Free Statistics

of Irreproducible Research!

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationTue, 23 Nov 2010 16:42:52 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Nov/23/t1290530539uqzymgn7wdm6up0.htm/, Retrieved Thu, 28 Mar 2024 15:41:03 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=99407, Retrieved Thu, 28 Mar 2024 15:41:03 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact167
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
-    D  [Multiple Regression] [ws7- mini tut] [2010-11-20 10:24:18] [4a7069087cf9e0eda253aeed7d8c30d6]
-   PD    [Multiple Regression] [ws7- mini tut] [2010-11-20 12:35:54] [4a7069087cf9e0eda253aeed7d8c30d6]
- R PD        [Multiple Regression] [Personal Standard...] [2010-11-23 16:42:52] [194b0dcd1d575718d8c1582a0112d12c] [Current]
Feedback Forum

Post a new message
Dataseries X:
24	24	14	11	12
25	25	11	7	8
30	17	6	17	8
19	18	12	10	8
22	18	8	12	9
22	16	10	12	7
25	20	10	11	4
23	16	11	11	11
17	18	16	12	7
21	17	11	13	7
19	23	13	14	12
19	30	12	16	10
15	23	8	11	10
16	18	12	10	8
23	15	11	11	8
27	12	4	15	4
22	21	9	9	9
14	15	8	11	8
22	20	8	17	7
23	31	14	17	11
23	27	15	11	9
21	34	16	18	11
19	21	9	14	13
18	31	14	10	8
20	19	11	11	8
23	16	8	15	9
25	20	9	15	6
19	21	9	13	9
24	22	9	16	9
22	17	9	13	6
25	24	10	9	6
26	25	16	18	16
29	26	11	18	5
32	25	8	12	7
25	17	9	17	9
29	32	16	9	6
28	33	11	9	6
17	13	16	12	5
28	32	12	18	12
29	25	12	12	7
26	29	14	18	10
25	22	9	14	9
14	18	10	15	8
25	17	9	16	5
26	20	10	10	8
20	15	12	11	8
18	20	14	14	10
32	33	14	9	6
25	29	10	12	8
25	23	14	17	7
23	26	16	5	4
21	18	9	12	8
20	20	10	12	8
15	11	6	6	4
30	28	8	24	20
24	26	13	12	8
26	22	10	12	8
24	17	8	14	6
22	12	7	7	4
14	14	15	13	8
24	17	9	12	9
24	21	10	13	6
24	19	12	14	7
24	18	13	8	9
19	10	10	11	5
31	29	11	9	5
22	31	8	11	8
27	19	9	13	8
19	9	13	10	6
25	20	11	11	8
20	28	8	12	7
21	19	9	9	7
27	30	9	15	9
23	29	15	18	11
25	26	9	15	6
20	23	10	12	8
21	13	14	13	6
22	21	12	14	9
23	19	12	10	8
25	28	11	13	6
25	23	14	13	10
17	18	6	11	8
19	21	12	13	8
25	20	8	16	10
19	23	14	8	5
20	21	11	16	7
26	21	10	11	5
23	15	14	9	8
27	28	12	16	14
17	19	10	12	7
17	26	14	14	8
19	10	5	8	6
17	16	11	9	5
22	22	10	15	6
21	19	9	11	10
32	31	10	21	12
21	31	16	14	9
21	29	13	18	12
18	19	9	12	7
18	22	10	13	8
23	23	10	15	10
19	15	7	12	6
20	20	9	19	10
21	18	8	15	10
20	23	14	11	10
17	25	14	11	5
18	21	8	10	7
19	24	9	13	10
22	25	14	15	11
15	17	14	12	6
14	13	8	12	7
18	28	8	16	12
24	21	8	9	11
35	25	7	18	11
29	9	6	8	11
21	16	8	13	5
25	19	6	17	8
20	17	11	9	6
22	25	14	15	9
13	20	11	8	4
26	29	11	7	4
17	14	11	12	7
25	22	14	14	11
20	15	8	6	6
19	19	20	8	7
21	20	11	17	8
22	15	8	10	4
24	20	11	11	8
21	18	10	14	9
26	33	14	11	8
24	22	11	13	11
16	16	9	12	8
23	17	9	11	5
18	16	8	9	4
16	21	10	12	8
26	26	13	20	10
19	18	13	12	6
21	18	12	13	9
21	17	8	12	9
22	22	13	12	13
23	30	14	9	9
29	30	12	15	10
21	24	14	24	20
21	21	15	7	5
23	21	13	17	11
27	29	16	11	6
25	31	9	17	9
21	20	9	11	7
10	16	9	12	9
20	22	8	14	10
26	20	7	11	9
24	28	16	16	8
29	38	11	21	7
19	22	9	14	6
24	20	11	20	13
19	17	9	13	6
24	28	14	11	8
22	22	13	15	10
17	31	16	19	16




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk

\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 & 8 seconds \tabularnewline
R Server & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99407&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]8 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'RServer@AstonUniversity' @ vre.aston.ac.uk[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99407&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99407&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 time8 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk







Multiple Linear Regression - Estimated Regression Equation
PS[t] = + 15.4323237613405 + 0.364294694129429CM[t] -0.321728388550003D[t] + 0.142655035713217PE[t] -0.0849617358950159PC[t] + 1.18753715676232M1[t] + 0.582940513559847M2[t] + 1.4142918417762M3[t] + 1.37572882776538M4[t] + 1.13169308310493M5[t] + 1.21435808901099M6[t] + 1.63067391786142M7[t] + 2.61302398862814M8[t] + 2.20955916043914M9[t] + 1.17050255859489M10[t] + 0.130689993576573M11[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
PS[t] =  +  15.4323237613405 +  0.364294694129429CM[t] -0.321728388550003D[t] +  0.142655035713217PE[t] -0.0849617358950159PC[t] +  1.18753715676232M1[t] +  0.582940513559847M2[t] +  1.4142918417762M3[t] +  1.37572882776538M4[t] +  1.13169308310493M5[t] +  1.21435808901099M6[t] +  1.63067391786142M7[t] +  2.61302398862814M8[t] +  2.20955916043914M9[t] +  1.17050255859489M10[t] +  0.130689993576573M11[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99407&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]PS[t] =  +  15.4323237613405 +  0.364294694129429CM[t] -0.321728388550003D[t] +  0.142655035713217PE[t] -0.0849617358950159PC[t] +  1.18753715676232M1[t] +  0.582940513559847M2[t] +  1.4142918417762M3[t] +  1.37572882776538M4[t] +  1.13169308310493M5[t] +  1.21435808901099M6[t] +  1.63067391786142M7[t] +  2.61302398862814M8[t] +  2.20955916043914M9[t] +  1.17050255859489M10[t] +  0.130689993576573M11[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99407&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99407&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
PS[t] = + 15.4323237613405 + 0.364294694129429CM[t] -0.321728388550003D[t] + 0.142655035713217PE[t] -0.0849617358950159PC[t] + 1.18753715676232M1[t] + 0.582940513559847M2[t] + 1.4142918417762M3[t] + 1.37572882776538M4[t] + 1.13169308310493M5[t] + 1.21435808901099M6[t] + 1.63067391786142M7[t] + 2.61302398862814M8[t] + 2.20955916043914M9[t] + 1.17050255859489M10[t] + 0.130689993576573M11[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)15.43232376134052.0466277.540400
CM0.3642946941294290.0648015.621700
D-0.3217283885500030.126426-2.54480.0119950.005997
PE0.1426550357132170.1162331.22730.2217190.110859
PC-0.08496173589501590.148896-0.57060.5691590.28458
M11.187537156762321.4675470.80920.4197450.209873
M20.5829405135598471.4928040.39050.6967480.348374
M31.41429184177621.477710.95710.340140.17007
M41.375728827765381.5108990.91050.3640720.182036
M51.131693083104931.5182840.74540.4572680.228634
M61.214358089010991.528340.79460.4281860.214093
M71.630673917861421.5321721.06430.2889920.144496
M82.613023988628141.5114231.72890.0859940.042997
M92.209559160439141.4982841.47470.1424840.071242
M101.170502558594891.4950980.78290.4349840.217492
M110.1306899935765731.5449460.08460.9327040.466352

\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) & 15.4323237613405 & 2.046627 & 7.5404 & 0 & 0 \tabularnewline
CM & 0.364294694129429 & 0.064801 & 5.6217 & 0 & 0 \tabularnewline
D & -0.321728388550003 & 0.126426 & -2.5448 & 0.011995 & 0.005997 \tabularnewline
PE & 0.142655035713217 & 0.116233 & 1.2273 & 0.221719 & 0.110859 \tabularnewline
PC & -0.0849617358950159 & 0.148896 & -0.5706 & 0.569159 & 0.28458 \tabularnewline
M1 & 1.18753715676232 & 1.467547 & 0.8092 & 0.419745 & 0.209873 \tabularnewline
M2 & 0.582940513559847 & 1.492804 & 0.3905 & 0.696748 & 0.348374 \tabularnewline
M3 & 1.4142918417762 & 1.47771 & 0.9571 & 0.34014 & 0.17007 \tabularnewline
M4 & 1.37572882776538 & 1.510899 & 0.9105 & 0.364072 & 0.182036 \tabularnewline
M5 & 1.13169308310493 & 1.518284 & 0.7454 & 0.457268 & 0.228634 \tabularnewline
M6 & 1.21435808901099 & 1.52834 & 0.7946 & 0.428186 & 0.214093 \tabularnewline
M7 & 1.63067391786142 & 1.532172 & 1.0643 & 0.288992 & 0.144496 \tabularnewline
M8 & 2.61302398862814 & 1.511423 & 1.7289 & 0.085994 & 0.042997 \tabularnewline
M9 & 2.20955916043914 & 1.498284 & 1.4747 & 0.142484 & 0.071242 \tabularnewline
M10 & 1.17050255859489 & 1.495098 & 0.7829 & 0.434984 & 0.217492 \tabularnewline
M11 & 0.130689993576573 & 1.544946 & 0.0846 & 0.932704 & 0.466352 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99407&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]15.4323237613405[/C][C]2.046627[/C][C]7.5404[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]CM[/C][C]0.364294694129429[/C][C]0.064801[/C][C]5.6217[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]D[/C][C]-0.321728388550003[/C][C]0.126426[/C][C]-2.5448[/C][C]0.011995[/C][C]0.005997[/C][/ROW]
[ROW][C]PE[/C][C]0.142655035713217[/C][C]0.116233[/C][C]1.2273[/C][C]0.221719[/C][C]0.110859[/C][/ROW]
[ROW][C]PC[/C][C]-0.0849617358950159[/C][C]0.148896[/C][C]-0.5706[/C][C]0.569159[/C][C]0.28458[/C][/ROW]
[ROW][C]M1[/C][C]1.18753715676232[/C][C]1.467547[/C][C]0.8092[/C][C]0.419745[/C][C]0.209873[/C][/ROW]
[ROW][C]M2[/C][C]0.582940513559847[/C][C]1.492804[/C][C]0.3905[/C][C]0.696748[/C][C]0.348374[/C][/ROW]
[ROW][C]M3[/C][C]1.4142918417762[/C][C]1.47771[/C][C]0.9571[/C][C]0.34014[/C][C]0.17007[/C][/ROW]
[ROW][C]M4[/C][C]1.37572882776538[/C][C]1.510899[/C][C]0.9105[/C][C]0.364072[/C][C]0.182036[/C][/ROW]
[ROW][C]M5[/C][C]1.13169308310493[/C][C]1.518284[/C][C]0.7454[/C][C]0.457268[/C][C]0.228634[/C][/ROW]
[ROW][C]M6[/C][C]1.21435808901099[/C][C]1.52834[/C][C]0.7946[/C][C]0.428186[/C][C]0.214093[/C][/ROW]
[ROW][C]M7[/C][C]1.63067391786142[/C][C]1.532172[/C][C]1.0643[/C][C]0.288992[/C][C]0.144496[/C][/ROW]
[ROW][C]M8[/C][C]2.61302398862814[/C][C]1.511423[/C][C]1.7289[/C][C]0.085994[/C][C]0.042997[/C][/ROW]
[ROW][C]M9[/C][C]2.20955916043914[/C][C]1.498284[/C][C]1.4747[/C][C]0.142484[/C][C]0.071242[/C][/ROW]
[ROW][C]M10[/C][C]1.17050255859489[/C][C]1.495098[/C][C]0.7829[/C][C]0.434984[/C][C]0.217492[/C][/ROW]
[ROW][C]M11[/C][C]0.130689993576573[/C][C]1.544946[/C][C]0.0846[/C][C]0.932704[/C][C]0.466352[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99407&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99407&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)15.43232376134052.0466277.540400
CM0.3642946941294290.0648015.621700
D-0.3217283885500030.126426-2.54480.0119950.005997
PE0.1426550357132170.1162331.22730.2217190.110859
PC-0.08496173589501590.148896-0.57060.5691590.28458
M11.187537156762321.4675470.80920.4197450.209873
M20.5829405135598471.4928040.39050.6967480.348374
M31.41429184177621.477710.95710.340140.17007
M41.375728827765381.5108990.91050.3640720.182036
M51.131693083104931.5182840.74540.4572680.228634
M61.214358089010991.528340.79460.4281860.214093
M71.630673917861421.5321721.06430.2889920.144496
M82.613023988628141.5114231.72890.0859940.042997
M92.209559160439141.4982841.47470.1424840.071242
M101.170502558594891.4950980.78290.4349840.217492
M110.1306899935765731.5449460.08460.9327040.466352







Multiple Linear Regression - Regression Statistics
Multiple R0.516397323766253
R-squared0.266666195992948
Adjusted R-squared0.189743069698502
F-TEST (value)3.46665832291065
F-TEST (DF numerator)15
F-TEST (DF denominator)143
p-value4.76876238936219e-05
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.79586465897246
Sum Squared Residuals2060.42815682077

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.516397323766253 \tabularnewline
R-squared & 0.266666195992948 \tabularnewline
Adjusted R-squared & 0.189743069698502 \tabularnewline
F-TEST (value) & 3.46665832291065 \tabularnewline
F-TEST (DF numerator) & 15 \tabularnewline
F-TEST (DF denominator) & 143 \tabularnewline
p-value & 4.76876238936219e-05 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 3.79586465897246 \tabularnewline
Sum Squared Residuals & 2060.42815682077 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99407&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.516397323766253[/C][/ROW]
[ROW][C]R-squared[/C][C]0.266666195992948[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.189743069698502[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]3.46665832291065[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]15[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]143[/C][/ROW]
[ROW][C]p-value[/C][C]4.76876238936219e-05[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]3.79586465897246[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]2060.42815682077[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99407&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99407&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 R0.516397323766253
R-squared0.266666195992948
Adjusted R-squared0.189743069698502
F-TEST (value)3.46665832291065
F-TEST (DF numerator)15
F-TEST (DF denominator)143
p-value4.76876238936219e-05
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.79586465897246
Sum Squared Residuals2060.42815682077







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
12421.40840069961432.59159930038574
22521.90251071691843.0974892830816
33022.85469679198157.1453032080185
41920.2514728908076-1.25147289080757
52221.49469903587860.505300964121442
62220.37524134821581.62475865178422
72522.36096612555582.63903387444424
82320.96967687998962.03032312001035
91720.1686614766028-3.16866147660277
102120.51660715909230.483392840907681
111920.7369523379887-1.73695233798871
121923.9332871350846-4.93328713508462
131523.1443998085749-8.14439980857486
141619.458684576602-3.45868457660204
152319.66153524669333.33846475330668
162721.69265395657725.30734604342283
172221.83788962257720.162110377422807
181420.4267866595781-6.42678665957812
192223.60546790925-1.60546790925002
202326.3248423405604-3.32484234056038
212323.4564636048144-0.456463604814387
222125.4744032515286-4.47440325152863
231921.2103147680349-2.21031476803485
241822.9681183096248-4.96811830962476
252020.8919593381972-0.891959338197159
262320.64532218521432.35467781478574
272522.86700910908342.13299089091662
281922.6525455100905-3.65254551009051
292423.20076956669910.79923043330086
302221.28888120250340.711118797496557
312523.3629113588571.63708864114299
322623.21346375492192.78653624507807
332925.71751465845763.28248534154245
343224.25349484206467.74650515793545
352520.52094804223684.47905195776315
362922.7162246627316.28377533726899
372825.87669845637282.12330154362723
381716.89049283086640.109507169133642
392826.19155496475611.80844503524392
402923.1718075570355.82819244296497
412624.34253881838651.65746118161345
422522.99812450117882.00187549882124
431421.8631499365697-7.8631499365697
442523.20047394515531.79952605484474
452622.45734938884023.5426506111598
462019.0960175749620.903982425037993
471819.4922633388405-1.49226333884045
483223.72397613396048.27602386603956
492524.99928970375470.00071029624532456
502521.72024825603673.27975174396329
512321.54405166866781.45594833133221
522121.501968127884-0.501968127884017
532021.6647933829324-1.66479338293243
541519.2396364251744-4.23963642517439
553026.41390814564283.58609185435724
562424.3667072875822-0.3667072875822
572623.47124884852552.52875115147451
582421.70940909635062.29059090364944
592218.34118967103263.65881032896738
601416.8813452280141-2.88134522801411
612420.86452002685653.13547997314349
622421.792914015022.20708598497998
632421.30991247769572.6900875223043
642419.55947269493614.44052730506388
651918.134076613610.86592338639004
663124.53130234799876.46869765200126
672226.6718175944994-4.67181759449943
682723.24621301858943.75378698141058
691917.65484605955651.34515394044351
702521.23921943415923.76078056584084
712024.3065663594345-4.30656635943452
722120.14753062300340.85246937699657
732726.02831615767870.971683842321257
742323.3870961243964-0.387096124396441
752525.05277727386-0.0527772738599558
762023.0017132099812-3.00171320998116
772118.14039547732972.85960452267035
782221.66864464139930.33135535860068
792320.8707126750332.12928732496696
802526.0513319604443-1.05133196044432
812522.52136155237812.47863844762191
821722.1192719886503-5.11927198865032
831920.5272832461467-1.5272832461467
842521.57725374799033.42274625200966
851921.2108730496103-2.21087304961026
862021.8141889977146-1.81418899771465
872622.4239170077053.57608299229505
882318.37247699560614.62752300439394
892723.99654388635063.00345611364937
901721.4681254306041-4.46812543060407
911723.3479388996919-6.3479388996919
921920.7111226188485-1.71112261884852
931720.7906723957443-3.79067239574431
942223.0300808256109-1.03008082561092
952120.30864548032140.69135451967861
963225.48439031309016.5156096869099
972123.9978570962449-2.99785709624493
982123.9455911656014-2.94559116560143
991821.9897875719193-3.98978757191928
1001822.7800735515649-4.78007355156495
1012323.0157191006703-0.0157191006703341
1021921.0610935556314-2.06109355563137
1032023.3141643844414-3.3141643844414
1042123.3190333126464-2.3190333126464
1052022.2360514809517-2.23605148095166
1061722.3503929468413-5.35039294684134
1071821.4711934291021-3.47119342910208
1081922.2847390288184-3.2847390288184
1092222.4282772724915-0.428277272491546
1101518.9061666485891-3.90616664858907
1111420.1257477956927-6.12574779569271
1121825.6974166570011-7.69741665700112
1132421.98969453933722.01030546066284
1143525.13516203172999.8648379682701
1152918.617940785927310.3820592140727
1162122.7299425324362-1.72994253243619
1172524.37855349890330.6214465010967
1182020.0309487521345-0.0309487521344678
1192221.54135358109580.458646418904167
1201319.9805987120047-6.98059871200469
1212624.30413308021871.69586691978135
1221718.6935059959558-1.69350599595577
1232521.41949283940393.58050716059607
1242020.0448056915565-0.0448056915564629
1251917.59755639634511.40244360365489
1262122.1390051788546-1.13900517885456
1272221.04029439629540.959705603704602
1282422.68174086419241.31825913580759
1292122.2144184075392-1.21441840753919
1302625.00986529219170.990134707808268
1312420.95842112114113.04157887885891
1321619.3976499118598-3.39764991185978
1332321.06171193472341.93828806527665
1341820.21420065041-2.21420065041004
1351622.3116868357331-6.31168683573312
1362624.10072894063511.89927105936486
1371920.1409423008136-1.14094230081359
1382120.43310552329780.566894476702186
1392121.6293851765056-0.629385176505612
1402222.4847198315894-0.484719831589402
1412324.5857660043262-1.58576600432624
1422924.96113465796634.03886534203371
1432121.5263751135402-0.526375113540181
1442118.83036308032592.16963691967413
1452321.57813695595031.42186304404974
1462722.4915911653294.508408834671
1472526.9046756082485-1.90467560824849
1482122.1728642163247-1.17286421632468
1491020.4443812590697-10.4443812590697
1502023.2348911538338-3.23489115383375
1512622.90134261173073.09865738826931
1522424.7007316530439-0.700731653043916
1532930.3470926233603-1.34709262336033
1541923.2091541784477-4.20915417844771
1552421.05849351108472.94150648891528
1561920.0745231134925-1.07452311349246
1572423.2054264197120.794573580287986
1582221.13748667134580.862513328654194
1591724.3431548085598-7.34315480855979

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 24 & 21.4084006996143 & 2.59159930038574 \tabularnewline
2 & 25 & 21.9025107169184 & 3.0974892830816 \tabularnewline
3 & 30 & 22.8546967919815 & 7.1453032080185 \tabularnewline
4 & 19 & 20.2514728908076 & -1.25147289080757 \tabularnewline
5 & 22 & 21.4946990358786 & 0.505300964121442 \tabularnewline
6 & 22 & 20.3752413482158 & 1.62475865178422 \tabularnewline
7 & 25 & 22.3609661255558 & 2.63903387444424 \tabularnewline
8 & 23 & 20.9696768799896 & 2.03032312001035 \tabularnewline
9 & 17 & 20.1686614766028 & -3.16866147660277 \tabularnewline
10 & 21 & 20.5166071590923 & 0.483392840907681 \tabularnewline
11 & 19 & 20.7369523379887 & -1.73695233798871 \tabularnewline
12 & 19 & 23.9332871350846 & -4.93328713508462 \tabularnewline
13 & 15 & 23.1443998085749 & -8.14439980857486 \tabularnewline
14 & 16 & 19.458684576602 & -3.45868457660204 \tabularnewline
15 & 23 & 19.6615352466933 & 3.33846475330668 \tabularnewline
16 & 27 & 21.6926539565772 & 5.30734604342283 \tabularnewline
17 & 22 & 21.8378896225772 & 0.162110377422807 \tabularnewline
18 & 14 & 20.4267866595781 & -6.42678665957812 \tabularnewline
19 & 22 & 23.60546790925 & -1.60546790925002 \tabularnewline
20 & 23 & 26.3248423405604 & -3.32484234056038 \tabularnewline
21 & 23 & 23.4564636048144 & -0.456463604814387 \tabularnewline
22 & 21 & 25.4744032515286 & -4.47440325152863 \tabularnewline
23 & 19 & 21.2103147680349 & -2.21031476803485 \tabularnewline
24 & 18 & 22.9681183096248 & -4.96811830962476 \tabularnewline
25 & 20 & 20.8919593381972 & -0.891959338197159 \tabularnewline
26 & 23 & 20.6453221852143 & 2.35467781478574 \tabularnewline
27 & 25 & 22.8670091090834 & 2.13299089091662 \tabularnewline
28 & 19 & 22.6525455100905 & -3.65254551009051 \tabularnewline
29 & 24 & 23.2007695666991 & 0.79923043330086 \tabularnewline
30 & 22 & 21.2888812025034 & 0.711118797496557 \tabularnewline
31 & 25 & 23.362911358857 & 1.63708864114299 \tabularnewline
32 & 26 & 23.2134637549219 & 2.78653624507807 \tabularnewline
33 & 29 & 25.7175146584576 & 3.28248534154245 \tabularnewline
34 & 32 & 24.2534948420646 & 7.74650515793545 \tabularnewline
35 & 25 & 20.5209480422368 & 4.47905195776315 \tabularnewline
36 & 29 & 22.716224662731 & 6.28377533726899 \tabularnewline
37 & 28 & 25.8766984563728 & 2.12330154362723 \tabularnewline
38 & 17 & 16.8904928308664 & 0.109507169133642 \tabularnewline
39 & 28 & 26.1915549647561 & 1.80844503524392 \tabularnewline
40 & 29 & 23.171807557035 & 5.82819244296497 \tabularnewline
41 & 26 & 24.3425388183865 & 1.65746118161345 \tabularnewline
42 & 25 & 22.9981245011788 & 2.00187549882124 \tabularnewline
43 & 14 & 21.8631499365697 & -7.8631499365697 \tabularnewline
44 & 25 & 23.2004739451553 & 1.79952605484474 \tabularnewline
45 & 26 & 22.4573493888402 & 3.5426506111598 \tabularnewline
46 & 20 & 19.096017574962 & 0.903982425037993 \tabularnewline
47 & 18 & 19.4922633388405 & -1.49226333884045 \tabularnewline
48 & 32 & 23.7239761339604 & 8.27602386603956 \tabularnewline
49 & 25 & 24.9992897037547 & 0.00071029624532456 \tabularnewline
50 & 25 & 21.7202482560367 & 3.27975174396329 \tabularnewline
51 & 23 & 21.5440516686678 & 1.45594833133221 \tabularnewline
52 & 21 & 21.501968127884 & -0.501968127884017 \tabularnewline
53 & 20 & 21.6647933829324 & -1.66479338293243 \tabularnewline
54 & 15 & 19.2396364251744 & -4.23963642517439 \tabularnewline
55 & 30 & 26.4139081456428 & 3.58609185435724 \tabularnewline
56 & 24 & 24.3667072875822 & -0.3667072875822 \tabularnewline
57 & 26 & 23.4712488485255 & 2.52875115147451 \tabularnewline
58 & 24 & 21.7094090963506 & 2.29059090364944 \tabularnewline
59 & 22 & 18.3411896710326 & 3.65881032896738 \tabularnewline
60 & 14 & 16.8813452280141 & -2.88134522801411 \tabularnewline
61 & 24 & 20.8645200268565 & 3.13547997314349 \tabularnewline
62 & 24 & 21.79291401502 & 2.20708598497998 \tabularnewline
63 & 24 & 21.3099124776957 & 2.6900875223043 \tabularnewline
64 & 24 & 19.5594726949361 & 4.44052730506388 \tabularnewline
65 & 19 & 18.13407661361 & 0.86592338639004 \tabularnewline
66 & 31 & 24.5313023479987 & 6.46869765200126 \tabularnewline
67 & 22 & 26.6718175944994 & -4.67181759449943 \tabularnewline
68 & 27 & 23.2462130185894 & 3.75378698141058 \tabularnewline
69 & 19 & 17.6548460595565 & 1.34515394044351 \tabularnewline
70 & 25 & 21.2392194341592 & 3.76078056584084 \tabularnewline
71 & 20 & 24.3065663594345 & -4.30656635943452 \tabularnewline
72 & 21 & 20.1475306230034 & 0.85246937699657 \tabularnewline
73 & 27 & 26.0283161576787 & 0.971683842321257 \tabularnewline
74 & 23 & 23.3870961243964 & -0.387096124396441 \tabularnewline
75 & 25 & 25.05277727386 & -0.0527772738599558 \tabularnewline
76 & 20 & 23.0017132099812 & -3.00171320998116 \tabularnewline
77 & 21 & 18.1403954773297 & 2.85960452267035 \tabularnewline
78 & 22 & 21.6686446413993 & 0.33135535860068 \tabularnewline
79 & 23 & 20.870712675033 & 2.12928732496696 \tabularnewline
80 & 25 & 26.0513319604443 & -1.05133196044432 \tabularnewline
81 & 25 & 22.5213615523781 & 2.47863844762191 \tabularnewline
82 & 17 & 22.1192719886503 & -5.11927198865032 \tabularnewline
83 & 19 & 20.5272832461467 & -1.5272832461467 \tabularnewline
84 & 25 & 21.5772537479903 & 3.42274625200966 \tabularnewline
85 & 19 & 21.2108730496103 & -2.21087304961026 \tabularnewline
86 & 20 & 21.8141889977146 & -1.81418899771465 \tabularnewline
87 & 26 & 22.423917007705 & 3.57608299229505 \tabularnewline
88 & 23 & 18.3724769956061 & 4.62752300439394 \tabularnewline
89 & 27 & 23.9965438863506 & 3.00345611364937 \tabularnewline
90 & 17 & 21.4681254306041 & -4.46812543060407 \tabularnewline
91 & 17 & 23.3479388996919 & -6.3479388996919 \tabularnewline
92 & 19 & 20.7111226188485 & -1.71112261884852 \tabularnewline
93 & 17 & 20.7906723957443 & -3.79067239574431 \tabularnewline
94 & 22 & 23.0300808256109 & -1.03008082561092 \tabularnewline
95 & 21 & 20.3086454803214 & 0.69135451967861 \tabularnewline
96 & 32 & 25.4843903130901 & 6.5156096869099 \tabularnewline
97 & 21 & 23.9978570962449 & -2.99785709624493 \tabularnewline
98 & 21 & 23.9455911656014 & -2.94559116560143 \tabularnewline
99 & 18 & 21.9897875719193 & -3.98978757191928 \tabularnewline
100 & 18 & 22.7800735515649 & -4.78007355156495 \tabularnewline
101 & 23 & 23.0157191006703 & -0.0157191006703341 \tabularnewline
102 & 19 & 21.0610935556314 & -2.06109355563137 \tabularnewline
103 & 20 & 23.3141643844414 & -3.3141643844414 \tabularnewline
104 & 21 & 23.3190333126464 & -2.3190333126464 \tabularnewline
105 & 20 & 22.2360514809517 & -2.23605148095166 \tabularnewline
106 & 17 & 22.3503929468413 & -5.35039294684134 \tabularnewline
107 & 18 & 21.4711934291021 & -3.47119342910208 \tabularnewline
108 & 19 & 22.2847390288184 & -3.2847390288184 \tabularnewline
109 & 22 & 22.4282772724915 & -0.428277272491546 \tabularnewline
110 & 15 & 18.9061666485891 & -3.90616664858907 \tabularnewline
111 & 14 & 20.1257477956927 & -6.12574779569271 \tabularnewline
112 & 18 & 25.6974166570011 & -7.69741665700112 \tabularnewline
113 & 24 & 21.9896945393372 & 2.01030546066284 \tabularnewline
114 & 35 & 25.1351620317299 & 9.8648379682701 \tabularnewline
115 & 29 & 18.6179407859273 & 10.3820592140727 \tabularnewline
116 & 21 & 22.7299425324362 & -1.72994253243619 \tabularnewline
117 & 25 & 24.3785534989033 & 0.6214465010967 \tabularnewline
118 & 20 & 20.0309487521345 & -0.0309487521344678 \tabularnewline
119 & 22 & 21.5413535810958 & 0.458646418904167 \tabularnewline
120 & 13 & 19.9805987120047 & -6.98059871200469 \tabularnewline
121 & 26 & 24.3041330802187 & 1.69586691978135 \tabularnewline
122 & 17 & 18.6935059959558 & -1.69350599595577 \tabularnewline
123 & 25 & 21.4194928394039 & 3.58050716059607 \tabularnewline
124 & 20 & 20.0448056915565 & -0.0448056915564629 \tabularnewline
125 & 19 & 17.5975563963451 & 1.40244360365489 \tabularnewline
126 & 21 & 22.1390051788546 & -1.13900517885456 \tabularnewline
127 & 22 & 21.0402943962954 & 0.959705603704602 \tabularnewline
128 & 24 & 22.6817408641924 & 1.31825913580759 \tabularnewline
129 & 21 & 22.2144184075392 & -1.21441840753919 \tabularnewline
130 & 26 & 25.0098652921917 & 0.990134707808268 \tabularnewline
131 & 24 & 20.9584211211411 & 3.04157887885891 \tabularnewline
132 & 16 & 19.3976499118598 & -3.39764991185978 \tabularnewline
133 & 23 & 21.0617119347234 & 1.93828806527665 \tabularnewline
134 & 18 & 20.21420065041 & -2.21420065041004 \tabularnewline
135 & 16 & 22.3116868357331 & -6.31168683573312 \tabularnewline
136 & 26 & 24.1007289406351 & 1.89927105936486 \tabularnewline
137 & 19 & 20.1409423008136 & -1.14094230081359 \tabularnewline
138 & 21 & 20.4331055232978 & 0.566894476702186 \tabularnewline
139 & 21 & 21.6293851765056 & -0.629385176505612 \tabularnewline
140 & 22 & 22.4847198315894 & -0.484719831589402 \tabularnewline
141 & 23 & 24.5857660043262 & -1.58576600432624 \tabularnewline
142 & 29 & 24.9611346579663 & 4.03886534203371 \tabularnewline
143 & 21 & 21.5263751135402 & -0.526375113540181 \tabularnewline
144 & 21 & 18.8303630803259 & 2.16963691967413 \tabularnewline
145 & 23 & 21.5781369559503 & 1.42186304404974 \tabularnewline
146 & 27 & 22.491591165329 & 4.508408834671 \tabularnewline
147 & 25 & 26.9046756082485 & -1.90467560824849 \tabularnewline
148 & 21 & 22.1728642163247 & -1.17286421632468 \tabularnewline
149 & 10 & 20.4443812590697 & -10.4443812590697 \tabularnewline
150 & 20 & 23.2348911538338 & -3.23489115383375 \tabularnewline
151 & 26 & 22.9013426117307 & 3.09865738826931 \tabularnewline
152 & 24 & 24.7007316530439 & -0.700731653043916 \tabularnewline
153 & 29 & 30.3470926233603 & -1.34709262336033 \tabularnewline
154 & 19 & 23.2091541784477 & -4.20915417844771 \tabularnewline
155 & 24 & 21.0584935110847 & 2.94150648891528 \tabularnewline
156 & 19 & 20.0745231134925 & -1.07452311349246 \tabularnewline
157 & 24 & 23.205426419712 & 0.794573580287986 \tabularnewline
158 & 22 & 21.1374866713458 & 0.862513328654194 \tabularnewline
159 & 17 & 24.3431548085598 & -7.34315480855979 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99407&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]24[/C][C]21.4084006996143[/C][C]2.59159930038574[/C][/ROW]
[ROW][C]2[/C][C]25[/C][C]21.9025107169184[/C][C]3.0974892830816[/C][/ROW]
[ROW][C]3[/C][C]30[/C][C]22.8546967919815[/C][C]7.1453032080185[/C][/ROW]
[ROW][C]4[/C][C]19[/C][C]20.2514728908076[/C][C]-1.25147289080757[/C][/ROW]
[ROW][C]5[/C][C]22[/C][C]21.4946990358786[/C][C]0.505300964121442[/C][/ROW]
[ROW][C]6[/C][C]22[/C][C]20.3752413482158[/C][C]1.62475865178422[/C][/ROW]
[ROW][C]7[/C][C]25[/C][C]22.3609661255558[/C][C]2.63903387444424[/C][/ROW]
[ROW][C]8[/C][C]23[/C][C]20.9696768799896[/C][C]2.03032312001035[/C][/ROW]
[ROW][C]9[/C][C]17[/C][C]20.1686614766028[/C][C]-3.16866147660277[/C][/ROW]
[ROW][C]10[/C][C]21[/C][C]20.5166071590923[/C][C]0.483392840907681[/C][/ROW]
[ROW][C]11[/C][C]19[/C][C]20.7369523379887[/C][C]-1.73695233798871[/C][/ROW]
[ROW][C]12[/C][C]19[/C][C]23.9332871350846[/C][C]-4.93328713508462[/C][/ROW]
[ROW][C]13[/C][C]15[/C][C]23.1443998085749[/C][C]-8.14439980857486[/C][/ROW]
[ROW][C]14[/C][C]16[/C][C]19.458684576602[/C][C]-3.45868457660204[/C][/ROW]
[ROW][C]15[/C][C]23[/C][C]19.6615352466933[/C][C]3.33846475330668[/C][/ROW]
[ROW][C]16[/C][C]27[/C][C]21.6926539565772[/C][C]5.30734604342283[/C][/ROW]
[ROW][C]17[/C][C]22[/C][C]21.8378896225772[/C][C]0.162110377422807[/C][/ROW]
[ROW][C]18[/C][C]14[/C][C]20.4267866595781[/C][C]-6.42678665957812[/C][/ROW]
[ROW][C]19[/C][C]22[/C][C]23.60546790925[/C][C]-1.60546790925002[/C][/ROW]
[ROW][C]20[/C][C]23[/C][C]26.3248423405604[/C][C]-3.32484234056038[/C][/ROW]
[ROW][C]21[/C][C]23[/C][C]23.4564636048144[/C][C]-0.456463604814387[/C][/ROW]
[ROW][C]22[/C][C]21[/C][C]25.4744032515286[/C][C]-4.47440325152863[/C][/ROW]
[ROW][C]23[/C][C]19[/C][C]21.2103147680349[/C][C]-2.21031476803485[/C][/ROW]
[ROW][C]24[/C][C]18[/C][C]22.9681183096248[/C][C]-4.96811830962476[/C][/ROW]
[ROW][C]25[/C][C]20[/C][C]20.8919593381972[/C][C]-0.891959338197159[/C][/ROW]
[ROW][C]26[/C][C]23[/C][C]20.6453221852143[/C][C]2.35467781478574[/C][/ROW]
[ROW][C]27[/C][C]25[/C][C]22.8670091090834[/C][C]2.13299089091662[/C][/ROW]
[ROW][C]28[/C][C]19[/C][C]22.6525455100905[/C][C]-3.65254551009051[/C][/ROW]
[ROW][C]29[/C][C]24[/C][C]23.2007695666991[/C][C]0.79923043330086[/C][/ROW]
[ROW][C]30[/C][C]22[/C][C]21.2888812025034[/C][C]0.711118797496557[/C][/ROW]
[ROW][C]31[/C][C]25[/C][C]23.362911358857[/C][C]1.63708864114299[/C][/ROW]
[ROW][C]32[/C][C]26[/C][C]23.2134637549219[/C][C]2.78653624507807[/C][/ROW]
[ROW][C]33[/C][C]29[/C][C]25.7175146584576[/C][C]3.28248534154245[/C][/ROW]
[ROW][C]34[/C][C]32[/C][C]24.2534948420646[/C][C]7.74650515793545[/C][/ROW]
[ROW][C]35[/C][C]25[/C][C]20.5209480422368[/C][C]4.47905195776315[/C][/ROW]
[ROW][C]36[/C][C]29[/C][C]22.716224662731[/C][C]6.28377533726899[/C][/ROW]
[ROW][C]37[/C][C]28[/C][C]25.8766984563728[/C][C]2.12330154362723[/C][/ROW]
[ROW][C]38[/C][C]17[/C][C]16.8904928308664[/C][C]0.109507169133642[/C][/ROW]
[ROW][C]39[/C][C]28[/C][C]26.1915549647561[/C][C]1.80844503524392[/C][/ROW]
[ROW][C]40[/C][C]29[/C][C]23.171807557035[/C][C]5.82819244296497[/C][/ROW]
[ROW][C]41[/C][C]26[/C][C]24.3425388183865[/C][C]1.65746118161345[/C][/ROW]
[ROW][C]42[/C][C]25[/C][C]22.9981245011788[/C][C]2.00187549882124[/C][/ROW]
[ROW][C]43[/C][C]14[/C][C]21.8631499365697[/C][C]-7.8631499365697[/C][/ROW]
[ROW][C]44[/C][C]25[/C][C]23.2004739451553[/C][C]1.79952605484474[/C][/ROW]
[ROW][C]45[/C][C]26[/C][C]22.4573493888402[/C][C]3.5426506111598[/C][/ROW]
[ROW][C]46[/C][C]20[/C][C]19.096017574962[/C][C]0.903982425037993[/C][/ROW]
[ROW][C]47[/C][C]18[/C][C]19.4922633388405[/C][C]-1.49226333884045[/C][/ROW]
[ROW][C]48[/C][C]32[/C][C]23.7239761339604[/C][C]8.27602386603956[/C][/ROW]
[ROW][C]49[/C][C]25[/C][C]24.9992897037547[/C][C]0.00071029624532456[/C][/ROW]
[ROW][C]50[/C][C]25[/C][C]21.7202482560367[/C][C]3.27975174396329[/C][/ROW]
[ROW][C]51[/C][C]23[/C][C]21.5440516686678[/C][C]1.45594833133221[/C][/ROW]
[ROW][C]52[/C][C]21[/C][C]21.501968127884[/C][C]-0.501968127884017[/C][/ROW]
[ROW][C]53[/C][C]20[/C][C]21.6647933829324[/C][C]-1.66479338293243[/C][/ROW]
[ROW][C]54[/C][C]15[/C][C]19.2396364251744[/C][C]-4.23963642517439[/C][/ROW]
[ROW][C]55[/C][C]30[/C][C]26.4139081456428[/C][C]3.58609185435724[/C][/ROW]
[ROW][C]56[/C][C]24[/C][C]24.3667072875822[/C][C]-0.3667072875822[/C][/ROW]
[ROW][C]57[/C][C]26[/C][C]23.4712488485255[/C][C]2.52875115147451[/C][/ROW]
[ROW][C]58[/C][C]24[/C][C]21.7094090963506[/C][C]2.29059090364944[/C][/ROW]
[ROW][C]59[/C][C]22[/C][C]18.3411896710326[/C][C]3.65881032896738[/C][/ROW]
[ROW][C]60[/C][C]14[/C][C]16.8813452280141[/C][C]-2.88134522801411[/C][/ROW]
[ROW][C]61[/C][C]24[/C][C]20.8645200268565[/C][C]3.13547997314349[/C][/ROW]
[ROW][C]62[/C][C]24[/C][C]21.79291401502[/C][C]2.20708598497998[/C][/ROW]
[ROW][C]63[/C][C]24[/C][C]21.3099124776957[/C][C]2.6900875223043[/C][/ROW]
[ROW][C]64[/C][C]24[/C][C]19.5594726949361[/C][C]4.44052730506388[/C][/ROW]
[ROW][C]65[/C][C]19[/C][C]18.13407661361[/C][C]0.86592338639004[/C][/ROW]
[ROW][C]66[/C][C]31[/C][C]24.5313023479987[/C][C]6.46869765200126[/C][/ROW]
[ROW][C]67[/C][C]22[/C][C]26.6718175944994[/C][C]-4.67181759449943[/C][/ROW]
[ROW][C]68[/C][C]27[/C][C]23.2462130185894[/C][C]3.75378698141058[/C][/ROW]
[ROW][C]69[/C][C]19[/C][C]17.6548460595565[/C][C]1.34515394044351[/C][/ROW]
[ROW][C]70[/C][C]25[/C][C]21.2392194341592[/C][C]3.76078056584084[/C][/ROW]
[ROW][C]71[/C][C]20[/C][C]24.3065663594345[/C][C]-4.30656635943452[/C][/ROW]
[ROW][C]72[/C][C]21[/C][C]20.1475306230034[/C][C]0.85246937699657[/C][/ROW]
[ROW][C]73[/C][C]27[/C][C]26.0283161576787[/C][C]0.971683842321257[/C][/ROW]
[ROW][C]74[/C][C]23[/C][C]23.3870961243964[/C][C]-0.387096124396441[/C][/ROW]
[ROW][C]75[/C][C]25[/C][C]25.05277727386[/C][C]-0.0527772738599558[/C][/ROW]
[ROW][C]76[/C][C]20[/C][C]23.0017132099812[/C][C]-3.00171320998116[/C][/ROW]
[ROW][C]77[/C][C]21[/C][C]18.1403954773297[/C][C]2.85960452267035[/C][/ROW]
[ROW][C]78[/C][C]22[/C][C]21.6686446413993[/C][C]0.33135535860068[/C][/ROW]
[ROW][C]79[/C][C]23[/C][C]20.870712675033[/C][C]2.12928732496696[/C][/ROW]
[ROW][C]80[/C][C]25[/C][C]26.0513319604443[/C][C]-1.05133196044432[/C][/ROW]
[ROW][C]81[/C][C]25[/C][C]22.5213615523781[/C][C]2.47863844762191[/C][/ROW]
[ROW][C]82[/C][C]17[/C][C]22.1192719886503[/C][C]-5.11927198865032[/C][/ROW]
[ROW][C]83[/C][C]19[/C][C]20.5272832461467[/C][C]-1.5272832461467[/C][/ROW]
[ROW][C]84[/C][C]25[/C][C]21.5772537479903[/C][C]3.42274625200966[/C][/ROW]
[ROW][C]85[/C][C]19[/C][C]21.2108730496103[/C][C]-2.21087304961026[/C][/ROW]
[ROW][C]86[/C][C]20[/C][C]21.8141889977146[/C][C]-1.81418899771465[/C][/ROW]
[ROW][C]87[/C][C]26[/C][C]22.423917007705[/C][C]3.57608299229505[/C][/ROW]
[ROW][C]88[/C][C]23[/C][C]18.3724769956061[/C][C]4.62752300439394[/C][/ROW]
[ROW][C]89[/C][C]27[/C][C]23.9965438863506[/C][C]3.00345611364937[/C][/ROW]
[ROW][C]90[/C][C]17[/C][C]21.4681254306041[/C][C]-4.46812543060407[/C][/ROW]
[ROW][C]91[/C][C]17[/C][C]23.3479388996919[/C][C]-6.3479388996919[/C][/ROW]
[ROW][C]92[/C][C]19[/C][C]20.7111226188485[/C][C]-1.71112261884852[/C][/ROW]
[ROW][C]93[/C][C]17[/C][C]20.7906723957443[/C][C]-3.79067239574431[/C][/ROW]
[ROW][C]94[/C][C]22[/C][C]23.0300808256109[/C][C]-1.03008082561092[/C][/ROW]
[ROW][C]95[/C][C]21[/C][C]20.3086454803214[/C][C]0.69135451967861[/C][/ROW]
[ROW][C]96[/C][C]32[/C][C]25.4843903130901[/C][C]6.5156096869099[/C][/ROW]
[ROW][C]97[/C][C]21[/C][C]23.9978570962449[/C][C]-2.99785709624493[/C][/ROW]
[ROW][C]98[/C][C]21[/C][C]23.9455911656014[/C][C]-2.94559116560143[/C][/ROW]
[ROW][C]99[/C][C]18[/C][C]21.9897875719193[/C][C]-3.98978757191928[/C][/ROW]
[ROW][C]100[/C][C]18[/C][C]22.7800735515649[/C][C]-4.78007355156495[/C][/ROW]
[ROW][C]101[/C][C]23[/C][C]23.0157191006703[/C][C]-0.0157191006703341[/C][/ROW]
[ROW][C]102[/C][C]19[/C][C]21.0610935556314[/C][C]-2.06109355563137[/C][/ROW]
[ROW][C]103[/C][C]20[/C][C]23.3141643844414[/C][C]-3.3141643844414[/C][/ROW]
[ROW][C]104[/C][C]21[/C][C]23.3190333126464[/C][C]-2.3190333126464[/C][/ROW]
[ROW][C]105[/C][C]20[/C][C]22.2360514809517[/C][C]-2.23605148095166[/C][/ROW]
[ROW][C]106[/C][C]17[/C][C]22.3503929468413[/C][C]-5.35039294684134[/C][/ROW]
[ROW][C]107[/C][C]18[/C][C]21.4711934291021[/C][C]-3.47119342910208[/C][/ROW]
[ROW][C]108[/C][C]19[/C][C]22.2847390288184[/C][C]-3.2847390288184[/C][/ROW]
[ROW][C]109[/C][C]22[/C][C]22.4282772724915[/C][C]-0.428277272491546[/C][/ROW]
[ROW][C]110[/C][C]15[/C][C]18.9061666485891[/C][C]-3.90616664858907[/C][/ROW]
[ROW][C]111[/C][C]14[/C][C]20.1257477956927[/C][C]-6.12574779569271[/C][/ROW]
[ROW][C]112[/C][C]18[/C][C]25.6974166570011[/C][C]-7.69741665700112[/C][/ROW]
[ROW][C]113[/C][C]24[/C][C]21.9896945393372[/C][C]2.01030546066284[/C][/ROW]
[ROW][C]114[/C][C]35[/C][C]25.1351620317299[/C][C]9.8648379682701[/C][/ROW]
[ROW][C]115[/C][C]29[/C][C]18.6179407859273[/C][C]10.3820592140727[/C][/ROW]
[ROW][C]116[/C][C]21[/C][C]22.7299425324362[/C][C]-1.72994253243619[/C][/ROW]
[ROW][C]117[/C][C]25[/C][C]24.3785534989033[/C][C]0.6214465010967[/C][/ROW]
[ROW][C]118[/C][C]20[/C][C]20.0309487521345[/C][C]-0.0309487521344678[/C][/ROW]
[ROW][C]119[/C][C]22[/C][C]21.5413535810958[/C][C]0.458646418904167[/C][/ROW]
[ROW][C]120[/C][C]13[/C][C]19.9805987120047[/C][C]-6.98059871200469[/C][/ROW]
[ROW][C]121[/C][C]26[/C][C]24.3041330802187[/C][C]1.69586691978135[/C][/ROW]
[ROW][C]122[/C][C]17[/C][C]18.6935059959558[/C][C]-1.69350599595577[/C][/ROW]
[ROW][C]123[/C][C]25[/C][C]21.4194928394039[/C][C]3.58050716059607[/C][/ROW]
[ROW][C]124[/C][C]20[/C][C]20.0448056915565[/C][C]-0.0448056915564629[/C][/ROW]
[ROW][C]125[/C][C]19[/C][C]17.5975563963451[/C][C]1.40244360365489[/C][/ROW]
[ROW][C]126[/C][C]21[/C][C]22.1390051788546[/C][C]-1.13900517885456[/C][/ROW]
[ROW][C]127[/C][C]22[/C][C]21.0402943962954[/C][C]0.959705603704602[/C][/ROW]
[ROW][C]128[/C][C]24[/C][C]22.6817408641924[/C][C]1.31825913580759[/C][/ROW]
[ROW][C]129[/C][C]21[/C][C]22.2144184075392[/C][C]-1.21441840753919[/C][/ROW]
[ROW][C]130[/C][C]26[/C][C]25.0098652921917[/C][C]0.990134707808268[/C][/ROW]
[ROW][C]131[/C][C]24[/C][C]20.9584211211411[/C][C]3.04157887885891[/C][/ROW]
[ROW][C]132[/C][C]16[/C][C]19.3976499118598[/C][C]-3.39764991185978[/C][/ROW]
[ROW][C]133[/C][C]23[/C][C]21.0617119347234[/C][C]1.93828806527665[/C][/ROW]
[ROW][C]134[/C][C]18[/C][C]20.21420065041[/C][C]-2.21420065041004[/C][/ROW]
[ROW][C]135[/C][C]16[/C][C]22.3116868357331[/C][C]-6.31168683573312[/C][/ROW]
[ROW][C]136[/C][C]26[/C][C]24.1007289406351[/C][C]1.89927105936486[/C][/ROW]
[ROW][C]137[/C][C]19[/C][C]20.1409423008136[/C][C]-1.14094230081359[/C][/ROW]
[ROW][C]138[/C][C]21[/C][C]20.4331055232978[/C][C]0.566894476702186[/C][/ROW]
[ROW][C]139[/C][C]21[/C][C]21.6293851765056[/C][C]-0.629385176505612[/C][/ROW]
[ROW][C]140[/C][C]22[/C][C]22.4847198315894[/C][C]-0.484719831589402[/C][/ROW]
[ROW][C]141[/C][C]23[/C][C]24.5857660043262[/C][C]-1.58576600432624[/C][/ROW]
[ROW][C]142[/C][C]29[/C][C]24.9611346579663[/C][C]4.03886534203371[/C][/ROW]
[ROW][C]143[/C][C]21[/C][C]21.5263751135402[/C][C]-0.526375113540181[/C][/ROW]
[ROW][C]144[/C][C]21[/C][C]18.8303630803259[/C][C]2.16963691967413[/C][/ROW]
[ROW][C]145[/C][C]23[/C][C]21.5781369559503[/C][C]1.42186304404974[/C][/ROW]
[ROW][C]146[/C][C]27[/C][C]22.491591165329[/C][C]4.508408834671[/C][/ROW]
[ROW][C]147[/C][C]25[/C][C]26.9046756082485[/C][C]-1.90467560824849[/C][/ROW]
[ROW][C]148[/C][C]21[/C][C]22.1728642163247[/C][C]-1.17286421632468[/C][/ROW]
[ROW][C]149[/C][C]10[/C][C]20.4443812590697[/C][C]-10.4443812590697[/C][/ROW]
[ROW][C]150[/C][C]20[/C][C]23.2348911538338[/C][C]-3.23489115383375[/C][/ROW]
[ROW][C]151[/C][C]26[/C][C]22.9013426117307[/C][C]3.09865738826931[/C][/ROW]
[ROW][C]152[/C][C]24[/C][C]24.7007316530439[/C][C]-0.700731653043916[/C][/ROW]
[ROW][C]153[/C][C]29[/C][C]30.3470926233603[/C][C]-1.34709262336033[/C][/ROW]
[ROW][C]154[/C][C]19[/C][C]23.2091541784477[/C][C]-4.20915417844771[/C][/ROW]
[ROW][C]155[/C][C]24[/C][C]21.0584935110847[/C][C]2.94150648891528[/C][/ROW]
[ROW][C]156[/C][C]19[/C][C]20.0745231134925[/C][C]-1.07452311349246[/C][/ROW]
[ROW][C]157[/C][C]24[/C][C]23.205426419712[/C][C]0.794573580287986[/C][/ROW]
[ROW][C]158[/C][C]22[/C][C]21.1374866713458[/C][C]0.862513328654194[/C][/ROW]
[ROW][C]159[/C][C]17[/C][C]24.3431548085598[/C][C]-7.34315480855979[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99407&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99407&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
12421.40840069961432.59159930038574
22521.90251071691843.0974892830816
33022.85469679198157.1453032080185
41920.2514728908076-1.25147289080757
52221.49469903587860.505300964121442
62220.37524134821581.62475865178422
72522.36096612555582.63903387444424
82320.96967687998962.03032312001035
91720.1686614766028-3.16866147660277
102120.51660715909230.483392840907681
111920.7369523379887-1.73695233798871
121923.9332871350846-4.93328713508462
131523.1443998085749-8.14439980857486
141619.458684576602-3.45868457660204
152319.66153524669333.33846475330668
162721.69265395657725.30734604342283
172221.83788962257720.162110377422807
181420.4267866595781-6.42678665957812
192223.60546790925-1.60546790925002
202326.3248423405604-3.32484234056038
212323.4564636048144-0.456463604814387
222125.4744032515286-4.47440325152863
231921.2103147680349-2.21031476803485
241822.9681183096248-4.96811830962476
252020.8919593381972-0.891959338197159
262320.64532218521432.35467781478574
272522.86700910908342.13299089091662
281922.6525455100905-3.65254551009051
292423.20076956669910.79923043330086
302221.28888120250340.711118797496557
312523.3629113588571.63708864114299
322623.21346375492192.78653624507807
332925.71751465845763.28248534154245
343224.25349484206467.74650515793545
352520.52094804223684.47905195776315
362922.7162246627316.28377533726899
372825.87669845637282.12330154362723
381716.89049283086640.109507169133642
392826.19155496475611.80844503524392
402923.1718075570355.82819244296497
412624.34253881838651.65746118161345
422522.99812450117882.00187549882124
431421.8631499365697-7.8631499365697
442523.20047394515531.79952605484474
452622.45734938884023.5426506111598
462019.0960175749620.903982425037993
471819.4922633388405-1.49226333884045
483223.72397613396048.27602386603956
492524.99928970375470.00071029624532456
502521.72024825603673.27975174396329
512321.54405166866781.45594833133221
522121.501968127884-0.501968127884017
532021.6647933829324-1.66479338293243
541519.2396364251744-4.23963642517439
553026.41390814564283.58609185435724
562424.3667072875822-0.3667072875822
572623.47124884852552.52875115147451
582421.70940909635062.29059090364944
592218.34118967103263.65881032896738
601416.8813452280141-2.88134522801411
612420.86452002685653.13547997314349
622421.792914015022.20708598497998
632421.30991247769572.6900875223043
642419.55947269493614.44052730506388
651918.134076613610.86592338639004
663124.53130234799876.46869765200126
672226.6718175944994-4.67181759449943
682723.24621301858943.75378698141058
691917.65484605955651.34515394044351
702521.23921943415923.76078056584084
712024.3065663594345-4.30656635943452
722120.14753062300340.85246937699657
732726.02831615767870.971683842321257
742323.3870961243964-0.387096124396441
752525.05277727386-0.0527772738599558
762023.0017132099812-3.00171320998116
772118.14039547732972.85960452267035
782221.66864464139930.33135535860068
792320.8707126750332.12928732496696
802526.0513319604443-1.05133196044432
812522.52136155237812.47863844762191
821722.1192719886503-5.11927198865032
831920.5272832461467-1.5272832461467
842521.57725374799033.42274625200966
851921.2108730496103-2.21087304961026
862021.8141889977146-1.81418899771465
872622.4239170077053.57608299229505
882318.37247699560614.62752300439394
892723.99654388635063.00345611364937
901721.4681254306041-4.46812543060407
911723.3479388996919-6.3479388996919
921920.7111226188485-1.71112261884852
931720.7906723957443-3.79067239574431
942223.0300808256109-1.03008082561092
952120.30864548032140.69135451967861
963225.48439031309016.5156096869099
972123.9978570962449-2.99785709624493
982123.9455911656014-2.94559116560143
991821.9897875719193-3.98978757191928
1001822.7800735515649-4.78007355156495
1012323.0157191006703-0.0157191006703341
1021921.0610935556314-2.06109355563137
1032023.3141643844414-3.3141643844414
1042123.3190333126464-2.3190333126464
1052022.2360514809517-2.23605148095166
1061722.3503929468413-5.35039294684134
1071821.4711934291021-3.47119342910208
1081922.2847390288184-3.2847390288184
1092222.4282772724915-0.428277272491546
1101518.9061666485891-3.90616664858907
1111420.1257477956927-6.12574779569271
1121825.6974166570011-7.69741665700112
1132421.98969453933722.01030546066284
1143525.13516203172999.8648379682701
1152918.617940785927310.3820592140727
1162122.7299425324362-1.72994253243619
1172524.37855349890330.6214465010967
1182020.0309487521345-0.0309487521344678
1192221.54135358109580.458646418904167
1201319.9805987120047-6.98059871200469
1212624.30413308021871.69586691978135
1221718.6935059959558-1.69350599595577
1232521.41949283940393.58050716059607
1242020.0448056915565-0.0448056915564629
1251917.59755639634511.40244360365489
1262122.1390051788546-1.13900517885456
1272221.04029439629540.959705603704602
1282422.68174086419241.31825913580759
1292122.2144184075392-1.21441840753919
1302625.00986529219170.990134707808268
1312420.95842112114113.04157887885891
1321619.3976499118598-3.39764991185978
1332321.06171193472341.93828806527665
1341820.21420065041-2.21420065041004
1351622.3116868357331-6.31168683573312
1362624.10072894063511.89927105936486
1371920.1409423008136-1.14094230081359
1382120.43310552329780.566894476702186
1392121.6293851765056-0.629385176505612
1402222.4847198315894-0.484719831589402
1412324.5857660043262-1.58576600432624
1422924.96113465796634.03886534203371
1432121.5263751135402-0.526375113540181
1442118.83036308032592.16963691967413
1452321.57813695595031.42186304404974
1462722.4915911653294.508408834671
1472526.9046756082485-1.90467560824849
1482122.1728642163247-1.17286421632468
1491020.4443812590697-10.4443812590697
1502023.2348911538338-3.23489115383375
1512622.90134261173073.09865738826931
1522424.7007316530439-0.700731653043916
1532930.3470926233603-1.34709262336033
1541923.2091541784477-4.20915417844771
1552421.05849351108472.94150648891528
1561920.0745231134925-1.07452311349246
1572423.2054264197120.794573580287986
1582221.13748667134580.862513328654194
1591724.3431548085598-7.34315480855979







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
190.6086084383836020.7827831232327950.391391561616398
200.959346335177930.081307329644140.04065366482207
210.9463857700889660.1072284598220690.0536142299110343
220.9143318353859030.1713363292281950.0856681646140974
230.8653233323755980.2693533352488050.134676667624402
240.8214060397954680.3571879204090630.178593960204532
250.7500971721920680.4998056556158650.249902827807932
260.6971559903827830.6056880192344350.302844009617217
270.6564182166444540.6871635667110920.343581783355546
280.6226969068641190.7546061862717620.377303093135881
290.5418777421320090.9162445157359810.458122257867991
300.4810206707071980.9620413414143950.518979329292802
310.4176530228651850.8353060457303690.582346977134815
320.4221321575684330.8442643151368660.577867842431567
330.4169220548097420.8338441096194840.583077945190258
340.5930280325506880.8139439348986240.406971967449312
350.585692581023030.8286148379539410.41430741897697
360.7508273699871060.4983452600257880.249172630012894
370.6975477420237850.604904515952430.302452257976215
380.6556382082924240.6887235834151520.344361791707576
390.606750134037920.7864997319241610.393249865962081
400.6566252803095140.6867494393809710.343374719690486
410.6095939225624350.780812154875130.390406077437565
420.6000411223261020.7999177553477960.399958877673898
430.6758213095672450.648357380865510.324178690432755
440.6723552168808230.6552895662383530.327644783119177
450.661296231333330.677407537333340.33870376866667
460.6065828404392080.7868343191215840.393417159560792
470.5613719560462010.8772560879075970.438628043953799
480.686902853405540.626194293188920.31309714659446
490.6345000730074180.7309998539851640.365499926992582
500.6153282238922460.7693435522155070.384671776107754
510.6366461617168620.7267076765662760.363353838283138
520.5868463810245570.8263072379508870.413153618975443
530.551872445966430.8962551080671410.448127554033571
540.5895291491707560.8209417016584880.410470850829244
550.6768214014324720.6463571971350560.323178598567528
560.634708013587380.730583972825240.36529198641262
570.5964196465681220.8071607068637550.403580353431878
580.5566816718088550.886636656382290.443318328191145
590.537175676442930.9256486471141410.46282432355707
600.4996978259454020.9993956518908050.500302174054598
610.498001479735070.996002959470140.501998520264929
620.4625417501207470.9250835002414930.537458249879253
630.4401340072092940.8802680144185870.559865992790706
640.4703028752998260.9406057505996520.529697124700174
650.4261795438136030.8523590876272050.573820456186397
660.5161802911801410.9676394176397170.483819708819859
670.5603952706868640.8792094586262730.439604729313136
680.5484804625030330.9030390749939350.451519537496967
690.5089717034412870.9820565931174250.491028296558713
700.502644060161460.9947118796770810.49735593983854
710.5489634570609480.9020730858781040.451036542939052
720.5002926040810470.9994147918379070.499707395918953
730.451237623740890.902475247481780.54876237625911
740.4035076955233490.8070153910466980.596492304476651
750.4004470733823020.8008941467646030.599552926617698
760.3930130857888490.7860261715776990.606986914211151
770.390192276767270.7803845535345410.60980772323273
780.3442847474931760.6885694949863510.655715252506824
790.3200799519747760.6401599039495510.679920048025224
800.2898455598319880.5796911196639770.710154440168012
810.2660922605639390.5321845211278790.733907739436061
820.3162564408858890.6325128817717780.683743559114111
830.2770609935556660.5541219871113320.722939006444334
840.2680755948601850.5361511897203690.731924405139815
850.2409239096433060.4818478192866110.759076090356694
860.2147809382067910.4295618764135830.785219061793209
870.2595894899845240.5191789799690490.740410510015476
880.3062719858786590.6125439717573170.693728014121341
890.2857269054967250.5714538109934510.714273094503275
900.3028983013767230.6057966027534470.697101698623277
910.4121505374969990.8243010749939980.587849462503001
920.375022109392850.75004421878570.62497789060715
930.3674173835386720.7348347670773440.632582616461328
940.3265175793643970.6530351587287940.673482420635603
950.2831096470064840.5662192940129670.716890352993516
960.4072355728564450.814471145712890.592764427143555
970.4057246414829210.8114492829658410.59427535851708
980.3894956482193710.7789912964387410.610504351780629
990.3941870942063990.7883741884127980.6058129057936
1000.4068782016695730.8137564033391460.593121798330427
1010.3701894152392930.7403788304785850.629810584760708
1020.339600198207610.679200396415220.66039980179239
1030.3626112538435390.7252225076870780.637388746156461
1040.3247477677641760.6494955355283510.675252232235824
1050.2970576872410270.5941153744820540.702942312758973
1060.3426094005195430.6852188010390860.657390599480457
1070.3427119203990370.6854238407980750.657288079600963
1080.3173565148245330.6347130296490660.682643485175467
1090.2782765293847820.5565530587695650.721723470615218
1100.2804275131519550.560855026303910.719572486848045
1110.2954879558547420.5909759117094840.704512044145258
1120.440337139522870.880674279045740.55966286047713
1130.4477858383804250.895571676760850.552214161619575
1140.8489129485226070.3021741029547860.151087051477393
1150.9734137678177840.05317246436443240.0265862321822162
1160.9623523639860020.07529527202799590.037647636013998
1170.9649046835145430.07019063297091430.0350953164854572
1180.9504456999654740.09910860006905270.0495543000345263
1190.9506926140084570.09861477198308530.0493073859915426
1200.981779937986440.03644012402712080.0182200620135604
1210.9729409389811770.05411812203764530.0270590610188226
1220.963120946961730.07375810607654010.0368790530382701
1230.9893429273909550.0213141452180910.0106570726090455
1240.9825457186359040.03490856272819160.0174542813640958
1250.9774269037145530.04514619257089410.0225730962854471
1260.9647402991454360.07051940170912770.0352597008545638
1270.9520954371054970.0958091257890050.0479045628945025
1280.9408560170160320.1182879659679350.0591439829839676
1290.9392123042277330.1215753915445350.0607876957722674
1300.9146382567359960.1707234865280080.085361743264004
1310.8747312810395740.2505374379208520.125268718960426
1320.8249443031452450.350111393709510.175055696854755
1330.7819606628630330.4360786742739350.218039337136967
1340.7133497658583210.5733004682833580.286650234141679
1350.63781995108140.7243600978371990.362180048918599
1360.549473688945260.901052622109480.45052631105474
1370.6641386909098550.671722618180290.335861309090145
1380.714450606654740.571098786690520.28554939334526
1390.583998470124260.832003059751480.41600152987574
1400.4150036068445830.8300072136891650.584996393155417

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
19 & 0.608608438383602 & 0.782783123232795 & 0.391391561616398 \tabularnewline
20 & 0.95934633517793 & 0.08130732964414 & 0.04065366482207 \tabularnewline
21 & 0.946385770088966 & 0.107228459822069 & 0.0536142299110343 \tabularnewline
22 & 0.914331835385903 & 0.171336329228195 & 0.0856681646140974 \tabularnewline
23 & 0.865323332375598 & 0.269353335248805 & 0.134676667624402 \tabularnewline
24 & 0.821406039795468 & 0.357187920409063 & 0.178593960204532 \tabularnewline
25 & 0.750097172192068 & 0.499805655615865 & 0.249902827807932 \tabularnewline
26 & 0.697155990382783 & 0.605688019234435 & 0.302844009617217 \tabularnewline
27 & 0.656418216644454 & 0.687163566711092 & 0.343581783355546 \tabularnewline
28 & 0.622696906864119 & 0.754606186271762 & 0.377303093135881 \tabularnewline
29 & 0.541877742132009 & 0.916244515735981 & 0.458122257867991 \tabularnewline
30 & 0.481020670707198 & 0.962041341414395 & 0.518979329292802 \tabularnewline
31 & 0.417653022865185 & 0.835306045730369 & 0.582346977134815 \tabularnewline
32 & 0.422132157568433 & 0.844264315136866 & 0.577867842431567 \tabularnewline
33 & 0.416922054809742 & 0.833844109619484 & 0.583077945190258 \tabularnewline
34 & 0.593028032550688 & 0.813943934898624 & 0.406971967449312 \tabularnewline
35 & 0.58569258102303 & 0.828614837953941 & 0.41430741897697 \tabularnewline
36 & 0.750827369987106 & 0.498345260025788 & 0.249172630012894 \tabularnewline
37 & 0.697547742023785 & 0.60490451595243 & 0.302452257976215 \tabularnewline
38 & 0.655638208292424 & 0.688723583415152 & 0.344361791707576 \tabularnewline
39 & 0.60675013403792 & 0.786499731924161 & 0.393249865962081 \tabularnewline
40 & 0.656625280309514 & 0.686749439380971 & 0.343374719690486 \tabularnewline
41 & 0.609593922562435 & 0.78081215487513 & 0.390406077437565 \tabularnewline
42 & 0.600041122326102 & 0.799917755347796 & 0.399958877673898 \tabularnewline
43 & 0.675821309567245 & 0.64835738086551 & 0.324178690432755 \tabularnewline
44 & 0.672355216880823 & 0.655289566238353 & 0.327644783119177 \tabularnewline
45 & 0.66129623133333 & 0.67740753733334 & 0.33870376866667 \tabularnewline
46 & 0.606582840439208 & 0.786834319121584 & 0.393417159560792 \tabularnewline
47 & 0.561371956046201 & 0.877256087907597 & 0.438628043953799 \tabularnewline
48 & 0.68690285340554 & 0.62619429318892 & 0.31309714659446 \tabularnewline
49 & 0.634500073007418 & 0.730999853985164 & 0.365499926992582 \tabularnewline
50 & 0.615328223892246 & 0.769343552215507 & 0.384671776107754 \tabularnewline
51 & 0.636646161716862 & 0.726707676566276 & 0.363353838283138 \tabularnewline
52 & 0.586846381024557 & 0.826307237950887 & 0.413153618975443 \tabularnewline
53 & 0.55187244596643 & 0.896255108067141 & 0.448127554033571 \tabularnewline
54 & 0.589529149170756 & 0.820941701658488 & 0.410470850829244 \tabularnewline
55 & 0.676821401432472 & 0.646357197135056 & 0.323178598567528 \tabularnewline
56 & 0.63470801358738 & 0.73058397282524 & 0.36529198641262 \tabularnewline
57 & 0.596419646568122 & 0.807160706863755 & 0.403580353431878 \tabularnewline
58 & 0.556681671808855 & 0.88663665638229 & 0.443318328191145 \tabularnewline
59 & 0.53717567644293 & 0.925648647114141 & 0.46282432355707 \tabularnewline
60 & 0.499697825945402 & 0.999395651890805 & 0.500302174054598 \tabularnewline
61 & 0.49800147973507 & 0.99600295947014 & 0.501998520264929 \tabularnewline
62 & 0.462541750120747 & 0.925083500241493 & 0.537458249879253 \tabularnewline
63 & 0.440134007209294 & 0.880268014418587 & 0.559865992790706 \tabularnewline
64 & 0.470302875299826 & 0.940605750599652 & 0.529697124700174 \tabularnewline
65 & 0.426179543813603 & 0.852359087627205 & 0.573820456186397 \tabularnewline
66 & 0.516180291180141 & 0.967639417639717 & 0.483819708819859 \tabularnewline
67 & 0.560395270686864 & 0.879209458626273 & 0.439604729313136 \tabularnewline
68 & 0.548480462503033 & 0.903039074993935 & 0.451519537496967 \tabularnewline
69 & 0.508971703441287 & 0.982056593117425 & 0.491028296558713 \tabularnewline
70 & 0.50264406016146 & 0.994711879677081 & 0.49735593983854 \tabularnewline
71 & 0.548963457060948 & 0.902073085878104 & 0.451036542939052 \tabularnewline
72 & 0.500292604081047 & 0.999414791837907 & 0.499707395918953 \tabularnewline
73 & 0.45123762374089 & 0.90247524748178 & 0.54876237625911 \tabularnewline
74 & 0.403507695523349 & 0.807015391046698 & 0.596492304476651 \tabularnewline
75 & 0.400447073382302 & 0.800894146764603 & 0.599552926617698 \tabularnewline
76 & 0.393013085788849 & 0.786026171577699 & 0.606986914211151 \tabularnewline
77 & 0.39019227676727 & 0.780384553534541 & 0.60980772323273 \tabularnewline
78 & 0.344284747493176 & 0.688569494986351 & 0.655715252506824 \tabularnewline
79 & 0.320079951974776 & 0.640159903949551 & 0.679920048025224 \tabularnewline
80 & 0.289845559831988 & 0.579691119663977 & 0.710154440168012 \tabularnewline
81 & 0.266092260563939 & 0.532184521127879 & 0.733907739436061 \tabularnewline
82 & 0.316256440885889 & 0.632512881771778 & 0.683743559114111 \tabularnewline
83 & 0.277060993555666 & 0.554121987111332 & 0.722939006444334 \tabularnewline
84 & 0.268075594860185 & 0.536151189720369 & 0.731924405139815 \tabularnewline
85 & 0.240923909643306 & 0.481847819286611 & 0.759076090356694 \tabularnewline
86 & 0.214780938206791 & 0.429561876413583 & 0.785219061793209 \tabularnewline
87 & 0.259589489984524 & 0.519178979969049 & 0.740410510015476 \tabularnewline
88 & 0.306271985878659 & 0.612543971757317 & 0.693728014121341 \tabularnewline
89 & 0.285726905496725 & 0.571453810993451 & 0.714273094503275 \tabularnewline
90 & 0.302898301376723 & 0.605796602753447 & 0.697101698623277 \tabularnewline
91 & 0.412150537496999 & 0.824301074993998 & 0.587849462503001 \tabularnewline
92 & 0.37502210939285 & 0.7500442187857 & 0.62497789060715 \tabularnewline
93 & 0.367417383538672 & 0.734834767077344 & 0.632582616461328 \tabularnewline
94 & 0.326517579364397 & 0.653035158728794 & 0.673482420635603 \tabularnewline
95 & 0.283109647006484 & 0.566219294012967 & 0.716890352993516 \tabularnewline
96 & 0.407235572856445 & 0.81447114571289 & 0.592764427143555 \tabularnewline
97 & 0.405724641482921 & 0.811449282965841 & 0.59427535851708 \tabularnewline
98 & 0.389495648219371 & 0.778991296438741 & 0.610504351780629 \tabularnewline
99 & 0.394187094206399 & 0.788374188412798 & 0.6058129057936 \tabularnewline
100 & 0.406878201669573 & 0.813756403339146 & 0.593121798330427 \tabularnewline
101 & 0.370189415239293 & 0.740378830478585 & 0.629810584760708 \tabularnewline
102 & 0.33960019820761 & 0.67920039641522 & 0.66039980179239 \tabularnewline
103 & 0.362611253843539 & 0.725222507687078 & 0.637388746156461 \tabularnewline
104 & 0.324747767764176 & 0.649495535528351 & 0.675252232235824 \tabularnewline
105 & 0.297057687241027 & 0.594115374482054 & 0.702942312758973 \tabularnewline
106 & 0.342609400519543 & 0.685218801039086 & 0.657390599480457 \tabularnewline
107 & 0.342711920399037 & 0.685423840798075 & 0.657288079600963 \tabularnewline
108 & 0.317356514824533 & 0.634713029649066 & 0.682643485175467 \tabularnewline
109 & 0.278276529384782 & 0.556553058769565 & 0.721723470615218 \tabularnewline
110 & 0.280427513151955 & 0.56085502630391 & 0.719572486848045 \tabularnewline
111 & 0.295487955854742 & 0.590975911709484 & 0.704512044145258 \tabularnewline
112 & 0.44033713952287 & 0.88067427904574 & 0.55966286047713 \tabularnewline
113 & 0.447785838380425 & 0.89557167676085 & 0.552214161619575 \tabularnewline
114 & 0.848912948522607 & 0.302174102954786 & 0.151087051477393 \tabularnewline
115 & 0.973413767817784 & 0.0531724643644324 & 0.0265862321822162 \tabularnewline
116 & 0.962352363986002 & 0.0752952720279959 & 0.037647636013998 \tabularnewline
117 & 0.964904683514543 & 0.0701906329709143 & 0.0350953164854572 \tabularnewline
118 & 0.950445699965474 & 0.0991086000690527 & 0.0495543000345263 \tabularnewline
119 & 0.950692614008457 & 0.0986147719830853 & 0.0493073859915426 \tabularnewline
120 & 0.98177993798644 & 0.0364401240271208 & 0.0182200620135604 \tabularnewline
121 & 0.972940938981177 & 0.0541181220376453 & 0.0270590610188226 \tabularnewline
122 & 0.96312094696173 & 0.0737581060765401 & 0.0368790530382701 \tabularnewline
123 & 0.989342927390955 & 0.021314145218091 & 0.0106570726090455 \tabularnewline
124 & 0.982545718635904 & 0.0349085627281916 & 0.0174542813640958 \tabularnewline
125 & 0.977426903714553 & 0.0451461925708941 & 0.0225730962854471 \tabularnewline
126 & 0.964740299145436 & 0.0705194017091277 & 0.0352597008545638 \tabularnewline
127 & 0.952095437105497 & 0.095809125789005 & 0.0479045628945025 \tabularnewline
128 & 0.940856017016032 & 0.118287965967935 & 0.0591439829839676 \tabularnewline
129 & 0.939212304227733 & 0.121575391544535 & 0.0607876957722674 \tabularnewline
130 & 0.914638256735996 & 0.170723486528008 & 0.085361743264004 \tabularnewline
131 & 0.874731281039574 & 0.250537437920852 & 0.125268718960426 \tabularnewline
132 & 0.824944303145245 & 0.35011139370951 & 0.175055696854755 \tabularnewline
133 & 0.781960662863033 & 0.436078674273935 & 0.218039337136967 \tabularnewline
134 & 0.713349765858321 & 0.573300468283358 & 0.286650234141679 \tabularnewline
135 & 0.6378199510814 & 0.724360097837199 & 0.362180048918599 \tabularnewline
136 & 0.54947368894526 & 0.90105262210948 & 0.45052631105474 \tabularnewline
137 & 0.664138690909855 & 0.67172261818029 & 0.335861309090145 \tabularnewline
138 & 0.71445060665474 & 0.57109878669052 & 0.28554939334526 \tabularnewline
139 & 0.58399847012426 & 0.83200305975148 & 0.41600152987574 \tabularnewline
140 & 0.415003606844583 & 0.830007213689165 & 0.584996393155417 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99407&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]19[/C][C]0.608608438383602[/C][C]0.782783123232795[/C][C]0.391391561616398[/C][/ROW]
[ROW][C]20[/C][C]0.95934633517793[/C][C]0.08130732964414[/C][C]0.04065366482207[/C][/ROW]
[ROW][C]21[/C][C]0.946385770088966[/C][C]0.107228459822069[/C][C]0.0536142299110343[/C][/ROW]
[ROW][C]22[/C][C]0.914331835385903[/C][C]0.171336329228195[/C][C]0.0856681646140974[/C][/ROW]
[ROW][C]23[/C][C]0.865323332375598[/C][C]0.269353335248805[/C][C]0.134676667624402[/C][/ROW]
[ROW][C]24[/C][C]0.821406039795468[/C][C]0.357187920409063[/C][C]0.178593960204532[/C][/ROW]
[ROW][C]25[/C][C]0.750097172192068[/C][C]0.499805655615865[/C][C]0.249902827807932[/C][/ROW]
[ROW][C]26[/C][C]0.697155990382783[/C][C]0.605688019234435[/C][C]0.302844009617217[/C][/ROW]
[ROW][C]27[/C][C]0.656418216644454[/C][C]0.687163566711092[/C][C]0.343581783355546[/C][/ROW]
[ROW][C]28[/C][C]0.622696906864119[/C][C]0.754606186271762[/C][C]0.377303093135881[/C][/ROW]
[ROW][C]29[/C][C]0.541877742132009[/C][C]0.916244515735981[/C][C]0.458122257867991[/C][/ROW]
[ROW][C]30[/C][C]0.481020670707198[/C][C]0.962041341414395[/C][C]0.518979329292802[/C][/ROW]
[ROW][C]31[/C][C]0.417653022865185[/C][C]0.835306045730369[/C][C]0.582346977134815[/C][/ROW]
[ROW][C]32[/C][C]0.422132157568433[/C][C]0.844264315136866[/C][C]0.577867842431567[/C][/ROW]
[ROW][C]33[/C][C]0.416922054809742[/C][C]0.833844109619484[/C][C]0.583077945190258[/C][/ROW]
[ROW][C]34[/C][C]0.593028032550688[/C][C]0.813943934898624[/C][C]0.406971967449312[/C][/ROW]
[ROW][C]35[/C][C]0.58569258102303[/C][C]0.828614837953941[/C][C]0.41430741897697[/C][/ROW]
[ROW][C]36[/C][C]0.750827369987106[/C][C]0.498345260025788[/C][C]0.249172630012894[/C][/ROW]
[ROW][C]37[/C][C]0.697547742023785[/C][C]0.60490451595243[/C][C]0.302452257976215[/C][/ROW]
[ROW][C]38[/C][C]0.655638208292424[/C][C]0.688723583415152[/C][C]0.344361791707576[/C][/ROW]
[ROW][C]39[/C][C]0.60675013403792[/C][C]0.786499731924161[/C][C]0.393249865962081[/C][/ROW]
[ROW][C]40[/C][C]0.656625280309514[/C][C]0.686749439380971[/C][C]0.343374719690486[/C][/ROW]
[ROW][C]41[/C][C]0.609593922562435[/C][C]0.78081215487513[/C][C]0.390406077437565[/C][/ROW]
[ROW][C]42[/C][C]0.600041122326102[/C][C]0.799917755347796[/C][C]0.399958877673898[/C][/ROW]
[ROW][C]43[/C][C]0.675821309567245[/C][C]0.64835738086551[/C][C]0.324178690432755[/C][/ROW]
[ROW][C]44[/C][C]0.672355216880823[/C][C]0.655289566238353[/C][C]0.327644783119177[/C][/ROW]
[ROW][C]45[/C][C]0.66129623133333[/C][C]0.67740753733334[/C][C]0.33870376866667[/C][/ROW]
[ROW][C]46[/C][C]0.606582840439208[/C][C]0.786834319121584[/C][C]0.393417159560792[/C][/ROW]
[ROW][C]47[/C][C]0.561371956046201[/C][C]0.877256087907597[/C][C]0.438628043953799[/C][/ROW]
[ROW][C]48[/C][C]0.68690285340554[/C][C]0.62619429318892[/C][C]0.31309714659446[/C][/ROW]
[ROW][C]49[/C][C]0.634500073007418[/C][C]0.730999853985164[/C][C]0.365499926992582[/C][/ROW]
[ROW][C]50[/C][C]0.615328223892246[/C][C]0.769343552215507[/C][C]0.384671776107754[/C][/ROW]
[ROW][C]51[/C][C]0.636646161716862[/C][C]0.726707676566276[/C][C]0.363353838283138[/C][/ROW]
[ROW][C]52[/C][C]0.586846381024557[/C][C]0.826307237950887[/C][C]0.413153618975443[/C][/ROW]
[ROW][C]53[/C][C]0.55187244596643[/C][C]0.896255108067141[/C][C]0.448127554033571[/C][/ROW]
[ROW][C]54[/C][C]0.589529149170756[/C][C]0.820941701658488[/C][C]0.410470850829244[/C][/ROW]
[ROW][C]55[/C][C]0.676821401432472[/C][C]0.646357197135056[/C][C]0.323178598567528[/C][/ROW]
[ROW][C]56[/C][C]0.63470801358738[/C][C]0.73058397282524[/C][C]0.36529198641262[/C][/ROW]
[ROW][C]57[/C][C]0.596419646568122[/C][C]0.807160706863755[/C][C]0.403580353431878[/C][/ROW]
[ROW][C]58[/C][C]0.556681671808855[/C][C]0.88663665638229[/C][C]0.443318328191145[/C][/ROW]
[ROW][C]59[/C][C]0.53717567644293[/C][C]0.925648647114141[/C][C]0.46282432355707[/C][/ROW]
[ROW][C]60[/C][C]0.499697825945402[/C][C]0.999395651890805[/C][C]0.500302174054598[/C][/ROW]
[ROW][C]61[/C][C]0.49800147973507[/C][C]0.99600295947014[/C][C]0.501998520264929[/C][/ROW]
[ROW][C]62[/C][C]0.462541750120747[/C][C]0.925083500241493[/C][C]0.537458249879253[/C][/ROW]
[ROW][C]63[/C][C]0.440134007209294[/C][C]0.880268014418587[/C][C]0.559865992790706[/C][/ROW]
[ROW][C]64[/C][C]0.470302875299826[/C][C]0.940605750599652[/C][C]0.529697124700174[/C][/ROW]
[ROW][C]65[/C][C]0.426179543813603[/C][C]0.852359087627205[/C][C]0.573820456186397[/C][/ROW]
[ROW][C]66[/C][C]0.516180291180141[/C][C]0.967639417639717[/C][C]0.483819708819859[/C][/ROW]
[ROW][C]67[/C][C]0.560395270686864[/C][C]0.879209458626273[/C][C]0.439604729313136[/C][/ROW]
[ROW][C]68[/C][C]0.548480462503033[/C][C]0.903039074993935[/C][C]0.451519537496967[/C][/ROW]
[ROW][C]69[/C][C]0.508971703441287[/C][C]0.982056593117425[/C][C]0.491028296558713[/C][/ROW]
[ROW][C]70[/C][C]0.50264406016146[/C][C]0.994711879677081[/C][C]0.49735593983854[/C][/ROW]
[ROW][C]71[/C][C]0.548963457060948[/C][C]0.902073085878104[/C][C]0.451036542939052[/C][/ROW]
[ROW][C]72[/C][C]0.500292604081047[/C][C]0.999414791837907[/C][C]0.499707395918953[/C][/ROW]
[ROW][C]73[/C][C]0.45123762374089[/C][C]0.90247524748178[/C][C]0.54876237625911[/C][/ROW]
[ROW][C]74[/C][C]0.403507695523349[/C][C]0.807015391046698[/C][C]0.596492304476651[/C][/ROW]
[ROW][C]75[/C][C]0.400447073382302[/C][C]0.800894146764603[/C][C]0.599552926617698[/C][/ROW]
[ROW][C]76[/C][C]0.393013085788849[/C][C]0.786026171577699[/C][C]0.606986914211151[/C][/ROW]
[ROW][C]77[/C][C]0.39019227676727[/C][C]0.780384553534541[/C][C]0.60980772323273[/C][/ROW]
[ROW][C]78[/C][C]0.344284747493176[/C][C]0.688569494986351[/C][C]0.655715252506824[/C][/ROW]
[ROW][C]79[/C][C]0.320079951974776[/C][C]0.640159903949551[/C][C]0.679920048025224[/C][/ROW]
[ROW][C]80[/C][C]0.289845559831988[/C][C]0.579691119663977[/C][C]0.710154440168012[/C][/ROW]
[ROW][C]81[/C][C]0.266092260563939[/C][C]0.532184521127879[/C][C]0.733907739436061[/C][/ROW]
[ROW][C]82[/C][C]0.316256440885889[/C][C]0.632512881771778[/C][C]0.683743559114111[/C][/ROW]
[ROW][C]83[/C][C]0.277060993555666[/C][C]0.554121987111332[/C][C]0.722939006444334[/C][/ROW]
[ROW][C]84[/C][C]0.268075594860185[/C][C]0.536151189720369[/C][C]0.731924405139815[/C][/ROW]
[ROW][C]85[/C][C]0.240923909643306[/C][C]0.481847819286611[/C][C]0.759076090356694[/C][/ROW]
[ROW][C]86[/C][C]0.214780938206791[/C][C]0.429561876413583[/C][C]0.785219061793209[/C][/ROW]
[ROW][C]87[/C][C]0.259589489984524[/C][C]0.519178979969049[/C][C]0.740410510015476[/C][/ROW]
[ROW][C]88[/C][C]0.306271985878659[/C][C]0.612543971757317[/C][C]0.693728014121341[/C][/ROW]
[ROW][C]89[/C][C]0.285726905496725[/C][C]0.571453810993451[/C][C]0.714273094503275[/C][/ROW]
[ROW][C]90[/C][C]0.302898301376723[/C][C]0.605796602753447[/C][C]0.697101698623277[/C][/ROW]
[ROW][C]91[/C][C]0.412150537496999[/C][C]0.824301074993998[/C][C]0.587849462503001[/C][/ROW]
[ROW][C]92[/C][C]0.37502210939285[/C][C]0.7500442187857[/C][C]0.62497789060715[/C][/ROW]
[ROW][C]93[/C][C]0.367417383538672[/C][C]0.734834767077344[/C][C]0.632582616461328[/C][/ROW]
[ROW][C]94[/C][C]0.326517579364397[/C][C]0.653035158728794[/C][C]0.673482420635603[/C][/ROW]
[ROW][C]95[/C][C]0.283109647006484[/C][C]0.566219294012967[/C][C]0.716890352993516[/C][/ROW]
[ROW][C]96[/C][C]0.407235572856445[/C][C]0.81447114571289[/C][C]0.592764427143555[/C][/ROW]
[ROW][C]97[/C][C]0.405724641482921[/C][C]0.811449282965841[/C][C]0.59427535851708[/C][/ROW]
[ROW][C]98[/C][C]0.389495648219371[/C][C]0.778991296438741[/C][C]0.610504351780629[/C][/ROW]
[ROW][C]99[/C][C]0.394187094206399[/C][C]0.788374188412798[/C][C]0.6058129057936[/C][/ROW]
[ROW][C]100[/C][C]0.406878201669573[/C][C]0.813756403339146[/C][C]0.593121798330427[/C][/ROW]
[ROW][C]101[/C][C]0.370189415239293[/C][C]0.740378830478585[/C][C]0.629810584760708[/C][/ROW]
[ROW][C]102[/C][C]0.33960019820761[/C][C]0.67920039641522[/C][C]0.66039980179239[/C][/ROW]
[ROW][C]103[/C][C]0.362611253843539[/C][C]0.725222507687078[/C][C]0.637388746156461[/C][/ROW]
[ROW][C]104[/C][C]0.324747767764176[/C][C]0.649495535528351[/C][C]0.675252232235824[/C][/ROW]
[ROW][C]105[/C][C]0.297057687241027[/C][C]0.594115374482054[/C][C]0.702942312758973[/C][/ROW]
[ROW][C]106[/C][C]0.342609400519543[/C][C]0.685218801039086[/C][C]0.657390599480457[/C][/ROW]
[ROW][C]107[/C][C]0.342711920399037[/C][C]0.685423840798075[/C][C]0.657288079600963[/C][/ROW]
[ROW][C]108[/C][C]0.317356514824533[/C][C]0.634713029649066[/C][C]0.682643485175467[/C][/ROW]
[ROW][C]109[/C][C]0.278276529384782[/C][C]0.556553058769565[/C][C]0.721723470615218[/C][/ROW]
[ROW][C]110[/C][C]0.280427513151955[/C][C]0.56085502630391[/C][C]0.719572486848045[/C][/ROW]
[ROW][C]111[/C][C]0.295487955854742[/C][C]0.590975911709484[/C][C]0.704512044145258[/C][/ROW]
[ROW][C]112[/C][C]0.44033713952287[/C][C]0.88067427904574[/C][C]0.55966286047713[/C][/ROW]
[ROW][C]113[/C][C]0.447785838380425[/C][C]0.89557167676085[/C][C]0.552214161619575[/C][/ROW]
[ROW][C]114[/C][C]0.848912948522607[/C][C]0.302174102954786[/C][C]0.151087051477393[/C][/ROW]
[ROW][C]115[/C][C]0.973413767817784[/C][C]0.0531724643644324[/C][C]0.0265862321822162[/C][/ROW]
[ROW][C]116[/C][C]0.962352363986002[/C][C]0.0752952720279959[/C][C]0.037647636013998[/C][/ROW]
[ROW][C]117[/C][C]0.964904683514543[/C][C]0.0701906329709143[/C][C]0.0350953164854572[/C][/ROW]
[ROW][C]118[/C][C]0.950445699965474[/C][C]0.0991086000690527[/C][C]0.0495543000345263[/C][/ROW]
[ROW][C]119[/C][C]0.950692614008457[/C][C]0.0986147719830853[/C][C]0.0493073859915426[/C][/ROW]
[ROW][C]120[/C][C]0.98177993798644[/C][C]0.0364401240271208[/C][C]0.0182200620135604[/C][/ROW]
[ROW][C]121[/C][C]0.972940938981177[/C][C]0.0541181220376453[/C][C]0.0270590610188226[/C][/ROW]
[ROW][C]122[/C][C]0.96312094696173[/C][C]0.0737581060765401[/C][C]0.0368790530382701[/C][/ROW]
[ROW][C]123[/C][C]0.989342927390955[/C][C]0.021314145218091[/C][C]0.0106570726090455[/C][/ROW]
[ROW][C]124[/C][C]0.982545718635904[/C][C]0.0349085627281916[/C][C]0.0174542813640958[/C][/ROW]
[ROW][C]125[/C][C]0.977426903714553[/C][C]0.0451461925708941[/C][C]0.0225730962854471[/C][/ROW]
[ROW][C]126[/C][C]0.964740299145436[/C][C]0.0705194017091277[/C][C]0.0352597008545638[/C][/ROW]
[ROW][C]127[/C][C]0.952095437105497[/C][C]0.095809125789005[/C][C]0.0479045628945025[/C][/ROW]
[ROW][C]128[/C][C]0.940856017016032[/C][C]0.118287965967935[/C][C]0.0591439829839676[/C][/ROW]
[ROW][C]129[/C][C]0.939212304227733[/C][C]0.121575391544535[/C][C]0.0607876957722674[/C][/ROW]
[ROW][C]130[/C][C]0.914638256735996[/C][C]0.170723486528008[/C][C]0.085361743264004[/C][/ROW]
[ROW][C]131[/C][C]0.874731281039574[/C][C]0.250537437920852[/C][C]0.125268718960426[/C][/ROW]
[ROW][C]132[/C][C]0.824944303145245[/C][C]0.35011139370951[/C][C]0.175055696854755[/C][/ROW]
[ROW][C]133[/C][C]0.781960662863033[/C][C]0.436078674273935[/C][C]0.218039337136967[/C][/ROW]
[ROW][C]134[/C][C]0.713349765858321[/C][C]0.573300468283358[/C][C]0.286650234141679[/C][/ROW]
[ROW][C]135[/C][C]0.6378199510814[/C][C]0.724360097837199[/C][C]0.362180048918599[/C][/ROW]
[ROW][C]136[/C][C]0.54947368894526[/C][C]0.90105262210948[/C][C]0.45052631105474[/C][/ROW]
[ROW][C]137[/C][C]0.664138690909855[/C][C]0.67172261818029[/C][C]0.335861309090145[/C][/ROW]
[ROW][C]138[/C][C]0.71445060665474[/C][C]0.57109878669052[/C][C]0.28554939334526[/C][/ROW]
[ROW][C]139[/C][C]0.58399847012426[/C][C]0.83200305975148[/C][C]0.41600152987574[/C][/ROW]
[ROW][C]140[/C][C]0.415003606844583[/C][C]0.830007213689165[/C][C]0.584996393155417[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99407&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99407&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
190.6086084383836020.7827831232327950.391391561616398
200.959346335177930.081307329644140.04065366482207
210.9463857700889660.1072284598220690.0536142299110343
220.9143318353859030.1713363292281950.0856681646140974
230.8653233323755980.2693533352488050.134676667624402
240.8214060397954680.3571879204090630.178593960204532
250.7500971721920680.4998056556158650.249902827807932
260.6971559903827830.6056880192344350.302844009617217
270.6564182166444540.6871635667110920.343581783355546
280.6226969068641190.7546061862717620.377303093135881
290.5418777421320090.9162445157359810.458122257867991
300.4810206707071980.9620413414143950.518979329292802
310.4176530228651850.8353060457303690.582346977134815
320.4221321575684330.8442643151368660.577867842431567
330.4169220548097420.8338441096194840.583077945190258
340.5930280325506880.8139439348986240.406971967449312
350.585692581023030.8286148379539410.41430741897697
360.7508273699871060.4983452600257880.249172630012894
370.6975477420237850.604904515952430.302452257976215
380.6556382082924240.6887235834151520.344361791707576
390.606750134037920.7864997319241610.393249865962081
400.6566252803095140.6867494393809710.343374719690486
410.6095939225624350.780812154875130.390406077437565
420.6000411223261020.7999177553477960.399958877673898
430.6758213095672450.648357380865510.324178690432755
440.6723552168808230.6552895662383530.327644783119177
450.661296231333330.677407537333340.33870376866667
460.6065828404392080.7868343191215840.393417159560792
470.5613719560462010.8772560879075970.438628043953799
480.686902853405540.626194293188920.31309714659446
490.6345000730074180.7309998539851640.365499926992582
500.6153282238922460.7693435522155070.384671776107754
510.6366461617168620.7267076765662760.363353838283138
520.5868463810245570.8263072379508870.413153618975443
530.551872445966430.8962551080671410.448127554033571
540.5895291491707560.8209417016584880.410470850829244
550.6768214014324720.6463571971350560.323178598567528
560.634708013587380.730583972825240.36529198641262
570.5964196465681220.8071607068637550.403580353431878
580.5566816718088550.886636656382290.443318328191145
590.537175676442930.9256486471141410.46282432355707
600.4996978259454020.9993956518908050.500302174054598
610.498001479735070.996002959470140.501998520264929
620.4625417501207470.9250835002414930.537458249879253
630.4401340072092940.8802680144185870.559865992790706
640.4703028752998260.9406057505996520.529697124700174
650.4261795438136030.8523590876272050.573820456186397
660.5161802911801410.9676394176397170.483819708819859
670.5603952706868640.8792094586262730.439604729313136
680.5484804625030330.9030390749939350.451519537496967
690.5089717034412870.9820565931174250.491028296558713
700.502644060161460.9947118796770810.49735593983854
710.5489634570609480.9020730858781040.451036542939052
720.5002926040810470.9994147918379070.499707395918953
730.451237623740890.902475247481780.54876237625911
740.4035076955233490.8070153910466980.596492304476651
750.4004470733823020.8008941467646030.599552926617698
760.3930130857888490.7860261715776990.606986914211151
770.390192276767270.7803845535345410.60980772323273
780.3442847474931760.6885694949863510.655715252506824
790.3200799519747760.6401599039495510.679920048025224
800.2898455598319880.5796911196639770.710154440168012
810.2660922605639390.5321845211278790.733907739436061
820.3162564408858890.6325128817717780.683743559114111
830.2770609935556660.5541219871113320.722939006444334
840.2680755948601850.5361511897203690.731924405139815
850.2409239096433060.4818478192866110.759076090356694
860.2147809382067910.4295618764135830.785219061793209
870.2595894899845240.5191789799690490.740410510015476
880.3062719858786590.6125439717573170.693728014121341
890.2857269054967250.5714538109934510.714273094503275
900.3028983013767230.6057966027534470.697101698623277
910.4121505374969990.8243010749939980.587849462503001
920.375022109392850.75004421878570.62497789060715
930.3674173835386720.7348347670773440.632582616461328
940.3265175793643970.6530351587287940.673482420635603
950.2831096470064840.5662192940129670.716890352993516
960.4072355728564450.814471145712890.592764427143555
970.4057246414829210.8114492829658410.59427535851708
980.3894956482193710.7789912964387410.610504351780629
990.3941870942063990.7883741884127980.6058129057936
1000.4068782016695730.8137564033391460.593121798330427
1010.3701894152392930.7403788304785850.629810584760708
1020.339600198207610.679200396415220.66039980179239
1030.3626112538435390.7252225076870780.637388746156461
1040.3247477677641760.6494955355283510.675252232235824
1050.2970576872410270.5941153744820540.702942312758973
1060.3426094005195430.6852188010390860.657390599480457
1070.3427119203990370.6854238407980750.657288079600963
1080.3173565148245330.6347130296490660.682643485175467
1090.2782765293847820.5565530587695650.721723470615218
1100.2804275131519550.560855026303910.719572486848045
1110.2954879558547420.5909759117094840.704512044145258
1120.440337139522870.880674279045740.55966286047713
1130.4477858383804250.895571676760850.552214161619575
1140.8489129485226070.3021741029547860.151087051477393
1150.9734137678177840.05317246436443240.0265862321822162
1160.9623523639860020.07529527202799590.037647636013998
1170.9649046835145430.07019063297091430.0350953164854572
1180.9504456999654740.09910860006905270.0495543000345263
1190.9506926140084570.09861477198308530.0493073859915426
1200.981779937986440.03644012402712080.0182200620135604
1210.9729409389811770.05411812203764530.0270590610188226
1220.963120946961730.07375810607654010.0368790530382701
1230.9893429273909550.0213141452180910.0106570726090455
1240.9825457186359040.03490856272819160.0174542813640958
1250.9774269037145530.04514619257089410.0225730962854471
1260.9647402991454360.07051940170912770.0352597008545638
1270.9520954371054970.0958091257890050.0479045628945025
1280.9408560170160320.1182879659679350.0591439829839676
1290.9392123042277330.1215753915445350.0607876957722674
1300.9146382567359960.1707234865280080.085361743264004
1310.8747312810395740.2505374379208520.125268718960426
1320.8249443031452450.350111393709510.175055696854755
1330.7819606628630330.4360786742739350.218039337136967
1340.7133497658583210.5733004682833580.286650234141679
1350.63781995108140.7243600978371990.362180048918599
1360.549473688945260.901052622109480.45052631105474
1370.6641386909098550.671722618180290.335861309090145
1380.714450606654740.571098786690520.28554939334526
1390.583998470124260.832003059751480.41600152987574
1400.4150036068445830.8300072136891650.584996393155417







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level40.0327868852459016OK
10% type I error level140.114754098360656NOK

\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 & 4 & 0.0327868852459016 & OK \tabularnewline
10% type I error level & 14 & 0.114754098360656 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99407&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]4[/C][C]0.0327868852459016[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]14[/C][C]0.114754098360656[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99407&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99407&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 level40.0327868852459016OK
10% type I error level140.114754098360656NOK



Parameters (Session):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Include Monthly 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')
}