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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationTue, 23 Nov 2010 18:21:32 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Nov/23/t1290536375zlnq75nkowexr0v.htm/, Retrieved Thu, 28 Mar 2024 12:37:25 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=99545, Retrieved Thu, 28 Mar 2024 12:37:25 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact143
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2010-11-17 09:20:01] [b98453cac15ba1066b407e146608df68]
-   PD    [Multiple Regression] [] [2010-11-23 18:21:32] [583fc5a74bfa894f261a865501f20e1c] [Current]
-    D      [Multiple Regression] [] [2010-11-23 18:49:43] [a9671b130b33f9fcb98554992ce4582f]
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Dataseries X:
24	14	11	12	24	26
25	11	7	8	25	23
17	6	17	8	30	25
18	12	10	8	19	23
18	8	12	9	22	19
16	10	12	7	22	29
20	10	11	4	25	25
16	11	11	11	23	21
18	16	12	7	17	22
17	11	13	7	21	25
23	13	14	12	19	24
30	12	16	10	19	18
23	8	11	10	15	22
18	12	10	8	16	15
15	11	11	8	23	22
12	4	15	4	27	28
21	9	9	9	22	20
15	8	11	8	14	12
20	8	17	7	22	24
31	14	17	11	23	20
27	15	11	9	23	21
34	16	18	11	21	20
21	9	14	13	19	21
31	14	10	8	18	23
19	11	11	8	20	28
16	8	15	9	23	24
20	9	15	6	25	24
21	9	13	9	19	24
22	9	16	9	24	23
17	9	13	6	22	23
24	10	9	6	25	29
25	16	18	16	26	24
26	11	18	5	29	18
25	8	12	7	32	25
17	9	17	9	25	21
32	16	9	6	29	26
33	11	9	6	28	22
13	16	12	5	17	22
32	12	18	12	28	22
25	12	12	7	29	23
29	14	18	10	26	30
22	9	14	9	25	23
18	10	15	8	14	17
17	9	16	5	25	23
20	10	10	8	26	23
15	12	11	8	20	25
20	14	14	10	18	24
33	14	9	6	32	24
29	10	12	8	25	23
23	14	17	7	25	21
26	16	5	4	23	24
18	9	12	8	21	24
20	10	12	8	20	28
11	6	6	4	15	16
28	8	24	20	30	20
26	13	12	8	24	29
22	10	12	8	26	27
17	8	14	6	24	22
12	7	7	4	22	28
14	15	13	8	14	16
17	9	12	9	24	25
21	10	13	6	24	24
19	12	14	7	24	28
18	13	8	9	24	24
10	10	11	5	19	23
29	11	9	5	31	30
31	8	11	8	22	24
19	9	13	8	27	21
9	13	10	6	19	25
20	11	11	8	25	25
28	8	12	7	20	22
19	9	9	7	21	23
30	9	15	9	27	26
29	15	18	11	23	23
26	9	15	6	25	25
23	10	12	8	20	21
13	14	13	6	21	25
21	12	14	9	22	24
19	12	10	8	23	29
28	11	13	6	25	22
23	14	13	10	25	27
18	6	11	8	17	26
21	12	13	8	19	22
20	8	16	10	25	24
23	14	8	5	19	27
21	11	16	7	20	24
21	10	11	5	26	24
15	14	9	8	23	29
28	12	16	14	27	22
19	10	12	7	17	21
26	14	14	8	17	24
10	5	8	6	19	24
16	11	9	5	17	23
22	10	15	6	22	20
19	9	11	10	21	27
31	10	21	12	32	26
31	16	14	9	21	25
29	13	18	12	21	21
19	9	12	7	18	21
22	10	13	8	18	19
23	10	15	10	23	21
15	7	12	6	19	21
20	9	19	10	20	16
18	8	15	10	21	22
23	14	11	10	20	29
25	14	11	5	17	15
21	8	10	7	18	17
24	9	13	10	19	15
25	14	15	11	22	21
17	14	12	6	15	21
13	8	12	7	14	19
28	8	16	12	18	24
21	8	9	11	24	20
25	7	18	11	35	17
9	6	8	11	29	23
16	8	13	5	21	24
19	6	17	8	25	14
17	11	9	6	20	19
25	14	15	9	22	24
20	11	8	4	13	13
29	11	7	4	26	22
14	11	12	7	17	16
22	14	14	11	25	19
15	8	6	6	20	25
19	20	8	7	19	25
20	11	17	8	21	23
15	8	10	4	22	24
20	11	11	8	24	26
18	10	14	9	21	26
33	14	11	8	26	25
22	11	13	11	24	18
16	9	12	8	16	21
17	9	11	5	23	26
16	8	9	4	18	23
21	10	12	8	16	23
26	13	20	10	26	22
18	13	12	6	19	20
18	12	13	9	21	13
17	8	12	9	21	24
22	13	12	13	22	15
30	14	9	9	23	14
30	12	15	10	29	22
24	14	24	20	21	10
21	15	7	5	21	24
21	13	17	11	23	22
29	16	11	6	27	24
31	9	17	9	25	19
20	9	11	7	21	20
16	9	12	9	10	13
22	8	14	10	20	20
20	7	11	9	26	22
28	16	16	8	24	24
38	11	21	7	29	29
22	9	14	6	19	12
20	11	20	13	24	20
17	9	13	6	19	21
28	14	11	8	24	24
22	13	15	10	22	22
31	16	19	16	17	20




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'George Udny Yule' @ 72.249.76.132

\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 & 9 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99545&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]9 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99545&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99545&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 time9 seconds
R Server'George Udny Yule' @ 72.249.76.132







Multiple Linear Regression - Estimated Regression Equation
O[t] = + 17.4684655763519 -0.0593663461768212CM[t] + 0.216789584253541D[t] -0.133176572068144PE[t] -0.253140065700683PC[t] + 0.395987020465247PS[t] -0.0148718920049206t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
O[t] =  +  17.4684655763519 -0.0593663461768212CM[t] +  0.216789584253541D[t] -0.133176572068144PE[t] -0.253140065700683PC[t] +  0.395987020465247PS[t] -0.0148718920049206t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99545&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]O[t] =  +  17.4684655763519 -0.0593663461768212CM[t] +  0.216789584253541D[t] -0.133176572068144PE[t] -0.253140065700683PC[t] +  0.395987020465247PS[t] -0.0148718920049206t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99545&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99545&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
O[t] = + 17.4684655763519 -0.0593663461768212CM[t] + 0.216789584253541D[t] -0.133176572068144PE[t] -0.253140065700683PC[t] + 0.395987020465247PS[t] -0.0148718920049206t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)17.46846557635192.0422618.553500
CM-0.05936634617682120.062074-0.95640.3403970.170199
D0.2167895842535410.1108011.95660.0522310.026115
PE-0.1331765720681440.102779-1.29580.1970250.098512
PC-0.2531400657006830.128304-1.9730.0503130.025157
PS0.3959870204652470.0751815.267100
t-0.01487189200492060.006034-2.46460.0148290.007414

\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) & 17.4684655763519 & 2.042261 & 8.5535 & 0 & 0 \tabularnewline
CM & -0.0593663461768212 & 0.062074 & -0.9564 & 0.340397 & 0.170199 \tabularnewline
D & 0.216789584253541 & 0.110801 & 1.9566 & 0.052231 & 0.026115 \tabularnewline
PE & -0.133176572068144 & 0.102779 & -1.2958 & 0.197025 & 0.098512 \tabularnewline
PC & -0.253140065700683 & 0.128304 & -1.973 & 0.050313 & 0.025157 \tabularnewline
PS & 0.395987020465247 & 0.075181 & 5.2671 & 0 & 0 \tabularnewline
t & -0.0148718920049206 & 0.006034 & -2.4646 & 0.014829 & 0.007414 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99545&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]17.4684655763519[/C][C]2.042261[/C][C]8.5535[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]CM[/C][C]-0.0593663461768212[/C][C]0.062074[/C][C]-0.9564[/C][C]0.340397[/C][C]0.170199[/C][/ROW]
[ROW][C]D[/C][C]0.216789584253541[/C][C]0.110801[/C][C]1.9566[/C][C]0.052231[/C][C]0.026115[/C][/ROW]
[ROW][C]PE[/C][C]-0.133176572068144[/C][C]0.102779[/C][C]-1.2958[/C][C]0.197025[/C][C]0.098512[/C][/ROW]
[ROW][C]PC[/C][C]-0.253140065700683[/C][C]0.128304[/C][C]-1.973[/C][C]0.050313[/C][C]0.025157[/C][/ROW]
[ROW][C]PS[/C][C]0.395987020465247[/C][C]0.075181[/C][C]5.2671[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]t[/C][C]-0.0148718920049206[/C][C]0.006034[/C][C]-2.4646[/C][C]0.014829[/C][C]0.007414[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99545&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99545&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)17.46846557635192.0422618.553500
CM-0.05936634617682120.062074-0.95640.3403970.170199
D0.2167895842535410.1108011.95660.0522310.026115
PE-0.1331765720681440.102779-1.29580.1970250.098512
PC-0.2531400657006830.128304-1.9730.0503130.025157
PS0.3959870204652470.0751815.267100
t-0.01487189200492060.006034-2.46460.0148290.007414







Multiple Linear Regression - Regression Statistics
Multiple R0.502249529664989
R-squared0.252254590048702
Adjusted R-squared0.222738323866414
F-TEST (value)8.5462906619291
F-TEST (DF numerator)6
F-TEST (DF denominator)152
p-value5.24699077519841e-08
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.44273447491298
Sum Squared Residuals1801.56794104266

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.502249529664989 \tabularnewline
R-squared & 0.252254590048702 \tabularnewline
Adjusted R-squared & 0.222738323866414 \tabularnewline
F-TEST (value) & 8.5462906619291 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 152 \tabularnewline
p-value & 5.24699077519841e-08 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 3.44273447491298 \tabularnewline
Sum Squared Residuals & 1801.56794104266 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99545&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.502249529664989[/C][/ROW]
[ROW][C]R-squared[/C][C]0.252254590048702[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.222738323866414[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]8.5462906619291[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]152[/C][/ROW]
[ROW][C]p-value[/C][C]5.24699077519841e-08[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]3.44273447491298[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1801.56794104266[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99545&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99545&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 R0.502249529664989
R-squared0.252254590048702
Adjusted R-squared0.222738323866414
F-TEST (value)8.5462906619291
F-TEST (DF numerator)6
F-TEST (DF denominator)152
p-value5.24699077519841e-08
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.44273447491298
Sum Squared Residuals1801.56794104266







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
12624.06492096566101.93507903433904
22325.2815675462592-2.28156754625919
32525.3058478840459-0.305847884045923
42323.1087259307447-0.108725930744718
51922.8951635532844-3.8951635532844
62923.93888365354165.06111634645841
72525.7671042073953-0.767104207395304
82123.6425327835159-2.64253278351593
92223.0963376883682-1.09633768836818
102523.50765573106521.49234426893478
112421.37931398900442.6206860109956
121820.9720150767733-2.97201507677327
132219.58748404947172.41251595052834
141521.7720459492998-6.77204594929977
152224.3572160827604-2.35721608276036
162825.06671819590232.93328180409774
172023.1549211111529-3.15492111115286
181220.0983484697977-8.09834846979775
192422.40862164392251.59137835607748
202022.4248842071563-2.42488420715633
212124.1696068479225-3.16960684792246
222021.7254699401245-1.72546994012447
232120.19928557458410.80071442541585
242322.07711773838950.922882261610536
252822.78307071660815.21692928339187
262422.69804381779551.30195618220447
272424.2138903633694-0.213890363369408
282421.27066294943042.72933705056958
292322.77683009737050.223169902629515
302323.4257658086257-0.425765808625656
312924.93278642730484.06721357269515
322422.82528290948951.17471709051053
331825.6395985341433-7.63959853414328
342526.5144645979578-1.51446459795780
352123.2472409246222-2.24724092462217
362627.2681817852479-1.26818178524791
372225.7140086053332-3.71400860533321
382223.4681646825110-1.46816468251095
392223.2429912089363-1.24299120893634
402326.1044305215467-3.10443052154669
413023.53923172243496.46076827756508
422323.2458356659081-0.245835665908052
431719.4493250113788-2.44932501137878
442324.2591307314488-1.25913073144876
452324.718575640939-1.71857564093898
462522.92501595346562.07498404653438
472421.34910761054742.65089238945262
482427.7847346279007-3.78473462790070
492323.4624507927264-0.462450792726371
502124.2581925201565-3.25819252015651
512426.0643657791175-2.06436577911748
522422.27012725854211.72987274145789
532821.95732523797186.04267476202816
541621.4412767174295-5.44127671742951
552020.3431420674669-0.343142067466904
562923.79082831951785.20917168048223
572724.155027100392.84497289961
582223.4513607170967-1.45136071709669
592824.16307306667023.83692693332979
601620.7842692974064-4.7842692974064
612523.13046757236971.86953242763029
622423.72116350494490.278836495055052
632823.87228683603194.12771316396808
642424.4263501754649-0.426350175464863
652322.86913574438600.130864255614046
663026.96129024899423.03870975100577
672421.58766038644952.41233961355052
682124.2155561910099-3.2155561910099
692523.39941978167121.60058021832882
702524.03440433253610.965595667463886
712221.03426130966230.965738690337696
722322.5659928541720.434007145828006
732622.96867371320333.03132628679671
742321.82414774342971.17585225657035
752523.14384147007231.85615852992771
762121.4371726833282-0.437172683328206
772523.65221316990411.34778683009587
782422.23222159127261.76777840872739
792923.51791576605985.48208423394016
802223.6506816303374-1.65068163033742
812723.57044995917453.42955004082551
822619.72283023584106.27716976415897
832221.35621770762110.643782292378905
842422.00366609996451.99633390003548
852723.06662345720753.93337654279254
862421.34440981731432.65559018268571
872424.6608334555894-0.660833455589397
882924.18828986329814.81171013670193
892222.1009479856673-0.100947985667272
902120.53161058427150.468389415728456
912420.44883939620613.55116060379393
922421.53003638948912.46996361051086
932321.78769337864661.21230662135342
942022.1275694295439-2.12756942954389
952721.19816599682055.80183400317952
962623.20549890798222.79450109201783
972521.82716349795983.17283650204017
982119.98852906017331.01147093982670
992120.57696099243900.423039007561035
1001920.2144630083883-1.21446300838829
1012121.3475265969951-0.347526596995136
1022120.98535861879030.0146413812096640
1031619.5584249175939-3.5584249175939
1042220.37418944242691.62581055757310
1052921.49994259286657.50005740713355
1061521.4440772756156-6.44407727561556
1071720.3888167239287-3.38881672392871
1081519.6496724848056-4.64967248480563
1092121.3278500194504-0.327850019450363
1102120.68122979831110.318770201688874
1111918.95395869932630.0460413006736854
1122417.83413307975416.16586692024592
1132021.7961238039561-1.79612380395608
1141724.4842650194948-7.48426501949476
1152324.1583086799554-1.15830867995539
1162421.84651290336122.1534870966388
1171421.5117844008051-7.5117844008051
1181922.2913507280418-3.29135072804181
1192421.68541123080252.31458876919748
1201319.9510554657143-6.95105546571428
1212224.6828942962343-2.68289429623433
1221620.5693313552517-4.56933135525173
1231922.6188802033758-3.61888020337582
1242522.07001303180972.92998696819027
1252523.50367053583781.49632946416220
1262320.81857086599072.18142913400929
1272422.79094523985431.20905476014574
1282622.77584757578553.22415242421453
1292620.82228794857985.1777120514202
1302523.41668408516811.58331591483193
1311821.5867258661787-3.58672586617873
1322119.21917348817591.78082651182413
1332622.80944116242113.19055883757895
1342321.17670413985011.82329586014985
1352319.09451566553053.90548433446946
1362221.82135829210810.178641707891903
1372021.5874808656089-1.58748086560890
1381321.2551966611107-8.25519666111074
1392420.56570935033663.43429064966338
1401520.7213804063778-5.72138040637781
1411422.2564443286843-8.25644432868428
1422223.1317158928542-1.13171589285421
1431017.0087352770752-7.0087352770752
1442423.44985471852300.550145281477033
1452220.94277158405591.05722841594408
1462424.7520455181703-0.752045518170321
1471920.7504601735956-1.75046017359557
1482021.1100095714849-1.11000957148492
1491316.3372891356001-3.33728913560005
1502019.18980657709620.810193422903832
1512222.1054696978879-0.105469697887947
1522422.36205645917981.63794354082021
1532922.23676549182526.76323450817484
1541219.9636818356675-7.96368183566751
1552019.91001701453590.0899829854640915
1562120.36394635460990.63605364539008
1572422.52000069098881.47999930901117
1582220.81357683118691.18642316881314
1592016.88329479154833.11670520845174

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 26 & 24.0649209656610 & 1.93507903433904 \tabularnewline
2 & 23 & 25.2815675462592 & -2.28156754625919 \tabularnewline
3 & 25 & 25.3058478840459 & -0.305847884045923 \tabularnewline
4 & 23 & 23.1087259307447 & -0.108725930744718 \tabularnewline
5 & 19 & 22.8951635532844 & -3.8951635532844 \tabularnewline
6 & 29 & 23.9388836535416 & 5.06111634645841 \tabularnewline
7 & 25 & 25.7671042073953 & -0.767104207395304 \tabularnewline
8 & 21 & 23.6425327835159 & -2.64253278351593 \tabularnewline
9 & 22 & 23.0963376883682 & -1.09633768836818 \tabularnewline
10 & 25 & 23.5076557310652 & 1.49234426893478 \tabularnewline
11 & 24 & 21.3793139890044 & 2.6206860109956 \tabularnewline
12 & 18 & 20.9720150767733 & -2.97201507677327 \tabularnewline
13 & 22 & 19.5874840494717 & 2.41251595052834 \tabularnewline
14 & 15 & 21.7720459492998 & -6.77204594929977 \tabularnewline
15 & 22 & 24.3572160827604 & -2.35721608276036 \tabularnewline
16 & 28 & 25.0667181959023 & 2.93328180409774 \tabularnewline
17 & 20 & 23.1549211111529 & -3.15492111115286 \tabularnewline
18 & 12 & 20.0983484697977 & -8.09834846979775 \tabularnewline
19 & 24 & 22.4086216439225 & 1.59137835607748 \tabularnewline
20 & 20 & 22.4248842071563 & -2.42488420715633 \tabularnewline
21 & 21 & 24.1696068479225 & -3.16960684792246 \tabularnewline
22 & 20 & 21.7254699401245 & -1.72546994012447 \tabularnewline
23 & 21 & 20.1992855745841 & 0.80071442541585 \tabularnewline
24 & 23 & 22.0771177383895 & 0.922882261610536 \tabularnewline
25 & 28 & 22.7830707166081 & 5.21692928339187 \tabularnewline
26 & 24 & 22.6980438177955 & 1.30195618220447 \tabularnewline
27 & 24 & 24.2138903633694 & -0.213890363369408 \tabularnewline
28 & 24 & 21.2706629494304 & 2.72933705056958 \tabularnewline
29 & 23 & 22.7768300973705 & 0.223169902629515 \tabularnewline
30 & 23 & 23.4257658086257 & -0.425765808625656 \tabularnewline
31 & 29 & 24.9327864273048 & 4.06721357269515 \tabularnewline
32 & 24 & 22.8252829094895 & 1.17471709051053 \tabularnewline
33 & 18 & 25.6395985341433 & -7.63959853414328 \tabularnewline
34 & 25 & 26.5144645979578 & -1.51446459795780 \tabularnewline
35 & 21 & 23.2472409246222 & -2.24724092462217 \tabularnewline
36 & 26 & 27.2681817852479 & -1.26818178524791 \tabularnewline
37 & 22 & 25.7140086053332 & -3.71400860533321 \tabularnewline
38 & 22 & 23.4681646825110 & -1.46816468251095 \tabularnewline
39 & 22 & 23.2429912089363 & -1.24299120893634 \tabularnewline
40 & 23 & 26.1044305215467 & -3.10443052154669 \tabularnewline
41 & 30 & 23.5392317224349 & 6.46076827756508 \tabularnewline
42 & 23 & 23.2458356659081 & -0.245835665908052 \tabularnewline
43 & 17 & 19.4493250113788 & -2.44932501137878 \tabularnewline
44 & 23 & 24.2591307314488 & -1.25913073144876 \tabularnewline
45 & 23 & 24.718575640939 & -1.71857564093898 \tabularnewline
46 & 25 & 22.9250159534656 & 2.07498404653438 \tabularnewline
47 & 24 & 21.3491076105474 & 2.65089238945262 \tabularnewline
48 & 24 & 27.7847346279007 & -3.78473462790070 \tabularnewline
49 & 23 & 23.4624507927264 & -0.462450792726371 \tabularnewline
50 & 21 & 24.2581925201565 & -3.25819252015651 \tabularnewline
51 & 24 & 26.0643657791175 & -2.06436577911748 \tabularnewline
52 & 24 & 22.2701272585421 & 1.72987274145789 \tabularnewline
53 & 28 & 21.9573252379718 & 6.04267476202816 \tabularnewline
54 & 16 & 21.4412767174295 & -5.44127671742951 \tabularnewline
55 & 20 & 20.3431420674669 & -0.343142067466904 \tabularnewline
56 & 29 & 23.7908283195178 & 5.20917168048223 \tabularnewline
57 & 27 & 24.15502710039 & 2.84497289961 \tabularnewline
58 & 22 & 23.4513607170967 & -1.45136071709669 \tabularnewline
59 & 28 & 24.1630730666702 & 3.83692693332979 \tabularnewline
60 & 16 & 20.7842692974064 & -4.7842692974064 \tabularnewline
61 & 25 & 23.1304675723697 & 1.86953242763029 \tabularnewline
62 & 24 & 23.7211635049449 & 0.278836495055052 \tabularnewline
63 & 28 & 23.8722868360319 & 4.12771316396808 \tabularnewline
64 & 24 & 24.4263501754649 & -0.426350175464863 \tabularnewline
65 & 23 & 22.8691357443860 & 0.130864255614046 \tabularnewline
66 & 30 & 26.9612902489942 & 3.03870975100577 \tabularnewline
67 & 24 & 21.5876603864495 & 2.41233961355052 \tabularnewline
68 & 21 & 24.2155561910099 & -3.2155561910099 \tabularnewline
69 & 25 & 23.3994197816712 & 1.60058021832882 \tabularnewline
70 & 25 & 24.0344043325361 & 0.965595667463886 \tabularnewline
71 & 22 & 21.0342613096623 & 0.965738690337696 \tabularnewline
72 & 23 & 22.565992854172 & 0.434007145828006 \tabularnewline
73 & 26 & 22.9686737132033 & 3.03132628679671 \tabularnewline
74 & 23 & 21.8241477434297 & 1.17585225657035 \tabularnewline
75 & 25 & 23.1438414700723 & 1.85615852992771 \tabularnewline
76 & 21 & 21.4371726833282 & -0.437172683328206 \tabularnewline
77 & 25 & 23.6522131699041 & 1.34778683009587 \tabularnewline
78 & 24 & 22.2322215912726 & 1.76777840872739 \tabularnewline
79 & 29 & 23.5179157660598 & 5.48208423394016 \tabularnewline
80 & 22 & 23.6506816303374 & -1.65068163033742 \tabularnewline
81 & 27 & 23.5704499591745 & 3.42955004082551 \tabularnewline
82 & 26 & 19.7228302358410 & 6.27716976415897 \tabularnewline
83 & 22 & 21.3562177076211 & 0.643782292378905 \tabularnewline
84 & 24 & 22.0036660999645 & 1.99633390003548 \tabularnewline
85 & 27 & 23.0666234572075 & 3.93337654279254 \tabularnewline
86 & 24 & 21.3444098173143 & 2.65559018268571 \tabularnewline
87 & 24 & 24.6608334555894 & -0.660833455589397 \tabularnewline
88 & 29 & 24.1882898632981 & 4.81171013670193 \tabularnewline
89 & 22 & 22.1009479856673 & -0.100947985667272 \tabularnewline
90 & 21 & 20.5316105842715 & 0.468389415728456 \tabularnewline
91 & 24 & 20.4488393962061 & 3.55116060379393 \tabularnewline
92 & 24 & 21.5300363894891 & 2.46996361051086 \tabularnewline
93 & 23 & 21.7876933786466 & 1.21230662135342 \tabularnewline
94 & 20 & 22.1275694295439 & -2.12756942954389 \tabularnewline
95 & 27 & 21.1981659968205 & 5.80183400317952 \tabularnewline
96 & 26 & 23.2054989079822 & 2.79450109201783 \tabularnewline
97 & 25 & 21.8271634979598 & 3.17283650204017 \tabularnewline
98 & 21 & 19.9885290601733 & 1.01147093982670 \tabularnewline
99 & 21 & 20.5769609924390 & 0.423039007561035 \tabularnewline
100 & 19 & 20.2144630083883 & -1.21446300838829 \tabularnewline
101 & 21 & 21.3475265969951 & -0.347526596995136 \tabularnewline
102 & 21 & 20.9853586187903 & 0.0146413812096640 \tabularnewline
103 & 16 & 19.5584249175939 & -3.5584249175939 \tabularnewline
104 & 22 & 20.3741894424269 & 1.62581055757310 \tabularnewline
105 & 29 & 21.4999425928665 & 7.50005740713355 \tabularnewline
106 & 15 & 21.4440772756156 & -6.44407727561556 \tabularnewline
107 & 17 & 20.3888167239287 & -3.38881672392871 \tabularnewline
108 & 15 & 19.6496724848056 & -4.64967248480563 \tabularnewline
109 & 21 & 21.3278500194504 & -0.327850019450363 \tabularnewline
110 & 21 & 20.6812297983111 & 0.318770201688874 \tabularnewline
111 & 19 & 18.9539586993263 & 0.0460413006736854 \tabularnewline
112 & 24 & 17.8341330797541 & 6.16586692024592 \tabularnewline
113 & 20 & 21.7961238039561 & -1.79612380395608 \tabularnewline
114 & 17 & 24.4842650194948 & -7.48426501949476 \tabularnewline
115 & 23 & 24.1583086799554 & -1.15830867995539 \tabularnewline
116 & 24 & 21.8465129033612 & 2.1534870966388 \tabularnewline
117 & 14 & 21.5117844008051 & -7.5117844008051 \tabularnewline
118 & 19 & 22.2913507280418 & -3.29135072804181 \tabularnewline
119 & 24 & 21.6854112308025 & 2.31458876919748 \tabularnewline
120 & 13 & 19.9510554657143 & -6.95105546571428 \tabularnewline
121 & 22 & 24.6828942962343 & -2.68289429623433 \tabularnewline
122 & 16 & 20.5693313552517 & -4.56933135525173 \tabularnewline
123 & 19 & 22.6188802033758 & -3.61888020337582 \tabularnewline
124 & 25 & 22.0700130318097 & 2.92998696819027 \tabularnewline
125 & 25 & 23.5036705358378 & 1.49632946416220 \tabularnewline
126 & 23 & 20.8185708659907 & 2.18142913400929 \tabularnewline
127 & 24 & 22.7909452398543 & 1.20905476014574 \tabularnewline
128 & 26 & 22.7758475757855 & 3.22415242421453 \tabularnewline
129 & 26 & 20.8222879485798 & 5.1777120514202 \tabularnewline
130 & 25 & 23.4166840851681 & 1.58331591483193 \tabularnewline
131 & 18 & 21.5867258661787 & -3.58672586617873 \tabularnewline
132 & 21 & 19.2191734881759 & 1.78082651182413 \tabularnewline
133 & 26 & 22.8094411624211 & 3.19055883757895 \tabularnewline
134 & 23 & 21.1767041398501 & 1.82329586014985 \tabularnewline
135 & 23 & 19.0945156655305 & 3.90548433446946 \tabularnewline
136 & 22 & 21.8213582921081 & 0.178641707891903 \tabularnewline
137 & 20 & 21.5874808656089 & -1.58748086560890 \tabularnewline
138 & 13 & 21.2551966611107 & -8.25519666111074 \tabularnewline
139 & 24 & 20.5657093503366 & 3.43429064966338 \tabularnewline
140 & 15 & 20.7213804063778 & -5.72138040637781 \tabularnewline
141 & 14 & 22.2564443286843 & -8.25644432868428 \tabularnewline
142 & 22 & 23.1317158928542 & -1.13171589285421 \tabularnewline
143 & 10 & 17.0087352770752 & -7.0087352770752 \tabularnewline
144 & 24 & 23.4498547185230 & 0.550145281477033 \tabularnewline
145 & 22 & 20.9427715840559 & 1.05722841594408 \tabularnewline
146 & 24 & 24.7520455181703 & -0.752045518170321 \tabularnewline
147 & 19 & 20.7504601735956 & -1.75046017359557 \tabularnewline
148 & 20 & 21.1100095714849 & -1.11000957148492 \tabularnewline
149 & 13 & 16.3372891356001 & -3.33728913560005 \tabularnewline
150 & 20 & 19.1898065770962 & 0.810193422903832 \tabularnewline
151 & 22 & 22.1054696978879 & -0.105469697887947 \tabularnewline
152 & 24 & 22.3620564591798 & 1.63794354082021 \tabularnewline
153 & 29 & 22.2367654918252 & 6.76323450817484 \tabularnewline
154 & 12 & 19.9636818356675 & -7.96368183566751 \tabularnewline
155 & 20 & 19.9100170145359 & 0.0899829854640915 \tabularnewline
156 & 21 & 20.3639463546099 & 0.63605364539008 \tabularnewline
157 & 24 & 22.5200006909888 & 1.47999930901117 \tabularnewline
158 & 22 & 20.8135768311869 & 1.18642316881314 \tabularnewline
159 & 20 & 16.8832947915483 & 3.11670520845174 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99545&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]26[/C][C]24.0649209656610[/C][C]1.93507903433904[/C][/ROW]
[ROW][C]2[/C][C]23[/C][C]25.2815675462592[/C][C]-2.28156754625919[/C][/ROW]
[ROW][C]3[/C][C]25[/C][C]25.3058478840459[/C][C]-0.305847884045923[/C][/ROW]
[ROW][C]4[/C][C]23[/C][C]23.1087259307447[/C][C]-0.108725930744718[/C][/ROW]
[ROW][C]5[/C][C]19[/C][C]22.8951635532844[/C][C]-3.8951635532844[/C][/ROW]
[ROW][C]6[/C][C]29[/C][C]23.9388836535416[/C][C]5.06111634645841[/C][/ROW]
[ROW][C]7[/C][C]25[/C][C]25.7671042073953[/C][C]-0.767104207395304[/C][/ROW]
[ROW][C]8[/C][C]21[/C][C]23.6425327835159[/C][C]-2.64253278351593[/C][/ROW]
[ROW][C]9[/C][C]22[/C][C]23.0963376883682[/C][C]-1.09633768836818[/C][/ROW]
[ROW][C]10[/C][C]25[/C][C]23.5076557310652[/C][C]1.49234426893478[/C][/ROW]
[ROW][C]11[/C][C]24[/C][C]21.3793139890044[/C][C]2.6206860109956[/C][/ROW]
[ROW][C]12[/C][C]18[/C][C]20.9720150767733[/C][C]-2.97201507677327[/C][/ROW]
[ROW][C]13[/C][C]22[/C][C]19.5874840494717[/C][C]2.41251595052834[/C][/ROW]
[ROW][C]14[/C][C]15[/C][C]21.7720459492998[/C][C]-6.77204594929977[/C][/ROW]
[ROW][C]15[/C][C]22[/C][C]24.3572160827604[/C][C]-2.35721608276036[/C][/ROW]
[ROW][C]16[/C][C]28[/C][C]25.0667181959023[/C][C]2.93328180409774[/C][/ROW]
[ROW][C]17[/C][C]20[/C][C]23.1549211111529[/C][C]-3.15492111115286[/C][/ROW]
[ROW][C]18[/C][C]12[/C][C]20.0983484697977[/C][C]-8.09834846979775[/C][/ROW]
[ROW][C]19[/C][C]24[/C][C]22.4086216439225[/C][C]1.59137835607748[/C][/ROW]
[ROW][C]20[/C][C]20[/C][C]22.4248842071563[/C][C]-2.42488420715633[/C][/ROW]
[ROW][C]21[/C][C]21[/C][C]24.1696068479225[/C][C]-3.16960684792246[/C][/ROW]
[ROW][C]22[/C][C]20[/C][C]21.7254699401245[/C][C]-1.72546994012447[/C][/ROW]
[ROW][C]23[/C][C]21[/C][C]20.1992855745841[/C][C]0.80071442541585[/C][/ROW]
[ROW][C]24[/C][C]23[/C][C]22.0771177383895[/C][C]0.922882261610536[/C][/ROW]
[ROW][C]25[/C][C]28[/C][C]22.7830707166081[/C][C]5.21692928339187[/C][/ROW]
[ROW][C]26[/C][C]24[/C][C]22.6980438177955[/C][C]1.30195618220447[/C][/ROW]
[ROW][C]27[/C][C]24[/C][C]24.2138903633694[/C][C]-0.213890363369408[/C][/ROW]
[ROW][C]28[/C][C]24[/C][C]21.2706629494304[/C][C]2.72933705056958[/C][/ROW]
[ROW][C]29[/C][C]23[/C][C]22.7768300973705[/C][C]0.223169902629515[/C][/ROW]
[ROW][C]30[/C][C]23[/C][C]23.4257658086257[/C][C]-0.425765808625656[/C][/ROW]
[ROW][C]31[/C][C]29[/C][C]24.9327864273048[/C][C]4.06721357269515[/C][/ROW]
[ROW][C]32[/C][C]24[/C][C]22.8252829094895[/C][C]1.17471709051053[/C][/ROW]
[ROW][C]33[/C][C]18[/C][C]25.6395985341433[/C][C]-7.63959853414328[/C][/ROW]
[ROW][C]34[/C][C]25[/C][C]26.5144645979578[/C][C]-1.51446459795780[/C][/ROW]
[ROW][C]35[/C][C]21[/C][C]23.2472409246222[/C][C]-2.24724092462217[/C][/ROW]
[ROW][C]36[/C][C]26[/C][C]27.2681817852479[/C][C]-1.26818178524791[/C][/ROW]
[ROW][C]37[/C][C]22[/C][C]25.7140086053332[/C][C]-3.71400860533321[/C][/ROW]
[ROW][C]38[/C][C]22[/C][C]23.4681646825110[/C][C]-1.46816468251095[/C][/ROW]
[ROW][C]39[/C][C]22[/C][C]23.2429912089363[/C][C]-1.24299120893634[/C][/ROW]
[ROW][C]40[/C][C]23[/C][C]26.1044305215467[/C][C]-3.10443052154669[/C][/ROW]
[ROW][C]41[/C][C]30[/C][C]23.5392317224349[/C][C]6.46076827756508[/C][/ROW]
[ROW][C]42[/C][C]23[/C][C]23.2458356659081[/C][C]-0.245835665908052[/C][/ROW]
[ROW][C]43[/C][C]17[/C][C]19.4493250113788[/C][C]-2.44932501137878[/C][/ROW]
[ROW][C]44[/C][C]23[/C][C]24.2591307314488[/C][C]-1.25913073144876[/C][/ROW]
[ROW][C]45[/C][C]23[/C][C]24.718575640939[/C][C]-1.71857564093898[/C][/ROW]
[ROW][C]46[/C][C]25[/C][C]22.9250159534656[/C][C]2.07498404653438[/C][/ROW]
[ROW][C]47[/C][C]24[/C][C]21.3491076105474[/C][C]2.65089238945262[/C][/ROW]
[ROW][C]48[/C][C]24[/C][C]27.7847346279007[/C][C]-3.78473462790070[/C][/ROW]
[ROW][C]49[/C][C]23[/C][C]23.4624507927264[/C][C]-0.462450792726371[/C][/ROW]
[ROW][C]50[/C][C]21[/C][C]24.2581925201565[/C][C]-3.25819252015651[/C][/ROW]
[ROW][C]51[/C][C]24[/C][C]26.0643657791175[/C][C]-2.06436577911748[/C][/ROW]
[ROW][C]52[/C][C]24[/C][C]22.2701272585421[/C][C]1.72987274145789[/C][/ROW]
[ROW][C]53[/C][C]28[/C][C]21.9573252379718[/C][C]6.04267476202816[/C][/ROW]
[ROW][C]54[/C][C]16[/C][C]21.4412767174295[/C][C]-5.44127671742951[/C][/ROW]
[ROW][C]55[/C][C]20[/C][C]20.3431420674669[/C][C]-0.343142067466904[/C][/ROW]
[ROW][C]56[/C][C]29[/C][C]23.7908283195178[/C][C]5.20917168048223[/C][/ROW]
[ROW][C]57[/C][C]27[/C][C]24.15502710039[/C][C]2.84497289961[/C][/ROW]
[ROW][C]58[/C][C]22[/C][C]23.4513607170967[/C][C]-1.45136071709669[/C][/ROW]
[ROW][C]59[/C][C]28[/C][C]24.1630730666702[/C][C]3.83692693332979[/C][/ROW]
[ROW][C]60[/C][C]16[/C][C]20.7842692974064[/C][C]-4.7842692974064[/C][/ROW]
[ROW][C]61[/C][C]25[/C][C]23.1304675723697[/C][C]1.86953242763029[/C][/ROW]
[ROW][C]62[/C][C]24[/C][C]23.7211635049449[/C][C]0.278836495055052[/C][/ROW]
[ROW][C]63[/C][C]28[/C][C]23.8722868360319[/C][C]4.12771316396808[/C][/ROW]
[ROW][C]64[/C][C]24[/C][C]24.4263501754649[/C][C]-0.426350175464863[/C][/ROW]
[ROW][C]65[/C][C]23[/C][C]22.8691357443860[/C][C]0.130864255614046[/C][/ROW]
[ROW][C]66[/C][C]30[/C][C]26.9612902489942[/C][C]3.03870975100577[/C][/ROW]
[ROW][C]67[/C][C]24[/C][C]21.5876603864495[/C][C]2.41233961355052[/C][/ROW]
[ROW][C]68[/C][C]21[/C][C]24.2155561910099[/C][C]-3.2155561910099[/C][/ROW]
[ROW][C]69[/C][C]25[/C][C]23.3994197816712[/C][C]1.60058021832882[/C][/ROW]
[ROW][C]70[/C][C]25[/C][C]24.0344043325361[/C][C]0.965595667463886[/C][/ROW]
[ROW][C]71[/C][C]22[/C][C]21.0342613096623[/C][C]0.965738690337696[/C][/ROW]
[ROW][C]72[/C][C]23[/C][C]22.565992854172[/C][C]0.434007145828006[/C][/ROW]
[ROW][C]73[/C][C]26[/C][C]22.9686737132033[/C][C]3.03132628679671[/C][/ROW]
[ROW][C]74[/C][C]23[/C][C]21.8241477434297[/C][C]1.17585225657035[/C][/ROW]
[ROW][C]75[/C][C]25[/C][C]23.1438414700723[/C][C]1.85615852992771[/C][/ROW]
[ROW][C]76[/C][C]21[/C][C]21.4371726833282[/C][C]-0.437172683328206[/C][/ROW]
[ROW][C]77[/C][C]25[/C][C]23.6522131699041[/C][C]1.34778683009587[/C][/ROW]
[ROW][C]78[/C][C]24[/C][C]22.2322215912726[/C][C]1.76777840872739[/C][/ROW]
[ROW][C]79[/C][C]29[/C][C]23.5179157660598[/C][C]5.48208423394016[/C][/ROW]
[ROW][C]80[/C][C]22[/C][C]23.6506816303374[/C][C]-1.65068163033742[/C][/ROW]
[ROW][C]81[/C][C]27[/C][C]23.5704499591745[/C][C]3.42955004082551[/C][/ROW]
[ROW][C]82[/C][C]26[/C][C]19.7228302358410[/C][C]6.27716976415897[/C][/ROW]
[ROW][C]83[/C][C]22[/C][C]21.3562177076211[/C][C]0.643782292378905[/C][/ROW]
[ROW][C]84[/C][C]24[/C][C]22.0036660999645[/C][C]1.99633390003548[/C][/ROW]
[ROW][C]85[/C][C]27[/C][C]23.0666234572075[/C][C]3.93337654279254[/C][/ROW]
[ROW][C]86[/C][C]24[/C][C]21.3444098173143[/C][C]2.65559018268571[/C][/ROW]
[ROW][C]87[/C][C]24[/C][C]24.6608334555894[/C][C]-0.660833455589397[/C][/ROW]
[ROW][C]88[/C][C]29[/C][C]24.1882898632981[/C][C]4.81171013670193[/C][/ROW]
[ROW][C]89[/C][C]22[/C][C]22.1009479856673[/C][C]-0.100947985667272[/C][/ROW]
[ROW][C]90[/C][C]21[/C][C]20.5316105842715[/C][C]0.468389415728456[/C][/ROW]
[ROW][C]91[/C][C]24[/C][C]20.4488393962061[/C][C]3.55116060379393[/C][/ROW]
[ROW][C]92[/C][C]24[/C][C]21.5300363894891[/C][C]2.46996361051086[/C][/ROW]
[ROW][C]93[/C][C]23[/C][C]21.7876933786466[/C][C]1.21230662135342[/C][/ROW]
[ROW][C]94[/C][C]20[/C][C]22.1275694295439[/C][C]-2.12756942954389[/C][/ROW]
[ROW][C]95[/C][C]27[/C][C]21.1981659968205[/C][C]5.80183400317952[/C][/ROW]
[ROW][C]96[/C][C]26[/C][C]23.2054989079822[/C][C]2.79450109201783[/C][/ROW]
[ROW][C]97[/C][C]25[/C][C]21.8271634979598[/C][C]3.17283650204017[/C][/ROW]
[ROW][C]98[/C][C]21[/C][C]19.9885290601733[/C][C]1.01147093982670[/C][/ROW]
[ROW][C]99[/C][C]21[/C][C]20.5769609924390[/C][C]0.423039007561035[/C][/ROW]
[ROW][C]100[/C][C]19[/C][C]20.2144630083883[/C][C]-1.21446300838829[/C][/ROW]
[ROW][C]101[/C][C]21[/C][C]21.3475265969951[/C][C]-0.347526596995136[/C][/ROW]
[ROW][C]102[/C][C]21[/C][C]20.9853586187903[/C][C]0.0146413812096640[/C][/ROW]
[ROW][C]103[/C][C]16[/C][C]19.5584249175939[/C][C]-3.5584249175939[/C][/ROW]
[ROW][C]104[/C][C]22[/C][C]20.3741894424269[/C][C]1.62581055757310[/C][/ROW]
[ROW][C]105[/C][C]29[/C][C]21.4999425928665[/C][C]7.50005740713355[/C][/ROW]
[ROW][C]106[/C][C]15[/C][C]21.4440772756156[/C][C]-6.44407727561556[/C][/ROW]
[ROW][C]107[/C][C]17[/C][C]20.3888167239287[/C][C]-3.38881672392871[/C][/ROW]
[ROW][C]108[/C][C]15[/C][C]19.6496724848056[/C][C]-4.64967248480563[/C][/ROW]
[ROW][C]109[/C][C]21[/C][C]21.3278500194504[/C][C]-0.327850019450363[/C][/ROW]
[ROW][C]110[/C][C]21[/C][C]20.6812297983111[/C][C]0.318770201688874[/C][/ROW]
[ROW][C]111[/C][C]19[/C][C]18.9539586993263[/C][C]0.0460413006736854[/C][/ROW]
[ROW][C]112[/C][C]24[/C][C]17.8341330797541[/C][C]6.16586692024592[/C][/ROW]
[ROW][C]113[/C][C]20[/C][C]21.7961238039561[/C][C]-1.79612380395608[/C][/ROW]
[ROW][C]114[/C][C]17[/C][C]24.4842650194948[/C][C]-7.48426501949476[/C][/ROW]
[ROW][C]115[/C][C]23[/C][C]24.1583086799554[/C][C]-1.15830867995539[/C][/ROW]
[ROW][C]116[/C][C]24[/C][C]21.8465129033612[/C][C]2.1534870966388[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]21.5117844008051[/C][C]-7.5117844008051[/C][/ROW]
[ROW][C]118[/C][C]19[/C][C]22.2913507280418[/C][C]-3.29135072804181[/C][/ROW]
[ROW][C]119[/C][C]24[/C][C]21.6854112308025[/C][C]2.31458876919748[/C][/ROW]
[ROW][C]120[/C][C]13[/C][C]19.9510554657143[/C][C]-6.95105546571428[/C][/ROW]
[ROW][C]121[/C][C]22[/C][C]24.6828942962343[/C][C]-2.68289429623433[/C][/ROW]
[ROW][C]122[/C][C]16[/C][C]20.5693313552517[/C][C]-4.56933135525173[/C][/ROW]
[ROW][C]123[/C][C]19[/C][C]22.6188802033758[/C][C]-3.61888020337582[/C][/ROW]
[ROW][C]124[/C][C]25[/C][C]22.0700130318097[/C][C]2.92998696819027[/C][/ROW]
[ROW][C]125[/C][C]25[/C][C]23.5036705358378[/C][C]1.49632946416220[/C][/ROW]
[ROW][C]126[/C][C]23[/C][C]20.8185708659907[/C][C]2.18142913400929[/C][/ROW]
[ROW][C]127[/C][C]24[/C][C]22.7909452398543[/C][C]1.20905476014574[/C][/ROW]
[ROW][C]128[/C][C]26[/C][C]22.7758475757855[/C][C]3.22415242421453[/C][/ROW]
[ROW][C]129[/C][C]26[/C][C]20.8222879485798[/C][C]5.1777120514202[/C][/ROW]
[ROW][C]130[/C][C]25[/C][C]23.4166840851681[/C][C]1.58331591483193[/C][/ROW]
[ROW][C]131[/C][C]18[/C][C]21.5867258661787[/C][C]-3.58672586617873[/C][/ROW]
[ROW][C]132[/C][C]21[/C][C]19.2191734881759[/C][C]1.78082651182413[/C][/ROW]
[ROW][C]133[/C][C]26[/C][C]22.8094411624211[/C][C]3.19055883757895[/C][/ROW]
[ROW][C]134[/C][C]23[/C][C]21.1767041398501[/C][C]1.82329586014985[/C][/ROW]
[ROW][C]135[/C][C]23[/C][C]19.0945156655305[/C][C]3.90548433446946[/C][/ROW]
[ROW][C]136[/C][C]22[/C][C]21.8213582921081[/C][C]0.178641707891903[/C][/ROW]
[ROW][C]137[/C][C]20[/C][C]21.5874808656089[/C][C]-1.58748086560890[/C][/ROW]
[ROW][C]138[/C][C]13[/C][C]21.2551966611107[/C][C]-8.25519666111074[/C][/ROW]
[ROW][C]139[/C][C]24[/C][C]20.5657093503366[/C][C]3.43429064966338[/C][/ROW]
[ROW][C]140[/C][C]15[/C][C]20.7213804063778[/C][C]-5.72138040637781[/C][/ROW]
[ROW][C]141[/C][C]14[/C][C]22.2564443286843[/C][C]-8.25644432868428[/C][/ROW]
[ROW][C]142[/C][C]22[/C][C]23.1317158928542[/C][C]-1.13171589285421[/C][/ROW]
[ROW][C]143[/C][C]10[/C][C]17.0087352770752[/C][C]-7.0087352770752[/C][/ROW]
[ROW][C]144[/C][C]24[/C][C]23.4498547185230[/C][C]0.550145281477033[/C][/ROW]
[ROW][C]145[/C][C]22[/C][C]20.9427715840559[/C][C]1.05722841594408[/C][/ROW]
[ROW][C]146[/C][C]24[/C][C]24.7520455181703[/C][C]-0.752045518170321[/C][/ROW]
[ROW][C]147[/C][C]19[/C][C]20.7504601735956[/C][C]-1.75046017359557[/C][/ROW]
[ROW][C]148[/C][C]20[/C][C]21.1100095714849[/C][C]-1.11000957148492[/C][/ROW]
[ROW][C]149[/C][C]13[/C][C]16.3372891356001[/C][C]-3.33728913560005[/C][/ROW]
[ROW][C]150[/C][C]20[/C][C]19.1898065770962[/C][C]0.810193422903832[/C][/ROW]
[ROW][C]151[/C][C]22[/C][C]22.1054696978879[/C][C]-0.105469697887947[/C][/ROW]
[ROW][C]152[/C][C]24[/C][C]22.3620564591798[/C][C]1.63794354082021[/C][/ROW]
[ROW][C]153[/C][C]29[/C][C]22.2367654918252[/C][C]6.76323450817484[/C][/ROW]
[ROW][C]154[/C][C]12[/C][C]19.9636818356675[/C][C]-7.96368183566751[/C][/ROW]
[ROW][C]155[/C][C]20[/C][C]19.9100170145359[/C][C]0.0899829854640915[/C][/ROW]
[ROW][C]156[/C][C]21[/C][C]20.3639463546099[/C][C]0.63605364539008[/C][/ROW]
[ROW][C]157[/C][C]24[/C][C]22.5200006909888[/C][C]1.47999930901117[/C][/ROW]
[ROW][C]158[/C][C]22[/C][C]20.8135768311869[/C][C]1.18642316881314[/C][/ROW]
[ROW][C]159[/C][C]20[/C][C]16.8832947915483[/C][C]3.11670520845174[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99545&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99545&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
12624.06492096566101.93507903433904
22325.2815675462592-2.28156754625919
32525.3058478840459-0.305847884045923
42323.1087259307447-0.108725930744718
51922.8951635532844-3.8951635532844
62923.93888365354165.06111634645841
72525.7671042073953-0.767104207395304
82123.6425327835159-2.64253278351593
92223.0963376883682-1.09633768836818
102523.50765573106521.49234426893478
112421.37931398900442.6206860109956
121820.9720150767733-2.97201507677327
132219.58748404947172.41251595052834
141521.7720459492998-6.77204594929977
152224.3572160827604-2.35721608276036
162825.06671819590232.93328180409774
172023.1549211111529-3.15492111115286
181220.0983484697977-8.09834846979775
192422.40862164392251.59137835607748
202022.4248842071563-2.42488420715633
212124.1696068479225-3.16960684792246
222021.7254699401245-1.72546994012447
232120.19928557458410.80071442541585
242322.07711773838950.922882261610536
252822.78307071660815.21692928339187
262422.69804381779551.30195618220447
272424.2138903633694-0.213890363369408
282421.27066294943042.72933705056958
292322.77683009737050.223169902629515
302323.4257658086257-0.425765808625656
312924.93278642730484.06721357269515
322422.82528290948951.17471709051053
331825.6395985341433-7.63959853414328
342526.5144645979578-1.51446459795780
352123.2472409246222-2.24724092462217
362627.2681817852479-1.26818178524791
372225.7140086053332-3.71400860533321
382223.4681646825110-1.46816468251095
392223.2429912089363-1.24299120893634
402326.1044305215467-3.10443052154669
413023.53923172243496.46076827756508
422323.2458356659081-0.245835665908052
431719.4493250113788-2.44932501137878
442324.2591307314488-1.25913073144876
452324.718575640939-1.71857564093898
462522.92501595346562.07498404653438
472421.34910761054742.65089238945262
482427.7847346279007-3.78473462790070
492323.4624507927264-0.462450792726371
502124.2581925201565-3.25819252015651
512426.0643657791175-2.06436577911748
522422.27012725854211.72987274145789
532821.95732523797186.04267476202816
541621.4412767174295-5.44127671742951
552020.3431420674669-0.343142067466904
562923.79082831951785.20917168048223
572724.155027100392.84497289961
582223.4513607170967-1.45136071709669
592824.16307306667023.83692693332979
601620.7842692974064-4.7842692974064
612523.13046757236971.86953242763029
622423.72116350494490.278836495055052
632823.87228683603194.12771316396808
642424.4263501754649-0.426350175464863
652322.86913574438600.130864255614046
663026.96129024899423.03870975100577
672421.58766038644952.41233961355052
682124.2155561910099-3.2155561910099
692523.39941978167121.60058021832882
702524.03440433253610.965595667463886
712221.03426130966230.965738690337696
722322.5659928541720.434007145828006
732622.96867371320333.03132628679671
742321.82414774342971.17585225657035
752523.14384147007231.85615852992771
762121.4371726833282-0.437172683328206
772523.65221316990411.34778683009587
782422.23222159127261.76777840872739
792923.51791576605985.48208423394016
802223.6506816303374-1.65068163033742
812723.57044995917453.42955004082551
822619.72283023584106.27716976415897
832221.35621770762110.643782292378905
842422.00366609996451.99633390003548
852723.06662345720753.93337654279254
862421.34440981731432.65559018268571
872424.6608334555894-0.660833455589397
882924.18828986329814.81171013670193
892222.1009479856673-0.100947985667272
902120.53161058427150.468389415728456
912420.44883939620613.55116060379393
922421.53003638948912.46996361051086
932321.78769337864661.21230662135342
942022.1275694295439-2.12756942954389
952721.19816599682055.80183400317952
962623.20549890798222.79450109201783
972521.82716349795983.17283650204017
982119.98852906017331.01147093982670
992120.57696099243900.423039007561035
1001920.2144630083883-1.21446300838829
1012121.3475265969951-0.347526596995136
1022120.98535861879030.0146413812096640
1031619.5584249175939-3.5584249175939
1042220.37418944242691.62581055757310
1052921.49994259286657.50005740713355
1061521.4440772756156-6.44407727561556
1071720.3888167239287-3.38881672392871
1081519.6496724848056-4.64967248480563
1092121.3278500194504-0.327850019450363
1102120.68122979831110.318770201688874
1111918.95395869932630.0460413006736854
1122417.83413307975416.16586692024592
1132021.7961238039561-1.79612380395608
1141724.4842650194948-7.48426501949476
1152324.1583086799554-1.15830867995539
1162421.84651290336122.1534870966388
1171421.5117844008051-7.5117844008051
1181922.2913507280418-3.29135072804181
1192421.68541123080252.31458876919748
1201319.9510554657143-6.95105546571428
1212224.6828942962343-2.68289429623433
1221620.5693313552517-4.56933135525173
1231922.6188802033758-3.61888020337582
1242522.07001303180972.92998696819027
1252523.50367053583781.49632946416220
1262320.81857086599072.18142913400929
1272422.79094523985431.20905476014574
1282622.77584757578553.22415242421453
1292620.82228794857985.1777120514202
1302523.41668408516811.58331591483193
1311821.5867258661787-3.58672586617873
1322119.21917348817591.78082651182413
1332622.80944116242113.19055883757895
1342321.17670413985011.82329586014985
1352319.09451566553053.90548433446946
1362221.82135829210810.178641707891903
1372021.5874808656089-1.58748086560890
1381321.2551966611107-8.25519666111074
1392420.56570935033663.43429064966338
1401520.7213804063778-5.72138040637781
1411422.2564443286843-8.25644432868428
1422223.1317158928542-1.13171589285421
1431017.0087352770752-7.0087352770752
1442423.44985471852300.550145281477033
1452220.94277158405591.05722841594408
1462424.7520455181703-0.752045518170321
1471920.7504601735956-1.75046017359557
1482021.1100095714849-1.11000957148492
1491316.3372891356001-3.33728913560005
1502019.18980657709620.810193422903832
1512222.1054696978879-0.105469697887947
1522422.36205645917981.63794354082021
1532922.23676549182526.76323450817484
1541219.9636818356675-7.96368183566751
1552019.91001701453590.0899829854640915
1562120.36394635460990.63605364539008
1572422.52000069098881.47999930901117
1582220.81357683118691.18642316881314
1592016.88329479154833.11670520845174







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.6804502239025810.6390995521948380.319549776097419
110.6359845469964390.7280309060071220.364015453003561
120.5773228279576070.8453543440847850.422677172042393
130.5810684053236670.8378631893526660.418931594676333
140.733906275182730.5321874496345410.266093724817270
150.6530798437767330.6938403124465340.346920156223267
160.6521921436071140.6956157127857710.347807856392886
170.5662070335618450.867585932876310.433792966438155
180.7101504908974650.579699018205070.289849509102535
190.6481914631670730.7036170736658540.351808536832927
200.5945304762171810.8109390475656370.405469523782818
210.5250896662042770.9498206675914460.474910333795723
220.4515235697875980.9030471395751950.548476430212402
230.4402156067594480.8804312135188950.559784393240552
240.4900986527266990.9801973054533980.509901347273301
250.6606311602823580.6787376794352840.339368839717642
260.5963980430771340.8072039138457310.403601956922866
270.5329678721165310.9340642557669380.467032127883469
280.5126520826509820.9746958346980370.487347917349018
290.4483144602000590.8966289204001180.551685539799941
300.3872217486997510.7744434973995010.612778251300249
310.3878114895475050.775622979095010.612188510452495
320.3278855329750050.6557710659500090.672114467024995
330.5978659080943530.8042681838112930.402134091905647
340.5567158145458840.8865683709082310.443284185454116
350.526900675558120.946198648883760.47309932444188
360.4715891415402040.9431782830804090.528410858459796
370.4576603193029050.9153206386058090.542339680697095
380.4060765840963740.8121531681927480.593923415903626
390.3577383449375550.715476689875110.642261655062445
400.3352454936230210.6704909872460410.66475450637698
410.4995362704088990.9990725408177990.500463729591101
420.4457672721413890.8915345442827770.554232727858611
430.4181992280897670.8363984561795340.581800771910233
440.3718402798165760.7436805596331520.628159720183424
450.3330373451354030.6660746902708070.666962654864597
460.3065409991508900.6130819983017790.69345900084911
470.2827636255501940.5655272511003870.717236374449806
480.2792870119279520.5585740238559050.720712988072048
490.2426668848939630.4853337697879250.757333115106037
500.2433026211488800.4866052422977610.75669737885112
510.2237179986766640.4474359973533270.776282001323336
520.1941482242719290.3882964485438580.80585177572807
530.2612842700944990.5225685401889980.738715729905501
540.3412991503500410.6825983007000830.658700849649959
550.3258444337044210.6516888674088420.674155566295579
560.3859336818903160.7718673637806310.614066318109684
570.3627597123779660.7255194247559310.637240287622034
580.3326722507384280.6653445014768560.667327749261572
590.3326737070879130.6653474141758260.667326292912087
600.4139420831919780.8278841663839560.586057916808022
610.3702021789942060.7404043579884120.629797821005794
620.3284034420652210.6568068841304430.671596557934778
630.3298858217698780.6597716435397550.670114178230122
640.2964275365194090.5928550730388190.70357246348059
650.2580870371891340.5161740743782670.741912962810866
660.2419390080926380.4838780161852760.758060991907362
670.2140437762634570.4280875525269130.785956223736543
680.2368802300568610.4737604601137230.763119769943139
690.2040719147096410.4081438294192810.79592808529036
700.1722071065926520.3444142131853040.827792893407348
710.1441737298473980.2883474596947950.855826270152602
720.1198343477874190.2396686955748380.880165652212581
730.1042005502491530.2084011004983050.895799449750847
740.084677170651680.1693543413033600.91532282934832
750.06887227522000150.1377445504400030.931127724779999
760.05760255258594770.1152051051718950.942397447414052
770.04561983679789520.09123967359579040.954380163202105
780.03554720378930120.07109440757860250.964452796210699
790.04059782692434970.08119565384869940.95940217307565
800.03731986826958480.07463973653916970.962680131730415
810.0312864294062720.0625728588125440.968713570593728
820.04152705267152830.08305410534305660.958472947328472
830.03243673017945630.06487346035891270.967563269820544
840.02539268363400810.05078536726801630.974607316365992
850.02331818975169050.0466363795033810.97668181024831
860.01845570837262840.03691141674525670.981544291627372
870.01503378454415620.03006756908831240.984966215455844
880.01546107106068400.03092214212136800.984538928939316
890.01300566474350190.02601132948700380.986994335256498
900.00983515067279130.01967030134558260.990164849327209
910.008402143395415430.01680428679083090.991597856604585
920.00686449231056660.01372898462113320.993135507689433
930.005058387650097560.01011677530019510.994941612349902
940.004820755449273880.009641510898547760.995179244550726
950.007192716380490160.01438543276098030.99280728361951
960.005871846263454150.01174369252690830.994128153736546
970.005023763093092380.01004752618618480.994976236906908
980.003851196784266960.007702393568533920.996148803215733
990.002911770444099860.005823540888199730.9970882295559
1000.002425581687420710.004851163374841420.99757441831258
1010.001912568333834190.003825136667668380.998087431666166
1020.001408128273150780.002816256546301550.99859187172685
1030.001735412085738810.003470824171477630.998264587914261
1040.001357701594012310.002715403188024620.998642298405988
1050.005962919851494480.01192583970298900.994037080148505
1060.01447366928777800.02894733857555610.985526330712222
1070.01530064899359650.03060129798719290.984699351006403
1080.02084117505882950.0416823501176590.97915882494117
1090.01631170216579540.03262340433159080.983688297834205
1100.01189681279474480.02379362558948960.988103187205255
1110.008646927914932930.01729385582986590.991353072085067
1120.02493434159919370.04986868319838740.975065658400806
1130.02507739235758050.05015478471516090.97492260764242
1140.06195122205291080.1239024441058220.938048777947089
1150.05330245910814070.1066049182162810.94669754089186
1160.04501058892531990.09002117785063970.95498941107468
1170.1160999017866240.2321998035732470.883900098213376
1180.1100225462523990.2200450925047990.8899774537476
1190.09882388731907080.1976477746381420.901176112680929
1200.1702983093600650.340596618720130.829701690639935
1210.1678873785131350.335774757026270.832112621486865
1220.2123224307058740.4246448614117480.787677569294126
1230.2125495016621850.4250990033243710.787450498337815
1240.2025957436443620.4051914872887240.797404256355638
1250.1772626885288180.3545253770576360.822737311471182
1260.1439701944702380.2879403889404770.856029805529762
1270.1143083954034210.2286167908068420.885691604596579
1280.1121201606485000.2242403212970010.8878798393515
1290.1560469377507490.3120938755014970.843953062249251
1300.1366434140822420.2732868281644840.863356585917758
1310.1161721983223270.2323443966446540.883827801677673
1320.1044256611782440.2088513223564880.895574338821756
1330.1030839063850610.2061678127701220.896916093614939
1340.09302892207644660.1860578441528930.906971077923553
1350.2102191741486180.4204383482972350.789780825851382
1360.1714376967481810.3428753934963620.828562303251819
1370.1466139592307180.2932279184614360.853386040769282
1380.2046025977773900.4092051955547790.79539740222261
1390.4642828774942640.9285657549885280.535717122505736
1400.4103980470633140.8207960941266290.589601952936686
1410.5654613320190320.8690773359619370.434538667980968
1420.4768371488295740.9536742976591490.523162851170426
1430.6484408287010840.7031183425978330.351559171298916
1440.603518416498860.7929631670022790.396481583501139
1450.5137474154065660.9725051691868690.486252584593434
1460.4098380138521340.8196760277042680.590161986147866
1470.4153038536241720.8306077072483450.584696146375828
1480.2855480842012730.5710961684025460.714451915798727
1490.1989602590167670.3979205180335340.801039740983233

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.680450223902581 & 0.639099552194838 & 0.319549776097419 \tabularnewline
11 & 0.635984546996439 & 0.728030906007122 & 0.364015453003561 \tabularnewline
12 & 0.577322827957607 & 0.845354344084785 & 0.422677172042393 \tabularnewline
13 & 0.581068405323667 & 0.837863189352666 & 0.418931594676333 \tabularnewline
14 & 0.73390627518273 & 0.532187449634541 & 0.266093724817270 \tabularnewline
15 & 0.653079843776733 & 0.693840312446534 & 0.346920156223267 \tabularnewline
16 & 0.652192143607114 & 0.695615712785771 & 0.347807856392886 \tabularnewline
17 & 0.566207033561845 & 0.86758593287631 & 0.433792966438155 \tabularnewline
18 & 0.710150490897465 & 0.57969901820507 & 0.289849509102535 \tabularnewline
19 & 0.648191463167073 & 0.703617073665854 & 0.351808536832927 \tabularnewline
20 & 0.594530476217181 & 0.810939047565637 & 0.405469523782818 \tabularnewline
21 & 0.525089666204277 & 0.949820667591446 & 0.474910333795723 \tabularnewline
22 & 0.451523569787598 & 0.903047139575195 & 0.548476430212402 \tabularnewline
23 & 0.440215606759448 & 0.880431213518895 & 0.559784393240552 \tabularnewline
24 & 0.490098652726699 & 0.980197305453398 & 0.509901347273301 \tabularnewline
25 & 0.660631160282358 & 0.678737679435284 & 0.339368839717642 \tabularnewline
26 & 0.596398043077134 & 0.807203913845731 & 0.403601956922866 \tabularnewline
27 & 0.532967872116531 & 0.934064255766938 & 0.467032127883469 \tabularnewline
28 & 0.512652082650982 & 0.974695834698037 & 0.487347917349018 \tabularnewline
29 & 0.448314460200059 & 0.896628920400118 & 0.551685539799941 \tabularnewline
30 & 0.387221748699751 & 0.774443497399501 & 0.612778251300249 \tabularnewline
31 & 0.387811489547505 & 0.77562297909501 & 0.612188510452495 \tabularnewline
32 & 0.327885532975005 & 0.655771065950009 & 0.672114467024995 \tabularnewline
33 & 0.597865908094353 & 0.804268183811293 & 0.402134091905647 \tabularnewline
34 & 0.556715814545884 & 0.886568370908231 & 0.443284185454116 \tabularnewline
35 & 0.52690067555812 & 0.94619864888376 & 0.47309932444188 \tabularnewline
36 & 0.471589141540204 & 0.943178283080409 & 0.528410858459796 \tabularnewline
37 & 0.457660319302905 & 0.915320638605809 & 0.542339680697095 \tabularnewline
38 & 0.406076584096374 & 0.812153168192748 & 0.593923415903626 \tabularnewline
39 & 0.357738344937555 & 0.71547668987511 & 0.642261655062445 \tabularnewline
40 & 0.335245493623021 & 0.670490987246041 & 0.66475450637698 \tabularnewline
41 & 0.499536270408899 & 0.999072540817799 & 0.500463729591101 \tabularnewline
42 & 0.445767272141389 & 0.891534544282777 & 0.554232727858611 \tabularnewline
43 & 0.418199228089767 & 0.836398456179534 & 0.581800771910233 \tabularnewline
44 & 0.371840279816576 & 0.743680559633152 & 0.628159720183424 \tabularnewline
45 & 0.333037345135403 & 0.666074690270807 & 0.666962654864597 \tabularnewline
46 & 0.306540999150890 & 0.613081998301779 & 0.69345900084911 \tabularnewline
47 & 0.282763625550194 & 0.565527251100387 & 0.717236374449806 \tabularnewline
48 & 0.279287011927952 & 0.558574023855905 & 0.720712988072048 \tabularnewline
49 & 0.242666884893963 & 0.485333769787925 & 0.757333115106037 \tabularnewline
50 & 0.243302621148880 & 0.486605242297761 & 0.75669737885112 \tabularnewline
51 & 0.223717998676664 & 0.447435997353327 & 0.776282001323336 \tabularnewline
52 & 0.194148224271929 & 0.388296448543858 & 0.80585177572807 \tabularnewline
53 & 0.261284270094499 & 0.522568540188998 & 0.738715729905501 \tabularnewline
54 & 0.341299150350041 & 0.682598300700083 & 0.658700849649959 \tabularnewline
55 & 0.325844433704421 & 0.651688867408842 & 0.674155566295579 \tabularnewline
56 & 0.385933681890316 & 0.771867363780631 & 0.614066318109684 \tabularnewline
57 & 0.362759712377966 & 0.725519424755931 & 0.637240287622034 \tabularnewline
58 & 0.332672250738428 & 0.665344501476856 & 0.667327749261572 \tabularnewline
59 & 0.332673707087913 & 0.665347414175826 & 0.667326292912087 \tabularnewline
60 & 0.413942083191978 & 0.827884166383956 & 0.586057916808022 \tabularnewline
61 & 0.370202178994206 & 0.740404357988412 & 0.629797821005794 \tabularnewline
62 & 0.328403442065221 & 0.656806884130443 & 0.671596557934778 \tabularnewline
63 & 0.329885821769878 & 0.659771643539755 & 0.670114178230122 \tabularnewline
64 & 0.296427536519409 & 0.592855073038819 & 0.70357246348059 \tabularnewline
65 & 0.258087037189134 & 0.516174074378267 & 0.741912962810866 \tabularnewline
66 & 0.241939008092638 & 0.483878016185276 & 0.758060991907362 \tabularnewline
67 & 0.214043776263457 & 0.428087552526913 & 0.785956223736543 \tabularnewline
68 & 0.236880230056861 & 0.473760460113723 & 0.763119769943139 \tabularnewline
69 & 0.204071914709641 & 0.408143829419281 & 0.79592808529036 \tabularnewline
70 & 0.172207106592652 & 0.344414213185304 & 0.827792893407348 \tabularnewline
71 & 0.144173729847398 & 0.288347459694795 & 0.855826270152602 \tabularnewline
72 & 0.119834347787419 & 0.239668695574838 & 0.880165652212581 \tabularnewline
73 & 0.104200550249153 & 0.208401100498305 & 0.895799449750847 \tabularnewline
74 & 0.08467717065168 & 0.169354341303360 & 0.91532282934832 \tabularnewline
75 & 0.0688722752200015 & 0.137744550440003 & 0.931127724779999 \tabularnewline
76 & 0.0576025525859477 & 0.115205105171895 & 0.942397447414052 \tabularnewline
77 & 0.0456198367978952 & 0.0912396735957904 & 0.954380163202105 \tabularnewline
78 & 0.0355472037893012 & 0.0710944075786025 & 0.964452796210699 \tabularnewline
79 & 0.0405978269243497 & 0.0811956538486994 & 0.95940217307565 \tabularnewline
80 & 0.0373198682695848 & 0.0746397365391697 & 0.962680131730415 \tabularnewline
81 & 0.031286429406272 & 0.062572858812544 & 0.968713570593728 \tabularnewline
82 & 0.0415270526715283 & 0.0830541053430566 & 0.958472947328472 \tabularnewline
83 & 0.0324367301794563 & 0.0648734603589127 & 0.967563269820544 \tabularnewline
84 & 0.0253926836340081 & 0.0507853672680163 & 0.974607316365992 \tabularnewline
85 & 0.0233181897516905 & 0.046636379503381 & 0.97668181024831 \tabularnewline
86 & 0.0184557083726284 & 0.0369114167452567 & 0.981544291627372 \tabularnewline
87 & 0.0150337845441562 & 0.0300675690883124 & 0.984966215455844 \tabularnewline
88 & 0.0154610710606840 & 0.0309221421213680 & 0.984538928939316 \tabularnewline
89 & 0.0130056647435019 & 0.0260113294870038 & 0.986994335256498 \tabularnewline
90 & 0.0098351506727913 & 0.0196703013455826 & 0.990164849327209 \tabularnewline
91 & 0.00840214339541543 & 0.0168042867908309 & 0.991597856604585 \tabularnewline
92 & 0.0068644923105666 & 0.0137289846211332 & 0.993135507689433 \tabularnewline
93 & 0.00505838765009756 & 0.0101167753001951 & 0.994941612349902 \tabularnewline
94 & 0.00482075544927388 & 0.00964151089854776 & 0.995179244550726 \tabularnewline
95 & 0.00719271638049016 & 0.0143854327609803 & 0.99280728361951 \tabularnewline
96 & 0.00587184626345415 & 0.0117436925269083 & 0.994128153736546 \tabularnewline
97 & 0.00502376309309238 & 0.0100475261861848 & 0.994976236906908 \tabularnewline
98 & 0.00385119678426696 & 0.00770239356853392 & 0.996148803215733 \tabularnewline
99 & 0.00291177044409986 & 0.00582354088819973 & 0.9970882295559 \tabularnewline
100 & 0.00242558168742071 & 0.00485116337484142 & 0.99757441831258 \tabularnewline
101 & 0.00191256833383419 & 0.00382513666766838 & 0.998087431666166 \tabularnewline
102 & 0.00140812827315078 & 0.00281625654630155 & 0.99859187172685 \tabularnewline
103 & 0.00173541208573881 & 0.00347082417147763 & 0.998264587914261 \tabularnewline
104 & 0.00135770159401231 & 0.00271540318802462 & 0.998642298405988 \tabularnewline
105 & 0.00596291985149448 & 0.0119258397029890 & 0.994037080148505 \tabularnewline
106 & 0.0144736692877780 & 0.0289473385755561 & 0.985526330712222 \tabularnewline
107 & 0.0153006489935965 & 0.0306012979871929 & 0.984699351006403 \tabularnewline
108 & 0.0208411750588295 & 0.041682350117659 & 0.97915882494117 \tabularnewline
109 & 0.0163117021657954 & 0.0326234043315908 & 0.983688297834205 \tabularnewline
110 & 0.0118968127947448 & 0.0237936255894896 & 0.988103187205255 \tabularnewline
111 & 0.00864692791493293 & 0.0172938558298659 & 0.991353072085067 \tabularnewline
112 & 0.0249343415991937 & 0.0498686831983874 & 0.975065658400806 \tabularnewline
113 & 0.0250773923575805 & 0.0501547847151609 & 0.97492260764242 \tabularnewline
114 & 0.0619512220529108 & 0.123902444105822 & 0.938048777947089 \tabularnewline
115 & 0.0533024591081407 & 0.106604918216281 & 0.94669754089186 \tabularnewline
116 & 0.0450105889253199 & 0.0900211778506397 & 0.95498941107468 \tabularnewline
117 & 0.116099901786624 & 0.232199803573247 & 0.883900098213376 \tabularnewline
118 & 0.110022546252399 & 0.220045092504799 & 0.8899774537476 \tabularnewline
119 & 0.0988238873190708 & 0.197647774638142 & 0.901176112680929 \tabularnewline
120 & 0.170298309360065 & 0.34059661872013 & 0.829701690639935 \tabularnewline
121 & 0.167887378513135 & 0.33577475702627 & 0.832112621486865 \tabularnewline
122 & 0.212322430705874 & 0.424644861411748 & 0.787677569294126 \tabularnewline
123 & 0.212549501662185 & 0.425099003324371 & 0.787450498337815 \tabularnewline
124 & 0.202595743644362 & 0.405191487288724 & 0.797404256355638 \tabularnewline
125 & 0.177262688528818 & 0.354525377057636 & 0.822737311471182 \tabularnewline
126 & 0.143970194470238 & 0.287940388940477 & 0.856029805529762 \tabularnewline
127 & 0.114308395403421 & 0.228616790806842 & 0.885691604596579 \tabularnewline
128 & 0.112120160648500 & 0.224240321297001 & 0.8878798393515 \tabularnewline
129 & 0.156046937750749 & 0.312093875501497 & 0.843953062249251 \tabularnewline
130 & 0.136643414082242 & 0.273286828164484 & 0.863356585917758 \tabularnewline
131 & 0.116172198322327 & 0.232344396644654 & 0.883827801677673 \tabularnewline
132 & 0.104425661178244 & 0.208851322356488 & 0.895574338821756 \tabularnewline
133 & 0.103083906385061 & 0.206167812770122 & 0.896916093614939 \tabularnewline
134 & 0.0930289220764466 & 0.186057844152893 & 0.906971077923553 \tabularnewline
135 & 0.210219174148618 & 0.420438348297235 & 0.789780825851382 \tabularnewline
136 & 0.171437696748181 & 0.342875393496362 & 0.828562303251819 \tabularnewline
137 & 0.146613959230718 & 0.293227918461436 & 0.853386040769282 \tabularnewline
138 & 0.204602597777390 & 0.409205195554779 & 0.79539740222261 \tabularnewline
139 & 0.464282877494264 & 0.928565754988528 & 0.535717122505736 \tabularnewline
140 & 0.410398047063314 & 0.820796094126629 & 0.589601952936686 \tabularnewline
141 & 0.565461332019032 & 0.869077335961937 & 0.434538667980968 \tabularnewline
142 & 0.476837148829574 & 0.953674297659149 & 0.523162851170426 \tabularnewline
143 & 0.648440828701084 & 0.703118342597833 & 0.351559171298916 \tabularnewline
144 & 0.60351841649886 & 0.792963167002279 & 0.396481583501139 \tabularnewline
145 & 0.513747415406566 & 0.972505169186869 & 0.486252584593434 \tabularnewline
146 & 0.409838013852134 & 0.819676027704268 & 0.590161986147866 \tabularnewline
147 & 0.415303853624172 & 0.830607707248345 & 0.584696146375828 \tabularnewline
148 & 0.285548084201273 & 0.571096168402546 & 0.714451915798727 \tabularnewline
149 & 0.198960259016767 & 0.397920518033534 & 0.801039740983233 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99545&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]10[/C][C]0.680450223902581[/C][C]0.639099552194838[/C][C]0.319549776097419[/C][/ROW]
[ROW][C]11[/C][C]0.635984546996439[/C][C]0.728030906007122[/C][C]0.364015453003561[/C][/ROW]
[ROW][C]12[/C][C]0.577322827957607[/C][C]0.845354344084785[/C][C]0.422677172042393[/C][/ROW]
[ROW][C]13[/C][C]0.581068405323667[/C][C]0.837863189352666[/C][C]0.418931594676333[/C][/ROW]
[ROW][C]14[/C][C]0.73390627518273[/C][C]0.532187449634541[/C][C]0.266093724817270[/C][/ROW]
[ROW][C]15[/C][C]0.653079843776733[/C][C]0.693840312446534[/C][C]0.346920156223267[/C][/ROW]
[ROW][C]16[/C][C]0.652192143607114[/C][C]0.695615712785771[/C][C]0.347807856392886[/C][/ROW]
[ROW][C]17[/C][C]0.566207033561845[/C][C]0.86758593287631[/C][C]0.433792966438155[/C][/ROW]
[ROW][C]18[/C][C]0.710150490897465[/C][C]0.57969901820507[/C][C]0.289849509102535[/C][/ROW]
[ROW][C]19[/C][C]0.648191463167073[/C][C]0.703617073665854[/C][C]0.351808536832927[/C][/ROW]
[ROW][C]20[/C][C]0.594530476217181[/C][C]0.810939047565637[/C][C]0.405469523782818[/C][/ROW]
[ROW][C]21[/C][C]0.525089666204277[/C][C]0.949820667591446[/C][C]0.474910333795723[/C][/ROW]
[ROW][C]22[/C][C]0.451523569787598[/C][C]0.903047139575195[/C][C]0.548476430212402[/C][/ROW]
[ROW][C]23[/C][C]0.440215606759448[/C][C]0.880431213518895[/C][C]0.559784393240552[/C][/ROW]
[ROW][C]24[/C][C]0.490098652726699[/C][C]0.980197305453398[/C][C]0.509901347273301[/C][/ROW]
[ROW][C]25[/C][C]0.660631160282358[/C][C]0.678737679435284[/C][C]0.339368839717642[/C][/ROW]
[ROW][C]26[/C][C]0.596398043077134[/C][C]0.807203913845731[/C][C]0.403601956922866[/C][/ROW]
[ROW][C]27[/C][C]0.532967872116531[/C][C]0.934064255766938[/C][C]0.467032127883469[/C][/ROW]
[ROW][C]28[/C][C]0.512652082650982[/C][C]0.974695834698037[/C][C]0.487347917349018[/C][/ROW]
[ROW][C]29[/C][C]0.448314460200059[/C][C]0.896628920400118[/C][C]0.551685539799941[/C][/ROW]
[ROW][C]30[/C][C]0.387221748699751[/C][C]0.774443497399501[/C][C]0.612778251300249[/C][/ROW]
[ROW][C]31[/C][C]0.387811489547505[/C][C]0.77562297909501[/C][C]0.612188510452495[/C][/ROW]
[ROW][C]32[/C][C]0.327885532975005[/C][C]0.655771065950009[/C][C]0.672114467024995[/C][/ROW]
[ROW][C]33[/C][C]0.597865908094353[/C][C]0.804268183811293[/C][C]0.402134091905647[/C][/ROW]
[ROW][C]34[/C][C]0.556715814545884[/C][C]0.886568370908231[/C][C]0.443284185454116[/C][/ROW]
[ROW][C]35[/C][C]0.52690067555812[/C][C]0.94619864888376[/C][C]0.47309932444188[/C][/ROW]
[ROW][C]36[/C][C]0.471589141540204[/C][C]0.943178283080409[/C][C]0.528410858459796[/C][/ROW]
[ROW][C]37[/C][C]0.457660319302905[/C][C]0.915320638605809[/C][C]0.542339680697095[/C][/ROW]
[ROW][C]38[/C][C]0.406076584096374[/C][C]0.812153168192748[/C][C]0.593923415903626[/C][/ROW]
[ROW][C]39[/C][C]0.357738344937555[/C][C]0.71547668987511[/C][C]0.642261655062445[/C][/ROW]
[ROW][C]40[/C][C]0.335245493623021[/C][C]0.670490987246041[/C][C]0.66475450637698[/C][/ROW]
[ROW][C]41[/C][C]0.499536270408899[/C][C]0.999072540817799[/C][C]0.500463729591101[/C][/ROW]
[ROW][C]42[/C][C]0.445767272141389[/C][C]0.891534544282777[/C][C]0.554232727858611[/C][/ROW]
[ROW][C]43[/C][C]0.418199228089767[/C][C]0.836398456179534[/C][C]0.581800771910233[/C][/ROW]
[ROW][C]44[/C][C]0.371840279816576[/C][C]0.743680559633152[/C][C]0.628159720183424[/C][/ROW]
[ROW][C]45[/C][C]0.333037345135403[/C][C]0.666074690270807[/C][C]0.666962654864597[/C][/ROW]
[ROW][C]46[/C][C]0.306540999150890[/C][C]0.613081998301779[/C][C]0.69345900084911[/C][/ROW]
[ROW][C]47[/C][C]0.282763625550194[/C][C]0.565527251100387[/C][C]0.717236374449806[/C][/ROW]
[ROW][C]48[/C][C]0.279287011927952[/C][C]0.558574023855905[/C][C]0.720712988072048[/C][/ROW]
[ROW][C]49[/C][C]0.242666884893963[/C][C]0.485333769787925[/C][C]0.757333115106037[/C][/ROW]
[ROW][C]50[/C][C]0.243302621148880[/C][C]0.486605242297761[/C][C]0.75669737885112[/C][/ROW]
[ROW][C]51[/C][C]0.223717998676664[/C][C]0.447435997353327[/C][C]0.776282001323336[/C][/ROW]
[ROW][C]52[/C][C]0.194148224271929[/C][C]0.388296448543858[/C][C]0.80585177572807[/C][/ROW]
[ROW][C]53[/C][C]0.261284270094499[/C][C]0.522568540188998[/C][C]0.738715729905501[/C][/ROW]
[ROW][C]54[/C][C]0.341299150350041[/C][C]0.682598300700083[/C][C]0.658700849649959[/C][/ROW]
[ROW][C]55[/C][C]0.325844433704421[/C][C]0.651688867408842[/C][C]0.674155566295579[/C][/ROW]
[ROW][C]56[/C][C]0.385933681890316[/C][C]0.771867363780631[/C][C]0.614066318109684[/C][/ROW]
[ROW][C]57[/C][C]0.362759712377966[/C][C]0.725519424755931[/C][C]0.637240287622034[/C][/ROW]
[ROW][C]58[/C][C]0.332672250738428[/C][C]0.665344501476856[/C][C]0.667327749261572[/C][/ROW]
[ROW][C]59[/C][C]0.332673707087913[/C][C]0.665347414175826[/C][C]0.667326292912087[/C][/ROW]
[ROW][C]60[/C][C]0.413942083191978[/C][C]0.827884166383956[/C][C]0.586057916808022[/C][/ROW]
[ROW][C]61[/C][C]0.370202178994206[/C][C]0.740404357988412[/C][C]0.629797821005794[/C][/ROW]
[ROW][C]62[/C][C]0.328403442065221[/C][C]0.656806884130443[/C][C]0.671596557934778[/C][/ROW]
[ROW][C]63[/C][C]0.329885821769878[/C][C]0.659771643539755[/C][C]0.670114178230122[/C][/ROW]
[ROW][C]64[/C][C]0.296427536519409[/C][C]0.592855073038819[/C][C]0.70357246348059[/C][/ROW]
[ROW][C]65[/C][C]0.258087037189134[/C][C]0.516174074378267[/C][C]0.741912962810866[/C][/ROW]
[ROW][C]66[/C][C]0.241939008092638[/C][C]0.483878016185276[/C][C]0.758060991907362[/C][/ROW]
[ROW][C]67[/C][C]0.214043776263457[/C][C]0.428087552526913[/C][C]0.785956223736543[/C][/ROW]
[ROW][C]68[/C][C]0.236880230056861[/C][C]0.473760460113723[/C][C]0.763119769943139[/C][/ROW]
[ROW][C]69[/C][C]0.204071914709641[/C][C]0.408143829419281[/C][C]0.79592808529036[/C][/ROW]
[ROW][C]70[/C][C]0.172207106592652[/C][C]0.344414213185304[/C][C]0.827792893407348[/C][/ROW]
[ROW][C]71[/C][C]0.144173729847398[/C][C]0.288347459694795[/C][C]0.855826270152602[/C][/ROW]
[ROW][C]72[/C][C]0.119834347787419[/C][C]0.239668695574838[/C][C]0.880165652212581[/C][/ROW]
[ROW][C]73[/C][C]0.104200550249153[/C][C]0.208401100498305[/C][C]0.895799449750847[/C][/ROW]
[ROW][C]74[/C][C]0.08467717065168[/C][C]0.169354341303360[/C][C]0.91532282934832[/C][/ROW]
[ROW][C]75[/C][C]0.0688722752200015[/C][C]0.137744550440003[/C][C]0.931127724779999[/C][/ROW]
[ROW][C]76[/C][C]0.0576025525859477[/C][C]0.115205105171895[/C][C]0.942397447414052[/C][/ROW]
[ROW][C]77[/C][C]0.0456198367978952[/C][C]0.0912396735957904[/C][C]0.954380163202105[/C][/ROW]
[ROW][C]78[/C][C]0.0355472037893012[/C][C]0.0710944075786025[/C][C]0.964452796210699[/C][/ROW]
[ROW][C]79[/C][C]0.0405978269243497[/C][C]0.0811956538486994[/C][C]0.95940217307565[/C][/ROW]
[ROW][C]80[/C][C]0.0373198682695848[/C][C]0.0746397365391697[/C][C]0.962680131730415[/C][/ROW]
[ROW][C]81[/C][C]0.031286429406272[/C][C]0.062572858812544[/C][C]0.968713570593728[/C][/ROW]
[ROW][C]82[/C][C]0.0415270526715283[/C][C]0.0830541053430566[/C][C]0.958472947328472[/C][/ROW]
[ROW][C]83[/C][C]0.0324367301794563[/C][C]0.0648734603589127[/C][C]0.967563269820544[/C][/ROW]
[ROW][C]84[/C][C]0.0253926836340081[/C][C]0.0507853672680163[/C][C]0.974607316365992[/C][/ROW]
[ROW][C]85[/C][C]0.0233181897516905[/C][C]0.046636379503381[/C][C]0.97668181024831[/C][/ROW]
[ROW][C]86[/C][C]0.0184557083726284[/C][C]0.0369114167452567[/C][C]0.981544291627372[/C][/ROW]
[ROW][C]87[/C][C]0.0150337845441562[/C][C]0.0300675690883124[/C][C]0.984966215455844[/C][/ROW]
[ROW][C]88[/C][C]0.0154610710606840[/C][C]0.0309221421213680[/C][C]0.984538928939316[/C][/ROW]
[ROW][C]89[/C][C]0.0130056647435019[/C][C]0.0260113294870038[/C][C]0.986994335256498[/C][/ROW]
[ROW][C]90[/C][C]0.0098351506727913[/C][C]0.0196703013455826[/C][C]0.990164849327209[/C][/ROW]
[ROW][C]91[/C][C]0.00840214339541543[/C][C]0.0168042867908309[/C][C]0.991597856604585[/C][/ROW]
[ROW][C]92[/C][C]0.0068644923105666[/C][C]0.0137289846211332[/C][C]0.993135507689433[/C][/ROW]
[ROW][C]93[/C][C]0.00505838765009756[/C][C]0.0101167753001951[/C][C]0.994941612349902[/C][/ROW]
[ROW][C]94[/C][C]0.00482075544927388[/C][C]0.00964151089854776[/C][C]0.995179244550726[/C][/ROW]
[ROW][C]95[/C][C]0.00719271638049016[/C][C]0.0143854327609803[/C][C]0.99280728361951[/C][/ROW]
[ROW][C]96[/C][C]0.00587184626345415[/C][C]0.0117436925269083[/C][C]0.994128153736546[/C][/ROW]
[ROW][C]97[/C][C]0.00502376309309238[/C][C]0.0100475261861848[/C][C]0.994976236906908[/C][/ROW]
[ROW][C]98[/C][C]0.00385119678426696[/C][C]0.00770239356853392[/C][C]0.996148803215733[/C][/ROW]
[ROW][C]99[/C][C]0.00291177044409986[/C][C]0.00582354088819973[/C][C]0.9970882295559[/C][/ROW]
[ROW][C]100[/C][C]0.00242558168742071[/C][C]0.00485116337484142[/C][C]0.99757441831258[/C][/ROW]
[ROW][C]101[/C][C]0.00191256833383419[/C][C]0.00382513666766838[/C][C]0.998087431666166[/C][/ROW]
[ROW][C]102[/C][C]0.00140812827315078[/C][C]0.00281625654630155[/C][C]0.99859187172685[/C][/ROW]
[ROW][C]103[/C][C]0.00173541208573881[/C][C]0.00347082417147763[/C][C]0.998264587914261[/C][/ROW]
[ROW][C]104[/C][C]0.00135770159401231[/C][C]0.00271540318802462[/C][C]0.998642298405988[/C][/ROW]
[ROW][C]105[/C][C]0.00596291985149448[/C][C]0.0119258397029890[/C][C]0.994037080148505[/C][/ROW]
[ROW][C]106[/C][C]0.0144736692877780[/C][C]0.0289473385755561[/C][C]0.985526330712222[/C][/ROW]
[ROW][C]107[/C][C]0.0153006489935965[/C][C]0.0306012979871929[/C][C]0.984699351006403[/C][/ROW]
[ROW][C]108[/C][C]0.0208411750588295[/C][C]0.041682350117659[/C][C]0.97915882494117[/C][/ROW]
[ROW][C]109[/C][C]0.0163117021657954[/C][C]0.0326234043315908[/C][C]0.983688297834205[/C][/ROW]
[ROW][C]110[/C][C]0.0118968127947448[/C][C]0.0237936255894896[/C][C]0.988103187205255[/C][/ROW]
[ROW][C]111[/C][C]0.00864692791493293[/C][C]0.0172938558298659[/C][C]0.991353072085067[/C][/ROW]
[ROW][C]112[/C][C]0.0249343415991937[/C][C]0.0498686831983874[/C][C]0.975065658400806[/C][/ROW]
[ROW][C]113[/C][C]0.0250773923575805[/C][C]0.0501547847151609[/C][C]0.97492260764242[/C][/ROW]
[ROW][C]114[/C][C]0.0619512220529108[/C][C]0.123902444105822[/C][C]0.938048777947089[/C][/ROW]
[ROW][C]115[/C][C]0.0533024591081407[/C][C]0.106604918216281[/C][C]0.94669754089186[/C][/ROW]
[ROW][C]116[/C][C]0.0450105889253199[/C][C]0.0900211778506397[/C][C]0.95498941107468[/C][/ROW]
[ROW][C]117[/C][C]0.116099901786624[/C][C]0.232199803573247[/C][C]0.883900098213376[/C][/ROW]
[ROW][C]118[/C][C]0.110022546252399[/C][C]0.220045092504799[/C][C]0.8899774537476[/C][/ROW]
[ROW][C]119[/C][C]0.0988238873190708[/C][C]0.197647774638142[/C][C]0.901176112680929[/C][/ROW]
[ROW][C]120[/C][C]0.170298309360065[/C][C]0.34059661872013[/C][C]0.829701690639935[/C][/ROW]
[ROW][C]121[/C][C]0.167887378513135[/C][C]0.33577475702627[/C][C]0.832112621486865[/C][/ROW]
[ROW][C]122[/C][C]0.212322430705874[/C][C]0.424644861411748[/C][C]0.787677569294126[/C][/ROW]
[ROW][C]123[/C][C]0.212549501662185[/C][C]0.425099003324371[/C][C]0.787450498337815[/C][/ROW]
[ROW][C]124[/C][C]0.202595743644362[/C][C]0.405191487288724[/C][C]0.797404256355638[/C][/ROW]
[ROW][C]125[/C][C]0.177262688528818[/C][C]0.354525377057636[/C][C]0.822737311471182[/C][/ROW]
[ROW][C]126[/C][C]0.143970194470238[/C][C]0.287940388940477[/C][C]0.856029805529762[/C][/ROW]
[ROW][C]127[/C][C]0.114308395403421[/C][C]0.228616790806842[/C][C]0.885691604596579[/C][/ROW]
[ROW][C]128[/C][C]0.112120160648500[/C][C]0.224240321297001[/C][C]0.8878798393515[/C][/ROW]
[ROW][C]129[/C][C]0.156046937750749[/C][C]0.312093875501497[/C][C]0.843953062249251[/C][/ROW]
[ROW][C]130[/C][C]0.136643414082242[/C][C]0.273286828164484[/C][C]0.863356585917758[/C][/ROW]
[ROW][C]131[/C][C]0.116172198322327[/C][C]0.232344396644654[/C][C]0.883827801677673[/C][/ROW]
[ROW][C]132[/C][C]0.104425661178244[/C][C]0.208851322356488[/C][C]0.895574338821756[/C][/ROW]
[ROW][C]133[/C][C]0.103083906385061[/C][C]0.206167812770122[/C][C]0.896916093614939[/C][/ROW]
[ROW][C]134[/C][C]0.0930289220764466[/C][C]0.186057844152893[/C][C]0.906971077923553[/C][/ROW]
[ROW][C]135[/C][C]0.210219174148618[/C][C]0.420438348297235[/C][C]0.789780825851382[/C][/ROW]
[ROW][C]136[/C][C]0.171437696748181[/C][C]0.342875393496362[/C][C]0.828562303251819[/C][/ROW]
[ROW][C]137[/C][C]0.146613959230718[/C][C]0.293227918461436[/C][C]0.853386040769282[/C][/ROW]
[ROW][C]138[/C][C]0.204602597777390[/C][C]0.409205195554779[/C][C]0.79539740222261[/C][/ROW]
[ROW][C]139[/C][C]0.464282877494264[/C][C]0.928565754988528[/C][C]0.535717122505736[/C][/ROW]
[ROW][C]140[/C][C]0.410398047063314[/C][C]0.820796094126629[/C][C]0.589601952936686[/C][/ROW]
[ROW][C]141[/C][C]0.565461332019032[/C][C]0.869077335961937[/C][C]0.434538667980968[/C][/ROW]
[ROW][C]142[/C][C]0.476837148829574[/C][C]0.953674297659149[/C][C]0.523162851170426[/C][/ROW]
[ROW][C]143[/C][C]0.648440828701084[/C][C]0.703118342597833[/C][C]0.351559171298916[/C][/ROW]
[ROW][C]144[/C][C]0.60351841649886[/C][C]0.792963167002279[/C][C]0.396481583501139[/C][/ROW]
[ROW][C]145[/C][C]0.513747415406566[/C][C]0.972505169186869[/C][C]0.486252584593434[/C][/ROW]
[ROW][C]146[/C][C]0.409838013852134[/C][C]0.819676027704268[/C][C]0.590161986147866[/C][/ROW]
[ROW][C]147[/C][C]0.415303853624172[/C][C]0.830607707248345[/C][C]0.584696146375828[/C][/ROW]
[ROW][C]148[/C][C]0.285548084201273[/C][C]0.571096168402546[/C][C]0.714451915798727[/C][/ROW]
[ROW][C]149[/C][C]0.198960259016767[/C][C]0.397920518033534[/C][C]0.801039740983233[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99545&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99545&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.6804502239025810.6390995521948380.319549776097419
110.6359845469964390.7280309060071220.364015453003561
120.5773228279576070.8453543440847850.422677172042393
130.5810684053236670.8378631893526660.418931594676333
140.733906275182730.5321874496345410.266093724817270
150.6530798437767330.6938403124465340.346920156223267
160.6521921436071140.6956157127857710.347807856392886
170.5662070335618450.867585932876310.433792966438155
180.7101504908974650.579699018205070.289849509102535
190.6481914631670730.7036170736658540.351808536832927
200.5945304762171810.8109390475656370.405469523782818
210.5250896662042770.9498206675914460.474910333795723
220.4515235697875980.9030471395751950.548476430212402
230.4402156067594480.8804312135188950.559784393240552
240.4900986527266990.9801973054533980.509901347273301
250.6606311602823580.6787376794352840.339368839717642
260.5963980430771340.8072039138457310.403601956922866
270.5329678721165310.9340642557669380.467032127883469
280.5126520826509820.9746958346980370.487347917349018
290.4483144602000590.8966289204001180.551685539799941
300.3872217486997510.7744434973995010.612778251300249
310.3878114895475050.775622979095010.612188510452495
320.3278855329750050.6557710659500090.672114467024995
330.5978659080943530.8042681838112930.402134091905647
340.5567158145458840.8865683709082310.443284185454116
350.526900675558120.946198648883760.47309932444188
360.4715891415402040.9431782830804090.528410858459796
370.4576603193029050.9153206386058090.542339680697095
380.4060765840963740.8121531681927480.593923415903626
390.3577383449375550.715476689875110.642261655062445
400.3352454936230210.6704909872460410.66475450637698
410.4995362704088990.9990725408177990.500463729591101
420.4457672721413890.8915345442827770.554232727858611
430.4181992280897670.8363984561795340.581800771910233
440.3718402798165760.7436805596331520.628159720183424
450.3330373451354030.6660746902708070.666962654864597
460.3065409991508900.6130819983017790.69345900084911
470.2827636255501940.5655272511003870.717236374449806
480.2792870119279520.5585740238559050.720712988072048
490.2426668848939630.4853337697879250.757333115106037
500.2433026211488800.4866052422977610.75669737885112
510.2237179986766640.4474359973533270.776282001323336
520.1941482242719290.3882964485438580.80585177572807
530.2612842700944990.5225685401889980.738715729905501
540.3412991503500410.6825983007000830.658700849649959
550.3258444337044210.6516888674088420.674155566295579
560.3859336818903160.7718673637806310.614066318109684
570.3627597123779660.7255194247559310.637240287622034
580.3326722507384280.6653445014768560.667327749261572
590.3326737070879130.6653474141758260.667326292912087
600.4139420831919780.8278841663839560.586057916808022
610.3702021789942060.7404043579884120.629797821005794
620.3284034420652210.6568068841304430.671596557934778
630.3298858217698780.6597716435397550.670114178230122
640.2964275365194090.5928550730388190.70357246348059
650.2580870371891340.5161740743782670.741912962810866
660.2419390080926380.4838780161852760.758060991907362
670.2140437762634570.4280875525269130.785956223736543
680.2368802300568610.4737604601137230.763119769943139
690.2040719147096410.4081438294192810.79592808529036
700.1722071065926520.3444142131853040.827792893407348
710.1441737298473980.2883474596947950.855826270152602
720.1198343477874190.2396686955748380.880165652212581
730.1042005502491530.2084011004983050.895799449750847
740.084677170651680.1693543413033600.91532282934832
750.06887227522000150.1377445504400030.931127724779999
760.05760255258594770.1152051051718950.942397447414052
770.04561983679789520.09123967359579040.954380163202105
780.03554720378930120.07109440757860250.964452796210699
790.04059782692434970.08119565384869940.95940217307565
800.03731986826958480.07463973653916970.962680131730415
810.0312864294062720.0625728588125440.968713570593728
820.04152705267152830.08305410534305660.958472947328472
830.03243673017945630.06487346035891270.967563269820544
840.02539268363400810.05078536726801630.974607316365992
850.02331818975169050.0466363795033810.97668181024831
860.01845570837262840.03691141674525670.981544291627372
870.01503378454415620.03006756908831240.984966215455844
880.01546107106068400.03092214212136800.984538928939316
890.01300566474350190.02601132948700380.986994335256498
900.00983515067279130.01967030134558260.990164849327209
910.008402143395415430.01680428679083090.991597856604585
920.00686449231056660.01372898462113320.993135507689433
930.005058387650097560.01011677530019510.994941612349902
940.004820755449273880.009641510898547760.995179244550726
950.007192716380490160.01438543276098030.99280728361951
960.005871846263454150.01174369252690830.994128153736546
970.005023763093092380.01004752618618480.994976236906908
980.003851196784266960.007702393568533920.996148803215733
990.002911770444099860.005823540888199730.9970882295559
1000.002425581687420710.004851163374841420.99757441831258
1010.001912568333834190.003825136667668380.998087431666166
1020.001408128273150780.002816256546301550.99859187172685
1030.001735412085738810.003470824171477630.998264587914261
1040.001357701594012310.002715403188024620.998642298405988
1050.005962919851494480.01192583970298900.994037080148505
1060.01447366928777800.02894733857555610.985526330712222
1070.01530064899359650.03060129798719290.984699351006403
1080.02084117505882950.0416823501176590.97915882494117
1090.01631170216579540.03262340433159080.983688297834205
1100.01189681279474480.02379362558948960.988103187205255
1110.008646927914932930.01729385582986590.991353072085067
1120.02493434159919370.04986868319838740.975065658400806
1130.02507739235758050.05015478471516090.97492260764242
1140.06195122205291080.1239024441058220.938048777947089
1150.05330245910814070.1066049182162810.94669754089186
1160.04501058892531990.09002117785063970.95498941107468
1170.1160999017866240.2321998035732470.883900098213376
1180.1100225462523990.2200450925047990.8899774537476
1190.09882388731907080.1976477746381420.901176112680929
1200.1702983093600650.340596618720130.829701690639935
1210.1678873785131350.335774757026270.832112621486865
1220.2123224307058740.4246448614117480.787677569294126
1230.2125495016621850.4250990033243710.787450498337815
1240.2025957436443620.4051914872887240.797404256355638
1250.1772626885288180.3545253770576360.822737311471182
1260.1439701944702380.2879403889404770.856029805529762
1270.1143083954034210.2286167908068420.885691604596579
1280.1121201606485000.2242403212970010.8878798393515
1290.1560469377507490.3120938755014970.843953062249251
1300.1366434140822420.2732868281644840.863356585917758
1310.1161721983223270.2323443966446540.883827801677673
1320.1044256611782440.2088513223564880.895574338821756
1330.1030839063850610.2061678127701220.896916093614939
1340.09302892207644660.1860578441528930.906971077923553
1350.2102191741486180.4204383482972350.789780825851382
1360.1714376967481810.3428753934963620.828562303251819
1370.1466139592307180.2932279184614360.853386040769282
1380.2046025977773900.4092051955547790.79539740222261
1390.4642828774942640.9285657549885280.535717122505736
1400.4103980470633140.8207960941266290.589601952936686
1410.5654613320190320.8690773359619370.434538667980968
1420.4768371488295740.9536742976591490.523162851170426
1430.6484408287010840.7031183425978330.351559171298916
1440.603518416498860.7929631670022790.396481583501139
1450.5137474154065660.9725051691868690.486252584593434
1460.4098380138521340.8196760277042680.590161986147866
1470.4153038536241720.8306077072483450.584696146375828
1480.2855480842012730.5710961684025460.714451915798727
1490.1989602590167670.3979205180335340.801039740983233







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level80.0571428571428571NOK
5% type I error level280.2NOK
10% type I error level380.271428571428571NOK

\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 & 8 & 0.0571428571428571 & NOK \tabularnewline
5% type I error level & 28 & 0.2 & NOK \tabularnewline
10% type I error level & 38 & 0.271428571428571 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99545&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]8[/C][C]0.0571428571428571[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]28[/C][C]0.2[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]38[/C][C]0.271428571428571[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99545&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99545&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 level80.0571428571428571NOK
5% type I error level280.2NOK
10% type I error level380.271428571428571NOK



Parameters (Session):
par1 = 6 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 6 ; 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')
}