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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationFri, 03 Dec 2010 20:50:37 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/03/t12914100979zpc1pduxhnq0tu.htm/, Retrieved Thu, 31 Oct 2024 23:27:50 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=105006, Retrieved Thu, 31 Oct 2024 23:27:50 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact225
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
-    D  [Multiple Regression] [ws4] [2010-11-30 12:25:45] [a2638725f7f7c6bd63902ba17eba666b]
-         [Multiple Regression] [ws4] [2010-11-30 13:06:41] [a2638725f7f7c6bd63902ba17eba666b]
-    D        [Multiple Regression] [] [2010-12-03 20:50:37] [d894319408e7bf490be5a864265f5d52] [Current]
-               [Multiple Regression] [] [2010-12-03 21:35:27] [dc73d270d5d96f29ff77294e1b86f79b]
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Dataseries X:
13	14	13	3
12	8	13	5
10	12	16	6
9	7	12	6
10	10	11	5
12	7	12	3
13	16	18	8
12	11	11	4
12	14	14	4
6	6	9	4
5	16	14	6
12	11	12	6
11	16	11	5
14	12	12	4
14	7	13	6
12	13	11	4
12	11	12	6
11	15	16	6
11	7	9	4
7	9	11	4
9	7	13	2
11	14	15	7
11	15	10	5
12	7	11	4
12	15	13	6
11	17	16	6
11	15	15	7
8	14	14	5
9	14	14	6
12	8	14	4
10	8	8	4
10	14	13	7
12	14	15	7
8	8	13	4
12	11	11	4
11	16	15	6
12	10	15	6
7	8	9	5
11	14	13	6
11	16	16	7
12	13	13	6
9	5	11	3
15	8	12	3
11	10	12	4
11	8	12	6
11	13	14	7
11	15	14	5
15	6	8	4
11	12	13	5
12	16	16	6
12	5	13	6
9	15	11	6
12	12	14	5
12	8	13	4
13	13	13	5
11	14	13	5
9	12	12	4
9	16	16	6
11	10	15	2
11	15	15	8
12	8	12	3
12	16	14	6
9	19	12	6
11	14	15	6
9	6	12	5
12	13	13	5
12	15	12	6
12	7	12	5
12	13	13	6
14	4	5	2
11	14	13	5
12	13	13	5
11	11	14	5
6	14	17	6
10	12	13	6
12	15	13	6
13	14	12	5
8	13	13	5
12	8	14	4
12	6	11	2
12	7	12	4
6	13	12	6
11	13	16	6
10	11	12	5
12	5	12	3
13	12	12	6
11	8	10	4
7	11	15	5
11	14	15	8
11	9	12	4
11	10	16	6
11	13	15	6
12	16	16	7
10	16	13	6
11	11	12	5
12	8	11	4
7	4	13	6
13	7	10	3
8	14	15	5
12	11	13	6
11	17	16	7
12	15	15	7
14	17	18	6
10	5	13	3
10	4	10	2
13	10	16	8
10	11	13	3
11	15	15	8
10	10	14	3
7	9	15	4
10	12	14	5
8	15	13	7
12	7	13	6
12	13	15	6
12	12	16	7
11	14	14	6
12	14	14	6
12	8	16	6
12	15	14	6
11	12	12	4
12	12	13	4
11	16	12	5
11	9	12	4
13	15	14	6
12	15	14	6
12	6	14	5
12	14	16	8
12	15	13	6
8	10	14	5
8	6	4	4
12	14	16	8
11	12	13	6
12	8	16	4
13	11	15	6
12	13	14	6
12	9	13	4
11	15	14	6
12	13	12	3
12	15	15	6
10	14	14	5
11	16	13	4
12	14	14	6
12	14	16	4
10	10	6	4
12	10	13	4
13	4	13	6
12	8	14	5
15	15	15	6
11	16	14	6
12	12	15	8
11	12	13	7
12	15	16	7
11	9	12	4
10	12	15	6
11	14	12	6
11	11	14	2




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time11 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\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 & 11 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=105006&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]11 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=105006&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=105006&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time11 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Multiple Linear Regression - Estimated Regression Equation
Liked[t] = + 7.07678977830615 + 0.0887064792188269FindingFriends[t] + 0.180163506492804KnowingFriends[t] + 0.583237749836951Celebrity[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Liked[t] =  +  7.07678977830615 +  0.0887064792188269FindingFriends[t] +  0.180163506492804KnowingFriends[t] +  0.583237749836951Celebrity[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=105006&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Liked[t] =  +  7.07678977830615 +  0.0887064792188269FindingFriends[t] +  0.180163506492804KnowingFriends[t] +  0.583237749836951Celebrity[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=105006&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=105006&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
Liked[t] = + 7.07678977830615 + 0.0887064792188269FindingFriends[t] + 0.180163506492804KnowingFriends[t] + 0.583237749836951Celebrity[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)7.076789778306151.0516546.729200
FindingFriends0.08870647921882690.0803511.1040.2713420.135671
KnowingFriends0.1801635064928040.0494853.64080.0003720.000186
Celebrity0.5832377498369510.122354.7674e-062e-06

\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) & 7.07678977830615 & 1.051654 & 6.7292 & 0 & 0 \tabularnewline
FindingFriends & 0.0887064792188269 & 0.080351 & 1.104 & 0.271342 & 0.135671 \tabularnewline
KnowingFriends & 0.180163506492804 & 0.049485 & 3.6408 & 0.000372 & 0.000186 \tabularnewline
Celebrity & 0.583237749836951 & 0.12235 & 4.767 & 4e-06 & 2e-06 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=105006&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]7.07678977830615[/C][C]1.051654[/C][C]6.7292[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]FindingFriends[/C][C]0.0887064792188269[/C][C]0.080351[/C][C]1.104[/C][C]0.271342[/C][C]0.135671[/C][/ROW]
[ROW][C]KnowingFriends[/C][C]0.180163506492804[/C][C]0.049485[/C][C]3.6408[/C][C]0.000372[/C][C]0.000186[/C][/ROW]
[ROW][C]Celebrity[/C][C]0.583237749836951[/C][C]0.12235[/C][C]4.767[/C][C]4e-06[/C][C]2e-06[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=105006&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=105006&T=2

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)7.076789778306151.0516546.729200
FindingFriends0.08870647921882690.0803511.1040.2713420.135671
KnowingFriends0.1801635064928040.0494853.64080.0003720.000186
Celebrity0.5832377498369510.122354.7674e-062e-06







Multiple Linear Regression - Regression Statistics
Multiple R0.592277490989078
R-squared0.350792626332318
Adjusted R-squared0.337979322904666
F-TEST (value)27.3772199583827
F-TEST (DF numerator)3
F-TEST (DF denominator)152
p-value3.26405569239796e-14
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.77011811147447
Sum Squared Residuals476.264355542632

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.592277490989078 \tabularnewline
R-squared & 0.350792626332318 \tabularnewline
Adjusted R-squared & 0.337979322904666 \tabularnewline
F-TEST (value) & 27.3772199583827 \tabularnewline
F-TEST (DF numerator) & 3 \tabularnewline
F-TEST (DF denominator) & 152 \tabularnewline
p-value & 3.26405569239796e-14 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.77011811147447 \tabularnewline
Sum Squared Residuals & 476.264355542632 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=105006&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.592277490989078[/C][/ROW]
[ROW][C]R-squared[/C][C]0.350792626332318[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.337979322904666[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]27.3772199583827[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]3[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]152[/C][/ROW]
[ROW][C]p-value[/C][C]3.26405569239796e-14[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.77011811147447[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]476.264355542632[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=105006&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=105006&T=3

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Regression Statistics
Multiple R0.592277490989078
R-squared0.350792626332318
Adjusted R-squared0.337979322904666
F-TEST (value)27.3772199583827
F-TEST (DF numerator)3
F-TEST (DF denominator)152
p-value3.26405569239796e-14
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.77011811147447
Sum Squared Residuals476.264355542632







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11312.50197634856130.498023651438748
21312.49876433005930.50123566994072
31613.62524314742982.37475685257020
41212.6357191357470-0.635719135746955
51112.6816783846072-1.68167838460724
61211.15212532389260.847874676107423
71815.77849211073142.2215078892686
81112.4560170997007-1.45601709970075
91412.99650761917921.00349238082084
10911.0229606919238-2.02296069192376
111413.90236477730690.0976352226931168
121213.6224925993746-1.62249259937465
131113.8513659027829-2.85136590278289
141212.8135935646312-0.813593564631204
151313.0792515318411-0.079251531841085
161112.8163441126864-1.81634411268635
171213.6224925993746-1.62249259937465
181614.25444014612701.74555985387296
19911.6466565945107-2.64665659451070
201111.652157690621-0.652157690621004
211310.30276813639912.69723186360085
221514.65751438947120.342485610528814
231013.6712023962901-3.67120239629009
241111.7353630737295-0.73536307372953
251314.3431466253459-1.34314662534587
261614.61476715911261.38523284088735
271514.8376778959640.16232210403601
281413.22491945214080.775080547859197
291413.89686368119660.103136318803419
301411.91552658022232.08447341977767
31811.7381136217847-3.73811362178468
321314.5688079102524-1.56880791025236
331514.746220868690.253779131309987
341311.56070066334701.43929933665297
351112.4560170997007-1.45601709970075
361514.43460365261980.565396347380157
371513.44232909288181.55767090711816
38912.0552319339651-3.05523193396515
391314.0742766396342-1.07427663963423
401615.01784140245680.982158597543205
411313.9828196123603-0.982819612360257
421110.52567887325050.474321126749511
431211.59840826804190.401591731958138
441212.1871471139891-0.187147113989115
451212.9932956006774-0.99329560067741
461414.4773508829784-0.477350882978382
471413.67120239629010.328797603709913
48811.8213190048932-3.82131900489321
491313.1307118768117-0.130711876811675
501614.52331013183871.47668986816133
511312.54151156041780.458488439582177
521114.0770271876894-3.07702718768939
531413.21941835603050.780581643969498
541311.91552658022231.08447341977767
551313.4882883417421-0.488288341742133
561313.4910388897973-0.491038889797283
571212.3700611685371-0.37006116853707
581614.25719069418221.74280930581781
591511.02067161431523.97932838568479
601515.4209156458009-0.420915645800941
611211.33228883038540.667711169614618
621414.5233101318387-0.52331013183867
631214.7976812136606-2.79768121366060
641514.07427663963420.925723360365765
651211.87231787941720.127682120582804
661313.3995818625233-0.399581862523306
671214.3431466253459-2.34314662534587
681212.3186008235665-0.318600823566480
691313.9828196123603-0.982819612360257
70510.2058100130149-5.20581001301487
711313.4910388897973-0.491038889797283
721313.3995818625233-0.399581862523306
731412.95054837031891.04945162968113
741713.63074424354013.3692557564599
751313.6252431474298-0.6252431474298
761314.3431466253459-1.34314662534587
771213.6684518482349-1.66845184823494
781313.044755945648-0.044755945647999
791411.91552658022232.08447341977767
801110.38872406756280.611275932437178
811211.73536307372950.264636926270471
821213.4505807370473-1.45058073704730
831613.89411313314142.10588686685857
841212.8618418911000-0.861841891100044
851210.79179831090701.20820168909303
861213.8913625850863-1.89136258508628
871011.8268201010035-1.82682010100351
881512.59572245344362.40427754655644
891515.2407521393081-0.240752139308137
901212.0069836074963-0.00698360749631072
911613.35362261366302.64637738633698
921513.89411313314141.10588686685857
931615.10654788167560.893452118324378
941314.3458971734010-1.34589717340102
951212.9505483703189-0.95054837031887
961111.9155265802223-0.915526580222334
971311.91781565783091.08218434216911
981011.2408318031114-1.24083180311140
991513.22491945214081.77508054785920
1001313.6224925993746-0.622492599374649
1011615.19800490894960.801995091050401
1021514.92638437518280.0736156248171831
1031814.88088659676913.11911340323087
1041310.61438535246932.38561464753068
105109.850984096139560.149015903860440
1061614.69751107177461.30248892822543
1071311.69536639142611.30463360857386
1081515.4209156458009-0.420915645800941
1091411.51520288493332.48479711506666
1101511.6521576906213.34784230937900
1111413.04200539759280.957994602407152
1121314.5715584583075-1.57155845830751
1131312.90183857340340.0981614265965683
1141513.98281961236031.01718038763974
1151614.38589385570441.61410614429560
1161414.0742766396342-0.0742766396342347
1171414.1629831188531-0.162983118853061
1181613.08200207989622.91799792010376
1191414.3431466253459-0.343146625345866
1201212.5474741269747-0.547474126974723
1211312.63618060619350.36381939380645
1221213.8513659027829-1.85136590278289
1231212.0069836074963-0.00698360749631072
1241414.4318531045647-0.431853104564693
1251414.3431466253459-0.343146625345866
1261412.13843731707371.86156268292632
1271615.32945861852700.670541381473036
1281314.3431466253459-1.34314662534587
1291412.50426542616961.49573457383041
130411.2003736503614-7.20037365036142
1311615.32945861852700.670541381473036
1321313.7139496266486-0.713949626648626
1331611.91552658022234.08447341977767
1341513.71119907859351.28880092140652
1351413.98281961236030.0171803876397428
1361312.09569008671510.904309913284862
1371414.2544401461270-0.254440146127039
1381212.2331063628494-0.233106362849403
1391514.34314662534590.656853374654135
1401413.40233241057850.597667589421543
1411313.2681281529459-0.268128152945941
1421414.1629831188531-0.162983118853061
1431612.99650761917923.00349238082084
144612.0984406347703-6.09844063477029
1451312.27585359320790.724146406792058
1461312.45005453314380.549945466856154
1471412.49876433005931.50123566994072
1481514.60926606300230.390733936997654
1491414.4346036526198-0.434603652619843
1501514.96913160554140.0308683944586446
1511314.2971873764856-1.29718737648558
1521614.92638437518281.07361562481718
1531212.0069836074963-0.00698360749631072
1541513.62524314742981.3747568525702
1551214.0742766396342-2.07427663963423
1561411.20083512080802.79916487919198

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 13 & 12.5019763485613 & 0.498023651438748 \tabularnewline
2 & 13 & 12.4987643300593 & 0.50123566994072 \tabularnewline
3 & 16 & 13.6252431474298 & 2.37475685257020 \tabularnewline
4 & 12 & 12.6357191357470 & -0.635719135746955 \tabularnewline
5 & 11 & 12.6816783846072 & -1.68167838460724 \tabularnewline
6 & 12 & 11.1521253238926 & 0.847874676107423 \tabularnewline
7 & 18 & 15.7784921107314 & 2.2215078892686 \tabularnewline
8 & 11 & 12.4560170997007 & -1.45601709970075 \tabularnewline
9 & 14 & 12.9965076191792 & 1.00349238082084 \tabularnewline
10 & 9 & 11.0229606919238 & -2.02296069192376 \tabularnewline
11 & 14 & 13.9023647773069 & 0.0976352226931168 \tabularnewline
12 & 12 & 13.6224925993746 & -1.62249259937465 \tabularnewline
13 & 11 & 13.8513659027829 & -2.85136590278289 \tabularnewline
14 & 12 & 12.8135935646312 & -0.813593564631204 \tabularnewline
15 & 13 & 13.0792515318411 & -0.079251531841085 \tabularnewline
16 & 11 & 12.8163441126864 & -1.81634411268635 \tabularnewline
17 & 12 & 13.6224925993746 & -1.62249259937465 \tabularnewline
18 & 16 & 14.2544401461270 & 1.74555985387296 \tabularnewline
19 & 9 & 11.6466565945107 & -2.64665659451070 \tabularnewline
20 & 11 & 11.652157690621 & -0.652157690621004 \tabularnewline
21 & 13 & 10.3027681363991 & 2.69723186360085 \tabularnewline
22 & 15 & 14.6575143894712 & 0.342485610528814 \tabularnewline
23 & 10 & 13.6712023962901 & -3.67120239629009 \tabularnewline
24 & 11 & 11.7353630737295 & -0.73536307372953 \tabularnewline
25 & 13 & 14.3431466253459 & -1.34314662534587 \tabularnewline
26 & 16 & 14.6147671591126 & 1.38523284088735 \tabularnewline
27 & 15 & 14.837677895964 & 0.16232210403601 \tabularnewline
28 & 14 & 13.2249194521408 & 0.775080547859197 \tabularnewline
29 & 14 & 13.8968636811966 & 0.103136318803419 \tabularnewline
30 & 14 & 11.9155265802223 & 2.08447341977767 \tabularnewline
31 & 8 & 11.7381136217847 & -3.73811362178468 \tabularnewline
32 & 13 & 14.5688079102524 & -1.56880791025236 \tabularnewline
33 & 15 & 14.74622086869 & 0.253779131309987 \tabularnewline
34 & 13 & 11.5607006633470 & 1.43929933665297 \tabularnewline
35 & 11 & 12.4560170997007 & -1.45601709970075 \tabularnewline
36 & 15 & 14.4346036526198 & 0.565396347380157 \tabularnewline
37 & 15 & 13.4423290928818 & 1.55767090711816 \tabularnewline
38 & 9 & 12.0552319339651 & -3.05523193396515 \tabularnewline
39 & 13 & 14.0742766396342 & -1.07427663963423 \tabularnewline
40 & 16 & 15.0178414024568 & 0.982158597543205 \tabularnewline
41 & 13 & 13.9828196123603 & -0.982819612360257 \tabularnewline
42 & 11 & 10.5256788732505 & 0.474321126749511 \tabularnewline
43 & 12 & 11.5984082680419 & 0.401591731958138 \tabularnewline
44 & 12 & 12.1871471139891 & -0.187147113989115 \tabularnewline
45 & 12 & 12.9932956006774 & -0.99329560067741 \tabularnewline
46 & 14 & 14.4773508829784 & -0.477350882978382 \tabularnewline
47 & 14 & 13.6712023962901 & 0.328797603709913 \tabularnewline
48 & 8 & 11.8213190048932 & -3.82131900489321 \tabularnewline
49 & 13 & 13.1307118768117 & -0.130711876811675 \tabularnewline
50 & 16 & 14.5233101318387 & 1.47668986816133 \tabularnewline
51 & 13 & 12.5415115604178 & 0.458488439582177 \tabularnewline
52 & 11 & 14.0770271876894 & -3.07702718768939 \tabularnewline
53 & 14 & 13.2194183560305 & 0.780581643969498 \tabularnewline
54 & 13 & 11.9155265802223 & 1.08447341977767 \tabularnewline
55 & 13 & 13.4882883417421 & -0.488288341742133 \tabularnewline
56 & 13 & 13.4910388897973 & -0.491038889797283 \tabularnewline
57 & 12 & 12.3700611685371 & -0.37006116853707 \tabularnewline
58 & 16 & 14.2571906941822 & 1.74280930581781 \tabularnewline
59 & 15 & 11.0206716143152 & 3.97932838568479 \tabularnewline
60 & 15 & 15.4209156458009 & -0.420915645800941 \tabularnewline
61 & 12 & 11.3322888303854 & 0.667711169614618 \tabularnewline
62 & 14 & 14.5233101318387 & -0.52331013183867 \tabularnewline
63 & 12 & 14.7976812136606 & -2.79768121366060 \tabularnewline
64 & 15 & 14.0742766396342 & 0.925723360365765 \tabularnewline
65 & 12 & 11.8723178794172 & 0.127682120582804 \tabularnewline
66 & 13 & 13.3995818625233 & -0.399581862523306 \tabularnewline
67 & 12 & 14.3431466253459 & -2.34314662534587 \tabularnewline
68 & 12 & 12.3186008235665 & -0.318600823566480 \tabularnewline
69 & 13 & 13.9828196123603 & -0.982819612360257 \tabularnewline
70 & 5 & 10.2058100130149 & -5.20581001301487 \tabularnewline
71 & 13 & 13.4910388897973 & -0.491038889797283 \tabularnewline
72 & 13 & 13.3995818625233 & -0.399581862523306 \tabularnewline
73 & 14 & 12.9505483703189 & 1.04945162968113 \tabularnewline
74 & 17 & 13.6307442435401 & 3.3692557564599 \tabularnewline
75 & 13 & 13.6252431474298 & -0.6252431474298 \tabularnewline
76 & 13 & 14.3431466253459 & -1.34314662534587 \tabularnewline
77 & 12 & 13.6684518482349 & -1.66845184823494 \tabularnewline
78 & 13 & 13.044755945648 & -0.044755945647999 \tabularnewline
79 & 14 & 11.9155265802223 & 2.08447341977767 \tabularnewline
80 & 11 & 10.3887240675628 & 0.611275932437178 \tabularnewline
81 & 12 & 11.7353630737295 & 0.264636926270471 \tabularnewline
82 & 12 & 13.4505807370473 & -1.45058073704730 \tabularnewline
83 & 16 & 13.8941131331414 & 2.10588686685857 \tabularnewline
84 & 12 & 12.8618418911000 & -0.861841891100044 \tabularnewline
85 & 12 & 10.7917983109070 & 1.20820168909303 \tabularnewline
86 & 12 & 13.8913625850863 & -1.89136258508628 \tabularnewline
87 & 10 & 11.8268201010035 & -1.82682010100351 \tabularnewline
88 & 15 & 12.5957224534436 & 2.40427754655644 \tabularnewline
89 & 15 & 15.2407521393081 & -0.240752139308137 \tabularnewline
90 & 12 & 12.0069836074963 & -0.00698360749631072 \tabularnewline
91 & 16 & 13.3536226136630 & 2.64637738633698 \tabularnewline
92 & 15 & 13.8941131331414 & 1.10588686685857 \tabularnewline
93 & 16 & 15.1065478816756 & 0.893452118324378 \tabularnewline
94 & 13 & 14.3458971734010 & -1.34589717340102 \tabularnewline
95 & 12 & 12.9505483703189 & -0.95054837031887 \tabularnewline
96 & 11 & 11.9155265802223 & -0.915526580222334 \tabularnewline
97 & 13 & 11.9178156578309 & 1.08218434216911 \tabularnewline
98 & 10 & 11.2408318031114 & -1.24083180311140 \tabularnewline
99 & 15 & 13.2249194521408 & 1.77508054785920 \tabularnewline
100 & 13 & 13.6224925993746 & -0.622492599374649 \tabularnewline
101 & 16 & 15.1980049089496 & 0.801995091050401 \tabularnewline
102 & 15 & 14.9263843751828 & 0.0736156248171831 \tabularnewline
103 & 18 & 14.8808865967691 & 3.11911340323087 \tabularnewline
104 & 13 & 10.6143853524693 & 2.38561464753068 \tabularnewline
105 & 10 & 9.85098409613956 & 0.149015903860440 \tabularnewline
106 & 16 & 14.6975110717746 & 1.30248892822543 \tabularnewline
107 & 13 & 11.6953663914261 & 1.30463360857386 \tabularnewline
108 & 15 & 15.4209156458009 & -0.420915645800941 \tabularnewline
109 & 14 & 11.5152028849333 & 2.48479711506666 \tabularnewline
110 & 15 & 11.652157690621 & 3.34784230937900 \tabularnewline
111 & 14 & 13.0420053975928 & 0.957994602407152 \tabularnewline
112 & 13 & 14.5715584583075 & -1.57155845830751 \tabularnewline
113 & 13 & 12.9018385734034 & 0.0981614265965683 \tabularnewline
114 & 15 & 13.9828196123603 & 1.01718038763974 \tabularnewline
115 & 16 & 14.3858938557044 & 1.61410614429560 \tabularnewline
116 & 14 & 14.0742766396342 & -0.0742766396342347 \tabularnewline
117 & 14 & 14.1629831188531 & -0.162983118853061 \tabularnewline
118 & 16 & 13.0820020798962 & 2.91799792010376 \tabularnewline
119 & 14 & 14.3431466253459 & -0.343146625345866 \tabularnewline
120 & 12 & 12.5474741269747 & -0.547474126974723 \tabularnewline
121 & 13 & 12.6361806061935 & 0.36381939380645 \tabularnewline
122 & 12 & 13.8513659027829 & -1.85136590278289 \tabularnewline
123 & 12 & 12.0069836074963 & -0.00698360749631072 \tabularnewline
124 & 14 & 14.4318531045647 & -0.431853104564693 \tabularnewline
125 & 14 & 14.3431466253459 & -0.343146625345866 \tabularnewline
126 & 14 & 12.1384373170737 & 1.86156268292632 \tabularnewline
127 & 16 & 15.3294586185270 & 0.670541381473036 \tabularnewline
128 & 13 & 14.3431466253459 & -1.34314662534587 \tabularnewline
129 & 14 & 12.5042654261696 & 1.49573457383041 \tabularnewline
130 & 4 & 11.2003736503614 & -7.20037365036142 \tabularnewline
131 & 16 & 15.3294586185270 & 0.670541381473036 \tabularnewline
132 & 13 & 13.7139496266486 & -0.713949626648626 \tabularnewline
133 & 16 & 11.9155265802223 & 4.08447341977767 \tabularnewline
134 & 15 & 13.7111990785935 & 1.28880092140652 \tabularnewline
135 & 14 & 13.9828196123603 & 0.0171803876397428 \tabularnewline
136 & 13 & 12.0956900867151 & 0.904309913284862 \tabularnewline
137 & 14 & 14.2544401461270 & -0.254440146127039 \tabularnewline
138 & 12 & 12.2331063628494 & -0.233106362849403 \tabularnewline
139 & 15 & 14.3431466253459 & 0.656853374654135 \tabularnewline
140 & 14 & 13.4023324105785 & 0.597667589421543 \tabularnewline
141 & 13 & 13.2681281529459 & -0.268128152945941 \tabularnewline
142 & 14 & 14.1629831188531 & -0.162983118853061 \tabularnewline
143 & 16 & 12.9965076191792 & 3.00349238082084 \tabularnewline
144 & 6 & 12.0984406347703 & -6.09844063477029 \tabularnewline
145 & 13 & 12.2758535932079 & 0.724146406792058 \tabularnewline
146 & 13 & 12.4500545331438 & 0.549945466856154 \tabularnewline
147 & 14 & 12.4987643300593 & 1.50123566994072 \tabularnewline
148 & 15 & 14.6092660630023 & 0.390733936997654 \tabularnewline
149 & 14 & 14.4346036526198 & -0.434603652619843 \tabularnewline
150 & 15 & 14.9691316055414 & 0.0308683944586446 \tabularnewline
151 & 13 & 14.2971873764856 & -1.29718737648558 \tabularnewline
152 & 16 & 14.9263843751828 & 1.07361562481718 \tabularnewline
153 & 12 & 12.0069836074963 & -0.00698360749631072 \tabularnewline
154 & 15 & 13.6252431474298 & 1.3747568525702 \tabularnewline
155 & 12 & 14.0742766396342 & -2.07427663963423 \tabularnewline
156 & 14 & 11.2008351208080 & 2.79916487919198 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=105006&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]13[/C][C]12.5019763485613[/C][C]0.498023651438748[/C][/ROW]
[ROW][C]2[/C][C]13[/C][C]12.4987643300593[/C][C]0.50123566994072[/C][/ROW]
[ROW][C]3[/C][C]16[/C][C]13.6252431474298[/C][C]2.37475685257020[/C][/ROW]
[ROW][C]4[/C][C]12[/C][C]12.6357191357470[/C][C]-0.635719135746955[/C][/ROW]
[ROW][C]5[/C][C]11[/C][C]12.6816783846072[/C][C]-1.68167838460724[/C][/ROW]
[ROW][C]6[/C][C]12[/C][C]11.1521253238926[/C][C]0.847874676107423[/C][/ROW]
[ROW][C]7[/C][C]18[/C][C]15.7784921107314[/C][C]2.2215078892686[/C][/ROW]
[ROW][C]8[/C][C]11[/C][C]12.4560170997007[/C][C]-1.45601709970075[/C][/ROW]
[ROW][C]9[/C][C]14[/C][C]12.9965076191792[/C][C]1.00349238082084[/C][/ROW]
[ROW][C]10[/C][C]9[/C][C]11.0229606919238[/C][C]-2.02296069192376[/C][/ROW]
[ROW][C]11[/C][C]14[/C][C]13.9023647773069[/C][C]0.0976352226931168[/C][/ROW]
[ROW][C]12[/C][C]12[/C][C]13.6224925993746[/C][C]-1.62249259937465[/C][/ROW]
[ROW][C]13[/C][C]11[/C][C]13.8513659027829[/C][C]-2.85136590278289[/C][/ROW]
[ROW][C]14[/C][C]12[/C][C]12.8135935646312[/C][C]-0.813593564631204[/C][/ROW]
[ROW][C]15[/C][C]13[/C][C]13.0792515318411[/C][C]-0.079251531841085[/C][/ROW]
[ROW][C]16[/C][C]11[/C][C]12.8163441126864[/C][C]-1.81634411268635[/C][/ROW]
[ROW][C]17[/C][C]12[/C][C]13.6224925993746[/C][C]-1.62249259937465[/C][/ROW]
[ROW][C]18[/C][C]16[/C][C]14.2544401461270[/C][C]1.74555985387296[/C][/ROW]
[ROW][C]19[/C][C]9[/C][C]11.6466565945107[/C][C]-2.64665659451070[/C][/ROW]
[ROW][C]20[/C][C]11[/C][C]11.652157690621[/C][C]-0.652157690621004[/C][/ROW]
[ROW][C]21[/C][C]13[/C][C]10.3027681363991[/C][C]2.69723186360085[/C][/ROW]
[ROW][C]22[/C][C]15[/C][C]14.6575143894712[/C][C]0.342485610528814[/C][/ROW]
[ROW][C]23[/C][C]10[/C][C]13.6712023962901[/C][C]-3.67120239629009[/C][/ROW]
[ROW][C]24[/C][C]11[/C][C]11.7353630737295[/C][C]-0.73536307372953[/C][/ROW]
[ROW][C]25[/C][C]13[/C][C]14.3431466253459[/C][C]-1.34314662534587[/C][/ROW]
[ROW][C]26[/C][C]16[/C][C]14.6147671591126[/C][C]1.38523284088735[/C][/ROW]
[ROW][C]27[/C][C]15[/C][C]14.837677895964[/C][C]0.16232210403601[/C][/ROW]
[ROW][C]28[/C][C]14[/C][C]13.2249194521408[/C][C]0.775080547859197[/C][/ROW]
[ROW][C]29[/C][C]14[/C][C]13.8968636811966[/C][C]0.103136318803419[/C][/ROW]
[ROW][C]30[/C][C]14[/C][C]11.9155265802223[/C][C]2.08447341977767[/C][/ROW]
[ROW][C]31[/C][C]8[/C][C]11.7381136217847[/C][C]-3.73811362178468[/C][/ROW]
[ROW][C]32[/C][C]13[/C][C]14.5688079102524[/C][C]-1.56880791025236[/C][/ROW]
[ROW][C]33[/C][C]15[/C][C]14.74622086869[/C][C]0.253779131309987[/C][/ROW]
[ROW][C]34[/C][C]13[/C][C]11.5607006633470[/C][C]1.43929933665297[/C][/ROW]
[ROW][C]35[/C][C]11[/C][C]12.4560170997007[/C][C]-1.45601709970075[/C][/ROW]
[ROW][C]36[/C][C]15[/C][C]14.4346036526198[/C][C]0.565396347380157[/C][/ROW]
[ROW][C]37[/C][C]15[/C][C]13.4423290928818[/C][C]1.55767090711816[/C][/ROW]
[ROW][C]38[/C][C]9[/C][C]12.0552319339651[/C][C]-3.05523193396515[/C][/ROW]
[ROW][C]39[/C][C]13[/C][C]14.0742766396342[/C][C]-1.07427663963423[/C][/ROW]
[ROW][C]40[/C][C]16[/C][C]15.0178414024568[/C][C]0.982158597543205[/C][/ROW]
[ROW][C]41[/C][C]13[/C][C]13.9828196123603[/C][C]-0.982819612360257[/C][/ROW]
[ROW][C]42[/C][C]11[/C][C]10.5256788732505[/C][C]0.474321126749511[/C][/ROW]
[ROW][C]43[/C][C]12[/C][C]11.5984082680419[/C][C]0.401591731958138[/C][/ROW]
[ROW][C]44[/C][C]12[/C][C]12.1871471139891[/C][C]-0.187147113989115[/C][/ROW]
[ROW][C]45[/C][C]12[/C][C]12.9932956006774[/C][C]-0.99329560067741[/C][/ROW]
[ROW][C]46[/C][C]14[/C][C]14.4773508829784[/C][C]-0.477350882978382[/C][/ROW]
[ROW][C]47[/C][C]14[/C][C]13.6712023962901[/C][C]0.328797603709913[/C][/ROW]
[ROW][C]48[/C][C]8[/C][C]11.8213190048932[/C][C]-3.82131900489321[/C][/ROW]
[ROW][C]49[/C][C]13[/C][C]13.1307118768117[/C][C]-0.130711876811675[/C][/ROW]
[ROW][C]50[/C][C]16[/C][C]14.5233101318387[/C][C]1.47668986816133[/C][/ROW]
[ROW][C]51[/C][C]13[/C][C]12.5415115604178[/C][C]0.458488439582177[/C][/ROW]
[ROW][C]52[/C][C]11[/C][C]14.0770271876894[/C][C]-3.07702718768939[/C][/ROW]
[ROW][C]53[/C][C]14[/C][C]13.2194183560305[/C][C]0.780581643969498[/C][/ROW]
[ROW][C]54[/C][C]13[/C][C]11.9155265802223[/C][C]1.08447341977767[/C][/ROW]
[ROW][C]55[/C][C]13[/C][C]13.4882883417421[/C][C]-0.488288341742133[/C][/ROW]
[ROW][C]56[/C][C]13[/C][C]13.4910388897973[/C][C]-0.491038889797283[/C][/ROW]
[ROW][C]57[/C][C]12[/C][C]12.3700611685371[/C][C]-0.37006116853707[/C][/ROW]
[ROW][C]58[/C][C]16[/C][C]14.2571906941822[/C][C]1.74280930581781[/C][/ROW]
[ROW][C]59[/C][C]15[/C][C]11.0206716143152[/C][C]3.97932838568479[/C][/ROW]
[ROW][C]60[/C][C]15[/C][C]15.4209156458009[/C][C]-0.420915645800941[/C][/ROW]
[ROW][C]61[/C][C]12[/C][C]11.3322888303854[/C][C]0.667711169614618[/C][/ROW]
[ROW][C]62[/C][C]14[/C][C]14.5233101318387[/C][C]-0.52331013183867[/C][/ROW]
[ROW][C]63[/C][C]12[/C][C]14.7976812136606[/C][C]-2.79768121366060[/C][/ROW]
[ROW][C]64[/C][C]15[/C][C]14.0742766396342[/C][C]0.925723360365765[/C][/ROW]
[ROW][C]65[/C][C]12[/C][C]11.8723178794172[/C][C]0.127682120582804[/C][/ROW]
[ROW][C]66[/C][C]13[/C][C]13.3995818625233[/C][C]-0.399581862523306[/C][/ROW]
[ROW][C]67[/C][C]12[/C][C]14.3431466253459[/C][C]-2.34314662534587[/C][/ROW]
[ROW][C]68[/C][C]12[/C][C]12.3186008235665[/C][C]-0.318600823566480[/C][/ROW]
[ROW][C]69[/C][C]13[/C][C]13.9828196123603[/C][C]-0.982819612360257[/C][/ROW]
[ROW][C]70[/C][C]5[/C][C]10.2058100130149[/C][C]-5.20581001301487[/C][/ROW]
[ROW][C]71[/C][C]13[/C][C]13.4910388897973[/C][C]-0.491038889797283[/C][/ROW]
[ROW][C]72[/C][C]13[/C][C]13.3995818625233[/C][C]-0.399581862523306[/C][/ROW]
[ROW][C]73[/C][C]14[/C][C]12.9505483703189[/C][C]1.04945162968113[/C][/ROW]
[ROW][C]74[/C][C]17[/C][C]13.6307442435401[/C][C]3.3692557564599[/C][/ROW]
[ROW][C]75[/C][C]13[/C][C]13.6252431474298[/C][C]-0.6252431474298[/C][/ROW]
[ROW][C]76[/C][C]13[/C][C]14.3431466253459[/C][C]-1.34314662534587[/C][/ROW]
[ROW][C]77[/C][C]12[/C][C]13.6684518482349[/C][C]-1.66845184823494[/C][/ROW]
[ROW][C]78[/C][C]13[/C][C]13.044755945648[/C][C]-0.044755945647999[/C][/ROW]
[ROW][C]79[/C][C]14[/C][C]11.9155265802223[/C][C]2.08447341977767[/C][/ROW]
[ROW][C]80[/C][C]11[/C][C]10.3887240675628[/C][C]0.611275932437178[/C][/ROW]
[ROW][C]81[/C][C]12[/C][C]11.7353630737295[/C][C]0.264636926270471[/C][/ROW]
[ROW][C]82[/C][C]12[/C][C]13.4505807370473[/C][C]-1.45058073704730[/C][/ROW]
[ROW][C]83[/C][C]16[/C][C]13.8941131331414[/C][C]2.10588686685857[/C][/ROW]
[ROW][C]84[/C][C]12[/C][C]12.8618418911000[/C][C]-0.861841891100044[/C][/ROW]
[ROW][C]85[/C][C]12[/C][C]10.7917983109070[/C][C]1.20820168909303[/C][/ROW]
[ROW][C]86[/C][C]12[/C][C]13.8913625850863[/C][C]-1.89136258508628[/C][/ROW]
[ROW][C]87[/C][C]10[/C][C]11.8268201010035[/C][C]-1.82682010100351[/C][/ROW]
[ROW][C]88[/C][C]15[/C][C]12.5957224534436[/C][C]2.40427754655644[/C][/ROW]
[ROW][C]89[/C][C]15[/C][C]15.2407521393081[/C][C]-0.240752139308137[/C][/ROW]
[ROW][C]90[/C][C]12[/C][C]12.0069836074963[/C][C]-0.00698360749631072[/C][/ROW]
[ROW][C]91[/C][C]16[/C][C]13.3536226136630[/C][C]2.64637738633698[/C][/ROW]
[ROW][C]92[/C][C]15[/C][C]13.8941131331414[/C][C]1.10588686685857[/C][/ROW]
[ROW][C]93[/C][C]16[/C][C]15.1065478816756[/C][C]0.893452118324378[/C][/ROW]
[ROW][C]94[/C][C]13[/C][C]14.3458971734010[/C][C]-1.34589717340102[/C][/ROW]
[ROW][C]95[/C][C]12[/C][C]12.9505483703189[/C][C]-0.95054837031887[/C][/ROW]
[ROW][C]96[/C][C]11[/C][C]11.9155265802223[/C][C]-0.915526580222334[/C][/ROW]
[ROW][C]97[/C][C]13[/C][C]11.9178156578309[/C][C]1.08218434216911[/C][/ROW]
[ROW][C]98[/C][C]10[/C][C]11.2408318031114[/C][C]-1.24083180311140[/C][/ROW]
[ROW][C]99[/C][C]15[/C][C]13.2249194521408[/C][C]1.77508054785920[/C][/ROW]
[ROW][C]100[/C][C]13[/C][C]13.6224925993746[/C][C]-0.622492599374649[/C][/ROW]
[ROW][C]101[/C][C]16[/C][C]15.1980049089496[/C][C]0.801995091050401[/C][/ROW]
[ROW][C]102[/C][C]15[/C][C]14.9263843751828[/C][C]0.0736156248171831[/C][/ROW]
[ROW][C]103[/C][C]18[/C][C]14.8808865967691[/C][C]3.11911340323087[/C][/ROW]
[ROW][C]104[/C][C]13[/C][C]10.6143853524693[/C][C]2.38561464753068[/C][/ROW]
[ROW][C]105[/C][C]10[/C][C]9.85098409613956[/C][C]0.149015903860440[/C][/ROW]
[ROW][C]106[/C][C]16[/C][C]14.6975110717746[/C][C]1.30248892822543[/C][/ROW]
[ROW][C]107[/C][C]13[/C][C]11.6953663914261[/C][C]1.30463360857386[/C][/ROW]
[ROW][C]108[/C][C]15[/C][C]15.4209156458009[/C][C]-0.420915645800941[/C][/ROW]
[ROW][C]109[/C][C]14[/C][C]11.5152028849333[/C][C]2.48479711506666[/C][/ROW]
[ROW][C]110[/C][C]15[/C][C]11.652157690621[/C][C]3.34784230937900[/C][/ROW]
[ROW][C]111[/C][C]14[/C][C]13.0420053975928[/C][C]0.957994602407152[/C][/ROW]
[ROW][C]112[/C][C]13[/C][C]14.5715584583075[/C][C]-1.57155845830751[/C][/ROW]
[ROW][C]113[/C][C]13[/C][C]12.9018385734034[/C][C]0.0981614265965683[/C][/ROW]
[ROW][C]114[/C][C]15[/C][C]13.9828196123603[/C][C]1.01718038763974[/C][/ROW]
[ROW][C]115[/C][C]16[/C][C]14.3858938557044[/C][C]1.61410614429560[/C][/ROW]
[ROW][C]116[/C][C]14[/C][C]14.0742766396342[/C][C]-0.0742766396342347[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]14.1629831188531[/C][C]-0.162983118853061[/C][/ROW]
[ROW][C]118[/C][C]16[/C][C]13.0820020798962[/C][C]2.91799792010376[/C][/ROW]
[ROW][C]119[/C][C]14[/C][C]14.3431466253459[/C][C]-0.343146625345866[/C][/ROW]
[ROW][C]120[/C][C]12[/C][C]12.5474741269747[/C][C]-0.547474126974723[/C][/ROW]
[ROW][C]121[/C][C]13[/C][C]12.6361806061935[/C][C]0.36381939380645[/C][/ROW]
[ROW][C]122[/C][C]12[/C][C]13.8513659027829[/C][C]-1.85136590278289[/C][/ROW]
[ROW][C]123[/C][C]12[/C][C]12.0069836074963[/C][C]-0.00698360749631072[/C][/ROW]
[ROW][C]124[/C][C]14[/C][C]14.4318531045647[/C][C]-0.431853104564693[/C][/ROW]
[ROW][C]125[/C][C]14[/C][C]14.3431466253459[/C][C]-0.343146625345866[/C][/ROW]
[ROW][C]126[/C][C]14[/C][C]12.1384373170737[/C][C]1.86156268292632[/C][/ROW]
[ROW][C]127[/C][C]16[/C][C]15.3294586185270[/C][C]0.670541381473036[/C][/ROW]
[ROW][C]128[/C][C]13[/C][C]14.3431466253459[/C][C]-1.34314662534587[/C][/ROW]
[ROW][C]129[/C][C]14[/C][C]12.5042654261696[/C][C]1.49573457383041[/C][/ROW]
[ROW][C]130[/C][C]4[/C][C]11.2003736503614[/C][C]-7.20037365036142[/C][/ROW]
[ROW][C]131[/C][C]16[/C][C]15.3294586185270[/C][C]0.670541381473036[/C][/ROW]
[ROW][C]132[/C][C]13[/C][C]13.7139496266486[/C][C]-0.713949626648626[/C][/ROW]
[ROW][C]133[/C][C]16[/C][C]11.9155265802223[/C][C]4.08447341977767[/C][/ROW]
[ROW][C]134[/C][C]15[/C][C]13.7111990785935[/C][C]1.28880092140652[/C][/ROW]
[ROW][C]135[/C][C]14[/C][C]13.9828196123603[/C][C]0.0171803876397428[/C][/ROW]
[ROW][C]136[/C][C]13[/C][C]12.0956900867151[/C][C]0.904309913284862[/C][/ROW]
[ROW][C]137[/C][C]14[/C][C]14.2544401461270[/C][C]-0.254440146127039[/C][/ROW]
[ROW][C]138[/C][C]12[/C][C]12.2331063628494[/C][C]-0.233106362849403[/C][/ROW]
[ROW][C]139[/C][C]15[/C][C]14.3431466253459[/C][C]0.656853374654135[/C][/ROW]
[ROW][C]140[/C][C]14[/C][C]13.4023324105785[/C][C]0.597667589421543[/C][/ROW]
[ROW][C]141[/C][C]13[/C][C]13.2681281529459[/C][C]-0.268128152945941[/C][/ROW]
[ROW][C]142[/C][C]14[/C][C]14.1629831188531[/C][C]-0.162983118853061[/C][/ROW]
[ROW][C]143[/C][C]16[/C][C]12.9965076191792[/C][C]3.00349238082084[/C][/ROW]
[ROW][C]144[/C][C]6[/C][C]12.0984406347703[/C][C]-6.09844063477029[/C][/ROW]
[ROW][C]145[/C][C]13[/C][C]12.2758535932079[/C][C]0.724146406792058[/C][/ROW]
[ROW][C]146[/C][C]13[/C][C]12.4500545331438[/C][C]0.549945466856154[/C][/ROW]
[ROW][C]147[/C][C]14[/C][C]12.4987643300593[/C][C]1.50123566994072[/C][/ROW]
[ROW][C]148[/C][C]15[/C][C]14.6092660630023[/C][C]0.390733936997654[/C][/ROW]
[ROW][C]149[/C][C]14[/C][C]14.4346036526198[/C][C]-0.434603652619843[/C][/ROW]
[ROW][C]150[/C][C]15[/C][C]14.9691316055414[/C][C]0.0308683944586446[/C][/ROW]
[ROW][C]151[/C][C]13[/C][C]14.2971873764856[/C][C]-1.29718737648558[/C][/ROW]
[ROW][C]152[/C][C]16[/C][C]14.9263843751828[/C][C]1.07361562481718[/C][/ROW]
[ROW][C]153[/C][C]12[/C][C]12.0069836074963[/C][C]-0.00698360749631072[/C][/ROW]
[ROW][C]154[/C][C]15[/C][C]13.6252431474298[/C][C]1.3747568525702[/C][/ROW]
[ROW][C]155[/C][C]12[/C][C]14.0742766396342[/C][C]-2.07427663963423[/C][/ROW]
[ROW][C]156[/C][C]14[/C][C]11.2008351208080[/C][C]2.79916487919198[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=105006&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=105006&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11312.50197634856130.498023651438748
21312.49876433005930.50123566994072
31613.62524314742982.37475685257020
41212.6357191357470-0.635719135746955
51112.6816783846072-1.68167838460724
61211.15212532389260.847874676107423
71815.77849211073142.2215078892686
81112.4560170997007-1.45601709970075
91412.99650761917921.00349238082084
10911.0229606919238-2.02296069192376
111413.90236477730690.0976352226931168
121213.6224925993746-1.62249259937465
131113.8513659027829-2.85136590278289
141212.8135935646312-0.813593564631204
151313.0792515318411-0.079251531841085
161112.8163441126864-1.81634411268635
171213.6224925993746-1.62249259937465
181614.25444014612701.74555985387296
19911.6466565945107-2.64665659451070
201111.652157690621-0.652157690621004
211310.30276813639912.69723186360085
221514.65751438947120.342485610528814
231013.6712023962901-3.67120239629009
241111.7353630737295-0.73536307372953
251314.3431466253459-1.34314662534587
261614.61476715911261.38523284088735
271514.8376778959640.16232210403601
281413.22491945214080.775080547859197
291413.89686368119660.103136318803419
301411.91552658022232.08447341977767
31811.7381136217847-3.73811362178468
321314.5688079102524-1.56880791025236
331514.746220868690.253779131309987
341311.56070066334701.43929933665297
351112.4560170997007-1.45601709970075
361514.43460365261980.565396347380157
371513.44232909288181.55767090711816
38912.0552319339651-3.05523193396515
391314.0742766396342-1.07427663963423
401615.01784140245680.982158597543205
411313.9828196123603-0.982819612360257
421110.52567887325050.474321126749511
431211.59840826804190.401591731958138
441212.1871471139891-0.187147113989115
451212.9932956006774-0.99329560067741
461414.4773508829784-0.477350882978382
471413.67120239629010.328797603709913
48811.8213190048932-3.82131900489321
491313.1307118768117-0.130711876811675
501614.52331013183871.47668986816133
511312.54151156041780.458488439582177
521114.0770271876894-3.07702718768939
531413.21941835603050.780581643969498
541311.91552658022231.08447341977767
551313.4882883417421-0.488288341742133
561313.4910388897973-0.491038889797283
571212.3700611685371-0.37006116853707
581614.25719069418221.74280930581781
591511.02067161431523.97932838568479
601515.4209156458009-0.420915645800941
611211.33228883038540.667711169614618
621414.5233101318387-0.52331013183867
631214.7976812136606-2.79768121366060
641514.07427663963420.925723360365765
651211.87231787941720.127682120582804
661313.3995818625233-0.399581862523306
671214.3431466253459-2.34314662534587
681212.3186008235665-0.318600823566480
691313.9828196123603-0.982819612360257
70510.2058100130149-5.20581001301487
711313.4910388897973-0.491038889797283
721313.3995818625233-0.399581862523306
731412.95054837031891.04945162968113
741713.63074424354013.3692557564599
751313.6252431474298-0.6252431474298
761314.3431466253459-1.34314662534587
771213.6684518482349-1.66845184823494
781313.044755945648-0.044755945647999
791411.91552658022232.08447341977767
801110.38872406756280.611275932437178
811211.73536307372950.264636926270471
821213.4505807370473-1.45058073704730
831613.89411313314142.10588686685857
841212.8618418911000-0.861841891100044
851210.79179831090701.20820168909303
861213.8913625850863-1.89136258508628
871011.8268201010035-1.82682010100351
881512.59572245344362.40427754655644
891515.2407521393081-0.240752139308137
901212.0069836074963-0.00698360749631072
911613.35362261366302.64637738633698
921513.89411313314141.10588686685857
931615.10654788167560.893452118324378
941314.3458971734010-1.34589717340102
951212.9505483703189-0.95054837031887
961111.9155265802223-0.915526580222334
971311.91781565783091.08218434216911
981011.2408318031114-1.24083180311140
991513.22491945214081.77508054785920
1001313.6224925993746-0.622492599374649
1011615.19800490894960.801995091050401
1021514.92638437518280.0736156248171831
1031814.88088659676913.11911340323087
1041310.61438535246932.38561464753068
105109.850984096139560.149015903860440
1061614.69751107177461.30248892822543
1071311.69536639142611.30463360857386
1081515.4209156458009-0.420915645800941
1091411.51520288493332.48479711506666
1101511.6521576906213.34784230937900
1111413.04200539759280.957994602407152
1121314.5715584583075-1.57155845830751
1131312.90183857340340.0981614265965683
1141513.98281961236031.01718038763974
1151614.38589385570441.61410614429560
1161414.0742766396342-0.0742766396342347
1171414.1629831188531-0.162983118853061
1181613.08200207989622.91799792010376
1191414.3431466253459-0.343146625345866
1201212.5474741269747-0.547474126974723
1211312.63618060619350.36381939380645
1221213.8513659027829-1.85136590278289
1231212.0069836074963-0.00698360749631072
1241414.4318531045647-0.431853104564693
1251414.3431466253459-0.343146625345866
1261412.13843731707371.86156268292632
1271615.32945861852700.670541381473036
1281314.3431466253459-1.34314662534587
1291412.50426542616961.49573457383041
130411.2003736503614-7.20037365036142
1311615.32945861852700.670541381473036
1321313.7139496266486-0.713949626648626
1331611.91552658022234.08447341977767
1341513.71119907859351.28880092140652
1351413.98281961236030.0171803876397428
1361312.09569008671510.904309913284862
1371414.2544401461270-0.254440146127039
1381212.2331063628494-0.233106362849403
1391514.34314662534590.656853374654135
1401413.40233241057850.597667589421543
1411313.2681281529459-0.268128152945941
1421414.1629831188531-0.162983118853061
1431612.99650761917923.00349238082084
144612.0984406347703-6.09844063477029
1451312.27585359320790.724146406792058
1461312.45005453314380.549945466856154
1471412.49876433005931.50123566994072
1481514.60926606300230.390733936997654
1491414.4346036526198-0.434603652619843
1501514.96913160554140.0308683944586446
1511314.2971873764856-1.29718737648558
1521614.92638437518281.07361562481718
1531212.0069836074963-0.00698360749631072
1541513.62524314742981.3747568525702
1551214.0742766396342-2.07427663963423
1561411.20083512080802.79916487919198







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
70.5062727062668060.9874545874663870.493727293733194
80.5100990293563190.9798019412873620.489900970643681
90.379627766006870.759255532013740.62037223399313
100.2620158204221770.5240316408443540.737984179577823
110.1748264403981210.3496528807962420.825173559601879
120.2643961776036020.5287923552072050.735603822396398
130.5241601247409240.9516797505181510.475839875259076
140.4491594904742470.8983189809484950.550840509525752
150.3618756805878970.7237513611757940.638124319412103
160.337075744091220.674151488182440.66292425590878
170.3315605855027960.6631211710055930.668439414497203
180.3255293538236210.6510587076472420.674470646176379
190.3255479630619120.6510959261238230.674452036938088
200.2680130221484590.5360260442969180.731986977851541
210.5391615117222230.9216769765555530.460838488277777
220.468510550345840.937021100691680.53148944965416
230.6607047197517950.6785905604964110.339295280248205
240.5985667778500630.8028664442998740.401433222149937
250.5566247330019370.8867505339961260.443375266998063
260.5425064656109610.9149870687780770.457493534389039
270.4782595576066920.9565191152133830.521740442393308
280.4342351830283520.8684703660567040.565764816971648
290.3731882090612070.7463764181224140.626811790938793
300.4294271228785850.858854245757170.570572877121415
310.5833443715425630.8333112569148740.416655628457437
320.5629421912085030.8741156175829940.437057808791497
330.5062324134170900.9875351731658190.493767586582910
340.5124484519051750.975103096189650.487551548094825
350.4798420478515840.959684095703170.520157952148416
360.4307744606665820.8615489213331650.569225539333418
370.4289609704426300.8579219408852590.571039029557370
380.5018349854461110.9963300291077780.498165014553889
390.4627983814969010.9255967629938020.537201618503099
400.425028502207860.850057004415720.57497149779214
410.3855407218877910.7710814437755810.614459278112209
420.3551362628665450.710272525733090.644863737133455
430.3120073379542700.6240146759085410.68799266204573
440.2670329110416560.5340658220833130.732967088958344
450.2332746097622550.466549219524510.766725390237745
460.196304411753820.392608823507640.80369558824618
470.1639837846700070.3279675693400140.836016215329993
480.2779436603001460.5558873206002920.722056339699854
490.2369794457837320.4739588915674640.763020554216268
500.2256527656222110.4513055312444220.77434723437779
510.2033927352193900.4067854704387800.79660726478061
520.2815287458485680.5630574916971370.718471254151432
530.252874797983490.505749595966980.74712520201651
540.2385500788292360.4771001576584730.761449921170764
550.2035554271587510.4071108543175010.79644457284125
560.1719273375501330.3438546751002650.828072662449867
570.1433846477540430.2867692955080850.856615352245957
580.1455955913926480.2911911827852970.854404408607352
590.2996252308228030.5992504616456060.700374769177197
600.2603906043428070.5207812086856140.739609395657193
610.2290472654304550.4580945308609110.770952734569545
620.1971469429965990.3942938859931980.802853057003401
630.2538094003364030.5076188006728060.746190599663597
640.2295878220083330.4591756440166650.770412177991667
650.1986563400975780.3973126801951550.801343659902423
660.1683079344465500.3366158688931000.83169206555345
670.1889072818863010.3778145637726020.811092718113699
680.1595138682152020.3190277364304030.840486131784798
690.1391723831296030.2783447662592050.860827616870397
700.4248549697481220.8497099394962440.575145030251878
710.3828065509501230.7656131019002470.617193449049877
720.3416154553820520.6832309107641050.658384544617948
730.3178505925721360.6357011851442730.682149407427864
740.443264900012320.886529800024640.55673509998768
750.4023246675184230.8046493350368460.597675332481577
760.3827195649266250.7654391298532510.617280435073375
770.3811759492570790.7623518985141580.618824050742921
780.3376441452416320.6752882904832650.662355854758367
790.3587767481959510.7175534963919010.64122325180405
800.3238450465398810.6476900930797620.676154953460119
810.2873658905539470.5747317811078940.712634109446053
820.2702472083181800.5404944166363610.72975279168182
830.2891312221944310.5782624443888620.710868777805569
840.2598541045270860.5197082090541710.740145895472914
850.2396210776248140.4792421552496280.760378922375186
860.2485528546694240.4971057093388490.751447145330576
870.2584666813554840.5169333627109670.741533318644516
880.3011081552469210.6022163104938420.698891844753079
890.2621780707299560.5243561414599120.737821929270044
900.2270587506383320.4541175012766640.772941249361668
910.2728276685310120.5456553370620240.727172331468988
920.2498854315558770.4997708631117540.750114568444123
930.2234477935873960.4468955871747920.776552206412604
940.2053136392789960.4106272785579920.794686360721004
950.1842921530102340.3685843060204680.815707846989766
960.1699702499487010.3399404998974010.830029750051299
970.1567226002642360.3134452005284730.843277399735764
980.1657215225897540.3314430451795080.834278477410246
990.1860079504642890.3720159009285790.81399204953571
1000.1635496165085770.3270992330171540.836450383491423
1010.1457143095462060.2914286190924120.854285690453794
1020.1199442361584240.2398884723168470.880055763841576
1030.1600768628634890.3201537257269780.839923137136511
1040.1717279599795640.3434559199591280.828272040020436
1050.1453736885462560.2907473770925110.854626311453744
1060.1290956286631980.2581912573263960.870904371336802
1070.1149234737390660.2298469474781310.885076526260934
1080.0927882278653660.1855764557307320.907211772134634
1090.1092806670736250.2185613341472490.890719332926375
1100.2952302042527680.5904604085055350.704769795747233
1110.2860200615761930.5720401231523850.713979938423807
1120.2690284375580520.5380568751161050.730971562441948
1130.2322976205459520.4645952410919040.767702379454048
1140.2027129198109820.4054258396219630.797287080189018
1150.1918664438531180.3837328877062360.808133556146882
1160.1590399875090600.3180799750181190.84096001249094
1170.1296646460120970.2593292920241950.870335353987903
1180.1640199028906180.3280398057812350.835980097109382
1190.1344131983816350.2688263967632690.865586801618365
1200.1085029270338130.2170058540676250.891497072966188
1210.08564308396225270.1712861679245050.914356916037747
1220.08122889154002860.1624577830800570.918771108459971
1230.06199366899223830.1239873379844770.938006331007762
1240.05387615439217830.1077523087843570.946123845607822
1250.04151565987862790.08303131975725580.958484340121372
1260.03817673010549070.07635346021098150.96182326989451
1270.02887205719317180.05774411438634350.971127942806828
1280.02729896898614720.05459793797229450.972701031013853
1290.09669132973672850.1933826594734570.903308670263271
1300.4061285923109230.8122571846218450.593871407689077
1310.3604488270009330.7208976540018660.639551172999067
1320.3029338441815650.605867688363130.697066155818435
1330.4731594249348570.9463188498697140.526840575065143
1340.4141080409850120.8282160819700230.585891959014988
1350.345802842065560.691605684131120.65419715793444
1360.2850323858755750.570064771751150.714967614124425
1370.2249406299751260.4498812599502520.775059370024874
1380.1926463609669710.3852927219339420.807353639033029
1390.1454032490305570.2908064980611150.854596750969443
1400.1211189090562750.2422378181125500.878881090943725
1410.08656971564180180.1731394312836040.913430284358198
1420.05838555365041760.1167711073008350.941614446349582
1430.06830533464796450.1366106692959290.931694665352035
1440.866930905827330.266138188345340.13306909417267
1450.7991122582092250.401775483581550.200887741790775
1460.704805556294470.5903888874110610.295194443705530
1470.5945562371682480.8108875256635040.405443762831752
1480.4557449324294880.9114898648589760.544255067570512
1490.3135031101961080.6270062203922160.686496889803892

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
7 & 0.506272706266806 & 0.987454587466387 & 0.493727293733194 \tabularnewline
8 & 0.510099029356319 & 0.979801941287362 & 0.489900970643681 \tabularnewline
9 & 0.37962776600687 & 0.75925553201374 & 0.62037223399313 \tabularnewline
10 & 0.262015820422177 & 0.524031640844354 & 0.737984179577823 \tabularnewline
11 & 0.174826440398121 & 0.349652880796242 & 0.825173559601879 \tabularnewline
12 & 0.264396177603602 & 0.528792355207205 & 0.735603822396398 \tabularnewline
13 & 0.524160124740924 & 0.951679750518151 & 0.475839875259076 \tabularnewline
14 & 0.449159490474247 & 0.898318980948495 & 0.550840509525752 \tabularnewline
15 & 0.361875680587897 & 0.723751361175794 & 0.638124319412103 \tabularnewline
16 & 0.33707574409122 & 0.67415148818244 & 0.66292425590878 \tabularnewline
17 & 0.331560585502796 & 0.663121171005593 & 0.668439414497203 \tabularnewline
18 & 0.325529353823621 & 0.651058707647242 & 0.674470646176379 \tabularnewline
19 & 0.325547963061912 & 0.651095926123823 & 0.674452036938088 \tabularnewline
20 & 0.268013022148459 & 0.536026044296918 & 0.731986977851541 \tabularnewline
21 & 0.539161511722223 & 0.921676976555553 & 0.460838488277777 \tabularnewline
22 & 0.46851055034584 & 0.93702110069168 & 0.53148944965416 \tabularnewline
23 & 0.660704719751795 & 0.678590560496411 & 0.339295280248205 \tabularnewline
24 & 0.598566777850063 & 0.802866444299874 & 0.401433222149937 \tabularnewline
25 & 0.556624733001937 & 0.886750533996126 & 0.443375266998063 \tabularnewline
26 & 0.542506465610961 & 0.914987068778077 & 0.457493534389039 \tabularnewline
27 & 0.478259557606692 & 0.956519115213383 & 0.521740442393308 \tabularnewline
28 & 0.434235183028352 & 0.868470366056704 & 0.565764816971648 \tabularnewline
29 & 0.373188209061207 & 0.746376418122414 & 0.626811790938793 \tabularnewline
30 & 0.429427122878585 & 0.85885424575717 & 0.570572877121415 \tabularnewline
31 & 0.583344371542563 & 0.833311256914874 & 0.416655628457437 \tabularnewline
32 & 0.562942191208503 & 0.874115617582994 & 0.437057808791497 \tabularnewline
33 & 0.506232413417090 & 0.987535173165819 & 0.493767586582910 \tabularnewline
34 & 0.512448451905175 & 0.97510309618965 & 0.487551548094825 \tabularnewline
35 & 0.479842047851584 & 0.95968409570317 & 0.520157952148416 \tabularnewline
36 & 0.430774460666582 & 0.861548921333165 & 0.569225539333418 \tabularnewline
37 & 0.428960970442630 & 0.857921940885259 & 0.571039029557370 \tabularnewline
38 & 0.501834985446111 & 0.996330029107778 & 0.498165014553889 \tabularnewline
39 & 0.462798381496901 & 0.925596762993802 & 0.537201618503099 \tabularnewline
40 & 0.42502850220786 & 0.85005700441572 & 0.57497149779214 \tabularnewline
41 & 0.385540721887791 & 0.771081443775581 & 0.614459278112209 \tabularnewline
42 & 0.355136262866545 & 0.71027252573309 & 0.644863737133455 \tabularnewline
43 & 0.312007337954270 & 0.624014675908541 & 0.68799266204573 \tabularnewline
44 & 0.267032911041656 & 0.534065822083313 & 0.732967088958344 \tabularnewline
45 & 0.233274609762255 & 0.46654921952451 & 0.766725390237745 \tabularnewline
46 & 0.19630441175382 & 0.39260882350764 & 0.80369558824618 \tabularnewline
47 & 0.163983784670007 & 0.327967569340014 & 0.836016215329993 \tabularnewline
48 & 0.277943660300146 & 0.555887320600292 & 0.722056339699854 \tabularnewline
49 & 0.236979445783732 & 0.473958891567464 & 0.763020554216268 \tabularnewline
50 & 0.225652765622211 & 0.451305531244422 & 0.77434723437779 \tabularnewline
51 & 0.203392735219390 & 0.406785470438780 & 0.79660726478061 \tabularnewline
52 & 0.281528745848568 & 0.563057491697137 & 0.718471254151432 \tabularnewline
53 & 0.25287479798349 & 0.50574959596698 & 0.74712520201651 \tabularnewline
54 & 0.238550078829236 & 0.477100157658473 & 0.761449921170764 \tabularnewline
55 & 0.203555427158751 & 0.407110854317501 & 0.79644457284125 \tabularnewline
56 & 0.171927337550133 & 0.343854675100265 & 0.828072662449867 \tabularnewline
57 & 0.143384647754043 & 0.286769295508085 & 0.856615352245957 \tabularnewline
58 & 0.145595591392648 & 0.291191182785297 & 0.854404408607352 \tabularnewline
59 & 0.299625230822803 & 0.599250461645606 & 0.700374769177197 \tabularnewline
60 & 0.260390604342807 & 0.520781208685614 & 0.739609395657193 \tabularnewline
61 & 0.229047265430455 & 0.458094530860911 & 0.770952734569545 \tabularnewline
62 & 0.197146942996599 & 0.394293885993198 & 0.802853057003401 \tabularnewline
63 & 0.253809400336403 & 0.507618800672806 & 0.746190599663597 \tabularnewline
64 & 0.229587822008333 & 0.459175644016665 & 0.770412177991667 \tabularnewline
65 & 0.198656340097578 & 0.397312680195155 & 0.801343659902423 \tabularnewline
66 & 0.168307934446550 & 0.336615868893100 & 0.83169206555345 \tabularnewline
67 & 0.188907281886301 & 0.377814563772602 & 0.811092718113699 \tabularnewline
68 & 0.159513868215202 & 0.319027736430403 & 0.840486131784798 \tabularnewline
69 & 0.139172383129603 & 0.278344766259205 & 0.860827616870397 \tabularnewline
70 & 0.424854969748122 & 0.849709939496244 & 0.575145030251878 \tabularnewline
71 & 0.382806550950123 & 0.765613101900247 & 0.617193449049877 \tabularnewline
72 & 0.341615455382052 & 0.683230910764105 & 0.658384544617948 \tabularnewline
73 & 0.317850592572136 & 0.635701185144273 & 0.682149407427864 \tabularnewline
74 & 0.44326490001232 & 0.88652980002464 & 0.55673509998768 \tabularnewline
75 & 0.402324667518423 & 0.804649335036846 & 0.597675332481577 \tabularnewline
76 & 0.382719564926625 & 0.765439129853251 & 0.617280435073375 \tabularnewline
77 & 0.381175949257079 & 0.762351898514158 & 0.618824050742921 \tabularnewline
78 & 0.337644145241632 & 0.675288290483265 & 0.662355854758367 \tabularnewline
79 & 0.358776748195951 & 0.717553496391901 & 0.64122325180405 \tabularnewline
80 & 0.323845046539881 & 0.647690093079762 & 0.676154953460119 \tabularnewline
81 & 0.287365890553947 & 0.574731781107894 & 0.712634109446053 \tabularnewline
82 & 0.270247208318180 & 0.540494416636361 & 0.72975279168182 \tabularnewline
83 & 0.289131222194431 & 0.578262444388862 & 0.710868777805569 \tabularnewline
84 & 0.259854104527086 & 0.519708209054171 & 0.740145895472914 \tabularnewline
85 & 0.239621077624814 & 0.479242155249628 & 0.760378922375186 \tabularnewline
86 & 0.248552854669424 & 0.497105709338849 & 0.751447145330576 \tabularnewline
87 & 0.258466681355484 & 0.516933362710967 & 0.741533318644516 \tabularnewline
88 & 0.301108155246921 & 0.602216310493842 & 0.698891844753079 \tabularnewline
89 & 0.262178070729956 & 0.524356141459912 & 0.737821929270044 \tabularnewline
90 & 0.227058750638332 & 0.454117501276664 & 0.772941249361668 \tabularnewline
91 & 0.272827668531012 & 0.545655337062024 & 0.727172331468988 \tabularnewline
92 & 0.249885431555877 & 0.499770863111754 & 0.750114568444123 \tabularnewline
93 & 0.223447793587396 & 0.446895587174792 & 0.776552206412604 \tabularnewline
94 & 0.205313639278996 & 0.410627278557992 & 0.794686360721004 \tabularnewline
95 & 0.184292153010234 & 0.368584306020468 & 0.815707846989766 \tabularnewline
96 & 0.169970249948701 & 0.339940499897401 & 0.830029750051299 \tabularnewline
97 & 0.156722600264236 & 0.313445200528473 & 0.843277399735764 \tabularnewline
98 & 0.165721522589754 & 0.331443045179508 & 0.834278477410246 \tabularnewline
99 & 0.186007950464289 & 0.372015900928579 & 0.81399204953571 \tabularnewline
100 & 0.163549616508577 & 0.327099233017154 & 0.836450383491423 \tabularnewline
101 & 0.145714309546206 & 0.291428619092412 & 0.854285690453794 \tabularnewline
102 & 0.119944236158424 & 0.239888472316847 & 0.880055763841576 \tabularnewline
103 & 0.160076862863489 & 0.320153725726978 & 0.839923137136511 \tabularnewline
104 & 0.171727959979564 & 0.343455919959128 & 0.828272040020436 \tabularnewline
105 & 0.145373688546256 & 0.290747377092511 & 0.854626311453744 \tabularnewline
106 & 0.129095628663198 & 0.258191257326396 & 0.870904371336802 \tabularnewline
107 & 0.114923473739066 & 0.229846947478131 & 0.885076526260934 \tabularnewline
108 & 0.092788227865366 & 0.185576455730732 & 0.907211772134634 \tabularnewline
109 & 0.109280667073625 & 0.218561334147249 & 0.890719332926375 \tabularnewline
110 & 0.295230204252768 & 0.590460408505535 & 0.704769795747233 \tabularnewline
111 & 0.286020061576193 & 0.572040123152385 & 0.713979938423807 \tabularnewline
112 & 0.269028437558052 & 0.538056875116105 & 0.730971562441948 \tabularnewline
113 & 0.232297620545952 & 0.464595241091904 & 0.767702379454048 \tabularnewline
114 & 0.202712919810982 & 0.405425839621963 & 0.797287080189018 \tabularnewline
115 & 0.191866443853118 & 0.383732887706236 & 0.808133556146882 \tabularnewline
116 & 0.159039987509060 & 0.318079975018119 & 0.84096001249094 \tabularnewline
117 & 0.129664646012097 & 0.259329292024195 & 0.870335353987903 \tabularnewline
118 & 0.164019902890618 & 0.328039805781235 & 0.835980097109382 \tabularnewline
119 & 0.134413198381635 & 0.268826396763269 & 0.865586801618365 \tabularnewline
120 & 0.108502927033813 & 0.217005854067625 & 0.891497072966188 \tabularnewline
121 & 0.0856430839622527 & 0.171286167924505 & 0.914356916037747 \tabularnewline
122 & 0.0812288915400286 & 0.162457783080057 & 0.918771108459971 \tabularnewline
123 & 0.0619936689922383 & 0.123987337984477 & 0.938006331007762 \tabularnewline
124 & 0.0538761543921783 & 0.107752308784357 & 0.946123845607822 \tabularnewline
125 & 0.0415156598786279 & 0.0830313197572558 & 0.958484340121372 \tabularnewline
126 & 0.0381767301054907 & 0.0763534602109815 & 0.96182326989451 \tabularnewline
127 & 0.0288720571931718 & 0.0577441143863435 & 0.971127942806828 \tabularnewline
128 & 0.0272989689861472 & 0.0545979379722945 & 0.972701031013853 \tabularnewline
129 & 0.0966913297367285 & 0.193382659473457 & 0.903308670263271 \tabularnewline
130 & 0.406128592310923 & 0.812257184621845 & 0.593871407689077 \tabularnewline
131 & 0.360448827000933 & 0.720897654001866 & 0.639551172999067 \tabularnewline
132 & 0.302933844181565 & 0.60586768836313 & 0.697066155818435 \tabularnewline
133 & 0.473159424934857 & 0.946318849869714 & 0.526840575065143 \tabularnewline
134 & 0.414108040985012 & 0.828216081970023 & 0.585891959014988 \tabularnewline
135 & 0.34580284206556 & 0.69160568413112 & 0.65419715793444 \tabularnewline
136 & 0.285032385875575 & 0.57006477175115 & 0.714967614124425 \tabularnewline
137 & 0.224940629975126 & 0.449881259950252 & 0.775059370024874 \tabularnewline
138 & 0.192646360966971 & 0.385292721933942 & 0.807353639033029 \tabularnewline
139 & 0.145403249030557 & 0.290806498061115 & 0.854596750969443 \tabularnewline
140 & 0.121118909056275 & 0.242237818112550 & 0.878881090943725 \tabularnewline
141 & 0.0865697156418018 & 0.173139431283604 & 0.913430284358198 \tabularnewline
142 & 0.0583855536504176 & 0.116771107300835 & 0.941614446349582 \tabularnewline
143 & 0.0683053346479645 & 0.136610669295929 & 0.931694665352035 \tabularnewline
144 & 0.86693090582733 & 0.26613818834534 & 0.13306909417267 \tabularnewline
145 & 0.799112258209225 & 0.40177548358155 & 0.200887741790775 \tabularnewline
146 & 0.70480555629447 & 0.590388887411061 & 0.295194443705530 \tabularnewline
147 & 0.594556237168248 & 0.810887525663504 & 0.405443762831752 \tabularnewline
148 & 0.455744932429488 & 0.911489864858976 & 0.544255067570512 \tabularnewline
149 & 0.313503110196108 & 0.627006220392216 & 0.686496889803892 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=105006&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]7[/C][C]0.506272706266806[/C][C]0.987454587466387[/C][C]0.493727293733194[/C][/ROW]
[ROW][C]8[/C][C]0.510099029356319[/C][C]0.979801941287362[/C][C]0.489900970643681[/C][/ROW]
[ROW][C]9[/C][C]0.37962776600687[/C][C]0.75925553201374[/C][C]0.62037223399313[/C][/ROW]
[ROW][C]10[/C][C]0.262015820422177[/C][C]0.524031640844354[/C][C]0.737984179577823[/C][/ROW]
[ROW][C]11[/C][C]0.174826440398121[/C][C]0.349652880796242[/C][C]0.825173559601879[/C][/ROW]
[ROW][C]12[/C][C]0.264396177603602[/C][C]0.528792355207205[/C][C]0.735603822396398[/C][/ROW]
[ROW][C]13[/C][C]0.524160124740924[/C][C]0.951679750518151[/C][C]0.475839875259076[/C][/ROW]
[ROW][C]14[/C][C]0.449159490474247[/C][C]0.898318980948495[/C][C]0.550840509525752[/C][/ROW]
[ROW][C]15[/C][C]0.361875680587897[/C][C]0.723751361175794[/C][C]0.638124319412103[/C][/ROW]
[ROW][C]16[/C][C]0.33707574409122[/C][C]0.67415148818244[/C][C]0.66292425590878[/C][/ROW]
[ROW][C]17[/C][C]0.331560585502796[/C][C]0.663121171005593[/C][C]0.668439414497203[/C][/ROW]
[ROW][C]18[/C][C]0.325529353823621[/C][C]0.651058707647242[/C][C]0.674470646176379[/C][/ROW]
[ROW][C]19[/C][C]0.325547963061912[/C][C]0.651095926123823[/C][C]0.674452036938088[/C][/ROW]
[ROW][C]20[/C][C]0.268013022148459[/C][C]0.536026044296918[/C][C]0.731986977851541[/C][/ROW]
[ROW][C]21[/C][C]0.539161511722223[/C][C]0.921676976555553[/C][C]0.460838488277777[/C][/ROW]
[ROW][C]22[/C][C]0.46851055034584[/C][C]0.93702110069168[/C][C]0.53148944965416[/C][/ROW]
[ROW][C]23[/C][C]0.660704719751795[/C][C]0.678590560496411[/C][C]0.339295280248205[/C][/ROW]
[ROW][C]24[/C][C]0.598566777850063[/C][C]0.802866444299874[/C][C]0.401433222149937[/C][/ROW]
[ROW][C]25[/C][C]0.556624733001937[/C][C]0.886750533996126[/C][C]0.443375266998063[/C][/ROW]
[ROW][C]26[/C][C]0.542506465610961[/C][C]0.914987068778077[/C][C]0.457493534389039[/C][/ROW]
[ROW][C]27[/C][C]0.478259557606692[/C][C]0.956519115213383[/C][C]0.521740442393308[/C][/ROW]
[ROW][C]28[/C][C]0.434235183028352[/C][C]0.868470366056704[/C][C]0.565764816971648[/C][/ROW]
[ROW][C]29[/C][C]0.373188209061207[/C][C]0.746376418122414[/C][C]0.626811790938793[/C][/ROW]
[ROW][C]30[/C][C]0.429427122878585[/C][C]0.85885424575717[/C][C]0.570572877121415[/C][/ROW]
[ROW][C]31[/C][C]0.583344371542563[/C][C]0.833311256914874[/C][C]0.416655628457437[/C][/ROW]
[ROW][C]32[/C][C]0.562942191208503[/C][C]0.874115617582994[/C][C]0.437057808791497[/C][/ROW]
[ROW][C]33[/C][C]0.506232413417090[/C][C]0.987535173165819[/C][C]0.493767586582910[/C][/ROW]
[ROW][C]34[/C][C]0.512448451905175[/C][C]0.97510309618965[/C][C]0.487551548094825[/C][/ROW]
[ROW][C]35[/C][C]0.479842047851584[/C][C]0.95968409570317[/C][C]0.520157952148416[/C][/ROW]
[ROW][C]36[/C][C]0.430774460666582[/C][C]0.861548921333165[/C][C]0.569225539333418[/C][/ROW]
[ROW][C]37[/C][C]0.428960970442630[/C][C]0.857921940885259[/C][C]0.571039029557370[/C][/ROW]
[ROW][C]38[/C][C]0.501834985446111[/C][C]0.996330029107778[/C][C]0.498165014553889[/C][/ROW]
[ROW][C]39[/C][C]0.462798381496901[/C][C]0.925596762993802[/C][C]0.537201618503099[/C][/ROW]
[ROW][C]40[/C][C]0.42502850220786[/C][C]0.85005700441572[/C][C]0.57497149779214[/C][/ROW]
[ROW][C]41[/C][C]0.385540721887791[/C][C]0.771081443775581[/C][C]0.614459278112209[/C][/ROW]
[ROW][C]42[/C][C]0.355136262866545[/C][C]0.71027252573309[/C][C]0.644863737133455[/C][/ROW]
[ROW][C]43[/C][C]0.312007337954270[/C][C]0.624014675908541[/C][C]0.68799266204573[/C][/ROW]
[ROW][C]44[/C][C]0.267032911041656[/C][C]0.534065822083313[/C][C]0.732967088958344[/C][/ROW]
[ROW][C]45[/C][C]0.233274609762255[/C][C]0.46654921952451[/C][C]0.766725390237745[/C][/ROW]
[ROW][C]46[/C][C]0.19630441175382[/C][C]0.39260882350764[/C][C]0.80369558824618[/C][/ROW]
[ROW][C]47[/C][C]0.163983784670007[/C][C]0.327967569340014[/C][C]0.836016215329993[/C][/ROW]
[ROW][C]48[/C][C]0.277943660300146[/C][C]0.555887320600292[/C][C]0.722056339699854[/C][/ROW]
[ROW][C]49[/C][C]0.236979445783732[/C][C]0.473958891567464[/C][C]0.763020554216268[/C][/ROW]
[ROW][C]50[/C][C]0.225652765622211[/C][C]0.451305531244422[/C][C]0.77434723437779[/C][/ROW]
[ROW][C]51[/C][C]0.203392735219390[/C][C]0.406785470438780[/C][C]0.79660726478061[/C][/ROW]
[ROW][C]52[/C][C]0.281528745848568[/C][C]0.563057491697137[/C][C]0.718471254151432[/C][/ROW]
[ROW][C]53[/C][C]0.25287479798349[/C][C]0.50574959596698[/C][C]0.74712520201651[/C][/ROW]
[ROW][C]54[/C][C]0.238550078829236[/C][C]0.477100157658473[/C][C]0.761449921170764[/C][/ROW]
[ROW][C]55[/C][C]0.203555427158751[/C][C]0.407110854317501[/C][C]0.79644457284125[/C][/ROW]
[ROW][C]56[/C][C]0.171927337550133[/C][C]0.343854675100265[/C][C]0.828072662449867[/C][/ROW]
[ROW][C]57[/C][C]0.143384647754043[/C][C]0.286769295508085[/C][C]0.856615352245957[/C][/ROW]
[ROW][C]58[/C][C]0.145595591392648[/C][C]0.291191182785297[/C][C]0.854404408607352[/C][/ROW]
[ROW][C]59[/C][C]0.299625230822803[/C][C]0.599250461645606[/C][C]0.700374769177197[/C][/ROW]
[ROW][C]60[/C][C]0.260390604342807[/C][C]0.520781208685614[/C][C]0.739609395657193[/C][/ROW]
[ROW][C]61[/C][C]0.229047265430455[/C][C]0.458094530860911[/C][C]0.770952734569545[/C][/ROW]
[ROW][C]62[/C][C]0.197146942996599[/C][C]0.394293885993198[/C][C]0.802853057003401[/C][/ROW]
[ROW][C]63[/C][C]0.253809400336403[/C][C]0.507618800672806[/C][C]0.746190599663597[/C][/ROW]
[ROW][C]64[/C][C]0.229587822008333[/C][C]0.459175644016665[/C][C]0.770412177991667[/C][/ROW]
[ROW][C]65[/C][C]0.198656340097578[/C][C]0.397312680195155[/C][C]0.801343659902423[/C][/ROW]
[ROW][C]66[/C][C]0.168307934446550[/C][C]0.336615868893100[/C][C]0.83169206555345[/C][/ROW]
[ROW][C]67[/C][C]0.188907281886301[/C][C]0.377814563772602[/C][C]0.811092718113699[/C][/ROW]
[ROW][C]68[/C][C]0.159513868215202[/C][C]0.319027736430403[/C][C]0.840486131784798[/C][/ROW]
[ROW][C]69[/C][C]0.139172383129603[/C][C]0.278344766259205[/C][C]0.860827616870397[/C][/ROW]
[ROW][C]70[/C][C]0.424854969748122[/C][C]0.849709939496244[/C][C]0.575145030251878[/C][/ROW]
[ROW][C]71[/C][C]0.382806550950123[/C][C]0.765613101900247[/C][C]0.617193449049877[/C][/ROW]
[ROW][C]72[/C][C]0.341615455382052[/C][C]0.683230910764105[/C][C]0.658384544617948[/C][/ROW]
[ROW][C]73[/C][C]0.317850592572136[/C][C]0.635701185144273[/C][C]0.682149407427864[/C][/ROW]
[ROW][C]74[/C][C]0.44326490001232[/C][C]0.88652980002464[/C][C]0.55673509998768[/C][/ROW]
[ROW][C]75[/C][C]0.402324667518423[/C][C]0.804649335036846[/C][C]0.597675332481577[/C][/ROW]
[ROW][C]76[/C][C]0.382719564926625[/C][C]0.765439129853251[/C][C]0.617280435073375[/C][/ROW]
[ROW][C]77[/C][C]0.381175949257079[/C][C]0.762351898514158[/C][C]0.618824050742921[/C][/ROW]
[ROW][C]78[/C][C]0.337644145241632[/C][C]0.675288290483265[/C][C]0.662355854758367[/C][/ROW]
[ROW][C]79[/C][C]0.358776748195951[/C][C]0.717553496391901[/C][C]0.64122325180405[/C][/ROW]
[ROW][C]80[/C][C]0.323845046539881[/C][C]0.647690093079762[/C][C]0.676154953460119[/C][/ROW]
[ROW][C]81[/C][C]0.287365890553947[/C][C]0.574731781107894[/C][C]0.712634109446053[/C][/ROW]
[ROW][C]82[/C][C]0.270247208318180[/C][C]0.540494416636361[/C][C]0.72975279168182[/C][/ROW]
[ROW][C]83[/C][C]0.289131222194431[/C][C]0.578262444388862[/C][C]0.710868777805569[/C][/ROW]
[ROW][C]84[/C][C]0.259854104527086[/C][C]0.519708209054171[/C][C]0.740145895472914[/C][/ROW]
[ROW][C]85[/C][C]0.239621077624814[/C][C]0.479242155249628[/C][C]0.760378922375186[/C][/ROW]
[ROW][C]86[/C][C]0.248552854669424[/C][C]0.497105709338849[/C][C]0.751447145330576[/C][/ROW]
[ROW][C]87[/C][C]0.258466681355484[/C][C]0.516933362710967[/C][C]0.741533318644516[/C][/ROW]
[ROW][C]88[/C][C]0.301108155246921[/C][C]0.602216310493842[/C][C]0.698891844753079[/C][/ROW]
[ROW][C]89[/C][C]0.262178070729956[/C][C]0.524356141459912[/C][C]0.737821929270044[/C][/ROW]
[ROW][C]90[/C][C]0.227058750638332[/C][C]0.454117501276664[/C][C]0.772941249361668[/C][/ROW]
[ROW][C]91[/C][C]0.272827668531012[/C][C]0.545655337062024[/C][C]0.727172331468988[/C][/ROW]
[ROW][C]92[/C][C]0.249885431555877[/C][C]0.499770863111754[/C][C]0.750114568444123[/C][/ROW]
[ROW][C]93[/C][C]0.223447793587396[/C][C]0.446895587174792[/C][C]0.776552206412604[/C][/ROW]
[ROW][C]94[/C][C]0.205313639278996[/C][C]0.410627278557992[/C][C]0.794686360721004[/C][/ROW]
[ROW][C]95[/C][C]0.184292153010234[/C][C]0.368584306020468[/C][C]0.815707846989766[/C][/ROW]
[ROW][C]96[/C][C]0.169970249948701[/C][C]0.339940499897401[/C][C]0.830029750051299[/C][/ROW]
[ROW][C]97[/C][C]0.156722600264236[/C][C]0.313445200528473[/C][C]0.843277399735764[/C][/ROW]
[ROW][C]98[/C][C]0.165721522589754[/C][C]0.331443045179508[/C][C]0.834278477410246[/C][/ROW]
[ROW][C]99[/C][C]0.186007950464289[/C][C]0.372015900928579[/C][C]0.81399204953571[/C][/ROW]
[ROW][C]100[/C][C]0.163549616508577[/C][C]0.327099233017154[/C][C]0.836450383491423[/C][/ROW]
[ROW][C]101[/C][C]0.145714309546206[/C][C]0.291428619092412[/C][C]0.854285690453794[/C][/ROW]
[ROW][C]102[/C][C]0.119944236158424[/C][C]0.239888472316847[/C][C]0.880055763841576[/C][/ROW]
[ROW][C]103[/C][C]0.160076862863489[/C][C]0.320153725726978[/C][C]0.839923137136511[/C][/ROW]
[ROW][C]104[/C][C]0.171727959979564[/C][C]0.343455919959128[/C][C]0.828272040020436[/C][/ROW]
[ROW][C]105[/C][C]0.145373688546256[/C][C]0.290747377092511[/C][C]0.854626311453744[/C][/ROW]
[ROW][C]106[/C][C]0.129095628663198[/C][C]0.258191257326396[/C][C]0.870904371336802[/C][/ROW]
[ROW][C]107[/C][C]0.114923473739066[/C][C]0.229846947478131[/C][C]0.885076526260934[/C][/ROW]
[ROW][C]108[/C][C]0.092788227865366[/C][C]0.185576455730732[/C][C]0.907211772134634[/C][/ROW]
[ROW][C]109[/C][C]0.109280667073625[/C][C]0.218561334147249[/C][C]0.890719332926375[/C][/ROW]
[ROW][C]110[/C][C]0.295230204252768[/C][C]0.590460408505535[/C][C]0.704769795747233[/C][/ROW]
[ROW][C]111[/C][C]0.286020061576193[/C][C]0.572040123152385[/C][C]0.713979938423807[/C][/ROW]
[ROW][C]112[/C][C]0.269028437558052[/C][C]0.538056875116105[/C][C]0.730971562441948[/C][/ROW]
[ROW][C]113[/C][C]0.232297620545952[/C][C]0.464595241091904[/C][C]0.767702379454048[/C][/ROW]
[ROW][C]114[/C][C]0.202712919810982[/C][C]0.405425839621963[/C][C]0.797287080189018[/C][/ROW]
[ROW][C]115[/C][C]0.191866443853118[/C][C]0.383732887706236[/C][C]0.808133556146882[/C][/ROW]
[ROW][C]116[/C][C]0.159039987509060[/C][C]0.318079975018119[/C][C]0.84096001249094[/C][/ROW]
[ROW][C]117[/C][C]0.129664646012097[/C][C]0.259329292024195[/C][C]0.870335353987903[/C][/ROW]
[ROW][C]118[/C][C]0.164019902890618[/C][C]0.328039805781235[/C][C]0.835980097109382[/C][/ROW]
[ROW][C]119[/C][C]0.134413198381635[/C][C]0.268826396763269[/C][C]0.865586801618365[/C][/ROW]
[ROW][C]120[/C][C]0.108502927033813[/C][C]0.217005854067625[/C][C]0.891497072966188[/C][/ROW]
[ROW][C]121[/C][C]0.0856430839622527[/C][C]0.171286167924505[/C][C]0.914356916037747[/C][/ROW]
[ROW][C]122[/C][C]0.0812288915400286[/C][C]0.162457783080057[/C][C]0.918771108459971[/C][/ROW]
[ROW][C]123[/C][C]0.0619936689922383[/C][C]0.123987337984477[/C][C]0.938006331007762[/C][/ROW]
[ROW][C]124[/C][C]0.0538761543921783[/C][C]0.107752308784357[/C][C]0.946123845607822[/C][/ROW]
[ROW][C]125[/C][C]0.0415156598786279[/C][C]0.0830313197572558[/C][C]0.958484340121372[/C][/ROW]
[ROW][C]126[/C][C]0.0381767301054907[/C][C]0.0763534602109815[/C][C]0.96182326989451[/C][/ROW]
[ROW][C]127[/C][C]0.0288720571931718[/C][C]0.0577441143863435[/C][C]0.971127942806828[/C][/ROW]
[ROW][C]128[/C][C]0.0272989689861472[/C][C]0.0545979379722945[/C][C]0.972701031013853[/C][/ROW]
[ROW][C]129[/C][C]0.0966913297367285[/C][C]0.193382659473457[/C][C]0.903308670263271[/C][/ROW]
[ROW][C]130[/C][C]0.406128592310923[/C][C]0.812257184621845[/C][C]0.593871407689077[/C][/ROW]
[ROW][C]131[/C][C]0.360448827000933[/C][C]0.720897654001866[/C][C]0.639551172999067[/C][/ROW]
[ROW][C]132[/C][C]0.302933844181565[/C][C]0.60586768836313[/C][C]0.697066155818435[/C][/ROW]
[ROW][C]133[/C][C]0.473159424934857[/C][C]0.946318849869714[/C][C]0.526840575065143[/C][/ROW]
[ROW][C]134[/C][C]0.414108040985012[/C][C]0.828216081970023[/C][C]0.585891959014988[/C][/ROW]
[ROW][C]135[/C][C]0.34580284206556[/C][C]0.69160568413112[/C][C]0.65419715793444[/C][/ROW]
[ROW][C]136[/C][C]0.285032385875575[/C][C]0.57006477175115[/C][C]0.714967614124425[/C][/ROW]
[ROW][C]137[/C][C]0.224940629975126[/C][C]0.449881259950252[/C][C]0.775059370024874[/C][/ROW]
[ROW][C]138[/C][C]0.192646360966971[/C][C]0.385292721933942[/C][C]0.807353639033029[/C][/ROW]
[ROW][C]139[/C][C]0.145403249030557[/C][C]0.290806498061115[/C][C]0.854596750969443[/C][/ROW]
[ROW][C]140[/C][C]0.121118909056275[/C][C]0.242237818112550[/C][C]0.878881090943725[/C][/ROW]
[ROW][C]141[/C][C]0.0865697156418018[/C][C]0.173139431283604[/C][C]0.913430284358198[/C][/ROW]
[ROW][C]142[/C][C]0.0583855536504176[/C][C]0.116771107300835[/C][C]0.941614446349582[/C][/ROW]
[ROW][C]143[/C][C]0.0683053346479645[/C][C]0.136610669295929[/C][C]0.931694665352035[/C][/ROW]
[ROW][C]144[/C][C]0.86693090582733[/C][C]0.26613818834534[/C][C]0.13306909417267[/C][/ROW]
[ROW][C]145[/C][C]0.799112258209225[/C][C]0.40177548358155[/C][C]0.200887741790775[/C][/ROW]
[ROW][C]146[/C][C]0.70480555629447[/C][C]0.590388887411061[/C][C]0.295194443705530[/C][/ROW]
[ROW][C]147[/C][C]0.594556237168248[/C][C]0.810887525663504[/C][C]0.405443762831752[/C][/ROW]
[ROW][C]148[/C][C]0.455744932429488[/C][C]0.911489864858976[/C][C]0.544255067570512[/C][/ROW]
[ROW][C]149[/C][C]0.313503110196108[/C][C]0.627006220392216[/C][C]0.686496889803892[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=105006&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=105006&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
70.5062727062668060.9874545874663870.493727293733194
80.5100990293563190.9798019412873620.489900970643681
90.379627766006870.759255532013740.62037223399313
100.2620158204221770.5240316408443540.737984179577823
110.1748264403981210.3496528807962420.825173559601879
120.2643961776036020.5287923552072050.735603822396398
130.5241601247409240.9516797505181510.475839875259076
140.4491594904742470.8983189809484950.550840509525752
150.3618756805878970.7237513611757940.638124319412103
160.337075744091220.674151488182440.66292425590878
170.3315605855027960.6631211710055930.668439414497203
180.3255293538236210.6510587076472420.674470646176379
190.3255479630619120.6510959261238230.674452036938088
200.2680130221484590.5360260442969180.731986977851541
210.5391615117222230.9216769765555530.460838488277777
220.468510550345840.937021100691680.53148944965416
230.6607047197517950.6785905604964110.339295280248205
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250.5566247330019370.8867505339961260.443375266998063
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320.5629421912085030.8741156175829940.437057808791497
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340.5124484519051750.975103096189650.487551548094825
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1490.3135031101961080.6270062203922160.686496889803892







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level40.0279720279720280OK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 4 & 0.0279720279720280 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=105006&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]4[/C][C]0.0279720279720280[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=105006&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=105006&T=6

As an alternative you can also use a QR Code:  

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level40.0279720279720280OK



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