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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 computationThu, 20 Dec 2012 10:04:29 -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/2012/Dec/20/t1356027649df9vyisfk8skkbq.htm/, Retrieved Thu, 28 Mar 2024 15:21:09 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=202989, Retrieved Thu, 28 Mar 2024 15:21:09 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact136
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
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Dataseries X:
13	14
16	18
19	11
15	12
14	16
13	18
19	14
15	14
14	15
15	15
16	17
16	19
16	10
16	16
17	18
15	14
15	14
20	17
18	14
16	16
16	18
16	11
19	14
16	12
17	17
17	9
16	16
15	14
16	15
14	11
15	16
12	13
14	17
16	15
14	14
7	16
10	9
14	15
16	17
16	13
16	15
14	16
20	16
14	12
14	12
11	11
14	15
15	15
16	17
14	13
16	16
14	14
12	11
16	12
9	12
14	15
16	16
16	15
15	12
16	12
12	8
16	13
16	11
14	14
16	15
17	10
18	11
18	12
12	15
16	15
10	14
14	16
18	15
18	15
16	13
17	12
16	17
16	13
13	15
16	13
16	15
20	16
16	15
15	16
15	15
16	14
14	15
16	14
16	13
15	7
12	17
17	13
16	15
15	14
13	13
16	16
16	12
16	14
16	17
14	15
16	17
16	12
20	16
15	11
16	15
13	9
17	16
16	15
16	10
12	10
16	15
16	11
17	13
13	14
12	18
18	16
14	14
14	14
13	14
16	14
13	12
16	14
13	15
16	15
15	15
16	13
15	17
17	17
15	19
12	15
16	13
10	9
16	15
12	15
14	15
15	16
13	11
15	14
11	11
12	15
8	13
16	15
15	16
17	14
16	15
10	16
18	16
13	11
16	12
13	9
10	16
15	13
16	16
16	12
14	9
10	13
17	13
13	14
15	19
16	13
12	12
13	13




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time10 seconds
R Server'Herman Ole Andreas Wold' @ wold.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 & 10 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202989&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]10 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202989&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202989&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 time10 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Hapiness[t] = + 11.6832354170298 + 0.175863833021828Learning[t] -0.0033800152342422t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Hapiness[t] =  +  11.6832354170298 +  0.175863833021828Learning[t] -0.0033800152342422t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202989&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Hapiness[t] =  +  11.6832354170298 +  0.175863833021828Learning[t] -0.0033800152342422t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202989&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202989&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
Hapiness[t] = + 11.6832354170298 + 0.175863833021828Learning[t] -0.0033800152342422t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)11.68323541702981.3409918.712400
Learning0.1758638330218280.082152.14080.0338170.016909
t-0.00338001523424220.003951-0.85540.3936020.196801

\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) & 11.6832354170298 & 1.340991 & 8.7124 & 0 & 0 \tabularnewline
Learning & 0.175863833021828 & 0.08215 & 2.1408 & 0.033817 & 0.016909 \tabularnewline
t & -0.0033800152342422 & 0.003951 & -0.8554 & 0.393602 & 0.196801 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202989&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]11.6832354170298[/C][C]1.340991[/C][C]8.7124[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Learning[/C][C]0.175863833021828[/C][C]0.08215[/C][C]2.1408[/C][C]0.033817[/C][C]0.016909[/C][/ROW]
[ROW][C]t[/C][C]-0.0033800152342422[/C][C]0.003951[/C][C]-0.8554[/C][C]0.393602[/C][C]0.196801[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202989&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202989&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)11.68323541702981.3409918.712400
Learning0.1758638330218280.082152.14080.0338170.016909
t-0.00338001523424220.003951-0.85540.3936020.196801







Multiple Linear Regression - Regression Statistics
Multiple R0.194612334477127
R-squared0.0378739607306371
Adjusted R-squared0.0257717464002049
F-TEST (value)3.12950669163074
F-TEST (DF numerator)2
F-TEST (DF denominator)159
p-value0.0464452719759907
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.30729920359476
Sum Squared Residuals846.457108770535

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.194612334477127 \tabularnewline
R-squared & 0.0378739607306371 \tabularnewline
Adjusted R-squared & 0.0257717464002049 \tabularnewline
F-TEST (value) & 3.12950669163074 \tabularnewline
F-TEST (DF numerator) & 2 \tabularnewline
F-TEST (DF denominator) & 159 \tabularnewline
p-value & 0.0464452719759907 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.30729920359476 \tabularnewline
Sum Squared Residuals & 846.457108770535 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202989&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.194612334477127[/C][/ROW]
[ROW][C]R-squared[/C][C]0.0378739607306371[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.0257717464002049[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]3.12950669163074[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]2[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]159[/C][/ROW]
[ROW][C]p-value[/C][C]0.0464452719759907[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.30729920359476[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]846.457108770535[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202989&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202989&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 R0.194612334477127
R-squared0.0378739607306371
Adjusted R-squared0.0257717464002049
F-TEST (value)3.12950669163074
F-TEST (DF numerator)2
F-TEST (DF denominator)159
p-value0.0464452719759907
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.30729920359476
Sum Squared Residuals846.457108770535







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11413.96608523107940.0339147689206149
21814.49029671491063.50970328508941
31115.0145081987418-4.01450819874183
41214.3076728514203-2.30767285142028
51614.12842900316421.87157099683579
61813.94918515490814.05081484509186
71415.0009881378049-1.00098813780486
81414.2941527904833-0.294152790483312
91514.11490894222720.885091057772758
101514.28739276001480.712607239985173
111714.45987657780242.54012342219759
121914.45649656256824.54350343743183
131014.4531165473339-4.45311654733393
141614.44973653209971.55026346790031
151814.62222034988733.37777965011273
161414.2671126686094-0.267112668609374
171414.2637326533751-0.263732653375132
181715.139671803251.86032819674997
191414.7845641219721-0.78456412197213
201614.42945644069421.57054355930577
211814.426076425463.57392357454001
221114.4226964102257-3.42269641022575
231414.946907894057-0.946907894056989
241214.4159363797573-2.41593637975726
251714.58842019754482.41157980245515
26914.5850401823106-5.58504018231061
271614.40579633405451.59420366594546
281414.2265524857985-0.226552485798468
291514.39903630358610.600963696413947
301114.0439286223082-3.04392862230815
311614.21641244009571.78358755990426
321313.685440925796-0.685440925796016
331714.03378857660542.96621142339457
341514.38213622741480.617863772585158
351414.0270285461369-0.0270285461369447
361612.79260169974993.20739830025009
37913.3168131835812-4.31681318358115
381514.01688850043420.983111499565782
391714.36523615124362.63476384875637
401314.3618561360094-1.36185613600939
411514.35847612077510.641523879224853
421614.00336843949721.99663156050275
431615.0551714223940.944828577606028
441213.9966084090288-1.99660840902876
451213.9932283937945-1.99322839379452
461113.4622568794948-2.4622568794948
471513.9864683633261.01353163667396
481514.15895218111360.841047818886376
491714.33143599890122.66856400109879
501313.9763283176233-0.976328317623312
511614.32467596843271.67532403156728
521413.96956828715480.0304317128451727
531113.6144606058769-2.61446060587693
541214.31453592273-2.31453592273
551213.080109076343-1.08010907634296
561513.95604822621791.04395177378214
571614.30439587702731.69560412297273
581514.3010158617930.698984138206971
591214.121772013537-2.12177201353696
601214.2942558313245-2.29425583132454
61813.587420484003-5.58742048400299
621314.2874958008561-1.28749580085606
631114.2841157856218-3.28411578562182
641413.92900810434390.0709918956560791
651514.27735575515330.722644244846666
661014.4498395729409-4.44983957294092
671114.6223233907285-3.6223233907285
681214.6189433754943-2.61894337549426
691513.56038036212911.43961963787095
701514.26045567898210.739544321017877
711413.20189266561690.798107334383085
721613.901967982472.09803201753002
731514.60204329932310.397956700676949
741514.59866328408880.401336715911191
751314.2435556028109-1.24355560281091
761214.4160394205985-2.4160394205985
771714.23679557234242.76320442765757
781314.2334155571082-1.23341555710819
791513.70244404280851.29755595719154
801314.2266555266397-1.2266555266397
811514.22327551140550.776724488594541
821614.92335082825851.07664917174147
831514.2165154809370.783484519063026
841614.03727163268091.9627283673191
851514.03389161744670.966108382553338
861414.2063754352342-0.206375435234248
871513.85126775395631.14873224604365
881414.1996154047658-0.199615404765763
891314.1962353895315-1.19623538953152
90714.0169915412755-7.01699154127545
911713.48602002697573.51397997302427
921314.3619591768506-1.36195917685062
931514.18271532859460.817284671405448
941414.0034714803385-0.00347148033848231
951313.6483637990606-0.648363799060585
961614.17257528289181.82742471710817
971214.1691952676576-2.16919526765758
981414.1658152524233-0.165815252423341
991714.16243523718912.8375647628109
1001513.80732755591121.1926724440888
1011714.15567520672062.84432479327939
1021214.1522951914864-2.15229519148637
1031614.85237050833941.14762949166056
1041113.9696713279961-2.96967132799606
1051514.14215514578360.857844854216354
106913.6111836314839-4.61118363148392
1071614.3112589483371.68874105166301
1081514.13201510008090.867984899919081
1091014.1286350848467-4.12863508484668
1101013.4217997375251-3.42179973752512
1111514.12187505437820.878124945621807
1121114.1184950391439-3.11849503914395
1131314.2909788569315-1.29097885693154
1141413.584143509610.415856490390017
1151813.40489966135394.59510033864609
1161614.45670264425061.54329735574936
1171413.74986729692910.250132703070916
1181413.74648728169480.253512718305158
1191413.56724343343880.432756566561228
1201414.09145491727-0.0914549172700126
1211213.5604834029703-1.56048340297029
1221414.0846948868015-0.0846948868015283
1231513.55372337250181.4462766274982
1241514.0779348563330.922065143666956
1251513.8986910080771.10130899192303
1261314.0711748258646-1.07117482586456
1271713.89193097760853.10806902239151
1281714.24027862841792.7597213715821
1291913.885170947145.11482905285999
1301513.35419943284031.64580056715972
1311314.0542747496933-1.05427474969335
132912.9957117363281-3.99571173632814
1331514.04751471922490.952485280775136
1341513.34067937190331.65932062809669
1351513.68902702271271.31097297728728
1361613.86151084050032.13848915949969
1371113.5064031592224-2.50640315922241
1381413.85475081003180.145249189968175
1391113.1479154627103-2.14791546271027
1401513.32039928049791.67960071950214
1411312.61356393317630.386436066823694
1421514.01709458211670.982905417883316
1431613.83785073386062.16214926613939
1441414.18619838467-0.186198384670027
1451514.0069545364140.993045463586042
1461612.94839152304883.05160847695125
1471614.35192217198911.64807782801087
1481113.4692229916457-2.46922299164575
1491213.993434475477-1.99343447547699
150913.4624629611773-4.46246296117726
1511612.93149144687753.06850855312246
1521313.8074305967524-0.807430596752435
1531613.979914414542.02008558545998
1541213.9765343993058-1.97653439930578
155913.6214267180279-4.62142671802788
1561312.91459137070630.0854086292936721
1571314.1422581866249-1.14225818662488
1581413.43542283930330.564577160696674
1591913.78377049011275.21622950988726
1601313.9562543079003-0.956254307900324
1611213.2494189605788-1.24941896057877
1621313.4219027783664-0.421902778366357

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 14 & 13.9660852310794 & 0.0339147689206149 \tabularnewline
2 & 18 & 14.4902967149106 & 3.50970328508941 \tabularnewline
3 & 11 & 15.0145081987418 & -4.01450819874183 \tabularnewline
4 & 12 & 14.3076728514203 & -2.30767285142028 \tabularnewline
5 & 16 & 14.1284290031642 & 1.87157099683579 \tabularnewline
6 & 18 & 13.9491851549081 & 4.05081484509186 \tabularnewline
7 & 14 & 15.0009881378049 & -1.00098813780486 \tabularnewline
8 & 14 & 14.2941527904833 & -0.294152790483312 \tabularnewline
9 & 15 & 14.1149089422272 & 0.885091057772758 \tabularnewline
10 & 15 & 14.2873927600148 & 0.712607239985173 \tabularnewline
11 & 17 & 14.4598765778024 & 2.54012342219759 \tabularnewline
12 & 19 & 14.4564965625682 & 4.54350343743183 \tabularnewline
13 & 10 & 14.4531165473339 & -4.45311654733393 \tabularnewline
14 & 16 & 14.4497365320997 & 1.55026346790031 \tabularnewline
15 & 18 & 14.6222203498873 & 3.37777965011273 \tabularnewline
16 & 14 & 14.2671126686094 & -0.267112668609374 \tabularnewline
17 & 14 & 14.2637326533751 & -0.263732653375132 \tabularnewline
18 & 17 & 15.13967180325 & 1.86032819674997 \tabularnewline
19 & 14 & 14.7845641219721 & -0.78456412197213 \tabularnewline
20 & 16 & 14.4294564406942 & 1.57054355930577 \tabularnewline
21 & 18 & 14.42607642546 & 3.57392357454001 \tabularnewline
22 & 11 & 14.4226964102257 & -3.42269641022575 \tabularnewline
23 & 14 & 14.946907894057 & -0.946907894056989 \tabularnewline
24 & 12 & 14.4159363797573 & -2.41593637975726 \tabularnewline
25 & 17 & 14.5884201975448 & 2.41157980245515 \tabularnewline
26 & 9 & 14.5850401823106 & -5.58504018231061 \tabularnewline
27 & 16 & 14.4057963340545 & 1.59420366594546 \tabularnewline
28 & 14 & 14.2265524857985 & -0.226552485798468 \tabularnewline
29 & 15 & 14.3990363035861 & 0.600963696413947 \tabularnewline
30 & 11 & 14.0439286223082 & -3.04392862230815 \tabularnewline
31 & 16 & 14.2164124400957 & 1.78358755990426 \tabularnewline
32 & 13 & 13.685440925796 & -0.685440925796016 \tabularnewline
33 & 17 & 14.0337885766054 & 2.96621142339457 \tabularnewline
34 & 15 & 14.3821362274148 & 0.617863772585158 \tabularnewline
35 & 14 & 14.0270285461369 & -0.0270285461369447 \tabularnewline
36 & 16 & 12.7926016997499 & 3.20739830025009 \tabularnewline
37 & 9 & 13.3168131835812 & -4.31681318358115 \tabularnewline
38 & 15 & 14.0168885004342 & 0.983111499565782 \tabularnewline
39 & 17 & 14.3652361512436 & 2.63476384875637 \tabularnewline
40 & 13 & 14.3618561360094 & -1.36185613600939 \tabularnewline
41 & 15 & 14.3584761207751 & 0.641523879224853 \tabularnewline
42 & 16 & 14.0033684394972 & 1.99663156050275 \tabularnewline
43 & 16 & 15.055171422394 & 0.944828577606028 \tabularnewline
44 & 12 & 13.9966084090288 & -1.99660840902876 \tabularnewline
45 & 12 & 13.9932283937945 & -1.99322839379452 \tabularnewline
46 & 11 & 13.4622568794948 & -2.4622568794948 \tabularnewline
47 & 15 & 13.986468363326 & 1.01353163667396 \tabularnewline
48 & 15 & 14.1589521811136 & 0.841047818886376 \tabularnewline
49 & 17 & 14.3314359989012 & 2.66856400109879 \tabularnewline
50 & 13 & 13.9763283176233 & -0.976328317623312 \tabularnewline
51 & 16 & 14.3246759684327 & 1.67532403156728 \tabularnewline
52 & 14 & 13.9695682871548 & 0.0304317128451727 \tabularnewline
53 & 11 & 13.6144606058769 & -2.61446060587693 \tabularnewline
54 & 12 & 14.31453592273 & -2.31453592273 \tabularnewline
55 & 12 & 13.080109076343 & -1.08010907634296 \tabularnewline
56 & 15 & 13.9560482262179 & 1.04395177378214 \tabularnewline
57 & 16 & 14.3043958770273 & 1.69560412297273 \tabularnewline
58 & 15 & 14.301015861793 & 0.698984138206971 \tabularnewline
59 & 12 & 14.121772013537 & -2.12177201353696 \tabularnewline
60 & 12 & 14.2942558313245 & -2.29425583132454 \tabularnewline
61 & 8 & 13.587420484003 & -5.58742048400299 \tabularnewline
62 & 13 & 14.2874958008561 & -1.28749580085606 \tabularnewline
63 & 11 & 14.2841157856218 & -3.28411578562182 \tabularnewline
64 & 14 & 13.9290081043439 & 0.0709918956560791 \tabularnewline
65 & 15 & 14.2773557551533 & 0.722644244846666 \tabularnewline
66 & 10 & 14.4498395729409 & -4.44983957294092 \tabularnewline
67 & 11 & 14.6223233907285 & -3.6223233907285 \tabularnewline
68 & 12 & 14.6189433754943 & -2.61894337549426 \tabularnewline
69 & 15 & 13.5603803621291 & 1.43961963787095 \tabularnewline
70 & 15 & 14.2604556789821 & 0.739544321017877 \tabularnewline
71 & 14 & 13.2018926656169 & 0.798107334383085 \tabularnewline
72 & 16 & 13.90196798247 & 2.09803201753002 \tabularnewline
73 & 15 & 14.6020432993231 & 0.397956700676949 \tabularnewline
74 & 15 & 14.5986632840888 & 0.401336715911191 \tabularnewline
75 & 13 & 14.2435556028109 & -1.24355560281091 \tabularnewline
76 & 12 & 14.4160394205985 & -2.4160394205985 \tabularnewline
77 & 17 & 14.2367955723424 & 2.76320442765757 \tabularnewline
78 & 13 & 14.2334155571082 & -1.23341555710819 \tabularnewline
79 & 15 & 13.7024440428085 & 1.29755595719154 \tabularnewline
80 & 13 & 14.2266555266397 & -1.2266555266397 \tabularnewline
81 & 15 & 14.2232755114055 & 0.776724488594541 \tabularnewline
82 & 16 & 14.9233508282585 & 1.07664917174147 \tabularnewline
83 & 15 & 14.216515480937 & 0.783484519063026 \tabularnewline
84 & 16 & 14.0372716326809 & 1.9627283673191 \tabularnewline
85 & 15 & 14.0338916174467 & 0.966108382553338 \tabularnewline
86 & 14 & 14.2063754352342 & -0.206375435234248 \tabularnewline
87 & 15 & 13.8512677539563 & 1.14873224604365 \tabularnewline
88 & 14 & 14.1996154047658 & -0.199615404765763 \tabularnewline
89 & 13 & 14.1962353895315 & -1.19623538953152 \tabularnewline
90 & 7 & 14.0169915412755 & -7.01699154127545 \tabularnewline
91 & 17 & 13.4860200269757 & 3.51397997302427 \tabularnewline
92 & 13 & 14.3619591768506 & -1.36195917685062 \tabularnewline
93 & 15 & 14.1827153285946 & 0.817284671405448 \tabularnewline
94 & 14 & 14.0034714803385 & -0.00347148033848231 \tabularnewline
95 & 13 & 13.6483637990606 & -0.648363799060585 \tabularnewline
96 & 16 & 14.1725752828918 & 1.82742471710817 \tabularnewline
97 & 12 & 14.1691952676576 & -2.16919526765758 \tabularnewline
98 & 14 & 14.1658152524233 & -0.165815252423341 \tabularnewline
99 & 17 & 14.1624352371891 & 2.8375647628109 \tabularnewline
100 & 15 & 13.8073275559112 & 1.1926724440888 \tabularnewline
101 & 17 & 14.1556752067206 & 2.84432479327939 \tabularnewline
102 & 12 & 14.1522951914864 & -2.15229519148637 \tabularnewline
103 & 16 & 14.8523705083394 & 1.14762949166056 \tabularnewline
104 & 11 & 13.9696713279961 & -2.96967132799606 \tabularnewline
105 & 15 & 14.1421551457836 & 0.857844854216354 \tabularnewline
106 & 9 & 13.6111836314839 & -4.61118363148392 \tabularnewline
107 & 16 & 14.311258948337 & 1.68874105166301 \tabularnewline
108 & 15 & 14.1320151000809 & 0.867984899919081 \tabularnewline
109 & 10 & 14.1286350848467 & -4.12863508484668 \tabularnewline
110 & 10 & 13.4217997375251 & -3.42179973752512 \tabularnewline
111 & 15 & 14.1218750543782 & 0.878124945621807 \tabularnewline
112 & 11 & 14.1184950391439 & -3.11849503914395 \tabularnewline
113 & 13 & 14.2909788569315 & -1.29097885693154 \tabularnewline
114 & 14 & 13.58414350961 & 0.415856490390017 \tabularnewline
115 & 18 & 13.4048996613539 & 4.59510033864609 \tabularnewline
116 & 16 & 14.4567026442506 & 1.54329735574936 \tabularnewline
117 & 14 & 13.7498672969291 & 0.250132703070916 \tabularnewline
118 & 14 & 13.7464872816948 & 0.253512718305158 \tabularnewline
119 & 14 & 13.5672434334388 & 0.432756566561228 \tabularnewline
120 & 14 & 14.09145491727 & -0.0914549172700126 \tabularnewline
121 & 12 & 13.5604834029703 & -1.56048340297029 \tabularnewline
122 & 14 & 14.0846948868015 & -0.0846948868015283 \tabularnewline
123 & 15 & 13.5537233725018 & 1.4462766274982 \tabularnewline
124 & 15 & 14.077934856333 & 0.922065143666956 \tabularnewline
125 & 15 & 13.898691008077 & 1.10130899192303 \tabularnewline
126 & 13 & 14.0711748258646 & -1.07117482586456 \tabularnewline
127 & 17 & 13.8919309776085 & 3.10806902239151 \tabularnewline
128 & 17 & 14.2402786284179 & 2.7597213715821 \tabularnewline
129 & 19 & 13.88517094714 & 5.11482905285999 \tabularnewline
130 & 15 & 13.3541994328403 & 1.64580056715972 \tabularnewline
131 & 13 & 14.0542747496933 & -1.05427474969335 \tabularnewline
132 & 9 & 12.9957117363281 & -3.99571173632814 \tabularnewline
133 & 15 & 14.0475147192249 & 0.952485280775136 \tabularnewline
134 & 15 & 13.3406793719033 & 1.65932062809669 \tabularnewline
135 & 15 & 13.6890270227127 & 1.31097297728728 \tabularnewline
136 & 16 & 13.8615108405003 & 2.13848915949969 \tabularnewline
137 & 11 & 13.5064031592224 & -2.50640315922241 \tabularnewline
138 & 14 & 13.8547508100318 & 0.145249189968175 \tabularnewline
139 & 11 & 13.1479154627103 & -2.14791546271027 \tabularnewline
140 & 15 & 13.3203992804979 & 1.67960071950214 \tabularnewline
141 & 13 & 12.6135639331763 & 0.386436066823694 \tabularnewline
142 & 15 & 14.0170945821167 & 0.982905417883316 \tabularnewline
143 & 16 & 13.8378507338606 & 2.16214926613939 \tabularnewline
144 & 14 & 14.18619838467 & -0.186198384670027 \tabularnewline
145 & 15 & 14.006954536414 & 0.993045463586042 \tabularnewline
146 & 16 & 12.9483915230488 & 3.05160847695125 \tabularnewline
147 & 16 & 14.3519221719891 & 1.64807782801087 \tabularnewline
148 & 11 & 13.4692229916457 & -2.46922299164575 \tabularnewline
149 & 12 & 13.993434475477 & -1.99343447547699 \tabularnewline
150 & 9 & 13.4624629611773 & -4.46246296117726 \tabularnewline
151 & 16 & 12.9314914468775 & 3.06850855312246 \tabularnewline
152 & 13 & 13.8074305967524 & -0.807430596752435 \tabularnewline
153 & 16 & 13.97991441454 & 2.02008558545998 \tabularnewline
154 & 12 & 13.9765343993058 & -1.97653439930578 \tabularnewline
155 & 9 & 13.6214267180279 & -4.62142671802788 \tabularnewline
156 & 13 & 12.9145913707063 & 0.0854086292936721 \tabularnewline
157 & 13 & 14.1422581866249 & -1.14225818662488 \tabularnewline
158 & 14 & 13.4354228393033 & 0.564577160696674 \tabularnewline
159 & 19 & 13.7837704901127 & 5.21622950988726 \tabularnewline
160 & 13 & 13.9562543079003 & -0.956254307900324 \tabularnewline
161 & 12 & 13.2494189605788 & -1.24941896057877 \tabularnewline
162 & 13 & 13.4219027783664 & -0.421902778366357 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202989&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]13.9660852310794[/C][C]0.0339147689206149[/C][/ROW]
[ROW][C]2[/C][C]18[/C][C]14.4902967149106[/C][C]3.50970328508941[/C][/ROW]
[ROW][C]3[/C][C]11[/C][C]15.0145081987418[/C][C]-4.01450819874183[/C][/ROW]
[ROW][C]4[/C][C]12[/C][C]14.3076728514203[/C][C]-2.30767285142028[/C][/ROW]
[ROW][C]5[/C][C]16[/C][C]14.1284290031642[/C][C]1.87157099683579[/C][/ROW]
[ROW][C]6[/C][C]18[/C][C]13.9491851549081[/C][C]4.05081484509186[/C][/ROW]
[ROW][C]7[/C][C]14[/C][C]15.0009881378049[/C][C]-1.00098813780486[/C][/ROW]
[ROW][C]8[/C][C]14[/C][C]14.2941527904833[/C][C]-0.294152790483312[/C][/ROW]
[ROW][C]9[/C][C]15[/C][C]14.1149089422272[/C][C]0.885091057772758[/C][/ROW]
[ROW][C]10[/C][C]15[/C][C]14.2873927600148[/C][C]0.712607239985173[/C][/ROW]
[ROW][C]11[/C][C]17[/C][C]14.4598765778024[/C][C]2.54012342219759[/C][/ROW]
[ROW][C]12[/C][C]19[/C][C]14.4564965625682[/C][C]4.54350343743183[/C][/ROW]
[ROW][C]13[/C][C]10[/C][C]14.4531165473339[/C][C]-4.45311654733393[/C][/ROW]
[ROW][C]14[/C][C]16[/C][C]14.4497365320997[/C][C]1.55026346790031[/C][/ROW]
[ROW][C]15[/C][C]18[/C][C]14.6222203498873[/C][C]3.37777965011273[/C][/ROW]
[ROW][C]16[/C][C]14[/C][C]14.2671126686094[/C][C]-0.267112668609374[/C][/ROW]
[ROW][C]17[/C][C]14[/C][C]14.2637326533751[/C][C]-0.263732653375132[/C][/ROW]
[ROW][C]18[/C][C]17[/C][C]15.13967180325[/C][C]1.86032819674997[/C][/ROW]
[ROW][C]19[/C][C]14[/C][C]14.7845641219721[/C][C]-0.78456412197213[/C][/ROW]
[ROW][C]20[/C][C]16[/C][C]14.4294564406942[/C][C]1.57054355930577[/C][/ROW]
[ROW][C]21[/C][C]18[/C][C]14.42607642546[/C][C]3.57392357454001[/C][/ROW]
[ROW][C]22[/C][C]11[/C][C]14.4226964102257[/C][C]-3.42269641022575[/C][/ROW]
[ROW][C]23[/C][C]14[/C][C]14.946907894057[/C][C]-0.946907894056989[/C][/ROW]
[ROW][C]24[/C][C]12[/C][C]14.4159363797573[/C][C]-2.41593637975726[/C][/ROW]
[ROW][C]25[/C][C]17[/C][C]14.5884201975448[/C][C]2.41157980245515[/C][/ROW]
[ROW][C]26[/C][C]9[/C][C]14.5850401823106[/C][C]-5.58504018231061[/C][/ROW]
[ROW][C]27[/C][C]16[/C][C]14.4057963340545[/C][C]1.59420366594546[/C][/ROW]
[ROW][C]28[/C][C]14[/C][C]14.2265524857985[/C][C]-0.226552485798468[/C][/ROW]
[ROW][C]29[/C][C]15[/C][C]14.3990363035861[/C][C]0.600963696413947[/C][/ROW]
[ROW][C]30[/C][C]11[/C][C]14.0439286223082[/C][C]-3.04392862230815[/C][/ROW]
[ROW][C]31[/C][C]16[/C][C]14.2164124400957[/C][C]1.78358755990426[/C][/ROW]
[ROW][C]32[/C][C]13[/C][C]13.685440925796[/C][C]-0.685440925796016[/C][/ROW]
[ROW][C]33[/C][C]17[/C][C]14.0337885766054[/C][C]2.96621142339457[/C][/ROW]
[ROW][C]34[/C][C]15[/C][C]14.3821362274148[/C][C]0.617863772585158[/C][/ROW]
[ROW][C]35[/C][C]14[/C][C]14.0270285461369[/C][C]-0.0270285461369447[/C][/ROW]
[ROW][C]36[/C][C]16[/C][C]12.7926016997499[/C][C]3.20739830025009[/C][/ROW]
[ROW][C]37[/C][C]9[/C][C]13.3168131835812[/C][C]-4.31681318358115[/C][/ROW]
[ROW][C]38[/C][C]15[/C][C]14.0168885004342[/C][C]0.983111499565782[/C][/ROW]
[ROW][C]39[/C][C]17[/C][C]14.3652361512436[/C][C]2.63476384875637[/C][/ROW]
[ROW][C]40[/C][C]13[/C][C]14.3618561360094[/C][C]-1.36185613600939[/C][/ROW]
[ROW][C]41[/C][C]15[/C][C]14.3584761207751[/C][C]0.641523879224853[/C][/ROW]
[ROW][C]42[/C][C]16[/C][C]14.0033684394972[/C][C]1.99663156050275[/C][/ROW]
[ROW][C]43[/C][C]16[/C][C]15.055171422394[/C][C]0.944828577606028[/C][/ROW]
[ROW][C]44[/C][C]12[/C][C]13.9966084090288[/C][C]-1.99660840902876[/C][/ROW]
[ROW][C]45[/C][C]12[/C][C]13.9932283937945[/C][C]-1.99322839379452[/C][/ROW]
[ROW][C]46[/C][C]11[/C][C]13.4622568794948[/C][C]-2.4622568794948[/C][/ROW]
[ROW][C]47[/C][C]15[/C][C]13.986468363326[/C][C]1.01353163667396[/C][/ROW]
[ROW][C]48[/C][C]15[/C][C]14.1589521811136[/C][C]0.841047818886376[/C][/ROW]
[ROW][C]49[/C][C]17[/C][C]14.3314359989012[/C][C]2.66856400109879[/C][/ROW]
[ROW][C]50[/C][C]13[/C][C]13.9763283176233[/C][C]-0.976328317623312[/C][/ROW]
[ROW][C]51[/C][C]16[/C][C]14.3246759684327[/C][C]1.67532403156728[/C][/ROW]
[ROW][C]52[/C][C]14[/C][C]13.9695682871548[/C][C]0.0304317128451727[/C][/ROW]
[ROW][C]53[/C][C]11[/C][C]13.6144606058769[/C][C]-2.61446060587693[/C][/ROW]
[ROW][C]54[/C][C]12[/C][C]14.31453592273[/C][C]-2.31453592273[/C][/ROW]
[ROW][C]55[/C][C]12[/C][C]13.080109076343[/C][C]-1.08010907634296[/C][/ROW]
[ROW][C]56[/C][C]15[/C][C]13.9560482262179[/C][C]1.04395177378214[/C][/ROW]
[ROW][C]57[/C][C]16[/C][C]14.3043958770273[/C][C]1.69560412297273[/C][/ROW]
[ROW][C]58[/C][C]15[/C][C]14.301015861793[/C][C]0.698984138206971[/C][/ROW]
[ROW][C]59[/C][C]12[/C][C]14.121772013537[/C][C]-2.12177201353696[/C][/ROW]
[ROW][C]60[/C][C]12[/C][C]14.2942558313245[/C][C]-2.29425583132454[/C][/ROW]
[ROW][C]61[/C][C]8[/C][C]13.587420484003[/C][C]-5.58742048400299[/C][/ROW]
[ROW][C]62[/C][C]13[/C][C]14.2874958008561[/C][C]-1.28749580085606[/C][/ROW]
[ROW][C]63[/C][C]11[/C][C]14.2841157856218[/C][C]-3.28411578562182[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]13.9290081043439[/C][C]0.0709918956560791[/C][/ROW]
[ROW][C]65[/C][C]15[/C][C]14.2773557551533[/C][C]0.722644244846666[/C][/ROW]
[ROW][C]66[/C][C]10[/C][C]14.4498395729409[/C][C]-4.44983957294092[/C][/ROW]
[ROW][C]67[/C][C]11[/C][C]14.6223233907285[/C][C]-3.6223233907285[/C][/ROW]
[ROW][C]68[/C][C]12[/C][C]14.6189433754943[/C][C]-2.61894337549426[/C][/ROW]
[ROW][C]69[/C][C]15[/C][C]13.5603803621291[/C][C]1.43961963787095[/C][/ROW]
[ROW][C]70[/C][C]15[/C][C]14.2604556789821[/C][C]0.739544321017877[/C][/ROW]
[ROW][C]71[/C][C]14[/C][C]13.2018926656169[/C][C]0.798107334383085[/C][/ROW]
[ROW][C]72[/C][C]16[/C][C]13.90196798247[/C][C]2.09803201753002[/C][/ROW]
[ROW][C]73[/C][C]15[/C][C]14.6020432993231[/C][C]0.397956700676949[/C][/ROW]
[ROW][C]74[/C][C]15[/C][C]14.5986632840888[/C][C]0.401336715911191[/C][/ROW]
[ROW][C]75[/C][C]13[/C][C]14.2435556028109[/C][C]-1.24355560281091[/C][/ROW]
[ROW][C]76[/C][C]12[/C][C]14.4160394205985[/C][C]-2.4160394205985[/C][/ROW]
[ROW][C]77[/C][C]17[/C][C]14.2367955723424[/C][C]2.76320442765757[/C][/ROW]
[ROW][C]78[/C][C]13[/C][C]14.2334155571082[/C][C]-1.23341555710819[/C][/ROW]
[ROW][C]79[/C][C]15[/C][C]13.7024440428085[/C][C]1.29755595719154[/C][/ROW]
[ROW][C]80[/C][C]13[/C][C]14.2266555266397[/C][C]-1.2266555266397[/C][/ROW]
[ROW][C]81[/C][C]15[/C][C]14.2232755114055[/C][C]0.776724488594541[/C][/ROW]
[ROW][C]82[/C][C]16[/C][C]14.9233508282585[/C][C]1.07664917174147[/C][/ROW]
[ROW][C]83[/C][C]15[/C][C]14.216515480937[/C][C]0.783484519063026[/C][/ROW]
[ROW][C]84[/C][C]16[/C][C]14.0372716326809[/C][C]1.9627283673191[/C][/ROW]
[ROW][C]85[/C][C]15[/C][C]14.0338916174467[/C][C]0.966108382553338[/C][/ROW]
[ROW][C]86[/C][C]14[/C][C]14.2063754352342[/C][C]-0.206375435234248[/C][/ROW]
[ROW][C]87[/C][C]15[/C][C]13.8512677539563[/C][C]1.14873224604365[/C][/ROW]
[ROW][C]88[/C][C]14[/C][C]14.1996154047658[/C][C]-0.199615404765763[/C][/ROW]
[ROW][C]89[/C][C]13[/C][C]14.1962353895315[/C][C]-1.19623538953152[/C][/ROW]
[ROW][C]90[/C][C]7[/C][C]14.0169915412755[/C][C]-7.01699154127545[/C][/ROW]
[ROW][C]91[/C][C]17[/C][C]13.4860200269757[/C][C]3.51397997302427[/C][/ROW]
[ROW][C]92[/C][C]13[/C][C]14.3619591768506[/C][C]-1.36195917685062[/C][/ROW]
[ROW][C]93[/C][C]15[/C][C]14.1827153285946[/C][C]0.817284671405448[/C][/ROW]
[ROW][C]94[/C][C]14[/C][C]14.0034714803385[/C][C]-0.00347148033848231[/C][/ROW]
[ROW][C]95[/C][C]13[/C][C]13.6483637990606[/C][C]-0.648363799060585[/C][/ROW]
[ROW][C]96[/C][C]16[/C][C]14.1725752828918[/C][C]1.82742471710817[/C][/ROW]
[ROW][C]97[/C][C]12[/C][C]14.1691952676576[/C][C]-2.16919526765758[/C][/ROW]
[ROW][C]98[/C][C]14[/C][C]14.1658152524233[/C][C]-0.165815252423341[/C][/ROW]
[ROW][C]99[/C][C]17[/C][C]14.1624352371891[/C][C]2.8375647628109[/C][/ROW]
[ROW][C]100[/C][C]15[/C][C]13.8073275559112[/C][C]1.1926724440888[/C][/ROW]
[ROW][C]101[/C][C]17[/C][C]14.1556752067206[/C][C]2.84432479327939[/C][/ROW]
[ROW][C]102[/C][C]12[/C][C]14.1522951914864[/C][C]-2.15229519148637[/C][/ROW]
[ROW][C]103[/C][C]16[/C][C]14.8523705083394[/C][C]1.14762949166056[/C][/ROW]
[ROW][C]104[/C][C]11[/C][C]13.9696713279961[/C][C]-2.96967132799606[/C][/ROW]
[ROW][C]105[/C][C]15[/C][C]14.1421551457836[/C][C]0.857844854216354[/C][/ROW]
[ROW][C]106[/C][C]9[/C][C]13.6111836314839[/C][C]-4.61118363148392[/C][/ROW]
[ROW][C]107[/C][C]16[/C][C]14.311258948337[/C][C]1.68874105166301[/C][/ROW]
[ROW][C]108[/C][C]15[/C][C]14.1320151000809[/C][C]0.867984899919081[/C][/ROW]
[ROW][C]109[/C][C]10[/C][C]14.1286350848467[/C][C]-4.12863508484668[/C][/ROW]
[ROW][C]110[/C][C]10[/C][C]13.4217997375251[/C][C]-3.42179973752512[/C][/ROW]
[ROW][C]111[/C][C]15[/C][C]14.1218750543782[/C][C]0.878124945621807[/C][/ROW]
[ROW][C]112[/C][C]11[/C][C]14.1184950391439[/C][C]-3.11849503914395[/C][/ROW]
[ROW][C]113[/C][C]13[/C][C]14.2909788569315[/C][C]-1.29097885693154[/C][/ROW]
[ROW][C]114[/C][C]14[/C][C]13.58414350961[/C][C]0.415856490390017[/C][/ROW]
[ROW][C]115[/C][C]18[/C][C]13.4048996613539[/C][C]4.59510033864609[/C][/ROW]
[ROW][C]116[/C][C]16[/C][C]14.4567026442506[/C][C]1.54329735574936[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]13.7498672969291[/C][C]0.250132703070916[/C][/ROW]
[ROW][C]118[/C][C]14[/C][C]13.7464872816948[/C][C]0.253512718305158[/C][/ROW]
[ROW][C]119[/C][C]14[/C][C]13.5672434334388[/C][C]0.432756566561228[/C][/ROW]
[ROW][C]120[/C][C]14[/C][C]14.09145491727[/C][C]-0.0914549172700126[/C][/ROW]
[ROW][C]121[/C][C]12[/C][C]13.5604834029703[/C][C]-1.56048340297029[/C][/ROW]
[ROW][C]122[/C][C]14[/C][C]14.0846948868015[/C][C]-0.0846948868015283[/C][/ROW]
[ROW][C]123[/C][C]15[/C][C]13.5537233725018[/C][C]1.4462766274982[/C][/ROW]
[ROW][C]124[/C][C]15[/C][C]14.077934856333[/C][C]0.922065143666956[/C][/ROW]
[ROW][C]125[/C][C]15[/C][C]13.898691008077[/C][C]1.10130899192303[/C][/ROW]
[ROW][C]126[/C][C]13[/C][C]14.0711748258646[/C][C]-1.07117482586456[/C][/ROW]
[ROW][C]127[/C][C]17[/C][C]13.8919309776085[/C][C]3.10806902239151[/C][/ROW]
[ROW][C]128[/C][C]17[/C][C]14.2402786284179[/C][C]2.7597213715821[/C][/ROW]
[ROW][C]129[/C][C]19[/C][C]13.88517094714[/C][C]5.11482905285999[/C][/ROW]
[ROW][C]130[/C][C]15[/C][C]13.3541994328403[/C][C]1.64580056715972[/C][/ROW]
[ROW][C]131[/C][C]13[/C][C]14.0542747496933[/C][C]-1.05427474969335[/C][/ROW]
[ROW][C]132[/C][C]9[/C][C]12.9957117363281[/C][C]-3.99571173632814[/C][/ROW]
[ROW][C]133[/C][C]15[/C][C]14.0475147192249[/C][C]0.952485280775136[/C][/ROW]
[ROW][C]134[/C][C]15[/C][C]13.3406793719033[/C][C]1.65932062809669[/C][/ROW]
[ROW][C]135[/C][C]15[/C][C]13.6890270227127[/C][C]1.31097297728728[/C][/ROW]
[ROW][C]136[/C][C]16[/C][C]13.8615108405003[/C][C]2.13848915949969[/C][/ROW]
[ROW][C]137[/C][C]11[/C][C]13.5064031592224[/C][C]-2.50640315922241[/C][/ROW]
[ROW][C]138[/C][C]14[/C][C]13.8547508100318[/C][C]0.145249189968175[/C][/ROW]
[ROW][C]139[/C][C]11[/C][C]13.1479154627103[/C][C]-2.14791546271027[/C][/ROW]
[ROW][C]140[/C][C]15[/C][C]13.3203992804979[/C][C]1.67960071950214[/C][/ROW]
[ROW][C]141[/C][C]13[/C][C]12.6135639331763[/C][C]0.386436066823694[/C][/ROW]
[ROW][C]142[/C][C]15[/C][C]14.0170945821167[/C][C]0.982905417883316[/C][/ROW]
[ROW][C]143[/C][C]16[/C][C]13.8378507338606[/C][C]2.16214926613939[/C][/ROW]
[ROW][C]144[/C][C]14[/C][C]14.18619838467[/C][C]-0.186198384670027[/C][/ROW]
[ROW][C]145[/C][C]15[/C][C]14.006954536414[/C][C]0.993045463586042[/C][/ROW]
[ROW][C]146[/C][C]16[/C][C]12.9483915230488[/C][C]3.05160847695125[/C][/ROW]
[ROW][C]147[/C][C]16[/C][C]14.3519221719891[/C][C]1.64807782801087[/C][/ROW]
[ROW][C]148[/C][C]11[/C][C]13.4692229916457[/C][C]-2.46922299164575[/C][/ROW]
[ROW][C]149[/C][C]12[/C][C]13.993434475477[/C][C]-1.99343447547699[/C][/ROW]
[ROW][C]150[/C][C]9[/C][C]13.4624629611773[/C][C]-4.46246296117726[/C][/ROW]
[ROW][C]151[/C][C]16[/C][C]12.9314914468775[/C][C]3.06850855312246[/C][/ROW]
[ROW][C]152[/C][C]13[/C][C]13.8074305967524[/C][C]-0.807430596752435[/C][/ROW]
[ROW][C]153[/C][C]16[/C][C]13.97991441454[/C][C]2.02008558545998[/C][/ROW]
[ROW][C]154[/C][C]12[/C][C]13.9765343993058[/C][C]-1.97653439930578[/C][/ROW]
[ROW][C]155[/C][C]9[/C][C]13.6214267180279[/C][C]-4.62142671802788[/C][/ROW]
[ROW][C]156[/C][C]13[/C][C]12.9145913707063[/C][C]0.0854086292936721[/C][/ROW]
[ROW][C]157[/C][C]13[/C][C]14.1422581866249[/C][C]-1.14225818662488[/C][/ROW]
[ROW][C]158[/C][C]14[/C][C]13.4354228393033[/C][C]0.564577160696674[/C][/ROW]
[ROW][C]159[/C][C]19[/C][C]13.7837704901127[/C][C]5.21622950988726[/C][/ROW]
[ROW][C]160[/C][C]13[/C][C]13.9562543079003[/C][C]-0.956254307900324[/C][/ROW]
[ROW][C]161[/C][C]12[/C][C]13.2494189605788[/C][C]-1.24941896057877[/C][/ROW]
[ROW][C]162[/C][C]13[/C][C]13.4219027783664[/C][C]-0.421902778366357[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202989&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202989&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
11413.96608523107940.0339147689206149
21814.49029671491063.50970328508941
31115.0145081987418-4.01450819874183
41214.3076728514203-2.30767285142028
51614.12842900316421.87157099683579
61813.94918515490814.05081484509186
71415.0009881378049-1.00098813780486
81414.2941527904833-0.294152790483312
91514.11490894222720.885091057772758
101514.28739276001480.712607239985173
111714.45987657780242.54012342219759
121914.45649656256824.54350343743183
131014.4531165473339-4.45311654733393
141614.44973653209971.55026346790031
151814.62222034988733.37777965011273
161414.2671126686094-0.267112668609374
171414.2637326533751-0.263732653375132
181715.139671803251.86032819674997
191414.7845641219721-0.78456412197213
201614.42945644069421.57054355930577
211814.426076425463.57392357454001
221114.4226964102257-3.42269641022575
231414.946907894057-0.946907894056989
241214.4159363797573-2.41593637975726
251714.58842019754482.41157980245515
26914.5850401823106-5.58504018231061
271614.40579633405451.59420366594546
281414.2265524857985-0.226552485798468
291514.39903630358610.600963696413947
301114.0439286223082-3.04392862230815
311614.21641244009571.78358755990426
321313.685440925796-0.685440925796016
331714.03378857660542.96621142339457
341514.38213622741480.617863772585158
351414.0270285461369-0.0270285461369447
361612.79260169974993.20739830025009
37913.3168131835812-4.31681318358115
381514.01688850043420.983111499565782
391714.36523615124362.63476384875637
401314.3618561360094-1.36185613600939
411514.35847612077510.641523879224853
421614.00336843949721.99663156050275
431615.0551714223940.944828577606028
441213.9966084090288-1.99660840902876
451213.9932283937945-1.99322839379452
461113.4622568794948-2.4622568794948
471513.9864683633261.01353163667396
481514.15895218111360.841047818886376
491714.33143599890122.66856400109879
501313.9763283176233-0.976328317623312
511614.32467596843271.67532403156728
521413.96956828715480.0304317128451727
531113.6144606058769-2.61446060587693
541214.31453592273-2.31453592273
551213.080109076343-1.08010907634296
561513.95604822621791.04395177378214
571614.30439587702731.69560412297273
581514.3010158617930.698984138206971
591214.121772013537-2.12177201353696
601214.2942558313245-2.29425583132454
61813.587420484003-5.58742048400299
621314.2874958008561-1.28749580085606
631114.2841157856218-3.28411578562182
641413.92900810434390.0709918956560791
651514.27735575515330.722644244846666
661014.4498395729409-4.44983957294092
671114.6223233907285-3.6223233907285
681214.6189433754943-2.61894337549426
691513.56038036212911.43961963787095
701514.26045567898210.739544321017877
711413.20189266561690.798107334383085
721613.901967982472.09803201753002
731514.60204329932310.397956700676949
741514.59866328408880.401336715911191
751314.2435556028109-1.24355560281091
761214.4160394205985-2.4160394205985
771714.23679557234242.76320442765757
781314.2334155571082-1.23341555710819
791513.70244404280851.29755595719154
801314.2266555266397-1.2266555266397
811514.22327551140550.776724488594541
821614.92335082825851.07664917174147
831514.2165154809370.783484519063026
841614.03727163268091.9627283673191
851514.03389161744670.966108382553338
861414.2063754352342-0.206375435234248
871513.85126775395631.14873224604365
881414.1996154047658-0.199615404765763
891314.1962353895315-1.19623538953152
90714.0169915412755-7.01699154127545
911713.48602002697573.51397997302427
921314.3619591768506-1.36195917685062
931514.18271532859460.817284671405448
941414.0034714803385-0.00347148033848231
951313.6483637990606-0.648363799060585
961614.17257528289181.82742471710817
971214.1691952676576-2.16919526765758
981414.1658152524233-0.165815252423341
991714.16243523718912.8375647628109
1001513.80732755591121.1926724440888
1011714.15567520672062.84432479327939
1021214.1522951914864-2.15229519148637
1031614.85237050833941.14762949166056
1041113.9696713279961-2.96967132799606
1051514.14215514578360.857844854216354
106913.6111836314839-4.61118363148392
1071614.3112589483371.68874105166301
1081514.13201510008090.867984899919081
1091014.1286350848467-4.12863508484668
1101013.4217997375251-3.42179973752512
1111514.12187505437820.878124945621807
1121114.1184950391439-3.11849503914395
1131314.2909788569315-1.29097885693154
1141413.584143509610.415856490390017
1151813.40489966135394.59510033864609
1161614.45670264425061.54329735574936
1171413.74986729692910.250132703070916
1181413.74648728169480.253512718305158
1191413.56724343343880.432756566561228
1201414.09145491727-0.0914549172700126
1211213.5604834029703-1.56048340297029
1221414.0846948868015-0.0846948868015283
1231513.55372337250181.4462766274982
1241514.0779348563330.922065143666956
1251513.8986910080771.10130899192303
1261314.0711748258646-1.07117482586456
1271713.89193097760853.10806902239151
1281714.24027862841792.7597213715821
1291913.885170947145.11482905285999
1301513.35419943284031.64580056715972
1311314.0542747496933-1.05427474969335
132912.9957117363281-3.99571173632814
1331514.04751471922490.952485280775136
1341513.34067937190331.65932062809669
1351513.68902702271271.31097297728728
1361613.86151084050032.13848915949969
1371113.5064031592224-2.50640315922241
1381413.85475081003180.145249189968175
1391113.1479154627103-2.14791546271027
1401513.32039928049791.67960071950214
1411312.61356393317630.386436066823694
1421514.01709458211670.982905417883316
1431613.83785073386062.16214926613939
1441414.18619838467-0.186198384670027
1451514.0069545364140.993045463586042
1461612.94839152304883.05160847695125
1471614.35192217198911.64807782801087
1481113.4692229916457-2.46922299164575
1491213.993434475477-1.99343447547699
150913.4624629611773-4.46246296117726
1511612.93149144687753.06850855312246
1521313.8074305967524-0.807430596752435
1531613.979914414542.02008558545998
1541213.9765343993058-1.97653439930578
155913.6214267180279-4.62142671802788
1561312.91459137070630.0854086292936721
1571314.1422581866249-1.14225818662488
1581413.43542283930330.564577160696674
1591913.78377049011275.21622950988726
1601313.9562543079003-0.956254307900324
1611213.2494189605788-1.24941896057877
1621313.4219027783664-0.421902778366357







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
60.8868005565857360.2263988868285280.113199443414264
70.8014175449230140.3971649101539720.198582455076986
80.7444887394003120.5110225211993770.255511260599688
90.6452541698890990.7094916602218020.354745830110901
100.53184530990410.93630938019180.4681546900959
110.5019651165453780.9960697669092450.498034883454622
120.5720948452043420.8558103095913160.427905154795658
130.8902343029324830.2195313941350340.109765697067517
140.8509345822653270.2981308354693460.149065417734673
150.8614452512315550.277109497536890.138554748768445
160.8512612821327690.2974774357344610.148738717867231
170.827397366587880.345205266824240.17260263341212
180.8186805990170340.3626388019659330.181319400982966
190.7831949696827180.4336100606345640.216805030317282
200.7315147230624370.5369705538751270.268485276937563
210.7275715690474530.5448568619050940.272428430952547
220.8565218024055060.2869563951889880.143478197594494
230.8205127815318950.358974436936210.179487218468105
240.8375615298538720.3248769402922570.162438470146128
250.8330645975971730.3338708048056540.166935402402827
260.943609371146540.112781257706920.0563906288534599
270.93385538909550.1322892218090.0661446109045001
280.9131184842021540.1737630315956910.0868815157978455
290.8893853217889150.2212293564221690.110614678211084
300.9065761448296070.1868477103407870.0934238551703933
310.8985174094493150.2029651811013690.101482590550685
320.8754797371263780.2490405257472440.124520262873622
330.8901781973950020.2196436052099960.109821802604998
340.8652286677128390.2695426645743220.134771332287161
350.8335976627980050.3328046744039910.166402337201995
360.8297727641172320.3404544717655350.170227235882768
370.9157117824166660.1685764351666680.084288217583334
380.8996154023531080.2007691952937830.100384597646892
390.9111127739389460.1777744521221070.0888872260610536
400.8931654203418980.2136691593162030.106834579658102
410.871604651554870.2567906968902590.12839534844513
420.8657210646911420.2685578706177150.134278935308858
430.8460628464916040.3078743070167920.153937153508396
440.8368439354207880.3263121291584230.163156064579212
450.8239373063363240.3521253873273520.176062693663676
460.8214200321769620.3571599356460770.178579967823038
470.7990954720786760.4018090558426480.200904527921324
480.7722330383697040.4555339232605930.227766961630296
490.7937086169342840.4125827661314320.206291383065716
500.7613436838672220.4773126322655560.238656316132778
510.7494066431664870.5011867136670260.250593356833513
520.7106783825616510.5786432348766970.289321617438349
530.7111777247301350.577644550539730.288822275269865
540.6983477544049790.6033044911900420.301652245595021
550.6585789098692410.6828421802615180.341421090130759
560.632086782761720.7358264344765610.36791321723828
570.6229307260798370.7541385478403260.377069273920163
580.5867278894208720.8265442211582550.413272110579128
590.5669758178990230.8660483642019540.433024182100977
600.5501989276695960.8996021446608090.449801072330405
610.71142399051460.5771520189707990.2885760094854
620.6743961409279190.6512077181441620.325603859072081
630.6879300907320720.6241398185358570.312069909267928
640.6512121636210180.6975756727579640.348787836378982
650.623129806539140.7537403869217210.376870193460861
660.696241415239110.607517169521780.30375858476089
670.7216864450609460.5566271098781070.278313554939054
680.713148197442390.5737036051152210.28685180255761
690.708828963644390.582342072711220.29117103635561
700.6878547712557870.6242904574884260.312145228744213
710.661172487106160.6776550257876790.33882751289384
720.6769560126578480.6460879746843030.323043987342152
730.6461282752774450.7077434494451110.353871724722555
740.6128868240787350.7742263518425290.387113175921265
750.575135337238180.849729325523640.42486466276182
760.5642848918438690.8714302163122630.435715108156131
770.6089002765184620.7821994469630750.391099723481538
780.5719904847893050.856019030421390.428009515210695
790.552838172100870.894323655798260.44716182789913
800.5152071873093650.969585625381270.484792812690635
810.483264121465390.9665282429307810.51673587853461
820.4576470153588650.9152940307177310.542352984641135
830.42424170539240.8484834107847990.5757582946076
840.4237837602027660.8475675204055320.576216239797234
850.3935728416087160.7871456832174310.606427158391284
860.3508341955562590.7016683911125180.649165804443741
870.3263009793340450.6526019586680910.673699020665955
880.2864866681628610.5729733363257230.713513331837139
890.2544127350022980.5088254700045960.745587264997702
900.575786842340670.8484263153186610.42421315765933
910.6509785351876430.6980429296247130.349021464812357
920.6209344924851310.7581310150297380.379065507514869
930.5859946160969330.8280107678061340.414005383903067
940.5416186409159370.9167627181681270.458381359084063
950.4970676915849260.9941353831698520.502932308415074
960.4857659931334510.9715319862669030.514234006866549
970.4756949036606350.951389807321270.524305096339365
980.4310404577679960.8620809155359910.568959542232004
990.4599458350778740.9198916701557490.540054164922126
1000.4308056605644670.8616113211289340.569194339435533
1010.4630311411949840.9260622823899670.536968858805016
1020.449392880322820.898785760645640.55060711967718
1030.4152755130502380.8305510261004760.584724486949762
1040.4358981372960150.871796274592030.564101862703985
1050.3973626234485460.7947252468970930.602637376551454
1060.5261262909589920.9477474180820150.473873709041008
1070.5027454717024720.9945090565950570.497254528297528
1080.4618196403134030.9236392806268050.538180359686597
1090.5752456429500040.8495087140999930.424754357049996
1100.6470038715894940.7059922568210130.352996128410506
1110.6059926064745130.7880147870509750.394007393525487
1120.6790888233144570.6418223533710870.320911176685543
1130.6754997585513790.6490004828972420.324500241448621
1140.6358109559999590.7283780880000810.364189044000041
1150.7400235110635180.5199529778729640.259976488936482
1160.7068367766108760.5863264467782480.293163223389124
1170.664035248675770.6719295026484610.33596475132423
1180.6188501217685060.7622997564629890.381149878231494
1190.5705194641809140.8589610716381730.429480535819086
1200.5284803246325380.9430393507349250.471519675367462
1210.5266746100546120.9466507798907750.473325389945388
1220.4892759321786560.9785518643573130.510724067821344
1230.4443431597467940.8886863194935880.555656840253206
1240.3964479262778720.7928958525557440.603552073722128
1250.3493288076690790.6986576153381580.650671192330921
1260.343940968642640.687881937285280.65605903135736
1270.3395205805324560.6790411610649120.660479419467544
1280.3225757563056070.6451515126112140.677424243694393
1290.4801645593047520.9603291186095030.519835440695248
1300.447864465364210.895728930728420.55213553463579
1310.407230234912920.8144604698258410.59276976508708
1320.5481630019443130.9036739961113750.451836998055687
1330.4898883800392140.9797767600784270.510111619960786
1340.4444510698861740.8889021397723470.555548930113826
1350.3940295650961920.7880591301923840.605970434903808
1360.3772883270231380.7545766540462760.622711672976862
1370.3948361926727390.7896723853454780.605163807327261
1380.3334640779517430.6669281559034870.666535922048257
1390.3562226468735330.7124452937470670.643777353126467
1400.3052148050643230.6104296101286470.694785194935677
1410.2522524530257190.5045049060514380.747747546974281
1420.2025840339386650.4051680678773290.797415966061335
1430.184565532454460.369131064908920.81543446754554
1440.1396259734757760.2792519469515530.860374026524224
1450.1111303220445880.2222606440891760.888869677955412
1460.1434647150487790.2869294300975570.856535284951221
1470.1702296471122280.3404592942244570.829770352887772
1480.1335357506962190.2670715013924380.866464249303781
1490.09582826076238510.191656521524770.904171739237615
1500.1738496988975730.3476993977951470.826150301102427
1510.2078634118104680.4157268236209370.792136588189532
1520.142255541831350.28451108366270.85774445816865
1530.1811686486921530.3623372973843070.818831351307847
1540.1153973544299530.2307947088599060.884602645570047
1550.2326502432543510.4653004865087010.767349756745649
1560.1376161675753070.2752323351506150.862383832424693

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
6 & 0.886800556585736 & 0.226398886828528 & 0.113199443414264 \tabularnewline
7 & 0.801417544923014 & 0.397164910153972 & 0.198582455076986 \tabularnewline
8 & 0.744488739400312 & 0.511022521199377 & 0.255511260599688 \tabularnewline
9 & 0.645254169889099 & 0.709491660221802 & 0.354745830110901 \tabularnewline
10 & 0.5318453099041 & 0.9363093801918 & 0.4681546900959 \tabularnewline
11 & 0.501965116545378 & 0.996069766909245 & 0.498034883454622 \tabularnewline
12 & 0.572094845204342 & 0.855810309591316 & 0.427905154795658 \tabularnewline
13 & 0.890234302932483 & 0.219531394135034 & 0.109765697067517 \tabularnewline
14 & 0.850934582265327 & 0.298130835469346 & 0.149065417734673 \tabularnewline
15 & 0.861445251231555 & 0.27710949753689 & 0.138554748768445 \tabularnewline
16 & 0.851261282132769 & 0.297477435734461 & 0.148738717867231 \tabularnewline
17 & 0.82739736658788 & 0.34520526682424 & 0.17260263341212 \tabularnewline
18 & 0.818680599017034 & 0.362638801965933 & 0.181319400982966 \tabularnewline
19 & 0.783194969682718 & 0.433610060634564 & 0.216805030317282 \tabularnewline
20 & 0.731514723062437 & 0.536970553875127 & 0.268485276937563 \tabularnewline
21 & 0.727571569047453 & 0.544856861905094 & 0.272428430952547 \tabularnewline
22 & 0.856521802405506 & 0.286956395188988 & 0.143478197594494 \tabularnewline
23 & 0.820512781531895 & 0.35897443693621 & 0.179487218468105 \tabularnewline
24 & 0.837561529853872 & 0.324876940292257 & 0.162438470146128 \tabularnewline
25 & 0.833064597597173 & 0.333870804805654 & 0.166935402402827 \tabularnewline
26 & 0.94360937114654 & 0.11278125770692 & 0.0563906288534599 \tabularnewline
27 & 0.9338553890955 & 0.132289221809 & 0.0661446109045001 \tabularnewline
28 & 0.913118484202154 & 0.173763031595691 & 0.0868815157978455 \tabularnewline
29 & 0.889385321788915 & 0.221229356422169 & 0.110614678211084 \tabularnewline
30 & 0.906576144829607 & 0.186847710340787 & 0.0934238551703933 \tabularnewline
31 & 0.898517409449315 & 0.202965181101369 & 0.101482590550685 \tabularnewline
32 & 0.875479737126378 & 0.249040525747244 & 0.124520262873622 \tabularnewline
33 & 0.890178197395002 & 0.219643605209996 & 0.109821802604998 \tabularnewline
34 & 0.865228667712839 & 0.269542664574322 & 0.134771332287161 \tabularnewline
35 & 0.833597662798005 & 0.332804674403991 & 0.166402337201995 \tabularnewline
36 & 0.829772764117232 & 0.340454471765535 & 0.170227235882768 \tabularnewline
37 & 0.915711782416666 & 0.168576435166668 & 0.084288217583334 \tabularnewline
38 & 0.899615402353108 & 0.200769195293783 & 0.100384597646892 \tabularnewline
39 & 0.911112773938946 & 0.177774452122107 & 0.0888872260610536 \tabularnewline
40 & 0.893165420341898 & 0.213669159316203 & 0.106834579658102 \tabularnewline
41 & 0.87160465155487 & 0.256790696890259 & 0.12839534844513 \tabularnewline
42 & 0.865721064691142 & 0.268557870617715 & 0.134278935308858 \tabularnewline
43 & 0.846062846491604 & 0.307874307016792 & 0.153937153508396 \tabularnewline
44 & 0.836843935420788 & 0.326312129158423 & 0.163156064579212 \tabularnewline
45 & 0.823937306336324 & 0.352125387327352 & 0.176062693663676 \tabularnewline
46 & 0.821420032176962 & 0.357159935646077 & 0.178579967823038 \tabularnewline
47 & 0.799095472078676 & 0.401809055842648 & 0.200904527921324 \tabularnewline
48 & 0.772233038369704 & 0.455533923260593 & 0.227766961630296 \tabularnewline
49 & 0.793708616934284 & 0.412582766131432 & 0.206291383065716 \tabularnewline
50 & 0.761343683867222 & 0.477312632265556 & 0.238656316132778 \tabularnewline
51 & 0.749406643166487 & 0.501186713667026 & 0.250593356833513 \tabularnewline
52 & 0.710678382561651 & 0.578643234876697 & 0.289321617438349 \tabularnewline
53 & 0.711177724730135 & 0.57764455053973 & 0.288822275269865 \tabularnewline
54 & 0.698347754404979 & 0.603304491190042 & 0.301652245595021 \tabularnewline
55 & 0.658578909869241 & 0.682842180261518 & 0.341421090130759 \tabularnewline
56 & 0.63208678276172 & 0.735826434476561 & 0.36791321723828 \tabularnewline
57 & 0.622930726079837 & 0.754138547840326 & 0.377069273920163 \tabularnewline
58 & 0.586727889420872 & 0.826544221158255 & 0.413272110579128 \tabularnewline
59 & 0.566975817899023 & 0.866048364201954 & 0.433024182100977 \tabularnewline
60 & 0.550198927669596 & 0.899602144660809 & 0.449801072330405 \tabularnewline
61 & 0.7114239905146 & 0.577152018970799 & 0.2885760094854 \tabularnewline
62 & 0.674396140927919 & 0.651207718144162 & 0.325603859072081 \tabularnewline
63 & 0.687930090732072 & 0.624139818535857 & 0.312069909267928 \tabularnewline
64 & 0.651212163621018 & 0.697575672757964 & 0.348787836378982 \tabularnewline
65 & 0.62312980653914 & 0.753740386921721 & 0.376870193460861 \tabularnewline
66 & 0.69624141523911 & 0.60751716952178 & 0.30375858476089 \tabularnewline
67 & 0.721686445060946 & 0.556627109878107 & 0.278313554939054 \tabularnewline
68 & 0.71314819744239 & 0.573703605115221 & 0.28685180255761 \tabularnewline
69 & 0.70882896364439 & 0.58234207271122 & 0.29117103635561 \tabularnewline
70 & 0.687854771255787 & 0.624290457488426 & 0.312145228744213 \tabularnewline
71 & 0.66117248710616 & 0.677655025787679 & 0.33882751289384 \tabularnewline
72 & 0.676956012657848 & 0.646087974684303 & 0.323043987342152 \tabularnewline
73 & 0.646128275277445 & 0.707743449445111 & 0.353871724722555 \tabularnewline
74 & 0.612886824078735 & 0.774226351842529 & 0.387113175921265 \tabularnewline
75 & 0.57513533723818 & 0.84972932552364 & 0.42486466276182 \tabularnewline
76 & 0.564284891843869 & 0.871430216312263 & 0.435715108156131 \tabularnewline
77 & 0.608900276518462 & 0.782199446963075 & 0.391099723481538 \tabularnewline
78 & 0.571990484789305 & 0.85601903042139 & 0.428009515210695 \tabularnewline
79 & 0.55283817210087 & 0.89432365579826 & 0.44716182789913 \tabularnewline
80 & 0.515207187309365 & 0.96958562538127 & 0.484792812690635 \tabularnewline
81 & 0.48326412146539 & 0.966528242930781 & 0.51673587853461 \tabularnewline
82 & 0.457647015358865 & 0.915294030717731 & 0.542352984641135 \tabularnewline
83 & 0.4242417053924 & 0.848483410784799 & 0.5757582946076 \tabularnewline
84 & 0.423783760202766 & 0.847567520405532 & 0.576216239797234 \tabularnewline
85 & 0.393572841608716 & 0.787145683217431 & 0.606427158391284 \tabularnewline
86 & 0.350834195556259 & 0.701668391112518 & 0.649165804443741 \tabularnewline
87 & 0.326300979334045 & 0.652601958668091 & 0.673699020665955 \tabularnewline
88 & 0.286486668162861 & 0.572973336325723 & 0.713513331837139 \tabularnewline
89 & 0.254412735002298 & 0.508825470004596 & 0.745587264997702 \tabularnewline
90 & 0.57578684234067 & 0.848426315318661 & 0.42421315765933 \tabularnewline
91 & 0.650978535187643 & 0.698042929624713 & 0.349021464812357 \tabularnewline
92 & 0.620934492485131 & 0.758131015029738 & 0.379065507514869 \tabularnewline
93 & 0.585994616096933 & 0.828010767806134 & 0.414005383903067 \tabularnewline
94 & 0.541618640915937 & 0.916762718168127 & 0.458381359084063 \tabularnewline
95 & 0.497067691584926 & 0.994135383169852 & 0.502932308415074 \tabularnewline
96 & 0.485765993133451 & 0.971531986266903 & 0.514234006866549 \tabularnewline
97 & 0.475694903660635 & 0.95138980732127 & 0.524305096339365 \tabularnewline
98 & 0.431040457767996 & 0.862080915535991 & 0.568959542232004 \tabularnewline
99 & 0.459945835077874 & 0.919891670155749 & 0.540054164922126 \tabularnewline
100 & 0.430805660564467 & 0.861611321128934 & 0.569194339435533 \tabularnewline
101 & 0.463031141194984 & 0.926062282389967 & 0.536968858805016 \tabularnewline
102 & 0.44939288032282 & 0.89878576064564 & 0.55060711967718 \tabularnewline
103 & 0.415275513050238 & 0.830551026100476 & 0.584724486949762 \tabularnewline
104 & 0.435898137296015 & 0.87179627459203 & 0.564101862703985 \tabularnewline
105 & 0.397362623448546 & 0.794725246897093 & 0.602637376551454 \tabularnewline
106 & 0.526126290958992 & 0.947747418082015 & 0.473873709041008 \tabularnewline
107 & 0.502745471702472 & 0.994509056595057 & 0.497254528297528 \tabularnewline
108 & 0.461819640313403 & 0.923639280626805 & 0.538180359686597 \tabularnewline
109 & 0.575245642950004 & 0.849508714099993 & 0.424754357049996 \tabularnewline
110 & 0.647003871589494 & 0.705992256821013 & 0.352996128410506 \tabularnewline
111 & 0.605992606474513 & 0.788014787050975 & 0.394007393525487 \tabularnewline
112 & 0.679088823314457 & 0.641822353371087 & 0.320911176685543 \tabularnewline
113 & 0.675499758551379 & 0.649000482897242 & 0.324500241448621 \tabularnewline
114 & 0.635810955999959 & 0.728378088000081 & 0.364189044000041 \tabularnewline
115 & 0.740023511063518 & 0.519952977872964 & 0.259976488936482 \tabularnewline
116 & 0.706836776610876 & 0.586326446778248 & 0.293163223389124 \tabularnewline
117 & 0.66403524867577 & 0.671929502648461 & 0.33596475132423 \tabularnewline
118 & 0.618850121768506 & 0.762299756462989 & 0.381149878231494 \tabularnewline
119 & 0.570519464180914 & 0.858961071638173 & 0.429480535819086 \tabularnewline
120 & 0.528480324632538 & 0.943039350734925 & 0.471519675367462 \tabularnewline
121 & 0.526674610054612 & 0.946650779890775 & 0.473325389945388 \tabularnewline
122 & 0.489275932178656 & 0.978551864357313 & 0.510724067821344 \tabularnewline
123 & 0.444343159746794 & 0.888686319493588 & 0.555656840253206 \tabularnewline
124 & 0.396447926277872 & 0.792895852555744 & 0.603552073722128 \tabularnewline
125 & 0.349328807669079 & 0.698657615338158 & 0.650671192330921 \tabularnewline
126 & 0.34394096864264 & 0.68788193728528 & 0.65605903135736 \tabularnewline
127 & 0.339520580532456 & 0.679041161064912 & 0.660479419467544 \tabularnewline
128 & 0.322575756305607 & 0.645151512611214 & 0.677424243694393 \tabularnewline
129 & 0.480164559304752 & 0.960329118609503 & 0.519835440695248 \tabularnewline
130 & 0.44786446536421 & 0.89572893072842 & 0.55213553463579 \tabularnewline
131 & 0.40723023491292 & 0.814460469825841 & 0.59276976508708 \tabularnewline
132 & 0.548163001944313 & 0.903673996111375 & 0.451836998055687 \tabularnewline
133 & 0.489888380039214 & 0.979776760078427 & 0.510111619960786 \tabularnewline
134 & 0.444451069886174 & 0.888902139772347 & 0.555548930113826 \tabularnewline
135 & 0.394029565096192 & 0.788059130192384 & 0.605970434903808 \tabularnewline
136 & 0.377288327023138 & 0.754576654046276 & 0.622711672976862 \tabularnewline
137 & 0.394836192672739 & 0.789672385345478 & 0.605163807327261 \tabularnewline
138 & 0.333464077951743 & 0.666928155903487 & 0.666535922048257 \tabularnewline
139 & 0.356222646873533 & 0.712445293747067 & 0.643777353126467 \tabularnewline
140 & 0.305214805064323 & 0.610429610128647 & 0.694785194935677 \tabularnewline
141 & 0.252252453025719 & 0.504504906051438 & 0.747747546974281 \tabularnewline
142 & 0.202584033938665 & 0.405168067877329 & 0.797415966061335 \tabularnewline
143 & 0.18456553245446 & 0.36913106490892 & 0.81543446754554 \tabularnewline
144 & 0.139625973475776 & 0.279251946951553 & 0.860374026524224 \tabularnewline
145 & 0.111130322044588 & 0.222260644089176 & 0.888869677955412 \tabularnewline
146 & 0.143464715048779 & 0.286929430097557 & 0.856535284951221 \tabularnewline
147 & 0.170229647112228 & 0.340459294224457 & 0.829770352887772 \tabularnewline
148 & 0.133535750696219 & 0.267071501392438 & 0.866464249303781 \tabularnewline
149 & 0.0958282607623851 & 0.19165652152477 & 0.904171739237615 \tabularnewline
150 & 0.173849698897573 & 0.347699397795147 & 0.826150301102427 \tabularnewline
151 & 0.207863411810468 & 0.415726823620937 & 0.792136588189532 \tabularnewline
152 & 0.14225554183135 & 0.2845110836627 & 0.85774445816865 \tabularnewline
153 & 0.181168648692153 & 0.362337297384307 & 0.818831351307847 \tabularnewline
154 & 0.115397354429953 & 0.230794708859906 & 0.884602645570047 \tabularnewline
155 & 0.232650243254351 & 0.465300486508701 & 0.767349756745649 \tabularnewline
156 & 0.137616167575307 & 0.275232335150615 & 0.862383832424693 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202989&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]6[/C][C]0.886800556585736[/C][C]0.226398886828528[/C][C]0.113199443414264[/C][/ROW]
[ROW][C]7[/C][C]0.801417544923014[/C][C]0.397164910153972[/C][C]0.198582455076986[/C][/ROW]
[ROW][C]8[/C][C]0.744488739400312[/C][C]0.511022521199377[/C][C]0.255511260599688[/C][/ROW]
[ROW][C]9[/C][C]0.645254169889099[/C][C]0.709491660221802[/C][C]0.354745830110901[/C][/ROW]
[ROW][C]10[/C][C]0.5318453099041[/C][C]0.9363093801918[/C][C]0.4681546900959[/C][/ROW]
[ROW][C]11[/C][C]0.501965116545378[/C][C]0.996069766909245[/C][C]0.498034883454622[/C][/ROW]
[ROW][C]12[/C][C]0.572094845204342[/C][C]0.855810309591316[/C][C]0.427905154795658[/C][/ROW]
[ROW][C]13[/C][C]0.890234302932483[/C][C]0.219531394135034[/C][C]0.109765697067517[/C][/ROW]
[ROW][C]14[/C][C]0.850934582265327[/C][C]0.298130835469346[/C][C]0.149065417734673[/C][/ROW]
[ROW][C]15[/C][C]0.861445251231555[/C][C]0.27710949753689[/C][C]0.138554748768445[/C][/ROW]
[ROW][C]16[/C][C]0.851261282132769[/C][C]0.297477435734461[/C][C]0.148738717867231[/C][/ROW]
[ROW][C]17[/C][C]0.82739736658788[/C][C]0.34520526682424[/C][C]0.17260263341212[/C][/ROW]
[ROW][C]18[/C][C]0.818680599017034[/C][C]0.362638801965933[/C][C]0.181319400982966[/C][/ROW]
[ROW][C]19[/C][C]0.783194969682718[/C][C]0.433610060634564[/C][C]0.216805030317282[/C][/ROW]
[ROW][C]20[/C][C]0.731514723062437[/C][C]0.536970553875127[/C][C]0.268485276937563[/C][/ROW]
[ROW][C]21[/C][C]0.727571569047453[/C][C]0.544856861905094[/C][C]0.272428430952547[/C][/ROW]
[ROW][C]22[/C][C]0.856521802405506[/C][C]0.286956395188988[/C][C]0.143478197594494[/C][/ROW]
[ROW][C]23[/C][C]0.820512781531895[/C][C]0.35897443693621[/C][C]0.179487218468105[/C][/ROW]
[ROW][C]24[/C][C]0.837561529853872[/C][C]0.324876940292257[/C][C]0.162438470146128[/C][/ROW]
[ROW][C]25[/C][C]0.833064597597173[/C][C]0.333870804805654[/C][C]0.166935402402827[/C][/ROW]
[ROW][C]26[/C][C]0.94360937114654[/C][C]0.11278125770692[/C][C]0.0563906288534599[/C][/ROW]
[ROW][C]27[/C][C]0.9338553890955[/C][C]0.132289221809[/C][C]0.0661446109045001[/C][/ROW]
[ROW][C]28[/C][C]0.913118484202154[/C][C]0.173763031595691[/C][C]0.0868815157978455[/C][/ROW]
[ROW][C]29[/C][C]0.889385321788915[/C][C]0.221229356422169[/C][C]0.110614678211084[/C][/ROW]
[ROW][C]30[/C][C]0.906576144829607[/C][C]0.186847710340787[/C][C]0.0934238551703933[/C][/ROW]
[ROW][C]31[/C][C]0.898517409449315[/C][C]0.202965181101369[/C][C]0.101482590550685[/C][/ROW]
[ROW][C]32[/C][C]0.875479737126378[/C][C]0.249040525747244[/C][C]0.124520262873622[/C][/ROW]
[ROW][C]33[/C][C]0.890178197395002[/C][C]0.219643605209996[/C][C]0.109821802604998[/C][/ROW]
[ROW][C]34[/C][C]0.865228667712839[/C][C]0.269542664574322[/C][C]0.134771332287161[/C][/ROW]
[ROW][C]35[/C][C]0.833597662798005[/C][C]0.332804674403991[/C][C]0.166402337201995[/C][/ROW]
[ROW][C]36[/C][C]0.829772764117232[/C][C]0.340454471765535[/C][C]0.170227235882768[/C][/ROW]
[ROW][C]37[/C][C]0.915711782416666[/C][C]0.168576435166668[/C][C]0.084288217583334[/C][/ROW]
[ROW][C]38[/C][C]0.899615402353108[/C][C]0.200769195293783[/C][C]0.100384597646892[/C][/ROW]
[ROW][C]39[/C][C]0.911112773938946[/C][C]0.177774452122107[/C][C]0.0888872260610536[/C][/ROW]
[ROW][C]40[/C][C]0.893165420341898[/C][C]0.213669159316203[/C][C]0.106834579658102[/C][/ROW]
[ROW][C]41[/C][C]0.87160465155487[/C][C]0.256790696890259[/C][C]0.12839534844513[/C][/ROW]
[ROW][C]42[/C][C]0.865721064691142[/C][C]0.268557870617715[/C][C]0.134278935308858[/C][/ROW]
[ROW][C]43[/C][C]0.846062846491604[/C][C]0.307874307016792[/C][C]0.153937153508396[/C][/ROW]
[ROW][C]44[/C][C]0.836843935420788[/C][C]0.326312129158423[/C][C]0.163156064579212[/C][/ROW]
[ROW][C]45[/C][C]0.823937306336324[/C][C]0.352125387327352[/C][C]0.176062693663676[/C][/ROW]
[ROW][C]46[/C][C]0.821420032176962[/C][C]0.357159935646077[/C][C]0.178579967823038[/C][/ROW]
[ROW][C]47[/C][C]0.799095472078676[/C][C]0.401809055842648[/C][C]0.200904527921324[/C][/ROW]
[ROW][C]48[/C][C]0.772233038369704[/C][C]0.455533923260593[/C][C]0.227766961630296[/C][/ROW]
[ROW][C]49[/C][C]0.793708616934284[/C][C]0.412582766131432[/C][C]0.206291383065716[/C][/ROW]
[ROW][C]50[/C][C]0.761343683867222[/C][C]0.477312632265556[/C][C]0.238656316132778[/C][/ROW]
[ROW][C]51[/C][C]0.749406643166487[/C][C]0.501186713667026[/C][C]0.250593356833513[/C][/ROW]
[ROW][C]52[/C][C]0.710678382561651[/C][C]0.578643234876697[/C][C]0.289321617438349[/C][/ROW]
[ROW][C]53[/C][C]0.711177724730135[/C][C]0.57764455053973[/C][C]0.288822275269865[/C][/ROW]
[ROW][C]54[/C][C]0.698347754404979[/C][C]0.603304491190042[/C][C]0.301652245595021[/C][/ROW]
[ROW][C]55[/C][C]0.658578909869241[/C][C]0.682842180261518[/C][C]0.341421090130759[/C][/ROW]
[ROW][C]56[/C][C]0.63208678276172[/C][C]0.735826434476561[/C][C]0.36791321723828[/C][/ROW]
[ROW][C]57[/C][C]0.622930726079837[/C][C]0.754138547840326[/C][C]0.377069273920163[/C][/ROW]
[ROW][C]58[/C][C]0.586727889420872[/C][C]0.826544221158255[/C][C]0.413272110579128[/C][/ROW]
[ROW][C]59[/C][C]0.566975817899023[/C][C]0.866048364201954[/C][C]0.433024182100977[/C][/ROW]
[ROW][C]60[/C][C]0.550198927669596[/C][C]0.899602144660809[/C][C]0.449801072330405[/C][/ROW]
[ROW][C]61[/C][C]0.7114239905146[/C][C]0.577152018970799[/C][C]0.2885760094854[/C][/ROW]
[ROW][C]62[/C][C]0.674396140927919[/C][C]0.651207718144162[/C][C]0.325603859072081[/C][/ROW]
[ROW][C]63[/C][C]0.687930090732072[/C][C]0.624139818535857[/C][C]0.312069909267928[/C][/ROW]
[ROW][C]64[/C][C]0.651212163621018[/C][C]0.697575672757964[/C][C]0.348787836378982[/C][/ROW]
[ROW][C]65[/C][C]0.62312980653914[/C][C]0.753740386921721[/C][C]0.376870193460861[/C][/ROW]
[ROW][C]66[/C][C]0.69624141523911[/C][C]0.60751716952178[/C][C]0.30375858476089[/C][/ROW]
[ROW][C]67[/C][C]0.721686445060946[/C][C]0.556627109878107[/C][C]0.278313554939054[/C][/ROW]
[ROW][C]68[/C][C]0.71314819744239[/C][C]0.573703605115221[/C][C]0.28685180255761[/C][/ROW]
[ROW][C]69[/C][C]0.70882896364439[/C][C]0.58234207271122[/C][C]0.29117103635561[/C][/ROW]
[ROW][C]70[/C][C]0.687854771255787[/C][C]0.624290457488426[/C][C]0.312145228744213[/C][/ROW]
[ROW][C]71[/C][C]0.66117248710616[/C][C]0.677655025787679[/C][C]0.33882751289384[/C][/ROW]
[ROW][C]72[/C][C]0.676956012657848[/C][C]0.646087974684303[/C][C]0.323043987342152[/C][/ROW]
[ROW][C]73[/C][C]0.646128275277445[/C][C]0.707743449445111[/C][C]0.353871724722555[/C][/ROW]
[ROW][C]74[/C][C]0.612886824078735[/C][C]0.774226351842529[/C][C]0.387113175921265[/C][/ROW]
[ROW][C]75[/C][C]0.57513533723818[/C][C]0.84972932552364[/C][C]0.42486466276182[/C][/ROW]
[ROW][C]76[/C][C]0.564284891843869[/C][C]0.871430216312263[/C][C]0.435715108156131[/C][/ROW]
[ROW][C]77[/C][C]0.608900276518462[/C][C]0.782199446963075[/C][C]0.391099723481538[/C][/ROW]
[ROW][C]78[/C][C]0.571990484789305[/C][C]0.85601903042139[/C][C]0.428009515210695[/C][/ROW]
[ROW][C]79[/C][C]0.55283817210087[/C][C]0.89432365579826[/C][C]0.44716182789913[/C][/ROW]
[ROW][C]80[/C][C]0.515207187309365[/C][C]0.96958562538127[/C][C]0.484792812690635[/C][/ROW]
[ROW][C]81[/C][C]0.48326412146539[/C][C]0.966528242930781[/C][C]0.51673587853461[/C][/ROW]
[ROW][C]82[/C][C]0.457647015358865[/C][C]0.915294030717731[/C][C]0.542352984641135[/C][/ROW]
[ROW][C]83[/C][C]0.4242417053924[/C][C]0.848483410784799[/C][C]0.5757582946076[/C][/ROW]
[ROW][C]84[/C][C]0.423783760202766[/C][C]0.847567520405532[/C][C]0.576216239797234[/C][/ROW]
[ROW][C]85[/C][C]0.393572841608716[/C][C]0.787145683217431[/C][C]0.606427158391284[/C][/ROW]
[ROW][C]86[/C][C]0.350834195556259[/C][C]0.701668391112518[/C][C]0.649165804443741[/C][/ROW]
[ROW][C]87[/C][C]0.326300979334045[/C][C]0.652601958668091[/C][C]0.673699020665955[/C][/ROW]
[ROW][C]88[/C][C]0.286486668162861[/C][C]0.572973336325723[/C][C]0.713513331837139[/C][/ROW]
[ROW][C]89[/C][C]0.254412735002298[/C][C]0.508825470004596[/C][C]0.745587264997702[/C][/ROW]
[ROW][C]90[/C][C]0.57578684234067[/C][C]0.848426315318661[/C][C]0.42421315765933[/C][/ROW]
[ROW][C]91[/C][C]0.650978535187643[/C][C]0.698042929624713[/C][C]0.349021464812357[/C][/ROW]
[ROW][C]92[/C][C]0.620934492485131[/C][C]0.758131015029738[/C][C]0.379065507514869[/C][/ROW]
[ROW][C]93[/C][C]0.585994616096933[/C][C]0.828010767806134[/C][C]0.414005383903067[/C][/ROW]
[ROW][C]94[/C][C]0.541618640915937[/C][C]0.916762718168127[/C][C]0.458381359084063[/C][/ROW]
[ROW][C]95[/C][C]0.497067691584926[/C][C]0.994135383169852[/C][C]0.502932308415074[/C][/ROW]
[ROW][C]96[/C][C]0.485765993133451[/C][C]0.971531986266903[/C][C]0.514234006866549[/C][/ROW]
[ROW][C]97[/C][C]0.475694903660635[/C][C]0.95138980732127[/C][C]0.524305096339365[/C][/ROW]
[ROW][C]98[/C][C]0.431040457767996[/C][C]0.862080915535991[/C][C]0.568959542232004[/C][/ROW]
[ROW][C]99[/C][C]0.459945835077874[/C][C]0.919891670155749[/C][C]0.540054164922126[/C][/ROW]
[ROW][C]100[/C][C]0.430805660564467[/C][C]0.861611321128934[/C][C]0.569194339435533[/C][/ROW]
[ROW][C]101[/C][C]0.463031141194984[/C][C]0.926062282389967[/C][C]0.536968858805016[/C][/ROW]
[ROW][C]102[/C][C]0.44939288032282[/C][C]0.89878576064564[/C][C]0.55060711967718[/C][/ROW]
[ROW][C]103[/C][C]0.415275513050238[/C][C]0.830551026100476[/C][C]0.584724486949762[/C][/ROW]
[ROW][C]104[/C][C]0.435898137296015[/C][C]0.87179627459203[/C][C]0.564101862703985[/C][/ROW]
[ROW][C]105[/C][C]0.397362623448546[/C][C]0.794725246897093[/C][C]0.602637376551454[/C][/ROW]
[ROW][C]106[/C][C]0.526126290958992[/C][C]0.947747418082015[/C][C]0.473873709041008[/C][/ROW]
[ROW][C]107[/C][C]0.502745471702472[/C][C]0.994509056595057[/C][C]0.497254528297528[/C][/ROW]
[ROW][C]108[/C][C]0.461819640313403[/C][C]0.923639280626805[/C][C]0.538180359686597[/C][/ROW]
[ROW][C]109[/C][C]0.575245642950004[/C][C]0.849508714099993[/C][C]0.424754357049996[/C][/ROW]
[ROW][C]110[/C][C]0.647003871589494[/C][C]0.705992256821013[/C][C]0.352996128410506[/C][/ROW]
[ROW][C]111[/C][C]0.605992606474513[/C][C]0.788014787050975[/C][C]0.394007393525487[/C][/ROW]
[ROW][C]112[/C][C]0.679088823314457[/C][C]0.641822353371087[/C][C]0.320911176685543[/C][/ROW]
[ROW][C]113[/C][C]0.675499758551379[/C][C]0.649000482897242[/C][C]0.324500241448621[/C][/ROW]
[ROW][C]114[/C][C]0.635810955999959[/C][C]0.728378088000081[/C][C]0.364189044000041[/C][/ROW]
[ROW][C]115[/C][C]0.740023511063518[/C][C]0.519952977872964[/C][C]0.259976488936482[/C][/ROW]
[ROW][C]116[/C][C]0.706836776610876[/C][C]0.586326446778248[/C][C]0.293163223389124[/C][/ROW]
[ROW][C]117[/C][C]0.66403524867577[/C][C]0.671929502648461[/C][C]0.33596475132423[/C][/ROW]
[ROW][C]118[/C][C]0.618850121768506[/C][C]0.762299756462989[/C][C]0.381149878231494[/C][/ROW]
[ROW][C]119[/C][C]0.570519464180914[/C][C]0.858961071638173[/C][C]0.429480535819086[/C][/ROW]
[ROW][C]120[/C][C]0.528480324632538[/C][C]0.943039350734925[/C][C]0.471519675367462[/C][/ROW]
[ROW][C]121[/C][C]0.526674610054612[/C][C]0.946650779890775[/C][C]0.473325389945388[/C][/ROW]
[ROW][C]122[/C][C]0.489275932178656[/C][C]0.978551864357313[/C][C]0.510724067821344[/C][/ROW]
[ROW][C]123[/C][C]0.444343159746794[/C][C]0.888686319493588[/C][C]0.555656840253206[/C][/ROW]
[ROW][C]124[/C][C]0.396447926277872[/C][C]0.792895852555744[/C][C]0.603552073722128[/C][/ROW]
[ROW][C]125[/C][C]0.349328807669079[/C][C]0.698657615338158[/C][C]0.650671192330921[/C][/ROW]
[ROW][C]126[/C][C]0.34394096864264[/C][C]0.68788193728528[/C][C]0.65605903135736[/C][/ROW]
[ROW][C]127[/C][C]0.339520580532456[/C][C]0.679041161064912[/C][C]0.660479419467544[/C][/ROW]
[ROW][C]128[/C][C]0.322575756305607[/C][C]0.645151512611214[/C][C]0.677424243694393[/C][/ROW]
[ROW][C]129[/C][C]0.480164559304752[/C][C]0.960329118609503[/C][C]0.519835440695248[/C][/ROW]
[ROW][C]130[/C][C]0.44786446536421[/C][C]0.89572893072842[/C][C]0.55213553463579[/C][/ROW]
[ROW][C]131[/C][C]0.40723023491292[/C][C]0.814460469825841[/C][C]0.59276976508708[/C][/ROW]
[ROW][C]132[/C][C]0.548163001944313[/C][C]0.903673996111375[/C][C]0.451836998055687[/C][/ROW]
[ROW][C]133[/C][C]0.489888380039214[/C][C]0.979776760078427[/C][C]0.510111619960786[/C][/ROW]
[ROW][C]134[/C][C]0.444451069886174[/C][C]0.888902139772347[/C][C]0.555548930113826[/C][/ROW]
[ROW][C]135[/C][C]0.394029565096192[/C][C]0.788059130192384[/C][C]0.605970434903808[/C][/ROW]
[ROW][C]136[/C][C]0.377288327023138[/C][C]0.754576654046276[/C][C]0.622711672976862[/C][/ROW]
[ROW][C]137[/C][C]0.394836192672739[/C][C]0.789672385345478[/C][C]0.605163807327261[/C][/ROW]
[ROW][C]138[/C][C]0.333464077951743[/C][C]0.666928155903487[/C][C]0.666535922048257[/C][/ROW]
[ROW][C]139[/C][C]0.356222646873533[/C][C]0.712445293747067[/C][C]0.643777353126467[/C][/ROW]
[ROW][C]140[/C][C]0.305214805064323[/C][C]0.610429610128647[/C][C]0.694785194935677[/C][/ROW]
[ROW][C]141[/C][C]0.252252453025719[/C][C]0.504504906051438[/C][C]0.747747546974281[/C][/ROW]
[ROW][C]142[/C][C]0.202584033938665[/C][C]0.405168067877329[/C][C]0.797415966061335[/C][/ROW]
[ROW][C]143[/C][C]0.18456553245446[/C][C]0.36913106490892[/C][C]0.81543446754554[/C][/ROW]
[ROW][C]144[/C][C]0.139625973475776[/C][C]0.279251946951553[/C][C]0.860374026524224[/C][/ROW]
[ROW][C]145[/C][C]0.111130322044588[/C][C]0.222260644089176[/C][C]0.888869677955412[/C][/ROW]
[ROW][C]146[/C][C]0.143464715048779[/C][C]0.286929430097557[/C][C]0.856535284951221[/C][/ROW]
[ROW][C]147[/C][C]0.170229647112228[/C][C]0.340459294224457[/C][C]0.829770352887772[/C][/ROW]
[ROW][C]148[/C][C]0.133535750696219[/C][C]0.267071501392438[/C][C]0.866464249303781[/C][/ROW]
[ROW][C]149[/C][C]0.0958282607623851[/C][C]0.19165652152477[/C][C]0.904171739237615[/C][/ROW]
[ROW][C]150[/C][C]0.173849698897573[/C][C]0.347699397795147[/C][C]0.826150301102427[/C][/ROW]
[ROW][C]151[/C][C]0.207863411810468[/C][C]0.415726823620937[/C][C]0.792136588189532[/C][/ROW]
[ROW][C]152[/C][C]0.14225554183135[/C][C]0.2845110836627[/C][C]0.85774445816865[/C][/ROW]
[ROW][C]153[/C][C]0.181168648692153[/C][C]0.362337297384307[/C][C]0.818831351307847[/C][/ROW]
[ROW][C]154[/C][C]0.115397354429953[/C][C]0.230794708859906[/C][C]0.884602645570047[/C][/ROW]
[ROW][C]155[/C][C]0.232650243254351[/C][C]0.465300486508701[/C][C]0.767349756745649[/C][/ROW]
[ROW][C]156[/C][C]0.137616167575307[/C][C]0.275232335150615[/C][C]0.862383832424693[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202989&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202989&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
60.8868005565857360.2263988868285280.113199443414264
70.8014175449230140.3971649101539720.198582455076986
80.7444887394003120.5110225211993770.255511260599688
90.6452541698890990.7094916602218020.354745830110901
100.53184530990410.93630938019180.4681546900959
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190.7831949696827180.4336100606345640.216805030317282
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270.93385538909550.1322892218090.0661446109045001
280.9131184842021540.1737630315956910.0868815157978455
290.8893853217889150.2212293564221690.110614678211084
300.9065761448296070.1868477103407870.0934238551703933
310.8985174094493150.2029651811013690.101482590550685
320.8754797371263780.2490405257472440.124520262873622
330.8901781973950020.2196436052099960.109821802604998
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350.8335976627980050.3328046744039910.166402337201995
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370.9157117824166660.1685764351666680.084288217583334
380.8996154023531080.2007691952937830.100384597646892
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400.8931654203418980.2136691593162030.106834579658102
410.871604651554870.2567906968902590.12839534844513
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430.8460628464916040.3078743070167920.153937153508396
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460.8214200321769620.3571599356460770.178579967823038
470.7990954720786760.4018090558426480.200904527921324
480.7722330383697040.4555339232605930.227766961630296
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500.7613436838672220.4773126322655560.238656316132778
510.7494066431664870.5011867136670260.250593356833513
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550.6585789098692410.6828421802615180.341421090130759
560.632086782761720.7358264344765610.36791321723828
570.6229307260798370.7541385478403260.377069273920163
580.5867278894208720.8265442211582550.413272110579128
590.5669758178990230.8660483642019540.433024182100977
600.5501989276695960.8996021446608090.449801072330405
610.71142399051460.5771520189707990.2885760094854
620.6743961409279190.6512077181441620.325603859072081
630.6879300907320720.6241398185358570.312069909267928
640.6512121636210180.6975756727579640.348787836378982
650.623129806539140.7537403869217210.376870193460861
660.696241415239110.607517169521780.30375858476089
670.7216864450609460.5566271098781070.278313554939054
680.713148197442390.5737036051152210.28685180255761
690.708828963644390.582342072711220.29117103635561
700.6878547712557870.6242904574884260.312145228744213
710.661172487106160.6776550257876790.33882751289384
720.6769560126578480.6460879746843030.323043987342152
730.6461282752774450.7077434494451110.353871724722555
740.6128868240787350.7742263518425290.387113175921265
750.575135337238180.849729325523640.42486466276182
760.5642848918438690.8714302163122630.435715108156131
770.6089002765184620.7821994469630750.391099723481538
780.5719904847893050.856019030421390.428009515210695
790.552838172100870.894323655798260.44716182789913
800.5152071873093650.969585625381270.484792812690635
810.483264121465390.9665282429307810.51673587853461
820.4576470153588650.9152940307177310.542352984641135
830.42424170539240.8484834107847990.5757582946076
840.4237837602027660.8475675204055320.576216239797234
850.3935728416087160.7871456832174310.606427158391284
860.3508341955562590.7016683911125180.649165804443741
870.3263009793340450.6526019586680910.673699020665955
880.2864866681628610.5729733363257230.713513331837139
890.2544127350022980.5088254700045960.745587264997702
900.575786842340670.8484263153186610.42421315765933
910.6509785351876430.6980429296247130.349021464812357
920.6209344924851310.7581310150297380.379065507514869
930.5859946160969330.8280107678061340.414005383903067
940.5416186409159370.9167627181681270.458381359084063
950.4970676915849260.9941353831698520.502932308415074
960.4857659931334510.9715319862669030.514234006866549
970.4756949036606350.951389807321270.524305096339365
980.4310404577679960.8620809155359910.568959542232004
990.4599458350778740.9198916701557490.540054164922126
1000.4308056605644670.8616113211289340.569194339435533
1010.4630311411949840.9260622823899670.536968858805016
1020.449392880322820.898785760645640.55060711967718
1030.4152755130502380.8305510261004760.584724486949762
1040.4358981372960150.871796274592030.564101862703985
1050.3973626234485460.7947252468970930.602637376551454
1060.5261262909589920.9477474180820150.473873709041008
1070.5027454717024720.9945090565950570.497254528297528
1080.4618196403134030.9236392806268050.538180359686597
1090.5752456429500040.8495087140999930.424754357049996
1100.6470038715894940.7059922568210130.352996128410506
1110.6059926064745130.7880147870509750.394007393525487
1120.6790888233144570.6418223533710870.320911176685543
1130.6754997585513790.6490004828972420.324500241448621
1140.6358109559999590.7283780880000810.364189044000041
1150.7400235110635180.5199529778729640.259976488936482
1160.7068367766108760.5863264467782480.293163223389124
1170.664035248675770.6719295026484610.33596475132423
1180.6188501217685060.7622997564629890.381149878231494
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1200.5284803246325380.9430393507349250.471519675367462
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1560.1376161675753070.2752323351506150.862383832424693







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=202989&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=202989&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202989&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 = 0 ;
Parameters (R input):
par1 = 2 ; 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')
}