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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, 02 Dec 2010 15:54:35 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/02/t129130517439i12ssse3ow4xe.htm/, Retrieved Sun, 05 May 2024 10:02:07 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=104337, Retrieved Sun, 05 May 2024 10:02:07 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact128

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
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-   PD  [Multiple Regression] [minitutorial work...] [2010-12-01 10:16:50] [47138a5b35b45ef255ae0d42cb04d202]
-   PD      [Multiple Regression] [gelukte blog mini...] [2010-12-02 15:54:35] [0dfe009a651fec1e160584d659799586] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
2	13	3	13	13	3
1	12	3	7	13	5
0	15	4	10	16	6
3	12	3	9	12	6
3	10	2	10	11	5
1	12	3	6	12	3
3	15	4	15	18	8
1	9	2	10	11	4
4	12	3	14	14	4
0	11	4	5	9	4
3	11	3	15	14	6
2	11	3	10	12	6
4	15	4	16	11	5
3	7	2	13	12	4
1	11	2	6	13	6
1	11	3	12	11	4
2	10	2	10	12	6
3	14	4	15	16	6
1	10	2	6	9	4
1	6	1	8	11	4
2	11	3	8	13	2
3	15	4	13	15	7
4	11	2	15	10	5
2	12	3	7	11	4
1	14	3	12	13	6
2	15	4	15	16	6
2	9	3	13	15	7
4	13	3	15	14	5
2	13	4	13	14	6
3	16	4	9	14	4
3	13	4	9	8	4
3	12	3	15	13	7
4	14	4	14	15	7
2	11	3	9	13	4
2	9	2	9	11	4
4	16	4	16	15	6
3	12	3	12	15	6
4	10	2	10	9	5
2	13	4	13	13	6
5	16	4	17	16	7
3	14	4	13	13	6
1	15	4	5	11	3
1	5	2	6	12	3
1	8	2	9	12	4
2	11	3	9	12	6
3	16	4	13	14	7
9	17	5	20	14	5
0	9	2	5	8	4
0	9	3	8	13	5
2	13	3	14	16	6
2	10	2	6	13	6
3	6	2	14	11	6
1	12	3	9	14	5
2	8	2	8	13	4
0	14	4	9	13	5
5	12	3	16	13	5
2	11	3	12	12	4
4	16	4	16	16	6
3	8	1	11	15	2
0	15	4	11	15	8
0	7	2	6	12	3
4	16	4	16	14	6
1	14	4	15	12	6
1	16	4	11	15	6
4	9	2	9	12	5
2	14	4	12	13	5
4	11	2	15	12	6
1	13	4	7	12	5
4	15	4	14	13	6
2	5	1	5	5	2
5	15	4	15	13	5
4	13	4	13	13	5
4	11	3	13	14	5
4	11	3	14	17	6
4	12	3	13	13	6
3	12	3	14	13	6
3	12	3	13	12	5
3	12	3	12	13	5
2	14	4	9	14	4
1	6	1	5	11	2
1	7	3	7	12	4
5	14	4	15	12	6
4	14	3	14	16	6
2	10	3	10	12	5
3	13	4	7	12	3
2	12	3	11	12	6
2	9	2	8	10	4
2	12	3	10	15	5
2	16	4	12	15	8
3	10	3	10	12	4
2	14	4	10	16	6
3	10	3	13	15	6
4	16	4	16	16	7
3	15	4	15	13	6
3	12	3	10	12	5
0	10	2	6	11	4
1	8	1	4	13	6
2	8	2	7	10	3
2	11	2	12	15	5
3	13	3	11	13	6
4	16	4	17	16	7
4	16	4	15	15	7
1	14	3	15	18	6
2	11	3	5	13	3
2	4	1	5	10	2
3	14	3	11	16	8
3	9	3	12	13	3
3	14	4	14	15	8
1	8	2	9	14	3
1	8	2	9	15	4
1	11	3	11	14	5
1	12	3	12	13	7
0	11	3	5	13	6
1	14	4	11	15	6
3	15	4	12	16	7
3	16	4	14	14	6
0	16	4	10	14	6
2	11	2	8	16	6
5	14	3	16	14	6
2	14	4	10	12	4
3	12	3	12	13	4
3	14	4	15	12	5
5	8	2	11	12	4
4	13	4	15	14	6
4	16	4	16	14	6
0	12	3	4	14	5
3	16	4	14	16	8
0	12	3	11	13	6
2	11	3	10	14	5
0	4	1	5	4	4
6	16	4	18	16	8
3	15	4	11	13	6
1	10	2	7	16	4
6	13	3	15	15	6
2	15	4	12	14	6
1	12	3	8	13	4
3	14	4	14	14	6
1	7	2	11	12	3
2	19	5	13	15	6
4	12	3	15	14	5
1	12	4	13	13	4
2	13	3	12	14	6
0	15	4	11	16	4
5	8	2	13	6	4
2	12	3	10	13	4
1	10	3	4	13	6
1	8	2	7	14	5
4	10	NA	16	15	6
3	15	4	15	14	6
0	16	4	9	15	8
3	13	3	12	13	7
3	16	4	14	16	7
0	9	2	7	12	4
2	14	4	10	15	6
5	14	4	16	12	6
2	12	3	11	14	2

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\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 & 7 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104337&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]7 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104337&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104337&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 time7 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24






Multiple Linear Regression - Estimated Regression Equation
Ihavemanyfriends[t] = + 0.0496203608625268 -0.0502689740190555sum[t] + 0.282977054518711Popularity[t] + 0.0269440633049747KnowingPeople[t] -0.0242599319071114Liked[t] -0.0409210098415009Celebrity[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Ihavemanyfriends[t] =  +  0.0496203608625268 -0.0502689740190555sum[t] +  0.282977054518711Popularity[t] +  0.0269440633049747KnowingPeople[t] -0.0242599319071114Liked[t] -0.0409210098415009Celebrity[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104337&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Ihavemanyfriends[t] =  +  0.0496203608625268 -0.0502689740190555sum[t] +  0.282977054518711Popularity[t] +  0.0269440633049747KnowingPeople[t] -0.0242599319071114Liked[t] -0.0409210098415009Celebrity[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104337&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104337&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
Ihavemanyfriends[t] = + 0.0496203608625268 -0.0502689740190555sum[t] + 0.282977054518711Popularity[t] + 0.0269440633049747KnowingPeople[t] -0.0242599319071114Liked[t] -0.0409210098415009Celebrity[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.04962036086252680.2022780.24530.8065560.403278
sum-0.05026897401905550.032126-1.56480.1197610.059881
Popularity0.2829770545187110.01589217.806800
KnowingPeople0.02694406330497470.0168691.59730.112320.05616
Liked-0.02425993190711140.019572-1.23950.2171090.108555
Celebrity-0.04092100984150090.031395-1.30340.1944450.097222

\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) & 0.0496203608625268 & 0.202278 & 0.2453 & 0.806556 & 0.403278 \tabularnewline
sum & -0.0502689740190555 & 0.032126 & -1.5648 & 0.119761 & 0.059881 \tabularnewline
Popularity & 0.282977054518711 & 0.015892 & 17.8068 & 0 & 0 \tabularnewline
KnowingPeople & 0.0269440633049747 & 0.016869 & 1.5973 & 0.11232 & 0.05616 \tabularnewline
Liked & -0.0242599319071114 & 0.019572 & -1.2395 & 0.217109 & 0.108555 \tabularnewline
Celebrity & -0.0409210098415009 & 0.031395 & -1.3034 & 0.194445 & 0.097222 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104337&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]0.0496203608625268[/C][C]0.202278[/C][C]0.2453[/C][C]0.806556[/C][C]0.403278[/C][/ROW]
[ROW][C]sum[/C][C]-0.0502689740190555[/C][C]0.032126[/C][C]-1.5648[/C][C]0.119761[/C][C]0.059881[/C][/ROW]
[ROW][C]Popularity[/C][C]0.282977054518711[/C][C]0.015892[/C][C]17.8068[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]KnowingPeople[/C][C]0.0269440633049747[/C][C]0.016869[/C][C]1.5973[/C][C]0.11232[/C][C]0.05616[/C][/ROW]
[ROW][C]Liked[/C][C]-0.0242599319071114[/C][C]0.019572[/C][C]-1.2395[/C][C]0.217109[/C][C]0.108555[/C][/ROW]
[ROW][C]Celebrity[/C][C]-0.0409210098415009[/C][C]0.031395[/C][C]-1.3034[/C][C]0.194445[/C][C]0.097222[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104337&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104337&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)0.04962036086252680.2022780.24530.8065560.403278
sum-0.05026897401905550.032126-1.56480.1197610.059881
Popularity0.2829770545187110.01589217.806800
KnowingPeople0.02694406330497470.0168691.59730.112320.05616
Liked-0.02425993190711140.019572-1.23950.2171090.108555
Celebrity-0.04092100984150090.031395-1.30340.1944450.097222






Multiple Linear Regression - Regression Statistics
Multiple R0.895389490189128
R-squared0.801722339141146
Adjusted R-squared0.795068726360648
F-TEST (value)120.494288680425
F-TEST (DF numerator)5
F-TEST (DF denominator)149
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.404795214699169
Sum Squared Residuals24.4150157106586

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.895389490189128 \tabularnewline
R-squared & 0.801722339141146 \tabularnewline
Adjusted R-squared & 0.795068726360648 \tabularnewline
F-TEST (value) & 120.494288680425 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 149 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.404795214699169 \tabularnewline
Sum Squared Residuals & 24.4150157106586 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104337&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.895389490189128[/C][/ROW]
[ROW][C]R-squared[/C][C]0.801722339141146[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.795068726360648[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]120.494288680425[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]149[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.404795214699169[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]24.4150157106586[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104337&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104337&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 R0.895389490189128
R-squared0.801722339141146
Adjusted R-squared0.795068726360648
F-TEST (value)120.494288680425
F-TEST (DF numerator)5
F-TEST (DF denominator)149
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.404795214699169
Sum Squared Residuals24.4150157106586






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
133.53991480021537-0.539914800215375
233.06370032020287-0.0637003202028701
343.930031842130150.0699681578698532
433.00038942084032-0.000389420840319157
522.52656031685648-0.526560316856485
633.14285820848801-0.142858208488009
743.783583353100630.216416646899371
822.38504222021739-0.385042220217386
933.11816291921492-0.118162919214916
1042.915064850563211.08493514943679
1132.830556882337230.169443117662766
1232.794625403645640.205374596354361
1344.05284099526083-0.0528409952608306
1421.775122421149670.224877578850334
1522.71285819253768-0.712858192537684
1633.00488445586476-0.00488445586475642
1722.51164834912693-0.511648349126928
1843.630968182079140.369031817920857
1922.60876288533042-0.60876288533042
2011.48222293005130-0.482222930051305
2132.880161384494580.119838615505419
2243.843396032053510.156603967946485
2322.91824864578812-0.918248645788125
2433.10287221983954-0.102872219839538
2533.72345373592366-0.723453735923664
2643.964214210616910.0357857893830908
2732.195802678960310.804197321039694
2833.3871630271971-0.387163027197100
2943.392891838783760.607108161216239
3044.16561979478394-0.165619794783941
3143.462248222670480.537751777329523
3233.09687285892156-0.0968728589215549
3343.537094066820720.462905933179277
3432.825263428116550.174736571883446
3522.30782918289336-0.307829182893356
3644.19785731230960-0.197857312309595
3733.00844181503391-0.008441815033909
3822.52481120665165-0.524811206651652
3943.417151770690870.582848229309127
4044.1093514598469-0.109351459846902
4143.649859851190530.350140148809472
4243.989105240646280.0108947593537228
4321.162018826857030.837981173142966
4422.05086117048659-0.050861170486589
4532.767681340340660.232318659659336
4644.15063301847934-0.150633018479337
4754.402446691701540.597553308298461
4822.3733706734329-0.373370673432902
4932.291982193970770.708017806029232
5033.37131603827451-0.371316038274513
5122.37961216399992-0.379612163999918
5221.461507342160040.53849265783996
5333.09332851490571-0.093328514905708
5421.949388201255450.0506117987445524
5543.733811529869300.266188470130704
5633.10512099387142-0.105120993871420
5732.930355549938590.0696444500614104
5844.17359738040248-0.173597380402484
5912.01327357302010-1.01327357302010
6043.899393817659230.100606182340769
6121.778241909913510.221758090086489
6244.22211724421671-0.222117244216706
6343.82854585774570.171454142254301
6444.21394391784189-0.213943917841888
6522.14211029310663-0.142110293106632
6643.714105771746110.285894228253891
6722.8288077721324-0.828807772132401
6843.370937306628690.629062693371308
6943.909511994995160.0904880050048423
7011.29554632272428-0.295546322724285
7143.927108094122580.0728919058774222
7243.357534832494260.642465167505738
7332.767320791549730.23267920845027
7432.680564049291870.319435950708131
7533.03363676813405-0.0336367681340510
7633.11084980545808-0.110849805458081
7733.14908668390172-0.149086683901719
7833.09788268868963-0.0978826886896326
7943.649934659765570.350065340234425
8011.48323275981938-0.483232759819382
8131.713995989357931.28600401064207
8243.627469961669480.372530038330522
8333.55375514475511-0.553755144755113
8432.552569358968430.447430641031571
8543.352241378273580.647758621726417
8633.10454652146932-0.104546521469324
8722.30514505149549-0.305145051495492
8833.04574367228452-0.045743672284516
8944.10877698744481-0.108776987444805
9032.543221394790870.456778605209126
9143.546516839573330.453483160426675
9232.469431769301460.530568230698538
9344.13267637056098-0.132676370560983
9443.986725032319190.0132749676808122
9533.06825449398679-0.0682544939867946
9622.61051199553525-0.610511995535253
9711.81003890237160-0.810038902371603
9822.03614494351331-0.0361449435133078
9922.81665474437575-0.816654744375754
10033.31299467006187-0.312994670061868
10144.15962043386596-0.159620433865957
10244.12999223916312-0.129992239163119
10333.68298626630303-0.682986266303031
10432.758408184738160.241591815261844
10510.8912696086700170.108730391329983
10633.44134990917624-0.441349909176243
10732.330793544816500.669206455183498
10843.546442030998280.453557969001722
10922.04326231651387-0.0432623165138671
11021.978081374765250.0219186252347451
11132.864239586996950.135760413003053
11233.11657861704474-0.116578617044742
11332.736183103251760.263816896748235
11443.647989808804470.352010191195533
11543.792192036841430.207807963158571
11644.21849809162581-0.218498091625812
11744.26152876046308-0.26152876046308
11822.64369754940724-0.643697549407243
11933.60589416116023-0.605894161160229
12043.725398586884770.274601413115228
12133.13880369853113-0.138803698531134
12243.768928919549090.231071080450911
12321.903673401020320.0963265989796834
12443.34624201735560.6537579826444
12544.22211724421671-0.222117244216706
12633.00887717239989-0.00887717239989009
12744.08813620812859-0.0881362081285879
12833.18082453760032-0.180824537600323
12932.787026549672920.212973450327083
13011.05552512846779-0.0555251284677942
13144.04510553929132-0.0451055392913202
13243.878948779099290.121051220900711
13322.46588742528562-0.465887425285616
13433.22144413741038-0.221444137410377
13543.931901884516210.0680981154837921
13633.13156539334935-0.131565393349346
13743.652543982588390.347456017411609
13821.862693252419330.137306747580671
13955.06649423398891-0.0664942339889137
14033.10418597267839-0.104185972678390
14143.266285709874220.733714290125781
14233.36594777547879-0.365947775478786
14344.03881792511812-0.0388179251181231
14422.10312111907293-0.103121119072934
14533.13518454594024-0.135184545940240
14632.375993011409020.624006988590976
14721.907532170220920.092467829779084
148NANA0.0375348995879236
14944.12848274556799-0.128482745567992
15044.29901772352534-0.299017723525342
15133.12905721797009-0.129057217970089
15244.33021907241441-0.330219072414406
15321.570776771480440.429223228519564
15443.654414024974450.345585975025548
15544.21971069702110-0.219710697021105
1563NANA

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 3 & 3.53991480021537 & -0.539914800215375 \tabularnewline
2 & 3 & 3.06370032020287 & -0.0637003202028701 \tabularnewline
3 & 4 & 3.93003184213015 & 0.0699681578698532 \tabularnewline
4 & 3 & 3.00038942084032 & -0.000389420840319157 \tabularnewline
5 & 2 & 2.52656031685648 & -0.526560316856485 \tabularnewline
6 & 3 & 3.14285820848801 & -0.142858208488009 \tabularnewline
7 & 4 & 3.78358335310063 & 0.216416646899371 \tabularnewline
8 & 2 & 2.38504222021739 & -0.385042220217386 \tabularnewline
9 & 3 & 3.11816291921492 & -0.118162919214916 \tabularnewline
10 & 4 & 2.91506485056321 & 1.08493514943679 \tabularnewline
11 & 3 & 2.83055688233723 & 0.169443117662766 \tabularnewline
12 & 3 & 2.79462540364564 & 0.205374596354361 \tabularnewline
13 & 4 & 4.05284099526083 & -0.0528409952608306 \tabularnewline
14 & 2 & 1.77512242114967 & 0.224877578850334 \tabularnewline
15 & 2 & 2.71285819253768 & -0.712858192537684 \tabularnewline
16 & 3 & 3.00488445586476 & -0.00488445586475642 \tabularnewline
17 & 2 & 2.51164834912693 & -0.511648349126928 \tabularnewline
18 & 4 & 3.63096818207914 & 0.369031817920857 \tabularnewline
19 & 2 & 2.60876288533042 & -0.60876288533042 \tabularnewline
20 & 1 & 1.48222293005130 & -0.482222930051305 \tabularnewline
21 & 3 & 2.88016138449458 & 0.119838615505419 \tabularnewline
22 & 4 & 3.84339603205351 & 0.156603967946485 \tabularnewline
23 & 2 & 2.91824864578812 & -0.918248645788125 \tabularnewline
24 & 3 & 3.10287221983954 & -0.102872219839538 \tabularnewline
25 & 3 & 3.72345373592366 & -0.723453735923664 \tabularnewline
26 & 4 & 3.96421421061691 & 0.0357857893830908 \tabularnewline
27 & 3 & 2.19580267896031 & 0.804197321039694 \tabularnewline
28 & 3 & 3.3871630271971 & -0.387163027197100 \tabularnewline
29 & 4 & 3.39289183878376 & 0.607108161216239 \tabularnewline
30 & 4 & 4.16561979478394 & -0.165619794783941 \tabularnewline
31 & 4 & 3.46224822267048 & 0.537751777329523 \tabularnewline
32 & 3 & 3.09687285892156 & -0.0968728589215549 \tabularnewline
33 & 4 & 3.53709406682072 & 0.462905933179277 \tabularnewline
34 & 3 & 2.82526342811655 & 0.174736571883446 \tabularnewline
35 & 2 & 2.30782918289336 & -0.307829182893356 \tabularnewline
36 & 4 & 4.19785731230960 & -0.197857312309595 \tabularnewline
37 & 3 & 3.00844181503391 & -0.008441815033909 \tabularnewline
38 & 2 & 2.52481120665165 & -0.524811206651652 \tabularnewline
39 & 4 & 3.41715177069087 & 0.582848229309127 \tabularnewline
40 & 4 & 4.1093514598469 & -0.109351459846902 \tabularnewline
41 & 4 & 3.64985985119053 & 0.350140148809472 \tabularnewline
42 & 4 & 3.98910524064628 & 0.0108947593537228 \tabularnewline
43 & 2 & 1.16201882685703 & 0.837981173142966 \tabularnewline
44 & 2 & 2.05086117048659 & -0.050861170486589 \tabularnewline
45 & 3 & 2.76768134034066 & 0.232318659659336 \tabularnewline
46 & 4 & 4.15063301847934 & -0.150633018479337 \tabularnewline
47 & 5 & 4.40244669170154 & 0.597553308298461 \tabularnewline
48 & 2 & 2.3733706734329 & -0.373370673432902 \tabularnewline
49 & 3 & 2.29198219397077 & 0.708017806029232 \tabularnewline
50 & 3 & 3.37131603827451 & -0.371316038274513 \tabularnewline
51 & 2 & 2.37961216399992 & -0.379612163999918 \tabularnewline
52 & 2 & 1.46150734216004 & 0.53849265783996 \tabularnewline
53 & 3 & 3.09332851490571 & -0.093328514905708 \tabularnewline
54 & 2 & 1.94938820125545 & 0.0506117987445524 \tabularnewline
55 & 4 & 3.73381152986930 & 0.266188470130704 \tabularnewline
56 & 3 & 3.10512099387142 & -0.105120993871420 \tabularnewline
57 & 3 & 2.93035554993859 & 0.0696444500614104 \tabularnewline
58 & 4 & 4.17359738040248 & -0.173597380402484 \tabularnewline
59 & 1 & 2.01327357302010 & -1.01327357302010 \tabularnewline
60 & 4 & 3.89939381765923 & 0.100606182340769 \tabularnewline
61 & 2 & 1.77824190991351 & 0.221758090086489 \tabularnewline
62 & 4 & 4.22211724421671 & -0.222117244216706 \tabularnewline
63 & 4 & 3.8285458577457 & 0.171454142254301 \tabularnewline
64 & 4 & 4.21394391784189 & -0.213943917841888 \tabularnewline
65 & 2 & 2.14211029310663 & -0.142110293106632 \tabularnewline
66 & 4 & 3.71410577174611 & 0.285894228253891 \tabularnewline
67 & 2 & 2.8288077721324 & -0.828807772132401 \tabularnewline
68 & 4 & 3.37093730662869 & 0.629062693371308 \tabularnewline
69 & 4 & 3.90951199499516 & 0.0904880050048423 \tabularnewline
70 & 1 & 1.29554632272428 & -0.295546322724285 \tabularnewline
71 & 4 & 3.92710809412258 & 0.0728919058774222 \tabularnewline
72 & 4 & 3.35753483249426 & 0.642465167505738 \tabularnewline
73 & 3 & 2.76732079154973 & 0.23267920845027 \tabularnewline
74 & 3 & 2.68056404929187 & 0.319435950708131 \tabularnewline
75 & 3 & 3.03363676813405 & -0.0336367681340510 \tabularnewline
76 & 3 & 3.11084980545808 & -0.110849805458081 \tabularnewline
77 & 3 & 3.14908668390172 & -0.149086683901719 \tabularnewline
78 & 3 & 3.09788268868963 & -0.0978826886896326 \tabularnewline
79 & 4 & 3.64993465976557 & 0.350065340234425 \tabularnewline
80 & 1 & 1.48323275981938 & -0.483232759819382 \tabularnewline
81 & 3 & 1.71399598935793 & 1.28600401064207 \tabularnewline
82 & 4 & 3.62746996166948 & 0.372530038330522 \tabularnewline
83 & 3 & 3.55375514475511 & -0.553755144755113 \tabularnewline
84 & 3 & 2.55256935896843 & 0.447430641031571 \tabularnewline
85 & 4 & 3.35224137827358 & 0.647758621726417 \tabularnewline
86 & 3 & 3.10454652146932 & -0.104546521469324 \tabularnewline
87 & 2 & 2.30514505149549 & -0.305145051495492 \tabularnewline
88 & 3 & 3.04574367228452 & -0.045743672284516 \tabularnewline
89 & 4 & 4.10877698744481 & -0.108776987444805 \tabularnewline
90 & 3 & 2.54322139479087 & 0.456778605209126 \tabularnewline
91 & 4 & 3.54651683957333 & 0.453483160426675 \tabularnewline
92 & 3 & 2.46943176930146 & 0.530568230698538 \tabularnewline
93 & 4 & 4.13267637056098 & -0.132676370560983 \tabularnewline
94 & 4 & 3.98672503231919 & 0.0132749676808122 \tabularnewline
95 & 3 & 3.06825449398679 & -0.0682544939867946 \tabularnewline
96 & 2 & 2.61051199553525 & -0.610511995535253 \tabularnewline
97 & 1 & 1.81003890237160 & -0.810038902371603 \tabularnewline
98 & 2 & 2.03614494351331 & -0.0361449435133078 \tabularnewline
99 & 2 & 2.81665474437575 & -0.816654744375754 \tabularnewline
100 & 3 & 3.31299467006187 & -0.312994670061868 \tabularnewline
101 & 4 & 4.15962043386596 & -0.159620433865957 \tabularnewline
102 & 4 & 4.12999223916312 & -0.129992239163119 \tabularnewline
103 & 3 & 3.68298626630303 & -0.682986266303031 \tabularnewline
104 & 3 & 2.75840818473816 & 0.241591815261844 \tabularnewline
105 & 1 & 0.891269608670017 & 0.108730391329983 \tabularnewline
106 & 3 & 3.44134990917624 & -0.441349909176243 \tabularnewline
107 & 3 & 2.33079354481650 & 0.669206455183498 \tabularnewline
108 & 4 & 3.54644203099828 & 0.453557969001722 \tabularnewline
109 & 2 & 2.04326231651387 & -0.0432623165138671 \tabularnewline
110 & 2 & 1.97808137476525 & 0.0219186252347451 \tabularnewline
111 & 3 & 2.86423958699695 & 0.135760413003053 \tabularnewline
112 & 3 & 3.11657861704474 & -0.116578617044742 \tabularnewline
113 & 3 & 2.73618310325176 & 0.263816896748235 \tabularnewline
114 & 4 & 3.64798980880447 & 0.352010191195533 \tabularnewline
115 & 4 & 3.79219203684143 & 0.207807963158571 \tabularnewline
116 & 4 & 4.21849809162581 & -0.218498091625812 \tabularnewline
117 & 4 & 4.26152876046308 & -0.26152876046308 \tabularnewline
118 & 2 & 2.64369754940724 & -0.643697549407243 \tabularnewline
119 & 3 & 3.60589416116023 & -0.605894161160229 \tabularnewline
120 & 4 & 3.72539858688477 & 0.274601413115228 \tabularnewline
121 & 3 & 3.13880369853113 & -0.138803698531134 \tabularnewline
122 & 4 & 3.76892891954909 & 0.231071080450911 \tabularnewline
123 & 2 & 1.90367340102032 & 0.0963265989796834 \tabularnewline
124 & 4 & 3.3462420173556 & 0.6537579826444 \tabularnewline
125 & 4 & 4.22211724421671 & -0.222117244216706 \tabularnewline
126 & 3 & 3.00887717239989 & -0.00887717239989009 \tabularnewline
127 & 4 & 4.08813620812859 & -0.0881362081285879 \tabularnewline
128 & 3 & 3.18082453760032 & -0.180824537600323 \tabularnewline
129 & 3 & 2.78702654967292 & 0.212973450327083 \tabularnewline
130 & 1 & 1.05552512846779 & -0.0555251284677942 \tabularnewline
131 & 4 & 4.04510553929132 & -0.0451055392913202 \tabularnewline
132 & 4 & 3.87894877909929 & 0.121051220900711 \tabularnewline
133 & 2 & 2.46588742528562 & -0.465887425285616 \tabularnewline
134 & 3 & 3.22144413741038 & -0.221444137410377 \tabularnewline
135 & 4 & 3.93190188451621 & 0.0680981154837921 \tabularnewline
136 & 3 & 3.13156539334935 & -0.131565393349346 \tabularnewline
137 & 4 & 3.65254398258839 & 0.347456017411609 \tabularnewline
138 & 2 & 1.86269325241933 & 0.137306747580671 \tabularnewline
139 & 5 & 5.06649423398891 & -0.0664942339889137 \tabularnewline
140 & 3 & 3.10418597267839 & -0.104185972678390 \tabularnewline
141 & 4 & 3.26628570987422 & 0.733714290125781 \tabularnewline
142 & 3 & 3.36594777547879 & -0.365947775478786 \tabularnewline
143 & 4 & 4.03881792511812 & -0.0388179251181231 \tabularnewline
144 & 2 & 2.10312111907293 & -0.103121119072934 \tabularnewline
145 & 3 & 3.13518454594024 & -0.135184545940240 \tabularnewline
146 & 3 & 2.37599301140902 & 0.624006988590976 \tabularnewline
147 & 2 & 1.90753217022092 & 0.092467829779084 \tabularnewline
148 & NA & NA & 0.0375348995879236 \tabularnewline
149 & 4 & 4.12848274556799 & -0.128482745567992 \tabularnewline
150 & 4 & 4.29901772352534 & -0.299017723525342 \tabularnewline
151 & 3 & 3.12905721797009 & -0.129057217970089 \tabularnewline
152 & 4 & 4.33021907241441 & -0.330219072414406 \tabularnewline
153 & 2 & 1.57077677148044 & 0.429223228519564 \tabularnewline
154 & 4 & 3.65441402497445 & 0.345585975025548 \tabularnewline
155 & 4 & 4.21971069702110 & -0.219710697021105 \tabularnewline
156 & 3 & NA & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104337&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]3[/C][C]3.53991480021537[/C][C]-0.539914800215375[/C][/ROW]
[ROW][C]2[/C][C]3[/C][C]3.06370032020287[/C][C]-0.0637003202028701[/C][/ROW]
[ROW][C]3[/C][C]4[/C][C]3.93003184213015[/C][C]0.0699681578698532[/C][/ROW]
[ROW][C]4[/C][C]3[/C][C]3.00038942084032[/C][C]-0.000389420840319157[/C][/ROW]
[ROW][C]5[/C][C]2[/C][C]2.52656031685648[/C][C]-0.526560316856485[/C][/ROW]
[ROW][C]6[/C][C]3[/C][C]3.14285820848801[/C][C]-0.142858208488009[/C][/ROW]
[ROW][C]7[/C][C]4[/C][C]3.78358335310063[/C][C]0.216416646899371[/C][/ROW]
[ROW][C]8[/C][C]2[/C][C]2.38504222021739[/C][C]-0.385042220217386[/C][/ROW]
[ROW][C]9[/C][C]3[/C][C]3.11816291921492[/C][C]-0.118162919214916[/C][/ROW]
[ROW][C]10[/C][C]4[/C][C]2.91506485056321[/C][C]1.08493514943679[/C][/ROW]
[ROW][C]11[/C][C]3[/C][C]2.83055688233723[/C][C]0.169443117662766[/C][/ROW]
[ROW][C]12[/C][C]3[/C][C]2.79462540364564[/C][C]0.205374596354361[/C][/ROW]
[ROW][C]13[/C][C]4[/C][C]4.05284099526083[/C][C]-0.0528409952608306[/C][/ROW]
[ROW][C]14[/C][C]2[/C][C]1.77512242114967[/C][C]0.224877578850334[/C][/ROW]
[ROW][C]15[/C][C]2[/C][C]2.71285819253768[/C][C]-0.712858192537684[/C][/ROW]
[ROW][C]16[/C][C]3[/C][C]3.00488445586476[/C][C]-0.00488445586475642[/C][/ROW]
[ROW][C]17[/C][C]2[/C][C]2.51164834912693[/C][C]-0.511648349126928[/C][/ROW]
[ROW][C]18[/C][C]4[/C][C]3.63096818207914[/C][C]0.369031817920857[/C][/ROW]
[ROW][C]19[/C][C]2[/C][C]2.60876288533042[/C][C]-0.60876288533042[/C][/ROW]
[ROW][C]20[/C][C]1[/C][C]1.48222293005130[/C][C]-0.482222930051305[/C][/ROW]
[ROW][C]21[/C][C]3[/C][C]2.88016138449458[/C][C]0.119838615505419[/C][/ROW]
[ROW][C]22[/C][C]4[/C][C]3.84339603205351[/C][C]0.156603967946485[/C][/ROW]
[ROW][C]23[/C][C]2[/C][C]2.91824864578812[/C][C]-0.918248645788125[/C][/ROW]
[ROW][C]24[/C][C]3[/C][C]3.10287221983954[/C][C]-0.102872219839538[/C][/ROW]
[ROW][C]25[/C][C]3[/C][C]3.72345373592366[/C][C]-0.723453735923664[/C][/ROW]
[ROW][C]26[/C][C]4[/C][C]3.96421421061691[/C][C]0.0357857893830908[/C][/ROW]
[ROW][C]27[/C][C]3[/C][C]2.19580267896031[/C][C]0.804197321039694[/C][/ROW]
[ROW][C]28[/C][C]3[/C][C]3.3871630271971[/C][C]-0.387163027197100[/C][/ROW]
[ROW][C]29[/C][C]4[/C][C]3.39289183878376[/C][C]0.607108161216239[/C][/ROW]
[ROW][C]30[/C][C]4[/C][C]4.16561979478394[/C][C]-0.165619794783941[/C][/ROW]
[ROW][C]31[/C][C]4[/C][C]3.46224822267048[/C][C]0.537751777329523[/C][/ROW]
[ROW][C]32[/C][C]3[/C][C]3.09687285892156[/C][C]-0.0968728589215549[/C][/ROW]
[ROW][C]33[/C][C]4[/C][C]3.53709406682072[/C][C]0.462905933179277[/C][/ROW]
[ROW][C]34[/C][C]3[/C][C]2.82526342811655[/C][C]0.174736571883446[/C][/ROW]
[ROW][C]35[/C][C]2[/C][C]2.30782918289336[/C][C]-0.307829182893356[/C][/ROW]
[ROW][C]36[/C][C]4[/C][C]4.19785731230960[/C][C]-0.197857312309595[/C][/ROW]
[ROW][C]37[/C][C]3[/C][C]3.00844181503391[/C][C]-0.008441815033909[/C][/ROW]
[ROW][C]38[/C][C]2[/C][C]2.52481120665165[/C][C]-0.524811206651652[/C][/ROW]
[ROW][C]39[/C][C]4[/C][C]3.41715177069087[/C][C]0.582848229309127[/C][/ROW]
[ROW][C]40[/C][C]4[/C][C]4.1093514598469[/C][C]-0.109351459846902[/C][/ROW]
[ROW][C]41[/C][C]4[/C][C]3.64985985119053[/C][C]0.350140148809472[/C][/ROW]
[ROW][C]42[/C][C]4[/C][C]3.98910524064628[/C][C]0.0108947593537228[/C][/ROW]
[ROW][C]43[/C][C]2[/C][C]1.16201882685703[/C][C]0.837981173142966[/C][/ROW]
[ROW][C]44[/C][C]2[/C][C]2.05086117048659[/C][C]-0.050861170486589[/C][/ROW]
[ROW][C]45[/C][C]3[/C][C]2.76768134034066[/C][C]0.232318659659336[/C][/ROW]
[ROW][C]46[/C][C]4[/C][C]4.15063301847934[/C][C]-0.150633018479337[/C][/ROW]
[ROW][C]47[/C][C]5[/C][C]4.40244669170154[/C][C]0.597553308298461[/C][/ROW]
[ROW][C]48[/C][C]2[/C][C]2.3733706734329[/C][C]-0.373370673432902[/C][/ROW]
[ROW][C]49[/C][C]3[/C][C]2.29198219397077[/C][C]0.708017806029232[/C][/ROW]
[ROW][C]50[/C][C]3[/C][C]3.37131603827451[/C][C]-0.371316038274513[/C][/ROW]
[ROW][C]51[/C][C]2[/C][C]2.37961216399992[/C][C]-0.379612163999918[/C][/ROW]
[ROW][C]52[/C][C]2[/C][C]1.46150734216004[/C][C]0.53849265783996[/C][/ROW]
[ROW][C]53[/C][C]3[/C][C]3.09332851490571[/C][C]-0.093328514905708[/C][/ROW]
[ROW][C]54[/C][C]2[/C][C]1.94938820125545[/C][C]0.0506117987445524[/C][/ROW]
[ROW][C]55[/C][C]4[/C][C]3.73381152986930[/C][C]0.266188470130704[/C][/ROW]
[ROW][C]56[/C][C]3[/C][C]3.10512099387142[/C][C]-0.105120993871420[/C][/ROW]
[ROW][C]57[/C][C]3[/C][C]2.93035554993859[/C][C]0.0696444500614104[/C][/ROW]
[ROW][C]58[/C][C]4[/C][C]4.17359738040248[/C][C]-0.173597380402484[/C][/ROW]
[ROW][C]59[/C][C]1[/C][C]2.01327357302010[/C][C]-1.01327357302010[/C][/ROW]
[ROW][C]60[/C][C]4[/C][C]3.89939381765923[/C][C]0.100606182340769[/C][/ROW]
[ROW][C]61[/C][C]2[/C][C]1.77824190991351[/C][C]0.221758090086489[/C][/ROW]
[ROW][C]62[/C][C]4[/C][C]4.22211724421671[/C][C]-0.222117244216706[/C][/ROW]
[ROW][C]63[/C][C]4[/C][C]3.8285458577457[/C][C]0.171454142254301[/C][/ROW]
[ROW][C]64[/C][C]4[/C][C]4.21394391784189[/C][C]-0.213943917841888[/C][/ROW]
[ROW][C]65[/C][C]2[/C][C]2.14211029310663[/C][C]-0.142110293106632[/C][/ROW]
[ROW][C]66[/C][C]4[/C][C]3.71410577174611[/C][C]0.285894228253891[/C][/ROW]
[ROW][C]67[/C][C]2[/C][C]2.8288077721324[/C][C]-0.828807772132401[/C][/ROW]
[ROW][C]68[/C][C]4[/C][C]3.37093730662869[/C][C]0.629062693371308[/C][/ROW]
[ROW][C]69[/C][C]4[/C][C]3.90951199499516[/C][C]0.0904880050048423[/C][/ROW]
[ROW][C]70[/C][C]1[/C][C]1.29554632272428[/C][C]-0.295546322724285[/C][/ROW]
[ROW][C]71[/C][C]4[/C][C]3.92710809412258[/C][C]0.0728919058774222[/C][/ROW]
[ROW][C]72[/C][C]4[/C][C]3.35753483249426[/C][C]0.642465167505738[/C][/ROW]
[ROW][C]73[/C][C]3[/C][C]2.76732079154973[/C][C]0.23267920845027[/C][/ROW]
[ROW][C]74[/C][C]3[/C][C]2.68056404929187[/C][C]0.319435950708131[/C][/ROW]
[ROW][C]75[/C][C]3[/C][C]3.03363676813405[/C][C]-0.0336367681340510[/C][/ROW]
[ROW][C]76[/C][C]3[/C][C]3.11084980545808[/C][C]-0.110849805458081[/C][/ROW]
[ROW][C]77[/C][C]3[/C][C]3.14908668390172[/C][C]-0.149086683901719[/C][/ROW]
[ROW][C]78[/C][C]3[/C][C]3.09788268868963[/C][C]-0.0978826886896326[/C][/ROW]
[ROW][C]79[/C][C]4[/C][C]3.64993465976557[/C][C]0.350065340234425[/C][/ROW]
[ROW][C]80[/C][C]1[/C][C]1.48323275981938[/C][C]-0.483232759819382[/C][/ROW]
[ROW][C]81[/C][C]3[/C][C]1.71399598935793[/C][C]1.28600401064207[/C][/ROW]
[ROW][C]82[/C][C]4[/C][C]3.62746996166948[/C][C]0.372530038330522[/C][/ROW]
[ROW][C]83[/C][C]3[/C][C]3.55375514475511[/C][C]-0.553755144755113[/C][/ROW]
[ROW][C]84[/C][C]3[/C][C]2.55256935896843[/C][C]0.447430641031571[/C][/ROW]
[ROW][C]85[/C][C]4[/C][C]3.35224137827358[/C][C]0.647758621726417[/C][/ROW]
[ROW][C]86[/C][C]3[/C][C]3.10454652146932[/C][C]-0.104546521469324[/C][/ROW]
[ROW][C]87[/C][C]2[/C][C]2.30514505149549[/C][C]-0.305145051495492[/C][/ROW]
[ROW][C]88[/C][C]3[/C][C]3.04574367228452[/C][C]-0.045743672284516[/C][/ROW]
[ROW][C]89[/C][C]4[/C][C]4.10877698744481[/C][C]-0.108776987444805[/C][/ROW]
[ROW][C]90[/C][C]3[/C][C]2.54322139479087[/C][C]0.456778605209126[/C][/ROW]
[ROW][C]91[/C][C]4[/C][C]3.54651683957333[/C][C]0.453483160426675[/C][/ROW]
[ROW][C]92[/C][C]3[/C][C]2.46943176930146[/C][C]0.530568230698538[/C][/ROW]
[ROW][C]93[/C][C]4[/C][C]4.13267637056098[/C][C]-0.132676370560983[/C][/ROW]
[ROW][C]94[/C][C]4[/C][C]3.98672503231919[/C][C]0.0132749676808122[/C][/ROW]
[ROW][C]95[/C][C]3[/C][C]3.06825449398679[/C][C]-0.0682544939867946[/C][/ROW]
[ROW][C]96[/C][C]2[/C][C]2.61051199553525[/C][C]-0.610511995535253[/C][/ROW]
[ROW][C]97[/C][C]1[/C][C]1.81003890237160[/C][C]-0.810038902371603[/C][/ROW]
[ROW][C]98[/C][C]2[/C][C]2.03614494351331[/C][C]-0.0361449435133078[/C][/ROW]
[ROW][C]99[/C][C]2[/C][C]2.81665474437575[/C][C]-0.816654744375754[/C][/ROW]
[ROW][C]100[/C][C]3[/C][C]3.31299467006187[/C][C]-0.312994670061868[/C][/ROW]
[ROW][C]101[/C][C]4[/C][C]4.15962043386596[/C][C]-0.159620433865957[/C][/ROW]
[ROW][C]102[/C][C]4[/C][C]4.12999223916312[/C][C]-0.129992239163119[/C][/ROW]
[ROW][C]103[/C][C]3[/C][C]3.68298626630303[/C][C]-0.682986266303031[/C][/ROW]
[ROW][C]104[/C][C]3[/C][C]2.75840818473816[/C][C]0.241591815261844[/C][/ROW]
[ROW][C]105[/C][C]1[/C][C]0.891269608670017[/C][C]0.108730391329983[/C][/ROW]
[ROW][C]106[/C][C]3[/C][C]3.44134990917624[/C][C]-0.441349909176243[/C][/ROW]
[ROW][C]107[/C][C]3[/C][C]2.33079354481650[/C][C]0.669206455183498[/C][/ROW]
[ROW][C]108[/C][C]4[/C][C]3.54644203099828[/C][C]0.453557969001722[/C][/ROW]
[ROW][C]109[/C][C]2[/C][C]2.04326231651387[/C][C]-0.0432623165138671[/C][/ROW]
[ROW][C]110[/C][C]2[/C][C]1.97808137476525[/C][C]0.0219186252347451[/C][/ROW]
[ROW][C]111[/C][C]3[/C][C]2.86423958699695[/C][C]0.135760413003053[/C][/ROW]
[ROW][C]112[/C][C]3[/C][C]3.11657861704474[/C][C]-0.116578617044742[/C][/ROW]
[ROW][C]113[/C][C]3[/C][C]2.73618310325176[/C][C]0.263816896748235[/C][/ROW]
[ROW][C]114[/C][C]4[/C][C]3.64798980880447[/C][C]0.352010191195533[/C][/ROW]
[ROW][C]115[/C][C]4[/C][C]3.79219203684143[/C][C]0.207807963158571[/C][/ROW]
[ROW][C]116[/C][C]4[/C][C]4.21849809162581[/C][C]-0.218498091625812[/C][/ROW]
[ROW][C]117[/C][C]4[/C][C]4.26152876046308[/C][C]-0.26152876046308[/C][/ROW]
[ROW][C]118[/C][C]2[/C][C]2.64369754940724[/C][C]-0.643697549407243[/C][/ROW]
[ROW][C]119[/C][C]3[/C][C]3.60589416116023[/C][C]-0.605894161160229[/C][/ROW]
[ROW][C]120[/C][C]4[/C][C]3.72539858688477[/C][C]0.274601413115228[/C][/ROW]
[ROW][C]121[/C][C]3[/C][C]3.13880369853113[/C][C]-0.138803698531134[/C][/ROW]
[ROW][C]122[/C][C]4[/C][C]3.76892891954909[/C][C]0.231071080450911[/C][/ROW]
[ROW][C]123[/C][C]2[/C][C]1.90367340102032[/C][C]0.0963265989796834[/C][/ROW]
[ROW][C]124[/C][C]4[/C][C]3.3462420173556[/C][C]0.6537579826444[/C][/ROW]
[ROW][C]125[/C][C]4[/C][C]4.22211724421671[/C][C]-0.222117244216706[/C][/ROW]
[ROW][C]126[/C][C]3[/C][C]3.00887717239989[/C][C]-0.00887717239989009[/C][/ROW]
[ROW][C]127[/C][C]4[/C][C]4.08813620812859[/C][C]-0.0881362081285879[/C][/ROW]
[ROW][C]128[/C][C]3[/C][C]3.18082453760032[/C][C]-0.180824537600323[/C][/ROW]
[ROW][C]129[/C][C]3[/C][C]2.78702654967292[/C][C]0.212973450327083[/C][/ROW]
[ROW][C]130[/C][C]1[/C][C]1.05552512846779[/C][C]-0.0555251284677942[/C][/ROW]
[ROW][C]131[/C][C]4[/C][C]4.04510553929132[/C][C]-0.0451055392913202[/C][/ROW]
[ROW][C]132[/C][C]4[/C][C]3.87894877909929[/C][C]0.121051220900711[/C][/ROW]
[ROW][C]133[/C][C]2[/C][C]2.46588742528562[/C][C]-0.465887425285616[/C][/ROW]
[ROW][C]134[/C][C]3[/C][C]3.22144413741038[/C][C]-0.221444137410377[/C][/ROW]
[ROW][C]135[/C][C]4[/C][C]3.93190188451621[/C][C]0.0680981154837921[/C][/ROW]
[ROW][C]136[/C][C]3[/C][C]3.13156539334935[/C][C]-0.131565393349346[/C][/ROW]
[ROW][C]137[/C][C]4[/C][C]3.65254398258839[/C][C]0.347456017411609[/C][/ROW]
[ROW][C]138[/C][C]2[/C][C]1.86269325241933[/C][C]0.137306747580671[/C][/ROW]
[ROW][C]139[/C][C]5[/C][C]5.06649423398891[/C][C]-0.0664942339889137[/C][/ROW]
[ROW][C]140[/C][C]3[/C][C]3.10418597267839[/C][C]-0.104185972678390[/C][/ROW]
[ROW][C]141[/C][C]4[/C][C]3.26628570987422[/C][C]0.733714290125781[/C][/ROW]
[ROW][C]142[/C][C]3[/C][C]3.36594777547879[/C][C]-0.365947775478786[/C][/ROW]
[ROW][C]143[/C][C]4[/C][C]4.03881792511812[/C][C]-0.0388179251181231[/C][/ROW]
[ROW][C]144[/C][C]2[/C][C]2.10312111907293[/C][C]-0.103121119072934[/C][/ROW]
[ROW][C]145[/C][C]3[/C][C]3.13518454594024[/C][C]-0.135184545940240[/C][/ROW]
[ROW][C]146[/C][C]3[/C][C]2.37599301140902[/C][C]0.624006988590976[/C][/ROW]
[ROW][C]147[/C][C]2[/C][C]1.90753217022092[/C][C]0.092467829779084[/C][/ROW]
[ROW][C]148[/C][C]NA[/C][C]NA[/C][C]0.0375348995879236[/C][/ROW]
[ROW][C]149[/C][C]4[/C][C]4.12848274556799[/C][C]-0.128482745567992[/C][/ROW]
[ROW][C]150[/C][C]4[/C][C]4.29901772352534[/C][C]-0.299017723525342[/C][/ROW]
[ROW][C]151[/C][C]3[/C][C]3.12905721797009[/C][C]-0.129057217970089[/C][/ROW]
[ROW][C]152[/C][C]4[/C][C]4.33021907241441[/C][C]-0.330219072414406[/C][/ROW]
[ROW][C]153[/C][C]2[/C][C]1.57077677148044[/C][C]0.429223228519564[/C][/ROW]
[ROW][C]154[/C][C]4[/C][C]3.65441402497445[/C][C]0.345585975025548[/C][/ROW]
[ROW][C]155[/C][C]4[/C][C]4.21971069702110[/C][C]-0.219710697021105[/C][/ROW]
[ROW][C]156[/C][C]3[/C][C]NA[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104337&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104337&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
133.53991480021537-0.539914800215375
233.06370032020287-0.0637003202028701
343.930031842130150.0699681578698532
433.00038942084032-0.000389420840319157
522.52656031685648-0.526560316856485
633.14285820848801-0.142858208488009
743.783583353100630.216416646899371
822.38504222021739-0.385042220217386
933.11816291921492-0.118162919214916
1042.915064850563211.08493514943679
1132.830556882337230.169443117662766
1232.794625403645640.205374596354361
1344.05284099526083-0.0528409952608306
1421.775122421149670.224877578850334
1522.71285819253768-0.712858192537684
1633.00488445586476-0.00488445586475642
1722.51164834912693-0.511648349126928
1843.630968182079140.369031817920857
1922.60876288533042-0.60876288533042
2011.48222293005130-0.482222930051305
2132.880161384494580.119838615505419
2243.843396032053510.156603967946485
2322.91824864578812-0.918248645788125
2433.10287221983954-0.102872219839538
2533.72345373592366-0.723453735923664
2643.964214210616910.0357857893830908
2732.195802678960310.804197321039694
2833.3871630271971-0.387163027197100
2943.392891838783760.607108161216239
3044.16561979478394-0.165619794783941
3143.462248222670480.537751777329523
3233.09687285892156-0.0968728589215549
3343.537094066820720.462905933179277
3432.825263428116550.174736571883446
3522.30782918289336-0.307829182893356
3644.19785731230960-0.197857312309595
3733.00844181503391-0.008441815033909
3822.52481120665165-0.524811206651652
3943.417151770690870.582848229309127
4044.1093514598469-0.109351459846902
4143.649859851190530.350140148809472
4243.989105240646280.0108947593537228
4321.162018826857030.837981173142966
4422.05086117048659-0.050861170486589
4532.767681340340660.232318659659336
4644.15063301847934-0.150633018479337
4754.402446691701540.597553308298461
4822.3733706734329-0.373370673432902
4932.291982193970770.708017806029232
5033.37131603827451-0.371316038274513
5122.37961216399992-0.379612163999918
5221.461507342160040.53849265783996
5333.09332851490571-0.093328514905708
5421.949388201255450.0506117987445524
5543.733811529869300.266188470130704
5633.10512099387142-0.105120993871420
5732.930355549938590.0696444500614104
5844.17359738040248-0.173597380402484
5912.01327357302010-1.01327357302010
6043.899393817659230.100606182340769
6121.778241909913510.221758090086489
6244.22211724421671-0.222117244216706
6343.82854585774570.171454142254301
6444.21394391784189-0.213943917841888
6522.14211029310663-0.142110293106632
6643.714105771746110.285894228253891
6722.8288077721324-0.828807772132401
6843.370937306628690.629062693371308
6943.909511994995160.0904880050048423
7011.29554632272428-0.295546322724285
7143.927108094122580.0728919058774222
7243.357534832494260.642465167505738
7332.767320791549730.23267920845027
7432.680564049291870.319435950708131
7533.03363676813405-0.0336367681340510
7633.11084980545808-0.110849805458081
7733.14908668390172-0.149086683901719
7833.09788268868963-0.0978826886896326
7943.649934659765570.350065340234425
8011.48323275981938-0.483232759819382
8131.713995989357931.28600401064207
8243.627469961669480.372530038330522
8333.55375514475511-0.553755144755113
8432.552569358968430.447430641031571
8543.352241378273580.647758621726417
8633.10454652146932-0.104546521469324
8722.30514505149549-0.305145051495492
8833.04574367228452-0.045743672284516
8944.10877698744481-0.108776987444805
9032.543221394790870.456778605209126
9143.546516839573330.453483160426675
9232.469431769301460.530568230698538
9344.13267637056098-0.132676370560983
9443.986725032319190.0132749676808122
9533.06825449398679-0.0682544939867946
9622.61051199553525-0.610511995535253
9711.81003890237160-0.810038902371603
9822.03614494351331-0.0361449435133078
9922.81665474437575-0.816654744375754
10033.31299467006187-0.312994670061868
10144.15962043386596-0.159620433865957
10244.12999223916312-0.129992239163119
10333.68298626630303-0.682986266303031
10432.758408184738160.241591815261844
10510.8912696086700170.108730391329983
10633.44134990917624-0.441349909176243
10732.330793544816500.669206455183498
10843.546442030998280.453557969001722
10922.04326231651387-0.0432623165138671
11021.978081374765250.0219186252347451
11132.864239586996950.135760413003053
11233.11657861704474-0.116578617044742
11332.736183103251760.263816896748235
11443.647989808804470.352010191195533
11543.792192036841430.207807963158571
11644.21849809162581-0.218498091625812
11744.26152876046308-0.26152876046308
11822.64369754940724-0.643697549407243
11933.60589416116023-0.605894161160229
12043.725398586884770.274601413115228
12133.13880369853113-0.138803698531134
12243.768928919549090.231071080450911
12321.903673401020320.0963265989796834
12443.34624201735560.6537579826444
12544.22211724421671-0.222117244216706
12633.00887717239989-0.00887717239989009
12744.08813620812859-0.0881362081285879
12833.18082453760032-0.180824537600323
12932.787026549672920.212973450327083
13011.05552512846779-0.0555251284677942
13144.04510553929132-0.0451055392913202
13243.878948779099290.121051220900711
13322.46588742528562-0.465887425285616
13433.22144413741038-0.221444137410377
13543.931901884516210.0680981154837921
13633.13156539334935-0.131565393349346
13743.652543982588390.347456017411609
13821.862693252419330.137306747580671
13955.06649423398891-0.0664942339889137
14033.10418597267839-0.104185972678390
14143.266285709874220.733714290125781
14233.36594777547879-0.365947775478786
14344.03881792511812-0.0388179251181231
14422.10312111907293-0.103121119072934
14533.13518454594024-0.135184545940240
14632.375993011409020.624006988590976
14721.907532170220920.092467829779084
148NANA0.0375348995879236
14944.12848274556799-0.128482745567992
15044.29901772352534-0.299017723525342
15133.12905721797009-0.129057217970089
15244.33021907241441-0.330219072414406
15321.570776771480440.429223228519564
15443.654414024974450.345585975025548
15544.21971069702110-0.219710697021105
1563NANA






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.07637492844870030.1527498568974010.9236250715513
100.7897130702693340.4205738594613310.210286929730666
110.7735615644846290.4528768710307430.226438435515371
120.6712506293495920.6574987413008150.328749370650408
130.6342168582773580.7315662834452840.365783141722642
140.6810951705095050.637809658980990.318904829490495
150.8519031688164550.2961936623670890.148096831183545
160.8030451209864220.3939097580271570.196954879013578
170.8305759602525610.3388480794948770.169424039747439
180.82790824564240.3441835087151990.172091754357600
190.8479657351605020.3040685296789950.152034264839498
200.8154657925832150.369068414833570.184534207416785
210.800129588590450.3997408228191010.199870411409551
220.7466819614719680.5066360770560640.253318038528032
230.8480456483195720.3039087033608560.151954351680428
240.8038858392756340.3922283214487320.196114160724366
250.87987401696820.2402519660635990.120125983031799
260.8429371578655230.3141256842689530.157062842134477
270.9219142737028450.1561714525943100.0780857262971552
280.909187721495620.181624557008760.09081227850438
290.9313923763985120.1372152472029760.0686076236014878
300.9100034936577660.1799930126844690.0899965063422344
310.9551242101108260.08975157977834780.0448757898891739
320.9398255513842470.1203488972315050.0601744486157527
330.9396755821705250.1206488356589510.0603244178294754
340.925812443276360.1483751134472800.0741875567236399
350.9101222788889430.1797554422221150.0898777211110573
360.8919026704308460.2161946591383080.108097329569154
370.8635241652168350.272951669566330.136475834783165
380.8608124255018720.2783751489962560.139187574498128
390.8845212062548420.2309575874903160.115478793745158
400.8584623714871660.2830752570256690.141537628512834
410.8484996698213380.3030006603573250.151500330178662
420.8154946725743350.369010654851330.184505327425665
430.9103135645791870.1793728708416260.089686435420813
440.8877800040990820.2244399918018370.112219995900918
450.868229185196440.2635416296071190.131770814803560
460.8429714510610060.3140570978779890.157028548938994
470.8855988253481030.2288023493037940.114401174651897
480.875166705895920.2496665882081600.124833294104080
490.913985970785950.1720280584280980.0860140292140492
500.9153882704017510.1692234591964980.084611729598249
510.9174841357129490.1650317285741030.0825158642870515
520.932614290379560.1347714192408790.0673857096204395
530.9162700292970470.1674599414059050.0837299707029527
540.8959526733773510.2080946532452980.104047326622649
550.8828166753500970.2343666492998050.117183324649903
560.8585492165961620.2829015668076770.141450783403838
570.830176060955050.33964787808990.16982393904495
580.8056864910529230.3886270178941540.194313508947077
590.9256563677048670.1486872645902650.0743436322951327
600.9100442019918370.1799115960163260.0899557980081628
610.897085852277420.2058282954451610.102914147722581
620.8815112983564150.2369774032871700.118488701643585
630.8609455740728680.2781088518542640.139054425927132
640.8423490037217780.3153019925564440.157650996278222
650.815305289831060.3693894203378790.184694710168940
660.7980382264572910.4039235470854180.201961773542709
670.881331634398690.2373367312026190.118668365601309
680.9084244021324880.1831511957350240.0915755978675119
690.8883408092445070.2233183815109850.111659190755493
700.880674485100660.2386510297986810.119325514899341
710.8578513624421070.2842972751157850.142148637557893
720.8918575363054090.2162849273891820.108142463694591
730.8759687342779820.2480625314440350.124031265722018
740.867721738604570.2645565227908610.132278261395431
750.840848052434430.318303895131140.15915194756557
760.8125522218479980.3748955563040050.187447778152002
770.7844498684807010.4311002630385980.215550131519299
780.7508740657349730.4982518685300540.249125934265027
790.7359443312183490.5281113375633020.264055668781651
800.7574742741723720.4850514516552560.242525725827628
810.9639583002446430.0720833995107130.0360416997553565
820.9615202125589590.07695957488208210.0384797874410411
830.9703766010278350.05924679794433060.0296233989721653
840.9727116352624020.05457672947519550.0272883647375977
850.9802350203296320.03952995934073570.0197649796703678
860.9741447668522050.05171046629558940.0258552331477947
870.971522305571270.056955388857460.02847769442873
880.9629318976822240.07413620463555260.0370681023177763
890.9534039803557780.09319203928844370.0465960196442218
900.9559959276737010.0880081446525980.044004072326299
910.9615519175176280.07689616496474420.0384480824823721
920.9727128504063840.05457429918723270.0272871495936164
930.9648639899378180.07027202012436330.0351360100621817
940.954112186270970.091775627458060.04588781372903
950.9414513926584350.1170972146831290.0585486073415647
960.961834493442050.07633101311590090.0381655065579505
970.9843128123418650.03137437531627020.0156871876581351
980.9793426144493670.04131477110126530.0206573855506326
990.9925893621190930.01482127576181410.00741063788090707
1000.9919030176761940.01619396464761130.00809698232380563
1010.9888992973524910.02220140529501790.0111007026475090
1020.9848998019199420.03020039616011510.0151001980800575
1030.9916489548293490.01670209034130290.00835104517065145
1040.9897508839842740.0204982320314510.0102491160157255
1050.9857083420697680.02858331586046480.0142916579302324
1060.9866315414182530.0267369171634940.013368458581747
1070.9938665102231540.01226697955369140.00613348977684568
1080.9947054435151490.01058911296970230.00529455648485116
1090.9921730727986750.01565385440265040.00782692720132518
1100.9886720019711020.02265599605779660.0113279980288983
1110.9845044214300630.03099115713987320.0154955785699366
1120.9791701550065390.04165968998692250.0208298449934613
1130.9751636968184870.04967260636302580.0248363031815129
1140.9745626041558480.05087479168830430.0254373958441522
1150.9698212733922150.06035745321556910.0301787266077846
1160.9625391173228570.07492176535428560.0374608826771428
1170.957352290742970.08529541851405950.0426477092570298
1180.972836094288680.05432781142264090.0271639057113204
1190.9863123007223030.02737539855539330.0136876992776967
1200.983656266942850.03268746611429970.0163437330571498
1210.977246050712110.04550789857578050.0227539492878903
1220.9703157799609790.05936844007804230.0296842200390212
1230.9580598207460890.08388035850782250.0419401792539112
1240.9812417265973760.03751654680524850.0187582734026242
1250.9757521610249390.04849567795012190.0242478389750609
1260.9639510713650130.07209785726997480.0360489286349874
1270.949130759394280.1017384812114380.0508692406057192
1280.9381776639149620.1236446721700770.0618223360850384
1290.9231054833738530.1537890332522940.0768945166261469
1300.9050116379566250.189976724086750.094988362043375
1310.8688048789130170.2623902421739650.131195121086983
1320.8283276472010640.3433447055978730.171672352798936
1330.8347513478947080.3304973042105840.165248652105292
1340.7991497693443680.4017004613112650.200850230655632
1350.7368142732400630.5263714535198740.263185726759937
1360.6723956961155750.6552086077688510.327604303884425
1370.6459223431671850.708155313665630.354077656832815
1380.5641763513083810.8716472973832380.435823648691619
1390.4725450688491170.9450901376982340.527454931150883
1400.3844843345671150.768968669134230.615515665432885
1410.9099753486458850.1800493027082310.0900246513541153
1420.8768515526320960.2462968947358080.123148447367904
1430.8706570749195970.2586858501608050.129342925080403
1440.7866679406672470.4266641186655060.213332059332753
1450.690515187497080.618969625005840.30948481250292
1460.5385351896925230.9229296206149540.461464810307477
1470.4299484199825760.8598968399651520.570051580017424

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.0763749284487003 & 0.152749856897401 & 0.9236250715513 \tabularnewline
10 & 0.789713070269334 & 0.420573859461331 & 0.210286929730666 \tabularnewline
11 & 0.773561564484629 & 0.452876871030743 & 0.226438435515371 \tabularnewline
12 & 0.671250629349592 & 0.657498741300815 & 0.328749370650408 \tabularnewline
13 & 0.634216858277358 & 0.731566283445284 & 0.365783141722642 \tabularnewline
14 & 0.681095170509505 & 0.63780965898099 & 0.318904829490495 \tabularnewline
15 & 0.851903168816455 & 0.296193662367089 & 0.148096831183545 \tabularnewline
16 & 0.803045120986422 & 0.393909758027157 & 0.196954879013578 \tabularnewline
17 & 0.830575960252561 & 0.338848079494877 & 0.169424039747439 \tabularnewline
18 & 0.8279082456424 & 0.344183508715199 & 0.172091754357600 \tabularnewline
19 & 0.847965735160502 & 0.304068529678995 & 0.152034264839498 \tabularnewline
20 & 0.815465792583215 & 0.36906841483357 & 0.184534207416785 \tabularnewline
21 & 0.80012958859045 & 0.399740822819101 & 0.199870411409551 \tabularnewline
22 & 0.746681961471968 & 0.506636077056064 & 0.253318038528032 \tabularnewline
23 & 0.848045648319572 & 0.303908703360856 & 0.151954351680428 \tabularnewline
24 & 0.803885839275634 & 0.392228321448732 & 0.196114160724366 \tabularnewline
25 & 0.8798740169682 & 0.240251966063599 & 0.120125983031799 \tabularnewline
26 & 0.842937157865523 & 0.314125684268953 & 0.157062842134477 \tabularnewline
27 & 0.921914273702845 & 0.156171452594310 & 0.0780857262971552 \tabularnewline
28 & 0.90918772149562 & 0.18162455700876 & 0.09081227850438 \tabularnewline
29 & 0.931392376398512 & 0.137215247202976 & 0.0686076236014878 \tabularnewline
30 & 0.910003493657766 & 0.179993012684469 & 0.0899965063422344 \tabularnewline
31 & 0.955124210110826 & 0.0897515797783478 & 0.0448757898891739 \tabularnewline
32 & 0.939825551384247 & 0.120348897231505 & 0.0601744486157527 \tabularnewline
33 & 0.939675582170525 & 0.120648835658951 & 0.0603244178294754 \tabularnewline
34 & 0.92581244327636 & 0.148375113447280 & 0.0741875567236399 \tabularnewline
35 & 0.910122278888943 & 0.179755442222115 & 0.0898777211110573 \tabularnewline
36 & 0.891902670430846 & 0.216194659138308 & 0.108097329569154 \tabularnewline
37 & 0.863524165216835 & 0.27295166956633 & 0.136475834783165 \tabularnewline
38 & 0.860812425501872 & 0.278375148996256 & 0.139187574498128 \tabularnewline
39 & 0.884521206254842 & 0.230957587490316 & 0.115478793745158 \tabularnewline
40 & 0.858462371487166 & 0.283075257025669 & 0.141537628512834 \tabularnewline
41 & 0.848499669821338 & 0.303000660357325 & 0.151500330178662 \tabularnewline
42 & 0.815494672574335 & 0.36901065485133 & 0.184505327425665 \tabularnewline
43 & 0.910313564579187 & 0.179372870841626 & 0.089686435420813 \tabularnewline
44 & 0.887780004099082 & 0.224439991801837 & 0.112219995900918 \tabularnewline
45 & 0.86822918519644 & 0.263541629607119 & 0.131770814803560 \tabularnewline
46 & 0.842971451061006 & 0.314057097877989 & 0.157028548938994 \tabularnewline
47 & 0.885598825348103 & 0.228802349303794 & 0.114401174651897 \tabularnewline
48 & 0.87516670589592 & 0.249666588208160 & 0.124833294104080 \tabularnewline
49 & 0.91398597078595 & 0.172028058428098 & 0.0860140292140492 \tabularnewline
50 & 0.915388270401751 & 0.169223459196498 & 0.084611729598249 \tabularnewline
51 & 0.917484135712949 & 0.165031728574103 & 0.0825158642870515 \tabularnewline
52 & 0.93261429037956 & 0.134771419240879 & 0.0673857096204395 \tabularnewline
53 & 0.916270029297047 & 0.167459941405905 & 0.0837299707029527 \tabularnewline
54 & 0.895952673377351 & 0.208094653245298 & 0.104047326622649 \tabularnewline
55 & 0.882816675350097 & 0.234366649299805 & 0.117183324649903 \tabularnewline
56 & 0.858549216596162 & 0.282901566807677 & 0.141450783403838 \tabularnewline
57 & 0.83017606095505 & 0.3396478780899 & 0.16982393904495 \tabularnewline
58 & 0.805686491052923 & 0.388627017894154 & 0.194313508947077 \tabularnewline
59 & 0.925656367704867 & 0.148687264590265 & 0.0743436322951327 \tabularnewline
60 & 0.910044201991837 & 0.179911596016326 & 0.0899557980081628 \tabularnewline
61 & 0.89708585227742 & 0.205828295445161 & 0.102914147722581 \tabularnewline
62 & 0.881511298356415 & 0.236977403287170 & 0.118488701643585 \tabularnewline
63 & 0.860945574072868 & 0.278108851854264 & 0.139054425927132 \tabularnewline
64 & 0.842349003721778 & 0.315301992556444 & 0.157650996278222 \tabularnewline
65 & 0.81530528983106 & 0.369389420337879 & 0.184694710168940 \tabularnewline
66 & 0.798038226457291 & 0.403923547085418 & 0.201961773542709 \tabularnewline
67 & 0.88133163439869 & 0.237336731202619 & 0.118668365601309 \tabularnewline
68 & 0.908424402132488 & 0.183151195735024 & 0.0915755978675119 \tabularnewline
69 & 0.888340809244507 & 0.223318381510985 & 0.111659190755493 \tabularnewline
70 & 0.88067448510066 & 0.238651029798681 & 0.119325514899341 \tabularnewline
71 & 0.857851362442107 & 0.284297275115785 & 0.142148637557893 \tabularnewline
72 & 0.891857536305409 & 0.216284927389182 & 0.108142463694591 \tabularnewline
73 & 0.875968734277982 & 0.248062531444035 & 0.124031265722018 \tabularnewline
74 & 0.86772173860457 & 0.264556522790861 & 0.132278261395431 \tabularnewline
75 & 0.84084805243443 & 0.31830389513114 & 0.15915194756557 \tabularnewline
76 & 0.812552221847998 & 0.374895556304005 & 0.187447778152002 \tabularnewline
77 & 0.784449868480701 & 0.431100263038598 & 0.215550131519299 \tabularnewline
78 & 0.750874065734973 & 0.498251868530054 & 0.249125934265027 \tabularnewline
79 & 0.735944331218349 & 0.528111337563302 & 0.264055668781651 \tabularnewline
80 & 0.757474274172372 & 0.485051451655256 & 0.242525725827628 \tabularnewline
81 & 0.963958300244643 & 0.072083399510713 & 0.0360416997553565 \tabularnewline
82 & 0.961520212558959 & 0.0769595748820821 & 0.0384797874410411 \tabularnewline
83 & 0.970376601027835 & 0.0592467979443306 & 0.0296233989721653 \tabularnewline
84 & 0.972711635262402 & 0.0545767294751955 & 0.0272883647375977 \tabularnewline
85 & 0.980235020329632 & 0.0395299593407357 & 0.0197649796703678 \tabularnewline
86 & 0.974144766852205 & 0.0517104662955894 & 0.0258552331477947 \tabularnewline
87 & 0.97152230557127 & 0.05695538885746 & 0.02847769442873 \tabularnewline
88 & 0.962931897682224 & 0.0741362046355526 & 0.0370681023177763 \tabularnewline
89 & 0.953403980355778 & 0.0931920392884437 & 0.0465960196442218 \tabularnewline
90 & 0.955995927673701 & 0.088008144652598 & 0.044004072326299 \tabularnewline
91 & 0.961551917517628 & 0.0768961649647442 & 0.0384480824823721 \tabularnewline
92 & 0.972712850406384 & 0.0545742991872327 & 0.0272871495936164 \tabularnewline
93 & 0.964863989937818 & 0.0702720201243633 & 0.0351360100621817 \tabularnewline
94 & 0.95411218627097 & 0.09177562745806 & 0.04588781372903 \tabularnewline
95 & 0.941451392658435 & 0.117097214683129 & 0.0585486073415647 \tabularnewline
96 & 0.96183449344205 & 0.0763310131159009 & 0.0381655065579505 \tabularnewline
97 & 0.984312812341865 & 0.0313743753162702 & 0.0156871876581351 \tabularnewline
98 & 0.979342614449367 & 0.0413147711012653 & 0.0206573855506326 \tabularnewline
99 & 0.992589362119093 & 0.0148212757618141 & 0.00741063788090707 \tabularnewline
100 & 0.991903017676194 & 0.0161939646476113 & 0.00809698232380563 \tabularnewline
101 & 0.988899297352491 & 0.0222014052950179 & 0.0111007026475090 \tabularnewline
102 & 0.984899801919942 & 0.0302003961601151 & 0.0151001980800575 \tabularnewline
103 & 0.991648954829349 & 0.0167020903413029 & 0.00835104517065145 \tabularnewline
104 & 0.989750883984274 & 0.020498232031451 & 0.0102491160157255 \tabularnewline
105 & 0.985708342069768 & 0.0285833158604648 & 0.0142916579302324 \tabularnewline
106 & 0.986631541418253 & 0.026736917163494 & 0.013368458581747 \tabularnewline
107 & 0.993866510223154 & 0.0122669795536914 & 0.00613348977684568 \tabularnewline
108 & 0.994705443515149 & 0.0105891129697023 & 0.00529455648485116 \tabularnewline
109 & 0.992173072798675 & 0.0156538544026504 & 0.00782692720132518 \tabularnewline
110 & 0.988672001971102 & 0.0226559960577966 & 0.0113279980288983 \tabularnewline
111 & 0.984504421430063 & 0.0309911571398732 & 0.0154955785699366 \tabularnewline
112 & 0.979170155006539 & 0.0416596899869225 & 0.0208298449934613 \tabularnewline
113 & 0.975163696818487 & 0.0496726063630258 & 0.0248363031815129 \tabularnewline
114 & 0.974562604155848 & 0.0508747916883043 & 0.0254373958441522 \tabularnewline
115 & 0.969821273392215 & 0.0603574532155691 & 0.0301787266077846 \tabularnewline
116 & 0.962539117322857 & 0.0749217653542856 & 0.0374608826771428 \tabularnewline
117 & 0.95735229074297 & 0.0852954185140595 & 0.0426477092570298 \tabularnewline
118 & 0.97283609428868 & 0.0543278114226409 & 0.0271639057113204 \tabularnewline
119 & 0.986312300722303 & 0.0273753985553933 & 0.0136876992776967 \tabularnewline
120 & 0.98365626694285 & 0.0326874661142997 & 0.0163437330571498 \tabularnewline
121 & 0.97724605071211 & 0.0455078985757805 & 0.0227539492878903 \tabularnewline
122 & 0.970315779960979 & 0.0593684400780423 & 0.0296842200390212 \tabularnewline
123 & 0.958059820746089 & 0.0838803585078225 & 0.0419401792539112 \tabularnewline
124 & 0.981241726597376 & 0.0375165468052485 & 0.0187582734026242 \tabularnewline
125 & 0.975752161024939 & 0.0484956779501219 & 0.0242478389750609 \tabularnewline
126 & 0.963951071365013 & 0.0720978572699748 & 0.0360489286349874 \tabularnewline
127 & 0.94913075939428 & 0.101738481211438 & 0.0508692406057192 \tabularnewline
128 & 0.938177663914962 & 0.123644672170077 & 0.0618223360850384 \tabularnewline
129 & 0.923105483373853 & 0.153789033252294 & 0.0768945166261469 \tabularnewline
130 & 0.905011637956625 & 0.18997672408675 & 0.094988362043375 \tabularnewline
131 & 0.868804878913017 & 0.262390242173965 & 0.131195121086983 \tabularnewline
132 & 0.828327647201064 & 0.343344705597873 & 0.171672352798936 \tabularnewline
133 & 0.834751347894708 & 0.330497304210584 & 0.165248652105292 \tabularnewline
134 & 0.799149769344368 & 0.401700461311265 & 0.200850230655632 \tabularnewline
135 & 0.736814273240063 & 0.526371453519874 & 0.263185726759937 \tabularnewline
136 & 0.672395696115575 & 0.655208607768851 & 0.327604303884425 \tabularnewline
137 & 0.645922343167185 & 0.70815531366563 & 0.354077656832815 \tabularnewline
138 & 0.564176351308381 & 0.871647297383238 & 0.435823648691619 \tabularnewline
139 & 0.472545068849117 & 0.945090137698234 & 0.527454931150883 \tabularnewline
140 & 0.384484334567115 & 0.76896866913423 & 0.615515665432885 \tabularnewline
141 & 0.909975348645885 & 0.180049302708231 & 0.0900246513541153 \tabularnewline
142 & 0.876851552632096 & 0.246296894735808 & 0.123148447367904 \tabularnewline
143 & 0.870657074919597 & 0.258685850160805 & 0.129342925080403 \tabularnewline
144 & 0.786667940667247 & 0.426664118665506 & 0.213332059332753 \tabularnewline
145 & 0.69051518749708 & 0.61896962500584 & 0.30948481250292 \tabularnewline
146 & 0.538535189692523 & 0.922929620614954 & 0.461464810307477 \tabularnewline
147 & 0.429948419982576 & 0.859896839965152 & 0.570051580017424 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104337&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]0.0763749284487003[/C][C]0.152749856897401[/C][C]0.9236250715513[/C][/ROW]
[ROW][C]10[/C][C]0.789713070269334[/C][C]0.420573859461331[/C][C]0.210286929730666[/C][/ROW]
[ROW][C]11[/C][C]0.773561564484629[/C][C]0.452876871030743[/C][C]0.226438435515371[/C][/ROW]
[ROW][C]12[/C][C]0.671250629349592[/C][C]0.657498741300815[/C][C]0.328749370650408[/C][/ROW]
[ROW][C]13[/C][C]0.634216858277358[/C][C]0.731566283445284[/C][C]0.365783141722642[/C][/ROW]
[ROW][C]14[/C][C]0.681095170509505[/C][C]0.63780965898099[/C][C]0.318904829490495[/C][/ROW]
[ROW][C]15[/C][C]0.851903168816455[/C][C]0.296193662367089[/C][C]0.148096831183545[/C][/ROW]
[ROW][C]16[/C][C]0.803045120986422[/C][C]0.393909758027157[/C][C]0.196954879013578[/C][/ROW]
[ROW][C]17[/C][C]0.830575960252561[/C][C]0.338848079494877[/C][C]0.169424039747439[/C][/ROW]
[ROW][C]18[/C][C]0.8279082456424[/C][C]0.344183508715199[/C][C]0.172091754357600[/C][/ROW]
[ROW][C]19[/C][C]0.847965735160502[/C][C]0.304068529678995[/C][C]0.152034264839498[/C][/ROW]
[ROW][C]20[/C][C]0.815465792583215[/C][C]0.36906841483357[/C][C]0.184534207416785[/C][/ROW]
[ROW][C]21[/C][C]0.80012958859045[/C][C]0.399740822819101[/C][C]0.199870411409551[/C][/ROW]
[ROW][C]22[/C][C]0.746681961471968[/C][C]0.506636077056064[/C][C]0.253318038528032[/C][/ROW]
[ROW][C]23[/C][C]0.848045648319572[/C][C]0.303908703360856[/C][C]0.151954351680428[/C][/ROW]
[ROW][C]24[/C][C]0.803885839275634[/C][C]0.392228321448732[/C][C]0.196114160724366[/C][/ROW]
[ROW][C]25[/C][C]0.8798740169682[/C][C]0.240251966063599[/C][C]0.120125983031799[/C][/ROW]
[ROW][C]26[/C][C]0.842937157865523[/C][C]0.314125684268953[/C][C]0.157062842134477[/C][/ROW]
[ROW][C]27[/C][C]0.921914273702845[/C][C]0.156171452594310[/C][C]0.0780857262971552[/C][/ROW]
[ROW][C]28[/C][C]0.90918772149562[/C][C]0.18162455700876[/C][C]0.09081227850438[/C][/ROW]
[ROW][C]29[/C][C]0.931392376398512[/C][C]0.137215247202976[/C][C]0.0686076236014878[/C][/ROW]
[ROW][C]30[/C][C]0.910003493657766[/C][C]0.179993012684469[/C][C]0.0899965063422344[/C][/ROW]
[ROW][C]31[/C][C]0.955124210110826[/C][C]0.0897515797783478[/C][C]0.0448757898891739[/C][/ROW]
[ROW][C]32[/C][C]0.939825551384247[/C][C]0.120348897231505[/C][C]0.0601744486157527[/C][/ROW]
[ROW][C]33[/C][C]0.939675582170525[/C][C]0.120648835658951[/C][C]0.0603244178294754[/C][/ROW]
[ROW][C]34[/C][C]0.92581244327636[/C][C]0.148375113447280[/C][C]0.0741875567236399[/C][/ROW]
[ROW][C]35[/C][C]0.910122278888943[/C][C]0.179755442222115[/C][C]0.0898777211110573[/C][/ROW]
[ROW][C]36[/C][C]0.891902670430846[/C][C]0.216194659138308[/C][C]0.108097329569154[/C][/ROW]
[ROW][C]37[/C][C]0.863524165216835[/C][C]0.27295166956633[/C][C]0.136475834783165[/C][/ROW]
[ROW][C]38[/C][C]0.860812425501872[/C][C]0.278375148996256[/C][C]0.139187574498128[/C][/ROW]
[ROW][C]39[/C][C]0.884521206254842[/C][C]0.230957587490316[/C][C]0.115478793745158[/C][/ROW]
[ROW][C]40[/C][C]0.858462371487166[/C][C]0.283075257025669[/C][C]0.141537628512834[/C][/ROW]
[ROW][C]41[/C][C]0.848499669821338[/C][C]0.303000660357325[/C][C]0.151500330178662[/C][/ROW]
[ROW][C]42[/C][C]0.815494672574335[/C][C]0.36901065485133[/C][C]0.184505327425665[/C][/ROW]
[ROW][C]43[/C][C]0.910313564579187[/C][C]0.179372870841626[/C][C]0.089686435420813[/C][/ROW]
[ROW][C]44[/C][C]0.887780004099082[/C][C]0.224439991801837[/C][C]0.112219995900918[/C][/ROW]
[ROW][C]45[/C][C]0.86822918519644[/C][C]0.263541629607119[/C][C]0.131770814803560[/C][/ROW]
[ROW][C]46[/C][C]0.842971451061006[/C][C]0.314057097877989[/C][C]0.157028548938994[/C][/ROW]
[ROW][C]47[/C][C]0.885598825348103[/C][C]0.228802349303794[/C][C]0.114401174651897[/C][/ROW]
[ROW][C]48[/C][C]0.87516670589592[/C][C]0.249666588208160[/C][C]0.124833294104080[/C][/ROW]
[ROW][C]49[/C][C]0.91398597078595[/C][C]0.172028058428098[/C][C]0.0860140292140492[/C][/ROW]
[ROW][C]50[/C][C]0.915388270401751[/C][C]0.169223459196498[/C][C]0.084611729598249[/C][/ROW]
[ROW][C]51[/C][C]0.917484135712949[/C][C]0.165031728574103[/C][C]0.0825158642870515[/C][/ROW]
[ROW][C]52[/C][C]0.93261429037956[/C][C]0.134771419240879[/C][C]0.0673857096204395[/C][/ROW]
[ROW][C]53[/C][C]0.916270029297047[/C][C]0.167459941405905[/C][C]0.0837299707029527[/C][/ROW]
[ROW][C]54[/C][C]0.895952673377351[/C][C]0.208094653245298[/C][C]0.104047326622649[/C][/ROW]
[ROW][C]55[/C][C]0.882816675350097[/C][C]0.234366649299805[/C][C]0.117183324649903[/C][/ROW]
[ROW][C]56[/C][C]0.858549216596162[/C][C]0.282901566807677[/C][C]0.141450783403838[/C][/ROW]
[ROW][C]57[/C][C]0.83017606095505[/C][C]0.3396478780899[/C][C]0.16982393904495[/C][/ROW]
[ROW][C]58[/C][C]0.805686491052923[/C][C]0.388627017894154[/C][C]0.194313508947077[/C][/ROW]
[ROW][C]59[/C][C]0.925656367704867[/C][C]0.148687264590265[/C][C]0.0743436322951327[/C][/ROW]
[ROW][C]60[/C][C]0.910044201991837[/C][C]0.179911596016326[/C][C]0.0899557980081628[/C][/ROW]
[ROW][C]61[/C][C]0.89708585227742[/C][C]0.205828295445161[/C][C]0.102914147722581[/C][/ROW]
[ROW][C]62[/C][C]0.881511298356415[/C][C]0.236977403287170[/C][C]0.118488701643585[/C][/ROW]
[ROW][C]63[/C][C]0.860945574072868[/C][C]0.278108851854264[/C][C]0.139054425927132[/C][/ROW]
[ROW][C]64[/C][C]0.842349003721778[/C][C]0.315301992556444[/C][C]0.157650996278222[/C][/ROW]
[ROW][C]65[/C][C]0.81530528983106[/C][C]0.369389420337879[/C][C]0.184694710168940[/C][/ROW]
[ROW][C]66[/C][C]0.798038226457291[/C][C]0.403923547085418[/C][C]0.201961773542709[/C][/ROW]
[ROW][C]67[/C][C]0.88133163439869[/C][C]0.237336731202619[/C][C]0.118668365601309[/C][/ROW]
[ROW][C]68[/C][C]0.908424402132488[/C][C]0.183151195735024[/C][C]0.0915755978675119[/C][/ROW]
[ROW][C]69[/C][C]0.888340809244507[/C][C]0.223318381510985[/C][C]0.111659190755493[/C][/ROW]
[ROW][C]70[/C][C]0.88067448510066[/C][C]0.238651029798681[/C][C]0.119325514899341[/C][/ROW]
[ROW][C]71[/C][C]0.857851362442107[/C][C]0.284297275115785[/C][C]0.142148637557893[/C][/ROW]
[ROW][C]72[/C][C]0.891857536305409[/C][C]0.216284927389182[/C][C]0.108142463694591[/C][/ROW]
[ROW][C]73[/C][C]0.875968734277982[/C][C]0.248062531444035[/C][C]0.124031265722018[/C][/ROW]
[ROW][C]74[/C][C]0.86772173860457[/C][C]0.264556522790861[/C][C]0.132278261395431[/C][/ROW]
[ROW][C]75[/C][C]0.84084805243443[/C][C]0.31830389513114[/C][C]0.15915194756557[/C][/ROW]
[ROW][C]76[/C][C]0.812552221847998[/C][C]0.374895556304005[/C][C]0.187447778152002[/C][/ROW]
[ROW][C]77[/C][C]0.784449868480701[/C][C]0.431100263038598[/C][C]0.215550131519299[/C][/ROW]
[ROW][C]78[/C][C]0.750874065734973[/C][C]0.498251868530054[/C][C]0.249125934265027[/C][/ROW]
[ROW][C]79[/C][C]0.735944331218349[/C][C]0.528111337563302[/C][C]0.264055668781651[/C][/ROW]
[ROW][C]80[/C][C]0.757474274172372[/C][C]0.485051451655256[/C][C]0.242525725827628[/C][/ROW]
[ROW][C]81[/C][C]0.963958300244643[/C][C]0.072083399510713[/C][C]0.0360416997553565[/C][/ROW]
[ROW][C]82[/C][C]0.961520212558959[/C][C]0.0769595748820821[/C][C]0.0384797874410411[/C][/ROW]
[ROW][C]83[/C][C]0.970376601027835[/C][C]0.0592467979443306[/C][C]0.0296233989721653[/C][/ROW]
[ROW][C]84[/C][C]0.972711635262402[/C][C]0.0545767294751955[/C][C]0.0272883647375977[/C][/ROW]
[ROW][C]85[/C][C]0.980235020329632[/C][C]0.0395299593407357[/C][C]0.0197649796703678[/C][/ROW]
[ROW][C]86[/C][C]0.974144766852205[/C][C]0.0517104662955894[/C][C]0.0258552331477947[/C][/ROW]
[ROW][C]87[/C][C]0.97152230557127[/C][C]0.05695538885746[/C][C]0.02847769442873[/C][/ROW]
[ROW][C]88[/C][C]0.962931897682224[/C][C]0.0741362046355526[/C][C]0.0370681023177763[/C][/ROW]
[ROW][C]89[/C][C]0.953403980355778[/C][C]0.0931920392884437[/C][C]0.0465960196442218[/C][/ROW]
[ROW][C]90[/C][C]0.955995927673701[/C][C]0.088008144652598[/C][C]0.044004072326299[/C][/ROW]
[ROW][C]91[/C][C]0.961551917517628[/C][C]0.0768961649647442[/C][C]0.0384480824823721[/C][/ROW]
[ROW][C]92[/C][C]0.972712850406384[/C][C]0.0545742991872327[/C][C]0.0272871495936164[/C][/ROW]
[ROW][C]93[/C][C]0.964863989937818[/C][C]0.0702720201243633[/C][C]0.0351360100621817[/C][/ROW]
[ROW][C]94[/C][C]0.95411218627097[/C][C]0.09177562745806[/C][C]0.04588781372903[/C][/ROW]
[ROW][C]95[/C][C]0.941451392658435[/C][C]0.117097214683129[/C][C]0.0585486073415647[/C][/ROW]
[ROW][C]96[/C][C]0.96183449344205[/C][C]0.0763310131159009[/C][C]0.0381655065579505[/C][/ROW]
[ROW][C]97[/C][C]0.984312812341865[/C][C]0.0313743753162702[/C][C]0.0156871876581351[/C][/ROW]
[ROW][C]98[/C][C]0.979342614449367[/C][C]0.0413147711012653[/C][C]0.0206573855506326[/C][/ROW]
[ROW][C]99[/C][C]0.992589362119093[/C][C]0.0148212757618141[/C][C]0.00741063788090707[/C][/ROW]
[ROW][C]100[/C][C]0.991903017676194[/C][C]0.0161939646476113[/C][C]0.00809698232380563[/C][/ROW]
[ROW][C]101[/C][C]0.988899297352491[/C][C]0.0222014052950179[/C][C]0.0111007026475090[/C][/ROW]
[ROW][C]102[/C][C]0.984899801919942[/C][C]0.0302003961601151[/C][C]0.0151001980800575[/C][/ROW]
[ROW][C]103[/C][C]0.991648954829349[/C][C]0.0167020903413029[/C][C]0.00835104517065145[/C][/ROW]
[ROW][C]104[/C][C]0.989750883984274[/C][C]0.020498232031451[/C][C]0.0102491160157255[/C][/ROW]
[ROW][C]105[/C][C]0.985708342069768[/C][C]0.0285833158604648[/C][C]0.0142916579302324[/C][/ROW]
[ROW][C]106[/C][C]0.986631541418253[/C][C]0.026736917163494[/C][C]0.013368458581747[/C][/ROW]
[ROW][C]107[/C][C]0.993866510223154[/C][C]0.0122669795536914[/C][C]0.00613348977684568[/C][/ROW]
[ROW][C]108[/C][C]0.994705443515149[/C][C]0.0105891129697023[/C][C]0.00529455648485116[/C][/ROW]
[ROW][C]109[/C][C]0.992173072798675[/C][C]0.0156538544026504[/C][C]0.00782692720132518[/C][/ROW]
[ROW][C]110[/C][C]0.988672001971102[/C][C]0.0226559960577966[/C][C]0.0113279980288983[/C][/ROW]
[ROW][C]111[/C][C]0.984504421430063[/C][C]0.0309911571398732[/C][C]0.0154955785699366[/C][/ROW]
[ROW][C]112[/C][C]0.979170155006539[/C][C]0.0416596899869225[/C][C]0.0208298449934613[/C][/ROW]
[ROW][C]113[/C][C]0.975163696818487[/C][C]0.0496726063630258[/C][C]0.0248363031815129[/C][/ROW]
[ROW][C]114[/C][C]0.974562604155848[/C][C]0.0508747916883043[/C][C]0.0254373958441522[/C][/ROW]
[ROW][C]115[/C][C]0.969821273392215[/C][C]0.0603574532155691[/C][C]0.0301787266077846[/C][/ROW]
[ROW][C]116[/C][C]0.962539117322857[/C][C]0.0749217653542856[/C][C]0.0374608826771428[/C][/ROW]
[ROW][C]117[/C][C]0.95735229074297[/C][C]0.0852954185140595[/C][C]0.0426477092570298[/C][/ROW]
[ROW][C]118[/C][C]0.97283609428868[/C][C]0.0543278114226409[/C][C]0.0271639057113204[/C][/ROW]
[ROW][C]119[/C][C]0.986312300722303[/C][C]0.0273753985553933[/C][C]0.0136876992776967[/C][/ROW]
[ROW][C]120[/C][C]0.98365626694285[/C][C]0.0326874661142997[/C][C]0.0163437330571498[/C][/ROW]
[ROW][C]121[/C][C]0.97724605071211[/C][C]0.0455078985757805[/C][C]0.0227539492878903[/C][/ROW]
[ROW][C]122[/C][C]0.970315779960979[/C][C]0.0593684400780423[/C][C]0.0296842200390212[/C][/ROW]
[ROW][C]123[/C][C]0.958059820746089[/C][C]0.0838803585078225[/C][C]0.0419401792539112[/C][/ROW]
[ROW][C]124[/C][C]0.981241726597376[/C][C]0.0375165468052485[/C][C]0.0187582734026242[/C][/ROW]
[ROW][C]125[/C][C]0.975752161024939[/C][C]0.0484956779501219[/C][C]0.0242478389750609[/C][/ROW]
[ROW][C]126[/C][C]0.963951071365013[/C][C]0.0720978572699748[/C][C]0.0360489286349874[/C][/ROW]
[ROW][C]127[/C][C]0.94913075939428[/C][C]0.101738481211438[/C][C]0.0508692406057192[/C][/ROW]
[ROW][C]128[/C][C]0.938177663914962[/C][C]0.123644672170077[/C][C]0.0618223360850384[/C][/ROW]
[ROW][C]129[/C][C]0.923105483373853[/C][C]0.153789033252294[/C][C]0.0768945166261469[/C][/ROW]
[ROW][C]130[/C][C]0.905011637956625[/C][C]0.18997672408675[/C][C]0.094988362043375[/C][/ROW]
[ROW][C]131[/C][C]0.868804878913017[/C][C]0.262390242173965[/C][C]0.131195121086983[/C][/ROW]
[ROW][C]132[/C][C]0.828327647201064[/C][C]0.343344705597873[/C][C]0.171672352798936[/C][/ROW]
[ROW][C]133[/C][C]0.834751347894708[/C][C]0.330497304210584[/C][C]0.165248652105292[/C][/ROW]
[ROW][C]134[/C][C]0.799149769344368[/C][C]0.401700461311265[/C][C]0.200850230655632[/C][/ROW]
[ROW][C]135[/C][C]0.736814273240063[/C][C]0.526371453519874[/C][C]0.263185726759937[/C][/ROW]
[ROW][C]136[/C][C]0.672395696115575[/C][C]0.655208607768851[/C][C]0.327604303884425[/C][/ROW]
[ROW][C]137[/C][C]0.645922343167185[/C][C]0.70815531366563[/C][C]0.354077656832815[/C][/ROW]
[ROW][C]138[/C][C]0.564176351308381[/C][C]0.871647297383238[/C][C]0.435823648691619[/C][/ROW]
[ROW][C]139[/C][C]0.472545068849117[/C][C]0.945090137698234[/C][C]0.527454931150883[/C][/ROW]
[ROW][C]140[/C][C]0.384484334567115[/C][C]0.76896866913423[/C][C]0.615515665432885[/C][/ROW]
[ROW][C]141[/C][C]0.909975348645885[/C][C]0.180049302708231[/C][C]0.0900246513541153[/C][/ROW]
[ROW][C]142[/C][C]0.876851552632096[/C][C]0.246296894735808[/C][C]0.123148447367904[/C][/ROW]
[ROW][C]143[/C][C]0.870657074919597[/C][C]0.258685850160805[/C][C]0.129342925080403[/C][/ROW]
[ROW][C]144[/C][C]0.786667940667247[/C][C]0.426664118665506[/C][C]0.213332059332753[/C][/ROW]
[ROW][C]145[/C][C]0.69051518749708[/C][C]0.61896962500584[/C][C]0.30948481250292[/C][/ROW]
[ROW][C]146[/C][C]0.538535189692523[/C][C]0.922929620614954[/C][C]0.461464810307477[/C][/ROW]
[ROW][C]147[/C][C]0.429948419982576[/C][C]0.859896839965152[/C][C]0.570051580017424[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104337&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104337&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
90.07637492844870030.1527498568974010.9236250715513
100.7897130702693340.4205738594613310.210286929730666
110.7735615644846290.4528768710307430.226438435515371
120.6712506293495920.6574987413008150.328749370650408
130.6342168582773580.7315662834452840.365783141722642
140.6810951705095050.637809658980990.318904829490495
150.8519031688164550.2961936623670890.148096831183545
160.8030451209864220.3939097580271570.196954879013578
170.8305759602525610.3388480794948770.169424039747439
180.82790824564240.3441835087151990.172091754357600
190.8479657351605020.3040685296789950.152034264839498
200.8154657925832150.369068414833570.184534207416785
210.800129588590450.3997408228191010.199870411409551
220.7466819614719680.5066360770560640.253318038528032
230.8480456483195720.3039087033608560.151954351680428
240.8038858392756340.3922283214487320.196114160724366
250.87987401696820.2402519660635990.120125983031799
260.8429371578655230.3141256842689530.157062842134477
270.9219142737028450.1561714525943100.0780857262971552
280.909187721495620.181624557008760.09081227850438
290.9313923763985120.1372152472029760.0686076236014878
300.9100034936577660.1799930126844690.0899965063422344
310.9551242101108260.08975157977834780.0448757898891739
320.9398255513842470.1203488972315050.0601744486157527
330.9396755821705250.1206488356589510.0603244178294754
340.925812443276360.1483751134472800.0741875567236399
350.9101222788889430.1797554422221150.0898777211110573
360.8919026704308460.2161946591383080.108097329569154
370.8635241652168350.272951669566330.136475834783165
380.8608124255018720.2783751489962560.139187574498128
390.8845212062548420.2309575874903160.115478793745158
400.8584623714871660.2830752570256690.141537628512834
410.8484996698213380.3030006603573250.151500330178662
420.8154946725743350.369010654851330.184505327425665
430.9103135645791870.1793728708416260.089686435420813
440.8877800040990820.2244399918018370.112219995900918
450.868229185196440.2635416296071190.131770814803560
460.8429714510610060.3140570978779890.157028548938994
470.8855988253481030.2288023493037940.114401174651897
480.875166705895920.2496665882081600.124833294104080
490.913985970785950.1720280584280980.0860140292140492
500.9153882704017510.1692234591964980.084611729598249
510.9174841357129490.1650317285741030.0825158642870515
520.932614290379560.1347714192408790.0673857096204395
530.9162700292970470.1674599414059050.0837299707029527
540.8959526733773510.2080946532452980.104047326622649
550.8828166753500970.2343666492998050.117183324649903
560.8585492165961620.2829015668076770.141450783403838
570.830176060955050.33964787808990.16982393904495
580.8056864910529230.3886270178941540.194313508947077
590.9256563677048670.1486872645902650.0743436322951327
600.9100442019918370.1799115960163260.0899557980081628
610.897085852277420.2058282954451610.102914147722581
620.8815112983564150.2369774032871700.118488701643585
630.8609455740728680.2781088518542640.139054425927132
640.8423490037217780.3153019925564440.157650996278222
650.815305289831060.3693894203378790.184694710168940
660.7980382264572910.4039235470854180.201961773542709
670.881331634398690.2373367312026190.118668365601309
680.9084244021324880.1831511957350240.0915755978675119
690.8883408092445070.2233183815109850.111659190755493
700.880674485100660.2386510297986810.119325514899341
710.8578513624421070.2842972751157850.142148637557893
720.8918575363054090.2162849273891820.108142463694591
730.8759687342779820.2480625314440350.124031265722018
740.867721738604570.2645565227908610.132278261395431
750.840848052434430.318303895131140.15915194756557
760.8125522218479980.3748955563040050.187447778152002
770.7844498684807010.4311002630385980.215550131519299
780.7508740657349730.4982518685300540.249125934265027
790.7359443312183490.5281113375633020.264055668781651
800.7574742741723720.4850514516552560.242525725827628
810.9639583002446430.0720833995107130.0360416997553565
820.9615202125589590.07695957488208210.0384797874410411
830.9703766010278350.05924679794433060.0296233989721653
840.9727116352624020.05457672947519550.0272883647375977
850.9802350203296320.03952995934073570.0197649796703678
860.9741447668522050.05171046629558940.0258552331477947
870.971522305571270.056955388857460.02847769442873
880.9629318976822240.07413620463555260.0370681023177763
890.9534039803557780.09319203928844370.0465960196442218
900.9559959276737010.0880081446525980.044004072326299
910.9615519175176280.07689616496474420.0384480824823721
920.9727128504063840.05457429918723270.0272871495936164
930.9648639899378180.07027202012436330.0351360100621817
940.954112186270970.091775627458060.04588781372903
950.9414513926584350.1170972146831290.0585486073415647
960.961834493442050.07633101311590090.0381655065579505
970.9843128123418650.03137437531627020.0156871876581351
980.9793426144493670.04131477110126530.0206573855506326
990.9925893621190930.01482127576181410.00741063788090707
1000.9919030176761940.01619396464761130.00809698232380563
1010.9888992973524910.02220140529501790.0111007026475090
1020.9848998019199420.03020039616011510.0151001980800575
1030.9916489548293490.01670209034130290.00835104517065145
1040.9897508839842740.0204982320314510.0102491160157255
1050.9857083420697680.02858331586046480.0142916579302324
1060.9866315414182530.0267369171634940.013368458581747
1070.9938665102231540.01226697955369140.00613348977684568
1080.9947054435151490.01058911296970230.00529455648485116
1090.9921730727986750.01565385440265040.00782692720132518
1100.9886720019711020.02265599605779660.0113279980288983
1110.9845044214300630.03099115713987320.0154955785699366
1120.9791701550065390.04165968998692250.0208298449934613
1130.9751636968184870.04967260636302580.0248363031815129
1140.9745626041558480.05087479168830430.0254373958441522
1150.9698212733922150.06035745321556910.0301787266077846
1160.9625391173228570.07492176535428560.0374608826771428
1170.957352290742970.08529541851405950.0426477092570298
1180.972836094288680.05432781142264090.0271639057113204
1190.9863123007223030.02737539855539330.0136876992776967
1200.983656266942850.03268746611429970.0163437330571498
1210.977246050712110.04550789857578050.0227539492878903
1220.9703157799609790.05936844007804230.0296842200390212
1230.9580598207460890.08388035850782250.0419401792539112
1240.9812417265973760.03751654680524850.0187582734026242
1250.9757521610249390.04849567795012190.0242478389750609
1260.9639510713650130.07209785726997480.0360489286349874
1270.949130759394280.1017384812114380.0508692406057192
1280.9381776639149620.1236446721700770.0618223360850384
1290.9231054833738530.1537890332522940.0768945166261469
1300.9050116379566250.189976724086750.094988362043375
1310.8688048789130170.2623902421739650.131195121086983
1320.8283276472010640.3433447055978730.171672352798936
1330.8347513478947080.3304973042105840.165248652105292
1340.7991497693443680.4017004613112650.200850230655632
1350.7368142732400630.5263714535198740.263185726759937
1360.6723956961155750.6552086077688510.327604303884425
1370.6459223431671850.708155313665630.354077656832815
1380.5641763513083810.8716472973832380.435823648691619
1390.4725450688491170.9450901376982340.527454931150883
1400.3844843345671150.768968669134230.615515665432885
1410.9099753486458850.1800493027082310.0900246513541153
1420.8768515526320960.2462968947358080.123148447367904
1430.8706570749195970.2586858501608050.129342925080403
1440.7866679406672470.4266641186655060.213332059332753
1450.690515187497080.618969625005840.30948481250292
1460.5385351896925230.9229296206149540.461464810307477
1470.4299484199825760.8598968399651520.570051580017424






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level230.165467625899281NOK
10% type I error level460.330935251798561NOK

\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 & 23 & 0.165467625899281 & NOK \tabularnewline
10% type I error level & 46 & 0.330935251798561 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104337&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]23[/C][C]0.165467625899281[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]46[/C][C]0.330935251798561[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104337&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104337&T=6

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


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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level230.165467625899281NOK
10% type I error level460.330935251798561NOK


Figures (Output of Computation)

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

Parameters (Session):
par1 = 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')
}