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Multiple Regression met monthly dummies, een lineaire trend en een autregre...

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 10:56:31 -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/t1258739950vrszalg1sg5235m.htm/, Retrieved Fri, 29 Mar 2024 00:14:13 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58375, Retrieved Fri, 29 Mar 2024 00:14:13 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact141
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] [Multiple Regressi...] [2009-11-20 17:56:31] [d1081bd6cdf1fed9ed45c42dbd523bf1] [Current]
-    D        [Multiple Regression] [Multiple Regressi...] [2009-11-20 18:33:52] [4395c69e961f9a13a0559fd2f0a72538]
-    D        [Multiple Regression] [Paper Multiple Re...] [2009-12-17 17:01:01] [4395c69e961f9a13a0559fd2f0a72538]
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Dataseries X:
7.9	9.1	7.6	7.5	7.6	7.3
7.9	9	7.9	7.6	7.5	7.6
8.1	9.3	7.9	7.9	7.6	7.5
8.2	9.9	8.1	7.9	7.9	7.6
8	9.8	8.2	8.1	7.9	7.9
7.5	9.3	8	8.2	8.1	7.9
6.8	8.3	7.5	8	8.2	8.1
6.5	8	6.8	7.5	8	8.2
6.6	8.5	6.5	6.8	7.5	8
7.6	10.4	6.6	6.5	6.8	7.5
8	11.1	7.6	6.6	6.5	6.8
8.1	10.9	8	7.6	6.6	6.5
7.7	10	8.1	8	7.6	6.6
7.5	9.2	7.7	8.1	8	7.6
7.6	9.2	7.5	7.7	8.1	8
7.8	9.5	7.6	7.5	7.7	8.1
7.8	9.6	7.8	7.6	7.5	7.7
7.8	9.5	7.8	7.8	7.6	7.5
7.5	9.1	7.8	7.8	7.8	7.6
7.5	8.9	7.5	7.8	7.8	7.8
7.1	9	7.5	7.5	7.8	7.8
7.5	10.1	7.1	7.5	7.5	7.8
7.5	10.3	7.5	7.1	7.5	7.5
7.6	10.2	7.5	7.5	7.1	7.5
7.7	9.6	7.6	7.5	7.5	7.1
7.7	9.2	7.7	7.6	7.5	7.5
7.9	9.3	7.7	7.7	7.6	7.5
8.1	9.4	7.9	7.7	7.7	7.6
8.2	9.4	8.1	7.9	7.7	7.7
8.2	9.2	8.2	8.1	7.9	7.7
8.2	9	8.2	8.2	8.1	7.9
7.9	9	8.2	8.2	8.2	8.1
7.3	9	7.9	8.2	8.2	8.2
6.9	9.8	7.3	7.9	8.2	8.2
6.6	10	6.9	7.3	7.9	8.2
6.7	9.8	6.6	6.9	7.3	7.9
6.9	9.3	6.7	6.6	6.9	7.3
7	9	6.9	6.7	6.6	6.9
7.1	9	7	6.9	6.7	6.6
7.2	9.1	7.1	7	6.9	6.7
7.1	9.1	7.2	7.1	7	6.9
6.9	9.1	7.1	7.2	7.1	7
7	9.2	6.9	7.1	7.2	7.1
6.8	8.8	7	6.9	7.1	7.2
6.4	8.3	6.8	7	6.9	7.1
6.7	8.4	6.4	6.8	7	6.9
6.6	8.1	6.7	6.4	6.8	7
6.4	7.7	6.6	6.7	6.4	6.8
6.3	7.9	6.4	6.6	6.7	6.4
6.2	7.9	6.3	6.4	6.6	6.7
6.5	8	6.2	6.3	6.4	6.6
6.8	7.9	6.5	6.2	6.3	6.4
6.8	7.6	6.8	6.5	6.2	6.3
6.4	7.1	6.8	6.8	6.5	6.2
6.1	6.8	6.4	6.8	6.8	6.5
5.8	6.5	6.1	6.4	6.8	6.8
6.1	6.9	5.8	6.1	6.4	6.8
7.2	8.2	6.1	5.8	6.1	6.4
7.3	8.7	7.2	6.1	5.8	6.1
6.9	8.3	7.3	7.2	6.1	5.8
6.1	7.9	6.9	7.3	7.2	6.1
5.8	7.5	6.1	6.9	7.3	7.2
6.2	7.8	5.8	6.1	6.9	7.3
7.1	8.3	6.2	5.8	6.1	6.9
7.7	8.4	7.1	6.2	5.8	6.1
7.9	8.2	7.7	7.1	6.2	5.8
7.7	7.7	7.9	7.7	7.1	6.2
7.4	7.2	7.7	7.9	7.7	7.1
7.5	7.3	7.4	7.7	7.9	7.7




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 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 & 3 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58375&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]3 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=58375&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58375&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 time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Multiple Linear Regression - Estimated Regression Equation
Y[t] = -0.00358137222472382 + 0.0430371388604634X[t] + 1.59792849135609Y1[t] -0.866541593526066Y2[t] -0.115378085010860Y3[t] + 0.314938073658562Y4[t] + 0.114618565818876M1[t] + 0.0598786116445835M2[t] + 0.297454773809027M3[t] + 0.163732537521505M4[t] -0.0342366300054975M5[t] + 0.029629519712166M6[t] + 0.0750588409022986M7[t] + 0.0084280845813315M8[t] -0.0150917423286754M9[t] + 0.503060824356568M10[t] -0.379811338849724M11[t] + 0.00157047152209919t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  -0.00358137222472382 +  0.0430371388604634X[t] +  1.59792849135609Y1[t] -0.866541593526066Y2[t] -0.115378085010860Y3[t] +  0.314938073658562Y4[t] +  0.114618565818876M1[t] +  0.0598786116445835M2[t] +  0.297454773809027M3[t] +  0.163732537521505M4[t] -0.0342366300054975M5[t] +  0.029629519712166M6[t] +  0.0750588409022986M7[t] +  0.0084280845813315M8[t] -0.0150917423286754M9[t] +  0.503060824356568M10[t] -0.379811338849724M11[t] +  0.00157047152209919t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58375&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  -0.00358137222472382 +  0.0430371388604634X[t] +  1.59792849135609Y1[t] -0.866541593526066Y2[t] -0.115378085010860Y3[t] +  0.314938073658562Y4[t] +  0.114618565818876M1[t] +  0.0598786116445835M2[t] +  0.297454773809027M3[t] +  0.163732537521505M4[t] -0.0342366300054975M5[t] +  0.029629519712166M6[t] +  0.0750588409022986M7[t] +  0.0084280845813315M8[t] -0.0150917423286754M9[t] +  0.503060824356568M10[t] -0.379811338849724M11[t] +  0.00157047152209919t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58375&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58375&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] = -0.00358137222472382 + 0.0430371388604634X[t] + 1.59792849135609Y1[t] -0.866541593526066Y2[t] -0.115378085010860Y3[t] + 0.314938073658562Y4[t] + 0.114618565818876M1[t] + 0.0598786116445835M2[t] + 0.297454773809027M3[t] + 0.163732537521505M4[t] -0.0342366300054975M5[t] + 0.029629519712166M6[t] + 0.0750588409022986M7[t] + 0.0084280845813315M8[t] -0.0150917423286754M9[t] + 0.503060824356568M10[t] -0.379811338849724M11[t] + 0.00157047152209919t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-0.003581372224723820.668581-0.00540.9957470.497873
X0.04303713886046340.0555750.77440.442270.221135
Y11.597928491356090.14807810.791100
Y2-0.8665415935260660.269219-3.21870.0022410.00112
Y3-0.1153780850108600.273253-0.42220.6746250.337313
Y40.3149380736585620.1544962.03850.0467010.023351
M10.1146185658188760.1943760.58970.5580130.279006
M20.05987861164458350.1576940.37970.7057340.352867
M30.2974547738090270.1629911.8250.0738650.036932
M40.1637325375215050.1731580.94560.3488290.174414
M5-0.03423663000549750.155588-0.220.8267140.413357
M60.0296295197121660.1539720.19240.8481660.424083
M70.07505884090229860.1716190.43740.6636990.33185
M80.00842808458133150.1763250.04780.9620630.481032
M9-0.01509174232867540.168227-0.08970.9288690.464434
M100.5030608243565680.1578993.1860.0024630.001231
M11-0.3798113388497240.202474-1.87590.0664040.033202
t0.001570471522099190.0022530.69710.4889320.244466

\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) & -0.00358137222472382 & 0.668581 & -0.0054 & 0.995747 & 0.497873 \tabularnewline
X & 0.0430371388604634 & 0.055575 & 0.7744 & 0.44227 & 0.221135 \tabularnewline
Y1 & 1.59792849135609 & 0.148078 & 10.7911 & 0 & 0 \tabularnewline
Y2 & -0.866541593526066 & 0.269219 & -3.2187 & 0.002241 & 0.00112 \tabularnewline
Y3 & -0.115378085010860 & 0.273253 & -0.4222 & 0.674625 & 0.337313 \tabularnewline
Y4 & 0.314938073658562 & 0.154496 & 2.0385 & 0.046701 & 0.023351 \tabularnewline
M1 & 0.114618565818876 & 0.194376 & 0.5897 & 0.558013 & 0.279006 \tabularnewline
M2 & 0.0598786116445835 & 0.157694 & 0.3797 & 0.705734 & 0.352867 \tabularnewline
M3 & 0.297454773809027 & 0.162991 & 1.825 & 0.073865 & 0.036932 \tabularnewline
M4 & 0.163732537521505 & 0.173158 & 0.9456 & 0.348829 & 0.174414 \tabularnewline
M5 & -0.0342366300054975 & 0.155588 & -0.22 & 0.826714 & 0.413357 \tabularnewline
M6 & 0.029629519712166 & 0.153972 & 0.1924 & 0.848166 & 0.424083 \tabularnewline
M7 & 0.0750588409022986 & 0.171619 & 0.4374 & 0.663699 & 0.33185 \tabularnewline
M8 & 0.0084280845813315 & 0.176325 & 0.0478 & 0.962063 & 0.481032 \tabularnewline
M9 & -0.0150917423286754 & 0.168227 & -0.0897 & 0.928869 & 0.464434 \tabularnewline
M10 & 0.503060824356568 & 0.157899 & 3.186 & 0.002463 & 0.001231 \tabularnewline
M11 & -0.379811338849724 & 0.202474 & -1.8759 & 0.066404 & 0.033202 \tabularnewline
t & 0.00157047152209919 & 0.002253 & 0.6971 & 0.488932 & 0.244466 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58375&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]-0.00358137222472382[/C][C]0.668581[/C][C]-0.0054[/C][C]0.995747[/C][C]0.497873[/C][/ROW]
[ROW][C]X[/C][C]0.0430371388604634[/C][C]0.055575[/C][C]0.7744[/C][C]0.44227[/C][C]0.221135[/C][/ROW]
[ROW][C]Y1[/C][C]1.59792849135609[/C][C]0.148078[/C][C]10.7911[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Y2[/C][C]-0.866541593526066[/C][C]0.269219[/C][C]-3.2187[/C][C]0.002241[/C][C]0.00112[/C][/ROW]
[ROW][C]Y3[/C][C]-0.115378085010860[/C][C]0.273253[/C][C]-0.4222[/C][C]0.674625[/C][C]0.337313[/C][/ROW]
[ROW][C]Y4[/C][C]0.314938073658562[/C][C]0.154496[/C][C]2.0385[/C][C]0.046701[/C][C]0.023351[/C][/ROW]
[ROW][C]M1[/C][C]0.114618565818876[/C][C]0.194376[/C][C]0.5897[/C][C]0.558013[/C][C]0.279006[/C][/ROW]
[ROW][C]M2[/C][C]0.0598786116445835[/C][C]0.157694[/C][C]0.3797[/C][C]0.705734[/C][C]0.352867[/C][/ROW]
[ROW][C]M3[/C][C]0.297454773809027[/C][C]0.162991[/C][C]1.825[/C][C]0.073865[/C][C]0.036932[/C][/ROW]
[ROW][C]M4[/C][C]0.163732537521505[/C][C]0.173158[/C][C]0.9456[/C][C]0.348829[/C][C]0.174414[/C][/ROW]
[ROW][C]M5[/C][C]-0.0342366300054975[/C][C]0.155588[/C][C]-0.22[/C][C]0.826714[/C][C]0.413357[/C][/ROW]
[ROW][C]M6[/C][C]0.029629519712166[/C][C]0.153972[/C][C]0.1924[/C][C]0.848166[/C][C]0.424083[/C][/ROW]
[ROW][C]M7[/C][C]0.0750588409022986[/C][C]0.171619[/C][C]0.4374[/C][C]0.663699[/C][C]0.33185[/C][/ROW]
[ROW][C]M8[/C][C]0.0084280845813315[/C][C]0.176325[/C][C]0.0478[/C][C]0.962063[/C][C]0.481032[/C][/ROW]
[ROW][C]M9[/C][C]-0.0150917423286754[/C][C]0.168227[/C][C]-0.0897[/C][C]0.928869[/C][C]0.464434[/C][/ROW]
[ROW][C]M10[/C][C]0.503060824356568[/C][C]0.157899[/C][C]3.186[/C][C]0.002463[/C][C]0.001231[/C][/ROW]
[ROW][C]M11[/C][C]-0.379811338849724[/C][C]0.202474[/C][C]-1.8759[/C][C]0.066404[/C][C]0.033202[/C][/ROW]
[ROW][C]t[/C][C]0.00157047152209919[/C][C]0.002253[/C][C]0.6971[/C][C]0.488932[/C][C]0.244466[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58375&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58375&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)-0.003581372224723820.668581-0.00540.9957470.497873
X0.04303713886046340.0555750.77440.442270.221135
Y11.597928491356090.14807810.791100
Y2-0.8665415935260660.269219-3.21870.0022410.00112
Y3-0.1153780850108600.273253-0.42220.6746250.337313
Y40.3149380736585620.1544962.03850.0467010.023351
M10.1146185658188760.1943760.58970.5580130.279006
M20.05987861164458350.1576940.37970.7057340.352867
M30.2974547738090270.1629911.8250.0738650.036932
M40.1637325375215050.1731580.94560.3488290.174414
M5-0.03423663000549750.155588-0.220.8267140.413357
M60.0296295197121660.1539720.19240.8481660.424083
M70.07505884090229860.1716190.43740.6636990.33185
M80.00842808458133150.1763250.04780.9620630.481032
M9-0.01509174232867540.168227-0.08970.9288690.464434
M100.5030608243565680.1578993.1860.0024630.001231
M11-0.3798113388497240.202474-1.87590.0664040.033202
t0.001570471522099190.0022530.69710.4889320.244466







Multiple Linear Regression - Regression Statistics
Multiple R0.963039353437112
R-squared0.927444796268571
Adjusted R-squared0.903259728358095
F-TEST (value)38.3478268368572
F-TEST (DF numerator)17
F-TEST (DF denominator)51
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.205259595827174
Sum Squared Residuals2.14870658563588

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.963039353437112 \tabularnewline
R-squared & 0.927444796268571 \tabularnewline
Adjusted R-squared & 0.903259728358095 \tabularnewline
F-TEST (value) & 38.3478268368572 \tabularnewline
F-TEST (DF numerator) & 17 \tabularnewline
F-TEST (DF denominator) & 51 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.205259595827174 \tabularnewline
Sum Squared Residuals & 2.14870658563588 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58375&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.963039353437112[/C][/ROW]
[ROW][C]R-squared[/C][C]0.927444796268571[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.903259728358095[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]38.3478268368572[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]17[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]51[/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]0.205259595827174[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]2.14870658563588[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58375&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58375&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 R0.963039353437112
R-squared0.927444796268571
Adjusted R-squared0.903259728358095
F-TEST (value)38.3478268368572
F-TEST (DF numerator)17
F-TEST (DF denominator)51
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.205259595827174
Sum Squared Residuals2.14870658563588







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
17.97.57161470323220.328385296767800
27.98.01288512534686-0.112885125346857
38.17.961948806766780.138051193233223
48.28.172085405451450.0279145945485524
588.05234894808846-0.0523489480884614
67.57.666951525272-0.166951525271997
76.87.09670805838156-0.296708058381559
86.56.388026909106350.111973090893651
96.66.509498118983810.090501881016189
107.67.454042670897780.145957329102217
1187.928298082361660.0717019176383355
128.17.967683037379110.132316962620889
137.77.77443058382585-0.0744305838258467
147.57.229792673844460.270207326155542
157.67.61040766763255-0.0104076676325478
167.87.90191325373629-0.101913253736286
177.87.83985019807479-0.0398501980747887
187.87.653149363490490.146850636509507
197.57.69135249102222-0.191352491022223
207.57.201293845776150.298706154223852
217.17.44361068233211-0.343610682332107
227.57.406116602246780.09388339775322
237.57.424728950189980.0752710498100245
247.67.501341643269670.0986583567303307
257.77.57937478296220.120625217037794
267.77.70810436401226-0.00810436401225563
277.97.853362743731150.0466372562688485
288.18.065056389987760.0349436100122358
298.28.046428880914720.153571119085279
308.28.066666987810620.133333012189385
318.28.058317191127690.141682808872312
327.98.04470671255945-0.144706712559445
337.37.57487261713057-0.274872617130569
346.97.43023074967045-0.530230749670445
356.66.472903470834810.127096529165192
366.76.687661372347080.0123386276529165
376.97.05927555726046-0.159275557260464
3877.13476466790858-0.134764667908577
397.17.25437660142686-0.154376601426861
407.27.20808543069417-0.00808543069417039
417.17.1362752307029-0.0362752307028975
426.96.97522084231921-0.0752208423192126
4376.813548808863650.186451191136349
446.87.10740645222836-0.307406452228362
456.46.64928047942271-0.249280479422713
466.76.632918730446080.0670812695539187
476.66.61927050628903-0.0192705062890313
486.46.54684575319587-0.14684575319587
496.36.278122024423650.0218779755763539
506.26.34448724193971-0.144487241939710
516.56.50638070936561-0.00638070936561317
526.86.88450813124295-0.0845081312429514
536.86.87465836406415-0.0746583640641462
546.46.59250670494674-0.192506704946743
556.16.047291956052710.0527080439472889
565.85.93104004169687-0.131040041696870
576.15.753040706508480.346959293491515
587.26.976691246738910.223308753261090
597.37.55479899032452-0.254798990324520
606.96.99646819380827-0.096468193808266
616.16.33718234829564-0.237182348295637
625.85.669965926948140.130034073051858
636.26.21352347107705-0.0135234710770500
647.16.968351388887380.13164861111262
657.77.650438378154990.0495616218450148
667.97.745504576160940.154495423839061
677.77.592781494552170.107218505447832
687.47.227526038632830.172473961367174
697.57.069697395622320.430302604377684

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 7.9 & 7.5716147032322 & 0.328385296767800 \tabularnewline
2 & 7.9 & 8.01288512534686 & -0.112885125346857 \tabularnewline
3 & 8.1 & 7.96194880676678 & 0.138051193233223 \tabularnewline
4 & 8.2 & 8.17208540545145 & 0.0279145945485524 \tabularnewline
5 & 8 & 8.05234894808846 & -0.0523489480884614 \tabularnewline
6 & 7.5 & 7.666951525272 & -0.166951525271997 \tabularnewline
7 & 6.8 & 7.09670805838156 & -0.296708058381559 \tabularnewline
8 & 6.5 & 6.38802690910635 & 0.111973090893651 \tabularnewline
9 & 6.6 & 6.50949811898381 & 0.090501881016189 \tabularnewline
10 & 7.6 & 7.45404267089778 & 0.145957329102217 \tabularnewline
11 & 8 & 7.92829808236166 & 0.0717019176383355 \tabularnewline
12 & 8.1 & 7.96768303737911 & 0.132316962620889 \tabularnewline
13 & 7.7 & 7.77443058382585 & -0.0744305838258467 \tabularnewline
14 & 7.5 & 7.22979267384446 & 0.270207326155542 \tabularnewline
15 & 7.6 & 7.61040766763255 & -0.0104076676325478 \tabularnewline
16 & 7.8 & 7.90191325373629 & -0.101913253736286 \tabularnewline
17 & 7.8 & 7.83985019807479 & -0.0398501980747887 \tabularnewline
18 & 7.8 & 7.65314936349049 & 0.146850636509507 \tabularnewline
19 & 7.5 & 7.69135249102222 & -0.191352491022223 \tabularnewline
20 & 7.5 & 7.20129384577615 & 0.298706154223852 \tabularnewline
21 & 7.1 & 7.44361068233211 & -0.343610682332107 \tabularnewline
22 & 7.5 & 7.40611660224678 & 0.09388339775322 \tabularnewline
23 & 7.5 & 7.42472895018998 & 0.0752710498100245 \tabularnewline
24 & 7.6 & 7.50134164326967 & 0.0986583567303307 \tabularnewline
25 & 7.7 & 7.5793747829622 & 0.120625217037794 \tabularnewline
26 & 7.7 & 7.70810436401226 & -0.00810436401225563 \tabularnewline
27 & 7.9 & 7.85336274373115 & 0.0466372562688485 \tabularnewline
28 & 8.1 & 8.06505638998776 & 0.0349436100122358 \tabularnewline
29 & 8.2 & 8.04642888091472 & 0.153571119085279 \tabularnewline
30 & 8.2 & 8.06666698781062 & 0.133333012189385 \tabularnewline
31 & 8.2 & 8.05831719112769 & 0.141682808872312 \tabularnewline
32 & 7.9 & 8.04470671255945 & -0.144706712559445 \tabularnewline
33 & 7.3 & 7.57487261713057 & -0.274872617130569 \tabularnewline
34 & 6.9 & 7.43023074967045 & -0.530230749670445 \tabularnewline
35 & 6.6 & 6.47290347083481 & 0.127096529165192 \tabularnewline
36 & 6.7 & 6.68766137234708 & 0.0123386276529165 \tabularnewline
37 & 6.9 & 7.05927555726046 & -0.159275557260464 \tabularnewline
38 & 7 & 7.13476466790858 & -0.134764667908577 \tabularnewline
39 & 7.1 & 7.25437660142686 & -0.154376601426861 \tabularnewline
40 & 7.2 & 7.20808543069417 & -0.00808543069417039 \tabularnewline
41 & 7.1 & 7.1362752307029 & -0.0362752307028975 \tabularnewline
42 & 6.9 & 6.97522084231921 & -0.0752208423192126 \tabularnewline
43 & 7 & 6.81354880886365 & 0.186451191136349 \tabularnewline
44 & 6.8 & 7.10740645222836 & -0.307406452228362 \tabularnewline
45 & 6.4 & 6.64928047942271 & -0.249280479422713 \tabularnewline
46 & 6.7 & 6.63291873044608 & 0.0670812695539187 \tabularnewline
47 & 6.6 & 6.61927050628903 & -0.0192705062890313 \tabularnewline
48 & 6.4 & 6.54684575319587 & -0.14684575319587 \tabularnewline
49 & 6.3 & 6.27812202442365 & 0.0218779755763539 \tabularnewline
50 & 6.2 & 6.34448724193971 & -0.144487241939710 \tabularnewline
51 & 6.5 & 6.50638070936561 & -0.00638070936561317 \tabularnewline
52 & 6.8 & 6.88450813124295 & -0.0845081312429514 \tabularnewline
53 & 6.8 & 6.87465836406415 & -0.0746583640641462 \tabularnewline
54 & 6.4 & 6.59250670494674 & -0.192506704946743 \tabularnewline
55 & 6.1 & 6.04729195605271 & 0.0527080439472889 \tabularnewline
56 & 5.8 & 5.93104004169687 & -0.131040041696870 \tabularnewline
57 & 6.1 & 5.75304070650848 & 0.346959293491515 \tabularnewline
58 & 7.2 & 6.97669124673891 & 0.223308753261090 \tabularnewline
59 & 7.3 & 7.55479899032452 & -0.254798990324520 \tabularnewline
60 & 6.9 & 6.99646819380827 & -0.096468193808266 \tabularnewline
61 & 6.1 & 6.33718234829564 & -0.237182348295637 \tabularnewline
62 & 5.8 & 5.66996592694814 & 0.130034073051858 \tabularnewline
63 & 6.2 & 6.21352347107705 & -0.0135234710770500 \tabularnewline
64 & 7.1 & 6.96835138888738 & 0.13164861111262 \tabularnewline
65 & 7.7 & 7.65043837815499 & 0.0495616218450148 \tabularnewline
66 & 7.9 & 7.74550457616094 & 0.154495423839061 \tabularnewline
67 & 7.7 & 7.59278149455217 & 0.107218505447832 \tabularnewline
68 & 7.4 & 7.22752603863283 & 0.172473961367174 \tabularnewline
69 & 7.5 & 7.06969739562232 & 0.430302604377684 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58375&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]7.9[/C][C]7.5716147032322[/C][C]0.328385296767800[/C][/ROW]
[ROW][C]2[/C][C]7.9[/C][C]8.01288512534686[/C][C]-0.112885125346857[/C][/ROW]
[ROW][C]3[/C][C]8.1[/C][C]7.96194880676678[/C][C]0.138051193233223[/C][/ROW]
[ROW][C]4[/C][C]8.2[/C][C]8.17208540545145[/C][C]0.0279145945485524[/C][/ROW]
[ROW][C]5[/C][C]8[/C][C]8.05234894808846[/C][C]-0.0523489480884614[/C][/ROW]
[ROW][C]6[/C][C]7.5[/C][C]7.666951525272[/C][C]-0.166951525271997[/C][/ROW]
[ROW][C]7[/C][C]6.8[/C][C]7.09670805838156[/C][C]-0.296708058381559[/C][/ROW]
[ROW][C]8[/C][C]6.5[/C][C]6.38802690910635[/C][C]0.111973090893651[/C][/ROW]
[ROW][C]9[/C][C]6.6[/C][C]6.50949811898381[/C][C]0.090501881016189[/C][/ROW]
[ROW][C]10[/C][C]7.6[/C][C]7.45404267089778[/C][C]0.145957329102217[/C][/ROW]
[ROW][C]11[/C][C]8[/C][C]7.92829808236166[/C][C]0.0717019176383355[/C][/ROW]
[ROW][C]12[/C][C]8.1[/C][C]7.96768303737911[/C][C]0.132316962620889[/C][/ROW]
[ROW][C]13[/C][C]7.7[/C][C]7.77443058382585[/C][C]-0.0744305838258467[/C][/ROW]
[ROW][C]14[/C][C]7.5[/C][C]7.22979267384446[/C][C]0.270207326155542[/C][/ROW]
[ROW][C]15[/C][C]7.6[/C][C]7.61040766763255[/C][C]-0.0104076676325478[/C][/ROW]
[ROW][C]16[/C][C]7.8[/C][C]7.90191325373629[/C][C]-0.101913253736286[/C][/ROW]
[ROW][C]17[/C][C]7.8[/C][C]7.83985019807479[/C][C]-0.0398501980747887[/C][/ROW]
[ROW][C]18[/C][C]7.8[/C][C]7.65314936349049[/C][C]0.146850636509507[/C][/ROW]
[ROW][C]19[/C][C]7.5[/C][C]7.69135249102222[/C][C]-0.191352491022223[/C][/ROW]
[ROW][C]20[/C][C]7.5[/C][C]7.20129384577615[/C][C]0.298706154223852[/C][/ROW]
[ROW][C]21[/C][C]7.1[/C][C]7.44361068233211[/C][C]-0.343610682332107[/C][/ROW]
[ROW][C]22[/C][C]7.5[/C][C]7.40611660224678[/C][C]0.09388339775322[/C][/ROW]
[ROW][C]23[/C][C]7.5[/C][C]7.42472895018998[/C][C]0.0752710498100245[/C][/ROW]
[ROW][C]24[/C][C]7.6[/C][C]7.50134164326967[/C][C]0.0986583567303307[/C][/ROW]
[ROW][C]25[/C][C]7.7[/C][C]7.5793747829622[/C][C]0.120625217037794[/C][/ROW]
[ROW][C]26[/C][C]7.7[/C][C]7.70810436401226[/C][C]-0.00810436401225563[/C][/ROW]
[ROW][C]27[/C][C]7.9[/C][C]7.85336274373115[/C][C]0.0466372562688485[/C][/ROW]
[ROW][C]28[/C][C]8.1[/C][C]8.06505638998776[/C][C]0.0349436100122358[/C][/ROW]
[ROW][C]29[/C][C]8.2[/C][C]8.04642888091472[/C][C]0.153571119085279[/C][/ROW]
[ROW][C]30[/C][C]8.2[/C][C]8.06666698781062[/C][C]0.133333012189385[/C][/ROW]
[ROW][C]31[/C][C]8.2[/C][C]8.05831719112769[/C][C]0.141682808872312[/C][/ROW]
[ROW][C]32[/C][C]7.9[/C][C]8.04470671255945[/C][C]-0.144706712559445[/C][/ROW]
[ROW][C]33[/C][C]7.3[/C][C]7.57487261713057[/C][C]-0.274872617130569[/C][/ROW]
[ROW][C]34[/C][C]6.9[/C][C]7.43023074967045[/C][C]-0.530230749670445[/C][/ROW]
[ROW][C]35[/C][C]6.6[/C][C]6.47290347083481[/C][C]0.127096529165192[/C][/ROW]
[ROW][C]36[/C][C]6.7[/C][C]6.68766137234708[/C][C]0.0123386276529165[/C][/ROW]
[ROW][C]37[/C][C]6.9[/C][C]7.05927555726046[/C][C]-0.159275557260464[/C][/ROW]
[ROW][C]38[/C][C]7[/C][C]7.13476466790858[/C][C]-0.134764667908577[/C][/ROW]
[ROW][C]39[/C][C]7.1[/C][C]7.25437660142686[/C][C]-0.154376601426861[/C][/ROW]
[ROW][C]40[/C][C]7.2[/C][C]7.20808543069417[/C][C]-0.00808543069417039[/C][/ROW]
[ROW][C]41[/C][C]7.1[/C][C]7.1362752307029[/C][C]-0.0362752307028975[/C][/ROW]
[ROW][C]42[/C][C]6.9[/C][C]6.97522084231921[/C][C]-0.0752208423192126[/C][/ROW]
[ROW][C]43[/C][C]7[/C][C]6.81354880886365[/C][C]0.186451191136349[/C][/ROW]
[ROW][C]44[/C][C]6.8[/C][C]7.10740645222836[/C][C]-0.307406452228362[/C][/ROW]
[ROW][C]45[/C][C]6.4[/C][C]6.64928047942271[/C][C]-0.249280479422713[/C][/ROW]
[ROW][C]46[/C][C]6.7[/C][C]6.63291873044608[/C][C]0.0670812695539187[/C][/ROW]
[ROW][C]47[/C][C]6.6[/C][C]6.61927050628903[/C][C]-0.0192705062890313[/C][/ROW]
[ROW][C]48[/C][C]6.4[/C][C]6.54684575319587[/C][C]-0.14684575319587[/C][/ROW]
[ROW][C]49[/C][C]6.3[/C][C]6.27812202442365[/C][C]0.0218779755763539[/C][/ROW]
[ROW][C]50[/C][C]6.2[/C][C]6.34448724193971[/C][C]-0.144487241939710[/C][/ROW]
[ROW][C]51[/C][C]6.5[/C][C]6.50638070936561[/C][C]-0.00638070936561317[/C][/ROW]
[ROW][C]52[/C][C]6.8[/C][C]6.88450813124295[/C][C]-0.0845081312429514[/C][/ROW]
[ROW][C]53[/C][C]6.8[/C][C]6.87465836406415[/C][C]-0.0746583640641462[/C][/ROW]
[ROW][C]54[/C][C]6.4[/C][C]6.59250670494674[/C][C]-0.192506704946743[/C][/ROW]
[ROW][C]55[/C][C]6.1[/C][C]6.04729195605271[/C][C]0.0527080439472889[/C][/ROW]
[ROW][C]56[/C][C]5.8[/C][C]5.93104004169687[/C][C]-0.131040041696870[/C][/ROW]
[ROW][C]57[/C][C]6.1[/C][C]5.75304070650848[/C][C]0.346959293491515[/C][/ROW]
[ROW][C]58[/C][C]7.2[/C][C]6.97669124673891[/C][C]0.223308753261090[/C][/ROW]
[ROW][C]59[/C][C]7.3[/C][C]7.55479899032452[/C][C]-0.254798990324520[/C][/ROW]
[ROW][C]60[/C][C]6.9[/C][C]6.99646819380827[/C][C]-0.096468193808266[/C][/ROW]
[ROW][C]61[/C][C]6.1[/C][C]6.33718234829564[/C][C]-0.237182348295637[/C][/ROW]
[ROW][C]62[/C][C]5.8[/C][C]5.66996592694814[/C][C]0.130034073051858[/C][/ROW]
[ROW][C]63[/C][C]6.2[/C][C]6.21352347107705[/C][C]-0.0135234710770500[/C][/ROW]
[ROW][C]64[/C][C]7.1[/C][C]6.96835138888738[/C][C]0.13164861111262[/C][/ROW]
[ROW][C]65[/C][C]7.7[/C][C]7.65043837815499[/C][C]0.0495616218450148[/C][/ROW]
[ROW][C]66[/C][C]7.9[/C][C]7.74550457616094[/C][C]0.154495423839061[/C][/ROW]
[ROW][C]67[/C][C]7.7[/C][C]7.59278149455217[/C][C]0.107218505447832[/C][/ROW]
[ROW][C]68[/C][C]7.4[/C][C]7.22752603863283[/C][C]0.172473961367174[/C][/ROW]
[ROW][C]69[/C][C]7.5[/C][C]7.06969739562232[/C][C]0.430302604377684[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58375&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58375&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
17.97.57161470323220.328385296767800
27.98.01288512534686-0.112885125346857
38.17.961948806766780.138051193233223
48.28.172085405451450.0279145945485524
588.05234894808846-0.0523489480884614
67.57.666951525272-0.166951525271997
76.87.09670805838156-0.296708058381559
86.56.388026909106350.111973090893651
96.66.509498118983810.090501881016189
107.67.454042670897780.145957329102217
1187.928298082361660.0717019176383355
128.17.967683037379110.132316962620889
137.77.77443058382585-0.0744305838258467
147.57.229792673844460.270207326155542
157.67.61040766763255-0.0104076676325478
167.87.90191325373629-0.101913253736286
177.87.83985019807479-0.0398501980747887
187.87.653149363490490.146850636509507
197.57.69135249102222-0.191352491022223
207.57.201293845776150.298706154223852
217.17.44361068233211-0.343610682332107
227.57.406116602246780.09388339775322
237.57.424728950189980.0752710498100245
247.67.501341643269670.0986583567303307
257.77.57937478296220.120625217037794
267.77.70810436401226-0.00810436401225563
277.97.853362743731150.0466372562688485
288.18.065056389987760.0349436100122358
298.28.046428880914720.153571119085279
308.28.066666987810620.133333012189385
318.28.058317191127690.141682808872312
327.98.04470671255945-0.144706712559445
337.37.57487261713057-0.274872617130569
346.97.43023074967045-0.530230749670445
356.66.472903470834810.127096529165192
366.76.687661372347080.0123386276529165
376.97.05927555726046-0.159275557260464
3877.13476466790858-0.134764667908577
397.17.25437660142686-0.154376601426861
407.27.20808543069417-0.00808543069417039
417.17.1362752307029-0.0362752307028975
426.96.97522084231921-0.0752208423192126
4376.813548808863650.186451191136349
446.87.10740645222836-0.307406452228362
456.46.64928047942271-0.249280479422713
466.76.632918730446080.0670812695539187
476.66.61927050628903-0.0192705062890313
486.46.54684575319587-0.14684575319587
496.36.278122024423650.0218779755763539
506.26.34448724193971-0.144487241939710
516.56.50638070936561-0.00638070936561317
526.86.88450813124295-0.0845081312429514
536.86.87465836406415-0.0746583640641462
546.46.59250670494674-0.192506704946743
556.16.047291956052710.0527080439472889
565.85.93104004169687-0.131040041696870
576.15.753040706508480.346959293491515
587.26.976691246738910.223308753261090
597.37.55479899032452-0.254798990324520
606.96.99646819380827-0.096468193808266
616.16.33718234829564-0.237182348295637
625.85.669965926948140.130034073051858
636.26.21352347107705-0.0135234710770500
647.16.968351388887380.13164861111262
657.77.650438378154990.0495616218450148
667.97.745504576160940.154495423839061
677.77.592781494552170.107218505447832
687.47.227526038632830.172473961367174
697.57.069697395622320.430302604377684







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
210.8164952588836850.3670094822326290.183504741116315
220.7745009636166330.4509980727667340.225499036383367
230.6996605415476210.6006789169047570.300339458452379
240.6053536719174110.7892926561651780.394646328082589
250.5258848910463450.948230217907310.474115108953655
260.4220845343502580.8441690687005150.577915465649742
270.3370099688742120.6740199377484230.662990031125788
280.2629252898999280.5258505797998560.737074710100072
290.2734108604480210.5468217208960410.72658913955198
300.2784694775229190.5569389550458380.721530522477081
310.4614121118986180.9228242237972360.538587888101382
320.4584464527303220.9168929054606430.541553547269678
330.3817213561027450.763442712205490.618278643897255
340.8532650026310510.2934699947378970.146734997368949
350.8020518976001160.3958962047997690.197948102399884
360.7390807632044530.5218384735910930.260919236795547
370.7949404448837940.4101191102324120.205059555116206
380.7699035706086070.4601928587827860.230096429391393
390.719544617727170.560910764545660.28045538227283
400.678373867998040.643252264003920.32162613200196
410.6040287639077240.7919424721845510.395971236092276
420.4977132834938340.9954265669876680.502286716506166
430.6154707734119630.7690584531760730.384529226588037
440.6251701753404740.7496596493190520.374829824659526
450.7429395121896610.5141209756206780.257060487810339
460.9758048730005640.04839025399887160.0241951269994358
470.9362907022739110.1274185954521780.0637092977260889
480.845422537646830.3091549247063410.154577462353171

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
21 & 0.816495258883685 & 0.367009482232629 & 0.183504741116315 \tabularnewline
22 & 0.774500963616633 & 0.450998072766734 & 0.225499036383367 \tabularnewline
23 & 0.699660541547621 & 0.600678916904757 & 0.300339458452379 \tabularnewline
24 & 0.605353671917411 & 0.789292656165178 & 0.394646328082589 \tabularnewline
25 & 0.525884891046345 & 0.94823021790731 & 0.474115108953655 \tabularnewline
26 & 0.422084534350258 & 0.844169068700515 & 0.577915465649742 \tabularnewline
27 & 0.337009968874212 & 0.674019937748423 & 0.662990031125788 \tabularnewline
28 & 0.262925289899928 & 0.525850579799856 & 0.737074710100072 \tabularnewline
29 & 0.273410860448021 & 0.546821720896041 & 0.72658913955198 \tabularnewline
30 & 0.278469477522919 & 0.556938955045838 & 0.721530522477081 \tabularnewline
31 & 0.461412111898618 & 0.922824223797236 & 0.538587888101382 \tabularnewline
32 & 0.458446452730322 & 0.916892905460643 & 0.541553547269678 \tabularnewline
33 & 0.381721356102745 & 0.76344271220549 & 0.618278643897255 \tabularnewline
34 & 0.853265002631051 & 0.293469994737897 & 0.146734997368949 \tabularnewline
35 & 0.802051897600116 & 0.395896204799769 & 0.197948102399884 \tabularnewline
36 & 0.739080763204453 & 0.521838473591093 & 0.260919236795547 \tabularnewline
37 & 0.794940444883794 & 0.410119110232412 & 0.205059555116206 \tabularnewline
38 & 0.769903570608607 & 0.460192858782786 & 0.230096429391393 \tabularnewline
39 & 0.71954461772717 & 0.56091076454566 & 0.28045538227283 \tabularnewline
40 & 0.67837386799804 & 0.64325226400392 & 0.32162613200196 \tabularnewline
41 & 0.604028763907724 & 0.791942472184551 & 0.395971236092276 \tabularnewline
42 & 0.497713283493834 & 0.995426566987668 & 0.502286716506166 \tabularnewline
43 & 0.615470773411963 & 0.769058453176073 & 0.384529226588037 \tabularnewline
44 & 0.625170175340474 & 0.749659649319052 & 0.374829824659526 \tabularnewline
45 & 0.742939512189661 & 0.514120975620678 & 0.257060487810339 \tabularnewline
46 & 0.975804873000564 & 0.0483902539988716 & 0.0241951269994358 \tabularnewline
47 & 0.936290702273911 & 0.127418595452178 & 0.0637092977260889 \tabularnewline
48 & 0.84542253764683 & 0.309154924706341 & 0.154577462353171 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58375&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]0.816495258883685[/C][C]0.367009482232629[/C][C]0.183504741116315[/C][/ROW]
[ROW][C]22[/C][C]0.774500963616633[/C][C]0.450998072766734[/C][C]0.225499036383367[/C][/ROW]
[ROW][C]23[/C][C]0.699660541547621[/C][C]0.600678916904757[/C][C]0.300339458452379[/C][/ROW]
[ROW][C]24[/C][C]0.605353671917411[/C][C]0.789292656165178[/C][C]0.394646328082589[/C][/ROW]
[ROW][C]25[/C][C]0.525884891046345[/C][C]0.94823021790731[/C][C]0.474115108953655[/C][/ROW]
[ROW][C]26[/C][C]0.422084534350258[/C][C]0.844169068700515[/C][C]0.577915465649742[/C][/ROW]
[ROW][C]27[/C][C]0.337009968874212[/C][C]0.674019937748423[/C][C]0.662990031125788[/C][/ROW]
[ROW][C]28[/C][C]0.262925289899928[/C][C]0.525850579799856[/C][C]0.737074710100072[/C][/ROW]
[ROW][C]29[/C][C]0.273410860448021[/C][C]0.546821720896041[/C][C]0.72658913955198[/C][/ROW]
[ROW][C]30[/C][C]0.278469477522919[/C][C]0.556938955045838[/C][C]0.721530522477081[/C][/ROW]
[ROW][C]31[/C][C]0.461412111898618[/C][C]0.922824223797236[/C][C]0.538587888101382[/C][/ROW]
[ROW][C]32[/C][C]0.458446452730322[/C][C]0.916892905460643[/C][C]0.541553547269678[/C][/ROW]
[ROW][C]33[/C][C]0.381721356102745[/C][C]0.76344271220549[/C][C]0.618278643897255[/C][/ROW]
[ROW][C]34[/C][C]0.853265002631051[/C][C]0.293469994737897[/C][C]0.146734997368949[/C][/ROW]
[ROW][C]35[/C][C]0.802051897600116[/C][C]0.395896204799769[/C][C]0.197948102399884[/C][/ROW]
[ROW][C]36[/C][C]0.739080763204453[/C][C]0.521838473591093[/C][C]0.260919236795547[/C][/ROW]
[ROW][C]37[/C][C]0.794940444883794[/C][C]0.410119110232412[/C][C]0.205059555116206[/C][/ROW]
[ROW][C]38[/C][C]0.769903570608607[/C][C]0.460192858782786[/C][C]0.230096429391393[/C][/ROW]
[ROW][C]39[/C][C]0.71954461772717[/C][C]0.56091076454566[/C][C]0.28045538227283[/C][/ROW]
[ROW][C]40[/C][C]0.67837386799804[/C][C]0.64325226400392[/C][C]0.32162613200196[/C][/ROW]
[ROW][C]41[/C][C]0.604028763907724[/C][C]0.791942472184551[/C][C]0.395971236092276[/C][/ROW]
[ROW][C]42[/C][C]0.497713283493834[/C][C]0.995426566987668[/C][C]0.502286716506166[/C][/ROW]
[ROW][C]43[/C][C]0.615470773411963[/C][C]0.769058453176073[/C][C]0.384529226588037[/C][/ROW]
[ROW][C]44[/C][C]0.625170175340474[/C][C]0.749659649319052[/C][C]0.374829824659526[/C][/ROW]
[ROW][C]45[/C][C]0.742939512189661[/C][C]0.514120975620678[/C][C]0.257060487810339[/C][/ROW]
[ROW][C]46[/C][C]0.975804873000564[/C][C]0.0483902539988716[/C][C]0.0241951269994358[/C][/ROW]
[ROW][C]47[/C][C]0.936290702273911[/C][C]0.127418595452178[/C][C]0.0637092977260889[/C][/ROW]
[ROW][C]48[/C][C]0.84542253764683[/C][C]0.309154924706341[/C][C]0.154577462353171[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58375&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58375&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
210.8164952588836850.3670094822326290.183504741116315
220.7745009636166330.4509980727667340.225499036383367
230.6996605415476210.6006789169047570.300339458452379
240.6053536719174110.7892926561651780.394646328082589
250.5258848910463450.948230217907310.474115108953655
260.4220845343502580.8441690687005150.577915465649742
270.3370099688742120.6740199377484230.662990031125788
280.2629252898999280.5258505797998560.737074710100072
290.2734108604480210.5468217208960410.72658913955198
300.2784694775229190.5569389550458380.721530522477081
310.4614121118986180.9228242237972360.538587888101382
320.4584464527303220.9168929054606430.541553547269678
330.3817213561027450.763442712205490.618278643897255
340.8532650026310510.2934699947378970.146734997368949
350.8020518976001160.3958962047997690.197948102399884
360.7390807632044530.5218384735910930.260919236795547
370.7949404448837940.4101191102324120.205059555116206
380.7699035706086070.4601928587827860.230096429391393
390.719544617727170.560910764545660.28045538227283
400.678373867998040.643252264003920.32162613200196
410.6040287639077240.7919424721845510.395971236092276
420.4977132834938340.9954265669876680.502286716506166
430.6154707734119630.7690584531760730.384529226588037
440.6251701753404740.7496596493190520.374829824659526
450.7429395121896610.5141209756206780.257060487810339
460.9758048730005640.04839025399887160.0241951269994358
470.9362907022739110.1274185954521780.0637092977260889
480.845422537646830.3091549247063410.154577462353171







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level10.0357142857142857OK
10% type I error level10.0357142857142857OK

\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 & 1 & 0.0357142857142857 & OK \tabularnewline
10% type I error level & 1 & 0.0357142857142857 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58375&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]1[/C][C]0.0357142857142857[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]1[/C][C]0.0357142857142857[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58375&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58375&T=6

As an alternative you can also use a QR Code:  

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level10.0357142857142857OK
10% type I error level10.0357142857142857OK



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')
}