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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationSun, 27 Dec 2009 04:57:17 -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/Dec/27/t1261915139shfleu7xtvohypv.htm/, Retrieved Sun, 28 Apr 2024 23:37:32 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=70857, Retrieved Sun, 28 Apr 2024 23:37:32 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact157

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RMPD  [Multiple Regression] [Seatbelt] [2009-11-12 13:54:52] [b98453cac15ba1066b407e146608df68]
-    D    [Multiple Regression] [JJ Workshop 7, Mu...] [2009-11-20 19:02:44] [96e597a9107bfe8c07649cce3d4f6fec]
-           [Multiple Regression] [Paper, Multiple R...] [2009-12-25 13:56:37] [96e597a9107bfe8c07649cce3d4f6fec]
-   PD        [Multiple Regression] [Paper, Multiple R...] [2009-12-26 14:04:00] [96e597a9107bfe8c07649cce3d4f6fec]
-    D            [Multiple Regression] [Paper, Multiple R...] [2009-12-27 11:57:17] [e31f2fa83f4a5291b9a51009566cf69b] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
95,1	93,8	96,9	98,6	111,7	109,8
97	93,8	95,1	96,9	98,6	111,7
112,7	107,6	97	95,1	96,9	98,6
102,9	101	112,7	97	95,1	96,9
97,4	95,4	102,9	112,7	97	95,1
111,4	96,5	97,4	102,9	112,7	97
87,4	89,2	111,4	97,4	102,9	112,7
96,8	87,1	87,4	111,4	97,4	102,9
114,1	110,5	96,8	87,4	111,4	97,4
110,3	110,8	114,1	96,8	87,4	111,4
103,9	104,2	110,3	114,1	96,8	87,4
101,6	88,9	103,9	110,3	114,1	96,8
94,6	89,8	101,6	103,9	110,3	114,1
95,9	90	94,6	101,6	103,9	110,3
104,7	93,9	95,9	94,6	101,6	103,9
102,8	91,3	104,7	95,9	94,6	101,6
98,1	87,8	102,8	104,7	95,9	94,6
113,9	99,7	98,1	102,8	104,7	95,9
80,9	73,5	113,9	98,1	102,8	104,7
95,7	79,2	80,9	113,9	98,1	102,8
113,2	96,9	95,7	80,9	113,9	98,1
105,9	95,2	113,2	95,7	80,9	113,9
108,8	95,6	105,9	113,2	95,7	80,9
102,3	89,7	108,8	105,9	113,2	95,7
99	92,8	102,3	108,8	105,9	113,2
100,7	88	99	102,3	108,8	105,9
115,5	101,1	100,7	99	102,3	108,8
100,7	92,7	115,5	100,7	99	102,3
109,9	95,8	100,7	115,5	100,7	99
114,6	103,8	109,9	100,7	115,5	100,7
85,4	81,8	114,6	109,9	100,7	115,5
100,5	87,1	85,4	114,6	109,9	100,7
114,8	105,9	100,5	85,4	114,6	109,9
116,5	108,1	114,8	100,5	85,4	114,6
112,9	102,6	116,5	114,8	100,5	85,4
102	93,7	112,9	116,5	114,8	100,5
106	103,5	102	112,9	116,5	114,8
105,3	100,6	106	102	112,9	116,5
118,8	113,3	105,3	106	102	112,9
106,1	102,4	118,8	105,3	106	102
109,3	102,1	106,1	118,8	105,3	106
117,2	106,9	109,3	106,1	118,8	105,3
92,5	87,3	117,2	109,3	106,1	118,8
104,2	93,1	92,5	117,2	109,3	106,1
112,5	109,1	104,2	92,5	117,2	109,3
122,4	120,3	112,5	104,2	92,5	117,2
113,3	104,9	122,4	112,5	104,2	92,5
100	92,6	113,3	122,4	112,5	104,2
110,7	109,8	100	113,3	122,4	112,5
112,8	111,4	110,7	100	113,3	122,4
109,8	117,9	112,8	110,7	100	113,3
117,3	121,6	109,8	112,8	110,7	100
109,1	117,8	117,3	109,8	112,8	110,7
115,9	124,2	109,1	117,3	109,8	112,8
96	106,8	115,9	109,1	117,3	109,8
99,8	102,7	96	115,9	109,1	117,3
116,8	116,8	99,8	96	115,9	109,1
115,7	113,6	116,8	99,8	96	115,9
99,4	96,1	115,7	116,8	99,8	96
94,3	85	99,4	115,7	116,8	99,8

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 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 & 4 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=70857&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]4 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=70857&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=70857&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Multiple Linear Regression - Estimated Regression Equation
Y(t)[t] = + 39.2600545642095 + 0.32005110995124`X(t)`[t] -0.0508039121660601`Y(t-1)`[t] + 0.088033584837062`Y(t-2)`[t] + 0.354570106951256`Y(t-3)`[t] -0.131980785927691`Y(t-4)`[t] + 0.823294556124404M1[t] + 5.23945758294206M2[t] + 13.7044910853491M3[t] + 8.24670612903364M4[t] + 6.116169736949M5[t] + 11.0269863471651M6[t] -5.04401421530769M7[t] + 2.6309225600948M8[t] + 10.9624252421365M9[t] + 20.6444943432124M10[t] + 8.34460830008787M11[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y(t)[t] =  +  39.2600545642095 +  0.32005110995124`X(t)`[t] -0.0508039121660601`Y(t-1)`[t] +  0.088033584837062`Y(t-2)`[t] +  0.354570106951256`Y(t-3)`[t] -0.131980785927691`Y(t-4)`[t] +  0.823294556124404M1[t] +  5.23945758294206M2[t] +  13.7044910853491M3[t] +  8.24670612903364M4[t] +  6.116169736949M5[t] +  11.0269863471651M6[t] -5.04401421530769M7[t] +  2.6309225600948M8[t] +  10.9624252421365M9[t] +  20.6444943432124M10[t] +  8.34460830008787M11[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=70857&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y(t)[t] =  +  39.2600545642095 +  0.32005110995124`X(t)`[t] -0.0508039121660601`Y(t-1)`[t] +  0.088033584837062`Y(t-2)`[t] +  0.354570106951256`Y(t-3)`[t] -0.131980785927691`Y(t-4)`[t] +  0.823294556124404M1[t] +  5.23945758294206M2[t] +  13.7044910853491M3[t] +  8.24670612903364M4[t] +  6.116169736949M5[t] +  11.0269863471651M6[t] -5.04401421530769M7[t] +  2.6309225600948M8[t] +  10.9624252421365M9[t] +  20.6444943432124M10[t] +  8.34460830008787M11[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=70857&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=70857&T=1

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


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

Multiple Linear Regression - Estimated Regression Equation
Y(t)[t] = + 39.2600545642095 + 0.32005110995124`X(t)`[t] -0.0508039121660601`Y(t-1)`[t] + 0.088033584837062`Y(t-2)`[t] + 0.354570106951256`Y(t-3)`[t] -0.131980785927691`Y(t-4)`[t] + 0.823294556124404M1[t] + 5.23945758294206M2[t] + 13.7044910853491M3[t] + 8.24670612903364M4[t] + 6.116169736949M5[t] + 11.0269863471651M6[t] -5.04401421530769M7[t] + 2.6309225600948M8[t] + 10.9624252421365M9[t] + 20.6444943432124M10[t] + 8.34460830008787M11[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)39.260054564209516.5572282.37120.0222820.011141
`X(t)`0.320051109951240.0876763.65040.0007050.000352
`Y(t-1)`-0.05080391216606010.143348-0.35440.7247640.362382
`Y(t-2)`0.0880335848370620.1247290.70580.4841180.242059
`Y(t-3)`0.3545701069512560.1264212.80470.0075320.003766
`Y(t-4)`-0.1319807859276910.144702-0.91210.3668070.183404
M10.8232945561244043.8878840.21180.8332960.416648
M25.239457582942064.4179461.18590.2421550.121077
M313.70449108534914.5784962.99320.0045610.002281
M48.246706129033643.7561482.19550.0335710.016785
M56.1161697369493.087521.98090.0540180.027009
M611.02698634716513.2666563.37560.0015710.000786
M7-5.044014215307693.281073-1.53730.1315450.065773
M82.63092256009484.0464980.65020.5190390.25952
M910.96242524213655.3028692.06730.0447620.022381
M1020.64449434321245.8100473.55320.0009390.000469
M118.344608300087874.1621282.00490.0512960.025648

\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) & 39.2600545642095 & 16.557228 & 2.3712 & 0.022282 & 0.011141 \tabularnewline
`X(t)` & 0.32005110995124 & 0.087676 & 3.6504 & 0.000705 & 0.000352 \tabularnewline
`Y(t-1)` & -0.0508039121660601 & 0.143348 & -0.3544 & 0.724764 & 0.362382 \tabularnewline
`Y(t-2)` & 0.088033584837062 & 0.124729 & 0.7058 & 0.484118 & 0.242059 \tabularnewline
`Y(t-3)` & 0.354570106951256 & 0.126421 & 2.8047 & 0.007532 & 0.003766 \tabularnewline
`Y(t-4)` & -0.131980785927691 & 0.144702 & -0.9121 & 0.366807 & 0.183404 \tabularnewline
M1 & 0.823294556124404 & 3.887884 & 0.2118 & 0.833296 & 0.416648 \tabularnewline
M2 & 5.23945758294206 & 4.417946 & 1.1859 & 0.242155 & 0.121077 \tabularnewline
M3 & 13.7044910853491 & 4.578496 & 2.9932 & 0.004561 & 0.002281 \tabularnewline
M4 & 8.24670612903364 & 3.756148 & 2.1955 & 0.033571 & 0.016785 \tabularnewline
M5 & 6.116169736949 & 3.08752 & 1.9809 & 0.054018 & 0.027009 \tabularnewline
M6 & 11.0269863471651 & 3.266656 & 3.3756 & 0.001571 & 0.000786 \tabularnewline
M7 & -5.04401421530769 & 3.281073 & -1.5373 & 0.131545 & 0.065773 \tabularnewline
M8 & 2.6309225600948 & 4.046498 & 0.6502 & 0.519039 & 0.25952 \tabularnewline
M9 & 10.9624252421365 & 5.302869 & 2.0673 & 0.044762 & 0.022381 \tabularnewline
M10 & 20.6444943432124 & 5.810047 & 3.5532 & 0.000939 & 0.000469 \tabularnewline
M11 & 8.34460830008787 & 4.162128 & 2.0049 & 0.051296 & 0.025648 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=70857&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]39.2600545642095[/C][C]16.557228[/C][C]2.3712[/C][C]0.022282[/C][C]0.011141[/C][/ROW]
[ROW][C]`X(t)`[/C][C]0.32005110995124[/C][C]0.087676[/C][C]3.6504[/C][C]0.000705[/C][C]0.000352[/C][/ROW]
[ROW][C]`Y(t-1)`[/C][C]-0.0508039121660601[/C][C]0.143348[/C][C]-0.3544[/C][C]0.724764[/C][C]0.362382[/C][/ROW]
[ROW][C]`Y(t-2)`[/C][C]0.088033584837062[/C][C]0.124729[/C][C]0.7058[/C][C]0.484118[/C][C]0.242059[/C][/ROW]
[ROW][C]`Y(t-3)`[/C][C]0.354570106951256[/C][C]0.126421[/C][C]2.8047[/C][C]0.007532[/C][C]0.003766[/C][/ROW]
[ROW][C]`Y(t-4)`[/C][C]-0.131980785927691[/C][C]0.144702[/C][C]-0.9121[/C][C]0.366807[/C][C]0.183404[/C][/ROW]
[ROW][C]M1[/C][C]0.823294556124404[/C][C]3.887884[/C][C]0.2118[/C][C]0.833296[/C][C]0.416648[/C][/ROW]
[ROW][C]M2[/C][C]5.23945758294206[/C][C]4.417946[/C][C]1.1859[/C][C]0.242155[/C][C]0.121077[/C][/ROW]
[ROW][C]M3[/C][C]13.7044910853491[/C][C]4.578496[/C][C]2.9932[/C][C]0.004561[/C][C]0.002281[/C][/ROW]
[ROW][C]M4[/C][C]8.24670612903364[/C][C]3.756148[/C][C]2.1955[/C][C]0.033571[/C][C]0.016785[/C][/ROW]
[ROW][C]M5[/C][C]6.116169736949[/C][C]3.08752[/C][C]1.9809[/C][C]0.054018[/C][C]0.027009[/C][/ROW]
[ROW][C]M6[/C][C]11.0269863471651[/C][C]3.266656[/C][C]3.3756[/C][C]0.001571[/C][C]0.000786[/C][/ROW]
[ROW][C]M7[/C][C]-5.04401421530769[/C][C]3.281073[/C][C]-1.5373[/C][C]0.131545[/C][C]0.065773[/C][/ROW]
[ROW][C]M8[/C][C]2.6309225600948[/C][C]4.046498[/C][C]0.6502[/C][C]0.519039[/C][C]0.25952[/C][/ROW]
[ROW][C]M9[/C][C]10.9624252421365[/C][C]5.302869[/C][C]2.0673[/C][C]0.044762[/C][C]0.022381[/C][/ROW]
[ROW][C]M10[/C][C]20.6444943432124[/C][C]5.810047[/C][C]3.5532[/C][C]0.000939[/C][C]0.000469[/C][/ROW]
[ROW][C]M11[/C][C]8.34460830008787[/C][C]4.162128[/C][C]2.0049[/C][C]0.051296[/C][C]0.025648[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=70857&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=70857&T=2

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


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)39.260054564209516.5572282.37120.0222820.011141
`X(t)`0.320051109951240.0876763.65040.0007050.000352
`Y(t-1)`-0.05080391216606010.143348-0.35440.7247640.362382
`Y(t-2)`0.0880335848370620.1247290.70580.4841180.242059
`Y(t-3)`0.3545701069512560.1264212.80470.0075320.003766
`Y(t-4)`-0.1319807859276910.144702-0.91210.3668070.183404
M10.8232945561244043.8878840.21180.8332960.416648
M25.239457582942064.4179461.18590.2421550.121077
M313.70449108534914.5784962.99320.0045610.002281
M48.246706129033643.7561482.19550.0335710.016785
M56.1161697369493.087521.98090.0540180.027009
M611.02698634716513.2666563.37560.0015710.000786
M7-5.044014215307693.281073-1.53730.1315450.065773
M82.63092256009484.0464980.65020.5190390.25952
M910.96242524213655.3028692.06730.0447620.022381
M1020.64449434321245.8100473.55320.0009390.000469
M118.344608300087874.1621282.00490.0512960.025648






Multiple Linear Regression - Regression Statistics
Multiple R0.941877335833354
R-squared0.887132915756537
Adjusted R-squared0.845135861154318
F-TEST (value)21.1236936532608
F-TEST (DF numerator)16
F-TEST (DF denominator)43
p-value2.66453525910038e-15
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.54162796179663
Sum Squared Residuals539.35453065053

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.941877335833354 \tabularnewline
R-squared & 0.887132915756537 \tabularnewline
Adjusted R-squared & 0.845135861154318 \tabularnewline
F-TEST (value) & 21.1236936532608 \tabularnewline
F-TEST (DF numerator) & 16 \tabularnewline
F-TEST (DF denominator) & 43 \tabularnewline
p-value & 2.66453525910038e-15 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 3.54162796179663 \tabularnewline
Sum Squared Residuals & 539.35453065053 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=70857&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.941877335833354[/C][/ROW]
[ROW][C]R-squared[/C][C]0.887132915756537[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.845135861154318[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]21.1236936532608[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]16[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]43[/C][/ROW]
[ROW][C]p-value[/C][C]2.66453525910038e-15[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]3.54162796179663[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]539.35453065053[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=70857&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=70857&T=3

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


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

Multiple Linear Regression - Regression Statistics
Multiple R0.941877335833354
R-squared0.887132915756537
Adjusted R-squared0.845135861154318
F-TEST (value)21.1236936532608
F-TEST (DF numerator)16
F-TEST (DF denominator)43
p-value2.66453525910038e-15
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.54162796179663
Sum Squared Residuals539.35453065053






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
195.198.9753462613978-3.87534626139785
29798.4376673415675-1.43766734156749
3112.7112.1905973893150.509402610684976
4102.9103.576258641070-0.676258641069492
597.4102.444690272304-5.04469027230441
6111.4112.440242674849-1.04024267484910
787.487.29054413561650.109455864383466
896.896.08841377368520.711586226314793
9114.1115.008625438055-0.908625438055306
10110.3114.377904319294-4.07790431929433
11103.9108.182214702010-4.28221470201022
12101.699.82488529768681.77511470231323
1394.696.8590259048282-2.25902590482819
1495.999.7246275957104-3.82462759571044
15104.7108.784746031201-4.08474603120131
16102.899.98376248121442.81623751878558
1798.198.9890768245126-0.88907682451258
18113.9110.7286581386043.17134186139646
1980.983.2207447150793-2.32074471507925
2095.794.3717165497011.32828345029891
21113.2110.9336350618892.26636493811105
22105.9106.699335921681-0.79933592168127
23108.8106.0419301344902.75806986550953
24102.399.27070501101533.02929498898469
259996.77365529861672.22634470138333
26100.7101.240720653806-0.540720653806237
27115.5110.8340962415564.66590375844358
28100.7101.773434911407-1.07343491140675
29109.9103.7281576911966.17184230880433
30114.6114.4523603803070.14763961969306
3185.484.7104127776190.68958722238106
32100.5101.194213135428-0.694213135427528
33114.8112.6572132057392.14278679426107
34116.5112.6724491189363.82755088106426
35112.9108.9926431476203.90735685238037
36102101.2095738088810.790426191118627
37106104.1216549227761.87834507722428
38105.3104.9460682862450.353931713755042
39118.8114.4737646264684.32623537353181
40106.1107.636817242473-1.53681724247337
41109.3106.4678063786362.83219362136399
42117.2116.5133522642470.68664773575268
4392.587.76492554379224.73507445620783
44104.2102.0572610311522.14273896884798
45112.5115.119511484541-2.61951148454149
46122.4119.1939436381623.20605636183815
47113.3109.6013861892363.69861381076446
48100100.052754019687-0.0527540196869052
49110.7108.6703176123822.02968238761843
50112.8107.3509161226715.44908387732913
51109.8115.216795711459-5.41679571145906
52117.3116.8297267238360.470273276164035
53109.1112.170268833351-3.07026883335133
54115.9118.865386541993-2.9653865419931
559699.213372827893-3.21337282789311
5699.8103.288395510034-3.48839551003415
57116.8117.681014809775-0.881014809775334
58115.7117.856367001927-2.15636700192681
5999.4105.481825826644-6.08182582664414
6094.399.8420818627296-5.54208186272964

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 95.1 & 98.9753462613978 & -3.87534626139785 \tabularnewline
2 & 97 & 98.4376673415675 & -1.43766734156749 \tabularnewline
3 & 112.7 & 112.190597389315 & 0.509402610684976 \tabularnewline
4 & 102.9 & 103.576258641070 & -0.676258641069492 \tabularnewline
5 & 97.4 & 102.444690272304 & -5.04469027230441 \tabularnewline
6 & 111.4 & 112.440242674849 & -1.04024267484910 \tabularnewline
7 & 87.4 & 87.2905441356165 & 0.109455864383466 \tabularnewline
8 & 96.8 & 96.0884137736852 & 0.711586226314793 \tabularnewline
9 & 114.1 & 115.008625438055 & -0.908625438055306 \tabularnewline
10 & 110.3 & 114.377904319294 & -4.07790431929433 \tabularnewline
11 & 103.9 & 108.182214702010 & -4.28221470201022 \tabularnewline
12 & 101.6 & 99.8248852976868 & 1.77511470231323 \tabularnewline
13 & 94.6 & 96.8590259048282 & -2.25902590482819 \tabularnewline
14 & 95.9 & 99.7246275957104 & -3.82462759571044 \tabularnewline
15 & 104.7 & 108.784746031201 & -4.08474603120131 \tabularnewline
16 & 102.8 & 99.9837624812144 & 2.81623751878558 \tabularnewline
17 & 98.1 & 98.9890768245126 & -0.88907682451258 \tabularnewline
18 & 113.9 & 110.728658138604 & 3.17134186139646 \tabularnewline
19 & 80.9 & 83.2207447150793 & -2.32074471507925 \tabularnewline
20 & 95.7 & 94.371716549701 & 1.32828345029891 \tabularnewline
21 & 113.2 & 110.933635061889 & 2.26636493811105 \tabularnewline
22 & 105.9 & 106.699335921681 & -0.79933592168127 \tabularnewline
23 & 108.8 & 106.041930134490 & 2.75806986550953 \tabularnewline
24 & 102.3 & 99.2707050110153 & 3.02929498898469 \tabularnewline
25 & 99 & 96.7736552986167 & 2.22634470138333 \tabularnewline
26 & 100.7 & 101.240720653806 & -0.540720653806237 \tabularnewline
27 & 115.5 & 110.834096241556 & 4.66590375844358 \tabularnewline
28 & 100.7 & 101.773434911407 & -1.07343491140675 \tabularnewline
29 & 109.9 & 103.728157691196 & 6.17184230880433 \tabularnewline
30 & 114.6 & 114.452360380307 & 0.14763961969306 \tabularnewline
31 & 85.4 & 84.710412777619 & 0.68958722238106 \tabularnewline
32 & 100.5 & 101.194213135428 & -0.694213135427528 \tabularnewline
33 & 114.8 & 112.657213205739 & 2.14278679426107 \tabularnewline
34 & 116.5 & 112.672449118936 & 3.82755088106426 \tabularnewline
35 & 112.9 & 108.992643147620 & 3.90735685238037 \tabularnewline
36 & 102 & 101.209573808881 & 0.790426191118627 \tabularnewline
37 & 106 & 104.121654922776 & 1.87834507722428 \tabularnewline
38 & 105.3 & 104.946068286245 & 0.353931713755042 \tabularnewline
39 & 118.8 & 114.473764626468 & 4.32623537353181 \tabularnewline
40 & 106.1 & 107.636817242473 & -1.53681724247337 \tabularnewline
41 & 109.3 & 106.467806378636 & 2.83219362136399 \tabularnewline
42 & 117.2 & 116.513352264247 & 0.68664773575268 \tabularnewline
43 & 92.5 & 87.7649255437922 & 4.73507445620783 \tabularnewline
44 & 104.2 & 102.057261031152 & 2.14273896884798 \tabularnewline
45 & 112.5 & 115.119511484541 & -2.61951148454149 \tabularnewline
46 & 122.4 & 119.193943638162 & 3.20605636183815 \tabularnewline
47 & 113.3 & 109.601386189236 & 3.69861381076446 \tabularnewline
48 & 100 & 100.052754019687 & -0.0527540196869052 \tabularnewline
49 & 110.7 & 108.670317612382 & 2.02968238761843 \tabularnewline
50 & 112.8 & 107.350916122671 & 5.44908387732913 \tabularnewline
51 & 109.8 & 115.216795711459 & -5.41679571145906 \tabularnewline
52 & 117.3 & 116.829726723836 & 0.470273276164035 \tabularnewline
53 & 109.1 & 112.170268833351 & -3.07026883335133 \tabularnewline
54 & 115.9 & 118.865386541993 & -2.9653865419931 \tabularnewline
55 & 96 & 99.213372827893 & -3.21337282789311 \tabularnewline
56 & 99.8 & 103.288395510034 & -3.48839551003415 \tabularnewline
57 & 116.8 & 117.681014809775 & -0.881014809775334 \tabularnewline
58 & 115.7 & 117.856367001927 & -2.15636700192681 \tabularnewline
59 & 99.4 & 105.481825826644 & -6.08182582664414 \tabularnewline
60 & 94.3 & 99.8420818627296 & -5.54208186272964 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=70857&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]95.1[/C][C]98.9753462613978[/C][C]-3.87534626139785[/C][/ROW]
[ROW][C]2[/C][C]97[/C][C]98.4376673415675[/C][C]-1.43766734156749[/C][/ROW]
[ROW][C]3[/C][C]112.7[/C][C]112.190597389315[/C][C]0.509402610684976[/C][/ROW]
[ROW][C]4[/C][C]102.9[/C][C]103.576258641070[/C][C]-0.676258641069492[/C][/ROW]
[ROW][C]5[/C][C]97.4[/C][C]102.444690272304[/C][C]-5.04469027230441[/C][/ROW]
[ROW][C]6[/C][C]111.4[/C][C]112.440242674849[/C][C]-1.04024267484910[/C][/ROW]
[ROW][C]7[/C][C]87.4[/C][C]87.2905441356165[/C][C]0.109455864383466[/C][/ROW]
[ROW][C]8[/C][C]96.8[/C][C]96.0884137736852[/C][C]0.711586226314793[/C][/ROW]
[ROW][C]9[/C][C]114.1[/C][C]115.008625438055[/C][C]-0.908625438055306[/C][/ROW]
[ROW][C]10[/C][C]110.3[/C][C]114.377904319294[/C][C]-4.07790431929433[/C][/ROW]
[ROW][C]11[/C][C]103.9[/C][C]108.182214702010[/C][C]-4.28221470201022[/C][/ROW]
[ROW][C]12[/C][C]101.6[/C][C]99.8248852976868[/C][C]1.77511470231323[/C][/ROW]
[ROW][C]13[/C][C]94.6[/C][C]96.8590259048282[/C][C]-2.25902590482819[/C][/ROW]
[ROW][C]14[/C][C]95.9[/C][C]99.7246275957104[/C][C]-3.82462759571044[/C][/ROW]
[ROW][C]15[/C][C]104.7[/C][C]108.784746031201[/C][C]-4.08474603120131[/C][/ROW]
[ROW][C]16[/C][C]102.8[/C][C]99.9837624812144[/C][C]2.81623751878558[/C][/ROW]
[ROW][C]17[/C][C]98.1[/C][C]98.9890768245126[/C][C]-0.88907682451258[/C][/ROW]
[ROW][C]18[/C][C]113.9[/C][C]110.728658138604[/C][C]3.17134186139646[/C][/ROW]
[ROW][C]19[/C][C]80.9[/C][C]83.2207447150793[/C][C]-2.32074471507925[/C][/ROW]
[ROW][C]20[/C][C]95.7[/C][C]94.371716549701[/C][C]1.32828345029891[/C][/ROW]
[ROW][C]21[/C][C]113.2[/C][C]110.933635061889[/C][C]2.26636493811105[/C][/ROW]
[ROW][C]22[/C][C]105.9[/C][C]106.699335921681[/C][C]-0.79933592168127[/C][/ROW]
[ROW][C]23[/C][C]108.8[/C][C]106.041930134490[/C][C]2.75806986550953[/C][/ROW]
[ROW][C]24[/C][C]102.3[/C][C]99.2707050110153[/C][C]3.02929498898469[/C][/ROW]
[ROW][C]25[/C][C]99[/C][C]96.7736552986167[/C][C]2.22634470138333[/C][/ROW]
[ROW][C]26[/C][C]100.7[/C][C]101.240720653806[/C][C]-0.540720653806237[/C][/ROW]
[ROW][C]27[/C][C]115.5[/C][C]110.834096241556[/C][C]4.66590375844358[/C][/ROW]
[ROW][C]28[/C][C]100.7[/C][C]101.773434911407[/C][C]-1.07343491140675[/C][/ROW]
[ROW][C]29[/C][C]109.9[/C][C]103.728157691196[/C][C]6.17184230880433[/C][/ROW]
[ROW][C]30[/C][C]114.6[/C][C]114.452360380307[/C][C]0.14763961969306[/C][/ROW]
[ROW][C]31[/C][C]85.4[/C][C]84.710412777619[/C][C]0.68958722238106[/C][/ROW]
[ROW][C]32[/C][C]100.5[/C][C]101.194213135428[/C][C]-0.694213135427528[/C][/ROW]
[ROW][C]33[/C][C]114.8[/C][C]112.657213205739[/C][C]2.14278679426107[/C][/ROW]
[ROW][C]34[/C][C]116.5[/C][C]112.672449118936[/C][C]3.82755088106426[/C][/ROW]
[ROW][C]35[/C][C]112.9[/C][C]108.992643147620[/C][C]3.90735685238037[/C][/ROW]
[ROW][C]36[/C][C]102[/C][C]101.209573808881[/C][C]0.790426191118627[/C][/ROW]
[ROW][C]37[/C][C]106[/C][C]104.121654922776[/C][C]1.87834507722428[/C][/ROW]
[ROW][C]38[/C][C]105.3[/C][C]104.946068286245[/C][C]0.353931713755042[/C][/ROW]
[ROW][C]39[/C][C]118.8[/C][C]114.473764626468[/C][C]4.32623537353181[/C][/ROW]
[ROW][C]40[/C][C]106.1[/C][C]107.636817242473[/C][C]-1.53681724247337[/C][/ROW]
[ROW][C]41[/C][C]109.3[/C][C]106.467806378636[/C][C]2.83219362136399[/C][/ROW]
[ROW][C]42[/C][C]117.2[/C][C]116.513352264247[/C][C]0.68664773575268[/C][/ROW]
[ROW][C]43[/C][C]92.5[/C][C]87.7649255437922[/C][C]4.73507445620783[/C][/ROW]
[ROW][C]44[/C][C]104.2[/C][C]102.057261031152[/C][C]2.14273896884798[/C][/ROW]
[ROW][C]45[/C][C]112.5[/C][C]115.119511484541[/C][C]-2.61951148454149[/C][/ROW]
[ROW][C]46[/C][C]122.4[/C][C]119.193943638162[/C][C]3.20605636183815[/C][/ROW]
[ROW][C]47[/C][C]113.3[/C][C]109.601386189236[/C][C]3.69861381076446[/C][/ROW]
[ROW][C]48[/C][C]100[/C][C]100.052754019687[/C][C]-0.0527540196869052[/C][/ROW]
[ROW][C]49[/C][C]110.7[/C][C]108.670317612382[/C][C]2.02968238761843[/C][/ROW]
[ROW][C]50[/C][C]112.8[/C][C]107.350916122671[/C][C]5.44908387732913[/C][/ROW]
[ROW][C]51[/C][C]109.8[/C][C]115.216795711459[/C][C]-5.41679571145906[/C][/ROW]
[ROW][C]52[/C][C]117.3[/C][C]116.829726723836[/C][C]0.470273276164035[/C][/ROW]
[ROW][C]53[/C][C]109.1[/C][C]112.170268833351[/C][C]-3.07026883335133[/C][/ROW]
[ROW][C]54[/C][C]115.9[/C][C]118.865386541993[/C][C]-2.9653865419931[/C][/ROW]
[ROW][C]55[/C][C]96[/C][C]99.213372827893[/C][C]-3.21337282789311[/C][/ROW]
[ROW][C]56[/C][C]99.8[/C][C]103.288395510034[/C][C]-3.48839551003415[/C][/ROW]
[ROW][C]57[/C][C]116.8[/C][C]117.681014809775[/C][C]-0.881014809775334[/C][/ROW]
[ROW][C]58[/C][C]115.7[/C][C]117.856367001927[/C][C]-2.15636700192681[/C][/ROW]
[ROW][C]59[/C][C]99.4[/C][C]105.481825826644[/C][C]-6.08182582664414[/C][/ROW]
[ROW][C]60[/C][C]94.3[/C][C]99.8420818627296[/C][C]-5.54208186272964[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=70857&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=70857&T=4

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


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
195.198.9753462613978-3.87534626139785
29798.4376673415675-1.43766734156749
3112.7112.1905973893150.509402610684976
4102.9103.576258641070-0.676258641069492
597.4102.444690272304-5.04469027230441
6111.4112.440242674849-1.04024267484910
787.487.29054413561650.109455864383466
896.896.08841377368520.711586226314793
9114.1115.008625438055-0.908625438055306
10110.3114.377904319294-4.07790431929433
11103.9108.182214702010-4.28221470201022
12101.699.82488529768681.77511470231323
1394.696.8590259048282-2.25902590482819
1495.999.7246275957104-3.82462759571044
15104.7108.784746031201-4.08474603120131
16102.899.98376248121442.81623751878558
1798.198.9890768245126-0.88907682451258
18113.9110.7286581386043.17134186139646
1980.983.2207447150793-2.32074471507925
2095.794.3717165497011.32828345029891
21113.2110.9336350618892.26636493811105
22105.9106.699335921681-0.79933592168127
23108.8106.0419301344902.75806986550953
24102.399.27070501101533.02929498898469
259996.77365529861672.22634470138333
26100.7101.240720653806-0.540720653806237
27115.5110.8340962415564.66590375844358
28100.7101.773434911407-1.07343491140675
29109.9103.7281576911966.17184230880433
30114.6114.4523603803070.14763961969306
3185.484.7104127776190.68958722238106
32100.5101.194213135428-0.694213135427528
33114.8112.6572132057392.14278679426107
34116.5112.6724491189363.82755088106426
35112.9108.9926431476203.90735685238037
36102101.2095738088810.790426191118627
37106104.1216549227761.87834507722428
38105.3104.9460682862450.353931713755042
39118.8114.4737646264684.32623537353181
40106.1107.636817242473-1.53681724247337
41109.3106.4678063786362.83219362136399
42117.2116.5133522642470.68664773575268
4392.587.76492554379224.73507445620783
44104.2102.0572610311522.14273896884798
45112.5115.119511484541-2.61951148454149
46122.4119.1939436381623.20605636183815
47113.3109.6013861892363.69861381076446
48100100.052754019687-0.0527540196869052
49110.7108.6703176123822.02968238761843
50112.8107.3509161226715.44908387732913
51109.8115.216795711459-5.41679571145906
52117.3116.8297267238360.470273276164035
53109.1112.170268833351-3.07026883335133
54115.9118.865386541993-2.9653865419931
559699.213372827893-3.21337282789311
5699.8103.288395510034-3.48839551003415
57116.8117.681014809775-0.881014809775334
58115.7117.856367001927-2.15636700192681
5999.4105.481825826644-6.08182582664414
6094.399.8420818627296-5.54208186272964






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
200.1548954542734290.3097909085468580.84510454572657
210.1063991817851620.2127983635703240.893600818214838
220.0575640515457790.1151281030915580.94243594845422
230.09087793002207350.1817558600441470.909122069977926
240.04607415908528860.09214831817057710.953925840914711
250.06400193018567970.1280038603713590.93599806981432
260.1538155681448910.3076311362897820.846184431855109
270.2987007283608250.597401456721650.701299271639175
280.2484194738577160.4968389477154310.751580526142284
290.391530237028450.78306047405690.60846976297155
300.3066374525551340.6132749051102680.693362547444866
310.2476048343945000.4952096687889990.7523951656055
320.1804530312765510.3609060625531020.819546968723449
330.1148834622416250.229766924483250.885116537758375
340.1116321630872010.2232643261744020.888367836912799
350.09025173524976230.1805034704995250.909748264750238
360.05868646006237180.1173729201247440.941313539937628
370.03719244558109320.07438489116218650.962807554418907
380.02822626588780920.05645253177561840.97177373411219
390.0644799691937180.1289599383874360.935520030806282
400.2175418431405630.4350836862811260.782458156859437

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
20 & 0.154895454273429 & 0.309790908546858 & 0.84510454572657 \tabularnewline
21 & 0.106399181785162 & 0.212798363570324 & 0.893600818214838 \tabularnewline
22 & 0.057564051545779 & 0.115128103091558 & 0.94243594845422 \tabularnewline
23 & 0.0908779300220735 & 0.181755860044147 & 0.909122069977926 \tabularnewline
24 & 0.0460741590852886 & 0.0921483181705771 & 0.953925840914711 \tabularnewline
25 & 0.0640019301856797 & 0.128003860371359 & 0.93599806981432 \tabularnewline
26 & 0.153815568144891 & 0.307631136289782 & 0.846184431855109 \tabularnewline
27 & 0.298700728360825 & 0.59740145672165 & 0.701299271639175 \tabularnewline
28 & 0.248419473857716 & 0.496838947715431 & 0.751580526142284 \tabularnewline
29 & 0.39153023702845 & 0.7830604740569 & 0.60846976297155 \tabularnewline
30 & 0.306637452555134 & 0.613274905110268 & 0.693362547444866 \tabularnewline
31 & 0.247604834394500 & 0.495209668788999 & 0.7523951656055 \tabularnewline
32 & 0.180453031276551 & 0.360906062553102 & 0.819546968723449 \tabularnewline
33 & 0.114883462241625 & 0.22976692448325 & 0.885116537758375 \tabularnewline
34 & 0.111632163087201 & 0.223264326174402 & 0.888367836912799 \tabularnewline
35 & 0.0902517352497623 & 0.180503470499525 & 0.909748264750238 \tabularnewline
36 & 0.0586864600623718 & 0.117372920124744 & 0.941313539937628 \tabularnewline
37 & 0.0371924455810932 & 0.0743848911621865 & 0.962807554418907 \tabularnewline
38 & 0.0282262658878092 & 0.0564525317756184 & 0.97177373411219 \tabularnewline
39 & 0.064479969193718 & 0.128959938387436 & 0.935520030806282 \tabularnewline
40 & 0.217541843140563 & 0.435083686281126 & 0.782458156859437 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=70857&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]20[/C][C]0.154895454273429[/C][C]0.309790908546858[/C][C]0.84510454572657[/C][/ROW]
[ROW][C]21[/C][C]0.106399181785162[/C][C]0.212798363570324[/C][C]0.893600818214838[/C][/ROW]
[ROW][C]22[/C][C]0.057564051545779[/C][C]0.115128103091558[/C][C]0.94243594845422[/C][/ROW]
[ROW][C]23[/C][C]0.0908779300220735[/C][C]0.181755860044147[/C][C]0.909122069977926[/C][/ROW]
[ROW][C]24[/C][C]0.0460741590852886[/C][C]0.0921483181705771[/C][C]0.953925840914711[/C][/ROW]
[ROW][C]25[/C][C]0.0640019301856797[/C][C]0.128003860371359[/C][C]0.93599806981432[/C][/ROW]
[ROW][C]26[/C][C]0.153815568144891[/C][C]0.307631136289782[/C][C]0.846184431855109[/C][/ROW]
[ROW][C]27[/C][C]0.298700728360825[/C][C]0.59740145672165[/C][C]0.701299271639175[/C][/ROW]
[ROW][C]28[/C][C]0.248419473857716[/C][C]0.496838947715431[/C][C]0.751580526142284[/C][/ROW]
[ROW][C]29[/C][C]0.39153023702845[/C][C]0.7830604740569[/C][C]0.60846976297155[/C][/ROW]
[ROW][C]30[/C][C]0.306637452555134[/C][C]0.613274905110268[/C][C]0.693362547444866[/C][/ROW]
[ROW][C]31[/C][C]0.247604834394500[/C][C]0.495209668788999[/C][C]0.7523951656055[/C][/ROW]
[ROW][C]32[/C][C]0.180453031276551[/C][C]0.360906062553102[/C][C]0.819546968723449[/C][/ROW]
[ROW][C]33[/C][C]0.114883462241625[/C][C]0.22976692448325[/C][C]0.885116537758375[/C][/ROW]
[ROW][C]34[/C][C]0.111632163087201[/C][C]0.223264326174402[/C][C]0.888367836912799[/C][/ROW]
[ROW][C]35[/C][C]0.0902517352497623[/C][C]0.180503470499525[/C][C]0.909748264750238[/C][/ROW]
[ROW][C]36[/C][C]0.0586864600623718[/C][C]0.117372920124744[/C][C]0.941313539937628[/C][/ROW]
[ROW][C]37[/C][C]0.0371924455810932[/C][C]0.0743848911621865[/C][C]0.962807554418907[/C][/ROW]
[ROW][C]38[/C][C]0.0282262658878092[/C][C]0.0564525317756184[/C][C]0.97177373411219[/C][/ROW]
[ROW][C]39[/C][C]0.064479969193718[/C][C]0.128959938387436[/C][C]0.935520030806282[/C][/ROW]
[ROW][C]40[/C][C]0.217541843140563[/C][C]0.435083686281126[/C][C]0.782458156859437[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=70857&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=70857&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
200.1548954542734290.3097909085468580.84510454572657
210.1063991817851620.2127983635703240.893600818214838
220.0575640515457790.1151281030915580.94243594845422
230.09087793002207350.1817558600441470.909122069977926
240.04607415908528860.09214831817057710.953925840914711
250.06400193018567970.1280038603713590.93599806981432
260.1538155681448910.3076311362897820.846184431855109
270.2987007283608250.597401456721650.701299271639175
280.2484194738577160.4968389477154310.751580526142284
290.391530237028450.78306047405690.60846976297155
300.3066374525551340.6132749051102680.693362547444866
310.2476048343945000.4952096687889990.7523951656055
320.1804530312765510.3609060625531020.819546968723449
330.1148834622416250.229766924483250.885116537758375
340.1116321630872010.2232643261744020.888367836912799
350.09025173524976230.1805034704995250.909748264750238
360.05868646006237180.1173729201247440.941313539937628
370.03719244558109320.07438489116218650.962807554418907
380.02822626588780920.05645253177561840.97177373411219
390.0644799691937180.1289599383874360.935520030806282
400.2175418431405630.4350836862811260.782458156859437






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 level30.142857142857143NOK

\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 & 3 & 0.142857142857143 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=70857&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]3[/C][C]0.142857142857143[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=70857&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=70857&T=6

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


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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level30.142857142857143NOK


Figures (Output of Computation)

Figure 1
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Figure 10
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Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No 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')
}