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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 computationSat, 11 Dec 2010 15:55:17 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/11/t12920828190l47edivypr80ly.htm/, Retrieved Sat, 27 Apr 2024 21:07:35 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=108229, Retrieved Sat, 27 Apr 2024 21:07:35 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact198

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Recursive Partitioning (Regression Trees)] [] [2010-12-05 18:59:57] [b98453cac15ba1066b407e146608df68]
- RMPD    [Multiple Regression] [WS 10 - Multiple ...] [2010-12-11 15:55:17] [a948b7c78e10e31abd3f68e640bbd8ba] [Current]
F           [Multiple Regression] [] [2010-12-13 18:20:27] [fb3a7008aea9486db3846dc25434607b]
-   PD        [Multiple Regression] [Multiple regressi...] [2010-12-21 14:10:34] [fb3a7008aea9486db3846dc25434607b]
-           [Multiple Regression] [] [2010-12-13 18:21:06] [d7b28a0391ab3b2ddc9f9fba95a43f33]
-   PD        [Multiple Regression] [] [2010-12-21 14:12:32] [d7b28a0391ab3b2ddc9f9fba95a43f33]
-             [Multiple Regression] [] [2010-12-21 18:58:21] [42a441ca3193af442aa2201743dfb347]
-    D          [Multiple Regression] [] [2010-12-22 17:06:05] [f82dc80ca9fc4fd83b66f6024d510f8c]
-   PD            [Multiple Regression] [] [2010-12-22 20:33:29] [4cec9a0c6d7fcfe819c8df12b51eb7f5]
-             [Multiple Regression] [] [2010-12-21 21:05:20] [07fa8844ca5618cd0482008937d9acea]
-             [Multiple Regression] [] [2010-12-24 12:20:54] [b07cd1964830aab808142229b1166ece]
- R           [Multiple Regression] [Multiple regression] [2010-12-24 12:24:12] [5b90046bcdf0f277a2c54de2210570b9]
-               [Multiple Regression] [] [2010-12-24 13:32:21] [59fbc0a3c8afb52467c3ea2d042f111d]
-             [Multiple Regression] [] [2010-12-25 21:49:52] [2e1e44f0ae3cb9513dc28781dfdb387b]
-   PD          [Multiple Regression] [Workshop 10 (3)] [2011-12-10 14:08:30] [3deae35ae8526e36953f595ad65f3a1f]
- R P             [Multiple Regression] [Workshop 10 (3)] [2011-12-10 14:28:47] [3deae35ae8526e36953f595ad65f3a1f]
-  M                [Multiple Regression] [Multiple Regression] [2013-12-09 21:48:38] [16ce55620e4b91ec00a4b56aea2a2582]
- RMP             [Recursive Partitioning (Regression Trees)] [Workshop 10 (4)] [2011-12-10 14:33:25] [3deae35ae8526e36953f595ad65f3a1f]
- R P               [Recursive Partitioning (Regression Trees)] [Workshop 10 (4)] [2011-12-12 15:13:36] [3deae35ae8526e36953f595ad65f3a1f]
-  M                  [Recursive Partitioning (Regression Trees)] [Recursive Partiti...] [2013-12-09 21:59:57] [16ce55620e4b91ec00a4b56aea2a2582]
-   PD              [Recursive Partitioning (Regression Trees)] [Workshop 10 (5)] [2011-12-12 15:18:00] [3deae35ae8526e36953f595ad65f3a1f]
- R  D                [Recursive Partitioning (Regression Trees)] [Workshop 10 (5)] [2011-12-12 15:28:11] [3deae35ae8526e36953f595ad65f3a1f]
-   P                   [Recursive Partitioning (Regression Trees)] [Workshop 10 (6)] [2011-12-12 16:25:11] [3deae35ae8526e36953f595ad65f3a1f]
-  M                      [Recursive Partitioning (Regression Trees)] [Recursive Partiti...] [2013-12-09 22:16:05] [16ce55620e4b91ec00a4b56aea2a2582]
-  M                    [Recursive Partitioning (Regression Trees)] [Recursive Partiti...] [2013-12-09 22:07:36] [16ce55620e4b91ec00a4b56aea2a2582]
- RM            [Multiple Regression] [WS10.4] [2011-12-12 22:23:35] [74be16979710d4c4e7c6647856088456]
-               [Multiple Regression] [] [2011-12-13 15:36:18] [9026c059d17255e641798e6a3c7c272a]
-   P           [Multiple Regression] [] [2011-12-14 20:14:22] [7d86e24de0a0f8503ecffdef58e8c96c]
-   PD      [Multiple Regression] [SP] [2010-12-14 18:22:33] [b1e5ec7263cdefe98ec76e1a1363de05]
-   PD      [Multiple Regression] [SP] [2010-12-14 18:25:57] [b1e5ec7263cdefe98ec76e1a1363de05]
-   PD      [Multiple Regression] [paper multiple re...] [2010-12-19 08:10:19] [033eb2749a430605d9b2be7c4aac4a0c]
-   P         [Multiple Regression] [paper - multiple ...] [2010-12-19 10:14:20] [033eb2749a430605d9b2be7c4aac4a0c]
-   P         [Multiple Regression] [paper - multiple ...] [2010-12-19 10:19:28] [033eb2749a430605d9b2be7c4aac4a0c]
-   P         [Multiple Regression] [paper - multiple ...] [2010-12-19 10:24:31] [74be16979710d4c4e7c6647856088456]
-   P         [Multiple Regression] [paper - multiple ...] [2010-12-19 10:24:31] [033eb2749a430605d9b2be7c4aac4a0c]
-   P         [Multiple Regression] [paper - multiple ...] [2010-12-19 10:30:11] [033eb2749a430605d9b2be7c4aac4a0c]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
2	24	14	11	12	24	26
2	25	11	7	8	25	23
2	17	6	17	8	30	25
1	18	12	10	8	19	23
2	18	8	12	9	22	19
2	16	10	12	7	22	29
2	20	10	11	4	25	25
2	16	11	11	11	23	21
2	18	16	12	7	17	22
2	17	11	13	7	21	25
1	23	13	14	12	19	24
2	30	12	16	10	19	18
1	23	8	11	10	15	22
2	18	12	10	8	16	15
2	15	11	11	8	23	22
1	12	4	15	4	27	28
1	21	9	9	9	22	20
2	15	8	11	8	14	12
1	20	8	17	7	22	24
2	31	14	17	11	23	20
1	27	15	11	9	23	21
2	34	16	18	11	21	20
2	21	9	14	13	19	21
2	31	14	10	8	18	23
1	19	11	11	8	20	28
2	16	8	15	9	23	24
1	20	9	15	6	25	24
2	21	9	13	9	19	24
2	22	9	16	9	24	23
1	17	9	13	6	22	23
2	24	10	9	6	25	29
1	25	16	18	16	26	24
2	26	11	18	5	29	18
2	25	8	12	7	32	25
1	17	9	17	9	25	21
1	32	16	9	6	29	26
1	33	11	9	6	28	22
1	13	16	12	5	17	22
2	32	12	18	12	28	22
1	25	12	12	7	29	23
1	29	14	18	10	26	30
2	22	9	14	9	25	23
1	18	10	15	8	14	17
1	17	9	16	5	25	23
2	20	10	10	8	26	23
2	15	12	11	8	20	25
2	20	14	14	10	18	24
2	33	14	9	6	32	24
2	29	10	12	8	25	23
1	23	14	17	7	25	21
2	26	16	5	4	23	24
1	18	9	12	8	21	24
1	20	10	12	8	20	28
2	11	6	6	4	15	16
1	28	8	24	20	30	20
2	26	13	12	8	24	29
2	22	10	12	8	26	27
2	17	8	14	6	24	22
1	12	7	7	4	22	28
2	14	15	13	8	14	16
1	17	9	12	9	24	25
1	21	10	13	6	24	24
2	19	12	14	7	24	28
2	18	13	8	9	24	24
2	10	10	11	5	19	23
1	29	11	9	5	31	30
2	31	8	11	8	22	24
1	19	9	13	8	27	21
2	9	13	10	6	19	25
1	20	11	11	8	25	25
1	28	8	12	7	20	22
2	19	9	9	7	21	23
2	30	9	15	9	27	26
1	29	15	18	11	23	23
1	26	9	15	6	25	25
2	23	10	12	8	20	21
2	13	14	13	6	21	25
2	21	12	14	9	22	24
1	19	12	10	8	23	29
1	28	11	13	6	25	22
1	23	14	13	10	25	27
1	18	6	11	8	17	26
2	21	12	13	8	19	22
1	20	8	16	10	25	24
2	23	14	8	5	19	27
2	21	11	16	7	20	24
1	21	10	11	5	26	24
2	15	14	9	8	23	29
2	28	12	16	14	27	22
2	19	10	12	7	17	21
2	26	14	14	8	17	24
2	10	5	8	6	19	24
2	16	11	9	5	17	23
2	22	10	15	6	22	20
2	19	9	11	10	21	27
2	31	10	21	12	32	26
2	31	16	14	9	21	25
2	29	13	18	12	21	21
1	19	9	12	7	18	21
1	22	10	13	8	18	19
2	23	10	15	10	23	21
1	15	7	12	6	19	21
2	20	9	19	10	20	16
1	18	8	15	10	21	22
2	23	14	11	10	20	29
1	25	14	11	5	17	15
2	21	8	10	7	18	17
1	24	9	13	10	19	15
1	25	14	15	11	22	21
2	17	14	12	6	15	21
2	13	8	12	7	14	19
2	28	8	16	12	18	24
2	21	8	9	11	24	20
1	25	7	18	11	35	17
2	9	6	8	11	29	23
1	16	8	13	5	21	24
2	19	6	17	8	25	14
2	17	11	9	6	20	19
2	25	14	15	9	22	24
2	20	11	8	4	13	13
2	29	11	7	4	26	22
2	14	11	12	7	17	16
2	22	14	14	11	25	19
2	15	8	6	6	20	25
2	19	20	8	7	19	25
2	20	11	17	8	21	23
1	15	8	10	4	22	24
2	20	11	11	8	24	26
2	18	10	14	9	21	26
2	33	14	11	8	26	25
1	22	11	13	11	24	18
1	16	9	12	8	16	21
2	17	9	11	5	23	26
1	16	8	9	4	18	23
1	21	10	12	8	16	23
2	26	13	20	10	26	22
1	18	13	12	6	19	20
1	18	12	13	9	21	13
2	17	8	12	9	21	24
2	22	13	12	13	22	15
1	30	14	9	9	23	14
2	30	12	15	10	29	22
1	24	14	24	20	21	10
2	21	15	7	5	21	24
1	21	13	17	11	23	22
2	29	16	11	6	27	24
2	31	9	17	9	25	19
1	20	9	11	7	21	20
1	16	9	12	9	10	13
1	22	8	14	10	20	20
2	20	7	11	9	26	22
2	28	16	16	8	24	24
1	38	11	21	7	29	29
2	22	9	14	6	19	12
2	20	11	20	13	24	20
2	17	9	13	6	19	21
2	28	14	11	8	24	24
2	22	13	15	10	22	22
2	31	16	19	16	17	20

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 7 seconds \tabularnewline
R Server & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
R Framework error message & 
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108229&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]7 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'RServer@AstonUniversity' @ vre.aston.ac.uk[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108229&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108229&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.






Multiple Linear Regression - Estimated Regression Equation
COM[t] = -1.76493979259821 -0.131393212690802G[t] + 0.812849739916337DA[t] + 0.24845330662011PE[t] + 0.190333948740538PC[t] + 0.56590043905433PS[t] -0.115683772583743`O `[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
COM[t] =  -1.76493979259821 -0.131393212690802G[t] +  0.812849739916337DA[t] +  0.24845330662011PE[t] +  0.190333948740538PC[t] +  0.56590043905433PS[t] -0.115683772583743`O
`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108229&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]COM[t] =  -1.76493979259821 -0.131393212690802G[t] +  0.812849739916337DA[t] +  0.24845330662011PE[t] +  0.190333948740538PC[t] +  0.56590043905433PS[t] -0.115683772583743`O
`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108229&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108229&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
COM[t] = -1.76493979259821 -0.131393212690802G[t] + 0.812849739916337DA[t] + 0.24845330662011PE[t] + 0.190333948740538PC[t] + 0.56590043905433PS[t] -0.115683772583743`O `[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-1.764939792598213.278744-0.53830.5911590.29558
G-0.1313932126908020.744476-0.17650.8601430.430072
DA0.8128497399163370.1316586.173900
PE0.248453306620110.1341241.85240.0659050.032953
PC0.1903339487405380.1691071.12550.2621410.13107
PS0.565900439054330.0961235.887300
`O `-0.1156837725837430.103352-1.11930.264770.132385

\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) & -1.76493979259821 & 3.278744 & -0.5383 & 0.591159 & 0.29558 \tabularnewline
G & -0.131393212690802 & 0.744476 & -0.1765 & 0.860143 & 0.430072 \tabularnewline
DA & 0.812849739916337 & 0.131658 & 6.1739 & 0 & 0 \tabularnewline
PE & 0.24845330662011 & 0.134124 & 1.8524 & 0.065905 & 0.032953 \tabularnewline
PC & 0.190333948740538 & 0.169107 & 1.1255 & 0.262141 & 0.13107 \tabularnewline
PS & 0.56590043905433 & 0.096123 & 5.8873 & 0 & 0 \tabularnewline
`O
` & -0.115683772583743 & 0.103352 & -1.1193 & 0.26477 & 0.132385 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108229&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]-1.76493979259821[/C][C]3.278744[/C][C]-0.5383[/C][C]0.591159[/C][C]0.29558[/C][/ROW]
[ROW][C]G[/C][C]-0.131393212690802[/C][C]0.744476[/C][C]-0.1765[/C][C]0.860143[/C][C]0.430072[/C][/ROW]
[ROW][C]DA[/C][C]0.812849739916337[/C][C]0.131658[/C][C]6.1739[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]PE[/C][C]0.24845330662011[/C][C]0.134124[/C][C]1.8524[/C][C]0.065905[/C][C]0.032953[/C][/ROW]
[ROW][C]PC[/C][C]0.190333948740538[/C][C]0.169107[/C][C]1.1255[/C][C]0.262141[/C][C]0.13107[/C][/ROW]
[ROW][C]PS[/C][C]0.56590043905433[/C][C]0.096123[/C][C]5.8873[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]`O
`[/C][C]-0.115683772583743[/C][C]0.103352[/C][C]-1.1193[/C][C]0.26477[/C][C]0.132385[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108229&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108229&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)-1.764939792598213.278744-0.53830.5911590.29558
G-0.1313932126908020.744476-0.17650.8601430.430072
DA0.8128497399163370.1316586.173900
PE0.248453306620110.1341241.85240.0659050.032953
PC0.1903339487405380.1691071.12550.2621410.13107
PS0.565900439054330.0961235.887300
`O `-0.1156837725837430.103352-1.11930.264770.132385






Multiple Linear Regression - Regression Statistics
Multiple R0.638198057370523
R-squared0.407296760431509
Adjusted R-squared0.383900579922227
F-TEST (value)17.4086860147926
F-TEST (DF numerator)6
F-TEST (DF denominator)152
p-value2.77555756156289e-15
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation4.49195510423555
Sum Squared Residuals3067.00442008711

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.638198057370523 \tabularnewline
R-squared & 0.407296760431509 \tabularnewline
Adjusted R-squared & 0.383900579922227 \tabularnewline
F-TEST (value) & 17.4086860147926 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 152 \tabularnewline
p-value & 2.77555756156289e-15 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 4.49195510423555 \tabularnewline
Sum Squared Residuals & 3067.00442008711 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108229&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.638198057370523[/C][/ROW]
[ROW][C]R-squared[/C][C]0.407296760431509[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.383900579922227[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]17.4086860147926[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]152[/C][/ROW]
[ROW][C]p-value[/C][C]2.77555756156289e-15[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]4.49195510423555[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]3067.00442008711[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108229&T=3

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

Multiple Linear Regression - Regression Statistics
Multiple R0.638198057370523
R-squared0.407296760431509
Adjusted R-squared0.383900579922227
F-TEST (value)17.4086860147926
F-TEST (DF numerator)6
F-TEST (DF denominator)152
p-value2.77555756156289e-15
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation4.49195510423555
Sum Squared Residuals3067.00442008711






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
12424.9429963486832-0.942996348683151
22521.66224986429713.33775013570285
31722.6806688810207-5.68066888102073
41819.9564501024386-1.95645010243863
51819.4213348995612-1.42133489956121
61619.5095287560754-3.50952875607538
72020.8505100107316-0.850510010731612
81622.326631604058-6.32663160405803
91822.3669114083879-4.36691140838794
101720.4676664538925-3.46766645389246
112322.40876509121380.591234908786182
123022.27486348986837.72513651013172
132315.18625436324097.8137456367591
141819.0528257532548-1.05282575325478
151521.6399459852527-6.63994598525267
161217.8833675707623-5.88336757076227
172119.50453415972431.49546584027573
181515.2651305398521-0.265130539852111
192019.83590788495280.164092115047235
203126.37158443611144.62841556388856
212725.32875587893311.6712441210669
223427.11393634445566.88606365554444
232119.56335818534941.43664181465056
243120.884855930526210.1151440694738
251919.379535245278-0.379535245278024
261620.1541763955572-4.15417639555715
272021.6592183800513-1.65921838005134
282118.20651776601592.79348223398405
292221.89706365373170.10293634626833
301719.5802942222319-2.58029422223187
312420.27153620463753.7284637953625
322530.5637664057857-5.56376640578574
332626.6662550100328-0.666255010032776
342524.0055687571210.994431242879022
351723.0741781572644-6.0741781572644
363227.89068093079494.10931906920513
373323.72326688249389.27673311750617
381322.1176367235977-9.11763672359766
393227.78280686174364.21719313825642
402525.9220271574816-0.922027157481621
412927.10196059800741.89803940199262
422221.96605747954580.0339425204542188
431817.43761759593730.562382404062688
441721.8330215105146-4.83302151051465
452022.1606604832955-2.16066048329547
461520.4080430902548-5.40804309025479
472022.1436532819039-2.14365328190395
483328.06265710060194.93734289939812
492922.09166665748146.90833334251864
502326.757758959365-3.757758959365
512623.22077150498412.77922849501594
521819.0309246014548-1.03092460145476
532018.81513881198181.1848611880182
541111.7389940806761-0.738994080676097
552829.0393609676901-1.03936096769014
562623.27021280267362.72978719732642
572222.1948320062007-0.19483200620072
581720.1319892269372-3.13198922693724
591215.5047881422787-3.50478814227875
601420.9892502421717-6.98925024217172
611720.8032760947745-3.80327609477455
622121.4092610676731-0.409261067673123
631922.8796194998407-3.87961949984067
641823.0451523878524-5.0451523878524
651017.8768088703137-7.87680887031365
662924.30516407024634.69483592975366
673118.404128781281812.5958712187182
681923.0218318601521-4.02183186015208
69920.0258711870156-11.0258711870156
702022.5560887583009-2.55608875830091
712817.69320801891110.306791981089
721918.07952129274680.920478707253165
733022.99926034652337.00073965347668
742927.21722937758751.78277062241254
752621.54353460746764.45646539253241
762319.49353200737723.50646799262281
771322.7158817249009-9.71588172490094
782122.5912216095481-1.59122160954806
791921.5259492231535-2.5259492231535
802823.01937879181134.98062120818873
812325.6408449436037-2.64084494360373
821813.84895277370084.15104722629917
832120.68610058219190.313899417808097
842021.8561577417173-1.85615774171726
852319.92011281978373.0798871802163
862120.76280970728220.237190292717798
872121.853821383801-0.853821383801027
881522.7718021836753-7.77180218367526
892827.10066770693010.899332293069894
901917.60549674147371.39450325852634
912621.19708494536854.80291505463146
921013.1418504270284-3.14185042702844
931617.0609511188811-1.06095111888111
942221.10570868044880.89429131955115
951918.68469466187370.315305338126302
963128.70333396765362.29666603234641
973125.16103635757535.83896364242467
982924.75003730086334.24996269913666
991917.48994065330251.51005934669754
1002218.97294519374693.02705480625307
1012322.31726114188160.68273885811841
1021516.2398076637836-1.23980766378358
1032021.3789421742014-1.37894217420141
1041819.5754702240473-1.57547022404732
1052321.95167537723361.04832462276643
1062521.05327034523113.94672965476892
1072116.51152617779014.48847382220993
1082419.5693988807014.43060111929903
1092525.3244868239239-0.324486823923948
1101719.5347608742898-2.53476087428981
1111314.5134634896455-1.51346348964548
1122818.14412935312729.85587064687278
1132120.07275998270690.92724001729313
1142528.1993393624112-3.19933936241119
115920.6810580737745-11.6810580737745
1161617.8955263219369-1.89552632193692
1171921.1236881841702-2.12368818417025
1181719.4117214751196-2.41172147511961
1192524.46537439600080.53462560399916
1202015.51539983314064.48460016685943
1212921.58249828097317.41750171902693
1221418.9967653443087-4.99676534430871
1232226.8737091669435-4.87370916694351
1241515.5337097000078-0.533709700007815
1251925.4092467019303-6.40924670193028
1262021.8831811742809-1.88318117428092
1271517.5257328923904-2.52573289239038
1282021.743111333972-1.74311133397203
1291820.1682541454936-2.16825414549357
1303325.42914520441347.57085479558655
1312223.8678831867946-1.86788318679461
1321616.5484737239343-0.548473723934336
1331718.9805095688634-1.98050956886341
1341615.12936160213670.870638397863306
1352117.12995591868323.87004408131681
1362627.5800944393104-1.5800944393104
1371821.2325898758653-3.23258987586534
1381823.1807825749856-5.18078257498559
1391718.2770155975882-1.27701559758816
1402224.70965448444-2.70965448444001
1413024.82878593386275.17121406613726
1423027.22267948345652.7773205165435
1432429.9801931815366-5.98019318153662
1442121.9633614489398-0.963361448939817
1452125.4587603637184-4.45876036371842
1462927.35576099840311.64423900159689
1473123.17415248974117.82584751025892
1482019.05487243642910.945127563570916
1491614.26887521901881.73112478098117
1502218.99248402354043.00751597645964
1512020.2765822914909-0.276582291490852
1522827.28099411182170.719005888178254
1533826.651154541643811.3488454583562
1542219.27217449741942.72782550258065
1552025.6249634727582-5.62496347275816
1561717.9825672375456-0.98256723754556
1572824.41302809888853.58697190111148
1582224.0742261499925-2.07422614999253
1593126.0504576385614.94954236143896

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 24 & 24.9429963486832 & -0.942996348683151 \tabularnewline
2 & 25 & 21.6622498642971 & 3.33775013570285 \tabularnewline
3 & 17 & 22.6806688810207 & -5.68066888102073 \tabularnewline
4 & 18 & 19.9564501024386 & -1.95645010243863 \tabularnewline
5 & 18 & 19.4213348995612 & -1.42133489956121 \tabularnewline
6 & 16 & 19.5095287560754 & -3.50952875607538 \tabularnewline
7 & 20 & 20.8505100107316 & -0.850510010731612 \tabularnewline
8 & 16 & 22.326631604058 & -6.32663160405803 \tabularnewline
9 & 18 & 22.3669114083879 & -4.36691140838794 \tabularnewline
10 & 17 & 20.4676664538925 & -3.46766645389246 \tabularnewline
11 & 23 & 22.4087650912138 & 0.591234908786182 \tabularnewline
12 & 30 & 22.2748634898683 & 7.72513651013172 \tabularnewline
13 & 23 & 15.1862543632409 & 7.8137456367591 \tabularnewline
14 & 18 & 19.0528257532548 & -1.05282575325478 \tabularnewline
15 & 15 & 21.6399459852527 & -6.63994598525267 \tabularnewline
16 & 12 & 17.8833675707623 & -5.88336757076227 \tabularnewline
17 & 21 & 19.5045341597243 & 1.49546584027573 \tabularnewline
18 & 15 & 15.2651305398521 & -0.265130539852111 \tabularnewline
19 & 20 & 19.8359078849528 & 0.164092115047235 \tabularnewline
20 & 31 & 26.3715844361114 & 4.62841556388856 \tabularnewline
21 & 27 & 25.3287558789331 & 1.6712441210669 \tabularnewline
22 & 34 & 27.1139363444556 & 6.88606365554444 \tabularnewline
23 & 21 & 19.5633581853494 & 1.43664181465056 \tabularnewline
24 & 31 & 20.8848559305262 & 10.1151440694738 \tabularnewline
25 & 19 & 19.379535245278 & -0.379535245278024 \tabularnewline
26 & 16 & 20.1541763955572 & -4.15417639555715 \tabularnewline
27 & 20 & 21.6592183800513 & -1.65921838005134 \tabularnewline
28 & 21 & 18.2065177660159 & 2.79348223398405 \tabularnewline
29 & 22 & 21.8970636537317 & 0.10293634626833 \tabularnewline
30 & 17 & 19.5802942222319 & -2.58029422223187 \tabularnewline
31 & 24 & 20.2715362046375 & 3.7284637953625 \tabularnewline
32 & 25 & 30.5637664057857 & -5.56376640578574 \tabularnewline
33 & 26 & 26.6662550100328 & -0.666255010032776 \tabularnewline
34 & 25 & 24.005568757121 & 0.994431242879022 \tabularnewline
35 & 17 & 23.0741781572644 & -6.0741781572644 \tabularnewline
36 & 32 & 27.8906809307949 & 4.10931906920513 \tabularnewline
37 & 33 & 23.7232668824938 & 9.27673311750617 \tabularnewline
38 & 13 & 22.1176367235977 & -9.11763672359766 \tabularnewline
39 & 32 & 27.7828068617436 & 4.21719313825642 \tabularnewline
40 & 25 & 25.9220271574816 & -0.922027157481621 \tabularnewline
41 & 29 & 27.1019605980074 & 1.89803940199262 \tabularnewline
42 & 22 & 21.9660574795458 & 0.0339425204542188 \tabularnewline
43 & 18 & 17.4376175959373 & 0.562382404062688 \tabularnewline
44 & 17 & 21.8330215105146 & -4.83302151051465 \tabularnewline
45 & 20 & 22.1606604832955 & -2.16066048329547 \tabularnewline
46 & 15 & 20.4080430902548 & -5.40804309025479 \tabularnewline
47 & 20 & 22.1436532819039 & -2.14365328190395 \tabularnewline
48 & 33 & 28.0626571006019 & 4.93734289939812 \tabularnewline
49 & 29 & 22.0916666574814 & 6.90833334251864 \tabularnewline
50 & 23 & 26.757758959365 & -3.757758959365 \tabularnewline
51 & 26 & 23.2207715049841 & 2.77922849501594 \tabularnewline
52 & 18 & 19.0309246014548 & -1.03092460145476 \tabularnewline
53 & 20 & 18.8151388119818 & 1.1848611880182 \tabularnewline
54 & 11 & 11.7389940806761 & -0.738994080676097 \tabularnewline
55 & 28 & 29.0393609676901 & -1.03936096769014 \tabularnewline
56 & 26 & 23.2702128026736 & 2.72978719732642 \tabularnewline
57 & 22 & 22.1948320062007 & -0.19483200620072 \tabularnewline
58 & 17 & 20.1319892269372 & -3.13198922693724 \tabularnewline
59 & 12 & 15.5047881422787 & -3.50478814227875 \tabularnewline
60 & 14 & 20.9892502421717 & -6.98925024217172 \tabularnewline
61 & 17 & 20.8032760947745 & -3.80327609477455 \tabularnewline
62 & 21 & 21.4092610676731 & -0.409261067673123 \tabularnewline
63 & 19 & 22.8796194998407 & -3.87961949984067 \tabularnewline
64 & 18 & 23.0451523878524 & -5.0451523878524 \tabularnewline
65 & 10 & 17.8768088703137 & -7.87680887031365 \tabularnewline
66 & 29 & 24.3051640702463 & 4.69483592975366 \tabularnewline
67 & 31 & 18.4041287812818 & 12.5958712187182 \tabularnewline
68 & 19 & 23.0218318601521 & -4.02183186015208 \tabularnewline
69 & 9 & 20.0258711870156 & -11.0258711870156 \tabularnewline
70 & 20 & 22.5560887583009 & -2.55608875830091 \tabularnewline
71 & 28 & 17.693208018911 & 10.306791981089 \tabularnewline
72 & 19 & 18.0795212927468 & 0.920478707253165 \tabularnewline
73 & 30 & 22.9992603465233 & 7.00073965347668 \tabularnewline
74 & 29 & 27.2172293775875 & 1.78277062241254 \tabularnewline
75 & 26 & 21.5435346074676 & 4.45646539253241 \tabularnewline
76 & 23 & 19.4935320073772 & 3.50646799262281 \tabularnewline
77 & 13 & 22.7158817249009 & -9.71588172490094 \tabularnewline
78 & 21 & 22.5912216095481 & -1.59122160954806 \tabularnewline
79 & 19 & 21.5259492231535 & -2.5259492231535 \tabularnewline
80 & 28 & 23.0193787918113 & 4.98062120818873 \tabularnewline
81 & 23 & 25.6408449436037 & -2.64084494360373 \tabularnewline
82 & 18 & 13.8489527737008 & 4.15104722629917 \tabularnewline
83 & 21 & 20.6861005821919 & 0.313899417808097 \tabularnewline
84 & 20 & 21.8561577417173 & -1.85615774171726 \tabularnewline
85 & 23 & 19.9201128197837 & 3.0798871802163 \tabularnewline
86 & 21 & 20.7628097072822 & 0.237190292717798 \tabularnewline
87 & 21 & 21.853821383801 & -0.853821383801027 \tabularnewline
88 & 15 & 22.7718021836753 & -7.77180218367526 \tabularnewline
89 & 28 & 27.1006677069301 & 0.899332293069894 \tabularnewline
90 & 19 & 17.6054967414737 & 1.39450325852634 \tabularnewline
91 & 26 & 21.1970849453685 & 4.80291505463146 \tabularnewline
92 & 10 & 13.1418504270284 & -3.14185042702844 \tabularnewline
93 & 16 & 17.0609511188811 & -1.06095111888111 \tabularnewline
94 & 22 & 21.1057086804488 & 0.89429131955115 \tabularnewline
95 & 19 & 18.6846946618737 & 0.315305338126302 \tabularnewline
96 & 31 & 28.7033339676536 & 2.29666603234641 \tabularnewline
97 & 31 & 25.1610363575753 & 5.83896364242467 \tabularnewline
98 & 29 & 24.7500373008633 & 4.24996269913666 \tabularnewline
99 & 19 & 17.4899406533025 & 1.51005934669754 \tabularnewline
100 & 22 & 18.9729451937469 & 3.02705480625307 \tabularnewline
101 & 23 & 22.3172611418816 & 0.68273885811841 \tabularnewline
102 & 15 & 16.2398076637836 & -1.23980766378358 \tabularnewline
103 & 20 & 21.3789421742014 & -1.37894217420141 \tabularnewline
104 & 18 & 19.5754702240473 & -1.57547022404732 \tabularnewline
105 & 23 & 21.9516753772336 & 1.04832462276643 \tabularnewline
106 & 25 & 21.0532703452311 & 3.94672965476892 \tabularnewline
107 & 21 & 16.5115261777901 & 4.48847382220993 \tabularnewline
108 & 24 & 19.569398880701 & 4.43060111929903 \tabularnewline
109 & 25 & 25.3244868239239 & -0.324486823923948 \tabularnewline
110 & 17 & 19.5347608742898 & -2.53476087428981 \tabularnewline
111 & 13 & 14.5134634896455 & -1.51346348964548 \tabularnewline
112 & 28 & 18.1441293531272 & 9.85587064687278 \tabularnewline
113 & 21 & 20.0727599827069 & 0.92724001729313 \tabularnewline
114 & 25 & 28.1993393624112 & -3.19933936241119 \tabularnewline
115 & 9 & 20.6810580737745 & -11.6810580737745 \tabularnewline
116 & 16 & 17.8955263219369 & -1.89552632193692 \tabularnewline
117 & 19 & 21.1236881841702 & -2.12368818417025 \tabularnewline
118 & 17 & 19.4117214751196 & -2.41172147511961 \tabularnewline
119 & 25 & 24.4653743960008 & 0.53462560399916 \tabularnewline
120 & 20 & 15.5153998331406 & 4.48460016685943 \tabularnewline
121 & 29 & 21.5824982809731 & 7.41750171902693 \tabularnewline
122 & 14 & 18.9967653443087 & -4.99676534430871 \tabularnewline
123 & 22 & 26.8737091669435 & -4.87370916694351 \tabularnewline
124 & 15 & 15.5337097000078 & -0.533709700007815 \tabularnewline
125 & 19 & 25.4092467019303 & -6.40924670193028 \tabularnewline
126 & 20 & 21.8831811742809 & -1.88318117428092 \tabularnewline
127 & 15 & 17.5257328923904 & -2.52573289239038 \tabularnewline
128 & 20 & 21.743111333972 & -1.74311133397203 \tabularnewline
129 & 18 & 20.1682541454936 & -2.16825414549357 \tabularnewline
130 & 33 & 25.4291452044134 & 7.57085479558655 \tabularnewline
131 & 22 & 23.8678831867946 & -1.86788318679461 \tabularnewline
132 & 16 & 16.5484737239343 & -0.548473723934336 \tabularnewline
133 & 17 & 18.9805095688634 & -1.98050956886341 \tabularnewline
134 & 16 & 15.1293616021367 & 0.870638397863306 \tabularnewline
135 & 21 & 17.1299559186832 & 3.87004408131681 \tabularnewline
136 & 26 & 27.5800944393104 & -1.5800944393104 \tabularnewline
137 & 18 & 21.2325898758653 & -3.23258987586534 \tabularnewline
138 & 18 & 23.1807825749856 & -5.18078257498559 \tabularnewline
139 & 17 & 18.2770155975882 & -1.27701559758816 \tabularnewline
140 & 22 & 24.70965448444 & -2.70965448444001 \tabularnewline
141 & 30 & 24.8287859338627 & 5.17121406613726 \tabularnewline
142 & 30 & 27.2226794834565 & 2.7773205165435 \tabularnewline
143 & 24 & 29.9801931815366 & -5.98019318153662 \tabularnewline
144 & 21 & 21.9633614489398 & -0.963361448939817 \tabularnewline
145 & 21 & 25.4587603637184 & -4.45876036371842 \tabularnewline
146 & 29 & 27.3557609984031 & 1.64423900159689 \tabularnewline
147 & 31 & 23.1741524897411 & 7.82584751025892 \tabularnewline
148 & 20 & 19.0548724364291 & 0.945127563570916 \tabularnewline
149 & 16 & 14.2688752190188 & 1.73112478098117 \tabularnewline
150 & 22 & 18.9924840235404 & 3.00751597645964 \tabularnewline
151 & 20 & 20.2765822914909 & -0.276582291490852 \tabularnewline
152 & 28 & 27.2809941118217 & 0.719005888178254 \tabularnewline
153 & 38 & 26.6511545416438 & 11.3488454583562 \tabularnewline
154 & 22 & 19.2721744974194 & 2.72782550258065 \tabularnewline
155 & 20 & 25.6249634727582 & -5.62496347275816 \tabularnewline
156 & 17 & 17.9825672375456 & -0.98256723754556 \tabularnewline
157 & 28 & 24.4130280988885 & 3.58697190111148 \tabularnewline
158 & 22 & 24.0742261499925 & -2.07422614999253 \tabularnewline
159 & 31 & 26.050457638561 & 4.94954236143896 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108229&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]24[/C][C]24.9429963486832[/C][C]-0.942996348683151[/C][/ROW]
[ROW][C]2[/C][C]25[/C][C]21.6622498642971[/C][C]3.33775013570285[/C][/ROW]
[ROW][C]3[/C][C]17[/C][C]22.6806688810207[/C][C]-5.68066888102073[/C][/ROW]
[ROW][C]4[/C][C]18[/C][C]19.9564501024386[/C][C]-1.95645010243863[/C][/ROW]
[ROW][C]5[/C][C]18[/C][C]19.4213348995612[/C][C]-1.42133489956121[/C][/ROW]
[ROW][C]6[/C][C]16[/C][C]19.5095287560754[/C][C]-3.50952875607538[/C][/ROW]
[ROW][C]7[/C][C]20[/C][C]20.8505100107316[/C][C]-0.850510010731612[/C][/ROW]
[ROW][C]8[/C][C]16[/C][C]22.326631604058[/C][C]-6.32663160405803[/C][/ROW]
[ROW][C]9[/C][C]18[/C][C]22.3669114083879[/C][C]-4.36691140838794[/C][/ROW]
[ROW][C]10[/C][C]17[/C][C]20.4676664538925[/C][C]-3.46766645389246[/C][/ROW]
[ROW][C]11[/C][C]23[/C][C]22.4087650912138[/C][C]0.591234908786182[/C][/ROW]
[ROW][C]12[/C][C]30[/C][C]22.2748634898683[/C][C]7.72513651013172[/C][/ROW]
[ROW][C]13[/C][C]23[/C][C]15.1862543632409[/C][C]7.8137456367591[/C][/ROW]
[ROW][C]14[/C][C]18[/C][C]19.0528257532548[/C][C]-1.05282575325478[/C][/ROW]
[ROW][C]15[/C][C]15[/C][C]21.6399459852527[/C][C]-6.63994598525267[/C][/ROW]
[ROW][C]16[/C][C]12[/C][C]17.8833675707623[/C][C]-5.88336757076227[/C][/ROW]
[ROW][C]17[/C][C]21[/C][C]19.5045341597243[/C][C]1.49546584027573[/C][/ROW]
[ROW][C]18[/C][C]15[/C][C]15.2651305398521[/C][C]-0.265130539852111[/C][/ROW]
[ROW][C]19[/C][C]20[/C][C]19.8359078849528[/C][C]0.164092115047235[/C][/ROW]
[ROW][C]20[/C][C]31[/C][C]26.3715844361114[/C][C]4.62841556388856[/C][/ROW]
[ROW][C]21[/C][C]27[/C][C]25.3287558789331[/C][C]1.6712441210669[/C][/ROW]
[ROW][C]22[/C][C]34[/C][C]27.1139363444556[/C][C]6.88606365554444[/C][/ROW]
[ROW][C]23[/C][C]21[/C][C]19.5633581853494[/C][C]1.43664181465056[/C][/ROW]
[ROW][C]24[/C][C]31[/C][C]20.8848559305262[/C][C]10.1151440694738[/C][/ROW]
[ROW][C]25[/C][C]19[/C][C]19.379535245278[/C][C]-0.379535245278024[/C][/ROW]
[ROW][C]26[/C][C]16[/C][C]20.1541763955572[/C][C]-4.15417639555715[/C][/ROW]
[ROW][C]27[/C][C]20[/C][C]21.6592183800513[/C][C]-1.65921838005134[/C][/ROW]
[ROW][C]28[/C][C]21[/C][C]18.2065177660159[/C][C]2.79348223398405[/C][/ROW]
[ROW][C]29[/C][C]22[/C][C]21.8970636537317[/C][C]0.10293634626833[/C][/ROW]
[ROW][C]30[/C][C]17[/C][C]19.5802942222319[/C][C]-2.58029422223187[/C][/ROW]
[ROW][C]31[/C][C]24[/C][C]20.2715362046375[/C][C]3.7284637953625[/C][/ROW]
[ROW][C]32[/C][C]25[/C][C]30.5637664057857[/C][C]-5.56376640578574[/C][/ROW]
[ROW][C]33[/C][C]26[/C][C]26.6662550100328[/C][C]-0.666255010032776[/C][/ROW]
[ROW][C]34[/C][C]25[/C][C]24.005568757121[/C][C]0.994431242879022[/C][/ROW]
[ROW][C]35[/C][C]17[/C][C]23.0741781572644[/C][C]-6.0741781572644[/C][/ROW]
[ROW][C]36[/C][C]32[/C][C]27.8906809307949[/C][C]4.10931906920513[/C][/ROW]
[ROW][C]37[/C][C]33[/C][C]23.7232668824938[/C][C]9.27673311750617[/C][/ROW]
[ROW][C]38[/C][C]13[/C][C]22.1176367235977[/C][C]-9.11763672359766[/C][/ROW]
[ROW][C]39[/C][C]32[/C][C]27.7828068617436[/C][C]4.21719313825642[/C][/ROW]
[ROW][C]40[/C][C]25[/C][C]25.9220271574816[/C][C]-0.922027157481621[/C][/ROW]
[ROW][C]41[/C][C]29[/C][C]27.1019605980074[/C][C]1.89803940199262[/C][/ROW]
[ROW][C]42[/C][C]22[/C][C]21.9660574795458[/C][C]0.0339425204542188[/C][/ROW]
[ROW][C]43[/C][C]18[/C][C]17.4376175959373[/C][C]0.562382404062688[/C][/ROW]
[ROW][C]44[/C][C]17[/C][C]21.8330215105146[/C][C]-4.83302151051465[/C][/ROW]
[ROW][C]45[/C][C]20[/C][C]22.1606604832955[/C][C]-2.16066048329547[/C][/ROW]
[ROW][C]46[/C][C]15[/C][C]20.4080430902548[/C][C]-5.40804309025479[/C][/ROW]
[ROW][C]47[/C][C]20[/C][C]22.1436532819039[/C][C]-2.14365328190395[/C][/ROW]
[ROW][C]48[/C][C]33[/C][C]28.0626571006019[/C][C]4.93734289939812[/C][/ROW]
[ROW][C]49[/C][C]29[/C][C]22.0916666574814[/C][C]6.90833334251864[/C][/ROW]
[ROW][C]50[/C][C]23[/C][C]26.757758959365[/C][C]-3.757758959365[/C][/ROW]
[ROW][C]51[/C][C]26[/C][C]23.2207715049841[/C][C]2.77922849501594[/C][/ROW]
[ROW][C]52[/C][C]18[/C][C]19.0309246014548[/C][C]-1.03092460145476[/C][/ROW]
[ROW][C]53[/C][C]20[/C][C]18.8151388119818[/C][C]1.1848611880182[/C][/ROW]
[ROW][C]54[/C][C]11[/C][C]11.7389940806761[/C][C]-0.738994080676097[/C][/ROW]
[ROW][C]55[/C][C]28[/C][C]29.0393609676901[/C][C]-1.03936096769014[/C][/ROW]
[ROW][C]56[/C][C]26[/C][C]23.2702128026736[/C][C]2.72978719732642[/C][/ROW]
[ROW][C]57[/C][C]22[/C][C]22.1948320062007[/C][C]-0.19483200620072[/C][/ROW]
[ROW][C]58[/C][C]17[/C][C]20.1319892269372[/C][C]-3.13198922693724[/C][/ROW]
[ROW][C]59[/C][C]12[/C][C]15.5047881422787[/C][C]-3.50478814227875[/C][/ROW]
[ROW][C]60[/C][C]14[/C][C]20.9892502421717[/C][C]-6.98925024217172[/C][/ROW]
[ROW][C]61[/C][C]17[/C][C]20.8032760947745[/C][C]-3.80327609477455[/C][/ROW]
[ROW][C]62[/C][C]21[/C][C]21.4092610676731[/C][C]-0.409261067673123[/C][/ROW]
[ROW][C]63[/C][C]19[/C][C]22.8796194998407[/C][C]-3.87961949984067[/C][/ROW]
[ROW][C]64[/C][C]18[/C][C]23.0451523878524[/C][C]-5.0451523878524[/C][/ROW]
[ROW][C]65[/C][C]10[/C][C]17.8768088703137[/C][C]-7.87680887031365[/C][/ROW]
[ROW][C]66[/C][C]29[/C][C]24.3051640702463[/C][C]4.69483592975366[/C][/ROW]
[ROW][C]67[/C][C]31[/C][C]18.4041287812818[/C][C]12.5958712187182[/C][/ROW]
[ROW][C]68[/C][C]19[/C][C]23.0218318601521[/C][C]-4.02183186015208[/C][/ROW]
[ROW][C]69[/C][C]9[/C][C]20.0258711870156[/C][C]-11.0258711870156[/C][/ROW]
[ROW][C]70[/C][C]20[/C][C]22.5560887583009[/C][C]-2.55608875830091[/C][/ROW]
[ROW][C]71[/C][C]28[/C][C]17.693208018911[/C][C]10.306791981089[/C][/ROW]
[ROW][C]72[/C][C]19[/C][C]18.0795212927468[/C][C]0.920478707253165[/C][/ROW]
[ROW][C]73[/C][C]30[/C][C]22.9992603465233[/C][C]7.00073965347668[/C][/ROW]
[ROW][C]74[/C][C]29[/C][C]27.2172293775875[/C][C]1.78277062241254[/C][/ROW]
[ROW][C]75[/C][C]26[/C][C]21.5435346074676[/C][C]4.45646539253241[/C][/ROW]
[ROW][C]76[/C][C]23[/C][C]19.4935320073772[/C][C]3.50646799262281[/C][/ROW]
[ROW][C]77[/C][C]13[/C][C]22.7158817249009[/C][C]-9.71588172490094[/C][/ROW]
[ROW][C]78[/C][C]21[/C][C]22.5912216095481[/C][C]-1.59122160954806[/C][/ROW]
[ROW][C]79[/C][C]19[/C][C]21.5259492231535[/C][C]-2.5259492231535[/C][/ROW]
[ROW][C]80[/C][C]28[/C][C]23.0193787918113[/C][C]4.98062120818873[/C][/ROW]
[ROW][C]81[/C][C]23[/C][C]25.6408449436037[/C][C]-2.64084494360373[/C][/ROW]
[ROW][C]82[/C][C]18[/C][C]13.8489527737008[/C][C]4.15104722629917[/C][/ROW]
[ROW][C]83[/C][C]21[/C][C]20.6861005821919[/C][C]0.313899417808097[/C][/ROW]
[ROW][C]84[/C][C]20[/C][C]21.8561577417173[/C][C]-1.85615774171726[/C][/ROW]
[ROW][C]85[/C][C]23[/C][C]19.9201128197837[/C][C]3.0798871802163[/C][/ROW]
[ROW][C]86[/C][C]21[/C][C]20.7628097072822[/C][C]0.237190292717798[/C][/ROW]
[ROW][C]87[/C][C]21[/C][C]21.853821383801[/C][C]-0.853821383801027[/C][/ROW]
[ROW][C]88[/C][C]15[/C][C]22.7718021836753[/C][C]-7.77180218367526[/C][/ROW]
[ROW][C]89[/C][C]28[/C][C]27.1006677069301[/C][C]0.899332293069894[/C][/ROW]
[ROW][C]90[/C][C]19[/C][C]17.6054967414737[/C][C]1.39450325852634[/C][/ROW]
[ROW][C]91[/C][C]26[/C][C]21.1970849453685[/C][C]4.80291505463146[/C][/ROW]
[ROW][C]92[/C][C]10[/C][C]13.1418504270284[/C][C]-3.14185042702844[/C][/ROW]
[ROW][C]93[/C][C]16[/C][C]17.0609511188811[/C][C]-1.06095111888111[/C][/ROW]
[ROW][C]94[/C][C]22[/C][C]21.1057086804488[/C][C]0.89429131955115[/C][/ROW]
[ROW][C]95[/C][C]19[/C][C]18.6846946618737[/C][C]0.315305338126302[/C][/ROW]
[ROW][C]96[/C][C]31[/C][C]28.7033339676536[/C][C]2.29666603234641[/C][/ROW]
[ROW][C]97[/C][C]31[/C][C]25.1610363575753[/C][C]5.83896364242467[/C][/ROW]
[ROW][C]98[/C][C]29[/C][C]24.7500373008633[/C][C]4.24996269913666[/C][/ROW]
[ROW][C]99[/C][C]19[/C][C]17.4899406533025[/C][C]1.51005934669754[/C][/ROW]
[ROW][C]100[/C][C]22[/C][C]18.9729451937469[/C][C]3.02705480625307[/C][/ROW]
[ROW][C]101[/C][C]23[/C][C]22.3172611418816[/C][C]0.68273885811841[/C][/ROW]
[ROW][C]102[/C][C]15[/C][C]16.2398076637836[/C][C]-1.23980766378358[/C][/ROW]
[ROW][C]103[/C][C]20[/C][C]21.3789421742014[/C][C]-1.37894217420141[/C][/ROW]
[ROW][C]104[/C][C]18[/C][C]19.5754702240473[/C][C]-1.57547022404732[/C][/ROW]
[ROW][C]105[/C][C]23[/C][C]21.9516753772336[/C][C]1.04832462276643[/C][/ROW]
[ROW][C]106[/C][C]25[/C][C]21.0532703452311[/C][C]3.94672965476892[/C][/ROW]
[ROW][C]107[/C][C]21[/C][C]16.5115261777901[/C][C]4.48847382220993[/C][/ROW]
[ROW][C]108[/C][C]24[/C][C]19.569398880701[/C][C]4.43060111929903[/C][/ROW]
[ROW][C]109[/C][C]25[/C][C]25.3244868239239[/C][C]-0.324486823923948[/C][/ROW]
[ROW][C]110[/C][C]17[/C][C]19.5347608742898[/C][C]-2.53476087428981[/C][/ROW]
[ROW][C]111[/C][C]13[/C][C]14.5134634896455[/C][C]-1.51346348964548[/C][/ROW]
[ROW][C]112[/C][C]28[/C][C]18.1441293531272[/C][C]9.85587064687278[/C][/ROW]
[ROW][C]113[/C][C]21[/C][C]20.0727599827069[/C][C]0.92724001729313[/C][/ROW]
[ROW][C]114[/C][C]25[/C][C]28.1993393624112[/C][C]-3.19933936241119[/C][/ROW]
[ROW][C]115[/C][C]9[/C][C]20.6810580737745[/C][C]-11.6810580737745[/C][/ROW]
[ROW][C]116[/C][C]16[/C][C]17.8955263219369[/C][C]-1.89552632193692[/C][/ROW]
[ROW][C]117[/C][C]19[/C][C]21.1236881841702[/C][C]-2.12368818417025[/C][/ROW]
[ROW][C]118[/C][C]17[/C][C]19.4117214751196[/C][C]-2.41172147511961[/C][/ROW]
[ROW][C]119[/C][C]25[/C][C]24.4653743960008[/C][C]0.53462560399916[/C][/ROW]
[ROW][C]120[/C][C]20[/C][C]15.5153998331406[/C][C]4.48460016685943[/C][/ROW]
[ROW][C]121[/C][C]29[/C][C]21.5824982809731[/C][C]7.41750171902693[/C][/ROW]
[ROW][C]122[/C][C]14[/C][C]18.9967653443087[/C][C]-4.99676534430871[/C][/ROW]
[ROW][C]123[/C][C]22[/C][C]26.8737091669435[/C][C]-4.87370916694351[/C][/ROW]
[ROW][C]124[/C][C]15[/C][C]15.5337097000078[/C][C]-0.533709700007815[/C][/ROW]
[ROW][C]125[/C][C]19[/C][C]25.4092467019303[/C][C]-6.40924670193028[/C][/ROW]
[ROW][C]126[/C][C]20[/C][C]21.8831811742809[/C][C]-1.88318117428092[/C][/ROW]
[ROW][C]127[/C][C]15[/C][C]17.5257328923904[/C][C]-2.52573289239038[/C][/ROW]
[ROW][C]128[/C][C]20[/C][C]21.743111333972[/C][C]-1.74311133397203[/C][/ROW]
[ROW][C]129[/C][C]18[/C][C]20.1682541454936[/C][C]-2.16825414549357[/C][/ROW]
[ROW][C]130[/C][C]33[/C][C]25.4291452044134[/C][C]7.57085479558655[/C][/ROW]
[ROW][C]131[/C][C]22[/C][C]23.8678831867946[/C][C]-1.86788318679461[/C][/ROW]
[ROW][C]132[/C][C]16[/C][C]16.5484737239343[/C][C]-0.548473723934336[/C][/ROW]
[ROW][C]133[/C][C]17[/C][C]18.9805095688634[/C][C]-1.98050956886341[/C][/ROW]
[ROW][C]134[/C][C]16[/C][C]15.1293616021367[/C][C]0.870638397863306[/C][/ROW]
[ROW][C]135[/C][C]21[/C][C]17.1299559186832[/C][C]3.87004408131681[/C][/ROW]
[ROW][C]136[/C][C]26[/C][C]27.5800944393104[/C][C]-1.5800944393104[/C][/ROW]
[ROW][C]137[/C][C]18[/C][C]21.2325898758653[/C][C]-3.23258987586534[/C][/ROW]
[ROW][C]138[/C][C]18[/C][C]23.1807825749856[/C][C]-5.18078257498559[/C][/ROW]
[ROW][C]139[/C][C]17[/C][C]18.2770155975882[/C][C]-1.27701559758816[/C][/ROW]
[ROW][C]140[/C][C]22[/C][C]24.70965448444[/C][C]-2.70965448444001[/C][/ROW]
[ROW][C]141[/C][C]30[/C][C]24.8287859338627[/C][C]5.17121406613726[/C][/ROW]
[ROW][C]142[/C][C]30[/C][C]27.2226794834565[/C][C]2.7773205165435[/C][/ROW]
[ROW][C]143[/C][C]24[/C][C]29.9801931815366[/C][C]-5.98019318153662[/C][/ROW]
[ROW][C]144[/C][C]21[/C][C]21.9633614489398[/C][C]-0.963361448939817[/C][/ROW]
[ROW][C]145[/C][C]21[/C][C]25.4587603637184[/C][C]-4.45876036371842[/C][/ROW]
[ROW][C]146[/C][C]29[/C][C]27.3557609984031[/C][C]1.64423900159689[/C][/ROW]
[ROW][C]147[/C][C]31[/C][C]23.1741524897411[/C][C]7.82584751025892[/C][/ROW]
[ROW][C]148[/C][C]20[/C][C]19.0548724364291[/C][C]0.945127563570916[/C][/ROW]
[ROW][C]149[/C][C]16[/C][C]14.2688752190188[/C][C]1.73112478098117[/C][/ROW]
[ROW][C]150[/C][C]22[/C][C]18.9924840235404[/C][C]3.00751597645964[/C][/ROW]
[ROW][C]151[/C][C]20[/C][C]20.2765822914909[/C][C]-0.276582291490852[/C][/ROW]
[ROW][C]152[/C][C]28[/C][C]27.2809941118217[/C][C]0.719005888178254[/C][/ROW]
[ROW][C]153[/C][C]38[/C][C]26.6511545416438[/C][C]11.3488454583562[/C][/ROW]
[ROW][C]154[/C][C]22[/C][C]19.2721744974194[/C][C]2.72782550258065[/C][/ROW]
[ROW][C]155[/C][C]20[/C][C]25.6249634727582[/C][C]-5.62496347275816[/C][/ROW]
[ROW][C]156[/C][C]17[/C][C]17.9825672375456[/C][C]-0.98256723754556[/C][/ROW]
[ROW][C]157[/C][C]28[/C][C]24.4130280988885[/C][C]3.58697190111148[/C][/ROW]
[ROW][C]158[/C][C]22[/C][C]24.0742261499925[/C][C]-2.07422614999253[/C][/ROW]
[ROW][C]159[/C][C]31[/C][C]26.050457638561[/C][C]4.94954236143896[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108229&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108229&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
12424.9429963486832-0.942996348683151
22521.66224986429713.33775013570285
31722.6806688810207-5.68066888102073
41819.9564501024386-1.95645010243863
51819.4213348995612-1.42133489956121
61619.5095287560754-3.50952875607538
72020.8505100107316-0.850510010731612
81622.326631604058-6.32663160405803
91822.3669114083879-4.36691140838794
101720.4676664538925-3.46766645389246
112322.40876509121380.591234908786182
123022.27486348986837.72513651013172
132315.18625436324097.8137456367591
141819.0528257532548-1.05282575325478
151521.6399459852527-6.63994598525267
161217.8833675707623-5.88336757076227
172119.50453415972431.49546584027573
181515.2651305398521-0.265130539852111
192019.83590788495280.164092115047235
203126.37158443611144.62841556388856
212725.32875587893311.6712441210669
223427.11393634445566.88606365554444
232119.56335818534941.43664181465056
243120.884855930526210.1151440694738
251919.379535245278-0.379535245278024
261620.1541763955572-4.15417639555715
272021.6592183800513-1.65921838005134
282118.20651776601592.79348223398405
292221.89706365373170.10293634626833
301719.5802942222319-2.58029422223187
312420.27153620463753.7284637953625
322530.5637664057857-5.56376640578574
332626.6662550100328-0.666255010032776
342524.0055687571210.994431242879022
351723.0741781572644-6.0741781572644
363227.89068093079494.10931906920513
373323.72326688249389.27673311750617
381322.1176367235977-9.11763672359766
393227.78280686174364.21719313825642
402525.9220271574816-0.922027157481621
412927.10196059800741.89803940199262
422221.96605747954580.0339425204542188
431817.43761759593730.562382404062688
441721.8330215105146-4.83302151051465
452022.1606604832955-2.16066048329547
461520.4080430902548-5.40804309025479
472022.1436532819039-2.14365328190395
483328.06265710060194.93734289939812
492922.09166665748146.90833334251864
502326.757758959365-3.757758959365
512623.22077150498412.77922849501594
521819.0309246014548-1.03092460145476
532018.81513881198181.1848611880182
541111.7389940806761-0.738994080676097
552829.0393609676901-1.03936096769014
562623.27021280267362.72978719732642
572222.1948320062007-0.19483200620072
581720.1319892269372-3.13198922693724
591215.5047881422787-3.50478814227875
601420.9892502421717-6.98925024217172
611720.8032760947745-3.80327609477455
622121.4092610676731-0.409261067673123
631922.8796194998407-3.87961949984067
641823.0451523878524-5.0451523878524
651017.8768088703137-7.87680887031365
662924.30516407024634.69483592975366
673118.404128781281812.5958712187182
681923.0218318601521-4.02183186015208
69920.0258711870156-11.0258711870156
702022.5560887583009-2.55608875830091
712817.69320801891110.306791981089
721918.07952129274680.920478707253165
733022.99926034652337.00073965347668
742927.21722937758751.78277062241254
752621.54353460746764.45646539253241
762319.49353200737723.50646799262281
771322.7158817249009-9.71588172490094
782122.5912216095481-1.59122160954806
791921.5259492231535-2.5259492231535
802823.01937879181134.98062120818873
812325.6408449436037-2.64084494360373
821813.84895277370084.15104722629917
832120.68610058219190.313899417808097
842021.8561577417173-1.85615774171726
852319.92011281978373.0798871802163
862120.76280970728220.237190292717798
872121.853821383801-0.853821383801027
881522.7718021836753-7.77180218367526
892827.10066770693010.899332293069894
901917.60549674147371.39450325852634
912621.19708494536854.80291505463146
921013.1418504270284-3.14185042702844
931617.0609511188811-1.06095111888111
942221.10570868044880.89429131955115
951918.68469466187370.315305338126302
963128.70333396765362.29666603234641
973125.16103635757535.83896364242467
982924.75003730086334.24996269913666
991917.48994065330251.51005934669754
1002218.97294519374693.02705480625307
1012322.31726114188160.68273885811841
1021516.2398076637836-1.23980766378358
1032021.3789421742014-1.37894217420141
1041819.5754702240473-1.57547022404732
1052321.95167537723361.04832462276643
1062521.05327034523113.94672965476892
1072116.51152617779014.48847382220993
1082419.5693988807014.43060111929903
1092525.3244868239239-0.324486823923948
1101719.5347608742898-2.53476087428981
1111314.5134634896455-1.51346348964548
1122818.14412935312729.85587064687278
1132120.07275998270690.92724001729313
1142528.1993393624112-3.19933936241119
115920.6810580737745-11.6810580737745
1161617.8955263219369-1.89552632193692
1171921.1236881841702-2.12368818417025
1181719.4117214751196-2.41172147511961
1192524.46537439600080.53462560399916
1202015.51539983314064.48460016685943
1212921.58249828097317.41750171902693
1221418.9967653443087-4.99676534430871
1232226.8737091669435-4.87370916694351
1241515.5337097000078-0.533709700007815
1251925.4092467019303-6.40924670193028
1262021.8831811742809-1.88318117428092
1271517.5257328923904-2.52573289239038
1282021.743111333972-1.74311133397203
1291820.1682541454936-2.16825414549357
1303325.42914520441347.57085479558655
1312223.8678831867946-1.86788318679461
1321616.5484737239343-0.548473723934336
1331718.9805095688634-1.98050956886341
1341615.12936160213670.870638397863306
1352117.12995591868323.87004408131681
1362627.5800944393104-1.5800944393104
1371821.2325898758653-3.23258987586534
1381823.1807825749856-5.18078257498559
1391718.2770155975882-1.27701559758816
1402224.70965448444-2.70965448444001
1413024.82878593386275.17121406613726
1423027.22267948345652.7773205165435
1432429.9801931815366-5.98019318153662
1442121.9633614489398-0.963361448939817
1452125.4587603637184-4.45876036371842
1462927.35576099840311.64423900159689
1473123.17415248974117.82584751025892
1482019.05487243642910.945127563570916
1491614.26887521901881.73112478098117
1502218.99248402354043.00751597645964
1512020.2765822914909-0.276582291490852
1522827.28099411182170.719005888178254
1533826.651154541643811.3488454583562
1542219.27217449741942.72782550258065
1552025.6249634727582-5.62496347275816
1561717.9825672375456-0.98256723754556
1572824.41302809888853.58697190111148
1582224.0742261499925-2.07422614999253
1593126.0504576385614.94954236143896






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.214465827965360.4289316559307190.78553417203464
110.2663908711114180.5327817422228350.733609128888583
120.7472688774787550.5054622450424910.252731122521245
130.7532428798811030.4935142402377950.246757120118897
140.7180392454127140.5639215091745710.281960754587286
150.7175828023617520.5648343952764960.282417197638248
160.6614078286186510.6771843427626980.338592171381349
170.5736850420040390.8526299159919210.426314957995961
180.5261676104314090.9476647791371820.473832389568591
190.4452702210870690.8905404421741390.554729778912931
200.4630582730357130.9261165460714250.536941726964287
210.383955783475350.76791156695070.61604421652465
220.3898462515551430.7796925031102860.610153748444857
230.3177975215178560.6355950430357110.682202478482144
240.6282749834381630.7434500331236740.371725016561837
250.5588288478036920.8823423043926160.441171152196308
260.5162502967718780.9674994064562440.483749703228122
270.4490476744089090.8980953488178190.550952325591091
280.4140436704437540.8280873408875080.585956329556246
290.3540557642646760.7081115285293520.645944235735324
300.3038982198375320.6077964396750630.696101780162468
310.38016864894430.76033729788860.6198313510557
320.4432963863532870.8865927727065740.556703613646713
330.3971713519071310.7943427038142620.602828648092869
340.4042854716908610.8085709433817220.595714528309139
350.4086264789174040.8172529578348080.591373521082596
360.4079786572159210.8159573144318430.592021342784078
370.585571960780440.828856078439120.41442803921956
380.7805943054182580.4388113891634830.219405694581741
390.7749891395815180.4500217208369640.225010860418482
400.7333678203505890.5332643592988230.266632179649411
410.7101911376498060.5796177247003890.289808862350194
420.6615038613379030.6769922773241940.338496138662097
430.6143736741653860.7712526516692270.385626325834614
440.5997504685099820.8004990629800370.400249531490018
450.5659475619960520.8681048760078960.434052438003948
460.5878941400570090.8242117198859820.412105859942991
470.5463841379008540.9072317241982920.453615862099146
480.5365914094759370.9268171810481250.463408590524063
490.5998327997603250.800334400479350.400167200239675
500.5789091579644150.842181684071170.421090842035585
510.5432060968395580.9135878063208850.456793903160442
520.4940078029377470.9880156058754940.505992197062253
530.4561600857131590.9123201714263190.543839914286841
540.4084775046339440.8169550092678880.591522495366056
550.3641220377638760.7282440755277510.635877962236124
560.3346955545072340.6693911090144680.665304445492766
570.2904446564091960.5808893128183920.709555343590804
580.2650275868661050.5300551737322090.734972413133895
590.2441927460448070.4883854920896150.755807253955193
600.3048602640035530.6097205280071060.695139735996447
610.2922149777193320.5844299554386640.707785022280668
620.2550569417682840.5101138835365670.744943058231716
630.2419586367770530.4839172735541060.758041363222947
640.2765486588784260.5530973177568520.723451341121574
650.3513751215637650.702750243127530.648624878436235
660.3511078114158780.7022156228317560.648892188584122
670.684177345712360.6316453085752810.31582265428764
680.6772179505465360.6455640989069280.322782049453464
690.849108315634780.3017833687304390.15089168436522
700.8304066907735750.339186618452850.169593309226425
710.931216028937710.1375679421245780.0687839710622892
720.915051954680360.1698960906392790.0849480453196393
730.9386147532336330.1227704935327330.0613852467663667
740.9265154648375450.146969070324910.0734845351624548
750.9272697765272180.1454604469455650.0727302234727823
760.9212959000026740.1574081999946530.0787040999973264
770.9693103644723740.06137927105525130.0306896355276256
780.9615953594511750.07680928109765010.0384046405488251
790.954408899354290.09118220129142080.0455911006457104
800.9560883093754810.08782338124903720.0439116906245186
810.9486539488534650.1026921022930710.0513460511465355
820.9472899762462630.1054200475074740.0527100237537368
830.9339765201254120.1320469597491760.0660234798745881
840.9210433014409310.1579133971181380.078956698559069
850.9125890638420910.1748218723158170.0874109361579086
860.8968603087233450.206279382553310.103139691276655
870.8755597001571060.2488805996857890.124440299842894
880.9196764586474180.1606470827051640.0803235413525821
890.9030478130783620.1939043738432760.0969521869216378
900.8832504007793710.2334991984412570.116749599220629
910.8858734398942570.2282531202114850.114126560105743
920.8737780360592140.2524439278815720.126221963940786
930.8512619044253560.2974761911492890.148738095574644
940.8235988998817240.3528022002365530.176401100118276
950.7905691830281850.418861633943630.209430816971815
960.7617144492819010.4765711014361980.238285550718099
970.7815174706851540.4369650586296910.218482529314846
980.7771350739962020.4457298520075970.222864926003798
990.7416724832098140.5166550335803720.258327516790186
1000.7155676491700530.5688647016598940.284432350829947
1010.6733622163766910.6532755672466190.326637783623309
1020.6360667066921360.7278665866157290.363933293307864
1030.5956196561791810.8087606876416380.404380343820819
1040.5545064969779880.8909870060440240.445493503022012
1050.5084612378933690.9830775242132620.491538762106631
1060.4854424980609470.9708849961218940.514557501939053
1070.4845708950659510.9691417901319020.515429104934049
1080.491336241946560.982672483893120.50866375805344
1090.4410942567141620.8821885134283240.558905743285838
1100.4167834439115440.8335668878230880.583216556088456
1110.3769381484277250.753876296855450.623061851572275
1120.6025865601156510.7948268797686970.397413439884349
1130.5965802709041860.8068394581916280.403419729095814
1140.5725362396754840.8549275206490330.427463760324516
1150.7721525378921290.4556949242157420.227847462107871
1160.754882203638880.4902355927222410.24511779636112
1170.7364412424292850.527117515141430.263558757570715
1180.7054290163632310.5891419672735380.294570983636769
1190.6558367803185080.6883264393629830.344163219681491
1200.6781286092404080.6437427815191850.321871390759592
1210.7363369631652580.5273260736694830.263663036834742
1220.7279613064783010.5440773870433980.272038693521699
1230.728988193114430.5420236137711410.271011806885571
1240.6765520994716970.6468958010566060.323447900528303
1250.7190642860193840.5618714279612320.280935713980616
1260.6856991281564180.6286017436871630.314300871843582
1270.6825034873078070.6349930253843870.317496512692193
1280.6443442721994220.7113114556011550.355655727800578
1290.6123617450268480.7752765099463040.387638254973152
1300.6861136783633330.6277726432733330.313886321636667
1310.6334238262997450.733152347400510.366576173700255
1320.5720686344216960.8558627311566070.427931365578304
1330.5622292109731650.875541578053670.437770789026835
1340.5050841767248880.9898316465502240.494915823275112
1350.455348068956030.910696137912060.54465193104397
1360.421753737161620.843507474323240.57824626283838
1370.4385729007873550.877145801574710.561427099212645
1380.5088201809509870.9823596380980270.491179819049013
1390.4332801923814180.8665603847628360.566719807618582
1400.3556699230677590.7113398461355190.64433007693224
1410.4246819981185060.8493639962370120.575318001881494
1420.3703251802935340.7406503605870670.629674819706466
1430.3047116303778640.6094232607557270.695288369622136
1440.2285587086849830.4571174173699670.771441291315017
1450.3682240643596840.7364481287193670.631775935640316
1460.2702762841063540.5405525682127090.729723715893646
1470.393719010786360.787438021572720.60628098921364
1480.3364928359229420.6729856718458850.663507164077058
1490.2529608164332760.5059216328665520.747039183566724

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.21446582796536 & 0.428931655930719 & 0.78553417203464 \tabularnewline
11 & 0.266390871111418 & 0.532781742222835 & 0.733609128888583 \tabularnewline
12 & 0.747268877478755 & 0.505462245042491 & 0.252731122521245 \tabularnewline
13 & 0.753242879881103 & 0.493514240237795 & 0.246757120118897 \tabularnewline
14 & 0.718039245412714 & 0.563921509174571 & 0.281960754587286 \tabularnewline
15 & 0.717582802361752 & 0.564834395276496 & 0.282417197638248 \tabularnewline
16 & 0.661407828618651 & 0.677184342762698 & 0.338592171381349 \tabularnewline
17 & 0.573685042004039 & 0.852629915991921 & 0.426314957995961 \tabularnewline
18 & 0.526167610431409 & 0.947664779137182 & 0.473832389568591 \tabularnewline
19 & 0.445270221087069 & 0.890540442174139 & 0.554729778912931 \tabularnewline
20 & 0.463058273035713 & 0.926116546071425 & 0.536941726964287 \tabularnewline
21 & 0.38395578347535 & 0.7679115669507 & 0.61604421652465 \tabularnewline
22 & 0.389846251555143 & 0.779692503110286 & 0.610153748444857 \tabularnewline
23 & 0.317797521517856 & 0.635595043035711 & 0.682202478482144 \tabularnewline
24 & 0.628274983438163 & 0.743450033123674 & 0.371725016561837 \tabularnewline
25 & 0.558828847803692 & 0.882342304392616 & 0.441171152196308 \tabularnewline
26 & 0.516250296771878 & 0.967499406456244 & 0.483749703228122 \tabularnewline
27 & 0.449047674408909 & 0.898095348817819 & 0.550952325591091 \tabularnewline
28 & 0.414043670443754 & 0.828087340887508 & 0.585956329556246 \tabularnewline
29 & 0.354055764264676 & 0.708111528529352 & 0.645944235735324 \tabularnewline
30 & 0.303898219837532 & 0.607796439675063 & 0.696101780162468 \tabularnewline
31 & 0.3801686489443 & 0.7603372978886 & 0.6198313510557 \tabularnewline
32 & 0.443296386353287 & 0.886592772706574 & 0.556703613646713 \tabularnewline
33 & 0.397171351907131 & 0.794342703814262 & 0.602828648092869 \tabularnewline
34 & 0.404285471690861 & 0.808570943381722 & 0.595714528309139 \tabularnewline
35 & 0.408626478917404 & 0.817252957834808 & 0.591373521082596 \tabularnewline
36 & 0.407978657215921 & 0.815957314431843 & 0.592021342784078 \tabularnewline
37 & 0.58557196078044 & 0.82885607843912 & 0.41442803921956 \tabularnewline
38 & 0.780594305418258 & 0.438811389163483 & 0.219405694581741 \tabularnewline
39 & 0.774989139581518 & 0.450021720836964 & 0.225010860418482 \tabularnewline
40 & 0.733367820350589 & 0.533264359298823 & 0.266632179649411 \tabularnewline
41 & 0.710191137649806 & 0.579617724700389 & 0.289808862350194 \tabularnewline
42 & 0.661503861337903 & 0.676992277324194 & 0.338496138662097 \tabularnewline
43 & 0.614373674165386 & 0.771252651669227 & 0.385626325834614 \tabularnewline
44 & 0.599750468509982 & 0.800499062980037 & 0.400249531490018 \tabularnewline
45 & 0.565947561996052 & 0.868104876007896 & 0.434052438003948 \tabularnewline
46 & 0.587894140057009 & 0.824211719885982 & 0.412105859942991 \tabularnewline
47 & 0.546384137900854 & 0.907231724198292 & 0.453615862099146 \tabularnewline
48 & 0.536591409475937 & 0.926817181048125 & 0.463408590524063 \tabularnewline
49 & 0.599832799760325 & 0.80033440047935 & 0.400167200239675 \tabularnewline
50 & 0.578909157964415 & 0.84218168407117 & 0.421090842035585 \tabularnewline
51 & 0.543206096839558 & 0.913587806320885 & 0.456793903160442 \tabularnewline
52 & 0.494007802937747 & 0.988015605875494 & 0.505992197062253 \tabularnewline
53 & 0.456160085713159 & 0.912320171426319 & 0.543839914286841 \tabularnewline
54 & 0.408477504633944 & 0.816955009267888 & 0.591522495366056 \tabularnewline
55 & 0.364122037763876 & 0.728244075527751 & 0.635877962236124 \tabularnewline
56 & 0.334695554507234 & 0.669391109014468 & 0.665304445492766 \tabularnewline
57 & 0.290444656409196 & 0.580889312818392 & 0.709555343590804 \tabularnewline
58 & 0.265027586866105 & 0.530055173732209 & 0.734972413133895 \tabularnewline
59 & 0.244192746044807 & 0.488385492089615 & 0.755807253955193 \tabularnewline
60 & 0.304860264003553 & 0.609720528007106 & 0.695139735996447 \tabularnewline
61 & 0.292214977719332 & 0.584429955438664 & 0.707785022280668 \tabularnewline
62 & 0.255056941768284 & 0.510113883536567 & 0.744943058231716 \tabularnewline
63 & 0.241958636777053 & 0.483917273554106 & 0.758041363222947 \tabularnewline
64 & 0.276548658878426 & 0.553097317756852 & 0.723451341121574 \tabularnewline
65 & 0.351375121563765 & 0.70275024312753 & 0.648624878436235 \tabularnewline
66 & 0.351107811415878 & 0.702215622831756 & 0.648892188584122 \tabularnewline
67 & 0.68417734571236 & 0.631645308575281 & 0.31582265428764 \tabularnewline
68 & 0.677217950546536 & 0.645564098906928 & 0.322782049453464 \tabularnewline
69 & 0.84910831563478 & 0.301783368730439 & 0.15089168436522 \tabularnewline
70 & 0.830406690773575 & 0.33918661845285 & 0.169593309226425 \tabularnewline
71 & 0.93121602893771 & 0.137567942124578 & 0.0687839710622892 \tabularnewline
72 & 0.91505195468036 & 0.169896090639279 & 0.0849480453196393 \tabularnewline
73 & 0.938614753233633 & 0.122770493532733 & 0.0613852467663667 \tabularnewline
74 & 0.926515464837545 & 0.14696907032491 & 0.0734845351624548 \tabularnewline
75 & 0.927269776527218 & 0.145460446945565 & 0.0727302234727823 \tabularnewline
76 & 0.921295900002674 & 0.157408199994653 & 0.0787040999973264 \tabularnewline
77 & 0.969310364472374 & 0.0613792710552513 & 0.0306896355276256 \tabularnewline
78 & 0.961595359451175 & 0.0768092810976501 & 0.0384046405488251 \tabularnewline
79 & 0.95440889935429 & 0.0911822012914208 & 0.0455911006457104 \tabularnewline
80 & 0.956088309375481 & 0.0878233812490372 & 0.0439116906245186 \tabularnewline
81 & 0.948653948853465 & 0.102692102293071 & 0.0513460511465355 \tabularnewline
82 & 0.947289976246263 & 0.105420047507474 & 0.0527100237537368 \tabularnewline
83 & 0.933976520125412 & 0.132046959749176 & 0.0660234798745881 \tabularnewline
84 & 0.921043301440931 & 0.157913397118138 & 0.078956698559069 \tabularnewline
85 & 0.912589063842091 & 0.174821872315817 & 0.0874109361579086 \tabularnewline
86 & 0.896860308723345 & 0.20627938255331 & 0.103139691276655 \tabularnewline
87 & 0.875559700157106 & 0.248880599685789 & 0.124440299842894 \tabularnewline
88 & 0.919676458647418 & 0.160647082705164 & 0.0803235413525821 \tabularnewline
89 & 0.903047813078362 & 0.193904373843276 & 0.0969521869216378 \tabularnewline
90 & 0.883250400779371 & 0.233499198441257 & 0.116749599220629 \tabularnewline
91 & 0.885873439894257 & 0.228253120211485 & 0.114126560105743 \tabularnewline
92 & 0.873778036059214 & 0.252443927881572 & 0.126221963940786 \tabularnewline
93 & 0.851261904425356 & 0.297476191149289 & 0.148738095574644 \tabularnewline
94 & 0.823598899881724 & 0.352802200236553 & 0.176401100118276 \tabularnewline
95 & 0.790569183028185 & 0.41886163394363 & 0.209430816971815 \tabularnewline
96 & 0.761714449281901 & 0.476571101436198 & 0.238285550718099 \tabularnewline
97 & 0.781517470685154 & 0.436965058629691 & 0.218482529314846 \tabularnewline
98 & 0.777135073996202 & 0.445729852007597 & 0.222864926003798 \tabularnewline
99 & 0.741672483209814 & 0.516655033580372 & 0.258327516790186 \tabularnewline
100 & 0.715567649170053 & 0.568864701659894 & 0.284432350829947 \tabularnewline
101 & 0.673362216376691 & 0.653275567246619 & 0.326637783623309 \tabularnewline
102 & 0.636066706692136 & 0.727866586615729 & 0.363933293307864 \tabularnewline
103 & 0.595619656179181 & 0.808760687641638 & 0.404380343820819 \tabularnewline
104 & 0.554506496977988 & 0.890987006044024 & 0.445493503022012 \tabularnewline
105 & 0.508461237893369 & 0.983077524213262 & 0.491538762106631 \tabularnewline
106 & 0.485442498060947 & 0.970884996121894 & 0.514557501939053 \tabularnewline
107 & 0.484570895065951 & 0.969141790131902 & 0.515429104934049 \tabularnewline
108 & 0.49133624194656 & 0.98267248389312 & 0.50866375805344 \tabularnewline
109 & 0.441094256714162 & 0.882188513428324 & 0.558905743285838 \tabularnewline
110 & 0.416783443911544 & 0.833566887823088 & 0.583216556088456 \tabularnewline
111 & 0.376938148427725 & 0.75387629685545 & 0.623061851572275 \tabularnewline
112 & 0.602586560115651 & 0.794826879768697 & 0.397413439884349 \tabularnewline
113 & 0.596580270904186 & 0.806839458191628 & 0.403419729095814 \tabularnewline
114 & 0.572536239675484 & 0.854927520649033 & 0.427463760324516 \tabularnewline
115 & 0.772152537892129 & 0.455694924215742 & 0.227847462107871 \tabularnewline
116 & 0.75488220363888 & 0.490235592722241 & 0.24511779636112 \tabularnewline
117 & 0.736441242429285 & 0.52711751514143 & 0.263558757570715 \tabularnewline
118 & 0.705429016363231 & 0.589141967273538 & 0.294570983636769 \tabularnewline
119 & 0.655836780318508 & 0.688326439362983 & 0.344163219681491 \tabularnewline
120 & 0.678128609240408 & 0.643742781519185 & 0.321871390759592 \tabularnewline
121 & 0.736336963165258 & 0.527326073669483 & 0.263663036834742 \tabularnewline
122 & 0.727961306478301 & 0.544077387043398 & 0.272038693521699 \tabularnewline
123 & 0.72898819311443 & 0.542023613771141 & 0.271011806885571 \tabularnewline
124 & 0.676552099471697 & 0.646895801056606 & 0.323447900528303 \tabularnewline
125 & 0.719064286019384 & 0.561871427961232 & 0.280935713980616 \tabularnewline
126 & 0.685699128156418 & 0.628601743687163 & 0.314300871843582 \tabularnewline
127 & 0.682503487307807 & 0.634993025384387 & 0.317496512692193 \tabularnewline
128 & 0.644344272199422 & 0.711311455601155 & 0.355655727800578 \tabularnewline
129 & 0.612361745026848 & 0.775276509946304 & 0.387638254973152 \tabularnewline
130 & 0.686113678363333 & 0.627772643273333 & 0.313886321636667 \tabularnewline
131 & 0.633423826299745 & 0.73315234740051 & 0.366576173700255 \tabularnewline
132 & 0.572068634421696 & 0.855862731156607 & 0.427931365578304 \tabularnewline
133 & 0.562229210973165 & 0.87554157805367 & 0.437770789026835 \tabularnewline
134 & 0.505084176724888 & 0.989831646550224 & 0.494915823275112 \tabularnewline
135 & 0.45534806895603 & 0.91069613791206 & 0.54465193104397 \tabularnewline
136 & 0.42175373716162 & 0.84350747432324 & 0.57824626283838 \tabularnewline
137 & 0.438572900787355 & 0.87714580157471 & 0.561427099212645 \tabularnewline
138 & 0.508820180950987 & 0.982359638098027 & 0.491179819049013 \tabularnewline
139 & 0.433280192381418 & 0.866560384762836 & 0.566719807618582 \tabularnewline
140 & 0.355669923067759 & 0.711339846135519 & 0.64433007693224 \tabularnewline
141 & 0.424681998118506 & 0.849363996237012 & 0.575318001881494 \tabularnewline
142 & 0.370325180293534 & 0.740650360587067 & 0.629674819706466 \tabularnewline
143 & 0.304711630377864 & 0.609423260755727 & 0.695288369622136 \tabularnewline
144 & 0.228558708684983 & 0.457117417369967 & 0.771441291315017 \tabularnewline
145 & 0.368224064359684 & 0.736448128719367 & 0.631775935640316 \tabularnewline
146 & 0.270276284106354 & 0.540552568212709 & 0.729723715893646 \tabularnewline
147 & 0.39371901078636 & 0.78743802157272 & 0.60628098921364 \tabularnewline
148 & 0.336492835922942 & 0.672985671845885 & 0.663507164077058 \tabularnewline
149 & 0.252960816433276 & 0.505921632866552 & 0.747039183566724 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108229&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]10[/C][C]0.21446582796536[/C][C]0.428931655930719[/C][C]0.78553417203464[/C][/ROW]
[ROW][C]11[/C][C]0.266390871111418[/C][C]0.532781742222835[/C][C]0.733609128888583[/C][/ROW]
[ROW][C]12[/C][C]0.747268877478755[/C][C]0.505462245042491[/C][C]0.252731122521245[/C][/ROW]
[ROW][C]13[/C][C]0.753242879881103[/C][C]0.493514240237795[/C][C]0.246757120118897[/C][/ROW]
[ROW][C]14[/C][C]0.718039245412714[/C][C]0.563921509174571[/C][C]0.281960754587286[/C][/ROW]
[ROW][C]15[/C][C]0.717582802361752[/C][C]0.564834395276496[/C][C]0.282417197638248[/C][/ROW]
[ROW][C]16[/C][C]0.661407828618651[/C][C]0.677184342762698[/C][C]0.338592171381349[/C][/ROW]
[ROW][C]17[/C][C]0.573685042004039[/C][C]0.852629915991921[/C][C]0.426314957995961[/C][/ROW]
[ROW][C]18[/C][C]0.526167610431409[/C][C]0.947664779137182[/C][C]0.473832389568591[/C][/ROW]
[ROW][C]19[/C][C]0.445270221087069[/C][C]0.890540442174139[/C][C]0.554729778912931[/C][/ROW]
[ROW][C]20[/C][C]0.463058273035713[/C][C]0.926116546071425[/C][C]0.536941726964287[/C][/ROW]
[ROW][C]21[/C][C]0.38395578347535[/C][C]0.7679115669507[/C][C]0.61604421652465[/C][/ROW]
[ROW][C]22[/C][C]0.389846251555143[/C][C]0.779692503110286[/C][C]0.610153748444857[/C][/ROW]
[ROW][C]23[/C][C]0.317797521517856[/C][C]0.635595043035711[/C][C]0.682202478482144[/C][/ROW]
[ROW][C]24[/C][C]0.628274983438163[/C][C]0.743450033123674[/C][C]0.371725016561837[/C][/ROW]
[ROW][C]25[/C][C]0.558828847803692[/C][C]0.882342304392616[/C][C]0.441171152196308[/C][/ROW]
[ROW][C]26[/C][C]0.516250296771878[/C][C]0.967499406456244[/C][C]0.483749703228122[/C][/ROW]
[ROW][C]27[/C][C]0.449047674408909[/C][C]0.898095348817819[/C][C]0.550952325591091[/C][/ROW]
[ROW][C]28[/C][C]0.414043670443754[/C][C]0.828087340887508[/C][C]0.585956329556246[/C][/ROW]
[ROW][C]29[/C][C]0.354055764264676[/C][C]0.708111528529352[/C][C]0.645944235735324[/C][/ROW]
[ROW][C]30[/C][C]0.303898219837532[/C][C]0.607796439675063[/C][C]0.696101780162468[/C][/ROW]
[ROW][C]31[/C][C]0.3801686489443[/C][C]0.7603372978886[/C][C]0.6198313510557[/C][/ROW]
[ROW][C]32[/C][C]0.443296386353287[/C][C]0.886592772706574[/C][C]0.556703613646713[/C][/ROW]
[ROW][C]33[/C][C]0.397171351907131[/C][C]0.794342703814262[/C][C]0.602828648092869[/C][/ROW]
[ROW][C]34[/C][C]0.404285471690861[/C][C]0.808570943381722[/C][C]0.595714528309139[/C][/ROW]
[ROW][C]35[/C][C]0.408626478917404[/C][C]0.817252957834808[/C][C]0.591373521082596[/C][/ROW]
[ROW][C]36[/C][C]0.407978657215921[/C][C]0.815957314431843[/C][C]0.592021342784078[/C][/ROW]
[ROW][C]37[/C][C]0.58557196078044[/C][C]0.82885607843912[/C][C]0.41442803921956[/C][/ROW]
[ROW][C]38[/C][C]0.780594305418258[/C][C]0.438811389163483[/C][C]0.219405694581741[/C][/ROW]
[ROW][C]39[/C][C]0.774989139581518[/C][C]0.450021720836964[/C][C]0.225010860418482[/C][/ROW]
[ROW][C]40[/C][C]0.733367820350589[/C][C]0.533264359298823[/C][C]0.266632179649411[/C][/ROW]
[ROW][C]41[/C][C]0.710191137649806[/C][C]0.579617724700389[/C][C]0.289808862350194[/C][/ROW]
[ROW][C]42[/C][C]0.661503861337903[/C][C]0.676992277324194[/C][C]0.338496138662097[/C][/ROW]
[ROW][C]43[/C][C]0.614373674165386[/C][C]0.771252651669227[/C][C]0.385626325834614[/C][/ROW]
[ROW][C]44[/C][C]0.599750468509982[/C][C]0.800499062980037[/C][C]0.400249531490018[/C][/ROW]
[ROW][C]45[/C][C]0.565947561996052[/C][C]0.868104876007896[/C][C]0.434052438003948[/C][/ROW]
[ROW][C]46[/C][C]0.587894140057009[/C][C]0.824211719885982[/C][C]0.412105859942991[/C][/ROW]
[ROW][C]47[/C][C]0.546384137900854[/C][C]0.907231724198292[/C][C]0.453615862099146[/C][/ROW]
[ROW][C]48[/C][C]0.536591409475937[/C][C]0.926817181048125[/C][C]0.463408590524063[/C][/ROW]
[ROW][C]49[/C][C]0.599832799760325[/C][C]0.80033440047935[/C][C]0.400167200239675[/C][/ROW]
[ROW][C]50[/C][C]0.578909157964415[/C][C]0.84218168407117[/C][C]0.421090842035585[/C][/ROW]
[ROW][C]51[/C][C]0.543206096839558[/C][C]0.913587806320885[/C][C]0.456793903160442[/C][/ROW]
[ROW][C]52[/C][C]0.494007802937747[/C][C]0.988015605875494[/C][C]0.505992197062253[/C][/ROW]
[ROW][C]53[/C][C]0.456160085713159[/C][C]0.912320171426319[/C][C]0.543839914286841[/C][/ROW]
[ROW][C]54[/C][C]0.408477504633944[/C][C]0.816955009267888[/C][C]0.591522495366056[/C][/ROW]
[ROW][C]55[/C][C]0.364122037763876[/C][C]0.728244075527751[/C][C]0.635877962236124[/C][/ROW]
[ROW][C]56[/C][C]0.334695554507234[/C][C]0.669391109014468[/C][C]0.665304445492766[/C][/ROW]
[ROW][C]57[/C][C]0.290444656409196[/C][C]0.580889312818392[/C][C]0.709555343590804[/C][/ROW]
[ROW][C]58[/C][C]0.265027586866105[/C][C]0.530055173732209[/C][C]0.734972413133895[/C][/ROW]
[ROW][C]59[/C][C]0.244192746044807[/C][C]0.488385492089615[/C][C]0.755807253955193[/C][/ROW]
[ROW][C]60[/C][C]0.304860264003553[/C][C]0.609720528007106[/C][C]0.695139735996447[/C][/ROW]
[ROW][C]61[/C][C]0.292214977719332[/C][C]0.584429955438664[/C][C]0.707785022280668[/C][/ROW]
[ROW][C]62[/C][C]0.255056941768284[/C][C]0.510113883536567[/C][C]0.744943058231716[/C][/ROW]
[ROW][C]63[/C][C]0.241958636777053[/C][C]0.483917273554106[/C][C]0.758041363222947[/C][/ROW]
[ROW][C]64[/C][C]0.276548658878426[/C][C]0.553097317756852[/C][C]0.723451341121574[/C][/ROW]
[ROW][C]65[/C][C]0.351375121563765[/C][C]0.70275024312753[/C][C]0.648624878436235[/C][/ROW]
[ROW][C]66[/C][C]0.351107811415878[/C][C]0.702215622831756[/C][C]0.648892188584122[/C][/ROW]
[ROW][C]67[/C][C]0.68417734571236[/C][C]0.631645308575281[/C][C]0.31582265428764[/C][/ROW]
[ROW][C]68[/C][C]0.677217950546536[/C][C]0.645564098906928[/C][C]0.322782049453464[/C][/ROW]
[ROW][C]69[/C][C]0.84910831563478[/C][C]0.301783368730439[/C][C]0.15089168436522[/C][/ROW]
[ROW][C]70[/C][C]0.830406690773575[/C][C]0.33918661845285[/C][C]0.169593309226425[/C][/ROW]
[ROW][C]71[/C][C]0.93121602893771[/C][C]0.137567942124578[/C][C]0.0687839710622892[/C][/ROW]
[ROW][C]72[/C][C]0.91505195468036[/C][C]0.169896090639279[/C][C]0.0849480453196393[/C][/ROW]
[ROW][C]73[/C][C]0.938614753233633[/C][C]0.122770493532733[/C][C]0.0613852467663667[/C][/ROW]
[ROW][C]74[/C][C]0.926515464837545[/C][C]0.14696907032491[/C][C]0.0734845351624548[/C][/ROW]
[ROW][C]75[/C][C]0.927269776527218[/C][C]0.145460446945565[/C][C]0.0727302234727823[/C][/ROW]
[ROW][C]76[/C][C]0.921295900002674[/C][C]0.157408199994653[/C][C]0.0787040999973264[/C][/ROW]
[ROW][C]77[/C][C]0.969310364472374[/C][C]0.0613792710552513[/C][C]0.0306896355276256[/C][/ROW]
[ROW][C]78[/C][C]0.961595359451175[/C][C]0.0768092810976501[/C][C]0.0384046405488251[/C][/ROW]
[ROW][C]79[/C][C]0.95440889935429[/C][C]0.0911822012914208[/C][C]0.0455911006457104[/C][/ROW]
[ROW][C]80[/C][C]0.956088309375481[/C][C]0.0878233812490372[/C][C]0.0439116906245186[/C][/ROW]
[ROW][C]81[/C][C]0.948653948853465[/C][C]0.102692102293071[/C][C]0.0513460511465355[/C][/ROW]
[ROW][C]82[/C][C]0.947289976246263[/C][C]0.105420047507474[/C][C]0.0527100237537368[/C][/ROW]
[ROW][C]83[/C][C]0.933976520125412[/C][C]0.132046959749176[/C][C]0.0660234798745881[/C][/ROW]
[ROW][C]84[/C][C]0.921043301440931[/C][C]0.157913397118138[/C][C]0.078956698559069[/C][/ROW]
[ROW][C]85[/C][C]0.912589063842091[/C][C]0.174821872315817[/C][C]0.0874109361579086[/C][/ROW]
[ROW][C]86[/C][C]0.896860308723345[/C][C]0.20627938255331[/C][C]0.103139691276655[/C][/ROW]
[ROW][C]87[/C][C]0.875559700157106[/C][C]0.248880599685789[/C][C]0.124440299842894[/C][/ROW]
[ROW][C]88[/C][C]0.919676458647418[/C][C]0.160647082705164[/C][C]0.0803235413525821[/C][/ROW]
[ROW][C]89[/C][C]0.903047813078362[/C][C]0.193904373843276[/C][C]0.0969521869216378[/C][/ROW]
[ROW][C]90[/C][C]0.883250400779371[/C][C]0.233499198441257[/C][C]0.116749599220629[/C][/ROW]
[ROW][C]91[/C][C]0.885873439894257[/C][C]0.228253120211485[/C][C]0.114126560105743[/C][/ROW]
[ROW][C]92[/C][C]0.873778036059214[/C][C]0.252443927881572[/C][C]0.126221963940786[/C][/ROW]
[ROW][C]93[/C][C]0.851261904425356[/C][C]0.297476191149289[/C][C]0.148738095574644[/C][/ROW]
[ROW][C]94[/C][C]0.823598899881724[/C][C]0.352802200236553[/C][C]0.176401100118276[/C][/ROW]
[ROW][C]95[/C][C]0.790569183028185[/C][C]0.41886163394363[/C][C]0.209430816971815[/C][/ROW]
[ROW][C]96[/C][C]0.761714449281901[/C][C]0.476571101436198[/C][C]0.238285550718099[/C][/ROW]
[ROW][C]97[/C][C]0.781517470685154[/C][C]0.436965058629691[/C][C]0.218482529314846[/C][/ROW]
[ROW][C]98[/C][C]0.777135073996202[/C][C]0.445729852007597[/C][C]0.222864926003798[/C][/ROW]
[ROW][C]99[/C][C]0.741672483209814[/C][C]0.516655033580372[/C][C]0.258327516790186[/C][/ROW]
[ROW][C]100[/C][C]0.715567649170053[/C][C]0.568864701659894[/C][C]0.284432350829947[/C][/ROW]
[ROW][C]101[/C][C]0.673362216376691[/C][C]0.653275567246619[/C][C]0.326637783623309[/C][/ROW]
[ROW][C]102[/C][C]0.636066706692136[/C][C]0.727866586615729[/C][C]0.363933293307864[/C][/ROW]
[ROW][C]103[/C][C]0.595619656179181[/C][C]0.808760687641638[/C][C]0.404380343820819[/C][/ROW]
[ROW][C]104[/C][C]0.554506496977988[/C][C]0.890987006044024[/C][C]0.445493503022012[/C][/ROW]
[ROW][C]105[/C][C]0.508461237893369[/C][C]0.983077524213262[/C][C]0.491538762106631[/C][/ROW]
[ROW][C]106[/C][C]0.485442498060947[/C][C]0.970884996121894[/C][C]0.514557501939053[/C][/ROW]
[ROW][C]107[/C][C]0.484570895065951[/C][C]0.969141790131902[/C][C]0.515429104934049[/C][/ROW]
[ROW][C]108[/C][C]0.49133624194656[/C][C]0.98267248389312[/C][C]0.50866375805344[/C][/ROW]
[ROW][C]109[/C][C]0.441094256714162[/C][C]0.882188513428324[/C][C]0.558905743285838[/C][/ROW]
[ROW][C]110[/C][C]0.416783443911544[/C][C]0.833566887823088[/C][C]0.583216556088456[/C][/ROW]
[ROW][C]111[/C][C]0.376938148427725[/C][C]0.75387629685545[/C][C]0.623061851572275[/C][/ROW]
[ROW][C]112[/C][C]0.602586560115651[/C][C]0.794826879768697[/C][C]0.397413439884349[/C][/ROW]
[ROW][C]113[/C][C]0.596580270904186[/C][C]0.806839458191628[/C][C]0.403419729095814[/C][/ROW]
[ROW][C]114[/C][C]0.572536239675484[/C][C]0.854927520649033[/C][C]0.427463760324516[/C][/ROW]
[ROW][C]115[/C][C]0.772152537892129[/C][C]0.455694924215742[/C][C]0.227847462107871[/C][/ROW]
[ROW][C]116[/C][C]0.75488220363888[/C][C]0.490235592722241[/C][C]0.24511779636112[/C][/ROW]
[ROW][C]117[/C][C]0.736441242429285[/C][C]0.52711751514143[/C][C]0.263558757570715[/C][/ROW]
[ROW][C]118[/C][C]0.705429016363231[/C][C]0.589141967273538[/C][C]0.294570983636769[/C][/ROW]
[ROW][C]119[/C][C]0.655836780318508[/C][C]0.688326439362983[/C][C]0.344163219681491[/C][/ROW]
[ROW][C]120[/C][C]0.678128609240408[/C][C]0.643742781519185[/C][C]0.321871390759592[/C][/ROW]
[ROW][C]121[/C][C]0.736336963165258[/C][C]0.527326073669483[/C][C]0.263663036834742[/C][/ROW]
[ROW][C]122[/C][C]0.727961306478301[/C][C]0.544077387043398[/C][C]0.272038693521699[/C][/ROW]
[ROW][C]123[/C][C]0.72898819311443[/C][C]0.542023613771141[/C][C]0.271011806885571[/C][/ROW]
[ROW][C]124[/C][C]0.676552099471697[/C][C]0.646895801056606[/C][C]0.323447900528303[/C][/ROW]
[ROW][C]125[/C][C]0.719064286019384[/C][C]0.561871427961232[/C][C]0.280935713980616[/C][/ROW]
[ROW][C]126[/C][C]0.685699128156418[/C][C]0.628601743687163[/C][C]0.314300871843582[/C][/ROW]
[ROW][C]127[/C][C]0.682503487307807[/C][C]0.634993025384387[/C][C]0.317496512692193[/C][/ROW]
[ROW][C]128[/C][C]0.644344272199422[/C][C]0.711311455601155[/C][C]0.355655727800578[/C][/ROW]
[ROW][C]129[/C][C]0.612361745026848[/C][C]0.775276509946304[/C][C]0.387638254973152[/C][/ROW]
[ROW][C]130[/C][C]0.686113678363333[/C][C]0.627772643273333[/C][C]0.313886321636667[/C][/ROW]
[ROW][C]131[/C][C]0.633423826299745[/C][C]0.73315234740051[/C][C]0.366576173700255[/C][/ROW]
[ROW][C]132[/C][C]0.572068634421696[/C][C]0.855862731156607[/C][C]0.427931365578304[/C][/ROW]
[ROW][C]133[/C][C]0.562229210973165[/C][C]0.87554157805367[/C][C]0.437770789026835[/C][/ROW]
[ROW][C]134[/C][C]0.505084176724888[/C][C]0.989831646550224[/C][C]0.494915823275112[/C][/ROW]
[ROW][C]135[/C][C]0.45534806895603[/C][C]0.91069613791206[/C][C]0.54465193104397[/C][/ROW]
[ROW][C]136[/C][C]0.42175373716162[/C][C]0.84350747432324[/C][C]0.57824626283838[/C][/ROW]
[ROW][C]137[/C][C]0.438572900787355[/C][C]0.87714580157471[/C][C]0.561427099212645[/C][/ROW]
[ROW][C]138[/C][C]0.508820180950987[/C][C]0.982359638098027[/C][C]0.491179819049013[/C][/ROW]
[ROW][C]139[/C][C]0.433280192381418[/C][C]0.866560384762836[/C][C]0.566719807618582[/C][/ROW]
[ROW][C]140[/C][C]0.355669923067759[/C][C]0.711339846135519[/C][C]0.64433007693224[/C][/ROW]
[ROW][C]141[/C][C]0.424681998118506[/C][C]0.849363996237012[/C][C]0.575318001881494[/C][/ROW]
[ROW][C]142[/C][C]0.370325180293534[/C][C]0.740650360587067[/C][C]0.629674819706466[/C][/ROW]
[ROW][C]143[/C][C]0.304711630377864[/C][C]0.609423260755727[/C][C]0.695288369622136[/C][/ROW]
[ROW][C]144[/C][C]0.228558708684983[/C][C]0.457117417369967[/C][C]0.771441291315017[/C][/ROW]
[ROW][C]145[/C][C]0.368224064359684[/C][C]0.736448128719367[/C][C]0.631775935640316[/C][/ROW]
[ROW][C]146[/C][C]0.270276284106354[/C][C]0.540552568212709[/C][C]0.729723715893646[/C][/ROW]
[ROW][C]147[/C][C]0.39371901078636[/C][C]0.78743802157272[/C][C]0.60628098921364[/C][/ROW]
[ROW][C]148[/C][C]0.336492835922942[/C][C]0.672985671845885[/C][C]0.663507164077058[/C][/ROW]
[ROW][C]149[/C][C]0.252960816433276[/C][C]0.505921632866552[/C][C]0.747039183566724[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108229&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108229&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.214465827965360.4289316559307190.78553417203464
110.2663908711114180.5327817422228350.733609128888583
120.7472688774787550.5054622450424910.252731122521245
130.7532428798811030.4935142402377950.246757120118897
140.7180392454127140.5639215091745710.281960754587286
150.7175828023617520.5648343952764960.282417197638248
160.6614078286186510.6771843427626980.338592171381349
170.5736850420040390.8526299159919210.426314957995961
180.5261676104314090.9476647791371820.473832389568591
190.4452702210870690.8905404421741390.554729778912931
200.4630582730357130.9261165460714250.536941726964287
210.383955783475350.76791156695070.61604421652465
220.3898462515551430.7796925031102860.610153748444857
230.3177975215178560.6355950430357110.682202478482144
240.6282749834381630.7434500331236740.371725016561837
250.5588288478036920.8823423043926160.441171152196308
260.5162502967718780.9674994064562440.483749703228122
270.4490476744089090.8980953488178190.550952325591091
280.4140436704437540.8280873408875080.585956329556246
290.3540557642646760.7081115285293520.645944235735324
300.3038982198375320.6077964396750630.696101780162468
310.38016864894430.76033729788860.6198313510557
320.4432963863532870.8865927727065740.556703613646713
330.3971713519071310.7943427038142620.602828648092869
340.4042854716908610.8085709433817220.595714528309139
350.4086264789174040.8172529578348080.591373521082596
360.4079786572159210.8159573144318430.592021342784078
370.585571960780440.828856078439120.41442803921956
380.7805943054182580.4388113891634830.219405694581741
390.7749891395815180.4500217208369640.225010860418482
400.7333678203505890.5332643592988230.266632179649411
410.7101911376498060.5796177247003890.289808862350194
420.6615038613379030.6769922773241940.338496138662097
430.6143736741653860.7712526516692270.385626325834614
440.5997504685099820.8004990629800370.400249531490018
450.5659475619960520.8681048760078960.434052438003948
460.5878941400570090.8242117198859820.412105859942991
470.5463841379008540.9072317241982920.453615862099146
480.5365914094759370.9268171810481250.463408590524063
490.5998327997603250.800334400479350.400167200239675
500.5789091579644150.842181684071170.421090842035585
510.5432060968395580.9135878063208850.456793903160442
520.4940078029377470.9880156058754940.505992197062253
530.4561600857131590.9123201714263190.543839914286841
540.4084775046339440.8169550092678880.591522495366056
550.3641220377638760.7282440755277510.635877962236124
560.3346955545072340.6693911090144680.665304445492766
570.2904446564091960.5808893128183920.709555343590804
580.2650275868661050.5300551737322090.734972413133895
590.2441927460448070.4883854920896150.755807253955193
600.3048602640035530.6097205280071060.695139735996447
610.2922149777193320.5844299554386640.707785022280668
620.2550569417682840.5101138835365670.744943058231716
630.2419586367770530.4839172735541060.758041363222947
640.2765486588784260.5530973177568520.723451341121574
650.3513751215637650.702750243127530.648624878436235
660.3511078114158780.7022156228317560.648892188584122
670.684177345712360.6316453085752810.31582265428764
680.6772179505465360.6455640989069280.322782049453464
690.849108315634780.3017833687304390.15089168436522
700.8304066907735750.339186618452850.169593309226425
710.931216028937710.1375679421245780.0687839710622892
720.915051954680360.1698960906392790.0849480453196393
730.9386147532336330.1227704935327330.0613852467663667
740.9265154648375450.146969070324910.0734845351624548
750.9272697765272180.1454604469455650.0727302234727823
760.9212959000026740.1574081999946530.0787040999973264
770.9693103644723740.06137927105525130.0306896355276256
780.9615953594511750.07680928109765010.0384046405488251
790.954408899354290.09118220129142080.0455911006457104
800.9560883093754810.08782338124903720.0439116906245186
810.9486539488534650.1026921022930710.0513460511465355
820.9472899762462630.1054200475074740.0527100237537368
830.9339765201254120.1320469597491760.0660234798745881
840.9210433014409310.1579133971181380.078956698559069
850.9125890638420910.1748218723158170.0874109361579086
860.8968603087233450.206279382553310.103139691276655
870.8755597001571060.2488805996857890.124440299842894
880.9196764586474180.1606470827051640.0803235413525821
890.9030478130783620.1939043738432760.0969521869216378
900.8832504007793710.2334991984412570.116749599220629
910.8858734398942570.2282531202114850.114126560105743
920.8737780360592140.2524439278815720.126221963940786
930.8512619044253560.2974761911492890.148738095574644
940.8235988998817240.3528022002365530.176401100118276
950.7905691830281850.418861633943630.209430816971815
960.7617144492819010.4765711014361980.238285550718099
970.7815174706851540.4369650586296910.218482529314846
980.7771350739962020.4457298520075970.222864926003798
990.7416724832098140.5166550335803720.258327516790186
1000.7155676491700530.5688647016598940.284432350829947
1010.6733622163766910.6532755672466190.326637783623309
1020.6360667066921360.7278665866157290.363933293307864
1030.5956196561791810.8087606876416380.404380343820819
1040.5545064969779880.8909870060440240.445493503022012
1050.5084612378933690.9830775242132620.491538762106631
1060.4854424980609470.9708849961218940.514557501939053
1070.4845708950659510.9691417901319020.515429104934049
1080.491336241946560.982672483893120.50866375805344
1090.4410942567141620.8821885134283240.558905743285838
1100.4167834439115440.8335668878230880.583216556088456
1110.3769381484277250.753876296855450.623061851572275
1120.6025865601156510.7948268797686970.397413439884349
1130.5965802709041860.8068394581916280.403419729095814
1140.5725362396754840.8549275206490330.427463760324516
1150.7721525378921290.4556949242157420.227847462107871
1160.754882203638880.4902355927222410.24511779636112
1170.7364412424292850.527117515141430.263558757570715
1180.7054290163632310.5891419672735380.294570983636769
1190.6558367803185080.6883264393629830.344163219681491
1200.6781286092404080.6437427815191850.321871390759592
1210.7363369631652580.5273260736694830.263663036834742
1220.7279613064783010.5440773870433980.272038693521699
1230.728988193114430.5420236137711410.271011806885571
1240.6765520994716970.6468958010566060.323447900528303
1250.7190642860193840.5618714279612320.280935713980616
1260.6856991281564180.6286017436871630.314300871843582
1270.6825034873078070.6349930253843870.317496512692193
1280.6443442721994220.7113114556011550.355655727800578
1290.6123617450268480.7752765099463040.387638254973152
1300.6861136783633330.6277726432733330.313886321636667
1310.6334238262997450.733152347400510.366576173700255
1320.5720686344216960.8558627311566070.427931365578304
1330.5622292109731650.875541578053670.437770789026835
1340.5050841767248880.9898316465502240.494915823275112
1350.455348068956030.910696137912060.54465193104397
1360.421753737161620.843507474323240.57824626283838
1370.4385729007873550.877145801574710.561427099212645
1380.5088201809509870.9823596380980270.491179819049013
1390.4332801923814180.8665603847628360.566719807618582
1400.3556699230677590.7113398461355190.64433007693224
1410.4246819981185060.8493639962370120.575318001881494
1420.3703251802935340.7406503605870670.629674819706466
1430.3047116303778640.6094232607557270.695288369622136
1440.2285587086849830.4571174173699670.771441291315017
1450.3682240643596840.7364481287193670.631775935640316
1460.2702762841063540.5405525682127090.729723715893646
1470.393719010786360.787438021572720.60628098921364
1480.3364928359229420.6729856718458850.663507164077058
1490.2529608164332760.5059216328665520.747039183566724






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 level40.0285714285714286OK

\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 & 4 & 0.0285714285714286 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108229&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]4[/C][C]0.0285714285714286[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108229&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108229&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 level40.0285714285714286OK


Figures (Output of Computation)

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