Free Statistics

of Irreproducible Research!

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationSun, 20 Nov 2011 10:44:07 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Nov/20/t1321803878jnztfbdf8pvz9he.htm/, Retrieved Thu, 28 Mar 2024 20:18:35 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=145608, Retrieved Thu, 28 Mar 2024 20:18:35 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact232
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
- R PD  [Multiple Regression] [WS7 Tutorial] [2010-11-18 16:04:53] [afe9379cca749d06b3d6872e02cc47ed]
-    D    [Multiple Regression] [WS7 Tutorial Popu...] [2010-11-22 10:41:15] [afe9379cca749d06b3d6872e02cc47ed]
- R  D      [Multiple Regression] [WS 7 Mini-tutorial] [2011-11-20 14:29:34] [f5fdea4413921432bb019d1f20c4f2ec]
- R  D        [Multiple Regression] [WS 7 Mini-tutorial] [2011-11-20 14:47:41] [f5fdea4413921432bb019d1f20c4f2ec]
-    D            [Multiple Regression] [Ws 7 Mini-tutoria...] [2011-11-20 15:44:07] [6140f0163e532fc168d2f211324acd0a] [Current]
-   P               [Multiple Regression] [Ws 7 Mini-tutoria...] [2011-11-20 15:51:02] [f5fdea4413921432bb019d1f20c4f2ec]
Feedback Forum

Post a new message
Dataseries X:
1	186.59	26	21	21	23	17	23	4
1	244.665	20	16	15	24	17	20	4
1	248.18	19	19	18	22	18	20	6
2	253.568	19	18	11	20	21	21	8
1	171.242	20	16	8	24	20	24	8
1	413.971	25	23	19	27	28	22	4
2	216.89	25	17	4	28	19	23	4
1	227.901	22	12	20	27	22	20	8
1	259.823	26	19	16	24	16	25	5
1	148.438	22	16	14	23	18	23	4
2	241.013	17	19	10	24	25	27	4
2	206.248	22	20	13	27	17	27	4
1	108.908	19	13	14	27	14	22	4
1	267.952	24	20	8	28	11	24	4
1	314.219	26	27	23	27	27	25	4
2	235.115	21	17	11	23	20	22	8
1	203.027	13	8	9	24	22	28	4
2	365.415	26	25	24	28	22	28	4
2	350.933	20	26	5	27	21	27	4
1	263.304	22	13	15	25	23	25	8
2	738.751	14	19	5	19	17	16	4
1	959.073	21	15	19	24	24	28	7
1	483.828	7	5	6	20	14	21	4
2	213.016	23	16	13	28	17	24	4
1	177.341	17	14	11	26	23	27	5
1	352.622	25	24	17	23	24	14	4
1	352.622	25	24	17	23	24	14	4
1	217.307	19	9	5	20	8	27	4
2	236.184	20	19	9	11	22	20	4
1	215.701	23	19	15	24	23	21	4
2	228.383	22	25	17	25	25	22	4
1	485.625	22	19	17	23	21	21	4
1	252.502	21	18	20	18	24	12	15
2	342.515	15	15	12	20	15	20	10
2	196.931	20	12	7	20	22	24	4
2	365.315	22	21	16	24	21	19	8
1	316.664	18	12	7	23	25	28	4
2	313.523	20	15	14	25	16	23	4
2	188.124	28	28	24	28	28	27	4
1	184.083	22	25	15	26	23	22	4
1	362.962	18	19	15	26	21	27	7
1	170.161	23	20	10	23	21	26	4
1	167.484	20	24	14	22	26	22	6
2	211.752	25	26	18	24	22	21	5
2	276.469	26	25	12	21	21	19	4
1	182.097	15	12	9	20	18	24	16
2	266.904	17	12	9	22	12	19	5
2	235.328	23	15	8	20	25	26	12
1	329.45	21	17	18	25	17	22	6
2	258.118	13	14	10	20	24	28	9
1	952.515	18	16	17	22	15	21	9
1	247.141	19	11	14	23	13	23	4
1	498.726	22	20	16	25	26	28	5
1	313.308	16	11	10	23	16	10	4
2	420.188	24	22	19	23	24	24	4
1	231.156	18	20	10	22	21	21	5
1	227.844	20	19	14	24	20	21	4
1	204.385	24	17	10	25	14	24	4
2	216.563	14	21	4	21	25	24	4
2	207.19	22	23	19	12	25	25	5
1	232.417	24	18	9	17	20	25	4
1	203.109	18	17	12	20	22	23	6
1	221.07	21	27	16	23	20	21	4
2	254.623	23	25	11	23	26	16	4
1	108.981	17	19	18	20	18	17	18
2	229.417	22	22	11	28	22	25	4
2	476.376	24	24	24	24	24	24	6
2	221.91	21	20	17	24	17	23	4
1	158.946	22	19	18	24	24	25	4
1	295.746	16	11	9	24	20	23	5
1	187.976	21	22	19	28	19	28	4
2	283.76	23	22	18	25	20	26	4
2	705.401	22	16	12	21	15	22	5
1	178.69	24	20	23	25	23	19	10
1	232.635	24	24	22	25	26	26	5
1	245.185	16	16	14	18	22	18	8
1	186.03	16	16	14	17	20	18	8
2	181.142	21	22	16	26	24	25	5
2	228.478	26	24	23	28	26	27	4
2	491.849	15	16	7	21	21	12	4
2	506.461	25	27	10	27	25	15	4
1	182.828	18	11	12	22	13	21	5
0	263.654	23	21	12	21	20	23	4
1	253.718	20	20	12	25	22	22	4
2	480.937	17	20	17	22	23	21	8
2	209.894	25	27	21	23	28	24	4
1	655.92	24	20	16	26	22	27	5
1	223.408	17	12	11	19	20	22	14
1	103.138	19	8	14	25	6	28	8
1	255.664	20	21	13	21	21	26	8
1	184.661	15	18	9	13	20	10	4
2	372.945	27	24	19	24	18	19	4
1	758.6	22	16	13	25	23	22	6
1	256.445	23	18	19	26	20	21	4
1	204.868	16	20	13	25	24	24	7
1	197.384	19	20	13	25	22	25	7
2	245.311	25	19	13	22	21	21	4
1	301.707	19	17	14	21	18	20	6
2	501.478	19	16	12	23	21	21	4
2	278.731	26	26	22	25	23	24	7
1	205.92	21	15	11	24	23	23	4
2	177.14	20	22	5	21	15	18	4
1	139.753	24	17	18	21	21	24	8
1	366.46	22	23	19	25	24	24	4
2	435.522	20	21	14	22	23	19	4
1	239.906	18	19	15	20	21	20	10
2	178.722	18	14	12	20	21	18	8
1	340.04	24	17	19	23	20	20	6
1	236.948	24	12	15	28	11	27	4
1	221.152	22	24	17	23	22	23	4
1	263.303	23	18	8	28	27	26	4
1	222.814	22	20	10	24	25	23	5
1	317.99	20	16	12	18	18	17	4
1	149.593	18	20	12	20	20	21	6
1	221.983	25	22	20	28	24	25	4
2	201.551	18	12	12	21	10	23	5
1	266.901	16	16	12	21	27	27	7
1	185.218	20	17	14	25	21	24	8
2	347.536	19	22	6	19	21	20	5
1	395.593	15	12	10	18	18	27	8
1	238.217	19	14	18	21	15	21	10
1	254.697	19	23	18	22	24	24	8
1	157.584	16	15	7	24	22	21	5
1	807.302	17	17	18	15	14	15	12
1	252.391	28	28	9	28	28	25	4
2	189.194	23	20	17	26	18	25	5
1	267.834	25	23	22	23	26	22	4
1	173.15	20	13	11	26	17	24	6
2	267.633	17	18	15	20	19	21	4
2	283.284	23	23	17	22	22	22	4
1	209.475	16	19	15	20	18	23	7
2	135.135	23	23	22	23	24	22	7
2	285.012	11	12	9	22	15	20	10
2	178.495	18	16	13	24	18	23	4
2	256.852	24	23	20	23	26	25	5
1	301.828	23	13	14	22	11	23	8
1	158.403	21	22	14	26	26	22	11
2	355.963	16	18	12	23	21	25	7
2	364.117	24	23	20	27	23	26	4
1	233.203	23	20	20	23	23	22	8
1	257.634	18	10	8	21	15	24	6
1	208.57	20	17	17	26	22	24	7
1	212.503	9	18	9	23	26	25	5
2	251.059	24	15	18	21	16	20	4
1	276.803	25	23	22	27	20	26	8
1	198.339	20	17	10	19	18	21	4
2	301.018	21	17	13	23	22	26	8
2	369.761	25	22	15	25	16	21	6
2	162.768	22	20	18	23	19	22	4
2	199.968	21	20	18	22	20	16	9
1	406.676	21	19	12	22	19	26	5
1	364.156	22	18	12	25	23	28	6
1	202.391	27	22	20	25	24	18	4
2	319.491	24	20	12	28	25	25	4
2	185.546	24	22	16	28	21	23	4
2	243.559	21	18	16	20	21	21	5
1	220.928	18	16	18	25	23	20	6
1	193.714	16	16	16	19	27	25	16
1	372.943	22	16	13	25	23	22	6
1	163.822	20	16	17	22	18	21	6
2	217.566	18	17	13	18	16	16	4
1	232.527	20	18	17	20	16	18	4




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'AstonUniversity' @ aston.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 6 seconds \tabularnewline
R Server & 'AstonUniversity' @ aston.wessa.net \tabularnewline
R Framework error message & 
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
\tabularnewline \hline \end{tabular} %Source: https://freestatistics.org/blog/index.php?pk=145608&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]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'AstonUniversity' @ aston.wessa.net[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=145608&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145608&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 time6 seconds
R Server'AstonUniversity' @ aston.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.







Multiple Linear Regression - Estimated Regression Equation
G[t] = + 1.40188117714777 + 0.000213047281008186Month[t] -0.00207923672899216I1[t] + 0.0429579390503465I2[t] -0.0155370398224378I3[t] -0.0117692258627864E1[t] -0.0135941498718194E2[t] + 0.00146277756554949E3[t] -0.0162112189045189`A `[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
G[t] =  +  1.40188117714777 +  0.000213047281008186Month[t] -0.00207923672899216I1[t] +  0.0429579390503465I2[t] -0.0155370398224378I3[t] -0.0117692258627864E1[t] -0.0135941498718194E2[t] +  0.00146277756554949E3[t] -0.0162112189045189`A
`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145608&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]G[t] =  +  1.40188117714777 +  0.000213047281008186Month[t] -0.00207923672899216I1[t] +  0.0429579390503465I2[t] -0.0155370398224378I3[t] -0.0117692258627864E1[t] -0.0135941498718194E2[t] +  0.00146277756554949E3[t] -0.0162112189045189`A
`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145608&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145608&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
G[t] = + 1.40188117714777 + 0.000213047281008186Month[t] -0.00207923672899216I1[t] + 0.0429579390503465I2[t] -0.0155370398224378I3[t] -0.0117692258627864E1[t] -0.0135941498718194E2[t] + 0.00146277756554949E3[t] -0.0162112189045189`A `[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)1.401881177147770.4139843.38639e-040.00045
Month0.0002130472810081860.0002740.77840.4375530.218776
I1-0.002079236728992160.015577-0.13350.8939880.446994
I20.04295793905034650.0134173.20170.0016620.000831
I3-0.01553703982243780.010512-1.4780.1414710.070736
E1-0.01176922586278640.015066-0.78120.4359050.217953
E2-0.01359414987181940.011533-1.17870.2403530.120177
E30.001462777565549490.0119020.12290.9023440.451172
`A `-0.01621121890451890.016624-0.97520.3310080.165504

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & 1.40188117714777 & 0.413984 & 3.3863 & 9e-04 & 0.00045 \tabularnewline
Month & 0.000213047281008186 & 0.000274 & 0.7784 & 0.437553 & 0.218776 \tabularnewline
I1 & -0.00207923672899216 & 0.015577 & -0.1335 & 0.893988 & 0.446994 \tabularnewline
I2 & 0.0429579390503465 & 0.013417 & 3.2017 & 0.001662 & 0.000831 \tabularnewline
I3 & -0.0155370398224378 & 0.010512 & -1.478 & 0.141471 & 0.070736 \tabularnewline
E1 & -0.0117692258627864 & 0.015066 & -0.7812 & 0.435905 & 0.217953 \tabularnewline
E2 & -0.0135941498718194 & 0.011533 & -1.1787 & 0.240353 & 0.120177 \tabularnewline
E3 & 0.00146277756554949 & 0.011902 & 0.1229 & 0.902344 & 0.451172 \tabularnewline
`A
` & -0.0162112189045189 & 0.016624 & -0.9752 & 0.331008 & 0.165504 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145608&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]1.40188117714777[/C][C]0.413984[/C][C]3.3863[/C][C]9e-04[/C][C]0.00045[/C][/ROW]
[ROW][C]Month[/C][C]0.000213047281008186[/C][C]0.000274[/C][C]0.7784[/C][C]0.437553[/C][C]0.218776[/C][/ROW]
[ROW][C]I1[/C][C]-0.00207923672899216[/C][C]0.015577[/C][C]-0.1335[/C][C]0.893988[/C][C]0.446994[/C][/ROW]
[ROW][C]I2[/C][C]0.0429579390503465[/C][C]0.013417[/C][C]3.2017[/C][C]0.001662[/C][C]0.000831[/C][/ROW]
[ROW][C]I3[/C][C]-0.0155370398224378[/C][C]0.010512[/C][C]-1.478[/C][C]0.141471[/C][C]0.070736[/C][/ROW]
[ROW][C]E1[/C][C]-0.0117692258627864[/C][C]0.015066[/C][C]-0.7812[/C][C]0.435905[/C][C]0.217953[/C][/ROW]
[ROW][C]E2[/C][C]-0.0135941498718194[/C][C]0.011533[/C][C]-1.1787[/C][C]0.240353[/C][C]0.120177[/C][/ROW]
[ROW][C]E3[/C][C]0.00146277756554949[/C][C]0.011902[/C][C]0.1229[/C][C]0.902344[/C][C]0.451172[/C][/ROW]
[ROW][C]`A
`[/C][C]-0.0162112189045189[/C][C]0.016624[/C][C]-0.9752[/C][C]0.331008[/C][C]0.165504[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145608&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145608&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)1.401881177147770.4139843.38639e-040.00045
Month0.0002130472810081860.0002740.77840.4375530.218776
I1-0.002079236728992160.015577-0.13350.8939880.446994
I20.04295793905034650.0134173.20170.0016620.000831
I3-0.01553703982243780.010512-1.4780.1414710.070736
E1-0.01176922586278640.015066-0.78120.4359050.217953
E2-0.01359414987181940.011533-1.17870.2403530.120177
E30.001462777565549490.0119020.12290.9023440.451172
`A `-0.01621121890451890.016624-0.97520.3310080.165504







Multiple Linear Regression - Regression Statistics
Multiple R0.329366620900901
R-squared0.108482370963678
Adjusted R-squared0.0618670700990334
F-TEST (value)2.32718375622363
F-TEST (DF numerator)8
F-TEST (DF denominator)153
p-value0.0219241616959551
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.484416269919223
Sum Squared Residuals35.9028457520554

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.329366620900901 \tabularnewline
R-squared & 0.108482370963678 \tabularnewline
Adjusted R-squared & 0.0618670700990334 \tabularnewline
F-TEST (value) & 2.32718375622363 \tabularnewline
F-TEST (DF numerator) & 8 \tabularnewline
F-TEST (DF denominator) & 153 \tabularnewline
p-value & 0.0219241616959551 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.484416269919223 \tabularnewline
Sum Squared Residuals & 35.9028457520554 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145608&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.329366620900901[/C][/ROW]
[ROW][C]R-squared[/C][C]0.108482370963678[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.0618670700990334[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]2.32718375622363[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]8[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]153[/C][/ROW]
[ROW][C]p-value[/C][C]0.0219241616959551[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.484416269919223[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]35.9028457520554[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145608&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145608&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 R0.329366620900901
R-squared0.108482370963678
Adjusted R-squared0.0618670700990334
F-TEST (value)2.32718375622363
F-TEST (DF numerator)8
F-TEST (DF denominator)153
p-value0.0219241616959551
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.484416269919223
Sum Squared Residuals35.9028457520554







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
111.43041866386793-0.43041866386793
211.31754179020989-0.317541790209887
311.38015444986006-0.380154449860065
421.398900030183480.601099969816518
511.31088228348215-0.310882283482152
611.3998554325387-0.399855432538699
721.445216724933950.55478327506605
810.8921715342539750.107828465746025
911.42632933664511-0.426329336645106
1011.31098286455238-0.310982864552381
1121.431046711974140.568953288025858
1221.483036280574160.51696371942584
1311.18576191704065-0.185761917040647
1411.63511621632718-0.635116216327176
1511.50419038291468-0.504190382914682
1621.327601506047720.672398493952284
1711.01651578232337-0.0165157823233669
1821.4742344897830.525765510216995
1921.845686853279120.154313146720882
2011.03761537498754-0.0376153749875439
2121.77252012389520.227479876104798
2211.2304686517794-0.230468651779397
2311.15214305403483-0.152143054034827
2421.294409633082210.70559036691779
2511.17459345953716-0.174593459537165
2611.55056849600538-0.550568496005378
2711.55056849600538-0.550568496005378
2811.34812099955338-0.348120999553376
2921.622860179162450.37713982083755
3011.35390507406146-0.353905074061463
3121.547864983024530.452135016975472
3211.42137433103086-0.421374331030858
3311.11079356161054-0.110793561610539
3421.329435720486140.670564279513863
3521.244794018030610.755205981969394
3621.417655873963930.582344126036067
3711.20422226464392-0.204222264643923
3821.311004157363320.688995842636679
3921.478150791182370.521849208817628
4011.58492014200159-0.584920142001594
4111.35946767005278-0.359467670052776
4211.51111745248106-0.511117452481058
4311.53199336044086-0.531993360440864
4421.600382661743190.39961733825681
4521.724542997017080.275457002982917
4611.08079775129727-0.0807977512972655
4721.323743146227050.676256853772953
4821.192526815900590.807473184099409
4911.28860647824348-0.288606478243482
5021.209295873745940.790704126254058
5111.41256513639681-0.412565136396807
5211.19143003462407-0.191430034624073
5311.38517946530179-0.385179465301788
5411.21411404557567-0.214114045575668
5521.46468030323290.535319696767105
5611.52275257416163-0.522752574161633
5711.41904930681962-0.419049306819617
5811.45615077099231-0.456150770992314
5921.64213287806750.357867121932501
6021.567536964260910.432463035739094
6111.53466957648324-0.534669576483244
6211.35348797835561-0.353487978355615
6311.73988554642976-0.739885546429759
6421.645765982344270.354234017655726
6511.17927244702369-0.179272447023688
6621.522296800420430.477703199579575
6721.440690019174860.559309980825137
6821.455760671854710.544239328145287
6911.2895386532799-0.2895386532799
7011.1625686131466-0.1625686131466
7111.43642160850976-0.436421608509762
7221.486994668223740.513005331776256
7321.507363301840380.492636698159618
7411.15064027046492-0.150640270464922
7511.40001498992872-0.400014989928715
7611.27638073660286-0.276380736602865
7711.30273545030125-0.302735450301251
7821.416544913623970.583455086376027
7921.361800157982850.638199842017149
8021.474125029119760.525874970880237
8121.761768296819080.238231703180915
8211.20351409304163-0.20351409304163
8301.57566401023057-1.57566401023057
8411.46109896282277-0.46109896282277
8521.393465738573870.606534261426129
8621.547137517711150.452862482288853
8711.45565534219355-0.455655342193546
8811.06844420321722-0.0684442032172213
8911.04596637945285-0.0459663794528463
9011.49061174002661-0.490611740026608
9111.56834366106025-0.56834366106025
9221.596775263990320.403224736009676
9311.33111884299008-0.331118842990079
9411.27472337202865-0.274723372028648
9511.37057510891295-0.370575108912954
9611.3913940301841-0.3913940301841
9721.437855761708220.562144238291782
9811.37955973872243-0.379559738722435
9921.379800861724810.620199138275194
10021.49702747871210.502972521287902
10111.24922169028337-0.24922169028337
10221.775844227287170.224155772712834
10311.20515775927702-0.205157759277025
10411.47681165970165-0.476811659701645
10521.529040865124540.470959134875456
10611.34499317934535-0.344993179345348
10721.193276401397660.806723598602338
10811.24891834591896-0.24891834591896
10911.18047643996361-0.180476439963609
11011.56915017799179-0.569150177991792
11111.33570827532806-0.335708275328056
11211.43766889074574-0.437668890745743
11311.43240747416538-0.432407474165376
11411.49522310182876-0.49522310182876
11511.34745363860086-0.347453638600855
11621.305938146943640.694061853056364
11711.23817914104912-0.238179141049122
11811.23823215666264-0.238232156662636
11921.727376717409630.272623282590373
12011.26836198943932-0.268361989439324
12111.15241174197337-0.152411741973366
12211.44523840841403-0.445238408414025
12311.30592555567947-0.305925555679471
12411.44969644634114-0.449696446341136
12511.72197274299639-0.721972742996393
12621.394213878180450.605786121819548
12711.39637542576148-0.396375425761477
12811.18547027677489-0.185470276774886
12921.435940029720810.564059970279191
13021.547656504173290.452343495826709
13111.43647285002064-0.436472850020636
13221.350817381107040.649182618892962
13321.219700660192990.780299339807008
13421.329471187553260.670528812446736
13521.415366170687440.584633829312557
13611.25479253245364-0.254792532453642
13711.3139305652786-0.313930565278603
13821.398170328465210.601829671534789
13921.449598229919170.550401770080833
14011.27129369532209-0.271293695322086
14111.21139956809541-0.21139956809541
14211.18744396047134-0.187443960471339
14311.39322403055198-0.393224030551976
14421.26991983655120.730080163448796
14511.37378047724873-0.373780477248733
14611.4750300570401-0.4750300570401
14721.289230691881680.710769308118318
14821.562409867297550.43759013270245
14921.408662501563490.591337498436505
15021.327009413221070.672990586778933
15111.51437909161338-0.514379091613377
15211.35731320459651-0.357313204596514
15311.36418937744287-0.364189377442871
15421.395092980213380.604907019786617
15521.441775125325860.558224874674138
15621.363557624091250.636442375908746
15711.14427547798853-0.144275477988526
15811.03515061685803-0.0351506168580286
15911.2489556677383-0.248955667738305
16011.24822897083673-0.248228970836732
16121.468317308881390.531682691118607
16211.42754311896069-0.427543118960693

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 1 & 1.43041866386793 & -0.43041866386793 \tabularnewline
2 & 1 & 1.31754179020989 & -0.317541790209887 \tabularnewline
3 & 1 & 1.38015444986006 & -0.380154449860065 \tabularnewline
4 & 2 & 1.39890003018348 & 0.601099969816518 \tabularnewline
5 & 1 & 1.31088228348215 & -0.310882283482152 \tabularnewline
6 & 1 & 1.3998554325387 & -0.399855432538699 \tabularnewline
7 & 2 & 1.44521672493395 & 0.55478327506605 \tabularnewline
8 & 1 & 0.892171534253975 & 0.107828465746025 \tabularnewline
9 & 1 & 1.42632933664511 & -0.426329336645106 \tabularnewline
10 & 1 & 1.31098286455238 & -0.310982864552381 \tabularnewline
11 & 2 & 1.43104671197414 & 0.568953288025858 \tabularnewline
12 & 2 & 1.48303628057416 & 0.51696371942584 \tabularnewline
13 & 1 & 1.18576191704065 & -0.185761917040647 \tabularnewline
14 & 1 & 1.63511621632718 & -0.635116216327176 \tabularnewline
15 & 1 & 1.50419038291468 & -0.504190382914682 \tabularnewline
16 & 2 & 1.32760150604772 & 0.672398493952284 \tabularnewline
17 & 1 & 1.01651578232337 & -0.0165157823233669 \tabularnewline
18 & 2 & 1.474234489783 & 0.525765510216995 \tabularnewline
19 & 2 & 1.84568685327912 & 0.154313146720882 \tabularnewline
20 & 1 & 1.03761537498754 & -0.0376153749875439 \tabularnewline
21 & 2 & 1.7725201238952 & 0.227479876104798 \tabularnewline
22 & 1 & 1.2304686517794 & -0.230468651779397 \tabularnewline
23 & 1 & 1.15214305403483 & -0.152143054034827 \tabularnewline
24 & 2 & 1.29440963308221 & 0.70559036691779 \tabularnewline
25 & 1 & 1.17459345953716 & -0.174593459537165 \tabularnewline
26 & 1 & 1.55056849600538 & -0.550568496005378 \tabularnewline
27 & 1 & 1.55056849600538 & -0.550568496005378 \tabularnewline
28 & 1 & 1.34812099955338 & -0.348120999553376 \tabularnewline
29 & 2 & 1.62286017916245 & 0.37713982083755 \tabularnewline
30 & 1 & 1.35390507406146 & -0.353905074061463 \tabularnewline
31 & 2 & 1.54786498302453 & 0.452135016975472 \tabularnewline
32 & 1 & 1.42137433103086 & -0.421374331030858 \tabularnewline
33 & 1 & 1.11079356161054 & -0.110793561610539 \tabularnewline
34 & 2 & 1.32943572048614 & 0.670564279513863 \tabularnewline
35 & 2 & 1.24479401803061 & 0.755205981969394 \tabularnewline
36 & 2 & 1.41765587396393 & 0.582344126036067 \tabularnewline
37 & 1 & 1.20422226464392 & -0.204222264643923 \tabularnewline
38 & 2 & 1.31100415736332 & 0.688995842636679 \tabularnewline
39 & 2 & 1.47815079118237 & 0.521849208817628 \tabularnewline
40 & 1 & 1.58492014200159 & -0.584920142001594 \tabularnewline
41 & 1 & 1.35946767005278 & -0.359467670052776 \tabularnewline
42 & 1 & 1.51111745248106 & -0.511117452481058 \tabularnewline
43 & 1 & 1.53199336044086 & -0.531993360440864 \tabularnewline
44 & 2 & 1.60038266174319 & 0.39961733825681 \tabularnewline
45 & 2 & 1.72454299701708 & 0.275457002982917 \tabularnewline
46 & 1 & 1.08079775129727 & -0.0807977512972655 \tabularnewline
47 & 2 & 1.32374314622705 & 0.676256853772953 \tabularnewline
48 & 2 & 1.19252681590059 & 0.807473184099409 \tabularnewline
49 & 1 & 1.28860647824348 & -0.288606478243482 \tabularnewline
50 & 2 & 1.20929587374594 & 0.790704126254058 \tabularnewline
51 & 1 & 1.41256513639681 & -0.412565136396807 \tabularnewline
52 & 1 & 1.19143003462407 & -0.191430034624073 \tabularnewline
53 & 1 & 1.38517946530179 & -0.385179465301788 \tabularnewline
54 & 1 & 1.21411404557567 & -0.214114045575668 \tabularnewline
55 & 2 & 1.4646803032329 & 0.535319696767105 \tabularnewline
56 & 1 & 1.52275257416163 & -0.522752574161633 \tabularnewline
57 & 1 & 1.41904930681962 & -0.419049306819617 \tabularnewline
58 & 1 & 1.45615077099231 & -0.456150770992314 \tabularnewline
59 & 2 & 1.6421328780675 & 0.357867121932501 \tabularnewline
60 & 2 & 1.56753696426091 & 0.432463035739094 \tabularnewline
61 & 1 & 1.53466957648324 & -0.534669576483244 \tabularnewline
62 & 1 & 1.35348797835561 & -0.353487978355615 \tabularnewline
63 & 1 & 1.73988554642976 & -0.739885546429759 \tabularnewline
64 & 2 & 1.64576598234427 & 0.354234017655726 \tabularnewline
65 & 1 & 1.17927244702369 & -0.179272447023688 \tabularnewline
66 & 2 & 1.52229680042043 & 0.477703199579575 \tabularnewline
67 & 2 & 1.44069001917486 & 0.559309980825137 \tabularnewline
68 & 2 & 1.45576067185471 & 0.544239328145287 \tabularnewline
69 & 1 & 1.2895386532799 & -0.2895386532799 \tabularnewline
70 & 1 & 1.1625686131466 & -0.1625686131466 \tabularnewline
71 & 1 & 1.43642160850976 & -0.436421608509762 \tabularnewline
72 & 2 & 1.48699466822374 & 0.513005331776256 \tabularnewline
73 & 2 & 1.50736330184038 & 0.492636698159618 \tabularnewline
74 & 1 & 1.15064027046492 & -0.150640270464922 \tabularnewline
75 & 1 & 1.40001498992872 & -0.400014989928715 \tabularnewline
76 & 1 & 1.27638073660286 & -0.276380736602865 \tabularnewline
77 & 1 & 1.30273545030125 & -0.302735450301251 \tabularnewline
78 & 2 & 1.41654491362397 & 0.583455086376027 \tabularnewline
79 & 2 & 1.36180015798285 & 0.638199842017149 \tabularnewline
80 & 2 & 1.47412502911976 & 0.525874970880237 \tabularnewline
81 & 2 & 1.76176829681908 & 0.238231703180915 \tabularnewline
82 & 1 & 1.20351409304163 & -0.20351409304163 \tabularnewline
83 & 0 & 1.57566401023057 & -1.57566401023057 \tabularnewline
84 & 1 & 1.46109896282277 & -0.46109896282277 \tabularnewline
85 & 2 & 1.39346573857387 & 0.606534261426129 \tabularnewline
86 & 2 & 1.54713751771115 & 0.452862482288853 \tabularnewline
87 & 1 & 1.45565534219355 & -0.455655342193546 \tabularnewline
88 & 1 & 1.06844420321722 & -0.0684442032172213 \tabularnewline
89 & 1 & 1.04596637945285 & -0.0459663794528463 \tabularnewline
90 & 1 & 1.49061174002661 & -0.490611740026608 \tabularnewline
91 & 1 & 1.56834366106025 & -0.56834366106025 \tabularnewline
92 & 2 & 1.59677526399032 & 0.403224736009676 \tabularnewline
93 & 1 & 1.33111884299008 & -0.331118842990079 \tabularnewline
94 & 1 & 1.27472337202865 & -0.274723372028648 \tabularnewline
95 & 1 & 1.37057510891295 & -0.370575108912954 \tabularnewline
96 & 1 & 1.3913940301841 & -0.3913940301841 \tabularnewline
97 & 2 & 1.43785576170822 & 0.562144238291782 \tabularnewline
98 & 1 & 1.37955973872243 & -0.379559738722435 \tabularnewline
99 & 2 & 1.37980086172481 & 0.620199138275194 \tabularnewline
100 & 2 & 1.4970274787121 & 0.502972521287902 \tabularnewline
101 & 1 & 1.24922169028337 & -0.24922169028337 \tabularnewline
102 & 2 & 1.77584422728717 & 0.224155772712834 \tabularnewline
103 & 1 & 1.20515775927702 & -0.205157759277025 \tabularnewline
104 & 1 & 1.47681165970165 & -0.476811659701645 \tabularnewline
105 & 2 & 1.52904086512454 & 0.470959134875456 \tabularnewline
106 & 1 & 1.34499317934535 & -0.344993179345348 \tabularnewline
107 & 2 & 1.19327640139766 & 0.806723598602338 \tabularnewline
108 & 1 & 1.24891834591896 & -0.24891834591896 \tabularnewline
109 & 1 & 1.18047643996361 & -0.180476439963609 \tabularnewline
110 & 1 & 1.56915017799179 & -0.569150177991792 \tabularnewline
111 & 1 & 1.33570827532806 & -0.335708275328056 \tabularnewline
112 & 1 & 1.43766889074574 & -0.437668890745743 \tabularnewline
113 & 1 & 1.43240747416538 & -0.432407474165376 \tabularnewline
114 & 1 & 1.49522310182876 & -0.49522310182876 \tabularnewline
115 & 1 & 1.34745363860086 & -0.347453638600855 \tabularnewline
116 & 2 & 1.30593814694364 & 0.694061853056364 \tabularnewline
117 & 1 & 1.23817914104912 & -0.238179141049122 \tabularnewline
118 & 1 & 1.23823215666264 & -0.238232156662636 \tabularnewline
119 & 2 & 1.72737671740963 & 0.272623282590373 \tabularnewline
120 & 1 & 1.26836198943932 & -0.268361989439324 \tabularnewline
121 & 1 & 1.15241174197337 & -0.152411741973366 \tabularnewline
122 & 1 & 1.44523840841403 & -0.445238408414025 \tabularnewline
123 & 1 & 1.30592555567947 & -0.305925555679471 \tabularnewline
124 & 1 & 1.44969644634114 & -0.449696446341136 \tabularnewline
125 & 1 & 1.72197274299639 & -0.721972742996393 \tabularnewline
126 & 2 & 1.39421387818045 & 0.605786121819548 \tabularnewline
127 & 1 & 1.39637542576148 & -0.396375425761477 \tabularnewline
128 & 1 & 1.18547027677489 & -0.185470276774886 \tabularnewline
129 & 2 & 1.43594002972081 & 0.564059970279191 \tabularnewline
130 & 2 & 1.54765650417329 & 0.452343495826709 \tabularnewline
131 & 1 & 1.43647285002064 & -0.436472850020636 \tabularnewline
132 & 2 & 1.35081738110704 & 0.649182618892962 \tabularnewline
133 & 2 & 1.21970066019299 & 0.780299339807008 \tabularnewline
134 & 2 & 1.32947118755326 & 0.670528812446736 \tabularnewline
135 & 2 & 1.41536617068744 & 0.584633829312557 \tabularnewline
136 & 1 & 1.25479253245364 & -0.254792532453642 \tabularnewline
137 & 1 & 1.3139305652786 & -0.313930565278603 \tabularnewline
138 & 2 & 1.39817032846521 & 0.601829671534789 \tabularnewline
139 & 2 & 1.44959822991917 & 0.550401770080833 \tabularnewline
140 & 1 & 1.27129369532209 & -0.271293695322086 \tabularnewline
141 & 1 & 1.21139956809541 & -0.21139956809541 \tabularnewline
142 & 1 & 1.18744396047134 & -0.187443960471339 \tabularnewline
143 & 1 & 1.39322403055198 & -0.393224030551976 \tabularnewline
144 & 2 & 1.2699198365512 & 0.730080163448796 \tabularnewline
145 & 1 & 1.37378047724873 & -0.373780477248733 \tabularnewline
146 & 1 & 1.4750300570401 & -0.4750300570401 \tabularnewline
147 & 2 & 1.28923069188168 & 0.710769308118318 \tabularnewline
148 & 2 & 1.56240986729755 & 0.43759013270245 \tabularnewline
149 & 2 & 1.40866250156349 & 0.591337498436505 \tabularnewline
150 & 2 & 1.32700941322107 & 0.672990586778933 \tabularnewline
151 & 1 & 1.51437909161338 & -0.514379091613377 \tabularnewline
152 & 1 & 1.35731320459651 & -0.357313204596514 \tabularnewline
153 & 1 & 1.36418937744287 & -0.364189377442871 \tabularnewline
154 & 2 & 1.39509298021338 & 0.604907019786617 \tabularnewline
155 & 2 & 1.44177512532586 & 0.558224874674138 \tabularnewline
156 & 2 & 1.36355762409125 & 0.636442375908746 \tabularnewline
157 & 1 & 1.14427547798853 & -0.144275477988526 \tabularnewline
158 & 1 & 1.03515061685803 & -0.0351506168580286 \tabularnewline
159 & 1 & 1.2489556677383 & -0.248955667738305 \tabularnewline
160 & 1 & 1.24822897083673 & -0.248228970836732 \tabularnewline
161 & 2 & 1.46831730888139 & 0.531682691118607 \tabularnewline
162 & 1 & 1.42754311896069 & -0.427543118960693 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145608&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]1[/C][C]1.43041866386793[/C][C]-0.43041866386793[/C][/ROW]
[ROW][C]2[/C][C]1[/C][C]1.31754179020989[/C][C]-0.317541790209887[/C][/ROW]
[ROW][C]3[/C][C]1[/C][C]1.38015444986006[/C][C]-0.380154449860065[/C][/ROW]
[ROW][C]4[/C][C]2[/C][C]1.39890003018348[/C][C]0.601099969816518[/C][/ROW]
[ROW][C]5[/C][C]1[/C][C]1.31088228348215[/C][C]-0.310882283482152[/C][/ROW]
[ROW][C]6[/C][C]1[/C][C]1.3998554325387[/C][C]-0.399855432538699[/C][/ROW]
[ROW][C]7[/C][C]2[/C][C]1.44521672493395[/C][C]0.55478327506605[/C][/ROW]
[ROW][C]8[/C][C]1[/C][C]0.892171534253975[/C][C]0.107828465746025[/C][/ROW]
[ROW][C]9[/C][C]1[/C][C]1.42632933664511[/C][C]-0.426329336645106[/C][/ROW]
[ROW][C]10[/C][C]1[/C][C]1.31098286455238[/C][C]-0.310982864552381[/C][/ROW]
[ROW][C]11[/C][C]2[/C][C]1.43104671197414[/C][C]0.568953288025858[/C][/ROW]
[ROW][C]12[/C][C]2[/C][C]1.48303628057416[/C][C]0.51696371942584[/C][/ROW]
[ROW][C]13[/C][C]1[/C][C]1.18576191704065[/C][C]-0.185761917040647[/C][/ROW]
[ROW][C]14[/C][C]1[/C][C]1.63511621632718[/C][C]-0.635116216327176[/C][/ROW]
[ROW][C]15[/C][C]1[/C][C]1.50419038291468[/C][C]-0.504190382914682[/C][/ROW]
[ROW][C]16[/C][C]2[/C][C]1.32760150604772[/C][C]0.672398493952284[/C][/ROW]
[ROW][C]17[/C][C]1[/C][C]1.01651578232337[/C][C]-0.0165157823233669[/C][/ROW]
[ROW][C]18[/C][C]2[/C][C]1.474234489783[/C][C]0.525765510216995[/C][/ROW]
[ROW][C]19[/C][C]2[/C][C]1.84568685327912[/C][C]0.154313146720882[/C][/ROW]
[ROW][C]20[/C][C]1[/C][C]1.03761537498754[/C][C]-0.0376153749875439[/C][/ROW]
[ROW][C]21[/C][C]2[/C][C]1.7725201238952[/C][C]0.227479876104798[/C][/ROW]
[ROW][C]22[/C][C]1[/C][C]1.2304686517794[/C][C]-0.230468651779397[/C][/ROW]
[ROW][C]23[/C][C]1[/C][C]1.15214305403483[/C][C]-0.152143054034827[/C][/ROW]
[ROW][C]24[/C][C]2[/C][C]1.29440963308221[/C][C]0.70559036691779[/C][/ROW]
[ROW][C]25[/C][C]1[/C][C]1.17459345953716[/C][C]-0.174593459537165[/C][/ROW]
[ROW][C]26[/C][C]1[/C][C]1.55056849600538[/C][C]-0.550568496005378[/C][/ROW]
[ROW][C]27[/C][C]1[/C][C]1.55056849600538[/C][C]-0.550568496005378[/C][/ROW]
[ROW][C]28[/C][C]1[/C][C]1.34812099955338[/C][C]-0.348120999553376[/C][/ROW]
[ROW][C]29[/C][C]2[/C][C]1.62286017916245[/C][C]0.37713982083755[/C][/ROW]
[ROW][C]30[/C][C]1[/C][C]1.35390507406146[/C][C]-0.353905074061463[/C][/ROW]
[ROW][C]31[/C][C]2[/C][C]1.54786498302453[/C][C]0.452135016975472[/C][/ROW]
[ROW][C]32[/C][C]1[/C][C]1.42137433103086[/C][C]-0.421374331030858[/C][/ROW]
[ROW][C]33[/C][C]1[/C][C]1.11079356161054[/C][C]-0.110793561610539[/C][/ROW]
[ROW][C]34[/C][C]2[/C][C]1.32943572048614[/C][C]0.670564279513863[/C][/ROW]
[ROW][C]35[/C][C]2[/C][C]1.24479401803061[/C][C]0.755205981969394[/C][/ROW]
[ROW][C]36[/C][C]2[/C][C]1.41765587396393[/C][C]0.582344126036067[/C][/ROW]
[ROW][C]37[/C][C]1[/C][C]1.20422226464392[/C][C]-0.204222264643923[/C][/ROW]
[ROW][C]38[/C][C]2[/C][C]1.31100415736332[/C][C]0.688995842636679[/C][/ROW]
[ROW][C]39[/C][C]2[/C][C]1.47815079118237[/C][C]0.521849208817628[/C][/ROW]
[ROW][C]40[/C][C]1[/C][C]1.58492014200159[/C][C]-0.584920142001594[/C][/ROW]
[ROW][C]41[/C][C]1[/C][C]1.35946767005278[/C][C]-0.359467670052776[/C][/ROW]
[ROW][C]42[/C][C]1[/C][C]1.51111745248106[/C][C]-0.511117452481058[/C][/ROW]
[ROW][C]43[/C][C]1[/C][C]1.53199336044086[/C][C]-0.531993360440864[/C][/ROW]
[ROW][C]44[/C][C]2[/C][C]1.60038266174319[/C][C]0.39961733825681[/C][/ROW]
[ROW][C]45[/C][C]2[/C][C]1.72454299701708[/C][C]0.275457002982917[/C][/ROW]
[ROW][C]46[/C][C]1[/C][C]1.08079775129727[/C][C]-0.0807977512972655[/C][/ROW]
[ROW][C]47[/C][C]2[/C][C]1.32374314622705[/C][C]0.676256853772953[/C][/ROW]
[ROW][C]48[/C][C]2[/C][C]1.19252681590059[/C][C]0.807473184099409[/C][/ROW]
[ROW][C]49[/C][C]1[/C][C]1.28860647824348[/C][C]-0.288606478243482[/C][/ROW]
[ROW][C]50[/C][C]2[/C][C]1.20929587374594[/C][C]0.790704126254058[/C][/ROW]
[ROW][C]51[/C][C]1[/C][C]1.41256513639681[/C][C]-0.412565136396807[/C][/ROW]
[ROW][C]52[/C][C]1[/C][C]1.19143003462407[/C][C]-0.191430034624073[/C][/ROW]
[ROW][C]53[/C][C]1[/C][C]1.38517946530179[/C][C]-0.385179465301788[/C][/ROW]
[ROW][C]54[/C][C]1[/C][C]1.21411404557567[/C][C]-0.214114045575668[/C][/ROW]
[ROW][C]55[/C][C]2[/C][C]1.4646803032329[/C][C]0.535319696767105[/C][/ROW]
[ROW][C]56[/C][C]1[/C][C]1.52275257416163[/C][C]-0.522752574161633[/C][/ROW]
[ROW][C]57[/C][C]1[/C][C]1.41904930681962[/C][C]-0.419049306819617[/C][/ROW]
[ROW][C]58[/C][C]1[/C][C]1.45615077099231[/C][C]-0.456150770992314[/C][/ROW]
[ROW][C]59[/C][C]2[/C][C]1.6421328780675[/C][C]0.357867121932501[/C][/ROW]
[ROW][C]60[/C][C]2[/C][C]1.56753696426091[/C][C]0.432463035739094[/C][/ROW]
[ROW][C]61[/C][C]1[/C][C]1.53466957648324[/C][C]-0.534669576483244[/C][/ROW]
[ROW][C]62[/C][C]1[/C][C]1.35348797835561[/C][C]-0.353487978355615[/C][/ROW]
[ROW][C]63[/C][C]1[/C][C]1.73988554642976[/C][C]-0.739885546429759[/C][/ROW]
[ROW][C]64[/C][C]2[/C][C]1.64576598234427[/C][C]0.354234017655726[/C][/ROW]
[ROW][C]65[/C][C]1[/C][C]1.17927244702369[/C][C]-0.179272447023688[/C][/ROW]
[ROW][C]66[/C][C]2[/C][C]1.52229680042043[/C][C]0.477703199579575[/C][/ROW]
[ROW][C]67[/C][C]2[/C][C]1.44069001917486[/C][C]0.559309980825137[/C][/ROW]
[ROW][C]68[/C][C]2[/C][C]1.45576067185471[/C][C]0.544239328145287[/C][/ROW]
[ROW][C]69[/C][C]1[/C][C]1.2895386532799[/C][C]-0.2895386532799[/C][/ROW]
[ROW][C]70[/C][C]1[/C][C]1.1625686131466[/C][C]-0.1625686131466[/C][/ROW]
[ROW][C]71[/C][C]1[/C][C]1.43642160850976[/C][C]-0.436421608509762[/C][/ROW]
[ROW][C]72[/C][C]2[/C][C]1.48699466822374[/C][C]0.513005331776256[/C][/ROW]
[ROW][C]73[/C][C]2[/C][C]1.50736330184038[/C][C]0.492636698159618[/C][/ROW]
[ROW][C]74[/C][C]1[/C][C]1.15064027046492[/C][C]-0.150640270464922[/C][/ROW]
[ROW][C]75[/C][C]1[/C][C]1.40001498992872[/C][C]-0.400014989928715[/C][/ROW]
[ROW][C]76[/C][C]1[/C][C]1.27638073660286[/C][C]-0.276380736602865[/C][/ROW]
[ROW][C]77[/C][C]1[/C][C]1.30273545030125[/C][C]-0.302735450301251[/C][/ROW]
[ROW][C]78[/C][C]2[/C][C]1.41654491362397[/C][C]0.583455086376027[/C][/ROW]
[ROW][C]79[/C][C]2[/C][C]1.36180015798285[/C][C]0.638199842017149[/C][/ROW]
[ROW][C]80[/C][C]2[/C][C]1.47412502911976[/C][C]0.525874970880237[/C][/ROW]
[ROW][C]81[/C][C]2[/C][C]1.76176829681908[/C][C]0.238231703180915[/C][/ROW]
[ROW][C]82[/C][C]1[/C][C]1.20351409304163[/C][C]-0.20351409304163[/C][/ROW]
[ROW][C]83[/C][C]0[/C][C]1.57566401023057[/C][C]-1.57566401023057[/C][/ROW]
[ROW][C]84[/C][C]1[/C][C]1.46109896282277[/C][C]-0.46109896282277[/C][/ROW]
[ROW][C]85[/C][C]2[/C][C]1.39346573857387[/C][C]0.606534261426129[/C][/ROW]
[ROW][C]86[/C][C]2[/C][C]1.54713751771115[/C][C]0.452862482288853[/C][/ROW]
[ROW][C]87[/C][C]1[/C][C]1.45565534219355[/C][C]-0.455655342193546[/C][/ROW]
[ROW][C]88[/C][C]1[/C][C]1.06844420321722[/C][C]-0.0684442032172213[/C][/ROW]
[ROW][C]89[/C][C]1[/C][C]1.04596637945285[/C][C]-0.0459663794528463[/C][/ROW]
[ROW][C]90[/C][C]1[/C][C]1.49061174002661[/C][C]-0.490611740026608[/C][/ROW]
[ROW][C]91[/C][C]1[/C][C]1.56834366106025[/C][C]-0.56834366106025[/C][/ROW]
[ROW][C]92[/C][C]2[/C][C]1.59677526399032[/C][C]0.403224736009676[/C][/ROW]
[ROW][C]93[/C][C]1[/C][C]1.33111884299008[/C][C]-0.331118842990079[/C][/ROW]
[ROW][C]94[/C][C]1[/C][C]1.27472337202865[/C][C]-0.274723372028648[/C][/ROW]
[ROW][C]95[/C][C]1[/C][C]1.37057510891295[/C][C]-0.370575108912954[/C][/ROW]
[ROW][C]96[/C][C]1[/C][C]1.3913940301841[/C][C]-0.3913940301841[/C][/ROW]
[ROW][C]97[/C][C]2[/C][C]1.43785576170822[/C][C]0.562144238291782[/C][/ROW]
[ROW][C]98[/C][C]1[/C][C]1.37955973872243[/C][C]-0.379559738722435[/C][/ROW]
[ROW][C]99[/C][C]2[/C][C]1.37980086172481[/C][C]0.620199138275194[/C][/ROW]
[ROW][C]100[/C][C]2[/C][C]1.4970274787121[/C][C]0.502972521287902[/C][/ROW]
[ROW][C]101[/C][C]1[/C][C]1.24922169028337[/C][C]-0.24922169028337[/C][/ROW]
[ROW][C]102[/C][C]2[/C][C]1.77584422728717[/C][C]0.224155772712834[/C][/ROW]
[ROW][C]103[/C][C]1[/C][C]1.20515775927702[/C][C]-0.205157759277025[/C][/ROW]
[ROW][C]104[/C][C]1[/C][C]1.47681165970165[/C][C]-0.476811659701645[/C][/ROW]
[ROW][C]105[/C][C]2[/C][C]1.52904086512454[/C][C]0.470959134875456[/C][/ROW]
[ROW][C]106[/C][C]1[/C][C]1.34499317934535[/C][C]-0.344993179345348[/C][/ROW]
[ROW][C]107[/C][C]2[/C][C]1.19327640139766[/C][C]0.806723598602338[/C][/ROW]
[ROW][C]108[/C][C]1[/C][C]1.24891834591896[/C][C]-0.24891834591896[/C][/ROW]
[ROW][C]109[/C][C]1[/C][C]1.18047643996361[/C][C]-0.180476439963609[/C][/ROW]
[ROW][C]110[/C][C]1[/C][C]1.56915017799179[/C][C]-0.569150177991792[/C][/ROW]
[ROW][C]111[/C][C]1[/C][C]1.33570827532806[/C][C]-0.335708275328056[/C][/ROW]
[ROW][C]112[/C][C]1[/C][C]1.43766889074574[/C][C]-0.437668890745743[/C][/ROW]
[ROW][C]113[/C][C]1[/C][C]1.43240747416538[/C][C]-0.432407474165376[/C][/ROW]
[ROW][C]114[/C][C]1[/C][C]1.49522310182876[/C][C]-0.49522310182876[/C][/ROW]
[ROW][C]115[/C][C]1[/C][C]1.34745363860086[/C][C]-0.347453638600855[/C][/ROW]
[ROW][C]116[/C][C]2[/C][C]1.30593814694364[/C][C]0.694061853056364[/C][/ROW]
[ROW][C]117[/C][C]1[/C][C]1.23817914104912[/C][C]-0.238179141049122[/C][/ROW]
[ROW][C]118[/C][C]1[/C][C]1.23823215666264[/C][C]-0.238232156662636[/C][/ROW]
[ROW][C]119[/C][C]2[/C][C]1.72737671740963[/C][C]0.272623282590373[/C][/ROW]
[ROW][C]120[/C][C]1[/C][C]1.26836198943932[/C][C]-0.268361989439324[/C][/ROW]
[ROW][C]121[/C][C]1[/C][C]1.15241174197337[/C][C]-0.152411741973366[/C][/ROW]
[ROW][C]122[/C][C]1[/C][C]1.44523840841403[/C][C]-0.445238408414025[/C][/ROW]
[ROW][C]123[/C][C]1[/C][C]1.30592555567947[/C][C]-0.305925555679471[/C][/ROW]
[ROW][C]124[/C][C]1[/C][C]1.44969644634114[/C][C]-0.449696446341136[/C][/ROW]
[ROW][C]125[/C][C]1[/C][C]1.72197274299639[/C][C]-0.721972742996393[/C][/ROW]
[ROW][C]126[/C][C]2[/C][C]1.39421387818045[/C][C]0.605786121819548[/C][/ROW]
[ROW][C]127[/C][C]1[/C][C]1.39637542576148[/C][C]-0.396375425761477[/C][/ROW]
[ROW][C]128[/C][C]1[/C][C]1.18547027677489[/C][C]-0.185470276774886[/C][/ROW]
[ROW][C]129[/C][C]2[/C][C]1.43594002972081[/C][C]0.564059970279191[/C][/ROW]
[ROW][C]130[/C][C]2[/C][C]1.54765650417329[/C][C]0.452343495826709[/C][/ROW]
[ROW][C]131[/C][C]1[/C][C]1.43647285002064[/C][C]-0.436472850020636[/C][/ROW]
[ROW][C]132[/C][C]2[/C][C]1.35081738110704[/C][C]0.649182618892962[/C][/ROW]
[ROW][C]133[/C][C]2[/C][C]1.21970066019299[/C][C]0.780299339807008[/C][/ROW]
[ROW][C]134[/C][C]2[/C][C]1.32947118755326[/C][C]0.670528812446736[/C][/ROW]
[ROW][C]135[/C][C]2[/C][C]1.41536617068744[/C][C]0.584633829312557[/C][/ROW]
[ROW][C]136[/C][C]1[/C][C]1.25479253245364[/C][C]-0.254792532453642[/C][/ROW]
[ROW][C]137[/C][C]1[/C][C]1.3139305652786[/C][C]-0.313930565278603[/C][/ROW]
[ROW][C]138[/C][C]2[/C][C]1.39817032846521[/C][C]0.601829671534789[/C][/ROW]
[ROW][C]139[/C][C]2[/C][C]1.44959822991917[/C][C]0.550401770080833[/C][/ROW]
[ROW][C]140[/C][C]1[/C][C]1.27129369532209[/C][C]-0.271293695322086[/C][/ROW]
[ROW][C]141[/C][C]1[/C][C]1.21139956809541[/C][C]-0.21139956809541[/C][/ROW]
[ROW][C]142[/C][C]1[/C][C]1.18744396047134[/C][C]-0.187443960471339[/C][/ROW]
[ROW][C]143[/C][C]1[/C][C]1.39322403055198[/C][C]-0.393224030551976[/C][/ROW]
[ROW][C]144[/C][C]2[/C][C]1.2699198365512[/C][C]0.730080163448796[/C][/ROW]
[ROW][C]145[/C][C]1[/C][C]1.37378047724873[/C][C]-0.373780477248733[/C][/ROW]
[ROW][C]146[/C][C]1[/C][C]1.4750300570401[/C][C]-0.4750300570401[/C][/ROW]
[ROW][C]147[/C][C]2[/C][C]1.28923069188168[/C][C]0.710769308118318[/C][/ROW]
[ROW][C]148[/C][C]2[/C][C]1.56240986729755[/C][C]0.43759013270245[/C][/ROW]
[ROW][C]149[/C][C]2[/C][C]1.40866250156349[/C][C]0.591337498436505[/C][/ROW]
[ROW][C]150[/C][C]2[/C][C]1.32700941322107[/C][C]0.672990586778933[/C][/ROW]
[ROW][C]151[/C][C]1[/C][C]1.51437909161338[/C][C]-0.514379091613377[/C][/ROW]
[ROW][C]152[/C][C]1[/C][C]1.35731320459651[/C][C]-0.357313204596514[/C][/ROW]
[ROW][C]153[/C][C]1[/C][C]1.36418937744287[/C][C]-0.364189377442871[/C][/ROW]
[ROW][C]154[/C][C]2[/C][C]1.39509298021338[/C][C]0.604907019786617[/C][/ROW]
[ROW][C]155[/C][C]2[/C][C]1.44177512532586[/C][C]0.558224874674138[/C][/ROW]
[ROW][C]156[/C][C]2[/C][C]1.36355762409125[/C][C]0.636442375908746[/C][/ROW]
[ROW][C]157[/C][C]1[/C][C]1.14427547798853[/C][C]-0.144275477988526[/C][/ROW]
[ROW][C]158[/C][C]1[/C][C]1.03515061685803[/C][C]-0.0351506168580286[/C][/ROW]
[ROW][C]159[/C][C]1[/C][C]1.2489556677383[/C][C]-0.248955667738305[/C][/ROW]
[ROW][C]160[/C][C]1[/C][C]1.24822897083673[/C][C]-0.248228970836732[/C][/ROW]
[ROW][C]161[/C][C]2[/C][C]1.46831730888139[/C][C]0.531682691118607[/C][/ROW]
[ROW][C]162[/C][C]1[/C][C]1.42754311896069[/C][C]-0.427543118960693[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145608&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145608&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
111.43041866386793-0.43041866386793
211.31754179020989-0.317541790209887
311.38015444986006-0.380154449860065
421.398900030183480.601099969816518
511.31088228348215-0.310882283482152
611.3998554325387-0.399855432538699
721.445216724933950.55478327506605
810.8921715342539750.107828465746025
911.42632933664511-0.426329336645106
1011.31098286455238-0.310982864552381
1121.431046711974140.568953288025858
1221.483036280574160.51696371942584
1311.18576191704065-0.185761917040647
1411.63511621632718-0.635116216327176
1511.50419038291468-0.504190382914682
1621.327601506047720.672398493952284
1711.01651578232337-0.0165157823233669
1821.4742344897830.525765510216995
1921.845686853279120.154313146720882
2011.03761537498754-0.0376153749875439
2121.77252012389520.227479876104798
2211.2304686517794-0.230468651779397
2311.15214305403483-0.152143054034827
2421.294409633082210.70559036691779
2511.17459345953716-0.174593459537165
2611.55056849600538-0.550568496005378
2711.55056849600538-0.550568496005378
2811.34812099955338-0.348120999553376
2921.622860179162450.37713982083755
3011.35390507406146-0.353905074061463
3121.547864983024530.452135016975472
3211.42137433103086-0.421374331030858
3311.11079356161054-0.110793561610539
3421.329435720486140.670564279513863
3521.244794018030610.755205981969394
3621.417655873963930.582344126036067
3711.20422226464392-0.204222264643923
3821.311004157363320.688995842636679
3921.478150791182370.521849208817628
4011.58492014200159-0.584920142001594
4111.35946767005278-0.359467670052776
4211.51111745248106-0.511117452481058
4311.53199336044086-0.531993360440864
4421.600382661743190.39961733825681
4521.724542997017080.275457002982917
4611.08079775129727-0.0807977512972655
4721.323743146227050.676256853772953
4821.192526815900590.807473184099409
4911.28860647824348-0.288606478243482
5021.209295873745940.790704126254058
5111.41256513639681-0.412565136396807
5211.19143003462407-0.191430034624073
5311.38517946530179-0.385179465301788
5411.21411404557567-0.214114045575668
5521.46468030323290.535319696767105
5611.52275257416163-0.522752574161633
5711.41904930681962-0.419049306819617
5811.45615077099231-0.456150770992314
5921.64213287806750.357867121932501
6021.567536964260910.432463035739094
6111.53466957648324-0.534669576483244
6211.35348797835561-0.353487978355615
6311.73988554642976-0.739885546429759
6421.645765982344270.354234017655726
6511.17927244702369-0.179272447023688
6621.522296800420430.477703199579575
6721.440690019174860.559309980825137
6821.455760671854710.544239328145287
6911.2895386532799-0.2895386532799
7011.1625686131466-0.1625686131466
7111.43642160850976-0.436421608509762
7221.486994668223740.513005331776256
7321.507363301840380.492636698159618
7411.15064027046492-0.150640270464922
7511.40001498992872-0.400014989928715
7611.27638073660286-0.276380736602865
7711.30273545030125-0.302735450301251
7821.416544913623970.583455086376027
7921.361800157982850.638199842017149
8021.474125029119760.525874970880237
8121.761768296819080.238231703180915
8211.20351409304163-0.20351409304163
8301.57566401023057-1.57566401023057
8411.46109896282277-0.46109896282277
8521.393465738573870.606534261426129
8621.547137517711150.452862482288853
8711.45565534219355-0.455655342193546
8811.06844420321722-0.0684442032172213
8911.04596637945285-0.0459663794528463
9011.49061174002661-0.490611740026608
9111.56834366106025-0.56834366106025
9221.596775263990320.403224736009676
9311.33111884299008-0.331118842990079
9411.27472337202865-0.274723372028648
9511.37057510891295-0.370575108912954
9611.3913940301841-0.3913940301841
9721.437855761708220.562144238291782
9811.37955973872243-0.379559738722435
9921.379800861724810.620199138275194
10021.49702747871210.502972521287902
10111.24922169028337-0.24922169028337
10221.775844227287170.224155772712834
10311.20515775927702-0.205157759277025
10411.47681165970165-0.476811659701645
10521.529040865124540.470959134875456
10611.34499317934535-0.344993179345348
10721.193276401397660.806723598602338
10811.24891834591896-0.24891834591896
10911.18047643996361-0.180476439963609
11011.56915017799179-0.569150177991792
11111.33570827532806-0.335708275328056
11211.43766889074574-0.437668890745743
11311.43240747416538-0.432407474165376
11411.49522310182876-0.49522310182876
11511.34745363860086-0.347453638600855
11621.305938146943640.694061853056364
11711.23817914104912-0.238179141049122
11811.23823215666264-0.238232156662636
11921.727376717409630.272623282590373
12011.26836198943932-0.268361989439324
12111.15241174197337-0.152411741973366
12211.44523840841403-0.445238408414025
12311.30592555567947-0.305925555679471
12411.44969644634114-0.449696446341136
12511.72197274299639-0.721972742996393
12621.394213878180450.605786121819548
12711.39637542576148-0.396375425761477
12811.18547027677489-0.185470276774886
12921.435940029720810.564059970279191
13021.547656504173290.452343495826709
13111.43647285002064-0.436472850020636
13221.350817381107040.649182618892962
13321.219700660192990.780299339807008
13421.329471187553260.670528812446736
13521.415366170687440.584633829312557
13611.25479253245364-0.254792532453642
13711.3139305652786-0.313930565278603
13821.398170328465210.601829671534789
13921.449598229919170.550401770080833
14011.27129369532209-0.271293695322086
14111.21139956809541-0.21139956809541
14211.18744396047134-0.187443960471339
14311.39322403055198-0.393224030551976
14421.26991983655120.730080163448796
14511.37378047724873-0.373780477248733
14611.4750300570401-0.4750300570401
14721.289230691881680.710769308118318
14821.562409867297550.43759013270245
14921.408662501563490.591337498436505
15021.327009413221070.672990586778933
15111.51437909161338-0.514379091613377
15211.35731320459651-0.357313204596514
15311.36418937744287-0.364189377442871
15421.395092980213380.604907019786617
15521.441775125325860.558224874674138
15621.363557624091250.636442375908746
15711.14427547798853-0.144275477988526
15811.03515061685803-0.0351506168580286
15911.2489556677383-0.248955667738305
16011.24822897083673-0.248228970836732
16121.468317308881390.531682691118607
16211.42754311896069-0.427543118960693







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
120.8133772575818640.3732454848362720.186622742418136
130.7055615341327920.5888769317344160.294438465867208
140.665891916893180.668216166213640.33410808310682
150.5644806175922150.8710387648155690.435519382407785
160.5704627527719680.8590744944560630.429537247228032
170.5310351629667750.937929674066450.468964837033225
180.682813768380270.6343724632394620.317186231619731
190.6056739460719340.7886521078561320.394326053928066
200.5522164529134720.8955670941730570.447783547086528
210.4930933529567880.9861867059135760.506906647043212
220.4363970514081850.872794102816370.563602948591815
230.3585217612508180.7170435225016350.641478238749182
240.4897860482653980.9795720965307960.510213951734602
250.4556035476282620.9112070952565240.544396452371738
260.4190296854794910.8380593709589820.580970314520509
270.3698953254877490.7397906509754990.63010467451225
280.3148289717915030.6296579435830050.685171028208497
290.3198273592364180.6396547184728350.680172640763583
300.2771417561400190.5542835122800370.722858243859982
310.2674821537056770.5349643074113540.732517846294323
320.2263026082967320.4526052165934650.773697391703268
330.1867084602413650.373416920482730.813291539758635
340.2000879399169990.4001758798339990.799912060083
350.2501049226037820.5002098452075640.749895077396218
360.2736565040441710.5473130080883430.726343495955829
370.2648582817251040.5297165634502080.735141718274896
380.3693482183631760.7386964367263520.630651781636824
390.3686756808142440.7373513616284890.631324319185756
400.4231518843440520.8463037686881040.576848115655948
410.4366272056881550.873254411376310.563372794311845
420.4694374568741590.9388749137483180.530562543125841
430.5051583153273120.9896833693453770.494841684672688
440.4950233928251460.9900467856502930.504976607174854
450.4600177175209040.9200354350418080.539982282479096
460.4431764633735420.8863529267470850.556823536626458
470.4941827333836470.9883654667672950.505817266616353
480.5405661329056080.9188677341887840.459433867094392
490.5012322911122890.9975354177754230.498767708887711
500.5492953933704420.9014092132591170.450704606629558
510.5280802265350910.9438395469298170.471919773464908
520.4806763252452680.9613526504905370.519323674754732
530.4634783573893460.9269567147786920.536521642610654
540.4238693091896820.8477386183793630.576130690810318
550.4471630141933490.8943260283866980.552836985806651
560.4678245188488360.9356490376976720.532175481151164
570.4531295028068280.9062590056136550.546870497193172
580.4445307208120680.8890614416241370.555469279187932
590.4199392442074630.8398784884149270.580060755792537
600.4198681349767640.8397362699535280.580131865023236
610.440889431054830.881778862109660.55911056894517
620.4251485296854580.8502970593709160.574851470314542
630.4885324296650340.9770648593300670.511467570334966
640.4662357744899480.9324715489798960.533764225510052
650.4318683491822940.8637366983645890.568131650817706
660.4261914532334650.852382906466930.573808546766535
670.4507143590767150.901428718153430.549285640923285
680.4717370408700720.9434740817401430.528262959129928
690.4402338165034570.8804676330069140.559766183496543
700.3998122888068180.7996245776136360.600187711193182
710.3980088814737240.7960177629474480.601991118526276
720.4054909523460540.8109819046921080.594509047653946
730.4114592451835920.8229184903671840.588540754816408
740.3756689450838570.7513378901677130.624331054916143
750.361628932369870.723257864739740.63837106763013
760.3306145059208680.6612290118417360.669385494079132
770.3013220793809750.602644158761950.698677920619025
780.3171853451896550.6343706903793110.682814654810345
790.3443278922308790.6886557844617590.655672107769121
800.3457414065280810.6914828130561620.654258593471919
810.3077352562852780.6154705125705560.692264743714722
820.2751859514066820.5503719028133640.724814048593318
830.6746616500720360.6506766998559290.325338349927964
840.6731000563123970.6537998873752060.326899943687603
850.6945559396575880.6108881206848240.305444060342412
860.6917830709797430.6164338580405140.308216929020257
870.6846282328596290.6307435342807420.315371767140371
880.6480389394713170.7039221210573670.351961060528683
890.6133585758963340.7732828482073320.386641424103666
900.6079283698978120.7841432602043770.392071630102188
910.6275727422360380.7448545155279240.372427257763962
920.6126411213660970.7747177572678050.387358878633903
930.5847593341949050.830481331610190.415240665805095
940.5635549682329430.8728900635341130.436445031767057
950.5458778539460370.9082442921079260.454122146053963
960.5283094334601760.9433811330796480.471690566539824
970.5497565927262530.9004868145474950.450243407273747
980.537347909714090.9253041805718210.46265209028591
990.5668327662903620.8663344674192770.433167233709638
1000.5731482091849540.8537035816300930.426851790815046
1010.5360857872423750.9278284255152510.463914212757625
1020.4936397630164360.9872795260328720.506360236983564
1030.4489256753186280.8978513506372560.551074324681372
1040.4464928280606060.8929856561212110.553507171939394
1050.4436478250594310.8872956501188620.556352174940569
1060.4172101211418690.8344202422837390.582789878858131
1070.5093028006480310.9813943987039380.490697199351969
1080.4702623664582520.9405247329165030.529737633541748
1090.4562555463379150.9125110926758310.543744453662085
1100.4904939359235460.9809878718470930.509506064076454
1110.4562068348345340.9124136696690680.543793165165466
1120.4307927996426220.8615855992852440.569207200357378
1130.4169726215722490.8339452431444980.583027378427751
1140.4327859727316830.8655719454633660.567214027268317
1150.4242204067618910.8484408135237810.575779593238109
1160.4352812214397150.870562442879430.564718778560285
1170.3874419278707880.7748838557415760.612558072129212
1180.3496061224539360.6992122449078710.650393877546064
1190.3327154223765550.665430844753110.667284577623445
1200.2895583428573540.5791166857147090.710441657142646
1210.258959797732850.5179195954656990.74104020226715
1220.2578537875188230.5157075750376450.742146212481177
1230.2288072596126480.4576145192252960.771192740387352
1240.2340550252307880.4681100504615760.765944974769212
1250.257192672403670.5143853448073390.74280732759633
1260.2571286375073360.5142572750146710.742871362492664
1270.2715242908563740.5430485817127480.728475709143626
1280.2257910296951140.4515820593902270.774208970304886
1290.2089204970633150.4178409941266310.791079502936685
1300.1809598611414510.3619197222829020.81904013885855
1310.1916182524559230.3832365049118450.808381747544077
1320.1943787066760890.3887574133521790.805621293323911
1330.2421209784092840.4842419568185680.757879021590716
1340.2825573319475690.5651146638951370.717442668052431
1350.2581303212216660.5162606424433320.741869678778334
1360.2208241198488530.4416482396977060.779175880151147
1370.197468414052690.3949368281053790.80253158594731
1380.2241493590893310.4482987181786620.775850640910669
1390.2597275697320170.5194551394640340.740272430267983
1400.2223711092153540.4447422184307070.777628890784646
1410.1927360342858340.3854720685716680.807263965714166
1420.1540934886407860.3081869772815710.845906511359214
1430.1130751741722170.2261503483444340.886924825827783
1440.186235323461380.372470646922760.81376467653862
1450.1603304720761440.3206609441522870.839669527923856
1460.3315317113170860.6630634226341730.668468288682914
1470.424613143005780.8492262860115590.57538685699422
1480.4315813261412080.8631626522824160.568418673858792
1490.4320665632647410.8641331265294820.567933436735259
1500.6285491911496250.7429016177007510.371450808850375

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
12 & 0.813377257581864 & 0.373245484836272 & 0.186622742418136 \tabularnewline
13 & 0.705561534132792 & 0.588876931734416 & 0.294438465867208 \tabularnewline
14 & 0.66589191689318 & 0.66821616621364 & 0.33410808310682 \tabularnewline
15 & 0.564480617592215 & 0.871038764815569 & 0.435519382407785 \tabularnewline
16 & 0.570462752771968 & 0.859074494456063 & 0.429537247228032 \tabularnewline
17 & 0.531035162966775 & 0.93792967406645 & 0.468964837033225 \tabularnewline
18 & 0.68281376838027 & 0.634372463239462 & 0.317186231619731 \tabularnewline
19 & 0.605673946071934 & 0.788652107856132 & 0.394326053928066 \tabularnewline
20 & 0.552216452913472 & 0.895567094173057 & 0.447783547086528 \tabularnewline
21 & 0.493093352956788 & 0.986186705913576 & 0.506906647043212 \tabularnewline
22 & 0.436397051408185 & 0.87279410281637 & 0.563602948591815 \tabularnewline
23 & 0.358521761250818 & 0.717043522501635 & 0.641478238749182 \tabularnewline
24 & 0.489786048265398 & 0.979572096530796 & 0.510213951734602 \tabularnewline
25 & 0.455603547628262 & 0.911207095256524 & 0.544396452371738 \tabularnewline
26 & 0.419029685479491 & 0.838059370958982 & 0.580970314520509 \tabularnewline
27 & 0.369895325487749 & 0.739790650975499 & 0.63010467451225 \tabularnewline
28 & 0.314828971791503 & 0.629657943583005 & 0.685171028208497 \tabularnewline
29 & 0.319827359236418 & 0.639654718472835 & 0.680172640763583 \tabularnewline
30 & 0.277141756140019 & 0.554283512280037 & 0.722858243859982 \tabularnewline
31 & 0.267482153705677 & 0.534964307411354 & 0.732517846294323 \tabularnewline
32 & 0.226302608296732 & 0.452605216593465 & 0.773697391703268 \tabularnewline
33 & 0.186708460241365 & 0.37341692048273 & 0.813291539758635 \tabularnewline
34 & 0.200087939916999 & 0.400175879833999 & 0.799912060083 \tabularnewline
35 & 0.250104922603782 & 0.500209845207564 & 0.749895077396218 \tabularnewline
36 & 0.273656504044171 & 0.547313008088343 & 0.726343495955829 \tabularnewline
37 & 0.264858281725104 & 0.529716563450208 & 0.735141718274896 \tabularnewline
38 & 0.369348218363176 & 0.738696436726352 & 0.630651781636824 \tabularnewline
39 & 0.368675680814244 & 0.737351361628489 & 0.631324319185756 \tabularnewline
40 & 0.423151884344052 & 0.846303768688104 & 0.576848115655948 \tabularnewline
41 & 0.436627205688155 & 0.87325441137631 & 0.563372794311845 \tabularnewline
42 & 0.469437456874159 & 0.938874913748318 & 0.530562543125841 \tabularnewline
43 & 0.505158315327312 & 0.989683369345377 & 0.494841684672688 \tabularnewline
44 & 0.495023392825146 & 0.990046785650293 & 0.504976607174854 \tabularnewline
45 & 0.460017717520904 & 0.920035435041808 & 0.539982282479096 \tabularnewline
46 & 0.443176463373542 & 0.886352926747085 & 0.556823536626458 \tabularnewline
47 & 0.494182733383647 & 0.988365466767295 & 0.505817266616353 \tabularnewline
48 & 0.540566132905608 & 0.918867734188784 & 0.459433867094392 \tabularnewline
49 & 0.501232291112289 & 0.997535417775423 & 0.498767708887711 \tabularnewline
50 & 0.549295393370442 & 0.901409213259117 & 0.450704606629558 \tabularnewline
51 & 0.528080226535091 & 0.943839546929817 & 0.471919773464908 \tabularnewline
52 & 0.480676325245268 & 0.961352650490537 & 0.519323674754732 \tabularnewline
53 & 0.463478357389346 & 0.926956714778692 & 0.536521642610654 \tabularnewline
54 & 0.423869309189682 & 0.847738618379363 & 0.576130690810318 \tabularnewline
55 & 0.447163014193349 & 0.894326028386698 & 0.552836985806651 \tabularnewline
56 & 0.467824518848836 & 0.935649037697672 & 0.532175481151164 \tabularnewline
57 & 0.453129502806828 & 0.906259005613655 & 0.546870497193172 \tabularnewline
58 & 0.444530720812068 & 0.889061441624137 & 0.555469279187932 \tabularnewline
59 & 0.419939244207463 & 0.839878488414927 & 0.580060755792537 \tabularnewline
60 & 0.419868134976764 & 0.839736269953528 & 0.580131865023236 \tabularnewline
61 & 0.44088943105483 & 0.88177886210966 & 0.55911056894517 \tabularnewline
62 & 0.425148529685458 & 0.850297059370916 & 0.574851470314542 \tabularnewline
63 & 0.488532429665034 & 0.977064859330067 & 0.511467570334966 \tabularnewline
64 & 0.466235774489948 & 0.932471548979896 & 0.533764225510052 \tabularnewline
65 & 0.431868349182294 & 0.863736698364589 & 0.568131650817706 \tabularnewline
66 & 0.426191453233465 & 0.85238290646693 & 0.573808546766535 \tabularnewline
67 & 0.450714359076715 & 0.90142871815343 & 0.549285640923285 \tabularnewline
68 & 0.471737040870072 & 0.943474081740143 & 0.528262959129928 \tabularnewline
69 & 0.440233816503457 & 0.880467633006914 & 0.559766183496543 \tabularnewline
70 & 0.399812288806818 & 0.799624577613636 & 0.600187711193182 \tabularnewline
71 & 0.398008881473724 & 0.796017762947448 & 0.601991118526276 \tabularnewline
72 & 0.405490952346054 & 0.810981904692108 & 0.594509047653946 \tabularnewline
73 & 0.411459245183592 & 0.822918490367184 & 0.588540754816408 \tabularnewline
74 & 0.375668945083857 & 0.751337890167713 & 0.624331054916143 \tabularnewline
75 & 0.36162893236987 & 0.72325786473974 & 0.63837106763013 \tabularnewline
76 & 0.330614505920868 & 0.661229011841736 & 0.669385494079132 \tabularnewline
77 & 0.301322079380975 & 0.60264415876195 & 0.698677920619025 \tabularnewline
78 & 0.317185345189655 & 0.634370690379311 & 0.682814654810345 \tabularnewline
79 & 0.344327892230879 & 0.688655784461759 & 0.655672107769121 \tabularnewline
80 & 0.345741406528081 & 0.691482813056162 & 0.654258593471919 \tabularnewline
81 & 0.307735256285278 & 0.615470512570556 & 0.692264743714722 \tabularnewline
82 & 0.275185951406682 & 0.550371902813364 & 0.724814048593318 \tabularnewline
83 & 0.674661650072036 & 0.650676699855929 & 0.325338349927964 \tabularnewline
84 & 0.673100056312397 & 0.653799887375206 & 0.326899943687603 \tabularnewline
85 & 0.694555939657588 & 0.610888120684824 & 0.305444060342412 \tabularnewline
86 & 0.691783070979743 & 0.616433858040514 & 0.308216929020257 \tabularnewline
87 & 0.684628232859629 & 0.630743534280742 & 0.315371767140371 \tabularnewline
88 & 0.648038939471317 & 0.703922121057367 & 0.351961060528683 \tabularnewline
89 & 0.613358575896334 & 0.773282848207332 & 0.386641424103666 \tabularnewline
90 & 0.607928369897812 & 0.784143260204377 & 0.392071630102188 \tabularnewline
91 & 0.627572742236038 & 0.744854515527924 & 0.372427257763962 \tabularnewline
92 & 0.612641121366097 & 0.774717757267805 & 0.387358878633903 \tabularnewline
93 & 0.584759334194905 & 0.83048133161019 & 0.415240665805095 \tabularnewline
94 & 0.563554968232943 & 0.872890063534113 & 0.436445031767057 \tabularnewline
95 & 0.545877853946037 & 0.908244292107926 & 0.454122146053963 \tabularnewline
96 & 0.528309433460176 & 0.943381133079648 & 0.471690566539824 \tabularnewline
97 & 0.549756592726253 & 0.900486814547495 & 0.450243407273747 \tabularnewline
98 & 0.53734790971409 & 0.925304180571821 & 0.46265209028591 \tabularnewline
99 & 0.566832766290362 & 0.866334467419277 & 0.433167233709638 \tabularnewline
100 & 0.573148209184954 & 0.853703581630093 & 0.426851790815046 \tabularnewline
101 & 0.536085787242375 & 0.927828425515251 & 0.463914212757625 \tabularnewline
102 & 0.493639763016436 & 0.987279526032872 & 0.506360236983564 \tabularnewline
103 & 0.448925675318628 & 0.897851350637256 & 0.551074324681372 \tabularnewline
104 & 0.446492828060606 & 0.892985656121211 & 0.553507171939394 \tabularnewline
105 & 0.443647825059431 & 0.887295650118862 & 0.556352174940569 \tabularnewline
106 & 0.417210121141869 & 0.834420242283739 & 0.582789878858131 \tabularnewline
107 & 0.509302800648031 & 0.981394398703938 & 0.490697199351969 \tabularnewline
108 & 0.470262366458252 & 0.940524732916503 & 0.529737633541748 \tabularnewline
109 & 0.456255546337915 & 0.912511092675831 & 0.543744453662085 \tabularnewline
110 & 0.490493935923546 & 0.980987871847093 & 0.509506064076454 \tabularnewline
111 & 0.456206834834534 & 0.912413669669068 & 0.543793165165466 \tabularnewline
112 & 0.430792799642622 & 0.861585599285244 & 0.569207200357378 \tabularnewline
113 & 0.416972621572249 & 0.833945243144498 & 0.583027378427751 \tabularnewline
114 & 0.432785972731683 & 0.865571945463366 & 0.567214027268317 \tabularnewline
115 & 0.424220406761891 & 0.848440813523781 & 0.575779593238109 \tabularnewline
116 & 0.435281221439715 & 0.87056244287943 & 0.564718778560285 \tabularnewline
117 & 0.387441927870788 & 0.774883855741576 & 0.612558072129212 \tabularnewline
118 & 0.349606122453936 & 0.699212244907871 & 0.650393877546064 \tabularnewline
119 & 0.332715422376555 & 0.66543084475311 & 0.667284577623445 \tabularnewline
120 & 0.289558342857354 & 0.579116685714709 & 0.710441657142646 \tabularnewline
121 & 0.25895979773285 & 0.517919595465699 & 0.74104020226715 \tabularnewline
122 & 0.257853787518823 & 0.515707575037645 & 0.742146212481177 \tabularnewline
123 & 0.228807259612648 & 0.457614519225296 & 0.771192740387352 \tabularnewline
124 & 0.234055025230788 & 0.468110050461576 & 0.765944974769212 \tabularnewline
125 & 0.25719267240367 & 0.514385344807339 & 0.74280732759633 \tabularnewline
126 & 0.257128637507336 & 0.514257275014671 & 0.742871362492664 \tabularnewline
127 & 0.271524290856374 & 0.543048581712748 & 0.728475709143626 \tabularnewline
128 & 0.225791029695114 & 0.451582059390227 & 0.774208970304886 \tabularnewline
129 & 0.208920497063315 & 0.417840994126631 & 0.791079502936685 \tabularnewline
130 & 0.180959861141451 & 0.361919722282902 & 0.81904013885855 \tabularnewline
131 & 0.191618252455923 & 0.383236504911845 & 0.808381747544077 \tabularnewline
132 & 0.194378706676089 & 0.388757413352179 & 0.805621293323911 \tabularnewline
133 & 0.242120978409284 & 0.484241956818568 & 0.757879021590716 \tabularnewline
134 & 0.282557331947569 & 0.565114663895137 & 0.717442668052431 \tabularnewline
135 & 0.258130321221666 & 0.516260642443332 & 0.741869678778334 \tabularnewline
136 & 0.220824119848853 & 0.441648239697706 & 0.779175880151147 \tabularnewline
137 & 0.19746841405269 & 0.394936828105379 & 0.80253158594731 \tabularnewline
138 & 0.224149359089331 & 0.448298718178662 & 0.775850640910669 \tabularnewline
139 & 0.259727569732017 & 0.519455139464034 & 0.740272430267983 \tabularnewline
140 & 0.222371109215354 & 0.444742218430707 & 0.777628890784646 \tabularnewline
141 & 0.192736034285834 & 0.385472068571668 & 0.807263965714166 \tabularnewline
142 & 0.154093488640786 & 0.308186977281571 & 0.845906511359214 \tabularnewline
143 & 0.113075174172217 & 0.226150348344434 & 0.886924825827783 \tabularnewline
144 & 0.18623532346138 & 0.37247064692276 & 0.81376467653862 \tabularnewline
145 & 0.160330472076144 & 0.320660944152287 & 0.839669527923856 \tabularnewline
146 & 0.331531711317086 & 0.663063422634173 & 0.668468288682914 \tabularnewline
147 & 0.42461314300578 & 0.849226286011559 & 0.57538685699422 \tabularnewline
148 & 0.431581326141208 & 0.863162652282416 & 0.568418673858792 \tabularnewline
149 & 0.432066563264741 & 0.864133126529482 & 0.567933436735259 \tabularnewline
150 & 0.628549191149625 & 0.742901617700751 & 0.371450808850375 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145608&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]12[/C][C]0.813377257581864[/C][C]0.373245484836272[/C][C]0.186622742418136[/C][/ROW]
[ROW][C]13[/C][C]0.705561534132792[/C][C]0.588876931734416[/C][C]0.294438465867208[/C][/ROW]
[ROW][C]14[/C][C]0.66589191689318[/C][C]0.66821616621364[/C][C]0.33410808310682[/C][/ROW]
[ROW][C]15[/C][C]0.564480617592215[/C][C]0.871038764815569[/C][C]0.435519382407785[/C][/ROW]
[ROW][C]16[/C][C]0.570462752771968[/C][C]0.859074494456063[/C][C]0.429537247228032[/C][/ROW]
[ROW][C]17[/C][C]0.531035162966775[/C][C]0.93792967406645[/C][C]0.468964837033225[/C][/ROW]
[ROW][C]18[/C][C]0.68281376838027[/C][C]0.634372463239462[/C][C]0.317186231619731[/C][/ROW]
[ROW][C]19[/C][C]0.605673946071934[/C][C]0.788652107856132[/C][C]0.394326053928066[/C][/ROW]
[ROW][C]20[/C][C]0.552216452913472[/C][C]0.895567094173057[/C][C]0.447783547086528[/C][/ROW]
[ROW][C]21[/C][C]0.493093352956788[/C][C]0.986186705913576[/C][C]0.506906647043212[/C][/ROW]
[ROW][C]22[/C][C]0.436397051408185[/C][C]0.87279410281637[/C][C]0.563602948591815[/C][/ROW]
[ROW][C]23[/C][C]0.358521761250818[/C][C]0.717043522501635[/C][C]0.641478238749182[/C][/ROW]
[ROW][C]24[/C][C]0.489786048265398[/C][C]0.979572096530796[/C][C]0.510213951734602[/C][/ROW]
[ROW][C]25[/C][C]0.455603547628262[/C][C]0.911207095256524[/C][C]0.544396452371738[/C][/ROW]
[ROW][C]26[/C][C]0.419029685479491[/C][C]0.838059370958982[/C][C]0.580970314520509[/C][/ROW]
[ROW][C]27[/C][C]0.369895325487749[/C][C]0.739790650975499[/C][C]0.63010467451225[/C][/ROW]
[ROW][C]28[/C][C]0.314828971791503[/C][C]0.629657943583005[/C][C]0.685171028208497[/C][/ROW]
[ROW][C]29[/C][C]0.319827359236418[/C][C]0.639654718472835[/C][C]0.680172640763583[/C][/ROW]
[ROW][C]30[/C][C]0.277141756140019[/C][C]0.554283512280037[/C][C]0.722858243859982[/C][/ROW]
[ROW][C]31[/C][C]0.267482153705677[/C][C]0.534964307411354[/C][C]0.732517846294323[/C][/ROW]
[ROW][C]32[/C][C]0.226302608296732[/C][C]0.452605216593465[/C][C]0.773697391703268[/C][/ROW]
[ROW][C]33[/C][C]0.186708460241365[/C][C]0.37341692048273[/C][C]0.813291539758635[/C][/ROW]
[ROW][C]34[/C][C]0.200087939916999[/C][C]0.400175879833999[/C][C]0.799912060083[/C][/ROW]
[ROW][C]35[/C][C]0.250104922603782[/C][C]0.500209845207564[/C][C]0.749895077396218[/C][/ROW]
[ROW][C]36[/C][C]0.273656504044171[/C][C]0.547313008088343[/C][C]0.726343495955829[/C][/ROW]
[ROW][C]37[/C][C]0.264858281725104[/C][C]0.529716563450208[/C][C]0.735141718274896[/C][/ROW]
[ROW][C]38[/C][C]0.369348218363176[/C][C]0.738696436726352[/C][C]0.630651781636824[/C][/ROW]
[ROW][C]39[/C][C]0.368675680814244[/C][C]0.737351361628489[/C][C]0.631324319185756[/C][/ROW]
[ROW][C]40[/C][C]0.423151884344052[/C][C]0.846303768688104[/C][C]0.576848115655948[/C][/ROW]
[ROW][C]41[/C][C]0.436627205688155[/C][C]0.87325441137631[/C][C]0.563372794311845[/C][/ROW]
[ROW][C]42[/C][C]0.469437456874159[/C][C]0.938874913748318[/C][C]0.530562543125841[/C][/ROW]
[ROW][C]43[/C][C]0.505158315327312[/C][C]0.989683369345377[/C][C]0.494841684672688[/C][/ROW]
[ROW][C]44[/C][C]0.495023392825146[/C][C]0.990046785650293[/C][C]0.504976607174854[/C][/ROW]
[ROW][C]45[/C][C]0.460017717520904[/C][C]0.920035435041808[/C][C]0.539982282479096[/C][/ROW]
[ROW][C]46[/C][C]0.443176463373542[/C][C]0.886352926747085[/C][C]0.556823536626458[/C][/ROW]
[ROW][C]47[/C][C]0.494182733383647[/C][C]0.988365466767295[/C][C]0.505817266616353[/C][/ROW]
[ROW][C]48[/C][C]0.540566132905608[/C][C]0.918867734188784[/C][C]0.459433867094392[/C][/ROW]
[ROW][C]49[/C][C]0.501232291112289[/C][C]0.997535417775423[/C][C]0.498767708887711[/C][/ROW]
[ROW][C]50[/C][C]0.549295393370442[/C][C]0.901409213259117[/C][C]0.450704606629558[/C][/ROW]
[ROW][C]51[/C][C]0.528080226535091[/C][C]0.943839546929817[/C][C]0.471919773464908[/C][/ROW]
[ROW][C]52[/C][C]0.480676325245268[/C][C]0.961352650490537[/C][C]0.519323674754732[/C][/ROW]
[ROW][C]53[/C][C]0.463478357389346[/C][C]0.926956714778692[/C][C]0.536521642610654[/C][/ROW]
[ROW][C]54[/C][C]0.423869309189682[/C][C]0.847738618379363[/C][C]0.576130690810318[/C][/ROW]
[ROW][C]55[/C][C]0.447163014193349[/C][C]0.894326028386698[/C][C]0.552836985806651[/C][/ROW]
[ROW][C]56[/C][C]0.467824518848836[/C][C]0.935649037697672[/C][C]0.532175481151164[/C][/ROW]
[ROW][C]57[/C][C]0.453129502806828[/C][C]0.906259005613655[/C][C]0.546870497193172[/C][/ROW]
[ROW][C]58[/C][C]0.444530720812068[/C][C]0.889061441624137[/C][C]0.555469279187932[/C][/ROW]
[ROW][C]59[/C][C]0.419939244207463[/C][C]0.839878488414927[/C][C]0.580060755792537[/C][/ROW]
[ROW][C]60[/C][C]0.419868134976764[/C][C]0.839736269953528[/C][C]0.580131865023236[/C][/ROW]
[ROW][C]61[/C][C]0.44088943105483[/C][C]0.88177886210966[/C][C]0.55911056894517[/C][/ROW]
[ROW][C]62[/C][C]0.425148529685458[/C][C]0.850297059370916[/C][C]0.574851470314542[/C][/ROW]
[ROW][C]63[/C][C]0.488532429665034[/C][C]0.977064859330067[/C][C]0.511467570334966[/C][/ROW]
[ROW][C]64[/C][C]0.466235774489948[/C][C]0.932471548979896[/C][C]0.533764225510052[/C][/ROW]
[ROW][C]65[/C][C]0.431868349182294[/C][C]0.863736698364589[/C][C]0.568131650817706[/C][/ROW]
[ROW][C]66[/C][C]0.426191453233465[/C][C]0.85238290646693[/C][C]0.573808546766535[/C][/ROW]
[ROW][C]67[/C][C]0.450714359076715[/C][C]0.90142871815343[/C][C]0.549285640923285[/C][/ROW]
[ROW][C]68[/C][C]0.471737040870072[/C][C]0.943474081740143[/C][C]0.528262959129928[/C][/ROW]
[ROW][C]69[/C][C]0.440233816503457[/C][C]0.880467633006914[/C][C]0.559766183496543[/C][/ROW]
[ROW][C]70[/C][C]0.399812288806818[/C][C]0.799624577613636[/C][C]0.600187711193182[/C][/ROW]
[ROW][C]71[/C][C]0.398008881473724[/C][C]0.796017762947448[/C][C]0.601991118526276[/C][/ROW]
[ROW][C]72[/C][C]0.405490952346054[/C][C]0.810981904692108[/C][C]0.594509047653946[/C][/ROW]
[ROW][C]73[/C][C]0.411459245183592[/C][C]0.822918490367184[/C][C]0.588540754816408[/C][/ROW]
[ROW][C]74[/C][C]0.375668945083857[/C][C]0.751337890167713[/C][C]0.624331054916143[/C][/ROW]
[ROW][C]75[/C][C]0.36162893236987[/C][C]0.72325786473974[/C][C]0.63837106763013[/C][/ROW]
[ROW][C]76[/C][C]0.330614505920868[/C][C]0.661229011841736[/C][C]0.669385494079132[/C][/ROW]
[ROW][C]77[/C][C]0.301322079380975[/C][C]0.60264415876195[/C][C]0.698677920619025[/C][/ROW]
[ROW][C]78[/C][C]0.317185345189655[/C][C]0.634370690379311[/C][C]0.682814654810345[/C][/ROW]
[ROW][C]79[/C][C]0.344327892230879[/C][C]0.688655784461759[/C][C]0.655672107769121[/C][/ROW]
[ROW][C]80[/C][C]0.345741406528081[/C][C]0.691482813056162[/C][C]0.654258593471919[/C][/ROW]
[ROW][C]81[/C][C]0.307735256285278[/C][C]0.615470512570556[/C][C]0.692264743714722[/C][/ROW]
[ROW][C]82[/C][C]0.275185951406682[/C][C]0.550371902813364[/C][C]0.724814048593318[/C][/ROW]
[ROW][C]83[/C][C]0.674661650072036[/C][C]0.650676699855929[/C][C]0.325338349927964[/C][/ROW]
[ROW][C]84[/C][C]0.673100056312397[/C][C]0.653799887375206[/C][C]0.326899943687603[/C][/ROW]
[ROW][C]85[/C][C]0.694555939657588[/C][C]0.610888120684824[/C][C]0.305444060342412[/C][/ROW]
[ROW][C]86[/C][C]0.691783070979743[/C][C]0.616433858040514[/C][C]0.308216929020257[/C][/ROW]
[ROW][C]87[/C][C]0.684628232859629[/C][C]0.630743534280742[/C][C]0.315371767140371[/C][/ROW]
[ROW][C]88[/C][C]0.648038939471317[/C][C]0.703922121057367[/C][C]0.351961060528683[/C][/ROW]
[ROW][C]89[/C][C]0.613358575896334[/C][C]0.773282848207332[/C][C]0.386641424103666[/C][/ROW]
[ROW][C]90[/C][C]0.607928369897812[/C][C]0.784143260204377[/C][C]0.392071630102188[/C][/ROW]
[ROW][C]91[/C][C]0.627572742236038[/C][C]0.744854515527924[/C][C]0.372427257763962[/C][/ROW]
[ROW][C]92[/C][C]0.612641121366097[/C][C]0.774717757267805[/C][C]0.387358878633903[/C][/ROW]
[ROW][C]93[/C][C]0.584759334194905[/C][C]0.83048133161019[/C][C]0.415240665805095[/C][/ROW]
[ROW][C]94[/C][C]0.563554968232943[/C][C]0.872890063534113[/C][C]0.436445031767057[/C][/ROW]
[ROW][C]95[/C][C]0.545877853946037[/C][C]0.908244292107926[/C][C]0.454122146053963[/C][/ROW]
[ROW][C]96[/C][C]0.528309433460176[/C][C]0.943381133079648[/C][C]0.471690566539824[/C][/ROW]
[ROW][C]97[/C][C]0.549756592726253[/C][C]0.900486814547495[/C][C]0.450243407273747[/C][/ROW]
[ROW][C]98[/C][C]0.53734790971409[/C][C]0.925304180571821[/C][C]0.46265209028591[/C][/ROW]
[ROW][C]99[/C][C]0.566832766290362[/C][C]0.866334467419277[/C][C]0.433167233709638[/C][/ROW]
[ROW][C]100[/C][C]0.573148209184954[/C][C]0.853703581630093[/C][C]0.426851790815046[/C][/ROW]
[ROW][C]101[/C][C]0.536085787242375[/C][C]0.927828425515251[/C][C]0.463914212757625[/C][/ROW]
[ROW][C]102[/C][C]0.493639763016436[/C][C]0.987279526032872[/C][C]0.506360236983564[/C][/ROW]
[ROW][C]103[/C][C]0.448925675318628[/C][C]0.897851350637256[/C][C]0.551074324681372[/C][/ROW]
[ROW][C]104[/C][C]0.446492828060606[/C][C]0.892985656121211[/C][C]0.553507171939394[/C][/ROW]
[ROW][C]105[/C][C]0.443647825059431[/C][C]0.887295650118862[/C][C]0.556352174940569[/C][/ROW]
[ROW][C]106[/C][C]0.417210121141869[/C][C]0.834420242283739[/C][C]0.582789878858131[/C][/ROW]
[ROW][C]107[/C][C]0.509302800648031[/C][C]0.981394398703938[/C][C]0.490697199351969[/C][/ROW]
[ROW][C]108[/C][C]0.470262366458252[/C][C]0.940524732916503[/C][C]0.529737633541748[/C][/ROW]
[ROW][C]109[/C][C]0.456255546337915[/C][C]0.912511092675831[/C][C]0.543744453662085[/C][/ROW]
[ROW][C]110[/C][C]0.490493935923546[/C][C]0.980987871847093[/C][C]0.509506064076454[/C][/ROW]
[ROW][C]111[/C][C]0.456206834834534[/C][C]0.912413669669068[/C][C]0.543793165165466[/C][/ROW]
[ROW][C]112[/C][C]0.430792799642622[/C][C]0.861585599285244[/C][C]0.569207200357378[/C][/ROW]
[ROW][C]113[/C][C]0.416972621572249[/C][C]0.833945243144498[/C][C]0.583027378427751[/C][/ROW]
[ROW][C]114[/C][C]0.432785972731683[/C][C]0.865571945463366[/C][C]0.567214027268317[/C][/ROW]
[ROW][C]115[/C][C]0.424220406761891[/C][C]0.848440813523781[/C][C]0.575779593238109[/C][/ROW]
[ROW][C]116[/C][C]0.435281221439715[/C][C]0.87056244287943[/C][C]0.564718778560285[/C][/ROW]
[ROW][C]117[/C][C]0.387441927870788[/C][C]0.774883855741576[/C][C]0.612558072129212[/C][/ROW]
[ROW][C]118[/C][C]0.349606122453936[/C][C]0.699212244907871[/C][C]0.650393877546064[/C][/ROW]
[ROW][C]119[/C][C]0.332715422376555[/C][C]0.66543084475311[/C][C]0.667284577623445[/C][/ROW]
[ROW][C]120[/C][C]0.289558342857354[/C][C]0.579116685714709[/C][C]0.710441657142646[/C][/ROW]
[ROW][C]121[/C][C]0.25895979773285[/C][C]0.517919595465699[/C][C]0.74104020226715[/C][/ROW]
[ROW][C]122[/C][C]0.257853787518823[/C][C]0.515707575037645[/C][C]0.742146212481177[/C][/ROW]
[ROW][C]123[/C][C]0.228807259612648[/C][C]0.457614519225296[/C][C]0.771192740387352[/C][/ROW]
[ROW][C]124[/C][C]0.234055025230788[/C][C]0.468110050461576[/C][C]0.765944974769212[/C][/ROW]
[ROW][C]125[/C][C]0.25719267240367[/C][C]0.514385344807339[/C][C]0.74280732759633[/C][/ROW]
[ROW][C]126[/C][C]0.257128637507336[/C][C]0.514257275014671[/C][C]0.742871362492664[/C][/ROW]
[ROW][C]127[/C][C]0.271524290856374[/C][C]0.543048581712748[/C][C]0.728475709143626[/C][/ROW]
[ROW][C]128[/C][C]0.225791029695114[/C][C]0.451582059390227[/C][C]0.774208970304886[/C][/ROW]
[ROW][C]129[/C][C]0.208920497063315[/C][C]0.417840994126631[/C][C]0.791079502936685[/C][/ROW]
[ROW][C]130[/C][C]0.180959861141451[/C][C]0.361919722282902[/C][C]0.81904013885855[/C][/ROW]
[ROW][C]131[/C][C]0.191618252455923[/C][C]0.383236504911845[/C][C]0.808381747544077[/C][/ROW]
[ROW][C]132[/C][C]0.194378706676089[/C][C]0.388757413352179[/C][C]0.805621293323911[/C][/ROW]
[ROW][C]133[/C][C]0.242120978409284[/C][C]0.484241956818568[/C][C]0.757879021590716[/C][/ROW]
[ROW][C]134[/C][C]0.282557331947569[/C][C]0.565114663895137[/C][C]0.717442668052431[/C][/ROW]
[ROW][C]135[/C][C]0.258130321221666[/C][C]0.516260642443332[/C][C]0.741869678778334[/C][/ROW]
[ROW][C]136[/C][C]0.220824119848853[/C][C]0.441648239697706[/C][C]0.779175880151147[/C][/ROW]
[ROW][C]137[/C][C]0.19746841405269[/C][C]0.394936828105379[/C][C]0.80253158594731[/C][/ROW]
[ROW][C]138[/C][C]0.224149359089331[/C][C]0.448298718178662[/C][C]0.775850640910669[/C][/ROW]
[ROW][C]139[/C][C]0.259727569732017[/C][C]0.519455139464034[/C][C]0.740272430267983[/C][/ROW]
[ROW][C]140[/C][C]0.222371109215354[/C][C]0.444742218430707[/C][C]0.777628890784646[/C][/ROW]
[ROW][C]141[/C][C]0.192736034285834[/C][C]0.385472068571668[/C][C]0.807263965714166[/C][/ROW]
[ROW][C]142[/C][C]0.154093488640786[/C][C]0.308186977281571[/C][C]0.845906511359214[/C][/ROW]
[ROW][C]143[/C][C]0.113075174172217[/C][C]0.226150348344434[/C][C]0.886924825827783[/C][/ROW]
[ROW][C]144[/C][C]0.18623532346138[/C][C]0.37247064692276[/C][C]0.81376467653862[/C][/ROW]
[ROW][C]145[/C][C]0.160330472076144[/C][C]0.320660944152287[/C][C]0.839669527923856[/C][/ROW]
[ROW][C]146[/C][C]0.331531711317086[/C][C]0.663063422634173[/C][C]0.668468288682914[/C][/ROW]
[ROW][C]147[/C][C]0.42461314300578[/C][C]0.849226286011559[/C][C]0.57538685699422[/C][/ROW]
[ROW][C]148[/C][C]0.431581326141208[/C][C]0.863162652282416[/C][C]0.568418673858792[/C][/ROW]
[ROW][C]149[/C][C]0.432066563264741[/C][C]0.864133126529482[/C][C]0.567933436735259[/C][/ROW]
[ROW][C]150[/C][C]0.628549191149625[/C][C]0.742901617700751[/C][C]0.371450808850375[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145608&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145608&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
120.8133772575818640.3732454848362720.186622742418136
130.7055615341327920.5888769317344160.294438465867208
140.665891916893180.668216166213640.33410808310682
150.5644806175922150.8710387648155690.435519382407785
160.5704627527719680.8590744944560630.429537247228032
170.5310351629667750.937929674066450.468964837033225
180.682813768380270.6343724632394620.317186231619731
190.6056739460719340.7886521078561320.394326053928066
200.5522164529134720.8955670941730570.447783547086528
210.4930933529567880.9861867059135760.506906647043212
220.4363970514081850.872794102816370.563602948591815
230.3585217612508180.7170435225016350.641478238749182
240.4897860482653980.9795720965307960.510213951734602
250.4556035476282620.9112070952565240.544396452371738
260.4190296854794910.8380593709589820.580970314520509
270.3698953254877490.7397906509754990.63010467451225
280.3148289717915030.6296579435830050.685171028208497
290.3198273592364180.6396547184728350.680172640763583
300.2771417561400190.5542835122800370.722858243859982
310.2674821537056770.5349643074113540.732517846294323
320.2263026082967320.4526052165934650.773697391703268
330.1867084602413650.373416920482730.813291539758635
340.2000879399169990.4001758798339990.799912060083
350.2501049226037820.5002098452075640.749895077396218
360.2736565040441710.5473130080883430.726343495955829
370.2648582817251040.5297165634502080.735141718274896
380.3693482183631760.7386964367263520.630651781636824
390.3686756808142440.7373513616284890.631324319185756
400.4231518843440520.8463037686881040.576848115655948
410.4366272056881550.873254411376310.563372794311845
420.4694374568741590.9388749137483180.530562543125841
430.5051583153273120.9896833693453770.494841684672688
440.4950233928251460.9900467856502930.504976607174854
450.4600177175209040.9200354350418080.539982282479096
460.4431764633735420.8863529267470850.556823536626458
470.4941827333836470.9883654667672950.505817266616353
480.5405661329056080.9188677341887840.459433867094392
490.5012322911122890.9975354177754230.498767708887711
500.5492953933704420.9014092132591170.450704606629558
510.5280802265350910.9438395469298170.471919773464908
520.4806763252452680.9613526504905370.519323674754732
530.4634783573893460.9269567147786920.536521642610654
540.4238693091896820.8477386183793630.576130690810318
550.4471630141933490.8943260283866980.552836985806651
560.4678245188488360.9356490376976720.532175481151164
570.4531295028068280.9062590056136550.546870497193172
580.4445307208120680.8890614416241370.555469279187932
590.4199392442074630.8398784884149270.580060755792537
600.4198681349767640.8397362699535280.580131865023236
610.440889431054830.881778862109660.55911056894517
620.4251485296854580.8502970593709160.574851470314542
630.4885324296650340.9770648593300670.511467570334966
640.4662357744899480.9324715489798960.533764225510052
650.4318683491822940.8637366983645890.568131650817706
660.4261914532334650.852382906466930.573808546766535
670.4507143590767150.901428718153430.549285640923285
680.4717370408700720.9434740817401430.528262959129928
690.4402338165034570.8804676330069140.559766183496543
700.3998122888068180.7996245776136360.600187711193182
710.3980088814737240.7960177629474480.601991118526276
720.4054909523460540.8109819046921080.594509047653946
730.4114592451835920.8229184903671840.588540754816408
740.3756689450838570.7513378901677130.624331054916143
750.361628932369870.723257864739740.63837106763013
760.3306145059208680.6612290118417360.669385494079132
770.3013220793809750.602644158761950.698677920619025
780.3171853451896550.6343706903793110.682814654810345
790.3443278922308790.6886557844617590.655672107769121
800.3457414065280810.6914828130561620.654258593471919
810.3077352562852780.6154705125705560.692264743714722
820.2751859514066820.5503719028133640.724814048593318
830.6746616500720360.6506766998559290.325338349927964
840.6731000563123970.6537998873752060.326899943687603
850.6945559396575880.6108881206848240.305444060342412
860.6917830709797430.6164338580405140.308216929020257
870.6846282328596290.6307435342807420.315371767140371
880.6480389394713170.7039221210573670.351961060528683
890.6133585758963340.7732828482073320.386641424103666
900.6079283698978120.7841432602043770.392071630102188
910.6275727422360380.7448545155279240.372427257763962
920.6126411213660970.7747177572678050.387358878633903
930.5847593341949050.830481331610190.415240665805095
940.5635549682329430.8728900635341130.436445031767057
950.5458778539460370.9082442921079260.454122146053963
960.5283094334601760.9433811330796480.471690566539824
970.5497565927262530.9004868145474950.450243407273747
980.537347909714090.9253041805718210.46265209028591
990.5668327662903620.8663344674192770.433167233709638
1000.5731482091849540.8537035816300930.426851790815046
1010.5360857872423750.9278284255152510.463914212757625
1020.4936397630164360.9872795260328720.506360236983564
1030.4489256753186280.8978513506372560.551074324681372
1040.4464928280606060.8929856561212110.553507171939394
1050.4436478250594310.8872956501188620.556352174940569
1060.4172101211418690.8344202422837390.582789878858131
1070.5093028006480310.9813943987039380.490697199351969
1080.4702623664582520.9405247329165030.529737633541748
1090.4562555463379150.9125110926758310.543744453662085
1100.4904939359235460.9809878718470930.509506064076454
1110.4562068348345340.9124136696690680.543793165165466
1120.4307927996426220.8615855992852440.569207200357378
1130.4169726215722490.8339452431444980.583027378427751
1140.4327859727316830.8655719454633660.567214027268317
1150.4242204067618910.8484408135237810.575779593238109
1160.4352812214397150.870562442879430.564718778560285
1170.3874419278707880.7748838557415760.612558072129212
1180.3496061224539360.6992122449078710.650393877546064
1190.3327154223765550.665430844753110.667284577623445
1200.2895583428573540.5791166857147090.710441657142646
1210.258959797732850.5179195954656990.74104020226715
1220.2578537875188230.5157075750376450.742146212481177
1230.2288072596126480.4576145192252960.771192740387352
1240.2340550252307880.4681100504615760.765944974769212
1250.257192672403670.5143853448073390.74280732759633
1260.2571286375073360.5142572750146710.742871362492664
1270.2715242908563740.5430485817127480.728475709143626
1280.2257910296951140.4515820593902270.774208970304886
1290.2089204970633150.4178409941266310.791079502936685
1300.1809598611414510.3619197222829020.81904013885855
1310.1916182524559230.3832365049118450.808381747544077
1320.1943787066760890.3887574133521790.805621293323911
1330.2421209784092840.4842419568185680.757879021590716
1340.2825573319475690.5651146638951370.717442668052431
1350.2581303212216660.5162606424433320.741869678778334
1360.2208241198488530.4416482396977060.779175880151147
1370.197468414052690.3949368281053790.80253158594731
1380.2241493590893310.4482987181786620.775850640910669
1390.2597275697320170.5194551394640340.740272430267983
1400.2223711092153540.4447422184307070.777628890784646
1410.1927360342858340.3854720685716680.807263965714166
1420.1540934886407860.3081869772815710.845906511359214
1430.1130751741722170.2261503483444340.886924825827783
1440.186235323461380.372470646922760.81376467653862
1450.1603304720761440.3206609441522870.839669527923856
1460.3315317113170860.6630634226341730.668468288682914
1470.424613143005780.8492262860115590.57538685699422
1480.4315813261412080.8631626522824160.568418673858792
1490.4320665632647410.8641331265294820.567933436735259
1500.6285491911496250.7429016177007510.371450808850375







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

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145608&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145608&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145608&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 level00OK
5% type I error level00OK
10% type I error level00OK



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