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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 13:35:54 -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/t13221597693x1l5cg8tqh6257.htm/, Retrieved Tue, 23 Apr 2024 19:22:50 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=147138, Retrieved Tue, 23 Apr 2024 19:22:50 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact91

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [] [2011-11-24 18:35:54] [46e17293cd0520480fa187e99449b207] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
14	13	41	12	53
18	16	39	11	86
11	19	30	14	66
12	15	31	12	67
16	14	34	21	76
18	13	35	12	78
14	19	39	22	53
14	15	34	11	80
15	14	36	10	74
15	15	37	13	76
17	16	38	10	79
19	16	36	8	54
10	16	38	15	67
16	16	39	14	54
18	17	33	10	87
14	15	32	14	58
14	15	36	14	75
17	20	38	11	88
14	18	39	10	64
16	16	32	13	57
18	16	32	7	66
11	16	31	14	68
14	19	39	12	54
12	16	37	14	56
17	17	39	11	86
9	17	41	9	80
16	16	36	11	76
14	15	33	15	69
15	16	33	14	78
11	14	34	13	67
16	15	31	9	80
13	12	27	15	54
17	14	37	10	71
15	16	34	11	84
14	14	34	13	74
16	7	32	8	71
9	10	29	20	63
15	14	36	12	71
17	16	29	10	76
13	16	35	10	69
15	16	37	9	74
16	14	34	14	75
16	20	38	8	54
12	14	35	14	52
12	14	38	11	69
11	11	37	13	68
15	14	38	9	65
15	15	33	11	75
17	16	36	15	74
13	14	38	11	75
16	16	32	10	72
14	14	32	14	67
11	12	32	18	63
12	16	34	14	62
12	9	32	11	63
15	14	37	12	76
16	16	39	13	74
15	16	29	9	67
12	15	37	10	73
12	16	35	15	70
8	12	30	20	53
13	16	38	12	77
11	16	34	12	77
14	14	31	14	52
15	16	34	13	54
10	17	35	11	80
11	18	36	17	66
12	18	30	12	73
15	12	39	13	63
15	16	35	14	69
14	10	38	13	67
16	14	31	15	54
15	18	34	13	81
15	18	38	10	69
13	16	34	11	84
12	17	39	19	80
17	16	37	13	70
13	16	34	17	69
15	13	28	13	77
13	16	37	9	54
15	16	33	11	79
16	20	37	10	30
15	16	35	9	71
16	15	37	12	73
15	15	32	12	72
14	16	33	13	77
15	14	38	13	75
14	16	33	12	69
13	16	29	15	54
7	15	33	22	70
17	12	31	13	73
13	17	36	15	54
15	16	35	13	77
14	15	32	15	82
13	13	29	10	80
16	16	39	11	80
12	16	37	16	69
14	16	35	11	78
17	16	37	11	81
15	14	32	10	76
17	16	38	10	76
12	16	37	16	73
16	20	36	12	85
11	15	32	11	66
15	16	33	16	79
9	13	40	19	68
16	17	38	11	76
15	16	41	16	71
10	16	36	15	54
10	12	43	24	46
15	16	30	14	82
11	16	31	15	74
13	17	32	11	88
14	13	32	15	38
18	12	37	12	76
16	18	37	10	86
14	14	33	14	54
14	14	34	13	70
14	13	33	9	69
14	16	38	15	90
12	13	33	15	54
14	16	31	14	76
15	13	38	11	89
15	16	37	8	76
15	15	33	11	73
13	16	31	11	79
17	15	39	8	90
17	17	44	10	74
19	15	33	11	81
15	12	35	13	72
13	16	32	11	71
9	10	28	20	66
15	16	40	10	77
15	12	27	15	65
15	14	37	12	74
16	15	32	14	82
11	13	28	23	54
14	15	34	14	63
11	11	30	16	54
15	12	35	11	64
13	8	31	12	69
15	16	32	10	54
16	15	30	14	84
14	17	30	12	86
15	16	31	12	77
16	10	40	11	89
16	18	32	12	76
11	13	36	13	60
12	16	32	11	75
9	13	35	19	73
16	10	38	12	85
13	15	42	17	79
16	16	34	9	71
12	16	35	12	72
9	14	35	19	69
13	10	33	18	78
13	17	36	15	54
14	13	32	14	69
19	15	33	11	81
13	16	34	9	84
12	12	32	18	84
13	13	34	16	69

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'Herman Ole Andreas Wold' @ wold.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.

\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 & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
R Framework error message & 
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147138&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]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147138&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147138&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'Herman Ole Andreas Wold' @ wold.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.






Multiple Linear Regression - Estimated Regression Equation
(1-B)Happiness[t] = + 5.30510914451663e-05 + 0.0740296120432005`(1-B)Learning`[t] + 0.0262250382991394`(1-B)Connected`[t] -0.350592682427525`(1-B)Depression`[t] + 0.0360877982550878`(1-B)Belonging `[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
(1-B)Happiness[t] =  +  5.30510914451663e-05 +  0.0740296120432005`(1-B)Learning`[t] +  0.0262250382991394`(1-B)Connected`[t] -0.350592682427525`(1-B)Depression`[t] +  0.0360877982550878`(1-B)Belonging
`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147138&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C](1-B)Happiness[t] =  +  5.30510914451663e-05 +  0.0740296120432005`(1-B)Learning`[t] +  0.0262250382991394`(1-B)Connected`[t] -0.350592682427525`(1-B)Depression`[t] +  0.0360877982550878`(1-B)Belonging
`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147138&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147138&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
(1-B)Happiness[t] = + 5.30510914451663e-05 + 0.0740296120432005`(1-B)Learning`[t] + 0.0262250382991394`(1-B)Connected`[t] -0.350592682427525`(1-B)Depression`[t] + 0.0360877982550878`(1-B)Belonging `[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)5.30510914451663e-050.2197412e-040.9998080.499904
`(1-B)Learning`0.07402961204320050.0746140.99220.3226510.161325
`(1-B)Connected`0.02622503829913940.0480290.5460.5858250.292913
`(1-B)Depression`-0.3505926824275250.053257-6.583100
`(1-B)Belonging `0.03608779825508780.0149182.41910.0167080.008354

\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) & 5.30510914451663e-05 & 0.219741 & 2e-04 & 0.999808 & 0.499904 \tabularnewline
`(1-B)Learning` & 0.0740296120432005 & 0.074614 & 0.9922 & 0.322651 & 0.161325 \tabularnewline
`(1-B)Connected` & 0.0262250382991394 & 0.048029 & 0.546 & 0.585825 & 0.292913 \tabularnewline
`(1-B)Depression` & -0.350592682427525 & 0.053257 & -6.5831 & 0 & 0 \tabularnewline
`(1-B)Belonging
` & 0.0360877982550878 & 0.014918 & 2.4191 & 0.016708 & 0.008354 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147138&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]5.30510914451663e-05[/C][C]0.219741[/C][C]2e-04[/C][C]0.999808[/C][C]0.499904[/C][/ROW]
[ROW][C]`(1-B)Learning`[/C][C]0.0740296120432005[/C][C]0.074614[/C][C]0.9922[/C][C]0.322651[/C][C]0.161325[/C][/ROW]
[ROW][C]`(1-B)Connected`[/C][C]0.0262250382991394[/C][C]0.048029[/C][C]0.546[/C][C]0.585825[/C][C]0.292913[/C][/ROW]
[ROW][C]`(1-B)Depression`[/C][C]-0.350592682427525[/C][C]0.053257[/C][C]-6.5831[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]`(1-B)Belonging
`[/C][C]0.0360877982550878[/C][C]0.014918[/C][C]2.4191[/C][C]0.016708[/C][C]0.008354[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147138&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147138&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)5.30510914451663e-050.2197412e-040.9998080.499904
`(1-B)Learning`0.07402961204320050.0746140.99220.3226510.161325
`(1-B)Connected`0.02622503829913940.0480290.5460.5858250.292913
`(1-B)Depression`-0.3505926824275250.053257-6.583100
`(1-B)Belonging `0.03608779825508780.0149182.41910.0167080.008354






Multiple Linear Regression - Regression Statistics
Multiple R0.563612578764377
R-squared0.31765913894143
Adjusted R-squared0.300163219427108
F-TEST (value)18.156184285221
F-TEST (DF numerator)4
F-TEST (DF denominator)156
p-value2.90634183386373e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.78792666271206
Sum Squared Residuals1212.51547195908

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.563612578764377 \tabularnewline
R-squared & 0.31765913894143 \tabularnewline
Adjusted R-squared & 0.300163219427108 \tabularnewline
F-TEST (value) & 18.156184285221 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 156 \tabularnewline
p-value & 2.90634183386373e-12 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.78792666271206 \tabularnewline
Sum Squared Residuals & 1212.51547195908 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147138&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.563612578764377[/C][/ROW]
[ROW][C]R-squared[/C][C]0.31765913894143[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.300163219427108[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]18.156184285221[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]156[/C][/ROW]
[ROW][C]p-value[/C][C]2.90634183386373e-12[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.78792666271206[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1212.51547195908[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147138&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147138&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 R0.563612578764377
R-squared0.31765913894143
Adjusted R-squared0.300163219427108
F-TEST (value)18.156184285221
F-TEST (DF numerator)4
F-TEST (DF denominator)156
p-value2.90634183386373e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.78792666271206
Sum Squared Residuals1212.51547195908






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
141.711181835468192.28881816453181
2-7-1.78741746985554-5.21258253014446
310.4674328043279190.532567195672081
44-2.825845403606276.82584540360627
523.17975821570528-1.17975821570528
6-4-3.85899090410523-0.141009095894765
704.40369947101309-4.40369947101309
810.1125394085435220.887460591456478
90-0.8792947493386130.879294749338613
1021.260349143481620.739650856518378
112-0.2534066170289792.25340661702898
12-9-1.93250427198681-7.06749572801319
136-0.09227060549803136.09227060549803
1422.5100005054678-0.510000505467803
15-4-2.62314809040174-1.37685190959826
1600.718445774624495-0.718445774624495
1731.943570612504441.05642938749556
18-3-0.637295610390397-2.3627043896096
192-1.635974076157123.63597407615712
2022.42839932995238-0.428399329952383
21-7-2.40814516769019-4.59185483230981
2230.6278983828979832.37210161710202
23-2-0.90349562998131-1.09650437001869
2452.260944734668132.73905526533187
25-80.537161703014243-8.53716170301424
267-1.050638310322858.05063831032285
27-2-1.80763699334489-0.192363006655113
2810.749465529857960.25053447014204
29-4-0.168154233074258-3.83184576692574
3051.866919655263473.13308034473653
31-3-3.368774787432140.368774787432144
3242.776818640643361.22318135935664
33-20.187985855169044-2.18798585516904
34-1-1.210069520400880.210069520400883
3521.074095707563120.925904292436877
36-7-4.35234780284737-2.64765219715263
3763.573190612819122.42680938718088
3820.8461613632143581.15383863678564
39-4-0.0952113068993317-3.90478869310067
4020.5835348013926871.41646519860731
411-1.943556901774912.94355690177491
4201.89484320775551-1.89484320775551
43-4-2.6985314271405-1.3014685728595
4401.74399878360793-1.74399878360793
45-1-0.985533986447433-0.0144660135525667
4641.542474260465022.45752573953498
470-0.3973499106652230.397349910665223
482-1.285700749933123.28570074993312
49-41.34290243156851-5.34290243156851
5030.2330913330452712.76690866695473
51-2-1.73081589398049-0.269184106019507
52-3-1.69472809572541-1.30527190427459
5311.71490450731754-0.714904507317538
5400.517261535728424-0.517261535728424
5530.6198749976917612.38012500230824
561-0.2222059271615751.22220592716157
57-10.887558810024535-1.88755881002454
58-30.00175785254436223-3.00175785254436
590-1.839594220366521.83959422036652
60-4-2.79364657105117-1.20635342894883
6154.176820423199670.823179576800334
62-2-0.104847102105112-1.89515289789489
633-1.830061609124624.83006160912462
6410.6495556690129650.350444330987035
65-51.73977582092112-6.73977582092112
661-2.508477568702593.50847756870259
6711.84828082121985-0.848280821219846
683-0.9195699414539063.91956994145391
6900.0572054531706917-0.0572054531706917
70-1-0.0870324203529905-0.912967579647009
712-1.057730511000923.05773051100092
72-12.05040253190408-3.05040253190408
7300.723677672509524-0.723677672509524
74-2-0.0621820347927218-1.93781796520728
75-1-2.743884797810211.74388479781021
7651.616251474464243.38374852553576
77-4-1.51708059177116-2.48291940822884
7821.311687100917810.688312899082193
79-21.03051860175638-3.03051860175638
8020.09616248941703211.90383751058297
811-1.016637779610972.01663777961097
82-11.48167693720649-2.48167693720649
831-1.001128935125872.00112893512587
84-1-0.16715993865934-0.83284006134066
85-1-0.0698459897183009-0.930154010281699
861-0.08905657800943411.08905657800943
87-10.151052976579147-1.15105297657915
88-1-1.6979421232140.697942123214003
89-6-1.84582041266647-4.15417958733353
90102.989111674976557.01088832502345
91-4-0.885527228898571-3.11447277110143
9221.431003125471170.568996874528828
93-1-0.673398049428785-0.326601950571215
94-11.45410652773507-2.45410652773507
9530.1337995877849172.86620041221508
96-4-2.20232621845042-1.79767378154958
9722.02535657092658-0.0253565709265791
9830.1607665224549872.83923347754501
99-2-0.108977673338568-1.89102232666143
10020.3054625049726831.69453749502732
101-5-2.23799147653811-2.76200852346189
10242.105370769736261.89462923026374
103-5-0.810070646740258-4.18992935325974
1044-1.18351433338775.1835143333877
105-6-1.48720434503272-4.51279565496728
10673.337165268126873.66283473187313
107-1-1.92870384946740.928703849467399
108-5-0.393972028313219-4.60602797168678
1090-3.55652665687583.5565266568758
11054.760333562833840.23966643716616
111-4-0.613016979077642-3.38698302092236
11222.00790760671511-0.00790760671511212
1131-3.502826039545844.50282603954584
11442.480263011519851.51973698848015
115-21.50629407075658-3.50629407075658
116-2-2.958145824150820.958145824150821
11700.954275543899513-0.954275543899513
11801.26608133220412-1.26608133220412
1190-0.9924452524915610.992445252491561
120-2-1.65232171371701-0.347678286282987
12121.314216054662220.685783945337777
12211.48245890765453-0.482458907654535
12300.778553518888341-0.778553518888341
1240-1.338918156196151.33891815619615
125-20.238159376066893-2.23815937606689
12641.58456757352992.4154324264701
1270-0.999352670262910.99935267026291
1282-0.53445968892742.5344596889274
129-4-1.19556125759072-2.80443874240928
130-20.882593950966791-2.88259395096679
131-4-3.88479790748748-0.115202092512524
13264.661823788021531.33817621197847
1330-2.823007886168852.82300788616885
13401.7869308897476-1.7869308897476
1351-0.4695255071753991.4695255071754
136-5-4.41869881918169-0.581301180818308
13733.78558683111619-0.785586831116194
138-3-1.42694109942875-1.57305890057125
13942.319049249318841.68095075068116
140-2-0.571119241430001-1.42888075857
14120.7783833767649221.22161662323508
1421-0.44616341960751.4461634196075
143-20.921473236543071-2.92147323654307
1441-0.3725417069484061.37254170694841
14510.5755469850130750.424453014986925
1460-0.4372444186997310.437244418699731
147-5-1.19319231043693-3.80680768956307
14811.35974407270585-0.359744072705855
149-3-3.020277726071110.0202777260711108
15072.743841685912994.25615831408701
151-3-1.49438893716414-1.50561106283586
15232.380321430121030.619678569878974
153-4-0.989412159636902-3.0105878403631
154-3-2.71041834475289-0.289581655247108
15540.3268673930436793.67313260695632
15600.782606339451736-0.782606339451736
15710.4909441059759260.509055894024074
15851.659168939820613.34083106017939
159-60.909756461054098-6.9097564610541
160-1-3.503849615527362.50384961552736
16110.2864011307616580.713598869238342
1623NANA

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 4 & 1.71118183546819 & 2.28881816453181 \tabularnewline
2 & -7 & -1.78741746985554 & -5.21258253014446 \tabularnewline
3 & 1 & 0.467432804327919 & 0.532567195672081 \tabularnewline
4 & 4 & -2.82584540360627 & 6.82584540360627 \tabularnewline
5 & 2 & 3.17975821570528 & -1.17975821570528 \tabularnewline
6 & -4 & -3.85899090410523 & -0.141009095894765 \tabularnewline
7 & 0 & 4.40369947101309 & -4.40369947101309 \tabularnewline
8 & 1 & 0.112539408543522 & 0.887460591456478 \tabularnewline
9 & 0 & -0.879294749338613 & 0.879294749338613 \tabularnewline
10 & 2 & 1.26034914348162 & 0.739650856518378 \tabularnewline
11 & 2 & -0.253406617028979 & 2.25340661702898 \tabularnewline
12 & -9 & -1.93250427198681 & -7.06749572801319 \tabularnewline
13 & 6 & -0.0922706054980313 & 6.09227060549803 \tabularnewline
14 & 2 & 2.5100005054678 & -0.510000505467803 \tabularnewline
15 & -4 & -2.62314809040174 & -1.37685190959826 \tabularnewline
16 & 0 & 0.718445774624495 & -0.718445774624495 \tabularnewline
17 & 3 & 1.94357061250444 & 1.05642938749556 \tabularnewline
18 & -3 & -0.637295610390397 & -2.3627043896096 \tabularnewline
19 & 2 & -1.63597407615712 & 3.63597407615712 \tabularnewline
20 & 2 & 2.42839932995238 & -0.428399329952383 \tabularnewline
21 & -7 & -2.40814516769019 & -4.59185483230981 \tabularnewline
22 & 3 & 0.627898382897983 & 2.37210161710202 \tabularnewline
23 & -2 & -0.90349562998131 & -1.09650437001869 \tabularnewline
24 & 5 & 2.26094473466813 & 2.73905526533187 \tabularnewline
25 & -8 & 0.537161703014243 & -8.53716170301424 \tabularnewline
26 & 7 & -1.05063831032285 & 8.05063831032285 \tabularnewline
27 & -2 & -1.80763699334489 & -0.192363006655113 \tabularnewline
28 & 1 & 0.74946552985796 & 0.25053447014204 \tabularnewline
29 & -4 & -0.168154233074258 & -3.83184576692574 \tabularnewline
30 & 5 & 1.86691965526347 & 3.13308034473653 \tabularnewline
31 & -3 & -3.36877478743214 & 0.368774787432144 \tabularnewline
32 & 4 & 2.77681864064336 & 1.22318135935664 \tabularnewline
33 & -2 & 0.187985855169044 & -2.18798585516904 \tabularnewline
34 & -1 & -1.21006952040088 & 0.210069520400883 \tabularnewline
35 & 2 & 1.07409570756312 & 0.925904292436877 \tabularnewline
36 & -7 & -4.35234780284737 & -2.64765219715263 \tabularnewline
37 & 6 & 3.57319061281912 & 2.42680938718088 \tabularnewline
38 & 2 & 0.846161363214358 & 1.15383863678564 \tabularnewline
39 & -4 & -0.0952113068993317 & -3.90478869310067 \tabularnewline
40 & 2 & 0.583534801392687 & 1.41646519860731 \tabularnewline
41 & 1 & -1.94355690177491 & 2.94355690177491 \tabularnewline
42 & 0 & 1.89484320775551 & -1.89484320775551 \tabularnewline
43 & -4 & -2.6985314271405 & -1.3014685728595 \tabularnewline
44 & 0 & 1.74399878360793 & -1.74399878360793 \tabularnewline
45 & -1 & -0.985533986447433 & -0.0144660135525667 \tabularnewline
46 & 4 & 1.54247426046502 & 2.45752573953498 \tabularnewline
47 & 0 & -0.397349910665223 & 0.397349910665223 \tabularnewline
48 & 2 & -1.28570074993312 & 3.28570074993312 \tabularnewline
49 & -4 & 1.34290243156851 & -5.34290243156851 \tabularnewline
50 & 3 & 0.233091333045271 & 2.76690866695473 \tabularnewline
51 & -2 & -1.73081589398049 & -0.269184106019507 \tabularnewline
52 & -3 & -1.69472809572541 & -1.30527190427459 \tabularnewline
53 & 1 & 1.71490450731754 & -0.714904507317538 \tabularnewline
54 & 0 & 0.517261535728424 & -0.517261535728424 \tabularnewline
55 & 3 & 0.619874997691761 & 2.38012500230824 \tabularnewline
56 & 1 & -0.222205927161575 & 1.22220592716157 \tabularnewline
57 & -1 & 0.887558810024535 & -1.88755881002454 \tabularnewline
58 & -3 & 0.00175785254436223 & -3.00175785254436 \tabularnewline
59 & 0 & -1.83959422036652 & 1.83959422036652 \tabularnewline
60 & -4 & -2.79364657105117 & -1.20635342894883 \tabularnewline
61 & 5 & 4.17682042319967 & 0.823179576800334 \tabularnewline
62 & -2 & -0.104847102105112 & -1.89515289789489 \tabularnewline
63 & 3 & -1.83006160912462 & 4.83006160912462 \tabularnewline
64 & 1 & 0.649555669012965 & 0.350444330987035 \tabularnewline
65 & -5 & 1.73977582092112 & -6.73977582092112 \tabularnewline
66 & 1 & -2.50847756870259 & 3.50847756870259 \tabularnewline
67 & 1 & 1.84828082121985 & -0.848280821219846 \tabularnewline
68 & 3 & -0.919569941453906 & 3.91956994145391 \tabularnewline
69 & 0 & 0.0572054531706917 & -0.0572054531706917 \tabularnewline
70 & -1 & -0.0870324203529905 & -0.912967579647009 \tabularnewline
71 & 2 & -1.05773051100092 & 3.05773051100092 \tabularnewline
72 & -1 & 2.05040253190408 & -3.05040253190408 \tabularnewline
73 & 0 & 0.723677672509524 & -0.723677672509524 \tabularnewline
74 & -2 & -0.0621820347927218 & -1.93781796520728 \tabularnewline
75 & -1 & -2.74388479781021 & 1.74388479781021 \tabularnewline
76 & 5 & 1.61625147446424 & 3.38374852553576 \tabularnewline
77 & -4 & -1.51708059177116 & -2.48291940822884 \tabularnewline
78 & 2 & 1.31168710091781 & 0.688312899082193 \tabularnewline
79 & -2 & 1.03051860175638 & -3.03051860175638 \tabularnewline
80 & 2 & 0.0961624894170321 & 1.90383751058297 \tabularnewline
81 & 1 & -1.01663777961097 & 2.01663777961097 \tabularnewline
82 & -1 & 1.48167693720649 & -2.48167693720649 \tabularnewline
83 & 1 & -1.00112893512587 & 2.00112893512587 \tabularnewline
84 & -1 & -0.16715993865934 & -0.83284006134066 \tabularnewline
85 & -1 & -0.0698459897183009 & -0.930154010281699 \tabularnewline
86 & 1 & -0.0890565780094341 & 1.08905657800943 \tabularnewline
87 & -1 & 0.151052976579147 & -1.15105297657915 \tabularnewline
88 & -1 & -1.697942123214 & 0.697942123214003 \tabularnewline
89 & -6 & -1.84582041266647 & -4.15417958733353 \tabularnewline
90 & 10 & 2.98911167497655 & 7.01088832502345 \tabularnewline
91 & -4 & -0.885527228898571 & -3.11447277110143 \tabularnewline
92 & 2 & 1.43100312547117 & 0.568996874528828 \tabularnewline
93 & -1 & -0.673398049428785 & -0.326601950571215 \tabularnewline
94 & -1 & 1.45410652773507 & -2.45410652773507 \tabularnewline
95 & 3 & 0.133799587784917 & 2.86620041221508 \tabularnewline
96 & -4 & -2.20232621845042 & -1.79767378154958 \tabularnewline
97 & 2 & 2.02535657092658 & -0.0253565709265791 \tabularnewline
98 & 3 & 0.160766522454987 & 2.83923347754501 \tabularnewline
99 & -2 & -0.108977673338568 & -1.89102232666143 \tabularnewline
100 & 2 & 0.305462504972683 & 1.69453749502732 \tabularnewline
101 & -5 & -2.23799147653811 & -2.76200852346189 \tabularnewline
102 & 4 & 2.10537076973626 & 1.89462923026374 \tabularnewline
103 & -5 & -0.810070646740258 & -4.18992935325974 \tabularnewline
104 & 4 & -1.1835143333877 & 5.1835143333877 \tabularnewline
105 & -6 & -1.48720434503272 & -4.51279565496728 \tabularnewline
106 & 7 & 3.33716526812687 & 3.66283473187313 \tabularnewline
107 & -1 & -1.9287038494674 & 0.928703849467399 \tabularnewline
108 & -5 & -0.393972028313219 & -4.60602797168678 \tabularnewline
109 & 0 & -3.5565266568758 & 3.5565266568758 \tabularnewline
110 & 5 & 4.76033356283384 & 0.23966643716616 \tabularnewline
111 & -4 & -0.613016979077642 & -3.38698302092236 \tabularnewline
112 & 2 & 2.00790760671511 & -0.00790760671511212 \tabularnewline
113 & 1 & -3.50282603954584 & 4.50282603954584 \tabularnewline
114 & 4 & 2.48026301151985 & 1.51973698848015 \tabularnewline
115 & -2 & 1.50629407075658 & -3.50629407075658 \tabularnewline
116 & -2 & -2.95814582415082 & 0.958145824150821 \tabularnewline
117 & 0 & 0.954275543899513 & -0.954275543899513 \tabularnewline
118 & 0 & 1.26608133220412 & -1.26608133220412 \tabularnewline
119 & 0 & -0.992445252491561 & 0.992445252491561 \tabularnewline
120 & -2 & -1.65232171371701 & -0.347678286282987 \tabularnewline
121 & 2 & 1.31421605466222 & 0.685783945337777 \tabularnewline
122 & 1 & 1.48245890765453 & -0.482458907654535 \tabularnewline
123 & 0 & 0.778553518888341 & -0.778553518888341 \tabularnewline
124 & 0 & -1.33891815619615 & 1.33891815619615 \tabularnewline
125 & -2 & 0.238159376066893 & -2.23815937606689 \tabularnewline
126 & 4 & 1.5845675735299 & 2.4154324264701 \tabularnewline
127 & 0 & -0.99935267026291 & 0.99935267026291 \tabularnewline
128 & 2 & -0.5344596889274 & 2.5344596889274 \tabularnewline
129 & -4 & -1.19556125759072 & -2.80443874240928 \tabularnewline
130 & -2 & 0.882593950966791 & -2.88259395096679 \tabularnewline
131 & -4 & -3.88479790748748 & -0.115202092512524 \tabularnewline
132 & 6 & 4.66182378802153 & 1.33817621197847 \tabularnewline
133 & 0 & -2.82300788616885 & 2.82300788616885 \tabularnewline
134 & 0 & 1.7869308897476 & -1.7869308897476 \tabularnewline
135 & 1 & -0.469525507175399 & 1.4695255071754 \tabularnewline
136 & -5 & -4.41869881918169 & -0.581301180818308 \tabularnewline
137 & 3 & 3.78558683111619 & -0.785586831116194 \tabularnewline
138 & -3 & -1.42694109942875 & -1.57305890057125 \tabularnewline
139 & 4 & 2.31904924931884 & 1.68095075068116 \tabularnewline
140 & -2 & -0.571119241430001 & -1.42888075857 \tabularnewline
141 & 2 & 0.778383376764922 & 1.22161662323508 \tabularnewline
142 & 1 & -0.4461634196075 & 1.4461634196075 \tabularnewline
143 & -2 & 0.921473236543071 & -2.92147323654307 \tabularnewline
144 & 1 & -0.372541706948406 & 1.37254170694841 \tabularnewline
145 & 1 & 0.575546985013075 & 0.424453014986925 \tabularnewline
146 & 0 & -0.437244418699731 & 0.437244418699731 \tabularnewline
147 & -5 & -1.19319231043693 & -3.80680768956307 \tabularnewline
148 & 1 & 1.35974407270585 & -0.359744072705855 \tabularnewline
149 & -3 & -3.02027772607111 & 0.0202777260711108 \tabularnewline
150 & 7 & 2.74384168591299 & 4.25615831408701 \tabularnewline
151 & -3 & -1.49438893716414 & -1.50561106283586 \tabularnewline
152 & 3 & 2.38032143012103 & 0.619678569878974 \tabularnewline
153 & -4 & -0.989412159636902 & -3.0105878403631 \tabularnewline
154 & -3 & -2.71041834475289 & -0.289581655247108 \tabularnewline
155 & 4 & 0.326867393043679 & 3.67313260695632 \tabularnewline
156 & 0 & 0.782606339451736 & -0.782606339451736 \tabularnewline
157 & 1 & 0.490944105975926 & 0.509055894024074 \tabularnewline
158 & 5 & 1.65916893982061 & 3.34083106017939 \tabularnewline
159 & -6 & 0.909756461054098 & -6.9097564610541 \tabularnewline
160 & -1 & -3.50384961552736 & 2.50384961552736 \tabularnewline
161 & 1 & 0.286401130761658 & 0.713598869238342 \tabularnewline
162 & 3 & NA & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147138&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]4[/C][C]1.71118183546819[/C][C]2.28881816453181[/C][/ROW]
[ROW][C]2[/C][C]-7[/C][C]-1.78741746985554[/C][C]-5.21258253014446[/C][/ROW]
[ROW][C]3[/C][C]1[/C][C]0.467432804327919[/C][C]0.532567195672081[/C][/ROW]
[ROW][C]4[/C][C]4[/C][C]-2.82584540360627[/C][C]6.82584540360627[/C][/ROW]
[ROW][C]5[/C][C]2[/C][C]3.17975821570528[/C][C]-1.17975821570528[/C][/ROW]
[ROW][C]6[/C][C]-4[/C][C]-3.85899090410523[/C][C]-0.141009095894765[/C][/ROW]
[ROW][C]7[/C][C]0[/C][C]4.40369947101309[/C][C]-4.40369947101309[/C][/ROW]
[ROW][C]8[/C][C]1[/C][C]0.112539408543522[/C][C]0.887460591456478[/C][/ROW]
[ROW][C]9[/C][C]0[/C][C]-0.879294749338613[/C][C]0.879294749338613[/C][/ROW]
[ROW][C]10[/C][C]2[/C][C]1.26034914348162[/C][C]0.739650856518378[/C][/ROW]
[ROW][C]11[/C][C]2[/C][C]-0.253406617028979[/C][C]2.25340661702898[/C][/ROW]
[ROW][C]12[/C][C]-9[/C][C]-1.93250427198681[/C][C]-7.06749572801319[/C][/ROW]
[ROW][C]13[/C][C]6[/C][C]-0.0922706054980313[/C][C]6.09227060549803[/C][/ROW]
[ROW][C]14[/C][C]2[/C][C]2.5100005054678[/C][C]-0.510000505467803[/C][/ROW]
[ROW][C]15[/C][C]-4[/C][C]-2.62314809040174[/C][C]-1.37685190959826[/C][/ROW]
[ROW][C]16[/C][C]0[/C][C]0.718445774624495[/C][C]-0.718445774624495[/C][/ROW]
[ROW][C]17[/C][C]3[/C][C]1.94357061250444[/C][C]1.05642938749556[/C][/ROW]
[ROW][C]18[/C][C]-3[/C][C]-0.637295610390397[/C][C]-2.3627043896096[/C][/ROW]
[ROW][C]19[/C][C]2[/C][C]-1.63597407615712[/C][C]3.63597407615712[/C][/ROW]
[ROW][C]20[/C][C]2[/C][C]2.42839932995238[/C][C]-0.428399329952383[/C][/ROW]
[ROW][C]21[/C][C]-7[/C][C]-2.40814516769019[/C][C]-4.59185483230981[/C][/ROW]
[ROW][C]22[/C][C]3[/C][C]0.627898382897983[/C][C]2.37210161710202[/C][/ROW]
[ROW][C]23[/C][C]-2[/C][C]-0.90349562998131[/C][C]-1.09650437001869[/C][/ROW]
[ROW][C]24[/C][C]5[/C][C]2.26094473466813[/C][C]2.73905526533187[/C][/ROW]
[ROW][C]25[/C][C]-8[/C][C]0.537161703014243[/C][C]-8.53716170301424[/C][/ROW]
[ROW][C]26[/C][C]7[/C][C]-1.05063831032285[/C][C]8.05063831032285[/C][/ROW]
[ROW][C]27[/C][C]-2[/C][C]-1.80763699334489[/C][C]-0.192363006655113[/C][/ROW]
[ROW][C]28[/C][C]1[/C][C]0.74946552985796[/C][C]0.25053447014204[/C][/ROW]
[ROW][C]29[/C][C]-4[/C][C]-0.168154233074258[/C][C]-3.83184576692574[/C][/ROW]
[ROW][C]30[/C][C]5[/C][C]1.86691965526347[/C][C]3.13308034473653[/C][/ROW]
[ROW][C]31[/C][C]-3[/C][C]-3.36877478743214[/C][C]0.368774787432144[/C][/ROW]
[ROW][C]32[/C][C]4[/C][C]2.77681864064336[/C][C]1.22318135935664[/C][/ROW]
[ROW][C]33[/C][C]-2[/C][C]0.187985855169044[/C][C]-2.18798585516904[/C][/ROW]
[ROW][C]34[/C][C]-1[/C][C]-1.21006952040088[/C][C]0.210069520400883[/C][/ROW]
[ROW][C]35[/C][C]2[/C][C]1.07409570756312[/C][C]0.925904292436877[/C][/ROW]
[ROW][C]36[/C][C]-7[/C][C]-4.35234780284737[/C][C]-2.64765219715263[/C][/ROW]
[ROW][C]37[/C][C]6[/C][C]3.57319061281912[/C][C]2.42680938718088[/C][/ROW]
[ROW][C]38[/C][C]2[/C][C]0.846161363214358[/C][C]1.15383863678564[/C][/ROW]
[ROW][C]39[/C][C]-4[/C][C]-0.0952113068993317[/C][C]-3.90478869310067[/C][/ROW]
[ROW][C]40[/C][C]2[/C][C]0.583534801392687[/C][C]1.41646519860731[/C][/ROW]
[ROW][C]41[/C][C]1[/C][C]-1.94355690177491[/C][C]2.94355690177491[/C][/ROW]
[ROW][C]42[/C][C]0[/C][C]1.89484320775551[/C][C]-1.89484320775551[/C][/ROW]
[ROW][C]43[/C][C]-4[/C][C]-2.6985314271405[/C][C]-1.3014685728595[/C][/ROW]
[ROW][C]44[/C][C]0[/C][C]1.74399878360793[/C][C]-1.74399878360793[/C][/ROW]
[ROW][C]45[/C][C]-1[/C][C]-0.985533986447433[/C][C]-0.0144660135525667[/C][/ROW]
[ROW][C]46[/C][C]4[/C][C]1.54247426046502[/C][C]2.45752573953498[/C][/ROW]
[ROW][C]47[/C][C]0[/C][C]-0.397349910665223[/C][C]0.397349910665223[/C][/ROW]
[ROW][C]48[/C][C]2[/C][C]-1.28570074993312[/C][C]3.28570074993312[/C][/ROW]
[ROW][C]49[/C][C]-4[/C][C]1.34290243156851[/C][C]-5.34290243156851[/C][/ROW]
[ROW][C]50[/C][C]3[/C][C]0.233091333045271[/C][C]2.76690866695473[/C][/ROW]
[ROW][C]51[/C][C]-2[/C][C]-1.73081589398049[/C][C]-0.269184106019507[/C][/ROW]
[ROW][C]52[/C][C]-3[/C][C]-1.69472809572541[/C][C]-1.30527190427459[/C][/ROW]
[ROW][C]53[/C][C]1[/C][C]1.71490450731754[/C][C]-0.714904507317538[/C][/ROW]
[ROW][C]54[/C][C]0[/C][C]0.517261535728424[/C][C]-0.517261535728424[/C][/ROW]
[ROW][C]55[/C][C]3[/C][C]0.619874997691761[/C][C]2.38012500230824[/C][/ROW]
[ROW][C]56[/C][C]1[/C][C]-0.222205927161575[/C][C]1.22220592716157[/C][/ROW]
[ROW][C]57[/C][C]-1[/C][C]0.887558810024535[/C][C]-1.88755881002454[/C][/ROW]
[ROW][C]58[/C][C]-3[/C][C]0.00175785254436223[/C][C]-3.00175785254436[/C][/ROW]
[ROW][C]59[/C][C]0[/C][C]-1.83959422036652[/C][C]1.83959422036652[/C][/ROW]
[ROW][C]60[/C][C]-4[/C][C]-2.79364657105117[/C][C]-1.20635342894883[/C][/ROW]
[ROW][C]61[/C][C]5[/C][C]4.17682042319967[/C][C]0.823179576800334[/C][/ROW]
[ROW][C]62[/C][C]-2[/C][C]-0.104847102105112[/C][C]-1.89515289789489[/C][/ROW]
[ROW][C]63[/C][C]3[/C][C]-1.83006160912462[/C][C]4.83006160912462[/C][/ROW]
[ROW][C]64[/C][C]1[/C][C]0.649555669012965[/C][C]0.350444330987035[/C][/ROW]
[ROW][C]65[/C][C]-5[/C][C]1.73977582092112[/C][C]-6.73977582092112[/C][/ROW]
[ROW][C]66[/C][C]1[/C][C]-2.50847756870259[/C][C]3.50847756870259[/C][/ROW]
[ROW][C]67[/C][C]1[/C][C]1.84828082121985[/C][C]-0.848280821219846[/C][/ROW]
[ROW][C]68[/C][C]3[/C][C]-0.919569941453906[/C][C]3.91956994145391[/C][/ROW]
[ROW][C]69[/C][C]0[/C][C]0.0572054531706917[/C][C]-0.0572054531706917[/C][/ROW]
[ROW][C]70[/C][C]-1[/C][C]-0.0870324203529905[/C][C]-0.912967579647009[/C][/ROW]
[ROW][C]71[/C][C]2[/C][C]-1.05773051100092[/C][C]3.05773051100092[/C][/ROW]
[ROW][C]72[/C][C]-1[/C][C]2.05040253190408[/C][C]-3.05040253190408[/C][/ROW]
[ROW][C]73[/C][C]0[/C][C]0.723677672509524[/C][C]-0.723677672509524[/C][/ROW]
[ROW][C]74[/C][C]-2[/C][C]-0.0621820347927218[/C][C]-1.93781796520728[/C][/ROW]
[ROW][C]75[/C][C]-1[/C][C]-2.74388479781021[/C][C]1.74388479781021[/C][/ROW]
[ROW][C]76[/C][C]5[/C][C]1.61625147446424[/C][C]3.38374852553576[/C][/ROW]
[ROW][C]77[/C][C]-4[/C][C]-1.51708059177116[/C][C]-2.48291940822884[/C][/ROW]
[ROW][C]78[/C][C]2[/C][C]1.31168710091781[/C][C]0.688312899082193[/C][/ROW]
[ROW][C]79[/C][C]-2[/C][C]1.03051860175638[/C][C]-3.03051860175638[/C][/ROW]
[ROW][C]80[/C][C]2[/C][C]0.0961624894170321[/C][C]1.90383751058297[/C][/ROW]
[ROW][C]81[/C][C]1[/C][C]-1.01663777961097[/C][C]2.01663777961097[/C][/ROW]
[ROW][C]82[/C][C]-1[/C][C]1.48167693720649[/C][C]-2.48167693720649[/C][/ROW]
[ROW][C]83[/C][C]1[/C][C]-1.00112893512587[/C][C]2.00112893512587[/C][/ROW]
[ROW][C]84[/C][C]-1[/C][C]-0.16715993865934[/C][C]-0.83284006134066[/C][/ROW]
[ROW][C]85[/C][C]-1[/C][C]-0.0698459897183009[/C][C]-0.930154010281699[/C][/ROW]
[ROW][C]86[/C][C]1[/C][C]-0.0890565780094341[/C][C]1.08905657800943[/C][/ROW]
[ROW][C]87[/C][C]-1[/C][C]0.151052976579147[/C][C]-1.15105297657915[/C][/ROW]
[ROW][C]88[/C][C]-1[/C][C]-1.697942123214[/C][C]0.697942123214003[/C][/ROW]
[ROW][C]89[/C][C]-6[/C][C]-1.84582041266647[/C][C]-4.15417958733353[/C][/ROW]
[ROW][C]90[/C][C]10[/C][C]2.98911167497655[/C][C]7.01088832502345[/C][/ROW]
[ROW][C]91[/C][C]-4[/C][C]-0.885527228898571[/C][C]-3.11447277110143[/C][/ROW]
[ROW][C]92[/C][C]2[/C][C]1.43100312547117[/C][C]0.568996874528828[/C][/ROW]
[ROW][C]93[/C][C]-1[/C][C]-0.673398049428785[/C][C]-0.326601950571215[/C][/ROW]
[ROW][C]94[/C][C]-1[/C][C]1.45410652773507[/C][C]-2.45410652773507[/C][/ROW]
[ROW][C]95[/C][C]3[/C][C]0.133799587784917[/C][C]2.86620041221508[/C][/ROW]
[ROW][C]96[/C][C]-4[/C][C]-2.20232621845042[/C][C]-1.79767378154958[/C][/ROW]
[ROW][C]97[/C][C]2[/C][C]2.02535657092658[/C][C]-0.0253565709265791[/C][/ROW]
[ROW][C]98[/C][C]3[/C][C]0.160766522454987[/C][C]2.83923347754501[/C][/ROW]
[ROW][C]99[/C][C]-2[/C][C]-0.108977673338568[/C][C]-1.89102232666143[/C][/ROW]
[ROW][C]100[/C][C]2[/C][C]0.305462504972683[/C][C]1.69453749502732[/C][/ROW]
[ROW][C]101[/C][C]-5[/C][C]-2.23799147653811[/C][C]-2.76200852346189[/C][/ROW]
[ROW][C]102[/C][C]4[/C][C]2.10537076973626[/C][C]1.89462923026374[/C][/ROW]
[ROW][C]103[/C][C]-5[/C][C]-0.810070646740258[/C][C]-4.18992935325974[/C][/ROW]
[ROW][C]104[/C][C]4[/C][C]-1.1835143333877[/C][C]5.1835143333877[/C][/ROW]
[ROW][C]105[/C][C]-6[/C][C]-1.48720434503272[/C][C]-4.51279565496728[/C][/ROW]
[ROW][C]106[/C][C]7[/C][C]3.33716526812687[/C][C]3.66283473187313[/C][/ROW]
[ROW][C]107[/C][C]-1[/C][C]-1.9287038494674[/C][C]0.928703849467399[/C][/ROW]
[ROW][C]108[/C][C]-5[/C][C]-0.393972028313219[/C][C]-4.60602797168678[/C][/ROW]
[ROW][C]109[/C][C]0[/C][C]-3.5565266568758[/C][C]3.5565266568758[/C][/ROW]
[ROW][C]110[/C][C]5[/C][C]4.76033356283384[/C][C]0.23966643716616[/C][/ROW]
[ROW][C]111[/C][C]-4[/C][C]-0.613016979077642[/C][C]-3.38698302092236[/C][/ROW]
[ROW][C]112[/C][C]2[/C][C]2.00790760671511[/C][C]-0.00790760671511212[/C][/ROW]
[ROW][C]113[/C][C]1[/C][C]-3.50282603954584[/C][C]4.50282603954584[/C][/ROW]
[ROW][C]114[/C][C]4[/C][C]2.48026301151985[/C][C]1.51973698848015[/C][/ROW]
[ROW][C]115[/C][C]-2[/C][C]1.50629407075658[/C][C]-3.50629407075658[/C][/ROW]
[ROW][C]116[/C][C]-2[/C][C]-2.95814582415082[/C][C]0.958145824150821[/C][/ROW]
[ROW][C]117[/C][C]0[/C][C]0.954275543899513[/C][C]-0.954275543899513[/C][/ROW]
[ROW][C]118[/C][C]0[/C][C]1.26608133220412[/C][C]-1.26608133220412[/C][/ROW]
[ROW][C]119[/C][C]0[/C][C]-0.992445252491561[/C][C]0.992445252491561[/C][/ROW]
[ROW][C]120[/C][C]-2[/C][C]-1.65232171371701[/C][C]-0.347678286282987[/C][/ROW]
[ROW][C]121[/C][C]2[/C][C]1.31421605466222[/C][C]0.685783945337777[/C][/ROW]
[ROW][C]122[/C][C]1[/C][C]1.48245890765453[/C][C]-0.482458907654535[/C][/ROW]
[ROW][C]123[/C][C]0[/C][C]0.778553518888341[/C][C]-0.778553518888341[/C][/ROW]
[ROW][C]124[/C][C]0[/C][C]-1.33891815619615[/C][C]1.33891815619615[/C][/ROW]
[ROW][C]125[/C][C]-2[/C][C]0.238159376066893[/C][C]-2.23815937606689[/C][/ROW]
[ROW][C]126[/C][C]4[/C][C]1.5845675735299[/C][C]2.4154324264701[/C][/ROW]
[ROW][C]127[/C][C]0[/C][C]-0.99935267026291[/C][C]0.99935267026291[/C][/ROW]
[ROW][C]128[/C][C]2[/C][C]-0.5344596889274[/C][C]2.5344596889274[/C][/ROW]
[ROW][C]129[/C][C]-4[/C][C]-1.19556125759072[/C][C]-2.80443874240928[/C][/ROW]
[ROW][C]130[/C][C]-2[/C][C]0.882593950966791[/C][C]-2.88259395096679[/C][/ROW]
[ROW][C]131[/C][C]-4[/C][C]-3.88479790748748[/C][C]-0.115202092512524[/C][/ROW]
[ROW][C]132[/C][C]6[/C][C]4.66182378802153[/C][C]1.33817621197847[/C][/ROW]
[ROW][C]133[/C][C]0[/C][C]-2.82300788616885[/C][C]2.82300788616885[/C][/ROW]
[ROW][C]134[/C][C]0[/C][C]1.7869308897476[/C][C]-1.7869308897476[/C][/ROW]
[ROW][C]135[/C][C]1[/C][C]-0.469525507175399[/C][C]1.4695255071754[/C][/ROW]
[ROW][C]136[/C][C]-5[/C][C]-4.41869881918169[/C][C]-0.581301180818308[/C][/ROW]
[ROW][C]137[/C][C]3[/C][C]3.78558683111619[/C][C]-0.785586831116194[/C][/ROW]
[ROW][C]138[/C][C]-3[/C][C]-1.42694109942875[/C][C]-1.57305890057125[/C][/ROW]
[ROW][C]139[/C][C]4[/C][C]2.31904924931884[/C][C]1.68095075068116[/C][/ROW]
[ROW][C]140[/C][C]-2[/C][C]-0.571119241430001[/C][C]-1.42888075857[/C][/ROW]
[ROW][C]141[/C][C]2[/C][C]0.778383376764922[/C][C]1.22161662323508[/C][/ROW]
[ROW][C]142[/C][C]1[/C][C]-0.4461634196075[/C][C]1.4461634196075[/C][/ROW]
[ROW][C]143[/C][C]-2[/C][C]0.921473236543071[/C][C]-2.92147323654307[/C][/ROW]
[ROW][C]144[/C][C]1[/C][C]-0.372541706948406[/C][C]1.37254170694841[/C][/ROW]
[ROW][C]145[/C][C]1[/C][C]0.575546985013075[/C][C]0.424453014986925[/C][/ROW]
[ROW][C]146[/C][C]0[/C][C]-0.437244418699731[/C][C]0.437244418699731[/C][/ROW]
[ROW][C]147[/C][C]-5[/C][C]-1.19319231043693[/C][C]-3.80680768956307[/C][/ROW]
[ROW][C]148[/C][C]1[/C][C]1.35974407270585[/C][C]-0.359744072705855[/C][/ROW]
[ROW][C]149[/C][C]-3[/C][C]-3.02027772607111[/C][C]0.0202777260711108[/C][/ROW]
[ROW][C]150[/C][C]7[/C][C]2.74384168591299[/C][C]4.25615831408701[/C][/ROW]
[ROW][C]151[/C][C]-3[/C][C]-1.49438893716414[/C][C]-1.50561106283586[/C][/ROW]
[ROW][C]152[/C][C]3[/C][C]2.38032143012103[/C][C]0.619678569878974[/C][/ROW]
[ROW][C]153[/C][C]-4[/C][C]-0.989412159636902[/C][C]-3.0105878403631[/C][/ROW]
[ROW][C]154[/C][C]-3[/C][C]-2.71041834475289[/C][C]-0.289581655247108[/C][/ROW]
[ROW][C]155[/C][C]4[/C][C]0.326867393043679[/C][C]3.67313260695632[/C][/ROW]
[ROW][C]156[/C][C]0[/C][C]0.782606339451736[/C][C]-0.782606339451736[/C][/ROW]
[ROW][C]157[/C][C]1[/C][C]0.490944105975926[/C][C]0.509055894024074[/C][/ROW]
[ROW][C]158[/C][C]5[/C][C]1.65916893982061[/C][C]3.34083106017939[/C][/ROW]
[ROW][C]159[/C][C]-6[/C][C]0.909756461054098[/C][C]-6.9097564610541[/C][/ROW]
[ROW][C]160[/C][C]-1[/C][C]-3.50384961552736[/C][C]2.50384961552736[/C][/ROW]
[ROW][C]161[/C][C]1[/C][C]0.286401130761658[/C][C]0.713598869238342[/C][/ROW]
[ROW][C]162[/C][C]3[/C][C]NA[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147138&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147138&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
141.711181835468192.28881816453181
2-7-1.78741746985554-5.21258253014446
310.4674328043279190.532567195672081
44-2.825845403606276.82584540360627
523.17975821570528-1.17975821570528
6-4-3.85899090410523-0.141009095894765
704.40369947101309-4.40369947101309
810.1125394085435220.887460591456478
90-0.8792947493386130.879294749338613
1021.260349143481620.739650856518378
112-0.2534066170289792.25340661702898
12-9-1.93250427198681-7.06749572801319
136-0.09227060549803136.09227060549803
1422.5100005054678-0.510000505467803
15-4-2.62314809040174-1.37685190959826
1600.718445774624495-0.718445774624495
1731.943570612504441.05642938749556
18-3-0.637295610390397-2.3627043896096
192-1.635974076157123.63597407615712
2022.42839932995238-0.428399329952383
21-7-2.40814516769019-4.59185483230981
2230.6278983828979832.37210161710202
23-2-0.90349562998131-1.09650437001869
2452.260944734668132.73905526533187
25-80.537161703014243-8.53716170301424
267-1.050638310322858.05063831032285
27-2-1.80763699334489-0.192363006655113
2810.749465529857960.25053447014204
29-4-0.168154233074258-3.83184576692574
3051.866919655263473.13308034473653
31-3-3.368774787432140.368774787432144
3242.776818640643361.22318135935664
33-20.187985855169044-2.18798585516904
34-1-1.210069520400880.210069520400883
3521.074095707563120.925904292436877
36-7-4.35234780284737-2.64765219715263
3763.573190612819122.42680938718088
3820.8461613632143581.15383863678564
39-4-0.0952113068993317-3.90478869310067
4020.5835348013926871.41646519860731
411-1.943556901774912.94355690177491
4201.89484320775551-1.89484320775551
43-4-2.6985314271405-1.3014685728595
4401.74399878360793-1.74399878360793
45-1-0.985533986447433-0.0144660135525667
4641.542474260465022.45752573953498
470-0.3973499106652230.397349910665223
482-1.285700749933123.28570074993312
49-41.34290243156851-5.34290243156851
5030.2330913330452712.76690866695473
51-2-1.73081589398049-0.269184106019507
52-3-1.69472809572541-1.30527190427459
5311.71490450731754-0.714904507317538
5400.517261535728424-0.517261535728424
5530.6198749976917612.38012500230824
561-0.2222059271615751.22220592716157
57-10.887558810024535-1.88755881002454
58-30.00175785254436223-3.00175785254436
590-1.839594220366521.83959422036652
60-4-2.79364657105117-1.20635342894883
6154.176820423199670.823179576800334
62-2-0.104847102105112-1.89515289789489
633-1.830061609124624.83006160912462
6410.6495556690129650.350444330987035
65-51.73977582092112-6.73977582092112
661-2.508477568702593.50847756870259
6711.84828082121985-0.848280821219846
683-0.9195699414539063.91956994145391
6900.0572054531706917-0.0572054531706917
70-1-0.0870324203529905-0.912967579647009
712-1.057730511000923.05773051100092
72-12.05040253190408-3.05040253190408
7300.723677672509524-0.723677672509524
74-2-0.0621820347927218-1.93781796520728
75-1-2.743884797810211.74388479781021
7651.616251474464243.38374852553576
77-4-1.51708059177116-2.48291940822884
7821.311687100917810.688312899082193
79-21.03051860175638-3.03051860175638
8020.09616248941703211.90383751058297
811-1.016637779610972.01663777961097
82-11.48167693720649-2.48167693720649
831-1.001128935125872.00112893512587
84-1-0.16715993865934-0.83284006134066
85-1-0.0698459897183009-0.930154010281699
861-0.08905657800943411.08905657800943
87-10.151052976579147-1.15105297657915
88-1-1.6979421232140.697942123214003
89-6-1.84582041266647-4.15417958733353
90102.989111674976557.01088832502345
91-4-0.885527228898571-3.11447277110143
9221.431003125471170.568996874528828
93-1-0.673398049428785-0.326601950571215
94-11.45410652773507-2.45410652773507
9530.1337995877849172.86620041221508
96-4-2.20232621845042-1.79767378154958
9722.02535657092658-0.0253565709265791
9830.1607665224549872.83923347754501
99-2-0.108977673338568-1.89102232666143
10020.3054625049726831.69453749502732
101-5-2.23799147653811-2.76200852346189
10242.105370769736261.89462923026374
103-5-0.810070646740258-4.18992935325974
1044-1.18351433338775.1835143333877
105-6-1.48720434503272-4.51279565496728
10673.337165268126873.66283473187313
107-1-1.92870384946740.928703849467399
108-5-0.393972028313219-4.60602797168678
1090-3.55652665687583.5565266568758
11054.760333562833840.23966643716616
111-4-0.613016979077642-3.38698302092236
11222.00790760671511-0.00790760671511212
1131-3.502826039545844.50282603954584
11442.480263011519851.51973698848015
115-21.50629407075658-3.50629407075658
116-2-2.958145824150820.958145824150821
11700.954275543899513-0.954275543899513
11801.26608133220412-1.26608133220412
1190-0.9924452524915610.992445252491561
120-2-1.65232171371701-0.347678286282987
12121.314216054662220.685783945337777
12211.48245890765453-0.482458907654535
12300.778553518888341-0.778553518888341
1240-1.338918156196151.33891815619615
125-20.238159376066893-2.23815937606689
12641.58456757352992.4154324264701
1270-0.999352670262910.99935267026291
1282-0.53445968892742.5344596889274
129-4-1.19556125759072-2.80443874240928
130-20.882593950966791-2.88259395096679
131-4-3.88479790748748-0.115202092512524
13264.661823788021531.33817621197847
1330-2.823007886168852.82300788616885
13401.7869308897476-1.7869308897476
1351-0.4695255071753991.4695255071754
136-5-4.41869881918169-0.581301180818308
13733.78558683111619-0.785586831116194
138-3-1.42694109942875-1.57305890057125
13942.319049249318841.68095075068116
140-2-0.571119241430001-1.42888075857
14120.7783833767649221.22161662323508
1421-0.44616341960751.4461634196075
143-20.921473236543071-2.92147323654307
1441-0.3725417069484061.37254170694841
14510.5755469850130750.424453014986925
1460-0.4372444186997310.437244418699731
147-5-1.19319231043693-3.80680768956307
14811.35974407270585-0.359744072705855
149-3-3.020277726071110.0202777260711108
15072.743841685912994.25615831408701
151-3-1.49438893716414-1.50561106283586
15232.380321430121030.619678569878974
153-4-0.989412159636902-3.0105878403631
154-3-2.71041834475289-0.289581655247108
15540.3268673930436793.67313260695632
15600.782606339451736-0.782606339451736
15710.4909441059759260.509055894024074
15851.659168939820613.34083106017939
159-60.909756461054098-6.9097564610541
160-1-3.503849615527362.50384961552736
16110.2864011307616580.713598869238342
1623NANA






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.2770211120287380.5540422240574770.722978887971262
90.1547616899375530.3095233798751070.845238310062447
100.09644961277801290.1928992255560260.903550387221987
110.4771675222088940.9543350444177880.522832477791106
120.979330593627610.041338812744780.02066940637239
130.9916862110070650.01662757798586980.00831378899293488
140.9884178311953280.02316433760934490.0115821688046725
150.9811672564558240.03766548708835190.018832743544176
160.9761976990725520.04760460185489670.0238023009274483
170.9627781885707510.07444362285849880.0372218114292494
180.9580505870015360.08389882599692770.0419494129984638
190.9792896930493970.04142061390120580.0207103069506029
200.9684910591636530.06301788167269440.0315089408363472
210.9800335429418170.03993291411636660.0199664570581833
220.9716608335427610.05667833291447790.0283391664572389
230.959384475690720.081231048618560.04061552430928
240.9529318579754450.09413628404911020.0470681420245551
250.9971244042651650.00575119146967030.00287559573483515
260.9998885277613340.0002229444773311960.000111472238665598
270.9998026385784430.000394722843113890.000197361421556945
280.9996599770886560.0006800458226886010.0003400229113443
290.9997166326709320.00056673465813510.00028336732906755
300.999717340205490.0005653195890204180.000282659794510209
310.9995387259056440.0009225481887125620.000461274094356281
320.9992828228566780.001434354286643770.000717177143321887
330.9991198794148090.001760241170382040.00088012058519102
340.9986147268366470.002770546326706180.00138527316335309
350.9980079660384390.003984067923121570.00199203396156079
360.9977527349101570.004494530179685640.00224726508984282
370.99719655006980.005606899860400490.00280344993020024
380.9961288110693370.007742377861325010.00387118893066251
390.9971141724503480.005771655099304120.00288582754965206
400.9960634681894010.007873063621197130.00393653181059857
410.9961068346663570.007786330667285350.00389316533364267
420.995019568830580.009960862338840310.00498043116942016
430.9933018145436820.01339637091263640.0066981854563182
440.991710874150290.01657825169941910.00828912584970953
450.9883789520910650.02324209581787080.0116210479089354
460.9871204032600540.0257591934798920.012879596739946
470.9824045703598140.03519085928037160.0175954296401858
480.9835406862676670.03291862746466530.0164593137323326
490.9919675924725560.01606481505488780.00803240752744388
500.9915072935707610.01698541285847860.0084927064292393
510.9882521597427490.02349568051450270.0117478402572514
520.9847676599921090.03046468001578160.0152323400078908
530.97995116373820.04009767252359990.0200488362617999
540.9735804008484430.05283919830311380.0264195991515569
550.9703992540040640.05920149199187250.0296007459959363
560.9630051685197210.07398966296055880.0369948314802794
570.9570774156779960.08584516864400850.0429225843220042
580.9575064740362320.08498705192753540.0424935259637677
590.9506016978605040.09879660427899140.0493983021394957
600.9392893863024910.1214212273950180.060710613697509
610.9248313822128480.1503372355743030.0751686177871516
620.9152162878176530.1695674243646940.084783712182347
630.9466486788169010.1067026423661990.0533513211830993
640.9328582937374330.1342834125251340.0671417062625668
650.9789867758584120.04202644828317670.0210132241415883
660.9819285640474990.03614287190500260.0180714359525013
670.9767604026585830.04647919468283360.0232395973414168
680.9822662475487790.03546750490244260.0177337524512213
690.9765325939841910.04693481203161830.0234674060158091
700.9702631033092680.05947379338146480.0297368966907324
710.9718854191796840.05622916164063290.0281145808203165
720.9728547614094590.05429047718108170.0271452385905409
730.9655072526281360.06898549474372710.0344927473718636
740.9604077186306260.07918456273874760.0395922813693738
750.9547151257669510.09056974846609860.0452848742330493
760.9585258885898810.08294822282023770.0414741114101188
770.9558246029238960.08835079415220830.0441753970761042
780.945007805882270.1099843882354590.0549921941177296
790.948473836174990.103052327650020.0515261638250102
800.9420263692910240.1159472614179530.0579736307089764
810.9367675696259850.126464860748030.063232430374015
820.9381546904850330.1236906190299340.0618453095149669
830.9312080093012110.1375839813975780.0687919906987888
840.916126351750240.167747296499520.0838736482497601
850.8989647676802860.2020704646394270.101035232319714
860.8803325796573070.2393348406853850.119667420342693
870.8596853643536620.2806292712926760.140314635646338
880.835164258738380.329671482523240.16483574126162
890.8705752041987080.2588495916025830.129424795801292
900.9609327036309230.07813459273815430.0390672963690772
910.9622230551461680.07555388970766420.0377769448538321
920.9519097562356940.09618048752861280.0480902437643064
930.9392229319067430.1215541361865140.060777068093257
940.9354204029469330.1291591941061350.0645795970530674
950.9373601277220010.1252797445559980.0626398722779988
960.9280824383786120.1438351232427750.0719175616213877
970.9101590259754310.1796819480491380.0898409740245689
980.9111198421068580.1777603157862840.0888801578931421
990.9003538741382290.1992922517235420.0996461258617711
1000.8878714003408160.2242571993183670.112128599659184
1010.8895103707433620.2209792585132760.110489629256638
1020.8763531957910730.2472936084178550.123646804208928
1030.9054683107287760.1890633785424490.0945316892712243
1040.94814031660110.1037193667977990.0518596833988997
1050.9675195657587360.0649608684825290.0324804342412645
1060.9763177049456280.04736459010874390.0236822950543719
1070.9693360840002880.06132783199942460.0306639159997123
1080.9832250047005830.03354999059883390.016774995299417
1090.9873774889208140.02524502215837190.012622511079186
1100.9825988942589460.03480221148210810.017401105741054
1110.9856967897279290.02860642054414280.0143032102720714
1120.9801265883169810.03974682336603710.0198734116830185
1130.9921155276688440.01576894466231160.00788447233115581
1140.9892217396883330.02155652062333350.0107782603116667
1150.9921015648812450.01579687023751070.00789843511875533
1160.9900642501841280.01987149963174480.00993574981587241
1170.9872187154778620.02556256904427630.0127812845221382
1180.9837519806820890.03249603863582230.0162480193179112
1190.9782217916316070.04355641673678660.0217782083683933
1200.9698595479714860.06028090405702860.0301404520285143
1210.9589025812304460.08219483753910780.0410974187695539
1220.9456270514528180.1087458970943640.0543729485471818
1230.9283237524195670.1433524951608660.071676247580433
1240.9114350939894080.1771298120211840.088564906010592
1250.9104813365455690.1790373269088620.0895186634544308
1260.9070316363315790.1859367273368420.0929683636684209
1270.9006523527441370.1986952945117260.0993476472558631
1280.8803213252770270.2393573494459470.119678674722973
1290.8725336794446270.2549326411107470.127466320555373
1300.8848859002208190.2302281995583620.115114099779181
1310.8503011683851730.2993976632296550.149698831614827
1320.8314668531813370.3370662936373250.168533146818663
1330.8133226509647360.3733546980705270.186677349035264
1340.779360403734640.441279192530720.22063959626536
1350.7326877031365210.5346245937269580.267312296863479
1360.6797139988948070.6405720022103850.320286001105193
1370.632965196666170.7340696066676590.36703480333383
1380.5764518094206260.8470963811587490.423548190579374
1390.5176678378871030.9646643242257950.482332162112897
1400.4836130134378150.967226026875630.516386986562185
1410.4671931529497840.9343863058995680.532806847050216
1420.39192801692850.7838560338570.6080719830715
1430.3946203741462710.7892407482925430.605379625853729
1440.3439761453001740.6879522906003480.656023854699826
1450.2686402074849560.5372804149699110.731359792515044
1460.2118734149215920.4237468298431830.788126585078408
1470.2900969757836870.5801939515673740.709903024216313
1480.2157713305036820.4315426610073630.784228669496318
1490.1497068174842630.2994136349685270.850293182515737
1500.1251777479866380.2503554959732760.874822252013362
1510.07672209359273140.1534441871854630.923277906407269
1520.046304133302650.09260826660530010.95369586669735
1530.03068070938504170.06136141877008340.969319290614958
1540.01256824456463510.02513648912927020.987431755435365

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 0.277021112028738 & 0.554042224057477 & 0.722978887971262 \tabularnewline
9 & 0.154761689937553 & 0.309523379875107 & 0.845238310062447 \tabularnewline
10 & 0.0964496127780129 & 0.192899225556026 & 0.903550387221987 \tabularnewline
11 & 0.477167522208894 & 0.954335044417788 & 0.522832477791106 \tabularnewline
12 & 0.97933059362761 & 0.04133881274478 & 0.02066940637239 \tabularnewline
13 & 0.991686211007065 & 0.0166275779858698 & 0.00831378899293488 \tabularnewline
14 & 0.988417831195328 & 0.0231643376093449 & 0.0115821688046725 \tabularnewline
15 & 0.981167256455824 & 0.0376654870883519 & 0.018832743544176 \tabularnewline
16 & 0.976197699072552 & 0.0476046018548967 & 0.0238023009274483 \tabularnewline
17 & 0.962778188570751 & 0.0744436228584988 & 0.0372218114292494 \tabularnewline
18 & 0.958050587001536 & 0.0838988259969277 & 0.0419494129984638 \tabularnewline
19 & 0.979289693049397 & 0.0414206139012058 & 0.0207103069506029 \tabularnewline
20 & 0.968491059163653 & 0.0630178816726944 & 0.0315089408363472 \tabularnewline
21 & 0.980033542941817 & 0.0399329141163666 & 0.0199664570581833 \tabularnewline
22 & 0.971660833542761 & 0.0566783329144779 & 0.0283391664572389 \tabularnewline
23 & 0.95938447569072 & 0.08123104861856 & 0.04061552430928 \tabularnewline
24 & 0.952931857975445 & 0.0941362840491102 & 0.0470681420245551 \tabularnewline
25 & 0.997124404265165 & 0.0057511914696703 & 0.00287559573483515 \tabularnewline
26 & 0.999888527761334 & 0.000222944477331196 & 0.000111472238665598 \tabularnewline
27 & 0.999802638578443 & 0.00039472284311389 & 0.000197361421556945 \tabularnewline
28 & 0.999659977088656 & 0.000680045822688601 & 0.0003400229113443 \tabularnewline
29 & 0.999716632670932 & 0.0005667346581351 & 0.00028336732906755 \tabularnewline
30 & 0.99971734020549 & 0.000565319589020418 & 0.000282659794510209 \tabularnewline
31 & 0.999538725905644 & 0.000922548188712562 & 0.000461274094356281 \tabularnewline
32 & 0.999282822856678 & 0.00143435428664377 & 0.000717177143321887 \tabularnewline
33 & 0.999119879414809 & 0.00176024117038204 & 0.00088012058519102 \tabularnewline
34 & 0.998614726836647 & 0.00277054632670618 & 0.00138527316335309 \tabularnewline
35 & 0.998007966038439 & 0.00398406792312157 & 0.00199203396156079 \tabularnewline
36 & 0.997752734910157 & 0.00449453017968564 & 0.00224726508984282 \tabularnewline
37 & 0.9971965500698 & 0.00560689986040049 & 0.00280344993020024 \tabularnewline
38 & 0.996128811069337 & 0.00774237786132501 & 0.00387118893066251 \tabularnewline
39 & 0.997114172450348 & 0.00577165509930412 & 0.00288582754965206 \tabularnewline
40 & 0.996063468189401 & 0.00787306362119713 & 0.00393653181059857 \tabularnewline
41 & 0.996106834666357 & 0.00778633066728535 & 0.00389316533364267 \tabularnewline
42 & 0.99501956883058 & 0.00996086233884031 & 0.00498043116942016 \tabularnewline
43 & 0.993301814543682 & 0.0133963709126364 & 0.0066981854563182 \tabularnewline
44 & 0.99171087415029 & 0.0165782516994191 & 0.00828912584970953 \tabularnewline
45 & 0.988378952091065 & 0.0232420958178708 & 0.0116210479089354 \tabularnewline
46 & 0.987120403260054 & 0.025759193479892 & 0.012879596739946 \tabularnewline
47 & 0.982404570359814 & 0.0351908592803716 & 0.0175954296401858 \tabularnewline
48 & 0.983540686267667 & 0.0329186274646653 & 0.0164593137323326 \tabularnewline
49 & 0.991967592472556 & 0.0160648150548878 & 0.00803240752744388 \tabularnewline
50 & 0.991507293570761 & 0.0169854128584786 & 0.0084927064292393 \tabularnewline
51 & 0.988252159742749 & 0.0234956805145027 & 0.0117478402572514 \tabularnewline
52 & 0.984767659992109 & 0.0304646800157816 & 0.0152323400078908 \tabularnewline
53 & 0.9799511637382 & 0.0400976725235999 & 0.0200488362617999 \tabularnewline
54 & 0.973580400848443 & 0.0528391983031138 & 0.0264195991515569 \tabularnewline
55 & 0.970399254004064 & 0.0592014919918725 & 0.0296007459959363 \tabularnewline
56 & 0.963005168519721 & 0.0739896629605588 & 0.0369948314802794 \tabularnewline
57 & 0.957077415677996 & 0.0858451686440085 & 0.0429225843220042 \tabularnewline
58 & 0.957506474036232 & 0.0849870519275354 & 0.0424935259637677 \tabularnewline
59 & 0.950601697860504 & 0.0987966042789914 & 0.0493983021394957 \tabularnewline
60 & 0.939289386302491 & 0.121421227395018 & 0.060710613697509 \tabularnewline
61 & 0.924831382212848 & 0.150337235574303 & 0.0751686177871516 \tabularnewline
62 & 0.915216287817653 & 0.169567424364694 & 0.084783712182347 \tabularnewline
63 & 0.946648678816901 & 0.106702642366199 & 0.0533513211830993 \tabularnewline
64 & 0.932858293737433 & 0.134283412525134 & 0.0671417062625668 \tabularnewline
65 & 0.978986775858412 & 0.0420264482831767 & 0.0210132241415883 \tabularnewline
66 & 0.981928564047499 & 0.0361428719050026 & 0.0180714359525013 \tabularnewline
67 & 0.976760402658583 & 0.0464791946828336 & 0.0232395973414168 \tabularnewline
68 & 0.982266247548779 & 0.0354675049024426 & 0.0177337524512213 \tabularnewline
69 & 0.976532593984191 & 0.0469348120316183 & 0.0234674060158091 \tabularnewline
70 & 0.970263103309268 & 0.0594737933814648 & 0.0297368966907324 \tabularnewline
71 & 0.971885419179684 & 0.0562291616406329 & 0.0281145808203165 \tabularnewline
72 & 0.972854761409459 & 0.0542904771810817 & 0.0271452385905409 \tabularnewline
73 & 0.965507252628136 & 0.0689854947437271 & 0.0344927473718636 \tabularnewline
74 & 0.960407718630626 & 0.0791845627387476 & 0.0395922813693738 \tabularnewline
75 & 0.954715125766951 & 0.0905697484660986 & 0.0452848742330493 \tabularnewline
76 & 0.958525888589881 & 0.0829482228202377 & 0.0414741114101188 \tabularnewline
77 & 0.955824602923896 & 0.0883507941522083 & 0.0441753970761042 \tabularnewline
78 & 0.94500780588227 & 0.109984388235459 & 0.0549921941177296 \tabularnewline
79 & 0.94847383617499 & 0.10305232765002 & 0.0515261638250102 \tabularnewline
80 & 0.942026369291024 & 0.115947261417953 & 0.0579736307089764 \tabularnewline
81 & 0.936767569625985 & 0.12646486074803 & 0.063232430374015 \tabularnewline
82 & 0.938154690485033 & 0.123690619029934 & 0.0618453095149669 \tabularnewline
83 & 0.931208009301211 & 0.137583981397578 & 0.0687919906987888 \tabularnewline
84 & 0.91612635175024 & 0.16774729649952 & 0.0838736482497601 \tabularnewline
85 & 0.898964767680286 & 0.202070464639427 & 0.101035232319714 \tabularnewline
86 & 0.880332579657307 & 0.239334840685385 & 0.119667420342693 \tabularnewline
87 & 0.859685364353662 & 0.280629271292676 & 0.140314635646338 \tabularnewline
88 & 0.83516425873838 & 0.32967148252324 & 0.16483574126162 \tabularnewline
89 & 0.870575204198708 & 0.258849591602583 & 0.129424795801292 \tabularnewline
90 & 0.960932703630923 & 0.0781345927381543 & 0.0390672963690772 \tabularnewline
91 & 0.962223055146168 & 0.0755538897076642 & 0.0377769448538321 \tabularnewline
92 & 0.951909756235694 & 0.0961804875286128 & 0.0480902437643064 \tabularnewline
93 & 0.939222931906743 & 0.121554136186514 & 0.060777068093257 \tabularnewline
94 & 0.935420402946933 & 0.129159194106135 & 0.0645795970530674 \tabularnewline
95 & 0.937360127722001 & 0.125279744555998 & 0.0626398722779988 \tabularnewline
96 & 0.928082438378612 & 0.143835123242775 & 0.0719175616213877 \tabularnewline
97 & 0.910159025975431 & 0.179681948049138 & 0.0898409740245689 \tabularnewline
98 & 0.911119842106858 & 0.177760315786284 & 0.0888801578931421 \tabularnewline
99 & 0.900353874138229 & 0.199292251723542 & 0.0996461258617711 \tabularnewline
100 & 0.887871400340816 & 0.224257199318367 & 0.112128599659184 \tabularnewline
101 & 0.889510370743362 & 0.220979258513276 & 0.110489629256638 \tabularnewline
102 & 0.876353195791073 & 0.247293608417855 & 0.123646804208928 \tabularnewline
103 & 0.905468310728776 & 0.189063378542449 & 0.0945316892712243 \tabularnewline
104 & 0.9481403166011 & 0.103719366797799 & 0.0518596833988997 \tabularnewline
105 & 0.967519565758736 & 0.064960868482529 & 0.0324804342412645 \tabularnewline
106 & 0.976317704945628 & 0.0473645901087439 & 0.0236822950543719 \tabularnewline
107 & 0.969336084000288 & 0.0613278319994246 & 0.0306639159997123 \tabularnewline
108 & 0.983225004700583 & 0.0335499905988339 & 0.016774995299417 \tabularnewline
109 & 0.987377488920814 & 0.0252450221583719 & 0.012622511079186 \tabularnewline
110 & 0.982598894258946 & 0.0348022114821081 & 0.017401105741054 \tabularnewline
111 & 0.985696789727929 & 0.0286064205441428 & 0.0143032102720714 \tabularnewline
112 & 0.980126588316981 & 0.0397468233660371 & 0.0198734116830185 \tabularnewline
113 & 0.992115527668844 & 0.0157689446623116 & 0.00788447233115581 \tabularnewline
114 & 0.989221739688333 & 0.0215565206233335 & 0.0107782603116667 \tabularnewline
115 & 0.992101564881245 & 0.0157968702375107 & 0.00789843511875533 \tabularnewline
116 & 0.990064250184128 & 0.0198714996317448 & 0.00993574981587241 \tabularnewline
117 & 0.987218715477862 & 0.0255625690442763 & 0.0127812845221382 \tabularnewline
118 & 0.983751980682089 & 0.0324960386358223 & 0.0162480193179112 \tabularnewline
119 & 0.978221791631607 & 0.0435564167367866 & 0.0217782083683933 \tabularnewline
120 & 0.969859547971486 & 0.0602809040570286 & 0.0301404520285143 \tabularnewline
121 & 0.958902581230446 & 0.0821948375391078 & 0.0410974187695539 \tabularnewline
122 & 0.945627051452818 & 0.108745897094364 & 0.0543729485471818 \tabularnewline
123 & 0.928323752419567 & 0.143352495160866 & 0.071676247580433 \tabularnewline
124 & 0.911435093989408 & 0.177129812021184 & 0.088564906010592 \tabularnewline
125 & 0.910481336545569 & 0.179037326908862 & 0.0895186634544308 \tabularnewline
126 & 0.907031636331579 & 0.185936727336842 & 0.0929683636684209 \tabularnewline
127 & 0.900652352744137 & 0.198695294511726 & 0.0993476472558631 \tabularnewline
128 & 0.880321325277027 & 0.239357349445947 & 0.119678674722973 \tabularnewline
129 & 0.872533679444627 & 0.254932641110747 & 0.127466320555373 \tabularnewline
130 & 0.884885900220819 & 0.230228199558362 & 0.115114099779181 \tabularnewline
131 & 0.850301168385173 & 0.299397663229655 & 0.149698831614827 \tabularnewline
132 & 0.831466853181337 & 0.337066293637325 & 0.168533146818663 \tabularnewline
133 & 0.813322650964736 & 0.373354698070527 & 0.186677349035264 \tabularnewline
134 & 0.77936040373464 & 0.44127919253072 & 0.22063959626536 \tabularnewline
135 & 0.732687703136521 & 0.534624593726958 & 0.267312296863479 \tabularnewline
136 & 0.679713998894807 & 0.640572002210385 & 0.320286001105193 \tabularnewline
137 & 0.63296519666617 & 0.734069606667659 & 0.36703480333383 \tabularnewline
138 & 0.576451809420626 & 0.847096381158749 & 0.423548190579374 \tabularnewline
139 & 0.517667837887103 & 0.964664324225795 & 0.482332162112897 \tabularnewline
140 & 0.483613013437815 & 0.96722602687563 & 0.516386986562185 \tabularnewline
141 & 0.467193152949784 & 0.934386305899568 & 0.532806847050216 \tabularnewline
142 & 0.3919280169285 & 0.783856033857 & 0.6080719830715 \tabularnewline
143 & 0.394620374146271 & 0.789240748292543 & 0.605379625853729 \tabularnewline
144 & 0.343976145300174 & 0.687952290600348 & 0.656023854699826 \tabularnewline
145 & 0.268640207484956 & 0.537280414969911 & 0.731359792515044 \tabularnewline
146 & 0.211873414921592 & 0.423746829843183 & 0.788126585078408 \tabularnewline
147 & 0.290096975783687 & 0.580193951567374 & 0.709903024216313 \tabularnewline
148 & 0.215771330503682 & 0.431542661007363 & 0.784228669496318 \tabularnewline
149 & 0.149706817484263 & 0.299413634968527 & 0.850293182515737 \tabularnewline
150 & 0.125177747986638 & 0.250355495973276 & 0.874822252013362 \tabularnewline
151 & 0.0767220935927314 & 0.153444187185463 & 0.923277906407269 \tabularnewline
152 & 0.04630413330265 & 0.0926082666053001 & 0.95369586669735 \tabularnewline
153 & 0.0306807093850417 & 0.0613614187700834 & 0.969319290614958 \tabularnewline
154 & 0.0125682445646351 & 0.0251364891292702 & 0.987431755435365 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147138&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]8[/C][C]0.277021112028738[/C][C]0.554042224057477[/C][C]0.722978887971262[/C][/ROW]
[ROW][C]9[/C][C]0.154761689937553[/C][C]0.309523379875107[/C][C]0.845238310062447[/C][/ROW]
[ROW][C]10[/C][C]0.0964496127780129[/C][C]0.192899225556026[/C][C]0.903550387221987[/C][/ROW]
[ROW][C]11[/C][C]0.477167522208894[/C][C]0.954335044417788[/C][C]0.522832477791106[/C][/ROW]
[ROW][C]12[/C][C]0.97933059362761[/C][C]0.04133881274478[/C][C]0.02066940637239[/C][/ROW]
[ROW][C]13[/C][C]0.991686211007065[/C][C]0.0166275779858698[/C][C]0.00831378899293488[/C][/ROW]
[ROW][C]14[/C][C]0.988417831195328[/C][C]0.0231643376093449[/C][C]0.0115821688046725[/C][/ROW]
[ROW][C]15[/C][C]0.981167256455824[/C][C]0.0376654870883519[/C][C]0.018832743544176[/C][/ROW]
[ROW][C]16[/C][C]0.976197699072552[/C][C]0.0476046018548967[/C][C]0.0238023009274483[/C][/ROW]
[ROW][C]17[/C][C]0.962778188570751[/C][C]0.0744436228584988[/C][C]0.0372218114292494[/C][/ROW]
[ROW][C]18[/C][C]0.958050587001536[/C][C]0.0838988259969277[/C][C]0.0419494129984638[/C][/ROW]
[ROW][C]19[/C][C]0.979289693049397[/C][C]0.0414206139012058[/C][C]0.0207103069506029[/C][/ROW]
[ROW][C]20[/C][C]0.968491059163653[/C][C]0.0630178816726944[/C][C]0.0315089408363472[/C][/ROW]
[ROW][C]21[/C][C]0.980033542941817[/C][C]0.0399329141163666[/C][C]0.0199664570581833[/C][/ROW]
[ROW][C]22[/C][C]0.971660833542761[/C][C]0.0566783329144779[/C][C]0.0283391664572389[/C][/ROW]
[ROW][C]23[/C][C]0.95938447569072[/C][C]0.08123104861856[/C][C]0.04061552430928[/C][/ROW]
[ROW][C]24[/C][C]0.952931857975445[/C][C]0.0941362840491102[/C][C]0.0470681420245551[/C][/ROW]
[ROW][C]25[/C][C]0.997124404265165[/C][C]0.0057511914696703[/C][C]0.00287559573483515[/C][/ROW]
[ROW][C]26[/C][C]0.999888527761334[/C][C]0.000222944477331196[/C][C]0.000111472238665598[/C][/ROW]
[ROW][C]27[/C][C]0.999802638578443[/C][C]0.00039472284311389[/C][C]0.000197361421556945[/C][/ROW]
[ROW][C]28[/C][C]0.999659977088656[/C][C]0.000680045822688601[/C][C]0.0003400229113443[/C][/ROW]
[ROW][C]29[/C][C]0.999716632670932[/C][C]0.0005667346581351[/C][C]0.00028336732906755[/C][/ROW]
[ROW][C]30[/C][C]0.99971734020549[/C][C]0.000565319589020418[/C][C]0.000282659794510209[/C][/ROW]
[ROW][C]31[/C][C]0.999538725905644[/C][C]0.000922548188712562[/C][C]0.000461274094356281[/C][/ROW]
[ROW][C]32[/C][C]0.999282822856678[/C][C]0.00143435428664377[/C][C]0.000717177143321887[/C][/ROW]
[ROW][C]33[/C][C]0.999119879414809[/C][C]0.00176024117038204[/C][C]0.00088012058519102[/C][/ROW]
[ROW][C]34[/C][C]0.998614726836647[/C][C]0.00277054632670618[/C][C]0.00138527316335309[/C][/ROW]
[ROW][C]35[/C][C]0.998007966038439[/C][C]0.00398406792312157[/C][C]0.00199203396156079[/C][/ROW]
[ROW][C]36[/C][C]0.997752734910157[/C][C]0.00449453017968564[/C][C]0.00224726508984282[/C][/ROW]
[ROW][C]37[/C][C]0.9971965500698[/C][C]0.00560689986040049[/C][C]0.00280344993020024[/C][/ROW]
[ROW][C]38[/C][C]0.996128811069337[/C][C]0.00774237786132501[/C][C]0.00387118893066251[/C][/ROW]
[ROW][C]39[/C][C]0.997114172450348[/C][C]0.00577165509930412[/C][C]0.00288582754965206[/C][/ROW]
[ROW][C]40[/C][C]0.996063468189401[/C][C]0.00787306362119713[/C][C]0.00393653181059857[/C][/ROW]
[ROW][C]41[/C][C]0.996106834666357[/C][C]0.00778633066728535[/C][C]0.00389316533364267[/C][/ROW]
[ROW][C]42[/C][C]0.99501956883058[/C][C]0.00996086233884031[/C][C]0.00498043116942016[/C][/ROW]
[ROW][C]43[/C][C]0.993301814543682[/C][C]0.0133963709126364[/C][C]0.0066981854563182[/C][/ROW]
[ROW][C]44[/C][C]0.99171087415029[/C][C]0.0165782516994191[/C][C]0.00828912584970953[/C][/ROW]
[ROW][C]45[/C][C]0.988378952091065[/C][C]0.0232420958178708[/C][C]0.0116210479089354[/C][/ROW]
[ROW][C]46[/C][C]0.987120403260054[/C][C]0.025759193479892[/C][C]0.012879596739946[/C][/ROW]
[ROW][C]47[/C][C]0.982404570359814[/C][C]0.0351908592803716[/C][C]0.0175954296401858[/C][/ROW]
[ROW][C]48[/C][C]0.983540686267667[/C][C]0.0329186274646653[/C][C]0.0164593137323326[/C][/ROW]
[ROW][C]49[/C][C]0.991967592472556[/C][C]0.0160648150548878[/C][C]0.00803240752744388[/C][/ROW]
[ROW][C]50[/C][C]0.991507293570761[/C][C]0.0169854128584786[/C][C]0.0084927064292393[/C][/ROW]
[ROW][C]51[/C][C]0.988252159742749[/C][C]0.0234956805145027[/C][C]0.0117478402572514[/C][/ROW]
[ROW][C]52[/C][C]0.984767659992109[/C][C]0.0304646800157816[/C][C]0.0152323400078908[/C][/ROW]
[ROW][C]53[/C][C]0.9799511637382[/C][C]0.0400976725235999[/C][C]0.0200488362617999[/C][/ROW]
[ROW][C]54[/C][C]0.973580400848443[/C][C]0.0528391983031138[/C][C]0.0264195991515569[/C][/ROW]
[ROW][C]55[/C][C]0.970399254004064[/C][C]0.0592014919918725[/C][C]0.0296007459959363[/C][/ROW]
[ROW][C]56[/C][C]0.963005168519721[/C][C]0.0739896629605588[/C][C]0.0369948314802794[/C][/ROW]
[ROW][C]57[/C][C]0.957077415677996[/C][C]0.0858451686440085[/C][C]0.0429225843220042[/C][/ROW]
[ROW][C]58[/C][C]0.957506474036232[/C][C]0.0849870519275354[/C][C]0.0424935259637677[/C][/ROW]
[ROW][C]59[/C][C]0.950601697860504[/C][C]0.0987966042789914[/C][C]0.0493983021394957[/C][/ROW]
[ROW][C]60[/C][C]0.939289386302491[/C][C]0.121421227395018[/C][C]0.060710613697509[/C][/ROW]
[ROW][C]61[/C][C]0.924831382212848[/C][C]0.150337235574303[/C][C]0.0751686177871516[/C][/ROW]
[ROW][C]62[/C][C]0.915216287817653[/C][C]0.169567424364694[/C][C]0.084783712182347[/C][/ROW]
[ROW][C]63[/C][C]0.946648678816901[/C][C]0.106702642366199[/C][C]0.0533513211830993[/C][/ROW]
[ROW][C]64[/C][C]0.932858293737433[/C][C]0.134283412525134[/C][C]0.0671417062625668[/C][/ROW]
[ROW][C]65[/C][C]0.978986775858412[/C][C]0.0420264482831767[/C][C]0.0210132241415883[/C][/ROW]
[ROW][C]66[/C][C]0.981928564047499[/C][C]0.0361428719050026[/C][C]0.0180714359525013[/C][/ROW]
[ROW][C]67[/C][C]0.976760402658583[/C][C]0.0464791946828336[/C][C]0.0232395973414168[/C][/ROW]
[ROW][C]68[/C][C]0.982266247548779[/C][C]0.0354675049024426[/C][C]0.0177337524512213[/C][/ROW]
[ROW][C]69[/C][C]0.976532593984191[/C][C]0.0469348120316183[/C][C]0.0234674060158091[/C][/ROW]
[ROW][C]70[/C][C]0.970263103309268[/C][C]0.0594737933814648[/C][C]0.0297368966907324[/C][/ROW]
[ROW][C]71[/C][C]0.971885419179684[/C][C]0.0562291616406329[/C][C]0.0281145808203165[/C][/ROW]
[ROW][C]72[/C][C]0.972854761409459[/C][C]0.0542904771810817[/C][C]0.0271452385905409[/C][/ROW]
[ROW][C]73[/C][C]0.965507252628136[/C][C]0.0689854947437271[/C][C]0.0344927473718636[/C][/ROW]
[ROW][C]74[/C][C]0.960407718630626[/C][C]0.0791845627387476[/C][C]0.0395922813693738[/C][/ROW]
[ROW][C]75[/C][C]0.954715125766951[/C][C]0.0905697484660986[/C][C]0.0452848742330493[/C][/ROW]
[ROW][C]76[/C][C]0.958525888589881[/C][C]0.0829482228202377[/C][C]0.0414741114101188[/C][/ROW]
[ROW][C]77[/C][C]0.955824602923896[/C][C]0.0883507941522083[/C][C]0.0441753970761042[/C][/ROW]
[ROW][C]78[/C][C]0.94500780588227[/C][C]0.109984388235459[/C][C]0.0549921941177296[/C][/ROW]
[ROW][C]79[/C][C]0.94847383617499[/C][C]0.10305232765002[/C][C]0.0515261638250102[/C][/ROW]
[ROW][C]80[/C][C]0.942026369291024[/C][C]0.115947261417953[/C][C]0.0579736307089764[/C][/ROW]
[ROW][C]81[/C][C]0.936767569625985[/C][C]0.12646486074803[/C][C]0.063232430374015[/C][/ROW]
[ROW][C]82[/C][C]0.938154690485033[/C][C]0.123690619029934[/C][C]0.0618453095149669[/C][/ROW]
[ROW][C]83[/C][C]0.931208009301211[/C][C]0.137583981397578[/C][C]0.0687919906987888[/C][/ROW]
[ROW][C]84[/C][C]0.91612635175024[/C][C]0.16774729649952[/C][C]0.0838736482497601[/C][/ROW]
[ROW][C]85[/C][C]0.898964767680286[/C][C]0.202070464639427[/C][C]0.101035232319714[/C][/ROW]
[ROW][C]86[/C][C]0.880332579657307[/C][C]0.239334840685385[/C][C]0.119667420342693[/C][/ROW]
[ROW][C]87[/C][C]0.859685364353662[/C][C]0.280629271292676[/C][C]0.140314635646338[/C][/ROW]
[ROW][C]88[/C][C]0.83516425873838[/C][C]0.32967148252324[/C][C]0.16483574126162[/C][/ROW]
[ROW][C]89[/C][C]0.870575204198708[/C][C]0.258849591602583[/C][C]0.129424795801292[/C][/ROW]
[ROW][C]90[/C][C]0.960932703630923[/C][C]0.0781345927381543[/C][C]0.0390672963690772[/C][/ROW]
[ROW][C]91[/C][C]0.962223055146168[/C][C]0.0755538897076642[/C][C]0.0377769448538321[/C][/ROW]
[ROW][C]92[/C][C]0.951909756235694[/C][C]0.0961804875286128[/C][C]0.0480902437643064[/C][/ROW]
[ROW][C]93[/C][C]0.939222931906743[/C][C]0.121554136186514[/C][C]0.060777068093257[/C][/ROW]
[ROW][C]94[/C][C]0.935420402946933[/C][C]0.129159194106135[/C][C]0.0645795970530674[/C][/ROW]
[ROW][C]95[/C][C]0.937360127722001[/C][C]0.125279744555998[/C][C]0.0626398722779988[/C][/ROW]
[ROW][C]96[/C][C]0.928082438378612[/C][C]0.143835123242775[/C][C]0.0719175616213877[/C][/ROW]
[ROW][C]97[/C][C]0.910159025975431[/C][C]0.179681948049138[/C][C]0.0898409740245689[/C][/ROW]
[ROW][C]98[/C][C]0.911119842106858[/C][C]0.177760315786284[/C][C]0.0888801578931421[/C][/ROW]
[ROW][C]99[/C][C]0.900353874138229[/C][C]0.199292251723542[/C][C]0.0996461258617711[/C][/ROW]
[ROW][C]100[/C][C]0.887871400340816[/C][C]0.224257199318367[/C][C]0.112128599659184[/C][/ROW]
[ROW][C]101[/C][C]0.889510370743362[/C][C]0.220979258513276[/C][C]0.110489629256638[/C][/ROW]
[ROW][C]102[/C][C]0.876353195791073[/C][C]0.247293608417855[/C][C]0.123646804208928[/C][/ROW]
[ROW][C]103[/C][C]0.905468310728776[/C][C]0.189063378542449[/C][C]0.0945316892712243[/C][/ROW]
[ROW][C]104[/C][C]0.9481403166011[/C][C]0.103719366797799[/C][C]0.0518596833988997[/C][/ROW]
[ROW][C]105[/C][C]0.967519565758736[/C][C]0.064960868482529[/C][C]0.0324804342412645[/C][/ROW]
[ROW][C]106[/C][C]0.976317704945628[/C][C]0.0473645901087439[/C][C]0.0236822950543719[/C][/ROW]
[ROW][C]107[/C][C]0.969336084000288[/C][C]0.0613278319994246[/C][C]0.0306639159997123[/C][/ROW]
[ROW][C]108[/C][C]0.983225004700583[/C][C]0.0335499905988339[/C][C]0.016774995299417[/C][/ROW]
[ROW][C]109[/C][C]0.987377488920814[/C][C]0.0252450221583719[/C][C]0.012622511079186[/C][/ROW]
[ROW][C]110[/C][C]0.982598894258946[/C][C]0.0348022114821081[/C][C]0.017401105741054[/C][/ROW]
[ROW][C]111[/C][C]0.985696789727929[/C][C]0.0286064205441428[/C][C]0.0143032102720714[/C][/ROW]
[ROW][C]112[/C][C]0.980126588316981[/C][C]0.0397468233660371[/C][C]0.0198734116830185[/C][/ROW]
[ROW][C]113[/C][C]0.992115527668844[/C][C]0.0157689446623116[/C][C]0.00788447233115581[/C][/ROW]
[ROW][C]114[/C][C]0.989221739688333[/C][C]0.0215565206233335[/C][C]0.0107782603116667[/C][/ROW]
[ROW][C]115[/C][C]0.992101564881245[/C][C]0.0157968702375107[/C][C]0.00789843511875533[/C][/ROW]
[ROW][C]116[/C][C]0.990064250184128[/C][C]0.0198714996317448[/C][C]0.00993574981587241[/C][/ROW]
[ROW][C]117[/C][C]0.987218715477862[/C][C]0.0255625690442763[/C][C]0.0127812845221382[/C][/ROW]
[ROW][C]118[/C][C]0.983751980682089[/C][C]0.0324960386358223[/C][C]0.0162480193179112[/C][/ROW]
[ROW][C]119[/C][C]0.978221791631607[/C][C]0.0435564167367866[/C][C]0.0217782083683933[/C][/ROW]
[ROW][C]120[/C][C]0.969859547971486[/C][C]0.0602809040570286[/C][C]0.0301404520285143[/C][/ROW]
[ROW][C]121[/C][C]0.958902581230446[/C][C]0.0821948375391078[/C][C]0.0410974187695539[/C][/ROW]
[ROW][C]122[/C][C]0.945627051452818[/C][C]0.108745897094364[/C][C]0.0543729485471818[/C][/ROW]
[ROW][C]123[/C][C]0.928323752419567[/C][C]0.143352495160866[/C][C]0.071676247580433[/C][/ROW]
[ROW][C]124[/C][C]0.911435093989408[/C][C]0.177129812021184[/C][C]0.088564906010592[/C][/ROW]
[ROW][C]125[/C][C]0.910481336545569[/C][C]0.179037326908862[/C][C]0.0895186634544308[/C][/ROW]
[ROW][C]126[/C][C]0.907031636331579[/C][C]0.185936727336842[/C][C]0.0929683636684209[/C][/ROW]
[ROW][C]127[/C][C]0.900652352744137[/C][C]0.198695294511726[/C][C]0.0993476472558631[/C][/ROW]
[ROW][C]128[/C][C]0.880321325277027[/C][C]0.239357349445947[/C][C]0.119678674722973[/C][/ROW]
[ROW][C]129[/C][C]0.872533679444627[/C][C]0.254932641110747[/C][C]0.127466320555373[/C][/ROW]
[ROW][C]130[/C][C]0.884885900220819[/C][C]0.230228199558362[/C][C]0.115114099779181[/C][/ROW]
[ROW][C]131[/C][C]0.850301168385173[/C][C]0.299397663229655[/C][C]0.149698831614827[/C][/ROW]
[ROW][C]132[/C][C]0.831466853181337[/C][C]0.337066293637325[/C][C]0.168533146818663[/C][/ROW]
[ROW][C]133[/C][C]0.813322650964736[/C][C]0.373354698070527[/C][C]0.186677349035264[/C][/ROW]
[ROW][C]134[/C][C]0.77936040373464[/C][C]0.44127919253072[/C][C]0.22063959626536[/C][/ROW]
[ROW][C]135[/C][C]0.732687703136521[/C][C]0.534624593726958[/C][C]0.267312296863479[/C][/ROW]
[ROW][C]136[/C][C]0.679713998894807[/C][C]0.640572002210385[/C][C]0.320286001105193[/C][/ROW]
[ROW][C]137[/C][C]0.63296519666617[/C][C]0.734069606667659[/C][C]0.36703480333383[/C][/ROW]
[ROW][C]138[/C][C]0.576451809420626[/C][C]0.847096381158749[/C][C]0.423548190579374[/C][/ROW]
[ROW][C]139[/C][C]0.517667837887103[/C][C]0.964664324225795[/C][C]0.482332162112897[/C][/ROW]
[ROW][C]140[/C][C]0.483613013437815[/C][C]0.96722602687563[/C][C]0.516386986562185[/C][/ROW]
[ROW][C]141[/C][C]0.467193152949784[/C][C]0.934386305899568[/C][C]0.532806847050216[/C][/ROW]
[ROW][C]142[/C][C]0.3919280169285[/C][C]0.783856033857[/C][C]0.6080719830715[/C][/ROW]
[ROW][C]143[/C][C]0.394620374146271[/C][C]0.789240748292543[/C][C]0.605379625853729[/C][/ROW]
[ROW][C]144[/C][C]0.343976145300174[/C][C]0.687952290600348[/C][C]0.656023854699826[/C][/ROW]
[ROW][C]145[/C][C]0.268640207484956[/C][C]0.537280414969911[/C][C]0.731359792515044[/C][/ROW]
[ROW][C]146[/C][C]0.211873414921592[/C][C]0.423746829843183[/C][C]0.788126585078408[/C][/ROW]
[ROW][C]147[/C][C]0.290096975783687[/C][C]0.580193951567374[/C][C]0.709903024216313[/C][/ROW]
[ROW][C]148[/C][C]0.215771330503682[/C][C]0.431542661007363[/C][C]0.784228669496318[/C][/ROW]
[ROW][C]149[/C][C]0.149706817484263[/C][C]0.299413634968527[/C][C]0.850293182515737[/C][/ROW]
[ROW][C]150[/C][C]0.125177747986638[/C][C]0.250355495973276[/C][C]0.874822252013362[/C][/ROW]
[ROW][C]151[/C][C]0.0767220935927314[/C][C]0.153444187185463[/C][C]0.923277906407269[/C][/ROW]
[ROW][C]152[/C][C]0.04630413330265[/C][C]0.0926082666053001[/C][C]0.95369586669735[/C][/ROW]
[ROW][C]153[/C][C]0.0306807093850417[/C][C]0.0613614187700834[/C][C]0.969319290614958[/C][/ROW]
[ROW][C]154[/C][C]0.0125682445646351[/C][C]0.0251364891292702[/C][C]0.987431755435365[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147138&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147138&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
80.2770211120287380.5540422240574770.722978887971262
90.1547616899375530.3095233798751070.845238310062447
100.09644961277801290.1928992255560260.903550387221987
110.4771675222088940.9543350444177880.522832477791106
120.979330593627610.041338812744780.02066940637239
130.9916862110070650.01662757798586980.00831378899293488
140.9884178311953280.02316433760934490.0115821688046725
150.9811672564558240.03766548708835190.018832743544176
160.9761976990725520.04760460185489670.0238023009274483
170.9627781885707510.07444362285849880.0372218114292494
180.9580505870015360.08389882599692770.0419494129984638
190.9792896930493970.04142061390120580.0207103069506029
200.9684910591636530.06301788167269440.0315089408363472
210.9800335429418170.03993291411636660.0199664570581833
220.9716608335427610.05667833291447790.0283391664572389
230.959384475690720.081231048618560.04061552430928
240.9529318579754450.09413628404911020.0470681420245551
250.9971244042651650.00575119146967030.00287559573483515
260.9998885277613340.0002229444773311960.000111472238665598
270.9998026385784430.000394722843113890.000197361421556945
280.9996599770886560.0006800458226886010.0003400229113443
290.9997166326709320.00056673465813510.00028336732906755
300.999717340205490.0005653195890204180.000282659794510209
310.9995387259056440.0009225481887125620.000461274094356281
320.9992828228566780.001434354286643770.000717177143321887
330.9991198794148090.001760241170382040.00088012058519102
340.9986147268366470.002770546326706180.00138527316335309
350.9980079660384390.003984067923121570.00199203396156079
360.9977527349101570.004494530179685640.00224726508984282
370.99719655006980.005606899860400490.00280344993020024
380.9961288110693370.007742377861325010.00387118893066251
390.9971141724503480.005771655099304120.00288582754965206
400.9960634681894010.007873063621197130.00393653181059857
410.9961068346663570.007786330667285350.00389316533364267
420.995019568830580.009960862338840310.00498043116942016
430.9933018145436820.01339637091263640.0066981854563182
440.991710874150290.01657825169941910.00828912584970953
450.9883789520910650.02324209581787080.0116210479089354
460.9871204032600540.0257591934798920.012879596739946
470.9824045703598140.03519085928037160.0175954296401858
480.9835406862676670.03291862746466530.0164593137323326
490.9919675924725560.01606481505488780.00803240752744388
500.9915072935707610.01698541285847860.0084927064292393
510.9882521597427490.02349568051450270.0117478402572514
520.9847676599921090.03046468001578160.0152323400078908
530.97995116373820.04009767252359990.0200488362617999
540.9735804008484430.05283919830311380.0264195991515569
550.9703992540040640.05920149199187250.0296007459959363
560.9630051685197210.07398966296055880.0369948314802794
570.9570774156779960.08584516864400850.0429225843220042
580.9575064740362320.08498705192753540.0424935259637677
590.9506016978605040.09879660427899140.0493983021394957
600.9392893863024910.1214212273950180.060710613697509
610.9248313822128480.1503372355743030.0751686177871516
620.9152162878176530.1695674243646940.084783712182347
630.9466486788169010.1067026423661990.0533513211830993
640.9328582937374330.1342834125251340.0671417062625668
650.9789867758584120.04202644828317670.0210132241415883
660.9819285640474990.03614287190500260.0180714359525013
670.9767604026585830.04647919468283360.0232395973414168
680.9822662475487790.03546750490244260.0177337524512213
690.9765325939841910.04693481203161830.0234674060158091
700.9702631033092680.05947379338146480.0297368966907324
710.9718854191796840.05622916164063290.0281145808203165
720.9728547614094590.05429047718108170.0271452385905409
730.9655072526281360.06898549474372710.0344927473718636
740.9604077186306260.07918456273874760.0395922813693738
750.9547151257669510.09056974846609860.0452848742330493
760.9585258885898810.08294822282023770.0414741114101188
770.9558246029238960.08835079415220830.0441753970761042
780.945007805882270.1099843882354590.0549921941177296
790.948473836174990.103052327650020.0515261638250102
800.9420263692910240.1159472614179530.0579736307089764
810.9367675696259850.126464860748030.063232430374015
820.9381546904850330.1236906190299340.0618453095149669
830.9312080093012110.1375839813975780.0687919906987888
840.916126351750240.167747296499520.0838736482497601
850.8989647676802860.2020704646394270.101035232319714
860.8803325796573070.2393348406853850.119667420342693
870.8596853643536620.2806292712926760.140314635646338
880.835164258738380.329671482523240.16483574126162
890.8705752041987080.2588495916025830.129424795801292
900.9609327036309230.07813459273815430.0390672963690772
910.9622230551461680.07555388970766420.0377769448538321
920.9519097562356940.09618048752861280.0480902437643064
930.9392229319067430.1215541361865140.060777068093257
940.9354204029469330.1291591941061350.0645795970530674
950.9373601277220010.1252797445559980.0626398722779988
960.9280824383786120.1438351232427750.0719175616213877
970.9101590259754310.1796819480491380.0898409740245689
980.9111198421068580.1777603157862840.0888801578931421
990.9003538741382290.1992922517235420.0996461258617711
1000.8878714003408160.2242571993183670.112128599659184
1010.8895103707433620.2209792585132760.110489629256638
1020.8763531957910730.2472936084178550.123646804208928
1030.9054683107287760.1890633785424490.0945316892712243
1040.94814031660110.1037193667977990.0518596833988997
1050.9675195657587360.0649608684825290.0324804342412645
1060.9763177049456280.04736459010874390.0236822950543719
1070.9693360840002880.06132783199942460.0306639159997123
1080.9832250047005830.03354999059883390.016774995299417
1090.9873774889208140.02524502215837190.012622511079186
1100.9825988942589460.03480221148210810.017401105741054
1110.9856967897279290.02860642054414280.0143032102720714
1120.9801265883169810.03974682336603710.0198734116830185
1130.9921155276688440.01576894466231160.00788447233115581
1140.9892217396883330.02155652062333350.0107782603116667
1150.9921015648812450.01579687023751070.00789843511875533
1160.9900642501841280.01987149963174480.00993574981587241
1170.9872187154778620.02556256904427630.0127812845221382
1180.9837519806820890.03249603863582230.0162480193179112
1190.9782217916316070.04355641673678660.0217782083683933
1200.9698595479714860.06028090405702860.0301404520285143
1210.9589025812304460.08219483753910780.0410974187695539
1220.9456270514528180.1087458970943640.0543729485471818
1230.9283237524195670.1433524951608660.071676247580433
1240.9114350939894080.1771298120211840.088564906010592
1250.9104813365455690.1790373269088620.0895186634544308
1260.9070316363315790.1859367273368420.0929683636684209
1270.9006523527441370.1986952945117260.0993476472558631
1280.8803213252770270.2393573494459470.119678674722973
1290.8725336794446270.2549326411107470.127466320555373
1300.8848859002208190.2302281995583620.115114099779181
1310.8503011683851730.2993976632296550.149698831614827
1320.8314668531813370.3370662936373250.168533146818663
1330.8133226509647360.3733546980705270.186677349035264
1340.779360403734640.441279192530720.22063959626536
1350.7326877031365210.5346245937269580.267312296863479
1360.6797139988948070.6405720022103850.320286001105193
1370.632965196666170.7340696066676590.36703480333383
1380.5764518094206260.8470963811587490.423548190579374
1390.5176678378871030.9646643242257950.482332162112897
1400.4836130134378150.967226026875630.516386986562185
1410.4671931529497840.9343863058995680.532806847050216
1420.39192801692850.7838560338570.6080719830715
1430.3946203741462710.7892407482925430.605379625853729
1440.3439761453001740.6879522906003480.656023854699826
1450.2686402074849560.5372804149699110.731359792515044
1460.2118734149215920.4237468298431830.788126585078408
1470.2900969757836870.5801939515673740.709903024216313
1480.2157713305036820.4315426610073630.784228669496318
1490.1497068174842630.2994136349685270.850293182515737
1500.1251777479866380.2503554959732760.874822252013362
1510.07672209359273140.1534441871854630.923277906407269
1520.046304133302650.09260826660530010.95369586669735
1530.03068070938504170.06136141877008340.969319290614958
1540.01256824456463510.02513648912927020.987431755435365






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level180.122448979591837NOK
5% type I error level550.374149659863946NOK
10% type I error level840.571428571428571NOK

\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 & 18 & 0.122448979591837 & NOK \tabularnewline
5% type I error level & 55 & 0.374149659863946 & NOK \tabularnewline
10% type I error level & 84 & 0.571428571428571 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147138&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]18[/C][C]0.122448979591837[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]55[/C][C]0.374149659863946[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]84[/C][C]0.571428571428571[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147138&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147138&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 level180.122448979591837NOK
5% type I error level550.374149659863946NOK
10% type I error level840.571428571428571NOK


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 4
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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 = First Differences ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = First Differences ;
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')
}