FreeStatistics.org

Free Statistics

of Irreproducible Research!

Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationMon, 19 Dec 2011 13:47:52 -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/2011/Dec/19/t1324320591fcxjxli12eysoda.htm/, Retrieved Wed, 15 May 2024 20:30:21 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=157616, Retrieved Wed, 15 May 2024 20:30:21 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact166

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Kendall tau Correlation Matrix] [paper statistiek,...] [2011-12-19 15:59:25] [4b648d52023f19d55c572f0eddd72b1f]
- R P   [Kendall tau Correlation Matrix] [Paper Kendall Tau] [2011-12-19 16:21:41] [74be16979710d4c4e7c6647856088456]
- RMP       [Multiple Regression] [paper statistiek] [2011-12-19 18:47:52] [d003b870c357302420e03293d5e8342f] [Current]
Feedback Forum

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
2	210907	79	94	112285	146283	30	-1
4	179321	108	103	101193	96933	30	3
0	149061	43	93	116174	95757	26	0
0	237213	78	123	66198	143983	38	3
-4	173326	86	148	71701	75851	44	4
4	133131	44	90	57793	59238	30	0
4	258873	104	124	80444	93163	40	0
0	324799	158	168	97668	151511	47	7
-1	230964	102	115	133824	136368	30	1
0	236785	77	71	101481	112642	31	0
1	344297	80	108	67654	127766	30	1
0	174724	123	120	69112	85646	34	4
3	174415	73	114	82753	98579	31	1
-1	223632	105	120	72654	131741	33	5
4	294424	107	124	101494	171975	33	13
3	325107	84	126	79215	159676	36	4
1	106408	33	37	31081	58391	14	0
0	96560	42	38	22996	31580	17	0
-2	265769	96	120	83122	136815	32	6
-3	269651	106	93	70106	120642	30	0
-4	149112	56	95	60578	69107	35	1
2	152871	59	90	79892	108016	28	3
2	362301	76	110	100708	79336	34	1
-4	183167	91	138	82875	93176	39	0
3	277965	115	133	139077	161632	39	2
2	218946	76	96	80670	102996	29	3
2	244052	101	164	143558	160604	44	4
0	341570	94	78	117105	158051	21	12
5	233328	92	102	120733	162647	28	0
-2	206161	75	99	73107	60622	28	3
0	311473	128	129	132068	179566	38	0
-2	207176	56	114	87011	96144	32	4
-3	196553	41	99	95260	129847	29	-1
2	143246	67	104	106671	71180	27	2
2	182192	77	138	70054	86767	40	1
2	194979	66	151	74011	93487	40	1
0	167488	69	72	83737	82981	28	0
4	143756	105	120	69094	73815	34	2
4	275541	116	115	93133	94552	33	0
2	152299	62	98	61370	67808	33	2
2	193339	100	71	84651	106175	35	4
-4	130585	67	107	95364	76669	29	0
3	112611	46	73	26706	57283	20	0
3	148446	135	129	126846	72413	37	6
2	182079	124	118	102860	96971	33	13
-1	243060	58	104	111813	120336	29	4
-3	162765	68	107	120293	93913	28	-1
0	85574	37	36	24266	32036	21	3
1	225060	93	139	109825	102255	41	0
-3	133328	56	56	40909	63506	20	2
3	100750	83	93	140867	68370	30	0
0	101523	59	87	61056	50517	22	1
0	243511	133	110	101338	103950	42	1
0	152474	106	83	65567	84396	32	0
3	132487	71	98	40735	55515	36	31
-3	317394	116	82	91413	209056	31	2
0	244749	98	115	76643	142775	33	5
-4	184510	64	140	110681	68847	40	1
2	128423	32	120	92696	20112	38	1
-1	97839	25	66	94785	61023	24	2
3	172494	46	139	86687	112494	43	13
2	229242	63	119	91721	78876	31	5
5	351619	95	141	115168	170745	40	3
2	324598	113	133	135777	122037	37	1
-2	195838	111	98	102372	112283	31	1
0	254488	120	117	103772	120691	39	4
3	199476	87	105	135400	122422	32	2
-2	92499	25	55	21399	25899	18	0
0	224330	131	132	130115	139296	39	4
6	181633	47	73	64466	89455	30	0
-3	271856	109	86	54990	147866	37	0
3	95227	37	48	34777	14336	32	0
0	98146	15	48	27114	30059	17	7
-2	118612	54	43	30080	41907	12	3
1	65475	16	46	69008	35885	13	4
0	108446	22	65	46300	55764	17	1
2	121848	37	52	30594	35619	17	0
2	76302	29	68	30976	40557	20	2
-3	98104	55	47	25568	44197	17	0
-2	30989	5	41	4154	4103	17	0
1	31774	0	47	4143	4694	17	0
-4	150580	27	71	45588	62991	22	2
0	54157	37	30	18625	24261	15	1
1	59382	29	24	26263	21425	12	0
0	84105	17	63	20055	27184	17	0

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 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 & 4 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=157616&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]4 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=157616&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=157616&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 time4 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net






Multiple Linear Regression - Estimated Regression Equation
time_in_rfc[t] = + 19762.366187134 + 905.833933759794estscore[t] + 323.294152339804blogged_computations[t] + 280.669133799413feedback_messages_p120[t] -0.188200853110265totsize[t] + 1.23813287142238totseconds[t] + 599.33252533969compendiums_reviewed[t] -566.576400883621`difference_hyperlinks-blogs`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
time_in_rfc[t] =  +  19762.366187134 +  905.833933759794estscore[t] +  323.294152339804blogged_computations[t] +  280.669133799413feedback_messages_p120[t] -0.188200853110265totsize[t] +  1.23813287142238totseconds[t] +  599.33252533969compendiums_reviewed[t] -566.576400883621`difference_hyperlinks-blogs`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=157616&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]time_in_rfc[t] =  +  19762.366187134 +  905.833933759794estscore[t] +  323.294152339804blogged_computations[t] +  280.669133799413feedback_messages_p120[t] -0.188200853110265totsize[t] +  1.23813287142238totseconds[t] +  599.33252533969compendiums_reviewed[t] -566.576400883621`difference_hyperlinks-blogs`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=157616&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=157616&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
time_in_rfc[t] = + 19762.366187134 + 905.833933759794estscore[t] + 323.294152339804blogged_computations[t] + 280.669133799413feedback_messages_p120[t] -0.188200853110265totsize[t] + 1.23813287142238totseconds[t] + 599.33252533969compendiums_reviewed[t] -566.576400883621`difference_hyperlinks-blogs`[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)19762.36618713416255.0818651.21580.2277890.113894
estscore905.8339337597941817.8234240.49830.6196880.309844
blogged_computations323.294152339804211.3393111.52970.1301790.06509
feedback_messages_p120280.669133799413321.2964480.87360.3850790.192539
totsize-0.1882008531102650.201903-0.93210.3541790.17709
totseconds1.238132871422380.1546788.004600
compendiums_reviewed599.332525339691211.1296430.49490.6221120.311056
`difference_hyperlinks-blogs`-566.5764008836211048.305402-0.54050.5904340.295217

\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) & 19762.366187134 & 16255.081865 & 1.2158 & 0.227789 & 0.113894 \tabularnewline
estscore & 905.833933759794 & 1817.823424 & 0.4983 & 0.619688 & 0.309844 \tabularnewline
blogged_computations & 323.294152339804 & 211.339311 & 1.5297 & 0.130179 & 0.06509 \tabularnewline
feedback_messages_p120 & 280.669133799413 & 321.296448 & 0.8736 & 0.385079 & 0.192539 \tabularnewline
totsize & -0.188200853110265 & 0.201903 & -0.9321 & 0.354179 & 0.17709 \tabularnewline
totseconds & 1.23813287142238 & 0.154678 & 8.0046 & 0 & 0 \tabularnewline
compendiums_reviewed & 599.33252533969 & 1211.129643 & 0.4949 & 0.622112 & 0.311056 \tabularnewline
`difference_hyperlinks-blogs` & -566.576400883621 & 1048.305402 & -0.5405 & 0.590434 & 0.295217 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=157616&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]19762.366187134[/C][C]16255.081865[/C][C]1.2158[/C][C]0.227789[/C][C]0.113894[/C][/ROW]
[ROW][C]estscore[/C][C]905.833933759794[/C][C]1817.823424[/C][C]0.4983[/C][C]0.619688[/C][C]0.309844[/C][/ROW]
[ROW][C]blogged_computations[/C][C]323.294152339804[/C][C]211.339311[/C][C]1.5297[/C][C]0.130179[/C][C]0.06509[/C][/ROW]
[ROW][C]feedback_messages_p120[/C][C]280.669133799413[/C][C]321.296448[/C][C]0.8736[/C][C]0.385079[/C][C]0.192539[/C][/ROW]
[ROW][C]totsize[/C][C]-0.188200853110265[/C][C]0.201903[/C][C]-0.9321[/C][C]0.354179[/C][C]0.17709[/C][/ROW]
[ROW][C]totseconds[/C][C]1.23813287142238[/C][C]0.154678[/C][C]8.0046[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]compendiums_reviewed[/C][C]599.33252533969[/C][C]1211.129643[/C][C]0.4949[/C][C]0.622112[/C][C]0.311056[/C][/ROW]
[ROW][C]`difference_hyperlinks-blogs`[/C][C]-566.576400883621[/C][C]1048.305402[/C][C]-0.5405[/C][C]0.590434[/C][C]0.295217[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=157616&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=157616&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)19762.36618713416255.0818651.21580.2277890.113894
estscore905.8339337597941817.8234240.49830.6196880.309844
blogged_computations323.294152339804211.3393111.52970.1301790.06509
feedback_messages_p120280.669133799413321.2964480.87360.3850790.192539
totsize-0.1882008531102650.201903-0.93210.3541790.17709
totseconds1.238132871422380.1546788.004600
compendiums_reviewed599.332525339691211.1296430.49490.6221120.311056
`difference_hyperlinks-blogs`-566.5764008836211048.305402-0.54050.5904340.295217






Multiple Linear Regression - Regression Statistics
Multiple R0.879802361857769
R-squared0.774052195930509
Adjusted R-squared0.753511486469647
F-TEST (value)37.6838101627088
F-TEST (DF numerator)7
F-TEST (DF denominator)77
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation39650.7916870487
Sum Squared Residuals121058266668.549

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.879802361857769 \tabularnewline
R-squared & 0.774052195930509 \tabularnewline
Adjusted R-squared & 0.753511486469647 \tabularnewline
F-TEST (value) & 37.6838101627088 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 77 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 39650.7916870487 \tabularnewline
Sum Squared Residuals & 121058266668.549 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=157616&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.879802361857769[/C][/ROW]
[ROW][C]R-squared[/C][C]0.774052195930509[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.753511486469647[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]37.6838101627088[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]77[/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]39650.7916870487[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]121058266668.549[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=157616&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=157616&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 R0.879802361857769
R-squared0.774052195930509
Adjusted R-squared0.753511486469647
F-TEST (value)37.6838101627088
F-TEST (DF numerator)7
F-TEST (DF denominator)77
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation39650.7916870487
Sum Squared Residuals121058266668.549






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1210907252029.380866511-41122.3808665112
2179321204461.96241055-25140.9624105499
3149061172044.733299484-22983.7332994839
4237213266388.085439039-29175.085439039
5173326190005.111928443-16679.1119284428
6133131143318.66556078-10187.6655607799
7258873215993.11064294942879.8893570514
8324799311407.39918040713391.6008195927
9230964245178.797981773-14214.7979817728
10236785203529.78483086533255.2151691355
11344297239716.142051552104580.857948448
12174724204353.033784559-29629.0337845589
13174415202569.069381512-28154.069381512
14223632252867.123468957-29235.1234689574
15294424299020.272115673-4596.27211567262
16325107287102.32675048838004.6732495125
17106408116558.666233145-10150.6662331453
189656088967.16986195337592.83013804667
19265769252197.93289715513571.0671028451
20269651231573.066603438077.9333965996
21149112155479.949745746-6367.94974574639
22152871199692.610274384-46821.6102743841
23362301176103.901783217186197.098216783
24183167207432.229993722-24265.2299937224
25277965293175.988215019-15210.9882150192
26218946201110.11091403717835.8890859632
27244052296192.160506606-52140.1605066058
28341570251480.15272266190089.8472773392
29233328278100.703767921-44772.7037679205
30206161146364.87666113759796.1233388626
31311473317596.228830926-6123.22883092595
32207176187627.28967116819548.7103288319
33196553218873.214201961-22320.214201961
34143246155527.882144684-12281.8821446837
35182192202851.601152761-20659.6011527606
36194979210519.606336616-15540.6063366162
37167488166041.2800082561446.71999174421
38143756188645.265192807-44889.2651928067
39275541212482.97652274363058.0234772568
40152299160174.094536538-7875.09453653822
41193339208068.657778381-14729.6577783811
42130585162190.802173325-31605.8021733251
43112611135724.769560721-23113.7695607208
44148446186891.132051362-38445.1320513621
45182079207898.58978162-25819.5897816205
46243060209860.17586220433199.8241377962
47162765180045.87830259-17280.8783025903
488557487812.5352372819-2238.53523728194
49225060220256.3175000024803.68249999814
50133328132650.063547339677.936452660835
51100750151535.342680218-50785.3426802177
52101523126929.641950465-25406.641950465
53243511227871.49676151515639.5032384849
54152474188659.421732057-36185.421732057
55132487138020.015007285-5533.01500728532
56317394336643.111495295-19249.1114952952
57244749263017.377567559-18268.3775675592
58184510163941.73472218320568.2652778166
5912842395263.664577619333159.3354223807
6097839116429.841049767-18590.8410497673
61172494217738.165856502-45244.165856502
62229242171484.61677348957757.3832265113
63351619306582.6812848845036.3187151205
64324598242588.66909807982009.3309019212
65195838219109.431693637-23271.4316936369
66254488242405.13146303512082.8685369648
67199476224214.513174158-24738.5131741582
689249980296.933124453312202.0668755467
69224330268249.091145094-43919.0911450939
70181633177485.6372936944147.36270630594
71271856271325.366188419530.63381158059
729522778297.322634323616929.6773656764
739814676420.673070045921725.3269299541
7411861299194.968981904919417.0310180951
756547575719.7375585441-10244.7375585441
76108446115069.949708455-6623.94970845498
7712184896662.603426708925185.3965732911
7876302105273.808516228-28971.8085162285
7998104108115.984089823-10011.9840898227
803098945561.5293254901-14572.5293254901
813177449080.3819042618-17306.3819042618
82150580126259.1710329124320.8289670898
835415775100.8360213006-20943.8360213006
845938265556.057819-6174.05781899995
858410583012.21100466811092.78899533194

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 210907 & 252029.380866511 & -41122.3808665112 \tabularnewline
2 & 179321 & 204461.96241055 & -25140.9624105499 \tabularnewline
3 & 149061 & 172044.733299484 & -22983.7332994839 \tabularnewline
4 & 237213 & 266388.085439039 & -29175.085439039 \tabularnewline
5 & 173326 & 190005.111928443 & -16679.1119284428 \tabularnewline
6 & 133131 & 143318.66556078 & -10187.6655607799 \tabularnewline
7 & 258873 & 215993.110642949 & 42879.8893570514 \tabularnewline
8 & 324799 & 311407.399180407 & 13391.6008195927 \tabularnewline
9 & 230964 & 245178.797981773 & -14214.7979817728 \tabularnewline
10 & 236785 & 203529.784830865 & 33255.2151691355 \tabularnewline
11 & 344297 & 239716.142051552 & 104580.857948448 \tabularnewline
12 & 174724 & 204353.033784559 & -29629.0337845589 \tabularnewline
13 & 174415 & 202569.069381512 & -28154.069381512 \tabularnewline
14 & 223632 & 252867.123468957 & -29235.1234689574 \tabularnewline
15 & 294424 & 299020.272115673 & -4596.27211567262 \tabularnewline
16 & 325107 & 287102.326750488 & 38004.6732495125 \tabularnewline
17 & 106408 & 116558.666233145 & -10150.6662331453 \tabularnewline
18 & 96560 & 88967.1698619533 & 7592.83013804667 \tabularnewline
19 & 265769 & 252197.932897155 & 13571.0671028451 \tabularnewline
20 & 269651 & 231573.0666034 & 38077.9333965996 \tabularnewline
21 & 149112 & 155479.949745746 & -6367.94974574639 \tabularnewline
22 & 152871 & 199692.610274384 & -46821.6102743841 \tabularnewline
23 & 362301 & 176103.901783217 & 186197.098216783 \tabularnewline
24 & 183167 & 207432.229993722 & -24265.2299937224 \tabularnewline
25 & 277965 & 293175.988215019 & -15210.9882150192 \tabularnewline
26 & 218946 & 201110.110914037 & 17835.8890859632 \tabularnewline
27 & 244052 & 296192.160506606 & -52140.1605066058 \tabularnewline
28 & 341570 & 251480.152722661 & 90089.8472773392 \tabularnewline
29 & 233328 & 278100.703767921 & -44772.7037679205 \tabularnewline
30 & 206161 & 146364.876661137 & 59796.1233388626 \tabularnewline
31 & 311473 & 317596.228830926 & -6123.22883092595 \tabularnewline
32 & 207176 & 187627.289671168 & 19548.7103288319 \tabularnewline
33 & 196553 & 218873.214201961 & -22320.214201961 \tabularnewline
34 & 143246 & 155527.882144684 & -12281.8821446837 \tabularnewline
35 & 182192 & 202851.601152761 & -20659.6011527606 \tabularnewline
36 & 194979 & 210519.606336616 & -15540.6063366162 \tabularnewline
37 & 167488 & 166041.280008256 & 1446.71999174421 \tabularnewline
38 & 143756 & 188645.265192807 & -44889.2651928067 \tabularnewline
39 & 275541 & 212482.976522743 & 63058.0234772568 \tabularnewline
40 & 152299 & 160174.094536538 & -7875.09453653822 \tabularnewline
41 & 193339 & 208068.657778381 & -14729.6577783811 \tabularnewline
42 & 130585 & 162190.802173325 & -31605.8021733251 \tabularnewline
43 & 112611 & 135724.769560721 & -23113.7695607208 \tabularnewline
44 & 148446 & 186891.132051362 & -38445.1320513621 \tabularnewline
45 & 182079 & 207898.58978162 & -25819.5897816205 \tabularnewline
46 & 243060 & 209860.175862204 & 33199.8241377962 \tabularnewline
47 & 162765 & 180045.87830259 & -17280.8783025903 \tabularnewline
48 & 85574 & 87812.5352372819 & -2238.53523728194 \tabularnewline
49 & 225060 & 220256.317500002 & 4803.68249999814 \tabularnewline
50 & 133328 & 132650.063547339 & 677.936452660835 \tabularnewline
51 & 100750 & 151535.342680218 & -50785.3426802177 \tabularnewline
52 & 101523 & 126929.641950465 & -25406.641950465 \tabularnewline
53 & 243511 & 227871.496761515 & 15639.5032384849 \tabularnewline
54 & 152474 & 188659.421732057 & -36185.421732057 \tabularnewline
55 & 132487 & 138020.015007285 & -5533.01500728532 \tabularnewline
56 & 317394 & 336643.111495295 & -19249.1114952952 \tabularnewline
57 & 244749 & 263017.377567559 & -18268.3775675592 \tabularnewline
58 & 184510 & 163941.734722183 & 20568.2652778166 \tabularnewline
59 & 128423 & 95263.6645776193 & 33159.3354223807 \tabularnewline
60 & 97839 & 116429.841049767 & -18590.8410497673 \tabularnewline
61 & 172494 & 217738.165856502 & -45244.165856502 \tabularnewline
62 & 229242 & 171484.616773489 & 57757.3832265113 \tabularnewline
63 & 351619 & 306582.68128488 & 45036.3187151205 \tabularnewline
64 & 324598 & 242588.669098079 & 82009.3309019212 \tabularnewline
65 & 195838 & 219109.431693637 & -23271.4316936369 \tabularnewline
66 & 254488 & 242405.131463035 & 12082.8685369648 \tabularnewline
67 & 199476 & 224214.513174158 & -24738.5131741582 \tabularnewline
68 & 92499 & 80296.9331244533 & 12202.0668755467 \tabularnewline
69 & 224330 & 268249.091145094 & -43919.0911450939 \tabularnewline
70 & 181633 & 177485.637293694 & 4147.36270630594 \tabularnewline
71 & 271856 & 271325.366188419 & 530.63381158059 \tabularnewline
72 & 95227 & 78297.3226343236 & 16929.6773656764 \tabularnewline
73 & 98146 & 76420.6730700459 & 21725.3269299541 \tabularnewline
74 & 118612 & 99194.9689819049 & 19417.0310180951 \tabularnewline
75 & 65475 & 75719.7375585441 & -10244.7375585441 \tabularnewline
76 & 108446 & 115069.949708455 & -6623.94970845498 \tabularnewline
77 & 121848 & 96662.6034267089 & 25185.3965732911 \tabularnewline
78 & 76302 & 105273.808516228 & -28971.8085162285 \tabularnewline
79 & 98104 & 108115.984089823 & -10011.9840898227 \tabularnewline
80 & 30989 & 45561.5293254901 & -14572.5293254901 \tabularnewline
81 & 31774 & 49080.3819042618 & -17306.3819042618 \tabularnewline
82 & 150580 & 126259.17103291 & 24320.8289670898 \tabularnewline
83 & 54157 & 75100.8360213006 & -20943.8360213006 \tabularnewline
84 & 59382 & 65556.057819 & -6174.05781899995 \tabularnewline
85 & 84105 & 83012.2110046681 & 1092.78899533194 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=157616&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]210907[/C][C]252029.380866511[/C][C]-41122.3808665112[/C][/ROW]
[ROW][C]2[/C][C]179321[/C][C]204461.96241055[/C][C]-25140.9624105499[/C][/ROW]
[ROW][C]3[/C][C]149061[/C][C]172044.733299484[/C][C]-22983.7332994839[/C][/ROW]
[ROW][C]4[/C][C]237213[/C][C]266388.085439039[/C][C]-29175.085439039[/C][/ROW]
[ROW][C]5[/C][C]173326[/C][C]190005.111928443[/C][C]-16679.1119284428[/C][/ROW]
[ROW][C]6[/C][C]133131[/C][C]143318.66556078[/C][C]-10187.6655607799[/C][/ROW]
[ROW][C]7[/C][C]258873[/C][C]215993.110642949[/C][C]42879.8893570514[/C][/ROW]
[ROW][C]8[/C][C]324799[/C][C]311407.399180407[/C][C]13391.6008195927[/C][/ROW]
[ROW][C]9[/C][C]230964[/C][C]245178.797981773[/C][C]-14214.7979817728[/C][/ROW]
[ROW][C]10[/C][C]236785[/C][C]203529.784830865[/C][C]33255.2151691355[/C][/ROW]
[ROW][C]11[/C][C]344297[/C][C]239716.142051552[/C][C]104580.857948448[/C][/ROW]
[ROW][C]12[/C][C]174724[/C][C]204353.033784559[/C][C]-29629.0337845589[/C][/ROW]
[ROW][C]13[/C][C]174415[/C][C]202569.069381512[/C][C]-28154.069381512[/C][/ROW]
[ROW][C]14[/C][C]223632[/C][C]252867.123468957[/C][C]-29235.1234689574[/C][/ROW]
[ROW][C]15[/C][C]294424[/C][C]299020.272115673[/C][C]-4596.27211567262[/C][/ROW]
[ROW][C]16[/C][C]325107[/C][C]287102.326750488[/C][C]38004.6732495125[/C][/ROW]
[ROW][C]17[/C][C]106408[/C][C]116558.666233145[/C][C]-10150.6662331453[/C][/ROW]
[ROW][C]18[/C][C]96560[/C][C]88967.1698619533[/C][C]7592.83013804667[/C][/ROW]
[ROW][C]19[/C][C]265769[/C][C]252197.932897155[/C][C]13571.0671028451[/C][/ROW]
[ROW][C]20[/C][C]269651[/C][C]231573.0666034[/C][C]38077.9333965996[/C][/ROW]
[ROW][C]21[/C][C]149112[/C][C]155479.949745746[/C][C]-6367.94974574639[/C][/ROW]
[ROW][C]22[/C][C]152871[/C][C]199692.610274384[/C][C]-46821.6102743841[/C][/ROW]
[ROW][C]23[/C][C]362301[/C][C]176103.901783217[/C][C]186197.098216783[/C][/ROW]
[ROW][C]24[/C][C]183167[/C][C]207432.229993722[/C][C]-24265.2299937224[/C][/ROW]
[ROW][C]25[/C][C]277965[/C][C]293175.988215019[/C][C]-15210.9882150192[/C][/ROW]
[ROW][C]26[/C][C]218946[/C][C]201110.110914037[/C][C]17835.8890859632[/C][/ROW]
[ROW][C]27[/C][C]244052[/C][C]296192.160506606[/C][C]-52140.1605066058[/C][/ROW]
[ROW][C]28[/C][C]341570[/C][C]251480.152722661[/C][C]90089.8472773392[/C][/ROW]
[ROW][C]29[/C][C]233328[/C][C]278100.703767921[/C][C]-44772.7037679205[/C][/ROW]
[ROW][C]30[/C][C]206161[/C][C]146364.876661137[/C][C]59796.1233388626[/C][/ROW]
[ROW][C]31[/C][C]311473[/C][C]317596.228830926[/C][C]-6123.22883092595[/C][/ROW]
[ROW][C]32[/C][C]207176[/C][C]187627.289671168[/C][C]19548.7103288319[/C][/ROW]
[ROW][C]33[/C][C]196553[/C][C]218873.214201961[/C][C]-22320.214201961[/C][/ROW]
[ROW][C]34[/C][C]143246[/C][C]155527.882144684[/C][C]-12281.8821446837[/C][/ROW]
[ROW][C]35[/C][C]182192[/C][C]202851.601152761[/C][C]-20659.6011527606[/C][/ROW]
[ROW][C]36[/C][C]194979[/C][C]210519.606336616[/C][C]-15540.6063366162[/C][/ROW]
[ROW][C]37[/C][C]167488[/C][C]166041.280008256[/C][C]1446.71999174421[/C][/ROW]
[ROW][C]38[/C][C]143756[/C][C]188645.265192807[/C][C]-44889.2651928067[/C][/ROW]
[ROW][C]39[/C][C]275541[/C][C]212482.976522743[/C][C]63058.0234772568[/C][/ROW]
[ROW][C]40[/C][C]152299[/C][C]160174.094536538[/C][C]-7875.09453653822[/C][/ROW]
[ROW][C]41[/C][C]193339[/C][C]208068.657778381[/C][C]-14729.6577783811[/C][/ROW]
[ROW][C]42[/C][C]130585[/C][C]162190.802173325[/C][C]-31605.8021733251[/C][/ROW]
[ROW][C]43[/C][C]112611[/C][C]135724.769560721[/C][C]-23113.7695607208[/C][/ROW]
[ROW][C]44[/C][C]148446[/C][C]186891.132051362[/C][C]-38445.1320513621[/C][/ROW]
[ROW][C]45[/C][C]182079[/C][C]207898.58978162[/C][C]-25819.5897816205[/C][/ROW]
[ROW][C]46[/C][C]243060[/C][C]209860.175862204[/C][C]33199.8241377962[/C][/ROW]
[ROW][C]47[/C][C]162765[/C][C]180045.87830259[/C][C]-17280.8783025903[/C][/ROW]
[ROW][C]48[/C][C]85574[/C][C]87812.5352372819[/C][C]-2238.53523728194[/C][/ROW]
[ROW][C]49[/C][C]225060[/C][C]220256.317500002[/C][C]4803.68249999814[/C][/ROW]
[ROW][C]50[/C][C]133328[/C][C]132650.063547339[/C][C]677.936452660835[/C][/ROW]
[ROW][C]51[/C][C]100750[/C][C]151535.342680218[/C][C]-50785.3426802177[/C][/ROW]
[ROW][C]52[/C][C]101523[/C][C]126929.641950465[/C][C]-25406.641950465[/C][/ROW]
[ROW][C]53[/C][C]243511[/C][C]227871.496761515[/C][C]15639.5032384849[/C][/ROW]
[ROW][C]54[/C][C]152474[/C][C]188659.421732057[/C][C]-36185.421732057[/C][/ROW]
[ROW][C]55[/C][C]132487[/C][C]138020.015007285[/C][C]-5533.01500728532[/C][/ROW]
[ROW][C]56[/C][C]317394[/C][C]336643.111495295[/C][C]-19249.1114952952[/C][/ROW]
[ROW][C]57[/C][C]244749[/C][C]263017.377567559[/C][C]-18268.3775675592[/C][/ROW]
[ROW][C]58[/C][C]184510[/C][C]163941.734722183[/C][C]20568.2652778166[/C][/ROW]
[ROW][C]59[/C][C]128423[/C][C]95263.6645776193[/C][C]33159.3354223807[/C][/ROW]
[ROW][C]60[/C][C]97839[/C][C]116429.841049767[/C][C]-18590.8410497673[/C][/ROW]
[ROW][C]61[/C][C]172494[/C][C]217738.165856502[/C][C]-45244.165856502[/C][/ROW]
[ROW][C]62[/C][C]229242[/C][C]171484.616773489[/C][C]57757.3832265113[/C][/ROW]
[ROW][C]63[/C][C]351619[/C][C]306582.68128488[/C][C]45036.3187151205[/C][/ROW]
[ROW][C]64[/C][C]324598[/C][C]242588.669098079[/C][C]82009.3309019212[/C][/ROW]
[ROW][C]65[/C][C]195838[/C][C]219109.431693637[/C][C]-23271.4316936369[/C][/ROW]
[ROW][C]66[/C][C]254488[/C][C]242405.131463035[/C][C]12082.8685369648[/C][/ROW]
[ROW][C]67[/C][C]199476[/C][C]224214.513174158[/C][C]-24738.5131741582[/C][/ROW]
[ROW][C]68[/C][C]92499[/C][C]80296.9331244533[/C][C]12202.0668755467[/C][/ROW]
[ROW][C]69[/C][C]224330[/C][C]268249.091145094[/C][C]-43919.0911450939[/C][/ROW]
[ROW][C]70[/C][C]181633[/C][C]177485.637293694[/C][C]4147.36270630594[/C][/ROW]
[ROW][C]71[/C][C]271856[/C][C]271325.366188419[/C][C]530.63381158059[/C][/ROW]
[ROW][C]72[/C][C]95227[/C][C]78297.3226343236[/C][C]16929.6773656764[/C][/ROW]
[ROW][C]73[/C][C]98146[/C][C]76420.6730700459[/C][C]21725.3269299541[/C][/ROW]
[ROW][C]74[/C][C]118612[/C][C]99194.9689819049[/C][C]19417.0310180951[/C][/ROW]
[ROW][C]75[/C][C]65475[/C][C]75719.7375585441[/C][C]-10244.7375585441[/C][/ROW]
[ROW][C]76[/C][C]108446[/C][C]115069.949708455[/C][C]-6623.94970845498[/C][/ROW]
[ROW][C]77[/C][C]121848[/C][C]96662.6034267089[/C][C]25185.3965732911[/C][/ROW]
[ROW][C]78[/C][C]76302[/C][C]105273.808516228[/C][C]-28971.8085162285[/C][/ROW]
[ROW][C]79[/C][C]98104[/C][C]108115.984089823[/C][C]-10011.9840898227[/C][/ROW]
[ROW][C]80[/C][C]30989[/C][C]45561.5293254901[/C][C]-14572.5293254901[/C][/ROW]
[ROW][C]81[/C][C]31774[/C][C]49080.3819042618[/C][C]-17306.3819042618[/C][/ROW]
[ROW][C]82[/C][C]150580[/C][C]126259.17103291[/C][C]24320.8289670898[/C][/ROW]
[ROW][C]83[/C][C]54157[/C][C]75100.8360213006[/C][C]-20943.8360213006[/C][/ROW]
[ROW][C]84[/C][C]59382[/C][C]65556.057819[/C][C]-6174.05781899995[/C][/ROW]
[ROW][C]85[/C][C]84105[/C][C]83012.2110046681[/C][C]1092.78899533194[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=157616&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=157616&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
1210907252029.380866511-41122.3808665112
2179321204461.96241055-25140.9624105499
3149061172044.733299484-22983.7332994839
4237213266388.085439039-29175.085439039
5173326190005.111928443-16679.1119284428
6133131143318.66556078-10187.6655607799
7258873215993.11064294942879.8893570514
8324799311407.39918040713391.6008195927
9230964245178.797981773-14214.7979817728
10236785203529.78483086533255.2151691355
11344297239716.142051552104580.857948448
12174724204353.033784559-29629.0337845589
13174415202569.069381512-28154.069381512
14223632252867.123468957-29235.1234689574
15294424299020.272115673-4596.27211567262
16325107287102.32675048838004.6732495125
17106408116558.666233145-10150.6662331453
189656088967.16986195337592.83013804667
19265769252197.93289715513571.0671028451
20269651231573.066603438077.9333965996
21149112155479.949745746-6367.94974574639
22152871199692.610274384-46821.6102743841
23362301176103.901783217186197.098216783
24183167207432.229993722-24265.2299937224
25277965293175.988215019-15210.9882150192
26218946201110.11091403717835.8890859632
27244052296192.160506606-52140.1605066058
28341570251480.15272266190089.8472773392
29233328278100.703767921-44772.7037679205
30206161146364.87666113759796.1233388626
31311473317596.228830926-6123.22883092595
32207176187627.28967116819548.7103288319
33196553218873.214201961-22320.214201961
34143246155527.882144684-12281.8821446837
35182192202851.601152761-20659.6011527606
36194979210519.606336616-15540.6063366162
37167488166041.2800082561446.71999174421
38143756188645.265192807-44889.2651928067
39275541212482.97652274363058.0234772568
40152299160174.094536538-7875.09453653822
41193339208068.657778381-14729.6577783811
42130585162190.802173325-31605.8021733251
43112611135724.769560721-23113.7695607208
44148446186891.132051362-38445.1320513621
45182079207898.58978162-25819.5897816205
46243060209860.17586220433199.8241377962
47162765180045.87830259-17280.8783025903
488557487812.5352372819-2238.53523728194
49225060220256.3175000024803.68249999814
50133328132650.063547339677.936452660835
51100750151535.342680218-50785.3426802177
52101523126929.641950465-25406.641950465
53243511227871.49676151515639.5032384849
54152474188659.421732057-36185.421732057
55132487138020.015007285-5533.01500728532
56317394336643.111495295-19249.1114952952
57244749263017.377567559-18268.3775675592
58184510163941.73472218320568.2652778166
5912842395263.664577619333159.3354223807
6097839116429.841049767-18590.8410497673
61172494217738.165856502-45244.165856502
62229242171484.61677348957757.3832265113
63351619306582.6812848845036.3187151205
64324598242588.66909807982009.3309019212
65195838219109.431693637-23271.4316936369
66254488242405.13146303512082.8685369648
67199476224214.513174158-24738.5131741582
689249980296.933124453312202.0668755467
69224330268249.091145094-43919.0911450939
70181633177485.6372936944147.36270630594
71271856271325.366188419530.63381158059
729522778297.322634323616929.6773656764
739814676420.673070045921725.3269299541
7411861299194.968981904919417.0310180951
756547575719.7375585441-10244.7375585441
76108446115069.949708455-6623.94970845498
7712184896662.603426708925185.3965732911
7876302105273.808516228-28971.8085162285
7998104108115.984089823-10011.9840898227
803098945561.5293254901-14572.5293254901
813177449080.3819042618-17306.3819042618
82150580126259.1710329124320.8289670898
835415775100.8360213006-20943.8360213006
845938265556.057819-6174.05781899995
858410583012.21100466811092.78899533194






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.772896161007570.4542076779848590.22710383899243
120.9213190743832040.1573618512335920.078680925616796
130.8762624380255680.2474751239488640.123737561974432
140.8212799689495610.3574400621008780.178720031050439
150.8307478352948080.3385043294103840.169252164705192
160.7785400693727620.4429198612544770.221459930627238
170.6959812114888290.6080375770223410.304018788511171
180.6125772339219250.7748455321561510.387422766078075
190.5432275924072820.9135448151854370.456772407592718
200.4736864896028850.9473729792057690.526313510397115
210.3842110997451280.7684221994902550.615788900254873
220.3805604250633540.7611208501267070.619439574936646
230.9995593942561260.0008812114877477010.00044060574387385
240.9993101233483030.001379753303394750.000689876651697374
250.9989524321893360.002095135621328190.00104756781066409
260.9982618375341590.003476324931681420.00173816246584071
270.9985423005865640.002915398826871240.00145769941343562
280.9998631373982130.0002737252035740610.000136862601787031
290.9998432294051020.0003135411897959960.000156770594897998
300.9999239866576710.0001520266846581657.60133423290826e-05
310.9998481237635740.0003037524728525170.000151876236426258
320.9997490443408420.0005019113183159310.000250955659157966
330.9995994659654580.0008010680690835840.000400534034541792
340.9993114207453540.001377158509292130.000688579254646065
350.9989569866963070.002086026607386420.00104301330369321
360.9985453254055630.002909349188874880.00145467459443744
370.9976423252548360.004715349490328610.00235767474516431
380.9984145862293270.003170827541346630.00158541377067331
390.9993968934342030.001206213131594440.000603106565797221
400.9989982241186720.002003551762655160.00100177588132758
410.9986149491036750.002770101792650410.00138505089632521
420.9984766587287760.003046682542447910.00152334127122395
430.9979949337995350.004010132400930750.00200506620046538
440.9980276984606680.003944603078664290.00197230153933214
450.9973342877762410.005331424447517430.00266571222375872
460.9973677724921240.005264455015751310.00263222750787566
470.9958099457292660.008380108541468830.00419005427073441
480.9933482830374590.01330343392508190.00665171696254094
490.9896771702614550.02064565947709030.0103228297385451
500.9840109103829270.03197817923414610.015989089617073
510.989074049007020.02185190198595990.0109259509929799
520.9887515007593580.02249699848128460.0112484992406423
530.9823820344981150.03523593100376970.0176179655018848
540.985220715823190.02955856835361970.0147792841768099
550.9823831880637750.03523362387245070.0176168119362254
560.9751271236004440.04974575279911280.0248728763995564
570.9650925526465610.06981489470687710.0349074473534385
580.9482773505160680.1034452989678640.0517226494839318
590.9271530609431130.1456938781137730.0728469390568865
600.8959914248066510.2080171503866990.104008575193349
610.9303303048858830.1393393902282350.0696696951141173
620.9263160742341620.1473678515316770.0736839257658385
630.9158694466218020.1682611067563970.0841305533781983
640.9979788884993430.004042223001314270.00202111150065714
650.9956554037891380.008689192421723730.00434459621086187
660.9948156659530680.01036866809386380.00518433404693189
670.988850175739210.02229964852158060.0111498242607903
680.9809926443300120.03801471133997650.0190073556699883
690.9839611342840580.03207773143188320.0160388657159416
700.9684730514160510.0630538971678970.0315269485839485
710.943035413677410.113929172645180.0569645863225901
720.9077328048727170.1845343902545650.0922671951272827
730.8622696908156110.2754606183687780.137730309184389
740.9506586260526720.09868274789465620.0493413739473281

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 & 0.77289616100757 & 0.454207677984859 & 0.22710383899243 \tabularnewline
12 & 0.921319074383204 & 0.157361851233592 & 0.078680925616796 \tabularnewline
13 & 0.876262438025568 & 0.247475123948864 & 0.123737561974432 \tabularnewline
14 & 0.821279968949561 & 0.357440062100878 & 0.178720031050439 \tabularnewline
15 & 0.830747835294808 & 0.338504329410384 & 0.169252164705192 \tabularnewline
16 & 0.778540069372762 & 0.442919861254477 & 0.221459930627238 \tabularnewline
17 & 0.695981211488829 & 0.608037577022341 & 0.304018788511171 \tabularnewline
18 & 0.612577233921925 & 0.774845532156151 & 0.387422766078075 \tabularnewline
19 & 0.543227592407282 & 0.913544815185437 & 0.456772407592718 \tabularnewline
20 & 0.473686489602885 & 0.947372979205769 & 0.526313510397115 \tabularnewline
21 & 0.384211099745128 & 0.768422199490255 & 0.615788900254873 \tabularnewline
22 & 0.380560425063354 & 0.761120850126707 & 0.619439574936646 \tabularnewline
23 & 0.999559394256126 & 0.000881211487747701 & 0.00044060574387385 \tabularnewline
24 & 0.999310123348303 & 0.00137975330339475 & 0.000689876651697374 \tabularnewline
25 & 0.998952432189336 & 0.00209513562132819 & 0.00104756781066409 \tabularnewline
26 & 0.998261837534159 & 0.00347632493168142 & 0.00173816246584071 \tabularnewline
27 & 0.998542300586564 & 0.00291539882687124 & 0.00145769941343562 \tabularnewline
28 & 0.999863137398213 & 0.000273725203574061 & 0.000136862601787031 \tabularnewline
29 & 0.999843229405102 & 0.000313541189795996 & 0.000156770594897998 \tabularnewline
30 & 0.999923986657671 & 0.000152026684658165 & 7.60133423290826e-05 \tabularnewline
31 & 0.999848123763574 & 0.000303752472852517 & 0.000151876236426258 \tabularnewline
32 & 0.999749044340842 & 0.000501911318315931 & 0.000250955659157966 \tabularnewline
33 & 0.999599465965458 & 0.000801068069083584 & 0.000400534034541792 \tabularnewline
34 & 0.999311420745354 & 0.00137715850929213 & 0.000688579254646065 \tabularnewline
35 & 0.998956986696307 & 0.00208602660738642 & 0.00104301330369321 \tabularnewline
36 & 0.998545325405563 & 0.00290934918887488 & 0.00145467459443744 \tabularnewline
37 & 0.997642325254836 & 0.00471534949032861 & 0.00235767474516431 \tabularnewline
38 & 0.998414586229327 & 0.00317082754134663 & 0.00158541377067331 \tabularnewline
39 & 0.999396893434203 & 0.00120621313159444 & 0.000603106565797221 \tabularnewline
40 & 0.998998224118672 & 0.00200355176265516 & 0.00100177588132758 \tabularnewline
41 & 0.998614949103675 & 0.00277010179265041 & 0.00138505089632521 \tabularnewline
42 & 0.998476658728776 & 0.00304668254244791 & 0.00152334127122395 \tabularnewline
43 & 0.997994933799535 & 0.00401013240093075 & 0.00200506620046538 \tabularnewline
44 & 0.998027698460668 & 0.00394460307866429 & 0.00197230153933214 \tabularnewline
45 & 0.997334287776241 & 0.00533142444751743 & 0.00266571222375872 \tabularnewline
46 & 0.997367772492124 & 0.00526445501575131 & 0.00263222750787566 \tabularnewline
47 & 0.995809945729266 & 0.00838010854146883 & 0.00419005427073441 \tabularnewline
48 & 0.993348283037459 & 0.0133034339250819 & 0.00665171696254094 \tabularnewline
49 & 0.989677170261455 & 0.0206456594770903 & 0.0103228297385451 \tabularnewline
50 & 0.984010910382927 & 0.0319781792341461 & 0.015989089617073 \tabularnewline
51 & 0.98907404900702 & 0.0218519019859599 & 0.0109259509929799 \tabularnewline
52 & 0.988751500759358 & 0.0224969984812846 & 0.0112484992406423 \tabularnewline
53 & 0.982382034498115 & 0.0352359310037697 & 0.0176179655018848 \tabularnewline
54 & 0.98522071582319 & 0.0295585683536197 & 0.0147792841768099 \tabularnewline
55 & 0.982383188063775 & 0.0352336238724507 & 0.0176168119362254 \tabularnewline
56 & 0.975127123600444 & 0.0497457527991128 & 0.0248728763995564 \tabularnewline
57 & 0.965092552646561 & 0.0698148947068771 & 0.0349074473534385 \tabularnewline
58 & 0.948277350516068 & 0.103445298967864 & 0.0517226494839318 \tabularnewline
59 & 0.927153060943113 & 0.145693878113773 & 0.0728469390568865 \tabularnewline
60 & 0.895991424806651 & 0.208017150386699 & 0.104008575193349 \tabularnewline
61 & 0.930330304885883 & 0.139339390228235 & 0.0696696951141173 \tabularnewline
62 & 0.926316074234162 & 0.147367851531677 & 0.0736839257658385 \tabularnewline
63 & 0.915869446621802 & 0.168261106756397 & 0.0841305533781983 \tabularnewline
64 & 0.997978888499343 & 0.00404222300131427 & 0.00202111150065714 \tabularnewline
65 & 0.995655403789138 & 0.00868919242172373 & 0.00434459621086187 \tabularnewline
66 & 0.994815665953068 & 0.0103686680938638 & 0.00518433404693189 \tabularnewline
67 & 0.98885017573921 & 0.0222996485215806 & 0.0111498242607903 \tabularnewline
68 & 0.980992644330012 & 0.0380147113399765 & 0.0190073556699883 \tabularnewline
69 & 0.983961134284058 & 0.0320777314318832 & 0.0160388657159416 \tabularnewline
70 & 0.968473051416051 & 0.063053897167897 & 0.0315269485839485 \tabularnewline
71 & 0.94303541367741 & 0.11392917264518 & 0.0569645863225901 \tabularnewline
72 & 0.907732804872717 & 0.184534390254565 & 0.0922671951272827 \tabularnewline
73 & 0.862269690815611 & 0.275460618368778 & 0.137730309184389 \tabularnewline
74 & 0.950658626052672 & 0.0986827478946562 & 0.0493413739473281 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=157616&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]11[/C][C]0.77289616100757[/C][C]0.454207677984859[/C][C]0.22710383899243[/C][/ROW]
[ROW][C]12[/C][C]0.921319074383204[/C][C]0.157361851233592[/C][C]0.078680925616796[/C][/ROW]
[ROW][C]13[/C][C]0.876262438025568[/C][C]0.247475123948864[/C][C]0.123737561974432[/C][/ROW]
[ROW][C]14[/C][C]0.821279968949561[/C][C]0.357440062100878[/C][C]0.178720031050439[/C][/ROW]
[ROW][C]15[/C][C]0.830747835294808[/C][C]0.338504329410384[/C][C]0.169252164705192[/C][/ROW]
[ROW][C]16[/C][C]0.778540069372762[/C][C]0.442919861254477[/C][C]0.221459930627238[/C][/ROW]
[ROW][C]17[/C][C]0.695981211488829[/C][C]0.608037577022341[/C][C]0.304018788511171[/C][/ROW]
[ROW][C]18[/C][C]0.612577233921925[/C][C]0.774845532156151[/C][C]0.387422766078075[/C][/ROW]
[ROW][C]19[/C][C]0.543227592407282[/C][C]0.913544815185437[/C][C]0.456772407592718[/C][/ROW]
[ROW][C]20[/C][C]0.473686489602885[/C][C]0.947372979205769[/C][C]0.526313510397115[/C][/ROW]
[ROW][C]21[/C][C]0.384211099745128[/C][C]0.768422199490255[/C][C]0.615788900254873[/C][/ROW]
[ROW][C]22[/C][C]0.380560425063354[/C][C]0.761120850126707[/C][C]0.619439574936646[/C][/ROW]
[ROW][C]23[/C][C]0.999559394256126[/C][C]0.000881211487747701[/C][C]0.00044060574387385[/C][/ROW]
[ROW][C]24[/C][C]0.999310123348303[/C][C]0.00137975330339475[/C][C]0.000689876651697374[/C][/ROW]
[ROW][C]25[/C][C]0.998952432189336[/C][C]0.00209513562132819[/C][C]0.00104756781066409[/C][/ROW]
[ROW][C]26[/C][C]0.998261837534159[/C][C]0.00347632493168142[/C][C]0.00173816246584071[/C][/ROW]
[ROW][C]27[/C][C]0.998542300586564[/C][C]0.00291539882687124[/C][C]0.00145769941343562[/C][/ROW]
[ROW][C]28[/C][C]0.999863137398213[/C][C]0.000273725203574061[/C][C]0.000136862601787031[/C][/ROW]
[ROW][C]29[/C][C]0.999843229405102[/C][C]0.000313541189795996[/C][C]0.000156770594897998[/C][/ROW]
[ROW][C]30[/C][C]0.999923986657671[/C][C]0.000152026684658165[/C][C]7.60133423290826e-05[/C][/ROW]
[ROW][C]31[/C][C]0.999848123763574[/C][C]0.000303752472852517[/C][C]0.000151876236426258[/C][/ROW]
[ROW][C]32[/C][C]0.999749044340842[/C][C]0.000501911318315931[/C][C]0.000250955659157966[/C][/ROW]
[ROW][C]33[/C][C]0.999599465965458[/C][C]0.000801068069083584[/C][C]0.000400534034541792[/C][/ROW]
[ROW][C]34[/C][C]0.999311420745354[/C][C]0.00137715850929213[/C][C]0.000688579254646065[/C][/ROW]
[ROW][C]35[/C][C]0.998956986696307[/C][C]0.00208602660738642[/C][C]0.00104301330369321[/C][/ROW]
[ROW][C]36[/C][C]0.998545325405563[/C][C]0.00290934918887488[/C][C]0.00145467459443744[/C][/ROW]
[ROW][C]37[/C][C]0.997642325254836[/C][C]0.00471534949032861[/C][C]0.00235767474516431[/C][/ROW]
[ROW][C]38[/C][C]0.998414586229327[/C][C]0.00317082754134663[/C][C]0.00158541377067331[/C][/ROW]
[ROW][C]39[/C][C]0.999396893434203[/C][C]0.00120621313159444[/C][C]0.000603106565797221[/C][/ROW]
[ROW][C]40[/C][C]0.998998224118672[/C][C]0.00200355176265516[/C][C]0.00100177588132758[/C][/ROW]
[ROW][C]41[/C][C]0.998614949103675[/C][C]0.00277010179265041[/C][C]0.00138505089632521[/C][/ROW]
[ROW][C]42[/C][C]0.998476658728776[/C][C]0.00304668254244791[/C][C]0.00152334127122395[/C][/ROW]
[ROW][C]43[/C][C]0.997994933799535[/C][C]0.00401013240093075[/C][C]0.00200506620046538[/C][/ROW]
[ROW][C]44[/C][C]0.998027698460668[/C][C]0.00394460307866429[/C][C]0.00197230153933214[/C][/ROW]
[ROW][C]45[/C][C]0.997334287776241[/C][C]0.00533142444751743[/C][C]0.00266571222375872[/C][/ROW]
[ROW][C]46[/C][C]0.997367772492124[/C][C]0.00526445501575131[/C][C]0.00263222750787566[/C][/ROW]
[ROW][C]47[/C][C]0.995809945729266[/C][C]0.00838010854146883[/C][C]0.00419005427073441[/C][/ROW]
[ROW][C]48[/C][C]0.993348283037459[/C][C]0.0133034339250819[/C][C]0.00665171696254094[/C][/ROW]
[ROW][C]49[/C][C]0.989677170261455[/C][C]0.0206456594770903[/C][C]0.0103228297385451[/C][/ROW]
[ROW][C]50[/C][C]0.984010910382927[/C][C]0.0319781792341461[/C][C]0.015989089617073[/C][/ROW]
[ROW][C]51[/C][C]0.98907404900702[/C][C]0.0218519019859599[/C][C]0.0109259509929799[/C][/ROW]
[ROW][C]52[/C][C]0.988751500759358[/C][C]0.0224969984812846[/C][C]0.0112484992406423[/C][/ROW]
[ROW][C]53[/C][C]0.982382034498115[/C][C]0.0352359310037697[/C][C]0.0176179655018848[/C][/ROW]
[ROW][C]54[/C][C]0.98522071582319[/C][C]0.0295585683536197[/C][C]0.0147792841768099[/C][/ROW]
[ROW][C]55[/C][C]0.982383188063775[/C][C]0.0352336238724507[/C][C]0.0176168119362254[/C][/ROW]
[ROW][C]56[/C][C]0.975127123600444[/C][C]0.0497457527991128[/C][C]0.0248728763995564[/C][/ROW]
[ROW][C]57[/C][C]0.965092552646561[/C][C]0.0698148947068771[/C][C]0.0349074473534385[/C][/ROW]
[ROW][C]58[/C][C]0.948277350516068[/C][C]0.103445298967864[/C][C]0.0517226494839318[/C][/ROW]
[ROW][C]59[/C][C]0.927153060943113[/C][C]0.145693878113773[/C][C]0.0728469390568865[/C][/ROW]
[ROW][C]60[/C][C]0.895991424806651[/C][C]0.208017150386699[/C][C]0.104008575193349[/C][/ROW]
[ROW][C]61[/C][C]0.930330304885883[/C][C]0.139339390228235[/C][C]0.0696696951141173[/C][/ROW]
[ROW][C]62[/C][C]0.926316074234162[/C][C]0.147367851531677[/C][C]0.0736839257658385[/C][/ROW]
[ROW][C]63[/C][C]0.915869446621802[/C][C]0.168261106756397[/C][C]0.0841305533781983[/C][/ROW]
[ROW][C]64[/C][C]0.997978888499343[/C][C]0.00404222300131427[/C][C]0.00202111150065714[/C][/ROW]
[ROW][C]65[/C][C]0.995655403789138[/C][C]0.00868919242172373[/C][C]0.00434459621086187[/C][/ROW]
[ROW][C]66[/C][C]0.994815665953068[/C][C]0.0103686680938638[/C][C]0.00518433404693189[/C][/ROW]
[ROW][C]67[/C][C]0.98885017573921[/C][C]0.0222996485215806[/C][C]0.0111498242607903[/C][/ROW]
[ROW][C]68[/C][C]0.980992644330012[/C][C]0.0380147113399765[/C][C]0.0190073556699883[/C][/ROW]
[ROW][C]69[/C][C]0.983961134284058[/C][C]0.0320777314318832[/C][C]0.0160388657159416[/C][/ROW]
[ROW][C]70[/C][C]0.968473051416051[/C][C]0.063053897167897[/C][C]0.0315269485839485[/C][/ROW]
[ROW][C]71[/C][C]0.94303541367741[/C][C]0.11392917264518[/C][C]0.0569645863225901[/C][/ROW]
[ROW][C]72[/C][C]0.907732804872717[/C][C]0.184534390254565[/C][C]0.0922671951272827[/C][/ROW]
[ROW][C]73[/C][C]0.862269690815611[/C][C]0.275460618368778[/C][C]0.137730309184389[/C][/ROW]
[ROW][C]74[/C][C]0.950658626052672[/C][C]0.0986827478946562[/C][C]0.0493413739473281[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=157616&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=157616&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
110.772896161007570.4542076779848590.22710383899243
120.9213190743832040.1573618512335920.078680925616796
130.8762624380255680.2474751239488640.123737561974432
140.8212799689495610.3574400621008780.178720031050439
150.8307478352948080.3385043294103840.169252164705192
160.7785400693727620.4429198612544770.221459930627238
170.6959812114888290.6080375770223410.304018788511171
180.6125772339219250.7748455321561510.387422766078075
190.5432275924072820.9135448151854370.456772407592718
200.4736864896028850.9473729792057690.526313510397115
210.3842110997451280.7684221994902550.615788900254873
220.3805604250633540.7611208501267070.619439574936646
230.9995593942561260.0008812114877477010.00044060574387385
240.9993101233483030.001379753303394750.000689876651697374
250.9989524321893360.002095135621328190.00104756781066409
260.9982618375341590.003476324931681420.00173816246584071
270.9985423005865640.002915398826871240.00145769941343562
280.9998631373982130.0002737252035740610.000136862601787031
290.9998432294051020.0003135411897959960.000156770594897998
300.9999239866576710.0001520266846581657.60133423290826e-05
310.9998481237635740.0003037524728525170.000151876236426258
320.9997490443408420.0005019113183159310.000250955659157966
330.9995994659654580.0008010680690835840.000400534034541792
340.9993114207453540.001377158509292130.000688579254646065
350.9989569866963070.002086026607386420.00104301330369321
360.9985453254055630.002909349188874880.00145467459443744
370.9976423252548360.004715349490328610.00235767474516431
380.9984145862293270.003170827541346630.00158541377067331
390.9993968934342030.001206213131594440.000603106565797221
400.9989982241186720.002003551762655160.00100177588132758
410.9986149491036750.002770101792650410.00138505089632521
420.9984766587287760.003046682542447910.00152334127122395
430.9979949337995350.004010132400930750.00200506620046538
440.9980276984606680.003944603078664290.00197230153933214
450.9973342877762410.005331424447517430.00266571222375872
460.9973677724921240.005264455015751310.00263222750787566
470.9958099457292660.008380108541468830.00419005427073441
480.9933482830374590.01330343392508190.00665171696254094
490.9896771702614550.02064565947709030.0103228297385451
500.9840109103829270.03197817923414610.015989089617073
510.989074049007020.02185190198595990.0109259509929799
520.9887515007593580.02249699848128460.0112484992406423
530.9823820344981150.03523593100376970.0176179655018848
540.985220715823190.02955856835361970.0147792841768099
550.9823831880637750.03523362387245070.0176168119362254
560.9751271236004440.04974575279911280.0248728763995564
570.9650925526465610.06981489470687710.0349074473534385
580.9482773505160680.1034452989678640.0517226494839318
590.9271530609431130.1456938781137730.0728469390568865
600.8959914248066510.2080171503866990.104008575193349
610.9303303048858830.1393393902282350.0696696951141173
620.9263160742341620.1473678515316770.0736839257658385
630.9158694466218020.1682611067563970.0841305533781983
640.9979788884993430.004042223001314270.00202111150065714
650.9956554037891380.008689192421723730.00434459621086187
660.9948156659530680.01036866809386380.00518433404693189
670.988850175739210.02229964852158060.0111498242607903
680.9809926443300120.03801471133997650.0190073556699883
690.9839611342840580.03207773143188320.0160388657159416
700.9684730514160510.0630538971678970.0315269485839485
710.943035413677410.113929172645180.0569645863225901
720.9077328048727170.1845343902545650.0922671951272827
730.8622696908156110.2754606183687780.137730309184389
740.9506586260526720.09868274789465620.0493413739473281






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level270.421875NOK
5% type I error level400.625NOK
10% type I error level430.671875NOK

\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 & 27 & 0.421875 & NOK \tabularnewline
5% type I error level & 40 & 0.625 & NOK \tabularnewline
10% type I error level & 43 & 0.671875 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=157616&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]27[/C][C]0.421875[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]40[/C][C]0.625[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]43[/C][C]0.671875[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=157616&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=157616&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 level270.421875NOK
5% type I error level400.625NOK
10% type I error level430.671875NOK


Figures (Output of Computation)

Figure 1
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 2
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 3
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 4
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 5
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 6
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 7
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 8
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 9
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 10
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Input Parameters & R Code

Parameters (Session):
par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 2 ; 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')
}