## 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 computationMon, 05 Nov 2012 07:10:35 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Nov/05/t1352117491du1l2uwsz83b7cy.htm/, Retrieved Sun, 16 Jun 2024 22:21:08 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=186000, Retrieved Sun, 16 Jun 2024 22:21:08 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact191
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]
-   P   [Multiple Regression] [] [2012-11-05 11:38:41] [4e21aa900c332d40e9c065e2c79814a0]
-   PD      [Multiple Regression] [] [2012-11-05 12:10:35] [70625068b3924f89f7a6efd1a4acaa7e] [Current]
- R P         [Multiple Regression] [multiple regression] [2012-12-20 19:16:48] [4e21aa900c332d40e9c065e2c79814a0]
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Dataseries X:
41	38	13	12	14	12	53	32
39	32	16	11	18	11	86	51
30	35	19	15	11	14	66	42
31	33	15	6	12	12	67	41
34	37	14	13	16	21	76	46
35	29	13	10	18	12	78	47
39	31	19	12	14	22	53	37
34	36	15	14	14	11	80	49
36	35	14	12	15	10	74	45
37	38	15	6	15	13	76	47
38	31	16	10	17	10	79	49
36	34	16	12	19	8	54	33
38	35	16	12	10	15	67	42
39	38	16	11	16	14	54	33
33	37	17	15	18	10	87	53
32	33	15	12	14	14	58	36
36	32	15	10	14	14	75	45
38	38	20	12	17	11	88	54
39	38	18	11	14	10	64	41
32	32	16	12	16	13	57	36
32	33	16	11	18	7	66	41
31	31	16	12	11	14	68	44
39	38	19	13	14	12	54	33
37	39	16	11	12	14	56	37
39	32	17	9	17	11	86	52
41	32	17	13	9	9	80	47
36	35	16	10	16	11	76	43
33	37	15	14	14	15	69	44
33	33	16	12	15	14	78	45
34	33	14	10	11	13	67	44
31	28	15	12	16	9	80	49
27	32	12	8	13	15	54	33
37	31	14	10	17	10	71	43
34	37	16	12	15	11	84	54
34	30	14	12	14	13	74	42
32	33	7	7	16	8	71	44
29	31	10	6	9	20	63	37
36	33	14	12	15	12	71	43
29	31	16	10	17	10	76	46
35	33	16	10	13	10	69	42
37	32	16	10	15	9	74	45
34	33	14	12	16	14	75	44
38	32	20	15	16	8	54	33
35	33	14	10	12	14	52	31
38	28	14	10	12	11	69	42
37	35	11	12	11	13	68	40
38	39	14	13	15	9	65	43
33	34	15	11	15	11	75	46
36	38	16	11	17	15	74	42
38	32	14	12	13	11	75	45
32	38	16	14	16	10	72	44
32	30	14	10	14	14	67	40
32	33	12	12	11	18	63	37
34	38	16	13	12	14	62	46
32	32	9	5	12	11	63	36
37	32	14	6	15	12	76	47
39	34	16	12	16	13	74	45
29	34	16	12	15	9	67	42
37	36	15	11	12	10	73	43
35	34	16	10	12	15	70	43
30	28	12	7	8	20	53	32
38	34	16	12	13	12	77	45
34	35	16	14	11	12	77	45
31	35	14	11	14	14	52	31
34	31	16	12	15	13	54	33
35	37	17	13	10	11	80	49
36	35	18	14	11	17	66	42
30	27	18	11	12	12	73	41
39	40	12	12	15	13	63	38
35	37	16	12	15	14	69	42
38	36	10	8	14	13	67	44
31	38	14	11	16	15	54	33
34	39	18	14	15	13	81	48
38	41	18	14	15	10	69	40
34	27	16	12	13	11	84	50
39	30	17	9	12	19	80	49
37	37	16	13	17	13	70	43
34	31	16	11	13	17	69	44
28	31	13	12	15	13	77	47
37	27	16	12	13	9	54	33
33	36	16	12	15	11	79	46
37	38	20	12	16	10	30	0
35	37	16	12	15	9	71	45
37	33	15	12	16	12	73	43
32	34	15	11	15	12	72	44
33	31	16	10	14	13	77	47
38	39	14	9	15	13	75	45
33	34	16	12	14	12	69	42
29	32	16	12	13	15	54	33
33	33	15	12	7	22	70	43
31	36	12	9	17	13	73	46
36	32	17	15	13	15	54	33
35	41	16	12	15	13	77	46
32	28	15	12	14	15	82	48
29	30	13	12	13	10	80	47
39	36	16	10	16	11	80	47
37	35	16	13	12	16	69	43
35	31	16	9	14	11	78	46
37	34	16	12	17	11	81	48
32	36	14	10	15	10	76	46
38	36	16	14	17	10	76	45
37	35	16	11	12	16	73	45
36	37	20	15	16	12	85	52
32	28	15	11	11	11	66	42
33	39	16	11	15	16	79	47
40	32	13	12	9	19	68	41
38	35	17	12	16	11	76	47
41	39	16	12	15	16	71	43
36	35	16	11	10	15	54	33
43	42	12	7	10	24	46	30
30	34	16	12	15	14	82	49
31	33	16	14	11	15	74	44
32	41	17	11	13	11	88	55
32	33	13	11	14	15	38	11
37	34	12	10	18	12	76	47
37	32	18	13	16	10	86	53
33	40	14	13	14	14	54	33
34	40	14	8	14	13	70	44
33	35	13	11	14	9	69	42
38	36	16	12	14	15	90	55
33	37	13	11	12	15	54	33
31	27	16	13	14	14	76	46
38	39	13	12	15	11	89	54
37	38	16	14	15	8	76	47
33	31	15	13	15	11	73	45
31	33	16	15	13	11	79	47
39	32	15	10	17	8	90	55
44	39	17	11	17	10	74	44
33	36	15	9	19	11	81	53
35	33	12	11	15	13	72	44
32	33	16	10	13	11	71	42
28	32	10	11	9	20	66	40
40	37	16	8	15	10	77	46
27	30	12	11	15	15	65	40
37	38	14	12	15	12	74	46
32	29	15	12	16	14	82	53
28	22	13	9	11	23	54	33
34	35	15	11	14	14	63	42
30	35	11	10	11	16	54	35
35	34	12	8	15	11	64	40
31	35	8	9	13	12	69	41
32	34	16	8	15	10	54	33
30	34	15	9	16	14	84	51
30	35	17	15	14	12	86	53
31	23	16	11	15	12	77	46
40	31	10	8	16	11	89	55
32	27	18	13	16	12	76	47
36	36	13	12	11	13	60	38
32	31	16	12	12	11	75	46
35	32	13	9	9	19	73	46
38	39	10	7	16	12	85	53
42	37	15	13	13	17	79	47
34	38	16	9	16	9	71	41
35	39	16	6	12	12	72	44
35	34	14	8	9	19	69	43
33	31	10	8	13	18	78	51
36	32	17	15	13	15	54	33
32	37	13	6	14	14	69	43
33	36	15	9	19	11	81	53
34	32	16	11	13	9	84	51
32	35	12	8	12	18	84	50
34	36	13	8	13	16	69	46

 Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 14 seconds R Server 'Gwilym Jenkins' @ jenkins.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 14 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186000&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]14 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186000&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186000&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 Input view raw input (R code) Raw Output view raw output of R engine Computing time 14 seconds R Server 'Gwilym Jenkins' @ jenkins.wessa.net

 Multiple Linear Regression - Estimated Regression Equation Learning[t] = + 5.51004602654412 + 0.114054780757305Connected[t] -0.0206657003468085Separate[t] + 0.542564264819151Software[t] + 0.0597091397316794Happiness[t] -0.0711620165288007Depression[t] + 0.0357610911025553Belonging[t] -0.0522339742838437Belonging_Final[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Learning[t] =  +  5.51004602654412 +  0.114054780757305Connected[t] -0.0206657003468085Separate[t] +  0.542564264819151Software[t] +  0.0597091397316794Happiness[t] -0.0711620165288007Depression[t] +  0.0357610911025553Belonging[t] -0.0522339742838437Belonging_Final[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186000&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Learning[t] =  +  5.51004602654412 +  0.114054780757305Connected[t] -0.0206657003468085Separate[t] +  0.542564264819151Software[t] +  0.0597091397316794Happiness[t] -0.0711620165288007Depression[t] +  0.0357610911025553Belonging[t] -0.0522339742838437Belonging_Final[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186000&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186000&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 Learning[t] = + 5.51004602654412 + 0.114054780757305Connected[t] -0.0206657003468085Separate[t] + 0.542564264819151Software[t] + 0.0597091397316794Happiness[t] -0.0711620165288007Depression[t] + 0.0357610911025553Belonging[t] -0.0522339742838437Belonging_Final[t] + e[t]

 Multiple Linear Regression - Ordinary Least Squares Variable Parameter S.D. T-STATH0: parameter = 0 2-tail p-value 1-tail p-value (Intercept) 5.51004602654412 2.596867 2.1218 0.035454 0.017727 Connected 0.114054780757305 0.04689 2.4324 0.016145 0.008073 Separate -0.0206657003468085 0.044811 -0.4612 0.645324 0.322662 Software 0.542564264819151 0.068952 7.8688 0 0 Happiness 0.0597091397316794 0.076381 0.7817 0.435576 0.217788 Depression -0.0711620165288007 0.05634 -1.2631 0.208471 0.104236 Belonging 0.0357610911025553 0.044531 0.8031 0.423173 0.211586 Belonging_Final -0.0522339742838437 0.063958 -0.8167 0.415366 0.207683

\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) & 5.51004602654412 & 2.596867 & 2.1218 & 0.035454 & 0.017727 \tabularnewline
Connected & 0.114054780757305 & 0.04689 & 2.4324 & 0.016145 & 0.008073 \tabularnewline
Separate & -0.0206657003468085 & 0.044811 & -0.4612 & 0.645324 & 0.322662 \tabularnewline
Software & 0.542564264819151 & 0.068952 & 7.8688 & 0 & 0 \tabularnewline
Happiness & 0.0597091397316794 & 0.076381 & 0.7817 & 0.435576 & 0.217788 \tabularnewline
Depression & -0.0711620165288007 & 0.05634 & -1.2631 & 0.208471 & 0.104236 \tabularnewline
Belonging & 0.0357610911025553 & 0.044531 & 0.8031 & 0.423173 & 0.211586 \tabularnewline
Belonging_Final & -0.0522339742838437 & 0.063958 & -0.8167 & 0.415366 & 0.207683 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186000&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]5.51004602654412[/C][C]2.596867[/C][C]2.1218[/C][C]0.035454[/C][C]0.017727[/C][/ROW]
[ROW][C]Connected[/C][C]0.114054780757305[/C][C]0.04689[/C][C]2.4324[/C][C]0.016145[/C][C]0.008073[/C][/ROW]
[ROW][C]Separate[/C][C]-0.0206657003468085[/C][C]0.044811[/C][C]-0.4612[/C][C]0.645324[/C][C]0.322662[/C][/ROW]
[ROW][C]Software[/C][C]0.542564264819151[/C][C]0.068952[/C][C]7.8688[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Happiness[/C][C]0.0597091397316794[/C][C]0.076381[/C][C]0.7817[/C][C]0.435576[/C][C]0.217788[/C][/ROW]
[ROW][C]Depression[/C][C]-0.0711620165288007[/C][C]0.05634[/C][C]-1.2631[/C][C]0.208471[/C][C]0.104236[/C][/ROW]
[ROW][C]Belonging[/C][C]0.0357610911025553[/C][C]0.044531[/C][C]0.8031[/C][C]0.423173[/C][C]0.211586[/C][/ROW]
[ROW][C]Belonging_Final[/C][C]-0.0522339742838437[/C][C]0.063958[/C][C]-0.8167[/C][C]0.415366[/C][C]0.207683[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186000&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186000&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 Variable Parameter S.D. T-STATH0: parameter = 0 2-tail p-value 1-tail p-value (Intercept) 5.51004602654412 2.596867 2.1218 0.035454 0.017727 Connected 0.114054780757305 0.04689 2.4324 0.016145 0.008073 Separate -0.0206657003468085 0.044811 -0.4612 0.645324 0.322662 Software 0.542564264819151 0.068952 7.8688 0 0 Happiness 0.0597091397316794 0.076381 0.7817 0.435576 0.217788 Depression -0.0711620165288007 0.05634 -1.2631 0.208471 0.104236 Belonging 0.0357610911025553 0.044531 0.8031 0.423173 0.211586 Belonging_Final -0.0522339742838437 0.063958 -0.8167 0.415366 0.207683

 Multiple Linear Regression - Regression Statistics Multiple R 0.597252140020402 R-squared 0.356710118758949 Adjusted R-squared 0.327469669611629 F-TEST (value) 12.1992010779916 F-TEST (DF numerator) 7 F-TEST (DF denominator) 154 p-value 2.35689245897674e-12 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 1.85031335255193 Sum Squared Residuals 527.24356340532

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.597252140020402 \tabularnewline
R-squared & 0.356710118758949 \tabularnewline
F-TEST (value) & 12.1992010779916 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 154 \tabularnewline
p-value & 2.35689245897674e-12 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.85031335255193 \tabularnewline
Sum Squared Residuals & 527.24356340532 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186000&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.597252140020402[/C][/ROW]
[ROW][C]R-squared[/C][C]0.356710118758949[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]12.1992010779916[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]154[/C][/ROW]
[ROW][C]p-value[/C][C]2.35689245897674e-12[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.85031335255193[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]527.24356340532[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186000&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186000&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 R 0.597252140020402 R-squared 0.356710118758949 Adjusted R-squared 0.327469669611629 F-TEST (value) 12.1992010779916 F-TEST (DF numerator) 7 F-TEST (DF denominator) 154 p-value 2.35689245897674e-12 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 1.85031335255193 Sum Squared Residuals 527.24356340532

 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals InterpolationForecast ResidualsPrediction Error 1 13 16.1176010114951 -3.11760101149508 2 16 15.968590457689 0.0314095423110451 3 19 16.1737913079047 2.82620869209528 4 15 11.736127344159 3.26387265584104 5 14 15.452637097449 -1.45263709744899 6 13 14.8834893226671 -1.88348932266714 7 19 15.0613613157009 3.93863868429912 8 15 16.5944113899983 -1.59441138999828 9 14 15.8832986290019 -1.88329862900193 10 15 12.4335389038549 2.56646109614508 11 16 15.1982303001062 0.801769699893772 12 16 16.1967107906881 -0.196710790688073 13 16 15.3634266943478 0.636573305652221 14 16 15.3075485483858 0.692451451614239 15 17 17.3536454897514 -0.353645489751352 16 15 15.0219820117331 -0.0219820117331483 17 15 14.5515710856597 0.448428914340278 18 20 16.1282168592918 3.87178314070815 19 18 15.4125174517924 2.58748254820759 20 16 15.1974669169696 0.802533083030439 21 16 15.2413072789435 0.758692721056456 22 16 14.7098703292292 1.29012967077078 23 19 16.4155828316183 2.58441716838169 24 16 14.6825230126674 1.31747698733264 25 17 14.7715188140351 2.22848118596487 26 17 16.8811396748344 0.118860325165605 27 16 14.9627073533393 1.0372926466607 28 15 16.0428407120701 -1.04284071207008 29 16 15.4408619857186 0.559138014281356 30 14 13.9609756665955 0.0390243334045331 31 15 15.5941864333835 -0.594186433383495 32 12 12.1849031511977 -0.184903151197658 33 14 15.1114906362315 -1.11149063623154 34 16 15.4302007927359 0.569799207264144 35 14 15.6420243027548 -1.64202430275481 36 7 12.902573456336 -5.90257345633603 37 10 10.8668171646377 -0.866817164637676 38 14 15.779490671898 -1.77949067189796 39 16 14.2211559228343 1.77884407716565 40 16 14.5839249071753 1.41607509282467 41 16 15.0453839976902 0.954616002309845 42 14 15.5595766071838 -1.55957660718381 43 20 17.9147171281587 2.08528287184129 44 14 14.2062028697073 -0.206202869707291 45 14 14.8985465949208 -0.898546594920809 46 11 15.59163412605 -4.59163412605 47 14 16.4250897991219 -2.42508979912194 48 15 14.9316008225476 0.0683991774524221 49 16 15.1990473828132 0.800952617186765 50 14 16.0185860866674 -2.01858608666736 51 16 16.490631866381 -0.490631866380993 52 14 14.1117645059229 -0.111764505922894 53 12 14.6847780076518 -2.68477800765184 54 16 15.1906136784413 0.809386321558708 55 9 11.5175710839817 -2.51757108398172 56 14 12.6286951224646 1.37130487753543 57 16 16.0923517617659 -0.0923517617659263 58 16 15.08311716571 0.916882834289958 59 15 15.3237028828634 -0.323702882863365 60 16 14.1312671012716 1.86873289872845 61 12 11.4292831319165 0.570716868083456 62 16 15.97761485165 0.0223851483499508 63 16 16.466440278449 -0.466440278448962 64 14 14.370634890267 -0.370634890266963 65 16 15.4359516886432 0.564048311356834 66 17 15.9063996466629 1.09360035333709 67 18 16.102069678043 1.89793032195701 68 18 14.6734546361936 3.32654536380641 69 12 15.8809142378122 -3.88091423781219 70 16 15.4211608487746 0.578839151225445 71 10 13.449196578141 -3.449196578141 72 14 14.3239482857985 -0.323948285798518 73 18 16.5377944610183 1.46220553898166 74 18 17.1549069339804 0.845093066019571 75 16 15.726375413876 0.273624586124044 76 17 13.8871437600761 3.11285623992386 77 16 16.3659420879192 -0.365942087919187 78 16 14.4511637276615 1.5488362723385 79 13 14.8428524594843 -1.84285245948429 80 16 16.0260086189542 -0.0260086189541596 81 16 15.5758780510833 0.424121948916667 82 20 16.772106282711 3.22789371728898 83 16 15.6917911907721 0.308208809227862 84 15 16.0247767745921 -1.02477677459206 85 15 14.7435687005215 0.256431299478507 86 16 14.2682886939008 1.73171130609916 87 14 14.223327636188 -0.223327636188002 88 16 15.3376632816263 0.662336718373708 89 16 14.5832697719889 1.41673022801113 90 15 14.212271955382 0.787728044617952 91 12 13.4826033949017 -1.4826033949017 92 17 17.0093460317475 -0.00934603174746024 93 16 15.4868128956012 0.513187104398811 94 15 15.2856069919936 -0.285606991993591 95 13 15.1789239840191 -2.17892398401912 96 16 15.2183144625393 0.781685537460747 97 16 15.8594806492654 0.140519350734551 98 16 14.1841530890403 1.8158469109597 99 16 16.159901087907 -0.159901087906952 100 14 14.3405734839089 -0.340573483908861 101 16 17.3668114814765 -1.3668114814765 102 16 14.8129285354697 1.18707146453032 103 20 17.414779311581 2.58522068841896 104 15 14.5897897621568 0.410210237843217 105 16 14.5632726282959 1.43672737170409 106 13 15.3971712164423 -2.39717121644233 107 17 16.0670095473568 0.932990452643163 108 16 15.9411223074884 0.0588776925115655 109 16 14.5979644522354 1.40203554776461 110 12 12.3115860011041 -0.311586001104149 111 16 15.0121404103748 0.987859589625233 112 16 15.8970719882604 0.10292801173956 113 17 14.5482562756779 2.45174372432212 114 13 14.9988832654302 -1.99888326543019 115 12 14.9367482002426 -2.9367482002426 116 18 16.6728852143104 1.3271147856896 117 14 15.5475987133233 -1.54759871332326 118 14 13.0175979270322 0.98240207296779 119 13 14.9879193660467 -1.98791936604673 120 16 15.7250609825965 0.27493901740351 121 13 14.3338869887332 -1.33388698873322 122 16 15.6958455948834 0.304154405116597 123 13 16.0238939705842 -3.02389397058424 124 16 17.1298631050521 -1.12986310505213 125 15 16.059438245305 -1.05943824530504 126 16 16.8658061313194 -0.865806131319397 127 15 15.5139115699994 -0.513911569999371 128 17 16.3421620626012 0.657837937398815 129 15 13.8929061777708 1.1070938222292 130 12 15.0352367266114 -3.03523672661142 131 16 14.2421207305797 1.75787926942027 132 10 13.3954993580855 -3.39549935808545 133 16 14.1829785910847 1.81702140891525 134 12 14.0010798079533 -2.00107980795329 135 14 15.7407983013774 -1.74079830137736 136 15 15.1943517162197 -0.194351716219724 137 13 12.3594647885484 0.640535211451566 138 15 14.5315975175447 0.468402482455295 139 11 13.2551506775076 -2.2551506775076 140 12 13.4520494331797 -1.45204943317969 141 8 13.4537200598596 -5.45372005985958 142 16 13.1890740163979 2.81092598360207 143 15 13.4112109892864 1.58878901071358 144 17 16.6358908650862 0.364109134913822 145 16 14.9311741305242 1.06882586947583 146 10 14.2545472410445 -4.25454724104455 147 18 16.0194087138778 1.98059128612218 148 13 15.2752928646931 -2.27529286469313 149 16 15.2429799884548 0.757020011545189 150 13 13.1168401022919 -0.116840102291911 151 10 13.208809379565 -3.20880937956498 152 15 16.5256452894514 -1.52564528945141 153 16 14.1980229520776 1.80197704792237 154 16 12.0904557977686 3.90954420223143 155 14 12.5466019952205 1.45339800477955 156 10 12.594466135854 -2.59446613585403 157 17 17.0093460317475 -0.00934603174746024 158 13 11.7116678035722 1.28833219642779 159 15 13.8929061777708 1.1070938222292 160 16 15.1705727060965 0.829427293903479 161 12 12.604839934877 -0.604839934876992 162 13 12.6868364994311 0.31316350056888

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 13 & 16.1176010114951 & -3.11760101149508 \tabularnewline
2 & 16 & 15.968590457689 & 0.0314095423110451 \tabularnewline
3 & 19 & 16.1737913079047 & 2.82620869209528 \tabularnewline
4 & 15 & 11.736127344159 & 3.26387265584104 \tabularnewline
5 & 14 & 15.452637097449 & -1.45263709744899 \tabularnewline
6 & 13 & 14.8834893226671 & -1.88348932266714 \tabularnewline
7 & 19 & 15.0613613157009 & 3.93863868429912 \tabularnewline
8 & 15 & 16.5944113899983 & -1.59441138999828 \tabularnewline
9 & 14 & 15.8832986290019 & -1.88329862900193 \tabularnewline
10 & 15 & 12.4335389038549 & 2.56646109614508 \tabularnewline
11 & 16 & 15.1982303001062 & 0.801769699893772 \tabularnewline
12 & 16 & 16.1967107906881 & -0.196710790688073 \tabularnewline
13 & 16 & 15.3634266943478 & 0.636573305652221 \tabularnewline
14 & 16 & 15.3075485483858 & 0.692451451614239 \tabularnewline
15 & 17 & 17.3536454897514 & -0.353645489751352 \tabularnewline
16 & 15 & 15.0219820117331 & -0.0219820117331483 \tabularnewline
17 & 15 & 14.5515710856597 & 0.448428914340278 \tabularnewline
18 & 20 & 16.1282168592918 & 3.87178314070815 \tabularnewline
19 & 18 & 15.4125174517924 & 2.58748254820759 \tabularnewline
20 & 16 & 15.1974669169696 & 0.802533083030439 \tabularnewline
21 & 16 & 15.2413072789435 & 0.758692721056456 \tabularnewline
22 & 16 & 14.7098703292292 & 1.29012967077078 \tabularnewline
23 & 19 & 16.4155828316183 & 2.58441716838169 \tabularnewline
24 & 16 & 14.6825230126674 & 1.31747698733264 \tabularnewline
25 & 17 & 14.7715188140351 & 2.22848118596487 \tabularnewline
26 & 17 & 16.8811396748344 & 0.118860325165605 \tabularnewline
27 & 16 & 14.9627073533393 & 1.0372926466607 \tabularnewline
28 & 15 & 16.0428407120701 & -1.04284071207008 \tabularnewline
29 & 16 & 15.4408619857186 & 0.559138014281356 \tabularnewline
30 & 14 & 13.9609756665955 & 0.0390243334045331 \tabularnewline
31 & 15 & 15.5941864333835 & -0.594186433383495 \tabularnewline
32 & 12 & 12.1849031511977 & -0.184903151197658 \tabularnewline
33 & 14 & 15.1114906362315 & -1.11149063623154 \tabularnewline
34 & 16 & 15.4302007927359 & 0.569799207264144 \tabularnewline
35 & 14 & 15.6420243027548 & -1.64202430275481 \tabularnewline
36 & 7 & 12.902573456336 & -5.90257345633603 \tabularnewline
37 & 10 & 10.8668171646377 & -0.866817164637676 \tabularnewline
38 & 14 & 15.779490671898 & -1.77949067189796 \tabularnewline
39 & 16 & 14.2211559228343 & 1.77884407716565 \tabularnewline
40 & 16 & 14.5839249071753 & 1.41607509282467 \tabularnewline
41 & 16 & 15.0453839976902 & 0.954616002309845 \tabularnewline
42 & 14 & 15.5595766071838 & -1.55957660718381 \tabularnewline
43 & 20 & 17.9147171281587 & 2.08528287184129 \tabularnewline
44 & 14 & 14.2062028697073 & -0.206202869707291 \tabularnewline
45 & 14 & 14.8985465949208 & -0.898546594920809 \tabularnewline
46 & 11 & 15.59163412605 & -4.59163412605 \tabularnewline
47 & 14 & 16.4250897991219 & -2.42508979912194 \tabularnewline
48 & 15 & 14.9316008225476 & 0.0683991774524221 \tabularnewline
49 & 16 & 15.1990473828132 & 0.800952617186765 \tabularnewline
50 & 14 & 16.0185860866674 & -2.01858608666736 \tabularnewline
51 & 16 & 16.490631866381 & -0.490631866380993 \tabularnewline
52 & 14 & 14.1117645059229 & -0.111764505922894 \tabularnewline
53 & 12 & 14.6847780076518 & -2.68477800765184 \tabularnewline
54 & 16 & 15.1906136784413 & 0.809386321558708 \tabularnewline
55 & 9 & 11.5175710839817 & -2.51757108398172 \tabularnewline
56 & 14 & 12.6286951224646 & 1.37130487753543 \tabularnewline
57 & 16 & 16.0923517617659 & -0.0923517617659263 \tabularnewline
58 & 16 & 15.08311716571 & 0.916882834289958 \tabularnewline
59 & 15 & 15.3237028828634 & -0.323702882863365 \tabularnewline
60 & 16 & 14.1312671012716 & 1.86873289872845 \tabularnewline
61 & 12 & 11.4292831319165 & 0.570716868083456 \tabularnewline
62 & 16 & 15.97761485165 & 0.0223851483499508 \tabularnewline
63 & 16 & 16.466440278449 & -0.466440278448962 \tabularnewline
64 & 14 & 14.370634890267 & -0.370634890266963 \tabularnewline
65 & 16 & 15.4359516886432 & 0.564048311356834 \tabularnewline
66 & 17 & 15.9063996466629 & 1.09360035333709 \tabularnewline
67 & 18 & 16.102069678043 & 1.89793032195701 \tabularnewline
68 & 18 & 14.6734546361936 & 3.32654536380641 \tabularnewline
69 & 12 & 15.8809142378122 & -3.88091423781219 \tabularnewline
70 & 16 & 15.4211608487746 & 0.578839151225445 \tabularnewline
71 & 10 & 13.449196578141 & -3.449196578141 \tabularnewline
72 & 14 & 14.3239482857985 & -0.323948285798518 \tabularnewline
73 & 18 & 16.5377944610183 & 1.46220553898166 \tabularnewline
74 & 18 & 17.1549069339804 & 0.845093066019571 \tabularnewline
75 & 16 & 15.726375413876 & 0.273624586124044 \tabularnewline
76 & 17 & 13.8871437600761 & 3.11285623992386 \tabularnewline
77 & 16 & 16.3659420879192 & -0.365942087919187 \tabularnewline
78 & 16 & 14.4511637276615 & 1.5488362723385 \tabularnewline
79 & 13 & 14.8428524594843 & -1.84285245948429 \tabularnewline
80 & 16 & 16.0260086189542 & -0.0260086189541596 \tabularnewline
81 & 16 & 15.5758780510833 & 0.424121948916667 \tabularnewline
82 & 20 & 16.772106282711 & 3.22789371728898 \tabularnewline
83 & 16 & 15.6917911907721 & 0.308208809227862 \tabularnewline
84 & 15 & 16.0247767745921 & -1.02477677459206 \tabularnewline
85 & 15 & 14.7435687005215 & 0.256431299478507 \tabularnewline
86 & 16 & 14.2682886939008 & 1.73171130609916 \tabularnewline
87 & 14 & 14.223327636188 & -0.223327636188002 \tabularnewline
88 & 16 & 15.3376632816263 & 0.662336718373708 \tabularnewline
89 & 16 & 14.5832697719889 & 1.41673022801113 \tabularnewline
90 & 15 & 14.212271955382 & 0.787728044617952 \tabularnewline
91 & 12 & 13.4826033949017 & -1.4826033949017 \tabularnewline
92 & 17 & 17.0093460317475 & -0.00934603174746024 \tabularnewline
93 & 16 & 15.4868128956012 & 0.513187104398811 \tabularnewline
94 & 15 & 15.2856069919936 & -0.285606991993591 \tabularnewline
95 & 13 & 15.1789239840191 & -2.17892398401912 \tabularnewline
96 & 16 & 15.2183144625393 & 0.781685537460747 \tabularnewline
97 & 16 & 15.8594806492654 & 0.140519350734551 \tabularnewline
98 & 16 & 14.1841530890403 & 1.8158469109597 \tabularnewline
99 & 16 & 16.159901087907 & -0.159901087906952 \tabularnewline
100 & 14 & 14.3405734839089 & -0.340573483908861 \tabularnewline
101 & 16 & 17.3668114814765 & -1.3668114814765 \tabularnewline
102 & 16 & 14.8129285354697 & 1.18707146453032 \tabularnewline
103 & 20 & 17.414779311581 & 2.58522068841896 \tabularnewline
104 & 15 & 14.5897897621568 & 0.410210237843217 \tabularnewline
105 & 16 & 14.5632726282959 & 1.43672737170409 \tabularnewline
106 & 13 & 15.3971712164423 & -2.39717121644233 \tabularnewline
107 & 17 & 16.0670095473568 & 0.932990452643163 \tabularnewline
108 & 16 & 15.9411223074884 & 0.0588776925115655 \tabularnewline
109 & 16 & 14.5979644522354 & 1.40203554776461 \tabularnewline
110 & 12 & 12.3115860011041 & -0.311586001104149 \tabularnewline
111 & 16 & 15.0121404103748 & 0.987859589625233 \tabularnewline
112 & 16 & 15.8970719882604 & 0.10292801173956 \tabularnewline
113 & 17 & 14.5482562756779 & 2.45174372432212 \tabularnewline
114 & 13 & 14.9988832654302 & -1.99888326543019 \tabularnewline
115 & 12 & 14.9367482002426 & -2.9367482002426 \tabularnewline
116 & 18 & 16.6728852143104 & 1.3271147856896 \tabularnewline
117 & 14 & 15.5475987133233 & -1.54759871332326 \tabularnewline
118 & 14 & 13.0175979270322 & 0.98240207296779 \tabularnewline
119 & 13 & 14.9879193660467 & -1.98791936604673 \tabularnewline
120 & 16 & 15.7250609825965 & 0.27493901740351 \tabularnewline
121 & 13 & 14.3338869887332 & -1.33388698873322 \tabularnewline
122 & 16 & 15.6958455948834 & 0.304154405116597 \tabularnewline
123 & 13 & 16.0238939705842 & -3.02389397058424 \tabularnewline
124 & 16 & 17.1298631050521 & -1.12986310505213 \tabularnewline
125 & 15 & 16.059438245305 & -1.05943824530504 \tabularnewline
126 & 16 & 16.8658061313194 & -0.865806131319397 \tabularnewline
127 & 15 & 15.5139115699994 & -0.513911569999371 \tabularnewline
128 & 17 & 16.3421620626012 & 0.657837937398815 \tabularnewline
129 & 15 & 13.8929061777708 & 1.1070938222292 \tabularnewline
130 & 12 & 15.0352367266114 & -3.03523672661142 \tabularnewline
131 & 16 & 14.2421207305797 & 1.75787926942027 \tabularnewline
132 & 10 & 13.3954993580855 & -3.39549935808545 \tabularnewline
133 & 16 & 14.1829785910847 & 1.81702140891525 \tabularnewline
134 & 12 & 14.0010798079533 & -2.00107980795329 \tabularnewline
135 & 14 & 15.7407983013774 & -1.74079830137736 \tabularnewline
136 & 15 & 15.1943517162197 & -0.194351716219724 \tabularnewline
137 & 13 & 12.3594647885484 & 0.640535211451566 \tabularnewline
138 & 15 & 14.5315975175447 & 0.468402482455295 \tabularnewline
139 & 11 & 13.2551506775076 & -2.2551506775076 \tabularnewline
140 & 12 & 13.4520494331797 & -1.45204943317969 \tabularnewline
141 & 8 & 13.4537200598596 & -5.45372005985958 \tabularnewline
142 & 16 & 13.1890740163979 & 2.81092598360207 \tabularnewline
143 & 15 & 13.4112109892864 & 1.58878901071358 \tabularnewline
144 & 17 & 16.6358908650862 & 0.364109134913822 \tabularnewline
145 & 16 & 14.9311741305242 & 1.06882586947583 \tabularnewline
146 & 10 & 14.2545472410445 & -4.25454724104455 \tabularnewline
147 & 18 & 16.0194087138778 & 1.98059128612218 \tabularnewline
148 & 13 & 15.2752928646931 & -2.27529286469313 \tabularnewline
149 & 16 & 15.2429799884548 & 0.757020011545189 \tabularnewline
150 & 13 & 13.1168401022919 & -0.116840102291911 \tabularnewline
151 & 10 & 13.208809379565 & -3.20880937956498 \tabularnewline
152 & 15 & 16.5256452894514 & -1.52564528945141 \tabularnewline
153 & 16 & 14.1980229520776 & 1.80197704792237 \tabularnewline
154 & 16 & 12.0904557977686 & 3.90954420223143 \tabularnewline
155 & 14 & 12.5466019952205 & 1.45339800477955 \tabularnewline
156 & 10 & 12.594466135854 & -2.59446613585403 \tabularnewline
157 & 17 & 17.0093460317475 & -0.00934603174746024 \tabularnewline
158 & 13 & 11.7116678035722 & 1.28833219642779 \tabularnewline
159 & 15 & 13.8929061777708 & 1.1070938222292 \tabularnewline
160 & 16 & 15.1705727060965 & 0.829427293903479 \tabularnewline
161 & 12 & 12.604839934877 & -0.604839934876992 \tabularnewline
162 & 13 & 12.6868364994311 & 0.31316350056888 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186000&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]13[/C][C]16.1176010114951[/C][C]-3.11760101149508[/C][/ROW]
[ROW][C]2[/C][C]16[/C][C]15.968590457689[/C][C]0.0314095423110451[/C][/ROW]
[ROW][C]3[/C][C]19[/C][C]16.1737913079047[/C][C]2.82620869209528[/C][/ROW]
[ROW][C]4[/C][C]15[/C][C]11.736127344159[/C][C]3.26387265584104[/C][/ROW]
[ROW][C]5[/C][C]14[/C][C]15.452637097449[/C][C]-1.45263709744899[/C][/ROW]
[ROW][C]6[/C][C]13[/C][C]14.8834893226671[/C][C]-1.88348932266714[/C][/ROW]
[ROW][C]7[/C][C]19[/C][C]15.0613613157009[/C][C]3.93863868429912[/C][/ROW]
[ROW][C]8[/C][C]15[/C][C]16.5944113899983[/C][C]-1.59441138999828[/C][/ROW]
[ROW][C]9[/C][C]14[/C][C]15.8832986290019[/C][C]-1.88329862900193[/C][/ROW]
[ROW][C]10[/C][C]15[/C][C]12.4335389038549[/C][C]2.56646109614508[/C][/ROW]
[ROW][C]11[/C][C]16[/C][C]15.1982303001062[/C][C]0.801769699893772[/C][/ROW]
[ROW][C]12[/C][C]16[/C][C]16.1967107906881[/C][C]-0.196710790688073[/C][/ROW]
[ROW][C]13[/C][C]16[/C][C]15.3634266943478[/C][C]0.636573305652221[/C][/ROW]
[ROW][C]14[/C][C]16[/C][C]15.3075485483858[/C][C]0.692451451614239[/C][/ROW]
[ROW][C]15[/C][C]17[/C][C]17.3536454897514[/C][C]-0.353645489751352[/C][/ROW]
[ROW][C]16[/C][C]15[/C][C]15.0219820117331[/C][C]-0.0219820117331483[/C][/ROW]
[ROW][C]17[/C][C]15[/C][C]14.5515710856597[/C][C]0.448428914340278[/C][/ROW]
[ROW][C]18[/C][C]20[/C][C]16.1282168592918[/C][C]3.87178314070815[/C][/ROW]
[ROW][C]19[/C][C]18[/C][C]15.4125174517924[/C][C]2.58748254820759[/C][/ROW]
[ROW][C]20[/C][C]16[/C][C]15.1974669169696[/C][C]0.802533083030439[/C][/ROW]
[ROW][C]21[/C][C]16[/C][C]15.2413072789435[/C][C]0.758692721056456[/C][/ROW]
[ROW][C]22[/C][C]16[/C][C]14.7098703292292[/C][C]1.29012967077078[/C][/ROW]
[ROW][C]23[/C][C]19[/C][C]16.4155828316183[/C][C]2.58441716838169[/C][/ROW]
[ROW][C]24[/C][C]16[/C][C]14.6825230126674[/C][C]1.31747698733264[/C][/ROW]
[ROW][C]25[/C][C]17[/C][C]14.7715188140351[/C][C]2.22848118596487[/C][/ROW]
[ROW][C]26[/C][C]17[/C][C]16.8811396748344[/C][C]0.118860325165605[/C][/ROW]
[ROW][C]27[/C][C]16[/C][C]14.9627073533393[/C][C]1.0372926466607[/C][/ROW]
[ROW][C]28[/C][C]15[/C][C]16.0428407120701[/C][C]-1.04284071207008[/C][/ROW]
[ROW][C]29[/C][C]16[/C][C]15.4408619857186[/C][C]0.559138014281356[/C][/ROW]
[ROW][C]30[/C][C]14[/C][C]13.9609756665955[/C][C]0.0390243334045331[/C][/ROW]
[ROW][C]31[/C][C]15[/C][C]15.5941864333835[/C][C]-0.594186433383495[/C][/ROW]
[ROW][C]32[/C][C]12[/C][C]12.1849031511977[/C][C]-0.184903151197658[/C][/ROW]
[ROW][C]33[/C][C]14[/C][C]15.1114906362315[/C][C]-1.11149063623154[/C][/ROW]
[ROW][C]34[/C][C]16[/C][C]15.4302007927359[/C][C]0.569799207264144[/C][/ROW]
[ROW][C]35[/C][C]14[/C][C]15.6420243027548[/C][C]-1.64202430275481[/C][/ROW]
[ROW][C]36[/C][C]7[/C][C]12.902573456336[/C][C]-5.90257345633603[/C][/ROW]
[ROW][C]37[/C][C]10[/C][C]10.8668171646377[/C][C]-0.866817164637676[/C][/ROW]
[ROW][C]38[/C][C]14[/C][C]15.779490671898[/C][C]-1.77949067189796[/C][/ROW]
[ROW][C]39[/C][C]16[/C][C]14.2211559228343[/C][C]1.77884407716565[/C][/ROW]
[ROW][C]40[/C][C]16[/C][C]14.5839249071753[/C][C]1.41607509282467[/C][/ROW]
[ROW][C]41[/C][C]16[/C][C]15.0453839976902[/C][C]0.954616002309845[/C][/ROW]
[ROW][C]42[/C][C]14[/C][C]15.5595766071838[/C][C]-1.55957660718381[/C][/ROW]
[ROW][C]43[/C][C]20[/C][C]17.9147171281587[/C][C]2.08528287184129[/C][/ROW]
[ROW][C]44[/C][C]14[/C][C]14.2062028697073[/C][C]-0.206202869707291[/C][/ROW]
[ROW][C]45[/C][C]14[/C][C]14.8985465949208[/C][C]-0.898546594920809[/C][/ROW]
[ROW][C]46[/C][C]11[/C][C]15.59163412605[/C][C]-4.59163412605[/C][/ROW]
[ROW][C]47[/C][C]14[/C][C]16.4250897991219[/C][C]-2.42508979912194[/C][/ROW]
[ROW][C]48[/C][C]15[/C][C]14.9316008225476[/C][C]0.0683991774524221[/C][/ROW]
[ROW][C]49[/C][C]16[/C][C]15.1990473828132[/C][C]0.800952617186765[/C][/ROW]
[ROW][C]50[/C][C]14[/C][C]16.0185860866674[/C][C]-2.01858608666736[/C][/ROW]
[ROW][C]51[/C][C]16[/C][C]16.490631866381[/C][C]-0.490631866380993[/C][/ROW]
[ROW][C]52[/C][C]14[/C][C]14.1117645059229[/C][C]-0.111764505922894[/C][/ROW]
[ROW][C]53[/C][C]12[/C][C]14.6847780076518[/C][C]-2.68477800765184[/C][/ROW]
[ROW][C]54[/C][C]16[/C][C]15.1906136784413[/C][C]0.809386321558708[/C][/ROW]
[ROW][C]55[/C][C]9[/C][C]11.5175710839817[/C][C]-2.51757108398172[/C][/ROW]
[ROW][C]56[/C][C]14[/C][C]12.6286951224646[/C][C]1.37130487753543[/C][/ROW]
[ROW][C]57[/C][C]16[/C][C]16.0923517617659[/C][C]-0.0923517617659263[/C][/ROW]
[ROW][C]58[/C][C]16[/C][C]15.08311716571[/C][C]0.916882834289958[/C][/ROW]
[ROW][C]59[/C][C]15[/C][C]15.3237028828634[/C][C]-0.323702882863365[/C][/ROW]
[ROW][C]60[/C][C]16[/C][C]14.1312671012716[/C][C]1.86873289872845[/C][/ROW]
[ROW][C]61[/C][C]12[/C][C]11.4292831319165[/C][C]0.570716868083456[/C][/ROW]
[ROW][C]62[/C][C]16[/C][C]15.97761485165[/C][C]0.0223851483499508[/C][/ROW]
[ROW][C]63[/C][C]16[/C][C]16.466440278449[/C][C]-0.466440278448962[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]14.370634890267[/C][C]-0.370634890266963[/C][/ROW]
[ROW][C]65[/C][C]16[/C][C]15.4359516886432[/C][C]0.564048311356834[/C][/ROW]
[ROW][C]66[/C][C]17[/C][C]15.9063996466629[/C][C]1.09360035333709[/C][/ROW]
[ROW][C]67[/C][C]18[/C][C]16.102069678043[/C][C]1.89793032195701[/C][/ROW]
[ROW][C]68[/C][C]18[/C][C]14.6734546361936[/C][C]3.32654536380641[/C][/ROW]
[ROW][C]69[/C][C]12[/C][C]15.8809142378122[/C][C]-3.88091423781219[/C][/ROW]
[ROW][C]70[/C][C]16[/C][C]15.4211608487746[/C][C]0.578839151225445[/C][/ROW]
[ROW][C]71[/C][C]10[/C][C]13.449196578141[/C][C]-3.449196578141[/C][/ROW]
[ROW][C]72[/C][C]14[/C][C]14.3239482857985[/C][C]-0.323948285798518[/C][/ROW]
[ROW][C]73[/C][C]18[/C][C]16.5377944610183[/C][C]1.46220553898166[/C][/ROW]
[ROW][C]74[/C][C]18[/C][C]17.1549069339804[/C][C]0.845093066019571[/C][/ROW]
[ROW][C]75[/C][C]16[/C][C]15.726375413876[/C][C]0.273624586124044[/C][/ROW]
[ROW][C]76[/C][C]17[/C][C]13.8871437600761[/C][C]3.11285623992386[/C][/ROW]
[ROW][C]77[/C][C]16[/C][C]16.3659420879192[/C][C]-0.365942087919187[/C][/ROW]
[ROW][C]78[/C][C]16[/C][C]14.4511637276615[/C][C]1.5488362723385[/C][/ROW]
[ROW][C]79[/C][C]13[/C][C]14.8428524594843[/C][C]-1.84285245948429[/C][/ROW]
[ROW][C]80[/C][C]16[/C][C]16.0260086189542[/C][C]-0.0260086189541596[/C][/ROW]
[ROW][C]81[/C][C]16[/C][C]15.5758780510833[/C][C]0.424121948916667[/C][/ROW]
[ROW][C]82[/C][C]20[/C][C]16.772106282711[/C][C]3.22789371728898[/C][/ROW]
[ROW][C]83[/C][C]16[/C][C]15.6917911907721[/C][C]0.308208809227862[/C][/ROW]
[ROW][C]84[/C][C]15[/C][C]16.0247767745921[/C][C]-1.02477677459206[/C][/ROW]
[ROW][C]85[/C][C]15[/C][C]14.7435687005215[/C][C]0.256431299478507[/C][/ROW]
[ROW][C]86[/C][C]16[/C][C]14.2682886939008[/C][C]1.73171130609916[/C][/ROW]
[ROW][C]87[/C][C]14[/C][C]14.223327636188[/C][C]-0.223327636188002[/C][/ROW]
[ROW][C]88[/C][C]16[/C][C]15.3376632816263[/C][C]0.662336718373708[/C][/ROW]
[ROW][C]89[/C][C]16[/C][C]14.5832697719889[/C][C]1.41673022801113[/C][/ROW]
[ROW][C]90[/C][C]15[/C][C]14.212271955382[/C][C]0.787728044617952[/C][/ROW]
[ROW][C]91[/C][C]12[/C][C]13.4826033949017[/C][C]-1.4826033949017[/C][/ROW]
[ROW][C]92[/C][C]17[/C][C]17.0093460317475[/C][C]-0.00934603174746024[/C][/ROW]
[ROW][C]93[/C][C]16[/C][C]15.4868128956012[/C][C]0.513187104398811[/C][/ROW]
[ROW][C]94[/C][C]15[/C][C]15.2856069919936[/C][C]-0.285606991993591[/C][/ROW]
[ROW][C]95[/C][C]13[/C][C]15.1789239840191[/C][C]-2.17892398401912[/C][/ROW]
[ROW][C]96[/C][C]16[/C][C]15.2183144625393[/C][C]0.781685537460747[/C][/ROW]
[ROW][C]97[/C][C]16[/C][C]15.8594806492654[/C][C]0.140519350734551[/C][/ROW]
[ROW][C]98[/C][C]16[/C][C]14.1841530890403[/C][C]1.8158469109597[/C][/ROW]
[ROW][C]99[/C][C]16[/C][C]16.159901087907[/C][C]-0.159901087906952[/C][/ROW]
[ROW][C]100[/C][C]14[/C][C]14.3405734839089[/C][C]-0.340573483908861[/C][/ROW]
[ROW][C]101[/C][C]16[/C][C]17.3668114814765[/C][C]-1.3668114814765[/C][/ROW]
[ROW][C]102[/C][C]16[/C][C]14.8129285354697[/C][C]1.18707146453032[/C][/ROW]
[ROW][C]103[/C][C]20[/C][C]17.414779311581[/C][C]2.58522068841896[/C][/ROW]
[ROW][C]104[/C][C]15[/C][C]14.5897897621568[/C][C]0.410210237843217[/C][/ROW]
[ROW][C]105[/C][C]16[/C][C]14.5632726282959[/C][C]1.43672737170409[/C][/ROW]
[ROW][C]106[/C][C]13[/C][C]15.3971712164423[/C][C]-2.39717121644233[/C][/ROW]
[ROW][C]107[/C][C]17[/C][C]16.0670095473568[/C][C]0.932990452643163[/C][/ROW]
[ROW][C]108[/C][C]16[/C][C]15.9411223074884[/C][C]0.0588776925115655[/C][/ROW]
[ROW][C]109[/C][C]16[/C][C]14.5979644522354[/C][C]1.40203554776461[/C][/ROW]
[ROW][C]110[/C][C]12[/C][C]12.3115860011041[/C][C]-0.311586001104149[/C][/ROW]
[ROW][C]111[/C][C]16[/C][C]15.0121404103748[/C][C]0.987859589625233[/C][/ROW]
[ROW][C]112[/C][C]16[/C][C]15.8970719882604[/C][C]0.10292801173956[/C][/ROW]
[ROW][C]113[/C][C]17[/C][C]14.5482562756779[/C][C]2.45174372432212[/C][/ROW]
[ROW][C]114[/C][C]13[/C][C]14.9988832654302[/C][C]-1.99888326543019[/C][/ROW]
[ROW][C]115[/C][C]12[/C][C]14.9367482002426[/C][C]-2.9367482002426[/C][/ROW]
[ROW][C]116[/C][C]18[/C][C]16.6728852143104[/C][C]1.3271147856896[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]15.5475987133233[/C][C]-1.54759871332326[/C][/ROW]
[ROW][C]118[/C][C]14[/C][C]13.0175979270322[/C][C]0.98240207296779[/C][/ROW]
[ROW][C]119[/C][C]13[/C][C]14.9879193660467[/C][C]-1.98791936604673[/C][/ROW]
[ROW][C]120[/C][C]16[/C][C]15.7250609825965[/C][C]0.27493901740351[/C][/ROW]
[ROW][C]121[/C][C]13[/C][C]14.3338869887332[/C][C]-1.33388698873322[/C][/ROW]
[ROW][C]122[/C][C]16[/C][C]15.6958455948834[/C][C]0.304154405116597[/C][/ROW]
[ROW][C]123[/C][C]13[/C][C]16.0238939705842[/C][C]-3.02389397058424[/C][/ROW]
[ROW][C]124[/C][C]16[/C][C]17.1298631050521[/C][C]-1.12986310505213[/C][/ROW]
[ROW][C]125[/C][C]15[/C][C]16.059438245305[/C][C]-1.05943824530504[/C][/ROW]
[ROW][C]126[/C][C]16[/C][C]16.8658061313194[/C][C]-0.865806131319397[/C][/ROW]
[ROW][C]127[/C][C]15[/C][C]15.5139115699994[/C][C]-0.513911569999371[/C][/ROW]
[ROW][C]128[/C][C]17[/C][C]16.3421620626012[/C][C]0.657837937398815[/C][/ROW]
[ROW][C]129[/C][C]15[/C][C]13.8929061777708[/C][C]1.1070938222292[/C][/ROW]
[ROW][C]130[/C][C]12[/C][C]15.0352367266114[/C][C]-3.03523672661142[/C][/ROW]
[ROW][C]131[/C][C]16[/C][C]14.2421207305797[/C][C]1.75787926942027[/C][/ROW]
[ROW][C]132[/C][C]10[/C][C]13.3954993580855[/C][C]-3.39549935808545[/C][/ROW]
[ROW][C]133[/C][C]16[/C][C]14.1829785910847[/C][C]1.81702140891525[/C][/ROW]
[ROW][C]134[/C][C]12[/C][C]14.0010798079533[/C][C]-2.00107980795329[/C][/ROW]
[ROW][C]135[/C][C]14[/C][C]15.7407983013774[/C][C]-1.74079830137736[/C][/ROW]
[ROW][C]136[/C][C]15[/C][C]15.1943517162197[/C][C]-0.194351716219724[/C][/ROW]
[ROW][C]137[/C][C]13[/C][C]12.3594647885484[/C][C]0.640535211451566[/C][/ROW]
[ROW][C]138[/C][C]15[/C][C]14.5315975175447[/C][C]0.468402482455295[/C][/ROW]
[ROW][C]139[/C][C]11[/C][C]13.2551506775076[/C][C]-2.2551506775076[/C][/ROW]
[ROW][C]140[/C][C]12[/C][C]13.4520494331797[/C][C]-1.45204943317969[/C][/ROW]
[ROW][C]141[/C][C]8[/C][C]13.4537200598596[/C][C]-5.45372005985958[/C][/ROW]
[ROW][C]142[/C][C]16[/C][C]13.1890740163979[/C][C]2.81092598360207[/C][/ROW]
[ROW][C]143[/C][C]15[/C][C]13.4112109892864[/C][C]1.58878901071358[/C][/ROW]
[ROW][C]144[/C][C]17[/C][C]16.6358908650862[/C][C]0.364109134913822[/C][/ROW]
[ROW][C]145[/C][C]16[/C][C]14.9311741305242[/C][C]1.06882586947583[/C][/ROW]
[ROW][C]146[/C][C]10[/C][C]14.2545472410445[/C][C]-4.25454724104455[/C][/ROW]
[ROW][C]147[/C][C]18[/C][C]16.0194087138778[/C][C]1.98059128612218[/C][/ROW]
[ROW][C]148[/C][C]13[/C][C]15.2752928646931[/C][C]-2.27529286469313[/C][/ROW]
[ROW][C]149[/C][C]16[/C][C]15.2429799884548[/C][C]0.757020011545189[/C][/ROW]
[ROW][C]150[/C][C]13[/C][C]13.1168401022919[/C][C]-0.116840102291911[/C][/ROW]
[ROW][C]151[/C][C]10[/C][C]13.208809379565[/C][C]-3.20880937956498[/C][/ROW]
[ROW][C]152[/C][C]15[/C][C]16.5256452894514[/C][C]-1.52564528945141[/C][/ROW]
[ROW][C]153[/C][C]16[/C][C]14.1980229520776[/C][C]1.80197704792237[/C][/ROW]
[ROW][C]154[/C][C]16[/C][C]12.0904557977686[/C][C]3.90954420223143[/C][/ROW]
[ROW][C]155[/C][C]14[/C][C]12.5466019952205[/C][C]1.45339800477955[/C][/ROW]
[ROW][C]156[/C][C]10[/C][C]12.594466135854[/C][C]-2.59446613585403[/C][/ROW]
[ROW][C]157[/C][C]17[/C][C]17.0093460317475[/C][C]-0.00934603174746024[/C][/ROW]
[ROW][C]158[/C][C]13[/C][C]11.7116678035722[/C][C]1.28833219642779[/C][/ROW]
[ROW][C]159[/C][C]15[/C][C]13.8929061777708[/C][C]1.1070938222292[/C][/ROW]
[ROW][C]160[/C][C]16[/C][C]15.1705727060965[/C][C]0.829427293903479[/C][/ROW]
[ROW][C]161[/C][C]12[/C][C]12.604839934877[/C][C]-0.604839934876992[/C][/ROW]
[ROW][C]162[/C][C]13[/C][C]12.6868364994311[/C][C]0.31316350056888[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186000&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186000&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 Index Actuals InterpolationForecast ResidualsPrediction Error 1 13 16.1176010114951 -3.11760101149508 2 16 15.968590457689 0.0314095423110451 3 19 16.1737913079047 2.82620869209528 4 15 11.736127344159 3.26387265584104 5 14 15.452637097449 -1.45263709744899 6 13 14.8834893226671 -1.88348932266714 7 19 15.0613613157009 3.93863868429912 8 15 16.5944113899983 -1.59441138999828 9 14 15.8832986290019 -1.88329862900193 10 15 12.4335389038549 2.56646109614508 11 16 15.1982303001062 0.801769699893772 12 16 16.1967107906881 -0.196710790688073 13 16 15.3634266943478 0.636573305652221 14 16 15.3075485483858 0.692451451614239 15 17 17.3536454897514 -0.353645489751352 16 15 15.0219820117331 -0.0219820117331483 17 15 14.5515710856597 0.448428914340278 18 20 16.1282168592918 3.87178314070815 19 18 15.4125174517924 2.58748254820759 20 16 15.1974669169696 0.802533083030439 21 16 15.2413072789435 0.758692721056456 22 16 14.7098703292292 1.29012967077078 23 19 16.4155828316183 2.58441716838169 24 16 14.6825230126674 1.31747698733264 25 17 14.7715188140351 2.22848118596487 26 17 16.8811396748344 0.118860325165605 27 16 14.9627073533393 1.0372926466607 28 15 16.0428407120701 -1.04284071207008 29 16 15.4408619857186 0.559138014281356 30 14 13.9609756665955 0.0390243334045331 31 15 15.5941864333835 -0.594186433383495 32 12 12.1849031511977 -0.184903151197658 33 14 15.1114906362315 -1.11149063623154 34 16 15.4302007927359 0.569799207264144 35 14 15.6420243027548 -1.64202430275481 36 7 12.902573456336 -5.90257345633603 37 10 10.8668171646377 -0.866817164637676 38 14 15.779490671898 -1.77949067189796 39 16 14.2211559228343 1.77884407716565 40 16 14.5839249071753 1.41607509282467 41 16 15.0453839976902 0.954616002309845 42 14 15.5595766071838 -1.55957660718381 43 20 17.9147171281587 2.08528287184129 44 14 14.2062028697073 -0.206202869707291 45 14 14.8985465949208 -0.898546594920809 46 11 15.59163412605 -4.59163412605 47 14 16.4250897991219 -2.42508979912194 48 15 14.9316008225476 0.0683991774524221 49 16 15.1990473828132 0.800952617186765 50 14 16.0185860866674 -2.01858608666736 51 16 16.490631866381 -0.490631866380993 52 14 14.1117645059229 -0.111764505922894 53 12 14.6847780076518 -2.68477800765184 54 16 15.1906136784413 0.809386321558708 55 9 11.5175710839817 -2.51757108398172 56 14 12.6286951224646 1.37130487753543 57 16 16.0923517617659 -0.0923517617659263 58 16 15.08311716571 0.916882834289958 59 15 15.3237028828634 -0.323702882863365 60 16 14.1312671012716 1.86873289872845 61 12 11.4292831319165 0.570716868083456 62 16 15.97761485165 0.0223851483499508 63 16 16.466440278449 -0.466440278448962 64 14 14.370634890267 -0.370634890266963 65 16 15.4359516886432 0.564048311356834 66 17 15.9063996466629 1.09360035333709 67 18 16.102069678043 1.89793032195701 68 18 14.6734546361936 3.32654536380641 69 12 15.8809142378122 -3.88091423781219 70 16 15.4211608487746 0.578839151225445 71 10 13.449196578141 -3.449196578141 72 14 14.3239482857985 -0.323948285798518 73 18 16.5377944610183 1.46220553898166 74 18 17.1549069339804 0.845093066019571 75 16 15.726375413876 0.273624586124044 76 17 13.8871437600761 3.11285623992386 77 16 16.3659420879192 -0.365942087919187 78 16 14.4511637276615 1.5488362723385 79 13 14.8428524594843 -1.84285245948429 80 16 16.0260086189542 -0.0260086189541596 81 16 15.5758780510833 0.424121948916667 82 20 16.772106282711 3.22789371728898 83 16 15.6917911907721 0.308208809227862 84 15 16.0247767745921 -1.02477677459206 85 15 14.7435687005215 0.256431299478507 86 16 14.2682886939008 1.73171130609916 87 14 14.223327636188 -0.223327636188002 88 16 15.3376632816263 0.662336718373708 89 16 14.5832697719889 1.41673022801113 90 15 14.212271955382 0.787728044617952 91 12 13.4826033949017 -1.4826033949017 92 17 17.0093460317475 -0.00934603174746024 93 16 15.4868128956012 0.513187104398811 94 15 15.2856069919936 -0.285606991993591 95 13 15.1789239840191 -2.17892398401912 96 16 15.2183144625393 0.781685537460747 97 16 15.8594806492654 0.140519350734551 98 16 14.1841530890403 1.8158469109597 99 16 16.159901087907 -0.159901087906952 100 14 14.3405734839089 -0.340573483908861 101 16 17.3668114814765 -1.3668114814765 102 16 14.8129285354697 1.18707146453032 103 20 17.414779311581 2.58522068841896 104 15 14.5897897621568 0.410210237843217 105 16 14.5632726282959 1.43672737170409 106 13 15.3971712164423 -2.39717121644233 107 17 16.0670095473568 0.932990452643163 108 16 15.9411223074884 0.0588776925115655 109 16 14.5979644522354 1.40203554776461 110 12 12.3115860011041 -0.311586001104149 111 16 15.0121404103748 0.987859589625233 112 16 15.8970719882604 0.10292801173956 113 17 14.5482562756779 2.45174372432212 114 13 14.9988832654302 -1.99888326543019 115 12 14.9367482002426 -2.9367482002426 116 18 16.6728852143104 1.3271147856896 117 14 15.5475987133233 -1.54759871332326 118 14 13.0175979270322 0.98240207296779 119 13 14.9879193660467 -1.98791936604673 120 16 15.7250609825965 0.27493901740351 121 13 14.3338869887332 -1.33388698873322 122 16 15.6958455948834 0.304154405116597 123 13 16.0238939705842 -3.02389397058424 124 16 17.1298631050521 -1.12986310505213 125 15 16.059438245305 -1.05943824530504 126 16 16.8658061313194 -0.865806131319397 127 15 15.5139115699994 -0.513911569999371 128 17 16.3421620626012 0.657837937398815 129 15 13.8929061777708 1.1070938222292 130 12 15.0352367266114 -3.03523672661142 131 16 14.2421207305797 1.75787926942027 132 10 13.3954993580855 -3.39549935808545 133 16 14.1829785910847 1.81702140891525 134 12 14.0010798079533 -2.00107980795329 135 14 15.7407983013774 -1.74079830137736 136 15 15.1943517162197 -0.194351716219724 137 13 12.3594647885484 0.640535211451566 138 15 14.5315975175447 0.468402482455295 139 11 13.2551506775076 -2.2551506775076 140 12 13.4520494331797 -1.45204943317969 141 8 13.4537200598596 -5.45372005985958 142 16 13.1890740163979 2.81092598360207 143 15 13.4112109892864 1.58878901071358 144 17 16.6358908650862 0.364109134913822 145 16 14.9311741305242 1.06882586947583 146 10 14.2545472410445 -4.25454724104455 147 18 16.0194087138778 1.98059128612218 148 13 15.2752928646931 -2.27529286469313 149 16 15.2429799884548 0.757020011545189 150 13 13.1168401022919 -0.116840102291911 151 10 13.208809379565 -3.20880937956498 152 15 16.5256452894514 -1.52564528945141 153 16 14.1980229520776 1.80197704792237 154 16 12.0904557977686 3.90954420223143 155 14 12.5466019952205 1.45339800477955 156 10 12.594466135854 -2.59446613585403 157 17 17.0093460317475 -0.00934603174746024 158 13 11.7116678035722 1.28833219642779 159 15 13.8929061777708 1.1070938222292 160 16 15.1705727060965 0.829427293903479 161 12 12.604839934877 -0.604839934876992 162 13 12.6868364994311 0.31316350056888

 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 11 0.391592737316465 0.78318547463293 0.608407262683535 12 0.625494324307599 0.749011351384803 0.374505675692401 13 0.486709110966601 0.973418221933201 0.513290889033399 14 0.460379032502383 0.920758065004766 0.539620967497617 15 0.351640382634583 0.703280765269166 0.648359617365417 16 0.267145938881104 0.534291877762207 0.732854061118896 17 0.217971969276912 0.435943938553823 0.782028030723088 18 0.419231086271126 0.838462172542252 0.580768913728874 19 0.342077757020335 0.68415551404067 0.657922242979665 20 0.262867097830819 0.525734195661639 0.73713290216918 21 0.203320554940347 0.406641109880693 0.796679445059653 22 0.185523847760742 0.371047695521484 0.814476152239258 23 0.370009803174088 0.740019606348176 0.629990196825912 24 0.40633422343729 0.81266844687458 0.59366577656271 25 0.395346365960102 0.790692731920205 0.604653634039897 26 0.357806583905504 0.715613167811007 0.642193416094496 27 0.417480679975857 0.834961359951714 0.582519320024143 28 0.423737909836793 0.847475819673586 0.576262090163207 29 0.404994581979495 0.80998916395899 0.595005418020505 30 0.446979809793504 0.893959619587008 0.553020190206496 31 0.388960387822833 0.777920775645666 0.611039612177167 32 0.342451535732889 0.684903071465779 0.657548464267111 33 0.319171912792343 0.638343825584685 0.680828087207658 34 0.286039861582077 0.572079723164155 0.713960138417922 35 0.245073281748214 0.490146563496428 0.754926718251786 36 0.829140931634888 0.341718136730225 0.170859068365112 37 0.802778452692215 0.394443094615569 0.197221547307785 38 0.796786492944592 0.406427014110815 0.203213507055408 39 0.818235582728629 0.363528834542742 0.181764417271371 40 0.7997937493677 0.4004125012646 0.2002062506323 41 0.766969363362431 0.466061273275139 0.233030636637569 42 0.741892014655346 0.516215970689307 0.258107985344654 43 0.767213510937847 0.465572978124306 0.232786489062153 44 0.72447243346753 0.55105513306494 0.27552756653247 45 0.695845745479517 0.608308509040966 0.304154254520483 46 0.861707847455688 0.276584305088624 0.138292152544312 47 0.90959795095478 0.18080409809044 0.09040204904522 48 0.886622330335456 0.226755339329089 0.113377669664544 49 0.870659535512732 0.258680928974536 0.129340464487268 50 0.869685546593415 0.26062890681317 0.130314453406585 51 0.84142877859404 0.317142442811919 0.15857122140596 52 0.808924528327297 0.382150943345407 0.191075471672703 53 0.826100561355736 0.347798877288529 0.173899438644264 54 0.813114570823474 0.373770858353052 0.186885429176526 55 0.827013016996463 0.345973966007075 0.172986983003537 56 0.801706626075065 0.396586747849869 0.198293373924935 57 0.76747248258696 0.465055034826079 0.23252751741304 58 0.744056101505625 0.511887796988749 0.255943898494375 59 0.70627686763562 0.587446264728759 0.29372313236438 60 0.702598452223429 0.594803095553142 0.297401547776571 61 0.664522974947353 0.670954050105294 0.335477025052647 62 0.62191823681952 0.756163526360959 0.37808176318048 63 0.582183275094972 0.835633449810055 0.417816724905028 64 0.534725232148106 0.930549535703788 0.465274767851894 65 0.495843302829506 0.991686605659013 0.504156697170494 66 0.466674057559933 0.933348115119866 0.533325942440067 67 0.463721146315528 0.927442292631055 0.536278853684472 68 0.613478939748883 0.773042120502234 0.386521060251117 69 0.729358335569008 0.541283328861983 0.270641664430992 70 0.694403882039748 0.611192235920505 0.305596117960252 71 0.802629523256327 0.394740953487347 0.197370476743673 72 0.768212893376977 0.463574213246045 0.231787106623023 73 0.758667373560066 0.482665252879868 0.241332626439934 74 0.739015604249321 0.521968791501358 0.260984395750679 75 0.699570990713306 0.600858018573388 0.300429009286694 76 0.750856404926374 0.498287190147252 0.249143595073626 77 0.713685952350372 0.572628095299256 0.286314047649628 78 0.700496476174032 0.599007047651935 0.299503523825968 79 0.706346825923367 0.587306348153265 0.293653174076633 80 0.664780080930307 0.670439838139385 0.335219919069693 81 0.625472431732697 0.749055136534605 0.374527568267303 82 0.749640195870538 0.500719608258924 0.250359804129462 83 0.712578525224244 0.574842949551511 0.287421474775756 84 0.682801845209802 0.634396309580396 0.317198154790198 85 0.640736724081722 0.718526551836556 0.359263275918278 86 0.634331470938219 0.731337058123562 0.365668529061781 87 0.589500320931648 0.820999358136705 0.410499679068352 88 0.549615687265225 0.900768625469551 0.450384312734775 89 0.532961058951719 0.934077882096562 0.467038941048281 90 0.498949539193853 0.997899078387706 0.501050460806147 91 0.480027937635368 0.960055875270737 0.519972062364632 92 0.441780647044689 0.883561294089377 0.558219352955311 93 0.399737648992291 0.799475297984583 0.600262351007709 94 0.35767206956834 0.715344139136679 0.64232793043166 95 0.373839994920099 0.747679989840198 0.626160005079901 96 0.338949483340487 0.677898966680973 0.661050516659513 97 0.30119430941359 0.602388618827181 0.69880569058641 98 0.299878852263542 0.599757704527084 0.700121147736458 99 0.260020361495584 0.520040722991169 0.739979638504416 100 0.224456210484302 0.448912420968604 0.775543789515698 101 0.202854737340541 0.405709474681082 0.797145262659459 102 0.188122179430185 0.376244358860369 0.811877820569815 103 0.231429393464024 0.462858786928048 0.768570606535976 104 0.197374118906996 0.394748237813991 0.802625881093004 105 0.191072048531601 0.382144097063202 0.808927951468399 106 0.203604934094425 0.40720986818885 0.796395065905575 107 0.18369426199133 0.36738852398266 0.81630573800867 108 0.163405207972352 0.326810415944704 0.836594792027648 109 0.157043647309579 0.314087294619158 0.842956352690421 110 0.147581451752345 0.29516290350469 0.852418548247655 111 0.130037575188798 0.260075150377596 0.869962424811202 112 0.108077816350241 0.216155632700483 0.891922183649759 113 0.126207472920557 0.252414945841115 0.873792527079443 114 0.116111809566638 0.232223619133276 0.883888190433362 115 0.148046379537207 0.296092759074413 0.851953620462793 116 0.141018350437075 0.282036700874151 0.858981649562925 117 0.122198121771532 0.244396243543065 0.877801878228468 118 0.105846938234546 0.211693876469092 0.894153061765454 119 0.108605312623334 0.217210625246668 0.891394687376666 120 0.100739824216558 0.201479648433115 0.899260175783442 121 0.085130992757172 0.170261985514344 0.914869007242828 122 0.0677193874578059 0.135438774915612 0.932280612542194 123 0.0768877108476768 0.153775421695354 0.923112289152323 124 0.061385519494432 0.122771038988864 0.938614480505568 125 0.0493717319861994 0.0987434639723987 0.950628268013801 126 0.0376353172064135 0.075270634412827 0.962364682793587 127 0.0281861879575797 0.0563723759151594 0.97181381204242 128 0.0222052869660717 0.0444105739321435 0.977794713033928 129 0.0176766917512319 0.0353533835024638 0.982323308248768 130 0.0247469295583477 0.0494938591166954 0.975253070441652 131 0.0205293694024478 0.0410587388048956 0.979470630597552 132 0.0323139979372079 0.0646279958744158 0.967686002062792 133 0.0334923448476136 0.0669846896952272 0.966507655152386 134 0.0450110794223706 0.0900221588447413 0.954988920577629 135 0.0357183324111285 0.071436664822257 0.964281667588871 136 0.0245208300915428 0.0490416601830857 0.975479169908457 137 0.0164419098923985 0.032883819784797 0.983558090107602 138 0.0114513389057443 0.0229026778114887 0.988548661094256 139 0.0187141566338838 0.0374283132677676 0.981285843366116 140 0.0156735971353231 0.0313471942706463 0.984326402864677 141 0.616099641298338 0.767800717403324 0.383900358701662 142 0.555282969656264 0.889434060687471 0.444717030343736 143 0.471948863858553 0.943897727717105 0.528051136141447 144 0.391835907226906 0.783671814453811 0.608164092773094 145 0.305190253535482 0.610380507070963 0.694809746464518 146 0.350444740839469 0.700889481678938 0.649555259160531 147 0.309883293317789 0.619766586635579 0.690116706682211 148 0.723212344308575 0.55357531138285 0.276787655691425 149 0.682313232972972 0.635373534054056 0.317686767027028 150 0.540947725608551 0.918104548782898 0.459052274391449 151 0.745961346148008 0.508077307703983 0.254038653851992

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 & 0.391592737316465 & 0.78318547463293 & 0.608407262683535 \tabularnewline
12 & 0.625494324307599 & 0.749011351384803 & 0.374505675692401 \tabularnewline
13 & 0.486709110966601 & 0.973418221933201 & 0.513290889033399 \tabularnewline
14 & 0.460379032502383 & 0.920758065004766 & 0.539620967497617 \tabularnewline
15 & 0.351640382634583 & 0.703280765269166 & 0.648359617365417 \tabularnewline
16 & 0.267145938881104 & 0.534291877762207 & 0.732854061118896 \tabularnewline
17 & 0.217971969276912 & 0.435943938553823 & 0.782028030723088 \tabularnewline
18 & 0.419231086271126 & 0.838462172542252 & 0.580768913728874 \tabularnewline
19 & 0.342077757020335 & 0.68415551404067 & 0.657922242979665 \tabularnewline
20 & 0.262867097830819 & 0.525734195661639 & 0.73713290216918 \tabularnewline
21 & 0.203320554940347 & 0.406641109880693 & 0.796679445059653 \tabularnewline
22 & 0.185523847760742 & 0.371047695521484 & 0.814476152239258 \tabularnewline
23 & 0.370009803174088 & 0.740019606348176 & 0.629990196825912 \tabularnewline
24 & 0.40633422343729 & 0.81266844687458 & 0.59366577656271 \tabularnewline
25 & 0.395346365960102 & 0.790692731920205 & 0.604653634039897 \tabularnewline
26 & 0.357806583905504 & 0.715613167811007 & 0.642193416094496 \tabularnewline
27 & 0.417480679975857 & 0.834961359951714 & 0.582519320024143 \tabularnewline
28 & 0.423737909836793 & 0.847475819673586 & 0.576262090163207 \tabularnewline
29 & 0.404994581979495 & 0.80998916395899 & 0.595005418020505 \tabularnewline
30 & 0.446979809793504 & 0.893959619587008 & 0.553020190206496 \tabularnewline
31 & 0.388960387822833 & 0.777920775645666 & 0.611039612177167 \tabularnewline
32 & 0.342451535732889 & 0.684903071465779 & 0.657548464267111 \tabularnewline
33 & 0.319171912792343 & 0.638343825584685 & 0.680828087207658 \tabularnewline
34 & 0.286039861582077 & 0.572079723164155 & 0.713960138417922 \tabularnewline
35 & 0.245073281748214 & 0.490146563496428 & 0.754926718251786 \tabularnewline
36 & 0.829140931634888 & 0.341718136730225 & 0.170859068365112 \tabularnewline
37 & 0.802778452692215 & 0.394443094615569 & 0.197221547307785 \tabularnewline
38 & 0.796786492944592 & 0.406427014110815 & 0.203213507055408 \tabularnewline
39 & 0.818235582728629 & 0.363528834542742 & 0.181764417271371 \tabularnewline
40 & 0.7997937493677 & 0.4004125012646 & 0.2002062506323 \tabularnewline
41 & 0.766969363362431 & 0.466061273275139 & 0.233030636637569 \tabularnewline
42 & 0.741892014655346 & 0.516215970689307 & 0.258107985344654 \tabularnewline
43 & 0.767213510937847 & 0.465572978124306 & 0.232786489062153 \tabularnewline
44 & 0.72447243346753 & 0.55105513306494 & 0.27552756653247 \tabularnewline
45 & 0.695845745479517 & 0.608308509040966 & 0.304154254520483 \tabularnewline
46 & 0.861707847455688 & 0.276584305088624 & 0.138292152544312 \tabularnewline
47 & 0.90959795095478 & 0.18080409809044 & 0.09040204904522 \tabularnewline
48 & 0.886622330335456 & 0.226755339329089 & 0.113377669664544 \tabularnewline
49 & 0.870659535512732 & 0.258680928974536 & 0.129340464487268 \tabularnewline
50 & 0.869685546593415 & 0.26062890681317 & 0.130314453406585 \tabularnewline
51 & 0.84142877859404 & 0.317142442811919 & 0.15857122140596 \tabularnewline
52 & 0.808924528327297 & 0.382150943345407 & 0.191075471672703 \tabularnewline
53 & 0.826100561355736 & 0.347798877288529 & 0.173899438644264 \tabularnewline
54 & 0.813114570823474 & 0.373770858353052 & 0.186885429176526 \tabularnewline
55 & 0.827013016996463 & 0.345973966007075 & 0.172986983003537 \tabularnewline
56 & 0.801706626075065 & 0.396586747849869 & 0.198293373924935 \tabularnewline
57 & 0.76747248258696 & 0.465055034826079 & 0.23252751741304 \tabularnewline
58 & 0.744056101505625 & 0.511887796988749 & 0.255943898494375 \tabularnewline
59 & 0.70627686763562 & 0.587446264728759 & 0.29372313236438 \tabularnewline
60 & 0.702598452223429 & 0.594803095553142 & 0.297401547776571 \tabularnewline
61 & 0.664522974947353 & 0.670954050105294 & 0.335477025052647 \tabularnewline
62 & 0.62191823681952 & 0.756163526360959 & 0.37808176318048 \tabularnewline
63 & 0.582183275094972 & 0.835633449810055 & 0.417816724905028 \tabularnewline
64 & 0.534725232148106 & 0.930549535703788 & 0.465274767851894 \tabularnewline
65 & 0.495843302829506 & 0.991686605659013 & 0.504156697170494 \tabularnewline
66 & 0.466674057559933 & 0.933348115119866 & 0.533325942440067 \tabularnewline
67 & 0.463721146315528 & 0.927442292631055 & 0.536278853684472 \tabularnewline
68 & 0.613478939748883 & 0.773042120502234 & 0.386521060251117 \tabularnewline
69 & 0.729358335569008 & 0.541283328861983 & 0.270641664430992 \tabularnewline
70 & 0.694403882039748 & 0.611192235920505 & 0.305596117960252 \tabularnewline
71 & 0.802629523256327 & 0.394740953487347 & 0.197370476743673 \tabularnewline
72 & 0.768212893376977 & 0.463574213246045 & 0.231787106623023 \tabularnewline
73 & 0.758667373560066 & 0.482665252879868 & 0.241332626439934 \tabularnewline
74 & 0.739015604249321 & 0.521968791501358 & 0.260984395750679 \tabularnewline
75 & 0.699570990713306 & 0.600858018573388 & 0.300429009286694 \tabularnewline
76 & 0.750856404926374 & 0.498287190147252 & 0.249143595073626 \tabularnewline
77 & 0.713685952350372 & 0.572628095299256 & 0.286314047649628 \tabularnewline
78 & 0.700496476174032 & 0.599007047651935 & 0.299503523825968 \tabularnewline
79 & 0.706346825923367 & 0.587306348153265 & 0.293653174076633 \tabularnewline
80 & 0.664780080930307 & 0.670439838139385 & 0.335219919069693 \tabularnewline
81 & 0.625472431732697 & 0.749055136534605 & 0.374527568267303 \tabularnewline
82 & 0.749640195870538 & 0.500719608258924 & 0.250359804129462 \tabularnewline
83 & 0.712578525224244 & 0.574842949551511 & 0.287421474775756 \tabularnewline
84 & 0.682801845209802 & 0.634396309580396 & 0.317198154790198 \tabularnewline
85 & 0.640736724081722 & 0.718526551836556 & 0.359263275918278 \tabularnewline
86 & 0.634331470938219 & 0.731337058123562 & 0.365668529061781 \tabularnewline
87 & 0.589500320931648 & 0.820999358136705 & 0.410499679068352 \tabularnewline
88 & 0.549615687265225 & 0.900768625469551 & 0.450384312734775 \tabularnewline
89 & 0.532961058951719 & 0.934077882096562 & 0.467038941048281 \tabularnewline
90 & 0.498949539193853 & 0.997899078387706 & 0.501050460806147 \tabularnewline
91 & 0.480027937635368 & 0.960055875270737 & 0.519972062364632 \tabularnewline
92 & 0.441780647044689 & 0.883561294089377 & 0.558219352955311 \tabularnewline
93 & 0.399737648992291 & 0.799475297984583 & 0.600262351007709 \tabularnewline
94 & 0.35767206956834 & 0.715344139136679 & 0.64232793043166 \tabularnewline
95 & 0.373839994920099 & 0.747679989840198 & 0.626160005079901 \tabularnewline
96 & 0.338949483340487 & 0.677898966680973 & 0.661050516659513 \tabularnewline
97 & 0.30119430941359 & 0.602388618827181 & 0.69880569058641 \tabularnewline
98 & 0.299878852263542 & 0.599757704527084 & 0.700121147736458 \tabularnewline
99 & 0.260020361495584 & 0.520040722991169 & 0.739979638504416 \tabularnewline
100 & 0.224456210484302 & 0.448912420968604 & 0.775543789515698 \tabularnewline
101 & 0.202854737340541 & 0.405709474681082 & 0.797145262659459 \tabularnewline
102 & 0.188122179430185 & 0.376244358860369 & 0.811877820569815 \tabularnewline
103 & 0.231429393464024 & 0.462858786928048 & 0.768570606535976 \tabularnewline
104 & 0.197374118906996 & 0.394748237813991 & 0.802625881093004 \tabularnewline
105 & 0.191072048531601 & 0.382144097063202 & 0.808927951468399 \tabularnewline
106 & 0.203604934094425 & 0.40720986818885 & 0.796395065905575 \tabularnewline
107 & 0.18369426199133 & 0.36738852398266 & 0.81630573800867 \tabularnewline
108 & 0.163405207972352 & 0.326810415944704 & 0.836594792027648 \tabularnewline
109 & 0.157043647309579 & 0.314087294619158 & 0.842956352690421 \tabularnewline
110 & 0.147581451752345 & 0.29516290350469 & 0.852418548247655 \tabularnewline
111 & 0.130037575188798 & 0.260075150377596 & 0.869962424811202 \tabularnewline
112 & 0.108077816350241 & 0.216155632700483 & 0.891922183649759 \tabularnewline
113 & 0.126207472920557 & 0.252414945841115 & 0.873792527079443 \tabularnewline
114 & 0.116111809566638 & 0.232223619133276 & 0.883888190433362 \tabularnewline
115 & 0.148046379537207 & 0.296092759074413 & 0.851953620462793 \tabularnewline
116 & 0.141018350437075 & 0.282036700874151 & 0.858981649562925 \tabularnewline
117 & 0.122198121771532 & 0.244396243543065 & 0.877801878228468 \tabularnewline
118 & 0.105846938234546 & 0.211693876469092 & 0.894153061765454 \tabularnewline
119 & 0.108605312623334 & 0.217210625246668 & 0.891394687376666 \tabularnewline
120 & 0.100739824216558 & 0.201479648433115 & 0.899260175783442 \tabularnewline
121 & 0.085130992757172 & 0.170261985514344 & 0.914869007242828 \tabularnewline
122 & 0.0677193874578059 & 0.135438774915612 & 0.932280612542194 \tabularnewline
123 & 0.0768877108476768 & 0.153775421695354 & 0.923112289152323 \tabularnewline
124 & 0.061385519494432 & 0.122771038988864 & 0.938614480505568 \tabularnewline
125 & 0.0493717319861994 & 0.0987434639723987 & 0.950628268013801 \tabularnewline
126 & 0.0376353172064135 & 0.075270634412827 & 0.962364682793587 \tabularnewline
127 & 0.0281861879575797 & 0.0563723759151594 & 0.97181381204242 \tabularnewline
128 & 0.0222052869660717 & 0.0444105739321435 & 0.977794713033928 \tabularnewline
129 & 0.0176766917512319 & 0.0353533835024638 & 0.982323308248768 \tabularnewline
130 & 0.0247469295583477 & 0.0494938591166954 & 0.975253070441652 \tabularnewline
131 & 0.0205293694024478 & 0.0410587388048956 & 0.979470630597552 \tabularnewline
132 & 0.0323139979372079 & 0.0646279958744158 & 0.967686002062792 \tabularnewline
133 & 0.0334923448476136 & 0.0669846896952272 & 0.966507655152386 \tabularnewline
134 & 0.0450110794223706 & 0.0900221588447413 & 0.954988920577629 \tabularnewline
135 & 0.0357183324111285 & 0.071436664822257 & 0.964281667588871 \tabularnewline
136 & 0.0245208300915428 & 0.0490416601830857 & 0.975479169908457 \tabularnewline
137 & 0.0164419098923985 & 0.032883819784797 & 0.983558090107602 \tabularnewline
138 & 0.0114513389057443 & 0.0229026778114887 & 0.988548661094256 \tabularnewline
139 & 0.0187141566338838 & 0.0374283132677676 & 0.981285843366116 \tabularnewline
140 & 0.0156735971353231 & 0.0313471942706463 & 0.984326402864677 \tabularnewline
141 & 0.616099641298338 & 0.767800717403324 & 0.383900358701662 \tabularnewline
142 & 0.555282969656264 & 0.889434060687471 & 0.444717030343736 \tabularnewline
143 & 0.471948863858553 & 0.943897727717105 & 0.528051136141447 \tabularnewline
144 & 0.391835907226906 & 0.783671814453811 & 0.608164092773094 \tabularnewline
145 & 0.305190253535482 & 0.610380507070963 & 0.694809746464518 \tabularnewline
146 & 0.350444740839469 & 0.700889481678938 & 0.649555259160531 \tabularnewline
147 & 0.309883293317789 & 0.619766586635579 & 0.690116706682211 \tabularnewline
148 & 0.723212344308575 & 0.55357531138285 & 0.276787655691425 \tabularnewline
149 & 0.682313232972972 & 0.635373534054056 & 0.317686767027028 \tabularnewline
150 & 0.540947725608551 & 0.918104548782898 & 0.459052274391449 \tabularnewline
151 & 0.745961346148008 & 0.508077307703983 & 0.254038653851992 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186000&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]11[/C][C]0.391592737316465[/C][C]0.78318547463293[/C][C]0.608407262683535[/C][/ROW]
[ROW][C]12[/C][C]0.625494324307599[/C][C]0.749011351384803[/C][C]0.374505675692401[/C][/ROW]
[ROW][C]13[/C][C]0.486709110966601[/C][C]0.973418221933201[/C][C]0.513290889033399[/C][/ROW]
[ROW][C]14[/C][C]0.460379032502383[/C][C]0.920758065004766[/C][C]0.539620967497617[/C][/ROW]
[ROW][C]15[/C][C]0.351640382634583[/C][C]0.703280765269166[/C][C]0.648359617365417[/C][/ROW]
[ROW][C]16[/C][C]0.267145938881104[/C][C]0.534291877762207[/C][C]0.732854061118896[/C][/ROW]
[ROW][C]17[/C][C]0.217971969276912[/C][C]0.435943938553823[/C][C]0.782028030723088[/C][/ROW]
[ROW][C]18[/C][C]0.419231086271126[/C][C]0.838462172542252[/C][C]0.580768913728874[/C][/ROW]
[ROW][C]19[/C][C]0.342077757020335[/C][C]0.68415551404067[/C][C]0.657922242979665[/C][/ROW]
[ROW][C]20[/C][C]0.262867097830819[/C][C]0.525734195661639[/C][C]0.73713290216918[/C][/ROW]
[ROW][C]21[/C][C]0.203320554940347[/C][C]0.406641109880693[/C][C]0.796679445059653[/C][/ROW]
[ROW][C]22[/C][C]0.185523847760742[/C][C]0.371047695521484[/C][C]0.814476152239258[/C][/ROW]
[ROW][C]23[/C][C]0.370009803174088[/C][C]0.740019606348176[/C][C]0.629990196825912[/C][/ROW]
[ROW][C]24[/C][C]0.40633422343729[/C][C]0.81266844687458[/C][C]0.59366577656271[/C][/ROW]
[ROW][C]25[/C][C]0.395346365960102[/C][C]0.790692731920205[/C][C]0.604653634039897[/C][/ROW]
[ROW][C]26[/C][C]0.357806583905504[/C][C]0.715613167811007[/C][C]0.642193416094496[/C][/ROW]
[ROW][C]27[/C][C]0.417480679975857[/C][C]0.834961359951714[/C][C]0.582519320024143[/C][/ROW]
[ROW][C]28[/C][C]0.423737909836793[/C][C]0.847475819673586[/C][C]0.576262090163207[/C][/ROW]
[ROW][C]29[/C][C]0.404994581979495[/C][C]0.80998916395899[/C][C]0.595005418020505[/C][/ROW]
[ROW][C]30[/C][C]0.446979809793504[/C][C]0.893959619587008[/C][C]0.553020190206496[/C][/ROW]
[ROW][C]31[/C][C]0.388960387822833[/C][C]0.777920775645666[/C][C]0.611039612177167[/C][/ROW]
[ROW][C]32[/C][C]0.342451535732889[/C][C]0.684903071465779[/C][C]0.657548464267111[/C][/ROW]
[ROW][C]33[/C][C]0.319171912792343[/C][C]0.638343825584685[/C][C]0.680828087207658[/C][/ROW]
[ROW][C]34[/C][C]0.286039861582077[/C][C]0.572079723164155[/C][C]0.713960138417922[/C][/ROW]
[ROW][C]35[/C][C]0.245073281748214[/C][C]0.490146563496428[/C][C]0.754926718251786[/C][/ROW]
[ROW][C]36[/C][C]0.829140931634888[/C][C]0.341718136730225[/C][C]0.170859068365112[/C][/ROW]
[ROW][C]37[/C][C]0.802778452692215[/C][C]0.394443094615569[/C][C]0.197221547307785[/C][/ROW]
[ROW][C]38[/C][C]0.796786492944592[/C][C]0.406427014110815[/C][C]0.203213507055408[/C][/ROW]
[ROW][C]39[/C][C]0.818235582728629[/C][C]0.363528834542742[/C][C]0.181764417271371[/C][/ROW]
[ROW][C]40[/C][C]0.7997937493677[/C][C]0.4004125012646[/C][C]0.2002062506323[/C][/ROW]
[ROW][C]41[/C][C]0.766969363362431[/C][C]0.466061273275139[/C][C]0.233030636637569[/C][/ROW]
[ROW][C]42[/C][C]0.741892014655346[/C][C]0.516215970689307[/C][C]0.258107985344654[/C][/ROW]
[ROW][C]43[/C][C]0.767213510937847[/C][C]0.465572978124306[/C][C]0.232786489062153[/C][/ROW]
[ROW][C]44[/C][C]0.72447243346753[/C][C]0.55105513306494[/C][C]0.27552756653247[/C][/ROW]
[ROW][C]45[/C][C]0.695845745479517[/C][C]0.608308509040966[/C][C]0.304154254520483[/C][/ROW]
[ROW][C]46[/C][C]0.861707847455688[/C][C]0.276584305088624[/C][C]0.138292152544312[/C][/ROW]
[ROW][C]47[/C][C]0.90959795095478[/C][C]0.18080409809044[/C][C]0.09040204904522[/C][/ROW]
[ROW][C]48[/C][C]0.886622330335456[/C][C]0.226755339329089[/C][C]0.113377669664544[/C][/ROW]
[ROW][C]49[/C][C]0.870659535512732[/C][C]0.258680928974536[/C][C]0.129340464487268[/C][/ROW]
[ROW][C]50[/C][C]0.869685546593415[/C][C]0.26062890681317[/C][C]0.130314453406585[/C][/ROW]
[ROW][C]51[/C][C]0.84142877859404[/C][C]0.317142442811919[/C][C]0.15857122140596[/C][/ROW]
[ROW][C]52[/C][C]0.808924528327297[/C][C]0.382150943345407[/C][C]0.191075471672703[/C][/ROW]
[ROW][C]53[/C][C]0.826100561355736[/C][C]0.347798877288529[/C][C]0.173899438644264[/C][/ROW]
[ROW][C]54[/C][C]0.813114570823474[/C][C]0.373770858353052[/C][C]0.186885429176526[/C][/ROW]
[ROW][C]55[/C][C]0.827013016996463[/C][C]0.345973966007075[/C][C]0.172986983003537[/C][/ROW]
[ROW][C]56[/C][C]0.801706626075065[/C][C]0.396586747849869[/C][C]0.198293373924935[/C][/ROW]
[ROW][C]57[/C][C]0.76747248258696[/C][C]0.465055034826079[/C][C]0.23252751741304[/C][/ROW]
[ROW][C]58[/C][C]0.744056101505625[/C][C]0.511887796988749[/C][C]0.255943898494375[/C][/ROW]
[ROW][C]59[/C][C]0.70627686763562[/C][C]0.587446264728759[/C][C]0.29372313236438[/C][/ROW]
[ROW][C]60[/C][C]0.702598452223429[/C][C]0.594803095553142[/C][C]0.297401547776571[/C][/ROW]
[ROW][C]61[/C][C]0.664522974947353[/C][C]0.670954050105294[/C][C]0.335477025052647[/C][/ROW]
[ROW][C]62[/C][C]0.62191823681952[/C][C]0.756163526360959[/C][C]0.37808176318048[/C][/ROW]
[ROW][C]63[/C][C]0.582183275094972[/C][C]0.835633449810055[/C][C]0.417816724905028[/C][/ROW]
[ROW][C]64[/C][C]0.534725232148106[/C][C]0.930549535703788[/C][C]0.465274767851894[/C][/ROW]
[ROW][C]65[/C][C]0.495843302829506[/C][C]0.991686605659013[/C][C]0.504156697170494[/C][/ROW]
[ROW][C]66[/C][C]0.466674057559933[/C][C]0.933348115119866[/C][C]0.533325942440067[/C][/ROW]
[ROW][C]67[/C][C]0.463721146315528[/C][C]0.927442292631055[/C][C]0.536278853684472[/C][/ROW]
[ROW][C]68[/C][C]0.613478939748883[/C][C]0.773042120502234[/C][C]0.386521060251117[/C][/ROW]
[ROW][C]69[/C][C]0.729358335569008[/C][C]0.541283328861983[/C][C]0.270641664430992[/C][/ROW]
[ROW][C]70[/C][C]0.694403882039748[/C][C]0.611192235920505[/C][C]0.305596117960252[/C][/ROW]
[ROW][C]71[/C][C]0.802629523256327[/C][C]0.394740953487347[/C][C]0.197370476743673[/C][/ROW]
[ROW][C]72[/C][C]0.768212893376977[/C][C]0.463574213246045[/C][C]0.231787106623023[/C][/ROW]
[ROW][C]73[/C][C]0.758667373560066[/C][C]0.482665252879868[/C][C]0.241332626439934[/C][/ROW]
[ROW][C]74[/C][C]0.739015604249321[/C][C]0.521968791501358[/C][C]0.260984395750679[/C][/ROW]
[ROW][C]75[/C][C]0.699570990713306[/C][C]0.600858018573388[/C][C]0.300429009286694[/C][/ROW]
[ROW][C]76[/C][C]0.750856404926374[/C][C]0.498287190147252[/C][C]0.249143595073626[/C][/ROW]
[ROW][C]77[/C][C]0.713685952350372[/C][C]0.572628095299256[/C][C]0.286314047649628[/C][/ROW]
[ROW][C]78[/C][C]0.700496476174032[/C][C]0.599007047651935[/C][C]0.299503523825968[/C][/ROW]
[ROW][C]79[/C][C]0.706346825923367[/C][C]0.587306348153265[/C][C]0.293653174076633[/C][/ROW]
[ROW][C]80[/C][C]0.664780080930307[/C][C]0.670439838139385[/C][C]0.335219919069693[/C][/ROW]
[ROW][C]81[/C][C]0.625472431732697[/C][C]0.749055136534605[/C][C]0.374527568267303[/C][/ROW]
[ROW][C]82[/C][C]0.749640195870538[/C][C]0.500719608258924[/C][C]0.250359804129462[/C][/ROW]
[ROW][C]83[/C][C]0.712578525224244[/C][C]0.574842949551511[/C][C]0.287421474775756[/C][/ROW]
[ROW][C]84[/C][C]0.682801845209802[/C][C]0.634396309580396[/C][C]0.317198154790198[/C][/ROW]
[ROW][C]85[/C][C]0.640736724081722[/C][C]0.718526551836556[/C][C]0.359263275918278[/C][/ROW]
[ROW][C]86[/C][C]0.634331470938219[/C][C]0.731337058123562[/C][C]0.365668529061781[/C][/ROW]
[ROW][C]87[/C][C]0.589500320931648[/C][C]0.820999358136705[/C][C]0.410499679068352[/C][/ROW]
[ROW][C]88[/C][C]0.549615687265225[/C][C]0.900768625469551[/C][C]0.450384312734775[/C][/ROW]
[ROW][C]89[/C][C]0.532961058951719[/C][C]0.934077882096562[/C][C]0.467038941048281[/C][/ROW]
[ROW][C]90[/C][C]0.498949539193853[/C][C]0.997899078387706[/C][C]0.501050460806147[/C][/ROW]
[ROW][C]91[/C][C]0.480027937635368[/C][C]0.960055875270737[/C][C]0.519972062364632[/C][/ROW]
[ROW][C]92[/C][C]0.441780647044689[/C][C]0.883561294089377[/C][C]0.558219352955311[/C][/ROW]
[ROW][C]93[/C][C]0.399737648992291[/C][C]0.799475297984583[/C][C]0.600262351007709[/C][/ROW]
[ROW][C]94[/C][C]0.35767206956834[/C][C]0.715344139136679[/C][C]0.64232793043166[/C][/ROW]
[ROW][C]95[/C][C]0.373839994920099[/C][C]0.747679989840198[/C][C]0.626160005079901[/C][/ROW]
[ROW][C]96[/C][C]0.338949483340487[/C][C]0.677898966680973[/C][C]0.661050516659513[/C][/ROW]
[ROW][C]97[/C][C]0.30119430941359[/C][C]0.602388618827181[/C][C]0.69880569058641[/C][/ROW]
[ROW][C]98[/C][C]0.299878852263542[/C][C]0.599757704527084[/C][C]0.700121147736458[/C][/ROW]
[ROW][C]99[/C][C]0.260020361495584[/C][C]0.520040722991169[/C][C]0.739979638504416[/C][/ROW]
[ROW][C]100[/C][C]0.224456210484302[/C][C]0.448912420968604[/C][C]0.775543789515698[/C][/ROW]
[ROW][C]101[/C][C]0.202854737340541[/C][C]0.405709474681082[/C][C]0.797145262659459[/C][/ROW]
[ROW][C]102[/C][C]0.188122179430185[/C][C]0.376244358860369[/C][C]0.811877820569815[/C][/ROW]
[ROW][C]103[/C][C]0.231429393464024[/C][C]0.462858786928048[/C][C]0.768570606535976[/C][/ROW]
[ROW][C]104[/C][C]0.197374118906996[/C][C]0.394748237813991[/C][C]0.802625881093004[/C][/ROW]
[ROW][C]105[/C][C]0.191072048531601[/C][C]0.382144097063202[/C][C]0.808927951468399[/C][/ROW]
[ROW][C]106[/C][C]0.203604934094425[/C][C]0.40720986818885[/C][C]0.796395065905575[/C][/ROW]
[ROW][C]107[/C][C]0.18369426199133[/C][C]0.36738852398266[/C][C]0.81630573800867[/C][/ROW]
[ROW][C]108[/C][C]0.163405207972352[/C][C]0.326810415944704[/C][C]0.836594792027648[/C][/ROW]
[ROW][C]109[/C][C]0.157043647309579[/C][C]0.314087294619158[/C][C]0.842956352690421[/C][/ROW]
[ROW][C]110[/C][C]0.147581451752345[/C][C]0.29516290350469[/C][C]0.852418548247655[/C][/ROW]
[ROW][C]111[/C][C]0.130037575188798[/C][C]0.260075150377596[/C][C]0.869962424811202[/C][/ROW]
[ROW][C]112[/C][C]0.108077816350241[/C][C]0.216155632700483[/C][C]0.891922183649759[/C][/ROW]
[ROW][C]113[/C][C]0.126207472920557[/C][C]0.252414945841115[/C][C]0.873792527079443[/C][/ROW]
[ROW][C]114[/C][C]0.116111809566638[/C][C]0.232223619133276[/C][C]0.883888190433362[/C][/ROW]
[ROW][C]115[/C][C]0.148046379537207[/C][C]0.296092759074413[/C][C]0.851953620462793[/C][/ROW]
[ROW][C]116[/C][C]0.141018350437075[/C][C]0.282036700874151[/C][C]0.858981649562925[/C][/ROW]
[ROW][C]117[/C][C]0.122198121771532[/C][C]0.244396243543065[/C][C]0.877801878228468[/C][/ROW]
[ROW][C]118[/C][C]0.105846938234546[/C][C]0.211693876469092[/C][C]0.894153061765454[/C][/ROW]
[ROW][C]119[/C][C]0.108605312623334[/C][C]0.217210625246668[/C][C]0.891394687376666[/C][/ROW]
[ROW][C]120[/C][C]0.100739824216558[/C][C]0.201479648433115[/C][C]0.899260175783442[/C][/ROW]
[ROW][C]121[/C][C]0.085130992757172[/C][C]0.170261985514344[/C][C]0.914869007242828[/C][/ROW]
[ROW][C]122[/C][C]0.0677193874578059[/C][C]0.135438774915612[/C][C]0.932280612542194[/C][/ROW]
[ROW][C]123[/C][C]0.0768877108476768[/C][C]0.153775421695354[/C][C]0.923112289152323[/C][/ROW]
[ROW][C]124[/C][C]0.061385519494432[/C][C]0.122771038988864[/C][C]0.938614480505568[/C][/ROW]
[ROW][C]125[/C][C]0.0493717319861994[/C][C]0.0987434639723987[/C][C]0.950628268013801[/C][/ROW]
[ROW][C]126[/C][C]0.0376353172064135[/C][C]0.075270634412827[/C][C]0.962364682793587[/C][/ROW]
[ROW][C]127[/C][C]0.0281861879575797[/C][C]0.0563723759151594[/C][C]0.97181381204242[/C][/ROW]
[ROW][C]128[/C][C]0.0222052869660717[/C][C]0.0444105739321435[/C][C]0.977794713033928[/C][/ROW]
[ROW][C]129[/C][C]0.0176766917512319[/C][C]0.0353533835024638[/C][C]0.982323308248768[/C][/ROW]
[ROW][C]130[/C][C]0.0247469295583477[/C][C]0.0494938591166954[/C][C]0.975253070441652[/C][/ROW]
[ROW][C]131[/C][C]0.0205293694024478[/C][C]0.0410587388048956[/C][C]0.979470630597552[/C][/ROW]
[ROW][C]132[/C][C]0.0323139979372079[/C][C]0.0646279958744158[/C][C]0.967686002062792[/C][/ROW]
[ROW][C]133[/C][C]0.0334923448476136[/C][C]0.0669846896952272[/C][C]0.966507655152386[/C][/ROW]
[ROW][C]134[/C][C]0.0450110794223706[/C][C]0.0900221588447413[/C][C]0.954988920577629[/C][/ROW]
[ROW][C]135[/C][C]0.0357183324111285[/C][C]0.071436664822257[/C][C]0.964281667588871[/C][/ROW]
[ROW][C]136[/C][C]0.0245208300915428[/C][C]0.0490416601830857[/C][C]0.975479169908457[/C][/ROW]
[ROW][C]137[/C][C]0.0164419098923985[/C][C]0.032883819784797[/C][C]0.983558090107602[/C][/ROW]
[ROW][C]138[/C][C]0.0114513389057443[/C][C]0.0229026778114887[/C][C]0.988548661094256[/C][/ROW]
[ROW][C]139[/C][C]0.0187141566338838[/C][C]0.0374283132677676[/C][C]0.981285843366116[/C][/ROW]
[ROW][C]140[/C][C]0.0156735971353231[/C][C]0.0313471942706463[/C][C]0.984326402864677[/C][/ROW]
[ROW][C]141[/C][C]0.616099641298338[/C][C]0.767800717403324[/C][C]0.383900358701662[/C][/ROW]
[ROW][C]142[/C][C]0.555282969656264[/C][C]0.889434060687471[/C][C]0.444717030343736[/C][/ROW]
[ROW][C]143[/C][C]0.471948863858553[/C][C]0.943897727717105[/C][C]0.528051136141447[/C][/ROW]
[ROW][C]144[/C][C]0.391835907226906[/C][C]0.783671814453811[/C][C]0.608164092773094[/C][/ROW]
[ROW][C]145[/C][C]0.305190253535482[/C][C]0.610380507070963[/C][C]0.694809746464518[/C][/ROW]
[ROW][C]146[/C][C]0.350444740839469[/C][C]0.700889481678938[/C][C]0.649555259160531[/C][/ROW]
[ROW][C]147[/C][C]0.309883293317789[/C][C]0.619766586635579[/C][C]0.690116706682211[/C][/ROW]
[ROW][C]148[/C][C]0.723212344308575[/C][C]0.55357531138285[/C][C]0.276787655691425[/C][/ROW]
[ROW][C]149[/C][C]0.682313232972972[/C][C]0.635373534054056[/C][C]0.317686767027028[/C][/ROW]
[ROW][C]150[/C][C]0.540947725608551[/C][C]0.918104548782898[/C][C]0.459052274391449[/C][/ROW]
[ROW][C]151[/C][C]0.745961346148008[/C][C]0.508077307703983[/C][C]0.254038653851992[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186000&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186000&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-values Alternative Hypothesis breakpoint index greater 2-sided less 11 0.391592737316465 0.78318547463293 0.608407262683535 12 0.625494324307599 0.749011351384803 0.374505675692401 13 0.486709110966601 0.973418221933201 0.513290889033399 14 0.460379032502383 0.920758065004766 0.539620967497617 15 0.351640382634583 0.703280765269166 0.648359617365417 16 0.267145938881104 0.534291877762207 0.732854061118896 17 0.217971969276912 0.435943938553823 0.782028030723088 18 0.419231086271126 0.838462172542252 0.580768913728874 19 0.342077757020335 0.68415551404067 0.657922242979665 20 0.262867097830819 0.525734195661639 0.73713290216918 21 0.203320554940347 0.406641109880693 0.796679445059653 22 0.185523847760742 0.371047695521484 0.814476152239258 23 0.370009803174088 0.740019606348176 0.629990196825912 24 0.40633422343729 0.81266844687458 0.59366577656271 25 0.395346365960102 0.790692731920205 0.604653634039897 26 0.357806583905504 0.715613167811007 0.642193416094496 27 0.417480679975857 0.834961359951714 0.582519320024143 28 0.423737909836793 0.847475819673586 0.576262090163207 29 0.404994581979495 0.80998916395899 0.595005418020505 30 0.446979809793504 0.893959619587008 0.553020190206496 31 0.388960387822833 0.777920775645666 0.611039612177167 32 0.342451535732889 0.684903071465779 0.657548464267111 33 0.319171912792343 0.638343825584685 0.680828087207658 34 0.286039861582077 0.572079723164155 0.713960138417922 35 0.245073281748214 0.490146563496428 0.754926718251786 36 0.829140931634888 0.341718136730225 0.170859068365112 37 0.802778452692215 0.394443094615569 0.197221547307785 38 0.796786492944592 0.406427014110815 0.203213507055408 39 0.818235582728629 0.363528834542742 0.181764417271371 40 0.7997937493677 0.4004125012646 0.2002062506323 41 0.766969363362431 0.466061273275139 0.233030636637569 42 0.741892014655346 0.516215970689307 0.258107985344654 43 0.767213510937847 0.465572978124306 0.232786489062153 44 0.72447243346753 0.55105513306494 0.27552756653247 45 0.695845745479517 0.608308509040966 0.304154254520483 46 0.861707847455688 0.276584305088624 0.138292152544312 47 0.90959795095478 0.18080409809044 0.09040204904522 48 0.886622330335456 0.226755339329089 0.113377669664544 49 0.870659535512732 0.258680928974536 0.129340464487268 50 0.869685546593415 0.26062890681317 0.130314453406585 51 0.84142877859404 0.317142442811919 0.15857122140596 52 0.808924528327297 0.382150943345407 0.191075471672703 53 0.826100561355736 0.347798877288529 0.173899438644264 54 0.813114570823474 0.373770858353052 0.186885429176526 55 0.827013016996463 0.345973966007075 0.172986983003537 56 0.801706626075065 0.396586747849869 0.198293373924935 57 0.76747248258696 0.465055034826079 0.23252751741304 58 0.744056101505625 0.511887796988749 0.255943898494375 59 0.70627686763562 0.587446264728759 0.29372313236438 60 0.702598452223429 0.594803095553142 0.297401547776571 61 0.664522974947353 0.670954050105294 0.335477025052647 62 0.62191823681952 0.756163526360959 0.37808176318048 63 0.582183275094972 0.835633449810055 0.417816724905028 64 0.534725232148106 0.930549535703788 0.465274767851894 65 0.495843302829506 0.991686605659013 0.504156697170494 66 0.466674057559933 0.933348115119866 0.533325942440067 67 0.463721146315528 0.927442292631055 0.536278853684472 68 0.613478939748883 0.773042120502234 0.386521060251117 69 0.729358335569008 0.541283328861983 0.270641664430992 70 0.694403882039748 0.611192235920505 0.305596117960252 71 0.802629523256327 0.394740953487347 0.197370476743673 72 0.768212893376977 0.463574213246045 0.231787106623023 73 0.758667373560066 0.482665252879868 0.241332626439934 74 0.739015604249321 0.521968791501358 0.260984395750679 75 0.699570990713306 0.600858018573388 0.300429009286694 76 0.750856404926374 0.498287190147252 0.249143595073626 77 0.713685952350372 0.572628095299256 0.286314047649628 78 0.700496476174032 0.599007047651935 0.299503523825968 79 0.706346825923367 0.587306348153265 0.293653174076633 80 0.664780080930307 0.670439838139385 0.335219919069693 81 0.625472431732697 0.749055136534605 0.374527568267303 82 0.749640195870538 0.500719608258924 0.250359804129462 83 0.712578525224244 0.574842949551511 0.287421474775756 84 0.682801845209802 0.634396309580396 0.317198154790198 85 0.640736724081722 0.718526551836556 0.359263275918278 86 0.634331470938219 0.731337058123562 0.365668529061781 87 0.589500320931648 0.820999358136705 0.410499679068352 88 0.549615687265225 0.900768625469551 0.450384312734775 89 0.532961058951719 0.934077882096562 0.467038941048281 90 0.498949539193853 0.997899078387706 0.501050460806147 91 0.480027937635368 0.960055875270737 0.519972062364632 92 0.441780647044689 0.883561294089377 0.558219352955311 93 0.399737648992291 0.799475297984583 0.600262351007709 94 0.35767206956834 0.715344139136679 0.64232793043166 95 0.373839994920099 0.747679989840198 0.626160005079901 96 0.338949483340487 0.677898966680973 0.661050516659513 97 0.30119430941359 0.602388618827181 0.69880569058641 98 0.299878852263542 0.599757704527084 0.700121147736458 99 0.260020361495584 0.520040722991169 0.739979638504416 100 0.224456210484302 0.448912420968604 0.775543789515698 101 0.202854737340541 0.405709474681082 0.797145262659459 102 0.188122179430185 0.376244358860369 0.811877820569815 103 0.231429393464024 0.462858786928048 0.768570606535976 104 0.197374118906996 0.394748237813991 0.802625881093004 105 0.191072048531601 0.382144097063202 0.808927951468399 106 0.203604934094425 0.40720986818885 0.796395065905575 107 0.18369426199133 0.36738852398266 0.81630573800867 108 0.163405207972352 0.326810415944704 0.836594792027648 109 0.157043647309579 0.314087294619158 0.842956352690421 110 0.147581451752345 0.29516290350469 0.852418548247655 111 0.130037575188798 0.260075150377596 0.869962424811202 112 0.108077816350241 0.216155632700483 0.891922183649759 113 0.126207472920557 0.252414945841115 0.873792527079443 114 0.116111809566638 0.232223619133276 0.883888190433362 115 0.148046379537207 0.296092759074413 0.851953620462793 116 0.141018350437075 0.282036700874151 0.858981649562925 117 0.122198121771532 0.244396243543065 0.877801878228468 118 0.105846938234546 0.211693876469092 0.894153061765454 119 0.108605312623334 0.217210625246668 0.891394687376666 120 0.100739824216558 0.201479648433115 0.899260175783442 121 0.085130992757172 0.170261985514344 0.914869007242828 122 0.0677193874578059 0.135438774915612 0.932280612542194 123 0.0768877108476768 0.153775421695354 0.923112289152323 124 0.061385519494432 0.122771038988864 0.938614480505568 125 0.0493717319861994 0.0987434639723987 0.950628268013801 126 0.0376353172064135 0.075270634412827 0.962364682793587 127 0.0281861879575797 0.0563723759151594 0.97181381204242 128 0.0222052869660717 0.0444105739321435 0.977794713033928 129 0.0176766917512319 0.0353533835024638 0.982323308248768 130 0.0247469295583477 0.0494938591166954 0.975253070441652 131 0.0205293694024478 0.0410587388048956 0.979470630597552 132 0.0323139979372079 0.0646279958744158 0.967686002062792 133 0.0334923448476136 0.0669846896952272 0.966507655152386 134 0.0450110794223706 0.0900221588447413 0.954988920577629 135 0.0357183324111285 0.071436664822257 0.964281667588871 136 0.0245208300915428 0.0490416601830857 0.975479169908457 137 0.0164419098923985 0.032883819784797 0.983558090107602 138 0.0114513389057443 0.0229026778114887 0.988548661094256 139 0.0187141566338838 0.0374283132677676 0.981285843366116 140 0.0156735971353231 0.0313471942706463 0.984326402864677 141 0.616099641298338 0.767800717403324 0.383900358701662 142 0.555282969656264 0.889434060687471 0.444717030343736 143 0.471948863858553 0.943897727717105 0.528051136141447 144 0.391835907226906 0.783671814453811 0.608164092773094 145 0.305190253535482 0.610380507070963 0.694809746464518 146 0.350444740839469 0.700889481678938 0.649555259160531 147 0.309883293317789 0.619766586635579 0.690116706682211 148 0.723212344308575 0.55357531138285 0.276787655691425 149 0.682313232972972 0.635373534054056 0.317686767027028 150 0.540947725608551 0.918104548782898 0.459052274391449 151 0.745961346148008 0.508077307703983 0.254038653851992

 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 0 0 OK 5% type I error level 9 0.0638297872340425 NOK 10% type I error level 16 0.113475177304965 NOK

\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 & 9 & 0.0638297872340425 & NOK \tabularnewline
10% type I error level & 16 & 0.113475177304965 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186000&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]9[/C][C]0.0638297872340425[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]16[/C][C]0.113475177304965[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186000&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186000&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 tests OK/NOK 1% type I error level 0 0 OK 5% type I error level 9 0.0638297872340425 NOK 10% type I error level 16 0.113475177304965 NOK

library(lattice)library(lmtest)n25 <- 25 #minimum number of obs. for Goldfeld-Quandt testpar1 <- 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 <- x1if (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'}xk <- length(x[1,])df <- as.data.frame(x)(mylm <- lm(df))(mysum <- summary(mylm))if (n > n25) {kp3 <- k + 3nmkm3 <- n - k - 3gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))numgqtests <- 0numsignificant1 <- 0numsignificant5 <- 0numsignificant10 <- 0for (mypoint in kp3:nmkm3) {j <- 0numgqtests <- numgqtests + 1for (myalt in c('greater', 'two.sided', 'less')) {j <- j + 1gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value}if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1}gqarr}bitmap(file='test0.png')plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')points(x[,1]-mysum$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)dumdum1 <- dum[2:length(myerror),]dum1z <- as.data.frame(dum1)zplot(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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')}}myeq <- paste(myeq, ' + e[t]')a<-table.row.start(a)a<-table.element(a, myeq)a<-table.row.end(a)a<-table.end(a)table.save(a,file='mytable1.tab')a<-table.start()a<-table.row.start(a)a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a,'Variable',header=TRUE)a<-table.element(a,'Parameter',header=TRUE)a<-table.element(a,'S.D.',header=TRUE)a<-table.element(a,'T-STATH0: parameter = 0',header=TRUE)a<-table.element(a,'2-tail p-value',header=TRUE)a<-table.element(a,'1-tail p-value',header=TRUE)a<-table.row.end(a)for (i in 1:k){a<-table.row.start(a)a<-table.element(a,rownames(mysum$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$sigma)a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a, 'Sum Squared Residuals',1,TRUE)a<-table.element(a, sum(myerror*myerror))a<-table.row.end(a)a<-table.end(a)table.save(a,file='mytable3.tab')a<-table.start()a<-table.row.start(a)a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a, 'Time or Index', 1, TRUE)a<-table.element(a, 'Actuals', 1, TRUE)a<-table.element(a, 'InterpolationForecast', 1, TRUE)a<-table.element(a, 'ResidualsPrediction Error', 1, TRUE)a<-table.row.end(a)for (i in 1:n) {a<-table.row.start(a)a<-table.element(a,i, 1, TRUE)a<-table.element(a,x[i])a<-table.element(a,x[i]-mysum$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')}