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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 computationMon, 20 Dec 2010 17:00:25 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/20/t1292867445hx46p4l8vbmknp6.htm/, Retrieved Fri, 03 May 2024 16:10:34 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=113038, Retrieved Fri, 03 May 2024 16:10:34 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact143

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Chi-Squared and McNemar Tests] [] [2010-11-25 11:32:31] [1c63f3c303537b65dfa698074d619a3e]
- RMPD    [Multiple Regression] [] [2010-12-20 17:00:25] [40b262140b988d7b8204c4955f8b7651] [Current]
-   PD      [Multiple Regression] [] [2010-12-20 23:12:06] [1c63f3c303537b65dfa698074d619a3e]
- RMPD      [Decomposition by Loess] [] [2010-12-21 11:55:29] [1c63f3c303537b65dfa698074d619a3e]
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Dataset

Dataseries X:
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9,4	0,5	5,1	-1,0	2504,7
9,4	0,8	5,0	3,0	2661,4
9,5	1,0	5,0	2,0	2880,4
9,5	1,3	5,1	3,0	3064,4
9,4	1,3	5,0	5,0	3141,1
9,4	1,2	4,9	5,0	3327,7
9,3	1,2	4,8	3,0	3565,0
9,4	1,0	4,5	2,0	3403,1
9,4	0,8	4,3	1,0	3149,9
9,2	0,7	4,3	-4,0	3006,8
9,1	0,6	4,2	1,0	3230,7
9,1	0,7	4,0	1,0	3361,1
9,1	1,0	3,8	6,0	3484,7
9,0	1,0	4,1	3,0	3411,1
9,0	1,3	4,2	2,0	3288,2
8,9	1,1	4,0	2,0	3280,4
8,8	0,8	4,3	2,0	3174,0
8,7	0,7	4,7	-8,0	3165,3
8,5	0,7	5,0	0,0	3092,7
8,3	0,9	5,1	-2,0	3053,1
8,1	1,3	5,4	3,0	3182,0
7,9	1,4	5,4	5,0	2999,9
7,8	1,6	5,4	8,0	3249,6
7,6	2,1	5,5	8,0	3210,5
7,4	0,3	5,8	9,0	3030,3
7,2	2,1	5,7	11,0	2803,5
7,0	2,5	5,5	13,0	2767,6
7,0	2,3	5,6	12,0	2882,6
6,8	2,4	5,6	13,0	2863,4
6,8	3,0	5,5	15,0	2897,1
6,7	1,7	5,5	13,0	3012,6
6,8	3,5	5,7	16,0	3143,0
6,7	4,0	5,6	10,0	3032,9
6,7	3,7	5,6	14,0	3045,8
6,7	3,7	5,4	14,0	3110,5
6,5	3,0	5,2	15,0	3013,2
6,3	2,7	5,1	13,0	2987,1
6,3	2,5	5,1	8,0	2995,6
6,3	2,2	5,0	7,0	2833,2
6,5	2,9	5,3	3,0	2849,0
6,6	3,1	5,4	3,0	2794,8
6,5	3,0	5,3	4,0	2845,3
6,3	2,8	5,1	4,0	2915,0
6,3	2,5	5,0	0,0	2892,6
6,5	1,9	5,0	-4,0	2604,4
7,0	1,9	4,6	-14,0	2641,7
7,1	1,8	4,8	-18,0	2659,8
7,3	2,0	5,1	-8,0	2638,5
7,3	2,6	5,1	-1,0	2720,3
7,4	2,5	5,1	1,0	2745,9
7,4	2,5	5,4	2,0	2735,7
7,3	1,6	5,3	0,0	2811,7
7,4	1,4	5,3	1,0	2799,4
7,5	0,8	5,1	0,0	2555,3
7,7	1,1	4,9	-1,0	2305,0
7,7	1,3	4,7	-3,0	2215,0
7,7	1,2	4,4	-3,0	2065,8
7,7	1,3	4,6	-3,0	1940,5
7,7	1,1	4,5	-4,0	2042,0
7,8	1,3	4,2	-8,0	1995,4
8,0	1,2	4,0	-9,0	1946,8
8,1	1,6	3,9	-13,0	1765,9
8,1	1,7	4,1	-18,0	1635,3
8,2	1,5	4,1	-11,0	1833,4
8,2	0,9	3,7	-9,0	1910,4
8,2	1,5	3,8	-10,0	1959,7
8,1	1,4	4,1	-13,0	1969,6
8,1	1,6	4,1	-11,0	2061,4
8,2	1,7	4,0	-5,0	2093,5
8,3	1,4	4,3	-15,0	2120,9
8,3	1,8	4,4	-6,0	2174,6
8,4	1,7	4,2	-6,0	2196,7
8,5	1,4	4,2	-3,0	2350,4
8,5	1,2	4,0	-1,0	2440,3
8,4	1,0	4,0	-3,0	2408,6
8,0	1,7	4,3	-4,0	2472,8
7,9	2,4	4,4	-6,0	2407,6
8,1	2,0	4,4	0,0	2454,6
8,5	2,1	4,3	-4,0	2448,1
8,8	2,0	4,1	-2,0	2497,8
8,8	1,8	4,1	-2,0	2645,6
8,6	2,7	3,9	-6,0	2756,8
8,3	2,3	3,8	-7,0	2849,3
8,3	1,9	3,7	-6,0	2921,4
8,3	2,0	3,5	-6,0	2981,9
8,4	2,3	3,7	-3,0	3080,6
8,4	2,8	3,7	-2,0	3106,2
8,5	2,4	3,5	-5,0	3119,3
8,6	2,3	3,3	-11,0	3061,3
8,6	2,7	3,2	-11,0	3097,3
8,6	2,7	3,3	-11,0	3161,7
8,6	2,9	3,1	-10,0	3257,2
8,6	3,0	3,2	-14,0	3277,0
8,5	2,2	3,4	-8,0	3295,3
8,4	2,3	3,5	-9,0	3364,0
8,4	2,8	3,3	-5,0	3494,2
8,4	2,8	3,5	-1,0	3667,0
8,5	2,8	3,5	-2,0	3813,1
8,5	2,2	3,8	-5,0	3918,0
8,6	2,6	4,0	-4,0	3895,5
8,6	2,8	4,0	-6,0	3801,1
8,4	2,5	4,1	-2,0	3570,1
8,2	2,4	4,0	-2,0	3701,6
8,0	2,3	3,8	-2,0	3862,3
8,0	1,9	3,7	-2,0	3970,1
8,0	1,7	3,8	2,0	4138,5
8,0	2,0	3,7	1,0	4199,8
7,9	2,1	4,0	-8,0	4290,9
7,9	1,7	4,2	-1,0	4443,9
7,8	1,8	4,0	1,0	4502,6
7,8	1,8	4,1	-1,0	4357,0
8,0	1,8	4,2	2,0	4591,3
7,8	1,3	4,5	2,0	4697,0
7,4	1,3	4,6	1,0	4621,4
7,2	1,3	4,5	-1,0	4562,8
7,0	1,2	4,5	-2,0	4202,5
7,0	1,4	4,5	-2,0	4296,5
7,2	2,2	4,4	-1,0	4435,2
7,2	2,9	4,3	-8,0	4105,2
7,2	3,1	4,5	-4,0	4116,7
7,0	3,5	4,1	-6,0	3844,5
6,9	3,6	4,1	-3,0	3721,0
6,8	4,4	4,3	-3,0	3674,4
6,8	4,1	4,4	-7,0	3857,6
6,8	5,1	4,7	-9,0	3801,1
6,9	5,8	5,0	-11,0	3504,4
7,2	5,9	4,7	-13,0	3032,6
7,2	5,4	4,5	-11,0	3047,0
7,2	5,5	4,5	-9,0	2962,3
7,1	4,8	4,5	-17,0	2197,8
7,2	3,2	5,5	-22,0	2014,5
7,3	2,7	4,5	-25,0	1862,8
7,5	2,1	4,4	-20,0	1905,4
7,6	1,9	4,2	-24,0	1811,0
7,7	0,6	3,9	-24,0	1670,1
7,7	0,7	3,9	-22,0	1864,4
7,7	-0,2	4,2	-19,0	2052,0
7,8	-1,0	4,0	-18,0	2029,6
8,0	-1,7	3,8	-17,0	2070,8
8,1	-0,7	3,7	-11,0	2293,4
8,1	-1,0	3,7	-11,0	2443,3
8,0	-0,9	3,7	-12,0	2513,2
8,1	0,0	3,7	-10,0	2466,9
8,2	0,3	3,7	-15,0	2502,7
8,3	0,8	3,8	-15,0	2539,9
8,4	0,8	3,7	-15,0	2482,6
8,4	1,9	3,5	-13,0	2626,2
8,4	2,1	3,5	-8,0	2656,3
8,5	2,5	3,1	-13,0	2446,7
8,5	2,7	3,4	-9,0	2467,4
8,6	2,4	3,3	-7,0	2462,3
8,6	2,4	2,8	-4,0	2504,6
8,5	2,9	3,2	-4,0	2579,4
8,5	3,1	3,4	-2,0	2649,2

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time11 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\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 & 11 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113038&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]11 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113038&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113038&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 time11 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Multiple Linear Regression - Estimated Regression Equation
werkloosheid[t] = + 12.8816810957190 -0.149333856903572hicp[t] -0.948425862863806rente[t] -0.0146403830228089consumer[t] + 0.000113042993792547bel20[t] -0.0119659263556134t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
werkloosheid[t] =  +  12.8816810957190 -0.149333856903572hicp[t] -0.948425862863806rente[t] -0.0146403830228089consumer[t] +  0.000113042993792547bel20[t] -0.0119659263556134t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113038&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]werkloosheid[t] =  +  12.8816810957190 -0.149333856903572hicp[t] -0.948425862863806rente[t] -0.0146403830228089consumer[t] +  0.000113042993792547bel20[t] -0.0119659263556134t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113038&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113038&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
werkloosheid[t] = + 12.8816810957190 -0.149333856903572hicp[t] -0.948425862863806rente[t] -0.0146403830228089consumer[t] + 0.000113042993792547bel20[t] -0.0119659263556134t + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)12.88168109571900.47097827.350900
hicp-0.1493338569035720.036618-4.07827.4e-053.7e-05
rente-0.9484258628638060.0816-11.622900
consumer-0.01464038302280890.007864-1.86180.0646140.032307
bel200.0001130429937925477.1e-051.60020.1116930.055847
t-0.01196592635561340.001441-8.304300

\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) & 12.8816810957190 & 0.470978 & 27.3509 & 0 & 0 \tabularnewline
hicp & -0.149333856903572 & 0.036618 & -4.0782 & 7.4e-05 & 3.7e-05 \tabularnewline
rente & -0.948425862863806 & 0.0816 & -11.6229 & 0 & 0 \tabularnewline
consumer & -0.0146403830228089 & 0.007864 & -1.8618 & 0.064614 & 0.032307 \tabularnewline
bel20 & 0.000113042993792547 & 7.1e-05 & 1.6002 & 0.111693 & 0.055847 \tabularnewline
t & -0.0119659263556134 & 0.001441 & -8.3043 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113038&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]12.8816810957190[/C][C]0.470978[/C][C]27.3509[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]hicp[/C][C]-0.149333856903572[/C][C]0.036618[/C][C]-4.0782[/C][C]7.4e-05[/C][C]3.7e-05[/C][/ROW]
[ROW][C]rente[/C][C]-0.948425862863806[/C][C]0.0816[/C][C]-11.6229[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]consumer[/C][C]-0.0146403830228089[/C][C]0.007864[/C][C]-1.8618[/C][C]0.064614[/C][C]0.032307[/C][/ROW]
[ROW][C]bel20[/C][C]0.000113042993792547[/C][C]7.1e-05[/C][C]1.6002[/C][C]0.111693[/C][C]0.055847[/C][/ROW]
[ROW][C]t[/C][C]-0.0119659263556134[/C][C]0.001441[/C][C]-8.3043[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113038&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113038&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)12.88168109571900.47097827.350900
hicp-0.1493338569035720.036618-4.07827.4e-053.7e-05
rente-0.9484258628638060.0816-11.622900
consumer-0.01464038302280890.007864-1.86180.0646140.032307
bel200.0001130429937925477.1e-051.60020.1116930.055847
t-0.01196592635561340.001441-8.304300






Multiple Linear Regression - Regression Statistics
Multiple R0.786066260077825
R-squared0.617900165232739
Adjusted R-squared0.604991387031142
F-TEST (value)47.866665270948
F-TEST (DF numerator)5
F-TEST (DF denominator)148
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.504543978966511
Sum Squared Residuals37.6755647532812

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.786066260077825 \tabularnewline
R-squared & 0.617900165232739 \tabularnewline
Adjusted R-squared & 0.604991387031142 \tabularnewline
F-TEST (value) & 47.866665270948 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 148 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.504543978966511 \tabularnewline
Sum Squared Residuals & 37.6755647532812 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113038&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.786066260077825[/C][/ROW]
[ROW][C]R-squared[/C][C]0.617900165232739[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.604991387031142[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]47.866665270948[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]148[/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]0.504543978966511[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]37.6755647532812[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113038&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113038&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 R0.786066260077825
R-squared0.617900165232739
Adjusted R-squared0.604991387031142
F-TEST (value)47.866665270948
F-TEST (DF numerator)5
F-TEST (DF denominator)148
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.504543978966511
Sum Squared Residuals37.6755647532812






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
19.48.255855509881271.14414449011873
29.48.2530843177771.14691568222300
39.58.250648418704041.24935158129596
49.58.1051992768261.394800723174
59.48.167465568335041.23253443166496
69.48.286369436597851.11363056340215
79.38.425351965001210.874648034998794
89.48.724119291213250.675880708786756
99.48.917723205805640.482276794194357
109.28.977716127842720.222283872157282
119.19.027634584659950.0723654153400512
129.19.20516125157729-0.105161251577288
139.19.27885053964208-0.178850539642079
1499.01795803915262-0.0179580391526186
1598.867096768525260.132903231474743
168.99.07380105077154-0.173801050771537
178.88.81007974808833-0.0100797480883266
188.78.579097218459640.120902781540358
198.58.157273547713080.342726452286924
208.38.04540252718180.254597472818199
218.17.630544625991430.469455374008568
227.97.553779418730220.34622058126978
237.87.496252407475470.303747592524535
247.67.31035698532440.289643014675603
257.47.247653512031850.152346487968155
267.27.006810312498420.193189687501585
2777.09145700643136-0.0914570064313644
2877.04215559247904-0.0421555924790369
296.86.99844547192944-0.198445471929441
306.86.96625060056326-0.166250600563256
316.77.19075592001094-0.490755920010942
326.86.691123535978260.10887646402174
336.76.77472953197754-0.074729531977535
346.76.75046048522168-0.0504604852216817
356.76.93549361313721-0.235493613137206
366.57.19210709286803-0.692107092868031
376.37.3461142537775-1.04611425377750
386.37.43817787936389-1.13817787936389
396.37.56213689719662-1.26213689719662
406.57.22145712354252-0.721457123542525
416.67.07865490925626-0.47865490925626
426.57.1675332430411-0.6675332430411
436.37.3829983573063-1.08299835730630
446.37.56670454333842-1.26670454333842
456.57.67032147240518-1.17032147240518
4678.18834622509164-1.18834622509164
477.18.0622361221325-0.9622361221325
487.37.58706401954116-0.28706401954116
497.37.39226201477597-0.0922620147759719
507.47.368842608706190.0311573912938133
517.47.056555501931940.343444498068062
527.37.31170466664977-0.0117046666497718
537.47.313574699828420.0864253001715847
547.57.56794084842575-0.0679408484257549
557.77.687205659248360.0127943407516353
567.77.85416503068909-0.154165030689087
577.78.12479423420912-0.424794234209124
587.77.89404546246819-0.194045462468187
597.78.03290314067242-0.332903140672421
607.88.32889193037574-0.528891930375738
6188.53069105580773-0.530691055807734
628.18.59194622749124-0.491946227491236
638.18.43380024299724-0.333800242997243
648.28.37161222393298-0.171612223932986
658.28.80804050134144-0.608040501341446
668.28.63184507717409-0.43184507717409
678.18.39532505235666-0.295325052356665
688.18.33458893540488-0.234588935404875
698.28.31831859160917-0.118318591609173
708.38.21612627172350.083873728276508
718.37.923891177881450.376108822118551
728.48.119042059951770.280957940048230
738.58.125329849744720.374670150255284
748.58.313797666438910.186202333561089
758.48.35739581460640.0426041853935949
7687.978266172783440.0217338272165596
777.97.788834323159290.111165676840711
788.17.75407266213650.345927337863499
798.57.87984268900850.620157310991504
808.88.048832791661870.751167208338128
818.88.083441391169510.716558608830489
828.68.197892079174410.402107920825588
838.38.36559914181523-0.0655991418152265
848.38.50171936133706-0.201719361337056
858.38.6713443229883-0.371344322988296
868.48.392129261407750.0078707385922525
878.48.293749924218630.106250075781372
888.58.57660472548431-0.0766047254843145
898.68.8505431618887-0.250543161888706
908.68.87775582683457-0.277755826834576
918.68.77822728299282-0.178227282992822
928.68.92223498071363-0.322234980713634
938.68.86129286574961-0.261292865749611
948.58.69333524099364-0.193335240993644
958.48.59399977935765-0.193999779357649
968.48.65320876282357-0.253208762823565
978.48.4125299611313-0.0125299611313071
988.58.43171999919160.068280000808406
998.58.280605987236250.219394012763753
1008.68.00203749516330.597962504836698
1018.67.978814304858580.621185695141424
1028.47.832131485630340.56786851436966
1038.27.944806684935180.255193315064816
10488.15562532594515-0.155625325945151
10588.31042156336818-0.310421563368183
10688.19395473017033-0.193954730170333
10788.25360115157232-0.253601151572321
1087.98.08423574460699-0.184235744606989
1097.97.857131085330640.0428689146693596
1107.87.99727180354744-0.197271803547436
1117.87.90328499705487-0.103284997054866
11287.779041308790040.220958691209962
1137.87.569163196470940.230836803529059
1147.47.46844901652104-0.068449016521039
1157.27.57398212306118-0.373982123061181
11677.55086057475528-0.550860574755279
11777.51965391843545-0.51965391843545
1187.27.48410217305958-0.284102173059577
1197.27.52762362636597-0.327623626365966
1207.27.23884421839426-0.0388442183942559
12177.54502555755802-0.545025557558023
1226.97.46024428671025-0.560244286710246
1236.87.13385829874828-0.333858298748282
1246.87.15112095173139-0.351120951731389
1256.86.72818724650940.0718127534905994
1266.96.322900771249510.577099228750486
1277.26.556476299636980.643523700363021
1287.26.78120952737090.418790472629092
1297.26.715454707705090.484545292294909
1307.16.838724176610050.261275823389953
1317.26.169747692788211.03025230721179
1327.37.207647084658290.0923529153417114
1337.57.311737775152720.188262224847284
1347.67.56721406622780.0327859337722017
1357.78.0179821548806-0.3179821548806
1367.77.9837663304829-0.283766330482903
1377.77.79895883304842-0.0989588330484179
1387.88.07897261870466-0.278972618704662
13988.35124255307575-0.351242553075754
1408.18.22210642838432-0.122106428384317
1418.18.27188580386928-0.171885803869278
14288.26752858011221-0.267528580112215
1438.18.086647525885170.0133524741148251
1448.28.10713029675030.0928697032496925
1458.37.929860055025610.370139944974391
1468.48.006259351412060.393740648587936
1478.48.006663562898280.393336437101725
1488.47.895031544161060.504968455838942
1498.58.252210523804670.247789476195335
1508.57.869628525089470.630371474910534
1518.67.967448056777350.632551943222654
1528.68.390555631422630.209444368577366
1538.57.93300804740540.566991952594606
1548.57.68009981201740.819900187982593

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 9.4 & 8.25585550988127 & 1.14414449011873 \tabularnewline
2 & 9.4 & 8.253084317777 & 1.14691568222300 \tabularnewline
3 & 9.5 & 8.25064841870404 & 1.24935158129596 \tabularnewline
4 & 9.5 & 8.105199276826 & 1.394800723174 \tabularnewline
5 & 9.4 & 8.16746556833504 & 1.23253443166496 \tabularnewline
6 & 9.4 & 8.28636943659785 & 1.11363056340215 \tabularnewline
7 & 9.3 & 8.42535196500121 & 0.874648034998794 \tabularnewline
8 & 9.4 & 8.72411929121325 & 0.675880708786756 \tabularnewline
9 & 9.4 & 8.91772320580564 & 0.482276794194357 \tabularnewline
10 & 9.2 & 8.97771612784272 & 0.222283872157282 \tabularnewline
11 & 9.1 & 9.02763458465995 & 0.0723654153400512 \tabularnewline
12 & 9.1 & 9.20516125157729 & -0.105161251577288 \tabularnewline
13 & 9.1 & 9.27885053964208 & -0.178850539642079 \tabularnewline
14 & 9 & 9.01795803915262 & -0.0179580391526186 \tabularnewline
15 & 9 & 8.86709676852526 & 0.132903231474743 \tabularnewline
16 & 8.9 & 9.07380105077154 & -0.173801050771537 \tabularnewline
17 & 8.8 & 8.81007974808833 & -0.0100797480883266 \tabularnewline
18 & 8.7 & 8.57909721845964 & 0.120902781540358 \tabularnewline
19 & 8.5 & 8.15727354771308 & 0.342726452286924 \tabularnewline
20 & 8.3 & 8.0454025271818 & 0.254597472818199 \tabularnewline
21 & 8.1 & 7.63054462599143 & 0.469455374008568 \tabularnewline
22 & 7.9 & 7.55377941873022 & 0.34622058126978 \tabularnewline
23 & 7.8 & 7.49625240747547 & 0.303747592524535 \tabularnewline
24 & 7.6 & 7.3103569853244 & 0.289643014675603 \tabularnewline
25 & 7.4 & 7.24765351203185 & 0.152346487968155 \tabularnewline
26 & 7.2 & 7.00681031249842 & 0.193189687501585 \tabularnewline
27 & 7 & 7.09145700643136 & -0.0914570064313644 \tabularnewline
28 & 7 & 7.04215559247904 & -0.0421555924790369 \tabularnewline
29 & 6.8 & 6.99844547192944 & -0.198445471929441 \tabularnewline
30 & 6.8 & 6.96625060056326 & -0.166250600563256 \tabularnewline
31 & 6.7 & 7.19075592001094 & -0.490755920010942 \tabularnewline
32 & 6.8 & 6.69112353597826 & 0.10887646402174 \tabularnewline
33 & 6.7 & 6.77472953197754 & -0.074729531977535 \tabularnewline
34 & 6.7 & 6.75046048522168 & -0.0504604852216817 \tabularnewline
35 & 6.7 & 6.93549361313721 & -0.235493613137206 \tabularnewline
36 & 6.5 & 7.19210709286803 & -0.692107092868031 \tabularnewline
37 & 6.3 & 7.3461142537775 & -1.04611425377750 \tabularnewline
38 & 6.3 & 7.43817787936389 & -1.13817787936389 \tabularnewline
39 & 6.3 & 7.56213689719662 & -1.26213689719662 \tabularnewline
40 & 6.5 & 7.22145712354252 & -0.721457123542525 \tabularnewline
41 & 6.6 & 7.07865490925626 & -0.47865490925626 \tabularnewline
42 & 6.5 & 7.1675332430411 & -0.6675332430411 \tabularnewline
43 & 6.3 & 7.3829983573063 & -1.08299835730630 \tabularnewline
44 & 6.3 & 7.56670454333842 & -1.26670454333842 \tabularnewline
45 & 6.5 & 7.67032147240518 & -1.17032147240518 \tabularnewline
46 & 7 & 8.18834622509164 & -1.18834622509164 \tabularnewline
47 & 7.1 & 8.0622361221325 & -0.9622361221325 \tabularnewline
48 & 7.3 & 7.58706401954116 & -0.28706401954116 \tabularnewline
49 & 7.3 & 7.39226201477597 & -0.0922620147759719 \tabularnewline
50 & 7.4 & 7.36884260870619 & 0.0311573912938133 \tabularnewline
51 & 7.4 & 7.05655550193194 & 0.343444498068062 \tabularnewline
52 & 7.3 & 7.31170466664977 & -0.0117046666497718 \tabularnewline
53 & 7.4 & 7.31357469982842 & 0.0864253001715847 \tabularnewline
54 & 7.5 & 7.56794084842575 & -0.0679408484257549 \tabularnewline
55 & 7.7 & 7.68720565924836 & 0.0127943407516353 \tabularnewline
56 & 7.7 & 7.85416503068909 & -0.154165030689087 \tabularnewline
57 & 7.7 & 8.12479423420912 & -0.424794234209124 \tabularnewline
58 & 7.7 & 7.89404546246819 & -0.194045462468187 \tabularnewline
59 & 7.7 & 8.03290314067242 & -0.332903140672421 \tabularnewline
60 & 7.8 & 8.32889193037574 & -0.528891930375738 \tabularnewline
61 & 8 & 8.53069105580773 & -0.530691055807734 \tabularnewline
62 & 8.1 & 8.59194622749124 & -0.491946227491236 \tabularnewline
63 & 8.1 & 8.43380024299724 & -0.333800242997243 \tabularnewline
64 & 8.2 & 8.37161222393298 & -0.171612223932986 \tabularnewline
65 & 8.2 & 8.80804050134144 & -0.608040501341446 \tabularnewline
66 & 8.2 & 8.63184507717409 & -0.43184507717409 \tabularnewline
67 & 8.1 & 8.39532505235666 & -0.295325052356665 \tabularnewline
68 & 8.1 & 8.33458893540488 & -0.234588935404875 \tabularnewline
69 & 8.2 & 8.31831859160917 & -0.118318591609173 \tabularnewline
70 & 8.3 & 8.2161262717235 & 0.083873728276508 \tabularnewline
71 & 8.3 & 7.92389117788145 & 0.376108822118551 \tabularnewline
72 & 8.4 & 8.11904205995177 & 0.280957940048230 \tabularnewline
73 & 8.5 & 8.12532984974472 & 0.374670150255284 \tabularnewline
74 & 8.5 & 8.31379766643891 & 0.186202333561089 \tabularnewline
75 & 8.4 & 8.3573958146064 & 0.0426041853935949 \tabularnewline
76 & 8 & 7.97826617278344 & 0.0217338272165596 \tabularnewline
77 & 7.9 & 7.78883432315929 & 0.111165676840711 \tabularnewline
78 & 8.1 & 7.7540726621365 & 0.345927337863499 \tabularnewline
79 & 8.5 & 7.8798426890085 & 0.620157310991504 \tabularnewline
80 & 8.8 & 8.04883279166187 & 0.751167208338128 \tabularnewline
81 & 8.8 & 8.08344139116951 & 0.716558608830489 \tabularnewline
82 & 8.6 & 8.19789207917441 & 0.402107920825588 \tabularnewline
83 & 8.3 & 8.36559914181523 & -0.0655991418152265 \tabularnewline
84 & 8.3 & 8.50171936133706 & -0.201719361337056 \tabularnewline
85 & 8.3 & 8.6713443229883 & -0.371344322988296 \tabularnewline
86 & 8.4 & 8.39212926140775 & 0.0078707385922525 \tabularnewline
87 & 8.4 & 8.29374992421863 & 0.106250075781372 \tabularnewline
88 & 8.5 & 8.57660472548431 & -0.0766047254843145 \tabularnewline
89 & 8.6 & 8.8505431618887 & -0.250543161888706 \tabularnewline
90 & 8.6 & 8.87775582683457 & -0.277755826834576 \tabularnewline
91 & 8.6 & 8.77822728299282 & -0.178227282992822 \tabularnewline
92 & 8.6 & 8.92223498071363 & -0.322234980713634 \tabularnewline
93 & 8.6 & 8.86129286574961 & -0.261292865749611 \tabularnewline
94 & 8.5 & 8.69333524099364 & -0.193335240993644 \tabularnewline
95 & 8.4 & 8.59399977935765 & -0.193999779357649 \tabularnewline
96 & 8.4 & 8.65320876282357 & -0.253208762823565 \tabularnewline
97 & 8.4 & 8.4125299611313 & -0.0125299611313071 \tabularnewline
98 & 8.5 & 8.4317199991916 & 0.068280000808406 \tabularnewline
99 & 8.5 & 8.28060598723625 & 0.219394012763753 \tabularnewline
100 & 8.6 & 8.0020374951633 & 0.597962504836698 \tabularnewline
101 & 8.6 & 7.97881430485858 & 0.621185695141424 \tabularnewline
102 & 8.4 & 7.83213148563034 & 0.56786851436966 \tabularnewline
103 & 8.2 & 7.94480668493518 & 0.255193315064816 \tabularnewline
104 & 8 & 8.15562532594515 & -0.155625325945151 \tabularnewline
105 & 8 & 8.31042156336818 & -0.310421563368183 \tabularnewline
106 & 8 & 8.19395473017033 & -0.193954730170333 \tabularnewline
107 & 8 & 8.25360115157232 & -0.253601151572321 \tabularnewline
108 & 7.9 & 8.08423574460699 & -0.184235744606989 \tabularnewline
109 & 7.9 & 7.85713108533064 & 0.0428689146693596 \tabularnewline
110 & 7.8 & 7.99727180354744 & -0.197271803547436 \tabularnewline
111 & 7.8 & 7.90328499705487 & -0.103284997054866 \tabularnewline
112 & 8 & 7.77904130879004 & 0.220958691209962 \tabularnewline
113 & 7.8 & 7.56916319647094 & 0.230836803529059 \tabularnewline
114 & 7.4 & 7.46844901652104 & -0.068449016521039 \tabularnewline
115 & 7.2 & 7.57398212306118 & -0.373982123061181 \tabularnewline
116 & 7 & 7.55086057475528 & -0.550860574755279 \tabularnewline
117 & 7 & 7.51965391843545 & -0.51965391843545 \tabularnewline
118 & 7.2 & 7.48410217305958 & -0.284102173059577 \tabularnewline
119 & 7.2 & 7.52762362636597 & -0.327623626365966 \tabularnewline
120 & 7.2 & 7.23884421839426 & -0.0388442183942559 \tabularnewline
121 & 7 & 7.54502555755802 & -0.545025557558023 \tabularnewline
122 & 6.9 & 7.46024428671025 & -0.560244286710246 \tabularnewline
123 & 6.8 & 7.13385829874828 & -0.333858298748282 \tabularnewline
124 & 6.8 & 7.15112095173139 & -0.351120951731389 \tabularnewline
125 & 6.8 & 6.7281872465094 & 0.0718127534905994 \tabularnewline
126 & 6.9 & 6.32290077124951 & 0.577099228750486 \tabularnewline
127 & 7.2 & 6.55647629963698 & 0.643523700363021 \tabularnewline
128 & 7.2 & 6.7812095273709 & 0.418790472629092 \tabularnewline
129 & 7.2 & 6.71545470770509 & 0.484545292294909 \tabularnewline
130 & 7.1 & 6.83872417661005 & 0.261275823389953 \tabularnewline
131 & 7.2 & 6.16974769278821 & 1.03025230721179 \tabularnewline
132 & 7.3 & 7.20764708465829 & 0.0923529153417114 \tabularnewline
133 & 7.5 & 7.31173777515272 & 0.188262224847284 \tabularnewline
134 & 7.6 & 7.5672140662278 & 0.0327859337722017 \tabularnewline
135 & 7.7 & 8.0179821548806 & -0.3179821548806 \tabularnewline
136 & 7.7 & 7.9837663304829 & -0.283766330482903 \tabularnewline
137 & 7.7 & 7.79895883304842 & -0.0989588330484179 \tabularnewline
138 & 7.8 & 8.07897261870466 & -0.278972618704662 \tabularnewline
139 & 8 & 8.35124255307575 & -0.351242553075754 \tabularnewline
140 & 8.1 & 8.22210642838432 & -0.122106428384317 \tabularnewline
141 & 8.1 & 8.27188580386928 & -0.171885803869278 \tabularnewline
142 & 8 & 8.26752858011221 & -0.267528580112215 \tabularnewline
143 & 8.1 & 8.08664752588517 & 0.0133524741148251 \tabularnewline
144 & 8.2 & 8.1071302967503 & 0.0928697032496925 \tabularnewline
145 & 8.3 & 7.92986005502561 & 0.370139944974391 \tabularnewline
146 & 8.4 & 8.00625935141206 & 0.393740648587936 \tabularnewline
147 & 8.4 & 8.00666356289828 & 0.393336437101725 \tabularnewline
148 & 8.4 & 7.89503154416106 & 0.504968455838942 \tabularnewline
149 & 8.5 & 8.25221052380467 & 0.247789476195335 \tabularnewline
150 & 8.5 & 7.86962852508947 & 0.630371474910534 \tabularnewline
151 & 8.6 & 7.96744805677735 & 0.632551943222654 \tabularnewline
152 & 8.6 & 8.39055563142263 & 0.209444368577366 \tabularnewline
153 & 8.5 & 7.9330080474054 & 0.566991952594606 \tabularnewline
154 & 8.5 & 7.6800998120174 & 0.819900187982593 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113038&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]9.4[/C][C]8.25585550988127[/C][C]1.14414449011873[/C][/ROW]
[ROW][C]2[/C][C]9.4[/C][C]8.253084317777[/C][C]1.14691568222300[/C][/ROW]
[ROW][C]3[/C][C]9.5[/C][C]8.25064841870404[/C][C]1.24935158129596[/C][/ROW]
[ROW][C]4[/C][C]9.5[/C][C]8.105199276826[/C][C]1.394800723174[/C][/ROW]
[ROW][C]5[/C][C]9.4[/C][C]8.16746556833504[/C][C]1.23253443166496[/C][/ROW]
[ROW][C]6[/C][C]9.4[/C][C]8.28636943659785[/C][C]1.11363056340215[/C][/ROW]
[ROW][C]7[/C][C]9.3[/C][C]8.42535196500121[/C][C]0.874648034998794[/C][/ROW]
[ROW][C]8[/C][C]9.4[/C][C]8.72411929121325[/C][C]0.675880708786756[/C][/ROW]
[ROW][C]9[/C][C]9.4[/C][C]8.91772320580564[/C][C]0.482276794194357[/C][/ROW]
[ROW][C]10[/C][C]9.2[/C][C]8.97771612784272[/C][C]0.222283872157282[/C][/ROW]
[ROW][C]11[/C][C]9.1[/C][C]9.02763458465995[/C][C]0.0723654153400512[/C][/ROW]
[ROW][C]12[/C][C]9.1[/C][C]9.20516125157729[/C][C]-0.105161251577288[/C][/ROW]
[ROW][C]13[/C][C]9.1[/C][C]9.27885053964208[/C][C]-0.178850539642079[/C][/ROW]
[ROW][C]14[/C][C]9[/C][C]9.01795803915262[/C][C]-0.0179580391526186[/C][/ROW]
[ROW][C]15[/C][C]9[/C][C]8.86709676852526[/C][C]0.132903231474743[/C][/ROW]
[ROW][C]16[/C][C]8.9[/C][C]9.07380105077154[/C][C]-0.173801050771537[/C][/ROW]
[ROW][C]17[/C][C]8.8[/C][C]8.81007974808833[/C][C]-0.0100797480883266[/C][/ROW]
[ROW][C]18[/C][C]8.7[/C][C]8.57909721845964[/C][C]0.120902781540358[/C][/ROW]
[ROW][C]19[/C][C]8.5[/C][C]8.15727354771308[/C][C]0.342726452286924[/C][/ROW]
[ROW][C]20[/C][C]8.3[/C][C]8.0454025271818[/C][C]0.254597472818199[/C][/ROW]
[ROW][C]21[/C][C]8.1[/C][C]7.63054462599143[/C][C]0.469455374008568[/C][/ROW]
[ROW][C]22[/C][C]7.9[/C][C]7.55377941873022[/C][C]0.34622058126978[/C][/ROW]
[ROW][C]23[/C][C]7.8[/C][C]7.49625240747547[/C][C]0.303747592524535[/C][/ROW]
[ROW][C]24[/C][C]7.6[/C][C]7.3103569853244[/C][C]0.289643014675603[/C][/ROW]
[ROW][C]25[/C][C]7.4[/C][C]7.24765351203185[/C][C]0.152346487968155[/C][/ROW]
[ROW][C]26[/C][C]7.2[/C][C]7.00681031249842[/C][C]0.193189687501585[/C][/ROW]
[ROW][C]27[/C][C]7[/C][C]7.09145700643136[/C][C]-0.0914570064313644[/C][/ROW]
[ROW][C]28[/C][C]7[/C][C]7.04215559247904[/C][C]-0.0421555924790369[/C][/ROW]
[ROW][C]29[/C][C]6.8[/C][C]6.99844547192944[/C][C]-0.198445471929441[/C][/ROW]
[ROW][C]30[/C][C]6.8[/C][C]6.96625060056326[/C][C]-0.166250600563256[/C][/ROW]
[ROW][C]31[/C][C]6.7[/C][C]7.19075592001094[/C][C]-0.490755920010942[/C][/ROW]
[ROW][C]32[/C][C]6.8[/C][C]6.69112353597826[/C][C]0.10887646402174[/C][/ROW]
[ROW][C]33[/C][C]6.7[/C][C]6.77472953197754[/C][C]-0.074729531977535[/C][/ROW]
[ROW][C]34[/C][C]6.7[/C][C]6.75046048522168[/C][C]-0.0504604852216817[/C][/ROW]
[ROW][C]35[/C][C]6.7[/C][C]6.93549361313721[/C][C]-0.235493613137206[/C][/ROW]
[ROW][C]36[/C][C]6.5[/C][C]7.19210709286803[/C][C]-0.692107092868031[/C][/ROW]
[ROW][C]37[/C][C]6.3[/C][C]7.3461142537775[/C][C]-1.04611425377750[/C][/ROW]
[ROW][C]38[/C][C]6.3[/C][C]7.43817787936389[/C][C]-1.13817787936389[/C][/ROW]
[ROW][C]39[/C][C]6.3[/C][C]7.56213689719662[/C][C]-1.26213689719662[/C][/ROW]
[ROW][C]40[/C][C]6.5[/C][C]7.22145712354252[/C][C]-0.721457123542525[/C][/ROW]
[ROW][C]41[/C][C]6.6[/C][C]7.07865490925626[/C][C]-0.47865490925626[/C][/ROW]
[ROW][C]42[/C][C]6.5[/C][C]7.1675332430411[/C][C]-0.6675332430411[/C][/ROW]
[ROW][C]43[/C][C]6.3[/C][C]7.3829983573063[/C][C]-1.08299835730630[/C][/ROW]
[ROW][C]44[/C][C]6.3[/C][C]7.56670454333842[/C][C]-1.26670454333842[/C][/ROW]
[ROW][C]45[/C][C]6.5[/C][C]7.67032147240518[/C][C]-1.17032147240518[/C][/ROW]
[ROW][C]46[/C][C]7[/C][C]8.18834622509164[/C][C]-1.18834622509164[/C][/ROW]
[ROW][C]47[/C][C]7.1[/C][C]8.0622361221325[/C][C]-0.9622361221325[/C][/ROW]
[ROW][C]48[/C][C]7.3[/C][C]7.58706401954116[/C][C]-0.28706401954116[/C][/ROW]
[ROW][C]49[/C][C]7.3[/C][C]7.39226201477597[/C][C]-0.0922620147759719[/C][/ROW]
[ROW][C]50[/C][C]7.4[/C][C]7.36884260870619[/C][C]0.0311573912938133[/C][/ROW]
[ROW][C]51[/C][C]7.4[/C][C]7.05655550193194[/C][C]0.343444498068062[/C][/ROW]
[ROW][C]52[/C][C]7.3[/C][C]7.31170466664977[/C][C]-0.0117046666497718[/C][/ROW]
[ROW][C]53[/C][C]7.4[/C][C]7.31357469982842[/C][C]0.0864253001715847[/C][/ROW]
[ROW][C]54[/C][C]7.5[/C][C]7.56794084842575[/C][C]-0.0679408484257549[/C][/ROW]
[ROW][C]55[/C][C]7.7[/C][C]7.68720565924836[/C][C]0.0127943407516353[/C][/ROW]
[ROW][C]56[/C][C]7.7[/C][C]7.85416503068909[/C][C]-0.154165030689087[/C][/ROW]
[ROW][C]57[/C][C]7.7[/C][C]8.12479423420912[/C][C]-0.424794234209124[/C][/ROW]
[ROW][C]58[/C][C]7.7[/C][C]7.89404546246819[/C][C]-0.194045462468187[/C][/ROW]
[ROW][C]59[/C][C]7.7[/C][C]8.03290314067242[/C][C]-0.332903140672421[/C][/ROW]
[ROW][C]60[/C][C]7.8[/C][C]8.32889193037574[/C][C]-0.528891930375738[/C][/ROW]
[ROW][C]61[/C][C]8[/C][C]8.53069105580773[/C][C]-0.530691055807734[/C][/ROW]
[ROW][C]62[/C][C]8.1[/C][C]8.59194622749124[/C][C]-0.491946227491236[/C][/ROW]
[ROW][C]63[/C][C]8.1[/C][C]8.43380024299724[/C][C]-0.333800242997243[/C][/ROW]
[ROW][C]64[/C][C]8.2[/C][C]8.37161222393298[/C][C]-0.171612223932986[/C][/ROW]
[ROW][C]65[/C][C]8.2[/C][C]8.80804050134144[/C][C]-0.608040501341446[/C][/ROW]
[ROW][C]66[/C][C]8.2[/C][C]8.63184507717409[/C][C]-0.43184507717409[/C][/ROW]
[ROW][C]67[/C][C]8.1[/C][C]8.39532505235666[/C][C]-0.295325052356665[/C][/ROW]
[ROW][C]68[/C][C]8.1[/C][C]8.33458893540488[/C][C]-0.234588935404875[/C][/ROW]
[ROW][C]69[/C][C]8.2[/C][C]8.31831859160917[/C][C]-0.118318591609173[/C][/ROW]
[ROW][C]70[/C][C]8.3[/C][C]8.2161262717235[/C][C]0.083873728276508[/C][/ROW]
[ROW][C]71[/C][C]8.3[/C][C]7.92389117788145[/C][C]0.376108822118551[/C][/ROW]
[ROW][C]72[/C][C]8.4[/C][C]8.11904205995177[/C][C]0.280957940048230[/C][/ROW]
[ROW][C]73[/C][C]8.5[/C][C]8.12532984974472[/C][C]0.374670150255284[/C][/ROW]
[ROW][C]74[/C][C]8.5[/C][C]8.31379766643891[/C][C]0.186202333561089[/C][/ROW]
[ROW][C]75[/C][C]8.4[/C][C]8.3573958146064[/C][C]0.0426041853935949[/C][/ROW]
[ROW][C]76[/C][C]8[/C][C]7.97826617278344[/C][C]0.0217338272165596[/C][/ROW]
[ROW][C]77[/C][C]7.9[/C][C]7.78883432315929[/C][C]0.111165676840711[/C][/ROW]
[ROW][C]78[/C][C]8.1[/C][C]7.7540726621365[/C][C]0.345927337863499[/C][/ROW]
[ROW][C]79[/C][C]8.5[/C][C]7.8798426890085[/C][C]0.620157310991504[/C][/ROW]
[ROW][C]80[/C][C]8.8[/C][C]8.04883279166187[/C][C]0.751167208338128[/C][/ROW]
[ROW][C]81[/C][C]8.8[/C][C]8.08344139116951[/C][C]0.716558608830489[/C][/ROW]
[ROW][C]82[/C][C]8.6[/C][C]8.19789207917441[/C][C]0.402107920825588[/C][/ROW]
[ROW][C]83[/C][C]8.3[/C][C]8.36559914181523[/C][C]-0.0655991418152265[/C][/ROW]
[ROW][C]84[/C][C]8.3[/C][C]8.50171936133706[/C][C]-0.201719361337056[/C][/ROW]
[ROW][C]85[/C][C]8.3[/C][C]8.6713443229883[/C][C]-0.371344322988296[/C][/ROW]
[ROW][C]86[/C][C]8.4[/C][C]8.39212926140775[/C][C]0.0078707385922525[/C][/ROW]
[ROW][C]87[/C][C]8.4[/C][C]8.29374992421863[/C][C]0.106250075781372[/C][/ROW]
[ROW][C]88[/C][C]8.5[/C][C]8.57660472548431[/C][C]-0.0766047254843145[/C][/ROW]
[ROW][C]89[/C][C]8.6[/C][C]8.8505431618887[/C][C]-0.250543161888706[/C][/ROW]
[ROW][C]90[/C][C]8.6[/C][C]8.87775582683457[/C][C]-0.277755826834576[/C][/ROW]
[ROW][C]91[/C][C]8.6[/C][C]8.77822728299282[/C][C]-0.178227282992822[/C][/ROW]
[ROW][C]92[/C][C]8.6[/C][C]8.92223498071363[/C][C]-0.322234980713634[/C][/ROW]
[ROW][C]93[/C][C]8.6[/C][C]8.86129286574961[/C][C]-0.261292865749611[/C][/ROW]
[ROW][C]94[/C][C]8.5[/C][C]8.69333524099364[/C][C]-0.193335240993644[/C][/ROW]
[ROW][C]95[/C][C]8.4[/C][C]8.59399977935765[/C][C]-0.193999779357649[/C][/ROW]
[ROW][C]96[/C][C]8.4[/C][C]8.65320876282357[/C][C]-0.253208762823565[/C][/ROW]
[ROW][C]97[/C][C]8.4[/C][C]8.4125299611313[/C][C]-0.0125299611313071[/C][/ROW]
[ROW][C]98[/C][C]8.5[/C][C]8.4317199991916[/C][C]0.068280000808406[/C][/ROW]
[ROW][C]99[/C][C]8.5[/C][C]8.28060598723625[/C][C]0.219394012763753[/C][/ROW]
[ROW][C]100[/C][C]8.6[/C][C]8.0020374951633[/C][C]0.597962504836698[/C][/ROW]
[ROW][C]101[/C][C]8.6[/C][C]7.97881430485858[/C][C]0.621185695141424[/C][/ROW]
[ROW][C]102[/C][C]8.4[/C][C]7.83213148563034[/C][C]0.56786851436966[/C][/ROW]
[ROW][C]103[/C][C]8.2[/C][C]7.94480668493518[/C][C]0.255193315064816[/C][/ROW]
[ROW][C]104[/C][C]8[/C][C]8.15562532594515[/C][C]-0.155625325945151[/C][/ROW]
[ROW][C]105[/C][C]8[/C][C]8.31042156336818[/C][C]-0.310421563368183[/C][/ROW]
[ROW][C]106[/C][C]8[/C][C]8.19395473017033[/C][C]-0.193954730170333[/C][/ROW]
[ROW][C]107[/C][C]8[/C][C]8.25360115157232[/C][C]-0.253601151572321[/C][/ROW]
[ROW][C]108[/C][C]7.9[/C][C]8.08423574460699[/C][C]-0.184235744606989[/C][/ROW]
[ROW][C]109[/C][C]7.9[/C][C]7.85713108533064[/C][C]0.0428689146693596[/C][/ROW]
[ROW][C]110[/C][C]7.8[/C][C]7.99727180354744[/C][C]-0.197271803547436[/C][/ROW]
[ROW][C]111[/C][C]7.8[/C][C]7.90328499705487[/C][C]-0.103284997054866[/C][/ROW]
[ROW][C]112[/C][C]8[/C][C]7.77904130879004[/C][C]0.220958691209962[/C][/ROW]
[ROW][C]113[/C][C]7.8[/C][C]7.56916319647094[/C][C]0.230836803529059[/C][/ROW]
[ROW][C]114[/C][C]7.4[/C][C]7.46844901652104[/C][C]-0.068449016521039[/C][/ROW]
[ROW][C]115[/C][C]7.2[/C][C]7.57398212306118[/C][C]-0.373982123061181[/C][/ROW]
[ROW][C]116[/C][C]7[/C][C]7.55086057475528[/C][C]-0.550860574755279[/C][/ROW]
[ROW][C]117[/C][C]7[/C][C]7.51965391843545[/C][C]-0.51965391843545[/C][/ROW]
[ROW][C]118[/C][C]7.2[/C][C]7.48410217305958[/C][C]-0.284102173059577[/C][/ROW]
[ROW][C]119[/C][C]7.2[/C][C]7.52762362636597[/C][C]-0.327623626365966[/C][/ROW]
[ROW][C]120[/C][C]7.2[/C][C]7.23884421839426[/C][C]-0.0388442183942559[/C][/ROW]
[ROW][C]121[/C][C]7[/C][C]7.54502555755802[/C][C]-0.545025557558023[/C][/ROW]
[ROW][C]122[/C][C]6.9[/C][C]7.46024428671025[/C][C]-0.560244286710246[/C][/ROW]
[ROW][C]123[/C][C]6.8[/C][C]7.13385829874828[/C][C]-0.333858298748282[/C][/ROW]
[ROW][C]124[/C][C]6.8[/C][C]7.15112095173139[/C][C]-0.351120951731389[/C][/ROW]
[ROW][C]125[/C][C]6.8[/C][C]6.7281872465094[/C][C]0.0718127534905994[/C][/ROW]
[ROW][C]126[/C][C]6.9[/C][C]6.32290077124951[/C][C]0.577099228750486[/C][/ROW]
[ROW][C]127[/C][C]7.2[/C][C]6.55647629963698[/C][C]0.643523700363021[/C][/ROW]
[ROW][C]128[/C][C]7.2[/C][C]6.7812095273709[/C][C]0.418790472629092[/C][/ROW]
[ROW][C]129[/C][C]7.2[/C][C]6.71545470770509[/C][C]0.484545292294909[/C][/ROW]
[ROW][C]130[/C][C]7.1[/C][C]6.83872417661005[/C][C]0.261275823389953[/C][/ROW]
[ROW][C]131[/C][C]7.2[/C][C]6.16974769278821[/C][C]1.03025230721179[/C][/ROW]
[ROW][C]132[/C][C]7.3[/C][C]7.20764708465829[/C][C]0.0923529153417114[/C][/ROW]
[ROW][C]133[/C][C]7.5[/C][C]7.31173777515272[/C][C]0.188262224847284[/C][/ROW]
[ROW][C]134[/C][C]7.6[/C][C]7.5672140662278[/C][C]0.0327859337722017[/C][/ROW]
[ROW][C]135[/C][C]7.7[/C][C]8.0179821548806[/C][C]-0.3179821548806[/C][/ROW]
[ROW][C]136[/C][C]7.7[/C][C]7.9837663304829[/C][C]-0.283766330482903[/C][/ROW]
[ROW][C]137[/C][C]7.7[/C][C]7.79895883304842[/C][C]-0.0989588330484179[/C][/ROW]
[ROW][C]138[/C][C]7.8[/C][C]8.07897261870466[/C][C]-0.278972618704662[/C][/ROW]
[ROW][C]139[/C][C]8[/C][C]8.35124255307575[/C][C]-0.351242553075754[/C][/ROW]
[ROW][C]140[/C][C]8.1[/C][C]8.22210642838432[/C][C]-0.122106428384317[/C][/ROW]
[ROW][C]141[/C][C]8.1[/C][C]8.27188580386928[/C][C]-0.171885803869278[/C][/ROW]
[ROW][C]142[/C][C]8[/C][C]8.26752858011221[/C][C]-0.267528580112215[/C][/ROW]
[ROW][C]143[/C][C]8.1[/C][C]8.08664752588517[/C][C]0.0133524741148251[/C][/ROW]
[ROW][C]144[/C][C]8.2[/C][C]8.1071302967503[/C][C]0.0928697032496925[/C][/ROW]
[ROW][C]145[/C][C]8.3[/C][C]7.92986005502561[/C][C]0.370139944974391[/C][/ROW]
[ROW][C]146[/C][C]8.4[/C][C]8.00625935141206[/C][C]0.393740648587936[/C][/ROW]
[ROW][C]147[/C][C]8.4[/C][C]8.00666356289828[/C][C]0.393336437101725[/C][/ROW]
[ROW][C]148[/C][C]8.4[/C][C]7.89503154416106[/C][C]0.504968455838942[/C][/ROW]
[ROW][C]149[/C][C]8.5[/C][C]8.25221052380467[/C][C]0.247789476195335[/C][/ROW]
[ROW][C]150[/C][C]8.5[/C][C]7.86962852508947[/C][C]0.630371474910534[/C][/ROW]
[ROW][C]151[/C][C]8.6[/C][C]7.96744805677735[/C][C]0.632551943222654[/C][/ROW]
[ROW][C]152[/C][C]8.6[/C][C]8.39055563142263[/C][C]0.209444368577366[/C][/ROW]
[ROW][C]153[/C][C]8.5[/C][C]7.9330080474054[/C][C]0.566991952594606[/C][/ROW]
[ROW][C]154[/C][C]8.5[/C][C]7.6800998120174[/C][C]0.819900187982593[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113038&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113038&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
19.48.255855509881271.14414449011873
29.48.2530843177771.14691568222300
39.58.250648418704041.24935158129596
49.58.1051992768261.394800723174
59.48.167465568335041.23253443166496
69.48.286369436597851.11363056340215
79.38.425351965001210.874648034998794
89.48.724119291213250.675880708786756
99.48.917723205805640.482276794194357
109.28.977716127842720.222283872157282
119.19.027634584659950.0723654153400512
129.19.20516125157729-0.105161251577288
139.19.27885053964208-0.178850539642079
1499.01795803915262-0.0179580391526186
1598.867096768525260.132903231474743
168.99.07380105077154-0.173801050771537
178.88.81007974808833-0.0100797480883266
188.78.579097218459640.120902781540358
198.58.157273547713080.342726452286924
208.38.04540252718180.254597472818199
218.17.630544625991430.469455374008568
227.97.553779418730220.34622058126978
237.87.496252407475470.303747592524535
247.67.31035698532440.289643014675603
257.47.247653512031850.152346487968155
267.27.006810312498420.193189687501585
2777.09145700643136-0.0914570064313644
2877.04215559247904-0.0421555924790369
296.86.99844547192944-0.198445471929441
306.86.96625060056326-0.166250600563256
316.77.19075592001094-0.490755920010942
326.86.691123535978260.10887646402174
336.76.77472953197754-0.074729531977535
346.76.75046048522168-0.0504604852216817
356.76.93549361313721-0.235493613137206
366.57.19210709286803-0.692107092868031
376.37.3461142537775-1.04611425377750
386.37.43817787936389-1.13817787936389
396.37.56213689719662-1.26213689719662
406.57.22145712354252-0.721457123542525
416.67.07865490925626-0.47865490925626
426.57.1675332430411-0.6675332430411
436.37.3829983573063-1.08299835730630
446.37.56670454333842-1.26670454333842
456.57.67032147240518-1.17032147240518
4678.18834622509164-1.18834622509164
477.18.0622361221325-0.9622361221325
487.37.58706401954116-0.28706401954116
497.37.39226201477597-0.0922620147759719
507.47.368842608706190.0311573912938133
517.47.056555501931940.343444498068062
527.37.31170466664977-0.0117046666497718
537.47.313574699828420.0864253001715847
547.57.56794084842575-0.0679408484257549
557.77.687205659248360.0127943407516353
567.77.85416503068909-0.154165030689087
577.78.12479423420912-0.424794234209124
587.77.89404546246819-0.194045462468187
597.78.03290314067242-0.332903140672421
607.88.32889193037574-0.528891930375738
6188.53069105580773-0.530691055807734
628.18.59194622749124-0.491946227491236
638.18.43380024299724-0.333800242997243
648.28.37161222393298-0.171612223932986
658.28.80804050134144-0.608040501341446
668.28.63184507717409-0.43184507717409
678.18.39532505235666-0.295325052356665
688.18.33458893540488-0.234588935404875
698.28.31831859160917-0.118318591609173
708.38.21612627172350.083873728276508
718.37.923891177881450.376108822118551
728.48.119042059951770.280957940048230
738.58.125329849744720.374670150255284
748.58.313797666438910.186202333561089
758.48.35739581460640.0426041853935949
7687.978266172783440.0217338272165596
777.97.788834323159290.111165676840711
788.17.75407266213650.345927337863499
798.57.87984268900850.620157310991504
808.88.048832791661870.751167208338128
818.88.083441391169510.716558608830489
828.68.197892079174410.402107920825588
838.38.36559914181523-0.0655991418152265
848.38.50171936133706-0.201719361337056
858.38.6713443229883-0.371344322988296
868.48.392129261407750.0078707385922525
878.48.293749924218630.106250075781372
888.58.57660472548431-0.0766047254843145
898.68.8505431618887-0.250543161888706
908.68.87775582683457-0.277755826834576
918.68.77822728299282-0.178227282992822
928.68.92223498071363-0.322234980713634
938.68.86129286574961-0.261292865749611
948.58.69333524099364-0.193335240993644
958.48.59399977935765-0.193999779357649
968.48.65320876282357-0.253208762823565
978.48.4125299611313-0.0125299611313071
988.58.43171999919160.068280000808406
998.58.280605987236250.219394012763753
1008.68.00203749516330.597962504836698
1018.67.978814304858580.621185695141424
1028.47.832131485630340.56786851436966
1038.27.944806684935180.255193315064816
10488.15562532594515-0.155625325945151
10588.31042156336818-0.310421563368183
10688.19395473017033-0.193954730170333
10788.25360115157232-0.253601151572321
1087.98.08423574460699-0.184235744606989
1097.97.857131085330640.0428689146693596
1107.87.99727180354744-0.197271803547436
1117.87.90328499705487-0.103284997054866
11287.779041308790040.220958691209962
1137.87.569163196470940.230836803529059
1147.47.46844901652104-0.068449016521039
1157.27.57398212306118-0.373982123061181
11677.55086057475528-0.550860574755279
11777.51965391843545-0.51965391843545
1187.27.48410217305958-0.284102173059577
1197.27.52762362636597-0.327623626365966
1207.27.23884421839426-0.0388442183942559
12177.54502555755802-0.545025557558023
1226.97.46024428671025-0.560244286710246
1236.87.13385829874828-0.333858298748282
1246.87.15112095173139-0.351120951731389
1256.86.72818724650940.0718127534905994
1266.96.322900771249510.577099228750486
1277.26.556476299636980.643523700363021
1287.26.78120952737090.418790472629092
1297.26.715454707705090.484545292294909
1307.16.838724176610050.261275823389953
1317.26.169747692788211.03025230721179
1327.37.207647084658290.0923529153417114
1337.57.311737775152720.188262224847284
1347.67.56721406622780.0327859337722017
1357.78.0179821548806-0.3179821548806
1367.77.9837663304829-0.283766330482903
1377.77.79895883304842-0.0989588330484179
1387.88.07897261870466-0.278972618704662
13988.35124255307575-0.351242553075754
1408.18.22210642838432-0.122106428384317
1418.18.27188580386928-0.171885803869278
14288.26752858011221-0.267528580112215
1438.18.086647525885170.0133524741148251
1448.28.10713029675030.0928697032496925
1458.37.929860055025610.370139944974391
1468.48.006259351412060.393740648587936
1478.48.006663562898280.393336437101725
1488.47.895031544161060.504968455838942
1498.58.252210523804670.247789476195335
1508.57.869628525089470.630371474910534
1518.67.967448056777350.632551943222654
1528.68.390555631422630.209444368577366
1538.57.93300804740540.566991952594606
1548.57.68009981201740.819900187982593






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.002591794995395430.005183589990790850.997408205004605
100.001254277462300300.002508554924600600.9987457225377
110.0001801105440675470.0003602210881350940.999819889455932
128.31308663298562e-050.0001662617326597120.99991686913367
134.15361081653174e-058.30722163306349e-050.999958463891835
147.07031090149108e-061.41406218029822e-050.999992929689099
151.17189380928912e-062.34378761857823e-060.99999882810619
161.85219213973971e-073.70438427947942e-070.999999814780786
171.04347295726033e-072.08694591452066e-070.999999895652704
181.92031223119275e-083.84062446238551e-080.999999980796878
193.7125008034218e-097.4250016068436e-090.9999999962875
204.90645696640869e-099.81291393281737e-090.999999995093543
217.84580934563705e-091.56916186912741e-080.99999999215419
228.0569879272158e-091.61139758544316e-080.999999991943012
236.2279099989468e-091.24558199978936e-080.99999999377209
241.28803882005952e-082.57607764011905e-080.999999987119612
254.38280521773852e-098.76561043547704e-090.999999995617195
262.00302508341381e-094.00605016682761e-090.999999997996975
271.33321340297769e-092.66642680595537e-090.999999998666787
284.37214752488268e-108.74429504976536e-100.999999999562785
292.48037724981095e-104.96075449962191e-100.999999999751962
306.03997072977186e-111.20799414595437e-100.9999999999396
314.99261524333757e-119.98523048667513e-110.999999999950074
322.56860893033364e-115.13721786066727e-110.999999999974314
338.22904525693566e-121.64580905138713e-110.999999999991771
344.63633693678049e-129.27267387356099e-120.999999999995364
351.95960901718554e-123.91921803437108e-120.99999999999804
365.9409644705727e-131.18819289411454e-120.999999999999406
379.99020094216675e-131.99804018843335e-120.999999999999
383.7210637987867e-127.4421275975734e-120.99999999999628
393.98282207141061e-127.96564414282121e-120.999999999996017
403.45162259591303e-126.90324519182606e-120.999999999996548
413.34716916541256e-116.69433833082512e-110.999999999966528
427.66185483482766e-111.53237096696553e-100.999999999923382
431.13121301235413e-102.26242602470825e-100.999999999886879
443.02700665323924e-106.05401330647848e-100.9999999996973
452.17731656021549e-094.35463312043099e-090.999999997822683
465.33584091738647e-091.06716818347729e-080.99999999466416
475.35694425197961e-091.07138885039592e-080.999999994643056
480.0002624192903708880.0005248385807417760.99973758070963
490.08719256684260050.1743851336852010.9128074331574
500.630143555694030.7397128886119390.369856444305969
510.9475155724120180.1049688551759650.0524844275879824
520.980686349639180.03862730072163950.0193136503608198
530.9925326168444380.01493476631112390.00746738315556194
540.9951932337764720.009613532447055360.00480676622352768
550.9973449261820.005310147636001630.00265507381800081
560.9978078298786280.004384340242743870.00219217012137193
570.998013101405410.003973797189179290.00198689859458964
580.9979822857577830.004035428484433430.00201771424221671
590.9979311651872560.004137669625487190.00206883481274359
600.9980854247571150.003829150485769180.00191457524288459
610.9981945927749770.003610814450045230.00180540722502261
620.998185973776450.00362805244709920.0018140262235496
630.997690304752650.004619390494700790.00230969524735039
640.9976843598714840.004631280257031310.00231564012851566
650.9982122910476930.003575417904613660.00178770895230683
660.9986412793006940.002717441398610960.00135872069930548
670.9986644287033120.002671142593375930.00133557129668797
680.9989597155503640.002080568899271690.00104028444963585
690.9994889754249210.001022049150157410.000511024575078705
700.9996473304333340.0007053391333313640.000352669566665682
710.999888792525240.0002224149495188070.000111207474759403
720.9999498325847130.0001003348305743755.01674152871874e-05
730.9999769829828484.60340343031472e-052.30170171515736e-05
740.9999805568657293.88862685411357e-051.94431342705678e-05
750.9999771022514284.57954971432442e-052.28977485716221e-05
760.9999802967272733.94065454542001e-051.97032727271000e-05
770.9999882146304742.35707390524654e-051.17853695262327e-05
780.9999926449448311.47101103372869e-057.35505516864346e-06
790.9999965806032266.8387935468657e-063.41939677343285e-06
800.9999986113401682.77731966346435e-061.38865983173217e-06
810.9999993350389131.32992217474706e-066.64961087373528e-07
820.999999522720829.54558357970348e-074.77279178985174e-07
830.9999991477091511.70458169722680e-068.52290848613398e-07
840.9999985671900492.86561990252088e-061.43280995126044e-06
850.9999982200829333.55983413465125e-061.77991706732563e-06
860.9999969902102876.01957942632581e-063.00978971316291e-06
870.9999954258099549.14838009153298e-064.57419004576649e-06
880.9999923860692011.52278615969034e-057.61393079845172e-06
890.9999868241525732.63516948536068e-051.31758474268034e-05
900.9999782111310974.35777378057213e-052.17888689028606e-05
910.9999630758884377.38482231264949e-053.69241115632474e-05
920.9999426310236130.0001147379527747095.73689763873543e-05
930.9999047371657650.00019052566846969.52628342348e-05
940.9998492414687820.0003015170624362050.000150758531218103
950.999760082037260.0004798359254816620.000239917962740831
960.9996935942453540.0006128115092922560.000306405754646128
970.9995584538253880.000883092349224430.000441546174612215
980.9993079723135910.001384055372817520.00069202768640876
990.9991409428854460.001718114229107190.000859057114553594
1000.9995769235755040.0008461528489929090.000423076424496454
1010.9998999479664790.0002001040670429290.000100052033521465
1020.9999510626603749.787467925159e-054.8937339625795e-05
1030.999960495859467.90082810817747e-053.95041405408874e-05
1040.9999470858157270.0001058283685456645.29141842728322e-05
1050.999935521183850.0001289576322995536.44788161497765e-05
1060.999925652750270.0001486944994595457.43472497297726e-05
1070.999914141633940.000171716732118568.585836605928e-05
1080.9999424074414480.0001151851171047795.75925585523896e-05
1090.999975677626224.86447475598347e-052.43223737799173e-05
1100.9999841518802463.16962395080822e-051.58481197540411e-05
1110.999995614775798.77044842192828e-064.38522421096414e-06
1120.9999999753715034.92569948629919e-082.46284974314959e-08
1130.9999999999728715.42577139579907e-112.71288569789953e-11
1140.999999999998922.16152244610829e-121.08076122305415e-12
1150.9999999999994181.1644085087537e-125.8220425437685e-13
1160.9999999999987562.48850019908797e-121.24425009954399e-12
1170.9999999999965456.91103139035736e-123.45551569517868e-12
1180.99999999999833.40153275783666e-121.70076637891833e-12
1190.9999999999993761.24709834114218e-126.23549170571092e-13
1200.9999999999999968.17259066743203e-154.08629533371602e-15
1210.9999999999999951.07871844894429e-145.39359224472144e-15
1220.999999999999984.13752972085881e-142.06876486042940e-14
1230.9999999999998952.09135049988995e-131.04567524994497e-13
1240.9999999999998233.52928343045464e-131.76464171522732e-13
1250.9999999999999637.41089789122334e-143.70544894561167e-14
1260.9999999999999872.68938125772153e-141.34469062886076e-14
1270.9999999999999676.5061801250305e-143.25309006251525e-14
1280.999999999999833.40553117939959e-131.70276558969979e-13
1290.9999999999994131.17436212431948e-125.8718106215974e-13
1300.9999999999973135.37456057696809e-122.68728028848404e-12
1310.9999999999909961.80079081017913e-119.00395405089566e-12
1320.9999999999795254.09496960923173e-112.04748480461586e-11
1330.9999999998796362.40727846275965e-101.20363923137983e-10
1340.9999999992707941.45841208157028e-097.2920604078514e-10
1350.999999995389339.22133885116278e-094.61066942558139e-09
1360.9999999747187365.05625275504592e-082.52812637752296e-08
1370.99999992333711.53325800586287e-077.66629002931435e-08
1380.9999999647287477.0542505930822e-083.5271252965411e-08
1390.999999860482152.79035701362843e-071.39517850681421e-07
1400.9999988957683232.20846335458023e-061.10423167729011e-06
1410.999995521810798.95637841953813e-064.47818920976906e-06
1420.99997654530254.69093950011839e-052.34546975005920e-05
1430.9998851674105370.0002296651789259590.000114832589462979
1440.9998727557370440.0002544885259124640.000127244262956232
1450.9992957465171230.001408506965754070.000704253482877036

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.00259179499539543 & 0.00518358999079085 & 0.997408205004605 \tabularnewline
10 & 0.00125427746230030 & 0.00250855492460060 & 0.9987457225377 \tabularnewline
11 & 0.000180110544067547 & 0.000360221088135094 & 0.999819889455932 \tabularnewline
12 & 8.31308663298562e-05 & 0.000166261732659712 & 0.99991686913367 \tabularnewline
13 & 4.15361081653174e-05 & 8.30722163306349e-05 & 0.999958463891835 \tabularnewline
14 & 7.07031090149108e-06 & 1.41406218029822e-05 & 0.999992929689099 \tabularnewline
15 & 1.17189380928912e-06 & 2.34378761857823e-06 & 0.99999882810619 \tabularnewline
16 & 1.85219213973971e-07 & 3.70438427947942e-07 & 0.999999814780786 \tabularnewline
17 & 1.04347295726033e-07 & 2.08694591452066e-07 & 0.999999895652704 \tabularnewline
18 & 1.92031223119275e-08 & 3.84062446238551e-08 & 0.999999980796878 \tabularnewline
19 & 3.7125008034218e-09 & 7.4250016068436e-09 & 0.9999999962875 \tabularnewline
20 & 4.90645696640869e-09 & 9.81291393281737e-09 & 0.999999995093543 \tabularnewline
21 & 7.84580934563705e-09 & 1.56916186912741e-08 & 0.99999999215419 \tabularnewline
22 & 8.0569879272158e-09 & 1.61139758544316e-08 & 0.999999991943012 \tabularnewline
23 & 6.2279099989468e-09 & 1.24558199978936e-08 & 0.99999999377209 \tabularnewline
24 & 1.28803882005952e-08 & 2.57607764011905e-08 & 0.999999987119612 \tabularnewline
25 & 4.38280521773852e-09 & 8.76561043547704e-09 & 0.999999995617195 \tabularnewline
26 & 2.00302508341381e-09 & 4.00605016682761e-09 & 0.999999997996975 \tabularnewline
27 & 1.33321340297769e-09 & 2.66642680595537e-09 & 0.999999998666787 \tabularnewline
28 & 4.37214752488268e-10 & 8.74429504976536e-10 & 0.999999999562785 \tabularnewline
29 & 2.48037724981095e-10 & 4.96075449962191e-10 & 0.999999999751962 \tabularnewline
30 & 6.03997072977186e-11 & 1.20799414595437e-10 & 0.9999999999396 \tabularnewline
31 & 4.99261524333757e-11 & 9.98523048667513e-11 & 0.999999999950074 \tabularnewline
32 & 2.56860893033364e-11 & 5.13721786066727e-11 & 0.999999999974314 \tabularnewline
33 & 8.22904525693566e-12 & 1.64580905138713e-11 & 0.999999999991771 \tabularnewline
34 & 4.63633693678049e-12 & 9.27267387356099e-12 & 0.999999999995364 \tabularnewline
35 & 1.95960901718554e-12 & 3.91921803437108e-12 & 0.99999999999804 \tabularnewline
36 & 5.9409644705727e-13 & 1.18819289411454e-12 & 0.999999999999406 \tabularnewline
37 & 9.99020094216675e-13 & 1.99804018843335e-12 & 0.999999999999 \tabularnewline
38 & 3.7210637987867e-12 & 7.4421275975734e-12 & 0.99999999999628 \tabularnewline
39 & 3.98282207141061e-12 & 7.96564414282121e-12 & 0.999999999996017 \tabularnewline
40 & 3.45162259591303e-12 & 6.90324519182606e-12 & 0.999999999996548 \tabularnewline
41 & 3.34716916541256e-11 & 6.69433833082512e-11 & 0.999999999966528 \tabularnewline
42 & 7.66185483482766e-11 & 1.53237096696553e-10 & 0.999999999923382 \tabularnewline
43 & 1.13121301235413e-10 & 2.26242602470825e-10 & 0.999999999886879 \tabularnewline
44 & 3.02700665323924e-10 & 6.05401330647848e-10 & 0.9999999996973 \tabularnewline
45 & 2.17731656021549e-09 & 4.35463312043099e-09 & 0.999999997822683 \tabularnewline
46 & 5.33584091738647e-09 & 1.06716818347729e-08 & 0.99999999466416 \tabularnewline
47 & 5.35694425197961e-09 & 1.07138885039592e-08 & 0.999999994643056 \tabularnewline
48 & 0.000262419290370888 & 0.000524838580741776 & 0.99973758070963 \tabularnewline
49 & 0.0871925668426005 & 0.174385133685201 & 0.9128074331574 \tabularnewline
50 & 0.63014355569403 & 0.739712888611939 & 0.369856444305969 \tabularnewline
51 & 0.947515572412018 & 0.104968855175965 & 0.0524844275879824 \tabularnewline
52 & 0.98068634963918 & 0.0386273007216395 & 0.0193136503608198 \tabularnewline
53 & 0.992532616844438 & 0.0149347663111239 & 0.00746738315556194 \tabularnewline
54 & 0.995193233776472 & 0.00961353244705536 & 0.00480676622352768 \tabularnewline
55 & 0.997344926182 & 0.00531014763600163 & 0.00265507381800081 \tabularnewline
56 & 0.997807829878628 & 0.00438434024274387 & 0.00219217012137193 \tabularnewline
57 & 0.99801310140541 & 0.00397379718917929 & 0.00198689859458964 \tabularnewline
58 & 0.997982285757783 & 0.00403542848443343 & 0.00201771424221671 \tabularnewline
59 & 0.997931165187256 & 0.00413766962548719 & 0.00206883481274359 \tabularnewline
60 & 0.998085424757115 & 0.00382915048576918 & 0.00191457524288459 \tabularnewline
61 & 0.998194592774977 & 0.00361081445004523 & 0.00180540722502261 \tabularnewline
62 & 0.99818597377645 & 0.0036280524470992 & 0.0018140262235496 \tabularnewline
63 & 0.99769030475265 & 0.00461939049470079 & 0.00230969524735039 \tabularnewline
64 & 0.997684359871484 & 0.00463128025703131 & 0.00231564012851566 \tabularnewline
65 & 0.998212291047693 & 0.00357541790461366 & 0.00178770895230683 \tabularnewline
66 & 0.998641279300694 & 0.00271744139861096 & 0.00135872069930548 \tabularnewline
67 & 0.998664428703312 & 0.00267114259337593 & 0.00133557129668797 \tabularnewline
68 & 0.998959715550364 & 0.00208056889927169 & 0.00104028444963585 \tabularnewline
69 & 0.999488975424921 & 0.00102204915015741 & 0.000511024575078705 \tabularnewline
70 & 0.999647330433334 & 0.000705339133331364 & 0.000352669566665682 \tabularnewline
71 & 0.99988879252524 & 0.000222414949518807 & 0.000111207474759403 \tabularnewline
72 & 0.999949832584713 & 0.000100334830574375 & 5.01674152871874e-05 \tabularnewline
73 & 0.999976982982848 & 4.60340343031472e-05 & 2.30170171515736e-05 \tabularnewline
74 & 0.999980556865729 & 3.88862685411357e-05 & 1.94431342705678e-05 \tabularnewline
75 & 0.999977102251428 & 4.57954971432442e-05 & 2.28977485716221e-05 \tabularnewline
76 & 0.999980296727273 & 3.94065454542001e-05 & 1.97032727271000e-05 \tabularnewline
77 & 0.999988214630474 & 2.35707390524654e-05 & 1.17853695262327e-05 \tabularnewline
78 & 0.999992644944831 & 1.47101103372869e-05 & 7.35505516864346e-06 \tabularnewline
79 & 0.999996580603226 & 6.8387935468657e-06 & 3.41939677343285e-06 \tabularnewline
80 & 0.999998611340168 & 2.77731966346435e-06 & 1.38865983173217e-06 \tabularnewline
81 & 0.999999335038913 & 1.32992217474706e-06 & 6.64961087373528e-07 \tabularnewline
82 & 0.99999952272082 & 9.54558357970348e-07 & 4.77279178985174e-07 \tabularnewline
83 & 0.999999147709151 & 1.70458169722680e-06 & 8.52290848613398e-07 \tabularnewline
84 & 0.999998567190049 & 2.86561990252088e-06 & 1.43280995126044e-06 \tabularnewline
85 & 0.999998220082933 & 3.55983413465125e-06 & 1.77991706732563e-06 \tabularnewline
86 & 0.999996990210287 & 6.01957942632581e-06 & 3.00978971316291e-06 \tabularnewline
87 & 0.999995425809954 & 9.14838009153298e-06 & 4.57419004576649e-06 \tabularnewline
88 & 0.999992386069201 & 1.52278615969034e-05 & 7.61393079845172e-06 \tabularnewline
89 & 0.999986824152573 & 2.63516948536068e-05 & 1.31758474268034e-05 \tabularnewline
90 & 0.999978211131097 & 4.35777378057213e-05 & 2.17888689028606e-05 \tabularnewline
91 & 0.999963075888437 & 7.38482231264949e-05 & 3.69241115632474e-05 \tabularnewline
92 & 0.999942631023613 & 0.000114737952774709 & 5.73689763873543e-05 \tabularnewline
93 & 0.999904737165765 & 0.0001905256684696 & 9.52628342348e-05 \tabularnewline
94 & 0.999849241468782 & 0.000301517062436205 & 0.000150758531218103 \tabularnewline
95 & 0.99976008203726 & 0.000479835925481662 & 0.000239917962740831 \tabularnewline
96 & 0.999693594245354 & 0.000612811509292256 & 0.000306405754646128 \tabularnewline
97 & 0.999558453825388 & 0.00088309234922443 & 0.000441546174612215 \tabularnewline
98 & 0.999307972313591 & 0.00138405537281752 & 0.00069202768640876 \tabularnewline
99 & 0.999140942885446 & 0.00171811422910719 & 0.000859057114553594 \tabularnewline
100 & 0.999576923575504 & 0.000846152848992909 & 0.000423076424496454 \tabularnewline
101 & 0.999899947966479 & 0.000200104067042929 & 0.000100052033521465 \tabularnewline
102 & 0.999951062660374 & 9.787467925159e-05 & 4.8937339625795e-05 \tabularnewline
103 & 0.99996049585946 & 7.90082810817747e-05 & 3.95041405408874e-05 \tabularnewline
104 & 0.999947085815727 & 0.000105828368545664 & 5.29141842728322e-05 \tabularnewline
105 & 0.99993552118385 & 0.000128957632299553 & 6.44788161497765e-05 \tabularnewline
106 & 0.99992565275027 & 0.000148694499459545 & 7.43472497297726e-05 \tabularnewline
107 & 0.99991414163394 & 0.00017171673211856 & 8.585836605928e-05 \tabularnewline
108 & 0.999942407441448 & 0.000115185117104779 & 5.75925585523896e-05 \tabularnewline
109 & 0.99997567762622 & 4.86447475598347e-05 & 2.43223737799173e-05 \tabularnewline
110 & 0.999984151880246 & 3.16962395080822e-05 & 1.58481197540411e-05 \tabularnewline
111 & 0.99999561477579 & 8.77044842192828e-06 & 4.38522421096414e-06 \tabularnewline
112 & 0.999999975371503 & 4.92569948629919e-08 & 2.46284974314959e-08 \tabularnewline
113 & 0.999999999972871 & 5.42577139579907e-11 & 2.71288569789953e-11 \tabularnewline
114 & 0.99999999999892 & 2.16152244610829e-12 & 1.08076122305415e-12 \tabularnewline
115 & 0.999999999999418 & 1.1644085087537e-12 & 5.8220425437685e-13 \tabularnewline
116 & 0.999999999998756 & 2.48850019908797e-12 & 1.24425009954399e-12 \tabularnewline
117 & 0.999999999996545 & 6.91103139035736e-12 & 3.45551569517868e-12 \tabularnewline
118 & 0.9999999999983 & 3.40153275783666e-12 & 1.70076637891833e-12 \tabularnewline
119 & 0.999999999999376 & 1.24709834114218e-12 & 6.23549170571092e-13 \tabularnewline
120 & 0.999999999999996 & 8.17259066743203e-15 & 4.08629533371602e-15 \tabularnewline
121 & 0.999999999999995 & 1.07871844894429e-14 & 5.39359224472144e-15 \tabularnewline
122 & 0.99999999999998 & 4.13752972085881e-14 & 2.06876486042940e-14 \tabularnewline
123 & 0.999999999999895 & 2.09135049988995e-13 & 1.04567524994497e-13 \tabularnewline
124 & 0.999999999999823 & 3.52928343045464e-13 & 1.76464171522732e-13 \tabularnewline
125 & 0.999999999999963 & 7.41089789122334e-14 & 3.70544894561167e-14 \tabularnewline
126 & 0.999999999999987 & 2.68938125772153e-14 & 1.34469062886076e-14 \tabularnewline
127 & 0.999999999999967 & 6.5061801250305e-14 & 3.25309006251525e-14 \tabularnewline
128 & 0.99999999999983 & 3.40553117939959e-13 & 1.70276558969979e-13 \tabularnewline
129 & 0.999999999999413 & 1.17436212431948e-12 & 5.8718106215974e-13 \tabularnewline
130 & 0.999999999997313 & 5.37456057696809e-12 & 2.68728028848404e-12 \tabularnewline
131 & 0.999999999990996 & 1.80079081017913e-11 & 9.00395405089566e-12 \tabularnewline
132 & 0.999999999979525 & 4.09496960923173e-11 & 2.04748480461586e-11 \tabularnewline
133 & 0.999999999879636 & 2.40727846275965e-10 & 1.20363923137983e-10 \tabularnewline
134 & 0.999999999270794 & 1.45841208157028e-09 & 7.2920604078514e-10 \tabularnewline
135 & 0.99999999538933 & 9.22133885116278e-09 & 4.61066942558139e-09 \tabularnewline
136 & 0.999999974718736 & 5.05625275504592e-08 & 2.52812637752296e-08 \tabularnewline
137 & 0.9999999233371 & 1.53325800586287e-07 & 7.66629002931435e-08 \tabularnewline
138 & 0.999999964728747 & 7.0542505930822e-08 & 3.5271252965411e-08 \tabularnewline
139 & 0.99999986048215 & 2.79035701362843e-07 & 1.39517850681421e-07 \tabularnewline
140 & 0.999998895768323 & 2.20846335458023e-06 & 1.10423167729011e-06 \tabularnewline
141 & 0.99999552181079 & 8.95637841953813e-06 & 4.47818920976906e-06 \tabularnewline
142 & 0.9999765453025 & 4.69093950011839e-05 & 2.34546975005920e-05 \tabularnewline
143 & 0.999885167410537 & 0.000229665178925959 & 0.000114832589462979 \tabularnewline
144 & 0.999872755737044 & 0.000254488525912464 & 0.000127244262956232 \tabularnewline
145 & 0.999295746517123 & 0.00140850696575407 & 0.000704253482877036 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113038&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]9[/C][C]0.00259179499539543[/C][C]0.00518358999079085[/C][C]0.997408205004605[/C][/ROW]
[ROW][C]10[/C][C]0.00125427746230030[/C][C]0.00250855492460060[/C][C]0.9987457225377[/C][/ROW]
[ROW][C]11[/C][C]0.000180110544067547[/C][C]0.000360221088135094[/C][C]0.999819889455932[/C][/ROW]
[ROW][C]12[/C][C]8.31308663298562e-05[/C][C]0.000166261732659712[/C][C]0.99991686913367[/C][/ROW]
[ROW][C]13[/C][C]4.15361081653174e-05[/C][C]8.30722163306349e-05[/C][C]0.999958463891835[/C][/ROW]
[ROW][C]14[/C][C]7.07031090149108e-06[/C][C]1.41406218029822e-05[/C][C]0.999992929689099[/C][/ROW]
[ROW][C]15[/C][C]1.17189380928912e-06[/C][C]2.34378761857823e-06[/C][C]0.99999882810619[/C][/ROW]
[ROW][C]16[/C][C]1.85219213973971e-07[/C][C]3.70438427947942e-07[/C][C]0.999999814780786[/C][/ROW]
[ROW][C]17[/C][C]1.04347295726033e-07[/C][C]2.08694591452066e-07[/C][C]0.999999895652704[/C][/ROW]
[ROW][C]18[/C][C]1.92031223119275e-08[/C][C]3.84062446238551e-08[/C][C]0.999999980796878[/C][/ROW]
[ROW][C]19[/C][C]3.7125008034218e-09[/C][C]7.4250016068436e-09[/C][C]0.9999999962875[/C][/ROW]
[ROW][C]20[/C][C]4.90645696640869e-09[/C][C]9.81291393281737e-09[/C][C]0.999999995093543[/C][/ROW]
[ROW][C]21[/C][C]7.84580934563705e-09[/C][C]1.56916186912741e-08[/C][C]0.99999999215419[/C][/ROW]
[ROW][C]22[/C][C]8.0569879272158e-09[/C][C]1.61139758544316e-08[/C][C]0.999999991943012[/C][/ROW]
[ROW][C]23[/C][C]6.2279099989468e-09[/C][C]1.24558199978936e-08[/C][C]0.99999999377209[/C][/ROW]
[ROW][C]24[/C][C]1.28803882005952e-08[/C][C]2.57607764011905e-08[/C][C]0.999999987119612[/C][/ROW]
[ROW][C]25[/C][C]4.38280521773852e-09[/C][C]8.76561043547704e-09[/C][C]0.999999995617195[/C][/ROW]
[ROW][C]26[/C][C]2.00302508341381e-09[/C][C]4.00605016682761e-09[/C][C]0.999999997996975[/C][/ROW]
[ROW][C]27[/C][C]1.33321340297769e-09[/C][C]2.66642680595537e-09[/C][C]0.999999998666787[/C][/ROW]
[ROW][C]28[/C][C]4.37214752488268e-10[/C][C]8.74429504976536e-10[/C][C]0.999999999562785[/C][/ROW]
[ROW][C]29[/C][C]2.48037724981095e-10[/C][C]4.96075449962191e-10[/C][C]0.999999999751962[/C][/ROW]
[ROW][C]30[/C][C]6.03997072977186e-11[/C][C]1.20799414595437e-10[/C][C]0.9999999999396[/C][/ROW]
[ROW][C]31[/C][C]4.99261524333757e-11[/C][C]9.98523048667513e-11[/C][C]0.999999999950074[/C][/ROW]
[ROW][C]32[/C][C]2.56860893033364e-11[/C][C]5.13721786066727e-11[/C][C]0.999999999974314[/C][/ROW]
[ROW][C]33[/C][C]8.22904525693566e-12[/C][C]1.64580905138713e-11[/C][C]0.999999999991771[/C][/ROW]
[ROW][C]34[/C][C]4.63633693678049e-12[/C][C]9.27267387356099e-12[/C][C]0.999999999995364[/C][/ROW]
[ROW][C]35[/C][C]1.95960901718554e-12[/C][C]3.91921803437108e-12[/C][C]0.99999999999804[/C][/ROW]
[ROW][C]36[/C][C]5.9409644705727e-13[/C][C]1.18819289411454e-12[/C][C]0.999999999999406[/C][/ROW]
[ROW][C]37[/C][C]9.99020094216675e-13[/C][C]1.99804018843335e-12[/C][C]0.999999999999[/C][/ROW]
[ROW][C]38[/C][C]3.7210637987867e-12[/C][C]7.4421275975734e-12[/C][C]0.99999999999628[/C][/ROW]
[ROW][C]39[/C][C]3.98282207141061e-12[/C][C]7.96564414282121e-12[/C][C]0.999999999996017[/C][/ROW]
[ROW][C]40[/C][C]3.45162259591303e-12[/C][C]6.90324519182606e-12[/C][C]0.999999999996548[/C][/ROW]
[ROW][C]41[/C][C]3.34716916541256e-11[/C][C]6.69433833082512e-11[/C][C]0.999999999966528[/C][/ROW]
[ROW][C]42[/C][C]7.66185483482766e-11[/C][C]1.53237096696553e-10[/C][C]0.999999999923382[/C][/ROW]
[ROW][C]43[/C][C]1.13121301235413e-10[/C][C]2.26242602470825e-10[/C][C]0.999999999886879[/C][/ROW]
[ROW][C]44[/C][C]3.02700665323924e-10[/C][C]6.05401330647848e-10[/C][C]0.9999999996973[/C][/ROW]
[ROW][C]45[/C][C]2.17731656021549e-09[/C][C]4.35463312043099e-09[/C][C]0.999999997822683[/C][/ROW]
[ROW][C]46[/C][C]5.33584091738647e-09[/C][C]1.06716818347729e-08[/C][C]0.99999999466416[/C][/ROW]
[ROW][C]47[/C][C]5.35694425197961e-09[/C][C]1.07138885039592e-08[/C][C]0.999999994643056[/C][/ROW]
[ROW][C]48[/C][C]0.000262419290370888[/C][C]0.000524838580741776[/C][C]0.99973758070963[/C][/ROW]
[ROW][C]49[/C][C]0.0871925668426005[/C][C]0.174385133685201[/C][C]0.9128074331574[/C][/ROW]
[ROW][C]50[/C][C]0.63014355569403[/C][C]0.739712888611939[/C][C]0.369856444305969[/C][/ROW]
[ROW][C]51[/C][C]0.947515572412018[/C][C]0.104968855175965[/C][C]0.0524844275879824[/C][/ROW]
[ROW][C]52[/C][C]0.98068634963918[/C][C]0.0386273007216395[/C][C]0.0193136503608198[/C][/ROW]
[ROW][C]53[/C][C]0.992532616844438[/C][C]0.0149347663111239[/C][C]0.00746738315556194[/C][/ROW]
[ROW][C]54[/C][C]0.995193233776472[/C][C]0.00961353244705536[/C][C]0.00480676622352768[/C][/ROW]
[ROW][C]55[/C][C]0.997344926182[/C][C]0.00531014763600163[/C][C]0.00265507381800081[/C][/ROW]
[ROW][C]56[/C][C]0.997807829878628[/C][C]0.00438434024274387[/C][C]0.00219217012137193[/C][/ROW]
[ROW][C]57[/C][C]0.99801310140541[/C][C]0.00397379718917929[/C][C]0.00198689859458964[/C][/ROW]
[ROW][C]58[/C][C]0.997982285757783[/C][C]0.00403542848443343[/C][C]0.00201771424221671[/C][/ROW]
[ROW][C]59[/C][C]0.997931165187256[/C][C]0.00413766962548719[/C][C]0.00206883481274359[/C][/ROW]
[ROW][C]60[/C][C]0.998085424757115[/C][C]0.00382915048576918[/C][C]0.00191457524288459[/C][/ROW]
[ROW][C]61[/C][C]0.998194592774977[/C][C]0.00361081445004523[/C][C]0.00180540722502261[/C][/ROW]
[ROW][C]62[/C][C]0.99818597377645[/C][C]0.0036280524470992[/C][C]0.0018140262235496[/C][/ROW]
[ROW][C]63[/C][C]0.99769030475265[/C][C]0.00461939049470079[/C][C]0.00230969524735039[/C][/ROW]
[ROW][C]64[/C][C]0.997684359871484[/C][C]0.00463128025703131[/C][C]0.00231564012851566[/C][/ROW]
[ROW][C]65[/C][C]0.998212291047693[/C][C]0.00357541790461366[/C][C]0.00178770895230683[/C][/ROW]
[ROW][C]66[/C][C]0.998641279300694[/C][C]0.00271744139861096[/C][C]0.00135872069930548[/C][/ROW]
[ROW][C]67[/C][C]0.998664428703312[/C][C]0.00267114259337593[/C][C]0.00133557129668797[/C][/ROW]
[ROW][C]68[/C][C]0.998959715550364[/C][C]0.00208056889927169[/C][C]0.00104028444963585[/C][/ROW]
[ROW][C]69[/C][C]0.999488975424921[/C][C]0.00102204915015741[/C][C]0.000511024575078705[/C][/ROW]
[ROW][C]70[/C][C]0.999647330433334[/C][C]0.000705339133331364[/C][C]0.000352669566665682[/C][/ROW]
[ROW][C]71[/C][C]0.99988879252524[/C][C]0.000222414949518807[/C][C]0.000111207474759403[/C][/ROW]
[ROW][C]72[/C][C]0.999949832584713[/C][C]0.000100334830574375[/C][C]5.01674152871874e-05[/C][/ROW]
[ROW][C]73[/C][C]0.999976982982848[/C][C]4.60340343031472e-05[/C][C]2.30170171515736e-05[/C][/ROW]
[ROW][C]74[/C][C]0.999980556865729[/C][C]3.88862685411357e-05[/C][C]1.94431342705678e-05[/C][/ROW]
[ROW][C]75[/C][C]0.999977102251428[/C][C]4.57954971432442e-05[/C][C]2.28977485716221e-05[/C][/ROW]
[ROW][C]76[/C][C]0.999980296727273[/C][C]3.94065454542001e-05[/C][C]1.97032727271000e-05[/C][/ROW]
[ROW][C]77[/C][C]0.999988214630474[/C][C]2.35707390524654e-05[/C][C]1.17853695262327e-05[/C][/ROW]
[ROW][C]78[/C][C]0.999992644944831[/C][C]1.47101103372869e-05[/C][C]7.35505516864346e-06[/C][/ROW]
[ROW][C]79[/C][C]0.999996580603226[/C][C]6.8387935468657e-06[/C][C]3.41939677343285e-06[/C][/ROW]
[ROW][C]80[/C][C]0.999998611340168[/C][C]2.77731966346435e-06[/C][C]1.38865983173217e-06[/C][/ROW]
[ROW][C]81[/C][C]0.999999335038913[/C][C]1.32992217474706e-06[/C][C]6.64961087373528e-07[/C][/ROW]
[ROW][C]82[/C][C]0.99999952272082[/C][C]9.54558357970348e-07[/C][C]4.77279178985174e-07[/C][/ROW]
[ROW][C]83[/C][C]0.999999147709151[/C][C]1.70458169722680e-06[/C][C]8.52290848613398e-07[/C][/ROW]
[ROW][C]84[/C][C]0.999998567190049[/C][C]2.86561990252088e-06[/C][C]1.43280995126044e-06[/C][/ROW]
[ROW][C]85[/C][C]0.999998220082933[/C][C]3.55983413465125e-06[/C][C]1.77991706732563e-06[/C][/ROW]
[ROW][C]86[/C][C]0.999996990210287[/C][C]6.01957942632581e-06[/C][C]3.00978971316291e-06[/C][/ROW]
[ROW][C]87[/C][C]0.999995425809954[/C][C]9.14838009153298e-06[/C][C]4.57419004576649e-06[/C][/ROW]
[ROW][C]88[/C][C]0.999992386069201[/C][C]1.52278615969034e-05[/C][C]7.61393079845172e-06[/C][/ROW]
[ROW][C]89[/C][C]0.999986824152573[/C][C]2.63516948536068e-05[/C][C]1.31758474268034e-05[/C][/ROW]
[ROW][C]90[/C][C]0.999978211131097[/C][C]4.35777378057213e-05[/C][C]2.17888689028606e-05[/C][/ROW]
[ROW][C]91[/C][C]0.999963075888437[/C][C]7.38482231264949e-05[/C][C]3.69241115632474e-05[/C][/ROW]
[ROW][C]92[/C][C]0.999942631023613[/C][C]0.000114737952774709[/C][C]5.73689763873543e-05[/C][/ROW]
[ROW][C]93[/C][C]0.999904737165765[/C][C]0.0001905256684696[/C][C]9.52628342348e-05[/C][/ROW]
[ROW][C]94[/C][C]0.999849241468782[/C][C]0.000301517062436205[/C][C]0.000150758531218103[/C][/ROW]
[ROW][C]95[/C][C]0.99976008203726[/C][C]0.000479835925481662[/C][C]0.000239917962740831[/C][/ROW]
[ROW][C]96[/C][C]0.999693594245354[/C][C]0.000612811509292256[/C][C]0.000306405754646128[/C][/ROW]
[ROW][C]97[/C][C]0.999558453825388[/C][C]0.00088309234922443[/C][C]0.000441546174612215[/C][/ROW]
[ROW][C]98[/C][C]0.999307972313591[/C][C]0.00138405537281752[/C][C]0.00069202768640876[/C][/ROW]
[ROW][C]99[/C][C]0.999140942885446[/C][C]0.00171811422910719[/C][C]0.000859057114553594[/C][/ROW]
[ROW][C]100[/C][C]0.999576923575504[/C][C]0.000846152848992909[/C][C]0.000423076424496454[/C][/ROW]
[ROW][C]101[/C][C]0.999899947966479[/C][C]0.000200104067042929[/C][C]0.000100052033521465[/C][/ROW]
[ROW][C]102[/C][C]0.999951062660374[/C][C]9.787467925159e-05[/C][C]4.8937339625795e-05[/C][/ROW]
[ROW][C]103[/C][C]0.99996049585946[/C][C]7.90082810817747e-05[/C][C]3.95041405408874e-05[/C][/ROW]
[ROW][C]104[/C][C]0.999947085815727[/C][C]0.000105828368545664[/C][C]5.29141842728322e-05[/C][/ROW]
[ROW][C]105[/C][C]0.99993552118385[/C][C]0.000128957632299553[/C][C]6.44788161497765e-05[/C][/ROW]
[ROW][C]106[/C][C]0.99992565275027[/C][C]0.000148694499459545[/C][C]7.43472497297726e-05[/C][/ROW]
[ROW][C]107[/C][C]0.99991414163394[/C][C]0.00017171673211856[/C][C]8.585836605928e-05[/C][/ROW]
[ROW][C]108[/C][C]0.999942407441448[/C][C]0.000115185117104779[/C][C]5.75925585523896e-05[/C][/ROW]
[ROW][C]109[/C][C]0.99997567762622[/C][C]4.86447475598347e-05[/C][C]2.43223737799173e-05[/C][/ROW]
[ROW][C]110[/C][C]0.999984151880246[/C][C]3.16962395080822e-05[/C][C]1.58481197540411e-05[/C][/ROW]
[ROW][C]111[/C][C]0.99999561477579[/C][C]8.77044842192828e-06[/C][C]4.38522421096414e-06[/C][/ROW]
[ROW][C]112[/C][C]0.999999975371503[/C][C]4.92569948629919e-08[/C][C]2.46284974314959e-08[/C][/ROW]
[ROW][C]113[/C][C]0.999999999972871[/C][C]5.42577139579907e-11[/C][C]2.71288569789953e-11[/C][/ROW]
[ROW][C]114[/C][C]0.99999999999892[/C][C]2.16152244610829e-12[/C][C]1.08076122305415e-12[/C][/ROW]
[ROW][C]115[/C][C]0.999999999999418[/C][C]1.1644085087537e-12[/C][C]5.8220425437685e-13[/C][/ROW]
[ROW][C]116[/C][C]0.999999999998756[/C][C]2.48850019908797e-12[/C][C]1.24425009954399e-12[/C][/ROW]
[ROW][C]117[/C][C]0.999999999996545[/C][C]6.91103139035736e-12[/C][C]3.45551569517868e-12[/C][/ROW]
[ROW][C]118[/C][C]0.9999999999983[/C][C]3.40153275783666e-12[/C][C]1.70076637891833e-12[/C][/ROW]
[ROW][C]119[/C][C]0.999999999999376[/C][C]1.24709834114218e-12[/C][C]6.23549170571092e-13[/C][/ROW]
[ROW][C]120[/C][C]0.999999999999996[/C][C]8.17259066743203e-15[/C][C]4.08629533371602e-15[/C][/ROW]
[ROW][C]121[/C][C]0.999999999999995[/C][C]1.07871844894429e-14[/C][C]5.39359224472144e-15[/C][/ROW]
[ROW][C]122[/C][C]0.99999999999998[/C][C]4.13752972085881e-14[/C][C]2.06876486042940e-14[/C][/ROW]
[ROW][C]123[/C][C]0.999999999999895[/C][C]2.09135049988995e-13[/C][C]1.04567524994497e-13[/C][/ROW]
[ROW][C]124[/C][C]0.999999999999823[/C][C]3.52928343045464e-13[/C][C]1.76464171522732e-13[/C][/ROW]
[ROW][C]125[/C][C]0.999999999999963[/C][C]7.41089789122334e-14[/C][C]3.70544894561167e-14[/C][/ROW]
[ROW][C]126[/C][C]0.999999999999987[/C][C]2.68938125772153e-14[/C][C]1.34469062886076e-14[/C][/ROW]
[ROW][C]127[/C][C]0.999999999999967[/C][C]6.5061801250305e-14[/C][C]3.25309006251525e-14[/C][/ROW]
[ROW][C]128[/C][C]0.99999999999983[/C][C]3.40553117939959e-13[/C][C]1.70276558969979e-13[/C][/ROW]
[ROW][C]129[/C][C]0.999999999999413[/C][C]1.17436212431948e-12[/C][C]5.8718106215974e-13[/C][/ROW]
[ROW][C]130[/C][C]0.999999999997313[/C][C]5.37456057696809e-12[/C][C]2.68728028848404e-12[/C][/ROW]
[ROW][C]131[/C][C]0.999999999990996[/C][C]1.80079081017913e-11[/C][C]9.00395405089566e-12[/C][/ROW]
[ROW][C]132[/C][C]0.999999999979525[/C][C]4.09496960923173e-11[/C][C]2.04748480461586e-11[/C][/ROW]
[ROW][C]133[/C][C]0.999999999879636[/C][C]2.40727846275965e-10[/C][C]1.20363923137983e-10[/C][/ROW]
[ROW][C]134[/C][C]0.999999999270794[/C][C]1.45841208157028e-09[/C][C]7.2920604078514e-10[/C][/ROW]
[ROW][C]135[/C][C]0.99999999538933[/C][C]9.22133885116278e-09[/C][C]4.61066942558139e-09[/C][/ROW]
[ROW][C]136[/C][C]0.999999974718736[/C][C]5.05625275504592e-08[/C][C]2.52812637752296e-08[/C][/ROW]
[ROW][C]137[/C][C]0.9999999233371[/C][C]1.53325800586287e-07[/C][C]7.66629002931435e-08[/C][/ROW]
[ROW][C]138[/C][C]0.999999964728747[/C][C]7.0542505930822e-08[/C][C]3.5271252965411e-08[/C][/ROW]
[ROW][C]139[/C][C]0.99999986048215[/C][C]2.79035701362843e-07[/C][C]1.39517850681421e-07[/C][/ROW]
[ROW][C]140[/C][C]0.999998895768323[/C][C]2.20846335458023e-06[/C][C]1.10423167729011e-06[/C][/ROW]
[ROW][C]141[/C][C]0.99999552181079[/C][C]8.95637841953813e-06[/C][C]4.47818920976906e-06[/C][/ROW]
[ROW][C]142[/C][C]0.9999765453025[/C][C]4.69093950011839e-05[/C][C]2.34546975005920e-05[/C][/ROW]
[ROW][C]143[/C][C]0.999885167410537[/C][C]0.000229665178925959[/C][C]0.000114832589462979[/C][/ROW]
[ROW][C]144[/C][C]0.999872755737044[/C][C]0.000254488525912464[/C][C]0.000127244262956232[/C][/ROW]
[ROW][C]145[/C][C]0.999295746517123[/C][C]0.00140850696575407[/C][C]0.000704253482877036[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113038&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113038&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
90.002591794995395430.005183589990790850.997408205004605
100.001254277462300300.002508554924600600.9987457225377
110.0001801105440675470.0003602210881350940.999819889455932
128.31308663298562e-050.0001662617326597120.99991686913367
134.15361081653174e-058.30722163306349e-050.999958463891835
147.07031090149108e-061.41406218029822e-050.999992929689099
151.17189380928912e-062.34378761857823e-060.99999882810619
161.85219213973971e-073.70438427947942e-070.999999814780786
171.04347295726033e-072.08694591452066e-070.999999895652704
181.92031223119275e-083.84062446238551e-080.999999980796878
193.7125008034218e-097.4250016068436e-090.9999999962875
204.90645696640869e-099.81291393281737e-090.999999995093543
217.84580934563705e-091.56916186912741e-080.99999999215419
228.0569879272158e-091.61139758544316e-080.999999991943012
236.2279099989468e-091.24558199978936e-080.99999999377209
241.28803882005952e-082.57607764011905e-080.999999987119612
254.38280521773852e-098.76561043547704e-090.999999995617195
262.00302508341381e-094.00605016682761e-090.999999997996975
271.33321340297769e-092.66642680595537e-090.999999998666787
284.37214752488268e-108.74429504976536e-100.999999999562785
292.48037724981095e-104.96075449962191e-100.999999999751962
306.03997072977186e-111.20799414595437e-100.9999999999396
314.99261524333757e-119.98523048667513e-110.999999999950074
322.56860893033364e-115.13721786066727e-110.999999999974314
338.22904525693566e-121.64580905138713e-110.999999999991771
344.63633693678049e-129.27267387356099e-120.999999999995364
351.95960901718554e-123.91921803437108e-120.99999999999804
365.9409644705727e-131.18819289411454e-120.999999999999406
379.99020094216675e-131.99804018843335e-120.999999999999
383.7210637987867e-127.4421275975734e-120.99999999999628
393.98282207141061e-127.96564414282121e-120.999999999996017
403.45162259591303e-126.90324519182606e-120.999999999996548
413.34716916541256e-116.69433833082512e-110.999999999966528
427.66185483482766e-111.53237096696553e-100.999999999923382
431.13121301235413e-102.26242602470825e-100.999999999886879
443.02700665323924e-106.05401330647848e-100.9999999996973
452.17731656021549e-094.35463312043099e-090.999999997822683
465.33584091738647e-091.06716818347729e-080.99999999466416
475.35694425197961e-091.07138885039592e-080.999999994643056
480.0002624192903708880.0005248385807417760.99973758070963
490.08719256684260050.1743851336852010.9128074331574
500.630143555694030.7397128886119390.369856444305969
510.9475155724120180.1049688551759650.0524844275879824
520.980686349639180.03862730072163950.0193136503608198
530.9925326168444380.01493476631112390.00746738315556194
540.9951932337764720.009613532447055360.00480676622352768
550.9973449261820.005310147636001630.00265507381800081
560.9978078298786280.004384340242743870.00219217012137193
570.998013101405410.003973797189179290.00198689859458964
580.9979822857577830.004035428484433430.00201771424221671
590.9979311651872560.004137669625487190.00206883481274359
600.9980854247571150.003829150485769180.00191457524288459
610.9981945927749770.003610814450045230.00180540722502261
620.998185973776450.00362805244709920.0018140262235496
630.997690304752650.004619390494700790.00230969524735039
640.9976843598714840.004631280257031310.00231564012851566
650.9982122910476930.003575417904613660.00178770895230683
660.9986412793006940.002717441398610960.00135872069930548
670.9986644287033120.002671142593375930.00133557129668797
680.9989597155503640.002080568899271690.00104028444963585
690.9994889754249210.001022049150157410.000511024575078705
700.9996473304333340.0007053391333313640.000352669566665682
710.999888792525240.0002224149495188070.000111207474759403
720.9999498325847130.0001003348305743755.01674152871874e-05
730.9999769829828484.60340343031472e-052.30170171515736e-05
740.9999805568657293.88862685411357e-051.94431342705678e-05
750.9999771022514284.57954971432442e-052.28977485716221e-05
760.9999802967272733.94065454542001e-051.97032727271000e-05
770.9999882146304742.35707390524654e-051.17853695262327e-05
780.9999926449448311.47101103372869e-057.35505516864346e-06
790.9999965806032266.8387935468657e-063.41939677343285e-06
800.9999986113401682.77731966346435e-061.38865983173217e-06
810.9999993350389131.32992217474706e-066.64961087373528e-07
820.999999522720829.54558357970348e-074.77279178985174e-07
830.9999991477091511.70458169722680e-068.52290848613398e-07
840.9999985671900492.86561990252088e-061.43280995126044e-06
850.9999982200829333.55983413465125e-061.77991706732563e-06
860.9999969902102876.01957942632581e-063.00978971316291e-06
870.9999954258099549.14838009153298e-064.57419004576649e-06
880.9999923860692011.52278615969034e-057.61393079845172e-06
890.9999868241525732.63516948536068e-051.31758474268034e-05
900.9999782111310974.35777378057213e-052.17888689028606e-05
910.9999630758884377.38482231264949e-053.69241115632474e-05
920.9999426310236130.0001147379527747095.73689763873543e-05
930.9999047371657650.00019052566846969.52628342348e-05
940.9998492414687820.0003015170624362050.000150758531218103
950.999760082037260.0004798359254816620.000239917962740831
960.9996935942453540.0006128115092922560.000306405754646128
970.9995584538253880.000883092349224430.000441546174612215
980.9993079723135910.001384055372817520.00069202768640876
990.9991409428854460.001718114229107190.000859057114553594
1000.9995769235755040.0008461528489929090.000423076424496454
1010.9998999479664790.0002001040670429290.000100052033521465
1020.9999510626603749.787467925159e-054.8937339625795e-05
1030.999960495859467.90082810817747e-053.95041405408874e-05
1040.9999470858157270.0001058283685456645.29141842728322e-05
1050.999935521183850.0001289576322995536.44788161497765e-05
1060.999925652750270.0001486944994595457.43472497297726e-05
1070.999914141633940.000171716732118568.585836605928e-05
1080.9999424074414480.0001151851171047795.75925585523896e-05
1090.999975677626224.86447475598347e-052.43223737799173e-05
1100.9999841518802463.16962395080822e-051.58481197540411e-05
1110.999995614775798.77044842192828e-064.38522421096414e-06
1120.9999999753715034.92569948629919e-082.46284974314959e-08
1130.9999999999728715.42577139579907e-112.71288569789953e-11
1140.999999999998922.16152244610829e-121.08076122305415e-12
1150.9999999999994181.1644085087537e-125.8220425437685e-13
1160.9999999999987562.48850019908797e-121.24425009954399e-12
1170.9999999999965456.91103139035736e-123.45551569517868e-12
1180.99999999999833.40153275783666e-121.70076637891833e-12
1190.9999999999993761.24709834114218e-126.23549170571092e-13
1200.9999999999999968.17259066743203e-154.08629533371602e-15
1210.9999999999999951.07871844894429e-145.39359224472144e-15
1220.999999999999984.13752972085881e-142.06876486042940e-14
1230.9999999999998952.09135049988995e-131.04567524994497e-13
1240.9999999999998233.52928343045464e-131.76464171522732e-13
1250.9999999999999637.41089789122334e-143.70544894561167e-14
1260.9999999999999872.68938125772153e-141.34469062886076e-14
1270.9999999999999676.5061801250305e-143.25309006251525e-14
1280.999999999999833.40553117939959e-131.70276558969979e-13
1290.9999999999994131.17436212431948e-125.8718106215974e-13
1300.9999999999973135.37456057696809e-122.68728028848404e-12
1310.9999999999909961.80079081017913e-119.00395405089566e-12
1320.9999999999795254.09496960923173e-112.04748480461586e-11
1330.9999999998796362.40727846275965e-101.20363923137983e-10
1340.9999999992707941.45841208157028e-097.2920604078514e-10
1350.999999995389339.22133885116278e-094.61066942558139e-09
1360.9999999747187365.05625275504592e-082.52812637752296e-08
1370.99999992333711.53325800586287e-077.66629002931435e-08
1380.9999999647287477.0542505930822e-083.5271252965411e-08
1390.999999860482152.79035701362843e-071.39517850681421e-07
1400.9999988957683232.20846335458023e-061.10423167729011e-06
1410.999995521810798.95637841953813e-064.47818920976906e-06
1420.99997654530254.69093950011839e-052.34546975005920e-05
1430.9998851674105370.0002296651789259590.000114832589462979
1440.9998727557370440.0002544885259124640.000127244262956232
1450.9992957465171230.001408506965754070.000704253482877036






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level1320.963503649635037NOK
5% type I error level1340.978102189781022NOK
10% type I error level1340.978102189781022NOK

\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 & 132 & 0.963503649635037 & NOK \tabularnewline
5% type I error level & 134 & 0.978102189781022 & NOK \tabularnewline
10% type I error level & 134 & 0.978102189781022 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113038&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]132[/C][C]0.963503649635037[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]134[/C][C]0.978102189781022[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]134[/C][C]0.978102189781022[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113038&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113038&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 level1320.963503649635037NOK
5% type I error level1340.978102189781022NOK
10% type I error level1340.978102189781022NOK


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')
}