Free Statistics

of Irreproducible Research!

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationFri, 07 Dec 2012 08:57:36 -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/Dec/07/t1354888671gx3kvmswacbd4iu.htm/, Retrieved Thu, 31 Oct 2024 23:30:39 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=197374, Retrieved Thu, 31 Oct 2024 23:30:39 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact231
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
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Dataseries X:
1966	1	41
1966	2	39
1966	3	50
1966	4	40
1966	5	43
1966	6	38
1966	7	44
1966	8	35
1966	9	39
1966	10	35
1966	11	29
1966	12	49
1967	1	50
1967	2	59
1967	3	63
1967	4	32
1967	5	39
1967	6	47
1967	7	53
1967	8	60
1967	9	57
1967	10	52
1967	11	70
1967	12	90
1968	1	74
1968	2	62
1968	3	55
1968	4	84
1968	5	94
1968	6	70
1968	7	108
1968	8	139
1968	9	120
1968	10	97
1968	11	126
1968	12	149
1969	1	158
1969	2	124
1969	3	140
1969	4	109
1969	5	114
1969	6	77
1969	7	120
1969	8	133
1969	9	110
1969	10	92
1969	11	97
1969	12	78
1970	1	99
1970	2	107
1970	3	112
1970	4	90
1970	5	98
1970	6	125
1970	7	155
1970	8	190
1970	9	236
1970	10	189
1970	11	174
1970	12	178
1971	1	136
1971	2	161
1971	3	171
1971	4	149
1971	5	184
1971	6	155
1971	7	276
1971	8	224
1971	9	213
1971	10	279
1971	11	268
1971	12	287
1972	1	238
1972	2	213
1972	3	257
1972	4	293
1972	5	212
1972	6	246
1972	7	353
1972	8	339
1972	9	308
1972	10	247
1972	11	257
1972	12	322
1973	1	298
1973	2	273
1973	3	312
1973	4	249
1973	5	286
1973	6	279
1973	7	309
1973	8	401
1973	9	309
1973	10	328
1973	11	353
1973	12	354
1974	1	327
1974	2	324
1974	3	285
1974	4	243
1974	5	241
1974	6	287
1974	7	355
1974	8	460
1974	9	364
1974	10	487
1974	11	452
1974	12	391
1975	1	500
1975	2	451
1975	3	375
1975	4	372
1975	5	302
1975	6	316
1975	7	398
1975	8	394
1975	9	431
1975	10	431




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R Server'Herman Ole Andreas Wold' @ wold.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 & 8 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197374&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]8 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197374&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197374&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net







Multiple Linear Regression - Estimated Regression Equation
year[t] = + 1967.10365283744 -0.125902148332274month[t] + 0.0210291378753831robberies[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
year[t] =  +  1967.10365283744 -0.125902148332274month[t] +  0.0210291378753831robberies[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197374&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]year[t] =  +  1967.10365283744 -0.125902148332274month[t] +  0.0210291378753831robberies[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197374&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197374&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
year[t] = + 1967.10365283744 -0.125902148332274month[t] + 0.0210291378753831robberies[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)1967.103652837440.226568682.475400
month-0.1259021483322740.026629-4.7286e-063e-06
robberies0.02102913787538310.00071429.457200

\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) & 1967.10365283744 & 0.22656 & 8682.4754 & 0 & 0 \tabularnewline
month & -0.125902148332274 & 0.026629 & -4.728 & 6e-06 & 3e-06 \tabularnewline
robberies & 0.0210291378753831 & 0.000714 & 29.4572 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197374&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]1967.10365283744[/C][C]0.22656[/C][C]8682.4754[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]month[/C][C]-0.125902148332274[/C][C]0.026629[/C][C]-4.728[/C][C]6e-06[/C][C]3e-06[/C][/ROW]
[ROW][C]robberies[/C][C]0.0210291378753831[/C][C]0.000714[/C][C]29.4572[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197374&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197374&T=2

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)1967.103652837440.226568682.475400
month-0.1259021483322740.026629-4.7286e-063e-06
robberies0.02102913787538310.00071429.457200







Multiple Linear Regression - Regression Statistics
Multiple R0.939769286485165
R-squared0.883166311820837
Adjusted R-squared0.881134421591634
F-TEST (value)434.652570856312
F-TEST (DF numerator)2
F-TEST (DF denominator)115
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.981805734481531
Sum Squared Residuals110.853387529994

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.939769286485165 \tabularnewline
R-squared & 0.883166311820837 \tabularnewline
Adjusted R-squared & 0.881134421591634 \tabularnewline
F-TEST (value) & 434.652570856312 \tabularnewline
F-TEST (DF numerator) & 2 \tabularnewline
F-TEST (DF denominator) & 115 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.981805734481531 \tabularnewline
Sum Squared Residuals & 110.853387529994 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197374&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.939769286485165[/C][/ROW]
[ROW][C]R-squared[/C][C]0.883166311820837[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.881134421591634[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]434.652570856312[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]2[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]115[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.981805734481531[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]110.853387529994[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197374&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197374&T=3

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Regression Statistics
Multiple R0.939769286485165
R-squared0.883166311820837
Adjusted R-squared0.881134421591634
F-TEST (value)434.652570856312
F-TEST (DF numerator)2
F-TEST (DF denominator)115
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.981805734481531
Sum Squared Residuals110.853387529994







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
119661967.839945342-1.83994534200346
219661967.67198491792-1.67198491791526
319661967.77740328621-1.7774032862122
419661967.44120975913-1.44120975912609
519661967.37839502442-1.37839502441997
619661967.14734718671-1.14734718671078
719661967.14761986563-1.1476198656308
819661966.83245547642-0.832455476420079
919661966.79066987959-0.790669879589338
1019661966.58065117976-0.580651179755531
1119661966.32857420417-0.32857420417096
1219661966.62325481335-0.623254813346346
1319671968.02920758288-1.02920758287674
1419671968.09256767542-1.09256767542292
1519671968.05078207859-1.05078207859217
1619671967.27297665612-0.272976656123026
1719671967.29427847292-0.294278472918433
1819671967.33660942759-0.336609427589224
1919671967.33688210651-0.336882106509248
2019671967.3581839233-0.358183923304655
2119671967.16919436135-0.169194361346232
2219671966.938146523640.0618534763629574
2319671967.19076885706-0.190768857061664
2419671967.48544946624-0.485449466237051
2519681968.53390689189-0.533906891885936
2619681968.15565508905-0.155655089049065
2719681967.882548975590.11745102441089
2819681968.36649182564-0.366491825642944
2919681968.45088105606-0.450881056064501
3019681967.820279598720.179720401276966
3119681968.49348468966-0.493484689655316
3219681969.01948581546-1.01948581545992
3319681968.4940300475-0.494030047495364
3419681967.884457728030.11554227197072
3519681968.36840057808-0.368400578083114
3619681968.72616860088-0.726168600884651
3719691970.30035447342-1.30035447341811
3819691969.45946163732-0.459461637322815
3919691969.670025695-0.67002569499667
4019691968.892220272530.107779727472479
4119691968.871463813570.128536186427838
4219691967.967483563851.03251643614928
4319691968.745834344160.254165655840088
4419691968.893310988210.106689011792382
4519691968.283738668740.716261331258466
4619691967.779312038651.22068796134764
4719691967.75855557971.24144442030299
4819691967.233099811731.76690018826755
4919701969.059635338770.940364661229488
5019701969.101966293440.898033706558697
5119701969.081209834490.918790165514056
5219701968.49266665291.50733334710476
5319701968.534997607571.46500239243397
5419701968.976882181871.0231178181309
5519701969.48185416980.518145830201681
5619701970.0919718471-0.0919718471044521
5719701970.93341004104-0.933410041039798
5819701969.819138412560.180861587435479
5919701969.37779919610.622200803898499
6019701969.336013599270.663986400729241
6119711969.837713440161.16228655984031
6219711970.237539738710.762460261288012
6319711970.321928969130.678071030866456
6419711969.733385787541.26661421245716
6519711970.343503464850.656496535151024
6619711969.607756318131.39224368186941
6719711972.02637985272-1.02637985271967
6819711970.806962534870.193037465132524
6919711970.449739869910.550260130094012
7019711971.71176082135-0.711760821348996
7119711971.35453815639-0.354538156387508
7219711971.62818962769-0.628189627687512
7319721971.982685503450.0173144965512435
7419721971.331054908230.668945091768093
7519721972.13043482642-0.130434826416487
7619721972.7615816416-0.761581641598003
7719721970.932319325361.0676806746403
7819721971.521407864790.478592135209549
7919721973.64562346912-1.64562346912416
8019721973.22531339054-1.22531339053653
8119721972.44750796807-0.447507968067379
8219721971.038828409340.961171590663262
8319721971.123217639760.876782360241705
8419721972.36420945333-0.364209453325919
8519731973.24443377597-0.24443377597174
8619731972.592803180750.40719681924511
8719731973.28703740956-0.287037409562555
8819731971.836299575081.16370042491885
8919731972.488475528140.511524471861953
9019731972.215369414680.784630585321908
9119731972.720341402610.27965859739269
9219731974.52911993881-1.52911993881028
9319731972.468537105940.531462894057238
9419731972.742188577240.257811422757234
9519731973.14201487579-0.142014875795068
9619731973.03714186534-0.0371418653381769
9719741973.854278774360.145721225642151
9819741973.66528921240.334710787600574
9919741972.719250686931.28074931307279
10019741971.710124747832.28987525217115
10119741971.542164323752.45783567625419
10219741972.383602517681.61639748231884
10319741973.687681744870.31231825512507
10419741975.76983907346-1.76983907345788
10519741973.625139689090.37486031091117
10619741976.08582149943-2.08582149942867
10719741975.22389952546-1.22389952545799
10819741973.815219966730.18478003327265
10919751977.4923196268-2.49231962679912
11019751976.33598972257-1.33598972257307
11119751974.611873095710.388126904288312
11219751974.422883533750.577116466246735
11319751972.824941734142.17505826585582
11419751972.993447516072.00655248393273
11519751974.591934673520.408065326483598
11619751974.381915973680.618084026317404
11719751975.03409192674-0.0340919267394948
11819751974.908189778410.0918102215927801

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 1966 & 1967.839945342 & -1.83994534200346 \tabularnewline
2 & 1966 & 1967.67198491792 & -1.67198491791526 \tabularnewline
3 & 1966 & 1967.77740328621 & -1.7774032862122 \tabularnewline
4 & 1966 & 1967.44120975913 & -1.44120975912609 \tabularnewline
5 & 1966 & 1967.37839502442 & -1.37839502441997 \tabularnewline
6 & 1966 & 1967.14734718671 & -1.14734718671078 \tabularnewline
7 & 1966 & 1967.14761986563 & -1.1476198656308 \tabularnewline
8 & 1966 & 1966.83245547642 & -0.832455476420079 \tabularnewline
9 & 1966 & 1966.79066987959 & -0.790669879589338 \tabularnewline
10 & 1966 & 1966.58065117976 & -0.580651179755531 \tabularnewline
11 & 1966 & 1966.32857420417 & -0.32857420417096 \tabularnewline
12 & 1966 & 1966.62325481335 & -0.623254813346346 \tabularnewline
13 & 1967 & 1968.02920758288 & -1.02920758287674 \tabularnewline
14 & 1967 & 1968.09256767542 & -1.09256767542292 \tabularnewline
15 & 1967 & 1968.05078207859 & -1.05078207859217 \tabularnewline
16 & 1967 & 1967.27297665612 & -0.272976656123026 \tabularnewline
17 & 1967 & 1967.29427847292 & -0.294278472918433 \tabularnewline
18 & 1967 & 1967.33660942759 & -0.336609427589224 \tabularnewline
19 & 1967 & 1967.33688210651 & -0.336882106509248 \tabularnewline
20 & 1967 & 1967.3581839233 & -0.358183923304655 \tabularnewline
21 & 1967 & 1967.16919436135 & -0.169194361346232 \tabularnewline
22 & 1967 & 1966.93814652364 & 0.0618534763629574 \tabularnewline
23 & 1967 & 1967.19076885706 & -0.190768857061664 \tabularnewline
24 & 1967 & 1967.48544946624 & -0.485449466237051 \tabularnewline
25 & 1968 & 1968.53390689189 & -0.533906891885936 \tabularnewline
26 & 1968 & 1968.15565508905 & -0.155655089049065 \tabularnewline
27 & 1968 & 1967.88254897559 & 0.11745102441089 \tabularnewline
28 & 1968 & 1968.36649182564 & -0.366491825642944 \tabularnewline
29 & 1968 & 1968.45088105606 & -0.450881056064501 \tabularnewline
30 & 1968 & 1967.82027959872 & 0.179720401276966 \tabularnewline
31 & 1968 & 1968.49348468966 & -0.493484689655316 \tabularnewline
32 & 1968 & 1969.01948581546 & -1.01948581545992 \tabularnewline
33 & 1968 & 1968.4940300475 & -0.494030047495364 \tabularnewline
34 & 1968 & 1967.88445772803 & 0.11554227197072 \tabularnewline
35 & 1968 & 1968.36840057808 & -0.368400578083114 \tabularnewline
36 & 1968 & 1968.72616860088 & -0.726168600884651 \tabularnewline
37 & 1969 & 1970.30035447342 & -1.30035447341811 \tabularnewline
38 & 1969 & 1969.45946163732 & -0.459461637322815 \tabularnewline
39 & 1969 & 1969.670025695 & -0.67002569499667 \tabularnewline
40 & 1969 & 1968.89222027253 & 0.107779727472479 \tabularnewline
41 & 1969 & 1968.87146381357 & 0.128536186427838 \tabularnewline
42 & 1969 & 1967.96748356385 & 1.03251643614928 \tabularnewline
43 & 1969 & 1968.74583434416 & 0.254165655840088 \tabularnewline
44 & 1969 & 1968.89331098821 & 0.106689011792382 \tabularnewline
45 & 1969 & 1968.28373866874 & 0.716261331258466 \tabularnewline
46 & 1969 & 1967.77931203865 & 1.22068796134764 \tabularnewline
47 & 1969 & 1967.7585555797 & 1.24144442030299 \tabularnewline
48 & 1969 & 1967.23309981173 & 1.76690018826755 \tabularnewline
49 & 1970 & 1969.05963533877 & 0.940364661229488 \tabularnewline
50 & 1970 & 1969.10196629344 & 0.898033706558697 \tabularnewline
51 & 1970 & 1969.08120983449 & 0.918790165514056 \tabularnewline
52 & 1970 & 1968.4926666529 & 1.50733334710476 \tabularnewline
53 & 1970 & 1968.53499760757 & 1.46500239243397 \tabularnewline
54 & 1970 & 1968.97688218187 & 1.0231178181309 \tabularnewline
55 & 1970 & 1969.4818541698 & 0.518145830201681 \tabularnewline
56 & 1970 & 1970.0919718471 & -0.0919718471044521 \tabularnewline
57 & 1970 & 1970.93341004104 & -0.933410041039798 \tabularnewline
58 & 1970 & 1969.81913841256 & 0.180861587435479 \tabularnewline
59 & 1970 & 1969.3777991961 & 0.622200803898499 \tabularnewline
60 & 1970 & 1969.33601359927 & 0.663986400729241 \tabularnewline
61 & 1971 & 1969.83771344016 & 1.16228655984031 \tabularnewline
62 & 1971 & 1970.23753973871 & 0.762460261288012 \tabularnewline
63 & 1971 & 1970.32192896913 & 0.678071030866456 \tabularnewline
64 & 1971 & 1969.73338578754 & 1.26661421245716 \tabularnewline
65 & 1971 & 1970.34350346485 & 0.656496535151024 \tabularnewline
66 & 1971 & 1969.60775631813 & 1.39224368186941 \tabularnewline
67 & 1971 & 1972.02637985272 & -1.02637985271967 \tabularnewline
68 & 1971 & 1970.80696253487 & 0.193037465132524 \tabularnewline
69 & 1971 & 1970.44973986991 & 0.550260130094012 \tabularnewline
70 & 1971 & 1971.71176082135 & -0.711760821348996 \tabularnewline
71 & 1971 & 1971.35453815639 & -0.354538156387508 \tabularnewline
72 & 1971 & 1971.62818962769 & -0.628189627687512 \tabularnewline
73 & 1972 & 1971.98268550345 & 0.0173144965512435 \tabularnewline
74 & 1972 & 1971.33105490823 & 0.668945091768093 \tabularnewline
75 & 1972 & 1972.13043482642 & -0.130434826416487 \tabularnewline
76 & 1972 & 1972.7615816416 & -0.761581641598003 \tabularnewline
77 & 1972 & 1970.93231932536 & 1.0676806746403 \tabularnewline
78 & 1972 & 1971.52140786479 & 0.478592135209549 \tabularnewline
79 & 1972 & 1973.64562346912 & -1.64562346912416 \tabularnewline
80 & 1972 & 1973.22531339054 & -1.22531339053653 \tabularnewline
81 & 1972 & 1972.44750796807 & -0.447507968067379 \tabularnewline
82 & 1972 & 1971.03882840934 & 0.961171590663262 \tabularnewline
83 & 1972 & 1971.12321763976 & 0.876782360241705 \tabularnewline
84 & 1972 & 1972.36420945333 & -0.364209453325919 \tabularnewline
85 & 1973 & 1973.24443377597 & -0.24443377597174 \tabularnewline
86 & 1973 & 1972.59280318075 & 0.40719681924511 \tabularnewline
87 & 1973 & 1973.28703740956 & -0.287037409562555 \tabularnewline
88 & 1973 & 1971.83629957508 & 1.16370042491885 \tabularnewline
89 & 1973 & 1972.48847552814 & 0.511524471861953 \tabularnewline
90 & 1973 & 1972.21536941468 & 0.784630585321908 \tabularnewline
91 & 1973 & 1972.72034140261 & 0.27965859739269 \tabularnewline
92 & 1973 & 1974.52911993881 & -1.52911993881028 \tabularnewline
93 & 1973 & 1972.46853710594 & 0.531462894057238 \tabularnewline
94 & 1973 & 1972.74218857724 & 0.257811422757234 \tabularnewline
95 & 1973 & 1973.14201487579 & -0.142014875795068 \tabularnewline
96 & 1973 & 1973.03714186534 & -0.0371418653381769 \tabularnewline
97 & 1974 & 1973.85427877436 & 0.145721225642151 \tabularnewline
98 & 1974 & 1973.6652892124 & 0.334710787600574 \tabularnewline
99 & 1974 & 1972.71925068693 & 1.28074931307279 \tabularnewline
100 & 1974 & 1971.71012474783 & 2.28987525217115 \tabularnewline
101 & 1974 & 1971.54216432375 & 2.45783567625419 \tabularnewline
102 & 1974 & 1972.38360251768 & 1.61639748231884 \tabularnewline
103 & 1974 & 1973.68768174487 & 0.31231825512507 \tabularnewline
104 & 1974 & 1975.76983907346 & -1.76983907345788 \tabularnewline
105 & 1974 & 1973.62513968909 & 0.37486031091117 \tabularnewline
106 & 1974 & 1976.08582149943 & -2.08582149942867 \tabularnewline
107 & 1974 & 1975.22389952546 & -1.22389952545799 \tabularnewline
108 & 1974 & 1973.81521996673 & 0.18478003327265 \tabularnewline
109 & 1975 & 1977.4923196268 & -2.49231962679912 \tabularnewline
110 & 1975 & 1976.33598972257 & -1.33598972257307 \tabularnewline
111 & 1975 & 1974.61187309571 & 0.388126904288312 \tabularnewline
112 & 1975 & 1974.42288353375 & 0.577116466246735 \tabularnewline
113 & 1975 & 1972.82494173414 & 2.17505826585582 \tabularnewline
114 & 1975 & 1972.99344751607 & 2.00655248393273 \tabularnewline
115 & 1975 & 1974.59193467352 & 0.408065326483598 \tabularnewline
116 & 1975 & 1974.38191597368 & 0.618084026317404 \tabularnewline
117 & 1975 & 1975.03409192674 & -0.0340919267394948 \tabularnewline
118 & 1975 & 1974.90818977841 & 0.0918102215927801 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197374&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]1966[/C][C]1967.839945342[/C][C]-1.83994534200346[/C][/ROW]
[ROW][C]2[/C][C]1966[/C][C]1967.67198491792[/C][C]-1.67198491791526[/C][/ROW]
[ROW][C]3[/C][C]1966[/C][C]1967.77740328621[/C][C]-1.7774032862122[/C][/ROW]
[ROW][C]4[/C][C]1966[/C][C]1967.44120975913[/C][C]-1.44120975912609[/C][/ROW]
[ROW][C]5[/C][C]1966[/C][C]1967.37839502442[/C][C]-1.37839502441997[/C][/ROW]
[ROW][C]6[/C][C]1966[/C][C]1967.14734718671[/C][C]-1.14734718671078[/C][/ROW]
[ROW][C]7[/C][C]1966[/C][C]1967.14761986563[/C][C]-1.1476198656308[/C][/ROW]
[ROW][C]8[/C][C]1966[/C][C]1966.83245547642[/C][C]-0.832455476420079[/C][/ROW]
[ROW][C]9[/C][C]1966[/C][C]1966.79066987959[/C][C]-0.790669879589338[/C][/ROW]
[ROW][C]10[/C][C]1966[/C][C]1966.58065117976[/C][C]-0.580651179755531[/C][/ROW]
[ROW][C]11[/C][C]1966[/C][C]1966.32857420417[/C][C]-0.32857420417096[/C][/ROW]
[ROW][C]12[/C][C]1966[/C][C]1966.62325481335[/C][C]-0.623254813346346[/C][/ROW]
[ROW][C]13[/C][C]1967[/C][C]1968.02920758288[/C][C]-1.02920758287674[/C][/ROW]
[ROW][C]14[/C][C]1967[/C][C]1968.09256767542[/C][C]-1.09256767542292[/C][/ROW]
[ROW][C]15[/C][C]1967[/C][C]1968.05078207859[/C][C]-1.05078207859217[/C][/ROW]
[ROW][C]16[/C][C]1967[/C][C]1967.27297665612[/C][C]-0.272976656123026[/C][/ROW]
[ROW][C]17[/C][C]1967[/C][C]1967.29427847292[/C][C]-0.294278472918433[/C][/ROW]
[ROW][C]18[/C][C]1967[/C][C]1967.33660942759[/C][C]-0.336609427589224[/C][/ROW]
[ROW][C]19[/C][C]1967[/C][C]1967.33688210651[/C][C]-0.336882106509248[/C][/ROW]
[ROW][C]20[/C][C]1967[/C][C]1967.3581839233[/C][C]-0.358183923304655[/C][/ROW]
[ROW][C]21[/C][C]1967[/C][C]1967.16919436135[/C][C]-0.169194361346232[/C][/ROW]
[ROW][C]22[/C][C]1967[/C][C]1966.93814652364[/C][C]0.0618534763629574[/C][/ROW]
[ROW][C]23[/C][C]1967[/C][C]1967.19076885706[/C][C]-0.190768857061664[/C][/ROW]
[ROW][C]24[/C][C]1967[/C][C]1967.48544946624[/C][C]-0.485449466237051[/C][/ROW]
[ROW][C]25[/C][C]1968[/C][C]1968.53390689189[/C][C]-0.533906891885936[/C][/ROW]
[ROW][C]26[/C][C]1968[/C][C]1968.15565508905[/C][C]-0.155655089049065[/C][/ROW]
[ROW][C]27[/C][C]1968[/C][C]1967.88254897559[/C][C]0.11745102441089[/C][/ROW]
[ROW][C]28[/C][C]1968[/C][C]1968.36649182564[/C][C]-0.366491825642944[/C][/ROW]
[ROW][C]29[/C][C]1968[/C][C]1968.45088105606[/C][C]-0.450881056064501[/C][/ROW]
[ROW][C]30[/C][C]1968[/C][C]1967.82027959872[/C][C]0.179720401276966[/C][/ROW]
[ROW][C]31[/C][C]1968[/C][C]1968.49348468966[/C][C]-0.493484689655316[/C][/ROW]
[ROW][C]32[/C][C]1968[/C][C]1969.01948581546[/C][C]-1.01948581545992[/C][/ROW]
[ROW][C]33[/C][C]1968[/C][C]1968.4940300475[/C][C]-0.494030047495364[/C][/ROW]
[ROW][C]34[/C][C]1968[/C][C]1967.88445772803[/C][C]0.11554227197072[/C][/ROW]
[ROW][C]35[/C][C]1968[/C][C]1968.36840057808[/C][C]-0.368400578083114[/C][/ROW]
[ROW][C]36[/C][C]1968[/C][C]1968.72616860088[/C][C]-0.726168600884651[/C][/ROW]
[ROW][C]37[/C][C]1969[/C][C]1970.30035447342[/C][C]-1.30035447341811[/C][/ROW]
[ROW][C]38[/C][C]1969[/C][C]1969.45946163732[/C][C]-0.459461637322815[/C][/ROW]
[ROW][C]39[/C][C]1969[/C][C]1969.670025695[/C][C]-0.67002569499667[/C][/ROW]
[ROW][C]40[/C][C]1969[/C][C]1968.89222027253[/C][C]0.107779727472479[/C][/ROW]
[ROW][C]41[/C][C]1969[/C][C]1968.87146381357[/C][C]0.128536186427838[/C][/ROW]
[ROW][C]42[/C][C]1969[/C][C]1967.96748356385[/C][C]1.03251643614928[/C][/ROW]
[ROW][C]43[/C][C]1969[/C][C]1968.74583434416[/C][C]0.254165655840088[/C][/ROW]
[ROW][C]44[/C][C]1969[/C][C]1968.89331098821[/C][C]0.106689011792382[/C][/ROW]
[ROW][C]45[/C][C]1969[/C][C]1968.28373866874[/C][C]0.716261331258466[/C][/ROW]
[ROW][C]46[/C][C]1969[/C][C]1967.77931203865[/C][C]1.22068796134764[/C][/ROW]
[ROW][C]47[/C][C]1969[/C][C]1967.7585555797[/C][C]1.24144442030299[/C][/ROW]
[ROW][C]48[/C][C]1969[/C][C]1967.23309981173[/C][C]1.76690018826755[/C][/ROW]
[ROW][C]49[/C][C]1970[/C][C]1969.05963533877[/C][C]0.940364661229488[/C][/ROW]
[ROW][C]50[/C][C]1970[/C][C]1969.10196629344[/C][C]0.898033706558697[/C][/ROW]
[ROW][C]51[/C][C]1970[/C][C]1969.08120983449[/C][C]0.918790165514056[/C][/ROW]
[ROW][C]52[/C][C]1970[/C][C]1968.4926666529[/C][C]1.50733334710476[/C][/ROW]
[ROW][C]53[/C][C]1970[/C][C]1968.53499760757[/C][C]1.46500239243397[/C][/ROW]
[ROW][C]54[/C][C]1970[/C][C]1968.97688218187[/C][C]1.0231178181309[/C][/ROW]
[ROW][C]55[/C][C]1970[/C][C]1969.4818541698[/C][C]0.518145830201681[/C][/ROW]
[ROW][C]56[/C][C]1970[/C][C]1970.0919718471[/C][C]-0.0919718471044521[/C][/ROW]
[ROW][C]57[/C][C]1970[/C][C]1970.93341004104[/C][C]-0.933410041039798[/C][/ROW]
[ROW][C]58[/C][C]1970[/C][C]1969.81913841256[/C][C]0.180861587435479[/C][/ROW]
[ROW][C]59[/C][C]1970[/C][C]1969.3777991961[/C][C]0.622200803898499[/C][/ROW]
[ROW][C]60[/C][C]1970[/C][C]1969.33601359927[/C][C]0.663986400729241[/C][/ROW]
[ROW][C]61[/C][C]1971[/C][C]1969.83771344016[/C][C]1.16228655984031[/C][/ROW]
[ROW][C]62[/C][C]1971[/C][C]1970.23753973871[/C][C]0.762460261288012[/C][/ROW]
[ROW][C]63[/C][C]1971[/C][C]1970.32192896913[/C][C]0.678071030866456[/C][/ROW]
[ROW][C]64[/C][C]1971[/C][C]1969.73338578754[/C][C]1.26661421245716[/C][/ROW]
[ROW][C]65[/C][C]1971[/C][C]1970.34350346485[/C][C]0.656496535151024[/C][/ROW]
[ROW][C]66[/C][C]1971[/C][C]1969.60775631813[/C][C]1.39224368186941[/C][/ROW]
[ROW][C]67[/C][C]1971[/C][C]1972.02637985272[/C][C]-1.02637985271967[/C][/ROW]
[ROW][C]68[/C][C]1971[/C][C]1970.80696253487[/C][C]0.193037465132524[/C][/ROW]
[ROW][C]69[/C][C]1971[/C][C]1970.44973986991[/C][C]0.550260130094012[/C][/ROW]
[ROW][C]70[/C][C]1971[/C][C]1971.71176082135[/C][C]-0.711760821348996[/C][/ROW]
[ROW][C]71[/C][C]1971[/C][C]1971.35453815639[/C][C]-0.354538156387508[/C][/ROW]
[ROW][C]72[/C][C]1971[/C][C]1971.62818962769[/C][C]-0.628189627687512[/C][/ROW]
[ROW][C]73[/C][C]1972[/C][C]1971.98268550345[/C][C]0.0173144965512435[/C][/ROW]
[ROW][C]74[/C][C]1972[/C][C]1971.33105490823[/C][C]0.668945091768093[/C][/ROW]
[ROW][C]75[/C][C]1972[/C][C]1972.13043482642[/C][C]-0.130434826416487[/C][/ROW]
[ROW][C]76[/C][C]1972[/C][C]1972.7615816416[/C][C]-0.761581641598003[/C][/ROW]
[ROW][C]77[/C][C]1972[/C][C]1970.93231932536[/C][C]1.0676806746403[/C][/ROW]
[ROW][C]78[/C][C]1972[/C][C]1971.52140786479[/C][C]0.478592135209549[/C][/ROW]
[ROW][C]79[/C][C]1972[/C][C]1973.64562346912[/C][C]-1.64562346912416[/C][/ROW]
[ROW][C]80[/C][C]1972[/C][C]1973.22531339054[/C][C]-1.22531339053653[/C][/ROW]
[ROW][C]81[/C][C]1972[/C][C]1972.44750796807[/C][C]-0.447507968067379[/C][/ROW]
[ROW][C]82[/C][C]1972[/C][C]1971.03882840934[/C][C]0.961171590663262[/C][/ROW]
[ROW][C]83[/C][C]1972[/C][C]1971.12321763976[/C][C]0.876782360241705[/C][/ROW]
[ROW][C]84[/C][C]1972[/C][C]1972.36420945333[/C][C]-0.364209453325919[/C][/ROW]
[ROW][C]85[/C][C]1973[/C][C]1973.24443377597[/C][C]-0.24443377597174[/C][/ROW]
[ROW][C]86[/C][C]1973[/C][C]1972.59280318075[/C][C]0.40719681924511[/C][/ROW]
[ROW][C]87[/C][C]1973[/C][C]1973.28703740956[/C][C]-0.287037409562555[/C][/ROW]
[ROW][C]88[/C][C]1973[/C][C]1971.83629957508[/C][C]1.16370042491885[/C][/ROW]
[ROW][C]89[/C][C]1973[/C][C]1972.48847552814[/C][C]0.511524471861953[/C][/ROW]
[ROW][C]90[/C][C]1973[/C][C]1972.21536941468[/C][C]0.784630585321908[/C][/ROW]
[ROW][C]91[/C][C]1973[/C][C]1972.72034140261[/C][C]0.27965859739269[/C][/ROW]
[ROW][C]92[/C][C]1973[/C][C]1974.52911993881[/C][C]-1.52911993881028[/C][/ROW]
[ROW][C]93[/C][C]1973[/C][C]1972.46853710594[/C][C]0.531462894057238[/C][/ROW]
[ROW][C]94[/C][C]1973[/C][C]1972.74218857724[/C][C]0.257811422757234[/C][/ROW]
[ROW][C]95[/C][C]1973[/C][C]1973.14201487579[/C][C]-0.142014875795068[/C][/ROW]
[ROW][C]96[/C][C]1973[/C][C]1973.03714186534[/C][C]-0.0371418653381769[/C][/ROW]
[ROW][C]97[/C][C]1974[/C][C]1973.85427877436[/C][C]0.145721225642151[/C][/ROW]
[ROW][C]98[/C][C]1974[/C][C]1973.6652892124[/C][C]0.334710787600574[/C][/ROW]
[ROW][C]99[/C][C]1974[/C][C]1972.71925068693[/C][C]1.28074931307279[/C][/ROW]
[ROW][C]100[/C][C]1974[/C][C]1971.71012474783[/C][C]2.28987525217115[/C][/ROW]
[ROW][C]101[/C][C]1974[/C][C]1971.54216432375[/C][C]2.45783567625419[/C][/ROW]
[ROW][C]102[/C][C]1974[/C][C]1972.38360251768[/C][C]1.61639748231884[/C][/ROW]
[ROW][C]103[/C][C]1974[/C][C]1973.68768174487[/C][C]0.31231825512507[/C][/ROW]
[ROW][C]104[/C][C]1974[/C][C]1975.76983907346[/C][C]-1.76983907345788[/C][/ROW]
[ROW][C]105[/C][C]1974[/C][C]1973.62513968909[/C][C]0.37486031091117[/C][/ROW]
[ROW][C]106[/C][C]1974[/C][C]1976.08582149943[/C][C]-2.08582149942867[/C][/ROW]
[ROW][C]107[/C][C]1974[/C][C]1975.22389952546[/C][C]-1.22389952545799[/C][/ROW]
[ROW][C]108[/C][C]1974[/C][C]1973.81521996673[/C][C]0.18478003327265[/C][/ROW]
[ROW][C]109[/C][C]1975[/C][C]1977.4923196268[/C][C]-2.49231962679912[/C][/ROW]
[ROW][C]110[/C][C]1975[/C][C]1976.33598972257[/C][C]-1.33598972257307[/C][/ROW]
[ROW][C]111[/C][C]1975[/C][C]1974.61187309571[/C][C]0.388126904288312[/C][/ROW]
[ROW][C]112[/C][C]1975[/C][C]1974.42288353375[/C][C]0.577116466246735[/C][/ROW]
[ROW][C]113[/C][C]1975[/C][C]1972.82494173414[/C][C]2.17505826585582[/C][/ROW]
[ROW][C]114[/C][C]1975[/C][C]1972.99344751607[/C][C]2.00655248393273[/C][/ROW]
[ROW][C]115[/C][C]1975[/C][C]1974.59193467352[/C][C]0.408065326483598[/C][/ROW]
[ROW][C]116[/C][C]1975[/C][C]1974.38191597368[/C][C]0.618084026317404[/C][/ROW]
[ROW][C]117[/C][C]1975[/C][C]1975.03409192674[/C][C]-0.0340919267394948[/C][/ROW]
[ROW][C]118[/C][C]1975[/C][C]1974.90818977841[/C][C]0.0918102215927801[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197374&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197374&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
119661967.839945342-1.83994534200346
219661967.67198491792-1.67198491791526
319661967.77740328621-1.7774032862122
419661967.44120975913-1.44120975912609
519661967.37839502442-1.37839502441997
619661967.14734718671-1.14734718671078
719661967.14761986563-1.1476198656308
819661966.83245547642-0.832455476420079
919661966.79066987959-0.790669879589338
1019661966.58065117976-0.580651179755531
1119661966.32857420417-0.32857420417096
1219661966.62325481335-0.623254813346346
1319671968.02920758288-1.02920758287674
1419671968.09256767542-1.09256767542292
1519671968.05078207859-1.05078207859217
1619671967.27297665612-0.272976656123026
1719671967.29427847292-0.294278472918433
1819671967.33660942759-0.336609427589224
1919671967.33688210651-0.336882106509248
2019671967.3581839233-0.358183923304655
2119671967.16919436135-0.169194361346232
2219671966.938146523640.0618534763629574
2319671967.19076885706-0.190768857061664
2419671967.48544946624-0.485449466237051
2519681968.53390689189-0.533906891885936
2619681968.15565508905-0.155655089049065
2719681967.882548975590.11745102441089
2819681968.36649182564-0.366491825642944
2919681968.45088105606-0.450881056064501
3019681967.820279598720.179720401276966
3119681968.49348468966-0.493484689655316
3219681969.01948581546-1.01948581545992
3319681968.4940300475-0.494030047495364
3419681967.884457728030.11554227197072
3519681968.36840057808-0.368400578083114
3619681968.72616860088-0.726168600884651
3719691970.30035447342-1.30035447341811
3819691969.45946163732-0.459461637322815
3919691969.670025695-0.67002569499667
4019691968.892220272530.107779727472479
4119691968.871463813570.128536186427838
4219691967.967483563851.03251643614928
4319691968.745834344160.254165655840088
4419691968.893310988210.106689011792382
4519691968.283738668740.716261331258466
4619691967.779312038651.22068796134764
4719691967.75855557971.24144442030299
4819691967.233099811731.76690018826755
4919701969.059635338770.940364661229488
5019701969.101966293440.898033706558697
5119701969.081209834490.918790165514056
5219701968.49266665291.50733334710476
5319701968.534997607571.46500239243397
5419701968.976882181871.0231178181309
5519701969.48185416980.518145830201681
5619701970.0919718471-0.0919718471044521
5719701970.93341004104-0.933410041039798
5819701969.819138412560.180861587435479
5919701969.37779919610.622200803898499
6019701969.336013599270.663986400729241
6119711969.837713440161.16228655984031
6219711970.237539738710.762460261288012
6319711970.321928969130.678071030866456
6419711969.733385787541.26661421245716
6519711970.343503464850.656496535151024
6619711969.607756318131.39224368186941
6719711972.02637985272-1.02637985271967
6819711970.806962534870.193037465132524
6919711970.449739869910.550260130094012
7019711971.71176082135-0.711760821348996
7119711971.35453815639-0.354538156387508
7219711971.62818962769-0.628189627687512
7319721971.982685503450.0173144965512435
7419721971.331054908230.668945091768093
7519721972.13043482642-0.130434826416487
7619721972.7615816416-0.761581641598003
7719721970.932319325361.0676806746403
7819721971.521407864790.478592135209549
7919721973.64562346912-1.64562346912416
8019721973.22531339054-1.22531339053653
8119721972.44750796807-0.447507968067379
8219721971.038828409340.961171590663262
8319721971.123217639760.876782360241705
8419721972.36420945333-0.364209453325919
8519731973.24443377597-0.24443377597174
8619731972.592803180750.40719681924511
8719731973.28703740956-0.287037409562555
8819731971.836299575081.16370042491885
8919731972.488475528140.511524471861953
9019731972.215369414680.784630585321908
9119731972.720341402610.27965859739269
9219731974.52911993881-1.52911993881028
9319731972.468537105940.531462894057238
9419731972.742188577240.257811422757234
9519731973.14201487579-0.142014875795068
9619731973.03714186534-0.0371418653381769
9719741973.854278774360.145721225642151
9819741973.66528921240.334710787600574
9919741972.719250686931.28074931307279
10019741971.710124747832.28987525217115
10119741971.542164323752.45783567625419
10219741972.383602517681.61639748231884
10319741973.687681744870.31231825512507
10419741975.76983907346-1.76983907345788
10519741973.625139689090.37486031091117
10619741976.08582149943-2.08582149942867
10719741975.22389952546-1.22389952545799
10819741973.815219966730.18478003327265
10919751977.4923196268-2.49231962679912
11019751976.33598972257-1.33598972257307
11119751974.611873095710.388126904288312
11219751974.422883533750.577116466246735
11319751972.824941734142.17505826585582
11419751972.993447516072.00655248393273
11519751974.591934673520.408065326483598
11619751974.381915973680.618084026317404
11719751975.03409192674-0.0340919267394948
11819751974.908189778410.0918102215927801







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
63.64720047330966e-397.29440094661932e-391
73.58906407244503e-567.17812814489006e-561
83.52826565266546e-637.05653130533092e-631
98.90797518222781e-781.78159503644556e-771
106.98523522154154e-911.39704704430831e-901
112.71577648095989e-1085.43155296191977e-1081
122.92123671309534e-1125.84247342619067e-1121
138.32683578043886e-050.0001665367156087770.999916731642196
148.82483957169387e-050.0001764967914338770.999911751604283
153.70938237516678e-057.41876475033356e-050.999962906176248
160.0007602751733009710.001520550346601940.999239724826699
170.001467583972739920.002935167945479850.99853241602726
180.001661495888093940.003322991776187870.998338504111906
190.00135300963886920.00270601927773840.998646990361131
200.0007927303361107770.001585460672221550.999207269663889
210.0004657325129545060.0009314650259090130.999534267487046
220.0003075150462773290.0006150300925546570.999692484953723
230.0001524284222007950.0003048568444015890.999847571577799
240.0001296806844798490.0002593613689596980.99987031931552
250.0001743662929449850.0003487325858899710.999825633707055
260.0003646489581216790.0007292979162433570.999635351041878
270.0009775864890155440.001955172978031090.999022413510984
280.0006220992158360950.001244198431672190.999377900784164
290.000402023249447780.000804046498895560.999597976750552
300.0004294451108362690.0008588902216725390.999570554889164
310.0003680907802685030.0007361815605370060.999631909219732
320.0009747422355045150.001949484471009030.999025257764495
330.0007129576755787470.001425915351157490.999287042324421
340.0005339316500370820.001067863300074160.999466068349963
350.0003883661748301410.0007767323496602830.99961163382517
360.0004856020518643260.0009712041037286510.999514397948136
370.0007130420757109280.001426084151421860.999286957924289
380.0007779854058876160.001555970811775230.999222014594112
390.0007976487089133020.00159529741782660.999202351291087
400.001292394931842120.002584789863684240.998707605068158
410.001721244346835610.003442488693671220.998278755653164
420.01030800846890120.02061601693780240.989691991531099
430.01067052105959780.02134104211919560.989329478940402
440.009438025009679940.01887605001935990.99056197499032
450.01221636753429020.02443273506858040.98778363246571
460.0258539953560680.0517079907121360.974146004643932
470.03898148668303160.07796297336606320.961018513316968
480.08596720531537870.1719344106307570.914032794684621
490.1574985969753990.3149971939507980.842501403024601
500.2067810420395870.4135620840791740.793218957960413
510.2378104571820790.4756209143641570.762189542817921
520.3431802356004330.6863604712008670.656819764399567
530.4132698984068940.8265397968137880.586730101593106
540.3986424716042780.7972849432085560.601357528395722
550.3571966732380690.7143933464761390.642803326761931
560.3604531064435230.7209062128870450.639546893556477
570.5036594898170960.9926810203658080.496340510182904
580.4718460743164650.9436921486329310.528153925683535
590.4278785506962210.8557571013924420.572121449303779
600.3829670096040830.7659340192081660.617032990395917
610.4045520983461480.8091041966922950.595447901653853
620.3839914305693180.7679828611386360.616008569430682
630.3565725241034230.7131450482068460.643427475896577
640.3516217978620770.7032435957241530.648378202137923
650.3172146826489360.6344293652978730.682785317351064
660.307710409643670.6154208192873390.69228959035633
670.4697085331193910.9394170662387820.530291466880609
680.4457584002122670.8915168004245340.554241599787733
690.4063941098498690.8127882196997370.593605890150131
700.4844550934764640.9689101869529270.515544906523536
710.5095500970561780.9808998058876440.490449902943822
720.5731215049056980.8537569901886050.426878495094302
730.5684957335026450.863008532994710.431504266497355
740.5495619246981820.9008761506036370.450438075301819
750.5645753579268740.8708492841462510.435424642073126
760.6461180098408840.7077639803182320.353881990159116
770.6376842669363160.7246314661273690.362315733063684
780.6266281516760820.7467436966478370.373371848323918
790.8055839052877610.3888321894244780.194416094712239
800.8846487845361840.2307024309276320.115351215463816
810.9051698985307940.1896602029384130.0948301014692065
820.900398045763440.199203908473120.0996019542365602
830.8958830599932010.2082338800135980.104116940006799
840.9215045308827040.1569909382345920.0784954691172958
850.9312180041516790.1375639916966430.0687819958483215
860.9344248120860690.1311503758278620.0655751879139308
870.9500610987781980.09987780244360390.049938901221802
880.9526029242841810.09479415143163710.0473970757158186
890.9564576662945760.08708466741084780.0435423337054239
900.9583839698605120.08323206027897650.0416160301394882
910.9624222537284790.07515549254304250.0375777462715212
920.9852069116421970.02958617671560660.0147930883578033
930.9858599392783520.02828012144329630.0141400607216481
940.987588215985670.02482356802866050.0124117840143302
950.9914976098886090.01700478022278210.00850239011139106
960.9959845541635090.008030891672982040.00401544583649102
970.9956440700016260.00871185999674770.00435592999837385
980.9956620390379450.008675921924110380.00433796096205519
990.9956570408919010.00868591821619810.00434295910809905
1000.9962880790432630.007423841913473390.00371192095673669
1010.9973216825237830.005356634952433850.00267831747621693
1020.9983999822785760.003200035442848920.00160001772142446
1030.9990341429967670.001931714006466740.000965857003233371
1040.9991007685577260.001798462884547130.000899231442273564
1050.9994162475499570.001167504900085730.000583752450042867
1060.999301758956340.001396482087319050.000698241043659525
1070.9995027777222840.0009944445554317470.000497222277715874
10811.14804331201647e-955.74021656008233e-96
10911.77637738632269e-788.88188693161344e-79
11013.24024151081195e-641.62012075540597e-64
11111.38622839575486e-596.9311419787743e-60
11214.68236943622658e-382.34118471811329e-38

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
6 & 3.64720047330966e-39 & 7.29440094661932e-39 & 1 \tabularnewline
7 & 3.58906407244503e-56 & 7.17812814489006e-56 & 1 \tabularnewline
8 & 3.52826565266546e-63 & 7.05653130533092e-63 & 1 \tabularnewline
9 & 8.90797518222781e-78 & 1.78159503644556e-77 & 1 \tabularnewline
10 & 6.98523522154154e-91 & 1.39704704430831e-90 & 1 \tabularnewline
11 & 2.71577648095989e-108 & 5.43155296191977e-108 & 1 \tabularnewline
12 & 2.92123671309534e-112 & 5.84247342619067e-112 & 1 \tabularnewline
13 & 8.32683578043886e-05 & 0.000166536715608777 & 0.999916731642196 \tabularnewline
14 & 8.82483957169387e-05 & 0.000176496791433877 & 0.999911751604283 \tabularnewline
15 & 3.70938237516678e-05 & 7.41876475033356e-05 & 0.999962906176248 \tabularnewline
16 & 0.000760275173300971 & 0.00152055034660194 & 0.999239724826699 \tabularnewline
17 & 0.00146758397273992 & 0.00293516794547985 & 0.99853241602726 \tabularnewline
18 & 0.00166149588809394 & 0.00332299177618787 & 0.998338504111906 \tabularnewline
19 & 0.0013530096388692 & 0.0027060192777384 & 0.998646990361131 \tabularnewline
20 & 0.000792730336110777 & 0.00158546067222155 & 0.999207269663889 \tabularnewline
21 & 0.000465732512954506 & 0.000931465025909013 & 0.999534267487046 \tabularnewline
22 & 0.000307515046277329 & 0.000615030092554657 & 0.999692484953723 \tabularnewline
23 & 0.000152428422200795 & 0.000304856844401589 & 0.999847571577799 \tabularnewline
24 & 0.000129680684479849 & 0.000259361368959698 & 0.99987031931552 \tabularnewline
25 & 0.000174366292944985 & 0.000348732585889971 & 0.999825633707055 \tabularnewline
26 & 0.000364648958121679 & 0.000729297916243357 & 0.999635351041878 \tabularnewline
27 & 0.000977586489015544 & 0.00195517297803109 & 0.999022413510984 \tabularnewline
28 & 0.000622099215836095 & 0.00124419843167219 & 0.999377900784164 \tabularnewline
29 & 0.00040202324944778 & 0.00080404649889556 & 0.999597976750552 \tabularnewline
30 & 0.000429445110836269 & 0.000858890221672539 & 0.999570554889164 \tabularnewline
31 & 0.000368090780268503 & 0.000736181560537006 & 0.999631909219732 \tabularnewline
32 & 0.000974742235504515 & 0.00194948447100903 & 0.999025257764495 \tabularnewline
33 & 0.000712957675578747 & 0.00142591535115749 & 0.999287042324421 \tabularnewline
34 & 0.000533931650037082 & 0.00106786330007416 & 0.999466068349963 \tabularnewline
35 & 0.000388366174830141 & 0.000776732349660283 & 0.99961163382517 \tabularnewline
36 & 0.000485602051864326 & 0.000971204103728651 & 0.999514397948136 \tabularnewline
37 & 0.000713042075710928 & 0.00142608415142186 & 0.999286957924289 \tabularnewline
38 & 0.000777985405887616 & 0.00155597081177523 & 0.999222014594112 \tabularnewline
39 & 0.000797648708913302 & 0.0015952974178266 & 0.999202351291087 \tabularnewline
40 & 0.00129239493184212 & 0.00258478986368424 & 0.998707605068158 \tabularnewline
41 & 0.00172124434683561 & 0.00344248869367122 & 0.998278755653164 \tabularnewline
42 & 0.0103080084689012 & 0.0206160169378024 & 0.989691991531099 \tabularnewline
43 & 0.0106705210595978 & 0.0213410421191956 & 0.989329478940402 \tabularnewline
44 & 0.00943802500967994 & 0.0188760500193599 & 0.99056197499032 \tabularnewline
45 & 0.0122163675342902 & 0.0244327350685804 & 0.98778363246571 \tabularnewline
46 & 0.025853995356068 & 0.051707990712136 & 0.974146004643932 \tabularnewline
47 & 0.0389814866830316 & 0.0779629733660632 & 0.961018513316968 \tabularnewline
48 & 0.0859672053153787 & 0.171934410630757 & 0.914032794684621 \tabularnewline
49 & 0.157498596975399 & 0.314997193950798 & 0.842501403024601 \tabularnewline
50 & 0.206781042039587 & 0.413562084079174 & 0.793218957960413 \tabularnewline
51 & 0.237810457182079 & 0.475620914364157 & 0.762189542817921 \tabularnewline
52 & 0.343180235600433 & 0.686360471200867 & 0.656819764399567 \tabularnewline
53 & 0.413269898406894 & 0.826539796813788 & 0.586730101593106 \tabularnewline
54 & 0.398642471604278 & 0.797284943208556 & 0.601357528395722 \tabularnewline
55 & 0.357196673238069 & 0.714393346476139 & 0.642803326761931 \tabularnewline
56 & 0.360453106443523 & 0.720906212887045 & 0.639546893556477 \tabularnewline
57 & 0.503659489817096 & 0.992681020365808 & 0.496340510182904 \tabularnewline
58 & 0.471846074316465 & 0.943692148632931 & 0.528153925683535 \tabularnewline
59 & 0.427878550696221 & 0.855757101392442 & 0.572121449303779 \tabularnewline
60 & 0.382967009604083 & 0.765934019208166 & 0.617032990395917 \tabularnewline
61 & 0.404552098346148 & 0.809104196692295 & 0.595447901653853 \tabularnewline
62 & 0.383991430569318 & 0.767982861138636 & 0.616008569430682 \tabularnewline
63 & 0.356572524103423 & 0.713145048206846 & 0.643427475896577 \tabularnewline
64 & 0.351621797862077 & 0.703243595724153 & 0.648378202137923 \tabularnewline
65 & 0.317214682648936 & 0.634429365297873 & 0.682785317351064 \tabularnewline
66 & 0.30771040964367 & 0.615420819287339 & 0.69228959035633 \tabularnewline
67 & 0.469708533119391 & 0.939417066238782 & 0.530291466880609 \tabularnewline
68 & 0.445758400212267 & 0.891516800424534 & 0.554241599787733 \tabularnewline
69 & 0.406394109849869 & 0.812788219699737 & 0.593605890150131 \tabularnewline
70 & 0.484455093476464 & 0.968910186952927 & 0.515544906523536 \tabularnewline
71 & 0.509550097056178 & 0.980899805887644 & 0.490449902943822 \tabularnewline
72 & 0.573121504905698 & 0.853756990188605 & 0.426878495094302 \tabularnewline
73 & 0.568495733502645 & 0.86300853299471 & 0.431504266497355 \tabularnewline
74 & 0.549561924698182 & 0.900876150603637 & 0.450438075301819 \tabularnewline
75 & 0.564575357926874 & 0.870849284146251 & 0.435424642073126 \tabularnewline
76 & 0.646118009840884 & 0.707763980318232 & 0.353881990159116 \tabularnewline
77 & 0.637684266936316 & 0.724631466127369 & 0.362315733063684 \tabularnewline
78 & 0.626628151676082 & 0.746743696647837 & 0.373371848323918 \tabularnewline
79 & 0.805583905287761 & 0.388832189424478 & 0.194416094712239 \tabularnewline
80 & 0.884648784536184 & 0.230702430927632 & 0.115351215463816 \tabularnewline
81 & 0.905169898530794 & 0.189660202938413 & 0.0948301014692065 \tabularnewline
82 & 0.90039804576344 & 0.19920390847312 & 0.0996019542365602 \tabularnewline
83 & 0.895883059993201 & 0.208233880013598 & 0.104116940006799 \tabularnewline
84 & 0.921504530882704 & 0.156990938234592 & 0.0784954691172958 \tabularnewline
85 & 0.931218004151679 & 0.137563991696643 & 0.0687819958483215 \tabularnewline
86 & 0.934424812086069 & 0.131150375827862 & 0.0655751879139308 \tabularnewline
87 & 0.950061098778198 & 0.0998778024436039 & 0.049938901221802 \tabularnewline
88 & 0.952602924284181 & 0.0947941514316371 & 0.0473970757158186 \tabularnewline
89 & 0.956457666294576 & 0.0870846674108478 & 0.0435423337054239 \tabularnewline
90 & 0.958383969860512 & 0.0832320602789765 & 0.0416160301394882 \tabularnewline
91 & 0.962422253728479 & 0.0751554925430425 & 0.0375777462715212 \tabularnewline
92 & 0.985206911642197 & 0.0295861767156066 & 0.0147930883578033 \tabularnewline
93 & 0.985859939278352 & 0.0282801214432963 & 0.0141400607216481 \tabularnewline
94 & 0.98758821598567 & 0.0248235680286605 & 0.0124117840143302 \tabularnewline
95 & 0.991497609888609 & 0.0170047802227821 & 0.00850239011139106 \tabularnewline
96 & 0.995984554163509 & 0.00803089167298204 & 0.00401544583649102 \tabularnewline
97 & 0.995644070001626 & 0.0087118599967477 & 0.00435592999837385 \tabularnewline
98 & 0.995662039037945 & 0.00867592192411038 & 0.00433796096205519 \tabularnewline
99 & 0.995657040891901 & 0.0086859182161981 & 0.00434295910809905 \tabularnewline
100 & 0.996288079043263 & 0.00742384191347339 & 0.00371192095673669 \tabularnewline
101 & 0.997321682523783 & 0.00535663495243385 & 0.00267831747621693 \tabularnewline
102 & 0.998399982278576 & 0.00320003544284892 & 0.00160001772142446 \tabularnewline
103 & 0.999034142996767 & 0.00193171400646674 & 0.000965857003233371 \tabularnewline
104 & 0.999100768557726 & 0.00179846288454713 & 0.000899231442273564 \tabularnewline
105 & 0.999416247549957 & 0.00116750490008573 & 0.000583752450042867 \tabularnewline
106 & 0.99930175895634 & 0.00139648208731905 & 0.000698241043659525 \tabularnewline
107 & 0.999502777722284 & 0.000994444555431747 & 0.000497222277715874 \tabularnewline
108 & 1 & 1.14804331201647e-95 & 5.74021656008233e-96 \tabularnewline
109 & 1 & 1.77637738632269e-78 & 8.88188693161344e-79 \tabularnewline
110 & 1 & 3.24024151081195e-64 & 1.62012075540597e-64 \tabularnewline
111 & 1 & 1.38622839575486e-59 & 6.9311419787743e-60 \tabularnewline
112 & 1 & 4.68236943622658e-38 & 2.34118471811329e-38 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197374&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]6[/C][C]3.64720047330966e-39[/C][C]7.29440094661932e-39[/C][C]1[/C][/ROW]
[ROW][C]7[/C][C]3.58906407244503e-56[/C][C]7.17812814489006e-56[/C][C]1[/C][/ROW]
[ROW][C]8[/C][C]3.52826565266546e-63[/C][C]7.05653130533092e-63[/C][C]1[/C][/ROW]
[ROW][C]9[/C][C]8.90797518222781e-78[/C][C]1.78159503644556e-77[/C][C]1[/C][/ROW]
[ROW][C]10[/C][C]6.98523522154154e-91[/C][C]1.39704704430831e-90[/C][C]1[/C][/ROW]
[ROW][C]11[/C][C]2.71577648095989e-108[/C][C]5.43155296191977e-108[/C][C]1[/C][/ROW]
[ROW][C]12[/C][C]2.92123671309534e-112[/C][C]5.84247342619067e-112[/C][C]1[/C][/ROW]
[ROW][C]13[/C][C]8.32683578043886e-05[/C][C]0.000166536715608777[/C][C]0.999916731642196[/C][/ROW]
[ROW][C]14[/C][C]8.82483957169387e-05[/C][C]0.000176496791433877[/C][C]0.999911751604283[/C][/ROW]
[ROW][C]15[/C][C]3.70938237516678e-05[/C][C]7.41876475033356e-05[/C][C]0.999962906176248[/C][/ROW]
[ROW][C]16[/C][C]0.000760275173300971[/C][C]0.00152055034660194[/C][C]0.999239724826699[/C][/ROW]
[ROW][C]17[/C][C]0.00146758397273992[/C][C]0.00293516794547985[/C][C]0.99853241602726[/C][/ROW]
[ROW][C]18[/C][C]0.00166149588809394[/C][C]0.00332299177618787[/C][C]0.998338504111906[/C][/ROW]
[ROW][C]19[/C][C]0.0013530096388692[/C][C]0.0027060192777384[/C][C]0.998646990361131[/C][/ROW]
[ROW][C]20[/C][C]0.000792730336110777[/C][C]0.00158546067222155[/C][C]0.999207269663889[/C][/ROW]
[ROW][C]21[/C][C]0.000465732512954506[/C][C]0.000931465025909013[/C][C]0.999534267487046[/C][/ROW]
[ROW][C]22[/C][C]0.000307515046277329[/C][C]0.000615030092554657[/C][C]0.999692484953723[/C][/ROW]
[ROW][C]23[/C][C]0.000152428422200795[/C][C]0.000304856844401589[/C][C]0.999847571577799[/C][/ROW]
[ROW][C]24[/C][C]0.000129680684479849[/C][C]0.000259361368959698[/C][C]0.99987031931552[/C][/ROW]
[ROW][C]25[/C][C]0.000174366292944985[/C][C]0.000348732585889971[/C][C]0.999825633707055[/C][/ROW]
[ROW][C]26[/C][C]0.000364648958121679[/C][C]0.000729297916243357[/C][C]0.999635351041878[/C][/ROW]
[ROW][C]27[/C][C]0.000977586489015544[/C][C]0.00195517297803109[/C][C]0.999022413510984[/C][/ROW]
[ROW][C]28[/C][C]0.000622099215836095[/C][C]0.00124419843167219[/C][C]0.999377900784164[/C][/ROW]
[ROW][C]29[/C][C]0.00040202324944778[/C][C]0.00080404649889556[/C][C]0.999597976750552[/C][/ROW]
[ROW][C]30[/C][C]0.000429445110836269[/C][C]0.000858890221672539[/C][C]0.999570554889164[/C][/ROW]
[ROW][C]31[/C][C]0.000368090780268503[/C][C]0.000736181560537006[/C][C]0.999631909219732[/C][/ROW]
[ROW][C]32[/C][C]0.000974742235504515[/C][C]0.00194948447100903[/C][C]0.999025257764495[/C][/ROW]
[ROW][C]33[/C][C]0.000712957675578747[/C][C]0.00142591535115749[/C][C]0.999287042324421[/C][/ROW]
[ROW][C]34[/C][C]0.000533931650037082[/C][C]0.00106786330007416[/C][C]0.999466068349963[/C][/ROW]
[ROW][C]35[/C][C]0.000388366174830141[/C][C]0.000776732349660283[/C][C]0.99961163382517[/C][/ROW]
[ROW][C]36[/C][C]0.000485602051864326[/C][C]0.000971204103728651[/C][C]0.999514397948136[/C][/ROW]
[ROW][C]37[/C][C]0.000713042075710928[/C][C]0.00142608415142186[/C][C]0.999286957924289[/C][/ROW]
[ROW][C]38[/C][C]0.000777985405887616[/C][C]0.00155597081177523[/C][C]0.999222014594112[/C][/ROW]
[ROW][C]39[/C][C]0.000797648708913302[/C][C]0.0015952974178266[/C][C]0.999202351291087[/C][/ROW]
[ROW][C]40[/C][C]0.00129239493184212[/C][C]0.00258478986368424[/C][C]0.998707605068158[/C][/ROW]
[ROW][C]41[/C][C]0.00172124434683561[/C][C]0.00344248869367122[/C][C]0.998278755653164[/C][/ROW]
[ROW][C]42[/C][C]0.0103080084689012[/C][C]0.0206160169378024[/C][C]0.989691991531099[/C][/ROW]
[ROW][C]43[/C][C]0.0106705210595978[/C][C]0.0213410421191956[/C][C]0.989329478940402[/C][/ROW]
[ROW][C]44[/C][C]0.00943802500967994[/C][C]0.0188760500193599[/C][C]0.99056197499032[/C][/ROW]
[ROW][C]45[/C][C]0.0122163675342902[/C][C]0.0244327350685804[/C][C]0.98778363246571[/C][/ROW]
[ROW][C]46[/C][C]0.025853995356068[/C][C]0.051707990712136[/C][C]0.974146004643932[/C][/ROW]
[ROW][C]47[/C][C]0.0389814866830316[/C][C]0.0779629733660632[/C][C]0.961018513316968[/C][/ROW]
[ROW][C]48[/C][C]0.0859672053153787[/C][C]0.171934410630757[/C][C]0.914032794684621[/C][/ROW]
[ROW][C]49[/C][C]0.157498596975399[/C][C]0.314997193950798[/C][C]0.842501403024601[/C][/ROW]
[ROW][C]50[/C][C]0.206781042039587[/C][C]0.413562084079174[/C][C]0.793218957960413[/C][/ROW]
[ROW][C]51[/C][C]0.237810457182079[/C][C]0.475620914364157[/C][C]0.762189542817921[/C][/ROW]
[ROW][C]52[/C][C]0.343180235600433[/C][C]0.686360471200867[/C][C]0.656819764399567[/C][/ROW]
[ROW][C]53[/C][C]0.413269898406894[/C][C]0.826539796813788[/C][C]0.586730101593106[/C][/ROW]
[ROW][C]54[/C][C]0.398642471604278[/C][C]0.797284943208556[/C][C]0.601357528395722[/C][/ROW]
[ROW][C]55[/C][C]0.357196673238069[/C][C]0.714393346476139[/C][C]0.642803326761931[/C][/ROW]
[ROW][C]56[/C][C]0.360453106443523[/C][C]0.720906212887045[/C][C]0.639546893556477[/C][/ROW]
[ROW][C]57[/C][C]0.503659489817096[/C][C]0.992681020365808[/C][C]0.496340510182904[/C][/ROW]
[ROW][C]58[/C][C]0.471846074316465[/C][C]0.943692148632931[/C][C]0.528153925683535[/C][/ROW]
[ROW][C]59[/C][C]0.427878550696221[/C][C]0.855757101392442[/C][C]0.572121449303779[/C][/ROW]
[ROW][C]60[/C][C]0.382967009604083[/C][C]0.765934019208166[/C][C]0.617032990395917[/C][/ROW]
[ROW][C]61[/C][C]0.404552098346148[/C][C]0.809104196692295[/C][C]0.595447901653853[/C][/ROW]
[ROW][C]62[/C][C]0.383991430569318[/C][C]0.767982861138636[/C][C]0.616008569430682[/C][/ROW]
[ROW][C]63[/C][C]0.356572524103423[/C][C]0.713145048206846[/C][C]0.643427475896577[/C][/ROW]
[ROW][C]64[/C][C]0.351621797862077[/C][C]0.703243595724153[/C][C]0.648378202137923[/C][/ROW]
[ROW][C]65[/C][C]0.317214682648936[/C][C]0.634429365297873[/C][C]0.682785317351064[/C][/ROW]
[ROW][C]66[/C][C]0.30771040964367[/C][C]0.615420819287339[/C][C]0.69228959035633[/C][/ROW]
[ROW][C]67[/C][C]0.469708533119391[/C][C]0.939417066238782[/C][C]0.530291466880609[/C][/ROW]
[ROW][C]68[/C][C]0.445758400212267[/C][C]0.891516800424534[/C][C]0.554241599787733[/C][/ROW]
[ROW][C]69[/C][C]0.406394109849869[/C][C]0.812788219699737[/C][C]0.593605890150131[/C][/ROW]
[ROW][C]70[/C][C]0.484455093476464[/C][C]0.968910186952927[/C][C]0.515544906523536[/C][/ROW]
[ROW][C]71[/C][C]0.509550097056178[/C][C]0.980899805887644[/C][C]0.490449902943822[/C][/ROW]
[ROW][C]72[/C][C]0.573121504905698[/C][C]0.853756990188605[/C][C]0.426878495094302[/C][/ROW]
[ROW][C]73[/C][C]0.568495733502645[/C][C]0.86300853299471[/C][C]0.431504266497355[/C][/ROW]
[ROW][C]74[/C][C]0.549561924698182[/C][C]0.900876150603637[/C][C]0.450438075301819[/C][/ROW]
[ROW][C]75[/C][C]0.564575357926874[/C][C]0.870849284146251[/C][C]0.435424642073126[/C][/ROW]
[ROW][C]76[/C][C]0.646118009840884[/C][C]0.707763980318232[/C][C]0.353881990159116[/C][/ROW]
[ROW][C]77[/C][C]0.637684266936316[/C][C]0.724631466127369[/C][C]0.362315733063684[/C][/ROW]
[ROW][C]78[/C][C]0.626628151676082[/C][C]0.746743696647837[/C][C]0.373371848323918[/C][/ROW]
[ROW][C]79[/C][C]0.805583905287761[/C][C]0.388832189424478[/C][C]0.194416094712239[/C][/ROW]
[ROW][C]80[/C][C]0.884648784536184[/C][C]0.230702430927632[/C][C]0.115351215463816[/C][/ROW]
[ROW][C]81[/C][C]0.905169898530794[/C][C]0.189660202938413[/C][C]0.0948301014692065[/C][/ROW]
[ROW][C]82[/C][C]0.90039804576344[/C][C]0.19920390847312[/C][C]0.0996019542365602[/C][/ROW]
[ROW][C]83[/C][C]0.895883059993201[/C][C]0.208233880013598[/C][C]0.104116940006799[/C][/ROW]
[ROW][C]84[/C][C]0.921504530882704[/C][C]0.156990938234592[/C][C]0.0784954691172958[/C][/ROW]
[ROW][C]85[/C][C]0.931218004151679[/C][C]0.137563991696643[/C][C]0.0687819958483215[/C][/ROW]
[ROW][C]86[/C][C]0.934424812086069[/C][C]0.131150375827862[/C][C]0.0655751879139308[/C][/ROW]
[ROW][C]87[/C][C]0.950061098778198[/C][C]0.0998778024436039[/C][C]0.049938901221802[/C][/ROW]
[ROW][C]88[/C][C]0.952602924284181[/C][C]0.0947941514316371[/C][C]0.0473970757158186[/C][/ROW]
[ROW][C]89[/C][C]0.956457666294576[/C][C]0.0870846674108478[/C][C]0.0435423337054239[/C][/ROW]
[ROW][C]90[/C][C]0.958383969860512[/C][C]0.0832320602789765[/C][C]0.0416160301394882[/C][/ROW]
[ROW][C]91[/C][C]0.962422253728479[/C][C]0.0751554925430425[/C][C]0.0375777462715212[/C][/ROW]
[ROW][C]92[/C][C]0.985206911642197[/C][C]0.0295861767156066[/C][C]0.0147930883578033[/C][/ROW]
[ROW][C]93[/C][C]0.985859939278352[/C][C]0.0282801214432963[/C][C]0.0141400607216481[/C][/ROW]
[ROW][C]94[/C][C]0.98758821598567[/C][C]0.0248235680286605[/C][C]0.0124117840143302[/C][/ROW]
[ROW][C]95[/C][C]0.991497609888609[/C][C]0.0170047802227821[/C][C]0.00850239011139106[/C][/ROW]
[ROW][C]96[/C][C]0.995984554163509[/C][C]0.00803089167298204[/C][C]0.00401544583649102[/C][/ROW]
[ROW][C]97[/C][C]0.995644070001626[/C][C]0.0087118599967477[/C][C]0.00435592999837385[/C][/ROW]
[ROW][C]98[/C][C]0.995662039037945[/C][C]0.00867592192411038[/C][C]0.00433796096205519[/C][/ROW]
[ROW][C]99[/C][C]0.995657040891901[/C][C]0.0086859182161981[/C][C]0.00434295910809905[/C][/ROW]
[ROW][C]100[/C][C]0.996288079043263[/C][C]0.00742384191347339[/C][C]0.00371192095673669[/C][/ROW]
[ROW][C]101[/C][C]0.997321682523783[/C][C]0.00535663495243385[/C][C]0.00267831747621693[/C][/ROW]
[ROW][C]102[/C][C]0.998399982278576[/C][C]0.00320003544284892[/C][C]0.00160001772142446[/C][/ROW]
[ROW][C]103[/C][C]0.999034142996767[/C][C]0.00193171400646674[/C][C]0.000965857003233371[/C][/ROW]
[ROW][C]104[/C][C]0.999100768557726[/C][C]0.00179846288454713[/C][C]0.000899231442273564[/C][/ROW]
[ROW][C]105[/C][C]0.999416247549957[/C][C]0.00116750490008573[/C][C]0.000583752450042867[/C][/ROW]
[ROW][C]106[/C][C]0.99930175895634[/C][C]0.00139648208731905[/C][C]0.000698241043659525[/C][/ROW]
[ROW][C]107[/C][C]0.999502777722284[/C][C]0.000994444555431747[/C][C]0.000497222277715874[/C][/ROW]
[ROW][C]108[/C][C]1[/C][C]1.14804331201647e-95[/C][C]5.74021656008233e-96[/C][/ROW]
[ROW][C]109[/C][C]1[/C][C]1.77637738632269e-78[/C][C]8.88188693161344e-79[/C][/ROW]
[ROW][C]110[/C][C]1[/C][C]3.24024151081195e-64[/C][C]1.62012075540597e-64[/C][/ROW]
[ROW][C]111[/C][C]1[/C][C]1.38622839575486e-59[/C][C]6.9311419787743e-60[/C][/ROW]
[ROW][C]112[/C][C]1[/C][C]4.68236943622658e-38[/C][C]2.34118471811329e-38[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197374&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197374&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
63.64720047330966e-397.29440094661932e-391
73.58906407244503e-567.17812814489006e-561
83.52826565266546e-637.05653130533092e-631
98.90797518222781e-781.78159503644556e-771
106.98523522154154e-911.39704704430831e-901
112.71577648095989e-1085.43155296191977e-1081
122.92123671309534e-1125.84247342619067e-1121
138.32683578043886e-050.0001665367156087770.999916731642196
148.82483957169387e-050.0001764967914338770.999911751604283
153.70938237516678e-057.41876475033356e-050.999962906176248
160.0007602751733009710.001520550346601940.999239724826699
170.001467583972739920.002935167945479850.99853241602726
180.001661495888093940.003322991776187870.998338504111906
190.00135300963886920.00270601927773840.998646990361131
200.0007927303361107770.001585460672221550.999207269663889
210.0004657325129545060.0009314650259090130.999534267487046
220.0003075150462773290.0006150300925546570.999692484953723
230.0001524284222007950.0003048568444015890.999847571577799
240.0001296806844798490.0002593613689596980.99987031931552
250.0001743662929449850.0003487325858899710.999825633707055
260.0003646489581216790.0007292979162433570.999635351041878
270.0009775864890155440.001955172978031090.999022413510984
280.0006220992158360950.001244198431672190.999377900784164
290.000402023249447780.000804046498895560.999597976750552
300.0004294451108362690.0008588902216725390.999570554889164
310.0003680907802685030.0007361815605370060.999631909219732
320.0009747422355045150.001949484471009030.999025257764495
330.0007129576755787470.001425915351157490.999287042324421
340.0005339316500370820.001067863300074160.999466068349963
350.0003883661748301410.0007767323496602830.99961163382517
360.0004856020518643260.0009712041037286510.999514397948136
370.0007130420757109280.001426084151421860.999286957924289
380.0007779854058876160.001555970811775230.999222014594112
390.0007976487089133020.00159529741782660.999202351291087
400.001292394931842120.002584789863684240.998707605068158
410.001721244346835610.003442488693671220.998278755653164
420.01030800846890120.02061601693780240.989691991531099
430.01067052105959780.02134104211919560.989329478940402
440.009438025009679940.01887605001935990.99056197499032
450.01221636753429020.02443273506858040.98778363246571
460.0258539953560680.0517079907121360.974146004643932
470.03898148668303160.07796297336606320.961018513316968
480.08596720531537870.1719344106307570.914032794684621
490.1574985969753990.3149971939507980.842501403024601
500.2067810420395870.4135620840791740.793218957960413
510.2378104571820790.4756209143641570.762189542817921
520.3431802356004330.6863604712008670.656819764399567
530.4132698984068940.8265397968137880.586730101593106
540.3986424716042780.7972849432085560.601357528395722
550.3571966732380690.7143933464761390.642803326761931
560.3604531064435230.7209062128870450.639546893556477
570.5036594898170960.9926810203658080.496340510182904
580.4718460743164650.9436921486329310.528153925683535
590.4278785506962210.8557571013924420.572121449303779
600.3829670096040830.7659340192081660.617032990395917
610.4045520983461480.8091041966922950.595447901653853
620.3839914305693180.7679828611386360.616008569430682
630.3565725241034230.7131450482068460.643427475896577
640.3516217978620770.7032435957241530.648378202137923
650.3172146826489360.6344293652978730.682785317351064
660.307710409643670.6154208192873390.69228959035633
670.4697085331193910.9394170662387820.530291466880609
680.4457584002122670.8915168004245340.554241599787733
690.4063941098498690.8127882196997370.593605890150131
700.4844550934764640.9689101869529270.515544906523536
710.5095500970561780.9808998058876440.490449902943822
720.5731215049056980.8537569901886050.426878495094302
730.5684957335026450.863008532994710.431504266497355
740.5495619246981820.9008761506036370.450438075301819
750.5645753579268740.8708492841462510.435424642073126
760.6461180098408840.7077639803182320.353881990159116
770.6376842669363160.7246314661273690.362315733063684
780.6266281516760820.7467436966478370.373371848323918
790.8055839052877610.3888321894244780.194416094712239
800.8846487845361840.2307024309276320.115351215463816
810.9051698985307940.1896602029384130.0948301014692065
820.900398045763440.199203908473120.0996019542365602
830.8958830599932010.2082338800135980.104116940006799
840.9215045308827040.1569909382345920.0784954691172958
850.9312180041516790.1375639916966430.0687819958483215
860.9344248120860690.1311503758278620.0655751879139308
870.9500610987781980.09987780244360390.049938901221802
880.9526029242841810.09479415143163710.0473970757158186
890.9564576662945760.08708466741084780.0435423337054239
900.9583839698605120.08323206027897650.0416160301394882
910.9624222537284790.07515549254304250.0375777462715212
920.9852069116421970.02958617671560660.0147930883578033
930.9858599392783520.02828012144329630.0141400607216481
940.987588215985670.02482356802866050.0124117840143302
950.9914976098886090.01700478022278210.00850239011139106
960.9959845541635090.008030891672982040.00401544583649102
970.9956440700016260.00871185999674770.00435592999837385
980.9956620390379450.008675921924110380.00433796096205519
990.9956570408919010.00868591821619810.00434295910809905
1000.9962880790432630.007423841913473390.00371192095673669
1010.9973216825237830.005356634952433850.00267831747621693
1020.9983999822785760.003200035442848920.00160001772142446
1030.9990341429967670.001931714006466740.000965857003233371
1040.9991007685577260.001798462884547130.000899231442273564
1050.9994162475499570.001167504900085730.000583752450042867
1060.999301758956340.001396482087319050.000698241043659525
1070.9995027777222840.0009944445554317470.000497222277715874
10811.14804331201647e-955.74021656008233e-96
10911.77637738632269e-788.88188693161344e-79
11013.24024151081195e-641.62012075540597e-64
11111.38622839575486e-596.9311419787743e-60
11214.68236943622658e-382.34118471811329e-38







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level530.495327102803738NOK
5% type I error level610.570093457943925NOK
10% type I error level680.635514018691589NOK

\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 & 53 & 0.495327102803738 & NOK \tabularnewline
5% type I error level & 61 & 0.570093457943925 & NOK \tabularnewline
10% type I error level & 68 & 0.635514018691589 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197374&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]53[/C][C]0.495327102803738[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]61[/C][C]0.570093457943925[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]68[/C][C]0.635514018691589[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197374&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197374&T=6

As an alternative you can also use a QR Code:  

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level530.495327102803738NOK
5% type I error level610.570093457943925NOK
10% type I error level680.635514018691589NOK



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')
}