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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 computationThu, 19 Nov 2009 11:13:01 -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/t1258654490xyi942icxr69vjp.htm/, Retrieved Fri, 29 Mar 2024 05:47:25 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=57866, Retrieved Fri, 29 Mar 2024 05:47:25 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsY= aantal bouwvergunningen X= rente
Estimated Impact138
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]
- R  D      [Multiple Regression] [multiple regressi...] [2009-11-19 18:13:01] [03368d751914a6c247d86aff8eac7cbf] [Current]
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Dataseries X:
2360	2
2214	2
2825	2
2355	2
2333	2
3016	2
2155	2
2172	2
2150	2
2533	2
2058	2
2160	2
2260	2
2498	2
2695	2
2799	2
2947	2
2930	2
2318	2
2540	2
2570	2
2669	2
2450	2
2842	2
3440	2
2678	2
2981	2
2260	2.21
2844	2.25
2546	2.25
2456	2.45
2295	2.5
2379	2.5
2479	2.64
2057	2.75
2280	2.93
2351	3
2276	3.17
2548	3.25
2311	3.39
2201	3.5
2725	3.5
2408	3.65
2139	3.75
1898	3.75
2537	3.9
2069	4
2063	4
2524	4
2437	4
2189	4
2793	4
2074	4
2622	4
2278	4
2144	4
2427	4
2139	4
1828	4.18
2072	4.25
1800	4.25




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=57866&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=57866&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57866&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] = + 2718.53693396615 -143.325735825476X[t] + 149.357889865432M1[t] + 79.583054198158M2[t] + 308.876265971366M3[t] + 174.909067479149M4[t] + 155.408839553913M5[t] + 443.408839553913M6[t] + 8.6416410616967M7[t] -52.0585868635391M8[t] -25.2585868635390M9[t] + 169.654305814339M10[t] -198.166286791274M11[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  2718.53693396615 -143.325735825476X[t] +  149.357889865432M1[t] +  79.583054198158M2[t] +  308.876265971366M3[t] +  174.909067479149M4[t] +  155.408839553913M5[t] +  443.408839553913M6[t] +  8.6416410616967M7[t] -52.0585868635391M8[t] -25.2585868635390M9[t] +  169.654305814339M10[t] -198.166286791274M11[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57866&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  2718.53693396615 -143.325735825476X[t] +  149.357889865432M1[t] +  79.583054198158M2[t] +  308.876265971366M3[t] +  174.909067479149M4[t] +  155.408839553913M5[t] +  443.408839553913M6[t] +  8.6416410616967M7[t] -52.0585868635391M8[t] -25.2585868635390M9[t] +  169.654305814339M10[t] -198.166286791274M11[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57866&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57866&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] = + 2718.53693396615 -143.325735825476X[t] + 149.357889865432M1[t] + 79.583054198158M2[t] + 308.876265971366M3[t] + 174.909067479149M4[t] + 155.408839553913M5[t] + 443.408839553913M6[t] + 8.6416410616967M7[t] -52.0585868635391M8[t] -25.2585868635390M9[t] + 169.654305814339M10[t] -198.166286791274M11[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)2718.53693396615165.41818416.434300
X-143.32573582547638.433263-3.72920.0005070.000254
M1149.357889865432158.8812560.94010.3518940.175947
M279.583054198158166.5383840.47790.6349150.317457
M3308.876265971366166.4824611.85530.0696990.034849
M4174.909067479149166.2643211.0520.298070.149035
M5155.408839553913166.1840780.93520.3543860.177193
M6443.408839553913166.1840782.66820.010370.005185
M78.6416410616967166.0278350.0520.9587050.479353
M8-52.0585868635391165.974178-0.31370.7551420.377571
M9-25.2585868635390165.974178-0.15220.879680.43984
M10169.654305814339165.8931181.02270.3115890.155795
M11-198.166286791274165.831295-1.1950.2379630.118982

\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) & 2718.53693396615 & 165.418184 & 16.4343 & 0 & 0 \tabularnewline
X & -143.325735825476 & 38.433263 & -3.7292 & 0.000507 & 0.000254 \tabularnewline
M1 & 149.357889865432 & 158.881256 & 0.9401 & 0.351894 & 0.175947 \tabularnewline
M2 & 79.583054198158 & 166.538384 & 0.4779 & 0.634915 & 0.317457 \tabularnewline
M3 & 308.876265971366 & 166.482461 & 1.8553 & 0.069699 & 0.034849 \tabularnewline
M4 & 174.909067479149 & 166.264321 & 1.052 & 0.29807 & 0.149035 \tabularnewline
M5 & 155.408839553913 & 166.184078 & 0.9352 & 0.354386 & 0.177193 \tabularnewline
M6 & 443.408839553913 & 166.184078 & 2.6682 & 0.01037 & 0.005185 \tabularnewline
M7 & 8.6416410616967 & 166.027835 & 0.052 & 0.958705 & 0.479353 \tabularnewline
M8 & -52.0585868635391 & 165.974178 & -0.3137 & 0.755142 & 0.377571 \tabularnewline
M9 & -25.2585868635390 & 165.974178 & -0.1522 & 0.87968 & 0.43984 \tabularnewline
M10 & 169.654305814339 & 165.893118 & 1.0227 & 0.311589 & 0.155795 \tabularnewline
M11 & -198.166286791274 & 165.831295 & -1.195 & 0.237963 & 0.118982 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57866&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]2718.53693396615[/C][C]165.418184[/C][C]16.4343[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]X[/C][C]-143.325735825476[/C][C]38.433263[/C][C]-3.7292[/C][C]0.000507[/C][C]0.000254[/C][/ROW]
[ROW][C]M1[/C][C]149.357889865432[/C][C]158.881256[/C][C]0.9401[/C][C]0.351894[/C][C]0.175947[/C][/ROW]
[ROW][C]M2[/C][C]79.583054198158[/C][C]166.538384[/C][C]0.4779[/C][C]0.634915[/C][C]0.317457[/C][/ROW]
[ROW][C]M3[/C][C]308.876265971366[/C][C]166.482461[/C][C]1.8553[/C][C]0.069699[/C][C]0.034849[/C][/ROW]
[ROW][C]M4[/C][C]174.909067479149[/C][C]166.264321[/C][C]1.052[/C][C]0.29807[/C][C]0.149035[/C][/ROW]
[ROW][C]M5[/C][C]155.408839553913[/C][C]166.184078[/C][C]0.9352[/C][C]0.354386[/C][C]0.177193[/C][/ROW]
[ROW][C]M6[/C][C]443.408839553913[/C][C]166.184078[/C][C]2.6682[/C][C]0.01037[/C][C]0.005185[/C][/ROW]
[ROW][C]M7[/C][C]8.6416410616967[/C][C]166.027835[/C][C]0.052[/C][C]0.958705[/C][C]0.479353[/C][/ROW]
[ROW][C]M8[/C][C]-52.0585868635391[/C][C]165.974178[/C][C]-0.3137[/C][C]0.755142[/C][C]0.377571[/C][/ROW]
[ROW][C]M9[/C][C]-25.2585868635390[/C][C]165.974178[/C][C]-0.1522[/C][C]0.87968[/C][C]0.43984[/C][/ROW]
[ROW][C]M10[/C][C]169.654305814339[/C][C]165.893118[/C][C]1.0227[/C][C]0.311589[/C][C]0.155795[/C][/ROW]
[ROW][C]M11[/C][C]-198.166286791274[/C][C]165.831295[/C][C]-1.195[/C][C]0.237963[/C][C]0.118982[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57866&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57866&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)2718.53693396615165.41818416.434300
X-143.32573582547638.433263-3.72920.0005070.000254
M1149.357889865432158.8812560.94010.3518940.175947
M279.583054198158166.5383840.47790.6349150.317457
M3308.876265971366166.4824611.85530.0696990.034849
M4174.909067479149166.2643211.0520.298070.149035
M5155.408839553913166.1840780.93520.3543860.177193
M6443.408839553913166.1840782.66820.010370.005185
M78.6416410616967166.0278350.0520.9587050.479353
M8-52.0585868635391165.974178-0.31370.7551420.377571
M9-25.2585868635390165.974178-0.15220.879680.43984
M10169.654305814339165.8931181.02270.3115890.155795
M11-198.166286791274165.831295-1.1950.2379630.118982







Multiple Linear Regression - Regression Statistics
Multiple R0.677027504049389
R-squared0.458366241239345
Adjusted R-squared0.322957801549182
F-TEST (value)3.38506404983442
F-TEST (DF numerator)12
F-TEST (DF denominator)48
p-value0.00125516766011968
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation262.184694437486
Sum Squared Residuals3299559.07186933

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.677027504049389 \tabularnewline
R-squared & 0.458366241239345 \tabularnewline
Adjusted R-squared & 0.322957801549182 \tabularnewline
F-TEST (value) & 3.38506404983442 \tabularnewline
F-TEST (DF numerator) & 12 \tabularnewline
F-TEST (DF denominator) & 48 \tabularnewline
p-value & 0.00125516766011968 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 262.184694437486 \tabularnewline
Sum Squared Residuals & 3299559.07186933 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57866&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.677027504049389[/C][/ROW]
[ROW][C]R-squared[/C][C]0.458366241239345[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.322957801549182[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]3.38506404983442[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]12[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]48[/C][/ROW]
[ROW][C]p-value[/C][C]0.00125516766011968[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]262.184694437486[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]3299559.07186933[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57866&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57866&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 R0.677027504049389
R-squared0.458366241239345
Adjusted R-squared0.322957801549182
F-TEST (value)3.38506404983442
F-TEST (DF numerator)12
F-TEST (DF denominator)48
p-value0.00125516766011968
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation262.184694437486
Sum Squared Residuals3299559.07186933







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
123602581.24335218062-221.243352180622
222142511.46851651335-297.468516513352
328252740.7617282865684.23827171344
423552606.79452979434-251.794529794343
523332587.29430186911-254.294301869108
630162875.29430186911140.705698130893
721552440.52710337689-285.527103376891
821722379.82687545165-207.826875451655
921502406.62687545165-256.626875451655
1025332601.53976812953-68.5397681295326
1120582233.71917552392-175.71917552392
1221602431.88546231519-271.885462315194
1322602581.24335218063-321.243352180626
1424982511.46851651335-13.4685165133519
1526952740.76172828656-45.7617282865597
1627992606.79452979434192.205470205657
1729472587.29430186911359.705698130893
1829302875.2943018691154.7056981308925
1923182440.52710337689-122.527103376891
2025402379.82687545165160.173124548345
2125702406.62687545165163.373124548345
2226692601.5397681295367.4602318704673
2324502233.71917552392216.280824476080
2428422431.88546231519410.114537684806
2534402581.24335218063858.756647819374
2626782511.46851651335166.531483486648
2729812740.76172828656240.238271713440
2822602576.69612527099-316.696125270993
2928442551.46286791274292.537132087262
3025462839.46286791274-293.462867912738
3124562376.0305222554379.9694777445737
3222952308.16400753892-13.1640075389167
3323792334.9640075389244.0359924610832
3424792509.81129720123-30.8112972012277
3520572126.22487365481-69.2248736548125
3622802298.5925279975-18.5925279975007
3723512437.91761635515-86.9176163551494
3822762343.77740559754-67.7774055975444
3925482561.60455850471-13.6045585047140
4023112407.57175699693-96.5717569969307
4122012372.30569813089-171.305698130893
4227252660.3056981308964.6943018691073
4324082204.03963926485203.960360735145
4421392129.006837757079.9931622429289
4518982155.80683775707-257.806837757071
4625372329.22087006113207.779129938873
4720691947.06770387297121.932296127033
4820632145.23399066424-82.2339906642408
4925242294.59188052967229.408119470327
5024372224.8170448624212.182955137601
5121892454.11025663561-265.110256635607
5227932320.14305814339472.85694185661
5320742300.64283021815-226.642830218154
5426222588.6428302181533.3571697818456
5522782153.87563172594124.124368274062
5621442093.175403800750.824596199298
5724272119.9754038007307.024596199298
5821392314.88829647858-175.888296478580
5918281921.26907142438-93.2690714243812
6020722109.40255670787-37.4025567078717
6118002258.76044657330-458.760446573304

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 2360 & 2581.24335218062 & -221.243352180622 \tabularnewline
2 & 2214 & 2511.46851651335 & -297.468516513352 \tabularnewline
3 & 2825 & 2740.76172828656 & 84.23827171344 \tabularnewline
4 & 2355 & 2606.79452979434 & -251.794529794343 \tabularnewline
5 & 2333 & 2587.29430186911 & -254.294301869108 \tabularnewline
6 & 3016 & 2875.29430186911 & 140.705698130893 \tabularnewline
7 & 2155 & 2440.52710337689 & -285.527103376891 \tabularnewline
8 & 2172 & 2379.82687545165 & -207.826875451655 \tabularnewline
9 & 2150 & 2406.62687545165 & -256.626875451655 \tabularnewline
10 & 2533 & 2601.53976812953 & -68.5397681295326 \tabularnewline
11 & 2058 & 2233.71917552392 & -175.71917552392 \tabularnewline
12 & 2160 & 2431.88546231519 & -271.885462315194 \tabularnewline
13 & 2260 & 2581.24335218063 & -321.243352180626 \tabularnewline
14 & 2498 & 2511.46851651335 & -13.4685165133519 \tabularnewline
15 & 2695 & 2740.76172828656 & -45.7617282865597 \tabularnewline
16 & 2799 & 2606.79452979434 & 192.205470205657 \tabularnewline
17 & 2947 & 2587.29430186911 & 359.705698130893 \tabularnewline
18 & 2930 & 2875.29430186911 & 54.7056981308925 \tabularnewline
19 & 2318 & 2440.52710337689 & -122.527103376891 \tabularnewline
20 & 2540 & 2379.82687545165 & 160.173124548345 \tabularnewline
21 & 2570 & 2406.62687545165 & 163.373124548345 \tabularnewline
22 & 2669 & 2601.53976812953 & 67.4602318704673 \tabularnewline
23 & 2450 & 2233.71917552392 & 216.280824476080 \tabularnewline
24 & 2842 & 2431.88546231519 & 410.114537684806 \tabularnewline
25 & 3440 & 2581.24335218063 & 858.756647819374 \tabularnewline
26 & 2678 & 2511.46851651335 & 166.531483486648 \tabularnewline
27 & 2981 & 2740.76172828656 & 240.238271713440 \tabularnewline
28 & 2260 & 2576.69612527099 & -316.696125270993 \tabularnewline
29 & 2844 & 2551.46286791274 & 292.537132087262 \tabularnewline
30 & 2546 & 2839.46286791274 & -293.462867912738 \tabularnewline
31 & 2456 & 2376.03052225543 & 79.9694777445737 \tabularnewline
32 & 2295 & 2308.16400753892 & -13.1640075389167 \tabularnewline
33 & 2379 & 2334.96400753892 & 44.0359924610832 \tabularnewline
34 & 2479 & 2509.81129720123 & -30.8112972012277 \tabularnewline
35 & 2057 & 2126.22487365481 & -69.2248736548125 \tabularnewline
36 & 2280 & 2298.5925279975 & -18.5925279975007 \tabularnewline
37 & 2351 & 2437.91761635515 & -86.9176163551494 \tabularnewline
38 & 2276 & 2343.77740559754 & -67.7774055975444 \tabularnewline
39 & 2548 & 2561.60455850471 & -13.6045585047140 \tabularnewline
40 & 2311 & 2407.57175699693 & -96.5717569969307 \tabularnewline
41 & 2201 & 2372.30569813089 & -171.305698130893 \tabularnewline
42 & 2725 & 2660.30569813089 & 64.6943018691073 \tabularnewline
43 & 2408 & 2204.03963926485 & 203.960360735145 \tabularnewline
44 & 2139 & 2129.00683775707 & 9.9931622429289 \tabularnewline
45 & 1898 & 2155.80683775707 & -257.806837757071 \tabularnewline
46 & 2537 & 2329.22087006113 & 207.779129938873 \tabularnewline
47 & 2069 & 1947.06770387297 & 121.932296127033 \tabularnewline
48 & 2063 & 2145.23399066424 & -82.2339906642408 \tabularnewline
49 & 2524 & 2294.59188052967 & 229.408119470327 \tabularnewline
50 & 2437 & 2224.8170448624 & 212.182955137601 \tabularnewline
51 & 2189 & 2454.11025663561 & -265.110256635607 \tabularnewline
52 & 2793 & 2320.14305814339 & 472.85694185661 \tabularnewline
53 & 2074 & 2300.64283021815 & -226.642830218154 \tabularnewline
54 & 2622 & 2588.64283021815 & 33.3571697818456 \tabularnewline
55 & 2278 & 2153.87563172594 & 124.124368274062 \tabularnewline
56 & 2144 & 2093.1754038007 & 50.824596199298 \tabularnewline
57 & 2427 & 2119.9754038007 & 307.024596199298 \tabularnewline
58 & 2139 & 2314.88829647858 & -175.888296478580 \tabularnewline
59 & 1828 & 1921.26907142438 & -93.2690714243812 \tabularnewline
60 & 2072 & 2109.40255670787 & -37.4025567078717 \tabularnewline
61 & 1800 & 2258.76044657330 & -458.760446573304 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57866&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]2360[/C][C]2581.24335218062[/C][C]-221.243352180622[/C][/ROW]
[ROW][C]2[/C][C]2214[/C][C]2511.46851651335[/C][C]-297.468516513352[/C][/ROW]
[ROW][C]3[/C][C]2825[/C][C]2740.76172828656[/C][C]84.23827171344[/C][/ROW]
[ROW][C]4[/C][C]2355[/C][C]2606.79452979434[/C][C]-251.794529794343[/C][/ROW]
[ROW][C]5[/C][C]2333[/C][C]2587.29430186911[/C][C]-254.294301869108[/C][/ROW]
[ROW][C]6[/C][C]3016[/C][C]2875.29430186911[/C][C]140.705698130893[/C][/ROW]
[ROW][C]7[/C][C]2155[/C][C]2440.52710337689[/C][C]-285.527103376891[/C][/ROW]
[ROW][C]8[/C][C]2172[/C][C]2379.82687545165[/C][C]-207.826875451655[/C][/ROW]
[ROW][C]9[/C][C]2150[/C][C]2406.62687545165[/C][C]-256.626875451655[/C][/ROW]
[ROW][C]10[/C][C]2533[/C][C]2601.53976812953[/C][C]-68.5397681295326[/C][/ROW]
[ROW][C]11[/C][C]2058[/C][C]2233.71917552392[/C][C]-175.71917552392[/C][/ROW]
[ROW][C]12[/C][C]2160[/C][C]2431.88546231519[/C][C]-271.885462315194[/C][/ROW]
[ROW][C]13[/C][C]2260[/C][C]2581.24335218063[/C][C]-321.243352180626[/C][/ROW]
[ROW][C]14[/C][C]2498[/C][C]2511.46851651335[/C][C]-13.4685165133519[/C][/ROW]
[ROW][C]15[/C][C]2695[/C][C]2740.76172828656[/C][C]-45.7617282865597[/C][/ROW]
[ROW][C]16[/C][C]2799[/C][C]2606.79452979434[/C][C]192.205470205657[/C][/ROW]
[ROW][C]17[/C][C]2947[/C][C]2587.29430186911[/C][C]359.705698130893[/C][/ROW]
[ROW][C]18[/C][C]2930[/C][C]2875.29430186911[/C][C]54.7056981308925[/C][/ROW]
[ROW][C]19[/C][C]2318[/C][C]2440.52710337689[/C][C]-122.527103376891[/C][/ROW]
[ROW][C]20[/C][C]2540[/C][C]2379.82687545165[/C][C]160.173124548345[/C][/ROW]
[ROW][C]21[/C][C]2570[/C][C]2406.62687545165[/C][C]163.373124548345[/C][/ROW]
[ROW][C]22[/C][C]2669[/C][C]2601.53976812953[/C][C]67.4602318704673[/C][/ROW]
[ROW][C]23[/C][C]2450[/C][C]2233.71917552392[/C][C]216.280824476080[/C][/ROW]
[ROW][C]24[/C][C]2842[/C][C]2431.88546231519[/C][C]410.114537684806[/C][/ROW]
[ROW][C]25[/C][C]3440[/C][C]2581.24335218063[/C][C]858.756647819374[/C][/ROW]
[ROW][C]26[/C][C]2678[/C][C]2511.46851651335[/C][C]166.531483486648[/C][/ROW]
[ROW][C]27[/C][C]2981[/C][C]2740.76172828656[/C][C]240.238271713440[/C][/ROW]
[ROW][C]28[/C][C]2260[/C][C]2576.69612527099[/C][C]-316.696125270993[/C][/ROW]
[ROW][C]29[/C][C]2844[/C][C]2551.46286791274[/C][C]292.537132087262[/C][/ROW]
[ROW][C]30[/C][C]2546[/C][C]2839.46286791274[/C][C]-293.462867912738[/C][/ROW]
[ROW][C]31[/C][C]2456[/C][C]2376.03052225543[/C][C]79.9694777445737[/C][/ROW]
[ROW][C]32[/C][C]2295[/C][C]2308.16400753892[/C][C]-13.1640075389167[/C][/ROW]
[ROW][C]33[/C][C]2379[/C][C]2334.96400753892[/C][C]44.0359924610832[/C][/ROW]
[ROW][C]34[/C][C]2479[/C][C]2509.81129720123[/C][C]-30.8112972012277[/C][/ROW]
[ROW][C]35[/C][C]2057[/C][C]2126.22487365481[/C][C]-69.2248736548125[/C][/ROW]
[ROW][C]36[/C][C]2280[/C][C]2298.5925279975[/C][C]-18.5925279975007[/C][/ROW]
[ROW][C]37[/C][C]2351[/C][C]2437.91761635515[/C][C]-86.9176163551494[/C][/ROW]
[ROW][C]38[/C][C]2276[/C][C]2343.77740559754[/C][C]-67.7774055975444[/C][/ROW]
[ROW][C]39[/C][C]2548[/C][C]2561.60455850471[/C][C]-13.6045585047140[/C][/ROW]
[ROW][C]40[/C][C]2311[/C][C]2407.57175699693[/C][C]-96.5717569969307[/C][/ROW]
[ROW][C]41[/C][C]2201[/C][C]2372.30569813089[/C][C]-171.305698130893[/C][/ROW]
[ROW][C]42[/C][C]2725[/C][C]2660.30569813089[/C][C]64.6943018691073[/C][/ROW]
[ROW][C]43[/C][C]2408[/C][C]2204.03963926485[/C][C]203.960360735145[/C][/ROW]
[ROW][C]44[/C][C]2139[/C][C]2129.00683775707[/C][C]9.9931622429289[/C][/ROW]
[ROW][C]45[/C][C]1898[/C][C]2155.80683775707[/C][C]-257.806837757071[/C][/ROW]
[ROW][C]46[/C][C]2537[/C][C]2329.22087006113[/C][C]207.779129938873[/C][/ROW]
[ROW][C]47[/C][C]2069[/C][C]1947.06770387297[/C][C]121.932296127033[/C][/ROW]
[ROW][C]48[/C][C]2063[/C][C]2145.23399066424[/C][C]-82.2339906642408[/C][/ROW]
[ROW][C]49[/C][C]2524[/C][C]2294.59188052967[/C][C]229.408119470327[/C][/ROW]
[ROW][C]50[/C][C]2437[/C][C]2224.8170448624[/C][C]212.182955137601[/C][/ROW]
[ROW][C]51[/C][C]2189[/C][C]2454.11025663561[/C][C]-265.110256635607[/C][/ROW]
[ROW][C]52[/C][C]2793[/C][C]2320.14305814339[/C][C]472.85694185661[/C][/ROW]
[ROW][C]53[/C][C]2074[/C][C]2300.64283021815[/C][C]-226.642830218154[/C][/ROW]
[ROW][C]54[/C][C]2622[/C][C]2588.64283021815[/C][C]33.3571697818456[/C][/ROW]
[ROW][C]55[/C][C]2278[/C][C]2153.87563172594[/C][C]124.124368274062[/C][/ROW]
[ROW][C]56[/C][C]2144[/C][C]2093.1754038007[/C][C]50.824596199298[/C][/ROW]
[ROW][C]57[/C][C]2427[/C][C]2119.9754038007[/C][C]307.024596199298[/C][/ROW]
[ROW][C]58[/C][C]2139[/C][C]2314.88829647858[/C][C]-175.888296478580[/C][/ROW]
[ROW][C]59[/C][C]1828[/C][C]1921.26907142438[/C][C]-93.2690714243812[/C][/ROW]
[ROW][C]60[/C][C]2072[/C][C]2109.40255670787[/C][C]-37.4025567078717[/C][/ROW]
[ROW][C]61[/C][C]1800[/C][C]2258.76044657330[/C][C]-458.760446573304[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57866&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57866&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
123602581.24335218062-221.243352180622
222142511.46851651335-297.468516513352
328252740.7617282865684.23827171344
423552606.79452979434-251.794529794343
523332587.29430186911-254.294301869108
630162875.29430186911140.705698130893
721552440.52710337689-285.527103376891
821722379.82687545165-207.826875451655
921502406.62687545165-256.626875451655
1025332601.53976812953-68.5397681295326
1120582233.71917552392-175.71917552392
1221602431.88546231519-271.885462315194
1322602581.24335218063-321.243352180626
1424982511.46851651335-13.4685165133519
1526952740.76172828656-45.7617282865597
1627992606.79452979434192.205470205657
1729472587.29430186911359.705698130893
1829302875.2943018691154.7056981308925
1923182440.52710337689-122.527103376891
2025402379.82687545165160.173124548345
2125702406.62687545165163.373124548345
2226692601.5397681295367.4602318704673
2324502233.71917552392216.280824476080
2428422431.88546231519410.114537684806
2534402581.24335218063858.756647819374
2626782511.46851651335166.531483486648
2729812740.76172828656240.238271713440
2822602576.69612527099-316.696125270993
2928442551.46286791274292.537132087262
3025462839.46286791274-293.462867912738
3124562376.0305222554379.9694777445737
3222952308.16400753892-13.1640075389167
3323792334.9640075389244.0359924610832
3424792509.81129720123-30.8112972012277
3520572126.22487365481-69.2248736548125
3622802298.5925279975-18.5925279975007
3723512437.91761635515-86.9176163551494
3822762343.77740559754-67.7774055975444
3925482561.60455850471-13.6045585047140
4023112407.57175699693-96.5717569969307
4122012372.30569813089-171.305698130893
4227252660.3056981308964.6943018691073
4324082204.03963926485203.960360735145
4421392129.006837757079.9931622429289
4518982155.80683775707-257.806837757071
4625372329.22087006113207.779129938873
4720691947.06770387297121.932296127033
4820632145.23399066424-82.2339906642408
4925242294.59188052967229.408119470327
5024372224.8170448624212.182955137601
5121892454.11025663561-265.110256635607
5227932320.14305814339472.85694185661
5320742300.64283021815-226.642830218154
5426222588.6428302181533.3571697818456
5522782153.87563172594124.124368274062
5621442093.175403800750.824596199298
5724272119.9754038007307.024596199298
5821392314.88829647858-175.888296478580
5918281921.26907142438-93.2690714243812
6020722109.40255670787-37.4025567078717
6118002258.76044657330-458.760446573304







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
160.4730084511424250.946016902284850.526991548857575
170.6966472341740360.6067055316519290.303352765825964
180.5614869300910360.8770261398179280.438513069908964
190.4732864221098780.9465728442197550.526713577890122
200.4502559961552340.9005119923104670.549744003844766
210.4509111060483820.9018222120967640.549088893951618
220.3502024866834070.7004049733668140.649797513316593
230.3362427303674310.6724854607348620.663757269632569
240.4963160872842580.9926321745685160.503683912715742
250.974835691618690.05032861676262090.0251643083813104
260.9643673210881650.07126535782366990.0356326789118350
270.9644166770229260.07116664595414870.0355833229770743
280.9666047634030130.06679047319397350.0333952365969867
290.9835554425860860.03288911482782720.0164445574139136
300.9813320007884030.0373359984231940.018667999211597
310.9728522527366740.05429549452665240.0271477472633262
320.9529034539574140.09419309208517210.0470965460425861
330.9233720048977640.1532559902044720.076627995102236
340.8801643269576260.2396713460847490.119835673042374
350.823195374355890.3536092512882190.176804625644109
360.7513490771680620.4973018456638760.248650922831938
370.670534397524960.6589312049500790.329465602475039
380.5965321939668510.8069356120662980.403467806033149
390.5342106023518740.9315787952962530.465789397648126
400.6144907328383020.7710185343233970.385509267161698
410.5046028561831960.9907942876336080.495397143816804
420.3908428719977750.781685743995550.609157128002225
430.2927953258390240.5855906516780470.707204674160976
440.1890239606713030.3780479213426060.810976039328697
450.5154442562872560.9691114874254880.484555743712744

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
16 & 0.473008451142425 & 0.94601690228485 & 0.526991548857575 \tabularnewline
17 & 0.696647234174036 & 0.606705531651929 & 0.303352765825964 \tabularnewline
18 & 0.561486930091036 & 0.877026139817928 & 0.438513069908964 \tabularnewline
19 & 0.473286422109878 & 0.946572844219755 & 0.526713577890122 \tabularnewline
20 & 0.450255996155234 & 0.900511992310467 & 0.549744003844766 \tabularnewline
21 & 0.450911106048382 & 0.901822212096764 & 0.549088893951618 \tabularnewline
22 & 0.350202486683407 & 0.700404973366814 & 0.649797513316593 \tabularnewline
23 & 0.336242730367431 & 0.672485460734862 & 0.663757269632569 \tabularnewline
24 & 0.496316087284258 & 0.992632174568516 & 0.503683912715742 \tabularnewline
25 & 0.97483569161869 & 0.0503286167626209 & 0.0251643083813104 \tabularnewline
26 & 0.964367321088165 & 0.0712653578236699 & 0.0356326789118350 \tabularnewline
27 & 0.964416677022926 & 0.0711666459541487 & 0.0355833229770743 \tabularnewline
28 & 0.966604763403013 & 0.0667904731939735 & 0.0333952365969867 \tabularnewline
29 & 0.983555442586086 & 0.0328891148278272 & 0.0164445574139136 \tabularnewline
30 & 0.981332000788403 & 0.037335998423194 & 0.018667999211597 \tabularnewline
31 & 0.972852252736674 & 0.0542954945266524 & 0.0271477472633262 \tabularnewline
32 & 0.952903453957414 & 0.0941930920851721 & 0.0470965460425861 \tabularnewline
33 & 0.923372004897764 & 0.153255990204472 & 0.076627995102236 \tabularnewline
34 & 0.880164326957626 & 0.239671346084749 & 0.119835673042374 \tabularnewline
35 & 0.82319537435589 & 0.353609251288219 & 0.176804625644109 \tabularnewline
36 & 0.751349077168062 & 0.497301845663876 & 0.248650922831938 \tabularnewline
37 & 0.67053439752496 & 0.658931204950079 & 0.329465602475039 \tabularnewline
38 & 0.596532193966851 & 0.806935612066298 & 0.403467806033149 \tabularnewline
39 & 0.534210602351874 & 0.931578795296253 & 0.465789397648126 \tabularnewline
40 & 0.614490732838302 & 0.771018534323397 & 0.385509267161698 \tabularnewline
41 & 0.504602856183196 & 0.990794287633608 & 0.495397143816804 \tabularnewline
42 & 0.390842871997775 & 0.78168574399555 & 0.609157128002225 \tabularnewline
43 & 0.292795325839024 & 0.585590651678047 & 0.707204674160976 \tabularnewline
44 & 0.189023960671303 & 0.378047921342606 & 0.810976039328697 \tabularnewline
45 & 0.515444256287256 & 0.969111487425488 & 0.484555743712744 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57866&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]0.473008451142425[/C][C]0.94601690228485[/C][C]0.526991548857575[/C][/ROW]
[ROW][C]17[/C][C]0.696647234174036[/C][C]0.606705531651929[/C][C]0.303352765825964[/C][/ROW]
[ROW][C]18[/C][C]0.561486930091036[/C][C]0.877026139817928[/C][C]0.438513069908964[/C][/ROW]
[ROW][C]19[/C][C]0.473286422109878[/C][C]0.946572844219755[/C][C]0.526713577890122[/C][/ROW]
[ROW][C]20[/C][C]0.450255996155234[/C][C]0.900511992310467[/C][C]0.549744003844766[/C][/ROW]
[ROW][C]21[/C][C]0.450911106048382[/C][C]0.901822212096764[/C][C]0.549088893951618[/C][/ROW]
[ROW][C]22[/C][C]0.350202486683407[/C][C]0.700404973366814[/C][C]0.649797513316593[/C][/ROW]
[ROW][C]23[/C][C]0.336242730367431[/C][C]0.672485460734862[/C][C]0.663757269632569[/C][/ROW]
[ROW][C]24[/C][C]0.496316087284258[/C][C]0.992632174568516[/C][C]0.503683912715742[/C][/ROW]
[ROW][C]25[/C][C]0.97483569161869[/C][C]0.0503286167626209[/C][C]0.0251643083813104[/C][/ROW]
[ROW][C]26[/C][C]0.964367321088165[/C][C]0.0712653578236699[/C][C]0.0356326789118350[/C][/ROW]
[ROW][C]27[/C][C]0.964416677022926[/C][C]0.0711666459541487[/C][C]0.0355833229770743[/C][/ROW]
[ROW][C]28[/C][C]0.966604763403013[/C][C]0.0667904731939735[/C][C]0.0333952365969867[/C][/ROW]
[ROW][C]29[/C][C]0.983555442586086[/C][C]0.0328891148278272[/C][C]0.0164445574139136[/C][/ROW]
[ROW][C]30[/C][C]0.981332000788403[/C][C]0.037335998423194[/C][C]0.018667999211597[/C][/ROW]
[ROW][C]31[/C][C]0.972852252736674[/C][C]0.0542954945266524[/C][C]0.0271477472633262[/C][/ROW]
[ROW][C]32[/C][C]0.952903453957414[/C][C]0.0941930920851721[/C][C]0.0470965460425861[/C][/ROW]
[ROW][C]33[/C][C]0.923372004897764[/C][C]0.153255990204472[/C][C]0.076627995102236[/C][/ROW]
[ROW][C]34[/C][C]0.880164326957626[/C][C]0.239671346084749[/C][C]0.119835673042374[/C][/ROW]
[ROW][C]35[/C][C]0.82319537435589[/C][C]0.353609251288219[/C][C]0.176804625644109[/C][/ROW]
[ROW][C]36[/C][C]0.751349077168062[/C][C]0.497301845663876[/C][C]0.248650922831938[/C][/ROW]
[ROW][C]37[/C][C]0.67053439752496[/C][C]0.658931204950079[/C][C]0.329465602475039[/C][/ROW]
[ROW][C]38[/C][C]0.596532193966851[/C][C]0.806935612066298[/C][C]0.403467806033149[/C][/ROW]
[ROW][C]39[/C][C]0.534210602351874[/C][C]0.931578795296253[/C][C]0.465789397648126[/C][/ROW]
[ROW][C]40[/C][C]0.614490732838302[/C][C]0.771018534323397[/C][C]0.385509267161698[/C][/ROW]
[ROW][C]41[/C][C]0.504602856183196[/C][C]0.990794287633608[/C][C]0.495397143816804[/C][/ROW]
[ROW][C]42[/C][C]0.390842871997775[/C][C]0.78168574399555[/C][C]0.609157128002225[/C][/ROW]
[ROW][C]43[/C][C]0.292795325839024[/C][C]0.585590651678047[/C][C]0.707204674160976[/C][/ROW]
[ROW][C]44[/C][C]0.189023960671303[/C][C]0.378047921342606[/C][C]0.810976039328697[/C][/ROW]
[ROW][C]45[/C][C]0.515444256287256[/C][C]0.969111487425488[/C][C]0.484555743712744[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57866&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57866&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
160.4730084511424250.946016902284850.526991548857575
170.6966472341740360.6067055316519290.303352765825964
180.5614869300910360.8770261398179280.438513069908964
190.4732864221098780.9465728442197550.526713577890122
200.4502559961552340.9005119923104670.549744003844766
210.4509111060483820.9018222120967640.549088893951618
220.3502024866834070.7004049733668140.649797513316593
230.3362427303674310.6724854607348620.663757269632569
240.4963160872842580.9926321745685160.503683912715742
250.974835691618690.05032861676262090.0251643083813104
260.9643673210881650.07126535782366990.0356326789118350
270.9644166770229260.07116664595414870.0355833229770743
280.9666047634030130.06679047319397350.0333952365969867
290.9835554425860860.03288911482782720.0164445574139136
300.9813320007884030.0373359984231940.018667999211597
310.9728522527366740.05429549452665240.0271477472633262
320.9529034539574140.09419309208517210.0470965460425861
330.9233720048977640.1532559902044720.076627995102236
340.8801643269576260.2396713460847490.119835673042374
350.823195374355890.3536092512882190.176804625644109
360.7513490771680620.4973018456638760.248650922831938
370.670534397524960.6589312049500790.329465602475039
380.5965321939668510.8069356120662980.403467806033149
390.5342106023518740.9315787952962530.465789397648126
400.6144907328383020.7710185343233970.385509267161698
410.5046028561831960.9907942876336080.495397143816804
420.3908428719977750.781685743995550.609157128002225
430.2927953258390240.5855906516780470.707204674160976
440.1890239606713030.3780479213426060.810976039328697
450.5154442562872560.9691114874254880.484555743712744







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level20.0666666666666667NOK
10% type I error level80.266666666666667NOK

\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 & 2 & 0.0666666666666667 & NOK \tabularnewline
10% type I error level & 8 & 0.266666666666667 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57866&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]2[/C][C]0.0666666666666667[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]8[/C][C]0.266666666666667[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57866&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57866&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 level20.0666666666666667NOK
10% type I error level80.266666666666667NOK



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')
}