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Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationThu, 19 Nov 2009 13:04:27 -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/t125866112128m1rk5wdjhclyk.htm/, Retrieved Fri, 29 Mar 2024 02:05:55 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=57926, Retrieved Fri, 29 Mar 2024 02:05:55 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact137
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RMPD  [Multiple Regression] [Seatbelt] [2009-11-12 14:03:14] [b98453cac15ba1066b407e146608df68]
-    D      [Multiple Regression] [multi regression ...] [2009-11-19 20:04:27] [244731fa3e7e6c85774b8c0902c58f85] [Current]
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Dataseries X:
8.9	6.3
8.2	6.2
7.6	6.1
7.7	6.3
8.1	6.5
8.3	6.6
8.3	6.5
7.9	6.2
7.8	6.2
8	5.9
8.5	6.1
8.6	6.1
8.5	6.1
8	6.1
7.8	6.1
8	6.4
8.2	6.7
8.3	6.9
8.2	7
8.1	7
8	6.8
7.8	6.4
7.8	5.9
7.7	5.5
7.6	5.5
7.6	5.6
7.6	5.8
7.8	5.9
8	6.1
8	6.1
7.9	6
7.7	6
7.4	5.9
6.9	5.5
6.7	5.6
6.5	5.4
6.4	5.2
6.7	5.2
6.8	5.2
6.9	5.5
6.9	5.8
6.7	5.8
6.4	5.5
6.2	5.3
5.9	5.1
6.1	5.2
6.7	5.8
6.8	5.8
6.6	5.5
6.4	5
6.4	4.9
6.7	5.3
7.1	6.1
7.1	6.5
6.9	6.8
6.4	6.6
6	6.4
6	6.4




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

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 3 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57926&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=57926&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57926&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
X[t] = + 1.05047871431013 + 1.11395110275261Y[t] + 0.177720977944961M1[t] + 0.0691160882202088M2[t] -0.0708839117797914M3[t] -0.180511198495470M4[t] -0.341533595486408M5[t] -0.477486749871774M6[t] -0.595207727816722M7[t] -0.719254573431356M8[t] -0.803301419045992M9[t] -0.64051119849547M10[t] -0.142092665412891M11[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
X[t] =  +  1.05047871431013 +  1.11395110275261Y[t] +  0.177720977944961M1[t] +  0.0691160882202088M2[t] -0.0708839117797914M3[t] -0.180511198495470M4[t] -0.341533595486408M5[t] -0.477486749871774M6[t] -0.595207727816722M7[t] -0.719254573431356M8[t] -0.803301419045992M9[t] -0.64051119849547M10[t] -0.142092665412891M11[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57926&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]X[t] =  +  1.05047871431013 +  1.11395110275261Y[t] +  0.177720977944961M1[t] +  0.0691160882202088M2[t] -0.0708839117797914M3[t] -0.180511198495470M4[t] -0.341533595486408M5[t] -0.477486749871774M6[t] -0.595207727816722M7[t] -0.719254573431356M8[t] -0.803301419045992M9[t] -0.64051119849547M10[t] -0.142092665412891M11[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57926&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57926&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
X[t] = + 1.05047871431013 + 1.11395110275261Y[t] + 0.177720977944961M1[t] + 0.0691160882202088M2[t] -0.0708839117797914M3[t] -0.180511198495470M4[t] -0.341533595486408M5[t] -0.477486749871774M6[t] -0.595207727816722M7[t] -0.719254573431356M8[t] -0.803301419045992M9[t] -0.64051119849547M10[t] -0.142092665412891M11[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)1.050478714310131.0993230.95560.3443960.172198
Y1.113951102752610.1847296.030200
M10.1777209779449610.4238510.41930.6769910.338496
M20.06911608822020880.4240920.1630.8712690.435634
M3-0.07088391177979140.424092-0.16710.8680070.434003
M4-0.1805111984954700.425137-0.42460.6731550.336578
M5-0.3415335954864080.435416-0.78440.4369240.218462
M6-0.4774867498717740.442058-1.08010.2858330.142916
M7-0.5952077278167220.441022-1.34960.1838920.091946
M8-0.7192545734313560.434584-1.6550.1048750.052438
M9-0.8033014190459920.429609-1.86980.068020.03401
M10-0.640511198495470.425137-1.50660.1389020.069451
M11-0.1420926654128910.44762-0.31740.7523780.376189

\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) & 1.05047871431013 & 1.099323 & 0.9556 & 0.344396 & 0.172198 \tabularnewline
Y & 1.11395110275261 & 0.184729 & 6.0302 & 0 & 0 \tabularnewline
M1 & 0.177720977944961 & 0.423851 & 0.4193 & 0.676991 & 0.338496 \tabularnewline
M2 & 0.0691160882202088 & 0.424092 & 0.163 & 0.871269 & 0.435634 \tabularnewline
M3 & -0.0708839117797914 & 0.424092 & -0.1671 & 0.868007 & 0.434003 \tabularnewline
M4 & -0.180511198495470 & 0.425137 & -0.4246 & 0.673155 & 0.336578 \tabularnewline
M5 & -0.341533595486408 & 0.435416 & -0.7844 & 0.436924 & 0.218462 \tabularnewline
M6 & -0.477486749871774 & 0.442058 & -1.0801 & 0.285833 & 0.142916 \tabularnewline
M7 & -0.595207727816722 & 0.441022 & -1.3496 & 0.183892 & 0.091946 \tabularnewline
M8 & -0.719254573431356 & 0.434584 & -1.655 & 0.104875 & 0.052438 \tabularnewline
M9 & -0.803301419045992 & 0.429609 & -1.8698 & 0.06802 & 0.03401 \tabularnewline
M10 & -0.64051119849547 & 0.425137 & -1.5066 & 0.138902 & 0.069451 \tabularnewline
M11 & -0.142092665412891 & 0.44762 & -0.3174 & 0.752378 & 0.376189 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57926&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]1.05047871431013[/C][C]1.099323[/C][C]0.9556[/C][C]0.344396[/C][C]0.172198[/C][/ROW]
[ROW][C]Y[/C][C]1.11395110275261[/C][C]0.184729[/C][C]6.0302[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M1[/C][C]0.177720977944961[/C][C]0.423851[/C][C]0.4193[/C][C]0.676991[/C][C]0.338496[/C][/ROW]
[ROW][C]M2[/C][C]0.0691160882202088[/C][C]0.424092[/C][C]0.163[/C][C]0.871269[/C][C]0.435634[/C][/ROW]
[ROW][C]M3[/C][C]-0.0708839117797914[/C][C]0.424092[/C][C]-0.1671[/C][C]0.868007[/C][C]0.434003[/C][/ROW]
[ROW][C]M4[/C][C]-0.180511198495470[/C][C]0.425137[/C][C]-0.4246[/C][C]0.673155[/C][C]0.336578[/C][/ROW]
[ROW][C]M5[/C][C]-0.341533595486408[/C][C]0.435416[/C][C]-0.7844[/C][C]0.436924[/C][C]0.218462[/C][/ROW]
[ROW][C]M6[/C][C]-0.477486749871774[/C][C]0.442058[/C][C]-1.0801[/C][C]0.285833[/C][C]0.142916[/C][/ROW]
[ROW][C]M7[/C][C]-0.595207727816722[/C][C]0.441022[/C][C]-1.3496[/C][C]0.183892[/C][C]0.091946[/C][/ROW]
[ROW][C]M8[/C][C]-0.719254573431356[/C][C]0.434584[/C][C]-1.655[/C][C]0.104875[/C][C]0.052438[/C][/ROW]
[ROW][C]M9[/C][C]-0.803301419045992[/C][C]0.429609[/C][C]-1.8698[/C][C]0.06802[/C][C]0.03401[/C][/ROW]
[ROW][C]M10[/C][C]-0.64051119849547[/C][C]0.425137[/C][C]-1.5066[/C][C]0.138902[/C][C]0.069451[/C][/ROW]
[ROW][C]M11[/C][C]-0.142092665412891[/C][C]0.44762[/C][C]-0.3174[/C][C]0.752378[/C][C]0.376189[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57926&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57926&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)1.050478714310131.0993230.95560.3443960.172198
Y1.113951102752610.1847296.030200
M10.1777209779449610.4238510.41930.6769910.338496
M20.06911608822020880.4240920.1630.8712690.435634
M3-0.07088391177979140.424092-0.16710.8680070.434003
M4-0.1805111984954700.425137-0.42460.6731550.336578
M5-0.3415335954864080.435416-0.78440.4369240.218462
M6-0.4774867498717740.442058-1.08010.2858330.142916
M7-0.5952077278167220.441022-1.34960.1838920.091946
M8-0.7192545734313560.434584-1.6550.1048750.052438
M9-0.8033014190459920.429609-1.86980.068020.03401
M10-0.640511198495470.425137-1.50660.1389020.069451
M11-0.1420926654128910.44762-0.31740.7523780.376189







Multiple Linear Regression - Regression Statistics
Multiple R0.70191490771637
R-squared0.492684537674481
Adjusted R-squared0.357400414387675
F-TEST (value)3.64185039385575
F-TEST (DF numerator)12
F-TEST (DF denominator)45
p-value0.000762777364173739
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.631815781520582
Sum Squared Residuals17.9636031800309

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.70191490771637 \tabularnewline
R-squared & 0.492684537674481 \tabularnewline
Adjusted R-squared & 0.357400414387675 \tabularnewline
F-TEST (value) & 3.64185039385575 \tabularnewline
F-TEST (DF numerator) & 12 \tabularnewline
F-TEST (DF denominator) & 45 \tabularnewline
p-value & 0.000762777364173739 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.631815781520582 \tabularnewline
Sum Squared Residuals & 17.9636031800309 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57926&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.70191490771637[/C][/ROW]
[ROW][C]R-squared[/C][C]0.492684537674481[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.357400414387675[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]3.64185039385575[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]12[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]45[/C][/ROW]
[ROW][C]p-value[/C][C]0.000762777364173739[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.631815781520582[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]17.9636031800309[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57926&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57926&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 R0.70191490771637
R-squared0.492684537674481
Adjusted R-squared0.357400414387675
F-TEST (value)3.64185039385575
F-TEST (DF numerator)12
F-TEST (DF denominator)45
p-value0.000762777364173739
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.631815781520582
Sum Squared Residuals17.9636031800309







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
18.98.246091639596460.653908360403539
28.28.026091639596510.173908360403488
37.67.77469652932125-0.174696529321252
47.77.8878594631561-0.187859463156095
58.17.949627286715680.150372713284322
68.37.925069242605570.374930757394427
78.37.695953154385370.604046845614634
87.97.237720977944950.662279022055052
97.87.153674132330310.646325867669687
1086.982279022055051.01772097794495
118.57.703487775688150.796512224311849
128.67.845580441101040.754419558898957
138.58.0233014190460.476698580953996
1487.914696529321250.085303470678748
157.87.774696529321250.0253034706787481
1687.999254573431360.000745426568643112
178.28.17241750726620.0275824927337991
188.38.259254573431360.0407454265686433
198.28.25292870576167-0.0529287057616704
208.18.12888186014703-0.0288818601470353
2187.822044793981880.177955206018122
227.87.539254573431360.260745426568643
237.87.480697555137630.319302444862369
247.77.177209779449480.522790220550521
257.67.354930757394440.245069242605561
267.67.357720977944950.242279022055052
277.67.440511198495470.159488801504530
287.87.442279022055050.357720977944947
2987.504046845614630.495953154385366
3087.368093691229270.63190630877073
317.97.138977603009060.761022396990939
327.77.014930757394430.685069242605574
337.46.819488801504530.580511198495469
346.96.536698580954010.363301419045992
356.77.14651222431185-0.446512224311848
366.57.06581466917422-0.565814669174218
376.47.02074542656866-0.620745426568656
386.76.9121405368439-0.212140536843905
396.86.77214053684390.0278594631560951
406.96.99669858095401-0.0966985809540086
416.97.16986151478885-0.269861514788852
426.77.03390836040349-0.333908360403487
436.46.58200205163276-0.182002051632756
446.26.2351649854676-0.0351649854676001
455.95.92832791930244-0.0283279193024429
466.16.20251325012823-0.102513250128226
476.77.36930244486237-0.66930244486237
486.87.51139511027526-0.711395110275261
496.67.35493075739444-0.75493075739444
506.46.68935031629338-0.289350316293383
516.46.43795520601812-0.0379552060181217
526.76.77390836040349-0.0739083604034868
537.17.50404684561463-0.404046845614634
547.17.81367413233031-0.713674132330314
556.98.03013848521115-1.13013848521115
566.47.68330141904599-1.28330141904599
5767.37646435288084-1.37646435288083
5867.53925457343136-1.53925457343136

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 8.9 & 8.24609163959646 & 0.653908360403539 \tabularnewline
2 & 8.2 & 8.02609163959651 & 0.173908360403488 \tabularnewline
3 & 7.6 & 7.77469652932125 & -0.174696529321252 \tabularnewline
4 & 7.7 & 7.8878594631561 & -0.187859463156095 \tabularnewline
5 & 8.1 & 7.94962728671568 & 0.150372713284322 \tabularnewline
6 & 8.3 & 7.92506924260557 & 0.374930757394427 \tabularnewline
7 & 8.3 & 7.69595315438537 & 0.604046845614634 \tabularnewline
8 & 7.9 & 7.23772097794495 & 0.662279022055052 \tabularnewline
9 & 7.8 & 7.15367413233031 & 0.646325867669687 \tabularnewline
10 & 8 & 6.98227902205505 & 1.01772097794495 \tabularnewline
11 & 8.5 & 7.70348777568815 & 0.796512224311849 \tabularnewline
12 & 8.6 & 7.84558044110104 & 0.754419558898957 \tabularnewline
13 & 8.5 & 8.023301419046 & 0.476698580953996 \tabularnewline
14 & 8 & 7.91469652932125 & 0.085303470678748 \tabularnewline
15 & 7.8 & 7.77469652932125 & 0.0253034706787481 \tabularnewline
16 & 8 & 7.99925457343136 & 0.000745426568643112 \tabularnewline
17 & 8.2 & 8.1724175072662 & 0.0275824927337991 \tabularnewline
18 & 8.3 & 8.25925457343136 & 0.0407454265686433 \tabularnewline
19 & 8.2 & 8.25292870576167 & -0.0529287057616704 \tabularnewline
20 & 8.1 & 8.12888186014703 & -0.0288818601470353 \tabularnewline
21 & 8 & 7.82204479398188 & 0.177955206018122 \tabularnewline
22 & 7.8 & 7.53925457343136 & 0.260745426568643 \tabularnewline
23 & 7.8 & 7.48069755513763 & 0.319302444862369 \tabularnewline
24 & 7.7 & 7.17720977944948 & 0.522790220550521 \tabularnewline
25 & 7.6 & 7.35493075739444 & 0.245069242605561 \tabularnewline
26 & 7.6 & 7.35772097794495 & 0.242279022055052 \tabularnewline
27 & 7.6 & 7.44051119849547 & 0.159488801504530 \tabularnewline
28 & 7.8 & 7.44227902205505 & 0.357720977944947 \tabularnewline
29 & 8 & 7.50404684561463 & 0.495953154385366 \tabularnewline
30 & 8 & 7.36809369122927 & 0.63190630877073 \tabularnewline
31 & 7.9 & 7.13897760300906 & 0.761022396990939 \tabularnewline
32 & 7.7 & 7.01493075739443 & 0.685069242605574 \tabularnewline
33 & 7.4 & 6.81948880150453 & 0.580511198495469 \tabularnewline
34 & 6.9 & 6.53669858095401 & 0.363301419045992 \tabularnewline
35 & 6.7 & 7.14651222431185 & -0.446512224311848 \tabularnewline
36 & 6.5 & 7.06581466917422 & -0.565814669174218 \tabularnewline
37 & 6.4 & 7.02074542656866 & -0.620745426568656 \tabularnewline
38 & 6.7 & 6.9121405368439 & -0.212140536843905 \tabularnewline
39 & 6.8 & 6.7721405368439 & 0.0278594631560951 \tabularnewline
40 & 6.9 & 6.99669858095401 & -0.0966985809540086 \tabularnewline
41 & 6.9 & 7.16986151478885 & -0.269861514788852 \tabularnewline
42 & 6.7 & 7.03390836040349 & -0.333908360403487 \tabularnewline
43 & 6.4 & 6.58200205163276 & -0.182002051632756 \tabularnewline
44 & 6.2 & 6.2351649854676 & -0.0351649854676001 \tabularnewline
45 & 5.9 & 5.92832791930244 & -0.0283279193024429 \tabularnewline
46 & 6.1 & 6.20251325012823 & -0.102513250128226 \tabularnewline
47 & 6.7 & 7.36930244486237 & -0.66930244486237 \tabularnewline
48 & 6.8 & 7.51139511027526 & -0.711395110275261 \tabularnewline
49 & 6.6 & 7.35493075739444 & -0.75493075739444 \tabularnewline
50 & 6.4 & 6.68935031629338 & -0.289350316293383 \tabularnewline
51 & 6.4 & 6.43795520601812 & -0.0379552060181217 \tabularnewline
52 & 6.7 & 6.77390836040349 & -0.0739083604034868 \tabularnewline
53 & 7.1 & 7.50404684561463 & -0.404046845614634 \tabularnewline
54 & 7.1 & 7.81367413233031 & -0.713674132330314 \tabularnewline
55 & 6.9 & 8.03013848521115 & -1.13013848521115 \tabularnewline
56 & 6.4 & 7.68330141904599 & -1.28330141904599 \tabularnewline
57 & 6 & 7.37646435288084 & -1.37646435288083 \tabularnewline
58 & 6 & 7.53925457343136 & -1.53925457343136 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57926&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]8.9[/C][C]8.24609163959646[/C][C]0.653908360403539[/C][/ROW]
[ROW][C]2[/C][C]8.2[/C][C]8.02609163959651[/C][C]0.173908360403488[/C][/ROW]
[ROW][C]3[/C][C]7.6[/C][C]7.77469652932125[/C][C]-0.174696529321252[/C][/ROW]
[ROW][C]4[/C][C]7.7[/C][C]7.8878594631561[/C][C]-0.187859463156095[/C][/ROW]
[ROW][C]5[/C][C]8.1[/C][C]7.94962728671568[/C][C]0.150372713284322[/C][/ROW]
[ROW][C]6[/C][C]8.3[/C][C]7.92506924260557[/C][C]0.374930757394427[/C][/ROW]
[ROW][C]7[/C][C]8.3[/C][C]7.69595315438537[/C][C]0.604046845614634[/C][/ROW]
[ROW][C]8[/C][C]7.9[/C][C]7.23772097794495[/C][C]0.662279022055052[/C][/ROW]
[ROW][C]9[/C][C]7.8[/C][C]7.15367413233031[/C][C]0.646325867669687[/C][/ROW]
[ROW][C]10[/C][C]8[/C][C]6.98227902205505[/C][C]1.01772097794495[/C][/ROW]
[ROW][C]11[/C][C]8.5[/C][C]7.70348777568815[/C][C]0.796512224311849[/C][/ROW]
[ROW][C]12[/C][C]8.6[/C][C]7.84558044110104[/C][C]0.754419558898957[/C][/ROW]
[ROW][C]13[/C][C]8.5[/C][C]8.023301419046[/C][C]0.476698580953996[/C][/ROW]
[ROW][C]14[/C][C]8[/C][C]7.91469652932125[/C][C]0.085303470678748[/C][/ROW]
[ROW][C]15[/C][C]7.8[/C][C]7.77469652932125[/C][C]0.0253034706787481[/C][/ROW]
[ROW][C]16[/C][C]8[/C][C]7.99925457343136[/C][C]0.000745426568643112[/C][/ROW]
[ROW][C]17[/C][C]8.2[/C][C]8.1724175072662[/C][C]0.0275824927337991[/C][/ROW]
[ROW][C]18[/C][C]8.3[/C][C]8.25925457343136[/C][C]0.0407454265686433[/C][/ROW]
[ROW][C]19[/C][C]8.2[/C][C]8.25292870576167[/C][C]-0.0529287057616704[/C][/ROW]
[ROW][C]20[/C][C]8.1[/C][C]8.12888186014703[/C][C]-0.0288818601470353[/C][/ROW]
[ROW][C]21[/C][C]8[/C][C]7.82204479398188[/C][C]0.177955206018122[/C][/ROW]
[ROW][C]22[/C][C]7.8[/C][C]7.53925457343136[/C][C]0.260745426568643[/C][/ROW]
[ROW][C]23[/C][C]7.8[/C][C]7.48069755513763[/C][C]0.319302444862369[/C][/ROW]
[ROW][C]24[/C][C]7.7[/C][C]7.17720977944948[/C][C]0.522790220550521[/C][/ROW]
[ROW][C]25[/C][C]7.6[/C][C]7.35493075739444[/C][C]0.245069242605561[/C][/ROW]
[ROW][C]26[/C][C]7.6[/C][C]7.35772097794495[/C][C]0.242279022055052[/C][/ROW]
[ROW][C]27[/C][C]7.6[/C][C]7.44051119849547[/C][C]0.159488801504530[/C][/ROW]
[ROW][C]28[/C][C]7.8[/C][C]7.44227902205505[/C][C]0.357720977944947[/C][/ROW]
[ROW][C]29[/C][C]8[/C][C]7.50404684561463[/C][C]0.495953154385366[/C][/ROW]
[ROW][C]30[/C][C]8[/C][C]7.36809369122927[/C][C]0.63190630877073[/C][/ROW]
[ROW][C]31[/C][C]7.9[/C][C]7.13897760300906[/C][C]0.761022396990939[/C][/ROW]
[ROW][C]32[/C][C]7.7[/C][C]7.01493075739443[/C][C]0.685069242605574[/C][/ROW]
[ROW][C]33[/C][C]7.4[/C][C]6.81948880150453[/C][C]0.580511198495469[/C][/ROW]
[ROW][C]34[/C][C]6.9[/C][C]6.53669858095401[/C][C]0.363301419045992[/C][/ROW]
[ROW][C]35[/C][C]6.7[/C][C]7.14651222431185[/C][C]-0.446512224311848[/C][/ROW]
[ROW][C]36[/C][C]6.5[/C][C]7.06581466917422[/C][C]-0.565814669174218[/C][/ROW]
[ROW][C]37[/C][C]6.4[/C][C]7.02074542656866[/C][C]-0.620745426568656[/C][/ROW]
[ROW][C]38[/C][C]6.7[/C][C]6.9121405368439[/C][C]-0.212140536843905[/C][/ROW]
[ROW][C]39[/C][C]6.8[/C][C]6.7721405368439[/C][C]0.0278594631560951[/C][/ROW]
[ROW][C]40[/C][C]6.9[/C][C]6.99669858095401[/C][C]-0.0966985809540086[/C][/ROW]
[ROW][C]41[/C][C]6.9[/C][C]7.16986151478885[/C][C]-0.269861514788852[/C][/ROW]
[ROW][C]42[/C][C]6.7[/C][C]7.03390836040349[/C][C]-0.333908360403487[/C][/ROW]
[ROW][C]43[/C][C]6.4[/C][C]6.58200205163276[/C][C]-0.182002051632756[/C][/ROW]
[ROW][C]44[/C][C]6.2[/C][C]6.2351649854676[/C][C]-0.0351649854676001[/C][/ROW]
[ROW][C]45[/C][C]5.9[/C][C]5.92832791930244[/C][C]-0.0283279193024429[/C][/ROW]
[ROW][C]46[/C][C]6.1[/C][C]6.20251325012823[/C][C]-0.102513250128226[/C][/ROW]
[ROW][C]47[/C][C]6.7[/C][C]7.36930244486237[/C][C]-0.66930244486237[/C][/ROW]
[ROW][C]48[/C][C]6.8[/C][C]7.51139511027526[/C][C]-0.711395110275261[/C][/ROW]
[ROW][C]49[/C][C]6.6[/C][C]7.35493075739444[/C][C]-0.75493075739444[/C][/ROW]
[ROW][C]50[/C][C]6.4[/C][C]6.68935031629338[/C][C]-0.289350316293383[/C][/ROW]
[ROW][C]51[/C][C]6.4[/C][C]6.43795520601812[/C][C]-0.0379552060181217[/C][/ROW]
[ROW][C]52[/C][C]6.7[/C][C]6.77390836040349[/C][C]-0.0739083604034868[/C][/ROW]
[ROW][C]53[/C][C]7.1[/C][C]7.50404684561463[/C][C]-0.404046845614634[/C][/ROW]
[ROW][C]54[/C][C]7.1[/C][C]7.81367413233031[/C][C]-0.713674132330314[/C][/ROW]
[ROW][C]55[/C][C]6.9[/C][C]8.03013848521115[/C][C]-1.13013848521115[/C][/ROW]
[ROW][C]56[/C][C]6.4[/C][C]7.68330141904599[/C][C]-1.28330141904599[/C][/ROW]
[ROW][C]57[/C][C]6[/C][C]7.37646435288084[/C][C]-1.37646435288083[/C][/ROW]
[ROW][C]58[/C][C]6[/C][C]7.53925457343136[/C][C]-1.53925457343136[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57926&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57926&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
18.98.246091639596460.653908360403539
28.28.026091639596510.173908360403488
37.67.77469652932125-0.174696529321252
47.77.8878594631561-0.187859463156095
58.17.949627286715680.150372713284322
68.37.925069242605570.374930757394427
78.37.695953154385370.604046845614634
87.97.237720977944950.662279022055052
97.87.153674132330310.646325867669687
1086.982279022055051.01772097794495
118.57.703487775688150.796512224311849
128.67.845580441101040.754419558898957
138.58.0233014190460.476698580953996
1487.914696529321250.085303470678748
157.87.774696529321250.0253034706787481
1687.999254573431360.000745426568643112
178.28.17241750726620.0275824927337991
188.38.259254573431360.0407454265686433
198.28.25292870576167-0.0529287057616704
208.18.12888186014703-0.0288818601470353
2187.822044793981880.177955206018122
227.87.539254573431360.260745426568643
237.87.480697555137630.319302444862369
247.77.177209779449480.522790220550521
257.67.354930757394440.245069242605561
267.67.357720977944950.242279022055052
277.67.440511198495470.159488801504530
287.87.442279022055050.357720977944947
2987.504046845614630.495953154385366
3087.368093691229270.63190630877073
317.97.138977603009060.761022396990939
327.77.014930757394430.685069242605574
337.46.819488801504530.580511198495469
346.96.536698580954010.363301419045992
356.77.14651222431185-0.446512224311848
366.57.06581466917422-0.565814669174218
376.47.02074542656866-0.620745426568656
386.76.9121405368439-0.212140536843905
396.86.77214053684390.0278594631560951
406.96.99669858095401-0.0966985809540086
416.97.16986151478885-0.269861514788852
426.77.03390836040349-0.333908360403487
436.46.58200205163276-0.182002051632756
446.26.2351649854676-0.0351649854676001
455.95.92832791930244-0.0283279193024429
466.16.20251325012823-0.102513250128226
476.77.36930244486237-0.66930244486237
486.87.51139511027526-0.711395110275261
496.67.35493075739444-0.75493075739444
506.46.68935031629338-0.289350316293383
516.46.43795520601812-0.0379552060181217
526.76.77390836040349-0.0739083604034868
537.17.50404684561463-0.404046845614634
547.17.81367413233031-0.713674132330314
556.98.03013848521115-1.13013848521115
566.47.68330141904599-1.28330141904599
5767.37646435288084-1.37646435288083
5867.53925457343136-1.53925457343136







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
160.003950589395669300.007901178791338610.99604941060433
170.002452565308533580.004905130617067150.997547434691466
180.001888929638408300.003777859276816590.998111070361592
190.001149643602190610.002299287204381220.99885035639781
200.0002657852228031650.0005315704456063290.999734214777197
216.8353876962633e-050.0001367077539252660.999931646123037
225.60455634651084e-050.0001120911269302170.999943954436535
230.0004184651772170270.0008369303544340530.999581534822783
240.001477604037459910.002955208074919830.99852239596254
250.004853357902947630.009706715805895250.995146642097052
260.002885291631497530.005770583262995060.997114708368502
270.001661119034465210.003322238068930430.998338880965535
280.001348452911008040.002696905822016080.998651547088992
290.001148499190866240.002296998381732490.998851500809134
300.001291079219638130.002582158439276260.998708920780362
310.002824112058721690.005648224117443390.997175887941278
320.01588093361161520.03176186722323050.984119066388385
330.3061961381630670.6123922763261340.693803861836933
340.9348666052829590.1302667894340820.0651333947170411
350.9788142917950490.04237141640990260.0211857082049513
360.9921400015705170.01571999685896550.00785999842948273
370.993076786209530.01384642758093890.00692321379046943
380.9891335003095370.02173299938092540.0108664996904627
390.9874735323238770.02505293535224640.0125264676761232
400.9715380609686840.05692387806263260.0284619390313163
410.9382934316896560.1234131366206890.0617065683103445
420.9131851856368770.1736296287262460.086814814363123

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
16 & 0.00395058939566930 & 0.00790117879133861 & 0.99604941060433 \tabularnewline
17 & 0.00245256530853358 & 0.00490513061706715 & 0.997547434691466 \tabularnewline
18 & 0.00188892963840830 & 0.00377785927681659 & 0.998111070361592 \tabularnewline
19 & 0.00114964360219061 & 0.00229928720438122 & 0.99885035639781 \tabularnewline
20 & 0.000265785222803165 & 0.000531570445606329 & 0.999734214777197 \tabularnewline
21 & 6.8353876962633e-05 & 0.000136707753925266 & 0.999931646123037 \tabularnewline
22 & 5.60455634651084e-05 & 0.000112091126930217 & 0.999943954436535 \tabularnewline
23 & 0.000418465177217027 & 0.000836930354434053 & 0.999581534822783 \tabularnewline
24 & 0.00147760403745991 & 0.00295520807491983 & 0.99852239596254 \tabularnewline
25 & 0.00485335790294763 & 0.00970671580589525 & 0.995146642097052 \tabularnewline
26 & 0.00288529163149753 & 0.00577058326299506 & 0.997114708368502 \tabularnewline
27 & 0.00166111903446521 & 0.00332223806893043 & 0.998338880965535 \tabularnewline
28 & 0.00134845291100804 & 0.00269690582201608 & 0.998651547088992 \tabularnewline
29 & 0.00114849919086624 & 0.00229699838173249 & 0.998851500809134 \tabularnewline
30 & 0.00129107921963813 & 0.00258215843927626 & 0.998708920780362 \tabularnewline
31 & 0.00282411205872169 & 0.00564822411744339 & 0.997175887941278 \tabularnewline
32 & 0.0158809336116152 & 0.0317618672232305 & 0.984119066388385 \tabularnewline
33 & 0.306196138163067 & 0.612392276326134 & 0.693803861836933 \tabularnewline
34 & 0.934866605282959 & 0.130266789434082 & 0.0651333947170411 \tabularnewline
35 & 0.978814291795049 & 0.0423714164099026 & 0.0211857082049513 \tabularnewline
36 & 0.992140001570517 & 0.0157199968589655 & 0.00785999842948273 \tabularnewline
37 & 0.99307678620953 & 0.0138464275809389 & 0.00692321379046943 \tabularnewline
38 & 0.989133500309537 & 0.0217329993809254 & 0.0108664996904627 \tabularnewline
39 & 0.987473532323877 & 0.0250529353522464 & 0.0125264676761232 \tabularnewline
40 & 0.971538060968684 & 0.0569238780626326 & 0.0284619390313163 \tabularnewline
41 & 0.938293431689656 & 0.123413136620689 & 0.0617065683103445 \tabularnewline
42 & 0.913185185636877 & 0.173629628726246 & 0.086814814363123 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57926&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]16[/C][C]0.00395058939566930[/C][C]0.00790117879133861[/C][C]0.99604941060433[/C][/ROW]
[ROW][C]17[/C][C]0.00245256530853358[/C][C]0.00490513061706715[/C][C]0.997547434691466[/C][/ROW]
[ROW][C]18[/C][C]0.00188892963840830[/C][C]0.00377785927681659[/C][C]0.998111070361592[/C][/ROW]
[ROW][C]19[/C][C]0.00114964360219061[/C][C]0.00229928720438122[/C][C]0.99885035639781[/C][/ROW]
[ROW][C]20[/C][C]0.000265785222803165[/C][C]0.000531570445606329[/C][C]0.999734214777197[/C][/ROW]
[ROW][C]21[/C][C]6.8353876962633e-05[/C][C]0.000136707753925266[/C][C]0.999931646123037[/C][/ROW]
[ROW][C]22[/C][C]5.60455634651084e-05[/C][C]0.000112091126930217[/C][C]0.999943954436535[/C][/ROW]
[ROW][C]23[/C][C]0.000418465177217027[/C][C]0.000836930354434053[/C][C]0.999581534822783[/C][/ROW]
[ROW][C]24[/C][C]0.00147760403745991[/C][C]0.00295520807491983[/C][C]0.99852239596254[/C][/ROW]
[ROW][C]25[/C][C]0.00485335790294763[/C][C]0.00970671580589525[/C][C]0.995146642097052[/C][/ROW]
[ROW][C]26[/C][C]0.00288529163149753[/C][C]0.00577058326299506[/C][C]0.997114708368502[/C][/ROW]
[ROW][C]27[/C][C]0.00166111903446521[/C][C]0.00332223806893043[/C][C]0.998338880965535[/C][/ROW]
[ROW][C]28[/C][C]0.00134845291100804[/C][C]0.00269690582201608[/C][C]0.998651547088992[/C][/ROW]
[ROW][C]29[/C][C]0.00114849919086624[/C][C]0.00229699838173249[/C][C]0.998851500809134[/C][/ROW]
[ROW][C]30[/C][C]0.00129107921963813[/C][C]0.00258215843927626[/C][C]0.998708920780362[/C][/ROW]
[ROW][C]31[/C][C]0.00282411205872169[/C][C]0.00564822411744339[/C][C]0.997175887941278[/C][/ROW]
[ROW][C]32[/C][C]0.0158809336116152[/C][C]0.0317618672232305[/C][C]0.984119066388385[/C][/ROW]
[ROW][C]33[/C][C]0.306196138163067[/C][C]0.612392276326134[/C][C]0.693803861836933[/C][/ROW]
[ROW][C]34[/C][C]0.934866605282959[/C][C]0.130266789434082[/C][C]0.0651333947170411[/C][/ROW]
[ROW][C]35[/C][C]0.978814291795049[/C][C]0.0423714164099026[/C][C]0.0211857082049513[/C][/ROW]
[ROW][C]36[/C][C]0.992140001570517[/C][C]0.0157199968589655[/C][C]0.00785999842948273[/C][/ROW]
[ROW][C]37[/C][C]0.99307678620953[/C][C]0.0138464275809389[/C][C]0.00692321379046943[/C][/ROW]
[ROW][C]38[/C][C]0.989133500309537[/C][C]0.0217329993809254[/C][C]0.0108664996904627[/C][/ROW]
[ROW][C]39[/C][C]0.987473532323877[/C][C]0.0250529353522464[/C][C]0.0125264676761232[/C][/ROW]
[ROW][C]40[/C][C]0.971538060968684[/C][C]0.0569238780626326[/C][C]0.0284619390313163[/C][/ROW]
[ROW][C]41[/C][C]0.938293431689656[/C][C]0.123413136620689[/C][C]0.0617065683103445[/C][/ROW]
[ROW][C]42[/C][C]0.913185185636877[/C][C]0.173629628726246[/C][C]0.086814814363123[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57926&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57926&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
160.003950589395669300.007901178791338610.99604941060433
170.002452565308533580.004905130617067150.997547434691466
180.001888929638408300.003777859276816590.998111070361592
190.001149643602190610.002299287204381220.99885035639781
200.0002657852228031650.0005315704456063290.999734214777197
216.8353876962633e-050.0001367077539252660.999931646123037
225.60455634651084e-050.0001120911269302170.999943954436535
230.0004184651772170270.0008369303544340530.999581534822783
240.001477604037459910.002955208074919830.99852239596254
250.004853357902947630.009706715805895250.995146642097052
260.002885291631497530.005770583262995060.997114708368502
270.001661119034465210.003322238068930430.998338880965535
280.001348452911008040.002696905822016080.998651547088992
290.001148499190866240.002296998381732490.998851500809134
300.001291079219638130.002582158439276260.998708920780362
310.002824112058721690.005648224117443390.997175887941278
320.01588093361161520.03176186722323050.984119066388385
330.3061961381630670.6123922763261340.693803861836933
340.9348666052829590.1302667894340820.0651333947170411
350.9788142917950490.04237141640990260.0211857082049513
360.9921400015705170.01571999685896550.00785999842948273
370.993076786209530.01384642758093890.00692321379046943
380.9891335003095370.02173299938092540.0108664996904627
390.9874735323238770.02505293535224640.0125264676761232
400.9715380609686840.05692387806263260.0284619390313163
410.9382934316896560.1234131366206890.0617065683103445
420.9131851856368770.1736296287262460.086814814363123







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level160.592592592592593NOK
5% type I error level220.814814814814815NOK
10% type I error level230.851851851851852NOK

\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 & 16 & 0.592592592592593 & NOK \tabularnewline
5% type I error level & 22 & 0.814814814814815 & NOK \tabularnewline
10% type I error level & 23 & 0.851851851851852 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57926&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]16[/C][C]0.592592592592593[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]22[/C][C]0.814814814814815[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]23[/C][C]0.851851851851852[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57926&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57926&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 level160.592592592592593NOK
5% type I error level220.814814814814815NOK
10% type I error level230.851851851851852NOK



Parameters (Session):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}