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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationFri, 19 Nov 2010 15:14:14 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Nov/19/t1290179617mwz5axq0by3ddkt.htm/, Retrieved Fri, 29 Mar 2024 10:17:34 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=98028, Retrieved Fri, 29 Mar 2024 10:17:34 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact142
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2010-11-17 09:55:05] [b98453cac15ba1066b407e146608df68]
- R PD    [Multiple Regression] [workshop 7 dummy ...] [2010-11-19 15:14:14] [e926a978b40506c05812140b9c5157ab] [Current]
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Dataseries X:
1	26	24	24	14	14	11	11	12	12	24	24
1	23	25	25	11	11	7	7	8	8	25	25
0	25	17	0	6	0	17	0	8	0	30	0
1	23	18	18	12	12	10	10	8	8	19	19
1	19	18	18	8	8	12	12	9	9	22	22
0	29	16	0	10	0	12	0	7	0	22	0
1	25	20	20	10	10	11	11	4	4	25	25
1	21	16	16	11	11	11	11	11	11	23	23
1	22	18	18	16	16	12	12	7	7	17	17
1	25	17	17	11	11	13	13	7	7	21	21
1	24	23	23	13	13	14	14	12	12	19	19
1	18	30	30	12	12	16	16	10	10	19	19
1	22	23	23	8	8	11	11	10	10	15	15
1	15	18	18	12	12	10	10	8	8	16	16
1	22	15	15	11	11	11	11	8	8	23	23
1	28	12	12	4	4	15	15	4	4	27	27
1	20	21	21	9	9	9	9	9	9	22	22
1	12	15	15	8	8	11	11	8	8	14	14
1	24	20	20	8	8	17	17	7	7	22	22
1	20	31	31	14	14	17	17	11	11	23	23
1	21	27	27	15	15	11	11	9	9	23	23
1	20	34	34	16	16	18	18	11	11	21	21
1	21	21	21	9	9	14	14	13	13	19	19
1	23	31	31	14	14	10	10	8	8	18	18
1	28	19	19	11	11	11	11	8	8	20	20
1	24	16	16	8	8	15	15	9	9	23	23
1	24	20	20	9	9	15	15	6	6	25	25
1	24	21	21	9	9	13	13	9	9	19	19
1	23	22	22	9	9	16	16	9	9	24	24
1	23	17	17	9	9	13	13	6	6	22	22
1	29	24	24	10	10	9	9	6	6	25	25
1	24	25	25	16	16	18	18	16	16	26	26
1	18	26	26	11	11	18	18	5	5	29	29
1	25	25	25	8	8	12	12	7	7	32	32
1	21	17	17	9	9	17	17	9	9	25	25
1	26	32	32	16	16	9	9	6	6	29	29
1	22	33	33	11	11	9	9	6	6	28	28
1	22	13	13	16	16	12	12	5	5	17	17
0	22	32	0	12	0	18	0	12	0	28	0
1	23	25	25	12	12	12	12	7	7	29	29
1	30	29	29	14	14	18	18	10	10	26	26
1	23	22	22	9	9	14	14	9	9	25	25
1	17	18	18	10	10	15	15	8	8	14	14
1	23	17	17	9	9	16	16	5	5	25	25
1	23	20	20	10	10	10	10	8	8	26	26
1	25	15	15	12	12	11	11	8	8	20	20
1	24	20	20	14	14	14	14	10	10	18	18
1	24	33	33	14	14	9	9	6	6	32	32
1	23	29	29	10	10	12	12	8	8	25	25
1	21	23	23	14	14	17	17	7	7	25	25
1	24	26	26	16	16	5	5	4	4	23	23
1	24	18	18	9	9	12	12	8	8	21	21
1	28	20	20	10	10	12	12	8	8	20	20
1	16	11	11	6	6	6	6	4	4	15	15
1	20	28	28	8	8	24	24	20	20	30	30
1	29	26	26	13	13	12	12	8	8	24	24
1	27	22	22	10	10	12	12	8	8	26	26
1	22	17	17	8	8	14	14	6	6	24	24
1	28	12	12	7	7	7	7	4	4	22	22
1	16	14	14	15	15	13	13	8	8	14	14
1	25	17	17	9	9	12	12	9	9	24	24
1	24	21	21	10	10	13	13	6	6	24	24
0	28	19	0	12	0	14	0	7	0	24	0
1	24	18	18	13	13	8	8	9	9	24	24
1	23	10	10	10	10	11	11	5	5	19	19
1	30	29	29	11	11	9	9	5	5	31	31
1	24	31	31	8	8	11	11	8	8	22	22
1	21	19	19	9	9	13	13	8	8	27	27
1	25	9	9	13	13	10	10	6	6	19	19
0	25	20	0	11	0	11	0	8	0	25	0
1	22	28	28	8	8	12	12	7	7	20	20
1	23	19	19	9	9	9	9	7	7	21	21
1	26	30	30	9	9	15	15	9	9	27	27
1	23	29	29	15	15	18	18	11	11	23	23
1	25	26	26	9	9	15	15	6	6	25	25
1	21	23	23	10	10	12	12	8	8	20	20
1	25	13	13	14	14	13	13	6	6	21	21
1	24	21	21	12	12	14	14	9	9	22	22
1	29	19	19	12	12	10	10	8	8	23	23
1	22	28	28	11	11	13	13	6	6	25	25
1	27	23	23	14	14	13	13	10	10	25	25
0	26	18	0	6	0	11	0	8	0	17	0
1	22	21	21	12	12	13	13	8	8	19	19
1	24	20	20	8	8	16	16	10	10	25	25
0	27	23	0	14	0	8	0	5	0	19	0
1	24	21	21	11	11	16	16	7	7	20	20
1	24	21	21	10	10	11	11	5	5	26	26
1	29	15	15	14	14	9	9	8	8	23	23
1	22	28	28	12	12	16	16	14	14	27	27
0	21	19	0	10	0	12	0	7	0	17	0
1	24	26	26	14	14	14	14	8	8	17	17
1	24	10	10	5	5	8	8	6	6	19	19
0	23	16	0	11	0	9	0	5	0	17	0
1	20	22	22	10	10	15	15	6	6	22	22
1	27	19	19	9	9	11	11	10	10	21	21
1	26	31	31	10	10	21	21	12	12	32	32
1	25	31	31	16	16	14	14	9	9	21	21
1	21	29	29	13	13	18	18	12	12	21	21
1	21	19	19	9	9	12	12	7	7	18	18
1	19	22	22	10	10	13	13	8	8	18	18
1	21	23	23	10	10	15	15	10	10	23	23
1	21	15	15	7	7	12	12	6	6	19	19
1	16	20	20	9	9	19	19	10	10	20	20
1	22	18	18	8	8	15	15	10	10	21	21
1	29	23	23	14	14	11	11	10	10	20	20
0	15	25	0	14	0	11	0	5	0	17	0
1	17	21	21	8	8	10	10	7	7	18	18
1	15	24	24	9	9	13	13	10	10	19	19
1	21	25	25	14	14	15	15	11	11	22	22
0	21	17	0	14	0	12	0	6	0	15	0
1	19	13	13	8	8	12	12	7	7	14	14
1	24	28	28	8	8	16	16	12	12	18	18
1	20	21	21	8	8	9	9	11	11	24	24
0	17	25	0	7	0	18	0	11	0	35	0
1	23	9	9	6	6	8	8	11	11	29	29
1	24	16	16	8	8	13	13	5	5	21	21
1	14	19	19	6	6	17	17	8	8	25	25
1	19	17	17	11	11	9	9	6	6	20	20
1	24	25	25	14	14	15	15	9	9	22	22
1	13	20	20	11	11	8	8	4	4	13	13
1	22	29	29	11	11	7	7	4	4	26	26
1	16	14	14	11	11	12	12	7	7	17	17
0	19	22	0	14	0	14	0	11	0	25	0
1	25	15	15	8	8	6	6	6	6	20	20
1	25	19	19	20	20	8	8	7	7	19	19
1	23	20	20	11	11	17	17	8	8	21	21
0	24	15	0	8	0	10	0	4	0	22	0
1	26	20	20	11	11	11	11	8	8	24	24
1	26	18	18	10	10	14	14	9	9	21	21
1	25	33	33	14	14	11	11	8	8	26	26
1	18	22	22	11	11	13	13	11	11	24	24
1	21	16	16	9	9	12	12	8	8	16	16
1	26	17	17	9	9	11	11	5	5	23	23
1	23	16	16	8	8	9	9	4	4	18	18
1	23	21	21	10	10	12	12	8	8	16	16
1	22	26	26	13	13	20	20	10	10	26	26
1	20	18	18	13	13	12	12	6	6	19	19
1	13	18	18	12	12	13	13	9	9	21	21
1	24	17	17	8	8	12	12	9	9	21	21
1	15	22	22	13	13	12	12	13	13	22	22
1	14	30	30	14	14	9	9	9	9	23	23
0	22	30	0	12	0	15	0	10	0	29	0
1	10	24	24	14	14	24	24	20	20	21	21
1	24	21	21	15	15	7	7	5	5	21	21
1	22	21	21	13	13	17	17	11	11	23	23
1	24	29	29	16	16	11	11	6	6	27	27
1	19	31	31	9	9	17	17	9	9	25	25
0	20	20	0	9	0	11	0	7	0	21	0
1	13	16	16	9	9	12	12	9	9	10	10
1	20	22	22	8	8	14	14	10	10	20	20
1	22	20	20	7	7	11	11	9	9	26	26
1	24	28	28	16	16	16	16	8	8	24	24
1	29	38	38	11	11	21	21	7	7	29	29
1	12	22	22	9	9	14	14	6	6	19	19
1	20	20	20	11	11	20	20	13	13	24	24
1	21	17	17	9	9	13	13	6	6	19	19
1	24	28	28	14	14	11	11	8	8	24	24
1	22	22	22	13	13	15	15	10	10	22	22
1	20	31	31	16	16	19	19	16	16	17	17




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time50 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\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 & 50 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98028&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]50 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98028&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98028&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 time50 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24







Multiple Linear Regression - Estimated Regression Equation
pe_b[t] = -3.3167593452465 + 3.35943414633357Br[t] + 0.00693286009409913org[t] -0.000531295134700565cm[t] + 0.00396717664181525cm_b[t] + 0.0574140713295431d[t] -0.0630114852430697d_b[t] + 0.973189821980576pe[t] -0.623529846461795pc[t] + 0.644309517450024pc_b[t] -0.223550340334766ps[t] + 0.221838290372535ps_b[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
pe_b[t] =  -3.3167593452465 +  3.35943414633357Br[t] +  0.00693286009409913org[t] -0.000531295134700565cm[t] +  0.00396717664181525cm_b[t] +  0.0574140713295431d[t] -0.0630114852430697d_b[t] +  0.973189821980576pe[t] -0.623529846461795pc[t] +  0.644309517450024pc_b[t] -0.223550340334766ps[t] +  0.221838290372535ps_b[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98028&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]pe_b[t] =  -3.3167593452465 +  3.35943414633357Br[t] +  0.00693286009409913org[t] -0.000531295134700565cm[t] +  0.00396717664181525cm_b[t] +  0.0574140713295431d[t] -0.0630114852430697d_b[t] +  0.973189821980576pe[t] -0.623529846461795pc[t] +  0.644309517450024pc_b[t] -0.223550340334766ps[t] +  0.221838290372535ps_b[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98028&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98028&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
pe_b[t] = -3.3167593452465 + 3.35943414633357Br[t] + 0.00693286009409913org[t] -0.000531295134700565cm[t] + 0.00396717664181525cm_b[t] + 0.0574140713295431d[t] -0.0630114852430697d_b[t] + 0.973189821980576pe[t] -0.623529846461795pc[t] + 0.644309517450024pc_b[t] -0.223550340334766ps[t] + 0.221838290372535ps_b[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-3.31675934524650.848095-3.91080.000147e-05
Br3.359434146333570.8492463.95580.0001185.9e-05
org0.006932860094099130.01070.64790.5180520.259026
cm-0.0005312951347005650.037055-0.01430.988580.49429
cm_b0.003967176641815250.0379310.10460.9168440.458422
d0.05741407132954310.0546431.05070.2951170.147559
d_b-0.06301148524306970.056719-1.11090.2684110.134206
pe0.9731898219805760.01362571.428100
pc-0.6235298464617950.082019-7.602300
pc_b0.6443095174500240.0828867.773400
ps-0.2235503403347660.032932-6.788300
ps_b0.2218382903725350.0346686.398900

\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) & -3.3167593452465 & 0.848095 & -3.9108 & 0.00014 & 7e-05 \tabularnewline
Br & 3.35943414633357 & 0.849246 & 3.9558 & 0.000118 & 5.9e-05 \tabularnewline
org & 0.00693286009409913 & 0.0107 & 0.6479 & 0.518052 & 0.259026 \tabularnewline
cm & -0.000531295134700565 & 0.037055 & -0.0143 & 0.98858 & 0.49429 \tabularnewline
cm_b & 0.00396717664181525 & 0.037931 & 0.1046 & 0.916844 & 0.458422 \tabularnewline
d & 0.0574140713295431 & 0.054643 & 1.0507 & 0.295117 & 0.147559 \tabularnewline
d_b & -0.0630114852430697 & 0.056719 & -1.1109 & 0.268411 & 0.134206 \tabularnewline
pe & 0.973189821980576 & 0.013625 & 71.4281 & 0 & 0 \tabularnewline
pc & -0.623529846461795 & 0.082019 & -7.6023 & 0 & 0 \tabularnewline
pc_b & 0.644309517450024 & 0.082886 & 7.7734 & 0 & 0 \tabularnewline
ps & -0.223550340334766 & 0.032932 & -6.7883 & 0 & 0 \tabularnewline
ps_b & 0.221838290372535 & 0.034668 & 6.3989 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98028&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]-3.3167593452465[/C][C]0.848095[/C][C]-3.9108[/C][C]0.00014[/C][C]7e-05[/C][/ROW]
[ROW][C]Br[/C][C]3.35943414633357[/C][C]0.849246[/C][C]3.9558[/C][C]0.000118[/C][C]5.9e-05[/C][/ROW]
[ROW][C]org[/C][C]0.00693286009409913[/C][C]0.0107[/C][C]0.6479[/C][C]0.518052[/C][C]0.259026[/C][/ROW]
[ROW][C]cm[/C][C]-0.000531295134700565[/C][C]0.037055[/C][C]-0.0143[/C][C]0.98858[/C][C]0.49429[/C][/ROW]
[ROW][C]cm_b[/C][C]0.00396717664181525[/C][C]0.037931[/C][C]0.1046[/C][C]0.916844[/C][C]0.458422[/C][/ROW]
[ROW][C]d[/C][C]0.0574140713295431[/C][C]0.054643[/C][C]1.0507[/C][C]0.295117[/C][C]0.147559[/C][/ROW]
[ROW][C]d_b[/C][C]-0.0630114852430697[/C][C]0.056719[/C][C]-1.1109[/C][C]0.268411[/C][C]0.134206[/C][/ROW]
[ROW][C]pe[/C][C]0.973189821980576[/C][C]0.013625[/C][C]71.4281[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]pc[/C][C]-0.623529846461795[/C][C]0.082019[/C][C]-7.6023[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]pc_b[/C][C]0.644309517450024[/C][C]0.082886[/C][C]7.7734[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]ps[/C][C]-0.223550340334766[/C][C]0.032932[/C][C]-6.7883[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]ps_b[/C][C]0.221838290372535[/C][C]0.034668[/C][C]6.3989[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98028&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98028&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)-3.31675934524650.848095-3.91080.000147e-05
Br3.359434146333570.8492463.95580.0001185.9e-05
org0.006932860094099130.01070.64790.5180520.259026
cm-0.0005312951347005650.037055-0.01430.988580.49429
cm_b0.003967176641815250.0379310.10460.9168440.458422
d0.05741407132954310.0546431.05070.2951170.147559
d_b-0.06301148524306970.056719-1.11090.2684110.134206
pe0.9731898219805760.01362571.428100
pc-0.6235298464617950.082019-7.602300
pc_b0.6443095174500240.0828867.773400
ps-0.2235503403347660.032932-6.788300
ps_b0.2218382903725350.0346686.398900







Multiple Linear Regression - Regression Statistics
Multiple R0.996336455957132
R-squared0.992686333469218
Adjusted R-squared0.992139052300248
F-TEST (value)1813.85070372019
F-TEST (DF numerator)11
F-TEST (DF denominator)147
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.452791176290144
Sum Squared Residuals30.1379178509532

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.996336455957132 \tabularnewline
R-squared & 0.992686333469218 \tabularnewline
Adjusted R-squared & 0.992139052300248 \tabularnewline
F-TEST (value) & 1813.85070372019 \tabularnewline
F-TEST (DF numerator) & 11 \tabularnewline
F-TEST (DF denominator) & 147 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.452791176290144 \tabularnewline
Sum Squared Residuals & 30.1379178509532 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98028&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.996336455957132[/C][/ROW]
[ROW][C]R-squared[/C][C]0.992686333469218[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.992139052300248[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]1813.85070372019[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]11[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]147[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.452791176290144[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]30.1379178509532[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98028&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98028&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 R0.996336455957132
R-squared0.992686333469218
Adjusted R-squared0.992139052300248
F-TEST (value)1813.85070372019
F-TEST (DF numerator)11
F-TEST (DF denominator)147
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.452791176290144
Sum Squared Residuals30.1379178509532







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11111.1403814194666-0.140381419466573
277.1622209405945-0.162220940594507
302.04149255972577-2.04149255972577
41010.0624141218463-0.0624141218462946
51212.0190955021867-0.0190955021866933
60-0.1546049602079140.154604960207914
71110.97414527113000.0258547288699511
81111.0759546876537-0.0759546876537369
91211.96211567899550.0378843210045264
101312.97380706946990.0261929305300589
111414.0568069474377-0.0568069474376607
121615.94967867332110.0503213266789092
131111.1166476887478-0.116647688747833
141010.0120873909802-0.0120873909801950
151111.0171126532760-0.0171126532760307
161514.89037647083440.109623529165571
1799.11116912694688-0.111169126946882
181110.97998474373570.0200152562643023
191716.88502133359780.114978666402161
201716.94290674030920.057093259690768
211111.0498003866014-0.0498003866014301
221817.91863347890860.0813665210914399
231414.0723059307835-0.0723059307834722
241010.0975978035740-0.097597803573965
251111.0775894897558-0.077589489755777
261514.96474545562250.0352545443775432
271514.90712845484820.0928715451517605
281313.0367960051323-0.0367960051322751
291615.94430824267590.0556917573241361
301312.94864445615830.051355543841663
3199.11079993555021-0.110799935550211
321818.0107800908509-0.0107800908509158
331817.76689335060390.233106649396147
341212.0260639917023-0.0260639917023221
351716.88474088697040.115259113029564
3699.07705572399475-0.0770557239947482
3799.08245928465533-0.0824592846553304
381211.90337692948340.0966230705165576
3901.28338009720315-1.28338009720315
401211.99494476574670.00505523425328857
411817.95263757934160.0473624206583547
421413.99621654875250.0037834512475189
431514.90652114882280.0934788511772117
441615.84229810122510.157701898774856
451010.0684963629520-0.0684963629519611
461111.0374499695315-0.0374499695314946
471414.0010545969886-0.00105459698856436
4899.07268456325402-0.0726845632540236
491212.0475109904394-0.0475109904393759
501716.83580976446900.164190235530967
5155.11852838463848-0.118528384638484
521212.0290947677177-0.0290947677176641
531212.059812607157-0.0598126071569941
5466.05438764209266-0.0543876420926603
552423.95354502229150.0464549777084812
561212.0637203147043-0.0637203147042778
571212.0494792103037-0.049479210303739
581413.91707473203390.082925267966122
5977.0966259030604-0.0966259030603979
601312.91147804917140.0885219508285541
611212.0482352674062-0.0482352674061845
621312.96029932844290.0397006715570933
6301.45097540024459-1.45097540024459
6488.12958934524279-0.129589345242790
651110.95697270663230.04302729336768
6699.09826281850474-0.0982628185047413
671111.1044567692809-0.104456769280875
681312.97464959114970.0253504088503282
69109.998200152580480.00179984741952341
700-2.394418199240242.39441819924024
711212.0361176554881-0.0361176554881409
7299.08524865220072-0.0852486522007223
731515.0142679031478-0.0142679031478093
741817.92442596564430.0755740343556527
751514.93467660398500.0653233960149735
761212.0215902310196-0.0215902310196441
771312.92249163071270.0775083692873257
781413.98805743548560.0119425645144205
791010.1005989640691-0.100598964069080
801312.96317531492880.0368246850712469
811313.046986650076-0.0469866500760132
820-0.8850903828463240.885090382846324
831312.98535837221530.0146416277847314
841615.96903437469530.030965625304742
850-1.917582035015261.91758203501526
861615.90189925130830.0981007486917389
871110.98971591356910.0102840864309359
8899.102470788233-0.102470788232993
891616.0399606349383-0.0399606349383254
9000.906089975309023-0.906089975309023
911413.98182259401700.0181774059829752
9288.09310284134055-0.0931028413405518
930-0.6935461207872730.693546120787273
941514.88580751345920.114192486540761
951111.1216987495030-0.121698749502958
962120.90502406577840.094975934221612
971414.0086715049589-0.00867150495894964
981817.94595884419590.0540411558041064
991211.99608854784090.00391145215905488
1001312.98090255122940.0190974487706307
1011514.97758288905110.0224171109488623
1021211.97104812868910.0289518713109204
1031918.83610379578180.163896204218179
1041514.98195526936120.0180447306388206
1051111.1230329761142-0.123032976114214
10601.36483120019741-1.36483120019741
1071010.0344466404312-0.0344466404311514
1081313.0054875797950-0.00548757979495669
1091514.98455671736170.0154432826382789
11002.20743937802792-2.20743937802792
1111211.97405315237250.0259468476274913
1121616.0500651184642-0.0500651184642499
11399.1549017829124-0.154901782912404
11400.0240419701462767-0.0240419701462767
11588.16391454114465-0.163914541144647
1161312.93867122763280.0613287723671505
1171716.83909520007830.160904799921677
11899.02038299995704-0.0203829999570453
1191514.96379595566760.0362040443324395
12087.986328669692380.0136713303076233
12177.08420087261372-0.0842008726137225
1221211.93476206197010.065237938029946
1230-1.215855809529261.21585580952926
12466.15233117330626-0.152331173306261
12588.06777709728402-0.0677770972840157
1261716.88378795271360.116212047286379
1270-0.3793562127785650.379356212778565
1281111.0603114512258-0.0603114512257693
1291414.0045223889417-0.00452238894171759
1301111.0778287090591-0.0778287090591191
1311313.0204389904130-0.0204389904130452
1321212.0099846742323-0.00998467423229209
1331111.0005716715290-0.000571671529022328
13499.02333555851491-0.0233355585149103
1351212.0354323880425-0.0354323880425376
1362019.83884411194220.161155888057811
1371211.94083842963520.0591615703648346
1381312.93001055791080.0699894420892005
1391212.0520359711123-0.052035971112305
1401212.0602392022240-0.0602392022240392
14199.07079578041646-0.0707957804164556
1420-0.611617425880350.61161742588035
1432423.8522968615010.147703138499008
14477.07752980589028-0.0775298058902813
1451716.92801105933980.0719889406601921
1461111.0026861031708-0.0026861031708185
1471716.91897750788180.0810224921181561
1480-1.026179434568961.02617943456896
1491211.98557376424110.0144262357588853
1501414.0103553031831-0.0103553031830943
1511111.0723252375672-0.072325237567246
1521615.91189482342970.0881051765702638
1532120.84551419764250.154485802357494
1541413.86788837452610.132111625473910
1552019.88132104342750.118678956572498
1561312.93991488585680.0600851141431682
1571111.0571405413539-0.0571405413539098
1581514.96599967585980.0340003241402262
1591918.99226221115790.0077377888421377

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 11 & 11.1403814194666 & -0.140381419466573 \tabularnewline
2 & 7 & 7.1622209405945 & -0.162220940594507 \tabularnewline
3 & 0 & 2.04149255972577 & -2.04149255972577 \tabularnewline
4 & 10 & 10.0624141218463 & -0.0624141218462946 \tabularnewline
5 & 12 & 12.0190955021867 & -0.0190955021866933 \tabularnewline
6 & 0 & -0.154604960207914 & 0.154604960207914 \tabularnewline
7 & 11 & 10.9741452711300 & 0.0258547288699511 \tabularnewline
8 & 11 & 11.0759546876537 & -0.0759546876537369 \tabularnewline
9 & 12 & 11.9621156789955 & 0.0378843210045264 \tabularnewline
10 & 13 & 12.9738070694699 & 0.0261929305300589 \tabularnewline
11 & 14 & 14.0568069474377 & -0.0568069474376607 \tabularnewline
12 & 16 & 15.9496786733211 & 0.0503213266789092 \tabularnewline
13 & 11 & 11.1166476887478 & -0.116647688747833 \tabularnewline
14 & 10 & 10.0120873909802 & -0.0120873909801950 \tabularnewline
15 & 11 & 11.0171126532760 & -0.0171126532760307 \tabularnewline
16 & 15 & 14.8903764708344 & 0.109623529165571 \tabularnewline
17 & 9 & 9.11116912694688 & -0.111169126946882 \tabularnewline
18 & 11 & 10.9799847437357 & 0.0200152562643023 \tabularnewline
19 & 17 & 16.8850213335978 & 0.114978666402161 \tabularnewline
20 & 17 & 16.9429067403092 & 0.057093259690768 \tabularnewline
21 & 11 & 11.0498003866014 & -0.0498003866014301 \tabularnewline
22 & 18 & 17.9186334789086 & 0.0813665210914399 \tabularnewline
23 & 14 & 14.0723059307835 & -0.0723059307834722 \tabularnewline
24 & 10 & 10.0975978035740 & -0.097597803573965 \tabularnewline
25 & 11 & 11.0775894897558 & -0.077589489755777 \tabularnewline
26 & 15 & 14.9647454556225 & 0.0352545443775432 \tabularnewline
27 & 15 & 14.9071284548482 & 0.0928715451517605 \tabularnewline
28 & 13 & 13.0367960051323 & -0.0367960051322751 \tabularnewline
29 & 16 & 15.9443082426759 & 0.0556917573241361 \tabularnewline
30 & 13 & 12.9486444561583 & 0.051355543841663 \tabularnewline
31 & 9 & 9.11079993555021 & -0.110799935550211 \tabularnewline
32 & 18 & 18.0107800908509 & -0.0107800908509158 \tabularnewline
33 & 18 & 17.7668933506039 & 0.233106649396147 \tabularnewline
34 & 12 & 12.0260639917023 & -0.0260639917023221 \tabularnewline
35 & 17 & 16.8847408869704 & 0.115259113029564 \tabularnewline
36 & 9 & 9.07705572399475 & -0.0770557239947482 \tabularnewline
37 & 9 & 9.08245928465533 & -0.0824592846553304 \tabularnewline
38 & 12 & 11.9033769294834 & 0.0966230705165576 \tabularnewline
39 & 0 & 1.28338009720315 & -1.28338009720315 \tabularnewline
40 & 12 & 11.9949447657467 & 0.00505523425328857 \tabularnewline
41 & 18 & 17.9526375793416 & 0.0473624206583547 \tabularnewline
42 & 14 & 13.9962165487525 & 0.0037834512475189 \tabularnewline
43 & 15 & 14.9065211488228 & 0.0934788511772117 \tabularnewline
44 & 16 & 15.8422981012251 & 0.157701898774856 \tabularnewline
45 & 10 & 10.0684963629520 & -0.0684963629519611 \tabularnewline
46 & 11 & 11.0374499695315 & -0.0374499695314946 \tabularnewline
47 & 14 & 14.0010545969886 & -0.00105459698856436 \tabularnewline
48 & 9 & 9.07268456325402 & -0.0726845632540236 \tabularnewline
49 & 12 & 12.0475109904394 & -0.0475109904393759 \tabularnewline
50 & 17 & 16.8358097644690 & 0.164190235530967 \tabularnewline
51 & 5 & 5.11852838463848 & -0.118528384638484 \tabularnewline
52 & 12 & 12.0290947677177 & -0.0290947677176641 \tabularnewline
53 & 12 & 12.059812607157 & -0.0598126071569941 \tabularnewline
54 & 6 & 6.05438764209266 & -0.0543876420926603 \tabularnewline
55 & 24 & 23.9535450222915 & 0.0464549777084812 \tabularnewline
56 & 12 & 12.0637203147043 & -0.0637203147042778 \tabularnewline
57 & 12 & 12.0494792103037 & -0.049479210303739 \tabularnewline
58 & 14 & 13.9170747320339 & 0.082925267966122 \tabularnewline
59 & 7 & 7.0966259030604 & -0.0966259030603979 \tabularnewline
60 & 13 & 12.9114780491714 & 0.0885219508285541 \tabularnewline
61 & 12 & 12.0482352674062 & -0.0482352674061845 \tabularnewline
62 & 13 & 12.9602993284429 & 0.0397006715570933 \tabularnewline
63 & 0 & 1.45097540024459 & -1.45097540024459 \tabularnewline
64 & 8 & 8.12958934524279 & -0.129589345242790 \tabularnewline
65 & 11 & 10.9569727066323 & 0.04302729336768 \tabularnewline
66 & 9 & 9.09826281850474 & -0.0982628185047413 \tabularnewline
67 & 11 & 11.1044567692809 & -0.104456769280875 \tabularnewline
68 & 13 & 12.9746495911497 & 0.0253504088503282 \tabularnewline
69 & 10 & 9.99820015258048 & 0.00179984741952341 \tabularnewline
70 & 0 & -2.39441819924024 & 2.39441819924024 \tabularnewline
71 & 12 & 12.0361176554881 & -0.0361176554881409 \tabularnewline
72 & 9 & 9.08524865220072 & -0.0852486522007223 \tabularnewline
73 & 15 & 15.0142679031478 & -0.0142679031478093 \tabularnewline
74 & 18 & 17.9244259656443 & 0.0755740343556527 \tabularnewline
75 & 15 & 14.9346766039850 & 0.0653233960149735 \tabularnewline
76 & 12 & 12.0215902310196 & -0.0215902310196441 \tabularnewline
77 & 13 & 12.9224916307127 & 0.0775083692873257 \tabularnewline
78 & 14 & 13.9880574354856 & 0.0119425645144205 \tabularnewline
79 & 10 & 10.1005989640691 & -0.100598964069080 \tabularnewline
80 & 13 & 12.9631753149288 & 0.0368246850712469 \tabularnewline
81 & 13 & 13.046986650076 & -0.0469866500760132 \tabularnewline
82 & 0 & -0.885090382846324 & 0.885090382846324 \tabularnewline
83 & 13 & 12.9853583722153 & 0.0146416277847314 \tabularnewline
84 & 16 & 15.9690343746953 & 0.030965625304742 \tabularnewline
85 & 0 & -1.91758203501526 & 1.91758203501526 \tabularnewline
86 & 16 & 15.9018992513083 & 0.0981007486917389 \tabularnewline
87 & 11 & 10.9897159135691 & 0.0102840864309359 \tabularnewline
88 & 9 & 9.102470788233 & -0.102470788232993 \tabularnewline
89 & 16 & 16.0399606349383 & -0.0399606349383254 \tabularnewline
90 & 0 & 0.906089975309023 & -0.906089975309023 \tabularnewline
91 & 14 & 13.9818225940170 & 0.0181774059829752 \tabularnewline
92 & 8 & 8.09310284134055 & -0.0931028413405518 \tabularnewline
93 & 0 & -0.693546120787273 & 0.693546120787273 \tabularnewline
94 & 15 & 14.8858075134592 & 0.114192486540761 \tabularnewline
95 & 11 & 11.1216987495030 & -0.121698749502958 \tabularnewline
96 & 21 & 20.9050240657784 & 0.094975934221612 \tabularnewline
97 & 14 & 14.0086715049589 & -0.00867150495894964 \tabularnewline
98 & 18 & 17.9459588441959 & 0.0540411558041064 \tabularnewline
99 & 12 & 11.9960885478409 & 0.00391145215905488 \tabularnewline
100 & 13 & 12.9809025512294 & 0.0190974487706307 \tabularnewline
101 & 15 & 14.9775828890511 & 0.0224171109488623 \tabularnewline
102 & 12 & 11.9710481286891 & 0.0289518713109204 \tabularnewline
103 & 19 & 18.8361037957818 & 0.163896204218179 \tabularnewline
104 & 15 & 14.9819552693612 & 0.0180447306388206 \tabularnewline
105 & 11 & 11.1230329761142 & -0.123032976114214 \tabularnewline
106 & 0 & 1.36483120019741 & -1.36483120019741 \tabularnewline
107 & 10 & 10.0344466404312 & -0.0344466404311514 \tabularnewline
108 & 13 & 13.0054875797950 & -0.00548757979495669 \tabularnewline
109 & 15 & 14.9845567173617 & 0.0154432826382789 \tabularnewline
110 & 0 & 2.20743937802792 & -2.20743937802792 \tabularnewline
111 & 12 & 11.9740531523725 & 0.0259468476274913 \tabularnewline
112 & 16 & 16.0500651184642 & -0.0500651184642499 \tabularnewline
113 & 9 & 9.1549017829124 & -0.154901782912404 \tabularnewline
114 & 0 & 0.0240419701462767 & -0.0240419701462767 \tabularnewline
115 & 8 & 8.16391454114465 & -0.163914541144647 \tabularnewline
116 & 13 & 12.9386712276328 & 0.0613287723671505 \tabularnewline
117 & 17 & 16.8390952000783 & 0.160904799921677 \tabularnewline
118 & 9 & 9.02038299995704 & -0.0203829999570453 \tabularnewline
119 & 15 & 14.9637959556676 & 0.0362040443324395 \tabularnewline
120 & 8 & 7.98632866969238 & 0.0136713303076233 \tabularnewline
121 & 7 & 7.08420087261372 & -0.0842008726137225 \tabularnewline
122 & 12 & 11.9347620619701 & 0.065237938029946 \tabularnewline
123 & 0 & -1.21585580952926 & 1.21585580952926 \tabularnewline
124 & 6 & 6.15233117330626 & -0.152331173306261 \tabularnewline
125 & 8 & 8.06777709728402 & -0.0677770972840157 \tabularnewline
126 & 17 & 16.8837879527136 & 0.116212047286379 \tabularnewline
127 & 0 & -0.379356212778565 & 0.379356212778565 \tabularnewline
128 & 11 & 11.0603114512258 & -0.0603114512257693 \tabularnewline
129 & 14 & 14.0045223889417 & -0.00452238894171759 \tabularnewline
130 & 11 & 11.0778287090591 & -0.0778287090591191 \tabularnewline
131 & 13 & 13.0204389904130 & -0.0204389904130452 \tabularnewline
132 & 12 & 12.0099846742323 & -0.00998467423229209 \tabularnewline
133 & 11 & 11.0005716715290 & -0.000571671529022328 \tabularnewline
134 & 9 & 9.02333555851491 & -0.0233355585149103 \tabularnewline
135 & 12 & 12.0354323880425 & -0.0354323880425376 \tabularnewline
136 & 20 & 19.8388441119422 & 0.161155888057811 \tabularnewline
137 & 12 & 11.9408384296352 & 0.0591615703648346 \tabularnewline
138 & 13 & 12.9300105579108 & 0.0699894420892005 \tabularnewline
139 & 12 & 12.0520359711123 & -0.052035971112305 \tabularnewline
140 & 12 & 12.0602392022240 & -0.0602392022240392 \tabularnewline
141 & 9 & 9.07079578041646 & -0.0707957804164556 \tabularnewline
142 & 0 & -0.61161742588035 & 0.61161742588035 \tabularnewline
143 & 24 & 23.852296861501 & 0.147703138499008 \tabularnewline
144 & 7 & 7.07752980589028 & -0.0775298058902813 \tabularnewline
145 & 17 & 16.9280110593398 & 0.0719889406601921 \tabularnewline
146 & 11 & 11.0026861031708 & -0.0026861031708185 \tabularnewline
147 & 17 & 16.9189775078818 & 0.0810224921181561 \tabularnewline
148 & 0 & -1.02617943456896 & 1.02617943456896 \tabularnewline
149 & 12 & 11.9855737642411 & 0.0144262357588853 \tabularnewline
150 & 14 & 14.0103553031831 & -0.0103553031830943 \tabularnewline
151 & 11 & 11.0723252375672 & -0.072325237567246 \tabularnewline
152 & 16 & 15.9118948234297 & 0.0881051765702638 \tabularnewline
153 & 21 & 20.8455141976425 & 0.154485802357494 \tabularnewline
154 & 14 & 13.8678883745261 & 0.132111625473910 \tabularnewline
155 & 20 & 19.8813210434275 & 0.118678956572498 \tabularnewline
156 & 13 & 12.9399148858568 & 0.0600851141431682 \tabularnewline
157 & 11 & 11.0571405413539 & -0.0571405413539098 \tabularnewline
158 & 15 & 14.9659996758598 & 0.0340003241402262 \tabularnewline
159 & 19 & 18.9922622111579 & 0.0077377888421377 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98028&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]11[/C][C]11.1403814194666[/C][C]-0.140381419466573[/C][/ROW]
[ROW][C]2[/C][C]7[/C][C]7.1622209405945[/C][C]-0.162220940594507[/C][/ROW]
[ROW][C]3[/C][C]0[/C][C]2.04149255972577[/C][C]-2.04149255972577[/C][/ROW]
[ROW][C]4[/C][C]10[/C][C]10.0624141218463[/C][C]-0.0624141218462946[/C][/ROW]
[ROW][C]5[/C][C]12[/C][C]12.0190955021867[/C][C]-0.0190955021866933[/C][/ROW]
[ROW][C]6[/C][C]0[/C][C]-0.154604960207914[/C][C]0.154604960207914[/C][/ROW]
[ROW][C]7[/C][C]11[/C][C]10.9741452711300[/C][C]0.0258547288699511[/C][/ROW]
[ROW][C]8[/C][C]11[/C][C]11.0759546876537[/C][C]-0.0759546876537369[/C][/ROW]
[ROW][C]9[/C][C]12[/C][C]11.9621156789955[/C][C]0.0378843210045264[/C][/ROW]
[ROW][C]10[/C][C]13[/C][C]12.9738070694699[/C][C]0.0261929305300589[/C][/ROW]
[ROW][C]11[/C][C]14[/C][C]14.0568069474377[/C][C]-0.0568069474376607[/C][/ROW]
[ROW][C]12[/C][C]16[/C][C]15.9496786733211[/C][C]0.0503213266789092[/C][/ROW]
[ROW][C]13[/C][C]11[/C][C]11.1166476887478[/C][C]-0.116647688747833[/C][/ROW]
[ROW][C]14[/C][C]10[/C][C]10.0120873909802[/C][C]-0.0120873909801950[/C][/ROW]
[ROW][C]15[/C][C]11[/C][C]11.0171126532760[/C][C]-0.0171126532760307[/C][/ROW]
[ROW][C]16[/C][C]15[/C][C]14.8903764708344[/C][C]0.109623529165571[/C][/ROW]
[ROW][C]17[/C][C]9[/C][C]9.11116912694688[/C][C]-0.111169126946882[/C][/ROW]
[ROW][C]18[/C][C]11[/C][C]10.9799847437357[/C][C]0.0200152562643023[/C][/ROW]
[ROW][C]19[/C][C]17[/C][C]16.8850213335978[/C][C]0.114978666402161[/C][/ROW]
[ROW][C]20[/C][C]17[/C][C]16.9429067403092[/C][C]0.057093259690768[/C][/ROW]
[ROW][C]21[/C][C]11[/C][C]11.0498003866014[/C][C]-0.0498003866014301[/C][/ROW]
[ROW][C]22[/C][C]18[/C][C]17.9186334789086[/C][C]0.0813665210914399[/C][/ROW]
[ROW][C]23[/C][C]14[/C][C]14.0723059307835[/C][C]-0.0723059307834722[/C][/ROW]
[ROW][C]24[/C][C]10[/C][C]10.0975978035740[/C][C]-0.097597803573965[/C][/ROW]
[ROW][C]25[/C][C]11[/C][C]11.0775894897558[/C][C]-0.077589489755777[/C][/ROW]
[ROW][C]26[/C][C]15[/C][C]14.9647454556225[/C][C]0.0352545443775432[/C][/ROW]
[ROW][C]27[/C][C]15[/C][C]14.9071284548482[/C][C]0.0928715451517605[/C][/ROW]
[ROW][C]28[/C][C]13[/C][C]13.0367960051323[/C][C]-0.0367960051322751[/C][/ROW]
[ROW][C]29[/C][C]16[/C][C]15.9443082426759[/C][C]0.0556917573241361[/C][/ROW]
[ROW][C]30[/C][C]13[/C][C]12.9486444561583[/C][C]0.051355543841663[/C][/ROW]
[ROW][C]31[/C][C]9[/C][C]9.11079993555021[/C][C]-0.110799935550211[/C][/ROW]
[ROW][C]32[/C][C]18[/C][C]18.0107800908509[/C][C]-0.0107800908509158[/C][/ROW]
[ROW][C]33[/C][C]18[/C][C]17.7668933506039[/C][C]0.233106649396147[/C][/ROW]
[ROW][C]34[/C][C]12[/C][C]12.0260639917023[/C][C]-0.0260639917023221[/C][/ROW]
[ROW][C]35[/C][C]17[/C][C]16.8847408869704[/C][C]0.115259113029564[/C][/ROW]
[ROW][C]36[/C][C]9[/C][C]9.07705572399475[/C][C]-0.0770557239947482[/C][/ROW]
[ROW][C]37[/C][C]9[/C][C]9.08245928465533[/C][C]-0.0824592846553304[/C][/ROW]
[ROW][C]38[/C][C]12[/C][C]11.9033769294834[/C][C]0.0966230705165576[/C][/ROW]
[ROW][C]39[/C][C]0[/C][C]1.28338009720315[/C][C]-1.28338009720315[/C][/ROW]
[ROW][C]40[/C][C]12[/C][C]11.9949447657467[/C][C]0.00505523425328857[/C][/ROW]
[ROW][C]41[/C][C]18[/C][C]17.9526375793416[/C][C]0.0473624206583547[/C][/ROW]
[ROW][C]42[/C][C]14[/C][C]13.9962165487525[/C][C]0.0037834512475189[/C][/ROW]
[ROW][C]43[/C][C]15[/C][C]14.9065211488228[/C][C]0.0934788511772117[/C][/ROW]
[ROW][C]44[/C][C]16[/C][C]15.8422981012251[/C][C]0.157701898774856[/C][/ROW]
[ROW][C]45[/C][C]10[/C][C]10.0684963629520[/C][C]-0.0684963629519611[/C][/ROW]
[ROW][C]46[/C][C]11[/C][C]11.0374499695315[/C][C]-0.0374499695314946[/C][/ROW]
[ROW][C]47[/C][C]14[/C][C]14.0010545969886[/C][C]-0.00105459698856436[/C][/ROW]
[ROW][C]48[/C][C]9[/C][C]9.07268456325402[/C][C]-0.0726845632540236[/C][/ROW]
[ROW][C]49[/C][C]12[/C][C]12.0475109904394[/C][C]-0.0475109904393759[/C][/ROW]
[ROW][C]50[/C][C]17[/C][C]16.8358097644690[/C][C]0.164190235530967[/C][/ROW]
[ROW][C]51[/C][C]5[/C][C]5.11852838463848[/C][C]-0.118528384638484[/C][/ROW]
[ROW][C]52[/C][C]12[/C][C]12.0290947677177[/C][C]-0.0290947677176641[/C][/ROW]
[ROW][C]53[/C][C]12[/C][C]12.059812607157[/C][C]-0.0598126071569941[/C][/ROW]
[ROW][C]54[/C][C]6[/C][C]6.05438764209266[/C][C]-0.0543876420926603[/C][/ROW]
[ROW][C]55[/C][C]24[/C][C]23.9535450222915[/C][C]0.0464549777084812[/C][/ROW]
[ROW][C]56[/C][C]12[/C][C]12.0637203147043[/C][C]-0.0637203147042778[/C][/ROW]
[ROW][C]57[/C][C]12[/C][C]12.0494792103037[/C][C]-0.049479210303739[/C][/ROW]
[ROW][C]58[/C][C]14[/C][C]13.9170747320339[/C][C]0.082925267966122[/C][/ROW]
[ROW][C]59[/C][C]7[/C][C]7.0966259030604[/C][C]-0.0966259030603979[/C][/ROW]
[ROW][C]60[/C][C]13[/C][C]12.9114780491714[/C][C]0.0885219508285541[/C][/ROW]
[ROW][C]61[/C][C]12[/C][C]12.0482352674062[/C][C]-0.0482352674061845[/C][/ROW]
[ROW][C]62[/C][C]13[/C][C]12.9602993284429[/C][C]0.0397006715570933[/C][/ROW]
[ROW][C]63[/C][C]0[/C][C]1.45097540024459[/C][C]-1.45097540024459[/C][/ROW]
[ROW][C]64[/C][C]8[/C][C]8.12958934524279[/C][C]-0.129589345242790[/C][/ROW]
[ROW][C]65[/C][C]11[/C][C]10.9569727066323[/C][C]0.04302729336768[/C][/ROW]
[ROW][C]66[/C][C]9[/C][C]9.09826281850474[/C][C]-0.0982628185047413[/C][/ROW]
[ROW][C]67[/C][C]11[/C][C]11.1044567692809[/C][C]-0.104456769280875[/C][/ROW]
[ROW][C]68[/C][C]13[/C][C]12.9746495911497[/C][C]0.0253504088503282[/C][/ROW]
[ROW][C]69[/C][C]10[/C][C]9.99820015258048[/C][C]0.00179984741952341[/C][/ROW]
[ROW][C]70[/C][C]0[/C][C]-2.39441819924024[/C][C]2.39441819924024[/C][/ROW]
[ROW][C]71[/C][C]12[/C][C]12.0361176554881[/C][C]-0.0361176554881409[/C][/ROW]
[ROW][C]72[/C][C]9[/C][C]9.08524865220072[/C][C]-0.0852486522007223[/C][/ROW]
[ROW][C]73[/C][C]15[/C][C]15.0142679031478[/C][C]-0.0142679031478093[/C][/ROW]
[ROW][C]74[/C][C]18[/C][C]17.9244259656443[/C][C]0.0755740343556527[/C][/ROW]
[ROW][C]75[/C][C]15[/C][C]14.9346766039850[/C][C]0.0653233960149735[/C][/ROW]
[ROW][C]76[/C][C]12[/C][C]12.0215902310196[/C][C]-0.0215902310196441[/C][/ROW]
[ROW][C]77[/C][C]13[/C][C]12.9224916307127[/C][C]0.0775083692873257[/C][/ROW]
[ROW][C]78[/C][C]14[/C][C]13.9880574354856[/C][C]0.0119425645144205[/C][/ROW]
[ROW][C]79[/C][C]10[/C][C]10.1005989640691[/C][C]-0.100598964069080[/C][/ROW]
[ROW][C]80[/C][C]13[/C][C]12.9631753149288[/C][C]0.0368246850712469[/C][/ROW]
[ROW][C]81[/C][C]13[/C][C]13.046986650076[/C][C]-0.0469866500760132[/C][/ROW]
[ROW][C]82[/C][C]0[/C][C]-0.885090382846324[/C][C]0.885090382846324[/C][/ROW]
[ROW][C]83[/C][C]13[/C][C]12.9853583722153[/C][C]0.0146416277847314[/C][/ROW]
[ROW][C]84[/C][C]16[/C][C]15.9690343746953[/C][C]0.030965625304742[/C][/ROW]
[ROW][C]85[/C][C]0[/C][C]-1.91758203501526[/C][C]1.91758203501526[/C][/ROW]
[ROW][C]86[/C][C]16[/C][C]15.9018992513083[/C][C]0.0981007486917389[/C][/ROW]
[ROW][C]87[/C][C]11[/C][C]10.9897159135691[/C][C]0.0102840864309359[/C][/ROW]
[ROW][C]88[/C][C]9[/C][C]9.102470788233[/C][C]-0.102470788232993[/C][/ROW]
[ROW][C]89[/C][C]16[/C][C]16.0399606349383[/C][C]-0.0399606349383254[/C][/ROW]
[ROW][C]90[/C][C]0[/C][C]0.906089975309023[/C][C]-0.906089975309023[/C][/ROW]
[ROW][C]91[/C][C]14[/C][C]13.9818225940170[/C][C]0.0181774059829752[/C][/ROW]
[ROW][C]92[/C][C]8[/C][C]8.09310284134055[/C][C]-0.0931028413405518[/C][/ROW]
[ROW][C]93[/C][C]0[/C][C]-0.693546120787273[/C][C]0.693546120787273[/C][/ROW]
[ROW][C]94[/C][C]15[/C][C]14.8858075134592[/C][C]0.114192486540761[/C][/ROW]
[ROW][C]95[/C][C]11[/C][C]11.1216987495030[/C][C]-0.121698749502958[/C][/ROW]
[ROW][C]96[/C][C]21[/C][C]20.9050240657784[/C][C]0.094975934221612[/C][/ROW]
[ROW][C]97[/C][C]14[/C][C]14.0086715049589[/C][C]-0.00867150495894964[/C][/ROW]
[ROW][C]98[/C][C]18[/C][C]17.9459588441959[/C][C]0.0540411558041064[/C][/ROW]
[ROW][C]99[/C][C]12[/C][C]11.9960885478409[/C][C]0.00391145215905488[/C][/ROW]
[ROW][C]100[/C][C]13[/C][C]12.9809025512294[/C][C]0.0190974487706307[/C][/ROW]
[ROW][C]101[/C][C]15[/C][C]14.9775828890511[/C][C]0.0224171109488623[/C][/ROW]
[ROW][C]102[/C][C]12[/C][C]11.9710481286891[/C][C]0.0289518713109204[/C][/ROW]
[ROW][C]103[/C][C]19[/C][C]18.8361037957818[/C][C]0.163896204218179[/C][/ROW]
[ROW][C]104[/C][C]15[/C][C]14.9819552693612[/C][C]0.0180447306388206[/C][/ROW]
[ROW][C]105[/C][C]11[/C][C]11.1230329761142[/C][C]-0.123032976114214[/C][/ROW]
[ROW][C]106[/C][C]0[/C][C]1.36483120019741[/C][C]-1.36483120019741[/C][/ROW]
[ROW][C]107[/C][C]10[/C][C]10.0344466404312[/C][C]-0.0344466404311514[/C][/ROW]
[ROW][C]108[/C][C]13[/C][C]13.0054875797950[/C][C]-0.00548757979495669[/C][/ROW]
[ROW][C]109[/C][C]15[/C][C]14.9845567173617[/C][C]0.0154432826382789[/C][/ROW]
[ROW][C]110[/C][C]0[/C][C]2.20743937802792[/C][C]-2.20743937802792[/C][/ROW]
[ROW][C]111[/C][C]12[/C][C]11.9740531523725[/C][C]0.0259468476274913[/C][/ROW]
[ROW][C]112[/C][C]16[/C][C]16.0500651184642[/C][C]-0.0500651184642499[/C][/ROW]
[ROW][C]113[/C][C]9[/C][C]9.1549017829124[/C][C]-0.154901782912404[/C][/ROW]
[ROW][C]114[/C][C]0[/C][C]0.0240419701462767[/C][C]-0.0240419701462767[/C][/ROW]
[ROW][C]115[/C][C]8[/C][C]8.16391454114465[/C][C]-0.163914541144647[/C][/ROW]
[ROW][C]116[/C][C]13[/C][C]12.9386712276328[/C][C]0.0613287723671505[/C][/ROW]
[ROW][C]117[/C][C]17[/C][C]16.8390952000783[/C][C]0.160904799921677[/C][/ROW]
[ROW][C]118[/C][C]9[/C][C]9.02038299995704[/C][C]-0.0203829999570453[/C][/ROW]
[ROW][C]119[/C][C]15[/C][C]14.9637959556676[/C][C]0.0362040443324395[/C][/ROW]
[ROW][C]120[/C][C]8[/C][C]7.98632866969238[/C][C]0.0136713303076233[/C][/ROW]
[ROW][C]121[/C][C]7[/C][C]7.08420087261372[/C][C]-0.0842008726137225[/C][/ROW]
[ROW][C]122[/C][C]12[/C][C]11.9347620619701[/C][C]0.065237938029946[/C][/ROW]
[ROW][C]123[/C][C]0[/C][C]-1.21585580952926[/C][C]1.21585580952926[/C][/ROW]
[ROW][C]124[/C][C]6[/C][C]6.15233117330626[/C][C]-0.152331173306261[/C][/ROW]
[ROW][C]125[/C][C]8[/C][C]8.06777709728402[/C][C]-0.0677770972840157[/C][/ROW]
[ROW][C]126[/C][C]17[/C][C]16.8837879527136[/C][C]0.116212047286379[/C][/ROW]
[ROW][C]127[/C][C]0[/C][C]-0.379356212778565[/C][C]0.379356212778565[/C][/ROW]
[ROW][C]128[/C][C]11[/C][C]11.0603114512258[/C][C]-0.0603114512257693[/C][/ROW]
[ROW][C]129[/C][C]14[/C][C]14.0045223889417[/C][C]-0.00452238894171759[/C][/ROW]
[ROW][C]130[/C][C]11[/C][C]11.0778287090591[/C][C]-0.0778287090591191[/C][/ROW]
[ROW][C]131[/C][C]13[/C][C]13.0204389904130[/C][C]-0.0204389904130452[/C][/ROW]
[ROW][C]132[/C][C]12[/C][C]12.0099846742323[/C][C]-0.00998467423229209[/C][/ROW]
[ROW][C]133[/C][C]11[/C][C]11.0005716715290[/C][C]-0.000571671529022328[/C][/ROW]
[ROW][C]134[/C][C]9[/C][C]9.02333555851491[/C][C]-0.0233355585149103[/C][/ROW]
[ROW][C]135[/C][C]12[/C][C]12.0354323880425[/C][C]-0.0354323880425376[/C][/ROW]
[ROW][C]136[/C][C]20[/C][C]19.8388441119422[/C][C]0.161155888057811[/C][/ROW]
[ROW][C]137[/C][C]12[/C][C]11.9408384296352[/C][C]0.0591615703648346[/C][/ROW]
[ROW][C]138[/C][C]13[/C][C]12.9300105579108[/C][C]0.0699894420892005[/C][/ROW]
[ROW][C]139[/C][C]12[/C][C]12.0520359711123[/C][C]-0.052035971112305[/C][/ROW]
[ROW][C]140[/C][C]12[/C][C]12.0602392022240[/C][C]-0.0602392022240392[/C][/ROW]
[ROW][C]141[/C][C]9[/C][C]9.07079578041646[/C][C]-0.0707957804164556[/C][/ROW]
[ROW][C]142[/C][C]0[/C][C]-0.61161742588035[/C][C]0.61161742588035[/C][/ROW]
[ROW][C]143[/C][C]24[/C][C]23.852296861501[/C][C]0.147703138499008[/C][/ROW]
[ROW][C]144[/C][C]7[/C][C]7.07752980589028[/C][C]-0.0775298058902813[/C][/ROW]
[ROW][C]145[/C][C]17[/C][C]16.9280110593398[/C][C]0.0719889406601921[/C][/ROW]
[ROW][C]146[/C][C]11[/C][C]11.0026861031708[/C][C]-0.0026861031708185[/C][/ROW]
[ROW][C]147[/C][C]17[/C][C]16.9189775078818[/C][C]0.0810224921181561[/C][/ROW]
[ROW][C]148[/C][C]0[/C][C]-1.02617943456896[/C][C]1.02617943456896[/C][/ROW]
[ROW][C]149[/C][C]12[/C][C]11.9855737642411[/C][C]0.0144262357588853[/C][/ROW]
[ROW][C]150[/C][C]14[/C][C]14.0103553031831[/C][C]-0.0103553031830943[/C][/ROW]
[ROW][C]151[/C][C]11[/C][C]11.0723252375672[/C][C]-0.072325237567246[/C][/ROW]
[ROW][C]152[/C][C]16[/C][C]15.9118948234297[/C][C]0.0881051765702638[/C][/ROW]
[ROW][C]153[/C][C]21[/C][C]20.8455141976425[/C][C]0.154485802357494[/C][/ROW]
[ROW][C]154[/C][C]14[/C][C]13.8678883745261[/C][C]0.132111625473910[/C][/ROW]
[ROW][C]155[/C][C]20[/C][C]19.8813210434275[/C][C]0.118678956572498[/C][/ROW]
[ROW][C]156[/C][C]13[/C][C]12.9399148858568[/C][C]0.0600851141431682[/C][/ROW]
[ROW][C]157[/C][C]11[/C][C]11.0571405413539[/C][C]-0.0571405413539098[/C][/ROW]
[ROW][C]158[/C][C]15[/C][C]14.9659996758598[/C][C]0.0340003241402262[/C][/ROW]
[ROW][C]159[/C][C]19[/C][C]18.9922622111579[/C][C]0.0077377888421377[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98028&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98028&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
11111.1403814194666-0.140381419466573
277.1622209405945-0.162220940594507
302.04149255972577-2.04149255972577
41010.0624141218463-0.0624141218462946
51212.0190955021867-0.0190955021866933
60-0.1546049602079140.154604960207914
71110.97414527113000.0258547288699511
81111.0759546876537-0.0759546876537369
91211.96211567899550.0378843210045264
101312.97380706946990.0261929305300589
111414.0568069474377-0.0568069474376607
121615.94967867332110.0503213266789092
131111.1166476887478-0.116647688747833
141010.0120873909802-0.0120873909801950
151111.0171126532760-0.0171126532760307
161514.89037647083440.109623529165571
1799.11116912694688-0.111169126946882
181110.97998474373570.0200152562643023
191716.88502133359780.114978666402161
201716.94290674030920.057093259690768
211111.0498003866014-0.0498003866014301
221817.91863347890860.0813665210914399
231414.0723059307835-0.0723059307834722
241010.0975978035740-0.097597803573965
251111.0775894897558-0.077589489755777
261514.96474545562250.0352545443775432
271514.90712845484820.0928715451517605
281313.0367960051323-0.0367960051322751
291615.94430824267590.0556917573241361
301312.94864445615830.051355543841663
3199.11079993555021-0.110799935550211
321818.0107800908509-0.0107800908509158
331817.76689335060390.233106649396147
341212.0260639917023-0.0260639917023221
351716.88474088697040.115259113029564
3699.07705572399475-0.0770557239947482
3799.08245928465533-0.0824592846553304
381211.90337692948340.0966230705165576
3901.28338009720315-1.28338009720315
401211.99494476574670.00505523425328857
411817.95263757934160.0473624206583547
421413.99621654875250.0037834512475189
431514.90652114882280.0934788511772117
441615.84229810122510.157701898774856
451010.0684963629520-0.0684963629519611
461111.0374499695315-0.0374499695314946
471414.0010545969886-0.00105459698856436
4899.07268456325402-0.0726845632540236
491212.0475109904394-0.0475109904393759
501716.83580976446900.164190235530967
5155.11852838463848-0.118528384638484
521212.0290947677177-0.0290947677176641
531212.059812607157-0.0598126071569941
5466.05438764209266-0.0543876420926603
552423.95354502229150.0464549777084812
561212.0637203147043-0.0637203147042778
571212.0494792103037-0.049479210303739
581413.91707473203390.082925267966122
5977.0966259030604-0.0966259030603979
601312.91147804917140.0885219508285541
611212.0482352674062-0.0482352674061845
621312.96029932844290.0397006715570933
6301.45097540024459-1.45097540024459
6488.12958934524279-0.129589345242790
651110.95697270663230.04302729336768
6699.09826281850474-0.0982628185047413
671111.1044567692809-0.104456769280875
681312.97464959114970.0253504088503282
69109.998200152580480.00179984741952341
700-2.394418199240242.39441819924024
711212.0361176554881-0.0361176554881409
7299.08524865220072-0.0852486522007223
731515.0142679031478-0.0142679031478093
741817.92442596564430.0755740343556527
751514.93467660398500.0653233960149735
761212.0215902310196-0.0215902310196441
771312.92249163071270.0775083692873257
781413.98805743548560.0119425645144205
791010.1005989640691-0.100598964069080
801312.96317531492880.0368246850712469
811313.046986650076-0.0469866500760132
820-0.8850903828463240.885090382846324
831312.98535837221530.0146416277847314
841615.96903437469530.030965625304742
850-1.917582035015261.91758203501526
861615.90189925130830.0981007486917389
871110.98971591356910.0102840864309359
8899.102470788233-0.102470788232993
891616.0399606349383-0.0399606349383254
9000.906089975309023-0.906089975309023
911413.98182259401700.0181774059829752
9288.09310284134055-0.0931028413405518
930-0.6935461207872730.693546120787273
941514.88580751345920.114192486540761
951111.1216987495030-0.121698749502958
962120.90502406577840.094975934221612
971414.0086715049589-0.00867150495894964
981817.94595884419590.0540411558041064
991211.99608854784090.00391145215905488
1001312.98090255122940.0190974487706307
1011514.97758288905110.0224171109488623
1021211.97104812868910.0289518713109204
1031918.83610379578180.163896204218179
1041514.98195526936120.0180447306388206
1051111.1230329761142-0.123032976114214
10601.36483120019741-1.36483120019741
1071010.0344466404312-0.0344466404311514
1081313.0054875797950-0.00548757979495669
1091514.98455671736170.0154432826382789
11002.20743937802792-2.20743937802792
1111211.97405315237250.0259468476274913
1121616.0500651184642-0.0500651184642499
11399.1549017829124-0.154901782912404
11400.0240419701462767-0.0240419701462767
11588.16391454114465-0.163914541144647
1161312.93867122763280.0613287723671505
1171716.83909520007830.160904799921677
11899.02038299995704-0.0203829999570453
1191514.96379595566760.0362040443324395
12087.986328669692380.0136713303076233
12177.08420087261372-0.0842008726137225
1221211.93476206197010.065237938029946
1230-1.215855809529261.21585580952926
12466.15233117330626-0.152331173306261
12588.06777709728402-0.0677770972840157
1261716.88378795271360.116212047286379
1270-0.3793562127785650.379356212778565
1281111.0603114512258-0.0603114512257693
1291414.0045223889417-0.00452238894171759
1301111.0778287090591-0.0778287090591191
1311313.0204389904130-0.0204389904130452
1321212.0099846742323-0.00998467423229209
1331111.0005716715290-0.000571671529022328
13499.02333555851491-0.0233355585149103
1351212.0354323880425-0.0354323880425376
1362019.83884411194220.161155888057811
1371211.94083842963520.0591615703648346
1381312.93001055791080.0699894420892005
1391212.0520359711123-0.052035971112305
1401212.0602392022240-0.0602392022240392
14199.07079578041646-0.0707957804164556
1420-0.611617425880350.61161742588035
1432423.8522968615010.147703138499008
14477.07752980589028-0.0775298058902813
1451716.92801105933980.0719889406601921
1461111.0026861031708-0.0026861031708185
1471716.91897750788180.0810224921181561
1480-1.026179434568961.02617943456896
1491211.98557376424110.0144262357588853
1501414.0103553031831-0.0103553031830943
1511111.0723252375672-0.072325237567246
1521615.91189482342970.0881051765702638
1532120.84551419764250.154485802357494
1541413.86788837452610.132111625473910
1552019.88132104342750.118678956572498
1561312.93991488585680.0600851141431682
1571111.0571405413539-0.0571405413539098
1581514.96599967585980.0340003241402262
1591918.99226221115790.0077377888421377







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
157.12352139600324e-401.42470427920065e-391
168.62983344447803e-541.72596668889561e-531
174.36699623746823e-688.73399247493646e-681
186.18005865344976e-821.23601173068995e-811
197.80798434956018e-931.56159686991204e-921
201.20551027143727e-1072.41102054287453e-1071
213.77957387486951e-1257.55914774973902e-1251
222.22125472940736e-1324.44250945881471e-1321
236.50921349735127e-1511.30184269947025e-1501
243.35171382141838e-1576.70342764283675e-1571
253.95511321780597e-1737.91022643561194e-1731
263.00649926631648e-1876.01299853263297e-1871
271.11092214173037e-2042.22184428346074e-2041
285.33479796323646e-2181.06695959264729e-2171
297.61007223513885e-2311.52201444702777e-2301
304.35196393477539e-2408.70392786955077e-2401
313.12009680586135e-2576.2401936117227e-2571
329.86845602833614e-2611.97369120566723e-2601
337.55669658911429e-2751.51133931782286e-2741
345.16232513458644e-2921.03246502691729e-2911
351.60824534574185e-3093.21649069148371e-3091
361.87744945419674e-3223.75489890839347e-3221
37001
38001
39001
40001
41001
42001
43001
44001
45001
46001
47001
48001
49001
50001
51001
52001
53001
54001
55001
56001
57001
58001
59001
60001
61001
62001
63001
64001
65001
66001
67001
68001
69001
70001
71001
72001
73001
74001
75001
76001
77001
78001
79001
80001
81001
820.01789703169751030.03579406339502060.98210296830249
830.01314533806559950.02629067613119910.9868546619344
840.009533223894599740.01906644778919950.9904667761054
850.5405595174543880.9188809650912240.459440482545612
860.4938891698449350.987778339689870.506110830155065
870.4450177489785330.8900354979570670.554982251021467
880.3975928961472900.7951857922945790.60240710385271
890.3514581755002370.7029163510004750.648541824499763
900.9055046268579770.1889907462840460.094495373142023
910.8827369170478420.2345261659043170.117263082952158
920.8570518721168070.2858962557663860.142948127883193
930.9411370356558270.1177259286883460.058862964344173
940.9253980742650020.1492038514699960.0746019257349978
950.9070348190002730.1859303619994550.0929651809997273
960.8844367227682120.2311265544635770.115563277231788
970.8576285553430170.2847428893139650.142371444656983
980.8269004110405750.3461991779188510.173099588959425
990.7918346101308740.4163307797382520.208165389869126
1000.7527839496048230.4944321007903540.247216050395177
1010.7099654227491390.5800691545017230.290034577250861
1020.6638184544050410.6723630911899180.336181545594959
1030.6188345508118730.7623308983762540.381165449188127
1040.5677848351062240.8644303297875510.432215164893776
1050.5199013321924760.9601973356150480.480098667807524
1060.8004971792659550.399005641468090.199502820734045
1070.7604648745688720.4790702508622570.239535125431128
1080.7163403408399290.5673193183201410.283659659160071
1090.6682234171193010.6635531657613980.331776582880699
110100
111100
112100
113100
114100
115100
116100
117100
118100
119100
120100
121100
122100
123100
124100
12514.33221529656821e-3062.16610764828411e-306
12614.98650200395513e-2992.49325100197757e-299
12714.17189273509107e-2832.08594636754554e-283
12812.77015547334519e-2681.38507773667259e-268
12915.9519889100069e-2582.97599445500345e-258
13011.68413000387365e-2408.42065001936827e-241
13117.93575159425184e-2303.96787579712592e-230
13216.43653674824289e-2183.21826837412144e-218
13316.77176507179549e-2023.38588253589775e-202
13411.3952761090742e-1886.976380545371e-189
13511.59156583494553e-1747.95782917472766e-175
13612.55493355510492e-1591.27746677755246e-159
13711.28900734946478e-1456.44503674732391e-146
13818.30721724397876e-1314.15360862198938e-131
13911.26120439086290e-1166.30602195431449e-117
14011.53789655179996e-1007.68948275899981e-101
14113.6270973435417e-871.81354867177085e-87
14214.79002646071558e-712.39501323035779e-71
14311.48258031049960e-567.41290155249802e-57
14419.65070375797895e-444.82535187898948e-44

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
15 & 7.12352139600324e-40 & 1.42470427920065e-39 & 1 \tabularnewline
16 & 8.62983344447803e-54 & 1.72596668889561e-53 & 1 \tabularnewline
17 & 4.36699623746823e-68 & 8.73399247493646e-68 & 1 \tabularnewline
18 & 6.18005865344976e-82 & 1.23601173068995e-81 & 1 \tabularnewline
19 & 7.80798434956018e-93 & 1.56159686991204e-92 & 1 \tabularnewline
20 & 1.20551027143727e-107 & 2.41102054287453e-107 & 1 \tabularnewline
21 & 3.77957387486951e-125 & 7.55914774973902e-125 & 1 \tabularnewline
22 & 2.22125472940736e-132 & 4.44250945881471e-132 & 1 \tabularnewline
23 & 6.50921349735127e-151 & 1.30184269947025e-150 & 1 \tabularnewline
24 & 3.35171382141838e-157 & 6.70342764283675e-157 & 1 \tabularnewline
25 & 3.95511321780597e-173 & 7.91022643561194e-173 & 1 \tabularnewline
26 & 3.00649926631648e-187 & 6.01299853263297e-187 & 1 \tabularnewline
27 & 1.11092214173037e-204 & 2.22184428346074e-204 & 1 \tabularnewline
28 & 5.33479796323646e-218 & 1.06695959264729e-217 & 1 \tabularnewline
29 & 7.61007223513885e-231 & 1.52201444702777e-230 & 1 \tabularnewline
30 & 4.35196393477539e-240 & 8.70392786955077e-240 & 1 \tabularnewline
31 & 3.12009680586135e-257 & 6.2401936117227e-257 & 1 \tabularnewline
32 & 9.86845602833614e-261 & 1.97369120566723e-260 & 1 \tabularnewline
33 & 7.55669658911429e-275 & 1.51133931782286e-274 & 1 \tabularnewline
34 & 5.16232513458644e-292 & 1.03246502691729e-291 & 1 \tabularnewline
35 & 1.60824534574185e-309 & 3.21649069148371e-309 & 1 \tabularnewline
36 & 1.87744945419674e-322 & 3.75489890839347e-322 & 1 \tabularnewline
37 & 0 & 0 & 1 \tabularnewline
38 & 0 & 0 & 1 \tabularnewline
39 & 0 & 0 & 1 \tabularnewline
40 & 0 & 0 & 1 \tabularnewline
41 & 0 & 0 & 1 \tabularnewline
42 & 0 & 0 & 1 \tabularnewline
43 & 0 & 0 & 1 \tabularnewline
44 & 0 & 0 & 1 \tabularnewline
45 & 0 & 0 & 1 \tabularnewline
46 & 0 & 0 & 1 \tabularnewline
47 & 0 & 0 & 1 \tabularnewline
48 & 0 & 0 & 1 \tabularnewline
49 & 0 & 0 & 1 \tabularnewline
50 & 0 & 0 & 1 \tabularnewline
51 & 0 & 0 & 1 \tabularnewline
52 & 0 & 0 & 1 \tabularnewline
53 & 0 & 0 & 1 \tabularnewline
54 & 0 & 0 & 1 \tabularnewline
55 & 0 & 0 & 1 \tabularnewline
56 & 0 & 0 & 1 \tabularnewline
57 & 0 & 0 & 1 \tabularnewline
58 & 0 & 0 & 1 \tabularnewline
59 & 0 & 0 & 1 \tabularnewline
60 & 0 & 0 & 1 \tabularnewline
61 & 0 & 0 & 1 \tabularnewline
62 & 0 & 0 & 1 \tabularnewline
63 & 0 & 0 & 1 \tabularnewline
64 & 0 & 0 & 1 \tabularnewline
65 & 0 & 0 & 1 \tabularnewline
66 & 0 & 0 & 1 \tabularnewline
67 & 0 & 0 & 1 \tabularnewline
68 & 0 & 0 & 1 \tabularnewline
69 & 0 & 0 & 1 \tabularnewline
70 & 0 & 0 & 1 \tabularnewline
71 & 0 & 0 & 1 \tabularnewline
72 & 0 & 0 & 1 \tabularnewline
73 & 0 & 0 & 1 \tabularnewline
74 & 0 & 0 & 1 \tabularnewline
75 & 0 & 0 & 1 \tabularnewline
76 & 0 & 0 & 1 \tabularnewline
77 & 0 & 0 & 1 \tabularnewline
78 & 0 & 0 & 1 \tabularnewline
79 & 0 & 0 & 1 \tabularnewline
80 & 0 & 0 & 1 \tabularnewline
81 & 0 & 0 & 1 \tabularnewline
82 & 0.0178970316975103 & 0.0357940633950206 & 0.98210296830249 \tabularnewline
83 & 0.0131453380655995 & 0.0262906761311991 & 0.9868546619344 \tabularnewline
84 & 0.00953322389459974 & 0.0190664477891995 & 0.9904667761054 \tabularnewline
85 & 0.540559517454388 & 0.918880965091224 & 0.459440482545612 \tabularnewline
86 & 0.493889169844935 & 0.98777833968987 & 0.506110830155065 \tabularnewline
87 & 0.445017748978533 & 0.890035497957067 & 0.554982251021467 \tabularnewline
88 & 0.397592896147290 & 0.795185792294579 & 0.60240710385271 \tabularnewline
89 & 0.351458175500237 & 0.702916351000475 & 0.648541824499763 \tabularnewline
90 & 0.905504626857977 & 0.188990746284046 & 0.094495373142023 \tabularnewline
91 & 0.882736917047842 & 0.234526165904317 & 0.117263082952158 \tabularnewline
92 & 0.857051872116807 & 0.285896255766386 & 0.142948127883193 \tabularnewline
93 & 0.941137035655827 & 0.117725928688346 & 0.058862964344173 \tabularnewline
94 & 0.925398074265002 & 0.149203851469996 & 0.0746019257349978 \tabularnewline
95 & 0.907034819000273 & 0.185930361999455 & 0.0929651809997273 \tabularnewline
96 & 0.884436722768212 & 0.231126554463577 & 0.115563277231788 \tabularnewline
97 & 0.857628555343017 & 0.284742889313965 & 0.142371444656983 \tabularnewline
98 & 0.826900411040575 & 0.346199177918851 & 0.173099588959425 \tabularnewline
99 & 0.791834610130874 & 0.416330779738252 & 0.208165389869126 \tabularnewline
100 & 0.752783949604823 & 0.494432100790354 & 0.247216050395177 \tabularnewline
101 & 0.709965422749139 & 0.580069154501723 & 0.290034577250861 \tabularnewline
102 & 0.663818454405041 & 0.672363091189918 & 0.336181545594959 \tabularnewline
103 & 0.618834550811873 & 0.762330898376254 & 0.381165449188127 \tabularnewline
104 & 0.567784835106224 & 0.864430329787551 & 0.432215164893776 \tabularnewline
105 & 0.519901332192476 & 0.960197335615048 & 0.480098667807524 \tabularnewline
106 & 0.800497179265955 & 0.39900564146809 & 0.199502820734045 \tabularnewline
107 & 0.760464874568872 & 0.479070250862257 & 0.239535125431128 \tabularnewline
108 & 0.716340340839929 & 0.567319318320141 & 0.283659659160071 \tabularnewline
109 & 0.668223417119301 & 0.663553165761398 & 0.331776582880699 \tabularnewline
110 & 1 & 0 & 0 \tabularnewline
111 & 1 & 0 & 0 \tabularnewline
112 & 1 & 0 & 0 \tabularnewline
113 & 1 & 0 & 0 \tabularnewline
114 & 1 & 0 & 0 \tabularnewline
115 & 1 & 0 & 0 \tabularnewline
116 & 1 & 0 & 0 \tabularnewline
117 & 1 & 0 & 0 \tabularnewline
118 & 1 & 0 & 0 \tabularnewline
119 & 1 & 0 & 0 \tabularnewline
120 & 1 & 0 & 0 \tabularnewline
121 & 1 & 0 & 0 \tabularnewline
122 & 1 & 0 & 0 \tabularnewline
123 & 1 & 0 & 0 \tabularnewline
124 & 1 & 0 & 0 \tabularnewline
125 & 1 & 4.33221529656821e-306 & 2.16610764828411e-306 \tabularnewline
126 & 1 & 4.98650200395513e-299 & 2.49325100197757e-299 \tabularnewline
127 & 1 & 4.17189273509107e-283 & 2.08594636754554e-283 \tabularnewline
128 & 1 & 2.77015547334519e-268 & 1.38507773667259e-268 \tabularnewline
129 & 1 & 5.9519889100069e-258 & 2.97599445500345e-258 \tabularnewline
130 & 1 & 1.68413000387365e-240 & 8.42065001936827e-241 \tabularnewline
131 & 1 & 7.93575159425184e-230 & 3.96787579712592e-230 \tabularnewline
132 & 1 & 6.43653674824289e-218 & 3.21826837412144e-218 \tabularnewline
133 & 1 & 6.77176507179549e-202 & 3.38588253589775e-202 \tabularnewline
134 & 1 & 1.3952761090742e-188 & 6.976380545371e-189 \tabularnewline
135 & 1 & 1.59156583494553e-174 & 7.95782917472766e-175 \tabularnewline
136 & 1 & 2.55493355510492e-159 & 1.27746677755246e-159 \tabularnewline
137 & 1 & 1.28900734946478e-145 & 6.44503674732391e-146 \tabularnewline
138 & 1 & 8.30721724397876e-131 & 4.15360862198938e-131 \tabularnewline
139 & 1 & 1.26120439086290e-116 & 6.30602195431449e-117 \tabularnewline
140 & 1 & 1.53789655179996e-100 & 7.68948275899981e-101 \tabularnewline
141 & 1 & 3.6270973435417e-87 & 1.81354867177085e-87 \tabularnewline
142 & 1 & 4.79002646071558e-71 & 2.39501323035779e-71 \tabularnewline
143 & 1 & 1.48258031049960e-56 & 7.41290155249802e-57 \tabularnewline
144 & 1 & 9.65070375797895e-44 & 4.82535187898948e-44 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98028&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]15[/C][C]7.12352139600324e-40[/C][C]1.42470427920065e-39[/C][C]1[/C][/ROW]
[ROW][C]16[/C][C]8.62983344447803e-54[/C][C]1.72596668889561e-53[/C][C]1[/C][/ROW]
[ROW][C]17[/C][C]4.36699623746823e-68[/C][C]8.73399247493646e-68[/C][C]1[/C][/ROW]
[ROW][C]18[/C][C]6.18005865344976e-82[/C][C]1.23601173068995e-81[/C][C]1[/C][/ROW]
[ROW][C]19[/C][C]7.80798434956018e-93[/C][C]1.56159686991204e-92[/C][C]1[/C][/ROW]
[ROW][C]20[/C][C]1.20551027143727e-107[/C][C]2.41102054287453e-107[/C][C]1[/C][/ROW]
[ROW][C]21[/C][C]3.77957387486951e-125[/C][C]7.55914774973902e-125[/C][C]1[/C][/ROW]
[ROW][C]22[/C][C]2.22125472940736e-132[/C][C]4.44250945881471e-132[/C][C]1[/C][/ROW]
[ROW][C]23[/C][C]6.50921349735127e-151[/C][C]1.30184269947025e-150[/C][C]1[/C][/ROW]
[ROW][C]24[/C][C]3.35171382141838e-157[/C][C]6.70342764283675e-157[/C][C]1[/C][/ROW]
[ROW][C]25[/C][C]3.95511321780597e-173[/C][C]7.91022643561194e-173[/C][C]1[/C][/ROW]
[ROW][C]26[/C][C]3.00649926631648e-187[/C][C]6.01299853263297e-187[/C][C]1[/C][/ROW]
[ROW][C]27[/C][C]1.11092214173037e-204[/C][C]2.22184428346074e-204[/C][C]1[/C][/ROW]
[ROW][C]28[/C][C]5.33479796323646e-218[/C][C]1.06695959264729e-217[/C][C]1[/C][/ROW]
[ROW][C]29[/C][C]7.61007223513885e-231[/C][C]1.52201444702777e-230[/C][C]1[/C][/ROW]
[ROW][C]30[/C][C]4.35196393477539e-240[/C][C]8.70392786955077e-240[/C][C]1[/C][/ROW]
[ROW][C]31[/C][C]3.12009680586135e-257[/C][C]6.2401936117227e-257[/C][C]1[/C][/ROW]
[ROW][C]32[/C][C]9.86845602833614e-261[/C][C]1.97369120566723e-260[/C][C]1[/C][/ROW]
[ROW][C]33[/C][C]7.55669658911429e-275[/C][C]1.51133931782286e-274[/C][C]1[/C][/ROW]
[ROW][C]34[/C][C]5.16232513458644e-292[/C][C]1.03246502691729e-291[/C][C]1[/C][/ROW]
[ROW][C]35[/C][C]1.60824534574185e-309[/C][C]3.21649069148371e-309[/C][C]1[/C][/ROW]
[ROW][C]36[/C][C]1.87744945419674e-322[/C][C]3.75489890839347e-322[/C][C]1[/C][/ROW]
[ROW][C]37[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]38[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]39[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]40[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]41[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]42[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]43[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]44[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]45[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]46[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]47[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]48[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]49[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]50[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]51[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]52[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]53[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]54[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]55[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]56[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]57[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]58[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]59[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]60[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]61[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]62[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]63[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]64[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]65[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]66[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]67[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]68[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]69[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]70[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]71[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]72[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]73[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]74[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]75[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]76[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]77[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]78[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]79[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]80[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]81[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]82[/C][C]0.0178970316975103[/C][C]0.0357940633950206[/C][C]0.98210296830249[/C][/ROW]
[ROW][C]83[/C][C]0.0131453380655995[/C][C]0.0262906761311991[/C][C]0.9868546619344[/C][/ROW]
[ROW][C]84[/C][C]0.00953322389459974[/C][C]0.0190664477891995[/C][C]0.9904667761054[/C][/ROW]
[ROW][C]85[/C][C]0.540559517454388[/C][C]0.918880965091224[/C][C]0.459440482545612[/C][/ROW]
[ROW][C]86[/C][C]0.493889169844935[/C][C]0.98777833968987[/C][C]0.506110830155065[/C][/ROW]
[ROW][C]87[/C][C]0.445017748978533[/C][C]0.890035497957067[/C][C]0.554982251021467[/C][/ROW]
[ROW][C]88[/C][C]0.397592896147290[/C][C]0.795185792294579[/C][C]0.60240710385271[/C][/ROW]
[ROW][C]89[/C][C]0.351458175500237[/C][C]0.702916351000475[/C][C]0.648541824499763[/C][/ROW]
[ROW][C]90[/C][C]0.905504626857977[/C][C]0.188990746284046[/C][C]0.094495373142023[/C][/ROW]
[ROW][C]91[/C][C]0.882736917047842[/C][C]0.234526165904317[/C][C]0.117263082952158[/C][/ROW]
[ROW][C]92[/C][C]0.857051872116807[/C][C]0.285896255766386[/C][C]0.142948127883193[/C][/ROW]
[ROW][C]93[/C][C]0.941137035655827[/C][C]0.117725928688346[/C][C]0.058862964344173[/C][/ROW]
[ROW][C]94[/C][C]0.925398074265002[/C][C]0.149203851469996[/C][C]0.0746019257349978[/C][/ROW]
[ROW][C]95[/C][C]0.907034819000273[/C][C]0.185930361999455[/C][C]0.0929651809997273[/C][/ROW]
[ROW][C]96[/C][C]0.884436722768212[/C][C]0.231126554463577[/C][C]0.115563277231788[/C][/ROW]
[ROW][C]97[/C][C]0.857628555343017[/C][C]0.284742889313965[/C][C]0.142371444656983[/C][/ROW]
[ROW][C]98[/C][C]0.826900411040575[/C][C]0.346199177918851[/C][C]0.173099588959425[/C][/ROW]
[ROW][C]99[/C][C]0.791834610130874[/C][C]0.416330779738252[/C][C]0.208165389869126[/C][/ROW]
[ROW][C]100[/C][C]0.752783949604823[/C][C]0.494432100790354[/C][C]0.247216050395177[/C][/ROW]
[ROW][C]101[/C][C]0.709965422749139[/C][C]0.580069154501723[/C][C]0.290034577250861[/C][/ROW]
[ROW][C]102[/C][C]0.663818454405041[/C][C]0.672363091189918[/C][C]0.336181545594959[/C][/ROW]
[ROW][C]103[/C][C]0.618834550811873[/C][C]0.762330898376254[/C][C]0.381165449188127[/C][/ROW]
[ROW][C]104[/C][C]0.567784835106224[/C][C]0.864430329787551[/C][C]0.432215164893776[/C][/ROW]
[ROW][C]105[/C][C]0.519901332192476[/C][C]0.960197335615048[/C][C]0.480098667807524[/C][/ROW]
[ROW][C]106[/C][C]0.800497179265955[/C][C]0.39900564146809[/C][C]0.199502820734045[/C][/ROW]
[ROW][C]107[/C][C]0.760464874568872[/C][C]0.479070250862257[/C][C]0.239535125431128[/C][/ROW]
[ROW][C]108[/C][C]0.716340340839929[/C][C]0.567319318320141[/C][C]0.283659659160071[/C][/ROW]
[ROW][C]109[/C][C]0.668223417119301[/C][C]0.663553165761398[/C][C]0.331776582880699[/C][/ROW]
[ROW][C]110[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]111[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]112[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]113[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]114[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]115[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]116[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]117[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]118[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]119[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]120[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]121[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]122[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]123[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]124[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]125[/C][C]1[/C][C]4.33221529656821e-306[/C][C]2.16610764828411e-306[/C][/ROW]
[ROW][C]126[/C][C]1[/C][C]4.98650200395513e-299[/C][C]2.49325100197757e-299[/C][/ROW]
[ROW][C]127[/C][C]1[/C][C]4.17189273509107e-283[/C][C]2.08594636754554e-283[/C][/ROW]
[ROW][C]128[/C][C]1[/C][C]2.77015547334519e-268[/C][C]1.38507773667259e-268[/C][/ROW]
[ROW][C]129[/C][C]1[/C][C]5.9519889100069e-258[/C][C]2.97599445500345e-258[/C][/ROW]
[ROW][C]130[/C][C]1[/C][C]1.68413000387365e-240[/C][C]8.42065001936827e-241[/C][/ROW]
[ROW][C]131[/C][C]1[/C][C]7.93575159425184e-230[/C][C]3.96787579712592e-230[/C][/ROW]
[ROW][C]132[/C][C]1[/C][C]6.43653674824289e-218[/C][C]3.21826837412144e-218[/C][/ROW]
[ROW][C]133[/C][C]1[/C][C]6.77176507179549e-202[/C][C]3.38588253589775e-202[/C][/ROW]
[ROW][C]134[/C][C]1[/C][C]1.3952761090742e-188[/C][C]6.976380545371e-189[/C][/ROW]
[ROW][C]135[/C][C]1[/C][C]1.59156583494553e-174[/C][C]7.95782917472766e-175[/C][/ROW]
[ROW][C]136[/C][C]1[/C][C]2.55493355510492e-159[/C][C]1.27746677755246e-159[/C][/ROW]
[ROW][C]137[/C][C]1[/C][C]1.28900734946478e-145[/C][C]6.44503674732391e-146[/C][/ROW]
[ROW][C]138[/C][C]1[/C][C]8.30721724397876e-131[/C][C]4.15360862198938e-131[/C][/ROW]
[ROW][C]139[/C][C]1[/C][C]1.26120439086290e-116[/C][C]6.30602195431449e-117[/C][/ROW]
[ROW][C]140[/C][C]1[/C][C]1.53789655179996e-100[/C][C]7.68948275899981e-101[/C][/ROW]
[ROW][C]141[/C][C]1[/C][C]3.6270973435417e-87[/C][C]1.81354867177085e-87[/C][/ROW]
[ROW][C]142[/C][C]1[/C][C]4.79002646071558e-71[/C][C]2.39501323035779e-71[/C][/ROW]
[ROW][C]143[/C][C]1[/C][C]1.48258031049960e-56[/C][C]7.41290155249802e-57[/C][/ROW]
[ROW][C]144[/C][C]1[/C][C]9.65070375797895e-44[/C][C]4.82535187898948e-44[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98028&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98028&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
157.12352139600324e-401.42470427920065e-391
168.62983344447803e-541.72596668889561e-531
174.36699623746823e-688.73399247493646e-681
186.18005865344976e-821.23601173068995e-811
197.80798434956018e-931.56159686991204e-921
201.20551027143727e-1072.41102054287453e-1071
213.77957387486951e-1257.55914774973902e-1251
222.22125472940736e-1324.44250945881471e-1321
236.50921349735127e-1511.30184269947025e-1501
243.35171382141838e-1576.70342764283675e-1571
253.95511321780597e-1737.91022643561194e-1731
263.00649926631648e-1876.01299853263297e-1871
271.11092214173037e-2042.22184428346074e-2041
285.33479796323646e-2181.06695959264729e-2171
297.61007223513885e-2311.52201444702777e-2301
304.35196393477539e-2408.70392786955077e-2401
313.12009680586135e-2576.2401936117227e-2571
329.86845602833614e-2611.97369120566723e-2601
337.55669658911429e-2751.51133931782286e-2741
345.16232513458644e-2921.03246502691729e-2911
351.60824534574185e-3093.21649069148371e-3091
361.87744945419674e-3223.75489890839347e-3221
37001
38001
39001
40001
41001
42001
43001
44001
45001
46001
47001
48001
49001
50001
51001
52001
53001
54001
55001
56001
57001
58001
59001
60001
61001
62001
63001
64001
65001
66001
67001
68001
69001
70001
71001
72001
73001
74001
75001
76001
77001
78001
79001
80001
81001
820.01789703169751030.03579406339502060.98210296830249
830.01314533806559950.02629067613119910.9868546619344
840.009533223894599740.01906644778919950.9904667761054
850.5405595174543880.9188809650912240.459440482545612
860.4938891698449350.987778339689870.506110830155065
870.4450177489785330.8900354979570670.554982251021467
880.3975928961472900.7951857922945790.60240710385271
890.3514581755002370.7029163510004750.648541824499763
900.9055046268579770.1889907462840460.094495373142023
910.8827369170478420.2345261659043170.117263082952158
920.8570518721168070.2858962557663860.142948127883193
930.9411370356558270.1177259286883460.058862964344173
940.9253980742650020.1492038514699960.0746019257349978
950.9070348190002730.1859303619994550.0929651809997273
960.8844367227682120.2311265544635770.115563277231788
970.8576285553430170.2847428893139650.142371444656983
980.8269004110405750.3461991779188510.173099588959425
990.7918346101308740.4163307797382520.208165389869126
1000.7527839496048230.4944321007903540.247216050395177
1010.7099654227491390.5800691545017230.290034577250861
1020.6638184544050410.6723630911899180.336181545594959
1030.6188345508118730.7623308983762540.381165449188127
1040.5677848351062240.8644303297875510.432215164893776
1050.5199013321924760.9601973356150480.480098667807524
1060.8004971792659550.399005641468090.199502820734045
1070.7604648745688720.4790702508622570.239535125431128
1080.7163403408399290.5673193183201410.283659659160071
1090.6682234171193010.6635531657613980.331776582880699
110100
111100
112100
113100
114100
115100
116100
117100
118100
119100
120100
121100
122100
123100
124100
12514.33221529656821e-3062.16610764828411e-306
12614.98650200395513e-2992.49325100197757e-299
12714.17189273509107e-2832.08594636754554e-283
12812.77015547334519e-2681.38507773667259e-268
12915.9519889100069e-2582.97599445500345e-258
13011.68413000387365e-2408.42065001936827e-241
13117.93575159425184e-2303.96787579712592e-230
13216.43653674824289e-2183.21826837412144e-218
13316.77176507179549e-2023.38588253589775e-202
13411.3952761090742e-1886.976380545371e-189
13511.59156583494553e-1747.95782917472766e-175
13612.55493355510492e-1591.27746677755246e-159
13711.28900734946478e-1456.44503674732391e-146
13818.30721724397876e-1314.15360862198938e-131
13911.26120439086290e-1166.30602195431449e-117
14011.53789655179996e-1007.68948275899981e-101
14113.6270973435417e-871.81354867177085e-87
14214.79002646071558e-712.39501323035779e-71
14311.48258031049960e-567.41290155249802e-57
14419.65070375797895e-444.82535187898948e-44







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level1020.784615384615385NOK
5% type I error level1050.807692307692308NOK
10% type I error level1050.807692307692308NOK

\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 & 102 & 0.784615384615385 & NOK \tabularnewline
5% type I error level & 105 & 0.807692307692308 & NOK \tabularnewline
10% type I error level & 105 & 0.807692307692308 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98028&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]102[/C][C]0.784615384615385[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]105[/C][C]0.807692307692308[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]105[/C][C]0.807692307692308[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98028&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98028&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 level1020.784615384615385NOK
5% type I error level1050.807692307692308NOK
10% type I error level1050.807692307692308NOK



Parameters (Session):
par1 = 3 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 8 ; 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')
}