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Description of Statistical Computation

Author's title

Werkeloosheid vergelijken met opleidingsniveau afkomst en aantal banen in N...

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationThu, 15 Nov 2012 12:53:40 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Nov/15/t1353002140y6oc6qov887s0cx.htm/, Retrieved Sat, 27 Apr 2024 22:11:00 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=189742, Retrieved Sat, 27 Apr 2024 22:11:00 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact155

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [Werkeloosheid ver...] [2012-11-15 17:53:40] [eace0511beeaae09dbb51bfebd62c02b] [Current]
- R  D    [Multiple Regression] [] [2012-11-19 15:56:31] [8ab8078357d7493428921287469fd527]
- R  D    [Multiple Regression] [] [2012-11-19 16:04:43] [8ab8078357d7493428921287469fd527]
- RMPD    [Histogram] [Frequency tabel ] [2012-11-19 17:43:14] [8ab8078357d7493428921287469fd527]
- RMPD    [Stem-and-leaf Plot] [] [2012-11-19 18:08:20] [8ab8078357d7493428921287469fd527]
- RMPD    [Histogram] [] [2012-11-19 18:13:18] [8ab8078357d7493428921287469fd527]
- R PD    [Multiple Regression] [] [2012-12-10 12:50:31] [8ab8078357d7493428921287469fd527]
- R PD    [Multiple Regression] [] [2012-12-10 12:50:31] [8ab8078357d7493428921287469fd527]
- R PD    [Multiple Regression] [] [2012-12-10 12:50:31] [8ab8078357d7493428921287469fd527]
- R PD    [Multiple Regression] [] [2012-12-10 12:50:31] [8ab8078357d7493428921287469fd527]
- R PD    [Multiple Regression] [] [2012-12-10 12:50:31] [8ab8078357d7493428921287469fd527]
- R PD    [Multiple Regression] [] [2012-12-10 12:50:31] [8ab8078357d7493428921287469fd527]
- R PD    [Multiple Regression] [] [2012-12-10 12:50:31] [8ab8078357d7493428921287469fd527]
- R PD    [Multiple Regression] [] [2012-12-10 12:50:31] [8ab8078357d7493428921287469fd527]
- R PD    [Multiple Regression] [] [2012-12-10 12:50:31] [8ab8078357d7493428921287469fd527]
- R PD    [Multiple Regression] [] [2012-12-10 12:50:31] [8ab8078357d7493428921287469fd527]
- R PD    [Multiple Regression] [] [2012-12-10 12:50:31] [8ab8078357d7493428921287469fd527]
- RMP       [Kendall tau Correlation Matrix] [] [2012-12-13 17:44:55] [8ab8078357d7493428921287469fd527]
- R PD    [Multiple Regression] [] [2012-12-10 12:50:31] [8ab8078357d7493428921287469fd527]
- R PD    [Multiple Regression] [] [2012-12-10 13:01:22] [8ab8078357d7493428921287469fd527]
- R PD    [Multiple Regression] [] [2012-12-10 13:04:06] [8ab8078357d7493428921287469fd527]
- RMPD    [Skewness and Kurtosis Test] [] [2012-12-10 13:31:08] [8ab8078357d7493428921287469fd527]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
277	52	99	104	172	79	8909	
232	50	81	125	183	93	8841	
256	59	95	98	162	68	8733	
242	52	93	100	179	77	8885	
302	66	109	93	162	78	8933	
282	62	103	123	206	95	8854	
288	59	101	116	194	88	8748	
321	70	121	124	198	88	8827	
316	74	112	126	219	102	8850	
396	84	151	126	212	103	8761	
362	71	142	156	265	131	8617	
392	81	144	141	234	127	8758	
414	92	154	163	259	133	8806	
417	89	164	164	287	127	8710	
476	100	188	156	278	138	8681	
488	103	189	180	317	158	8819	
489	97	188	187	320	167	8834	
467	107	185	194	326	162	8742	
460	93	188	168	316	149	8766	
482	97	200	170	306	153	8902	
510	100	211	177	315	166	8980	
493	89	202	189	329	179	9031	
476	102	198	194	316	176	8984	
448	96	189	170	316	159	9150	
410	81	174	156	297	151	9231	
466	91	199	148	266	143	9230	
417	84	175	167	296	169	9194	
387	78	160	150	275	141	9307	
370	70	160	141	252	134	9336	
344	67	145	134	239	130	9310	
396	76	172	127	231	112	9236	
349	65	147	142	256	141	9244	
326	66	138	132	232	116	9222	
303	62	122	118	230	95	9186	
300	66	118	115	205	98	9095	
329	68	133	113	195	104	9216	
304	59	118	123	207	121	9237	
286	68	112	123	197	106	9207	
281	68	109	103	194	90	9189	
377	84	152	101	181	99	9183	
344	75	141	135	246	130	9277	
369	79	144	122	220	123	9305	
390	92	152	142	234	133	9268	
406	88	172	140	264	126	9259	
426	98	168	138	266	137	9197	
467	104	185	153	282	142	9293	
437	95	174	172	312	153	9270	
410	99	159	160	297	138	9395	
390	93	155	146	269	139	9316	
418	102	171	136	252	137	9237	
398	91	161	139	265	152	9207	
422	105	173	139	246	151	9189	
439	100	179	140	263	158	9183	
419	99	171	150	274	162	9277	
484	111	202	142	262	156	9305	
491	110	199	130	298	186	9268

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net

\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 & 9 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=189742&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]9 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=189742&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=189742&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net






Multiple Linear Regression - Estimated Regression Equation
werkeloosheid[t] = + 347.577586584537 + 1.04313174226083onderwijshoog[t] + 1.65856049191535onderwijsmiddelbaar[t] + 0.0963565106816862onderwijslaag[t] -0.0195889378182551autochtoon[t] + 0.0729893635415538allochtonen[t] -0.0354772937823839banen[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
werkeloosheid[t] =  +  347.577586584537 +  1.04313174226083onderwijshoog[t] +  1.65856049191535onderwijsmiddelbaar[t] +  0.0963565106816862onderwijslaag[t] -0.0195889378182551autochtoon[t] +  0.0729893635415538allochtonen[t] -0.0354772937823839banen[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=189742&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]werkeloosheid[t] =  +  347.577586584537 +  1.04313174226083onderwijshoog[t] +  1.65856049191535onderwijsmiddelbaar[t] +  0.0963565106816862onderwijslaag[t] -0.0195889378182551autochtoon[t] +  0.0729893635415538allochtonen[t] -0.0354772937823839banen[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=189742&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=189742&T=1

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


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

Multiple Linear Regression - Estimated Regression Equation
werkeloosheid[t] = + 347.577586584537 + 1.04313174226083onderwijshoog[t] + 1.65856049191535onderwijsmiddelbaar[t] + 0.0963565106816862onderwijslaag[t] -0.0195889378182551autochtoon[t] + 0.0729893635415538allochtonen[t] -0.0354772937823839banen[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)347.57758658453765.3577595.31813e-061e-06
onderwijshoog1.043131742260830.1522716.850500
onderwijsmiddelbaar1.658560491915350.10456715.861200
onderwijslaag0.09635651068168620.1628610.59160.5568070.278403
autochtoon-0.01958893781825510.107456-0.18230.8561010.428051
allochtonen0.07298936354155380.1284040.56840.5723350.286167
banen-0.03547729378238390.006974-5.08686e-063e-06

\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) & 347.577586584537 & 65.357759 & 5.3181 & 3e-06 & 1e-06 \tabularnewline
onderwijshoog & 1.04313174226083 & 0.152271 & 6.8505 & 0 & 0 \tabularnewline
onderwijsmiddelbaar & 1.65856049191535 & 0.104567 & 15.8612 & 0 & 0 \tabularnewline
onderwijslaag & 0.0963565106816862 & 0.162861 & 0.5916 & 0.556807 & 0.278403 \tabularnewline
autochtoon & -0.0195889378182551 & 0.107456 & -0.1823 & 0.856101 & 0.428051 \tabularnewline
allochtonen & 0.0729893635415538 & 0.128404 & 0.5684 & 0.572335 & 0.286167 \tabularnewline
banen & -0.0354772937823839 & 0.006974 & -5.0868 & 6e-06 & 3e-06 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=189742&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]347.577586584537[/C][C]65.357759[/C][C]5.3181[/C][C]3e-06[/C][C]1e-06[/C][/ROW]
[ROW][C]onderwijshoog[/C][C]1.04313174226083[/C][C]0.152271[/C][C]6.8505[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]onderwijsmiddelbaar[/C][C]1.65856049191535[/C][C]0.104567[/C][C]15.8612[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]onderwijslaag[/C][C]0.0963565106816862[/C][C]0.162861[/C][C]0.5916[/C][C]0.556807[/C][C]0.278403[/C][/ROW]
[ROW][C]autochtoon[/C][C]-0.0195889378182551[/C][C]0.107456[/C][C]-0.1823[/C][C]0.856101[/C][C]0.428051[/C][/ROW]
[ROW][C]allochtonen[/C][C]0.0729893635415538[/C][C]0.128404[/C][C]0.5684[/C][C]0.572335[/C][C]0.286167[/C][/ROW]
[ROW][C]banen[/C][C]-0.0354772937823839[/C][C]0.006974[/C][C]-5.0868[/C][C]6e-06[/C][C]3e-06[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=189742&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=189742&T=2

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


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)347.57758658453765.3577595.31813e-061e-06
onderwijshoog1.043131742260830.1522716.850500
onderwijsmiddelbaar1.658560491915350.10456715.861200
onderwijslaag0.09635651068168620.1628610.59160.5568070.278403
autochtoon-0.01958893781825510.107456-0.18230.8561010.428051
allochtonen0.07298936354155380.1284040.56840.5723350.286167
banen-0.03547729378238390.006974-5.08686e-063e-06






Multiple Linear Regression - Regression Statistics
Multiple R0.993849707145605
R-squared0.987737240393404
Adjusted R-squared0.986235677992596
F-TEST (value)657.806322176237
F-TEST (DF numerator)6
F-TEST (DF denominator)49
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation8.70251469796598
Sum Squared Residuals3710.95434134739

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.993849707145605 \tabularnewline
R-squared & 0.987737240393404 \tabularnewline
Adjusted R-squared & 0.986235677992596 \tabularnewline
F-TEST (value) & 657.806322176237 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 49 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 8.70251469796598 \tabularnewline
Sum Squared Residuals & 3710.95434134739 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=189742&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.993849707145605[/C][/ROW]
[ROW][C]R-squared[/C][C]0.987737240393404[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.986235677992596[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]657.806322176237[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]49[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]8.70251469796598[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]3710.95434134739[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=189742&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=189742&T=3

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


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

Multiple Linear Regression - Regression Statistics
Multiple R0.993849707145605
R-squared0.987737240393404
Adjusted R-squared0.986235677992596
F-TEST (value)657.806322176237
F-TEST (DF numerator)6
F-TEST (DF denominator)49
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation8.70251469796598
Sum Squared Residuals3710.95434134739






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1277262.368655100414.6313448996001
2232235.670618236501-3.6706182365008
3256268.0952063494-12.0952063493997
4242252.600219865148-10.6002198651478
5302291.76962775757110.2303722424293
6282283.718045282497-1.71804528249739
7288280.0817683470737.91823165292671
8321322.617217475622-1.61721747562161
9316311.8499186771944.15008132280635
10396390.3326863594025.66731364059774
11362370.849843382683-8.84984338268318
12392378.4659353237813.5340646762196
13414406.8911352770397.10886472296133
14417422.86309524304-5.86309524303982
15476475.3801690874340.619830912565559
16488478.2806332164439.71936678355712
17489471.10375589741817.8962441025822
18467480.015318002414-13.0153180024139
19460466.27745841015-6.27745841014966
20482486.208359181485-4.20835918148514
21510506.2617477647583.73825223524173
22493479.88180691451413.1181930854857
23476488.993181058437-12.9931810584367
24448458.364739973191-10.364739973191
25410413.404979424846-3.40497942484599
26466464.58827651771.41172348229952
27417421.898914112557-4.8989141125568
28387383.4823869162833.51761308371668
29370373.180902887401-3.18090288740138
30344345.38371308293-1.38371308292987
31396400.346759168915-4.34675916891457
32349350.196795113377-1.19679511337652
33326334.775218203894-8.77521820389401
34303302.5003160320910.499683967908872
35300303.686656771706-3.68665677170593
36329326.7996876253582.20031237464205
37304293.75738843005410.2426115699457
38286293.35957889744-7.35957889744041
39281285.986295492933-4.98629549293344
40377374.9262157263092.07378427369086
41344348.224509694128-4.22450969412763
42369355.12510611263213.8748938873679
43390385.6497412868884.35025871311244
44406413.676413099491-7.6764130994909
45426420.2440728709035.75592712909724
46467452.78944295676114.2105570432391
47437428.0190581897018.98094181029916
48410400.9212315431869.07876845681388
49390390.103393803677-0.103393803677157
50418428.054721672489-10.054721672489
51398402.188240195469-4.1882401954689
52422437.632602233193-15.6326022331929
53439442.855440348637-3.85544034863745
54419426.249003300492-7.24900330049198
55484488.214874218207-4.21487421820737
56491483.8369218867587.16307811324212

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 277 & 262.3686551004 & 14.6313448996001 \tabularnewline
2 & 232 & 235.670618236501 & -3.6706182365008 \tabularnewline
3 & 256 & 268.0952063494 & -12.0952063493997 \tabularnewline
4 & 242 & 252.600219865148 & -10.6002198651478 \tabularnewline
5 & 302 & 291.769627757571 & 10.2303722424293 \tabularnewline
6 & 282 & 283.718045282497 & -1.71804528249739 \tabularnewline
7 & 288 & 280.081768347073 & 7.91823165292671 \tabularnewline
8 & 321 & 322.617217475622 & -1.61721747562161 \tabularnewline
9 & 316 & 311.849918677194 & 4.15008132280635 \tabularnewline
10 & 396 & 390.332686359402 & 5.66731364059774 \tabularnewline
11 & 362 & 370.849843382683 & -8.84984338268318 \tabularnewline
12 & 392 & 378.46593532378 & 13.5340646762196 \tabularnewline
13 & 414 & 406.891135277039 & 7.10886472296133 \tabularnewline
14 & 417 & 422.86309524304 & -5.86309524303982 \tabularnewline
15 & 476 & 475.380169087434 & 0.619830912565559 \tabularnewline
16 & 488 & 478.280633216443 & 9.71936678355712 \tabularnewline
17 & 489 & 471.103755897418 & 17.8962441025822 \tabularnewline
18 & 467 & 480.015318002414 & -13.0153180024139 \tabularnewline
19 & 460 & 466.27745841015 & -6.27745841014966 \tabularnewline
20 & 482 & 486.208359181485 & -4.20835918148514 \tabularnewline
21 & 510 & 506.261747764758 & 3.73825223524173 \tabularnewline
22 & 493 & 479.881806914514 & 13.1181930854857 \tabularnewline
23 & 476 & 488.993181058437 & -12.9931810584367 \tabularnewline
24 & 448 & 458.364739973191 & -10.364739973191 \tabularnewline
25 & 410 & 413.404979424846 & -3.40497942484599 \tabularnewline
26 & 466 & 464.5882765177 & 1.41172348229952 \tabularnewline
27 & 417 & 421.898914112557 & -4.8989141125568 \tabularnewline
28 & 387 & 383.482386916283 & 3.51761308371668 \tabularnewline
29 & 370 & 373.180902887401 & -3.18090288740138 \tabularnewline
30 & 344 & 345.38371308293 & -1.38371308292987 \tabularnewline
31 & 396 & 400.346759168915 & -4.34675916891457 \tabularnewline
32 & 349 & 350.196795113377 & -1.19679511337652 \tabularnewline
33 & 326 & 334.775218203894 & -8.77521820389401 \tabularnewline
34 & 303 & 302.500316032091 & 0.499683967908872 \tabularnewline
35 & 300 & 303.686656771706 & -3.68665677170593 \tabularnewline
36 & 329 & 326.799687625358 & 2.20031237464205 \tabularnewline
37 & 304 & 293.757388430054 & 10.2426115699457 \tabularnewline
38 & 286 & 293.35957889744 & -7.35957889744041 \tabularnewline
39 & 281 & 285.986295492933 & -4.98629549293344 \tabularnewline
40 & 377 & 374.926215726309 & 2.07378427369086 \tabularnewline
41 & 344 & 348.224509694128 & -4.22450969412763 \tabularnewline
42 & 369 & 355.125106112632 & 13.8748938873679 \tabularnewline
43 & 390 & 385.649741286888 & 4.35025871311244 \tabularnewline
44 & 406 & 413.676413099491 & -7.6764130994909 \tabularnewline
45 & 426 & 420.244072870903 & 5.75592712909724 \tabularnewline
46 & 467 & 452.789442956761 & 14.2105570432391 \tabularnewline
47 & 437 & 428.019058189701 & 8.98094181029916 \tabularnewline
48 & 410 & 400.921231543186 & 9.07876845681388 \tabularnewline
49 & 390 & 390.103393803677 & -0.103393803677157 \tabularnewline
50 & 418 & 428.054721672489 & -10.054721672489 \tabularnewline
51 & 398 & 402.188240195469 & -4.1882401954689 \tabularnewline
52 & 422 & 437.632602233193 & -15.6326022331929 \tabularnewline
53 & 439 & 442.855440348637 & -3.85544034863745 \tabularnewline
54 & 419 & 426.249003300492 & -7.24900330049198 \tabularnewline
55 & 484 & 488.214874218207 & -4.21487421820737 \tabularnewline
56 & 491 & 483.836921886758 & 7.16307811324212 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=189742&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]277[/C][C]262.3686551004[/C][C]14.6313448996001[/C][/ROW]
[ROW][C]2[/C][C]232[/C][C]235.670618236501[/C][C]-3.6706182365008[/C][/ROW]
[ROW][C]3[/C][C]256[/C][C]268.0952063494[/C][C]-12.0952063493997[/C][/ROW]
[ROW][C]4[/C][C]242[/C][C]252.600219865148[/C][C]-10.6002198651478[/C][/ROW]
[ROW][C]5[/C][C]302[/C][C]291.769627757571[/C][C]10.2303722424293[/C][/ROW]
[ROW][C]6[/C][C]282[/C][C]283.718045282497[/C][C]-1.71804528249739[/C][/ROW]
[ROW][C]7[/C][C]288[/C][C]280.081768347073[/C][C]7.91823165292671[/C][/ROW]
[ROW][C]8[/C][C]321[/C][C]322.617217475622[/C][C]-1.61721747562161[/C][/ROW]
[ROW][C]9[/C][C]316[/C][C]311.849918677194[/C][C]4.15008132280635[/C][/ROW]
[ROW][C]10[/C][C]396[/C][C]390.332686359402[/C][C]5.66731364059774[/C][/ROW]
[ROW][C]11[/C][C]362[/C][C]370.849843382683[/C][C]-8.84984338268318[/C][/ROW]
[ROW][C]12[/C][C]392[/C][C]378.46593532378[/C][C]13.5340646762196[/C][/ROW]
[ROW][C]13[/C][C]414[/C][C]406.891135277039[/C][C]7.10886472296133[/C][/ROW]
[ROW][C]14[/C][C]417[/C][C]422.86309524304[/C][C]-5.86309524303982[/C][/ROW]
[ROW][C]15[/C][C]476[/C][C]475.380169087434[/C][C]0.619830912565559[/C][/ROW]
[ROW][C]16[/C][C]488[/C][C]478.280633216443[/C][C]9.71936678355712[/C][/ROW]
[ROW][C]17[/C][C]489[/C][C]471.103755897418[/C][C]17.8962441025822[/C][/ROW]
[ROW][C]18[/C][C]467[/C][C]480.015318002414[/C][C]-13.0153180024139[/C][/ROW]
[ROW][C]19[/C][C]460[/C][C]466.27745841015[/C][C]-6.27745841014966[/C][/ROW]
[ROW][C]20[/C][C]482[/C][C]486.208359181485[/C][C]-4.20835918148514[/C][/ROW]
[ROW][C]21[/C][C]510[/C][C]506.261747764758[/C][C]3.73825223524173[/C][/ROW]
[ROW][C]22[/C][C]493[/C][C]479.881806914514[/C][C]13.1181930854857[/C][/ROW]
[ROW][C]23[/C][C]476[/C][C]488.993181058437[/C][C]-12.9931810584367[/C][/ROW]
[ROW][C]24[/C][C]448[/C][C]458.364739973191[/C][C]-10.364739973191[/C][/ROW]
[ROW][C]25[/C][C]410[/C][C]413.404979424846[/C][C]-3.40497942484599[/C][/ROW]
[ROW][C]26[/C][C]466[/C][C]464.5882765177[/C][C]1.41172348229952[/C][/ROW]
[ROW][C]27[/C][C]417[/C][C]421.898914112557[/C][C]-4.8989141125568[/C][/ROW]
[ROW][C]28[/C][C]387[/C][C]383.482386916283[/C][C]3.51761308371668[/C][/ROW]
[ROW][C]29[/C][C]370[/C][C]373.180902887401[/C][C]-3.18090288740138[/C][/ROW]
[ROW][C]30[/C][C]344[/C][C]345.38371308293[/C][C]-1.38371308292987[/C][/ROW]
[ROW][C]31[/C][C]396[/C][C]400.346759168915[/C][C]-4.34675916891457[/C][/ROW]
[ROW][C]32[/C][C]349[/C][C]350.196795113377[/C][C]-1.19679511337652[/C][/ROW]
[ROW][C]33[/C][C]326[/C][C]334.775218203894[/C][C]-8.77521820389401[/C][/ROW]
[ROW][C]34[/C][C]303[/C][C]302.500316032091[/C][C]0.499683967908872[/C][/ROW]
[ROW][C]35[/C][C]300[/C][C]303.686656771706[/C][C]-3.68665677170593[/C][/ROW]
[ROW][C]36[/C][C]329[/C][C]326.799687625358[/C][C]2.20031237464205[/C][/ROW]
[ROW][C]37[/C][C]304[/C][C]293.757388430054[/C][C]10.2426115699457[/C][/ROW]
[ROW][C]38[/C][C]286[/C][C]293.35957889744[/C][C]-7.35957889744041[/C][/ROW]
[ROW][C]39[/C][C]281[/C][C]285.986295492933[/C][C]-4.98629549293344[/C][/ROW]
[ROW][C]40[/C][C]377[/C][C]374.926215726309[/C][C]2.07378427369086[/C][/ROW]
[ROW][C]41[/C][C]344[/C][C]348.224509694128[/C][C]-4.22450969412763[/C][/ROW]
[ROW][C]42[/C][C]369[/C][C]355.125106112632[/C][C]13.8748938873679[/C][/ROW]
[ROW][C]43[/C][C]390[/C][C]385.649741286888[/C][C]4.35025871311244[/C][/ROW]
[ROW][C]44[/C][C]406[/C][C]413.676413099491[/C][C]-7.6764130994909[/C][/ROW]
[ROW][C]45[/C][C]426[/C][C]420.244072870903[/C][C]5.75592712909724[/C][/ROW]
[ROW][C]46[/C][C]467[/C][C]452.789442956761[/C][C]14.2105570432391[/C][/ROW]
[ROW][C]47[/C][C]437[/C][C]428.019058189701[/C][C]8.98094181029916[/C][/ROW]
[ROW][C]48[/C][C]410[/C][C]400.921231543186[/C][C]9.07876845681388[/C][/ROW]
[ROW][C]49[/C][C]390[/C][C]390.103393803677[/C][C]-0.103393803677157[/C][/ROW]
[ROW][C]50[/C][C]418[/C][C]428.054721672489[/C][C]-10.054721672489[/C][/ROW]
[ROW][C]51[/C][C]398[/C][C]402.188240195469[/C][C]-4.1882401954689[/C][/ROW]
[ROW][C]52[/C][C]422[/C][C]437.632602233193[/C][C]-15.6326022331929[/C][/ROW]
[ROW][C]53[/C][C]439[/C][C]442.855440348637[/C][C]-3.85544034863745[/C][/ROW]
[ROW][C]54[/C][C]419[/C][C]426.249003300492[/C][C]-7.24900330049198[/C][/ROW]
[ROW][C]55[/C][C]484[/C][C]488.214874218207[/C][C]-4.21487421820737[/C][/ROW]
[ROW][C]56[/C][C]491[/C][C]483.836921886758[/C][C]7.16307811324212[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=189742&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=189742&T=4

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


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1277262.368655100414.6313448996001
2232235.670618236501-3.6706182365008
3256268.0952063494-12.0952063493997
4242252.600219865148-10.6002198651478
5302291.76962775757110.2303722424293
6282283.718045282497-1.71804528249739
7288280.0817683470737.91823165292671
8321322.617217475622-1.61721747562161
9316311.8499186771944.15008132280635
10396390.3326863594025.66731364059774
11362370.849843382683-8.84984338268318
12392378.4659353237813.5340646762196
13414406.8911352770397.10886472296133
14417422.86309524304-5.86309524303982
15476475.3801690874340.619830912565559
16488478.2806332164439.71936678355712
17489471.10375589741817.8962441025822
18467480.015318002414-13.0153180024139
19460466.27745841015-6.27745841014966
20482486.208359181485-4.20835918148514
21510506.2617477647583.73825223524173
22493479.88180691451413.1181930854857
23476488.993181058437-12.9931810584367
24448458.364739973191-10.364739973191
25410413.404979424846-3.40497942484599
26466464.58827651771.41172348229952
27417421.898914112557-4.8989141125568
28387383.4823869162833.51761308371668
29370373.180902887401-3.18090288740138
30344345.38371308293-1.38371308292987
31396400.346759168915-4.34675916891457
32349350.196795113377-1.19679511337652
33326334.775218203894-8.77521820389401
34303302.5003160320910.499683967908872
35300303.686656771706-3.68665677170593
36329326.7996876253582.20031237464205
37304293.75738843005410.2426115699457
38286293.35957889744-7.35957889744041
39281285.986295492933-4.98629549293344
40377374.9262157263092.07378427369086
41344348.224509694128-4.22450969412763
42369355.12510611263213.8748938873679
43390385.6497412868884.35025871311244
44406413.676413099491-7.6764130994909
45426420.2440728709035.75592712909724
46467452.78944295676114.2105570432391
47437428.0190581897018.98094181029916
48410400.9212315431869.07876845681388
49390390.103393803677-0.103393803677157
50418428.054721672489-10.054721672489
51398402.188240195469-4.1882401954689
52422437.632602233193-15.6326022331929
53439442.855440348637-3.85544034863745
54419426.249003300492-7.24900330049198
55484488.214874218207-4.21487421820737
56491483.8369218867587.16307811324212






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.815578847023450.36884230595310.18442115297655
110.7736456032058960.4527087935882080.226354396794104
120.6984093887038640.6031812225922710.301590611296136
130.6017597428443510.7964805143112980.398240257155649
140.4843217363624830.9686434727249660.515678263637517
150.4060221894811370.8120443789622740.593977810518863
160.3372208195594040.6744416391188080.662779180440596
170.4233040818683040.8466081637366080.576695918131696
180.5600929895266320.8798140209467370.439907010473368
190.5262310240243020.9475379519513970.473768975975698
200.6550297644376470.6899404711247060.344970235562353
210.6578762898966270.6842474202067460.342123710103373
220.8044663248201390.3910673503597220.195533675179861
230.948117631339990.1037647373200190.0518823686600095
240.9476271585304140.1047456829391720.0523728414695862
250.9225522205195120.1548955589609750.0774477794804877
260.8979595584578320.2040808830843360.102040441542168
270.8843872323561290.2312255352877410.11561276764387
280.8511440489583480.2977119020833050.148855951041652
290.8159391237851360.3681217524297290.184060876214864
300.7694577851387660.4610844297224680.230542214861234
310.7134862486726170.5730275026547660.286513751327383
320.6415090671953710.7169818656092580.358490932804629
330.6670693843686840.6658612312626320.332930615631316
340.6308901262930830.7382197474138330.369109873706917
350.548832045776450.90233590844710.45116795422355
360.4534795220811980.9069590441623970.546520477918802
370.4541143689795750.908228737959150.545885631020425
380.3789374253515180.7578748507030360.621062574648482
390.313544015366510.6270880307330210.68645598463349
400.2526535600638150.5053071201276310.747346439936185
410.2352824171943060.4705648343886120.764717582805694
420.3675740718393360.7351481436786720.632425928160664
430.7941925354895910.4116149290208180.205807464510409
440.9079548190108260.1840903619783480.092045180989174
450.8294967048093690.3410065903812620.170503295190631
460.9440279758382550.111944048323490.0559720241617448

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.81557884702345 & 0.3688423059531 & 0.18442115297655 \tabularnewline
11 & 0.773645603205896 & 0.452708793588208 & 0.226354396794104 \tabularnewline
12 & 0.698409388703864 & 0.603181222592271 & 0.301590611296136 \tabularnewline
13 & 0.601759742844351 & 0.796480514311298 & 0.398240257155649 \tabularnewline
14 & 0.484321736362483 & 0.968643472724966 & 0.515678263637517 \tabularnewline
15 & 0.406022189481137 & 0.812044378962274 & 0.593977810518863 \tabularnewline
16 & 0.337220819559404 & 0.674441639118808 & 0.662779180440596 \tabularnewline
17 & 0.423304081868304 & 0.846608163736608 & 0.576695918131696 \tabularnewline
18 & 0.560092989526632 & 0.879814020946737 & 0.439907010473368 \tabularnewline
19 & 0.526231024024302 & 0.947537951951397 & 0.473768975975698 \tabularnewline
20 & 0.655029764437647 & 0.689940471124706 & 0.344970235562353 \tabularnewline
21 & 0.657876289896627 & 0.684247420206746 & 0.342123710103373 \tabularnewline
22 & 0.804466324820139 & 0.391067350359722 & 0.195533675179861 \tabularnewline
23 & 0.94811763133999 & 0.103764737320019 & 0.0518823686600095 \tabularnewline
24 & 0.947627158530414 & 0.104745682939172 & 0.0523728414695862 \tabularnewline
25 & 0.922552220519512 & 0.154895558960975 & 0.0774477794804877 \tabularnewline
26 & 0.897959558457832 & 0.204080883084336 & 0.102040441542168 \tabularnewline
27 & 0.884387232356129 & 0.231225535287741 & 0.11561276764387 \tabularnewline
28 & 0.851144048958348 & 0.297711902083305 & 0.148855951041652 \tabularnewline
29 & 0.815939123785136 & 0.368121752429729 & 0.184060876214864 \tabularnewline
30 & 0.769457785138766 & 0.461084429722468 & 0.230542214861234 \tabularnewline
31 & 0.713486248672617 & 0.573027502654766 & 0.286513751327383 \tabularnewline
32 & 0.641509067195371 & 0.716981865609258 & 0.358490932804629 \tabularnewline
33 & 0.667069384368684 & 0.665861231262632 & 0.332930615631316 \tabularnewline
34 & 0.630890126293083 & 0.738219747413833 & 0.369109873706917 \tabularnewline
35 & 0.54883204577645 & 0.9023359084471 & 0.45116795422355 \tabularnewline
36 & 0.453479522081198 & 0.906959044162397 & 0.546520477918802 \tabularnewline
37 & 0.454114368979575 & 0.90822873795915 & 0.545885631020425 \tabularnewline
38 & 0.378937425351518 & 0.757874850703036 & 0.621062574648482 \tabularnewline
39 & 0.31354401536651 & 0.627088030733021 & 0.68645598463349 \tabularnewline
40 & 0.252653560063815 & 0.505307120127631 & 0.747346439936185 \tabularnewline
41 & 0.235282417194306 & 0.470564834388612 & 0.764717582805694 \tabularnewline
42 & 0.367574071839336 & 0.735148143678672 & 0.632425928160664 \tabularnewline
43 & 0.794192535489591 & 0.411614929020818 & 0.205807464510409 \tabularnewline
44 & 0.907954819010826 & 0.184090361978348 & 0.092045180989174 \tabularnewline
45 & 0.829496704809369 & 0.341006590381262 & 0.170503295190631 \tabularnewline
46 & 0.944027975838255 & 0.11194404832349 & 0.0559720241617448 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=189742&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]10[/C][C]0.81557884702345[/C][C]0.3688423059531[/C][C]0.18442115297655[/C][/ROW]
[ROW][C]11[/C][C]0.773645603205896[/C][C]0.452708793588208[/C][C]0.226354396794104[/C][/ROW]
[ROW][C]12[/C][C]0.698409388703864[/C][C]0.603181222592271[/C][C]0.301590611296136[/C][/ROW]
[ROW][C]13[/C][C]0.601759742844351[/C][C]0.796480514311298[/C][C]0.398240257155649[/C][/ROW]
[ROW][C]14[/C][C]0.484321736362483[/C][C]0.968643472724966[/C][C]0.515678263637517[/C][/ROW]
[ROW][C]15[/C][C]0.406022189481137[/C][C]0.812044378962274[/C][C]0.593977810518863[/C][/ROW]
[ROW][C]16[/C][C]0.337220819559404[/C][C]0.674441639118808[/C][C]0.662779180440596[/C][/ROW]
[ROW][C]17[/C][C]0.423304081868304[/C][C]0.846608163736608[/C][C]0.576695918131696[/C][/ROW]
[ROW][C]18[/C][C]0.560092989526632[/C][C]0.879814020946737[/C][C]0.439907010473368[/C][/ROW]
[ROW][C]19[/C][C]0.526231024024302[/C][C]0.947537951951397[/C][C]0.473768975975698[/C][/ROW]
[ROW][C]20[/C][C]0.655029764437647[/C][C]0.689940471124706[/C][C]0.344970235562353[/C][/ROW]
[ROW][C]21[/C][C]0.657876289896627[/C][C]0.684247420206746[/C][C]0.342123710103373[/C][/ROW]
[ROW][C]22[/C][C]0.804466324820139[/C][C]0.391067350359722[/C][C]0.195533675179861[/C][/ROW]
[ROW][C]23[/C][C]0.94811763133999[/C][C]0.103764737320019[/C][C]0.0518823686600095[/C][/ROW]
[ROW][C]24[/C][C]0.947627158530414[/C][C]0.104745682939172[/C][C]0.0523728414695862[/C][/ROW]
[ROW][C]25[/C][C]0.922552220519512[/C][C]0.154895558960975[/C][C]0.0774477794804877[/C][/ROW]
[ROW][C]26[/C][C]0.897959558457832[/C][C]0.204080883084336[/C][C]0.102040441542168[/C][/ROW]
[ROW][C]27[/C][C]0.884387232356129[/C][C]0.231225535287741[/C][C]0.11561276764387[/C][/ROW]
[ROW][C]28[/C][C]0.851144048958348[/C][C]0.297711902083305[/C][C]0.148855951041652[/C][/ROW]
[ROW][C]29[/C][C]0.815939123785136[/C][C]0.368121752429729[/C][C]0.184060876214864[/C][/ROW]
[ROW][C]30[/C][C]0.769457785138766[/C][C]0.461084429722468[/C][C]0.230542214861234[/C][/ROW]
[ROW][C]31[/C][C]0.713486248672617[/C][C]0.573027502654766[/C][C]0.286513751327383[/C][/ROW]
[ROW][C]32[/C][C]0.641509067195371[/C][C]0.716981865609258[/C][C]0.358490932804629[/C][/ROW]
[ROW][C]33[/C][C]0.667069384368684[/C][C]0.665861231262632[/C][C]0.332930615631316[/C][/ROW]
[ROW][C]34[/C][C]0.630890126293083[/C][C]0.738219747413833[/C][C]0.369109873706917[/C][/ROW]
[ROW][C]35[/C][C]0.54883204577645[/C][C]0.9023359084471[/C][C]0.45116795422355[/C][/ROW]
[ROW][C]36[/C][C]0.453479522081198[/C][C]0.906959044162397[/C][C]0.546520477918802[/C][/ROW]
[ROW][C]37[/C][C]0.454114368979575[/C][C]0.90822873795915[/C][C]0.545885631020425[/C][/ROW]
[ROW][C]38[/C][C]0.378937425351518[/C][C]0.757874850703036[/C][C]0.621062574648482[/C][/ROW]
[ROW][C]39[/C][C]0.31354401536651[/C][C]0.627088030733021[/C][C]0.68645598463349[/C][/ROW]
[ROW][C]40[/C][C]0.252653560063815[/C][C]0.505307120127631[/C][C]0.747346439936185[/C][/ROW]
[ROW][C]41[/C][C]0.235282417194306[/C][C]0.470564834388612[/C][C]0.764717582805694[/C][/ROW]
[ROW][C]42[/C][C]0.367574071839336[/C][C]0.735148143678672[/C][C]0.632425928160664[/C][/ROW]
[ROW][C]43[/C][C]0.794192535489591[/C][C]0.411614929020818[/C][C]0.205807464510409[/C][/ROW]
[ROW][C]44[/C][C]0.907954819010826[/C][C]0.184090361978348[/C][C]0.092045180989174[/C][/ROW]
[ROW][C]45[/C][C]0.829496704809369[/C][C]0.341006590381262[/C][C]0.170503295190631[/C][/ROW]
[ROW][C]46[/C][C]0.944027975838255[/C][C]0.11194404832349[/C][C]0.0559720241617448[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=189742&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=189742&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.815578847023450.36884230595310.18442115297655
110.7736456032058960.4527087935882080.226354396794104
120.6984093887038640.6031812225922710.301590611296136
130.6017597428443510.7964805143112980.398240257155649
140.4843217363624830.9686434727249660.515678263637517
150.4060221894811370.8120443789622740.593977810518863
160.3372208195594040.6744416391188080.662779180440596
170.4233040818683040.8466081637366080.576695918131696
180.5600929895266320.8798140209467370.439907010473368
190.5262310240243020.9475379519513970.473768975975698
200.6550297644376470.6899404711247060.344970235562353
210.6578762898966270.6842474202067460.342123710103373
220.8044663248201390.3910673503597220.195533675179861
230.948117631339990.1037647373200190.0518823686600095
240.9476271585304140.1047456829391720.0523728414695862
250.9225522205195120.1548955589609750.0774477794804877
260.8979595584578320.2040808830843360.102040441542168
270.8843872323561290.2312255352877410.11561276764387
280.8511440489583480.2977119020833050.148855951041652
290.8159391237851360.3681217524297290.184060876214864
300.7694577851387660.4610844297224680.230542214861234
310.7134862486726170.5730275026547660.286513751327383
320.6415090671953710.7169818656092580.358490932804629
330.6670693843686840.6658612312626320.332930615631316
340.6308901262930830.7382197474138330.369109873706917
350.548832045776450.90233590844710.45116795422355
360.4534795220811980.9069590441623970.546520477918802
370.4541143689795750.908228737959150.545885631020425
380.3789374253515180.7578748507030360.621062574648482
390.313544015366510.6270880307330210.68645598463349
400.2526535600638150.5053071201276310.747346439936185
410.2352824171943060.4705648343886120.764717582805694
420.3675740718393360.7351481436786720.632425928160664
430.7941925354895910.4116149290208180.205807464510409
440.9079548190108260.1840903619783480.092045180989174
450.8294967048093690.3410065903812620.170503295190631
460.9440279758382550.111944048323490.0559720241617448






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

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=189742&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=189742&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=189742&T=6

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


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

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


Figures (Output of Computation)

Figure 1
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Figure 10
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Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal 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')
}