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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, 07 Dec 2012 09:38:23 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Dec/07/t1354891121u1bplnz6s1kqnt0.htm/, Retrieved Thu, 31 Oct 2024 23:05:59 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=197403, Retrieved Thu, 31 Oct 2024 23:05:59 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact249

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
- R PD  [Multiple Regression] [WS7 Tutorial] [2010-11-18 16:04:53] [afe9379cca749d06b3d6872e02cc47ed]
-    D    [Multiple Regression] [WS7 Tutorial Popu...] [2010-11-22 10:41:15] [afe9379cca749d06b3d6872e02cc47ed]
- R  D      [Multiple Regression] [WS 7 Mini-tutorial] [2011-11-20 14:29:34] [f5fdea4413921432bb019d1f20c4f2ec]
- R  D        [Multiple Regression] [WS 7 Mini-tutorial] [2011-11-20 14:47:41] [f5fdea4413921432bb019d1f20c4f2ec]
- RMP           [Kendall tau Correlation Matrix] [workshop 10 a] [2012-12-07 13:55:33] [dbae308bdff61c0f4902cc85498d0d35]
- RMP               [Multiple Regression] [workshop 10 c] [2012-12-07 14:38:23] [7915dafcfdccff56a257085e1714b048] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
1	26	21	21	23	17	23	4
1	20	16	15	24	17	20	4
1	19	19	18	22	18	20	6
2	19	18	11	20	21	21	8
1	20	16	8	24	20	24	8
1	25	23	19	27	28	22	4
2	25	17	4	28	19	23	4
1	22	12	20	27	22	20	8
1	26	19	16	24	16	25	5
1	22	16	14	23	18	23	4
2	17	19	10	24	25	27	4
2	22	20	13	27	17	27	4
1	19	13	14	27	14	22	4
1	24	20	8	28	11	24	4
1	26	27	23	27	27	25	4
2	21	17	11	23	20	22	8
1	13	8	9	24	22	28	4
2	26	25	24	28	22	28	4
2	20	26	5	27	21	27	4
1	22	13	15	25	23	25	8
2	14	19	5	19	17	16	4
1	21	15	19	24	24	28	7
1	7	5	6	20	14	21	4
2	23	16	13	28	17	24	4
1	17	14	11	26	23	27	5
1	25	24	17	23	24	14	4
1	25	24	17	23	24	14	4
1	19	9	5	20	8	27	4
2	20	19	9	11	22	20	4
1	23	19	15	24	23	21	4
2	22	25	17	25	25	22	4
1	22	19	17	23	21	21	4
1	21	18	20	18	24	12	15
2	15	15	12	20	15	20	10
2	20	12	7	20	22	24	4
2	22	21	16	24	21	19	8
1	18	12	7	23	25	28	4
2	20	15	14	25	16	23	4
2	28	28	24	28	28	27	4
1	22	25	15	26	23	22	4
1	18	19	15	26	21	27	7
1	23	20	10	23	21	26	4
1	20	24	14	22	26	22	6
2	25	26	18	24	22	21	5
2	26	25	12	21	21	19	4
1	15	12	9	20	18	24	16
2	17	12	9	22	12	19	5
2	23	15	8	20	25	26	12
1	21	17	18	25	17	22	6
2	13	14	10	20	24	28	9
1	18	16	17	22	15	21	9
1	19	11	14	23	13	23	4
1	22	20	16	25	26	28	5
1	16	11	10	23	16	10	4
2	24	22	19	23	24	24	4
1	18	20	10	22	21	21	5
1	20	19	14	24	20	21	4
1	24	17	10	25	14	24	4
2	14	21	4	21	25	24	4
2	22	23	19	12	25	25	5
1	24	18	9	17	20	25	4
1	18	17	12	20	22	23	6
1	21	27	16	23	20	21	4
2	23	25	11	23	26	16	4
1	17	19	18	20	18	17	18
2	22	22	11	28	22	25	4
2	24	24	24	24	24	24	6
2	21	20	17	24	17	23	4
1	22	19	18	24	24	25	4
1	16	11	9	24	20	23	5
1	21	22	19	28	19	28	4
2	23	22	18	25	20	26	4
2	22	16	12	21	15	22	5
1	24	20	23	25	23	19	10
1	24	24	22	25	26	26	5
1	16	16	14	18	22	18	8
1	16	16	14	17	20	18	8
2	21	22	16	26	24	25	5
2	26	24	23	28	26	27	4
2	15	16	7	21	21	12	4
2	25	27	10	27	25	15	4
1	18	11	12	22	13	21	5
0	23	21	12	21	20	23	4
1	20	20	12	25	22	22	4
2	17	20	17	22	23	21	8
2	25	27	21	23	28	24	4
1	24	20	16	26	22	27	5
1	17	12	11	19	20	22	14
1	19	8	14	25	6	28	8
1	20	21	13	21	21	26	8
1	15	18	9	13	20	10	4
2	27	24	19	24	18	19	4
1	22	16	13	25	23	22	6
1	23	18	19	26	20	21	4
1	16	20	13	25	24	24	7
1	19	20	13	25	22	25	7
2	25	19	13	22	21	21	4
1	19	17	14	21	18	20	6
2	19	16	12	23	21	21	4
2	26	26	22	25	23	24	7
1	21	15	11	24	23	23	4
2	20	22	5	21	15	18	4
1	24	17	18	21	21	24	8
1	22	23	19	25	24	24	4
2	20	21	14	22	23	19	4
1	18	19	15	20	21	20	10
2	18	14	12	20	21	18	8
1	24	17	19	23	20	20	6
1	24	12	15	28	11	27	4
1	22	24	17	23	22	23	4
1	23	18	8	28	27	26	4
1	22	20	10	24	25	23	5
1	20	16	12	18	18	17	4
1	18	20	12	20	20	21	6
1	25	22	20	28	24	25	4
2	18	12	12	21	10	23	5
1	16	16	12	21	27	27	7
1	20	17	14	25	21	24	8
2	19	22	6	19	21	20	5
1	15	12	10	18	18	27	8
1	19	14	18	21	15	21	10
1	19	23	18	22	24	24	8
1	16	15	7	24	22	21	5
1	17	17	18	15	14	15	12
1	28	28	9	28	28	25	4
2	23	20	17	26	18	25	5
1	25	23	22	23	26	22	4
1	20	13	11	26	17	24	6
2	17	18	15	20	19	21	4
2	23	23	17	22	22	22	4
1	16	19	15	20	18	23	7
2	23	23	22	23	24	22	7
2	11	12	9	22	15	20	10
2	18	16	13	24	18	23	4
2	24	23	20	23	26	25	5
1	23	13	14	22	11	23	8
1	21	22	14	26	26	22	11
2	16	18	12	23	21	25	7
2	24	23	20	27	23	26	4
1	23	20	20	23	23	22	8
1	18	10	8	21	15	24	6
1	20	17	17	26	22	24	7
1	9	18	9	23	26	25	5
2	24	15	18	21	16	20	4
1	25	23	22	27	20	26	8
1	20	17	10	19	18	21	4
2	21	17	13	23	22	26	8
2	25	22	15	25	16	21	6
2	22	20	18	23	19	22	4
2	21	20	18	22	20	16	9
1	21	19	12	22	19	26	5
1	22	18	12	25	23	28	6
1	27	22	20	25	24	18	4
2	24	20	12	28	25	25	4
2	24	22	16	28	21	23	4
2	21	18	16	20	21	21	5
1	18	16	18	25	23	20	6
1	16	16	16	19	27	25	16
1	22	16	13	25	23	22	6
1	20	16	17	22	18	21	6
2	18	17	13	18	16	16	4
1	20	18	17	20	16	18	4

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time10 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 10 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197403&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]10 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197403&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197403&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 time10 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net






Multiple Linear Regression - Estimated Regression Equation
G[t] = + 1.48601458299977 -0.00258388724390901I1[t] + 0.0428607603342156I2[t] -0.0150765680836861I3[t] -0.0118362110423281E1[t] -0.0135081669900305E2[t] + 0.000765601244283087E3[t] -0.016760125297127A[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
G[t] =  +  1.48601458299977 -0.00258388724390901I1[t] +  0.0428607603342156I2[t] -0.0150765680836861I3[t] -0.0118362110423281E1[t] -0.0135081669900305E2[t] +  0.000765601244283087E3[t] -0.016760125297127A[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197403&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]G[t] =  +  1.48601458299977 -0.00258388724390901I1[t] +  0.0428607603342156I2[t] -0.0150765680836861I3[t] -0.0118362110423281E1[t] -0.0135081669900305E2[t] +  0.000765601244283087E3[t] -0.016760125297127A[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197403&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197403&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
G[t] = + 1.48601458299977 -0.00258388724390901I1[t] + 0.0428607603342156I2[t] -0.0150765680836861I3[t] -0.0118362110423281E1[t] -0.0135081669900305E2[t] + 0.000765601244283087E3[t] -0.016760125297127A[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)1.486014582999770.3991133.72330.0002750.000138
I1-0.002583887243909010.015544-0.16620.868190.434095
I20.04286076033421560.0133993.19870.0016760.000838
I3-0.01507656808368610.010482-1.43830.1523820.076191
E1-0.01183621104232810.015046-0.78660.4326990.21635
E2-0.01350816699003050.011518-1.17280.2426930.121346
E30.0007656012442830870.0118530.06460.9485820.474291
A-0.0167601252971270.016588-1.01040.3138860.156943

\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) & 1.48601458299977 & 0.399113 & 3.7233 & 0.000275 & 0.000138 \tabularnewline
I1 & -0.00258388724390901 & 0.015544 & -0.1662 & 0.86819 & 0.434095 \tabularnewline
I2 & 0.0428607603342156 & 0.013399 & 3.1987 & 0.001676 & 0.000838 \tabularnewline
I3 & -0.0150765680836861 & 0.010482 & -1.4383 & 0.152382 & 0.076191 \tabularnewline
E1 & -0.0118362110423281 & 0.015046 & -0.7866 & 0.432699 & 0.21635 \tabularnewline
E2 & -0.0135081669900305 & 0.011518 & -1.1728 & 0.242693 & 0.121346 \tabularnewline
E3 & 0.000765601244283087 & 0.011853 & 0.0646 & 0.948582 & 0.474291 \tabularnewline
A & -0.016760125297127 & 0.016588 & -1.0104 & 0.313886 & 0.156943 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197403&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]1.48601458299977[/C][C]0.399113[/C][C]3.7233[/C][C]0.000275[/C][C]0.000138[/C][/ROW]
[ROW][C]I1[/C][C]-0.00258388724390901[/C][C]0.015544[/C][C]-0.1662[/C][C]0.86819[/C][C]0.434095[/C][/ROW]
[ROW][C]I2[/C][C]0.0428607603342156[/C][C]0.013399[/C][C]3.1987[/C][C]0.001676[/C][C]0.000838[/C][/ROW]
[ROW][C]I3[/C][C]-0.0150765680836861[/C][C]0.010482[/C][C]-1.4383[/C][C]0.152382[/C][C]0.076191[/C][/ROW]
[ROW][C]E1[/C][C]-0.0118362110423281[/C][C]0.015046[/C][C]-0.7866[/C][C]0.432699[/C][C]0.21635[/C][/ROW]
[ROW][C]E2[/C][C]-0.0135081669900305[/C][C]0.011518[/C][C]-1.1728[/C][C]0.242693[/C][C]0.121346[/C][/ROW]
[ROW][C]E3[/C][C]0.000765601244283087[/C][C]0.011853[/C][C]0.0646[/C][C]0.948582[/C][C]0.474291[/C][/ROW]
[ROW][C]A[/C][C]-0.016760125297127[/C][C]0.016588[/C][C]-1.0104[/C][C]0.313886[/C][C]0.156943[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197403&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197403&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)1.486014582999770.3991133.72330.0002750.000138
I1-0.002583887243909010.015544-0.16620.868190.434095
I20.04286076033421560.0133993.19870.0016760.000838
I3-0.01507656808368610.010482-1.43830.1523820.076191
E1-0.01183621104232810.015046-0.78660.4326990.21635
E2-0.01350816699003050.011518-1.17280.2426930.121346
E30.0007656012442830870.0118530.06460.9485820.474291
A-0.0167601252971270.016588-1.01040.3138860.156943






Multiple Linear Regression - Regression Statistics
Multiple R0.323963077424294
R-squared0.104952075534219
Adjusted R-squared0.064268078967593
F-TEST (value)2.57968942068767
F-TEST (DF numerator)7
F-TEST (DF denominator)154
p-value0.0152819344889854
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.483795977813726
Sum Squared Residuals36.0450164149059

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.323963077424294 \tabularnewline
R-squared & 0.104952075534219 \tabularnewline
Adjusted R-squared & 0.064268078967593 \tabularnewline
F-TEST (value) & 2.57968942068767 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 154 \tabularnewline
p-value & 0.0152819344889854 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.483795977813726 \tabularnewline
Sum Squared Residuals & 36.0450164149059 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197403&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.323963077424294[/C][/ROW]
[ROW][C]R-squared[/C][C]0.104952075534219[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.064268078967593[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]2.57968942068767[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]154[/C][/ROW]
[ROW][C]p-value[/C][C]0.0152819344889854[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.483795977813726[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]36.0450164149059[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197403&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197403&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 R0.323963077424294
R-squared0.104952075534219
Adjusted R-squared0.064268078967593
F-TEST (value)2.57968942068767
F-TEST (DF numerator)7
F-TEST (DF denominator)154
p-value0.0152819344889854
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.483795977813726
Sum Squared Residuals36.0450164149059






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
111.4509981865452-0.450998186545205
211.32852410206451-0.328524102064514
311.39110457056038-0.391104570560383
421.404173058576560.595826941423436
511.32955748146885-0.329557481468849
611.37275644832098-0.37275644832098
721.452243300683210.547756699316793
810.9064404765557580.0935595234442419
911.42710253943434-0.42710253943434
1011.33905774344553-0.339057743445529
1121.437534758007060.562465241992944
1221.494803080663930.505196919336072
1311.22414934672114-0.224149346721135
1411.63193413375955-0.631934133759546
1511.49711430080207-0.49711430080207
1621.334909658861860.66509034113814
1711.03276861360438-0.0327686136043802
1821.454317639690630.545682360309374
1921.823715293866410.176284706133594
2011.03867633862445-0.0386763386244454
2121.679494037601980.320505962398016
2211.08406044728432-0.0840604472843152
2311.11497033169582-0.11497033169582
2421.306643137307980.693356862692021
2511.19473817485057-0.194738174850569
2611.54102304699774-0.541023046997738
2711.54102304699774-0.541023046997738
2811.35612590359534-0.356125903595342
2921.633895700169390.36610429983061
3011.36907132063953-0.369071320639534
3121.560579689943260.439420310056742
3211.38035461673846-0.38035461673846
3311.1222528041717-0.1222528041717
3421.317612908567510.682387091432487
3521.260560019593430.739439980406568
3621.400744790771170.599255209228825
3711.19275706496131-0.192757064961307
3821.304708669494540.695291330505464
3921.495917543020990.504082456979011
4011.60591294904836-0.605912949048364
4111.3396479003288-0.339647900328801
4211.52999547263598-0.529995472635984
4311.55659662389062-0.556596623890619
4421.615447205933070.384552794066929
4521.724707689782640.275292310217362
4611.0962374840402-0.0962374840402035
4721.328979661454890.671020338545107
4821.193239769922120.806760230077875
4911.27974601175575-0.279746011755746
5021.21138449122960.788615508770395
5111.27119247120833-0.271192471208325
5211.20004643845633-0.20004643845633
5311.33567777153441-0.335677771534408
5411.21762705537703-0.21762705537703
5521.435388289848670.564611710151326
5611.53416298837932-0.534162988379315
5711.43242405142504-0.432424051425038
5811.46818284874642-0.468182848746419
5921.654679178303470.345320821696534
6021.584112455093440.415887544906557
6111.54052646480705-0.540526464807046
6211.37036290349536-0.370362903495361
6311.75440932172981-0.754409321729811
6421.654025858830390.345974141169607
6511.21652645983448-0.216526459834485
6621.529769489018680.470230510981316
6721.400370508462090.599629491537907
6821.469526923722940.530473076277056
6911.31597974161949-0.315979741619486
7011.16002743533682-0.160027435336819
7111.45456213629605-0.454562136296046
7221.48494019354030.515059806459699
7321.415882076126240.584117923873761
7411.1748074837467-0.174807483746696
7511.40996242739277-0.409962427392769
7611.28884094675058-0.288840946750578
7711.32769349177297-0.327693491772967
7821.436866498651630.563133501348368
7921.371735178235690.628264821764309
8021.437407238166140.562592761833855
8121.715053894671130.284946105328873
8211.22632834512425-0.226328345124246
8301.57758688214467-1.57758688214467
8411.46735100414852-0.46735100414852
8521.353914189165980.64608581083402
8621.562922400148350.437077599851651
8711.3719408527201-0.3719408527201
8811.07772549855297-0.0777254985529682
8911.07913640933215-0.0791364093321479
9011.49201011134702-0.492010111347017
9111.59964227550729-0.599642275507293
9221.578742933461820.421257066538182
9311.22863520265587-0.228635202655869
9411.28275636685601-0.282756366856009
9511.38684447765759-0.386844477657594
9611.40687475115021-0.406874751150212
9721.44474543838380.555254561616195
9811.37752553326902-0.377525533269024
9921.334906837885970.66509316211403
10021.495989221460650.504010778539346
10111.2646335286138-0.2646335286138
10221.797448109525150.202551890474853
10311.23331747812752-0.233317478127522
10411.45974440258605-0.459744402586052
10521.499762290719470.500237709280532
10611.35502558198141-0.355025581981407
10721.217940532667080.782059467332925
10811.23853450056633-0.238534500566332
10911.18580887823287-0.185808878232867
11011.58268145390807-0.582681453908074
11111.3341970309831-0.334197030983102
11211.44765355184747-0.447653551847467
11311.42896610184666-0.428966101846661
11411.5244303159895-0.524430315989503
11511.35931238055372-0.359312380553722
11621.323081019959450.676918980040553
11711.23959515133649-0.239595151336488
11811.25661445526859-0.25661445526859
11921.712349926021280.287650073978717
12011.24121114415104-0.241211144151039
12111.1628865809575-0.1628865809575
12211.45104076433994-0.451040764339941
12311.33307607637243-0.333076076372426
12411.34304821163201-0.343048211632012
12511.72053486178771-0.720534861787714
12621.411949637351880.588050362648121
12711.4018879222193-0.401887922219296
12811.20611782569483-0.206117825694833
12921.443091395898210.556908604101793
13021.548307416127990.451692583872006
13111.45329503706355-0.453295037063547
13221.383791654795790.616208345204207
13321.22092345870690.779076541293096
13421.352633649462520.647366350537477
13521.42016162406630.579838375933701
13611.24724445398301-0.247244453983006
13711.33714574532333-0.337145745323332
13821.381162049371880.61883795062812
13921.430867007408490.569132992591512
14011.28211055165342-0.282110551653423
14111.21413041262276-0.214130412622764
14211.2028004982823-0.2028004982823
14311.4104583799744-0.410458379974403
14421.279114888620620.720885111379381
14511.37161398377879-0.371613983778791
14611.49320619228305-0.493206192283053
14721.280802593691560.719197406308441
14821.541686534447990.458313465552006
14921.435920744213330.564079255786667
15021.348438441558210.651561558441793
15111.48424177034748-0.484241770347477
15211.33402689887368-0.334026898873685
15311.38429403048291-0.384294030482907
15421.383279124808660.616720875191342
15521.46119583861390.538804161386099
15621.37390281956170.626097180438304
15711.16205670872451-0.162056708724509
15811.05058897092369-0.0505889709236913
15911.22863520265587-0.228635202655869
16011.2757805716418-0.275780571641796
16121.488168801320790.511831198679214
16211.44341429523635-0.44341429523635

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 1 & 1.4509981865452 & -0.450998186545205 \tabularnewline
2 & 1 & 1.32852410206451 & -0.328524102064514 \tabularnewline
3 & 1 & 1.39110457056038 & -0.391104570560383 \tabularnewline
4 & 2 & 1.40417305857656 & 0.595826941423436 \tabularnewline
5 & 1 & 1.32955748146885 & -0.329557481468849 \tabularnewline
6 & 1 & 1.37275644832098 & -0.37275644832098 \tabularnewline
7 & 2 & 1.45224330068321 & 0.547756699316793 \tabularnewline
8 & 1 & 0.906440476555758 & 0.0935595234442419 \tabularnewline
9 & 1 & 1.42710253943434 & -0.42710253943434 \tabularnewline
10 & 1 & 1.33905774344553 & -0.339057743445529 \tabularnewline
11 & 2 & 1.43753475800706 & 0.562465241992944 \tabularnewline
12 & 2 & 1.49480308066393 & 0.505196919336072 \tabularnewline
13 & 1 & 1.22414934672114 & -0.224149346721135 \tabularnewline
14 & 1 & 1.63193413375955 & -0.631934133759546 \tabularnewline
15 & 1 & 1.49711430080207 & -0.49711430080207 \tabularnewline
16 & 2 & 1.33490965886186 & 0.66509034113814 \tabularnewline
17 & 1 & 1.03276861360438 & -0.0327686136043802 \tabularnewline
18 & 2 & 1.45431763969063 & 0.545682360309374 \tabularnewline
19 & 2 & 1.82371529386641 & 0.176284706133594 \tabularnewline
20 & 1 & 1.03867633862445 & -0.0386763386244454 \tabularnewline
21 & 2 & 1.67949403760198 & 0.320505962398016 \tabularnewline
22 & 1 & 1.08406044728432 & -0.0840604472843152 \tabularnewline
23 & 1 & 1.11497033169582 & -0.11497033169582 \tabularnewline
24 & 2 & 1.30664313730798 & 0.693356862692021 \tabularnewline
25 & 1 & 1.19473817485057 & -0.194738174850569 \tabularnewline
26 & 1 & 1.54102304699774 & -0.541023046997738 \tabularnewline
27 & 1 & 1.54102304699774 & -0.541023046997738 \tabularnewline
28 & 1 & 1.35612590359534 & -0.356125903595342 \tabularnewline
29 & 2 & 1.63389570016939 & 0.36610429983061 \tabularnewline
30 & 1 & 1.36907132063953 & -0.369071320639534 \tabularnewline
31 & 2 & 1.56057968994326 & 0.439420310056742 \tabularnewline
32 & 1 & 1.38035461673846 & -0.38035461673846 \tabularnewline
33 & 1 & 1.1222528041717 & -0.1222528041717 \tabularnewline
34 & 2 & 1.31761290856751 & 0.682387091432487 \tabularnewline
35 & 2 & 1.26056001959343 & 0.739439980406568 \tabularnewline
36 & 2 & 1.40074479077117 & 0.599255209228825 \tabularnewline
37 & 1 & 1.19275706496131 & -0.192757064961307 \tabularnewline
38 & 2 & 1.30470866949454 & 0.695291330505464 \tabularnewline
39 & 2 & 1.49591754302099 & 0.504082456979011 \tabularnewline
40 & 1 & 1.60591294904836 & -0.605912949048364 \tabularnewline
41 & 1 & 1.3396479003288 & -0.339647900328801 \tabularnewline
42 & 1 & 1.52999547263598 & -0.529995472635984 \tabularnewline
43 & 1 & 1.55659662389062 & -0.556596623890619 \tabularnewline
44 & 2 & 1.61544720593307 & 0.384552794066929 \tabularnewline
45 & 2 & 1.72470768978264 & 0.275292310217362 \tabularnewline
46 & 1 & 1.0962374840402 & -0.0962374840402035 \tabularnewline
47 & 2 & 1.32897966145489 & 0.671020338545107 \tabularnewline
48 & 2 & 1.19323976992212 & 0.806760230077875 \tabularnewline
49 & 1 & 1.27974601175575 & -0.279746011755746 \tabularnewline
50 & 2 & 1.2113844912296 & 0.788615508770395 \tabularnewline
51 & 1 & 1.27119247120833 & -0.271192471208325 \tabularnewline
52 & 1 & 1.20004643845633 & -0.20004643845633 \tabularnewline
53 & 1 & 1.33567777153441 & -0.335677771534408 \tabularnewline
54 & 1 & 1.21762705537703 & -0.21762705537703 \tabularnewline
55 & 2 & 1.43538828984867 & 0.564611710151326 \tabularnewline
56 & 1 & 1.53416298837932 & -0.534162988379315 \tabularnewline
57 & 1 & 1.43242405142504 & -0.432424051425038 \tabularnewline
58 & 1 & 1.46818284874642 & -0.468182848746419 \tabularnewline
59 & 2 & 1.65467917830347 & 0.345320821696534 \tabularnewline
60 & 2 & 1.58411245509344 & 0.415887544906557 \tabularnewline
61 & 1 & 1.54052646480705 & -0.540526464807046 \tabularnewline
62 & 1 & 1.37036290349536 & -0.370362903495361 \tabularnewline
63 & 1 & 1.75440932172981 & -0.754409321729811 \tabularnewline
64 & 2 & 1.65402585883039 & 0.345974141169607 \tabularnewline
65 & 1 & 1.21652645983448 & -0.216526459834485 \tabularnewline
66 & 2 & 1.52976948901868 & 0.470230510981316 \tabularnewline
67 & 2 & 1.40037050846209 & 0.599629491537907 \tabularnewline
68 & 2 & 1.46952692372294 & 0.530473076277056 \tabularnewline
69 & 1 & 1.31597974161949 & -0.315979741619486 \tabularnewline
70 & 1 & 1.16002743533682 & -0.160027435336819 \tabularnewline
71 & 1 & 1.45456213629605 & -0.454562136296046 \tabularnewline
72 & 2 & 1.4849401935403 & 0.515059806459699 \tabularnewline
73 & 2 & 1.41588207612624 & 0.584117923873761 \tabularnewline
74 & 1 & 1.1748074837467 & -0.174807483746696 \tabularnewline
75 & 1 & 1.40996242739277 & -0.409962427392769 \tabularnewline
76 & 1 & 1.28884094675058 & -0.288840946750578 \tabularnewline
77 & 1 & 1.32769349177297 & -0.327693491772967 \tabularnewline
78 & 2 & 1.43686649865163 & 0.563133501348368 \tabularnewline
79 & 2 & 1.37173517823569 & 0.628264821764309 \tabularnewline
80 & 2 & 1.43740723816614 & 0.562592761833855 \tabularnewline
81 & 2 & 1.71505389467113 & 0.284946105328873 \tabularnewline
82 & 1 & 1.22632834512425 & -0.226328345124246 \tabularnewline
83 & 0 & 1.57758688214467 & -1.57758688214467 \tabularnewline
84 & 1 & 1.46735100414852 & -0.46735100414852 \tabularnewline
85 & 2 & 1.35391418916598 & 0.64608581083402 \tabularnewline
86 & 2 & 1.56292240014835 & 0.437077599851651 \tabularnewline
87 & 1 & 1.3719408527201 & -0.3719408527201 \tabularnewline
88 & 1 & 1.07772549855297 & -0.0777254985529682 \tabularnewline
89 & 1 & 1.07913640933215 & -0.0791364093321479 \tabularnewline
90 & 1 & 1.49201011134702 & -0.492010111347017 \tabularnewline
91 & 1 & 1.59964227550729 & -0.599642275507293 \tabularnewline
92 & 2 & 1.57874293346182 & 0.421257066538182 \tabularnewline
93 & 1 & 1.22863520265587 & -0.228635202655869 \tabularnewline
94 & 1 & 1.28275636685601 & -0.282756366856009 \tabularnewline
95 & 1 & 1.38684447765759 & -0.386844477657594 \tabularnewline
96 & 1 & 1.40687475115021 & -0.406874751150212 \tabularnewline
97 & 2 & 1.4447454383838 & 0.555254561616195 \tabularnewline
98 & 1 & 1.37752553326902 & -0.377525533269024 \tabularnewline
99 & 2 & 1.33490683788597 & 0.66509316211403 \tabularnewline
100 & 2 & 1.49598922146065 & 0.504010778539346 \tabularnewline
101 & 1 & 1.2646335286138 & -0.2646335286138 \tabularnewline
102 & 2 & 1.79744810952515 & 0.202551890474853 \tabularnewline
103 & 1 & 1.23331747812752 & -0.233317478127522 \tabularnewline
104 & 1 & 1.45974440258605 & -0.459744402586052 \tabularnewline
105 & 2 & 1.49976229071947 & 0.500237709280532 \tabularnewline
106 & 1 & 1.35502558198141 & -0.355025581981407 \tabularnewline
107 & 2 & 1.21794053266708 & 0.782059467332925 \tabularnewline
108 & 1 & 1.23853450056633 & -0.238534500566332 \tabularnewline
109 & 1 & 1.18580887823287 & -0.185808878232867 \tabularnewline
110 & 1 & 1.58268145390807 & -0.582681453908074 \tabularnewline
111 & 1 & 1.3341970309831 & -0.334197030983102 \tabularnewline
112 & 1 & 1.44765355184747 & -0.447653551847467 \tabularnewline
113 & 1 & 1.42896610184666 & -0.428966101846661 \tabularnewline
114 & 1 & 1.5244303159895 & -0.524430315989503 \tabularnewline
115 & 1 & 1.35931238055372 & -0.359312380553722 \tabularnewline
116 & 2 & 1.32308101995945 & 0.676918980040553 \tabularnewline
117 & 1 & 1.23959515133649 & -0.239595151336488 \tabularnewline
118 & 1 & 1.25661445526859 & -0.25661445526859 \tabularnewline
119 & 2 & 1.71234992602128 & 0.287650073978717 \tabularnewline
120 & 1 & 1.24121114415104 & -0.241211144151039 \tabularnewline
121 & 1 & 1.1628865809575 & -0.1628865809575 \tabularnewline
122 & 1 & 1.45104076433994 & -0.451040764339941 \tabularnewline
123 & 1 & 1.33307607637243 & -0.333076076372426 \tabularnewline
124 & 1 & 1.34304821163201 & -0.343048211632012 \tabularnewline
125 & 1 & 1.72053486178771 & -0.720534861787714 \tabularnewline
126 & 2 & 1.41194963735188 & 0.588050362648121 \tabularnewline
127 & 1 & 1.4018879222193 & -0.401887922219296 \tabularnewline
128 & 1 & 1.20611782569483 & -0.206117825694833 \tabularnewline
129 & 2 & 1.44309139589821 & 0.556908604101793 \tabularnewline
130 & 2 & 1.54830741612799 & 0.451692583872006 \tabularnewline
131 & 1 & 1.45329503706355 & -0.453295037063547 \tabularnewline
132 & 2 & 1.38379165479579 & 0.616208345204207 \tabularnewline
133 & 2 & 1.2209234587069 & 0.779076541293096 \tabularnewline
134 & 2 & 1.35263364946252 & 0.647366350537477 \tabularnewline
135 & 2 & 1.4201616240663 & 0.579838375933701 \tabularnewline
136 & 1 & 1.24724445398301 & -0.247244453983006 \tabularnewline
137 & 1 & 1.33714574532333 & -0.337145745323332 \tabularnewline
138 & 2 & 1.38116204937188 & 0.61883795062812 \tabularnewline
139 & 2 & 1.43086700740849 & 0.569132992591512 \tabularnewline
140 & 1 & 1.28211055165342 & -0.282110551653423 \tabularnewline
141 & 1 & 1.21413041262276 & -0.214130412622764 \tabularnewline
142 & 1 & 1.2028004982823 & -0.2028004982823 \tabularnewline
143 & 1 & 1.4104583799744 & -0.410458379974403 \tabularnewline
144 & 2 & 1.27911488862062 & 0.720885111379381 \tabularnewline
145 & 1 & 1.37161398377879 & -0.371613983778791 \tabularnewline
146 & 1 & 1.49320619228305 & -0.493206192283053 \tabularnewline
147 & 2 & 1.28080259369156 & 0.719197406308441 \tabularnewline
148 & 2 & 1.54168653444799 & 0.458313465552006 \tabularnewline
149 & 2 & 1.43592074421333 & 0.564079255786667 \tabularnewline
150 & 2 & 1.34843844155821 & 0.651561558441793 \tabularnewline
151 & 1 & 1.48424177034748 & -0.484241770347477 \tabularnewline
152 & 1 & 1.33402689887368 & -0.334026898873685 \tabularnewline
153 & 1 & 1.38429403048291 & -0.384294030482907 \tabularnewline
154 & 2 & 1.38327912480866 & 0.616720875191342 \tabularnewline
155 & 2 & 1.4611958386139 & 0.538804161386099 \tabularnewline
156 & 2 & 1.3739028195617 & 0.626097180438304 \tabularnewline
157 & 1 & 1.16205670872451 & -0.162056708724509 \tabularnewline
158 & 1 & 1.05058897092369 & -0.0505889709236913 \tabularnewline
159 & 1 & 1.22863520265587 & -0.228635202655869 \tabularnewline
160 & 1 & 1.2757805716418 & -0.275780571641796 \tabularnewline
161 & 2 & 1.48816880132079 & 0.511831198679214 \tabularnewline
162 & 1 & 1.44341429523635 & -0.44341429523635 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197403&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]1[/C][C]1.4509981865452[/C][C]-0.450998186545205[/C][/ROW]
[ROW][C]2[/C][C]1[/C][C]1.32852410206451[/C][C]-0.328524102064514[/C][/ROW]
[ROW][C]3[/C][C]1[/C][C]1.39110457056038[/C][C]-0.391104570560383[/C][/ROW]
[ROW][C]4[/C][C]2[/C][C]1.40417305857656[/C][C]0.595826941423436[/C][/ROW]
[ROW][C]5[/C][C]1[/C][C]1.32955748146885[/C][C]-0.329557481468849[/C][/ROW]
[ROW][C]6[/C][C]1[/C][C]1.37275644832098[/C][C]-0.37275644832098[/C][/ROW]
[ROW][C]7[/C][C]2[/C][C]1.45224330068321[/C][C]0.547756699316793[/C][/ROW]
[ROW][C]8[/C][C]1[/C][C]0.906440476555758[/C][C]0.0935595234442419[/C][/ROW]
[ROW][C]9[/C][C]1[/C][C]1.42710253943434[/C][C]-0.42710253943434[/C][/ROW]
[ROW][C]10[/C][C]1[/C][C]1.33905774344553[/C][C]-0.339057743445529[/C][/ROW]
[ROW][C]11[/C][C]2[/C][C]1.43753475800706[/C][C]0.562465241992944[/C][/ROW]
[ROW][C]12[/C][C]2[/C][C]1.49480308066393[/C][C]0.505196919336072[/C][/ROW]
[ROW][C]13[/C][C]1[/C][C]1.22414934672114[/C][C]-0.224149346721135[/C][/ROW]
[ROW][C]14[/C][C]1[/C][C]1.63193413375955[/C][C]-0.631934133759546[/C][/ROW]
[ROW][C]15[/C][C]1[/C][C]1.49711430080207[/C][C]-0.49711430080207[/C][/ROW]
[ROW][C]16[/C][C]2[/C][C]1.33490965886186[/C][C]0.66509034113814[/C][/ROW]
[ROW][C]17[/C][C]1[/C][C]1.03276861360438[/C][C]-0.0327686136043802[/C][/ROW]
[ROW][C]18[/C][C]2[/C][C]1.45431763969063[/C][C]0.545682360309374[/C][/ROW]
[ROW][C]19[/C][C]2[/C][C]1.82371529386641[/C][C]0.176284706133594[/C][/ROW]
[ROW][C]20[/C][C]1[/C][C]1.03867633862445[/C][C]-0.0386763386244454[/C][/ROW]
[ROW][C]21[/C][C]2[/C][C]1.67949403760198[/C][C]0.320505962398016[/C][/ROW]
[ROW][C]22[/C][C]1[/C][C]1.08406044728432[/C][C]-0.0840604472843152[/C][/ROW]
[ROW][C]23[/C][C]1[/C][C]1.11497033169582[/C][C]-0.11497033169582[/C][/ROW]
[ROW][C]24[/C][C]2[/C][C]1.30664313730798[/C][C]0.693356862692021[/C][/ROW]
[ROW][C]25[/C][C]1[/C][C]1.19473817485057[/C][C]-0.194738174850569[/C][/ROW]
[ROW][C]26[/C][C]1[/C][C]1.54102304699774[/C][C]-0.541023046997738[/C][/ROW]
[ROW][C]27[/C][C]1[/C][C]1.54102304699774[/C][C]-0.541023046997738[/C][/ROW]
[ROW][C]28[/C][C]1[/C][C]1.35612590359534[/C][C]-0.356125903595342[/C][/ROW]
[ROW][C]29[/C][C]2[/C][C]1.63389570016939[/C][C]0.36610429983061[/C][/ROW]
[ROW][C]30[/C][C]1[/C][C]1.36907132063953[/C][C]-0.369071320639534[/C][/ROW]
[ROW][C]31[/C][C]2[/C][C]1.56057968994326[/C][C]0.439420310056742[/C][/ROW]
[ROW][C]32[/C][C]1[/C][C]1.38035461673846[/C][C]-0.38035461673846[/C][/ROW]
[ROW][C]33[/C][C]1[/C][C]1.1222528041717[/C][C]-0.1222528041717[/C][/ROW]
[ROW][C]34[/C][C]2[/C][C]1.31761290856751[/C][C]0.682387091432487[/C][/ROW]
[ROW][C]35[/C][C]2[/C][C]1.26056001959343[/C][C]0.739439980406568[/C][/ROW]
[ROW][C]36[/C][C]2[/C][C]1.40074479077117[/C][C]0.599255209228825[/C][/ROW]
[ROW][C]37[/C][C]1[/C][C]1.19275706496131[/C][C]-0.192757064961307[/C][/ROW]
[ROW][C]38[/C][C]2[/C][C]1.30470866949454[/C][C]0.695291330505464[/C][/ROW]
[ROW][C]39[/C][C]2[/C][C]1.49591754302099[/C][C]0.504082456979011[/C][/ROW]
[ROW][C]40[/C][C]1[/C][C]1.60591294904836[/C][C]-0.605912949048364[/C][/ROW]
[ROW][C]41[/C][C]1[/C][C]1.3396479003288[/C][C]-0.339647900328801[/C][/ROW]
[ROW][C]42[/C][C]1[/C][C]1.52999547263598[/C][C]-0.529995472635984[/C][/ROW]
[ROW][C]43[/C][C]1[/C][C]1.55659662389062[/C][C]-0.556596623890619[/C][/ROW]
[ROW][C]44[/C][C]2[/C][C]1.61544720593307[/C][C]0.384552794066929[/C][/ROW]
[ROW][C]45[/C][C]2[/C][C]1.72470768978264[/C][C]0.275292310217362[/C][/ROW]
[ROW][C]46[/C][C]1[/C][C]1.0962374840402[/C][C]-0.0962374840402035[/C][/ROW]
[ROW][C]47[/C][C]2[/C][C]1.32897966145489[/C][C]0.671020338545107[/C][/ROW]
[ROW][C]48[/C][C]2[/C][C]1.19323976992212[/C][C]0.806760230077875[/C][/ROW]
[ROW][C]49[/C][C]1[/C][C]1.27974601175575[/C][C]-0.279746011755746[/C][/ROW]
[ROW][C]50[/C][C]2[/C][C]1.2113844912296[/C][C]0.788615508770395[/C][/ROW]
[ROW][C]51[/C][C]1[/C][C]1.27119247120833[/C][C]-0.271192471208325[/C][/ROW]
[ROW][C]52[/C][C]1[/C][C]1.20004643845633[/C][C]-0.20004643845633[/C][/ROW]
[ROW][C]53[/C][C]1[/C][C]1.33567777153441[/C][C]-0.335677771534408[/C][/ROW]
[ROW][C]54[/C][C]1[/C][C]1.21762705537703[/C][C]-0.21762705537703[/C][/ROW]
[ROW][C]55[/C][C]2[/C][C]1.43538828984867[/C][C]0.564611710151326[/C][/ROW]
[ROW][C]56[/C][C]1[/C][C]1.53416298837932[/C][C]-0.534162988379315[/C][/ROW]
[ROW][C]57[/C][C]1[/C][C]1.43242405142504[/C][C]-0.432424051425038[/C][/ROW]
[ROW][C]58[/C][C]1[/C][C]1.46818284874642[/C][C]-0.468182848746419[/C][/ROW]
[ROW][C]59[/C][C]2[/C][C]1.65467917830347[/C][C]0.345320821696534[/C][/ROW]
[ROW][C]60[/C][C]2[/C][C]1.58411245509344[/C][C]0.415887544906557[/C][/ROW]
[ROW][C]61[/C][C]1[/C][C]1.54052646480705[/C][C]-0.540526464807046[/C][/ROW]
[ROW][C]62[/C][C]1[/C][C]1.37036290349536[/C][C]-0.370362903495361[/C][/ROW]
[ROW][C]63[/C][C]1[/C][C]1.75440932172981[/C][C]-0.754409321729811[/C][/ROW]
[ROW][C]64[/C][C]2[/C][C]1.65402585883039[/C][C]0.345974141169607[/C][/ROW]
[ROW][C]65[/C][C]1[/C][C]1.21652645983448[/C][C]-0.216526459834485[/C][/ROW]
[ROW][C]66[/C][C]2[/C][C]1.52976948901868[/C][C]0.470230510981316[/C][/ROW]
[ROW][C]67[/C][C]2[/C][C]1.40037050846209[/C][C]0.599629491537907[/C][/ROW]
[ROW][C]68[/C][C]2[/C][C]1.46952692372294[/C][C]0.530473076277056[/C][/ROW]
[ROW][C]69[/C][C]1[/C][C]1.31597974161949[/C][C]-0.315979741619486[/C][/ROW]
[ROW][C]70[/C][C]1[/C][C]1.16002743533682[/C][C]-0.160027435336819[/C][/ROW]
[ROW][C]71[/C][C]1[/C][C]1.45456213629605[/C][C]-0.454562136296046[/C][/ROW]
[ROW][C]72[/C][C]2[/C][C]1.4849401935403[/C][C]0.515059806459699[/C][/ROW]
[ROW][C]73[/C][C]2[/C][C]1.41588207612624[/C][C]0.584117923873761[/C][/ROW]
[ROW][C]74[/C][C]1[/C][C]1.1748074837467[/C][C]-0.174807483746696[/C][/ROW]
[ROW][C]75[/C][C]1[/C][C]1.40996242739277[/C][C]-0.409962427392769[/C][/ROW]
[ROW][C]76[/C][C]1[/C][C]1.28884094675058[/C][C]-0.288840946750578[/C][/ROW]
[ROW][C]77[/C][C]1[/C][C]1.32769349177297[/C][C]-0.327693491772967[/C][/ROW]
[ROW][C]78[/C][C]2[/C][C]1.43686649865163[/C][C]0.563133501348368[/C][/ROW]
[ROW][C]79[/C][C]2[/C][C]1.37173517823569[/C][C]0.628264821764309[/C][/ROW]
[ROW][C]80[/C][C]2[/C][C]1.43740723816614[/C][C]0.562592761833855[/C][/ROW]
[ROW][C]81[/C][C]2[/C][C]1.71505389467113[/C][C]0.284946105328873[/C][/ROW]
[ROW][C]82[/C][C]1[/C][C]1.22632834512425[/C][C]-0.226328345124246[/C][/ROW]
[ROW][C]83[/C][C]0[/C][C]1.57758688214467[/C][C]-1.57758688214467[/C][/ROW]
[ROW][C]84[/C][C]1[/C][C]1.46735100414852[/C][C]-0.46735100414852[/C][/ROW]
[ROW][C]85[/C][C]2[/C][C]1.35391418916598[/C][C]0.64608581083402[/C][/ROW]
[ROW][C]86[/C][C]2[/C][C]1.56292240014835[/C][C]0.437077599851651[/C][/ROW]
[ROW][C]87[/C][C]1[/C][C]1.3719408527201[/C][C]-0.3719408527201[/C][/ROW]
[ROW][C]88[/C][C]1[/C][C]1.07772549855297[/C][C]-0.0777254985529682[/C][/ROW]
[ROW][C]89[/C][C]1[/C][C]1.07913640933215[/C][C]-0.0791364093321479[/C][/ROW]
[ROW][C]90[/C][C]1[/C][C]1.49201011134702[/C][C]-0.492010111347017[/C][/ROW]
[ROW][C]91[/C][C]1[/C][C]1.59964227550729[/C][C]-0.599642275507293[/C][/ROW]
[ROW][C]92[/C][C]2[/C][C]1.57874293346182[/C][C]0.421257066538182[/C][/ROW]
[ROW][C]93[/C][C]1[/C][C]1.22863520265587[/C][C]-0.228635202655869[/C][/ROW]
[ROW][C]94[/C][C]1[/C][C]1.28275636685601[/C][C]-0.282756366856009[/C][/ROW]
[ROW][C]95[/C][C]1[/C][C]1.38684447765759[/C][C]-0.386844477657594[/C][/ROW]
[ROW][C]96[/C][C]1[/C][C]1.40687475115021[/C][C]-0.406874751150212[/C][/ROW]
[ROW][C]97[/C][C]2[/C][C]1.4447454383838[/C][C]0.555254561616195[/C][/ROW]
[ROW][C]98[/C][C]1[/C][C]1.37752553326902[/C][C]-0.377525533269024[/C][/ROW]
[ROW][C]99[/C][C]2[/C][C]1.33490683788597[/C][C]0.66509316211403[/C][/ROW]
[ROW][C]100[/C][C]2[/C][C]1.49598922146065[/C][C]0.504010778539346[/C][/ROW]
[ROW][C]101[/C][C]1[/C][C]1.2646335286138[/C][C]-0.2646335286138[/C][/ROW]
[ROW][C]102[/C][C]2[/C][C]1.79744810952515[/C][C]0.202551890474853[/C][/ROW]
[ROW][C]103[/C][C]1[/C][C]1.23331747812752[/C][C]-0.233317478127522[/C][/ROW]
[ROW][C]104[/C][C]1[/C][C]1.45974440258605[/C][C]-0.459744402586052[/C][/ROW]
[ROW][C]105[/C][C]2[/C][C]1.49976229071947[/C][C]0.500237709280532[/C][/ROW]
[ROW][C]106[/C][C]1[/C][C]1.35502558198141[/C][C]-0.355025581981407[/C][/ROW]
[ROW][C]107[/C][C]2[/C][C]1.21794053266708[/C][C]0.782059467332925[/C][/ROW]
[ROW][C]108[/C][C]1[/C][C]1.23853450056633[/C][C]-0.238534500566332[/C][/ROW]
[ROW][C]109[/C][C]1[/C][C]1.18580887823287[/C][C]-0.185808878232867[/C][/ROW]
[ROW][C]110[/C][C]1[/C][C]1.58268145390807[/C][C]-0.582681453908074[/C][/ROW]
[ROW][C]111[/C][C]1[/C][C]1.3341970309831[/C][C]-0.334197030983102[/C][/ROW]
[ROW][C]112[/C][C]1[/C][C]1.44765355184747[/C][C]-0.447653551847467[/C][/ROW]
[ROW][C]113[/C][C]1[/C][C]1.42896610184666[/C][C]-0.428966101846661[/C][/ROW]
[ROW][C]114[/C][C]1[/C][C]1.5244303159895[/C][C]-0.524430315989503[/C][/ROW]
[ROW][C]115[/C][C]1[/C][C]1.35931238055372[/C][C]-0.359312380553722[/C][/ROW]
[ROW][C]116[/C][C]2[/C][C]1.32308101995945[/C][C]0.676918980040553[/C][/ROW]
[ROW][C]117[/C][C]1[/C][C]1.23959515133649[/C][C]-0.239595151336488[/C][/ROW]
[ROW][C]118[/C][C]1[/C][C]1.25661445526859[/C][C]-0.25661445526859[/C][/ROW]
[ROW][C]119[/C][C]2[/C][C]1.71234992602128[/C][C]0.287650073978717[/C][/ROW]
[ROW][C]120[/C][C]1[/C][C]1.24121114415104[/C][C]-0.241211144151039[/C][/ROW]
[ROW][C]121[/C][C]1[/C][C]1.1628865809575[/C][C]-0.1628865809575[/C][/ROW]
[ROW][C]122[/C][C]1[/C][C]1.45104076433994[/C][C]-0.451040764339941[/C][/ROW]
[ROW][C]123[/C][C]1[/C][C]1.33307607637243[/C][C]-0.333076076372426[/C][/ROW]
[ROW][C]124[/C][C]1[/C][C]1.34304821163201[/C][C]-0.343048211632012[/C][/ROW]
[ROW][C]125[/C][C]1[/C][C]1.72053486178771[/C][C]-0.720534861787714[/C][/ROW]
[ROW][C]126[/C][C]2[/C][C]1.41194963735188[/C][C]0.588050362648121[/C][/ROW]
[ROW][C]127[/C][C]1[/C][C]1.4018879222193[/C][C]-0.401887922219296[/C][/ROW]
[ROW][C]128[/C][C]1[/C][C]1.20611782569483[/C][C]-0.206117825694833[/C][/ROW]
[ROW][C]129[/C][C]2[/C][C]1.44309139589821[/C][C]0.556908604101793[/C][/ROW]
[ROW][C]130[/C][C]2[/C][C]1.54830741612799[/C][C]0.451692583872006[/C][/ROW]
[ROW][C]131[/C][C]1[/C][C]1.45329503706355[/C][C]-0.453295037063547[/C][/ROW]
[ROW][C]132[/C][C]2[/C][C]1.38379165479579[/C][C]0.616208345204207[/C][/ROW]
[ROW][C]133[/C][C]2[/C][C]1.2209234587069[/C][C]0.779076541293096[/C][/ROW]
[ROW][C]134[/C][C]2[/C][C]1.35263364946252[/C][C]0.647366350537477[/C][/ROW]
[ROW][C]135[/C][C]2[/C][C]1.4201616240663[/C][C]0.579838375933701[/C][/ROW]
[ROW][C]136[/C][C]1[/C][C]1.24724445398301[/C][C]-0.247244453983006[/C][/ROW]
[ROW][C]137[/C][C]1[/C][C]1.33714574532333[/C][C]-0.337145745323332[/C][/ROW]
[ROW][C]138[/C][C]2[/C][C]1.38116204937188[/C][C]0.61883795062812[/C][/ROW]
[ROW][C]139[/C][C]2[/C][C]1.43086700740849[/C][C]0.569132992591512[/C][/ROW]
[ROW][C]140[/C][C]1[/C][C]1.28211055165342[/C][C]-0.282110551653423[/C][/ROW]
[ROW][C]141[/C][C]1[/C][C]1.21413041262276[/C][C]-0.214130412622764[/C][/ROW]
[ROW][C]142[/C][C]1[/C][C]1.2028004982823[/C][C]-0.2028004982823[/C][/ROW]
[ROW][C]143[/C][C]1[/C][C]1.4104583799744[/C][C]-0.410458379974403[/C][/ROW]
[ROW][C]144[/C][C]2[/C][C]1.27911488862062[/C][C]0.720885111379381[/C][/ROW]
[ROW][C]145[/C][C]1[/C][C]1.37161398377879[/C][C]-0.371613983778791[/C][/ROW]
[ROW][C]146[/C][C]1[/C][C]1.49320619228305[/C][C]-0.493206192283053[/C][/ROW]
[ROW][C]147[/C][C]2[/C][C]1.28080259369156[/C][C]0.719197406308441[/C][/ROW]
[ROW][C]148[/C][C]2[/C][C]1.54168653444799[/C][C]0.458313465552006[/C][/ROW]
[ROW][C]149[/C][C]2[/C][C]1.43592074421333[/C][C]0.564079255786667[/C][/ROW]
[ROW][C]150[/C][C]2[/C][C]1.34843844155821[/C][C]0.651561558441793[/C][/ROW]
[ROW][C]151[/C][C]1[/C][C]1.48424177034748[/C][C]-0.484241770347477[/C][/ROW]
[ROW][C]152[/C][C]1[/C][C]1.33402689887368[/C][C]-0.334026898873685[/C][/ROW]
[ROW][C]153[/C][C]1[/C][C]1.38429403048291[/C][C]-0.384294030482907[/C][/ROW]
[ROW][C]154[/C][C]2[/C][C]1.38327912480866[/C][C]0.616720875191342[/C][/ROW]
[ROW][C]155[/C][C]2[/C][C]1.4611958386139[/C][C]0.538804161386099[/C][/ROW]
[ROW][C]156[/C][C]2[/C][C]1.3739028195617[/C][C]0.626097180438304[/C][/ROW]
[ROW][C]157[/C][C]1[/C][C]1.16205670872451[/C][C]-0.162056708724509[/C][/ROW]
[ROW][C]158[/C][C]1[/C][C]1.05058897092369[/C][C]-0.0505889709236913[/C][/ROW]
[ROW][C]159[/C][C]1[/C][C]1.22863520265587[/C][C]-0.228635202655869[/C][/ROW]
[ROW][C]160[/C][C]1[/C][C]1.2757805716418[/C][C]-0.275780571641796[/C][/ROW]
[ROW][C]161[/C][C]2[/C][C]1.48816880132079[/C][C]0.511831198679214[/C][/ROW]
[ROW][C]162[/C][C]1[/C][C]1.44341429523635[/C][C]-0.44341429523635[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197403&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197403&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
111.4509981865452-0.450998186545205
211.32852410206451-0.328524102064514
311.39110457056038-0.391104570560383
421.404173058576560.595826941423436
511.32955748146885-0.329557481468849
611.37275644832098-0.37275644832098
721.452243300683210.547756699316793
810.9064404765557580.0935595234442419
911.42710253943434-0.42710253943434
1011.33905774344553-0.339057743445529
1121.437534758007060.562465241992944
1221.494803080663930.505196919336072
1311.22414934672114-0.224149346721135
1411.63193413375955-0.631934133759546
1511.49711430080207-0.49711430080207
1621.334909658861860.66509034113814
1711.03276861360438-0.0327686136043802
1821.454317639690630.545682360309374
1921.823715293866410.176284706133594
2011.03867633862445-0.0386763386244454
2121.679494037601980.320505962398016
2211.08406044728432-0.0840604472843152
2311.11497033169582-0.11497033169582
2421.306643137307980.693356862692021
2511.19473817485057-0.194738174850569
2611.54102304699774-0.541023046997738
2711.54102304699774-0.541023046997738
2811.35612590359534-0.356125903595342
2921.633895700169390.36610429983061
3011.36907132063953-0.369071320639534
3121.560579689943260.439420310056742
3211.38035461673846-0.38035461673846
3311.1222528041717-0.1222528041717
3421.317612908567510.682387091432487
3521.260560019593430.739439980406568
3621.400744790771170.599255209228825
3711.19275706496131-0.192757064961307
3821.304708669494540.695291330505464
3921.495917543020990.504082456979011
4011.60591294904836-0.605912949048364
4111.3396479003288-0.339647900328801
4211.52999547263598-0.529995472635984
4311.55659662389062-0.556596623890619
4421.615447205933070.384552794066929
4521.724707689782640.275292310217362
4611.0962374840402-0.0962374840402035
4721.328979661454890.671020338545107
4821.193239769922120.806760230077875
4911.27974601175575-0.279746011755746
5021.21138449122960.788615508770395
5111.27119247120833-0.271192471208325
5211.20004643845633-0.20004643845633
5311.33567777153441-0.335677771534408
5411.21762705537703-0.21762705537703
5521.435388289848670.564611710151326
5611.53416298837932-0.534162988379315
5711.43242405142504-0.432424051425038
5811.46818284874642-0.468182848746419
5921.654679178303470.345320821696534
6021.584112455093440.415887544906557
6111.54052646480705-0.540526464807046
6211.37036290349536-0.370362903495361
6311.75440932172981-0.754409321729811
6421.654025858830390.345974141169607
6511.21652645983448-0.216526459834485
6621.529769489018680.470230510981316
6721.400370508462090.599629491537907
6821.469526923722940.530473076277056
6911.31597974161949-0.315979741619486
7011.16002743533682-0.160027435336819
7111.45456213629605-0.454562136296046
7221.48494019354030.515059806459699
7321.415882076126240.584117923873761
7411.1748074837467-0.174807483746696
7511.40996242739277-0.409962427392769
7611.28884094675058-0.288840946750578
7711.32769349177297-0.327693491772967
7821.436866498651630.563133501348368
7921.371735178235690.628264821764309
8021.437407238166140.562592761833855
8121.715053894671130.284946105328873
8211.22632834512425-0.226328345124246
8301.57758688214467-1.57758688214467
8411.46735100414852-0.46735100414852
8521.353914189165980.64608581083402
8621.562922400148350.437077599851651
8711.3719408527201-0.3719408527201
8811.07772549855297-0.0777254985529682
8911.07913640933215-0.0791364093321479
9011.49201011134702-0.492010111347017
9111.59964227550729-0.599642275507293
9221.578742933461820.421257066538182
9311.22863520265587-0.228635202655869
9411.28275636685601-0.282756366856009
9511.38684447765759-0.386844477657594
9611.40687475115021-0.406874751150212
9721.44474543838380.555254561616195
9811.37752553326902-0.377525533269024
9921.334906837885970.66509316211403
10021.495989221460650.504010778539346
10111.2646335286138-0.2646335286138
10221.797448109525150.202551890474853
10311.23331747812752-0.233317478127522
10411.45974440258605-0.459744402586052
10521.499762290719470.500237709280532
10611.35502558198141-0.355025581981407
10721.217940532667080.782059467332925
10811.23853450056633-0.238534500566332
10911.18580887823287-0.185808878232867
11011.58268145390807-0.582681453908074
11111.3341970309831-0.334197030983102
11211.44765355184747-0.447653551847467
11311.42896610184666-0.428966101846661
11411.5244303159895-0.524430315989503
11511.35931238055372-0.359312380553722
11621.323081019959450.676918980040553
11711.23959515133649-0.239595151336488
11811.25661445526859-0.25661445526859
11921.712349926021280.287650073978717
12011.24121114415104-0.241211144151039
12111.1628865809575-0.1628865809575
12211.45104076433994-0.451040764339941
12311.33307607637243-0.333076076372426
12411.34304821163201-0.343048211632012
12511.72053486178771-0.720534861787714
12621.411949637351880.588050362648121
12711.4018879222193-0.401887922219296
12811.20611782569483-0.206117825694833
12921.443091395898210.556908604101793
13021.548307416127990.451692583872006
13111.45329503706355-0.453295037063547
13221.383791654795790.616208345204207
13321.22092345870690.779076541293096
13421.352633649462520.647366350537477
13521.42016162406630.579838375933701
13611.24724445398301-0.247244453983006
13711.33714574532333-0.337145745323332
13821.381162049371880.61883795062812
13921.430867007408490.569132992591512
14011.28211055165342-0.282110551653423
14111.21413041262276-0.214130412622764
14211.2028004982823-0.2028004982823
14311.4104583799744-0.410458379974403
14421.279114888620620.720885111379381
14511.37161398377879-0.371613983778791
14611.49320619228305-0.493206192283053
14721.280802593691560.719197406308441
14821.541686534447990.458313465552006
14921.435920744213330.564079255786667
15021.348438441558210.651561558441793
15111.48424177034748-0.484241770347477
15211.33402689887368-0.334026898873685
15311.38429403048291-0.384294030482907
15421.383279124808660.616720875191342
15521.46119583861390.538804161386099
15621.37390281956170.626097180438304
15711.16205670872451-0.162056708724509
15811.05058897092369-0.0505889709236913
15911.22863520265587-0.228635202655869
16011.2757805716418-0.275780571641796
16121.488168801320790.511831198679214
16211.44341429523635-0.44341429523635






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.6597400665186730.6805198669626540.340259933481327
120.6957171905973030.6085656188053950.304282809402697
130.5817418276970290.8365163446059430.418258172302971
140.5564906449245040.8870187101509920.443509355075496
150.4524202141939170.9048404283878340.547579785806083
160.4679634611770160.9359269223540310.532036538822984
170.4734035709135540.9468071418271090.526596429086446
180.6026480584029350.794703883194130.397351941597065
190.5255847636113720.9488304727772560.474415236388628
200.4793741023398550.9587482046797090.520625897660145
210.438907627635880.8778152552717610.56109237236412
220.3664528311848720.7329056623697450.633547168815128
230.2952750834747580.5905501669495160.704724916525242
240.4239020948533060.8478041897066110.576097905146694
250.3938328094002410.7876656188004820.606167190599759
260.3602300831879030.7204601663758070.639769916812097
270.3147309964568460.6294619929136920.685269003543154
280.2650052166299690.5300104332599380.734994783370031
290.270780675896610.541561351793220.72921932410339
300.2327069363891860.4654138727783710.767293063610815
310.2269641140477130.4539282280954260.773035885952287
320.1904769077384310.3809538154768630.809523092261569
330.1547935301065530.3095870602131060.845206469893447
340.1683362638495040.3366725276990080.831663736150496
350.2157460687021810.4314921374043620.784253931297819
360.2377171375659980.4754342751319960.762282862434002
370.2311286393662580.4622572787325150.768871360633742
380.3305864024185180.6611728048370350.669413597581482
390.334059662941440.668119325882880.66594033705856
400.3839829220473910.7679658440947830.616017077952609
410.3976674618351310.7953349236702610.60233253816487
420.4312428131206910.8624856262413830.568757186879309
430.4713420227233880.9426840454467750.528657977276612
440.4602083748501940.9204167497003880.539791625149806
450.4262806758565580.8525613517131160.573719324143442
460.4119433658857480.8238867317714970.588056634114252
470.4621624614579590.9243249229159180.537837538542041
480.508973692928340.9820526141433210.49102630707166
490.4692470196863440.9384940393726890.530752980313656
500.5172640861304320.9654718277391360.482735913869568
510.4816251110987690.9632502221975370.518374888901231
520.4349551311351720.8699102622703450.565044868864828
530.4154930843089340.8309861686178690.584506915691066
540.3769947688394570.7539895376789140.623005231160543
550.4032517484350.8065034968700010.596748251565
560.4245305600844130.8490611201688270.575469439915587
570.4117039027851330.8234078055702660.588296097214867
580.4059578080992590.8119156161985190.594042191900741
590.3805676994085520.7611353988171040.619432300591448
600.3770787500480620.7541575000961240.622921249951938
610.3993907238992910.7987814477985810.600609276100709
620.3851128507449870.7702257014899740.614887149255013
630.4511441575886960.9022883151773930.548855842411303
640.4277676107142790.8555352214285580.572232389285721
650.3979551315725410.7959102631450820.602044868427459
660.3909283083101610.7818566166203220.609071691689839
670.4256036374334930.8512072748669860.574396362566507
680.4437990573183290.8875981146366580.556200942681671
690.4159376023731040.8318752047462090.584062397626896
700.3761173021307370.7522346042614750.623882697869263
710.3776040497173030.7552080994346050.622395950282697
720.3859120517905650.7718241035811290.614087948209435
730.4175513264121070.8351026528242150.582448673587893
740.3841970112112060.7683940224224120.615802988788794
750.372533756459140.7450675129182810.62746624354086
760.3436117481896730.6872234963793470.656388251810327
770.3178851684394270.6357703368788550.682114831560573
780.3290843971325580.6581687942651160.670915602867442
790.3541238562902440.7082477125804870.645876143709756
800.3647683887816740.7295367775633470.635231611218326
810.3299800910822530.6599601821645060.670019908917747
820.297897524068360.595795048136720.70210247593164
830.699110402368020.6017791952639610.30088959763198
840.6991882831718220.6016234336563560.300811716828178
850.7258187738625670.5483624522748660.274181226137433
860.7215803509559790.5568392980880420.278419649044021
870.7036072840257720.5927854319484560.296392715974228
880.6683129648812970.6633740702374070.331687035118704
890.634509344649110.7309813107017810.36549065535089
900.6298089067445160.7403821865109680.370191093255484
910.6536713369212080.6926573261575840.346328663078792
920.6416800235352150.716639952929570.358319976464785
930.6064143302154740.7871713395690530.393585669784526
940.5862200969789950.8275598060420110.413779903021005
950.5714949100884690.8570101798230610.428505089911531
960.5565754420884480.8868491158231040.443424557911552
970.5769403505455060.8461192989089880.423059649454494
980.564833931043760.8703321379124810.43516606895624
990.5986018762713960.8027962474572070.401398123728604
1000.6053356017856180.7893287964287630.394664398214382
1010.5706354053033140.8587291893933710.429364594696686
1020.528331766141310.943336467717380.47166823385869
1030.4845828668550370.9691657337100740.515417133144963
1040.4835453576835670.9670907153671340.516454642316433
1050.4801564909988670.9603129819977350.519843509001133
1060.4530220159392570.9060440318785140.546977984060743
1070.5455455093258960.9089089813482080.454454490674104
1080.5074969119316720.9850061761366560.492503088068328
1090.4942704392909490.9885408785818980.505729560709051
1100.5301142699338740.9397714601322510.469885730066126
1110.4968300935858070.9936601871716130.503169906414193
1120.472675284164450.9453505683289010.52732471583555
1130.4595801229790050.919160245958010.540419877020995
1140.4729083818424190.9458167636848380.527091618157581
1150.4680362213917330.9360724427834660.531963778608267
1160.4798221214275490.9596442428550980.520177878572451
1170.4318878918397350.8637757836794710.568112108160264
1180.3933710499529590.7867420999059180.606628950047041
1190.3776584427920780.7553168855841550.622341557207922
1200.331537309784940.6630746195698810.66846269021506
1210.2981194689384080.5962389378768160.701880531061592
1220.296361374413280.592722748826560.70363862558672
1230.2662005460688640.5324010921377280.733799453931136
1240.2578329097024070.5156658194048140.742167090297593
1250.2927204128048160.5854408256096320.707279587195184
1260.283435890122580.566871780245160.71656410987742
1270.2910143587966050.5820287175932110.708985641203395
1280.2507697663795380.5015395327590760.749230233620462
1290.2369921585731660.4739843171463320.763007841426834
1300.2091842537064430.4183685074128870.790815746293557
1310.2257616794156370.4515233588312750.774238320584363
1320.2200771115770080.4401542231540160.779922888422992
1330.2849450422807120.5698900845614240.715054957719288
1340.3150265362972350.630053072594470.684973463702765
1350.2878344646010260.5756689292020510.712165535398974
1360.2493886641792550.498777328358510.750611335820745
1370.2382888839680830.4765777679361670.761711116031917
1380.2741620200118320.5483240400236640.725837979988168
1390.2974904720503260.5949809441006530.702509527949674
1400.2611556575503160.5223113151006320.738844342449684
1410.2152788490638670.4305576981277340.784721150936133
1420.1680088504310150.3360177008620290.831991149568985
1430.125022802141690.2500456042833790.87497719785831
1440.2214832602842830.4429665205685660.778516739715717
1450.2028020029197270.4056040058394530.797197997080273
1460.2157752062301020.4315504124602040.784224793769898
1470.3684594207460210.7369188414920430.631540579253979
1480.2872187875636380.5744375751272770.712781212436362
1490.3412184928766230.6824369857532460.658781507123377
1500.2606348410286320.5212696820572640.739365158971368
1510.3108565529015880.6217131058031750.689143447098412

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 & 0.659740066518673 & 0.680519866962654 & 0.340259933481327 \tabularnewline
12 & 0.695717190597303 & 0.608565618805395 & 0.304282809402697 \tabularnewline
13 & 0.581741827697029 & 0.836516344605943 & 0.418258172302971 \tabularnewline
14 & 0.556490644924504 & 0.887018710150992 & 0.443509355075496 \tabularnewline
15 & 0.452420214193917 & 0.904840428387834 & 0.547579785806083 \tabularnewline
16 & 0.467963461177016 & 0.935926922354031 & 0.532036538822984 \tabularnewline
17 & 0.473403570913554 & 0.946807141827109 & 0.526596429086446 \tabularnewline
18 & 0.602648058402935 & 0.79470388319413 & 0.397351941597065 \tabularnewline
19 & 0.525584763611372 & 0.948830472777256 & 0.474415236388628 \tabularnewline
20 & 0.479374102339855 & 0.958748204679709 & 0.520625897660145 \tabularnewline
21 & 0.43890762763588 & 0.877815255271761 & 0.56109237236412 \tabularnewline
22 & 0.366452831184872 & 0.732905662369745 & 0.633547168815128 \tabularnewline
23 & 0.295275083474758 & 0.590550166949516 & 0.704724916525242 \tabularnewline
24 & 0.423902094853306 & 0.847804189706611 & 0.576097905146694 \tabularnewline
25 & 0.393832809400241 & 0.787665618800482 & 0.606167190599759 \tabularnewline
26 & 0.360230083187903 & 0.720460166375807 & 0.639769916812097 \tabularnewline
27 & 0.314730996456846 & 0.629461992913692 & 0.685269003543154 \tabularnewline
28 & 0.265005216629969 & 0.530010433259938 & 0.734994783370031 \tabularnewline
29 & 0.27078067589661 & 0.54156135179322 & 0.72921932410339 \tabularnewline
30 & 0.232706936389186 & 0.465413872778371 & 0.767293063610815 \tabularnewline
31 & 0.226964114047713 & 0.453928228095426 & 0.773035885952287 \tabularnewline
32 & 0.190476907738431 & 0.380953815476863 & 0.809523092261569 \tabularnewline
33 & 0.154793530106553 & 0.309587060213106 & 0.845206469893447 \tabularnewline
34 & 0.168336263849504 & 0.336672527699008 & 0.831663736150496 \tabularnewline
35 & 0.215746068702181 & 0.431492137404362 & 0.784253931297819 \tabularnewline
36 & 0.237717137565998 & 0.475434275131996 & 0.762282862434002 \tabularnewline
37 & 0.231128639366258 & 0.462257278732515 & 0.768871360633742 \tabularnewline
38 & 0.330586402418518 & 0.661172804837035 & 0.669413597581482 \tabularnewline
39 & 0.33405966294144 & 0.66811932588288 & 0.66594033705856 \tabularnewline
40 & 0.383982922047391 & 0.767965844094783 & 0.616017077952609 \tabularnewline
41 & 0.397667461835131 & 0.795334923670261 & 0.60233253816487 \tabularnewline
42 & 0.431242813120691 & 0.862485626241383 & 0.568757186879309 \tabularnewline
43 & 0.471342022723388 & 0.942684045446775 & 0.528657977276612 \tabularnewline
44 & 0.460208374850194 & 0.920416749700388 & 0.539791625149806 \tabularnewline
45 & 0.426280675856558 & 0.852561351713116 & 0.573719324143442 \tabularnewline
46 & 0.411943365885748 & 0.823886731771497 & 0.588056634114252 \tabularnewline
47 & 0.462162461457959 & 0.924324922915918 & 0.537837538542041 \tabularnewline
48 & 0.50897369292834 & 0.982052614143321 & 0.49102630707166 \tabularnewline
49 & 0.469247019686344 & 0.938494039372689 & 0.530752980313656 \tabularnewline
50 & 0.517264086130432 & 0.965471827739136 & 0.482735913869568 \tabularnewline
51 & 0.481625111098769 & 0.963250222197537 & 0.518374888901231 \tabularnewline
52 & 0.434955131135172 & 0.869910262270345 & 0.565044868864828 \tabularnewline
53 & 0.415493084308934 & 0.830986168617869 & 0.584506915691066 \tabularnewline
54 & 0.376994768839457 & 0.753989537678914 & 0.623005231160543 \tabularnewline
55 & 0.403251748435 & 0.806503496870001 & 0.596748251565 \tabularnewline
56 & 0.424530560084413 & 0.849061120168827 & 0.575469439915587 \tabularnewline
57 & 0.411703902785133 & 0.823407805570266 & 0.588296097214867 \tabularnewline
58 & 0.405957808099259 & 0.811915616198519 & 0.594042191900741 \tabularnewline
59 & 0.380567699408552 & 0.761135398817104 & 0.619432300591448 \tabularnewline
60 & 0.377078750048062 & 0.754157500096124 & 0.622921249951938 \tabularnewline
61 & 0.399390723899291 & 0.798781447798581 & 0.600609276100709 \tabularnewline
62 & 0.385112850744987 & 0.770225701489974 & 0.614887149255013 \tabularnewline
63 & 0.451144157588696 & 0.902288315177393 & 0.548855842411303 \tabularnewline
64 & 0.427767610714279 & 0.855535221428558 & 0.572232389285721 \tabularnewline
65 & 0.397955131572541 & 0.795910263145082 & 0.602044868427459 \tabularnewline
66 & 0.390928308310161 & 0.781856616620322 & 0.609071691689839 \tabularnewline
67 & 0.425603637433493 & 0.851207274866986 & 0.574396362566507 \tabularnewline
68 & 0.443799057318329 & 0.887598114636658 & 0.556200942681671 \tabularnewline
69 & 0.415937602373104 & 0.831875204746209 & 0.584062397626896 \tabularnewline
70 & 0.376117302130737 & 0.752234604261475 & 0.623882697869263 \tabularnewline
71 & 0.377604049717303 & 0.755208099434605 & 0.622395950282697 \tabularnewline
72 & 0.385912051790565 & 0.771824103581129 & 0.614087948209435 \tabularnewline
73 & 0.417551326412107 & 0.835102652824215 & 0.582448673587893 \tabularnewline
74 & 0.384197011211206 & 0.768394022422412 & 0.615802988788794 \tabularnewline
75 & 0.37253375645914 & 0.745067512918281 & 0.62746624354086 \tabularnewline
76 & 0.343611748189673 & 0.687223496379347 & 0.656388251810327 \tabularnewline
77 & 0.317885168439427 & 0.635770336878855 & 0.682114831560573 \tabularnewline
78 & 0.329084397132558 & 0.658168794265116 & 0.670915602867442 \tabularnewline
79 & 0.354123856290244 & 0.708247712580487 & 0.645876143709756 \tabularnewline
80 & 0.364768388781674 & 0.729536777563347 & 0.635231611218326 \tabularnewline
81 & 0.329980091082253 & 0.659960182164506 & 0.670019908917747 \tabularnewline
82 & 0.29789752406836 & 0.59579504813672 & 0.70210247593164 \tabularnewline
83 & 0.69911040236802 & 0.601779195263961 & 0.30088959763198 \tabularnewline
84 & 0.699188283171822 & 0.601623433656356 & 0.300811716828178 \tabularnewline
85 & 0.725818773862567 & 0.548362452274866 & 0.274181226137433 \tabularnewline
86 & 0.721580350955979 & 0.556839298088042 & 0.278419649044021 \tabularnewline
87 & 0.703607284025772 & 0.592785431948456 & 0.296392715974228 \tabularnewline
88 & 0.668312964881297 & 0.663374070237407 & 0.331687035118704 \tabularnewline
89 & 0.63450934464911 & 0.730981310701781 & 0.36549065535089 \tabularnewline
90 & 0.629808906744516 & 0.740382186510968 & 0.370191093255484 \tabularnewline
91 & 0.653671336921208 & 0.692657326157584 & 0.346328663078792 \tabularnewline
92 & 0.641680023535215 & 0.71663995292957 & 0.358319976464785 \tabularnewline
93 & 0.606414330215474 & 0.787171339569053 & 0.393585669784526 \tabularnewline
94 & 0.586220096978995 & 0.827559806042011 & 0.413779903021005 \tabularnewline
95 & 0.571494910088469 & 0.857010179823061 & 0.428505089911531 \tabularnewline
96 & 0.556575442088448 & 0.886849115823104 & 0.443424557911552 \tabularnewline
97 & 0.576940350545506 & 0.846119298908988 & 0.423059649454494 \tabularnewline
98 & 0.56483393104376 & 0.870332137912481 & 0.43516606895624 \tabularnewline
99 & 0.598601876271396 & 0.802796247457207 & 0.401398123728604 \tabularnewline
100 & 0.605335601785618 & 0.789328796428763 & 0.394664398214382 \tabularnewline
101 & 0.570635405303314 & 0.858729189393371 & 0.429364594696686 \tabularnewline
102 & 0.52833176614131 & 0.94333646771738 & 0.47166823385869 \tabularnewline
103 & 0.484582866855037 & 0.969165733710074 & 0.515417133144963 \tabularnewline
104 & 0.483545357683567 & 0.967090715367134 & 0.516454642316433 \tabularnewline
105 & 0.480156490998867 & 0.960312981997735 & 0.519843509001133 \tabularnewline
106 & 0.453022015939257 & 0.906044031878514 & 0.546977984060743 \tabularnewline
107 & 0.545545509325896 & 0.908908981348208 & 0.454454490674104 \tabularnewline
108 & 0.507496911931672 & 0.985006176136656 & 0.492503088068328 \tabularnewline
109 & 0.494270439290949 & 0.988540878581898 & 0.505729560709051 \tabularnewline
110 & 0.530114269933874 & 0.939771460132251 & 0.469885730066126 \tabularnewline
111 & 0.496830093585807 & 0.993660187171613 & 0.503169906414193 \tabularnewline
112 & 0.47267528416445 & 0.945350568328901 & 0.52732471583555 \tabularnewline
113 & 0.459580122979005 & 0.91916024595801 & 0.540419877020995 \tabularnewline
114 & 0.472908381842419 & 0.945816763684838 & 0.527091618157581 \tabularnewline
115 & 0.468036221391733 & 0.936072442783466 & 0.531963778608267 \tabularnewline
116 & 0.479822121427549 & 0.959644242855098 & 0.520177878572451 \tabularnewline
117 & 0.431887891839735 & 0.863775783679471 & 0.568112108160264 \tabularnewline
118 & 0.393371049952959 & 0.786742099905918 & 0.606628950047041 \tabularnewline
119 & 0.377658442792078 & 0.755316885584155 & 0.622341557207922 \tabularnewline
120 & 0.33153730978494 & 0.663074619569881 & 0.66846269021506 \tabularnewline
121 & 0.298119468938408 & 0.596238937876816 & 0.701880531061592 \tabularnewline
122 & 0.29636137441328 & 0.59272274882656 & 0.70363862558672 \tabularnewline
123 & 0.266200546068864 & 0.532401092137728 & 0.733799453931136 \tabularnewline
124 & 0.257832909702407 & 0.515665819404814 & 0.742167090297593 \tabularnewline
125 & 0.292720412804816 & 0.585440825609632 & 0.707279587195184 \tabularnewline
126 & 0.28343589012258 & 0.56687178024516 & 0.71656410987742 \tabularnewline
127 & 0.291014358796605 & 0.582028717593211 & 0.708985641203395 \tabularnewline
128 & 0.250769766379538 & 0.501539532759076 & 0.749230233620462 \tabularnewline
129 & 0.236992158573166 & 0.473984317146332 & 0.763007841426834 \tabularnewline
130 & 0.209184253706443 & 0.418368507412887 & 0.790815746293557 \tabularnewline
131 & 0.225761679415637 & 0.451523358831275 & 0.774238320584363 \tabularnewline
132 & 0.220077111577008 & 0.440154223154016 & 0.779922888422992 \tabularnewline
133 & 0.284945042280712 & 0.569890084561424 & 0.715054957719288 \tabularnewline
134 & 0.315026536297235 & 0.63005307259447 & 0.684973463702765 \tabularnewline
135 & 0.287834464601026 & 0.575668929202051 & 0.712165535398974 \tabularnewline
136 & 0.249388664179255 & 0.49877732835851 & 0.750611335820745 \tabularnewline
137 & 0.238288883968083 & 0.476577767936167 & 0.761711116031917 \tabularnewline
138 & 0.274162020011832 & 0.548324040023664 & 0.725837979988168 \tabularnewline
139 & 0.297490472050326 & 0.594980944100653 & 0.702509527949674 \tabularnewline
140 & 0.261155657550316 & 0.522311315100632 & 0.738844342449684 \tabularnewline
141 & 0.215278849063867 & 0.430557698127734 & 0.784721150936133 \tabularnewline
142 & 0.168008850431015 & 0.336017700862029 & 0.831991149568985 \tabularnewline
143 & 0.12502280214169 & 0.250045604283379 & 0.87497719785831 \tabularnewline
144 & 0.221483260284283 & 0.442966520568566 & 0.778516739715717 \tabularnewline
145 & 0.202802002919727 & 0.405604005839453 & 0.797197997080273 \tabularnewline
146 & 0.215775206230102 & 0.431550412460204 & 0.784224793769898 \tabularnewline
147 & 0.368459420746021 & 0.736918841492043 & 0.631540579253979 \tabularnewline
148 & 0.287218787563638 & 0.574437575127277 & 0.712781212436362 \tabularnewline
149 & 0.341218492876623 & 0.682436985753246 & 0.658781507123377 \tabularnewline
150 & 0.260634841028632 & 0.521269682057264 & 0.739365158971368 \tabularnewline
151 & 0.310856552901588 & 0.621713105803175 & 0.689143447098412 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197403&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]11[/C][C]0.659740066518673[/C][C]0.680519866962654[/C][C]0.340259933481327[/C][/ROW]
[ROW][C]12[/C][C]0.695717190597303[/C][C]0.608565618805395[/C][C]0.304282809402697[/C][/ROW]
[ROW][C]13[/C][C]0.581741827697029[/C][C]0.836516344605943[/C][C]0.418258172302971[/C][/ROW]
[ROW][C]14[/C][C]0.556490644924504[/C][C]0.887018710150992[/C][C]0.443509355075496[/C][/ROW]
[ROW][C]15[/C][C]0.452420214193917[/C][C]0.904840428387834[/C][C]0.547579785806083[/C][/ROW]
[ROW][C]16[/C][C]0.467963461177016[/C][C]0.935926922354031[/C][C]0.532036538822984[/C][/ROW]
[ROW][C]17[/C][C]0.473403570913554[/C][C]0.946807141827109[/C][C]0.526596429086446[/C][/ROW]
[ROW][C]18[/C][C]0.602648058402935[/C][C]0.79470388319413[/C][C]0.397351941597065[/C][/ROW]
[ROW][C]19[/C][C]0.525584763611372[/C][C]0.948830472777256[/C][C]0.474415236388628[/C][/ROW]
[ROW][C]20[/C][C]0.479374102339855[/C][C]0.958748204679709[/C][C]0.520625897660145[/C][/ROW]
[ROW][C]21[/C][C]0.43890762763588[/C][C]0.877815255271761[/C][C]0.56109237236412[/C][/ROW]
[ROW][C]22[/C][C]0.366452831184872[/C][C]0.732905662369745[/C][C]0.633547168815128[/C][/ROW]
[ROW][C]23[/C][C]0.295275083474758[/C][C]0.590550166949516[/C][C]0.704724916525242[/C][/ROW]
[ROW][C]24[/C][C]0.423902094853306[/C][C]0.847804189706611[/C][C]0.576097905146694[/C][/ROW]
[ROW][C]25[/C][C]0.393832809400241[/C][C]0.787665618800482[/C][C]0.606167190599759[/C][/ROW]
[ROW][C]26[/C][C]0.360230083187903[/C][C]0.720460166375807[/C][C]0.639769916812097[/C][/ROW]
[ROW][C]27[/C][C]0.314730996456846[/C][C]0.629461992913692[/C][C]0.685269003543154[/C][/ROW]
[ROW][C]28[/C][C]0.265005216629969[/C][C]0.530010433259938[/C][C]0.734994783370031[/C][/ROW]
[ROW][C]29[/C][C]0.27078067589661[/C][C]0.54156135179322[/C][C]0.72921932410339[/C][/ROW]
[ROW][C]30[/C][C]0.232706936389186[/C][C]0.465413872778371[/C][C]0.767293063610815[/C][/ROW]
[ROW][C]31[/C][C]0.226964114047713[/C][C]0.453928228095426[/C][C]0.773035885952287[/C][/ROW]
[ROW][C]32[/C][C]0.190476907738431[/C][C]0.380953815476863[/C][C]0.809523092261569[/C][/ROW]
[ROW][C]33[/C][C]0.154793530106553[/C][C]0.309587060213106[/C][C]0.845206469893447[/C][/ROW]
[ROW][C]34[/C][C]0.168336263849504[/C][C]0.336672527699008[/C][C]0.831663736150496[/C][/ROW]
[ROW][C]35[/C][C]0.215746068702181[/C][C]0.431492137404362[/C][C]0.784253931297819[/C][/ROW]
[ROW][C]36[/C][C]0.237717137565998[/C][C]0.475434275131996[/C][C]0.762282862434002[/C][/ROW]
[ROW][C]37[/C][C]0.231128639366258[/C][C]0.462257278732515[/C][C]0.768871360633742[/C][/ROW]
[ROW][C]38[/C][C]0.330586402418518[/C][C]0.661172804837035[/C][C]0.669413597581482[/C][/ROW]
[ROW][C]39[/C][C]0.33405966294144[/C][C]0.66811932588288[/C][C]0.66594033705856[/C][/ROW]
[ROW][C]40[/C][C]0.383982922047391[/C][C]0.767965844094783[/C][C]0.616017077952609[/C][/ROW]
[ROW][C]41[/C][C]0.397667461835131[/C][C]0.795334923670261[/C][C]0.60233253816487[/C][/ROW]
[ROW][C]42[/C][C]0.431242813120691[/C][C]0.862485626241383[/C][C]0.568757186879309[/C][/ROW]
[ROW][C]43[/C][C]0.471342022723388[/C][C]0.942684045446775[/C][C]0.528657977276612[/C][/ROW]
[ROW][C]44[/C][C]0.460208374850194[/C][C]0.920416749700388[/C][C]0.539791625149806[/C][/ROW]
[ROW][C]45[/C][C]0.426280675856558[/C][C]0.852561351713116[/C][C]0.573719324143442[/C][/ROW]
[ROW][C]46[/C][C]0.411943365885748[/C][C]0.823886731771497[/C][C]0.588056634114252[/C][/ROW]
[ROW][C]47[/C][C]0.462162461457959[/C][C]0.924324922915918[/C][C]0.537837538542041[/C][/ROW]
[ROW][C]48[/C][C]0.50897369292834[/C][C]0.982052614143321[/C][C]0.49102630707166[/C][/ROW]
[ROW][C]49[/C][C]0.469247019686344[/C][C]0.938494039372689[/C][C]0.530752980313656[/C][/ROW]
[ROW][C]50[/C][C]0.517264086130432[/C][C]0.965471827739136[/C][C]0.482735913869568[/C][/ROW]
[ROW][C]51[/C][C]0.481625111098769[/C][C]0.963250222197537[/C][C]0.518374888901231[/C][/ROW]
[ROW][C]52[/C][C]0.434955131135172[/C][C]0.869910262270345[/C][C]0.565044868864828[/C][/ROW]
[ROW][C]53[/C][C]0.415493084308934[/C][C]0.830986168617869[/C][C]0.584506915691066[/C][/ROW]
[ROW][C]54[/C][C]0.376994768839457[/C][C]0.753989537678914[/C][C]0.623005231160543[/C][/ROW]
[ROW][C]55[/C][C]0.403251748435[/C][C]0.806503496870001[/C][C]0.596748251565[/C][/ROW]
[ROW][C]56[/C][C]0.424530560084413[/C][C]0.849061120168827[/C][C]0.575469439915587[/C][/ROW]
[ROW][C]57[/C][C]0.411703902785133[/C][C]0.823407805570266[/C][C]0.588296097214867[/C][/ROW]
[ROW][C]58[/C][C]0.405957808099259[/C][C]0.811915616198519[/C][C]0.594042191900741[/C][/ROW]
[ROW][C]59[/C][C]0.380567699408552[/C][C]0.761135398817104[/C][C]0.619432300591448[/C][/ROW]
[ROW][C]60[/C][C]0.377078750048062[/C][C]0.754157500096124[/C][C]0.622921249951938[/C][/ROW]
[ROW][C]61[/C][C]0.399390723899291[/C][C]0.798781447798581[/C][C]0.600609276100709[/C][/ROW]
[ROW][C]62[/C][C]0.385112850744987[/C][C]0.770225701489974[/C][C]0.614887149255013[/C][/ROW]
[ROW][C]63[/C][C]0.451144157588696[/C][C]0.902288315177393[/C][C]0.548855842411303[/C][/ROW]
[ROW][C]64[/C][C]0.427767610714279[/C][C]0.855535221428558[/C][C]0.572232389285721[/C][/ROW]
[ROW][C]65[/C][C]0.397955131572541[/C][C]0.795910263145082[/C][C]0.602044868427459[/C][/ROW]
[ROW][C]66[/C][C]0.390928308310161[/C][C]0.781856616620322[/C][C]0.609071691689839[/C][/ROW]
[ROW][C]67[/C][C]0.425603637433493[/C][C]0.851207274866986[/C][C]0.574396362566507[/C][/ROW]
[ROW][C]68[/C][C]0.443799057318329[/C][C]0.887598114636658[/C][C]0.556200942681671[/C][/ROW]
[ROW][C]69[/C][C]0.415937602373104[/C][C]0.831875204746209[/C][C]0.584062397626896[/C][/ROW]
[ROW][C]70[/C][C]0.376117302130737[/C][C]0.752234604261475[/C][C]0.623882697869263[/C][/ROW]
[ROW][C]71[/C][C]0.377604049717303[/C][C]0.755208099434605[/C][C]0.622395950282697[/C][/ROW]
[ROW][C]72[/C][C]0.385912051790565[/C][C]0.771824103581129[/C][C]0.614087948209435[/C][/ROW]
[ROW][C]73[/C][C]0.417551326412107[/C][C]0.835102652824215[/C][C]0.582448673587893[/C][/ROW]
[ROW][C]74[/C][C]0.384197011211206[/C][C]0.768394022422412[/C][C]0.615802988788794[/C][/ROW]
[ROW][C]75[/C][C]0.37253375645914[/C][C]0.745067512918281[/C][C]0.62746624354086[/C][/ROW]
[ROW][C]76[/C][C]0.343611748189673[/C][C]0.687223496379347[/C][C]0.656388251810327[/C][/ROW]
[ROW][C]77[/C][C]0.317885168439427[/C][C]0.635770336878855[/C][C]0.682114831560573[/C][/ROW]
[ROW][C]78[/C][C]0.329084397132558[/C][C]0.658168794265116[/C][C]0.670915602867442[/C][/ROW]
[ROW][C]79[/C][C]0.354123856290244[/C][C]0.708247712580487[/C][C]0.645876143709756[/C][/ROW]
[ROW][C]80[/C][C]0.364768388781674[/C][C]0.729536777563347[/C][C]0.635231611218326[/C][/ROW]
[ROW][C]81[/C][C]0.329980091082253[/C][C]0.659960182164506[/C][C]0.670019908917747[/C][/ROW]
[ROW][C]82[/C][C]0.29789752406836[/C][C]0.59579504813672[/C][C]0.70210247593164[/C][/ROW]
[ROW][C]83[/C][C]0.69911040236802[/C][C]0.601779195263961[/C][C]0.30088959763198[/C][/ROW]
[ROW][C]84[/C][C]0.699188283171822[/C][C]0.601623433656356[/C][C]0.300811716828178[/C][/ROW]
[ROW][C]85[/C][C]0.725818773862567[/C][C]0.548362452274866[/C][C]0.274181226137433[/C][/ROW]
[ROW][C]86[/C][C]0.721580350955979[/C][C]0.556839298088042[/C][C]0.278419649044021[/C][/ROW]
[ROW][C]87[/C][C]0.703607284025772[/C][C]0.592785431948456[/C][C]0.296392715974228[/C][/ROW]
[ROW][C]88[/C][C]0.668312964881297[/C][C]0.663374070237407[/C][C]0.331687035118704[/C][/ROW]
[ROW][C]89[/C][C]0.63450934464911[/C][C]0.730981310701781[/C][C]0.36549065535089[/C][/ROW]
[ROW][C]90[/C][C]0.629808906744516[/C][C]0.740382186510968[/C][C]0.370191093255484[/C][/ROW]
[ROW][C]91[/C][C]0.653671336921208[/C][C]0.692657326157584[/C][C]0.346328663078792[/C][/ROW]
[ROW][C]92[/C][C]0.641680023535215[/C][C]0.71663995292957[/C][C]0.358319976464785[/C][/ROW]
[ROW][C]93[/C][C]0.606414330215474[/C][C]0.787171339569053[/C][C]0.393585669784526[/C][/ROW]
[ROW][C]94[/C][C]0.586220096978995[/C][C]0.827559806042011[/C][C]0.413779903021005[/C][/ROW]
[ROW][C]95[/C][C]0.571494910088469[/C][C]0.857010179823061[/C][C]0.428505089911531[/C][/ROW]
[ROW][C]96[/C][C]0.556575442088448[/C][C]0.886849115823104[/C][C]0.443424557911552[/C][/ROW]
[ROW][C]97[/C][C]0.576940350545506[/C][C]0.846119298908988[/C][C]0.423059649454494[/C][/ROW]
[ROW][C]98[/C][C]0.56483393104376[/C][C]0.870332137912481[/C][C]0.43516606895624[/C][/ROW]
[ROW][C]99[/C][C]0.598601876271396[/C][C]0.802796247457207[/C][C]0.401398123728604[/C][/ROW]
[ROW][C]100[/C][C]0.605335601785618[/C][C]0.789328796428763[/C][C]0.394664398214382[/C][/ROW]
[ROW][C]101[/C][C]0.570635405303314[/C][C]0.858729189393371[/C][C]0.429364594696686[/C][/ROW]
[ROW][C]102[/C][C]0.52833176614131[/C][C]0.94333646771738[/C][C]0.47166823385869[/C][/ROW]
[ROW][C]103[/C][C]0.484582866855037[/C][C]0.969165733710074[/C][C]0.515417133144963[/C][/ROW]
[ROW][C]104[/C][C]0.483545357683567[/C][C]0.967090715367134[/C][C]0.516454642316433[/C][/ROW]
[ROW][C]105[/C][C]0.480156490998867[/C][C]0.960312981997735[/C][C]0.519843509001133[/C][/ROW]
[ROW][C]106[/C][C]0.453022015939257[/C][C]0.906044031878514[/C][C]0.546977984060743[/C][/ROW]
[ROW][C]107[/C][C]0.545545509325896[/C][C]0.908908981348208[/C][C]0.454454490674104[/C][/ROW]
[ROW][C]108[/C][C]0.507496911931672[/C][C]0.985006176136656[/C][C]0.492503088068328[/C][/ROW]
[ROW][C]109[/C][C]0.494270439290949[/C][C]0.988540878581898[/C][C]0.505729560709051[/C][/ROW]
[ROW][C]110[/C][C]0.530114269933874[/C][C]0.939771460132251[/C][C]0.469885730066126[/C][/ROW]
[ROW][C]111[/C][C]0.496830093585807[/C][C]0.993660187171613[/C][C]0.503169906414193[/C][/ROW]
[ROW][C]112[/C][C]0.47267528416445[/C][C]0.945350568328901[/C][C]0.52732471583555[/C][/ROW]
[ROW][C]113[/C][C]0.459580122979005[/C][C]0.91916024595801[/C][C]0.540419877020995[/C][/ROW]
[ROW][C]114[/C][C]0.472908381842419[/C][C]0.945816763684838[/C][C]0.527091618157581[/C][/ROW]
[ROW][C]115[/C][C]0.468036221391733[/C][C]0.936072442783466[/C][C]0.531963778608267[/C][/ROW]
[ROW][C]116[/C][C]0.479822121427549[/C][C]0.959644242855098[/C][C]0.520177878572451[/C][/ROW]
[ROW][C]117[/C][C]0.431887891839735[/C][C]0.863775783679471[/C][C]0.568112108160264[/C][/ROW]
[ROW][C]118[/C][C]0.393371049952959[/C][C]0.786742099905918[/C][C]0.606628950047041[/C][/ROW]
[ROW][C]119[/C][C]0.377658442792078[/C][C]0.755316885584155[/C][C]0.622341557207922[/C][/ROW]
[ROW][C]120[/C][C]0.33153730978494[/C][C]0.663074619569881[/C][C]0.66846269021506[/C][/ROW]
[ROW][C]121[/C][C]0.298119468938408[/C][C]0.596238937876816[/C][C]0.701880531061592[/C][/ROW]
[ROW][C]122[/C][C]0.29636137441328[/C][C]0.59272274882656[/C][C]0.70363862558672[/C][/ROW]
[ROW][C]123[/C][C]0.266200546068864[/C][C]0.532401092137728[/C][C]0.733799453931136[/C][/ROW]
[ROW][C]124[/C][C]0.257832909702407[/C][C]0.515665819404814[/C][C]0.742167090297593[/C][/ROW]
[ROW][C]125[/C][C]0.292720412804816[/C][C]0.585440825609632[/C][C]0.707279587195184[/C][/ROW]
[ROW][C]126[/C][C]0.28343589012258[/C][C]0.56687178024516[/C][C]0.71656410987742[/C][/ROW]
[ROW][C]127[/C][C]0.291014358796605[/C][C]0.582028717593211[/C][C]0.708985641203395[/C][/ROW]
[ROW][C]128[/C][C]0.250769766379538[/C][C]0.501539532759076[/C][C]0.749230233620462[/C][/ROW]
[ROW][C]129[/C][C]0.236992158573166[/C][C]0.473984317146332[/C][C]0.763007841426834[/C][/ROW]
[ROW][C]130[/C][C]0.209184253706443[/C][C]0.418368507412887[/C][C]0.790815746293557[/C][/ROW]
[ROW][C]131[/C][C]0.225761679415637[/C][C]0.451523358831275[/C][C]0.774238320584363[/C][/ROW]
[ROW][C]132[/C][C]0.220077111577008[/C][C]0.440154223154016[/C][C]0.779922888422992[/C][/ROW]
[ROW][C]133[/C][C]0.284945042280712[/C][C]0.569890084561424[/C][C]0.715054957719288[/C][/ROW]
[ROW][C]134[/C][C]0.315026536297235[/C][C]0.63005307259447[/C][C]0.684973463702765[/C][/ROW]
[ROW][C]135[/C][C]0.287834464601026[/C][C]0.575668929202051[/C][C]0.712165535398974[/C][/ROW]
[ROW][C]136[/C][C]0.249388664179255[/C][C]0.49877732835851[/C][C]0.750611335820745[/C][/ROW]
[ROW][C]137[/C][C]0.238288883968083[/C][C]0.476577767936167[/C][C]0.761711116031917[/C][/ROW]
[ROW][C]138[/C][C]0.274162020011832[/C][C]0.548324040023664[/C][C]0.725837979988168[/C][/ROW]
[ROW][C]139[/C][C]0.297490472050326[/C][C]0.594980944100653[/C][C]0.702509527949674[/C][/ROW]
[ROW][C]140[/C][C]0.261155657550316[/C][C]0.522311315100632[/C][C]0.738844342449684[/C][/ROW]
[ROW][C]141[/C][C]0.215278849063867[/C][C]0.430557698127734[/C][C]0.784721150936133[/C][/ROW]
[ROW][C]142[/C][C]0.168008850431015[/C][C]0.336017700862029[/C][C]0.831991149568985[/C][/ROW]
[ROW][C]143[/C][C]0.12502280214169[/C][C]0.250045604283379[/C][C]0.87497719785831[/C][/ROW]
[ROW][C]144[/C][C]0.221483260284283[/C][C]0.442966520568566[/C][C]0.778516739715717[/C][/ROW]
[ROW][C]145[/C][C]0.202802002919727[/C][C]0.405604005839453[/C][C]0.797197997080273[/C][/ROW]
[ROW][C]146[/C][C]0.215775206230102[/C][C]0.431550412460204[/C][C]0.784224793769898[/C][/ROW]
[ROW][C]147[/C][C]0.368459420746021[/C][C]0.736918841492043[/C][C]0.631540579253979[/C][/ROW]
[ROW][C]148[/C][C]0.287218787563638[/C][C]0.574437575127277[/C][C]0.712781212436362[/C][/ROW]
[ROW][C]149[/C][C]0.341218492876623[/C][C]0.682436985753246[/C][C]0.658781507123377[/C][/ROW]
[ROW][C]150[/C][C]0.260634841028632[/C][C]0.521269682057264[/C][C]0.739365158971368[/C][/ROW]
[ROW][C]151[/C][C]0.310856552901588[/C][C]0.621713105803175[/C][C]0.689143447098412[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197403&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197403&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
110.6597400665186730.6805198669626540.340259933481327
120.6957171905973030.6085656188053950.304282809402697
130.5817418276970290.8365163446059430.418258172302971
140.5564906449245040.8870187101509920.443509355075496
150.4524202141939170.9048404283878340.547579785806083
160.4679634611770160.9359269223540310.532036538822984
170.4734035709135540.9468071418271090.526596429086446
180.6026480584029350.794703883194130.397351941597065
190.5255847636113720.9488304727772560.474415236388628
200.4793741023398550.9587482046797090.520625897660145
210.438907627635880.8778152552717610.56109237236412
220.3664528311848720.7329056623697450.633547168815128
230.2952750834747580.5905501669495160.704724916525242
240.4239020948533060.8478041897066110.576097905146694
250.3938328094002410.7876656188004820.606167190599759
260.3602300831879030.7204601663758070.639769916812097
270.3147309964568460.6294619929136920.685269003543154
280.2650052166299690.5300104332599380.734994783370031
290.270780675896610.541561351793220.72921932410339
300.2327069363891860.4654138727783710.767293063610815
310.2269641140477130.4539282280954260.773035885952287
320.1904769077384310.3809538154768630.809523092261569
330.1547935301065530.3095870602131060.845206469893447
340.1683362638495040.3366725276990080.831663736150496
350.2157460687021810.4314921374043620.784253931297819
360.2377171375659980.4754342751319960.762282862434002
370.2311286393662580.4622572787325150.768871360633742
380.3305864024185180.6611728048370350.669413597581482
390.334059662941440.668119325882880.66594033705856
400.3839829220473910.7679658440947830.616017077952609
410.3976674618351310.7953349236702610.60233253816487
420.4312428131206910.8624856262413830.568757186879309
430.4713420227233880.9426840454467750.528657977276612
440.4602083748501940.9204167497003880.539791625149806
450.4262806758565580.8525613517131160.573719324143442
460.4119433658857480.8238867317714970.588056634114252
470.4621624614579590.9243249229159180.537837538542041
480.508973692928340.9820526141433210.49102630707166
490.4692470196863440.9384940393726890.530752980313656
500.5172640861304320.9654718277391360.482735913869568
510.4816251110987690.9632502221975370.518374888901231
520.4349551311351720.8699102622703450.565044868864828
530.4154930843089340.8309861686178690.584506915691066
540.3769947688394570.7539895376789140.623005231160543
550.4032517484350.8065034968700010.596748251565
560.4245305600844130.8490611201688270.575469439915587
570.4117039027851330.8234078055702660.588296097214867
580.4059578080992590.8119156161985190.594042191900741
590.3805676994085520.7611353988171040.619432300591448
600.3770787500480620.7541575000961240.622921249951938
610.3993907238992910.7987814477985810.600609276100709
620.3851128507449870.7702257014899740.614887149255013
630.4511441575886960.9022883151773930.548855842411303
640.4277676107142790.8555352214285580.572232389285721
650.3979551315725410.7959102631450820.602044868427459
660.3909283083101610.7818566166203220.609071691689839
670.4256036374334930.8512072748669860.574396362566507
680.4437990573183290.8875981146366580.556200942681671
690.4159376023731040.8318752047462090.584062397626896
700.3761173021307370.7522346042614750.623882697869263
710.3776040497173030.7552080994346050.622395950282697
720.3859120517905650.7718241035811290.614087948209435
730.4175513264121070.8351026528242150.582448673587893
740.3841970112112060.7683940224224120.615802988788794
750.372533756459140.7450675129182810.62746624354086
760.3436117481896730.6872234963793470.656388251810327
770.3178851684394270.6357703368788550.682114831560573
780.3290843971325580.6581687942651160.670915602867442
790.3541238562902440.7082477125804870.645876143709756
800.3647683887816740.7295367775633470.635231611218326
810.3299800910822530.6599601821645060.670019908917747
820.297897524068360.595795048136720.70210247593164
830.699110402368020.6017791952639610.30088959763198
840.6991882831718220.6016234336563560.300811716828178
850.7258187738625670.5483624522748660.274181226137433
860.7215803509559790.5568392980880420.278419649044021
870.7036072840257720.5927854319484560.296392715974228
880.6683129648812970.6633740702374070.331687035118704
890.634509344649110.7309813107017810.36549065535089
900.6298089067445160.7403821865109680.370191093255484
910.6536713369212080.6926573261575840.346328663078792
920.6416800235352150.716639952929570.358319976464785
930.6064143302154740.7871713395690530.393585669784526
940.5862200969789950.8275598060420110.413779903021005
950.5714949100884690.8570101798230610.428505089911531
960.5565754420884480.8868491158231040.443424557911552
970.5769403505455060.8461192989089880.423059649454494
980.564833931043760.8703321379124810.43516606895624
990.5986018762713960.8027962474572070.401398123728604
1000.6053356017856180.7893287964287630.394664398214382
1010.5706354053033140.8587291893933710.429364594696686
1020.528331766141310.943336467717380.47166823385869
1030.4845828668550370.9691657337100740.515417133144963
1040.4835453576835670.9670907153671340.516454642316433
1050.4801564909988670.9603129819977350.519843509001133
1060.4530220159392570.9060440318785140.546977984060743
1070.5455455093258960.9089089813482080.454454490674104
1080.5074969119316720.9850061761366560.492503088068328
1090.4942704392909490.9885408785818980.505729560709051
1100.5301142699338740.9397714601322510.469885730066126
1110.4968300935858070.9936601871716130.503169906414193
1120.472675284164450.9453505683289010.52732471583555
1130.4595801229790050.919160245958010.540419877020995
1140.4729083818424190.9458167636848380.527091618157581
1150.4680362213917330.9360724427834660.531963778608267
1160.4798221214275490.9596442428550980.520177878572451
1170.4318878918397350.8637757836794710.568112108160264
1180.3933710499529590.7867420999059180.606628950047041
1190.3776584427920780.7553168855841550.622341557207922
1200.331537309784940.6630746195698810.66846269021506
1210.2981194689384080.5962389378768160.701880531061592
1220.296361374413280.592722748826560.70363862558672
1230.2662005460688640.5324010921377280.733799453931136
1240.2578329097024070.5156658194048140.742167090297593
1250.2927204128048160.5854408256096320.707279587195184
1260.283435890122580.566871780245160.71656410987742
1270.2910143587966050.5820287175932110.708985641203395
1280.2507697663795380.5015395327590760.749230233620462
1290.2369921585731660.4739843171463320.763007841426834
1300.2091842537064430.4183685074128870.790815746293557
1310.2257616794156370.4515233588312750.774238320584363
1320.2200771115770080.4401542231540160.779922888422992
1330.2849450422807120.5698900845614240.715054957719288
1340.3150265362972350.630053072594470.684973463702765
1350.2878344646010260.5756689292020510.712165535398974
1360.2493886641792550.498777328358510.750611335820745
1370.2382888839680830.4765777679361670.761711116031917
1380.2741620200118320.5483240400236640.725837979988168
1390.2974904720503260.5949809441006530.702509527949674
1400.2611556575503160.5223113151006320.738844342449684
1410.2152788490638670.4305576981277340.784721150936133
1420.1680088504310150.3360177008620290.831991149568985
1430.125022802141690.2500456042833790.87497719785831
1440.2214832602842830.4429665205685660.778516739715717
1450.2028020029197270.4056040058394530.797197997080273
1460.2157752062301020.4315504124602040.784224793769898
1470.3684594207460210.7369188414920430.631540579253979
1480.2872187875636380.5744375751272770.712781212436362
1490.3412184928766230.6824369857532460.658781507123377
1500.2606348410286320.5212696820572640.739365158971368
1510.3108565529015880.6217131058031750.689143447098412






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

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197403&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197403&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197403&T=6

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


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

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


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 4
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Figure 5
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Figure 6
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Figure 7
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Figure 8
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Figure 9
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Figure 10
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Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; 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')
}