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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationThu, 15 Nov 2012 11:33:38 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Nov/15/t13529972330m284zfiauo6d4b.htm/, Retrieved Thu, 02 May 2024 11:25:48 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=189713, Retrieved Thu, 02 May 2024 11:25:48 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact72

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [WS7] [2012-11-15 14:32:47] [f4f48270c576c45d216b84daa061a85b]
- R  D    [Multiple Regression] [WS7-3] [2012-11-15 16:33:38] [78cbff3691fb0cc454e192ff02249329] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
26	21	21	23	17	4
20	16	15	24	17	4
19	19	18	22	18	6
19	18	11	20	21	8
20	16	8	24	20	8
25	23	19	27	28	4
25	17	4	28	19	4
22	12	20	27	22	8
26	19	16	24	16	5
22	16	14	23	18	4
17	19	10	24	25	4
22	20	13	27	17	4
19	13	14	27	14	4
24	20	8	28	11	4
26	27	23	27	27	4
21	17	11	23	20	8
13	8	9	24	22	4
26	25	24	28	22	4
20	26	5	27	21	4
22	13	15	25	23	8
14	19	5	19	17	4
21	15	19	24	24	7
7	5	6	20	14	4
23	16	13	28	17	4
17	14	11	26	23	5
25	24	17	23	24	4
25	24	17	23	24	4
19	9	5	20	8	4
20	19	9	11	22	4
23	19	15	24	23	4
22	25	17	25	25	4
22	19	17	23	21	4
21	18	20	18	24	15
15	15	12	20	15	10
20	12	7	20	22	4
22	21	16	24	21	8
18	12	7	23	25	4
20	15	14	25	16	4
28	28	24	28	28	4
22	25	15	26	23	4
18	19	15	26	21	7
23	20	10	23	21	4
20	24	14	22	26	6
25	26	18	24	22	5
26	25	12	21	21	4
15	12	9	20	18	16
17	12	9	22	12	5
23	15	8	20	25	12
21	17	18	25	17	6
13	14	10	20	24	9
18	16	17	22	15	9
19	11	14	23	13	4
22	20	16	25	26	5
16	11	10	23	16	4
24	22	19	23	24	4
18	20	10	22	21	5
20	19	14	24	20	4
24	17	10	25	14	4
14	21	4	21	25	4
22	23	19	12	25	5
24	18	9	17	20	4
18	17	12	20	22	6
21	27	16	23	20	4
23	25	11	23	26	4
17	19	18	20	18	18
22	22	11	28	22	4
24	24	24	24	24	6
21	20	17	24	17	4
22	19	18	24	24	4
16	11	9	24	20	5
21	22	19	28	19	4
23	22	18	25	20	4
22	16	12	21	15	5
24	20	23	25	23	10
24	24	22	25	26	5
16	16	14	18	22	8
16	16	14	17	20	8
21	22	16	26	24	5
26	24	23	28	26	4
15	16	7	21	21	4
25	27	10	27	25	4
18	11	12	22	13	5
23	21	12	21	20	4
20	20	12	25	22	4
17	20	17	22	23	8
25	27	21	23	28	4
24	20	16	26	22	5
17	12	11	19	20	14
19	8	14	25	6	8
20	21	13	21	21	8
15	18	9	13	20	4
27	24	19	24	18	4
22	16	13	25	23	6
23	18	19	26	20	4
16	20	13	25	24	7
19	20	13	25	22	7
25	19	13	22	21	4
19	17	14	21	18	6
19	16	12	23	21	4
26	26	22	25	23	7
21	15	11	24	23	4
20	22	5	21	15	4
24	17	18	21	21	8
22	23	19	25	24	4
20	21	14	22	23	4
18	19	15	20	21	10
18	14	12	20	21	8
24	17	19	23	20	6
24	12	15	28	11	4
22	24	17	23	22	4
23	18	8	28	27	4
22	20	10	24	25	5
20	16	12	18	18	4
18	20	12	20	20	6
25	22	20	28	24	4
18	12	12	21	10	5
16	16	12	21	27	7
20	17	14	25	21	8
19	22	6	19	21	5
15	12	10	18	18	8
19	14	18	21	15	10
19	23	18	22	24	8
16	15	7	24	22	5
17	17	18	15	14	12
28	28	9	28	28	4
23	20	17	26	18	5
25	23	22	23	26	4
20	13	11	26	17	6
17	18	15	20	19	4
23	23	17	22	22	4
16	19	15	20	18	7
23	23	22	23	24	7
11	12	9	22	15	10
18	16	13	24	18	4
24	23	20	23	26	5
23	13	14	22	11	8
21	22	14	26	26	11
16	18	12	23	21	7
24	23	20	27	23	4
23	20	20	23	23	8
18	10	8	21	15	6
20	17	17	26	22	7
9	18	9	23	26	5
24	15	18	21	16	4
25	23	22	27	20	8
20	17	10	19	18	4
21	17	13	23	22	8
25	22	15	25	16	6
22	20	18	23	19	4
21	20	18	22	20	9
21	19	12	22	19	5
22	18	12	25	23	6
27	22	20	25	24	4
24	20	12	28	25	4
24	22	16	28	21	4
21	18	16	20	21	5
18	16	18	25	23	6
16	16	16	19	27	16
22	16	13	25	23	6
20	16	17	22	18	6
18	17	13	18	16	4
20	18	17	20	16	4

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time13 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 13 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=189713&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]13 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=189713&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=189713&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time13 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net






Multiple Linear Regression - Estimated Regression Equation
I1[t] = + 7.49731615796313 + 0.35255519637078I2[t] + 0.250814374015268I3[t] + 0.283005495004579E1[t] -0.108731229414337E2[t] -0.213156884669359A[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
I1[t] =  +  7.49731615796313 +  0.35255519637078I2[t] +  0.250814374015268I3[t] +  0.283005495004579E1[t] -0.108731229414337E2[t] -0.213156884669359A[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=189713&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]I1[t] =  +  7.49731615796313 +  0.35255519637078I2[t] +  0.250814374015268I3[t] +  0.283005495004579E1[t] -0.108731229414337E2[t] -0.213156884669359A[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=189713&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=189713&T=1

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


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

Multiple Linear Regression - Estimated Regression Equation
I1[t] = + 7.49731615796313 + 0.35255519637078I2[t] + 0.250814374015268I3[t] + 0.283005495004579E1[t] -0.108731229414337E2[t] -0.213156884669359A[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)7.497316157963131.864984.02019e-054.5e-05
I20.352555196370780.0616485.718800
I30.2508143740152680.050174.99922e-061e-06
E10.2830054950045790.0679244.16655.1e-052.6e-05
E2-0.1087312294143370.057642-1.88630.061110.030555
A-0.2131568846693590.083831-2.54270.0119740.005987

\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) & 7.49731615796313 & 1.86498 & 4.0201 & 9e-05 & 4.5e-05 \tabularnewline
I2 & 0.35255519637078 & 0.061648 & 5.7188 & 0 & 0 \tabularnewline
I3 & 0.250814374015268 & 0.05017 & 4.9992 & 2e-06 & 1e-06 \tabularnewline
E1 & 0.283005495004579 & 0.067924 & 4.1665 & 5.1e-05 & 2.6e-05 \tabularnewline
E2 & -0.108731229414337 & 0.057642 & -1.8863 & 0.06111 & 0.030555 \tabularnewline
A & -0.213156884669359 & 0.083831 & -2.5427 & 0.011974 & 0.005987 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=189713&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]7.49731615796313[/C][C]1.86498[/C][C]4.0201[/C][C]9e-05[/C][C]4.5e-05[/C][/ROW]
[ROW][C]I2[/C][C]0.35255519637078[/C][C]0.061648[/C][C]5.7188[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]I3[/C][C]0.250814374015268[/C][C]0.05017[/C][C]4.9992[/C][C]2e-06[/C][C]1e-06[/C][/ROW]
[ROW][C]E1[/C][C]0.283005495004579[/C][C]0.067924[/C][C]4.1665[/C][C]5.1e-05[/C][C]2.6e-05[/C][/ROW]
[ROW][C]E2[/C][C]-0.108731229414337[/C][C]0.057642[/C][C]-1.8863[/C][C]0.06111[/C][C]0.030555[/C][/ROW]
[ROW][C]A[/C][C]-0.213156884669359[/C][C]0.083831[/C][C]-2.5427[/C][C]0.011974[/C][C]0.005987[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=189713&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=189713&T=2

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


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)7.497316157963131.864984.02019e-054.5e-05
I20.352555196370780.0616485.718800
I30.2508143740152680.050174.99922e-061e-06
E10.2830054950045790.0679244.16655.1e-052.6e-05
E2-0.1087312294143370.057642-1.88630.061110.030555
A-0.2131568846693590.083831-2.54270.0119740.005987






Multiple Linear Regression - Regression Statistics
Multiple R0.740900485805513
R-squared0.548933529866845
Adjusted R-squared0.534476271208731
F-TEST (value)37.9694064309185
F-TEST (DF numerator)5
F-TEST (DF denominator)156
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.49519013703326
Sum Squared Residuals971.2519159119

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.740900485805513 \tabularnewline
R-squared & 0.548933529866845 \tabularnewline
Adjusted R-squared & 0.534476271208731 \tabularnewline
F-TEST (value) & 37.9694064309185 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 156 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.49519013703326 \tabularnewline
Sum Squared Residuals & 971.2519159119 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=189713&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.740900485805513[/C][/ROW]
[ROW][C]R-squared[/C][C]0.548933529866845[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.534476271208731[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]37.9694064309185[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]156[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.49519013703326[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]971.2519159119[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=189713&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=189713&T=3

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


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

Multiple Linear Regression - Regression Statistics
Multiple R0.740900485805513
R-squared0.548933529866845
Adjusted R-squared0.534476271208731
F-TEST (value)37.9694064309185
F-TEST (DF numerator)5
F-TEST (DF denominator)156
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.49519013703326
Sum Squared Residuals971.2519159119






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
12623.97614508245432.0238549175457
22020.9914883515134-0.991488351513359
31921.7005410739093-2.70054107390929
41918.27376681184080.726233188159249
52018.0569665064861.94303349351396
62524.11560518362590.884394816374078
72519.49964495490585.50035504509417
82220.28807223537121.71192776462881
92622.19554265938593.80445734061405
102220.34893725307921.65106274692082
111719.9252322352347-2.92523223523466
122222.7490968739797-0.749096873979683
131920.8582185616425-1.8582185616425
142422.43041787539391.56958212460605
152626.6378146945845-0.637814694584451
162118.8789593298982.12104067010196
171316.1225043893838-3.12250438938382
182627.0101803179344-1.01018031793442
192022.4229881424249-2.42298814242487
202218.71181334224213.28818665775786
211418.1259827254501-4.12598272545013
222120.24160139129520.758398608704785
23714.0502235335221-7.05022353352206
242321.62188158350111.37811841649886
251718.9835871915645-1.9835871915645
262523.26943456960521.7305654303948
272523.26943456960521.7305654303948
281915.86201732147593.13798267852406
292016.32154011440293.67845988559712
302321.39676656413971.60323343586032
312224.0792695265708-2.0792695265708
322221.83285227599430.16714772400569
332118.14679330704052.85320669295952
341517.692989203891-2.69298920389098
352015.89907444681814.10092555318191
362221.71752625104770.282473748952254
371816.42189724358881.57810275641118
382020.7798555055462-0.779855505546229
392827.41545853056070.584541469439259
402224.0781087323735-2.07810873237352
411821.5407693589694-3.54076935896943
422320.42970685425822.57029314574179
432021.5902097243874-1.59020972438742
442524.51267040552590.487329594474077
452622.12810059413353.87189940586651
461514.27774549647370.722254503526328
471717.8408695943318-0.840869594331801
482315.17610564434787.82389435565218
492121.9531783955958-0.953178395595806
501316.07338107943-3.07338107942999
511820.0787841450166-2.07878414501662
521919.129817418297-0.129817418296961
532221.74379105661790.256208943382066
541617.8003662339929-1.80036623399288
552423.06595292489420.934047075105825
561819.9335444745843-1.93354447458428
572021.4721458783674-1.47214587836742
582420.69917086105543.30082913894461
591418.2764398988709-4.27643989887088
602219.98355956213092.01644043786911
612417.88448034688826.11551965311175
621818.4896085294096-0.489608529409618
632124.5112107023596-3.51121070235962
642321.89964106305571.1003589369443
651718.5766474678678-1.57664746786783
662222.6919278666236-0.6919278666236
672424.8818269133779-0.881826913377937
682122.903337885027-1.90333788502702
692222.0404784567711-0.0404784567711447
701617.1844755526555-1.18447555265548
712125.0246365469888-4.02463654698876
722323.8160744585454-0.816074458545414
732219.39433431861312.60566568138686
742422.7599009396211.24009906037897
752424.6588980861927-0.658898086192664
761617.6463573217215-1.6463573217215
771617.5808142855456-1.58081428554559
782122.9493694031927-1.94936940319275
792625.9718858298910.0281141701089734
801517.7010319567201-2.70103195672013
812523.59469029121461.40530970878536
821818.1320262905925-0.132026290592488
832320.82661103806472.17338896193529
842021.3886153628836-1.38861536288357
851720.8323119798544-3.8323119798544
862524.89543273712130.104567262878736
872422.46172146927991.53827853072014
881714.70522006000972.29477993999033
891918.54665388641670.453346113583296
902020.1160666439882-0.116066643988206
911516.7524583668699-1.75245836686993
922724.70645618912632.29354381087366
932219.69416395266272.30583604733734
942322.93967354208210.0603264579178607
951620.7824966240621-4.78249662406209
961920.9999590828908-1.99995908289076
972520.54658928492874.45341071507134
981919.7091676901021-0.709167690102082
991919.5211148168056-0.521114816805629
1002625.26388839783850.736111602161482
1012118.98328828259552.01671171740452
1022019.96712176340030.0328782365996985
1032419.95991772858144.04008227141858
1042223.9845191112741-1.98451911127411
1052021.2850515928568-1.28505159285681
1061819.2032657349339-1.20326573493388
1071817.11436040037290.885639599627104
1082421.31178809135892.6882119086411
1092421.36567692253462.63432307746542
1102223.4868970284339-1.48689702843387
1112319.9856078120233.01439218797701
1122220.06463054693611.93536945306392
1132018.43228103002571.56771896997425
1141819.7647365773506-1.76473657735063
1152524.73179477393230.268205226067663
1161818.5277696802017-0.527769680201702
1171617.6632457963024-1.66324579630237
1182020.0886822125387-0.0886822125386673
1191918.7863808862510.213619113748971
1201515.6678039578347-0.667803957834653
1211919.1283257466164-0.128325746616389
1221922.0320607135677-3.03206071356767
1231617.8756051312794-1.87560513127939
1241718.1703758257769-1.17037582577688
1252823.65324292033174.34675707966828
1262323.1474607609525-0.14746076095248
1272523.95348878448211.04651121551791
1282019.07026248701040.929737512989613
1291720.3471143054079-3.34711430540793
1302322.85133633705850.148663662941484
1311620.168930077185-4.16893007718497
1322323.5314805893027-0.531480589302683
1331116.448891482742-5.44889148274199
1341820.3811283740685-2.38112837406849
1352423.23870315178220.76129684821781
1362318.91675723618524.08324276381482
1372120.95133688831740.0486631116826158
1381619.5867545555391-3.58675455553911
1392424.9100757047129-0.910075704712876
1402321.86776059690481.13223940309522
1411816.0625887596581.93741124034198
1422021.2285564848441-1.22855648484407
143918.7169690557603-9.71696905576034
1442420.6510910215893.34890897841101
1452524.8852706023090.114729397691011
1462018.56621297337061.43378702662943
1472119.16312561909991.8368743809001
1482523.07224248481821.92775751518176
1492222.653684305209-0.653684305209033
1502121.1961631574433-0.196163157443323
1512120.30008048507270.699919514927293
1522220.1484599713891.85154002861105
1532723.88277828891863.1172217110814
1542421.91143815965432.0885618403457
1552424.0547309661143-0.054730966114277
1562120.16730933592520.832690664074833
1571820.948235822739-2.948235822739
1581616.1820803403301-0.182080340330055
1592219.69416395266272.30583604733734
1602020.3920611107817-0.392061110781685
1611819.2531130592405-1.25311305924047
1622021.1749367416815-1.17493674168148

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 26 & 23.9761450824543 & 2.0238549175457 \tabularnewline
2 & 20 & 20.9914883515134 & -0.991488351513359 \tabularnewline
3 & 19 & 21.7005410739093 & -2.70054107390929 \tabularnewline
4 & 19 & 18.2737668118408 & 0.726233188159249 \tabularnewline
5 & 20 & 18.056966506486 & 1.94303349351396 \tabularnewline
6 & 25 & 24.1156051836259 & 0.884394816374078 \tabularnewline
7 & 25 & 19.4996449549058 & 5.50035504509417 \tabularnewline
8 & 22 & 20.2880722353712 & 1.71192776462881 \tabularnewline
9 & 26 & 22.1955426593859 & 3.80445734061405 \tabularnewline
10 & 22 & 20.3489372530792 & 1.65106274692082 \tabularnewline
11 & 17 & 19.9252322352347 & -2.92523223523466 \tabularnewline
12 & 22 & 22.7490968739797 & -0.749096873979683 \tabularnewline
13 & 19 & 20.8582185616425 & -1.8582185616425 \tabularnewline
14 & 24 & 22.4304178753939 & 1.56958212460605 \tabularnewline
15 & 26 & 26.6378146945845 & -0.637814694584451 \tabularnewline
16 & 21 & 18.878959329898 & 2.12104067010196 \tabularnewline
17 & 13 & 16.1225043893838 & -3.12250438938382 \tabularnewline
18 & 26 & 27.0101803179344 & -1.01018031793442 \tabularnewline
19 & 20 & 22.4229881424249 & -2.42298814242487 \tabularnewline
20 & 22 & 18.7118133422421 & 3.28818665775786 \tabularnewline
21 & 14 & 18.1259827254501 & -4.12598272545013 \tabularnewline
22 & 21 & 20.2416013912952 & 0.758398608704785 \tabularnewline
23 & 7 & 14.0502235335221 & -7.05022353352206 \tabularnewline
24 & 23 & 21.6218815835011 & 1.37811841649886 \tabularnewline
25 & 17 & 18.9835871915645 & -1.9835871915645 \tabularnewline
26 & 25 & 23.2694345696052 & 1.7305654303948 \tabularnewline
27 & 25 & 23.2694345696052 & 1.7305654303948 \tabularnewline
28 & 19 & 15.8620173214759 & 3.13798267852406 \tabularnewline
29 & 20 & 16.3215401144029 & 3.67845988559712 \tabularnewline
30 & 23 & 21.3967665641397 & 1.60323343586032 \tabularnewline
31 & 22 & 24.0792695265708 & -2.0792695265708 \tabularnewline
32 & 22 & 21.8328522759943 & 0.16714772400569 \tabularnewline
33 & 21 & 18.1467933070405 & 2.85320669295952 \tabularnewline
34 & 15 & 17.692989203891 & -2.69298920389098 \tabularnewline
35 & 20 & 15.8990744468181 & 4.10092555318191 \tabularnewline
36 & 22 & 21.7175262510477 & 0.282473748952254 \tabularnewline
37 & 18 & 16.4218972435888 & 1.57810275641118 \tabularnewline
38 & 20 & 20.7798555055462 & -0.779855505546229 \tabularnewline
39 & 28 & 27.4154585305607 & 0.584541469439259 \tabularnewline
40 & 22 & 24.0781087323735 & -2.07810873237352 \tabularnewline
41 & 18 & 21.5407693589694 & -3.54076935896943 \tabularnewline
42 & 23 & 20.4297068542582 & 2.57029314574179 \tabularnewline
43 & 20 & 21.5902097243874 & -1.59020972438742 \tabularnewline
44 & 25 & 24.5126704055259 & 0.487329594474077 \tabularnewline
45 & 26 & 22.1281005941335 & 3.87189940586651 \tabularnewline
46 & 15 & 14.2777454964737 & 0.722254503526328 \tabularnewline
47 & 17 & 17.8408695943318 & -0.840869594331801 \tabularnewline
48 & 23 & 15.1761056443478 & 7.82389435565218 \tabularnewline
49 & 21 & 21.9531783955958 & -0.953178395595806 \tabularnewline
50 & 13 & 16.07338107943 & -3.07338107942999 \tabularnewline
51 & 18 & 20.0787841450166 & -2.07878414501662 \tabularnewline
52 & 19 & 19.129817418297 & -0.129817418296961 \tabularnewline
53 & 22 & 21.7437910566179 & 0.256208943382066 \tabularnewline
54 & 16 & 17.8003662339929 & -1.80036623399288 \tabularnewline
55 & 24 & 23.0659529248942 & 0.934047075105825 \tabularnewline
56 & 18 & 19.9335444745843 & -1.93354447458428 \tabularnewline
57 & 20 & 21.4721458783674 & -1.47214587836742 \tabularnewline
58 & 24 & 20.6991708610554 & 3.30082913894461 \tabularnewline
59 & 14 & 18.2764398988709 & -4.27643989887088 \tabularnewline
60 & 22 & 19.9835595621309 & 2.01644043786911 \tabularnewline
61 & 24 & 17.8844803468882 & 6.11551965311175 \tabularnewline
62 & 18 & 18.4896085294096 & -0.489608529409618 \tabularnewline
63 & 21 & 24.5112107023596 & -3.51121070235962 \tabularnewline
64 & 23 & 21.8996410630557 & 1.1003589369443 \tabularnewline
65 & 17 & 18.5766474678678 & -1.57664746786783 \tabularnewline
66 & 22 & 22.6919278666236 & -0.6919278666236 \tabularnewline
67 & 24 & 24.8818269133779 & -0.881826913377937 \tabularnewline
68 & 21 & 22.903337885027 & -1.90333788502702 \tabularnewline
69 & 22 & 22.0404784567711 & -0.0404784567711447 \tabularnewline
70 & 16 & 17.1844755526555 & -1.18447555265548 \tabularnewline
71 & 21 & 25.0246365469888 & -4.02463654698876 \tabularnewline
72 & 23 & 23.8160744585454 & -0.816074458545414 \tabularnewline
73 & 22 & 19.3943343186131 & 2.60566568138686 \tabularnewline
74 & 24 & 22.759900939621 & 1.24009906037897 \tabularnewline
75 & 24 & 24.6588980861927 & -0.658898086192664 \tabularnewline
76 & 16 & 17.6463573217215 & -1.6463573217215 \tabularnewline
77 & 16 & 17.5808142855456 & -1.58081428554559 \tabularnewline
78 & 21 & 22.9493694031927 & -1.94936940319275 \tabularnewline
79 & 26 & 25.971885829891 & 0.0281141701089734 \tabularnewline
80 & 15 & 17.7010319567201 & -2.70103195672013 \tabularnewline
81 & 25 & 23.5946902912146 & 1.40530970878536 \tabularnewline
82 & 18 & 18.1320262905925 & -0.132026290592488 \tabularnewline
83 & 23 & 20.8266110380647 & 2.17338896193529 \tabularnewline
84 & 20 & 21.3886153628836 & -1.38861536288357 \tabularnewline
85 & 17 & 20.8323119798544 & -3.8323119798544 \tabularnewline
86 & 25 & 24.8954327371213 & 0.104567262878736 \tabularnewline
87 & 24 & 22.4617214692799 & 1.53827853072014 \tabularnewline
88 & 17 & 14.7052200600097 & 2.29477993999033 \tabularnewline
89 & 19 & 18.5466538864167 & 0.453346113583296 \tabularnewline
90 & 20 & 20.1160666439882 & -0.116066643988206 \tabularnewline
91 & 15 & 16.7524583668699 & -1.75245836686993 \tabularnewline
92 & 27 & 24.7064561891263 & 2.29354381087366 \tabularnewline
93 & 22 & 19.6941639526627 & 2.30583604733734 \tabularnewline
94 & 23 & 22.9396735420821 & 0.0603264579178607 \tabularnewline
95 & 16 & 20.7824966240621 & -4.78249662406209 \tabularnewline
96 & 19 & 20.9999590828908 & -1.99995908289076 \tabularnewline
97 & 25 & 20.5465892849287 & 4.45341071507134 \tabularnewline
98 & 19 & 19.7091676901021 & -0.709167690102082 \tabularnewline
99 & 19 & 19.5211148168056 & -0.521114816805629 \tabularnewline
100 & 26 & 25.2638883978385 & 0.736111602161482 \tabularnewline
101 & 21 & 18.9832882825955 & 2.01671171740452 \tabularnewline
102 & 20 & 19.9671217634003 & 0.0328782365996985 \tabularnewline
103 & 24 & 19.9599177285814 & 4.04008227141858 \tabularnewline
104 & 22 & 23.9845191112741 & -1.98451911127411 \tabularnewline
105 & 20 & 21.2850515928568 & -1.28505159285681 \tabularnewline
106 & 18 & 19.2032657349339 & -1.20326573493388 \tabularnewline
107 & 18 & 17.1143604003729 & 0.885639599627104 \tabularnewline
108 & 24 & 21.3117880913589 & 2.6882119086411 \tabularnewline
109 & 24 & 21.3656769225346 & 2.63432307746542 \tabularnewline
110 & 22 & 23.4868970284339 & -1.48689702843387 \tabularnewline
111 & 23 & 19.985607812023 & 3.01439218797701 \tabularnewline
112 & 22 & 20.0646305469361 & 1.93536945306392 \tabularnewline
113 & 20 & 18.4322810300257 & 1.56771896997425 \tabularnewline
114 & 18 & 19.7647365773506 & -1.76473657735063 \tabularnewline
115 & 25 & 24.7317947739323 & 0.268205226067663 \tabularnewline
116 & 18 & 18.5277696802017 & -0.527769680201702 \tabularnewline
117 & 16 & 17.6632457963024 & -1.66324579630237 \tabularnewline
118 & 20 & 20.0886822125387 & -0.0886822125386673 \tabularnewline
119 & 19 & 18.786380886251 & 0.213619113748971 \tabularnewline
120 & 15 & 15.6678039578347 & -0.667803957834653 \tabularnewline
121 & 19 & 19.1283257466164 & -0.128325746616389 \tabularnewline
122 & 19 & 22.0320607135677 & -3.03206071356767 \tabularnewline
123 & 16 & 17.8756051312794 & -1.87560513127939 \tabularnewline
124 & 17 & 18.1703758257769 & -1.17037582577688 \tabularnewline
125 & 28 & 23.6532429203317 & 4.34675707966828 \tabularnewline
126 & 23 & 23.1474607609525 & -0.14746076095248 \tabularnewline
127 & 25 & 23.9534887844821 & 1.04651121551791 \tabularnewline
128 & 20 & 19.0702624870104 & 0.929737512989613 \tabularnewline
129 & 17 & 20.3471143054079 & -3.34711430540793 \tabularnewline
130 & 23 & 22.8513363370585 & 0.148663662941484 \tabularnewline
131 & 16 & 20.168930077185 & -4.16893007718497 \tabularnewline
132 & 23 & 23.5314805893027 & -0.531480589302683 \tabularnewline
133 & 11 & 16.448891482742 & -5.44889148274199 \tabularnewline
134 & 18 & 20.3811283740685 & -2.38112837406849 \tabularnewline
135 & 24 & 23.2387031517822 & 0.76129684821781 \tabularnewline
136 & 23 & 18.9167572361852 & 4.08324276381482 \tabularnewline
137 & 21 & 20.9513368883174 & 0.0486631116826158 \tabularnewline
138 & 16 & 19.5867545555391 & -3.58675455553911 \tabularnewline
139 & 24 & 24.9100757047129 & -0.910075704712876 \tabularnewline
140 & 23 & 21.8677605969048 & 1.13223940309522 \tabularnewline
141 & 18 & 16.062588759658 & 1.93741124034198 \tabularnewline
142 & 20 & 21.2285564848441 & -1.22855648484407 \tabularnewline
143 & 9 & 18.7169690557603 & -9.71696905576034 \tabularnewline
144 & 24 & 20.651091021589 & 3.34890897841101 \tabularnewline
145 & 25 & 24.885270602309 & 0.114729397691011 \tabularnewline
146 & 20 & 18.5662129733706 & 1.43378702662943 \tabularnewline
147 & 21 & 19.1631256190999 & 1.8368743809001 \tabularnewline
148 & 25 & 23.0722424848182 & 1.92775751518176 \tabularnewline
149 & 22 & 22.653684305209 & -0.653684305209033 \tabularnewline
150 & 21 & 21.1961631574433 & -0.196163157443323 \tabularnewline
151 & 21 & 20.3000804850727 & 0.699919514927293 \tabularnewline
152 & 22 & 20.148459971389 & 1.85154002861105 \tabularnewline
153 & 27 & 23.8827782889186 & 3.1172217110814 \tabularnewline
154 & 24 & 21.9114381596543 & 2.0885618403457 \tabularnewline
155 & 24 & 24.0547309661143 & -0.054730966114277 \tabularnewline
156 & 21 & 20.1673093359252 & 0.832690664074833 \tabularnewline
157 & 18 & 20.948235822739 & -2.948235822739 \tabularnewline
158 & 16 & 16.1820803403301 & -0.182080340330055 \tabularnewline
159 & 22 & 19.6941639526627 & 2.30583604733734 \tabularnewline
160 & 20 & 20.3920611107817 & -0.392061110781685 \tabularnewline
161 & 18 & 19.2531130592405 & -1.25311305924047 \tabularnewline
162 & 20 & 21.1749367416815 & -1.17493674168148 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=189713&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]23.9761450824543[/C][C]2.0238549175457[/C][/ROW]
[ROW][C]2[/C][C]20[/C][C]20.9914883515134[/C][C]-0.991488351513359[/C][/ROW]
[ROW][C]3[/C][C]19[/C][C]21.7005410739093[/C][C]-2.70054107390929[/C][/ROW]
[ROW][C]4[/C][C]19[/C][C]18.2737668118408[/C][C]0.726233188159249[/C][/ROW]
[ROW][C]5[/C][C]20[/C][C]18.056966506486[/C][C]1.94303349351396[/C][/ROW]
[ROW][C]6[/C][C]25[/C][C]24.1156051836259[/C][C]0.884394816374078[/C][/ROW]
[ROW][C]7[/C][C]25[/C][C]19.4996449549058[/C][C]5.50035504509417[/C][/ROW]
[ROW][C]8[/C][C]22[/C][C]20.2880722353712[/C][C]1.71192776462881[/C][/ROW]
[ROW][C]9[/C][C]26[/C][C]22.1955426593859[/C][C]3.80445734061405[/C][/ROW]
[ROW][C]10[/C][C]22[/C][C]20.3489372530792[/C][C]1.65106274692082[/C][/ROW]
[ROW][C]11[/C][C]17[/C][C]19.9252322352347[/C][C]-2.92523223523466[/C][/ROW]
[ROW][C]12[/C][C]22[/C][C]22.7490968739797[/C][C]-0.749096873979683[/C][/ROW]
[ROW][C]13[/C][C]19[/C][C]20.8582185616425[/C][C]-1.8582185616425[/C][/ROW]
[ROW][C]14[/C][C]24[/C][C]22.4304178753939[/C][C]1.56958212460605[/C][/ROW]
[ROW][C]15[/C][C]26[/C][C]26.6378146945845[/C][C]-0.637814694584451[/C][/ROW]
[ROW][C]16[/C][C]21[/C][C]18.878959329898[/C][C]2.12104067010196[/C][/ROW]
[ROW][C]17[/C][C]13[/C][C]16.1225043893838[/C][C]-3.12250438938382[/C][/ROW]
[ROW][C]18[/C][C]26[/C][C]27.0101803179344[/C][C]-1.01018031793442[/C][/ROW]
[ROW][C]19[/C][C]20[/C][C]22.4229881424249[/C][C]-2.42298814242487[/C][/ROW]
[ROW][C]20[/C][C]22[/C][C]18.7118133422421[/C][C]3.28818665775786[/C][/ROW]
[ROW][C]21[/C][C]14[/C][C]18.1259827254501[/C][C]-4.12598272545013[/C][/ROW]
[ROW][C]22[/C][C]21[/C][C]20.2416013912952[/C][C]0.758398608704785[/C][/ROW]
[ROW][C]23[/C][C]7[/C][C]14.0502235335221[/C][C]-7.05022353352206[/C][/ROW]
[ROW][C]24[/C][C]23[/C][C]21.6218815835011[/C][C]1.37811841649886[/C][/ROW]
[ROW][C]25[/C][C]17[/C][C]18.9835871915645[/C][C]-1.9835871915645[/C][/ROW]
[ROW][C]26[/C][C]25[/C][C]23.2694345696052[/C][C]1.7305654303948[/C][/ROW]
[ROW][C]27[/C][C]25[/C][C]23.2694345696052[/C][C]1.7305654303948[/C][/ROW]
[ROW][C]28[/C][C]19[/C][C]15.8620173214759[/C][C]3.13798267852406[/C][/ROW]
[ROW][C]29[/C][C]20[/C][C]16.3215401144029[/C][C]3.67845988559712[/C][/ROW]
[ROW][C]30[/C][C]23[/C][C]21.3967665641397[/C][C]1.60323343586032[/C][/ROW]
[ROW][C]31[/C][C]22[/C][C]24.0792695265708[/C][C]-2.0792695265708[/C][/ROW]
[ROW][C]32[/C][C]22[/C][C]21.8328522759943[/C][C]0.16714772400569[/C][/ROW]
[ROW][C]33[/C][C]21[/C][C]18.1467933070405[/C][C]2.85320669295952[/C][/ROW]
[ROW][C]34[/C][C]15[/C][C]17.692989203891[/C][C]-2.69298920389098[/C][/ROW]
[ROW][C]35[/C][C]20[/C][C]15.8990744468181[/C][C]4.10092555318191[/C][/ROW]
[ROW][C]36[/C][C]22[/C][C]21.7175262510477[/C][C]0.282473748952254[/C][/ROW]
[ROW][C]37[/C][C]18[/C][C]16.4218972435888[/C][C]1.57810275641118[/C][/ROW]
[ROW][C]38[/C][C]20[/C][C]20.7798555055462[/C][C]-0.779855505546229[/C][/ROW]
[ROW][C]39[/C][C]28[/C][C]27.4154585305607[/C][C]0.584541469439259[/C][/ROW]
[ROW][C]40[/C][C]22[/C][C]24.0781087323735[/C][C]-2.07810873237352[/C][/ROW]
[ROW][C]41[/C][C]18[/C][C]21.5407693589694[/C][C]-3.54076935896943[/C][/ROW]
[ROW][C]42[/C][C]23[/C][C]20.4297068542582[/C][C]2.57029314574179[/C][/ROW]
[ROW][C]43[/C][C]20[/C][C]21.5902097243874[/C][C]-1.59020972438742[/C][/ROW]
[ROW][C]44[/C][C]25[/C][C]24.5126704055259[/C][C]0.487329594474077[/C][/ROW]
[ROW][C]45[/C][C]26[/C][C]22.1281005941335[/C][C]3.87189940586651[/C][/ROW]
[ROW][C]46[/C][C]15[/C][C]14.2777454964737[/C][C]0.722254503526328[/C][/ROW]
[ROW][C]47[/C][C]17[/C][C]17.8408695943318[/C][C]-0.840869594331801[/C][/ROW]
[ROW][C]48[/C][C]23[/C][C]15.1761056443478[/C][C]7.82389435565218[/C][/ROW]
[ROW][C]49[/C][C]21[/C][C]21.9531783955958[/C][C]-0.953178395595806[/C][/ROW]
[ROW][C]50[/C][C]13[/C][C]16.07338107943[/C][C]-3.07338107942999[/C][/ROW]
[ROW][C]51[/C][C]18[/C][C]20.0787841450166[/C][C]-2.07878414501662[/C][/ROW]
[ROW][C]52[/C][C]19[/C][C]19.129817418297[/C][C]-0.129817418296961[/C][/ROW]
[ROW][C]53[/C][C]22[/C][C]21.7437910566179[/C][C]0.256208943382066[/C][/ROW]
[ROW][C]54[/C][C]16[/C][C]17.8003662339929[/C][C]-1.80036623399288[/C][/ROW]
[ROW][C]55[/C][C]24[/C][C]23.0659529248942[/C][C]0.934047075105825[/C][/ROW]
[ROW][C]56[/C][C]18[/C][C]19.9335444745843[/C][C]-1.93354447458428[/C][/ROW]
[ROW][C]57[/C][C]20[/C][C]21.4721458783674[/C][C]-1.47214587836742[/C][/ROW]
[ROW][C]58[/C][C]24[/C][C]20.6991708610554[/C][C]3.30082913894461[/C][/ROW]
[ROW][C]59[/C][C]14[/C][C]18.2764398988709[/C][C]-4.27643989887088[/C][/ROW]
[ROW][C]60[/C][C]22[/C][C]19.9835595621309[/C][C]2.01644043786911[/C][/ROW]
[ROW][C]61[/C][C]24[/C][C]17.8844803468882[/C][C]6.11551965311175[/C][/ROW]
[ROW][C]62[/C][C]18[/C][C]18.4896085294096[/C][C]-0.489608529409618[/C][/ROW]
[ROW][C]63[/C][C]21[/C][C]24.5112107023596[/C][C]-3.51121070235962[/C][/ROW]
[ROW][C]64[/C][C]23[/C][C]21.8996410630557[/C][C]1.1003589369443[/C][/ROW]
[ROW][C]65[/C][C]17[/C][C]18.5766474678678[/C][C]-1.57664746786783[/C][/ROW]
[ROW][C]66[/C][C]22[/C][C]22.6919278666236[/C][C]-0.6919278666236[/C][/ROW]
[ROW][C]67[/C][C]24[/C][C]24.8818269133779[/C][C]-0.881826913377937[/C][/ROW]
[ROW][C]68[/C][C]21[/C][C]22.903337885027[/C][C]-1.90333788502702[/C][/ROW]
[ROW][C]69[/C][C]22[/C][C]22.0404784567711[/C][C]-0.0404784567711447[/C][/ROW]
[ROW][C]70[/C][C]16[/C][C]17.1844755526555[/C][C]-1.18447555265548[/C][/ROW]
[ROW][C]71[/C][C]21[/C][C]25.0246365469888[/C][C]-4.02463654698876[/C][/ROW]
[ROW][C]72[/C][C]23[/C][C]23.8160744585454[/C][C]-0.816074458545414[/C][/ROW]
[ROW][C]73[/C][C]22[/C][C]19.3943343186131[/C][C]2.60566568138686[/C][/ROW]
[ROW][C]74[/C][C]24[/C][C]22.759900939621[/C][C]1.24009906037897[/C][/ROW]
[ROW][C]75[/C][C]24[/C][C]24.6588980861927[/C][C]-0.658898086192664[/C][/ROW]
[ROW][C]76[/C][C]16[/C][C]17.6463573217215[/C][C]-1.6463573217215[/C][/ROW]
[ROW][C]77[/C][C]16[/C][C]17.5808142855456[/C][C]-1.58081428554559[/C][/ROW]
[ROW][C]78[/C][C]21[/C][C]22.9493694031927[/C][C]-1.94936940319275[/C][/ROW]
[ROW][C]79[/C][C]26[/C][C]25.971885829891[/C][C]0.0281141701089734[/C][/ROW]
[ROW][C]80[/C][C]15[/C][C]17.7010319567201[/C][C]-2.70103195672013[/C][/ROW]
[ROW][C]81[/C][C]25[/C][C]23.5946902912146[/C][C]1.40530970878536[/C][/ROW]
[ROW][C]82[/C][C]18[/C][C]18.1320262905925[/C][C]-0.132026290592488[/C][/ROW]
[ROW][C]83[/C][C]23[/C][C]20.8266110380647[/C][C]2.17338896193529[/C][/ROW]
[ROW][C]84[/C][C]20[/C][C]21.3886153628836[/C][C]-1.38861536288357[/C][/ROW]
[ROW][C]85[/C][C]17[/C][C]20.8323119798544[/C][C]-3.8323119798544[/C][/ROW]
[ROW][C]86[/C][C]25[/C][C]24.8954327371213[/C][C]0.104567262878736[/C][/ROW]
[ROW][C]87[/C][C]24[/C][C]22.4617214692799[/C][C]1.53827853072014[/C][/ROW]
[ROW][C]88[/C][C]17[/C][C]14.7052200600097[/C][C]2.29477993999033[/C][/ROW]
[ROW][C]89[/C][C]19[/C][C]18.5466538864167[/C][C]0.453346113583296[/C][/ROW]
[ROW][C]90[/C][C]20[/C][C]20.1160666439882[/C][C]-0.116066643988206[/C][/ROW]
[ROW][C]91[/C][C]15[/C][C]16.7524583668699[/C][C]-1.75245836686993[/C][/ROW]
[ROW][C]92[/C][C]27[/C][C]24.7064561891263[/C][C]2.29354381087366[/C][/ROW]
[ROW][C]93[/C][C]22[/C][C]19.6941639526627[/C][C]2.30583604733734[/C][/ROW]
[ROW][C]94[/C][C]23[/C][C]22.9396735420821[/C][C]0.0603264579178607[/C][/ROW]
[ROW][C]95[/C][C]16[/C][C]20.7824966240621[/C][C]-4.78249662406209[/C][/ROW]
[ROW][C]96[/C][C]19[/C][C]20.9999590828908[/C][C]-1.99995908289076[/C][/ROW]
[ROW][C]97[/C][C]25[/C][C]20.5465892849287[/C][C]4.45341071507134[/C][/ROW]
[ROW][C]98[/C][C]19[/C][C]19.7091676901021[/C][C]-0.709167690102082[/C][/ROW]
[ROW][C]99[/C][C]19[/C][C]19.5211148168056[/C][C]-0.521114816805629[/C][/ROW]
[ROW][C]100[/C][C]26[/C][C]25.2638883978385[/C][C]0.736111602161482[/C][/ROW]
[ROW][C]101[/C][C]21[/C][C]18.9832882825955[/C][C]2.01671171740452[/C][/ROW]
[ROW][C]102[/C][C]20[/C][C]19.9671217634003[/C][C]0.0328782365996985[/C][/ROW]
[ROW][C]103[/C][C]24[/C][C]19.9599177285814[/C][C]4.04008227141858[/C][/ROW]
[ROW][C]104[/C][C]22[/C][C]23.9845191112741[/C][C]-1.98451911127411[/C][/ROW]
[ROW][C]105[/C][C]20[/C][C]21.2850515928568[/C][C]-1.28505159285681[/C][/ROW]
[ROW][C]106[/C][C]18[/C][C]19.2032657349339[/C][C]-1.20326573493388[/C][/ROW]
[ROW][C]107[/C][C]18[/C][C]17.1143604003729[/C][C]0.885639599627104[/C][/ROW]
[ROW][C]108[/C][C]24[/C][C]21.3117880913589[/C][C]2.6882119086411[/C][/ROW]
[ROW][C]109[/C][C]24[/C][C]21.3656769225346[/C][C]2.63432307746542[/C][/ROW]
[ROW][C]110[/C][C]22[/C][C]23.4868970284339[/C][C]-1.48689702843387[/C][/ROW]
[ROW][C]111[/C][C]23[/C][C]19.985607812023[/C][C]3.01439218797701[/C][/ROW]
[ROW][C]112[/C][C]22[/C][C]20.0646305469361[/C][C]1.93536945306392[/C][/ROW]
[ROW][C]113[/C][C]20[/C][C]18.4322810300257[/C][C]1.56771896997425[/C][/ROW]
[ROW][C]114[/C][C]18[/C][C]19.7647365773506[/C][C]-1.76473657735063[/C][/ROW]
[ROW][C]115[/C][C]25[/C][C]24.7317947739323[/C][C]0.268205226067663[/C][/ROW]
[ROW][C]116[/C][C]18[/C][C]18.5277696802017[/C][C]-0.527769680201702[/C][/ROW]
[ROW][C]117[/C][C]16[/C][C]17.6632457963024[/C][C]-1.66324579630237[/C][/ROW]
[ROW][C]118[/C][C]20[/C][C]20.0886822125387[/C][C]-0.0886822125386673[/C][/ROW]
[ROW][C]119[/C][C]19[/C][C]18.786380886251[/C][C]0.213619113748971[/C][/ROW]
[ROW][C]120[/C][C]15[/C][C]15.6678039578347[/C][C]-0.667803957834653[/C][/ROW]
[ROW][C]121[/C][C]19[/C][C]19.1283257466164[/C][C]-0.128325746616389[/C][/ROW]
[ROW][C]122[/C][C]19[/C][C]22.0320607135677[/C][C]-3.03206071356767[/C][/ROW]
[ROW][C]123[/C][C]16[/C][C]17.8756051312794[/C][C]-1.87560513127939[/C][/ROW]
[ROW][C]124[/C][C]17[/C][C]18.1703758257769[/C][C]-1.17037582577688[/C][/ROW]
[ROW][C]125[/C][C]28[/C][C]23.6532429203317[/C][C]4.34675707966828[/C][/ROW]
[ROW][C]126[/C][C]23[/C][C]23.1474607609525[/C][C]-0.14746076095248[/C][/ROW]
[ROW][C]127[/C][C]25[/C][C]23.9534887844821[/C][C]1.04651121551791[/C][/ROW]
[ROW][C]128[/C][C]20[/C][C]19.0702624870104[/C][C]0.929737512989613[/C][/ROW]
[ROW][C]129[/C][C]17[/C][C]20.3471143054079[/C][C]-3.34711430540793[/C][/ROW]
[ROW][C]130[/C][C]23[/C][C]22.8513363370585[/C][C]0.148663662941484[/C][/ROW]
[ROW][C]131[/C][C]16[/C][C]20.168930077185[/C][C]-4.16893007718497[/C][/ROW]
[ROW][C]132[/C][C]23[/C][C]23.5314805893027[/C][C]-0.531480589302683[/C][/ROW]
[ROW][C]133[/C][C]11[/C][C]16.448891482742[/C][C]-5.44889148274199[/C][/ROW]
[ROW][C]134[/C][C]18[/C][C]20.3811283740685[/C][C]-2.38112837406849[/C][/ROW]
[ROW][C]135[/C][C]24[/C][C]23.2387031517822[/C][C]0.76129684821781[/C][/ROW]
[ROW][C]136[/C][C]23[/C][C]18.9167572361852[/C][C]4.08324276381482[/C][/ROW]
[ROW][C]137[/C][C]21[/C][C]20.9513368883174[/C][C]0.0486631116826158[/C][/ROW]
[ROW][C]138[/C][C]16[/C][C]19.5867545555391[/C][C]-3.58675455553911[/C][/ROW]
[ROW][C]139[/C][C]24[/C][C]24.9100757047129[/C][C]-0.910075704712876[/C][/ROW]
[ROW][C]140[/C][C]23[/C][C]21.8677605969048[/C][C]1.13223940309522[/C][/ROW]
[ROW][C]141[/C][C]18[/C][C]16.062588759658[/C][C]1.93741124034198[/C][/ROW]
[ROW][C]142[/C][C]20[/C][C]21.2285564848441[/C][C]-1.22855648484407[/C][/ROW]
[ROW][C]143[/C][C]9[/C][C]18.7169690557603[/C][C]-9.71696905576034[/C][/ROW]
[ROW][C]144[/C][C]24[/C][C]20.651091021589[/C][C]3.34890897841101[/C][/ROW]
[ROW][C]145[/C][C]25[/C][C]24.885270602309[/C][C]0.114729397691011[/C][/ROW]
[ROW][C]146[/C][C]20[/C][C]18.5662129733706[/C][C]1.43378702662943[/C][/ROW]
[ROW][C]147[/C][C]21[/C][C]19.1631256190999[/C][C]1.8368743809001[/C][/ROW]
[ROW][C]148[/C][C]25[/C][C]23.0722424848182[/C][C]1.92775751518176[/C][/ROW]
[ROW][C]149[/C][C]22[/C][C]22.653684305209[/C][C]-0.653684305209033[/C][/ROW]
[ROW][C]150[/C][C]21[/C][C]21.1961631574433[/C][C]-0.196163157443323[/C][/ROW]
[ROW][C]151[/C][C]21[/C][C]20.3000804850727[/C][C]0.699919514927293[/C][/ROW]
[ROW][C]152[/C][C]22[/C][C]20.148459971389[/C][C]1.85154002861105[/C][/ROW]
[ROW][C]153[/C][C]27[/C][C]23.8827782889186[/C][C]3.1172217110814[/C][/ROW]
[ROW][C]154[/C][C]24[/C][C]21.9114381596543[/C][C]2.0885618403457[/C][/ROW]
[ROW][C]155[/C][C]24[/C][C]24.0547309661143[/C][C]-0.054730966114277[/C][/ROW]
[ROW][C]156[/C][C]21[/C][C]20.1673093359252[/C][C]0.832690664074833[/C][/ROW]
[ROW][C]157[/C][C]18[/C][C]20.948235822739[/C][C]-2.948235822739[/C][/ROW]
[ROW][C]158[/C][C]16[/C][C]16.1820803403301[/C][C]-0.182080340330055[/C][/ROW]
[ROW][C]159[/C][C]22[/C][C]19.6941639526627[/C][C]2.30583604733734[/C][/ROW]
[ROW][C]160[/C][C]20[/C][C]20.3920611107817[/C][C]-0.392061110781685[/C][/ROW]
[ROW][C]161[/C][C]18[/C][C]19.2531130592405[/C][C]-1.25311305924047[/C][/ROW]
[ROW][C]162[/C][C]20[/C][C]21.1749367416815[/C][C]-1.17493674168148[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=189713&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=189713&T=4

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


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
12623.97614508245432.0238549175457
22020.9914883515134-0.991488351513359
31921.7005410739093-2.70054107390929
41918.27376681184080.726233188159249
52018.0569665064861.94303349351396
62524.11560518362590.884394816374078
72519.49964495490585.50035504509417
82220.28807223537121.71192776462881
92622.19554265938593.80445734061405
102220.34893725307921.65106274692082
111719.9252322352347-2.92523223523466
122222.7490968739797-0.749096873979683
131920.8582185616425-1.8582185616425
142422.43041787539391.56958212460605
152626.6378146945845-0.637814694584451
162118.8789593298982.12104067010196
171316.1225043893838-3.12250438938382
182627.0101803179344-1.01018031793442
192022.4229881424249-2.42298814242487
202218.71181334224213.28818665775786
211418.1259827254501-4.12598272545013
222120.24160139129520.758398608704785
23714.0502235335221-7.05022353352206
242321.62188158350111.37811841649886
251718.9835871915645-1.9835871915645
262523.26943456960521.7305654303948
272523.26943456960521.7305654303948
281915.86201732147593.13798267852406
292016.32154011440293.67845988559712
302321.39676656413971.60323343586032
312224.0792695265708-2.0792695265708
322221.83285227599430.16714772400569
332118.14679330704052.85320669295952
341517.692989203891-2.69298920389098
352015.89907444681814.10092555318191
362221.71752625104770.282473748952254
371816.42189724358881.57810275641118
382020.7798555055462-0.779855505546229
392827.41545853056070.584541469439259
402224.0781087323735-2.07810873237352
411821.5407693589694-3.54076935896943
422320.42970685425822.57029314574179
432021.5902097243874-1.59020972438742
442524.51267040552590.487329594474077
452622.12810059413353.87189940586651
461514.27774549647370.722254503526328
471717.8408695943318-0.840869594331801
482315.17610564434787.82389435565218
492121.9531783955958-0.953178395595806
501316.07338107943-3.07338107942999
511820.0787841450166-2.07878414501662
521919.129817418297-0.129817418296961
532221.74379105661790.256208943382066
541617.8003662339929-1.80036623399288
552423.06595292489420.934047075105825
561819.9335444745843-1.93354447458428
572021.4721458783674-1.47214587836742
582420.69917086105543.30082913894461
591418.2764398988709-4.27643989887088
602219.98355956213092.01644043786911
612417.88448034688826.11551965311175
621818.4896085294096-0.489608529409618
632124.5112107023596-3.51121070235962
642321.89964106305571.1003589369443
651718.5766474678678-1.57664746786783
662222.6919278666236-0.6919278666236
672424.8818269133779-0.881826913377937
682122.903337885027-1.90333788502702
692222.0404784567711-0.0404784567711447
701617.1844755526555-1.18447555265548
712125.0246365469888-4.02463654698876
722323.8160744585454-0.816074458545414
732219.39433431861312.60566568138686
742422.7599009396211.24009906037897
752424.6588980861927-0.658898086192664
761617.6463573217215-1.6463573217215
771617.5808142855456-1.58081428554559
782122.9493694031927-1.94936940319275
792625.9718858298910.0281141701089734
801517.7010319567201-2.70103195672013
812523.59469029121461.40530970878536
821818.1320262905925-0.132026290592488
832320.82661103806472.17338896193529
842021.3886153628836-1.38861536288357
851720.8323119798544-3.8323119798544
862524.89543273712130.104567262878736
872422.46172146927991.53827853072014
881714.70522006000972.29477993999033
891918.54665388641670.453346113583296
902020.1160666439882-0.116066643988206
911516.7524583668699-1.75245836686993
922724.70645618912632.29354381087366
932219.69416395266272.30583604733734
942322.93967354208210.0603264579178607
951620.7824966240621-4.78249662406209
961920.9999590828908-1.99995908289076
972520.54658928492874.45341071507134
981919.7091676901021-0.709167690102082
991919.5211148168056-0.521114816805629
1002625.26388839783850.736111602161482
1012118.98328828259552.01671171740452
1022019.96712176340030.0328782365996985
1032419.95991772858144.04008227141858
1042223.9845191112741-1.98451911127411
1052021.2850515928568-1.28505159285681
1061819.2032657349339-1.20326573493388
1071817.11436040037290.885639599627104
1082421.31178809135892.6882119086411
1092421.36567692253462.63432307746542
1102223.4868970284339-1.48689702843387
1112319.9856078120233.01439218797701
1122220.06463054693611.93536945306392
1132018.43228103002571.56771896997425
1141819.7647365773506-1.76473657735063
1152524.73179477393230.268205226067663
1161818.5277696802017-0.527769680201702
1171617.6632457963024-1.66324579630237
1182020.0886822125387-0.0886822125386673
1191918.7863808862510.213619113748971
1201515.6678039578347-0.667803957834653
1211919.1283257466164-0.128325746616389
1221922.0320607135677-3.03206071356767
1231617.8756051312794-1.87560513127939
1241718.1703758257769-1.17037582577688
1252823.65324292033174.34675707966828
1262323.1474607609525-0.14746076095248
1272523.95348878448211.04651121551791
1282019.07026248701040.929737512989613
1291720.3471143054079-3.34711430540793
1302322.85133633705850.148663662941484
1311620.168930077185-4.16893007718497
1322323.5314805893027-0.531480589302683
1331116.448891482742-5.44889148274199
1341820.3811283740685-2.38112837406849
1352423.23870315178220.76129684821781
1362318.91675723618524.08324276381482
1372120.95133688831740.0486631116826158
1381619.5867545555391-3.58675455553911
1392424.9100757047129-0.910075704712876
1402321.86776059690481.13223940309522
1411816.0625887596581.93741124034198
1422021.2285564848441-1.22855648484407
143918.7169690557603-9.71696905576034
1442420.6510910215893.34890897841101
1452524.8852706023090.114729397691011
1462018.56621297337061.43378702662943
1472119.16312561909991.8368743809001
1482523.07224248481821.92775751518176
1492222.653684305209-0.653684305209033
1502121.1961631574433-0.196163157443323
1512120.30008048507270.699919514927293
1522220.1484599713891.85154002861105
1532723.88277828891863.1172217110814
1542421.91143815965432.0885618403457
1552424.0547309661143-0.054730966114277
1562120.16730933592520.832690664074833
1571820.948235822739-2.948235822739
1581616.1820803403301-0.182080340330055
1592219.69416395266272.30583604733734
1602020.3920611107817-0.392061110781685
1611819.2531130592405-1.25311305924047
1622021.1749367416815-1.17493674168148






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.5643744049511110.8712511900977790.435625595048889
100.519585431808430.9608291363831390.48041456819157
110.4884963400974880.9769926801949760.511503659902512
120.7324588739658080.5350822520683830.267541126034192
130.7770993309106080.4458013381787840.222900669089392
140.7302783024295030.5394433951409950.269721697570497
150.6562109793659670.6875780412680660.343789020634033
160.5720428107815950.855914378436810.427957189218405
170.5163652009512820.9672695980974370.483634799048718
180.4623388118713920.9246776237427840.537661188128608
190.5680623090496710.8638753819006580.431937690950329
200.5349626578851780.9300746842296450.465037342114822
210.5209826723134330.9580346553731350.479017327686567
220.444593946436570.889187892873140.55540605356343
230.5925480348946710.8149039302106570.407451965105329
240.5283266834920480.9433466330159050.471673316507952
250.48810156163910.97620312327820.5118984383609
260.5240218657769120.9519562684461760.475978134223088
270.5271070151829710.9457859696340590.472892984817029
280.6997051137405080.6005897725189830.300294886259492
290.8495963603617850.300807279276430.150403639638215
300.8321012575275990.3357974849448020.167898742472401
310.8236504463989260.3526991072021480.176349553601074
320.7821251296524950.435749740695010.217874870347505
330.7765940533189180.4468118933621640.223405946681082
340.843450954652560.313098090694880.15654904534744
350.901562298309390.196875403381220.0984377016906102
360.876887342808260.2462253143834790.12311265719174
370.8584446739300420.2831106521399160.141555326069958
380.8272888523924270.3454222952151450.172711147607573
390.7905411576061760.4189176847876480.209458842393824
400.7827791642255770.4344416715488460.217220835774423
410.8274243955603730.3451512088792540.172575604439627
420.8225498240437290.3549003519125420.177450175956271
430.8099350682137280.3801298635725450.190064931786272
440.7726359113029890.4547281773940210.227364088697011
450.8056191807579370.3887616384841250.194380819242063
460.7710251570675540.4579496858648930.228974842932446
470.734745921017880.5305081579642390.26525407898212
480.9258060836936850.1483878326126310.0741939163063154
490.9091458784496040.1817082431007920.0908541215503959
500.9320291190179530.1359417619640940.0679708809820468
510.9279954538924940.1440090922150110.0720045461075056
520.9109333133630850.1781333732738290.0890666866369146
530.8896224859371420.2207550281257160.110377514062858
540.8762056797713520.2475886404572950.123794320228648
550.8521321137011250.295735772597750.147867886298875
560.8456082394467670.3087835211064660.154391760553233
570.8259660628488990.3480678743022010.174033937151101
580.8491141087665980.3017717824668050.150885891233402
590.9006000472563260.1987999054873480.0993999527436738
600.8907731034152450.2184537931695110.109226896584755
610.9627576766595030.0744846466809950.0372423233404975
620.9533695898355710.09326082032885760.0466304101644288
630.9644042296131220.07119154077375520.0355957703868776
640.9566514173586620.08669716528267560.0433485826413378
650.9537447294777610.09251054104447720.0462552705222386
660.9424294970643860.1151410058712280.057570502935614
670.9297437237347980.1405125525304050.0702562762652025
680.9233348612421870.1533302775156270.0766651387578134
690.9050745420229060.1898509159541870.0949254579770935
700.8892315822611050.221536835477790.110768417738895
710.9238761925830250.152247614833950.0761238074169752
720.9088966349676120.1822067300647760.0911033650323881
730.9094477305344480.1811045389311050.0905522694655523
740.8939971973368160.2120056053263670.106002802663184
750.8730133929290920.2539732141418170.126986607070908
760.861251528189390.277496943621220.13874847181061
770.8466235262381410.3067529475237190.153376473761859
780.8370934922455430.3258130155089150.162906507754457
790.8090540370503250.3818919258993490.190945962949675
800.813221601632010.3735567967359810.18677839836799
810.7896687797830250.4206624404339490.210331220216975
820.7559588070504430.4880823858991140.244041192949557
830.748527520740390.5029449585192190.25147247925961
840.7238156047671840.5523687904656310.276184395232816
850.771202530499820.4575949390003610.22879746950018
860.7347109836760110.5305780326479790.265289016323989
870.7086071515047960.5827856969904070.291392848495204
880.7214710374843210.5570579250313590.278528962515679
890.6859821229124930.6280357541750150.314017877087507
900.645922473586360.708155052827280.35407752641364
910.6200451998378590.7599096003242820.379954800162141
920.6071835698705040.7856328602589910.392816430129496
930.597988103931940.8040237921361190.40201189606806
940.5555476827494790.8889046345010410.444452317250521
950.6720843745258110.6558312509483790.327915625474189
960.6581008336248010.6837983327503970.341899166375199
970.7506992494724790.4986015010550410.249300750527521
980.7139649573992550.5720700852014890.286035042600745
990.6739947312433590.6520105375132820.326005268756641
1000.6323114778628550.7353770442742890.367688522137145
1010.6152518415341640.7694963169316720.384748158465836
1020.5691671586464470.8616656827071070.430832841353553
1030.6592526842536250.681494631492750.340747315746375
1040.6484214543722480.7031570912555050.351578545627752
1050.6118734736775960.7762530526448090.388126526322404
1060.5712908286179580.8574183427640840.428709171382042
1070.5420627900796050.9158744198407890.457937209920394
1080.5498565982178190.9002868035643620.450143401782181
1090.5289928975332330.9420142049335330.471007102466767
1100.5008240212115020.9983519575769950.499175978788498
1110.5228861496126690.9542277007746620.477113850387331
1120.5137895488656160.9724209022687670.486210451134384
1130.5058900477290420.9882199045419160.494109952270958
1140.4706540007091920.9413080014183830.529345999290808
1150.421494434069660.8429888681393190.57850556593034
1160.3740087943833020.7480175887666040.625991205616698
1170.3353378994609930.6706757989219860.664662100539007
1180.2888087919702490.5776175839404980.711191208029751
1190.2544567022528710.5089134045057420.745543297747129
1200.2225245635223110.4450491270446220.777475436477689
1210.1847221201264590.3694442402529170.815277879873541
1220.1916916190966340.3833832381932690.808308380903366
1230.16404656050350.3280931210070010.8359534394965
1240.1347892503168810.2695785006337620.865210749683119
1250.2362246460023650.4724492920047290.763775353997635
1260.1998500398785440.3997000797570890.800149960121456
1270.1681222025829890.3362444051659780.831877797417011
1280.1363619341663610.2727238683327220.863638065833639
1290.1441253748385420.2882507496770840.855874625161458
1300.114760945325250.2295218906505010.88523905467475
1310.1565487041279270.3130974082558530.843451295872073
1320.1281335771530390.2562671543060780.871866422846961
1330.29958291558310.5991658311661990.7004170844169
1340.3097554061215540.6195108122431070.690244593878446
1350.2922107934159540.5844215868319080.707789206584046
1360.2634858703408470.5269717406816940.736514129659153
1370.2156646441253550.431329288250710.784335355874645
1380.2509835317141290.5019670634282570.749016468285871
1390.2037689943209430.4075379886418870.796231005679057
1400.1656176136235550.3312352272471090.834382386376445
1410.1275093663451090.2550187326902180.872490633654891
1420.1113046897756740.2226093795513480.888695310224326
1430.9324852734533990.1350294530932020.067514726546601
1440.9941838318213380.01163233635732480.00581616817866241
1450.9881211628086440.0237576743827120.011878837191356
1460.9771720284935370.04565594301292560.0228279715064628
1470.9666952059542680.06660958809146460.0333047940457323
1480.9637422214501930.07251555709961510.0362577785498075
1490.9337479448888450.132504110222310.0662520551111548
1500.881676733826970.2366465323460610.11832326617303
1510.7935897574639530.4128204850720940.206410242536047
1520.6765274555805630.6469450888388740.323472544419437
1530.6902477005222710.6195045989554580.309752299477729

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.564374404951111 & 0.871251190097779 & 0.435625595048889 \tabularnewline
10 & 0.51958543180843 & 0.960829136383139 & 0.48041456819157 \tabularnewline
11 & 0.488496340097488 & 0.976992680194976 & 0.511503659902512 \tabularnewline
12 & 0.732458873965808 & 0.535082252068383 & 0.267541126034192 \tabularnewline
13 & 0.777099330910608 & 0.445801338178784 & 0.222900669089392 \tabularnewline
14 & 0.730278302429503 & 0.539443395140995 & 0.269721697570497 \tabularnewline
15 & 0.656210979365967 & 0.687578041268066 & 0.343789020634033 \tabularnewline
16 & 0.572042810781595 & 0.85591437843681 & 0.427957189218405 \tabularnewline
17 & 0.516365200951282 & 0.967269598097437 & 0.483634799048718 \tabularnewline
18 & 0.462338811871392 & 0.924677623742784 & 0.537661188128608 \tabularnewline
19 & 0.568062309049671 & 0.863875381900658 & 0.431937690950329 \tabularnewline
20 & 0.534962657885178 & 0.930074684229645 & 0.465037342114822 \tabularnewline
21 & 0.520982672313433 & 0.958034655373135 & 0.479017327686567 \tabularnewline
22 & 0.44459394643657 & 0.88918789287314 & 0.55540605356343 \tabularnewline
23 & 0.592548034894671 & 0.814903930210657 & 0.407451965105329 \tabularnewline
24 & 0.528326683492048 & 0.943346633015905 & 0.471673316507952 \tabularnewline
25 & 0.4881015616391 & 0.9762031232782 & 0.5118984383609 \tabularnewline
26 & 0.524021865776912 & 0.951956268446176 & 0.475978134223088 \tabularnewline
27 & 0.527107015182971 & 0.945785969634059 & 0.472892984817029 \tabularnewline
28 & 0.699705113740508 & 0.600589772518983 & 0.300294886259492 \tabularnewline
29 & 0.849596360361785 & 0.30080727927643 & 0.150403639638215 \tabularnewline
30 & 0.832101257527599 & 0.335797484944802 & 0.167898742472401 \tabularnewline
31 & 0.823650446398926 & 0.352699107202148 & 0.176349553601074 \tabularnewline
32 & 0.782125129652495 & 0.43574974069501 & 0.217874870347505 \tabularnewline
33 & 0.776594053318918 & 0.446811893362164 & 0.223405946681082 \tabularnewline
34 & 0.84345095465256 & 0.31309809069488 & 0.15654904534744 \tabularnewline
35 & 0.90156229830939 & 0.19687540338122 & 0.0984377016906102 \tabularnewline
36 & 0.87688734280826 & 0.246225314383479 & 0.12311265719174 \tabularnewline
37 & 0.858444673930042 & 0.283110652139916 & 0.141555326069958 \tabularnewline
38 & 0.827288852392427 & 0.345422295215145 & 0.172711147607573 \tabularnewline
39 & 0.790541157606176 & 0.418917684787648 & 0.209458842393824 \tabularnewline
40 & 0.782779164225577 & 0.434441671548846 & 0.217220835774423 \tabularnewline
41 & 0.827424395560373 & 0.345151208879254 & 0.172575604439627 \tabularnewline
42 & 0.822549824043729 & 0.354900351912542 & 0.177450175956271 \tabularnewline
43 & 0.809935068213728 & 0.380129863572545 & 0.190064931786272 \tabularnewline
44 & 0.772635911302989 & 0.454728177394021 & 0.227364088697011 \tabularnewline
45 & 0.805619180757937 & 0.388761638484125 & 0.194380819242063 \tabularnewline
46 & 0.771025157067554 & 0.457949685864893 & 0.228974842932446 \tabularnewline
47 & 0.73474592101788 & 0.530508157964239 & 0.26525407898212 \tabularnewline
48 & 0.925806083693685 & 0.148387832612631 & 0.0741939163063154 \tabularnewline
49 & 0.909145878449604 & 0.181708243100792 & 0.0908541215503959 \tabularnewline
50 & 0.932029119017953 & 0.135941761964094 & 0.0679708809820468 \tabularnewline
51 & 0.927995453892494 & 0.144009092215011 & 0.0720045461075056 \tabularnewline
52 & 0.910933313363085 & 0.178133373273829 & 0.0890666866369146 \tabularnewline
53 & 0.889622485937142 & 0.220755028125716 & 0.110377514062858 \tabularnewline
54 & 0.876205679771352 & 0.247588640457295 & 0.123794320228648 \tabularnewline
55 & 0.852132113701125 & 0.29573577259775 & 0.147867886298875 \tabularnewline
56 & 0.845608239446767 & 0.308783521106466 & 0.154391760553233 \tabularnewline
57 & 0.825966062848899 & 0.348067874302201 & 0.174033937151101 \tabularnewline
58 & 0.849114108766598 & 0.301771782466805 & 0.150885891233402 \tabularnewline
59 & 0.900600047256326 & 0.198799905487348 & 0.0993999527436738 \tabularnewline
60 & 0.890773103415245 & 0.218453793169511 & 0.109226896584755 \tabularnewline
61 & 0.962757676659503 & 0.074484646680995 & 0.0372423233404975 \tabularnewline
62 & 0.953369589835571 & 0.0932608203288576 & 0.0466304101644288 \tabularnewline
63 & 0.964404229613122 & 0.0711915407737552 & 0.0355957703868776 \tabularnewline
64 & 0.956651417358662 & 0.0866971652826756 & 0.0433485826413378 \tabularnewline
65 & 0.953744729477761 & 0.0925105410444772 & 0.0462552705222386 \tabularnewline
66 & 0.942429497064386 & 0.115141005871228 & 0.057570502935614 \tabularnewline
67 & 0.929743723734798 & 0.140512552530405 & 0.0702562762652025 \tabularnewline
68 & 0.923334861242187 & 0.153330277515627 & 0.0766651387578134 \tabularnewline
69 & 0.905074542022906 & 0.189850915954187 & 0.0949254579770935 \tabularnewline
70 & 0.889231582261105 & 0.22153683547779 & 0.110768417738895 \tabularnewline
71 & 0.923876192583025 & 0.15224761483395 & 0.0761238074169752 \tabularnewline
72 & 0.908896634967612 & 0.182206730064776 & 0.0911033650323881 \tabularnewline
73 & 0.909447730534448 & 0.181104538931105 & 0.0905522694655523 \tabularnewline
74 & 0.893997197336816 & 0.212005605326367 & 0.106002802663184 \tabularnewline
75 & 0.873013392929092 & 0.253973214141817 & 0.126986607070908 \tabularnewline
76 & 0.86125152818939 & 0.27749694362122 & 0.13874847181061 \tabularnewline
77 & 0.846623526238141 & 0.306752947523719 & 0.153376473761859 \tabularnewline
78 & 0.837093492245543 & 0.325813015508915 & 0.162906507754457 \tabularnewline
79 & 0.809054037050325 & 0.381891925899349 & 0.190945962949675 \tabularnewline
80 & 0.81322160163201 & 0.373556796735981 & 0.18677839836799 \tabularnewline
81 & 0.789668779783025 & 0.420662440433949 & 0.210331220216975 \tabularnewline
82 & 0.755958807050443 & 0.488082385899114 & 0.244041192949557 \tabularnewline
83 & 0.74852752074039 & 0.502944958519219 & 0.25147247925961 \tabularnewline
84 & 0.723815604767184 & 0.552368790465631 & 0.276184395232816 \tabularnewline
85 & 0.77120253049982 & 0.457594939000361 & 0.22879746950018 \tabularnewline
86 & 0.734710983676011 & 0.530578032647979 & 0.265289016323989 \tabularnewline
87 & 0.708607151504796 & 0.582785696990407 & 0.291392848495204 \tabularnewline
88 & 0.721471037484321 & 0.557057925031359 & 0.278528962515679 \tabularnewline
89 & 0.685982122912493 & 0.628035754175015 & 0.314017877087507 \tabularnewline
90 & 0.64592247358636 & 0.70815505282728 & 0.35407752641364 \tabularnewline
91 & 0.620045199837859 & 0.759909600324282 & 0.379954800162141 \tabularnewline
92 & 0.607183569870504 & 0.785632860258991 & 0.392816430129496 \tabularnewline
93 & 0.59798810393194 & 0.804023792136119 & 0.40201189606806 \tabularnewline
94 & 0.555547682749479 & 0.888904634501041 & 0.444452317250521 \tabularnewline
95 & 0.672084374525811 & 0.655831250948379 & 0.327915625474189 \tabularnewline
96 & 0.658100833624801 & 0.683798332750397 & 0.341899166375199 \tabularnewline
97 & 0.750699249472479 & 0.498601501055041 & 0.249300750527521 \tabularnewline
98 & 0.713964957399255 & 0.572070085201489 & 0.286035042600745 \tabularnewline
99 & 0.673994731243359 & 0.652010537513282 & 0.326005268756641 \tabularnewline
100 & 0.632311477862855 & 0.735377044274289 & 0.367688522137145 \tabularnewline
101 & 0.615251841534164 & 0.769496316931672 & 0.384748158465836 \tabularnewline
102 & 0.569167158646447 & 0.861665682707107 & 0.430832841353553 \tabularnewline
103 & 0.659252684253625 & 0.68149463149275 & 0.340747315746375 \tabularnewline
104 & 0.648421454372248 & 0.703157091255505 & 0.351578545627752 \tabularnewline
105 & 0.611873473677596 & 0.776253052644809 & 0.388126526322404 \tabularnewline
106 & 0.571290828617958 & 0.857418342764084 & 0.428709171382042 \tabularnewline
107 & 0.542062790079605 & 0.915874419840789 & 0.457937209920394 \tabularnewline
108 & 0.549856598217819 & 0.900286803564362 & 0.450143401782181 \tabularnewline
109 & 0.528992897533233 & 0.942014204933533 & 0.471007102466767 \tabularnewline
110 & 0.500824021211502 & 0.998351957576995 & 0.499175978788498 \tabularnewline
111 & 0.522886149612669 & 0.954227700774662 & 0.477113850387331 \tabularnewline
112 & 0.513789548865616 & 0.972420902268767 & 0.486210451134384 \tabularnewline
113 & 0.505890047729042 & 0.988219904541916 & 0.494109952270958 \tabularnewline
114 & 0.470654000709192 & 0.941308001418383 & 0.529345999290808 \tabularnewline
115 & 0.42149443406966 & 0.842988868139319 & 0.57850556593034 \tabularnewline
116 & 0.374008794383302 & 0.748017588766604 & 0.625991205616698 \tabularnewline
117 & 0.335337899460993 & 0.670675798921986 & 0.664662100539007 \tabularnewline
118 & 0.288808791970249 & 0.577617583940498 & 0.711191208029751 \tabularnewline
119 & 0.254456702252871 & 0.508913404505742 & 0.745543297747129 \tabularnewline
120 & 0.222524563522311 & 0.445049127044622 & 0.777475436477689 \tabularnewline
121 & 0.184722120126459 & 0.369444240252917 & 0.815277879873541 \tabularnewline
122 & 0.191691619096634 & 0.383383238193269 & 0.808308380903366 \tabularnewline
123 & 0.1640465605035 & 0.328093121007001 & 0.8359534394965 \tabularnewline
124 & 0.134789250316881 & 0.269578500633762 & 0.865210749683119 \tabularnewline
125 & 0.236224646002365 & 0.472449292004729 & 0.763775353997635 \tabularnewline
126 & 0.199850039878544 & 0.399700079757089 & 0.800149960121456 \tabularnewline
127 & 0.168122202582989 & 0.336244405165978 & 0.831877797417011 \tabularnewline
128 & 0.136361934166361 & 0.272723868332722 & 0.863638065833639 \tabularnewline
129 & 0.144125374838542 & 0.288250749677084 & 0.855874625161458 \tabularnewline
130 & 0.11476094532525 & 0.229521890650501 & 0.88523905467475 \tabularnewline
131 & 0.156548704127927 & 0.313097408255853 & 0.843451295872073 \tabularnewline
132 & 0.128133577153039 & 0.256267154306078 & 0.871866422846961 \tabularnewline
133 & 0.2995829155831 & 0.599165831166199 & 0.7004170844169 \tabularnewline
134 & 0.309755406121554 & 0.619510812243107 & 0.690244593878446 \tabularnewline
135 & 0.292210793415954 & 0.584421586831908 & 0.707789206584046 \tabularnewline
136 & 0.263485870340847 & 0.526971740681694 & 0.736514129659153 \tabularnewline
137 & 0.215664644125355 & 0.43132928825071 & 0.784335355874645 \tabularnewline
138 & 0.250983531714129 & 0.501967063428257 & 0.749016468285871 \tabularnewline
139 & 0.203768994320943 & 0.407537988641887 & 0.796231005679057 \tabularnewline
140 & 0.165617613623555 & 0.331235227247109 & 0.834382386376445 \tabularnewline
141 & 0.127509366345109 & 0.255018732690218 & 0.872490633654891 \tabularnewline
142 & 0.111304689775674 & 0.222609379551348 & 0.888695310224326 \tabularnewline
143 & 0.932485273453399 & 0.135029453093202 & 0.067514726546601 \tabularnewline
144 & 0.994183831821338 & 0.0116323363573248 & 0.00581616817866241 \tabularnewline
145 & 0.988121162808644 & 0.023757674382712 & 0.011878837191356 \tabularnewline
146 & 0.977172028493537 & 0.0456559430129256 & 0.0228279715064628 \tabularnewline
147 & 0.966695205954268 & 0.0666095880914646 & 0.0333047940457323 \tabularnewline
148 & 0.963742221450193 & 0.0725155570996151 & 0.0362577785498075 \tabularnewline
149 & 0.933747944888845 & 0.13250411022231 & 0.0662520551111548 \tabularnewline
150 & 0.88167673382697 & 0.236646532346061 & 0.11832326617303 \tabularnewline
151 & 0.793589757463953 & 0.412820485072094 & 0.206410242536047 \tabularnewline
152 & 0.676527455580563 & 0.646945088838874 & 0.323472544419437 \tabularnewline
153 & 0.690247700522271 & 0.619504598955458 & 0.309752299477729 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=189713&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]9[/C][C]0.564374404951111[/C][C]0.871251190097779[/C][C]0.435625595048889[/C][/ROW]
[ROW][C]10[/C][C]0.51958543180843[/C][C]0.960829136383139[/C][C]0.48041456819157[/C][/ROW]
[ROW][C]11[/C][C]0.488496340097488[/C][C]0.976992680194976[/C][C]0.511503659902512[/C][/ROW]
[ROW][C]12[/C][C]0.732458873965808[/C][C]0.535082252068383[/C][C]0.267541126034192[/C][/ROW]
[ROW][C]13[/C][C]0.777099330910608[/C][C]0.445801338178784[/C][C]0.222900669089392[/C][/ROW]
[ROW][C]14[/C][C]0.730278302429503[/C][C]0.539443395140995[/C][C]0.269721697570497[/C][/ROW]
[ROW][C]15[/C][C]0.656210979365967[/C][C]0.687578041268066[/C][C]0.343789020634033[/C][/ROW]
[ROW][C]16[/C][C]0.572042810781595[/C][C]0.85591437843681[/C][C]0.427957189218405[/C][/ROW]
[ROW][C]17[/C][C]0.516365200951282[/C][C]0.967269598097437[/C][C]0.483634799048718[/C][/ROW]
[ROW][C]18[/C][C]0.462338811871392[/C][C]0.924677623742784[/C][C]0.537661188128608[/C][/ROW]
[ROW][C]19[/C][C]0.568062309049671[/C][C]0.863875381900658[/C][C]0.431937690950329[/C][/ROW]
[ROW][C]20[/C][C]0.534962657885178[/C][C]0.930074684229645[/C][C]0.465037342114822[/C][/ROW]
[ROW][C]21[/C][C]0.520982672313433[/C][C]0.958034655373135[/C][C]0.479017327686567[/C][/ROW]
[ROW][C]22[/C][C]0.44459394643657[/C][C]0.88918789287314[/C][C]0.55540605356343[/C][/ROW]
[ROW][C]23[/C][C]0.592548034894671[/C][C]0.814903930210657[/C][C]0.407451965105329[/C][/ROW]
[ROW][C]24[/C][C]0.528326683492048[/C][C]0.943346633015905[/C][C]0.471673316507952[/C][/ROW]
[ROW][C]25[/C][C]0.4881015616391[/C][C]0.9762031232782[/C][C]0.5118984383609[/C][/ROW]
[ROW][C]26[/C][C]0.524021865776912[/C][C]0.951956268446176[/C][C]0.475978134223088[/C][/ROW]
[ROW][C]27[/C][C]0.527107015182971[/C][C]0.945785969634059[/C][C]0.472892984817029[/C][/ROW]
[ROW][C]28[/C][C]0.699705113740508[/C][C]0.600589772518983[/C][C]0.300294886259492[/C][/ROW]
[ROW][C]29[/C][C]0.849596360361785[/C][C]0.30080727927643[/C][C]0.150403639638215[/C][/ROW]
[ROW][C]30[/C][C]0.832101257527599[/C][C]0.335797484944802[/C][C]0.167898742472401[/C][/ROW]
[ROW][C]31[/C][C]0.823650446398926[/C][C]0.352699107202148[/C][C]0.176349553601074[/C][/ROW]
[ROW][C]32[/C][C]0.782125129652495[/C][C]0.43574974069501[/C][C]0.217874870347505[/C][/ROW]
[ROW][C]33[/C][C]0.776594053318918[/C][C]0.446811893362164[/C][C]0.223405946681082[/C][/ROW]
[ROW][C]34[/C][C]0.84345095465256[/C][C]0.31309809069488[/C][C]0.15654904534744[/C][/ROW]
[ROW][C]35[/C][C]0.90156229830939[/C][C]0.19687540338122[/C][C]0.0984377016906102[/C][/ROW]
[ROW][C]36[/C][C]0.87688734280826[/C][C]0.246225314383479[/C][C]0.12311265719174[/C][/ROW]
[ROW][C]37[/C][C]0.858444673930042[/C][C]0.283110652139916[/C][C]0.141555326069958[/C][/ROW]
[ROW][C]38[/C][C]0.827288852392427[/C][C]0.345422295215145[/C][C]0.172711147607573[/C][/ROW]
[ROW][C]39[/C][C]0.790541157606176[/C][C]0.418917684787648[/C][C]0.209458842393824[/C][/ROW]
[ROW][C]40[/C][C]0.782779164225577[/C][C]0.434441671548846[/C][C]0.217220835774423[/C][/ROW]
[ROW][C]41[/C][C]0.827424395560373[/C][C]0.345151208879254[/C][C]0.172575604439627[/C][/ROW]
[ROW][C]42[/C][C]0.822549824043729[/C][C]0.354900351912542[/C][C]0.177450175956271[/C][/ROW]
[ROW][C]43[/C][C]0.809935068213728[/C][C]0.380129863572545[/C][C]0.190064931786272[/C][/ROW]
[ROW][C]44[/C][C]0.772635911302989[/C][C]0.454728177394021[/C][C]0.227364088697011[/C][/ROW]
[ROW][C]45[/C][C]0.805619180757937[/C][C]0.388761638484125[/C][C]0.194380819242063[/C][/ROW]
[ROW][C]46[/C][C]0.771025157067554[/C][C]0.457949685864893[/C][C]0.228974842932446[/C][/ROW]
[ROW][C]47[/C][C]0.73474592101788[/C][C]0.530508157964239[/C][C]0.26525407898212[/C][/ROW]
[ROW][C]48[/C][C]0.925806083693685[/C][C]0.148387832612631[/C][C]0.0741939163063154[/C][/ROW]
[ROW][C]49[/C][C]0.909145878449604[/C][C]0.181708243100792[/C][C]0.0908541215503959[/C][/ROW]
[ROW][C]50[/C][C]0.932029119017953[/C][C]0.135941761964094[/C][C]0.0679708809820468[/C][/ROW]
[ROW][C]51[/C][C]0.927995453892494[/C][C]0.144009092215011[/C][C]0.0720045461075056[/C][/ROW]
[ROW][C]52[/C][C]0.910933313363085[/C][C]0.178133373273829[/C][C]0.0890666866369146[/C][/ROW]
[ROW][C]53[/C][C]0.889622485937142[/C][C]0.220755028125716[/C][C]0.110377514062858[/C][/ROW]
[ROW][C]54[/C][C]0.876205679771352[/C][C]0.247588640457295[/C][C]0.123794320228648[/C][/ROW]
[ROW][C]55[/C][C]0.852132113701125[/C][C]0.29573577259775[/C][C]0.147867886298875[/C][/ROW]
[ROW][C]56[/C][C]0.845608239446767[/C][C]0.308783521106466[/C][C]0.154391760553233[/C][/ROW]
[ROW][C]57[/C][C]0.825966062848899[/C][C]0.348067874302201[/C][C]0.174033937151101[/C][/ROW]
[ROW][C]58[/C][C]0.849114108766598[/C][C]0.301771782466805[/C][C]0.150885891233402[/C][/ROW]
[ROW][C]59[/C][C]0.900600047256326[/C][C]0.198799905487348[/C][C]0.0993999527436738[/C][/ROW]
[ROW][C]60[/C][C]0.890773103415245[/C][C]0.218453793169511[/C][C]0.109226896584755[/C][/ROW]
[ROW][C]61[/C][C]0.962757676659503[/C][C]0.074484646680995[/C][C]0.0372423233404975[/C][/ROW]
[ROW][C]62[/C][C]0.953369589835571[/C][C]0.0932608203288576[/C][C]0.0466304101644288[/C][/ROW]
[ROW][C]63[/C][C]0.964404229613122[/C][C]0.0711915407737552[/C][C]0.0355957703868776[/C][/ROW]
[ROW][C]64[/C][C]0.956651417358662[/C][C]0.0866971652826756[/C][C]0.0433485826413378[/C][/ROW]
[ROW][C]65[/C][C]0.953744729477761[/C][C]0.0925105410444772[/C][C]0.0462552705222386[/C][/ROW]
[ROW][C]66[/C][C]0.942429497064386[/C][C]0.115141005871228[/C][C]0.057570502935614[/C][/ROW]
[ROW][C]67[/C][C]0.929743723734798[/C][C]0.140512552530405[/C][C]0.0702562762652025[/C][/ROW]
[ROW][C]68[/C][C]0.923334861242187[/C][C]0.153330277515627[/C][C]0.0766651387578134[/C][/ROW]
[ROW][C]69[/C][C]0.905074542022906[/C][C]0.189850915954187[/C][C]0.0949254579770935[/C][/ROW]
[ROW][C]70[/C][C]0.889231582261105[/C][C]0.22153683547779[/C][C]0.110768417738895[/C][/ROW]
[ROW][C]71[/C][C]0.923876192583025[/C][C]0.15224761483395[/C][C]0.0761238074169752[/C][/ROW]
[ROW][C]72[/C][C]0.908896634967612[/C][C]0.182206730064776[/C][C]0.0911033650323881[/C][/ROW]
[ROW][C]73[/C][C]0.909447730534448[/C][C]0.181104538931105[/C][C]0.0905522694655523[/C][/ROW]
[ROW][C]74[/C][C]0.893997197336816[/C][C]0.212005605326367[/C][C]0.106002802663184[/C][/ROW]
[ROW][C]75[/C][C]0.873013392929092[/C][C]0.253973214141817[/C][C]0.126986607070908[/C][/ROW]
[ROW][C]76[/C][C]0.86125152818939[/C][C]0.27749694362122[/C][C]0.13874847181061[/C][/ROW]
[ROW][C]77[/C][C]0.846623526238141[/C][C]0.306752947523719[/C][C]0.153376473761859[/C][/ROW]
[ROW][C]78[/C][C]0.837093492245543[/C][C]0.325813015508915[/C][C]0.162906507754457[/C][/ROW]
[ROW][C]79[/C][C]0.809054037050325[/C][C]0.381891925899349[/C][C]0.190945962949675[/C][/ROW]
[ROW][C]80[/C][C]0.81322160163201[/C][C]0.373556796735981[/C][C]0.18677839836799[/C][/ROW]
[ROW][C]81[/C][C]0.789668779783025[/C][C]0.420662440433949[/C][C]0.210331220216975[/C][/ROW]
[ROW][C]82[/C][C]0.755958807050443[/C][C]0.488082385899114[/C][C]0.244041192949557[/C][/ROW]
[ROW][C]83[/C][C]0.74852752074039[/C][C]0.502944958519219[/C][C]0.25147247925961[/C][/ROW]
[ROW][C]84[/C][C]0.723815604767184[/C][C]0.552368790465631[/C][C]0.276184395232816[/C][/ROW]
[ROW][C]85[/C][C]0.77120253049982[/C][C]0.457594939000361[/C][C]0.22879746950018[/C][/ROW]
[ROW][C]86[/C][C]0.734710983676011[/C][C]0.530578032647979[/C][C]0.265289016323989[/C][/ROW]
[ROW][C]87[/C][C]0.708607151504796[/C][C]0.582785696990407[/C][C]0.291392848495204[/C][/ROW]
[ROW][C]88[/C][C]0.721471037484321[/C][C]0.557057925031359[/C][C]0.278528962515679[/C][/ROW]
[ROW][C]89[/C][C]0.685982122912493[/C][C]0.628035754175015[/C][C]0.314017877087507[/C][/ROW]
[ROW][C]90[/C][C]0.64592247358636[/C][C]0.70815505282728[/C][C]0.35407752641364[/C][/ROW]
[ROW][C]91[/C][C]0.620045199837859[/C][C]0.759909600324282[/C][C]0.379954800162141[/C][/ROW]
[ROW][C]92[/C][C]0.607183569870504[/C][C]0.785632860258991[/C][C]0.392816430129496[/C][/ROW]
[ROW][C]93[/C][C]0.59798810393194[/C][C]0.804023792136119[/C][C]0.40201189606806[/C][/ROW]
[ROW][C]94[/C][C]0.555547682749479[/C][C]0.888904634501041[/C][C]0.444452317250521[/C][/ROW]
[ROW][C]95[/C][C]0.672084374525811[/C][C]0.655831250948379[/C][C]0.327915625474189[/C][/ROW]
[ROW][C]96[/C][C]0.658100833624801[/C][C]0.683798332750397[/C][C]0.341899166375199[/C][/ROW]
[ROW][C]97[/C][C]0.750699249472479[/C][C]0.498601501055041[/C][C]0.249300750527521[/C][/ROW]
[ROW][C]98[/C][C]0.713964957399255[/C][C]0.572070085201489[/C][C]0.286035042600745[/C][/ROW]
[ROW][C]99[/C][C]0.673994731243359[/C][C]0.652010537513282[/C][C]0.326005268756641[/C][/ROW]
[ROW][C]100[/C][C]0.632311477862855[/C][C]0.735377044274289[/C][C]0.367688522137145[/C][/ROW]
[ROW][C]101[/C][C]0.615251841534164[/C][C]0.769496316931672[/C][C]0.384748158465836[/C][/ROW]
[ROW][C]102[/C][C]0.569167158646447[/C][C]0.861665682707107[/C][C]0.430832841353553[/C][/ROW]
[ROW][C]103[/C][C]0.659252684253625[/C][C]0.68149463149275[/C][C]0.340747315746375[/C][/ROW]
[ROW][C]104[/C][C]0.648421454372248[/C][C]0.703157091255505[/C][C]0.351578545627752[/C][/ROW]
[ROW][C]105[/C][C]0.611873473677596[/C][C]0.776253052644809[/C][C]0.388126526322404[/C][/ROW]
[ROW][C]106[/C][C]0.571290828617958[/C][C]0.857418342764084[/C][C]0.428709171382042[/C][/ROW]
[ROW][C]107[/C][C]0.542062790079605[/C][C]0.915874419840789[/C][C]0.457937209920394[/C][/ROW]
[ROW][C]108[/C][C]0.549856598217819[/C][C]0.900286803564362[/C][C]0.450143401782181[/C][/ROW]
[ROW][C]109[/C][C]0.528992897533233[/C][C]0.942014204933533[/C][C]0.471007102466767[/C][/ROW]
[ROW][C]110[/C][C]0.500824021211502[/C][C]0.998351957576995[/C][C]0.499175978788498[/C][/ROW]
[ROW][C]111[/C][C]0.522886149612669[/C][C]0.954227700774662[/C][C]0.477113850387331[/C][/ROW]
[ROW][C]112[/C][C]0.513789548865616[/C][C]0.972420902268767[/C][C]0.486210451134384[/C][/ROW]
[ROW][C]113[/C][C]0.505890047729042[/C][C]0.988219904541916[/C][C]0.494109952270958[/C][/ROW]
[ROW][C]114[/C][C]0.470654000709192[/C][C]0.941308001418383[/C][C]0.529345999290808[/C][/ROW]
[ROW][C]115[/C][C]0.42149443406966[/C][C]0.842988868139319[/C][C]0.57850556593034[/C][/ROW]
[ROW][C]116[/C][C]0.374008794383302[/C][C]0.748017588766604[/C][C]0.625991205616698[/C][/ROW]
[ROW][C]117[/C][C]0.335337899460993[/C][C]0.670675798921986[/C][C]0.664662100539007[/C][/ROW]
[ROW][C]118[/C][C]0.288808791970249[/C][C]0.577617583940498[/C][C]0.711191208029751[/C][/ROW]
[ROW][C]119[/C][C]0.254456702252871[/C][C]0.508913404505742[/C][C]0.745543297747129[/C][/ROW]
[ROW][C]120[/C][C]0.222524563522311[/C][C]0.445049127044622[/C][C]0.777475436477689[/C][/ROW]
[ROW][C]121[/C][C]0.184722120126459[/C][C]0.369444240252917[/C][C]0.815277879873541[/C][/ROW]
[ROW][C]122[/C][C]0.191691619096634[/C][C]0.383383238193269[/C][C]0.808308380903366[/C][/ROW]
[ROW][C]123[/C][C]0.1640465605035[/C][C]0.328093121007001[/C][C]0.8359534394965[/C][/ROW]
[ROW][C]124[/C][C]0.134789250316881[/C][C]0.269578500633762[/C][C]0.865210749683119[/C][/ROW]
[ROW][C]125[/C][C]0.236224646002365[/C][C]0.472449292004729[/C][C]0.763775353997635[/C][/ROW]
[ROW][C]126[/C][C]0.199850039878544[/C][C]0.399700079757089[/C][C]0.800149960121456[/C][/ROW]
[ROW][C]127[/C][C]0.168122202582989[/C][C]0.336244405165978[/C][C]0.831877797417011[/C][/ROW]
[ROW][C]128[/C][C]0.136361934166361[/C][C]0.272723868332722[/C][C]0.863638065833639[/C][/ROW]
[ROW][C]129[/C][C]0.144125374838542[/C][C]0.288250749677084[/C][C]0.855874625161458[/C][/ROW]
[ROW][C]130[/C][C]0.11476094532525[/C][C]0.229521890650501[/C][C]0.88523905467475[/C][/ROW]
[ROW][C]131[/C][C]0.156548704127927[/C][C]0.313097408255853[/C][C]0.843451295872073[/C][/ROW]
[ROW][C]132[/C][C]0.128133577153039[/C][C]0.256267154306078[/C][C]0.871866422846961[/C][/ROW]
[ROW][C]133[/C][C]0.2995829155831[/C][C]0.599165831166199[/C][C]0.7004170844169[/C][/ROW]
[ROW][C]134[/C][C]0.309755406121554[/C][C]0.619510812243107[/C][C]0.690244593878446[/C][/ROW]
[ROW][C]135[/C][C]0.292210793415954[/C][C]0.584421586831908[/C][C]0.707789206584046[/C][/ROW]
[ROW][C]136[/C][C]0.263485870340847[/C][C]0.526971740681694[/C][C]0.736514129659153[/C][/ROW]
[ROW][C]137[/C][C]0.215664644125355[/C][C]0.43132928825071[/C][C]0.784335355874645[/C][/ROW]
[ROW][C]138[/C][C]0.250983531714129[/C][C]0.501967063428257[/C][C]0.749016468285871[/C][/ROW]
[ROW][C]139[/C][C]0.203768994320943[/C][C]0.407537988641887[/C][C]0.796231005679057[/C][/ROW]
[ROW][C]140[/C][C]0.165617613623555[/C][C]0.331235227247109[/C][C]0.834382386376445[/C][/ROW]
[ROW][C]141[/C][C]0.127509366345109[/C][C]0.255018732690218[/C][C]0.872490633654891[/C][/ROW]
[ROW][C]142[/C][C]0.111304689775674[/C][C]0.222609379551348[/C][C]0.888695310224326[/C][/ROW]
[ROW][C]143[/C][C]0.932485273453399[/C][C]0.135029453093202[/C][C]0.067514726546601[/C][/ROW]
[ROW][C]144[/C][C]0.994183831821338[/C][C]0.0116323363573248[/C][C]0.00581616817866241[/C][/ROW]
[ROW][C]145[/C][C]0.988121162808644[/C][C]0.023757674382712[/C][C]0.011878837191356[/C][/ROW]
[ROW][C]146[/C][C]0.977172028493537[/C][C]0.0456559430129256[/C][C]0.0228279715064628[/C][/ROW]
[ROW][C]147[/C][C]0.966695205954268[/C][C]0.0666095880914646[/C][C]0.0333047940457323[/C][/ROW]
[ROW][C]148[/C][C]0.963742221450193[/C][C]0.0725155570996151[/C][C]0.0362577785498075[/C][/ROW]
[ROW][C]149[/C][C]0.933747944888845[/C][C]0.13250411022231[/C][C]0.0662520551111548[/C][/ROW]
[ROW][C]150[/C][C]0.88167673382697[/C][C]0.236646532346061[/C][C]0.11832326617303[/C][/ROW]
[ROW][C]151[/C][C]0.793589757463953[/C][C]0.412820485072094[/C][C]0.206410242536047[/C][/ROW]
[ROW][C]152[/C][C]0.676527455580563[/C][C]0.646945088838874[/C][C]0.323472544419437[/C][/ROW]
[ROW][C]153[/C][C]0.690247700522271[/C][C]0.619504598955458[/C][C]0.309752299477729[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=189713&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=189713&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.5643744049511110.8712511900977790.435625595048889
100.519585431808430.9608291363831390.48041456819157
110.4884963400974880.9769926801949760.511503659902512
120.7324588739658080.5350822520683830.267541126034192
130.7770993309106080.4458013381787840.222900669089392
140.7302783024295030.5394433951409950.269721697570497
150.6562109793659670.6875780412680660.343789020634033
160.5720428107815950.855914378436810.427957189218405
170.5163652009512820.9672695980974370.483634799048718
180.4623388118713920.9246776237427840.537661188128608
190.5680623090496710.8638753819006580.431937690950329
200.5349626578851780.9300746842296450.465037342114822
210.5209826723134330.9580346553731350.479017327686567
220.444593946436570.889187892873140.55540605356343
230.5925480348946710.8149039302106570.407451965105329
240.5283266834920480.9433466330159050.471673316507952
250.48810156163910.97620312327820.5118984383609
260.5240218657769120.9519562684461760.475978134223088
270.5271070151829710.9457859696340590.472892984817029
280.6997051137405080.6005897725189830.300294886259492
290.8495963603617850.300807279276430.150403639638215
300.8321012575275990.3357974849448020.167898742472401
310.8236504463989260.3526991072021480.176349553601074
320.7821251296524950.435749740695010.217874870347505
330.7765940533189180.4468118933621640.223405946681082
340.843450954652560.313098090694880.15654904534744
350.901562298309390.196875403381220.0984377016906102
360.876887342808260.2462253143834790.12311265719174
370.8584446739300420.2831106521399160.141555326069958
380.8272888523924270.3454222952151450.172711147607573
390.7905411576061760.4189176847876480.209458842393824
400.7827791642255770.4344416715488460.217220835774423
410.8274243955603730.3451512088792540.172575604439627
420.8225498240437290.3549003519125420.177450175956271
430.8099350682137280.3801298635725450.190064931786272
440.7726359113029890.4547281773940210.227364088697011
450.8056191807579370.3887616384841250.194380819242063
460.7710251570675540.4579496858648930.228974842932446
470.734745921017880.5305081579642390.26525407898212
480.9258060836936850.1483878326126310.0741939163063154
490.9091458784496040.1817082431007920.0908541215503959
500.9320291190179530.1359417619640940.0679708809820468
510.9279954538924940.1440090922150110.0720045461075056
520.9109333133630850.1781333732738290.0890666866369146
530.8896224859371420.2207550281257160.110377514062858
540.8762056797713520.2475886404572950.123794320228648
550.8521321137011250.295735772597750.147867886298875
560.8456082394467670.3087835211064660.154391760553233
570.8259660628488990.3480678743022010.174033937151101
580.8491141087665980.3017717824668050.150885891233402
590.9006000472563260.1987999054873480.0993999527436738
600.8907731034152450.2184537931695110.109226896584755
610.9627576766595030.0744846466809950.0372423233404975
620.9533695898355710.09326082032885760.0466304101644288
630.9644042296131220.07119154077375520.0355957703868776
640.9566514173586620.08669716528267560.0433485826413378
650.9537447294777610.09251054104447720.0462552705222386
660.9424294970643860.1151410058712280.057570502935614
670.9297437237347980.1405125525304050.0702562762652025
680.9233348612421870.1533302775156270.0766651387578134
690.9050745420229060.1898509159541870.0949254579770935
700.8892315822611050.221536835477790.110768417738895
710.9238761925830250.152247614833950.0761238074169752
720.9088966349676120.1822067300647760.0911033650323881
730.9094477305344480.1811045389311050.0905522694655523
740.8939971973368160.2120056053263670.106002802663184
750.8730133929290920.2539732141418170.126986607070908
760.861251528189390.277496943621220.13874847181061
770.8466235262381410.3067529475237190.153376473761859
780.8370934922455430.3258130155089150.162906507754457
790.8090540370503250.3818919258993490.190945962949675
800.813221601632010.3735567967359810.18677839836799
810.7896687797830250.4206624404339490.210331220216975
820.7559588070504430.4880823858991140.244041192949557
830.748527520740390.5029449585192190.25147247925961
840.7238156047671840.5523687904656310.276184395232816
850.771202530499820.4575949390003610.22879746950018
860.7347109836760110.5305780326479790.265289016323989
870.7086071515047960.5827856969904070.291392848495204
880.7214710374843210.5570579250313590.278528962515679
890.6859821229124930.6280357541750150.314017877087507
900.645922473586360.708155052827280.35407752641364
910.6200451998378590.7599096003242820.379954800162141
920.6071835698705040.7856328602589910.392816430129496
930.597988103931940.8040237921361190.40201189606806
940.5555476827494790.8889046345010410.444452317250521
950.6720843745258110.6558312509483790.327915625474189
960.6581008336248010.6837983327503970.341899166375199
970.7506992494724790.4986015010550410.249300750527521
980.7139649573992550.5720700852014890.286035042600745
990.6739947312433590.6520105375132820.326005268756641
1000.6323114778628550.7353770442742890.367688522137145
1010.6152518415341640.7694963169316720.384748158465836
1020.5691671586464470.8616656827071070.430832841353553
1030.6592526842536250.681494631492750.340747315746375
1040.6484214543722480.7031570912555050.351578545627752
1050.6118734736775960.7762530526448090.388126526322404
1060.5712908286179580.8574183427640840.428709171382042
1070.5420627900796050.9158744198407890.457937209920394
1080.5498565982178190.9002868035643620.450143401782181
1090.5289928975332330.9420142049335330.471007102466767
1100.5008240212115020.9983519575769950.499175978788498
1110.5228861496126690.9542277007746620.477113850387331
1120.5137895488656160.9724209022687670.486210451134384
1130.5058900477290420.9882199045419160.494109952270958
1140.4706540007091920.9413080014183830.529345999290808
1150.421494434069660.8429888681393190.57850556593034
1160.3740087943833020.7480175887666040.625991205616698
1170.3353378994609930.6706757989219860.664662100539007
1180.2888087919702490.5776175839404980.711191208029751
1190.2544567022528710.5089134045057420.745543297747129
1200.2225245635223110.4450491270446220.777475436477689
1210.1847221201264590.3694442402529170.815277879873541
1220.1916916190966340.3833832381932690.808308380903366
1230.16404656050350.3280931210070010.8359534394965
1240.1347892503168810.2695785006337620.865210749683119
1250.2362246460023650.4724492920047290.763775353997635
1260.1998500398785440.3997000797570890.800149960121456
1270.1681222025829890.3362444051659780.831877797417011
1280.1363619341663610.2727238683327220.863638065833639
1290.1441253748385420.2882507496770840.855874625161458
1300.114760945325250.2295218906505010.88523905467475
1310.1565487041279270.3130974082558530.843451295872073
1320.1281335771530390.2562671543060780.871866422846961
1330.29958291558310.5991658311661990.7004170844169
1340.3097554061215540.6195108122431070.690244593878446
1350.2922107934159540.5844215868319080.707789206584046
1360.2634858703408470.5269717406816940.736514129659153
1370.2156646441253550.431329288250710.784335355874645
1380.2509835317141290.5019670634282570.749016468285871
1390.2037689943209430.4075379886418870.796231005679057
1400.1656176136235550.3312352272471090.834382386376445
1410.1275093663451090.2550187326902180.872490633654891
1420.1113046897756740.2226093795513480.888695310224326
1430.9324852734533990.1350294530932020.067514726546601
1440.9941838318213380.01163233635732480.00581616817866241
1450.9881211628086440.0237576743827120.011878837191356
1460.9771720284935370.04565594301292560.0228279715064628
1470.9666952059542680.06660958809146460.0333047940457323
1480.9637422214501930.07251555709961510.0362577785498075
1490.9337479448888450.132504110222310.0662520551111548
1500.881676733826970.2366465323460610.11832326617303
1510.7935897574639530.4128204850720940.206410242536047
1520.6765274555805630.6469450888388740.323472544419437
1530.6902477005222710.6195045989554580.309752299477729






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level30.0206896551724138OK
10% type I error level100.0689655172413793OK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 3 & 0.0206896551724138 & OK \tabularnewline
10% type I error level & 10 & 0.0689655172413793 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=189713&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]3[/C][C]0.0206896551724138[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]10[/C][C]0.0689655172413793[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=189713&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=189713&T=6

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


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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level30.0206896551724138OK
10% type I error level100.0689655172413793OK


Figures (Output of Computation)

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

Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}