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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationMon, 29 Nov 2010 18:31:52 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Nov/29/t1291055459824okt6ukprehys.htm/, Retrieved Mon, 29 Apr 2024 15:14:11 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=103025, Retrieved Mon, 29 Apr 2024 15:14:11 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact238

Tree of Dependent Computations

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]
-    D      [Multiple Regression] [ws 8 auitoregressie] [2010-11-29 18:31:52] [09489ba95453d3f5c9e6f2eaeda915af] [Current]
-    D        [Multiple Regression] [autoregressie] [2010-12-19 09:44:42] [bd591a1ebb67d263a02e7adae3fa1a4d]
-    D        [Multiple Regression] [paper - autoregre...] [2010-12-24 09:54:39] [9894f466352df31a128e82ec8d720241]
-   PD        [Multiple Regression] [autoregressie] [2010-12-24 13:28:49] [bd591a1ebb67d263a02e7adae3fa1a4d]
-   PD        [Multiple Regression] [autoregressie] [2010-12-24 13:37:13] [bd591a1ebb67d263a02e7adae3fa1a4d]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
98,1	102,8	104,7	95,9	94,6
113,9	98,1	102,8	104,7	95,9
80,9	113,9	98,1	102,8	104,7
95,7	80,9	113,9	98,1	102,8
113,2	95,7	80,9	113,9	98,1
105,9	113,2	95,7	80,9	113,9
108,8	105,9	113,2	95,7	80,9
102,3	108,8	105,9	113,2	95,7
99	102,3	108,8	105,9	113,2
100,7	99	102,3	108,8	105,9
115,5	100,7	99	102,3	108,8
100,7	115,5	100,7	99	102,3
109,9	100,7	115,5	100,7	99
114,6	109,9	100,7	115,5	100,7
85,4	114,6	109,9	100,7	115,5
100,5	85,4	114,6	109,9	100,7
114,8	100,5	85,4	114,6	109,9
116,5	114,8	100,5	85,4	114,6
112,9	116,5	114,8	100,5	85,4
102	112,9	116,5	114,8	100,5
106	102	112,9	116,5	114,8
105,3	106	102	112,9	116,5
118,8	105,3	106	102	112,9
106,1	118,8	105,3	106	102
109,3	106,1	118,8	105,3	106
117,2	109,3	106,1	118,8	105,3
92,5	117,2	109,3	106,1	118,8
104,2	92,5	117,2	109,3	106,1
112,5	104,2	92,5	117,2	109,3
122,4	112,5	104,2	92,5	117,2
113,3	122,4	112,5	104,2	92,5
100	113,3	122,4	112,5	104,2
110,7	100	113,3	122,4	112,5
112,8	110,7	100	113,3	122,4
109,8	112,8	110,7	100	113,3
117,3	109,8	112,8	110,7	100
109,1	117,3	109,8	112,8	110,7
115,9	109,1	117,3	109,8	112,8
96	115,9	109,1	117,3	109,8
99,8	96	115,9	109,1	117,3
116,8	99,8	96	115,9	109,1
115,7	116,8	99,8	96	115,9
99,4	115,7	116,8	99,8	96
94,3	99,4	115,7	116,8	99,8
91	94,3	99,4	115,7	116,8
93,2	91	94,3	99,4	115,7
103,1	93,2	91	94,3	99,4
94,1	103,1	93,2	91	94,3
91,8	94,1	103,1	93,2	91
102,7	91,8	94,1	103,1	93,2
82,6	102,7	91,8	94,1	103,1
89,1	82,6	102,7	91,8	94,1
104,5	89,1	82,6	102,7	91,8
105,1	104,5	89,1	82,6	102,7
95,1	105,1	104,5	89,1	82,6
88,7	95,1	105,1	104,5	89,1
86,3	88,7	95,1	105,1	104,5
91,8	86,3	88,7	95,1	105,1
111,5	91,8	86,3	88,7	95,1
99,7	111,5	91,8	86,3	88,7
97,5	99,7	111,5	91,8	86,3
111,7	97,5	99,7	111,5	91,8
86,2	111,7	97,5	99,7	111,5
95,4	86,2	111,7	97,5	99,7

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 6 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103025&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103025&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103025&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24






Multiple Linear Regression - Estimated Regression Equation
y[t] = + 18.1864437368423 + 0.206751675048763y1[t] + 0.333777650199015y2[t] + 0.520824147742392y3[t] -0.220361994936251y4[t] -3.29045670333491M1[t] + 4.25600909331611M2[t] -16.8638473658139M3[t] -5.88390147598205M4[t] + 9.7805082769888M5[t] + 19.3620595783345M6[t] -3.81998157203203M7[t] -16.2516899915533M8[t] -8.12360050980905M9[t] + 0.579419546771023M10[t] + 13.5708797465492M11[t] -0.0338081356043498t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
y[t] =  +  18.1864437368423 +  0.206751675048763y1[t] +  0.333777650199015y2[t] +  0.520824147742392y3[t] -0.220361994936251y4[t] -3.29045670333491M1[t] +  4.25600909331611M2[t] -16.8638473658139M3[t] -5.88390147598205M4[t] +  9.7805082769888M5[t] +  19.3620595783345M6[t] -3.81998157203203M7[t] -16.2516899915533M8[t] -8.12360050980905M9[t] +  0.579419546771023M10[t] +  13.5708797465492M11[t] -0.0338081356043498t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103025&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]y[t] =  +  18.1864437368423 +  0.206751675048763y1[t] +  0.333777650199015y2[t] +  0.520824147742392y3[t] -0.220361994936251y4[t] -3.29045670333491M1[t] +  4.25600909331611M2[t] -16.8638473658139M3[t] -5.88390147598205M4[t] +  9.7805082769888M5[t] +  19.3620595783345M6[t] -3.81998157203203M7[t] -16.2516899915533M8[t] -8.12360050980905M9[t] +  0.579419546771023M10[t] +  13.5708797465492M11[t] -0.0338081356043498t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103025&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103025&T=1

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


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

Multiple Linear Regression - Estimated Regression Equation
y[t] = + 18.1864437368423 + 0.206751675048763y1[t] + 0.333777650199015y2[t] + 0.520824147742392y3[t] -0.220361994936251y4[t] -3.29045670333491M1[t] + 4.25600909331611M2[t] -16.8638473658139M3[t] -5.88390147598205M4[t] + 9.7805082769888M5[t] + 19.3620595783345M6[t] -3.81998157203203M7[t] -16.2516899915533M8[t] -8.12360050980905M9[t] + 0.579419546771023M10[t] + 13.5708797465492M11[t] -0.0338081356043498t + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)18.186443736842311.1640531.6290.1099950.054997
y10.2067516750487630.1424451.45140.1532980.076649
y20.3337776501990150.1222112.73120.0088590.00443
y30.5208241477423920.1207294.3148.2e-054.1e-05
y4-0.2203619949362510.140102-1.57290.1224580.061229
M1-3.290456703334913.05909-1.07560.2875820.143791
M24.256009093316113.3798071.25920.214160.10708
M3-16.86384736581392.998161-5.62471e-060
M4-5.883901475982055.004684-1.17570.2456440.122822
M59.78050827698884.7646162.05270.0456890.022844
M619.36205957833453.8004055.09476e-063e-06
M7-3.819981572032033.504889-1.08990.2813130.140657
M8-16.25168999155333.468919-4.68492.4e-051.2e-05
M9-8.123600509809054.788879-1.69630.0964350.048217
M100.5794195467710234.6238160.12530.9008110.450406
M1113.57087974654923.5508633.82190.0003880.000194
t-0.03380813560434980.031047-1.08890.2817330.140866

\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) & 18.1864437368423 & 11.164053 & 1.629 & 0.109995 & 0.054997 \tabularnewline
y1 & 0.206751675048763 & 0.142445 & 1.4514 & 0.153298 & 0.076649 \tabularnewline
y2 & 0.333777650199015 & 0.122211 & 2.7312 & 0.008859 & 0.00443 \tabularnewline
y3 & 0.520824147742392 & 0.120729 & 4.314 & 8.2e-05 & 4.1e-05 \tabularnewline
y4 & -0.220361994936251 & 0.140102 & -1.5729 & 0.122458 & 0.061229 \tabularnewline
M1 & -3.29045670333491 & 3.05909 & -1.0756 & 0.287582 & 0.143791 \tabularnewline
M2 & 4.25600909331611 & 3.379807 & 1.2592 & 0.21416 & 0.10708 \tabularnewline
M3 & -16.8638473658139 & 2.998161 & -5.6247 & 1e-06 & 0 \tabularnewline
M4 & -5.88390147598205 & 5.004684 & -1.1757 & 0.245644 & 0.122822 \tabularnewline
M5 & 9.7805082769888 & 4.764616 & 2.0527 & 0.045689 & 0.022844 \tabularnewline
M6 & 19.3620595783345 & 3.800405 & 5.0947 & 6e-06 & 3e-06 \tabularnewline
M7 & -3.81998157203203 & 3.504889 & -1.0899 & 0.281313 & 0.140657 \tabularnewline
M8 & -16.2516899915533 & 3.468919 & -4.6849 & 2.4e-05 & 1.2e-05 \tabularnewline
M9 & -8.12360050980905 & 4.788879 & -1.6963 & 0.096435 & 0.048217 \tabularnewline
M10 & 0.579419546771023 & 4.623816 & 0.1253 & 0.900811 & 0.450406 \tabularnewline
M11 & 13.5708797465492 & 3.550863 & 3.8219 & 0.000388 & 0.000194 \tabularnewline
t & -0.0338081356043498 & 0.031047 & -1.0889 & 0.281733 & 0.140866 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103025&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]18.1864437368423[/C][C]11.164053[/C][C]1.629[/C][C]0.109995[/C][C]0.054997[/C][/ROW]
[ROW][C]y1[/C][C]0.206751675048763[/C][C]0.142445[/C][C]1.4514[/C][C]0.153298[/C][C]0.076649[/C][/ROW]
[ROW][C]y2[/C][C]0.333777650199015[/C][C]0.122211[/C][C]2.7312[/C][C]0.008859[/C][C]0.00443[/C][/ROW]
[ROW][C]y3[/C][C]0.520824147742392[/C][C]0.120729[/C][C]4.314[/C][C]8.2e-05[/C][C]4.1e-05[/C][/ROW]
[ROW][C]y4[/C][C]-0.220361994936251[/C][C]0.140102[/C][C]-1.5729[/C][C]0.122458[/C][C]0.061229[/C][/ROW]
[ROW][C]M1[/C][C]-3.29045670333491[/C][C]3.05909[/C][C]-1.0756[/C][C]0.287582[/C][C]0.143791[/C][/ROW]
[ROW][C]M2[/C][C]4.25600909331611[/C][C]3.379807[/C][C]1.2592[/C][C]0.21416[/C][C]0.10708[/C][/ROW]
[ROW][C]M3[/C][C]-16.8638473658139[/C][C]2.998161[/C][C]-5.6247[/C][C]1e-06[/C][C]0[/C][/ROW]
[ROW][C]M4[/C][C]-5.88390147598205[/C][C]5.004684[/C][C]-1.1757[/C][C]0.245644[/C][C]0.122822[/C][/ROW]
[ROW][C]M5[/C][C]9.7805082769888[/C][C]4.764616[/C][C]2.0527[/C][C]0.045689[/C][C]0.022844[/C][/ROW]
[ROW][C]M6[/C][C]19.3620595783345[/C][C]3.800405[/C][C]5.0947[/C][C]6e-06[/C][C]3e-06[/C][/ROW]
[ROW][C]M7[/C][C]-3.81998157203203[/C][C]3.504889[/C][C]-1.0899[/C][C]0.281313[/C][C]0.140657[/C][/ROW]
[ROW][C]M8[/C][C]-16.2516899915533[/C][C]3.468919[/C][C]-4.6849[/C][C]2.4e-05[/C][C]1.2e-05[/C][/ROW]
[ROW][C]M9[/C][C]-8.12360050980905[/C][C]4.788879[/C][C]-1.6963[/C][C]0.096435[/C][C]0.048217[/C][/ROW]
[ROW][C]M10[/C][C]0.579419546771023[/C][C]4.623816[/C][C]0.1253[/C][C]0.900811[/C][C]0.450406[/C][/ROW]
[ROW][C]M11[/C][C]13.5708797465492[/C][C]3.550863[/C][C]3.8219[/C][C]0.000388[/C][C]0.000194[/C][/ROW]
[ROW][C]t[/C][C]-0.0338081356043498[/C][C]0.031047[/C][C]-1.0889[/C][C]0.281733[/C][C]0.140866[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103025&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103025&T=2

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


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)18.186443736842311.1640531.6290.1099950.054997
y10.2067516750487630.1424451.45140.1532980.076649
y20.3337776501990150.1222112.73120.0088590.00443
y30.5208241477423920.1207294.3148.2e-054.1e-05
y4-0.2203619949362510.140102-1.57290.1224580.061229
M1-3.290456703334913.05909-1.07560.2875820.143791
M24.256009093316113.3798071.25920.214160.10708
M3-16.86384736581392.998161-5.62471e-060
M4-5.883901475982055.004684-1.17570.2456440.122822
M59.78050827698884.7646162.05270.0456890.022844
M619.36205957833453.8004055.09476e-063e-06
M7-3.819981572032033.504889-1.08990.2813130.140657
M8-16.25168999155333.468919-4.68492.4e-051.2e-05
M9-8.123600509809054.788879-1.69630.0964350.048217
M100.5794195467710234.6238160.12530.9008110.450406
M1113.57087974654923.5508633.82190.0003880.000194
t-0.03380813560434980.031047-1.08890.2817330.140866






Multiple Linear Regression - Regression Statistics
Multiple R0.937289648886527
R-squared0.878511885909829
Adjusted R-squared0.837154230049345
F-TEST (value)21.2418201087945
F-TEST (DF numerator)16
F-TEST (DF denominator)47
p-value3.33066907387547e-16
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation4.09930331129844
Sum Squared Residuals789.80151898705

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.937289648886527 \tabularnewline
R-squared & 0.878511885909829 \tabularnewline
Adjusted R-squared & 0.837154230049345 \tabularnewline
F-TEST (value) & 21.2418201087945 \tabularnewline
F-TEST (DF numerator) & 16 \tabularnewline
F-TEST (DF denominator) & 47 \tabularnewline
p-value & 3.33066907387547e-16 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 4.09930331129844 \tabularnewline
Sum Squared Residuals & 789.80151898705 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103025&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.937289648886527[/C][/ROW]
[ROW][C]R-squared[/C][C]0.878511885909829[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.837154230049345[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]21.2418201087945[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]16[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]47[/C][/ROW]
[ROW][C]p-value[/C][C]3.33066907387547e-16[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]4.09930331129844[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]789.80151898705[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103025&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103025&T=3

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


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

Multiple Linear Regression - Regression Statistics
Multiple R0.937289648886527
R-squared0.878511885909829
Adjusted R-squared0.837154230049345
F-TEST (value)21.2418201087945
F-TEST (DF numerator)16
F-TEST (DF denominator)47
p-value3.33066907387547e-16
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation4.09930331129844
Sum Squared Residuals789.80151898705






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
198.1100.163562116279-2.0635621162787
2113.9110.3670912759343.53290872406601
380.987.9825967548852-7.08259675488519
495.795.35043040163760.349569598362421
5113.2112.2910172636880.908982736311528
6105.9109.727907570237-3.82790757023684
7108.8105.8240231543762.97597684562353
8102.397.37457467087484.92542532912516
99997.4345741248711.56542587512897
10100.7106.370983383380-5.67098338337979
11115.5114.5542403038390.945759696160877
12100.7104.290532497281-3.5905324972813
13109.9104.4588477250175.44115227498254
14114.6116.267293568763-1.66729356876312
1585.488.186561316945-2.78656131694498
16100.5102.717244799970-2.21724479997049
17114.8112.1440324657382.65596753426219
18116.5113.4446006124033.05539938759667
19112.9109.652264454913.24773554509007
20102101.1301830641260.869816935874058
21106103.5034961350922.49650386490848
22105.3107.111956045829-1.81195604582879
23118.8116.3763125096432.42368749035701
24106.1109.814370221283-3.71437022128312
25109.3107.1243325037472.17566749625341
26117.2118.244998758399-1.04499875839947
2792.590.20340726921942.29659273078056
28104.2103.1448566947811.05514330521926
29112.5116.359497333671-3.85949733367101
30122.4116.9232617004125.47673829958794
31113.3110.0611922975873.23880770241283
32100100.763239321996-0.763239321995807
33110.7106.3975012778554.30249872214511
34112.8105.9186298798816.88137012011918
35109.8117.960214307733-8.1602143077326
36117.3112.9398373793464.36016262065359
37109.1108.9007345171170.199265482883037
38115.9115.1961281866630.703871813336926
399697.278665343505-1.2786653435051
4099.8100.456659812106-0.65665981210601
41116.8115.5793151188231.22068488117705
42115.7118.047329725509-2.34732972550937
4399.4106.642609111021-7.24260911102061
4494.398.4565197682442-4.15651976824419
459195.7367313969585-4.73673139695853
4693.293.7743613604872-0.57436136048724
47103.1107.021098228086-3.92109822808631
4894.195.6026892459783-1.50268924597832
4991.895.5950657768933-3.79506577689333
50102.7104.299558407327-1.59955840732663
5182.677.76279739561574.83720260438433
5289.188.9767652831510.123234716848961
53104.5105.426137818080-0.926137818079758
54105.1107.456900391438-2.3569003914384
5595.197.3199109821058-2.21991098210582
5688.789.5754831747592-0.875483174759216
5786.389.927697065224-3.62769706522403
5891.890.62406933042341.17593066957665
59111.5102.7881346506998.71186534930103
6099.795.25257065611094.44742934388914
6197.599.457457360947-1.95745736094696
62111.7111.6249298029140.0750701970862843
6386.282.18597191982964.01402808017039
6495.494.05404300835411.34595699164586

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 98.1 & 100.163562116279 & -2.0635621162787 \tabularnewline
2 & 113.9 & 110.367091275934 & 3.53290872406601 \tabularnewline
3 & 80.9 & 87.9825967548852 & -7.08259675488519 \tabularnewline
4 & 95.7 & 95.3504304016376 & 0.349569598362421 \tabularnewline
5 & 113.2 & 112.291017263688 & 0.908982736311528 \tabularnewline
6 & 105.9 & 109.727907570237 & -3.82790757023684 \tabularnewline
7 & 108.8 & 105.824023154376 & 2.97597684562353 \tabularnewline
8 & 102.3 & 97.3745746708748 & 4.92542532912516 \tabularnewline
9 & 99 & 97.434574124871 & 1.56542587512897 \tabularnewline
10 & 100.7 & 106.370983383380 & -5.67098338337979 \tabularnewline
11 & 115.5 & 114.554240303839 & 0.945759696160877 \tabularnewline
12 & 100.7 & 104.290532497281 & -3.5905324972813 \tabularnewline
13 & 109.9 & 104.458847725017 & 5.44115227498254 \tabularnewline
14 & 114.6 & 116.267293568763 & -1.66729356876312 \tabularnewline
15 & 85.4 & 88.186561316945 & -2.78656131694498 \tabularnewline
16 & 100.5 & 102.717244799970 & -2.21724479997049 \tabularnewline
17 & 114.8 & 112.144032465738 & 2.65596753426219 \tabularnewline
18 & 116.5 & 113.444600612403 & 3.05539938759667 \tabularnewline
19 & 112.9 & 109.65226445491 & 3.24773554509007 \tabularnewline
20 & 102 & 101.130183064126 & 0.869816935874058 \tabularnewline
21 & 106 & 103.503496135092 & 2.49650386490848 \tabularnewline
22 & 105.3 & 107.111956045829 & -1.81195604582879 \tabularnewline
23 & 118.8 & 116.376312509643 & 2.42368749035701 \tabularnewline
24 & 106.1 & 109.814370221283 & -3.71437022128312 \tabularnewline
25 & 109.3 & 107.124332503747 & 2.17566749625341 \tabularnewline
26 & 117.2 & 118.244998758399 & -1.04499875839947 \tabularnewline
27 & 92.5 & 90.2034072692194 & 2.29659273078056 \tabularnewline
28 & 104.2 & 103.144856694781 & 1.05514330521926 \tabularnewline
29 & 112.5 & 116.359497333671 & -3.85949733367101 \tabularnewline
30 & 122.4 & 116.923261700412 & 5.47673829958794 \tabularnewline
31 & 113.3 & 110.061192297587 & 3.23880770241283 \tabularnewline
32 & 100 & 100.763239321996 & -0.763239321995807 \tabularnewline
33 & 110.7 & 106.397501277855 & 4.30249872214511 \tabularnewline
34 & 112.8 & 105.918629879881 & 6.88137012011918 \tabularnewline
35 & 109.8 & 117.960214307733 & -8.1602143077326 \tabularnewline
36 & 117.3 & 112.939837379346 & 4.36016262065359 \tabularnewline
37 & 109.1 & 108.900734517117 & 0.199265482883037 \tabularnewline
38 & 115.9 & 115.196128186663 & 0.703871813336926 \tabularnewline
39 & 96 & 97.278665343505 & -1.2786653435051 \tabularnewline
40 & 99.8 & 100.456659812106 & -0.65665981210601 \tabularnewline
41 & 116.8 & 115.579315118823 & 1.22068488117705 \tabularnewline
42 & 115.7 & 118.047329725509 & -2.34732972550937 \tabularnewline
43 & 99.4 & 106.642609111021 & -7.24260911102061 \tabularnewline
44 & 94.3 & 98.4565197682442 & -4.15651976824419 \tabularnewline
45 & 91 & 95.7367313969585 & -4.73673139695853 \tabularnewline
46 & 93.2 & 93.7743613604872 & -0.57436136048724 \tabularnewline
47 & 103.1 & 107.021098228086 & -3.92109822808631 \tabularnewline
48 & 94.1 & 95.6026892459783 & -1.50268924597832 \tabularnewline
49 & 91.8 & 95.5950657768933 & -3.79506577689333 \tabularnewline
50 & 102.7 & 104.299558407327 & -1.59955840732663 \tabularnewline
51 & 82.6 & 77.7627973956157 & 4.83720260438433 \tabularnewline
52 & 89.1 & 88.976765283151 & 0.123234716848961 \tabularnewline
53 & 104.5 & 105.426137818080 & -0.926137818079758 \tabularnewline
54 & 105.1 & 107.456900391438 & -2.3569003914384 \tabularnewline
55 & 95.1 & 97.3199109821058 & -2.21991098210582 \tabularnewline
56 & 88.7 & 89.5754831747592 & -0.875483174759216 \tabularnewline
57 & 86.3 & 89.927697065224 & -3.62769706522403 \tabularnewline
58 & 91.8 & 90.6240693304234 & 1.17593066957665 \tabularnewline
59 & 111.5 & 102.788134650699 & 8.71186534930103 \tabularnewline
60 & 99.7 & 95.2525706561109 & 4.44742934388914 \tabularnewline
61 & 97.5 & 99.457457360947 & -1.95745736094696 \tabularnewline
62 & 111.7 & 111.624929802914 & 0.0750701970862843 \tabularnewline
63 & 86.2 & 82.1859719198296 & 4.01402808017039 \tabularnewline
64 & 95.4 & 94.0540430083541 & 1.34595699164586 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103025&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]98.1[/C][C]100.163562116279[/C][C]-2.0635621162787[/C][/ROW]
[ROW][C]2[/C][C]113.9[/C][C]110.367091275934[/C][C]3.53290872406601[/C][/ROW]
[ROW][C]3[/C][C]80.9[/C][C]87.9825967548852[/C][C]-7.08259675488519[/C][/ROW]
[ROW][C]4[/C][C]95.7[/C][C]95.3504304016376[/C][C]0.349569598362421[/C][/ROW]
[ROW][C]5[/C][C]113.2[/C][C]112.291017263688[/C][C]0.908982736311528[/C][/ROW]
[ROW][C]6[/C][C]105.9[/C][C]109.727907570237[/C][C]-3.82790757023684[/C][/ROW]
[ROW][C]7[/C][C]108.8[/C][C]105.824023154376[/C][C]2.97597684562353[/C][/ROW]
[ROW][C]8[/C][C]102.3[/C][C]97.3745746708748[/C][C]4.92542532912516[/C][/ROW]
[ROW][C]9[/C][C]99[/C][C]97.434574124871[/C][C]1.56542587512897[/C][/ROW]
[ROW][C]10[/C][C]100.7[/C][C]106.370983383380[/C][C]-5.67098338337979[/C][/ROW]
[ROW][C]11[/C][C]115.5[/C][C]114.554240303839[/C][C]0.945759696160877[/C][/ROW]
[ROW][C]12[/C][C]100.7[/C][C]104.290532497281[/C][C]-3.5905324972813[/C][/ROW]
[ROW][C]13[/C][C]109.9[/C][C]104.458847725017[/C][C]5.44115227498254[/C][/ROW]
[ROW][C]14[/C][C]114.6[/C][C]116.267293568763[/C][C]-1.66729356876312[/C][/ROW]
[ROW][C]15[/C][C]85.4[/C][C]88.186561316945[/C][C]-2.78656131694498[/C][/ROW]
[ROW][C]16[/C][C]100.5[/C][C]102.717244799970[/C][C]-2.21724479997049[/C][/ROW]
[ROW][C]17[/C][C]114.8[/C][C]112.144032465738[/C][C]2.65596753426219[/C][/ROW]
[ROW][C]18[/C][C]116.5[/C][C]113.444600612403[/C][C]3.05539938759667[/C][/ROW]
[ROW][C]19[/C][C]112.9[/C][C]109.65226445491[/C][C]3.24773554509007[/C][/ROW]
[ROW][C]20[/C][C]102[/C][C]101.130183064126[/C][C]0.869816935874058[/C][/ROW]
[ROW][C]21[/C][C]106[/C][C]103.503496135092[/C][C]2.49650386490848[/C][/ROW]
[ROW][C]22[/C][C]105.3[/C][C]107.111956045829[/C][C]-1.81195604582879[/C][/ROW]
[ROW][C]23[/C][C]118.8[/C][C]116.376312509643[/C][C]2.42368749035701[/C][/ROW]
[ROW][C]24[/C][C]106.1[/C][C]109.814370221283[/C][C]-3.71437022128312[/C][/ROW]
[ROW][C]25[/C][C]109.3[/C][C]107.124332503747[/C][C]2.17566749625341[/C][/ROW]
[ROW][C]26[/C][C]117.2[/C][C]118.244998758399[/C][C]-1.04499875839947[/C][/ROW]
[ROW][C]27[/C][C]92.5[/C][C]90.2034072692194[/C][C]2.29659273078056[/C][/ROW]
[ROW][C]28[/C][C]104.2[/C][C]103.144856694781[/C][C]1.05514330521926[/C][/ROW]
[ROW][C]29[/C][C]112.5[/C][C]116.359497333671[/C][C]-3.85949733367101[/C][/ROW]
[ROW][C]30[/C][C]122.4[/C][C]116.923261700412[/C][C]5.47673829958794[/C][/ROW]
[ROW][C]31[/C][C]113.3[/C][C]110.061192297587[/C][C]3.23880770241283[/C][/ROW]
[ROW][C]32[/C][C]100[/C][C]100.763239321996[/C][C]-0.763239321995807[/C][/ROW]
[ROW][C]33[/C][C]110.7[/C][C]106.397501277855[/C][C]4.30249872214511[/C][/ROW]
[ROW][C]34[/C][C]112.8[/C][C]105.918629879881[/C][C]6.88137012011918[/C][/ROW]
[ROW][C]35[/C][C]109.8[/C][C]117.960214307733[/C][C]-8.1602143077326[/C][/ROW]
[ROW][C]36[/C][C]117.3[/C][C]112.939837379346[/C][C]4.36016262065359[/C][/ROW]
[ROW][C]37[/C][C]109.1[/C][C]108.900734517117[/C][C]0.199265482883037[/C][/ROW]
[ROW][C]38[/C][C]115.9[/C][C]115.196128186663[/C][C]0.703871813336926[/C][/ROW]
[ROW][C]39[/C][C]96[/C][C]97.278665343505[/C][C]-1.2786653435051[/C][/ROW]
[ROW][C]40[/C][C]99.8[/C][C]100.456659812106[/C][C]-0.65665981210601[/C][/ROW]
[ROW][C]41[/C][C]116.8[/C][C]115.579315118823[/C][C]1.22068488117705[/C][/ROW]
[ROW][C]42[/C][C]115.7[/C][C]118.047329725509[/C][C]-2.34732972550937[/C][/ROW]
[ROW][C]43[/C][C]99.4[/C][C]106.642609111021[/C][C]-7.24260911102061[/C][/ROW]
[ROW][C]44[/C][C]94.3[/C][C]98.4565197682442[/C][C]-4.15651976824419[/C][/ROW]
[ROW][C]45[/C][C]91[/C][C]95.7367313969585[/C][C]-4.73673139695853[/C][/ROW]
[ROW][C]46[/C][C]93.2[/C][C]93.7743613604872[/C][C]-0.57436136048724[/C][/ROW]
[ROW][C]47[/C][C]103.1[/C][C]107.021098228086[/C][C]-3.92109822808631[/C][/ROW]
[ROW][C]48[/C][C]94.1[/C][C]95.6026892459783[/C][C]-1.50268924597832[/C][/ROW]
[ROW][C]49[/C][C]91.8[/C][C]95.5950657768933[/C][C]-3.79506577689333[/C][/ROW]
[ROW][C]50[/C][C]102.7[/C][C]104.299558407327[/C][C]-1.59955840732663[/C][/ROW]
[ROW][C]51[/C][C]82.6[/C][C]77.7627973956157[/C][C]4.83720260438433[/C][/ROW]
[ROW][C]52[/C][C]89.1[/C][C]88.976765283151[/C][C]0.123234716848961[/C][/ROW]
[ROW][C]53[/C][C]104.5[/C][C]105.426137818080[/C][C]-0.926137818079758[/C][/ROW]
[ROW][C]54[/C][C]105.1[/C][C]107.456900391438[/C][C]-2.3569003914384[/C][/ROW]
[ROW][C]55[/C][C]95.1[/C][C]97.3199109821058[/C][C]-2.21991098210582[/C][/ROW]
[ROW][C]56[/C][C]88.7[/C][C]89.5754831747592[/C][C]-0.875483174759216[/C][/ROW]
[ROW][C]57[/C][C]86.3[/C][C]89.927697065224[/C][C]-3.62769706522403[/C][/ROW]
[ROW][C]58[/C][C]91.8[/C][C]90.6240693304234[/C][C]1.17593066957665[/C][/ROW]
[ROW][C]59[/C][C]111.5[/C][C]102.788134650699[/C][C]8.71186534930103[/C][/ROW]
[ROW][C]60[/C][C]99.7[/C][C]95.2525706561109[/C][C]4.44742934388914[/C][/ROW]
[ROW][C]61[/C][C]97.5[/C][C]99.457457360947[/C][C]-1.95745736094696[/C][/ROW]
[ROW][C]62[/C][C]111.7[/C][C]111.624929802914[/C][C]0.0750701970862843[/C][/ROW]
[ROW][C]63[/C][C]86.2[/C][C]82.1859719198296[/C][C]4.01402808017039[/C][/ROW]
[ROW][C]64[/C][C]95.4[/C][C]94.0540430083541[/C][C]1.34595699164586[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103025&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103025&T=4

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


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
198.1100.163562116279-2.0635621162787
2113.9110.3670912759343.53290872406601
380.987.9825967548852-7.08259675488519
495.795.35043040163760.349569598362421
5113.2112.2910172636880.908982736311528
6105.9109.727907570237-3.82790757023684
7108.8105.8240231543762.97597684562353
8102.397.37457467087484.92542532912516
99997.4345741248711.56542587512897
10100.7106.370983383380-5.67098338337979
11115.5114.5542403038390.945759696160877
12100.7104.290532497281-3.5905324972813
13109.9104.4588477250175.44115227498254
14114.6116.267293568763-1.66729356876312
1585.488.186561316945-2.78656131694498
16100.5102.717244799970-2.21724479997049
17114.8112.1440324657382.65596753426219
18116.5113.4446006124033.05539938759667
19112.9109.652264454913.24773554509007
20102101.1301830641260.869816935874058
21106103.5034961350922.49650386490848
22105.3107.111956045829-1.81195604582879
23118.8116.3763125096432.42368749035701
24106.1109.814370221283-3.71437022128312
25109.3107.1243325037472.17566749625341
26117.2118.244998758399-1.04499875839947
2792.590.20340726921942.29659273078056
28104.2103.1448566947811.05514330521926
29112.5116.359497333671-3.85949733367101
30122.4116.9232617004125.47673829958794
31113.3110.0611922975873.23880770241283
32100100.763239321996-0.763239321995807
33110.7106.3975012778554.30249872214511
34112.8105.9186298798816.88137012011918
35109.8117.960214307733-8.1602143077326
36117.3112.9398373793464.36016262065359
37109.1108.9007345171170.199265482883037
38115.9115.1961281866630.703871813336926
399697.278665343505-1.2786653435051
4099.8100.456659812106-0.65665981210601
41116.8115.5793151188231.22068488117705
42115.7118.047329725509-2.34732972550937
4399.4106.642609111021-7.24260911102061
4494.398.4565197682442-4.15651976824419
459195.7367313969585-4.73673139695853
4693.293.7743613604872-0.57436136048724
47103.1107.021098228086-3.92109822808631
4894.195.6026892459783-1.50268924597832
4991.895.5950657768933-3.79506577689333
50102.7104.299558407327-1.59955840732663
5182.677.76279739561574.83720260438433
5289.188.9767652831510.123234716848961
53104.5105.426137818080-0.926137818079758
54105.1107.456900391438-2.3569003914384
5595.197.3199109821058-2.21991098210582
5688.789.5754831747592-0.875483174759216
5786.389.927697065224-3.62769706522403
5891.890.62406933042341.17593066957665
59111.5102.7881346506998.71186534930103
6099.795.25257065611094.44742934388914
6197.599.457457360947-1.95745736094696
62111.7111.6249298029140.0750701970862843
6386.282.18597191982964.01402808017039
6495.494.05404300835411.34595699164586






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
200.2851846371117760.5703692742235510.714815362888224
210.1689788818670900.3379577637341810.83102111813291
220.09145895738025150.1829179147605030.908541042619749
230.05146547536062560.1029309507212510.948534524639374
240.03035395709358690.06070791418717380.969646042906413
250.02073343865890610.04146687731781230.979266561341094
260.03599519008788490.07199038017576990.964004809912115
270.01758783884889750.0351756776977950.982412161151103
280.007904094835652420.01580818967130480.992095905164348
290.03231645711571840.06463291423143670.967683542884282
300.0363801230469270.0727602460938540.963619876953073
310.02578289594669760.05156579189339520.974217104053302
320.04383921936913780.08767843873827570.956160780630862
330.08828410864401640.1765682172880330.911715891355984
340.1600335942156830.3200671884313670.839966405784317
350.2695343210200450.5390686420400910.730465678979955
360.5723020333800810.8553959332398370.427697966619919
370.5173580336449480.9652839327101040.482641966355052
380.4640703511462510.9281407022925030.535929648853749
390.3632515680125980.7265031360251960.636748431987402
400.3154689152187510.6309378304375020.684531084781249
410.3368455613489060.6736911226978110.663154438651094
420.3449006222868970.6898012445737940.655099377713103
430.4544250297632870.9088500595265740.545574970236713
440.3917203193386170.7834406386772330.608279680661383

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
20 & 0.285184637111776 & 0.570369274223551 & 0.714815362888224 \tabularnewline
21 & 0.168978881867090 & 0.337957763734181 & 0.83102111813291 \tabularnewline
22 & 0.0914589573802515 & 0.182917914760503 & 0.908541042619749 \tabularnewline
23 & 0.0514654753606256 & 0.102930950721251 & 0.948534524639374 \tabularnewline
24 & 0.0303539570935869 & 0.0607079141871738 & 0.969646042906413 \tabularnewline
25 & 0.0207334386589061 & 0.0414668773178123 & 0.979266561341094 \tabularnewline
26 & 0.0359951900878849 & 0.0719903801757699 & 0.964004809912115 \tabularnewline
27 & 0.0175878388488975 & 0.035175677697795 & 0.982412161151103 \tabularnewline
28 & 0.00790409483565242 & 0.0158081896713048 & 0.992095905164348 \tabularnewline
29 & 0.0323164571157184 & 0.0646329142314367 & 0.967683542884282 \tabularnewline
30 & 0.036380123046927 & 0.072760246093854 & 0.963619876953073 \tabularnewline
31 & 0.0257828959466976 & 0.0515657918933952 & 0.974217104053302 \tabularnewline
32 & 0.0438392193691378 & 0.0876784387382757 & 0.956160780630862 \tabularnewline
33 & 0.0882841086440164 & 0.176568217288033 & 0.911715891355984 \tabularnewline
34 & 0.160033594215683 & 0.320067188431367 & 0.839966405784317 \tabularnewline
35 & 0.269534321020045 & 0.539068642040091 & 0.730465678979955 \tabularnewline
36 & 0.572302033380081 & 0.855395933239837 & 0.427697966619919 \tabularnewline
37 & 0.517358033644948 & 0.965283932710104 & 0.482641966355052 \tabularnewline
38 & 0.464070351146251 & 0.928140702292503 & 0.535929648853749 \tabularnewline
39 & 0.363251568012598 & 0.726503136025196 & 0.636748431987402 \tabularnewline
40 & 0.315468915218751 & 0.630937830437502 & 0.684531084781249 \tabularnewline
41 & 0.336845561348906 & 0.673691122697811 & 0.663154438651094 \tabularnewline
42 & 0.344900622286897 & 0.689801244573794 & 0.655099377713103 \tabularnewline
43 & 0.454425029763287 & 0.908850059526574 & 0.545574970236713 \tabularnewline
44 & 0.391720319338617 & 0.783440638677233 & 0.608279680661383 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103025&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]20[/C][C]0.285184637111776[/C][C]0.570369274223551[/C][C]0.714815362888224[/C][/ROW]
[ROW][C]21[/C][C]0.168978881867090[/C][C]0.337957763734181[/C][C]0.83102111813291[/C][/ROW]
[ROW][C]22[/C][C]0.0914589573802515[/C][C]0.182917914760503[/C][C]0.908541042619749[/C][/ROW]
[ROW][C]23[/C][C]0.0514654753606256[/C][C]0.102930950721251[/C][C]0.948534524639374[/C][/ROW]
[ROW][C]24[/C][C]0.0303539570935869[/C][C]0.0607079141871738[/C][C]0.969646042906413[/C][/ROW]
[ROW][C]25[/C][C]0.0207334386589061[/C][C]0.0414668773178123[/C][C]0.979266561341094[/C][/ROW]
[ROW][C]26[/C][C]0.0359951900878849[/C][C]0.0719903801757699[/C][C]0.964004809912115[/C][/ROW]
[ROW][C]27[/C][C]0.0175878388488975[/C][C]0.035175677697795[/C][C]0.982412161151103[/C][/ROW]
[ROW][C]28[/C][C]0.00790409483565242[/C][C]0.0158081896713048[/C][C]0.992095905164348[/C][/ROW]
[ROW][C]29[/C][C]0.0323164571157184[/C][C]0.0646329142314367[/C][C]0.967683542884282[/C][/ROW]
[ROW][C]30[/C][C]0.036380123046927[/C][C]0.072760246093854[/C][C]0.963619876953073[/C][/ROW]
[ROW][C]31[/C][C]0.0257828959466976[/C][C]0.0515657918933952[/C][C]0.974217104053302[/C][/ROW]
[ROW][C]32[/C][C]0.0438392193691378[/C][C]0.0876784387382757[/C][C]0.956160780630862[/C][/ROW]
[ROW][C]33[/C][C]0.0882841086440164[/C][C]0.176568217288033[/C][C]0.911715891355984[/C][/ROW]
[ROW][C]34[/C][C]0.160033594215683[/C][C]0.320067188431367[/C][C]0.839966405784317[/C][/ROW]
[ROW][C]35[/C][C]0.269534321020045[/C][C]0.539068642040091[/C][C]0.730465678979955[/C][/ROW]
[ROW][C]36[/C][C]0.572302033380081[/C][C]0.855395933239837[/C][C]0.427697966619919[/C][/ROW]
[ROW][C]37[/C][C]0.517358033644948[/C][C]0.965283932710104[/C][C]0.482641966355052[/C][/ROW]
[ROW][C]38[/C][C]0.464070351146251[/C][C]0.928140702292503[/C][C]0.535929648853749[/C][/ROW]
[ROW][C]39[/C][C]0.363251568012598[/C][C]0.726503136025196[/C][C]0.636748431987402[/C][/ROW]
[ROW][C]40[/C][C]0.315468915218751[/C][C]0.630937830437502[/C][C]0.684531084781249[/C][/ROW]
[ROW][C]41[/C][C]0.336845561348906[/C][C]0.673691122697811[/C][C]0.663154438651094[/C][/ROW]
[ROW][C]42[/C][C]0.344900622286897[/C][C]0.689801244573794[/C][C]0.655099377713103[/C][/ROW]
[ROW][C]43[/C][C]0.454425029763287[/C][C]0.908850059526574[/C][C]0.545574970236713[/C][/ROW]
[ROW][C]44[/C][C]0.391720319338617[/C][C]0.783440638677233[/C][C]0.608279680661383[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103025&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103025&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
200.2851846371117760.5703692742235510.714815362888224
210.1689788818670900.3379577637341810.83102111813291
220.09145895738025150.1829179147605030.908541042619749
230.05146547536062560.1029309507212510.948534524639374
240.03035395709358690.06070791418717380.969646042906413
250.02073343865890610.04146687731781230.979266561341094
260.03599519008788490.07199038017576990.964004809912115
270.01758783884889750.0351756776977950.982412161151103
280.007904094835652420.01580818967130480.992095905164348
290.03231645711571840.06463291423143670.967683542884282
300.0363801230469270.0727602460938540.963619876953073
310.02578289594669760.05156579189339520.974217104053302
320.04383921936913780.08767843873827570.956160780630862
330.08828410864401640.1765682172880330.911715891355984
340.1600335942156830.3200671884313670.839966405784317
350.2695343210200450.5390686420400910.730465678979955
360.5723020333800810.8553959332398370.427697966619919
370.5173580336449480.9652839327101040.482641966355052
380.4640703511462510.9281407022925030.535929648853749
390.3632515680125980.7265031360251960.636748431987402
400.3154689152187510.6309378304375020.684531084781249
410.3368455613489060.6736911226978110.663154438651094
420.3449006222868970.6898012445737940.655099377713103
430.4544250297632870.9088500595265740.545574970236713
440.3917203193386170.7834406386772330.608279680661383






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level30.12NOK
10% type I error level90.36NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 3 & 0.12 & NOK \tabularnewline
10% type I error level & 9 & 0.36 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103025&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]3[/C][C]0.12[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]9[/C][C]0.36[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103025&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103025&T=6

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


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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level30.12NOK
10% type I error level90.36NOK


Figures (Output of Computation)

Figure 1
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Figure 10
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Input Parameters & R Code

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')
}