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Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationThu, 19 Nov 2009 14:57:32 -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/19/t12586679315bezu9vmz4kccag.htm/, Retrieved Fri, 29 Mar 2024 09:04:47 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=57968, Retrieved Fri, 29 Mar 2024 09:04:47 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact152
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]
-   PD      [Multiple Regression] [Model 4 - WZM & W...] [2009-11-19 21:57:32] [acc980be4047884b6edd254cd7beb9fa] [Current]
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Dataseries X:
6.5	15.8	6.8	7.5	8	8.2
6.6	15.8	6.5	6.8	7.5	8
7.6	23.2	6.6	6.5	6.8	7.5
8	23.2	7.6	6.6	6.5	6.8
8.1	23.2	8	7.6	6.6	6.5
7.7	20.9	8.1	8	7.6	6.6
7.5	20.9	7.7	8.1	8	7.6
7.6	20.9	7.5	7.7	8.1	8
7.8	19.8	7.6	7.5	7.7	8.1
7.8	19.8	7.8	7.6	7.5	7.7
7.8	19.8	7.8	7.8	7.6	7.5
7.5	20.6	7.8	7.8	7.8	7.6
7.5	20.6	7.5	7.8	7.8	7.8
7.1	20.6	7.5	7.5	7.8	7.8
7.5	21.1	7.1	7.5	7.5	7.8
7.5	21.1	7.5	7.1	7.5	7.5
7.6	21.1	7.5	7.5	7.1	7.5
7.7	22.4	7.6	7.5	7.5	7.1
7.7	22.4	7.7	7.6	7.5	7.5
7.9	22.4	7.7	7.7	7.6	7.5
8.1	20.5	7.9	7.7	7.7	7.6
8.2	20.5	8.1	7.9	7.7	7.7
8.2	20.5	8.2	8.1	7.9	7.7
8.2	18.4	8.2	8.2	8.1	7.9
7.9	18.4	8.2	8.2	8.2	8.1
7.3	18.4	7.9	8.2	8.2	8.2
6.9	17.6	7.3	7.9	8.2	8.2
6.6	17.6	6.9	7.3	7.9	8.2
6.7	17.6	6.6	6.9	7.3	7.9
6.9	18.5	6.7	6.6	6.9	7.3
7	18.5	6.9	6.7	6.6	6.9
7.1	18.5	7	6.9	6.7	6.6
7.2	17.3	7.1	7	6.9	6.7
7.1	17.3	7.2	7.1	7	6.9
6.9	17.3	7.1	7.2	7.1	7
7	16.2	6.9	7.1	7.2	7.1
6.8	16.2	7	6.9	7.1	7.2
6.4	16.2	6.8	7	6.9	7.1
6.7	18.5	6.4	6.8	7	6.9
6.6	18.5	6.7	6.4	6.8	7
6.4	18.5	6.6	6.7	6.4	6.8
6.3	16.3	6.4	6.6	6.7	6.4
6.2	16.3	6.3	6.4	6.6	6.7
6.5	16.3	6.2	6.3	6.4	6.6
6.8	16.8	6.5	6.2	6.3	6.4
6.8	16.8	6.8	6.5	6.2	6.3
6.4	16.8	6.8	6.8	6.5	6.2
6.1	14.8	6.4	6.8	6.8	6.5
5.8	14.8	6.1	6.4	6.8	6.8
6.1	14.8	5.8	6.1	6.4	6.8
7.2	21.4	6.1	5.8	6.1	6.4
7.3	21.4	7.2	6.1	5.8	6.1
6.9	21.4	7.3	7.2	6.1	5.8
6.1	16.1	6.9	7.3	7.2	6.1
5.8	16.1	6.1	6.9	7.3	7.2
6.2	16.1	5.8	6.1	6.9	7.3
7.1	19.6	6.2	5.8	6.1	6.9
7.7	19.6	7.1	6.2	5.8	6.1
7.9	19.6	7.7	7.1	6.2	5.8
7.7	18.9	7.9	7.7	7.1	6.2
7.4	18.9	7.7	7.9	7.7	7.1
7.5	18.9	7.4	7.7	7.9	7.7
8	21.9	7.5	7.4	7.7	7.9
8.1	21.9	8	7.5	7.4	7.7
8	21.9	8.1	8	7.5	7.4




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=57968&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=57968&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57968&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] = + 0.0438440963714834 + 0.091191104880777X[t] + 1.10242811026445`Y(t-1)`[t] -0.486419144668766`Y(t-2)`[t] -0.188441799569958`Y(t-3)`[t] + 0.342364461886313`Y(t-4)`[t] -0.189339107617867M1[t] -0.248582058103744M2[t] + 0.00334296687708956M3[t] -0.53509934530751M4[t] -0.353680066285393M5[t] -0.188467403632904M6[t] -0.259620844371123M7[t] -0.0467034678873066M8[t] -0.0132662230148949M9[t] -0.113338664654363M10[t] -0.0864416950016453M11[t] + 0.00188780839695149t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  0.0438440963714834 +  0.091191104880777X[t] +  1.10242811026445`Y(t-1)`[t] -0.486419144668766`Y(t-2)`[t] -0.188441799569958`Y(t-3)`[t] +  0.342364461886313`Y(t-4)`[t] -0.189339107617867M1[t] -0.248582058103744M2[t] +  0.00334296687708956M3[t] -0.53509934530751M4[t] -0.353680066285393M5[t] -0.188467403632904M6[t] -0.259620844371123M7[t] -0.0467034678873066M8[t] -0.0132662230148949M9[t] -0.113338664654363M10[t] -0.0864416950016453M11[t] +  0.00188780839695149t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57968&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  0.0438440963714834 +  0.091191104880777X[t] +  1.10242811026445`Y(t-1)`[t] -0.486419144668766`Y(t-2)`[t] -0.188441799569958`Y(t-3)`[t] +  0.342364461886313`Y(t-4)`[t] -0.189339107617867M1[t] -0.248582058103744M2[t] +  0.00334296687708956M3[t] -0.53509934530751M4[t] -0.353680066285393M5[t] -0.188467403632904M6[t] -0.259620844371123M7[t] -0.0467034678873066M8[t] -0.0132662230148949M9[t] -0.113338664654363M10[t] -0.0864416950016453M11[t] +  0.00188780839695149t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57968&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57968&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] = + 0.0438440963714834 + 0.091191104880777X[t] + 1.10242811026445`Y(t-1)`[t] -0.486419144668766`Y(t-2)`[t] -0.188441799569958`Y(t-3)`[t] + 0.342364461886313`Y(t-4)`[t] -0.189339107617867M1[t] -0.248582058103744M2[t] + 0.00334296687708956M3[t] -0.53509934530751M4[t] -0.353680066285393M5[t] -0.188467403632904M6[t] -0.259620844371123M7[t] -0.0467034678873066M8[t] -0.0132662230148949M9[t] -0.113338664654363M10[t] -0.0864416950016453M11[t] + 0.00188780839695149t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.04384409637148340.4019430.10910.9136030.456802
X0.0911911048807770.02154.24150.0001035.2e-05
`Y(t-1)`1.102428110264450.1564437.046800
`Y(t-2)`-0.4864191446687660.229403-2.12040.0392810.019641
`Y(t-3)`-0.1884417995699580.221128-0.85220.3984340.199217
`Y(t-4)`0.3423644618863130.1189042.87930.0059820.002991
M1-0.1893391076178670.106143-1.78380.0809110.040456
M2-0.2485820581037440.11166-2.22620.0308290.015415
M30.003342966877089560.140870.02370.9811680.490584
M4-0.535099345307510.126357-4.23480.0001065.3e-05
M5-0.3536800662853930.143484-2.46490.0174130.008707
M6-0.1884674036329040.119133-1.5820.1203580.060179
M7-0.2596208443711230.121117-2.14360.0372740.018637
M8-0.04670346788730660.121031-0.38590.7013260.350663
M9-0.01326622301489490.117847-0.11260.910850.455425
M10-0.1133386646543630.117581-0.96390.3400210.170011
M11-0.08644169500164530.111035-0.77850.4401730.220086
t0.001887808396951490.0014411.30990.196590.098295

\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) & 0.0438440963714834 & 0.401943 & 0.1091 & 0.913603 & 0.456802 \tabularnewline
X & 0.091191104880777 & 0.0215 & 4.2415 & 0.000103 & 5.2e-05 \tabularnewline
`Y(t-1)` & 1.10242811026445 & 0.156443 & 7.0468 & 0 & 0 \tabularnewline
`Y(t-2)` & -0.486419144668766 & 0.229403 & -2.1204 & 0.039281 & 0.019641 \tabularnewline
`Y(t-3)` & -0.188441799569958 & 0.221128 & -0.8522 & 0.398434 & 0.199217 \tabularnewline
`Y(t-4)` & 0.342364461886313 & 0.118904 & 2.8793 & 0.005982 & 0.002991 \tabularnewline
M1 & -0.189339107617867 & 0.106143 & -1.7838 & 0.080911 & 0.040456 \tabularnewline
M2 & -0.248582058103744 & 0.11166 & -2.2262 & 0.030829 & 0.015415 \tabularnewline
M3 & 0.00334296687708956 & 0.14087 & 0.0237 & 0.981168 & 0.490584 \tabularnewline
M4 & -0.53509934530751 & 0.126357 & -4.2348 & 0.000106 & 5.3e-05 \tabularnewline
M5 & -0.353680066285393 & 0.143484 & -2.4649 & 0.017413 & 0.008707 \tabularnewline
M6 & -0.188467403632904 & 0.119133 & -1.582 & 0.120358 & 0.060179 \tabularnewline
M7 & -0.259620844371123 & 0.121117 & -2.1436 & 0.037274 & 0.018637 \tabularnewline
M8 & -0.0467034678873066 & 0.121031 & -0.3859 & 0.701326 & 0.350663 \tabularnewline
M9 & -0.0132662230148949 & 0.117847 & -0.1126 & 0.91085 & 0.455425 \tabularnewline
M10 & -0.113338664654363 & 0.117581 & -0.9639 & 0.340021 & 0.170011 \tabularnewline
M11 & -0.0864416950016453 & 0.111035 & -0.7785 & 0.440173 & 0.220086 \tabularnewline
t & 0.00188780839695149 & 0.001441 & 1.3099 & 0.19659 & 0.098295 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57968&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]0.0438440963714834[/C][C]0.401943[/C][C]0.1091[/C][C]0.913603[/C][C]0.456802[/C][/ROW]
[ROW][C]X[/C][C]0.091191104880777[/C][C]0.0215[/C][C]4.2415[/C][C]0.000103[/C][C]5.2e-05[/C][/ROW]
[ROW][C]`Y(t-1)`[/C][C]1.10242811026445[/C][C]0.156443[/C][C]7.0468[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]`Y(t-2)`[/C][C]-0.486419144668766[/C][C]0.229403[/C][C]-2.1204[/C][C]0.039281[/C][C]0.019641[/C][/ROW]
[ROW][C]`Y(t-3)`[/C][C]-0.188441799569958[/C][C]0.221128[/C][C]-0.8522[/C][C]0.398434[/C][C]0.199217[/C][/ROW]
[ROW][C]`Y(t-4)`[/C][C]0.342364461886313[/C][C]0.118904[/C][C]2.8793[/C][C]0.005982[/C][C]0.002991[/C][/ROW]
[ROW][C]M1[/C][C]-0.189339107617867[/C][C]0.106143[/C][C]-1.7838[/C][C]0.080911[/C][C]0.040456[/C][/ROW]
[ROW][C]M2[/C][C]-0.248582058103744[/C][C]0.11166[/C][C]-2.2262[/C][C]0.030829[/C][C]0.015415[/C][/ROW]
[ROW][C]M3[/C][C]0.00334296687708956[/C][C]0.14087[/C][C]0.0237[/C][C]0.981168[/C][C]0.490584[/C][/ROW]
[ROW][C]M4[/C][C]-0.53509934530751[/C][C]0.126357[/C][C]-4.2348[/C][C]0.000106[/C][C]5.3e-05[/C][/ROW]
[ROW][C]M5[/C][C]-0.353680066285393[/C][C]0.143484[/C][C]-2.4649[/C][C]0.017413[/C][C]0.008707[/C][/ROW]
[ROW][C]M6[/C][C]-0.188467403632904[/C][C]0.119133[/C][C]-1.582[/C][C]0.120358[/C][C]0.060179[/C][/ROW]
[ROW][C]M7[/C][C]-0.259620844371123[/C][C]0.121117[/C][C]-2.1436[/C][C]0.037274[/C][C]0.018637[/C][/ROW]
[ROW][C]M8[/C][C]-0.0467034678873066[/C][C]0.121031[/C][C]-0.3859[/C][C]0.701326[/C][C]0.350663[/C][/ROW]
[ROW][C]M9[/C][C]-0.0132662230148949[/C][C]0.117847[/C][C]-0.1126[/C][C]0.91085[/C][C]0.455425[/C][/ROW]
[ROW][C]M10[/C][C]-0.113338664654363[/C][C]0.117581[/C][C]-0.9639[/C][C]0.340021[/C][C]0.170011[/C][/ROW]
[ROW][C]M11[/C][C]-0.0864416950016453[/C][C]0.111035[/C][C]-0.7785[/C][C]0.440173[/C][C]0.220086[/C][/ROW]
[ROW][C]t[/C][C]0.00188780839695149[/C][C]0.001441[/C][C]1.3099[/C][C]0.19659[/C][C]0.098295[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57968&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57968&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)0.04384409637148340.4019430.10910.9136030.456802
X0.0911911048807770.02154.24150.0001035.2e-05
`Y(t-1)`1.102428110264450.1564437.046800
`Y(t-2)`-0.4864191446687660.229403-2.12040.0392810.019641
`Y(t-3)`-0.1884417995699580.221128-0.85220.3984340.199217
`Y(t-4)`0.3423644618863130.1189042.87930.0059820.002991
M1-0.1893391076178670.106143-1.78380.0809110.040456
M2-0.2485820581037440.11166-2.22620.0308290.015415
M30.003342966877089560.140870.02370.9811680.490584
M4-0.535099345307510.126357-4.23480.0001065.3e-05
M5-0.3536800662853930.143484-2.46490.0174130.008707
M6-0.1884674036329040.119133-1.5820.1203580.060179
M7-0.2596208443711230.121117-2.14360.0372740.018637
M8-0.04670346788730660.121031-0.38590.7013260.350663
M9-0.01326622301489490.117847-0.11260.910850.455425
M10-0.1133386646543630.117581-0.96390.3400210.170011
M11-0.08644169500164530.111035-0.77850.4401730.220086
t0.001887808396951490.0014411.30990.196590.098295







Multiple Linear Regression - Regression Statistics
Multiple R0.97556268486673
R-squared0.951722552104384
Adjusted R-squared0.934260496482565
F-TEST (value)54.5023205008703
F-TEST (DF numerator)17
F-TEST (DF denominator)47
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.169643885658411
Sum Squared Residuals1.35261525324035

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.97556268486673 \tabularnewline
R-squared & 0.951722552104384 \tabularnewline
Adjusted R-squared & 0.934260496482565 \tabularnewline
F-TEST (value) & 54.5023205008703 \tabularnewline
F-TEST (DF numerator) & 17 \tabularnewline
F-TEST (DF denominator) & 47 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.169643885658411 \tabularnewline
Sum Squared Residuals & 1.35261525324035 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57968&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.97556268486673[/C][/ROW]
[ROW][C]R-squared[/C][C]0.951722552104384[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.934260496482565[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]54.5023205008703[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]17[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]47[/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]0.169643885658411[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1.35261525324035[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57968&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57968&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 R0.97556268486673
R-squared0.951722552104384
Adjusted R-squared0.934260496482565
F-TEST (value)54.5023205008703
F-TEST (DF numerator)17
F-TEST (DF denominator)47
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.169643885658411
Sum Squared Residuals1.35261525324035







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
16.56.445434009957430.054565990042572
26.66.423591843465040.176408156534963
37.67.569114436143460.03088556385654
487.903223544703950.0967764552960502
58.17.919529213037140.180470786962857
67.77.638359942638410.061640057361593
77.57.346468893782810.153531106217185
87.67.65345771927573-0.0534577192757292
97.87.90561236315305-0.105612363153049
107.87.88001401265601-0.0800140126560126
117.87.724197889437670.0758021105623299
127.57.88202836301553-0.382028363015528
137.57.432321523092540.0676784769074585
147.17.52089212440425-0.420892124404246
157.57.435861805987630.0641381940123728
167.57.432136865607370.0678631343926295
177.67.496253014986920.103746985013084
187.77.67982222882530.0201777711746977
197.77.80910327779813-0.109103277798129
207.97.95642236825502-0.0564223682550237
218.18.054362210535440.0456377894645638
228.28.113615816600690.0863841833993132
238.28.117671216829060.0823287831709441
248.27.996642017974410.203357982025585
257.97.858819431173770.0411805688262343
267.37.50497230219414-0.204972302194139
276.97.17030112890926-0.270301128909262
286.66.541159407688080.0588405923119164
296.76.59866146107140.101338538928596
306.96.97396052363681-0.0739605236368146
3176.996125353998020.00387464600197746
327.17.10233600244859-0.00233600244859139
337.27.086380712695230.113619287304771
347.17.099425688432550.000574311567450727
356.96.98471800722053-0.0847180072205312
3676.81628585389590.183714146104103
376.86.8894418207808-0.089441820780806
386.46.56641105589747-0.166411055897474
396.76.598958942994760.101041057005237
406.66.65962533625658-0.0596253362565784
416.46.59366769669929-0.193667696699291
426.36.19482570479950.105174295200500
436.26.23415460888843-0.0341546088884289
446.56.390810810934990.109189189065011
456.86.80147305177069-0.00147305177068493
466.86.87269884197524-0.0726988419752369
476.46.66478889056466-0.264788890564657
486.16.17594173879083-0.075941738790829
495.85.95503900292398-0.155039002923979
506.15.788257890984330.311742109015668
517.27.040172948171670.159827051828328
527.37.52418682357938-0.224186823579379
536.97.12343378445237-0.223433784452367
546.16.21303160009998-0.113031600099976
555.85.81414786553260-0.0141478655326054
566.26.196973099085670.00302690091433393
577.17.1521716618456-0.0521716618456001
587.77.634245640335510.0657543596644856
597.97.708623995948090.191376004051914
607.77.629102026323330.0708979736766684
617.47.318944212071480.0810557879285197
627.57.195874783054770.304125216945229
6388.08559073779321-0.085590737793215
648.18.039668022164640.0603319778353614
6587.968454829752880.0315451702471214

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 6.5 & 6.44543400995743 & 0.054565990042572 \tabularnewline
2 & 6.6 & 6.42359184346504 & 0.176408156534963 \tabularnewline
3 & 7.6 & 7.56911443614346 & 0.03088556385654 \tabularnewline
4 & 8 & 7.90322354470395 & 0.0967764552960502 \tabularnewline
5 & 8.1 & 7.91952921303714 & 0.180470786962857 \tabularnewline
6 & 7.7 & 7.63835994263841 & 0.061640057361593 \tabularnewline
7 & 7.5 & 7.34646889378281 & 0.153531106217185 \tabularnewline
8 & 7.6 & 7.65345771927573 & -0.0534577192757292 \tabularnewline
9 & 7.8 & 7.90561236315305 & -0.105612363153049 \tabularnewline
10 & 7.8 & 7.88001401265601 & -0.0800140126560126 \tabularnewline
11 & 7.8 & 7.72419788943767 & 0.0758021105623299 \tabularnewline
12 & 7.5 & 7.88202836301553 & -0.382028363015528 \tabularnewline
13 & 7.5 & 7.43232152309254 & 0.0676784769074585 \tabularnewline
14 & 7.1 & 7.52089212440425 & -0.420892124404246 \tabularnewline
15 & 7.5 & 7.43586180598763 & 0.0641381940123728 \tabularnewline
16 & 7.5 & 7.43213686560737 & 0.0678631343926295 \tabularnewline
17 & 7.6 & 7.49625301498692 & 0.103746985013084 \tabularnewline
18 & 7.7 & 7.6798222288253 & 0.0201777711746977 \tabularnewline
19 & 7.7 & 7.80910327779813 & -0.109103277798129 \tabularnewline
20 & 7.9 & 7.95642236825502 & -0.0564223682550237 \tabularnewline
21 & 8.1 & 8.05436221053544 & 0.0456377894645638 \tabularnewline
22 & 8.2 & 8.11361581660069 & 0.0863841833993132 \tabularnewline
23 & 8.2 & 8.11767121682906 & 0.0823287831709441 \tabularnewline
24 & 8.2 & 7.99664201797441 & 0.203357982025585 \tabularnewline
25 & 7.9 & 7.85881943117377 & 0.0411805688262343 \tabularnewline
26 & 7.3 & 7.50497230219414 & -0.204972302194139 \tabularnewline
27 & 6.9 & 7.17030112890926 & -0.270301128909262 \tabularnewline
28 & 6.6 & 6.54115940768808 & 0.0588405923119164 \tabularnewline
29 & 6.7 & 6.5986614610714 & 0.101338538928596 \tabularnewline
30 & 6.9 & 6.97396052363681 & -0.0739605236368146 \tabularnewline
31 & 7 & 6.99612535399802 & 0.00387464600197746 \tabularnewline
32 & 7.1 & 7.10233600244859 & -0.00233600244859139 \tabularnewline
33 & 7.2 & 7.08638071269523 & 0.113619287304771 \tabularnewline
34 & 7.1 & 7.09942568843255 & 0.000574311567450727 \tabularnewline
35 & 6.9 & 6.98471800722053 & -0.0847180072205312 \tabularnewline
36 & 7 & 6.8162858538959 & 0.183714146104103 \tabularnewline
37 & 6.8 & 6.8894418207808 & -0.089441820780806 \tabularnewline
38 & 6.4 & 6.56641105589747 & -0.166411055897474 \tabularnewline
39 & 6.7 & 6.59895894299476 & 0.101041057005237 \tabularnewline
40 & 6.6 & 6.65962533625658 & -0.0596253362565784 \tabularnewline
41 & 6.4 & 6.59366769669929 & -0.193667696699291 \tabularnewline
42 & 6.3 & 6.1948257047995 & 0.105174295200500 \tabularnewline
43 & 6.2 & 6.23415460888843 & -0.0341546088884289 \tabularnewline
44 & 6.5 & 6.39081081093499 & 0.109189189065011 \tabularnewline
45 & 6.8 & 6.80147305177069 & -0.00147305177068493 \tabularnewline
46 & 6.8 & 6.87269884197524 & -0.0726988419752369 \tabularnewline
47 & 6.4 & 6.66478889056466 & -0.264788890564657 \tabularnewline
48 & 6.1 & 6.17594173879083 & -0.075941738790829 \tabularnewline
49 & 5.8 & 5.95503900292398 & -0.155039002923979 \tabularnewline
50 & 6.1 & 5.78825789098433 & 0.311742109015668 \tabularnewline
51 & 7.2 & 7.04017294817167 & 0.159827051828328 \tabularnewline
52 & 7.3 & 7.52418682357938 & -0.224186823579379 \tabularnewline
53 & 6.9 & 7.12343378445237 & -0.223433784452367 \tabularnewline
54 & 6.1 & 6.21303160009998 & -0.113031600099976 \tabularnewline
55 & 5.8 & 5.81414786553260 & -0.0141478655326054 \tabularnewline
56 & 6.2 & 6.19697309908567 & 0.00302690091433393 \tabularnewline
57 & 7.1 & 7.1521716618456 & -0.0521716618456001 \tabularnewline
58 & 7.7 & 7.63424564033551 & 0.0657543596644856 \tabularnewline
59 & 7.9 & 7.70862399594809 & 0.191376004051914 \tabularnewline
60 & 7.7 & 7.62910202632333 & 0.0708979736766684 \tabularnewline
61 & 7.4 & 7.31894421207148 & 0.0810557879285197 \tabularnewline
62 & 7.5 & 7.19587478305477 & 0.304125216945229 \tabularnewline
63 & 8 & 8.08559073779321 & -0.085590737793215 \tabularnewline
64 & 8.1 & 8.03966802216464 & 0.0603319778353614 \tabularnewline
65 & 8 & 7.96845482975288 & 0.0315451702471214 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57968&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]6.5[/C][C]6.44543400995743[/C][C]0.054565990042572[/C][/ROW]
[ROW][C]2[/C][C]6.6[/C][C]6.42359184346504[/C][C]0.176408156534963[/C][/ROW]
[ROW][C]3[/C][C]7.6[/C][C]7.56911443614346[/C][C]0.03088556385654[/C][/ROW]
[ROW][C]4[/C][C]8[/C][C]7.90322354470395[/C][C]0.0967764552960502[/C][/ROW]
[ROW][C]5[/C][C]8.1[/C][C]7.91952921303714[/C][C]0.180470786962857[/C][/ROW]
[ROW][C]6[/C][C]7.7[/C][C]7.63835994263841[/C][C]0.061640057361593[/C][/ROW]
[ROW][C]7[/C][C]7.5[/C][C]7.34646889378281[/C][C]0.153531106217185[/C][/ROW]
[ROW][C]8[/C][C]7.6[/C][C]7.65345771927573[/C][C]-0.0534577192757292[/C][/ROW]
[ROW][C]9[/C][C]7.8[/C][C]7.90561236315305[/C][C]-0.105612363153049[/C][/ROW]
[ROW][C]10[/C][C]7.8[/C][C]7.88001401265601[/C][C]-0.0800140126560126[/C][/ROW]
[ROW][C]11[/C][C]7.8[/C][C]7.72419788943767[/C][C]0.0758021105623299[/C][/ROW]
[ROW][C]12[/C][C]7.5[/C][C]7.88202836301553[/C][C]-0.382028363015528[/C][/ROW]
[ROW][C]13[/C][C]7.5[/C][C]7.43232152309254[/C][C]0.0676784769074585[/C][/ROW]
[ROW][C]14[/C][C]7.1[/C][C]7.52089212440425[/C][C]-0.420892124404246[/C][/ROW]
[ROW][C]15[/C][C]7.5[/C][C]7.43586180598763[/C][C]0.0641381940123728[/C][/ROW]
[ROW][C]16[/C][C]7.5[/C][C]7.43213686560737[/C][C]0.0678631343926295[/C][/ROW]
[ROW][C]17[/C][C]7.6[/C][C]7.49625301498692[/C][C]0.103746985013084[/C][/ROW]
[ROW][C]18[/C][C]7.7[/C][C]7.6798222288253[/C][C]0.0201777711746977[/C][/ROW]
[ROW][C]19[/C][C]7.7[/C][C]7.80910327779813[/C][C]-0.109103277798129[/C][/ROW]
[ROW][C]20[/C][C]7.9[/C][C]7.95642236825502[/C][C]-0.0564223682550237[/C][/ROW]
[ROW][C]21[/C][C]8.1[/C][C]8.05436221053544[/C][C]0.0456377894645638[/C][/ROW]
[ROW][C]22[/C][C]8.2[/C][C]8.11361581660069[/C][C]0.0863841833993132[/C][/ROW]
[ROW][C]23[/C][C]8.2[/C][C]8.11767121682906[/C][C]0.0823287831709441[/C][/ROW]
[ROW][C]24[/C][C]8.2[/C][C]7.99664201797441[/C][C]0.203357982025585[/C][/ROW]
[ROW][C]25[/C][C]7.9[/C][C]7.85881943117377[/C][C]0.0411805688262343[/C][/ROW]
[ROW][C]26[/C][C]7.3[/C][C]7.50497230219414[/C][C]-0.204972302194139[/C][/ROW]
[ROW][C]27[/C][C]6.9[/C][C]7.17030112890926[/C][C]-0.270301128909262[/C][/ROW]
[ROW][C]28[/C][C]6.6[/C][C]6.54115940768808[/C][C]0.0588405923119164[/C][/ROW]
[ROW][C]29[/C][C]6.7[/C][C]6.5986614610714[/C][C]0.101338538928596[/C][/ROW]
[ROW][C]30[/C][C]6.9[/C][C]6.97396052363681[/C][C]-0.0739605236368146[/C][/ROW]
[ROW][C]31[/C][C]7[/C][C]6.99612535399802[/C][C]0.00387464600197746[/C][/ROW]
[ROW][C]32[/C][C]7.1[/C][C]7.10233600244859[/C][C]-0.00233600244859139[/C][/ROW]
[ROW][C]33[/C][C]7.2[/C][C]7.08638071269523[/C][C]0.113619287304771[/C][/ROW]
[ROW][C]34[/C][C]7.1[/C][C]7.09942568843255[/C][C]0.000574311567450727[/C][/ROW]
[ROW][C]35[/C][C]6.9[/C][C]6.98471800722053[/C][C]-0.0847180072205312[/C][/ROW]
[ROW][C]36[/C][C]7[/C][C]6.8162858538959[/C][C]0.183714146104103[/C][/ROW]
[ROW][C]37[/C][C]6.8[/C][C]6.8894418207808[/C][C]-0.089441820780806[/C][/ROW]
[ROW][C]38[/C][C]6.4[/C][C]6.56641105589747[/C][C]-0.166411055897474[/C][/ROW]
[ROW][C]39[/C][C]6.7[/C][C]6.59895894299476[/C][C]0.101041057005237[/C][/ROW]
[ROW][C]40[/C][C]6.6[/C][C]6.65962533625658[/C][C]-0.0596253362565784[/C][/ROW]
[ROW][C]41[/C][C]6.4[/C][C]6.59366769669929[/C][C]-0.193667696699291[/C][/ROW]
[ROW][C]42[/C][C]6.3[/C][C]6.1948257047995[/C][C]0.105174295200500[/C][/ROW]
[ROW][C]43[/C][C]6.2[/C][C]6.23415460888843[/C][C]-0.0341546088884289[/C][/ROW]
[ROW][C]44[/C][C]6.5[/C][C]6.39081081093499[/C][C]0.109189189065011[/C][/ROW]
[ROW][C]45[/C][C]6.8[/C][C]6.80147305177069[/C][C]-0.00147305177068493[/C][/ROW]
[ROW][C]46[/C][C]6.8[/C][C]6.87269884197524[/C][C]-0.0726988419752369[/C][/ROW]
[ROW][C]47[/C][C]6.4[/C][C]6.66478889056466[/C][C]-0.264788890564657[/C][/ROW]
[ROW][C]48[/C][C]6.1[/C][C]6.17594173879083[/C][C]-0.075941738790829[/C][/ROW]
[ROW][C]49[/C][C]5.8[/C][C]5.95503900292398[/C][C]-0.155039002923979[/C][/ROW]
[ROW][C]50[/C][C]6.1[/C][C]5.78825789098433[/C][C]0.311742109015668[/C][/ROW]
[ROW][C]51[/C][C]7.2[/C][C]7.04017294817167[/C][C]0.159827051828328[/C][/ROW]
[ROW][C]52[/C][C]7.3[/C][C]7.52418682357938[/C][C]-0.224186823579379[/C][/ROW]
[ROW][C]53[/C][C]6.9[/C][C]7.12343378445237[/C][C]-0.223433784452367[/C][/ROW]
[ROW][C]54[/C][C]6.1[/C][C]6.21303160009998[/C][C]-0.113031600099976[/C][/ROW]
[ROW][C]55[/C][C]5.8[/C][C]5.81414786553260[/C][C]-0.0141478655326054[/C][/ROW]
[ROW][C]56[/C][C]6.2[/C][C]6.19697309908567[/C][C]0.00302690091433393[/C][/ROW]
[ROW][C]57[/C][C]7.1[/C][C]7.1521716618456[/C][C]-0.0521716618456001[/C][/ROW]
[ROW][C]58[/C][C]7.7[/C][C]7.63424564033551[/C][C]0.0657543596644856[/C][/ROW]
[ROW][C]59[/C][C]7.9[/C][C]7.70862399594809[/C][C]0.191376004051914[/C][/ROW]
[ROW][C]60[/C][C]7.7[/C][C]7.62910202632333[/C][C]0.0708979736766684[/C][/ROW]
[ROW][C]61[/C][C]7.4[/C][C]7.31894421207148[/C][C]0.0810557879285197[/C][/ROW]
[ROW][C]62[/C][C]7.5[/C][C]7.19587478305477[/C][C]0.304125216945229[/C][/ROW]
[ROW][C]63[/C][C]8[/C][C]8.08559073779321[/C][C]-0.085590737793215[/C][/ROW]
[ROW][C]64[/C][C]8.1[/C][C]8.03966802216464[/C][C]0.0603319778353614[/C][/ROW]
[ROW][C]65[/C][C]8[/C][C]7.96845482975288[/C][C]0.0315451702471214[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57968&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57968&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
16.56.445434009957430.054565990042572
26.66.423591843465040.176408156534963
37.67.569114436143460.03088556385654
487.903223544703950.0967764552960502
58.17.919529213037140.180470786962857
67.77.638359942638410.061640057361593
77.57.346468893782810.153531106217185
87.67.65345771927573-0.0534577192757292
97.87.90561236315305-0.105612363153049
107.87.88001401265601-0.0800140126560126
117.87.724197889437670.0758021105623299
127.57.88202836301553-0.382028363015528
137.57.432321523092540.0676784769074585
147.17.52089212440425-0.420892124404246
157.57.435861805987630.0641381940123728
167.57.432136865607370.0678631343926295
177.67.496253014986920.103746985013084
187.77.67982222882530.0201777711746977
197.77.80910327779813-0.109103277798129
207.97.95642236825502-0.0564223682550237
218.18.054362210535440.0456377894645638
228.28.113615816600690.0863841833993132
238.28.117671216829060.0823287831709441
248.27.996642017974410.203357982025585
257.97.858819431173770.0411805688262343
267.37.50497230219414-0.204972302194139
276.97.17030112890926-0.270301128909262
286.66.541159407688080.0588405923119164
296.76.59866146107140.101338538928596
306.96.97396052363681-0.0739605236368146
3176.996125353998020.00387464600197746
327.17.10233600244859-0.00233600244859139
337.27.086380712695230.113619287304771
347.17.099425688432550.000574311567450727
356.96.98471800722053-0.0847180072205312
3676.81628585389590.183714146104103
376.86.8894418207808-0.089441820780806
386.46.56641105589747-0.166411055897474
396.76.598958942994760.101041057005237
406.66.65962533625658-0.0596253362565784
416.46.59366769669929-0.193667696699291
426.36.19482570479950.105174295200500
436.26.23415460888843-0.0341546088884289
446.56.390810810934990.109189189065011
456.86.80147305177069-0.00147305177068493
466.86.87269884197524-0.0726988419752369
476.46.66478889056466-0.264788890564657
486.16.17594173879083-0.075941738790829
495.85.95503900292398-0.155039002923979
506.15.788257890984330.311742109015668
517.27.040172948171670.159827051828328
527.37.52418682357938-0.224186823579379
536.97.12343378445237-0.223433784452367
546.16.21303160009998-0.113031600099976
555.85.81414786553260-0.0141478655326054
566.26.196973099085670.00302690091433393
577.17.1521716618456-0.0521716618456001
587.77.634245640335510.0657543596644856
597.97.708623995948090.191376004051914
607.77.629102026323330.0708979736766684
617.47.318944212071480.0810557879285197
627.57.195874783054770.304125216945229
6388.08559073779321-0.085590737793215
648.18.039668022164640.0603319778353614
6587.968454829752880.0315451702471214







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
210.4909449354361380.9818898708722760.509055064563862
220.5095054004777140.9809891990445720.490494599522286
230.4933359675569630.9866719351139270.506664032443037
240.6925895780582370.6148208438835270.307410421941763
250.6257946277413290.7484107445173420.374205372258671
260.6244045839758420.7511908320483160.375595416024158
270.8470316896452920.3059366207094160.152968310354708
280.788594421625030.4228111567499410.211405578374970
290.7475940511650280.5048118976699440.252405948834972
300.7332659918232360.5334680163535270.266734008176764
310.6506797800704380.6986404398591240.349320219929562
320.5580440157978430.8839119684043150.441955984202157
330.4995483548714920.9990967097429830.500451645128508
340.39365431735210.78730863470420.6063456826479
350.3241271497964490.6482542995928970.675872850203551
360.3421524127591020.6843048255182040.657847587240898
370.2585591347875220.5171182695750430.741440865212478
380.4562748359792650.912549671958530.543725164020735
390.4100850510873230.8201701021746460.589914948912677
400.3977336826754060.7954673653508130.602266317324593
410.3400303594545690.6800607189091380.659969640545431
420.3019275834193180.6038551668386360.698072416580682
430.1881430872535420.3762861745070840.811856912746458
440.2682375200178970.5364750400357940.731762479982103

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
21 & 0.490944935436138 & 0.981889870872276 & 0.509055064563862 \tabularnewline
22 & 0.509505400477714 & 0.980989199044572 & 0.490494599522286 \tabularnewline
23 & 0.493335967556963 & 0.986671935113927 & 0.506664032443037 \tabularnewline
24 & 0.692589578058237 & 0.614820843883527 & 0.307410421941763 \tabularnewline
25 & 0.625794627741329 & 0.748410744517342 & 0.374205372258671 \tabularnewline
26 & 0.624404583975842 & 0.751190832048316 & 0.375595416024158 \tabularnewline
27 & 0.847031689645292 & 0.305936620709416 & 0.152968310354708 \tabularnewline
28 & 0.78859442162503 & 0.422811156749941 & 0.211405578374970 \tabularnewline
29 & 0.747594051165028 & 0.504811897669944 & 0.252405948834972 \tabularnewline
30 & 0.733265991823236 & 0.533468016353527 & 0.266734008176764 \tabularnewline
31 & 0.650679780070438 & 0.698640439859124 & 0.349320219929562 \tabularnewline
32 & 0.558044015797843 & 0.883911968404315 & 0.441955984202157 \tabularnewline
33 & 0.499548354871492 & 0.999096709742983 & 0.500451645128508 \tabularnewline
34 & 0.3936543173521 & 0.7873086347042 & 0.6063456826479 \tabularnewline
35 & 0.324127149796449 & 0.648254299592897 & 0.675872850203551 \tabularnewline
36 & 0.342152412759102 & 0.684304825518204 & 0.657847587240898 \tabularnewline
37 & 0.258559134787522 & 0.517118269575043 & 0.741440865212478 \tabularnewline
38 & 0.456274835979265 & 0.91254967195853 & 0.543725164020735 \tabularnewline
39 & 0.410085051087323 & 0.820170102174646 & 0.589914948912677 \tabularnewline
40 & 0.397733682675406 & 0.795467365350813 & 0.602266317324593 \tabularnewline
41 & 0.340030359454569 & 0.680060718909138 & 0.659969640545431 \tabularnewline
42 & 0.301927583419318 & 0.603855166838636 & 0.698072416580682 \tabularnewline
43 & 0.188143087253542 & 0.376286174507084 & 0.811856912746458 \tabularnewline
44 & 0.268237520017897 & 0.536475040035794 & 0.731762479982103 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57968&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]0.490944935436138[/C][C]0.981889870872276[/C][C]0.509055064563862[/C][/ROW]
[ROW][C]22[/C][C]0.509505400477714[/C][C]0.980989199044572[/C][C]0.490494599522286[/C][/ROW]
[ROW][C]23[/C][C]0.493335967556963[/C][C]0.986671935113927[/C][C]0.506664032443037[/C][/ROW]
[ROW][C]24[/C][C]0.692589578058237[/C][C]0.614820843883527[/C][C]0.307410421941763[/C][/ROW]
[ROW][C]25[/C][C]0.625794627741329[/C][C]0.748410744517342[/C][C]0.374205372258671[/C][/ROW]
[ROW][C]26[/C][C]0.624404583975842[/C][C]0.751190832048316[/C][C]0.375595416024158[/C][/ROW]
[ROW][C]27[/C][C]0.847031689645292[/C][C]0.305936620709416[/C][C]0.152968310354708[/C][/ROW]
[ROW][C]28[/C][C]0.78859442162503[/C][C]0.422811156749941[/C][C]0.211405578374970[/C][/ROW]
[ROW][C]29[/C][C]0.747594051165028[/C][C]0.504811897669944[/C][C]0.252405948834972[/C][/ROW]
[ROW][C]30[/C][C]0.733265991823236[/C][C]0.533468016353527[/C][C]0.266734008176764[/C][/ROW]
[ROW][C]31[/C][C]0.650679780070438[/C][C]0.698640439859124[/C][C]0.349320219929562[/C][/ROW]
[ROW][C]32[/C][C]0.558044015797843[/C][C]0.883911968404315[/C][C]0.441955984202157[/C][/ROW]
[ROW][C]33[/C][C]0.499548354871492[/C][C]0.999096709742983[/C][C]0.500451645128508[/C][/ROW]
[ROW][C]34[/C][C]0.3936543173521[/C][C]0.7873086347042[/C][C]0.6063456826479[/C][/ROW]
[ROW][C]35[/C][C]0.324127149796449[/C][C]0.648254299592897[/C][C]0.675872850203551[/C][/ROW]
[ROW][C]36[/C][C]0.342152412759102[/C][C]0.684304825518204[/C][C]0.657847587240898[/C][/ROW]
[ROW][C]37[/C][C]0.258559134787522[/C][C]0.517118269575043[/C][C]0.741440865212478[/C][/ROW]
[ROW][C]38[/C][C]0.456274835979265[/C][C]0.91254967195853[/C][C]0.543725164020735[/C][/ROW]
[ROW][C]39[/C][C]0.410085051087323[/C][C]0.820170102174646[/C][C]0.589914948912677[/C][/ROW]
[ROW][C]40[/C][C]0.397733682675406[/C][C]0.795467365350813[/C][C]0.602266317324593[/C][/ROW]
[ROW][C]41[/C][C]0.340030359454569[/C][C]0.680060718909138[/C][C]0.659969640545431[/C][/ROW]
[ROW][C]42[/C][C]0.301927583419318[/C][C]0.603855166838636[/C][C]0.698072416580682[/C][/ROW]
[ROW][C]43[/C][C]0.188143087253542[/C][C]0.376286174507084[/C][C]0.811856912746458[/C][/ROW]
[ROW][C]44[/C][C]0.268237520017897[/C][C]0.536475040035794[/C][C]0.731762479982103[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57968&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57968&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
210.4909449354361380.9818898708722760.509055064563862
220.5095054004777140.9809891990445720.490494599522286
230.4933359675569630.9866719351139270.506664032443037
240.6925895780582370.6148208438835270.307410421941763
250.6257946277413290.7484107445173420.374205372258671
260.6244045839758420.7511908320483160.375595416024158
270.8470316896452920.3059366207094160.152968310354708
280.788594421625030.4228111567499410.211405578374970
290.7475940511650280.5048118976699440.252405948834972
300.7332659918232360.5334680163535270.266734008176764
310.6506797800704380.6986404398591240.349320219929562
320.5580440157978430.8839119684043150.441955984202157
330.4995483548714920.9990967097429830.500451645128508
340.39365431735210.78730863470420.6063456826479
350.3241271497964490.6482542995928970.675872850203551
360.3421524127591020.6843048255182040.657847587240898
370.2585591347875220.5171182695750430.741440865212478
380.4562748359792650.912549671958530.543725164020735
390.4100850510873230.8201701021746460.589914948912677
400.3977336826754060.7954673653508130.602266317324593
410.3400303594545690.6800607189091380.659969640545431
420.3019275834193180.6038551668386360.698072416580682
430.1881430872535420.3762861745070840.811856912746458
440.2682375200178970.5364750400357940.731762479982103







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

\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 & 0 & 0 & OK \tabularnewline
10% type I error level & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57968&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]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57968&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57968&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 level00OK
10% type I error level00OK



Parameters (Session):
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')
}