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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, 20 Nov 2009 07:31:26 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Nov/20/t1258727590js7ojf76qskiydm.htm/, Retrieved Fri, 29 Mar 2024 11:28:27 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58212, Retrieved Fri, 29 Mar 2024 11:28:27 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact129
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RM D  [Multiple Regression] [Seatbelt] [2009-11-12 14:10:54] [b98453cac15ba1066b407e146608df68]
- R  D      [Multiple Regression] [workshop 7] [2009-11-20 14:31:26] [e81f30a5c3daacfe71a556c99a478849] [Current]
-    D        [Multiple Regression] [paper] [2009-12-13 10:32:52] [3d8acb8ffdb376c5fec19e610f8198c2]
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Dataseries X:
8.0	2.77	8.0	7.8	7.6	7.6
8.0	2.93	8.0	8.0	7.8	7.6
7.9	2.91	8.0	8.0	8.0	7.8
7.9	2.69	7.9	8.0	8.0	8.0
8.0	2.38	7.9	7.9	8.0	8.0
8.5	2.58	8.0	7.9	7.9	8.0
9.2	3.19	8.5	8.0	7.9	7.9
9.4	2.82	9.2	8.5	8.0	7.9
9.5	2.72	9.4	9.2	8.5	8.0
9.5	2.53	9.5	9.4	9.2	8.5
9.6	2.70	9.5	9.5	9.4	9.2
9.7	2.42	9.6	9.5	9.5	9.4
9.7	2.50	9.7	9.6	9.5	9.5
9.6	2.31	9.7	9.7	9.6	9.5
9.5	2.41	9.6	9.7	9.7	9.6
9.4	2.56	9.5	9.6	9.7	9.7
9.3	2.76	9.4	9.5	9.6	9.7
9.6	2.71	9.3	9.4	9.5	9.6
10.2	2.44	9.6	9.3	9.4	9.5
10.2	2.46	10.2	9.6	9.3	9.4
10.1	2.12	10.2	10.2	9.6	9.3
9.9	1.99	10.1	10.2	10.2	9.6
9.8	1.86	9.9	10.1	10.2	10.2
9.8	1.88	9.8	9.9	10.1	10.2
9.7	1.82	9.8	9.8	9.9	10.1
9.5	1.74	9.7	9.8	9.8	9.9
9.3	1.71	9.5	9.7	9.8	9.8
9.1	1.38	9.3	9.5	9.7	9.8
9.0	1.27	9.1	9.3	9.5	9.7
9.5	1.19	9.0	9.1	9.3	9.5
10.0	1.28	9.5	9.0	9.1	9.3
10.2	1.19	10.0	9.5	9.0	9.1
10.1	1.22	10.2	10.0	9.5	9.0
10.0	1.47	10.1	10.2	10.0	9.5
9.9	1.46	10.0	10.1	10.2	10.0
10.0	1.96	9.9	10.0	10.1	10.2
9.9	1.88	10.0	9.9	10.0	10.1
9.7	2.03	9.9	10.0	9.9	10.0
9.5	2.04	9.7	9.9	10.0	9.9
9.2	1.90	9.5	9.7	9.9	10.0
9.0	1.80	9.2	9.5	9.7	9.9
9.3	1.92	9.0	9.2	9.5	9.7
9.8	1.92	9.3	9.0	9.2	9.5
9.8	1.97	9.8	9.3	9.0	9.2
9.6	2.46	9.8	9.8	9.3	9.0
9.4	2.36	9.6	9.8	9.8	9.3
9.3	2.53	9.4	9.6	9.8	9.8
9.2	2.31	9.3	9.4	9.6	9.8
9.2	1.98	9.2	9.3	9.4	9.6
9.0	1.46	9.2	9.2	9.3	9.4
8.8	1.26	9.0	9.2	9.2	9.3
8.7	1.58	8.8	9.0	9.2	9.2
8.7	1.74	8.7	8.8	9.0	9.2
9.1	1.89	8.7	8.7	8.8	9.0
9.7	1.85	9.1	8.7	8.7	8.8
9.8	1.62	9.7	9.1	8.7	8.7
9.6	1.30	9.8	9.7	9.1	8.7
9.4	1.42	9.6	9.8	9.7	9.1
9.4	1.15	9.4	9.6	9.8	9.7
9.5	0.42	9.4	9.4	9.6	9.8
9.4	0.74	9.5	9.4	9.4	9.6
9.3	1.02	9.4	9.5	9.4	9.4
9.2	1.51	9.3	9.4	9.5	9.4
9.0	1.86	9.2	9.3	9.4	9.5
8.9	1.59	9.0	9.2	9.3	9.4
9.2	1.03	8.9	9.0	9.2	9.3
9.8	0.44	9.2	8.9	9.0	9.2
9.9	0.82	9.8	9.2	8.9	9.0
9.6	0.86	9.9	9.8	9.2	8.9
9.2	0.58	9.6	9.9	9.8	9.2
9.1	0.59	9.2	9.6	9.9	9.8
9.1	0.95	9.1	9.2	9.6	9.9
9.0	0.98	9.1	9.1	9.2	9.6
8.9	1.23	9.0	9.1	9.1	9.2
8.7	1.17	8.9	9.0	9.1	9.1
8.5	0.84	8.7	8.9	9.0	9.1
8.3	0.74	8.5	8.7	8.9	9.0
8.5	0.65	8.3	8.5	8.7	8.9
8.7	0.91	8.5	8.3	8.5	8.7
8.4	1.19	8.7	8.5	8.3	8.5
8.1	1.30	8.4	8.7	8.5	8.3
7.8	1.53	8.1	8.4	8.7	8.5
7.7	1.94	7.8	8.1	8.4	8.7
7.5	1.79	7.7	7.8	8.1	8.4
7.2	1.95	7.5	7.7	7.8	8.1
6.8	2.26	7.2	7.5	7.7	7.8
6.7	2.04	6.8	7.2	7.5	7.7
6.4	2.16	6.7	6.8	7.2	7.5
6.3	2.75	6.4	6.7	6.8	7.2
6.8	2.79	6.3	6.4	6.7	6.8
7.3	2.88	6.8	6.3	6.4	6.7
7.1	3.36	7.3	6.8	6.3	6.4
7.0	2.97	7.1	7.3	6.8	6.3
6.8	3.10	7.0	7.1	7.3	6.8
6.6	2.49	6.8	7.0	7.1	7.3
6.3	2.20	6.6	6.8	7.0	7.1
6.1	2.25	6.3	6.6	6.8	7.0
6.1	2.09	6.1	6.3	6.6	6.8
6.3	2.79	6.1	6.1	6.3	6.6
6.3	3.14	6.3	6.1	6.1	6.3
6.0	2.93	6.3	6.3	6.1	6.1
6.2	2.65	6.0	6.3	6.3	6.1
6.4	2.67	6.2	6.0	6.3	6.3
6.8	2.26	6.4	6.2	6.0	6.3
7.5	2.35	6.8	6.4	6.2	6.0
7.5	2.13	7.5	6.8	6.4	6.2
7.6	2.18	7.5	7.5	6.8	6.4
7.6	2.90	7.6	7.5	7.5	6.8
7.4	2.63	7.6	7.6	7.5	7.5
7.3	2.67	7.4	7.6	7.6	7.5
7.1	1.81	7.3	7.4	7.6	7.6
6.9	1.33	7.1	7.3	7.4	7.6
6.8	0.88	6.9	7.1	7.3	7.4
7.5	1.28	6.8	6.9	7.1	7.3
7.6	1.26	7.5	6.8	6.9	7.1
7.8	1.26	7.6	7.5	6.8	6.9
8.0	1.29	7.8	7.6	7.5	6.8
8.1	1.10	8.0	7.8	7.6	7.5
8.2	1.37	8.1	8.0	7.8	7.6
8.3	1.21	8.2	8.1	8.0	7.8
8.2	1.74	8.3	8.2	8.1	8.0
8.0	1.76	8.2	8.3	8.2	8.1
7.9	1.48	8.0	8.2	8.3	8.2
7.6	1.04	7.9	8.0	8.2	8.3
7.6	1.62	7.6	7.9	8.0	8.2
8.3	1.49	7.6	7.6	7.9	8.0
8.4	1.79	8.3	7.6	7.6	7.9
8.4	1.80	8.4	8.3	7.6	7.6
8.4	1.58	8.4	8.4	8.3	7.6
8.4	1.86	8.4	8.4	8.4	8.3
8.6	1.74	8.4	8.4	8.4	8.4
8.9	1.59	8.6	8.4	8.4	8.4
8.8	1.26	8.9	8.6	8.4	8.4
8.3	1.13	8.8	8.9	8.6	8.4
7.5	1.92	8.3	8.8	8.9	8.6
7.2	2.61	7.5	8.3	8.8	8.9
7.4	2.26	7.2	7.5	8.3	8.8
8.8	2.41	7.4	7.2	7.5	8.3
9.3	2.26	8.8	7.4	7.2	7.5
9.3	2.03	9.3	8.8	7.4	7.2
8.7	2.86	9.3	9.3	8.8	7.4
8.2	2.55	8.7	9.3	9.3	8.8
8.3	2.27	8.2	8.7	9.3	9.3
8.5	2.26	8.3	8.2	8.7	9.3
8.6	2.57	8.5	8.3	8.2	8.7
8.5	3.07	8.6	8.5	8.3	8.2
8.2	2.76	8.5	8.6	8.5	8.3
8.1	2.51	8.2	8.5	8.6	8.5
7.9	2.87	8.1	8.2	8.5	8.6
8.6	3.14	7.9	8.1	8.2	8.5
8.7	3.11	8.6	7.9	8.1	8.2
8.7	3.16	8.7	8.6	7.9	8.1
8.5	2.47	8.7	8.7	8.6	7.9
8.4	2.57	8.5	8.7	8.7	8.6
8.5	2.89	8.4	8.5	8.7	8.7
8.7	2.63	8.5	8.4	8.5	8.7
8.7	2.38	8.7	8.5	8.4	8.5
8.6	1.69	8.7	8.7	8.5	8.4
8.5	1.96	8.6	8.7	8.7	8.5
8.3	2.19	8.5	8.6	8.7	8.7
8.0	1.87	8.3	8.5	8.6	8.7
8.2	1.6	8.0	8.3	8.5	8.6
8.1	1.63	8.2	8.0	8.3	8.5
8.1	1.22	8.1	8.2	8.0	8.3




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\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 & 5 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58212&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]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58212&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58212&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 time5 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 0.218696812913364 + 0.0214033923580752X[t] + 1.38294217269954Y1[t] -0.440570189496239Y2[t] -0.255229751929124Y3[t] + 0.289096046118345Y4[t] -0.161991160311381M1[t] -0.138075749392786M2[t] -0.118629659389799M3[t] -0.158456291128355M4[t] -0.106289572157702M5[t] + 0.448837334806040M6[t] + 0.0935944507375918M7[t] -0.115701160265447M8[t] + 0.0501367232538751M9[t] -0.0165214692594234M10[t] + 0.0682679561748867M11[t] -0.000532440156977438t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  0.218696812913364 +  0.0214033923580752X[t] +  1.38294217269954Y1[t] -0.440570189496239Y2[t] -0.255229751929124Y3[t] +  0.289096046118345Y4[t] -0.161991160311381M1[t] -0.138075749392786M2[t] -0.118629659389799M3[t] -0.158456291128355M4[t] -0.106289572157702M5[t] +  0.448837334806040M6[t] +  0.0935944507375918M7[t] -0.115701160265447M8[t] +  0.0501367232538751M9[t] -0.0165214692594234M10[t] +  0.0682679561748867M11[t] -0.000532440156977438t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58212&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  0.218696812913364 +  0.0214033923580752X[t] +  1.38294217269954Y1[t] -0.440570189496239Y2[t] -0.255229751929124Y3[t] +  0.289096046118345Y4[t] -0.161991160311381M1[t] -0.138075749392786M2[t] -0.118629659389799M3[t] -0.158456291128355M4[t] -0.106289572157702M5[t] +  0.448837334806040M6[t] +  0.0935944507375918M7[t] -0.115701160265447M8[t] +  0.0501367232538751M9[t] -0.0165214692594234M10[t] +  0.0682679561748867M11[t] -0.000532440156977438t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58212&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58212&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
Y[t] = + 0.218696812913364 + 0.0214033923580752X[t] + 1.38294217269954Y1[t] -0.440570189496239Y2[t] -0.255229751929124Y3[t] + 0.289096046118345Y4[t] -0.161991160311381M1[t] -0.138075749392786M2[t] -0.118629659389799M3[t] -0.158456291128355M4[t] -0.106289572157702M5[t] + 0.448837334806040M6[t] + 0.0935944507375918M7[t] -0.115701160265447M8[t] + 0.0501367232538751M9[t] -0.0165214692594234M10[t] + 0.0682679561748867M11[t] -0.000532440156977438t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.2186968129133640.1896271.15330.2506730.125337
X0.02140339235807520.0216470.98870.3244250.162213
Y11.382942172699540.07954717.385200
Y2-0.4405701894962390.139215-3.16470.001890.000945
Y3-0.2552297519291240.139883-1.82460.0701060.035053
Y40.2890960461183450.0807913.57830.000470.000235
M1-0.1619911603113810.068284-2.37230.018980.00949
M2-0.1380757493927860.069296-1.99260.0481740.024087
M3-0.1186296593897990.068567-1.73010.0857230.042861
M4-0.1584562911283550.06797-2.33130.0211070.010553
M5-0.1062895721577020.068547-1.55060.1231610.06158
M60.4488373348060400.0678496.615200
M70.09359445073759180.0757131.23620.218380.10919
M8-0.1157011602654470.084064-1.37630.1708230.085411
M90.05013672325387510.0848890.59060.5556930.277847
M10-0.01652146925942340.076516-0.21590.8293490.414675
M110.06826795617488670.0694990.98230.327580.16379
t-0.0005324401569774380.000333-1.59750.1123120.056156

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & 0.218696812913364 & 0.189627 & 1.1533 & 0.250673 & 0.125337 \tabularnewline
X & 0.0214033923580752 & 0.021647 & 0.9887 & 0.324425 & 0.162213 \tabularnewline
Y1 & 1.38294217269954 & 0.079547 & 17.3852 & 0 & 0 \tabularnewline
Y2 & -0.440570189496239 & 0.139215 & -3.1647 & 0.00189 & 0.000945 \tabularnewline
Y3 & -0.255229751929124 & 0.139883 & -1.8246 & 0.070106 & 0.035053 \tabularnewline
Y4 & 0.289096046118345 & 0.080791 & 3.5783 & 0.00047 & 0.000235 \tabularnewline
M1 & -0.161991160311381 & 0.068284 & -2.3723 & 0.01898 & 0.00949 \tabularnewline
M2 & -0.138075749392786 & 0.069296 & -1.9926 & 0.048174 & 0.024087 \tabularnewline
M3 & -0.118629659389799 & 0.068567 & -1.7301 & 0.085723 & 0.042861 \tabularnewline
M4 & -0.158456291128355 & 0.06797 & -2.3313 & 0.021107 & 0.010553 \tabularnewline
M5 & -0.106289572157702 & 0.068547 & -1.5506 & 0.123161 & 0.06158 \tabularnewline
M6 & 0.448837334806040 & 0.067849 & 6.6152 & 0 & 0 \tabularnewline
M7 & 0.0935944507375918 & 0.075713 & 1.2362 & 0.21838 & 0.10919 \tabularnewline
M8 & -0.115701160265447 & 0.084064 & -1.3763 & 0.170823 & 0.085411 \tabularnewline
M9 & 0.0501367232538751 & 0.084889 & 0.5906 & 0.555693 & 0.277847 \tabularnewline
M10 & -0.0165214692594234 & 0.076516 & -0.2159 & 0.829349 & 0.414675 \tabularnewline
M11 & 0.0682679561748867 & 0.069499 & 0.9823 & 0.32758 & 0.16379 \tabularnewline
t & -0.000532440156977438 & 0.000333 & -1.5975 & 0.112312 & 0.056156 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58212&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]0.218696812913364[/C][C]0.189627[/C][C]1.1533[/C][C]0.250673[/C][C]0.125337[/C][/ROW]
[ROW][C]X[/C][C]0.0214033923580752[/C][C]0.021647[/C][C]0.9887[/C][C]0.324425[/C][C]0.162213[/C][/ROW]
[ROW][C]Y1[/C][C]1.38294217269954[/C][C]0.079547[/C][C]17.3852[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Y2[/C][C]-0.440570189496239[/C][C]0.139215[/C][C]-3.1647[/C][C]0.00189[/C][C]0.000945[/C][/ROW]
[ROW][C]Y3[/C][C]-0.255229751929124[/C][C]0.139883[/C][C]-1.8246[/C][C]0.070106[/C][C]0.035053[/C][/ROW]
[ROW][C]Y4[/C][C]0.289096046118345[/C][C]0.080791[/C][C]3.5783[/C][C]0.00047[/C][C]0.000235[/C][/ROW]
[ROW][C]M1[/C][C]-0.161991160311381[/C][C]0.068284[/C][C]-2.3723[/C][C]0.01898[/C][C]0.00949[/C][/ROW]
[ROW][C]M2[/C][C]-0.138075749392786[/C][C]0.069296[/C][C]-1.9926[/C][C]0.048174[/C][C]0.024087[/C][/ROW]
[ROW][C]M3[/C][C]-0.118629659389799[/C][C]0.068567[/C][C]-1.7301[/C][C]0.085723[/C][C]0.042861[/C][/ROW]
[ROW][C]M4[/C][C]-0.158456291128355[/C][C]0.06797[/C][C]-2.3313[/C][C]0.021107[/C][C]0.010553[/C][/ROW]
[ROW][C]M5[/C][C]-0.106289572157702[/C][C]0.068547[/C][C]-1.5506[/C][C]0.123161[/C][C]0.06158[/C][/ROW]
[ROW][C]M6[/C][C]0.448837334806040[/C][C]0.067849[/C][C]6.6152[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M7[/C][C]0.0935944507375918[/C][C]0.075713[/C][C]1.2362[/C][C]0.21838[/C][C]0.10919[/C][/ROW]
[ROW][C]M8[/C][C]-0.115701160265447[/C][C]0.084064[/C][C]-1.3763[/C][C]0.170823[/C][C]0.085411[/C][/ROW]
[ROW][C]M9[/C][C]0.0501367232538751[/C][C]0.084889[/C][C]0.5906[/C][C]0.555693[/C][C]0.277847[/C][/ROW]
[ROW][C]M10[/C][C]-0.0165214692594234[/C][C]0.076516[/C][C]-0.2159[/C][C]0.829349[/C][C]0.414675[/C][/ROW]
[ROW][C]M11[/C][C]0.0682679561748867[/C][C]0.069499[/C][C]0.9823[/C][C]0.32758[/C][C]0.16379[/C][/ROW]
[ROW][C]t[/C][C]-0.000532440156977438[/C][C]0.000333[/C][C]-1.5975[/C][C]0.112312[/C][C]0.056156[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58212&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58212&T=2

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.2186968129133640.1896271.15330.2506730.125337
X0.02140339235807520.0216470.98870.3244250.162213
Y11.382942172699540.07954717.385200
Y2-0.4405701894962390.139215-3.16470.001890.000945
Y3-0.2552297519291240.139883-1.82460.0701060.035053
Y40.2890960461183450.0807913.57830.000470.000235
M1-0.1619911603113810.068284-2.37230.018980.00949
M2-0.1380757493927860.069296-1.99260.0481740.024087
M3-0.1186296593897990.068567-1.73010.0857230.042861
M4-0.1584562911283550.06797-2.33130.0211070.010553
M5-0.1062895721577020.068547-1.55060.1231610.06158
M60.4488373348060400.0678496.615200
M70.09359445073759180.0757131.23620.218380.10919
M8-0.1157011602654470.084064-1.37630.1708230.085411
M90.05013672325387510.0848890.59060.5556930.277847
M10-0.01652146925942340.076516-0.21590.8293490.414675
M110.06826795617488670.0694990.98230.327580.16379
t-0.0005324401569774380.000333-1.59750.1123120.056156







Multiple Linear Regression - Regression Statistics
Multiple R0.988075861397124
R-squared0.97629390787567
Adjusted R-squared0.97353360947763
F-TEST (value)353.691437334976
F-TEST (DF numerator)17
F-TEST (DF denominator)146
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.171762517264902
Sum Squared Residuals4.30734490122769

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.988075861397124 \tabularnewline
R-squared & 0.97629390787567 \tabularnewline
Adjusted R-squared & 0.97353360947763 \tabularnewline
F-TEST (value) & 353.691437334976 \tabularnewline
F-TEST (DF numerator) & 17 \tabularnewline
F-TEST (DF denominator) & 146 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.171762517264902 \tabularnewline
Sum Squared Residuals & 4.30734490122769 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58212&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.988075861397124[/C][/ROW]
[ROW][C]R-squared[/C][C]0.97629390787567[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.97353360947763[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]353.691437334976[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]17[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]146[/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]0.171762517264902[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]4.30734490122769[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58212&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58212&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 R0.988075861397124
R-squared0.97629390787567
Adjusted R-squared0.97353360947763
F-TEST (value)353.691437334976
F-TEST (DF numerator)17
F-TEST (DF denominator)146
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.171762517264902
Sum Squared Residuals4.30734490122769







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
187.999934348640626.56513593800204e-05
287.887581873894460.112418126105540
37.97.91284071473113-0.0128407147311306
47.97.787297888470550.112702111529449
587.876354134602850.123645865397153
68.58.5990464723441-0.0990464723440924
79.28.87483168024540.325168319754592
89.49.379335824861550.0206641751384513
99.59.41198495952790.0880150404720975
109.59.356795058389080.143204941610919
119.69.551954883314680.0480451166853189
129.79.647751988423270.0522480115767324
139.79.610087462275720.0899125377242815
149.69.559823794346770.0401762056532345
159.59.445970195577550.0540298044224499
169.49.343494038827230.0565059611727672
179.39.3306947729851-0.0306947729851053
189.69.78659524243471-0.186595242434714
1910.29.880594043613170.319405956386828
2010.210.3654016776522-0.165401677652248
2110.110.1536093237245-0.0536093237245325
229.99.878932995455780.0210670045442195
239.89.90133375180729-0.101333751807287
249.89.80830421914479-0.00830421914478972
259.79.71068977985856-0.010689779858562
269.59.56177002793082-0.0617700279308206
279.39.31860055580397-0.0186005558039693
289.19.10822694298252-0.00822694298252507
2998.991168797770140.00883120222986119
309.59.48709755497970.0129024450202934
31109.862003382528060.137996617471940
3210.210.08913878362670.110861216373288
3310.110.1548651879752-0.0548651879751908
34109.883550295319840.116449704680158
359.99.96685812102661-0.0668581210266119
36109.897864406970040.102135593029963
379.99.91259314191369-0.0125931419136879
389.79.75344875589052-0.053448755890519
399.59.485612444265070.0143875557349250
409.29.3082150806035-0.108215080603500
4199.05307675204474-0.0530767520447411
429.39.4590489894056-0.159048989405593
439.89.625020071244350.174979928755652
449.89.93987935575346-0.139879355753456
459.69.76099923182073-0.160999231820733
469.49.374193763245680.0258062367543196
479.39.4181629516424-0.118162951642399
489.29.34551958000688-0.145519580006878
499.29.074922402902180.125077597097823
5099.09893639455646-0.0989363945564631
518.88.83359430197203-0.033594301972027
528.78.582700314378580.117299685621415
538.78.638624906984670.0613750930153314
549.19.23371364275692-0.133713642756924
559.79.397962817886240.302037182113762
569.89.80783960969326-0.00783960969325669
579.69.73815617030158-0.138156170301581
589.49.31538905851460.0846109414853975
599.49.353327383692690.0466726163073101
609.59.436972103836340.0630278961636623
619.49.41281854735467-0.0128185473546724
629.39.202024022533310.0979759774666949
639.29.111665161121530.0883348388784693
6499.03899265803574-0.0389926580357383
658.98.848929975903530.0510700240964726
669.29.33797173420014-0.137971734200141
679.89.450644425016930.349355574983075
689.99.91424767569308-0.0142476756930777
699.69.84888282813138-0.248882828131385
709.29.25035053751939-0.0503505375193881
719.19.061750396967450.0382496030325490
729.19.1440676106036-0.0440676106036082
7399.04160621779176-0.0416062177917615
748.98.841930376118520.0580696238814814
758.78.73641301949088-0.0364130194908797
768.58.48198238771980.0180176122801927
778.38.3396153012381-0.0396153012380927
788.58.72594541186596-0.225945411865961
798.78.73366418325495-0.0336641832549470
808.48.711530219758-0.311530219758007
818.18.26732818696114-0.167328186961138
827.87.92912199841007-0.129121998410071
837.77.87383091439563-0.173830914395631
847.57.7855369605322-0.285536960532207
857.27.38374659899409-0.183746598994089
866.87.0257901688335-0.225790168833505
876.76.641125605903780.0588743940962178
886.46.66001851597483-0.260018515974828
896.36.36881825035568-0.068818250355677
906.86.82803024918125-0.0280302491812544
917.37.257368656534350.0426313434656451
927.17.46779438666528-0.367794386665276
9376.971354497143550.0286455028564546
946.86.87369927320372-0.0736992732037239
956.66.90796274699734-0.307962746997343
966.36.61218473621022-0.312184736210219
976.16.14609903722314-0.04609903722314
986.16.014866828678580.0851331713214155
996.36.155626607429560.144373392570437
1006.36.36366429394959-0.0636642939495871
10166.26487061324515-0.264870613245149
1026.26.34754352799597-0.147543527995965
1036.46.45877497223015-0.0587749722301495
1046.86.505214852422720.294785147577279
1057.56.999734668056530.500265331943468
1067.57.72643999299651-0.226439992996508
1077.67.45909532369640.140904676303603
1087.67.480977179229250.119022820770747
1097.47.47098487615743-0.0709848761574307
1107.37.193112572880550.106887427119448
1117.17.17234873053974-0.0723487305397426
1126.96.94023056510787-0.0402305651078727
1136.86.7614626866890.0385373133110015
1147.57.296574676842280.203425323157725
1157.67.94571456577115-0.345714565771149
1167.87.533485365202960.266514634797038
11787.724393894964110.275606105035888
1188.18.01845527147640.0815447285236073
1198.28.136535006287120.0634649937128786
1208.38.165320524336140.134679475663862
1218.28.140674154168650.0593258458313496
12287.985520585976770.0144794140232293
1237.97.769296499791160.130703500208844
1247.67.72377233569211-0.123772335692112
1257.67.439131294987220.160868705012778
1268.38.090818143605550.209181856394451
1278.48.75718267894413-0.357182678944133
1288.48.290734932494780.109265067505221
1298.48.228613784238340.171386215761664
1308.48.344260358518250.0557396414817489
1318.68.454858541324450.145141458675550
1328.98.659436070678780.240563929321219
1338.88.81661796464287-0.0166179646428731
1348.38.51570726989329-0.215707269893291
1357.57.88536581594696-0.385365815946965
1367.27.085958230395410.114041769604595
1377.47.166380093023610.23361990697639
1388.88.1925803385570.607419661443008
1399.39.52689159804203-0.226891598042029
1409.39.249038823473360.0509611765266366
1418.78.91232154426769-0.212321544267688
1428.28.2858501449478-0.0858501449478024
1438.38.081533230772020.218466769227983
1448.58.52423596369212-0.0242359636921239
1458.68.55503607873860.0449639212613928
1468.58.469229926797880.0307700732021162
1478.28.27702094301932-0.0770209430193227
1488.17.892781624204790.207218375795213
1497.98.00043054365104-0.100430543651034
1508.68.37593183177110.224068168228902
1518.79.01448212592127-0.314482125921266
1528.78.657755674775730.0422443252242692
1538.58.52775572288732-0.0277557228873226
1548.48.362961252002880.0370387479971244
1558.58.432796748075920.0672032519240788
1568.78.591828656336360.108171343663640
1578.78.6241893893380.0758106106619902
1588.68.490257401668560.109742598331439
1598.58.35451940440730.145480595592694
1608.38.282665123657470.0173348763425314
16188.12044187651919-0.120441876519189
1628.28.33910218405973-0.139102184059734
1638.18.41486479876782-0.314864798767821
1648.17.988602817926860.111397182073139

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 8 & 7.99993434864062 & 6.56513593800204e-05 \tabularnewline
2 & 8 & 7.88758187389446 & 0.112418126105540 \tabularnewline
3 & 7.9 & 7.91284071473113 & -0.0128407147311306 \tabularnewline
4 & 7.9 & 7.78729788847055 & 0.112702111529449 \tabularnewline
5 & 8 & 7.87635413460285 & 0.123645865397153 \tabularnewline
6 & 8.5 & 8.5990464723441 & -0.0990464723440924 \tabularnewline
7 & 9.2 & 8.8748316802454 & 0.325168319754592 \tabularnewline
8 & 9.4 & 9.37933582486155 & 0.0206641751384513 \tabularnewline
9 & 9.5 & 9.4119849595279 & 0.0880150404720975 \tabularnewline
10 & 9.5 & 9.35679505838908 & 0.143204941610919 \tabularnewline
11 & 9.6 & 9.55195488331468 & 0.0480451166853189 \tabularnewline
12 & 9.7 & 9.64775198842327 & 0.0522480115767324 \tabularnewline
13 & 9.7 & 9.61008746227572 & 0.0899125377242815 \tabularnewline
14 & 9.6 & 9.55982379434677 & 0.0401762056532345 \tabularnewline
15 & 9.5 & 9.44597019557755 & 0.0540298044224499 \tabularnewline
16 & 9.4 & 9.34349403882723 & 0.0565059611727672 \tabularnewline
17 & 9.3 & 9.3306947729851 & -0.0306947729851053 \tabularnewline
18 & 9.6 & 9.78659524243471 & -0.186595242434714 \tabularnewline
19 & 10.2 & 9.88059404361317 & 0.319405956386828 \tabularnewline
20 & 10.2 & 10.3654016776522 & -0.165401677652248 \tabularnewline
21 & 10.1 & 10.1536093237245 & -0.0536093237245325 \tabularnewline
22 & 9.9 & 9.87893299545578 & 0.0210670045442195 \tabularnewline
23 & 9.8 & 9.90133375180729 & -0.101333751807287 \tabularnewline
24 & 9.8 & 9.80830421914479 & -0.00830421914478972 \tabularnewline
25 & 9.7 & 9.71068977985856 & -0.010689779858562 \tabularnewline
26 & 9.5 & 9.56177002793082 & -0.0617700279308206 \tabularnewline
27 & 9.3 & 9.31860055580397 & -0.0186005558039693 \tabularnewline
28 & 9.1 & 9.10822694298252 & -0.00822694298252507 \tabularnewline
29 & 9 & 8.99116879777014 & 0.00883120222986119 \tabularnewline
30 & 9.5 & 9.4870975549797 & 0.0129024450202934 \tabularnewline
31 & 10 & 9.86200338252806 & 0.137996617471940 \tabularnewline
32 & 10.2 & 10.0891387836267 & 0.110861216373288 \tabularnewline
33 & 10.1 & 10.1548651879752 & -0.0548651879751908 \tabularnewline
34 & 10 & 9.88355029531984 & 0.116449704680158 \tabularnewline
35 & 9.9 & 9.96685812102661 & -0.0668581210266119 \tabularnewline
36 & 10 & 9.89786440697004 & 0.102135593029963 \tabularnewline
37 & 9.9 & 9.91259314191369 & -0.0125931419136879 \tabularnewline
38 & 9.7 & 9.75344875589052 & -0.053448755890519 \tabularnewline
39 & 9.5 & 9.48561244426507 & 0.0143875557349250 \tabularnewline
40 & 9.2 & 9.3082150806035 & -0.108215080603500 \tabularnewline
41 & 9 & 9.05307675204474 & -0.0530767520447411 \tabularnewline
42 & 9.3 & 9.4590489894056 & -0.159048989405593 \tabularnewline
43 & 9.8 & 9.62502007124435 & 0.174979928755652 \tabularnewline
44 & 9.8 & 9.93987935575346 & -0.139879355753456 \tabularnewline
45 & 9.6 & 9.76099923182073 & -0.160999231820733 \tabularnewline
46 & 9.4 & 9.37419376324568 & 0.0258062367543196 \tabularnewline
47 & 9.3 & 9.4181629516424 & -0.118162951642399 \tabularnewline
48 & 9.2 & 9.34551958000688 & -0.145519580006878 \tabularnewline
49 & 9.2 & 9.07492240290218 & 0.125077597097823 \tabularnewline
50 & 9 & 9.09893639455646 & -0.0989363945564631 \tabularnewline
51 & 8.8 & 8.83359430197203 & -0.033594301972027 \tabularnewline
52 & 8.7 & 8.58270031437858 & 0.117299685621415 \tabularnewline
53 & 8.7 & 8.63862490698467 & 0.0613750930153314 \tabularnewline
54 & 9.1 & 9.23371364275692 & -0.133713642756924 \tabularnewline
55 & 9.7 & 9.39796281788624 & 0.302037182113762 \tabularnewline
56 & 9.8 & 9.80783960969326 & -0.00783960969325669 \tabularnewline
57 & 9.6 & 9.73815617030158 & -0.138156170301581 \tabularnewline
58 & 9.4 & 9.3153890585146 & 0.0846109414853975 \tabularnewline
59 & 9.4 & 9.35332738369269 & 0.0466726163073101 \tabularnewline
60 & 9.5 & 9.43697210383634 & 0.0630278961636623 \tabularnewline
61 & 9.4 & 9.41281854735467 & -0.0128185473546724 \tabularnewline
62 & 9.3 & 9.20202402253331 & 0.0979759774666949 \tabularnewline
63 & 9.2 & 9.11166516112153 & 0.0883348388784693 \tabularnewline
64 & 9 & 9.03899265803574 & -0.0389926580357383 \tabularnewline
65 & 8.9 & 8.84892997590353 & 0.0510700240964726 \tabularnewline
66 & 9.2 & 9.33797173420014 & -0.137971734200141 \tabularnewline
67 & 9.8 & 9.45064442501693 & 0.349355574983075 \tabularnewline
68 & 9.9 & 9.91424767569308 & -0.0142476756930777 \tabularnewline
69 & 9.6 & 9.84888282813138 & -0.248882828131385 \tabularnewline
70 & 9.2 & 9.25035053751939 & -0.0503505375193881 \tabularnewline
71 & 9.1 & 9.06175039696745 & 0.0382496030325490 \tabularnewline
72 & 9.1 & 9.1440676106036 & -0.0440676106036082 \tabularnewline
73 & 9 & 9.04160621779176 & -0.0416062177917615 \tabularnewline
74 & 8.9 & 8.84193037611852 & 0.0580696238814814 \tabularnewline
75 & 8.7 & 8.73641301949088 & -0.0364130194908797 \tabularnewline
76 & 8.5 & 8.4819823877198 & 0.0180176122801927 \tabularnewline
77 & 8.3 & 8.3396153012381 & -0.0396153012380927 \tabularnewline
78 & 8.5 & 8.72594541186596 & -0.225945411865961 \tabularnewline
79 & 8.7 & 8.73366418325495 & -0.0336641832549470 \tabularnewline
80 & 8.4 & 8.711530219758 & -0.311530219758007 \tabularnewline
81 & 8.1 & 8.26732818696114 & -0.167328186961138 \tabularnewline
82 & 7.8 & 7.92912199841007 & -0.129121998410071 \tabularnewline
83 & 7.7 & 7.87383091439563 & -0.173830914395631 \tabularnewline
84 & 7.5 & 7.7855369605322 & -0.285536960532207 \tabularnewline
85 & 7.2 & 7.38374659899409 & -0.183746598994089 \tabularnewline
86 & 6.8 & 7.0257901688335 & -0.225790168833505 \tabularnewline
87 & 6.7 & 6.64112560590378 & 0.0588743940962178 \tabularnewline
88 & 6.4 & 6.66001851597483 & -0.260018515974828 \tabularnewline
89 & 6.3 & 6.36881825035568 & -0.068818250355677 \tabularnewline
90 & 6.8 & 6.82803024918125 & -0.0280302491812544 \tabularnewline
91 & 7.3 & 7.25736865653435 & 0.0426313434656451 \tabularnewline
92 & 7.1 & 7.46779438666528 & -0.367794386665276 \tabularnewline
93 & 7 & 6.97135449714355 & 0.0286455028564546 \tabularnewline
94 & 6.8 & 6.87369927320372 & -0.0736992732037239 \tabularnewline
95 & 6.6 & 6.90796274699734 & -0.307962746997343 \tabularnewline
96 & 6.3 & 6.61218473621022 & -0.312184736210219 \tabularnewline
97 & 6.1 & 6.14609903722314 & -0.04609903722314 \tabularnewline
98 & 6.1 & 6.01486682867858 & 0.0851331713214155 \tabularnewline
99 & 6.3 & 6.15562660742956 & 0.144373392570437 \tabularnewline
100 & 6.3 & 6.36366429394959 & -0.0636642939495871 \tabularnewline
101 & 6 & 6.26487061324515 & -0.264870613245149 \tabularnewline
102 & 6.2 & 6.34754352799597 & -0.147543527995965 \tabularnewline
103 & 6.4 & 6.45877497223015 & -0.0587749722301495 \tabularnewline
104 & 6.8 & 6.50521485242272 & 0.294785147577279 \tabularnewline
105 & 7.5 & 6.99973466805653 & 0.500265331943468 \tabularnewline
106 & 7.5 & 7.72643999299651 & -0.226439992996508 \tabularnewline
107 & 7.6 & 7.4590953236964 & 0.140904676303603 \tabularnewline
108 & 7.6 & 7.48097717922925 & 0.119022820770747 \tabularnewline
109 & 7.4 & 7.47098487615743 & -0.0709848761574307 \tabularnewline
110 & 7.3 & 7.19311257288055 & 0.106887427119448 \tabularnewline
111 & 7.1 & 7.17234873053974 & -0.0723487305397426 \tabularnewline
112 & 6.9 & 6.94023056510787 & -0.0402305651078727 \tabularnewline
113 & 6.8 & 6.761462686689 & 0.0385373133110015 \tabularnewline
114 & 7.5 & 7.29657467684228 & 0.203425323157725 \tabularnewline
115 & 7.6 & 7.94571456577115 & -0.345714565771149 \tabularnewline
116 & 7.8 & 7.53348536520296 & 0.266514634797038 \tabularnewline
117 & 8 & 7.72439389496411 & 0.275606105035888 \tabularnewline
118 & 8.1 & 8.0184552714764 & 0.0815447285236073 \tabularnewline
119 & 8.2 & 8.13653500628712 & 0.0634649937128786 \tabularnewline
120 & 8.3 & 8.16532052433614 & 0.134679475663862 \tabularnewline
121 & 8.2 & 8.14067415416865 & 0.0593258458313496 \tabularnewline
122 & 8 & 7.98552058597677 & 0.0144794140232293 \tabularnewline
123 & 7.9 & 7.76929649979116 & 0.130703500208844 \tabularnewline
124 & 7.6 & 7.72377233569211 & -0.123772335692112 \tabularnewline
125 & 7.6 & 7.43913129498722 & 0.160868705012778 \tabularnewline
126 & 8.3 & 8.09081814360555 & 0.209181856394451 \tabularnewline
127 & 8.4 & 8.75718267894413 & -0.357182678944133 \tabularnewline
128 & 8.4 & 8.29073493249478 & 0.109265067505221 \tabularnewline
129 & 8.4 & 8.22861378423834 & 0.171386215761664 \tabularnewline
130 & 8.4 & 8.34426035851825 & 0.0557396414817489 \tabularnewline
131 & 8.6 & 8.45485854132445 & 0.145141458675550 \tabularnewline
132 & 8.9 & 8.65943607067878 & 0.240563929321219 \tabularnewline
133 & 8.8 & 8.81661796464287 & -0.0166179646428731 \tabularnewline
134 & 8.3 & 8.51570726989329 & -0.215707269893291 \tabularnewline
135 & 7.5 & 7.88536581594696 & -0.385365815946965 \tabularnewline
136 & 7.2 & 7.08595823039541 & 0.114041769604595 \tabularnewline
137 & 7.4 & 7.16638009302361 & 0.23361990697639 \tabularnewline
138 & 8.8 & 8.192580338557 & 0.607419661443008 \tabularnewline
139 & 9.3 & 9.52689159804203 & -0.226891598042029 \tabularnewline
140 & 9.3 & 9.24903882347336 & 0.0509611765266366 \tabularnewline
141 & 8.7 & 8.91232154426769 & -0.212321544267688 \tabularnewline
142 & 8.2 & 8.2858501449478 & -0.0858501449478024 \tabularnewline
143 & 8.3 & 8.08153323077202 & 0.218466769227983 \tabularnewline
144 & 8.5 & 8.52423596369212 & -0.0242359636921239 \tabularnewline
145 & 8.6 & 8.5550360787386 & 0.0449639212613928 \tabularnewline
146 & 8.5 & 8.46922992679788 & 0.0307700732021162 \tabularnewline
147 & 8.2 & 8.27702094301932 & -0.0770209430193227 \tabularnewline
148 & 8.1 & 7.89278162420479 & 0.207218375795213 \tabularnewline
149 & 7.9 & 8.00043054365104 & -0.100430543651034 \tabularnewline
150 & 8.6 & 8.3759318317711 & 0.224068168228902 \tabularnewline
151 & 8.7 & 9.01448212592127 & -0.314482125921266 \tabularnewline
152 & 8.7 & 8.65775567477573 & 0.0422443252242692 \tabularnewline
153 & 8.5 & 8.52775572288732 & -0.0277557228873226 \tabularnewline
154 & 8.4 & 8.36296125200288 & 0.0370387479971244 \tabularnewline
155 & 8.5 & 8.43279674807592 & 0.0672032519240788 \tabularnewline
156 & 8.7 & 8.59182865633636 & 0.108171343663640 \tabularnewline
157 & 8.7 & 8.624189389338 & 0.0758106106619902 \tabularnewline
158 & 8.6 & 8.49025740166856 & 0.109742598331439 \tabularnewline
159 & 8.5 & 8.3545194044073 & 0.145480595592694 \tabularnewline
160 & 8.3 & 8.28266512365747 & 0.0173348763425314 \tabularnewline
161 & 8 & 8.12044187651919 & -0.120441876519189 \tabularnewline
162 & 8.2 & 8.33910218405973 & -0.139102184059734 \tabularnewline
163 & 8.1 & 8.41486479876782 & -0.314864798767821 \tabularnewline
164 & 8.1 & 7.98860281792686 & 0.111397182073139 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58212&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]8[/C][C]7.99993434864062[/C][C]6.56513593800204e-05[/C][/ROW]
[ROW][C]2[/C][C]8[/C][C]7.88758187389446[/C][C]0.112418126105540[/C][/ROW]
[ROW][C]3[/C][C]7.9[/C][C]7.91284071473113[/C][C]-0.0128407147311306[/C][/ROW]
[ROW][C]4[/C][C]7.9[/C][C]7.78729788847055[/C][C]0.112702111529449[/C][/ROW]
[ROW][C]5[/C][C]8[/C][C]7.87635413460285[/C][C]0.123645865397153[/C][/ROW]
[ROW][C]6[/C][C]8.5[/C][C]8.5990464723441[/C][C]-0.0990464723440924[/C][/ROW]
[ROW][C]7[/C][C]9.2[/C][C]8.8748316802454[/C][C]0.325168319754592[/C][/ROW]
[ROW][C]8[/C][C]9.4[/C][C]9.37933582486155[/C][C]0.0206641751384513[/C][/ROW]
[ROW][C]9[/C][C]9.5[/C][C]9.4119849595279[/C][C]0.0880150404720975[/C][/ROW]
[ROW][C]10[/C][C]9.5[/C][C]9.35679505838908[/C][C]0.143204941610919[/C][/ROW]
[ROW][C]11[/C][C]9.6[/C][C]9.55195488331468[/C][C]0.0480451166853189[/C][/ROW]
[ROW][C]12[/C][C]9.7[/C][C]9.64775198842327[/C][C]0.0522480115767324[/C][/ROW]
[ROW][C]13[/C][C]9.7[/C][C]9.61008746227572[/C][C]0.0899125377242815[/C][/ROW]
[ROW][C]14[/C][C]9.6[/C][C]9.55982379434677[/C][C]0.0401762056532345[/C][/ROW]
[ROW][C]15[/C][C]9.5[/C][C]9.44597019557755[/C][C]0.0540298044224499[/C][/ROW]
[ROW][C]16[/C][C]9.4[/C][C]9.34349403882723[/C][C]0.0565059611727672[/C][/ROW]
[ROW][C]17[/C][C]9.3[/C][C]9.3306947729851[/C][C]-0.0306947729851053[/C][/ROW]
[ROW][C]18[/C][C]9.6[/C][C]9.78659524243471[/C][C]-0.186595242434714[/C][/ROW]
[ROW][C]19[/C][C]10.2[/C][C]9.88059404361317[/C][C]0.319405956386828[/C][/ROW]
[ROW][C]20[/C][C]10.2[/C][C]10.3654016776522[/C][C]-0.165401677652248[/C][/ROW]
[ROW][C]21[/C][C]10.1[/C][C]10.1536093237245[/C][C]-0.0536093237245325[/C][/ROW]
[ROW][C]22[/C][C]9.9[/C][C]9.87893299545578[/C][C]0.0210670045442195[/C][/ROW]
[ROW][C]23[/C][C]9.8[/C][C]9.90133375180729[/C][C]-0.101333751807287[/C][/ROW]
[ROW][C]24[/C][C]9.8[/C][C]9.80830421914479[/C][C]-0.00830421914478972[/C][/ROW]
[ROW][C]25[/C][C]9.7[/C][C]9.71068977985856[/C][C]-0.010689779858562[/C][/ROW]
[ROW][C]26[/C][C]9.5[/C][C]9.56177002793082[/C][C]-0.0617700279308206[/C][/ROW]
[ROW][C]27[/C][C]9.3[/C][C]9.31860055580397[/C][C]-0.0186005558039693[/C][/ROW]
[ROW][C]28[/C][C]9.1[/C][C]9.10822694298252[/C][C]-0.00822694298252507[/C][/ROW]
[ROW][C]29[/C][C]9[/C][C]8.99116879777014[/C][C]0.00883120222986119[/C][/ROW]
[ROW][C]30[/C][C]9.5[/C][C]9.4870975549797[/C][C]0.0129024450202934[/C][/ROW]
[ROW][C]31[/C][C]10[/C][C]9.86200338252806[/C][C]0.137996617471940[/C][/ROW]
[ROW][C]32[/C][C]10.2[/C][C]10.0891387836267[/C][C]0.110861216373288[/C][/ROW]
[ROW][C]33[/C][C]10.1[/C][C]10.1548651879752[/C][C]-0.0548651879751908[/C][/ROW]
[ROW][C]34[/C][C]10[/C][C]9.88355029531984[/C][C]0.116449704680158[/C][/ROW]
[ROW][C]35[/C][C]9.9[/C][C]9.96685812102661[/C][C]-0.0668581210266119[/C][/ROW]
[ROW][C]36[/C][C]10[/C][C]9.89786440697004[/C][C]0.102135593029963[/C][/ROW]
[ROW][C]37[/C][C]9.9[/C][C]9.91259314191369[/C][C]-0.0125931419136879[/C][/ROW]
[ROW][C]38[/C][C]9.7[/C][C]9.75344875589052[/C][C]-0.053448755890519[/C][/ROW]
[ROW][C]39[/C][C]9.5[/C][C]9.48561244426507[/C][C]0.0143875557349250[/C][/ROW]
[ROW][C]40[/C][C]9.2[/C][C]9.3082150806035[/C][C]-0.108215080603500[/C][/ROW]
[ROW][C]41[/C][C]9[/C][C]9.05307675204474[/C][C]-0.0530767520447411[/C][/ROW]
[ROW][C]42[/C][C]9.3[/C][C]9.4590489894056[/C][C]-0.159048989405593[/C][/ROW]
[ROW][C]43[/C][C]9.8[/C][C]9.62502007124435[/C][C]0.174979928755652[/C][/ROW]
[ROW][C]44[/C][C]9.8[/C][C]9.93987935575346[/C][C]-0.139879355753456[/C][/ROW]
[ROW][C]45[/C][C]9.6[/C][C]9.76099923182073[/C][C]-0.160999231820733[/C][/ROW]
[ROW][C]46[/C][C]9.4[/C][C]9.37419376324568[/C][C]0.0258062367543196[/C][/ROW]
[ROW][C]47[/C][C]9.3[/C][C]9.4181629516424[/C][C]-0.118162951642399[/C][/ROW]
[ROW][C]48[/C][C]9.2[/C][C]9.34551958000688[/C][C]-0.145519580006878[/C][/ROW]
[ROW][C]49[/C][C]9.2[/C][C]9.07492240290218[/C][C]0.125077597097823[/C][/ROW]
[ROW][C]50[/C][C]9[/C][C]9.09893639455646[/C][C]-0.0989363945564631[/C][/ROW]
[ROW][C]51[/C][C]8.8[/C][C]8.83359430197203[/C][C]-0.033594301972027[/C][/ROW]
[ROW][C]52[/C][C]8.7[/C][C]8.58270031437858[/C][C]0.117299685621415[/C][/ROW]
[ROW][C]53[/C][C]8.7[/C][C]8.63862490698467[/C][C]0.0613750930153314[/C][/ROW]
[ROW][C]54[/C][C]9.1[/C][C]9.23371364275692[/C][C]-0.133713642756924[/C][/ROW]
[ROW][C]55[/C][C]9.7[/C][C]9.39796281788624[/C][C]0.302037182113762[/C][/ROW]
[ROW][C]56[/C][C]9.8[/C][C]9.80783960969326[/C][C]-0.00783960969325669[/C][/ROW]
[ROW][C]57[/C][C]9.6[/C][C]9.73815617030158[/C][C]-0.138156170301581[/C][/ROW]
[ROW][C]58[/C][C]9.4[/C][C]9.3153890585146[/C][C]0.0846109414853975[/C][/ROW]
[ROW][C]59[/C][C]9.4[/C][C]9.35332738369269[/C][C]0.0466726163073101[/C][/ROW]
[ROW][C]60[/C][C]9.5[/C][C]9.43697210383634[/C][C]0.0630278961636623[/C][/ROW]
[ROW][C]61[/C][C]9.4[/C][C]9.41281854735467[/C][C]-0.0128185473546724[/C][/ROW]
[ROW][C]62[/C][C]9.3[/C][C]9.20202402253331[/C][C]0.0979759774666949[/C][/ROW]
[ROW][C]63[/C][C]9.2[/C][C]9.11166516112153[/C][C]0.0883348388784693[/C][/ROW]
[ROW][C]64[/C][C]9[/C][C]9.03899265803574[/C][C]-0.0389926580357383[/C][/ROW]
[ROW][C]65[/C][C]8.9[/C][C]8.84892997590353[/C][C]0.0510700240964726[/C][/ROW]
[ROW][C]66[/C][C]9.2[/C][C]9.33797173420014[/C][C]-0.137971734200141[/C][/ROW]
[ROW][C]67[/C][C]9.8[/C][C]9.45064442501693[/C][C]0.349355574983075[/C][/ROW]
[ROW][C]68[/C][C]9.9[/C][C]9.91424767569308[/C][C]-0.0142476756930777[/C][/ROW]
[ROW][C]69[/C][C]9.6[/C][C]9.84888282813138[/C][C]-0.248882828131385[/C][/ROW]
[ROW][C]70[/C][C]9.2[/C][C]9.25035053751939[/C][C]-0.0503505375193881[/C][/ROW]
[ROW][C]71[/C][C]9.1[/C][C]9.06175039696745[/C][C]0.0382496030325490[/C][/ROW]
[ROW][C]72[/C][C]9.1[/C][C]9.1440676106036[/C][C]-0.0440676106036082[/C][/ROW]
[ROW][C]73[/C][C]9[/C][C]9.04160621779176[/C][C]-0.0416062177917615[/C][/ROW]
[ROW][C]74[/C][C]8.9[/C][C]8.84193037611852[/C][C]0.0580696238814814[/C][/ROW]
[ROW][C]75[/C][C]8.7[/C][C]8.73641301949088[/C][C]-0.0364130194908797[/C][/ROW]
[ROW][C]76[/C][C]8.5[/C][C]8.4819823877198[/C][C]0.0180176122801927[/C][/ROW]
[ROW][C]77[/C][C]8.3[/C][C]8.3396153012381[/C][C]-0.0396153012380927[/C][/ROW]
[ROW][C]78[/C][C]8.5[/C][C]8.72594541186596[/C][C]-0.225945411865961[/C][/ROW]
[ROW][C]79[/C][C]8.7[/C][C]8.73366418325495[/C][C]-0.0336641832549470[/C][/ROW]
[ROW][C]80[/C][C]8.4[/C][C]8.711530219758[/C][C]-0.311530219758007[/C][/ROW]
[ROW][C]81[/C][C]8.1[/C][C]8.26732818696114[/C][C]-0.167328186961138[/C][/ROW]
[ROW][C]82[/C][C]7.8[/C][C]7.92912199841007[/C][C]-0.129121998410071[/C][/ROW]
[ROW][C]83[/C][C]7.7[/C][C]7.87383091439563[/C][C]-0.173830914395631[/C][/ROW]
[ROW][C]84[/C][C]7.5[/C][C]7.7855369605322[/C][C]-0.285536960532207[/C][/ROW]
[ROW][C]85[/C][C]7.2[/C][C]7.38374659899409[/C][C]-0.183746598994089[/C][/ROW]
[ROW][C]86[/C][C]6.8[/C][C]7.0257901688335[/C][C]-0.225790168833505[/C][/ROW]
[ROW][C]87[/C][C]6.7[/C][C]6.64112560590378[/C][C]0.0588743940962178[/C][/ROW]
[ROW][C]88[/C][C]6.4[/C][C]6.66001851597483[/C][C]-0.260018515974828[/C][/ROW]
[ROW][C]89[/C][C]6.3[/C][C]6.36881825035568[/C][C]-0.068818250355677[/C][/ROW]
[ROW][C]90[/C][C]6.8[/C][C]6.82803024918125[/C][C]-0.0280302491812544[/C][/ROW]
[ROW][C]91[/C][C]7.3[/C][C]7.25736865653435[/C][C]0.0426313434656451[/C][/ROW]
[ROW][C]92[/C][C]7.1[/C][C]7.46779438666528[/C][C]-0.367794386665276[/C][/ROW]
[ROW][C]93[/C][C]7[/C][C]6.97135449714355[/C][C]0.0286455028564546[/C][/ROW]
[ROW][C]94[/C][C]6.8[/C][C]6.87369927320372[/C][C]-0.0736992732037239[/C][/ROW]
[ROW][C]95[/C][C]6.6[/C][C]6.90796274699734[/C][C]-0.307962746997343[/C][/ROW]
[ROW][C]96[/C][C]6.3[/C][C]6.61218473621022[/C][C]-0.312184736210219[/C][/ROW]
[ROW][C]97[/C][C]6.1[/C][C]6.14609903722314[/C][C]-0.04609903722314[/C][/ROW]
[ROW][C]98[/C][C]6.1[/C][C]6.01486682867858[/C][C]0.0851331713214155[/C][/ROW]
[ROW][C]99[/C][C]6.3[/C][C]6.15562660742956[/C][C]0.144373392570437[/C][/ROW]
[ROW][C]100[/C][C]6.3[/C][C]6.36366429394959[/C][C]-0.0636642939495871[/C][/ROW]
[ROW][C]101[/C][C]6[/C][C]6.26487061324515[/C][C]-0.264870613245149[/C][/ROW]
[ROW][C]102[/C][C]6.2[/C][C]6.34754352799597[/C][C]-0.147543527995965[/C][/ROW]
[ROW][C]103[/C][C]6.4[/C][C]6.45877497223015[/C][C]-0.0587749722301495[/C][/ROW]
[ROW][C]104[/C][C]6.8[/C][C]6.50521485242272[/C][C]0.294785147577279[/C][/ROW]
[ROW][C]105[/C][C]7.5[/C][C]6.99973466805653[/C][C]0.500265331943468[/C][/ROW]
[ROW][C]106[/C][C]7.5[/C][C]7.72643999299651[/C][C]-0.226439992996508[/C][/ROW]
[ROW][C]107[/C][C]7.6[/C][C]7.4590953236964[/C][C]0.140904676303603[/C][/ROW]
[ROW][C]108[/C][C]7.6[/C][C]7.48097717922925[/C][C]0.119022820770747[/C][/ROW]
[ROW][C]109[/C][C]7.4[/C][C]7.47098487615743[/C][C]-0.0709848761574307[/C][/ROW]
[ROW][C]110[/C][C]7.3[/C][C]7.19311257288055[/C][C]0.106887427119448[/C][/ROW]
[ROW][C]111[/C][C]7.1[/C][C]7.17234873053974[/C][C]-0.0723487305397426[/C][/ROW]
[ROW][C]112[/C][C]6.9[/C][C]6.94023056510787[/C][C]-0.0402305651078727[/C][/ROW]
[ROW][C]113[/C][C]6.8[/C][C]6.761462686689[/C][C]0.0385373133110015[/C][/ROW]
[ROW][C]114[/C][C]7.5[/C][C]7.29657467684228[/C][C]0.203425323157725[/C][/ROW]
[ROW][C]115[/C][C]7.6[/C][C]7.94571456577115[/C][C]-0.345714565771149[/C][/ROW]
[ROW][C]116[/C][C]7.8[/C][C]7.53348536520296[/C][C]0.266514634797038[/C][/ROW]
[ROW][C]117[/C][C]8[/C][C]7.72439389496411[/C][C]0.275606105035888[/C][/ROW]
[ROW][C]118[/C][C]8.1[/C][C]8.0184552714764[/C][C]0.0815447285236073[/C][/ROW]
[ROW][C]119[/C][C]8.2[/C][C]8.13653500628712[/C][C]0.0634649937128786[/C][/ROW]
[ROW][C]120[/C][C]8.3[/C][C]8.16532052433614[/C][C]0.134679475663862[/C][/ROW]
[ROW][C]121[/C][C]8.2[/C][C]8.14067415416865[/C][C]0.0593258458313496[/C][/ROW]
[ROW][C]122[/C][C]8[/C][C]7.98552058597677[/C][C]0.0144794140232293[/C][/ROW]
[ROW][C]123[/C][C]7.9[/C][C]7.76929649979116[/C][C]0.130703500208844[/C][/ROW]
[ROW][C]124[/C][C]7.6[/C][C]7.72377233569211[/C][C]-0.123772335692112[/C][/ROW]
[ROW][C]125[/C][C]7.6[/C][C]7.43913129498722[/C][C]0.160868705012778[/C][/ROW]
[ROW][C]126[/C][C]8.3[/C][C]8.09081814360555[/C][C]0.209181856394451[/C][/ROW]
[ROW][C]127[/C][C]8.4[/C][C]8.75718267894413[/C][C]-0.357182678944133[/C][/ROW]
[ROW][C]128[/C][C]8.4[/C][C]8.29073493249478[/C][C]0.109265067505221[/C][/ROW]
[ROW][C]129[/C][C]8.4[/C][C]8.22861378423834[/C][C]0.171386215761664[/C][/ROW]
[ROW][C]130[/C][C]8.4[/C][C]8.34426035851825[/C][C]0.0557396414817489[/C][/ROW]
[ROW][C]131[/C][C]8.6[/C][C]8.45485854132445[/C][C]0.145141458675550[/C][/ROW]
[ROW][C]132[/C][C]8.9[/C][C]8.65943607067878[/C][C]0.240563929321219[/C][/ROW]
[ROW][C]133[/C][C]8.8[/C][C]8.81661796464287[/C][C]-0.0166179646428731[/C][/ROW]
[ROW][C]134[/C][C]8.3[/C][C]8.51570726989329[/C][C]-0.215707269893291[/C][/ROW]
[ROW][C]135[/C][C]7.5[/C][C]7.88536581594696[/C][C]-0.385365815946965[/C][/ROW]
[ROW][C]136[/C][C]7.2[/C][C]7.08595823039541[/C][C]0.114041769604595[/C][/ROW]
[ROW][C]137[/C][C]7.4[/C][C]7.16638009302361[/C][C]0.23361990697639[/C][/ROW]
[ROW][C]138[/C][C]8.8[/C][C]8.192580338557[/C][C]0.607419661443008[/C][/ROW]
[ROW][C]139[/C][C]9.3[/C][C]9.52689159804203[/C][C]-0.226891598042029[/C][/ROW]
[ROW][C]140[/C][C]9.3[/C][C]9.24903882347336[/C][C]0.0509611765266366[/C][/ROW]
[ROW][C]141[/C][C]8.7[/C][C]8.91232154426769[/C][C]-0.212321544267688[/C][/ROW]
[ROW][C]142[/C][C]8.2[/C][C]8.2858501449478[/C][C]-0.0858501449478024[/C][/ROW]
[ROW][C]143[/C][C]8.3[/C][C]8.08153323077202[/C][C]0.218466769227983[/C][/ROW]
[ROW][C]144[/C][C]8.5[/C][C]8.52423596369212[/C][C]-0.0242359636921239[/C][/ROW]
[ROW][C]145[/C][C]8.6[/C][C]8.5550360787386[/C][C]0.0449639212613928[/C][/ROW]
[ROW][C]146[/C][C]8.5[/C][C]8.46922992679788[/C][C]0.0307700732021162[/C][/ROW]
[ROW][C]147[/C][C]8.2[/C][C]8.27702094301932[/C][C]-0.0770209430193227[/C][/ROW]
[ROW][C]148[/C][C]8.1[/C][C]7.89278162420479[/C][C]0.207218375795213[/C][/ROW]
[ROW][C]149[/C][C]7.9[/C][C]8.00043054365104[/C][C]-0.100430543651034[/C][/ROW]
[ROW][C]150[/C][C]8.6[/C][C]8.3759318317711[/C][C]0.224068168228902[/C][/ROW]
[ROW][C]151[/C][C]8.7[/C][C]9.01448212592127[/C][C]-0.314482125921266[/C][/ROW]
[ROW][C]152[/C][C]8.7[/C][C]8.65775567477573[/C][C]0.0422443252242692[/C][/ROW]
[ROW][C]153[/C][C]8.5[/C][C]8.52775572288732[/C][C]-0.0277557228873226[/C][/ROW]
[ROW][C]154[/C][C]8.4[/C][C]8.36296125200288[/C][C]0.0370387479971244[/C][/ROW]
[ROW][C]155[/C][C]8.5[/C][C]8.43279674807592[/C][C]0.0672032519240788[/C][/ROW]
[ROW][C]156[/C][C]8.7[/C][C]8.59182865633636[/C][C]0.108171343663640[/C][/ROW]
[ROW][C]157[/C][C]8.7[/C][C]8.624189389338[/C][C]0.0758106106619902[/C][/ROW]
[ROW][C]158[/C][C]8.6[/C][C]8.49025740166856[/C][C]0.109742598331439[/C][/ROW]
[ROW][C]159[/C][C]8.5[/C][C]8.3545194044073[/C][C]0.145480595592694[/C][/ROW]
[ROW][C]160[/C][C]8.3[/C][C]8.28266512365747[/C][C]0.0173348763425314[/C][/ROW]
[ROW][C]161[/C][C]8[/C][C]8.12044187651919[/C][C]-0.120441876519189[/C][/ROW]
[ROW][C]162[/C][C]8.2[/C][C]8.33910218405973[/C][C]-0.139102184059734[/C][/ROW]
[ROW][C]163[/C][C]8.1[/C][C]8.41486479876782[/C][C]-0.314864798767821[/C][/ROW]
[ROW][C]164[/C][C]8.1[/C][C]7.98860281792686[/C][C]0.111397182073139[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58212&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58212&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
187.999934348640626.56513593800204e-05
287.887581873894460.112418126105540
37.97.91284071473113-0.0128407147311306
47.97.787297888470550.112702111529449
587.876354134602850.123645865397153
68.58.5990464723441-0.0990464723440924
79.28.87483168024540.325168319754592
89.49.379335824861550.0206641751384513
99.59.41198495952790.0880150404720975
109.59.356795058389080.143204941610919
119.69.551954883314680.0480451166853189
129.79.647751988423270.0522480115767324
139.79.610087462275720.0899125377242815
149.69.559823794346770.0401762056532345
159.59.445970195577550.0540298044224499
169.49.343494038827230.0565059611727672
179.39.3306947729851-0.0306947729851053
189.69.78659524243471-0.186595242434714
1910.29.880594043613170.319405956386828
2010.210.3654016776522-0.165401677652248
2110.110.1536093237245-0.0536093237245325
229.99.878932995455780.0210670045442195
239.89.90133375180729-0.101333751807287
249.89.80830421914479-0.00830421914478972
259.79.71068977985856-0.010689779858562
269.59.56177002793082-0.0617700279308206
279.39.31860055580397-0.0186005558039693
289.19.10822694298252-0.00822694298252507
2998.991168797770140.00883120222986119
309.59.48709755497970.0129024450202934
31109.862003382528060.137996617471940
3210.210.08913878362670.110861216373288
3310.110.1548651879752-0.0548651879751908
34109.883550295319840.116449704680158
359.99.96685812102661-0.0668581210266119
36109.897864406970040.102135593029963
379.99.91259314191369-0.0125931419136879
389.79.75344875589052-0.053448755890519
399.59.485612444265070.0143875557349250
409.29.3082150806035-0.108215080603500
4199.05307675204474-0.0530767520447411
429.39.4590489894056-0.159048989405593
439.89.625020071244350.174979928755652
449.89.93987935575346-0.139879355753456
459.69.76099923182073-0.160999231820733
469.49.374193763245680.0258062367543196
479.39.4181629516424-0.118162951642399
489.29.34551958000688-0.145519580006878
499.29.074922402902180.125077597097823
5099.09893639455646-0.0989363945564631
518.88.83359430197203-0.033594301972027
528.78.582700314378580.117299685621415
538.78.638624906984670.0613750930153314
549.19.23371364275692-0.133713642756924
559.79.397962817886240.302037182113762
569.89.80783960969326-0.00783960969325669
579.69.73815617030158-0.138156170301581
589.49.31538905851460.0846109414853975
599.49.353327383692690.0466726163073101
609.59.436972103836340.0630278961636623
619.49.41281854735467-0.0128185473546724
629.39.202024022533310.0979759774666949
639.29.111665161121530.0883348388784693
6499.03899265803574-0.0389926580357383
658.98.848929975903530.0510700240964726
669.29.33797173420014-0.137971734200141
679.89.450644425016930.349355574983075
689.99.91424767569308-0.0142476756930777
699.69.84888282813138-0.248882828131385
709.29.25035053751939-0.0503505375193881
719.19.061750396967450.0382496030325490
729.19.1440676106036-0.0440676106036082
7399.04160621779176-0.0416062177917615
748.98.841930376118520.0580696238814814
758.78.73641301949088-0.0364130194908797
768.58.48198238771980.0180176122801927
778.38.3396153012381-0.0396153012380927
788.58.72594541186596-0.225945411865961
798.78.73366418325495-0.0336641832549470
808.48.711530219758-0.311530219758007
818.18.26732818696114-0.167328186961138
827.87.92912199841007-0.129121998410071
837.77.87383091439563-0.173830914395631
847.57.7855369605322-0.285536960532207
857.27.38374659899409-0.183746598994089
866.87.0257901688335-0.225790168833505
876.76.641125605903780.0588743940962178
886.46.66001851597483-0.260018515974828
896.36.36881825035568-0.068818250355677
906.86.82803024918125-0.0280302491812544
917.37.257368656534350.0426313434656451
927.17.46779438666528-0.367794386665276
9376.971354497143550.0286455028564546
946.86.87369927320372-0.0736992732037239
956.66.90796274699734-0.307962746997343
966.36.61218473621022-0.312184736210219
976.16.14609903722314-0.04609903722314
986.16.014866828678580.0851331713214155
996.36.155626607429560.144373392570437
1006.36.36366429394959-0.0636642939495871
10166.26487061324515-0.264870613245149
1026.26.34754352799597-0.147543527995965
1036.46.45877497223015-0.0587749722301495
1046.86.505214852422720.294785147577279
1057.56.999734668056530.500265331943468
1067.57.72643999299651-0.226439992996508
1077.67.45909532369640.140904676303603
1087.67.480977179229250.119022820770747
1097.47.47098487615743-0.0709848761574307
1107.37.193112572880550.106887427119448
1117.17.17234873053974-0.0723487305397426
1126.96.94023056510787-0.0402305651078727
1136.86.7614626866890.0385373133110015
1147.57.296574676842280.203425323157725
1157.67.94571456577115-0.345714565771149
1167.87.533485365202960.266514634797038
11787.724393894964110.275606105035888
1188.18.01845527147640.0815447285236073
1198.28.136535006287120.0634649937128786
1208.38.165320524336140.134679475663862
1218.28.140674154168650.0593258458313496
12287.985520585976770.0144794140232293
1237.97.769296499791160.130703500208844
1247.67.72377233569211-0.123772335692112
1257.67.439131294987220.160868705012778
1268.38.090818143605550.209181856394451
1278.48.75718267894413-0.357182678944133
1288.48.290734932494780.109265067505221
1298.48.228613784238340.171386215761664
1308.48.344260358518250.0557396414817489
1318.68.454858541324450.145141458675550
1328.98.659436070678780.240563929321219
1338.88.81661796464287-0.0166179646428731
1348.38.51570726989329-0.215707269893291
1357.57.88536581594696-0.385365815946965
1367.27.085958230395410.114041769604595
1377.47.166380093023610.23361990697639
1388.88.1925803385570.607419661443008
1399.39.52689159804203-0.226891598042029
1409.39.249038823473360.0509611765266366
1418.78.91232154426769-0.212321544267688
1428.28.2858501449478-0.0858501449478024
1438.38.081533230772020.218466769227983
1448.58.52423596369212-0.0242359636921239
1458.68.55503607873860.0449639212613928
1468.58.469229926797880.0307700732021162
1478.28.27702094301932-0.0770209430193227
1488.17.892781624204790.207218375795213
1497.98.00043054365104-0.100430543651034
1508.68.37593183177110.224068168228902
1518.79.01448212592127-0.314482125921266
1528.78.657755674775730.0422443252242692
1538.58.52775572288732-0.0277557228873226
1548.48.362961252002880.0370387479971244
1558.58.432796748075920.0672032519240788
1568.78.591828656336360.108171343663640
1578.78.6241893893380.0758106106619902
1588.68.490257401668560.109742598331439
1598.58.35451940440730.145480595592694
1608.38.282665123657470.0173348763425314
16188.12044187651919-0.120441876519189
1628.28.33910218405973-0.139102184059734
1638.18.41486479876782-0.314864798767821
1648.17.988602817926860.111397182073139







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
210.01633576860740600.03267153721481210.983664231392594
220.003348292180772740.006696584361545490.996651707819227
230.0006279853542179490.001255970708435900.999372014645782
240.002166749682453840.004333499364907680.997833250317546
250.0008579206306181360.001715841261236270.999142079369382
260.0002984303393325070.0005968606786650130.999701569660667
278.8473222566525e-050.000176946445133050.999911526777433
287.33684069859946e-050.0001467368139719890.999926631593014
291.92346611245317e-053.84693222490634e-050.999980765338875
304.11374083538347e-058.22748167076694e-050.999958862591646
317.00045475054603e-050.0001400090950109210.999929995452495
322.94832686394765e-055.8966537278953e-050.99997051673136
331.27262529376994e-052.54525058753989e-050.999987273747062
344.97329109433351e-069.94658218866703e-060.999995026708906
351.60252834949155e-063.20505669898311e-060.99999839747165
363.54621980007247e-067.09243960014494e-060.9999964537802
371.99721306392272e-063.99442612784544e-060.999998002786936
386.91888596496511e-071.38377719299302e-060.999999308111404
392.26592062592814e-074.53184125185629e-070.999999773407937
401.43158813397703e-072.86317626795407e-070.999999856841187
416.5376260532538e-081.30752521065076e-070.99999993462374
422.51617845274439e-085.03235690548878e-080.999999974838215
431.52183432237809e-083.04366864475618e-080.999999984781657
444.94393360118467e-099.88786720236934e-090.999999995056066
451.63997542157751e-093.27995084315503e-090.999999998360025
466.24852466291475e-101.24970493258295e-090.999999999375148
474.09357080156452e-108.18714160312904e-100.999999999590643
481.70913854819413e-103.41827709638826e-100.999999999829086
495.84910290747484e-101.16982058149497e-090.99999999941509
502.39178133890418e-104.78356267780836e-100.999999999760822
518.5855446840273e-111.71710893680546e-100.999999999914145
524.420510174586e-118.841020349172e-110.999999999955795
535.42119935740874e-111.08423987148175e-100.999999999945788
541.85663213541309e-113.71326427082619e-110.999999999981434
552.23924783037401e-114.47849566074802e-110.999999999977607
567.6881190342904e-121.53762380685808e-110.999999999992312
571.7243596343106e-113.4487192686212e-110.999999999982756
581.22749157836800e-112.45498315673600e-110.999999999987725
597.66765689909576e-121.53353137981915e-110.999999999992332
604.54497325590115e-129.08994651180231e-120.999999999995455
611.86311092168957e-123.72622184337915e-120.999999999998137
627.98045748313551e-131.59609149662710e-120.999999999999202
636.88591880002961e-131.37718376000592e-120.999999999999311
642.53374393601352e-135.06748787202704e-130.999999999999747
651.25864909964112e-132.51729819928224e-130.999999999999874
668.69348486020997e-141.73869697204199e-130.999999999999913
675.86863166892948e-131.17372633378590e-120.999999999999413
682.26804408845946e-134.53608817691891e-130.999999999999773
691.73224548716259e-123.46449097432517e-120.999999999998268
704.88195073662597e-119.76390147325194e-110.99999999995118
712.38619373968637e-114.77238747937274e-110.999999999976138
721.12506749867814e-112.25013499735628e-110.99999999998875
734.98351452800972e-129.96702905601944e-120.999999999995016
744.82383798642279e-129.64767597284558e-120.999999999995176
752.35688423201181e-124.71376846402361e-120.999999999997643
761.64621893326811e-123.29243786653623e-120.999999999998354
772.24912145522489e-124.49824291044979e-120.99999999999775
782.90436416481181e-125.80872832962361e-120.999999999997096
798.67104538361769e-101.73420907672354e-090.999999999132895
801.98815290012723e-093.97630580025446e-090.999999998011847
811.56792090286952e-093.13584180573904e-090.99999999843208
828.27460677297655e-101.65492135459531e-090.99999999917254
834.08507591418838e-108.17015182837677e-100.999999999591492
841.04466830262749e-092.08933660525498e-090.999999998955332
856.28386355368256e-101.25677271073651e-090.999999999371614
864.51835107943857e-109.03670215887715e-100.999999999548165
872.69893375264926e-095.39786750529853e-090.999999997301066
883.43094130953231e-096.86188261906462e-090.999999996569059
891.90995312236469e-093.81990624472938e-090.999999998090047
904.69310062633659e-099.38620125267318e-090.9999999953069
911.25977690588088e-082.51955381176177e-080.99999998740223
922.64527356865801e-075.29054713731602e-070.999999735472643
933.55623996672693e-077.11247993345387e-070.999999644376003
941.90691352123106e-073.81382704246212e-070.999999809308648
951.9511354252912e-063.9022708505824e-060.999998048864575
964.77896846057607e-059.55793692115214e-050.999952210315394
973.86734850744292e-057.73469701488584e-050.999961326514926
989.10359430658777e-050.0001820718861317550.999908964056934
990.0003229856092401110.0006459712184802220.99967701439076
1000.0003046847031273050.0006093694062546100.999695315296873
1010.001241047941891700.002482095883783390.998758952058108
1020.003542249779055920.007084499558111840.996457750220944
1030.004872074005739160.009744148011478330.99512792599426
1040.03721217250703680.07442434501407360.962787827492963
1050.1864700137271030.3729400274542060.813529986272897
1060.3959830830145870.7919661660291750.604016916985413
1070.423512180325270.847024360650540.57648781967473
1080.3760432724408500.7520865448816990.62395672755915
1090.3650539774572890.7301079549145780.634946022542711
1100.3372505469200590.6745010938401180.662749453079941
1110.3599408338428360.7198816676856730.640059166157164
1120.4152103734374720.8304207468749440.584789626562528
1130.3799964190916060.7599928381832110.620003580908394
1140.4594269323931810.9188538647863630.540573067606819
1150.6678991395903940.6642017208192130.332100860409606
1160.6834973104144540.6330053791710910.316502689585546
1170.6518418747009230.6963162505981550.348158125299077
1180.6591988341677270.6816023316645460.340801165832273
1190.7129720351246550.574055929750690.287027964875345
1200.67415373680760.6516925263848010.325846263192401
1210.6239107130119320.7521785739761360.376089286988068
1220.5729403341419050.8541193317161890.427059665858094
1230.5403738502301880.9192522995396250.459626149769812
1240.6910561575996040.6178876848007920.308943842400396
1250.666167467503390.667665064993220.33383253249661
1260.6617742382655370.6764515234689250.338225761734463
1270.6714930737903990.6570138524192030.328506926209601
1280.614274477758740.771451044482520.38572552224126
1290.5701018696188590.8597962607622820.429898130381141
1300.5433824157100490.9132351685799020.456617584289951
1310.5053335324868430.9893329350263140.494666467513157
1320.5501418115837260.8997163768325490.449858188416274
1330.4840876300397030.9681752600794060.515912369960297
1340.440353062351080.880706124702160.55964693764892
1350.6990383844586340.6019232310827330.300961615541366
1360.7121074636077920.5757850727844150.287892536392208
1370.6447276740884710.7105446518230580.355272325911529
1380.8042239155572460.3915521688855070.195776084442753
1390.7362084801565240.5275830396869520.263791519843476
1400.7180947129658410.5638105740683170.281905287034159
1410.6199941972322530.7600116055354950.380005802767747
1420.4983992009249070.9967984018498140.501600799075093
1430.4116998592619080.8233997185238160.588300140738092

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
21 & 0.0163357686074060 & 0.0326715372148121 & 0.983664231392594 \tabularnewline
22 & 0.00334829218077274 & 0.00669658436154549 & 0.996651707819227 \tabularnewline
23 & 0.000627985354217949 & 0.00125597070843590 & 0.999372014645782 \tabularnewline
24 & 0.00216674968245384 & 0.00433349936490768 & 0.997833250317546 \tabularnewline
25 & 0.000857920630618136 & 0.00171584126123627 & 0.999142079369382 \tabularnewline
26 & 0.000298430339332507 & 0.000596860678665013 & 0.999701569660667 \tabularnewline
27 & 8.8473222566525e-05 & 0.00017694644513305 & 0.999911526777433 \tabularnewline
28 & 7.33684069859946e-05 & 0.000146736813971989 & 0.999926631593014 \tabularnewline
29 & 1.92346611245317e-05 & 3.84693222490634e-05 & 0.999980765338875 \tabularnewline
30 & 4.11374083538347e-05 & 8.22748167076694e-05 & 0.999958862591646 \tabularnewline
31 & 7.00045475054603e-05 & 0.000140009095010921 & 0.999929995452495 \tabularnewline
32 & 2.94832686394765e-05 & 5.8966537278953e-05 & 0.99997051673136 \tabularnewline
33 & 1.27262529376994e-05 & 2.54525058753989e-05 & 0.999987273747062 \tabularnewline
34 & 4.97329109433351e-06 & 9.94658218866703e-06 & 0.999995026708906 \tabularnewline
35 & 1.60252834949155e-06 & 3.20505669898311e-06 & 0.99999839747165 \tabularnewline
36 & 3.54621980007247e-06 & 7.09243960014494e-06 & 0.9999964537802 \tabularnewline
37 & 1.99721306392272e-06 & 3.99442612784544e-06 & 0.999998002786936 \tabularnewline
38 & 6.91888596496511e-07 & 1.38377719299302e-06 & 0.999999308111404 \tabularnewline
39 & 2.26592062592814e-07 & 4.53184125185629e-07 & 0.999999773407937 \tabularnewline
40 & 1.43158813397703e-07 & 2.86317626795407e-07 & 0.999999856841187 \tabularnewline
41 & 6.5376260532538e-08 & 1.30752521065076e-07 & 0.99999993462374 \tabularnewline
42 & 2.51617845274439e-08 & 5.03235690548878e-08 & 0.999999974838215 \tabularnewline
43 & 1.52183432237809e-08 & 3.04366864475618e-08 & 0.999999984781657 \tabularnewline
44 & 4.94393360118467e-09 & 9.88786720236934e-09 & 0.999999995056066 \tabularnewline
45 & 1.63997542157751e-09 & 3.27995084315503e-09 & 0.999999998360025 \tabularnewline
46 & 6.24852466291475e-10 & 1.24970493258295e-09 & 0.999999999375148 \tabularnewline
47 & 4.09357080156452e-10 & 8.18714160312904e-10 & 0.999999999590643 \tabularnewline
48 & 1.70913854819413e-10 & 3.41827709638826e-10 & 0.999999999829086 \tabularnewline
49 & 5.84910290747484e-10 & 1.16982058149497e-09 & 0.99999999941509 \tabularnewline
50 & 2.39178133890418e-10 & 4.78356267780836e-10 & 0.999999999760822 \tabularnewline
51 & 8.5855446840273e-11 & 1.71710893680546e-10 & 0.999999999914145 \tabularnewline
52 & 4.420510174586e-11 & 8.841020349172e-11 & 0.999999999955795 \tabularnewline
53 & 5.42119935740874e-11 & 1.08423987148175e-10 & 0.999999999945788 \tabularnewline
54 & 1.85663213541309e-11 & 3.71326427082619e-11 & 0.999999999981434 \tabularnewline
55 & 2.23924783037401e-11 & 4.47849566074802e-11 & 0.999999999977607 \tabularnewline
56 & 7.6881190342904e-12 & 1.53762380685808e-11 & 0.999999999992312 \tabularnewline
57 & 1.7243596343106e-11 & 3.4487192686212e-11 & 0.999999999982756 \tabularnewline
58 & 1.22749157836800e-11 & 2.45498315673600e-11 & 0.999999999987725 \tabularnewline
59 & 7.66765689909576e-12 & 1.53353137981915e-11 & 0.999999999992332 \tabularnewline
60 & 4.54497325590115e-12 & 9.08994651180231e-12 & 0.999999999995455 \tabularnewline
61 & 1.86311092168957e-12 & 3.72622184337915e-12 & 0.999999999998137 \tabularnewline
62 & 7.98045748313551e-13 & 1.59609149662710e-12 & 0.999999999999202 \tabularnewline
63 & 6.88591880002961e-13 & 1.37718376000592e-12 & 0.999999999999311 \tabularnewline
64 & 2.53374393601352e-13 & 5.06748787202704e-13 & 0.999999999999747 \tabularnewline
65 & 1.25864909964112e-13 & 2.51729819928224e-13 & 0.999999999999874 \tabularnewline
66 & 8.69348486020997e-14 & 1.73869697204199e-13 & 0.999999999999913 \tabularnewline
67 & 5.86863166892948e-13 & 1.17372633378590e-12 & 0.999999999999413 \tabularnewline
68 & 2.26804408845946e-13 & 4.53608817691891e-13 & 0.999999999999773 \tabularnewline
69 & 1.73224548716259e-12 & 3.46449097432517e-12 & 0.999999999998268 \tabularnewline
70 & 4.88195073662597e-11 & 9.76390147325194e-11 & 0.99999999995118 \tabularnewline
71 & 2.38619373968637e-11 & 4.77238747937274e-11 & 0.999999999976138 \tabularnewline
72 & 1.12506749867814e-11 & 2.25013499735628e-11 & 0.99999999998875 \tabularnewline
73 & 4.98351452800972e-12 & 9.96702905601944e-12 & 0.999999999995016 \tabularnewline
74 & 4.82383798642279e-12 & 9.64767597284558e-12 & 0.999999999995176 \tabularnewline
75 & 2.35688423201181e-12 & 4.71376846402361e-12 & 0.999999999997643 \tabularnewline
76 & 1.64621893326811e-12 & 3.29243786653623e-12 & 0.999999999998354 \tabularnewline
77 & 2.24912145522489e-12 & 4.49824291044979e-12 & 0.99999999999775 \tabularnewline
78 & 2.90436416481181e-12 & 5.80872832962361e-12 & 0.999999999997096 \tabularnewline
79 & 8.67104538361769e-10 & 1.73420907672354e-09 & 0.999999999132895 \tabularnewline
80 & 1.98815290012723e-09 & 3.97630580025446e-09 & 0.999999998011847 \tabularnewline
81 & 1.56792090286952e-09 & 3.13584180573904e-09 & 0.99999999843208 \tabularnewline
82 & 8.27460677297655e-10 & 1.65492135459531e-09 & 0.99999999917254 \tabularnewline
83 & 4.08507591418838e-10 & 8.17015182837677e-10 & 0.999999999591492 \tabularnewline
84 & 1.04466830262749e-09 & 2.08933660525498e-09 & 0.999999998955332 \tabularnewline
85 & 6.28386355368256e-10 & 1.25677271073651e-09 & 0.999999999371614 \tabularnewline
86 & 4.51835107943857e-10 & 9.03670215887715e-10 & 0.999999999548165 \tabularnewline
87 & 2.69893375264926e-09 & 5.39786750529853e-09 & 0.999999997301066 \tabularnewline
88 & 3.43094130953231e-09 & 6.86188261906462e-09 & 0.999999996569059 \tabularnewline
89 & 1.90995312236469e-09 & 3.81990624472938e-09 & 0.999999998090047 \tabularnewline
90 & 4.69310062633659e-09 & 9.38620125267318e-09 & 0.9999999953069 \tabularnewline
91 & 1.25977690588088e-08 & 2.51955381176177e-08 & 0.99999998740223 \tabularnewline
92 & 2.64527356865801e-07 & 5.29054713731602e-07 & 0.999999735472643 \tabularnewline
93 & 3.55623996672693e-07 & 7.11247993345387e-07 & 0.999999644376003 \tabularnewline
94 & 1.90691352123106e-07 & 3.81382704246212e-07 & 0.999999809308648 \tabularnewline
95 & 1.9511354252912e-06 & 3.9022708505824e-06 & 0.999998048864575 \tabularnewline
96 & 4.77896846057607e-05 & 9.55793692115214e-05 & 0.999952210315394 \tabularnewline
97 & 3.86734850744292e-05 & 7.73469701488584e-05 & 0.999961326514926 \tabularnewline
98 & 9.10359430658777e-05 & 0.000182071886131755 & 0.999908964056934 \tabularnewline
99 & 0.000322985609240111 & 0.000645971218480222 & 0.99967701439076 \tabularnewline
100 & 0.000304684703127305 & 0.000609369406254610 & 0.999695315296873 \tabularnewline
101 & 0.00124104794189170 & 0.00248209588378339 & 0.998758952058108 \tabularnewline
102 & 0.00354224977905592 & 0.00708449955811184 & 0.996457750220944 \tabularnewline
103 & 0.00487207400573916 & 0.00974414801147833 & 0.99512792599426 \tabularnewline
104 & 0.0372121725070368 & 0.0744243450140736 & 0.962787827492963 \tabularnewline
105 & 0.186470013727103 & 0.372940027454206 & 0.813529986272897 \tabularnewline
106 & 0.395983083014587 & 0.791966166029175 & 0.604016916985413 \tabularnewline
107 & 0.42351218032527 & 0.84702436065054 & 0.57648781967473 \tabularnewline
108 & 0.376043272440850 & 0.752086544881699 & 0.62395672755915 \tabularnewline
109 & 0.365053977457289 & 0.730107954914578 & 0.634946022542711 \tabularnewline
110 & 0.337250546920059 & 0.674501093840118 & 0.662749453079941 \tabularnewline
111 & 0.359940833842836 & 0.719881667685673 & 0.640059166157164 \tabularnewline
112 & 0.415210373437472 & 0.830420746874944 & 0.584789626562528 \tabularnewline
113 & 0.379996419091606 & 0.759992838183211 & 0.620003580908394 \tabularnewline
114 & 0.459426932393181 & 0.918853864786363 & 0.540573067606819 \tabularnewline
115 & 0.667899139590394 & 0.664201720819213 & 0.332100860409606 \tabularnewline
116 & 0.683497310414454 & 0.633005379171091 & 0.316502689585546 \tabularnewline
117 & 0.651841874700923 & 0.696316250598155 & 0.348158125299077 \tabularnewline
118 & 0.659198834167727 & 0.681602331664546 & 0.340801165832273 \tabularnewline
119 & 0.712972035124655 & 0.57405592975069 & 0.287027964875345 \tabularnewline
120 & 0.6741537368076 & 0.651692526384801 & 0.325846263192401 \tabularnewline
121 & 0.623910713011932 & 0.752178573976136 & 0.376089286988068 \tabularnewline
122 & 0.572940334141905 & 0.854119331716189 & 0.427059665858094 \tabularnewline
123 & 0.540373850230188 & 0.919252299539625 & 0.459626149769812 \tabularnewline
124 & 0.691056157599604 & 0.617887684800792 & 0.308943842400396 \tabularnewline
125 & 0.66616746750339 & 0.66766506499322 & 0.33383253249661 \tabularnewline
126 & 0.661774238265537 & 0.676451523468925 & 0.338225761734463 \tabularnewline
127 & 0.671493073790399 & 0.657013852419203 & 0.328506926209601 \tabularnewline
128 & 0.61427447775874 & 0.77145104448252 & 0.38572552224126 \tabularnewline
129 & 0.570101869618859 & 0.859796260762282 & 0.429898130381141 \tabularnewline
130 & 0.543382415710049 & 0.913235168579902 & 0.456617584289951 \tabularnewline
131 & 0.505333532486843 & 0.989332935026314 & 0.494666467513157 \tabularnewline
132 & 0.550141811583726 & 0.899716376832549 & 0.449858188416274 \tabularnewline
133 & 0.484087630039703 & 0.968175260079406 & 0.515912369960297 \tabularnewline
134 & 0.44035306235108 & 0.88070612470216 & 0.55964693764892 \tabularnewline
135 & 0.699038384458634 & 0.601923231082733 & 0.300961615541366 \tabularnewline
136 & 0.712107463607792 & 0.575785072784415 & 0.287892536392208 \tabularnewline
137 & 0.644727674088471 & 0.710544651823058 & 0.355272325911529 \tabularnewline
138 & 0.804223915557246 & 0.391552168885507 & 0.195776084442753 \tabularnewline
139 & 0.736208480156524 & 0.527583039686952 & 0.263791519843476 \tabularnewline
140 & 0.718094712965841 & 0.563810574068317 & 0.281905287034159 \tabularnewline
141 & 0.619994197232253 & 0.760011605535495 & 0.380005802767747 \tabularnewline
142 & 0.498399200924907 & 0.996798401849814 & 0.501600799075093 \tabularnewline
143 & 0.411699859261908 & 0.823399718523816 & 0.588300140738092 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58212&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]21[/C][C]0.0163357686074060[/C][C]0.0326715372148121[/C][C]0.983664231392594[/C][/ROW]
[ROW][C]22[/C][C]0.00334829218077274[/C][C]0.00669658436154549[/C][C]0.996651707819227[/C][/ROW]
[ROW][C]23[/C][C]0.000627985354217949[/C][C]0.00125597070843590[/C][C]0.999372014645782[/C][/ROW]
[ROW][C]24[/C][C]0.00216674968245384[/C][C]0.00433349936490768[/C][C]0.997833250317546[/C][/ROW]
[ROW][C]25[/C][C]0.000857920630618136[/C][C]0.00171584126123627[/C][C]0.999142079369382[/C][/ROW]
[ROW][C]26[/C][C]0.000298430339332507[/C][C]0.000596860678665013[/C][C]0.999701569660667[/C][/ROW]
[ROW][C]27[/C][C]8.8473222566525e-05[/C][C]0.00017694644513305[/C][C]0.999911526777433[/C][/ROW]
[ROW][C]28[/C][C]7.33684069859946e-05[/C][C]0.000146736813971989[/C][C]0.999926631593014[/C][/ROW]
[ROW][C]29[/C][C]1.92346611245317e-05[/C][C]3.84693222490634e-05[/C][C]0.999980765338875[/C][/ROW]
[ROW][C]30[/C][C]4.11374083538347e-05[/C][C]8.22748167076694e-05[/C][C]0.999958862591646[/C][/ROW]
[ROW][C]31[/C][C]7.00045475054603e-05[/C][C]0.000140009095010921[/C][C]0.999929995452495[/C][/ROW]
[ROW][C]32[/C][C]2.94832686394765e-05[/C][C]5.8966537278953e-05[/C][C]0.99997051673136[/C][/ROW]
[ROW][C]33[/C][C]1.27262529376994e-05[/C][C]2.54525058753989e-05[/C][C]0.999987273747062[/C][/ROW]
[ROW][C]34[/C][C]4.97329109433351e-06[/C][C]9.94658218866703e-06[/C][C]0.999995026708906[/C][/ROW]
[ROW][C]35[/C][C]1.60252834949155e-06[/C][C]3.20505669898311e-06[/C][C]0.99999839747165[/C][/ROW]
[ROW][C]36[/C][C]3.54621980007247e-06[/C][C]7.09243960014494e-06[/C][C]0.9999964537802[/C][/ROW]
[ROW][C]37[/C][C]1.99721306392272e-06[/C][C]3.99442612784544e-06[/C][C]0.999998002786936[/C][/ROW]
[ROW][C]38[/C][C]6.91888596496511e-07[/C][C]1.38377719299302e-06[/C][C]0.999999308111404[/C][/ROW]
[ROW][C]39[/C][C]2.26592062592814e-07[/C][C]4.53184125185629e-07[/C][C]0.999999773407937[/C][/ROW]
[ROW][C]40[/C][C]1.43158813397703e-07[/C][C]2.86317626795407e-07[/C][C]0.999999856841187[/C][/ROW]
[ROW][C]41[/C][C]6.5376260532538e-08[/C][C]1.30752521065076e-07[/C][C]0.99999993462374[/C][/ROW]
[ROW][C]42[/C][C]2.51617845274439e-08[/C][C]5.03235690548878e-08[/C][C]0.999999974838215[/C][/ROW]
[ROW][C]43[/C][C]1.52183432237809e-08[/C][C]3.04366864475618e-08[/C][C]0.999999984781657[/C][/ROW]
[ROW][C]44[/C][C]4.94393360118467e-09[/C][C]9.88786720236934e-09[/C][C]0.999999995056066[/C][/ROW]
[ROW][C]45[/C][C]1.63997542157751e-09[/C][C]3.27995084315503e-09[/C][C]0.999999998360025[/C][/ROW]
[ROW][C]46[/C][C]6.24852466291475e-10[/C][C]1.24970493258295e-09[/C][C]0.999999999375148[/C][/ROW]
[ROW][C]47[/C][C]4.09357080156452e-10[/C][C]8.18714160312904e-10[/C][C]0.999999999590643[/C][/ROW]
[ROW][C]48[/C][C]1.70913854819413e-10[/C][C]3.41827709638826e-10[/C][C]0.999999999829086[/C][/ROW]
[ROW][C]49[/C][C]5.84910290747484e-10[/C][C]1.16982058149497e-09[/C][C]0.99999999941509[/C][/ROW]
[ROW][C]50[/C][C]2.39178133890418e-10[/C][C]4.78356267780836e-10[/C][C]0.999999999760822[/C][/ROW]
[ROW][C]51[/C][C]8.5855446840273e-11[/C][C]1.71710893680546e-10[/C][C]0.999999999914145[/C][/ROW]
[ROW][C]52[/C][C]4.420510174586e-11[/C][C]8.841020349172e-11[/C][C]0.999999999955795[/C][/ROW]
[ROW][C]53[/C][C]5.42119935740874e-11[/C][C]1.08423987148175e-10[/C][C]0.999999999945788[/C][/ROW]
[ROW][C]54[/C][C]1.85663213541309e-11[/C][C]3.71326427082619e-11[/C][C]0.999999999981434[/C][/ROW]
[ROW][C]55[/C][C]2.23924783037401e-11[/C][C]4.47849566074802e-11[/C][C]0.999999999977607[/C][/ROW]
[ROW][C]56[/C][C]7.6881190342904e-12[/C][C]1.53762380685808e-11[/C][C]0.999999999992312[/C][/ROW]
[ROW][C]57[/C][C]1.7243596343106e-11[/C][C]3.4487192686212e-11[/C][C]0.999999999982756[/C][/ROW]
[ROW][C]58[/C][C]1.22749157836800e-11[/C][C]2.45498315673600e-11[/C][C]0.999999999987725[/C][/ROW]
[ROW][C]59[/C][C]7.66765689909576e-12[/C][C]1.53353137981915e-11[/C][C]0.999999999992332[/C][/ROW]
[ROW][C]60[/C][C]4.54497325590115e-12[/C][C]9.08994651180231e-12[/C][C]0.999999999995455[/C][/ROW]
[ROW][C]61[/C][C]1.86311092168957e-12[/C][C]3.72622184337915e-12[/C][C]0.999999999998137[/C][/ROW]
[ROW][C]62[/C][C]7.98045748313551e-13[/C][C]1.59609149662710e-12[/C][C]0.999999999999202[/C][/ROW]
[ROW][C]63[/C][C]6.88591880002961e-13[/C][C]1.37718376000592e-12[/C][C]0.999999999999311[/C][/ROW]
[ROW][C]64[/C][C]2.53374393601352e-13[/C][C]5.06748787202704e-13[/C][C]0.999999999999747[/C][/ROW]
[ROW][C]65[/C][C]1.25864909964112e-13[/C][C]2.51729819928224e-13[/C][C]0.999999999999874[/C][/ROW]
[ROW][C]66[/C][C]8.69348486020997e-14[/C][C]1.73869697204199e-13[/C][C]0.999999999999913[/C][/ROW]
[ROW][C]67[/C][C]5.86863166892948e-13[/C][C]1.17372633378590e-12[/C][C]0.999999999999413[/C][/ROW]
[ROW][C]68[/C][C]2.26804408845946e-13[/C][C]4.53608817691891e-13[/C][C]0.999999999999773[/C][/ROW]
[ROW][C]69[/C][C]1.73224548716259e-12[/C][C]3.46449097432517e-12[/C][C]0.999999999998268[/C][/ROW]
[ROW][C]70[/C][C]4.88195073662597e-11[/C][C]9.76390147325194e-11[/C][C]0.99999999995118[/C][/ROW]
[ROW][C]71[/C][C]2.38619373968637e-11[/C][C]4.77238747937274e-11[/C][C]0.999999999976138[/C][/ROW]
[ROW][C]72[/C][C]1.12506749867814e-11[/C][C]2.25013499735628e-11[/C][C]0.99999999998875[/C][/ROW]
[ROW][C]73[/C][C]4.98351452800972e-12[/C][C]9.96702905601944e-12[/C][C]0.999999999995016[/C][/ROW]
[ROW][C]74[/C][C]4.82383798642279e-12[/C][C]9.64767597284558e-12[/C][C]0.999999999995176[/C][/ROW]
[ROW][C]75[/C][C]2.35688423201181e-12[/C][C]4.71376846402361e-12[/C][C]0.999999999997643[/C][/ROW]
[ROW][C]76[/C][C]1.64621893326811e-12[/C][C]3.29243786653623e-12[/C][C]0.999999999998354[/C][/ROW]
[ROW][C]77[/C][C]2.24912145522489e-12[/C][C]4.49824291044979e-12[/C][C]0.99999999999775[/C][/ROW]
[ROW][C]78[/C][C]2.90436416481181e-12[/C][C]5.80872832962361e-12[/C][C]0.999999999997096[/C][/ROW]
[ROW][C]79[/C][C]8.67104538361769e-10[/C][C]1.73420907672354e-09[/C][C]0.999999999132895[/C][/ROW]
[ROW][C]80[/C][C]1.98815290012723e-09[/C][C]3.97630580025446e-09[/C][C]0.999999998011847[/C][/ROW]
[ROW][C]81[/C][C]1.56792090286952e-09[/C][C]3.13584180573904e-09[/C][C]0.99999999843208[/C][/ROW]
[ROW][C]82[/C][C]8.27460677297655e-10[/C][C]1.65492135459531e-09[/C][C]0.99999999917254[/C][/ROW]
[ROW][C]83[/C][C]4.08507591418838e-10[/C][C]8.17015182837677e-10[/C][C]0.999999999591492[/C][/ROW]
[ROW][C]84[/C][C]1.04466830262749e-09[/C][C]2.08933660525498e-09[/C][C]0.999999998955332[/C][/ROW]
[ROW][C]85[/C][C]6.28386355368256e-10[/C][C]1.25677271073651e-09[/C][C]0.999999999371614[/C][/ROW]
[ROW][C]86[/C][C]4.51835107943857e-10[/C][C]9.03670215887715e-10[/C][C]0.999999999548165[/C][/ROW]
[ROW][C]87[/C][C]2.69893375264926e-09[/C][C]5.39786750529853e-09[/C][C]0.999999997301066[/C][/ROW]
[ROW][C]88[/C][C]3.43094130953231e-09[/C][C]6.86188261906462e-09[/C][C]0.999999996569059[/C][/ROW]
[ROW][C]89[/C][C]1.90995312236469e-09[/C][C]3.81990624472938e-09[/C][C]0.999999998090047[/C][/ROW]
[ROW][C]90[/C][C]4.69310062633659e-09[/C][C]9.38620125267318e-09[/C][C]0.9999999953069[/C][/ROW]
[ROW][C]91[/C][C]1.25977690588088e-08[/C][C]2.51955381176177e-08[/C][C]0.99999998740223[/C][/ROW]
[ROW][C]92[/C][C]2.64527356865801e-07[/C][C]5.29054713731602e-07[/C][C]0.999999735472643[/C][/ROW]
[ROW][C]93[/C][C]3.55623996672693e-07[/C][C]7.11247993345387e-07[/C][C]0.999999644376003[/C][/ROW]
[ROW][C]94[/C][C]1.90691352123106e-07[/C][C]3.81382704246212e-07[/C][C]0.999999809308648[/C][/ROW]
[ROW][C]95[/C][C]1.9511354252912e-06[/C][C]3.9022708505824e-06[/C][C]0.999998048864575[/C][/ROW]
[ROW][C]96[/C][C]4.77896846057607e-05[/C][C]9.55793692115214e-05[/C][C]0.999952210315394[/C][/ROW]
[ROW][C]97[/C][C]3.86734850744292e-05[/C][C]7.73469701488584e-05[/C][C]0.999961326514926[/C][/ROW]
[ROW][C]98[/C][C]9.10359430658777e-05[/C][C]0.000182071886131755[/C][C]0.999908964056934[/C][/ROW]
[ROW][C]99[/C][C]0.000322985609240111[/C][C]0.000645971218480222[/C][C]0.99967701439076[/C][/ROW]
[ROW][C]100[/C][C]0.000304684703127305[/C][C]0.000609369406254610[/C][C]0.999695315296873[/C][/ROW]
[ROW][C]101[/C][C]0.00124104794189170[/C][C]0.00248209588378339[/C][C]0.998758952058108[/C][/ROW]
[ROW][C]102[/C][C]0.00354224977905592[/C][C]0.00708449955811184[/C][C]0.996457750220944[/C][/ROW]
[ROW][C]103[/C][C]0.00487207400573916[/C][C]0.00974414801147833[/C][C]0.99512792599426[/C][/ROW]
[ROW][C]104[/C][C]0.0372121725070368[/C][C]0.0744243450140736[/C][C]0.962787827492963[/C][/ROW]
[ROW][C]105[/C][C]0.186470013727103[/C][C]0.372940027454206[/C][C]0.813529986272897[/C][/ROW]
[ROW][C]106[/C][C]0.395983083014587[/C][C]0.791966166029175[/C][C]0.604016916985413[/C][/ROW]
[ROW][C]107[/C][C]0.42351218032527[/C][C]0.84702436065054[/C][C]0.57648781967473[/C][/ROW]
[ROW][C]108[/C][C]0.376043272440850[/C][C]0.752086544881699[/C][C]0.62395672755915[/C][/ROW]
[ROW][C]109[/C][C]0.365053977457289[/C][C]0.730107954914578[/C][C]0.634946022542711[/C][/ROW]
[ROW][C]110[/C][C]0.337250546920059[/C][C]0.674501093840118[/C][C]0.662749453079941[/C][/ROW]
[ROW][C]111[/C][C]0.359940833842836[/C][C]0.719881667685673[/C][C]0.640059166157164[/C][/ROW]
[ROW][C]112[/C][C]0.415210373437472[/C][C]0.830420746874944[/C][C]0.584789626562528[/C][/ROW]
[ROW][C]113[/C][C]0.379996419091606[/C][C]0.759992838183211[/C][C]0.620003580908394[/C][/ROW]
[ROW][C]114[/C][C]0.459426932393181[/C][C]0.918853864786363[/C][C]0.540573067606819[/C][/ROW]
[ROW][C]115[/C][C]0.667899139590394[/C][C]0.664201720819213[/C][C]0.332100860409606[/C][/ROW]
[ROW][C]116[/C][C]0.683497310414454[/C][C]0.633005379171091[/C][C]0.316502689585546[/C][/ROW]
[ROW][C]117[/C][C]0.651841874700923[/C][C]0.696316250598155[/C][C]0.348158125299077[/C][/ROW]
[ROW][C]118[/C][C]0.659198834167727[/C][C]0.681602331664546[/C][C]0.340801165832273[/C][/ROW]
[ROW][C]119[/C][C]0.712972035124655[/C][C]0.57405592975069[/C][C]0.287027964875345[/C][/ROW]
[ROW][C]120[/C][C]0.6741537368076[/C][C]0.651692526384801[/C][C]0.325846263192401[/C][/ROW]
[ROW][C]121[/C][C]0.623910713011932[/C][C]0.752178573976136[/C][C]0.376089286988068[/C][/ROW]
[ROW][C]122[/C][C]0.572940334141905[/C][C]0.854119331716189[/C][C]0.427059665858094[/C][/ROW]
[ROW][C]123[/C][C]0.540373850230188[/C][C]0.919252299539625[/C][C]0.459626149769812[/C][/ROW]
[ROW][C]124[/C][C]0.691056157599604[/C][C]0.617887684800792[/C][C]0.308943842400396[/C][/ROW]
[ROW][C]125[/C][C]0.66616746750339[/C][C]0.66766506499322[/C][C]0.33383253249661[/C][/ROW]
[ROW][C]126[/C][C]0.661774238265537[/C][C]0.676451523468925[/C][C]0.338225761734463[/C][/ROW]
[ROW][C]127[/C][C]0.671493073790399[/C][C]0.657013852419203[/C][C]0.328506926209601[/C][/ROW]
[ROW][C]128[/C][C]0.61427447775874[/C][C]0.77145104448252[/C][C]0.38572552224126[/C][/ROW]
[ROW][C]129[/C][C]0.570101869618859[/C][C]0.859796260762282[/C][C]0.429898130381141[/C][/ROW]
[ROW][C]130[/C][C]0.543382415710049[/C][C]0.913235168579902[/C][C]0.456617584289951[/C][/ROW]
[ROW][C]131[/C][C]0.505333532486843[/C][C]0.989332935026314[/C][C]0.494666467513157[/C][/ROW]
[ROW][C]132[/C][C]0.550141811583726[/C][C]0.899716376832549[/C][C]0.449858188416274[/C][/ROW]
[ROW][C]133[/C][C]0.484087630039703[/C][C]0.968175260079406[/C][C]0.515912369960297[/C][/ROW]
[ROW][C]134[/C][C]0.44035306235108[/C][C]0.88070612470216[/C][C]0.55964693764892[/C][/ROW]
[ROW][C]135[/C][C]0.699038384458634[/C][C]0.601923231082733[/C][C]0.300961615541366[/C][/ROW]
[ROW][C]136[/C][C]0.712107463607792[/C][C]0.575785072784415[/C][C]0.287892536392208[/C][/ROW]
[ROW][C]137[/C][C]0.644727674088471[/C][C]0.710544651823058[/C][C]0.355272325911529[/C][/ROW]
[ROW][C]138[/C][C]0.804223915557246[/C][C]0.391552168885507[/C][C]0.195776084442753[/C][/ROW]
[ROW][C]139[/C][C]0.736208480156524[/C][C]0.527583039686952[/C][C]0.263791519843476[/C][/ROW]
[ROW][C]140[/C][C]0.718094712965841[/C][C]0.563810574068317[/C][C]0.281905287034159[/C][/ROW]
[ROW][C]141[/C][C]0.619994197232253[/C][C]0.760011605535495[/C][C]0.380005802767747[/C][/ROW]
[ROW][C]142[/C][C]0.498399200924907[/C][C]0.996798401849814[/C][C]0.501600799075093[/C][/ROW]
[ROW][C]143[/C][C]0.411699859261908[/C][C]0.823399718523816[/C][C]0.588300140738092[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58212&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58212&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
210.01633576860740600.03267153721481210.983664231392594
220.003348292180772740.006696584361545490.996651707819227
230.0006279853542179490.001255970708435900.999372014645782
240.002166749682453840.004333499364907680.997833250317546
250.0008579206306181360.001715841261236270.999142079369382
260.0002984303393325070.0005968606786650130.999701569660667
278.8473222566525e-050.000176946445133050.999911526777433
287.33684069859946e-050.0001467368139719890.999926631593014
291.92346611245317e-053.84693222490634e-050.999980765338875
304.11374083538347e-058.22748167076694e-050.999958862591646
317.00045475054603e-050.0001400090950109210.999929995452495
322.94832686394765e-055.8966537278953e-050.99997051673136
331.27262529376994e-052.54525058753989e-050.999987273747062
344.97329109433351e-069.94658218866703e-060.999995026708906
351.60252834949155e-063.20505669898311e-060.99999839747165
363.54621980007247e-067.09243960014494e-060.9999964537802
371.99721306392272e-063.99442612784544e-060.999998002786936
386.91888596496511e-071.38377719299302e-060.999999308111404
392.26592062592814e-074.53184125185629e-070.999999773407937
401.43158813397703e-072.86317626795407e-070.999999856841187
416.5376260532538e-081.30752521065076e-070.99999993462374
422.51617845274439e-085.03235690548878e-080.999999974838215
431.52183432237809e-083.04366864475618e-080.999999984781657
444.94393360118467e-099.88786720236934e-090.999999995056066
451.63997542157751e-093.27995084315503e-090.999999998360025
466.24852466291475e-101.24970493258295e-090.999999999375148
474.09357080156452e-108.18714160312904e-100.999999999590643
481.70913854819413e-103.41827709638826e-100.999999999829086
495.84910290747484e-101.16982058149497e-090.99999999941509
502.39178133890418e-104.78356267780836e-100.999999999760822
518.5855446840273e-111.71710893680546e-100.999999999914145
524.420510174586e-118.841020349172e-110.999999999955795
535.42119935740874e-111.08423987148175e-100.999999999945788
541.85663213541309e-113.71326427082619e-110.999999999981434
552.23924783037401e-114.47849566074802e-110.999999999977607
567.6881190342904e-121.53762380685808e-110.999999999992312
571.7243596343106e-113.4487192686212e-110.999999999982756
581.22749157836800e-112.45498315673600e-110.999999999987725
597.66765689909576e-121.53353137981915e-110.999999999992332
604.54497325590115e-129.08994651180231e-120.999999999995455
611.86311092168957e-123.72622184337915e-120.999999999998137
627.98045748313551e-131.59609149662710e-120.999999999999202
636.88591880002961e-131.37718376000592e-120.999999999999311
642.53374393601352e-135.06748787202704e-130.999999999999747
651.25864909964112e-132.51729819928224e-130.999999999999874
668.69348486020997e-141.73869697204199e-130.999999999999913
675.86863166892948e-131.17372633378590e-120.999999999999413
682.26804408845946e-134.53608817691891e-130.999999999999773
691.73224548716259e-123.46449097432517e-120.999999999998268
704.88195073662597e-119.76390147325194e-110.99999999995118
712.38619373968637e-114.77238747937274e-110.999999999976138
721.12506749867814e-112.25013499735628e-110.99999999998875
734.98351452800972e-129.96702905601944e-120.999999999995016
744.82383798642279e-129.64767597284558e-120.999999999995176
752.35688423201181e-124.71376846402361e-120.999999999997643
761.64621893326811e-123.29243786653623e-120.999999999998354
772.24912145522489e-124.49824291044979e-120.99999999999775
782.90436416481181e-125.80872832962361e-120.999999999997096
798.67104538361769e-101.73420907672354e-090.999999999132895
801.98815290012723e-093.97630580025446e-090.999999998011847
811.56792090286952e-093.13584180573904e-090.99999999843208
828.27460677297655e-101.65492135459531e-090.99999999917254
834.08507591418838e-108.17015182837677e-100.999999999591492
841.04466830262749e-092.08933660525498e-090.999999998955332
856.28386355368256e-101.25677271073651e-090.999999999371614
864.51835107943857e-109.03670215887715e-100.999999999548165
872.69893375264926e-095.39786750529853e-090.999999997301066
883.43094130953231e-096.86188261906462e-090.999999996569059
891.90995312236469e-093.81990624472938e-090.999999998090047
904.69310062633659e-099.38620125267318e-090.9999999953069
911.25977690588088e-082.51955381176177e-080.99999998740223
922.64527356865801e-075.29054713731602e-070.999999735472643
933.55623996672693e-077.11247993345387e-070.999999644376003
941.90691352123106e-073.81382704246212e-070.999999809308648
951.9511354252912e-063.9022708505824e-060.999998048864575
964.77896846057607e-059.55793692115214e-050.999952210315394
973.86734850744292e-057.73469701488584e-050.999961326514926
989.10359430658777e-050.0001820718861317550.999908964056934
990.0003229856092401110.0006459712184802220.99967701439076
1000.0003046847031273050.0006093694062546100.999695315296873
1010.001241047941891700.002482095883783390.998758952058108
1020.003542249779055920.007084499558111840.996457750220944
1030.004872074005739160.009744148011478330.99512792599426
1040.03721217250703680.07442434501407360.962787827492963
1050.1864700137271030.3729400274542060.813529986272897
1060.3959830830145870.7919661660291750.604016916985413
1070.423512180325270.847024360650540.57648781967473
1080.3760432724408500.7520865448816990.62395672755915
1090.3650539774572890.7301079549145780.634946022542711
1100.3372505469200590.6745010938401180.662749453079941
1110.3599408338428360.7198816676856730.640059166157164
1120.4152103734374720.8304207468749440.584789626562528
1130.3799964190916060.7599928381832110.620003580908394
1140.4594269323931810.9188538647863630.540573067606819
1150.6678991395903940.6642017208192130.332100860409606
1160.6834973104144540.6330053791710910.316502689585546
1170.6518418747009230.6963162505981550.348158125299077
1180.6591988341677270.6816023316645460.340801165832273
1190.7129720351246550.574055929750690.287027964875345
1200.67415373680760.6516925263848010.325846263192401
1210.6239107130119320.7521785739761360.376089286988068
1220.5729403341419050.8541193317161890.427059665858094
1230.5403738502301880.9192522995396250.459626149769812
1240.6910561575996040.6178876848007920.308943842400396
1250.666167467503390.667665064993220.33383253249661
1260.6617742382655370.6764515234689250.338225761734463
1270.6714930737903990.6570138524192030.328506926209601
1280.614274477758740.771451044482520.38572552224126
1290.5701018696188590.8597962607622820.429898130381141
1300.5433824157100490.9132351685799020.456617584289951
1310.5053335324868430.9893329350263140.494666467513157
1320.5501418115837260.8997163768325490.449858188416274
1330.4840876300397030.9681752600794060.515912369960297
1340.440353062351080.880706124702160.55964693764892
1350.6990383844586340.6019232310827330.300961615541366
1360.7121074636077920.5757850727844150.287892536392208
1370.6447276740884710.7105446518230580.355272325911529
1380.8042239155572460.3915521688855070.195776084442753
1390.7362084801565240.5275830396869520.263791519843476
1400.7180947129658410.5638105740683170.281905287034159
1410.6199941972322530.7600116055354950.380005802767747
1420.4983992009249070.9967984018498140.501600799075093
1430.4116998592619080.8233997185238160.588300140738092







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level820.666666666666667NOK
5% type I error level830.67479674796748NOK
10% type I error level840.682926829268293NOK

\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 & 82 & 0.666666666666667 & NOK \tabularnewline
5% type I error level & 83 & 0.67479674796748 & NOK \tabularnewline
10% type I error level & 84 & 0.682926829268293 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58212&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]82[/C][C]0.666666666666667[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]83[/C][C]0.67479674796748[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]84[/C][C]0.682926829268293[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58212&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58212&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 level820.666666666666667NOK
5% type I error level830.67479674796748NOK
10% type I error level840.682926829268293NOK



Parameters (Session):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = 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')
}