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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationThu, 17 Nov 2011 19:56:59 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Nov/17/t1321578547d7s4yjudkp2l8u4.htm/, Retrieved Thu, 31 Oct 2024 23:54:04 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=145377, Retrieved Thu, 31 Oct 2024 23:54:04 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact149
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Chi-Squared Test, McNemar Test, and Fisher Exact Test] [] [2011-11-18 00:02:05] [2ba7ee2cbaa966a49160c7cfb7436069]
- R P   [Chi-Squared Test, McNemar Test, and Fisher Exact Test] [] [2011-11-18 00:03:30] [2ba7ee2cbaa966a49160c7cfb7436069]
-   P     [Chi-Squared Test, McNemar Test, and Fisher Exact Test] [] [2011-11-18 00:04:38] [2ba7ee2cbaa966a49160c7cfb7436069]
-   P       [Chi-Squared Test, McNemar Test, and Fisher Exact Test] [] [2011-11-18 00:05:54] [2ba7ee2cbaa966a49160c7cfb7436069]
- RMPD          [Multiple Regression] [] [2011-11-18 00:56:59] [393d554610c677f923bed472882d0fdb] [Current]
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Dataseries X:
12	14
11	18
15	11
6	12
13	16
10	18
12	14
14	14
12	15
6	15
10	17
12	19
12	10
11	16
15	18
12	14
10	14
12	17
11	14
12	16
11	18
12	11
13	14
11	12
9	17
13	9
10	16
14	14
12	15
10	11
12	16
8	13
10	17
12	15
12	14
7	16
6	9
12	15
10	17
10	13
10	15
12	16
15	16
10	12
10	12
12	11
13	15
11	15
11	17
12	13
14	16
10	14
12	11
13	12
5	12
6	15
12	16
12	15
11	12
10	12
7	8
12	13
14	11
11	14
12	15
13	10
14	11
11	12
12	15
12	15
8	14
11	16
14	15
14	15
12	13
9	12
13	17
11	13
12	15
12	13
12	15
12	16
12	15
12	16
11	15
10	14
9	15
12	14
12	13
12	7
9	17
15	13
12	15
12	14
12	13
10	16
13	12
9	14
12	17
10	15
14	17
11	12
15	16
11	11
11	15
12	9
12	16
12	15
11	10
7	10
12	15
14	11
11	13
11	14
10	18
13	16
13	14
8	14
11	14
12	14
11	12
13	14
12	15
14	15
13	15
15	13
10	17
11	17
9	19
11	15
10	13
11	9
8	15
11	15
12	15
12	16
9	11
11	14
10	11
8	15
9	13
8	15
9	16
15	14
11	15
8	16
13	16
12	11
12	12
9	9
7	16
13	13
9	16
6	12
8	9
8	13
15	13
6	14
9	19
11	13
8	12
8	13




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 5 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145377&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145377&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145377&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Happiness[t] = + 13.1438100253266 + 0.0807944030692845Software[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Happiness[t] =  +  13.1438100253266 +  0.0807944030692845Software[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145377&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Happiness[t] =  +  13.1438100253266 +  0.0807944030692845Software[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145377&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145377&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
Happiness[t] = + 13.1438100253266 + 0.0807944030692845Software[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)13.14381002532660.96892513.565400
Software0.08079440306928450.0860510.93890.3491930.174597

\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) & 13.1438100253266 & 0.968925 & 13.5654 & 0 & 0 \tabularnewline
Software & 0.0807944030692845 & 0.086051 & 0.9389 & 0.349193 & 0.174597 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145377&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]13.1438100253266[/C][C]0.968925[/C][C]13.5654[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Software[/C][C]0.0807944030692845[/C][C]0.086051[/C][C]0.9389[/C][C]0.349193[/C][C]0.174597[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145377&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145377&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)13.14381002532660.96892513.565400
Software0.08079440306928450.0860510.93890.3491930.174597







Multiple Linear Regression - Regression Statistics
Multiple R0.0740235710364125
R-squared0.0054794890689828
Adjusted R-squared-0.00073626412433625
F-TEST (value)0.881548687433818
F-TEST (DF numerator)1
F-TEST (DF denominator)160
p-value0.349193073077933
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.33847846507792
Sum Squared Residuals874.957045061311

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.0740235710364125 \tabularnewline
R-squared & 0.0054794890689828 \tabularnewline
Adjusted R-squared & -0.00073626412433625 \tabularnewline
F-TEST (value) & 0.881548687433818 \tabularnewline
F-TEST (DF numerator) & 1 \tabularnewline
F-TEST (DF denominator) & 160 \tabularnewline
p-value & 0.349193073077933 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.33847846507792 \tabularnewline
Sum Squared Residuals & 874.957045061311 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145377&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.0740235710364125[/C][/ROW]
[ROW][C]R-squared[/C][C]0.0054794890689828[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]-0.00073626412433625[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]0.881548687433818[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]1[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]160[/C][/ROW]
[ROW][C]p-value[/C][C]0.349193073077933[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.33847846507792[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]874.957045061311[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145377&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145377&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 R0.0740235710364125
R-squared0.0054794890689828
Adjusted R-squared-0.00073626412433625
F-TEST (value)0.881548687433818
F-TEST (DF numerator)1
F-TEST (DF denominator)160
p-value0.349193073077933
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.33847846507792
Sum Squared Residuals874.957045061311







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11414.1133428621581-0.113342862158066
21814.03254845908873.96745154091126
31114.3557260713659-3.35572607136588
41213.6285764437423-1.62857644374232
51614.19413726522731.80586273477269
61813.95175405601954.04824594398054
71414.113342862158-0.113342862158028
81414.2749316682966-0.274931668296597
91514.1133428621580.886657137841972
101513.62857644374231.37142355625768
111713.95175405601953.04824594398054
121914.1133428621584.88665713784197
131014.113342862158-4.11334286215803
141614.03254845908871.96745154091126
151814.35572607136593.64427392863412
161414.113342862158-0.113342862158028
171413.95175405601950.0482459439805413
181714.1133428621582.88665713784197
191414.0325484590887-0.0325484590887432
201614.1133428621581.88665713784197
211814.03254845908873.96745154091126
221114.113342862158-3.11334286215803
231414.1941372652273-0.194137265227312
241214.0325484590887-2.03254845908874
251713.87095965295023.12904034704983
26914.1941372652273-5.19413726522731
271613.95175405601952.04824594398054
281414.2749316682966-0.274931668296597
291514.1133428621580.886657137841972
301113.9517540560195-2.95175405601946
311614.1133428621581.88665713784197
321313.7901652498809-0.79016524988089
331713.95175405601953.04824594398054
341514.1133428621580.886657137841972
351414.113342862158-0.113342862158028
361613.70937084681162.29062915318839
37913.6285764437423-4.62857644374232
381514.1133428621580.886657137841972
391713.95175405601953.04824594398054
401313.9517540560195-0.951754056019459
411513.95175405601951.04824594398054
421614.1133428621581.88665713784197
431614.35572607136591.64427392863412
441213.9517540560195-1.95175405601946
451213.9517540560195-1.95175405601946
461114.113342862158-3.11334286215803
471514.19413726522730.805862734772688
481514.03254845908870.967451540911257
491714.03254845908872.96745154091126
501314.113342862158-1.11334286215803
511614.27493166829661.7250683317034
521413.95175405601950.0482459439805413
531114.113342862158-3.11334286215803
541214.1941372652273-2.19413726522731
551213.547782040673-1.54778204067304
561513.62857644374231.37142355625768
571614.1133428621581.88665713784197
581514.1133428621580.886657137841972
591214.0325484590887-2.03254845908874
601213.9517540560195-1.95175405601946
61813.7093708468116-5.7093708468116
621314.113342862158-1.11334286215803
631114.2749316682966-3.2749316682966
641414.0325484590887-0.0325484590887432
651514.1133428621580.886657137841972
661014.1941372652273-4.19413726522731
671114.2749316682966-3.2749316682966
681214.0325484590887-2.03254845908874
691514.1133428621580.886657137841972
701514.1133428621580.886657137841972
711413.79016524988090.20983475011911
721614.03254845908871.96745154091126
731514.27493166829660.725068331703403
741514.27493166829660.725068331703403
751314.113342862158-1.11334286215803
761213.8709596529502-1.87095965295017
771714.19413726522732.80586273477269
781314.0325484590887-1.03254845908874
791514.1133428621580.886657137841972
801314.113342862158-1.11334286215803
811514.1133428621580.886657137841972
821614.1133428621581.88665713784197
831514.1133428621580.886657137841972
841614.1133428621581.88665713784197
851514.03254845908870.967451540911257
861413.95175405601950.0482459439805413
871513.87095965295021.12904034704983
881414.113342862158-0.113342862158028
891314.113342862158-1.11334286215803
90714.113342862158-7.11334286215803
911713.87095965295023.12904034704983
921314.3557260713659-1.35572607136588
931514.1133428621580.886657137841972
941414.113342862158-0.113342862158028
951314.113342862158-1.11334286215803
961613.95175405601952.04824594398054
971214.1941372652273-2.19413726522731
981413.87095965295020.129040347049826
991714.1133428621582.88665713784197
1001513.95175405601951.04824594398054
1011714.27493166829662.7250683317034
1021214.0325484590887-2.03254845908874
1031614.35572607136591.64427392863412
1041114.0325484590887-3.03254845908874
1051514.03254845908870.967451540911257
106914.113342862158-5.11334286215803
1071614.1133428621581.88665713784197
1081514.1133428621580.886657137841972
1091014.0325484590887-4.03254845908874
1101013.7093708468116-3.70937084681161
1111514.1133428621580.886657137841972
1121114.2749316682966-3.2749316682966
1131314.0325484590887-1.03254845908874
1141414.0325484590887-0.0325484590887432
1151813.95175405601954.04824594398054
1161614.19413726522731.80586273477269
1171414.1941372652273-0.194137265227312
1181413.79016524988090.20983475011911
1191414.0325484590887-0.0325484590887432
1201414.113342862158-0.113342862158028
1211214.0325484590887-2.03254845908874
1221414.1941372652273-0.194137265227312
1231514.1133428621580.886657137841972
1241514.27493166829660.725068331703403
1251514.19413726522730.805862734772688
1261314.3557260713659-1.35572607136588
1271713.95175405601953.04824594398054
1281714.03254845908872.96745154091126
1291913.87095965295025.12904034704983
1301514.03254845908870.967451540911257
1311313.9517540560195-0.951754056019459
132914.0325484590887-5.03254845908874
1331513.79016524988091.20983475011911
1341514.03254845908870.967451540911257
1351514.1133428621580.886657137841972
1361614.1133428621581.88665713784197
1371113.8709596529502-2.87095965295017
1381414.0325484590887-0.0325484590887432
1391113.9517540560195-2.95175405601946
1401513.79016524988091.20983475011911
1411313.8709596529502-0.870959652950174
1421513.79016524988091.20983475011911
1431613.87095965295022.12904034704983
1441414.3557260713659-0.355726071365881
1451514.03254845908870.967451540911257
1461613.79016524988092.20983475011911
1471614.19413726522731.80586273477269
1481114.113342862158-3.11334286215803
1491214.113342862158-2.11334286215803
150913.8709596529502-4.87095965295017
1511613.70937084681162.29062915318839
1521314.1941372652273-1.19413726522731
1531613.87095965295022.12904034704983
1541213.6285764437423-1.62857644374232
155913.7901652498809-4.79016524988089
1561313.7901652498809-0.79016524988089
1571314.3557260713659-1.35572607136588
1581413.62857644374230.371423556257679
1591913.87095965295025.12904034704983
1601314.0325484590887-1.03254845908874
1611213.7901652498809-1.79016524988089
1621313.7901652498809-0.79016524988089

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 14 & 14.1133428621581 & -0.113342862158066 \tabularnewline
2 & 18 & 14.0325484590887 & 3.96745154091126 \tabularnewline
3 & 11 & 14.3557260713659 & -3.35572607136588 \tabularnewline
4 & 12 & 13.6285764437423 & -1.62857644374232 \tabularnewline
5 & 16 & 14.1941372652273 & 1.80586273477269 \tabularnewline
6 & 18 & 13.9517540560195 & 4.04824594398054 \tabularnewline
7 & 14 & 14.113342862158 & -0.113342862158028 \tabularnewline
8 & 14 & 14.2749316682966 & -0.274931668296597 \tabularnewline
9 & 15 & 14.113342862158 & 0.886657137841972 \tabularnewline
10 & 15 & 13.6285764437423 & 1.37142355625768 \tabularnewline
11 & 17 & 13.9517540560195 & 3.04824594398054 \tabularnewline
12 & 19 & 14.113342862158 & 4.88665713784197 \tabularnewline
13 & 10 & 14.113342862158 & -4.11334286215803 \tabularnewline
14 & 16 & 14.0325484590887 & 1.96745154091126 \tabularnewline
15 & 18 & 14.3557260713659 & 3.64427392863412 \tabularnewline
16 & 14 & 14.113342862158 & -0.113342862158028 \tabularnewline
17 & 14 & 13.9517540560195 & 0.0482459439805413 \tabularnewline
18 & 17 & 14.113342862158 & 2.88665713784197 \tabularnewline
19 & 14 & 14.0325484590887 & -0.0325484590887432 \tabularnewline
20 & 16 & 14.113342862158 & 1.88665713784197 \tabularnewline
21 & 18 & 14.0325484590887 & 3.96745154091126 \tabularnewline
22 & 11 & 14.113342862158 & -3.11334286215803 \tabularnewline
23 & 14 & 14.1941372652273 & -0.194137265227312 \tabularnewline
24 & 12 & 14.0325484590887 & -2.03254845908874 \tabularnewline
25 & 17 & 13.8709596529502 & 3.12904034704983 \tabularnewline
26 & 9 & 14.1941372652273 & -5.19413726522731 \tabularnewline
27 & 16 & 13.9517540560195 & 2.04824594398054 \tabularnewline
28 & 14 & 14.2749316682966 & -0.274931668296597 \tabularnewline
29 & 15 & 14.113342862158 & 0.886657137841972 \tabularnewline
30 & 11 & 13.9517540560195 & -2.95175405601946 \tabularnewline
31 & 16 & 14.113342862158 & 1.88665713784197 \tabularnewline
32 & 13 & 13.7901652498809 & -0.79016524988089 \tabularnewline
33 & 17 & 13.9517540560195 & 3.04824594398054 \tabularnewline
34 & 15 & 14.113342862158 & 0.886657137841972 \tabularnewline
35 & 14 & 14.113342862158 & -0.113342862158028 \tabularnewline
36 & 16 & 13.7093708468116 & 2.29062915318839 \tabularnewline
37 & 9 & 13.6285764437423 & -4.62857644374232 \tabularnewline
38 & 15 & 14.113342862158 & 0.886657137841972 \tabularnewline
39 & 17 & 13.9517540560195 & 3.04824594398054 \tabularnewline
40 & 13 & 13.9517540560195 & -0.951754056019459 \tabularnewline
41 & 15 & 13.9517540560195 & 1.04824594398054 \tabularnewline
42 & 16 & 14.113342862158 & 1.88665713784197 \tabularnewline
43 & 16 & 14.3557260713659 & 1.64427392863412 \tabularnewline
44 & 12 & 13.9517540560195 & -1.95175405601946 \tabularnewline
45 & 12 & 13.9517540560195 & -1.95175405601946 \tabularnewline
46 & 11 & 14.113342862158 & -3.11334286215803 \tabularnewline
47 & 15 & 14.1941372652273 & 0.805862734772688 \tabularnewline
48 & 15 & 14.0325484590887 & 0.967451540911257 \tabularnewline
49 & 17 & 14.0325484590887 & 2.96745154091126 \tabularnewline
50 & 13 & 14.113342862158 & -1.11334286215803 \tabularnewline
51 & 16 & 14.2749316682966 & 1.7250683317034 \tabularnewline
52 & 14 & 13.9517540560195 & 0.0482459439805413 \tabularnewline
53 & 11 & 14.113342862158 & -3.11334286215803 \tabularnewline
54 & 12 & 14.1941372652273 & -2.19413726522731 \tabularnewline
55 & 12 & 13.547782040673 & -1.54778204067304 \tabularnewline
56 & 15 & 13.6285764437423 & 1.37142355625768 \tabularnewline
57 & 16 & 14.113342862158 & 1.88665713784197 \tabularnewline
58 & 15 & 14.113342862158 & 0.886657137841972 \tabularnewline
59 & 12 & 14.0325484590887 & -2.03254845908874 \tabularnewline
60 & 12 & 13.9517540560195 & -1.95175405601946 \tabularnewline
61 & 8 & 13.7093708468116 & -5.7093708468116 \tabularnewline
62 & 13 & 14.113342862158 & -1.11334286215803 \tabularnewline
63 & 11 & 14.2749316682966 & -3.2749316682966 \tabularnewline
64 & 14 & 14.0325484590887 & -0.0325484590887432 \tabularnewline
65 & 15 & 14.113342862158 & 0.886657137841972 \tabularnewline
66 & 10 & 14.1941372652273 & -4.19413726522731 \tabularnewline
67 & 11 & 14.2749316682966 & -3.2749316682966 \tabularnewline
68 & 12 & 14.0325484590887 & -2.03254845908874 \tabularnewline
69 & 15 & 14.113342862158 & 0.886657137841972 \tabularnewline
70 & 15 & 14.113342862158 & 0.886657137841972 \tabularnewline
71 & 14 & 13.7901652498809 & 0.20983475011911 \tabularnewline
72 & 16 & 14.0325484590887 & 1.96745154091126 \tabularnewline
73 & 15 & 14.2749316682966 & 0.725068331703403 \tabularnewline
74 & 15 & 14.2749316682966 & 0.725068331703403 \tabularnewline
75 & 13 & 14.113342862158 & -1.11334286215803 \tabularnewline
76 & 12 & 13.8709596529502 & -1.87095965295017 \tabularnewline
77 & 17 & 14.1941372652273 & 2.80586273477269 \tabularnewline
78 & 13 & 14.0325484590887 & -1.03254845908874 \tabularnewline
79 & 15 & 14.113342862158 & 0.886657137841972 \tabularnewline
80 & 13 & 14.113342862158 & -1.11334286215803 \tabularnewline
81 & 15 & 14.113342862158 & 0.886657137841972 \tabularnewline
82 & 16 & 14.113342862158 & 1.88665713784197 \tabularnewline
83 & 15 & 14.113342862158 & 0.886657137841972 \tabularnewline
84 & 16 & 14.113342862158 & 1.88665713784197 \tabularnewline
85 & 15 & 14.0325484590887 & 0.967451540911257 \tabularnewline
86 & 14 & 13.9517540560195 & 0.0482459439805413 \tabularnewline
87 & 15 & 13.8709596529502 & 1.12904034704983 \tabularnewline
88 & 14 & 14.113342862158 & -0.113342862158028 \tabularnewline
89 & 13 & 14.113342862158 & -1.11334286215803 \tabularnewline
90 & 7 & 14.113342862158 & -7.11334286215803 \tabularnewline
91 & 17 & 13.8709596529502 & 3.12904034704983 \tabularnewline
92 & 13 & 14.3557260713659 & -1.35572607136588 \tabularnewline
93 & 15 & 14.113342862158 & 0.886657137841972 \tabularnewline
94 & 14 & 14.113342862158 & -0.113342862158028 \tabularnewline
95 & 13 & 14.113342862158 & -1.11334286215803 \tabularnewline
96 & 16 & 13.9517540560195 & 2.04824594398054 \tabularnewline
97 & 12 & 14.1941372652273 & -2.19413726522731 \tabularnewline
98 & 14 & 13.8709596529502 & 0.129040347049826 \tabularnewline
99 & 17 & 14.113342862158 & 2.88665713784197 \tabularnewline
100 & 15 & 13.9517540560195 & 1.04824594398054 \tabularnewline
101 & 17 & 14.2749316682966 & 2.7250683317034 \tabularnewline
102 & 12 & 14.0325484590887 & -2.03254845908874 \tabularnewline
103 & 16 & 14.3557260713659 & 1.64427392863412 \tabularnewline
104 & 11 & 14.0325484590887 & -3.03254845908874 \tabularnewline
105 & 15 & 14.0325484590887 & 0.967451540911257 \tabularnewline
106 & 9 & 14.113342862158 & -5.11334286215803 \tabularnewline
107 & 16 & 14.113342862158 & 1.88665713784197 \tabularnewline
108 & 15 & 14.113342862158 & 0.886657137841972 \tabularnewline
109 & 10 & 14.0325484590887 & -4.03254845908874 \tabularnewline
110 & 10 & 13.7093708468116 & -3.70937084681161 \tabularnewline
111 & 15 & 14.113342862158 & 0.886657137841972 \tabularnewline
112 & 11 & 14.2749316682966 & -3.2749316682966 \tabularnewline
113 & 13 & 14.0325484590887 & -1.03254845908874 \tabularnewline
114 & 14 & 14.0325484590887 & -0.0325484590887432 \tabularnewline
115 & 18 & 13.9517540560195 & 4.04824594398054 \tabularnewline
116 & 16 & 14.1941372652273 & 1.80586273477269 \tabularnewline
117 & 14 & 14.1941372652273 & -0.194137265227312 \tabularnewline
118 & 14 & 13.7901652498809 & 0.20983475011911 \tabularnewline
119 & 14 & 14.0325484590887 & -0.0325484590887432 \tabularnewline
120 & 14 & 14.113342862158 & -0.113342862158028 \tabularnewline
121 & 12 & 14.0325484590887 & -2.03254845908874 \tabularnewline
122 & 14 & 14.1941372652273 & -0.194137265227312 \tabularnewline
123 & 15 & 14.113342862158 & 0.886657137841972 \tabularnewline
124 & 15 & 14.2749316682966 & 0.725068331703403 \tabularnewline
125 & 15 & 14.1941372652273 & 0.805862734772688 \tabularnewline
126 & 13 & 14.3557260713659 & -1.35572607136588 \tabularnewline
127 & 17 & 13.9517540560195 & 3.04824594398054 \tabularnewline
128 & 17 & 14.0325484590887 & 2.96745154091126 \tabularnewline
129 & 19 & 13.8709596529502 & 5.12904034704983 \tabularnewline
130 & 15 & 14.0325484590887 & 0.967451540911257 \tabularnewline
131 & 13 & 13.9517540560195 & -0.951754056019459 \tabularnewline
132 & 9 & 14.0325484590887 & -5.03254845908874 \tabularnewline
133 & 15 & 13.7901652498809 & 1.20983475011911 \tabularnewline
134 & 15 & 14.0325484590887 & 0.967451540911257 \tabularnewline
135 & 15 & 14.113342862158 & 0.886657137841972 \tabularnewline
136 & 16 & 14.113342862158 & 1.88665713784197 \tabularnewline
137 & 11 & 13.8709596529502 & -2.87095965295017 \tabularnewline
138 & 14 & 14.0325484590887 & -0.0325484590887432 \tabularnewline
139 & 11 & 13.9517540560195 & -2.95175405601946 \tabularnewline
140 & 15 & 13.7901652498809 & 1.20983475011911 \tabularnewline
141 & 13 & 13.8709596529502 & -0.870959652950174 \tabularnewline
142 & 15 & 13.7901652498809 & 1.20983475011911 \tabularnewline
143 & 16 & 13.8709596529502 & 2.12904034704983 \tabularnewline
144 & 14 & 14.3557260713659 & -0.355726071365881 \tabularnewline
145 & 15 & 14.0325484590887 & 0.967451540911257 \tabularnewline
146 & 16 & 13.7901652498809 & 2.20983475011911 \tabularnewline
147 & 16 & 14.1941372652273 & 1.80586273477269 \tabularnewline
148 & 11 & 14.113342862158 & -3.11334286215803 \tabularnewline
149 & 12 & 14.113342862158 & -2.11334286215803 \tabularnewline
150 & 9 & 13.8709596529502 & -4.87095965295017 \tabularnewline
151 & 16 & 13.7093708468116 & 2.29062915318839 \tabularnewline
152 & 13 & 14.1941372652273 & -1.19413726522731 \tabularnewline
153 & 16 & 13.8709596529502 & 2.12904034704983 \tabularnewline
154 & 12 & 13.6285764437423 & -1.62857644374232 \tabularnewline
155 & 9 & 13.7901652498809 & -4.79016524988089 \tabularnewline
156 & 13 & 13.7901652498809 & -0.79016524988089 \tabularnewline
157 & 13 & 14.3557260713659 & -1.35572607136588 \tabularnewline
158 & 14 & 13.6285764437423 & 0.371423556257679 \tabularnewline
159 & 19 & 13.8709596529502 & 5.12904034704983 \tabularnewline
160 & 13 & 14.0325484590887 & -1.03254845908874 \tabularnewline
161 & 12 & 13.7901652498809 & -1.79016524988089 \tabularnewline
162 & 13 & 13.7901652498809 & -0.79016524988089 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145377&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]14[/C][C]14.1133428621581[/C][C]-0.113342862158066[/C][/ROW]
[ROW][C]2[/C][C]18[/C][C]14.0325484590887[/C][C]3.96745154091126[/C][/ROW]
[ROW][C]3[/C][C]11[/C][C]14.3557260713659[/C][C]-3.35572607136588[/C][/ROW]
[ROW][C]4[/C][C]12[/C][C]13.6285764437423[/C][C]-1.62857644374232[/C][/ROW]
[ROW][C]5[/C][C]16[/C][C]14.1941372652273[/C][C]1.80586273477269[/C][/ROW]
[ROW][C]6[/C][C]18[/C][C]13.9517540560195[/C][C]4.04824594398054[/C][/ROW]
[ROW][C]7[/C][C]14[/C][C]14.113342862158[/C][C]-0.113342862158028[/C][/ROW]
[ROW][C]8[/C][C]14[/C][C]14.2749316682966[/C][C]-0.274931668296597[/C][/ROW]
[ROW][C]9[/C][C]15[/C][C]14.113342862158[/C][C]0.886657137841972[/C][/ROW]
[ROW][C]10[/C][C]15[/C][C]13.6285764437423[/C][C]1.37142355625768[/C][/ROW]
[ROW][C]11[/C][C]17[/C][C]13.9517540560195[/C][C]3.04824594398054[/C][/ROW]
[ROW][C]12[/C][C]19[/C][C]14.113342862158[/C][C]4.88665713784197[/C][/ROW]
[ROW][C]13[/C][C]10[/C][C]14.113342862158[/C][C]-4.11334286215803[/C][/ROW]
[ROW][C]14[/C][C]16[/C][C]14.0325484590887[/C][C]1.96745154091126[/C][/ROW]
[ROW][C]15[/C][C]18[/C][C]14.3557260713659[/C][C]3.64427392863412[/C][/ROW]
[ROW][C]16[/C][C]14[/C][C]14.113342862158[/C][C]-0.113342862158028[/C][/ROW]
[ROW][C]17[/C][C]14[/C][C]13.9517540560195[/C][C]0.0482459439805413[/C][/ROW]
[ROW][C]18[/C][C]17[/C][C]14.113342862158[/C][C]2.88665713784197[/C][/ROW]
[ROW][C]19[/C][C]14[/C][C]14.0325484590887[/C][C]-0.0325484590887432[/C][/ROW]
[ROW][C]20[/C][C]16[/C][C]14.113342862158[/C][C]1.88665713784197[/C][/ROW]
[ROW][C]21[/C][C]18[/C][C]14.0325484590887[/C][C]3.96745154091126[/C][/ROW]
[ROW][C]22[/C][C]11[/C][C]14.113342862158[/C][C]-3.11334286215803[/C][/ROW]
[ROW][C]23[/C][C]14[/C][C]14.1941372652273[/C][C]-0.194137265227312[/C][/ROW]
[ROW][C]24[/C][C]12[/C][C]14.0325484590887[/C][C]-2.03254845908874[/C][/ROW]
[ROW][C]25[/C][C]17[/C][C]13.8709596529502[/C][C]3.12904034704983[/C][/ROW]
[ROW][C]26[/C][C]9[/C][C]14.1941372652273[/C][C]-5.19413726522731[/C][/ROW]
[ROW][C]27[/C][C]16[/C][C]13.9517540560195[/C][C]2.04824594398054[/C][/ROW]
[ROW][C]28[/C][C]14[/C][C]14.2749316682966[/C][C]-0.274931668296597[/C][/ROW]
[ROW][C]29[/C][C]15[/C][C]14.113342862158[/C][C]0.886657137841972[/C][/ROW]
[ROW][C]30[/C][C]11[/C][C]13.9517540560195[/C][C]-2.95175405601946[/C][/ROW]
[ROW][C]31[/C][C]16[/C][C]14.113342862158[/C][C]1.88665713784197[/C][/ROW]
[ROW][C]32[/C][C]13[/C][C]13.7901652498809[/C][C]-0.79016524988089[/C][/ROW]
[ROW][C]33[/C][C]17[/C][C]13.9517540560195[/C][C]3.04824594398054[/C][/ROW]
[ROW][C]34[/C][C]15[/C][C]14.113342862158[/C][C]0.886657137841972[/C][/ROW]
[ROW][C]35[/C][C]14[/C][C]14.113342862158[/C][C]-0.113342862158028[/C][/ROW]
[ROW][C]36[/C][C]16[/C][C]13.7093708468116[/C][C]2.29062915318839[/C][/ROW]
[ROW][C]37[/C][C]9[/C][C]13.6285764437423[/C][C]-4.62857644374232[/C][/ROW]
[ROW][C]38[/C][C]15[/C][C]14.113342862158[/C][C]0.886657137841972[/C][/ROW]
[ROW][C]39[/C][C]17[/C][C]13.9517540560195[/C][C]3.04824594398054[/C][/ROW]
[ROW][C]40[/C][C]13[/C][C]13.9517540560195[/C][C]-0.951754056019459[/C][/ROW]
[ROW][C]41[/C][C]15[/C][C]13.9517540560195[/C][C]1.04824594398054[/C][/ROW]
[ROW][C]42[/C][C]16[/C][C]14.113342862158[/C][C]1.88665713784197[/C][/ROW]
[ROW][C]43[/C][C]16[/C][C]14.3557260713659[/C][C]1.64427392863412[/C][/ROW]
[ROW][C]44[/C][C]12[/C][C]13.9517540560195[/C][C]-1.95175405601946[/C][/ROW]
[ROW][C]45[/C][C]12[/C][C]13.9517540560195[/C][C]-1.95175405601946[/C][/ROW]
[ROW][C]46[/C][C]11[/C][C]14.113342862158[/C][C]-3.11334286215803[/C][/ROW]
[ROW][C]47[/C][C]15[/C][C]14.1941372652273[/C][C]0.805862734772688[/C][/ROW]
[ROW][C]48[/C][C]15[/C][C]14.0325484590887[/C][C]0.967451540911257[/C][/ROW]
[ROW][C]49[/C][C]17[/C][C]14.0325484590887[/C][C]2.96745154091126[/C][/ROW]
[ROW][C]50[/C][C]13[/C][C]14.113342862158[/C][C]-1.11334286215803[/C][/ROW]
[ROW][C]51[/C][C]16[/C][C]14.2749316682966[/C][C]1.7250683317034[/C][/ROW]
[ROW][C]52[/C][C]14[/C][C]13.9517540560195[/C][C]0.0482459439805413[/C][/ROW]
[ROW][C]53[/C][C]11[/C][C]14.113342862158[/C][C]-3.11334286215803[/C][/ROW]
[ROW][C]54[/C][C]12[/C][C]14.1941372652273[/C][C]-2.19413726522731[/C][/ROW]
[ROW][C]55[/C][C]12[/C][C]13.547782040673[/C][C]-1.54778204067304[/C][/ROW]
[ROW][C]56[/C][C]15[/C][C]13.6285764437423[/C][C]1.37142355625768[/C][/ROW]
[ROW][C]57[/C][C]16[/C][C]14.113342862158[/C][C]1.88665713784197[/C][/ROW]
[ROW][C]58[/C][C]15[/C][C]14.113342862158[/C][C]0.886657137841972[/C][/ROW]
[ROW][C]59[/C][C]12[/C][C]14.0325484590887[/C][C]-2.03254845908874[/C][/ROW]
[ROW][C]60[/C][C]12[/C][C]13.9517540560195[/C][C]-1.95175405601946[/C][/ROW]
[ROW][C]61[/C][C]8[/C][C]13.7093708468116[/C][C]-5.7093708468116[/C][/ROW]
[ROW][C]62[/C][C]13[/C][C]14.113342862158[/C][C]-1.11334286215803[/C][/ROW]
[ROW][C]63[/C][C]11[/C][C]14.2749316682966[/C][C]-3.2749316682966[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]14.0325484590887[/C][C]-0.0325484590887432[/C][/ROW]
[ROW][C]65[/C][C]15[/C][C]14.113342862158[/C][C]0.886657137841972[/C][/ROW]
[ROW][C]66[/C][C]10[/C][C]14.1941372652273[/C][C]-4.19413726522731[/C][/ROW]
[ROW][C]67[/C][C]11[/C][C]14.2749316682966[/C][C]-3.2749316682966[/C][/ROW]
[ROW][C]68[/C][C]12[/C][C]14.0325484590887[/C][C]-2.03254845908874[/C][/ROW]
[ROW][C]69[/C][C]15[/C][C]14.113342862158[/C][C]0.886657137841972[/C][/ROW]
[ROW][C]70[/C][C]15[/C][C]14.113342862158[/C][C]0.886657137841972[/C][/ROW]
[ROW][C]71[/C][C]14[/C][C]13.7901652498809[/C][C]0.20983475011911[/C][/ROW]
[ROW][C]72[/C][C]16[/C][C]14.0325484590887[/C][C]1.96745154091126[/C][/ROW]
[ROW][C]73[/C][C]15[/C][C]14.2749316682966[/C][C]0.725068331703403[/C][/ROW]
[ROW][C]74[/C][C]15[/C][C]14.2749316682966[/C][C]0.725068331703403[/C][/ROW]
[ROW][C]75[/C][C]13[/C][C]14.113342862158[/C][C]-1.11334286215803[/C][/ROW]
[ROW][C]76[/C][C]12[/C][C]13.8709596529502[/C][C]-1.87095965295017[/C][/ROW]
[ROW][C]77[/C][C]17[/C][C]14.1941372652273[/C][C]2.80586273477269[/C][/ROW]
[ROW][C]78[/C][C]13[/C][C]14.0325484590887[/C][C]-1.03254845908874[/C][/ROW]
[ROW][C]79[/C][C]15[/C][C]14.113342862158[/C][C]0.886657137841972[/C][/ROW]
[ROW][C]80[/C][C]13[/C][C]14.113342862158[/C][C]-1.11334286215803[/C][/ROW]
[ROW][C]81[/C][C]15[/C][C]14.113342862158[/C][C]0.886657137841972[/C][/ROW]
[ROW][C]82[/C][C]16[/C][C]14.113342862158[/C][C]1.88665713784197[/C][/ROW]
[ROW][C]83[/C][C]15[/C][C]14.113342862158[/C][C]0.886657137841972[/C][/ROW]
[ROW][C]84[/C][C]16[/C][C]14.113342862158[/C][C]1.88665713784197[/C][/ROW]
[ROW][C]85[/C][C]15[/C][C]14.0325484590887[/C][C]0.967451540911257[/C][/ROW]
[ROW][C]86[/C][C]14[/C][C]13.9517540560195[/C][C]0.0482459439805413[/C][/ROW]
[ROW][C]87[/C][C]15[/C][C]13.8709596529502[/C][C]1.12904034704983[/C][/ROW]
[ROW][C]88[/C][C]14[/C][C]14.113342862158[/C][C]-0.113342862158028[/C][/ROW]
[ROW][C]89[/C][C]13[/C][C]14.113342862158[/C][C]-1.11334286215803[/C][/ROW]
[ROW][C]90[/C][C]7[/C][C]14.113342862158[/C][C]-7.11334286215803[/C][/ROW]
[ROW][C]91[/C][C]17[/C][C]13.8709596529502[/C][C]3.12904034704983[/C][/ROW]
[ROW][C]92[/C][C]13[/C][C]14.3557260713659[/C][C]-1.35572607136588[/C][/ROW]
[ROW][C]93[/C][C]15[/C][C]14.113342862158[/C][C]0.886657137841972[/C][/ROW]
[ROW][C]94[/C][C]14[/C][C]14.113342862158[/C][C]-0.113342862158028[/C][/ROW]
[ROW][C]95[/C][C]13[/C][C]14.113342862158[/C][C]-1.11334286215803[/C][/ROW]
[ROW][C]96[/C][C]16[/C][C]13.9517540560195[/C][C]2.04824594398054[/C][/ROW]
[ROW][C]97[/C][C]12[/C][C]14.1941372652273[/C][C]-2.19413726522731[/C][/ROW]
[ROW][C]98[/C][C]14[/C][C]13.8709596529502[/C][C]0.129040347049826[/C][/ROW]
[ROW][C]99[/C][C]17[/C][C]14.113342862158[/C][C]2.88665713784197[/C][/ROW]
[ROW][C]100[/C][C]15[/C][C]13.9517540560195[/C][C]1.04824594398054[/C][/ROW]
[ROW][C]101[/C][C]17[/C][C]14.2749316682966[/C][C]2.7250683317034[/C][/ROW]
[ROW][C]102[/C][C]12[/C][C]14.0325484590887[/C][C]-2.03254845908874[/C][/ROW]
[ROW][C]103[/C][C]16[/C][C]14.3557260713659[/C][C]1.64427392863412[/C][/ROW]
[ROW][C]104[/C][C]11[/C][C]14.0325484590887[/C][C]-3.03254845908874[/C][/ROW]
[ROW][C]105[/C][C]15[/C][C]14.0325484590887[/C][C]0.967451540911257[/C][/ROW]
[ROW][C]106[/C][C]9[/C][C]14.113342862158[/C][C]-5.11334286215803[/C][/ROW]
[ROW][C]107[/C][C]16[/C][C]14.113342862158[/C][C]1.88665713784197[/C][/ROW]
[ROW][C]108[/C][C]15[/C][C]14.113342862158[/C][C]0.886657137841972[/C][/ROW]
[ROW][C]109[/C][C]10[/C][C]14.0325484590887[/C][C]-4.03254845908874[/C][/ROW]
[ROW][C]110[/C][C]10[/C][C]13.7093708468116[/C][C]-3.70937084681161[/C][/ROW]
[ROW][C]111[/C][C]15[/C][C]14.113342862158[/C][C]0.886657137841972[/C][/ROW]
[ROW][C]112[/C][C]11[/C][C]14.2749316682966[/C][C]-3.2749316682966[/C][/ROW]
[ROW][C]113[/C][C]13[/C][C]14.0325484590887[/C][C]-1.03254845908874[/C][/ROW]
[ROW][C]114[/C][C]14[/C][C]14.0325484590887[/C][C]-0.0325484590887432[/C][/ROW]
[ROW][C]115[/C][C]18[/C][C]13.9517540560195[/C][C]4.04824594398054[/C][/ROW]
[ROW][C]116[/C][C]16[/C][C]14.1941372652273[/C][C]1.80586273477269[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]14.1941372652273[/C][C]-0.194137265227312[/C][/ROW]
[ROW][C]118[/C][C]14[/C][C]13.7901652498809[/C][C]0.20983475011911[/C][/ROW]
[ROW][C]119[/C][C]14[/C][C]14.0325484590887[/C][C]-0.0325484590887432[/C][/ROW]
[ROW][C]120[/C][C]14[/C][C]14.113342862158[/C][C]-0.113342862158028[/C][/ROW]
[ROW][C]121[/C][C]12[/C][C]14.0325484590887[/C][C]-2.03254845908874[/C][/ROW]
[ROW][C]122[/C][C]14[/C][C]14.1941372652273[/C][C]-0.194137265227312[/C][/ROW]
[ROW][C]123[/C][C]15[/C][C]14.113342862158[/C][C]0.886657137841972[/C][/ROW]
[ROW][C]124[/C][C]15[/C][C]14.2749316682966[/C][C]0.725068331703403[/C][/ROW]
[ROW][C]125[/C][C]15[/C][C]14.1941372652273[/C][C]0.805862734772688[/C][/ROW]
[ROW][C]126[/C][C]13[/C][C]14.3557260713659[/C][C]-1.35572607136588[/C][/ROW]
[ROW][C]127[/C][C]17[/C][C]13.9517540560195[/C][C]3.04824594398054[/C][/ROW]
[ROW][C]128[/C][C]17[/C][C]14.0325484590887[/C][C]2.96745154091126[/C][/ROW]
[ROW][C]129[/C][C]19[/C][C]13.8709596529502[/C][C]5.12904034704983[/C][/ROW]
[ROW][C]130[/C][C]15[/C][C]14.0325484590887[/C][C]0.967451540911257[/C][/ROW]
[ROW][C]131[/C][C]13[/C][C]13.9517540560195[/C][C]-0.951754056019459[/C][/ROW]
[ROW][C]132[/C][C]9[/C][C]14.0325484590887[/C][C]-5.03254845908874[/C][/ROW]
[ROW][C]133[/C][C]15[/C][C]13.7901652498809[/C][C]1.20983475011911[/C][/ROW]
[ROW][C]134[/C][C]15[/C][C]14.0325484590887[/C][C]0.967451540911257[/C][/ROW]
[ROW][C]135[/C][C]15[/C][C]14.113342862158[/C][C]0.886657137841972[/C][/ROW]
[ROW][C]136[/C][C]16[/C][C]14.113342862158[/C][C]1.88665713784197[/C][/ROW]
[ROW][C]137[/C][C]11[/C][C]13.8709596529502[/C][C]-2.87095965295017[/C][/ROW]
[ROW][C]138[/C][C]14[/C][C]14.0325484590887[/C][C]-0.0325484590887432[/C][/ROW]
[ROW][C]139[/C][C]11[/C][C]13.9517540560195[/C][C]-2.95175405601946[/C][/ROW]
[ROW][C]140[/C][C]15[/C][C]13.7901652498809[/C][C]1.20983475011911[/C][/ROW]
[ROW][C]141[/C][C]13[/C][C]13.8709596529502[/C][C]-0.870959652950174[/C][/ROW]
[ROW][C]142[/C][C]15[/C][C]13.7901652498809[/C][C]1.20983475011911[/C][/ROW]
[ROW][C]143[/C][C]16[/C][C]13.8709596529502[/C][C]2.12904034704983[/C][/ROW]
[ROW][C]144[/C][C]14[/C][C]14.3557260713659[/C][C]-0.355726071365881[/C][/ROW]
[ROW][C]145[/C][C]15[/C][C]14.0325484590887[/C][C]0.967451540911257[/C][/ROW]
[ROW][C]146[/C][C]16[/C][C]13.7901652498809[/C][C]2.20983475011911[/C][/ROW]
[ROW][C]147[/C][C]16[/C][C]14.1941372652273[/C][C]1.80586273477269[/C][/ROW]
[ROW][C]148[/C][C]11[/C][C]14.113342862158[/C][C]-3.11334286215803[/C][/ROW]
[ROW][C]149[/C][C]12[/C][C]14.113342862158[/C][C]-2.11334286215803[/C][/ROW]
[ROW][C]150[/C][C]9[/C][C]13.8709596529502[/C][C]-4.87095965295017[/C][/ROW]
[ROW][C]151[/C][C]16[/C][C]13.7093708468116[/C][C]2.29062915318839[/C][/ROW]
[ROW][C]152[/C][C]13[/C][C]14.1941372652273[/C][C]-1.19413726522731[/C][/ROW]
[ROW][C]153[/C][C]16[/C][C]13.8709596529502[/C][C]2.12904034704983[/C][/ROW]
[ROW][C]154[/C][C]12[/C][C]13.6285764437423[/C][C]-1.62857644374232[/C][/ROW]
[ROW][C]155[/C][C]9[/C][C]13.7901652498809[/C][C]-4.79016524988089[/C][/ROW]
[ROW][C]156[/C][C]13[/C][C]13.7901652498809[/C][C]-0.79016524988089[/C][/ROW]
[ROW][C]157[/C][C]13[/C][C]14.3557260713659[/C][C]-1.35572607136588[/C][/ROW]
[ROW][C]158[/C][C]14[/C][C]13.6285764437423[/C][C]0.371423556257679[/C][/ROW]
[ROW][C]159[/C][C]19[/C][C]13.8709596529502[/C][C]5.12904034704983[/C][/ROW]
[ROW][C]160[/C][C]13[/C][C]14.0325484590887[/C][C]-1.03254845908874[/C][/ROW]
[ROW][C]161[/C][C]12[/C][C]13.7901652498809[/C][C]-1.79016524988089[/C][/ROW]
[ROW][C]162[/C][C]13[/C][C]13.7901652498809[/C][C]-0.79016524988089[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145377&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145377&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
11414.1133428621581-0.113342862158066
21814.03254845908873.96745154091126
31114.3557260713659-3.35572607136588
41213.6285764437423-1.62857644374232
51614.19413726522731.80586273477269
61813.95175405601954.04824594398054
71414.113342862158-0.113342862158028
81414.2749316682966-0.274931668296597
91514.1133428621580.886657137841972
101513.62857644374231.37142355625768
111713.95175405601953.04824594398054
121914.1133428621584.88665713784197
131014.113342862158-4.11334286215803
141614.03254845908871.96745154091126
151814.35572607136593.64427392863412
161414.113342862158-0.113342862158028
171413.95175405601950.0482459439805413
181714.1133428621582.88665713784197
191414.0325484590887-0.0325484590887432
201614.1133428621581.88665713784197
211814.03254845908873.96745154091126
221114.113342862158-3.11334286215803
231414.1941372652273-0.194137265227312
241214.0325484590887-2.03254845908874
251713.87095965295023.12904034704983
26914.1941372652273-5.19413726522731
271613.95175405601952.04824594398054
281414.2749316682966-0.274931668296597
291514.1133428621580.886657137841972
301113.9517540560195-2.95175405601946
311614.1133428621581.88665713784197
321313.7901652498809-0.79016524988089
331713.95175405601953.04824594398054
341514.1133428621580.886657137841972
351414.113342862158-0.113342862158028
361613.70937084681162.29062915318839
37913.6285764437423-4.62857644374232
381514.1133428621580.886657137841972
391713.95175405601953.04824594398054
401313.9517540560195-0.951754056019459
411513.95175405601951.04824594398054
421614.1133428621581.88665713784197
431614.35572607136591.64427392863412
441213.9517540560195-1.95175405601946
451213.9517540560195-1.95175405601946
461114.113342862158-3.11334286215803
471514.19413726522730.805862734772688
481514.03254845908870.967451540911257
491714.03254845908872.96745154091126
501314.113342862158-1.11334286215803
511614.27493166829661.7250683317034
521413.95175405601950.0482459439805413
531114.113342862158-3.11334286215803
541214.1941372652273-2.19413726522731
551213.547782040673-1.54778204067304
561513.62857644374231.37142355625768
571614.1133428621581.88665713784197
581514.1133428621580.886657137841972
591214.0325484590887-2.03254845908874
601213.9517540560195-1.95175405601946
61813.7093708468116-5.7093708468116
621314.113342862158-1.11334286215803
631114.2749316682966-3.2749316682966
641414.0325484590887-0.0325484590887432
651514.1133428621580.886657137841972
661014.1941372652273-4.19413726522731
671114.2749316682966-3.2749316682966
681214.0325484590887-2.03254845908874
691514.1133428621580.886657137841972
701514.1133428621580.886657137841972
711413.79016524988090.20983475011911
721614.03254845908871.96745154091126
731514.27493166829660.725068331703403
741514.27493166829660.725068331703403
751314.113342862158-1.11334286215803
761213.8709596529502-1.87095965295017
771714.19413726522732.80586273477269
781314.0325484590887-1.03254845908874
791514.1133428621580.886657137841972
801314.113342862158-1.11334286215803
811514.1133428621580.886657137841972
821614.1133428621581.88665713784197
831514.1133428621580.886657137841972
841614.1133428621581.88665713784197
851514.03254845908870.967451540911257
861413.95175405601950.0482459439805413
871513.87095965295021.12904034704983
881414.113342862158-0.113342862158028
891314.113342862158-1.11334286215803
90714.113342862158-7.11334286215803
911713.87095965295023.12904034704983
921314.3557260713659-1.35572607136588
931514.1133428621580.886657137841972
941414.113342862158-0.113342862158028
951314.113342862158-1.11334286215803
961613.95175405601952.04824594398054
971214.1941372652273-2.19413726522731
981413.87095965295020.129040347049826
991714.1133428621582.88665713784197
1001513.95175405601951.04824594398054
1011714.27493166829662.7250683317034
1021214.0325484590887-2.03254845908874
1031614.35572607136591.64427392863412
1041114.0325484590887-3.03254845908874
1051514.03254845908870.967451540911257
106914.113342862158-5.11334286215803
1071614.1133428621581.88665713784197
1081514.1133428621580.886657137841972
1091014.0325484590887-4.03254845908874
1101013.7093708468116-3.70937084681161
1111514.1133428621580.886657137841972
1121114.2749316682966-3.2749316682966
1131314.0325484590887-1.03254845908874
1141414.0325484590887-0.0325484590887432
1151813.95175405601954.04824594398054
1161614.19413726522731.80586273477269
1171414.1941372652273-0.194137265227312
1181413.79016524988090.20983475011911
1191414.0325484590887-0.0325484590887432
1201414.113342862158-0.113342862158028
1211214.0325484590887-2.03254845908874
1221414.1941372652273-0.194137265227312
1231514.1133428621580.886657137841972
1241514.27493166829660.725068331703403
1251514.19413726522730.805862734772688
1261314.3557260713659-1.35572607136588
1271713.95175405601953.04824594398054
1281714.03254845908872.96745154091126
1291913.87095965295025.12904034704983
1301514.03254845908870.967451540911257
1311313.9517540560195-0.951754056019459
132914.0325484590887-5.03254845908874
1331513.79016524988091.20983475011911
1341514.03254845908870.967451540911257
1351514.1133428621580.886657137841972
1361614.1133428621581.88665713784197
1371113.8709596529502-2.87095965295017
1381414.0325484590887-0.0325484590887432
1391113.9517540560195-2.95175405601946
1401513.79016524988091.20983475011911
1411313.8709596529502-0.870959652950174
1421513.79016524988091.20983475011911
1431613.87095965295022.12904034704983
1441414.3557260713659-0.355726071365881
1451514.03254845908870.967451540911257
1461613.79016524988092.20983475011911
1471614.19413726522731.80586273477269
1481114.113342862158-3.11334286215803
1491214.113342862158-2.11334286215803
150913.8709596529502-4.87095965295017
1511613.70937084681162.29062915318839
1521314.1941372652273-1.19413726522731
1531613.87095965295022.12904034704983
1541213.6285764437423-1.62857644374232
155913.7901652498809-4.79016524988089
1561313.7901652498809-0.79016524988089
1571314.3557260713659-1.35572607136588
1581413.62857644374230.371423556257679
1591913.87095965295025.12904034704983
1601314.0325484590887-1.03254845908874
1611213.7901652498809-1.79016524988089
1621313.7901652498809-0.79016524988089







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
50.8850414762654360.2299170474691280.114958523734564
60.9143542998562270.1712914002875460.0856457001437731
70.8561293100842310.2877413798315380.143870689915769
80.7791665058098590.4416669883802810.220833494190141
90.6889627900134850.6220744199730290.311037209986515
100.5904006464065780.8191987071868440.409599353593422
110.5713919462494230.8572161075011550.428608053750577
120.7285469232140010.5429061535719980.271453076785999
130.8863389593518210.2273220812963580.113661040648179
140.8522383711716520.2955232576566960.147761628828348
150.8779798822117280.2440402355765450.122020117788272
160.8419200005820170.3161599988359660.158079999417983
170.7993868178351120.4012263643297760.200613182164888
180.7841926778314360.4316146443371270.215807322168564
190.7372625693056930.5254748613886150.262737430694307
200.6894107432586720.6211785134826570.310589256741328
210.7255722370559770.5488555258880460.274427762944023
220.8125253378534690.3749493242930620.187474662146531
230.7723477843217290.4553044313565410.227652215678271
240.78742261978450.4251547604310.2125773802155
250.7825098393128410.4349803213743170.217490160687159
260.9227126727503860.1545746544992280.0772873272496138
270.9066112280939630.1867775438120740.0933887719060371
280.8799544210103820.2400911579792360.120045578989618
290.8496175196185320.3007649607629350.150382480381468
300.887469656490620.2250606870187590.11253034350938
310.8703032159971640.2593935680056710.129696784002836
320.8536380045657330.2927239908685350.146361995434267
330.8561281380819410.2877437238361180.143871861918059
340.8245282078540240.3509435842919510.175471792145976
350.7889913556206580.4220172887586850.211008644379342
360.7648779486038490.4702441027923020.235122051396151
370.8939762030769880.2120475938460250.106023796923012
380.8696083107558390.2607833784883210.130391689244161
390.8763780877995290.2472438244009430.123621912200471
400.8573381083263950.285323783347210.142661891673605
410.8296048748642980.3407902502714030.170395125135702
420.8104193464583240.3791613070833530.189580653541676
430.7855404306515640.4289191386968710.214459569348436
440.7835695052967860.4328609894064290.216430494703214
450.7799221668100510.4401556663798990.220077833189949
460.8163268593560640.3673462812878730.183673140643936
470.7840387118345150.4319225763309690.215961288165485
480.7503745544578090.4992508910843810.249625445542191
490.7627749197632360.4744501604735280.237225080236764
500.737861403975460.524277192049080.26213859602454
510.7135542682638130.5728914634723740.286445731736187
520.6719682320951610.6560635358096780.328031767904839
530.7152667058996050.569466588200790.284733294100395
540.7165359253650020.5669281492699960.283464074634998
550.6953644056682350.6092711886635290.304635594331765
560.6656455807885520.6687088384228960.334354419211448
570.6456218906053980.7087562187892040.354378109394602
580.6057054932934110.7885890134131790.394294506706589
590.6001352121639320.7997295756721360.399864787836068
600.590206289881920.819587420236160.40979371011808
610.785099457325990.4298010853480210.21490054267401
620.7597596732613340.4804806534773320.240240326738666
630.7970015026366440.4059969947267110.202998497363356
640.7629892587044030.4740214825911930.237010741295597
650.7309083552344560.5381832895310890.269091644765544
660.8079177913077730.3841644173844530.192082208692227
670.8358692123383570.3282615753232870.164130787661643
680.8282153193586670.3435693612826650.171784680641333
690.8020327159178450.3959345681643110.197967284082155
700.7736054065981030.4527891868037950.226394593401897
710.7382579236848010.5234841526303970.261742076315199
720.7265377173238620.5469245653522760.273462282676138
730.6912773679538670.6174452640922670.308722632046133
740.6542885507367470.6914228985265070.345711449263253
750.6213501910667210.7572996178665580.378649808933279
760.6051239170588160.7897521658823670.394876082941184
770.6232107941222940.7535784117554110.376789205877706
780.5879605618825480.8240788762349050.412039438117452
790.5499928583475810.9000142833048370.450007141652418
800.5149907900732390.9700184198535230.485009209926761
810.476509912103770.9530198242075410.52349008789623
820.4608916967395840.9217833934791680.539108303260416
830.4230279666949020.8460559333898050.576972033305098
840.4081500097270360.8163000194540720.591849990272964
850.372501206069650.7450024121393010.62749879393035
860.3305824689902230.6611649379804460.669417531009777
870.2997076165773710.5994152331547420.700292383422629
880.2618823718083320.5237647436166640.738117628191668
890.2337769765051170.4675539530102340.766223023494883
900.5722717436312370.8554565127375260.427728256368763
910.6063217118627060.7873565762745870.393678288137294
920.5756265168897760.8487469662204470.424373483110224
930.5373538876199490.9252922247601030.462646112380051
940.4918058112212740.9836116224425490.508194188778726
950.4557200346134820.9114400692269640.544279965386518
960.4451191996437840.8902383992875680.554880800356216
970.4375288265469190.8750576530938390.562471173453081
980.3927384551525310.7854769103050620.607261544847469
990.41626941957770.83253883915540.5837305804223
1000.3810250197391140.7620500394782280.618974980260886
1010.4001680695069560.8003361390139120.599831930493044
1020.3861050654128250.772210130825650.613894934587175
1030.3684273933360210.7368547866720420.631572606663979
1040.3930766541065920.7861533082131840.606923345893408
1050.3571804428082020.7143608856164050.642819557191798
1060.5201354388423470.9597291223153050.479864561157653
1070.5056081453108460.9887837093783070.494391854689154
1080.4662349948885780.9324699897771570.533765005111422
1090.5542670105982630.8914659788034750.445732989401737
1100.6326556396542160.7346887206915680.367344360345784
1110.5938140286104120.8123719427791770.406185971389588
1120.6311996258034270.7376007483931460.368800374196573
1130.593292203359130.813415593281740.40670779664087
1140.5443693625737820.9112612748524360.455630637426218
1150.638022064278610.723955871442780.36197793572139
1160.6223852016673380.7552295966653240.377614798332662
1170.5732471390328490.8535057219343030.426752860967151
1180.5227741882237320.9544516235525360.477225811776268
1190.4714186441556030.9428372883112060.528581355844397
1200.4203057960002430.8406115920004860.579694203999757
1210.4052283765391480.8104567530782950.594771623460852
1220.3554045317545880.7108090635091750.644595468245412
1230.3151012153814570.6302024307629150.684898784618543
1240.2765495643026790.5530991286053590.723450435697321
1250.2413290634634180.4826581269268360.758670936536582
1260.2083711874603290.4167423749206570.791628812539671
1270.23359508973210.4671901794641990.7664049102679
1280.2638219494088660.5276438988177330.736178050591134
1290.4631682849571340.9263365699142680.536831715042866
1300.4244181989629750.8488363979259490.575581801037025
1310.3732019212539570.7464038425079130.626798078746043
1320.548438362526030.9031232749479410.451561637473971
1330.5072061856688720.9855876286622570.492793814331129
1340.4632128874296260.9264257748592520.536787112570374
1350.4197597498545440.8395194997090890.580240250145456
1360.4196484209563920.8392968419127840.580351579043608
1370.4336203448972870.8672406897945750.566379655102712
1380.3729763523803880.7459527047607760.627023647619612
1390.3885449099515540.7770898199031070.611455090048446
1400.3430999538843980.6861999077687960.656900046115602
1410.2876143919484080.5752287838968160.712385608051592
1420.2470318956185280.4940637912370570.752968104381472
1430.2447289586666530.4894579173333050.755271041333347
1440.1938949464931780.3877898929863560.806105053506822
1450.1643481385227410.3286962770454820.835651861477259
1460.1686903324358110.3373806648716210.831309667564189
1470.1882101180462630.3764202360925260.811789881953737
1480.1699235654042770.3398471308085550.830076434595723
1490.1324645101213910.2649290202427820.867535489878609
1500.2535984322525380.5071968645050770.746401567747462
1510.255115938052550.51023187610510.74488406194745
1520.1880693128951790.3761386257903590.811930687104821
1530.1927616858418830.3855233716837660.807238314158117
1540.1357846823083720.2715693646167440.864215317691628
1550.318780623411750.6375612468234990.68121937658825
1560.2231817309400270.4463634618800540.776818269059973
1570.1361600205175370.2723200410350740.863839979482463

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
5 & 0.885041476265436 & 0.229917047469128 & 0.114958523734564 \tabularnewline
6 & 0.914354299856227 & 0.171291400287546 & 0.0856457001437731 \tabularnewline
7 & 0.856129310084231 & 0.287741379831538 & 0.143870689915769 \tabularnewline
8 & 0.779166505809859 & 0.441666988380281 & 0.220833494190141 \tabularnewline
9 & 0.688962790013485 & 0.622074419973029 & 0.311037209986515 \tabularnewline
10 & 0.590400646406578 & 0.819198707186844 & 0.409599353593422 \tabularnewline
11 & 0.571391946249423 & 0.857216107501155 & 0.428608053750577 \tabularnewline
12 & 0.728546923214001 & 0.542906153571998 & 0.271453076785999 \tabularnewline
13 & 0.886338959351821 & 0.227322081296358 & 0.113661040648179 \tabularnewline
14 & 0.852238371171652 & 0.295523257656696 & 0.147761628828348 \tabularnewline
15 & 0.877979882211728 & 0.244040235576545 & 0.122020117788272 \tabularnewline
16 & 0.841920000582017 & 0.316159998835966 & 0.158079999417983 \tabularnewline
17 & 0.799386817835112 & 0.401226364329776 & 0.200613182164888 \tabularnewline
18 & 0.784192677831436 & 0.431614644337127 & 0.215807322168564 \tabularnewline
19 & 0.737262569305693 & 0.525474861388615 & 0.262737430694307 \tabularnewline
20 & 0.689410743258672 & 0.621178513482657 & 0.310589256741328 \tabularnewline
21 & 0.725572237055977 & 0.548855525888046 & 0.274427762944023 \tabularnewline
22 & 0.812525337853469 & 0.374949324293062 & 0.187474662146531 \tabularnewline
23 & 0.772347784321729 & 0.455304431356541 & 0.227652215678271 \tabularnewline
24 & 0.7874226197845 & 0.425154760431 & 0.2125773802155 \tabularnewline
25 & 0.782509839312841 & 0.434980321374317 & 0.217490160687159 \tabularnewline
26 & 0.922712672750386 & 0.154574654499228 & 0.0772873272496138 \tabularnewline
27 & 0.906611228093963 & 0.186777543812074 & 0.0933887719060371 \tabularnewline
28 & 0.879954421010382 & 0.240091157979236 & 0.120045578989618 \tabularnewline
29 & 0.849617519618532 & 0.300764960762935 & 0.150382480381468 \tabularnewline
30 & 0.88746965649062 & 0.225060687018759 & 0.11253034350938 \tabularnewline
31 & 0.870303215997164 & 0.259393568005671 & 0.129696784002836 \tabularnewline
32 & 0.853638004565733 & 0.292723990868535 & 0.146361995434267 \tabularnewline
33 & 0.856128138081941 & 0.287743723836118 & 0.143871861918059 \tabularnewline
34 & 0.824528207854024 & 0.350943584291951 & 0.175471792145976 \tabularnewline
35 & 0.788991355620658 & 0.422017288758685 & 0.211008644379342 \tabularnewline
36 & 0.764877948603849 & 0.470244102792302 & 0.235122051396151 \tabularnewline
37 & 0.893976203076988 & 0.212047593846025 & 0.106023796923012 \tabularnewline
38 & 0.869608310755839 & 0.260783378488321 & 0.130391689244161 \tabularnewline
39 & 0.876378087799529 & 0.247243824400943 & 0.123621912200471 \tabularnewline
40 & 0.857338108326395 & 0.28532378334721 & 0.142661891673605 \tabularnewline
41 & 0.829604874864298 & 0.340790250271403 & 0.170395125135702 \tabularnewline
42 & 0.810419346458324 & 0.379161307083353 & 0.189580653541676 \tabularnewline
43 & 0.785540430651564 & 0.428919138696871 & 0.214459569348436 \tabularnewline
44 & 0.783569505296786 & 0.432860989406429 & 0.216430494703214 \tabularnewline
45 & 0.779922166810051 & 0.440155666379899 & 0.220077833189949 \tabularnewline
46 & 0.816326859356064 & 0.367346281287873 & 0.183673140643936 \tabularnewline
47 & 0.784038711834515 & 0.431922576330969 & 0.215961288165485 \tabularnewline
48 & 0.750374554457809 & 0.499250891084381 & 0.249625445542191 \tabularnewline
49 & 0.762774919763236 & 0.474450160473528 & 0.237225080236764 \tabularnewline
50 & 0.73786140397546 & 0.52427719204908 & 0.26213859602454 \tabularnewline
51 & 0.713554268263813 & 0.572891463472374 & 0.286445731736187 \tabularnewline
52 & 0.671968232095161 & 0.656063535809678 & 0.328031767904839 \tabularnewline
53 & 0.715266705899605 & 0.56946658820079 & 0.284733294100395 \tabularnewline
54 & 0.716535925365002 & 0.566928149269996 & 0.283464074634998 \tabularnewline
55 & 0.695364405668235 & 0.609271188663529 & 0.304635594331765 \tabularnewline
56 & 0.665645580788552 & 0.668708838422896 & 0.334354419211448 \tabularnewline
57 & 0.645621890605398 & 0.708756218789204 & 0.354378109394602 \tabularnewline
58 & 0.605705493293411 & 0.788589013413179 & 0.394294506706589 \tabularnewline
59 & 0.600135212163932 & 0.799729575672136 & 0.399864787836068 \tabularnewline
60 & 0.59020628988192 & 0.81958742023616 & 0.40979371011808 \tabularnewline
61 & 0.78509945732599 & 0.429801085348021 & 0.21490054267401 \tabularnewline
62 & 0.759759673261334 & 0.480480653477332 & 0.240240326738666 \tabularnewline
63 & 0.797001502636644 & 0.405996994726711 & 0.202998497363356 \tabularnewline
64 & 0.762989258704403 & 0.474021482591193 & 0.237010741295597 \tabularnewline
65 & 0.730908355234456 & 0.538183289531089 & 0.269091644765544 \tabularnewline
66 & 0.807917791307773 & 0.384164417384453 & 0.192082208692227 \tabularnewline
67 & 0.835869212338357 & 0.328261575323287 & 0.164130787661643 \tabularnewline
68 & 0.828215319358667 & 0.343569361282665 & 0.171784680641333 \tabularnewline
69 & 0.802032715917845 & 0.395934568164311 & 0.197967284082155 \tabularnewline
70 & 0.773605406598103 & 0.452789186803795 & 0.226394593401897 \tabularnewline
71 & 0.738257923684801 & 0.523484152630397 & 0.261742076315199 \tabularnewline
72 & 0.726537717323862 & 0.546924565352276 & 0.273462282676138 \tabularnewline
73 & 0.691277367953867 & 0.617445264092267 & 0.308722632046133 \tabularnewline
74 & 0.654288550736747 & 0.691422898526507 & 0.345711449263253 \tabularnewline
75 & 0.621350191066721 & 0.757299617866558 & 0.378649808933279 \tabularnewline
76 & 0.605123917058816 & 0.789752165882367 & 0.394876082941184 \tabularnewline
77 & 0.623210794122294 & 0.753578411755411 & 0.376789205877706 \tabularnewline
78 & 0.587960561882548 & 0.824078876234905 & 0.412039438117452 \tabularnewline
79 & 0.549992858347581 & 0.900014283304837 & 0.450007141652418 \tabularnewline
80 & 0.514990790073239 & 0.970018419853523 & 0.485009209926761 \tabularnewline
81 & 0.47650991210377 & 0.953019824207541 & 0.52349008789623 \tabularnewline
82 & 0.460891696739584 & 0.921783393479168 & 0.539108303260416 \tabularnewline
83 & 0.423027966694902 & 0.846055933389805 & 0.576972033305098 \tabularnewline
84 & 0.408150009727036 & 0.816300019454072 & 0.591849990272964 \tabularnewline
85 & 0.37250120606965 & 0.745002412139301 & 0.62749879393035 \tabularnewline
86 & 0.330582468990223 & 0.661164937980446 & 0.669417531009777 \tabularnewline
87 & 0.299707616577371 & 0.599415233154742 & 0.700292383422629 \tabularnewline
88 & 0.261882371808332 & 0.523764743616664 & 0.738117628191668 \tabularnewline
89 & 0.233776976505117 & 0.467553953010234 & 0.766223023494883 \tabularnewline
90 & 0.572271743631237 & 0.855456512737526 & 0.427728256368763 \tabularnewline
91 & 0.606321711862706 & 0.787356576274587 & 0.393678288137294 \tabularnewline
92 & 0.575626516889776 & 0.848746966220447 & 0.424373483110224 \tabularnewline
93 & 0.537353887619949 & 0.925292224760103 & 0.462646112380051 \tabularnewline
94 & 0.491805811221274 & 0.983611622442549 & 0.508194188778726 \tabularnewline
95 & 0.455720034613482 & 0.911440069226964 & 0.544279965386518 \tabularnewline
96 & 0.445119199643784 & 0.890238399287568 & 0.554880800356216 \tabularnewline
97 & 0.437528826546919 & 0.875057653093839 & 0.562471173453081 \tabularnewline
98 & 0.392738455152531 & 0.785476910305062 & 0.607261544847469 \tabularnewline
99 & 0.4162694195777 & 0.8325388391554 & 0.5837305804223 \tabularnewline
100 & 0.381025019739114 & 0.762050039478228 & 0.618974980260886 \tabularnewline
101 & 0.400168069506956 & 0.800336139013912 & 0.599831930493044 \tabularnewline
102 & 0.386105065412825 & 0.77221013082565 & 0.613894934587175 \tabularnewline
103 & 0.368427393336021 & 0.736854786672042 & 0.631572606663979 \tabularnewline
104 & 0.393076654106592 & 0.786153308213184 & 0.606923345893408 \tabularnewline
105 & 0.357180442808202 & 0.714360885616405 & 0.642819557191798 \tabularnewline
106 & 0.520135438842347 & 0.959729122315305 & 0.479864561157653 \tabularnewline
107 & 0.505608145310846 & 0.988783709378307 & 0.494391854689154 \tabularnewline
108 & 0.466234994888578 & 0.932469989777157 & 0.533765005111422 \tabularnewline
109 & 0.554267010598263 & 0.891465978803475 & 0.445732989401737 \tabularnewline
110 & 0.632655639654216 & 0.734688720691568 & 0.367344360345784 \tabularnewline
111 & 0.593814028610412 & 0.812371942779177 & 0.406185971389588 \tabularnewline
112 & 0.631199625803427 & 0.737600748393146 & 0.368800374196573 \tabularnewline
113 & 0.59329220335913 & 0.81341559328174 & 0.40670779664087 \tabularnewline
114 & 0.544369362573782 & 0.911261274852436 & 0.455630637426218 \tabularnewline
115 & 0.63802206427861 & 0.72395587144278 & 0.36197793572139 \tabularnewline
116 & 0.622385201667338 & 0.755229596665324 & 0.377614798332662 \tabularnewline
117 & 0.573247139032849 & 0.853505721934303 & 0.426752860967151 \tabularnewline
118 & 0.522774188223732 & 0.954451623552536 & 0.477225811776268 \tabularnewline
119 & 0.471418644155603 & 0.942837288311206 & 0.528581355844397 \tabularnewline
120 & 0.420305796000243 & 0.840611592000486 & 0.579694203999757 \tabularnewline
121 & 0.405228376539148 & 0.810456753078295 & 0.594771623460852 \tabularnewline
122 & 0.355404531754588 & 0.710809063509175 & 0.644595468245412 \tabularnewline
123 & 0.315101215381457 & 0.630202430762915 & 0.684898784618543 \tabularnewline
124 & 0.276549564302679 & 0.553099128605359 & 0.723450435697321 \tabularnewline
125 & 0.241329063463418 & 0.482658126926836 & 0.758670936536582 \tabularnewline
126 & 0.208371187460329 & 0.416742374920657 & 0.791628812539671 \tabularnewline
127 & 0.2335950897321 & 0.467190179464199 & 0.7664049102679 \tabularnewline
128 & 0.263821949408866 & 0.527643898817733 & 0.736178050591134 \tabularnewline
129 & 0.463168284957134 & 0.926336569914268 & 0.536831715042866 \tabularnewline
130 & 0.424418198962975 & 0.848836397925949 & 0.575581801037025 \tabularnewline
131 & 0.373201921253957 & 0.746403842507913 & 0.626798078746043 \tabularnewline
132 & 0.54843836252603 & 0.903123274947941 & 0.451561637473971 \tabularnewline
133 & 0.507206185668872 & 0.985587628662257 & 0.492793814331129 \tabularnewline
134 & 0.463212887429626 & 0.926425774859252 & 0.536787112570374 \tabularnewline
135 & 0.419759749854544 & 0.839519499709089 & 0.580240250145456 \tabularnewline
136 & 0.419648420956392 & 0.839296841912784 & 0.580351579043608 \tabularnewline
137 & 0.433620344897287 & 0.867240689794575 & 0.566379655102712 \tabularnewline
138 & 0.372976352380388 & 0.745952704760776 & 0.627023647619612 \tabularnewline
139 & 0.388544909951554 & 0.777089819903107 & 0.611455090048446 \tabularnewline
140 & 0.343099953884398 & 0.686199907768796 & 0.656900046115602 \tabularnewline
141 & 0.287614391948408 & 0.575228783896816 & 0.712385608051592 \tabularnewline
142 & 0.247031895618528 & 0.494063791237057 & 0.752968104381472 \tabularnewline
143 & 0.244728958666653 & 0.489457917333305 & 0.755271041333347 \tabularnewline
144 & 0.193894946493178 & 0.387789892986356 & 0.806105053506822 \tabularnewline
145 & 0.164348138522741 & 0.328696277045482 & 0.835651861477259 \tabularnewline
146 & 0.168690332435811 & 0.337380664871621 & 0.831309667564189 \tabularnewline
147 & 0.188210118046263 & 0.376420236092526 & 0.811789881953737 \tabularnewline
148 & 0.169923565404277 & 0.339847130808555 & 0.830076434595723 \tabularnewline
149 & 0.132464510121391 & 0.264929020242782 & 0.867535489878609 \tabularnewline
150 & 0.253598432252538 & 0.507196864505077 & 0.746401567747462 \tabularnewline
151 & 0.25511593805255 & 0.5102318761051 & 0.74488406194745 \tabularnewline
152 & 0.188069312895179 & 0.376138625790359 & 0.811930687104821 \tabularnewline
153 & 0.192761685841883 & 0.385523371683766 & 0.807238314158117 \tabularnewline
154 & 0.135784682308372 & 0.271569364616744 & 0.864215317691628 \tabularnewline
155 & 0.31878062341175 & 0.637561246823499 & 0.68121937658825 \tabularnewline
156 & 0.223181730940027 & 0.446363461880054 & 0.776818269059973 \tabularnewline
157 & 0.136160020517537 & 0.272320041035074 & 0.863839979482463 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145377&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]5[/C][C]0.885041476265436[/C][C]0.229917047469128[/C][C]0.114958523734564[/C][/ROW]
[ROW][C]6[/C][C]0.914354299856227[/C][C]0.171291400287546[/C][C]0.0856457001437731[/C][/ROW]
[ROW][C]7[/C][C]0.856129310084231[/C][C]0.287741379831538[/C][C]0.143870689915769[/C][/ROW]
[ROW][C]8[/C][C]0.779166505809859[/C][C]0.441666988380281[/C][C]0.220833494190141[/C][/ROW]
[ROW][C]9[/C][C]0.688962790013485[/C][C]0.622074419973029[/C][C]0.311037209986515[/C][/ROW]
[ROW][C]10[/C][C]0.590400646406578[/C][C]0.819198707186844[/C][C]0.409599353593422[/C][/ROW]
[ROW][C]11[/C][C]0.571391946249423[/C][C]0.857216107501155[/C][C]0.428608053750577[/C][/ROW]
[ROW][C]12[/C][C]0.728546923214001[/C][C]0.542906153571998[/C][C]0.271453076785999[/C][/ROW]
[ROW][C]13[/C][C]0.886338959351821[/C][C]0.227322081296358[/C][C]0.113661040648179[/C][/ROW]
[ROW][C]14[/C][C]0.852238371171652[/C][C]0.295523257656696[/C][C]0.147761628828348[/C][/ROW]
[ROW][C]15[/C][C]0.877979882211728[/C][C]0.244040235576545[/C][C]0.122020117788272[/C][/ROW]
[ROW][C]16[/C][C]0.841920000582017[/C][C]0.316159998835966[/C][C]0.158079999417983[/C][/ROW]
[ROW][C]17[/C][C]0.799386817835112[/C][C]0.401226364329776[/C][C]0.200613182164888[/C][/ROW]
[ROW][C]18[/C][C]0.784192677831436[/C][C]0.431614644337127[/C][C]0.215807322168564[/C][/ROW]
[ROW][C]19[/C][C]0.737262569305693[/C][C]0.525474861388615[/C][C]0.262737430694307[/C][/ROW]
[ROW][C]20[/C][C]0.689410743258672[/C][C]0.621178513482657[/C][C]0.310589256741328[/C][/ROW]
[ROW][C]21[/C][C]0.725572237055977[/C][C]0.548855525888046[/C][C]0.274427762944023[/C][/ROW]
[ROW][C]22[/C][C]0.812525337853469[/C][C]0.374949324293062[/C][C]0.187474662146531[/C][/ROW]
[ROW][C]23[/C][C]0.772347784321729[/C][C]0.455304431356541[/C][C]0.227652215678271[/C][/ROW]
[ROW][C]24[/C][C]0.7874226197845[/C][C]0.425154760431[/C][C]0.2125773802155[/C][/ROW]
[ROW][C]25[/C][C]0.782509839312841[/C][C]0.434980321374317[/C][C]0.217490160687159[/C][/ROW]
[ROW][C]26[/C][C]0.922712672750386[/C][C]0.154574654499228[/C][C]0.0772873272496138[/C][/ROW]
[ROW][C]27[/C][C]0.906611228093963[/C][C]0.186777543812074[/C][C]0.0933887719060371[/C][/ROW]
[ROW][C]28[/C][C]0.879954421010382[/C][C]0.240091157979236[/C][C]0.120045578989618[/C][/ROW]
[ROW][C]29[/C][C]0.849617519618532[/C][C]0.300764960762935[/C][C]0.150382480381468[/C][/ROW]
[ROW][C]30[/C][C]0.88746965649062[/C][C]0.225060687018759[/C][C]0.11253034350938[/C][/ROW]
[ROW][C]31[/C][C]0.870303215997164[/C][C]0.259393568005671[/C][C]0.129696784002836[/C][/ROW]
[ROW][C]32[/C][C]0.853638004565733[/C][C]0.292723990868535[/C][C]0.146361995434267[/C][/ROW]
[ROW][C]33[/C][C]0.856128138081941[/C][C]0.287743723836118[/C][C]0.143871861918059[/C][/ROW]
[ROW][C]34[/C][C]0.824528207854024[/C][C]0.350943584291951[/C][C]0.175471792145976[/C][/ROW]
[ROW][C]35[/C][C]0.788991355620658[/C][C]0.422017288758685[/C][C]0.211008644379342[/C][/ROW]
[ROW][C]36[/C][C]0.764877948603849[/C][C]0.470244102792302[/C][C]0.235122051396151[/C][/ROW]
[ROW][C]37[/C][C]0.893976203076988[/C][C]0.212047593846025[/C][C]0.106023796923012[/C][/ROW]
[ROW][C]38[/C][C]0.869608310755839[/C][C]0.260783378488321[/C][C]0.130391689244161[/C][/ROW]
[ROW][C]39[/C][C]0.876378087799529[/C][C]0.247243824400943[/C][C]0.123621912200471[/C][/ROW]
[ROW][C]40[/C][C]0.857338108326395[/C][C]0.28532378334721[/C][C]0.142661891673605[/C][/ROW]
[ROW][C]41[/C][C]0.829604874864298[/C][C]0.340790250271403[/C][C]0.170395125135702[/C][/ROW]
[ROW][C]42[/C][C]0.810419346458324[/C][C]0.379161307083353[/C][C]0.189580653541676[/C][/ROW]
[ROW][C]43[/C][C]0.785540430651564[/C][C]0.428919138696871[/C][C]0.214459569348436[/C][/ROW]
[ROW][C]44[/C][C]0.783569505296786[/C][C]0.432860989406429[/C][C]0.216430494703214[/C][/ROW]
[ROW][C]45[/C][C]0.779922166810051[/C][C]0.440155666379899[/C][C]0.220077833189949[/C][/ROW]
[ROW][C]46[/C][C]0.816326859356064[/C][C]0.367346281287873[/C][C]0.183673140643936[/C][/ROW]
[ROW][C]47[/C][C]0.784038711834515[/C][C]0.431922576330969[/C][C]0.215961288165485[/C][/ROW]
[ROW][C]48[/C][C]0.750374554457809[/C][C]0.499250891084381[/C][C]0.249625445542191[/C][/ROW]
[ROW][C]49[/C][C]0.762774919763236[/C][C]0.474450160473528[/C][C]0.237225080236764[/C][/ROW]
[ROW][C]50[/C][C]0.73786140397546[/C][C]0.52427719204908[/C][C]0.26213859602454[/C][/ROW]
[ROW][C]51[/C][C]0.713554268263813[/C][C]0.572891463472374[/C][C]0.286445731736187[/C][/ROW]
[ROW][C]52[/C][C]0.671968232095161[/C][C]0.656063535809678[/C][C]0.328031767904839[/C][/ROW]
[ROW][C]53[/C][C]0.715266705899605[/C][C]0.56946658820079[/C][C]0.284733294100395[/C][/ROW]
[ROW][C]54[/C][C]0.716535925365002[/C][C]0.566928149269996[/C][C]0.283464074634998[/C][/ROW]
[ROW][C]55[/C][C]0.695364405668235[/C][C]0.609271188663529[/C][C]0.304635594331765[/C][/ROW]
[ROW][C]56[/C][C]0.665645580788552[/C][C]0.668708838422896[/C][C]0.334354419211448[/C][/ROW]
[ROW][C]57[/C][C]0.645621890605398[/C][C]0.708756218789204[/C][C]0.354378109394602[/C][/ROW]
[ROW][C]58[/C][C]0.605705493293411[/C][C]0.788589013413179[/C][C]0.394294506706589[/C][/ROW]
[ROW][C]59[/C][C]0.600135212163932[/C][C]0.799729575672136[/C][C]0.399864787836068[/C][/ROW]
[ROW][C]60[/C][C]0.59020628988192[/C][C]0.81958742023616[/C][C]0.40979371011808[/C][/ROW]
[ROW][C]61[/C][C]0.78509945732599[/C][C]0.429801085348021[/C][C]0.21490054267401[/C][/ROW]
[ROW][C]62[/C][C]0.759759673261334[/C][C]0.480480653477332[/C][C]0.240240326738666[/C][/ROW]
[ROW][C]63[/C][C]0.797001502636644[/C][C]0.405996994726711[/C][C]0.202998497363356[/C][/ROW]
[ROW][C]64[/C][C]0.762989258704403[/C][C]0.474021482591193[/C][C]0.237010741295597[/C][/ROW]
[ROW][C]65[/C][C]0.730908355234456[/C][C]0.538183289531089[/C][C]0.269091644765544[/C][/ROW]
[ROW][C]66[/C][C]0.807917791307773[/C][C]0.384164417384453[/C][C]0.192082208692227[/C][/ROW]
[ROW][C]67[/C][C]0.835869212338357[/C][C]0.328261575323287[/C][C]0.164130787661643[/C][/ROW]
[ROW][C]68[/C][C]0.828215319358667[/C][C]0.343569361282665[/C][C]0.171784680641333[/C][/ROW]
[ROW][C]69[/C][C]0.802032715917845[/C][C]0.395934568164311[/C][C]0.197967284082155[/C][/ROW]
[ROW][C]70[/C][C]0.773605406598103[/C][C]0.452789186803795[/C][C]0.226394593401897[/C][/ROW]
[ROW][C]71[/C][C]0.738257923684801[/C][C]0.523484152630397[/C][C]0.261742076315199[/C][/ROW]
[ROW][C]72[/C][C]0.726537717323862[/C][C]0.546924565352276[/C][C]0.273462282676138[/C][/ROW]
[ROW][C]73[/C][C]0.691277367953867[/C][C]0.617445264092267[/C][C]0.308722632046133[/C][/ROW]
[ROW][C]74[/C][C]0.654288550736747[/C][C]0.691422898526507[/C][C]0.345711449263253[/C][/ROW]
[ROW][C]75[/C][C]0.621350191066721[/C][C]0.757299617866558[/C][C]0.378649808933279[/C][/ROW]
[ROW][C]76[/C][C]0.605123917058816[/C][C]0.789752165882367[/C][C]0.394876082941184[/C][/ROW]
[ROW][C]77[/C][C]0.623210794122294[/C][C]0.753578411755411[/C][C]0.376789205877706[/C][/ROW]
[ROW][C]78[/C][C]0.587960561882548[/C][C]0.824078876234905[/C][C]0.412039438117452[/C][/ROW]
[ROW][C]79[/C][C]0.549992858347581[/C][C]0.900014283304837[/C][C]0.450007141652418[/C][/ROW]
[ROW][C]80[/C][C]0.514990790073239[/C][C]0.970018419853523[/C][C]0.485009209926761[/C][/ROW]
[ROW][C]81[/C][C]0.47650991210377[/C][C]0.953019824207541[/C][C]0.52349008789623[/C][/ROW]
[ROW][C]82[/C][C]0.460891696739584[/C][C]0.921783393479168[/C][C]0.539108303260416[/C][/ROW]
[ROW][C]83[/C][C]0.423027966694902[/C][C]0.846055933389805[/C][C]0.576972033305098[/C][/ROW]
[ROW][C]84[/C][C]0.408150009727036[/C][C]0.816300019454072[/C][C]0.591849990272964[/C][/ROW]
[ROW][C]85[/C][C]0.37250120606965[/C][C]0.745002412139301[/C][C]0.62749879393035[/C][/ROW]
[ROW][C]86[/C][C]0.330582468990223[/C][C]0.661164937980446[/C][C]0.669417531009777[/C][/ROW]
[ROW][C]87[/C][C]0.299707616577371[/C][C]0.599415233154742[/C][C]0.700292383422629[/C][/ROW]
[ROW][C]88[/C][C]0.261882371808332[/C][C]0.523764743616664[/C][C]0.738117628191668[/C][/ROW]
[ROW][C]89[/C][C]0.233776976505117[/C][C]0.467553953010234[/C][C]0.766223023494883[/C][/ROW]
[ROW][C]90[/C][C]0.572271743631237[/C][C]0.855456512737526[/C][C]0.427728256368763[/C][/ROW]
[ROW][C]91[/C][C]0.606321711862706[/C][C]0.787356576274587[/C][C]0.393678288137294[/C][/ROW]
[ROW][C]92[/C][C]0.575626516889776[/C][C]0.848746966220447[/C][C]0.424373483110224[/C][/ROW]
[ROW][C]93[/C][C]0.537353887619949[/C][C]0.925292224760103[/C][C]0.462646112380051[/C][/ROW]
[ROW][C]94[/C][C]0.491805811221274[/C][C]0.983611622442549[/C][C]0.508194188778726[/C][/ROW]
[ROW][C]95[/C][C]0.455720034613482[/C][C]0.911440069226964[/C][C]0.544279965386518[/C][/ROW]
[ROW][C]96[/C][C]0.445119199643784[/C][C]0.890238399287568[/C][C]0.554880800356216[/C][/ROW]
[ROW][C]97[/C][C]0.437528826546919[/C][C]0.875057653093839[/C][C]0.562471173453081[/C][/ROW]
[ROW][C]98[/C][C]0.392738455152531[/C][C]0.785476910305062[/C][C]0.607261544847469[/C][/ROW]
[ROW][C]99[/C][C]0.4162694195777[/C][C]0.8325388391554[/C][C]0.5837305804223[/C][/ROW]
[ROW][C]100[/C][C]0.381025019739114[/C][C]0.762050039478228[/C][C]0.618974980260886[/C][/ROW]
[ROW][C]101[/C][C]0.400168069506956[/C][C]0.800336139013912[/C][C]0.599831930493044[/C][/ROW]
[ROW][C]102[/C][C]0.386105065412825[/C][C]0.77221013082565[/C][C]0.613894934587175[/C][/ROW]
[ROW][C]103[/C][C]0.368427393336021[/C][C]0.736854786672042[/C][C]0.631572606663979[/C][/ROW]
[ROW][C]104[/C][C]0.393076654106592[/C][C]0.786153308213184[/C][C]0.606923345893408[/C][/ROW]
[ROW][C]105[/C][C]0.357180442808202[/C][C]0.714360885616405[/C][C]0.642819557191798[/C][/ROW]
[ROW][C]106[/C][C]0.520135438842347[/C][C]0.959729122315305[/C][C]0.479864561157653[/C][/ROW]
[ROW][C]107[/C][C]0.505608145310846[/C][C]0.988783709378307[/C][C]0.494391854689154[/C][/ROW]
[ROW][C]108[/C][C]0.466234994888578[/C][C]0.932469989777157[/C][C]0.533765005111422[/C][/ROW]
[ROW][C]109[/C][C]0.554267010598263[/C][C]0.891465978803475[/C][C]0.445732989401737[/C][/ROW]
[ROW][C]110[/C][C]0.632655639654216[/C][C]0.734688720691568[/C][C]0.367344360345784[/C][/ROW]
[ROW][C]111[/C][C]0.593814028610412[/C][C]0.812371942779177[/C][C]0.406185971389588[/C][/ROW]
[ROW][C]112[/C][C]0.631199625803427[/C][C]0.737600748393146[/C][C]0.368800374196573[/C][/ROW]
[ROW][C]113[/C][C]0.59329220335913[/C][C]0.81341559328174[/C][C]0.40670779664087[/C][/ROW]
[ROW][C]114[/C][C]0.544369362573782[/C][C]0.911261274852436[/C][C]0.455630637426218[/C][/ROW]
[ROW][C]115[/C][C]0.63802206427861[/C][C]0.72395587144278[/C][C]0.36197793572139[/C][/ROW]
[ROW][C]116[/C][C]0.622385201667338[/C][C]0.755229596665324[/C][C]0.377614798332662[/C][/ROW]
[ROW][C]117[/C][C]0.573247139032849[/C][C]0.853505721934303[/C][C]0.426752860967151[/C][/ROW]
[ROW][C]118[/C][C]0.522774188223732[/C][C]0.954451623552536[/C][C]0.477225811776268[/C][/ROW]
[ROW][C]119[/C][C]0.471418644155603[/C][C]0.942837288311206[/C][C]0.528581355844397[/C][/ROW]
[ROW][C]120[/C][C]0.420305796000243[/C][C]0.840611592000486[/C][C]0.579694203999757[/C][/ROW]
[ROW][C]121[/C][C]0.405228376539148[/C][C]0.810456753078295[/C][C]0.594771623460852[/C][/ROW]
[ROW][C]122[/C][C]0.355404531754588[/C][C]0.710809063509175[/C][C]0.644595468245412[/C][/ROW]
[ROW][C]123[/C][C]0.315101215381457[/C][C]0.630202430762915[/C][C]0.684898784618543[/C][/ROW]
[ROW][C]124[/C][C]0.276549564302679[/C][C]0.553099128605359[/C][C]0.723450435697321[/C][/ROW]
[ROW][C]125[/C][C]0.241329063463418[/C][C]0.482658126926836[/C][C]0.758670936536582[/C][/ROW]
[ROW][C]126[/C][C]0.208371187460329[/C][C]0.416742374920657[/C][C]0.791628812539671[/C][/ROW]
[ROW][C]127[/C][C]0.2335950897321[/C][C]0.467190179464199[/C][C]0.7664049102679[/C][/ROW]
[ROW][C]128[/C][C]0.263821949408866[/C][C]0.527643898817733[/C][C]0.736178050591134[/C][/ROW]
[ROW][C]129[/C][C]0.463168284957134[/C][C]0.926336569914268[/C][C]0.536831715042866[/C][/ROW]
[ROW][C]130[/C][C]0.424418198962975[/C][C]0.848836397925949[/C][C]0.575581801037025[/C][/ROW]
[ROW][C]131[/C][C]0.373201921253957[/C][C]0.746403842507913[/C][C]0.626798078746043[/C][/ROW]
[ROW][C]132[/C][C]0.54843836252603[/C][C]0.903123274947941[/C][C]0.451561637473971[/C][/ROW]
[ROW][C]133[/C][C]0.507206185668872[/C][C]0.985587628662257[/C][C]0.492793814331129[/C][/ROW]
[ROW][C]134[/C][C]0.463212887429626[/C][C]0.926425774859252[/C][C]0.536787112570374[/C][/ROW]
[ROW][C]135[/C][C]0.419759749854544[/C][C]0.839519499709089[/C][C]0.580240250145456[/C][/ROW]
[ROW][C]136[/C][C]0.419648420956392[/C][C]0.839296841912784[/C][C]0.580351579043608[/C][/ROW]
[ROW][C]137[/C][C]0.433620344897287[/C][C]0.867240689794575[/C][C]0.566379655102712[/C][/ROW]
[ROW][C]138[/C][C]0.372976352380388[/C][C]0.745952704760776[/C][C]0.627023647619612[/C][/ROW]
[ROW][C]139[/C][C]0.388544909951554[/C][C]0.777089819903107[/C][C]0.611455090048446[/C][/ROW]
[ROW][C]140[/C][C]0.343099953884398[/C][C]0.686199907768796[/C][C]0.656900046115602[/C][/ROW]
[ROW][C]141[/C][C]0.287614391948408[/C][C]0.575228783896816[/C][C]0.712385608051592[/C][/ROW]
[ROW][C]142[/C][C]0.247031895618528[/C][C]0.494063791237057[/C][C]0.752968104381472[/C][/ROW]
[ROW][C]143[/C][C]0.244728958666653[/C][C]0.489457917333305[/C][C]0.755271041333347[/C][/ROW]
[ROW][C]144[/C][C]0.193894946493178[/C][C]0.387789892986356[/C][C]0.806105053506822[/C][/ROW]
[ROW][C]145[/C][C]0.164348138522741[/C][C]0.328696277045482[/C][C]0.835651861477259[/C][/ROW]
[ROW][C]146[/C][C]0.168690332435811[/C][C]0.337380664871621[/C][C]0.831309667564189[/C][/ROW]
[ROW][C]147[/C][C]0.188210118046263[/C][C]0.376420236092526[/C][C]0.811789881953737[/C][/ROW]
[ROW][C]148[/C][C]0.169923565404277[/C][C]0.339847130808555[/C][C]0.830076434595723[/C][/ROW]
[ROW][C]149[/C][C]0.132464510121391[/C][C]0.264929020242782[/C][C]0.867535489878609[/C][/ROW]
[ROW][C]150[/C][C]0.253598432252538[/C][C]0.507196864505077[/C][C]0.746401567747462[/C][/ROW]
[ROW][C]151[/C][C]0.25511593805255[/C][C]0.5102318761051[/C][C]0.74488406194745[/C][/ROW]
[ROW][C]152[/C][C]0.188069312895179[/C][C]0.376138625790359[/C][C]0.811930687104821[/C][/ROW]
[ROW][C]153[/C][C]0.192761685841883[/C][C]0.385523371683766[/C][C]0.807238314158117[/C][/ROW]
[ROW][C]154[/C][C]0.135784682308372[/C][C]0.271569364616744[/C][C]0.864215317691628[/C][/ROW]
[ROW][C]155[/C][C]0.31878062341175[/C][C]0.637561246823499[/C][C]0.68121937658825[/C][/ROW]
[ROW][C]156[/C][C]0.223181730940027[/C][C]0.446363461880054[/C][C]0.776818269059973[/C][/ROW]
[ROW][C]157[/C][C]0.136160020517537[/C][C]0.272320041035074[/C][C]0.863839979482463[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145377&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145377&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
50.8850414762654360.2299170474691280.114958523734564
60.9143542998562270.1712914002875460.0856457001437731
70.8561293100842310.2877413798315380.143870689915769
80.7791665058098590.4416669883802810.220833494190141
90.6889627900134850.6220744199730290.311037209986515
100.5904006464065780.8191987071868440.409599353593422
110.5713919462494230.8572161075011550.428608053750577
120.7285469232140010.5429061535719980.271453076785999
130.8863389593518210.2273220812963580.113661040648179
140.8522383711716520.2955232576566960.147761628828348
150.8779798822117280.2440402355765450.122020117788272
160.8419200005820170.3161599988359660.158079999417983
170.7993868178351120.4012263643297760.200613182164888
180.7841926778314360.4316146443371270.215807322168564
190.7372625693056930.5254748613886150.262737430694307
200.6894107432586720.6211785134826570.310589256741328
210.7255722370559770.5488555258880460.274427762944023
220.8125253378534690.3749493242930620.187474662146531
230.7723477843217290.4553044313565410.227652215678271
240.78742261978450.4251547604310.2125773802155
250.7825098393128410.4349803213743170.217490160687159
260.9227126727503860.1545746544992280.0772873272496138
270.9066112280939630.1867775438120740.0933887719060371
280.8799544210103820.2400911579792360.120045578989618
290.8496175196185320.3007649607629350.150382480381468
300.887469656490620.2250606870187590.11253034350938
310.8703032159971640.2593935680056710.129696784002836
320.8536380045657330.2927239908685350.146361995434267
330.8561281380819410.2877437238361180.143871861918059
340.8245282078540240.3509435842919510.175471792145976
350.7889913556206580.4220172887586850.211008644379342
360.7648779486038490.4702441027923020.235122051396151
370.8939762030769880.2120475938460250.106023796923012
380.8696083107558390.2607833784883210.130391689244161
390.8763780877995290.2472438244009430.123621912200471
400.8573381083263950.285323783347210.142661891673605
410.8296048748642980.3407902502714030.170395125135702
420.8104193464583240.3791613070833530.189580653541676
430.7855404306515640.4289191386968710.214459569348436
440.7835695052967860.4328609894064290.216430494703214
450.7799221668100510.4401556663798990.220077833189949
460.8163268593560640.3673462812878730.183673140643936
470.7840387118345150.4319225763309690.215961288165485
480.7503745544578090.4992508910843810.249625445542191
490.7627749197632360.4744501604735280.237225080236764
500.737861403975460.524277192049080.26213859602454
510.7135542682638130.5728914634723740.286445731736187
520.6719682320951610.6560635358096780.328031767904839
530.7152667058996050.569466588200790.284733294100395
540.7165359253650020.5669281492699960.283464074634998
550.6953644056682350.6092711886635290.304635594331765
560.6656455807885520.6687088384228960.334354419211448
570.6456218906053980.7087562187892040.354378109394602
580.6057054932934110.7885890134131790.394294506706589
590.6001352121639320.7997295756721360.399864787836068
600.590206289881920.819587420236160.40979371011808
610.785099457325990.4298010853480210.21490054267401
620.7597596732613340.4804806534773320.240240326738666
630.7970015026366440.4059969947267110.202998497363356
640.7629892587044030.4740214825911930.237010741295597
650.7309083552344560.5381832895310890.269091644765544
660.8079177913077730.3841644173844530.192082208692227
670.8358692123383570.3282615753232870.164130787661643
680.8282153193586670.3435693612826650.171784680641333
690.8020327159178450.3959345681643110.197967284082155
700.7736054065981030.4527891868037950.226394593401897
710.7382579236848010.5234841526303970.261742076315199
720.7265377173238620.5469245653522760.273462282676138
730.6912773679538670.6174452640922670.308722632046133
740.6542885507367470.6914228985265070.345711449263253
750.6213501910667210.7572996178665580.378649808933279
760.6051239170588160.7897521658823670.394876082941184
770.6232107941222940.7535784117554110.376789205877706
780.5879605618825480.8240788762349050.412039438117452
790.5499928583475810.9000142833048370.450007141652418
800.5149907900732390.9700184198535230.485009209926761
810.476509912103770.9530198242075410.52349008789623
820.4608916967395840.9217833934791680.539108303260416
830.4230279666949020.8460559333898050.576972033305098
840.4081500097270360.8163000194540720.591849990272964
850.372501206069650.7450024121393010.62749879393035
860.3305824689902230.6611649379804460.669417531009777
870.2997076165773710.5994152331547420.700292383422629
880.2618823718083320.5237647436166640.738117628191668
890.2337769765051170.4675539530102340.766223023494883
900.5722717436312370.8554565127375260.427728256368763
910.6063217118627060.7873565762745870.393678288137294
920.5756265168897760.8487469662204470.424373483110224
930.5373538876199490.9252922247601030.462646112380051
940.4918058112212740.9836116224425490.508194188778726
950.4557200346134820.9114400692269640.544279965386518
960.4451191996437840.8902383992875680.554880800356216
970.4375288265469190.8750576530938390.562471173453081
980.3927384551525310.7854769103050620.607261544847469
990.41626941957770.83253883915540.5837305804223
1000.3810250197391140.7620500394782280.618974980260886
1010.4001680695069560.8003361390139120.599831930493044
1020.3861050654128250.772210130825650.613894934587175
1030.3684273933360210.7368547866720420.631572606663979
1040.3930766541065920.7861533082131840.606923345893408
1050.3571804428082020.7143608856164050.642819557191798
1060.5201354388423470.9597291223153050.479864561157653
1070.5056081453108460.9887837093783070.494391854689154
1080.4662349948885780.9324699897771570.533765005111422
1090.5542670105982630.8914659788034750.445732989401737
1100.6326556396542160.7346887206915680.367344360345784
1110.5938140286104120.8123719427791770.406185971389588
1120.6311996258034270.7376007483931460.368800374196573
1130.593292203359130.813415593281740.40670779664087
1140.5443693625737820.9112612748524360.455630637426218
1150.638022064278610.723955871442780.36197793572139
1160.6223852016673380.7552295966653240.377614798332662
1170.5732471390328490.8535057219343030.426752860967151
1180.5227741882237320.9544516235525360.477225811776268
1190.4714186441556030.9428372883112060.528581355844397
1200.4203057960002430.8406115920004860.579694203999757
1210.4052283765391480.8104567530782950.594771623460852
1220.3554045317545880.7108090635091750.644595468245412
1230.3151012153814570.6302024307629150.684898784618543
1240.2765495643026790.5530991286053590.723450435697321
1250.2413290634634180.4826581269268360.758670936536582
1260.2083711874603290.4167423749206570.791628812539671
1270.23359508973210.4671901794641990.7664049102679
1280.2638219494088660.5276438988177330.736178050591134
1290.4631682849571340.9263365699142680.536831715042866
1300.4244181989629750.8488363979259490.575581801037025
1310.3732019212539570.7464038425079130.626798078746043
1320.548438362526030.9031232749479410.451561637473971
1330.5072061856688720.9855876286622570.492793814331129
1340.4632128874296260.9264257748592520.536787112570374
1350.4197597498545440.8395194997090890.580240250145456
1360.4196484209563920.8392968419127840.580351579043608
1370.4336203448972870.8672406897945750.566379655102712
1380.3729763523803880.7459527047607760.627023647619612
1390.3885449099515540.7770898199031070.611455090048446
1400.3430999538843980.6861999077687960.656900046115602
1410.2876143919484080.5752287838968160.712385608051592
1420.2470318956185280.4940637912370570.752968104381472
1430.2447289586666530.4894579173333050.755271041333347
1440.1938949464931780.3877898929863560.806105053506822
1450.1643481385227410.3286962770454820.835651861477259
1460.1686903324358110.3373806648716210.831309667564189
1470.1882101180462630.3764202360925260.811789881953737
1480.1699235654042770.3398471308085550.830076434595723
1490.1324645101213910.2649290202427820.867535489878609
1500.2535984322525380.5071968645050770.746401567747462
1510.255115938052550.51023187610510.74488406194745
1520.1880693128951790.3761386257903590.811930687104821
1530.1927616858418830.3855233716837660.807238314158117
1540.1357846823083720.2715693646167440.864215317691628
1550.318780623411750.6375612468234990.68121937658825
1560.2231817309400270.4463634618800540.776818269059973
1570.1361600205175370.2723200410350740.863839979482463







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

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145377&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145377&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145377&T=6

As an alternative you can also use a QR Code:  

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

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



Parameters (Session):
par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 2 ; 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')
}