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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationTue, 30 Oct 2012 11:05:45 -0400
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Oct/30/t1351609643dj5ig5791j1adj1.htm/, Retrieved Sat, 27 Apr 2024 10:41:25 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=185123, Retrieved Sat, 27 Apr 2024 10:41:25 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact134

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [] [2012-10-30 15:05:45] [a5d65c007476aeb95d503c7a121a195d] [Current]
- RMPD    [ARIMA Forecasting] [] [2012-12-07 14:02:03] [d2c1a12335a0e7c18f8727e39be21dbc]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
75.5	78.4	67.3	75.3	106.1	125.7	101.6
83.2	79.3	75.2	83.6	112.7	153.8	113.4
94.5	84.3	91.1	91.2	123.2	134.9	122.2
83.3	81.2	83.7	85.2	101.7	95.3	102.2
92.7	88.4	105.0	100.0	118.7	96.6	113.2
89.8	83.1	106.2	89.8	107.1	100.5	115.3
74.8	76.6	88.5	88.9	93.6	106.2	87.4
81.5	82.6	100.1	85.6	77.5	153.4	98.7
92.8	84.4	90.3	83.2	117.2	132.1	117.3
92.8	94.6	85.3	97.1	124.5	110.9	121.2
91.7	91.8	81.9	85.8	120.8	94.3	118.7
83.5	89.3	77.2	80.9	97.0	91.7	112.1
92.8	87.7	78.6	81.3	115.1	138.6	102.9
91.3	83.1	75.1	83.2	112.9	154.3	108.8
99.5	93.6	90.3	90.7	122.7	149.8	118.6
87.6	85.1	88.5	88.4	106.9	99.2	99.2
95.3	90.8	112.5	94.1	115.0	97.7	102.2
98.5	90.5	101.1	92.0	114.9	107.7	108.8
80.1	86.1	114.0	92.0	103.1	120.1	94.0
84.2	93.3	107.7	89.3	80.8	164.5	96.2
92.4	94.9	77.8	87.0	118.2	136.1	118.4
98.0	102.6	101.4	97.7	129.6	117.5	120.0
92.2	98.3	87.2	82.5	118.7	98.2	117.5
80.0	93.4	75.9	96.5	88.4	91.9	102.6
88.7	92.8	78.8	86.2	113.1	141.8	92.8
87.4	86.5	82.3	84.9	109.8	154.2	100.3
96.1	93.8	89.1	100.0	116.1	138.6	106.3
94.1	90.4	100.1	92.7	113.6	97.9	103.9
91.9	91.0	101.8	96.7	107.9	90.3	102.4
93.6	89.1	98.5	105.8	107.4	90.9	114.5
83.5	89.6	106.6	88.5	102.7	127.0	89.0
80.8	89.3	101.8	78.7	78.3	156.8	94.3
96.3	95.3	92.4	99.9	121.0	127.2	115.7
101.5	104.1	94.4	107.8	132.2	111.3	120.2
91.6	94.7	81.0	102.4	113.2	93.0	109.5
84.0	97.6	94.6	106.0	89.2	89.5	99.4
91.8	96.8	83.8	87.3	113.2	141.8	86.4
90.4	92.8	79.4	93.3	107.6	152.0	95.1
98.0	94.7	95.6	98.2	107.3	120.2	101.5
95.5	95.8	106.0	102.0	110.9	88.8	92.9
90.5	88.9	106.2	93.9	96.4	82.8	90.8
97.1	91.2	115.0	106.6	101.2	82.8	100.4
87.9	91.6	122.4	92.9	94.0	121.7	82.2
79.8	87.3	113.7	78.0	70.5	147.1	75.3
102.0	97.8	98.0	104.2	116.4	132.5	110.3
104.3	105.1	105.8	115.9	121.9	107.5	113.5
92.1	93.8	88.3	99.9	109.5	77.9	94.9
95.9	99.0	95.7	103.9	91.1	85.5	95.7
89.1	91.4	85.8	93.5	104.0	126.5	85.3
92.2	89.0	83.9	101.7	101.2	135.4	92.5
107.5	101.4	114.1	124.6	118.4	122.5	107.7
99.7	95.4	102.0	124.2	106.9	79.2	97.9
92.2	90.5	108.1	103.3	95.6	66.1	93.9
108.9	98.7	125.4	120.5	114.2	77.9	111.5
89.8	91.2	108.1	98.0	92.4	109.6	88.6
89.4	91.7	110.4	100.4	75.3	142.9	82.5
107.6	102.9	102.4	126.8	120.4	120.5	108.6
105.6	105.5	89.6	120.2	115.9	96.3	113.8
100.9	102.6	95.0	114.0	109.8	82.6	103.4
102.9	107.2	93.7	109.1	94.9	78.4	99.0
96.2	96.9	77.7	94.2	97.5	104.5	89.9
94.7	88.9	80.1	86.0	101.3	137.9	97.9
107.3	99.6	103.6	112.9	108.7	125.8	107.8
103.0	96.7	103.1	99.7	105.1	78.0	103.7
96.1	93.8	112.4	104.5	94.9	67.7	98.2
109.8	101.9	119.2	111.6	108.9	78.4	111.7
85.4	87.6	105.3	99.2	87.5	101.7	82.6
89.9	100.0	107.2	90.9	73.0	154.1	86.1
109.3	105.8	108.7	111.4	115.2	107.3	111.2
101.2	105.5	93.7	98.2	107.5	86.5	105.3
104.7	111.3	96.1	101.7	109.8	82.1	106.3
102.4	112.1	92.9	89.7	90.7	76.1	99.4
97.7	102.0	81.1	89.5	97.6	115.5	91.9
98.9	93.2	83.2	85.1	98.7	129.6	96.2
115.0	108.4	99.7	95.9	113.9	121.6	105.4
97.5	97.9	96.8	88.9	96.6	64.0	95.0
107.3	106.4	108.7	98.1	104.4	58.1	100.5
112.3	102.8	120.9	109.7	115.1	79.7	111.6
88.5	96.3	114.8	92.0	91.4	108.9	88.5
92.9	105.7	108.7	74.3	76.2	138.5	83.7
108.8	108.4	97.4	96.9	117.4	117.9	113.9
112.3	115.8	98.6	100.3	122.0	96.7	115.2
107.3	113.8	91.7	97.1	120.2	78.6	111.0
101.8	106.4	91.2	86.0	93.6	64.1	96.9
105.0	107.9	83.5	97.3	106.6	112.0	102.1
103.4	98.2	82.4	86.4	108.4	139.4	101.5
116.7	111.1	103.1	97.7	121.4	116.2	115.0
103.6	99.8	110.3	90.6	104.8	63.4	105.0
108.8	103.5	115.8	99.2	104.2	61.1	105.4
117.0	105.4	120.1	107.4	115.0	65.5	119.7
100.9	102.6	105.1	107.1	99.0	90.9	91.8
100.8	107.4	108.6	78.9	82.8	115.3	89.1
109.7	108.2	95.7	92.8	112.5	85.2	106.2
121.0	121.7	103.2	106.2	127.9	87.0	119.9
114.1	118.0	96.9	97.2	114.4	62.6	111.6
105.5	109.6	95.7	80.0	83.7	62.7	95.1
112.5	116.7	92.7	109.3	108.5	91.6	101.3
113.8	110.6	81.3	111.3	109.7	104.3	118.3
115.3	109.6	94.5	119.5	104.7	88.1	126.2
120.4	117.4	105.6	119.8	112.2	62.3	113.2
111.1	109.2	112.9	112.5	96.9	50.3	103.6
120.1	110.8	102.6	125.6	103.8	64.1	116.2
106.1	112.8	116.2	105.1	95.1	75.7	98.3
95.9	106.5	104.9	91.9	66.7	85.5	84.2
119.4	119.6	100.4	128.2	103.4	71.9	118.3
117.4	127.2	97.1	122.6	105.4	66.9	117.4
98.6	113.9	90.2	109.6	89.2	50.5	94.5
99.7	120.0	100.5	120.4	72.5	57.9	93.3
87.4	107.6	81.1	103.8	78.0	84.1	90.2
90.8	105.2	87.2	96.6	77.3	87.0	88.5
101.3	115.3	102.0	110.7	85.1	71.9	101.0
93.2	113.9	107.0	111.7	80.9	45.0	87.0
95.1	106.1	107.6	111.9	72.5	39.5	81.2
101.9	114.3	123.5	131.5	82.1	53.8	98.1
87.0	112.0	116.6	122.8	78.3	59.5	75.5
86.2	109.0	103.2	98.3	57.8	68.4	70.7
105.0	119.1	103.9	133.7	89.3	56.9	103.7
104.1	124.4	95.4	120.0	91.4	61.9	100.4
99.2	116.6	93.6	119.6	84.2	40.4	91.3
95.2	118.5	102.1	108.7	72.5	49.4	97.2
92.7	108.9	69.0	112.5	74.6	65.2	85.4
99.3	107.5	88.9	102.7	80.3	82.1	86.5
113.5	125.9	106.2	123.4	92.6	69.0	105.3
104.7	117.7	103.0	116.5	86.3	45.9	97.7
100.5	109.2	103.5	102.3	80.3	39.1	84.3
116.2	118.8	124.5	148.4	93.6	56.9	109.8
94.1	108.1	117.9	126.6	79.5	51.6	79.1
94.8	112.1	104.2	106.6	61.8	62.9	83.4
115.1	117.8	99.9	144.4	94.8	58.3	101.9
110.0	121.8	89.4	132.4	91.6	56.9	113.0
108.4	121.0	93.5	136.2	89.2	41.3	98.6
103.9	121.7	89.6	121.6	74.1	46.9	94.7
102.9	114.2	85.0	135.1	78.6	61.9	94.5
107.7	109.8	90.0	124.7	78.2	74.8	90.7
126.7	124.1	113.7	148.8	95.1	67.0	113.0
108.8	112.9	112.1	145.6	78.7	53.3	89.9
117.1	118.7	129.8	140.3	85.9	51.4	98.7
112.2	113.3	119.1	138.5	81.2	50.3	102.2
94.7	106.8	103.5	127.3	73.1	52.7	74.3
102.7	119.3	105.5	117.9	58.7	70.3	84.5
119.1	126.4	111.7	145.3	85.7	59.7	110.1
110.6	126.6	98.6	120.7	81.8	52.0	100.4
109.1	127.2	102.8	134.7	79.6	36.1	92.8
105.3	123.8	101.1	124.4	70.7	39.7	92.2
103.4	116.8	94.2	128.3	74.5	67.6	94.0
103.7	113.8	92.6	128.4	84.8	72.8	100.7
117.0	130.4	112.0	134.1	80.7	53.8	111.9
101.2	112.8	108.6	133.3	69.9	39.6	95.9
105.4	119.4	125.8	130.6	74.1	39.4	88.8
110.3	117.5	138.7	165.7	76.1	41.2	102.0
97.7	117.5	115.2	146.8	71.3	49.6	81.6

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time15 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.

\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 & 15 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
R Framework error message & 
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185123&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]15 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185123&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185123&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 time15 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.






Multiple Linear Regression - Estimated Regression Equation
Totaal[t] = -28.2818825546516 + 0.648098234696843Voeding[t] + 0.143333989241817Dranken[t] + 0.0623369283047743Tabaksproducten[t] + 0.211281568915149Textiel[t] + 0.00973820999478853Kleding[t] + 0.183562087492253`Apparatuur\r`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Totaal[t] =  -28.2818825546516 +  0.648098234696843Voeding[t] +  0.143333989241817Dranken[t] +  0.0623369283047743Tabaksproducten[t] +  0.211281568915149Textiel[t] +  0.00973820999478853Kleding[t] +  0.183562087492253`Apparatuur\r`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185123&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Totaal[t] =  -28.2818825546516 +  0.648098234696843Voeding[t] +  0.143333989241817Dranken[t] +  0.0623369283047743Tabaksproducten[t] +  0.211281568915149Textiel[t] +  0.00973820999478853Kleding[t] +  0.183562087492253`Apparatuur\r`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185123&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185123&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
Totaal[t] = -28.2818825546516 + 0.648098234696843Voeding[t] + 0.143333989241817Dranken[t] + 0.0623369283047743Tabaksproducten[t] + 0.211281568915149Textiel[t] + 0.00973820999478853Kleding[t] + 0.183562087492253`Apparatuur\r`[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-28.28188255465166.95572-4.0667.8e-053.9e-05
Voeding0.6480982346968430.04933513.136700
Dranken0.1433339892418170.0324294.41991.9e-051e-05
Tabaksproducten0.06233692830477430.0330871.8840.0615770.030788
Textiel0.2112815689151490.0411125.13921e-060
Kleding0.009738209994788530.017620.55270.5813380.290669
`Apparatuur\r`0.1835620874922530.0547853.35060.001030.000515

\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) & -28.2818825546516 & 6.95572 & -4.066 & 7.8e-05 & 3.9e-05 \tabularnewline
Voeding & 0.648098234696843 & 0.049335 & 13.1367 & 0 & 0 \tabularnewline
Dranken & 0.143333989241817 & 0.032429 & 4.4199 & 1.9e-05 & 1e-05 \tabularnewline
Tabaksproducten & 0.0623369283047743 & 0.033087 & 1.884 & 0.061577 & 0.030788 \tabularnewline
Textiel & 0.211281568915149 & 0.041112 & 5.1392 & 1e-06 & 0 \tabularnewline
Kleding & 0.00973820999478853 & 0.01762 & 0.5527 & 0.581338 & 0.290669 \tabularnewline
`Apparatuur\r` & 0.183562087492253 & 0.054785 & 3.3506 & 0.00103 & 0.000515 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185123&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]-28.2818825546516[/C][C]6.95572[/C][C]-4.066[/C][C]7.8e-05[/C][C]3.9e-05[/C][/ROW]
[ROW][C]Voeding[/C][C]0.648098234696843[/C][C]0.049335[/C][C]13.1367[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Dranken[/C][C]0.143333989241817[/C][C]0.032429[/C][C]4.4199[/C][C]1.9e-05[/C][C]1e-05[/C][/ROW]
[ROW][C]Tabaksproducten[/C][C]0.0623369283047743[/C][C]0.033087[/C][C]1.884[/C][C]0.061577[/C][C]0.030788[/C][/ROW]
[ROW][C]Textiel[/C][C]0.211281568915149[/C][C]0.041112[/C][C]5.1392[/C][C]1e-06[/C][C]0[/C][/ROW]
[ROW][C]Kleding[/C][C]0.00973820999478853[/C][C]0.01762[/C][C]0.5527[/C][C]0.581338[/C][C]0.290669[/C][/ROW]
[ROW][C]`Apparatuur\r`[/C][C]0.183562087492253[/C][C]0.054785[/C][C]3.3506[/C][C]0.00103[/C][C]0.000515[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185123&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185123&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)-28.28188255465166.95572-4.0667.8e-053.9e-05
Voeding0.6480982346968430.04933513.136700
Dranken0.1433339892418170.0324294.41991.9e-051e-05
Tabaksproducten0.06233692830477430.0330871.8840.0615770.030788
Textiel0.2112815689151490.0411125.13921e-060
Kleding0.009738209994788530.017620.55270.5813380.290669
`Apparatuur\r`0.1835620874922530.0547853.35060.001030.000515






Multiple Linear Regression - Regression Statistics
Multiple R0.906700222167259
R-squared0.822105292878156
Adjusted R-squared0.814693013414746
F-TEST (value)110.911265142711
F-TEST (DF numerator)6
F-TEST (DF denominator)144
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation4.54036505298097
Sum Squared Residuals2968.54773326364

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.906700222167259 \tabularnewline
R-squared & 0.822105292878156 \tabularnewline
Adjusted R-squared & 0.814693013414746 \tabularnewline
F-TEST (value) & 110.911265142711 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 144 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 4.54036505298097 \tabularnewline
Sum Squared Residuals & 2968.54773326364 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185123&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.906700222167259[/C][/ROW]
[ROW][C]R-squared[/C][C]0.822105292878156[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.814693013414746[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]110.911265142711[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]144[/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]4.54036505298097[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]2968.54773326364[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185123&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185123&T=3

As an alternative you can also use a QR Code:  
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The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Regression Statistics
Multiple R0.906700222167259
R-squared0.822105292878156
Adjusted R-squared0.814693013414746
F-TEST (value)110.911265142711
F-TEST (DF numerator)6
F-TEST (DF denominator)144
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation4.54036505298097
Sum Squared Residuals2968.54773326364






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
175.579.1603427703599-3.6603427703599
283.285.2275008896291-2.02750088962913
394.594.8705138218139-0.370513821813915
483.382.82728760672120.472712393278777
592.797.0928247132654-4.39282471326541
689.891.1666613910513-1.36666139105128
774.876.4427323960493-1.64273239604927
881.580.92054605691220.579453943087781
992.892.12755039726360.672449602736385
1092.8100.93976329081-8.13976329080955
1191.796.5310440707621-4.83104407076208
1283.587.6663473222742-4.16634732227417
1392.889.44713974421073.35286025578926
1491.386.85374582754714.44625417245289
1599.5100.130626778441-0.63062677844138
1687.686.82830895583660.771691044163396
1795.396.5652647824467-1.26526478244671
1898.595.89368400574622.60631599425384
1980.189.8019726301508-9.70197263015076
2084.289.5216002107654-5.32160021076543
2192.497.8299400287517-5.42994002875168
2298109.391162234603-11.3911622346026
2392.2100.671654095135-8.4716540951346
248087.5507582982236-7.55075829822363
2588.790.8411805381877-2.14118053818766
2687.487.979032897855-0.57903289785498
2796.196.9066390885886-0.80663908858865
2894.194.4596213165973-0.359621316597349
2991.993.7878372823305-1.88783728233051
3093.694.7720179196775-1.17201791967752
3183.589.8563362660703-6.35633626607027
3280.884.4708091899377-3.67080918993771
3396.3100.995302628472-4.69530262847225
34101.5110.515242232543-9.01524223254349
3591.696.0091505692475-4.40915056924748
368493.1035721728371-9.10357217283711
3791.893.0651448412605-1.26514484126048
3890.490.7292470368424-0.329247036842349
399895.38983306861882.61016693138121
4095.596.7064948442832-1.20649484428316
4190.588.25086231048252.24913768951749
4297.194.57085391580222.52914608419778
4387.990.5535078925442-2.65350789254417
4479.879.60651480586790.193485194132115
45102101.7747493701840.225250629816409
46104.3109.859205719861-5.5592057198614
4792.192.7075627054294-0.607562705429391
4895.993.7209719573772.17902804262304
4989.187.94386796468961.15613203531035
5092.287.44398913989234.75601086010767
51107.5107.535173189705-0.0351731897054373
5299.797.23699674765382.46300325234616
5392.290.38351030080271.81648969919733
54108.9106.5252298056662.37477019433372
5589.889.28142539561110.518574604388948
5689.485.67639014682183.72360985317824
57107.6107.5357467064760.0642532935242961
58105.6106.742794440548-1.14279444054838
59100.9101.919547389113-1.01954738911329
60102.9100.4123450902312.48765490976873
6196.289.64785357710756.55214642289255
6294.786.89252933725547.80747066274456
63107.3102.1353085023015.16469149769874
6410397.38260452887285.61739547112718
6596.193.87037595697562.22962404302445
66109.8106.077463968733.72253603127002
6785.484.40815682319450.991843176805537
6889.990.2886797687453-0.388679768745294
69109.3108.6082999206160.691700079383662
70101.2102.528573993214-1.32857399321446
71104.7107.476386149723-2.77638614972324
72102.4101.4276672023050.972332797695014
7397.793.64287921626984.05712078373022
7498.989.12536910675429.77463089324576
75115106.8370572948088.1629427051915
7697.593.05486101570564.44513898429439
77107.3103.4430025027873.85699749721336
78112.3108.0902291894074.20977081059292
7988.592.9365880259957-4.43658802599575
8092.993.2466836151492-0.346683615149146
81108.8108.833457905764-0.0334579057635461
82112.3115.017407064707-2.71740706470669
83107.3111.205198706549-3.90519870654879
84101.897.29614565928014.50385434071992
85105102.0366720936142.96332790638568
86103.495.4499758357767.95002416422398
87116.7112.4806860356784.21931396432188
88103.699.88951610854213.71048389145786
89108.8103.5361721118315.26382788816869
90117110.8446846429946.15531535700583
91100.998.60676185891572.2932381410843
92100.896.78063424083064.01936575916938
93109.7105.4374418428594.26255815714066
94121121.883153307792-0.883153307792118
95114.1113.4076745220340.69232547796567
96105.597.20530860833088.29469139166924
97112.5109.862578226682.63742177332015
98113.8107.8976140112825.90238598871777
99115.3109.8886608913745.411339108626
100120.4115.5005942926844.89940570731624
101111.1105.6658047487455.43419525125481
102120.1109.94814802170710.151851978293
103106.1106.904831934808-0.804831934807909
10495.991.88610399128024.01389600871984
105119.4115.8750795184273.52492048157339
106117.4120.187203348231-2.78720334823122
10798.6101.98207236912-3.38207236912007
10899.7104.408436563741-4.70843656374082
10987.493.4046933120203-6.00469331202025
11090.891.843057161336-1.043057161336
111101.3105.18461842192-3.88461842192034
11293.2101.337078104664-8.13707810466365
11395.193.48739421192061.61260578807934
114101.9107.572372703283-5.67237270328321
1158799.6545456192282-12.6545456192282
11686.289.1366206020606-2.93662060206061
117105110.590402720088-5.59040272008849
118104.1111.839595993622-7.73959599362225
11999.2103.100480003773-3.90048000377314
12095.2103.569398889581-8.36939888958069
12192.791.27170350037591.42829649962411
12299.394.17660944829545.12339055170457
123113.5115.793829388089-2.29382938808857
124104.7106.639531892712-1.93953189271152
125100.596.52353829662983.97646170337018
126116.2116.293245754179-0.0932457541789287
12794.198.3876065561945-4.28760655619449
12894.894.9292602556331-0.129260255633142
129115.1110.6868145564164.41318544358361
130110112.37396212515-2.37396212515021
131108.4109.377747319639-0.977747319638823
132103.9104.510584516766-0.610584516766098
133102.9100.8921877306832.00781226931658
134107.797.452495738752910.2475042612471
135126.7119.2077710398787.49222896012221
136108.8103.6815428297035.11845717029713
137117.1112.765209547644.33479045235977
138112.2107.2583308257694.94166917423136
13994.793.30211722579431.39788277420574
140102.7100.1053172078752.59468279212533
141119.1117.6040840176381.4959159823617
142110.6111.643005384809-1.04300538480861
143109.1111.491855221238-2.3918552212376
144105.3106.447097420159-1.14709742015912
145103.4102.569497050120.830502949880484
146103.7103.85880649407-0.158806494069589
147117118.757852030126-1.7578520301258
148101.2101.45700106731-0.257001067309696
149105.4107.613628451095-2.2136284510949
150110.3113.282387920607-2.98238792060657
15197.7104.058854076785-6.35885407678518

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 75.5 & 79.1603427703599 & -3.6603427703599 \tabularnewline
2 & 83.2 & 85.2275008896291 & -2.02750088962913 \tabularnewline
3 & 94.5 & 94.8705138218139 & -0.370513821813915 \tabularnewline
4 & 83.3 & 82.8272876067212 & 0.472712393278777 \tabularnewline
5 & 92.7 & 97.0928247132654 & -4.39282471326541 \tabularnewline
6 & 89.8 & 91.1666613910513 & -1.36666139105128 \tabularnewline
7 & 74.8 & 76.4427323960493 & -1.64273239604927 \tabularnewline
8 & 81.5 & 80.9205460569122 & 0.579453943087781 \tabularnewline
9 & 92.8 & 92.1275503972636 & 0.672449602736385 \tabularnewline
10 & 92.8 & 100.93976329081 & -8.13976329080955 \tabularnewline
11 & 91.7 & 96.5310440707621 & -4.83104407076208 \tabularnewline
12 & 83.5 & 87.6663473222742 & -4.16634732227417 \tabularnewline
13 & 92.8 & 89.4471397442107 & 3.35286025578926 \tabularnewline
14 & 91.3 & 86.8537458275471 & 4.44625417245289 \tabularnewline
15 & 99.5 & 100.130626778441 & -0.63062677844138 \tabularnewline
16 & 87.6 & 86.8283089558366 & 0.771691044163396 \tabularnewline
17 & 95.3 & 96.5652647824467 & -1.26526478244671 \tabularnewline
18 & 98.5 & 95.8936840057462 & 2.60631599425384 \tabularnewline
19 & 80.1 & 89.8019726301508 & -9.70197263015076 \tabularnewline
20 & 84.2 & 89.5216002107654 & -5.32160021076543 \tabularnewline
21 & 92.4 & 97.8299400287517 & -5.42994002875168 \tabularnewline
22 & 98 & 109.391162234603 & -11.3911622346026 \tabularnewline
23 & 92.2 & 100.671654095135 & -8.4716540951346 \tabularnewline
24 & 80 & 87.5507582982236 & -7.55075829822363 \tabularnewline
25 & 88.7 & 90.8411805381877 & -2.14118053818766 \tabularnewline
26 & 87.4 & 87.979032897855 & -0.57903289785498 \tabularnewline
27 & 96.1 & 96.9066390885886 & -0.80663908858865 \tabularnewline
28 & 94.1 & 94.4596213165973 & -0.359621316597349 \tabularnewline
29 & 91.9 & 93.7878372823305 & -1.88783728233051 \tabularnewline
30 & 93.6 & 94.7720179196775 & -1.17201791967752 \tabularnewline
31 & 83.5 & 89.8563362660703 & -6.35633626607027 \tabularnewline
32 & 80.8 & 84.4708091899377 & -3.67080918993771 \tabularnewline
33 & 96.3 & 100.995302628472 & -4.69530262847225 \tabularnewline
34 & 101.5 & 110.515242232543 & -9.01524223254349 \tabularnewline
35 & 91.6 & 96.0091505692475 & -4.40915056924748 \tabularnewline
36 & 84 & 93.1035721728371 & -9.10357217283711 \tabularnewline
37 & 91.8 & 93.0651448412605 & -1.26514484126048 \tabularnewline
38 & 90.4 & 90.7292470368424 & -0.329247036842349 \tabularnewline
39 & 98 & 95.3898330686188 & 2.61016693138121 \tabularnewline
40 & 95.5 & 96.7064948442832 & -1.20649484428316 \tabularnewline
41 & 90.5 & 88.2508623104825 & 2.24913768951749 \tabularnewline
42 & 97.1 & 94.5708539158022 & 2.52914608419778 \tabularnewline
43 & 87.9 & 90.5535078925442 & -2.65350789254417 \tabularnewline
44 & 79.8 & 79.6065148058679 & 0.193485194132115 \tabularnewline
45 & 102 & 101.774749370184 & 0.225250629816409 \tabularnewline
46 & 104.3 & 109.859205719861 & -5.5592057198614 \tabularnewline
47 & 92.1 & 92.7075627054294 & -0.607562705429391 \tabularnewline
48 & 95.9 & 93.720971957377 & 2.17902804262304 \tabularnewline
49 & 89.1 & 87.9438679646896 & 1.15613203531035 \tabularnewline
50 & 92.2 & 87.4439891398923 & 4.75601086010767 \tabularnewline
51 & 107.5 & 107.535173189705 & -0.0351731897054373 \tabularnewline
52 & 99.7 & 97.2369967476538 & 2.46300325234616 \tabularnewline
53 & 92.2 & 90.3835103008027 & 1.81648969919733 \tabularnewline
54 & 108.9 & 106.525229805666 & 2.37477019433372 \tabularnewline
55 & 89.8 & 89.2814253956111 & 0.518574604388948 \tabularnewline
56 & 89.4 & 85.6763901468218 & 3.72360985317824 \tabularnewline
57 & 107.6 & 107.535746706476 & 0.0642532935242961 \tabularnewline
58 & 105.6 & 106.742794440548 & -1.14279444054838 \tabularnewline
59 & 100.9 & 101.919547389113 & -1.01954738911329 \tabularnewline
60 & 102.9 & 100.412345090231 & 2.48765490976873 \tabularnewline
61 & 96.2 & 89.6478535771075 & 6.55214642289255 \tabularnewline
62 & 94.7 & 86.8925293372554 & 7.80747066274456 \tabularnewline
63 & 107.3 & 102.135308502301 & 5.16469149769874 \tabularnewline
64 & 103 & 97.3826045288728 & 5.61739547112718 \tabularnewline
65 & 96.1 & 93.8703759569756 & 2.22962404302445 \tabularnewline
66 & 109.8 & 106.07746396873 & 3.72253603127002 \tabularnewline
67 & 85.4 & 84.4081568231945 & 0.991843176805537 \tabularnewline
68 & 89.9 & 90.2886797687453 & -0.388679768745294 \tabularnewline
69 & 109.3 & 108.608299920616 & 0.691700079383662 \tabularnewline
70 & 101.2 & 102.528573993214 & -1.32857399321446 \tabularnewline
71 & 104.7 & 107.476386149723 & -2.77638614972324 \tabularnewline
72 & 102.4 & 101.427667202305 & 0.972332797695014 \tabularnewline
73 & 97.7 & 93.6428792162698 & 4.05712078373022 \tabularnewline
74 & 98.9 & 89.1253691067542 & 9.77463089324576 \tabularnewline
75 & 115 & 106.837057294808 & 8.1629427051915 \tabularnewline
76 & 97.5 & 93.0548610157056 & 4.44513898429439 \tabularnewline
77 & 107.3 & 103.443002502787 & 3.85699749721336 \tabularnewline
78 & 112.3 & 108.090229189407 & 4.20977081059292 \tabularnewline
79 & 88.5 & 92.9365880259957 & -4.43658802599575 \tabularnewline
80 & 92.9 & 93.2466836151492 & -0.346683615149146 \tabularnewline
81 & 108.8 & 108.833457905764 & -0.0334579057635461 \tabularnewline
82 & 112.3 & 115.017407064707 & -2.71740706470669 \tabularnewline
83 & 107.3 & 111.205198706549 & -3.90519870654879 \tabularnewline
84 & 101.8 & 97.2961456592801 & 4.50385434071992 \tabularnewline
85 & 105 & 102.036672093614 & 2.96332790638568 \tabularnewline
86 & 103.4 & 95.449975835776 & 7.95002416422398 \tabularnewline
87 & 116.7 & 112.480686035678 & 4.21931396432188 \tabularnewline
88 & 103.6 & 99.8895161085421 & 3.71048389145786 \tabularnewline
89 & 108.8 & 103.536172111831 & 5.26382788816869 \tabularnewline
90 & 117 & 110.844684642994 & 6.15531535700583 \tabularnewline
91 & 100.9 & 98.6067618589157 & 2.2932381410843 \tabularnewline
92 & 100.8 & 96.7806342408306 & 4.01936575916938 \tabularnewline
93 & 109.7 & 105.437441842859 & 4.26255815714066 \tabularnewline
94 & 121 & 121.883153307792 & -0.883153307792118 \tabularnewline
95 & 114.1 & 113.407674522034 & 0.69232547796567 \tabularnewline
96 & 105.5 & 97.2053086083308 & 8.29469139166924 \tabularnewline
97 & 112.5 & 109.86257822668 & 2.63742177332015 \tabularnewline
98 & 113.8 & 107.897614011282 & 5.90238598871777 \tabularnewline
99 & 115.3 & 109.888660891374 & 5.411339108626 \tabularnewline
100 & 120.4 & 115.500594292684 & 4.89940570731624 \tabularnewline
101 & 111.1 & 105.665804748745 & 5.43419525125481 \tabularnewline
102 & 120.1 & 109.948148021707 & 10.151851978293 \tabularnewline
103 & 106.1 & 106.904831934808 & -0.804831934807909 \tabularnewline
104 & 95.9 & 91.8861039912802 & 4.01389600871984 \tabularnewline
105 & 119.4 & 115.875079518427 & 3.52492048157339 \tabularnewline
106 & 117.4 & 120.187203348231 & -2.78720334823122 \tabularnewline
107 & 98.6 & 101.98207236912 & -3.38207236912007 \tabularnewline
108 & 99.7 & 104.408436563741 & -4.70843656374082 \tabularnewline
109 & 87.4 & 93.4046933120203 & -6.00469331202025 \tabularnewline
110 & 90.8 & 91.843057161336 & -1.043057161336 \tabularnewline
111 & 101.3 & 105.18461842192 & -3.88461842192034 \tabularnewline
112 & 93.2 & 101.337078104664 & -8.13707810466365 \tabularnewline
113 & 95.1 & 93.4873942119206 & 1.61260578807934 \tabularnewline
114 & 101.9 & 107.572372703283 & -5.67237270328321 \tabularnewline
115 & 87 & 99.6545456192282 & -12.6545456192282 \tabularnewline
116 & 86.2 & 89.1366206020606 & -2.93662060206061 \tabularnewline
117 & 105 & 110.590402720088 & -5.59040272008849 \tabularnewline
118 & 104.1 & 111.839595993622 & -7.73959599362225 \tabularnewline
119 & 99.2 & 103.100480003773 & -3.90048000377314 \tabularnewline
120 & 95.2 & 103.569398889581 & -8.36939888958069 \tabularnewline
121 & 92.7 & 91.2717035003759 & 1.42829649962411 \tabularnewline
122 & 99.3 & 94.1766094482954 & 5.12339055170457 \tabularnewline
123 & 113.5 & 115.793829388089 & -2.29382938808857 \tabularnewline
124 & 104.7 & 106.639531892712 & -1.93953189271152 \tabularnewline
125 & 100.5 & 96.5235382966298 & 3.97646170337018 \tabularnewline
126 & 116.2 & 116.293245754179 & -0.0932457541789287 \tabularnewline
127 & 94.1 & 98.3876065561945 & -4.28760655619449 \tabularnewline
128 & 94.8 & 94.9292602556331 & -0.129260255633142 \tabularnewline
129 & 115.1 & 110.686814556416 & 4.41318544358361 \tabularnewline
130 & 110 & 112.37396212515 & -2.37396212515021 \tabularnewline
131 & 108.4 & 109.377747319639 & -0.977747319638823 \tabularnewline
132 & 103.9 & 104.510584516766 & -0.610584516766098 \tabularnewline
133 & 102.9 & 100.892187730683 & 2.00781226931658 \tabularnewline
134 & 107.7 & 97.4524957387529 & 10.2475042612471 \tabularnewline
135 & 126.7 & 119.207771039878 & 7.49222896012221 \tabularnewline
136 & 108.8 & 103.681542829703 & 5.11845717029713 \tabularnewline
137 & 117.1 & 112.76520954764 & 4.33479045235977 \tabularnewline
138 & 112.2 & 107.258330825769 & 4.94166917423136 \tabularnewline
139 & 94.7 & 93.3021172257943 & 1.39788277420574 \tabularnewline
140 & 102.7 & 100.105317207875 & 2.59468279212533 \tabularnewline
141 & 119.1 & 117.604084017638 & 1.4959159823617 \tabularnewline
142 & 110.6 & 111.643005384809 & -1.04300538480861 \tabularnewline
143 & 109.1 & 111.491855221238 & -2.3918552212376 \tabularnewline
144 & 105.3 & 106.447097420159 & -1.14709742015912 \tabularnewline
145 & 103.4 & 102.56949705012 & 0.830502949880484 \tabularnewline
146 & 103.7 & 103.85880649407 & -0.158806494069589 \tabularnewline
147 & 117 & 118.757852030126 & -1.7578520301258 \tabularnewline
148 & 101.2 & 101.45700106731 & -0.257001067309696 \tabularnewline
149 & 105.4 & 107.613628451095 & -2.2136284510949 \tabularnewline
150 & 110.3 & 113.282387920607 & -2.98238792060657 \tabularnewline
151 & 97.7 & 104.058854076785 & -6.35885407678518 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185123&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]75.5[/C][C]79.1603427703599[/C][C]-3.6603427703599[/C][/ROW]
[ROW][C]2[/C][C]83.2[/C][C]85.2275008896291[/C][C]-2.02750088962913[/C][/ROW]
[ROW][C]3[/C][C]94.5[/C][C]94.8705138218139[/C][C]-0.370513821813915[/C][/ROW]
[ROW][C]4[/C][C]83.3[/C][C]82.8272876067212[/C][C]0.472712393278777[/C][/ROW]
[ROW][C]5[/C][C]92.7[/C][C]97.0928247132654[/C][C]-4.39282471326541[/C][/ROW]
[ROW][C]6[/C][C]89.8[/C][C]91.1666613910513[/C][C]-1.36666139105128[/C][/ROW]
[ROW][C]7[/C][C]74.8[/C][C]76.4427323960493[/C][C]-1.64273239604927[/C][/ROW]
[ROW][C]8[/C][C]81.5[/C][C]80.9205460569122[/C][C]0.579453943087781[/C][/ROW]
[ROW][C]9[/C][C]92.8[/C][C]92.1275503972636[/C][C]0.672449602736385[/C][/ROW]
[ROW][C]10[/C][C]92.8[/C][C]100.93976329081[/C][C]-8.13976329080955[/C][/ROW]
[ROW][C]11[/C][C]91.7[/C][C]96.5310440707621[/C][C]-4.83104407076208[/C][/ROW]
[ROW][C]12[/C][C]83.5[/C][C]87.6663473222742[/C][C]-4.16634732227417[/C][/ROW]
[ROW][C]13[/C][C]92.8[/C][C]89.4471397442107[/C][C]3.35286025578926[/C][/ROW]
[ROW][C]14[/C][C]91.3[/C][C]86.8537458275471[/C][C]4.44625417245289[/C][/ROW]
[ROW][C]15[/C][C]99.5[/C][C]100.130626778441[/C][C]-0.63062677844138[/C][/ROW]
[ROW][C]16[/C][C]87.6[/C][C]86.8283089558366[/C][C]0.771691044163396[/C][/ROW]
[ROW][C]17[/C][C]95.3[/C][C]96.5652647824467[/C][C]-1.26526478244671[/C][/ROW]
[ROW][C]18[/C][C]98.5[/C][C]95.8936840057462[/C][C]2.60631599425384[/C][/ROW]
[ROW][C]19[/C][C]80.1[/C][C]89.8019726301508[/C][C]-9.70197263015076[/C][/ROW]
[ROW][C]20[/C][C]84.2[/C][C]89.5216002107654[/C][C]-5.32160021076543[/C][/ROW]
[ROW][C]21[/C][C]92.4[/C][C]97.8299400287517[/C][C]-5.42994002875168[/C][/ROW]
[ROW][C]22[/C][C]98[/C][C]109.391162234603[/C][C]-11.3911622346026[/C][/ROW]
[ROW][C]23[/C][C]92.2[/C][C]100.671654095135[/C][C]-8.4716540951346[/C][/ROW]
[ROW][C]24[/C][C]80[/C][C]87.5507582982236[/C][C]-7.55075829822363[/C][/ROW]
[ROW][C]25[/C][C]88.7[/C][C]90.8411805381877[/C][C]-2.14118053818766[/C][/ROW]
[ROW][C]26[/C][C]87.4[/C][C]87.979032897855[/C][C]-0.57903289785498[/C][/ROW]
[ROW][C]27[/C][C]96.1[/C][C]96.9066390885886[/C][C]-0.80663908858865[/C][/ROW]
[ROW][C]28[/C][C]94.1[/C][C]94.4596213165973[/C][C]-0.359621316597349[/C][/ROW]
[ROW][C]29[/C][C]91.9[/C][C]93.7878372823305[/C][C]-1.88783728233051[/C][/ROW]
[ROW][C]30[/C][C]93.6[/C][C]94.7720179196775[/C][C]-1.17201791967752[/C][/ROW]
[ROW][C]31[/C][C]83.5[/C][C]89.8563362660703[/C][C]-6.35633626607027[/C][/ROW]
[ROW][C]32[/C][C]80.8[/C][C]84.4708091899377[/C][C]-3.67080918993771[/C][/ROW]
[ROW][C]33[/C][C]96.3[/C][C]100.995302628472[/C][C]-4.69530262847225[/C][/ROW]
[ROW][C]34[/C][C]101.5[/C][C]110.515242232543[/C][C]-9.01524223254349[/C][/ROW]
[ROW][C]35[/C][C]91.6[/C][C]96.0091505692475[/C][C]-4.40915056924748[/C][/ROW]
[ROW][C]36[/C][C]84[/C][C]93.1035721728371[/C][C]-9.10357217283711[/C][/ROW]
[ROW][C]37[/C][C]91.8[/C][C]93.0651448412605[/C][C]-1.26514484126048[/C][/ROW]
[ROW][C]38[/C][C]90.4[/C][C]90.7292470368424[/C][C]-0.329247036842349[/C][/ROW]
[ROW][C]39[/C][C]98[/C][C]95.3898330686188[/C][C]2.61016693138121[/C][/ROW]
[ROW][C]40[/C][C]95.5[/C][C]96.7064948442832[/C][C]-1.20649484428316[/C][/ROW]
[ROW][C]41[/C][C]90.5[/C][C]88.2508623104825[/C][C]2.24913768951749[/C][/ROW]
[ROW][C]42[/C][C]97.1[/C][C]94.5708539158022[/C][C]2.52914608419778[/C][/ROW]
[ROW][C]43[/C][C]87.9[/C][C]90.5535078925442[/C][C]-2.65350789254417[/C][/ROW]
[ROW][C]44[/C][C]79.8[/C][C]79.6065148058679[/C][C]0.193485194132115[/C][/ROW]
[ROW][C]45[/C][C]102[/C][C]101.774749370184[/C][C]0.225250629816409[/C][/ROW]
[ROW][C]46[/C][C]104.3[/C][C]109.859205719861[/C][C]-5.5592057198614[/C][/ROW]
[ROW][C]47[/C][C]92.1[/C][C]92.7075627054294[/C][C]-0.607562705429391[/C][/ROW]
[ROW][C]48[/C][C]95.9[/C][C]93.720971957377[/C][C]2.17902804262304[/C][/ROW]
[ROW][C]49[/C][C]89.1[/C][C]87.9438679646896[/C][C]1.15613203531035[/C][/ROW]
[ROW][C]50[/C][C]92.2[/C][C]87.4439891398923[/C][C]4.75601086010767[/C][/ROW]
[ROW][C]51[/C][C]107.5[/C][C]107.535173189705[/C][C]-0.0351731897054373[/C][/ROW]
[ROW][C]52[/C][C]99.7[/C][C]97.2369967476538[/C][C]2.46300325234616[/C][/ROW]
[ROW][C]53[/C][C]92.2[/C][C]90.3835103008027[/C][C]1.81648969919733[/C][/ROW]
[ROW][C]54[/C][C]108.9[/C][C]106.525229805666[/C][C]2.37477019433372[/C][/ROW]
[ROW][C]55[/C][C]89.8[/C][C]89.2814253956111[/C][C]0.518574604388948[/C][/ROW]
[ROW][C]56[/C][C]89.4[/C][C]85.6763901468218[/C][C]3.72360985317824[/C][/ROW]
[ROW][C]57[/C][C]107.6[/C][C]107.535746706476[/C][C]0.0642532935242961[/C][/ROW]
[ROW][C]58[/C][C]105.6[/C][C]106.742794440548[/C][C]-1.14279444054838[/C][/ROW]
[ROW][C]59[/C][C]100.9[/C][C]101.919547389113[/C][C]-1.01954738911329[/C][/ROW]
[ROW][C]60[/C][C]102.9[/C][C]100.412345090231[/C][C]2.48765490976873[/C][/ROW]
[ROW][C]61[/C][C]96.2[/C][C]89.6478535771075[/C][C]6.55214642289255[/C][/ROW]
[ROW][C]62[/C][C]94.7[/C][C]86.8925293372554[/C][C]7.80747066274456[/C][/ROW]
[ROW][C]63[/C][C]107.3[/C][C]102.135308502301[/C][C]5.16469149769874[/C][/ROW]
[ROW][C]64[/C][C]103[/C][C]97.3826045288728[/C][C]5.61739547112718[/C][/ROW]
[ROW][C]65[/C][C]96.1[/C][C]93.8703759569756[/C][C]2.22962404302445[/C][/ROW]
[ROW][C]66[/C][C]109.8[/C][C]106.07746396873[/C][C]3.72253603127002[/C][/ROW]
[ROW][C]67[/C][C]85.4[/C][C]84.4081568231945[/C][C]0.991843176805537[/C][/ROW]
[ROW][C]68[/C][C]89.9[/C][C]90.2886797687453[/C][C]-0.388679768745294[/C][/ROW]
[ROW][C]69[/C][C]109.3[/C][C]108.608299920616[/C][C]0.691700079383662[/C][/ROW]
[ROW][C]70[/C][C]101.2[/C][C]102.528573993214[/C][C]-1.32857399321446[/C][/ROW]
[ROW][C]71[/C][C]104.7[/C][C]107.476386149723[/C][C]-2.77638614972324[/C][/ROW]
[ROW][C]72[/C][C]102.4[/C][C]101.427667202305[/C][C]0.972332797695014[/C][/ROW]
[ROW][C]73[/C][C]97.7[/C][C]93.6428792162698[/C][C]4.05712078373022[/C][/ROW]
[ROW][C]74[/C][C]98.9[/C][C]89.1253691067542[/C][C]9.77463089324576[/C][/ROW]
[ROW][C]75[/C][C]115[/C][C]106.837057294808[/C][C]8.1629427051915[/C][/ROW]
[ROW][C]76[/C][C]97.5[/C][C]93.0548610157056[/C][C]4.44513898429439[/C][/ROW]
[ROW][C]77[/C][C]107.3[/C][C]103.443002502787[/C][C]3.85699749721336[/C][/ROW]
[ROW][C]78[/C][C]112.3[/C][C]108.090229189407[/C][C]4.20977081059292[/C][/ROW]
[ROW][C]79[/C][C]88.5[/C][C]92.9365880259957[/C][C]-4.43658802599575[/C][/ROW]
[ROW][C]80[/C][C]92.9[/C][C]93.2466836151492[/C][C]-0.346683615149146[/C][/ROW]
[ROW][C]81[/C][C]108.8[/C][C]108.833457905764[/C][C]-0.0334579057635461[/C][/ROW]
[ROW][C]82[/C][C]112.3[/C][C]115.017407064707[/C][C]-2.71740706470669[/C][/ROW]
[ROW][C]83[/C][C]107.3[/C][C]111.205198706549[/C][C]-3.90519870654879[/C][/ROW]
[ROW][C]84[/C][C]101.8[/C][C]97.2961456592801[/C][C]4.50385434071992[/C][/ROW]
[ROW][C]85[/C][C]105[/C][C]102.036672093614[/C][C]2.96332790638568[/C][/ROW]
[ROW][C]86[/C][C]103.4[/C][C]95.449975835776[/C][C]7.95002416422398[/C][/ROW]
[ROW][C]87[/C][C]116.7[/C][C]112.480686035678[/C][C]4.21931396432188[/C][/ROW]
[ROW][C]88[/C][C]103.6[/C][C]99.8895161085421[/C][C]3.71048389145786[/C][/ROW]
[ROW][C]89[/C][C]108.8[/C][C]103.536172111831[/C][C]5.26382788816869[/C][/ROW]
[ROW][C]90[/C][C]117[/C][C]110.844684642994[/C][C]6.15531535700583[/C][/ROW]
[ROW][C]91[/C][C]100.9[/C][C]98.6067618589157[/C][C]2.2932381410843[/C][/ROW]
[ROW][C]92[/C][C]100.8[/C][C]96.7806342408306[/C][C]4.01936575916938[/C][/ROW]
[ROW][C]93[/C][C]109.7[/C][C]105.437441842859[/C][C]4.26255815714066[/C][/ROW]
[ROW][C]94[/C][C]121[/C][C]121.883153307792[/C][C]-0.883153307792118[/C][/ROW]
[ROW][C]95[/C][C]114.1[/C][C]113.407674522034[/C][C]0.69232547796567[/C][/ROW]
[ROW][C]96[/C][C]105.5[/C][C]97.2053086083308[/C][C]8.29469139166924[/C][/ROW]
[ROW][C]97[/C][C]112.5[/C][C]109.86257822668[/C][C]2.63742177332015[/C][/ROW]
[ROW][C]98[/C][C]113.8[/C][C]107.897614011282[/C][C]5.90238598871777[/C][/ROW]
[ROW][C]99[/C][C]115.3[/C][C]109.888660891374[/C][C]5.411339108626[/C][/ROW]
[ROW][C]100[/C][C]120.4[/C][C]115.500594292684[/C][C]4.89940570731624[/C][/ROW]
[ROW][C]101[/C][C]111.1[/C][C]105.665804748745[/C][C]5.43419525125481[/C][/ROW]
[ROW][C]102[/C][C]120.1[/C][C]109.948148021707[/C][C]10.151851978293[/C][/ROW]
[ROW][C]103[/C][C]106.1[/C][C]106.904831934808[/C][C]-0.804831934807909[/C][/ROW]
[ROW][C]104[/C][C]95.9[/C][C]91.8861039912802[/C][C]4.01389600871984[/C][/ROW]
[ROW][C]105[/C][C]119.4[/C][C]115.875079518427[/C][C]3.52492048157339[/C][/ROW]
[ROW][C]106[/C][C]117.4[/C][C]120.187203348231[/C][C]-2.78720334823122[/C][/ROW]
[ROW][C]107[/C][C]98.6[/C][C]101.98207236912[/C][C]-3.38207236912007[/C][/ROW]
[ROW][C]108[/C][C]99.7[/C][C]104.408436563741[/C][C]-4.70843656374082[/C][/ROW]
[ROW][C]109[/C][C]87.4[/C][C]93.4046933120203[/C][C]-6.00469331202025[/C][/ROW]
[ROW][C]110[/C][C]90.8[/C][C]91.843057161336[/C][C]-1.043057161336[/C][/ROW]
[ROW][C]111[/C][C]101.3[/C][C]105.18461842192[/C][C]-3.88461842192034[/C][/ROW]
[ROW][C]112[/C][C]93.2[/C][C]101.337078104664[/C][C]-8.13707810466365[/C][/ROW]
[ROW][C]113[/C][C]95.1[/C][C]93.4873942119206[/C][C]1.61260578807934[/C][/ROW]
[ROW][C]114[/C][C]101.9[/C][C]107.572372703283[/C][C]-5.67237270328321[/C][/ROW]
[ROW][C]115[/C][C]87[/C][C]99.6545456192282[/C][C]-12.6545456192282[/C][/ROW]
[ROW][C]116[/C][C]86.2[/C][C]89.1366206020606[/C][C]-2.93662060206061[/C][/ROW]
[ROW][C]117[/C][C]105[/C][C]110.590402720088[/C][C]-5.59040272008849[/C][/ROW]
[ROW][C]118[/C][C]104.1[/C][C]111.839595993622[/C][C]-7.73959599362225[/C][/ROW]
[ROW][C]119[/C][C]99.2[/C][C]103.100480003773[/C][C]-3.90048000377314[/C][/ROW]
[ROW][C]120[/C][C]95.2[/C][C]103.569398889581[/C][C]-8.36939888958069[/C][/ROW]
[ROW][C]121[/C][C]92.7[/C][C]91.2717035003759[/C][C]1.42829649962411[/C][/ROW]
[ROW][C]122[/C][C]99.3[/C][C]94.1766094482954[/C][C]5.12339055170457[/C][/ROW]
[ROW][C]123[/C][C]113.5[/C][C]115.793829388089[/C][C]-2.29382938808857[/C][/ROW]
[ROW][C]124[/C][C]104.7[/C][C]106.639531892712[/C][C]-1.93953189271152[/C][/ROW]
[ROW][C]125[/C][C]100.5[/C][C]96.5235382966298[/C][C]3.97646170337018[/C][/ROW]
[ROW][C]126[/C][C]116.2[/C][C]116.293245754179[/C][C]-0.0932457541789287[/C][/ROW]
[ROW][C]127[/C][C]94.1[/C][C]98.3876065561945[/C][C]-4.28760655619449[/C][/ROW]
[ROW][C]128[/C][C]94.8[/C][C]94.9292602556331[/C][C]-0.129260255633142[/C][/ROW]
[ROW][C]129[/C][C]115.1[/C][C]110.686814556416[/C][C]4.41318544358361[/C][/ROW]
[ROW][C]130[/C][C]110[/C][C]112.37396212515[/C][C]-2.37396212515021[/C][/ROW]
[ROW][C]131[/C][C]108.4[/C][C]109.377747319639[/C][C]-0.977747319638823[/C][/ROW]
[ROW][C]132[/C][C]103.9[/C][C]104.510584516766[/C][C]-0.610584516766098[/C][/ROW]
[ROW][C]133[/C][C]102.9[/C][C]100.892187730683[/C][C]2.00781226931658[/C][/ROW]
[ROW][C]134[/C][C]107.7[/C][C]97.4524957387529[/C][C]10.2475042612471[/C][/ROW]
[ROW][C]135[/C][C]126.7[/C][C]119.207771039878[/C][C]7.49222896012221[/C][/ROW]
[ROW][C]136[/C][C]108.8[/C][C]103.681542829703[/C][C]5.11845717029713[/C][/ROW]
[ROW][C]137[/C][C]117.1[/C][C]112.76520954764[/C][C]4.33479045235977[/C][/ROW]
[ROW][C]138[/C][C]112.2[/C][C]107.258330825769[/C][C]4.94166917423136[/C][/ROW]
[ROW][C]139[/C][C]94.7[/C][C]93.3021172257943[/C][C]1.39788277420574[/C][/ROW]
[ROW][C]140[/C][C]102.7[/C][C]100.105317207875[/C][C]2.59468279212533[/C][/ROW]
[ROW][C]141[/C][C]119.1[/C][C]117.604084017638[/C][C]1.4959159823617[/C][/ROW]
[ROW][C]142[/C][C]110.6[/C][C]111.643005384809[/C][C]-1.04300538480861[/C][/ROW]
[ROW][C]143[/C][C]109.1[/C][C]111.491855221238[/C][C]-2.3918552212376[/C][/ROW]
[ROW][C]144[/C][C]105.3[/C][C]106.447097420159[/C][C]-1.14709742015912[/C][/ROW]
[ROW][C]145[/C][C]103.4[/C][C]102.56949705012[/C][C]0.830502949880484[/C][/ROW]
[ROW][C]146[/C][C]103.7[/C][C]103.85880649407[/C][C]-0.158806494069589[/C][/ROW]
[ROW][C]147[/C][C]117[/C][C]118.757852030126[/C][C]-1.7578520301258[/C][/ROW]
[ROW][C]148[/C][C]101.2[/C][C]101.45700106731[/C][C]-0.257001067309696[/C][/ROW]
[ROW][C]149[/C][C]105.4[/C][C]107.613628451095[/C][C]-2.2136284510949[/C][/ROW]
[ROW][C]150[/C][C]110.3[/C][C]113.282387920607[/C][C]-2.98238792060657[/C][/ROW]
[ROW][C]151[/C][C]97.7[/C][C]104.058854076785[/C][C]-6.35885407678518[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185123&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185123&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
175.579.1603427703599-3.6603427703599
283.285.2275008896291-2.02750088962913
394.594.8705138218139-0.370513821813915
483.382.82728760672120.472712393278777
592.797.0928247132654-4.39282471326541
689.891.1666613910513-1.36666139105128
774.876.4427323960493-1.64273239604927
881.580.92054605691220.579453943087781
992.892.12755039726360.672449602736385
1092.8100.93976329081-8.13976329080955
1191.796.5310440707621-4.83104407076208
1283.587.6663473222742-4.16634732227417
1392.889.44713974421073.35286025578926
1491.386.85374582754714.44625417245289
1599.5100.130626778441-0.63062677844138
1687.686.82830895583660.771691044163396
1795.396.5652647824467-1.26526478244671
1898.595.89368400574622.60631599425384
1980.189.8019726301508-9.70197263015076
2084.289.5216002107654-5.32160021076543
2192.497.8299400287517-5.42994002875168
2298109.391162234603-11.3911622346026
2392.2100.671654095135-8.4716540951346
248087.5507582982236-7.55075829822363
2588.790.8411805381877-2.14118053818766
2687.487.979032897855-0.57903289785498
2796.196.9066390885886-0.80663908858865
2894.194.4596213165973-0.359621316597349
2991.993.7878372823305-1.88783728233051
3093.694.7720179196775-1.17201791967752
3183.589.8563362660703-6.35633626607027
3280.884.4708091899377-3.67080918993771
3396.3100.995302628472-4.69530262847225
34101.5110.515242232543-9.01524223254349
3591.696.0091505692475-4.40915056924748
368493.1035721728371-9.10357217283711
3791.893.0651448412605-1.26514484126048
3890.490.7292470368424-0.329247036842349
399895.38983306861882.61016693138121
4095.596.7064948442832-1.20649484428316
4190.588.25086231048252.24913768951749
4297.194.57085391580222.52914608419778
4387.990.5535078925442-2.65350789254417
4479.879.60651480586790.193485194132115
45102101.7747493701840.225250629816409
46104.3109.859205719861-5.5592057198614
4792.192.7075627054294-0.607562705429391
4895.993.7209719573772.17902804262304
4989.187.94386796468961.15613203531035
5092.287.44398913989234.75601086010767
51107.5107.535173189705-0.0351731897054373
5299.797.23699674765382.46300325234616
5392.290.38351030080271.81648969919733
54108.9106.5252298056662.37477019433372
5589.889.28142539561110.518574604388948
5689.485.67639014682183.72360985317824
57107.6107.5357467064760.0642532935242961
58105.6106.742794440548-1.14279444054838
59100.9101.919547389113-1.01954738911329
60102.9100.4123450902312.48765490976873
6196.289.64785357710756.55214642289255
6294.786.89252933725547.80747066274456
63107.3102.1353085023015.16469149769874
6410397.38260452887285.61739547112718
6596.193.87037595697562.22962404302445
66109.8106.077463968733.72253603127002
6785.484.40815682319450.991843176805537
6889.990.2886797687453-0.388679768745294
69109.3108.6082999206160.691700079383662
70101.2102.528573993214-1.32857399321446
71104.7107.476386149723-2.77638614972324
72102.4101.4276672023050.972332797695014
7397.793.64287921626984.05712078373022
7498.989.12536910675429.77463089324576
75115106.8370572948088.1629427051915
7697.593.05486101570564.44513898429439
77107.3103.4430025027873.85699749721336
78112.3108.0902291894074.20977081059292
7988.592.9365880259957-4.43658802599575
8092.993.2466836151492-0.346683615149146
81108.8108.833457905764-0.0334579057635461
82112.3115.017407064707-2.71740706470669
83107.3111.205198706549-3.90519870654879
84101.897.29614565928014.50385434071992
85105102.0366720936142.96332790638568
86103.495.4499758357767.95002416422398
87116.7112.4806860356784.21931396432188
88103.699.88951610854213.71048389145786
89108.8103.5361721118315.26382788816869
90117110.8446846429946.15531535700583
91100.998.60676185891572.2932381410843
92100.896.78063424083064.01936575916938
93109.7105.4374418428594.26255815714066
94121121.883153307792-0.883153307792118
95114.1113.4076745220340.69232547796567
96105.597.20530860833088.29469139166924
97112.5109.862578226682.63742177332015
98113.8107.8976140112825.90238598871777
99115.3109.8886608913745.411339108626
100120.4115.5005942926844.89940570731624
101111.1105.6658047487455.43419525125481
102120.1109.94814802170710.151851978293
103106.1106.904831934808-0.804831934807909
10495.991.88610399128024.01389600871984
105119.4115.8750795184273.52492048157339
106117.4120.187203348231-2.78720334823122
10798.6101.98207236912-3.38207236912007
10899.7104.408436563741-4.70843656374082
10987.493.4046933120203-6.00469331202025
11090.891.843057161336-1.043057161336
111101.3105.18461842192-3.88461842192034
11293.2101.337078104664-8.13707810466365
11395.193.48739421192061.61260578807934
114101.9107.572372703283-5.67237270328321
1158799.6545456192282-12.6545456192282
11686.289.1366206020606-2.93662060206061
117105110.590402720088-5.59040272008849
118104.1111.839595993622-7.73959599362225
11999.2103.100480003773-3.90048000377314
12095.2103.569398889581-8.36939888958069
12192.791.27170350037591.42829649962411
12299.394.17660944829545.12339055170457
123113.5115.793829388089-2.29382938808857
124104.7106.639531892712-1.93953189271152
125100.596.52353829662983.97646170337018
126116.2116.293245754179-0.0932457541789287
12794.198.3876065561945-4.28760655619449
12894.894.9292602556331-0.129260255633142
129115.1110.6868145564164.41318544358361
130110112.37396212515-2.37396212515021
131108.4109.377747319639-0.977747319638823
132103.9104.510584516766-0.610584516766098
133102.9100.8921877306832.00781226931658
134107.797.452495738752910.2475042612471
135126.7119.2077710398787.49222896012221
136108.8103.6815428297035.11845717029713
137117.1112.765209547644.33479045235977
138112.2107.2583308257694.94166917423136
13994.793.30211722579431.39788277420574
140102.7100.1053172078752.59468279212533
141119.1117.6040840176381.4959159823617
142110.6111.643005384809-1.04300538480861
143109.1111.491855221238-2.3918552212376
144105.3106.447097420159-1.14709742015912
145103.4102.569497050120.830502949880484
146103.7103.85880649407-0.158806494069589
147117118.757852030126-1.7578520301258
148101.2101.45700106731-0.257001067309696
149105.4107.613628451095-2.2136284510949
150110.3113.282387920607-2.98238792060657
15197.7104.058854076785-6.35885407678518






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.1863452992935650.372690598587130.813654700706435
110.08459674956295530.1691934991259110.915403250437045
120.03530376973362320.07060753946724640.964696230266377
130.04824401064725330.09648802129450660.951755989352747
140.08229502921633610.1645900584326720.917704970783664
150.04504096219906820.09008192439813640.954959037800932
160.02899389270240110.05798778540480220.971006107297599
170.02152775503575350.0430555100715070.978472244964246
180.02095861530065340.04191723060130680.979041384699347
190.3046068038693660.6092136077387330.695393196130634
200.2674353048420570.5348706096841130.732564695157943
210.2368527986232380.4737055972464770.763147201376762
220.3055651673322730.6111303346645460.694434832667727
230.3144020596399880.6288041192799760.685597940360012
240.2951967482284170.5903934964568350.704803251771583
250.238865625229770.477731250459540.76113437477023
260.1879676620635090.3759353241270170.812032337936491
270.1746163940353820.3492327880707630.825383605964618
280.1633418431551960.3266836863103920.836658156844804
290.1433081457405240.2866162914810490.856691854259476
300.1340676206441180.2681352412882370.865932379355882
310.1400782176685860.2801564353371720.859921782331414
320.1257162103080050.251432420616010.874283789691995
330.1176967494475230.2353934988950470.882303250552477
340.1377620660007880.2755241320015770.862237933999212
350.1390280481324370.2780560962648740.860971951867563
360.1978779881577640.3957559763155280.802122011842236
370.1799667076864360.3599334153728710.820033292313564
380.1590285731748320.3180571463496640.840971426825168
390.2515727459923490.5031454919846990.748427254007651
400.2379797066127150.475959413225430.762020293387285
410.2709973829048180.5419947658096350.729002617095182
420.3126678969528070.6253357939056140.687332103047193
430.2727272891912320.5454545783824640.727272710808768
440.2425593508789620.4851187017579240.757440649121038
450.255387042460540.510774084921080.74461295753946
460.2658128502240050.5316257004480090.734187149775995
470.2456257045944940.4912514091889880.754374295405506
480.3277565548344580.6555131096689160.672243445165542
490.2859433491553060.5718866983106120.714056650844694
500.271393081949210.5427861638984210.72860691805079
510.23866881912360.47733763824720.7613311808764
520.2046877742328820.4093755484657630.795312225767118
530.1915175591022430.3830351182044870.808482440897757
540.1983435155321890.3966870310643770.801656484467811
550.1741228375155850.348245675031170.825877162484415
560.157539265501890.3150785310037790.84246073449811
570.1351491658398750.2702983316797490.864850834160125
580.1386863067481340.2773726134962680.861313693251866
590.1306429934539390.2612859869078780.869357006546061
600.1724818526241330.3449637052482660.827518147375867
610.2408226412671120.4816452825342230.759177358732888
620.3352224319501270.6704448639002540.664777568049873
630.3643935995052380.7287871990104770.635606400494762
640.4534967409742390.9069934819484790.546503259025761
650.4362880353936250.8725760707872510.563711964606375
660.4721722217700160.9443444435400320.527827778229984
670.4547061160284530.9094122320569060.545293883971547
680.427561321622140.8551226432442810.57243867837786
690.4182102524785330.8364205049570660.581789747521467
700.4140902110877820.8281804221755640.585909788912218
710.3975016858107580.7950033716215170.602498314189241
720.3882533955613540.7765067911227070.611746604438646
730.3792056294498870.7584112588997740.620794370550113
740.5109969644734180.9780060710531640.489003035526582
750.6698071926344240.6603856147311520.330192807365576
760.6530185280470410.6939629439059190.346981471952959
770.6381048441465550.723790311706890.361895155853445
780.6333791930464140.7332416139071710.366620806953586
790.7161836687645710.5676326624708580.283816331235429
800.675517659218490.6489646815630190.32448234078151
810.6686675155485950.6626649689028090.331332484451405
820.6543350904346240.6913298191307510.345664909565376
830.6658446380305140.6683107239389730.334155361969486
840.6495649749278650.700870050144270.350435025072135
850.6133390854041360.7733218291917270.386660914595864
860.6479476057333140.7041047885333730.352052394266686
870.6397631518339160.7204736963321680.360236848166084
880.6215709128508960.7568581742982070.378429087149104
890.6050561338447830.7898877323104330.394943866155217
900.6128507349744180.7742985300511640.387149265025582
910.5670685993961410.8658628012077180.432931400603859
920.5368462424398260.9263075151203470.463153757560174
930.5016914162882830.9966171674234350.498308583711717
940.4572342084724140.9144684169448270.542765791527586
950.40787493715520.8157498743104010.5921250628448
960.515679269763850.9686414604722990.48432073023615
970.4837186637175790.9674373274351590.516281336282421
980.463473565305740.926947130611480.53652643469426
990.4578870057565770.9157740115131550.542112994243423
1000.4453471451167750.8906942902335510.554652854883225
1010.4341717087313960.8683434174627910.565828291268604
1020.5046022489561930.9907955020876140.495397751043807
1030.4721866119260530.9443732238521070.527813388073947
1040.4717629092911430.9435258185822850.528237090708857
1050.4349105139596570.8698210279193140.565089486040343
1060.4217964202854580.8435928405709150.578203579714542
1070.4206785309508650.841357061901730.579321469049135
1080.4554573341858510.9109146683717010.544542665814149
1090.5839800005157180.8320399989685640.416019999484282
1100.5571706756201890.8856586487596220.442829324379811
1110.5624448506691760.8751102986616480.437555149330824
1120.6273113989079460.7453772021841080.372688601092054
1130.590363150625380.819273698749240.40963684937462
1140.6372037530014530.7255924939970940.362796246998547
1150.8862897415334990.2274205169330020.113710258466501
1160.8727858785876680.2544282428246640.127214121412332
1170.905671518065380.188656963869240.0943284819346199
1180.9505435397748940.09891292045021140.0494564602251057
1190.9392891859925480.1214216280149040.0607108140074518
1200.9763853517210.04722929655800040.0236146482790002
1210.96781323835890.06437352328219960.0321867616410998
1220.9560777450466070.08784450990678610.0439222549533931
1230.9573232611234270.08535347775314650.0426767388765732
1240.9488403329727320.1023193340545350.0511596670272677
1250.944972030806530.1100559383869390.0550279691934697
1260.9357095719526150.1285808560947690.0642904280473847
1270.9718125846201620.05637483075967610.0281874153798381
1280.9661629342986350.06767413140273080.0338370657013654
1290.9523618192371880.09527636152562310.0476381807628116
1300.9642265583840120.07154688323197540.0357734416159877
1310.9429541760462930.1140916479074140.0570458239537068
1320.9113671695302070.1772656609395850.0886328304697925
1330.8674698007461660.2650603985076680.132530199253834
1340.9102529888780960.1794940222438080.0897470111219041
1350.9260859963711610.1478280072576780.0739140036288392
1360.9691482339503710.06170353209925780.0308517660496289
1370.9597536898841880.08049262023162380.0402463101158119
1380.9681548498648660.06369030027026840.0318451501351342
1390.9709480902958650.05810381940826960.0290519097041348
1400.9542190948075610.09156181038487790.0457809051924389
1410.9527759107109330.09444817857813290.0472240892890665

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.186345299293565 & 0.37269059858713 & 0.813654700706435 \tabularnewline
11 & 0.0845967495629553 & 0.169193499125911 & 0.915403250437045 \tabularnewline
12 & 0.0353037697336232 & 0.0706075394672464 & 0.964696230266377 \tabularnewline
13 & 0.0482440106472533 & 0.0964880212945066 & 0.951755989352747 \tabularnewline
14 & 0.0822950292163361 & 0.164590058432672 & 0.917704970783664 \tabularnewline
15 & 0.0450409621990682 & 0.0900819243981364 & 0.954959037800932 \tabularnewline
16 & 0.0289938927024011 & 0.0579877854048022 & 0.971006107297599 \tabularnewline
17 & 0.0215277550357535 & 0.043055510071507 & 0.978472244964246 \tabularnewline
18 & 0.0209586153006534 & 0.0419172306013068 & 0.979041384699347 \tabularnewline
19 & 0.304606803869366 & 0.609213607738733 & 0.695393196130634 \tabularnewline
20 & 0.267435304842057 & 0.534870609684113 & 0.732564695157943 \tabularnewline
21 & 0.236852798623238 & 0.473705597246477 & 0.763147201376762 \tabularnewline
22 & 0.305565167332273 & 0.611130334664546 & 0.694434832667727 \tabularnewline
23 & 0.314402059639988 & 0.628804119279976 & 0.685597940360012 \tabularnewline
24 & 0.295196748228417 & 0.590393496456835 & 0.704803251771583 \tabularnewline
25 & 0.23886562522977 & 0.47773125045954 & 0.76113437477023 \tabularnewline
26 & 0.187967662063509 & 0.375935324127017 & 0.812032337936491 \tabularnewline
27 & 0.174616394035382 & 0.349232788070763 & 0.825383605964618 \tabularnewline
28 & 0.163341843155196 & 0.326683686310392 & 0.836658156844804 \tabularnewline
29 & 0.143308145740524 & 0.286616291481049 & 0.856691854259476 \tabularnewline
30 & 0.134067620644118 & 0.268135241288237 & 0.865932379355882 \tabularnewline
31 & 0.140078217668586 & 0.280156435337172 & 0.859921782331414 \tabularnewline
32 & 0.125716210308005 & 0.25143242061601 & 0.874283789691995 \tabularnewline
33 & 0.117696749447523 & 0.235393498895047 & 0.882303250552477 \tabularnewline
34 & 0.137762066000788 & 0.275524132001577 & 0.862237933999212 \tabularnewline
35 & 0.139028048132437 & 0.278056096264874 & 0.860971951867563 \tabularnewline
36 & 0.197877988157764 & 0.395755976315528 & 0.802122011842236 \tabularnewline
37 & 0.179966707686436 & 0.359933415372871 & 0.820033292313564 \tabularnewline
38 & 0.159028573174832 & 0.318057146349664 & 0.840971426825168 \tabularnewline
39 & 0.251572745992349 & 0.503145491984699 & 0.748427254007651 \tabularnewline
40 & 0.237979706612715 & 0.47595941322543 & 0.762020293387285 \tabularnewline
41 & 0.270997382904818 & 0.541994765809635 & 0.729002617095182 \tabularnewline
42 & 0.312667896952807 & 0.625335793905614 & 0.687332103047193 \tabularnewline
43 & 0.272727289191232 & 0.545454578382464 & 0.727272710808768 \tabularnewline
44 & 0.242559350878962 & 0.485118701757924 & 0.757440649121038 \tabularnewline
45 & 0.25538704246054 & 0.51077408492108 & 0.74461295753946 \tabularnewline
46 & 0.265812850224005 & 0.531625700448009 & 0.734187149775995 \tabularnewline
47 & 0.245625704594494 & 0.491251409188988 & 0.754374295405506 \tabularnewline
48 & 0.327756554834458 & 0.655513109668916 & 0.672243445165542 \tabularnewline
49 & 0.285943349155306 & 0.571886698310612 & 0.714056650844694 \tabularnewline
50 & 0.27139308194921 & 0.542786163898421 & 0.72860691805079 \tabularnewline
51 & 0.2386688191236 & 0.4773376382472 & 0.7613311808764 \tabularnewline
52 & 0.204687774232882 & 0.409375548465763 & 0.795312225767118 \tabularnewline
53 & 0.191517559102243 & 0.383035118204487 & 0.808482440897757 \tabularnewline
54 & 0.198343515532189 & 0.396687031064377 & 0.801656484467811 \tabularnewline
55 & 0.174122837515585 & 0.34824567503117 & 0.825877162484415 \tabularnewline
56 & 0.15753926550189 & 0.315078531003779 & 0.84246073449811 \tabularnewline
57 & 0.135149165839875 & 0.270298331679749 & 0.864850834160125 \tabularnewline
58 & 0.138686306748134 & 0.277372613496268 & 0.861313693251866 \tabularnewline
59 & 0.130642993453939 & 0.261285986907878 & 0.869357006546061 \tabularnewline
60 & 0.172481852624133 & 0.344963705248266 & 0.827518147375867 \tabularnewline
61 & 0.240822641267112 & 0.481645282534223 & 0.759177358732888 \tabularnewline
62 & 0.335222431950127 & 0.670444863900254 & 0.664777568049873 \tabularnewline
63 & 0.364393599505238 & 0.728787199010477 & 0.635606400494762 \tabularnewline
64 & 0.453496740974239 & 0.906993481948479 & 0.546503259025761 \tabularnewline
65 & 0.436288035393625 & 0.872576070787251 & 0.563711964606375 \tabularnewline
66 & 0.472172221770016 & 0.944344443540032 & 0.527827778229984 \tabularnewline
67 & 0.454706116028453 & 0.909412232056906 & 0.545293883971547 \tabularnewline
68 & 0.42756132162214 & 0.855122643244281 & 0.57243867837786 \tabularnewline
69 & 0.418210252478533 & 0.836420504957066 & 0.581789747521467 \tabularnewline
70 & 0.414090211087782 & 0.828180422175564 & 0.585909788912218 \tabularnewline
71 & 0.397501685810758 & 0.795003371621517 & 0.602498314189241 \tabularnewline
72 & 0.388253395561354 & 0.776506791122707 & 0.611746604438646 \tabularnewline
73 & 0.379205629449887 & 0.758411258899774 & 0.620794370550113 \tabularnewline
74 & 0.510996964473418 & 0.978006071053164 & 0.489003035526582 \tabularnewline
75 & 0.669807192634424 & 0.660385614731152 & 0.330192807365576 \tabularnewline
76 & 0.653018528047041 & 0.693962943905919 & 0.346981471952959 \tabularnewline
77 & 0.638104844146555 & 0.72379031170689 & 0.361895155853445 \tabularnewline
78 & 0.633379193046414 & 0.733241613907171 & 0.366620806953586 \tabularnewline
79 & 0.716183668764571 & 0.567632662470858 & 0.283816331235429 \tabularnewline
80 & 0.67551765921849 & 0.648964681563019 & 0.32448234078151 \tabularnewline
81 & 0.668667515548595 & 0.662664968902809 & 0.331332484451405 \tabularnewline
82 & 0.654335090434624 & 0.691329819130751 & 0.345664909565376 \tabularnewline
83 & 0.665844638030514 & 0.668310723938973 & 0.334155361969486 \tabularnewline
84 & 0.649564974927865 & 0.70087005014427 & 0.350435025072135 \tabularnewline
85 & 0.613339085404136 & 0.773321829191727 & 0.386660914595864 \tabularnewline
86 & 0.647947605733314 & 0.704104788533373 & 0.352052394266686 \tabularnewline
87 & 0.639763151833916 & 0.720473696332168 & 0.360236848166084 \tabularnewline
88 & 0.621570912850896 & 0.756858174298207 & 0.378429087149104 \tabularnewline
89 & 0.605056133844783 & 0.789887732310433 & 0.394943866155217 \tabularnewline
90 & 0.612850734974418 & 0.774298530051164 & 0.387149265025582 \tabularnewline
91 & 0.567068599396141 & 0.865862801207718 & 0.432931400603859 \tabularnewline
92 & 0.536846242439826 & 0.926307515120347 & 0.463153757560174 \tabularnewline
93 & 0.501691416288283 & 0.996617167423435 & 0.498308583711717 \tabularnewline
94 & 0.457234208472414 & 0.914468416944827 & 0.542765791527586 \tabularnewline
95 & 0.4078749371552 & 0.815749874310401 & 0.5921250628448 \tabularnewline
96 & 0.51567926976385 & 0.968641460472299 & 0.48432073023615 \tabularnewline
97 & 0.483718663717579 & 0.967437327435159 & 0.516281336282421 \tabularnewline
98 & 0.46347356530574 & 0.92694713061148 & 0.53652643469426 \tabularnewline
99 & 0.457887005756577 & 0.915774011513155 & 0.542112994243423 \tabularnewline
100 & 0.445347145116775 & 0.890694290233551 & 0.554652854883225 \tabularnewline
101 & 0.434171708731396 & 0.868343417462791 & 0.565828291268604 \tabularnewline
102 & 0.504602248956193 & 0.990795502087614 & 0.495397751043807 \tabularnewline
103 & 0.472186611926053 & 0.944373223852107 & 0.527813388073947 \tabularnewline
104 & 0.471762909291143 & 0.943525818582285 & 0.528237090708857 \tabularnewline
105 & 0.434910513959657 & 0.869821027919314 & 0.565089486040343 \tabularnewline
106 & 0.421796420285458 & 0.843592840570915 & 0.578203579714542 \tabularnewline
107 & 0.420678530950865 & 0.84135706190173 & 0.579321469049135 \tabularnewline
108 & 0.455457334185851 & 0.910914668371701 & 0.544542665814149 \tabularnewline
109 & 0.583980000515718 & 0.832039998968564 & 0.416019999484282 \tabularnewline
110 & 0.557170675620189 & 0.885658648759622 & 0.442829324379811 \tabularnewline
111 & 0.562444850669176 & 0.875110298661648 & 0.437555149330824 \tabularnewline
112 & 0.627311398907946 & 0.745377202184108 & 0.372688601092054 \tabularnewline
113 & 0.59036315062538 & 0.81927369874924 & 0.40963684937462 \tabularnewline
114 & 0.637203753001453 & 0.725592493997094 & 0.362796246998547 \tabularnewline
115 & 0.886289741533499 & 0.227420516933002 & 0.113710258466501 \tabularnewline
116 & 0.872785878587668 & 0.254428242824664 & 0.127214121412332 \tabularnewline
117 & 0.90567151806538 & 0.18865696386924 & 0.0943284819346199 \tabularnewline
118 & 0.950543539774894 & 0.0989129204502114 & 0.0494564602251057 \tabularnewline
119 & 0.939289185992548 & 0.121421628014904 & 0.0607108140074518 \tabularnewline
120 & 0.976385351721 & 0.0472292965580004 & 0.0236146482790002 \tabularnewline
121 & 0.9678132383589 & 0.0643735232821996 & 0.0321867616410998 \tabularnewline
122 & 0.956077745046607 & 0.0878445099067861 & 0.0439222549533931 \tabularnewline
123 & 0.957323261123427 & 0.0853534777531465 & 0.0426767388765732 \tabularnewline
124 & 0.948840332972732 & 0.102319334054535 & 0.0511596670272677 \tabularnewline
125 & 0.94497203080653 & 0.110055938386939 & 0.0550279691934697 \tabularnewline
126 & 0.935709571952615 & 0.128580856094769 & 0.0642904280473847 \tabularnewline
127 & 0.971812584620162 & 0.0563748307596761 & 0.0281874153798381 \tabularnewline
128 & 0.966162934298635 & 0.0676741314027308 & 0.0338370657013654 \tabularnewline
129 & 0.952361819237188 & 0.0952763615256231 & 0.0476381807628116 \tabularnewline
130 & 0.964226558384012 & 0.0715468832319754 & 0.0357734416159877 \tabularnewline
131 & 0.942954176046293 & 0.114091647907414 & 0.0570458239537068 \tabularnewline
132 & 0.911367169530207 & 0.177265660939585 & 0.0886328304697925 \tabularnewline
133 & 0.867469800746166 & 0.265060398507668 & 0.132530199253834 \tabularnewline
134 & 0.910252988878096 & 0.179494022243808 & 0.0897470111219041 \tabularnewline
135 & 0.926085996371161 & 0.147828007257678 & 0.0739140036288392 \tabularnewline
136 & 0.969148233950371 & 0.0617035320992578 & 0.0308517660496289 \tabularnewline
137 & 0.959753689884188 & 0.0804926202316238 & 0.0402463101158119 \tabularnewline
138 & 0.968154849864866 & 0.0636903002702684 & 0.0318451501351342 \tabularnewline
139 & 0.970948090295865 & 0.0581038194082696 & 0.0290519097041348 \tabularnewline
140 & 0.954219094807561 & 0.0915618103848779 & 0.0457809051924389 \tabularnewline
141 & 0.952775910710933 & 0.0944481785781329 & 0.0472240892890665 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185123&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]10[/C][C]0.186345299293565[/C][C]0.37269059858713[/C][C]0.813654700706435[/C][/ROW]
[ROW][C]11[/C][C]0.0845967495629553[/C][C]0.169193499125911[/C][C]0.915403250437045[/C][/ROW]
[ROW][C]12[/C][C]0.0353037697336232[/C][C]0.0706075394672464[/C][C]0.964696230266377[/C][/ROW]
[ROW][C]13[/C][C]0.0482440106472533[/C][C]0.0964880212945066[/C][C]0.951755989352747[/C][/ROW]
[ROW][C]14[/C][C]0.0822950292163361[/C][C]0.164590058432672[/C][C]0.917704970783664[/C][/ROW]
[ROW][C]15[/C][C]0.0450409621990682[/C][C]0.0900819243981364[/C][C]0.954959037800932[/C][/ROW]
[ROW][C]16[/C][C]0.0289938927024011[/C][C]0.0579877854048022[/C][C]0.971006107297599[/C][/ROW]
[ROW][C]17[/C][C]0.0215277550357535[/C][C]0.043055510071507[/C][C]0.978472244964246[/C][/ROW]
[ROW][C]18[/C][C]0.0209586153006534[/C][C]0.0419172306013068[/C][C]0.979041384699347[/C][/ROW]
[ROW][C]19[/C][C]0.304606803869366[/C][C]0.609213607738733[/C][C]0.695393196130634[/C][/ROW]
[ROW][C]20[/C][C]0.267435304842057[/C][C]0.534870609684113[/C][C]0.732564695157943[/C][/ROW]
[ROW][C]21[/C][C]0.236852798623238[/C][C]0.473705597246477[/C][C]0.763147201376762[/C][/ROW]
[ROW][C]22[/C][C]0.305565167332273[/C][C]0.611130334664546[/C][C]0.694434832667727[/C][/ROW]
[ROW][C]23[/C][C]0.314402059639988[/C][C]0.628804119279976[/C][C]0.685597940360012[/C][/ROW]
[ROW][C]24[/C][C]0.295196748228417[/C][C]0.590393496456835[/C][C]0.704803251771583[/C][/ROW]
[ROW][C]25[/C][C]0.23886562522977[/C][C]0.47773125045954[/C][C]0.76113437477023[/C][/ROW]
[ROW][C]26[/C][C]0.187967662063509[/C][C]0.375935324127017[/C][C]0.812032337936491[/C][/ROW]
[ROW][C]27[/C][C]0.174616394035382[/C][C]0.349232788070763[/C][C]0.825383605964618[/C][/ROW]
[ROW][C]28[/C][C]0.163341843155196[/C][C]0.326683686310392[/C][C]0.836658156844804[/C][/ROW]
[ROW][C]29[/C][C]0.143308145740524[/C][C]0.286616291481049[/C][C]0.856691854259476[/C][/ROW]
[ROW][C]30[/C][C]0.134067620644118[/C][C]0.268135241288237[/C][C]0.865932379355882[/C][/ROW]
[ROW][C]31[/C][C]0.140078217668586[/C][C]0.280156435337172[/C][C]0.859921782331414[/C][/ROW]
[ROW][C]32[/C][C]0.125716210308005[/C][C]0.25143242061601[/C][C]0.874283789691995[/C][/ROW]
[ROW][C]33[/C][C]0.117696749447523[/C][C]0.235393498895047[/C][C]0.882303250552477[/C][/ROW]
[ROW][C]34[/C][C]0.137762066000788[/C][C]0.275524132001577[/C][C]0.862237933999212[/C][/ROW]
[ROW][C]35[/C][C]0.139028048132437[/C][C]0.278056096264874[/C][C]0.860971951867563[/C][/ROW]
[ROW][C]36[/C][C]0.197877988157764[/C][C]0.395755976315528[/C][C]0.802122011842236[/C][/ROW]
[ROW][C]37[/C][C]0.179966707686436[/C][C]0.359933415372871[/C][C]0.820033292313564[/C][/ROW]
[ROW][C]38[/C][C]0.159028573174832[/C][C]0.318057146349664[/C][C]0.840971426825168[/C][/ROW]
[ROW][C]39[/C][C]0.251572745992349[/C][C]0.503145491984699[/C][C]0.748427254007651[/C][/ROW]
[ROW][C]40[/C][C]0.237979706612715[/C][C]0.47595941322543[/C][C]0.762020293387285[/C][/ROW]
[ROW][C]41[/C][C]0.270997382904818[/C][C]0.541994765809635[/C][C]0.729002617095182[/C][/ROW]
[ROW][C]42[/C][C]0.312667896952807[/C][C]0.625335793905614[/C][C]0.687332103047193[/C][/ROW]
[ROW][C]43[/C][C]0.272727289191232[/C][C]0.545454578382464[/C][C]0.727272710808768[/C][/ROW]
[ROW][C]44[/C][C]0.242559350878962[/C][C]0.485118701757924[/C][C]0.757440649121038[/C][/ROW]
[ROW][C]45[/C][C]0.25538704246054[/C][C]0.51077408492108[/C][C]0.74461295753946[/C][/ROW]
[ROW][C]46[/C][C]0.265812850224005[/C][C]0.531625700448009[/C][C]0.734187149775995[/C][/ROW]
[ROW][C]47[/C][C]0.245625704594494[/C][C]0.491251409188988[/C][C]0.754374295405506[/C][/ROW]
[ROW][C]48[/C][C]0.327756554834458[/C][C]0.655513109668916[/C][C]0.672243445165542[/C][/ROW]
[ROW][C]49[/C][C]0.285943349155306[/C][C]0.571886698310612[/C][C]0.714056650844694[/C][/ROW]
[ROW][C]50[/C][C]0.27139308194921[/C][C]0.542786163898421[/C][C]0.72860691805079[/C][/ROW]
[ROW][C]51[/C][C]0.2386688191236[/C][C]0.4773376382472[/C][C]0.7613311808764[/C][/ROW]
[ROW][C]52[/C][C]0.204687774232882[/C][C]0.409375548465763[/C][C]0.795312225767118[/C][/ROW]
[ROW][C]53[/C][C]0.191517559102243[/C][C]0.383035118204487[/C][C]0.808482440897757[/C][/ROW]
[ROW][C]54[/C][C]0.198343515532189[/C][C]0.396687031064377[/C][C]0.801656484467811[/C][/ROW]
[ROW][C]55[/C][C]0.174122837515585[/C][C]0.34824567503117[/C][C]0.825877162484415[/C][/ROW]
[ROW][C]56[/C][C]0.15753926550189[/C][C]0.315078531003779[/C][C]0.84246073449811[/C][/ROW]
[ROW][C]57[/C][C]0.135149165839875[/C][C]0.270298331679749[/C][C]0.864850834160125[/C][/ROW]
[ROW][C]58[/C][C]0.138686306748134[/C][C]0.277372613496268[/C][C]0.861313693251866[/C][/ROW]
[ROW][C]59[/C][C]0.130642993453939[/C][C]0.261285986907878[/C][C]0.869357006546061[/C][/ROW]
[ROW][C]60[/C][C]0.172481852624133[/C][C]0.344963705248266[/C][C]0.827518147375867[/C][/ROW]
[ROW][C]61[/C][C]0.240822641267112[/C][C]0.481645282534223[/C][C]0.759177358732888[/C][/ROW]
[ROW][C]62[/C][C]0.335222431950127[/C][C]0.670444863900254[/C][C]0.664777568049873[/C][/ROW]
[ROW][C]63[/C][C]0.364393599505238[/C][C]0.728787199010477[/C][C]0.635606400494762[/C][/ROW]
[ROW][C]64[/C][C]0.453496740974239[/C][C]0.906993481948479[/C][C]0.546503259025761[/C][/ROW]
[ROW][C]65[/C][C]0.436288035393625[/C][C]0.872576070787251[/C][C]0.563711964606375[/C][/ROW]
[ROW][C]66[/C][C]0.472172221770016[/C][C]0.944344443540032[/C][C]0.527827778229984[/C][/ROW]
[ROW][C]67[/C][C]0.454706116028453[/C][C]0.909412232056906[/C][C]0.545293883971547[/C][/ROW]
[ROW][C]68[/C][C]0.42756132162214[/C][C]0.855122643244281[/C][C]0.57243867837786[/C][/ROW]
[ROW][C]69[/C][C]0.418210252478533[/C][C]0.836420504957066[/C][C]0.581789747521467[/C][/ROW]
[ROW][C]70[/C][C]0.414090211087782[/C][C]0.828180422175564[/C][C]0.585909788912218[/C][/ROW]
[ROW][C]71[/C][C]0.397501685810758[/C][C]0.795003371621517[/C][C]0.602498314189241[/C][/ROW]
[ROW][C]72[/C][C]0.388253395561354[/C][C]0.776506791122707[/C][C]0.611746604438646[/C][/ROW]
[ROW][C]73[/C][C]0.379205629449887[/C][C]0.758411258899774[/C][C]0.620794370550113[/C][/ROW]
[ROW][C]74[/C][C]0.510996964473418[/C][C]0.978006071053164[/C][C]0.489003035526582[/C][/ROW]
[ROW][C]75[/C][C]0.669807192634424[/C][C]0.660385614731152[/C][C]0.330192807365576[/C][/ROW]
[ROW][C]76[/C][C]0.653018528047041[/C][C]0.693962943905919[/C][C]0.346981471952959[/C][/ROW]
[ROW][C]77[/C][C]0.638104844146555[/C][C]0.72379031170689[/C][C]0.361895155853445[/C][/ROW]
[ROW][C]78[/C][C]0.633379193046414[/C][C]0.733241613907171[/C][C]0.366620806953586[/C][/ROW]
[ROW][C]79[/C][C]0.716183668764571[/C][C]0.567632662470858[/C][C]0.283816331235429[/C][/ROW]
[ROW][C]80[/C][C]0.67551765921849[/C][C]0.648964681563019[/C][C]0.32448234078151[/C][/ROW]
[ROW][C]81[/C][C]0.668667515548595[/C][C]0.662664968902809[/C][C]0.331332484451405[/C][/ROW]
[ROW][C]82[/C][C]0.654335090434624[/C][C]0.691329819130751[/C][C]0.345664909565376[/C][/ROW]
[ROW][C]83[/C][C]0.665844638030514[/C][C]0.668310723938973[/C][C]0.334155361969486[/C][/ROW]
[ROW][C]84[/C][C]0.649564974927865[/C][C]0.70087005014427[/C][C]0.350435025072135[/C][/ROW]
[ROW][C]85[/C][C]0.613339085404136[/C][C]0.773321829191727[/C][C]0.386660914595864[/C][/ROW]
[ROW][C]86[/C][C]0.647947605733314[/C][C]0.704104788533373[/C][C]0.352052394266686[/C][/ROW]
[ROW][C]87[/C][C]0.639763151833916[/C][C]0.720473696332168[/C][C]0.360236848166084[/C][/ROW]
[ROW][C]88[/C][C]0.621570912850896[/C][C]0.756858174298207[/C][C]0.378429087149104[/C][/ROW]
[ROW][C]89[/C][C]0.605056133844783[/C][C]0.789887732310433[/C][C]0.394943866155217[/C][/ROW]
[ROW][C]90[/C][C]0.612850734974418[/C][C]0.774298530051164[/C][C]0.387149265025582[/C][/ROW]
[ROW][C]91[/C][C]0.567068599396141[/C][C]0.865862801207718[/C][C]0.432931400603859[/C][/ROW]
[ROW][C]92[/C][C]0.536846242439826[/C][C]0.926307515120347[/C][C]0.463153757560174[/C][/ROW]
[ROW][C]93[/C][C]0.501691416288283[/C][C]0.996617167423435[/C][C]0.498308583711717[/C][/ROW]
[ROW][C]94[/C][C]0.457234208472414[/C][C]0.914468416944827[/C][C]0.542765791527586[/C][/ROW]
[ROW][C]95[/C][C]0.4078749371552[/C][C]0.815749874310401[/C][C]0.5921250628448[/C][/ROW]
[ROW][C]96[/C][C]0.51567926976385[/C][C]0.968641460472299[/C][C]0.48432073023615[/C][/ROW]
[ROW][C]97[/C][C]0.483718663717579[/C][C]0.967437327435159[/C][C]0.516281336282421[/C][/ROW]
[ROW][C]98[/C][C]0.46347356530574[/C][C]0.92694713061148[/C][C]0.53652643469426[/C][/ROW]
[ROW][C]99[/C][C]0.457887005756577[/C][C]0.915774011513155[/C][C]0.542112994243423[/C][/ROW]
[ROW][C]100[/C][C]0.445347145116775[/C][C]0.890694290233551[/C][C]0.554652854883225[/C][/ROW]
[ROW][C]101[/C][C]0.434171708731396[/C][C]0.868343417462791[/C][C]0.565828291268604[/C][/ROW]
[ROW][C]102[/C][C]0.504602248956193[/C][C]0.990795502087614[/C][C]0.495397751043807[/C][/ROW]
[ROW][C]103[/C][C]0.472186611926053[/C][C]0.944373223852107[/C][C]0.527813388073947[/C][/ROW]
[ROW][C]104[/C][C]0.471762909291143[/C][C]0.943525818582285[/C][C]0.528237090708857[/C][/ROW]
[ROW][C]105[/C][C]0.434910513959657[/C][C]0.869821027919314[/C][C]0.565089486040343[/C][/ROW]
[ROW][C]106[/C][C]0.421796420285458[/C][C]0.843592840570915[/C][C]0.578203579714542[/C][/ROW]
[ROW][C]107[/C][C]0.420678530950865[/C][C]0.84135706190173[/C][C]0.579321469049135[/C][/ROW]
[ROW][C]108[/C][C]0.455457334185851[/C][C]0.910914668371701[/C][C]0.544542665814149[/C][/ROW]
[ROW][C]109[/C][C]0.583980000515718[/C][C]0.832039998968564[/C][C]0.416019999484282[/C][/ROW]
[ROW][C]110[/C][C]0.557170675620189[/C][C]0.885658648759622[/C][C]0.442829324379811[/C][/ROW]
[ROW][C]111[/C][C]0.562444850669176[/C][C]0.875110298661648[/C][C]0.437555149330824[/C][/ROW]
[ROW][C]112[/C][C]0.627311398907946[/C][C]0.745377202184108[/C][C]0.372688601092054[/C][/ROW]
[ROW][C]113[/C][C]0.59036315062538[/C][C]0.81927369874924[/C][C]0.40963684937462[/C][/ROW]
[ROW][C]114[/C][C]0.637203753001453[/C][C]0.725592493997094[/C][C]0.362796246998547[/C][/ROW]
[ROW][C]115[/C][C]0.886289741533499[/C][C]0.227420516933002[/C][C]0.113710258466501[/C][/ROW]
[ROW][C]116[/C][C]0.872785878587668[/C][C]0.254428242824664[/C][C]0.127214121412332[/C][/ROW]
[ROW][C]117[/C][C]0.90567151806538[/C][C]0.18865696386924[/C][C]0.0943284819346199[/C][/ROW]
[ROW][C]118[/C][C]0.950543539774894[/C][C]0.0989129204502114[/C][C]0.0494564602251057[/C][/ROW]
[ROW][C]119[/C][C]0.939289185992548[/C][C]0.121421628014904[/C][C]0.0607108140074518[/C][/ROW]
[ROW][C]120[/C][C]0.976385351721[/C][C]0.0472292965580004[/C][C]0.0236146482790002[/C][/ROW]
[ROW][C]121[/C][C]0.9678132383589[/C][C]0.0643735232821996[/C][C]0.0321867616410998[/C][/ROW]
[ROW][C]122[/C][C]0.956077745046607[/C][C]0.0878445099067861[/C][C]0.0439222549533931[/C][/ROW]
[ROW][C]123[/C][C]0.957323261123427[/C][C]0.0853534777531465[/C][C]0.0426767388765732[/C][/ROW]
[ROW][C]124[/C][C]0.948840332972732[/C][C]0.102319334054535[/C][C]0.0511596670272677[/C][/ROW]
[ROW][C]125[/C][C]0.94497203080653[/C][C]0.110055938386939[/C][C]0.0550279691934697[/C][/ROW]
[ROW][C]126[/C][C]0.935709571952615[/C][C]0.128580856094769[/C][C]0.0642904280473847[/C][/ROW]
[ROW][C]127[/C][C]0.971812584620162[/C][C]0.0563748307596761[/C][C]0.0281874153798381[/C][/ROW]
[ROW][C]128[/C][C]0.966162934298635[/C][C]0.0676741314027308[/C][C]0.0338370657013654[/C][/ROW]
[ROW][C]129[/C][C]0.952361819237188[/C][C]0.0952763615256231[/C][C]0.0476381807628116[/C][/ROW]
[ROW][C]130[/C][C]0.964226558384012[/C][C]0.0715468832319754[/C][C]0.0357734416159877[/C][/ROW]
[ROW][C]131[/C][C]0.942954176046293[/C][C]0.114091647907414[/C][C]0.0570458239537068[/C][/ROW]
[ROW][C]132[/C][C]0.911367169530207[/C][C]0.177265660939585[/C][C]0.0886328304697925[/C][/ROW]
[ROW][C]133[/C][C]0.867469800746166[/C][C]0.265060398507668[/C][C]0.132530199253834[/C][/ROW]
[ROW][C]134[/C][C]0.910252988878096[/C][C]0.179494022243808[/C][C]0.0897470111219041[/C][/ROW]
[ROW][C]135[/C][C]0.926085996371161[/C][C]0.147828007257678[/C][C]0.0739140036288392[/C][/ROW]
[ROW][C]136[/C][C]0.969148233950371[/C][C]0.0617035320992578[/C][C]0.0308517660496289[/C][/ROW]
[ROW][C]137[/C][C]0.959753689884188[/C][C]0.0804926202316238[/C][C]0.0402463101158119[/C][/ROW]
[ROW][C]138[/C][C]0.968154849864866[/C][C]0.0636903002702684[/C][C]0.0318451501351342[/C][/ROW]
[ROW][C]139[/C][C]0.970948090295865[/C][C]0.0581038194082696[/C][C]0.0290519097041348[/C][/ROW]
[ROW][C]140[/C][C]0.954219094807561[/C][C]0.0915618103848779[/C][C]0.0457809051924389[/C][/ROW]
[ROW][C]141[/C][C]0.952775910710933[/C][C]0.0944481785781329[/C][C]0.0472240892890665[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185123&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185123&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
100.1863452992935650.372690598587130.813654700706435
110.08459674956295530.1691934991259110.915403250437045
120.03530376973362320.07060753946724640.964696230266377
130.04824401064725330.09648802129450660.951755989352747
140.08229502921633610.1645900584326720.917704970783664
150.04504096219906820.09008192439813640.954959037800932
160.02899389270240110.05798778540480220.971006107297599
170.02152775503575350.0430555100715070.978472244964246
180.02095861530065340.04191723060130680.979041384699347
190.3046068038693660.6092136077387330.695393196130634
200.2674353048420570.5348706096841130.732564695157943
210.2368527986232380.4737055972464770.763147201376762
220.3055651673322730.6111303346645460.694434832667727
230.3144020596399880.6288041192799760.685597940360012
240.2951967482284170.5903934964568350.704803251771583
250.238865625229770.477731250459540.76113437477023
260.1879676620635090.3759353241270170.812032337936491
270.1746163940353820.3492327880707630.825383605964618
280.1633418431551960.3266836863103920.836658156844804
290.1433081457405240.2866162914810490.856691854259476
300.1340676206441180.2681352412882370.865932379355882
310.1400782176685860.2801564353371720.859921782331414
320.1257162103080050.251432420616010.874283789691995
330.1176967494475230.2353934988950470.882303250552477
340.1377620660007880.2755241320015770.862237933999212
350.1390280481324370.2780560962648740.860971951867563
360.1978779881577640.3957559763155280.802122011842236
370.1799667076864360.3599334153728710.820033292313564
380.1590285731748320.3180571463496640.840971426825168
390.2515727459923490.5031454919846990.748427254007651
400.2379797066127150.475959413225430.762020293387285
410.2709973829048180.5419947658096350.729002617095182
420.3126678969528070.6253357939056140.687332103047193
430.2727272891912320.5454545783824640.727272710808768
440.2425593508789620.4851187017579240.757440649121038
450.255387042460540.510774084921080.74461295753946
460.2658128502240050.5316257004480090.734187149775995
470.2456257045944940.4912514091889880.754374295405506
480.3277565548344580.6555131096689160.672243445165542
490.2859433491553060.5718866983106120.714056650844694
500.271393081949210.5427861638984210.72860691805079
510.23866881912360.47733763824720.7613311808764
520.2046877742328820.4093755484657630.795312225767118
530.1915175591022430.3830351182044870.808482440897757
540.1983435155321890.3966870310643770.801656484467811
550.1741228375155850.348245675031170.825877162484415
560.157539265501890.3150785310037790.84246073449811
570.1351491658398750.2702983316797490.864850834160125
580.1386863067481340.2773726134962680.861313693251866
590.1306429934539390.2612859869078780.869357006546061
600.1724818526241330.3449637052482660.827518147375867
610.2408226412671120.4816452825342230.759177358732888
620.3352224319501270.6704448639002540.664777568049873
630.3643935995052380.7287871990104770.635606400494762
640.4534967409742390.9069934819484790.546503259025761
650.4362880353936250.8725760707872510.563711964606375
660.4721722217700160.9443444435400320.527827778229984
670.4547061160284530.9094122320569060.545293883971547
680.427561321622140.8551226432442810.57243867837786
690.4182102524785330.8364205049570660.581789747521467
700.4140902110877820.8281804221755640.585909788912218
710.3975016858107580.7950033716215170.602498314189241
720.3882533955613540.7765067911227070.611746604438646
730.3792056294498870.7584112588997740.620794370550113
740.5109969644734180.9780060710531640.489003035526582
750.6698071926344240.6603856147311520.330192807365576
760.6530185280470410.6939629439059190.346981471952959
770.6381048441465550.723790311706890.361895155853445
780.6333791930464140.7332416139071710.366620806953586
790.7161836687645710.5676326624708580.283816331235429
800.675517659218490.6489646815630190.32448234078151
810.6686675155485950.6626649689028090.331332484451405
820.6543350904346240.6913298191307510.345664909565376
830.6658446380305140.6683107239389730.334155361969486
840.6495649749278650.700870050144270.350435025072135
850.6133390854041360.7733218291917270.386660914595864
860.6479476057333140.7041047885333730.352052394266686
870.6397631518339160.7204736963321680.360236848166084
880.6215709128508960.7568581742982070.378429087149104
890.6050561338447830.7898877323104330.394943866155217
900.6128507349744180.7742985300511640.387149265025582
910.5670685993961410.8658628012077180.432931400603859
920.5368462424398260.9263075151203470.463153757560174
930.5016914162882830.9966171674234350.498308583711717
940.4572342084724140.9144684169448270.542765791527586
950.40787493715520.8157498743104010.5921250628448
960.515679269763850.9686414604722990.48432073023615
970.4837186637175790.9674373274351590.516281336282421
980.463473565305740.926947130611480.53652643469426
990.4578870057565770.9157740115131550.542112994243423
1000.4453471451167750.8906942902335510.554652854883225
1010.4341717087313960.8683434174627910.565828291268604
1020.5046022489561930.9907955020876140.495397751043807
1030.4721866119260530.9443732238521070.527813388073947
1040.4717629092911430.9435258185822850.528237090708857
1050.4349105139596570.8698210279193140.565089486040343
1060.4217964202854580.8435928405709150.578203579714542
1070.4206785309508650.841357061901730.579321469049135
1080.4554573341858510.9109146683717010.544542665814149
1090.5839800005157180.8320399989685640.416019999484282
1100.5571706756201890.8856586487596220.442829324379811
1110.5624448506691760.8751102986616480.437555149330824
1120.6273113989079460.7453772021841080.372688601092054
1130.590363150625380.819273698749240.40963684937462
1140.6372037530014530.7255924939970940.362796246998547
1150.8862897415334990.2274205169330020.113710258466501
1160.8727858785876680.2544282428246640.127214121412332
1170.905671518065380.188656963869240.0943284819346199
1180.9505435397748940.09891292045021140.0494564602251057
1190.9392891859925480.1214216280149040.0607108140074518
1200.9763853517210.04722929655800040.0236146482790002
1210.96781323835890.06437352328219960.0321867616410998
1220.9560777450466070.08784450990678610.0439222549533931
1230.9573232611234270.08535347775314650.0426767388765732
1240.9488403329727320.1023193340545350.0511596670272677
1250.944972030806530.1100559383869390.0550279691934697
1260.9357095719526150.1285808560947690.0642904280473847
1270.9718125846201620.05637483075967610.0281874153798381
1280.9661629342986350.06767413140273080.0338370657013654
1290.9523618192371880.09527636152562310.0476381807628116
1300.9642265583840120.07154688323197540.0357734416159877
1310.9429541760462930.1140916479074140.0570458239537068
1320.9113671695302070.1772656609395850.0886328304697925
1330.8674698007461660.2650603985076680.132530199253834
1340.9102529888780960.1794940222438080.0897470111219041
1350.9260859963711610.1478280072576780.0739140036288392
1360.9691482339503710.06170353209925780.0308517660496289
1370.9597536898841880.08049262023162380.0402463101158119
1380.9681548498648660.06369030027026840.0318451501351342
1390.9709480902958650.05810381940826960.0290519097041348
1400.9542190948075610.09156181038487790.0457809051924389
1410.9527759107109330.09444817857813290.0472240892890665






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level30.0227272727272727OK
10% type I error level210.159090909090909NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 3 & 0.0227272727272727 & OK \tabularnewline
10% type I error level & 21 & 0.159090909090909 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185123&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]3[/C][C]0.0227272727272727[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]21[/C][C]0.159090909090909[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185123&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185123&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 level00OK
5% type I error level30.0227272727272727OK
10% type I error level210.159090909090909NOK


Figures (Output of Computation)

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