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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 computationThu, 19 Nov 2009 10:16:31 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Nov/19/t12586511819w26nl9wb5okb08.htm/, Retrieved Thu, 25 Apr 2024 02:20:34 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=57841, Retrieved Thu, 25 Apr 2024 02:20:34 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsws7l4
Estimated Impact146

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RM D  [Multiple Regression] [Seatbelt] [2009-11-12 14:10:54] [b98453cac15ba1066b407e146608df68]
-    D      [Multiple Regression] [] [2009-11-19 17:16:31] [42ed2e0ab6f351a3dce7cf3f388e378d] [Current]
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Dataset

Dataseries X:
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Histogram
Boxplots 
6,3	101,9	6,3	6,1	6,1	6,3
6	106,2	6,3	6,3	6,1	6,1
6,2	81	6	6,3	6,3	6,1
6,4	94,7	6,2	6	6,3	6,3
6,8	101	6,4	6,2	6	6,3
7,5	109,4	6,8	6,4	6,2	6
7,5	102,3	7,5	6,8	6,4	6,2
7,6	90,7	7,5	7,5	6,8	6,4
7,6	96,2	7,6	7,5	7,5	6,8
7,4	96,1	7,6	7,6	7,5	7,5
7,3	106	7,4	7,6	7,6	7,5
7,1	103,1	7,3	7,4	7,6	7,6
6,9	102	7,1	7,3	7,4	7,6
6,8	104,7	6,9	7,1	7,3	7,4
7,5	86	6,8	6,9	7,1	7,3
7,6	92,1	7,5	6,8	6,9	7,1
7,8	106,9	7,6	7,5	6,8	6,9
8	112,6	7,8	7,6	7,5	6,8
8,1	101,7	8	7,8	7,6	7,5
8,2	92	8,1	8	7,8	7,6
8,3	97,4	8,2	8,1	8	7,8
8,2	97	8,3	8,2	8,1	8
8	105,4	8,2	8,3	8,2	8,1
7,9	102,7	8	8,2	8,3	8,2
7,6	98,1	7,9	8	8,2	8,3
7,6	104,5	7,6	7,9	8	8,2
8,3	87,4	7,6	7,6	7,9	8
8,4	89,9	8,3	7,6	7,6	7,9
8,4	109,8	8,4	8,3	7,6	7,6
8,4	111,7	8,4	8,4	8,3	7,6
8,4	98,6	8,4	8,4	8,4	8,3
8,6	96,9	8,4	8,4	8,4	8,4
8,9	95,1	8,6	8,4	8,4	8,4
8,8	97	8,9	8,6	8,4	8,4
8,3	112,7	8,8	8,9	8,6	8,4
7,5	102,9	8,3	8,8	8,9	8,6
7,2	97,4	7,5	8,3	8,8	8,9
7,4	111,4	7,2	7,5	8,3	8,8
8,8	87,4	7,4	7,2	7,5	8,3
9,3	96,8	8,8	7,4	7,2	7,5
9,3	114,1	9,3	8,8	7,4	7,2
8,7	110,3	9,3	9,3	8,8	7,4
8,2	103,9	8,7	9,3	9,3	8,8
8,3	101,6	8,2	8,7	9,3	9,3
8,5	94,6	8,3	8,2	8,7	9,3
8,6	95,9	8,5	8,3	8,2	8,7
8,5	104,7	8,6	8,5	8,3	8,2
8,2	102,8	8,5	8,6	8,5	8,3
8,1	98,1	8,2	8,5	8,6	8,5
7,9	113,9	8,1	8,2	8,5	8,6
8,6	80,9	7,9	8,1	8,2	8,5
8,7	95,7	8,6	7,9	8,1	8,2
8,7	113,2	8,7	8,6	7,9	8,1
8,5	105,9	8,7	8,7	8,6	7,9
8,4	108,8	8,5	8,7	8,7	8,6
8,5	102,3	8,4	8,5	8,7	8,7
8,7	99	8,5	8,4	8,5	8,7
8,7	100,7	8,7	8,5	8,4	8,5
8,6	115,5	8,7	8,7	8,5	8,4
8,5	100,7	8,6	8,7	8,7	8,5
8,3	109,9	8,5	8,6	8,7	8,7
8	114,6	8,3	8,5	8,6	8,7
8,2	85,4	8	8,3	8,5	8,6
8,1	100,5	8,2	8	8,3	8,5
8,1	114,8	8,1	8,2	8	8,3
8	116,5	8,1	8,1	8,2	8
7,9	112,9	8	8,1	8,1	8,2
7,9	102	7,9	8	8,1	8,1
8	106	7,9	7,9	8	8,1
8	105,3	8	7,9	7,9	8
7,9	118,8	8	8	7,9	7,9
8	106,1	7,9	8	8	7,9
7,7	109,3	8	7,9	8	8
7,2	117,2	7,7	8	7,9	8
7,5	92,5	7,2	7,7	8	7,9
7,3	104,2	7,5	7,2	7,7	8
7	112,5	7,3	7,5	7,2	7,7
7	122,4	7	7,3	7,5	7,2
7	113,3	7	7	7,3	7,5
7,2	100	7	7	7	7,3
7,3	110,7	7,2	7	7	7
7,1	112,8	7,3	7,2	7	7
6,8	109,8	7,1	7,3	7,2	7
6,4	117,3	6,8	7,1	7,3	7,2
6,1	109,1	6,4	6,8	7,1	7,3
6,5	115,9	6,1	6,4	6,8	7,1
7,7	96	6,5	6,1	6,4	6,8
7,9	99,8	7,7	6,5	6,1	6,4
7,5	116,8	7,9	7,7	6,5	6,1
6,9	115,7	7,5	7,9	7,7	6,5
6,6	99,4	6,9	7,5	7,9	7,7
6,9	94,3	6,6	6,9	7,5	7,9
7,7	91	6,9	6,6	6,9	7,5

Tables (Output of Computation)





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=57841&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=57841&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57841&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 time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 0.933141956395697 -0.00599446355432254X[t] + 1.55409357038416Y1[t] -0.862743246928274Y2[t] -0.133170668301423Y3[t] + 0.394880751487633Y4[t] -0.107835895318313M1[t] + 0.00865868146602026M2[t] + 0.555132299015706M3[t] -0.358561693549813M4[t] + 0.194164167438996M5[t] + 0.377211037290883M6[t] + 0.0719094385043453M7[t] + 0.228312254709038M8[t] + 0.145801390152894M9[t] -0.0769332094556072M10[t] + 0.0653509691361015M11[t] -0.000644466348359865t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  0.933141956395697 -0.00599446355432254X[t] +  1.55409357038416Y1[t] -0.862743246928274Y2[t] -0.133170668301423Y3[t] +  0.394880751487633Y4[t] -0.107835895318313M1[t] +  0.00865868146602026M2[t] +  0.555132299015706M3[t] -0.358561693549813M4[t] +  0.194164167438996M5[t] +  0.377211037290883M6[t] +  0.0719094385043453M7[t] +  0.228312254709038M8[t] +  0.145801390152894M9[t] -0.0769332094556072M10[t] +  0.0653509691361015M11[t] -0.000644466348359865t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57841&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  0.933141956395697 -0.00599446355432254X[t] +  1.55409357038416Y1[t] -0.862743246928274Y2[t] -0.133170668301423Y3[t] +  0.394880751487633Y4[t] -0.107835895318313M1[t] +  0.00865868146602026M2[t] +  0.555132299015706M3[t] -0.358561693549813M4[t] +  0.194164167438996M5[t] +  0.377211037290883M6[t] +  0.0719094385043453M7[t] +  0.228312254709038M8[t] +  0.145801390152894M9[t] -0.0769332094556072M10[t] +  0.0653509691361015M11[t] -0.000644466348359865t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57841&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57841&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] = + 0.933141956395697 -0.00599446355432254X[t] + 1.55409357038416Y1[t] -0.862743246928274Y2[t] -0.133170668301423Y3[t] + 0.394880751487633Y4[t] -0.107835895318313M1[t] + 0.00865868146602026M2[t] + 0.555132299015706M3[t] -0.358561693549813M4[t] + 0.194164167438996M5[t] + 0.377211037290883M6[t] + 0.0719094385043453M7[t] + 0.228312254709038M8[t] + 0.145801390152894M9[t] -0.0769332094556072M10[t] + 0.0653509691361015M11[t] -0.000644466348359865t + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.9331419563956970.5003751.86490.0661090.033055
X-0.005994463554322540.004393-1.36450.1764790.08824
Y11.554093570384160.10742214.467200
Y2-0.8627432469282740.208198-4.14398.9e-054.4e-05
Y3-0.1331706683014230.20837-0.63910.52470.26235
Y40.3948807514876330.106413.71090.0003940.000197
M1-0.1078358953183130.086434-1.24760.2160550.108028
M20.008658681466020260.0938060.09230.9267020.463351
M30.5551322990157060.1173544.73041e-055e-06
M4-0.3585616935498130.121642-2.94770.0042660.002133
M50.1941641674389960.1254271.5480.1258260.062913
M60.3772110372908830.1039363.62930.0005160.000258
M70.07190943850434530.0845640.85040.3978320.198916
M80.2283122547090380.0916222.49190.0149150.007458
M90.1458013901528940.0947451.53890.128040.06402
M10-0.07693320945560720.093945-0.81890.415430.207715
M110.06535096913610150.0923640.70750.4814260.240713
t-0.0006444663483598650.000878-0.73430.4650760.232538

\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) & 0.933141956395697 & 0.500375 & 1.8649 & 0.066109 & 0.033055 \tabularnewline
X & -0.00599446355432254 & 0.004393 & -1.3645 & 0.176479 & 0.08824 \tabularnewline
Y1 & 1.55409357038416 & 0.107422 & 14.4672 & 0 & 0 \tabularnewline
Y2 & -0.862743246928274 & 0.208198 & -4.1439 & 8.9e-05 & 4.4e-05 \tabularnewline
Y3 & -0.133170668301423 & 0.20837 & -0.6391 & 0.5247 & 0.26235 \tabularnewline
Y4 & 0.394880751487633 & 0.10641 & 3.7109 & 0.000394 & 0.000197 \tabularnewline
M1 & -0.107835895318313 & 0.086434 & -1.2476 & 0.216055 & 0.108028 \tabularnewline
M2 & 0.00865868146602026 & 0.093806 & 0.0923 & 0.926702 & 0.463351 \tabularnewline
M3 & 0.555132299015706 & 0.117354 & 4.7304 & 1e-05 & 5e-06 \tabularnewline
M4 & -0.358561693549813 & 0.121642 & -2.9477 & 0.004266 & 0.002133 \tabularnewline
M5 & 0.194164167438996 & 0.125427 & 1.548 & 0.125826 & 0.062913 \tabularnewline
M6 & 0.377211037290883 & 0.103936 & 3.6293 & 0.000516 & 0.000258 \tabularnewline
M7 & 0.0719094385043453 & 0.084564 & 0.8504 & 0.397832 & 0.198916 \tabularnewline
M8 & 0.228312254709038 & 0.091622 & 2.4919 & 0.014915 & 0.007458 \tabularnewline
M9 & 0.145801390152894 & 0.094745 & 1.5389 & 0.12804 & 0.06402 \tabularnewline
M10 & -0.0769332094556072 & 0.093945 & -0.8189 & 0.41543 & 0.207715 \tabularnewline
M11 & 0.0653509691361015 & 0.092364 & 0.7075 & 0.481426 & 0.240713 \tabularnewline
t & -0.000644466348359865 & 0.000878 & -0.7343 & 0.465076 & 0.232538 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57841&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]0.933141956395697[/C][C]0.500375[/C][C]1.8649[/C][C]0.066109[/C][C]0.033055[/C][/ROW]
[ROW][C]X[/C][C]-0.00599446355432254[/C][C]0.004393[/C][C]-1.3645[/C][C]0.176479[/C][C]0.08824[/C][/ROW]
[ROW][C]Y1[/C][C]1.55409357038416[/C][C]0.107422[/C][C]14.4672[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Y2[/C][C]-0.862743246928274[/C][C]0.208198[/C][C]-4.1439[/C][C]8.9e-05[/C][C]4.4e-05[/C][/ROW]
[ROW][C]Y3[/C][C]-0.133170668301423[/C][C]0.20837[/C][C]-0.6391[/C][C]0.5247[/C][C]0.26235[/C][/ROW]
[ROW][C]Y4[/C][C]0.394880751487633[/C][C]0.10641[/C][C]3.7109[/C][C]0.000394[/C][C]0.000197[/C][/ROW]
[ROW][C]M1[/C][C]-0.107835895318313[/C][C]0.086434[/C][C]-1.2476[/C][C]0.216055[/C][C]0.108028[/C][/ROW]
[ROW][C]M2[/C][C]0.00865868146602026[/C][C]0.093806[/C][C]0.0923[/C][C]0.926702[/C][C]0.463351[/C][/ROW]
[ROW][C]M3[/C][C]0.555132299015706[/C][C]0.117354[/C][C]4.7304[/C][C]1e-05[/C][C]5e-06[/C][/ROW]
[ROW][C]M4[/C][C]-0.358561693549813[/C][C]0.121642[/C][C]-2.9477[/C][C]0.004266[/C][C]0.002133[/C][/ROW]
[ROW][C]M5[/C][C]0.194164167438996[/C][C]0.125427[/C][C]1.548[/C][C]0.125826[/C][C]0.062913[/C][/ROW]
[ROW][C]M6[/C][C]0.377211037290883[/C][C]0.103936[/C][C]3.6293[/C][C]0.000516[/C][C]0.000258[/C][/ROW]
[ROW][C]M7[/C][C]0.0719094385043453[/C][C]0.084564[/C][C]0.8504[/C][C]0.397832[/C][C]0.198916[/C][/ROW]
[ROW][C]M8[/C][C]0.228312254709038[/C][C]0.091622[/C][C]2.4919[/C][C]0.014915[/C][C]0.007458[/C][/ROW]
[ROW][C]M9[/C][C]0.145801390152894[/C][C]0.094745[/C][C]1.5389[/C][C]0.12804[/C][C]0.06402[/C][/ROW]
[ROW][C]M10[/C][C]-0.0769332094556072[/C][C]0.093945[/C][C]-0.8189[/C][C]0.41543[/C][C]0.207715[/C][/ROW]
[ROW][C]M11[/C][C]0.0653509691361015[/C][C]0.092364[/C][C]0.7075[/C][C]0.481426[/C][C]0.240713[/C][/ROW]
[ROW][C]t[/C][C]-0.000644466348359865[/C][C]0.000878[/C][C]-0.7343[/C][C]0.465076[/C][C]0.232538[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57841&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57841&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)0.9331419563956970.5003751.86490.0661090.033055
X-0.005994463554322540.004393-1.36450.1764790.08824
Y11.554093570384160.10742214.467200
Y2-0.8627432469282740.208198-4.14398.9e-054.4e-05
Y3-0.1331706683014230.20837-0.63910.52470.26235
Y40.3948807514876330.106413.71090.0003940.000197
M1-0.1078358953183130.086434-1.24760.2160550.108028
M20.008658681466020260.0938060.09230.9267020.463351
M30.5551322990157060.1173544.73041e-055e-06
M4-0.3585616935498130.121642-2.94770.0042660.002133
M50.1941641674389960.1254271.5480.1258260.062913
M60.3772110372908830.1039363.62930.0005160.000258
M70.07190943850434530.0845640.85040.3978320.198916
M80.2283122547090380.0916222.49190.0149150.007458
M90.1458013901528940.0947451.53890.128040.06402
M10-0.07693320945560720.093945-0.81890.415430.207715
M110.06535096913610150.0923640.70750.4814260.240713
t-0.0006444663483598650.000878-0.73430.4650760.232538






Multiple Linear Regression - Regression Statistics
Multiple R0.980250524311518
R-squared0.960891090413006
Adjusted R-squared0.952026404239954
F-TEST (value)108.395387231424
F-TEST (DF numerator)17
F-TEST (DF denominator)75
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.162365640018282
Sum Squared Residuals1.97719507939096

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.980250524311518 \tabularnewline
R-squared & 0.960891090413006 \tabularnewline
Adjusted R-squared & 0.952026404239954 \tabularnewline
F-TEST (value) & 108.395387231424 \tabularnewline
F-TEST (DF numerator) & 17 \tabularnewline
F-TEST (DF denominator) & 75 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.162365640018282 \tabularnewline
Sum Squared Residuals & 1.97719507939096 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57841&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.980250524311518[/C][/ROW]
[ROW][C]R-squared[/C][C]0.960891090413006[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.952026404239954[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]108.395387231424[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]17[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]75[/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]0.162365640018282[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1.97719507939096[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57841&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57841&T=3

As an alternative you can also use a QR Code:  
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The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Regression Statistics
Multiple R0.980250524311518
R-squared0.960891090413006
Adjusted R-squared0.952026404239954
F-TEST (value)108.395387231424
F-TEST (DF numerator)17
F-TEST (DF denominator)75
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.162365640018282
Sum Squared Residuals1.97719507939096






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
16.36.4172891034347-0.1172891034347
266.2558382209039-0.255838220903900
36.26.45986564889862-0.259865648898616
46.46.112020877743370.287979122256635
56.86.80455841717319-0.00455841717318884
67.57.240597746481840.259402253518158
77.57.77232258971748-0.27232258971748
87.67.419404326931120.180595673068879
97.67.523421636300320.0765783636996842
107.47.4907842180474-0.090784218047402
117.37.248942960195980.0510570398040157
127.17.25695883651506-0.156958836515059
136.96.95716212903442-0.0571621290344202
146.86.85289803971516-0.0528980397151625
157.57.51510901024108-0.0151090102410793
167.67.68600213097033-0.0860021309703315
177.87.635195465728050.164804534271954
1887.875266273396180.124733726603819
198.18.036029384905780.0639706150942221
208.28.24564868038028-0.0456486803802792
218.38.251600295265260.0483997047347368
228.28.16541313054310.0345868694568924
2388.04118667551752-0.0411866755175201
247.97.793002910564350.106997089435653
257.67.7820415155737-0.182041515573704
267.67.466719371351110.133280628648889
278.38.30821773994245-0.00821773994245075
288.48.46722174675334-0.0672217467533438
298.48.33303817540510.066961824594893
308.48.32455730565160.0754426943484016
318.48.360238172289530.0397618277104728
328.68.565675185336970.0343248146630277
338.98.804128602907080.095871397092920
348.88.8630394779266-0.0630394779266007
358.38.4696996475899-0.169699647589903
367.57.81070244424565-0.310702444245649
377.27.055069691560990.144930308439011
387.47.338061097665770.0619389023342349
398.88.506495221223470.29350477877653
409.39.263037753351450.0369622466485494
419.39.135522808888040.164477191111958
428.78.8018697651094-0.101869765109394
438.28.088079842423640.111920157576364
448.38.195664997163610.10433500283639
458.58.82115429262278-0.321154292622775
468.68.64418369668743-0.0441836966874283
478.58.50517539473154-0.00517539473154078
488.28.22173969975753-0.0217396997575299
498.17.827138653841130.272861346158865
507.98.00449499913778-0.104494999137783
518.68.524060183589410.0759398164105859
528.78.676270654109980.0237293458900206
538.78.662084079249930.0379159207500705
548.58.62977612389866-0.129776123898662
558.48.25872685959060.141273140409403
568.58.51007659004603-0.0100765900460287
578.78.71502080426232-0.015020804262318
588.78.640336456179730.0596635438202719
598.68.467904316454540.132095683545457
608.58.348071526024120.151928473975882
618.38.194283217609620.105716782390385
6288.07073202678641-0.0707320267864107
638.28.47174908372574-0.271749083725740
648.18.023681971808430.0763180281915747
658.18.12305958139089-0.0230595813908927
6688.23644736243832-0.236447362438324
677.97.888965226188240.0110347738117600
687.98.00144012129234-0.101440121292337
6987.993898327693510.00610167230648676
7087.903953734944470.0960462650555279
717.97.838905789362880.0610942106371242
7287.680313617149750.319686382850246
737.77.83382272898925-0.133822728989254
747.27.36313124836815-0.163131248368146
757.57.485994696268740.0140053037312642
767.37.47855998398786-0.178559983987857
7777.35936675167654-0.359366751676536
7876.951352968028430.0486470319715659
7977.10387785442293-0.103877854422928
807.27.30033761974465-0.100337619744651
817.37.34539601743944-0.0453960174394394
827.17.092289285671260.00771071432873867
836.86.82818521614763-0.0281852161476333
846.46.48921096574354-0.0892109657435433
856.16.13319295995618-0.0331929599561825
866.56.048124996071720.451875003928279
877.77.528508416110490.171491583889507
887.97.99320488127525-0.0932048812752471
897.57.54717472048826-0.0471747204882575
906.96.94013245499557-0.0401324549955651
916.66.591760070461810.00823992953818632
926.96.961752479105-0.061752479105001
937.77.54538002350930.154619976490705

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 6.3 & 6.4172891034347 & -0.1172891034347 \tabularnewline
2 & 6 & 6.2558382209039 & -0.255838220903900 \tabularnewline
3 & 6.2 & 6.45986564889862 & -0.259865648898616 \tabularnewline
4 & 6.4 & 6.11202087774337 & 0.287979122256635 \tabularnewline
5 & 6.8 & 6.80455841717319 & -0.00455841717318884 \tabularnewline
6 & 7.5 & 7.24059774648184 & 0.259402253518158 \tabularnewline
7 & 7.5 & 7.77232258971748 & -0.27232258971748 \tabularnewline
8 & 7.6 & 7.41940432693112 & 0.180595673068879 \tabularnewline
9 & 7.6 & 7.52342163630032 & 0.0765783636996842 \tabularnewline
10 & 7.4 & 7.4907842180474 & -0.090784218047402 \tabularnewline
11 & 7.3 & 7.24894296019598 & 0.0510570398040157 \tabularnewline
12 & 7.1 & 7.25695883651506 & -0.156958836515059 \tabularnewline
13 & 6.9 & 6.95716212903442 & -0.0571621290344202 \tabularnewline
14 & 6.8 & 6.85289803971516 & -0.0528980397151625 \tabularnewline
15 & 7.5 & 7.51510901024108 & -0.0151090102410793 \tabularnewline
16 & 7.6 & 7.68600213097033 & -0.0860021309703315 \tabularnewline
17 & 7.8 & 7.63519546572805 & 0.164804534271954 \tabularnewline
18 & 8 & 7.87526627339618 & 0.124733726603819 \tabularnewline
19 & 8.1 & 8.03602938490578 & 0.0639706150942221 \tabularnewline
20 & 8.2 & 8.24564868038028 & -0.0456486803802792 \tabularnewline
21 & 8.3 & 8.25160029526526 & 0.0483997047347368 \tabularnewline
22 & 8.2 & 8.1654131305431 & 0.0345868694568924 \tabularnewline
23 & 8 & 8.04118667551752 & -0.0411866755175201 \tabularnewline
24 & 7.9 & 7.79300291056435 & 0.106997089435653 \tabularnewline
25 & 7.6 & 7.7820415155737 & -0.182041515573704 \tabularnewline
26 & 7.6 & 7.46671937135111 & 0.133280628648889 \tabularnewline
27 & 8.3 & 8.30821773994245 & -0.00821773994245075 \tabularnewline
28 & 8.4 & 8.46722174675334 & -0.0672217467533438 \tabularnewline
29 & 8.4 & 8.3330381754051 & 0.066961824594893 \tabularnewline
30 & 8.4 & 8.3245573056516 & 0.0754426943484016 \tabularnewline
31 & 8.4 & 8.36023817228953 & 0.0397618277104728 \tabularnewline
32 & 8.6 & 8.56567518533697 & 0.0343248146630277 \tabularnewline
33 & 8.9 & 8.80412860290708 & 0.095871397092920 \tabularnewline
34 & 8.8 & 8.8630394779266 & -0.0630394779266007 \tabularnewline
35 & 8.3 & 8.4696996475899 & -0.169699647589903 \tabularnewline
36 & 7.5 & 7.81070244424565 & -0.310702444245649 \tabularnewline
37 & 7.2 & 7.05506969156099 & 0.144930308439011 \tabularnewline
38 & 7.4 & 7.33806109766577 & 0.0619389023342349 \tabularnewline
39 & 8.8 & 8.50649522122347 & 0.29350477877653 \tabularnewline
40 & 9.3 & 9.26303775335145 & 0.0369622466485494 \tabularnewline
41 & 9.3 & 9.13552280888804 & 0.164477191111958 \tabularnewline
42 & 8.7 & 8.8018697651094 & -0.101869765109394 \tabularnewline
43 & 8.2 & 8.08807984242364 & 0.111920157576364 \tabularnewline
44 & 8.3 & 8.19566499716361 & 0.10433500283639 \tabularnewline
45 & 8.5 & 8.82115429262278 & -0.321154292622775 \tabularnewline
46 & 8.6 & 8.64418369668743 & -0.0441836966874283 \tabularnewline
47 & 8.5 & 8.50517539473154 & -0.00517539473154078 \tabularnewline
48 & 8.2 & 8.22173969975753 & -0.0217396997575299 \tabularnewline
49 & 8.1 & 7.82713865384113 & 0.272861346158865 \tabularnewline
50 & 7.9 & 8.00449499913778 & -0.104494999137783 \tabularnewline
51 & 8.6 & 8.52406018358941 & 0.0759398164105859 \tabularnewline
52 & 8.7 & 8.67627065410998 & 0.0237293458900206 \tabularnewline
53 & 8.7 & 8.66208407924993 & 0.0379159207500705 \tabularnewline
54 & 8.5 & 8.62977612389866 & -0.129776123898662 \tabularnewline
55 & 8.4 & 8.2587268595906 & 0.141273140409403 \tabularnewline
56 & 8.5 & 8.51007659004603 & -0.0100765900460287 \tabularnewline
57 & 8.7 & 8.71502080426232 & -0.015020804262318 \tabularnewline
58 & 8.7 & 8.64033645617973 & 0.0596635438202719 \tabularnewline
59 & 8.6 & 8.46790431645454 & 0.132095683545457 \tabularnewline
60 & 8.5 & 8.34807152602412 & 0.151928473975882 \tabularnewline
61 & 8.3 & 8.19428321760962 & 0.105716782390385 \tabularnewline
62 & 8 & 8.07073202678641 & -0.0707320267864107 \tabularnewline
63 & 8.2 & 8.47174908372574 & -0.271749083725740 \tabularnewline
64 & 8.1 & 8.02368197180843 & 0.0763180281915747 \tabularnewline
65 & 8.1 & 8.12305958139089 & -0.0230595813908927 \tabularnewline
66 & 8 & 8.23644736243832 & -0.236447362438324 \tabularnewline
67 & 7.9 & 7.88896522618824 & 0.0110347738117600 \tabularnewline
68 & 7.9 & 8.00144012129234 & -0.101440121292337 \tabularnewline
69 & 8 & 7.99389832769351 & 0.00610167230648676 \tabularnewline
70 & 8 & 7.90395373494447 & 0.0960462650555279 \tabularnewline
71 & 7.9 & 7.83890578936288 & 0.0610942106371242 \tabularnewline
72 & 8 & 7.68031361714975 & 0.319686382850246 \tabularnewline
73 & 7.7 & 7.83382272898925 & -0.133822728989254 \tabularnewline
74 & 7.2 & 7.36313124836815 & -0.163131248368146 \tabularnewline
75 & 7.5 & 7.48599469626874 & 0.0140053037312642 \tabularnewline
76 & 7.3 & 7.47855998398786 & -0.178559983987857 \tabularnewline
77 & 7 & 7.35936675167654 & -0.359366751676536 \tabularnewline
78 & 7 & 6.95135296802843 & 0.0486470319715659 \tabularnewline
79 & 7 & 7.10387785442293 & -0.103877854422928 \tabularnewline
80 & 7.2 & 7.30033761974465 & -0.100337619744651 \tabularnewline
81 & 7.3 & 7.34539601743944 & -0.0453960174394394 \tabularnewline
82 & 7.1 & 7.09228928567126 & 0.00771071432873867 \tabularnewline
83 & 6.8 & 6.82818521614763 & -0.0281852161476333 \tabularnewline
84 & 6.4 & 6.48921096574354 & -0.0892109657435433 \tabularnewline
85 & 6.1 & 6.13319295995618 & -0.0331929599561825 \tabularnewline
86 & 6.5 & 6.04812499607172 & 0.451875003928279 \tabularnewline
87 & 7.7 & 7.52850841611049 & 0.171491583889507 \tabularnewline
88 & 7.9 & 7.99320488127525 & -0.0932048812752471 \tabularnewline
89 & 7.5 & 7.54717472048826 & -0.0471747204882575 \tabularnewline
90 & 6.9 & 6.94013245499557 & -0.0401324549955651 \tabularnewline
91 & 6.6 & 6.59176007046181 & 0.00823992953818632 \tabularnewline
92 & 6.9 & 6.961752479105 & -0.061752479105001 \tabularnewline
93 & 7.7 & 7.5453800235093 & 0.154619976490705 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57841&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]6.3[/C][C]6.4172891034347[/C][C]-0.1172891034347[/C][/ROW]
[ROW][C]2[/C][C]6[/C][C]6.2558382209039[/C][C]-0.255838220903900[/C][/ROW]
[ROW][C]3[/C][C]6.2[/C][C]6.45986564889862[/C][C]-0.259865648898616[/C][/ROW]
[ROW][C]4[/C][C]6.4[/C][C]6.11202087774337[/C][C]0.287979122256635[/C][/ROW]
[ROW][C]5[/C][C]6.8[/C][C]6.80455841717319[/C][C]-0.00455841717318884[/C][/ROW]
[ROW][C]6[/C][C]7.5[/C][C]7.24059774648184[/C][C]0.259402253518158[/C][/ROW]
[ROW][C]7[/C][C]7.5[/C][C]7.77232258971748[/C][C]-0.27232258971748[/C][/ROW]
[ROW][C]8[/C][C]7.6[/C][C]7.41940432693112[/C][C]0.180595673068879[/C][/ROW]
[ROW][C]9[/C][C]7.6[/C][C]7.52342163630032[/C][C]0.0765783636996842[/C][/ROW]
[ROW][C]10[/C][C]7.4[/C][C]7.4907842180474[/C][C]-0.090784218047402[/C][/ROW]
[ROW][C]11[/C][C]7.3[/C][C]7.24894296019598[/C][C]0.0510570398040157[/C][/ROW]
[ROW][C]12[/C][C]7.1[/C][C]7.25695883651506[/C][C]-0.156958836515059[/C][/ROW]
[ROW][C]13[/C][C]6.9[/C][C]6.95716212903442[/C][C]-0.0571621290344202[/C][/ROW]
[ROW][C]14[/C][C]6.8[/C][C]6.85289803971516[/C][C]-0.0528980397151625[/C][/ROW]
[ROW][C]15[/C][C]7.5[/C][C]7.51510901024108[/C][C]-0.0151090102410793[/C][/ROW]
[ROW][C]16[/C][C]7.6[/C][C]7.68600213097033[/C][C]-0.0860021309703315[/C][/ROW]
[ROW][C]17[/C][C]7.8[/C][C]7.63519546572805[/C][C]0.164804534271954[/C][/ROW]
[ROW][C]18[/C][C]8[/C][C]7.87526627339618[/C][C]0.124733726603819[/C][/ROW]
[ROW][C]19[/C][C]8.1[/C][C]8.03602938490578[/C][C]0.0639706150942221[/C][/ROW]
[ROW][C]20[/C][C]8.2[/C][C]8.24564868038028[/C][C]-0.0456486803802792[/C][/ROW]
[ROW][C]21[/C][C]8.3[/C][C]8.25160029526526[/C][C]0.0483997047347368[/C][/ROW]
[ROW][C]22[/C][C]8.2[/C][C]8.1654131305431[/C][C]0.0345868694568924[/C][/ROW]
[ROW][C]23[/C][C]8[/C][C]8.04118667551752[/C][C]-0.0411866755175201[/C][/ROW]
[ROW][C]24[/C][C]7.9[/C][C]7.79300291056435[/C][C]0.106997089435653[/C][/ROW]
[ROW][C]25[/C][C]7.6[/C][C]7.7820415155737[/C][C]-0.182041515573704[/C][/ROW]
[ROW][C]26[/C][C]7.6[/C][C]7.46671937135111[/C][C]0.133280628648889[/C][/ROW]
[ROW][C]27[/C][C]8.3[/C][C]8.30821773994245[/C][C]-0.00821773994245075[/C][/ROW]
[ROW][C]28[/C][C]8.4[/C][C]8.46722174675334[/C][C]-0.0672217467533438[/C][/ROW]
[ROW][C]29[/C][C]8.4[/C][C]8.3330381754051[/C][C]0.066961824594893[/C][/ROW]
[ROW][C]30[/C][C]8.4[/C][C]8.3245573056516[/C][C]0.0754426943484016[/C][/ROW]
[ROW][C]31[/C][C]8.4[/C][C]8.36023817228953[/C][C]0.0397618277104728[/C][/ROW]
[ROW][C]32[/C][C]8.6[/C][C]8.56567518533697[/C][C]0.0343248146630277[/C][/ROW]
[ROW][C]33[/C][C]8.9[/C][C]8.80412860290708[/C][C]0.095871397092920[/C][/ROW]
[ROW][C]34[/C][C]8.8[/C][C]8.8630394779266[/C][C]-0.0630394779266007[/C][/ROW]
[ROW][C]35[/C][C]8.3[/C][C]8.4696996475899[/C][C]-0.169699647589903[/C][/ROW]
[ROW][C]36[/C][C]7.5[/C][C]7.81070244424565[/C][C]-0.310702444245649[/C][/ROW]
[ROW][C]37[/C][C]7.2[/C][C]7.05506969156099[/C][C]0.144930308439011[/C][/ROW]
[ROW][C]38[/C][C]7.4[/C][C]7.33806109766577[/C][C]0.0619389023342349[/C][/ROW]
[ROW][C]39[/C][C]8.8[/C][C]8.50649522122347[/C][C]0.29350477877653[/C][/ROW]
[ROW][C]40[/C][C]9.3[/C][C]9.26303775335145[/C][C]0.0369622466485494[/C][/ROW]
[ROW][C]41[/C][C]9.3[/C][C]9.13552280888804[/C][C]0.164477191111958[/C][/ROW]
[ROW][C]42[/C][C]8.7[/C][C]8.8018697651094[/C][C]-0.101869765109394[/C][/ROW]
[ROW][C]43[/C][C]8.2[/C][C]8.08807984242364[/C][C]0.111920157576364[/C][/ROW]
[ROW][C]44[/C][C]8.3[/C][C]8.19566499716361[/C][C]0.10433500283639[/C][/ROW]
[ROW][C]45[/C][C]8.5[/C][C]8.82115429262278[/C][C]-0.321154292622775[/C][/ROW]
[ROW][C]46[/C][C]8.6[/C][C]8.64418369668743[/C][C]-0.0441836966874283[/C][/ROW]
[ROW][C]47[/C][C]8.5[/C][C]8.50517539473154[/C][C]-0.00517539473154078[/C][/ROW]
[ROW][C]48[/C][C]8.2[/C][C]8.22173969975753[/C][C]-0.0217396997575299[/C][/ROW]
[ROW][C]49[/C][C]8.1[/C][C]7.82713865384113[/C][C]0.272861346158865[/C][/ROW]
[ROW][C]50[/C][C]7.9[/C][C]8.00449499913778[/C][C]-0.104494999137783[/C][/ROW]
[ROW][C]51[/C][C]8.6[/C][C]8.52406018358941[/C][C]0.0759398164105859[/C][/ROW]
[ROW][C]52[/C][C]8.7[/C][C]8.67627065410998[/C][C]0.0237293458900206[/C][/ROW]
[ROW][C]53[/C][C]8.7[/C][C]8.66208407924993[/C][C]0.0379159207500705[/C][/ROW]
[ROW][C]54[/C][C]8.5[/C][C]8.62977612389866[/C][C]-0.129776123898662[/C][/ROW]
[ROW][C]55[/C][C]8.4[/C][C]8.2587268595906[/C][C]0.141273140409403[/C][/ROW]
[ROW][C]56[/C][C]8.5[/C][C]8.51007659004603[/C][C]-0.0100765900460287[/C][/ROW]
[ROW][C]57[/C][C]8.7[/C][C]8.71502080426232[/C][C]-0.015020804262318[/C][/ROW]
[ROW][C]58[/C][C]8.7[/C][C]8.64033645617973[/C][C]0.0596635438202719[/C][/ROW]
[ROW][C]59[/C][C]8.6[/C][C]8.46790431645454[/C][C]0.132095683545457[/C][/ROW]
[ROW][C]60[/C][C]8.5[/C][C]8.34807152602412[/C][C]0.151928473975882[/C][/ROW]
[ROW][C]61[/C][C]8.3[/C][C]8.19428321760962[/C][C]0.105716782390385[/C][/ROW]
[ROW][C]62[/C][C]8[/C][C]8.07073202678641[/C][C]-0.0707320267864107[/C][/ROW]
[ROW][C]63[/C][C]8.2[/C][C]8.47174908372574[/C][C]-0.271749083725740[/C][/ROW]
[ROW][C]64[/C][C]8.1[/C][C]8.02368197180843[/C][C]0.0763180281915747[/C][/ROW]
[ROW][C]65[/C][C]8.1[/C][C]8.12305958139089[/C][C]-0.0230595813908927[/C][/ROW]
[ROW][C]66[/C][C]8[/C][C]8.23644736243832[/C][C]-0.236447362438324[/C][/ROW]
[ROW][C]67[/C][C]7.9[/C][C]7.88896522618824[/C][C]0.0110347738117600[/C][/ROW]
[ROW][C]68[/C][C]7.9[/C][C]8.00144012129234[/C][C]-0.101440121292337[/C][/ROW]
[ROW][C]69[/C][C]8[/C][C]7.99389832769351[/C][C]0.00610167230648676[/C][/ROW]
[ROW][C]70[/C][C]8[/C][C]7.90395373494447[/C][C]0.0960462650555279[/C][/ROW]
[ROW][C]71[/C][C]7.9[/C][C]7.83890578936288[/C][C]0.0610942106371242[/C][/ROW]
[ROW][C]72[/C][C]8[/C][C]7.68031361714975[/C][C]0.319686382850246[/C][/ROW]
[ROW][C]73[/C][C]7.7[/C][C]7.83382272898925[/C][C]-0.133822728989254[/C][/ROW]
[ROW][C]74[/C][C]7.2[/C][C]7.36313124836815[/C][C]-0.163131248368146[/C][/ROW]
[ROW][C]75[/C][C]7.5[/C][C]7.48599469626874[/C][C]0.0140053037312642[/C][/ROW]
[ROW][C]76[/C][C]7.3[/C][C]7.47855998398786[/C][C]-0.178559983987857[/C][/ROW]
[ROW][C]77[/C][C]7[/C][C]7.35936675167654[/C][C]-0.359366751676536[/C][/ROW]
[ROW][C]78[/C][C]7[/C][C]6.95135296802843[/C][C]0.0486470319715659[/C][/ROW]
[ROW][C]79[/C][C]7[/C][C]7.10387785442293[/C][C]-0.103877854422928[/C][/ROW]
[ROW][C]80[/C][C]7.2[/C][C]7.30033761974465[/C][C]-0.100337619744651[/C][/ROW]
[ROW][C]81[/C][C]7.3[/C][C]7.34539601743944[/C][C]-0.0453960174394394[/C][/ROW]
[ROW][C]82[/C][C]7.1[/C][C]7.09228928567126[/C][C]0.00771071432873867[/C][/ROW]
[ROW][C]83[/C][C]6.8[/C][C]6.82818521614763[/C][C]-0.0281852161476333[/C][/ROW]
[ROW][C]84[/C][C]6.4[/C][C]6.48921096574354[/C][C]-0.0892109657435433[/C][/ROW]
[ROW][C]85[/C][C]6.1[/C][C]6.13319295995618[/C][C]-0.0331929599561825[/C][/ROW]
[ROW][C]86[/C][C]6.5[/C][C]6.04812499607172[/C][C]0.451875003928279[/C][/ROW]
[ROW][C]87[/C][C]7.7[/C][C]7.52850841611049[/C][C]0.171491583889507[/C][/ROW]
[ROW][C]88[/C][C]7.9[/C][C]7.99320488127525[/C][C]-0.0932048812752471[/C][/ROW]
[ROW][C]89[/C][C]7.5[/C][C]7.54717472048826[/C][C]-0.0471747204882575[/C][/ROW]
[ROW][C]90[/C][C]6.9[/C][C]6.94013245499557[/C][C]-0.0401324549955651[/C][/ROW]
[ROW][C]91[/C][C]6.6[/C][C]6.59176007046181[/C][C]0.00823992953818632[/C][/ROW]
[ROW][C]92[/C][C]6.9[/C][C]6.961752479105[/C][C]-0.061752479105001[/C][/ROW]
[ROW][C]93[/C][C]7.7[/C][C]7.5453800235093[/C][C]0.154619976490705[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57841&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57841&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
16.36.4172891034347-0.1172891034347
266.2558382209039-0.255838220903900
36.26.45986564889862-0.259865648898616
46.46.112020877743370.287979122256635
56.86.80455841717319-0.00455841717318884
67.57.240597746481840.259402253518158
77.57.77232258971748-0.27232258971748
87.67.419404326931120.180595673068879
97.67.523421636300320.0765783636996842
107.47.4907842180474-0.090784218047402
117.37.248942960195980.0510570398040157
127.17.25695883651506-0.156958836515059
136.96.95716212903442-0.0571621290344202
146.86.85289803971516-0.0528980397151625
157.57.51510901024108-0.0151090102410793
167.67.68600213097033-0.0860021309703315
177.87.635195465728050.164804534271954
1887.875266273396180.124733726603819
198.18.036029384905780.0639706150942221
208.28.24564868038028-0.0456486803802792
218.38.251600295265260.0483997047347368
228.28.16541313054310.0345868694568924
2388.04118667551752-0.0411866755175201
247.97.793002910564350.106997089435653
257.67.7820415155737-0.182041515573704
267.67.466719371351110.133280628648889
278.38.30821773994245-0.00821773994245075
288.48.46722174675334-0.0672217467533438
298.48.33303817540510.066961824594893
308.48.32455730565160.0754426943484016
318.48.360238172289530.0397618277104728
328.68.565675185336970.0343248146630277
338.98.804128602907080.095871397092920
348.88.8630394779266-0.0630394779266007
358.38.4696996475899-0.169699647589903
367.57.81070244424565-0.310702444245649
377.27.055069691560990.144930308439011
387.47.338061097665770.0619389023342349
398.88.506495221223470.29350477877653
409.39.263037753351450.0369622466485494
419.39.135522808888040.164477191111958
428.78.8018697651094-0.101869765109394
438.28.088079842423640.111920157576364
448.38.195664997163610.10433500283639
458.58.82115429262278-0.321154292622775
468.68.64418369668743-0.0441836966874283
478.58.50517539473154-0.00517539473154078
488.28.22173969975753-0.0217396997575299
498.17.827138653841130.272861346158865
507.98.00449499913778-0.104494999137783
518.68.524060183589410.0759398164105859
528.78.676270654109980.0237293458900206
538.78.662084079249930.0379159207500705
548.58.62977612389866-0.129776123898662
558.48.25872685959060.141273140409403
568.58.51007659004603-0.0100765900460287
578.78.71502080426232-0.015020804262318
588.78.640336456179730.0596635438202719
598.68.467904316454540.132095683545457
608.58.348071526024120.151928473975882
618.38.194283217609620.105716782390385
6288.07073202678641-0.0707320267864107
638.28.47174908372574-0.271749083725740
648.18.023681971808430.0763180281915747
658.18.12305958139089-0.0230595813908927
6688.23644736243832-0.236447362438324
677.97.888965226188240.0110347738117600
687.98.00144012129234-0.101440121292337
6987.993898327693510.00610167230648676
7087.903953734944470.0960462650555279
717.97.838905789362880.0610942106371242
7287.680313617149750.319686382850246
737.77.83382272898925-0.133822728989254
747.27.36313124836815-0.163131248368146
757.57.485994696268740.0140053037312642
767.37.47855998398786-0.178559983987857
7777.35936675167654-0.359366751676536
7876.951352968028430.0486470319715659
7977.10387785442293-0.103877854422928
807.27.30033761974465-0.100337619744651
817.37.34539601743944-0.0453960174394394
827.17.092289285671260.00771071432873867
836.86.82818521614763-0.0281852161476333
846.46.48921096574354-0.0892109657435433
856.16.13319295995618-0.0331929599561825
866.56.048124996071720.451875003928279
877.77.528508416110490.171491583889507
887.97.99320488127525-0.0932048812752471
897.57.54717472048826-0.0471747204882575
906.96.94013245499557-0.0401324549955651
916.66.591760070461810.00823992953818632
926.96.961752479105-0.061752479105001
937.77.54538002350930.154619976490705






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
210.5351504339810290.9296991320379420.464849566018971
220.5797160290729340.8405679418541320.420283970927066
230.4364369942332550.872873988466510.563563005766745
240.3892927074654310.7785854149308620.610707292534569
250.3073328570457530.6146657140915060.692667142954247
260.2507415252751340.5014830505502670.749258474724867
270.1971040305744970.3942080611489930.802895969425503
280.1318904349410660.2637808698821320.868109565058934
290.1204448567051010.2408897134102020.8795551432949
300.1623992377152620.3247984754305240.837600762284738
310.1195985515294980.2391971030589960.880401448470502
320.1564998201141300.3129996402282610.84350017988587
330.1103961794660620.2207923589321230.889603820533938
340.07937873661642380.1587574732328480.920621263383576
350.09569508077917980.1913901615583600.90430491922082
360.2244018399151920.4488036798303840.775598160084808
370.1893039795516310.3786079591032620.810696020448369
380.1418259012267890.2836518024535790.85817409877321
390.2171479105978660.4342958211957320.782852089402134
400.2150615484652360.4301230969304730.784938451534763
410.2116913814947330.4233827629894670.788308618505267
420.1693991872283920.3387983744567830.830600812771608
430.1259610013363440.2519220026726880.874038998663656
440.1186527064383420.2373054128766840.881347293561658
450.5136460143213370.9727079713573260.486353985678663
460.4513698224353870.9027396448707740.548630177564613
470.44045170610660.88090341221320.5595482938934
480.4416078734621050.883215746924210.558392126537895
490.5136285991545780.9727428016908440.486371400845422
500.5626287425523520.8747425148952970.437371257447648
510.494455509108970.988911018217940.50554449089103
520.4321793199517590.8643586399035180.567820680048241
530.4132402162592830.8264804325185650.586759783740717
540.4646253908798380.9292507817596750.535374609120162
550.4257160324439680.8514320648879370.574283967556032
560.3623251586861200.7246503173722410.63767484131388
570.2965716606510030.5931433213020060.703428339348997
580.2389606144913480.4779212289826970.761039385508652
590.2142577874986690.4285155749973370.785742212501331
600.1961190387598100.3922380775196210.80388096124019
610.209189332210820.418378664421640.79081066778918
620.1855107241826130.3710214483652260.814489275817387
630.3128326241557820.6256652483115630.687167375844218
640.3220991314307910.6441982628615830.677900868569209
650.2828996574387080.5657993148774160.717100342561292
660.3720837311929120.7441674623858240.627916268807088
670.3628264655343030.7256529310686060.637173534465697
680.2709769485788660.5419538971577310.729023051421134
690.1954868012897790.3909736025795580.804513198710221
700.1277572518313110.2555145036626220.872242748168689
710.3001829612021680.6003659224043360.699817038797832
720.3859123594920830.7718247189841660.614087640507917

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
21 & 0.535150433981029 & 0.929699132037942 & 0.464849566018971 \tabularnewline
22 & 0.579716029072934 & 0.840567941854132 & 0.420283970927066 \tabularnewline
23 & 0.436436994233255 & 0.87287398846651 & 0.563563005766745 \tabularnewline
24 & 0.389292707465431 & 0.778585414930862 & 0.610707292534569 \tabularnewline
25 & 0.307332857045753 & 0.614665714091506 & 0.692667142954247 \tabularnewline
26 & 0.250741525275134 & 0.501483050550267 & 0.749258474724867 \tabularnewline
27 & 0.197104030574497 & 0.394208061148993 & 0.802895969425503 \tabularnewline
28 & 0.131890434941066 & 0.263780869882132 & 0.868109565058934 \tabularnewline
29 & 0.120444856705101 & 0.240889713410202 & 0.8795551432949 \tabularnewline
30 & 0.162399237715262 & 0.324798475430524 & 0.837600762284738 \tabularnewline
31 & 0.119598551529498 & 0.239197103058996 & 0.880401448470502 \tabularnewline
32 & 0.156499820114130 & 0.312999640228261 & 0.84350017988587 \tabularnewline
33 & 0.110396179466062 & 0.220792358932123 & 0.889603820533938 \tabularnewline
34 & 0.0793787366164238 & 0.158757473232848 & 0.920621263383576 \tabularnewline
35 & 0.0956950807791798 & 0.191390161558360 & 0.90430491922082 \tabularnewline
36 & 0.224401839915192 & 0.448803679830384 & 0.775598160084808 \tabularnewline
37 & 0.189303979551631 & 0.378607959103262 & 0.810696020448369 \tabularnewline
38 & 0.141825901226789 & 0.283651802453579 & 0.85817409877321 \tabularnewline
39 & 0.217147910597866 & 0.434295821195732 & 0.782852089402134 \tabularnewline
40 & 0.215061548465236 & 0.430123096930473 & 0.784938451534763 \tabularnewline
41 & 0.211691381494733 & 0.423382762989467 & 0.788308618505267 \tabularnewline
42 & 0.169399187228392 & 0.338798374456783 & 0.830600812771608 \tabularnewline
43 & 0.125961001336344 & 0.251922002672688 & 0.874038998663656 \tabularnewline
44 & 0.118652706438342 & 0.237305412876684 & 0.881347293561658 \tabularnewline
45 & 0.513646014321337 & 0.972707971357326 & 0.486353985678663 \tabularnewline
46 & 0.451369822435387 & 0.902739644870774 & 0.548630177564613 \tabularnewline
47 & 0.4404517061066 & 0.8809034122132 & 0.5595482938934 \tabularnewline
48 & 0.441607873462105 & 0.88321574692421 & 0.558392126537895 \tabularnewline
49 & 0.513628599154578 & 0.972742801690844 & 0.486371400845422 \tabularnewline
50 & 0.562628742552352 & 0.874742514895297 & 0.437371257447648 \tabularnewline
51 & 0.49445550910897 & 0.98891101821794 & 0.50554449089103 \tabularnewline
52 & 0.432179319951759 & 0.864358639903518 & 0.567820680048241 \tabularnewline
53 & 0.413240216259283 & 0.826480432518565 & 0.586759783740717 \tabularnewline
54 & 0.464625390879838 & 0.929250781759675 & 0.535374609120162 \tabularnewline
55 & 0.425716032443968 & 0.851432064887937 & 0.574283967556032 \tabularnewline
56 & 0.362325158686120 & 0.724650317372241 & 0.63767484131388 \tabularnewline
57 & 0.296571660651003 & 0.593143321302006 & 0.703428339348997 \tabularnewline
58 & 0.238960614491348 & 0.477921228982697 & 0.761039385508652 \tabularnewline
59 & 0.214257787498669 & 0.428515574997337 & 0.785742212501331 \tabularnewline
60 & 0.196119038759810 & 0.392238077519621 & 0.80388096124019 \tabularnewline
61 & 0.20918933221082 & 0.41837866442164 & 0.79081066778918 \tabularnewline
62 & 0.185510724182613 & 0.371021448365226 & 0.814489275817387 \tabularnewline
63 & 0.312832624155782 & 0.625665248311563 & 0.687167375844218 \tabularnewline
64 & 0.322099131430791 & 0.644198262861583 & 0.677900868569209 \tabularnewline
65 & 0.282899657438708 & 0.565799314877416 & 0.717100342561292 \tabularnewline
66 & 0.372083731192912 & 0.744167462385824 & 0.627916268807088 \tabularnewline
67 & 0.362826465534303 & 0.725652931068606 & 0.637173534465697 \tabularnewline
68 & 0.270976948578866 & 0.541953897157731 & 0.729023051421134 \tabularnewline
69 & 0.195486801289779 & 0.390973602579558 & 0.804513198710221 \tabularnewline
70 & 0.127757251831311 & 0.255514503662622 & 0.872242748168689 \tabularnewline
71 & 0.300182961202168 & 0.600365922404336 & 0.699817038797832 \tabularnewline
72 & 0.385912359492083 & 0.771824718984166 & 0.614087640507917 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57841&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]0.535150433981029[/C][C]0.929699132037942[/C][C]0.464849566018971[/C][/ROW]
[ROW][C]22[/C][C]0.579716029072934[/C][C]0.840567941854132[/C][C]0.420283970927066[/C][/ROW]
[ROW][C]23[/C][C]0.436436994233255[/C][C]0.87287398846651[/C][C]0.563563005766745[/C][/ROW]
[ROW][C]24[/C][C]0.389292707465431[/C][C]0.778585414930862[/C][C]0.610707292534569[/C][/ROW]
[ROW][C]25[/C][C]0.307332857045753[/C][C]0.614665714091506[/C][C]0.692667142954247[/C][/ROW]
[ROW][C]26[/C][C]0.250741525275134[/C][C]0.501483050550267[/C][C]0.749258474724867[/C][/ROW]
[ROW][C]27[/C][C]0.197104030574497[/C][C]0.394208061148993[/C][C]0.802895969425503[/C][/ROW]
[ROW][C]28[/C][C]0.131890434941066[/C][C]0.263780869882132[/C][C]0.868109565058934[/C][/ROW]
[ROW][C]29[/C][C]0.120444856705101[/C][C]0.240889713410202[/C][C]0.8795551432949[/C][/ROW]
[ROW][C]30[/C][C]0.162399237715262[/C][C]0.324798475430524[/C][C]0.837600762284738[/C][/ROW]
[ROW][C]31[/C][C]0.119598551529498[/C][C]0.239197103058996[/C][C]0.880401448470502[/C][/ROW]
[ROW][C]32[/C][C]0.156499820114130[/C][C]0.312999640228261[/C][C]0.84350017988587[/C][/ROW]
[ROW][C]33[/C][C]0.110396179466062[/C][C]0.220792358932123[/C][C]0.889603820533938[/C][/ROW]
[ROW][C]34[/C][C]0.0793787366164238[/C][C]0.158757473232848[/C][C]0.920621263383576[/C][/ROW]
[ROW][C]35[/C][C]0.0956950807791798[/C][C]0.191390161558360[/C][C]0.90430491922082[/C][/ROW]
[ROW][C]36[/C][C]0.224401839915192[/C][C]0.448803679830384[/C][C]0.775598160084808[/C][/ROW]
[ROW][C]37[/C][C]0.189303979551631[/C][C]0.378607959103262[/C][C]0.810696020448369[/C][/ROW]
[ROW][C]38[/C][C]0.141825901226789[/C][C]0.283651802453579[/C][C]0.85817409877321[/C][/ROW]
[ROW][C]39[/C][C]0.217147910597866[/C][C]0.434295821195732[/C][C]0.782852089402134[/C][/ROW]
[ROW][C]40[/C][C]0.215061548465236[/C][C]0.430123096930473[/C][C]0.784938451534763[/C][/ROW]
[ROW][C]41[/C][C]0.211691381494733[/C][C]0.423382762989467[/C][C]0.788308618505267[/C][/ROW]
[ROW][C]42[/C][C]0.169399187228392[/C][C]0.338798374456783[/C][C]0.830600812771608[/C][/ROW]
[ROW][C]43[/C][C]0.125961001336344[/C][C]0.251922002672688[/C][C]0.874038998663656[/C][/ROW]
[ROW][C]44[/C][C]0.118652706438342[/C][C]0.237305412876684[/C][C]0.881347293561658[/C][/ROW]
[ROW][C]45[/C][C]0.513646014321337[/C][C]0.972707971357326[/C][C]0.486353985678663[/C][/ROW]
[ROW][C]46[/C][C]0.451369822435387[/C][C]0.902739644870774[/C][C]0.548630177564613[/C][/ROW]
[ROW][C]47[/C][C]0.4404517061066[/C][C]0.8809034122132[/C][C]0.5595482938934[/C][/ROW]
[ROW][C]48[/C][C]0.441607873462105[/C][C]0.88321574692421[/C][C]0.558392126537895[/C][/ROW]
[ROW][C]49[/C][C]0.513628599154578[/C][C]0.972742801690844[/C][C]0.486371400845422[/C][/ROW]
[ROW][C]50[/C][C]0.562628742552352[/C][C]0.874742514895297[/C][C]0.437371257447648[/C][/ROW]
[ROW][C]51[/C][C]0.49445550910897[/C][C]0.98891101821794[/C][C]0.50554449089103[/C][/ROW]
[ROW][C]52[/C][C]0.432179319951759[/C][C]0.864358639903518[/C][C]0.567820680048241[/C][/ROW]
[ROW][C]53[/C][C]0.413240216259283[/C][C]0.826480432518565[/C][C]0.586759783740717[/C][/ROW]
[ROW][C]54[/C][C]0.464625390879838[/C][C]0.929250781759675[/C][C]0.535374609120162[/C][/ROW]
[ROW][C]55[/C][C]0.425716032443968[/C][C]0.851432064887937[/C][C]0.574283967556032[/C][/ROW]
[ROW][C]56[/C][C]0.362325158686120[/C][C]0.724650317372241[/C][C]0.63767484131388[/C][/ROW]
[ROW][C]57[/C][C]0.296571660651003[/C][C]0.593143321302006[/C][C]0.703428339348997[/C][/ROW]
[ROW][C]58[/C][C]0.238960614491348[/C][C]0.477921228982697[/C][C]0.761039385508652[/C][/ROW]
[ROW][C]59[/C][C]0.214257787498669[/C][C]0.428515574997337[/C][C]0.785742212501331[/C][/ROW]
[ROW][C]60[/C][C]0.196119038759810[/C][C]0.392238077519621[/C][C]0.80388096124019[/C][/ROW]
[ROW][C]61[/C][C]0.20918933221082[/C][C]0.41837866442164[/C][C]0.79081066778918[/C][/ROW]
[ROW][C]62[/C][C]0.185510724182613[/C][C]0.371021448365226[/C][C]0.814489275817387[/C][/ROW]
[ROW][C]63[/C][C]0.312832624155782[/C][C]0.625665248311563[/C][C]0.687167375844218[/C][/ROW]
[ROW][C]64[/C][C]0.322099131430791[/C][C]0.644198262861583[/C][C]0.677900868569209[/C][/ROW]
[ROW][C]65[/C][C]0.282899657438708[/C][C]0.565799314877416[/C][C]0.717100342561292[/C][/ROW]
[ROW][C]66[/C][C]0.372083731192912[/C][C]0.744167462385824[/C][C]0.627916268807088[/C][/ROW]
[ROW][C]67[/C][C]0.362826465534303[/C][C]0.725652931068606[/C][C]0.637173534465697[/C][/ROW]
[ROW][C]68[/C][C]0.270976948578866[/C][C]0.541953897157731[/C][C]0.729023051421134[/C][/ROW]
[ROW][C]69[/C][C]0.195486801289779[/C][C]0.390973602579558[/C][C]0.804513198710221[/C][/ROW]
[ROW][C]70[/C][C]0.127757251831311[/C][C]0.255514503662622[/C][C]0.872242748168689[/C][/ROW]
[ROW][C]71[/C][C]0.300182961202168[/C][C]0.600365922404336[/C][C]0.699817038797832[/C][/ROW]
[ROW][C]72[/C][C]0.385912359492083[/C][C]0.771824718984166[/C][C]0.614087640507917[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57841&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57841&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
210.5351504339810290.9296991320379420.464849566018971
220.5797160290729340.8405679418541320.420283970927066
230.4364369942332550.872873988466510.563563005766745
240.3892927074654310.7785854149308620.610707292534569
250.3073328570457530.6146657140915060.692667142954247
260.2507415252751340.5014830505502670.749258474724867
270.1971040305744970.3942080611489930.802895969425503
280.1318904349410660.2637808698821320.868109565058934
290.1204448567051010.2408897134102020.8795551432949
300.1623992377152620.3247984754305240.837600762284738
310.1195985515294980.2391971030589960.880401448470502
320.1564998201141300.3129996402282610.84350017988587
330.1103961794660620.2207923589321230.889603820533938
340.07937873661642380.1587574732328480.920621263383576
350.09569508077917980.1913901615583600.90430491922082
360.2244018399151920.4488036798303840.775598160084808
370.1893039795516310.3786079591032620.810696020448369
380.1418259012267890.2836518024535790.85817409877321
390.2171479105978660.4342958211957320.782852089402134
400.2150615484652360.4301230969304730.784938451534763
410.2116913814947330.4233827629894670.788308618505267
420.1693991872283920.3387983744567830.830600812771608
430.1259610013363440.2519220026726880.874038998663656
440.1186527064383420.2373054128766840.881347293561658
450.5136460143213370.9727079713573260.486353985678663
460.4513698224353870.9027396448707740.548630177564613
470.44045170610660.88090341221320.5595482938934
480.4416078734621050.883215746924210.558392126537895
490.5136285991545780.9727428016908440.486371400845422
500.5626287425523520.8747425148952970.437371257447648
510.494455509108970.988911018217940.50554449089103
520.4321793199517590.8643586399035180.567820680048241
530.4132402162592830.8264804325185650.586759783740717
540.4646253908798380.9292507817596750.535374609120162
550.4257160324439680.8514320648879370.574283967556032
560.3623251586861200.7246503173722410.63767484131388
570.2965716606510030.5931433213020060.703428339348997
580.2389606144913480.4779212289826970.761039385508652
590.2142577874986690.4285155749973370.785742212501331
600.1961190387598100.3922380775196210.80388096124019
610.209189332210820.418378664421640.79081066778918
620.1855107241826130.3710214483652260.814489275817387
630.3128326241557820.6256652483115630.687167375844218
640.3220991314307910.6441982628615830.677900868569209
650.2828996574387080.5657993148774160.717100342561292
660.3720837311929120.7441674623858240.627916268807088
670.3628264655343030.7256529310686060.637173534465697
680.2709769485788660.5419538971577310.729023051421134
690.1954868012897790.3909736025795580.804513198710221
700.1277572518313110.2555145036626220.872242748168689
710.3001829612021680.6003659224043360.699817038797832
720.3859123594920830.7718247189841660.614087640507917






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=57841&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=57841&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57841&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 level00OK


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 6
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Figure 7
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Figure 8
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Figure 9
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Figure 10
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Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}