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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationMon, 05 Nov 2012 15:31:41 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Nov/05/t1352147536f7rfyc7997mxzkw.htm/, Retrieved Sun, 05 Feb 2023 23:46:37 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=186283, Retrieved Sun, 05 Feb 2023 23:46:37 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact92
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Decreasing Compet...] [2010-11-17 09:04:39] [b98453cac15ba1066b407e146608df68]
- R PD  [Multiple Regression] [Vermindering van ...] [2012-11-05 19:01:54] [86dcce9422b96d4554cb918e531c1d5d]
- R PD      [Multiple Regression] [Variabele maand i...] [2012-11-05 20:31:41] [5f6cd87c5735ffe37dbfae854ce1e663] [Current]
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Dataseries X:
9	13	12	14	12	53	32	41	38
9	16	11	18	11	86	51	39	32
9	19	15	11	14	66	42	30	35
9	15	6	12	12	67	41	31	33
9	14	13	16	21	76	46	34	37
9	13	10	18	12	78	47	35	29
9	19	12	14	22	53	37	39	31
9	15	14	14	11	80	49	34	36
9	14	12	15	10	74	45	36	35
9	15	6	15	13	76	47	37	38
9	16	10	17	10	79	49	38	31
9	16	12	19	8	54	33	36	34
9	16	12	10	15	67	42	38	35
9	16	11	16	14	54	33	39	38
9	17	15	18	10	87	53	33	37
9	15	12	14	14	58	36	32	33
9	15	10	14	14	75	45	36	32
9	20	12	17	11	88	54	38	38
9	18	11	14	10	64	41	39	38
9	16	12	16	13	57	36	32	32
9	16	11	18	7	66	41	32	33
9	16	12	11	14	68	44	31	31
9	19	13	14	12	54	33	39	38
9	16	11	12	14	56	37	37	39
9	17	9	17	11	86	52	39	32
9	17	13	9	9	80	47	41	32
9	16	10	16	11	76	43	36	35
9	15	14	14	15	69	44	33	37
9	16	12	15	14	78	45	33	33
9	14	10	11	13	67	44	34	33
9	15	12	16	9	80	49	31	28
9	12	8	13	15	54	33	27	32
9	14	10	17	10	71	43	37	31
9	16	12	15	11	84	54	34	37
9	14	12	14	13	74	42	34	30
9	7	7	16	8	71	44	32	33
9	10	6	9	20	63	37	29	31
9	14	12	15	12	71	43	36	33
9	16	10	17	10	76	46	29	31
9	16	10	13	10	69	42	35	33
9	16	10	15	9	74	45	37	32
9	14	12	16	14	75	44	34	33
9	20	15	16	8	54	33	38	32
9	14	10	12	14	52	31	35	33
9	14	10	12	11	69	42	38	28
9	11	12	11	13	68	40	37	35
9	14	13	15	9	65	43	38	39
9	15	11	15	11	75	46	33	34
9	16	11	17	15	74	42	36	38
9	14	12	13	11	75	45	38	32
9	16	14	16	10	72	44	32	38
9	14	10	14	14	67	40	32	30
9	12	12	11	18	63	37	32	33
9	16	13	12	14	62	46	34	38
9	9	5	12	11	63	36	32	32
9	14	6	15	12	76	47	37	32
9	16	12	16	13	74	45	39	34
9	16	12	15	9	67	42	29	34
9	15	11	12	10	73	43	37	36
9	16	10	12	15	70	43	35	34
9	12	7	8	20	53	32	30	28
9	16	12	13	12	77	45	38	34
9	16	14	11	12	77	45	34	35
9	14	11	14	14	52	31	31	35
9	16	12	15	13	54	33	34	31
10	17	13	10	11	80	49	35	37
10	18	14	11	17	66	42	36	35
10	18	11	12	12	73	41	30	27
10	12	12	15	13	63	38	39	40
10	16	12	15	14	69	42	35	37
10	10	8	14	13	67	44	38	36
10	14	11	16	15	54	33	31	38
10	18	14	15	13	81	48	34	39
10	18	14	15	10	69	40	38	41
10	16	12	13	11	84	50	34	27
10	17	9	12	19	80	49	39	30
10	16	13	17	13	70	43	37	37
10	16	11	13	17	69	44	34	31
10	13	12	15	13	77	47	28	31
10	16	12	13	9	54	33	37	27
10	16	12	15	11	79	46	33	36
10	20	12	16	10	30	0	37	38
10	16	12	15	9	71	45	35	37
10	15	12	16	12	73	43	37	33
10	15	11	15	12	72	44	32	34
10	16	10	14	13	77	47	33	31
10	14	9	15	13	75	45	38	39
10	16	12	14	12	69	42	33	34
10	16	12	13	15	54	33	29	32
10	15	12	7	22	70	43	33	33
10	12	9	17	13	73	46	31	36
10	17	15	13	15	54	33	36	32
10	16	12	15	13	77	46	35	41
10	15	12	14	15	82	48	32	28
10	13	12	13	10	80	47	29	30
10	16	10	16	11	80	47	39	36
10	16	13	12	16	69	43	37	35
10	16	9	14	11	78	46	35	31
10	16	12	17	11	81	48	37	34
10	14	10	15	10	76	46	32	36
10	16	14	17	10	76	45	38	36
10	16	11	12	16	73	45	37	35
10	20	15	16	12	85	52	36	37
10	15	11	11	11	66	42	32	28
10	16	11	15	16	79	47	33	39
10	13	12	9	19	68	41	40	32
10	17	12	16	11	76	47	38	35
10	16	12	15	16	71	43	41	39
10	16	11	10	15	54	33	36	35
10	12	7	10	24	46	30	43	42
10	16	12	15	14	82	49	30	34
10	16	14	11	15	74	44	31	33
10	17	11	13	11	88	55	32	41
10	13	11	14	15	38	11	32	33
10	12	10	18	12	76	47	37	34
10	18	13	16	10	86	53	37	32
10	14	13	14	14	54	33	33	40
10	14	8	14	13	70	44	34	40
10	13	11	14	9	69	42	33	35
10	16	12	14	15	90	55	38	36
10	13	11	12	15	54	33	33	37
10	16	13	14	14	76	46	31	27
10	13	12	15	11	89	54	38	39
10	16	14	15	8	76	47	37	38
10	15	13	15	11	73	45	33	31
10	16	15	13	11	79	47	31	33
10	15	10	17	8	90	55	39	32
10	17	11	17	10	74	44	44	39
10	15	9	19	11	81	53	33	36
10	12	11	15	13	72	44	35	33
10	16	10	13	11	71	42	32	33
10	10	11	9	20	66	40	28	32
10	16	8	15	10	77	46	40	37
10	12	11	15	15	65	40	27	30
10	14	12	15	12	74	46	37	38
10	15	12	16	14	82	53	32	29
10	13	9	11	23	54	33	28	22
10	15	11	14	14	63	42	34	35
10	11	10	11	16	54	35	30	35
10	12	8	15	11	64	40	35	34
10	8	9	13	12	69	41	31	35
10	16	8	15	10	54	33	32	34
10	15	9	16	14	84	51	30	34
10	17	15	14	12	86	53	30	35
10	16	11	15	12	77	46	31	23
10	10	8	16	11	89	55	40	31
10	18	13	16	12	76	47	32	27
10	13	12	11	13	60	38	36	36
10	16	12	12	11	75	46	32	31
10	13	9	9	19	73	46	35	32
10	10	7	16	12	85	53	38	39
10	15	13	13	17	79	47	42	37
10	16	9	16	9	71	41	34	38
9	16	6	12	12	72	44	35	39
10	14	8	9	19	69	43	35	34
10	10	8	13	18	78	51	33	31
10	17	15	13	15	54	33	36	32
10	13	6	14	14	69	43	32	37
10	15	9	19	11	81	53	33	36
10	16	11	13	9	84	51	34	32
10	12	8	12	18	84	50	32	35
11	13	8	13	16	69	46	34	36




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time10 seconds
R Server'George Udny Yule' @ yule.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 & 10 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186283&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]10 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186283&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186283&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time10 seconds
R Server'George Udny Yule' @ yule.wessa.net







Multiple Linear Regression - Estimated Regression Equation
learning[t] = + 7.13849143549936 -0.19541119211959month[t] + 0.543562867871674software[t] + 0.0595549258757396happiness[t] -0.0646170437037978depression[t] + 0.0400975540319473belonging[t] -0.0562249832541988belonging_final[t] + 0.111120048323479connected[t] -0.0171256307170541separate[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
learning[t] =  +  7.13849143549936 -0.19541119211959month[t] +  0.543562867871674software[t] +  0.0595549258757396happiness[t] -0.0646170437037978depression[t] +  0.0400975540319473belonging[t] -0.0562249832541988belonging_final[t] +  0.111120048323479connected[t] -0.0171256307170541separate[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186283&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]learning[t] =  +  7.13849143549936 -0.19541119211959month[t] +  0.543562867871674software[t] +  0.0595549258757396happiness[t] -0.0646170437037978depression[t] +  0.0400975540319473belonging[t] -0.0562249832541988belonging_final[t] +  0.111120048323479connected[t] -0.0171256307170541separate[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186283&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186283&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
learning[t] = + 7.13849143549936 -0.19541119211959month[t] + 0.543562867871674software[t] + 0.0595549258757396happiness[t] -0.0646170437037978depression[t] + 0.0400975540319473belonging[t] -0.0562249832541988belonging_final[t] + 0.111120048323479connected[t] -0.0171256307170541separate[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)7.138491435499363.6143341.97510.0500620.025031
month-0.195411192119590.301057-0.64910.5172570.258629
software0.5435628678716740.0690997.866500
happiness0.05955492587573960.0765250.77820.4376310.218816
depression-0.06461704370379780.05734-1.12690.2615450.130772
belonging0.04009755403194730.0451120.88880.3754810.187741
belonging_final-0.05622498325419880.064373-0.87340.3837990.191899
connected0.1111200483234790.0471952.35450.0198190.009909
separate-0.01712563071705410.045226-0.37870.7054570.352728

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & 7.13849143549936 & 3.614334 & 1.9751 & 0.050062 & 0.025031 \tabularnewline
month & -0.19541119211959 & 0.301057 & -0.6491 & 0.517257 & 0.258629 \tabularnewline
software & 0.543562867871674 & 0.069099 & 7.8665 & 0 & 0 \tabularnewline
happiness & 0.0595549258757396 & 0.076525 & 0.7782 & 0.437631 & 0.218816 \tabularnewline
depression & -0.0646170437037978 & 0.05734 & -1.1269 & 0.261545 & 0.130772 \tabularnewline
belonging & 0.0400975540319473 & 0.045112 & 0.8888 & 0.375481 & 0.187741 \tabularnewline
belonging_final & -0.0562249832541988 & 0.064373 & -0.8734 & 0.383799 & 0.191899 \tabularnewline
connected & 0.111120048323479 & 0.047195 & 2.3545 & 0.019819 & 0.009909 \tabularnewline
separate & -0.0171256307170541 & 0.045226 & -0.3787 & 0.705457 & 0.352728 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186283&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]7.13849143549936[/C][C]3.614334[/C][C]1.9751[/C][C]0.050062[/C][C]0.025031[/C][/ROW]
[ROW][C]month[/C][C]-0.19541119211959[/C][C]0.301057[/C][C]-0.6491[/C][C]0.517257[/C][C]0.258629[/C][/ROW]
[ROW][C]software[/C][C]0.543562867871674[/C][C]0.069099[/C][C]7.8665[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]happiness[/C][C]0.0595549258757396[/C][C]0.076525[/C][C]0.7782[/C][C]0.437631[/C][C]0.218816[/C][/ROW]
[ROW][C]depression[/C][C]-0.0646170437037978[/C][C]0.05734[/C][C]-1.1269[/C][C]0.261545[/C][C]0.130772[/C][/ROW]
[ROW][C]belonging[/C][C]0.0400975540319473[/C][C]0.045112[/C][C]0.8888[/C][C]0.375481[/C][C]0.187741[/C][/ROW]
[ROW][C]belonging_final[/C][C]-0.0562249832541988[/C][C]0.064373[/C][C]-0.8734[/C][C]0.383799[/C][C]0.191899[/C][/ROW]
[ROW][C]connected[/C][C]0.111120048323479[/C][C]0.047195[/C][C]2.3545[/C][C]0.019819[/C][C]0.009909[/C][/ROW]
[ROW][C]separate[/C][C]-0.0171256307170541[/C][C]0.045226[/C][C]-0.3787[/C][C]0.705457[/C][C]0.352728[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186283&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186283&T=2

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)7.138491435499363.6143341.97510.0500620.025031
month-0.195411192119590.301057-0.64910.5172570.258629
software0.5435628678716740.0690997.866500
happiness0.05955492587573960.0765250.77820.4376310.218816
depression-0.06461704370379780.05734-1.12690.2615450.130772
belonging0.04009755403194730.0451120.88880.3754810.187741
belonging_final-0.05622498325419880.064373-0.87340.3837990.191899
connected0.1111200483234790.0471952.35450.0198190.009909
separate-0.01712563071705410.045226-0.37870.7054570.352728







Multiple Linear Regression - Regression Statistics
Multiple R0.598729202174744
R-squared0.358476657536806
Adjusted R-squared0.324932953355723
F-TEST (value)10.6868536522266
F-TEST (DF numerator)8
F-TEST (DF denominator)153
p-value7.11086745042167e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.85379966537436
Sum Squared Residuals525.79569949934

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.598729202174744 \tabularnewline
R-squared & 0.358476657536806 \tabularnewline
Adjusted R-squared & 0.324932953355723 \tabularnewline
F-TEST (value) & 10.6868536522266 \tabularnewline
F-TEST (DF numerator) & 8 \tabularnewline
F-TEST (DF denominator) & 153 \tabularnewline
p-value & 7.11086745042167e-12 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.85379966537436 \tabularnewline
Sum Squared Residuals & 525.79569949934 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186283&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.598729202174744[/C][/ROW]
[ROW][C]R-squared[/C][C]0.358476657536806[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.324932953355723[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]10.6868536522266[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]8[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]153[/C][/ROW]
[ROW][C]p-value[/C][C]7.11086745042167e-12[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.85379966537436[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]525.79569949934[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186283&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186283&T=3

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Regression Statistics
Multiple R0.598729202174744
R-squared0.358476657536806
Adjusted R-squared0.324932953355723
F-TEST (value)10.6868536522266
F-TEST (DF numerator)8
F-TEST (DF denominator)153
p-value7.11086745042167e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.85379966537436
Sum Squared Residuals525.79569949934







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11316.1920284722714-3.19202847227137
21616.0867606404863-0.086760640486277
31916.30289294131782.69710705868223
41511.84130999079983.15869000920022
51415.647527068189-1.64752706818902
61314.9895969285293-1.98959692852927
71915.16237243733483.83762756266521
81516.7269914184294-1.72699141842944
91415.9877179884548-1.98771798845476
101512.55997794784112.44002205215887
111615.2860325611210.713967438878996
121616.2450461284938-0.245046128493805
131615.4770903087430.522909691257007
141615.39997384287430.600026157125654
151717.5009282996742-0.500928299674175
161515.1239299406807-0.123929940680651
171514.67404359820360.325956401796412
182016.26841490815773.73158509184229
191815.49050784022392.50949215977606
201615.2846849128210.715315087178965
211615.31056159822310.689438401776896
221614.81957205044991.18042794955006
231916.49722381427382.50277618572619
241614.77768358705451.22231641294552
251714.8838549956132.11614500438701
261716.97368083622760.0263191637723657
271615.08817520945560.911824790544357
281516.1803293864933-1.18032938649329
291615.5905311462310.409468853768975
301414.0560746879148-0.0560746879147753
311515.6918545226112-0.691854522611165
321212.2953166223489-0.295316622348885
331415.1914819800671-1.19148198006714
341615.56156027770160.438439722298424
351415.7663749381685-1.76637493816853
36712.9843960516783-5.98439605167827
371011.0222297452183-1.02222974521833
381415.8848924668938-1.88489246689382
391614.33433441387641.66566558612355
401614.67280079367341.32719920632658
411615.12770623688980.872293763110152
421415.6971384415886-1.6971384415886
432017.95356131256272.04643868743733
441414.2915940902356-0.291594090235572
451414.9676171226496-0.967617122649589
461115.7073067942432-4.70730679424319
471416.5012074540298-2.50120745402984
481515.0471761334037-0.0471761334036503
491615.3574778114270.642522188572993
501416.1177056358295-2.11770563582955
511616.6145714398191-0.61457143981907
521414.2241591503592-0.224159150359196
531214.8310597751438-2.83105977514378
541615.28313528344830.716864716551666
55911.6113445458156-2.61134454581564
561412.72734877584731.27265122415274
571616.2039075589066-0.203907558906594
581615.17961239615020.820387603849779
591515.4318371730387-0.431837173038739
601614.25690758933941.74309241066061
611211.54888400364040.451115996359571
621616.0990324387555-0.0990324387555359
631616.6054424987364-0.605442498736434
641414.4755353551307-0.475535355130701
651615.51287800197610.487121998023938
661715.84379207254161.15620792745835
671816.0367880401561.96321195984401
681814.59593219820913.40406780179092
691215.7986894450369-3.79868944503691
701615.35665449136520.643345508634752
711013.3453058388218-3.34530583882181
721414.2509852204631-0.250985220463126
731816.5068467099131.49315329008702
741817.07955599053440.920444009465595
751615.64319547401780.356804525982157
761713.83607371148933.16392628851066
771616.2900569221169-0.290056922116905
781614.37931441010121.62068558989885
791314.7858404970916-1.7858404970916
801615.85868780075880.141312199241163
811615.52146676384940.478533236150585
822016.67743674741653.32256325258351
831615.59125986818550.408740131814465
841515.9403513570373-0.940351357037348
851514.66818515366930.331814846330661
861614.19476007708991.80523992291009
871414.1616021894194-0.161602189419441
881615.25547044840130.744529551598692
891614.49639699836291.50360300163705
901514.1934137317280.806586268272044
911213.4178285037396-1.41782850373964
921716.90492594024230.0950740597576777
931615.44864951143960.551350488560388
941515.2371713561589-0.237171356158882
951315.1091201175879-2.10912011758789
961615.14448881470040.85551118529957
971615.7925848690290.207415130971025
981614.09899393055891.90100606944106
991616.0870532118844-0.087053211884415
1001414.2675453613905-0.267545361390544
1011617.2838519578238-1.28385195782379
1021614.7533993829051.24660061709498
1032017.36656318855632.63343681144375
1041514.46920092048920.530799079510783
1051614.54721680205341.45278319794661
1061315.3335955420167-2.33359554201674
1071715.97722991670961.02277008329044
1081615.88385955727410.116140442725885
1091614.50063279897721.49936720102275
1101212.2506833749084-0.250683374908364
1111614.98012446153491.01987553846506
1121615.85300361312750.146996386872521
1131714.51689897931722.48310102068275
1141314.9240123377016-1.92401233770162
1151214.8506025714075-2.85060257140747
1161816.5892923129071.410707687093
1171415.4716069833716-1.47160698337164
1181412.95261578475551.04738421524448
1191314.888633080924-1.88863308092397
1201615.69409214983950.30590785016049
1211314.2521312443242-1.25213124432418
1221615.62322149244460.376778507555358
1231315.9768657876015-2.97686578760153
1241617.0361549172139-1.03615491721392
1251515.9662974443689-0.966297444368874
1261616.805957327963-0.805957327962962
1271515.4175730688417-0.417573068841661
1281716.24453662718880.755463372811213
1291513.81561808902191.18438191097815
1301214.954053885653-2.95405388565303
1311614.15960752094351.84039247905651
1321013.3680049257498-3.36800492574982
1331614.09235193554991.90764806445008
1341213.9314533586019-1.93145335860193
1351415.6665908818457-1.66659088184569
1361515.1226477046261-0.12264770462611
1371312.28979845221310.710201547786873
1381514.43608258653680.56391741346322
1391113.1728375568282-2.17283755682824
1401213.3395932394898-1.33959323948977
141813.3820861748007-5.38208617480073
1421613.0634494806832.93655051931695
1431513.37673592535121.62326407464885
1441716.59885687907580.401143120924245
1451614.83348484688481.16651515311521
1461014.1651894011198-4.16518940111977
1471815.9264604966732.073539503327
1481315.1753194573359-2.17531945733589
1491615.15691987535620.843080124643801
1501313.0666695506731-0.0666695506730648
1511013.1498240975415-3.14982409754151
1521516.4849473386869-1.48494733868688
1531614.11678044442251.88321955557748
1541612.21484922018853.78515077981149
1551412.49714015500211.50285984499791
1561012.540191817967-2.54019181796698
1571716.90492594024230.0950740597576777
1581311.64613723003481.35386276996517
1591513.81561808902191.18438191097815
1601615.08701355671430.912986443285705
1611212.5978246283454-0.597824628345433
1621312.41975353797670.580246462023333

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 13 & 16.1920284722714 & -3.19202847227137 \tabularnewline
2 & 16 & 16.0867606404863 & -0.086760640486277 \tabularnewline
3 & 19 & 16.3028929413178 & 2.69710705868223 \tabularnewline
4 & 15 & 11.8413099907998 & 3.15869000920022 \tabularnewline
5 & 14 & 15.647527068189 & -1.64752706818902 \tabularnewline
6 & 13 & 14.9895969285293 & -1.98959692852927 \tabularnewline
7 & 19 & 15.1623724373348 & 3.83762756266521 \tabularnewline
8 & 15 & 16.7269914184294 & -1.72699141842944 \tabularnewline
9 & 14 & 15.9877179884548 & -1.98771798845476 \tabularnewline
10 & 15 & 12.5599779478411 & 2.44002205215887 \tabularnewline
11 & 16 & 15.286032561121 & 0.713967438878996 \tabularnewline
12 & 16 & 16.2450461284938 & -0.245046128493805 \tabularnewline
13 & 16 & 15.477090308743 & 0.522909691257007 \tabularnewline
14 & 16 & 15.3999738428743 & 0.600026157125654 \tabularnewline
15 & 17 & 17.5009282996742 & -0.500928299674175 \tabularnewline
16 & 15 & 15.1239299406807 & -0.123929940680651 \tabularnewline
17 & 15 & 14.6740435982036 & 0.325956401796412 \tabularnewline
18 & 20 & 16.2684149081577 & 3.73158509184229 \tabularnewline
19 & 18 & 15.4905078402239 & 2.50949215977606 \tabularnewline
20 & 16 & 15.284684912821 & 0.715315087178965 \tabularnewline
21 & 16 & 15.3105615982231 & 0.689438401776896 \tabularnewline
22 & 16 & 14.8195720504499 & 1.18042794955006 \tabularnewline
23 & 19 & 16.4972238142738 & 2.50277618572619 \tabularnewline
24 & 16 & 14.7776835870545 & 1.22231641294552 \tabularnewline
25 & 17 & 14.883854995613 & 2.11614500438701 \tabularnewline
26 & 17 & 16.9736808362276 & 0.0263191637723657 \tabularnewline
27 & 16 & 15.0881752094556 & 0.911824790544357 \tabularnewline
28 & 15 & 16.1803293864933 & -1.18032938649329 \tabularnewline
29 & 16 & 15.590531146231 & 0.409468853768975 \tabularnewline
30 & 14 & 14.0560746879148 & -0.0560746879147753 \tabularnewline
31 & 15 & 15.6918545226112 & -0.691854522611165 \tabularnewline
32 & 12 & 12.2953166223489 & -0.295316622348885 \tabularnewline
33 & 14 & 15.1914819800671 & -1.19148198006714 \tabularnewline
34 & 16 & 15.5615602777016 & 0.438439722298424 \tabularnewline
35 & 14 & 15.7663749381685 & -1.76637493816853 \tabularnewline
36 & 7 & 12.9843960516783 & -5.98439605167827 \tabularnewline
37 & 10 & 11.0222297452183 & -1.02222974521833 \tabularnewline
38 & 14 & 15.8848924668938 & -1.88489246689382 \tabularnewline
39 & 16 & 14.3343344138764 & 1.66566558612355 \tabularnewline
40 & 16 & 14.6728007936734 & 1.32719920632658 \tabularnewline
41 & 16 & 15.1277062368898 & 0.872293763110152 \tabularnewline
42 & 14 & 15.6971384415886 & -1.6971384415886 \tabularnewline
43 & 20 & 17.9535613125627 & 2.04643868743733 \tabularnewline
44 & 14 & 14.2915940902356 & -0.291594090235572 \tabularnewline
45 & 14 & 14.9676171226496 & -0.967617122649589 \tabularnewline
46 & 11 & 15.7073067942432 & -4.70730679424319 \tabularnewline
47 & 14 & 16.5012074540298 & -2.50120745402984 \tabularnewline
48 & 15 & 15.0471761334037 & -0.0471761334036503 \tabularnewline
49 & 16 & 15.357477811427 & 0.642522188572993 \tabularnewline
50 & 14 & 16.1177056358295 & -2.11770563582955 \tabularnewline
51 & 16 & 16.6145714398191 & -0.61457143981907 \tabularnewline
52 & 14 & 14.2241591503592 & -0.224159150359196 \tabularnewline
53 & 12 & 14.8310597751438 & -2.83105977514378 \tabularnewline
54 & 16 & 15.2831352834483 & 0.716864716551666 \tabularnewline
55 & 9 & 11.6113445458156 & -2.61134454581564 \tabularnewline
56 & 14 & 12.7273487758473 & 1.27265122415274 \tabularnewline
57 & 16 & 16.2039075589066 & -0.203907558906594 \tabularnewline
58 & 16 & 15.1796123961502 & 0.820387603849779 \tabularnewline
59 & 15 & 15.4318371730387 & -0.431837173038739 \tabularnewline
60 & 16 & 14.2569075893394 & 1.74309241066061 \tabularnewline
61 & 12 & 11.5488840036404 & 0.451115996359571 \tabularnewline
62 & 16 & 16.0990324387555 & -0.0990324387555359 \tabularnewline
63 & 16 & 16.6054424987364 & -0.605442498736434 \tabularnewline
64 & 14 & 14.4755353551307 & -0.475535355130701 \tabularnewline
65 & 16 & 15.5128780019761 & 0.487121998023938 \tabularnewline
66 & 17 & 15.8437920725416 & 1.15620792745835 \tabularnewline
67 & 18 & 16.036788040156 & 1.96321195984401 \tabularnewline
68 & 18 & 14.5959321982091 & 3.40406780179092 \tabularnewline
69 & 12 & 15.7986894450369 & -3.79868944503691 \tabularnewline
70 & 16 & 15.3566544913652 & 0.643345508634752 \tabularnewline
71 & 10 & 13.3453058388218 & -3.34530583882181 \tabularnewline
72 & 14 & 14.2509852204631 & -0.250985220463126 \tabularnewline
73 & 18 & 16.506846709913 & 1.49315329008702 \tabularnewline
74 & 18 & 17.0795559905344 & 0.920444009465595 \tabularnewline
75 & 16 & 15.6431954740178 & 0.356804525982157 \tabularnewline
76 & 17 & 13.8360737114893 & 3.16392628851066 \tabularnewline
77 & 16 & 16.2900569221169 & -0.290056922116905 \tabularnewline
78 & 16 & 14.3793144101012 & 1.62068558989885 \tabularnewline
79 & 13 & 14.7858404970916 & -1.7858404970916 \tabularnewline
80 & 16 & 15.8586878007588 & 0.141312199241163 \tabularnewline
81 & 16 & 15.5214667638494 & 0.478533236150585 \tabularnewline
82 & 20 & 16.6774367474165 & 3.32256325258351 \tabularnewline
83 & 16 & 15.5912598681855 & 0.408740131814465 \tabularnewline
84 & 15 & 15.9403513570373 & -0.940351357037348 \tabularnewline
85 & 15 & 14.6681851536693 & 0.331814846330661 \tabularnewline
86 & 16 & 14.1947600770899 & 1.80523992291009 \tabularnewline
87 & 14 & 14.1616021894194 & -0.161602189419441 \tabularnewline
88 & 16 & 15.2554704484013 & 0.744529551598692 \tabularnewline
89 & 16 & 14.4963969983629 & 1.50360300163705 \tabularnewline
90 & 15 & 14.193413731728 & 0.806586268272044 \tabularnewline
91 & 12 & 13.4178285037396 & -1.41782850373964 \tabularnewline
92 & 17 & 16.9049259402423 & 0.0950740597576777 \tabularnewline
93 & 16 & 15.4486495114396 & 0.551350488560388 \tabularnewline
94 & 15 & 15.2371713561589 & -0.237171356158882 \tabularnewline
95 & 13 & 15.1091201175879 & -2.10912011758789 \tabularnewline
96 & 16 & 15.1444888147004 & 0.85551118529957 \tabularnewline
97 & 16 & 15.792584869029 & 0.207415130971025 \tabularnewline
98 & 16 & 14.0989939305589 & 1.90100606944106 \tabularnewline
99 & 16 & 16.0870532118844 & -0.087053211884415 \tabularnewline
100 & 14 & 14.2675453613905 & -0.267545361390544 \tabularnewline
101 & 16 & 17.2838519578238 & -1.28385195782379 \tabularnewline
102 & 16 & 14.753399382905 & 1.24660061709498 \tabularnewline
103 & 20 & 17.3665631885563 & 2.63343681144375 \tabularnewline
104 & 15 & 14.4692009204892 & 0.530799079510783 \tabularnewline
105 & 16 & 14.5472168020534 & 1.45278319794661 \tabularnewline
106 & 13 & 15.3335955420167 & -2.33359554201674 \tabularnewline
107 & 17 & 15.9772299167096 & 1.02277008329044 \tabularnewline
108 & 16 & 15.8838595572741 & 0.116140442725885 \tabularnewline
109 & 16 & 14.5006327989772 & 1.49936720102275 \tabularnewline
110 & 12 & 12.2506833749084 & -0.250683374908364 \tabularnewline
111 & 16 & 14.9801244615349 & 1.01987553846506 \tabularnewline
112 & 16 & 15.8530036131275 & 0.146996386872521 \tabularnewline
113 & 17 & 14.5168989793172 & 2.48310102068275 \tabularnewline
114 & 13 & 14.9240123377016 & -1.92401233770162 \tabularnewline
115 & 12 & 14.8506025714075 & -2.85060257140747 \tabularnewline
116 & 18 & 16.589292312907 & 1.410707687093 \tabularnewline
117 & 14 & 15.4716069833716 & -1.47160698337164 \tabularnewline
118 & 14 & 12.9526157847555 & 1.04738421524448 \tabularnewline
119 & 13 & 14.888633080924 & -1.88863308092397 \tabularnewline
120 & 16 & 15.6940921498395 & 0.30590785016049 \tabularnewline
121 & 13 & 14.2521312443242 & -1.25213124432418 \tabularnewline
122 & 16 & 15.6232214924446 & 0.376778507555358 \tabularnewline
123 & 13 & 15.9768657876015 & -2.97686578760153 \tabularnewline
124 & 16 & 17.0361549172139 & -1.03615491721392 \tabularnewline
125 & 15 & 15.9662974443689 & -0.966297444368874 \tabularnewline
126 & 16 & 16.805957327963 & -0.805957327962962 \tabularnewline
127 & 15 & 15.4175730688417 & -0.417573068841661 \tabularnewline
128 & 17 & 16.2445366271888 & 0.755463372811213 \tabularnewline
129 & 15 & 13.8156180890219 & 1.18438191097815 \tabularnewline
130 & 12 & 14.954053885653 & -2.95405388565303 \tabularnewline
131 & 16 & 14.1596075209435 & 1.84039247905651 \tabularnewline
132 & 10 & 13.3680049257498 & -3.36800492574982 \tabularnewline
133 & 16 & 14.0923519355499 & 1.90764806445008 \tabularnewline
134 & 12 & 13.9314533586019 & -1.93145335860193 \tabularnewline
135 & 14 & 15.6665908818457 & -1.66659088184569 \tabularnewline
136 & 15 & 15.1226477046261 & -0.12264770462611 \tabularnewline
137 & 13 & 12.2897984522131 & 0.710201547786873 \tabularnewline
138 & 15 & 14.4360825865368 & 0.56391741346322 \tabularnewline
139 & 11 & 13.1728375568282 & -2.17283755682824 \tabularnewline
140 & 12 & 13.3395932394898 & -1.33959323948977 \tabularnewline
141 & 8 & 13.3820861748007 & -5.38208617480073 \tabularnewline
142 & 16 & 13.063449480683 & 2.93655051931695 \tabularnewline
143 & 15 & 13.3767359253512 & 1.62326407464885 \tabularnewline
144 & 17 & 16.5988568790758 & 0.401143120924245 \tabularnewline
145 & 16 & 14.8334848468848 & 1.16651515311521 \tabularnewline
146 & 10 & 14.1651894011198 & -4.16518940111977 \tabularnewline
147 & 18 & 15.926460496673 & 2.073539503327 \tabularnewline
148 & 13 & 15.1753194573359 & -2.17531945733589 \tabularnewline
149 & 16 & 15.1569198753562 & 0.843080124643801 \tabularnewline
150 & 13 & 13.0666695506731 & -0.0666695506730648 \tabularnewline
151 & 10 & 13.1498240975415 & -3.14982409754151 \tabularnewline
152 & 15 & 16.4849473386869 & -1.48494733868688 \tabularnewline
153 & 16 & 14.1167804444225 & 1.88321955557748 \tabularnewline
154 & 16 & 12.2148492201885 & 3.78515077981149 \tabularnewline
155 & 14 & 12.4971401550021 & 1.50285984499791 \tabularnewline
156 & 10 & 12.540191817967 & -2.54019181796698 \tabularnewline
157 & 17 & 16.9049259402423 & 0.0950740597576777 \tabularnewline
158 & 13 & 11.6461372300348 & 1.35386276996517 \tabularnewline
159 & 15 & 13.8156180890219 & 1.18438191097815 \tabularnewline
160 & 16 & 15.0870135567143 & 0.912986443285705 \tabularnewline
161 & 12 & 12.5978246283454 & -0.597824628345433 \tabularnewline
162 & 13 & 12.4197535379767 & 0.580246462023333 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186283&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]16.1920284722714[/C][C]-3.19202847227137[/C][/ROW]
[ROW][C]2[/C][C]16[/C][C]16.0867606404863[/C][C]-0.086760640486277[/C][/ROW]
[ROW][C]3[/C][C]19[/C][C]16.3028929413178[/C][C]2.69710705868223[/C][/ROW]
[ROW][C]4[/C][C]15[/C][C]11.8413099907998[/C][C]3.15869000920022[/C][/ROW]
[ROW][C]5[/C][C]14[/C][C]15.647527068189[/C][C]-1.64752706818902[/C][/ROW]
[ROW][C]6[/C][C]13[/C][C]14.9895969285293[/C][C]-1.98959692852927[/C][/ROW]
[ROW][C]7[/C][C]19[/C][C]15.1623724373348[/C][C]3.83762756266521[/C][/ROW]
[ROW][C]8[/C][C]15[/C][C]16.7269914184294[/C][C]-1.72699141842944[/C][/ROW]
[ROW][C]9[/C][C]14[/C][C]15.9877179884548[/C][C]-1.98771798845476[/C][/ROW]
[ROW][C]10[/C][C]15[/C][C]12.5599779478411[/C][C]2.44002205215887[/C][/ROW]
[ROW][C]11[/C][C]16[/C][C]15.286032561121[/C][C]0.713967438878996[/C][/ROW]
[ROW][C]12[/C][C]16[/C][C]16.2450461284938[/C][C]-0.245046128493805[/C][/ROW]
[ROW][C]13[/C][C]16[/C][C]15.477090308743[/C][C]0.522909691257007[/C][/ROW]
[ROW][C]14[/C][C]16[/C][C]15.3999738428743[/C][C]0.600026157125654[/C][/ROW]
[ROW][C]15[/C][C]17[/C][C]17.5009282996742[/C][C]-0.500928299674175[/C][/ROW]
[ROW][C]16[/C][C]15[/C][C]15.1239299406807[/C][C]-0.123929940680651[/C][/ROW]
[ROW][C]17[/C][C]15[/C][C]14.6740435982036[/C][C]0.325956401796412[/C][/ROW]
[ROW][C]18[/C][C]20[/C][C]16.2684149081577[/C][C]3.73158509184229[/C][/ROW]
[ROW][C]19[/C][C]18[/C][C]15.4905078402239[/C][C]2.50949215977606[/C][/ROW]
[ROW][C]20[/C][C]16[/C][C]15.284684912821[/C][C]0.715315087178965[/C][/ROW]
[ROW][C]21[/C][C]16[/C][C]15.3105615982231[/C][C]0.689438401776896[/C][/ROW]
[ROW][C]22[/C][C]16[/C][C]14.8195720504499[/C][C]1.18042794955006[/C][/ROW]
[ROW][C]23[/C][C]19[/C][C]16.4972238142738[/C][C]2.50277618572619[/C][/ROW]
[ROW][C]24[/C][C]16[/C][C]14.7776835870545[/C][C]1.22231641294552[/C][/ROW]
[ROW][C]25[/C][C]17[/C][C]14.883854995613[/C][C]2.11614500438701[/C][/ROW]
[ROW][C]26[/C][C]17[/C][C]16.9736808362276[/C][C]0.0263191637723657[/C][/ROW]
[ROW][C]27[/C][C]16[/C][C]15.0881752094556[/C][C]0.911824790544357[/C][/ROW]
[ROW][C]28[/C][C]15[/C][C]16.1803293864933[/C][C]-1.18032938649329[/C][/ROW]
[ROW][C]29[/C][C]16[/C][C]15.590531146231[/C][C]0.409468853768975[/C][/ROW]
[ROW][C]30[/C][C]14[/C][C]14.0560746879148[/C][C]-0.0560746879147753[/C][/ROW]
[ROW][C]31[/C][C]15[/C][C]15.6918545226112[/C][C]-0.691854522611165[/C][/ROW]
[ROW][C]32[/C][C]12[/C][C]12.2953166223489[/C][C]-0.295316622348885[/C][/ROW]
[ROW][C]33[/C][C]14[/C][C]15.1914819800671[/C][C]-1.19148198006714[/C][/ROW]
[ROW][C]34[/C][C]16[/C][C]15.5615602777016[/C][C]0.438439722298424[/C][/ROW]
[ROW][C]35[/C][C]14[/C][C]15.7663749381685[/C][C]-1.76637493816853[/C][/ROW]
[ROW][C]36[/C][C]7[/C][C]12.9843960516783[/C][C]-5.98439605167827[/C][/ROW]
[ROW][C]37[/C][C]10[/C][C]11.0222297452183[/C][C]-1.02222974521833[/C][/ROW]
[ROW][C]38[/C][C]14[/C][C]15.8848924668938[/C][C]-1.88489246689382[/C][/ROW]
[ROW][C]39[/C][C]16[/C][C]14.3343344138764[/C][C]1.66566558612355[/C][/ROW]
[ROW][C]40[/C][C]16[/C][C]14.6728007936734[/C][C]1.32719920632658[/C][/ROW]
[ROW][C]41[/C][C]16[/C][C]15.1277062368898[/C][C]0.872293763110152[/C][/ROW]
[ROW][C]42[/C][C]14[/C][C]15.6971384415886[/C][C]-1.6971384415886[/C][/ROW]
[ROW][C]43[/C][C]20[/C][C]17.9535613125627[/C][C]2.04643868743733[/C][/ROW]
[ROW][C]44[/C][C]14[/C][C]14.2915940902356[/C][C]-0.291594090235572[/C][/ROW]
[ROW][C]45[/C][C]14[/C][C]14.9676171226496[/C][C]-0.967617122649589[/C][/ROW]
[ROW][C]46[/C][C]11[/C][C]15.7073067942432[/C][C]-4.70730679424319[/C][/ROW]
[ROW][C]47[/C][C]14[/C][C]16.5012074540298[/C][C]-2.50120745402984[/C][/ROW]
[ROW][C]48[/C][C]15[/C][C]15.0471761334037[/C][C]-0.0471761334036503[/C][/ROW]
[ROW][C]49[/C][C]16[/C][C]15.357477811427[/C][C]0.642522188572993[/C][/ROW]
[ROW][C]50[/C][C]14[/C][C]16.1177056358295[/C][C]-2.11770563582955[/C][/ROW]
[ROW][C]51[/C][C]16[/C][C]16.6145714398191[/C][C]-0.61457143981907[/C][/ROW]
[ROW][C]52[/C][C]14[/C][C]14.2241591503592[/C][C]-0.224159150359196[/C][/ROW]
[ROW][C]53[/C][C]12[/C][C]14.8310597751438[/C][C]-2.83105977514378[/C][/ROW]
[ROW][C]54[/C][C]16[/C][C]15.2831352834483[/C][C]0.716864716551666[/C][/ROW]
[ROW][C]55[/C][C]9[/C][C]11.6113445458156[/C][C]-2.61134454581564[/C][/ROW]
[ROW][C]56[/C][C]14[/C][C]12.7273487758473[/C][C]1.27265122415274[/C][/ROW]
[ROW][C]57[/C][C]16[/C][C]16.2039075589066[/C][C]-0.203907558906594[/C][/ROW]
[ROW][C]58[/C][C]16[/C][C]15.1796123961502[/C][C]0.820387603849779[/C][/ROW]
[ROW][C]59[/C][C]15[/C][C]15.4318371730387[/C][C]-0.431837173038739[/C][/ROW]
[ROW][C]60[/C][C]16[/C][C]14.2569075893394[/C][C]1.74309241066061[/C][/ROW]
[ROW][C]61[/C][C]12[/C][C]11.5488840036404[/C][C]0.451115996359571[/C][/ROW]
[ROW][C]62[/C][C]16[/C][C]16.0990324387555[/C][C]-0.0990324387555359[/C][/ROW]
[ROW][C]63[/C][C]16[/C][C]16.6054424987364[/C][C]-0.605442498736434[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]14.4755353551307[/C][C]-0.475535355130701[/C][/ROW]
[ROW][C]65[/C][C]16[/C][C]15.5128780019761[/C][C]0.487121998023938[/C][/ROW]
[ROW][C]66[/C][C]17[/C][C]15.8437920725416[/C][C]1.15620792745835[/C][/ROW]
[ROW][C]67[/C][C]18[/C][C]16.036788040156[/C][C]1.96321195984401[/C][/ROW]
[ROW][C]68[/C][C]18[/C][C]14.5959321982091[/C][C]3.40406780179092[/C][/ROW]
[ROW][C]69[/C][C]12[/C][C]15.7986894450369[/C][C]-3.79868944503691[/C][/ROW]
[ROW][C]70[/C][C]16[/C][C]15.3566544913652[/C][C]0.643345508634752[/C][/ROW]
[ROW][C]71[/C][C]10[/C][C]13.3453058388218[/C][C]-3.34530583882181[/C][/ROW]
[ROW][C]72[/C][C]14[/C][C]14.2509852204631[/C][C]-0.250985220463126[/C][/ROW]
[ROW][C]73[/C][C]18[/C][C]16.506846709913[/C][C]1.49315329008702[/C][/ROW]
[ROW][C]74[/C][C]18[/C][C]17.0795559905344[/C][C]0.920444009465595[/C][/ROW]
[ROW][C]75[/C][C]16[/C][C]15.6431954740178[/C][C]0.356804525982157[/C][/ROW]
[ROW][C]76[/C][C]17[/C][C]13.8360737114893[/C][C]3.16392628851066[/C][/ROW]
[ROW][C]77[/C][C]16[/C][C]16.2900569221169[/C][C]-0.290056922116905[/C][/ROW]
[ROW][C]78[/C][C]16[/C][C]14.3793144101012[/C][C]1.62068558989885[/C][/ROW]
[ROW][C]79[/C][C]13[/C][C]14.7858404970916[/C][C]-1.7858404970916[/C][/ROW]
[ROW][C]80[/C][C]16[/C][C]15.8586878007588[/C][C]0.141312199241163[/C][/ROW]
[ROW][C]81[/C][C]16[/C][C]15.5214667638494[/C][C]0.478533236150585[/C][/ROW]
[ROW][C]82[/C][C]20[/C][C]16.6774367474165[/C][C]3.32256325258351[/C][/ROW]
[ROW][C]83[/C][C]16[/C][C]15.5912598681855[/C][C]0.408740131814465[/C][/ROW]
[ROW][C]84[/C][C]15[/C][C]15.9403513570373[/C][C]-0.940351357037348[/C][/ROW]
[ROW][C]85[/C][C]15[/C][C]14.6681851536693[/C][C]0.331814846330661[/C][/ROW]
[ROW][C]86[/C][C]16[/C][C]14.1947600770899[/C][C]1.80523992291009[/C][/ROW]
[ROW][C]87[/C][C]14[/C][C]14.1616021894194[/C][C]-0.161602189419441[/C][/ROW]
[ROW][C]88[/C][C]16[/C][C]15.2554704484013[/C][C]0.744529551598692[/C][/ROW]
[ROW][C]89[/C][C]16[/C][C]14.4963969983629[/C][C]1.50360300163705[/C][/ROW]
[ROW][C]90[/C][C]15[/C][C]14.193413731728[/C][C]0.806586268272044[/C][/ROW]
[ROW][C]91[/C][C]12[/C][C]13.4178285037396[/C][C]-1.41782850373964[/C][/ROW]
[ROW][C]92[/C][C]17[/C][C]16.9049259402423[/C][C]0.0950740597576777[/C][/ROW]
[ROW][C]93[/C][C]16[/C][C]15.4486495114396[/C][C]0.551350488560388[/C][/ROW]
[ROW][C]94[/C][C]15[/C][C]15.2371713561589[/C][C]-0.237171356158882[/C][/ROW]
[ROW][C]95[/C][C]13[/C][C]15.1091201175879[/C][C]-2.10912011758789[/C][/ROW]
[ROW][C]96[/C][C]16[/C][C]15.1444888147004[/C][C]0.85551118529957[/C][/ROW]
[ROW][C]97[/C][C]16[/C][C]15.792584869029[/C][C]0.207415130971025[/C][/ROW]
[ROW][C]98[/C][C]16[/C][C]14.0989939305589[/C][C]1.90100606944106[/C][/ROW]
[ROW][C]99[/C][C]16[/C][C]16.0870532118844[/C][C]-0.087053211884415[/C][/ROW]
[ROW][C]100[/C][C]14[/C][C]14.2675453613905[/C][C]-0.267545361390544[/C][/ROW]
[ROW][C]101[/C][C]16[/C][C]17.2838519578238[/C][C]-1.28385195782379[/C][/ROW]
[ROW][C]102[/C][C]16[/C][C]14.753399382905[/C][C]1.24660061709498[/C][/ROW]
[ROW][C]103[/C][C]20[/C][C]17.3665631885563[/C][C]2.63343681144375[/C][/ROW]
[ROW][C]104[/C][C]15[/C][C]14.4692009204892[/C][C]0.530799079510783[/C][/ROW]
[ROW][C]105[/C][C]16[/C][C]14.5472168020534[/C][C]1.45278319794661[/C][/ROW]
[ROW][C]106[/C][C]13[/C][C]15.3335955420167[/C][C]-2.33359554201674[/C][/ROW]
[ROW][C]107[/C][C]17[/C][C]15.9772299167096[/C][C]1.02277008329044[/C][/ROW]
[ROW][C]108[/C][C]16[/C][C]15.8838595572741[/C][C]0.116140442725885[/C][/ROW]
[ROW][C]109[/C][C]16[/C][C]14.5006327989772[/C][C]1.49936720102275[/C][/ROW]
[ROW][C]110[/C][C]12[/C][C]12.2506833749084[/C][C]-0.250683374908364[/C][/ROW]
[ROW][C]111[/C][C]16[/C][C]14.9801244615349[/C][C]1.01987553846506[/C][/ROW]
[ROW][C]112[/C][C]16[/C][C]15.8530036131275[/C][C]0.146996386872521[/C][/ROW]
[ROW][C]113[/C][C]17[/C][C]14.5168989793172[/C][C]2.48310102068275[/C][/ROW]
[ROW][C]114[/C][C]13[/C][C]14.9240123377016[/C][C]-1.92401233770162[/C][/ROW]
[ROW][C]115[/C][C]12[/C][C]14.8506025714075[/C][C]-2.85060257140747[/C][/ROW]
[ROW][C]116[/C][C]18[/C][C]16.589292312907[/C][C]1.410707687093[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]15.4716069833716[/C][C]-1.47160698337164[/C][/ROW]
[ROW][C]118[/C][C]14[/C][C]12.9526157847555[/C][C]1.04738421524448[/C][/ROW]
[ROW][C]119[/C][C]13[/C][C]14.888633080924[/C][C]-1.88863308092397[/C][/ROW]
[ROW][C]120[/C][C]16[/C][C]15.6940921498395[/C][C]0.30590785016049[/C][/ROW]
[ROW][C]121[/C][C]13[/C][C]14.2521312443242[/C][C]-1.25213124432418[/C][/ROW]
[ROW][C]122[/C][C]16[/C][C]15.6232214924446[/C][C]0.376778507555358[/C][/ROW]
[ROW][C]123[/C][C]13[/C][C]15.9768657876015[/C][C]-2.97686578760153[/C][/ROW]
[ROW][C]124[/C][C]16[/C][C]17.0361549172139[/C][C]-1.03615491721392[/C][/ROW]
[ROW][C]125[/C][C]15[/C][C]15.9662974443689[/C][C]-0.966297444368874[/C][/ROW]
[ROW][C]126[/C][C]16[/C][C]16.805957327963[/C][C]-0.805957327962962[/C][/ROW]
[ROW][C]127[/C][C]15[/C][C]15.4175730688417[/C][C]-0.417573068841661[/C][/ROW]
[ROW][C]128[/C][C]17[/C][C]16.2445366271888[/C][C]0.755463372811213[/C][/ROW]
[ROW][C]129[/C][C]15[/C][C]13.8156180890219[/C][C]1.18438191097815[/C][/ROW]
[ROW][C]130[/C][C]12[/C][C]14.954053885653[/C][C]-2.95405388565303[/C][/ROW]
[ROW][C]131[/C][C]16[/C][C]14.1596075209435[/C][C]1.84039247905651[/C][/ROW]
[ROW][C]132[/C][C]10[/C][C]13.3680049257498[/C][C]-3.36800492574982[/C][/ROW]
[ROW][C]133[/C][C]16[/C][C]14.0923519355499[/C][C]1.90764806445008[/C][/ROW]
[ROW][C]134[/C][C]12[/C][C]13.9314533586019[/C][C]-1.93145335860193[/C][/ROW]
[ROW][C]135[/C][C]14[/C][C]15.6665908818457[/C][C]-1.66659088184569[/C][/ROW]
[ROW][C]136[/C][C]15[/C][C]15.1226477046261[/C][C]-0.12264770462611[/C][/ROW]
[ROW][C]137[/C][C]13[/C][C]12.2897984522131[/C][C]0.710201547786873[/C][/ROW]
[ROW][C]138[/C][C]15[/C][C]14.4360825865368[/C][C]0.56391741346322[/C][/ROW]
[ROW][C]139[/C][C]11[/C][C]13.1728375568282[/C][C]-2.17283755682824[/C][/ROW]
[ROW][C]140[/C][C]12[/C][C]13.3395932394898[/C][C]-1.33959323948977[/C][/ROW]
[ROW][C]141[/C][C]8[/C][C]13.3820861748007[/C][C]-5.38208617480073[/C][/ROW]
[ROW][C]142[/C][C]16[/C][C]13.063449480683[/C][C]2.93655051931695[/C][/ROW]
[ROW][C]143[/C][C]15[/C][C]13.3767359253512[/C][C]1.62326407464885[/C][/ROW]
[ROW][C]144[/C][C]17[/C][C]16.5988568790758[/C][C]0.401143120924245[/C][/ROW]
[ROW][C]145[/C][C]16[/C][C]14.8334848468848[/C][C]1.16651515311521[/C][/ROW]
[ROW][C]146[/C][C]10[/C][C]14.1651894011198[/C][C]-4.16518940111977[/C][/ROW]
[ROW][C]147[/C][C]18[/C][C]15.926460496673[/C][C]2.073539503327[/C][/ROW]
[ROW][C]148[/C][C]13[/C][C]15.1753194573359[/C][C]-2.17531945733589[/C][/ROW]
[ROW][C]149[/C][C]16[/C][C]15.1569198753562[/C][C]0.843080124643801[/C][/ROW]
[ROW][C]150[/C][C]13[/C][C]13.0666695506731[/C][C]-0.0666695506730648[/C][/ROW]
[ROW][C]151[/C][C]10[/C][C]13.1498240975415[/C][C]-3.14982409754151[/C][/ROW]
[ROW][C]152[/C][C]15[/C][C]16.4849473386869[/C][C]-1.48494733868688[/C][/ROW]
[ROW][C]153[/C][C]16[/C][C]14.1167804444225[/C][C]1.88321955557748[/C][/ROW]
[ROW][C]154[/C][C]16[/C][C]12.2148492201885[/C][C]3.78515077981149[/C][/ROW]
[ROW][C]155[/C][C]14[/C][C]12.4971401550021[/C][C]1.50285984499791[/C][/ROW]
[ROW][C]156[/C][C]10[/C][C]12.540191817967[/C][C]-2.54019181796698[/C][/ROW]
[ROW][C]157[/C][C]17[/C][C]16.9049259402423[/C][C]0.0950740597576777[/C][/ROW]
[ROW][C]158[/C][C]13[/C][C]11.6461372300348[/C][C]1.35386276996517[/C][/ROW]
[ROW][C]159[/C][C]15[/C][C]13.8156180890219[/C][C]1.18438191097815[/C][/ROW]
[ROW][C]160[/C][C]16[/C][C]15.0870135567143[/C][C]0.912986443285705[/C][/ROW]
[ROW][C]161[/C][C]12[/C][C]12.5978246283454[/C][C]-0.597824628345433[/C][/ROW]
[ROW][C]162[/C][C]13[/C][C]12.4197535379767[/C][C]0.580246462023333[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186283&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186283&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11316.1920284722714-3.19202847227137
21616.0867606404863-0.086760640486277
31916.30289294131782.69710705868223
41511.84130999079983.15869000920022
51415.647527068189-1.64752706818902
61314.9895969285293-1.98959692852927
71915.16237243733483.83762756266521
81516.7269914184294-1.72699141842944
91415.9877179884548-1.98771798845476
101512.55997794784112.44002205215887
111615.2860325611210.713967438878996
121616.2450461284938-0.245046128493805
131615.4770903087430.522909691257007
141615.39997384287430.600026157125654
151717.5009282996742-0.500928299674175
161515.1239299406807-0.123929940680651
171514.67404359820360.325956401796412
182016.26841490815773.73158509184229
191815.49050784022392.50949215977606
201615.2846849128210.715315087178965
211615.31056159822310.689438401776896
221614.81957205044991.18042794955006
231916.49722381427382.50277618572619
241614.77768358705451.22231641294552
251714.8838549956132.11614500438701
261716.97368083622760.0263191637723657
271615.08817520945560.911824790544357
281516.1803293864933-1.18032938649329
291615.5905311462310.409468853768975
301414.0560746879148-0.0560746879147753
311515.6918545226112-0.691854522611165
321212.2953166223489-0.295316622348885
331415.1914819800671-1.19148198006714
341615.56156027770160.438439722298424
351415.7663749381685-1.76637493816853
36712.9843960516783-5.98439605167827
371011.0222297452183-1.02222974521833
381415.8848924668938-1.88489246689382
391614.33433441387641.66566558612355
401614.67280079367341.32719920632658
411615.12770623688980.872293763110152
421415.6971384415886-1.6971384415886
432017.95356131256272.04643868743733
441414.2915940902356-0.291594090235572
451414.9676171226496-0.967617122649589
461115.7073067942432-4.70730679424319
471416.5012074540298-2.50120745402984
481515.0471761334037-0.0471761334036503
491615.3574778114270.642522188572993
501416.1177056358295-2.11770563582955
511616.6145714398191-0.61457143981907
521414.2241591503592-0.224159150359196
531214.8310597751438-2.83105977514378
541615.28313528344830.716864716551666
55911.6113445458156-2.61134454581564
561412.72734877584731.27265122415274
571616.2039075589066-0.203907558906594
581615.17961239615020.820387603849779
591515.4318371730387-0.431837173038739
601614.25690758933941.74309241066061
611211.54888400364040.451115996359571
621616.0990324387555-0.0990324387555359
631616.6054424987364-0.605442498736434
641414.4755353551307-0.475535355130701
651615.51287800197610.487121998023938
661715.84379207254161.15620792745835
671816.0367880401561.96321195984401
681814.59593219820913.40406780179092
691215.7986894450369-3.79868944503691
701615.35665449136520.643345508634752
711013.3453058388218-3.34530583882181
721414.2509852204631-0.250985220463126
731816.5068467099131.49315329008702
741817.07955599053440.920444009465595
751615.64319547401780.356804525982157
761713.83607371148933.16392628851066
771616.2900569221169-0.290056922116905
781614.37931441010121.62068558989885
791314.7858404970916-1.7858404970916
801615.85868780075880.141312199241163
811615.52146676384940.478533236150585
822016.67743674741653.32256325258351
831615.59125986818550.408740131814465
841515.9403513570373-0.940351357037348
851514.66818515366930.331814846330661
861614.19476007708991.80523992291009
871414.1616021894194-0.161602189419441
881615.25547044840130.744529551598692
891614.49639699836291.50360300163705
901514.1934137317280.806586268272044
911213.4178285037396-1.41782850373964
921716.90492594024230.0950740597576777
931615.44864951143960.551350488560388
941515.2371713561589-0.237171356158882
951315.1091201175879-2.10912011758789
961615.14448881470040.85551118529957
971615.7925848690290.207415130971025
981614.09899393055891.90100606944106
991616.0870532118844-0.087053211884415
1001414.2675453613905-0.267545361390544
1011617.2838519578238-1.28385195782379
1021614.7533993829051.24660061709498
1032017.36656318855632.63343681144375
1041514.46920092048920.530799079510783
1051614.54721680205341.45278319794661
1061315.3335955420167-2.33359554201674
1071715.97722991670961.02277008329044
1081615.88385955727410.116140442725885
1091614.50063279897721.49936720102275
1101212.2506833749084-0.250683374908364
1111614.98012446153491.01987553846506
1121615.85300361312750.146996386872521
1131714.51689897931722.48310102068275
1141314.9240123377016-1.92401233770162
1151214.8506025714075-2.85060257140747
1161816.5892923129071.410707687093
1171415.4716069833716-1.47160698337164
1181412.95261578475551.04738421524448
1191314.888633080924-1.88863308092397
1201615.69409214983950.30590785016049
1211314.2521312443242-1.25213124432418
1221615.62322149244460.376778507555358
1231315.9768657876015-2.97686578760153
1241617.0361549172139-1.03615491721392
1251515.9662974443689-0.966297444368874
1261616.805957327963-0.805957327962962
1271515.4175730688417-0.417573068841661
1281716.24453662718880.755463372811213
1291513.81561808902191.18438191097815
1301214.954053885653-2.95405388565303
1311614.15960752094351.84039247905651
1321013.3680049257498-3.36800492574982
1331614.09235193554991.90764806445008
1341213.9314533586019-1.93145335860193
1351415.6665908818457-1.66659088184569
1361515.1226477046261-0.12264770462611
1371312.28979845221310.710201547786873
1381514.43608258653680.56391741346322
1391113.1728375568282-2.17283755682824
1401213.3395932394898-1.33959323948977
141813.3820861748007-5.38208617480073
1421613.0634494806832.93655051931695
1431513.37673592535121.62326407464885
1441716.59885687907580.401143120924245
1451614.83348484688481.16651515311521
1461014.1651894011198-4.16518940111977
1471815.9264604966732.073539503327
1481315.1753194573359-2.17531945733589
1491615.15691987535620.843080124643801
1501313.0666695506731-0.0666695506730648
1511013.1498240975415-3.14982409754151
1521516.4849473386869-1.48494733868688
1531614.11678044442251.88321955557748
1541612.21484922018853.78515077981149
1551412.49714015500211.50285984499791
1561012.540191817967-2.54019181796698
1571716.90492594024230.0950740597576777
1581311.64613723003481.35386276996517
1591513.81561808902191.18438191097815
1601615.08701355671430.912986443285705
1611212.5978246283454-0.597824628345433
1621312.41975353797670.580246462023333







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
120.7596690053381850.480661989323630.240330994661815
130.62257921916330.7548415616734010.3774207808367
140.582435660136410.835128679727180.41756433986359
150.462540002938730.925080005877460.53745999706127
160.3622408958746050.724481791749210.637759104125395
170.2990779281940360.5981558563880710.700922071805964
180.5059720583861910.9880558832276180.494027941613809
190.4215329146461660.8430658292923320.578467085353834
200.3322772163013920.6645544326027840.667722783698608
210.2622514671290490.5245029342580980.737748532870951
220.238943387702370.477886775404740.76105661229763
230.4351154878678040.8702309757356070.564884512132196
240.4699077599461940.9398155198923880.530092240053806
250.457754826968690.9155096539373790.54224517303131
260.4169426969809750.8338853939619490.583057303019025
270.476615581918380.953231163836760.52338441808162
280.4798453343453820.9596906686907650.520154665654618
290.4596599136559860.9193198273119720.540340086344014
300.5004823615828510.9990352768342970.499517638417149
310.439749298400250.8794985968004990.56025070159975
320.3905079462838960.7810158925677910.609492053716104
330.3641152359369270.7282304718738530.635884764063073
340.3291504737229260.6583009474458520.670849526277074
350.282675334072610.5653506681452210.71732466592739
360.8504199732150570.2991600535698870.149580026784943
370.8252866636581690.3494266726836630.174713336341831
380.8186983945868210.3626032108263570.181301605413179
390.8379888225505820.3240223548988360.162011177449418
400.8206577088210760.3586845823578470.179342291178923
410.7898858929962780.4202282140074450.210114107003722
420.7644694388271030.4710611223457940.235530561172897
430.7886004021524450.422799195695110.211399597847555
440.7476329501345320.5047340997309350.252367049865468
450.7192074334670230.5615851330659550.280792566532977
460.8738921093053380.2522157813893240.126107890694662
470.9182901492177370.1634197015645270.0817098507822634
480.8966855929673350.206628814065330.103314407032665
490.88102642674180.23794714651640.1189735732582
500.8807955969224320.2384088061551350.119204403077568
510.8545307814530670.2909384370938660.145469218546933
520.8237402254396240.3525195491207520.176259774560376
530.8455528540990440.3088942918019110.154447145900956
540.8297596104375910.3404807791248180.170240389562409
550.8486720515476780.3026558969046440.151327948452322
560.8216258575186630.3567482849626730.178374142481337
570.7894124656218410.4211750687563180.210587534378159
580.7640719102430830.4718561795138340.235928089756917
590.7311477036118970.5377045927762060.268852296388103
600.7197987525568820.5604024948862360.280201247443118
610.6816203758149760.6367592483700480.318379624185024
620.6416240325248210.7167519349503570.358375967475179
630.6117437621786270.7765124756427460.388256237821373
640.5728294353243060.8543411293513890.427170564675695
650.5297223977096350.9405552045807310.470277602290365
660.4851516067124480.9703032134248960.514848393287552
670.4676022649839940.9352045299679890.532397735016006
680.5206035877399340.9587928245201330.479396412260066
690.7438164883735150.512367023252970.256183511626485
700.7064414341483660.5871171317032670.293558565851634
710.8250744128803290.3498511742393410.174925587119671
720.792959734714190.4140805305716210.20704026528581
730.7834528390827180.4330943218345640.216547160917282
740.7629144169235890.4741711661528230.237085583076412
750.7257852152687060.5484295694625880.274214784731294
760.769231148572830.4615377028543390.23076885142717
770.7354340769941580.5291318460116830.264565923005842
780.7197139508112370.5605720983775260.280286049188763
790.7310829677823260.5378340644353490.268917032217674
800.6909507592880340.6180984814239320.309049240711966
810.6514618982532710.6970762034934570.348538101746729
820.7764896446436570.4470207107126870.223510355356343
830.740072589345880.519854821308240.25992741065412
840.7145583612882030.5708832774235950.285441638711797
850.6734620735144320.6530758529711370.326537926485568
860.6637893559818070.6724212880363850.336210644018193
870.620735744174410.7585285116511790.37926425582559
880.5800451746192190.8399096507615620.419954825380781
890.5590294675765870.8819410648468250.440970532423412
900.5251460464966220.9497079070067550.474853953503378
910.5121713902623710.9756572194752570.487828609737629
920.4735040355578210.9470080711156420.526495964442179
930.4301479542291810.8602959084583620.569852045770819
940.3880519679100680.7761039358201360.611948032089932
950.4038162259568290.8076324519136580.596183774043171
960.3681601265115440.7363202530230870.631839873488456
970.3285478977437850.657095795487570.671452102256215
980.3283240653788460.6566481307576930.671675934621154
990.2868331556780290.5736663113560570.713166844321971
1000.2486021538691490.4972043077382970.751397846130851
1010.2270318969726570.4540637939453140.772968103027343
1020.2105418507509720.4210837015019440.789458149249028
1030.25227104120740.50454208241480.7477289587926
1040.2158456635028730.4316913270057460.784154336497127
1050.2079588024658430.4159176049316870.792041197534157
1060.2233972476691210.4467944953382430.776602752330879
1070.2007847873446180.4015695746892360.799215212655382
1080.1779909179140550.3559818358281090.822009082085945
1090.1711716537887630.3423433075775250.828828346211237
1100.1587196149629460.3174392299258920.841280385037054
1110.1396190980834090.2792381961668170.860380901916591
1120.1165357531166340.2330715062332680.883464246883366
1130.1377232003429820.2754464006859640.862276799657018
1140.1242092448742260.2484184897484520.875790755125774
1150.1622174224928430.3244348449856860.837782577507157
1160.1530936601779370.3061873203558750.846906339822063
1170.1337791789393020.2675583578786030.866220821060698
1180.116180459638330.232360919276660.88381954036167
1190.117513986612960.235027973225920.88248601338704
1200.1083083644260110.2166167288520230.891691635573989
1210.09165090288227060.1833018057645410.908349097117729
1220.07280449323434490.145608986468690.927195506765655
1230.08166672309071620.1633334461814320.918333276909284
1240.06518883315833880.1303776663166780.934811166841661
1250.05257482712081110.1051496542416220.947425172879189
1260.03985327662072020.07970655324144050.96014672337928
1270.02963179551335610.05926359102671220.970368204486644
1280.0234937781425810.0469875562851620.976506221857419
1290.01808832298732340.03617664597464680.981911677012677
1300.02529721998784680.05059443997569360.974702780012153
1310.02175252500704760.04350505001409520.978247474992952
1320.03329249012054050.0665849802410810.96670750987946
1330.03702455293664460.07404910587328910.962975447063355
1340.05177437893577580.1035487578715520.948225621064224
1350.04124945978984080.08249891957968170.958750540210159
1360.02851822841742420.05703645683484850.971481771582576
1370.01933955422362360.03867910844724710.980660445776376
1380.01277528417228270.02555056834456540.987224715827717
1390.02451277325421610.04902554650843230.975487226745784
1400.0203819144764140.04076382895282810.979618085523586
1410.6147471192417470.7705057615165060.385252880758253
1420.5489522954172480.9020954091655040.451047704582752
1430.4615448914374960.9230897828749920.538455108562504
1440.385534252991920.7710685059838410.61446574700808
1450.29610470506230.5922094101245990.7038952949377
1460.2989241218499750.5978482436999510.701075878150025
1470.2550264584221610.5100529168443230.744973541577838
1480.7574024365827470.4851951268345060.242597563417253
1490.7424493127090660.5151013745818680.257550687290934
1500.5827096385395150.8345807229209710.417290361460485

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
12 & 0.759669005338185 & 0.48066198932363 & 0.240330994661815 \tabularnewline
13 & 0.6225792191633 & 0.754841561673401 & 0.3774207808367 \tabularnewline
14 & 0.58243566013641 & 0.83512867972718 & 0.41756433986359 \tabularnewline
15 & 0.46254000293873 & 0.92508000587746 & 0.53745999706127 \tabularnewline
16 & 0.362240895874605 & 0.72448179174921 & 0.637759104125395 \tabularnewline
17 & 0.299077928194036 & 0.598155856388071 & 0.700922071805964 \tabularnewline
18 & 0.505972058386191 & 0.988055883227618 & 0.494027941613809 \tabularnewline
19 & 0.421532914646166 & 0.843065829292332 & 0.578467085353834 \tabularnewline
20 & 0.332277216301392 & 0.664554432602784 & 0.667722783698608 \tabularnewline
21 & 0.262251467129049 & 0.524502934258098 & 0.737748532870951 \tabularnewline
22 & 0.23894338770237 & 0.47788677540474 & 0.76105661229763 \tabularnewline
23 & 0.435115487867804 & 0.870230975735607 & 0.564884512132196 \tabularnewline
24 & 0.469907759946194 & 0.939815519892388 & 0.530092240053806 \tabularnewline
25 & 0.45775482696869 & 0.915509653937379 & 0.54224517303131 \tabularnewline
26 & 0.416942696980975 & 0.833885393961949 & 0.583057303019025 \tabularnewline
27 & 0.47661558191838 & 0.95323116383676 & 0.52338441808162 \tabularnewline
28 & 0.479845334345382 & 0.959690668690765 & 0.520154665654618 \tabularnewline
29 & 0.459659913655986 & 0.919319827311972 & 0.540340086344014 \tabularnewline
30 & 0.500482361582851 & 0.999035276834297 & 0.499517638417149 \tabularnewline
31 & 0.43974929840025 & 0.879498596800499 & 0.56025070159975 \tabularnewline
32 & 0.390507946283896 & 0.781015892567791 & 0.609492053716104 \tabularnewline
33 & 0.364115235936927 & 0.728230471873853 & 0.635884764063073 \tabularnewline
34 & 0.329150473722926 & 0.658300947445852 & 0.670849526277074 \tabularnewline
35 & 0.28267533407261 & 0.565350668145221 & 0.71732466592739 \tabularnewline
36 & 0.850419973215057 & 0.299160053569887 & 0.149580026784943 \tabularnewline
37 & 0.825286663658169 & 0.349426672683663 & 0.174713336341831 \tabularnewline
38 & 0.818698394586821 & 0.362603210826357 & 0.181301605413179 \tabularnewline
39 & 0.837988822550582 & 0.324022354898836 & 0.162011177449418 \tabularnewline
40 & 0.820657708821076 & 0.358684582357847 & 0.179342291178923 \tabularnewline
41 & 0.789885892996278 & 0.420228214007445 & 0.210114107003722 \tabularnewline
42 & 0.764469438827103 & 0.471061122345794 & 0.235530561172897 \tabularnewline
43 & 0.788600402152445 & 0.42279919569511 & 0.211399597847555 \tabularnewline
44 & 0.747632950134532 & 0.504734099730935 & 0.252367049865468 \tabularnewline
45 & 0.719207433467023 & 0.561585133065955 & 0.280792566532977 \tabularnewline
46 & 0.873892109305338 & 0.252215781389324 & 0.126107890694662 \tabularnewline
47 & 0.918290149217737 & 0.163419701564527 & 0.0817098507822634 \tabularnewline
48 & 0.896685592967335 & 0.20662881406533 & 0.103314407032665 \tabularnewline
49 & 0.8810264267418 & 0.2379471465164 & 0.1189735732582 \tabularnewline
50 & 0.880795596922432 & 0.238408806155135 & 0.119204403077568 \tabularnewline
51 & 0.854530781453067 & 0.290938437093866 & 0.145469218546933 \tabularnewline
52 & 0.823740225439624 & 0.352519549120752 & 0.176259774560376 \tabularnewline
53 & 0.845552854099044 & 0.308894291801911 & 0.154447145900956 \tabularnewline
54 & 0.829759610437591 & 0.340480779124818 & 0.170240389562409 \tabularnewline
55 & 0.848672051547678 & 0.302655896904644 & 0.151327948452322 \tabularnewline
56 & 0.821625857518663 & 0.356748284962673 & 0.178374142481337 \tabularnewline
57 & 0.789412465621841 & 0.421175068756318 & 0.210587534378159 \tabularnewline
58 & 0.764071910243083 & 0.471856179513834 & 0.235928089756917 \tabularnewline
59 & 0.731147703611897 & 0.537704592776206 & 0.268852296388103 \tabularnewline
60 & 0.719798752556882 & 0.560402494886236 & 0.280201247443118 \tabularnewline
61 & 0.681620375814976 & 0.636759248370048 & 0.318379624185024 \tabularnewline
62 & 0.641624032524821 & 0.716751934950357 & 0.358375967475179 \tabularnewline
63 & 0.611743762178627 & 0.776512475642746 & 0.388256237821373 \tabularnewline
64 & 0.572829435324306 & 0.854341129351389 & 0.427170564675695 \tabularnewline
65 & 0.529722397709635 & 0.940555204580731 & 0.470277602290365 \tabularnewline
66 & 0.485151606712448 & 0.970303213424896 & 0.514848393287552 \tabularnewline
67 & 0.467602264983994 & 0.935204529967989 & 0.532397735016006 \tabularnewline
68 & 0.520603587739934 & 0.958792824520133 & 0.479396412260066 \tabularnewline
69 & 0.743816488373515 & 0.51236702325297 & 0.256183511626485 \tabularnewline
70 & 0.706441434148366 & 0.587117131703267 & 0.293558565851634 \tabularnewline
71 & 0.825074412880329 & 0.349851174239341 & 0.174925587119671 \tabularnewline
72 & 0.79295973471419 & 0.414080530571621 & 0.20704026528581 \tabularnewline
73 & 0.783452839082718 & 0.433094321834564 & 0.216547160917282 \tabularnewline
74 & 0.762914416923589 & 0.474171166152823 & 0.237085583076412 \tabularnewline
75 & 0.725785215268706 & 0.548429569462588 & 0.274214784731294 \tabularnewline
76 & 0.76923114857283 & 0.461537702854339 & 0.23076885142717 \tabularnewline
77 & 0.735434076994158 & 0.529131846011683 & 0.264565923005842 \tabularnewline
78 & 0.719713950811237 & 0.560572098377526 & 0.280286049188763 \tabularnewline
79 & 0.731082967782326 & 0.537834064435349 & 0.268917032217674 \tabularnewline
80 & 0.690950759288034 & 0.618098481423932 & 0.309049240711966 \tabularnewline
81 & 0.651461898253271 & 0.697076203493457 & 0.348538101746729 \tabularnewline
82 & 0.776489644643657 & 0.447020710712687 & 0.223510355356343 \tabularnewline
83 & 0.74007258934588 & 0.51985482130824 & 0.25992741065412 \tabularnewline
84 & 0.714558361288203 & 0.570883277423595 & 0.285441638711797 \tabularnewline
85 & 0.673462073514432 & 0.653075852971137 & 0.326537926485568 \tabularnewline
86 & 0.663789355981807 & 0.672421288036385 & 0.336210644018193 \tabularnewline
87 & 0.62073574417441 & 0.758528511651179 & 0.37926425582559 \tabularnewline
88 & 0.580045174619219 & 0.839909650761562 & 0.419954825380781 \tabularnewline
89 & 0.559029467576587 & 0.881941064846825 & 0.440970532423412 \tabularnewline
90 & 0.525146046496622 & 0.949707907006755 & 0.474853953503378 \tabularnewline
91 & 0.512171390262371 & 0.975657219475257 & 0.487828609737629 \tabularnewline
92 & 0.473504035557821 & 0.947008071115642 & 0.526495964442179 \tabularnewline
93 & 0.430147954229181 & 0.860295908458362 & 0.569852045770819 \tabularnewline
94 & 0.388051967910068 & 0.776103935820136 & 0.611948032089932 \tabularnewline
95 & 0.403816225956829 & 0.807632451913658 & 0.596183774043171 \tabularnewline
96 & 0.368160126511544 & 0.736320253023087 & 0.631839873488456 \tabularnewline
97 & 0.328547897743785 & 0.65709579548757 & 0.671452102256215 \tabularnewline
98 & 0.328324065378846 & 0.656648130757693 & 0.671675934621154 \tabularnewline
99 & 0.286833155678029 & 0.573666311356057 & 0.713166844321971 \tabularnewline
100 & 0.248602153869149 & 0.497204307738297 & 0.751397846130851 \tabularnewline
101 & 0.227031896972657 & 0.454063793945314 & 0.772968103027343 \tabularnewline
102 & 0.210541850750972 & 0.421083701501944 & 0.789458149249028 \tabularnewline
103 & 0.2522710412074 & 0.5045420824148 & 0.7477289587926 \tabularnewline
104 & 0.215845663502873 & 0.431691327005746 & 0.784154336497127 \tabularnewline
105 & 0.207958802465843 & 0.415917604931687 & 0.792041197534157 \tabularnewline
106 & 0.223397247669121 & 0.446794495338243 & 0.776602752330879 \tabularnewline
107 & 0.200784787344618 & 0.401569574689236 & 0.799215212655382 \tabularnewline
108 & 0.177990917914055 & 0.355981835828109 & 0.822009082085945 \tabularnewline
109 & 0.171171653788763 & 0.342343307577525 & 0.828828346211237 \tabularnewline
110 & 0.158719614962946 & 0.317439229925892 & 0.841280385037054 \tabularnewline
111 & 0.139619098083409 & 0.279238196166817 & 0.860380901916591 \tabularnewline
112 & 0.116535753116634 & 0.233071506233268 & 0.883464246883366 \tabularnewline
113 & 0.137723200342982 & 0.275446400685964 & 0.862276799657018 \tabularnewline
114 & 0.124209244874226 & 0.248418489748452 & 0.875790755125774 \tabularnewline
115 & 0.162217422492843 & 0.324434844985686 & 0.837782577507157 \tabularnewline
116 & 0.153093660177937 & 0.306187320355875 & 0.846906339822063 \tabularnewline
117 & 0.133779178939302 & 0.267558357878603 & 0.866220821060698 \tabularnewline
118 & 0.11618045963833 & 0.23236091927666 & 0.88381954036167 \tabularnewline
119 & 0.11751398661296 & 0.23502797322592 & 0.88248601338704 \tabularnewline
120 & 0.108308364426011 & 0.216616728852023 & 0.891691635573989 \tabularnewline
121 & 0.0916509028822706 & 0.183301805764541 & 0.908349097117729 \tabularnewline
122 & 0.0728044932343449 & 0.14560898646869 & 0.927195506765655 \tabularnewline
123 & 0.0816667230907162 & 0.163333446181432 & 0.918333276909284 \tabularnewline
124 & 0.0651888331583388 & 0.130377666316678 & 0.934811166841661 \tabularnewline
125 & 0.0525748271208111 & 0.105149654241622 & 0.947425172879189 \tabularnewline
126 & 0.0398532766207202 & 0.0797065532414405 & 0.96014672337928 \tabularnewline
127 & 0.0296317955133561 & 0.0592635910267122 & 0.970368204486644 \tabularnewline
128 & 0.023493778142581 & 0.046987556285162 & 0.976506221857419 \tabularnewline
129 & 0.0180883229873234 & 0.0361766459746468 & 0.981911677012677 \tabularnewline
130 & 0.0252972199878468 & 0.0505944399756936 & 0.974702780012153 \tabularnewline
131 & 0.0217525250070476 & 0.0435050500140952 & 0.978247474992952 \tabularnewline
132 & 0.0332924901205405 & 0.066584980241081 & 0.96670750987946 \tabularnewline
133 & 0.0370245529366446 & 0.0740491058732891 & 0.962975447063355 \tabularnewline
134 & 0.0517743789357758 & 0.103548757871552 & 0.948225621064224 \tabularnewline
135 & 0.0412494597898408 & 0.0824989195796817 & 0.958750540210159 \tabularnewline
136 & 0.0285182284174242 & 0.0570364568348485 & 0.971481771582576 \tabularnewline
137 & 0.0193395542236236 & 0.0386791084472471 & 0.980660445776376 \tabularnewline
138 & 0.0127752841722827 & 0.0255505683445654 & 0.987224715827717 \tabularnewline
139 & 0.0245127732542161 & 0.0490255465084323 & 0.975487226745784 \tabularnewline
140 & 0.020381914476414 & 0.0407638289528281 & 0.979618085523586 \tabularnewline
141 & 0.614747119241747 & 0.770505761516506 & 0.385252880758253 \tabularnewline
142 & 0.548952295417248 & 0.902095409165504 & 0.451047704582752 \tabularnewline
143 & 0.461544891437496 & 0.923089782874992 & 0.538455108562504 \tabularnewline
144 & 0.38553425299192 & 0.771068505983841 & 0.61446574700808 \tabularnewline
145 & 0.2961047050623 & 0.592209410124599 & 0.7038952949377 \tabularnewline
146 & 0.298924121849975 & 0.597848243699951 & 0.701075878150025 \tabularnewline
147 & 0.255026458422161 & 0.510052916844323 & 0.744973541577838 \tabularnewline
148 & 0.757402436582747 & 0.485195126834506 & 0.242597563417253 \tabularnewline
149 & 0.742449312709066 & 0.515101374581868 & 0.257550687290934 \tabularnewline
150 & 0.582709638539515 & 0.834580722920971 & 0.417290361460485 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186283&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]12[/C][C]0.759669005338185[/C][C]0.48066198932363[/C][C]0.240330994661815[/C][/ROW]
[ROW][C]13[/C][C]0.6225792191633[/C][C]0.754841561673401[/C][C]0.3774207808367[/C][/ROW]
[ROW][C]14[/C][C]0.58243566013641[/C][C]0.83512867972718[/C][C]0.41756433986359[/C][/ROW]
[ROW][C]15[/C][C]0.46254000293873[/C][C]0.92508000587746[/C][C]0.53745999706127[/C][/ROW]
[ROW][C]16[/C][C]0.362240895874605[/C][C]0.72448179174921[/C][C]0.637759104125395[/C][/ROW]
[ROW][C]17[/C][C]0.299077928194036[/C][C]0.598155856388071[/C][C]0.700922071805964[/C][/ROW]
[ROW][C]18[/C][C]0.505972058386191[/C][C]0.988055883227618[/C][C]0.494027941613809[/C][/ROW]
[ROW][C]19[/C][C]0.421532914646166[/C][C]0.843065829292332[/C][C]0.578467085353834[/C][/ROW]
[ROW][C]20[/C][C]0.332277216301392[/C][C]0.664554432602784[/C][C]0.667722783698608[/C][/ROW]
[ROW][C]21[/C][C]0.262251467129049[/C][C]0.524502934258098[/C][C]0.737748532870951[/C][/ROW]
[ROW][C]22[/C][C]0.23894338770237[/C][C]0.47788677540474[/C][C]0.76105661229763[/C][/ROW]
[ROW][C]23[/C][C]0.435115487867804[/C][C]0.870230975735607[/C][C]0.564884512132196[/C][/ROW]
[ROW][C]24[/C][C]0.469907759946194[/C][C]0.939815519892388[/C][C]0.530092240053806[/C][/ROW]
[ROW][C]25[/C][C]0.45775482696869[/C][C]0.915509653937379[/C][C]0.54224517303131[/C][/ROW]
[ROW][C]26[/C][C]0.416942696980975[/C][C]0.833885393961949[/C][C]0.583057303019025[/C][/ROW]
[ROW][C]27[/C][C]0.47661558191838[/C][C]0.95323116383676[/C][C]0.52338441808162[/C][/ROW]
[ROW][C]28[/C][C]0.479845334345382[/C][C]0.959690668690765[/C][C]0.520154665654618[/C][/ROW]
[ROW][C]29[/C][C]0.459659913655986[/C][C]0.919319827311972[/C][C]0.540340086344014[/C][/ROW]
[ROW][C]30[/C][C]0.500482361582851[/C][C]0.999035276834297[/C][C]0.499517638417149[/C][/ROW]
[ROW][C]31[/C][C]0.43974929840025[/C][C]0.879498596800499[/C][C]0.56025070159975[/C][/ROW]
[ROW][C]32[/C][C]0.390507946283896[/C][C]0.781015892567791[/C][C]0.609492053716104[/C][/ROW]
[ROW][C]33[/C][C]0.364115235936927[/C][C]0.728230471873853[/C][C]0.635884764063073[/C][/ROW]
[ROW][C]34[/C][C]0.329150473722926[/C][C]0.658300947445852[/C][C]0.670849526277074[/C][/ROW]
[ROW][C]35[/C][C]0.28267533407261[/C][C]0.565350668145221[/C][C]0.71732466592739[/C][/ROW]
[ROW][C]36[/C][C]0.850419973215057[/C][C]0.299160053569887[/C][C]0.149580026784943[/C][/ROW]
[ROW][C]37[/C][C]0.825286663658169[/C][C]0.349426672683663[/C][C]0.174713336341831[/C][/ROW]
[ROW][C]38[/C][C]0.818698394586821[/C][C]0.362603210826357[/C][C]0.181301605413179[/C][/ROW]
[ROW][C]39[/C][C]0.837988822550582[/C][C]0.324022354898836[/C][C]0.162011177449418[/C][/ROW]
[ROW][C]40[/C][C]0.820657708821076[/C][C]0.358684582357847[/C][C]0.179342291178923[/C][/ROW]
[ROW][C]41[/C][C]0.789885892996278[/C][C]0.420228214007445[/C][C]0.210114107003722[/C][/ROW]
[ROW][C]42[/C][C]0.764469438827103[/C][C]0.471061122345794[/C][C]0.235530561172897[/C][/ROW]
[ROW][C]43[/C][C]0.788600402152445[/C][C]0.42279919569511[/C][C]0.211399597847555[/C][/ROW]
[ROW][C]44[/C][C]0.747632950134532[/C][C]0.504734099730935[/C][C]0.252367049865468[/C][/ROW]
[ROW][C]45[/C][C]0.719207433467023[/C][C]0.561585133065955[/C][C]0.280792566532977[/C][/ROW]
[ROW][C]46[/C][C]0.873892109305338[/C][C]0.252215781389324[/C][C]0.126107890694662[/C][/ROW]
[ROW][C]47[/C][C]0.918290149217737[/C][C]0.163419701564527[/C][C]0.0817098507822634[/C][/ROW]
[ROW][C]48[/C][C]0.896685592967335[/C][C]0.20662881406533[/C][C]0.103314407032665[/C][/ROW]
[ROW][C]49[/C][C]0.8810264267418[/C][C]0.2379471465164[/C][C]0.1189735732582[/C][/ROW]
[ROW][C]50[/C][C]0.880795596922432[/C][C]0.238408806155135[/C][C]0.119204403077568[/C][/ROW]
[ROW][C]51[/C][C]0.854530781453067[/C][C]0.290938437093866[/C][C]0.145469218546933[/C][/ROW]
[ROW][C]52[/C][C]0.823740225439624[/C][C]0.352519549120752[/C][C]0.176259774560376[/C][/ROW]
[ROW][C]53[/C][C]0.845552854099044[/C][C]0.308894291801911[/C][C]0.154447145900956[/C][/ROW]
[ROW][C]54[/C][C]0.829759610437591[/C][C]0.340480779124818[/C][C]0.170240389562409[/C][/ROW]
[ROW][C]55[/C][C]0.848672051547678[/C][C]0.302655896904644[/C][C]0.151327948452322[/C][/ROW]
[ROW][C]56[/C][C]0.821625857518663[/C][C]0.356748284962673[/C][C]0.178374142481337[/C][/ROW]
[ROW][C]57[/C][C]0.789412465621841[/C][C]0.421175068756318[/C][C]0.210587534378159[/C][/ROW]
[ROW][C]58[/C][C]0.764071910243083[/C][C]0.471856179513834[/C][C]0.235928089756917[/C][/ROW]
[ROW][C]59[/C][C]0.731147703611897[/C][C]0.537704592776206[/C][C]0.268852296388103[/C][/ROW]
[ROW][C]60[/C][C]0.719798752556882[/C][C]0.560402494886236[/C][C]0.280201247443118[/C][/ROW]
[ROW][C]61[/C][C]0.681620375814976[/C][C]0.636759248370048[/C][C]0.318379624185024[/C][/ROW]
[ROW][C]62[/C][C]0.641624032524821[/C][C]0.716751934950357[/C][C]0.358375967475179[/C][/ROW]
[ROW][C]63[/C][C]0.611743762178627[/C][C]0.776512475642746[/C][C]0.388256237821373[/C][/ROW]
[ROW][C]64[/C][C]0.572829435324306[/C][C]0.854341129351389[/C][C]0.427170564675695[/C][/ROW]
[ROW][C]65[/C][C]0.529722397709635[/C][C]0.940555204580731[/C][C]0.470277602290365[/C][/ROW]
[ROW][C]66[/C][C]0.485151606712448[/C][C]0.970303213424896[/C][C]0.514848393287552[/C][/ROW]
[ROW][C]67[/C][C]0.467602264983994[/C][C]0.935204529967989[/C][C]0.532397735016006[/C][/ROW]
[ROW][C]68[/C][C]0.520603587739934[/C][C]0.958792824520133[/C][C]0.479396412260066[/C][/ROW]
[ROW][C]69[/C][C]0.743816488373515[/C][C]0.51236702325297[/C][C]0.256183511626485[/C][/ROW]
[ROW][C]70[/C][C]0.706441434148366[/C][C]0.587117131703267[/C][C]0.293558565851634[/C][/ROW]
[ROW][C]71[/C][C]0.825074412880329[/C][C]0.349851174239341[/C][C]0.174925587119671[/C][/ROW]
[ROW][C]72[/C][C]0.79295973471419[/C][C]0.414080530571621[/C][C]0.20704026528581[/C][/ROW]
[ROW][C]73[/C][C]0.783452839082718[/C][C]0.433094321834564[/C][C]0.216547160917282[/C][/ROW]
[ROW][C]74[/C][C]0.762914416923589[/C][C]0.474171166152823[/C][C]0.237085583076412[/C][/ROW]
[ROW][C]75[/C][C]0.725785215268706[/C][C]0.548429569462588[/C][C]0.274214784731294[/C][/ROW]
[ROW][C]76[/C][C]0.76923114857283[/C][C]0.461537702854339[/C][C]0.23076885142717[/C][/ROW]
[ROW][C]77[/C][C]0.735434076994158[/C][C]0.529131846011683[/C][C]0.264565923005842[/C][/ROW]
[ROW][C]78[/C][C]0.719713950811237[/C][C]0.560572098377526[/C][C]0.280286049188763[/C][/ROW]
[ROW][C]79[/C][C]0.731082967782326[/C][C]0.537834064435349[/C][C]0.268917032217674[/C][/ROW]
[ROW][C]80[/C][C]0.690950759288034[/C][C]0.618098481423932[/C][C]0.309049240711966[/C][/ROW]
[ROW][C]81[/C][C]0.651461898253271[/C][C]0.697076203493457[/C][C]0.348538101746729[/C][/ROW]
[ROW][C]82[/C][C]0.776489644643657[/C][C]0.447020710712687[/C][C]0.223510355356343[/C][/ROW]
[ROW][C]83[/C][C]0.74007258934588[/C][C]0.51985482130824[/C][C]0.25992741065412[/C][/ROW]
[ROW][C]84[/C][C]0.714558361288203[/C][C]0.570883277423595[/C][C]0.285441638711797[/C][/ROW]
[ROW][C]85[/C][C]0.673462073514432[/C][C]0.653075852971137[/C][C]0.326537926485568[/C][/ROW]
[ROW][C]86[/C][C]0.663789355981807[/C][C]0.672421288036385[/C][C]0.336210644018193[/C][/ROW]
[ROW][C]87[/C][C]0.62073574417441[/C][C]0.758528511651179[/C][C]0.37926425582559[/C][/ROW]
[ROW][C]88[/C][C]0.580045174619219[/C][C]0.839909650761562[/C][C]0.419954825380781[/C][/ROW]
[ROW][C]89[/C][C]0.559029467576587[/C][C]0.881941064846825[/C][C]0.440970532423412[/C][/ROW]
[ROW][C]90[/C][C]0.525146046496622[/C][C]0.949707907006755[/C][C]0.474853953503378[/C][/ROW]
[ROW][C]91[/C][C]0.512171390262371[/C][C]0.975657219475257[/C][C]0.487828609737629[/C][/ROW]
[ROW][C]92[/C][C]0.473504035557821[/C][C]0.947008071115642[/C][C]0.526495964442179[/C][/ROW]
[ROW][C]93[/C][C]0.430147954229181[/C][C]0.860295908458362[/C][C]0.569852045770819[/C][/ROW]
[ROW][C]94[/C][C]0.388051967910068[/C][C]0.776103935820136[/C][C]0.611948032089932[/C][/ROW]
[ROW][C]95[/C][C]0.403816225956829[/C][C]0.807632451913658[/C][C]0.596183774043171[/C][/ROW]
[ROW][C]96[/C][C]0.368160126511544[/C][C]0.736320253023087[/C][C]0.631839873488456[/C][/ROW]
[ROW][C]97[/C][C]0.328547897743785[/C][C]0.65709579548757[/C][C]0.671452102256215[/C][/ROW]
[ROW][C]98[/C][C]0.328324065378846[/C][C]0.656648130757693[/C][C]0.671675934621154[/C][/ROW]
[ROW][C]99[/C][C]0.286833155678029[/C][C]0.573666311356057[/C][C]0.713166844321971[/C][/ROW]
[ROW][C]100[/C][C]0.248602153869149[/C][C]0.497204307738297[/C][C]0.751397846130851[/C][/ROW]
[ROW][C]101[/C][C]0.227031896972657[/C][C]0.454063793945314[/C][C]0.772968103027343[/C][/ROW]
[ROW][C]102[/C][C]0.210541850750972[/C][C]0.421083701501944[/C][C]0.789458149249028[/C][/ROW]
[ROW][C]103[/C][C]0.2522710412074[/C][C]0.5045420824148[/C][C]0.7477289587926[/C][/ROW]
[ROW][C]104[/C][C]0.215845663502873[/C][C]0.431691327005746[/C][C]0.784154336497127[/C][/ROW]
[ROW][C]105[/C][C]0.207958802465843[/C][C]0.415917604931687[/C][C]0.792041197534157[/C][/ROW]
[ROW][C]106[/C][C]0.223397247669121[/C][C]0.446794495338243[/C][C]0.776602752330879[/C][/ROW]
[ROW][C]107[/C][C]0.200784787344618[/C][C]0.401569574689236[/C][C]0.799215212655382[/C][/ROW]
[ROW][C]108[/C][C]0.177990917914055[/C][C]0.355981835828109[/C][C]0.822009082085945[/C][/ROW]
[ROW][C]109[/C][C]0.171171653788763[/C][C]0.342343307577525[/C][C]0.828828346211237[/C][/ROW]
[ROW][C]110[/C][C]0.158719614962946[/C][C]0.317439229925892[/C][C]0.841280385037054[/C][/ROW]
[ROW][C]111[/C][C]0.139619098083409[/C][C]0.279238196166817[/C][C]0.860380901916591[/C][/ROW]
[ROW][C]112[/C][C]0.116535753116634[/C][C]0.233071506233268[/C][C]0.883464246883366[/C][/ROW]
[ROW][C]113[/C][C]0.137723200342982[/C][C]0.275446400685964[/C][C]0.862276799657018[/C][/ROW]
[ROW][C]114[/C][C]0.124209244874226[/C][C]0.248418489748452[/C][C]0.875790755125774[/C][/ROW]
[ROW][C]115[/C][C]0.162217422492843[/C][C]0.324434844985686[/C][C]0.837782577507157[/C][/ROW]
[ROW][C]116[/C][C]0.153093660177937[/C][C]0.306187320355875[/C][C]0.846906339822063[/C][/ROW]
[ROW][C]117[/C][C]0.133779178939302[/C][C]0.267558357878603[/C][C]0.866220821060698[/C][/ROW]
[ROW][C]118[/C][C]0.11618045963833[/C][C]0.23236091927666[/C][C]0.88381954036167[/C][/ROW]
[ROW][C]119[/C][C]0.11751398661296[/C][C]0.23502797322592[/C][C]0.88248601338704[/C][/ROW]
[ROW][C]120[/C][C]0.108308364426011[/C][C]0.216616728852023[/C][C]0.891691635573989[/C][/ROW]
[ROW][C]121[/C][C]0.0916509028822706[/C][C]0.183301805764541[/C][C]0.908349097117729[/C][/ROW]
[ROW][C]122[/C][C]0.0728044932343449[/C][C]0.14560898646869[/C][C]0.927195506765655[/C][/ROW]
[ROW][C]123[/C][C]0.0816667230907162[/C][C]0.163333446181432[/C][C]0.918333276909284[/C][/ROW]
[ROW][C]124[/C][C]0.0651888331583388[/C][C]0.130377666316678[/C][C]0.934811166841661[/C][/ROW]
[ROW][C]125[/C][C]0.0525748271208111[/C][C]0.105149654241622[/C][C]0.947425172879189[/C][/ROW]
[ROW][C]126[/C][C]0.0398532766207202[/C][C]0.0797065532414405[/C][C]0.96014672337928[/C][/ROW]
[ROW][C]127[/C][C]0.0296317955133561[/C][C]0.0592635910267122[/C][C]0.970368204486644[/C][/ROW]
[ROW][C]128[/C][C]0.023493778142581[/C][C]0.046987556285162[/C][C]0.976506221857419[/C][/ROW]
[ROW][C]129[/C][C]0.0180883229873234[/C][C]0.0361766459746468[/C][C]0.981911677012677[/C][/ROW]
[ROW][C]130[/C][C]0.0252972199878468[/C][C]0.0505944399756936[/C][C]0.974702780012153[/C][/ROW]
[ROW][C]131[/C][C]0.0217525250070476[/C][C]0.0435050500140952[/C][C]0.978247474992952[/C][/ROW]
[ROW][C]132[/C][C]0.0332924901205405[/C][C]0.066584980241081[/C][C]0.96670750987946[/C][/ROW]
[ROW][C]133[/C][C]0.0370245529366446[/C][C]0.0740491058732891[/C][C]0.962975447063355[/C][/ROW]
[ROW][C]134[/C][C]0.0517743789357758[/C][C]0.103548757871552[/C][C]0.948225621064224[/C][/ROW]
[ROW][C]135[/C][C]0.0412494597898408[/C][C]0.0824989195796817[/C][C]0.958750540210159[/C][/ROW]
[ROW][C]136[/C][C]0.0285182284174242[/C][C]0.0570364568348485[/C][C]0.971481771582576[/C][/ROW]
[ROW][C]137[/C][C]0.0193395542236236[/C][C]0.0386791084472471[/C][C]0.980660445776376[/C][/ROW]
[ROW][C]138[/C][C]0.0127752841722827[/C][C]0.0255505683445654[/C][C]0.987224715827717[/C][/ROW]
[ROW][C]139[/C][C]0.0245127732542161[/C][C]0.0490255465084323[/C][C]0.975487226745784[/C][/ROW]
[ROW][C]140[/C][C]0.020381914476414[/C][C]0.0407638289528281[/C][C]0.979618085523586[/C][/ROW]
[ROW][C]141[/C][C]0.614747119241747[/C][C]0.770505761516506[/C][C]0.385252880758253[/C][/ROW]
[ROW][C]142[/C][C]0.548952295417248[/C][C]0.902095409165504[/C][C]0.451047704582752[/C][/ROW]
[ROW][C]143[/C][C]0.461544891437496[/C][C]0.923089782874992[/C][C]0.538455108562504[/C][/ROW]
[ROW][C]144[/C][C]0.38553425299192[/C][C]0.771068505983841[/C][C]0.61446574700808[/C][/ROW]
[ROW][C]145[/C][C]0.2961047050623[/C][C]0.592209410124599[/C][C]0.7038952949377[/C][/ROW]
[ROW][C]146[/C][C]0.298924121849975[/C][C]0.597848243699951[/C][C]0.701075878150025[/C][/ROW]
[ROW][C]147[/C][C]0.255026458422161[/C][C]0.510052916844323[/C][C]0.744973541577838[/C][/ROW]
[ROW][C]148[/C][C]0.757402436582747[/C][C]0.485195126834506[/C][C]0.242597563417253[/C][/ROW]
[ROW][C]149[/C][C]0.742449312709066[/C][C]0.515101374581868[/C][C]0.257550687290934[/C][/ROW]
[ROW][C]150[/C][C]0.582709638539515[/C][C]0.834580722920971[/C][C]0.417290361460485[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186283&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186283&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
120.7596690053381850.480661989323630.240330994661815
130.62257921916330.7548415616734010.3774207808367
140.582435660136410.835128679727180.41756433986359
150.462540002938730.925080005877460.53745999706127
160.3622408958746050.724481791749210.637759104125395
170.2990779281940360.5981558563880710.700922071805964
180.5059720583861910.9880558832276180.494027941613809
190.4215329146461660.8430658292923320.578467085353834
200.3322772163013920.6645544326027840.667722783698608
210.2622514671290490.5245029342580980.737748532870951
220.238943387702370.477886775404740.76105661229763
230.4351154878678040.8702309757356070.564884512132196
240.4699077599461940.9398155198923880.530092240053806
250.457754826968690.9155096539373790.54224517303131
260.4169426969809750.8338853939619490.583057303019025
270.476615581918380.953231163836760.52338441808162
280.4798453343453820.9596906686907650.520154665654618
290.4596599136559860.9193198273119720.540340086344014
300.5004823615828510.9990352768342970.499517638417149
310.439749298400250.8794985968004990.56025070159975
320.3905079462838960.7810158925677910.609492053716104
330.3641152359369270.7282304718738530.635884764063073
340.3291504737229260.6583009474458520.670849526277074
350.282675334072610.5653506681452210.71732466592739
360.8504199732150570.2991600535698870.149580026784943
370.8252866636581690.3494266726836630.174713336341831
380.8186983945868210.3626032108263570.181301605413179
390.8379888225505820.3240223548988360.162011177449418
400.8206577088210760.3586845823578470.179342291178923
410.7898858929962780.4202282140074450.210114107003722
420.7644694388271030.4710611223457940.235530561172897
430.7886004021524450.422799195695110.211399597847555
440.7476329501345320.5047340997309350.252367049865468
450.7192074334670230.5615851330659550.280792566532977
460.8738921093053380.2522157813893240.126107890694662
470.9182901492177370.1634197015645270.0817098507822634
480.8966855929673350.206628814065330.103314407032665
490.88102642674180.23794714651640.1189735732582
500.8807955969224320.2384088061551350.119204403077568
510.8545307814530670.2909384370938660.145469218546933
520.8237402254396240.3525195491207520.176259774560376
530.8455528540990440.3088942918019110.154447145900956
540.8297596104375910.3404807791248180.170240389562409
550.8486720515476780.3026558969046440.151327948452322
560.8216258575186630.3567482849626730.178374142481337
570.7894124656218410.4211750687563180.210587534378159
580.7640719102430830.4718561795138340.235928089756917
590.7311477036118970.5377045927762060.268852296388103
600.7197987525568820.5604024948862360.280201247443118
610.6816203758149760.6367592483700480.318379624185024
620.6416240325248210.7167519349503570.358375967475179
630.6117437621786270.7765124756427460.388256237821373
640.5728294353243060.8543411293513890.427170564675695
650.5297223977096350.9405552045807310.470277602290365
660.4851516067124480.9703032134248960.514848393287552
670.4676022649839940.9352045299679890.532397735016006
680.5206035877399340.9587928245201330.479396412260066
690.7438164883735150.512367023252970.256183511626485
700.7064414341483660.5871171317032670.293558565851634
710.8250744128803290.3498511742393410.174925587119671
720.792959734714190.4140805305716210.20704026528581
730.7834528390827180.4330943218345640.216547160917282
740.7629144169235890.4741711661528230.237085583076412
750.7257852152687060.5484295694625880.274214784731294
760.769231148572830.4615377028543390.23076885142717
770.7354340769941580.5291318460116830.264565923005842
780.7197139508112370.5605720983775260.280286049188763
790.7310829677823260.5378340644353490.268917032217674
800.6909507592880340.6180984814239320.309049240711966
810.6514618982532710.6970762034934570.348538101746729
820.7764896446436570.4470207107126870.223510355356343
830.740072589345880.519854821308240.25992741065412
840.7145583612882030.5708832774235950.285441638711797
850.6734620735144320.6530758529711370.326537926485568
860.6637893559818070.6724212880363850.336210644018193
870.620735744174410.7585285116511790.37926425582559
880.5800451746192190.8399096507615620.419954825380781
890.5590294675765870.8819410648468250.440970532423412
900.5251460464966220.9497079070067550.474853953503378
910.5121713902623710.9756572194752570.487828609737629
920.4735040355578210.9470080711156420.526495964442179
930.4301479542291810.8602959084583620.569852045770819
940.3880519679100680.7761039358201360.611948032089932
950.4038162259568290.8076324519136580.596183774043171
960.3681601265115440.7363202530230870.631839873488456
970.3285478977437850.657095795487570.671452102256215
980.3283240653788460.6566481307576930.671675934621154
990.2868331556780290.5736663113560570.713166844321971
1000.2486021538691490.4972043077382970.751397846130851
1010.2270318969726570.4540637939453140.772968103027343
1020.2105418507509720.4210837015019440.789458149249028
1030.25227104120740.50454208241480.7477289587926
1040.2158456635028730.4316913270057460.784154336497127
1050.2079588024658430.4159176049316870.792041197534157
1060.2233972476691210.4467944953382430.776602752330879
1070.2007847873446180.4015695746892360.799215212655382
1080.1779909179140550.3559818358281090.822009082085945
1090.1711716537887630.3423433075775250.828828346211237
1100.1587196149629460.3174392299258920.841280385037054
1110.1396190980834090.2792381961668170.860380901916591
1120.1165357531166340.2330715062332680.883464246883366
1130.1377232003429820.2754464006859640.862276799657018
1140.1242092448742260.2484184897484520.875790755125774
1150.1622174224928430.3244348449856860.837782577507157
1160.1530936601779370.3061873203558750.846906339822063
1170.1337791789393020.2675583578786030.866220821060698
1180.116180459638330.232360919276660.88381954036167
1190.117513986612960.235027973225920.88248601338704
1200.1083083644260110.2166167288520230.891691635573989
1210.09165090288227060.1833018057645410.908349097117729
1220.07280449323434490.145608986468690.927195506765655
1230.08166672309071620.1633334461814320.918333276909284
1240.06518883315833880.1303776663166780.934811166841661
1250.05257482712081110.1051496542416220.947425172879189
1260.03985327662072020.07970655324144050.96014672337928
1270.02963179551335610.05926359102671220.970368204486644
1280.0234937781425810.0469875562851620.976506221857419
1290.01808832298732340.03617664597464680.981911677012677
1300.02529721998784680.05059443997569360.974702780012153
1310.02175252500704760.04350505001409520.978247474992952
1320.03329249012054050.0665849802410810.96670750987946
1330.03702455293664460.07404910587328910.962975447063355
1340.05177437893577580.1035487578715520.948225621064224
1350.04124945978984080.08249891957968170.958750540210159
1360.02851822841742420.05703645683484850.971481771582576
1370.01933955422362360.03867910844724710.980660445776376
1380.01277528417228270.02555056834456540.987224715827717
1390.02451277325421610.04902554650843230.975487226745784
1400.0203819144764140.04076382895282810.979618085523586
1410.6147471192417470.7705057615165060.385252880758253
1420.5489522954172480.9020954091655040.451047704582752
1430.4615448914374960.9230897828749920.538455108562504
1440.385534252991920.7710685059838410.61446574700808
1450.29610470506230.5922094101245990.7038952949377
1460.2989241218499750.5978482436999510.701075878150025
1470.2550264584221610.5100529168443230.744973541577838
1480.7574024365827470.4851951268345060.242597563417253
1490.7424493127090660.5151013745818680.257550687290934
1500.5827096385395150.8345807229209710.417290361460485







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level70.0503597122302158NOK
10% type I error level140.100719424460432NOK

\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 & 7 & 0.0503597122302158 & NOK \tabularnewline
10% type I error level & 14 & 0.100719424460432 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186283&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]7[/C][C]0.0503597122302158[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]14[/C][C]0.100719424460432[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186283&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186283&T=6

As an alternative you can also use a QR Code:  

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level70.0503597122302158NOK
10% type I error level140.100719424460432NOK



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