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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationFri, 20 Nov 2009 06:27:42 -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/t1258723756pbdmg6ot5avlopf.htm/, Retrieved Thu, 28 Mar 2024 19:41:07 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58140, Retrieved Thu, 28 Mar 2024 19:41:07 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsSHW WS 7 Multiple Regression - autoregression met 4 lags
Estimated Impact184
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 7 Multiple Reg...] [2009-11-20 13:27:42] [a45cc820faa25ce30779915639528ec2] [Current]
-   PD        [Multiple Regression] [Workshop 7] [2009-11-20 16:10:52] [dc3c82a565f0b2cd85906905748a1f2c]
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Dataseries X:
15.6	0	14.6	11.9	13.5	14.2
14.1	-0.2	15.6	14.6	11.9	13.5
14.9	1	14.1	15.6	14.6	11.9
14.2	0.4	14.9	14.1	15.6	14.6
14.6	1	14.2	14.9	14.1	15.6
17.2	1.7	14.6	14.2	14.9	14.1
15.4	3.1	17.2	14.6	14.2	14.9
14.3	3.3	15.4	17.2	14.6	14.2
17.5	3.1	14.3	15.4	17.2	14.6
14.5	3.5	17.5	14.3	15.4	17.2
14.4	6	14.5	17.5	14.3	15.4
16.6	5.7	14.4	14.5	17.5	14.3
16.7	4.7	16.6	14.4	14.5	17.5
16.6	4.2	16.7	16.6	14.4	14.5
16.9	3.6	16.6	16.7	16.6	14.4
15.7	4.4	16.9	16.6	16.7	16.6
16.4	2.5	15.7	16.9	16.6	16.7
18.4	-0.6	16.4	15.7	16.9	16.6
16.9	-1.9	18.4	16.4	15.7	16.9
16.5	-1.9	16.9	18.4	16.4	15.7
18.3	0.7	16.5	16.9	18.4	16.4
15.1	-0.9	18.3	16.5	16.9	18.4
15.7	-1.7	15.1	18.3	16.5	16.9
18.1	-3.1	15.7	15.1	18.3	16.5
16.8	-2.1	18.1	15.7	15.1	18.3
18.9	0.2	16.8	18.1	15.7	15.1
19	1.2	18.9	16.8	18.1	15.7
18.1	3.8	19	18.9	16.8	18.1
17.8	4	18.1	19	18.9	16.8
21.5	6.6	17.8	18.1	19	18.9
17.1	5.3	21.5	17.8	18.1	19
18.7	7.6	17.1	21.5	17.8	18.1
19	4.7	18.7	17.1	21.5	17.8
16.4	6.6	19	18.7	17.1	21.5
16.9	4.4	16.4	19	18.7	17.1
18.6	4.6	16.9	16.4	19	18.7
19.3	6	18.6	16.9	16.4	19
19.4	4.8	19.3	18.6	16.9	16.4
17.6	4	19.4	19.3	18.6	16.9
18.6	2.7	17.6	19.4	19.3	18.6
18.1	3	18.6	17.6	19.4	19.3
20.4	4.1	18.1	18.6	17.6	19.4
18.1	4	20.4	18.1	18.6	17.6
19.6	2.7	18.1	20.4	18.1	18.6
19.9	2.6	19.6	18.1	20.4	18.1
19.2	3.1	19.9	19.6	18.1	20.4
17.8	4.4	19.2	19.9	19.6	18.1
19.2	3	17.8	19.2	19.9	19.6
22	2	19.2	17.8	19.2	19.9
21.1	1.3	22	19.2	17.8	19.2
19.5	1.5	21.1	22	19.2	17.8
22.2	1.3	19.5	21.1	22	19.2
20.9	3.2	22.2	19.5	21.1	22
22.2	1.8	20.9	22.2	19.5	21.1
23.5	3.3	22.2	20.9	22.2	19.5
21.5	1	23.5	22.2	20.9	22.2
24.3	2.4	21.5	23.5	22.2	20.9
22.8	0.4	24.3	21.5	23.5	22.2
20.3	-0.1	22.8	24.3	21.5	23.5
23.7	1.3	20.3	22.8	24.3	21.5
23.3	-1.1	23.7	20.3	22.8	24.3
19.6	-4.4	23.3	23.7	20.3	22.8
18	-7.5	19.6	23.3	23.7	20.3
17.3	-12.2	18	19.6	23.3	23.7
16.8	-14.5	17.3	18	19.6	23.3
18.2	-16	16.8	17.3	18	19.6
16.5	-16.7	18.2	16.8	17.3	18
16	-16.3	16.5	18.2	16.8	17.3
18.4	-16.9	16	16.5	18.2	16.8




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=58140&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=58140&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58140&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] = + 6.08577545459943 + 0.0937233198980662X[t] + 0.353117280750058Y1[t] + 0.297388312914167Y2[t] + 0.390002938543844Y3[t] -0.412813823279897Y4[t] + 1.32708847505859M1[t] -0.690848167073465M2[t] -2.44667437247958M3[t] -1.21771370872316M4[t] -0.853217792826271M5[t] + 1.39824803727689M6[t] -1.35047284337896M7[t] -1.46577081148973M8[t] -0.108516967765969M9[t] -1.34277021223409M10[t] -2.37859652074837M11[t] + 0.0403527438512034t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  6.08577545459943 +  0.0937233198980662X[t] +  0.353117280750058Y1[t] +  0.297388312914167Y2[t] +  0.390002938543844Y3[t] -0.412813823279897Y4[t] +  1.32708847505859M1[t] -0.690848167073465M2[t] -2.44667437247958M3[t] -1.21771370872316M4[t] -0.853217792826271M5[t] +  1.39824803727689M6[t] -1.35047284337896M7[t] -1.46577081148973M8[t] -0.108516967765969M9[t] -1.34277021223409M10[t] -2.37859652074837M11[t] +  0.0403527438512034t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58140&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  6.08577545459943 +  0.0937233198980662X[t] +  0.353117280750058Y1[t] +  0.297388312914167Y2[t] +  0.390002938543844Y3[t] -0.412813823279897Y4[t] +  1.32708847505859M1[t] -0.690848167073465M2[t] -2.44667437247958M3[t] -1.21771370872316M4[t] -0.853217792826271M5[t] +  1.39824803727689M6[t] -1.35047284337896M7[t] -1.46577081148973M8[t] -0.108516967765969M9[t] -1.34277021223409M10[t] -2.37859652074837M11[t] +  0.0403527438512034t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58140&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58140&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] = + 6.08577545459943 + 0.0937233198980662X[t] + 0.353117280750058Y1[t] + 0.297388312914167Y2[t] + 0.390002938543844Y3[t] -0.412813823279897Y4[t] + 1.32708847505859M1[t] -0.690848167073465M2[t] -2.44667437247958M3[t] -1.21771370872316M4[t] -0.853217792826271M5[t] + 1.39824803727689M6[t] -1.35047284337896M7[t] -1.46577081148973M8[t] -0.108516967765969M9[t] -1.34277021223409M10[t] -2.37859652074837M11[t] + 0.0403527438512034t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)6.085775454599431.8458083.29710.0017840.000892
X0.09372331989806620.0382342.45130.0176990.00885
Y10.3531172807500580.1339952.63530.0111070.005554
Y20.2973883129141670.1318082.25620.0283730.014186
Y30.3900029385438440.1245093.13230.0028720.001436
Y4-0.4128138232798970.137136-3.01020.0040520.002026
M11.327088475058590.7627061.740.0878950.043948
M2-0.6908481670734650.889778-0.77640.4410830.220541
M3-2.446674372479580.790693-3.09430.0031990.001599
M4-1.217713708723160.641573-1.8980.0633610.03168
M5-0.8532177928262710.656872-1.29890.1998170.099909
M61.398248037276890.6731952.0770.0428540.021427
M7-1.350472843378960.809549-1.66820.1014090.050705
M8-1.465770811489730.797244-1.83850.0718080.035904
M9-0.1085169677659690.639952-0.16960.8660190.433009
M10-1.342770212234090.78138-1.71850.0917790.045889
M11-2.378596520748370.742783-3.20230.002350.001175
t0.04035274385120340.0177832.26920.0275190.013759

\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) & 6.08577545459943 & 1.845808 & 3.2971 & 0.001784 & 0.000892 \tabularnewline
X & 0.0937233198980662 & 0.038234 & 2.4513 & 0.017699 & 0.00885 \tabularnewline
Y1 & 0.353117280750058 & 0.133995 & 2.6353 & 0.011107 & 0.005554 \tabularnewline
Y2 & 0.297388312914167 & 0.131808 & 2.2562 & 0.028373 & 0.014186 \tabularnewline
Y3 & 0.390002938543844 & 0.124509 & 3.1323 & 0.002872 & 0.001436 \tabularnewline
Y4 & -0.412813823279897 & 0.137136 & -3.0102 & 0.004052 & 0.002026 \tabularnewline
M1 & 1.32708847505859 & 0.762706 & 1.74 & 0.087895 & 0.043948 \tabularnewline
M2 & -0.690848167073465 & 0.889778 & -0.7764 & 0.441083 & 0.220541 \tabularnewline
M3 & -2.44667437247958 & 0.790693 & -3.0943 & 0.003199 & 0.001599 \tabularnewline
M4 & -1.21771370872316 & 0.641573 & -1.898 & 0.063361 & 0.03168 \tabularnewline
M5 & -0.853217792826271 & 0.656872 & -1.2989 & 0.199817 & 0.099909 \tabularnewline
M6 & 1.39824803727689 & 0.673195 & 2.077 & 0.042854 & 0.021427 \tabularnewline
M7 & -1.35047284337896 & 0.809549 & -1.6682 & 0.101409 & 0.050705 \tabularnewline
M8 & -1.46577081148973 & 0.797244 & -1.8385 & 0.071808 & 0.035904 \tabularnewline
M9 & -0.108516967765969 & 0.639952 & -0.1696 & 0.866019 & 0.433009 \tabularnewline
M10 & -1.34277021223409 & 0.78138 & -1.7185 & 0.091779 & 0.045889 \tabularnewline
M11 & -2.37859652074837 & 0.742783 & -3.2023 & 0.00235 & 0.001175 \tabularnewline
t & 0.0403527438512034 & 0.017783 & 2.2692 & 0.027519 & 0.013759 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58140&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]6.08577545459943[/C][C]1.845808[/C][C]3.2971[/C][C]0.001784[/C][C]0.000892[/C][/ROW]
[ROW][C]X[/C][C]0.0937233198980662[/C][C]0.038234[/C][C]2.4513[/C][C]0.017699[/C][C]0.00885[/C][/ROW]
[ROW][C]Y1[/C][C]0.353117280750058[/C][C]0.133995[/C][C]2.6353[/C][C]0.011107[/C][C]0.005554[/C][/ROW]
[ROW][C]Y2[/C][C]0.297388312914167[/C][C]0.131808[/C][C]2.2562[/C][C]0.028373[/C][C]0.014186[/C][/ROW]
[ROW][C]Y3[/C][C]0.390002938543844[/C][C]0.124509[/C][C]3.1323[/C][C]0.002872[/C][C]0.001436[/C][/ROW]
[ROW][C]Y4[/C][C]-0.412813823279897[/C][C]0.137136[/C][C]-3.0102[/C][C]0.004052[/C][C]0.002026[/C][/ROW]
[ROW][C]M1[/C][C]1.32708847505859[/C][C]0.762706[/C][C]1.74[/C][C]0.087895[/C][C]0.043948[/C][/ROW]
[ROW][C]M2[/C][C]-0.690848167073465[/C][C]0.889778[/C][C]-0.7764[/C][C]0.441083[/C][C]0.220541[/C][/ROW]
[ROW][C]M3[/C][C]-2.44667437247958[/C][C]0.790693[/C][C]-3.0943[/C][C]0.003199[/C][C]0.001599[/C][/ROW]
[ROW][C]M4[/C][C]-1.21771370872316[/C][C]0.641573[/C][C]-1.898[/C][C]0.063361[/C][C]0.03168[/C][/ROW]
[ROW][C]M5[/C][C]-0.853217792826271[/C][C]0.656872[/C][C]-1.2989[/C][C]0.199817[/C][C]0.099909[/C][/ROW]
[ROW][C]M6[/C][C]1.39824803727689[/C][C]0.673195[/C][C]2.077[/C][C]0.042854[/C][C]0.021427[/C][/ROW]
[ROW][C]M7[/C][C]-1.35047284337896[/C][C]0.809549[/C][C]-1.6682[/C][C]0.101409[/C][C]0.050705[/C][/ROW]
[ROW][C]M8[/C][C]-1.46577081148973[/C][C]0.797244[/C][C]-1.8385[/C][C]0.071808[/C][C]0.035904[/C][/ROW]
[ROW][C]M9[/C][C]-0.108516967765969[/C][C]0.639952[/C][C]-0.1696[/C][C]0.866019[/C][C]0.433009[/C][/ROW]
[ROW][C]M10[/C][C]-1.34277021223409[/C][C]0.78138[/C][C]-1.7185[/C][C]0.091779[/C][C]0.045889[/C][/ROW]
[ROW][C]M11[/C][C]-2.37859652074837[/C][C]0.742783[/C][C]-3.2023[/C][C]0.00235[/C][C]0.001175[/C][/ROW]
[ROW][C]t[/C][C]0.0403527438512034[/C][C]0.017783[/C][C]2.2692[/C][C]0.027519[/C][C]0.013759[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58140&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58140&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)6.085775454599431.8458083.29710.0017840.000892
X0.09372331989806620.0382342.45130.0176990.00885
Y10.3531172807500580.1339952.63530.0111070.005554
Y20.2973883129141670.1318082.25620.0283730.014186
Y30.3900029385438440.1245093.13230.0028720.001436
Y4-0.4128138232798970.137136-3.01020.0040520.002026
M11.327088475058590.7627061.740.0878950.043948
M2-0.6908481670734650.889778-0.77640.4410830.220541
M3-2.446674372479580.790693-3.09430.0031990.001599
M4-1.217713708723160.641573-1.8980.0633610.03168
M5-0.8532177928262710.656872-1.29890.1998170.099909
M61.398248037276890.6731952.0770.0428540.021427
M7-1.350472843378960.809549-1.66820.1014090.050705
M8-1.465770811489730.797244-1.83850.0718080.035904
M9-0.1085169677659690.639952-0.16960.8660190.433009
M10-1.342770212234090.78138-1.71850.0917790.045889
M11-2.378596520748370.742783-3.20230.002350.001175
t0.04035274385120340.0177832.26920.0275190.013759







Multiple Linear Regression - Regression Statistics
Multiple R0.940286933379321
R-squared0.884139517083888
Adjusted R-squared0.84551935611185
F-TEST (value)22.8932115980574
F-TEST (DF numerator)17
F-TEST (DF denominator)51
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.983511283994648
Sum Squared Residuals49.3320167329849

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.940286933379321 \tabularnewline
R-squared & 0.884139517083888 \tabularnewline
Adjusted R-squared & 0.84551935611185 \tabularnewline
F-TEST (value) & 22.8932115980574 \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 & 0.983511283994648 \tabularnewline
Sum Squared Residuals & 49.3320167329849 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58140&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.940286933379321[/C][/ROW]
[ROW][C]R-squared[/C][C]0.884139517083888[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.84551935611185[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]22.8932115980574[/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]0.983511283994648[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]49.3320167329849[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58140&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58140&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 R0.940286933379321
R-squared0.884139517083888
Adjusted R-squared0.84551935611185
F-TEST (value)22.8932115980574
F-TEST (DF numerator)17
F-TEST (DF denominator)51
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.983511283994648
Sum Squared Residuals49.3320167329849







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
115.615.5507332759060.049266724093985
214.114.3754354138896-0.275435413889626
314.914.25365237931770.646347620682323
414.214.5785487659034-0.378548765903390
514.614.03254174030100.567458259699044
617.217.254264817199-0.0542648171989887
715.415.11091646776280.289083532237153
814.315.6372852674231-1.33728526742308
917.516.94130132984990.558698670150091
1014.514.8124170814802-0.312417081480165
1114.414.2576042251430.142395774856993
1216.617.4230644359038-0.82306443590384
1316.716.9528884711471-0.252888471147079
1416.616.8174501053887-0.217450105388667
1516.915.93945760223580.960542397764226
1615.716.4907559013341-0.790755901334058
1716.416.30272433406760.0972756659323625
1818.418.35259900125720.0474009987428246
1916.915.84494925588851.0550507441115
2016.516.6034833812487-0.103483381248741
2118.318.14847741967920.151522580320833
2215.115.9106433330342-0.810643333034218
2315.714.70873433680030.991265663199682
2418.117.12382953935820.976170460641827
2516.817.6198342544706-0.81983425447056
2618.917.66749947559621.23250052440384
271917.08900957526331.91099042473669
2818.117.76407380382190.335926196178095
2917.819.2552645473719-1.45526454737192
3021.520.58927035218010.910729647819914
3117.118.5840953173915-1.48409531739147
3218.718.52586601076840.174133989231630
331920.4750090626129-1.47500906261290
3416.416.7975152789615-0.397515278961452
3516.917.2073474985484-0.307347498548432
3618.618.50488921824110.0951107817588718
3719.319.6146848315424-0.314684831542436
3819.419.5456925876627-0.145692587662684
3917.618.4553221011888-0.855322101188823
4018.618.56814147627510.0318585237248646
4118.118.5689560670557-0.468956067055667
4220.420.34141329373010.0585867062698637
4318.119.4202162346514-1.32021623465139
4419.618.48743877595001.11256122405004
4519.920.8247695032484-0.924769503248421
4619.218.38326976398220.816730236017805
4717.818.8861471138953-1.08614711389535
4819.219.9691278651909-0.769127865190936
492220.92402011520831.07597988479175
5021.120.02886747951331.07113252048670
5119.519.9709638719758-0.470963871975847
5222.220.90296436011191.29703563988808
5320.920.45660133515570.443398664844337
5422.222.7086309804277-0.508630980427662
5523.521.9268055329731.57319446702701
5621.520.86035280174860.639647198251358
5724.323.11320407284011.18679592715994
5822.822.09615454254200.70384545745803
5920.320.04016682561290.259833174387105
6023.723.17908894130590.520911058694078
6123.323.03783905172570.262160948274342
6219.621.2650549379496-1.66505493794956
631820.1915944700186-2.19159447001858
6417.317.7955156925536-0.49551569255359
6516.815.98391197604820.816088023951838
6618.218.6538215552059-0.453821555205949
6716.516.6130171913328-0.113017191332803
681616.4855737628612-0.485573762861198
6918.417.89723861176950.502761388230456

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 15.6 & 15.550733275906 & 0.049266724093985 \tabularnewline
2 & 14.1 & 14.3754354138896 & -0.275435413889626 \tabularnewline
3 & 14.9 & 14.2536523793177 & 0.646347620682323 \tabularnewline
4 & 14.2 & 14.5785487659034 & -0.378548765903390 \tabularnewline
5 & 14.6 & 14.0325417403010 & 0.567458259699044 \tabularnewline
6 & 17.2 & 17.254264817199 & -0.0542648171989887 \tabularnewline
7 & 15.4 & 15.1109164677628 & 0.289083532237153 \tabularnewline
8 & 14.3 & 15.6372852674231 & -1.33728526742308 \tabularnewline
9 & 17.5 & 16.9413013298499 & 0.558698670150091 \tabularnewline
10 & 14.5 & 14.8124170814802 & -0.312417081480165 \tabularnewline
11 & 14.4 & 14.257604225143 & 0.142395774856993 \tabularnewline
12 & 16.6 & 17.4230644359038 & -0.82306443590384 \tabularnewline
13 & 16.7 & 16.9528884711471 & -0.252888471147079 \tabularnewline
14 & 16.6 & 16.8174501053887 & -0.217450105388667 \tabularnewline
15 & 16.9 & 15.9394576022358 & 0.960542397764226 \tabularnewline
16 & 15.7 & 16.4907559013341 & -0.790755901334058 \tabularnewline
17 & 16.4 & 16.3027243340676 & 0.0972756659323625 \tabularnewline
18 & 18.4 & 18.3525990012572 & 0.0474009987428246 \tabularnewline
19 & 16.9 & 15.8449492558885 & 1.0550507441115 \tabularnewline
20 & 16.5 & 16.6034833812487 & -0.103483381248741 \tabularnewline
21 & 18.3 & 18.1484774196792 & 0.151522580320833 \tabularnewline
22 & 15.1 & 15.9106433330342 & -0.810643333034218 \tabularnewline
23 & 15.7 & 14.7087343368003 & 0.991265663199682 \tabularnewline
24 & 18.1 & 17.1238295393582 & 0.976170460641827 \tabularnewline
25 & 16.8 & 17.6198342544706 & -0.81983425447056 \tabularnewline
26 & 18.9 & 17.6674994755962 & 1.23250052440384 \tabularnewline
27 & 19 & 17.0890095752633 & 1.91099042473669 \tabularnewline
28 & 18.1 & 17.7640738038219 & 0.335926196178095 \tabularnewline
29 & 17.8 & 19.2552645473719 & -1.45526454737192 \tabularnewline
30 & 21.5 & 20.5892703521801 & 0.910729647819914 \tabularnewline
31 & 17.1 & 18.5840953173915 & -1.48409531739147 \tabularnewline
32 & 18.7 & 18.5258660107684 & 0.174133989231630 \tabularnewline
33 & 19 & 20.4750090626129 & -1.47500906261290 \tabularnewline
34 & 16.4 & 16.7975152789615 & -0.397515278961452 \tabularnewline
35 & 16.9 & 17.2073474985484 & -0.307347498548432 \tabularnewline
36 & 18.6 & 18.5048892182411 & 0.0951107817588718 \tabularnewline
37 & 19.3 & 19.6146848315424 & -0.314684831542436 \tabularnewline
38 & 19.4 & 19.5456925876627 & -0.145692587662684 \tabularnewline
39 & 17.6 & 18.4553221011888 & -0.855322101188823 \tabularnewline
40 & 18.6 & 18.5681414762751 & 0.0318585237248646 \tabularnewline
41 & 18.1 & 18.5689560670557 & -0.468956067055667 \tabularnewline
42 & 20.4 & 20.3414132937301 & 0.0585867062698637 \tabularnewline
43 & 18.1 & 19.4202162346514 & -1.32021623465139 \tabularnewline
44 & 19.6 & 18.4874387759500 & 1.11256122405004 \tabularnewline
45 & 19.9 & 20.8247695032484 & -0.924769503248421 \tabularnewline
46 & 19.2 & 18.3832697639822 & 0.816730236017805 \tabularnewline
47 & 17.8 & 18.8861471138953 & -1.08614711389535 \tabularnewline
48 & 19.2 & 19.9691278651909 & -0.769127865190936 \tabularnewline
49 & 22 & 20.9240201152083 & 1.07597988479175 \tabularnewline
50 & 21.1 & 20.0288674795133 & 1.07113252048670 \tabularnewline
51 & 19.5 & 19.9709638719758 & -0.470963871975847 \tabularnewline
52 & 22.2 & 20.9029643601119 & 1.29703563988808 \tabularnewline
53 & 20.9 & 20.4566013351557 & 0.443398664844337 \tabularnewline
54 & 22.2 & 22.7086309804277 & -0.508630980427662 \tabularnewline
55 & 23.5 & 21.926805532973 & 1.57319446702701 \tabularnewline
56 & 21.5 & 20.8603528017486 & 0.639647198251358 \tabularnewline
57 & 24.3 & 23.1132040728401 & 1.18679592715994 \tabularnewline
58 & 22.8 & 22.0961545425420 & 0.70384545745803 \tabularnewline
59 & 20.3 & 20.0401668256129 & 0.259833174387105 \tabularnewline
60 & 23.7 & 23.1790889413059 & 0.520911058694078 \tabularnewline
61 & 23.3 & 23.0378390517257 & 0.262160948274342 \tabularnewline
62 & 19.6 & 21.2650549379496 & -1.66505493794956 \tabularnewline
63 & 18 & 20.1915944700186 & -2.19159447001858 \tabularnewline
64 & 17.3 & 17.7955156925536 & -0.49551569255359 \tabularnewline
65 & 16.8 & 15.9839119760482 & 0.816088023951838 \tabularnewline
66 & 18.2 & 18.6538215552059 & -0.453821555205949 \tabularnewline
67 & 16.5 & 16.6130171913328 & -0.113017191332803 \tabularnewline
68 & 16 & 16.4855737628612 & -0.485573762861198 \tabularnewline
69 & 18.4 & 17.8972386117695 & 0.502761388230456 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58140&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]15.6[/C][C]15.550733275906[/C][C]0.049266724093985[/C][/ROW]
[ROW][C]2[/C][C]14.1[/C][C]14.3754354138896[/C][C]-0.275435413889626[/C][/ROW]
[ROW][C]3[/C][C]14.9[/C][C]14.2536523793177[/C][C]0.646347620682323[/C][/ROW]
[ROW][C]4[/C][C]14.2[/C][C]14.5785487659034[/C][C]-0.378548765903390[/C][/ROW]
[ROW][C]5[/C][C]14.6[/C][C]14.0325417403010[/C][C]0.567458259699044[/C][/ROW]
[ROW][C]6[/C][C]17.2[/C][C]17.254264817199[/C][C]-0.0542648171989887[/C][/ROW]
[ROW][C]7[/C][C]15.4[/C][C]15.1109164677628[/C][C]0.289083532237153[/C][/ROW]
[ROW][C]8[/C][C]14.3[/C][C]15.6372852674231[/C][C]-1.33728526742308[/C][/ROW]
[ROW][C]9[/C][C]17.5[/C][C]16.9413013298499[/C][C]0.558698670150091[/C][/ROW]
[ROW][C]10[/C][C]14.5[/C][C]14.8124170814802[/C][C]-0.312417081480165[/C][/ROW]
[ROW][C]11[/C][C]14.4[/C][C]14.257604225143[/C][C]0.142395774856993[/C][/ROW]
[ROW][C]12[/C][C]16.6[/C][C]17.4230644359038[/C][C]-0.82306443590384[/C][/ROW]
[ROW][C]13[/C][C]16.7[/C][C]16.9528884711471[/C][C]-0.252888471147079[/C][/ROW]
[ROW][C]14[/C][C]16.6[/C][C]16.8174501053887[/C][C]-0.217450105388667[/C][/ROW]
[ROW][C]15[/C][C]16.9[/C][C]15.9394576022358[/C][C]0.960542397764226[/C][/ROW]
[ROW][C]16[/C][C]15.7[/C][C]16.4907559013341[/C][C]-0.790755901334058[/C][/ROW]
[ROW][C]17[/C][C]16.4[/C][C]16.3027243340676[/C][C]0.0972756659323625[/C][/ROW]
[ROW][C]18[/C][C]18.4[/C][C]18.3525990012572[/C][C]0.0474009987428246[/C][/ROW]
[ROW][C]19[/C][C]16.9[/C][C]15.8449492558885[/C][C]1.0550507441115[/C][/ROW]
[ROW][C]20[/C][C]16.5[/C][C]16.6034833812487[/C][C]-0.103483381248741[/C][/ROW]
[ROW][C]21[/C][C]18.3[/C][C]18.1484774196792[/C][C]0.151522580320833[/C][/ROW]
[ROW][C]22[/C][C]15.1[/C][C]15.9106433330342[/C][C]-0.810643333034218[/C][/ROW]
[ROW][C]23[/C][C]15.7[/C][C]14.7087343368003[/C][C]0.991265663199682[/C][/ROW]
[ROW][C]24[/C][C]18.1[/C][C]17.1238295393582[/C][C]0.976170460641827[/C][/ROW]
[ROW][C]25[/C][C]16.8[/C][C]17.6198342544706[/C][C]-0.81983425447056[/C][/ROW]
[ROW][C]26[/C][C]18.9[/C][C]17.6674994755962[/C][C]1.23250052440384[/C][/ROW]
[ROW][C]27[/C][C]19[/C][C]17.0890095752633[/C][C]1.91099042473669[/C][/ROW]
[ROW][C]28[/C][C]18.1[/C][C]17.7640738038219[/C][C]0.335926196178095[/C][/ROW]
[ROW][C]29[/C][C]17.8[/C][C]19.2552645473719[/C][C]-1.45526454737192[/C][/ROW]
[ROW][C]30[/C][C]21.5[/C][C]20.5892703521801[/C][C]0.910729647819914[/C][/ROW]
[ROW][C]31[/C][C]17.1[/C][C]18.5840953173915[/C][C]-1.48409531739147[/C][/ROW]
[ROW][C]32[/C][C]18.7[/C][C]18.5258660107684[/C][C]0.174133989231630[/C][/ROW]
[ROW][C]33[/C][C]19[/C][C]20.4750090626129[/C][C]-1.47500906261290[/C][/ROW]
[ROW][C]34[/C][C]16.4[/C][C]16.7975152789615[/C][C]-0.397515278961452[/C][/ROW]
[ROW][C]35[/C][C]16.9[/C][C]17.2073474985484[/C][C]-0.307347498548432[/C][/ROW]
[ROW][C]36[/C][C]18.6[/C][C]18.5048892182411[/C][C]0.0951107817588718[/C][/ROW]
[ROW][C]37[/C][C]19.3[/C][C]19.6146848315424[/C][C]-0.314684831542436[/C][/ROW]
[ROW][C]38[/C][C]19.4[/C][C]19.5456925876627[/C][C]-0.145692587662684[/C][/ROW]
[ROW][C]39[/C][C]17.6[/C][C]18.4553221011888[/C][C]-0.855322101188823[/C][/ROW]
[ROW][C]40[/C][C]18.6[/C][C]18.5681414762751[/C][C]0.0318585237248646[/C][/ROW]
[ROW][C]41[/C][C]18.1[/C][C]18.5689560670557[/C][C]-0.468956067055667[/C][/ROW]
[ROW][C]42[/C][C]20.4[/C][C]20.3414132937301[/C][C]0.0585867062698637[/C][/ROW]
[ROW][C]43[/C][C]18.1[/C][C]19.4202162346514[/C][C]-1.32021623465139[/C][/ROW]
[ROW][C]44[/C][C]19.6[/C][C]18.4874387759500[/C][C]1.11256122405004[/C][/ROW]
[ROW][C]45[/C][C]19.9[/C][C]20.8247695032484[/C][C]-0.924769503248421[/C][/ROW]
[ROW][C]46[/C][C]19.2[/C][C]18.3832697639822[/C][C]0.816730236017805[/C][/ROW]
[ROW][C]47[/C][C]17.8[/C][C]18.8861471138953[/C][C]-1.08614711389535[/C][/ROW]
[ROW][C]48[/C][C]19.2[/C][C]19.9691278651909[/C][C]-0.769127865190936[/C][/ROW]
[ROW][C]49[/C][C]22[/C][C]20.9240201152083[/C][C]1.07597988479175[/C][/ROW]
[ROW][C]50[/C][C]21.1[/C][C]20.0288674795133[/C][C]1.07113252048670[/C][/ROW]
[ROW][C]51[/C][C]19.5[/C][C]19.9709638719758[/C][C]-0.470963871975847[/C][/ROW]
[ROW][C]52[/C][C]22.2[/C][C]20.9029643601119[/C][C]1.29703563988808[/C][/ROW]
[ROW][C]53[/C][C]20.9[/C][C]20.4566013351557[/C][C]0.443398664844337[/C][/ROW]
[ROW][C]54[/C][C]22.2[/C][C]22.7086309804277[/C][C]-0.508630980427662[/C][/ROW]
[ROW][C]55[/C][C]23.5[/C][C]21.926805532973[/C][C]1.57319446702701[/C][/ROW]
[ROW][C]56[/C][C]21.5[/C][C]20.8603528017486[/C][C]0.639647198251358[/C][/ROW]
[ROW][C]57[/C][C]24.3[/C][C]23.1132040728401[/C][C]1.18679592715994[/C][/ROW]
[ROW][C]58[/C][C]22.8[/C][C]22.0961545425420[/C][C]0.70384545745803[/C][/ROW]
[ROW][C]59[/C][C]20.3[/C][C]20.0401668256129[/C][C]0.259833174387105[/C][/ROW]
[ROW][C]60[/C][C]23.7[/C][C]23.1790889413059[/C][C]0.520911058694078[/C][/ROW]
[ROW][C]61[/C][C]23.3[/C][C]23.0378390517257[/C][C]0.262160948274342[/C][/ROW]
[ROW][C]62[/C][C]19.6[/C][C]21.2650549379496[/C][C]-1.66505493794956[/C][/ROW]
[ROW][C]63[/C][C]18[/C][C]20.1915944700186[/C][C]-2.19159447001858[/C][/ROW]
[ROW][C]64[/C][C]17.3[/C][C]17.7955156925536[/C][C]-0.49551569255359[/C][/ROW]
[ROW][C]65[/C][C]16.8[/C][C]15.9839119760482[/C][C]0.816088023951838[/C][/ROW]
[ROW][C]66[/C][C]18.2[/C][C]18.6538215552059[/C][C]-0.453821555205949[/C][/ROW]
[ROW][C]67[/C][C]16.5[/C][C]16.6130171913328[/C][C]-0.113017191332803[/C][/ROW]
[ROW][C]68[/C][C]16[/C][C]16.4855737628612[/C][C]-0.485573762861198[/C][/ROW]
[ROW][C]69[/C][C]18.4[/C][C]17.8972386117695[/C][C]0.502761388230456[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58140&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58140&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
115.615.5507332759060.049266724093985
214.114.3754354138896-0.275435413889626
314.914.25365237931770.646347620682323
414.214.5785487659034-0.378548765903390
514.614.03254174030100.567458259699044
617.217.254264817199-0.0542648171989887
715.415.11091646776280.289083532237153
814.315.6372852674231-1.33728526742308
917.516.94130132984990.558698670150091
1014.514.8124170814802-0.312417081480165
1114.414.2576042251430.142395774856993
1216.617.4230644359038-0.82306443590384
1316.716.9528884711471-0.252888471147079
1416.616.8174501053887-0.217450105388667
1516.915.93945760223580.960542397764226
1615.716.4907559013341-0.790755901334058
1716.416.30272433406760.0972756659323625
1818.418.35259900125720.0474009987428246
1916.915.84494925588851.0550507441115
2016.516.6034833812487-0.103483381248741
2118.318.14847741967920.151522580320833
2215.115.9106433330342-0.810643333034218
2315.714.70873433680030.991265663199682
2418.117.12382953935820.976170460641827
2516.817.6198342544706-0.81983425447056
2618.917.66749947559621.23250052440384
271917.08900957526331.91099042473669
2818.117.76407380382190.335926196178095
2917.819.2552645473719-1.45526454737192
3021.520.58927035218010.910729647819914
3117.118.5840953173915-1.48409531739147
3218.718.52586601076840.174133989231630
331920.4750090626129-1.47500906261290
3416.416.7975152789615-0.397515278961452
3516.917.2073474985484-0.307347498548432
3618.618.50488921824110.0951107817588718
3719.319.6146848315424-0.314684831542436
3819.419.5456925876627-0.145692587662684
3917.618.4553221011888-0.855322101188823
4018.618.56814147627510.0318585237248646
4118.118.5689560670557-0.468956067055667
4220.420.34141329373010.0585867062698637
4318.119.4202162346514-1.32021623465139
4419.618.48743877595001.11256122405004
4519.920.8247695032484-0.924769503248421
4619.218.38326976398220.816730236017805
4717.818.8861471138953-1.08614711389535
4819.219.9691278651909-0.769127865190936
492220.92402011520831.07597988479175
5021.120.02886747951331.07113252048670
5119.519.9709638719758-0.470963871975847
5222.220.90296436011191.29703563988808
5320.920.45660133515570.443398664844337
5422.222.7086309804277-0.508630980427662
5523.521.9268055329731.57319446702701
5621.520.86035280174860.639647198251358
5724.323.11320407284011.18679592715994
5822.822.09615454254200.70384545745803
5920.320.04016682561290.259833174387105
6023.723.17908894130590.520911058694078
6123.323.03783905172570.262160948274342
6219.621.2650549379496-1.66505493794956
631820.1915944700186-2.19159447001858
6417.317.7955156925536-0.49551569255359
6516.815.98391197604820.816088023951838
6618.218.6538215552059-0.453821555205949
6716.516.6130171913328-0.113017191332803
681616.4855737628612-0.485573762861198
6918.417.89723861176950.502761388230456







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
210.02055455327151870.04110910654303750.979445446728481
220.01885258256247310.03770516512494630.981147417437527
230.01152968069278790.02305936138557590.988470319307212
240.003973835618004730.007947671236009460.996026164381995
250.003734760836442490.007469521672884980.996265239163558
260.002485302672558160.004970605345116310.997514697327442
270.004028698638648680.008057397277297350.995971301361351
280.04497323378564370.08994646757128750.955026766214356
290.02524283485377290.05048566970754580.974757165146227
300.03461284212321810.06922568424643620.965387157876782
310.08987248209735750.1797449641947150.910127517902642
320.06206696446636090.1241339289327220.93793303553364
330.1008498226520590.2016996453041190.89915017734794
340.1355407604855400.2710815209710790.86445923951446
350.1324856142924850.2649712285849690.867514385707515
360.1329707290209640.2659414580419280.867029270979036
370.09136608945618510.1827321789123700.908633910543815
380.06535201866248920.1307040373249780.934647981337511
390.1372866028713770.2745732057427530.862713397128623
400.09544834987740540.1908966997548110.904551650122595
410.05967484861972790.1193496972394560.940325151380272
420.03796517677147820.07593035354295640.962034823228522
430.04190862759068830.08381725518137660.958091372409312
440.06393656808160040.1278731361632010.9360634319184
450.04202495228361880.08404990456723760.957975047716381
460.0323466355872910.0646932711745820.967653364412709
470.05873583364508670.1174716672901730.941264166354913
480.1984066036888570.3968132073777140.801593396311143

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
21 & 0.0205545532715187 & 0.0411091065430375 & 0.979445446728481 \tabularnewline
22 & 0.0188525825624731 & 0.0377051651249463 & 0.981147417437527 \tabularnewline
23 & 0.0115296806927879 & 0.0230593613855759 & 0.988470319307212 \tabularnewline
24 & 0.00397383561800473 & 0.00794767123600946 & 0.996026164381995 \tabularnewline
25 & 0.00373476083644249 & 0.00746952167288498 & 0.996265239163558 \tabularnewline
26 & 0.00248530267255816 & 0.00497060534511631 & 0.997514697327442 \tabularnewline
27 & 0.00402869863864868 & 0.00805739727729735 & 0.995971301361351 \tabularnewline
28 & 0.0449732337856437 & 0.0899464675712875 & 0.955026766214356 \tabularnewline
29 & 0.0252428348537729 & 0.0504856697075458 & 0.974757165146227 \tabularnewline
30 & 0.0346128421232181 & 0.0692256842464362 & 0.965387157876782 \tabularnewline
31 & 0.0898724820973575 & 0.179744964194715 & 0.910127517902642 \tabularnewline
32 & 0.0620669644663609 & 0.124133928932722 & 0.93793303553364 \tabularnewline
33 & 0.100849822652059 & 0.201699645304119 & 0.89915017734794 \tabularnewline
34 & 0.135540760485540 & 0.271081520971079 & 0.86445923951446 \tabularnewline
35 & 0.132485614292485 & 0.264971228584969 & 0.867514385707515 \tabularnewline
36 & 0.132970729020964 & 0.265941458041928 & 0.867029270979036 \tabularnewline
37 & 0.0913660894561851 & 0.182732178912370 & 0.908633910543815 \tabularnewline
38 & 0.0653520186624892 & 0.130704037324978 & 0.934647981337511 \tabularnewline
39 & 0.137286602871377 & 0.274573205742753 & 0.862713397128623 \tabularnewline
40 & 0.0954483498774054 & 0.190896699754811 & 0.904551650122595 \tabularnewline
41 & 0.0596748486197279 & 0.119349697239456 & 0.940325151380272 \tabularnewline
42 & 0.0379651767714782 & 0.0759303535429564 & 0.962034823228522 \tabularnewline
43 & 0.0419086275906883 & 0.0838172551813766 & 0.958091372409312 \tabularnewline
44 & 0.0639365680816004 & 0.127873136163201 & 0.9360634319184 \tabularnewline
45 & 0.0420249522836188 & 0.0840499045672376 & 0.957975047716381 \tabularnewline
46 & 0.032346635587291 & 0.064693271174582 & 0.967653364412709 \tabularnewline
47 & 0.0587358336450867 & 0.117471667290173 & 0.941264166354913 \tabularnewline
48 & 0.198406603688857 & 0.396813207377714 & 0.801593396311143 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58140&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]0.0205545532715187[/C][C]0.0411091065430375[/C][C]0.979445446728481[/C][/ROW]
[ROW][C]22[/C][C]0.0188525825624731[/C][C]0.0377051651249463[/C][C]0.981147417437527[/C][/ROW]
[ROW][C]23[/C][C]0.0115296806927879[/C][C]0.0230593613855759[/C][C]0.988470319307212[/C][/ROW]
[ROW][C]24[/C][C]0.00397383561800473[/C][C]0.00794767123600946[/C][C]0.996026164381995[/C][/ROW]
[ROW][C]25[/C][C]0.00373476083644249[/C][C]0.00746952167288498[/C][C]0.996265239163558[/C][/ROW]
[ROW][C]26[/C][C]0.00248530267255816[/C][C]0.00497060534511631[/C][C]0.997514697327442[/C][/ROW]
[ROW][C]27[/C][C]0.00402869863864868[/C][C]0.00805739727729735[/C][C]0.995971301361351[/C][/ROW]
[ROW][C]28[/C][C]0.0449732337856437[/C][C]0.0899464675712875[/C][C]0.955026766214356[/C][/ROW]
[ROW][C]29[/C][C]0.0252428348537729[/C][C]0.0504856697075458[/C][C]0.974757165146227[/C][/ROW]
[ROW][C]30[/C][C]0.0346128421232181[/C][C]0.0692256842464362[/C][C]0.965387157876782[/C][/ROW]
[ROW][C]31[/C][C]0.0898724820973575[/C][C]0.179744964194715[/C][C]0.910127517902642[/C][/ROW]
[ROW][C]32[/C][C]0.0620669644663609[/C][C]0.124133928932722[/C][C]0.93793303553364[/C][/ROW]
[ROW][C]33[/C][C]0.100849822652059[/C][C]0.201699645304119[/C][C]0.89915017734794[/C][/ROW]
[ROW][C]34[/C][C]0.135540760485540[/C][C]0.271081520971079[/C][C]0.86445923951446[/C][/ROW]
[ROW][C]35[/C][C]0.132485614292485[/C][C]0.264971228584969[/C][C]0.867514385707515[/C][/ROW]
[ROW][C]36[/C][C]0.132970729020964[/C][C]0.265941458041928[/C][C]0.867029270979036[/C][/ROW]
[ROW][C]37[/C][C]0.0913660894561851[/C][C]0.182732178912370[/C][C]0.908633910543815[/C][/ROW]
[ROW][C]38[/C][C]0.0653520186624892[/C][C]0.130704037324978[/C][C]0.934647981337511[/C][/ROW]
[ROW][C]39[/C][C]0.137286602871377[/C][C]0.274573205742753[/C][C]0.862713397128623[/C][/ROW]
[ROW][C]40[/C][C]0.0954483498774054[/C][C]0.190896699754811[/C][C]0.904551650122595[/C][/ROW]
[ROW][C]41[/C][C]0.0596748486197279[/C][C]0.119349697239456[/C][C]0.940325151380272[/C][/ROW]
[ROW][C]42[/C][C]0.0379651767714782[/C][C]0.0759303535429564[/C][C]0.962034823228522[/C][/ROW]
[ROW][C]43[/C][C]0.0419086275906883[/C][C]0.0838172551813766[/C][C]0.958091372409312[/C][/ROW]
[ROW][C]44[/C][C]0.0639365680816004[/C][C]0.127873136163201[/C][C]0.9360634319184[/C][/ROW]
[ROW][C]45[/C][C]0.0420249522836188[/C][C]0.0840499045672376[/C][C]0.957975047716381[/C][/ROW]
[ROW][C]46[/C][C]0.032346635587291[/C][C]0.064693271174582[/C][C]0.967653364412709[/C][/ROW]
[ROW][C]47[/C][C]0.0587358336450867[/C][C]0.117471667290173[/C][C]0.941264166354913[/C][/ROW]
[ROW][C]48[/C][C]0.198406603688857[/C][C]0.396813207377714[/C][C]0.801593396311143[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58140&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58140&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
210.02055455327151870.04110910654303750.979445446728481
220.01885258256247310.03770516512494630.981147417437527
230.01152968069278790.02305936138557590.988470319307212
240.003973835618004730.007947671236009460.996026164381995
250.003734760836442490.007469521672884980.996265239163558
260.002485302672558160.004970605345116310.997514697327442
270.004028698638648680.008057397277297350.995971301361351
280.04497323378564370.08994646757128750.955026766214356
290.02524283485377290.05048566970754580.974757165146227
300.03461284212321810.06922568424643620.965387157876782
310.08987248209735750.1797449641947150.910127517902642
320.06206696446636090.1241339289327220.93793303553364
330.1008498226520590.2016996453041190.89915017734794
340.1355407604855400.2710815209710790.86445923951446
350.1324856142924850.2649712285849690.867514385707515
360.1329707290209640.2659414580419280.867029270979036
370.09136608945618510.1827321789123700.908633910543815
380.06535201866248920.1307040373249780.934647981337511
390.1372866028713770.2745732057427530.862713397128623
400.09544834987740540.1908966997548110.904551650122595
410.05967484861972790.1193496972394560.940325151380272
420.03796517677147820.07593035354295640.962034823228522
430.04190862759068830.08381725518137660.958091372409312
440.06393656808160040.1278731361632010.9360634319184
450.04202495228361880.08404990456723760.957975047716381
460.0323466355872910.0646932711745820.967653364412709
470.05873583364508670.1174716672901730.941264166354913
480.1984066036888570.3968132073777140.801593396311143







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level40.142857142857143NOK
5% type I error level70.25NOK
10% type I error level140.5NOK

\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 & 4 & 0.142857142857143 & NOK \tabularnewline
5% type I error level & 7 & 0.25 & NOK \tabularnewline
10% type I error level & 14 & 0.5 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58140&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]4[/C][C]0.142857142857143[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]7[/C][C]0.25[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]14[/C][C]0.5[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58140&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58140&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 level40.142857142857143NOK
5% type I error level70.25NOK
10% type I error level140.5NOK



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')
}