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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationFri, 20 Nov 2009 14:16:42 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Nov/20/t1258751854emy122lwcxvzflj.htm/, Retrieved Fri, 29 Mar 2024 11:09:00 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58470, Retrieved Fri, 29 Mar 2024 11:09:00 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact248
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 14:14:11] [b98453cac15ba1066b407e146608df68]
-    D    [Multiple Regression] [ws 7] [2009-11-19 18:07:30] [b5908418e3090fddbd22f5f0f774653d]
-    D        [Multiple Regression] [WS 7-5] [2009-11-20 21:16:42] [a53416c107f5e7e1e12bb9940270d09d] [Current]
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Dataseries X:
8.3	98.6	8.2	8.7
8.5	96.5	8.3	8.2
8.6	95.9	8.5	8.3
8.5	103.7	8.6	8.5
8.2	103.1	8.5	8.6
8.1	103.7	8.2	8.5
7.9	112.1	8.1	8.2
8.6	86.9	7.9	8.1
8.7	95	8.6	7.9
8.7	111.8	8.7	8.6
8.5	108.8	8.7	8.7
8.4	109.3	8.5	8.7
8.5	101.4	8.4	8.5
8.7	100.5	8.5	8.4
8.7	100.7	8.7	8.5
8.6	113.5	8.7	8.7
8.5	106.1	8.6	8.7
8.3	111.6	8.5	8.6
8	114.9	8.3	8.5
8.2	88.6	8	8.3
8.1	99.5	8.2	8
8.1	115.1	8.1	8.2
8	118	8.1	8.1
7.9	111.4	8	8.1
7.9	107.3	7.9	8
8	105.3	7.9	7.9
8	105.3	8	7.9
7.9	117.9	8	8
8	110.2	7.9	8
7.7	112.4	8	7.9
7.2	117.5	7.7	8
7.5	93	7.2	7.7
7.3	103.5	7.5	7.2
7	116.3	7.3	7.5
7	120	7	7.3
7	114.3	7	7
7.2	104.7	7	7
7.3	109.8	7.2	7
7.1	112.6	7.3	7.2
6.8	114.4	7.1	7.3
6.4	115.7	6.8	7.1
6.1	114.7	6.4	6.8
6.5	118.4	6.1	6.4
7.7	94.9	6.5	6.1
7.9	103.8	7.7	6.5
7.5	115.1	7.9	7.7
6.9	113.7	7.5	7.9
6.6	104	6.9	7.5
6.9	94.3	6.6	6.9
7.7	92.5	6.9	6.6
8	93.2	7.7	6.9
8	104.7	8	7.7
7.7	94	8	8
7.3	98.1	7.7	8
7.4	102.7	7.3	7.7
8.1	82.4	7.4	7.3




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=58470&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=58470&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58470&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 4.14999041251723 -0.0152438597880977X[t] + 1.34171092585125Y1[t] -0.632193949965465Y2[t] + 0.0835740790049867M1[t] + 0.0525149196902091M2[t] -0.176803720858912M3[t] -0.0232878832478820M4[t] -0.105116917657922M5[t] -0.129479933541223M6[t] + 0.0778523659487016M7[t] + 0.310812223645426M8[t] -0.447340912981457M9[t] -0.0193029508698561M10[t] + 0.00728265666300871M11[t] -0.00840207262544193t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  4.14999041251723 -0.0152438597880977X[t] +  1.34171092585125Y1[t] -0.632193949965465Y2[t] +  0.0835740790049867M1[t] +  0.0525149196902091M2[t] -0.176803720858912M3[t] -0.0232878832478820M4[t] -0.105116917657922M5[t] -0.129479933541223M6[t] +  0.0778523659487016M7[t] +  0.310812223645426M8[t] -0.447340912981457M9[t] -0.0193029508698561M10[t] +  0.00728265666300871M11[t] -0.00840207262544193t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58470&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  4.14999041251723 -0.0152438597880977X[t] +  1.34171092585125Y1[t] -0.632193949965465Y2[t] +  0.0835740790049867M1[t] +  0.0525149196902091M2[t] -0.176803720858912M3[t] -0.0232878832478820M4[t] -0.105116917657922M5[t] -0.129479933541223M6[t] +  0.0778523659487016M7[t] +  0.310812223645426M8[t] -0.447340912981457M9[t] -0.0193029508698561M10[t] +  0.00728265666300871M11[t] -0.00840207262544193t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58470&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58470&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 4.14999041251723 -0.0152438597880977X[t] + 1.34171092585125Y1[t] -0.632193949965465Y2[t] + 0.0835740790049867M1[t] + 0.0525149196902091M2[t] -0.176803720858912M3[t] -0.0232878832478820M4[t] -0.105116917657922M5[t] -0.129479933541223M6[t] + 0.0778523659487016M7[t] + 0.310812223645426M8[t] -0.447340912981457M9[t] -0.0193029508698561M10[t] + 0.00728265666300871M11[t] -0.00840207262544193t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)4.149990412517230.9906314.18920.000157.5e-05
X-0.01524385978809770.004999-3.04910.0040580.002029
Y11.341710925851250.11603111.563400
Y2-0.6321939499654650.118245-5.34654e-062e-06
M10.08357407900498670.1269530.65830.5141130.257056
M20.05251491969020910.133460.39350.6960490.348025
M3-0.1768037208589120.134934-1.31030.1975690.098784
M4-0.02328788324788200.123139-0.18910.8509570.425478
M5-0.1051169176579220.11934-0.88080.383680.19184
M6-0.1294799335412230.117649-1.10060.2776660.138833
M70.07785236594870160.1183050.65810.5142660.257133
M80.3108122236454260.1610861.92950.0607860.030393
M9-0.4473409129814570.161306-2.77320.008390.004195
M10-0.01930295086985610.13103-0.14730.8836220.441811
M110.007282656663008710.1283880.05670.9550480.477524
t-0.008402072625441930.002576-3.26150.0022690.001135

\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) & 4.14999041251723 & 0.990631 & 4.1892 & 0.00015 & 7.5e-05 \tabularnewline
X & -0.0152438597880977 & 0.004999 & -3.0491 & 0.004058 & 0.002029 \tabularnewline
Y1 & 1.34171092585125 & 0.116031 & 11.5634 & 0 & 0 \tabularnewline
Y2 & -0.632193949965465 & 0.118245 & -5.3465 & 4e-06 & 2e-06 \tabularnewline
M1 & 0.0835740790049867 & 0.126953 & 0.6583 & 0.514113 & 0.257056 \tabularnewline
M2 & 0.0525149196902091 & 0.13346 & 0.3935 & 0.696049 & 0.348025 \tabularnewline
M3 & -0.176803720858912 & 0.134934 & -1.3103 & 0.197569 & 0.098784 \tabularnewline
M4 & -0.0232878832478820 & 0.123139 & -0.1891 & 0.850957 & 0.425478 \tabularnewline
M5 & -0.105116917657922 & 0.11934 & -0.8808 & 0.38368 & 0.19184 \tabularnewline
M6 & -0.129479933541223 & 0.117649 & -1.1006 & 0.277666 & 0.138833 \tabularnewline
M7 & 0.0778523659487016 & 0.118305 & 0.6581 & 0.514266 & 0.257133 \tabularnewline
M8 & 0.310812223645426 & 0.161086 & 1.9295 & 0.060786 & 0.030393 \tabularnewline
M9 & -0.447340912981457 & 0.161306 & -2.7732 & 0.00839 & 0.004195 \tabularnewline
M10 & -0.0193029508698561 & 0.13103 & -0.1473 & 0.883622 & 0.441811 \tabularnewline
M11 & 0.00728265666300871 & 0.128388 & 0.0567 & 0.955048 & 0.477524 \tabularnewline
t & -0.00840207262544193 & 0.002576 & -3.2615 & 0.002269 & 0.001135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58470&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]4.14999041251723[/C][C]0.990631[/C][C]4.1892[/C][C]0.00015[/C][C]7.5e-05[/C][/ROW]
[ROW][C]X[/C][C]-0.0152438597880977[/C][C]0.004999[/C][C]-3.0491[/C][C]0.004058[/C][C]0.002029[/C][/ROW]
[ROW][C]Y1[/C][C]1.34171092585125[/C][C]0.116031[/C][C]11.5634[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Y2[/C][C]-0.632193949965465[/C][C]0.118245[/C][C]-5.3465[/C][C]4e-06[/C][C]2e-06[/C][/ROW]
[ROW][C]M1[/C][C]0.0835740790049867[/C][C]0.126953[/C][C]0.6583[/C][C]0.514113[/C][C]0.257056[/C][/ROW]
[ROW][C]M2[/C][C]0.0525149196902091[/C][C]0.13346[/C][C]0.3935[/C][C]0.696049[/C][C]0.348025[/C][/ROW]
[ROW][C]M3[/C][C]-0.176803720858912[/C][C]0.134934[/C][C]-1.3103[/C][C]0.197569[/C][C]0.098784[/C][/ROW]
[ROW][C]M4[/C][C]-0.0232878832478820[/C][C]0.123139[/C][C]-0.1891[/C][C]0.850957[/C][C]0.425478[/C][/ROW]
[ROW][C]M5[/C][C]-0.105116917657922[/C][C]0.11934[/C][C]-0.8808[/C][C]0.38368[/C][C]0.19184[/C][/ROW]
[ROW][C]M6[/C][C]-0.129479933541223[/C][C]0.117649[/C][C]-1.1006[/C][C]0.277666[/C][C]0.138833[/C][/ROW]
[ROW][C]M7[/C][C]0.0778523659487016[/C][C]0.118305[/C][C]0.6581[/C][C]0.514266[/C][C]0.257133[/C][/ROW]
[ROW][C]M8[/C][C]0.310812223645426[/C][C]0.161086[/C][C]1.9295[/C][C]0.060786[/C][C]0.030393[/C][/ROW]
[ROW][C]M9[/C][C]-0.447340912981457[/C][C]0.161306[/C][C]-2.7732[/C][C]0.00839[/C][C]0.004195[/C][/ROW]
[ROW][C]M10[/C][C]-0.0193029508698561[/C][C]0.13103[/C][C]-0.1473[/C][C]0.883622[/C][C]0.441811[/C][/ROW]
[ROW][C]M11[/C][C]0.00728265666300871[/C][C]0.128388[/C][C]0.0567[/C][C]0.955048[/C][C]0.477524[/C][/ROW]
[ROW][C]t[/C][C]-0.00840207262544193[/C][C]0.002576[/C][C]-3.2615[/C][C]0.002269[/C][C]0.001135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58470&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58470&T=2

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)4.149990412517230.9906314.18920.000157.5e-05
X-0.01524385978809770.004999-3.04910.0040580.002029
Y11.341710925851250.11603111.563400
Y2-0.6321939499654650.118245-5.34654e-062e-06
M10.08357407900498670.1269530.65830.5141130.257056
M20.05251491969020910.133460.39350.6960490.348025
M3-0.1768037208589120.134934-1.31030.1975690.098784
M4-0.02328788324788200.123139-0.18910.8509570.425478
M5-0.1051169176579220.11934-0.88080.383680.19184
M6-0.1294799335412230.117649-1.10060.2776660.138833
M70.07785236594870160.1183050.65810.5142660.257133
M80.3108122236454260.1610861.92950.0607860.030393
M9-0.4473409129814570.161306-2.77320.008390.004195
M10-0.01930295086985610.13103-0.14730.8836220.441811
M110.007282656663008710.1283880.05670.9550480.477524
t-0.008402072625441930.002576-3.26150.0022690.001135







Multiple Linear Regression - Regression Statistics
Multiple R0.974568997085734
R-squared0.949784730080693
Adjusted R-squared0.930954003860953
F-TEST (value)50.4380297922358
F-TEST (DF numerator)15
F-TEST (DF denominator)40
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.174779636784900
Sum Squared Residuals1.22191685738646

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.974568997085734 \tabularnewline
R-squared & 0.949784730080693 \tabularnewline
Adjusted R-squared & 0.930954003860953 \tabularnewline
F-TEST (value) & 50.4380297922358 \tabularnewline
F-TEST (DF numerator) & 15 \tabularnewline
F-TEST (DF denominator) & 40 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.174779636784900 \tabularnewline
Sum Squared Residuals & 1.22191685738646 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58470&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.974568997085734[/C][/ROW]
[ROW][C]R-squared[/C][C]0.949784730080693[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.930954003860953[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]50.4380297922358[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]15[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]40[/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]0.174779636784900[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1.22191685738646[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58470&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58470&T=3

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Regression Statistics
Multiple R0.974568997085734
R-squared0.949784730080693
Adjusted R-squared0.930954003860953
F-TEST (value)50.4380297922358
F-TEST (DF numerator)15
F-TEST (DF denominator)40
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.174779636784900
Sum Squared Residuals1.22191685738646







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
18.38.224060071071070.0759399289289281
28.58.6668790122537-0.166879012253704
38.68.6434274051257-0.0434274051257002
48.58.67737136635616-0.177371366356160
58.28.39889608761187-0.198896087611866
68.18.017690800471430.0823091995285654
77.98.14405969752041-0.244059697520411
88.68.547639959078050.0523600409219463
98.78.7232459236311-0.0232459236311051
108.78.578420296286520.121579703713478
118.58.5791160155617-0.0791160155616911
128.48.287467171208940.112532828791058
138.58.475333367322430.0246666326775733
148.78.646982096773170.0530179032268333
158.78.611335401814690.0886645981853136
168.68.434888971519530.165111028480468
178.58.323291334330850.176708665669152
188.38.135733319398990.164266680601011
1988.07923601878905-0.0792360187890467
208.28.42863282852502-0.228632828525016
218.17.953919917742310.146080082257685
228.17.875141711955930.224858288044067
2387.912337448474420.0876625515255819
247.97.863091101202290.0369088987977128
257.97.92981123512445-0.0298112351244543
2687.984057117756980.0159428822430233
2787.880507497167540.119492502832462
287.97.770329233826550.129670766173451
2987.66330475457430.336695245425706
307.77.7943936621134-0.0943936621134074
317.27.44984753130667-0.249847531306671
327.57.56668260325036-0.0666826032503617
337.37.35867711896112-0.0586771189611185
3477.12519123299974-0.125191232999737
3576.810897998928920.189102001071084
3677.07176145542226-0.071761455422262
377.27.29327451576754-0.0932745157675443
387.37.44441178407828-0.144411784078277
397.17.17174056608907-0.0717405660890719
406.86.95785380328929-0.157853803289287
416.46.571731190767-0.171731190766996
426.16.20718377669549-0.107183776695492
436.56.200076024574820.299923975425178
447.77.509207069996540.190792930003459
457.97.96415703966546-0.0641570396654613
467.57.72124675875781-0.221246758757809
476.97.09764853703497-0.197648537034974
486.66.67768027216651-0.0776802721665084
496.96.87752081071450.0224791892854972
507.77.457669989137870.242330010862125
5188.092989129803-0.0929891298030035
5287.959556625008470.0404433749915284
537.77.842776632716-0.142776632715995
547.37.34499844132068-0.0449984413206777
557.47.126780727809050.273219272190951
568.18.047837539150030.0521624608499723

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 8.3 & 8.22406007107107 & 0.0759399289289281 \tabularnewline
2 & 8.5 & 8.6668790122537 & -0.166879012253704 \tabularnewline
3 & 8.6 & 8.6434274051257 & -0.0434274051257002 \tabularnewline
4 & 8.5 & 8.67737136635616 & -0.177371366356160 \tabularnewline
5 & 8.2 & 8.39889608761187 & -0.198896087611866 \tabularnewline
6 & 8.1 & 8.01769080047143 & 0.0823091995285654 \tabularnewline
7 & 7.9 & 8.14405969752041 & -0.244059697520411 \tabularnewline
8 & 8.6 & 8.54763995907805 & 0.0523600409219463 \tabularnewline
9 & 8.7 & 8.7232459236311 & -0.0232459236311051 \tabularnewline
10 & 8.7 & 8.57842029628652 & 0.121579703713478 \tabularnewline
11 & 8.5 & 8.5791160155617 & -0.0791160155616911 \tabularnewline
12 & 8.4 & 8.28746717120894 & 0.112532828791058 \tabularnewline
13 & 8.5 & 8.47533336732243 & 0.0246666326775733 \tabularnewline
14 & 8.7 & 8.64698209677317 & 0.0530179032268333 \tabularnewline
15 & 8.7 & 8.61133540181469 & 0.0886645981853136 \tabularnewline
16 & 8.6 & 8.43488897151953 & 0.165111028480468 \tabularnewline
17 & 8.5 & 8.32329133433085 & 0.176708665669152 \tabularnewline
18 & 8.3 & 8.13573331939899 & 0.164266680601011 \tabularnewline
19 & 8 & 8.07923601878905 & -0.0792360187890467 \tabularnewline
20 & 8.2 & 8.42863282852502 & -0.228632828525016 \tabularnewline
21 & 8.1 & 7.95391991774231 & 0.146080082257685 \tabularnewline
22 & 8.1 & 7.87514171195593 & 0.224858288044067 \tabularnewline
23 & 8 & 7.91233744847442 & 0.0876625515255819 \tabularnewline
24 & 7.9 & 7.86309110120229 & 0.0369088987977128 \tabularnewline
25 & 7.9 & 7.92981123512445 & -0.0298112351244543 \tabularnewline
26 & 8 & 7.98405711775698 & 0.0159428822430233 \tabularnewline
27 & 8 & 7.88050749716754 & 0.119492502832462 \tabularnewline
28 & 7.9 & 7.77032923382655 & 0.129670766173451 \tabularnewline
29 & 8 & 7.6633047545743 & 0.336695245425706 \tabularnewline
30 & 7.7 & 7.7943936621134 & -0.0943936621134074 \tabularnewline
31 & 7.2 & 7.44984753130667 & -0.249847531306671 \tabularnewline
32 & 7.5 & 7.56668260325036 & -0.0666826032503617 \tabularnewline
33 & 7.3 & 7.35867711896112 & -0.0586771189611185 \tabularnewline
34 & 7 & 7.12519123299974 & -0.125191232999737 \tabularnewline
35 & 7 & 6.81089799892892 & 0.189102001071084 \tabularnewline
36 & 7 & 7.07176145542226 & -0.071761455422262 \tabularnewline
37 & 7.2 & 7.29327451576754 & -0.0932745157675443 \tabularnewline
38 & 7.3 & 7.44441178407828 & -0.144411784078277 \tabularnewline
39 & 7.1 & 7.17174056608907 & -0.0717405660890719 \tabularnewline
40 & 6.8 & 6.95785380328929 & -0.157853803289287 \tabularnewline
41 & 6.4 & 6.571731190767 & -0.171731190766996 \tabularnewline
42 & 6.1 & 6.20718377669549 & -0.107183776695492 \tabularnewline
43 & 6.5 & 6.20007602457482 & 0.299923975425178 \tabularnewline
44 & 7.7 & 7.50920706999654 & 0.190792930003459 \tabularnewline
45 & 7.9 & 7.96415703966546 & -0.0641570396654613 \tabularnewline
46 & 7.5 & 7.72124675875781 & -0.221246758757809 \tabularnewline
47 & 6.9 & 7.09764853703497 & -0.197648537034974 \tabularnewline
48 & 6.6 & 6.67768027216651 & -0.0776802721665084 \tabularnewline
49 & 6.9 & 6.8775208107145 & 0.0224791892854972 \tabularnewline
50 & 7.7 & 7.45766998913787 & 0.242330010862125 \tabularnewline
51 & 8 & 8.092989129803 & -0.0929891298030035 \tabularnewline
52 & 8 & 7.95955662500847 & 0.0404433749915284 \tabularnewline
53 & 7.7 & 7.842776632716 & -0.142776632715995 \tabularnewline
54 & 7.3 & 7.34499844132068 & -0.0449984413206777 \tabularnewline
55 & 7.4 & 7.12678072780905 & 0.273219272190951 \tabularnewline
56 & 8.1 & 8.04783753915003 & 0.0521624608499723 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58470&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]8.3[/C][C]8.22406007107107[/C][C]0.0759399289289281[/C][/ROW]
[ROW][C]2[/C][C]8.5[/C][C]8.6668790122537[/C][C]-0.166879012253704[/C][/ROW]
[ROW][C]3[/C][C]8.6[/C][C]8.6434274051257[/C][C]-0.0434274051257002[/C][/ROW]
[ROW][C]4[/C][C]8.5[/C][C]8.67737136635616[/C][C]-0.177371366356160[/C][/ROW]
[ROW][C]5[/C][C]8.2[/C][C]8.39889608761187[/C][C]-0.198896087611866[/C][/ROW]
[ROW][C]6[/C][C]8.1[/C][C]8.01769080047143[/C][C]0.0823091995285654[/C][/ROW]
[ROW][C]7[/C][C]7.9[/C][C]8.14405969752041[/C][C]-0.244059697520411[/C][/ROW]
[ROW][C]8[/C][C]8.6[/C][C]8.54763995907805[/C][C]0.0523600409219463[/C][/ROW]
[ROW][C]9[/C][C]8.7[/C][C]8.7232459236311[/C][C]-0.0232459236311051[/C][/ROW]
[ROW][C]10[/C][C]8.7[/C][C]8.57842029628652[/C][C]0.121579703713478[/C][/ROW]
[ROW][C]11[/C][C]8.5[/C][C]8.5791160155617[/C][C]-0.0791160155616911[/C][/ROW]
[ROW][C]12[/C][C]8.4[/C][C]8.28746717120894[/C][C]0.112532828791058[/C][/ROW]
[ROW][C]13[/C][C]8.5[/C][C]8.47533336732243[/C][C]0.0246666326775733[/C][/ROW]
[ROW][C]14[/C][C]8.7[/C][C]8.64698209677317[/C][C]0.0530179032268333[/C][/ROW]
[ROW][C]15[/C][C]8.7[/C][C]8.61133540181469[/C][C]0.0886645981853136[/C][/ROW]
[ROW][C]16[/C][C]8.6[/C][C]8.43488897151953[/C][C]0.165111028480468[/C][/ROW]
[ROW][C]17[/C][C]8.5[/C][C]8.32329133433085[/C][C]0.176708665669152[/C][/ROW]
[ROW][C]18[/C][C]8.3[/C][C]8.13573331939899[/C][C]0.164266680601011[/C][/ROW]
[ROW][C]19[/C][C]8[/C][C]8.07923601878905[/C][C]-0.0792360187890467[/C][/ROW]
[ROW][C]20[/C][C]8.2[/C][C]8.42863282852502[/C][C]-0.228632828525016[/C][/ROW]
[ROW][C]21[/C][C]8.1[/C][C]7.95391991774231[/C][C]0.146080082257685[/C][/ROW]
[ROW][C]22[/C][C]8.1[/C][C]7.87514171195593[/C][C]0.224858288044067[/C][/ROW]
[ROW][C]23[/C][C]8[/C][C]7.91233744847442[/C][C]0.0876625515255819[/C][/ROW]
[ROW][C]24[/C][C]7.9[/C][C]7.86309110120229[/C][C]0.0369088987977128[/C][/ROW]
[ROW][C]25[/C][C]7.9[/C][C]7.92981123512445[/C][C]-0.0298112351244543[/C][/ROW]
[ROW][C]26[/C][C]8[/C][C]7.98405711775698[/C][C]0.0159428822430233[/C][/ROW]
[ROW][C]27[/C][C]8[/C][C]7.88050749716754[/C][C]0.119492502832462[/C][/ROW]
[ROW][C]28[/C][C]7.9[/C][C]7.77032923382655[/C][C]0.129670766173451[/C][/ROW]
[ROW][C]29[/C][C]8[/C][C]7.6633047545743[/C][C]0.336695245425706[/C][/ROW]
[ROW][C]30[/C][C]7.7[/C][C]7.7943936621134[/C][C]-0.0943936621134074[/C][/ROW]
[ROW][C]31[/C][C]7.2[/C][C]7.44984753130667[/C][C]-0.249847531306671[/C][/ROW]
[ROW][C]32[/C][C]7.5[/C][C]7.56668260325036[/C][C]-0.0666826032503617[/C][/ROW]
[ROW][C]33[/C][C]7.3[/C][C]7.35867711896112[/C][C]-0.0586771189611185[/C][/ROW]
[ROW][C]34[/C][C]7[/C][C]7.12519123299974[/C][C]-0.125191232999737[/C][/ROW]
[ROW][C]35[/C][C]7[/C][C]6.81089799892892[/C][C]0.189102001071084[/C][/ROW]
[ROW][C]36[/C][C]7[/C][C]7.07176145542226[/C][C]-0.071761455422262[/C][/ROW]
[ROW][C]37[/C][C]7.2[/C][C]7.29327451576754[/C][C]-0.0932745157675443[/C][/ROW]
[ROW][C]38[/C][C]7.3[/C][C]7.44441178407828[/C][C]-0.144411784078277[/C][/ROW]
[ROW][C]39[/C][C]7.1[/C][C]7.17174056608907[/C][C]-0.0717405660890719[/C][/ROW]
[ROW][C]40[/C][C]6.8[/C][C]6.95785380328929[/C][C]-0.157853803289287[/C][/ROW]
[ROW][C]41[/C][C]6.4[/C][C]6.571731190767[/C][C]-0.171731190766996[/C][/ROW]
[ROW][C]42[/C][C]6.1[/C][C]6.20718377669549[/C][C]-0.107183776695492[/C][/ROW]
[ROW][C]43[/C][C]6.5[/C][C]6.20007602457482[/C][C]0.299923975425178[/C][/ROW]
[ROW][C]44[/C][C]7.7[/C][C]7.50920706999654[/C][C]0.190792930003459[/C][/ROW]
[ROW][C]45[/C][C]7.9[/C][C]7.96415703966546[/C][C]-0.0641570396654613[/C][/ROW]
[ROW][C]46[/C][C]7.5[/C][C]7.72124675875781[/C][C]-0.221246758757809[/C][/ROW]
[ROW][C]47[/C][C]6.9[/C][C]7.09764853703497[/C][C]-0.197648537034974[/C][/ROW]
[ROW][C]48[/C][C]6.6[/C][C]6.67768027216651[/C][C]-0.0776802721665084[/C][/ROW]
[ROW][C]49[/C][C]6.9[/C][C]6.8775208107145[/C][C]0.0224791892854972[/C][/ROW]
[ROW][C]50[/C][C]7.7[/C][C]7.45766998913787[/C][C]0.242330010862125[/C][/ROW]
[ROW][C]51[/C][C]8[/C][C]8.092989129803[/C][C]-0.0929891298030035[/C][/ROW]
[ROW][C]52[/C][C]8[/C][C]7.95955662500847[/C][C]0.0404433749915284[/C][/ROW]
[ROW][C]53[/C][C]7.7[/C][C]7.842776632716[/C][C]-0.142776632715995[/C][/ROW]
[ROW][C]54[/C][C]7.3[/C][C]7.34499844132068[/C][C]-0.0449984413206777[/C][/ROW]
[ROW][C]55[/C][C]7.4[/C][C]7.12678072780905[/C][C]0.273219272190951[/C][/ROW]
[ROW][C]56[/C][C]8.1[/C][C]8.04783753915003[/C][C]0.0521624608499723[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58470&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58470&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
18.38.224060071071070.0759399289289281
28.58.6668790122537-0.166879012253704
38.68.6434274051257-0.0434274051257002
48.58.67737136635616-0.177371366356160
58.28.39889608761187-0.198896087611866
68.18.017690800471430.0823091995285654
77.98.14405969752041-0.244059697520411
88.68.547639959078050.0523600409219463
98.78.7232459236311-0.0232459236311051
108.78.578420296286520.121579703713478
118.58.5791160155617-0.0791160155616911
128.48.287467171208940.112532828791058
138.58.475333367322430.0246666326775733
148.78.646982096773170.0530179032268333
158.78.611335401814690.0886645981853136
168.68.434888971519530.165111028480468
178.58.323291334330850.176708665669152
188.38.135733319398990.164266680601011
1988.07923601878905-0.0792360187890467
208.28.42863282852502-0.228632828525016
218.17.953919917742310.146080082257685
228.17.875141711955930.224858288044067
2387.912337448474420.0876625515255819
247.97.863091101202290.0369088987977128
257.97.92981123512445-0.0298112351244543
2687.984057117756980.0159428822430233
2787.880507497167540.119492502832462
287.97.770329233826550.129670766173451
2987.66330475457430.336695245425706
307.77.7943936621134-0.0943936621134074
317.27.44984753130667-0.249847531306671
327.57.56668260325036-0.0666826032503617
337.37.35867711896112-0.0586771189611185
3477.12519123299974-0.125191232999737
3576.810897998928920.189102001071084
3677.07176145542226-0.071761455422262
377.27.29327451576754-0.0932745157675443
387.37.44441178407828-0.144411784078277
397.17.17174056608907-0.0717405660890719
406.86.95785380328929-0.157853803289287
416.46.571731190767-0.171731190766996
426.16.20718377669549-0.107183776695492
436.56.200076024574820.299923975425178
447.77.509207069996540.190792930003459
457.97.96415703966546-0.0641570396654613
467.57.72124675875781-0.221246758757809
476.97.09764853703497-0.197648537034974
486.66.67768027216651-0.0776802721665084
496.96.87752081071450.0224791892854972
507.77.457669989137870.242330010862125
5188.092989129803-0.0929891298030035
5287.959556625008470.0404433749915284
537.77.842776632716-0.142776632715995
547.37.34499844132068-0.0449984413206777
557.47.126780727809050.273219272190951
568.18.047837539150030.0521624608499723







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
190.05795178137616580.1159035627523320.942048218623834
200.6330953284919590.7338093430160820.366904671508041
210.4891421592256570.9782843184513130.510857840774343
220.3785958695346540.7571917390693070.621404130465346
230.261443653799090.522887307598180.73855634620091
240.1661707514661740.3323415029323480.833829248533826
250.1236683401753870.2473366803507740.876331659824613
260.07027431950391030.1405486390078210.92972568049609
270.043086557881920.086173115763840.95691344211808
280.03003588486034940.06007176972069870.96996411513965
290.3702315022330120.7404630044660240.629768497766988
300.4076825450585580.8153650901171160.592317454941442
310.5323416964417340.9353166071165320.467658303558266
320.4433870633822720.8867741267645440.556612936617728
330.3618297562975960.7236595125951920.638170243702404
340.2830282996283910.5660565992567830.716971700371609
350.5792985034909780.8414029930180440.420701496509022
360.4515881260531340.9031762521062680.548411873946866
370.3626165374487290.7252330748974570.637383462551271

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
19 & 0.0579517813761658 & 0.115903562752332 & 0.942048218623834 \tabularnewline
20 & 0.633095328491959 & 0.733809343016082 & 0.366904671508041 \tabularnewline
21 & 0.489142159225657 & 0.978284318451313 & 0.510857840774343 \tabularnewline
22 & 0.378595869534654 & 0.757191739069307 & 0.621404130465346 \tabularnewline
23 & 0.26144365379909 & 0.52288730759818 & 0.73855634620091 \tabularnewline
24 & 0.166170751466174 & 0.332341502932348 & 0.833829248533826 \tabularnewline
25 & 0.123668340175387 & 0.247336680350774 & 0.876331659824613 \tabularnewline
26 & 0.0702743195039103 & 0.140548639007821 & 0.92972568049609 \tabularnewline
27 & 0.04308655788192 & 0.08617311576384 & 0.95691344211808 \tabularnewline
28 & 0.0300358848603494 & 0.0600717697206987 & 0.96996411513965 \tabularnewline
29 & 0.370231502233012 & 0.740463004466024 & 0.629768497766988 \tabularnewline
30 & 0.407682545058558 & 0.815365090117116 & 0.592317454941442 \tabularnewline
31 & 0.532341696441734 & 0.935316607116532 & 0.467658303558266 \tabularnewline
32 & 0.443387063382272 & 0.886774126764544 & 0.556612936617728 \tabularnewline
33 & 0.361829756297596 & 0.723659512595192 & 0.638170243702404 \tabularnewline
34 & 0.283028299628391 & 0.566056599256783 & 0.716971700371609 \tabularnewline
35 & 0.579298503490978 & 0.841402993018044 & 0.420701496509022 \tabularnewline
36 & 0.451588126053134 & 0.903176252106268 & 0.548411873946866 \tabularnewline
37 & 0.362616537448729 & 0.725233074897457 & 0.637383462551271 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58470&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]19[/C][C]0.0579517813761658[/C][C]0.115903562752332[/C][C]0.942048218623834[/C][/ROW]
[ROW][C]20[/C][C]0.633095328491959[/C][C]0.733809343016082[/C][C]0.366904671508041[/C][/ROW]
[ROW][C]21[/C][C]0.489142159225657[/C][C]0.978284318451313[/C][C]0.510857840774343[/C][/ROW]
[ROW][C]22[/C][C]0.378595869534654[/C][C]0.757191739069307[/C][C]0.621404130465346[/C][/ROW]
[ROW][C]23[/C][C]0.26144365379909[/C][C]0.52288730759818[/C][C]0.73855634620091[/C][/ROW]
[ROW][C]24[/C][C]0.166170751466174[/C][C]0.332341502932348[/C][C]0.833829248533826[/C][/ROW]
[ROW][C]25[/C][C]0.123668340175387[/C][C]0.247336680350774[/C][C]0.876331659824613[/C][/ROW]
[ROW][C]26[/C][C]0.0702743195039103[/C][C]0.140548639007821[/C][C]0.92972568049609[/C][/ROW]
[ROW][C]27[/C][C]0.04308655788192[/C][C]0.08617311576384[/C][C]0.95691344211808[/C][/ROW]
[ROW][C]28[/C][C]0.0300358848603494[/C][C]0.0600717697206987[/C][C]0.96996411513965[/C][/ROW]
[ROW][C]29[/C][C]0.370231502233012[/C][C]0.740463004466024[/C][C]0.629768497766988[/C][/ROW]
[ROW][C]30[/C][C]0.407682545058558[/C][C]0.815365090117116[/C][C]0.592317454941442[/C][/ROW]
[ROW][C]31[/C][C]0.532341696441734[/C][C]0.935316607116532[/C][C]0.467658303558266[/C][/ROW]
[ROW][C]32[/C][C]0.443387063382272[/C][C]0.886774126764544[/C][C]0.556612936617728[/C][/ROW]
[ROW][C]33[/C][C]0.361829756297596[/C][C]0.723659512595192[/C][C]0.638170243702404[/C][/ROW]
[ROW][C]34[/C][C]0.283028299628391[/C][C]0.566056599256783[/C][C]0.716971700371609[/C][/ROW]
[ROW][C]35[/C][C]0.579298503490978[/C][C]0.841402993018044[/C][C]0.420701496509022[/C][/ROW]
[ROW][C]36[/C][C]0.451588126053134[/C][C]0.903176252106268[/C][C]0.548411873946866[/C][/ROW]
[ROW][C]37[/C][C]0.362616537448729[/C][C]0.725233074897457[/C][C]0.637383462551271[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58470&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58470&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
190.05795178137616580.1159035627523320.942048218623834
200.6330953284919590.7338093430160820.366904671508041
210.4891421592256570.9782843184513130.510857840774343
220.3785958695346540.7571917390693070.621404130465346
230.261443653799090.522887307598180.73855634620091
240.1661707514661740.3323415029323480.833829248533826
250.1236683401753870.2473366803507740.876331659824613
260.07027431950391030.1405486390078210.92972568049609
270.043086557881920.086173115763840.95691344211808
280.03003588486034940.06007176972069870.96996411513965
290.3702315022330120.7404630044660240.629768497766988
300.4076825450585580.8153650901171160.592317454941442
310.5323416964417340.9353166071165320.467658303558266
320.4433870633822720.8867741267645440.556612936617728
330.3618297562975960.7236595125951920.638170243702404
340.2830282996283910.5660565992567830.716971700371609
350.5792985034909780.8414029930180440.420701496509022
360.4515881260531340.9031762521062680.548411873946866
370.3626165374487290.7252330748974570.637383462551271







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 level20.105263157894737NOK

\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 & 2 & 0.105263157894737 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58470&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]2[/C][C]0.105263157894737[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58470&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58470&T=6

As an alternative you can also use a QR Code:  

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level20.105263157894737NOK



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')
}