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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 computationMon, 12 Dec 2011 11:38:32 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Dec/12/t1323707929hgfnty9v200z0z5.htm/, Retrieved Fri, 03 May 2024 13:40:26 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=154113, Retrieved Fri, 03 May 2024 13:40:26 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact75

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2010-12-05 18:56:24] [b98453cac15ba1066b407e146608df68]
- R PD  [Multiple Regression] [] [2011-12-12 16:34:43] [0fa8c500575976cf9d2f7efbe256ddfb]
-   P       [Multiple Regression] [] [2011-12-12 16:38:32] [2e63149daec6ba44c7d6fab36a0b0c34] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
13	15	2	9	42	12
12	18	1	9	51	15
15	11	1	9	42	14
12	16	1	8	46	10
10	12	2	14	41	10
12	17	2	14	49	9
15	15	1	15	47	18
9	19	1	11	33	11
11	18	1	8	47	12
11	10	2	14	42	11
11	14	1	9	32	15
15	18	1	6	53	17
7	18	2	14	41	14
11	14	2	8	41	24
11	14	1	11	33	7
10	12	1	16	37	18
14	16	2	11	43	11
6	13	2	13	33	14
11	16	1	7	49	18
15	14	2	9	42	12
11	9	1	15	43	11
12	9	2	16	37	5
14	17	1	10	43	12
15	13	2	14	42	11
9	15	2	12	43	10
13	17	1	6	46	11
13	16	2	4	33	15
16	12	1	12	42	16
13	11	1	14	40	14
12	16	2	13	44	8
14	17	1	9	42	13
11	17	2	14	52	18
9	16	1	14	44	17
16	13	2	10	45	10
12	12	1	14	46	13
10	12	2	8	36	11
13	16	1	8	45	12
16	14	1	10	49	12
14	12	2	9	43	12
15	12	1	9	43	9
5	14	1	11	37	18
8	8	2	15	32	7
11	15	1	9	45	14
16	14	2	9	45	16
17	11	1	10	45	12
9	13	2	8	45	17
9	14	1	8	31	12
13	15	1	14	33	9
10	16	1	10	44	12
6	10	2	11	49	9
12	11	2	9	44	13
8	12	2	12	41	10
14	14	2	13	44	10
12	15	1	14	38	11
11	16	1	15	33	13
16	9	1	11	47	13
8	11	2	9	37	13
15	15	1	8	48	6
7	15	2	7	40	7
16	13	2	10	50	13
14	17	1	10	54	21
16	17	1	10	43	11
9	15	1	9	54	9
14	13	1	13	44	18
11	15	2	11	47	9
13	13	2	8	33	9
15	15	1	10	45	15
5	10	2	14	33	9
15	15	1	11	44	11
13	14	1	10	47	14
11	15	2	16	45	14
11	16	2	11	43	8
12	7	1	16	43	12
12	13	1	6	33	8
12	15	1	11	46	11
14	13	1	14	47	17
6	16	1	9	47	16
7	16	2	9	0	11
14	12	1	11	43	13
13	15	2	12	46	11
12	14	2	20	36	8
9	11	2	11	42	11
12	14	1	12	44	13
16	15	1	9	47	13
10	9	2	10	41	15
14	15	1	14	47	15
10	17	1	8	46	12
16	16	1	10	47	12
15	14	1	8	46	15
12	15	2	7	46	12
10	16	1	11	36	21
8	10	1	14	30	24
8	17	2	8	48	11
11	15	2	14	45	12
13	15	1	10	49	15
16	13	1	9	55	17
14	14	2	16	11	12
11	16	1	8	52	16
4	11	2	12	33	13
14	18	1	8	47	15
9	14	1	16	33	11
14	14	1	13	44	15
8	14	1	13	42	12
8	14	1	8	55	14
11	15	1	9	42	12
12	14	1	11	46	20
14	15	1	9	46	17
15	15	2	8	47	12
16	12	1	14	33	11
16	19	1	7	53	11
14	13	2	11	42	9
12	15	1	11	44	12
14	17	2	10	55	11
8	9	2	14	40	8
16	15	2	10	46	12
12	16	1	9	53	15
12	17	1	8	44	10
11	11	1	14	35	14
4	15	1	12	40	16
16	11	1	12	44	18
15	15	1	6	46	6
10	17	1	16	45	16
13	14	1	8	53	11
15	12	2	13	45	20
12	14	1	12	48	10
14	15	2	11	46	16
7	16	1	12	55	15
19	16	1	9	47	14
12	14	1	11	43	7
12	11	2	16	38	9
8	14	2	10	40	12
12	13	1	13	47	12
10	13	1	11	47	13
8	14	2	11	42	17
10	16	2	9	53	11
14	16	2	11	43	11
16	12	1	12	44	14
13	11	1	10	42	13
16	13	1	13	51	12
9	15	1	9	54	11
14	13	2	14	41	15
14	16	2	14	51	11
12	13	1	8	51	13

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 5 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154113&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154113&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154113&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net






Multiple Linear Regression - Estimated Regression Equation
Belonging[t] = + 2.54375969417852 -0.0238711991697093Popularity[t] -0.0194029797756343Gender[t] + 0.0090580287877587Happiness[t] -0.00680253845882531Doubtsaboutexpectations[t] -0.0307346221701844Parentalexpectations[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Belonging[t] =  +  2.54375969417852 -0.0238711991697093Popularity[t] -0.0194029797756343Gender[t] +  0.0090580287877587Happiness[t] -0.00680253845882531Doubtsaboutexpectations[t] -0.0307346221701844Parentalexpectations[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154113&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Belonging[t] =  +  2.54375969417852 -0.0238711991697093Popularity[t] -0.0194029797756343Gender[t] +  0.0090580287877587Happiness[t] -0.00680253845882531Doubtsaboutexpectations[t] -0.0307346221701844Parentalexpectations[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154113&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154113&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
Belonging[t] = + 2.54375969417852 -0.0238711991697093Popularity[t] -0.0194029797756343Gender[t] + 0.0090580287877587Happiness[t] -0.00680253845882531Doubtsaboutexpectations[t] -0.0307346221701844Parentalexpectations[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)2.543759694178520.4452145.713600
Popularity-0.02387119916970930.013755-1.73540.0849120.042456
Gender-0.01940297977563430.01817-1.06780.2874730.143737
Happiness0.00905802878775870.0155910.5810.5622010.281101
Doubtsaboutexpectations-0.006802538458825310.005856-1.16160.2474280.123714
Parentalexpectations-0.03073462217018440.011634-2.64170.0092080.004604

\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) & 2.54375969417852 & 0.445214 & 5.7136 & 0 & 0 \tabularnewline
Popularity & -0.0238711991697093 & 0.013755 & -1.7354 & 0.084912 & 0.042456 \tabularnewline
Gender & -0.0194029797756343 & 0.01817 & -1.0678 & 0.287473 & 0.143737 \tabularnewline
Happiness & 0.0090580287877587 & 0.015591 & 0.581 & 0.562201 & 0.281101 \tabularnewline
Doubtsaboutexpectations & -0.00680253845882531 & 0.005856 & -1.1616 & 0.247428 & 0.123714 \tabularnewline
Parentalexpectations & -0.0307346221701844 & 0.011634 & -2.6417 & 0.009208 & 0.004604 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154113&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]2.54375969417852[/C][C]0.445214[/C][C]5.7136[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Popularity[/C][C]-0.0238711991697093[/C][C]0.013755[/C][C]-1.7354[/C][C]0.084912[/C][C]0.042456[/C][/ROW]
[ROW][C]Gender[/C][C]-0.0194029797756343[/C][C]0.01817[/C][C]-1.0678[/C][C]0.287473[/C][C]0.143737[/C][/ROW]
[ROW][C]Happiness[/C][C]0.0090580287877587[/C][C]0.015591[/C][C]0.581[/C][C]0.562201[/C][C]0.281101[/C][/ROW]
[ROW][C]Doubtsaboutexpectations[/C][C]-0.00680253845882531[/C][C]0.005856[/C][C]-1.1616[/C][C]0.247428[/C][C]0.123714[/C][/ROW]
[ROW][C]Parentalexpectations[/C][C]-0.0307346221701844[/C][C]0.011634[/C][C]-2.6417[/C][C]0.009208[/C][C]0.004604[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154113&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154113&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)2.543759694178520.4452145.713600
Popularity-0.02387119916970930.013755-1.73540.0849120.042456
Gender-0.01940297977563430.01817-1.06780.2874730.143737
Happiness0.00905802878775870.0155910.5810.5622010.281101
Doubtsaboutexpectations-0.006802538458825310.005856-1.16160.2474280.123714
Parentalexpectations-0.03073462217018440.011634-2.64170.0092080.004604






Multiple Linear Regression - Regression Statistics
Multiple R0.348486029133161
R-squared0.121442512500998
Adjusted R-squared0.0893783706214725
F-TEST (value)3.78748674944408
F-TEST (DF numerator)5
F-TEST (DF denominator)137
p-value0.00303642309716856
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.46886028081709
Sum Squared Residuals30.1167049211196

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.348486029133161 \tabularnewline
R-squared & 0.121442512500998 \tabularnewline
Adjusted R-squared & 0.0893783706214725 \tabularnewline
F-TEST (value) & 3.78748674944408 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 137 \tabularnewline
p-value & 0.00303642309716856 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.46886028081709 \tabularnewline
Sum Squared Residuals & 30.1167049211196 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154113&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.348486029133161[/C][/ROW]
[ROW][C]R-squared[/C][C]0.121442512500998[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.0893783706214725[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]3.78748674944408[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]137[/C][/ROW]
[ROW][C]p-value[/C][C]0.00303642309716856[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.46886028081709[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]30.1167049211196[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154113&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154113&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 R0.348486029133161
R-squared0.121442512500998
Adjusted R-squared0.0893783706214725
F-TEST (value)3.78748674944408
F-TEST (DF numerator)5
F-TEST (DF denominator)137
p-value0.00303642309716856
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.46886028081709
Sum Squared Residuals30.1167049211196






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
121.369389586114740.630610413885263
211.18162513331756-0.181625133317563
311.33778986253749-0.337789862537488
411.39905886722612-0.399058867226122
521.612774049688760.387225950311244
621.444331066970750.555668933029252
711.15757493518664-0.157574935186638
811.49733598956617-0.497335989566167
911.31585232404537-0.315852324045368
1021.590171649441310.409828350558694
1111.41235648230749-0.412356482307491
1211.00776312818714-0.0077631281871396
1321.445031279863340.554968720136659
1421.065464007858640.934535992141355
1511.66954697878566-0.669546978785658
1611.4122232837381-0.4122232837381
1721.368163548456270.63183645154373
1821.611279656794070.388720343205934
1911.14758744487012-0.147587444870121
2021.341050167550950.658949832449047
2111.61183011954587-0.611830119545874
2221.822239912937980.177760087062019
2311.30896791772269-0.308967917722693
2421.436477913435570.563522086564435
2521.546715175038390.453284824961605
2611.30693400853508-0.306934008535076
2721.273715442019180.726284557980816
2811.26022052561505-0.260220525615051
2911.44442748173335-0.44442748173335
3021.519423332422930.480576667577066
3111.27597780522358-0.275977805223575
3221.171183051232320.828816948767678
3311.32348335918816-0.323483359188162
3421.386701605908530.61329839409147
3511.43881509254466-0.438815092544658
3621.561703947086150.438296052913854
3711.32052096217487-0.320520962174869
3811.27861922795723-0.278619227957226
3921.396924787813110.603075212186894
4011.46525745515395-0.46525745515395
4111.44748317609658-0.447483176096584
4221.900613108558450.099386891441548
4311.33525512473731-0.335255124737312
4421.173832864324030.82616713567597
4511.34016712194972-0.34016712194972
4621.320541587329690.679458412670313
4711.55004725682853-0.550047256828529
4811.56810644269351-0.568106442693512
4911.41705315571834-0.41705315571834
5021.696205034055170.303794965944832
5121.426533005299150.573466994700851
5221.642400390452660.357599609547342
5321.449017649294420.550982350705584
5411.49649570522873-0.496495705228726
5511.48256540136432-0.482565401364318
5611.36756261037062-0.367562610370622
5721.569635571189760.430364428810236
5811.45618166125571-0.456181661255715
5921.661778911326050.338221088673951
6021.260485047103850.73951495289615
6110.9575283951439550.0424716048560448
6211.29196014155346-0.291960141553459
6311.47544778779822-0.475447787798225
6411.22254365170857-0.222543651708575
6521.49343921624610.5065607837539
6621.552564229518230.447435770481771
6711.21809373467605-0.218093734676049
6821.856090934929360.143909065070641
6911.35689279060337-0.35689279060337
7011.30236865804364-0.302368658043635
7121.398661326251620.601338673748377
7221.531981012475950.468018987524048
7311.60508828654501-0.605088286545007
7411.5890539932826-0.589053993282605
7511.41490131119485-0.414901311194848
7611.24192868729004-0.241928687290042
7711.3601338195522-0.360133819552205
7821.809655038798210.190344961201793
7911.38430622321844-0.384306223218439
8021.40008814081290.599911859187103
8121.676055801159120.323944198840884
8221.591336981641810.408663018358186
8311.39549815233552-0.395498152335522
8411.2330286741413-0.233028674141299
8521.48107776301370.518922236986297
8611.26459197207914-0.264591972079142
8711.36592904144954-0.365929041449537
8811.25341834532361-0.253418345323607
8911.21257811841734-0.21257811841734
9021.347934573873630.652065426126372
9111.20391989264504-0.203919892645041
9211.34386562024394-0.343865620243941
9321.430800985041490.56919901495851
9421.442014513016470.557985486983526
9511.23862597918017-0.238625979180166
9611.09447583734123-0.0944758373412273
9721.639206260458560.360793739541442
9811.19770710262177-0.197707102621773
9921.719504608067180.280495391932821
10011.15203486002569-0.152034860025687
10111.63964103238313-0.639641032383133
10211.29534453844349-0.295344538443494
10311.54438067688995-0.544380676889954
10411.34918828864606-0.349188288646062
10511.41713198445416-0.417131984454156
10611.15769269143882-0.157692691438822
10711.16463512225881-0.164635122258805
10821.278576466693430.721423533306566
10911.49323254017092-0.493232540170918
11011.15795471105066-0.157954711050661
11121.494644270582370.505355729417633
11211.39777176594231-0.397771765942314
11321.258072078386970.741927921613026
11421.786997170154270.213002829845728
11521.279623863558070.720376136441933
11611.20682601595118-0.206826015951181
11711.39326096436814-0.393260964368138
11811.5261825723669-0.526182572366896
11911.50207105324231-0.502071053242311
12011.20454918413267-0.204549184132666
12111.45167068059785-0.451670680597848
12211.32225732152969-0.322257321529694
12311.33564123622572-0.335641236225719
12421.149803649515310.850196350484694
12511.46049186501077-0.460491865010774
12621.213485802004510.786514197995493
12711.33975102124535-0.339751021245353
12811.11127747468635-0.111277474686352
12911.5776503950277-0.577650395027695
13021.653692926247150.34630707375285
13121.530811667444330.469188332555672
13211.43428616769262-0.434286167692624
13311.43317788628634-0.433177886286341
13421.372591508463510.627408491536486
13521.377506902971340.622493097028663
13621.368163548456270.63183645154373
13711.30808469303777-0.308084693037769
13811.42532491183485-0.425324911834849
13911.31159121717849-0.311591217178485
14011.41397854345786-0.413978543457856
14121.344213162383360.655786837616637
14221.340917327148940.659082672851056
14311.33105124774834-0.331051247748345

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 2 & 1.36938958611474 & 0.630610413885263 \tabularnewline
2 & 1 & 1.18162513331756 & -0.181625133317563 \tabularnewline
3 & 1 & 1.33778986253749 & -0.337789862537488 \tabularnewline
4 & 1 & 1.39905886722612 & -0.399058867226122 \tabularnewline
5 & 2 & 1.61277404968876 & 0.387225950311244 \tabularnewline
6 & 2 & 1.44433106697075 & 0.555668933029252 \tabularnewline
7 & 1 & 1.15757493518664 & -0.157574935186638 \tabularnewline
8 & 1 & 1.49733598956617 & -0.497335989566167 \tabularnewline
9 & 1 & 1.31585232404537 & -0.315852324045368 \tabularnewline
10 & 2 & 1.59017164944131 & 0.409828350558694 \tabularnewline
11 & 1 & 1.41235648230749 & -0.412356482307491 \tabularnewline
12 & 1 & 1.00776312818714 & -0.0077631281871396 \tabularnewline
13 & 2 & 1.44503127986334 & 0.554968720136659 \tabularnewline
14 & 2 & 1.06546400785864 & 0.934535992141355 \tabularnewline
15 & 1 & 1.66954697878566 & -0.669546978785658 \tabularnewline
16 & 1 & 1.4122232837381 & -0.4122232837381 \tabularnewline
17 & 2 & 1.36816354845627 & 0.63183645154373 \tabularnewline
18 & 2 & 1.61127965679407 & 0.388720343205934 \tabularnewline
19 & 1 & 1.14758744487012 & -0.147587444870121 \tabularnewline
20 & 2 & 1.34105016755095 & 0.658949832449047 \tabularnewline
21 & 1 & 1.61183011954587 & -0.611830119545874 \tabularnewline
22 & 2 & 1.82223991293798 & 0.177760087062019 \tabularnewline
23 & 1 & 1.30896791772269 & -0.308967917722693 \tabularnewline
24 & 2 & 1.43647791343557 & 0.563522086564435 \tabularnewline
25 & 2 & 1.54671517503839 & 0.453284824961605 \tabularnewline
26 & 1 & 1.30693400853508 & -0.306934008535076 \tabularnewline
27 & 2 & 1.27371544201918 & 0.726284557980816 \tabularnewline
28 & 1 & 1.26022052561505 & -0.260220525615051 \tabularnewline
29 & 1 & 1.44442748173335 & -0.44442748173335 \tabularnewline
30 & 2 & 1.51942333242293 & 0.480576667577066 \tabularnewline
31 & 1 & 1.27597780522358 & -0.275977805223575 \tabularnewline
32 & 2 & 1.17118305123232 & 0.828816948767678 \tabularnewline
33 & 1 & 1.32348335918816 & -0.323483359188162 \tabularnewline
34 & 2 & 1.38670160590853 & 0.61329839409147 \tabularnewline
35 & 1 & 1.43881509254466 & -0.438815092544658 \tabularnewline
36 & 2 & 1.56170394708615 & 0.438296052913854 \tabularnewline
37 & 1 & 1.32052096217487 & -0.320520962174869 \tabularnewline
38 & 1 & 1.27861922795723 & -0.278619227957226 \tabularnewline
39 & 2 & 1.39692478781311 & 0.603075212186894 \tabularnewline
40 & 1 & 1.46525745515395 & -0.46525745515395 \tabularnewline
41 & 1 & 1.44748317609658 & -0.447483176096584 \tabularnewline
42 & 2 & 1.90061310855845 & 0.099386891441548 \tabularnewline
43 & 1 & 1.33525512473731 & -0.335255124737312 \tabularnewline
44 & 2 & 1.17383286432403 & 0.82616713567597 \tabularnewline
45 & 1 & 1.34016712194972 & -0.34016712194972 \tabularnewline
46 & 2 & 1.32054158732969 & 0.679458412670313 \tabularnewline
47 & 1 & 1.55004725682853 & -0.550047256828529 \tabularnewline
48 & 1 & 1.56810644269351 & -0.568106442693512 \tabularnewline
49 & 1 & 1.41705315571834 & -0.41705315571834 \tabularnewline
50 & 2 & 1.69620503405517 & 0.303794965944832 \tabularnewline
51 & 2 & 1.42653300529915 & 0.573466994700851 \tabularnewline
52 & 2 & 1.64240039045266 & 0.357599609547342 \tabularnewline
53 & 2 & 1.44901764929442 & 0.550982350705584 \tabularnewline
54 & 1 & 1.49649570522873 & -0.496495705228726 \tabularnewline
55 & 1 & 1.48256540136432 & -0.482565401364318 \tabularnewline
56 & 1 & 1.36756261037062 & -0.367562610370622 \tabularnewline
57 & 2 & 1.56963557118976 & 0.430364428810236 \tabularnewline
58 & 1 & 1.45618166125571 & -0.456181661255715 \tabularnewline
59 & 2 & 1.66177891132605 & 0.338221088673951 \tabularnewline
60 & 2 & 1.26048504710385 & 0.73951495289615 \tabularnewline
61 & 1 & 0.957528395143955 & 0.0424716048560448 \tabularnewline
62 & 1 & 1.29196014155346 & -0.291960141553459 \tabularnewline
63 & 1 & 1.47544778779822 & -0.475447787798225 \tabularnewline
64 & 1 & 1.22254365170857 & -0.222543651708575 \tabularnewline
65 & 2 & 1.4934392162461 & 0.5065607837539 \tabularnewline
66 & 2 & 1.55256422951823 & 0.447435770481771 \tabularnewline
67 & 1 & 1.21809373467605 & -0.218093734676049 \tabularnewline
68 & 2 & 1.85609093492936 & 0.143909065070641 \tabularnewline
69 & 1 & 1.35689279060337 & -0.35689279060337 \tabularnewline
70 & 1 & 1.30236865804364 & -0.302368658043635 \tabularnewline
71 & 2 & 1.39866132625162 & 0.601338673748377 \tabularnewline
72 & 2 & 1.53198101247595 & 0.468018987524048 \tabularnewline
73 & 1 & 1.60508828654501 & -0.605088286545007 \tabularnewline
74 & 1 & 1.5890539932826 & -0.589053993282605 \tabularnewline
75 & 1 & 1.41490131119485 & -0.414901311194848 \tabularnewline
76 & 1 & 1.24192868729004 & -0.241928687290042 \tabularnewline
77 & 1 & 1.3601338195522 & -0.360133819552205 \tabularnewline
78 & 2 & 1.80965503879821 & 0.190344961201793 \tabularnewline
79 & 1 & 1.38430622321844 & -0.384306223218439 \tabularnewline
80 & 2 & 1.4000881408129 & 0.599911859187103 \tabularnewline
81 & 2 & 1.67605580115912 & 0.323944198840884 \tabularnewline
82 & 2 & 1.59133698164181 & 0.408663018358186 \tabularnewline
83 & 1 & 1.39549815233552 & -0.395498152335522 \tabularnewline
84 & 1 & 1.2330286741413 & -0.233028674141299 \tabularnewline
85 & 2 & 1.4810777630137 & 0.518922236986297 \tabularnewline
86 & 1 & 1.26459197207914 & -0.264591972079142 \tabularnewline
87 & 1 & 1.36592904144954 & -0.365929041449537 \tabularnewline
88 & 1 & 1.25341834532361 & -0.253418345323607 \tabularnewline
89 & 1 & 1.21257811841734 & -0.21257811841734 \tabularnewline
90 & 2 & 1.34793457387363 & 0.652065426126372 \tabularnewline
91 & 1 & 1.20391989264504 & -0.203919892645041 \tabularnewline
92 & 1 & 1.34386562024394 & -0.343865620243941 \tabularnewline
93 & 2 & 1.43080098504149 & 0.56919901495851 \tabularnewline
94 & 2 & 1.44201451301647 & 0.557985486983526 \tabularnewline
95 & 1 & 1.23862597918017 & -0.238625979180166 \tabularnewline
96 & 1 & 1.09447583734123 & -0.0944758373412273 \tabularnewline
97 & 2 & 1.63920626045856 & 0.360793739541442 \tabularnewline
98 & 1 & 1.19770710262177 & -0.197707102621773 \tabularnewline
99 & 2 & 1.71950460806718 & 0.280495391932821 \tabularnewline
100 & 1 & 1.15203486002569 & -0.152034860025687 \tabularnewline
101 & 1 & 1.63964103238313 & -0.639641032383133 \tabularnewline
102 & 1 & 1.29534453844349 & -0.295344538443494 \tabularnewline
103 & 1 & 1.54438067688995 & -0.544380676889954 \tabularnewline
104 & 1 & 1.34918828864606 & -0.349188288646062 \tabularnewline
105 & 1 & 1.41713198445416 & -0.417131984454156 \tabularnewline
106 & 1 & 1.15769269143882 & -0.157692691438822 \tabularnewline
107 & 1 & 1.16463512225881 & -0.164635122258805 \tabularnewline
108 & 2 & 1.27857646669343 & 0.721423533306566 \tabularnewline
109 & 1 & 1.49323254017092 & -0.493232540170918 \tabularnewline
110 & 1 & 1.15795471105066 & -0.157954711050661 \tabularnewline
111 & 2 & 1.49464427058237 & 0.505355729417633 \tabularnewline
112 & 1 & 1.39777176594231 & -0.397771765942314 \tabularnewline
113 & 2 & 1.25807207838697 & 0.741927921613026 \tabularnewline
114 & 2 & 1.78699717015427 & 0.213002829845728 \tabularnewline
115 & 2 & 1.27962386355807 & 0.720376136441933 \tabularnewline
116 & 1 & 1.20682601595118 & -0.206826015951181 \tabularnewline
117 & 1 & 1.39326096436814 & -0.393260964368138 \tabularnewline
118 & 1 & 1.5261825723669 & -0.526182572366896 \tabularnewline
119 & 1 & 1.50207105324231 & -0.502071053242311 \tabularnewline
120 & 1 & 1.20454918413267 & -0.204549184132666 \tabularnewline
121 & 1 & 1.45167068059785 & -0.451670680597848 \tabularnewline
122 & 1 & 1.32225732152969 & -0.322257321529694 \tabularnewline
123 & 1 & 1.33564123622572 & -0.335641236225719 \tabularnewline
124 & 2 & 1.14980364951531 & 0.850196350484694 \tabularnewline
125 & 1 & 1.46049186501077 & -0.460491865010774 \tabularnewline
126 & 2 & 1.21348580200451 & 0.786514197995493 \tabularnewline
127 & 1 & 1.33975102124535 & -0.339751021245353 \tabularnewline
128 & 1 & 1.11127747468635 & -0.111277474686352 \tabularnewline
129 & 1 & 1.5776503950277 & -0.577650395027695 \tabularnewline
130 & 2 & 1.65369292624715 & 0.34630707375285 \tabularnewline
131 & 2 & 1.53081166744433 & 0.469188332555672 \tabularnewline
132 & 1 & 1.43428616769262 & -0.434286167692624 \tabularnewline
133 & 1 & 1.43317788628634 & -0.433177886286341 \tabularnewline
134 & 2 & 1.37259150846351 & 0.627408491536486 \tabularnewline
135 & 2 & 1.37750690297134 & 0.622493097028663 \tabularnewline
136 & 2 & 1.36816354845627 & 0.63183645154373 \tabularnewline
137 & 1 & 1.30808469303777 & -0.308084693037769 \tabularnewline
138 & 1 & 1.42532491183485 & -0.425324911834849 \tabularnewline
139 & 1 & 1.31159121717849 & -0.311591217178485 \tabularnewline
140 & 1 & 1.41397854345786 & -0.413978543457856 \tabularnewline
141 & 2 & 1.34421316238336 & 0.655786837616637 \tabularnewline
142 & 2 & 1.34091732714894 & 0.659082672851056 \tabularnewline
143 & 1 & 1.33105124774834 & -0.331051247748345 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154113&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]2[/C][C]1.36938958611474[/C][C]0.630610413885263[/C][/ROW]
[ROW][C]2[/C][C]1[/C][C]1.18162513331756[/C][C]-0.181625133317563[/C][/ROW]
[ROW][C]3[/C][C]1[/C][C]1.33778986253749[/C][C]-0.337789862537488[/C][/ROW]
[ROW][C]4[/C][C]1[/C][C]1.39905886722612[/C][C]-0.399058867226122[/C][/ROW]
[ROW][C]5[/C][C]2[/C][C]1.61277404968876[/C][C]0.387225950311244[/C][/ROW]
[ROW][C]6[/C][C]2[/C][C]1.44433106697075[/C][C]0.555668933029252[/C][/ROW]
[ROW][C]7[/C][C]1[/C][C]1.15757493518664[/C][C]-0.157574935186638[/C][/ROW]
[ROW][C]8[/C][C]1[/C][C]1.49733598956617[/C][C]-0.497335989566167[/C][/ROW]
[ROW][C]9[/C][C]1[/C][C]1.31585232404537[/C][C]-0.315852324045368[/C][/ROW]
[ROW][C]10[/C][C]2[/C][C]1.59017164944131[/C][C]0.409828350558694[/C][/ROW]
[ROW][C]11[/C][C]1[/C][C]1.41235648230749[/C][C]-0.412356482307491[/C][/ROW]
[ROW][C]12[/C][C]1[/C][C]1.00776312818714[/C][C]-0.0077631281871396[/C][/ROW]
[ROW][C]13[/C][C]2[/C][C]1.44503127986334[/C][C]0.554968720136659[/C][/ROW]
[ROW][C]14[/C][C]2[/C][C]1.06546400785864[/C][C]0.934535992141355[/C][/ROW]
[ROW][C]15[/C][C]1[/C][C]1.66954697878566[/C][C]-0.669546978785658[/C][/ROW]
[ROW][C]16[/C][C]1[/C][C]1.4122232837381[/C][C]-0.4122232837381[/C][/ROW]
[ROW][C]17[/C][C]2[/C][C]1.36816354845627[/C][C]0.63183645154373[/C][/ROW]
[ROW][C]18[/C][C]2[/C][C]1.61127965679407[/C][C]0.388720343205934[/C][/ROW]
[ROW][C]19[/C][C]1[/C][C]1.14758744487012[/C][C]-0.147587444870121[/C][/ROW]
[ROW][C]20[/C][C]2[/C][C]1.34105016755095[/C][C]0.658949832449047[/C][/ROW]
[ROW][C]21[/C][C]1[/C][C]1.61183011954587[/C][C]-0.611830119545874[/C][/ROW]
[ROW][C]22[/C][C]2[/C][C]1.82223991293798[/C][C]0.177760087062019[/C][/ROW]
[ROW][C]23[/C][C]1[/C][C]1.30896791772269[/C][C]-0.308967917722693[/C][/ROW]
[ROW][C]24[/C][C]2[/C][C]1.43647791343557[/C][C]0.563522086564435[/C][/ROW]
[ROW][C]25[/C][C]2[/C][C]1.54671517503839[/C][C]0.453284824961605[/C][/ROW]
[ROW][C]26[/C][C]1[/C][C]1.30693400853508[/C][C]-0.306934008535076[/C][/ROW]
[ROW][C]27[/C][C]2[/C][C]1.27371544201918[/C][C]0.726284557980816[/C][/ROW]
[ROW][C]28[/C][C]1[/C][C]1.26022052561505[/C][C]-0.260220525615051[/C][/ROW]
[ROW][C]29[/C][C]1[/C][C]1.44442748173335[/C][C]-0.44442748173335[/C][/ROW]
[ROW][C]30[/C][C]2[/C][C]1.51942333242293[/C][C]0.480576667577066[/C][/ROW]
[ROW][C]31[/C][C]1[/C][C]1.27597780522358[/C][C]-0.275977805223575[/C][/ROW]
[ROW][C]32[/C][C]2[/C][C]1.17118305123232[/C][C]0.828816948767678[/C][/ROW]
[ROW][C]33[/C][C]1[/C][C]1.32348335918816[/C][C]-0.323483359188162[/C][/ROW]
[ROW][C]34[/C][C]2[/C][C]1.38670160590853[/C][C]0.61329839409147[/C][/ROW]
[ROW][C]35[/C][C]1[/C][C]1.43881509254466[/C][C]-0.438815092544658[/C][/ROW]
[ROW][C]36[/C][C]2[/C][C]1.56170394708615[/C][C]0.438296052913854[/C][/ROW]
[ROW][C]37[/C][C]1[/C][C]1.32052096217487[/C][C]-0.320520962174869[/C][/ROW]
[ROW][C]38[/C][C]1[/C][C]1.27861922795723[/C][C]-0.278619227957226[/C][/ROW]
[ROW][C]39[/C][C]2[/C][C]1.39692478781311[/C][C]0.603075212186894[/C][/ROW]
[ROW][C]40[/C][C]1[/C][C]1.46525745515395[/C][C]-0.46525745515395[/C][/ROW]
[ROW][C]41[/C][C]1[/C][C]1.44748317609658[/C][C]-0.447483176096584[/C][/ROW]
[ROW][C]42[/C][C]2[/C][C]1.90061310855845[/C][C]0.099386891441548[/C][/ROW]
[ROW][C]43[/C][C]1[/C][C]1.33525512473731[/C][C]-0.335255124737312[/C][/ROW]
[ROW][C]44[/C][C]2[/C][C]1.17383286432403[/C][C]0.82616713567597[/C][/ROW]
[ROW][C]45[/C][C]1[/C][C]1.34016712194972[/C][C]-0.34016712194972[/C][/ROW]
[ROW][C]46[/C][C]2[/C][C]1.32054158732969[/C][C]0.679458412670313[/C][/ROW]
[ROW][C]47[/C][C]1[/C][C]1.55004725682853[/C][C]-0.550047256828529[/C][/ROW]
[ROW][C]48[/C][C]1[/C][C]1.56810644269351[/C][C]-0.568106442693512[/C][/ROW]
[ROW][C]49[/C][C]1[/C][C]1.41705315571834[/C][C]-0.41705315571834[/C][/ROW]
[ROW][C]50[/C][C]2[/C][C]1.69620503405517[/C][C]0.303794965944832[/C][/ROW]
[ROW][C]51[/C][C]2[/C][C]1.42653300529915[/C][C]0.573466994700851[/C][/ROW]
[ROW][C]52[/C][C]2[/C][C]1.64240039045266[/C][C]0.357599609547342[/C][/ROW]
[ROW][C]53[/C][C]2[/C][C]1.44901764929442[/C][C]0.550982350705584[/C][/ROW]
[ROW][C]54[/C][C]1[/C][C]1.49649570522873[/C][C]-0.496495705228726[/C][/ROW]
[ROW][C]55[/C][C]1[/C][C]1.48256540136432[/C][C]-0.482565401364318[/C][/ROW]
[ROW][C]56[/C][C]1[/C][C]1.36756261037062[/C][C]-0.367562610370622[/C][/ROW]
[ROW][C]57[/C][C]2[/C][C]1.56963557118976[/C][C]0.430364428810236[/C][/ROW]
[ROW][C]58[/C][C]1[/C][C]1.45618166125571[/C][C]-0.456181661255715[/C][/ROW]
[ROW][C]59[/C][C]2[/C][C]1.66177891132605[/C][C]0.338221088673951[/C][/ROW]
[ROW][C]60[/C][C]2[/C][C]1.26048504710385[/C][C]0.73951495289615[/C][/ROW]
[ROW][C]61[/C][C]1[/C][C]0.957528395143955[/C][C]0.0424716048560448[/C][/ROW]
[ROW][C]62[/C][C]1[/C][C]1.29196014155346[/C][C]-0.291960141553459[/C][/ROW]
[ROW][C]63[/C][C]1[/C][C]1.47544778779822[/C][C]-0.475447787798225[/C][/ROW]
[ROW][C]64[/C][C]1[/C][C]1.22254365170857[/C][C]-0.222543651708575[/C][/ROW]
[ROW][C]65[/C][C]2[/C][C]1.4934392162461[/C][C]0.5065607837539[/C][/ROW]
[ROW][C]66[/C][C]2[/C][C]1.55256422951823[/C][C]0.447435770481771[/C][/ROW]
[ROW][C]67[/C][C]1[/C][C]1.21809373467605[/C][C]-0.218093734676049[/C][/ROW]
[ROW][C]68[/C][C]2[/C][C]1.85609093492936[/C][C]0.143909065070641[/C][/ROW]
[ROW][C]69[/C][C]1[/C][C]1.35689279060337[/C][C]-0.35689279060337[/C][/ROW]
[ROW][C]70[/C][C]1[/C][C]1.30236865804364[/C][C]-0.302368658043635[/C][/ROW]
[ROW][C]71[/C][C]2[/C][C]1.39866132625162[/C][C]0.601338673748377[/C][/ROW]
[ROW][C]72[/C][C]2[/C][C]1.53198101247595[/C][C]0.468018987524048[/C][/ROW]
[ROW][C]73[/C][C]1[/C][C]1.60508828654501[/C][C]-0.605088286545007[/C][/ROW]
[ROW][C]74[/C][C]1[/C][C]1.5890539932826[/C][C]-0.589053993282605[/C][/ROW]
[ROW][C]75[/C][C]1[/C][C]1.41490131119485[/C][C]-0.414901311194848[/C][/ROW]
[ROW][C]76[/C][C]1[/C][C]1.24192868729004[/C][C]-0.241928687290042[/C][/ROW]
[ROW][C]77[/C][C]1[/C][C]1.3601338195522[/C][C]-0.360133819552205[/C][/ROW]
[ROW][C]78[/C][C]2[/C][C]1.80965503879821[/C][C]0.190344961201793[/C][/ROW]
[ROW][C]79[/C][C]1[/C][C]1.38430622321844[/C][C]-0.384306223218439[/C][/ROW]
[ROW][C]80[/C][C]2[/C][C]1.4000881408129[/C][C]0.599911859187103[/C][/ROW]
[ROW][C]81[/C][C]2[/C][C]1.67605580115912[/C][C]0.323944198840884[/C][/ROW]
[ROW][C]82[/C][C]2[/C][C]1.59133698164181[/C][C]0.408663018358186[/C][/ROW]
[ROW][C]83[/C][C]1[/C][C]1.39549815233552[/C][C]-0.395498152335522[/C][/ROW]
[ROW][C]84[/C][C]1[/C][C]1.2330286741413[/C][C]-0.233028674141299[/C][/ROW]
[ROW][C]85[/C][C]2[/C][C]1.4810777630137[/C][C]0.518922236986297[/C][/ROW]
[ROW][C]86[/C][C]1[/C][C]1.26459197207914[/C][C]-0.264591972079142[/C][/ROW]
[ROW][C]87[/C][C]1[/C][C]1.36592904144954[/C][C]-0.365929041449537[/C][/ROW]
[ROW][C]88[/C][C]1[/C][C]1.25341834532361[/C][C]-0.253418345323607[/C][/ROW]
[ROW][C]89[/C][C]1[/C][C]1.21257811841734[/C][C]-0.21257811841734[/C][/ROW]
[ROW][C]90[/C][C]2[/C][C]1.34793457387363[/C][C]0.652065426126372[/C][/ROW]
[ROW][C]91[/C][C]1[/C][C]1.20391989264504[/C][C]-0.203919892645041[/C][/ROW]
[ROW][C]92[/C][C]1[/C][C]1.34386562024394[/C][C]-0.343865620243941[/C][/ROW]
[ROW][C]93[/C][C]2[/C][C]1.43080098504149[/C][C]0.56919901495851[/C][/ROW]
[ROW][C]94[/C][C]2[/C][C]1.44201451301647[/C][C]0.557985486983526[/C][/ROW]
[ROW][C]95[/C][C]1[/C][C]1.23862597918017[/C][C]-0.238625979180166[/C][/ROW]
[ROW][C]96[/C][C]1[/C][C]1.09447583734123[/C][C]-0.0944758373412273[/C][/ROW]
[ROW][C]97[/C][C]2[/C][C]1.63920626045856[/C][C]0.360793739541442[/C][/ROW]
[ROW][C]98[/C][C]1[/C][C]1.19770710262177[/C][C]-0.197707102621773[/C][/ROW]
[ROW][C]99[/C][C]2[/C][C]1.71950460806718[/C][C]0.280495391932821[/C][/ROW]
[ROW][C]100[/C][C]1[/C][C]1.15203486002569[/C][C]-0.152034860025687[/C][/ROW]
[ROW][C]101[/C][C]1[/C][C]1.63964103238313[/C][C]-0.639641032383133[/C][/ROW]
[ROW][C]102[/C][C]1[/C][C]1.29534453844349[/C][C]-0.295344538443494[/C][/ROW]
[ROW][C]103[/C][C]1[/C][C]1.54438067688995[/C][C]-0.544380676889954[/C][/ROW]
[ROW][C]104[/C][C]1[/C][C]1.34918828864606[/C][C]-0.349188288646062[/C][/ROW]
[ROW][C]105[/C][C]1[/C][C]1.41713198445416[/C][C]-0.417131984454156[/C][/ROW]
[ROW][C]106[/C][C]1[/C][C]1.15769269143882[/C][C]-0.157692691438822[/C][/ROW]
[ROW][C]107[/C][C]1[/C][C]1.16463512225881[/C][C]-0.164635122258805[/C][/ROW]
[ROW][C]108[/C][C]2[/C][C]1.27857646669343[/C][C]0.721423533306566[/C][/ROW]
[ROW][C]109[/C][C]1[/C][C]1.49323254017092[/C][C]-0.493232540170918[/C][/ROW]
[ROW][C]110[/C][C]1[/C][C]1.15795471105066[/C][C]-0.157954711050661[/C][/ROW]
[ROW][C]111[/C][C]2[/C][C]1.49464427058237[/C][C]0.505355729417633[/C][/ROW]
[ROW][C]112[/C][C]1[/C][C]1.39777176594231[/C][C]-0.397771765942314[/C][/ROW]
[ROW][C]113[/C][C]2[/C][C]1.25807207838697[/C][C]0.741927921613026[/C][/ROW]
[ROW][C]114[/C][C]2[/C][C]1.78699717015427[/C][C]0.213002829845728[/C][/ROW]
[ROW][C]115[/C][C]2[/C][C]1.27962386355807[/C][C]0.720376136441933[/C][/ROW]
[ROW][C]116[/C][C]1[/C][C]1.20682601595118[/C][C]-0.206826015951181[/C][/ROW]
[ROW][C]117[/C][C]1[/C][C]1.39326096436814[/C][C]-0.393260964368138[/C][/ROW]
[ROW][C]118[/C][C]1[/C][C]1.5261825723669[/C][C]-0.526182572366896[/C][/ROW]
[ROW][C]119[/C][C]1[/C][C]1.50207105324231[/C][C]-0.502071053242311[/C][/ROW]
[ROW][C]120[/C][C]1[/C][C]1.20454918413267[/C][C]-0.204549184132666[/C][/ROW]
[ROW][C]121[/C][C]1[/C][C]1.45167068059785[/C][C]-0.451670680597848[/C][/ROW]
[ROW][C]122[/C][C]1[/C][C]1.32225732152969[/C][C]-0.322257321529694[/C][/ROW]
[ROW][C]123[/C][C]1[/C][C]1.33564123622572[/C][C]-0.335641236225719[/C][/ROW]
[ROW][C]124[/C][C]2[/C][C]1.14980364951531[/C][C]0.850196350484694[/C][/ROW]
[ROW][C]125[/C][C]1[/C][C]1.46049186501077[/C][C]-0.460491865010774[/C][/ROW]
[ROW][C]126[/C][C]2[/C][C]1.21348580200451[/C][C]0.786514197995493[/C][/ROW]
[ROW][C]127[/C][C]1[/C][C]1.33975102124535[/C][C]-0.339751021245353[/C][/ROW]
[ROW][C]128[/C][C]1[/C][C]1.11127747468635[/C][C]-0.111277474686352[/C][/ROW]
[ROW][C]129[/C][C]1[/C][C]1.5776503950277[/C][C]-0.577650395027695[/C][/ROW]
[ROW][C]130[/C][C]2[/C][C]1.65369292624715[/C][C]0.34630707375285[/C][/ROW]
[ROW][C]131[/C][C]2[/C][C]1.53081166744433[/C][C]0.469188332555672[/C][/ROW]
[ROW][C]132[/C][C]1[/C][C]1.43428616769262[/C][C]-0.434286167692624[/C][/ROW]
[ROW][C]133[/C][C]1[/C][C]1.43317788628634[/C][C]-0.433177886286341[/C][/ROW]
[ROW][C]134[/C][C]2[/C][C]1.37259150846351[/C][C]0.627408491536486[/C][/ROW]
[ROW][C]135[/C][C]2[/C][C]1.37750690297134[/C][C]0.622493097028663[/C][/ROW]
[ROW][C]136[/C][C]2[/C][C]1.36816354845627[/C][C]0.63183645154373[/C][/ROW]
[ROW][C]137[/C][C]1[/C][C]1.30808469303777[/C][C]-0.308084693037769[/C][/ROW]
[ROW][C]138[/C][C]1[/C][C]1.42532491183485[/C][C]-0.425324911834849[/C][/ROW]
[ROW][C]139[/C][C]1[/C][C]1.31159121717849[/C][C]-0.311591217178485[/C][/ROW]
[ROW][C]140[/C][C]1[/C][C]1.41397854345786[/C][C]-0.413978543457856[/C][/ROW]
[ROW][C]141[/C][C]2[/C][C]1.34421316238336[/C][C]0.655786837616637[/C][/ROW]
[ROW][C]142[/C][C]2[/C][C]1.34091732714894[/C][C]0.659082672851056[/C][/ROW]
[ROW][C]143[/C][C]1[/C][C]1.33105124774834[/C][C]-0.331051247748345[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154113&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154113&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
121.369389586114740.630610413885263
211.18162513331756-0.181625133317563
311.33778986253749-0.337789862537488
411.39905886722612-0.399058867226122
521.612774049688760.387225950311244
621.444331066970750.555668933029252
711.15757493518664-0.157574935186638
811.49733598956617-0.497335989566167
911.31585232404537-0.315852324045368
1021.590171649441310.409828350558694
1111.41235648230749-0.412356482307491
1211.00776312818714-0.0077631281871396
1321.445031279863340.554968720136659
1421.065464007858640.934535992141355
1511.66954697878566-0.669546978785658
1611.4122232837381-0.4122232837381
1721.368163548456270.63183645154373
1821.611279656794070.388720343205934
1911.14758744487012-0.147587444870121
2021.341050167550950.658949832449047
2111.61183011954587-0.611830119545874
2221.822239912937980.177760087062019
2311.30896791772269-0.308967917722693
2421.436477913435570.563522086564435
2521.546715175038390.453284824961605
2611.30693400853508-0.306934008535076
2721.273715442019180.726284557980816
2811.26022052561505-0.260220525615051
2911.44442748173335-0.44442748173335
3021.519423332422930.480576667577066
3111.27597780522358-0.275977805223575
3221.171183051232320.828816948767678
3311.32348335918816-0.323483359188162
3421.386701605908530.61329839409147
3511.43881509254466-0.438815092544658
3621.561703947086150.438296052913854
3711.32052096217487-0.320520962174869
3811.27861922795723-0.278619227957226
3921.396924787813110.603075212186894
4011.46525745515395-0.46525745515395
4111.44748317609658-0.447483176096584
4221.900613108558450.099386891441548
4311.33525512473731-0.335255124737312
4421.173832864324030.82616713567597
4511.34016712194972-0.34016712194972
4621.320541587329690.679458412670313
4711.55004725682853-0.550047256828529
4811.56810644269351-0.568106442693512
4911.41705315571834-0.41705315571834
5021.696205034055170.303794965944832
5121.426533005299150.573466994700851
5221.642400390452660.357599609547342
5321.449017649294420.550982350705584
5411.49649570522873-0.496495705228726
5511.48256540136432-0.482565401364318
5611.36756261037062-0.367562610370622
5721.569635571189760.430364428810236
5811.45618166125571-0.456181661255715
5921.661778911326050.338221088673951
6021.260485047103850.73951495289615
6110.9575283951439550.0424716048560448
6211.29196014155346-0.291960141553459
6311.47544778779822-0.475447787798225
6411.22254365170857-0.222543651708575
6521.49343921624610.5065607837539
6621.552564229518230.447435770481771
6711.21809373467605-0.218093734676049
6821.856090934929360.143909065070641
6911.35689279060337-0.35689279060337
7011.30236865804364-0.302368658043635
7121.398661326251620.601338673748377
7221.531981012475950.468018987524048
7311.60508828654501-0.605088286545007
7411.5890539932826-0.589053993282605
7511.41490131119485-0.414901311194848
7611.24192868729004-0.241928687290042
7711.3601338195522-0.360133819552205
7821.809655038798210.190344961201793
7911.38430622321844-0.384306223218439
8021.40008814081290.599911859187103
8121.676055801159120.323944198840884
8221.591336981641810.408663018358186
8311.39549815233552-0.395498152335522
8411.2330286741413-0.233028674141299
8521.48107776301370.518922236986297
8611.26459197207914-0.264591972079142
8711.36592904144954-0.365929041449537
8811.25341834532361-0.253418345323607
8911.21257811841734-0.21257811841734
9021.347934573873630.652065426126372
9111.20391989264504-0.203919892645041
9211.34386562024394-0.343865620243941
9321.430800985041490.56919901495851
9421.442014513016470.557985486983526
9511.23862597918017-0.238625979180166
9611.09447583734123-0.0944758373412273
9721.639206260458560.360793739541442
9811.19770710262177-0.197707102621773
9921.719504608067180.280495391932821
10011.15203486002569-0.152034860025687
10111.63964103238313-0.639641032383133
10211.29534453844349-0.295344538443494
10311.54438067688995-0.544380676889954
10411.34918828864606-0.349188288646062
10511.41713198445416-0.417131984454156
10611.15769269143882-0.157692691438822
10711.16463512225881-0.164635122258805
10821.278576466693430.721423533306566
10911.49323254017092-0.493232540170918
11011.15795471105066-0.157954711050661
11121.494644270582370.505355729417633
11211.39777176594231-0.397771765942314
11321.258072078386970.741927921613026
11421.786997170154270.213002829845728
11521.279623863558070.720376136441933
11611.20682601595118-0.206826015951181
11711.39326096436814-0.393260964368138
11811.5261825723669-0.526182572366896
11911.50207105324231-0.502071053242311
12011.20454918413267-0.204549184132666
12111.45167068059785-0.451670680597848
12211.32225732152969-0.322257321529694
12311.33564123622572-0.335641236225719
12421.149803649515310.850196350484694
12511.46049186501077-0.460491865010774
12621.213485802004510.786514197995493
12711.33975102124535-0.339751021245353
12811.11127747468635-0.111277474686352
12911.5776503950277-0.577650395027695
13021.653692926247150.34630707375285
13121.530811667444330.469188332555672
13211.43428616769262-0.434286167692624
13311.43317788628634-0.433177886286341
13421.372591508463510.627408491536486
13521.377506902971340.622493097028663
13621.368163548456270.63183645154373
13711.30808469303777-0.308084693037769
13811.42532491183485-0.425324911834849
13911.31159121717849-0.311591217178485
14011.41397854345786-0.413978543457856
14121.344213162383360.655786837616637
14221.340917327148940.659082672851056
14311.33105124774834-0.331051247748345






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.7051660335984790.5896679328030430.294833966401521
100.5560999757339460.8878000485321080.443900024266054
110.4275821044583190.8551642089166390.572417895541681
120.3788240128665340.7576480257330680.621175987133466
130.3944806107392170.7889612214784350.605519389260783
140.5248009235526840.9503981528946320.475199076447316
150.4462416271748090.8924832543496190.553758372825191
160.634262092770330.7314758144593390.36573790722967
170.8007201132086990.3985597735826020.199279886791301
180.7468128342749190.5063743314501630.253187165725081
190.7288495815229270.5423008369541470.271150418477073
200.809570593490910.3808588130181810.19042940650909
210.8710205252307090.2579589495385820.128979474769291
220.8392483840718060.3215032318563890.160751615928194
230.8079510655928230.3840978688143540.192048934407177
240.8089567592170940.3820864815658120.191043240782906
250.7918667421786980.4162665156426050.208133257821302
260.7532443394295620.4935113211408760.246755660570438
270.8269834497109030.3460331005781930.173016550289097
280.8067260251385090.3865479497229820.193273974861491
290.8054305463722750.389138907255450.194569453627725
300.7970976998818130.4058046002363740.202902300118187
310.7689349673732090.4621300652535820.231065032626791
320.8049408120589260.3901183758821480.195059187941074
330.8049937978765710.3900124042468580.195006202123429
340.8210638908442090.3578722183115830.178936109155791
350.8240228148314530.3519543703370930.175977185168547
360.8130668139091080.3738663721817850.186933186090892
370.7971591488041650.405681702391670.202840851195835
380.7719674847642250.4560650304715490.228032515235775
390.7860204853615710.4279590292768580.213979514638429
400.7851641590518130.4296716818963740.214835840948187
410.787833325506340.424333348987320.21216667449366
420.7478697644559010.5042604710881980.252130235544099
430.7275657952423370.5448684095153260.272434204757663
440.792272006931870.4154559861362610.20772799306813
450.7778574122916890.4442851754166220.222142587708311
460.8074652773701890.3850694452596220.192534722629811
470.813441457631510.3731170847369790.18655854236849
480.8161883480297590.3676233039404810.18381165197024
490.8101879246884750.3796241506230510.189812075311525
500.7802827944289380.4394344111421250.219717205571062
510.7859216181142180.4281567637715640.214078381885782
520.7645689949676250.4708620100647490.235431005032375
530.7747564376931660.4504871246136690.225243562306834
540.7716545333628940.4566909332742110.228345466637106
550.7617559501062680.4764880997874650.238244049893732
560.7590104046559470.4819791906881060.240989595344053
570.7502585837798150.4994828324403710.249741416220185
580.7510646390424870.4978707219150260.248935360957513
590.7280315920768390.5439368158463220.271968407923161
600.7759448315178830.4481103369642340.224055168482117
610.741629179085360.5167416418292790.25837082091464
620.7141009382707620.5717981234584760.285899061729238
630.7380968157289690.5238063685420620.261903184271031
640.7075628781832730.5848742436334540.292437121816727
650.7083515500510020.5832968998979960.291648449948998
660.7133253301149390.5733493397701210.286674669885061
670.6790696375673440.6418607248653130.320930362432656
680.6396964223841290.7206071552317410.360303577615871
690.6186004902977770.7627990194044470.381399509702223
700.593522208820360.8129555823592790.40647779117964
710.6176693023006070.7646613953987860.382330697699393
720.615972990791750.7680540184165010.38402700920825
730.6453742373319250.7092515253361510.354625762668075
740.6625492751334940.6749014497330120.337450724866506
750.6545101174594690.6909797650810610.345489882540531
760.620211877623210.759576244753580.37978812237679
770.6075467634194820.7849064731610370.392453236580518
780.57510538907210.84978922185580.4248946109279
790.5575197787266530.8849604425466950.442480221273347
800.5847641328695080.8304717342609830.415235867130492
810.5589097680699430.8821804638601130.441090231930057
820.5485801255166680.9028397489666640.451419874483332
830.5316952476724060.9366095046551890.468304752327594
840.4939643708005570.9879287416011150.506035629199443
850.5175472926135720.9649054147728570.482452707386428
860.4858948550886610.9717897101773220.514105144911339
870.4651930591951960.9303861183903930.534806940804804
880.4336898825527080.8673797651054160.566310117447292
890.3927557251214040.7855114502428080.607244274878596
900.4456298397644780.8912596795289550.554370160235522
910.4052143689349820.8104287378699640.594785631065018
920.3742998502722130.7485997005444260.625700149727787
930.4098631158050910.8197262316101830.590136884194909
940.4272945449277830.8545890898555670.572705455072217
950.3891974158533840.7783948317067670.610802584146616
960.3423150606341360.6846301212682720.657684939365864
970.3244403310149650.648880662029930.675559668985035
980.2847887761869710.5695775523739410.715211223813029
990.3028993851990270.6057987703980550.697100614800973
1000.2637579663380140.5275159326760280.736242033661986
1010.281500808716220.563001617432440.71849919128378
1020.2630266423252090.5260532846504170.736973357674791
1030.2632361429160860.5264722858321710.736763857083914
1040.2310611035932910.4621222071865820.768938896406709
1050.2112106368297970.4224212736595940.788789363170203
1060.1802466033654160.3604932067308320.819753396634584
1070.1537594095614040.3075188191228070.846240590438596
1080.19894308256010.39788616512020.8010569174399
1090.2214285104619220.4428570209238440.778571489538078
1100.1901885170610390.3803770341220770.809811482938961
1110.1985319110540360.3970638221080720.801468088945964
1120.1892978559981910.3785957119963830.810702144001809
1130.2425618799994550.485123759998910.757438120000545
1140.2795491946845110.5590983893690220.720450805315489
1150.3203426746888140.6406853493776270.679657325311186
1160.2763675572110290.5527351144220570.723632442788971
1170.2681742261578480.5363484523156960.731825773842152
1180.2821080094802820.5642160189605650.717891990519718
1190.3880463184829290.7760926369658580.611953681517071
1200.3573400182391520.7146800364783030.642659981760848
1210.3105948513705110.6211897027410220.689405148629489
1220.7056626817844780.5886746364310450.294337318215522
1230.6751675008725180.6496649982549640.324832499127482
1240.7053861503863730.5892276992272530.294613849613627
1250.6759962599408560.6480074801182880.324003740059144
1260.6675286300309960.6649427399380080.332471369969004
1270.8301665768659650.339666846268070.169833423134035
1280.8012742698629860.3974514602740280.198725730137014
1290.8860276112474110.2279447775051770.113972388752589
1300.9343831456583010.1312337086833990.0656168543416994
1310.9145835738978050.170832852204390.085416426102195
1320.8517598264654890.2964803470690210.148240173534511
1330.7864983215880610.4270033568238780.213501678411939
1340.6916219891409650.6167560217180690.308378010859035

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.705166033598479 & 0.589667932803043 & 0.294833966401521 \tabularnewline
10 & 0.556099975733946 & 0.887800048532108 & 0.443900024266054 \tabularnewline
11 & 0.427582104458319 & 0.855164208916639 & 0.572417895541681 \tabularnewline
12 & 0.378824012866534 & 0.757648025733068 & 0.621175987133466 \tabularnewline
13 & 0.394480610739217 & 0.788961221478435 & 0.605519389260783 \tabularnewline
14 & 0.524800923552684 & 0.950398152894632 & 0.475199076447316 \tabularnewline
15 & 0.446241627174809 & 0.892483254349619 & 0.553758372825191 \tabularnewline
16 & 0.63426209277033 & 0.731475814459339 & 0.36573790722967 \tabularnewline
17 & 0.800720113208699 & 0.398559773582602 & 0.199279886791301 \tabularnewline
18 & 0.746812834274919 & 0.506374331450163 & 0.253187165725081 \tabularnewline
19 & 0.728849581522927 & 0.542300836954147 & 0.271150418477073 \tabularnewline
20 & 0.80957059349091 & 0.380858813018181 & 0.19042940650909 \tabularnewline
21 & 0.871020525230709 & 0.257958949538582 & 0.128979474769291 \tabularnewline
22 & 0.839248384071806 & 0.321503231856389 & 0.160751615928194 \tabularnewline
23 & 0.807951065592823 & 0.384097868814354 & 0.192048934407177 \tabularnewline
24 & 0.808956759217094 & 0.382086481565812 & 0.191043240782906 \tabularnewline
25 & 0.791866742178698 & 0.416266515642605 & 0.208133257821302 \tabularnewline
26 & 0.753244339429562 & 0.493511321140876 & 0.246755660570438 \tabularnewline
27 & 0.826983449710903 & 0.346033100578193 & 0.173016550289097 \tabularnewline
28 & 0.806726025138509 & 0.386547949722982 & 0.193273974861491 \tabularnewline
29 & 0.805430546372275 & 0.38913890725545 & 0.194569453627725 \tabularnewline
30 & 0.797097699881813 & 0.405804600236374 & 0.202902300118187 \tabularnewline
31 & 0.768934967373209 & 0.462130065253582 & 0.231065032626791 \tabularnewline
32 & 0.804940812058926 & 0.390118375882148 & 0.195059187941074 \tabularnewline
33 & 0.804993797876571 & 0.390012404246858 & 0.195006202123429 \tabularnewline
34 & 0.821063890844209 & 0.357872218311583 & 0.178936109155791 \tabularnewline
35 & 0.824022814831453 & 0.351954370337093 & 0.175977185168547 \tabularnewline
36 & 0.813066813909108 & 0.373866372181785 & 0.186933186090892 \tabularnewline
37 & 0.797159148804165 & 0.40568170239167 & 0.202840851195835 \tabularnewline
38 & 0.771967484764225 & 0.456065030471549 & 0.228032515235775 \tabularnewline
39 & 0.786020485361571 & 0.427959029276858 & 0.213979514638429 \tabularnewline
40 & 0.785164159051813 & 0.429671681896374 & 0.214835840948187 \tabularnewline
41 & 0.78783332550634 & 0.42433334898732 & 0.21216667449366 \tabularnewline
42 & 0.747869764455901 & 0.504260471088198 & 0.252130235544099 \tabularnewline
43 & 0.727565795242337 & 0.544868409515326 & 0.272434204757663 \tabularnewline
44 & 0.79227200693187 & 0.415455986136261 & 0.20772799306813 \tabularnewline
45 & 0.777857412291689 & 0.444285175416622 & 0.222142587708311 \tabularnewline
46 & 0.807465277370189 & 0.385069445259622 & 0.192534722629811 \tabularnewline
47 & 0.81344145763151 & 0.373117084736979 & 0.18655854236849 \tabularnewline
48 & 0.816188348029759 & 0.367623303940481 & 0.18381165197024 \tabularnewline
49 & 0.810187924688475 & 0.379624150623051 & 0.189812075311525 \tabularnewline
50 & 0.780282794428938 & 0.439434411142125 & 0.219717205571062 \tabularnewline
51 & 0.785921618114218 & 0.428156763771564 & 0.214078381885782 \tabularnewline
52 & 0.764568994967625 & 0.470862010064749 & 0.235431005032375 \tabularnewline
53 & 0.774756437693166 & 0.450487124613669 & 0.225243562306834 \tabularnewline
54 & 0.771654533362894 & 0.456690933274211 & 0.228345466637106 \tabularnewline
55 & 0.761755950106268 & 0.476488099787465 & 0.238244049893732 \tabularnewline
56 & 0.759010404655947 & 0.481979190688106 & 0.240989595344053 \tabularnewline
57 & 0.750258583779815 & 0.499482832440371 & 0.249741416220185 \tabularnewline
58 & 0.751064639042487 & 0.497870721915026 & 0.248935360957513 \tabularnewline
59 & 0.728031592076839 & 0.543936815846322 & 0.271968407923161 \tabularnewline
60 & 0.775944831517883 & 0.448110336964234 & 0.224055168482117 \tabularnewline
61 & 0.74162917908536 & 0.516741641829279 & 0.25837082091464 \tabularnewline
62 & 0.714100938270762 & 0.571798123458476 & 0.285899061729238 \tabularnewline
63 & 0.738096815728969 & 0.523806368542062 & 0.261903184271031 \tabularnewline
64 & 0.707562878183273 & 0.584874243633454 & 0.292437121816727 \tabularnewline
65 & 0.708351550051002 & 0.583296899897996 & 0.291648449948998 \tabularnewline
66 & 0.713325330114939 & 0.573349339770121 & 0.286674669885061 \tabularnewline
67 & 0.679069637567344 & 0.641860724865313 & 0.320930362432656 \tabularnewline
68 & 0.639696422384129 & 0.720607155231741 & 0.360303577615871 \tabularnewline
69 & 0.618600490297777 & 0.762799019404447 & 0.381399509702223 \tabularnewline
70 & 0.59352220882036 & 0.812955582359279 & 0.40647779117964 \tabularnewline
71 & 0.617669302300607 & 0.764661395398786 & 0.382330697699393 \tabularnewline
72 & 0.61597299079175 & 0.768054018416501 & 0.38402700920825 \tabularnewline
73 & 0.645374237331925 & 0.709251525336151 & 0.354625762668075 \tabularnewline
74 & 0.662549275133494 & 0.674901449733012 & 0.337450724866506 \tabularnewline
75 & 0.654510117459469 & 0.690979765081061 & 0.345489882540531 \tabularnewline
76 & 0.62021187762321 & 0.75957624475358 & 0.37978812237679 \tabularnewline
77 & 0.607546763419482 & 0.784906473161037 & 0.392453236580518 \tabularnewline
78 & 0.5751053890721 & 0.8497892218558 & 0.4248946109279 \tabularnewline
79 & 0.557519778726653 & 0.884960442546695 & 0.442480221273347 \tabularnewline
80 & 0.584764132869508 & 0.830471734260983 & 0.415235867130492 \tabularnewline
81 & 0.558909768069943 & 0.882180463860113 & 0.441090231930057 \tabularnewline
82 & 0.548580125516668 & 0.902839748966664 & 0.451419874483332 \tabularnewline
83 & 0.531695247672406 & 0.936609504655189 & 0.468304752327594 \tabularnewline
84 & 0.493964370800557 & 0.987928741601115 & 0.506035629199443 \tabularnewline
85 & 0.517547292613572 & 0.964905414772857 & 0.482452707386428 \tabularnewline
86 & 0.485894855088661 & 0.971789710177322 & 0.514105144911339 \tabularnewline
87 & 0.465193059195196 & 0.930386118390393 & 0.534806940804804 \tabularnewline
88 & 0.433689882552708 & 0.867379765105416 & 0.566310117447292 \tabularnewline
89 & 0.392755725121404 & 0.785511450242808 & 0.607244274878596 \tabularnewline
90 & 0.445629839764478 & 0.891259679528955 & 0.554370160235522 \tabularnewline
91 & 0.405214368934982 & 0.810428737869964 & 0.594785631065018 \tabularnewline
92 & 0.374299850272213 & 0.748599700544426 & 0.625700149727787 \tabularnewline
93 & 0.409863115805091 & 0.819726231610183 & 0.590136884194909 \tabularnewline
94 & 0.427294544927783 & 0.854589089855567 & 0.572705455072217 \tabularnewline
95 & 0.389197415853384 & 0.778394831706767 & 0.610802584146616 \tabularnewline
96 & 0.342315060634136 & 0.684630121268272 & 0.657684939365864 \tabularnewline
97 & 0.324440331014965 & 0.64888066202993 & 0.675559668985035 \tabularnewline
98 & 0.284788776186971 & 0.569577552373941 & 0.715211223813029 \tabularnewline
99 & 0.302899385199027 & 0.605798770398055 & 0.697100614800973 \tabularnewline
100 & 0.263757966338014 & 0.527515932676028 & 0.736242033661986 \tabularnewline
101 & 0.28150080871622 & 0.56300161743244 & 0.71849919128378 \tabularnewline
102 & 0.263026642325209 & 0.526053284650417 & 0.736973357674791 \tabularnewline
103 & 0.263236142916086 & 0.526472285832171 & 0.736763857083914 \tabularnewline
104 & 0.231061103593291 & 0.462122207186582 & 0.768938896406709 \tabularnewline
105 & 0.211210636829797 & 0.422421273659594 & 0.788789363170203 \tabularnewline
106 & 0.180246603365416 & 0.360493206730832 & 0.819753396634584 \tabularnewline
107 & 0.153759409561404 & 0.307518819122807 & 0.846240590438596 \tabularnewline
108 & 0.1989430825601 & 0.3978861651202 & 0.8010569174399 \tabularnewline
109 & 0.221428510461922 & 0.442857020923844 & 0.778571489538078 \tabularnewline
110 & 0.190188517061039 & 0.380377034122077 & 0.809811482938961 \tabularnewline
111 & 0.198531911054036 & 0.397063822108072 & 0.801468088945964 \tabularnewline
112 & 0.189297855998191 & 0.378595711996383 & 0.810702144001809 \tabularnewline
113 & 0.242561879999455 & 0.48512375999891 & 0.757438120000545 \tabularnewline
114 & 0.279549194684511 & 0.559098389369022 & 0.720450805315489 \tabularnewline
115 & 0.320342674688814 & 0.640685349377627 & 0.679657325311186 \tabularnewline
116 & 0.276367557211029 & 0.552735114422057 & 0.723632442788971 \tabularnewline
117 & 0.268174226157848 & 0.536348452315696 & 0.731825773842152 \tabularnewline
118 & 0.282108009480282 & 0.564216018960565 & 0.717891990519718 \tabularnewline
119 & 0.388046318482929 & 0.776092636965858 & 0.611953681517071 \tabularnewline
120 & 0.357340018239152 & 0.714680036478303 & 0.642659981760848 \tabularnewline
121 & 0.310594851370511 & 0.621189702741022 & 0.689405148629489 \tabularnewline
122 & 0.705662681784478 & 0.588674636431045 & 0.294337318215522 \tabularnewline
123 & 0.675167500872518 & 0.649664998254964 & 0.324832499127482 \tabularnewline
124 & 0.705386150386373 & 0.589227699227253 & 0.294613849613627 \tabularnewline
125 & 0.675996259940856 & 0.648007480118288 & 0.324003740059144 \tabularnewline
126 & 0.667528630030996 & 0.664942739938008 & 0.332471369969004 \tabularnewline
127 & 0.830166576865965 & 0.33966684626807 & 0.169833423134035 \tabularnewline
128 & 0.801274269862986 & 0.397451460274028 & 0.198725730137014 \tabularnewline
129 & 0.886027611247411 & 0.227944777505177 & 0.113972388752589 \tabularnewline
130 & 0.934383145658301 & 0.131233708683399 & 0.0656168543416994 \tabularnewline
131 & 0.914583573897805 & 0.17083285220439 & 0.085416426102195 \tabularnewline
132 & 0.851759826465489 & 0.296480347069021 & 0.148240173534511 \tabularnewline
133 & 0.786498321588061 & 0.427003356823878 & 0.213501678411939 \tabularnewline
134 & 0.691621989140965 & 0.616756021718069 & 0.308378010859035 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154113&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]0.705166033598479[/C][C]0.589667932803043[/C][C]0.294833966401521[/C][/ROW]
[ROW][C]10[/C][C]0.556099975733946[/C][C]0.887800048532108[/C][C]0.443900024266054[/C][/ROW]
[ROW][C]11[/C][C]0.427582104458319[/C][C]0.855164208916639[/C][C]0.572417895541681[/C][/ROW]
[ROW][C]12[/C][C]0.378824012866534[/C][C]0.757648025733068[/C][C]0.621175987133466[/C][/ROW]
[ROW][C]13[/C][C]0.394480610739217[/C][C]0.788961221478435[/C][C]0.605519389260783[/C][/ROW]
[ROW][C]14[/C][C]0.524800923552684[/C][C]0.950398152894632[/C][C]0.475199076447316[/C][/ROW]
[ROW][C]15[/C][C]0.446241627174809[/C][C]0.892483254349619[/C][C]0.553758372825191[/C][/ROW]
[ROW][C]16[/C][C]0.63426209277033[/C][C]0.731475814459339[/C][C]0.36573790722967[/C][/ROW]
[ROW][C]17[/C][C]0.800720113208699[/C][C]0.398559773582602[/C][C]0.199279886791301[/C][/ROW]
[ROW][C]18[/C][C]0.746812834274919[/C][C]0.506374331450163[/C][C]0.253187165725081[/C][/ROW]
[ROW][C]19[/C][C]0.728849581522927[/C][C]0.542300836954147[/C][C]0.271150418477073[/C][/ROW]
[ROW][C]20[/C][C]0.80957059349091[/C][C]0.380858813018181[/C][C]0.19042940650909[/C][/ROW]
[ROW][C]21[/C][C]0.871020525230709[/C][C]0.257958949538582[/C][C]0.128979474769291[/C][/ROW]
[ROW][C]22[/C][C]0.839248384071806[/C][C]0.321503231856389[/C][C]0.160751615928194[/C][/ROW]
[ROW][C]23[/C][C]0.807951065592823[/C][C]0.384097868814354[/C][C]0.192048934407177[/C][/ROW]
[ROW][C]24[/C][C]0.808956759217094[/C][C]0.382086481565812[/C][C]0.191043240782906[/C][/ROW]
[ROW][C]25[/C][C]0.791866742178698[/C][C]0.416266515642605[/C][C]0.208133257821302[/C][/ROW]
[ROW][C]26[/C][C]0.753244339429562[/C][C]0.493511321140876[/C][C]0.246755660570438[/C][/ROW]
[ROW][C]27[/C][C]0.826983449710903[/C][C]0.346033100578193[/C][C]0.173016550289097[/C][/ROW]
[ROW][C]28[/C][C]0.806726025138509[/C][C]0.386547949722982[/C][C]0.193273974861491[/C][/ROW]
[ROW][C]29[/C][C]0.805430546372275[/C][C]0.38913890725545[/C][C]0.194569453627725[/C][/ROW]
[ROW][C]30[/C][C]0.797097699881813[/C][C]0.405804600236374[/C][C]0.202902300118187[/C][/ROW]
[ROW][C]31[/C][C]0.768934967373209[/C][C]0.462130065253582[/C][C]0.231065032626791[/C][/ROW]
[ROW][C]32[/C][C]0.804940812058926[/C][C]0.390118375882148[/C][C]0.195059187941074[/C][/ROW]
[ROW][C]33[/C][C]0.804993797876571[/C][C]0.390012404246858[/C][C]0.195006202123429[/C][/ROW]
[ROW][C]34[/C][C]0.821063890844209[/C][C]0.357872218311583[/C][C]0.178936109155791[/C][/ROW]
[ROW][C]35[/C][C]0.824022814831453[/C][C]0.351954370337093[/C][C]0.175977185168547[/C][/ROW]
[ROW][C]36[/C][C]0.813066813909108[/C][C]0.373866372181785[/C][C]0.186933186090892[/C][/ROW]
[ROW][C]37[/C][C]0.797159148804165[/C][C]0.40568170239167[/C][C]0.202840851195835[/C][/ROW]
[ROW][C]38[/C][C]0.771967484764225[/C][C]0.456065030471549[/C][C]0.228032515235775[/C][/ROW]
[ROW][C]39[/C][C]0.786020485361571[/C][C]0.427959029276858[/C][C]0.213979514638429[/C][/ROW]
[ROW][C]40[/C][C]0.785164159051813[/C][C]0.429671681896374[/C][C]0.214835840948187[/C][/ROW]
[ROW][C]41[/C][C]0.78783332550634[/C][C]0.42433334898732[/C][C]0.21216667449366[/C][/ROW]
[ROW][C]42[/C][C]0.747869764455901[/C][C]0.504260471088198[/C][C]0.252130235544099[/C][/ROW]
[ROW][C]43[/C][C]0.727565795242337[/C][C]0.544868409515326[/C][C]0.272434204757663[/C][/ROW]
[ROW][C]44[/C][C]0.79227200693187[/C][C]0.415455986136261[/C][C]0.20772799306813[/C][/ROW]
[ROW][C]45[/C][C]0.777857412291689[/C][C]0.444285175416622[/C][C]0.222142587708311[/C][/ROW]
[ROW][C]46[/C][C]0.807465277370189[/C][C]0.385069445259622[/C][C]0.192534722629811[/C][/ROW]
[ROW][C]47[/C][C]0.81344145763151[/C][C]0.373117084736979[/C][C]0.18655854236849[/C][/ROW]
[ROW][C]48[/C][C]0.816188348029759[/C][C]0.367623303940481[/C][C]0.18381165197024[/C][/ROW]
[ROW][C]49[/C][C]0.810187924688475[/C][C]0.379624150623051[/C][C]0.189812075311525[/C][/ROW]
[ROW][C]50[/C][C]0.780282794428938[/C][C]0.439434411142125[/C][C]0.219717205571062[/C][/ROW]
[ROW][C]51[/C][C]0.785921618114218[/C][C]0.428156763771564[/C][C]0.214078381885782[/C][/ROW]
[ROW][C]52[/C][C]0.764568994967625[/C][C]0.470862010064749[/C][C]0.235431005032375[/C][/ROW]
[ROW][C]53[/C][C]0.774756437693166[/C][C]0.450487124613669[/C][C]0.225243562306834[/C][/ROW]
[ROW][C]54[/C][C]0.771654533362894[/C][C]0.456690933274211[/C][C]0.228345466637106[/C][/ROW]
[ROW][C]55[/C][C]0.761755950106268[/C][C]0.476488099787465[/C][C]0.238244049893732[/C][/ROW]
[ROW][C]56[/C][C]0.759010404655947[/C][C]0.481979190688106[/C][C]0.240989595344053[/C][/ROW]
[ROW][C]57[/C][C]0.750258583779815[/C][C]0.499482832440371[/C][C]0.249741416220185[/C][/ROW]
[ROW][C]58[/C][C]0.751064639042487[/C][C]0.497870721915026[/C][C]0.248935360957513[/C][/ROW]
[ROW][C]59[/C][C]0.728031592076839[/C][C]0.543936815846322[/C][C]0.271968407923161[/C][/ROW]
[ROW][C]60[/C][C]0.775944831517883[/C][C]0.448110336964234[/C][C]0.224055168482117[/C][/ROW]
[ROW][C]61[/C][C]0.74162917908536[/C][C]0.516741641829279[/C][C]0.25837082091464[/C][/ROW]
[ROW][C]62[/C][C]0.714100938270762[/C][C]0.571798123458476[/C][C]0.285899061729238[/C][/ROW]
[ROW][C]63[/C][C]0.738096815728969[/C][C]0.523806368542062[/C][C]0.261903184271031[/C][/ROW]
[ROW][C]64[/C][C]0.707562878183273[/C][C]0.584874243633454[/C][C]0.292437121816727[/C][/ROW]
[ROW][C]65[/C][C]0.708351550051002[/C][C]0.583296899897996[/C][C]0.291648449948998[/C][/ROW]
[ROW][C]66[/C][C]0.713325330114939[/C][C]0.573349339770121[/C][C]0.286674669885061[/C][/ROW]
[ROW][C]67[/C][C]0.679069637567344[/C][C]0.641860724865313[/C][C]0.320930362432656[/C][/ROW]
[ROW][C]68[/C][C]0.639696422384129[/C][C]0.720607155231741[/C][C]0.360303577615871[/C][/ROW]
[ROW][C]69[/C][C]0.618600490297777[/C][C]0.762799019404447[/C][C]0.381399509702223[/C][/ROW]
[ROW][C]70[/C][C]0.59352220882036[/C][C]0.812955582359279[/C][C]0.40647779117964[/C][/ROW]
[ROW][C]71[/C][C]0.617669302300607[/C][C]0.764661395398786[/C][C]0.382330697699393[/C][/ROW]
[ROW][C]72[/C][C]0.61597299079175[/C][C]0.768054018416501[/C][C]0.38402700920825[/C][/ROW]
[ROW][C]73[/C][C]0.645374237331925[/C][C]0.709251525336151[/C][C]0.354625762668075[/C][/ROW]
[ROW][C]74[/C][C]0.662549275133494[/C][C]0.674901449733012[/C][C]0.337450724866506[/C][/ROW]
[ROW][C]75[/C][C]0.654510117459469[/C][C]0.690979765081061[/C][C]0.345489882540531[/C][/ROW]
[ROW][C]76[/C][C]0.62021187762321[/C][C]0.75957624475358[/C][C]0.37978812237679[/C][/ROW]
[ROW][C]77[/C][C]0.607546763419482[/C][C]0.784906473161037[/C][C]0.392453236580518[/C][/ROW]
[ROW][C]78[/C][C]0.5751053890721[/C][C]0.8497892218558[/C][C]0.4248946109279[/C][/ROW]
[ROW][C]79[/C][C]0.557519778726653[/C][C]0.884960442546695[/C][C]0.442480221273347[/C][/ROW]
[ROW][C]80[/C][C]0.584764132869508[/C][C]0.830471734260983[/C][C]0.415235867130492[/C][/ROW]
[ROW][C]81[/C][C]0.558909768069943[/C][C]0.882180463860113[/C][C]0.441090231930057[/C][/ROW]
[ROW][C]82[/C][C]0.548580125516668[/C][C]0.902839748966664[/C][C]0.451419874483332[/C][/ROW]
[ROW][C]83[/C][C]0.531695247672406[/C][C]0.936609504655189[/C][C]0.468304752327594[/C][/ROW]
[ROW][C]84[/C][C]0.493964370800557[/C][C]0.987928741601115[/C][C]0.506035629199443[/C][/ROW]
[ROW][C]85[/C][C]0.517547292613572[/C][C]0.964905414772857[/C][C]0.482452707386428[/C][/ROW]
[ROW][C]86[/C][C]0.485894855088661[/C][C]0.971789710177322[/C][C]0.514105144911339[/C][/ROW]
[ROW][C]87[/C][C]0.465193059195196[/C][C]0.930386118390393[/C][C]0.534806940804804[/C][/ROW]
[ROW][C]88[/C][C]0.433689882552708[/C][C]0.867379765105416[/C][C]0.566310117447292[/C][/ROW]
[ROW][C]89[/C][C]0.392755725121404[/C][C]0.785511450242808[/C][C]0.607244274878596[/C][/ROW]
[ROW][C]90[/C][C]0.445629839764478[/C][C]0.891259679528955[/C][C]0.554370160235522[/C][/ROW]
[ROW][C]91[/C][C]0.405214368934982[/C][C]0.810428737869964[/C][C]0.594785631065018[/C][/ROW]
[ROW][C]92[/C][C]0.374299850272213[/C][C]0.748599700544426[/C][C]0.625700149727787[/C][/ROW]
[ROW][C]93[/C][C]0.409863115805091[/C][C]0.819726231610183[/C][C]0.590136884194909[/C][/ROW]
[ROW][C]94[/C][C]0.427294544927783[/C][C]0.854589089855567[/C][C]0.572705455072217[/C][/ROW]
[ROW][C]95[/C][C]0.389197415853384[/C][C]0.778394831706767[/C][C]0.610802584146616[/C][/ROW]
[ROW][C]96[/C][C]0.342315060634136[/C][C]0.684630121268272[/C][C]0.657684939365864[/C][/ROW]
[ROW][C]97[/C][C]0.324440331014965[/C][C]0.64888066202993[/C][C]0.675559668985035[/C][/ROW]
[ROW][C]98[/C][C]0.284788776186971[/C][C]0.569577552373941[/C][C]0.715211223813029[/C][/ROW]
[ROW][C]99[/C][C]0.302899385199027[/C][C]0.605798770398055[/C][C]0.697100614800973[/C][/ROW]
[ROW][C]100[/C][C]0.263757966338014[/C][C]0.527515932676028[/C][C]0.736242033661986[/C][/ROW]
[ROW][C]101[/C][C]0.28150080871622[/C][C]0.56300161743244[/C][C]0.71849919128378[/C][/ROW]
[ROW][C]102[/C][C]0.263026642325209[/C][C]0.526053284650417[/C][C]0.736973357674791[/C][/ROW]
[ROW][C]103[/C][C]0.263236142916086[/C][C]0.526472285832171[/C][C]0.736763857083914[/C][/ROW]
[ROW][C]104[/C][C]0.231061103593291[/C][C]0.462122207186582[/C][C]0.768938896406709[/C][/ROW]
[ROW][C]105[/C][C]0.211210636829797[/C][C]0.422421273659594[/C][C]0.788789363170203[/C][/ROW]
[ROW][C]106[/C][C]0.180246603365416[/C][C]0.360493206730832[/C][C]0.819753396634584[/C][/ROW]
[ROW][C]107[/C][C]0.153759409561404[/C][C]0.307518819122807[/C][C]0.846240590438596[/C][/ROW]
[ROW][C]108[/C][C]0.1989430825601[/C][C]0.3978861651202[/C][C]0.8010569174399[/C][/ROW]
[ROW][C]109[/C][C]0.221428510461922[/C][C]0.442857020923844[/C][C]0.778571489538078[/C][/ROW]
[ROW][C]110[/C][C]0.190188517061039[/C][C]0.380377034122077[/C][C]0.809811482938961[/C][/ROW]
[ROW][C]111[/C][C]0.198531911054036[/C][C]0.397063822108072[/C][C]0.801468088945964[/C][/ROW]
[ROW][C]112[/C][C]0.189297855998191[/C][C]0.378595711996383[/C][C]0.810702144001809[/C][/ROW]
[ROW][C]113[/C][C]0.242561879999455[/C][C]0.48512375999891[/C][C]0.757438120000545[/C][/ROW]
[ROW][C]114[/C][C]0.279549194684511[/C][C]0.559098389369022[/C][C]0.720450805315489[/C][/ROW]
[ROW][C]115[/C][C]0.320342674688814[/C][C]0.640685349377627[/C][C]0.679657325311186[/C][/ROW]
[ROW][C]116[/C][C]0.276367557211029[/C][C]0.552735114422057[/C][C]0.723632442788971[/C][/ROW]
[ROW][C]117[/C][C]0.268174226157848[/C][C]0.536348452315696[/C][C]0.731825773842152[/C][/ROW]
[ROW][C]118[/C][C]0.282108009480282[/C][C]0.564216018960565[/C][C]0.717891990519718[/C][/ROW]
[ROW][C]119[/C][C]0.388046318482929[/C][C]0.776092636965858[/C][C]0.611953681517071[/C][/ROW]
[ROW][C]120[/C][C]0.357340018239152[/C][C]0.714680036478303[/C][C]0.642659981760848[/C][/ROW]
[ROW][C]121[/C][C]0.310594851370511[/C][C]0.621189702741022[/C][C]0.689405148629489[/C][/ROW]
[ROW][C]122[/C][C]0.705662681784478[/C][C]0.588674636431045[/C][C]0.294337318215522[/C][/ROW]
[ROW][C]123[/C][C]0.675167500872518[/C][C]0.649664998254964[/C][C]0.324832499127482[/C][/ROW]
[ROW][C]124[/C][C]0.705386150386373[/C][C]0.589227699227253[/C][C]0.294613849613627[/C][/ROW]
[ROW][C]125[/C][C]0.675996259940856[/C][C]0.648007480118288[/C][C]0.324003740059144[/C][/ROW]
[ROW][C]126[/C][C]0.667528630030996[/C][C]0.664942739938008[/C][C]0.332471369969004[/C][/ROW]
[ROW][C]127[/C][C]0.830166576865965[/C][C]0.33966684626807[/C][C]0.169833423134035[/C][/ROW]
[ROW][C]128[/C][C]0.801274269862986[/C][C]0.397451460274028[/C][C]0.198725730137014[/C][/ROW]
[ROW][C]129[/C][C]0.886027611247411[/C][C]0.227944777505177[/C][C]0.113972388752589[/C][/ROW]
[ROW][C]130[/C][C]0.934383145658301[/C][C]0.131233708683399[/C][C]0.0656168543416994[/C][/ROW]
[ROW][C]131[/C][C]0.914583573897805[/C][C]0.17083285220439[/C][C]0.085416426102195[/C][/ROW]
[ROW][C]132[/C][C]0.851759826465489[/C][C]0.296480347069021[/C][C]0.148240173534511[/C][/ROW]
[ROW][C]133[/C][C]0.786498321588061[/C][C]0.427003356823878[/C][C]0.213501678411939[/C][/ROW]
[ROW][C]134[/C][C]0.691621989140965[/C][C]0.616756021718069[/C][C]0.308378010859035[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154113&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154113&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
90.7051660335984790.5896679328030430.294833966401521
100.5560999757339460.8878000485321080.443900024266054
110.4275821044583190.8551642089166390.572417895541681
120.3788240128665340.7576480257330680.621175987133466
130.3944806107392170.7889612214784350.605519389260783
140.5248009235526840.9503981528946320.475199076447316
150.4462416271748090.8924832543496190.553758372825191
160.634262092770330.7314758144593390.36573790722967
170.8007201132086990.3985597735826020.199279886791301
180.7468128342749190.5063743314501630.253187165725081
190.7288495815229270.5423008369541470.271150418477073
200.809570593490910.3808588130181810.19042940650909
210.8710205252307090.2579589495385820.128979474769291
220.8392483840718060.3215032318563890.160751615928194
230.8079510655928230.3840978688143540.192048934407177
240.8089567592170940.3820864815658120.191043240782906
250.7918667421786980.4162665156426050.208133257821302
260.7532443394295620.4935113211408760.246755660570438
270.8269834497109030.3460331005781930.173016550289097
280.8067260251385090.3865479497229820.193273974861491
290.8054305463722750.389138907255450.194569453627725
300.7970976998818130.4058046002363740.202902300118187
310.7689349673732090.4621300652535820.231065032626791
320.8049408120589260.3901183758821480.195059187941074
330.8049937978765710.3900124042468580.195006202123429
340.8210638908442090.3578722183115830.178936109155791
350.8240228148314530.3519543703370930.175977185168547
360.8130668139091080.3738663721817850.186933186090892
370.7971591488041650.405681702391670.202840851195835
380.7719674847642250.4560650304715490.228032515235775
390.7860204853615710.4279590292768580.213979514638429
400.7851641590518130.4296716818963740.214835840948187
410.787833325506340.424333348987320.21216667449366
420.7478697644559010.5042604710881980.252130235544099
430.7275657952423370.5448684095153260.272434204757663
440.792272006931870.4154559861362610.20772799306813
450.7778574122916890.4442851754166220.222142587708311
460.8074652773701890.3850694452596220.192534722629811
470.813441457631510.3731170847369790.18655854236849
480.8161883480297590.3676233039404810.18381165197024
490.8101879246884750.3796241506230510.189812075311525
500.7802827944289380.4394344111421250.219717205571062
510.7859216181142180.4281567637715640.214078381885782
520.7645689949676250.4708620100647490.235431005032375
530.7747564376931660.4504871246136690.225243562306834
540.7716545333628940.4566909332742110.228345466637106
550.7617559501062680.4764880997874650.238244049893732
560.7590104046559470.4819791906881060.240989595344053
570.7502585837798150.4994828324403710.249741416220185
580.7510646390424870.4978707219150260.248935360957513
590.7280315920768390.5439368158463220.271968407923161
600.7759448315178830.4481103369642340.224055168482117
610.741629179085360.5167416418292790.25837082091464
620.7141009382707620.5717981234584760.285899061729238
630.7380968157289690.5238063685420620.261903184271031
640.7075628781832730.5848742436334540.292437121816727
650.7083515500510020.5832968998979960.291648449948998
660.7133253301149390.5733493397701210.286674669885061
670.6790696375673440.6418607248653130.320930362432656
680.6396964223841290.7206071552317410.360303577615871
690.6186004902977770.7627990194044470.381399509702223
700.593522208820360.8129555823592790.40647779117964
710.6176693023006070.7646613953987860.382330697699393
720.615972990791750.7680540184165010.38402700920825
730.6453742373319250.7092515253361510.354625762668075
740.6625492751334940.6749014497330120.337450724866506
750.6545101174594690.6909797650810610.345489882540531
760.620211877623210.759576244753580.37978812237679
770.6075467634194820.7849064731610370.392453236580518
780.57510538907210.84978922185580.4248946109279
790.5575197787266530.8849604425466950.442480221273347
800.5847641328695080.8304717342609830.415235867130492
810.5589097680699430.8821804638601130.441090231930057
820.5485801255166680.9028397489666640.451419874483332
830.5316952476724060.9366095046551890.468304752327594
840.4939643708005570.9879287416011150.506035629199443
850.5175472926135720.9649054147728570.482452707386428
860.4858948550886610.9717897101773220.514105144911339
870.4651930591951960.9303861183903930.534806940804804
880.4336898825527080.8673797651054160.566310117447292
890.3927557251214040.7855114502428080.607244274878596
900.4456298397644780.8912596795289550.554370160235522
910.4052143689349820.8104287378699640.594785631065018
920.3742998502722130.7485997005444260.625700149727787
930.4098631158050910.8197262316101830.590136884194909
940.4272945449277830.8545890898555670.572705455072217
950.3891974158533840.7783948317067670.610802584146616
960.3423150606341360.6846301212682720.657684939365864
970.3244403310149650.648880662029930.675559668985035
980.2847887761869710.5695775523739410.715211223813029
990.3028993851990270.6057987703980550.697100614800973
1000.2637579663380140.5275159326760280.736242033661986
1010.281500808716220.563001617432440.71849919128378
1020.2630266423252090.5260532846504170.736973357674791
1030.2632361429160860.5264722858321710.736763857083914
1040.2310611035932910.4621222071865820.768938896406709
1050.2112106368297970.4224212736595940.788789363170203
1060.1802466033654160.3604932067308320.819753396634584
1070.1537594095614040.3075188191228070.846240590438596
1080.19894308256010.39788616512020.8010569174399
1090.2214285104619220.4428570209238440.778571489538078
1100.1901885170610390.3803770341220770.809811482938961
1110.1985319110540360.3970638221080720.801468088945964
1120.1892978559981910.3785957119963830.810702144001809
1130.2425618799994550.485123759998910.757438120000545
1140.2795491946845110.5590983893690220.720450805315489
1150.3203426746888140.6406853493776270.679657325311186
1160.2763675572110290.5527351144220570.723632442788971
1170.2681742261578480.5363484523156960.731825773842152
1180.2821080094802820.5642160189605650.717891990519718
1190.3880463184829290.7760926369658580.611953681517071
1200.3573400182391520.7146800364783030.642659981760848
1210.3105948513705110.6211897027410220.689405148629489
1220.7056626817844780.5886746364310450.294337318215522
1230.6751675008725180.6496649982549640.324832499127482
1240.7053861503863730.5892276992272530.294613849613627
1250.6759962599408560.6480074801182880.324003740059144
1260.6675286300309960.6649427399380080.332471369969004
1270.8301665768659650.339666846268070.169833423134035
1280.8012742698629860.3974514602740280.198725730137014
1290.8860276112474110.2279447775051770.113972388752589
1300.9343831456583010.1312337086833990.0656168543416994
1310.9145835738978050.170832852204390.085416426102195
1320.8517598264654890.2964803470690210.148240173534511
1330.7864983215880610.4270033568238780.213501678411939
1340.6916219891409650.6167560217180690.308378010859035






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

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154113&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154113&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154113&T=6

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

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


Figures (Output of Computation)

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Input Parameters & R Code

Parameters (Session):
par1 = 3 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 3 ; 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')
}