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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationTue, 22 Nov 2011 04:30:16 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Nov/22/t1321954260j90rloojew26v2q.htm/, Retrieved Sat, 20 Apr 2024 08:07:47 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=146087, Retrieved Sat, 20 Apr 2024 08:07:47 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact129

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
- RMPD  [Pearson Correlation] [Correlatie belong...] [2010-11-19 18:01:17] [97ad38b1c3b35a5feca8b85f7bc7b3ff]
- RMPD      [Multiple Regression] [C7.3] [2011-11-22 09:30:16] [51aabe75794be7f34bed5d3096a085df] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
15	15	77	10	5	4	11	12	13	6
12	9	63	20	6	4	12	7	11	4
15	12	73	16	4	10	12	13	14	6
12	15	76	10	6	6	11	11	12	5
14	17	90	8	3	5	11	16	12	5
8	14	67	14	10	8	10	10	6	4
11	9	69	19	8	9	11	15	10	5
15	12	70	15	3	6	9	5	11	3
4	11	54	23	4	8	10	4	10	2
13	13	54	9	3	11	12	7	12	5
19	16	76	12	5	6	12	15	15	6
10	16	75	14	5	8	12	5	13	6
15	15	76	13	6	11	13	16	18	8
6	10	80	11	5	5	9	15	11	6
7	16	89	11	3	10	12	13	12	3
14	12	73	10	4	7	12	13	13	6
16	15	74	12	8	7	12	15	14	6
16	13	78	18	8	13	12	15	16	7
14	18	76	12	8	10	13	10	16	8
15	13	69	10	5	8	11	17	16	6
14	17	74	15	8	6	12	14	15	7
12	14	82	15	2	8	12	9	13	4
9	13	77	12	0	7	15	6	8	4
12	13	84	9	5	5	11	11	14	2
14	15	75	11	2	9	12	13	15	6
12	13	54	15	7	9	10	12	13	6
14	15	79	16	5	11	11	10	16	6
10	13	79	17	2	11	13	4	13	6
14	14	69	12	12	11	6	13	12	6
16	13	88	11	7	9	12	15	15	7
10	16	57	13	0	7	12	8	11	4
8	14	69	9	2	6	10	10	14	3
12	18	86	11	3	6	12	8	13	5
11	15	65	9	0	6	12	7	13	6
8	9	66	20	9	5	11	9	12	4
13	16	54	8	2	4	9	14	14	6
11	16	85	12	3	10	10	5	13	3
12	17	79	10	1	8	12	7	12	3
16	13	84	11	10	6	12	16	14	6
16	17	70	13	1	5	11	14	15	6
13	15	54	13	4	9	12	16	16	6
14	14	70	13	6	10	11	15	15	8
5	10	54	15	6	6	14	4	5	2
14	13	69	12	4	9	10	12	15	6
13	11	68	13	4	10	10	8	8	4
16	11	68	13	7	6	11	17	16	7
14	16	71	9	7	6	11	15	16	6
15	16	71	9	7	6	11	16	14	6
15	11	66	14	0	13	10	12	16	6
11	15	67	9	3	8	10	12	14	5
15	15	71	9	8	10	12	13	13	6
16	12	54	15	8	5	11	14	14	6
13	17	76	10	10	8	8	14	14	5
11	15	77	13	11	6	12	15	12	6
12	16	71	8	6	9	10	14	13	7
12	14	69	15	2	9	7	11	15	5
10	17	73	13	6	7	11	13	15	6
8	10	46	24	1	20	7	4	13	6
9	11	66	11	5	8	11	8	10	4
12	15	77	13	4	8	8	13	13	5
14	15	77	12	6	7	11	15	14	6
12	7	70	22	6	7	12	15	13	6
11	17	86	11	4	10	8	8	13	4
14	14	38	15	1	5	14	17	18	6
7	18	66	7	6	8	14	12	12	4
16	14	75	14	7	9	11	13	14	7
16	12	80	19	7	9	12	14	16	8
11	14	64	10	2	20	14	7	13	6
16	9	80	9	7	6	9	16	16	6
13	14	86	12	8	10	13	11	15	6
11	11	54	16	5	11	8	10	14	5
13	16	74	13	4	7	11	14	13	6
14	17	88	11	2	12	9	19	12	6
15	16	85	12	0	12	12	14	16	4
10	12	63	11	7	8	7	8	9	5
15	15	81	13	0	6	11	15	15	8
11	15	81	13	5	6	12	8	16	6
11	15	74	10	3	9	11	8	12	6
6	16	80	11	3	5	12	6	11	2
11	16	80	9	3	11	9	7	13	2
12	11	60	13	3	6	11	16	13	4
13	15	65	15	7	6	13	15	14	6
12	12	62	14	6	10	12	10	15	6
8	14	63	14	3	8	12	8	14	5
9	15	89	11	0	7	11	9	12	4
10	17	76	10	2	8	12	8	16	4
16	19	81	11	0	9	12	14	14	6
15	15	72	12	9	8	11	14	13	5
14	16	84	14	10	10	11	14	12	6
12	14	76	14	3	13	8	15	13	7
12	16	76	21	7	7	9	7	12	6
10	15	78	14	3	7	11	7	9	4
12	15	72	13	6	7	12	12	13	4
8	17	81	11	5	8	13	7	10	3
16	12	72	12	0	9	12	12	15	8
11	18	78	12	0	9	6	6	9	4
12	13	79	11	4	8	12	10	13	4
9	14	52	14	0	7	11	12	13	5
14	14	67	13	0	6	13	13	13	5
15	14	74	13	7	8	11	14	15	7
8	12	73	12	3	8	12	8	13	4
12	14	69	14	9	4	10	14	14	5
10	12	67	12	4	8	10	10	11	5
16	15	76	12	4	10	11	14	15	8
17	11	77	12	15	7	11	15	14	5
8	11	63	18	7	8	11	10	15	2
9	15	84	11	8	7	9	6	12	5
8	14	90	15	2	10	7	9	15	4
11	15	75	13	8	9	11	11	14	5
16	16	76	11	7	8	12	16	16	7
13	12	75	11	3	8	12	14	14	6
5	14	53	22	3	5	15	8	12	3
15	18	87	10	6	8	11	16	11	5
15	14	78	11	8	9	10	16	13	6
12	13	54	15	5	11	13	14	12	5
12	14	58	14	6	7	13	12	12	6
16	14	80	11	10	8	11	16	16	7
12	17	74	10	0	4	12	15	13	6
10	12	56	14	5	16	12	11	12	6
12	16	82	14	0	9	12	6	14	5
4	15	64	11	0	16	8	6	4	4
11	10	67	15	5	12	5	16	14	6
16	13	75	11	10	8	11	16	15	6
7	15	69	10	0	4	12	8	12	3
9	16	72	10	5	11	12	11	11	4
14	15	71	16	6	11	11	12	12	4
11	14	54	12	1	8	12	13	11	4
10	11	68	14	5	8	10	11	12	5
6	13	54	15	3	12	7	9	11	4
14	17	71	10	3	8	12	15	13	6
11	14	53	12	6	6	12	11	12	6
11	16	54	15	2	8	9	12	12	4
9	15	71	12	5	6	11	15	15	7
16	12	69	11	6	14	12	8	14	4
7	16	30	10	2	10	12	7	12	4
8	8	53	20	3	5	11	10	12	4
10	9	68	19	7	8	11	9	12	4
14	13	69	17	6	12	12	13	13	5
9	19	54	8	3	11	12	11	11	4
13	11	66	17	6	8	11	12	13	7
13	15	79	11	9	8	12	5	12	3
12	11	67	13	2	9	12	12	14	5
11	15	74	9	5	6	8	14	15	5
10	16	86	10	10	5	15	15	15	6
12	15	63	13	9	8	11	14	13	5
14	12	69	16	8	7	11	13	16	6
11	16	73	12	8	4	6	14	17	6
13	15	69	14	5	9	13	14	13	3
14	13	71	11	9	5	12	15	14	6
13	14	77	13	9	9	12	13	13	5
16	11	74	15	14	12	12	14	16	8
13	15	82	14	5	6	12	11	13	6
12	16	54	14	12	4	12	14	14	4
9	14	54	14	6	6	10	11	13	3
14	13	80	10	6	7	12	8	14	4
15	15	76	8	8	9	12	12	16	7

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Herman Ole Andreas Wold' @ wold.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 & 6 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146087&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]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146087&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146087&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net






Multiple Linear Regression - Estimated Regression Equation
Popularity[t] = -1.16892826150713 -0.0572765637003981Happiness[t] + 0.0453253788739521Belonging[t] -0.080730628713802Depression[t] + 0.0964153521063372Weighted_popularity[t] + 0.077276519014147Parental_criticism[t] + 0.117596535487944Finding_Friends[t] + 0.227711407897117Knowing_People[t] + 0.34708334464486Perceived_Liked[t] + 0.520075961070442Celebrity[t] -0.00640747869671335t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Popularity[t] =  -1.16892826150713 -0.0572765637003981Happiness[t] +  0.0453253788739521Belonging[t] -0.080730628713802Depression[t] +  0.0964153521063372Weighted_popularity[t] +  0.077276519014147Parental_criticism[t] +  0.117596535487944Finding_Friends[t] +  0.227711407897117Knowing_People[t] +  0.34708334464486Perceived_Liked[t] +  0.520075961070442Celebrity[t] -0.00640747869671335t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146087&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Popularity[t] =  -1.16892826150713 -0.0572765637003981Happiness[t] +  0.0453253788739521Belonging[t] -0.080730628713802Depression[t] +  0.0964153521063372Weighted_popularity[t] +  0.077276519014147Parental_criticism[t] +  0.117596535487944Finding_Friends[t] +  0.227711407897117Knowing_People[t] +  0.34708334464486Perceived_Liked[t] +  0.520075961070442Celebrity[t] -0.00640747869671335t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146087&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146087&T=1

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


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

Multiple Linear Regression - Estimated Regression Equation
Popularity[t] = -1.16892826150713 -0.0572765637003981Happiness[t] + 0.0453253788739521Belonging[t] -0.080730628713802Depression[t] + 0.0964153521063372Weighted_popularity[t] + 0.077276519014147Parental_criticism[t] + 0.117596535487944Finding_Friends[t] + 0.227711407897117Knowing_People[t] + 0.34708334464486Perceived_Liked[t] + 0.520075961070442Celebrity[t] -0.00640747869671335t + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-1.168928261507132.523234-0.46330.6438680.321934
Happiness-0.05727656370039810.085478-0.67010.5038760.251938
Belonging0.04532537887395210.0169622.67220.0083990.0042
Depression-0.0807306287138020.063126-1.27890.2029840.101492
Weighted_popularity0.09641535210633720.058111.65920.099240.04962
Parental_criticism0.0772765190141470.0646611.19510.2339990.117
Finding_Friends0.1175965354879440.0936611.25550.2112990.10565
Knowing_People0.2277114078971170.0642943.54170.0005350.000268
Perceived_Liked0.347083344644860.0938253.69930.0003060.000153
Celebrity0.5200759610704420.1584053.28320.0012860.000643
t-0.006407478696713350.003735-1.71560.0883660.044183

\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) & -1.16892826150713 & 2.523234 & -0.4633 & 0.643868 & 0.321934 \tabularnewline
Happiness & -0.0572765637003981 & 0.085478 & -0.6701 & 0.503876 & 0.251938 \tabularnewline
Belonging & 0.0453253788739521 & 0.016962 & 2.6722 & 0.008399 & 0.0042 \tabularnewline
Depression & -0.080730628713802 & 0.063126 & -1.2789 & 0.202984 & 0.101492 \tabularnewline
Weighted_popularity & 0.0964153521063372 & 0.05811 & 1.6592 & 0.09924 & 0.04962 \tabularnewline
Parental_criticism & 0.077276519014147 & 0.064661 & 1.1951 & 0.233999 & 0.117 \tabularnewline
Finding_Friends & 0.117596535487944 & 0.093661 & 1.2555 & 0.211299 & 0.10565 \tabularnewline
Knowing_People & 0.227711407897117 & 0.064294 & 3.5417 & 0.000535 & 0.000268 \tabularnewline
Perceived_Liked & 0.34708334464486 & 0.093825 & 3.6993 & 0.000306 & 0.000153 \tabularnewline
Celebrity & 0.520075961070442 & 0.158405 & 3.2832 & 0.001286 & 0.000643 \tabularnewline
t & -0.00640747869671335 & 0.003735 & -1.7156 & 0.088366 & 0.044183 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146087&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]-1.16892826150713[/C][C]2.523234[/C][C]-0.4633[/C][C]0.643868[/C][C]0.321934[/C][/ROW]
[ROW][C]Happiness[/C][C]-0.0572765637003981[/C][C]0.085478[/C][C]-0.6701[/C][C]0.503876[/C][C]0.251938[/C][/ROW]
[ROW][C]Belonging[/C][C]0.0453253788739521[/C][C]0.016962[/C][C]2.6722[/C][C]0.008399[/C][C]0.0042[/C][/ROW]
[ROW][C]Depression[/C][C]-0.080730628713802[/C][C]0.063126[/C][C]-1.2789[/C][C]0.202984[/C][C]0.101492[/C][/ROW]
[ROW][C]Weighted_popularity[/C][C]0.0964153521063372[/C][C]0.05811[/C][C]1.6592[/C][C]0.09924[/C][C]0.04962[/C][/ROW]
[ROW][C]Parental_criticism[/C][C]0.077276519014147[/C][C]0.064661[/C][C]1.1951[/C][C]0.233999[/C][C]0.117[/C][/ROW]
[ROW][C]Finding_Friends[/C][C]0.117596535487944[/C][C]0.093661[/C][C]1.2555[/C][C]0.211299[/C][C]0.10565[/C][/ROW]
[ROW][C]Knowing_People[/C][C]0.227711407897117[/C][C]0.064294[/C][C]3.5417[/C][C]0.000535[/C][C]0.000268[/C][/ROW]
[ROW][C]Perceived_Liked[/C][C]0.34708334464486[/C][C]0.093825[/C][C]3.6993[/C][C]0.000306[/C][C]0.000153[/C][/ROW]
[ROW][C]Celebrity[/C][C]0.520075961070442[/C][C]0.158405[/C][C]3.2832[/C][C]0.001286[/C][C]0.000643[/C][/ROW]
[ROW][C]t[/C][C]-0.00640747869671335[/C][C]0.003735[/C][C]-1.7156[/C][C]0.088366[/C][C]0.044183[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146087&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146087&T=2

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


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-1.168928261507132.523234-0.46330.6438680.321934
Happiness-0.05727656370039810.085478-0.67010.5038760.251938
Belonging0.04532537887395210.0169622.67220.0083990.0042
Depression-0.0807306287138020.063126-1.27890.2029840.101492
Weighted_popularity0.09641535210633720.058111.65920.099240.04962
Parental_criticism0.0772765190141470.0646611.19510.2339990.117
Finding_Friends0.1175965354879440.0936611.25550.2112990.10565
Knowing_People0.2277114078971170.0642943.54170.0005350.000268
Perceived_Liked0.347083344644860.0938253.69930.0003060.000153
Celebrity0.5200759610704420.1584053.28320.0012860.000643
t-0.006407478696713350.003735-1.71560.0883660.044183






Multiple Linear Regression - Regression Statistics
Multiple R0.746865837404828
R-squared0.557808579082415
Adjusted R-squared0.527312619019133
F-TEST (value)18.2912286717622
F-TEST (DF numerator)10
F-TEST (DF denominator)145
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.01899776486645
Sum Squared Residuals591.071036307677

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.746865837404828 \tabularnewline
R-squared & 0.557808579082415 \tabularnewline
Adjusted R-squared & 0.527312619019133 \tabularnewline
F-TEST (value) & 18.2912286717622 \tabularnewline
F-TEST (DF numerator) & 10 \tabularnewline
F-TEST (DF denominator) & 145 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.01899776486645 \tabularnewline
Sum Squared Residuals & 591.071036307677 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146087&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.746865837404828[/C][/ROW]
[ROW][C]R-squared[/C][C]0.557808579082415[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.527312619019133[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]18.2912286717622[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]10[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]145[/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]2.01899776486645[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]591.071036307677[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146087&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146087&T=3

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


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

Multiple Linear Regression - Regression Statistics
Multiple R0.746865837404828
R-squared0.557808579082415
Adjusted R-squared0.527312619019133
F-TEST (value)18.2912286717622
F-TEST (DF numerator)10
F-TEST (DF denominator)145
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.01899776486645
Sum Squared Residuals591.071036307677






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11513.09808455897341.90191544102663
2129.334611107783792.66538889221621
31513.6510490549111.34895094508902
41212.1896344205315-0.189634420531493
51413.63672484024930.363275159750658
689.09557557624667-1.09557557624667
71112.1115572596557-1.11155725965572
8158.382285938569766.61771406143024
946.33579814388996-2.33579814388996
101310.65320241189012.34679758810994
111914.41939705850164.58060294149841
121011.3894752132707-1.38947521327073
131517.2926358827324-2.29263588273236
14613.187466644766-7.18746664476603
15712.1231023366937-5.12310233669369
161413.47322270244920.526777297550778
171614.36701722296211.63298277703789
181615.84998237932420.150017620675773
191415.2182109966908-1.21821099669079
201515.2172057096217-0.21720570962175
211414.5468136734371-0.546813673437087
221211.25794823023680.742051769763175
2398.988513743333510.0114862566664853
241211.57961856866130.420381431338652
251413.9095348552170.0904651447829504
261212.0679306250819-0.0679306250818922
271413.66451994956330.335480050436709
281011.2303635028932-1.23036350289317
291412.96037591440251.0396240855975
301616.038856051892-0.0388560518920171
31108.922069259730751.07793074026925
32810.7539999726585-2.75399997265848
331211.69681060737380.30318939262623
341111.0749796177072-0.0749796177071758
35810.3105726476984-2.31057264769841
361312.21359081735890.786409182641093
371110.01030467164840.989695328351576
38129.83227841331952.1677215866805
391615.21785582958420.782144170415825
401613.01537483981442.98462516018556
411313.9167692548379-0.916769254837924
421415.3107122591626-1.31071225916263
4355.59431254652437-0.594312546524372
441413.27958921076240.720410789237553
45138.958374404649554.04162559535045
461615.43600075314750.563999246852537
471414.6266103305611-0.626610330561149
481514.15374757047180.846252429528168
491513.33519556290631.66480443709368
501112.2372811627391-1.23728116273909
511513.68470209339671.31529790660325
521612.66602471464453.33397528535547
531313.3258405899626-0.325840589962623
541113.7029888671069-2.70298886710691
551213.9250133183881-1.92501331838815
561211.6098233369160.390176663083983
571013.451342229255-3.45134222925503
5889.04261103534925-1.04261103534925
5999.69310574143097-0.693105741430971
601212.045387994039-0.0453879940389715
611413.9106370572280.0893629427720446
621213.0083713397218-1.00837133972183
631110.97692213531350.0230778646865252
641413.49872552489020.501274475109798
65711.6308647176515-4.63086471765147
661613.99938574768012.00061425231988
671615.49005574093020.509944259069826
681112.2982409395182-1.29824093951819
691615.2870284919230.712971508076961
701314.4142657678109-1.4142657678109
711110.91153041303670.0884695869632992
721312.79854600577340.201453994226599
731414.2807110397621-0.28071103976212
741513.48445667863431.51554332136573
75109.292770560647780.707229439352217
761514.64656239869280.353437601307165
771112.9527797832399-1.95277978323991
781111.4043554773293-0.40435547732932
7968.45757353417261-2.45757353417261
80119.645374917711391.35462508228861
811211.63428523430250.365714765697492
821313.2443154760362-0.244315476036222
831212.6581126741542-0.658112674154231
84810.8160962310738-2.81609623107383
85910.7024135722567-1.70241357225673
861011.6212793989087-1.62127939890874
871613.24291455350252.75708544649748
881512.78266009976772.21733990023233
891413.52538035299060.474619647009403
901213.5699261701497-1.56992617014973
911210.23459942399181.76540057600822
92108.709363374215061.29063662578494
931211.44546726086040.554532739139586
9489.29247098996122-1.29247098996122
951614.03574408224371.96425591775626
96117.722977922127063.27702207787294
971211.44215238202690.557847617973135
98910.3074555378289-1.30745553782888
991411.44728733081412.55271266918589
1001514.31445495535950.685545044640456
101810.5692779610823-2.56927796108232
1021212.373175300765-0.373175300764953
1031010.4270650991506-0.42706509915059
1041614.78831280611371.21168719388628
1051714.20547645726082.79452354273925
106810.0343820261573-2.0343820261573
107910.7078936298132-1.70789362981318
108811.3302451431894-3.33024514318939
1091112.3481588429061-1.34815884290607
1101615.30804175209420.69195824790584
1111313.4300882747453-0.430088274745342
11257.92422944710671-2.92422944710671
1131512.7639954519712.23600454802897
1141513.86478852759561.13521147240442
1151211.40044047143280.599559528567213
1161211.45075099457120.549249005428758
1171615.73069356494480.269306435055162
1181212.4165342860829-0.416534286082865
1191011.7091963037699-1.70919630376992
1201210.66466371609731.33533628390266
12146.22150795113951-2.22150795113951
1221112.9228174382834-1.92281743828336
1231614.65573905637991.34426094362011
12478.76472456379761-1.76472456379761
125910.71615589177-1.71615589177001
1261410.79092939477733.2090706052227
127119.678507834945081.32149216505492
1281010.8792289215278-0.879228921527817
12968.48388602635172-2.48388602635172
1301412.8020205374761.19797946252395
1311110.86688871477920.133111285220822
132119.152723110309561.84727688969044
133913.8708137524157-4.8708137524157
1341611.35724879235754.64275120764247
13577.81813032982012-0.818130329820121
13688.78068323292848-0.780683232928479
137109.867390059925120.132609940074876
1381411.94695515947032.05304484052966
13999.60739523239862-0.60739523239862
1401312.29845509555510.701544904444912
141139.522030607301483.47796939269852
1421211.77003110047150.22996889952846
1431112.56425405171-1.56425405170999
1441014.9395072859872-4.93950728598721
1451211.92877477547560.0712252245243581
1461413.28388009096990.71611990903015
1471113.3075729058824-2.30757290588238
1481310.98743024333922.01256975666079
1491413.52239996856670.477600031433273
1501312.55573089653280.444269103467224
1511615.96681135750490.0331886424950528
1521312.07869782090330.921302179096672
1531211.2563232429470.743676757053049
15499.15504321665876-0.155043216658764
1551411.20378933925442.7962106607456
1561514.57561242141920.424387578580833

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 15 & 13.0980845589734 & 1.90191544102663 \tabularnewline
2 & 12 & 9.33461110778379 & 2.66538889221621 \tabularnewline
3 & 15 & 13.651049054911 & 1.34895094508902 \tabularnewline
4 & 12 & 12.1896344205315 & -0.189634420531493 \tabularnewline
5 & 14 & 13.6367248402493 & 0.363275159750658 \tabularnewline
6 & 8 & 9.09557557624667 & -1.09557557624667 \tabularnewline
7 & 11 & 12.1115572596557 & -1.11155725965572 \tabularnewline
8 & 15 & 8.38228593856976 & 6.61771406143024 \tabularnewline
9 & 4 & 6.33579814388996 & -2.33579814388996 \tabularnewline
10 & 13 & 10.6532024118901 & 2.34679758810994 \tabularnewline
11 & 19 & 14.4193970585016 & 4.58060294149841 \tabularnewline
12 & 10 & 11.3894752132707 & -1.38947521327073 \tabularnewline
13 & 15 & 17.2926358827324 & -2.29263588273236 \tabularnewline
14 & 6 & 13.187466644766 & -7.18746664476603 \tabularnewline
15 & 7 & 12.1231023366937 & -5.12310233669369 \tabularnewline
16 & 14 & 13.4732227024492 & 0.526777297550778 \tabularnewline
17 & 16 & 14.3670172229621 & 1.63298277703789 \tabularnewline
18 & 16 & 15.8499823793242 & 0.150017620675773 \tabularnewline
19 & 14 & 15.2182109966908 & -1.21821099669079 \tabularnewline
20 & 15 & 15.2172057096217 & -0.21720570962175 \tabularnewline
21 & 14 & 14.5468136734371 & -0.546813673437087 \tabularnewline
22 & 12 & 11.2579482302368 & 0.742051769763175 \tabularnewline
23 & 9 & 8.98851374333351 & 0.0114862566664853 \tabularnewline
24 & 12 & 11.5796185686613 & 0.420381431338652 \tabularnewline
25 & 14 & 13.909534855217 & 0.0904651447829504 \tabularnewline
26 & 12 & 12.0679306250819 & -0.0679306250818922 \tabularnewline
27 & 14 & 13.6645199495633 & 0.335480050436709 \tabularnewline
28 & 10 & 11.2303635028932 & -1.23036350289317 \tabularnewline
29 & 14 & 12.9603759144025 & 1.0396240855975 \tabularnewline
30 & 16 & 16.038856051892 & -0.0388560518920171 \tabularnewline
31 & 10 & 8.92206925973075 & 1.07793074026925 \tabularnewline
32 & 8 & 10.7539999726585 & -2.75399997265848 \tabularnewline
33 & 12 & 11.6968106073738 & 0.30318939262623 \tabularnewline
34 & 11 & 11.0749796177072 & -0.0749796177071758 \tabularnewline
35 & 8 & 10.3105726476984 & -2.31057264769841 \tabularnewline
36 & 13 & 12.2135908173589 & 0.786409182641093 \tabularnewline
37 & 11 & 10.0103046716484 & 0.989695328351576 \tabularnewline
38 & 12 & 9.8322784133195 & 2.1677215866805 \tabularnewline
39 & 16 & 15.2178558295842 & 0.782144170415825 \tabularnewline
40 & 16 & 13.0153748398144 & 2.98462516018556 \tabularnewline
41 & 13 & 13.9167692548379 & -0.916769254837924 \tabularnewline
42 & 14 & 15.3107122591626 & -1.31071225916263 \tabularnewline
43 & 5 & 5.59431254652437 & -0.594312546524372 \tabularnewline
44 & 14 & 13.2795892107624 & 0.720410789237553 \tabularnewline
45 & 13 & 8.95837440464955 & 4.04162559535045 \tabularnewline
46 & 16 & 15.4360007531475 & 0.563999246852537 \tabularnewline
47 & 14 & 14.6266103305611 & -0.626610330561149 \tabularnewline
48 & 15 & 14.1537475704718 & 0.846252429528168 \tabularnewline
49 & 15 & 13.3351955629063 & 1.66480443709368 \tabularnewline
50 & 11 & 12.2372811627391 & -1.23728116273909 \tabularnewline
51 & 15 & 13.6847020933967 & 1.31529790660325 \tabularnewline
52 & 16 & 12.6660247146445 & 3.33397528535547 \tabularnewline
53 & 13 & 13.3258405899626 & -0.325840589962623 \tabularnewline
54 & 11 & 13.7029888671069 & -2.70298886710691 \tabularnewline
55 & 12 & 13.9250133183881 & -1.92501331838815 \tabularnewline
56 & 12 & 11.609823336916 & 0.390176663083983 \tabularnewline
57 & 10 & 13.451342229255 & -3.45134222925503 \tabularnewline
58 & 8 & 9.04261103534925 & -1.04261103534925 \tabularnewline
59 & 9 & 9.69310574143097 & -0.693105741430971 \tabularnewline
60 & 12 & 12.045387994039 & -0.0453879940389715 \tabularnewline
61 & 14 & 13.910637057228 & 0.0893629427720446 \tabularnewline
62 & 12 & 13.0083713397218 & -1.00837133972183 \tabularnewline
63 & 11 & 10.9769221353135 & 0.0230778646865252 \tabularnewline
64 & 14 & 13.4987255248902 & 0.501274475109798 \tabularnewline
65 & 7 & 11.6308647176515 & -4.63086471765147 \tabularnewline
66 & 16 & 13.9993857476801 & 2.00061425231988 \tabularnewline
67 & 16 & 15.4900557409302 & 0.509944259069826 \tabularnewline
68 & 11 & 12.2982409395182 & -1.29824093951819 \tabularnewline
69 & 16 & 15.287028491923 & 0.712971508076961 \tabularnewline
70 & 13 & 14.4142657678109 & -1.4142657678109 \tabularnewline
71 & 11 & 10.9115304130367 & 0.0884695869632992 \tabularnewline
72 & 13 & 12.7985460057734 & 0.201453994226599 \tabularnewline
73 & 14 & 14.2807110397621 & -0.28071103976212 \tabularnewline
74 & 15 & 13.4844566786343 & 1.51554332136573 \tabularnewline
75 & 10 & 9.29277056064778 & 0.707229439352217 \tabularnewline
76 & 15 & 14.6465623986928 & 0.353437601307165 \tabularnewline
77 & 11 & 12.9527797832399 & -1.95277978323991 \tabularnewline
78 & 11 & 11.4043554773293 & -0.40435547732932 \tabularnewline
79 & 6 & 8.45757353417261 & -2.45757353417261 \tabularnewline
80 & 11 & 9.64537491771139 & 1.35462508228861 \tabularnewline
81 & 12 & 11.6342852343025 & 0.365714765697492 \tabularnewline
82 & 13 & 13.2443154760362 & -0.244315476036222 \tabularnewline
83 & 12 & 12.6581126741542 & -0.658112674154231 \tabularnewline
84 & 8 & 10.8160962310738 & -2.81609623107383 \tabularnewline
85 & 9 & 10.7024135722567 & -1.70241357225673 \tabularnewline
86 & 10 & 11.6212793989087 & -1.62127939890874 \tabularnewline
87 & 16 & 13.2429145535025 & 2.75708544649748 \tabularnewline
88 & 15 & 12.7826600997677 & 2.21733990023233 \tabularnewline
89 & 14 & 13.5253803529906 & 0.474619647009403 \tabularnewline
90 & 12 & 13.5699261701497 & -1.56992617014973 \tabularnewline
91 & 12 & 10.2345994239918 & 1.76540057600822 \tabularnewline
92 & 10 & 8.70936337421506 & 1.29063662578494 \tabularnewline
93 & 12 & 11.4454672608604 & 0.554532739139586 \tabularnewline
94 & 8 & 9.29247098996122 & -1.29247098996122 \tabularnewline
95 & 16 & 14.0357440822437 & 1.96425591775626 \tabularnewline
96 & 11 & 7.72297792212706 & 3.27702207787294 \tabularnewline
97 & 12 & 11.4421523820269 & 0.557847617973135 \tabularnewline
98 & 9 & 10.3074555378289 & -1.30745553782888 \tabularnewline
99 & 14 & 11.4472873308141 & 2.55271266918589 \tabularnewline
100 & 15 & 14.3144549553595 & 0.685545044640456 \tabularnewline
101 & 8 & 10.5692779610823 & -2.56927796108232 \tabularnewline
102 & 12 & 12.373175300765 & -0.373175300764953 \tabularnewline
103 & 10 & 10.4270650991506 & -0.42706509915059 \tabularnewline
104 & 16 & 14.7883128061137 & 1.21168719388628 \tabularnewline
105 & 17 & 14.2054764572608 & 2.79452354273925 \tabularnewline
106 & 8 & 10.0343820261573 & -2.0343820261573 \tabularnewline
107 & 9 & 10.7078936298132 & -1.70789362981318 \tabularnewline
108 & 8 & 11.3302451431894 & -3.33024514318939 \tabularnewline
109 & 11 & 12.3481588429061 & -1.34815884290607 \tabularnewline
110 & 16 & 15.3080417520942 & 0.69195824790584 \tabularnewline
111 & 13 & 13.4300882747453 & -0.430088274745342 \tabularnewline
112 & 5 & 7.92422944710671 & -2.92422944710671 \tabularnewline
113 & 15 & 12.763995451971 & 2.23600454802897 \tabularnewline
114 & 15 & 13.8647885275956 & 1.13521147240442 \tabularnewline
115 & 12 & 11.4004404714328 & 0.599559528567213 \tabularnewline
116 & 12 & 11.4507509945712 & 0.549249005428758 \tabularnewline
117 & 16 & 15.7306935649448 & 0.269306435055162 \tabularnewline
118 & 12 & 12.4165342860829 & -0.416534286082865 \tabularnewline
119 & 10 & 11.7091963037699 & -1.70919630376992 \tabularnewline
120 & 12 & 10.6646637160973 & 1.33533628390266 \tabularnewline
121 & 4 & 6.22150795113951 & -2.22150795113951 \tabularnewline
122 & 11 & 12.9228174382834 & -1.92281743828336 \tabularnewline
123 & 16 & 14.6557390563799 & 1.34426094362011 \tabularnewline
124 & 7 & 8.76472456379761 & -1.76472456379761 \tabularnewline
125 & 9 & 10.71615589177 & -1.71615589177001 \tabularnewline
126 & 14 & 10.7909293947773 & 3.2090706052227 \tabularnewline
127 & 11 & 9.67850783494508 & 1.32149216505492 \tabularnewline
128 & 10 & 10.8792289215278 & -0.879228921527817 \tabularnewline
129 & 6 & 8.48388602635172 & -2.48388602635172 \tabularnewline
130 & 14 & 12.802020537476 & 1.19797946252395 \tabularnewline
131 & 11 & 10.8668887147792 & 0.133111285220822 \tabularnewline
132 & 11 & 9.15272311030956 & 1.84727688969044 \tabularnewline
133 & 9 & 13.8708137524157 & -4.8708137524157 \tabularnewline
134 & 16 & 11.3572487923575 & 4.64275120764247 \tabularnewline
135 & 7 & 7.81813032982012 & -0.818130329820121 \tabularnewline
136 & 8 & 8.78068323292848 & -0.780683232928479 \tabularnewline
137 & 10 & 9.86739005992512 & 0.132609940074876 \tabularnewline
138 & 14 & 11.9469551594703 & 2.05304484052966 \tabularnewline
139 & 9 & 9.60739523239862 & -0.60739523239862 \tabularnewline
140 & 13 & 12.2984550955551 & 0.701544904444912 \tabularnewline
141 & 13 & 9.52203060730148 & 3.47796939269852 \tabularnewline
142 & 12 & 11.7700311004715 & 0.22996889952846 \tabularnewline
143 & 11 & 12.56425405171 & -1.56425405170999 \tabularnewline
144 & 10 & 14.9395072859872 & -4.93950728598721 \tabularnewline
145 & 12 & 11.9287747754756 & 0.0712252245243581 \tabularnewline
146 & 14 & 13.2838800909699 & 0.71611990903015 \tabularnewline
147 & 11 & 13.3075729058824 & -2.30757290588238 \tabularnewline
148 & 13 & 10.9874302433392 & 2.01256975666079 \tabularnewline
149 & 14 & 13.5223999685667 & 0.477600031433273 \tabularnewline
150 & 13 & 12.5557308965328 & 0.444269103467224 \tabularnewline
151 & 16 & 15.9668113575049 & 0.0331886424950528 \tabularnewline
152 & 13 & 12.0786978209033 & 0.921302179096672 \tabularnewline
153 & 12 & 11.256323242947 & 0.743676757053049 \tabularnewline
154 & 9 & 9.15504321665876 & -0.155043216658764 \tabularnewline
155 & 14 & 11.2037893392544 & 2.7962106607456 \tabularnewline
156 & 15 & 14.5756124214192 & 0.424387578580833 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146087&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]15[/C][C]13.0980845589734[/C][C]1.90191544102663[/C][/ROW]
[ROW][C]2[/C][C]12[/C][C]9.33461110778379[/C][C]2.66538889221621[/C][/ROW]
[ROW][C]3[/C][C]15[/C][C]13.651049054911[/C][C]1.34895094508902[/C][/ROW]
[ROW][C]4[/C][C]12[/C][C]12.1896344205315[/C][C]-0.189634420531493[/C][/ROW]
[ROW][C]5[/C][C]14[/C][C]13.6367248402493[/C][C]0.363275159750658[/C][/ROW]
[ROW][C]6[/C][C]8[/C][C]9.09557557624667[/C][C]-1.09557557624667[/C][/ROW]
[ROW][C]7[/C][C]11[/C][C]12.1115572596557[/C][C]-1.11155725965572[/C][/ROW]
[ROW][C]8[/C][C]15[/C][C]8.38228593856976[/C][C]6.61771406143024[/C][/ROW]
[ROW][C]9[/C][C]4[/C][C]6.33579814388996[/C][C]-2.33579814388996[/C][/ROW]
[ROW][C]10[/C][C]13[/C][C]10.6532024118901[/C][C]2.34679758810994[/C][/ROW]
[ROW][C]11[/C][C]19[/C][C]14.4193970585016[/C][C]4.58060294149841[/C][/ROW]
[ROW][C]12[/C][C]10[/C][C]11.3894752132707[/C][C]-1.38947521327073[/C][/ROW]
[ROW][C]13[/C][C]15[/C][C]17.2926358827324[/C][C]-2.29263588273236[/C][/ROW]
[ROW][C]14[/C][C]6[/C][C]13.187466644766[/C][C]-7.18746664476603[/C][/ROW]
[ROW][C]15[/C][C]7[/C][C]12.1231023366937[/C][C]-5.12310233669369[/C][/ROW]
[ROW][C]16[/C][C]14[/C][C]13.4732227024492[/C][C]0.526777297550778[/C][/ROW]
[ROW][C]17[/C][C]16[/C][C]14.3670172229621[/C][C]1.63298277703789[/C][/ROW]
[ROW][C]18[/C][C]16[/C][C]15.8499823793242[/C][C]0.150017620675773[/C][/ROW]
[ROW][C]19[/C][C]14[/C][C]15.2182109966908[/C][C]-1.21821099669079[/C][/ROW]
[ROW][C]20[/C][C]15[/C][C]15.2172057096217[/C][C]-0.21720570962175[/C][/ROW]
[ROW][C]21[/C][C]14[/C][C]14.5468136734371[/C][C]-0.546813673437087[/C][/ROW]
[ROW][C]22[/C][C]12[/C][C]11.2579482302368[/C][C]0.742051769763175[/C][/ROW]
[ROW][C]23[/C][C]9[/C][C]8.98851374333351[/C][C]0.0114862566664853[/C][/ROW]
[ROW][C]24[/C][C]12[/C][C]11.5796185686613[/C][C]0.420381431338652[/C][/ROW]
[ROW][C]25[/C][C]14[/C][C]13.909534855217[/C][C]0.0904651447829504[/C][/ROW]
[ROW][C]26[/C][C]12[/C][C]12.0679306250819[/C][C]-0.0679306250818922[/C][/ROW]
[ROW][C]27[/C][C]14[/C][C]13.6645199495633[/C][C]0.335480050436709[/C][/ROW]
[ROW][C]28[/C][C]10[/C][C]11.2303635028932[/C][C]-1.23036350289317[/C][/ROW]
[ROW][C]29[/C][C]14[/C][C]12.9603759144025[/C][C]1.0396240855975[/C][/ROW]
[ROW][C]30[/C][C]16[/C][C]16.038856051892[/C][C]-0.0388560518920171[/C][/ROW]
[ROW][C]31[/C][C]10[/C][C]8.92206925973075[/C][C]1.07793074026925[/C][/ROW]
[ROW][C]32[/C][C]8[/C][C]10.7539999726585[/C][C]-2.75399997265848[/C][/ROW]
[ROW][C]33[/C][C]12[/C][C]11.6968106073738[/C][C]0.30318939262623[/C][/ROW]
[ROW][C]34[/C][C]11[/C][C]11.0749796177072[/C][C]-0.0749796177071758[/C][/ROW]
[ROW][C]35[/C][C]8[/C][C]10.3105726476984[/C][C]-2.31057264769841[/C][/ROW]
[ROW][C]36[/C][C]13[/C][C]12.2135908173589[/C][C]0.786409182641093[/C][/ROW]
[ROW][C]37[/C][C]11[/C][C]10.0103046716484[/C][C]0.989695328351576[/C][/ROW]
[ROW][C]38[/C][C]12[/C][C]9.8322784133195[/C][C]2.1677215866805[/C][/ROW]
[ROW][C]39[/C][C]16[/C][C]15.2178558295842[/C][C]0.782144170415825[/C][/ROW]
[ROW][C]40[/C][C]16[/C][C]13.0153748398144[/C][C]2.98462516018556[/C][/ROW]
[ROW][C]41[/C][C]13[/C][C]13.9167692548379[/C][C]-0.916769254837924[/C][/ROW]
[ROW][C]42[/C][C]14[/C][C]15.3107122591626[/C][C]-1.31071225916263[/C][/ROW]
[ROW][C]43[/C][C]5[/C][C]5.59431254652437[/C][C]-0.594312546524372[/C][/ROW]
[ROW][C]44[/C][C]14[/C][C]13.2795892107624[/C][C]0.720410789237553[/C][/ROW]
[ROW][C]45[/C][C]13[/C][C]8.95837440464955[/C][C]4.04162559535045[/C][/ROW]
[ROW][C]46[/C][C]16[/C][C]15.4360007531475[/C][C]0.563999246852537[/C][/ROW]
[ROW][C]47[/C][C]14[/C][C]14.6266103305611[/C][C]-0.626610330561149[/C][/ROW]
[ROW][C]48[/C][C]15[/C][C]14.1537475704718[/C][C]0.846252429528168[/C][/ROW]
[ROW][C]49[/C][C]15[/C][C]13.3351955629063[/C][C]1.66480443709368[/C][/ROW]
[ROW][C]50[/C][C]11[/C][C]12.2372811627391[/C][C]-1.23728116273909[/C][/ROW]
[ROW][C]51[/C][C]15[/C][C]13.6847020933967[/C][C]1.31529790660325[/C][/ROW]
[ROW][C]52[/C][C]16[/C][C]12.6660247146445[/C][C]3.33397528535547[/C][/ROW]
[ROW][C]53[/C][C]13[/C][C]13.3258405899626[/C][C]-0.325840589962623[/C][/ROW]
[ROW][C]54[/C][C]11[/C][C]13.7029888671069[/C][C]-2.70298886710691[/C][/ROW]
[ROW][C]55[/C][C]12[/C][C]13.9250133183881[/C][C]-1.92501331838815[/C][/ROW]
[ROW][C]56[/C][C]12[/C][C]11.609823336916[/C][C]0.390176663083983[/C][/ROW]
[ROW][C]57[/C][C]10[/C][C]13.451342229255[/C][C]-3.45134222925503[/C][/ROW]
[ROW][C]58[/C][C]8[/C][C]9.04261103534925[/C][C]-1.04261103534925[/C][/ROW]
[ROW][C]59[/C][C]9[/C][C]9.69310574143097[/C][C]-0.693105741430971[/C][/ROW]
[ROW][C]60[/C][C]12[/C][C]12.045387994039[/C][C]-0.0453879940389715[/C][/ROW]
[ROW][C]61[/C][C]14[/C][C]13.910637057228[/C][C]0.0893629427720446[/C][/ROW]
[ROW][C]62[/C][C]12[/C][C]13.0083713397218[/C][C]-1.00837133972183[/C][/ROW]
[ROW][C]63[/C][C]11[/C][C]10.9769221353135[/C][C]0.0230778646865252[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]13.4987255248902[/C][C]0.501274475109798[/C][/ROW]
[ROW][C]65[/C][C]7[/C][C]11.6308647176515[/C][C]-4.63086471765147[/C][/ROW]
[ROW][C]66[/C][C]16[/C][C]13.9993857476801[/C][C]2.00061425231988[/C][/ROW]
[ROW][C]67[/C][C]16[/C][C]15.4900557409302[/C][C]0.509944259069826[/C][/ROW]
[ROW][C]68[/C][C]11[/C][C]12.2982409395182[/C][C]-1.29824093951819[/C][/ROW]
[ROW][C]69[/C][C]16[/C][C]15.287028491923[/C][C]0.712971508076961[/C][/ROW]
[ROW][C]70[/C][C]13[/C][C]14.4142657678109[/C][C]-1.4142657678109[/C][/ROW]
[ROW][C]71[/C][C]11[/C][C]10.9115304130367[/C][C]0.0884695869632992[/C][/ROW]
[ROW][C]72[/C][C]13[/C][C]12.7985460057734[/C][C]0.201453994226599[/C][/ROW]
[ROW][C]73[/C][C]14[/C][C]14.2807110397621[/C][C]-0.28071103976212[/C][/ROW]
[ROW][C]74[/C][C]15[/C][C]13.4844566786343[/C][C]1.51554332136573[/C][/ROW]
[ROW][C]75[/C][C]10[/C][C]9.29277056064778[/C][C]0.707229439352217[/C][/ROW]
[ROW][C]76[/C][C]15[/C][C]14.6465623986928[/C][C]0.353437601307165[/C][/ROW]
[ROW][C]77[/C][C]11[/C][C]12.9527797832399[/C][C]-1.95277978323991[/C][/ROW]
[ROW][C]78[/C][C]11[/C][C]11.4043554773293[/C][C]-0.40435547732932[/C][/ROW]
[ROW][C]79[/C][C]6[/C][C]8.45757353417261[/C][C]-2.45757353417261[/C][/ROW]
[ROW][C]80[/C][C]11[/C][C]9.64537491771139[/C][C]1.35462508228861[/C][/ROW]
[ROW][C]81[/C][C]12[/C][C]11.6342852343025[/C][C]0.365714765697492[/C][/ROW]
[ROW][C]82[/C][C]13[/C][C]13.2443154760362[/C][C]-0.244315476036222[/C][/ROW]
[ROW][C]83[/C][C]12[/C][C]12.6581126741542[/C][C]-0.658112674154231[/C][/ROW]
[ROW][C]84[/C][C]8[/C][C]10.8160962310738[/C][C]-2.81609623107383[/C][/ROW]
[ROW][C]85[/C][C]9[/C][C]10.7024135722567[/C][C]-1.70241357225673[/C][/ROW]
[ROW][C]86[/C][C]10[/C][C]11.6212793989087[/C][C]-1.62127939890874[/C][/ROW]
[ROW][C]87[/C][C]16[/C][C]13.2429145535025[/C][C]2.75708544649748[/C][/ROW]
[ROW][C]88[/C][C]15[/C][C]12.7826600997677[/C][C]2.21733990023233[/C][/ROW]
[ROW][C]89[/C][C]14[/C][C]13.5253803529906[/C][C]0.474619647009403[/C][/ROW]
[ROW][C]90[/C][C]12[/C][C]13.5699261701497[/C][C]-1.56992617014973[/C][/ROW]
[ROW][C]91[/C][C]12[/C][C]10.2345994239918[/C][C]1.76540057600822[/C][/ROW]
[ROW][C]92[/C][C]10[/C][C]8.70936337421506[/C][C]1.29063662578494[/C][/ROW]
[ROW][C]93[/C][C]12[/C][C]11.4454672608604[/C][C]0.554532739139586[/C][/ROW]
[ROW][C]94[/C][C]8[/C][C]9.29247098996122[/C][C]-1.29247098996122[/C][/ROW]
[ROW][C]95[/C][C]16[/C][C]14.0357440822437[/C][C]1.96425591775626[/C][/ROW]
[ROW][C]96[/C][C]11[/C][C]7.72297792212706[/C][C]3.27702207787294[/C][/ROW]
[ROW][C]97[/C][C]12[/C][C]11.4421523820269[/C][C]0.557847617973135[/C][/ROW]
[ROW][C]98[/C][C]9[/C][C]10.3074555378289[/C][C]-1.30745553782888[/C][/ROW]
[ROW][C]99[/C][C]14[/C][C]11.4472873308141[/C][C]2.55271266918589[/C][/ROW]
[ROW][C]100[/C][C]15[/C][C]14.3144549553595[/C][C]0.685545044640456[/C][/ROW]
[ROW][C]101[/C][C]8[/C][C]10.5692779610823[/C][C]-2.56927796108232[/C][/ROW]
[ROW][C]102[/C][C]12[/C][C]12.373175300765[/C][C]-0.373175300764953[/C][/ROW]
[ROW][C]103[/C][C]10[/C][C]10.4270650991506[/C][C]-0.42706509915059[/C][/ROW]
[ROW][C]104[/C][C]16[/C][C]14.7883128061137[/C][C]1.21168719388628[/C][/ROW]
[ROW][C]105[/C][C]17[/C][C]14.2054764572608[/C][C]2.79452354273925[/C][/ROW]
[ROW][C]106[/C][C]8[/C][C]10.0343820261573[/C][C]-2.0343820261573[/C][/ROW]
[ROW][C]107[/C][C]9[/C][C]10.7078936298132[/C][C]-1.70789362981318[/C][/ROW]
[ROW][C]108[/C][C]8[/C][C]11.3302451431894[/C][C]-3.33024514318939[/C][/ROW]
[ROW][C]109[/C][C]11[/C][C]12.3481588429061[/C][C]-1.34815884290607[/C][/ROW]
[ROW][C]110[/C][C]16[/C][C]15.3080417520942[/C][C]0.69195824790584[/C][/ROW]
[ROW][C]111[/C][C]13[/C][C]13.4300882747453[/C][C]-0.430088274745342[/C][/ROW]
[ROW][C]112[/C][C]5[/C][C]7.92422944710671[/C][C]-2.92422944710671[/C][/ROW]
[ROW][C]113[/C][C]15[/C][C]12.763995451971[/C][C]2.23600454802897[/C][/ROW]
[ROW][C]114[/C][C]15[/C][C]13.8647885275956[/C][C]1.13521147240442[/C][/ROW]
[ROW][C]115[/C][C]12[/C][C]11.4004404714328[/C][C]0.599559528567213[/C][/ROW]
[ROW][C]116[/C][C]12[/C][C]11.4507509945712[/C][C]0.549249005428758[/C][/ROW]
[ROW][C]117[/C][C]16[/C][C]15.7306935649448[/C][C]0.269306435055162[/C][/ROW]
[ROW][C]118[/C][C]12[/C][C]12.4165342860829[/C][C]-0.416534286082865[/C][/ROW]
[ROW][C]119[/C][C]10[/C][C]11.7091963037699[/C][C]-1.70919630376992[/C][/ROW]
[ROW][C]120[/C][C]12[/C][C]10.6646637160973[/C][C]1.33533628390266[/C][/ROW]
[ROW][C]121[/C][C]4[/C][C]6.22150795113951[/C][C]-2.22150795113951[/C][/ROW]
[ROW][C]122[/C][C]11[/C][C]12.9228174382834[/C][C]-1.92281743828336[/C][/ROW]
[ROW][C]123[/C][C]16[/C][C]14.6557390563799[/C][C]1.34426094362011[/C][/ROW]
[ROW][C]124[/C][C]7[/C][C]8.76472456379761[/C][C]-1.76472456379761[/C][/ROW]
[ROW][C]125[/C][C]9[/C][C]10.71615589177[/C][C]-1.71615589177001[/C][/ROW]
[ROW][C]126[/C][C]14[/C][C]10.7909293947773[/C][C]3.2090706052227[/C][/ROW]
[ROW][C]127[/C][C]11[/C][C]9.67850783494508[/C][C]1.32149216505492[/C][/ROW]
[ROW][C]128[/C][C]10[/C][C]10.8792289215278[/C][C]-0.879228921527817[/C][/ROW]
[ROW][C]129[/C][C]6[/C][C]8.48388602635172[/C][C]-2.48388602635172[/C][/ROW]
[ROW][C]130[/C][C]14[/C][C]12.802020537476[/C][C]1.19797946252395[/C][/ROW]
[ROW][C]131[/C][C]11[/C][C]10.8668887147792[/C][C]0.133111285220822[/C][/ROW]
[ROW][C]132[/C][C]11[/C][C]9.15272311030956[/C][C]1.84727688969044[/C][/ROW]
[ROW][C]133[/C][C]9[/C][C]13.8708137524157[/C][C]-4.8708137524157[/C][/ROW]
[ROW][C]134[/C][C]16[/C][C]11.3572487923575[/C][C]4.64275120764247[/C][/ROW]
[ROW][C]135[/C][C]7[/C][C]7.81813032982012[/C][C]-0.818130329820121[/C][/ROW]
[ROW][C]136[/C][C]8[/C][C]8.78068323292848[/C][C]-0.780683232928479[/C][/ROW]
[ROW][C]137[/C][C]10[/C][C]9.86739005992512[/C][C]0.132609940074876[/C][/ROW]
[ROW][C]138[/C][C]14[/C][C]11.9469551594703[/C][C]2.05304484052966[/C][/ROW]
[ROW][C]139[/C][C]9[/C][C]9.60739523239862[/C][C]-0.60739523239862[/C][/ROW]
[ROW][C]140[/C][C]13[/C][C]12.2984550955551[/C][C]0.701544904444912[/C][/ROW]
[ROW][C]141[/C][C]13[/C][C]9.52203060730148[/C][C]3.47796939269852[/C][/ROW]
[ROW][C]142[/C][C]12[/C][C]11.7700311004715[/C][C]0.22996889952846[/C][/ROW]
[ROW][C]143[/C][C]11[/C][C]12.56425405171[/C][C]-1.56425405170999[/C][/ROW]
[ROW][C]144[/C][C]10[/C][C]14.9395072859872[/C][C]-4.93950728598721[/C][/ROW]
[ROW][C]145[/C][C]12[/C][C]11.9287747754756[/C][C]0.0712252245243581[/C][/ROW]
[ROW][C]146[/C][C]14[/C][C]13.2838800909699[/C][C]0.71611990903015[/C][/ROW]
[ROW][C]147[/C][C]11[/C][C]13.3075729058824[/C][C]-2.30757290588238[/C][/ROW]
[ROW][C]148[/C][C]13[/C][C]10.9874302433392[/C][C]2.01256975666079[/C][/ROW]
[ROW][C]149[/C][C]14[/C][C]13.5223999685667[/C][C]0.477600031433273[/C][/ROW]
[ROW][C]150[/C][C]13[/C][C]12.5557308965328[/C][C]0.444269103467224[/C][/ROW]
[ROW][C]151[/C][C]16[/C][C]15.9668113575049[/C][C]0.0331886424950528[/C][/ROW]
[ROW][C]152[/C][C]13[/C][C]12.0786978209033[/C][C]0.921302179096672[/C][/ROW]
[ROW][C]153[/C][C]12[/C][C]11.256323242947[/C][C]0.743676757053049[/C][/ROW]
[ROW][C]154[/C][C]9[/C][C]9.15504321665876[/C][C]-0.155043216658764[/C][/ROW]
[ROW][C]155[/C][C]14[/C][C]11.2037893392544[/C][C]2.7962106607456[/C][/ROW]
[ROW][C]156[/C][C]15[/C][C]14.5756124214192[/C][C]0.424387578580833[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146087&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146087&T=4

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


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11513.09808455897341.90191544102663
2129.334611107783792.66538889221621
31513.6510490549111.34895094508902
41212.1896344205315-0.189634420531493
51413.63672484024930.363275159750658
689.09557557624667-1.09557557624667
71112.1115572596557-1.11155725965572
8158.382285938569766.61771406143024
946.33579814388996-2.33579814388996
101310.65320241189012.34679758810994
111914.41939705850164.58060294149841
121011.3894752132707-1.38947521327073
131517.2926358827324-2.29263588273236
14613.187466644766-7.18746664476603
15712.1231023366937-5.12310233669369
161413.47322270244920.526777297550778
171614.36701722296211.63298277703789
181615.84998237932420.150017620675773
191415.2182109966908-1.21821099669079
201515.2172057096217-0.21720570962175
211414.5468136734371-0.546813673437087
221211.25794823023680.742051769763175
2398.988513743333510.0114862566664853
241211.57961856866130.420381431338652
251413.9095348552170.0904651447829504
261212.0679306250819-0.0679306250818922
271413.66451994956330.335480050436709
281011.2303635028932-1.23036350289317
291412.96037591440251.0396240855975
301616.038856051892-0.0388560518920171
31108.922069259730751.07793074026925
32810.7539999726585-2.75399997265848
331211.69681060737380.30318939262623
341111.0749796177072-0.0749796177071758
35810.3105726476984-2.31057264769841
361312.21359081735890.786409182641093
371110.01030467164840.989695328351576
38129.83227841331952.1677215866805
391615.21785582958420.782144170415825
401613.01537483981442.98462516018556
411313.9167692548379-0.916769254837924
421415.3107122591626-1.31071225916263
4355.59431254652437-0.594312546524372
441413.27958921076240.720410789237553
45138.958374404649554.04162559535045
461615.43600075314750.563999246852537
471414.6266103305611-0.626610330561149
481514.15374757047180.846252429528168
491513.33519556290631.66480443709368
501112.2372811627391-1.23728116273909
511513.68470209339671.31529790660325
521612.66602471464453.33397528535547
531313.3258405899626-0.325840589962623
541113.7029888671069-2.70298886710691
551213.9250133183881-1.92501331838815
561211.6098233369160.390176663083983
571013.451342229255-3.45134222925503
5889.04261103534925-1.04261103534925
5999.69310574143097-0.693105741430971
601212.045387994039-0.0453879940389715
611413.9106370572280.0893629427720446
621213.0083713397218-1.00837133972183
631110.97692213531350.0230778646865252
641413.49872552489020.501274475109798
65711.6308647176515-4.63086471765147
661613.99938574768012.00061425231988
671615.49005574093020.509944259069826
681112.2982409395182-1.29824093951819
691615.2870284919230.712971508076961
701314.4142657678109-1.4142657678109
711110.91153041303670.0884695869632992
721312.79854600577340.201453994226599
731414.2807110397621-0.28071103976212
741513.48445667863431.51554332136573
75109.292770560647780.707229439352217
761514.64656239869280.353437601307165
771112.9527797832399-1.95277978323991
781111.4043554773293-0.40435547732932
7968.45757353417261-2.45757353417261
80119.645374917711391.35462508228861
811211.63428523430250.365714765697492
821313.2443154760362-0.244315476036222
831212.6581126741542-0.658112674154231
84810.8160962310738-2.81609623107383
85910.7024135722567-1.70241357225673
861011.6212793989087-1.62127939890874
871613.24291455350252.75708544649748
881512.78266009976772.21733990023233
891413.52538035299060.474619647009403
901213.5699261701497-1.56992617014973
911210.23459942399181.76540057600822
92108.709363374215061.29063662578494
931211.44546726086040.554532739139586
9489.29247098996122-1.29247098996122
951614.03574408224371.96425591775626
96117.722977922127063.27702207787294
971211.44215238202690.557847617973135
98910.3074555378289-1.30745553782888
991411.44728733081412.55271266918589
1001514.31445495535950.685545044640456
101810.5692779610823-2.56927796108232
1021212.373175300765-0.373175300764953
1031010.4270650991506-0.42706509915059
1041614.78831280611371.21168719388628
1051714.20547645726082.79452354273925
106810.0343820261573-2.0343820261573
107910.7078936298132-1.70789362981318
108811.3302451431894-3.33024514318939
1091112.3481588429061-1.34815884290607
1101615.30804175209420.69195824790584
1111313.4300882747453-0.430088274745342
11257.92422944710671-2.92422944710671
1131512.7639954519712.23600454802897
1141513.86478852759561.13521147240442
1151211.40044047143280.599559528567213
1161211.45075099457120.549249005428758
1171615.73069356494480.269306435055162
1181212.4165342860829-0.416534286082865
1191011.7091963037699-1.70919630376992
1201210.66466371609731.33533628390266
12146.22150795113951-2.22150795113951
1221112.9228174382834-1.92281743828336
1231614.65573905637991.34426094362011
12478.76472456379761-1.76472456379761
125910.71615589177-1.71615589177001
1261410.79092939477733.2090706052227
127119.678507834945081.32149216505492
1281010.8792289215278-0.879228921527817
12968.48388602635172-2.48388602635172
1301412.8020205374761.19797946252395
1311110.86688871477920.133111285220822
132119.152723110309561.84727688969044
133913.8708137524157-4.8708137524157
1341611.35724879235754.64275120764247
13577.81813032982012-0.818130329820121
13688.78068323292848-0.780683232928479
137109.867390059925120.132609940074876
1381411.94695515947032.05304484052966
13999.60739523239862-0.60739523239862
1401312.29845509555510.701544904444912
141139.522030607301483.47796939269852
1421211.77003110047150.22996889952846
1431112.56425405171-1.56425405170999
1441014.9395072859872-4.93950728598721
1451211.92877477547560.0712252245243581
1461413.28388009096990.71611990903015
1471113.3075729058824-2.30757290588238
1481310.98743024333922.01256975666079
1491413.52239996856670.477600031433273
1501312.55573089653280.444269103467224
1511615.96681135750490.0331886424950528
1521312.07869782090330.921302179096672
1531211.2563232429470.743676757053049
15499.15504321665876-0.155043216658764
1551411.20378933925442.7962106607456
1561514.57561242141920.424387578580833






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
140.9997492612568940.0005014774862120820.000250738743106041
150.999897136847380.0002057263052405210.000102863152620261
160.9998951477071390.0002097045857227920.000104852292861396
170.9998315700222490.0003368599555028110.000168429977751405
180.9998796455022720.0002407089954564890.000120354497728245
190.9997378545297440.0005242909405124840.000262145470256242
200.9995654370285150.000869125942969170.000434562971484585
210.999137460356580.001725079286839450.000862539643419727
220.999452924095780.00109415180844060.000547075904220299
230.9992083203828090.001583359234381860.000791679617190932
240.9985830162137010.002833967572597930.00141698378629897
250.9976450218883550.004709956223289410.0023549781116447
260.9960943352579950.007811329484010880.00390566474200544
270.995320812576780.009358374846439420.00467918742321971
280.9926112195267690.01477756094646260.0073887804732313
290.9954354374067310.009129125186537410.0045645625932687
300.9942476637566470.01150467248670660.00575233624335331
310.9914964421529550.01700711569409030.00850355784704516
320.9954311589920260.009137682015948550.00456884100797427
330.9932089017751560.01358219644968720.00679109822484358
340.9899418917989390.02011621640212190.0100581082010609
350.9885356329761390.02292873404772260.0114643670238613
360.9839023346807050.03219533063858930.0160976653192947
370.9805971214720980.03880575705580360.0194028785279018
380.9824818204152470.03503635916950620.0175181795847531
390.9812120621090440.03757587578191140.0187879378909557
400.9867396987244780.02652060255104320.0132603012755216
410.9823696646598140.03526067068037190.017630335340186
420.976205184821190.047589630357620.02379481517881
430.9678150684793230.06436986304135350.0321849315206768
440.9602667505273680.07946649894526380.0397332494726319
450.989203529823550.02159294035289990.0107964701764499
460.9854827407696040.02903451846079290.0145172592303965
470.9805813563355850.03883728732883090.0194186436644155
480.9751229755244260.04975404895114770.0248770244755738
490.9720529445409580.05589411091808390.0279470554590419
500.9675090998195380.06498180036092480.0324909001804624
510.9635855328752970.07282893424940650.0364144671247032
520.9775419898325130.04491602033497370.0224580101674869
530.9702407884678590.05951842306428280.0297592115321414
540.9712801644903880.05743967101922310.0287198355096115
550.9679156955536210.0641686088927590.0320843044463795
560.9593089862514420.08138202749711650.0406910137485582
570.9718415897918080.05631682041638340.0281584102081917
580.9645182704996930.07096345900061340.0354817295003067
590.9540984762727360.09180304745452770.0459015237272639
600.9415396066020830.1169207867958340.058460393397917
610.9272512266280610.1454975467438770.0727487733719386
620.9124334765069090.1751330469861810.0875665234930907
630.8918937967409010.2162124065181990.108106203259099
640.8761863779879960.2476272440240070.123813622012004
650.93509498119810.12981003760380.0649050188019
660.9414434120754380.1171131758491240.0585565879245619
670.9284019536291210.1431960927417590.0715980463708794
680.9163987282966980.1672025434066030.0836012717033017
690.9000105078924780.1999789842150430.0999894921075217
700.8873821522202740.2252356955594520.112617847779726
710.8649266577201260.2701466845597480.135073342279874
720.839251605670030.3214967886599390.16074839432997
730.8238246260399710.3523507479200570.176175373960029
740.8172288642915420.3655422714169160.182771135708458
750.7936048529359540.4127902941280920.206395147064046
760.7609771100858490.4780457798283010.239022889914151
770.751013752577390.4979724948452190.24898624742261
780.7107362792671440.5785274414657130.289263720732856
790.7152341394659130.5695317210681740.284765860534087
800.7011333650869880.5977332698260240.298866634913012
810.6644571489291580.6710857021416840.335542851070842
820.6217935672504750.7564128654990490.378206432749525
830.5746544625480040.8506910749039910.425345537451996
840.5968753401092120.8062493197815770.403124659890788
850.5780599061909950.843880187618010.421940093809005
860.5545419791813820.8909160416372370.445458020818618
870.5971582603009830.8056834793980350.402841739699018
880.6252310135678020.7495379728643960.374768986432198
890.5912221789544480.8175556420911040.408777821045552
900.5718954488561880.8562091022876240.428104551143812
910.5641121564393510.8717756871212980.435887843560649
920.5372589310704550.925482137859090.462741068929545
930.4938645032188870.9877290064377740.506135496781113
940.4751315789452470.9502631578904940.524868421054753
950.490170899331650.98034179866330.50982910066835
960.63433053850410.73133892299180.3656694614959
970.590805468357350.81838906328530.40919453164265
980.5557193654086330.8885612691827330.444280634591367
990.6135686294146040.7728627411707930.386431370585396
1000.5845495580907640.8309008838184730.415450441909236
1010.5983612438312660.8032775123374690.401638756168734
1020.5514689781239760.8970620437520470.448531021876024
1030.5014317908163590.9971364183672820.498568209183641
1040.509095427668290.981809144663420.49090457233171
1050.5666181835178960.8667636329642090.433381816482104
1060.5669412906700460.8661174186599070.433058709329954
1070.5265533044676240.9468933910647510.473446695532376
1080.6113309046650180.7773381906699650.388669095334982
1090.592419206520890.815161586958220.40758079347911
1100.5493061972848160.9013876054303680.450693802715184
1110.4939868535680090.9879737071360180.506013146431991
1120.6451668519738180.7096662960523650.354833148026182
1130.6595099309755260.6809801380489490.340490069024474
1140.6518289861665060.6963420276669880.348171013833494
1150.5989864859986710.8020270280026590.401013514001329
1160.5614663665922060.8770672668155890.438533633407794
1170.5184822119699760.9630355760600470.481517788030024
1180.5057864211651750.988427157669650.494213578834825
1190.5265866491714060.9468267016571890.473413350828594
1200.4719280996890090.9438561993780180.528071900310991
1210.4466560398485890.8933120796971790.553343960151411
1220.4062971632009450.8125943264018910.593702836799055
1230.4513333974960030.9026667949920060.548666602503997
1240.4109431067698680.8218862135397350.589056893230133
1250.4445165454474140.8890330908948270.555483454552586
1260.4640486629798170.9280973259596340.535951337020183
1270.4522297360553760.9044594721107520.547770263944624
1280.3826198011587810.7652396023175620.617380198841219
1290.6069338593179040.7861322813641910.393066140682096
1300.7167583950213690.5664832099572630.283241604978631
1310.7712682568877770.4574634862244450.228731743112223
1320.8290645162296220.3418709675407570.170935483770378
1330.8068867699170450.386226460165910.193113230082955
1340.8620868893475240.2758262213049510.137913110652476
1350.8029249787245340.3941500425509320.197075021275466
1360.7477576402932160.5044847194135680.252242359706784
1370.8073846023370450.3852307953259110.192615397662955
1380.748551388071170.5028972238576610.25144861192883
1390.644129329121650.71174134175670.35587067087835
1400.5136510207734960.9726979584530090.486348979226504
1410.4818939207945280.9637878415890550.518106079205472
1420.3364232238923890.6728464477847780.663576776107611

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
14 & 0.999749261256894 & 0.000501477486212082 & 0.000250738743106041 \tabularnewline
15 & 0.99989713684738 & 0.000205726305240521 & 0.000102863152620261 \tabularnewline
16 & 0.999895147707139 & 0.000209704585722792 & 0.000104852292861396 \tabularnewline
17 & 0.999831570022249 & 0.000336859955502811 & 0.000168429977751405 \tabularnewline
18 & 0.999879645502272 & 0.000240708995456489 & 0.000120354497728245 \tabularnewline
19 & 0.999737854529744 & 0.000524290940512484 & 0.000262145470256242 \tabularnewline
20 & 0.999565437028515 & 0.00086912594296917 & 0.000434562971484585 \tabularnewline
21 & 0.99913746035658 & 0.00172507928683945 & 0.000862539643419727 \tabularnewline
22 & 0.99945292409578 & 0.0010941518084406 & 0.000547075904220299 \tabularnewline
23 & 0.999208320382809 & 0.00158335923438186 & 0.000791679617190932 \tabularnewline
24 & 0.998583016213701 & 0.00283396757259793 & 0.00141698378629897 \tabularnewline
25 & 0.997645021888355 & 0.00470995622328941 & 0.0023549781116447 \tabularnewline
26 & 0.996094335257995 & 0.00781132948401088 & 0.00390566474200544 \tabularnewline
27 & 0.99532081257678 & 0.00935837484643942 & 0.00467918742321971 \tabularnewline
28 & 0.992611219526769 & 0.0147775609464626 & 0.0073887804732313 \tabularnewline
29 & 0.995435437406731 & 0.00912912518653741 & 0.0045645625932687 \tabularnewline
30 & 0.994247663756647 & 0.0115046724867066 & 0.00575233624335331 \tabularnewline
31 & 0.991496442152955 & 0.0170071156940903 & 0.00850355784704516 \tabularnewline
32 & 0.995431158992026 & 0.00913768201594855 & 0.00456884100797427 \tabularnewline
33 & 0.993208901775156 & 0.0135821964496872 & 0.00679109822484358 \tabularnewline
34 & 0.989941891798939 & 0.0201162164021219 & 0.0100581082010609 \tabularnewline
35 & 0.988535632976139 & 0.0229287340477226 & 0.0114643670238613 \tabularnewline
36 & 0.983902334680705 & 0.0321953306385893 & 0.0160976653192947 \tabularnewline
37 & 0.980597121472098 & 0.0388057570558036 & 0.0194028785279018 \tabularnewline
38 & 0.982481820415247 & 0.0350363591695062 & 0.0175181795847531 \tabularnewline
39 & 0.981212062109044 & 0.0375758757819114 & 0.0187879378909557 \tabularnewline
40 & 0.986739698724478 & 0.0265206025510432 & 0.0132603012755216 \tabularnewline
41 & 0.982369664659814 & 0.0352606706803719 & 0.017630335340186 \tabularnewline
42 & 0.97620518482119 & 0.04758963035762 & 0.02379481517881 \tabularnewline
43 & 0.967815068479323 & 0.0643698630413535 & 0.0321849315206768 \tabularnewline
44 & 0.960266750527368 & 0.0794664989452638 & 0.0397332494726319 \tabularnewline
45 & 0.98920352982355 & 0.0215929403528999 & 0.0107964701764499 \tabularnewline
46 & 0.985482740769604 & 0.0290345184607929 & 0.0145172592303965 \tabularnewline
47 & 0.980581356335585 & 0.0388372873288309 & 0.0194186436644155 \tabularnewline
48 & 0.975122975524426 & 0.0497540489511477 & 0.0248770244755738 \tabularnewline
49 & 0.972052944540958 & 0.0558941109180839 & 0.0279470554590419 \tabularnewline
50 & 0.967509099819538 & 0.0649818003609248 & 0.0324909001804624 \tabularnewline
51 & 0.963585532875297 & 0.0728289342494065 & 0.0364144671247032 \tabularnewline
52 & 0.977541989832513 & 0.0449160203349737 & 0.0224580101674869 \tabularnewline
53 & 0.970240788467859 & 0.0595184230642828 & 0.0297592115321414 \tabularnewline
54 & 0.971280164490388 & 0.0574396710192231 & 0.0287198355096115 \tabularnewline
55 & 0.967915695553621 & 0.064168608892759 & 0.0320843044463795 \tabularnewline
56 & 0.959308986251442 & 0.0813820274971165 & 0.0406910137485582 \tabularnewline
57 & 0.971841589791808 & 0.0563168204163834 & 0.0281584102081917 \tabularnewline
58 & 0.964518270499693 & 0.0709634590006134 & 0.0354817295003067 \tabularnewline
59 & 0.954098476272736 & 0.0918030474545277 & 0.0459015237272639 \tabularnewline
60 & 0.941539606602083 & 0.116920786795834 & 0.058460393397917 \tabularnewline
61 & 0.927251226628061 & 0.145497546743877 & 0.0727487733719386 \tabularnewline
62 & 0.912433476506909 & 0.175133046986181 & 0.0875665234930907 \tabularnewline
63 & 0.891893796740901 & 0.216212406518199 & 0.108106203259099 \tabularnewline
64 & 0.876186377987996 & 0.247627244024007 & 0.123813622012004 \tabularnewline
65 & 0.9350949811981 & 0.1298100376038 & 0.0649050188019 \tabularnewline
66 & 0.941443412075438 & 0.117113175849124 & 0.0585565879245619 \tabularnewline
67 & 0.928401953629121 & 0.143196092741759 & 0.0715980463708794 \tabularnewline
68 & 0.916398728296698 & 0.167202543406603 & 0.0836012717033017 \tabularnewline
69 & 0.900010507892478 & 0.199978984215043 & 0.0999894921075217 \tabularnewline
70 & 0.887382152220274 & 0.225235695559452 & 0.112617847779726 \tabularnewline
71 & 0.864926657720126 & 0.270146684559748 & 0.135073342279874 \tabularnewline
72 & 0.83925160567003 & 0.321496788659939 & 0.16074839432997 \tabularnewline
73 & 0.823824626039971 & 0.352350747920057 & 0.176175373960029 \tabularnewline
74 & 0.817228864291542 & 0.365542271416916 & 0.182771135708458 \tabularnewline
75 & 0.793604852935954 & 0.412790294128092 & 0.206395147064046 \tabularnewline
76 & 0.760977110085849 & 0.478045779828301 & 0.239022889914151 \tabularnewline
77 & 0.75101375257739 & 0.497972494845219 & 0.24898624742261 \tabularnewline
78 & 0.710736279267144 & 0.578527441465713 & 0.289263720732856 \tabularnewline
79 & 0.715234139465913 & 0.569531721068174 & 0.284765860534087 \tabularnewline
80 & 0.701133365086988 & 0.597733269826024 & 0.298866634913012 \tabularnewline
81 & 0.664457148929158 & 0.671085702141684 & 0.335542851070842 \tabularnewline
82 & 0.621793567250475 & 0.756412865499049 & 0.378206432749525 \tabularnewline
83 & 0.574654462548004 & 0.850691074903991 & 0.425345537451996 \tabularnewline
84 & 0.596875340109212 & 0.806249319781577 & 0.403124659890788 \tabularnewline
85 & 0.578059906190995 & 0.84388018761801 & 0.421940093809005 \tabularnewline
86 & 0.554541979181382 & 0.890916041637237 & 0.445458020818618 \tabularnewline
87 & 0.597158260300983 & 0.805683479398035 & 0.402841739699018 \tabularnewline
88 & 0.625231013567802 & 0.749537972864396 & 0.374768986432198 \tabularnewline
89 & 0.591222178954448 & 0.817555642091104 & 0.408777821045552 \tabularnewline
90 & 0.571895448856188 & 0.856209102287624 & 0.428104551143812 \tabularnewline
91 & 0.564112156439351 & 0.871775687121298 & 0.435887843560649 \tabularnewline
92 & 0.537258931070455 & 0.92548213785909 & 0.462741068929545 \tabularnewline
93 & 0.493864503218887 & 0.987729006437774 & 0.506135496781113 \tabularnewline
94 & 0.475131578945247 & 0.950263157890494 & 0.524868421054753 \tabularnewline
95 & 0.49017089933165 & 0.9803417986633 & 0.50982910066835 \tabularnewline
96 & 0.6343305385041 & 0.7313389229918 & 0.3656694614959 \tabularnewline
97 & 0.59080546835735 & 0.8183890632853 & 0.40919453164265 \tabularnewline
98 & 0.555719365408633 & 0.888561269182733 & 0.444280634591367 \tabularnewline
99 & 0.613568629414604 & 0.772862741170793 & 0.386431370585396 \tabularnewline
100 & 0.584549558090764 & 0.830900883818473 & 0.415450441909236 \tabularnewline
101 & 0.598361243831266 & 0.803277512337469 & 0.401638756168734 \tabularnewline
102 & 0.551468978123976 & 0.897062043752047 & 0.448531021876024 \tabularnewline
103 & 0.501431790816359 & 0.997136418367282 & 0.498568209183641 \tabularnewline
104 & 0.50909542766829 & 0.98180914466342 & 0.49090457233171 \tabularnewline
105 & 0.566618183517896 & 0.866763632964209 & 0.433381816482104 \tabularnewline
106 & 0.566941290670046 & 0.866117418659907 & 0.433058709329954 \tabularnewline
107 & 0.526553304467624 & 0.946893391064751 & 0.473446695532376 \tabularnewline
108 & 0.611330904665018 & 0.777338190669965 & 0.388669095334982 \tabularnewline
109 & 0.59241920652089 & 0.81516158695822 & 0.40758079347911 \tabularnewline
110 & 0.549306197284816 & 0.901387605430368 & 0.450693802715184 \tabularnewline
111 & 0.493986853568009 & 0.987973707136018 & 0.506013146431991 \tabularnewline
112 & 0.645166851973818 & 0.709666296052365 & 0.354833148026182 \tabularnewline
113 & 0.659509930975526 & 0.680980138048949 & 0.340490069024474 \tabularnewline
114 & 0.651828986166506 & 0.696342027666988 & 0.348171013833494 \tabularnewline
115 & 0.598986485998671 & 0.802027028002659 & 0.401013514001329 \tabularnewline
116 & 0.561466366592206 & 0.877067266815589 & 0.438533633407794 \tabularnewline
117 & 0.518482211969976 & 0.963035576060047 & 0.481517788030024 \tabularnewline
118 & 0.505786421165175 & 0.98842715766965 & 0.494213578834825 \tabularnewline
119 & 0.526586649171406 & 0.946826701657189 & 0.473413350828594 \tabularnewline
120 & 0.471928099689009 & 0.943856199378018 & 0.528071900310991 \tabularnewline
121 & 0.446656039848589 & 0.893312079697179 & 0.553343960151411 \tabularnewline
122 & 0.406297163200945 & 0.812594326401891 & 0.593702836799055 \tabularnewline
123 & 0.451333397496003 & 0.902666794992006 & 0.548666602503997 \tabularnewline
124 & 0.410943106769868 & 0.821886213539735 & 0.589056893230133 \tabularnewline
125 & 0.444516545447414 & 0.889033090894827 & 0.555483454552586 \tabularnewline
126 & 0.464048662979817 & 0.928097325959634 & 0.535951337020183 \tabularnewline
127 & 0.452229736055376 & 0.904459472110752 & 0.547770263944624 \tabularnewline
128 & 0.382619801158781 & 0.765239602317562 & 0.617380198841219 \tabularnewline
129 & 0.606933859317904 & 0.786132281364191 & 0.393066140682096 \tabularnewline
130 & 0.716758395021369 & 0.566483209957263 & 0.283241604978631 \tabularnewline
131 & 0.771268256887777 & 0.457463486224445 & 0.228731743112223 \tabularnewline
132 & 0.829064516229622 & 0.341870967540757 & 0.170935483770378 \tabularnewline
133 & 0.806886769917045 & 0.38622646016591 & 0.193113230082955 \tabularnewline
134 & 0.862086889347524 & 0.275826221304951 & 0.137913110652476 \tabularnewline
135 & 0.802924978724534 & 0.394150042550932 & 0.197075021275466 \tabularnewline
136 & 0.747757640293216 & 0.504484719413568 & 0.252242359706784 \tabularnewline
137 & 0.807384602337045 & 0.385230795325911 & 0.192615397662955 \tabularnewline
138 & 0.74855138807117 & 0.502897223857661 & 0.25144861192883 \tabularnewline
139 & 0.64412932912165 & 0.7117413417567 & 0.35587067087835 \tabularnewline
140 & 0.513651020773496 & 0.972697958453009 & 0.486348979226504 \tabularnewline
141 & 0.481893920794528 & 0.963787841589055 & 0.518106079205472 \tabularnewline
142 & 0.336423223892389 & 0.672846447784778 & 0.663576776107611 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146087&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]14[/C][C]0.999749261256894[/C][C]0.000501477486212082[/C][C]0.000250738743106041[/C][/ROW]
[ROW][C]15[/C][C]0.99989713684738[/C][C]0.000205726305240521[/C][C]0.000102863152620261[/C][/ROW]
[ROW][C]16[/C][C]0.999895147707139[/C][C]0.000209704585722792[/C][C]0.000104852292861396[/C][/ROW]
[ROW][C]17[/C][C]0.999831570022249[/C][C]0.000336859955502811[/C][C]0.000168429977751405[/C][/ROW]
[ROW][C]18[/C][C]0.999879645502272[/C][C]0.000240708995456489[/C][C]0.000120354497728245[/C][/ROW]
[ROW][C]19[/C][C]0.999737854529744[/C][C]0.000524290940512484[/C][C]0.000262145470256242[/C][/ROW]
[ROW][C]20[/C][C]0.999565437028515[/C][C]0.00086912594296917[/C][C]0.000434562971484585[/C][/ROW]
[ROW][C]21[/C][C]0.99913746035658[/C][C]0.00172507928683945[/C][C]0.000862539643419727[/C][/ROW]
[ROW][C]22[/C][C]0.99945292409578[/C][C]0.0010941518084406[/C][C]0.000547075904220299[/C][/ROW]
[ROW][C]23[/C][C]0.999208320382809[/C][C]0.00158335923438186[/C][C]0.000791679617190932[/C][/ROW]
[ROW][C]24[/C][C]0.998583016213701[/C][C]0.00283396757259793[/C][C]0.00141698378629897[/C][/ROW]
[ROW][C]25[/C][C]0.997645021888355[/C][C]0.00470995622328941[/C][C]0.0023549781116447[/C][/ROW]
[ROW][C]26[/C][C]0.996094335257995[/C][C]0.00781132948401088[/C][C]0.00390566474200544[/C][/ROW]
[ROW][C]27[/C][C]0.99532081257678[/C][C]0.00935837484643942[/C][C]0.00467918742321971[/C][/ROW]
[ROW][C]28[/C][C]0.992611219526769[/C][C]0.0147775609464626[/C][C]0.0073887804732313[/C][/ROW]
[ROW][C]29[/C][C]0.995435437406731[/C][C]0.00912912518653741[/C][C]0.0045645625932687[/C][/ROW]
[ROW][C]30[/C][C]0.994247663756647[/C][C]0.0115046724867066[/C][C]0.00575233624335331[/C][/ROW]
[ROW][C]31[/C][C]0.991496442152955[/C][C]0.0170071156940903[/C][C]0.00850355784704516[/C][/ROW]
[ROW][C]32[/C][C]0.995431158992026[/C][C]0.00913768201594855[/C][C]0.00456884100797427[/C][/ROW]
[ROW][C]33[/C][C]0.993208901775156[/C][C]0.0135821964496872[/C][C]0.00679109822484358[/C][/ROW]
[ROW][C]34[/C][C]0.989941891798939[/C][C]0.0201162164021219[/C][C]0.0100581082010609[/C][/ROW]
[ROW][C]35[/C][C]0.988535632976139[/C][C]0.0229287340477226[/C][C]0.0114643670238613[/C][/ROW]
[ROW][C]36[/C][C]0.983902334680705[/C][C]0.0321953306385893[/C][C]0.0160976653192947[/C][/ROW]
[ROW][C]37[/C][C]0.980597121472098[/C][C]0.0388057570558036[/C][C]0.0194028785279018[/C][/ROW]
[ROW][C]38[/C][C]0.982481820415247[/C][C]0.0350363591695062[/C][C]0.0175181795847531[/C][/ROW]
[ROW][C]39[/C][C]0.981212062109044[/C][C]0.0375758757819114[/C][C]0.0187879378909557[/C][/ROW]
[ROW][C]40[/C][C]0.986739698724478[/C][C]0.0265206025510432[/C][C]0.0132603012755216[/C][/ROW]
[ROW][C]41[/C][C]0.982369664659814[/C][C]0.0352606706803719[/C][C]0.017630335340186[/C][/ROW]
[ROW][C]42[/C][C]0.97620518482119[/C][C]0.04758963035762[/C][C]0.02379481517881[/C][/ROW]
[ROW][C]43[/C][C]0.967815068479323[/C][C]0.0643698630413535[/C][C]0.0321849315206768[/C][/ROW]
[ROW][C]44[/C][C]0.960266750527368[/C][C]0.0794664989452638[/C][C]0.0397332494726319[/C][/ROW]
[ROW][C]45[/C][C]0.98920352982355[/C][C]0.0215929403528999[/C][C]0.0107964701764499[/C][/ROW]
[ROW][C]46[/C][C]0.985482740769604[/C][C]0.0290345184607929[/C][C]0.0145172592303965[/C][/ROW]
[ROW][C]47[/C][C]0.980581356335585[/C][C]0.0388372873288309[/C][C]0.0194186436644155[/C][/ROW]
[ROW][C]48[/C][C]0.975122975524426[/C][C]0.0497540489511477[/C][C]0.0248770244755738[/C][/ROW]
[ROW][C]49[/C][C]0.972052944540958[/C][C]0.0558941109180839[/C][C]0.0279470554590419[/C][/ROW]
[ROW][C]50[/C][C]0.967509099819538[/C][C]0.0649818003609248[/C][C]0.0324909001804624[/C][/ROW]
[ROW][C]51[/C][C]0.963585532875297[/C][C]0.0728289342494065[/C][C]0.0364144671247032[/C][/ROW]
[ROW][C]52[/C][C]0.977541989832513[/C][C]0.0449160203349737[/C][C]0.0224580101674869[/C][/ROW]
[ROW][C]53[/C][C]0.970240788467859[/C][C]0.0595184230642828[/C][C]0.0297592115321414[/C][/ROW]
[ROW][C]54[/C][C]0.971280164490388[/C][C]0.0574396710192231[/C][C]0.0287198355096115[/C][/ROW]
[ROW][C]55[/C][C]0.967915695553621[/C][C]0.064168608892759[/C][C]0.0320843044463795[/C][/ROW]
[ROW][C]56[/C][C]0.959308986251442[/C][C]0.0813820274971165[/C][C]0.0406910137485582[/C][/ROW]
[ROW][C]57[/C][C]0.971841589791808[/C][C]0.0563168204163834[/C][C]0.0281584102081917[/C][/ROW]
[ROW][C]58[/C][C]0.964518270499693[/C][C]0.0709634590006134[/C][C]0.0354817295003067[/C][/ROW]
[ROW][C]59[/C][C]0.954098476272736[/C][C]0.0918030474545277[/C][C]0.0459015237272639[/C][/ROW]
[ROW][C]60[/C][C]0.941539606602083[/C][C]0.116920786795834[/C][C]0.058460393397917[/C][/ROW]
[ROW][C]61[/C][C]0.927251226628061[/C][C]0.145497546743877[/C][C]0.0727487733719386[/C][/ROW]
[ROW][C]62[/C][C]0.912433476506909[/C][C]0.175133046986181[/C][C]0.0875665234930907[/C][/ROW]
[ROW][C]63[/C][C]0.891893796740901[/C][C]0.216212406518199[/C][C]0.108106203259099[/C][/ROW]
[ROW][C]64[/C][C]0.876186377987996[/C][C]0.247627244024007[/C][C]0.123813622012004[/C][/ROW]
[ROW][C]65[/C][C]0.9350949811981[/C][C]0.1298100376038[/C][C]0.0649050188019[/C][/ROW]
[ROW][C]66[/C][C]0.941443412075438[/C][C]0.117113175849124[/C][C]0.0585565879245619[/C][/ROW]
[ROW][C]67[/C][C]0.928401953629121[/C][C]0.143196092741759[/C][C]0.0715980463708794[/C][/ROW]
[ROW][C]68[/C][C]0.916398728296698[/C][C]0.167202543406603[/C][C]0.0836012717033017[/C][/ROW]
[ROW][C]69[/C][C]0.900010507892478[/C][C]0.199978984215043[/C][C]0.0999894921075217[/C][/ROW]
[ROW][C]70[/C][C]0.887382152220274[/C][C]0.225235695559452[/C][C]0.112617847779726[/C][/ROW]
[ROW][C]71[/C][C]0.864926657720126[/C][C]0.270146684559748[/C][C]0.135073342279874[/C][/ROW]
[ROW][C]72[/C][C]0.83925160567003[/C][C]0.321496788659939[/C][C]0.16074839432997[/C][/ROW]
[ROW][C]73[/C][C]0.823824626039971[/C][C]0.352350747920057[/C][C]0.176175373960029[/C][/ROW]
[ROW][C]74[/C][C]0.817228864291542[/C][C]0.365542271416916[/C][C]0.182771135708458[/C][/ROW]
[ROW][C]75[/C][C]0.793604852935954[/C][C]0.412790294128092[/C][C]0.206395147064046[/C][/ROW]
[ROW][C]76[/C][C]0.760977110085849[/C][C]0.478045779828301[/C][C]0.239022889914151[/C][/ROW]
[ROW][C]77[/C][C]0.75101375257739[/C][C]0.497972494845219[/C][C]0.24898624742261[/C][/ROW]
[ROW][C]78[/C][C]0.710736279267144[/C][C]0.578527441465713[/C][C]0.289263720732856[/C][/ROW]
[ROW][C]79[/C][C]0.715234139465913[/C][C]0.569531721068174[/C][C]0.284765860534087[/C][/ROW]
[ROW][C]80[/C][C]0.701133365086988[/C][C]0.597733269826024[/C][C]0.298866634913012[/C][/ROW]
[ROW][C]81[/C][C]0.664457148929158[/C][C]0.671085702141684[/C][C]0.335542851070842[/C][/ROW]
[ROW][C]82[/C][C]0.621793567250475[/C][C]0.756412865499049[/C][C]0.378206432749525[/C][/ROW]
[ROW][C]83[/C][C]0.574654462548004[/C][C]0.850691074903991[/C][C]0.425345537451996[/C][/ROW]
[ROW][C]84[/C][C]0.596875340109212[/C][C]0.806249319781577[/C][C]0.403124659890788[/C][/ROW]
[ROW][C]85[/C][C]0.578059906190995[/C][C]0.84388018761801[/C][C]0.421940093809005[/C][/ROW]
[ROW][C]86[/C][C]0.554541979181382[/C][C]0.890916041637237[/C][C]0.445458020818618[/C][/ROW]
[ROW][C]87[/C][C]0.597158260300983[/C][C]0.805683479398035[/C][C]0.402841739699018[/C][/ROW]
[ROW][C]88[/C][C]0.625231013567802[/C][C]0.749537972864396[/C][C]0.374768986432198[/C][/ROW]
[ROW][C]89[/C][C]0.591222178954448[/C][C]0.817555642091104[/C][C]0.408777821045552[/C][/ROW]
[ROW][C]90[/C][C]0.571895448856188[/C][C]0.856209102287624[/C][C]0.428104551143812[/C][/ROW]
[ROW][C]91[/C][C]0.564112156439351[/C][C]0.871775687121298[/C][C]0.435887843560649[/C][/ROW]
[ROW][C]92[/C][C]0.537258931070455[/C][C]0.92548213785909[/C][C]0.462741068929545[/C][/ROW]
[ROW][C]93[/C][C]0.493864503218887[/C][C]0.987729006437774[/C][C]0.506135496781113[/C][/ROW]
[ROW][C]94[/C][C]0.475131578945247[/C][C]0.950263157890494[/C][C]0.524868421054753[/C][/ROW]
[ROW][C]95[/C][C]0.49017089933165[/C][C]0.9803417986633[/C][C]0.50982910066835[/C][/ROW]
[ROW][C]96[/C][C]0.6343305385041[/C][C]0.7313389229918[/C][C]0.3656694614959[/C][/ROW]
[ROW][C]97[/C][C]0.59080546835735[/C][C]0.8183890632853[/C][C]0.40919453164265[/C][/ROW]
[ROW][C]98[/C][C]0.555719365408633[/C][C]0.888561269182733[/C][C]0.444280634591367[/C][/ROW]
[ROW][C]99[/C][C]0.613568629414604[/C][C]0.772862741170793[/C][C]0.386431370585396[/C][/ROW]
[ROW][C]100[/C][C]0.584549558090764[/C][C]0.830900883818473[/C][C]0.415450441909236[/C][/ROW]
[ROW][C]101[/C][C]0.598361243831266[/C][C]0.803277512337469[/C][C]0.401638756168734[/C][/ROW]
[ROW][C]102[/C][C]0.551468978123976[/C][C]0.897062043752047[/C][C]0.448531021876024[/C][/ROW]
[ROW][C]103[/C][C]0.501431790816359[/C][C]0.997136418367282[/C][C]0.498568209183641[/C][/ROW]
[ROW][C]104[/C][C]0.50909542766829[/C][C]0.98180914466342[/C][C]0.49090457233171[/C][/ROW]
[ROW][C]105[/C][C]0.566618183517896[/C][C]0.866763632964209[/C][C]0.433381816482104[/C][/ROW]
[ROW][C]106[/C][C]0.566941290670046[/C][C]0.866117418659907[/C][C]0.433058709329954[/C][/ROW]
[ROW][C]107[/C][C]0.526553304467624[/C][C]0.946893391064751[/C][C]0.473446695532376[/C][/ROW]
[ROW][C]108[/C][C]0.611330904665018[/C][C]0.777338190669965[/C][C]0.388669095334982[/C][/ROW]
[ROW][C]109[/C][C]0.59241920652089[/C][C]0.81516158695822[/C][C]0.40758079347911[/C][/ROW]
[ROW][C]110[/C][C]0.549306197284816[/C][C]0.901387605430368[/C][C]0.450693802715184[/C][/ROW]
[ROW][C]111[/C][C]0.493986853568009[/C][C]0.987973707136018[/C][C]0.506013146431991[/C][/ROW]
[ROW][C]112[/C][C]0.645166851973818[/C][C]0.709666296052365[/C][C]0.354833148026182[/C][/ROW]
[ROW][C]113[/C][C]0.659509930975526[/C][C]0.680980138048949[/C][C]0.340490069024474[/C][/ROW]
[ROW][C]114[/C][C]0.651828986166506[/C][C]0.696342027666988[/C][C]0.348171013833494[/C][/ROW]
[ROW][C]115[/C][C]0.598986485998671[/C][C]0.802027028002659[/C][C]0.401013514001329[/C][/ROW]
[ROW][C]116[/C][C]0.561466366592206[/C][C]0.877067266815589[/C][C]0.438533633407794[/C][/ROW]
[ROW][C]117[/C][C]0.518482211969976[/C][C]0.963035576060047[/C][C]0.481517788030024[/C][/ROW]
[ROW][C]118[/C][C]0.505786421165175[/C][C]0.98842715766965[/C][C]0.494213578834825[/C][/ROW]
[ROW][C]119[/C][C]0.526586649171406[/C][C]0.946826701657189[/C][C]0.473413350828594[/C][/ROW]
[ROW][C]120[/C][C]0.471928099689009[/C][C]0.943856199378018[/C][C]0.528071900310991[/C][/ROW]
[ROW][C]121[/C][C]0.446656039848589[/C][C]0.893312079697179[/C][C]0.553343960151411[/C][/ROW]
[ROW][C]122[/C][C]0.406297163200945[/C][C]0.812594326401891[/C][C]0.593702836799055[/C][/ROW]
[ROW][C]123[/C][C]0.451333397496003[/C][C]0.902666794992006[/C][C]0.548666602503997[/C][/ROW]
[ROW][C]124[/C][C]0.410943106769868[/C][C]0.821886213539735[/C][C]0.589056893230133[/C][/ROW]
[ROW][C]125[/C][C]0.444516545447414[/C][C]0.889033090894827[/C][C]0.555483454552586[/C][/ROW]
[ROW][C]126[/C][C]0.464048662979817[/C][C]0.928097325959634[/C][C]0.535951337020183[/C][/ROW]
[ROW][C]127[/C][C]0.452229736055376[/C][C]0.904459472110752[/C][C]0.547770263944624[/C][/ROW]
[ROW][C]128[/C][C]0.382619801158781[/C][C]0.765239602317562[/C][C]0.617380198841219[/C][/ROW]
[ROW][C]129[/C][C]0.606933859317904[/C][C]0.786132281364191[/C][C]0.393066140682096[/C][/ROW]
[ROW][C]130[/C][C]0.716758395021369[/C][C]0.566483209957263[/C][C]0.283241604978631[/C][/ROW]
[ROW][C]131[/C][C]0.771268256887777[/C][C]0.457463486224445[/C][C]0.228731743112223[/C][/ROW]
[ROW][C]132[/C][C]0.829064516229622[/C][C]0.341870967540757[/C][C]0.170935483770378[/C][/ROW]
[ROW][C]133[/C][C]0.806886769917045[/C][C]0.38622646016591[/C][C]0.193113230082955[/C][/ROW]
[ROW][C]134[/C][C]0.862086889347524[/C][C]0.275826221304951[/C][C]0.137913110652476[/C][/ROW]
[ROW][C]135[/C][C]0.802924978724534[/C][C]0.394150042550932[/C][C]0.197075021275466[/C][/ROW]
[ROW][C]136[/C][C]0.747757640293216[/C][C]0.504484719413568[/C][C]0.252242359706784[/C][/ROW]
[ROW][C]137[/C][C]0.807384602337045[/C][C]0.385230795325911[/C][C]0.192615397662955[/C][/ROW]
[ROW][C]138[/C][C]0.74855138807117[/C][C]0.502897223857661[/C][C]0.25144861192883[/C][/ROW]
[ROW][C]139[/C][C]0.64412932912165[/C][C]0.7117413417567[/C][C]0.35587067087835[/C][/ROW]
[ROW][C]140[/C][C]0.513651020773496[/C][C]0.972697958453009[/C][C]0.486348979226504[/C][/ROW]
[ROW][C]141[/C][C]0.481893920794528[/C][C]0.963787841589055[/C][C]0.518106079205472[/C][/ROW]
[ROW][C]142[/C][C]0.336423223892389[/C][C]0.672846447784778[/C][C]0.663576776107611[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146087&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146087&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
140.9997492612568940.0005014774862120820.000250738743106041
150.999897136847380.0002057263052405210.000102863152620261
160.9998951477071390.0002097045857227920.000104852292861396
170.9998315700222490.0003368599555028110.000168429977751405
180.9998796455022720.0002407089954564890.000120354497728245
190.9997378545297440.0005242909405124840.000262145470256242
200.9995654370285150.000869125942969170.000434562971484585
210.999137460356580.001725079286839450.000862539643419727
220.999452924095780.00109415180844060.000547075904220299
230.9992083203828090.001583359234381860.000791679617190932
240.9985830162137010.002833967572597930.00141698378629897
250.9976450218883550.004709956223289410.0023549781116447
260.9960943352579950.007811329484010880.00390566474200544
270.995320812576780.009358374846439420.00467918742321971
280.9926112195267690.01477756094646260.0073887804732313
290.9954354374067310.009129125186537410.0045645625932687
300.9942476637566470.01150467248670660.00575233624335331
310.9914964421529550.01700711569409030.00850355784704516
320.9954311589920260.009137682015948550.00456884100797427
330.9932089017751560.01358219644968720.00679109822484358
340.9899418917989390.02011621640212190.0100581082010609
350.9885356329761390.02292873404772260.0114643670238613
360.9839023346807050.03219533063858930.0160976653192947
370.9805971214720980.03880575705580360.0194028785279018
380.9824818204152470.03503635916950620.0175181795847531
390.9812120621090440.03757587578191140.0187879378909557
400.9867396987244780.02652060255104320.0132603012755216
410.9823696646598140.03526067068037190.017630335340186
420.976205184821190.047589630357620.02379481517881
430.9678150684793230.06436986304135350.0321849315206768
440.9602667505273680.07946649894526380.0397332494726319
450.989203529823550.02159294035289990.0107964701764499
460.9854827407696040.02903451846079290.0145172592303965
470.9805813563355850.03883728732883090.0194186436644155
480.9751229755244260.04975404895114770.0248770244755738
490.9720529445409580.05589411091808390.0279470554590419
500.9675090998195380.06498180036092480.0324909001804624
510.9635855328752970.07282893424940650.0364144671247032
520.9775419898325130.04491602033497370.0224580101674869
530.9702407884678590.05951842306428280.0297592115321414
540.9712801644903880.05743967101922310.0287198355096115
550.9679156955536210.0641686088927590.0320843044463795
560.9593089862514420.08138202749711650.0406910137485582
570.9718415897918080.05631682041638340.0281584102081917
580.9645182704996930.07096345900061340.0354817295003067
590.9540984762727360.09180304745452770.0459015237272639
600.9415396066020830.1169207867958340.058460393397917
610.9272512266280610.1454975467438770.0727487733719386
620.9124334765069090.1751330469861810.0875665234930907
630.8918937967409010.2162124065181990.108106203259099
640.8761863779879960.2476272440240070.123813622012004
650.93509498119810.12981003760380.0649050188019
660.9414434120754380.1171131758491240.0585565879245619
670.9284019536291210.1431960927417590.0715980463708794
680.9163987282966980.1672025434066030.0836012717033017
690.9000105078924780.1999789842150430.0999894921075217
700.8873821522202740.2252356955594520.112617847779726
710.8649266577201260.2701466845597480.135073342279874
720.839251605670030.3214967886599390.16074839432997
730.8238246260399710.3523507479200570.176175373960029
740.8172288642915420.3655422714169160.182771135708458
750.7936048529359540.4127902941280920.206395147064046
760.7609771100858490.4780457798283010.239022889914151
770.751013752577390.4979724948452190.24898624742261
780.7107362792671440.5785274414657130.289263720732856
790.7152341394659130.5695317210681740.284765860534087
800.7011333650869880.5977332698260240.298866634913012
810.6644571489291580.6710857021416840.335542851070842
820.6217935672504750.7564128654990490.378206432749525
830.5746544625480040.8506910749039910.425345537451996
840.5968753401092120.8062493197815770.403124659890788
850.5780599061909950.843880187618010.421940093809005
860.5545419791813820.8909160416372370.445458020818618
870.5971582603009830.8056834793980350.402841739699018
880.6252310135678020.7495379728643960.374768986432198
890.5912221789544480.8175556420911040.408777821045552
900.5718954488561880.8562091022876240.428104551143812
910.5641121564393510.8717756871212980.435887843560649
920.5372589310704550.925482137859090.462741068929545
930.4938645032188870.9877290064377740.506135496781113
940.4751315789452470.9502631578904940.524868421054753
950.490170899331650.98034179866330.50982910066835
960.63433053850410.73133892299180.3656694614959
970.590805468357350.81838906328530.40919453164265
980.5557193654086330.8885612691827330.444280634591367
990.6135686294146040.7728627411707930.386431370585396
1000.5845495580907640.8309008838184730.415450441909236
1010.5983612438312660.8032775123374690.401638756168734
1020.5514689781239760.8970620437520470.448531021876024
1030.5014317908163590.9971364183672820.498568209183641
1040.509095427668290.981809144663420.49090457233171
1050.5666181835178960.8667636329642090.433381816482104
1060.5669412906700460.8661174186599070.433058709329954
1070.5265533044676240.9468933910647510.473446695532376
1080.6113309046650180.7773381906699650.388669095334982
1090.592419206520890.815161586958220.40758079347911
1100.5493061972848160.9013876054303680.450693802715184
1110.4939868535680090.9879737071360180.506013146431991
1120.6451668519738180.7096662960523650.354833148026182
1130.6595099309755260.6809801380489490.340490069024474
1140.6518289861665060.6963420276669880.348171013833494
1150.5989864859986710.8020270280026590.401013514001329
1160.5614663665922060.8770672668155890.438533633407794
1170.5184822119699760.9630355760600470.481517788030024
1180.5057864211651750.988427157669650.494213578834825
1190.5265866491714060.9468267016571890.473413350828594
1200.4719280996890090.9438561993780180.528071900310991
1210.4466560398485890.8933120796971790.553343960151411
1220.4062971632009450.8125943264018910.593702836799055
1230.4513333974960030.9026667949920060.548666602503997
1240.4109431067698680.8218862135397350.589056893230133
1250.4445165454474140.8890330908948270.555483454552586
1260.4640486629798170.9280973259596340.535951337020183
1270.4522297360553760.9044594721107520.547770263944624
1280.3826198011587810.7652396023175620.617380198841219
1290.6069338593179040.7861322813641910.393066140682096
1300.7167583950213690.5664832099572630.283241604978631
1310.7712682568877770.4574634862244450.228731743112223
1320.8290645162296220.3418709675407570.170935483770378
1330.8068867699170450.386226460165910.193113230082955
1340.8620868893475240.2758262213049510.137913110652476
1350.8029249787245340.3941500425509320.197075021275466
1360.7477576402932160.5044847194135680.252242359706784
1370.8073846023370450.3852307953259110.192615397662955
1380.748551388071170.5028972238576610.25144861192883
1390.644129329121650.71174134175670.35587067087835
1400.5136510207734960.9726979584530090.486348979226504
1410.4818939207945280.9637878415890550.518106079205472
1420.3364232238923890.6728464477847780.663576776107611






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level160.124031007751938NOK
5% type I error level340.263565891472868NOK
10% type I error level460.356589147286822NOK

\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 & 16 & 0.124031007751938 & NOK \tabularnewline
5% type I error level & 34 & 0.263565891472868 & NOK \tabularnewline
10% type I error level & 46 & 0.356589147286822 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146087&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]16[/C][C]0.124031007751938[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]34[/C][C]0.263565891472868[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]46[/C][C]0.356589147286822[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146087&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146087&T=6

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


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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level160.124031007751938NOK
5% type I error level340.263565891472868NOK
10% type I error level460.356589147286822NOK


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 4
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Figure 5
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Figure 6
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Figure 7
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Figure 8
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Figure 9
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Figure 10
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Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = 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')
}