 0.813912439370541 & 0.372175121258917 & 0.186087560629459 \tabularnewline
10 & 0.720282442022533 & 0.559435115954934 & 0.279717557977467 \tabularnewline
11 & 0.657904630900664 & 0.684190738198672 & 0.342095369099336 \tabularnewline
12 & 0.682046198411008 & 0.635907603177984 & 0.317953801588992 \tabularnewline
13 & 0.839697007354928 & 0.320605985290144 & 0.160302992645072 \tabularnewline
14 & 0.777653251711927 & 0.444693496576146 & 0.222346748288073 \tabularnewline
15 & 0.708630852715897 & 0.582738294568205 & 0.291369147284103 \tabularnewline
16 & 0.626709851704733 & 0.746580296590534 & 0.373290148295267 \tabularnewline
17 & 0.551477162208686 & 0.897045675582628 & 0.448522837791314 \tabularnewline
18 & 0.468531778426622 & 0.937063556853245 & 0.531468221573378 \tabularnewline
19 & 0.403363486957646 & 0.806726973915293 & 0.596636513042354 \tabularnewline
20 & 0.358130058318999 & 0.716260116637998 & 0.641869941681001 \tabularnewline
21 & 0.28966382044265 & 0.579327640885301 & 0.71033617955735 \tabularnewline
22 & 0.233659622251139 & 0.467319244502279 & 0.76634037774886 \tabularnewline
23 & 0.499896904590234 & 0.999793809180468 & 0.500103095409766 \tabularnewline
24 & 0.430998638418712 & 0.861997276837425 & 0.569001361581288 \tabularnewline
25 & 0.439681655794539 & 0.879363311589077 & 0.560318344205461 \tabularnewline
26 & 0.481729327812443 & 0.963458655624887 & 0.518270672187557 \tabularnewline
27 & 0.498678804679855 & 0.99735760935971 & 0.501321195320146 \tabularnewline
28 & 0.569867922415226 & 0.860264155169548 & 0.430132077584774 \tabularnewline
29 & 0.515370753626273 & 0.969258492747453 & 0.484629246373727 \tabularnewline
30 & 0.548234400854444 & 0.903531198291111 & 0.451765599145556 \tabularnewline
31 & 0.624874159201382 & 0.750251681597237 & 0.375125840798618 \tabularnewline
32 & 0.631707968350881 & 0.736584063298238 & 0.368292031649119 \tabularnewline
33 & 0.574397600474495 & 0.85120479905101 & 0.425602399525505 \tabularnewline
34 & 0.580294796736032 & 0.839410406527936 & 0.419705203263968 \tabularnewline
35 & 0.586781163401302 & 0.826437673197396 & 0.413218836598698 \tabularnewline
36 & 0.644065551357884 & 0.711868897284232 & 0.355934448642116 \tabularnewline
37 & 0.603717052393996 & 0.792565895212008 & 0.396282947606004 \tabularnewline
38 & 0.601984516906267 & 0.796030966187465 & 0.398015483093733 \tabularnewline
39 & 0.583203706676927 & 0.833592586646146 & 0.416796293323073 \tabularnewline
40 & 0.580066870365003 & 0.839866259269995 & 0.419933129634997 \tabularnewline
41 & 0.695676359134982 & 0.608647281730037 & 0.304323640865018 \tabularnewline
42 & 0.65619042799185 & 0.6876191440163 & 0.34380957200815 \tabularnewline
43 & 0.670473732113247 & 0.659052535773506 & 0.329526267886753 \tabularnewline
44 & 0.660285123595949 & 0.679429752808103 & 0.339714876404051 \tabularnewline
45 & 0.62617417477356 & 0.74765165045288 & 0.37382582522644 \tabularnewline
46 & 0.61998798838026 & 0.760024023239479 & 0.380012011619739 \tabularnewline
47 & 0.579848754068995 & 0.84030249186201 & 0.420151245931005 \tabularnewline
48 & 0.581942907240775 & 0.83611418551845 & 0.418057092759225 \tabularnewline
49 & 0.81042378888093 & 0.379152422238139 & 0.189576211119069 \tabularnewline
50 & 0.774466482259161 & 0.451067035481678 & 0.225533517740839 \tabularnewline
51 & 0.811814022922836 & 0.376371954154329 & 0.188185977077164 \tabularnewline
52 & 0.777312596684577 & 0.445374806630846 & 0.222687403315423 \tabularnewline
53 & 0.74779586820326 & 0.504408263593481 & 0.25220413179674 \tabularnewline
54 & 0.706753456388361 & 0.586493087223278 & 0.293246543611639 \tabularnewline
55 & 0.718277548377823 & 0.563444903244353 & 0.281722451622177 \tabularnewline
56 & 0.736460049504134 & 0.527079900991732 & 0.263539950495866 \tabularnewline
57 & 0.700307838092254 & 0.599384323815491 & 0.299692161907746 \tabularnewline
58 & 0.658798741344757 & 0.682402517310487 & 0.341201258655243 \tabularnewline
59 & 0.616290895837243 & 0.767418208325513 & 0.383709104162757 \tabularnewline
60 & 0.582054694067227 & 0.835890611865546 & 0.417945305932773 \tabularnewline
61 & 0.536891857456761 & 0.926216285086478 & 0.463108142543239 \tabularnewline
62 & 0.584150091951162 & 0.831699816097676 & 0.415849908048838 \tabularnewline
63 & 0.803997488403913 & 0.392005023192174 & 0.196002511596087 \tabularnewline
64 & 0.769463358116071 & 0.461073283767858 & 0.230536641883929 \tabularnewline
65 & 0.774496164697185 & 0.45100767060563 & 0.225503835302815 \tabularnewline
66 & 0.742936915828695 & 0.51412616834261 & 0.257063084171305 \tabularnewline
67 & 0.714202970445463 & 0.571594059109073 & 0.285797029554537 \tabularnewline
68 & 0.692396593295833 & 0.615206813408334 & 0.307603406704167 \tabularnewline
69 & 0.802577236250145 & 0.394845527499711 & 0.197422763749855 \tabularnewline
70 & 0.768988189568734 & 0.462023620862533 & 0.231011810431266 \tabularnewline
71 & 0.739377756622804 & 0.521244486754391 & 0.260622243377195 \tabularnewline
72 & 0.724088293007418 & 0.551823413985163 & 0.275911706992582 \tabularnewline
73 & 0.760644566536628 & 0.478710866926743 & 0.239355433463372 \tabularnewline
74 & 0.728830710316429 & 0.542338579367143 & 0.271169289683571 \tabularnewline
75 & 0.7643704794303 & 0.4712590411394 & 0.2356295205697 \tabularnewline
76 & 0.728692344838109 & 0.542615310323783 & 0.271307655161891 \tabularnewline
77 & 0.730177165149184 & 0.539645669701633 & 0.269822834850816 \tabularnewline
78 & 0.699317670653394 & 0.601364658693212 & 0.300682329346606 \tabularnewline
79 & 0.765478729499877 & 0.469042541000245 & 0.234521270500123 \tabularnewline
80 & 0.739000621862728 & 0.521998756274545 & 0.260999378137272 \tabularnewline
81 & 0.723468840464586 & 0.553062319070828 & 0.276531159535414 \tabularnewline
82 & 0.690590363025762 & 0.618819273948476 & 0.309409636974238 \tabularnewline
83 & 0.782130746554753 & 0.435738506890493 & 0.217869253445247 \tabularnewline
84 & 0.797744286272776 & 0.404511427454447 & 0.202255713727224 \tabularnewline
85 & 0.789890124073659 & 0.420219751852682 & 0.210109875926341 \tabularnewline
86 & 0.762185964628367 & 0.475628070743266 & 0.237814035371633 \tabularnewline
87 & 0.726870927854069 & 0.546258144291863 & 0.273129072145931 \tabularnewline
88 & 0.747737393204246 & 0.504525213591509 & 0.252262606795754 \tabularnewline
89 & 0.780336376572614 & 0.439327246854772 & 0.219663623427386 \tabularnewline
90 & 0.75583202787052 & 0.488335944258959 & 0.24416797212948 \tabularnewline
91 & 0.727607012040002 & 0.544785975919997 & 0.272392987959998 \tabularnewline
92 & 0.716740285973181 & 0.566519428053638 & 0.283259714026819 \tabularnewline
93 & 0.877625854630127 & 0.244748290739745 & 0.122374145369873 \tabularnewline
94 & 0.855787215748143 & 0.288425568503713 & 0.144212784251857 \tabularnewline
95 & 0.832242968578095 & 0.33551406284381 & 0.167757031421905 \tabularnewline
96 & 0.80888118792366 & 0.38223762415268 & 0.19111881207634 \tabularnewline
97 & 0.802656015006272 & 0.394687969987456 & 0.197343984993728 \tabularnewline
98 & 0.78319860469908 & 0.433602790601839 & 0.216801395300919 \tabularnewline
99 & 0.842832883917236 & 0.314334232165529 & 0.157167116082764 \tabularnewline
100 & 0.81455668488876 & 0.370886630222479 & 0.185443315111239 \tabularnewline
101 & 0.881530249623283 & 0.236939500753434 & 0.118469750376717 \tabularnewline
102 & 0.855550917160246 & 0.288898165679508 & 0.144449082839754 \tabularnewline
103 & 0.825768040765712 & 0.348463918468576 & 0.174231959234288 \tabularnewline
104 & 0.796432934037716 & 0.407134131924568 & 0.203567065962284 \tabularnewline
105 & 0.770951486315743 & 0.458097027368514 & 0.229048513684257 \tabularnewline
106 & 0.73754494637703 & 0.524910107245939 & 0.26245505362297 \tabularnewline
107 & 0.703376028025047 & 0.593247943949906 & 0.296623971974953 \tabularnewline
108 & 0.670582510712749 & 0.658834978574503 & 0.329417489287251 \tabularnewline
109 & 0.860121246894954 & 0.279757506210093 & 0.139878753105046 \tabularnewline
110 & 0.840381921531612 & 0.319236156936777 & 0.159618078468388 \tabularnewline
111 & 0.873124361524316 & 0.253751276951368 & 0.126875638475684 \tabularnewline
112 & 0.85392590597044 & 0.29214818805912 & 0.14607409402956 \tabularnewline
113 & 0.833972325086582 & 0.332055349826837 & 0.166027674913418 \tabularnewline
114 & 0.798757818568279 & 0.402484362863442 & 0.201242181431721 \tabularnewline
115 & 0.78768239008474 & 0.424635219830518 & 0.212317609915259 \tabularnewline
116 & 0.752908017185312 & 0.494183965629375 & 0.247091982814688 \tabularnewline
117 & 0.738231106252623 & 0.523537787494755 & 0.261768893747377 \tabularnewline
118 & 0.930693090432811 & 0.138613819134377 & 0.0693069095671885 \tabularnewline
119 & 0.932698513760833 & 0.134602972478335 & 0.0673014862391673 \tabularnewline
120 & 0.920788290912893 & 0.158423418174214 & 0.0792117090871072 \tabularnewline
121 & 0.914796076020215 & 0.17040784795957 & 0.0852039239797852 \tabularnewline
122 & 0.889904602882723 & 0.220190794234555 & 0.110095397117277 \tabularnewline
123 & 0.868364877436704 & 0.263270245126592 & 0.131635122563296 \tabularnewline
124 & 0.877255813765586 & 0.245488372468829 & 0.122744186234414 \tabularnewline
125 & 0.895469021121869 & 0.209061957756263 & 0.104530978878132 \tabularnewline
126 & 0.864269945431438 & 0.271460109137123 & 0.135730054568562 \tabularnewline
127 & 0.849755409570497 & 0.300489180859006 & 0.150244590429503 \tabularnewline
128 & 0.844825428846148 & 0.310349142307703 & 0.155174571153852 \tabularnewline
129 & 0.891889546096321 & 0.216220907807358 & 0.108110453903679 \tabularnewline
130 & 0.966916836963612 & 0.066166326072776 & 0.033083163036388 \tabularnewline
131 & 0.953589414823093 & 0.0928211703538136 & 0.0464105851769068 \tabularnewline
132 & 0.960197664715152 & 0.0796046705696953 & 0.0398023352848476 \tabularnewline
133 & 0.943404306878609 & 0.113191386242783 & 0.0565956931213915 \tabularnewline
134 & 0.91879201435309 & 0.162415971293818 & 0.081207985646909 \tabularnewline
135 & 0.893000201585837 & 0.213999596828325 & 0.106999798414163 \tabularnewline
136 & 0.89252853052965 & 0.214942938940698 & 0.107471469470349 \tabularnewline
137 & 0.889520207481531 & 0.220959585036938 & 0.110479792518469 \tabularnewline
138 & 0.852426825790329 & 0.295146348419341 & 0.147573174209671 \tabularnewline
139 & 0.98656178859673 & 0.0268764228065406 & 0.0134382114032703 \tabularnewline
140 & 0.984893949433326 & 0.0302121011333485 & 0.0151060505666742 \tabularnewline
141 & 0.994031928664284 & 0.0119361426714318 & 0.00596807133571591 \tabularnewline
142 & 0.988255436741953 & 0.0234891265160943 & 0.0117445632580471 \tabularnewline
143 & 0.978153409662698 & 0.0436931806746039 & 0.021846590337302 \tabularnewline
144 & 0.96058750577567 & 0.0788249884486611 & 0.0394124942243306 \tabularnewline
145 & 0.93049243234045 & 0.139015135319098 & 0.0695075676595491 \tabularnewline
146 & 0.918651611480295 & 0.16269677703941 & 0.0813483885197051 \tabularnewline
147 & 0.929817199673893 & 0.140365600652214 & 0.070182800326107 \tabularnewline
148 & 0.90869868548233 & 0.182602629035338 & 0.0913013145176692 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=111464&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]8[/C][C]0.531517602314718[/C][C]0.936964795370565[/C][C]0.468482397685282[/C][/ROW]
[ROW][C]9[/C][C]0.813912439370541[/C][C]0.372175121258917[/C][C]0.186087560629459[/C][/ROW]
[ROW][C]10[/C][C]0.720282442022533[/C][C]0.559435115954934[/C][C]0.279717557977467[/C][/ROW]
[ROW][C]11[/C][C]0.657904630900664[/C][C]0.684190738198672[/C][C]0.342095369099336[/C][/ROW]
[ROW][C]12[/C][C]0.682046198411008[/C][C]0.635907603177984[/C][C]0.317953801588992[/C][/ROW]
[ROW][C]13[/C][C]0.839697007354928[/C][C]0.320605985290144[/C][C]0.160302992645072[/C][/ROW]
[ROW][C]14[/C][C]0.777653251711927[/C][C]0.444693496576146[/C][C]0.222346748288073[/C][/ROW]
[ROW][C]15[/C][C]0.708630852715897[/C][C]0.582738294568205[/C][C]0.291369147284103[/C][/ROW]
[ROW][C]16[/C][C]0.626709851704733[/C][C]0.746580296590534[/C][C]0.373290148295267[/C][/ROW]
[ROW][C]17[/C][C]0.551477162208686[/C][C]0.897045675582628[/C][C]0.448522837791314[/C][/ROW]
[ROW][C]18[/C][C]0.468531778426622[/C][C]0.937063556853245[/C][C]0.531468221573378[/C][/ROW]
[ROW][C]19[/C][C]0.403363486957646[/C][C]0.806726973915293[/C][C]0.596636513042354[/C][/ROW]
[ROW][C]20[/C][C]0.358130058318999[/C][C]0.716260116637998[/C][C]0.641869941681001[/C][/ROW]
[ROW][C]21[/C][C]0.28966382044265[/C][C]0.579327640885301[/C][C]0.71033617955735[/C][/ROW]
[ROW][C]22[/C][C]0.233659622251139[/C][C]0.467319244502279[/C][C]0.76634037774886[/C][/ROW]
[ROW][C]23[/C][C]0.499896904590234[/C][C]0.999793809180468[/C][C]0.500103095409766[/C][/ROW]
[ROW][C]24[/C][C]0.430998638418712[/C][C]0.861997276837425[/C][C]0.569001361581288[/C][/ROW]
[ROW][C]25[/C][C]0.439681655794539[/C][C]0.879363311589077[/C][C]0.560318344205461[/C][/ROW]
[ROW][C]26[/C][C]0.481729327812443[/C][C]0.963458655624887[/C][C]0.518270672187557[/C][/ROW]
[ROW][C]27[/C][C]0.498678804679855[/C][C]0.99735760935971[/C][C]0.501321195320146[/C][/ROW]
[ROW][C]28[/C][C]0.569867922415226[/C][C]0.860264155169548[/C][C]0.430132077584774[/C][/ROW]
[ROW][C]29[/C][C]0.515370753626273[/C][C]0.969258492747453[/C][C]0.484629246373727[/C][/ROW]
[ROW][C]30[/C][C]0.548234400854444[/C][C]0.903531198291111[/C][C]0.451765599145556[/C][/ROW]
[ROW][C]31[/C][C]0.624874159201382[/C][C]0.750251681597237[/C][C]0.375125840798618[/C][/ROW]
[ROW][C]32[/C][C]0.631707968350881[/C][C]0.736584063298238[/C][C]0.368292031649119[/C][/ROW]
[ROW][C]33[/C][C]0.574397600474495[/C][C]0.85120479905101[/C][C]0.425602399525505[/C][/ROW]
[ROW][C]34[/C][C]0.580294796736032[/C][C]0.839410406527936[/C][C]0.419705203263968[/C][/ROW]
[ROW][C]35[/C][C]0.586781163401302[/C][C]0.826437673197396[/C][C]0.413218836598698[/C][/ROW]
[ROW][C]36[/C][C]0.644065551357884[/C][C]0.711868897284232[/C][C]0.355934448642116[/C][/ROW]
[ROW][C]37[/C][C]0.603717052393996[/C][C]0.792565895212008[/C][C]0.396282947606004[/C][/ROW]
[ROW][C]38[/C][C]0.601984516906267[/C][C]0.796030966187465[/C][C]0.398015483093733[/C][/ROW]
[ROW][C]39[/C][C]0.583203706676927[/C][C]0.833592586646146[/C][C]0.416796293323073[/C][/ROW]
[ROW][C]40[/C][C]0.580066870365003[/C][C]0.839866259269995[/C][C]0.419933129634997[/C][/ROW]
[ROW][C]41[/C][C]0.695676359134982[/C][C]0.608647281730037[/C][C]0.304323640865018[/C][/ROW]
[ROW][C]42[/C][C]0.65619042799185[/C][C]0.6876191440163[/C][C]0.34380957200815[/C][/ROW]
[ROW][C]43[/C][C]0.670473732113247[/C][C]0.659052535773506[/C][C]0.329526267886753[/C][/ROW]
[ROW][C]44[/C][C]0.660285123595949[/C][C]0.679429752808103[/C][C]0.339714876404051[/C][/ROW]
[ROW][C]45[/C][C]0.62617417477356[/C][C]0.74765165045288[/C][C]0.37382582522644[/C][/ROW]
[ROW][C]46[/C][C]0.61998798838026[/C][C]0.760024023239479[/C][C]0.380012011619739[/C][/ROW]
[ROW][C]47[/C][C]0.579848754068995[/C][C]0.84030249186201[/C][C]0.420151245931005[/C][/ROW]
[ROW][C]48[/C][C]0.581942907240775[/C][C]0.83611418551845[/C][C]0.418057092759225[/C][/ROW]
[ROW][C]49[/C][C]0.81042378888093[/C][C]0.379152422238139[/C][C]0.189576211119069[/C][/ROW]
[ROW][C]50[/C][C]0.774466482259161[/C][C]0.451067035481678[/C][C]0.225533517740839[/C][/ROW]
[ROW][C]51[/C][C]0.811814022922836[/C][C]0.376371954154329[/C][C]0.188185977077164[/C][/ROW]
[ROW][C]52[/C][C]0.777312596684577[/C][C]0.445374806630846[/C][C]0.222687403315423[/C][/ROW]
[ROW][C]53[/C][C]0.74779586820326[/C][C]0.504408263593481[/C][C]0.25220413179674[/C][/ROW]
[ROW][C]54[/C][C]0.706753456388361[/C][C]0.586493087223278[/C][C]0.293246543611639[/C][/ROW]
[ROW][C]55[/C][C]0.718277548377823[/C][C]0.563444903244353[/C][C]0.281722451622177[/C][/ROW]
[ROW][C]56[/C][C]0.736460049504134[/C][C]0.527079900991732[/C][C]0.263539950495866[/C][/ROW]
[ROW][C]57[/C][C]0.700307838092254[/C][C]0.599384323815491[/C][C]0.299692161907746[/C][/ROW]
[ROW][C]58[/C][C]0.658798741344757[/C][C]0.682402517310487[/C][C]0.341201258655243[/C][/ROW]
[ROW][C]59[/C][C]0.616290895837243[/C][C]0.767418208325513[/C][C]0.383709104162757[/C][/ROW]
[ROW][C]60[/C][C]0.582054694067227[/C][C]0.835890611865546[/C][C]0.417945305932773[/C][/ROW]
[ROW][C]61[/C][C]0.536891857456761[/C][C]0.926216285086478[/C][C]0.463108142543239[/C][/ROW]
[ROW][C]62[/C][C]0.584150091951162[/C][C]0.831699816097676[/C][C]0.415849908048838[/C][/ROW]
[ROW][C]63[/C][C]0.803997488403913[/C][C]0.392005023192174[/C][C]0.196002511596087[/C][/ROW]
[ROW][C]64[/C][C]0.769463358116071[/C][C]0.461073283767858[/C][C]0.230536641883929[/C][/ROW]
[ROW][C]65[/C][C]0.774496164697185[/C][C]0.45100767060563[/C][C]0.225503835302815[/C][/ROW]
[ROW][C]66[/C][C]0.742936915828695[/C][C]0.51412616834261[/C][C]0.257063084171305[/C][/ROW]
[ROW][C]67[/C][C]0.714202970445463[/C][C]0.571594059109073[/C][C]0.285797029554537[/C][/ROW]
[ROW][C]68[/C][C]0.692396593295833[/C][C]0.615206813408334[/C][C]0.307603406704167[/C][/ROW]
[ROW][C]69[/C][C]0.802577236250145[/C][C]0.394845527499711[/C][C]0.197422763749855[/C][/ROW]
[ROW][C]70[/C][C]0.768988189568734[/C][C]0.462023620862533[/C][C]0.231011810431266[/C][/ROW]
[ROW][C]71[/C][C]0.739377756622804[/C][C]0.521244486754391[/C][C]0.260622243377195[/C][/ROW]
[ROW][C]72[/C][C]0.724088293007418[/C][C]0.551823413985163[/C][C]0.275911706992582[/C][/ROW]
[ROW][C]73[/C][C]0.760644566536628[/C][C]0.478710866926743[/C][C]0.239355433463372[/C][/ROW]
[ROW][C]74[/C][C]0.728830710316429[/C][C]0.542338579367143[/C][C]0.271169289683571[/C][/ROW]
[ROW][C]75[/C][C]0.7643704794303[/C][C]0.4712590411394[/C][C]0.2356295205697[/C][/ROW]
[ROW][C]76[/C][C]0.728692344838109[/C][C]0.542615310323783[/C][C]0.271307655161891[/C][/ROW]
[ROW][C]77[/C][C]0.730177165149184[/C][C]0.539645669701633[/C][C]0.269822834850816[/C][/ROW]
[ROW][C]78[/C][C]0.699317670653394[/C][C]0.601364658693212[/C][C]0.300682329346606[/C][/ROW]
[ROW][C]79[/C][C]0.765478729499877[/C][C]0.469042541000245[/C][C]0.234521270500123[/C][/ROW]
[ROW][C]80[/C][C]0.739000621862728[/C][C]0.521998756274545[/C][C]0.260999378137272[/C][/ROW]
[ROW][C]81[/C][C]0.723468840464586[/C][C]0.553062319070828[/C][C]0.276531159535414[/C][/ROW]
[ROW][C]82[/C][C]0.690590363025762[/C][C]0.618819273948476[/C][C]0.309409636974238[/C][/ROW]
[ROW][C]83[/C][C]0.782130746554753[/C][C]0.435738506890493[/C][C]0.217869253445247[/C][/ROW]
[ROW][C]84[/C][C]0.797744286272776[/C][C]0.404511427454447[/C][C]0.202255713727224[/C][/ROW]
[ROW][C]85[/C][C]0.789890124073659[/C][C]0.420219751852682[/C][C]0.210109875926341[/C][/ROW]
[ROW][C]86[/C][C]0.762185964628367[/C][C]0.475628070743266[/C][C]0.237814035371633[/C][/ROW]
[ROW][C]87[/C][C]0.726870927854069[/C][C]0.546258144291863[/C][C]0.273129072145931[/C][/ROW]
[ROW][C]88[/C][C]0.747737393204246[/C][C]0.504525213591509[/C][C]0.252262606795754[/C][/ROW]
[ROW][C]89[/C][C]0.780336376572614[/C][C]0.439327246854772[/C][C]0.219663623427386[/C][/ROW]
[ROW][C]90[/C][C]0.75583202787052[/C][C]0.488335944258959[/C][C]0.24416797212948[/C][/ROW]
[ROW][C]91[/C][C]0.727607012040002[/C][C]0.544785975919997[/C][C]0.272392987959998[/C][/ROW]
[ROW][C]92[/C][C]0.716740285973181[/C][C]0.566519428053638[/C][C]0.283259714026819[/C][/ROW]
[ROW][C]93[/C][C]0.877625854630127[/C][C]0.244748290739745[/C][C]0.122374145369873[/C][/ROW]
[ROW][C]94[/C][C]0.855787215748143[/C][C]0.288425568503713[/C][C]0.144212784251857[/C][/ROW]
[ROW][C]95[/C][C]0.832242968578095[/C][C]0.33551406284381[/C][C]0.167757031421905[/C][/ROW]
[ROW][C]96[/C][C]0.80888118792366[/C][C]0.38223762415268[/C][C]0.19111881207634[/C][/ROW]
[ROW][C]97[/C][C]0.802656015006272[/C][C]0.394687969987456[/C][C]0.197343984993728[/C][/ROW]
[ROW][C]98[/C][C]0.78319860469908[/C][C]0.433602790601839[/C][C]0.216801395300919[/C][/ROW]
[ROW][C]99[/C][C]0.842832883917236[/C][C]0.314334232165529[/C][C]0.157167116082764[/C][/ROW]
[ROW][C]100[/C][C]0.81455668488876[/C][C]0.370886630222479[/C][C]0.185443315111239[/C][/ROW]
[ROW][C]101[/C][C]0.881530249623283[/C][C]0.236939500753434[/C][C]0.118469750376717[/C][/ROW]
[ROW][C]102[/C][C]0.855550917160246[/C][C]0.288898165679508[/C][C]0.144449082839754[/C][/ROW]
[ROW][C]103[/C][C]0.825768040765712[/C][C]0.348463918468576[/C][C]0.174231959234288[/C][/ROW]
[ROW][C]104[/C][C]0.796432934037716[/C][C]0.407134131924568[/C][C]0.203567065962284[/C][/ROW]
[ROW][C]105[/C][C]0.770951486315743[/C][C]0.458097027368514[/C][C]0.229048513684257[/C][/ROW]
[ROW][C]106[/C][C]0.73754494637703[/C][C]0.524910107245939[/C][C]0.26245505362297[/C][/ROW]
[ROW][C]107[/C][C]0.703376028025047[/C][C]0.593247943949906[/C][C]0.296623971974953[/C][/ROW]
[ROW][C]108[/C][C]0.670582510712749[/C][C]0.658834978574503[/C][C]0.329417489287251[/C][/ROW]
[ROW][C]109[/C][C]0.860121246894954[/C][C]0.279757506210093[/C][C]0.139878753105046[/C][/ROW]
[ROW][C]110[/C][C]0.840381921531612[/C][C]0.319236156936777[/C][C]0.159618078468388[/C][/ROW]
[ROW][C]111[/C][C]0.873124361524316[/C][C]0.253751276951368[/C][C]0.126875638475684[/C][/ROW]
[ROW][C]112[/C][C]0.85392590597044[/C][C]0.29214818805912[/C][C]0.14607409402956[/C][/ROW]
[ROW][C]113[/C][C]0.833972325086582[/C][C]0.332055349826837[/C][C]0.166027674913418[/C][/ROW]
[ROW][C]114[/C][C]0.798757818568279[/C][C]0.402484362863442[/C][C]0.201242181431721[/C][/ROW]
[ROW][C]115[/C][C]0.78768239008474[/C][C]0.424635219830518[/C][C]0.212317609915259[/C][/ROW]
[ROW][C]116[/C][C]0.752908017185312[/C][C]0.494183965629375[/C][C]0.247091982814688[/C][/ROW]
[ROW][C]117[/C][C]0.738231106252623[/C][C]0.523537787494755[/C][C]0.261768893747377[/C][/ROW]
[ROW][C]118[/C][C]0.930693090432811[/C][C]0.138613819134377[/C][C]0.0693069095671885[/C][/ROW]
[ROW][C]119[/C][C]0.932698513760833[/C][C]0.134602972478335[/C][C]0.0673014862391673[/C][/ROW]
[ROW][C]120[/C][C]0.920788290912893[/C][C]0.158423418174214[/C][C]0.0792117090871072[/C][/ROW]
[ROW][C]121[/C][C]0.914796076020215[/C][C]0.17040784795957[/C][C]0.0852039239797852[/C][/ROW]
[ROW][C]122[/C][C]0.889904602882723[/C][C]0.220190794234555[/C][C]0.110095397117277[/C][/ROW]
[ROW][C]123[/C][C]0.868364877436704[/C][C]0.263270245126592[/C][C]0.131635122563296[/C][/ROW]
[ROW][C]124[/C][C]0.877255813765586[/C][C]0.245488372468829[/C][C]0.122744186234414[/C][/ROW]
[ROW][C]125[/C][C]0.895469021121869[/C][C]0.209061957756263[/C][C]0.104530978878132[/C][/ROW]
[ROW][C]126[/C][C]0.864269945431438[/C][C]0.271460109137123[/C][C]0.135730054568562[/C][/ROW]
[ROW][C]127[/C][C]0.849755409570497[/C][C]0.300489180859006[/C][C]0.150244590429503[/C][/ROW]
[ROW][C]128[/C][C]0.844825428846148[/C][C]0.310349142307703[/C][C]0.155174571153852[/C][/ROW]
[ROW][C]129[/C][C]0.891889546096321[/C][C]0.216220907807358[/C][C]0.108110453903679[/C][/ROW]
[ROW][C]130[/C][C]0.966916836963612[/C][C]0.066166326072776[/C][C]0.033083163036388[/C][/ROW]
[ROW][C]131[/C][C]0.953589414823093[/C][C]0.0928211703538136[/C][C]0.0464105851769068[/C][/ROW]
[ROW][C]132[/C][C]0.960197664715152[/C][C]0.0796046705696953[/C][C]0.0398023352848476[/C][/ROW]
[ROW][C]133[/C][C]0.943404306878609[/C][C]0.113191386242783[/C][C]0.0565956931213915[/C][/ROW]
[ROW][C]134[/C][C]0.91879201435309[/C][C]0.162415971293818[/C][C]0.081207985646909[/C][/ROW]
[ROW][C]135[/C][C]0.893000201585837[/C][C]0.213999596828325[/C][C]0.106999798414163[/C][/ROW]
[ROW][C]136[/C][C]0.89252853052965[/C][C]0.214942938940698[/C][C]0.107471469470349[/C][/ROW]
[ROW][C]137[/C][C]0.889520207481531[/C][C]0.220959585036938[/C][C]0.110479792518469[/C][/ROW]
[ROW][C]138[/C][C]0.852426825790329[/C][C]0.295146348419341[/C][C]0.147573174209671[/C][/ROW]
[ROW][C]139[/C][C]0.98656178859673[/C][C]0.0268764228065406[/C][C]0.0134382114032703[/C][/ROW]
[ROW][C]140[/C][C]0.984893949433326[/C][C]0.0302121011333485[/C][C]0.0151060505666742[/C][/ROW]
[ROW][C]141[/C][C]0.994031928664284[/C][C]0.0119361426714318[/C][C]0.00596807133571591[/C][/ROW]
[ROW][C]142[/C][C]0.988255436741953[/C][C]0.0234891265160943[/C][C]0.0117445632580471[/C][/ROW]
[ROW][C]143[/C][C]0.978153409662698[/C][C]0.0436931806746039[/C][C]0.021846590337302[/C][/ROW]
[ROW][C]144[/C][C]0.96058750577567[/C][C]0.0788249884486611[/C][C]0.0394124942243306[/C][/ROW]
[ROW][C]145[/C][C]0.93049243234045[/C][C]0.139015135319098[/C][C]0.0695075676595491[/C][/ROW]
[ROW][C]146[/C][C]0.918651611480295[/C][C]0.16269677703941[/C][C]0.0813483885197051[/C][/ROW]
[ROW][C]147[/C][C]0.929817199673893[/C][C]0.140365600652214[/C][C]0.070182800326107[/C][/ROW]
[ROW][C]148[/C][C]0.90869868548233[/C][C]0.182602629035338[/C][C]0.0913013145176692[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=111464&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=111464&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.5315176023147180.9369647953705650.468482397685282
90.8139124393705410.3721751212589170.186087560629459
100.7202824420225330.5594351159549340.279717557977467
110.6579046309006640.6841907381986720.342095369099336
120.6820461984110080.6359076031779840.317953801588992
130.8396970073549280.3206059852901440.160302992645072
140.7776532517119270.4446934965761460.222346748288073
150.7086308527158970.5827382945682050.291369147284103
160.6267098517047330.7465802965905340.373290148295267
170.5514771622086860.8970456755826280.448522837791314
180.4685317784266220.9370635568532450.531468221573378
190.4033634869576460.8067269739152930.596636513042354
200.3581300583189990.7162601166379980.641869941681001
210.289663820442650.5793276408853010.71033617955735
220.2336596222511390.4673192445022790.76634037774886
230.4998969045902340.9997938091804680.500103095409766
240.4309986384187120.8619972768374250.569001361581288
250.4396816557945390.8793633115890770.560318344205461
260.4817293278124430.9634586556248870.518270672187557
270.4986788046798550.997357609359710.501321195320146
280.5698679224152260.8602641551695480.430132077584774
290.5153707536262730.9692584927474530.484629246373727
300.5482344008544440.9035311982911110.451765599145556
310.6248741592013820.7502516815972370.375125840798618
320.6317079683508810.7365840632982380.368292031649119
330.5743976004744950.851204799051010.425602399525505
340.5802947967360320.8394104065279360.419705203263968
350.5867811634013020.8264376731973960.413218836598698
360.6440655513578840.7118688972842320.355934448642116
370.6037170523939960.7925658952120080.396282947606004
380.6019845169062670.7960309661874650.398015483093733
390.5832037066769270.8335925866461460.416796293323073
400.5800668703650030.8398662592699950.419933129634997
410.6956763591349820.6086472817300370.304323640865018
420.656190427991850.68761914401630.34380957200815
430.6704737321132470.6590525357735060.329526267886753
440.6602851235959490.6794297528081030.339714876404051
450.626174174773560.747651650452880.37382582522644
460.619987988380260.7600240232394790.380012011619739
470.5798487540689950.840302491862010.420151245931005
480.5819429072407750.836114185518450.418057092759225
490.810423788880930.3791524222381390.189576211119069
500.7744664822591610.4510670354816780.225533517740839
510.8118140229228360.3763719541543290.188185977077164
520.7773125966845770.4453748066308460.222687403315423
530.747795868203260.5044082635934810.25220413179674
540.7067534563883610.5864930872232780.293246543611639
550.7182775483778230.5634449032443530.281722451622177
560.7364600495041340.5270799009917320.263539950495866
570.7003078380922540.5993843238154910.299692161907746
580.6587987413447570.6824025173104870.341201258655243
590.6162908958372430.7674182083255130.383709104162757
600.5820546940672270.8358906118655460.417945305932773
610.5368918574567610.9262162850864780.463108142543239
620.5841500919511620.8316998160976760.415849908048838
630.8039974884039130.3920050231921740.196002511596087
640.7694633581160710.4610732837678580.230536641883929
650.7744961646971850.451007670605630.225503835302815
660.7429369158286950.514126168342610.257063084171305
670.7142029704454630.5715940591090730.285797029554537
680.6923965932958330.6152068134083340.307603406704167
690.8025772362501450.3948455274997110.197422763749855
700.7689881895687340.4620236208625330.231011810431266
710.7393777566228040.5212444867543910.260622243377195
720.7240882930074180.5518234139851630.275911706992582
730.7606445665366280.4787108669267430.239355433463372
740.7288307103164290.5423385793671430.271169289683571
750.76437047943030.47125904113940.2356295205697
760.7286923448381090.5426153103237830.271307655161891
770.7301771651491840.5396456697016330.269822834850816
780.6993176706533940.6013646586932120.300682329346606
790.7654787294998770.4690425410002450.234521270500123
800.7390006218627280.5219987562745450.260999378137272
810.7234688404645860.5530623190708280.276531159535414
820.6905903630257620.6188192739484760.309409636974238
830.7821307465547530.4357385068904930.217869253445247
840.7977442862727760.4045114274544470.202255713727224
850.7898901240736590.4202197518526820.210109875926341
860.7621859646283670.4756280707432660.237814035371633
870.7268709278540690.5462581442918630.273129072145931
880.7477373932042460.5045252135915090.252262606795754
890.7803363765726140.4393272468547720.219663623427386
900.755832027870520.4883359442589590.24416797212948
910.7276070120400020.5447859759199970.272392987959998
920.7167402859731810.5665194280536380.283259714026819
930.8776258546301270.2447482907397450.122374145369873
940.8557872157481430.2884255685037130.144212784251857
950.8322429685780950.335514062843810.167757031421905
960.808881187923660.382237624152680.19111881207634
970.8026560150062720.3946879699874560.197343984993728
980.783198604699080.4336027906018390.216801395300919
990.8428328839172360.3143342321655290.157167116082764
1000.814556684888760.3708866302224790.185443315111239
1010.8815302496232830.2369395007534340.118469750376717
1020.8555509171602460.2888981656795080.144449082839754
1030.8257680407657120.3484639184685760.174231959234288
1040.7964329340377160.4071341319245680.203567065962284
1050.7709514863157430.4580970273685140.229048513684257
1060.737544946377030.5249101072459390.26245505362297
1070.7033760280250470.5932479439499060.296623971974953
1080.6705825107127490.6588349785745030.329417489287251
1090.8601212468949540.2797575062100930.139878753105046
1100.8403819215316120.3192361569367770.159618078468388
1110.8731243615243160.2537512769513680.126875638475684
1120.853925905970440.292148188059120.14607409402956
1130.8339723250865820.3320553498268370.166027674913418
1140.7987578185682790.4024843628634420.201242181431721
1150.787682390084740.4246352198305180.212317609915259
1160.7529080171853120.4941839656293750.247091982814688
1170.7382311062526230.5235377874947550.261768893747377
1180.9306930904328110.1386138191343770.0693069095671885
1190.9326985137608330.1346029724783350.0673014862391673
1200.9207882909128930.1584234181742140.0792117090871072
1210.9147960760202150.170407847959570.0852039239797852
1220.8899046028827230.2201907942345550.110095397117277
1230.8683648774367040.2632702451265920.131635122563296
1240.8772558137655860.2454883724688290.122744186234414
1250.8954690211218690.2090619577562630.104530978878132
1260.8642699454314380.2714601091371230.135730054568562
1270.8497554095704970.3004891808590060.150244590429503
1280.8448254288461480.3103491423077030.155174571153852
1290.8918895460963210.2162209078073580.108110453903679
1300.9669168369636120.0661663260727760.033083163036388
1310.9535894148230930.09282117035381360.0464105851769068
1320.9601976647151520.07960467056969530.0398023352848476
1330.9434043068786090.1131913862427830.0565956931213915
1340.918792014353090.1624159712938180.081207985646909
1350.8930002015858370.2139995968283250.106999798414163
1360.892528530529650.2149429389406980.107471469470349
1370.8895202074815310.2209595850369380.110479792518469
1380.8524268257903290.2951463484193410.147573174209671
1390.986561788596730.02687642280654060.0134382114032703
1400.9848939494333260.03021210113334850.0151060505666742
1410.9940319286642840.01193614267143180.00596807133571591
1420.9882554367419530.02348912651609430.0117445632580471
1430.9781534096626980.04369318067460390.021846590337302
1440.960587505775670.07882498844866110.0394124942243306
1450.930492432340450.1390151353190980.0695075676595491
1460.9186516114802950.162696777039410.0813483885197051
1470.9298171996738930.1403656006522140.070182800326107
1480.908698685482330.1826026290353380.0913013145176692






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level50.0354609929078014OK
10% type I error level90.0638297872340425OK

\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 & 5 & 0.0354609929078014 & OK \tabularnewline
10% type I error level & 9 & 0.0638297872340425 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=111464&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]5[/C][C]0.0354609929078014[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]9[/C][C]0.0638297872340425[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=111464&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=111464&T=6

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


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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level50.0354609929078014OK
10% type I error level90.0638297872340425OK


Figures (Output of Computation)

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

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