## 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 10:54:43 -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/t1351954517jy4srqlx1myjdb3.htm/, Retrieved Thu, 18 Aug 2022 14:21:16 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=185743, Retrieved Thu, 18 Aug 2022 14:21:16 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact102
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] [2c4ddb4bf62114b8025bb962e2c7a2b5]
-             [Multiple Regression] [Multiple Regressi...] [2012-11-03 14:54:43] [b4b733de199089e913cc2b6ea19b06b9] [Current]
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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 Input view raw input (R code) Raw Output view raw output of R engine Computing time 8 seconds R Server 'Sir Ronald Aylmer Fisher' @ fisher.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 Ronald Aylmer Fisher' @ fisher.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185743&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 Ronald Aylmer Fisher' @ fisher.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185743&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185743&T=0

As an alternative you can also use a QR Code:

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

 Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 8 seconds R Server 'Sir Ronald Aylmer Fisher' @ fisher.wessa.net

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

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y_t[t] =  +  31.3471573638543 +  0.444702753166022X_1t[t] -0.362675102423084X_2t[t] -0.262539402740327X_3t[t] -0.207318027182089X_4t[t] -0.274389035067997X_5t[t] -0.327297113875413X_6t[t] -0.209605852221708X_7t[t] +  0.0459801204750859X_8t[t] +  0.887743425105732X_9t[t] -0.000478451727605654X_10t[t] -0.241762602800191X_11t[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185743&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y_t[t] =  +  31.3471573638543 +  0.444702753166022X_1t[t] -0.362675102423084X_2t[t] -0.262539402740327X_3t[t] -0.207318027182089X_4t[t] -0.274389035067997X_5t[t] -0.327297113875413X_6t[t] -0.209605852221708X_7t[t] +  0.0459801204750859X_8t[t] +  0.887743425105732X_9t[t] -0.000478451727605654X_10t[t] -0.241762602800191X_11t[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185743&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185743&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 Y_t[t] = + 31.3471573638543 + 0.444702753166022X_1t[t] -0.362675102423084X_2t[t] -0.262539402740327X_3t[t] -0.207318027182089X_4t[t] -0.274389035067997X_5t[t] -0.327297113875413X_6t[t] -0.209605852221708X_7t[t] + 0.0459801204750859X_8t[t] + 0.887743425105732X_9t[t] -0.000478451727605654X_10t[t] -0.241762602800191X_11t[t] + e[t]

 Multiple Linear Regression - Ordinary Least Squares Variable Parameter S.D. T-STATH0: parameter = 0 2-tail p-value 1-tail p-value (Intercept) 31.3471573638543 9.23636 3.3939 0.001138 0.000569 X_1t 0.444702753166022 0.053459 8.3185 0 0 X_2t -0.362675102423084 0.058971 -6.1501 0 0 X_3t -0.262539402740327 0.059547 -4.4089 3.7e-05 1.8e-05 X_4t -0.207318027182089 0.056577 -3.6644 0.000478 0.000239 X_5t -0.274389035067997 0.079743 -3.4409 0.000982 0.000491 X_6t -0.327297113875413 0.136247 -2.4022 0.018952 0.009476 X_7t -0.209605852221708 0.269963 -0.7764 0.440115 0.220057 X_8t 0.0459801204750859 0.297767 0.1544 0.877726 0.438863 X_9t 0.887743425105732 0.312265 2.8429 0.005854 0.002927 X_10t -0.000478451727605654 0.066048 -0.0072 0.994241 0.49712 X_11t -0.241762602800191 0.158891 -1.5216 0.132624 0.066312

\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_2t & -0.362675102423084 & 0.058971 & -6.1501 & 0 & 0 \tabularnewline
X_3t & -0.262539402740327 & 0.059547 & -4.4089 & 3.7e-05 & 1.8e-05 \tabularnewline
X_4t & -0.207318027182089 & 0.056577 & -3.6644 & 0.000478 & 0.000239 \tabularnewline
X_5t & -0.274389035067997 & 0.079743 & -3.4409 & 0.000982 & 0.000491 \tabularnewline
X_6t & -0.327297113875413 & 0.136247 & -2.4022 & 0.018952 & 0.009476 \tabularnewline
X_7t & -0.209605852221708 & 0.269963 & -0.7764 & 0.440115 & 0.220057 \tabularnewline
X_8t & 0.0459801204750859 & 0.297767 & 0.1544 & 0.877726 & 0.438863 \tabularnewline
X_9t & 0.887743425105732 & 0.312265 & 2.8429 & 0.005854 & 0.002927 \tabularnewline
X_10t & -0.000478451727605654 & 0.066048 & -0.0072 & 0.994241 & 0.49712 \tabularnewline
X_11t & -0.241762602800191 & 0.158891 & -1.5216 & 0.132624 & 0.066312 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185743&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_2t[/C][C]-0.362675102423084[/C][C]0.058971[/C][C]-6.1501[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]X_3t[/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_4t[/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_5t[/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_6t[/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_7t[/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_8t[/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_9t[/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_10t[/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_11t[/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=185743&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185743&T=2

As an alternative you can also use a QR Code:

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

 Multiple Linear Regression - Ordinary Least Squares Variable Parameter S.D. T-STATH0: parameter = 0 2-tail p-value 1-tail p-value (Intercept) 31.3471573638543 9.23636 3.3939 0.001138 0.000569 X_1t 0.444702753166022 0.053459 8.3185 0 0 X_2t -0.362675102423084 0.058971 -6.1501 0 0 X_3t -0.262539402740327 0.059547 -4.4089 3.7e-05 1.8e-05 X_4t -0.207318027182089 0.056577 -3.6644 0.000478 0.000239 X_5t -0.274389035067997 0.079743 -3.4409 0.000982 0.000491 X_6t -0.327297113875413 0.136247 -2.4022 0.018952 0.009476 X_7t -0.209605852221708 0.269963 -0.7764 0.440115 0.220057 X_8t 0.0459801204750859 0.297767 0.1544 0.877726 0.438863 X_9t 0.887743425105732 0.312265 2.8429 0.005854 0.002927 X_10t -0.000478451727605654 0.066048 -0.0072 0.994241 0.49712 X_11t -0.241762602800191 0.158891 -1.5216 0.132624 0.066312

 Multiple Linear Regression - Regression Statistics Multiple R 0.957117519497325 R-squared 0.916073946128713 Adjusted R-squared 0.902885566234654 F-TEST (value) 69.460688385338 F-TEST (DF numerator) 11 F-TEST (DF denominator) 70 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 3.0240654350445 Sum Squared Residuals 640.148022880163

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.957117519497325 \tabularnewline
R-squared & 0.916073946128713 \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=185743&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]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=185743&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185743&T=3

As an alternative you can also use a QR Code:

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

 Multiple Linear Regression - Regression Statistics Multiple R 0.957117519497325 R-squared 0.916073946128713 Adjusted R-squared 0.902885566234654 F-TEST (value) 69.460688385338 F-TEST (DF numerator) 11 F-TEST (DF denominator) 70 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 3.0240654350445 Sum Squared Residuals 640.148022880163

 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals InterpolationForecast ResidualsPrediction Error 1 -3 -4.94808082486269 1.94808082486269 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 7 0 -1.78500251453288 1.78500251453288 8 -5 -1.86842044012433 -3.13157955987567 9 -3 -3.07434783929724 0.0743478392972435 10 3 0.564505095473401 2.4354949045266 11 2 -0.632129383592598 2.6321293835926 12 -7 -11.1663388195651 4.16633881956507 13 -1 1.69080317608383 -2.69080317608383 14 0 1.94923548223378 -1.94923548223378 15 -3 -0.948930336808768 -2.05106966319123 16 4 7.10114994268736 -3.10114994268736 17 2 2.58329321015519 -0.583293210155192 18 3 1.21672499531602 1.78327500468398 19 0 -3.04277854650954 3.04277854650954 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 -16.823827801967 0.823827801967045 25 -18 -21.377552846466 3.37755284646597 26 -14 -14.2583025565495 0.258302556549502 27 -12 -16.1476454519459 4.14764545194594 28 -17 -18.5571169442532 1.55711694425317 29 -23 -20.6407854145687 -2.35921458543134 30 -28 -24.2630396191683 -3.73696038083171 31 -31 -28.0235085879537 -2.97649141204635 32 -21 -21.1020592383068 0.102059238306841 33 -19 -17.4096709779125 -1.59032902208746 34 -22 -25.0019751407929 3.0019751407929 35 -22 -23.8570898503263 1.85708985032628 36 -25 -22.0598870066226 -2.94011299337739 37 -16 -18.4456709644692 2.44567096446921 38 -22 -17.8341257308486 -4.16587426915142 39 -21 -16.7685618316674 -4.23143816833263 40 -10 -11.0306703421397 1.0306703421397 41 -7 -6.4366553049305 -0.563344695069504 42 -5 -8.40780507108695 3.40780507108696 43 -4 -5.61479460237621 1.61479460237621 44 7 2.14591340850389 4.85408659149611 45 6 1.42993911934804 4.57006088065196 46 3 2.62980878894998 0.370191211050021 47 10 7.87258694746923 2.12741305253077 48 0 5.80434527654845 -5.80434527654845 49 -2 -0.288555804068115 -1.71144419593188 50 -1 1.20563724070957 -2.20563724070957 51 2 0.900108328206155 1.09989167179384 52 8 6.0843590203522 1.9156409796478 53 -6 -4.3397445194819 -1.6602554805181 54 -4 -1.01767324966563 -2.98232675033437 55 4 4.81947387797566 -0.819473877975659 56 7 3.39324563089569 3.60675436910431 57 3 2.09695799721404 0.903042002785955 58 3 -0.0809167571807361 3.08091675718074 59 8 3.01423765824375 4.98576234175625 60 3 0.551300428954482 2.44869957104552 61 -3 -2.56320995451276 -0.436790045487237 62 4 3.3630497294552 0.6369502705448 63 -5 -8.21313854701562 3.21313854701562 64 -1 1.21843408563981 -2.21843408563981 65 5 5.34078981836698 -0.340789818366985 66 0 -2.70501667147466 2.70501667147466 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 -20.4896206863158 0.489620686315809 72 -10 -19.3364214441201 9.33642144412008 73 -22 -23.2399015742188 1.23990157421877 74 -25 -24.4844514739612 -0.515548526038803 75 -10 -11.8386881013632 1.83868810136319 76 -8 -11.8417462537851 3.84174625378515 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 -17.0097690902306 1.0097690902306

\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=185743&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=185743&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185743&T=4

As an alternative you can also use a QR Code:

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

 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals InterpolationForecast ResidualsPrediction Error 1 -3 -4.94808082486269 1.94808082486269 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 7 0 -1.78500251453288 1.78500251453288 8 -5 -1.86842044012433 -3.13157955987567 9 -3 -3.07434783929724 0.0743478392972435 10 3 0.564505095473401 2.4354949045266 11 2 -0.632129383592598 2.6321293835926 12 -7 -11.1663388195651 4.16633881956507 13 -1 1.69080317608383 -2.69080317608383 14 0 1.94923548223378 -1.94923548223378 15 -3 -0.948930336808768 -2.05106966319123 16 4 7.10114994268736 -3.10114994268736 17 2 2.58329321015519 -0.583293210155192 18 3 1.21672499531602 1.78327500468398 19 0 -3.04277854650954 3.04277854650954 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 -16.823827801967 0.823827801967045 25 -18 -21.377552846466 3.37755284646597 26 -14 -14.2583025565495 0.258302556549502 27 -12 -16.1476454519459 4.14764545194594 28 -17 -18.5571169442532 1.55711694425317 29 -23 -20.6407854145687 -2.35921458543134 30 -28 -24.2630396191683 -3.73696038083171 31 -31 -28.0235085879537 -2.97649141204635 32 -21 -21.1020592383068 0.102059238306841 33 -19 -17.4096709779125 -1.59032902208746 34 -22 -25.0019751407929 3.0019751407929 35 -22 -23.8570898503263 1.85708985032628 36 -25 -22.0598870066226 -2.94011299337739 37 -16 -18.4456709644692 2.44567096446921 38 -22 -17.8341257308486 -4.16587426915142 39 -21 -16.7685618316674 -4.23143816833263 40 -10 -11.0306703421397 1.0306703421397 41 -7 -6.4366553049305 -0.563344695069504 42 -5 -8.40780507108695 3.40780507108696 43 -4 -5.61479460237621 1.61479460237621 44 7 2.14591340850389 4.85408659149611 45 6 1.42993911934804 4.57006088065196 46 3 2.62980878894998 0.370191211050021 47 10 7.87258694746923 2.12741305253077 48 0 5.80434527654845 -5.80434527654845 49 -2 -0.288555804068115 -1.71144419593188 50 -1 1.20563724070957 -2.20563724070957 51 2 0.900108328206155 1.09989167179384 52 8 6.0843590203522 1.9156409796478 53 -6 -4.3397445194819 -1.6602554805181 54 -4 -1.01767324966563 -2.98232675033437 55 4 4.81947387797566 -0.819473877975659 56 7 3.39324563089569 3.60675436910431 57 3 2.09695799721404 0.903042002785955 58 3 -0.0809167571807361 3.08091675718074 59 8 3.01423765824375 4.98576234175625 60 3 0.551300428954482 2.44869957104552 61 -3 -2.56320995451276 -0.436790045487237 62 4 3.3630497294552 0.6369502705448 63 -5 -8.21313854701562 3.21313854701562 64 -1 1.21843408563981 -2.21843408563981 65 5 5.34078981836698 -0.340789818366985 66 0 -2.70501667147466 2.70501667147466 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 -20.4896206863158 0.489620686315809 72 -10 -19.3364214441201 9.33642144412008 73 -22 -23.2399015742188 1.23990157421877 74 -25 -24.4844514739612 -0.515548526038803 75 -10 -11.8386881013632 1.83868810136319 76 -8 -11.8417462537851 3.84174625378515 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 -17.0097690902306 1.0097690902306

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

\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=185743&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=185743&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185743&T=5

As an alternative you can also use a QR Code:

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

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

 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 1 0.0188679245283019 NOK 5% type I error level 17 0.320754716981132 NOK 10% type I error level 26 0.490566037735849 NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 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=185743&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=185743&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185743&T=6

As an alternative you can also use a QR Code:

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

 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 1 0.0188679245283019 NOK 5% type I error level 17 0.320754716981132 NOK 10% type I error level 26 0.490566037735849 NOK

library(lattice)library(lmtest)n25 <- 25 #minimum number of obs. for Goldfeld-Quandt testpar1 <- as.numeric(par1)x <- t(y)k <- length(x[1,])n <- length(x[,1])x1 <- cbind(x[,par1], x[,1:k!=par1])mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])colnames(x1) <- mycolnames #colnames(x)[par1]x <- x1if (par3 == 'First Differences'){x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))for (i in 1:n-1) {for (j in 1:k) {x2[i,j] <- x[i+1,j] - x[i,j]}}x <- x2}if (par2 == 'Include Monthly Dummies'){x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))for (i in 1:11){x2[seq(i,n,12),i] <- 1}x <- cbind(x, x2)}if (par2 == 'Include Quarterly Dummies'){x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))for (i in 1:3){x2[seq(i,n,4),i] <- 1}x <- cbind(x, x2)}k <- length(x[1,])if (par3 == 'Linear Trend'){x <- cbind(x, c(1:n))colnames(x)[k+1] <- 't'}xk <- length(x[1,])df <- as.data.frame(x)(mylm <- lm(df))(mysum <- summary(mylm))if (n > n25) {kp3 <- k + 3nmkm3 <- n - k - 3gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))numgqtests <- 0numsignificant1 <- 0numsignificant5 <- 0numsignificant10 <- 0for (mypoint in kp3:nmkm3) {j <- 0numgqtests <- numgqtests + 1for (myalt in c('greater', 'two.sided', 'less')) {j <- j + 1gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value}if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1}gqarr}bitmap(file='test0.png')plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')points(x[,1]-mysum$resid)grid()dev.off()bitmap(file='test1.png')plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')grid()dev.off()bitmap(file='test2.png')hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')grid()dev.off()bitmap(file='test3.png')densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')dev.off()bitmap(file='test4.png')qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')qqline(mysum$resid)grid()dev.off()(myerror <- as.ts(mysum$resid))bitmap(file='test5.png')dum <- cbind(lag(myerror,k=1),myerror)dumdum1 <- dum[2:length(myerror),]dum1z <- as.data.frame(dum1)zplot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')lines(lowess(z))abline(lm(z))grid()dev.off()bitmap(file='test6.png')acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')grid()dev.off()bitmap(file='test7.png')pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')grid()dev.off()bitmap(file='test8.png')opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))plot(mylm, las = 1, sub='Residual Diagnostics')par(opar)dev.off()if (n > n25) {bitmap(file='test9.png')plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')grid()dev.off()}load(file='createtable')a<-table.start()a<-table.row.start(a)a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)a<-table.row.end(a)myeq <- colnames(x)[1]myeq <- paste(myeq, '[t] = ', sep='')for (i in 1:k){if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')if (rownames(mysum$coefficients)[i] != '(Intercept)') {myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')}}myeq <- paste(myeq, ' + e[t]')a<-table.row.start(a)a<-table.element(a, myeq)a<-table.row.end(a)a<-table.end(a)table.save(a,file='mytable1.tab')a<-table.start()a<-table.row.start(a)a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a,'Variable',header=TRUE)a<-table.element(a,'Parameter',header=TRUE)a<-table.element(a,'S.D.',header=TRUE)a<-table.element(a,'T-STATH0: parameter = 0',header=TRUE)a<-table.element(a,'2-tail p-value',header=TRUE)a<-table.element(a,'1-tail p-value',header=TRUE)a<-table.row.end(a)for (i in 1:k){a<-table.row.start(a)a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)a<-table.element(a,mysum$coefficients[i,1])a<-table.element(a, round(mysum$coefficients[i,2],6))a<-table.element(a, round(mysum$coefficients[i,3],4))a<-table.element(a, round(mysum$coefficients[i,4],6))a<-table.element(a, round(mysum$coefficients[i,4]/2,6))a<-table.row.end(a)}a<-table.end(a)table.save(a,file='mytable2.tab')a<-table.start()a<-table.row.start(a)a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a, 'Multiple R',1,TRUE)a<-table.element(a, sqrt(mysum$r.squared))a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a, 'R-squared',1,TRUE)a<-table.element(a, mysum$r.squared)a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a, 'Adjusted R-squared',1,TRUE)a<-table.element(a, mysum$adj.r.squared)a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a, 'F-TEST (value)',1,TRUE)a<-table.element(a, mysum$fstatistic[1])a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)a<-table.element(a, mysum$fstatistic[2])a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)a<-table.element(a, mysum$fstatistic[3])a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a, 'p-value',1,TRUE)a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a, 'Residual Standard Deviation',1,TRUE)a<-table.element(a, mysum$sigma)a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a, 'Sum Squared Residuals',1,TRUE)a<-table.element(a, sum(myerror*myerror))a<-table.row.end(a)a<-table.end(a)table.save(a,file='mytable3.tab')a<-table.start()a<-table.row.start(a)a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a, 'Time or Index', 1, TRUE)a<-table.element(a, 'Actuals', 1, TRUE)a<-table.element(a, 'InterpolationForecast', 1, TRUE)a<-table.element(a, 'ResidualsPrediction Error', 1, TRUE)a<-table.row.end(a)for (i in 1:n) {a<-table.row.start(a)a<-table.element(a,i, 1, TRUE)a<-table.element(a,x[i])a<-table.element(a,x[i]-mysum$resid[i])a<-table.element(a,mysum\$resid[i])a<-table.row.end(a)}a<-table.end(a)table.save(a,file='mytable4.tab')if (n > n25) {a<-table.start()a<-table.row.start(a)a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a,'p-values',header=TRUE)a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a,'breakpoint index',header=TRUE)a<-table.element(a,'greater',header=TRUE)a<-table.element(a,'2-sided',header=TRUE)a<-table.element(a,'less',header=TRUE)a<-table.row.end(a)for (mypoint in kp3:nmkm3) {a<-table.row.start(a)a<-table.element(a,mypoint,header=TRUE)a<-table.element(a,gqarr[mypoint-kp3+1,1])a<-table.element(a,gqarr[mypoint-kp3+1,2])a<-table.element(a,gqarr[mypoint-kp3+1,3])a<-table.row.end(a)}a<-table.end(a)table.save(a,file='mytable5.tab')a<-table.start()a<-table.row.start(a)a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a,'Description',header=TRUE)a<-table.element(a,'# significant tests',header=TRUE)a<-table.element(a,'% significant tests',header=TRUE)a<-table.element(a,'OK/NOK',header=TRUE)a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a,'1% type I error level',header=TRUE)a<-table.element(a,numsignificant1)a<-table.element(a,numsignificant1/numgqtests)if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'a<-table.element(a,dum)a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a,'5% type I error level',header=TRUE)a<-table.element(a,numsignificant5)a<-table.element(a,numsignificant5/numgqtests)if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'a<-table.element(a,dum)a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a,'10% type I error level',header=TRUE)a<-table.element(a,numsignificant10)a<-table.element(a,numsignificant10/numgqtests)if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'a<-table.element(a,dum)a<-table.row.end(a)a<-table.end(a)table.save(a,file='mytable6.tab')}