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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 16:28:34 -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/t1322170148vhqv4n1or9oupvc.htm/, Retrieved Fri, 19 Apr 2024 03:19:10 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=147219, Retrieved Fri, 19 Apr 2024 03:19:10 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact101

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
-   PD  [Multiple Regression] [workshop 7] [2010-11-21 17:50:24] [65eb19f81eab2b6e672eafaed2a27190]
-   PD    [Multiple Regression] [workshop 7] [2010-11-21 18:15:20] [65eb19f81eab2b6e672eafaed2a27190]
F   P       [Multiple Regression] [WS7 Celebrity] [2010-11-21 18:23:11] [65eb19f81eab2b6e672eafaed2a27190]
-   PD        [Multiple Regression] [Workshop 7 - Doub...] [2010-11-22 17:46:53] [1429a1a14191a86916b95357f6de790b]
-    D          [Multiple Regression] [Workshop 7 - Doub...] [2010-11-22 18:43:58] [1429a1a14191a86916b95357f6de790b]
-   PD              [Multiple Regression] [WS7 Computation 2] [2011-11-24 21:28:34] [ef8d8c90df4ff8d053d0205bd6ba250c] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
12	13	12	53	41	38
11	16	11	86	39	32
14	19	15	66	30	35
12	15	6	67	31	33
21	14	13	76	34	37
12	13	10	78	35	29
22	19	12	53	39	31
11	15	14	80	34	36
10	14	12	74	36	35
13	15	6	76	37	38
10	16	10	79	38	31
8	16	12	54	36	34
15	16	12	67	38	35
14	16	11	54	39	38
10	17	15	87	33	37
14	15	12	58	32	33
14	15	10	75	36	32
11	20	12	88	38	38
10	18	11	64	39	38
13	16	12	57	32	32
7	16	11	66	32	33
14	16	12	68	31	31
12	19	13	54	39	38
14	16	11	56	37	39
11	17	9	86	39	32
9	17	13	80	41	32
11	16	10	76	36	35
15	15	14	69	33	37
14	16	12	78	33	33
13	14	10	67	34	33
9	15	12	80	31	28
15	12	8	54	27	32
10	14	10	71	37	31
11	16	12	84	34	37
13	14	12	74	34	30
8	7	7	71	32	33
20	10	6	63	29	31
12	14	12	71	36	33
10	16	10	76	29	31
10	16	10	69	35	33
9	16	10	74	37	32
14	14	12	75	34	33
8	20	15	54	38	32
14	14	10	52	35	33
11	14	10	69	38	28
13	11	12	68	37	35
9	14	13	65	38	39
11	15	11	75	33	34
15	16	11	74	36	38
11	14	12	75	38	32
10	16	14	72	32	38
14	14	10	67	32	30
18	12	12	63	32	33
14	16	13	62	34	38
11	9	5	63	32	32
12	14	6	76	37	32
13	16	12	74	39	34
9	16	12	67	29	34
10	15	11	73	37	36
15	16	10	70	35	34
20	12	7	53	30	28
12	16	12	77	38	34
12	16	14	77	34	35
14	14	11	52	31	35
13	16	12	54	34	31
11	17	13	80	35	37
17	18	14	66	36	35
12	18	11	73	30	27
13	12	12	63	39	40
14	16	12	69	35	37
13	10	8	67	38	36
15	14	11	54	31	38
13	18	14	81	34	39
10	18	14	69	38	41
11	16	12	84	34	27
19	17	9	80	39	30
13	16	13	70	37	37
17	16	11	69	34	31
13	13	12	77	28	31
9	16	12	54	37	27
11	16	12	79	33	36
10	20	12	30	37	38
9	16	12	71	35	37
12	15	12	73	37	33
12	15	11	72	32	34
13	16	10	77	33	31
13	14	9	75	38	39
12	16	12	69	33	34
15	16	12	54	29	32
22	15	12	70	33	33
13	12	9	73	31	36
15	17	15	54	36	32
13	16	12	77	35	41
15	15	12	82	32	28
10	13	12	80	29	30
11	16	10	80	39	36
16	16	13	69	37	35
11	16	9	78	35	31
11	16	12	81	37	34
10	14	10	76	32	36
10	16	14	76	38	36
16	16	11	73	37	35
12	20	15	85	36	37
11	15	11	66	32	28
16	16	11	79	33	39
19	13	12	68	40	32
11	17	12	76	38	35
16	16	12	71	41	39
15	16	11	54	36	35
24	12	7	46	43	42
14	16	12	82	30	34
15	16	14	74	31	33
11	17	11	88	32	41
15	13	11	38	32	33
12	12	10	76	37	34
10	18	13	86	37	32
14	14	13	54	33	40
13	14	8	70	34	40
9	13	11	69	33	35
15	16	12	90	38	36
15	13	11	54	33	37
14	16	13	76	31	27
11	13	12	89	38	39
8	16	14	76	37	38
11	15	13	73	33	31
11	16	15	79	31	33
8	15	10	90	39	32
10	17	11	74	44	39
11	15	9	81	33	36
13	12	11	72	35	33
11	16	10	71	32	33
20	10	11	66	28	32
10	16	8	77	40	37
15	12	11	65	27	30
12	14	12	74	37	38
14	15	12	82	32	29
23	13	9	54	28	22
14	15	11	63	34	35
16	11	10	54	30	35
11	12	8	64	35	34
12	8	9	69	31	35
10	16	8	54	32	34
14	15	9	84	30	34
12	17	15	86	30	35
12	16	11	77	31	23
11	10	8	89	40	31
12	18	13	76	32	27
13	13	12	60	36	36
11	16	12	75	32	31
19	13	9	73	35	32
12	10	7	85	38	39
17	15	13	79	42	37
9	16	9	71	34	38
12	16	6	72	35	39
19	14	8	69	35	34
18	10	8	78	33	31
15	17	15	54	36	32
14	13	6	69	32	37
11	15	9	81	33	36
9	16	11	84	34	32
18	12	8	84	32	35
16	13	8	69	34	36

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 6 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147219&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]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147219&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147219&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 time6 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net






Multiple Linear Regression - Estimated Regression Equation
CESDS[t] = + 25.3479105408615 -0.264708357488973PCL[t] -0.0240263804038775PCC[t] -0.0752023466977504PBS[t] -0.0362534889626793ATC[t] -0.0478757771978142ATS[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
CESDS[t] =  +  25.3479105408615 -0.264708357488973PCL[t] -0.0240263804038775PCC[t] -0.0752023466977504PBS[t] -0.0362534889626793ATC[t] -0.0478757771978142ATS[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147219&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]CESDS[t] =  +  25.3479105408615 -0.264708357488973PCL[t] -0.0240263804038775PCC[t] -0.0752023466977504PBS[t] -0.0362534889626793ATC[t] -0.0478757771978142ATS[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147219&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147219&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
CESDS[t] = + 25.3479105408615 -0.264708357488973PCL[t] -0.0240263804038775PCC[t] -0.0752023466977504PBS[t] -0.0362534889626793ATC[t] -0.0478757771978142ATS[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)25.34791054086153.3913837.474200
PCL-0.2647083574889730.128751-2.0560.0414520.020726
PCC-0.02402638040387750.13264-0.18110.8564930.428247
PBS-0.07520234669775040.022285-3.37460.0009330.000466
ATC-0.03625348896267930.077621-0.46710.6411110.320556
ATS-0.04787577719781420.072038-0.66460.5072930.253646

\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) & 25.3479105408615 & 3.391383 & 7.4742 & 0 & 0 \tabularnewline
PCL & -0.264708357488973 & 0.128751 & -2.056 & 0.041452 & 0.020726 \tabularnewline
PCC & -0.0240263804038775 & 0.13264 & -0.1811 & 0.856493 & 0.428247 \tabularnewline
PBS & -0.0752023466977504 & 0.022285 & -3.3746 & 0.000933 & 0.000466 \tabularnewline
ATC & -0.0362534889626793 & 0.077621 & -0.4671 & 0.641111 & 0.320556 \tabularnewline
ATS & -0.0478757771978142 & 0.072038 & -0.6646 & 0.507293 & 0.253646 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147219&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]25.3479105408615[/C][C]3.391383[/C][C]7.4742[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]PCL[/C][C]-0.264708357488973[/C][C]0.128751[/C][C]-2.056[/C][C]0.041452[/C][C]0.020726[/C][/ROW]
[ROW][C]PCC[/C][C]-0.0240263804038775[/C][C]0.13264[/C][C]-0.1811[/C][C]0.856493[/C][C]0.428247[/C][/ROW]
[ROW][C]PBS[/C][C]-0.0752023466977504[/C][C]0.022285[/C][C]-3.3746[/C][C]0.000933[/C][C]0.000466[/C][/ROW]
[ROW][C]ATC[/C][C]-0.0362534889626793[/C][C]0.077621[/C][C]-0.4671[/C][C]0.641111[/C][C]0.320556[/C][/ROW]
[ROW][C]ATS[/C][C]-0.0478757771978142[/C][C]0.072038[/C][C]-0.6646[/C][C]0.507293[/C][C]0.253646[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147219&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147219&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)25.34791054086153.3913837.474200
PCL-0.2647083574889730.128751-2.0560.0414520.020726
PCC-0.02402638040387750.13264-0.18110.8564930.428247
PBS-0.07520234669775040.022285-3.37460.0009330.000466
ATC-0.03625348896267930.077621-0.46710.6411110.320556
ATS-0.04787577719781420.072038-0.66460.5072930.253646






Multiple Linear Regression - Regression Statistics
Multiple R0.353173104743026
R-squared0.124731241913829
Adjusted R-squared0.0966777560777333
F-TEST (value)4.44619405383634
F-TEST (DF numerator)5
F-TEST (DF denominator)156
p-value0.000817184987821462
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.00922295457808
Sum Squared Residuals1412.6459552961

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.353173104743026 \tabularnewline
R-squared & 0.124731241913829 \tabularnewline
Adjusted R-squared & 0.0966777560777333 \tabularnewline
F-TEST (value) & 4.44619405383634 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 156 \tabularnewline
p-value & 0.000817184987821462 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 3.00922295457808 \tabularnewline
Sum Squared Residuals & 1412.6459552961 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147219&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.353173104743026[/C][/ROW]
[ROW][C]R-squared[/C][C]0.124731241913829[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.0966777560777333[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]4.44619405383634[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]156[/C][/ROW]
[ROW][C]p-value[/C][C]0.000817184987821462[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]3.00922295457808[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1412.6459552961[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147219&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147219&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 R0.353173104743026
R-squared0.124731241913829
Adjusted R-squared0.0966777560777333
F-TEST (value)4.44619405383634
F-TEST (DF numerator)5
F-TEST (DF denominator)156
p-value0.000817184987821462
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.00922295457808
Sum Squared Residuals1412.6459552961






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11214.3269883726908-2.32698837269083
21111.4349738807142-0.434973880714205
31412.23144428965751.76855571034254
41213.4908108619834-1.49081086198344
52112.61024986068628.38975013931378
61213.1433853946112-1.14338539461116
72213.1463756460678.85362435393301
81112.0685815132002-1.06858151320019
91012.8079255109559-2.80792551095587
101312.35708992193850.642910078061458
111012.0695459541628-2.06954595416283
12813.8304315071308-5.83043150713075
131512.73241824493682.26758175506318
141413.55419431185530.445805688144667
151010.977099702699-0.977099702698976
161413.98722021087730.012779789122748
171412.65969489917031.34030510082965
18119.950708202734731.04929179726527
191012.2727541298999-2.27275412989988
201313.8455899772838-0.845589977283844
21713.1449194602102-6.14491946021016
221413.10249342976910.897506570230917
231212.7120164785807-0.712016478580659
241413.42842081918740.571579180812624
251111.218318284033-0.218318284032987
26911.5009198646786-2.50091986467862
271112.1761568633902-1.17615686339018
281512.88418503864032.11581496135969
291412.18221143047061.81778856952941
301313.5506532309689-0.550653230968867
31912.6084009584785-3.60840095847849
321515.4074034137619-0.407403413761892
331013.2368349316855-3.23683493168546
341111.5032407525302-0.503240752530153
351313.1198113748703-0.119811374870302
36815.2473884657377-7.24738846573767
372015.28342056854034.71657943145971
381213.1292841054448-1.12928410544475
391012.6214343949202-2.62143439492019
401012.8345783336327-2.83457833363274
41912.4339353994164-3.43393539941644
421412.90098169657911.09901830342089
43812.7227635124335-4.7227635124335
441414.6424349424724-0.642434942472444
451113.4946134677117-2.49461346771172
461314.0170111746466-1.01701117464661
47913.1967101641151-4.19671016411513
481112.6486774312589-1.64867743125888
491512.15890784478842.84109215521164
501112.8038435179262-1.80384351792621
511012.3822473528229-2.38224735282295
521413.76678754048770.233212459512331
531814.40533354985543.59466645014458
541413.0857902222790.91420977772103
551115.4155190623473-4.41551906234729
561212.9090529426144-0.9090529426144
571312.21762410628770.782375893712297
58913.1065754227988-4.10657542279875
591012.558316614408-2.55831661440804
601512.71150020973722.28849979026282
612015.58937478276674.41062521723326
621212.0282705551571-0.0282705551571312
631212.0773559730023-0.0773559730022793
641414.6676709635237-0.667670963523656
651314.0465658166496-1.04656581664955
661111.4790619124656-0.479061912465625
671712.30265809377424.69734190622577
681212.4488479594602-0.448847959460199
691313.816428686732-0.816428686731963
701412.59502246403371.40497753596627
711314.3688981342884-1.36889813428835
721514.37363893853470.626361061465288
731311.05562676244211.94437323755792
741011.7172894125687-1.71728941256873
751111.9819985245083-0.981998524508295
761911.76528391861517.23471608138488
771312.42328675900670.576713240993257
781712.94255699658724.05744300341283
791313.3285578488443-0.328557848844285
80914.1293084585528-5.12930845855277
811111.9633817521794-0.963381752179399
821014.3486978001669-4.34869780016693
83912.4446177706382-3.44461777063823
841212.6779175655976-0.677917565597599
851212.9105379603148-0.910537960314809
861312.40121809237170.598781907628274
871312.54079221875310.459207781246861
881212.8111567735525-0.811156773552531
891514.17995748426510.820042515734867
902213.04853856154168.95146143845843
911313.6180153814588-0.618015381458783
921513.58939556282581.41060443717423
931311.80190058166051.19809941833953
941512.42174277612032.57825722387969
951013.1145730969862-3.11457309698617
961111.7187112325133-0.718711232513327
971612.59424066010013.40575933989988
981112.2775351481525-1.27753514815249
991111.7637146573288-0.763714657328809
1001012.802711757021-2.80271175702103
1011011.9596685866515-1.9596685866515
1021612.34148403411693.65851596588312
1031210.22461885673951.77538114326048
1041113.6490067036882-2.6490067036882
1051611.84378080098984.15621919901017
1061913.52246132437415.47753867562593
1071111.7908887671681-0.790888767168095
1081612.13134528246653.86865471753348
1091513.80658211033681.19341788966319
1102414.97423497236689.02576502763324
1111411.94228673336982.05771326663019
1121512.50747503437922.4925249656208
1131110.84275325778820.157246742211842
1141516.0447102402141-1.04471024021408
1151213.2466125815812-1.24661258158121
1161010.9300113828539-0.930011382853863
1171414.157327645406-0.15732764540597
1181313.0379685112987-0.0379685112986713
119913.5814324492255-4.58143244922551
1201510.95488849369074.04511150630925
1211514.61371609529610.38628390470386
1221412.66835138457451.33164861542554
1231111.680588581262-0.680588581261973
124811.9001705212186-3.90017052121855
1251112.8946566954401-1.89465669544007
1261112.1074369204866-1.10743692048657
127811.4228992318161-3.42289923181605
1281011.5562957984001-1.55629579840014
1291112.1497645574845-1.1497645574845
1301313.6437783430915-0.643778343091504
1311112.7929341071253-1.79293410712528
1322014.92605933819255.07394066180748
1331011.9082417672538-1.90824176725384
1341514.60385001327060.396149986729366
1351212.6280446903998-0.62804469039975
1361412.37386699892251.6261330010775
1372315.56117295888457.43882704111549
1381413.46697632547140.53302367452861
1391615.37167121196160.628328788038371
1401114.2696004806873-3.26960048068733
1411215.0255339754035-3.02553397540349
1421014.071550984597-4.07155098459698
1431412.12866953867491.87133046132508
1441211.25681407068040.743185929319607
1451212.8327049074757-0.832704907475721
1461112.8813184150016-1.88131841500156
1471212.1026811806338-0.102681180633835
1481314.0775909450155-1.07759094501554
1491112.5398235139222-1.53982351392215
1501913.39979617691035.60020382308965
1511212.8956549425393-0.895654942539281
1521711.82990655140265.17009344859744
153912.5050746236147-3.50507462361473
1541212.4178221519681-0.417822151968115
1551913.36417203222065.63582796777937
1561813.96231865141564.03768134858443
1571513.58939556282581.41060443717423
1581413.6420662858120.357933714188049
1591112.1497645574845-1.1497645574845
160911.7666460189231-2.7666460189231
1611812.82643823642255.17356176357746
1621613.56938232427672.43061767572335

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 12 & 14.3269883726908 & -2.32698837269083 \tabularnewline
2 & 11 & 11.4349738807142 & -0.434973880714205 \tabularnewline
3 & 14 & 12.2314442896575 & 1.76855571034254 \tabularnewline
4 & 12 & 13.4908108619834 & -1.49081086198344 \tabularnewline
5 & 21 & 12.6102498606862 & 8.38975013931378 \tabularnewline
6 & 12 & 13.1433853946112 & -1.14338539461116 \tabularnewline
7 & 22 & 13.146375646067 & 8.85362435393301 \tabularnewline
8 & 11 & 12.0685815132002 & -1.06858151320019 \tabularnewline
9 & 10 & 12.8079255109559 & -2.80792551095587 \tabularnewline
10 & 13 & 12.3570899219385 & 0.642910078061458 \tabularnewline
11 & 10 & 12.0695459541628 & -2.06954595416283 \tabularnewline
12 & 8 & 13.8304315071308 & -5.83043150713075 \tabularnewline
13 & 15 & 12.7324182449368 & 2.26758175506318 \tabularnewline
14 & 14 & 13.5541943118553 & 0.445805688144667 \tabularnewline
15 & 10 & 10.977099702699 & -0.977099702698976 \tabularnewline
16 & 14 & 13.9872202108773 & 0.012779789122748 \tabularnewline
17 & 14 & 12.6596948991703 & 1.34030510082965 \tabularnewline
18 & 11 & 9.95070820273473 & 1.04929179726527 \tabularnewline
19 & 10 & 12.2727541298999 & -2.27275412989988 \tabularnewline
20 & 13 & 13.8455899772838 & -0.845589977283844 \tabularnewline
21 & 7 & 13.1449194602102 & -6.14491946021016 \tabularnewline
22 & 14 & 13.1024934297691 & 0.897506570230917 \tabularnewline
23 & 12 & 12.7120164785807 & -0.712016478580659 \tabularnewline
24 & 14 & 13.4284208191874 & 0.571579180812624 \tabularnewline
25 & 11 & 11.218318284033 & -0.218318284032987 \tabularnewline
26 & 9 & 11.5009198646786 & -2.50091986467862 \tabularnewline
27 & 11 & 12.1761568633902 & -1.17615686339018 \tabularnewline
28 & 15 & 12.8841850386403 & 2.11581496135969 \tabularnewline
29 & 14 & 12.1822114304706 & 1.81778856952941 \tabularnewline
30 & 13 & 13.5506532309689 & -0.550653230968867 \tabularnewline
31 & 9 & 12.6084009584785 & -3.60840095847849 \tabularnewline
32 & 15 & 15.4074034137619 & -0.407403413761892 \tabularnewline
33 & 10 & 13.2368349316855 & -3.23683493168546 \tabularnewline
34 & 11 & 11.5032407525302 & -0.503240752530153 \tabularnewline
35 & 13 & 13.1198113748703 & -0.119811374870302 \tabularnewline
36 & 8 & 15.2473884657377 & -7.24738846573767 \tabularnewline
37 & 20 & 15.2834205685403 & 4.71657943145971 \tabularnewline
38 & 12 & 13.1292841054448 & -1.12928410544475 \tabularnewline
39 & 10 & 12.6214343949202 & -2.62143439492019 \tabularnewline
40 & 10 & 12.8345783336327 & -2.83457833363274 \tabularnewline
41 & 9 & 12.4339353994164 & -3.43393539941644 \tabularnewline
42 & 14 & 12.9009816965791 & 1.09901830342089 \tabularnewline
43 & 8 & 12.7227635124335 & -4.7227635124335 \tabularnewline
44 & 14 & 14.6424349424724 & -0.642434942472444 \tabularnewline
45 & 11 & 13.4946134677117 & -2.49461346771172 \tabularnewline
46 & 13 & 14.0170111746466 & -1.01701117464661 \tabularnewline
47 & 9 & 13.1967101641151 & -4.19671016411513 \tabularnewline
48 & 11 & 12.6486774312589 & -1.64867743125888 \tabularnewline
49 & 15 & 12.1589078447884 & 2.84109215521164 \tabularnewline
50 & 11 & 12.8038435179262 & -1.80384351792621 \tabularnewline
51 & 10 & 12.3822473528229 & -2.38224735282295 \tabularnewline
52 & 14 & 13.7667875404877 & 0.233212459512331 \tabularnewline
53 & 18 & 14.4053335498554 & 3.59466645014458 \tabularnewline
54 & 14 & 13.085790222279 & 0.91420977772103 \tabularnewline
55 & 11 & 15.4155190623473 & -4.41551906234729 \tabularnewline
56 & 12 & 12.9090529426144 & -0.9090529426144 \tabularnewline
57 & 13 & 12.2176241062877 & 0.782375893712297 \tabularnewline
58 & 9 & 13.1065754227988 & -4.10657542279875 \tabularnewline
59 & 10 & 12.558316614408 & -2.55831661440804 \tabularnewline
60 & 15 & 12.7115002097372 & 2.28849979026282 \tabularnewline
61 & 20 & 15.5893747827667 & 4.41062521723326 \tabularnewline
62 & 12 & 12.0282705551571 & -0.0282705551571312 \tabularnewline
63 & 12 & 12.0773559730023 & -0.0773559730022793 \tabularnewline
64 & 14 & 14.6676709635237 & -0.667670963523656 \tabularnewline
65 & 13 & 14.0465658166496 & -1.04656581664955 \tabularnewline
66 & 11 & 11.4790619124656 & -0.479061912465625 \tabularnewline
67 & 17 & 12.3026580937742 & 4.69734190622577 \tabularnewline
68 & 12 & 12.4488479594602 & -0.448847959460199 \tabularnewline
69 & 13 & 13.816428686732 & -0.816428686731963 \tabularnewline
70 & 14 & 12.5950224640337 & 1.40497753596627 \tabularnewline
71 & 13 & 14.3688981342884 & -1.36889813428835 \tabularnewline
72 & 15 & 14.3736389385347 & 0.626361061465288 \tabularnewline
73 & 13 & 11.0556267624421 & 1.94437323755792 \tabularnewline
74 & 10 & 11.7172894125687 & -1.71728941256873 \tabularnewline
75 & 11 & 11.9819985245083 & -0.981998524508295 \tabularnewline
76 & 19 & 11.7652839186151 & 7.23471608138488 \tabularnewline
77 & 13 & 12.4232867590067 & 0.576713240993257 \tabularnewline
78 & 17 & 12.9425569965872 & 4.05744300341283 \tabularnewline
79 & 13 & 13.3285578488443 & -0.328557848844285 \tabularnewline
80 & 9 & 14.1293084585528 & -5.12930845855277 \tabularnewline
81 & 11 & 11.9633817521794 & -0.963381752179399 \tabularnewline
82 & 10 & 14.3486978001669 & -4.34869780016693 \tabularnewline
83 & 9 & 12.4446177706382 & -3.44461777063823 \tabularnewline
84 & 12 & 12.6779175655976 & -0.677917565597599 \tabularnewline
85 & 12 & 12.9105379603148 & -0.910537960314809 \tabularnewline
86 & 13 & 12.4012180923717 & 0.598781907628274 \tabularnewline
87 & 13 & 12.5407922187531 & 0.459207781246861 \tabularnewline
88 & 12 & 12.8111567735525 & -0.811156773552531 \tabularnewline
89 & 15 & 14.1799574842651 & 0.820042515734867 \tabularnewline
90 & 22 & 13.0485385615416 & 8.95146143845843 \tabularnewline
91 & 13 & 13.6180153814588 & -0.618015381458783 \tabularnewline
92 & 15 & 13.5893955628258 & 1.41060443717423 \tabularnewline
93 & 13 & 11.8019005816605 & 1.19809941833953 \tabularnewline
94 & 15 & 12.4217427761203 & 2.57825722387969 \tabularnewline
95 & 10 & 13.1145730969862 & -3.11457309698617 \tabularnewline
96 & 11 & 11.7187112325133 & -0.718711232513327 \tabularnewline
97 & 16 & 12.5942406601001 & 3.40575933989988 \tabularnewline
98 & 11 & 12.2775351481525 & -1.27753514815249 \tabularnewline
99 & 11 & 11.7637146573288 & -0.763714657328809 \tabularnewline
100 & 10 & 12.802711757021 & -2.80271175702103 \tabularnewline
101 & 10 & 11.9596685866515 & -1.9596685866515 \tabularnewline
102 & 16 & 12.3414840341169 & 3.65851596588312 \tabularnewline
103 & 12 & 10.2246188567395 & 1.77538114326048 \tabularnewline
104 & 11 & 13.6490067036882 & -2.6490067036882 \tabularnewline
105 & 16 & 11.8437808009898 & 4.15621919901017 \tabularnewline
106 & 19 & 13.5224613243741 & 5.47753867562593 \tabularnewline
107 & 11 & 11.7908887671681 & -0.790888767168095 \tabularnewline
108 & 16 & 12.1313452824665 & 3.86865471753348 \tabularnewline
109 & 15 & 13.8065821103368 & 1.19341788966319 \tabularnewline
110 & 24 & 14.9742349723668 & 9.02576502763324 \tabularnewline
111 & 14 & 11.9422867333698 & 2.05771326663019 \tabularnewline
112 & 15 & 12.5074750343792 & 2.4925249656208 \tabularnewline
113 & 11 & 10.8427532577882 & 0.157246742211842 \tabularnewline
114 & 15 & 16.0447102402141 & -1.04471024021408 \tabularnewline
115 & 12 & 13.2466125815812 & -1.24661258158121 \tabularnewline
116 & 10 & 10.9300113828539 & -0.930011382853863 \tabularnewline
117 & 14 & 14.157327645406 & -0.15732764540597 \tabularnewline
118 & 13 & 13.0379685112987 & -0.0379685112986713 \tabularnewline
119 & 9 & 13.5814324492255 & -4.58143244922551 \tabularnewline
120 & 15 & 10.9548884936907 & 4.04511150630925 \tabularnewline
121 & 15 & 14.6137160952961 & 0.38628390470386 \tabularnewline
122 & 14 & 12.6683513845745 & 1.33164861542554 \tabularnewline
123 & 11 & 11.680588581262 & -0.680588581261973 \tabularnewline
124 & 8 & 11.9001705212186 & -3.90017052121855 \tabularnewline
125 & 11 & 12.8946566954401 & -1.89465669544007 \tabularnewline
126 & 11 & 12.1074369204866 & -1.10743692048657 \tabularnewline
127 & 8 & 11.4228992318161 & -3.42289923181605 \tabularnewline
128 & 10 & 11.5562957984001 & -1.55629579840014 \tabularnewline
129 & 11 & 12.1497645574845 & -1.1497645574845 \tabularnewline
130 & 13 & 13.6437783430915 & -0.643778343091504 \tabularnewline
131 & 11 & 12.7929341071253 & -1.79293410712528 \tabularnewline
132 & 20 & 14.9260593381925 & 5.07394066180748 \tabularnewline
133 & 10 & 11.9082417672538 & -1.90824176725384 \tabularnewline
134 & 15 & 14.6038500132706 & 0.396149986729366 \tabularnewline
135 & 12 & 12.6280446903998 & -0.62804469039975 \tabularnewline
136 & 14 & 12.3738669989225 & 1.6261330010775 \tabularnewline
137 & 23 & 15.5611729588845 & 7.43882704111549 \tabularnewline
138 & 14 & 13.4669763254714 & 0.53302367452861 \tabularnewline
139 & 16 & 15.3716712119616 & 0.628328788038371 \tabularnewline
140 & 11 & 14.2696004806873 & -3.26960048068733 \tabularnewline
141 & 12 & 15.0255339754035 & -3.02553397540349 \tabularnewline
142 & 10 & 14.071550984597 & -4.07155098459698 \tabularnewline
143 & 14 & 12.1286695386749 & 1.87133046132508 \tabularnewline
144 & 12 & 11.2568140706804 & 0.743185929319607 \tabularnewline
145 & 12 & 12.8327049074757 & -0.832704907475721 \tabularnewline
146 & 11 & 12.8813184150016 & -1.88131841500156 \tabularnewline
147 & 12 & 12.1026811806338 & -0.102681180633835 \tabularnewline
148 & 13 & 14.0775909450155 & -1.07759094501554 \tabularnewline
149 & 11 & 12.5398235139222 & -1.53982351392215 \tabularnewline
150 & 19 & 13.3997961769103 & 5.60020382308965 \tabularnewline
151 & 12 & 12.8956549425393 & -0.895654942539281 \tabularnewline
152 & 17 & 11.8299065514026 & 5.17009344859744 \tabularnewline
153 & 9 & 12.5050746236147 & -3.50507462361473 \tabularnewline
154 & 12 & 12.4178221519681 & -0.417822151968115 \tabularnewline
155 & 19 & 13.3641720322206 & 5.63582796777937 \tabularnewline
156 & 18 & 13.9623186514156 & 4.03768134858443 \tabularnewline
157 & 15 & 13.5893955628258 & 1.41060443717423 \tabularnewline
158 & 14 & 13.642066285812 & 0.357933714188049 \tabularnewline
159 & 11 & 12.1497645574845 & -1.1497645574845 \tabularnewline
160 & 9 & 11.7666460189231 & -2.7666460189231 \tabularnewline
161 & 18 & 12.8264382364225 & 5.17356176357746 \tabularnewline
162 & 16 & 13.5693823242767 & 2.43061767572335 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147219&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]12[/C][C]14.3269883726908[/C][C]-2.32698837269083[/C][/ROW]
[ROW][C]2[/C][C]11[/C][C]11.4349738807142[/C][C]-0.434973880714205[/C][/ROW]
[ROW][C]3[/C][C]14[/C][C]12.2314442896575[/C][C]1.76855571034254[/C][/ROW]
[ROW][C]4[/C][C]12[/C][C]13.4908108619834[/C][C]-1.49081086198344[/C][/ROW]
[ROW][C]5[/C][C]21[/C][C]12.6102498606862[/C][C]8.38975013931378[/C][/ROW]
[ROW][C]6[/C][C]12[/C][C]13.1433853946112[/C][C]-1.14338539461116[/C][/ROW]
[ROW][C]7[/C][C]22[/C][C]13.146375646067[/C][C]8.85362435393301[/C][/ROW]
[ROW][C]8[/C][C]11[/C][C]12.0685815132002[/C][C]-1.06858151320019[/C][/ROW]
[ROW][C]9[/C][C]10[/C][C]12.8079255109559[/C][C]-2.80792551095587[/C][/ROW]
[ROW][C]10[/C][C]13[/C][C]12.3570899219385[/C][C]0.642910078061458[/C][/ROW]
[ROW][C]11[/C][C]10[/C][C]12.0695459541628[/C][C]-2.06954595416283[/C][/ROW]
[ROW][C]12[/C][C]8[/C][C]13.8304315071308[/C][C]-5.83043150713075[/C][/ROW]
[ROW][C]13[/C][C]15[/C][C]12.7324182449368[/C][C]2.26758175506318[/C][/ROW]
[ROW][C]14[/C][C]14[/C][C]13.5541943118553[/C][C]0.445805688144667[/C][/ROW]
[ROW][C]15[/C][C]10[/C][C]10.977099702699[/C][C]-0.977099702698976[/C][/ROW]
[ROW][C]16[/C][C]14[/C][C]13.9872202108773[/C][C]0.012779789122748[/C][/ROW]
[ROW][C]17[/C][C]14[/C][C]12.6596948991703[/C][C]1.34030510082965[/C][/ROW]
[ROW][C]18[/C][C]11[/C][C]9.95070820273473[/C][C]1.04929179726527[/C][/ROW]
[ROW][C]19[/C][C]10[/C][C]12.2727541298999[/C][C]-2.27275412989988[/C][/ROW]
[ROW][C]20[/C][C]13[/C][C]13.8455899772838[/C][C]-0.845589977283844[/C][/ROW]
[ROW][C]21[/C][C]7[/C][C]13.1449194602102[/C][C]-6.14491946021016[/C][/ROW]
[ROW][C]22[/C][C]14[/C][C]13.1024934297691[/C][C]0.897506570230917[/C][/ROW]
[ROW][C]23[/C][C]12[/C][C]12.7120164785807[/C][C]-0.712016478580659[/C][/ROW]
[ROW][C]24[/C][C]14[/C][C]13.4284208191874[/C][C]0.571579180812624[/C][/ROW]
[ROW][C]25[/C][C]11[/C][C]11.218318284033[/C][C]-0.218318284032987[/C][/ROW]
[ROW][C]26[/C][C]9[/C][C]11.5009198646786[/C][C]-2.50091986467862[/C][/ROW]
[ROW][C]27[/C][C]11[/C][C]12.1761568633902[/C][C]-1.17615686339018[/C][/ROW]
[ROW][C]28[/C][C]15[/C][C]12.8841850386403[/C][C]2.11581496135969[/C][/ROW]
[ROW][C]29[/C][C]14[/C][C]12.1822114304706[/C][C]1.81778856952941[/C][/ROW]
[ROW][C]30[/C][C]13[/C][C]13.5506532309689[/C][C]-0.550653230968867[/C][/ROW]
[ROW][C]31[/C][C]9[/C][C]12.6084009584785[/C][C]-3.60840095847849[/C][/ROW]
[ROW][C]32[/C][C]15[/C][C]15.4074034137619[/C][C]-0.407403413761892[/C][/ROW]
[ROW][C]33[/C][C]10[/C][C]13.2368349316855[/C][C]-3.23683493168546[/C][/ROW]
[ROW][C]34[/C][C]11[/C][C]11.5032407525302[/C][C]-0.503240752530153[/C][/ROW]
[ROW][C]35[/C][C]13[/C][C]13.1198113748703[/C][C]-0.119811374870302[/C][/ROW]
[ROW][C]36[/C][C]8[/C][C]15.2473884657377[/C][C]-7.24738846573767[/C][/ROW]
[ROW][C]37[/C][C]20[/C][C]15.2834205685403[/C][C]4.71657943145971[/C][/ROW]
[ROW][C]38[/C][C]12[/C][C]13.1292841054448[/C][C]-1.12928410544475[/C][/ROW]
[ROW][C]39[/C][C]10[/C][C]12.6214343949202[/C][C]-2.62143439492019[/C][/ROW]
[ROW][C]40[/C][C]10[/C][C]12.8345783336327[/C][C]-2.83457833363274[/C][/ROW]
[ROW][C]41[/C][C]9[/C][C]12.4339353994164[/C][C]-3.43393539941644[/C][/ROW]
[ROW][C]42[/C][C]14[/C][C]12.9009816965791[/C][C]1.09901830342089[/C][/ROW]
[ROW][C]43[/C][C]8[/C][C]12.7227635124335[/C][C]-4.7227635124335[/C][/ROW]
[ROW][C]44[/C][C]14[/C][C]14.6424349424724[/C][C]-0.642434942472444[/C][/ROW]
[ROW][C]45[/C][C]11[/C][C]13.4946134677117[/C][C]-2.49461346771172[/C][/ROW]
[ROW][C]46[/C][C]13[/C][C]14.0170111746466[/C][C]-1.01701117464661[/C][/ROW]
[ROW][C]47[/C][C]9[/C][C]13.1967101641151[/C][C]-4.19671016411513[/C][/ROW]
[ROW][C]48[/C][C]11[/C][C]12.6486774312589[/C][C]-1.64867743125888[/C][/ROW]
[ROW][C]49[/C][C]15[/C][C]12.1589078447884[/C][C]2.84109215521164[/C][/ROW]
[ROW][C]50[/C][C]11[/C][C]12.8038435179262[/C][C]-1.80384351792621[/C][/ROW]
[ROW][C]51[/C][C]10[/C][C]12.3822473528229[/C][C]-2.38224735282295[/C][/ROW]
[ROW][C]52[/C][C]14[/C][C]13.7667875404877[/C][C]0.233212459512331[/C][/ROW]
[ROW][C]53[/C][C]18[/C][C]14.4053335498554[/C][C]3.59466645014458[/C][/ROW]
[ROW][C]54[/C][C]14[/C][C]13.085790222279[/C][C]0.91420977772103[/C][/ROW]
[ROW][C]55[/C][C]11[/C][C]15.4155190623473[/C][C]-4.41551906234729[/C][/ROW]
[ROW][C]56[/C][C]12[/C][C]12.9090529426144[/C][C]-0.9090529426144[/C][/ROW]
[ROW][C]57[/C][C]13[/C][C]12.2176241062877[/C][C]0.782375893712297[/C][/ROW]
[ROW][C]58[/C][C]9[/C][C]13.1065754227988[/C][C]-4.10657542279875[/C][/ROW]
[ROW][C]59[/C][C]10[/C][C]12.558316614408[/C][C]-2.55831661440804[/C][/ROW]
[ROW][C]60[/C][C]15[/C][C]12.7115002097372[/C][C]2.28849979026282[/C][/ROW]
[ROW][C]61[/C][C]20[/C][C]15.5893747827667[/C][C]4.41062521723326[/C][/ROW]
[ROW][C]62[/C][C]12[/C][C]12.0282705551571[/C][C]-0.0282705551571312[/C][/ROW]
[ROW][C]63[/C][C]12[/C][C]12.0773559730023[/C][C]-0.0773559730022793[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]14.6676709635237[/C][C]-0.667670963523656[/C][/ROW]
[ROW][C]65[/C][C]13[/C][C]14.0465658166496[/C][C]-1.04656581664955[/C][/ROW]
[ROW][C]66[/C][C]11[/C][C]11.4790619124656[/C][C]-0.479061912465625[/C][/ROW]
[ROW][C]67[/C][C]17[/C][C]12.3026580937742[/C][C]4.69734190622577[/C][/ROW]
[ROW][C]68[/C][C]12[/C][C]12.4488479594602[/C][C]-0.448847959460199[/C][/ROW]
[ROW][C]69[/C][C]13[/C][C]13.816428686732[/C][C]-0.816428686731963[/C][/ROW]
[ROW][C]70[/C][C]14[/C][C]12.5950224640337[/C][C]1.40497753596627[/C][/ROW]
[ROW][C]71[/C][C]13[/C][C]14.3688981342884[/C][C]-1.36889813428835[/C][/ROW]
[ROW][C]72[/C][C]15[/C][C]14.3736389385347[/C][C]0.626361061465288[/C][/ROW]
[ROW][C]73[/C][C]13[/C][C]11.0556267624421[/C][C]1.94437323755792[/C][/ROW]
[ROW][C]74[/C][C]10[/C][C]11.7172894125687[/C][C]-1.71728941256873[/C][/ROW]
[ROW][C]75[/C][C]11[/C][C]11.9819985245083[/C][C]-0.981998524508295[/C][/ROW]
[ROW][C]76[/C][C]19[/C][C]11.7652839186151[/C][C]7.23471608138488[/C][/ROW]
[ROW][C]77[/C][C]13[/C][C]12.4232867590067[/C][C]0.576713240993257[/C][/ROW]
[ROW][C]78[/C][C]17[/C][C]12.9425569965872[/C][C]4.05744300341283[/C][/ROW]
[ROW][C]79[/C][C]13[/C][C]13.3285578488443[/C][C]-0.328557848844285[/C][/ROW]
[ROW][C]80[/C][C]9[/C][C]14.1293084585528[/C][C]-5.12930845855277[/C][/ROW]
[ROW][C]81[/C][C]11[/C][C]11.9633817521794[/C][C]-0.963381752179399[/C][/ROW]
[ROW][C]82[/C][C]10[/C][C]14.3486978001669[/C][C]-4.34869780016693[/C][/ROW]
[ROW][C]83[/C][C]9[/C][C]12.4446177706382[/C][C]-3.44461777063823[/C][/ROW]
[ROW][C]84[/C][C]12[/C][C]12.6779175655976[/C][C]-0.677917565597599[/C][/ROW]
[ROW][C]85[/C][C]12[/C][C]12.9105379603148[/C][C]-0.910537960314809[/C][/ROW]
[ROW][C]86[/C][C]13[/C][C]12.4012180923717[/C][C]0.598781907628274[/C][/ROW]
[ROW][C]87[/C][C]13[/C][C]12.5407922187531[/C][C]0.459207781246861[/C][/ROW]
[ROW][C]88[/C][C]12[/C][C]12.8111567735525[/C][C]-0.811156773552531[/C][/ROW]
[ROW][C]89[/C][C]15[/C][C]14.1799574842651[/C][C]0.820042515734867[/C][/ROW]
[ROW][C]90[/C][C]22[/C][C]13.0485385615416[/C][C]8.95146143845843[/C][/ROW]
[ROW][C]91[/C][C]13[/C][C]13.6180153814588[/C][C]-0.618015381458783[/C][/ROW]
[ROW][C]92[/C][C]15[/C][C]13.5893955628258[/C][C]1.41060443717423[/C][/ROW]
[ROW][C]93[/C][C]13[/C][C]11.8019005816605[/C][C]1.19809941833953[/C][/ROW]
[ROW][C]94[/C][C]15[/C][C]12.4217427761203[/C][C]2.57825722387969[/C][/ROW]
[ROW][C]95[/C][C]10[/C][C]13.1145730969862[/C][C]-3.11457309698617[/C][/ROW]
[ROW][C]96[/C][C]11[/C][C]11.7187112325133[/C][C]-0.718711232513327[/C][/ROW]
[ROW][C]97[/C][C]16[/C][C]12.5942406601001[/C][C]3.40575933989988[/C][/ROW]
[ROW][C]98[/C][C]11[/C][C]12.2775351481525[/C][C]-1.27753514815249[/C][/ROW]
[ROW][C]99[/C][C]11[/C][C]11.7637146573288[/C][C]-0.763714657328809[/C][/ROW]
[ROW][C]100[/C][C]10[/C][C]12.802711757021[/C][C]-2.80271175702103[/C][/ROW]
[ROW][C]101[/C][C]10[/C][C]11.9596685866515[/C][C]-1.9596685866515[/C][/ROW]
[ROW][C]102[/C][C]16[/C][C]12.3414840341169[/C][C]3.65851596588312[/C][/ROW]
[ROW][C]103[/C][C]12[/C][C]10.2246188567395[/C][C]1.77538114326048[/C][/ROW]
[ROW][C]104[/C][C]11[/C][C]13.6490067036882[/C][C]-2.6490067036882[/C][/ROW]
[ROW][C]105[/C][C]16[/C][C]11.8437808009898[/C][C]4.15621919901017[/C][/ROW]
[ROW][C]106[/C][C]19[/C][C]13.5224613243741[/C][C]5.47753867562593[/C][/ROW]
[ROW][C]107[/C][C]11[/C][C]11.7908887671681[/C][C]-0.790888767168095[/C][/ROW]
[ROW][C]108[/C][C]16[/C][C]12.1313452824665[/C][C]3.86865471753348[/C][/ROW]
[ROW][C]109[/C][C]15[/C][C]13.8065821103368[/C][C]1.19341788966319[/C][/ROW]
[ROW][C]110[/C][C]24[/C][C]14.9742349723668[/C][C]9.02576502763324[/C][/ROW]
[ROW][C]111[/C][C]14[/C][C]11.9422867333698[/C][C]2.05771326663019[/C][/ROW]
[ROW][C]112[/C][C]15[/C][C]12.5074750343792[/C][C]2.4925249656208[/C][/ROW]
[ROW][C]113[/C][C]11[/C][C]10.8427532577882[/C][C]0.157246742211842[/C][/ROW]
[ROW][C]114[/C][C]15[/C][C]16.0447102402141[/C][C]-1.04471024021408[/C][/ROW]
[ROW][C]115[/C][C]12[/C][C]13.2466125815812[/C][C]-1.24661258158121[/C][/ROW]
[ROW][C]116[/C][C]10[/C][C]10.9300113828539[/C][C]-0.930011382853863[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]14.157327645406[/C][C]-0.15732764540597[/C][/ROW]
[ROW][C]118[/C][C]13[/C][C]13.0379685112987[/C][C]-0.0379685112986713[/C][/ROW]
[ROW][C]119[/C][C]9[/C][C]13.5814324492255[/C][C]-4.58143244922551[/C][/ROW]
[ROW][C]120[/C][C]15[/C][C]10.9548884936907[/C][C]4.04511150630925[/C][/ROW]
[ROW][C]121[/C][C]15[/C][C]14.6137160952961[/C][C]0.38628390470386[/C][/ROW]
[ROW][C]122[/C][C]14[/C][C]12.6683513845745[/C][C]1.33164861542554[/C][/ROW]
[ROW][C]123[/C][C]11[/C][C]11.680588581262[/C][C]-0.680588581261973[/C][/ROW]
[ROW][C]124[/C][C]8[/C][C]11.9001705212186[/C][C]-3.90017052121855[/C][/ROW]
[ROW][C]125[/C][C]11[/C][C]12.8946566954401[/C][C]-1.89465669544007[/C][/ROW]
[ROW][C]126[/C][C]11[/C][C]12.1074369204866[/C][C]-1.10743692048657[/C][/ROW]
[ROW][C]127[/C][C]8[/C][C]11.4228992318161[/C][C]-3.42289923181605[/C][/ROW]
[ROW][C]128[/C][C]10[/C][C]11.5562957984001[/C][C]-1.55629579840014[/C][/ROW]
[ROW][C]129[/C][C]11[/C][C]12.1497645574845[/C][C]-1.1497645574845[/C][/ROW]
[ROW][C]130[/C][C]13[/C][C]13.6437783430915[/C][C]-0.643778343091504[/C][/ROW]
[ROW][C]131[/C][C]11[/C][C]12.7929341071253[/C][C]-1.79293410712528[/C][/ROW]
[ROW][C]132[/C][C]20[/C][C]14.9260593381925[/C][C]5.07394066180748[/C][/ROW]
[ROW][C]133[/C][C]10[/C][C]11.9082417672538[/C][C]-1.90824176725384[/C][/ROW]
[ROW][C]134[/C][C]15[/C][C]14.6038500132706[/C][C]0.396149986729366[/C][/ROW]
[ROW][C]135[/C][C]12[/C][C]12.6280446903998[/C][C]-0.62804469039975[/C][/ROW]
[ROW][C]136[/C][C]14[/C][C]12.3738669989225[/C][C]1.6261330010775[/C][/ROW]
[ROW][C]137[/C][C]23[/C][C]15.5611729588845[/C][C]7.43882704111549[/C][/ROW]
[ROW][C]138[/C][C]14[/C][C]13.4669763254714[/C][C]0.53302367452861[/C][/ROW]
[ROW][C]139[/C][C]16[/C][C]15.3716712119616[/C][C]0.628328788038371[/C][/ROW]
[ROW][C]140[/C][C]11[/C][C]14.2696004806873[/C][C]-3.26960048068733[/C][/ROW]
[ROW][C]141[/C][C]12[/C][C]15.0255339754035[/C][C]-3.02553397540349[/C][/ROW]
[ROW][C]142[/C][C]10[/C][C]14.071550984597[/C][C]-4.07155098459698[/C][/ROW]
[ROW][C]143[/C][C]14[/C][C]12.1286695386749[/C][C]1.87133046132508[/C][/ROW]
[ROW][C]144[/C][C]12[/C][C]11.2568140706804[/C][C]0.743185929319607[/C][/ROW]
[ROW][C]145[/C][C]12[/C][C]12.8327049074757[/C][C]-0.832704907475721[/C][/ROW]
[ROW][C]146[/C][C]11[/C][C]12.8813184150016[/C][C]-1.88131841500156[/C][/ROW]
[ROW][C]147[/C][C]12[/C][C]12.1026811806338[/C][C]-0.102681180633835[/C][/ROW]
[ROW][C]148[/C][C]13[/C][C]14.0775909450155[/C][C]-1.07759094501554[/C][/ROW]
[ROW][C]149[/C][C]11[/C][C]12.5398235139222[/C][C]-1.53982351392215[/C][/ROW]
[ROW][C]150[/C][C]19[/C][C]13.3997961769103[/C][C]5.60020382308965[/C][/ROW]
[ROW][C]151[/C][C]12[/C][C]12.8956549425393[/C][C]-0.895654942539281[/C][/ROW]
[ROW][C]152[/C][C]17[/C][C]11.8299065514026[/C][C]5.17009344859744[/C][/ROW]
[ROW][C]153[/C][C]9[/C][C]12.5050746236147[/C][C]-3.50507462361473[/C][/ROW]
[ROW][C]154[/C][C]12[/C][C]12.4178221519681[/C][C]-0.417822151968115[/C][/ROW]
[ROW][C]155[/C][C]19[/C][C]13.3641720322206[/C][C]5.63582796777937[/C][/ROW]
[ROW][C]156[/C][C]18[/C][C]13.9623186514156[/C][C]4.03768134858443[/C][/ROW]
[ROW][C]157[/C][C]15[/C][C]13.5893955628258[/C][C]1.41060443717423[/C][/ROW]
[ROW][C]158[/C][C]14[/C][C]13.642066285812[/C][C]0.357933714188049[/C][/ROW]
[ROW][C]159[/C][C]11[/C][C]12.1497645574845[/C][C]-1.1497645574845[/C][/ROW]
[ROW][C]160[/C][C]9[/C][C]11.7666460189231[/C][C]-2.7666460189231[/C][/ROW]
[ROW][C]161[/C][C]18[/C][C]12.8264382364225[/C][C]5.17356176357746[/C][/ROW]
[ROW][C]162[/C][C]16[/C][C]13.5693823242767[/C][C]2.43061767572335[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147219&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147219&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
11214.3269883726908-2.32698837269083
21111.4349738807142-0.434973880714205
31412.23144428965751.76855571034254
41213.4908108619834-1.49081086198344
52112.61024986068628.38975013931378
61213.1433853946112-1.14338539461116
72213.1463756460678.85362435393301
81112.0685815132002-1.06858151320019
91012.8079255109559-2.80792551095587
101312.35708992193850.642910078061458
111012.0695459541628-2.06954595416283
12813.8304315071308-5.83043150713075
131512.73241824493682.26758175506318
141413.55419431185530.445805688144667
151010.977099702699-0.977099702698976
161413.98722021087730.012779789122748
171412.65969489917031.34030510082965
18119.950708202734731.04929179726527
191012.2727541298999-2.27275412989988
201313.8455899772838-0.845589977283844
21713.1449194602102-6.14491946021016
221413.10249342976910.897506570230917
231212.7120164785807-0.712016478580659
241413.42842081918740.571579180812624
251111.218318284033-0.218318284032987
26911.5009198646786-2.50091986467862
271112.1761568633902-1.17615686339018
281512.88418503864032.11581496135969
291412.18221143047061.81778856952941
301313.5506532309689-0.550653230968867
31912.6084009584785-3.60840095847849
321515.4074034137619-0.407403413761892
331013.2368349316855-3.23683493168546
341111.5032407525302-0.503240752530153
351313.1198113748703-0.119811374870302
36815.2473884657377-7.24738846573767
372015.28342056854034.71657943145971
381213.1292841054448-1.12928410544475
391012.6214343949202-2.62143439492019
401012.8345783336327-2.83457833363274
41912.4339353994164-3.43393539941644
421412.90098169657911.09901830342089
43812.7227635124335-4.7227635124335
441414.6424349424724-0.642434942472444
451113.4946134677117-2.49461346771172
461314.0170111746466-1.01701117464661
47913.1967101641151-4.19671016411513
481112.6486774312589-1.64867743125888
491512.15890784478842.84109215521164
501112.8038435179262-1.80384351792621
511012.3822473528229-2.38224735282295
521413.76678754048770.233212459512331
531814.40533354985543.59466645014458
541413.0857902222790.91420977772103
551115.4155190623473-4.41551906234729
561212.9090529426144-0.9090529426144
571312.21762410628770.782375893712297
58913.1065754227988-4.10657542279875
591012.558316614408-2.55831661440804
601512.71150020973722.28849979026282
612015.58937478276674.41062521723326
621212.0282705551571-0.0282705551571312
631212.0773559730023-0.0773559730022793
641414.6676709635237-0.667670963523656
651314.0465658166496-1.04656581664955
661111.4790619124656-0.479061912465625
671712.30265809377424.69734190622577
681212.4488479594602-0.448847959460199
691313.816428686732-0.816428686731963
701412.59502246403371.40497753596627
711314.3688981342884-1.36889813428835
721514.37363893853470.626361061465288
731311.05562676244211.94437323755792
741011.7172894125687-1.71728941256873
751111.9819985245083-0.981998524508295
761911.76528391861517.23471608138488
771312.42328675900670.576713240993257
781712.94255699658724.05744300341283
791313.3285578488443-0.328557848844285
80914.1293084585528-5.12930845855277
811111.9633817521794-0.963381752179399
821014.3486978001669-4.34869780016693
83912.4446177706382-3.44461777063823
841212.6779175655976-0.677917565597599
851212.9105379603148-0.910537960314809
861312.40121809237170.598781907628274
871312.54079221875310.459207781246861
881212.8111567735525-0.811156773552531
891514.17995748426510.820042515734867
902213.04853856154168.95146143845843
911313.6180153814588-0.618015381458783
921513.58939556282581.41060443717423
931311.80190058166051.19809941833953
941512.42174277612032.57825722387969
951013.1145730969862-3.11457309698617
961111.7187112325133-0.718711232513327
971612.59424066010013.40575933989988
981112.2775351481525-1.27753514815249
991111.7637146573288-0.763714657328809
1001012.802711757021-2.80271175702103
1011011.9596685866515-1.9596685866515
1021612.34148403411693.65851596588312
1031210.22461885673951.77538114326048
1041113.6490067036882-2.6490067036882
1051611.84378080098984.15621919901017
1061913.52246132437415.47753867562593
1071111.7908887671681-0.790888767168095
1081612.13134528246653.86865471753348
1091513.80658211033681.19341788966319
1102414.97423497236689.02576502763324
1111411.94228673336982.05771326663019
1121512.50747503437922.4925249656208
1131110.84275325778820.157246742211842
1141516.0447102402141-1.04471024021408
1151213.2466125815812-1.24661258158121
1161010.9300113828539-0.930011382853863
1171414.157327645406-0.15732764540597
1181313.0379685112987-0.0379685112986713
119913.5814324492255-4.58143244922551
1201510.95488849369074.04511150630925
1211514.61371609529610.38628390470386
1221412.66835138457451.33164861542554
1231111.680588581262-0.680588581261973
124811.9001705212186-3.90017052121855
1251112.8946566954401-1.89465669544007
1261112.1074369204866-1.10743692048657
127811.4228992318161-3.42289923181605
1281011.5562957984001-1.55629579840014
1291112.1497645574845-1.1497645574845
1301313.6437783430915-0.643778343091504
1311112.7929341071253-1.79293410712528
1322014.92605933819255.07394066180748
1331011.9082417672538-1.90824176725384
1341514.60385001327060.396149986729366
1351212.6280446903998-0.62804469039975
1361412.37386699892251.6261330010775
1372315.56117295888457.43882704111549
1381413.46697632547140.53302367452861
1391615.37167121196160.628328788038371
1401114.2696004806873-3.26960048068733
1411215.0255339754035-3.02553397540349
1421014.071550984597-4.07155098459698
1431412.12866953867491.87133046132508
1441211.25681407068040.743185929319607
1451212.8327049074757-0.832704907475721
1461112.8813184150016-1.88131841500156
1471212.1026811806338-0.102681180633835
1481314.0775909450155-1.07759094501554
1491112.5398235139222-1.53982351392215
1501913.39979617691035.60020382308965
1511212.8956549425393-0.895654942539281
1521711.82990655140265.17009344859744
153912.5050746236147-3.50507462361473
1541212.4178221519681-0.417822151968115
1551913.36417203222065.63582796777937
1561813.96231865141564.03768134858443
1571513.58939556282581.41060443717423
1581413.6420662858120.357933714188049
1591112.1497645574845-1.1497645574845
160911.7666460189231-2.7666460189231
1611812.82643823642255.17356176357746
1621613.56938232427672.43061767572335






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.9953432604557380.009313479088524330.00465673954426217
100.9894052438612230.02118951227755440.0105947561387772
110.9847076768417660.0305846463164690.0152923231582345
120.996978624333890.006042751332219410.0030213756661097
130.9942890030165240.01142199396695140.00571099698347569
140.989344743530590.02131051293881910.0106552564694096
150.9871944005387070.02561119892258530.0128055994612927
160.9783875082767710.04322498344645860.0216124917232293
170.9681224040340350.06375519193193080.0318775959659654
180.956120258385180.087759483229640.04387974161482
190.9543077185654230.09138456286915330.0456922814345766
200.9367746848937840.1264506302124320.0632253151062162
210.9712313792614130.05753724147717330.0287686207385867
220.9592723484292090.08145530314158130.0407276515707907
230.9463809219900870.1072381560198270.0536190780099134
240.9275879355656270.1448241288687450.0724120644343726
250.9023331105506960.1953337788986080.0976668894493039
260.8990470257439850.2019059485120310.100952974256015
270.8696552830480750.2606894339038490.130344716951925
280.8500168670850830.2999662658298340.149983132914917
290.8234220160709340.3531559678581320.176577983929066
300.7804792747968080.4390414504063830.219520725203192
310.7803139784585060.4393720430829880.219686021541494
320.7379879216832790.5240241566334420.262012078316721
330.7162154862567210.5675690274865580.283784513743279
340.6657054579935050.6685890840129890.334294542006495
350.6147211393726440.7705577212547120.385278860627356
360.683848676036910.632302647926180.31615132396309
370.8360514687242430.3278970625515140.163948531275757
380.8010870173701850.397825965259630.198912982629815
390.7910292117226440.4179415765547120.208970788277356
400.7792814857424420.4414370285151160.220718514257558
410.775246337542740.4495073249145210.22475366245726
420.7473369762434220.5053260475131560.252663023756578
430.8056816185449280.3886367629101450.194318381455072
440.7689317039514630.4621365920970740.231068296048537
450.7424039011586070.5151921976827870.257596098841394
460.7022676083223920.5954647833552160.297732391677608
470.7313613610819940.5372772778360110.268638638918006
480.6968197456159360.6063605087681290.303180254384064
490.6911315099723770.6177369800552460.308868490027623
500.6563042585038080.6873914829923830.343695741496192
510.6421105356802390.7157789286395220.357889464319761
520.6006804316146380.7986391367707230.399319568385362
530.6453665701557510.7092668596884970.354633429844249
540.6015350111918530.7969299776162940.398464988808147
550.6292102020002550.741579595999490.370789797999745
560.5902426107023750.819514778595250.409757389297625
570.5523207825579480.8953584348841040.447679217442052
580.5932344222656320.8135311554687360.406765577734368
590.5749383458718140.8501233082563730.425061654128186
600.5612480778010890.8775038443978210.438751922198911
610.6332251117003690.7335497765992630.366774888299631
620.5901302251216680.8197395497566640.409869774878332
630.5437959993190190.9124080013619630.456204000680981
640.4986513494968810.9973026989937610.501348650503119
650.4577693905537380.9155387811074770.542230609446262
660.4116667408591430.8233334817182860.588333259140857
670.4777680724175190.9555361448350370.522231927582481
680.4326594225608770.8653188451217540.567340577439123
690.3911525089817710.7823050179635420.608847491018229
700.3571123092386290.7142246184772580.642887690761371
710.3279663778650040.6559327557300090.672033622134996
720.2880308205392170.5760616410784340.711969179460783
730.2670449152467130.5340898304934260.732955084753287
740.2430872727724230.4861745455448450.756912727227577
750.2124633831389320.4249267662778630.787536616861068
760.411580709500170.823161419000340.58841929049983
770.3694101443826610.7388202887653230.630589855617338
780.4059931965630950.8119863931261890.594006803436905
790.3636520866590810.7273041733181620.636347913340919
800.4568796668135420.9137593336270830.543120333186458
810.4154077201315590.8308154402631190.584592279868441
820.4816614472217350.9633228944434690.518338552778265
830.4987996345797650.9975992691595310.501200365420235
840.4587087916826640.9174175833653290.541291208317336
850.4177918912362410.8355837824724830.582208108763759
860.3742785480488120.7485570960976230.625721451951188
870.3327812083969120.6655624167938240.667218791603088
880.2959406464349840.5918812928699670.704059353565016
890.25997087104250.5199417420849990.7400291289575
900.6000139454388870.7999721091222260.399986054561113
910.5563593025003450.8872813949993090.443640697499655
920.5190081913813160.9619836172373680.480991808618684
930.4808357087659360.9616714175318720.519164291234064
940.465610345417330.9312206908346610.53438965458267
950.4688540810565860.9377081621131710.531145918943414
960.425833110446230.8516662208924610.57416688955377
970.4326704494969060.8653408989938130.567329550503094
980.3971137305636120.7942274611272240.602886269436388
990.3558062848133330.7116125696266670.644193715186667
1000.3493270392861650.698654078572330.650672960713835
1010.3275843576724480.6551687153448970.672415642327552
1020.3414497998543090.6828995997086180.658550200145691
1030.3191911128649120.6383822257298250.680808887135088
1040.3209906926945450.6419813853890890.679009307305455
1050.3760682084769620.7521364169539240.623931791523038
1060.4581361894776860.9162723789553730.541863810522314
1070.4130146351072820.8260292702145640.586985364892718
1080.4460577558162130.8921155116324260.553942244183787
1090.4030694334002470.8061388668004950.596930566599753
1100.7923063463776510.4153873072446980.207693653622349
1110.7700264003619120.4599471992761760.229973599638088
1120.7593731282244850.481253743551030.240626871775515
1130.7261625739487890.5476748521024210.273837426051211
1140.6902890223107220.6194219553785570.309710977689278
1150.6561384912393570.6877230175212870.343861508760643
1160.6083573529790550.7832852940418890.391642647020945
1170.559884745668360.880230508663280.44011525433164
1180.5112428406059360.9775143187881280.488757159394064
1190.5884803486244560.8230393027510880.411519651375544
1200.6743361732401790.6513276535196420.325663826759821
1210.6238204196858220.7523591606283570.376179580314178
1220.5767052101484510.8465895797030990.423294789851549
1230.5233532739799240.9532934520401520.476646726020076
1240.5213415766633070.9573168466733860.478658423336693
1250.4974994802409690.9949989604819370.502500519759031
1260.4496987628671660.8993975257343330.550301237132834
1270.4696423318637850.9392846637275690.530357668136215
1280.4173628389241770.8347256778483540.582637161075823
1290.3647467669354590.7294935338709190.635253233064541
1300.3265896836444210.6531793672888410.673410316355579
1310.2925093857718340.5850187715436690.707490614228166
1320.3262354634809940.6524709269619880.673764536519006
1330.2893633488634760.5787266977269520.710636651136524
1340.2401842235387040.4803684470774080.759815776461296
1350.193780762945680.3875615258913590.80621923705432
1360.1549251546241090.3098503092482180.845074845375891
1370.3190266983779830.6380533967559660.680973301622017
1380.2610220918601570.5220441837203130.738977908139843
1390.216407663451520.4328153269030390.78359233654848
1400.2219393494771880.4438786989543750.778060650522812
1410.2902440388738990.5804880777477970.709755961126101
1420.3388847959217430.6777695918434850.661115204078257
1430.2914410196616730.5828820393233460.708558980338327
1440.2419332523304550.483866504660910.758066747669545
1450.1961756266517430.3923512533034870.803824373348257
1460.5518532352147550.896293529570490.448146764785245
1470.4730984235604170.9461968471208340.526901576439583
1480.4322195392160660.8644390784321310.567780460783934
1490.3511304338597240.7022608677194490.648869566140276
1500.2792918131141550.5585836262283090.720708186885845
1510.5989370370295110.8021259259409780.401062962970489
1520.4853087297983280.9706174595966550.514691270201672
1530.4634820749993730.9269641499987460.536517925000627

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.995343260455738 & 0.00931347908852433 & 0.00465673954426217 \tabularnewline
10 & 0.989405243861223 & 0.0211895122775544 & 0.0105947561387772 \tabularnewline
11 & 0.984707676841766 & 0.030584646316469 & 0.0152923231582345 \tabularnewline
12 & 0.99697862433389 & 0.00604275133221941 & 0.0030213756661097 \tabularnewline
13 & 0.994289003016524 & 0.0114219939669514 & 0.00571099698347569 \tabularnewline
14 & 0.98934474353059 & 0.0213105129388191 & 0.0106552564694096 \tabularnewline
15 & 0.987194400538707 & 0.0256111989225853 & 0.0128055994612927 \tabularnewline
16 & 0.978387508276771 & 0.0432249834464586 & 0.0216124917232293 \tabularnewline
17 & 0.968122404034035 & 0.0637551919319308 & 0.0318775959659654 \tabularnewline
18 & 0.95612025838518 & 0.08775948322964 & 0.04387974161482 \tabularnewline
19 & 0.954307718565423 & 0.0913845628691533 & 0.0456922814345766 \tabularnewline
20 & 0.936774684893784 & 0.126450630212432 & 0.0632253151062162 \tabularnewline
21 & 0.971231379261413 & 0.0575372414771733 & 0.0287686207385867 \tabularnewline
22 & 0.959272348429209 & 0.0814553031415813 & 0.0407276515707907 \tabularnewline
23 & 0.946380921990087 & 0.107238156019827 & 0.0536190780099134 \tabularnewline
24 & 0.927587935565627 & 0.144824128868745 & 0.0724120644343726 \tabularnewline
25 & 0.902333110550696 & 0.195333778898608 & 0.0976668894493039 \tabularnewline
26 & 0.899047025743985 & 0.201905948512031 & 0.100952974256015 \tabularnewline
27 & 0.869655283048075 & 0.260689433903849 & 0.130344716951925 \tabularnewline
28 & 0.850016867085083 & 0.299966265829834 & 0.149983132914917 \tabularnewline
29 & 0.823422016070934 & 0.353155967858132 & 0.176577983929066 \tabularnewline
30 & 0.780479274796808 & 0.439041450406383 & 0.219520725203192 \tabularnewline
31 & 0.780313978458506 & 0.439372043082988 & 0.219686021541494 \tabularnewline
32 & 0.737987921683279 & 0.524024156633442 & 0.262012078316721 \tabularnewline
33 & 0.716215486256721 & 0.567569027486558 & 0.283784513743279 \tabularnewline
34 & 0.665705457993505 & 0.668589084012989 & 0.334294542006495 \tabularnewline
35 & 0.614721139372644 & 0.770557721254712 & 0.385278860627356 \tabularnewline
36 & 0.68384867603691 & 0.63230264792618 & 0.31615132396309 \tabularnewline
37 & 0.836051468724243 & 0.327897062551514 & 0.163948531275757 \tabularnewline
38 & 0.801087017370185 & 0.39782596525963 & 0.198912982629815 \tabularnewline
39 & 0.791029211722644 & 0.417941576554712 & 0.208970788277356 \tabularnewline
40 & 0.779281485742442 & 0.441437028515116 & 0.220718514257558 \tabularnewline
41 & 0.77524633754274 & 0.449507324914521 & 0.22475366245726 \tabularnewline
42 & 0.747336976243422 & 0.505326047513156 & 0.252663023756578 \tabularnewline
43 & 0.805681618544928 & 0.388636762910145 & 0.194318381455072 \tabularnewline
44 & 0.768931703951463 & 0.462136592097074 & 0.231068296048537 \tabularnewline
45 & 0.742403901158607 & 0.515192197682787 & 0.257596098841394 \tabularnewline
46 & 0.702267608322392 & 0.595464783355216 & 0.297732391677608 \tabularnewline
47 & 0.731361361081994 & 0.537277277836011 & 0.268638638918006 \tabularnewline
48 & 0.696819745615936 & 0.606360508768129 & 0.303180254384064 \tabularnewline
49 & 0.691131509972377 & 0.617736980055246 & 0.308868490027623 \tabularnewline
50 & 0.656304258503808 & 0.687391482992383 & 0.343695741496192 \tabularnewline
51 & 0.642110535680239 & 0.715778928639522 & 0.357889464319761 \tabularnewline
52 & 0.600680431614638 & 0.798639136770723 & 0.399319568385362 \tabularnewline
53 & 0.645366570155751 & 0.709266859688497 & 0.354633429844249 \tabularnewline
54 & 0.601535011191853 & 0.796929977616294 & 0.398464988808147 \tabularnewline
55 & 0.629210202000255 & 0.74157959599949 & 0.370789797999745 \tabularnewline
56 & 0.590242610702375 & 0.81951477859525 & 0.409757389297625 \tabularnewline
57 & 0.552320782557948 & 0.895358434884104 & 0.447679217442052 \tabularnewline
58 & 0.593234422265632 & 0.813531155468736 & 0.406765577734368 \tabularnewline
59 & 0.574938345871814 & 0.850123308256373 & 0.425061654128186 \tabularnewline
60 & 0.561248077801089 & 0.877503844397821 & 0.438751922198911 \tabularnewline
61 & 0.633225111700369 & 0.733549776599263 & 0.366774888299631 \tabularnewline
62 & 0.590130225121668 & 0.819739549756664 & 0.409869774878332 \tabularnewline
63 & 0.543795999319019 & 0.912408001361963 & 0.456204000680981 \tabularnewline
64 & 0.498651349496881 & 0.997302698993761 & 0.501348650503119 \tabularnewline
65 & 0.457769390553738 & 0.915538781107477 & 0.542230609446262 \tabularnewline
66 & 0.411666740859143 & 0.823333481718286 & 0.588333259140857 \tabularnewline
67 & 0.477768072417519 & 0.955536144835037 & 0.522231927582481 \tabularnewline
68 & 0.432659422560877 & 0.865318845121754 & 0.567340577439123 \tabularnewline
69 & 0.391152508981771 & 0.782305017963542 & 0.608847491018229 \tabularnewline
70 & 0.357112309238629 & 0.714224618477258 & 0.642887690761371 \tabularnewline
71 & 0.327966377865004 & 0.655932755730009 & 0.672033622134996 \tabularnewline
72 & 0.288030820539217 & 0.576061641078434 & 0.711969179460783 \tabularnewline
73 & 0.267044915246713 & 0.534089830493426 & 0.732955084753287 \tabularnewline
74 & 0.243087272772423 & 0.486174545544845 & 0.756912727227577 \tabularnewline
75 & 0.212463383138932 & 0.424926766277863 & 0.787536616861068 \tabularnewline
76 & 0.41158070950017 & 0.82316141900034 & 0.58841929049983 \tabularnewline
77 & 0.369410144382661 & 0.738820288765323 & 0.630589855617338 \tabularnewline
78 & 0.405993196563095 & 0.811986393126189 & 0.594006803436905 \tabularnewline
79 & 0.363652086659081 & 0.727304173318162 & 0.636347913340919 \tabularnewline
80 & 0.456879666813542 & 0.913759333627083 & 0.543120333186458 \tabularnewline
81 & 0.415407720131559 & 0.830815440263119 & 0.584592279868441 \tabularnewline
82 & 0.481661447221735 & 0.963322894443469 & 0.518338552778265 \tabularnewline
83 & 0.498799634579765 & 0.997599269159531 & 0.501200365420235 \tabularnewline
84 & 0.458708791682664 & 0.917417583365329 & 0.541291208317336 \tabularnewline
85 & 0.417791891236241 & 0.835583782472483 & 0.582208108763759 \tabularnewline
86 & 0.374278548048812 & 0.748557096097623 & 0.625721451951188 \tabularnewline
87 & 0.332781208396912 & 0.665562416793824 & 0.667218791603088 \tabularnewline
88 & 0.295940646434984 & 0.591881292869967 & 0.704059353565016 \tabularnewline
89 & 0.2599708710425 & 0.519941742084999 & 0.7400291289575 \tabularnewline
90 & 0.600013945438887 & 0.799972109122226 & 0.399986054561113 \tabularnewline
91 & 0.556359302500345 & 0.887281394999309 & 0.443640697499655 \tabularnewline
92 & 0.519008191381316 & 0.961983617237368 & 0.480991808618684 \tabularnewline
93 & 0.480835708765936 & 0.961671417531872 & 0.519164291234064 \tabularnewline
94 & 0.46561034541733 & 0.931220690834661 & 0.53438965458267 \tabularnewline
95 & 0.468854081056586 & 0.937708162113171 & 0.531145918943414 \tabularnewline
96 & 0.42583311044623 & 0.851666220892461 & 0.57416688955377 \tabularnewline
97 & 0.432670449496906 & 0.865340898993813 & 0.567329550503094 \tabularnewline
98 & 0.397113730563612 & 0.794227461127224 & 0.602886269436388 \tabularnewline
99 & 0.355806284813333 & 0.711612569626667 & 0.644193715186667 \tabularnewline
100 & 0.349327039286165 & 0.69865407857233 & 0.650672960713835 \tabularnewline
101 & 0.327584357672448 & 0.655168715344897 & 0.672415642327552 \tabularnewline
102 & 0.341449799854309 & 0.682899599708618 & 0.658550200145691 \tabularnewline
103 & 0.319191112864912 & 0.638382225729825 & 0.680808887135088 \tabularnewline
104 & 0.320990692694545 & 0.641981385389089 & 0.679009307305455 \tabularnewline
105 & 0.376068208476962 & 0.752136416953924 & 0.623931791523038 \tabularnewline
106 & 0.458136189477686 & 0.916272378955373 & 0.541863810522314 \tabularnewline
107 & 0.413014635107282 & 0.826029270214564 & 0.586985364892718 \tabularnewline
108 & 0.446057755816213 & 0.892115511632426 & 0.553942244183787 \tabularnewline
109 & 0.403069433400247 & 0.806138866800495 & 0.596930566599753 \tabularnewline
110 & 0.792306346377651 & 0.415387307244698 & 0.207693653622349 \tabularnewline
111 & 0.770026400361912 & 0.459947199276176 & 0.229973599638088 \tabularnewline
112 & 0.759373128224485 & 0.48125374355103 & 0.240626871775515 \tabularnewline
113 & 0.726162573948789 & 0.547674852102421 & 0.273837426051211 \tabularnewline
114 & 0.690289022310722 & 0.619421955378557 & 0.309710977689278 \tabularnewline
115 & 0.656138491239357 & 0.687723017521287 & 0.343861508760643 \tabularnewline
116 & 0.608357352979055 & 0.783285294041889 & 0.391642647020945 \tabularnewline
117 & 0.55988474566836 & 0.88023050866328 & 0.44011525433164 \tabularnewline
118 & 0.511242840605936 & 0.977514318788128 & 0.488757159394064 \tabularnewline
119 & 0.588480348624456 & 0.823039302751088 & 0.411519651375544 \tabularnewline
120 & 0.674336173240179 & 0.651327653519642 & 0.325663826759821 \tabularnewline
121 & 0.623820419685822 & 0.752359160628357 & 0.376179580314178 \tabularnewline
122 & 0.576705210148451 & 0.846589579703099 & 0.423294789851549 \tabularnewline
123 & 0.523353273979924 & 0.953293452040152 & 0.476646726020076 \tabularnewline
124 & 0.521341576663307 & 0.957316846673386 & 0.478658423336693 \tabularnewline
125 & 0.497499480240969 & 0.994998960481937 & 0.502500519759031 \tabularnewline
126 & 0.449698762867166 & 0.899397525734333 & 0.550301237132834 \tabularnewline
127 & 0.469642331863785 & 0.939284663727569 & 0.530357668136215 \tabularnewline
128 & 0.417362838924177 & 0.834725677848354 & 0.582637161075823 \tabularnewline
129 & 0.364746766935459 & 0.729493533870919 & 0.635253233064541 \tabularnewline
130 & 0.326589683644421 & 0.653179367288841 & 0.673410316355579 \tabularnewline
131 & 0.292509385771834 & 0.585018771543669 & 0.707490614228166 \tabularnewline
132 & 0.326235463480994 & 0.652470926961988 & 0.673764536519006 \tabularnewline
133 & 0.289363348863476 & 0.578726697726952 & 0.710636651136524 \tabularnewline
134 & 0.240184223538704 & 0.480368447077408 & 0.759815776461296 \tabularnewline
135 & 0.19378076294568 & 0.387561525891359 & 0.80621923705432 \tabularnewline
136 & 0.154925154624109 & 0.309850309248218 & 0.845074845375891 \tabularnewline
137 & 0.319026698377983 & 0.638053396755966 & 0.680973301622017 \tabularnewline
138 & 0.261022091860157 & 0.522044183720313 & 0.738977908139843 \tabularnewline
139 & 0.21640766345152 & 0.432815326903039 & 0.78359233654848 \tabularnewline
140 & 0.221939349477188 & 0.443878698954375 & 0.778060650522812 \tabularnewline
141 & 0.290244038873899 & 0.580488077747797 & 0.709755961126101 \tabularnewline
142 & 0.338884795921743 & 0.677769591843485 & 0.661115204078257 \tabularnewline
143 & 0.291441019661673 & 0.582882039323346 & 0.708558980338327 \tabularnewline
144 & 0.241933252330455 & 0.48386650466091 & 0.758066747669545 \tabularnewline
145 & 0.196175626651743 & 0.392351253303487 & 0.803824373348257 \tabularnewline
146 & 0.551853235214755 & 0.89629352957049 & 0.448146764785245 \tabularnewline
147 & 0.473098423560417 & 0.946196847120834 & 0.526901576439583 \tabularnewline
148 & 0.432219539216066 & 0.864439078432131 & 0.567780460783934 \tabularnewline
149 & 0.351130433859724 & 0.702260867719449 & 0.648869566140276 \tabularnewline
150 & 0.279291813114155 & 0.558583626228309 & 0.720708186885845 \tabularnewline
151 & 0.598937037029511 & 0.802125925940978 & 0.401062962970489 \tabularnewline
152 & 0.485308729798328 & 0.970617459596655 & 0.514691270201672 \tabularnewline
153 & 0.463482074999373 & 0.926964149998746 & 0.536517925000627 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147219&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]9[/C][C]0.995343260455738[/C][C]0.00931347908852433[/C][C]0.00465673954426217[/C][/ROW]
[ROW][C]10[/C][C]0.989405243861223[/C][C]0.0211895122775544[/C][C]0.0105947561387772[/C][/ROW]
[ROW][C]11[/C][C]0.984707676841766[/C][C]0.030584646316469[/C][C]0.0152923231582345[/C][/ROW]
[ROW][C]12[/C][C]0.99697862433389[/C][C]0.00604275133221941[/C][C]0.0030213756661097[/C][/ROW]
[ROW][C]13[/C][C]0.994289003016524[/C][C]0.0114219939669514[/C][C]0.00571099698347569[/C][/ROW]
[ROW][C]14[/C][C]0.98934474353059[/C][C]0.0213105129388191[/C][C]0.0106552564694096[/C][/ROW]
[ROW][C]15[/C][C]0.987194400538707[/C][C]0.0256111989225853[/C][C]0.0128055994612927[/C][/ROW]
[ROW][C]16[/C][C]0.978387508276771[/C][C]0.0432249834464586[/C][C]0.0216124917232293[/C][/ROW]
[ROW][C]17[/C][C]0.968122404034035[/C][C]0.0637551919319308[/C][C]0.0318775959659654[/C][/ROW]
[ROW][C]18[/C][C]0.95612025838518[/C][C]0.08775948322964[/C][C]0.04387974161482[/C][/ROW]
[ROW][C]19[/C][C]0.954307718565423[/C][C]0.0913845628691533[/C][C]0.0456922814345766[/C][/ROW]
[ROW][C]20[/C][C]0.936774684893784[/C][C]0.126450630212432[/C][C]0.0632253151062162[/C][/ROW]
[ROW][C]21[/C][C]0.971231379261413[/C][C]0.0575372414771733[/C][C]0.0287686207385867[/C][/ROW]
[ROW][C]22[/C][C]0.959272348429209[/C][C]0.0814553031415813[/C][C]0.0407276515707907[/C][/ROW]
[ROW][C]23[/C][C]0.946380921990087[/C][C]0.107238156019827[/C][C]0.0536190780099134[/C][/ROW]
[ROW][C]24[/C][C]0.927587935565627[/C][C]0.144824128868745[/C][C]0.0724120644343726[/C][/ROW]
[ROW][C]25[/C][C]0.902333110550696[/C][C]0.195333778898608[/C][C]0.0976668894493039[/C][/ROW]
[ROW][C]26[/C][C]0.899047025743985[/C][C]0.201905948512031[/C][C]0.100952974256015[/C][/ROW]
[ROW][C]27[/C][C]0.869655283048075[/C][C]0.260689433903849[/C][C]0.130344716951925[/C][/ROW]
[ROW][C]28[/C][C]0.850016867085083[/C][C]0.299966265829834[/C][C]0.149983132914917[/C][/ROW]
[ROW][C]29[/C][C]0.823422016070934[/C][C]0.353155967858132[/C][C]0.176577983929066[/C][/ROW]
[ROW][C]30[/C][C]0.780479274796808[/C][C]0.439041450406383[/C][C]0.219520725203192[/C][/ROW]
[ROW][C]31[/C][C]0.780313978458506[/C][C]0.439372043082988[/C][C]0.219686021541494[/C][/ROW]
[ROW][C]32[/C][C]0.737987921683279[/C][C]0.524024156633442[/C][C]0.262012078316721[/C][/ROW]
[ROW][C]33[/C][C]0.716215486256721[/C][C]0.567569027486558[/C][C]0.283784513743279[/C][/ROW]
[ROW][C]34[/C][C]0.665705457993505[/C][C]0.668589084012989[/C][C]0.334294542006495[/C][/ROW]
[ROW][C]35[/C][C]0.614721139372644[/C][C]0.770557721254712[/C][C]0.385278860627356[/C][/ROW]
[ROW][C]36[/C][C]0.68384867603691[/C][C]0.63230264792618[/C][C]0.31615132396309[/C][/ROW]
[ROW][C]37[/C][C]0.836051468724243[/C][C]0.327897062551514[/C][C]0.163948531275757[/C][/ROW]
[ROW][C]38[/C][C]0.801087017370185[/C][C]0.39782596525963[/C][C]0.198912982629815[/C][/ROW]
[ROW][C]39[/C][C]0.791029211722644[/C][C]0.417941576554712[/C][C]0.208970788277356[/C][/ROW]
[ROW][C]40[/C][C]0.779281485742442[/C][C]0.441437028515116[/C][C]0.220718514257558[/C][/ROW]
[ROW][C]41[/C][C]0.77524633754274[/C][C]0.449507324914521[/C][C]0.22475366245726[/C][/ROW]
[ROW][C]42[/C][C]0.747336976243422[/C][C]0.505326047513156[/C][C]0.252663023756578[/C][/ROW]
[ROW][C]43[/C][C]0.805681618544928[/C][C]0.388636762910145[/C][C]0.194318381455072[/C][/ROW]
[ROW][C]44[/C][C]0.768931703951463[/C][C]0.462136592097074[/C][C]0.231068296048537[/C][/ROW]
[ROW][C]45[/C][C]0.742403901158607[/C][C]0.515192197682787[/C][C]0.257596098841394[/C][/ROW]
[ROW][C]46[/C][C]0.702267608322392[/C][C]0.595464783355216[/C][C]0.297732391677608[/C][/ROW]
[ROW][C]47[/C][C]0.731361361081994[/C][C]0.537277277836011[/C][C]0.268638638918006[/C][/ROW]
[ROW][C]48[/C][C]0.696819745615936[/C][C]0.606360508768129[/C][C]0.303180254384064[/C][/ROW]
[ROW][C]49[/C][C]0.691131509972377[/C][C]0.617736980055246[/C][C]0.308868490027623[/C][/ROW]
[ROW][C]50[/C][C]0.656304258503808[/C][C]0.687391482992383[/C][C]0.343695741496192[/C][/ROW]
[ROW][C]51[/C][C]0.642110535680239[/C][C]0.715778928639522[/C][C]0.357889464319761[/C][/ROW]
[ROW][C]52[/C][C]0.600680431614638[/C][C]0.798639136770723[/C][C]0.399319568385362[/C][/ROW]
[ROW][C]53[/C][C]0.645366570155751[/C][C]0.709266859688497[/C][C]0.354633429844249[/C][/ROW]
[ROW][C]54[/C][C]0.601535011191853[/C][C]0.796929977616294[/C][C]0.398464988808147[/C][/ROW]
[ROW][C]55[/C][C]0.629210202000255[/C][C]0.74157959599949[/C][C]0.370789797999745[/C][/ROW]
[ROW][C]56[/C][C]0.590242610702375[/C][C]0.81951477859525[/C][C]0.409757389297625[/C][/ROW]
[ROW][C]57[/C][C]0.552320782557948[/C][C]0.895358434884104[/C][C]0.447679217442052[/C][/ROW]
[ROW][C]58[/C][C]0.593234422265632[/C][C]0.813531155468736[/C][C]0.406765577734368[/C][/ROW]
[ROW][C]59[/C][C]0.574938345871814[/C][C]0.850123308256373[/C][C]0.425061654128186[/C][/ROW]
[ROW][C]60[/C][C]0.561248077801089[/C][C]0.877503844397821[/C][C]0.438751922198911[/C][/ROW]
[ROW][C]61[/C][C]0.633225111700369[/C][C]0.733549776599263[/C][C]0.366774888299631[/C][/ROW]
[ROW][C]62[/C][C]0.590130225121668[/C][C]0.819739549756664[/C][C]0.409869774878332[/C][/ROW]
[ROW][C]63[/C][C]0.543795999319019[/C][C]0.912408001361963[/C][C]0.456204000680981[/C][/ROW]
[ROW][C]64[/C][C]0.498651349496881[/C][C]0.997302698993761[/C][C]0.501348650503119[/C][/ROW]
[ROW][C]65[/C][C]0.457769390553738[/C][C]0.915538781107477[/C][C]0.542230609446262[/C][/ROW]
[ROW][C]66[/C][C]0.411666740859143[/C][C]0.823333481718286[/C][C]0.588333259140857[/C][/ROW]
[ROW][C]67[/C][C]0.477768072417519[/C][C]0.955536144835037[/C][C]0.522231927582481[/C][/ROW]
[ROW][C]68[/C][C]0.432659422560877[/C][C]0.865318845121754[/C][C]0.567340577439123[/C][/ROW]
[ROW][C]69[/C][C]0.391152508981771[/C][C]0.782305017963542[/C][C]0.608847491018229[/C][/ROW]
[ROW][C]70[/C][C]0.357112309238629[/C][C]0.714224618477258[/C][C]0.642887690761371[/C][/ROW]
[ROW][C]71[/C][C]0.327966377865004[/C][C]0.655932755730009[/C][C]0.672033622134996[/C][/ROW]
[ROW][C]72[/C][C]0.288030820539217[/C][C]0.576061641078434[/C][C]0.711969179460783[/C][/ROW]
[ROW][C]73[/C][C]0.267044915246713[/C][C]0.534089830493426[/C][C]0.732955084753287[/C][/ROW]
[ROW][C]74[/C][C]0.243087272772423[/C][C]0.486174545544845[/C][C]0.756912727227577[/C][/ROW]
[ROW][C]75[/C][C]0.212463383138932[/C][C]0.424926766277863[/C][C]0.787536616861068[/C][/ROW]
[ROW][C]76[/C][C]0.41158070950017[/C][C]0.82316141900034[/C][C]0.58841929049983[/C][/ROW]
[ROW][C]77[/C][C]0.369410144382661[/C][C]0.738820288765323[/C][C]0.630589855617338[/C][/ROW]
[ROW][C]78[/C][C]0.405993196563095[/C][C]0.811986393126189[/C][C]0.594006803436905[/C][/ROW]
[ROW][C]79[/C][C]0.363652086659081[/C][C]0.727304173318162[/C][C]0.636347913340919[/C][/ROW]
[ROW][C]80[/C][C]0.456879666813542[/C][C]0.913759333627083[/C][C]0.543120333186458[/C][/ROW]
[ROW][C]81[/C][C]0.415407720131559[/C][C]0.830815440263119[/C][C]0.584592279868441[/C][/ROW]
[ROW][C]82[/C][C]0.481661447221735[/C][C]0.963322894443469[/C][C]0.518338552778265[/C][/ROW]
[ROW][C]83[/C][C]0.498799634579765[/C][C]0.997599269159531[/C][C]0.501200365420235[/C][/ROW]
[ROW][C]84[/C][C]0.458708791682664[/C][C]0.917417583365329[/C][C]0.541291208317336[/C][/ROW]
[ROW][C]85[/C][C]0.417791891236241[/C][C]0.835583782472483[/C][C]0.582208108763759[/C][/ROW]
[ROW][C]86[/C][C]0.374278548048812[/C][C]0.748557096097623[/C][C]0.625721451951188[/C][/ROW]
[ROW][C]87[/C][C]0.332781208396912[/C][C]0.665562416793824[/C][C]0.667218791603088[/C][/ROW]
[ROW][C]88[/C][C]0.295940646434984[/C][C]0.591881292869967[/C][C]0.704059353565016[/C][/ROW]
[ROW][C]89[/C][C]0.2599708710425[/C][C]0.519941742084999[/C][C]0.7400291289575[/C][/ROW]
[ROW][C]90[/C][C]0.600013945438887[/C][C]0.799972109122226[/C][C]0.399986054561113[/C][/ROW]
[ROW][C]91[/C][C]0.556359302500345[/C][C]0.887281394999309[/C][C]0.443640697499655[/C][/ROW]
[ROW][C]92[/C][C]0.519008191381316[/C][C]0.961983617237368[/C][C]0.480991808618684[/C][/ROW]
[ROW][C]93[/C][C]0.480835708765936[/C][C]0.961671417531872[/C][C]0.519164291234064[/C][/ROW]
[ROW][C]94[/C][C]0.46561034541733[/C][C]0.931220690834661[/C][C]0.53438965458267[/C][/ROW]
[ROW][C]95[/C][C]0.468854081056586[/C][C]0.937708162113171[/C][C]0.531145918943414[/C][/ROW]
[ROW][C]96[/C][C]0.42583311044623[/C][C]0.851666220892461[/C][C]0.57416688955377[/C][/ROW]
[ROW][C]97[/C][C]0.432670449496906[/C][C]0.865340898993813[/C][C]0.567329550503094[/C][/ROW]
[ROW][C]98[/C][C]0.397113730563612[/C][C]0.794227461127224[/C][C]0.602886269436388[/C][/ROW]
[ROW][C]99[/C][C]0.355806284813333[/C][C]0.711612569626667[/C][C]0.644193715186667[/C][/ROW]
[ROW][C]100[/C][C]0.349327039286165[/C][C]0.69865407857233[/C][C]0.650672960713835[/C][/ROW]
[ROW][C]101[/C][C]0.327584357672448[/C][C]0.655168715344897[/C][C]0.672415642327552[/C][/ROW]
[ROW][C]102[/C][C]0.341449799854309[/C][C]0.682899599708618[/C][C]0.658550200145691[/C][/ROW]
[ROW][C]103[/C][C]0.319191112864912[/C][C]0.638382225729825[/C][C]0.680808887135088[/C][/ROW]
[ROW][C]104[/C][C]0.320990692694545[/C][C]0.641981385389089[/C][C]0.679009307305455[/C][/ROW]
[ROW][C]105[/C][C]0.376068208476962[/C][C]0.752136416953924[/C][C]0.623931791523038[/C][/ROW]
[ROW][C]106[/C][C]0.458136189477686[/C][C]0.916272378955373[/C][C]0.541863810522314[/C][/ROW]
[ROW][C]107[/C][C]0.413014635107282[/C][C]0.826029270214564[/C][C]0.586985364892718[/C][/ROW]
[ROW][C]108[/C][C]0.446057755816213[/C][C]0.892115511632426[/C][C]0.553942244183787[/C][/ROW]
[ROW][C]109[/C][C]0.403069433400247[/C][C]0.806138866800495[/C][C]0.596930566599753[/C][/ROW]
[ROW][C]110[/C][C]0.792306346377651[/C][C]0.415387307244698[/C][C]0.207693653622349[/C][/ROW]
[ROW][C]111[/C][C]0.770026400361912[/C][C]0.459947199276176[/C][C]0.229973599638088[/C][/ROW]
[ROW][C]112[/C][C]0.759373128224485[/C][C]0.48125374355103[/C][C]0.240626871775515[/C][/ROW]
[ROW][C]113[/C][C]0.726162573948789[/C][C]0.547674852102421[/C][C]0.273837426051211[/C][/ROW]
[ROW][C]114[/C][C]0.690289022310722[/C][C]0.619421955378557[/C][C]0.309710977689278[/C][/ROW]
[ROW][C]115[/C][C]0.656138491239357[/C][C]0.687723017521287[/C][C]0.343861508760643[/C][/ROW]
[ROW][C]116[/C][C]0.608357352979055[/C][C]0.783285294041889[/C][C]0.391642647020945[/C][/ROW]
[ROW][C]117[/C][C]0.55988474566836[/C][C]0.88023050866328[/C][C]0.44011525433164[/C][/ROW]
[ROW][C]118[/C][C]0.511242840605936[/C][C]0.977514318788128[/C][C]0.488757159394064[/C][/ROW]
[ROW][C]119[/C][C]0.588480348624456[/C][C]0.823039302751088[/C][C]0.411519651375544[/C][/ROW]
[ROW][C]120[/C][C]0.674336173240179[/C][C]0.651327653519642[/C][C]0.325663826759821[/C][/ROW]
[ROW][C]121[/C][C]0.623820419685822[/C][C]0.752359160628357[/C][C]0.376179580314178[/C][/ROW]
[ROW][C]122[/C][C]0.576705210148451[/C][C]0.846589579703099[/C][C]0.423294789851549[/C][/ROW]
[ROW][C]123[/C][C]0.523353273979924[/C][C]0.953293452040152[/C][C]0.476646726020076[/C][/ROW]
[ROW][C]124[/C][C]0.521341576663307[/C][C]0.957316846673386[/C][C]0.478658423336693[/C][/ROW]
[ROW][C]125[/C][C]0.497499480240969[/C][C]0.994998960481937[/C][C]0.502500519759031[/C][/ROW]
[ROW][C]126[/C][C]0.449698762867166[/C][C]0.899397525734333[/C][C]0.550301237132834[/C][/ROW]
[ROW][C]127[/C][C]0.469642331863785[/C][C]0.939284663727569[/C][C]0.530357668136215[/C][/ROW]
[ROW][C]128[/C][C]0.417362838924177[/C][C]0.834725677848354[/C][C]0.582637161075823[/C][/ROW]
[ROW][C]129[/C][C]0.364746766935459[/C][C]0.729493533870919[/C][C]0.635253233064541[/C][/ROW]
[ROW][C]130[/C][C]0.326589683644421[/C][C]0.653179367288841[/C][C]0.673410316355579[/C][/ROW]
[ROW][C]131[/C][C]0.292509385771834[/C][C]0.585018771543669[/C][C]0.707490614228166[/C][/ROW]
[ROW][C]132[/C][C]0.326235463480994[/C][C]0.652470926961988[/C][C]0.673764536519006[/C][/ROW]
[ROW][C]133[/C][C]0.289363348863476[/C][C]0.578726697726952[/C][C]0.710636651136524[/C][/ROW]
[ROW][C]134[/C][C]0.240184223538704[/C][C]0.480368447077408[/C][C]0.759815776461296[/C][/ROW]
[ROW][C]135[/C][C]0.19378076294568[/C][C]0.387561525891359[/C][C]0.80621923705432[/C][/ROW]
[ROW][C]136[/C][C]0.154925154624109[/C][C]0.309850309248218[/C][C]0.845074845375891[/C][/ROW]
[ROW][C]137[/C][C]0.319026698377983[/C][C]0.638053396755966[/C][C]0.680973301622017[/C][/ROW]
[ROW][C]138[/C][C]0.261022091860157[/C][C]0.522044183720313[/C][C]0.738977908139843[/C][/ROW]
[ROW][C]139[/C][C]0.21640766345152[/C][C]0.432815326903039[/C][C]0.78359233654848[/C][/ROW]
[ROW][C]140[/C][C]0.221939349477188[/C][C]0.443878698954375[/C][C]0.778060650522812[/C][/ROW]
[ROW][C]141[/C][C]0.290244038873899[/C][C]0.580488077747797[/C][C]0.709755961126101[/C][/ROW]
[ROW][C]142[/C][C]0.338884795921743[/C][C]0.677769591843485[/C][C]0.661115204078257[/C][/ROW]
[ROW][C]143[/C][C]0.291441019661673[/C][C]0.582882039323346[/C][C]0.708558980338327[/C][/ROW]
[ROW][C]144[/C][C]0.241933252330455[/C][C]0.48386650466091[/C][C]0.758066747669545[/C][/ROW]
[ROW][C]145[/C][C]0.196175626651743[/C][C]0.392351253303487[/C][C]0.803824373348257[/C][/ROW]
[ROW][C]146[/C][C]0.551853235214755[/C][C]0.89629352957049[/C][C]0.448146764785245[/C][/ROW]
[ROW][C]147[/C][C]0.473098423560417[/C][C]0.946196847120834[/C][C]0.526901576439583[/C][/ROW]
[ROW][C]148[/C][C]0.432219539216066[/C][C]0.864439078432131[/C][C]0.567780460783934[/C][/ROW]
[ROW][C]149[/C][C]0.351130433859724[/C][C]0.702260867719449[/C][C]0.648869566140276[/C][/ROW]
[ROW][C]150[/C][C]0.279291813114155[/C][C]0.558583626228309[/C][C]0.720708186885845[/C][/ROW]
[ROW][C]151[/C][C]0.598937037029511[/C][C]0.802125925940978[/C][C]0.401062962970489[/C][/ROW]
[ROW][C]152[/C][C]0.485308729798328[/C][C]0.970617459596655[/C][C]0.514691270201672[/C][/ROW]
[ROW][C]153[/C][C]0.463482074999373[/C][C]0.926964149998746[/C][C]0.536517925000627[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147219&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147219&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
90.9953432604557380.009313479088524330.00465673954426217
100.9894052438612230.02118951227755440.0105947561387772
110.9847076768417660.0305846463164690.0152923231582345
120.996978624333890.006042751332219410.0030213756661097
130.9942890030165240.01142199396695140.00571099698347569
140.989344743530590.02131051293881910.0106552564694096
150.9871944005387070.02561119892258530.0128055994612927
160.9783875082767710.04322498344645860.0216124917232293
170.9681224040340350.06375519193193080.0318775959659654
180.956120258385180.087759483229640.04387974161482
190.9543077185654230.09138456286915330.0456922814345766
200.9367746848937840.1264506302124320.0632253151062162
210.9712313792614130.05753724147717330.0287686207385867
220.9592723484292090.08145530314158130.0407276515707907
230.9463809219900870.1072381560198270.0536190780099134
240.9275879355656270.1448241288687450.0724120644343726
250.9023331105506960.1953337788986080.0976668894493039
260.8990470257439850.2019059485120310.100952974256015
270.8696552830480750.2606894339038490.130344716951925
280.8500168670850830.2999662658298340.149983132914917
290.8234220160709340.3531559678581320.176577983929066
300.7804792747968080.4390414504063830.219520725203192
310.7803139784585060.4393720430829880.219686021541494
320.7379879216832790.5240241566334420.262012078316721
330.7162154862567210.5675690274865580.283784513743279
340.6657054579935050.6685890840129890.334294542006495
350.6147211393726440.7705577212547120.385278860627356
360.683848676036910.632302647926180.31615132396309
370.8360514687242430.3278970625515140.163948531275757
380.8010870173701850.397825965259630.198912982629815
390.7910292117226440.4179415765547120.208970788277356
400.7792814857424420.4414370285151160.220718514257558
410.775246337542740.4495073249145210.22475366245726
420.7473369762434220.5053260475131560.252663023756578
430.8056816185449280.3886367629101450.194318381455072
440.7689317039514630.4621365920970740.231068296048537
450.7424039011586070.5151921976827870.257596098841394
460.7022676083223920.5954647833552160.297732391677608
470.7313613610819940.5372772778360110.268638638918006
480.6968197456159360.6063605087681290.303180254384064
490.6911315099723770.6177369800552460.308868490027623
500.6563042585038080.6873914829923830.343695741496192
510.6421105356802390.7157789286395220.357889464319761
520.6006804316146380.7986391367707230.399319568385362
530.6453665701557510.7092668596884970.354633429844249
540.6015350111918530.7969299776162940.398464988808147
550.6292102020002550.741579595999490.370789797999745
560.5902426107023750.819514778595250.409757389297625
570.5523207825579480.8953584348841040.447679217442052
580.5932344222656320.8135311554687360.406765577734368
590.5749383458718140.8501233082563730.425061654128186
600.5612480778010890.8775038443978210.438751922198911
610.6332251117003690.7335497765992630.366774888299631
620.5901302251216680.8197395497566640.409869774878332
630.5437959993190190.9124080013619630.456204000680981
640.4986513494968810.9973026989937610.501348650503119
650.4577693905537380.9155387811074770.542230609446262
660.4116667408591430.8233334817182860.588333259140857
670.4777680724175190.9555361448350370.522231927582481
680.4326594225608770.8653188451217540.567340577439123
690.3911525089817710.7823050179635420.608847491018229
700.3571123092386290.7142246184772580.642887690761371
710.3279663778650040.6559327557300090.672033622134996
720.2880308205392170.5760616410784340.711969179460783
730.2670449152467130.5340898304934260.732955084753287
740.2430872727724230.4861745455448450.756912727227577
750.2124633831389320.4249267662778630.787536616861068
760.411580709500170.823161419000340.58841929049983
770.3694101443826610.7388202887653230.630589855617338
780.4059931965630950.8119863931261890.594006803436905
790.3636520866590810.7273041733181620.636347913340919
800.4568796668135420.9137593336270830.543120333186458
810.4154077201315590.8308154402631190.584592279868441
820.4816614472217350.9633228944434690.518338552778265
830.4987996345797650.9975992691595310.501200365420235
840.4587087916826640.9174175833653290.541291208317336
850.4177918912362410.8355837824724830.582208108763759
860.3742785480488120.7485570960976230.625721451951188
870.3327812083969120.6655624167938240.667218791603088
880.2959406464349840.5918812928699670.704059353565016
890.25997087104250.5199417420849990.7400291289575
900.6000139454388870.7999721091222260.399986054561113
910.5563593025003450.8872813949993090.443640697499655
920.5190081913813160.9619836172373680.480991808618684
930.4808357087659360.9616714175318720.519164291234064
940.465610345417330.9312206908346610.53438965458267
950.4688540810565860.9377081621131710.531145918943414
960.425833110446230.8516662208924610.57416688955377
970.4326704494969060.8653408989938130.567329550503094
980.3971137305636120.7942274611272240.602886269436388
990.3558062848133330.7116125696266670.644193715186667
1000.3493270392861650.698654078572330.650672960713835
1010.3275843576724480.6551687153448970.672415642327552
1020.3414497998543090.6828995997086180.658550200145691
1030.3191911128649120.6383822257298250.680808887135088
1040.3209906926945450.6419813853890890.679009307305455
1050.3760682084769620.7521364169539240.623931791523038
1060.4581361894776860.9162723789553730.541863810522314
1070.4130146351072820.8260292702145640.586985364892718
1080.4460577558162130.8921155116324260.553942244183787
1090.4030694334002470.8061388668004950.596930566599753
1100.7923063463776510.4153873072446980.207693653622349
1110.7700264003619120.4599471992761760.229973599638088
1120.7593731282244850.481253743551030.240626871775515
1130.7261625739487890.5476748521024210.273837426051211
1140.6902890223107220.6194219553785570.309710977689278
1150.6561384912393570.6877230175212870.343861508760643
1160.6083573529790550.7832852940418890.391642647020945
1170.559884745668360.880230508663280.44011525433164
1180.5112428406059360.9775143187881280.488757159394064
1190.5884803486244560.8230393027510880.411519651375544
1200.6743361732401790.6513276535196420.325663826759821
1210.6238204196858220.7523591606283570.376179580314178
1220.5767052101484510.8465895797030990.423294789851549
1230.5233532739799240.9532934520401520.476646726020076
1240.5213415766633070.9573168466733860.478658423336693
1250.4974994802409690.9949989604819370.502500519759031
1260.4496987628671660.8993975257343330.550301237132834
1270.4696423318637850.9392846637275690.530357668136215
1280.4173628389241770.8347256778483540.582637161075823
1290.3647467669354590.7294935338709190.635253233064541
1300.3265896836444210.6531793672888410.673410316355579
1310.2925093857718340.5850187715436690.707490614228166
1320.3262354634809940.6524709269619880.673764536519006
1330.2893633488634760.5787266977269520.710636651136524
1340.2401842235387040.4803684470774080.759815776461296
1350.193780762945680.3875615258913590.80621923705432
1360.1549251546241090.3098503092482180.845074845375891
1370.3190266983779830.6380533967559660.680973301622017
1380.2610220918601570.5220441837203130.738977908139843
1390.216407663451520.4328153269030390.78359233654848
1400.2219393494771880.4438786989543750.778060650522812
1410.2902440388738990.5804880777477970.709755961126101
1420.3388847959217430.6777695918434850.661115204078257
1430.2914410196616730.5828820393233460.708558980338327
1440.2419332523304550.483866504660910.758066747669545
1450.1961756266517430.3923512533034870.803824373348257
1460.5518532352147550.896293529570490.448146764785245
1470.4730984235604170.9461968471208340.526901576439583
1480.4322195392160660.8644390784321310.567780460783934
1490.3511304338597240.7022608677194490.648869566140276
1500.2792918131141550.5585836262283090.720708186885845
1510.5989370370295110.8021259259409780.401062962970489
1520.4853087297983280.9706174595966550.514691270201672
1530.4634820749993730.9269641499987460.536517925000627






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level20.0137931034482759NOK
5% type I error level80.0551724137931034NOK
10% type I error level130.0896551724137931OK

\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 & 2 & 0.0137931034482759 & NOK \tabularnewline
5% type I error level & 8 & 0.0551724137931034 & NOK \tabularnewline
10% type I error level & 13 & 0.0896551724137931 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147219&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]2[/C][C]0.0137931034482759[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]8[/C][C]0.0551724137931034[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]13[/C][C]0.0896551724137931[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147219&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147219&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 level20.0137931034482759NOK
5% type I error level80.0551724137931034NOK
10% type I error level130.0896551724137931OK


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