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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 computationThu, 24 Nov 2011 05:59:43 -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/Nov/24/t1322132448x4norxdzmfp9cfh.htm/, Retrieved Thu, 25 Apr 2024 10:27:03 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=146620, Retrieved Thu, 25 Apr 2024 10:27:03 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact137

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
-   PD    [Multiple Regression] [Multiple regression] [2011-11-24 10:59:43] [ea5ba587a62f29790549e6d9c8da7af9] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
14	41	53	12
18	39	86	11
11	30	66	15
12	31	67	6
16	34	76	13
18	35	78	10
14	39	53	12
14	34	80	14
15	36	74	12
15	37	76	6
17	38	79	10
19	36	54	12
10	38	67	12
16	39	54	11
18	33	87	15
14	32	58	12
14	36	75	10
17	38	88	12
14	39	64	11
16	32	57	12
18	32	66	11
11	31	68	12
14	39	54	13
12	37	56	11
17	39	86	9
9	41	80	13
16	36	76	10
14	33	69	14
15	33	78	12
11	34	67	10
16	31	80	12
13	27	54	8
17	37	71	10
15	34	84	12
14	34	74	12
16	32	71	7
9	29	63	6
15	36	71	12
17	29	76	10
13	35	69	10
15	37	74	10
16	34	75	12
16	38	54	15
12	35	52	10
12	38	69	10
11	37	68	12
15	38	65	13
15	33	75	11
17	36	74	11
13	38	75	12
16	32	72	14
14	32	67	10
11	32	63	12
12	34	62	13
12	32	63	5
15	37	76	6
16	39	74	12
15	29	67	12
12	37	73	11
12	35	70	10
8	30	53	7
13	38	77	12
11	34	77	14
14	31	52	11
15	34	54	12
10	35	80	13
11	36	66	14
12	30	73	11
15	39	63	12
15	35	69	12
14	38	67	8
16	31	54	11
15	34	81	14
15	38	69	14
13	34	84	12
12	39	80	9
17	37	70	13
13	34	69	11
15	28	77	12
13	37	54	12
15	33	79	12
16	37	30	12
15	35	71	12
16	37	73	12
15	32	72	11
14	33	77	10
15	38	75	9
14	33	69	12
13	29	54	12
7	33	70	12
17	31	73	9
13	36	54	15
15	35	77	12
14	32	82	12
13	29	80	12
16	39	80	10
12	37	69	13
14	35	78	9
17	37	81	12
15	32	76	10
17	38	76	14
12	37	73	11
16	36	85	15
11	32	66	11
15	33	79	11
9	40	68	12
16	38	76	12
15	41	71	12
10	36	54	11
10	43	46	7
15	30	82	12
11	31	74	14
13	32	88	11
14	32	38	11
18	37	76	10
16	37	86	13
14	33	54	13
14	34	70	8
14	33	69	11
14	38	90	12
12	33	54	11
14	31	76	13
15	38	89	12
15	37	76	14
15	33	73	13
13	31	79	15
17	39	90	10
17	44	74	11
19	33	81	9
15	35	72	11
13	32	71	10
9	28	66	11
15	40	77	8
15	27	65	11
15	37	74	12
16	32	82	12
11	28	54	9
14	34	63	11
11	30	54	10
15	35	64	8
13	31	69	9
15	32	54	8
16	30	84	9
14	30	86	15
15	31	77	11
16	40	89	8
16	32	76	13
11	36	60	12
12	32	75	12
9	35	73	9
16	38	85	7
13	42	79	13
16	34	71	9
12	35	72	6
9	35	69	8
13	33	78	8
13	36	54	15
14	32	69	6
19	33	81	9
13	34	84	11
12	32	84	8
13	34	69	8

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 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 & 5 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146620&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]5 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=146620&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146620&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 time5 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net






Multiple Linear Regression - Estimated Regression Equation
Happiness[t] = + 6.32453227714989 + 0.0811177693027703Connected[t] + 0.059742187056915Belonging[t] + 0.0614681464799096Software[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Happiness[t] =  +  6.32453227714989 +  0.0811177693027703Connected[t] +  0.059742187056915Belonging[t] +  0.0614681464799096Software[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146620&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Happiness[t] =  +  6.32453227714989 +  0.0811177693027703Connected[t] +  0.059742187056915Belonging[t] +  0.0614681464799096Software[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146620&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146620&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
Happiness[t] = + 6.32453227714989 + 0.0811177693027703Connected[t] + 0.059742187056915Belonging[t] + 0.0614681464799096Software[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)6.324532277149892.2227332.84540.0050240.002512
Connected0.08111776930277030.052561.54330.1247520.062376
Belonging0.0597421870569150.0165233.61570.0004020.000201
Software0.06146814647990960.082620.7440.4579920.228996

\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) & 6.32453227714989 & 2.222733 & 2.8454 & 0.005024 & 0.002512 \tabularnewline
Connected & 0.0811177693027703 & 0.05256 & 1.5433 & 0.124752 & 0.062376 \tabularnewline
Belonging & 0.059742187056915 & 0.016523 & 3.6157 & 0.000402 & 0.000201 \tabularnewline
Software & 0.0614681464799096 & 0.08262 & 0.744 & 0.457992 & 0.228996 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146620&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]6.32453227714989[/C][C]2.222733[/C][C]2.8454[/C][C]0.005024[/C][C]0.002512[/C][/ROW]
[ROW][C]Connected[/C][C]0.0811177693027703[/C][C]0.05256[/C][C]1.5433[/C][C]0.124752[/C][C]0.062376[/C][/ROW]
[ROW][C]Belonging[/C][C]0.059742187056915[/C][C]0.016523[/C][C]3.6157[/C][C]0.000402[/C][C]0.000201[/C][/ROW]
[ROW][C]Software[/C][C]0.0614681464799096[/C][C]0.08262[/C][C]0.744[/C][C]0.457992[/C][C]0.228996[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146620&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146620&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)6.324532277149892.2227332.84540.0050240.002512
Connected0.08111776930277030.052561.54330.1247520.062376
Belonging0.0597421870569150.0165233.61570.0004020.000201
Software0.06146814647990960.082620.7440.4579920.228996






Multiple Linear Regression - Regression Statistics
Multiple R0.314895788240558
R-squared0.0991593574516424
Adjusted R-squared0.0820547882893318
F-TEST (value)5.79724379554307
F-TEST (DF numerator)3
F-TEST (DF denominator)158
p-value0.000874029415911526
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.23965918891424
Sum Squared Residuals792.539578633099

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.314895788240558 \tabularnewline
R-squared & 0.0991593574516424 \tabularnewline
Adjusted R-squared & 0.0820547882893318 \tabularnewline
F-TEST (value) & 5.79724379554307 \tabularnewline
F-TEST (DF numerator) & 3 \tabularnewline
F-TEST (DF denominator) & 158 \tabularnewline
p-value & 0.000874029415911526 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.23965918891424 \tabularnewline
Sum Squared Residuals & 792.539578633099 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146620&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.314895788240558[/C][/ROW]
[ROW][C]R-squared[/C][C]0.0991593574516424[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.0820547882893318[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]5.79724379554307[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]3[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]158[/C][/ROW]
[ROW][C]p-value[/C][C]0.000874029415911526[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.23965918891424[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]792.539578633099[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146620&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146620&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 R0.314895788240558
R-squared0.0991593574516424
Adjusted R-squared0.0820547882893318
F-TEST (value)5.79724379554307
F-TEST (DF numerator)3
F-TEST (DF denominator)158
p-value0.000874029415911526
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.23965918891424
Sum Squared Residuals792.539578633099






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11413.55431449033890.4456855096611
21815.30210297813162.69789702186837
31113.623071899188-2.62307189918803
41213.2107185372285-1.21071853722853
51614.42202855400841.57797144599156
61814.43822625798533.56177374201469
71413.39207895173330.60792104826666
81414.722465448716-0.722465448716013
91514.40331157202020.596688427979755
101514.23510483655740.764895163442613
111714.74132175295052.25867824704946
121913.20846783088195.79153216911805
131014.1473518012274-4.14735180122738
141613.39035299231032.60964700768965
151815.12101113529162.87898886470844
161413.12296550189850.877034498101477
171414.3401174661173-0.34011746611734
181715.40193772942261.5980622705774
191413.98777486287950.012225137120504
201613.06322331484162.93677668515839
211813.53943485187394.46056514812607
221113.6392696031649-2.6392696031649
231413.51328928527020.486710714729835
241213.3476018278186-1.34760182781864
251715.17916668517181.82083331482819
26915.2288216873555-6.2288216873555
271614.39985965317431.60014034682574
281413.98418362178720.0158163782128221
291514.39892701233960.601072987660406
301113.6999444310565-2.69994443105648
311614.35617584784791.64382415215212
321312.23253532123740.767464678762626
331714.18226648719252.81773351280755
341514.83849790398390.161502096016146
351414.2410760334147-0.241076033414704
361613.59227320123892.40772679876113
37912.8095142503953-3.80951425039533
381514.22408501084950.7759149891505
391713.83203526805493.16796473194514
401313.9005465744731-0.90054657447308
411514.36149304836320.638506951636804
421614.30081822047161.69918177952838
431613.55510780892722.44489219107279
441212.8849293945055-0.884929394505525
451214.1438998823814-2.14389988238139
461114.1259762189815-3.12597621898153
471514.08933557359350.91066442640654
481514.15823230468890.841767695311061
491714.34184342554032.65815657445966
501314.6252892976827-1.6252892976827
511614.08229241365521.91770758634485
521413.53770889245090.462291107549061
531113.4216764371831-2.4216764371831
541213.5856379352116-1.58563793521163
551212.9913994118237-0.991399411823731
561514.23510483655740.764895163442613
571614.64666487992861.35333512007144
581513.41729187750241.58270812249755
591214.3632190077862-2.36321900778619
601213.96028876153-1.96028876153
61812.3546782956089-4.35467829560886
621314.7447736717965-1.74477367179653
631114.5432388875453-3.54323888754527
641412.62192646377441.37807353622565
651513.04623229227641.9537677077236
661014.7421150715389-4.74211507153887
671114.0483103685247-3.04831036852474
681213.7953946226668-1.7953946226668
691513.98950082230251.01049917769751
701514.02348286743290.976517132567101
711413.90147921530770.0985207846922581
721612.74141083788823.25858916211182
731514.78220763577290.217792364227072
741514.3897724683010.610227531698971
751314.8384979039839-1.83849790398385
761214.8207135628303-2.82071356283032
771714.30692873957532.69307126042474
781313.8808969516502-0.880896951650219
791513.93359597876881.06640402123117
801313.2895856001847-0.289585600184715
811514.45866919939650.541330800603491
821611.85577311081884.14422688918124
831514.14296724154670.857032758453271
841614.42468715426611.5753128457339
851513.89788797421541.10211202578458
861414.2162485323229-0.216248532322859
871514.4408848582430.559115141757029
881413.86124732882740.138752671172641
891312.64064344576260.359356554237448
90713.9209895158843-6.92098951588427
911713.75357609900973.24642390099025
921313.3928722703217-0.392872270321673
931514.50142036388820.498579636111781
941414.5567779912645-0.556777991264483
951314.1939403092423-1.19394030924234
961614.88218170931021.11781829068977
971214.2471865525184-2.24718655251835
981414.3767581115054-0.376758111505406
991714.90262465072142.09737534927858
1001514.07538857596320.924611424036826
1011714.80796777769942.19203222230057
1021214.3632190077862-2.36321900778619
1031615.2448800690860.755119930913962
1041113.5394348518739-2.53943485187393
1051514.39720105291660.602798947083401
106914.3693295268898-5.36932952688984
1071614.68503148473961.31496851526038
1081514.62967385736340.370326142636649
1091013.146999684402-3.14699968440204
1101012.9910139871465-2.99101398714647
1111514.39454245265890.605457547341058
1121114.1206590184662-3.12065901846621
1131314.8537629671261-1.85376296712606
1141411.86665361428032.13334638571969
1151814.4809774224773.51902257752297
1161615.26280373248590.737196267514096
1171413.02658266945350.973417330546457
1181413.75623469926740.243765300732594
1191413.79977918234750.200220817652551
1201415.5214221035364-1.52142210353643
1211212.9036463764937-0.903646376493724
1221414.1786752461001-0.178675246100133
1231515.4616799164795-0.46167991647951
1241514.72685000839670.273149991603336
1251514.16168422353490.838315776465072
1261314.4808381002307-1.4808381002307
1271715.47960357987941.52039642012062
1281714.99078557996252.0092144200375
1291914.39374913407064.60625086592939
1301514.14124128212370.858758717876265
1311313.7766776406786-0.776677640678599
132913.2149637746629-4.21496377466285
1331514.66113662448240.338863375517567
1341513.07410381830321.92589618169683
1351514.4844293413230.515570658676985
1361614.55677799126451.44322200873552
1371112.3751212370201-1.37512123702005
1381413.52244382930870.47755617069127
1391112.5988249221055-1.5988249221055
1401513.47889934622871.52110065377131
1411313.5146073507821-0.514607350782089
1421512.63812416775122.36187583224877
1431614.3296223873331.67037761266696
1441414.8179156403263-0.817915640326331
1451514.11548114019720.884518859802772
1461615.37804286916540.621957130834587
1471614.25979301540291.7402069845971
1481113.5669209532234-2.56692095322343
1491214.1385826818661-2.13858268186608
150914.0780471762208-5.07804717622083
1511614.91537043585231.0846295641477
1521315.2501972696014-2.25019726960135
1531613.87744503280422.12255496719577
1541213.8339005497242-1.83390054972419
155913.7776102815133-4.77761028151326
1561314.15305442642-1.15305442641996
1571313.3928722703217-0.392872270321673
1581413.41132068064510.588679319354869
1591914.39374913407064.60625086592939
1601314.7770297575039-1.77702975750394
1611214.4303897794587-2.43038977945867
1621313.6964925122105-0.696492512210491

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 14 & 13.5543144903389 & 0.4456855096611 \tabularnewline
2 & 18 & 15.3021029781316 & 2.69789702186837 \tabularnewline
3 & 11 & 13.623071899188 & -2.62307189918803 \tabularnewline
4 & 12 & 13.2107185372285 & -1.21071853722853 \tabularnewline
5 & 16 & 14.4220285540084 & 1.57797144599156 \tabularnewline
6 & 18 & 14.4382262579853 & 3.56177374201469 \tabularnewline
7 & 14 & 13.3920789517333 & 0.60792104826666 \tabularnewline
8 & 14 & 14.722465448716 & -0.722465448716013 \tabularnewline
9 & 15 & 14.4033115720202 & 0.596688427979755 \tabularnewline
10 & 15 & 14.2351048365574 & 0.764895163442613 \tabularnewline
11 & 17 & 14.7413217529505 & 2.25867824704946 \tabularnewline
12 & 19 & 13.2084678308819 & 5.79153216911805 \tabularnewline
13 & 10 & 14.1473518012274 & -4.14735180122738 \tabularnewline
14 & 16 & 13.3903529923103 & 2.60964700768965 \tabularnewline
15 & 18 & 15.1210111352916 & 2.87898886470844 \tabularnewline
16 & 14 & 13.1229655018985 & 0.877034498101477 \tabularnewline
17 & 14 & 14.3401174661173 & -0.34011746611734 \tabularnewline
18 & 17 & 15.4019377294226 & 1.5980622705774 \tabularnewline
19 & 14 & 13.9877748628795 & 0.012225137120504 \tabularnewline
20 & 16 & 13.0632233148416 & 2.93677668515839 \tabularnewline
21 & 18 & 13.5394348518739 & 4.46056514812607 \tabularnewline
22 & 11 & 13.6392696031649 & -2.6392696031649 \tabularnewline
23 & 14 & 13.5132892852702 & 0.486710714729835 \tabularnewline
24 & 12 & 13.3476018278186 & -1.34760182781864 \tabularnewline
25 & 17 & 15.1791666851718 & 1.82083331482819 \tabularnewline
26 & 9 & 15.2288216873555 & -6.2288216873555 \tabularnewline
27 & 16 & 14.3998596531743 & 1.60014034682574 \tabularnewline
28 & 14 & 13.9841836217872 & 0.0158163782128221 \tabularnewline
29 & 15 & 14.3989270123396 & 0.601072987660406 \tabularnewline
30 & 11 & 13.6999444310565 & -2.69994443105648 \tabularnewline
31 & 16 & 14.3561758478479 & 1.64382415215212 \tabularnewline
32 & 13 & 12.2325353212374 & 0.767464678762626 \tabularnewline
33 & 17 & 14.1822664871925 & 2.81773351280755 \tabularnewline
34 & 15 & 14.8384979039839 & 0.161502096016146 \tabularnewline
35 & 14 & 14.2410760334147 & -0.241076033414704 \tabularnewline
36 & 16 & 13.5922732012389 & 2.40772679876113 \tabularnewline
37 & 9 & 12.8095142503953 & -3.80951425039533 \tabularnewline
38 & 15 & 14.2240850108495 & 0.7759149891505 \tabularnewline
39 & 17 & 13.8320352680549 & 3.16796473194514 \tabularnewline
40 & 13 & 13.9005465744731 & -0.90054657447308 \tabularnewline
41 & 15 & 14.3614930483632 & 0.638506951636804 \tabularnewline
42 & 16 & 14.3008182204716 & 1.69918177952838 \tabularnewline
43 & 16 & 13.5551078089272 & 2.44489219107279 \tabularnewline
44 & 12 & 12.8849293945055 & -0.884929394505525 \tabularnewline
45 & 12 & 14.1438998823814 & -2.14389988238139 \tabularnewline
46 & 11 & 14.1259762189815 & -3.12597621898153 \tabularnewline
47 & 15 & 14.0893355735935 & 0.91066442640654 \tabularnewline
48 & 15 & 14.1582323046889 & 0.841767695311061 \tabularnewline
49 & 17 & 14.3418434255403 & 2.65815657445966 \tabularnewline
50 & 13 & 14.6252892976827 & -1.6252892976827 \tabularnewline
51 & 16 & 14.0822924136552 & 1.91770758634485 \tabularnewline
52 & 14 & 13.5377088924509 & 0.462291107549061 \tabularnewline
53 & 11 & 13.4216764371831 & -2.4216764371831 \tabularnewline
54 & 12 & 13.5856379352116 & -1.58563793521163 \tabularnewline
55 & 12 & 12.9913994118237 & -0.991399411823731 \tabularnewline
56 & 15 & 14.2351048365574 & 0.764895163442613 \tabularnewline
57 & 16 & 14.6466648799286 & 1.35333512007144 \tabularnewline
58 & 15 & 13.4172918775024 & 1.58270812249755 \tabularnewline
59 & 12 & 14.3632190077862 & -2.36321900778619 \tabularnewline
60 & 12 & 13.96028876153 & -1.96028876153 \tabularnewline
61 & 8 & 12.3546782956089 & -4.35467829560886 \tabularnewline
62 & 13 & 14.7447736717965 & -1.74477367179653 \tabularnewline
63 & 11 & 14.5432388875453 & -3.54323888754527 \tabularnewline
64 & 14 & 12.6219264637744 & 1.37807353622565 \tabularnewline
65 & 15 & 13.0462322922764 & 1.9537677077236 \tabularnewline
66 & 10 & 14.7421150715389 & -4.74211507153887 \tabularnewline
67 & 11 & 14.0483103685247 & -3.04831036852474 \tabularnewline
68 & 12 & 13.7953946226668 & -1.7953946226668 \tabularnewline
69 & 15 & 13.9895008223025 & 1.01049917769751 \tabularnewline
70 & 15 & 14.0234828674329 & 0.976517132567101 \tabularnewline
71 & 14 & 13.9014792153077 & 0.0985207846922581 \tabularnewline
72 & 16 & 12.7414108378882 & 3.25858916211182 \tabularnewline
73 & 15 & 14.7822076357729 & 0.217792364227072 \tabularnewline
74 & 15 & 14.389772468301 & 0.610227531698971 \tabularnewline
75 & 13 & 14.8384979039839 & -1.83849790398385 \tabularnewline
76 & 12 & 14.8207135628303 & -2.82071356283032 \tabularnewline
77 & 17 & 14.3069287395753 & 2.69307126042474 \tabularnewline
78 & 13 & 13.8808969516502 & -0.880896951650219 \tabularnewline
79 & 15 & 13.9335959787688 & 1.06640402123117 \tabularnewline
80 & 13 & 13.2895856001847 & -0.289585600184715 \tabularnewline
81 & 15 & 14.4586691993965 & 0.541330800603491 \tabularnewline
82 & 16 & 11.8557731108188 & 4.14422688918124 \tabularnewline
83 & 15 & 14.1429672415467 & 0.857032758453271 \tabularnewline
84 & 16 & 14.4246871542661 & 1.5753128457339 \tabularnewline
85 & 15 & 13.8978879742154 & 1.10211202578458 \tabularnewline
86 & 14 & 14.2162485323229 & -0.216248532322859 \tabularnewline
87 & 15 & 14.440884858243 & 0.559115141757029 \tabularnewline
88 & 14 & 13.8612473288274 & 0.138752671172641 \tabularnewline
89 & 13 & 12.6406434457626 & 0.359356554237448 \tabularnewline
90 & 7 & 13.9209895158843 & -6.92098951588427 \tabularnewline
91 & 17 & 13.7535760990097 & 3.24642390099025 \tabularnewline
92 & 13 & 13.3928722703217 & -0.392872270321673 \tabularnewline
93 & 15 & 14.5014203638882 & 0.498579636111781 \tabularnewline
94 & 14 & 14.5567779912645 & -0.556777991264483 \tabularnewline
95 & 13 & 14.1939403092423 & -1.19394030924234 \tabularnewline
96 & 16 & 14.8821817093102 & 1.11781829068977 \tabularnewline
97 & 12 & 14.2471865525184 & -2.24718655251835 \tabularnewline
98 & 14 & 14.3767581115054 & -0.376758111505406 \tabularnewline
99 & 17 & 14.9026246507214 & 2.09737534927858 \tabularnewline
100 & 15 & 14.0753885759632 & 0.924611424036826 \tabularnewline
101 & 17 & 14.8079677776994 & 2.19203222230057 \tabularnewline
102 & 12 & 14.3632190077862 & -2.36321900778619 \tabularnewline
103 & 16 & 15.244880069086 & 0.755119930913962 \tabularnewline
104 & 11 & 13.5394348518739 & -2.53943485187393 \tabularnewline
105 & 15 & 14.3972010529166 & 0.602798947083401 \tabularnewline
106 & 9 & 14.3693295268898 & -5.36932952688984 \tabularnewline
107 & 16 & 14.6850314847396 & 1.31496851526038 \tabularnewline
108 & 15 & 14.6296738573634 & 0.370326142636649 \tabularnewline
109 & 10 & 13.146999684402 & -3.14699968440204 \tabularnewline
110 & 10 & 12.9910139871465 & -2.99101398714647 \tabularnewline
111 & 15 & 14.3945424526589 & 0.605457547341058 \tabularnewline
112 & 11 & 14.1206590184662 & -3.12065901846621 \tabularnewline
113 & 13 & 14.8537629671261 & -1.85376296712606 \tabularnewline
114 & 14 & 11.8666536142803 & 2.13334638571969 \tabularnewline
115 & 18 & 14.480977422477 & 3.51902257752297 \tabularnewline
116 & 16 & 15.2628037324859 & 0.737196267514096 \tabularnewline
117 & 14 & 13.0265826694535 & 0.973417330546457 \tabularnewline
118 & 14 & 13.7562346992674 & 0.243765300732594 \tabularnewline
119 & 14 & 13.7997791823475 & 0.200220817652551 \tabularnewline
120 & 14 & 15.5214221035364 & -1.52142210353643 \tabularnewline
121 & 12 & 12.9036463764937 & -0.903646376493724 \tabularnewline
122 & 14 & 14.1786752461001 & -0.178675246100133 \tabularnewline
123 & 15 & 15.4616799164795 & -0.46167991647951 \tabularnewline
124 & 15 & 14.7268500083967 & 0.273149991603336 \tabularnewline
125 & 15 & 14.1616842235349 & 0.838315776465072 \tabularnewline
126 & 13 & 14.4808381002307 & -1.4808381002307 \tabularnewline
127 & 17 & 15.4796035798794 & 1.52039642012062 \tabularnewline
128 & 17 & 14.9907855799625 & 2.0092144200375 \tabularnewline
129 & 19 & 14.3937491340706 & 4.60625086592939 \tabularnewline
130 & 15 & 14.1412412821237 & 0.858758717876265 \tabularnewline
131 & 13 & 13.7766776406786 & -0.776677640678599 \tabularnewline
132 & 9 & 13.2149637746629 & -4.21496377466285 \tabularnewline
133 & 15 & 14.6611366244824 & 0.338863375517567 \tabularnewline
134 & 15 & 13.0741038183032 & 1.92589618169683 \tabularnewline
135 & 15 & 14.484429341323 & 0.515570658676985 \tabularnewline
136 & 16 & 14.5567779912645 & 1.44322200873552 \tabularnewline
137 & 11 & 12.3751212370201 & -1.37512123702005 \tabularnewline
138 & 14 & 13.5224438293087 & 0.47755617069127 \tabularnewline
139 & 11 & 12.5988249221055 & -1.5988249221055 \tabularnewline
140 & 15 & 13.4788993462287 & 1.52110065377131 \tabularnewline
141 & 13 & 13.5146073507821 & -0.514607350782089 \tabularnewline
142 & 15 & 12.6381241677512 & 2.36187583224877 \tabularnewline
143 & 16 & 14.329622387333 & 1.67037761266696 \tabularnewline
144 & 14 & 14.8179156403263 & -0.817915640326331 \tabularnewline
145 & 15 & 14.1154811401972 & 0.884518859802772 \tabularnewline
146 & 16 & 15.3780428691654 & 0.621957130834587 \tabularnewline
147 & 16 & 14.2597930154029 & 1.7402069845971 \tabularnewline
148 & 11 & 13.5669209532234 & -2.56692095322343 \tabularnewline
149 & 12 & 14.1385826818661 & -2.13858268186608 \tabularnewline
150 & 9 & 14.0780471762208 & -5.07804717622083 \tabularnewline
151 & 16 & 14.9153704358523 & 1.0846295641477 \tabularnewline
152 & 13 & 15.2501972696014 & -2.25019726960135 \tabularnewline
153 & 16 & 13.8774450328042 & 2.12255496719577 \tabularnewline
154 & 12 & 13.8339005497242 & -1.83390054972419 \tabularnewline
155 & 9 & 13.7776102815133 & -4.77761028151326 \tabularnewline
156 & 13 & 14.15305442642 & -1.15305442641996 \tabularnewline
157 & 13 & 13.3928722703217 & -0.392872270321673 \tabularnewline
158 & 14 & 13.4113206806451 & 0.588679319354869 \tabularnewline
159 & 19 & 14.3937491340706 & 4.60625086592939 \tabularnewline
160 & 13 & 14.7770297575039 & -1.77702975750394 \tabularnewline
161 & 12 & 14.4303897794587 & -2.43038977945867 \tabularnewline
162 & 13 & 13.6964925122105 & -0.696492512210491 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146620&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]14[/C][C]13.5543144903389[/C][C]0.4456855096611[/C][/ROW]
[ROW][C]2[/C][C]18[/C][C]15.3021029781316[/C][C]2.69789702186837[/C][/ROW]
[ROW][C]3[/C][C]11[/C][C]13.623071899188[/C][C]-2.62307189918803[/C][/ROW]
[ROW][C]4[/C][C]12[/C][C]13.2107185372285[/C][C]-1.21071853722853[/C][/ROW]
[ROW][C]5[/C][C]16[/C][C]14.4220285540084[/C][C]1.57797144599156[/C][/ROW]
[ROW][C]6[/C][C]18[/C][C]14.4382262579853[/C][C]3.56177374201469[/C][/ROW]
[ROW][C]7[/C][C]14[/C][C]13.3920789517333[/C][C]0.60792104826666[/C][/ROW]
[ROW][C]8[/C][C]14[/C][C]14.722465448716[/C][C]-0.722465448716013[/C][/ROW]
[ROW][C]9[/C][C]15[/C][C]14.4033115720202[/C][C]0.596688427979755[/C][/ROW]
[ROW][C]10[/C][C]15[/C][C]14.2351048365574[/C][C]0.764895163442613[/C][/ROW]
[ROW][C]11[/C][C]17[/C][C]14.7413217529505[/C][C]2.25867824704946[/C][/ROW]
[ROW][C]12[/C][C]19[/C][C]13.2084678308819[/C][C]5.79153216911805[/C][/ROW]
[ROW][C]13[/C][C]10[/C][C]14.1473518012274[/C][C]-4.14735180122738[/C][/ROW]
[ROW][C]14[/C][C]16[/C][C]13.3903529923103[/C][C]2.60964700768965[/C][/ROW]
[ROW][C]15[/C][C]18[/C][C]15.1210111352916[/C][C]2.87898886470844[/C][/ROW]
[ROW][C]16[/C][C]14[/C][C]13.1229655018985[/C][C]0.877034498101477[/C][/ROW]
[ROW][C]17[/C][C]14[/C][C]14.3401174661173[/C][C]-0.34011746611734[/C][/ROW]
[ROW][C]18[/C][C]17[/C][C]15.4019377294226[/C][C]1.5980622705774[/C][/ROW]
[ROW][C]19[/C][C]14[/C][C]13.9877748628795[/C][C]0.012225137120504[/C][/ROW]
[ROW][C]20[/C][C]16[/C][C]13.0632233148416[/C][C]2.93677668515839[/C][/ROW]
[ROW][C]21[/C][C]18[/C][C]13.5394348518739[/C][C]4.46056514812607[/C][/ROW]
[ROW][C]22[/C][C]11[/C][C]13.6392696031649[/C][C]-2.6392696031649[/C][/ROW]
[ROW][C]23[/C][C]14[/C][C]13.5132892852702[/C][C]0.486710714729835[/C][/ROW]
[ROW][C]24[/C][C]12[/C][C]13.3476018278186[/C][C]-1.34760182781864[/C][/ROW]
[ROW][C]25[/C][C]17[/C][C]15.1791666851718[/C][C]1.82083331482819[/C][/ROW]
[ROW][C]26[/C][C]9[/C][C]15.2288216873555[/C][C]-6.2288216873555[/C][/ROW]
[ROW][C]27[/C][C]16[/C][C]14.3998596531743[/C][C]1.60014034682574[/C][/ROW]
[ROW][C]28[/C][C]14[/C][C]13.9841836217872[/C][C]0.0158163782128221[/C][/ROW]
[ROW][C]29[/C][C]15[/C][C]14.3989270123396[/C][C]0.601072987660406[/C][/ROW]
[ROW][C]30[/C][C]11[/C][C]13.6999444310565[/C][C]-2.69994443105648[/C][/ROW]
[ROW][C]31[/C][C]16[/C][C]14.3561758478479[/C][C]1.64382415215212[/C][/ROW]
[ROW][C]32[/C][C]13[/C][C]12.2325353212374[/C][C]0.767464678762626[/C][/ROW]
[ROW][C]33[/C][C]17[/C][C]14.1822664871925[/C][C]2.81773351280755[/C][/ROW]
[ROW][C]34[/C][C]15[/C][C]14.8384979039839[/C][C]0.161502096016146[/C][/ROW]
[ROW][C]35[/C][C]14[/C][C]14.2410760334147[/C][C]-0.241076033414704[/C][/ROW]
[ROW][C]36[/C][C]16[/C][C]13.5922732012389[/C][C]2.40772679876113[/C][/ROW]
[ROW][C]37[/C][C]9[/C][C]12.8095142503953[/C][C]-3.80951425039533[/C][/ROW]
[ROW][C]38[/C][C]15[/C][C]14.2240850108495[/C][C]0.7759149891505[/C][/ROW]
[ROW][C]39[/C][C]17[/C][C]13.8320352680549[/C][C]3.16796473194514[/C][/ROW]
[ROW][C]40[/C][C]13[/C][C]13.9005465744731[/C][C]-0.90054657447308[/C][/ROW]
[ROW][C]41[/C][C]15[/C][C]14.3614930483632[/C][C]0.638506951636804[/C][/ROW]
[ROW][C]42[/C][C]16[/C][C]14.3008182204716[/C][C]1.69918177952838[/C][/ROW]
[ROW][C]43[/C][C]16[/C][C]13.5551078089272[/C][C]2.44489219107279[/C][/ROW]
[ROW][C]44[/C][C]12[/C][C]12.8849293945055[/C][C]-0.884929394505525[/C][/ROW]
[ROW][C]45[/C][C]12[/C][C]14.1438998823814[/C][C]-2.14389988238139[/C][/ROW]
[ROW][C]46[/C][C]11[/C][C]14.1259762189815[/C][C]-3.12597621898153[/C][/ROW]
[ROW][C]47[/C][C]15[/C][C]14.0893355735935[/C][C]0.91066442640654[/C][/ROW]
[ROW][C]48[/C][C]15[/C][C]14.1582323046889[/C][C]0.841767695311061[/C][/ROW]
[ROW][C]49[/C][C]17[/C][C]14.3418434255403[/C][C]2.65815657445966[/C][/ROW]
[ROW][C]50[/C][C]13[/C][C]14.6252892976827[/C][C]-1.6252892976827[/C][/ROW]
[ROW][C]51[/C][C]16[/C][C]14.0822924136552[/C][C]1.91770758634485[/C][/ROW]
[ROW][C]52[/C][C]14[/C][C]13.5377088924509[/C][C]0.462291107549061[/C][/ROW]
[ROW][C]53[/C][C]11[/C][C]13.4216764371831[/C][C]-2.4216764371831[/C][/ROW]
[ROW][C]54[/C][C]12[/C][C]13.5856379352116[/C][C]-1.58563793521163[/C][/ROW]
[ROW][C]55[/C][C]12[/C][C]12.9913994118237[/C][C]-0.991399411823731[/C][/ROW]
[ROW][C]56[/C][C]15[/C][C]14.2351048365574[/C][C]0.764895163442613[/C][/ROW]
[ROW][C]57[/C][C]16[/C][C]14.6466648799286[/C][C]1.35333512007144[/C][/ROW]
[ROW][C]58[/C][C]15[/C][C]13.4172918775024[/C][C]1.58270812249755[/C][/ROW]
[ROW][C]59[/C][C]12[/C][C]14.3632190077862[/C][C]-2.36321900778619[/C][/ROW]
[ROW][C]60[/C][C]12[/C][C]13.96028876153[/C][C]-1.96028876153[/C][/ROW]
[ROW][C]61[/C][C]8[/C][C]12.3546782956089[/C][C]-4.35467829560886[/C][/ROW]
[ROW][C]62[/C][C]13[/C][C]14.7447736717965[/C][C]-1.74477367179653[/C][/ROW]
[ROW][C]63[/C][C]11[/C][C]14.5432388875453[/C][C]-3.54323888754527[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]12.6219264637744[/C][C]1.37807353622565[/C][/ROW]
[ROW][C]65[/C][C]15[/C][C]13.0462322922764[/C][C]1.9537677077236[/C][/ROW]
[ROW][C]66[/C][C]10[/C][C]14.7421150715389[/C][C]-4.74211507153887[/C][/ROW]
[ROW][C]67[/C][C]11[/C][C]14.0483103685247[/C][C]-3.04831036852474[/C][/ROW]
[ROW][C]68[/C][C]12[/C][C]13.7953946226668[/C][C]-1.7953946226668[/C][/ROW]
[ROW][C]69[/C][C]15[/C][C]13.9895008223025[/C][C]1.01049917769751[/C][/ROW]
[ROW][C]70[/C][C]15[/C][C]14.0234828674329[/C][C]0.976517132567101[/C][/ROW]
[ROW][C]71[/C][C]14[/C][C]13.9014792153077[/C][C]0.0985207846922581[/C][/ROW]
[ROW][C]72[/C][C]16[/C][C]12.7414108378882[/C][C]3.25858916211182[/C][/ROW]
[ROW][C]73[/C][C]15[/C][C]14.7822076357729[/C][C]0.217792364227072[/C][/ROW]
[ROW][C]74[/C][C]15[/C][C]14.389772468301[/C][C]0.610227531698971[/C][/ROW]
[ROW][C]75[/C][C]13[/C][C]14.8384979039839[/C][C]-1.83849790398385[/C][/ROW]
[ROW][C]76[/C][C]12[/C][C]14.8207135628303[/C][C]-2.82071356283032[/C][/ROW]
[ROW][C]77[/C][C]17[/C][C]14.3069287395753[/C][C]2.69307126042474[/C][/ROW]
[ROW][C]78[/C][C]13[/C][C]13.8808969516502[/C][C]-0.880896951650219[/C][/ROW]
[ROW][C]79[/C][C]15[/C][C]13.9335959787688[/C][C]1.06640402123117[/C][/ROW]
[ROW][C]80[/C][C]13[/C][C]13.2895856001847[/C][C]-0.289585600184715[/C][/ROW]
[ROW][C]81[/C][C]15[/C][C]14.4586691993965[/C][C]0.541330800603491[/C][/ROW]
[ROW][C]82[/C][C]16[/C][C]11.8557731108188[/C][C]4.14422688918124[/C][/ROW]
[ROW][C]83[/C][C]15[/C][C]14.1429672415467[/C][C]0.857032758453271[/C][/ROW]
[ROW][C]84[/C][C]16[/C][C]14.4246871542661[/C][C]1.5753128457339[/C][/ROW]
[ROW][C]85[/C][C]15[/C][C]13.8978879742154[/C][C]1.10211202578458[/C][/ROW]
[ROW][C]86[/C][C]14[/C][C]14.2162485323229[/C][C]-0.216248532322859[/C][/ROW]
[ROW][C]87[/C][C]15[/C][C]14.440884858243[/C][C]0.559115141757029[/C][/ROW]
[ROW][C]88[/C][C]14[/C][C]13.8612473288274[/C][C]0.138752671172641[/C][/ROW]
[ROW][C]89[/C][C]13[/C][C]12.6406434457626[/C][C]0.359356554237448[/C][/ROW]
[ROW][C]90[/C][C]7[/C][C]13.9209895158843[/C][C]-6.92098951588427[/C][/ROW]
[ROW][C]91[/C][C]17[/C][C]13.7535760990097[/C][C]3.24642390099025[/C][/ROW]
[ROW][C]92[/C][C]13[/C][C]13.3928722703217[/C][C]-0.392872270321673[/C][/ROW]
[ROW][C]93[/C][C]15[/C][C]14.5014203638882[/C][C]0.498579636111781[/C][/ROW]
[ROW][C]94[/C][C]14[/C][C]14.5567779912645[/C][C]-0.556777991264483[/C][/ROW]
[ROW][C]95[/C][C]13[/C][C]14.1939403092423[/C][C]-1.19394030924234[/C][/ROW]
[ROW][C]96[/C][C]16[/C][C]14.8821817093102[/C][C]1.11781829068977[/C][/ROW]
[ROW][C]97[/C][C]12[/C][C]14.2471865525184[/C][C]-2.24718655251835[/C][/ROW]
[ROW][C]98[/C][C]14[/C][C]14.3767581115054[/C][C]-0.376758111505406[/C][/ROW]
[ROW][C]99[/C][C]17[/C][C]14.9026246507214[/C][C]2.09737534927858[/C][/ROW]
[ROW][C]100[/C][C]15[/C][C]14.0753885759632[/C][C]0.924611424036826[/C][/ROW]
[ROW][C]101[/C][C]17[/C][C]14.8079677776994[/C][C]2.19203222230057[/C][/ROW]
[ROW][C]102[/C][C]12[/C][C]14.3632190077862[/C][C]-2.36321900778619[/C][/ROW]
[ROW][C]103[/C][C]16[/C][C]15.244880069086[/C][C]0.755119930913962[/C][/ROW]
[ROW][C]104[/C][C]11[/C][C]13.5394348518739[/C][C]-2.53943485187393[/C][/ROW]
[ROW][C]105[/C][C]15[/C][C]14.3972010529166[/C][C]0.602798947083401[/C][/ROW]
[ROW][C]106[/C][C]9[/C][C]14.3693295268898[/C][C]-5.36932952688984[/C][/ROW]
[ROW][C]107[/C][C]16[/C][C]14.6850314847396[/C][C]1.31496851526038[/C][/ROW]
[ROW][C]108[/C][C]15[/C][C]14.6296738573634[/C][C]0.370326142636649[/C][/ROW]
[ROW][C]109[/C][C]10[/C][C]13.146999684402[/C][C]-3.14699968440204[/C][/ROW]
[ROW][C]110[/C][C]10[/C][C]12.9910139871465[/C][C]-2.99101398714647[/C][/ROW]
[ROW][C]111[/C][C]15[/C][C]14.3945424526589[/C][C]0.605457547341058[/C][/ROW]
[ROW][C]112[/C][C]11[/C][C]14.1206590184662[/C][C]-3.12065901846621[/C][/ROW]
[ROW][C]113[/C][C]13[/C][C]14.8537629671261[/C][C]-1.85376296712606[/C][/ROW]
[ROW][C]114[/C][C]14[/C][C]11.8666536142803[/C][C]2.13334638571969[/C][/ROW]
[ROW][C]115[/C][C]18[/C][C]14.480977422477[/C][C]3.51902257752297[/C][/ROW]
[ROW][C]116[/C][C]16[/C][C]15.2628037324859[/C][C]0.737196267514096[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]13.0265826694535[/C][C]0.973417330546457[/C][/ROW]
[ROW][C]118[/C][C]14[/C][C]13.7562346992674[/C][C]0.243765300732594[/C][/ROW]
[ROW][C]119[/C][C]14[/C][C]13.7997791823475[/C][C]0.200220817652551[/C][/ROW]
[ROW][C]120[/C][C]14[/C][C]15.5214221035364[/C][C]-1.52142210353643[/C][/ROW]
[ROW][C]121[/C][C]12[/C][C]12.9036463764937[/C][C]-0.903646376493724[/C][/ROW]
[ROW][C]122[/C][C]14[/C][C]14.1786752461001[/C][C]-0.178675246100133[/C][/ROW]
[ROW][C]123[/C][C]15[/C][C]15.4616799164795[/C][C]-0.46167991647951[/C][/ROW]
[ROW][C]124[/C][C]15[/C][C]14.7268500083967[/C][C]0.273149991603336[/C][/ROW]
[ROW][C]125[/C][C]15[/C][C]14.1616842235349[/C][C]0.838315776465072[/C][/ROW]
[ROW][C]126[/C][C]13[/C][C]14.4808381002307[/C][C]-1.4808381002307[/C][/ROW]
[ROW][C]127[/C][C]17[/C][C]15.4796035798794[/C][C]1.52039642012062[/C][/ROW]
[ROW][C]128[/C][C]17[/C][C]14.9907855799625[/C][C]2.0092144200375[/C][/ROW]
[ROW][C]129[/C][C]19[/C][C]14.3937491340706[/C][C]4.60625086592939[/C][/ROW]
[ROW][C]130[/C][C]15[/C][C]14.1412412821237[/C][C]0.858758717876265[/C][/ROW]
[ROW][C]131[/C][C]13[/C][C]13.7766776406786[/C][C]-0.776677640678599[/C][/ROW]
[ROW][C]132[/C][C]9[/C][C]13.2149637746629[/C][C]-4.21496377466285[/C][/ROW]
[ROW][C]133[/C][C]15[/C][C]14.6611366244824[/C][C]0.338863375517567[/C][/ROW]
[ROW][C]134[/C][C]15[/C][C]13.0741038183032[/C][C]1.92589618169683[/C][/ROW]
[ROW][C]135[/C][C]15[/C][C]14.484429341323[/C][C]0.515570658676985[/C][/ROW]
[ROW][C]136[/C][C]16[/C][C]14.5567779912645[/C][C]1.44322200873552[/C][/ROW]
[ROW][C]137[/C][C]11[/C][C]12.3751212370201[/C][C]-1.37512123702005[/C][/ROW]
[ROW][C]138[/C][C]14[/C][C]13.5224438293087[/C][C]0.47755617069127[/C][/ROW]
[ROW][C]139[/C][C]11[/C][C]12.5988249221055[/C][C]-1.5988249221055[/C][/ROW]
[ROW][C]140[/C][C]15[/C][C]13.4788993462287[/C][C]1.52110065377131[/C][/ROW]
[ROW][C]141[/C][C]13[/C][C]13.5146073507821[/C][C]-0.514607350782089[/C][/ROW]
[ROW][C]142[/C][C]15[/C][C]12.6381241677512[/C][C]2.36187583224877[/C][/ROW]
[ROW][C]143[/C][C]16[/C][C]14.329622387333[/C][C]1.67037761266696[/C][/ROW]
[ROW][C]144[/C][C]14[/C][C]14.8179156403263[/C][C]-0.817915640326331[/C][/ROW]
[ROW][C]145[/C][C]15[/C][C]14.1154811401972[/C][C]0.884518859802772[/C][/ROW]
[ROW][C]146[/C][C]16[/C][C]15.3780428691654[/C][C]0.621957130834587[/C][/ROW]
[ROW][C]147[/C][C]16[/C][C]14.2597930154029[/C][C]1.7402069845971[/C][/ROW]
[ROW][C]148[/C][C]11[/C][C]13.5669209532234[/C][C]-2.56692095322343[/C][/ROW]
[ROW][C]149[/C][C]12[/C][C]14.1385826818661[/C][C]-2.13858268186608[/C][/ROW]
[ROW][C]150[/C][C]9[/C][C]14.0780471762208[/C][C]-5.07804717622083[/C][/ROW]
[ROW][C]151[/C][C]16[/C][C]14.9153704358523[/C][C]1.0846295641477[/C][/ROW]
[ROW][C]152[/C][C]13[/C][C]15.2501972696014[/C][C]-2.25019726960135[/C][/ROW]
[ROW][C]153[/C][C]16[/C][C]13.8774450328042[/C][C]2.12255496719577[/C][/ROW]
[ROW][C]154[/C][C]12[/C][C]13.8339005497242[/C][C]-1.83390054972419[/C][/ROW]
[ROW][C]155[/C][C]9[/C][C]13.7776102815133[/C][C]-4.77761028151326[/C][/ROW]
[ROW][C]156[/C][C]13[/C][C]14.15305442642[/C][C]-1.15305442641996[/C][/ROW]
[ROW][C]157[/C][C]13[/C][C]13.3928722703217[/C][C]-0.392872270321673[/C][/ROW]
[ROW][C]158[/C][C]14[/C][C]13.4113206806451[/C][C]0.588679319354869[/C][/ROW]
[ROW][C]159[/C][C]19[/C][C]14.3937491340706[/C][C]4.60625086592939[/C][/ROW]
[ROW][C]160[/C][C]13[/C][C]14.7770297575039[/C][C]-1.77702975750394[/C][/ROW]
[ROW][C]161[/C][C]12[/C][C]14.4303897794587[/C][C]-2.43038977945867[/C][/ROW]
[ROW][C]162[/C][C]13[/C][C]13.6964925122105[/C][C]-0.696492512210491[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146620&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146620&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
11413.55431449033890.4456855096611
21815.30210297813162.69789702186837
31113.623071899188-2.62307189918803
41213.2107185372285-1.21071853722853
51614.42202855400841.57797144599156
61814.43822625798533.56177374201469
71413.39207895173330.60792104826666
81414.722465448716-0.722465448716013
91514.40331157202020.596688427979755
101514.23510483655740.764895163442613
111714.74132175295052.25867824704946
121913.20846783088195.79153216911805
131014.1473518012274-4.14735180122738
141613.39035299231032.60964700768965
151815.12101113529162.87898886470844
161413.12296550189850.877034498101477
171414.3401174661173-0.34011746611734
181715.40193772942261.5980622705774
191413.98777486287950.012225137120504
201613.06322331484162.93677668515839
211813.53943485187394.46056514812607
221113.6392696031649-2.6392696031649
231413.51328928527020.486710714729835
241213.3476018278186-1.34760182781864
251715.17916668517181.82083331482819
26915.2288216873555-6.2288216873555
271614.39985965317431.60014034682574
281413.98418362178720.0158163782128221
291514.39892701233960.601072987660406
301113.6999444310565-2.69994443105648
311614.35617584784791.64382415215212
321312.23253532123740.767464678762626
331714.18226648719252.81773351280755
341514.83849790398390.161502096016146
351414.2410760334147-0.241076033414704
361613.59227320123892.40772679876113
37912.8095142503953-3.80951425039533
381514.22408501084950.7759149891505
391713.83203526805493.16796473194514
401313.9005465744731-0.90054657447308
411514.36149304836320.638506951636804
421614.30081822047161.69918177952838
431613.55510780892722.44489219107279
441212.8849293945055-0.884929394505525
451214.1438998823814-2.14389988238139
461114.1259762189815-3.12597621898153
471514.08933557359350.91066442640654
481514.15823230468890.841767695311061
491714.34184342554032.65815657445966
501314.6252892976827-1.6252892976827
511614.08229241365521.91770758634485
521413.53770889245090.462291107549061
531113.4216764371831-2.4216764371831
541213.5856379352116-1.58563793521163
551212.9913994118237-0.991399411823731
561514.23510483655740.764895163442613
571614.64666487992861.35333512007144
581513.41729187750241.58270812249755
591214.3632190077862-2.36321900778619
601213.96028876153-1.96028876153
61812.3546782956089-4.35467829560886
621314.7447736717965-1.74477367179653
631114.5432388875453-3.54323888754527
641412.62192646377441.37807353622565
651513.04623229227641.9537677077236
661014.7421150715389-4.74211507153887
671114.0483103685247-3.04831036852474
681213.7953946226668-1.7953946226668
691513.98950082230251.01049917769751
701514.02348286743290.976517132567101
711413.90147921530770.0985207846922581
721612.74141083788823.25858916211182
731514.78220763577290.217792364227072
741514.3897724683010.610227531698971
751314.8384979039839-1.83849790398385
761214.8207135628303-2.82071356283032
771714.30692873957532.69307126042474
781313.8808969516502-0.880896951650219
791513.93359597876881.06640402123117
801313.2895856001847-0.289585600184715
811514.45866919939650.541330800603491
821611.85577311081884.14422688918124
831514.14296724154670.857032758453271
841614.42468715426611.5753128457339
851513.89788797421541.10211202578458
861414.2162485323229-0.216248532322859
871514.4408848582430.559115141757029
881413.86124732882740.138752671172641
891312.64064344576260.359356554237448
90713.9209895158843-6.92098951588427
911713.75357609900973.24642390099025
921313.3928722703217-0.392872270321673
931514.50142036388820.498579636111781
941414.5567779912645-0.556777991264483
951314.1939403092423-1.19394030924234
961614.88218170931021.11781829068977
971214.2471865525184-2.24718655251835
981414.3767581115054-0.376758111505406
991714.90262465072142.09737534927858
1001514.07538857596320.924611424036826
1011714.80796777769942.19203222230057
1021214.3632190077862-2.36321900778619
1031615.2448800690860.755119930913962
1041113.5394348518739-2.53943485187393
1051514.39720105291660.602798947083401
106914.3693295268898-5.36932952688984
1071614.68503148473961.31496851526038
1081514.62967385736340.370326142636649
1091013.146999684402-3.14699968440204
1101012.9910139871465-2.99101398714647
1111514.39454245265890.605457547341058
1121114.1206590184662-3.12065901846621
1131314.8537629671261-1.85376296712606
1141411.86665361428032.13334638571969
1151814.4809774224773.51902257752297
1161615.26280373248590.737196267514096
1171413.02658266945350.973417330546457
1181413.75623469926740.243765300732594
1191413.79977918234750.200220817652551
1201415.5214221035364-1.52142210353643
1211212.9036463764937-0.903646376493724
1221414.1786752461001-0.178675246100133
1231515.4616799164795-0.46167991647951
1241514.72685000839670.273149991603336
1251514.16168422353490.838315776465072
1261314.4808381002307-1.4808381002307
1271715.47960357987941.52039642012062
1281714.99078557996252.0092144200375
1291914.39374913407064.60625086592939
1301514.14124128212370.858758717876265
1311313.7766776406786-0.776677640678599
132913.2149637746629-4.21496377466285
1331514.66113662448240.338863375517567
1341513.07410381830321.92589618169683
1351514.4844293413230.515570658676985
1361614.55677799126451.44322200873552
1371112.3751212370201-1.37512123702005
1381413.52244382930870.47755617069127
1391112.5988249221055-1.5988249221055
1401513.47889934622871.52110065377131
1411313.5146073507821-0.514607350782089
1421512.63812416775122.36187583224877
1431614.3296223873331.67037761266696
1441414.8179156403263-0.817915640326331
1451514.11548114019720.884518859802772
1461615.37804286916540.621957130834587
1471614.25979301540291.7402069845971
1481113.5669209532234-2.56692095322343
1491214.1385826818661-2.13858268186608
150914.0780471762208-5.07804717622083
1511614.91537043585231.0846295641477
1521315.2501972696014-2.25019726960135
1531613.87744503280422.12255496719577
1541213.8339005497242-1.83390054972419
155913.7776102815133-4.77761028151326
1561314.15305442642-1.15305442641996
1571313.3928722703217-0.392872270321673
1581413.41132068064510.588679319354869
1591914.39374913407064.60625086592939
1601314.7770297575039-1.77702975750394
1611214.4303897794587-2.43038977945867
1621313.6964925122105-0.696492512210491






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
70.2835844986832880.5671689973665760.716415501316712
80.2383520653915660.4767041307831320.761647934608434
90.1370244160636480.2740488321272970.862975583936352
100.1104354556168010.2208709112336030.889564544383199
110.05975624386085190.1195124877217040.940243756139148
120.6464666727994990.7070666544010020.353533327200501
130.9081829363759820.1836341272480360.091817063624018
140.8824182122049160.2351635755901670.117581787795084
150.8844931574243880.2310136851512230.115506842575612
160.8458994781379940.3082010437240110.154100521862006
170.810906032322250.37818793535550.18909396767775
180.7567050213001030.4865899573997940.243294978699897
190.7102568189644650.579486362071070.289743181035535
200.729038112994170.541923774011660.27096188700583
210.8113207331637040.3773585336725910.188679266836296
220.8546520553879920.2906958892240160.145347944612008
230.8161439761289990.3677120477420020.183856023871001
240.8072371680632730.3855256638734540.192762831936727
250.7662725786204890.4674548427590210.233727421379511
260.9656123630293270.06877527394134520.0343876369706726
270.954929766936060.09014046612787980.0450702330639399
280.9389446475489530.1221107049020930.0610553524510466
290.9190251795438340.1619496409123330.0809748204561664
300.9396043550427330.1207912899145340.060395644957267
310.9251231150547360.1497537698905280.0748768849452638
320.9047207429057990.1905585141884030.0952792570942013
330.9043122710133140.1913754579733730.0956877289866864
340.8789275724285770.2421448551428450.121072427571423
350.8513361062976250.2973277874047490.148663893702375
360.8337262083201430.3325475833597130.166273791679857
370.9126641439529570.1746717120940860.0873358560470431
380.8905291373485920.2189417253028170.109470862651408
390.9006499454145370.1987001091709250.0993500545854626
400.8840910938255830.2318178123488330.115908906174417
410.8574702977524480.2850594044951030.142529702247552
420.8373437842325660.3253124315348690.162656215767434
430.8349229423794370.3301541152411260.165077057620563
440.8104048214167280.3791903571665440.189595178583272
450.814880140414550.3702397191709010.18511985958545
460.8517426761759970.2965146476480070.148257323824003
470.8242751940970370.3514496118059260.175724805902963
480.7925960040216150.4148079919567690.207403995978385
490.7960982995288570.4078034009422860.203901700471143
500.7851206228010990.4297587543978030.214879377198902
510.7660990533420370.4678018933159260.233900946657963
520.7280999027435640.5438001945128730.271900097256436
530.7451097822512410.5097804354975180.254890217748759
540.729425226594650.54114954681070.27057477340535
550.6978768825441620.6042462349116750.302123117455837
560.6580984542126180.6838030915747650.341901545787382
570.6258140619535310.7483718760929390.374185938046469
580.5967296227759330.8065407544481340.403270377224067
590.6099296589083310.7801406821833380.390070341091669
600.6041042029017140.7917915941965730.395895797098286
610.7127463522246310.5745072955507380.287253647775369
620.7031212847931320.5937574304137370.296878715206869
630.7712288150367910.4575423699264180.228771184963209
640.749125666473780.501748667052440.25087433352622
650.7395853192055550.520829361588890.260414680794445
660.8536565032696450.292686993460710.146343496730355
670.8738743346410390.2522513307179220.126125665358961
680.8651573105558830.2696853788882350.134842689444117
690.843973386511890.3120532269762210.15602661348811
700.8202496603162440.3595006793675110.179750339683755
710.7885572782179620.4228854435640750.211442721782038
720.8215486391227540.3569027217544910.178451360877246
730.7903193550244680.4193612899510630.209680644975531
740.7586399978541420.4827200042917160.241360002145858
750.7461784223497480.5076431553005040.253821577650252
760.764384326931250.47123134613750.23561567306875
770.77945441473210.4410911705357990.2205455852679
780.7494926090434640.5010147819130720.250507390956536
790.7202748649722810.5594502700554370.279725135027719
800.6816826594193750.636634681161250.318317340580625
810.6424265657405750.715146868518850.357573434259425
820.7545092531813970.4909814936372060.245490746818603
830.7245411917444470.5509176165111050.275458808255553
840.7084774891647650.5830450216704690.291522510835235
850.6791434872420330.6417130255159350.320856512757967
860.6373356597444510.7253286805110980.362664340255549
870.5970754092750160.8058491814499690.402924590724984
880.5536227437099430.8927545125801140.446377256290057
890.5159485802853210.9681028394293570.484051419714679
900.8392196390946350.3215607218107290.160780360905365
910.8700768691787410.2598462616425180.129923130821259
920.8471454729901810.3057090540196380.152854527009819
930.8201829822827060.3596340354345880.179817017717294
940.7900452368755790.4199095262488420.209954763124421
950.765512943733720.4689741125325590.23448705626628
960.7384200858918140.5231598282163720.261579914108186
970.7315427896608580.5369144206782840.268457210339142
980.6925545314612780.6148909370774430.307445468538722
990.6895634688899440.6208730622201120.310436531110056
1000.6542593358652320.6914813282695360.345740664134768
1010.6633808090999290.6732383818001430.336619190900071
1020.6614212487278220.6771575025443550.338578751272178
1030.6245096761685020.7509806476629950.375490323831498
1040.6294874972369510.7410250055260980.370512502763049
1050.5867608838830850.826478232233830.413239116116915
1060.7665315838075070.4669368323849850.233468416192493
1070.7438349515759950.5123300968480110.256165048424005
1080.7060062801853810.5879874396292380.293993719814619
1090.7318607743559970.5362784512880060.268139225644003
1100.7648710178971360.4702579642057290.235128982102864
1110.73119081800540.53761836398920.2688091819946
1120.7583944222411210.4832111555177580.241605577758879
1130.7444086784123720.5111826431752560.255591321587628
1140.7556652178469680.4886695643060640.244334782153032
1150.8123310959656570.3753378080686860.187668904034343
1160.7791245117937720.4417509764124560.220875488206228
1170.7598727911216190.4802544177567620.240127208878381
1180.7177279635205590.5645440729588810.282272036479441
1190.6732929692042380.6534140615915240.326707030795762
1200.6541301833316260.6917396333367490.345869816668374
1210.6061136170125550.787772765974890.393886382987445
1220.5532090232014920.8935819535970160.446790976798508
1230.5055649852262560.9888700295474870.494435014773744
1240.4525862611548040.9051725223096070.547413738845196
1250.4106322170775890.8212644341551770.589367782922412
1260.3756122422842230.7512244845684450.624387757715777
1270.3364266727924730.6728533455849470.663573327207527
1280.3435026794020.6870053588040.656497320598
1290.5067543099920560.9864913800158870.493245690007944
1300.4666305902895310.9332611805790620.533369409710469
1310.4110374109917550.822074821983510.588962589008245
1320.5571221461711210.8857557076577590.442877853828879
1330.5124122383622820.9751755232754370.487587761637718
1340.4835982826344410.9671965652688810.516401717365559
1350.4387133711618730.8774267423237460.561286628838127
1360.4031554827858230.8063109655716450.596844517214177
1370.3627552311787530.7255104623575060.637244768821247
1380.3163110492886720.6326220985773450.683688950711328
1390.2813293284405180.5626586568810350.718670671559482
1400.2688581372684110.5377162745368220.731141862731589
1410.2163649616838910.4327299233677810.783635038316109
1420.2620040886775070.5240081773550150.737995911322493
1430.2217861342773630.4435722685547260.778213865722637
1440.1906038445671430.3812076891342860.809396155432857
1450.1464411499307970.2928822998615940.853558850069203
1460.1206468101069040.2412936202138070.879353189893096
1470.1062620782025510.2125241564051020.893737921797449
1480.07827503615152360.1565500723030470.921724963848476
1490.06380544111334620.1276108822266920.936194558886654
1500.1397729683795050.2795459367590110.860227031620495
1510.1521684258362950.3043368516725890.847831574163705
1520.1314046928053980.2628093856107960.868595307194602
1530.1456589314052290.2913178628104580.854341068594771
1540.1157330828160370.2314661656320750.884266917183963
1550.1013707368529970.2027414737059950.898629263147003

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
7 & 0.283584498683288 & 0.567168997366576 & 0.716415501316712 \tabularnewline
8 & 0.238352065391566 & 0.476704130783132 & 0.761647934608434 \tabularnewline
9 & 0.137024416063648 & 0.274048832127297 & 0.862975583936352 \tabularnewline
10 & 0.110435455616801 & 0.220870911233603 & 0.889564544383199 \tabularnewline
11 & 0.0597562438608519 & 0.119512487721704 & 0.940243756139148 \tabularnewline
12 & 0.646466672799499 & 0.707066654401002 & 0.353533327200501 \tabularnewline
13 & 0.908182936375982 & 0.183634127248036 & 0.091817063624018 \tabularnewline
14 & 0.882418212204916 & 0.235163575590167 & 0.117581787795084 \tabularnewline
15 & 0.884493157424388 & 0.231013685151223 & 0.115506842575612 \tabularnewline
16 & 0.845899478137994 & 0.308201043724011 & 0.154100521862006 \tabularnewline
17 & 0.81090603232225 & 0.3781879353555 & 0.18909396767775 \tabularnewline
18 & 0.756705021300103 & 0.486589957399794 & 0.243294978699897 \tabularnewline
19 & 0.710256818964465 & 0.57948636207107 & 0.289743181035535 \tabularnewline
20 & 0.72903811299417 & 0.54192377401166 & 0.27096188700583 \tabularnewline
21 & 0.811320733163704 & 0.377358533672591 & 0.188679266836296 \tabularnewline
22 & 0.854652055387992 & 0.290695889224016 & 0.145347944612008 \tabularnewline
23 & 0.816143976128999 & 0.367712047742002 & 0.183856023871001 \tabularnewline
24 & 0.807237168063273 & 0.385525663873454 & 0.192762831936727 \tabularnewline
25 & 0.766272578620489 & 0.467454842759021 & 0.233727421379511 \tabularnewline
26 & 0.965612363029327 & 0.0687752739413452 & 0.0343876369706726 \tabularnewline
27 & 0.95492976693606 & 0.0901404661278798 & 0.0450702330639399 \tabularnewline
28 & 0.938944647548953 & 0.122110704902093 & 0.0610553524510466 \tabularnewline
29 & 0.919025179543834 & 0.161949640912333 & 0.0809748204561664 \tabularnewline
30 & 0.939604355042733 & 0.120791289914534 & 0.060395644957267 \tabularnewline
31 & 0.925123115054736 & 0.149753769890528 & 0.0748768849452638 \tabularnewline
32 & 0.904720742905799 & 0.190558514188403 & 0.0952792570942013 \tabularnewline
33 & 0.904312271013314 & 0.191375457973373 & 0.0956877289866864 \tabularnewline
34 & 0.878927572428577 & 0.242144855142845 & 0.121072427571423 \tabularnewline
35 & 0.851336106297625 & 0.297327787404749 & 0.148663893702375 \tabularnewline
36 & 0.833726208320143 & 0.332547583359713 & 0.166273791679857 \tabularnewline
37 & 0.912664143952957 & 0.174671712094086 & 0.0873358560470431 \tabularnewline
38 & 0.890529137348592 & 0.218941725302817 & 0.109470862651408 \tabularnewline
39 & 0.900649945414537 & 0.198700109170925 & 0.0993500545854626 \tabularnewline
40 & 0.884091093825583 & 0.231817812348833 & 0.115908906174417 \tabularnewline
41 & 0.857470297752448 & 0.285059404495103 & 0.142529702247552 \tabularnewline
42 & 0.837343784232566 & 0.325312431534869 & 0.162656215767434 \tabularnewline
43 & 0.834922942379437 & 0.330154115241126 & 0.165077057620563 \tabularnewline
44 & 0.810404821416728 & 0.379190357166544 & 0.189595178583272 \tabularnewline
45 & 0.81488014041455 & 0.370239719170901 & 0.18511985958545 \tabularnewline
46 & 0.851742676175997 & 0.296514647648007 & 0.148257323824003 \tabularnewline
47 & 0.824275194097037 & 0.351449611805926 & 0.175724805902963 \tabularnewline
48 & 0.792596004021615 & 0.414807991956769 & 0.207403995978385 \tabularnewline
49 & 0.796098299528857 & 0.407803400942286 & 0.203901700471143 \tabularnewline
50 & 0.785120622801099 & 0.429758754397803 & 0.214879377198902 \tabularnewline
51 & 0.766099053342037 & 0.467801893315926 & 0.233900946657963 \tabularnewline
52 & 0.728099902743564 & 0.543800194512873 & 0.271900097256436 \tabularnewline
53 & 0.745109782251241 & 0.509780435497518 & 0.254890217748759 \tabularnewline
54 & 0.72942522659465 & 0.5411495468107 & 0.27057477340535 \tabularnewline
55 & 0.697876882544162 & 0.604246234911675 & 0.302123117455837 \tabularnewline
56 & 0.658098454212618 & 0.683803091574765 & 0.341901545787382 \tabularnewline
57 & 0.625814061953531 & 0.748371876092939 & 0.374185938046469 \tabularnewline
58 & 0.596729622775933 & 0.806540754448134 & 0.403270377224067 \tabularnewline
59 & 0.609929658908331 & 0.780140682183338 & 0.390070341091669 \tabularnewline
60 & 0.604104202901714 & 0.791791594196573 & 0.395895797098286 \tabularnewline
61 & 0.712746352224631 & 0.574507295550738 & 0.287253647775369 \tabularnewline
62 & 0.703121284793132 & 0.593757430413737 & 0.296878715206869 \tabularnewline
63 & 0.771228815036791 & 0.457542369926418 & 0.228771184963209 \tabularnewline
64 & 0.74912566647378 & 0.50174866705244 & 0.25087433352622 \tabularnewline
65 & 0.739585319205555 & 0.52082936158889 & 0.260414680794445 \tabularnewline
66 & 0.853656503269645 & 0.29268699346071 & 0.146343496730355 \tabularnewline
67 & 0.873874334641039 & 0.252251330717922 & 0.126125665358961 \tabularnewline
68 & 0.865157310555883 & 0.269685378888235 & 0.134842689444117 \tabularnewline
69 & 0.84397338651189 & 0.312053226976221 & 0.15602661348811 \tabularnewline
70 & 0.820249660316244 & 0.359500679367511 & 0.179750339683755 \tabularnewline
71 & 0.788557278217962 & 0.422885443564075 & 0.211442721782038 \tabularnewline
72 & 0.821548639122754 & 0.356902721754491 & 0.178451360877246 \tabularnewline
73 & 0.790319355024468 & 0.419361289951063 & 0.209680644975531 \tabularnewline
74 & 0.758639997854142 & 0.482720004291716 & 0.241360002145858 \tabularnewline
75 & 0.746178422349748 & 0.507643155300504 & 0.253821577650252 \tabularnewline
76 & 0.76438432693125 & 0.4712313461375 & 0.23561567306875 \tabularnewline
77 & 0.7794544147321 & 0.441091170535799 & 0.2205455852679 \tabularnewline
78 & 0.749492609043464 & 0.501014781913072 & 0.250507390956536 \tabularnewline
79 & 0.720274864972281 & 0.559450270055437 & 0.279725135027719 \tabularnewline
80 & 0.681682659419375 & 0.63663468116125 & 0.318317340580625 \tabularnewline
81 & 0.642426565740575 & 0.71514686851885 & 0.357573434259425 \tabularnewline
82 & 0.754509253181397 & 0.490981493637206 & 0.245490746818603 \tabularnewline
83 & 0.724541191744447 & 0.550917616511105 & 0.275458808255553 \tabularnewline
84 & 0.708477489164765 & 0.583045021670469 & 0.291522510835235 \tabularnewline
85 & 0.679143487242033 & 0.641713025515935 & 0.320856512757967 \tabularnewline
86 & 0.637335659744451 & 0.725328680511098 & 0.362664340255549 \tabularnewline
87 & 0.597075409275016 & 0.805849181449969 & 0.402924590724984 \tabularnewline
88 & 0.553622743709943 & 0.892754512580114 & 0.446377256290057 \tabularnewline
89 & 0.515948580285321 & 0.968102839429357 & 0.484051419714679 \tabularnewline
90 & 0.839219639094635 & 0.321560721810729 & 0.160780360905365 \tabularnewline
91 & 0.870076869178741 & 0.259846261642518 & 0.129923130821259 \tabularnewline
92 & 0.847145472990181 & 0.305709054019638 & 0.152854527009819 \tabularnewline
93 & 0.820182982282706 & 0.359634035434588 & 0.179817017717294 \tabularnewline
94 & 0.790045236875579 & 0.419909526248842 & 0.209954763124421 \tabularnewline
95 & 0.76551294373372 & 0.468974112532559 & 0.23448705626628 \tabularnewline
96 & 0.738420085891814 & 0.523159828216372 & 0.261579914108186 \tabularnewline
97 & 0.731542789660858 & 0.536914420678284 & 0.268457210339142 \tabularnewline
98 & 0.692554531461278 & 0.614890937077443 & 0.307445468538722 \tabularnewline
99 & 0.689563468889944 & 0.620873062220112 & 0.310436531110056 \tabularnewline
100 & 0.654259335865232 & 0.691481328269536 & 0.345740664134768 \tabularnewline
101 & 0.663380809099929 & 0.673238381800143 & 0.336619190900071 \tabularnewline
102 & 0.661421248727822 & 0.677157502544355 & 0.338578751272178 \tabularnewline
103 & 0.624509676168502 & 0.750980647662995 & 0.375490323831498 \tabularnewline
104 & 0.629487497236951 & 0.741025005526098 & 0.370512502763049 \tabularnewline
105 & 0.586760883883085 & 0.82647823223383 & 0.413239116116915 \tabularnewline
106 & 0.766531583807507 & 0.466936832384985 & 0.233468416192493 \tabularnewline
107 & 0.743834951575995 & 0.512330096848011 & 0.256165048424005 \tabularnewline
108 & 0.706006280185381 & 0.587987439629238 & 0.293993719814619 \tabularnewline
109 & 0.731860774355997 & 0.536278451288006 & 0.268139225644003 \tabularnewline
110 & 0.764871017897136 & 0.470257964205729 & 0.235128982102864 \tabularnewline
111 & 0.7311908180054 & 0.5376183639892 & 0.2688091819946 \tabularnewline
112 & 0.758394422241121 & 0.483211155517758 & 0.241605577758879 \tabularnewline
113 & 0.744408678412372 & 0.511182643175256 & 0.255591321587628 \tabularnewline
114 & 0.755665217846968 & 0.488669564306064 & 0.244334782153032 \tabularnewline
115 & 0.812331095965657 & 0.375337808068686 & 0.187668904034343 \tabularnewline
116 & 0.779124511793772 & 0.441750976412456 & 0.220875488206228 \tabularnewline
117 & 0.759872791121619 & 0.480254417756762 & 0.240127208878381 \tabularnewline
118 & 0.717727963520559 & 0.564544072958881 & 0.282272036479441 \tabularnewline
119 & 0.673292969204238 & 0.653414061591524 & 0.326707030795762 \tabularnewline
120 & 0.654130183331626 & 0.691739633336749 & 0.345869816668374 \tabularnewline
121 & 0.606113617012555 & 0.78777276597489 & 0.393886382987445 \tabularnewline
122 & 0.553209023201492 & 0.893581953597016 & 0.446790976798508 \tabularnewline
123 & 0.505564985226256 & 0.988870029547487 & 0.494435014773744 \tabularnewline
124 & 0.452586261154804 & 0.905172522309607 & 0.547413738845196 \tabularnewline
125 & 0.410632217077589 & 0.821264434155177 & 0.589367782922412 \tabularnewline
126 & 0.375612242284223 & 0.751224484568445 & 0.624387757715777 \tabularnewline
127 & 0.336426672792473 & 0.672853345584947 & 0.663573327207527 \tabularnewline
128 & 0.343502679402 & 0.687005358804 & 0.656497320598 \tabularnewline
129 & 0.506754309992056 & 0.986491380015887 & 0.493245690007944 \tabularnewline
130 & 0.466630590289531 & 0.933261180579062 & 0.533369409710469 \tabularnewline
131 & 0.411037410991755 & 0.82207482198351 & 0.588962589008245 \tabularnewline
132 & 0.557122146171121 & 0.885755707657759 & 0.442877853828879 \tabularnewline
133 & 0.512412238362282 & 0.975175523275437 & 0.487587761637718 \tabularnewline
134 & 0.483598282634441 & 0.967196565268881 & 0.516401717365559 \tabularnewline
135 & 0.438713371161873 & 0.877426742323746 & 0.561286628838127 \tabularnewline
136 & 0.403155482785823 & 0.806310965571645 & 0.596844517214177 \tabularnewline
137 & 0.362755231178753 & 0.725510462357506 & 0.637244768821247 \tabularnewline
138 & 0.316311049288672 & 0.632622098577345 & 0.683688950711328 \tabularnewline
139 & 0.281329328440518 & 0.562658656881035 & 0.718670671559482 \tabularnewline
140 & 0.268858137268411 & 0.537716274536822 & 0.731141862731589 \tabularnewline
141 & 0.216364961683891 & 0.432729923367781 & 0.783635038316109 \tabularnewline
142 & 0.262004088677507 & 0.524008177355015 & 0.737995911322493 \tabularnewline
143 & 0.221786134277363 & 0.443572268554726 & 0.778213865722637 \tabularnewline
144 & 0.190603844567143 & 0.381207689134286 & 0.809396155432857 \tabularnewline
145 & 0.146441149930797 & 0.292882299861594 & 0.853558850069203 \tabularnewline
146 & 0.120646810106904 & 0.241293620213807 & 0.879353189893096 \tabularnewline
147 & 0.106262078202551 & 0.212524156405102 & 0.893737921797449 \tabularnewline
148 & 0.0782750361515236 & 0.156550072303047 & 0.921724963848476 \tabularnewline
149 & 0.0638054411133462 & 0.127610882226692 & 0.936194558886654 \tabularnewline
150 & 0.139772968379505 & 0.279545936759011 & 0.860227031620495 \tabularnewline
151 & 0.152168425836295 & 0.304336851672589 & 0.847831574163705 \tabularnewline
152 & 0.131404692805398 & 0.262809385610796 & 0.868595307194602 \tabularnewline
153 & 0.145658931405229 & 0.291317862810458 & 0.854341068594771 \tabularnewline
154 & 0.115733082816037 & 0.231466165632075 & 0.884266917183963 \tabularnewline
155 & 0.101370736852997 & 0.202741473705995 & 0.898629263147003 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146620&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]7[/C][C]0.283584498683288[/C][C]0.567168997366576[/C][C]0.716415501316712[/C][/ROW]
[ROW][C]8[/C][C]0.238352065391566[/C][C]0.476704130783132[/C][C]0.761647934608434[/C][/ROW]
[ROW][C]9[/C][C]0.137024416063648[/C][C]0.274048832127297[/C][C]0.862975583936352[/C][/ROW]
[ROW][C]10[/C][C]0.110435455616801[/C][C]0.220870911233603[/C][C]0.889564544383199[/C][/ROW]
[ROW][C]11[/C][C]0.0597562438608519[/C][C]0.119512487721704[/C][C]0.940243756139148[/C][/ROW]
[ROW][C]12[/C][C]0.646466672799499[/C][C]0.707066654401002[/C][C]0.353533327200501[/C][/ROW]
[ROW][C]13[/C][C]0.908182936375982[/C][C]0.183634127248036[/C][C]0.091817063624018[/C][/ROW]
[ROW][C]14[/C][C]0.882418212204916[/C][C]0.235163575590167[/C][C]0.117581787795084[/C][/ROW]
[ROW][C]15[/C][C]0.884493157424388[/C][C]0.231013685151223[/C][C]0.115506842575612[/C][/ROW]
[ROW][C]16[/C][C]0.845899478137994[/C][C]0.308201043724011[/C][C]0.154100521862006[/C][/ROW]
[ROW][C]17[/C][C]0.81090603232225[/C][C]0.3781879353555[/C][C]0.18909396767775[/C][/ROW]
[ROW][C]18[/C][C]0.756705021300103[/C][C]0.486589957399794[/C][C]0.243294978699897[/C][/ROW]
[ROW][C]19[/C][C]0.710256818964465[/C][C]0.57948636207107[/C][C]0.289743181035535[/C][/ROW]
[ROW][C]20[/C][C]0.72903811299417[/C][C]0.54192377401166[/C][C]0.27096188700583[/C][/ROW]
[ROW][C]21[/C][C]0.811320733163704[/C][C]0.377358533672591[/C][C]0.188679266836296[/C][/ROW]
[ROW][C]22[/C][C]0.854652055387992[/C][C]0.290695889224016[/C][C]0.145347944612008[/C][/ROW]
[ROW][C]23[/C][C]0.816143976128999[/C][C]0.367712047742002[/C][C]0.183856023871001[/C][/ROW]
[ROW][C]24[/C][C]0.807237168063273[/C][C]0.385525663873454[/C][C]0.192762831936727[/C][/ROW]
[ROW][C]25[/C][C]0.766272578620489[/C][C]0.467454842759021[/C][C]0.233727421379511[/C][/ROW]
[ROW][C]26[/C][C]0.965612363029327[/C][C]0.0687752739413452[/C][C]0.0343876369706726[/C][/ROW]
[ROW][C]27[/C][C]0.95492976693606[/C][C]0.0901404661278798[/C][C]0.0450702330639399[/C][/ROW]
[ROW][C]28[/C][C]0.938944647548953[/C][C]0.122110704902093[/C][C]0.0610553524510466[/C][/ROW]
[ROW][C]29[/C][C]0.919025179543834[/C][C]0.161949640912333[/C][C]0.0809748204561664[/C][/ROW]
[ROW][C]30[/C][C]0.939604355042733[/C][C]0.120791289914534[/C][C]0.060395644957267[/C][/ROW]
[ROW][C]31[/C][C]0.925123115054736[/C][C]0.149753769890528[/C][C]0.0748768849452638[/C][/ROW]
[ROW][C]32[/C][C]0.904720742905799[/C][C]0.190558514188403[/C][C]0.0952792570942013[/C][/ROW]
[ROW][C]33[/C][C]0.904312271013314[/C][C]0.191375457973373[/C][C]0.0956877289866864[/C][/ROW]
[ROW][C]34[/C][C]0.878927572428577[/C][C]0.242144855142845[/C][C]0.121072427571423[/C][/ROW]
[ROW][C]35[/C][C]0.851336106297625[/C][C]0.297327787404749[/C][C]0.148663893702375[/C][/ROW]
[ROW][C]36[/C][C]0.833726208320143[/C][C]0.332547583359713[/C][C]0.166273791679857[/C][/ROW]
[ROW][C]37[/C][C]0.912664143952957[/C][C]0.174671712094086[/C][C]0.0873358560470431[/C][/ROW]
[ROW][C]38[/C][C]0.890529137348592[/C][C]0.218941725302817[/C][C]0.109470862651408[/C][/ROW]
[ROW][C]39[/C][C]0.900649945414537[/C][C]0.198700109170925[/C][C]0.0993500545854626[/C][/ROW]
[ROW][C]40[/C][C]0.884091093825583[/C][C]0.231817812348833[/C][C]0.115908906174417[/C][/ROW]
[ROW][C]41[/C][C]0.857470297752448[/C][C]0.285059404495103[/C][C]0.142529702247552[/C][/ROW]
[ROW][C]42[/C][C]0.837343784232566[/C][C]0.325312431534869[/C][C]0.162656215767434[/C][/ROW]
[ROW][C]43[/C][C]0.834922942379437[/C][C]0.330154115241126[/C][C]0.165077057620563[/C][/ROW]
[ROW][C]44[/C][C]0.810404821416728[/C][C]0.379190357166544[/C][C]0.189595178583272[/C][/ROW]
[ROW][C]45[/C][C]0.81488014041455[/C][C]0.370239719170901[/C][C]0.18511985958545[/C][/ROW]
[ROW][C]46[/C][C]0.851742676175997[/C][C]0.296514647648007[/C][C]0.148257323824003[/C][/ROW]
[ROW][C]47[/C][C]0.824275194097037[/C][C]0.351449611805926[/C][C]0.175724805902963[/C][/ROW]
[ROW][C]48[/C][C]0.792596004021615[/C][C]0.414807991956769[/C][C]0.207403995978385[/C][/ROW]
[ROW][C]49[/C][C]0.796098299528857[/C][C]0.407803400942286[/C][C]0.203901700471143[/C][/ROW]
[ROW][C]50[/C][C]0.785120622801099[/C][C]0.429758754397803[/C][C]0.214879377198902[/C][/ROW]
[ROW][C]51[/C][C]0.766099053342037[/C][C]0.467801893315926[/C][C]0.233900946657963[/C][/ROW]
[ROW][C]52[/C][C]0.728099902743564[/C][C]0.543800194512873[/C][C]0.271900097256436[/C][/ROW]
[ROW][C]53[/C][C]0.745109782251241[/C][C]0.509780435497518[/C][C]0.254890217748759[/C][/ROW]
[ROW][C]54[/C][C]0.72942522659465[/C][C]0.5411495468107[/C][C]0.27057477340535[/C][/ROW]
[ROW][C]55[/C][C]0.697876882544162[/C][C]0.604246234911675[/C][C]0.302123117455837[/C][/ROW]
[ROW][C]56[/C][C]0.658098454212618[/C][C]0.683803091574765[/C][C]0.341901545787382[/C][/ROW]
[ROW][C]57[/C][C]0.625814061953531[/C][C]0.748371876092939[/C][C]0.374185938046469[/C][/ROW]
[ROW][C]58[/C][C]0.596729622775933[/C][C]0.806540754448134[/C][C]0.403270377224067[/C][/ROW]
[ROW][C]59[/C][C]0.609929658908331[/C][C]0.780140682183338[/C][C]0.390070341091669[/C][/ROW]
[ROW][C]60[/C][C]0.604104202901714[/C][C]0.791791594196573[/C][C]0.395895797098286[/C][/ROW]
[ROW][C]61[/C][C]0.712746352224631[/C][C]0.574507295550738[/C][C]0.287253647775369[/C][/ROW]
[ROW][C]62[/C][C]0.703121284793132[/C][C]0.593757430413737[/C][C]0.296878715206869[/C][/ROW]
[ROW][C]63[/C][C]0.771228815036791[/C][C]0.457542369926418[/C][C]0.228771184963209[/C][/ROW]
[ROW][C]64[/C][C]0.74912566647378[/C][C]0.50174866705244[/C][C]0.25087433352622[/C][/ROW]
[ROW][C]65[/C][C]0.739585319205555[/C][C]0.52082936158889[/C][C]0.260414680794445[/C][/ROW]
[ROW][C]66[/C][C]0.853656503269645[/C][C]0.29268699346071[/C][C]0.146343496730355[/C][/ROW]
[ROW][C]67[/C][C]0.873874334641039[/C][C]0.252251330717922[/C][C]0.126125665358961[/C][/ROW]
[ROW][C]68[/C][C]0.865157310555883[/C][C]0.269685378888235[/C][C]0.134842689444117[/C][/ROW]
[ROW][C]69[/C][C]0.84397338651189[/C][C]0.312053226976221[/C][C]0.15602661348811[/C][/ROW]
[ROW][C]70[/C][C]0.820249660316244[/C][C]0.359500679367511[/C][C]0.179750339683755[/C][/ROW]
[ROW][C]71[/C][C]0.788557278217962[/C][C]0.422885443564075[/C][C]0.211442721782038[/C][/ROW]
[ROW][C]72[/C][C]0.821548639122754[/C][C]0.356902721754491[/C][C]0.178451360877246[/C][/ROW]
[ROW][C]73[/C][C]0.790319355024468[/C][C]0.419361289951063[/C][C]0.209680644975531[/C][/ROW]
[ROW][C]74[/C][C]0.758639997854142[/C][C]0.482720004291716[/C][C]0.241360002145858[/C][/ROW]
[ROW][C]75[/C][C]0.746178422349748[/C][C]0.507643155300504[/C][C]0.253821577650252[/C][/ROW]
[ROW][C]76[/C][C]0.76438432693125[/C][C]0.4712313461375[/C][C]0.23561567306875[/C][/ROW]
[ROW][C]77[/C][C]0.7794544147321[/C][C]0.441091170535799[/C][C]0.2205455852679[/C][/ROW]
[ROW][C]78[/C][C]0.749492609043464[/C][C]0.501014781913072[/C][C]0.250507390956536[/C][/ROW]
[ROW][C]79[/C][C]0.720274864972281[/C][C]0.559450270055437[/C][C]0.279725135027719[/C][/ROW]
[ROW][C]80[/C][C]0.681682659419375[/C][C]0.63663468116125[/C][C]0.318317340580625[/C][/ROW]
[ROW][C]81[/C][C]0.642426565740575[/C][C]0.71514686851885[/C][C]0.357573434259425[/C][/ROW]
[ROW][C]82[/C][C]0.754509253181397[/C][C]0.490981493637206[/C][C]0.245490746818603[/C][/ROW]
[ROW][C]83[/C][C]0.724541191744447[/C][C]0.550917616511105[/C][C]0.275458808255553[/C][/ROW]
[ROW][C]84[/C][C]0.708477489164765[/C][C]0.583045021670469[/C][C]0.291522510835235[/C][/ROW]
[ROW][C]85[/C][C]0.679143487242033[/C][C]0.641713025515935[/C][C]0.320856512757967[/C][/ROW]
[ROW][C]86[/C][C]0.637335659744451[/C][C]0.725328680511098[/C][C]0.362664340255549[/C][/ROW]
[ROW][C]87[/C][C]0.597075409275016[/C][C]0.805849181449969[/C][C]0.402924590724984[/C][/ROW]
[ROW][C]88[/C][C]0.553622743709943[/C][C]0.892754512580114[/C][C]0.446377256290057[/C][/ROW]
[ROW][C]89[/C][C]0.515948580285321[/C][C]0.968102839429357[/C][C]0.484051419714679[/C][/ROW]
[ROW][C]90[/C][C]0.839219639094635[/C][C]0.321560721810729[/C][C]0.160780360905365[/C][/ROW]
[ROW][C]91[/C][C]0.870076869178741[/C][C]0.259846261642518[/C][C]0.129923130821259[/C][/ROW]
[ROW][C]92[/C][C]0.847145472990181[/C][C]0.305709054019638[/C][C]0.152854527009819[/C][/ROW]
[ROW][C]93[/C][C]0.820182982282706[/C][C]0.359634035434588[/C][C]0.179817017717294[/C][/ROW]
[ROW][C]94[/C][C]0.790045236875579[/C][C]0.419909526248842[/C][C]0.209954763124421[/C][/ROW]
[ROW][C]95[/C][C]0.76551294373372[/C][C]0.468974112532559[/C][C]0.23448705626628[/C][/ROW]
[ROW][C]96[/C][C]0.738420085891814[/C][C]0.523159828216372[/C][C]0.261579914108186[/C][/ROW]
[ROW][C]97[/C][C]0.731542789660858[/C][C]0.536914420678284[/C][C]0.268457210339142[/C][/ROW]
[ROW][C]98[/C][C]0.692554531461278[/C][C]0.614890937077443[/C][C]0.307445468538722[/C][/ROW]
[ROW][C]99[/C][C]0.689563468889944[/C][C]0.620873062220112[/C][C]0.310436531110056[/C][/ROW]
[ROW][C]100[/C][C]0.654259335865232[/C][C]0.691481328269536[/C][C]0.345740664134768[/C][/ROW]
[ROW][C]101[/C][C]0.663380809099929[/C][C]0.673238381800143[/C][C]0.336619190900071[/C][/ROW]
[ROW][C]102[/C][C]0.661421248727822[/C][C]0.677157502544355[/C][C]0.338578751272178[/C][/ROW]
[ROW][C]103[/C][C]0.624509676168502[/C][C]0.750980647662995[/C][C]0.375490323831498[/C][/ROW]
[ROW][C]104[/C][C]0.629487497236951[/C][C]0.741025005526098[/C][C]0.370512502763049[/C][/ROW]
[ROW][C]105[/C][C]0.586760883883085[/C][C]0.82647823223383[/C][C]0.413239116116915[/C][/ROW]
[ROW][C]106[/C][C]0.766531583807507[/C][C]0.466936832384985[/C][C]0.233468416192493[/C][/ROW]
[ROW][C]107[/C][C]0.743834951575995[/C][C]0.512330096848011[/C][C]0.256165048424005[/C][/ROW]
[ROW][C]108[/C][C]0.706006280185381[/C][C]0.587987439629238[/C][C]0.293993719814619[/C][/ROW]
[ROW][C]109[/C][C]0.731860774355997[/C][C]0.536278451288006[/C][C]0.268139225644003[/C][/ROW]
[ROW][C]110[/C][C]0.764871017897136[/C][C]0.470257964205729[/C][C]0.235128982102864[/C][/ROW]
[ROW][C]111[/C][C]0.7311908180054[/C][C]0.5376183639892[/C][C]0.2688091819946[/C][/ROW]
[ROW][C]112[/C][C]0.758394422241121[/C][C]0.483211155517758[/C][C]0.241605577758879[/C][/ROW]
[ROW][C]113[/C][C]0.744408678412372[/C][C]0.511182643175256[/C][C]0.255591321587628[/C][/ROW]
[ROW][C]114[/C][C]0.755665217846968[/C][C]0.488669564306064[/C][C]0.244334782153032[/C][/ROW]
[ROW][C]115[/C][C]0.812331095965657[/C][C]0.375337808068686[/C][C]0.187668904034343[/C][/ROW]
[ROW][C]116[/C][C]0.779124511793772[/C][C]0.441750976412456[/C][C]0.220875488206228[/C][/ROW]
[ROW][C]117[/C][C]0.759872791121619[/C][C]0.480254417756762[/C][C]0.240127208878381[/C][/ROW]
[ROW][C]118[/C][C]0.717727963520559[/C][C]0.564544072958881[/C][C]0.282272036479441[/C][/ROW]
[ROW][C]119[/C][C]0.673292969204238[/C][C]0.653414061591524[/C][C]0.326707030795762[/C][/ROW]
[ROW][C]120[/C][C]0.654130183331626[/C][C]0.691739633336749[/C][C]0.345869816668374[/C][/ROW]
[ROW][C]121[/C][C]0.606113617012555[/C][C]0.78777276597489[/C][C]0.393886382987445[/C][/ROW]
[ROW][C]122[/C][C]0.553209023201492[/C][C]0.893581953597016[/C][C]0.446790976798508[/C][/ROW]
[ROW][C]123[/C][C]0.505564985226256[/C][C]0.988870029547487[/C][C]0.494435014773744[/C][/ROW]
[ROW][C]124[/C][C]0.452586261154804[/C][C]0.905172522309607[/C][C]0.547413738845196[/C][/ROW]
[ROW][C]125[/C][C]0.410632217077589[/C][C]0.821264434155177[/C][C]0.589367782922412[/C][/ROW]
[ROW][C]126[/C][C]0.375612242284223[/C][C]0.751224484568445[/C][C]0.624387757715777[/C][/ROW]
[ROW][C]127[/C][C]0.336426672792473[/C][C]0.672853345584947[/C][C]0.663573327207527[/C][/ROW]
[ROW][C]128[/C][C]0.343502679402[/C][C]0.687005358804[/C][C]0.656497320598[/C][/ROW]
[ROW][C]129[/C][C]0.506754309992056[/C][C]0.986491380015887[/C][C]0.493245690007944[/C][/ROW]
[ROW][C]130[/C][C]0.466630590289531[/C][C]0.933261180579062[/C][C]0.533369409710469[/C][/ROW]
[ROW][C]131[/C][C]0.411037410991755[/C][C]0.82207482198351[/C][C]0.588962589008245[/C][/ROW]
[ROW][C]132[/C][C]0.557122146171121[/C][C]0.885755707657759[/C][C]0.442877853828879[/C][/ROW]
[ROW][C]133[/C][C]0.512412238362282[/C][C]0.975175523275437[/C][C]0.487587761637718[/C][/ROW]
[ROW][C]134[/C][C]0.483598282634441[/C][C]0.967196565268881[/C][C]0.516401717365559[/C][/ROW]
[ROW][C]135[/C][C]0.438713371161873[/C][C]0.877426742323746[/C][C]0.561286628838127[/C][/ROW]
[ROW][C]136[/C][C]0.403155482785823[/C][C]0.806310965571645[/C][C]0.596844517214177[/C][/ROW]
[ROW][C]137[/C][C]0.362755231178753[/C][C]0.725510462357506[/C][C]0.637244768821247[/C][/ROW]
[ROW][C]138[/C][C]0.316311049288672[/C][C]0.632622098577345[/C][C]0.683688950711328[/C][/ROW]
[ROW][C]139[/C][C]0.281329328440518[/C][C]0.562658656881035[/C][C]0.718670671559482[/C][/ROW]
[ROW][C]140[/C][C]0.268858137268411[/C][C]0.537716274536822[/C][C]0.731141862731589[/C][/ROW]
[ROW][C]141[/C][C]0.216364961683891[/C][C]0.432729923367781[/C][C]0.783635038316109[/C][/ROW]
[ROW][C]142[/C][C]0.262004088677507[/C][C]0.524008177355015[/C][C]0.737995911322493[/C][/ROW]
[ROW][C]143[/C][C]0.221786134277363[/C][C]0.443572268554726[/C][C]0.778213865722637[/C][/ROW]
[ROW][C]144[/C][C]0.190603844567143[/C][C]0.381207689134286[/C][C]0.809396155432857[/C][/ROW]
[ROW][C]145[/C][C]0.146441149930797[/C][C]0.292882299861594[/C][C]0.853558850069203[/C][/ROW]
[ROW][C]146[/C][C]0.120646810106904[/C][C]0.241293620213807[/C][C]0.879353189893096[/C][/ROW]
[ROW][C]147[/C][C]0.106262078202551[/C][C]0.212524156405102[/C][C]0.893737921797449[/C][/ROW]
[ROW][C]148[/C][C]0.0782750361515236[/C][C]0.156550072303047[/C][C]0.921724963848476[/C][/ROW]
[ROW][C]149[/C][C]0.0638054411133462[/C][C]0.127610882226692[/C][C]0.936194558886654[/C][/ROW]
[ROW][C]150[/C][C]0.139772968379505[/C][C]0.279545936759011[/C][C]0.860227031620495[/C][/ROW]
[ROW][C]151[/C][C]0.152168425836295[/C][C]0.304336851672589[/C][C]0.847831574163705[/C][/ROW]
[ROW][C]152[/C][C]0.131404692805398[/C][C]0.262809385610796[/C][C]0.868595307194602[/C][/ROW]
[ROW][C]153[/C][C]0.145658931405229[/C][C]0.291317862810458[/C][C]0.854341068594771[/C][/ROW]
[ROW][C]154[/C][C]0.115733082816037[/C][C]0.231466165632075[/C][C]0.884266917183963[/C][/ROW]
[ROW][C]155[/C][C]0.101370736852997[/C][C]0.202741473705995[/C][C]0.898629263147003[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146620&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146620&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
70.2835844986832880.5671689973665760.716415501316712
80.2383520653915660.4767041307831320.761647934608434
90.1370244160636480.2740488321272970.862975583936352
100.1104354556168010.2208709112336030.889564544383199
110.05975624386085190.1195124877217040.940243756139148
120.6464666727994990.7070666544010020.353533327200501
130.9081829363759820.1836341272480360.091817063624018
140.8824182122049160.2351635755901670.117581787795084
150.8844931574243880.2310136851512230.115506842575612
160.8458994781379940.3082010437240110.154100521862006
170.810906032322250.37818793535550.18909396767775
180.7567050213001030.4865899573997940.243294978699897
190.7102568189644650.579486362071070.289743181035535
200.729038112994170.541923774011660.27096188700583
210.8113207331637040.3773585336725910.188679266836296
220.8546520553879920.2906958892240160.145347944612008
230.8161439761289990.3677120477420020.183856023871001
240.8072371680632730.3855256638734540.192762831936727
250.7662725786204890.4674548427590210.233727421379511
260.9656123630293270.06877527394134520.0343876369706726
270.954929766936060.09014046612787980.0450702330639399
280.9389446475489530.1221107049020930.0610553524510466
290.9190251795438340.1619496409123330.0809748204561664
300.9396043550427330.1207912899145340.060395644957267
310.9251231150547360.1497537698905280.0748768849452638
320.9047207429057990.1905585141884030.0952792570942013
330.9043122710133140.1913754579733730.0956877289866864
340.8789275724285770.2421448551428450.121072427571423
350.8513361062976250.2973277874047490.148663893702375
360.8337262083201430.3325475833597130.166273791679857
370.9126641439529570.1746717120940860.0873358560470431
380.8905291373485920.2189417253028170.109470862651408
390.9006499454145370.1987001091709250.0993500545854626
400.8840910938255830.2318178123488330.115908906174417
410.8574702977524480.2850594044951030.142529702247552
420.8373437842325660.3253124315348690.162656215767434
430.8349229423794370.3301541152411260.165077057620563
440.8104048214167280.3791903571665440.189595178583272
450.814880140414550.3702397191709010.18511985958545
460.8517426761759970.2965146476480070.148257323824003
470.8242751940970370.3514496118059260.175724805902963
480.7925960040216150.4148079919567690.207403995978385
490.7960982995288570.4078034009422860.203901700471143
500.7851206228010990.4297587543978030.214879377198902
510.7660990533420370.4678018933159260.233900946657963
520.7280999027435640.5438001945128730.271900097256436
530.7451097822512410.5097804354975180.254890217748759
540.729425226594650.54114954681070.27057477340535
550.6978768825441620.6042462349116750.302123117455837
560.6580984542126180.6838030915747650.341901545787382
570.6258140619535310.7483718760929390.374185938046469
580.5967296227759330.8065407544481340.403270377224067
590.6099296589083310.7801406821833380.390070341091669
600.6041042029017140.7917915941965730.395895797098286
610.7127463522246310.5745072955507380.287253647775369
620.7031212847931320.5937574304137370.296878715206869
630.7712288150367910.4575423699264180.228771184963209
640.749125666473780.501748667052440.25087433352622
650.7395853192055550.520829361588890.260414680794445
660.8536565032696450.292686993460710.146343496730355
670.8738743346410390.2522513307179220.126125665358961
680.8651573105558830.2696853788882350.134842689444117
690.843973386511890.3120532269762210.15602661348811
700.8202496603162440.3595006793675110.179750339683755
710.7885572782179620.4228854435640750.211442721782038
720.8215486391227540.3569027217544910.178451360877246
730.7903193550244680.4193612899510630.209680644975531
740.7586399978541420.4827200042917160.241360002145858
750.7461784223497480.5076431553005040.253821577650252
760.764384326931250.47123134613750.23561567306875
770.77945441473210.4410911705357990.2205455852679
780.7494926090434640.5010147819130720.250507390956536
790.7202748649722810.5594502700554370.279725135027719
800.6816826594193750.636634681161250.318317340580625
810.6424265657405750.715146868518850.357573434259425
820.7545092531813970.4909814936372060.245490746818603
830.7245411917444470.5509176165111050.275458808255553
840.7084774891647650.5830450216704690.291522510835235
850.6791434872420330.6417130255159350.320856512757967
860.6373356597444510.7253286805110980.362664340255549
870.5970754092750160.8058491814499690.402924590724984
880.5536227437099430.8927545125801140.446377256290057
890.5159485802853210.9681028394293570.484051419714679
900.8392196390946350.3215607218107290.160780360905365
910.8700768691787410.2598462616425180.129923130821259
920.8471454729901810.3057090540196380.152854527009819
930.8201829822827060.3596340354345880.179817017717294
940.7900452368755790.4199095262488420.209954763124421
950.765512943733720.4689741125325590.23448705626628
960.7384200858918140.5231598282163720.261579914108186
970.7315427896608580.5369144206782840.268457210339142
980.6925545314612780.6148909370774430.307445468538722
990.6895634688899440.6208730622201120.310436531110056
1000.6542593358652320.6914813282695360.345740664134768
1010.6633808090999290.6732383818001430.336619190900071
1020.6614212487278220.6771575025443550.338578751272178
1030.6245096761685020.7509806476629950.375490323831498
1040.6294874972369510.7410250055260980.370512502763049
1050.5867608838830850.826478232233830.413239116116915
1060.7665315838075070.4669368323849850.233468416192493
1070.7438349515759950.5123300968480110.256165048424005
1080.7060062801853810.5879874396292380.293993719814619
1090.7318607743559970.5362784512880060.268139225644003
1100.7648710178971360.4702579642057290.235128982102864
1110.73119081800540.53761836398920.2688091819946
1120.7583944222411210.4832111555177580.241605577758879
1130.7444086784123720.5111826431752560.255591321587628
1140.7556652178469680.4886695643060640.244334782153032
1150.8123310959656570.3753378080686860.187668904034343
1160.7791245117937720.4417509764124560.220875488206228
1170.7598727911216190.4802544177567620.240127208878381
1180.7177279635205590.5645440729588810.282272036479441
1190.6732929692042380.6534140615915240.326707030795762
1200.6541301833316260.6917396333367490.345869816668374
1210.6061136170125550.787772765974890.393886382987445
1220.5532090232014920.8935819535970160.446790976798508
1230.5055649852262560.9888700295474870.494435014773744
1240.4525862611548040.9051725223096070.547413738845196
1250.4106322170775890.8212644341551770.589367782922412
1260.3756122422842230.7512244845684450.624387757715777
1270.3364266727924730.6728533455849470.663573327207527
1280.3435026794020.6870053588040.656497320598
1290.5067543099920560.9864913800158870.493245690007944
1300.4666305902895310.9332611805790620.533369409710469
1310.4110374109917550.822074821983510.588962589008245
1320.5571221461711210.8857557076577590.442877853828879
1330.5124122383622820.9751755232754370.487587761637718
1340.4835982826344410.9671965652688810.516401717365559
1350.4387133711618730.8774267423237460.561286628838127
1360.4031554827858230.8063109655716450.596844517214177
1370.3627552311787530.7255104623575060.637244768821247
1380.3163110492886720.6326220985773450.683688950711328
1390.2813293284405180.5626586568810350.718670671559482
1400.2688581372684110.5377162745368220.731141862731589
1410.2163649616838910.4327299233677810.783635038316109
1420.2620040886775070.5240081773550150.737995911322493
1430.2217861342773630.4435722685547260.778213865722637
1440.1906038445671430.3812076891342860.809396155432857
1450.1464411499307970.2928822998615940.853558850069203
1460.1206468101069040.2412936202138070.879353189893096
1470.1062620782025510.2125241564051020.893737921797449
1480.07827503615152360.1565500723030470.921724963848476
1490.06380544111334620.1276108822266920.936194558886654
1500.1397729683795050.2795459367590110.860227031620495
1510.1521684258362950.3043368516725890.847831574163705
1520.1314046928053980.2628093856107960.868595307194602
1530.1456589314052290.2913178628104580.854341068594771
1540.1157330828160370.2314661656320750.884266917183963
1550.1013707368529970.2027414737059950.898629263147003






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level20.0134228187919463OK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 2 & 0.0134228187919463 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146620&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]2[/C][C]0.0134228187919463[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146620&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146620&T=6

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


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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level20.0134228187919463OK


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 5
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Figure 6
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Figure 7
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Figure 8
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Figure 9
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Figure 10
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Input Parameters & R Code

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