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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, 19 Nov 2010 12:57:20 +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/Nov/19/t1290171345xt36yhb5bwiv6mn.htm/, Retrieved Wed, 19 Nov 2025 23:24:42 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=97952, Retrieved Wed, 19 Nov 2025 23:24:42 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact459

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Decreasing Compet...] [2010-11-17 09:04:39] [b98453cac15ba1066b407e146608df68]
- R PD    [Multiple Regression] [Workshop 7 - Regr...] [2010-11-19 12:57:20] [3de277db83c2673156e9464be2ef6f69] [Current]
-   P       [Multiple Regression] [Workshop 7 - Regr...] [2010-11-19 13:07:16] [8b017ffbf7b0eded54d8efebfb3e4cfa]
-   P         [Multiple Regression] [workshop 7 determ...] [2010-11-19 14:10:30] [4eaa304e6a28c475ba490fccf4c01ad3]
-             [Multiple Regression] [workshop 7 multip...] [2010-11-23 14:08:09] [af8eb90b4bf1bcfcc4325c143dbee260]
- RM          [Multiple Regression] [fdbvdfb] [2011-11-20 13:58:58] [a9671b130b33f9fcb98554992ce4582f]
-           [Multiple Regression] [workshop 7 multip...] [2010-11-23 14:03:11] [af8eb90b4bf1bcfcc4325c143dbee260]
- RM        [Multiple Regression] [fhfhg] [2011-11-20 13:58:17] [a9671b130b33f9fcb98554992ce4582f]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
9	26	24	14	11	12	24
9	23	25	11	7	8	25
9	25	17	6	17	8	30
9	23	18	12	10	8	19
9	19	18	8	12	9	22
9	29	16	10	12	7	22
10	25	20	10	11	4	25
10	21	16	11	11	11	23
10	22	18	16	12	7	17
10	25	17	11	13	7	21
10	24	23	13	14	12	19
10	18	30	12	16	10	19
10	22	23	8	11	10	15
10	15	18	12	10	8	16
10	22	15	11	11	8	23
10	28	12	4	15	4	27
10	20	21	9	9	9	22
10	12	15	8	11	8	14
10	24	20	8	17	7	22
10	20	31	14	17	11	23
10	21	27	15	11	9	23
10	20	34	16	18	11	21
10	21	21	9	14	13	19
10	23	31	14	10	8	18
10	28	19	11	11	8	20
10	24	16	8	15	9	23
10	24	20	9	15	6	25
10	24	21	9	13	9	19
10	23	22	9	16	9	24
10	23	17	9	13	6	22
10	29	24	10	9	6	25
10	24	25	16	18	16	26
10	18	26	11	18	5	29
10	25	25	8	12	7	32
10	21	17	9	17	9	25
10	26	32	16	9	6	29
10	22	33	11	9	6	28
10	22	13	16	12	5	17
10	22	32	12	18	12	28
10	23	25	12	12	7	29
10	30	29	14	18	10	26
10	23	22	9	14	9	25
10	17	18	10	15	8	14
10	23	17	9	16	5	25
10	23	20	10	10	8	26
10	25	15	12	11	8	20
10	24	20	14	14	10	18
10	24	33	14	9	6	32
10	23	29	10	12	8	25
10	21	23	14	17	7	25
10	24	26	16	5	4	23
10	24	18	9	12	8	21
10	28	20	10	12	8	20
10	16	11	6	6	4	15
10	20	28	8	24	20	30
10	29	26	13	12	8	24
10	27	22	10	12	8	26
10	22	17	8	14	6	24
10	28	12	7	7	4	22
10	16	14	15	13	8	14
10	25	17	9	12	9	24
10	24	21	10	13	6	24
10	28	19	12	14	7	24
10	24	18	13	8	9	24
10	23	10	10	11	5	19
10	30	29	11	9	5	31
10	24	31	8	11	8	22
10	21	19	9	13	8	27
10	25	9	13	10	6	19
10	25	20	11	11	8	25
10	22	28	8	12	7	20
10	23	19	9	9	7	21
10	26	30	9	15	9	27
10	23	29	15	18	11	23
10	25	26	9	15	6	25
10	21	23	10	12	8	20
10	25	13	14	13	6	21
10	24	21	12	14	9	22
10	29	19	12	10	8	23
10	22	28	11	13	6	25
10	27	23	14	13	10	25
10	26	18	6	11	8	17
10	22	21	12	13	8	19
10	24	20	8	16	10	25
10	27	23	14	8	5	19
10	24	21	11	16	7	20
10	24	21	10	11	5	26
10	29	15	14	9	8	23
10	22	28	12	16	14	27
10	21	19	10	12	7	17
10	24	26	14	14	8	17
10	24	10	5	8	6	19
10	23	16	11	9	5	17
10	20	22	10	15	6	22
10	27	19	9	11	10	21
10	26	31	10	21	12	32
10	25	31	16	14	9	21
10	21	29	13	18	12	21
10	21	19	9	12	7	18
10	19	22	10	13	8	18
10	21	23	10	15	10	23
10	21	15	7	12	6	19
10	16	20	9	19	10	20
10	22	18	8	15	10	21
10	29	23	14	11	10	20
10	15	25	14	11	5	17
10	17	21	8	10	7	18
10	15	24	9	13	10	19
10	21	25	14	15	11	22
10	21	17	14	12	6	15
10	19	13	8	12	7	14
10	24	28	8	16	12	18
10	20	21	8	9	11	24
10	17	25	7	18	11	35
10	23	9	6	8	11	29
10	24	16	8	13	5	21
10	14	19	6	17	8	25
10	19	17	11	9	6	20
10	24	25	14	15	9	22
10	13	20	11	8	4	13
10	22	29	11	7	4	26
10	16	14	11	12	7	17
10	19	22	14	14	11	25
10	25	15	8	6	6	20
10	25	19	20	8	7	19
10	23	20	11	17	8	21
10	24	15	8	10	4	22
10	26	20	11	11	8	24
10	26	18	10	14	9	21
10	25	33	14	11	8	26
10	18	22	11	13	11	24
10	21	16	9	12	8	16
10	26	17	9	11	5	23
10	23	16	8	9	4	18
10	23	21	10	12	8	16
10	22	26	13	20	10	26
10	20	18	13	12	6	19
10	13	18	12	13	9	21
10	24	17	8	12	9	21
10	15	22	13	12	13	22
10	14	30	14	9	9	23
10	22	30	12	15	10	29
10	10	24	14	24	20	21
10	24	21	15	7	5	21
10	22	21	13	17	11	23
10	24	29	16	11	6	27
10	19	31	9	17	9	25
10	20	20	9	11	7	21
10	13	16	9	12	9	10
10	20	22	8	14	10	20
10	22	20	7	11	9	26
10	24	28	16	16	8	24
10	29	38	11	21	7	29
10	12	22	9	14	6	19
10	20	20	11	20	13	24
10	21	17	9	13	6	19
10	24	28	14	11	8	24
10	22	22	13	15	10	22
10	20	31	16	19	16	17

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'George Udny Yule' @ 72.249.76.132

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 9 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=97952&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]9 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=97952&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=97952&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 time9 seconds
R Server'George Udny Yule' @ 72.249.76.132






Multiple Linear Regression - Estimated Regression Equation
O[t] = + 28.2103187556997 -1.21064258019503Month[t] -0.0662911074151968CM[t] + 0.220046465031066D[t] -0.137980253016112PE[t] -0.267847267331188PC[t] + 0.415218299040598PS[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
O[t] =  +  28.2103187556997 -1.21064258019503Month[t] -0.0662911074151968CM[t] +  0.220046465031066D[t] -0.137980253016112PE[t] -0.267847267331188PC[t] +  0.415218299040598PS[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=97952&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]O[t] =  +  28.2103187556997 -1.21064258019503Month[t] -0.0662911074151968CM[t] +  0.220046465031066D[t] -0.137980253016112PE[t] -0.267847267331188PC[t] +  0.415218299040598PS[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=97952&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=97952&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
O[t] = + 28.2103187556997 -1.21064258019503Month[t] -0.0662911074151968CM[t] + 0.220046465031066D[t] -0.137980253016112PE[t] -0.267847267331188PC[t] + 0.415218299040598PS[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)28.210318755699714.9452051.88760.0609880.030494
Month-1.210642580195031.484765-0.81540.4161330.208066
CM-0.06629110741519680.06322-1.04860.2960390.148019
D0.2200464650310660.1127691.95130.0528590.02643
PE-0.1379802530161120.105245-1.3110.1918230.095912
PC-0.2678472673311880.131479-2.03720.0433660.021683
PS0.4152182990405980.0762625.444700

\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) & 28.2103187556997 & 14.945205 & 1.8876 & 0.060988 & 0.030494 \tabularnewline
Month & -1.21064258019503 & 1.484765 & -0.8154 & 0.416133 & 0.208066 \tabularnewline
CM & -0.0662911074151968 & 0.06322 & -1.0486 & 0.296039 & 0.148019 \tabularnewline
D & 0.220046465031066 & 0.112769 & 1.9513 & 0.052859 & 0.02643 \tabularnewline
PE & -0.137980253016112 & 0.105245 & -1.311 & 0.191823 & 0.095912 \tabularnewline
PC & -0.267847267331188 & 0.131479 & -2.0372 & 0.043366 & 0.021683 \tabularnewline
PS & 0.415218299040598 & 0.076262 & 5.4447 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=97952&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]28.2103187556997[/C][C]14.945205[/C][C]1.8876[/C][C]0.060988[/C][C]0.030494[/C][/ROW]
[ROW][C]Month[/C][C]-1.21064258019503[/C][C]1.484765[/C][C]-0.8154[/C][C]0.416133[/C][C]0.208066[/C][/ROW]
[ROW][C]CM[/C][C]-0.0662911074151968[/C][C]0.06322[/C][C]-1.0486[/C][C]0.296039[/C][C]0.148019[/C][/ROW]
[ROW][C]D[/C][C]0.220046465031066[/C][C]0.112769[/C][C]1.9513[/C][C]0.052859[/C][C]0.02643[/C][/ROW]
[ROW][C]PE[/C][C]-0.137980253016112[/C][C]0.105245[/C][C]-1.311[/C][C]0.191823[/C][C]0.095912[/C][/ROW]
[ROW][C]PC[/C][C]-0.267847267331188[/C][C]0.131479[/C][C]-2.0372[/C][C]0.043366[/C][C]0.021683[/C][/ROW]
[ROW][C]PS[/C][C]0.415218299040598[/C][C]0.076262[/C][C]5.4447[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=97952&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=97952&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)28.210318755699714.9452051.88760.0609880.030494
Month-1.210642580195031.484765-0.81540.4161330.208066
CM-0.06629110741519680.06322-1.04860.2960390.148019
D0.2200464650310660.1127691.95130.0528590.02643
PE-0.1379802530161120.105245-1.3110.1918230.095912
PC-0.2678472673311880.131479-2.03720.0433660.021683
PS0.4152182990405980.0762625.444700






Multiple Linear Regression - Regression Statistics
Multiple R0.475140658091823
R-squared0.225758644971930
Adjusted R-squared0.195196486220822
F-TEST (value)7.3868684084283
F-TEST (DF numerator)6
F-TEST (DF denominator)152
p-value5.99882120422279e-07
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.50319920019948
Sum Squared Residuals1865.40550471430

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.475140658091823 \tabularnewline
R-squared & 0.225758644971930 \tabularnewline
Adjusted R-squared & 0.195196486220822 \tabularnewline
F-TEST (value) & 7.3868684084283 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 152 \tabularnewline
p-value & 5.99882120422279e-07 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 3.50319920019948 \tabularnewline
Sum Squared Residuals & 1865.40550471430 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=97952&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.475140658091823[/C][/ROW]
[ROW][C]R-squared[/C][C]0.225758644971930[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.195196486220822[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]7.3868684084283[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]152[/C][/ROW]
[ROW][C]p-value[/C][C]5.99882120422279e-07[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]3.50319920019948[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1865.40550471430[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=97952&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=97952&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 R0.475140658091823
R-squared0.225758644971930
Adjusted R-squared0.195196486220822
F-TEST (value)7.3868684084283
F-TEST (DF numerator)6
F-TEST (DF denominator)152
p-value5.99882120422279e-07
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.50319920019948
Sum Squared Residuals1865.40550471430






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
12624.03748865223741.96251134776263
22325.3495865301589-2.34958653015892
32525.4759720293670-0.47597202936704
42323.1284201938044-0.128420193804447
51922.9500814574386-3.95008145743856
62924.05845113699354.94154886300653
72524.76982107926910.23017892073088
82122.5496645045615-1.54966450456147
92221.95941363695150.0405863630485405
102522.44836536235762.55163463764240
112420.18305846017533.81694153982470
121819.758708271868-1.75870827186801
132218.37158823256833.6284117674317
141520.6721227164876-5.67212271648763
152223.4194974139702-1.41949741397023
162824.25838673442113.74161326557895
172022.1745527790774-2.17455277907735
181219.0223933275116-7.02239332751165
192421.45264993199502.54735006800504
202021.3875557703300-1.38755577033004
212123.2363427177809-2.23634271778094
222020.6603585270493-0.660358527049277
232119.16760754755021.83239245244975
242321.08086784823341.91913215176660
252821.90867808718766.09132191281235
262421.87329863206622.12670136793380
272423.46215906751120.537840932488764
282420.37697686989113.62302313010889
292321.97283649863061.02716350136943
302322.69133799866730.308662001332743
312924.24492262097824.75507737902181
322421.99383365233312.00616634766691
331825.0192850575274-7.01928505752743
342525.9632786504055-0.963278650405518
352122.5815300817310-1.58153008173104
362626.6957457480054-0.695745748005403
372225.1140040163943-3.11400401639428
382222.8265637086898-0.82656370868982
392222.5514357077084-0.551435707708401
402325.597809613408-2.59780961340799
413022.89565989659737.1043401034027
422322.66401530370340.335984696296611
431718.7116919232637-1.71169192326374
442323.7908994040719-0.790899404071903
452324.2516305620011-1.25163056200108
462522.39388898187952.6061110181205
472420.72245448307373.27754551692626
482427.4350166076499-3.43501660764987
492322.96383179019150.0361682098085093
502123.8197102970576-2.81971029705757
512425.6897981449798-1.68979814497982
522421.81211431056522.18788568943480
532821.48436026172536.51563973827473
541621.0239734605562-5.02397346055621
552019.79619121857990.203808781420062
562923.40762620848975.59237379151032
572723.84308784113853.15691215886153
582223.1637478787013-1.16374787870127
592823.94627665844024.05372334155984
601620.3530491841121-4.35304918411208
612522.8562130477712.143786952229
622423.47665663211870.523343367881268
632823.64350425666404.35649574333604
642424.2220288125445-0.222028812544515
652322.67357509184630.326424908153681
663026.89267061050803.10732938949196
672421.28348200119332.71651799880672
682124.0991527443775-3.09915274437747
692523.27013858003961.72986141996036
702523.91847847497541.08152152502456
712220.78178573967281.21821426032725
722322.42761122952950.572388770470475
732622.82614278944693.1738572105531
742321.60220419717541.39779580282461
752523.06441242302011.93558757697994
762121.2854869394797-0.285486939479683
772523.64151645444281.35848354555722
782422.144790909091.85520909091001
792923.51235970235665.48764029764338
802223.647883644284-1.64788364428402
812723.56808950712843.43191049287155
822619.62908197232576.37091802767428
832221.30496353231550.695036467684501
842422.03274328013931.96725671986070
852723.10591731462143.89408268537864
862421.35404187460792.64595812539212
872424.8509010035633-0.85090100356334
882924.35559731509574.64440268490435
892222.1416478096984-0.141647809698437
902120.57284373934990.427156260650136
912420.44518407420433.55481592579566
922421.71943625840812.28056374159187
932321.94139882033721.05860117966277
942022.3039684205901-2.30396842059012
952721.34810892150375.65189107849627
962623.42456632217552.57543367782448
972521.94684739602173.05315260397831
982120.06382740170090.936172598299126
992120.76801557335940.231984426640604
1001920.3833611957976-1.38336119579757
1012121.5815065428908-0.581506542890763
1022121.2761526393298-0.276152639329836
1031619.7627574909190-3.76275749091905
1042220.64243255182341.35756744817658
1052921.76795851795777.23204148204232
1061521.7289577426614-6.72895774266144
1071720.6913473995302-3.69134739953016
1081519.9102562803143-4.91025628031433
1092121.6460446218128-0.646044621812849
1102121.0230224835545-0.0230224835545169
1111919.2848425566571-0.284842556657119
1122418.06019179287125.93980820712883
1132022.2492483774651-2.24924837746510
1141725.0896164950748-8.08961649507482
1152324.8187204846044-1.81872048460443
1162422.3902116093421.60978839065802
1171422.0566557391386-8.05665573913863
1181922.8529153427126-3.85291534271264
1192422.18173915647521.81826084352477
1201320.4211887148614-7.42118871486136
1212225.3603868886685-3.36038688866847
1221621.1243457414569-5.12434574145691
1231923.2285530941963-4.22855309419634
1242522.73929892149822.26070107850183
1252524.15566599980620.844334000193828
1262321.42972376071641.57027623928362
1272423.55350904217730.446490957822704
1282623.50326017593482.49673982406516
1292621.48835300223284.51164699776715
1302524.13205177271170.86794822728832
1311822.2911756530787-4.29117565307866
1322119.86860503019261.13139496980740
1332623.65036407107132.34963592892873
1342321.96432499161581.03567500838418
1352319.75719595814773.24280404185232
1362222.5985262477796-0.598526247779605
1372022.3975581072706-2.39755810727064
1381322.0664261853111-9.0664261853111
1392421.39051168561812.60948831438186
1401521.5031177034133-6.50311770341333
1411423.0933834365365-9.09338343653651
1422224.0488715152901-2.04887151529010
1431017.6446697470617-7.64466974706174
1442424.4269628455801-0.42696284558013
1452221.83042037945090.169579620549057
1462425.7882219661376-1.78822196613757
1471921.6534545779183-2.65345457791828
1482022.0853596160821-2.08535961608210
1491317.1094479686178-4.10944796861783
1502020.1000300761381-0.100030076138146
1512223.1856636465606-1.18566364656058
1522423.38326237668800.616737623311959
1532923.27415647483445.72584352516564
1541220.9762473114534-8.97624731145337
1552020.9222015621339-0.922201562133892
1562121.4456831015455-0.445683101545463
1572423.63307071170650.366929288293533
1582221.89271874635860.107281253641439
1592017.72114206346042.27885793653958

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 26 & 24.0374886522374 & 1.96251134776263 \tabularnewline
2 & 23 & 25.3495865301589 & -2.34958653015892 \tabularnewline
3 & 25 & 25.4759720293670 & -0.47597202936704 \tabularnewline
4 & 23 & 23.1284201938044 & -0.128420193804447 \tabularnewline
5 & 19 & 22.9500814574386 & -3.95008145743856 \tabularnewline
6 & 29 & 24.0584511369935 & 4.94154886300653 \tabularnewline
7 & 25 & 24.7698210792691 & 0.23017892073088 \tabularnewline
8 & 21 & 22.5496645045615 & -1.54966450456147 \tabularnewline
9 & 22 & 21.9594136369515 & 0.0405863630485405 \tabularnewline
10 & 25 & 22.4483653623576 & 2.55163463764240 \tabularnewline
11 & 24 & 20.1830584601753 & 3.81694153982470 \tabularnewline
12 & 18 & 19.758708271868 & -1.75870827186801 \tabularnewline
13 & 22 & 18.3715882325683 & 3.6284117674317 \tabularnewline
14 & 15 & 20.6721227164876 & -5.67212271648763 \tabularnewline
15 & 22 & 23.4194974139702 & -1.41949741397023 \tabularnewline
16 & 28 & 24.2583867344211 & 3.74161326557895 \tabularnewline
17 & 20 & 22.1745527790774 & -2.17455277907735 \tabularnewline
18 & 12 & 19.0223933275116 & -7.02239332751165 \tabularnewline
19 & 24 & 21.4526499319950 & 2.54735006800504 \tabularnewline
20 & 20 & 21.3875557703300 & -1.38755577033004 \tabularnewline
21 & 21 & 23.2363427177809 & -2.23634271778094 \tabularnewline
22 & 20 & 20.6603585270493 & -0.660358527049277 \tabularnewline
23 & 21 & 19.1676075475502 & 1.83239245244975 \tabularnewline
24 & 23 & 21.0808678482334 & 1.91913215176660 \tabularnewline
25 & 28 & 21.9086780871876 & 6.09132191281235 \tabularnewline
26 & 24 & 21.8732986320662 & 2.12670136793380 \tabularnewline
27 & 24 & 23.4621590675112 & 0.537840932488764 \tabularnewline
28 & 24 & 20.3769768698911 & 3.62302313010889 \tabularnewline
29 & 23 & 21.9728364986306 & 1.02716350136943 \tabularnewline
30 & 23 & 22.6913379986673 & 0.308662001332743 \tabularnewline
31 & 29 & 24.2449226209782 & 4.75507737902181 \tabularnewline
32 & 24 & 21.9938336523331 & 2.00616634766691 \tabularnewline
33 & 18 & 25.0192850575274 & -7.01928505752743 \tabularnewline
34 & 25 & 25.9632786504055 & -0.963278650405518 \tabularnewline
35 & 21 & 22.5815300817310 & -1.58153008173104 \tabularnewline
36 & 26 & 26.6957457480054 & -0.695745748005403 \tabularnewline
37 & 22 & 25.1140040163943 & -3.11400401639428 \tabularnewline
38 & 22 & 22.8265637086898 & -0.82656370868982 \tabularnewline
39 & 22 & 22.5514357077084 & -0.551435707708401 \tabularnewline
40 & 23 & 25.597809613408 & -2.59780961340799 \tabularnewline
41 & 30 & 22.8956598965973 & 7.1043401034027 \tabularnewline
42 & 23 & 22.6640153037034 & 0.335984696296611 \tabularnewline
43 & 17 & 18.7116919232637 & -1.71169192326374 \tabularnewline
44 & 23 & 23.7908994040719 & -0.790899404071903 \tabularnewline
45 & 23 & 24.2516305620011 & -1.25163056200108 \tabularnewline
46 & 25 & 22.3938889818795 & 2.6061110181205 \tabularnewline
47 & 24 & 20.7224544830737 & 3.27754551692626 \tabularnewline
48 & 24 & 27.4350166076499 & -3.43501660764987 \tabularnewline
49 & 23 & 22.9638317901915 & 0.0361682098085093 \tabularnewline
50 & 21 & 23.8197102970576 & -2.81971029705757 \tabularnewline
51 & 24 & 25.6897981449798 & -1.68979814497982 \tabularnewline
52 & 24 & 21.8121143105652 & 2.18788568943480 \tabularnewline
53 & 28 & 21.4843602617253 & 6.51563973827473 \tabularnewline
54 & 16 & 21.0239734605562 & -5.02397346055621 \tabularnewline
55 & 20 & 19.7961912185799 & 0.203808781420062 \tabularnewline
56 & 29 & 23.4076262084897 & 5.59237379151032 \tabularnewline
57 & 27 & 23.8430878411385 & 3.15691215886153 \tabularnewline
58 & 22 & 23.1637478787013 & -1.16374787870127 \tabularnewline
59 & 28 & 23.9462766584402 & 4.05372334155984 \tabularnewline
60 & 16 & 20.3530491841121 & -4.35304918411208 \tabularnewline
61 & 25 & 22.856213047771 & 2.143786952229 \tabularnewline
62 & 24 & 23.4766566321187 & 0.523343367881268 \tabularnewline
63 & 28 & 23.6435042566640 & 4.35649574333604 \tabularnewline
64 & 24 & 24.2220288125445 & -0.222028812544515 \tabularnewline
65 & 23 & 22.6735750918463 & 0.326424908153681 \tabularnewline
66 & 30 & 26.8926706105080 & 3.10732938949196 \tabularnewline
67 & 24 & 21.2834820011933 & 2.71651799880672 \tabularnewline
68 & 21 & 24.0991527443775 & -3.09915274437747 \tabularnewline
69 & 25 & 23.2701385800396 & 1.72986141996036 \tabularnewline
70 & 25 & 23.9184784749754 & 1.08152152502456 \tabularnewline
71 & 22 & 20.7817857396728 & 1.21821426032725 \tabularnewline
72 & 23 & 22.4276112295295 & 0.572388770470475 \tabularnewline
73 & 26 & 22.8261427894469 & 3.1738572105531 \tabularnewline
74 & 23 & 21.6022041971754 & 1.39779580282461 \tabularnewline
75 & 25 & 23.0644124230201 & 1.93558757697994 \tabularnewline
76 & 21 & 21.2854869394797 & -0.285486939479683 \tabularnewline
77 & 25 & 23.6415164544428 & 1.35848354555722 \tabularnewline
78 & 24 & 22.14479090909 & 1.85520909091001 \tabularnewline
79 & 29 & 23.5123597023566 & 5.48764029764338 \tabularnewline
80 & 22 & 23.647883644284 & -1.64788364428402 \tabularnewline
81 & 27 & 23.5680895071284 & 3.43191049287155 \tabularnewline
82 & 26 & 19.6290819723257 & 6.37091802767428 \tabularnewline
83 & 22 & 21.3049635323155 & 0.695036467684501 \tabularnewline
84 & 24 & 22.0327432801393 & 1.96725671986070 \tabularnewline
85 & 27 & 23.1059173146214 & 3.89408268537864 \tabularnewline
86 & 24 & 21.3540418746079 & 2.64595812539212 \tabularnewline
87 & 24 & 24.8509010035633 & -0.85090100356334 \tabularnewline
88 & 29 & 24.3555973150957 & 4.64440268490435 \tabularnewline
89 & 22 & 22.1416478096984 & -0.141647809698437 \tabularnewline
90 & 21 & 20.5728437393499 & 0.427156260650136 \tabularnewline
91 & 24 & 20.4451840742043 & 3.55481592579566 \tabularnewline
92 & 24 & 21.7194362584081 & 2.28056374159187 \tabularnewline
93 & 23 & 21.9413988203372 & 1.05860117966277 \tabularnewline
94 & 20 & 22.3039684205901 & -2.30396842059012 \tabularnewline
95 & 27 & 21.3481089215037 & 5.65189107849627 \tabularnewline
96 & 26 & 23.4245663221755 & 2.57543367782448 \tabularnewline
97 & 25 & 21.9468473960217 & 3.05315260397831 \tabularnewline
98 & 21 & 20.0638274017009 & 0.936172598299126 \tabularnewline
99 & 21 & 20.7680155733594 & 0.231984426640604 \tabularnewline
100 & 19 & 20.3833611957976 & -1.38336119579757 \tabularnewline
101 & 21 & 21.5815065428908 & -0.581506542890763 \tabularnewline
102 & 21 & 21.2761526393298 & -0.276152639329836 \tabularnewline
103 & 16 & 19.7627574909190 & -3.76275749091905 \tabularnewline
104 & 22 & 20.6424325518234 & 1.35756744817658 \tabularnewline
105 & 29 & 21.7679585179577 & 7.23204148204232 \tabularnewline
106 & 15 & 21.7289577426614 & -6.72895774266144 \tabularnewline
107 & 17 & 20.6913473995302 & -3.69134739953016 \tabularnewline
108 & 15 & 19.9102562803143 & -4.91025628031433 \tabularnewline
109 & 21 & 21.6460446218128 & -0.646044621812849 \tabularnewline
110 & 21 & 21.0230224835545 & -0.0230224835545169 \tabularnewline
111 & 19 & 19.2848425566571 & -0.284842556657119 \tabularnewline
112 & 24 & 18.0601917928712 & 5.93980820712883 \tabularnewline
113 & 20 & 22.2492483774651 & -2.24924837746510 \tabularnewline
114 & 17 & 25.0896164950748 & -8.08961649507482 \tabularnewline
115 & 23 & 24.8187204846044 & -1.81872048460443 \tabularnewline
116 & 24 & 22.390211609342 & 1.60978839065802 \tabularnewline
117 & 14 & 22.0566557391386 & -8.05665573913863 \tabularnewline
118 & 19 & 22.8529153427126 & -3.85291534271264 \tabularnewline
119 & 24 & 22.1817391564752 & 1.81826084352477 \tabularnewline
120 & 13 & 20.4211887148614 & -7.42118871486136 \tabularnewline
121 & 22 & 25.3603868886685 & -3.36038688866847 \tabularnewline
122 & 16 & 21.1243457414569 & -5.12434574145691 \tabularnewline
123 & 19 & 23.2285530941963 & -4.22855309419634 \tabularnewline
124 & 25 & 22.7392989214982 & 2.26070107850183 \tabularnewline
125 & 25 & 24.1556659998062 & 0.844334000193828 \tabularnewline
126 & 23 & 21.4297237607164 & 1.57027623928362 \tabularnewline
127 & 24 & 23.5535090421773 & 0.446490957822704 \tabularnewline
128 & 26 & 23.5032601759348 & 2.49673982406516 \tabularnewline
129 & 26 & 21.4883530022328 & 4.51164699776715 \tabularnewline
130 & 25 & 24.1320517727117 & 0.86794822728832 \tabularnewline
131 & 18 & 22.2911756530787 & -4.29117565307866 \tabularnewline
132 & 21 & 19.8686050301926 & 1.13139496980740 \tabularnewline
133 & 26 & 23.6503640710713 & 2.34963592892873 \tabularnewline
134 & 23 & 21.9643249916158 & 1.03567500838418 \tabularnewline
135 & 23 & 19.7571959581477 & 3.24280404185232 \tabularnewline
136 & 22 & 22.5985262477796 & -0.598526247779605 \tabularnewline
137 & 20 & 22.3975581072706 & -2.39755810727064 \tabularnewline
138 & 13 & 22.0664261853111 & -9.0664261853111 \tabularnewline
139 & 24 & 21.3905116856181 & 2.60948831438186 \tabularnewline
140 & 15 & 21.5031177034133 & -6.50311770341333 \tabularnewline
141 & 14 & 23.0933834365365 & -9.09338343653651 \tabularnewline
142 & 22 & 24.0488715152901 & -2.04887151529010 \tabularnewline
143 & 10 & 17.6446697470617 & -7.64466974706174 \tabularnewline
144 & 24 & 24.4269628455801 & -0.42696284558013 \tabularnewline
145 & 22 & 21.8304203794509 & 0.169579620549057 \tabularnewline
146 & 24 & 25.7882219661376 & -1.78822196613757 \tabularnewline
147 & 19 & 21.6534545779183 & -2.65345457791828 \tabularnewline
148 & 20 & 22.0853596160821 & -2.08535961608210 \tabularnewline
149 & 13 & 17.1094479686178 & -4.10944796861783 \tabularnewline
150 & 20 & 20.1000300761381 & -0.100030076138146 \tabularnewline
151 & 22 & 23.1856636465606 & -1.18566364656058 \tabularnewline
152 & 24 & 23.3832623766880 & 0.616737623311959 \tabularnewline
153 & 29 & 23.2741564748344 & 5.72584352516564 \tabularnewline
154 & 12 & 20.9762473114534 & -8.97624731145337 \tabularnewline
155 & 20 & 20.9222015621339 & -0.922201562133892 \tabularnewline
156 & 21 & 21.4456831015455 & -0.445683101545463 \tabularnewline
157 & 24 & 23.6330707117065 & 0.366929288293533 \tabularnewline
158 & 22 & 21.8927187463586 & 0.107281253641439 \tabularnewline
159 & 20 & 17.7211420634604 & 2.27885793653958 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=97952&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]26[/C][C]24.0374886522374[/C][C]1.96251134776263[/C][/ROW]
[ROW][C]2[/C][C]23[/C][C]25.3495865301589[/C][C]-2.34958653015892[/C][/ROW]
[ROW][C]3[/C][C]25[/C][C]25.4759720293670[/C][C]-0.47597202936704[/C][/ROW]
[ROW][C]4[/C][C]23[/C][C]23.1284201938044[/C][C]-0.128420193804447[/C][/ROW]
[ROW][C]5[/C][C]19[/C][C]22.9500814574386[/C][C]-3.95008145743856[/C][/ROW]
[ROW][C]6[/C][C]29[/C][C]24.0584511369935[/C][C]4.94154886300653[/C][/ROW]
[ROW][C]7[/C][C]25[/C][C]24.7698210792691[/C][C]0.23017892073088[/C][/ROW]
[ROW][C]8[/C][C]21[/C][C]22.5496645045615[/C][C]-1.54966450456147[/C][/ROW]
[ROW][C]9[/C][C]22[/C][C]21.9594136369515[/C][C]0.0405863630485405[/C][/ROW]
[ROW][C]10[/C][C]25[/C][C]22.4483653623576[/C][C]2.55163463764240[/C][/ROW]
[ROW][C]11[/C][C]24[/C][C]20.1830584601753[/C][C]3.81694153982470[/C][/ROW]
[ROW][C]12[/C][C]18[/C][C]19.758708271868[/C][C]-1.75870827186801[/C][/ROW]
[ROW][C]13[/C][C]22[/C][C]18.3715882325683[/C][C]3.6284117674317[/C][/ROW]
[ROW][C]14[/C][C]15[/C][C]20.6721227164876[/C][C]-5.67212271648763[/C][/ROW]
[ROW][C]15[/C][C]22[/C][C]23.4194974139702[/C][C]-1.41949741397023[/C][/ROW]
[ROW][C]16[/C][C]28[/C][C]24.2583867344211[/C][C]3.74161326557895[/C][/ROW]
[ROW][C]17[/C][C]20[/C][C]22.1745527790774[/C][C]-2.17455277907735[/C][/ROW]
[ROW][C]18[/C][C]12[/C][C]19.0223933275116[/C][C]-7.02239332751165[/C][/ROW]
[ROW][C]19[/C][C]24[/C][C]21.4526499319950[/C][C]2.54735006800504[/C][/ROW]
[ROW][C]20[/C][C]20[/C][C]21.3875557703300[/C][C]-1.38755577033004[/C][/ROW]
[ROW][C]21[/C][C]21[/C][C]23.2363427177809[/C][C]-2.23634271778094[/C][/ROW]
[ROW][C]22[/C][C]20[/C][C]20.6603585270493[/C][C]-0.660358527049277[/C][/ROW]
[ROW][C]23[/C][C]21[/C][C]19.1676075475502[/C][C]1.83239245244975[/C][/ROW]
[ROW][C]24[/C][C]23[/C][C]21.0808678482334[/C][C]1.91913215176660[/C][/ROW]
[ROW][C]25[/C][C]28[/C][C]21.9086780871876[/C][C]6.09132191281235[/C][/ROW]
[ROW][C]26[/C][C]24[/C][C]21.8732986320662[/C][C]2.12670136793380[/C][/ROW]
[ROW][C]27[/C][C]24[/C][C]23.4621590675112[/C][C]0.537840932488764[/C][/ROW]
[ROW][C]28[/C][C]24[/C][C]20.3769768698911[/C][C]3.62302313010889[/C][/ROW]
[ROW][C]29[/C][C]23[/C][C]21.9728364986306[/C][C]1.02716350136943[/C][/ROW]
[ROW][C]30[/C][C]23[/C][C]22.6913379986673[/C][C]0.308662001332743[/C][/ROW]
[ROW][C]31[/C][C]29[/C][C]24.2449226209782[/C][C]4.75507737902181[/C][/ROW]
[ROW][C]32[/C][C]24[/C][C]21.9938336523331[/C][C]2.00616634766691[/C][/ROW]
[ROW][C]33[/C][C]18[/C][C]25.0192850575274[/C][C]-7.01928505752743[/C][/ROW]
[ROW][C]34[/C][C]25[/C][C]25.9632786504055[/C][C]-0.963278650405518[/C][/ROW]
[ROW][C]35[/C][C]21[/C][C]22.5815300817310[/C][C]-1.58153008173104[/C][/ROW]
[ROW][C]36[/C][C]26[/C][C]26.6957457480054[/C][C]-0.695745748005403[/C][/ROW]
[ROW][C]37[/C][C]22[/C][C]25.1140040163943[/C][C]-3.11400401639428[/C][/ROW]
[ROW][C]38[/C][C]22[/C][C]22.8265637086898[/C][C]-0.82656370868982[/C][/ROW]
[ROW][C]39[/C][C]22[/C][C]22.5514357077084[/C][C]-0.551435707708401[/C][/ROW]
[ROW][C]40[/C][C]23[/C][C]25.597809613408[/C][C]-2.59780961340799[/C][/ROW]
[ROW][C]41[/C][C]30[/C][C]22.8956598965973[/C][C]7.1043401034027[/C][/ROW]
[ROW][C]42[/C][C]23[/C][C]22.6640153037034[/C][C]0.335984696296611[/C][/ROW]
[ROW][C]43[/C][C]17[/C][C]18.7116919232637[/C][C]-1.71169192326374[/C][/ROW]
[ROW][C]44[/C][C]23[/C][C]23.7908994040719[/C][C]-0.790899404071903[/C][/ROW]
[ROW][C]45[/C][C]23[/C][C]24.2516305620011[/C][C]-1.25163056200108[/C][/ROW]
[ROW][C]46[/C][C]25[/C][C]22.3938889818795[/C][C]2.6061110181205[/C][/ROW]
[ROW][C]47[/C][C]24[/C][C]20.7224544830737[/C][C]3.27754551692626[/C][/ROW]
[ROW][C]48[/C][C]24[/C][C]27.4350166076499[/C][C]-3.43501660764987[/C][/ROW]
[ROW][C]49[/C][C]23[/C][C]22.9638317901915[/C][C]0.0361682098085093[/C][/ROW]
[ROW][C]50[/C][C]21[/C][C]23.8197102970576[/C][C]-2.81971029705757[/C][/ROW]
[ROW][C]51[/C][C]24[/C][C]25.6897981449798[/C][C]-1.68979814497982[/C][/ROW]
[ROW][C]52[/C][C]24[/C][C]21.8121143105652[/C][C]2.18788568943480[/C][/ROW]
[ROW][C]53[/C][C]28[/C][C]21.4843602617253[/C][C]6.51563973827473[/C][/ROW]
[ROW][C]54[/C][C]16[/C][C]21.0239734605562[/C][C]-5.02397346055621[/C][/ROW]
[ROW][C]55[/C][C]20[/C][C]19.7961912185799[/C][C]0.203808781420062[/C][/ROW]
[ROW][C]56[/C][C]29[/C][C]23.4076262084897[/C][C]5.59237379151032[/C][/ROW]
[ROW][C]57[/C][C]27[/C][C]23.8430878411385[/C][C]3.15691215886153[/C][/ROW]
[ROW][C]58[/C][C]22[/C][C]23.1637478787013[/C][C]-1.16374787870127[/C][/ROW]
[ROW][C]59[/C][C]28[/C][C]23.9462766584402[/C][C]4.05372334155984[/C][/ROW]
[ROW][C]60[/C][C]16[/C][C]20.3530491841121[/C][C]-4.35304918411208[/C][/ROW]
[ROW][C]61[/C][C]25[/C][C]22.856213047771[/C][C]2.143786952229[/C][/ROW]
[ROW][C]62[/C][C]24[/C][C]23.4766566321187[/C][C]0.523343367881268[/C][/ROW]
[ROW][C]63[/C][C]28[/C][C]23.6435042566640[/C][C]4.35649574333604[/C][/ROW]
[ROW][C]64[/C][C]24[/C][C]24.2220288125445[/C][C]-0.222028812544515[/C][/ROW]
[ROW][C]65[/C][C]23[/C][C]22.6735750918463[/C][C]0.326424908153681[/C][/ROW]
[ROW][C]66[/C][C]30[/C][C]26.8926706105080[/C][C]3.10732938949196[/C][/ROW]
[ROW][C]67[/C][C]24[/C][C]21.2834820011933[/C][C]2.71651799880672[/C][/ROW]
[ROW][C]68[/C][C]21[/C][C]24.0991527443775[/C][C]-3.09915274437747[/C][/ROW]
[ROW][C]69[/C][C]25[/C][C]23.2701385800396[/C][C]1.72986141996036[/C][/ROW]
[ROW][C]70[/C][C]25[/C][C]23.9184784749754[/C][C]1.08152152502456[/C][/ROW]
[ROW][C]71[/C][C]22[/C][C]20.7817857396728[/C][C]1.21821426032725[/C][/ROW]
[ROW][C]72[/C][C]23[/C][C]22.4276112295295[/C][C]0.572388770470475[/C][/ROW]
[ROW][C]73[/C][C]26[/C][C]22.8261427894469[/C][C]3.1738572105531[/C][/ROW]
[ROW][C]74[/C][C]23[/C][C]21.6022041971754[/C][C]1.39779580282461[/C][/ROW]
[ROW][C]75[/C][C]25[/C][C]23.0644124230201[/C][C]1.93558757697994[/C][/ROW]
[ROW][C]76[/C][C]21[/C][C]21.2854869394797[/C][C]-0.285486939479683[/C][/ROW]
[ROW][C]77[/C][C]25[/C][C]23.6415164544428[/C][C]1.35848354555722[/C][/ROW]
[ROW][C]78[/C][C]24[/C][C]22.14479090909[/C][C]1.85520909091001[/C][/ROW]
[ROW][C]79[/C][C]29[/C][C]23.5123597023566[/C][C]5.48764029764338[/C][/ROW]
[ROW][C]80[/C][C]22[/C][C]23.647883644284[/C][C]-1.64788364428402[/C][/ROW]
[ROW][C]81[/C][C]27[/C][C]23.5680895071284[/C][C]3.43191049287155[/C][/ROW]
[ROW][C]82[/C][C]26[/C][C]19.6290819723257[/C][C]6.37091802767428[/C][/ROW]
[ROW][C]83[/C][C]22[/C][C]21.3049635323155[/C][C]0.695036467684501[/C][/ROW]
[ROW][C]84[/C][C]24[/C][C]22.0327432801393[/C][C]1.96725671986070[/C][/ROW]
[ROW][C]85[/C][C]27[/C][C]23.1059173146214[/C][C]3.89408268537864[/C][/ROW]
[ROW][C]86[/C][C]24[/C][C]21.3540418746079[/C][C]2.64595812539212[/C][/ROW]
[ROW][C]87[/C][C]24[/C][C]24.8509010035633[/C][C]-0.85090100356334[/C][/ROW]
[ROW][C]88[/C][C]29[/C][C]24.3555973150957[/C][C]4.64440268490435[/C][/ROW]
[ROW][C]89[/C][C]22[/C][C]22.1416478096984[/C][C]-0.141647809698437[/C][/ROW]
[ROW][C]90[/C][C]21[/C][C]20.5728437393499[/C][C]0.427156260650136[/C][/ROW]
[ROW][C]91[/C][C]24[/C][C]20.4451840742043[/C][C]3.55481592579566[/C][/ROW]
[ROW][C]92[/C][C]24[/C][C]21.7194362584081[/C][C]2.28056374159187[/C][/ROW]
[ROW][C]93[/C][C]23[/C][C]21.9413988203372[/C][C]1.05860117966277[/C][/ROW]
[ROW][C]94[/C][C]20[/C][C]22.3039684205901[/C][C]-2.30396842059012[/C][/ROW]
[ROW][C]95[/C][C]27[/C][C]21.3481089215037[/C][C]5.65189107849627[/C][/ROW]
[ROW][C]96[/C][C]26[/C][C]23.4245663221755[/C][C]2.57543367782448[/C][/ROW]
[ROW][C]97[/C][C]25[/C][C]21.9468473960217[/C][C]3.05315260397831[/C][/ROW]
[ROW][C]98[/C][C]21[/C][C]20.0638274017009[/C][C]0.936172598299126[/C][/ROW]
[ROW][C]99[/C][C]21[/C][C]20.7680155733594[/C][C]0.231984426640604[/C][/ROW]
[ROW][C]100[/C][C]19[/C][C]20.3833611957976[/C][C]-1.38336119579757[/C][/ROW]
[ROW][C]101[/C][C]21[/C][C]21.5815065428908[/C][C]-0.581506542890763[/C][/ROW]
[ROW][C]102[/C][C]21[/C][C]21.2761526393298[/C][C]-0.276152639329836[/C][/ROW]
[ROW][C]103[/C][C]16[/C][C]19.7627574909190[/C][C]-3.76275749091905[/C][/ROW]
[ROW][C]104[/C][C]22[/C][C]20.6424325518234[/C][C]1.35756744817658[/C][/ROW]
[ROW][C]105[/C][C]29[/C][C]21.7679585179577[/C][C]7.23204148204232[/C][/ROW]
[ROW][C]106[/C][C]15[/C][C]21.7289577426614[/C][C]-6.72895774266144[/C][/ROW]
[ROW][C]107[/C][C]17[/C][C]20.6913473995302[/C][C]-3.69134739953016[/C][/ROW]
[ROW][C]108[/C][C]15[/C][C]19.9102562803143[/C][C]-4.91025628031433[/C][/ROW]
[ROW][C]109[/C][C]21[/C][C]21.6460446218128[/C][C]-0.646044621812849[/C][/ROW]
[ROW][C]110[/C][C]21[/C][C]21.0230224835545[/C][C]-0.0230224835545169[/C][/ROW]
[ROW][C]111[/C][C]19[/C][C]19.2848425566571[/C][C]-0.284842556657119[/C][/ROW]
[ROW][C]112[/C][C]24[/C][C]18.0601917928712[/C][C]5.93980820712883[/C][/ROW]
[ROW][C]113[/C][C]20[/C][C]22.2492483774651[/C][C]-2.24924837746510[/C][/ROW]
[ROW][C]114[/C][C]17[/C][C]25.0896164950748[/C][C]-8.08961649507482[/C][/ROW]
[ROW][C]115[/C][C]23[/C][C]24.8187204846044[/C][C]-1.81872048460443[/C][/ROW]
[ROW][C]116[/C][C]24[/C][C]22.390211609342[/C][C]1.60978839065802[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]22.0566557391386[/C][C]-8.05665573913863[/C][/ROW]
[ROW][C]118[/C][C]19[/C][C]22.8529153427126[/C][C]-3.85291534271264[/C][/ROW]
[ROW][C]119[/C][C]24[/C][C]22.1817391564752[/C][C]1.81826084352477[/C][/ROW]
[ROW][C]120[/C][C]13[/C][C]20.4211887148614[/C][C]-7.42118871486136[/C][/ROW]
[ROW][C]121[/C][C]22[/C][C]25.3603868886685[/C][C]-3.36038688866847[/C][/ROW]
[ROW][C]122[/C][C]16[/C][C]21.1243457414569[/C][C]-5.12434574145691[/C][/ROW]
[ROW][C]123[/C][C]19[/C][C]23.2285530941963[/C][C]-4.22855309419634[/C][/ROW]
[ROW][C]124[/C][C]25[/C][C]22.7392989214982[/C][C]2.26070107850183[/C][/ROW]
[ROW][C]125[/C][C]25[/C][C]24.1556659998062[/C][C]0.844334000193828[/C][/ROW]
[ROW][C]126[/C][C]23[/C][C]21.4297237607164[/C][C]1.57027623928362[/C][/ROW]
[ROW][C]127[/C][C]24[/C][C]23.5535090421773[/C][C]0.446490957822704[/C][/ROW]
[ROW][C]128[/C][C]26[/C][C]23.5032601759348[/C][C]2.49673982406516[/C][/ROW]
[ROW][C]129[/C][C]26[/C][C]21.4883530022328[/C][C]4.51164699776715[/C][/ROW]
[ROW][C]130[/C][C]25[/C][C]24.1320517727117[/C][C]0.86794822728832[/C][/ROW]
[ROW][C]131[/C][C]18[/C][C]22.2911756530787[/C][C]-4.29117565307866[/C][/ROW]
[ROW][C]132[/C][C]21[/C][C]19.8686050301926[/C][C]1.13139496980740[/C][/ROW]
[ROW][C]133[/C][C]26[/C][C]23.6503640710713[/C][C]2.34963592892873[/C][/ROW]
[ROW][C]134[/C][C]23[/C][C]21.9643249916158[/C][C]1.03567500838418[/C][/ROW]
[ROW][C]135[/C][C]23[/C][C]19.7571959581477[/C][C]3.24280404185232[/C][/ROW]
[ROW][C]136[/C][C]22[/C][C]22.5985262477796[/C][C]-0.598526247779605[/C][/ROW]
[ROW][C]137[/C][C]20[/C][C]22.3975581072706[/C][C]-2.39755810727064[/C][/ROW]
[ROW][C]138[/C][C]13[/C][C]22.0664261853111[/C][C]-9.0664261853111[/C][/ROW]
[ROW][C]139[/C][C]24[/C][C]21.3905116856181[/C][C]2.60948831438186[/C][/ROW]
[ROW][C]140[/C][C]15[/C][C]21.5031177034133[/C][C]-6.50311770341333[/C][/ROW]
[ROW][C]141[/C][C]14[/C][C]23.0933834365365[/C][C]-9.09338343653651[/C][/ROW]
[ROW][C]142[/C][C]22[/C][C]24.0488715152901[/C][C]-2.04887151529010[/C][/ROW]
[ROW][C]143[/C][C]10[/C][C]17.6446697470617[/C][C]-7.64466974706174[/C][/ROW]
[ROW][C]144[/C][C]24[/C][C]24.4269628455801[/C][C]-0.42696284558013[/C][/ROW]
[ROW][C]145[/C][C]22[/C][C]21.8304203794509[/C][C]0.169579620549057[/C][/ROW]
[ROW][C]146[/C][C]24[/C][C]25.7882219661376[/C][C]-1.78822196613757[/C][/ROW]
[ROW][C]147[/C][C]19[/C][C]21.6534545779183[/C][C]-2.65345457791828[/C][/ROW]
[ROW][C]148[/C][C]20[/C][C]22.0853596160821[/C][C]-2.08535961608210[/C][/ROW]
[ROW][C]149[/C][C]13[/C][C]17.1094479686178[/C][C]-4.10944796861783[/C][/ROW]
[ROW][C]150[/C][C]20[/C][C]20.1000300761381[/C][C]-0.100030076138146[/C][/ROW]
[ROW][C]151[/C][C]22[/C][C]23.1856636465606[/C][C]-1.18566364656058[/C][/ROW]
[ROW][C]152[/C][C]24[/C][C]23.3832623766880[/C][C]0.616737623311959[/C][/ROW]
[ROW][C]153[/C][C]29[/C][C]23.2741564748344[/C][C]5.72584352516564[/C][/ROW]
[ROW][C]154[/C][C]12[/C][C]20.9762473114534[/C][C]-8.97624731145337[/C][/ROW]
[ROW][C]155[/C][C]20[/C][C]20.9222015621339[/C][C]-0.922201562133892[/C][/ROW]
[ROW][C]156[/C][C]21[/C][C]21.4456831015455[/C][C]-0.445683101545463[/C][/ROW]
[ROW][C]157[/C][C]24[/C][C]23.6330707117065[/C][C]0.366929288293533[/C][/ROW]
[ROW][C]158[/C][C]22[/C][C]21.8927187463586[/C][C]0.107281253641439[/C][/ROW]
[ROW][C]159[/C][C]20[/C][C]17.7211420634604[/C][C]2.27885793653958[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=97952&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=97952&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
12624.03748865223741.96251134776263
22325.3495865301589-2.34958653015892
32525.4759720293670-0.47597202936704
42323.1284201938044-0.128420193804447
51922.9500814574386-3.95008145743856
62924.05845113699354.94154886300653
72524.76982107926910.23017892073088
82122.5496645045615-1.54966450456147
92221.95941363695150.0405863630485405
102522.44836536235762.55163463764240
112420.18305846017533.81694153982470
121819.758708271868-1.75870827186801
132218.37158823256833.6284117674317
141520.6721227164876-5.67212271648763
152223.4194974139702-1.41949741397023
162824.25838673442113.74161326557895
172022.1745527790774-2.17455277907735
181219.0223933275116-7.02239332751165
192421.45264993199502.54735006800504
202021.3875557703300-1.38755577033004
212123.2363427177809-2.23634271778094
222020.6603585270493-0.660358527049277
232119.16760754755021.83239245244975
242321.08086784823341.91913215176660
252821.90867808718766.09132191281235
262421.87329863206622.12670136793380
272423.46215906751120.537840932488764
282420.37697686989113.62302313010889
292321.97283649863061.02716350136943
302322.69133799866730.308662001332743
312924.24492262097824.75507737902181
322421.99383365233312.00616634766691
331825.0192850575274-7.01928505752743
342525.9632786504055-0.963278650405518
352122.5815300817310-1.58153008173104
362626.6957457480054-0.695745748005403
372225.1140040163943-3.11400401639428
382222.8265637086898-0.82656370868982
392222.5514357077084-0.551435707708401
402325.597809613408-2.59780961340799
413022.89565989659737.1043401034027
422322.66401530370340.335984696296611
431718.7116919232637-1.71169192326374
442323.7908994040719-0.790899404071903
452324.2516305620011-1.25163056200108
462522.39388898187952.6061110181205
472420.72245448307373.27754551692626
482427.4350166076499-3.43501660764987
492322.96383179019150.0361682098085093
502123.8197102970576-2.81971029705757
512425.6897981449798-1.68979814497982
522421.81211431056522.18788568943480
532821.48436026172536.51563973827473
541621.0239734605562-5.02397346055621
552019.79619121857990.203808781420062
562923.40762620848975.59237379151032
572723.84308784113853.15691215886153
582223.1637478787013-1.16374787870127
592823.94627665844024.05372334155984
601620.3530491841121-4.35304918411208
612522.8562130477712.143786952229
622423.47665663211870.523343367881268
632823.64350425666404.35649574333604
642424.2220288125445-0.222028812544515
652322.67357509184630.326424908153681
663026.89267061050803.10732938949196
672421.28348200119332.71651799880672
682124.0991527443775-3.09915274437747
692523.27013858003961.72986141996036
702523.91847847497541.08152152502456
712220.78178573967281.21821426032725
722322.42761122952950.572388770470475
732622.82614278944693.1738572105531
742321.60220419717541.39779580282461
752523.06441242302011.93558757697994
762121.2854869394797-0.285486939479683
772523.64151645444281.35848354555722
782422.144790909091.85520909091001
792923.51235970235665.48764029764338
802223.647883644284-1.64788364428402
812723.56808950712843.43191049287155
822619.62908197232576.37091802767428
832221.30496353231550.695036467684501
842422.03274328013931.96725671986070
852723.10591731462143.89408268537864
862421.35404187460792.64595812539212
872424.8509010035633-0.85090100356334
882924.35559731509574.64440268490435
892222.1416478096984-0.141647809698437
902120.57284373934990.427156260650136
912420.44518407420433.55481592579566
922421.71943625840812.28056374159187
932321.94139882033721.05860117966277
942022.3039684205901-2.30396842059012
952721.34810892150375.65189107849627
962623.42456632217552.57543367782448
972521.94684739602173.05315260397831
982120.06382740170090.936172598299126
992120.76801557335940.231984426640604
1001920.3833611957976-1.38336119579757
1012121.5815065428908-0.581506542890763
1022121.2761526393298-0.276152639329836
1031619.7627574909190-3.76275749091905
1042220.64243255182341.35756744817658
1052921.76795851795777.23204148204232
1061521.7289577426614-6.72895774266144
1071720.6913473995302-3.69134739953016
1081519.9102562803143-4.91025628031433
1092121.6460446218128-0.646044621812849
1102121.0230224835545-0.0230224835545169
1111919.2848425566571-0.284842556657119
1122418.06019179287125.93980820712883
1132022.2492483774651-2.24924837746510
1141725.0896164950748-8.08961649507482
1152324.8187204846044-1.81872048460443
1162422.3902116093421.60978839065802
1171422.0566557391386-8.05665573913863
1181922.8529153427126-3.85291534271264
1192422.18173915647521.81826084352477
1201320.4211887148614-7.42118871486136
1212225.3603868886685-3.36038688866847
1221621.1243457414569-5.12434574145691
1231923.2285530941963-4.22855309419634
1242522.73929892149822.26070107850183
1252524.15566599980620.844334000193828
1262321.42972376071641.57027623928362
1272423.55350904217730.446490957822704
1282623.50326017593482.49673982406516
1292621.48835300223284.51164699776715
1302524.13205177271170.86794822728832
1311822.2911756530787-4.29117565307866
1322119.86860503019261.13139496980740
1332623.65036407107132.34963592892873
1342321.96432499161581.03567500838418
1352319.75719595814773.24280404185232
1362222.5985262477796-0.598526247779605
1372022.3975581072706-2.39755810727064
1381322.0664261853111-9.0664261853111
1392421.39051168561812.60948831438186
1401521.5031177034133-6.50311770341333
1411423.0933834365365-9.09338343653651
1422224.0488715152901-2.04887151529010
1431017.6446697470617-7.64466974706174
1442424.4269628455801-0.42696284558013
1452221.83042037945090.169579620549057
1462425.7882219661376-1.78822196613757
1471921.6534545779183-2.65345457791828
1482022.0853596160821-2.08535961608210
1491317.1094479686178-4.10944796861783
1502020.1000300761381-0.100030076138146
1512223.1856636465606-1.18566364656058
1522423.38326237668800.616737623311959
1532923.27415647483445.72584352516564
1541220.9762473114534-8.97624731145337
1552020.9222015621339-0.922201562133892
1562121.4456831015455-0.445683101545463
1572423.63307071170650.366929288293533
1582221.89271874635860.107281253641439
1592017.72114206346042.27885793653958






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.5270283643144650.945943271371070.472971635685535
110.5620721769234440.8758556461531130.437927823076556
120.4888201218553440.9776402437106880.511179878144656
130.540340997121710.919318005756580.45965900287829
140.7416341416927360.5167317166145270.258365858307264
150.6547677799223590.6904644401552820.345232220077641
160.6238402036212980.7523195927574040.376159796378702
170.5381604118785770.9236791762428460.461839588121423
180.676102822720440.6477943545591190.323897177279559
190.6013206563170240.7973586873659520.398679343682976
200.5885070534294230.8229858931411550.411492946570577
210.5246008119634860.9507983760730290.475399188036514
220.4551930008433730.9103860016867460.544806999156627
230.4045132330494310.8090264660988620.595486766950569
240.4089286325352480.8178572650704950.591071367464752
250.5713124779890450.857375044021910.428687522010955
260.510036637596390.979926724807220.48996336240361
270.4430170306403400.8860340612806810.55698296935966
280.4374636088203230.8749272176406460.562536391179677
290.3733175255846280.7466350511692550.626682474415373
300.3129254693000450.6258509386000890.687074530699955
310.3467053025993480.6934106051986960.653294697400652
320.2956848691865100.5913697383730210.70431513081349
330.5218562366792980.9562875266414050.478143763320702
340.4707206954930720.9414413909861440.529279304506928
350.4327585476229680.8655170952459370.567241452377032
360.3754389188255470.7508778376510950.624561081174453
370.3488126340243320.6976252680486640.651187365975668
380.2972001395534950.5944002791069910.702799860446505
390.2498292770490700.4996585540981390.75017072295093
400.2224962417419930.4449924834839860.777503758258007
410.3751097500675780.7502195001351560.624890249932422
420.3233293513738100.6466587027476190.67667064862619
430.2913097444098280.5826194888196550.708690255590172
440.2477411429354410.4954822858708820.752258857064559
450.2113446507543240.4226893015086480.788655349245676
460.1919434164478600.3838868328957200.80805658355214
470.1795112931879290.3590225863758580.820488706812071
480.1644349986815210.3288699973630430.835565001318479
490.1343471198614540.2686942397229090.865652880138546
500.1247300837685840.2494601675371690.875269916231415
510.1029539738582300.2059079477164600.89704602614177
520.08783550096624450.1756710019324890.912164499033756
530.1473975884524600.2947951769049200.85260241154754
540.176789105538170.353578211076340.82321089446183
550.1652671865387720.3305343730775440.834732813461228
560.2226869754661200.4453739509322410.77731302453388
570.2151319088197600.4302638176395190.78486809118024
580.1841536430785530.3683072861571060.815846356921447
590.1973511380899140.3947022761798280.802648861910086
600.2250919989972290.4501839979944580.774908001002771
610.1988802369784360.3977604739568710.801119763021564
620.1671182584235650.3342365168471300.832881741576435
630.1823448707505040.3646897415010090.817655129249496
640.1532548126361260.3065096252722520.846745187363874
650.1264389685164600.2528779370329210.87356103148354
660.1228832953083450.245766590616690.877116704691655
670.1122482173709430.2244964347418850.887751782629058
680.1113449234101170.2226898468202340.888655076589883
690.09475658474965190.1895131694993040.905243415250348
700.07736051432496710.1547210286499340.922639485675033
710.0628681336664160.1257362673328320.937131866333584
720.04965621586309260.09931243172618510.950343784136907
730.04661542310497250.0932308462099450.953384576895028
740.0373413642259390.0746827284518780.96265863577406
750.03103143747197540.06206287494395080.968968562528025
760.02380097559040180.04760195118080350.976199024409598
770.01868590141621320.03737180283242640.981314098583787
780.01495714068057460.02991428136114930.985042859319425
790.02247810815643580.04495621631287170.977521891843564
800.01792120620812370.03584241241624740.982078793791876
810.01752536410636600.03505072821273210.982474635893634
820.03135058596689540.06270117193379080.968649414033105
830.02417738786077950.04835477572155910.97582261213922
840.02017265224147270.04034530448294550.979827347758527
850.02209130363799680.04418260727599360.977908696362003
860.01957790681058910.03915581362117830.98042209318941
870.01488339564409600.02976679128819210.985116604355904
880.01943484492832480.03886968985664960.980565155071675
890.01528193402292590.03056386804585170.984718065977074
900.01143503976743200.02287007953486390.988564960232568
910.01149360150002350.0229872030000470.988506398499976
920.01004879652614640.02009759305229280.989951203473854
930.007706348891424210.01541269778284840.992293651108576
940.006370954451671760.01274190890334350.993629045548328
950.01167643040904610.02335286081809220.988323569590954
960.01057507286336610.02115014572673230.989424927136634
970.01005793173773190.02011586347546380.989942068262268
980.00769645881097110.01539291762194220.992303541189029
990.005678852227648470.01135770445529690.994321147772351
1000.004347520484855510.008695040969711020.995652479515144
1010.003212265659623610.006424531319247220.996787734340376
1020.002285633287830200.004571266575660400.99771436671217
1030.002443964708573920.004887929417147840.997556035291426
1040.001914651603452040.003829303206904080.998085348396548
1050.008114129825284910.01622825965056980.991885870174715
1060.01816718460084600.03633436920169190.981832815399154
1070.01802955125972420.03605910251944850.981970448740276
1080.02272115350154470.04544230700308940.977278846498455
1090.01752965006428280.03505930012856560.982470349935717
1100.01280354570534050.02560709141068100.98719645429466
1110.009269762016710840.01853952403342170.99073023798329
1120.02273309451267340.04546618902534670.977266905487327
1130.02059644815267280.04119289630534570.979403551847327
1140.05719326271489970.1143865254297990.9428067372851
1150.04845018371725440.09690036743450880.951549816282746
1160.039264729060880.078529458121760.96073527093912
1170.1157125762394330.2314251524788660.884287423760567
1180.1108706235566050.2217412471132090.889129376443395
1190.09829755436003970.1965951087200790.90170244563996
1200.1718239045700470.3436478091400940.828176095429953
1210.1637067916363790.3274135832727580.83629320836362
1220.1950620238804540.3901240477609090.804937976119546
1230.1880429805116190.3760859610232380.811957019488381
1240.1872256420395590.3744512840791190.81277435796044
1250.1699933065863640.3399866131727280.830006693413636
1260.1383323830819520.2766647661639030.861667616918048
1270.1081702103029710.2163404206059430.891829789697029
1280.1116225072733080.2232450145466150.888377492726692
1290.1601248309655510.3202496619311030.839875169034449
1300.1379440028202490.2758880056404980.862055997179751
1310.1204113511155700.2408227022311390.87958864888443
1320.1042886228655730.2085772457311460.895711377134427
1330.09566304356770120.1913260871354020.904336956432299
1340.07809052629471880.1561810525894380.921909473705281
1350.1065685015349170.2131370030698340.893431498465083
1360.07901952437298270.1580390487459650.920980475627017
1370.05762954179662790.1152590835932560.942370458203372
1380.1468580666087150.293716133217430.853141933391285
1390.2081200446700520.4162400893401040.791879955329948
1400.1885131002139580.3770262004279150.811486899786042
1410.4060256499171640.8120512998343270.593974350082836
1420.3563760407383630.7127520814767260.643623959261637
1430.6660043732679610.6679912534640770.333995626732039
1440.604797946544660.790404106910680.39520205345534
1450.494173129371250.98834625874250.50582687062875
1460.4232865036024230.8465730072048450.576713496397577
1470.4335832749889490.8671665499778970.566416725011051
1480.3032838121562240.6065676243124490.696716187843776
1490.2114026001644030.4228052003288050.788597399835597

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.527028364314465 & 0.94594327137107 & 0.472971635685535 \tabularnewline
11 & 0.562072176923444 & 0.875855646153113 & 0.437927823076556 \tabularnewline
12 & 0.488820121855344 & 0.977640243710688 & 0.511179878144656 \tabularnewline
13 & 0.54034099712171 & 0.91931800575658 & 0.45965900287829 \tabularnewline
14 & 0.741634141692736 & 0.516731716614527 & 0.258365858307264 \tabularnewline
15 & 0.654767779922359 & 0.690464440155282 & 0.345232220077641 \tabularnewline
16 & 0.623840203621298 & 0.752319592757404 & 0.376159796378702 \tabularnewline
17 & 0.538160411878577 & 0.923679176242846 & 0.461839588121423 \tabularnewline
18 & 0.67610282272044 & 0.647794354559119 & 0.323897177279559 \tabularnewline
19 & 0.601320656317024 & 0.797358687365952 & 0.398679343682976 \tabularnewline
20 & 0.588507053429423 & 0.822985893141155 & 0.411492946570577 \tabularnewline
21 & 0.524600811963486 & 0.950798376073029 & 0.475399188036514 \tabularnewline
22 & 0.455193000843373 & 0.910386001686746 & 0.544806999156627 \tabularnewline
23 & 0.404513233049431 & 0.809026466098862 & 0.595486766950569 \tabularnewline
24 & 0.408928632535248 & 0.817857265070495 & 0.591071367464752 \tabularnewline
25 & 0.571312477989045 & 0.85737504402191 & 0.428687522010955 \tabularnewline
26 & 0.51003663759639 & 0.97992672480722 & 0.48996336240361 \tabularnewline
27 & 0.443017030640340 & 0.886034061280681 & 0.55698296935966 \tabularnewline
28 & 0.437463608820323 & 0.874927217640646 & 0.562536391179677 \tabularnewline
29 & 0.373317525584628 & 0.746635051169255 & 0.626682474415373 \tabularnewline
30 & 0.312925469300045 & 0.625850938600089 & 0.687074530699955 \tabularnewline
31 & 0.346705302599348 & 0.693410605198696 & 0.653294697400652 \tabularnewline
32 & 0.295684869186510 & 0.591369738373021 & 0.70431513081349 \tabularnewline
33 & 0.521856236679298 & 0.956287526641405 & 0.478143763320702 \tabularnewline
34 & 0.470720695493072 & 0.941441390986144 & 0.529279304506928 \tabularnewline
35 & 0.432758547622968 & 0.865517095245937 & 0.567241452377032 \tabularnewline
36 & 0.375438918825547 & 0.750877837651095 & 0.624561081174453 \tabularnewline
37 & 0.348812634024332 & 0.697625268048664 & 0.651187365975668 \tabularnewline
38 & 0.297200139553495 & 0.594400279106991 & 0.702799860446505 \tabularnewline
39 & 0.249829277049070 & 0.499658554098139 & 0.75017072295093 \tabularnewline
40 & 0.222496241741993 & 0.444992483483986 & 0.777503758258007 \tabularnewline
41 & 0.375109750067578 & 0.750219500135156 & 0.624890249932422 \tabularnewline
42 & 0.323329351373810 & 0.646658702747619 & 0.67667064862619 \tabularnewline
43 & 0.291309744409828 & 0.582619488819655 & 0.708690255590172 \tabularnewline
44 & 0.247741142935441 & 0.495482285870882 & 0.752258857064559 \tabularnewline
45 & 0.211344650754324 & 0.422689301508648 & 0.788655349245676 \tabularnewline
46 & 0.191943416447860 & 0.383886832895720 & 0.80805658355214 \tabularnewline
47 & 0.179511293187929 & 0.359022586375858 & 0.820488706812071 \tabularnewline
48 & 0.164434998681521 & 0.328869997363043 & 0.835565001318479 \tabularnewline
49 & 0.134347119861454 & 0.268694239722909 & 0.865652880138546 \tabularnewline
50 & 0.124730083768584 & 0.249460167537169 & 0.875269916231415 \tabularnewline
51 & 0.102953973858230 & 0.205907947716460 & 0.89704602614177 \tabularnewline
52 & 0.0878355009662445 & 0.175671001932489 & 0.912164499033756 \tabularnewline
53 & 0.147397588452460 & 0.294795176904920 & 0.85260241154754 \tabularnewline
54 & 0.17678910553817 & 0.35357821107634 & 0.82321089446183 \tabularnewline
55 & 0.165267186538772 & 0.330534373077544 & 0.834732813461228 \tabularnewline
56 & 0.222686975466120 & 0.445373950932241 & 0.77731302453388 \tabularnewline
57 & 0.215131908819760 & 0.430263817639519 & 0.78486809118024 \tabularnewline
58 & 0.184153643078553 & 0.368307286157106 & 0.815846356921447 \tabularnewline
59 & 0.197351138089914 & 0.394702276179828 & 0.802648861910086 \tabularnewline
60 & 0.225091998997229 & 0.450183997994458 & 0.774908001002771 \tabularnewline
61 & 0.198880236978436 & 0.397760473956871 & 0.801119763021564 \tabularnewline
62 & 0.167118258423565 & 0.334236516847130 & 0.832881741576435 \tabularnewline
63 & 0.182344870750504 & 0.364689741501009 & 0.817655129249496 \tabularnewline
64 & 0.153254812636126 & 0.306509625272252 & 0.846745187363874 \tabularnewline
65 & 0.126438968516460 & 0.252877937032921 & 0.87356103148354 \tabularnewline
66 & 0.122883295308345 & 0.24576659061669 & 0.877116704691655 \tabularnewline
67 & 0.112248217370943 & 0.224496434741885 & 0.887751782629058 \tabularnewline
68 & 0.111344923410117 & 0.222689846820234 & 0.888655076589883 \tabularnewline
69 & 0.0947565847496519 & 0.189513169499304 & 0.905243415250348 \tabularnewline
70 & 0.0773605143249671 & 0.154721028649934 & 0.922639485675033 \tabularnewline
71 & 0.062868133666416 & 0.125736267332832 & 0.937131866333584 \tabularnewline
72 & 0.0496562158630926 & 0.0993124317261851 & 0.950343784136907 \tabularnewline
73 & 0.0466154231049725 & 0.093230846209945 & 0.953384576895028 \tabularnewline
74 & 0.037341364225939 & 0.074682728451878 & 0.96265863577406 \tabularnewline
75 & 0.0310314374719754 & 0.0620628749439508 & 0.968968562528025 \tabularnewline
76 & 0.0238009755904018 & 0.0476019511808035 & 0.976199024409598 \tabularnewline
77 & 0.0186859014162132 & 0.0373718028324264 & 0.981314098583787 \tabularnewline
78 & 0.0149571406805746 & 0.0299142813611493 & 0.985042859319425 \tabularnewline
79 & 0.0224781081564358 & 0.0449562163128717 & 0.977521891843564 \tabularnewline
80 & 0.0179212062081237 & 0.0358424124162474 & 0.982078793791876 \tabularnewline
81 & 0.0175253641063660 & 0.0350507282127321 & 0.982474635893634 \tabularnewline
82 & 0.0313505859668954 & 0.0627011719337908 & 0.968649414033105 \tabularnewline
83 & 0.0241773878607795 & 0.0483547757215591 & 0.97582261213922 \tabularnewline
84 & 0.0201726522414727 & 0.0403453044829455 & 0.979827347758527 \tabularnewline
85 & 0.0220913036379968 & 0.0441826072759936 & 0.977908696362003 \tabularnewline
86 & 0.0195779068105891 & 0.0391558136211783 & 0.98042209318941 \tabularnewline
87 & 0.0148833956440960 & 0.0297667912881921 & 0.985116604355904 \tabularnewline
88 & 0.0194348449283248 & 0.0388696898566496 & 0.980565155071675 \tabularnewline
89 & 0.0152819340229259 & 0.0305638680458517 & 0.984718065977074 \tabularnewline
90 & 0.0114350397674320 & 0.0228700795348639 & 0.988564960232568 \tabularnewline
91 & 0.0114936015000235 & 0.022987203000047 & 0.988506398499976 \tabularnewline
92 & 0.0100487965261464 & 0.0200975930522928 & 0.989951203473854 \tabularnewline
93 & 0.00770634889142421 & 0.0154126977828484 & 0.992293651108576 \tabularnewline
94 & 0.00637095445167176 & 0.0127419089033435 & 0.993629045548328 \tabularnewline
95 & 0.0116764304090461 & 0.0233528608180922 & 0.988323569590954 \tabularnewline
96 & 0.0105750728633661 & 0.0211501457267323 & 0.989424927136634 \tabularnewline
97 & 0.0100579317377319 & 0.0201158634754638 & 0.989942068262268 \tabularnewline
98 & 0.0076964588109711 & 0.0153929176219422 & 0.992303541189029 \tabularnewline
99 & 0.00567885222764847 & 0.0113577044552969 & 0.994321147772351 \tabularnewline
100 & 0.00434752048485551 & 0.00869504096971102 & 0.995652479515144 \tabularnewline
101 & 0.00321226565962361 & 0.00642453131924722 & 0.996787734340376 \tabularnewline
102 & 0.00228563328783020 & 0.00457126657566040 & 0.99771436671217 \tabularnewline
103 & 0.00244396470857392 & 0.00488792941714784 & 0.997556035291426 \tabularnewline
104 & 0.00191465160345204 & 0.00382930320690408 & 0.998085348396548 \tabularnewline
105 & 0.00811412982528491 & 0.0162282596505698 & 0.991885870174715 \tabularnewline
106 & 0.0181671846008460 & 0.0363343692016919 & 0.981832815399154 \tabularnewline
107 & 0.0180295512597242 & 0.0360591025194485 & 0.981970448740276 \tabularnewline
108 & 0.0227211535015447 & 0.0454423070030894 & 0.977278846498455 \tabularnewline
109 & 0.0175296500642828 & 0.0350593001285656 & 0.982470349935717 \tabularnewline
110 & 0.0128035457053405 & 0.0256070914106810 & 0.98719645429466 \tabularnewline
111 & 0.00926976201671084 & 0.0185395240334217 & 0.99073023798329 \tabularnewline
112 & 0.0227330945126734 & 0.0454661890253467 & 0.977266905487327 \tabularnewline
113 & 0.0205964481526728 & 0.0411928963053457 & 0.979403551847327 \tabularnewline
114 & 0.0571932627148997 & 0.114386525429799 & 0.9428067372851 \tabularnewline
115 & 0.0484501837172544 & 0.0969003674345088 & 0.951549816282746 \tabularnewline
116 & 0.03926472906088 & 0.07852945812176 & 0.96073527093912 \tabularnewline
117 & 0.115712576239433 & 0.231425152478866 & 0.884287423760567 \tabularnewline
118 & 0.110870623556605 & 0.221741247113209 & 0.889129376443395 \tabularnewline
119 & 0.0982975543600397 & 0.196595108720079 & 0.90170244563996 \tabularnewline
120 & 0.171823904570047 & 0.343647809140094 & 0.828176095429953 \tabularnewline
121 & 0.163706791636379 & 0.327413583272758 & 0.83629320836362 \tabularnewline
122 & 0.195062023880454 & 0.390124047760909 & 0.804937976119546 \tabularnewline
123 & 0.188042980511619 & 0.376085961023238 & 0.811957019488381 \tabularnewline
124 & 0.187225642039559 & 0.374451284079119 & 0.81277435796044 \tabularnewline
125 & 0.169993306586364 & 0.339986613172728 & 0.830006693413636 \tabularnewline
126 & 0.138332383081952 & 0.276664766163903 & 0.861667616918048 \tabularnewline
127 & 0.108170210302971 & 0.216340420605943 & 0.891829789697029 \tabularnewline
128 & 0.111622507273308 & 0.223245014546615 & 0.888377492726692 \tabularnewline
129 & 0.160124830965551 & 0.320249661931103 & 0.839875169034449 \tabularnewline
130 & 0.137944002820249 & 0.275888005640498 & 0.862055997179751 \tabularnewline
131 & 0.120411351115570 & 0.240822702231139 & 0.87958864888443 \tabularnewline
132 & 0.104288622865573 & 0.208577245731146 & 0.895711377134427 \tabularnewline
133 & 0.0956630435677012 & 0.191326087135402 & 0.904336956432299 \tabularnewline
134 & 0.0780905262947188 & 0.156181052589438 & 0.921909473705281 \tabularnewline
135 & 0.106568501534917 & 0.213137003069834 & 0.893431498465083 \tabularnewline
136 & 0.0790195243729827 & 0.158039048745965 & 0.920980475627017 \tabularnewline
137 & 0.0576295417966279 & 0.115259083593256 & 0.942370458203372 \tabularnewline
138 & 0.146858066608715 & 0.29371613321743 & 0.853141933391285 \tabularnewline
139 & 0.208120044670052 & 0.416240089340104 & 0.791879955329948 \tabularnewline
140 & 0.188513100213958 & 0.377026200427915 & 0.811486899786042 \tabularnewline
141 & 0.406025649917164 & 0.812051299834327 & 0.593974350082836 \tabularnewline
142 & 0.356376040738363 & 0.712752081476726 & 0.643623959261637 \tabularnewline
143 & 0.666004373267961 & 0.667991253464077 & 0.333995626732039 \tabularnewline
144 & 0.60479794654466 & 0.79040410691068 & 0.39520205345534 \tabularnewline
145 & 0.49417312937125 & 0.9883462587425 & 0.50582687062875 \tabularnewline
146 & 0.423286503602423 & 0.846573007204845 & 0.576713496397577 \tabularnewline
147 & 0.433583274988949 & 0.867166549977897 & 0.566416725011051 \tabularnewline
148 & 0.303283812156224 & 0.606567624312449 & 0.696716187843776 \tabularnewline
149 & 0.211402600164403 & 0.422805200328805 & 0.788597399835597 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=97952&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]10[/C][C]0.527028364314465[/C][C]0.94594327137107[/C][C]0.472971635685535[/C][/ROW]
[ROW][C]11[/C][C]0.562072176923444[/C][C]0.875855646153113[/C][C]0.437927823076556[/C][/ROW]
[ROW][C]12[/C][C]0.488820121855344[/C][C]0.977640243710688[/C][C]0.511179878144656[/C][/ROW]
[ROW][C]13[/C][C]0.54034099712171[/C][C]0.91931800575658[/C][C]0.45965900287829[/C][/ROW]
[ROW][C]14[/C][C]0.741634141692736[/C][C]0.516731716614527[/C][C]0.258365858307264[/C][/ROW]
[ROW][C]15[/C][C]0.654767779922359[/C][C]0.690464440155282[/C][C]0.345232220077641[/C][/ROW]
[ROW][C]16[/C][C]0.623840203621298[/C][C]0.752319592757404[/C][C]0.376159796378702[/C][/ROW]
[ROW][C]17[/C][C]0.538160411878577[/C][C]0.923679176242846[/C][C]0.461839588121423[/C][/ROW]
[ROW][C]18[/C][C]0.67610282272044[/C][C]0.647794354559119[/C][C]0.323897177279559[/C][/ROW]
[ROW][C]19[/C][C]0.601320656317024[/C][C]0.797358687365952[/C][C]0.398679343682976[/C][/ROW]
[ROW][C]20[/C][C]0.588507053429423[/C][C]0.822985893141155[/C][C]0.411492946570577[/C][/ROW]
[ROW][C]21[/C][C]0.524600811963486[/C][C]0.950798376073029[/C][C]0.475399188036514[/C][/ROW]
[ROW][C]22[/C][C]0.455193000843373[/C][C]0.910386001686746[/C][C]0.544806999156627[/C][/ROW]
[ROW][C]23[/C][C]0.404513233049431[/C][C]0.809026466098862[/C][C]0.595486766950569[/C][/ROW]
[ROW][C]24[/C][C]0.408928632535248[/C][C]0.817857265070495[/C][C]0.591071367464752[/C][/ROW]
[ROW][C]25[/C][C]0.571312477989045[/C][C]0.85737504402191[/C][C]0.428687522010955[/C][/ROW]
[ROW][C]26[/C][C]0.51003663759639[/C][C]0.97992672480722[/C][C]0.48996336240361[/C][/ROW]
[ROW][C]27[/C][C]0.443017030640340[/C][C]0.886034061280681[/C][C]0.55698296935966[/C][/ROW]
[ROW][C]28[/C][C]0.437463608820323[/C][C]0.874927217640646[/C][C]0.562536391179677[/C][/ROW]
[ROW][C]29[/C][C]0.373317525584628[/C][C]0.746635051169255[/C][C]0.626682474415373[/C][/ROW]
[ROW][C]30[/C][C]0.312925469300045[/C][C]0.625850938600089[/C][C]0.687074530699955[/C][/ROW]
[ROW][C]31[/C][C]0.346705302599348[/C][C]0.693410605198696[/C][C]0.653294697400652[/C][/ROW]
[ROW][C]32[/C][C]0.295684869186510[/C][C]0.591369738373021[/C][C]0.70431513081349[/C][/ROW]
[ROW][C]33[/C][C]0.521856236679298[/C][C]0.956287526641405[/C][C]0.478143763320702[/C][/ROW]
[ROW][C]34[/C][C]0.470720695493072[/C][C]0.941441390986144[/C][C]0.529279304506928[/C][/ROW]
[ROW][C]35[/C][C]0.432758547622968[/C][C]0.865517095245937[/C][C]0.567241452377032[/C][/ROW]
[ROW][C]36[/C][C]0.375438918825547[/C][C]0.750877837651095[/C][C]0.624561081174453[/C][/ROW]
[ROW][C]37[/C][C]0.348812634024332[/C][C]0.697625268048664[/C][C]0.651187365975668[/C][/ROW]
[ROW][C]38[/C][C]0.297200139553495[/C][C]0.594400279106991[/C][C]0.702799860446505[/C][/ROW]
[ROW][C]39[/C][C]0.249829277049070[/C][C]0.499658554098139[/C][C]0.75017072295093[/C][/ROW]
[ROW][C]40[/C][C]0.222496241741993[/C][C]0.444992483483986[/C][C]0.777503758258007[/C][/ROW]
[ROW][C]41[/C][C]0.375109750067578[/C][C]0.750219500135156[/C][C]0.624890249932422[/C][/ROW]
[ROW][C]42[/C][C]0.323329351373810[/C][C]0.646658702747619[/C][C]0.67667064862619[/C][/ROW]
[ROW][C]43[/C][C]0.291309744409828[/C][C]0.582619488819655[/C][C]0.708690255590172[/C][/ROW]
[ROW][C]44[/C][C]0.247741142935441[/C][C]0.495482285870882[/C][C]0.752258857064559[/C][/ROW]
[ROW][C]45[/C][C]0.211344650754324[/C][C]0.422689301508648[/C][C]0.788655349245676[/C][/ROW]
[ROW][C]46[/C][C]0.191943416447860[/C][C]0.383886832895720[/C][C]0.80805658355214[/C][/ROW]
[ROW][C]47[/C][C]0.179511293187929[/C][C]0.359022586375858[/C][C]0.820488706812071[/C][/ROW]
[ROW][C]48[/C][C]0.164434998681521[/C][C]0.328869997363043[/C][C]0.835565001318479[/C][/ROW]
[ROW][C]49[/C][C]0.134347119861454[/C][C]0.268694239722909[/C][C]0.865652880138546[/C][/ROW]
[ROW][C]50[/C][C]0.124730083768584[/C][C]0.249460167537169[/C][C]0.875269916231415[/C][/ROW]
[ROW][C]51[/C][C]0.102953973858230[/C][C]0.205907947716460[/C][C]0.89704602614177[/C][/ROW]
[ROW][C]52[/C][C]0.0878355009662445[/C][C]0.175671001932489[/C][C]0.912164499033756[/C][/ROW]
[ROW][C]53[/C][C]0.147397588452460[/C][C]0.294795176904920[/C][C]0.85260241154754[/C][/ROW]
[ROW][C]54[/C][C]0.17678910553817[/C][C]0.35357821107634[/C][C]0.82321089446183[/C][/ROW]
[ROW][C]55[/C][C]0.165267186538772[/C][C]0.330534373077544[/C][C]0.834732813461228[/C][/ROW]
[ROW][C]56[/C][C]0.222686975466120[/C][C]0.445373950932241[/C][C]0.77731302453388[/C][/ROW]
[ROW][C]57[/C][C]0.215131908819760[/C][C]0.430263817639519[/C][C]0.78486809118024[/C][/ROW]
[ROW][C]58[/C][C]0.184153643078553[/C][C]0.368307286157106[/C][C]0.815846356921447[/C][/ROW]
[ROW][C]59[/C][C]0.197351138089914[/C][C]0.394702276179828[/C][C]0.802648861910086[/C][/ROW]
[ROW][C]60[/C][C]0.225091998997229[/C][C]0.450183997994458[/C][C]0.774908001002771[/C][/ROW]
[ROW][C]61[/C][C]0.198880236978436[/C][C]0.397760473956871[/C][C]0.801119763021564[/C][/ROW]
[ROW][C]62[/C][C]0.167118258423565[/C][C]0.334236516847130[/C][C]0.832881741576435[/C][/ROW]
[ROW][C]63[/C][C]0.182344870750504[/C][C]0.364689741501009[/C][C]0.817655129249496[/C][/ROW]
[ROW][C]64[/C][C]0.153254812636126[/C][C]0.306509625272252[/C][C]0.846745187363874[/C][/ROW]
[ROW][C]65[/C][C]0.126438968516460[/C][C]0.252877937032921[/C][C]0.87356103148354[/C][/ROW]
[ROW][C]66[/C][C]0.122883295308345[/C][C]0.24576659061669[/C][C]0.877116704691655[/C][/ROW]
[ROW][C]67[/C][C]0.112248217370943[/C][C]0.224496434741885[/C][C]0.887751782629058[/C][/ROW]
[ROW][C]68[/C][C]0.111344923410117[/C][C]0.222689846820234[/C][C]0.888655076589883[/C][/ROW]
[ROW][C]69[/C][C]0.0947565847496519[/C][C]0.189513169499304[/C][C]0.905243415250348[/C][/ROW]
[ROW][C]70[/C][C]0.0773605143249671[/C][C]0.154721028649934[/C][C]0.922639485675033[/C][/ROW]
[ROW][C]71[/C][C]0.062868133666416[/C][C]0.125736267332832[/C][C]0.937131866333584[/C][/ROW]
[ROW][C]72[/C][C]0.0496562158630926[/C][C]0.0993124317261851[/C][C]0.950343784136907[/C][/ROW]
[ROW][C]73[/C][C]0.0466154231049725[/C][C]0.093230846209945[/C][C]0.953384576895028[/C][/ROW]
[ROW][C]74[/C][C]0.037341364225939[/C][C]0.074682728451878[/C][C]0.96265863577406[/C][/ROW]
[ROW][C]75[/C][C]0.0310314374719754[/C][C]0.0620628749439508[/C][C]0.968968562528025[/C][/ROW]
[ROW][C]76[/C][C]0.0238009755904018[/C][C]0.0476019511808035[/C][C]0.976199024409598[/C][/ROW]
[ROW][C]77[/C][C]0.0186859014162132[/C][C]0.0373718028324264[/C][C]0.981314098583787[/C][/ROW]
[ROW][C]78[/C][C]0.0149571406805746[/C][C]0.0299142813611493[/C][C]0.985042859319425[/C][/ROW]
[ROW][C]79[/C][C]0.0224781081564358[/C][C]0.0449562163128717[/C][C]0.977521891843564[/C][/ROW]
[ROW][C]80[/C][C]0.0179212062081237[/C][C]0.0358424124162474[/C][C]0.982078793791876[/C][/ROW]
[ROW][C]81[/C][C]0.0175253641063660[/C][C]0.0350507282127321[/C][C]0.982474635893634[/C][/ROW]
[ROW][C]82[/C][C]0.0313505859668954[/C][C]0.0627011719337908[/C][C]0.968649414033105[/C][/ROW]
[ROW][C]83[/C][C]0.0241773878607795[/C][C]0.0483547757215591[/C][C]0.97582261213922[/C][/ROW]
[ROW][C]84[/C][C]0.0201726522414727[/C][C]0.0403453044829455[/C][C]0.979827347758527[/C][/ROW]
[ROW][C]85[/C][C]0.0220913036379968[/C][C]0.0441826072759936[/C][C]0.977908696362003[/C][/ROW]
[ROW][C]86[/C][C]0.0195779068105891[/C][C]0.0391558136211783[/C][C]0.98042209318941[/C][/ROW]
[ROW][C]87[/C][C]0.0148833956440960[/C][C]0.0297667912881921[/C][C]0.985116604355904[/C][/ROW]
[ROW][C]88[/C][C]0.0194348449283248[/C][C]0.0388696898566496[/C][C]0.980565155071675[/C][/ROW]
[ROW][C]89[/C][C]0.0152819340229259[/C][C]0.0305638680458517[/C][C]0.984718065977074[/C][/ROW]
[ROW][C]90[/C][C]0.0114350397674320[/C][C]0.0228700795348639[/C][C]0.988564960232568[/C][/ROW]
[ROW][C]91[/C][C]0.0114936015000235[/C][C]0.022987203000047[/C][C]0.988506398499976[/C][/ROW]
[ROW][C]92[/C][C]0.0100487965261464[/C][C]0.0200975930522928[/C][C]0.989951203473854[/C][/ROW]
[ROW][C]93[/C][C]0.00770634889142421[/C][C]0.0154126977828484[/C][C]0.992293651108576[/C][/ROW]
[ROW][C]94[/C][C]0.00637095445167176[/C][C]0.0127419089033435[/C][C]0.993629045548328[/C][/ROW]
[ROW][C]95[/C][C]0.0116764304090461[/C][C]0.0233528608180922[/C][C]0.988323569590954[/C][/ROW]
[ROW][C]96[/C][C]0.0105750728633661[/C][C]0.0211501457267323[/C][C]0.989424927136634[/C][/ROW]
[ROW][C]97[/C][C]0.0100579317377319[/C][C]0.0201158634754638[/C][C]0.989942068262268[/C][/ROW]
[ROW][C]98[/C][C]0.0076964588109711[/C][C]0.0153929176219422[/C][C]0.992303541189029[/C][/ROW]
[ROW][C]99[/C][C]0.00567885222764847[/C][C]0.0113577044552969[/C][C]0.994321147772351[/C][/ROW]
[ROW][C]100[/C][C]0.00434752048485551[/C][C]0.00869504096971102[/C][C]0.995652479515144[/C][/ROW]
[ROW][C]101[/C][C]0.00321226565962361[/C][C]0.00642453131924722[/C][C]0.996787734340376[/C][/ROW]
[ROW][C]102[/C][C]0.00228563328783020[/C][C]0.00457126657566040[/C][C]0.99771436671217[/C][/ROW]
[ROW][C]103[/C][C]0.00244396470857392[/C][C]0.00488792941714784[/C][C]0.997556035291426[/C][/ROW]
[ROW][C]104[/C][C]0.00191465160345204[/C][C]0.00382930320690408[/C][C]0.998085348396548[/C][/ROW]
[ROW][C]105[/C][C]0.00811412982528491[/C][C]0.0162282596505698[/C][C]0.991885870174715[/C][/ROW]
[ROW][C]106[/C][C]0.0181671846008460[/C][C]0.0363343692016919[/C][C]0.981832815399154[/C][/ROW]
[ROW][C]107[/C][C]0.0180295512597242[/C][C]0.0360591025194485[/C][C]0.981970448740276[/C][/ROW]
[ROW][C]108[/C][C]0.0227211535015447[/C][C]0.0454423070030894[/C][C]0.977278846498455[/C][/ROW]
[ROW][C]109[/C][C]0.0175296500642828[/C][C]0.0350593001285656[/C][C]0.982470349935717[/C][/ROW]
[ROW][C]110[/C][C]0.0128035457053405[/C][C]0.0256070914106810[/C][C]0.98719645429466[/C][/ROW]
[ROW][C]111[/C][C]0.00926976201671084[/C][C]0.0185395240334217[/C][C]0.99073023798329[/C][/ROW]
[ROW][C]112[/C][C]0.0227330945126734[/C][C]0.0454661890253467[/C][C]0.977266905487327[/C][/ROW]
[ROW][C]113[/C][C]0.0205964481526728[/C][C]0.0411928963053457[/C][C]0.979403551847327[/C][/ROW]
[ROW][C]114[/C][C]0.0571932627148997[/C][C]0.114386525429799[/C][C]0.9428067372851[/C][/ROW]
[ROW][C]115[/C][C]0.0484501837172544[/C][C]0.0969003674345088[/C][C]0.951549816282746[/C][/ROW]
[ROW][C]116[/C][C]0.03926472906088[/C][C]0.07852945812176[/C][C]0.96073527093912[/C][/ROW]
[ROW][C]117[/C][C]0.115712576239433[/C][C]0.231425152478866[/C][C]0.884287423760567[/C][/ROW]
[ROW][C]118[/C][C]0.110870623556605[/C][C]0.221741247113209[/C][C]0.889129376443395[/C][/ROW]
[ROW][C]119[/C][C]0.0982975543600397[/C][C]0.196595108720079[/C][C]0.90170244563996[/C][/ROW]
[ROW][C]120[/C][C]0.171823904570047[/C][C]0.343647809140094[/C][C]0.828176095429953[/C][/ROW]
[ROW][C]121[/C][C]0.163706791636379[/C][C]0.327413583272758[/C][C]0.83629320836362[/C][/ROW]
[ROW][C]122[/C][C]0.195062023880454[/C][C]0.390124047760909[/C][C]0.804937976119546[/C][/ROW]
[ROW][C]123[/C][C]0.188042980511619[/C][C]0.376085961023238[/C][C]0.811957019488381[/C][/ROW]
[ROW][C]124[/C][C]0.187225642039559[/C][C]0.374451284079119[/C][C]0.81277435796044[/C][/ROW]
[ROW][C]125[/C][C]0.169993306586364[/C][C]0.339986613172728[/C][C]0.830006693413636[/C][/ROW]
[ROW][C]126[/C][C]0.138332383081952[/C][C]0.276664766163903[/C][C]0.861667616918048[/C][/ROW]
[ROW][C]127[/C][C]0.108170210302971[/C][C]0.216340420605943[/C][C]0.891829789697029[/C][/ROW]
[ROW][C]128[/C][C]0.111622507273308[/C][C]0.223245014546615[/C][C]0.888377492726692[/C][/ROW]
[ROW][C]129[/C][C]0.160124830965551[/C][C]0.320249661931103[/C][C]0.839875169034449[/C][/ROW]
[ROW][C]130[/C][C]0.137944002820249[/C][C]0.275888005640498[/C][C]0.862055997179751[/C][/ROW]
[ROW][C]131[/C][C]0.120411351115570[/C][C]0.240822702231139[/C][C]0.87958864888443[/C][/ROW]
[ROW][C]132[/C][C]0.104288622865573[/C][C]0.208577245731146[/C][C]0.895711377134427[/C][/ROW]
[ROW][C]133[/C][C]0.0956630435677012[/C][C]0.191326087135402[/C][C]0.904336956432299[/C][/ROW]
[ROW][C]134[/C][C]0.0780905262947188[/C][C]0.156181052589438[/C][C]0.921909473705281[/C][/ROW]
[ROW][C]135[/C][C]0.106568501534917[/C][C]0.213137003069834[/C][C]0.893431498465083[/C][/ROW]
[ROW][C]136[/C][C]0.0790195243729827[/C][C]0.158039048745965[/C][C]0.920980475627017[/C][/ROW]
[ROW][C]137[/C][C]0.0576295417966279[/C][C]0.115259083593256[/C][C]0.942370458203372[/C][/ROW]
[ROW][C]138[/C][C]0.146858066608715[/C][C]0.29371613321743[/C][C]0.853141933391285[/C][/ROW]
[ROW][C]139[/C][C]0.208120044670052[/C][C]0.416240089340104[/C][C]0.791879955329948[/C][/ROW]
[ROW][C]140[/C][C]0.188513100213958[/C][C]0.377026200427915[/C][C]0.811486899786042[/C][/ROW]
[ROW][C]141[/C][C]0.406025649917164[/C][C]0.812051299834327[/C][C]0.593974350082836[/C][/ROW]
[ROW][C]142[/C][C]0.356376040738363[/C][C]0.712752081476726[/C][C]0.643623959261637[/C][/ROW]
[ROW][C]143[/C][C]0.666004373267961[/C][C]0.667991253464077[/C][C]0.333995626732039[/C][/ROW]
[ROW][C]144[/C][C]0.60479794654466[/C][C]0.79040410691068[/C][C]0.39520205345534[/C][/ROW]
[ROW][C]145[/C][C]0.49417312937125[/C][C]0.9883462587425[/C][C]0.50582687062875[/C][/ROW]
[ROW][C]146[/C][C]0.423286503602423[/C][C]0.846573007204845[/C][C]0.576713496397577[/C][/ROW]
[ROW][C]147[/C][C]0.433583274988949[/C][C]0.867166549977897[/C][C]0.566416725011051[/C][/ROW]
[ROW][C]148[/C][C]0.303283812156224[/C][C]0.606567624312449[/C][C]0.696716187843776[/C][/ROW]
[ROW][C]149[/C][C]0.211402600164403[/C][C]0.422805200328805[/C][C]0.788597399835597[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=97952&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=97952&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
100.5270283643144650.945943271371070.472971635685535
110.5620721769234440.8758556461531130.437927823076556
120.4888201218553440.9776402437106880.511179878144656
130.540340997121710.919318005756580.45965900287829
140.7416341416927360.5167317166145270.258365858307264
150.6547677799223590.6904644401552820.345232220077641
160.6238402036212980.7523195927574040.376159796378702
170.5381604118785770.9236791762428460.461839588121423
180.676102822720440.6477943545591190.323897177279559
190.6013206563170240.7973586873659520.398679343682976
200.5885070534294230.8229858931411550.411492946570577
210.5246008119634860.9507983760730290.475399188036514
220.4551930008433730.9103860016867460.544806999156627
230.4045132330494310.8090264660988620.595486766950569
240.4089286325352480.8178572650704950.591071367464752
250.5713124779890450.857375044021910.428687522010955
260.510036637596390.979926724807220.48996336240361
270.4430170306403400.8860340612806810.55698296935966
280.4374636088203230.8749272176406460.562536391179677
290.3733175255846280.7466350511692550.626682474415373
300.3129254693000450.6258509386000890.687074530699955
310.3467053025993480.6934106051986960.653294697400652
320.2956848691865100.5913697383730210.70431513081349
330.5218562366792980.9562875266414050.478143763320702
340.4707206954930720.9414413909861440.529279304506928
350.4327585476229680.8655170952459370.567241452377032
360.3754389188255470.7508778376510950.624561081174453
370.3488126340243320.6976252680486640.651187365975668
380.2972001395534950.5944002791069910.702799860446505
390.2498292770490700.4996585540981390.75017072295093
400.2224962417419930.4449924834839860.777503758258007
410.3751097500675780.7502195001351560.624890249932422
420.3233293513738100.6466587027476190.67667064862619
430.2913097444098280.5826194888196550.708690255590172
440.2477411429354410.4954822858708820.752258857064559
450.2113446507543240.4226893015086480.788655349245676
460.1919434164478600.3838868328957200.80805658355214
470.1795112931879290.3590225863758580.820488706812071
480.1644349986815210.3288699973630430.835565001318479
490.1343471198614540.2686942397229090.865652880138546
500.1247300837685840.2494601675371690.875269916231415
510.1029539738582300.2059079477164600.89704602614177
520.08783550096624450.1756710019324890.912164499033756
530.1473975884524600.2947951769049200.85260241154754
540.176789105538170.353578211076340.82321089446183
550.1652671865387720.3305343730775440.834732813461228
560.2226869754661200.4453739509322410.77731302453388
570.2151319088197600.4302638176395190.78486809118024
580.1841536430785530.3683072861571060.815846356921447
590.1973511380899140.3947022761798280.802648861910086
600.2250919989972290.4501839979944580.774908001002771
610.1988802369784360.3977604739568710.801119763021564
620.1671182584235650.3342365168471300.832881741576435
630.1823448707505040.3646897415010090.817655129249496
640.1532548126361260.3065096252722520.846745187363874
650.1264389685164600.2528779370329210.87356103148354
660.1228832953083450.245766590616690.877116704691655
670.1122482173709430.2244964347418850.887751782629058
680.1113449234101170.2226898468202340.888655076589883
690.09475658474965190.1895131694993040.905243415250348
700.07736051432496710.1547210286499340.922639485675033
710.0628681336664160.1257362673328320.937131866333584
720.04965621586309260.09931243172618510.950343784136907
730.04661542310497250.0932308462099450.953384576895028
740.0373413642259390.0746827284518780.96265863577406
750.03103143747197540.06206287494395080.968968562528025
760.02380097559040180.04760195118080350.976199024409598
770.01868590141621320.03737180283242640.981314098583787
780.01495714068057460.02991428136114930.985042859319425
790.02247810815643580.04495621631287170.977521891843564
800.01792120620812370.03584241241624740.982078793791876
810.01752536410636600.03505072821273210.982474635893634
820.03135058596689540.06270117193379080.968649414033105
830.02417738786077950.04835477572155910.97582261213922
840.02017265224147270.04034530448294550.979827347758527
850.02209130363799680.04418260727599360.977908696362003
860.01957790681058910.03915581362117830.98042209318941
870.01488339564409600.02976679128819210.985116604355904
880.01943484492832480.03886968985664960.980565155071675
890.01528193402292590.03056386804585170.984718065977074
900.01143503976743200.02287007953486390.988564960232568
910.01149360150002350.0229872030000470.988506398499976
920.01004879652614640.02009759305229280.989951203473854
930.007706348891424210.01541269778284840.992293651108576
940.006370954451671760.01274190890334350.993629045548328
950.01167643040904610.02335286081809220.988323569590954
960.01057507286336610.02115014572673230.989424927136634
970.01005793173773190.02011586347546380.989942068262268
980.00769645881097110.01539291762194220.992303541189029
990.005678852227648470.01135770445529690.994321147772351
1000.004347520484855510.008695040969711020.995652479515144
1010.003212265659623610.006424531319247220.996787734340376
1020.002285633287830200.004571266575660400.99771436671217
1030.002443964708573920.004887929417147840.997556035291426
1040.001914651603452040.003829303206904080.998085348396548
1050.008114129825284910.01622825965056980.991885870174715
1060.01816718460084600.03633436920169190.981832815399154
1070.01802955125972420.03605910251944850.981970448740276
1080.02272115350154470.04544230700308940.977278846498455
1090.01752965006428280.03505930012856560.982470349935717
1100.01280354570534050.02560709141068100.98719645429466
1110.009269762016710840.01853952403342170.99073023798329
1120.02273309451267340.04546618902534670.977266905487327
1130.02059644815267280.04119289630534570.979403551847327
1140.05719326271489970.1143865254297990.9428067372851
1150.04845018371725440.09690036743450880.951549816282746
1160.039264729060880.078529458121760.96073527093912
1170.1157125762394330.2314251524788660.884287423760567
1180.1108706235566050.2217412471132090.889129376443395
1190.09829755436003970.1965951087200790.90170244563996
1200.1718239045700470.3436478091400940.828176095429953
1210.1637067916363790.3274135832727580.83629320836362
1220.1950620238804540.3901240477609090.804937976119546
1230.1880429805116190.3760859610232380.811957019488381
1240.1872256420395590.3744512840791190.81277435796044
1250.1699933065863640.3399866131727280.830006693413636
1260.1383323830819520.2766647661639030.861667616918048
1270.1081702103029710.2163404206059430.891829789697029
1280.1116225072733080.2232450145466150.888377492726692
1290.1601248309655510.3202496619311030.839875169034449
1300.1379440028202490.2758880056404980.862055997179751
1310.1204113511155700.2408227022311390.87958864888443
1320.1042886228655730.2085772457311460.895711377134427
1330.09566304356770120.1913260871354020.904336956432299
1340.07809052629471880.1561810525894380.921909473705281
1350.1065685015349170.2131370030698340.893431498465083
1360.07901952437298270.1580390487459650.920980475627017
1370.05762954179662790.1152590835932560.942370458203372
1380.1468580666087150.293716133217430.853141933391285
1390.2081200446700520.4162400893401040.791879955329948
1400.1885131002139580.3770262004279150.811486899786042
1410.4060256499171640.8120512998343270.593974350082836
1420.3563760407383630.7127520814767260.643623959261637
1430.6660043732679610.6679912534640770.333995626732039
1440.604797946544660.790404106910680.39520205345534
1450.494173129371250.98834625874250.50582687062875
1460.4232865036024230.8465730072048450.576713496397577
1470.4335832749889490.8671665499778970.566416725011051
1480.3032838121562240.6065676243124490.696716187843776
1490.2114026001644030.4228052003288050.788597399835597






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level50.0357142857142857NOK
5% type I error level370.264285714285714NOK
10% type I error level440.314285714285714NOK

\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 & 5 & 0.0357142857142857 & NOK \tabularnewline
5% type I error level & 37 & 0.264285714285714 & NOK \tabularnewline
10% type I error level & 44 & 0.314285714285714 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=97952&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]5[/C][C]0.0357142857142857[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]37[/C][C]0.264285714285714[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]44[/C][C]0.314285714285714[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=97952&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=97952&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 level50.0357142857142857NOK
5% type I error level370.264285714285714NOK
10% type I error level440.314285714285714NOK


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 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 2 ; 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')
}