Free Statistics

of Irreproducible Research!

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationSat, 03 Nov 2012 06:29:14 -0400
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/03/t1351938605p911luo51relhge.htm/, Retrieved Thu, 06 Oct 2022 01:00:34 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=185706, Retrieved Thu, 06 Oct 2022 01:00:34 +0000
QR Codes:

Original text written by user:vanaf 2006
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] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
- R PD  [Multiple Regression] [Multiple Regression?] [2012-11-03 10:18:20] [2c4ddb4bf62114b8025bb962e2c7a2b5]
- R  D      [Multiple Regression] [Multiple Regressi...] [2012-11-03 10:29:14] [b4b733de199089e913cc2b6ea19b06b9] [Current]
-             [Multiple Regression] [Multiple Regressi...] [2012-11-03 14:54:43] [2c4ddb4bf62114b8025bb962e2c7a2b5]
Feedback Forum

Post a new message
Dataseries X:
-19	-3	53	14	24	20	-9	-2	20	6	-29	17
-20	-4	50	16	24	19	-12	-4	21	6	-29	13
-21	-7	50	19	31	21	-10	-5	20	5	-27	12
-19	-7	51	18	25	17	-10	-2	21	5	-29	13
-17	-7	53	19	28	15	-11	-4	19	3	-24	10
-16	-3	49	20	24	18	-11	-4	22	5	-29	14
-10	0	54	20	25	19	-10	-5	20	5	-21	13
-16	-5	57	24	16	16	-13	-7	18	5	-20	10
-10	-3	58	18	17	21	-10	-5	16	3	-26	11
-8	3	56	15	11	26	-6	-6	17	6	-19	12
-7	2	60	25	12	23	-9	-4	18	6	-22	7
-15	-7	55	23	39	24	-8	-2	19	4	-22	11
-7	-1	54	20	19	23	-12	-3	18	6	-15	9
-6	0	52	20	14	19	-10	0	20	5	-16	13
-6	-3	55	22	15	25	-11	-4	21	4	-22	12
2	4	56	25	7	21	-13	-3	18	5	-21	5
-4	2	54	22	12	19	-10	-3	19	5	-11	13
-4	3	53	26	12	20	-10	-3	19	4	-10	11
-8	0	59	27	14	20	-11	-4	19	3	-6	8
-10	-10	62	41	9	17	-11	-5	21	2	-8	8
-16	-10	63	29	8	25	-11	-5	19	3	-15	8
-14	-9	64	33	4	19	-10	-6	19	2	-16	8
-30	-22	75	39	7	13	-13	-10	17	-1	-24	0
-33	-16	77	27	3	15	-12	-11	16	0	-27	3
-40	-18	79	27	5	15	-13	-13	16	-2	-33	0
-38	-14	77	25	0	13	-15	-12	17	1	-29	-1
-39	-12	82	19	-2	11	-16	-13	16	-2	-34	-1
-46	-17	83	15	6	9	-18	-12	15	-2	-37	-4
-50	-23	81	19	11	2	-17	-15	16	-2	-31	1
-55	-28	78	23	9	-2	-18	-14	16	-6	-33	-1
-66	-31	79	23	17	-4	-20	-16	16	-4	-25	0
-63	-21	79	7	21	-2	-22	-16	18	-2	-27	-1
-56	-19	73	1	21	1	-17	-12	19	0	-21	6
-66	-22	72	7	41	-13	-19	-16	16	-5	-32	0
-63	-22	67	4	57	-11	-18	-15	16	-4	-31	-3
-69	-25	67	-8	65	-14	-26	-17	16	-5	-32	-3
-69	-16	50	-14	68	-4	-19	-15	18	-1	-30	4
-72	-22	45	-10	73	-9	-23	-14	16	-2	-34	1
-69	-21	39	-11	71	-5	-21	-15	15	-4	-35	0
-67	-10	39	-10	71	-4	-27	-14	15	-1	-37	-4
-64	-7	37	-8	70	-8	-27	-16	16	1	-32	-2
-61	-5	30	-8	69	-1	-21	-11	18	1	-28	3
-58	-4	24	-7	65	-2	-22	-14	16	-2	-26	2
-47	7	27	-8	57	-1	-24	-12	19	1	-24	5
-44	6	19	-4	57	8	-21	-11	19	1	-27	6
-42	3	19	3	57	8	-21	-13	18	3	-26	6
-34	10	25	-5	55	6	-22	-12	17	3	-27	3
-38	0	16	-4	65	7	-25	-12	19	1	-27	4
-41	-2	20	5	65	2	-21	-10	22	1	-24	7
-38	-1	25	3	64	3	-26	-12	19	0	-28	5
-37	2	34	6	60	0	-27	-11	19	2	-23	6
-22	8	39	10	43	5	-22	-10	16	2	-23	1
-37	-6	40	16	47	-1	-22	-12	18	-1	-29	3
-36	-4	38	11	40	3	-20	-12	20	1	-25	6
-25	4	42	10	31	4	-21	-11	17	0	-24	0
-15	7	46	21	27	8	-16	-12	17	1	-20	3
-17	3	48	18	24	10	-17	-9	17	1	-22	4
-19	3	51	20	23	14	-19	-6	20	3	-24	7
-12	8	55	18	17	15	-20	-7	21	2	-27	6
-17	3	52	23	16	9	-20	-7	19	0	-25	6
-21	-3	55	28	15	8	-20	-10	18	0	-26	6
-10	4	58	31	8	10	-19	-8	20	3	-24	6
-19	-5	72	38	5	5	-20	-11	17	-2	-26	2
-14	-1	70	27	6	4	-25	-12	15	0	-22	2
-8	5	70	21	5	8	-25	-11	17	1	-20	2
-16	0	63	31	12	8	-22	-11	18	-1	-26	3
-14	-6	66	31	8	10	-19	-9	20	-2	-22	-1
-30	-13	65	29	17	8	-20	-9	19	-1	-29	-4
-33	-15	55	24	22	10	-18	-12	20	-1	-30	4
-37	-8	57	27	24	-8	-17	-10	22	1	-26	5
-47	-20	60	36	36	-6	-17	-10	20	-2	-30	3
-48	-10	63	35	31	-10	-21	-13	21	-5	-33	-1
-50	-22	65	44	34	-15	-17	-13	19	-5	-33	-4
-56	-25	61	39	47	-21	-22	-12	22	-6	-31	0
-47	-10	65	26	33	-24	-24	-14	19	-4	-36	-1
-37	-8	63	27	35	-15	-18	-9	21	-3	-43	-1
-35	-9	59	17	31	-12	-20	-12	19	-3	-40	3
-29	-5	56	20	35	-11	-21	-10	21	-1	-38	2
-28	-7	54	22	39	-11	-17	-13	18	-2	-41	-4
-29	-11	56	32	46	-13	-17	-11	18	-3	-38	-3
-33	-11	54	28	40	-10	-17	-11	20	-3	-40	-1
-41	-16	58	30	50	-9	-21	-11	19	-3	-41	3




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R Server'Sir Maurice George Kendall' @ kendall.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 & 8 seconds \tabularnewline
R Server & 'Sir Maurice George Kendall' @ kendall.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185706&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]8 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Maurice George Kendall' @ kendall.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185706&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185706&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R Server'Sir Maurice George Kendall' @ kendall.wessa.net







Multiple Linear Regression - Estimated Regression Equation
X_2t[t] = + 31.3471573638543 + 0.444702753166022X_1t[t] -0.362675102423084X_3t[t] -0.262539402740327X_4t[t] -0.207318027182089X_5t[t] -0.274389035067997X_6t[t] -0.327297113875413X_7t[t] -0.209605852221708X_8t[t] + 0.0459801204750859X_9t[t] + 0.887743425105732X_10t[t] -0.000478451727605654X_11t[t] -0.241762602800191X_12t[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
X_2t[t] =  +  31.3471573638543 +  0.444702753166022X_1t[t] -0.362675102423084X_3t[t] -0.262539402740327X_4t[t] -0.207318027182089X_5t[t] -0.274389035067997X_6t[t] -0.327297113875413X_7t[t] -0.209605852221708X_8t[t] +  0.0459801204750859X_9t[t] +  0.887743425105732X_10t[t] -0.000478451727605654X_11t[t] -0.241762602800191X_12t[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185706&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]X_2t[t] =  +  31.3471573638543 +  0.444702753166022X_1t[t] -0.362675102423084X_3t[t] -0.262539402740327X_4t[t] -0.207318027182089X_5t[t] -0.274389035067997X_6t[t] -0.327297113875413X_7t[t] -0.209605852221708X_8t[t] +  0.0459801204750859X_9t[t] +  0.887743425105732X_10t[t] -0.000478451727605654X_11t[t] -0.241762602800191X_12t[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185706&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185706&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
X_2t[t] = + 31.3471573638543 + 0.444702753166022X_1t[t] -0.362675102423084X_3t[t] -0.262539402740327X_4t[t] -0.207318027182089X_5t[t] -0.274389035067997X_6t[t] -0.327297113875413X_7t[t] -0.209605852221708X_8t[t] + 0.0459801204750859X_9t[t] + 0.887743425105732X_10t[t] -0.000478451727605654X_11t[t] -0.241762602800191X_12t[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)31.34715736385439.236363.39390.0011380.000569
X_1t0.4447027531660220.0534598.318500
X_3t-0.3626751024230840.058971-6.150100
X_4t-0.2625394027403270.059547-4.40893.7e-051.8e-05
X_5t-0.2073180271820890.056577-3.66440.0004780.000239
X_6t-0.2743890350679970.079743-3.44090.0009820.000491
X_7t-0.3272971138754130.136247-2.40220.0189520.009476
X_8t-0.2096058522217080.269963-0.77640.4401150.220057
X_9t0.04598012047508590.2977670.15440.8777260.438863
X_10t0.8877434251057320.3122652.84290.0058540.002927
X_11t-0.0004784517276056540.066048-0.00720.9942410.49712
X_12t-0.2417626028001910.158891-1.52160.1326240.066312

\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) & 31.3471573638543 & 9.23636 & 3.3939 & 0.001138 & 0.000569 \tabularnewline
X_1t & 0.444702753166022 & 0.053459 & 8.3185 & 0 & 0 \tabularnewline
X_3t & -0.362675102423084 & 0.058971 & -6.1501 & 0 & 0 \tabularnewline
X_4t & -0.262539402740327 & 0.059547 & -4.4089 & 3.7e-05 & 1.8e-05 \tabularnewline
X_5t & -0.207318027182089 & 0.056577 & -3.6644 & 0.000478 & 0.000239 \tabularnewline
X_6t & -0.274389035067997 & 0.079743 & -3.4409 & 0.000982 & 0.000491 \tabularnewline
X_7t & -0.327297113875413 & 0.136247 & -2.4022 & 0.018952 & 0.009476 \tabularnewline
X_8t & -0.209605852221708 & 0.269963 & -0.7764 & 0.440115 & 0.220057 \tabularnewline
X_9t & 0.0459801204750859 & 0.297767 & 0.1544 & 0.877726 & 0.438863 \tabularnewline
X_10t & 0.887743425105732 & 0.312265 & 2.8429 & 0.005854 & 0.002927 \tabularnewline
X_11t & -0.000478451727605654 & 0.066048 & -0.0072 & 0.994241 & 0.49712 \tabularnewline
X_12t & -0.241762602800191 & 0.158891 & -1.5216 & 0.132624 & 0.066312 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185706&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]31.3471573638543[/C][C]9.23636[/C][C]3.3939[/C][C]0.001138[/C][C]0.000569[/C][/ROW]
[ROW][C]X_1t[/C][C]0.444702753166022[/C][C]0.053459[/C][C]8.3185[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]X_3t[/C][C]-0.362675102423084[/C][C]0.058971[/C][C]-6.1501[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]X_4t[/C][C]-0.262539402740327[/C][C]0.059547[/C][C]-4.4089[/C][C]3.7e-05[/C][C]1.8e-05[/C][/ROW]
[ROW][C]X_5t[/C][C]-0.207318027182089[/C][C]0.056577[/C][C]-3.6644[/C][C]0.000478[/C][C]0.000239[/C][/ROW]
[ROW][C]X_6t[/C][C]-0.274389035067997[/C][C]0.079743[/C][C]-3.4409[/C][C]0.000982[/C][C]0.000491[/C][/ROW]
[ROW][C]X_7t[/C][C]-0.327297113875413[/C][C]0.136247[/C][C]-2.4022[/C][C]0.018952[/C][C]0.009476[/C][/ROW]
[ROW][C]X_8t[/C][C]-0.209605852221708[/C][C]0.269963[/C][C]-0.7764[/C][C]0.440115[/C][C]0.220057[/C][/ROW]
[ROW][C]X_9t[/C][C]0.0459801204750859[/C][C]0.297767[/C][C]0.1544[/C][C]0.877726[/C][C]0.438863[/C][/ROW]
[ROW][C]X_10t[/C][C]0.887743425105732[/C][C]0.312265[/C][C]2.8429[/C][C]0.005854[/C][C]0.002927[/C][/ROW]
[ROW][C]X_11t[/C][C]-0.000478451727605654[/C][C]0.066048[/C][C]-0.0072[/C][C]0.994241[/C][C]0.49712[/C][/ROW]
[ROW][C]X_12t[/C][C]-0.241762602800191[/C][C]0.158891[/C][C]-1.5216[/C][C]0.132624[/C][C]0.066312[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185706&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185706&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)31.34715736385439.236363.39390.0011380.000569
X_1t0.4447027531660220.0534598.318500
X_3t-0.3626751024230840.058971-6.150100
X_4t-0.2625394027403270.059547-4.40893.7e-051.8e-05
X_5t-0.2073180271820890.056577-3.66440.0004780.000239
X_6t-0.2743890350679970.079743-3.44090.0009820.000491
X_7t-0.3272971138754130.136247-2.40220.0189520.009476
X_8t-0.2096058522217080.269963-0.77640.4401150.220057
X_9t0.04598012047508590.2977670.15440.8777260.438863
X_10t0.8877434251057320.3122652.84290.0058540.002927
X_11t-0.0004784517276056540.066048-0.00720.9942410.49712
X_12t-0.2417626028001910.158891-1.52160.1326240.066312







Multiple Linear Regression - Regression Statistics
Multiple R0.957117519497325
R-squared0.916073946128713
Adjusted R-squared0.902885566234654
F-TEST (value)69.460688385338
F-TEST (DF numerator)11
F-TEST (DF denominator)70
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.0240654350445
Sum Squared Residuals640.148022880163

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.957117519497325 \tabularnewline
R-squared & 0.916073946128713 \tabularnewline
Adjusted R-squared & 0.902885566234654 \tabularnewline
F-TEST (value) & 69.460688385338 \tabularnewline
F-TEST (DF numerator) & 11 \tabularnewline
F-TEST (DF denominator) & 70 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 3.0240654350445 \tabularnewline
Sum Squared Residuals & 640.148022880163 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185706&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.957117519497325[/C][/ROW]
[ROW][C]R-squared[/C][C]0.916073946128713[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.902885566234654[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]69.460688385338[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]11[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]70[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]3.0240654350445[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]640.148022880163[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185706&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185706&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 R0.957117519497325
R-squared0.916073946128713
Adjusted R-squared0.902885566234654
F-TEST (value)69.460688385338
F-TEST (DF numerator)11
F-TEST (DF denominator)70
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.0240654350445
Sum Squared Residuals640.148022880163







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1-3-4.948080824862691.94808082486269
2-4-2.14131446342658-1.85868553657342
3-7-6.51154590698915-0.488454093010852
4-7-4.20445493251037-2.79554506748963
5-7-4.77415776825536-2.22584223174464
6-3-2.18641994553898-0.813580054461025
70-1.785002514532881.78500251453288
8-5-1.86842044012433-3.13157955987567
9-3-3.074347839297240.0743478392972435
1030.5645050954734012.4354949045266
112-0.6321293835925982.6321293835926
12-7-11.16633881956514.16633881956507
13-11.69080317608383-2.69080317608383
1401.94923548223378-1.94923548223378
15-3-0.948930336808768-2.05106966319123
1647.10114994268736-3.10114994268736
1722.58329321015519-0.583293210155192
1831.216724995316021.78327500468398
190-3.042778546509543.04277854650954
20-10-7.42122418583962-2.57877581416038
21-10-8.49030488148799-1.50969511851201
22-9-7.54308200443584-1.45691799556416
23-22-18.1955557988583-3.80444420114172
24-16-16.8238278019670.823827801967045
25-18-21.3775528464663.37755284646597
26-14-14.25830255654950.258302556549502
27-12-16.14764545194594.14764545194594
28-17-18.55711694425321.55711694425317
29-23-20.6407854145687-2.35921458543134
30-28-24.2630396191683-3.73696038083171
31-31-28.0235085879537-2.97649141204635
32-21-21.10205923830680.102059238306841
33-19-17.4096709779125-1.59032902208746
34-22-25.00197514079293.0019751407929
35-22-23.85708985032631.85708985032628
36-25-22.0598870066226-2.94011299337739
37-16-18.44567096446922.44567096446921
38-22-17.8341257308486-4.16587426915142
39-21-16.7685618316674-4.23143816833263
40-10-11.03067034213971.0306703421397
41-7-6.4366553049305-0.563344695069504
42-5-8.407805071086953.40780507108696
43-4-5.614794602376211.61479460237621
4472.145913408503894.85408659149611
4561.429939119348044.57006088065196
4632.629808788949980.370191211050021
47107.872586947469232.12741305253077
4805.80434527654845-5.80434527654845
49-2-0.288555804068115-1.71144419593188
50-11.20563724070957-2.20563724070957
5120.9001083282061551.09989167179384
5286.08435902035221.9156409796478
53-6-4.3397445194819-1.6602554805181
54-4-1.01767324966563-2.98232675033437
5544.81947387797566-0.819473877975659
5673.393245630895693.60675436910431
5732.096957997214040.903042002785955
583-0.08091675718073613.08091675718074
5983.014237658243754.98576234175625
6030.5513004289544822.44869957104552
61-3-2.56320995451276-0.436790045487237
6243.36304972945520.6369502705448
63-5-8.213138547015623.21313854701562
64-11.21843408563981-2.21843408563981
6555.34078981836698-0.340789818366985
660-2.705016671474662.70501667147466
67-6-3.85489205975439-2.14510794024561
68-13-9.50176898800993-3.49823101199007
69-15-9.3952163369065-5.6047836630935
70-8-7.28536732264578-0.714632677354223
71-20-20.48962068631580.489620686315809
72-10-19.33642144412019.33642144412008
73-22-23.23990157421881.23990157421877
74-25-24.4844514739612-0.515548526038803
75-10-11.83868810136321.83868810136319
76-8-11.84174625378513.84174625378515
77-9-6.64717552975094-2.35282447024906
78-5-2.76587485556428-2.23412514443572
79-7-3.20421652514465-3.79578347485535
80-11-9.4522647182309-1.5477352817691
81-11-9.42543491839396-1.57456508160604
82-16-17.00976909023061.0097690902306

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & -3 & -4.94808082486269 & 1.94808082486269 \tabularnewline
2 & -4 & -2.14131446342658 & -1.85868553657342 \tabularnewline
3 & -7 & -6.51154590698915 & -0.488454093010852 \tabularnewline
4 & -7 & -4.20445493251037 & -2.79554506748963 \tabularnewline
5 & -7 & -4.77415776825536 & -2.22584223174464 \tabularnewline
6 & -3 & -2.18641994553898 & -0.813580054461025 \tabularnewline
7 & 0 & -1.78500251453288 & 1.78500251453288 \tabularnewline
8 & -5 & -1.86842044012433 & -3.13157955987567 \tabularnewline
9 & -3 & -3.07434783929724 & 0.0743478392972435 \tabularnewline
10 & 3 & 0.564505095473401 & 2.4354949045266 \tabularnewline
11 & 2 & -0.632129383592598 & 2.6321293835926 \tabularnewline
12 & -7 & -11.1663388195651 & 4.16633881956507 \tabularnewline
13 & -1 & 1.69080317608383 & -2.69080317608383 \tabularnewline
14 & 0 & 1.94923548223378 & -1.94923548223378 \tabularnewline
15 & -3 & -0.948930336808768 & -2.05106966319123 \tabularnewline
16 & 4 & 7.10114994268736 & -3.10114994268736 \tabularnewline
17 & 2 & 2.58329321015519 & -0.583293210155192 \tabularnewline
18 & 3 & 1.21672499531602 & 1.78327500468398 \tabularnewline
19 & 0 & -3.04277854650954 & 3.04277854650954 \tabularnewline
20 & -10 & -7.42122418583962 & -2.57877581416038 \tabularnewline
21 & -10 & -8.49030488148799 & -1.50969511851201 \tabularnewline
22 & -9 & -7.54308200443584 & -1.45691799556416 \tabularnewline
23 & -22 & -18.1955557988583 & -3.80444420114172 \tabularnewline
24 & -16 & -16.823827801967 & 0.823827801967045 \tabularnewline
25 & -18 & -21.377552846466 & 3.37755284646597 \tabularnewline
26 & -14 & -14.2583025565495 & 0.258302556549502 \tabularnewline
27 & -12 & -16.1476454519459 & 4.14764545194594 \tabularnewline
28 & -17 & -18.5571169442532 & 1.55711694425317 \tabularnewline
29 & -23 & -20.6407854145687 & -2.35921458543134 \tabularnewline
30 & -28 & -24.2630396191683 & -3.73696038083171 \tabularnewline
31 & -31 & -28.0235085879537 & -2.97649141204635 \tabularnewline
32 & -21 & -21.1020592383068 & 0.102059238306841 \tabularnewline
33 & -19 & -17.4096709779125 & -1.59032902208746 \tabularnewline
34 & -22 & -25.0019751407929 & 3.0019751407929 \tabularnewline
35 & -22 & -23.8570898503263 & 1.85708985032628 \tabularnewline
36 & -25 & -22.0598870066226 & -2.94011299337739 \tabularnewline
37 & -16 & -18.4456709644692 & 2.44567096446921 \tabularnewline
38 & -22 & -17.8341257308486 & -4.16587426915142 \tabularnewline
39 & -21 & -16.7685618316674 & -4.23143816833263 \tabularnewline
40 & -10 & -11.0306703421397 & 1.0306703421397 \tabularnewline
41 & -7 & -6.4366553049305 & -0.563344695069504 \tabularnewline
42 & -5 & -8.40780507108695 & 3.40780507108696 \tabularnewline
43 & -4 & -5.61479460237621 & 1.61479460237621 \tabularnewline
44 & 7 & 2.14591340850389 & 4.85408659149611 \tabularnewline
45 & 6 & 1.42993911934804 & 4.57006088065196 \tabularnewline
46 & 3 & 2.62980878894998 & 0.370191211050021 \tabularnewline
47 & 10 & 7.87258694746923 & 2.12741305253077 \tabularnewline
48 & 0 & 5.80434527654845 & -5.80434527654845 \tabularnewline
49 & -2 & -0.288555804068115 & -1.71144419593188 \tabularnewline
50 & -1 & 1.20563724070957 & -2.20563724070957 \tabularnewline
51 & 2 & 0.900108328206155 & 1.09989167179384 \tabularnewline
52 & 8 & 6.0843590203522 & 1.9156409796478 \tabularnewline
53 & -6 & -4.3397445194819 & -1.6602554805181 \tabularnewline
54 & -4 & -1.01767324966563 & -2.98232675033437 \tabularnewline
55 & 4 & 4.81947387797566 & -0.819473877975659 \tabularnewline
56 & 7 & 3.39324563089569 & 3.60675436910431 \tabularnewline
57 & 3 & 2.09695799721404 & 0.903042002785955 \tabularnewline
58 & 3 & -0.0809167571807361 & 3.08091675718074 \tabularnewline
59 & 8 & 3.01423765824375 & 4.98576234175625 \tabularnewline
60 & 3 & 0.551300428954482 & 2.44869957104552 \tabularnewline
61 & -3 & -2.56320995451276 & -0.436790045487237 \tabularnewline
62 & 4 & 3.3630497294552 & 0.6369502705448 \tabularnewline
63 & -5 & -8.21313854701562 & 3.21313854701562 \tabularnewline
64 & -1 & 1.21843408563981 & -2.21843408563981 \tabularnewline
65 & 5 & 5.34078981836698 & -0.340789818366985 \tabularnewline
66 & 0 & -2.70501667147466 & 2.70501667147466 \tabularnewline
67 & -6 & -3.85489205975439 & -2.14510794024561 \tabularnewline
68 & -13 & -9.50176898800993 & -3.49823101199007 \tabularnewline
69 & -15 & -9.3952163369065 & -5.6047836630935 \tabularnewline
70 & -8 & -7.28536732264578 & -0.714632677354223 \tabularnewline
71 & -20 & -20.4896206863158 & 0.489620686315809 \tabularnewline
72 & -10 & -19.3364214441201 & 9.33642144412008 \tabularnewline
73 & -22 & -23.2399015742188 & 1.23990157421877 \tabularnewline
74 & -25 & -24.4844514739612 & -0.515548526038803 \tabularnewline
75 & -10 & -11.8386881013632 & 1.83868810136319 \tabularnewline
76 & -8 & -11.8417462537851 & 3.84174625378515 \tabularnewline
77 & -9 & -6.64717552975094 & -2.35282447024906 \tabularnewline
78 & -5 & -2.76587485556428 & -2.23412514443572 \tabularnewline
79 & -7 & -3.20421652514465 & -3.79578347485535 \tabularnewline
80 & -11 & -9.4522647182309 & -1.5477352817691 \tabularnewline
81 & -11 & -9.42543491839396 & -1.57456508160604 \tabularnewline
82 & -16 & -17.0097690902306 & 1.0097690902306 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185706&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]-3[/C][C]-4.94808082486269[/C][C]1.94808082486269[/C][/ROW]
[ROW][C]2[/C][C]-4[/C][C]-2.14131446342658[/C][C]-1.85868553657342[/C][/ROW]
[ROW][C]3[/C][C]-7[/C][C]-6.51154590698915[/C][C]-0.488454093010852[/C][/ROW]
[ROW][C]4[/C][C]-7[/C][C]-4.20445493251037[/C][C]-2.79554506748963[/C][/ROW]
[ROW][C]5[/C][C]-7[/C][C]-4.77415776825536[/C][C]-2.22584223174464[/C][/ROW]
[ROW][C]6[/C][C]-3[/C][C]-2.18641994553898[/C][C]-0.813580054461025[/C][/ROW]
[ROW][C]7[/C][C]0[/C][C]-1.78500251453288[/C][C]1.78500251453288[/C][/ROW]
[ROW][C]8[/C][C]-5[/C][C]-1.86842044012433[/C][C]-3.13157955987567[/C][/ROW]
[ROW][C]9[/C][C]-3[/C][C]-3.07434783929724[/C][C]0.0743478392972435[/C][/ROW]
[ROW][C]10[/C][C]3[/C][C]0.564505095473401[/C][C]2.4354949045266[/C][/ROW]
[ROW][C]11[/C][C]2[/C][C]-0.632129383592598[/C][C]2.6321293835926[/C][/ROW]
[ROW][C]12[/C][C]-7[/C][C]-11.1663388195651[/C][C]4.16633881956507[/C][/ROW]
[ROW][C]13[/C][C]-1[/C][C]1.69080317608383[/C][C]-2.69080317608383[/C][/ROW]
[ROW][C]14[/C][C]0[/C][C]1.94923548223378[/C][C]-1.94923548223378[/C][/ROW]
[ROW][C]15[/C][C]-3[/C][C]-0.948930336808768[/C][C]-2.05106966319123[/C][/ROW]
[ROW][C]16[/C][C]4[/C][C]7.10114994268736[/C][C]-3.10114994268736[/C][/ROW]
[ROW][C]17[/C][C]2[/C][C]2.58329321015519[/C][C]-0.583293210155192[/C][/ROW]
[ROW][C]18[/C][C]3[/C][C]1.21672499531602[/C][C]1.78327500468398[/C][/ROW]
[ROW][C]19[/C][C]0[/C][C]-3.04277854650954[/C][C]3.04277854650954[/C][/ROW]
[ROW][C]20[/C][C]-10[/C][C]-7.42122418583962[/C][C]-2.57877581416038[/C][/ROW]
[ROW][C]21[/C][C]-10[/C][C]-8.49030488148799[/C][C]-1.50969511851201[/C][/ROW]
[ROW][C]22[/C][C]-9[/C][C]-7.54308200443584[/C][C]-1.45691799556416[/C][/ROW]
[ROW][C]23[/C][C]-22[/C][C]-18.1955557988583[/C][C]-3.80444420114172[/C][/ROW]
[ROW][C]24[/C][C]-16[/C][C]-16.823827801967[/C][C]0.823827801967045[/C][/ROW]
[ROW][C]25[/C][C]-18[/C][C]-21.377552846466[/C][C]3.37755284646597[/C][/ROW]
[ROW][C]26[/C][C]-14[/C][C]-14.2583025565495[/C][C]0.258302556549502[/C][/ROW]
[ROW][C]27[/C][C]-12[/C][C]-16.1476454519459[/C][C]4.14764545194594[/C][/ROW]
[ROW][C]28[/C][C]-17[/C][C]-18.5571169442532[/C][C]1.55711694425317[/C][/ROW]
[ROW][C]29[/C][C]-23[/C][C]-20.6407854145687[/C][C]-2.35921458543134[/C][/ROW]
[ROW][C]30[/C][C]-28[/C][C]-24.2630396191683[/C][C]-3.73696038083171[/C][/ROW]
[ROW][C]31[/C][C]-31[/C][C]-28.0235085879537[/C][C]-2.97649141204635[/C][/ROW]
[ROW][C]32[/C][C]-21[/C][C]-21.1020592383068[/C][C]0.102059238306841[/C][/ROW]
[ROW][C]33[/C][C]-19[/C][C]-17.4096709779125[/C][C]-1.59032902208746[/C][/ROW]
[ROW][C]34[/C][C]-22[/C][C]-25.0019751407929[/C][C]3.0019751407929[/C][/ROW]
[ROW][C]35[/C][C]-22[/C][C]-23.8570898503263[/C][C]1.85708985032628[/C][/ROW]
[ROW][C]36[/C][C]-25[/C][C]-22.0598870066226[/C][C]-2.94011299337739[/C][/ROW]
[ROW][C]37[/C][C]-16[/C][C]-18.4456709644692[/C][C]2.44567096446921[/C][/ROW]
[ROW][C]38[/C][C]-22[/C][C]-17.8341257308486[/C][C]-4.16587426915142[/C][/ROW]
[ROW][C]39[/C][C]-21[/C][C]-16.7685618316674[/C][C]-4.23143816833263[/C][/ROW]
[ROW][C]40[/C][C]-10[/C][C]-11.0306703421397[/C][C]1.0306703421397[/C][/ROW]
[ROW][C]41[/C][C]-7[/C][C]-6.4366553049305[/C][C]-0.563344695069504[/C][/ROW]
[ROW][C]42[/C][C]-5[/C][C]-8.40780507108695[/C][C]3.40780507108696[/C][/ROW]
[ROW][C]43[/C][C]-4[/C][C]-5.61479460237621[/C][C]1.61479460237621[/C][/ROW]
[ROW][C]44[/C][C]7[/C][C]2.14591340850389[/C][C]4.85408659149611[/C][/ROW]
[ROW][C]45[/C][C]6[/C][C]1.42993911934804[/C][C]4.57006088065196[/C][/ROW]
[ROW][C]46[/C][C]3[/C][C]2.62980878894998[/C][C]0.370191211050021[/C][/ROW]
[ROW][C]47[/C][C]10[/C][C]7.87258694746923[/C][C]2.12741305253077[/C][/ROW]
[ROW][C]48[/C][C]0[/C][C]5.80434527654845[/C][C]-5.80434527654845[/C][/ROW]
[ROW][C]49[/C][C]-2[/C][C]-0.288555804068115[/C][C]-1.71144419593188[/C][/ROW]
[ROW][C]50[/C][C]-1[/C][C]1.20563724070957[/C][C]-2.20563724070957[/C][/ROW]
[ROW][C]51[/C][C]2[/C][C]0.900108328206155[/C][C]1.09989167179384[/C][/ROW]
[ROW][C]52[/C][C]8[/C][C]6.0843590203522[/C][C]1.9156409796478[/C][/ROW]
[ROW][C]53[/C][C]-6[/C][C]-4.3397445194819[/C][C]-1.6602554805181[/C][/ROW]
[ROW][C]54[/C][C]-4[/C][C]-1.01767324966563[/C][C]-2.98232675033437[/C][/ROW]
[ROW][C]55[/C][C]4[/C][C]4.81947387797566[/C][C]-0.819473877975659[/C][/ROW]
[ROW][C]56[/C][C]7[/C][C]3.39324563089569[/C][C]3.60675436910431[/C][/ROW]
[ROW][C]57[/C][C]3[/C][C]2.09695799721404[/C][C]0.903042002785955[/C][/ROW]
[ROW][C]58[/C][C]3[/C][C]-0.0809167571807361[/C][C]3.08091675718074[/C][/ROW]
[ROW][C]59[/C][C]8[/C][C]3.01423765824375[/C][C]4.98576234175625[/C][/ROW]
[ROW][C]60[/C][C]3[/C][C]0.551300428954482[/C][C]2.44869957104552[/C][/ROW]
[ROW][C]61[/C][C]-3[/C][C]-2.56320995451276[/C][C]-0.436790045487237[/C][/ROW]
[ROW][C]62[/C][C]4[/C][C]3.3630497294552[/C][C]0.6369502705448[/C][/ROW]
[ROW][C]63[/C][C]-5[/C][C]-8.21313854701562[/C][C]3.21313854701562[/C][/ROW]
[ROW][C]64[/C][C]-1[/C][C]1.21843408563981[/C][C]-2.21843408563981[/C][/ROW]
[ROW][C]65[/C][C]5[/C][C]5.34078981836698[/C][C]-0.340789818366985[/C][/ROW]
[ROW][C]66[/C][C]0[/C][C]-2.70501667147466[/C][C]2.70501667147466[/C][/ROW]
[ROW][C]67[/C][C]-6[/C][C]-3.85489205975439[/C][C]-2.14510794024561[/C][/ROW]
[ROW][C]68[/C][C]-13[/C][C]-9.50176898800993[/C][C]-3.49823101199007[/C][/ROW]
[ROW][C]69[/C][C]-15[/C][C]-9.3952163369065[/C][C]-5.6047836630935[/C][/ROW]
[ROW][C]70[/C][C]-8[/C][C]-7.28536732264578[/C][C]-0.714632677354223[/C][/ROW]
[ROW][C]71[/C][C]-20[/C][C]-20.4896206863158[/C][C]0.489620686315809[/C][/ROW]
[ROW][C]72[/C][C]-10[/C][C]-19.3364214441201[/C][C]9.33642144412008[/C][/ROW]
[ROW][C]73[/C][C]-22[/C][C]-23.2399015742188[/C][C]1.23990157421877[/C][/ROW]
[ROW][C]74[/C][C]-25[/C][C]-24.4844514739612[/C][C]-0.515548526038803[/C][/ROW]
[ROW][C]75[/C][C]-10[/C][C]-11.8386881013632[/C][C]1.83868810136319[/C][/ROW]
[ROW][C]76[/C][C]-8[/C][C]-11.8417462537851[/C][C]3.84174625378515[/C][/ROW]
[ROW][C]77[/C][C]-9[/C][C]-6.64717552975094[/C][C]-2.35282447024906[/C][/ROW]
[ROW][C]78[/C][C]-5[/C][C]-2.76587485556428[/C][C]-2.23412514443572[/C][/ROW]
[ROW][C]79[/C][C]-7[/C][C]-3.20421652514465[/C][C]-3.79578347485535[/C][/ROW]
[ROW][C]80[/C][C]-11[/C][C]-9.4522647182309[/C][C]-1.5477352817691[/C][/ROW]
[ROW][C]81[/C][C]-11[/C][C]-9.42543491839396[/C][C]-1.57456508160604[/C][/ROW]
[ROW][C]82[/C][C]-16[/C][C]-17.0097690902306[/C][C]1.0097690902306[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185706&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185706&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
1-3-4.948080824862691.94808082486269
2-4-2.14131446342658-1.85868553657342
3-7-6.51154590698915-0.488454093010852
4-7-4.20445493251037-2.79554506748963
5-7-4.77415776825536-2.22584223174464
6-3-2.18641994553898-0.813580054461025
70-1.785002514532881.78500251453288
8-5-1.86842044012433-3.13157955987567
9-3-3.074347839297240.0743478392972435
1030.5645050954734012.4354949045266
112-0.6321293835925982.6321293835926
12-7-11.16633881956514.16633881956507
13-11.69080317608383-2.69080317608383
1401.94923548223378-1.94923548223378
15-3-0.948930336808768-2.05106966319123
1647.10114994268736-3.10114994268736
1722.58329321015519-0.583293210155192
1831.216724995316021.78327500468398
190-3.042778546509543.04277854650954
20-10-7.42122418583962-2.57877581416038
21-10-8.49030488148799-1.50969511851201
22-9-7.54308200443584-1.45691799556416
23-22-18.1955557988583-3.80444420114172
24-16-16.8238278019670.823827801967045
25-18-21.3775528464663.37755284646597
26-14-14.25830255654950.258302556549502
27-12-16.14764545194594.14764545194594
28-17-18.55711694425321.55711694425317
29-23-20.6407854145687-2.35921458543134
30-28-24.2630396191683-3.73696038083171
31-31-28.0235085879537-2.97649141204635
32-21-21.10205923830680.102059238306841
33-19-17.4096709779125-1.59032902208746
34-22-25.00197514079293.0019751407929
35-22-23.85708985032631.85708985032628
36-25-22.0598870066226-2.94011299337739
37-16-18.44567096446922.44567096446921
38-22-17.8341257308486-4.16587426915142
39-21-16.7685618316674-4.23143816833263
40-10-11.03067034213971.0306703421397
41-7-6.4366553049305-0.563344695069504
42-5-8.407805071086953.40780507108696
43-4-5.614794602376211.61479460237621
4472.145913408503894.85408659149611
4561.429939119348044.57006088065196
4632.629808788949980.370191211050021
47107.872586947469232.12741305253077
4805.80434527654845-5.80434527654845
49-2-0.288555804068115-1.71144419593188
50-11.20563724070957-2.20563724070957
5120.9001083282061551.09989167179384
5286.08435902035221.9156409796478
53-6-4.3397445194819-1.6602554805181
54-4-1.01767324966563-2.98232675033437
5544.81947387797566-0.819473877975659
5673.393245630895693.60675436910431
5732.096957997214040.903042002785955
583-0.08091675718073613.08091675718074
5983.014237658243754.98576234175625
6030.5513004289544822.44869957104552
61-3-2.56320995451276-0.436790045487237
6243.36304972945520.6369502705448
63-5-8.213138547015623.21313854701562
64-11.21843408563981-2.21843408563981
6555.34078981836698-0.340789818366985
660-2.705016671474662.70501667147466
67-6-3.85489205975439-2.14510794024561
68-13-9.50176898800993-3.49823101199007
69-15-9.3952163369065-5.6047836630935
70-8-7.28536732264578-0.714632677354223
71-20-20.48962068631580.489620686315809
72-10-19.33642144412019.33642144412008
73-22-23.23990157421881.23990157421877
74-25-24.4844514739612-0.515548526038803
75-10-11.83868810136321.83868810136319
76-8-11.84174625378513.84174625378515
77-9-6.64717552975094-2.35282447024906
78-5-2.76587485556428-2.23412514443572
79-7-3.20421652514465-3.79578347485535
80-11-9.4522647182309-1.5477352817691
81-11-9.42543491839396-1.57456508160604
82-16-17.00976909023061.0097690902306







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
150.07411431615227090.1482286323045420.925885683847729
160.02536208276093270.05072416552186540.974637917239067
170.008773900770656630.01754780154131330.991226099229343
180.02540408715178310.05080817430356620.974595912848217
190.05535210261350380.1107042052270080.944647897386496
200.10005045801980.2001009160395990.8999495419802
210.05861570845728170.1172314169145630.941384291542718
220.03360300935835970.06720601871671940.96639699064164
230.02111454341707260.04222908683414520.978885456582927
240.01643071254088220.03286142508176440.983569287459118
250.02806333137487810.05612666274975620.971936668625122
260.01558909592475630.03117819184951270.984410904075244
270.01334874259238540.02669748518477090.986651257407615
280.009296013188278760.01859202637655750.990703986811721
290.01613996660029630.03227993320059250.983860033399704
300.01007450917346420.02014901834692830.989925490826536
310.007815615022830060.01563123004566010.99218438497717
320.004700378484466640.009400756968933270.995299621515533
330.01166318830007580.02332637660015150.988336811699924
340.01170531806747450.0234106361349490.988294681932525
350.0092407040585130.0184814081170260.990759295941487
360.007749412787456220.01549882557491240.992250587212544
370.007246685001114860.01449337000222970.992753314998885
380.005410781940706770.01082156388141350.994589218059293
390.005704008205154420.01140801641030880.994295991794846
400.03252940268012240.06505880536024470.967470597319878
410.02971386264151490.05942772528302980.970286137358485
420.04058839074683610.08117678149367210.959411609253164
430.03482045359859920.06964090719719840.965179546401401
440.07474391143897060.1494878228779410.925256088561029
450.09301864802941680.1860372960588340.906981351970583
460.07443044301069780.1488608860213960.925569556989302
470.09385995574456320.1877199114891260.906140044255437
480.1399536625810180.2799073251620360.860046337418982
490.1073206659215140.2146413318430280.892679334078486
500.08626952436685770.1725390487337150.913730475633142
510.1009007663948630.2018015327897270.899099233605137
520.09497516430162680.1899503286032540.905024835698373
530.06693115093946320.1338623018789260.933068849060537
540.05422099663559150.1084419932711830.945779003364408
550.03553661767557160.07107323535114320.964463382324428
560.06452257541498180.1290451508299640.935477424585018
570.07815322946276250.1563064589255250.921846770537238
580.1216775614360780.2433551228721560.878322438563922
590.3148139566615770.6296279133231530.685186043338423
600.4563504022707690.9127008045415380.543649597729231
610.4974880195793180.9949760391586360.502511980420682
620.3941762704787480.7883525409574970.605823729521252
630.3966888381181270.7933776762362550.603311161881873
640.3187588451359750.637517690271950.681241154864025
650.2197087954918740.4394175909837480.780291204508126
660.1449922133042090.2899844266084170.855007786695791
670.08688470895951740.1737694179190350.913115291040483

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
15 & 0.0741143161522709 & 0.148228632304542 & 0.925885683847729 \tabularnewline
16 & 0.0253620827609327 & 0.0507241655218654 & 0.974637917239067 \tabularnewline
17 & 0.00877390077065663 & 0.0175478015413133 & 0.991226099229343 \tabularnewline
18 & 0.0254040871517831 & 0.0508081743035662 & 0.974595912848217 \tabularnewline
19 & 0.0553521026135038 & 0.110704205227008 & 0.944647897386496 \tabularnewline
20 & 0.1000504580198 & 0.200100916039599 & 0.8999495419802 \tabularnewline
21 & 0.0586157084572817 & 0.117231416914563 & 0.941384291542718 \tabularnewline
22 & 0.0336030093583597 & 0.0672060187167194 & 0.96639699064164 \tabularnewline
23 & 0.0211145434170726 & 0.0422290868341452 & 0.978885456582927 \tabularnewline
24 & 0.0164307125408822 & 0.0328614250817644 & 0.983569287459118 \tabularnewline
25 & 0.0280633313748781 & 0.0561266627497562 & 0.971936668625122 \tabularnewline
26 & 0.0155890959247563 & 0.0311781918495127 & 0.984410904075244 \tabularnewline
27 & 0.0133487425923854 & 0.0266974851847709 & 0.986651257407615 \tabularnewline
28 & 0.00929601318827876 & 0.0185920263765575 & 0.990703986811721 \tabularnewline
29 & 0.0161399666002963 & 0.0322799332005925 & 0.983860033399704 \tabularnewline
30 & 0.0100745091734642 & 0.0201490183469283 & 0.989925490826536 \tabularnewline
31 & 0.00781561502283006 & 0.0156312300456601 & 0.99218438497717 \tabularnewline
32 & 0.00470037848446664 & 0.00940075696893327 & 0.995299621515533 \tabularnewline
33 & 0.0116631883000758 & 0.0233263766001515 & 0.988336811699924 \tabularnewline
34 & 0.0117053180674745 & 0.023410636134949 & 0.988294681932525 \tabularnewline
35 & 0.009240704058513 & 0.018481408117026 & 0.990759295941487 \tabularnewline
36 & 0.00774941278745622 & 0.0154988255749124 & 0.992250587212544 \tabularnewline
37 & 0.00724668500111486 & 0.0144933700022297 & 0.992753314998885 \tabularnewline
38 & 0.00541078194070677 & 0.0108215638814135 & 0.994589218059293 \tabularnewline
39 & 0.00570400820515442 & 0.0114080164103088 & 0.994295991794846 \tabularnewline
40 & 0.0325294026801224 & 0.0650588053602447 & 0.967470597319878 \tabularnewline
41 & 0.0297138626415149 & 0.0594277252830298 & 0.970286137358485 \tabularnewline
42 & 0.0405883907468361 & 0.0811767814936721 & 0.959411609253164 \tabularnewline
43 & 0.0348204535985992 & 0.0696409071971984 & 0.965179546401401 \tabularnewline
44 & 0.0747439114389706 & 0.149487822877941 & 0.925256088561029 \tabularnewline
45 & 0.0930186480294168 & 0.186037296058834 & 0.906981351970583 \tabularnewline
46 & 0.0744304430106978 & 0.148860886021396 & 0.925569556989302 \tabularnewline
47 & 0.0938599557445632 & 0.187719911489126 & 0.906140044255437 \tabularnewline
48 & 0.139953662581018 & 0.279907325162036 & 0.860046337418982 \tabularnewline
49 & 0.107320665921514 & 0.214641331843028 & 0.892679334078486 \tabularnewline
50 & 0.0862695243668577 & 0.172539048733715 & 0.913730475633142 \tabularnewline
51 & 0.100900766394863 & 0.201801532789727 & 0.899099233605137 \tabularnewline
52 & 0.0949751643016268 & 0.189950328603254 & 0.905024835698373 \tabularnewline
53 & 0.0669311509394632 & 0.133862301878926 & 0.933068849060537 \tabularnewline
54 & 0.0542209966355915 & 0.108441993271183 & 0.945779003364408 \tabularnewline
55 & 0.0355366176755716 & 0.0710732353511432 & 0.964463382324428 \tabularnewline
56 & 0.0645225754149818 & 0.129045150829964 & 0.935477424585018 \tabularnewline
57 & 0.0781532294627625 & 0.156306458925525 & 0.921846770537238 \tabularnewline
58 & 0.121677561436078 & 0.243355122872156 & 0.878322438563922 \tabularnewline
59 & 0.314813956661577 & 0.629627913323153 & 0.685186043338423 \tabularnewline
60 & 0.456350402270769 & 0.912700804541538 & 0.543649597729231 \tabularnewline
61 & 0.497488019579318 & 0.994976039158636 & 0.502511980420682 \tabularnewline
62 & 0.394176270478748 & 0.788352540957497 & 0.605823729521252 \tabularnewline
63 & 0.396688838118127 & 0.793377676236255 & 0.603311161881873 \tabularnewline
64 & 0.318758845135975 & 0.63751769027195 & 0.681241154864025 \tabularnewline
65 & 0.219708795491874 & 0.439417590983748 & 0.780291204508126 \tabularnewline
66 & 0.144992213304209 & 0.289984426608417 & 0.855007786695791 \tabularnewline
67 & 0.0868847089595174 & 0.173769417919035 & 0.913115291040483 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185706&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]15[/C][C]0.0741143161522709[/C][C]0.148228632304542[/C][C]0.925885683847729[/C][/ROW]
[ROW][C]16[/C][C]0.0253620827609327[/C][C]0.0507241655218654[/C][C]0.974637917239067[/C][/ROW]
[ROW][C]17[/C][C]0.00877390077065663[/C][C]0.0175478015413133[/C][C]0.991226099229343[/C][/ROW]
[ROW][C]18[/C][C]0.0254040871517831[/C][C]0.0508081743035662[/C][C]0.974595912848217[/C][/ROW]
[ROW][C]19[/C][C]0.0553521026135038[/C][C]0.110704205227008[/C][C]0.944647897386496[/C][/ROW]
[ROW][C]20[/C][C]0.1000504580198[/C][C]0.200100916039599[/C][C]0.8999495419802[/C][/ROW]
[ROW][C]21[/C][C]0.0586157084572817[/C][C]0.117231416914563[/C][C]0.941384291542718[/C][/ROW]
[ROW][C]22[/C][C]0.0336030093583597[/C][C]0.0672060187167194[/C][C]0.96639699064164[/C][/ROW]
[ROW][C]23[/C][C]0.0211145434170726[/C][C]0.0422290868341452[/C][C]0.978885456582927[/C][/ROW]
[ROW][C]24[/C][C]0.0164307125408822[/C][C]0.0328614250817644[/C][C]0.983569287459118[/C][/ROW]
[ROW][C]25[/C][C]0.0280633313748781[/C][C]0.0561266627497562[/C][C]0.971936668625122[/C][/ROW]
[ROW][C]26[/C][C]0.0155890959247563[/C][C]0.0311781918495127[/C][C]0.984410904075244[/C][/ROW]
[ROW][C]27[/C][C]0.0133487425923854[/C][C]0.0266974851847709[/C][C]0.986651257407615[/C][/ROW]
[ROW][C]28[/C][C]0.00929601318827876[/C][C]0.0185920263765575[/C][C]0.990703986811721[/C][/ROW]
[ROW][C]29[/C][C]0.0161399666002963[/C][C]0.0322799332005925[/C][C]0.983860033399704[/C][/ROW]
[ROW][C]30[/C][C]0.0100745091734642[/C][C]0.0201490183469283[/C][C]0.989925490826536[/C][/ROW]
[ROW][C]31[/C][C]0.00781561502283006[/C][C]0.0156312300456601[/C][C]0.99218438497717[/C][/ROW]
[ROW][C]32[/C][C]0.00470037848446664[/C][C]0.00940075696893327[/C][C]0.995299621515533[/C][/ROW]
[ROW][C]33[/C][C]0.0116631883000758[/C][C]0.0233263766001515[/C][C]0.988336811699924[/C][/ROW]
[ROW][C]34[/C][C]0.0117053180674745[/C][C]0.023410636134949[/C][C]0.988294681932525[/C][/ROW]
[ROW][C]35[/C][C]0.009240704058513[/C][C]0.018481408117026[/C][C]0.990759295941487[/C][/ROW]
[ROW][C]36[/C][C]0.00774941278745622[/C][C]0.0154988255749124[/C][C]0.992250587212544[/C][/ROW]
[ROW][C]37[/C][C]0.00724668500111486[/C][C]0.0144933700022297[/C][C]0.992753314998885[/C][/ROW]
[ROW][C]38[/C][C]0.00541078194070677[/C][C]0.0108215638814135[/C][C]0.994589218059293[/C][/ROW]
[ROW][C]39[/C][C]0.00570400820515442[/C][C]0.0114080164103088[/C][C]0.994295991794846[/C][/ROW]
[ROW][C]40[/C][C]0.0325294026801224[/C][C]0.0650588053602447[/C][C]0.967470597319878[/C][/ROW]
[ROW][C]41[/C][C]0.0297138626415149[/C][C]0.0594277252830298[/C][C]0.970286137358485[/C][/ROW]
[ROW][C]42[/C][C]0.0405883907468361[/C][C]0.0811767814936721[/C][C]0.959411609253164[/C][/ROW]
[ROW][C]43[/C][C]0.0348204535985992[/C][C]0.0696409071971984[/C][C]0.965179546401401[/C][/ROW]
[ROW][C]44[/C][C]0.0747439114389706[/C][C]0.149487822877941[/C][C]0.925256088561029[/C][/ROW]
[ROW][C]45[/C][C]0.0930186480294168[/C][C]0.186037296058834[/C][C]0.906981351970583[/C][/ROW]
[ROW][C]46[/C][C]0.0744304430106978[/C][C]0.148860886021396[/C][C]0.925569556989302[/C][/ROW]
[ROW][C]47[/C][C]0.0938599557445632[/C][C]0.187719911489126[/C][C]0.906140044255437[/C][/ROW]
[ROW][C]48[/C][C]0.139953662581018[/C][C]0.279907325162036[/C][C]0.860046337418982[/C][/ROW]
[ROW][C]49[/C][C]0.107320665921514[/C][C]0.214641331843028[/C][C]0.892679334078486[/C][/ROW]
[ROW][C]50[/C][C]0.0862695243668577[/C][C]0.172539048733715[/C][C]0.913730475633142[/C][/ROW]
[ROW][C]51[/C][C]0.100900766394863[/C][C]0.201801532789727[/C][C]0.899099233605137[/C][/ROW]
[ROW][C]52[/C][C]0.0949751643016268[/C][C]0.189950328603254[/C][C]0.905024835698373[/C][/ROW]
[ROW][C]53[/C][C]0.0669311509394632[/C][C]0.133862301878926[/C][C]0.933068849060537[/C][/ROW]
[ROW][C]54[/C][C]0.0542209966355915[/C][C]0.108441993271183[/C][C]0.945779003364408[/C][/ROW]
[ROW][C]55[/C][C]0.0355366176755716[/C][C]0.0710732353511432[/C][C]0.964463382324428[/C][/ROW]
[ROW][C]56[/C][C]0.0645225754149818[/C][C]0.129045150829964[/C][C]0.935477424585018[/C][/ROW]
[ROW][C]57[/C][C]0.0781532294627625[/C][C]0.156306458925525[/C][C]0.921846770537238[/C][/ROW]
[ROW][C]58[/C][C]0.121677561436078[/C][C]0.243355122872156[/C][C]0.878322438563922[/C][/ROW]
[ROW][C]59[/C][C]0.314813956661577[/C][C]0.629627913323153[/C][C]0.685186043338423[/C][/ROW]
[ROW][C]60[/C][C]0.456350402270769[/C][C]0.912700804541538[/C][C]0.543649597729231[/C][/ROW]
[ROW][C]61[/C][C]0.497488019579318[/C][C]0.994976039158636[/C][C]0.502511980420682[/C][/ROW]
[ROW][C]62[/C][C]0.394176270478748[/C][C]0.788352540957497[/C][C]0.605823729521252[/C][/ROW]
[ROW][C]63[/C][C]0.396688838118127[/C][C]0.793377676236255[/C][C]0.603311161881873[/C][/ROW]
[ROW][C]64[/C][C]0.318758845135975[/C][C]0.63751769027195[/C][C]0.681241154864025[/C][/ROW]
[ROW][C]65[/C][C]0.219708795491874[/C][C]0.439417590983748[/C][C]0.780291204508126[/C][/ROW]
[ROW][C]66[/C][C]0.144992213304209[/C][C]0.289984426608417[/C][C]0.855007786695791[/C][/ROW]
[ROW][C]67[/C][C]0.0868847089595174[/C][C]0.173769417919035[/C][C]0.913115291040483[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185706&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185706&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
150.07411431615227090.1482286323045420.925885683847729
160.02536208276093270.05072416552186540.974637917239067
170.008773900770656630.01754780154131330.991226099229343
180.02540408715178310.05080817430356620.974595912848217
190.05535210261350380.1107042052270080.944647897386496
200.10005045801980.2001009160395990.8999495419802
210.05861570845728170.1172314169145630.941384291542718
220.03360300935835970.06720601871671940.96639699064164
230.02111454341707260.04222908683414520.978885456582927
240.01643071254088220.03286142508176440.983569287459118
250.02806333137487810.05612666274975620.971936668625122
260.01558909592475630.03117819184951270.984410904075244
270.01334874259238540.02669748518477090.986651257407615
280.009296013188278760.01859202637655750.990703986811721
290.01613996660029630.03227993320059250.983860033399704
300.01007450917346420.02014901834692830.989925490826536
310.007815615022830060.01563123004566010.99218438497717
320.004700378484466640.009400756968933270.995299621515533
330.01166318830007580.02332637660015150.988336811699924
340.01170531806747450.0234106361349490.988294681932525
350.0092407040585130.0184814081170260.990759295941487
360.007749412787456220.01549882557491240.992250587212544
370.007246685001114860.01449337000222970.992753314998885
380.005410781940706770.01082156388141350.994589218059293
390.005704008205154420.01140801641030880.994295991794846
400.03252940268012240.06505880536024470.967470597319878
410.02971386264151490.05942772528302980.970286137358485
420.04058839074683610.08117678149367210.959411609253164
430.03482045359859920.06964090719719840.965179546401401
440.07474391143897060.1494878228779410.925256088561029
450.09301864802941680.1860372960588340.906981351970583
460.07443044301069780.1488608860213960.925569556989302
470.09385995574456320.1877199114891260.906140044255437
480.1399536625810180.2799073251620360.860046337418982
490.1073206659215140.2146413318430280.892679334078486
500.08626952436685770.1725390487337150.913730475633142
510.1009007663948630.2018015327897270.899099233605137
520.09497516430162680.1899503286032540.905024835698373
530.06693115093946320.1338623018789260.933068849060537
540.05422099663559150.1084419932711830.945779003364408
550.03553661767557160.07107323535114320.964463382324428
560.06452257541498180.1290451508299640.935477424585018
570.07815322946276250.1563064589255250.921846770537238
580.1216775614360780.2433551228721560.878322438563922
590.3148139566615770.6296279133231530.685186043338423
600.4563504022707690.9127008045415380.543649597729231
610.4974880195793180.9949760391586360.502511980420682
620.3941762704787480.7883525409574970.605823729521252
630.3966888381181270.7933776762362550.603311161881873
640.3187588451359750.637517690271950.681241154864025
650.2197087954918740.4394175909837480.780291204508126
660.1449922133042090.2899844266084170.855007786695791
670.08688470895951740.1737694179190350.913115291040483







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level10.0188679245283019NOK
5% type I error level170.320754716981132NOK
10% type I error level260.490566037735849NOK

\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 & 1 & 0.0188679245283019 & NOK \tabularnewline
5% type I error level & 17 & 0.320754716981132 & NOK \tabularnewline
10% type I error level & 26 & 0.490566037735849 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185706&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]1[/C][C]0.0188679245283019[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]17[/C][C]0.320754716981132[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]26[/C][C]0.490566037735849[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185706&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185706&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 level10.0188679245283019NOK
5% type I error level170.320754716981132NOK
10% type I error level260.490566037735849NOK



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