FreeStatistics.org

Free Statistics

of Irreproducible Research!

Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationWed, 19 Dec 2012 07:57:37 -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/Dec/19/t1355921886n1w9hq06p01745e.htm/, Retrieved Thu, 31 Oct 2024 22:58:51 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=201898, Retrieved Thu, 31 Oct 2024 22:58:51 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact157

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
-    D  [Multiple Regression] [Multiple linear r...] [2012-11-03 11:19:15] [0dc867bfbaab36a894719867823e3cb9]
- R       [Multiple Regression] [Multiple Regression] [2012-12-16 15:11:34] [0dc867bfbaab36a894719867823e3cb9]
- R P         [Multiple Regression] [Multiple Regression] [2012-12-19 12:57:37] [b7b610b08ce09537f4b16b68ce5f31b7] [Current]
Feedback Forum

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
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

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'Gertrude Mary Cox' @ cox.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 & 9 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=201898&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]9 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=201898&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=201898&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'Gertrude Mary Cox' @ cox.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=201898&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=201898&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=201898&T=1

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


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

Multiple Linear Regression - Estimated Regression Equation
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
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)5.510046026544122.5968672.12180.0354540.017727
Connected0.1140547807573050.046892.43240.0161450.008073
Separate-0.02066570034680850.044811-0.46120.6453240.322662
Software0.5425642648191510.0689527.868800
Happiness0.05970913973167940.0763810.78170.4355760.217788
Depression-0.07116201652880070.05634-1.26310.2084710.104236
Belonging0.03576109110255530.0445310.80310.4231730.211586
Belonging_Final-0.05223397428384370.063958-0.81670.4153660.207683

\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=201898&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=201898&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=201898&T=2

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


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)5.510046026544122.5968672.12180.0354540.017727
Connected0.1140547807573050.046892.43240.0161450.008073
Separate-0.02066570034680850.044811-0.46120.6453240.322662
Software0.5425642648191510.0689527.868800
Happiness0.05970913973167940.0763810.78170.4355760.217788
Depression-0.07116201652880070.05634-1.26310.2084710.104236
Belonging0.03576109110255530.0445310.80310.4231730.211586
Belonging_Final-0.05223397428384370.063958-0.81670.4153660.207683






Multiple Linear Regression - Regression Statistics
Multiple R0.597252140020402
R-squared0.356710118758949
Adjusted R-squared0.327469669611629
F-TEST (value)12.1992010779916
F-TEST (DF numerator)7
F-TEST (DF denominator)154
p-value2.35689245897674e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.85031335255193
Sum Squared Residuals527.24356340532

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.597252140020402 \tabularnewline
R-squared & 0.356710118758949 \tabularnewline
Adjusted R-squared & 0.327469669611629 \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=201898&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]Adjusted R-squared[/C][C]0.327469669611629[/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=201898&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=201898&T=3

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


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

Multiple Linear Regression - Regression Statistics
Multiple R0.597252140020402
R-squared0.356710118758949
Adjusted R-squared0.327469669611629
F-TEST (value)12.1992010779916
F-TEST (DF numerator)7
F-TEST (DF denominator)154
p-value2.35689245897674e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.85031335255193
Sum Squared Residuals527.24356340532






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11316.1176010114951-3.11760101149508
21615.9685904576890.0314095423110451
31916.17379130790472.82620869209528
41511.7361273441593.26387265584104
51415.452637097449-1.45263709744899
61314.8834893226671-1.88348932266714
71915.06136131570093.93863868429912
81516.5944113899983-1.59441138999828
91415.8832986290019-1.88329862900193
101512.43353890385492.56646109614508
111615.19823030010620.801769699893772
121616.1967107906881-0.196710790688073
131615.36342669434780.636573305652221
141615.30754854838580.692451451614239
151717.3536454897514-0.353645489751352
161515.0219820117331-0.0219820117331483
171514.55157108565970.448428914340278
182016.12821685929183.87178314070815
191815.41251745179242.58748254820759
201615.19746691696960.802533083030439
211615.24130727894350.758692721056456
221614.70987032922921.29012967077078
231916.41558283161832.58441716838169
241614.68252301266741.31747698733264
251714.77151881403512.22848118596487
261716.88113967483440.118860325165605
271614.96270735333931.0372926466607
281516.0428407120701-1.04284071207008
291615.44086198571860.559138014281356
301413.96097566659550.0390243334045331
311515.5941864333835-0.594186433383495
321212.1849031511977-0.184903151197658
331415.1114906362315-1.11149063623154
341615.43020079273590.569799207264144
351415.6420243027548-1.64202430275481
36712.902573456336-5.90257345633603
371010.8668171646377-0.866817164637676
381415.779490671898-1.77949067189796
391614.22115592283431.77884407716565
401614.58392490717531.41607509282467
411615.04538399769020.954616002309845
421415.5595766071838-1.55957660718381
432017.91471712815872.08528287184129
441414.2062028697073-0.206202869707291
451414.8985465949208-0.898546594920809
461115.59163412605-4.59163412605
471416.4250897991219-2.42508979912194
481514.93160082254760.0683991774524221
491615.19904738281320.800952617186765
501416.0185860866674-2.01858608666736
511616.490631866381-0.490631866380993
521414.1117645059229-0.111764505922894
531214.6847780076518-2.68477800765184
541615.19061367844130.809386321558708
55911.5175710839817-2.51757108398172
561412.62869512246461.37130487753543
571616.0923517617659-0.0923517617659263
581615.083117165710.916882834289958
591515.3237028828634-0.323702882863365
601614.13126710127161.86873289872845
611211.42928313191650.570716868083456
621615.977614851650.0223851483499508
631616.466440278449-0.466440278448962
641414.370634890267-0.370634890266963
651615.43595168864320.564048311356834
661715.90639964666291.09360035333709
671816.1020696780431.89793032195701
681814.67345463619363.32654536380641
691215.8809142378122-3.88091423781219
701615.42116084877460.578839151225445
711013.449196578141-3.449196578141
721414.3239482857985-0.323948285798518
731816.53779446101831.46220553898166
741817.15490693398040.845093066019571
751615.7263754138760.273624586124044
761713.88714376007613.11285623992386
771616.3659420879192-0.365942087919187
781614.45116372766151.5488362723385
791314.8428524594843-1.84285245948429
801616.0260086189542-0.0260086189541596
811615.57587805108330.424121948916667
822016.7721062827113.22789371728898
831615.69179119077210.308208809227862
841516.0247767745921-1.02477677459206
851514.74356870052150.256431299478507
861614.26828869390081.73171130609916
871414.223327636188-0.223327636188002
881615.33766328162630.662336718373708
891614.58326977198891.41673022801113
901514.2122719553820.787728044617952
911213.4826033949017-1.4826033949017
921717.0093460317475-0.00934603174746024
931615.48681289560120.513187104398811
941515.2856069919936-0.285606991993591
951315.1789239840191-2.17892398401912
961615.21831446253930.781685537460747
971615.85948064926540.140519350734551
981614.18415308904031.8158469109597
991616.159901087907-0.159901087906952
1001414.3405734839089-0.340573483908861
1011617.3668114814765-1.3668114814765
1021614.81292853546971.18707146453032
1032017.4147793115812.58522068841896
1041514.58978976215680.410210237843217
1051614.56327262829591.43672737170409
1061315.3971712164423-2.39717121644233
1071716.06700954735680.932990452643163
1081615.94112230748840.0588776925115655
1091614.59796445223541.40203554776461
1101212.3115860011041-0.311586001104149
1111615.01214041037480.987859589625233
1121615.89707198826040.10292801173956
1131714.54825627567792.45174372432212
1141314.9988832654302-1.99888326543019
1151214.9367482002426-2.9367482002426
1161816.67288521431041.3271147856896
1171415.5475987133233-1.54759871332326
1181413.01759792703220.98240207296779
1191314.9879193660467-1.98791936604673
1201615.72506098259650.27493901740351
1211314.3338869887332-1.33388698873322
1221615.69584559488340.304154405116597
1231316.0238939705842-3.02389397058424
1241617.1298631050521-1.12986310505213
1251516.059438245305-1.05943824530504
1261616.8658061313194-0.865806131319397
1271515.5139115699994-0.513911569999371
1281716.34216206260120.657837937398815
1291513.89290617777081.1070938222292
1301215.0352367266114-3.03523672661142
1311614.24212073057971.75787926942027
1321013.3954993580855-3.39549935808545
1331614.18297859108471.81702140891525
1341214.0010798079533-2.00107980795329
1351415.7407983013774-1.74079830137736
1361515.1943517162197-0.194351716219724
1371312.35946478854840.640535211451566
1381514.53159751754470.468402482455295
1391113.2551506775076-2.2551506775076
1401213.4520494331797-1.45204943317969
141813.4537200598596-5.45372005985958
1421613.18907401639792.81092598360207
1431513.41121098928641.58878901071358
1441716.63589086508620.364109134913822
1451614.93117413052421.06882586947583
1461014.2545472410445-4.25454724104455
1471816.01940871387781.98059128612218
1481315.2752928646931-2.27529286469313
1491615.24297998845480.757020011545189
1501313.1168401022919-0.116840102291911
1511013.208809379565-3.20880937956498
1521516.5256452894514-1.52564528945141
1531614.19802295207761.80197704792237
1541612.09045579776863.90954420223143
1551412.54660199522051.45339800477955
1561012.594466135854-2.59446613585403
1571717.0093460317475-0.00934603174746024
1581311.71166780357221.28833219642779
1591513.89290617777081.1070938222292
1601615.17057270609650.829427293903479
1611212.604839934877-0.604839934876992
1621312.68683649943110.31316350056888

\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=201898&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=201898&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=201898&T=4

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


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11316.1176010114951-3.11760101149508
21615.9685904576890.0314095423110451
31916.17379130790472.82620869209528
41511.7361273441593.26387265584104
51415.452637097449-1.45263709744899
61314.8834893226671-1.88348932266714
71915.06136131570093.93863868429912
81516.5944113899983-1.59441138999828
91415.8832986290019-1.88329862900193
101512.43353890385492.56646109614508
111615.19823030010620.801769699893772
121616.1967107906881-0.196710790688073
131615.36342669434780.636573305652221
141615.30754854838580.692451451614239
151717.3536454897514-0.353645489751352
161515.0219820117331-0.0219820117331483
171514.55157108565970.448428914340278
182016.12821685929183.87178314070815
191815.41251745179242.58748254820759
201615.19746691696960.802533083030439
211615.24130727894350.758692721056456
221614.70987032922921.29012967077078
231916.41558283161832.58441716838169
241614.68252301266741.31747698733264
251714.77151881403512.22848118596487
261716.88113967483440.118860325165605
271614.96270735333931.0372926466607
281516.0428407120701-1.04284071207008
291615.44086198571860.559138014281356
301413.96097566659550.0390243334045331
311515.5941864333835-0.594186433383495
321212.1849031511977-0.184903151197658
331415.1114906362315-1.11149063623154
341615.43020079273590.569799207264144
351415.6420243027548-1.64202430275481
36712.902573456336-5.90257345633603
371010.8668171646377-0.866817164637676
381415.779490671898-1.77949067189796
391614.22115592283431.77884407716565
401614.58392490717531.41607509282467
411615.04538399769020.954616002309845
421415.5595766071838-1.55957660718381
432017.91471712815872.08528287184129
441414.2062028697073-0.206202869707291
451414.8985465949208-0.898546594920809
461115.59163412605-4.59163412605
471416.4250897991219-2.42508979912194
481514.93160082254760.0683991774524221
491615.19904738281320.800952617186765
501416.0185860866674-2.01858608666736
511616.490631866381-0.490631866380993
521414.1117645059229-0.111764505922894
531214.6847780076518-2.68477800765184
541615.19061367844130.809386321558708
55911.5175710839817-2.51757108398172
561412.62869512246461.37130487753543
571616.0923517617659-0.0923517617659263
581615.083117165710.916882834289958
591515.3237028828634-0.323702882863365
601614.13126710127161.86873289872845
611211.42928313191650.570716868083456
621615.977614851650.0223851483499508
631616.466440278449-0.466440278448962
641414.370634890267-0.370634890266963
651615.43595168864320.564048311356834
661715.90639964666291.09360035333709
671816.1020696780431.89793032195701
681814.67345463619363.32654536380641
691215.8809142378122-3.88091423781219
701615.42116084877460.578839151225445
711013.449196578141-3.449196578141
721414.3239482857985-0.323948285798518
731816.53779446101831.46220553898166
741817.15490693398040.845093066019571
751615.7263754138760.273624586124044
761713.88714376007613.11285623992386
771616.3659420879192-0.365942087919187
781614.45116372766151.5488362723385
791314.8428524594843-1.84285245948429
801616.0260086189542-0.0260086189541596
811615.57587805108330.424121948916667
822016.7721062827113.22789371728898
831615.69179119077210.308208809227862
841516.0247767745921-1.02477677459206
851514.74356870052150.256431299478507
861614.26828869390081.73171130609916
871414.223327636188-0.223327636188002
881615.33766328162630.662336718373708
891614.58326977198891.41673022801113
901514.2122719553820.787728044617952
911213.4826033949017-1.4826033949017
921717.0093460317475-0.00934603174746024
931615.48681289560120.513187104398811
941515.2856069919936-0.285606991993591
951315.1789239840191-2.17892398401912
961615.21831446253930.781685537460747
971615.85948064926540.140519350734551
981614.18415308904031.8158469109597
991616.159901087907-0.159901087906952
1001414.3405734839089-0.340573483908861
1011617.3668114814765-1.3668114814765
1021614.81292853546971.18707146453032
1032017.4147793115812.58522068841896
1041514.58978976215680.410210237843217
1051614.56327262829591.43672737170409
1061315.3971712164423-2.39717121644233
1071716.06700954735680.932990452643163
1081615.94112230748840.0588776925115655
1091614.59796445223541.40203554776461
1101212.3115860011041-0.311586001104149
1111615.01214041037480.987859589625233
1121615.89707198826040.10292801173956
1131714.54825627567792.45174372432212
1141314.9988832654302-1.99888326543019
1151214.9367482002426-2.9367482002426
1161816.67288521431041.3271147856896
1171415.5475987133233-1.54759871332326
1181413.01759792703220.98240207296779
1191314.9879193660467-1.98791936604673
1201615.72506098259650.27493901740351
1211314.3338869887332-1.33388698873322
1221615.69584559488340.304154405116597
1231316.0238939705842-3.02389397058424
1241617.1298631050521-1.12986310505213
1251516.059438245305-1.05943824530504
1261616.8658061313194-0.865806131319397
1271515.5139115699994-0.513911569999371
1281716.34216206260120.657837937398815
1291513.89290617777081.1070938222292
1301215.0352367266114-3.03523672661142
1311614.24212073057971.75787926942027
1321013.3954993580855-3.39549935808545
1331614.18297859108471.81702140891525
1341214.0010798079533-2.00107980795329
1351415.7407983013774-1.74079830137736
1361515.1943517162197-0.194351716219724
1371312.35946478854840.640535211451566
1381514.53159751754470.468402482455295
1391113.2551506775076-2.2551506775076
1401213.4520494331797-1.45204943317969
141813.4537200598596-5.45372005985958
1421613.18907401639792.81092598360207
1431513.41121098928641.58878901071358
1441716.63589086508620.364109134913822
1451614.93117413052421.06882586947583
1461014.2545472410445-4.25454724104455
1471816.01940871387781.98059128612218
1481315.2752928646931-2.27529286469313
1491615.24297998845480.757020011545189
1501313.1168401022919-0.116840102291911
1511013.208809379565-3.20880937956498
1521516.5256452894514-1.52564528945141
1531614.19802295207761.80197704792237
1541612.09045579776863.90954420223143
1551412.54660199522051.45339800477955
1561012.594466135854-2.59446613585403
1571717.0093460317475-0.00934603174746024
1581311.71166780357221.28833219642779
1591513.89290617777081.1070938222292
1601615.17057270609650.829427293903479
1611212.604839934877-0.604839934876992
1621312.68683649943110.31316350056888






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.3915927373164650.783185474632930.608407262683535
120.6254943243075990.7490113513848030.374505675692401
130.4867091109666010.9734182219332010.513290889033399
140.4603790325023830.9207580650047660.539620967497617
150.3516403826345830.7032807652691660.648359617365417
160.2671459388811040.5342918777622070.732854061118896
170.2179719692769120.4359439385538230.782028030723088
180.4192310862711260.8384621725422520.580768913728874
190.3420777570203350.684155514040670.657922242979665
200.2628670978308190.5257341956616390.73713290216918
210.2033205549403470.4066411098806930.796679445059653
220.1855238477607420.3710476955214840.814476152239258
230.3700098031740880.7400196063481760.629990196825912
240.406334223437290.812668446874580.59366577656271
250.3953463659601020.7906927319202050.604653634039897
260.3578065839055040.7156131678110070.642193416094496
270.4174806799758570.8349613599517140.582519320024143
280.4237379098367930.8474758196735860.576262090163207
290.4049945819794950.809989163958990.595005418020505
300.4469798097935040.8939596195870080.553020190206496
310.3889603878228330.7779207756456660.611039612177167
320.3424515357328890.6849030714657790.657548464267111
330.3191719127923430.6383438255846850.680828087207658
340.2860398615820770.5720797231641550.713960138417922
350.2450732817482140.4901465634964280.754926718251786
360.8291409316348880.3417181367302250.170859068365112
370.8027784526922150.3944430946155690.197221547307785
380.7967864929445920.4064270141108150.203213507055408
390.8182355827286290.3635288345427420.181764417271371
400.79979374936770.40041250126460.2002062506323
410.7669693633624310.4660612732751390.233030636637569
420.7418920146553460.5162159706893070.258107985344654
430.7672135109378470.4655729781243060.232786489062153
440.724472433467530.551055133064940.27552756653247
450.6958457454795170.6083085090409660.304154254520483
460.8617078474556880.2765843050886240.138292152544312
470.909597950954780.180804098090440.09040204904522
480.8866223303354560.2267553393290890.113377669664544
490.8706595355127320.2586809289745360.129340464487268
500.8696855465934150.260628906813170.130314453406585
510.841428778594040.3171424428119190.15857122140596
520.8089245283272970.3821509433454070.191075471672703
530.8261005613557360.3477988772885290.173899438644264
540.8131145708234740.3737708583530520.186885429176526
550.8270130169964630.3459739660070750.172986983003537
560.8017066260750650.3965867478498690.198293373924935
570.767472482586960.4650550348260790.23252751741304
580.7440561015056250.5118877969887490.255943898494375
590.706276867635620.5874462647287590.29372313236438
600.7025984522234290.5948030955531420.297401547776571
610.6645229749473530.6709540501052940.335477025052647
620.621918236819520.7561635263609590.37808176318048
630.5821832750949720.8356334498100550.417816724905028
640.5347252321481060.9305495357037880.465274767851894
650.4958433028295060.9916866056590130.504156697170494
660.4666740575599330.9333481151198660.533325942440067
670.4637211463155280.9274422926310550.536278853684472
680.6134789397488830.7730421205022340.386521060251117
690.7293583355690080.5412833288619830.270641664430992
700.6944038820397480.6111922359205050.305596117960252
710.8026295232563270.3947409534873470.197370476743673
720.7682128933769770.4635742132460450.231787106623023
730.7586673735600660.4826652528798680.241332626439934
740.7390156042493210.5219687915013580.260984395750679
750.6995709907133060.6008580185733880.300429009286694
760.7508564049263740.4982871901472520.249143595073626
770.7136859523503720.5726280952992560.286314047649628
780.7004964761740320.5990070476519350.299503523825968
790.7063468259233670.5873063481532650.293653174076633
800.6647800809303070.6704398381393850.335219919069693
810.6254724317326970.7490551365346050.374527568267303
820.7496401958705380.5007196082589240.250359804129462
830.7125785252242440.5748429495515110.287421474775756
840.6828018452098020.6343963095803960.317198154790198
850.6407367240817220.7185265518365560.359263275918278
860.6343314709382190.7313370581235620.365668529061781
870.5895003209316480.8209993581367050.410499679068352
880.5496156872652250.9007686254695510.450384312734775
890.5329610589517190.9340778820965620.467038941048281
900.4989495391938530.9978990783877060.501050460806147
910.4800279376353680.9600558752707370.519972062364632
920.4417806470446890.8835612940893770.558219352955311
930.3997376489922910.7994752979845830.600262351007709
940.357672069568340.7153441391366790.64232793043166
950.3738399949200990.7476799898401980.626160005079901
960.3389494833404870.6778989666809730.661050516659513
970.301194309413590.6023886188271810.69880569058641
980.2998788522635420.5997577045270840.700121147736458
990.2600203614955840.5200407229911690.739979638504416
1000.2244562104843020.4489124209686040.775543789515698
1010.2028547373405410.4057094746810820.797145262659459
1020.1881221794301850.3762443588603690.811877820569815
1030.2314293934640240.4628587869280480.768570606535976
1040.1973741189069960.3947482378139910.802625881093004
1050.1910720485316010.3821440970632020.808927951468399
1060.2036049340944250.407209868188850.796395065905575
1070.183694261991330.367388523982660.81630573800867
1080.1634052079723520.3268104159447040.836594792027648
1090.1570436473095790.3140872946191580.842956352690421
1100.1475814517523450.295162903504690.852418548247655
1110.1300375751887980.2600751503775960.869962424811202
1120.1080778163502410.2161556327004830.891922183649759
1130.1262074729205570.2524149458411150.873792527079443
1140.1161118095666380.2322236191332760.883888190433362
1150.1480463795372070.2960927590744130.851953620462793
1160.1410183504370750.2820367008741510.858981649562925
1170.1221981217715320.2443962435430650.877801878228468
1180.1058469382345460.2116938764690920.894153061765454
1190.1086053126233340.2172106252466680.891394687376666
1200.1007398242165580.2014796484331150.899260175783442
1210.0851309927571720.1702619855143440.914869007242828
1220.06771938745780590.1354387749156120.932280612542194
1230.07688771084767680.1537754216953540.923112289152323
1240.0613855194944320.1227710389888640.938614480505568
1250.04937173198619940.09874346397239870.950628268013801
1260.03763531720641350.0752706344128270.962364682793587
1270.02818618795757970.05637237591515940.97181381204242
1280.02220528696607170.04441057393214350.977794713033928
1290.01767669175123190.03535338350246380.982323308248768
1300.02474692955834770.04949385911669540.975253070441652
1310.02052936940244780.04105873880489560.979470630597552
1320.03231399793720790.06462799587441580.967686002062792
1330.03349234484761360.06698468969522720.966507655152386
1340.04501107942237060.09002215884474130.954988920577629
1350.03571833241112850.0714366648222570.964281667588871
1360.02452083009154280.04904166018308570.975479169908457
1370.01644190989239850.0328838197847970.983558090107602
1380.01145133890574430.02290267781148870.988548661094256
1390.01871415663388380.03742831326776760.981285843366116
1400.01567359713532310.03134719427064630.984326402864677
1410.6160996412983380.7678007174033240.383900358701662
1420.5552829696562640.8894340606874710.444717030343736
1430.4719488638585530.9438977277171050.528051136141447
1440.3918359072269060.7836718144538110.608164092773094
1450.3051902535354820.6103805070709630.694809746464518
1460.3504447408394690.7008894816789380.649555259160531
1470.3098832933177890.6197665866355790.690116706682211
1480.7232123443085750.553575311382850.276787655691425
1490.6823132329729720.6353735340540560.317686767027028
1500.5409477256085510.9181045487828980.459052274391449
1510.7459613461480080.5080773077039830.254038653851992

\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=201898&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=201898&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=201898&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.3915927373164650.783185474632930.608407262683535
120.6254943243075990.7490113513848030.374505675692401
130.4867091109666010.9734182219332010.513290889033399
140.4603790325023830.9207580650047660.539620967497617
150.3516403826345830.7032807652691660.648359617365417
160.2671459388811040.5342918777622070.732854061118896
170.2179719692769120.4359439385538230.782028030723088
180.4192310862711260.8384621725422520.580768913728874
190.3420777570203350.684155514040670.657922242979665
200.2628670978308190.5257341956616390.73713290216918
210.2033205549403470.4066411098806930.796679445059653
220.1855238477607420.3710476955214840.814476152239258
230.3700098031740880.7400196063481760.629990196825912
240.406334223437290.812668446874580.59366577656271
250.3953463659601020.7906927319202050.604653634039897
260.3578065839055040.7156131678110070.642193416094496
270.4174806799758570.8349613599517140.582519320024143
280.4237379098367930.8474758196735860.576262090163207
290.4049945819794950.809989163958990.595005418020505
300.4469798097935040.8939596195870080.553020190206496
310.3889603878228330.7779207756456660.611039612177167
320.3424515357328890.6849030714657790.657548464267111
330.3191719127923430.6383438255846850.680828087207658
340.2860398615820770.5720797231641550.713960138417922
350.2450732817482140.4901465634964280.754926718251786
360.8291409316348880.3417181367302250.170859068365112
370.8027784526922150.3944430946155690.197221547307785
380.7967864929445920.4064270141108150.203213507055408
390.8182355827286290.3635288345427420.181764417271371
400.79979374936770.40041250126460.2002062506323
410.7669693633624310.4660612732751390.233030636637569
420.7418920146553460.5162159706893070.258107985344654
430.7672135109378470.4655729781243060.232786489062153
440.724472433467530.551055133064940.27552756653247
450.6958457454795170.6083085090409660.304154254520483
460.8617078474556880.2765843050886240.138292152544312
470.909597950954780.180804098090440.09040204904522
480.8866223303354560.2267553393290890.113377669664544
490.8706595355127320.2586809289745360.129340464487268
500.8696855465934150.260628906813170.130314453406585
510.841428778594040.3171424428119190.15857122140596
520.8089245283272970.3821509433454070.191075471672703
530.8261005613557360.3477988772885290.173899438644264
540.8131145708234740.3737708583530520.186885429176526
550.8270130169964630.3459739660070750.172986983003537
560.8017066260750650.3965867478498690.198293373924935
570.767472482586960.4650550348260790.23252751741304
580.7440561015056250.5118877969887490.255943898494375
590.706276867635620.5874462647287590.29372313236438
600.7025984522234290.5948030955531420.297401547776571
610.6645229749473530.6709540501052940.335477025052647
620.621918236819520.7561635263609590.37808176318048
630.5821832750949720.8356334498100550.417816724905028
640.5347252321481060.9305495357037880.465274767851894
650.4958433028295060.9916866056590130.504156697170494
660.4666740575599330.9333481151198660.533325942440067
670.4637211463155280.9274422926310550.536278853684472
680.6134789397488830.7730421205022340.386521060251117
690.7293583355690080.5412833288619830.270641664430992
700.6944038820397480.6111922359205050.305596117960252
710.8026295232563270.3947409534873470.197370476743673
720.7682128933769770.4635742132460450.231787106623023
730.7586673735600660.4826652528798680.241332626439934
740.7390156042493210.5219687915013580.260984395750679
750.6995709907133060.6008580185733880.300429009286694
760.7508564049263740.4982871901472520.249143595073626
770.7136859523503720.5726280952992560.286314047649628
780.7004964761740320.5990070476519350.299503523825968
790.7063468259233670.5873063481532650.293653174076633
800.6647800809303070.6704398381393850.335219919069693
810.6254724317326970.7490551365346050.374527568267303
820.7496401958705380.5007196082589240.250359804129462
830.7125785252242440.5748429495515110.287421474775756
840.6828018452098020.6343963095803960.317198154790198
850.6407367240817220.7185265518365560.359263275918278
860.6343314709382190.7313370581235620.365668529061781
870.5895003209316480.8209993581367050.410499679068352
880.5496156872652250.9007686254695510.450384312734775
890.5329610589517190.9340778820965620.467038941048281
900.4989495391938530.9978990783877060.501050460806147
910.4800279376353680.9600558752707370.519972062364632
920.4417806470446890.8835612940893770.558219352955311
930.3997376489922910.7994752979845830.600262351007709
940.357672069568340.7153441391366790.64232793043166
950.3738399949200990.7476799898401980.626160005079901
960.3389494833404870.6778989666809730.661050516659513
970.301194309413590.6023886188271810.69880569058641
980.2998788522635420.5997577045270840.700121147736458
990.2600203614955840.5200407229911690.739979638504416
1000.2244562104843020.4489124209686040.775543789515698
1010.2028547373405410.4057094746810820.797145262659459
1020.1881221794301850.3762443588603690.811877820569815
1030.2314293934640240.4628587869280480.768570606535976
1040.1973741189069960.3947482378139910.802625881093004
1050.1910720485316010.3821440970632020.808927951468399
1060.2036049340944250.407209868188850.796395065905575
1070.183694261991330.367388523982660.81630573800867
1080.1634052079723520.3268104159447040.836594792027648
1090.1570436473095790.3140872946191580.842956352690421
1100.1475814517523450.295162903504690.852418548247655
1110.1300375751887980.2600751503775960.869962424811202
1120.1080778163502410.2161556327004830.891922183649759
1130.1262074729205570.2524149458411150.873792527079443
1140.1161118095666380.2322236191332760.883888190433362
1150.1480463795372070.2960927590744130.851953620462793
1160.1410183504370750.2820367008741510.858981649562925
1170.1221981217715320.2443962435430650.877801878228468
1180.1058469382345460.2116938764690920.894153061765454
1190.1086053126233340.2172106252466680.891394687376666
1200.1007398242165580.2014796484331150.899260175783442
1210.0851309927571720.1702619855143440.914869007242828
1220.06771938745780590.1354387749156120.932280612542194
1230.07688771084767680.1537754216953540.923112289152323
1240.0613855194944320.1227710389888640.938614480505568
1250.04937173198619940.09874346397239870.950628268013801
1260.03763531720641350.0752706344128270.962364682793587
1270.02818618795757970.05637237591515940.97181381204242
1280.02220528696607170.04441057393214350.977794713033928
1290.01767669175123190.03535338350246380.982323308248768
1300.02474692955834770.04949385911669540.975253070441652
1310.02052936940244780.04105873880489560.979470630597552
1320.03231399793720790.06462799587441580.967686002062792
1330.03349234484761360.06698468969522720.966507655152386
1340.04501107942237060.09002215884474130.954988920577629
1350.03571833241112850.0714366648222570.964281667588871
1360.02452083009154280.04904166018308570.975479169908457
1370.01644190989239850.0328838197847970.983558090107602
1380.01145133890574430.02290267781148870.988548661094256
1390.01871415663388380.03742831326776760.981285843366116
1400.01567359713532310.03134719427064630.984326402864677
1410.6160996412983380.7678007174033240.383900358701662
1420.5552829696562640.8894340606874710.444717030343736
1430.4719488638585530.9438977277171050.528051136141447
1440.3918359072269060.7836718144538110.608164092773094
1450.3051902535354820.6103805070709630.694809746464518
1460.3504447408394690.7008894816789380.649555259160531
1470.3098832933177890.6197665866355790.690116706682211
1480.7232123443085750.553575311382850.276787655691425
1490.6823132329729720.6353735340540560.317686767027028
1500.5409477256085510.9181045487828980.459052274391449
1510.7459613461480080.5080773077039830.254038653851992






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level90.0638297872340425NOK
10% type I error level160.113475177304965NOK

\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=201898&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=201898&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=201898&T=6

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


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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level90.0638297872340425NOK
10% type I error level160.113475177304965NOK


Figures (Output of Computation)

Figure 1
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 2
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 3
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 4
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 5
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 6
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 7
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 8
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 9
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 10
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Input Parameters & R Code

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