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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationTue, 22 Nov 2011 14:42:43 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Nov/22/t132199101643m5cprur1wc4fm.htm/, Retrieved Thu, 28 Mar 2024 18:57:48 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=146389, Retrieved Thu, 28 Mar 2024 18:57:48 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact112
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2010-11-17 09:14:55] [b98453cac15ba1066b407e146608df68]
- R PD  [Multiple Regression] [ws7 Tutorial Popu...] [2010-11-22 11:00:33] [afe9379cca749d06b3d6872e02cc47ed]
- R  D    [Multiple Regression] [ws7-4] [2011-11-22 18:15:17] [f7a862281046b7153543b12c78921b36]
-    D      [Multiple Regression] [ws7-5] [2011-11-22 19:16:13] [f7a862281046b7153543b12c78921b36]
-               [Multiple Regression] [ws7-5] [2011-11-22 19:42:43] [47995d3a8fac585eeb070a274b466f8c] [Current]
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Dataseries X:
14	3	2	3	3	7
8	5	6	0	7	2
12	6	6	0	6	3
7	6	6	6	6	8
10	7	8	5	5	7
9	3	1	0	7	7
16	8	9	8	8	9
7	4	4	0	2	2
14	7	7	0	4	4
6	4	4	9	9	4
16	6	6	6	6	6
11	6	5	6	6	4
17	7	7	5	5	9
12	4	5	4	4	8
7	6	6	0	2	7
13	5	5	0	4	4
9	0	2	2	2	2
15	9	9	6	6	8
7	4	4	0	4	4
9	4	4	4	4	4
7	2	5	5	5	2
14	7	7	7	7	9
15	5	5	5	5	3
7	9	9	4	4	4
13	6	6	6	6	6
17	6	6	6	6	6
15	7	3	0	7	7
14	3	3	1	2	2
14	6	5	0	6	6
8	6	5	4	4	4
8	4	4	4	4	2
12	7	7	7	7	9
14	7	6	7	7	7
8	7	7	0	4	4
11	4	4	4	4	4
16	5	5	5	5	7
11	6	6	0	6	6
8	5	5	5	5	5
14	6	0	1	6	6
16	6	6	2	2	2
14	6	5	0	6	2
5	3	3	9	9	7
8	3	3	3	3	3
10	3	3	0	4	4
8	6	7	6	6	6
13	7	7	1	5	5
15	5	1	5	5	7
6	5	5	0	4	4
12	5	5	0	2	2
14	6	6	0	6	6
5	6	2	6	6	9
15	6	6	7	7	8
11	5	5	0	5	5
8	4	2	4	4	4
13	7	7	5	5	2
14	5	5	1	5	9
12	3	3	4	4	4
16	6	6	9	9	6
10	2	2	2	2	2
15	8	8	8	8	8
8	3	5	3	3	3
16	0	2	1	6	3
19	6	6	0	6	7
14	8	2	6	6	2
7	4	1	0	5	9
13	5	5	0	5	5
15	6	6	6	6	4
7	5	2	2	2	2
13	6	6	1	6	6
4	2	2	5	5	5
14	6	6	5	5	5
13	5	5	5	5	9
11	5	0	5	5	2
14	6	2	6	6	6
12	4	4	6	6	6
15	6	1	0	9	6
14	5	5	0	5	5
13	5	5	1	5	3
7	4	2	7	7	2
5	2	2	2	2	2
7	7	7	4	4	4
13	5	5	0	6	8
13	6	2	5	5	5
11	5	5	5	5	9
6	3	3	3	3	2
12	6	6	0	6	6
8	4	1	4	4	4
11	5	5	9	9	5
12	7	7	0	8	8
9	4	2	4	4	3
12	6	6	2	2	2
13	8	8	7	7	7
16	7	7	7	7	7
16	6	6	6	6	9
11	7	7	0	5	5
8	4	4	5	5	5
4	0	5	6	6	2
7	3	2	0	3	3
14	5	5	5	5	5
11	6	2	9	9	2
17	5	5	0	7	7
15	7	7	7	7	7
14	6	5	1	6	6
5	8	8	3	3	3
4	7	2	7	7	3
19	8	8	8	8	2
11	3	3	0	3	3
15	8	2	5	5	5
10	3	3	3	3	3
9	4	5	0	4	4
12	2	2	5	5	5
15	7	2	7	7	7
7	6	6	0	6	6
13	2	2	0	7	7
14	7	7	0	9	2
14	6	6	6	6	6
14	6	2	0	6	9
8	6	2	6	6	4
15	6	5	6	6	6
15	6	6	2	2	2
9	4	4	5	5	2
16	5	5	0	5	5
9	7	7	4	4	4
15	6	6	0	7	7
15	6	6	6	6	6
6	5	5	5	5	7
8	8	2	8	8	8
15	6	6	6	6	6
10	0	3	5	5	3
9	4	2	0	4	4
14	8	8	8	8	8
12	6	6	0	6	9
8	4	4	9	9	2
11	6	6	5	5	5
13	2	5	0	6	6
9	4	4	0	4	4
15	6	2	0	6	6
13	3	3	3	3	3
15	6	6	6	6	6
14	5	5	0	5	5
16	4	4	4	4	8
12	6	6	6	6	6
14	1	1	0	5	5
10	4	5	4	4	3
10	4	2	7	7	2
4	6	6	0	6	6
8	5	5	5	5	5
17	9	2	6	6	6
16	6	6	6	6	6
12	8	8	8	8	9
12	7	7	2	2	4
15	7	7	7	7	7
9	0	9	0	4	4
13	6	2	0	6	7
14	6	6	5	5	5
11	5	5	0	2	2




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

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 5 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146389&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146389&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146389&T=0

As an alternative you can also use a QR Code:  

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

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







Multiple Linear Regression - Estimated Regression Equation
Schoolprestaties[t] = + 5.94451224803447 + 0.431702468220128Sport[t] + 0.096186434920722Goingout[t] -0.134927436537078Relation[t] + 0.260354564152351Family[t] + 0.312539014805456Coach[t] + 0.00558676759994745t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Schoolprestaties[t] =  +  5.94451224803447 +  0.431702468220128Sport[t] +  0.096186434920722Goingout[t] -0.134927436537078Relation[t] +  0.260354564152351Family[t] +  0.312539014805456Coach[t] +  0.00558676759994745t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146389&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Schoolprestaties[t] =  +  5.94451224803447 +  0.431702468220128Sport[t] +  0.096186434920722Goingout[t] -0.134927436537078Relation[t] +  0.260354564152351Family[t] +  0.312539014805456Coach[t] +  0.00558676759994745t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146389&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146389&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
Schoolprestaties[t] = + 5.94451224803447 + 0.431702468220128Sport[t] + 0.096186434920722Goingout[t] -0.134927436537078Relation[t] + 0.260354564152351Family[t] + 0.312539014805456Coach[t] + 0.00558676759994745t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)5.944512248034471.0999745.404200
Sport0.4317024682201280.1719642.51040.0131260.006563
Goingout0.0961864349207220.1427640.67370.5015160.250758
Relation-0.1349274365370780.099817-1.35180.1785040.089252
Family0.2603545641523510.185271.40530.1620230.081011
Coach0.3125390148054560.1366152.28770.023560.01178
t0.005586767599947450.0057430.97290.33220.1661

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & 5.94451224803447 & 1.099974 & 5.4042 & 0 & 0 \tabularnewline
Sport & 0.431702468220128 & 0.171964 & 2.5104 & 0.013126 & 0.006563 \tabularnewline
Goingout & 0.096186434920722 & 0.142764 & 0.6737 & 0.501516 & 0.250758 \tabularnewline
Relation & -0.134927436537078 & 0.099817 & -1.3518 & 0.178504 & 0.089252 \tabularnewline
Family & 0.260354564152351 & 0.18527 & 1.4053 & 0.162023 & 0.081011 \tabularnewline
Coach & 0.312539014805456 & 0.136615 & 2.2877 & 0.02356 & 0.01178 \tabularnewline
t & 0.00558676759994745 & 0.005743 & 0.9729 & 0.3322 & 0.1661 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146389&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]5.94451224803447[/C][C]1.099974[/C][C]5.4042[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Sport[/C][C]0.431702468220128[/C][C]0.171964[/C][C]2.5104[/C][C]0.013126[/C][C]0.006563[/C][/ROW]
[ROW][C]Goingout[/C][C]0.096186434920722[/C][C]0.142764[/C][C]0.6737[/C][C]0.501516[/C][C]0.250758[/C][/ROW]
[ROW][C]Relation[/C][C]-0.134927436537078[/C][C]0.099817[/C][C]-1.3518[/C][C]0.178504[/C][C]0.089252[/C][/ROW]
[ROW][C]Family[/C][C]0.260354564152351[/C][C]0.18527[/C][C]1.4053[/C][C]0.162023[/C][C]0.081011[/C][/ROW]
[ROW][C]Coach[/C][C]0.312539014805456[/C][C]0.136615[/C][C]2.2877[/C][C]0.02356[/C][C]0.01178[/C][/ROW]
[ROW][C]t[/C][C]0.00558676759994745[/C][C]0.005743[/C][C]0.9729[/C][C]0.3322[/C][C]0.1661[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146389&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146389&T=2

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)5.944512248034471.0999745.404200
Sport0.4317024682201280.1719642.51040.0131260.006563
Goingout0.0961864349207220.1427640.67370.5015160.250758
Relation-0.1349274365370780.099817-1.35180.1785040.089252
Family0.2603545641523510.185271.40530.1620230.081011
Coach0.3125390148054560.1366152.28770.023560.01178
t0.005586767599947450.0057430.97290.33220.1661







Multiple Linear Regression - Regression Statistics
Multiple R0.435590747177052
R-squared0.189739299026262
Adjusted R-squared0.157111351336045
F-TEST (value)5.81523854419901
F-TEST (DF numerator)6
F-TEST (DF denominator)149
p-value1.81317053578045e-05
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.20737979482366
Sum Squared Residuals1532.80548708822

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.435590747177052 \tabularnewline
R-squared & 0.189739299026262 \tabularnewline
Adjusted R-squared & 0.157111351336045 \tabularnewline
F-TEST (value) & 5.81523854419901 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 149 \tabularnewline
p-value & 1.81317053578045e-05 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 3.20737979482366 \tabularnewline
Sum Squared Residuals & 1532.80548708822 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146389&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.435590747177052[/C][/ROW]
[ROW][C]R-squared[/C][C]0.189739299026262[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.157111351336045[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]5.81523854419901[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]149[/C][/ROW]
[ROW][C]p-value[/C][C]1.81317053578045e-05[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]3.20737979482366[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1532.80548708822[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146389&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146389&T=3

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Regression Statistics
Multiple R0.435590747177052
R-squared0.189739299026262
Adjusted R-squared0.157111351336045
F-TEST (value)5.81523854419901
F-TEST (DF numerator)6
F-TEST (DF denominator)149
p-value1.81317053578045e-05
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.20737979482366
Sum Squared Residuals1532.80548708822







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11410.00163377662033.99836622337973
2811.1388767125367-3.1388767125367
31211.62835039900990.371649600990115
4712.3870676214146-5.38706762141464
51012.5787635846554-2.57876358465543
6911.3795817459199-2.37958174591991
71614.11918543545291.88081456454709
879.24654915931306-2.24654915931306
91411.98158979425122.01841020574883
10610.4909357443566-4.49093574435662
111611.80109696500344.19890303499664
121111.0854192680717-0.0854192680716782
131713.15234932014523.8476506798548
141211.23248967082260.767510329177406
15711.9041294128217-4.90412941282168
161310.96491936116912.03508063883089
1797.107792451916481.89220754808352
181514.04894907723650.951050922763542
19710.4537907608281-3.4537907608281
2099.91966778227973-0.919667782279734
2178.65838514636451-1.65838514636451
221413.45348448377530.546515516224724
231510.27720510103024.72279489896976
24712.5814593683838-5.58145936838377
251311.87931171140261.12068828859737
261711.88489847900265.11510152099742
271513.41608660824081.58391339175924
28148.695468171634095.30453182836591
291412.61503696610421.38496303389584
30810.9351268296402-2.93512682964019
3189.35604419626825-1.35604419626824
321213.5093521597748-1.50935215977475
331412.79367446284311.20632553715694
34812.1212589842499-4.12125898424986
351110.00346929627890.996530703721054
361611.59998913905144.40001086094862
371112.7559175418245-1.75591754182447
38810.9860846446404-2.98608464464037
391412.0550450309631.94495496903705
401610.21124865571895.78875134428107
411411.43192211808172.56807788191829
42511.0794404488305-6.07944044883046
4389.08230839151695-1.08230839151695
441010.0655710476859-0.0655710476859349
45812.0872334983223-4.0872334983223
461312.626266337870.373733662130041
471511.27669784296793.72330215703208
48611.1436959243674-5.14369592436742
491210.00349553405181.99650446594824
501412.82854552062381.17145447937622
51512.5774389737347-7.57743897373474
521512.78065959382742.21934040617261
531111.744523341325-0.744523341324969
5489.9172450108365-1.9172450108365
551311.19922045570481.80077954429519
561412.87651226680961.12348773319044
57129.598489280336942.40151071966306
581612.43995642504673.56004357495329
59108.205841627554531.79415837244547
601514.00655866852390.993441331476054
6189.37524307815744-1.37524307815744
62168.848081701866057.15191829813395
631913.21371251422865.78628748577144
641411.32569878533612.67430121466388
65712.2452724038431-5.24527240384314
661311.81715132012431.18284867987571
671511.48887792098953.51112207901049
6879.55122994061444-2.55122994061444
691312.79976666848570.200233331514294
7049.58119449841614-5.58119449841614
711411.69833687857952.30166312142052
721312.42619080226040.573809197739597
73119.763072291618551.23692770838145
741411.76831758411722.23168241588283
751211.10287228511830.897127714881699
761513.27393299607591.72606700392414
771411.87860576372372.12139423627629
781311.12418706517571.87581293482434
7979.8081175540701-2.8081175540701
8058.32316374715342-3.32316374715342
81711.8441273152991-4.84412731529908
821313.1045112102922-0.104511210292163
831311.3806323500961.61936764990404
841112.4932320134598-1.49323201345977
8569.00441361590928-3.00441361590928
861213.0296691542219-1.02966915422189
87810.005421906714-2.00542190671405
881111.7671315350988-0.76713153509883
891214.7201055180782-2.7201055180782
9099.80582962962916-0.805829629629156
911210.49617380331621.50382619668375
921313.7473690893015-0.747369089301537
931613.22506695376062.77493304623937
941613.20241572021542.79758427978463
951113.0349453868045-2.03494538680446
96810.7822282622965-2.78222826229647
9748.34500167513553-4.34500167513553
9879.69817648420457-2.69817648420457
991411.32687746823722.67312253176284
1001111.0396988653398-0.0396988653397946
1011713.15847534403813.84152465596194
1021513.27534786216021.72465213783984
1031412.89353033196321.1064696680368
104512.062545730818-7.06254573081799
105411.5610199311346-7.56101993113457
1061912.38831588928886.6116841107112
107119.844643827524821.15535617247518
1081512.38370647653492.61629352346509
109109.451035053113480.548964946886522
110911.058373047344-2.05837304734404
111129.810251970013982.18974802998602
1121512.8502833635562.14971663644397
113713.1805118794205-6.18051187942047
1141311.64743661341481.35256338658517
1151413.25048195099640.749518049003554
1161412.38770756299781.61229243700215
1171413.75573025455370.244269745446258
118811.3890573289039-3.38905732890394
1191512.3082814308772.69171856912303
1201510.65819006371474.34180993628528
12199.98428040787879-0.984280407878788
1221612.13001030572133.86998969427866
123912.0787715544969-3.07877155449687
1241513.81485990197771.1851400980223
1251512.43798847139742.56201152860263
126612.1027982230467-6.10279822304665
127813.8037534881961-5.80375348819609
1281512.45474877419722.54525122580278
129108.518517255682591.48148274431741
130910.8815490945808-1.88154909458082
1311414.4032191681202-0.403219168120215
1321214.2242775082358-2.22427750823584
133810.5530301295392-2.55303012953925
1341112.0503032373762-1.05030323737617
1351311.48042445881811.51957554118192
136911.107442570022-2.10744257002195
1371512.92984856213632.07015143786368
138139.613051313511953.38694868648805
1391512.51620321779662.48379678220336
1401412.23057212252041.7694278774796
1411611.84582272109524.1541772789048
1421212.5329635205965-0.53296352059648
1431410.13577681275683.86422318724316
1441010.3960743847885-0.396074384788484
1451010.1768442156666-0.176844215666628
146413.3648752102187-9.36487521021874
147811.5950423130346-3.59504231303464
1481713.47684579117373.52315420882634
1491612.57207089379613.42792910620389
1501214.8219067673247-2.82190676732467
1511211.98434679206490.0156532079351452
1521513.55468624215751.44531375784247
15399.95653992094415-0.956539920944155
1541313.3373626261409-0.337362626140886
1551412.16762535697511.83237464302493
1561110.60127966724610.398720332753864

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 14 & 10.0016337766203 & 3.99836622337973 \tabularnewline
2 & 8 & 11.1388767125367 & -3.1388767125367 \tabularnewline
3 & 12 & 11.6283503990099 & 0.371649600990115 \tabularnewline
4 & 7 & 12.3870676214146 & -5.38706762141464 \tabularnewline
5 & 10 & 12.5787635846554 & -2.57876358465543 \tabularnewline
6 & 9 & 11.3795817459199 & -2.37958174591991 \tabularnewline
7 & 16 & 14.1191854354529 & 1.88081456454709 \tabularnewline
8 & 7 & 9.24654915931306 & -2.24654915931306 \tabularnewline
9 & 14 & 11.9815897942512 & 2.01841020574883 \tabularnewline
10 & 6 & 10.4909357443566 & -4.49093574435662 \tabularnewline
11 & 16 & 11.8010969650034 & 4.19890303499664 \tabularnewline
12 & 11 & 11.0854192680717 & -0.0854192680716782 \tabularnewline
13 & 17 & 13.1523493201452 & 3.8476506798548 \tabularnewline
14 & 12 & 11.2324896708226 & 0.767510329177406 \tabularnewline
15 & 7 & 11.9041294128217 & -4.90412941282168 \tabularnewline
16 & 13 & 10.9649193611691 & 2.03508063883089 \tabularnewline
17 & 9 & 7.10779245191648 & 1.89220754808352 \tabularnewline
18 & 15 & 14.0489490772365 & 0.951050922763542 \tabularnewline
19 & 7 & 10.4537907608281 & -3.4537907608281 \tabularnewline
20 & 9 & 9.91966778227973 & -0.919667782279734 \tabularnewline
21 & 7 & 8.65838514636451 & -1.65838514636451 \tabularnewline
22 & 14 & 13.4534844837753 & 0.546515516224724 \tabularnewline
23 & 15 & 10.2772051010302 & 4.72279489896976 \tabularnewline
24 & 7 & 12.5814593683838 & -5.58145936838377 \tabularnewline
25 & 13 & 11.8793117114026 & 1.12068828859737 \tabularnewline
26 & 17 & 11.8848984790026 & 5.11510152099742 \tabularnewline
27 & 15 & 13.4160866082408 & 1.58391339175924 \tabularnewline
28 & 14 & 8.69546817163409 & 5.30453182836591 \tabularnewline
29 & 14 & 12.6150369661042 & 1.38496303389584 \tabularnewline
30 & 8 & 10.9351268296402 & -2.93512682964019 \tabularnewline
31 & 8 & 9.35604419626825 & -1.35604419626824 \tabularnewline
32 & 12 & 13.5093521597748 & -1.50935215977475 \tabularnewline
33 & 14 & 12.7936744628431 & 1.20632553715694 \tabularnewline
34 & 8 & 12.1212589842499 & -4.12125898424986 \tabularnewline
35 & 11 & 10.0034692962789 & 0.996530703721054 \tabularnewline
36 & 16 & 11.5999891390514 & 4.40001086094862 \tabularnewline
37 & 11 & 12.7559175418245 & -1.75591754182447 \tabularnewline
38 & 8 & 10.9860846446404 & -2.98608464464037 \tabularnewline
39 & 14 & 12.055045030963 & 1.94495496903705 \tabularnewline
40 & 16 & 10.2112486557189 & 5.78875134428107 \tabularnewline
41 & 14 & 11.4319221180817 & 2.56807788191829 \tabularnewline
42 & 5 & 11.0794404488305 & -6.07944044883046 \tabularnewline
43 & 8 & 9.08230839151695 & -1.08230839151695 \tabularnewline
44 & 10 & 10.0655710476859 & -0.0655710476859349 \tabularnewline
45 & 8 & 12.0872334983223 & -4.0872334983223 \tabularnewline
46 & 13 & 12.62626633787 & 0.373733662130041 \tabularnewline
47 & 15 & 11.2766978429679 & 3.72330215703208 \tabularnewline
48 & 6 & 11.1436959243674 & -5.14369592436742 \tabularnewline
49 & 12 & 10.0034955340518 & 1.99650446594824 \tabularnewline
50 & 14 & 12.8285455206238 & 1.17145447937622 \tabularnewline
51 & 5 & 12.5774389737347 & -7.57743897373474 \tabularnewline
52 & 15 & 12.7806595938274 & 2.21934040617261 \tabularnewline
53 & 11 & 11.744523341325 & -0.744523341324969 \tabularnewline
54 & 8 & 9.9172450108365 & -1.9172450108365 \tabularnewline
55 & 13 & 11.1992204557048 & 1.80077954429519 \tabularnewline
56 & 14 & 12.8765122668096 & 1.12348773319044 \tabularnewline
57 & 12 & 9.59848928033694 & 2.40151071966306 \tabularnewline
58 & 16 & 12.4399564250467 & 3.56004357495329 \tabularnewline
59 & 10 & 8.20584162755453 & 1.79415837244547 \tabularnewline
60 & 15 & 14.0065586685239 & 0.993441331476054 \tabularnewline
61 & 8 & 9.37524307815744 & -1.37524307815744 \tabularnewline
62 & 16 & 8.84808170186605 & 7.15191829813395 \tabularnewline
63 & 19 & 13.2137125142286 & 5.78628748577144 \tabularnewline
64 & 14 & 11.3256987853361 & 2.67430121466388 \tabularnewline
65 & 7 & 12.2452724038431 & -5.24527240384314 \tabularnewline
66 & 13 & 11.8171513201243 & 1.18284867987571 \tabularnewline
67 & 15 & 11.4888779209895 & 3.51112207901049 \tabularnewline
68 & 7 & 9.55122994061444 & -2.55122994061444 \tabularnewline
69 & 13 & 12.7997666684857 & 0.200233331514294 \tabularnewline
70 & 4 & 9.58119449841614 & -5.58119449841614 \tabularnewline
71 & 14 & 11.6983368785795 & 2.30166312142052 \tabularnewline
72 & 13 & 12.4261908022604 & 0.573809197739597 \tabularnewline
73 & 11 & 9.76307229161855 & 1.23692770838145 \tabularnewline
74 & 14 & 11.7683175841172 & 2.23168241588283 \tabularnewline
75 & 12 & 11.1028722851183 & 0.897127714881699 \tabularnewline
76 & 15 & 13.2739329960759 & 1.72606700392414 \tabularnewline
77 & 14 & 11.8786057637237 & 2.12139423627629 \tabularnewline
78 & 13 & 11.1241870651757 & 1.87581293482434 \tabularnewline
79 & 7 & 9.8081175540701 & -2.8081175540701 \tabularnewline
80 & 5 & 8.32316374715342 & -3.32316374715342 \tabularnewline
81 & 7 & 11.8441273152991 & -4.84412731529908 \tabularnewline
82 & 13 & 13.1045112102922 & -0.104511210292163 \tabularnewline
83 & 13 & 11.380632350096 & 1.61936764990404 \tabularnewline
84 & 11 & 12.4932320134598 & -1.49323201345977 \tabularnewline
85 & 6 & 9.00441361590928 & -3.00441361590928 \tabularnewline
86 & 12 & 13.0296691542219 & -1.02966915422189 \tabularnewline
87 & 8 & 10.005421906714 & -2.00542190671405 \tabularnewline
88 & 11 & 11.7671315350988 & -0.76713153509883 \tabularnewline
89 & 12 & 14.7201055180782 & -2.7201055180782 \tabularnewline
90 & 9 & 9.80582962962916 & -0.805829629629156 \tabularnewline
91 & 12 & 10.4961738033162 & 1.50382619668375 \tabularnewline
92 & 13 & 13.7473690893015 & -0.747369089301537 \tabularnewline
93 & 16 & 13.2250669537606 & 2.77493304623937 \tabularnewline
94 & 16 & 13.2024157202154 & 2.79758427978463 \tabularnewline
95 & 11 & 13.0349453868045 & -2.03494538680446 \tabularnewline
96 & 8 & 10.7822282622965 & -2.78222826229647 \tabularnewline
97 & 4 & 8.34500167513553 & -4.34500167513553 \tabularnewline
98 & 7 & 9.69817648420457 & -2.69817648420457 \tabularnewline
99 & 14 & 11.3268774682372 & 2.67312253176284 \tabularnewline
100 & 11 & 11.0396988653398 & -0.0396988653397946 \tabularnewline
101 & 17 & 13.1584753440381 & 3.84152465596194 \tabularnewline
102 & 15 & 13.2753478621602 & 1.72465213783984 \tabularnewline
103 & 14 & 12.8935303319632 & 1.1064696680368 \tabularnewline
104 & 5 & 12.062545730818 & -7.06254573081799 \tabularnewline
105 & 4 & 11.5610199311346 & -7.56101993113457 \tabularnewline
106 & 19 & 12.3883158892888 & 6.6116841107112 \tabularnewline
107 & 11 & 9.84464382752482 & 1.15535617247518 \tabularnewline
108 & 15 & 12.3837064765349 & 2.61629352346509 \tabularnewline
109 & 10 & 9.45103505311348 & 0.548964946886522 \tabularnewline
110 & 9 & 11.058373047344 & -2.05837304734404 \tabularnewline
111 & 12 & 9.81025197001398 & 2.18974802998602 \tabularnewline
112 & 15 & 12.850283363556 & 2.14971663644397 \tabularnewline
113 & 7 & 13.1805118794205 & -6.18051187942047 \tabularnewline
114 & 13 & 11.6474366134148 & 1.35256338658517 \tabularnewline
115 & 14 & 13.2504819509964 & 0.749518049003554 \tabularnewline
116 & 14 & 12.3877075629978 & 1.61229243700215 \tabularnewline
117 & 14 & 13.7557302545537 & 0.244269745446258 \tabularnewline
118 & 8 & 11.3890573289039 & -3.38905732890394 \tabularnewline
119 & 15 & 12.308281430877 & 2.69171856912303 \tabularnewline
120 & 15 & 10.6581900637147 & 4.34180993628528 \tabularnewline
121 & 9 & 9.98428040787879 & -0.984280407878788 \tabularnewline
122 & 16 & 12.1300103057213 & 3.86998969427866 \tabularnewline
123 & 9 & 12.0787715544969 & -3.07877155449687 \tabularnewline
124 & 15 & 13.8148599019777 & 1.1851400980223 \tabularnewline
125 & 15 & 12.4379884713974 & 2.56201152860263 \tabularnewline
126 & 6 & 12.1027982230467 & -6.10279822304665 \tabularnewline
127 & 8 & 13.8037534881961 & -5.80375348819609 \tabularnewline
128 & 15 & 12.4547487741972 & 2.54525122580278 \tabularnewline
129 & 10 & 8.51851725568259 & 1.48148274431741 \tabularnewline
130 & 9 & 10.8815490945808 & -1.88154909458082 \tabularnewline
131 & 14 & 14.4032191681202 & -0.403219168120215 \tabularnewline
132 & 12 & 14.2242775082358 & -2.22427750823584 \tabularnewline
133 & 8 & 10.5530301295392 & -2.55303012953925 \tabularnewline
134 & 11 & 12.0503032373762 & -1.05030323737617 \tabularnewline
135 & 13 & 11.4804244588181 & 1.51957554118192 \tabularnewline
136 & 9 & 11.107442570022 & -2.10744257002195 \tabularnewline
137 & 15 & 12.9298485621363 & 2.07015143786368 \tabularnewline
138 & 13 & 9.61305131351195 & 3.38694868648805 \tabularnewline
139 & 15 & 12.5162032177966 & 2.48379678220336 \tabularnewline
140 & 14 & 12.2305721225204 & 1.7694278774796 \tabularnewline
141 & 16 & 11.8458227210952 & 4.1541772789048 \tabularnewline
142 & 12 & 12.5329635205965 & -0.53296352059648 \tabularnewline
143 & 14 & 10.1357768127568 & 3.86422318724316 \tabularnewline
144 & 10 & 10.3960743847885 & -0.396074384788484 \tabularnewline
145 & 10 & 10.1768442156666 & -0.176844215666628 \tabularnewline
146 & 4 & 13.3648752102187 & -9.36487521021874 \tabularnewline
147 & 8 & 11.5950423130346 & -3.59504231303464 \tabularnewline
148 & 17 & 13.4768457911737 & 3.52315420882634 \tabularnewline
149 & 16 & 12.5720708937961 & 3.42792910620389 \tabularnewline
150 & 12 & 14.8219067673247 & -2.82190676732467 \tabularnewline
151 & 12 & 11.9843467920649 & 0.0156532079351452 \tabularnewline
152 & 15 & 13.5546862421575 & 1.44531375784247 \tabularnewline
153 & 9 & 9.95653992094415 & -0.956539920944155 \tabularnewline
154 & 13 & 13.3373626261409 & -0.337362626140886 \tabularnewline
155 & 14 & 12.1676253569751 & 1.83237464302493 \tabularnewline
156 & 11 & 10.6012796672461 & 0.398720332753864 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146389&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]14[/C][C]10.0016337766203[/C][C]3.99836622337973[/C][/ROW]
[ROW][C]2[/C][C]8[/C][C]11.1388767125367[/C][C]-3.1388767125367[/C][/ROW]
[ROW][C]3[/C][C]12[/C][C]11.6283503990099[/C][C]0.371649600990115[/C][/ROW]
[ROW][C]4[/C][C]7[/C][C]12.3870676214146[/C][C]-5.38706762141464[/C][/ROW]
[ROW][C]5[/C][C]10[/C][C]12.5787635846554[/C][C]-2.57876358465543[/C][/ROW]
[ROW][C]6[/C][C]9[/C][C]11.3795817459199[/C][C]-2.37958174591991[/C][/ROW]
[ROW][C]7[/C][C]16[/C][C]14.1191854354529[/C][C]1.88081456454709[/C][/ROW]
[ROW][C]8[/C][C]7[/C][C]9.24654915931306[/C][C]-2.24654915931306[/C][/ROW]
[ROW][C]9[/C][C]14[/C][C]11.9815897942512[/C][C]2.01841020574883[/C][/ROW]
[ROW][C]10[/C][C]6[/C][C]10.4909357443566[/C][C]-4.49093574435662[/C][/ROW]
[ROW][C]11[/C][C]16[/C][C]11.8010969650034[/C][C]4.19890303499664[/C][/ROW]
[ROW][C]12[/C][C]11[/C][C]11.0854192680717[/C][C]-0.0854192680716782[/C][/ROW]
[ROW][C]13[/C][C]17[/C][C]13.1523493201452[/C][C]3.8476506798548[/C][/ROW]
[ROW][C]14[/C][C]12[/C][C]11.2324896708226[/C][C]0.767510329177406[/C][/ROW]
[ROW][C]15[/C][C]7[/C][C]11.9041294128217[/C][C]-4.90412941282168[/C][/ROW]
[ROW][C]16[/C][C]13[/C][C]10.9649193611691[/C][C]2.03508063883089[/C][/ROW]
[ROW][C]17[/C][C]9[/C][C]7.10779245191648[/C][C]1.89220754808352[/C][/ROW]
[ROW][C]18[/C][C]15[/C][C]14.0489490772365[/C][C]0.951050922763542[/C][/ROW]
[ROW][C]19[/C][C]7[/C][C]10.4537907608281[/C][C]-3.4537907608281[/C][/ROW]
[ROW][C]20[/C][C]9[/C][C]9.91966778227973[/C][C]-0.919667782279734[/C][/ROW]
[ROW][C]21[/C][C]7[/C][C]8.65838514636451[/C][C]-1.65838514636451[/C][/ROW]
[ROW][C]22[/C][C]14[/C][C]13.4534844837753[/C][C]0.546515516224724[/C][/ROW]
[ROW][C]23[/C][C]15[/C][C]10.2772051010302[/C][C]4.72279489896976[/C][/ROW]
[ROW][C]24[/C][C]7[/C][C]12.5814593683838[/C][C]-5.58145936838377[/C][/ROW]
[ROW][C]25[/C][C]13[/C][C]11.8793117114026[/C][C]1.12068828859737[/C][/ROW]
[ROW][C]26[/C][C]17[/C][C]11.8848984790026[/C][C]5.11510152099742[/C][/ROW]
[ROW][C]27[/C][C]15[/C][C]13.4160866082408[/C][C]1.58391339175924[/C][/ROW]
[ROW][C]28[/C][C]14[/C][C]8.69546817163409[/C][C]5.30453182836591[/C][/ROW]
[ROW][C]29[/C][C]14[/C][C]12.6150369661042[/C][C]1.38496303389584[/C][/ROW]
[ROW][C]30[/C][C]8[/C][C]10.9351268296402[/C][C]-2.93512682964019[/C][/ROW]
[ROW][C]31[/C][C]8[/C][C]9.35604419626825[/C][C]-1.35604419626824[/C][/ROW]
[ROW][C]32[/C][C]12[/C][C]13.5093521597748[/C][C]-1.50935215977475[/C][/ROW]
[ROW][C]33[/C][C]14[/C][C]12.7936744628431[/C][C]1.20632553715694[/C][/ROW]
[ROW][C]34[/C][C]8[/C][C]12.1212589842499[/C][C]-4.12125898424986[/C][/ROW]
[ROW][C]35[/C][C]11[/C][C]10.0034692962789[/C][C]0.996530703721054[/C][/ROW]
[ROW][C]36[/C][C]16[/C][C]11.5999891390514[/C][C]4.40001086094862[/C][/ROW]
[ROW][C]37[/C][C]11[/C][C]12.7559175418245[/C][C]-1.75591754182447[/C][/ROW]
[ROW][C]38[/C][C]8[/C][C]10.9860846446404[/C][C]-2.98608464464037[/C][/ROW]
[ROW][C]39[/C][C]14[/C][C]12.055045030963[/C][C]1.94495496903705[/C][/ROW]
[ROW][C]40[/C][C]16[/C][C]10.2112486557189[/C][C]5.78875134428107[/C][/ROW]
[ROW][C]41[/C][C]14[/C][C]11.4319221180817[/C][C]2.56807788191829[/C][/ROW]
[ROW][C]42[/C][C]5[/C][C]11.0794404488305[/C][C]-6.07944044883046[/C][/ROW]
[ROW][C]43[/C][C]8[/C][C]9.08230839151695[/C][C]-1.08230839151695[/C][/ROW]
[ROW][C]44[/C][C]10[/C][C]10.0655710476859[/C][C]-0.0655710476859349[/C][/ROW]
[ROW][C]45[/C][C]8[/C][C]12.0872334983223[/C][C]-4.0872334983223[/C][/ROW]
[ROW][C]46[/C][C]13[/C][C]12.62626633787[/C][C]0.373733662130041[/C][/ROW]
[ROW][C]47[/C][C]15[/C][C]11.2766978429679[/C][C]3.72330215703208[/C][/ROW]
[ROW][C]48[/C][C]6[/C][C]11.1436959243674[/C][C]-5.14369592436742[/C][/ROW]
[ROW][C]49[/C][C]12[/C][C]10.0034955340518[/C][C]1.99650446594824[/C][/ROW]
[ROW][C]50[/C][C]14[/C][C]12.8285455206238[/C][C]1.17145447937622[/C][/ROW]
[ROW][C]51[/C][C]5[/C][C]12.5774389737347[/C][C]-7.57743897373474[/C][/ROW]
[ROW][C]52[/C][C]15[/C][C]12.7806595938274[/C][C]2.21934040617261[/C][/ROW]
[ROW][C]53[/C][C]11[/C][C]11.744523341325[/C][C]-0.744523341324969[/C][/ROW]
[ROW][C]54[/C][C]8[/C][C]9.9172450108365[/C][C]-1.9172450108365[/C][/ROW]
[ROW][C]55[/C][C]13[/C][C]11.1992204557048[/C][C]1.80077954429519[/C][/ROW]
[ROW][C]56[/C][C]14[/C][C]12.8765122668096[/C][C]1.12348773319044[/C][/ROW]
[ROW][C]57[/C][C]12[/C][C]9.59848928033694[/C][C]2.40151071966306[/C][/ROW]
[ROW][C]58[/C][C]16[/C][C]12.4399564250467[/C][C]3.56004357495329[/C][/ROW]
[ROW][C]59[/C][C]10[/C][C]8.20584162755453[/C][C]1.79415837244547[/C][/ROW]
[ROW][C]60[/C][C]15[/C][C]14.0065586685239[/C][C]0.993441331476054[/C][/ROW]
[ROW][C]61[/C][C]8[/C][C]9.37524307815744[/C][C]-1.37524307815744[/C][/ROW]
[ROW][C]62[/C][C]16[/C][C]8.84808170186605[/C][C]7.15191829813395[/C][/ROW]
[ROW][C]63[/C][C]19[/C][C]13.2137125142286[/C][C]5.78628748577144[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]11.3256987853361[/C][C]2.67430121466388[/C][/ROW]
[ROW][C]65[/C][C]7[/C][C]12.2452724038431[/C][C]-5.24527240384314[/C][/ROW]
[ROW][C]66[/C][C]13[/C][C]11.8171513201243[/C][C]1.18284867987571[/C][/ROW]
[ROW][C]67[/C][C]15[/C][C]11.4888779209895[/C][C]3.51112207901049[/C][/ROW]
[ROW][C]68[/C][C]7[/C][C]9.55122994061444[/C][C]-2.55122994061444[/C][/ROW]
[ROW][C]69[/C][C]13[/C][C]12.7997666684857[/C][C]0.200233331514294[/C][/ROW]
[ROW][C]70[/C][C]4[/C][C]9.58119449841614[/C][C]-5.58119449841614[/C][/ROW]
[ROW][C]71[/C][C]14[/C][C]11.6983368785795[/C][C]2.30166312142052[/C][/ROW]
[ROW][C]72[/C][C]13[/C][C]12.4261908022604[/C][C]0.573809197739597[/C][/ROW]
[ROW][C]73[/C][C]11[/C][C]9.76307229161855[/C][C]1.23692770838145[/C][/ROW]
[ROW][C]74[/C][C]14[/C][C]11.7683175841172[/C][C]2.23168241588283[/C][/ROW]
[ROW][C]75[/C][C]12[/C][C]11.1028722851183[/C][C]0.897127714881699[/C][/ROW]
[ROW][C]76[/C][C]15[/C][C]13.2739329960759[/C][C]1.72606700392414[/C][/ROW]
[ROW][C]77[/C][C]14[/C][C]11.8786057637237[/C][C]2.12139423627629[/C][/ROW]
[ROW][C]78[/C][C]13[/C][C]11.1241870651757[/C][C]1.87581293482434[/C][/ROW]
[ROW][C]79[/C][C]7[/C][C]9.8081175540701[/C][C]-2.8081175540701[/C][/ROW]
[ROW][C]80[/C][C]5[/C][C]8.32316374715342[/C][C]-3.32316374715342[/C][/ROW]
[ROW][C]81[/C][C]7[/C][C]11.8441273152991[/C][C]-4.84412731529908[/C][/ROW]
[ROW][C]82[/C][C]13[/C][C]13.1045112102922[/C][C]-0.104511210292163[/C][/ROW]
[ROW][C]83[/C][C]13[/C][C]11.380632350096[/C][C]1.61936764990404[/C][/ROW]
[ROW][C]84[/C][C]11[/C][C]12.4932320134598[/C][C]-1.49323201345977[/C][/ROW]
[ROW][C]85[/C][C]6[/C][C]9.00441361590928[/C][C]-3.00441361590928[/C][/ROW]
[ROW][C]86[/C][C]12[/C][C]13.0296691542219[/C][C]-1.02966915422189[/C][/ROW]
[ROW][C]87[/C][C]8[/C][C]10.005421906714[/C][C]-2.00542190671405[/C][/ROW]
[ROW][C]88[/C][C]11[/C][C]11.7671315350988[/C][C]-0.76713153509883[/C][/ROW]
[ROW][C]89[/C][C]12[/C][C]14.7201055180782[/C][C]-2.7201055180782[/C][/ROW]
[ROW][C]90[/C][C]9[/C][C]9.80582962962916[/C][C]-0.805829629629156[/C][/ROW]
[ROW][C]91[/C][C]12[/C][C]10.4961738033162[/C][C]1.50382619668375[/C][/ROW]
[ROW][C]92[/C][C]13[/C][C]13.7473690893015[/C][C]-0.747369089301537[/C][/ROW]
[ROW][C]93[/C][C]16[/C][C]13.2250669537606[/C][C]2.77493304623937[/C][/ROW]
[ROW][C]94[/C][C]16[/C][C]13.2024157202154[/C][C]2.79758427978463[/C][/ROW]
[ROW][C]95[/C][C]11[/C][C]13.0349453868045[/C][C]-2.03494538680446[/C][/ROW]
[ROW][C]96[/C][C]8[/C][C]10.7822282622965[/C][C]-2.78222826229647[/C][/ROW]
[ROW][C]97[/C][C]4[/C][C]8.34500167513553[/C][C]-4.34500167513553[/C][/ROW]
[ROW][C]98[/C][C]7[/C][C]9.69817648420457[/C][C]-2.69817648420457[/C][/ROW]
[ROW][C]99[/C][C]14[/C][C]11.3268774682372[/C][C]2.67312253176284[/C][/ROW]
[ROW][C]100[/C][C]11[/C][C]11.0396988653398[/C][C]-0.0396988653397946[/C][/ROW]
[ROW][C]101[/C][C]17[/C][C]13.1584753440381[/C][C]3.84152465596194[/C][/ROW]
[ROW][C]102[/C][C]15[/C][C]13.2753478621602[/C][C]1.72465213783984[/C][/ROW]
[ROW][C]103[/C][C]14[/C][C]12.8935303319632[/C][C]1.1064696680368[/C][/ROW]
[ROW][C]104[/C][C]5[/C][C]12.062545730818[/C][C]-7.06254573081799[/C][/ROW]
[ROW][C]105[/C][C]4[/C][C]11.5610199311346[/C][C]-7.56101993113457[/C][/ROW]
[ROW][C]106[/C][C]19[/C][C]12.3883158892888[/C][C]6.6116841107112[/C][/ROW]
[ROW][C]107[/C][C]11[/C][C]9.84464382752482[/C][C]1.15535617247518[/C][/ROW]
[ROW][C]108[/C][C]15[/C][C]12.3837064765349[/C][C]2.61629352346509[/C][/ROW]
[ROW][C]109[/C][C]10[/C][C]9.45103505311348[/C][C]0.548964946886522[/C][/ROW]
[ROW][C]110[/C][C]9[/C][C]11.058373047344[/C][C]-2.05837304734404[/C][/ROW]
[ROW][C]111[/C][C]12[/C][C]9.81025197001398[/C][C]2.18974802998602[/C][/ROW]
[ROW][C]112[/C][C]15[/C][C]12.850283363556[/C][C]2.14971663644397[/C][/ROW]
[ROW][C]113[/C][C]7[/C][C]13.1805118794205[/C][C]-6.18051187942047[/C][/ROW]
[ROW][C]114[/C][C]13[/C][C]11.6474366134148[/C][C]1.35256338658517[/C][/ROW]
[ROW][C]115[/C][C]14[/C][C]13.2504819509964[/C][C]0.749518049003554[/C][/ROW]
[ROW][C]116[/C][C]14[/C][C]12.3877075629978[/C][C]1.61229243700215[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]13.7557302545537[/C][C]0.244269745446258[/C][/ROW]
[ROW][C]118[/C][C]8[/C][C]11.3890573289039[/C][C]-3.38905732890394[/C][/ROW]
[ROW][C]119[/C][C]15[/C][C]12.308281430877[/C][C]2.69171856912303[/C][/ROW]
[ROW][C]120[/C][C]15[/C][C]10.6581900637147[/C][C]4.34180993628528[/C][/ROW]
[ROW][C]121[/C][C]9[/C][C]9.98428040787879[/C][C]-0.984280407878788[/C][/ROW]
[ROW][C]122[/C][C]16[/C][C]12.1300103057213[/C][C]3.86998969427866[/C][/ROW]
[ROW][C]123[/C][C]9[/C][C]12.0787715544969[/C][C]-3.07877155449687[/C][/ROW]
[ROW][C]124[/C][C]15[/C][C]13.8148599019777[/C][C]1.1851400980223[/C][/ROW]
[ROW][C]125[/C][C]15[/C][C]12.4379884713974[/C][C]2.56201152860263[/C][/ROW]
[ROW][C]126[/C][C]6[/C][C]12.1027982230467[/C][C]-6.10279822304665[/C][/ROW]
[ROW][C]127[/C][C]8[/C][C]13.8037534881961[/C][C]-5.80375348819609[/C][/ROW]
[ROW][C]128[/C][C]15[/C][C]12.4547487741972[/C][C]2.54525122580278[/C][/ROW]
[ROW][C]129[/C][C]10[/C][C]8.51851725568259[/C][C]1.48148274431741[/C][/ROW]
[ROW][C]130[/C][C]9[/C][C]10.8815490945808[/C][C]-1.88154909458082[/C][/ROW]
[ROW][C]131[/C][C]14[/C][C]14.4032191681202[/C][C]-0.403219168120215[/C][/ROW]
[ROW][C]132[/C][C]12[/C][C]14.2242775082358[/C][C]-2.22427750823584[/C][/ROW]
[ROW][C]133[/C][C]8[/C][C]10.5530301295392[/C][C]-2.55303012953925[/C][/ROW]
[ROW][C]134[/C][C]11[/C][C]12.0503032373762[/C][C]-1.05030323737617[/C][/ROW]
[ROW][C]135[/C][C]13[/C][C]11.4804244588181[/C][C]1.51957554118192[/C][/ROW]
[ROW][C]136[/C][C]9[/C][C]11.107442570022[/C][C]-2.10744257002195[/C][/ROW]
[ROW][C]137[/C][C]15[/C][C]12.9298485621363[/C][C]2.07015143786368[/C][/ROW]
[ROW][C]138[/C][C]13[/C][C]9.61305131351195[/C][C]3.38694868648805[/C][/ROW]
[ROW][C]139[/C][C]15[/C][C]12.5162032177966[/C][C]2.48379678220336[/C][/ROW]
[ROW][C]140[/C][C]14[/C][C]12.2305721225204[/C][C]1.7694278774796[/C][/ROW]
[ROW][C]141[/C][C]16[/C][C]11.8458227210952[/C][C]4.1541772789048[/C][/ROW]
[ROW][C]142[/C][C]12[/C][C]12.5329635205965[/C][C]-0.53296352059648[/C][/ROW]
[ROW][C]143[/C][C]14[/C][C]10.1357768127568[/C][C]3.86422318724316[/C][/ROW]
[ROW][C]144[/C][C]10[/C][C]10.3960743847885[/C][C]-0.396074384788484[/C][/ROW]
[ROW][C]145[/C][C]10[/C][C]10.1768442156666[/C][C]-0.176844215666628[/C][/ROW]
[ROW][C]146[/C][C]4[/C][C]13.3648752102187[/C][C]-9.36487521021874[/C][/ROW]
[ROW][C]147[/C][C]8[/C][C]11.5950423130346[/C][C]-3.59504231303464[/C][/ROW]
[ROW][C]148[/C][C]17[/C][C]13.4768457911737[/C][C]3.52315420882634[/C][/ROW]
[ROW][C]149[/C][C]16[/C][C]12.5720708937961[/C][C]3.42792910620389[/C][/ROW]
[ROW][C]150[/C][C]12[/C][C]14.8219067673247[/C][C]-2.82190676732467[/C][/ROW]
[ROW][C]151[/C][C]12[/C][C]11.9843467920649[/C][C]0.0156532079351452[/C][/ROW]
[ROW][C]152[/C][C]15[/C][C]13.5546862421575[/C][C]1.44531375784247[/C][/ROW]
[ROW][C]153[/C][C]9[/C][C]9.95653992094415[/C][C]-0.956539920944155[/C][/ROW]
[ROW][C]154[/C][C]13[/C][C]13.3373626261409[/C][C]-0.337362626140886[/C][/ROW]
[ROW][C]155[/C][C]14[/C][C]12.1676253569751[/C][C]1.83237464302493[/C][/ROW]
[ROW][C]156[/C][C]11[/C][C]10.6012796672461[/C][C]0.398720332753864[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146389&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146389&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11410.00163377662033.99836622337973
2811.1388767125367-3.1388767125367
31211.62835039900990.371649600990115
4712.3870676214146-5.38706762141464
51012.5787635846554-2.57876358465543
6911.3795817459199-2.37958174591991
71614.11918543545291.88081456454709
879.24654915931306-2.24654915931306
91411.98158979425122.01841020574883
10610.4909357443566-4.49093574435662
111611.80109696500344.19890303499664
121111.0854192680717-0.0854192680716782
131713.15234932014523.8476506798548
141211.23248967082260.767510329177406
15711.9041294128217-4.90412941282168
161310.96491936116912.03508063883089
1797.107792451916481.89220754808352
181514.04894907723650.951050922763542
19710.4537907608281-3.4537907608281
2099.91966778227973-0.919667782279734
2178.65838514636451-1.65838514636451
221413.45348448377530.546515516224724
231510.27720510103024.72279489896976
24712.5814593683838-5.58145936838377
251311.87931171140261.12068828859737
261711.88489847900265.11510152099742
271513.41608660824081.58391339175924
28148.695468171634095.30453182836591
291412.61503696610421.38496303389584
30810.9351268296402-2.93512682964019
3189.35604419626825-1.35604419626824
321213.5093521597748-1.50935215977475
331412.79367446284311.20632553715694
34812.1212589842499-4.12125898424986
351110.00346929627890.996530703721054
361611.59998913905144.40001086094862
371112.7559175418245-1.75591754182447
38810.9860846446404-2.98608464464037
391412.0550450309631.94495496903705
401610.21124865571895.78875134428107
411411.43192211808172.56807788191829
42511.0794404488305-6.07944044883046
4389.08230839151695-1.08230839151695
441010.0655710476859-0.0655710476859349
45812.0872334983223-4.0872334983223
461312.626266337870.373733662130041
471511.27669784296793.72330215703208
48611.1436959243674-5.14369592436742
491210.00349553405181.99650446594824
501412.82854552062381.17145447937622
51512.5774389737347-7.57743897373474
521512.78065959382742.21934040617261
531111.744523341325-0.744523341324969
5489.9172450108365-1.9172450108365
551311.19922045570481.80077954429519
561412.87651226680961.12348773319044
57129.598489280336942.40151071966306
581612.43995642504673.56004357495329
59108.205841627554531.79415837244547
601514.00655866852390.993441331476054
6189.37524307815744-1.37524307815744
62168.848081701866057.15191829813395
631913.21371251422865.78628748577144
641411.32569878533612.67430121466388
65712.2452724038431-5.24527240384314
661311.81715132012431.18284867987571
671511.48887792098953.51112207901049
6879.55122994061444-2.55122994061444
691312.79976666848570.200233331514294
7049.58119449841614-5.58119449841614
711411.69833687857952.30166312142052
721312.42619080226040.573809197739597
73119.763072291618551.23692770838145
741411.76831758411722.23168241588283
751211.10287228511830.897127714881699
761513.27393299607591.72606700392414
771411.87860576372372.12139423627629
781311.12418706517571.87581293482434
7979.8081175540701-2.8081175540701
8058.32316374715342-3.32316374715342
81711.8441273152991-4.84412731529908
821313.1045112102922-0.104511210292163
831311.3806323500961.61936764990404
841112.4932320134598-1.49323201345977
8569.00441361590928-3.00441361590928
861213.0296691542219-1.02966915422189
87810.005421906714-2.00542190671405
881111.7671315350988-0.76713153509883
891214.7201055180782-2.7201055180782
9099.80582962962916-0.805829629629156
911210.49617380331621.50382619668375
921313.7473690893015-0.747369089301537
931613.22506695376062.77493304623937
941613.20241572021542.79758427978463
951113.0349453868045-2.03494538680446
96810.7822282622965-2.78222826229647
9748.34500167513553-4.34500167513553
9879.69817648420457-2.69817648420457
991411.32687746823722.67312253176284
1001111.0396988653398-0.0396988653397946
1011713.15847534403813.84152465596194
1021513.27534786216021.72465213783984
1031412.89353033196321.1064696680368
104512.062545730818-7.06254573081799
105411.5610199311346-7.56101993113457
1061912.38831588928886.6116841107112
107119.844643827524821.15535617247518
1081512.38370647653492.61629352346509
109109.451035053113480.548964946886522
110911.058373047344-2.05837304734404
111129.810251970013982.18974802998602
1121512.8502833635562.14971663644397
113713.1805118794205-6.18051187942047
1141311.64743661341481.35256338658517
1151413.25048195099640.749518049003554
1161412.38770756299781.61229243700215
1171413.75573025455370.244269745446258
118811.3890573289039-3.38905732890394
1191512.3082814308772.69171856912303
1201510.65819006371474.34180993628528
12199.98428040787879-0.984280407878788
1221612.13001030572133.86998969427866
123912.0787715544969-3.07877155449687
1241513.81485990197771.1851400980223
1251512.43798847139742.56201152860263
126612.1027982230467-6.10279822304665
127813.8037534881961-5.80375348819609
1281512.45474877419722.54525122580278
129108.518517255682591.48148274431741
130910.8815490945808-1.88154909458082
1311414.4032191681202-0.403219168120215
1321214.2242775082358-2.22427750823584
133810.5530301295392-2.55303012953925
1341112.0503032373762-1.05030323737617
1351311.48042445881811.51957554118192
136911.107442570022-2.10744257002195
1371512.92984856213632.07015143786368
138139.613051313511953.38694868648805
1391512.51620321779662.48379678220336
1401412.23057212252041.7694278774796
1411611.84582272109524.1541772789048
1421212.5329635205965-0.53296352059648
1431410.13577681275683.86422318724316
1441010.3960743847885-0.396074384788484
1451010.1768442156666-0.176844215666628
146413.3648752102187-9.36487521021874
147811.5950423130346-3.59504231303464
1481713.47684579117373.52315420882634
1491612.57207089379613.42792910620389
1501214.8219067673247-2.82190676732467
1511211.98434679206490.0156532079351452
1521513.55468624215751.44531375784247
15399.95653992094415-0.956539920944155
1541313.3373626261409-0.337362626140886
1551412.16762535697511.83237464302493
1561110.60127966724610.398720332753864







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.8719442297376880.2561115405246230.128055770262312
110.9039688614430270.1920622771139460.0960311385569732
120.8468079136256540.3063841727486930.153192086374346
130.7787362197787790.4425275604424410.221263780221221
140.6857937600057330.6284124799885340.314206239994267
150.8774081867291470.2451836265417070.122591813270853
160.8585267808397110.2829464383205770.141473219160289
170.8339695408696440.3320609182607110.166030459130356
180.7731393546949470.4537212906101060.226860645305053
190.7647240994297910.4705518011404190.235275900570209
200.7018475989390760.5963048021218480.298152401060924
210.6330874317034370.7338251365931250.366912568296563
220.5582165498261760.8835669003476480.441783450173824
230.6107153247558320.7785693504883370.389284675244168
240.7539764789770120.4920470420459760.246023521022988
250.7006949540101750.5986100919796490.299305045989825
260.7499470326776990.5001059346446020.250052967322301
270.6951286321953920.6097427356092170.304871367804608
280.7262449231377090.5475101537245810.273755076862291
290.6711115326853810.6577769346292390.328888467314619
300.7094391520328180.5811216959343650.290560847967182
310.6756545537141050.6486908925717890.324345446285895
320.6533804576959060.6932390846081880.346619542304094
330.5964710675750350.807057864849930.403528932424965
340.6135991287738070.7728017424523860.386400871226193
350.5557926836658560.8884146326682870.444207316334144
360.5601280002250830.8797439995498350.439871999774917
370.5145181609795820.9709636780408370.485481839020418
380.5284061176942950.9431877646114110.471593882305705
390.4755383001812560.9510766003625120.524461699818744
400.5783234794464760.8433530411070480.421676520553524
410.564249826307220.871500347385560.43575017369278
420.6941632110458180.6116735779083630.305836788954182
430.6611258379706070.6777483240587850.338874162029393
440.6100859353810580.7798281292378840.389914064618942
450.6224342678468890.7551314643062220.377565732153111
460.5739238074994230.8521523850011530.426076192500577
470.5498209222759690.9003581554480610.450179077724031
480.6130708624383580.7738582751232840.386929137561642
490.5758055716893530.8483888566212930.424194428310647
500.552130059614260.895739880771480.44786994038574
510.8000513633314770.3998972733370450.199948636668523
520.7928581411103340.4142837177793330.207141858889666
530.756553287003420.4868934259931610.24344671299658
540.734317923876530.531364152246940.26568207612347
550.7034107057600360.5931785884799280.296589294239964
560.668874159834130.662251680331740.33112584016587
570.6459712509375070.7080574981249860.354028749062493
580.6648529472334020.6702941055331950.335147052766597
590.6280660326040020.7438679347919960.371933967395998
600.5840541500886140.8318916998227710.415945849911386
610.5475696458668660.9048607082662690.452430354133135
620.7159858988511830.5680282022976340.284014101148817
630.7811106755223540.4377786489552920.218889324477646
640.7658618985580550.468276202883890.234138101441945
650.8295143797037940.3409712405924110.170485620296206
660.8011961311328370.3976077377343250.198803868867163
670.8006238144081120.3987523711837750.199376185591888
680.7942051396247240.4115897207505510.205794860375276
690.7600643098811560.4798713802376880.239935690118844
700.8333110622142650.333377875571470.166688937785735
710.816527245526840.3669455089463190.18347275447316
720.7835219109099340.4329561781801330.216478089090066
730.7529925683378790.4940148633242430.247007431662121
740.7310263669946490.5379472660107030.268973633005351
750.6932259365995390.6135481268009230.306774063400461
760.6635957201848240.6728085596303510.336404279815176
770.6418462872466750.716307425506650.358153712753325
780.6212339454922660.7575321090154680.378766054507734
790.6139719884316040.7720560231367920.386028011568396
800.6156302620972530.7687394758054950.384369737902747
810.6688875679493090.6622248641013820.331112432050691
820.625460343043690.749079313912620.37453965695631
830.5929924705300140.8140150589399720.407007529469986
840.5569942759371440.8860114481257120.443005724062856
850.5465145870161010.9069708259677980.453485412983899
860.5035845735092560.9928308529814870.496415426490744
870.4737241925709160.9474483851418320.526275807429084
880.4287519419373460.8575038838746920.571248058062654
890.4096478333894020.8192956667788030.590352166610598
900.3662069738908270.7324139477816530.633793026109173
910.3339514314972580.6679028629945160.666048568502742
920.2930307614998510.5860615229997030.706969238500149
930.2811354415597110.5622708831194210.718864558440289
940.2693208765087380.5386417530174760.730679123491262
950.2413363465780110.4826726931560210.758663653421989
960.2295208188994610.4590416377989220.770479181100539
970.2651720354629370.5303440709258740.734827964537063
980.2564588959173610.5129177918347220.743541104082639
990.2393168436083770.4786336872167530.760683156391623
1000.20266060078280.4053212015655990.7973393992172
1010.2200786575864010.4401573151728020.779921342413599
1020.196310952960330.392621905920660.80368904703967
1030.1709813191688410.3419626383376810.829018680831159
1040.2979585059709550.595917011941910.702041494029045
1050.5343351040099940.9313297919800130.465664895990007
1060.7057841442840970.5884317114318050.294215855715903
1070.6624173747561910.6751652504876180.337582625243809
1080.6396990628035290.7206018743929410.360300937196471
1090.5925386603286370.8149226793427260.407461339671363
1100.5655425736945840.8689148526108320.434457426305416
1110.5231890074802760.9536219850394470.476810992519724
1120.4918589928933490.9837179857866990.508141007106651
1130.6143850415073770.7712299169852450.385614958492623
1140.5668965785722510.8662068428554980.433103421427749
1150.5571238920047360.8857522159905290.442876107995264
1160.5170689199028360.9658621601943280.482931080097164
1170.4603356945387980.9206713890775960.539664305461202
1180.4715546495273360.9431092990546720.528445350472664
1190.4509402468410360.9018804936820720.549059753158964
1200.487172161723850.97434432344770.51282783827615
1210.4310078273479190.8620156546958380.568992172652081
1220.5070307968196960.9859384063606080.492969203180304
1230.466925307459370.933850614918740.53307469254063
1240.5042332196769840.9915335606460320.495766780323016
1250.5364682405571380.9270635188857240.463531759442862
1260.7304760714395390.5390478571209220.269523928560461
1270.9117920674751090.1764158650497820.088207932524891
1280.9062295390908830.1875409218182340.0937704609091172
1290.8809826479372960.2380347041254080.119017352062704
1300.8755122624425870.2489754751148250.124487737557413
1310.8462217445339730.3075565109320550.153778255466027
1320.8027162761216660.3945674477566680.197283723878334
1330.7492870280538550.5014259438922890.250712971946145
1340.6906546644060750.6186906711878490.309345335593925
1350.6605825416411030.6788349167177940.339417458358897
1360.6152685245579150.769462950884170.384731475442085
1370.5952192242934320.8095615514131360.404780775706568
1380.5156080128466990.9687839743066020.484391987153301
1390.4920123703264670.9840247406529350.507987629673533
1400.8283364135631860.3433271728736290.171663586436814
1410.7841967048309950.4316065903380090.215803295169005
1420.7151132041152940.5697735917694110.284886795884706
1430.6801173101422230.6397653797155550.319882689857777
1440.5713282003278570.8573435993442870.428671799672143
1450.4267282010560620.8534564021121250.573271798943938
1460.3886525459222470.7773050918444950.611347454077753

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.871944229737688 & 0.256111540524623 & 0.128055770262312 \tabularnewline
11 & 0.903968861443027 & 0.192062277113946 & 0.0960311385569732 \tabularnewline
12 & 0.846807913625654 & 0.306384172748693 & 0.153192086374346 \tabularnewline
13 & 0.778736219778779 & 0.442527560442441 & 0.221263780221221 \tabularnewline
14 & 0.685793760005733 & 0.628412479988534 & 0.314206239994267 \tabularnewline
15 & 0.877408186729147 & 0.245183626541707 & 0.122591813270853 \tabularnewline
16 & 0.858526780839711 & 0.282946438320577 & 0.141473219160289 \tabularnewline
17 & 0.833969540869644 & 0.332060918260711 & 0.166030459130356 \tabularnewline
18 & 0.773139354694947 & 0.453721290610106 & 0.226860645305053 \tabularnewline
19 & 0.764724099429791 & 0.470551801140419 & 0.235275900570209 \tabularnewline
20 & 0.701847598939076 & 0.596304802121848 & 0.298152401060924 \tabularnewline
21 & 0.633087431703437 & 0.733825136593125 & 0.366912568296563 \tabularnewline
22 & 0.558216549826176 & 0.883566900347648 & 0.441783450173824 \tabularnewline
23 & 0.610715324755832 & 0.778569350488337 & 0.389284675244168 \tabularnewline
24 & 0.753976478977012 & 0.492047042045976 & 0.246023521022988 \tabularnewline
25 & 0.700694954010175 & 0.598610091979649 & 0.299305045989825 \tabularnewline
26 & 0.749947032677699 & 0.500105934644602 & 0.250052967322301 \tabularnewline
27 & 0.695128632195392 & 0.609742735609217 & 0.304871367804608 \tabularnewline
28 & 0.726244923137709 & 0.547510153724581 & 0.273755076862291 \tabularnewline
29 & 0.671111532685381 & 0.657776934629239 & 0.328888467314619 \tabularnewline
30 & 0.709439152032818 & 0.581121695934365 & 0.290560847967182 \tabularnewline
31 & 0.675654553714105 & 0.648690892571789 & 0.324345446285895 \tabularnewline
32 & 0.653380457695906 & 0.693239084608188 & 0.346619542304094 \tabularnewline
33 & 0.596471067575035 & 0.80705786484993 & 0.403528932424965 \tabularnewline
34 & 0.613599128773807 & 0.772801742452386 & 0.386400871226193 \tabularnewline
35 & 0.555792683665856 & 0.888414632668287 & 0.444207316334144 \tabularnewline
36 & 0.560128000225083 & 0.879743999549835 & 0.439871999774917 \tabularnewline
37 & 0.514518160979582 & 0.970963678040837 & 0.485481839020418 \tabularnewline
38 & 0.528406117694295 & 0.943187764611411 & 0.471593882305705 \tabularnewline
39 & 0.475538300181256 & 0.951076600362512 & 0.524461699818744 \tabularnewline
40 & 0.578323479446476 & 0.843353041107048 & 0.421676520553524 \tabularnewline
41 & 0.56424982630722 & 0.87150034738556 & 0.43575017369278 \tabularnewline
42 & 0.694163211045818 & 0.611673577908363 & 0.305836788954182 \tabularnewline
43 & 0.661125837970607 & 0.677748324058785 & 0.338874162029393 \tabularnewline
44 & 0.610085935381058 & 0.779828129237884 & 0.389914064618942 \tabularnewline
45 & 0.622434267846889 & 0.755131464306222 & 0.377565732153111 \tabularnewline
46 & 0.573923807499423 & 0.852152385001153 & 0.426076192500577 \tabularnewline
47 & 0.549820922275969 & 0.900358155448061 & 0.450179077724031 \tabularnewline
48 & 0.613070862438358 & 0.773858275123284 & 0.386929137561642 \tabularnewline
49 & 0.575805571689353 & 0.848388856621293 & 0.424194428310647 \tabularnewline
50 & 0.55213005961426 & 0.89573988077148 & 0.44786994038574 \tabularnewline
51 & 0.800051363331477 & 0.399897273337045 & 0.199948636668523 \tabularnewline
52 & 0.792858141110334 & 0.414283717779333 & 0.207141858889666 \tabularnewline
53 & 0.75655328700342 & 0.486893425993161 & 0.24344671299658 \tabularnewline
54 & 0.73431792387653 & 0.53136415224694 & 0.26568207612347 \tabularnewline
55 & 0.703410705760036 & 0.593178588479928 & 0.296589294239964 \tabularnewline
56 & 0.66887415983413 & 0.66225168033174 & 0.33112584016587 \tabularnewline
57 & 0.645971250937507 & 0.708057498124986 & 0.354028749062493 \tabularnewline
58 & 0.664852947233402 & 0.670294105533195 & 0.335147052766597 \tabularnewline
59 & 0.628066032604002 & 0.743867934791996 & 0.371933967395998 \tabularnewline
60 & 0.584054150088614 & 0.831891699822771 & 0.415945849911386 \tabularnewline
61 & 0.547569645866866 & 0.904860708266269 & 0.452430354133135 \tabularnewline
62 & 0.715985898851183 & 0.568028202297634 & 0.284014101148817 \tabularnewline
63 & 0.781110675522354 & 0.437778648955292 & 0.218889324477646 \tabularnewline
64 & 0.765861898558055 & 0.46827620288389 & 0.234138101441945 \tabularnewline
65 & 0.829514379703794 & 0.340971240592411 & 0.170485620296206 \tabularnewline
66 & 0.801196131132837 & 0.397607737734325 & 0.198803868867163 \tabularnewline
67 & 0.800623814408112 & 0.398752371183775 & 0.199376185591888 \tabularnewline
68 & 0.794205139624724 & 0.411589720750551 & 0.205794860375276 \tabularnewline
69 & 0.760064309881156 & 0.479871380237688 & 0.239935690118844 \tabularnewline
70 & 0.833311062214265 & 0.33337787557147 & 0.166688937785735 \tabularnewline
71 & 0.81652724552684 & 0.366945508946319 & 0.18347275447316 \tabularnewline
72 & 0.783521910909934 & 0.432956178180133 & 0.216478089090066 \tabularnewline
73 & 0.752992568337879 & 0.494014863324243 & 0.247007431662121 \tabularnewline
74 & 0.731026366994649 & 0.537947266010703 & 0.268973633005351 \tabularnewline
75 & 0.693225936599539 & 0.613548126800923 & 0.306774063400461 \tabularnewline
76 & 0.663595720184824 & 0.672808559630351 & 0.336404279815176 \tabularnewline
77 & 0.641846287246675 & 0.71630742550665 & 0.358153712753325 \tabularnewline
78 & 0.621233945492266 & 0.757532109015468 & 0.378766054507734 \tabularnewline
79 & 0.613971988431604 & 0.772056023136792 & 0.386028011568396 \tabularnewline
80 & 0.615630262097253 & 0.768739475805495 & 0.384369737902747 \tabularnewline
81 & 0.668887567949309 & 0.662224864101382 & 0.331112432050691 \tabularnewline
82 & 0.62546034304369 & 0.74907931391262 & 0.37453965695631 \tabularnewline
83 & 0.592992470530014 & 0.814015058939972 & 0.407007529469986 \tabularnewline
84 & 0.556994275937144 & 0.886011448125712 & 0.443005724062856 \tabularnewline
85 & 0.546514587016101 & 0.906970825967798 & 0.453485412983899 \tabularnewline
86 & 0.503584573509256 & 0.992830852981487 & 0.496415426490744 \tabularnewline
87 & 0.473724192570916 & 0.947448385141832 & 0.526275807429084 \tabularnewline
88 & 0.428751941937346 & 0.857503883874692 & 0.571248058062654 \tabularnewline
89 & 0.409647833389402 & 0.819295666778803 & 0.590352166610598 \tabularnewline
90 & 0.366206973890827 & 0.732413947781653 & 0.633793026109173 \tabularnewline
91 & 0.333951431497258 & 0.667902862994516 & 0.666048568502742 \tabularnewline
92 & 0.293030761499851 & 0.586061522999703 & 0.706969238500149 \tabularnewline
93 & 0.281135441559711 & 0.562270883119421 & 0.718864558440289 \tabularnewline
94 & 0.269320876508738 & 0.538641753017476 & 0.730679123491262 \tabularnewline
95 & 0.241336346578011 & 0.482672693156021 & 0.758663653421989 \tabularnewline
96 & 0.229520818899461 & 0.459041637798922 & 0.770479181100539 \tabularnewline
97 & 0.265172035462937 & 0.530344070925874 & 0.734827964537063 \tabularnewline
98 & 0.256458895917361 & 0.512917791834722 & 0.743541104082639 \tabularnewline
99 & 0.239316843608377 & 0.478633687216753 & 0.760683156391623 \tabularnewline
100 & 0.2026606007828 & 0.405321201565599 & 0.7973393992172 \tabularnewline
101 & 0.220078657586401 & 0.440157315172802 & 0.779921342413599 \tabularnewline
102 & 0.19631095296033 & 0.39262190592066 & 0.80368904703967 \tabularnewline
103 & 0.170981319168841 & 0.341962638337681 & 0.829018680831159 \tabularnewline
104 & 0.297958505970955 & 0.59591701194191 & 0.702041494029045 \tabularnewline
105 & 0.534335104009994 & 0.931329791980013 & 0.465664895990007 \tabularnewline
106 & 0.705784144284097 & 0.588431711431805 & 0.294215855715903 \tabularnewline
107 & 0.662417374756191 & 0.675165250487618 & 0.337582625243809 \tabularnewline
108 & 0.639699062803529 & 0.720601874392941 & 0.360300937196471 \tabularnewline
109 & 0.592538660328637 & 0.814922679342726 & 0.407461339671363 \tabularnewline
110 & 0.565542573694584 & 0.868914852610832 & 0.434457426305416 \tabularnewline
111 & 0.523189007480276 & 0.953621985039447 & 0.476810992519724 \tabularnewline
112 & 0.491858992893349 & 0.983717985786699 & 0.508141007106651 \tabularnewline
113 & 0.614385041507377 & 0.771229916985245 & 0.385614958492623 \tabularnewline
114 & 0.566896578572251 & 0.866206842855498 & 0.433103421427749 \tabularnewline
115 & 0.557123892004736 & 0.885752215990529 & 0.442876107995264 \tabularnewline
116 & 0.517068919902836 & 0.965862160194328 & 0.482931080097164 \tabularnewline
117 & 0.460335694538798 & 0.920671389077596 & 0.539664305461202 \tabularnewline
118 & 0.471554649527336 & 0.943109299054672 & 0.528445350472664 \tabularnewline
119 & 0.450940246841036 & 0.901880493682072 & 0.549059753158964 \tabularnewline
120 & 0.48717216172385 & 0.9743443234477 & 0.51282783827615 \tabularnewline
121 & 0.431007827347919 & 0.862015654695838 & 0.568992172652081 \tabularnewline
122 & 0.507030796819696 & 0.985938406360608 & 0.492969203180304 \tabularnewline
123 & 0.46692530745937 & 0.93385061491874 & 0.53307469254063 \tabularnewline
124 & 0.504233219676984 & 0.991533560646032 & 0.495766780323016 \tabularnewline
125 & 0.536468240557138 & 0.927063518885724 & 0.463531759442862 \tabularnewline
126 & 0.730476071439539 & 0.539047857120922 & 0.269523928560461 \tabularnewline
127 & 0.911792067475109 & 0.176415865049782 & 0.088207932524891 \tabularnewline
128 & 0.906229539090883 & 0.187540921818234 & 0.0937704609091172 \tabularnewline
129 & 0.880982647937296 & 0.238034704125408 & 0.119017352062704 \tabularnewline
130 & 0.875512262442587 & 0.248975475114825 & 0.124487737557413 \tabularnewline
131 & 0.846221744533973 & 0.307556510932055 & 0.153778255466027 \tabularnewline
132 & 0.802716276121666 & 0.394567447756668 & 0.197283723878334 \tabularnewline
133 & 0.749287028053855 & 0.501425943892289 & 0.250712971946145 \tabularnewline
134 & 0.690654664406075 & 0.618690671187849 & 0.309345335593925 \tabularnewline
135 & 0.660582541641103 & 0.678834916717794 & 0.339417458358897 \tabularnewline
136 & 0.615268524557915 & 0.76946295088417 & 0.384731475442085 \tabularnewline
137 & 0.595219224293432 & 0.809561551413136 & 0.404780775706568 \tabularnewline
138 & 0.515608012846699 & 0.968783974306602 & 0.484391987153301 \tabularnewline
139 & 0.492012370326467 & 0.984024740652935 & 0.507987629673533 \tabularnewline
140 & 0.828336413563186 & 0.343327172873629 & 0.171663586436814 \tabularnewline
141 & 0.784196704830995 & 0.431606590338009 & 0.215803295169005 \tabularnewline
142 & 0.715113204115294 & 0.569773591769411 & 0.284886795884706 \tabularnewline
143 & 0.680117310142223 & 0.639765379715555 & 0.319882689857777 \tabularnewline
144 & 0.571328200327857 & 0.857343599344287 & 0.428671799672143 \tabularnewline
145 & 0.426728201056062 & 0.853456402112125 & 0.573271798943938 \tabularnewline
146 & 0.388652545922247 & 0.777305091844495 & 0.611347454077753 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146389&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]10[/C][C]0.871944229737688[/C][C]0.256111540524623[/C][C]0.128055770262312[/C][/ROW]
[ROW][C]11[/C][C]0.903968861443027[/C][C]0.192062277113946[/C][C]0.0960311385569732[/C][/ROW]
[ROW][C]12[/C][C]0.846807913625654[/C][C]0.306384172748693[/C][C]0.153192086374346[/C][/ROW]
[ROW][C]13[/C][C]0.778736219778779[/C][C]0.442527560442441[/C][C]0.221263780221221[/C][/ROW]
[ROW][C]14[/C][C]0.685793760005733[/C][C]0.628412479988534[/C][C]0.314206239994267[/C][/ROW]
[ROW][C]15[/C][C]0.877408186729147[/C][C]0.245183626541707[/C][C]0.122591813270853[/C][/ROW]
[ROW][C]16[/C][C]0.858526780839711[/C][C]0.282946438320577[/C][C]0.141473219160289[/C][/ROW]
[ROW][C]17[/C][C]0.833969540869644[/C][C]0.332060918260711[/C][C]0.166030459130356[/C][/ROW]
[ROW][C]18[/C][C]0.773139354694947[/C][C]0.453721290610106[/C][C]0.226860645305053[/C][/ROW]
[ROW][C]19[/C][C]0.764724099429791[/C][C]0.470551801140419[/C][C]0.235275900570209[/C][/ROW]
[ROW][C]20[/C][C]0.701847598939076[/C][C]0.596304802121848[/C][C]0.298152401060924[/C][/ROW]
[ROW][C]21[/C][C]0.633087431703437[/C][C]0.733825136593125[/C][C]0.366912568296563[/C][/ROW]
[ROW][C]22[/C][C]0.558216549826176[/C][C]0.883566900347648[/C][C]0.441783450173824[/C][/ROW]
[ROW][C]23[/C][C]0.610715324755832[/C][C]0.778569350488337[/C][C]0.389284675244168[/C][/ROW]
[ROW][C]24[/C][C]0.753976478977012[/C][C]0.492047042045976[/C][C]0.246023521022988[/C][/ROW]
[ROW][C]25[/C][C]0.700694954010175[/C][C]0.598610091979649[/C][C]0.299305045989825[/C][/ROW]
[ROW][C]26[/C][C]0.749947032677699[/C][C]0.500105934644602[/C][C]0.250052967322301[/C][/ROW]
[ROW][C]27[/C][C]0.695128632195392[/C][C]0.609742735609217[/C][C]0.304871367804608[/C][/ROW]
[ROW][C]28[/C][C]0.726244923137709[/C][C]0.547510153724581[/C][C]0.273755076862291[/C][/ROW]
[ROW][C]29[/C][C]0.671111532685381[/C][C]0.657776934629239[/C][C]0.328888467314619[/C][/ROW]
[ROW][C]30[/C][C]0.709439152032818[/C][C]0.581121695934365[/C][C]0.290560847967182[/C][/ROW]
[ROW][C]31[/C][C]0.675654553714105[/C][C]0.648690892571789[/C][C]0.324345446285895[/C][/ROW]
[ROW][C]32[/C][C]0.653380457695906[/C][C]0.693239084608188[/C][C]0.346619542304094[/C][/ROW]
[ROW][C]33[/C][C]0.596471067575035[/C][C]0.80705786484993[/C][C]0.403528932424965[/C][/ROW]
[ROW][C]34[/C][C]0.613599128773807[/C][C]0.772801742452386[/C][C]0.386400871226193[/C][/ROW]
[ROW][C]35[/C][C]0.555792683665856[/C][C]0.888414632668287[/C][C]0.444207316334144[/C][/ROW]
[ROW][C]36[/C][C]0.560128000225083[/C][C]0.879743999549835[/C][C]0.439871999774917[/C][/ROW]
[ROW][C]37[/C][C]0.514518160979582[/C][C]0.970963678040837[/C][C]0.485481839020418[/C][/ROW]
[ROW][C]38[/C][C]0.528406117694295[/C][C]0.943187764611411[/C][C]0.471593882305705[/C][/ROW]
[ROW][C]39[/C][C]0.475538300181256[/C][C]0.951076600362512[/C][C]0.524461699818744[/C][/ROW]
[ROW][C]40[/C][C]0.578323479446476[/C][C]0.843353041107048[/C][C]0.421676520553524[/C][/ROW]
[ROW][C]41[/C][C]0.56424982630722[/C][C]0.87150034738556[/C][C]0.43575017369278[/C][/ROW]
[ROW][C]42[/C][C]0.694163211045818[/C][C]0.611673577908363[/C][C]0.305836788954182[/C][/ROW]
[ROW][C]43[/C][C]0.661125837970607[/C][C]0.677748324058785[/C][C]0.338874162029393[/C][/ROW]
[ROW][C]44[/C][C]0.610085935381058[/C][C]0.779828129237884[/C][C]0.389914064618942[/C][/ROW]
[ROW][C]45[/C][C]0.622434267846889[/C][C]0.755131464306222[/C][C]0.377565732153111[/C][/ROW]
[ROW][C]46[/C][C]0.573923807499423[/C][C]0.852152385001153[/C][C]0.426076192500577[/C][/ROW]
[ROW][C]47[/C][C]0.549820922275969[/C][C]0.900358155448061[/C][C]0.450179077724031[/C][/ROW]
[ROW][C]48[/C][C]0.613070862438358[/C][C]0.773858275123284[/C][C]0.386929137561642[/C][/ROW]
[ROW][C]49[/C][C]0.575805571689353[/C][C]0.848388856621293[/C][C]0.424194428310647[/C][/ROW]
[ROW][C]50[/C][C]0.55213005961426[/C][C]0.89573988077148[/C][C]0.44786994038574[/C][/ROW]
[ROW][C]51[/C][C]0.800051363331477[/C][C]0.399897273337045[/C][C]0.199948636668523[/C][/ROW]
[ROW][C]52[/C][C]0.792858141110334[/C][C]0.414283717779333[/C][C]0.207141858889666[/C][/ROW]
[ROW][C]53[/C][C]0.75655328700342[/C][C]0.486893425993161[/C][C]0.24344671299658[/C][/ROW]
[ROW][C]54[/C][C]0.73431792387653[/C][C]0.53136415224694[/C][C]0.26568207612347[/C][/ROW]
[ROW][C]55[/C][C]0.703410705760036[/C][C]0.593178588479928[/C][C]0.296589294239964[/C][/ROW]
[ROW][C]56[/C][C]0.66887415983413[/C][C]0.66225168033174[/C][C]0.33112584016587[/C][/ROW]
[ROW][C]57[/C][C]0.645971250937507[/C][C]0.708057498124986[/C][C]0.354028749062493[/C][/ROW]
[ROW][C]58[/C][C]0.664852947233402[/C][C]0.670294105533195[/C][C]0.335147052766597[/C][/ROW]
[ROW][C]59[/C][C]0.628066032604002[/C][C]0.743867934791996[/C][C]0.371933967395998[/C][/ROW]
[ROW][C]60[/C][C]0.584054150088614[/C][C]0.831891699822771[/C][C]0.415945849911386[/C][/ROW]
[ROW][C]61[/C][C]0.547569645866866[/C][C]0.904860708266269[/C][C]0.452430354133135[/C][/ROW]
[ROW][C]62[/C][C]0.715985898851183[/C][C]0.568028202297634[/C][C]0.284014101148817[/C][/ROW]
[ROW][C]63[/C][C]0.781110675522354[/C][C]0.437778648955292[/C][C]0.218889324477646[/C][/ROW]
[ROW][C]64[/C][C]0.765861898558055[/C][C]0.46827620288389[/C][C]0.234138101441945[/C][/ROW]
[ROW][C]65[/C][C]0.829514379703794[/C][C]0.340971240592411[/C][C]0.170485620296206[/C][/ROW]
[ROW][C]66[/C][C]0.801196131132837[/C][C]0.397607737734325[/C][C]0.198803868867163[/C][/ROW]
[ROW][C]67[/C][C]0.800623814408112[/C][C]0.398752371183775[/C][C]0.199376185591888[/C][/ROW]
[ROW][C]68[/C][C]0.794205139624724[/C][C]0.411589720750551[/C][C]0.205794860375276[/C][/ROW]
[ROW][C]69[/C][C]0.760064309881156[/C][C]0.479871380237688[/C][C]0.239935690118844[/C][/ROW]
[ROW][C]70[/C][C]0.833311062214265[/C][C]0.33337787557147[/C][C]0.166688937785735[/C][/ROW]
[ROW][C]71[/C][C]0.81652724552684[/C][C]0.366945508946319[/C][C]0.18347275447316[/C][/ROW]
[ROW][C]72[/C][C]0.783521910909934[/C][C]0.432956178180133[/C][C]0.216478089090066[/C][/ROW]
[ROW][C]73[/C][C]0.752992568337879[/C][C]0.494014863324243[/C][C]0.247007431662121[/C][/ROW]
[ROW][C]74[/C][C]0.731026366994649[/C][C]0.537947266010703[/C][C]0.268973633005351[/C][/ROW]
[ROW][C]75[/C][C]0.693225936599539[/C][C]0.613548126800923[/C][C]0.306774063400461[/C][/ROW]
[ROW][C]76[/C][C]0.663595720184824[/C][C]0.672808559630351[/C][C]0.336404279815176[/C][/ROW]
[ROW][C]77[/C][C]0.641846287246675[/C][C]0.71630742550665[/C][C]0.358153712753325[/C][/ROW]
[ROW][C]78[/C][C]0.621233945492266[/C][C]0.757532109015468[/C][C]0.378766054507734[/C][/ROW]
[ROW][C]79[/C][C]0.613971988431604[/C][C]0.772056023136792[/C][C]0.386028011568396[/C][/ROW]
[ROW][C]80[/C][C]0.615630262097253[/C][C]0.768739475805495[/C][C]0.384369737902747[/C][/ROW]
[ROW][C]81[/C][C]0.668887567949309[/C][C]0.662224864101382[/C][C]0.331112432050691[/C][/ROW]
[ROW][C]82[/C][C]0.62546034304369[/C][C]0.74907931391262[/C][C]0.37453965695631[/C][/ROW]
[ROW][C]83[/C][C]0.592992470530014[/C][C]0.814015058939972[/C][C]0.407007529469986[/C][/ROW]
[ROW][C]84[/C][C]0.556994275937144[/C][C]0.886011448125712[/C][C]0.443005724062856[/C][/ROW]
[ROW][C]85[/C][C]0.546514587016101[/C][C]0.906970825967798[/C][C]0.453485412983899[/C][/ROW]
[ROW][C]86[/C][C]0.503584573509256[/C][C]0.992830852981487[/C][C]0.496415426490744[/C][/ROW]
[ROW][C]87[/C][C]0.473724192570916[/C][C]0.947448385141832[/C][C]0.526275807429084[/C][/ROW]
[ROW][C]88[/C][C]0.428751941937346[/C][C]0.857503883874692[/C][C]0.571248058062654[/C][/ROW]
[ROW][C]89[/C][C]0.409647833389402[/C][C]0.819295666778803[/C][C]0.590352166610598[/C][/ROW]
[ROW][C]90[/C][C]0.366206973890827[/C][C]0.732413947781653[/C][C]0.633793026109173[/C][/ROW]
[ROW][C]91[/C][C]0.333951431497258[/C][C]0.667902862994516[/C][C]0.666048568502742[/C][/ROW]
[ROW][C]92[/C][C]0.293030761499851[/C][C]0.586061522999703[/C][C]0.706969238500149[/C][/ROW]
[ROW][C]93[/C][C]0.281135441559711[/C][C]0.562270883119421[/C][C]0.718864558440289[/C][/ROW]
[ROW][C]94[/C][C]0.269320876508738[/C][C]0.538641753017476[/C][C]0.730679123491262[/C][/ROW]
[ROW][C]95[/C][C]0.241336346578011[/C][C]0.482672693156021[/C][C]0.758663653421989[/C][/ROW]
[ROW][C]96[/C][C]0.229520818899461[/C][C]0.459041637798922[/C][C]0.770479181100539[/C][/ROW]
[ROW][C]97[/C][C]0.265172035462937[/C][C]0.530344070925874[/C][C]0.734827964537063[/C][/ROW]
[ROW][C]98[/C][C]0.256458895917361[/C][C]0.512917791834722[/C][C]0.743541104082639[/C][/ROW]
[ROW][C]99[/C][C]0.239316843608377[/C][C]0.478633687216753[/C][C]0.760683156391623[/C][/ROW]
[ROW][C]100[/C][C]0.2026606007828[/C][C]0.405321201565599[/C][C]0.7973393992172[/C][/ROW]
[ROW][C]101[/C][C]0.220078657586401[/C][C]0.440157315172802[/C][C]0.779921342413599[/C][/ROW]
[ROW][C]102[/C][C]0.19631095296033[/C][C]0.39262190592066[/C][C]0.80368904703967[/C][/ROW]
[ROW][C]103[/C][C]0.170981319168841[/C][C]0.341962638337681[/C][C]0.829018680831159[/C][/ROW]
[ROW][C]104[/C][C]0.297958505970955[/C][C]0.59591701194191[/C][C]0.702041494029045[/C][/ROW]
[ROW][C]105[/C][C]0.534335104009994[/C][C]0.931329791980013[/C][C]0.465664895990007[/C][/ROW]
[ROW][C]106[/C][C]0.705784144284097[/C][C]0.588431711431805[/C][C]0.294215855715903[/C][/ROW]
[ROW][C]107[/C][C]0.662417374756191[/C][C]0.675165250487618[/C][C]0.337582625243809[/C][/ROW]
[ROW][C]108[/C][C]0.639699062803529[/C][C]0.720601874392941[/C][C]0.360300937196471[/C][/ROW]
[ROW][C]109[/C][C]0.592538660328637[/C][C]0.814922679342726[/C][C]0.407461339671363[/C][/ROW]
[ROW][C]110[/C][C]0.565542573694584[/C][C]0.868914852610832[/C][C]0.434457426305416[/C][/ROW]
[ROW][C]111[/C][C]0.523189007480276[/C][C]0.953621985039447[/C][C]0.476810992519724[/C][/ROW]
[ROW][C]112[/C][C]0.491858992893349[/C][C]0.983717985786699[/C][C]0.508141007106651[/C][/ROW]
[ROW][C]113[/C][C]0.614385041507377[/C][C]0.771229916985245[/C][C]0.385614958492623[/C][/ROW]
[ROW][C]114[/C][C]0.566896578572251[/C][C]0.866206842855498[/C][C]0.433103421427749[/C][/ROW]
[ROW][C]115[/C][C]0.557123892004736[/C][C]0.885752215990529[/C][C]0.442876107995264[/C][/ROW]
[ROW][C]116[/C][C]0.517068919902836[/C][C]0.965862160194328[/C][C]0.482931080097164[/C][/ROW]
[ROW][C]117[/C][C]0.460335694538798[/C][C]0.920671389077596[/C][C]0.539664305461202[/C][/ROW]
[ROW][C]118[/C][C]0.471554649527336[/C][C]0.943109299054672[/C][C]0.528445350472664[/C][/ROW]
[ROW][C]119[/C][C]0.450940246841036[/C][C]0.901880493682072[/C][C]0.549059753158964[/C][/ROW]
[ROW][C]120[/C][C]0.48717216172385[/C][C]0.9743443234477[/C][C]0.51282783827615[/C][/ROW]
[ROW][C]121[/C][C]0.431007827347919[/C][C]0.862015654695838[/C][C]0.568992172652081[/C][/ROW]
[ROW][C]122[/C][C]0.507030796819696[/C][C]0.985938406360608[/C][C]0.492969203180304[/C][/ROW]
[ROW][C]123[/C][C]0.46692530745937[/C][C]0.93385061491874[/C][C]0.53307469254063[/C][/ROW]
[ROW][C]124[/C][C]0.504233219676984[/C][C]0.991533560646032[/C][C]0.495766780323016[/C][/ROW]
[ROW][C]125[/C][C]0.536468240557138[/C][C]0.927063518885724[/C][C]0.463531759442862[/C][/ROW]
[ROW][C]126[/C][C]0.730476071439539[/C][C]0.539047857120922[/C][C]0.269523928560461[/C][/ROW]
[ROW][C]127[/C][C]0.911792067475109[/C][C]0.176415865049782[/C][C]0.088207932524891[/C][/ROW]
[ROW][C]128[/C][C]0.906229539090883[/C][C]0.187540921818234[/C][C]0.0937704609091172[/C][/ROW]
[ROW][C]129[/C][C]0.880982647937296[/C][C]0.238034704125408[/C][C]0.119017352062704[/C][/ROW]
[ROW][C]130[/C][C]0.875512262442587[/C][C]0.248975475114825[/C][C]0.124487737557413[/C][/ROW]
[ROW][C]131[/C][C]0.846221744533973[/C][C]0.307556510932055[/C][C]0.153778255466027[/C][/ROW]
[ROW][C]132[/C][C]0.802716276121666[/C][C]0.394567447756668[/C][C]0.197283723878334[/C][/ROW]
[ROW][C]133[/C][C]0.749287028053855[/C][C]0.501425943892289[/C][C]0.250712971946145[/C][/ROW]
[ROW][C]134[/C][C]0.690654664406075[/C][C]0.618690671187849[/C][C]0.309345335593925[/C][/ROW]
[ROW][C]135[/C][C]0.660582541641103[/C][C]0.678834916717794[/C][C]0.339417458358897[/C][/ROW]
[ROW][C]136[/C][C]0.615268524557915[/C][C]0.76946295088417[/C][C]0.384731475442085[/C][/ROW]
[ROW][C]137[/C][C]0.595219224293432[/C][C]0.809561551413136[/C][C]0.404780775706568[/C][/ROW]
[ROW][C]138[/C][C]0.515608012846699[/C][C]0.968783974306602[/C][C]0.484391987153301[/C][/ROW]
[ROW][C]139[/C][C]0.492012370326467[/C][C]0.984024740652935[/C][C]0.507987629673533[/C][/ROW]
[ROW][C]140[/C][C]0.828336413563186[/C][C]0.343327172873629[/C][C]0.171663586436814[/C][/ROW]
[ROW][C]141[/C][C]0.784196704830995[/C][C]0.431606590338009[/C][C]0.215803295169005[/C][/ROW]
[ROW][C]142[/C][C]0.715113204115294[/C][C]0.569773591769411[/C][C]0.284886795884706[/C][/ROW]
[ROW][C]143[/C][C]0.680117310142223[/C][C]0.639765379715555[/C][C]0.319882689857777[/C][/ROW]
[ROW][C]144[/C][C]0.571328200327857[/C][C]0.857343599344287[/C][C]0.428671799672143[/C][/ROW]
[ROW][C]145[/C][C]0.426728201056062[/C][C]0.853456402112125[/C][C]0.573271798943938[/C][/ROW]
[ROW][C]146[/C][C]0.388652545922247[/C][C]0.777305091844495[/C][C]0.611347454077753[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146389&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146389&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.8719442297376880.2561115405246230.128055770262312
110.9039688614430270.1920622771139460.0960311385569732
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1460.3886525459222470.7773050918444950.611347454077753







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

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146389&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146389&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146389&T=6

As an alternative you can also use a QR Code:  

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

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



Parameters (Session):
par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = 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')
}