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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationTue, 23 Nov 2010 09:33:39 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Nov/23/t1290504768gi2xs1nih1xserd.htm/, Retrieved Fri, 29 Mar 2024 11:00:32 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=98873, Retrieved Fri, 29 Mar 2024 11:00:32 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact198
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2010-11-17 09:55:05] [b98453cac15ba1066b407e146608df68]
- R PD  [Multiple Regression] [Workshop 7 - Regr...] [2010-11-19 15:10:35] [8b017ffbf7b0eded54d8efebfb3e4cfa]
-         [Multiple Regression] [workshop 7 - tuto...] [2010-11-19 16:27:16] [956e8df26b41c50d9c6c2ec1b6a122a8]
-    D        [Multiple Regression] [WS7 comp 6] [2010-11-23 09:33:39] [4f70e6cd0867f10d298e58e8e27859b5] [Current]
-               [Multiple Regression] [] [2010-12-02 15:22:24] [2e1e44f0ae3cb9513dc28781dfdb387b]
-               [Multiple Regression] [] [2010-12-03 17:49:16] [b07cd1964830aab808142229b1166ece]
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Dataseries X:
1	26	24	24	14	14	11	11	12	12	24	24
1	23	25	25	11	11	7	7	8	8	25	25
0	25	17	0	6	0	17	0	8	0	30	0
1	23	18	18	12	12	10	10	8	8	19	19
1	20	18	18	8	8	12	12	9	9	22	22
0	29	16		10	0	12	0	7	0	22	
1	25	20	20	10	10	11	11	4	4	25	25
1	21	16	16	11	11	11	11	11	11	23	23
1	22	18	18	16	16	12	12	7	7	17	17
1	25	17	17	11	11	13	13	7	7	21	21
1	24	23	23	13	13	14	14	12	12	19	19
1	18	30	30	12	12	16	16	10	10	19	19
1	22	23	23	8	8	11	11	10	10	15	15
1	15	18	18	12	12	10	10	8	8	16	16
1	22	15	15	11	11	11	11	8	8	23	23
1	28	12	12	4	4	15	15	4	4	27	27
1	20	21	21	9	9	9	9	9	9	22	22
1	12	15	15	8	8	11	11	8	8	14	14
1	24	20	20	8	8	17	17	7	7	22	22
1	20	31	31	14	14	17	17	11	11	23	23
1	21	27	27	15	15	11	11	9	9	23	23
1	20	34	34	16	16	18	18	11	11	21	21
1	21	21	21	9	9	14	14	13	13	19	19
1	23	31	31	14	14	10	10	8	8	18	18
1	28	19	19	11	11	11	11	8	8	20	20
1	24	16	16	8	8	15	15	9	9	23	23
1	24	20	20	9	9	15	15	6	6	25	25
1	24	21	21	9	9	13	13	9	9	19	19
1	23	22	22	9	9	16	16	9	9	24	24
1	23	17	17	9	9	13	13	6	6	22	22
1	29	24	24	10	10	9	9	6	6	25	25
1	24	25	25	16	16	18	18	16	16	26	26
1	18	26	26	11	11	18	18	5	5	29	29
1	25	25	25	8	8	12	12	7	7	32	32
1	21	17	17	9	9	17	17	9	9	25	25
1	26	32	32	16	16	9	9	6	6	29	29
1	22	33	33	11	11	9	9	6	6	28	28
1	22	13	13	16	16	12	12	5	5	17	17
0	22	32	0	12	0	18	0	12	0	28	0
1	23	25	25	12	12	12	12	7	7	29	29
1	30	29	29	14	14	18	18	10	10	26	26
1	23	22	22	9	9	14	14	9	9	25	25
1	17	18	18	10	10	15	15	8	8	14	14
1	23	17	17	9	9	16	16	5	5	25	25
1	23	20	20	10	10	10	10	8	8	26	26
1	25	15	15	12	12	11	11	8	8	20	20
1	24	20	20	14	14	14	14	10	10	18	18
1	24	33	33	14	14	9	9	6	6	32	32
1	23	29	29	10	10	12	12	8	8	25	25
1	21	23	23	14	14	17	17	7	7	25	25
1	24	26	26	16	16	5	5	4	4	23	23
1	24	18	18	9	9	12	12	8	8	21	21
1	28	20	20	10	10	12	12	8	8	20	20
1	16	11	11	6	6	6	6	4	4	15	15
1	20	28	28	8	8	24	24	20	20	30	30
1	29	26	26	13	13	12	12	8	8	24	24
1	27	22	22	10	10	12	12	8	8	26	26
1	22	17	17	8	8	14	14	6	6	24	24
1	28	12	12	7	7	7	7	4	4	22	22
1	16	14	14	15	15	13	13	8	8	14	14
1	25	17	17	9	9	12	12	9	9	24	24
1	24	21	21	10	10	13	13	6	6	24	24
0	28	19	0	12	0	14	0	7	0	24	0
1	24	18	18	13	13	8	8	9	9	24	24
1	23	10	10	10	10	11	11	5	5	19	19
1	30	29	29	11	11	9	9	5	5	31	31
1	24	31	31	8	8	11	11	8	8	22	22
1	21	19	19	9	9	13	13	8	8	27	27
1	25	9	9	13	13	10	10	6	6	19	19
0	25	20	0	11	0	11	0	8	0	25	0
1	22	28	28	8	8	12	12	7	7	20	20
1	23	19	19	9	9	9	9	7	7	21	21
1	26	30	30	9	9	15	15	9	9	27	27
1	23	29	29	15	15	18	18	11	11	23	23
1	25	26	26	9	9	15	15	6	6	25	25
1	21	23	23	10	10	12	12	8	8	20	20
1	25	13	13	14	14	13	13	6	6	21	21
1	24	21	21	12	12	14	14	9	9	22	22
1	29	19	19	12	12	10	10	8	8	23	23
1	22	28	28	11	11	13	13	6	6	25	25
1	27	23	23	14	14	13	13	10	10	25	25
0	26	18	0	6	0	11	0	8	0	17	0
1	22	21	21	12	12	13	13	8	8	19	19
1	24	20	20	8	8	16	16	10	10	25	25
0	27	23	0	14	0	8	0	5	0	19	0
1	24	21	21	11	11	16	16	7	7	20	20
1	24	21	21	10	10	11	11	5	5	26	26
1	29	15	15	14	14	9	9	8	8	23	23
1	22	28	28	12	12	16	16	14	14	27	27
0	21	19	0	10	0	12	0	7	0	17	0
1	24	26	26	14	14	14	14	8	8	17	17
1	24	10	10	5	5	8	8	6	6	19	19
0	23	16	0	11	0	9	0	5	0	17	0
1	20	22	22	10	10	15	15	6	6	22	22
1	27	19	19	9	9	11	11	10	10	21	21
1	26	31	31	10	10	21	21	12	12	32	32
1	25	31	31	16	16	14	14	9	9	21	21
1	21	29	29	13	13	18	18	12	12	21	21
1	21	19	19	9	9	12	12	7	7	18	18
1	19	22	22	10	10	13	13	8	8	18	18
1	21	23	23	10	10	15	15	10	10	23	23
1	21	15	15	7	7	12	12	6	6	19	19
1	16	20	20	9	9	19	19	10	10	20	20
1	22	18	18	8	8	15	15	10	10	21	21
1	29	23	23	14	14	11	11	10	10	20	20
0	15	25	0	14	0	11	0	5	0	17	0
1	17	21	21	8	8	10	10	7	7	18	18
1	15	24	24	9	9	13	13	10	10	19	19
1	21	25	25	14	14	15	15	11	11	22	22
0	21	17	0	14	0	12	0	6	0	15	0
1	19	13	13	8	8	12	12	7	7	14	14
1	24	28	28	8	8	16	16	12	12	18	18
1	20	21	21	8	8	9	9	11	11	24	24
0	17	25	0	7	0	18	0	11	0	35	0
1	23	9	9	6	6	8	8	11	11	29	29
1	24	16	16	8	8	13	13	5	5	21	21
1	14	19	19	6	6	17	17	8	8	25	25
1	19	17	17	11	11	9	9	6	6	20	20
1	24	25	25	14	14	15	15	9	9	22	22
1	13	20	20	11	11	8	8	4	4	13	13
1	22	29	29	11	11	7	7	4	4	26	26
1	16	14	14	11	11	12	12	7	7	17	17
0	19	22	0	14	0	14	0	11	0	25	0
1	25	15	15	8	8	6	6	6	6	20	20
1	25	19	19	20	20	8	8	7	7	19	19
1	23	20	20	11	11	17	17	8	8	21	21
0	24	15	0	8	0	10	0	4	0	22	0
1	26	20	20	11	11	11	11	8	8	24	24
1	26	18	18	10	10	14	14	9	9	21	21
1	25	33	33	14	14	11	11	8	8	26	26
1	18	22	22	11	11	13	13	11	11	24	24
1	21	16	16	9	9	12	12	8	8	16	16
1	26	17	17	9	9	11	11	5	5	23	23
1	23	16	16	8	8	9	9	4	4	18	18
1	23	21	21	10	10	12	12	8	8	16	16
1	22	26	26	13	13	20	20	10	10	26	26
1	20	18	18	13	13	12	12	6	6	19	19
1	13	18	18	12	12	13	13	9	9	21	21
1	24	17	17	8	8	12	12	9	9	21	21
1	15	22	22	13	13	12	12	13	13	22	22
1	14	30	30	14	14	9	9	9	9	23	23
0	22	30	0	12	0	15	0	10	0	29	0
1	10	24	24	14	14	24	24	20	20	21	21
1	24	21	21	15	15	7	7	5	5	21	21
1	22	21	21	13	13	17	17	11	11	23	23
1	24	29	29	16	16	11	11	6	6	27	27
1	19	31	31	9	9	17	17	9	9	25	25
0	20	20	0	9	0	11	0	7	0	21	0
1	13	16	16	9	9	12	12	9	9	10	10
1	20	22	22	8	8	14	14	10	10	20	20
1	22	20	20	7	7	11	11	9	9	26	26
1	24	28	28	16	16	16	16	8	8	24	24
1	29	38	38	11	11	21	21	7	7	29	29
1	12	22	22	9	9	14	14	6	6	19	19
1	20	20	20	11	11	20	20	13	13	24	24
1	21	17	17	9	9	13	13	6	6	19	19
1	24	28	28	14	14	11	11	8	8	24	24
1	22	22	22	13	13	15	15	10	10	22	22
1	20	31	31	16	16	19	19	16	16	17	17




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time10 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24
R Framework error message
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 10 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
R Framework error message & 
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.
\tabularnewline \hline \end{tabular} %Source: https://freestatistics.org/blog/index.php?pk=98873&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]10 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.
[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=98873&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98873&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time10 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24
R Framework error message
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.







Multiple Linear Regression - Estimated Regression Equation
O[t] = -0.578264633807956 + 0.92553541241128B[t] + 0.0132183880731032CM[t] + 0.0160035232163502CM_B[t] -1.31048736184262D[t] + 1.32524772719412D_B[t] + 0.406327339245112PE[t] -0.394585656390905PE_B[t] -0.341990685460131PC[t] + 0.363464570658179PC_B[t] + 0.942105175460257PS[t] + 0.0146495184249108PS_B[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
O[t] =  -0.578264633807956 +  0.92553541241128B[t] +  0.0132183880731032CM[t] +  0.0160035232163502CM_B[t] -1.31048736184262D[t] +  1.32524772719412D_B[t] +  0.406327339245112PE[t] -0.394585656390905PE_B[t] -0.341990685460131PC[t] +  0.363464570658179PC_B[t] +  0.942105175460257PS[t] +  0.0146495184249108PS_B[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98873&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]O[t] =  -0.578264633807956 +  0.92553541241128B[t] +  0.0132183880731032CM[t] +  0.0160035232163502CM_B[t] -1.31048736184262D[t] +  1.32524772719412D_B[t] +  0.406327339245112PE[t] -0.394585656390905PE_B[t] -0.341990685460131PC[t] +  0.363464570658179PC_B[t] +  0.942105175460257PS[t] +  0.0146495184249108PS_B[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98873&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98873&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
O[t] = -0.578264633807956 + 0.92553541241128B[t] + 0.0132183880731032CM[t] + 0.0160035232163502CM_B[t] -1.31048736184262D[t] + 1.32524772719412D_B[t] + 0.406327339245112PE[t] -0.394585656390905PE_B[t] -0.341990685460131PC[t] + 0.363464570658179PC_B[t] + 0.942105175460257PS[t] + 0.0146495184249108PS_B[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-0.5782646338079561.018728-0.56760.571150.285575
B0.925535412411280.02645834.981300
CM0.01321838807310320.081170.16280.8708620.435431
CM_B0.01600352321635020.0790440.20250.8398340.419917
D-1.310487361842620.218181-6.006400
D_B1.325247727194120.2210445.995400
PE0.4063273392451120.1435492.83060.0052970.002649
PE_B-0.3945856563909050.151846-2.59860.0103140.005157
PC-0.3419906854601310.108022-3.16590.001880.00094
PC_B0.3634645706581790.1092793.3260.0011130.000556
PS0.9421051754602570.04818519.551900
PS_B0.01464951842491080.0305660.47930.6324590.316229

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & -0.578264633807956 & 1.018728 & -0.5676 & 0.57115 & 0.285575 \tabularnewline
B & 0.92553541241128 & 0.026458 & 34.9813 & 0 & 0 \tabularnewline
CM & 0.0132183880731032 & 0.08117 & 0.1628 & 0.870862 & 0.435431 \tabularnewline
CM_B & 0.0160035232163502 & 0.079044 & 0.2025 & 0.839834 & 0.419917 \tabularnewline
D & -1.31048736184262 & 0.218181 & -6.0064 & 0 & 0 \tabularnewline
D_B & 1.32524772719412 & 0.221044 & 5.9954 & 0 & 0 \tabularnewline
PE & 0.406327339245112 & 0.143549 & 2.8306 & 0.005297 & 0.002649 \tabularnewline
PE_B & -0.394585656390905 & 0.151846 & -2.5986 & 0.010314 & 0.005157 \tabularnewline
PC & -0.341990685460131 & 0.108022 & -3.1659 & 0.00188 & 0.00094 \tabularnewline
PC_B & 0.363464570658179 & 0.109279 & 3.326 & 0.001113 & 0.000556 \tabularnewline
PS & 0.942105175460257 & 0.048185 & 19.5519 & 0 & 0 \tabularnewline
PS_B & 0.0146495184249108 & 0.030566 & 0.4793 & 0.632459 & 0.316229 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98873&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]-0.578264633807956[/C][C]1.018728[/C][C]-0.5676[/C][C]0.57115[/C][C]0.285575[/C][/ROW]
[ROW][C]B[/C][C]0.92553541241128[/C][C]0.026458[/C][C]34.9813[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]CM[/C][C]0.0132183880731032[/C][C]0.08117[/C][C]0.1628[/C][C]0.870862[/C][C]0.435431[/C][/ROW]
[ROW][C]CM_B[/C][C]0.0160035232163502[/C][C]0.079044[/C][C]0.2025[/C][C]0.839834[/C][C]0.419917[/C][/ROW]
[ROW][C]D[/C][C]-1.31048736184262[/C][C]0.218181[/C][C]-6.0064[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]D_B[/C][C]1.32524772719412[/C][C]0.221044[/C][C]5.9954[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]PE[/C][C]0.406327339245112[/C][C]0.143549[/C][C]2.8306[/C][C]0.005297[/C][C]0.002649[/C][/ROW]
[ROW][C]PE_B[/C][C]-0.394585656390905[/C][C]0.151846[/C][C]-2.5986[/C][C]0.010314[/C][C]0.005157[/C][/ROW]
[ROW][C]PC[/C][C]-0.341990685460131[/C][C]0.108022[/C][C]-3.1659[/C][C]0.00188[/C][C]0.00094[/C][/ROW]
[ROW][C]PC_B[/C][C]0.363464570658179[/C][C]0.109279[/C][C]3.326[/C][C]0.001113[/C][C]0.000556[/C][/ROW]
[ROW][C]PS[/C][C]0.942105175460257[/C][C]0.048185[/C][C]19.5519[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]PS_B[/C][C]0.0146495184249108[/C][C]0.030566[/C][C]0.4793[/C][C]0.632459[/C][C]0.316229[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98873&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98873&T=2

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-0.5782646338079561.018728-0.56760.571150.285575
B0.925535412411280.02645834.981300
CM0.01321838807310320.081170.16280.8708620.435431
CM_B0.01600352321635020.0790440.20250.8398340.419917
D-1.310487361842620.218181-6.006400
D_B1.325247727194120.2210445.995400
PE0.4063273392451120.1435492.83060.0052970.002649
PE_B-0.3945856563909050.151846-2.59860.0103140.005157
PC-0.3419906854601310.108022-3.16590.001880.00094
PC_B0.3634645706581790.1092793.3260.0011130.000556
PS0.9421051754602570.04818519.551900
PS_B0.01464951842491080.0305660.47930.6324590.316229







Multiple Linear Regression - Regression Statistics
Multiple R0.986054290328691
R-squared0.972303063475619
Adjusted R-squared0.970230503599645
F-TEST (value)469.131471059977
F-TEST (DF numerator)11
F-TEST (DF denominator)147
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.43270045551944
Sum Squared Residuals301.736697501106

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.986054290328691 \tabularnewline
R-squared & 0.972303063475619 \tabularnewline
Adjusted R-squared & 0.970230503599645 \tabularnewline
F-TEST (value) & 469.131471059977 \tabularnewline
F-TEST (DF numerator) & 11 \tabularnewline
F-TEST (DF denominator) & 147 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.43270045551944 \tabularnewline
Sum Squared Residuals & 301.736697501106 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98873&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.986054290328691[/C][/ROW]
[ROW][C]R-squared[/C][C]0.972303063475619[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.970230503599645[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]469.131471059977[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]11[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]147[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.43270045551944[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]301.736697501106[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98873&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98873&T=3

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Regression Statistics
Multiple R0.986054290328691
R-squared0.972303063475619
Adjusted R-squared0.970230503599645
F-TEST (value)469.131471059977
F-TEST (DF numerator)11
F-TEST (DF denominator)147
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.43270045551944
Sum Squared Residuals301.736697501106







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
12624.60419955148811.39580044851185
22325.4130327883993-2.41303278839925
32524.21831833967270.78168166032734
42319.51793665997623.48206334002381
52022.3741165311321-2.37411653113214
62922.23870162968136.76129837031869
72020.2205856699302-0.220585669930177
81616.6015594595828-0.601559459582769
91818.4816387188737-0.481638718873717
101717.4960001377340-0.49600013773396
112323.0502803334575-0.0502803334575275
123029.56444174774130.435558252258724
132322.70656221918040.293437780819601
141818.2815495853943-0.281549585394317
151515.7286961091583-0.728696109158332
161212.7283106162804-0.728310616280428
172120.94981953320170.0501804667983207
181515.3891673348079-0.389167334807894
192020.2068579140039-0.206857914003871
203130.64616905330450.353830946695542
212726.84655423870640.153445761293572
223433.45303170807260.546968291927387
232121.1673111384559-0.167311138455912
243130.50279865026540.497201349734574
251919.3078180295097-0.307818029509666
261616.5787508582618-0.578750858261844
272020.3178371410299-0.317837141029893
282121.1055840416876-0.105584041687578
292222.1827699761436-0.182769976143621
301717.5205361096234-0.52053610962345
312423.96063850985540.0393614901445705
322525.2453422813576-0.245342281357636
332626.0625779104939-0.0625779104938981
342524.99012151970810.00987848029186374
351717.6352757199115-0.635275719911508
363231.59684978082480.403150219175239
373332.35480175159070.645198248409274
381312.84442456037390.155575439626082
3902.18794968435404-2.18794968435404
402525.174433175096-0.174433175096001
412928.89183760342320.108162396576836
422222.0868260200892-0.0868260200891926
431818.2686093376018-0.268609337601757
441717.5296000678684-0.529600067868443
452020.3328639948932-0.332863994893188
461515.634898291154-0.634898291154
472020.3458358671561-0.345835867156141
483332.54301254467620.456987455323809
492928.61213147840000.38786852159997
502323.2818153482682-0.281815348268219
512625.86156820490420.138431795095829
521818.4186711187687-0.418671118768677
532020.1016957485837-0.101695748583711
541111.4706891159079-0.470689115907916
552828.1707423952679-0.170742395267928
562625.98961420038600.0103857996140351
572222.1695069951440-0.169506995144028
581717.5343728156567-0.53437281565669
591212.5319259276469-0.531925927646852
601414.7123555505507-0.712355550550712
611717.5107009711061-0.510700971106106
622120.33809274706900.661907252931047
630-5.251085665582265.25108566558226
641818.4794330488443-0.479433048844261
651010.9799755833075-0.979975583307488
662928.73463911153540.265360888464633
673130.32557645969800.67442354030197
681919.4438616524450-0.443861652445022
6999.12373445468246-0.123734454682459
700-0.3722689387810720.372268938781072
712827.53834017141520.461659828584786
721919.2588847154214-0.258884715421411
733029.63671456567650.363285434323499
742928.89767857882250.102321421177499
752625.82710106022280.172898939777158
762323.0101476516417-0.0101476516417497
771313.869782539103-0.869782539102996
782121.3603289070511-0.360328907051057
791919.3574021941919-0.357402194191937
802827.79499208747080.205007912529240
812322.34519279681440.654807203185622
8200.446493779711898-0.446493779711898
832121.1961576111266-0.196157611126643
842019.45218570632320.54781429367675
8507.2377156800752-7.2377156800752
862121.2209489982356-0.220948998235552
872121.296532797793-0.296532797792997
881515.6989440017742-0.698944001774215
892826.97537404203481.02462595796524
900-0.2626129198223200.262612919822320
912625.85409109071740.145908909282640
92109.756674809225280.243325190774723
9300.0342085643549303-0.0342085643549303
942222.1776567768225-0.177656776822495
951919.3236304946870-0.323630494687042
963130.85192759288900.148072407111043
973130.59390064372430.406099356275664
982928.74943059500210.250569404997940
991919.1361975269074-0.136197526907407
1002221.99782676048620.0021732395137644
1012323.0837356952992-0.083735695299185
1021515.3413957017525-0.341395701752489
1032020.2871768710127-0.287176871012682
1041818.4718631876671-0.471863187667068
1052322.04715793744710.952842062552895
10605.73875916131579-5.73875916131579
1072120.86992763603790.130072363962143
1082423.86463292492580.135367075074196
1092524.09985678747550.90014321252454
1100-1.856403752801871.85640375280187
1111313.5411151924826-0.541115192482634
1122827.56919372142140.430806278578555
1132120.11817117468100.881828825318965
1140-7.157152280356477.15715228035647
115910.0905632682401-1.09056326824013
1161616.3128204414968-0.312820441496799
1171919.2843924990371-0.284392499037124
1181717.4037431082757-0.40374310827568
1192524.9012824498280.0987175501719828
1202019.96248938121340.0375106187865633
1212928.46881112458860.531188875411382
1221413.60338522677160.396614773228392
1230-1.150074462256981.15007446225698
1241515.4353749719551-0.435374971955149
1251919.4786690485831-0.47866904858306
1262019.40128434376780.598715656232224
1270-4.269853030114584.26985303011458
1282020.3485480195630-0.348548019562960
1291818.4452068883406-0.445206888340618
1303332.39392573777480.606074262225229
1312222.1911170315266-0.191117031526600
1321616.4309318311061-0.430931831106053
1331717.4128504707148-0.412850470714814
1341616.3094613074666-0.309461307466619
1352121.0292327307523-0.029232730752263
1362626.0715816303282-0.0715816303281501
1371818.2493848514483-0.249384851448318
1381818.4742408271431-0.474240827143106
1391717.2852117383983-0.285211738398310
1402222.1127894550919-0.11278945509194
1413028.65161169533741.34838830466263
14202.92325184158514-2.92325184158514
1432424.3774196544525-0.377419654452538
1442121.1736848378696-0.173684837869617
1452121.4055415731769-0.405541573176936
1462928.76286794862310.237132051376895
1473129.56276920765971.43723079234029
14800.431084021843932-0.431084021843932
1491616.2259330922225-0.225933092222503
1502222.0352239577883-0.0352239577882566
1512020.2570507908056-0.25705079080563
1522828.0166912573327-0.0166912573327332
1533837.04632358166840.953676418331649
1542221.9667062156130.0332937843869862
1552020.466852129873-0.466852129872996
1561717.3828668619047-0.38286686190475
1572827.78189897902190.218101020978083
1582222.2097426131333-0.209742613133305
1593130.73724630179370.262753698206326

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 26 & 24.6041995514881 & 1.39580044851185 \tabularnewline
2 & 23 & 25.4130327883993 & -2.41303278839925 \tabularnewline
3 & 25 & 24.2183183396727 & 0.78168166032734 \tabularnewline
4 & 23 & 19.5179366599762 & 3.48206334002381 \tabularnewline
5 & 20 & 22.3741165311321 & -2.37411653113214 \tabularnewline
6 & 29 & 22.2387016296813 & 6.76129837031869 \tabularnewline
7 & 20 & 20.2205856699302 & -0.220585669930177 \tabularnewline
8 & 16 & 16.6015594595828 & -0.601559459582769 \tabularnewline
9 & 18 & 18.4816387188737 & -0.481638718873717 \tabularnewline
10 & 17 & 17.4960001377340 & -0.49600013773396 \tabularnewline
11 & 23 & 23.0502803334575 & -0.0502803334575275 \tabularnewline
12 & 30 & 29.5644417477413 & 0.435558252258724 \tabularnewline
13 & 23 & 22.7065622191804 & 0.293437780819601 \tabularnewline
14 & 18 & 18.2815495853943 & -0.281549585394317 \tabularnewline
15 & 15 & 15.7286961091583 & -0.728696109158332 \tabularnewline
16 & 12 & 12.7283106162804 & -0.728310616280428 \tabularnewline
17 & 21 & 20.9498195332017 & 0.0501804667983207 \tabularnewline
18 & 15 & 15.3891673348079 & -0.389167334807894 \tabularnewline
19 & 20 & 20.2068579140039 & -0.206857914003871 \tabularnewline
20 & 31 & 30.6461690533045 & 0.353830946695542 \tabularnewline
21 & 27 & 26.8465542387064 & 0.153445761293572 \tabularnewline
22 & 34 & 33.4530317080726 & 0.546968291927387 \tabularnewline
23 & 21 & 21.1673111384559 & -0.167311138455912 \tabularnewline
24 & 31 & 30.5027986502654 & 0.497201349734574 \tabularnewline
25 & 19 & 19.3078180295097 & -0.307818029509666 \tabularnewline
26 & 16 & 16.5787508582618 & -0.578750858261844 \tabularnewline
27 & 20 & 20.3178371410299 & -0.317837141029893 \tabularnewline
28 & 21 & 21.1055840416876 & -0.105584041687578 \tabularnewline
29 & 22 & 22.1827699761436 & -0.182769976143621 \tabularnewline
30 & 17 & 17.5205361096234 & -0.52053610962345 \tabularnewline
31 & 24 & 23.9606385098554 & 0.0393614901445705 \tabularnewline
32 & 25 & 25.2453422813576 & -0.245342281357636 \tabularnewline
33 & 26 & 26.0625779104939 & -0.0625779104938981 \tabularnewline
34 & 25 & 24.9901215197081 & 0.00987848029186374 \tabularnewline
35 & 17 & 17.6352757199115 & -0.635275719911508 \tabularnewline
36 & 32 & 31.5968497808248 & 0.403150219175239 \tabularnewline
37 & 33 & 32.3548017515907 & 0.645198248409274 \tabularnewline
38 & 13 & 12.8444245603739 & 0.155575439626082 \tabularnewline
39 & 0 & 2.18794968435404 & -2.18794968435404 \tabularnewline
40 & 25 & 25.174433175096 & -0.174433175096001 \tabularnewline
41 & 29 & 28.8918376034232 & 0.108162396576836 \tabularnewline
42 & 22 & 22.0868260200892 & -0.0868260200891926 \tabularnewline
43 & 18 & 18.2686093376018 & -0.268609337601757 \tabularnewline
44 & 17 & 17.5296000678684 & -0.529600067868443 \tabularnewline
45 & 20 & 20.3328639948932 & -0.332863994893188 \tabularnewline
46 & 15 & 15.634898291154 & -0.634898291154 \tabularnewline
47 & 20 & 20.3458358671561 & -0.345835867156141 \tabularnewline
48 & 33 & 32.5430125446762 & 0.456987455323809 \tabularnewline
49 & 29 & 28.6121314784000 & 0.38786852159997 \tabularnewline
50 & 23 & 23.2818153482682 & -0.281815348268219 \tabularnewline
51 & 26 & 25.8615682049042 & 0.138431795095829 \tabularnewline
52 & 18 & 18.4186711187687 & -0.418671118768677 \tabularnewline
53 & 20 & 20.1016957485837 & -0.101695748583711 \tabularnewline
54 & 11 & 11.4706891159079 & -0.470689115907916 \tabularnewline
55 & 28 & 28.1707423952679 & -0.170742395267928 \tabularnewline
56 & 26 & 25.9896142003860 & 0.0103857996140351 \tabularnewline
57 & 22 & 22.1695069951440 & -0.169506995144028 \tabularnewline
58 & 17 & 17.5343728156567 & -0.53437281565669 \tabularnewline
59 & 12 & 12.5319259276469 & -0.531925927646852 \tabularnewline
60 & 14 & 14.7123555505507 & -0.712355550550712 \tabularnewline
61 & 17 & 17.5107009711061 & -0.510700971106106 \tabularnewline
62 & 21 & 20.3380927470690 & 0.661907252931047 \tabularnewline
63 & 0 & -5.25108566558226 & 5.25108566558226 \tabularnewline
64 & 18 & 18.4794330488443 & -0.479433048844261 \tabularnewline
65 & 10 & 10.9799755833075 & -0.979975583307488 \tabularnewline
66 & 29 & 28.7346391115354 & 0.265360888464633 \tabularnewline
67 & 31 & 30.3255764596980 & 0.67442354030197 \tabularnewline
68 & 19 & 19.4438616524450 & -0.443861652445022 \tabularnewline
69 & 9 & 9.12373445468246 & -0.123734454682459 \tabularnewline
70 & 0 & -0.372268938781072 & 0.372268938781072 \tabularnewline
71 & 28 & 27.5383401714152 & 0.461659828584786 \tabularnewline
72 & 19 & 19.2588847154214 & -0.258884715421411 \tabularnewline
73 & 30 & 29.6367145656765 & 0.363285434323499 \tabularnewline
74 & 29 & 28.8976785788225 & 0.102321421177499 \tabularnewline
75 & 26 & 25.8271010602228 & 0.172898939777158 \tabularnewline
76 & 23 & 23.0101476516417 & -0.0101476516417497 \tabularnewline
77 & 13 & 13.869782539103 & -0.869782539102996 \tabularnewline
78 & 21 & 21.3603289070511 & -0.360328907051057 \tabularnewline
79 & 19 & 19.3574021941919 & -0.357402194191937 \tabularnewline
80 & 28 & 27.7949920874708 & 0.205007912529240 \tabularnewline
81 & 23 & 22.3451927968144 & 0.654807203185622 \tabularnewline
82 & 0 & 0.446493779711898 & -0.446493779711898 \tabularnewline
83 & 21 & 21.1961576111266 & -0.196157611126643 \tabularnewline
84 & 20 & 19.4521857063232 & 0.54781429367675 \tabularnewline
85 & 0 & 7.2377156800752 & -7.2377156800752 \tabularnewline
86 & 21 & 21.2209489982356 & -0.220948998235552 \tabularnewline
87 & 21 & 21.296532797793 & -0.296532797792997 \tabularnewline
88 & 15 & 15.6989440017742 & -0.698944001774215 \tabularnewline
89 & 28 & 26.9753740420348 & 1.02462595796524 \tabularnewline
90 & 0 & -0.262612919822320 & 0.262612919822320 \tabularnewline
91 & 26 & 25.8540910907174 & 0.145908909282640 \tabularnewline
92 & 10 & 9.75667480922528 & 0.243325190774723 \tabularnewline
93 & 0 & 0.0342085643549303 & -0.0342085643549303 \tabularnewline
94 & 22 & 22.1776567768225 & -0.177656776822495 \tabularnewline
95 & 19 & 19.3236304946870 & -0.323630494687042 \tabularnewline
96 & 31 & 30.8519275928890 & 0.148072407111043 \tabularnewline
97 & 31 & 30.5939006437243 & 0.406099356275664 \tabularnewline
98 & 29 & 28.7494305950021 & 0.250569404997940 \tabularnewline
99 & 19 & 19.1361975269074 & -0.136197526907407 \tabularnewline
100 & 22 & 21.9978267604862 & 0.0021732395137644 \tabularnewline
101 & 23 & 23.0837356952992 & -0.083735695299185 \tabularnewline
102 & 15 & 15.3413957017525 & -0.341395701752489 \tabularnewline
103 & 20 & 20.2871768710127 & -0.287176871012682 \tabularnewline
104 & 18 & 18.4718631876671 & -0.471863187667068 \tabularnewline
105 & 23 & 22.0471579374471 & 0.952842062552895 \tabularnewline
106 & 0 & 5.73875916131579 & -5.73875916131579 \tabularnewline
107 & 21 & 20.8699276360379 & 0.130072363962143 \tabularnewline
108 & 24 & 23.8646329249258 & 0.135367075074196 \tabularnewline
109 & 25 & 24.0998567874755 & 0.90014321252454 \tabularnewline
110 & 0 & -1.85640375280187 & 1.85640375280187 \tabularnewline
111 & 13 & 13.5411151924826 & -0.541115192482634 \tabularnewline
112 & 28 & 27.5691937214214 & 0.430806278578555 \tabularnewline
113 & 21 & 20.1181711746810 & 0.881828825318965 \tabularnewline
114 & 0 & -7.15715228035647 & 7.15715228035647 \tabularnewline
115 & 9 & 10.0905632682401 & -1.09056326824013 \tabularnewline
116 & 16 & 16.3128204414968 & -0.312820441496799 \tabularnewline
117 & 19 & 19.2843924990371 & -0.284392499037124 \tabularnewline
118 & 17 & 17.4037431082757 & -0.40374310827568 \tabularnewline
119 & 25 & 24.901282449828 & 0.0987175501719828 \tabularnewline
120 & 20 & 19.9624893812134 & 0.0375106187865633 \tabularnewline
121 & 29 & 28.4688111245886 & 0.531188875411382 \tabularnewline
122 & 14 & 13.6033852267716 & 0.396614773228392 \tabularnewline
123 & 0 & -1.15007446225698 & 1.15007446225698 \tabularnewline
124 & 15 & 15.4353749719551 & -0.435374971955149 \tabularnewline
125 & 19 & 19.4786690485831 & -0.47866904858306 \tabularnewline
126 & 20 & 19.4012843437678 & 0.598715656232224 \tabularnewline
127 & 0 & -4.26985303011458 & 4.26985303011458 \tabularnewline
128 & 20 & 20.3485480195630 & -0.348548019562960 \tabularnewline
129 & 18 & 18.4452068883406 & -0.445206888340618 \tabularnewline
130 & 33 & 32.3939257377748 & 0.606074262225229 \tabularnewline
131 & 22 & 22.1911170315266 & -0.191117031526600 \tabularnewline
132 & 16 & 16.4309318311061 & -0.430931831106053 \tabularnewline
133 & 17 & 17.4128504707148 & -0.412850470714814 \tabularnewline
134 & 16 & 16.3094613074666 & -0.309461307466619 \tabularnewline
135 & 21 & 21.0292327307523 & -0.029232730752263 \tabularnewline
136 & 26 & 26.0715816303282 & -0.0715816303281501 \tabularnewline
137 & 18 & 18.2493848514483 & -0.249384851448318 \tabularnewline
138 & 18 & 18.4742408271431 & -0.474240827143106 \tabularnewline
139 & 17 & 17.2852117383983 & -0.285211738398310 \tabularnewline
140 & 22 & 22.1127894550919 & -0.11278945509194 \tabularnewline
141 & 30 & 28.6516116953374 & 1.34838830466263 \tabularnewline
142 & 0 & 2.92325184158514 & -2.92325184158514 \tabularnewline
143 & 24 & 24.3774196544525 & -0.377419654452538 \tabularnewline
144 & 21 & 21.1736848378696 & -0.173684837869617 \tabularnewline
145 & 21 & 21.4055415731769 & -0.405541573176936 \tabularnewline
146 & 29 & 28.7628679486231 & 0.237132051376895 \tabularnewline
147 & 31 & 29.5627692076597 & 1.43723079234029 \tabularnewline
148 & 0 & 0.431084021843932 & -0.431084021843932 \tabularnewline
149 & 16 & 16.2259330922225 & -0.225933092222503 \tabularnewline
150 & 22 & 22.0352239577883 & -0.0352239577882566 \tabularnewline
151 & 20 & 20.2570507908056 & -0.25705079080563 \tabularnewline
152 & 28 & 28.0166912573327 & -0.0166912573327332 \tabularnewline
153 & 38 & 37.0463235816684 & 0.953676418331649 \tabularnewline
154 & 22 & 21.966706215613 & 0.0332937843869862 \tabularnewline
155 & 20 & 20.466852129873 & -0.466852129872996 \tabularnewline
156 & 17 & 17.3828668619047 & -0.38286686190475 \tabularnewline
157 & 28 & 27.7818989790219 & 0.218101020978083 \tabularnewline
158 & 22 & 22.2097426131333 & -0.209742613133305 \tabularnewline
159 & 31 & 30.7372463017937 & 0.262753698206326 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98873&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]26[/C][C]24.6041995514881[/C][C]1.39580044851185[/C][/ROW]
[ROW][C]2[/C][C]23[/C][C]25.4130327883993[/C][C]-2.41303278839925[/C][/ROW]
[ROW][C]3[/C][C]25[/C][C]24.2183183396727[/C][C]0.78168166032734[/C][/ROW]
[ROW][C]4[/C][C]23[/C][C]19.5179366599762[/C][C]3.48206334002381[/C][/ROW]
[ROW][C]5[/C][C]20[/C][C]22.3741165311321[/C][C]-2.37411653113214[/C][/ROW]
[ROW][C]6[/C][C]29[/C][C]22.2387016296813[/C][C]6.76129837031869[/C][/ROW]
[ROW][C]7[/C][C]20[/C][C]20.2205856699302[/C][C]-0.220585669930177[/C][/ROW]
[ROW][C]8[/C][C]16[/C][C]16.6015594595828[/C][C]-0.601559459582769[/C][/ROW]
[ROW][C]9[/C][C]18[/C][C]18.4816387188737[/C][C]-0.481638718873717[/C][/ROW]
[ROW][C]10[/C][C]17[/C][C]17.4960001377340[/C][C]-0.49600013773396[/C][/ROW]
[ROW][C]11[/C][C]23[/C][C]23.0502803334575[/C][C]-0.0502803334575275[/C][/ROW]
[ROW][C]12[/C][C]30[/C][C]29.5644417477413[/C][C]0.435558252258724[/C][/ROW]
[ROW][C]13[/C][C]23[/C][C]22.7065622191804[/C][C]0.293437780819601[/C][/ROW]
[ROW][C]14[/C][C]18[/C][C]18.2815495853943[/C][C]-0.281549585394317[/C][/ROW]
[ROW][C]15[/C][C]15[/C][C]15.7286961091583[/C][C]-0.728696109158332[/C][/ROW]
[ROW][C]16[/C][C]12[/C][C]12.7283106162804[/C][C]-0.728310616280428[/C][/ROW]
[ROW][C]17[/C][C]21[/C][C]20.9498195332017[/C][C]0.0501804667983207[/C][/ROW]
[ROW][C]18[/C][C]15[/C][C]15.3891673348079[/C][C]-0.389167334807894[/C][/ROW]
[ROW][C]19[/C][C]20[/C][C]20.2068579140039[/C][C]-0.206857914003871[/C][/ROW]
[ROW][C]20[/C][C]31[/C][C]30.6461690533045[/C][C]0.353830946695542[/C][/ROW]
[ROW][C]21[/C][C]27[/C][C]26.8465542387064[/C][C]0.153445761293572[/C][/ROW]
[ROW][C]22[/C][C]34[/C][C]33.4530317080726[/C][C]0.546968291927387[/C][/ROW]
[ROW][C]23[/C][C]21[/C][C]21.1673111384559[/C][C]-0.167311138455912[/C][/ROW]
[ROW][C]24[/C][C]31[/C][C]30.5027986502654[/C][C]0.497201349734574[/C][/ROW]
[ROW][C]25[/C][C]19[/C][C]19.3078180295097[/C][C]-0.307818029509666[/C][/ROW]
[ROW][C]26[/C][C]16[/C][C]16.5787508582618[/C][C]-0.578750858261844[/C][/ROW]
[ROW][C]27[/C][C]20[/C][C]20.3178371410299[/C][C]-0.317837141029893[/C][/ROW]
[ROW][C]28[/C][C]21[/C][C]21.1055840416876[/C][C]-0.105584041687578[/C][/ROW]
[ROW][C]29[/C][C]22[/C][C]22.1827699761436[/C][C]-0.182769976143621[/C][/ROW]
[ROW][C]30[/C][C]17[/C][C]17.5205361096234[/C][C]-0.52053610962345[/C][/ROW]
[ROW][C]31[/C][C]24[/C][C]23.9606385098554[/C][C]0.0393614901445705[/C][/ROW]
[ROW][C]32[/C][C]25[/C][C]25.2453422813576[/C][C]-0.245342281357636[/C][/ROW]
[ROW][C]33[/C][C]26[/C][C]26.0625779104939[/C][C]-0.0625779104938981[/C][/ROW]
[ROW][C]34[/C][C]25[/C][C]24.9901215197081[/C][C]0.00987848029186374[/C][/ROW]
[ROW][C]35[/C][C]17[/C][C]17.6352757199115[/C][C]-0.635275719911508[/C][/ROW]
[ROW][C]36[/C][C]32[/C][C]31.5968497808248[/C][C]0.403150219175239[/C][/ROW]
[ROW][C]37[/C][C]33[/C][C]32.3548017515907[/C][C]0.645198248409274[/C][/ROW]
[ROW][C]38[/C][C]13[/C][C]12.8444245603739[/C][C]0.155575439626082[/C][/ROW]
[ROW][C]39[/C][C]0[/C][C]2.18794968435404[/C][C]-2.18794968435404[/C][/ROW]
[ROW][C]40[/C][C]25[/C][C]25.174433175096[/C][C]-0.174433175096001[/C][/ROW]
[ROW][C]41[/C][C]29[/C][C]28.8918376034232[/C][C]0.108162396576836[/C][/ROW]
[ROW][C]42[/C][C]22[/C][C]22.0868260200892[/C][C]-0.0868260200891926[/C][/ROW]
[ROW][C]43[/C][C]18[/C][C]18.2686093376018[/C][C]-0.268609337601757[/C][/ROW]
[ROW][C]44[/C][C]17[/C][C]17.5296000678684[/C][C]-0.529600067868443[/C][/ROW]
[ROW][C]45[/C][C]20[/C][C]20.3328639948932[/C][C]-0.332863994893188[/C][/ROW]
[ROW][C]46[/C][C]15[/C][C]15.634898291154[/C][C]-0.634898291154[/C][/ROW]
[ROW][C]47[/C][C]20[/C][C]20.3458358671561[/C][C]-0.345835867156141[/C][/ROW]
[ROW][C]48[/C][C]33[/C][C]32.5430125446762[/C][C]0.456987455323809[/C][/ROW]
[ROW][C]49[/C][C]29[/C][C]28.6121314784000[/C][C]0.38786852159997[/C][/ROW]
[ROW][C]50[/C][C]23[/C][C]23.2818153482682[/C][C]-0.281815348268219[/C][/ROW]
[ROW][C]51[/C][C]26[/C][C]25.8615682049042[/C][C]0.138431795095829[/C][/ROW]
[ROW][C]52[/C][C]18[/C][C]18.4186711187687[/C][C]-0.418671118768677[/C][/ROW]
[ROW][C]53[/C][C]20[/C][C]20.1016957485837[/C][C]-0.101695748583711[/C][/ROW]
[ROW][C]54[/C][C]11[/C][C]11.4706891159079[/C][C]-0.470689115907916[/C][/ROW]
[ROW][C]55[/C][C]28[/C][C]28.1707423952679[/C][C]-0.170742395267928[/C][/ROW]
[ROW][C]56[/C][C]26[/C][C]25.9896142003860[/C][C]0.0103857996140351[/C][/ROW]
[ROW][C]57[/C][C]22[/C][C]22.1695069951440[/C][C]-0.169506995144028[/C][/ROW]
[ROW][C]58[/C][C]17[/C][C]17.5343728156567[/C][C]-0.53437281565669[/C][/ROW]
[ROW][C]59[/C][C]12[/C][C]12.5319259276469[/C][C]-0.531925927646852[/C][/ROW]
[ROW][C]60[/C][C]14[/C][C]14.7123555505507[/C][C]-0.712355550550712[/C][/ROW]
[ROW][C]61[/C][C]17[/C][C]17.5107009711061[/C][C]-0.510700971106106[/C][/ROW]
[ROW][C]62[/C][C]21[/C][C]20.3380927470690[/C][C]0.661907252931047[/C][/ROW]
[ROW][C]63[/C][C]0[/C][C]-5.25108566558226[/C][C]5.25108566558226[/C][/ROW]
[ROW][C]64[/C][C]18[/C][C]18.4794330488443[/C][C]-0.479433048844261[/C][/ROW]
[ROW][C]65[/C][C]10[/C][C]10.9799755833075[/C][C]-0.979975583307488[/C][/ROW]
[ROW][C]66[/C][C]29[/C][C]28.7346391115354[/C][C]0.265360888464633[/C][/ROW]
[ROW][C]67[/C][C]31[/C][C]30.3255764596980[/C][C]0.67442354030197[/C][/ROW]
[ROW][C]68[/C][C]19[/C][C]19.4438616524450[/C][C]-0.443861652445022[/C][/ROW]
[ROW][C]69[/C][C]9[/C][C]9.12373445468246[/C][C]-0.123734454682459[/C][/ROW]
[ROW][C]70[/C][C]0[/C][C]-0.372268938781072[/C][C]0.372268938781072[/C][/ROW]
[ROW][C]71[/C][C]28[/C][C]27.5383401714152[/C][C]0.461659828584786[/C][/ROW]
[ROW][C]72[/C][C]19[/C][C]19.2588847154214[/C][C]-0.258884715421411[/C][/ROW]
[ROW][C]73[/C][C]30[/C][C]29.6367145656765[/C][C]0.363285434323499[/C][/ROW]
[ROW][C]74[/C][C]29[/C][C]28.8976785788225[/C][C]0.102321421177499[/C][/ROW]
[ROW][C]75[/C][C]26[/C][C]25.8271010602228[/C][C]0.172898939777158[/C][/ROW]
[ROW][C]76[/C][C]23[/C][C]23.0101476516417[/C][C]-0.0101476516417497[/C][/ROW]
[ROW][C]77[/C][C]13[/C][C]13.869782539103[/C][C]-0.869782539102996[/C][/ROW]
[ROW][C]78[/C][C]21[/C][C]21.3603289070511[/C][C]-0.360328907051057[/C][/ROW]
[ROW][C]79[/C][C]19[/C][C]19.3574021941919[/C][C]-0.357402194191937[/C][/ROW]
[ROW][C]80[/C][C]28[/C][C]27.7949920874708[/C][C]0.205007912529240[/C][/ROW]
[ROW][C]81[/C][C]23[/C][C]22.3451927968144[/C][C]0.654807203185622[/C][/ROW]
[ROW][C]82[/C][C]0[/C][C]0.446493779711898[/C][C]-0.446493779711898[/C][/ROW]
[ROW][C]83[/C][C]21[/C][C]21.1961576111266[/C][C]-0.196157611126643[/C][/ROW]
[ROW][C]84[/C][C]20[/C][C]19.4521857063232[/C][C]0.54781429367675[/C][/ROW]
[ROW][C]85[/C][C]0[/C][C]7.2377156800752[/C][C]-7.2377156800752[/C][/ROW]
[ROW][C]86[/C][C]21[/C][C]21.2209489982356[/C][C]-0.220948998235552[/C][/ROW]
[ROW][C]87[/C][C]21[/C][C]21.296532797793[/C][C]-0.296532797792997[/C][/ROW]
[ROW][C]88[/C][C]15[/C][C]15.6989440017742[/C][C]-0.698944001774215[/C][/ROW]
[ROW][C]89[/C][C]28[/C][C]26.9753740420348[/C][C]1.02462595796524[/C][/ROW]
[ROW][C]90[/C][C]0[/C][C]-0.262612919822320[/C][C]0.262612919822320[/C][/ROW]
[ROW][C]91[/C][C]26[/C][C]25.8540910907174[/C][C]0.145908909282640[/C][/ROW]
[ROW][C]92[/C][C]10[/C][C]9.75667480922528[/C][C]0.243325190774723[/C][/ROW]
[ROW][C]93[/C][C]0[/C][C]0.0342085643549303[/C][C]-0.0342085643549303[/C][/ROW]
[ROW][C]94[/C][C]22[/C][C]22.1776567768225[/C][C]-0.177656776822495[/C][/ROW]
[ROW][C]95[/C][C]19[/C][C]19.3236304946870[/C][C]-0.323630494687042[/C][/ROW]
[ROW][C]96[/C][C]31[/C][C]30.8519275928890[/C][C]0.148072407111043[/C][/ROW]
[ROW][C]97[/C][C]31[/C][C]30.5939006437243[/C][C]0.406099356275664[/C][/ROW]
[ROW][C]98[/C][C]29[/C][C]28.7494305950021[/C][C]0.250569404997940[/C][/ROW]
[ROW][C]99[/C][C]19[/C][C]19.1361975269074[/C][C]-0.136197526907407[/C][/ROW]
[ROW][C]100[/C][C]22[/C][C]21.9978267604862[/C][C]0.0021732395137644[/C][/ROW]
[ROW][C]101[/C][C]23[/C][C]23.0837356952992[/C][C]-0.083735695299185[/C][/ROW]
[ROW][C]102[/C][C]15[/C][C]15.3413957017525[/C][C]-0.341395701752489[/C][/ROW]
[ROW][C]103[/C][C]20[/C][C]20.2871768710127[/C][C]-0.287176871012682[/C][/ROW]
[ROW][C]104[/C][C]18[/C][C]18.4718631876671[/C][C]-0.471863187667068[/C][/ROW]
[ROW][C]105[/C][C]23[/C][C]22.0471579374471[/C][C]0.952842062552895[/C][/ROW]
[ROW][C]106[/C][C]0[/C][C]5.73875916131579[/C][C]-5.73875916131579[/C][/ROW]
[ROW][C]107[/C][C]21[/C][C]20.8699276360379[/C][C]0.130072363962143[/C][/ROW]
[ROW][C]108[/C][C]24[/C][C]23.8646329249258[/C][C]0.135367075074196[/C][/ROW]
[ROW][C]109[/C][C]25[/C][C]24.0998567874755[/C][C]0.90014321252454[/C][/ROW]
[ROW][C]110[/C][C]0[/C][C]-1.85640375280187[/C][C]1.85640375280187[/C][/ROW]
[ROW][C]111[/C][C]13[/C][C]13.5411151924826[/C][C]-0.541115192482634[/C][/ROW]
[ROW][C]112[/C][C]28[/C][C]27.5691937214214[/C][C]0.430806278578555[/C][/ROW]
[ROW][C]113[/C][C]21[/C][C]20.1181711746810[/C][C]0.881828825318965[/C][/ROW]
[ROW][C]114[/C][C]0[/C][C]-7.15715228035647[/C][C]7.15715228035647[/C][/ROW]
[ROW][C]115[/C][C]9[/C][C]10.0905632682401[/C][C]-1.09056326824013[/C][/ROW]
[ROW][C]116[/C][C]16[/C][C]16.3128204414968[/C][C]-0.312820441496799[/C][/ROW]
[ROW][C]117[/C][C]19[/C][C]19.2843924990371[/C][C]-0.284392499037124[/C][/ROW]
[ROW][C]118[/C][C]17[/C][C]17.4037431082757[/C][C]-0.40374310827568[/C][/ROW]
[ROW][C]119[/C][C]25[/C][C]24.901282449828[/C][C]0.0987175501719828[/C][/ROW]
[ROW][C]120[/C][C]20[/C][C]19.9624893812134[/C][C]0.0375106187865633[/C][/ROW]
[ROW][C]121[/C][C]29[/C][C]28.4688111245886[/C][C]0.531188875411382[/C][/ROW]
[ROW][C]122[/C][C]14[/C][C]13.6033852267716[/C][C]0.396614773228392[/C][/ROW]
[ROW][C]123[/C][C]0[/C][C]-1.15007446225698[/C][C]1.15007446225698[/C][/ROW]
[ROW][C]124[/C][C]15[/C][C]15.4353749719551[/C][C]-0.435374971955149[/C][/ROW]
[ROW][C]125[/C][C]19[/C][C]19.4786690485831[/C][C]-0.47866904858306[/C][/ROW]
[ROW][C]126[/C][C]20[/C][C]19.4012843437678[/C][C]0.598715656232224[/C][/ROW]
[ROW][C]127[/C][C]0[/C][C]-4.26985303011458[/C][C]4.26985303011458[/C][/ROW]
[ROW][C]128[/C][C]20[/C][C]20.3485480195630[/C][C]-0.348548019562960[/C][/ROW]
[ROW][C]129[/C][C]18[/C][C]18.4452068883406[/C][C]-0.445206888340618[/C][/ROW]
[ROW][C]130[/C][C]33[/C][C]32.3939257377748[/C][C]0.606074262225229[/C][/ROW]
[ROW][C]131[/C][C]22[/C][C]22.1911170315266[/C][C]-0.191117031526600[/C][/ROW]
[ROW][C]132[/C][C]16[/C][C]16.4309318311061[/C][C]-0.430931831106053[/C][/ROW]
[ROW][C]133[/C][C]17[/C][C]17.4128504707148[/C][C]-0.412850470714814[/C][/ROW]
[ROW][C]134[/C][C]16[/C][C]16.3094613074666[/C][C]-0.309461307466619[/C][/ROW]
[ROW][C]135[/C][C]21[/C][C]21.0292327307523[/C][C]-0.029232730752263[/C][/ROW]
[ROW][C]136[/C][C]26[/C][C]26.0715816303282[/C][C]-0.0715816303281501[/C][/ROW]
[ROW][C]137[/C][C]18[/C][C]18.2493848514483[/C][C]-0.249384851448318[/C][/ROW]
[ROW][C]138[/C][C]18[/C][C]18.4742408271431[/C][C]-0.474240827143106[/C][/ROW]
[ROW][C]139[/C][C]17[/C][C]17.2852117383983[/C][C]-0.285211738398310[/C][/ROW]
[ROW][C]140[/C][C]22[/C][C]22.1127894550919[/C][C]-0.11278945509194[/C][/ROW]
[ROW][C]141[/C][C]30[/C][C]28.6516116953374[/C][C]1.34838830466263[/C][/ROW]
[ROW][C]142[/C][C]0[/C][C]2.92325184158514[/C][C]-2.92325184158514[/C][/ROW]
[ROW][C]143[/C][C]24[/C][C]24.3774196544525[/C][C]-0.377419654452538[/C][/ROW]
[ROW][C]144[/C][C]21[/C][C]21.1736848378696[/C][C]-0.173684837869617[/C][/ROW]
[ROW][C]145[/C][C]21[/C][C]21.4055415731769[/C][C]-0.405541573176936[/C][/ROW]
[ROW][C]146[/C][C]29[/C][C]28.7628679486231[/C][C]0.237132051376895[/C][/ROW]
[ROW][C]147[/C][C]31[/C][C]29.5627692076597[/C][C]1.43723079234029[/C][/ROW]
[ROW][C]148[/C][C]0[/C][C]0.431084021843932[/C][C]-0.431084021843932[/C][/ROW]
[ROW][C]149[/C][C]16[/C][C]16.2259330922225[/C][C]-0.225933092222503[/C][/ROW]
[ROW][C]150[/C][C]22[/C][C]22.0352239577883[/C][C]-0.0352239577882566[/C][/ROW]
[ROW][C]151[/C][C]20[/C][C]20.2570507908056[/C][C]-0.25705079080563[/C][/ROW]
[ROW][C]152[/C][C]28[/C][C]28.0166912573327[/C][C]-0.0166912573327332[/C][/ROW]
[ROW][C]153[/C][C]38[/C][C]37.0463235816684[/C][C]0.953676418331649[/C][/ROW]
[ROW][C]154[/C][C]22[/C][C]21.966706215613[/C][C]0.0332937843869862[/C][/ROW]
[ROW][C]155[/C][C]20[/C][C]20.466852129873[/C][C]-0.466852129872996[/C][/ROW]
[ROW][C]156[/C][C]17[/C][C]17.3828668619047[/C][C]-0.38286686190475[/C][/ROW]
[ROW][C]157[/C][C]28[/C][C]27.7818989790219[/C][C]0.218101020978083[/C][/ROW]
[ROW][C]158[/C][C]22[/C][C]22.2097426131333[/C][C]-0.209742613133305[/C][/ROW]
[ROW][C]159[/C][C]31[/C][C]30.7372463017937[/C][C]0.262753698206326[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98873&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98873&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
12624.60419955148811.39580044851185
22325.4130327883993-2.41303278839925
32524.21831833967270.78168166032734
42319.51793665997623.48206334002381
52022.3741165311321-2.37411653113214
62922.23870162968136.76129837031869
72020.2205856699302-0.220585669930177
81616.6015594595828-0.601559459582769
91818.4816387188737-0.481638718873717
101717.4960001377340-0.49600013773396
112323.0502803334575-0.0502803334575275
123029.56444174774130.435558252258724
132322.70656221918040.293437780819601
141818.2815495853943-0.281549585394317
151515.7286961091583-0.728696109158332
161212.7283106162804-0.728310616280428
172120.94981953320170.0501804667983207
181515.3891673348079-0.389167334807894
192020.2068579140039-0.206857914003871
203130.64616905330450.353830946695542
212726.84655423870640.153445761293572
223433.45303170807260.546968291927387
232121.1673111384559-0.167311138455912
243130.50279865026540.497201349734574
251919.3078180295097-0.307818029509666
261616.5787508582618-0.578750858261844
272020.3178371410299-0.317837141029893
282121.1055840416876-0.105584041687578
292222.1827699761436-0.182769976143621
301717.5205361096234-0.52053610962345
312423.96063850985540.0393614901445705
322525.2453422813576-0.245342281357636
332626.0625779104939-0.0625779104938981
342524.99012151970810.00987848029186374
351717.6352757199115-0.635275719911508
363231.59684978082480.403150219175239
373332.35480175159070.645198248409274
381312.84442456037390.155575439626082
3902.18794968435404-2.18794968435404
402525.174433175096-0.174433175096001
412928.89183760342320.108162396576836
422222.0868260200892-0.0868260200891926
431818.2686093376018-0.268609337601757
441717.5296000678684-0.529600067868443
452020.3328639948932-0.332863994893188
461515.634898291154-0.634898291154
472020.3458358671561-0.345835867156141
483332.54301254467620.456987455323809
492928.61213147840000.38786852159997
502323.2818153482682-0.281815348268219
512625.86156820490420.138431795095829
521818.4186711187687-0.418671118768677
532020.1016957485837-0.101695748583711
541111.4706891159079-0.470689115907916
552828.1707423952679-0.170742395267928
562625.98961420038600.0103857996140351
572222.1695069951440-0.169506995144028
581717.5343728156567-0.53437281565669
591212.5319259276469-0.531925927646852
601414.7123555505507-0.712355550550712
611717.5107009711061-0.510700971106106
622120.33809274706900.661907252931047
630-5.251085665582265.25108566558226
641818.4794330488443-0.479433048844261
651010.9799755833075-0.979975583307488
662928.73463911153540.265360888464633
673130.32557645969800.67442354030197
681919.4438616524450-0.443861652445022
6999.12373445468246-0.123734454682459
700-0.3722689387810720.372268938781072
712827.53834017141520.461659828584786
721919.2588847154214-0.258884715421411
733029.63671456567650.363285434323499
742928.89767857882250.102321421177499
752625.82710106022280.172898939777158
762323.0101476516417-0.0101476516417497
771313.869782539103-0.869782539102996
782121.3603289070511-0.360328907051057
791919.3574021941919-0.357402194191937
802827.79499208747080.205007912529240
812322.34519279681440.654807203185622
8200.446493779711898-0.446493779711898
832121.1961576111266-0.196157611126643
842019.45218570632320.54781429367675
8507.2377156800752-7.2377156800752
862121.2209489982356-0.220948998235552
872121.296532797793-0.296532797792997
881515.6989440017742-0.698944001774215
892826.97537404203481.02462595796524
900-0.2626129198223200.262612919822320
912625.85409109071740.145908909282640
92109.756674809225280.243325190774723
9300.0342085643549303-0.0342085643549303
942222.1776567768225-0.177656776822495
951919.3236304946870-0.323630494687042
963130.85192759288900.148072407111043
973130.59390064372430.406099356275664
982928.74943059500210.250569404997940
991919.1361975269074-0.136197526907407
1002221.99782676048620.0021732395137644
1012323.0837356952992-0.083735695299185
1021515.3413957017525-0.341395701752489
1032020.2871768710127-0.287176871012682
1041818.4718631876671-0.471863187667068
1052322.04715793744710.952842062552895
10605.73875916131579-5.73875916131579
1072120.86992763603790.130072363962143
1082423.86463292492580.135367075074196
1092524.09985678747550.90014321252454
1100-1.856403752801871.85640375280187
1111313.5411151924826-0.541115192482634
1122827.56919372142140.430806278578555
1132120.11817117468100.881828825318965
1140-7.157152280356477.15715228035647
115910.0905632682401-1.09056326824013
1161616.3128204414968-0.312820441496799
1171919.2843924990371-0.284392499037124
1181717.4037431082757-0.40374310827568
1192524.9012824498280.0987175501719828
1202019.96248938121340.0375106187865633
1212928.46881112458860.531188875411382
1221413.60338522677160.396614773228392
1230-1.150074462256981.15007446225698
1241515.4353749719551-0.435374971955149
1251919.4786690485831-0.47866904858306
1262019.40128434376780.598715656232224
1270-4.269853030114584.26985303011458
1282020.3485480195630-0.348548019562960
1291818.4452068883406-0.445206888340618
1303332.39392573777480.606074262225229
1312222.1911170315266-0.191117031526600
1321616.4309318311061-0.430931831106053
1331717.4128504707148-0.412850470714814
1341616.3094613074666-0.309461307466619
1352121.0292327307523-0.029232730752263
1362626.0715816303282-0.0715816303281501
1371818.2493848514483-0.249384851448318
1381818.4742408271431-0.474240827143106
1391717.2852117383983-0.285211738398310
1402222.1127894550919-0.11278945509194
1413028.65161169533741.34838830466263
14202.92325184158514-2.92325184158514
1432424.3774196544525-0.377419654452538
1442121.1736848378696-0.173684837869617
1452121.4055415731769-0.405541573176936
1462928.76286794862310.237132051376895
1473129.56276920765971.43723079234029
14800.431084021843932-0.431084021843932
1491616.2259330922225-0.225933092222503
1502222.0352239577883-0.0352239577882566
1512020.2570507908056-0.25705079080563
1522828.0166912573327-0.0166912573327332
1533837.04632358166840.953676418331649
1542221.9667062156130.0332937843869862
1552020.466852129873-0.466852129872996
1561717.3828668619047-0.38286686190475
1572827.78189897902190.218101020978083
1582222.2097426131333-0.209742613133305
1593130.73724630179370.262753698206326







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
150.9318903247751490.1362193504497030.0681096752248514
160.9968050819216950.00638983615661050.00319491807830525
170.9928763114391920.01424737712161610.00712368856080807
180.985384367544230.02923126491153830.0146156324557692
190.9830952896631740.03380942067365160.0169047103368258
200.970856332054660.05828733589067940.0291436679453397
210.9632075221269360.07358495574612870.0367924778730644
220.942149601773130.1157007964537390.0578503982268694
230.91273228538190.1745354292361990.0872677146180996
240.9650100132423320.06997997351533690.0349899867576685
250.947600648535720.1047987029285590.0523993514642797
260.9247262686917880.1505474626164250.0752737313082124
270.8952002199360970.2095995601278060.104799780063903
280.8580809797363610.2838380405272770.141919020263639
290.8133118387392060.3733763225215890.186688161260794
300.76977837469880.4604432506023990.230221625301199
310.7621131474026870.4757737051946270.237886852597313
320.7226346498817690.5547307002364630.277365350118231
330.6638504872041680.6722990255916640.336149512795832
340.6207090854084030.7585818291831940.379290914591597
350.5605564879901360.8788870240197280.439443512009864
360.5138125757638770.9723748484722450.486187424236123
370.4703941428274720.9407882856549450.529605857172528
380.4136575781733380.8273151563466760.586342421826662
390.5989743697082970.8020512605834050.401025630291703
400.5544929200017280.8910141599965440.445507079998272
410.4956622767746560.9913245535493120.504337723225344
420.4393055887427550.878611177485510.560694411257245
430.3854785274069980.7709570548139960.614521472593002
440.3360576254323690.6721152508647380.663942374567631
450.2875672835197010.5751345670394030.712432716480299
460.2422175044405640.4844350088811280.757782495559436
470.2011319766039760.4022639532079520.798868023396024
480.1671707873424520.3343415746849050.832829212657548
490.1360074515731750.2720149031463510.863992548426824
500.1100517112446260.2201034224892510.889948288755374
510.08633583343720960.1726716668744190.91366416656279
520.06765830747217390.1353166149443480.932341692527826
530.05291754103910860.1058350820782170.947082458960891
540.03980308500496550.0796061700099310.960196914995035
550.03047492367887720.06094984735775440.969525076321123
560.02248201920744220.04496403841488450.977517980792558
570.01616870357703830.03233740715407650.983831296422962
580.01170990679544470.02341981359088940.988290093204555
590.008294450049298920.01658890009859780.991705549950701
600.00599323754579840.01198647509159680.994006762454202
610.004111996227775750.00822399245555150.995888003772224
620.004409942937015090.008819885874030180.995590057062985
630.01138282136996620.02276564273993240.988617178630034
640.008065125352211930.01613025070442390.991934874647788
650.005984439018535020.01196887803707000.994015560981465
660.004203602593084690.008407205186169380.995796397406915
670.003076827440668010.006153654881336030.996923172559332
680.002100395818528080.004200791637056170.997899604181472
690.001843086301977760.003686172603955520.998156913698022
700.08573419243754130.1714683848750830.914265807562459
710.07045936205421710.1409187241084340.929540637945783
720.0555877519990650.111175503998130.944412248000935
730.04347856092208660.08695712184417310.956521439077913
740.03321796963960980.06643593927921950.96678203036039
750.02516763575563530.05033527151127060.974832364244365
760.01884149364322320.03768298728644630.981158506356777
770.01538034887803060.03076069775606120.98461965112197
780.01155659907173130.02311319814346260.988443400928269
790.008360040055652880.01672008011130580.991639959944347
800.006000730943367130.01200146188673430.993999269056633
810.005696938941721140.01139387788344230.994303061058279
820.0193095068878420.0386190137756840.980690493112158
830.01432014855753490.02864029711506980.985679851442465
840.01334727978265770.02669455956531530.986652720217342
850.9695889427609940.06082211447801170.0304110572390058
860.96050888406810.07898223186380160.0394911159319008
870.9512000026986010.0975999946027970.0487999973013985
880.9398053505464370.1203892989071260.060194649453563
890.9270992476348850.145801504730230.072900752365115
900.9356620442592170.1286759114815660.064337955740783
910.918652260359560.1626954792808800.0813477396404401
920.9039739508304750.1920520983390490.0960260491695245
930.9251920402201290.1496159195597420.0748079597798712
940.9093440251176690.1813119497646630.0906559748823313
950.888459864581470.2230802708370610.111540135418531
960.8625002887995370.2749994224009260.137499711200463
970.8339358747198880.3321282505602230.166064125280112
980.8014632964055970.3970734071888070.198536703594403
990.7641360607240270.4717278785519470.235863939275974
1000.7225615711659650.554876857668070.277438428834035
1010.6778705345918290.6442589308163420.322129465408171
1020.6331065565270830.7337868869458340.366893443472917
1030.5846249278468480.8307501443063030.415375072153152
1040.5497089087106820.9005821825786360.450291091289318
1050.5093418522958230.9813162954083550.490658147704177
1060.9995336153260940.0009327693478126840.000466384673906342
1070.9993365405526180.001326918894763350.000663459447381676
1080.9989210751268820.002157849746236070.00107892487311803
1090.9983174234904550.00336515301908930.00168257650954465
1100.9996767310327040.0006465379345928520.000323268967296426
1110.9994556260540850.00108874789182910.00054437394591455
1120.9991427143280520.001714571343896470.000857285671948233
1130.9987752998681780.002449400263644210.00122470013182210
114100
115100
116100
117100
118100
119100
120100
121100
122100
123100
124100
12514.25610936260445e-3042.12805468130223e-304
12614.84385149583064e-2942.42192574791532e-294
12718.7512927611759e-2784.37564638058795e-278
12815.65416621894546e-2632.82708310947273e-263
12912.30484563619042e-2451.15242281809521e-245
13013.05972870069150e-2301.52986435034575e-230
13111.32410842359731e-2236.62054211798654e-224
13213.79293484597089e-2091.89646742298545e-209
13318.4189297430175e-1954.20946487150875e-195
13411.01646719401544e-1825.08233597007721e-183
13515.71862489918074e-1682.85931244959037e-168
13612.35328575929728e-1541.17664287964864e-154
13714.30226771089198e-1422.15113385544599e-142
13819.92764614521413e-1264.96382307260707e-126
13911.06614260278708e-1125.33071301393539e-113
14011.9543900810254e-979.771950405127e-98
14117.62792043350856e-823.81396021675428e-82
14212.03877361448461e-731.01938680724230e-73
14315.91295474588529e-592.95647737294264e-59
14412.51294829863939e-451.25647414931970e-45

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
15 & 0.931890324775149 & 0.136219350449703 & 0.0681096752248514 \tabularnewline
16 & 0.996805081921695 & 0.0063898361566105 & 0.00319491807830525 \tabularnewline
17 & 0.992876311439192 & 0.0142473771216161 & 0.00712368856080807 \tabularnewline
18 & 0.98538436754423 & 0.0292312649115383 & 0.0146156324557692 \tabularnewline
19 & 0.983095289663174 & 0.0338094206736516 & 0.0169047103368258 \tabularnewline
20 & 0.97085633205466 & 0.0582873358906794 & 0.0291436679453397 \tabularnewline
21 & 0.963207522126936 & 0.0735849557461287 & 0.0367924778730644 \tabularnewline
22 & 0.94214960177313 & 0.115700796453739 & 0.0578503982268694 \tabularnewline
23 & 0.9127322853819 & 0.174535429236199 & 0.0872677146180996 \tabularnewline
24 & 0.965010013242332 & 0.0699799735153369 & 0.0349899867576685 \tabularnewline
25 & 0.94760064853572 & 0.104798702928559 & 0.0523993514642797 \tabularnewline
26 & 0.924726268691788 & 0.150547462616425 & 0.0752737313082124 \tabularnewline
27 & 0.895200219936097 & 0.209599560127806 & 0.104799780063903 \tabularnewline
28 & 0.858080979736361 & 0.283838040527277 & 0.141919020263639 \tabularnewline
29 & 0.813311838739206 & 0.373376322521589 & 0.186688161260794 \tabularnewline
30 & 0.7697783746988 & 0.460443250602399 & 0.230221625301199 \tabularnewline
31 & 0.762113147402687 & 0.475773705194627 & 0.237886852597313 \tabularnewline
32 & 0.722634649881769 & 0.554730700236463 & 0.277365350118231 \tabularnewline
33 & 0.663850487204168 & 0.672299025591664 & 0.336149512795832 \tabularnewline
34 & 0.620709085408403 & 0.758581829183194 & 0.379290914591597 \tabularnewline
35 & 0.560556487990136 & 0.878887024019728 & 0.439443512009864 \tabularnewline
36 & 0.513812575763877 & 0.972374848472245 & 0.486187424236123 \tabularnewline
37 & 0.470394142827472 & 0.940788285654945 & 0.529605857172528 \tabularnewline
38 & 0.413657578173338 & 0.827315156346676 & 0.586342421826662 \tabularnewline
39 & 0.598974369708297 & 0.802051260583405 & 0.401025630291703 \tabularnewline
40 & 0.554492920001728 & 0.891014159996544 & 0.445507079998272 \tabularnewline
41 & 0.495662276774656 & 0.991324553549312 & 0.504337723225344 \tabularnewline
42 & 0.439305588742755 & 0.87861117748551 & 0.560694411257245 \tabularnewline
43 & 0.385478527406998 & 0.770957054813996 & 0.614521472593002 \tabularnewline
44 & 0.336057625432369 & 0.672115250864738 & 0.663942374567631 \tabularnewline
45 & 0.287567283519701 & 0.575134567039403 & 0.712432716480299 \tabularnewline
46 & 0.242217504440564 & 0.484435008881128 & 0.757782495559436 \tabularnewline
47 & 0.201131976603976 & 0.402263953207952 & 0.798868023396024 \tabularnewline
48 & 0.167170787342452 & 0.334341574684905 & 0.832829212657548 \tabularnewline
49 & 0.136007451573175 & 0.272014903146351 & 0.863992548426824 \tabularnewline
50 & 0.110051711244626 & 0.220103422489251 & 0.889948288755374 \tabularnewline
51 & 0.0863358334372096 & 0.172671666874419 & 0.91366416656279 \tabularnewline
52 & 0.0676583074721739 & 0.135316614944348 & 0.932341692527826 \tabularnewline
53 & 0.0529175410391086 & 0.105835082078217 & 0.947082458960891 \tabularnewline
54 & 0.0398030850049655 & 0.079606170009931 & 0.960196914995035 \tabularnewline
55 & 0.0304749236788772 & 0.0609498473577544 & 0.969525076321123 \tabularnewline
56 & 0.0224820192074422 & 0.0449640384148845 & 0.977517980792558 \tabularnewline
57 & 0.0161687035770383 & 0.0323374071540765 & 0.983831296422962 \tabularnewline
58 & 0.0117099067954447 & 0.0234198135908894 & 0.988290093204555 \tabularnewline
59 & 0.00829445004929892 & 0.0165889000985978 & 0.991705549950701 \tabularnewline
60 & 0.0059932375457984 & 0.0119864750915968 & 0.994006762454202 \tabularnewline
61 & 0.00411199622777575 & 0.0082239924555515 & 0.995888003772224 \tabularnewline
62 & 0.00440994293701509 & 0.00881988587403018 & 0.995590057062985 \tabularnewline
63 & 0.0113828213699662 & 0.0227656427399324 & 0.988617178630034 \tabularnewline
64 & 0.00806512535221193 & 0.0161302507044239 & 0.991934874647788 \tabularnewline
65 & 0.00598443901853502 & 0.0119688780370700 & 0.994015560981465 \tabularnewline
66 & 0.00420360259308469 & 0.00840720518616938 & 0.995796397406915 \tabularnewline
67 & 0.00307682744066801 & 0.00615365488133603 & 0.996923172559332 \tabularnewline
68 & 0.00210039581852808 & 0.00420079163705617 & 0.997899604181472 \tabularnewline
69 & 0.00184308630197776 & 0.00368617260395552 & 0.998156913698022 \tabularnewline
70 & 0.0857341924375413 & 0.171468384875083 & 0.914265807562459 \tabularnewline
71 & 0.0704593620542171 & 0.140918724108434 & 0.929540637945783 \tabularnewline
72 & 0.055587751999065 & 0.11117550399813 & 0.944412248000935 \tabularnewline
73 & 0.0434785609220866 & 0.0869571218441731 & 0.956521439077913 \tabularnewline
74 & 0.0332179696396098 & 0.0664359392792195 & 0.96678203036039 \tabularnewline
75 & 0.0251676357556353 & 0.0503352715112706 & 0.974832364244365 \tabularnewline
76 & 0.0188414936432232 & 0.0376829872864463 & 0.981158506356777 \tabularnewline
77 & 0.0153803488780306 & 0.0307606977560612 & 0.98461965112197 \tabularnewline
78 & 0.0115565990717313 & 0.0231131981434626 & 0.988443400928269 \tabularnewline
79 & 0.00836004005565288 & 0.0167200801113058 & 0.991639959944347 \tabularnewline
80 & 0.00600073094336713 & 0.0120014618867343 & 0.993999269056633 \tabularnewline
81 & 0.00569693894172114 & 0.0113938778834423 & 0.994303061058279 \tabularnewline
82 & 0.019309506887842 & 0.038619013775684 & 0.980690493112158 \tabularnewline
83 & 0.0143201485575349 & 0.0286402971150698 & 0.985679851442465 \tabularnewline
84 & 0.0133472797826577 & 0.0266945595653153 & 0.986652720217342 \tabularnewline
85 & 0.969588942760994 & 0.0608221144780117 & 0.0304110572390058 \tabularnewline
86 & 0.9605088840681 & 0.0789822318638016 & 0.0394911159319008 \tabularnewline
87 & 0.951200002698601 & 0.097599994602797 & 0.0487999973013985 \tabularnewline
88 & 0.939805350546437 & 0.120389298907126 & 0.060194649453563 \tabularnewline
89 & 0.927099247634885 & 0.14580150473023 & 0.072900752365115 \tabularnewline
90 & 0.935662044259217 & 0.128675911481566 & 0.064337955740783 \tabularnewline
91 & 0.91865226035956 & 0.162695479280880 & 0.0813477396404401 \tabularnewline
92 & 0.903973950830475 & 0.192052098339049 & 0.0960260491695245 \tabularnewline
93 & 0.925192040220129 & 0.149615919559742 & 0.0748079597798712 \tabularnewline
94 & 0.909344025117669 & 0.181311949764663 & 0.0906559748823313 \tabularnewline
95 & 0.88845986458147 & 0.223080270837061 & 0.111540135418531 \tabularnewline
96 & 0.862500288799537 & 0.274999422400926 & 0.137499711200463 \tabularnewline
97 & 0.833935874719888 & 0.332128250560223 & 0.166064125280112 \tabularnewline
98 & 0.801463296405597 & 0.397073407188807 & 0.198536703594403 \tabularnewline
99 & 0.764136060724027 & 0.471727878551947 & 0.235863939275974 \tabularnewline
100 & 0.722561571165965 & 0.55487685766807 & 0.277438428834035 \tabularnewline
101 & 0.677870534591829 & 0.644258930816342 & 0.322129465408171 \tabularnewline
102 & 0.633106556527083 & 0.733786886945834 & 0.366893443472917 \tabularnewline
103 & 0.584624927846848 & 0.830750144306303 & 0.415375072153152 \tabularnewline
104 & 0.549708908710682 & 0.900582182578636 & 0.450291091289318 \tabularnewline
105 & 0.509341852295823 & 0.981316295408355 & 0.490658147704177 \tabularnewline
106 & 0.999533615326094 & 0.000932769347812684 & 0.000466384673906342 \tabularnewline
107 & 0.999336540552618 & 0.00132691889476335 & 0.000663459447381676 \tabularnewline
108 & 0.998921075126882 & 0.00215784974623607 & 0.00107892487311803 \tabularnewline
109 & 0.998317423490455 & 0.0033651530190893 & 0.00168257650954465 \tabularnewline
110 & 0.999676731032704 & 0.000646537934592852 & 0.000323268967296426 \tabularnewline
111 & 0.999455626054085 & 0.0010887478918291 & 0.00054437394591455 \tabularnewline
112 & 0.999142714328052 & 0.00171457134389647 & 0.000857285671948233 \tabularnewline
113 & 0.998775299868178 & 0.00244940026364421 & 0.00122470013182210 \tabularnewline
114 & 1 & 0 & 0 \tabularnewline
115 & 1 & 0 & 0 \tabularnewline
116 & 1 & 0 & 0 \tabularnewline
117 & 1 & 0 & 0 \tabularnewline
118 & 1 & 0 & 0 \tabularnewline
119 & 1 & 0 & 0 \tabularnewline
120 & 1 & 0 & 0 \tabularnewline
121 & 1 & 0 & 0 \tabularnewline
122 & 1 & 0 & 0 \tabularnewline
123 & 1 & 0 & 0 \tabularnewline
124 & 1 & 0 & 0 \tabularnewline
125 & 1 & 4.25610936260445e-304 & 2.12805468130223e-304 \tabularnewline
126 & 1 & 4.84385149583064e-294 & 2.42192574791532e-294 \tabularnewline
127 & 1 & 8.7512927611759e-278 & 4.37564638058795e-278 \tabularnewline
128 & 1 & 5.65416621894546e-263 & 2.82708310947273e-263 \tabularnewline
129 & 1 & 2.30484563619042e-245 & 1.15242281809521e-245 \tabularnewline
130 & 1 & 3.05972870069150e-230 & 1.52986435034575e-230 \tabularnewline
131 & 1 & 1.32410842359731e-223 & 6.62054211798654e-224 \tabularnewline
132 & 1 & 3.79293484597089e-209 & 1.89646742298545e-209 \tabularnewline
133 & 1 & 8.4189297430175e-195 & 4.20946487150875e-195 \tabularnewline
134 & 1 & 1.01646719401544e-182 & 5.08233597007721e-183 \tabularnewline
135 & 1 & 5.71862489918074e-168 & 2.85931244959037e-168 \tabularnewline
136 & 1 & 2.35328575929728e-154 & 1.17664287964864e-154 \tabularnewline
137 & 1 & 4.30226771089198e-142 & 2.15113385544599e-142 \tabularnewline
138 & 1 & 9.92764614521413e-126 & 4.96382307260707e-126 \tabularnewline
139 & 1 & 1.06614260278708e-112 & 5.33071301393539e-113 \tabularnewline
140 & 1 & 1.9543900810254e-97 & 9.771950405127e-98 \tabularnewline
141 & 1 & 7.62792043350856e-82 & 3.81396021675428e-82 \tabularnewline
142 & 1 & 2.03877361448461e-73 & 1.01938680724230e-73 \tabularnewline
143 & 1 & 5.91295474588529e-59 & 2.95647737294264e-59 \tabularnewline
144 & 1 & 2.51294829863939e-45 & 1.25647414931970e-45 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98873&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]15[/C][C]0.931890324775149[/C][C]0.136219350449703[/C][C]0.0681096752248514[/C][/ROW]
[ROW][C]16[/C][C]0.996805081921695[/C][C]0.0063898361566105[/C][C]0.00319491807830525[/C][/ROW]
[ROW][C]17[/C][C]0.992876311439192[/C][C]0.0142473771216161[/C][C]0.00712368856080807[/C][/ROW]
[ROW][C]18[/C][C]0.98538436754423[/C][C]0.0292312649115383[/C][C]0.0146156324557692[/C][/ROW]
[ROW][C]19[/C][C]0.983095289663174[/C][C]0.0338094206736516[/C][C]0.0169047103368258[/C][/ROW]
[ROW][C]20[/C][C]0.97085633205466[/C][C]0.0582873358906794[/C][C]0.0291436679453397[/C][/ROW]
[ROW][C]21[/C][C]0.963207522126936[/C][C]0.0735849557461287[/C][C]0.0367924778730644[/C][/ROW]
[ROW][C]22[/C][C]0.94214960177313[/C][C]0.115700796453739[/C][C]0.0578503982268694[/C][/ROW]
[ROW][C]23[/C][C]0.9127322853819[/C][C]0.174535429236199[/C][C]0.0872677146180996[/C][/ROW]
[ROW][C]24[/C][C]0.965010013242332[/C][C]0.0699799735153369[/C][C]0.0349899867576685[/C][/ROW]
[ROW][C]25[/C][C]0.94760064853572[/C][C]0.104798702928559[/C][C]0.0523993514642797[/C][/ROW]
[ROW][C]26[/C][C]0.924726268691788[/C][C]0.150547462616425[/C][C]0.0752737313082124[/C][/ROW]
[ROW][C]27[/C][C]0.895200219936097[/C][C]0.209599560127806[/C][C]0.104799780063903[/C][/ROW]
[ROW][C]28[/C][C]0.858080979736361[/C][C]0.283838040527277[/C][C]0.141919020263639[/C][/ROW]
[ROW][C]29[/C][C]0.813311838739206[/C][C]0.373376322521589[/C][C]0.186688161260794[/C][/ROW]
[ROW][C]30[/C][C]0.7697783746988[/C][C]0.460443250602399[/C][C]0.230221625301199[/C][/ROW]
[ROW][C]31[/C][C]0.762113147402687[/C][C]0.475773705194627[/C][C]0.237886852597313[/C][/ROW]
[ROW][C]32[/C][C]0.722634649881769[/C][C]0.554730700236463[/C][C]0.277365350118231[/C][/ROW]
[ROW][C]33[/C][C]0.663850487204168[/C][C]0.672299025591664[/C][C]0.336149512795832[/C][/ROW]
[ROW][C]34[/C][C]0.620709085408403[/C][C]0.758581829183194[/C][C]0.379290914591597[/C][/ROW]
[ROW][C]35[/C][C]0.560556487990136[/C][C]0.878887024019728[/C][C]0.439443512009864[/C][/ROW]
[ROW][C]36[/C][C]0.513812575763877[/C][C]0.972374848472245[/C][C]0.486187424236123[/C][/ROW]
[ROW][C]37[/C][C]0.470394142827472[/C][C]0.940788285654945[/C][C]0.529605857172528[/C][/ROW]
[ROW][C]38[/C][C]0.413657578173338[/C][C]0.827315156346676[/C][C]0.586342421826662[/C][/ROW]
[ROW][C]39[/C][C]0.598974369708297[/C][C]0.802051260583405[/C][C]0.401025630291703[/C][/ROW]
[ROW][C]40[/C][C]0.554492920001728[/C][C]0.891014159996544[/C][C]0.445507079998272[/C][/ROW]
[ROW][C]41[/C][C]0.495662276774656[/C][C]0.991324553549312[/C][C]0.504337723225344[/C][/ROW]
[ROW][C]42[/C][C]0.439305588742755[/C][C]0.87861117748551[/C][C]0.560694411257245[/C][/ROW]
[ROW][C]43[/C][C]0.385478527406998[/C][C]0.770957054813996[/C][C]0.614521472593002[/C][/ROW]
[ROW][C]44[/C][C]0.336057625432369[/C][C]0.672115250864738[/C][C]0.663942374567631[/C][/ROW]
[ROW][C]45[/C][C]0.287567283519701[/C][C]0.575134567039403[/C][C]0.712432716480299[/C][/ROW]
[ROW][C]46[/C][C]0.242217504440564[/C][C]0.484435008881128[/C][C]0.757782495559436[/C][/ROW]
[ROW][C]47[/C][C]0.201131976603976[/C][C]0.402263953207952[/C][C]0.798868023396024[/C][/ROW]
[ROW][C]48[/C][C]0.167170787342452[/C][C]0.334341574684905[/C][C]0.832829212657548[/C][/ROW]
[ROW][C]49[/C][C]0.136007451573175[/C][C]0.272014903146351[/C][C]0.863992548426824[/C][/ROW]
[ROW][C]50[/C][C]0.110051711244626[/C][C]0.220103422489251[/C][C]0.889948288755374[/C][/ROW]
[ROW][C]51[/C][C]0.0863358334372096[/C][C]0.172671666874419[/C][C]0.91366416656279[/C][/ROW]
[ROW][C]52[/C][C]0.0676583074721739[/C][C]0.135316614944348[/C][C]0.932341692527826[/C][/ROW]
[ROW][C]53[/C][C]0.0529175410391086[/C][C]0.105835082078217[/C][C]0.947082458960891[/C][/ROW]
[ROW][C]54[/C][C]0.0398030850049655[/C][C]0.079606170009931[/C][C]0.960196914995035[/C][/ROW]
[ROW][C]55[/C][C]0.0304749236788772[/C][C]0.0609498473577544[/C][C]0.969525076321123[/C][/ROW]
[ROW][C]56[/C][C]0.0224820192074422[/C][C]0.0449640384148845[/C][C]0.977517980792558[/C][/ROW]
[ROW][C]57[/C][C]0.0161687035770383[/C][C]0.0323374071540765[/C][C]0.983831296422962[/C][/ROW]
[ROW][C]58[/C][C]0.0117099067954447[/C][C]0.0234198135908894[/C][C]0.988290093204555[/C][/ROW]
[ROW][C]59[/C][C]0.00829445004929892[/C][C]0.0165889000985978[/C][C]0.991705549950701[/C][/ROW]
[ROW][C]60[/C][C]0.0059932375457984[/C][C]0.0119864750915968[/C][C]0.994006762454202[/C][/ROW]
[ROW][C]61[/C][C]0.00411199622777575[/C][C]0.0082239924555515[/C][C]0.995888003772224[/C][/ROW]
[ROW][C]62[/C][C]0.00440994293701509[/C][C]0.00881988587403018[/C][C]0.995590057062985[/C][/ROW]
[ROW][C]63[/C][C]0.0113828213699662[/C][C]0.0227656427399324[/C][C]0.988617178630034[/C][/ROW]
[ROW][C]64[/C][C]0.00806512535221193[/C][C]0.0161302507044239[/C][C]0.991934874647788[/C][/ROW]
[ROW][C]65[/C][C]0.00598443901853502[/C][C]0.0119688780370700[/C][C]0.994015560981465[/C][/ROW]
[ROW][C]66[/C][C]0.00420360259308469[/C][C]0.00840720518616938[/C][C]0.995796397406915[/C][/ROW]
[ROW][C]67[/C][C]0.00307682744066801[/C][C]0.00615365488133603[/C][C]0.996923172559332[/C][/ROW]
[ROW][C]68[/C][C]0.00210039581852808[/C][C]0.00420079163705617[/C][C]0.997899604181472[/C][/ROW]
[ROW][C]69[/C][C]0.00184308630197776[/C][C]0.00368617260395552[/C][C]0.998156913698022[/C][/ROW]
[ROW][C]70[/C][C]0.0857341924375413[/C][C]0.171468384875083[/C][C]0.914265807562459[/C][/ROW]
[ROW][C]71[/C][C]0.0704593620542171[/C][C]0.140918724108434[/C][C]0.929540637945783[/C][/ROW]
[ROW][C]72[/C][C]0.055587751999065[/C][C]0.11117550399813[/C][C]0.944412248000935[/C][/ROW]
[ROW][C]73[/C][C]0.0434785609220866[/C][C]0.0869571218441731[/C][C]0.956521439077913[/C][/ROW]
[ROW][C]74[/C][C]0.0332179696396098[/C][C]0.0664359392792195[/C][C]0.96678203036039[/C][/ROW]
[ROW][C]75[/C][C]0.0251676357556353[/C][C]0.0503352715112706[/C][C]0.974832364244365[/C][/ROW]
[ROW][C]76[/C][C]0.0188414936432232[/C][C]0.0376829872864463[/C][C]0.981158506356777[/C][/ROW]
[ROW][C]77[/C][C]0.0153803488780306[/C][C]0.0307606977560612[/C][C]0.98461965112197[/C][/ROW]
[ROW][C]78[/C][C]0.0115565990717313[/C][C]0.0231131981434626[/C][C]0.988443400928269[/C][/ROW]
[ROW][C]79[/C][C]0.00836004005565288[/C][C]0.0167200801113058[/C][C]0.991639959944347[/C][/ROW]
[ROW][C]80[/C][C]0.00600073094336713[/C][C]0.0120014618867343[/C][C]0.993999269056633[/C][/ROW]
[ROW][C]81[/C][C]0.00569693894172114[/C][C]0.0113938778834423[/C][C]0.994303061058279[/C][/ROW]
[ROW][C]82[/C][C]0.019309506887842[/C][C]0.038619013775684[/C][C]0.980690493112158[/C][/ROW]
[ROW][C]83[/C][C]0.0143201485575349[/C][C]0.0286402971150698[/C][C]0.985679851442465[/C][/ROW]
[ROW][C]84[/C][C]0.0133472797826577[/C][C]0.0266945595653153[/C][C]0.986652720217342[/C][/ROW]
[ROW][C]85[/C][C]0.969588942760994[/C][C]0.0608221144780117[/C][C]0.0304110572390058[/C][/ROW]
[ROW][C]86[/C][C]0.9605088840681[/C][C]0.0789822318638016[/C][C]0.0394911159319008[/C][/ROW]
[ROW][C]87[/C][C]0.951200002698601[/C][C]0.097599994602797[/C][C]0.0487999973013985[/C][/ROW]
[ROW][C]88[/C][C]0.939805350546437[/C][C]0.120389298907126[/C][C]0.060194649453563[/C][/ROW]
[ROW][C]89[/C][C]0.927099247634885[/C][C]0.14580150473023[/C][C]0.072900752365115[/C][/ROW]
[ROW][C]90[/C][C]0.935662044259217[/C][C]0.128675911481566[/C][C]0.064337955740783[/C][/ROW]
[ROW][C]91[/C][C]0.91865226035956[/C][C]0.162695479280880[/C][C]0.0813477396404401[/C][/ROW]
[ROW][C]92[/C][C]0.903973950830475[/C][C]0.192052098339049[/C][C]0.0960260491695245[/C][/ROW]
[ROW][C]93[/C][C]0.925192040220129[/C][C]0.149615919559742[/C][C]0.0748079597798712[/C][/ROW]
[ROW][C]94[/C][C]0.909344025117669[/C][C]0.181311949764663[/C][C]0.0906559748823313[/C][/ROW]
[ROW][C]95[/C][C]0.88845986458147[/C][C]0.223080270837061[/C][C]0.111540135418531[/C][/ROW]
[ROW][C]96[/C][C]0.862500288799537[/C][C]0.274999422400926[/C][C]0.137499711200463[/C][/ROW]
[ROW][C]97[/C][C]0.833935874719888[/C][C]0.332128250560223[/C][C]0.166064125280112[/C][/ROW]
[ROW][C]98[/C][C]0.801463296405597[/C][C]0.397073407188807[/C][C]0.198536703594403[/C][/ROW]
[ROW][C]99[/C][C]0.764136060724027[/C][C]0.471727878551947[/C][C]0.235863939275974[/C][/ROW]
[ROW][C]100[/C][C]0.722561571165965[/C][C]0.55487685766807[/C][C]0.277438428834035[/C][/ROW]
[ROW][C]101[/C][C]0.677870534591829[/C][C]0.644258930816342[/C][C]0.322129465408171[/C][/ROW]
[ROW][C]102[/C][C]0.633106556527083[/C][C]0.733786886945834[/C][C]0.366893443472917[/C][/ROW]
[ROW][C]103[/C][C]0.584624927846848[/C][C]0.830750144306303[/C][C]0.415375072153152[/C][/ROW]
[ROW][C]104[/C][C]0.549708908710682[/C][C]0.900582182578636[/C][C]0.450291091289318[/C][/ROW]
[ROW][C]105[/C][C]0.509341852295823[/C][C]0.981316295408355[/C][C]0.490658147704177[/C][/ROW]
[ROW][C]106[/C][C]0.999533615326094[/C][C]0.000932769347812684[/C][C]0.000466384673906342[/C][/ROW]
[ROW][C]107[/C][C]0.999336540552618[/C][C]0.00132691889476335[/C][C]0.000663459447381676[/C][/ROW]
[ROW][C]108[/C][C]0.998921075126882[/C][C]0.00215784974623607[/C][C]0.00107892487311803[/C][/ROW]
[ROW][C]109[/C][C]0.998317423490455[/C][C]0.0033651530190893[/C][C]0.00168257650954465[/C][/ROW]
[ROW][C]110[/C][C]0.999676731032704[/C][C]0.000646537934592852[/C][C]0.000323268967296426[/C][/ROW]
[ROW][C]111[/C][C]0.999455626054085[/C][C]0.0010887478918291[/C][C]0.00054437394591455[/C][/ROW]
[ROW][C]112[/C][C]0.999142714328052[/C][C]0.00171457134389647[/C][C]0.000857285671948233[/C][/ROW]
[ROW][C]113[/C][C]0.998775299868178[/C][C]0.00244940026364421[/C][C]0.00122470013182210[/C][/ROW]
[ROW][C]114[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]115[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]116[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]117[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]118[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]119[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]120[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]121[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]122[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]123[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]124[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]125[/C][C]1[/C][C]4.25610936260445e-304[/C][C]2.12805468130223e-304[/C][/ROW]
[ROW][C]126[/C][C]1[/C][C]4.84385149583064e-294[/C][C]2.42192574791532e-294[/C][/ROW]
[ROW][C]127[/C][C]1[/C][C]8.7512927611759e-278[/C][C]4.37564638058795e-278[/C][/ROW]
[ROW][C]128[/C][C]1[/C][C]5.65416621894546e-263[/C][C]2.82708310947273e-263[/C][/ROW]
[ROW][C]129[/C][C]1[/C][C]2.30484563619042e-245[/C][C]1.15242281809521e-245[/C][/ROW]
[ROW][C]130[/C][C]1[/C][C]3.05972870069150e-230[/C][C]1.52986435034575e-230[/C][/ROW]
[ROW][C]131[/C][C]1[/C][C]1.32410842359731e-223[/C][C]6.62054211798654e-224[/C][/ROW]
[ROW][C]132[/C][C]1[/C][C]3.79293484597089e-209[/C][C]1.89646742298545e-209[/C][/ROW]
[ROW][C]133[/C][C]1[/C][C]8.4189297430175e-195[/C][C]4.20946487150875e-195[/C][/ROW]
[ROW][C]134[/C][C]1[/C][C]1.01646719401544e-182[/C][C]5.08233597007721e-183[/C][/ROW]
[ROW][C]135[/C][C]1[/C][C]5.71862489918074e-168[/C][C]2.85931244959037e-168[/C][/ROW]
[ROW][C]136[/C][C]1[/C][C]2.35328575929728e-154[/C][C]1.17664287964864e-154[/C][/ROW]
[ROW][C]137[/C][C]1[/C][C]4.30226771089198e-142[/C][C]2.15113385544599e-142[/C][/ROW]
[ROW][C]138[/C][C]1[/C][C]9.92764614521413e-126[/C][C]4.96382307260707e-126[/C][/ROW]
[ROW][C]139[/C][C]1[/C][C]1.06614260278708e-112[/C][C]5.33071301393539e-113[/C][/ROW]
[ROW][C]140[/C][C]1[/C][C]1.9543900810254e-97[/C][C]9.771950405127e-98[/C][/ROW]
[ROW][C]141[/C][C]1[/C][C]7.62792043350856e-82[/C][C]3.81396021675428e-82[/C][/ROW]
[ROW][C]142[/C][C]1[/C][C]2.03877361448461e-73[/C][C]1.01938680724230e-73[/C][/ROW]
[ROW][C]143[/C][C]1[/C][C]5.91295474588529e-59[/C][C]2.95647737294264e-59[/C][/ROW]
[ROW][C]144[/C][C]1[/C][C]2.51294829863939e-45[/C][C]1.25647414931970e-45[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98873&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98873&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
150.9318903247751490.1362193504497030.0681096752248514
160.9968050819216950.00638983615661050.00319491807830525
170.9928763114391920.01424737712161610.00712368856080807
180.985384367544230.02923126491153830.0146156324557692
190.9830952896631740.03380942067365160.0169047103368258
200.970856332054660.05828733589067940.0291436679453397
210.9632075221269360.07358495574612870.0367924778730644
220.942149601773130.1157007964537390.0578503982268694
230.91273228538190.1745354292361990.0872677146180996
240.9650100132423320.06997997351533690.0349899867576685
250.947600648535720.1047987029285590.0523993514642797
260.9247262686917880.1505474626164250.0752737313082124
270.8952002199360970.2095995601278060.104799780063903
280.8580809797363610.2838380405272770.141919020263639
290.8133118387392060.3733763225215890.186688161260794
300.76977837469880.4604432506023990.230221625301199
310.7621131474026870.4757737051946270.237886852597313
320.7226346498817690.5547307002364630.277365350118231
330.6638504872041680.6722990255916640.336149512795832
340.6207090854084030.7585818291831940.379290914591597
350.5605564879901360.8788870240197280.439443512009864
360.5138125757638770.9723748484722450.486187424236123
370.4703941428274720.9407882856549450.529605857172528
380.4136575781733380.8273151563466760.586342421826662
390.5989743697082970.8020512605834050.401025630291703
400.5544929200017280.8910141599965440.445507079998272
410.4956622767746560.9913245535493120.504337723225344
420.4393055887427550.878611177485510.560694411257245
430.3854785274069980.7709570548139960.614521472593002
440.3360576254323690.6721152508647380.663942374567631
450.2875672835197010.5751345670394030.712432716480299
460.2422175044405640.4844350088811280.757782495559436
470.2011319766039760.4022639532079520.798868023396024
480.1671707873424520.3343415746849050.832829212657548
490.1360074515731750.2720149031463510.863992548426824
500.1100517112446260.2201034224892510.889948288755374
510.08633583343720960.1726716668744190.91366416656279
520.06765830747217390.1353166149443480.932341692527826
530.05291754103910860.1058350820782170.947082458960891
540.03980308500496550.0796061700099310.960196914995035
550.03047492367887720.06094984735775440.969525076321123
560.02248201920744220.04496403841488450.977517980792558
570.01616870357703830.03233740715407650.983831296422962
580.01170990679544470.02341981359088940.988290093204555
590.008294450049298920.01658890009859780.991705549950701
600.00599323754579840.01198647509159680.994006762454202
610.004111996227775750.00822399245555150.995888003772224
620.004409942937015090.008819885874030180.995590057062985
630.01138282136996620.02276564273993240.988617178630034
640.008065125352211930.01613025070442390.991934874647788
650.005984439018535020.01196887803707000.994015560981465
660.004203602593084690.008407205186169380.995796397406915
670.003076827440668010.006153654881336030.996923172559332
680.002100395818528080.004200791637056170.997899604181472
690.001843086301977760.003686172603955520.998156913698022
700.08573419243754130.1714683848750830.914265807562459
710.07045936205421710.1409187241084340.929540637945783
720.0555877519990650.111175503998130.944412248000935
730.04347856092208660.08695712184417310.956521439077913
740.03321796963960980.06643593927921950.96678203036039
750.02516763575563530.05033527151127060.974832364244365
760.01884149364322320.03768298728644630.981158506356777
770.01538034887803060.03076069775606120.98461965112197
780.01155659907173130.02311319814346260.988443400928269
790.008360040055652880.01672008011130580.991639959944347
800.006000730943367130.01200146188673430.993999269056633
810.005696938941721140.01139387788344230.994303061058279
820.0193095068878420.0386190137756840.980690493112158
830.01432014855753490.02864029711506980.985679851442465
840.01334727978265770.02669455956531530.986652720217342
850.9695889427609940.06082211447801170.0304110572390058
860.96050888406810.07898223186380160.0394911159319008
870.9512000026986010.0975999946027970.0487999973013985
880.9398053505464370.1203892989071260.060194649453563
890.9270992476348850.145801504730230.072900752365115
900.9356620442592170.1286759114815660.064337955740783
910.918652260359560.1626954792808800.0813477396404401
920.9039739508304750.1920520983390490.0960260491695245
930.9251920402201290.1496159195597420.0748079597798712
940.9093440251176690.1813119497646630.0906559748823313
950.888459864581470.2230802708370610.111540135418531
960.8625002887995370.2749994224009260.137499711200463
970.8339358747198880.3321282505602230.166064125280112
980.8014632964055970.3970734071888070.198536703594403
990.7641360607240270.4717278785519470.235863939275974
1000.7225615711659650.554876857668070.277438428834035
1010.6778705345918290.6442589308163420.322129465408171
1020.6331065565270830.7337868869458340.366893443472917
1030.5846249278468480.8307501443063030.415375072153152
1040.5497089087106820.9005821825786360.450291091289318
1050.5093418522958230.9813162954083550.490658147704177
1060.9995336153260940.0009327693478126840.000466384673906342
1070.9993365405526180.001326918894763350.000663459447381676
1080.9989210751268820.002157849746236070.00107892487311803
1090.9983174234904550.00336515301908930.00168257650954465
1100.9996767310327040.0006465379345928520.000323268967296426
1110.9994556260540850.00108874789182910.00054437394591455
1120.9991427143280520.001714571343896470.000857285671948233
1130.9987752998681780.002449400263644210.00122470013182210
114100
115100
116100
117100
118100
119100
120100
121100
122100
123100
124100
12514.25610936260445e-3042.12805468130223e-304
12614.84385149583064e-2942.42192574791532e-294
12718.7512927611759e-2784.37564638058795e-278
12815.65416621894546e-2632.82708310947273e-263
12912.30484563619042e-2451.15242281809521e-245
13013.05972870069150e-2301.52986435034575e-230
13111.32410842359731e-2236.62054211798654e-224
13213.79293484597089e-2091.89646742298545e-209
13318.4189297430175e-1954.20946487150875e-195
13411.01646719401544e-1825.08233597007721e-183
13515.71862489918074e-1682.85931244959037e-168
13612.35328575929728e-1541.17664287964864e-154
13714.30226771089198e-1422.15113385544599e-142
13819.92764614521413e-1264.96382307260707e-126
13911.06614260278708e-1125.33071301393539e-113
14011.9543900810254e-979.771950405127e-98
14117.62792043350856e-823.81396021675428e-82
14212.03877361448461e-731.01938680724230e-73
14315.91295474588529e-592.95647737294264e-59
14412.51294829863939e-451.25647414931970e-45







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level460.353846153846154NOK
5% type I error level660.507692307692308NOK
10% type I error level770.592307692307692NOK

\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 & 46 & 0.353846153846154 & NOK \tabularnewline
5% type I error level & 66 & 0.507692307692308 & NOK \tabularnewline
10% type I error level & 77 & 0.592307692307692 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98873&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]46[/C][C]0.353846153846154[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]66[/C][C]0.507692307692308[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]77[/C][C]0.592307692307692[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98873&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98873&T=6

As an alternative you can also use a QR Code:  

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level460.353846153846154NOK
5% type I error level660.507692307692308NOK
10% type I error level770.592307692307692NOK



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')
}