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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Fri, 15 Dec 2017 09:59:34 +0100

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2017/Dec/15/t15133284222p3wzv09l8azge3.htm/, Retrieved Wed, 15 May 2024 17:25:22 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=309617, Retrieved Wed, 15 May 2024 17:25:22 +0000

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Estimated Impact

105

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-       [Multiple Regression] [] [2017-12-15 08:59:34] [ca643b0c409f93e6a7ce1fd0961340ec] [Current]

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Dataseries X:

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52.20	78.70	56.90
63.90	88.60	69.60
70.30	104.20	82.60
64.30	88.20	71.20
77.20	94.70	74.10
71.90	112.00	67.60
46.30	78.90	56.30
61.50	111.40	54.40
73.30	132.50	65.00
75.00	121.60	68.60
74.40	116.10	80.30
74.70	123.30	72.50
71.70	107.90	88.30
66.60	107.00	89.80
75.10	115.80	103.50
67.50	91.80	78.80
74.60	93.50	85.70
76.40	107.10	96.90
53.90	80.50	66.90
70.10	100.50	71.50
76.10	100.20	89.50
79.40	100.30	86.60
74.80	96.60	91.20
65.30	86.00	75.60
63.50	76.90	75.60
64.40	79.70	80.40
70.30	93.10	91.60
74.50	79.50	90.10
69.40	80.30	90.80
74.50	88.80	94.60
52.80	72.40	62.60
61.50	75.50	65.00
73.90	92.90	87.60
79.40	101.50	99.90
69.80	94.70	85.10
77.40	93.00	71.00
69.40	79.80	73.00
75.00	82.20	83.50
76.40	87.60	86.50
75.90	83.20	80.90
70.30	81.60	80.80
89.50	85.90	81.20
62.50	71.90	61.90
59.00	71.80	49.40
89.50	98.30	79.20
83.50	93.60	76.80
76.00	86.10	81.20
85.80	96.20	79.40
66.90	78.60	74.00
75.40	82.10	78.20
84.60	94.40	98.90
81.80	86.40	88.30
75.00	82.20	79.30
92.60	96.70	104.00
66.40	84.20	60.50
75.70	73.60	75.30
91.30	94.90	106.20
88.60	96.90	106.70
85.80	90.20	95.40
86.70	104.20	90.50
71.00	78.40	113.80
83.20	81.50	94.10
85.00	96.70	109.90
79.30	87.50	104.30
77.50	86.20	80.70
96.50	105.10	121.10
56.50	72.90	68.80
75.20	76.40	73.70
86.30	100.50	104.20
84.80	92.40	87.20
91.60	96.30	94.50
110.70	103.60	120.90
81.00	75.10	88.50
81.50	78.80	102.50
91.00	93.70	118.60
81.30	82.50	86.00
93.50	88.30	110.60
100.70	95.70	114.00
68.50	73.30	72.60
77.60	72.40	76.00
102.70	94.00	114.60
113.10	96.90	113.50
98.50	92.40	115.20
108.20	90.90	102.00
89.60	93.50	101.50
93.30	92.00	99.60
104.60	115.90	113.80
94.30	97.80	94.80
100.70	97.70	102.00
111.80	116.90	119.50
76.10	96.70	88.00
102.10	97.70	82.80
149.20	103.90	112.10
172.30	124.10	131.50
125.60	117.30	110.00
132.20	113.80	96.50
106.50	100.00	101.90
116.60	114.20	103.10
110.80	116.30	103.50
121.90	111.40	111.80
117.20	103.40	100.30
123.90	125.30	111.00
98.00	92.50	84.60
93.50	92.00	73.30
136.30	121.60	112.00
131.00	113.30	111.20
113.20	92.50	82.40
101.00	100.30	75.60
88.70	83.20	64.20
96.90	81.20	72.20
105.80	94.50	80.80
95.20	87.70	71.10
88.00	82.30	153.20
107.70	99.00	89.80
71.10	72.40	57.30
72.30	80.80	83.60
101.50	105.50	88.40
103.20	98.40	84.10
103.00	94.50	95.50
88.30	109.20	74.60
78.00	84.10	79.80
91.80	88.40	85.40
111.50	111.30	106.40
100.20	93.20	94.60
94.30	86.30	94.60
118.20	111.40	113.70
80.50	85.40	66.70
92.60	89.70	78.90
113.10	110.90	126.30
111.80	119.40	118.10
101.70	109.30	117.30
106.50	110.70	118.10
88.90	101.30	108.60
101.20	99.00	118.10
119.00	117.90	141.00
104.60	89.30	112.70
120.20	105.40	131.90
112.60	99.90	123.50
88.10	79.50	81.30
99.20	88.30	85.40
126.50	116.20	138.50
113.20	110.60	124.60
114.20	99.30	125.80
128.10	105.40	125.30
109.20	89.90	111.00
107.00	100.70	120.40
142.30	122.50	141.40
106.00	97.40	113.10
115.20	97.90	114.00
129.70	124.30	131.30
90.40	94.70	77.80
97.50	85.20	105.10
118.30	101.90	125.40
121.20	110.90	123.60
117.50	102.00	107.90
105.50	95.80	86.10
97.30	86.90	97.80
98.00	90.30	98.40
114.80	97.90	118.00
109.80	91.90	115.60
121.90	90.40	114.50
123.00	98.90	124.00
104.10	81.30	101.80
99.90	79.80	80.60
128.50	93.70	129.70
127.70	101.50	137.00
116.70	88.60	127.30
112.10	94.60	110.30
102.80	84.20	134.90
110.80	86.50	126.20
117.80	92.60	130.50
122.40	84.20	127.60
120.40	85.90	134.80
119.20	90.00	128.90
101.30	79.10	101.10
101.20	75.60	86.00
136.10	97.00	139.20
133.60	96.40	126.80
109.60	85.20	117.10
115.80	100.30	103.00
104.30	76.70	108.70
115.00	79.00	115.00
124.60	94.40	133.20
123.10	82.80	131.30
120.00	74.60	119.60
132.00	92.80	146.70
107.20	69.70	101.00
101.00	68.90	88.70
153.10	97.50	143.70
144.50	92.90	138.10
125.80	93.40	139.80
125.40	92.10	121.60
111.70	80.60	112.60
118.40	86.00	136.70
135.60	93.60	147.40
130.70	90.30	128.10
128.50	81.30	117.50
137.10	98.40	148.20
92.10	73.30	101.60
103.70	77.10	90.40
139.00	91.40	148.60
125.00	89.00	133.80
130.20	94.10	130.30
116.40	94.70	113.60
106.40	80.70	105.80
121.20	85.20	136.10
147.60	107.90	160.30
116.00	81.60	127.70
137.50	83.80	141.80
136.40	98.80	149.30
95.80	75.60	94.50
127.00	80.70	95.20

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	10 seconds
R Server	Big Analytics Cloud Computing Center

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time10 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309617&T=0

[TABLE]
[ROW]

Summary of computational transaction[/C][/ROW] [ROW]

Raw Input[/C]

view raw input (R code) [/C][/ROW] [ROW]

Raw Output[/C]

view raw output of R engine [/C][/ROW] [ROW]

Computing time[/C]

10 seconds[/C][/ROW] [ROW]

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=309617&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309617&T=0

As an alternative you can also use a QR Code:

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

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	10 seconds
R Server	Big Analytics Cloud Computing Center

Multiple Linear Regression - Estimated Regression Equation
(1-Bs)(1-B)a[t] = + 0.126992 + 0.377941`(1-Bs)(1-B)b`[t] + 0.11906`(1-Bs)(1-B)c`[t] -0.306729`(1-Bs)(1-B)a(t-1s)`[t] -0.31441`(1-Bs)(1-B)a(t-2s)`[t] -0.239971`(1-Bs)(1-B)a(t-3s)`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
(1-Bs)(1-B)a[t] =  +  0.126992 +  0.377941`(1-Bs)(1-B)b`[t] +  0.11906`(1-Bs)(1-B)c`[t] -0.306729`(1-Bs)(1-B)a(t-1s)`[t] -0.31441`(1-Bs)(1-B)a(t-2s)`[t] -0.239971`(1-Bs)(1-B)a(t-3s)`[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309617&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C](1-Bs)(1-B)a[t] =  +  0.126992 +  0.377941`(1-Bs)(1-B)b`[t] +  0.11906`(1-Bs)(1-B)c`[t] -0.306729`(1-Bs)(1-B)a(t-1s)`[t] -0.31441`(1-Bs)(1-B)a(t-2s)`[t] -0.239971`(1-Bs)(1-B)a(t-3s)`[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309617&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309617&T=1

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Estimated Regression Equation
(1-Bs)(1-B)a[t] = + 0.126992 + 0.377941`(1-Bs)(1-B)b`[t] + 0.11906`(1-Bs)(1-B)c`[t] -0.306729`(1-Bs)(1-B)a(t-1s)`[t] -0.31441`(1-Bs)(1-B)a(t-2s)`[t] -0.239971`(1-Bs)(1-B)a(t-3s)`[t] + e[t]

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	+0.127	0.7813	+1.6250e-01	0.8711	0.4355
`(1-Bs)(1-B)b`	+0.3779	0.08215	+4.6010e+00	8.625e-06	4.312e-06
`(1-Bs)(1-B)c`	+0.1191	0.03885	+3.0640e+00	0.002569	0.001284
`(1-Bs)(1-B)a(t-1s)`	-0.3067	0.07306	-4.1980e+00	4.491e-05	2.246e-05
`(1-Bs)(1-B)a(t-2s)`	-0.3144	0.07448	-4.2220e+00	4.095e-05	2.047e-05
`(1-Bs)(1-B)a(t-3s)`	-0.24	0.07312	-3.2820e+00	0.001272	0.0006359

\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) & +0.127 &  0.7813 & +1.6250e-01 &  0.8711 &  0.4355 \tabularnewline
`(1-Bs)(1-B)b` & +0.3779 &  0.08215 & +4.6010e+00 &  8.625e-06 &  4.312e-06 \tabularnewline
`(1-Bs)(1-B)c` & +0.1191 &  0.03885 & +3.0640e+00 &  0.002569 &  0.001284 \tabularnewline
`(1-Bs)(1-B)a(t-1s)` & -0.3067 &  0.07306 & -4.1980e+00 &  4.491e-05 &  2.246e-05 \tabularnewline
`(1-Bs)(1-B)a(t-2s)` & -0.3144 &  0.07448 & -4.2220e+00 &  4.095e-05 &  2.047e-05 \tabularnewline
`(1-Bs)(1-B)a(t-3s)` & -0.24 &  0.07312 & -3.2820e+00 &  0.001272 &  0.0006359 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309617&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]+0.127[/C][C] 0.7813[/C][C]+1.6250e-01[/C][C] 0.8711[/C][C] 0.4355[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)b`[/C][C]+0.3779[/C][C] 0.08215[/C][C]+4.6010e+00[/C][C] 8.625e-06[/C][C] 4.312e-06[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)c`[/C][C]+0.1191[/C][C] 0.03885[/C][C]+3.0640e+00[/C][C] 0.002569[/C][C] 0.001284[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)a(t-1s)`[/C][C]-0.3067[/C][C] 0.07306[/C][C]-4.1980e+00[/C][C] 4.491e-05[/C][C] 2.246e-05[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)a(t-2s)`[/C][C]-0.3144[/C][C] 0.07448[/C][C]-4.2220e+00[/C][C] 4.095e-05[/C][C] 2.047e-05[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)a(t-3s)`[/C][C]-0.24[/C][C] 0.07312[/C][C]-3.2820e+00[/C][C] 0.001272[/C][C] 0.0006359[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309617&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309617&T=2

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	+0.127	0.7813	+1.6250e-01	0.8711	0.4355
`(1-Bs)(1-B)b`	+0.3779	0.08215	+4.6010e+00	8.625e-06	4.312e-06
`(1-Bs)(1-B)c`	+0.1191	0.03885	+3.0640e+00	0.002569	0.001284
`(1-Bs)(1-B)a(t-1s)`	-0.3067	0.07306	-4.1980e+00	4.491e-05	2.246e-05
`(1-Bs)(1-B)a(t-2s)`	-0.3144	0.07448	-4.2220e+00	4.095e-05	2.047e-05
`(1-Bs)(1-B)a(t-3s)`	-0.24	0.07312	-3.2820e+00	0.001272	0.0006359

Multiple Linear Regression - Regression Statistics
Multiple R	0.6028
R-squared	0.3634
Adjusted R-squared	0.3431
F-TEST (value)	17.93
F-TEST (DF numerator)	5
F-TEST (DF denominator)	157
p-value	4.863e-14
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	9.974
Sum Squared Residuals	1.562e+04

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.6028 \tabularnewline
R-squared &  0.3634 \tabularnewline
Adjusted R-squared &  0.3431 \tabularnewline
F-TEST (value) &  17.93 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 157 \tabularnewline
p-value &  4.863e-14 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  9.974 \tabularnewline
Sum Squared Residuals &  1.562e+04 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309617&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.6028[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.3634[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.3431[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 17.93[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]157[/C][/ROW]
[ROW][C]p-value[/C][C] 4.863e-14[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 9.974[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 1.562e+04[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309617&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309617&T=3

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Regression Statistics
Multiple R	0.6028
R-squared	0.3634
Adjusted R-squared	0.3431
F-TEST (value)	17.93
F-TEST (DF numerator)	5
F-TEST (DF denominator)	157
p-value	4.863e-14
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	9.974
Sum Squared Residuals	1.562e+04

Menu of Residual Diagnostics
Description	Link
Histogram	Compute
Central Tendency	Compute
QQ Plot	Compute
Kernel Density Plot	Compute
Skewness/Kurtosis Test	Compute
Skewness-Kurtosis Plot	Compute
Harrell-Davis Plot	Compute
Bootstrap Plot -- Central Tendency	Compute
Blocked Bootstrap Plot -- Central Tendency	Compute
(Partial) Autocorrelation Plot	Compute
Spectral Analysis	Compute
Tukey lambda PPCC Plot	Compute
Box-Cox Normality Plot	Compute
Summary Statistics	Compute

\begin{tabular}{lllllllll}
\hline
Menu of Residual Diagnostics \tabularnewline
Description & Link \tabularnewline
Histogram & Compute \tabularnewline
Central Tendency & Compute \tabularnewline
QQ Plot & Compute \tabularnewline
Kernel Density Plot & Compute \tabularnewline
Skewness/Kurtosis Test & Compute \tabularnewline
Skewness-Kurtosis Plot & Compute \tabularnewline
Harrell-Davis Plot & Compute \tabularnewline
Bootstrap Plot -- Central Tendency & Compute \tabularnewline
Blocked Bootstrap Plot -- Central Tendency & Compute \tabularnewline
(Partial) Autocorrelation Plot & Compute \tabularnewline
Spectral Analysis & Compute \tabularnewline
Tukey lambda PPCC Plot & Compute \tabularnewline
Box-Cox Normality Plot & Compute \tabularnewline
Summary Statistics & Compute \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309617&T=4

[TABLE]
[ROW][C]Menu of Residual Diagnostics[/C][/ROW]
[ROW][C]Description[/C][C]Link[/C][/ROW]
[ROW][C]Histogram[/C][C]Compute[/C][/ROW]
[ROW][C]Central Tendency[/C][C]Compute[/C][/ROW]
[ROW][C]QQ Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Kernel Density Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Skewness/Kurtosis Test[/C][C]Compute[/C][/ROW]
[ROW][C]Skewness-Kurtosis Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Harrell-Davis Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Bootstrap Plot -- Central Tendency[/C][C]Compute[/C][/ROW]
[ROW][C]Blocked Bootstrap Plot -- Central Tendency[/C][C]Compute[/C][/ROW]
[ROW][C](Partial) Autocorrelation Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Spectral Analysis[/C][C]Compute[/C][/ROW]
[ROW][C]Tukey lambda PPCC Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Box-Cox Normality Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Summary Statistics[/C][C]Compute[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309617&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309617&T=4

As an alternative you can also use a QR Code:

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

Menu of Residual Diagnostics
Description	Link
Histogram	Compute
Central Tendency	Compute
QQ Plot	Compute
Kernel Density Plot	Compute
Skewness/Kurtosis Test	Compute
Skewness-Kurtosis Plot	Compute
Harrell-Davis Plot	Compute
Bootstrap Plot -- Central Tendency	Compute
Blocked Bootstrap Plot -- Central Tendency	Compute
(Partial) Autocorrelation Plot	Compute
Spectral Analysis	Compute
Tukey lambda PPCC Plot	Compute
Box-Cox Normality Plot	Compute
Summary Statistics	Compute

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	2.9	0.4961	2.404
2	7.8	6.536	1.264
3	-2.3	-3.713	1.413
4	-1.2	3.466	-4.666
5	-1.6	-0.1911	-1.409
6	0.8	-1.557	2.357
7	12.8	5.269	7.531
8	-14.9	-7.88	-7.02
9	3.3	5.456	-2.156
10	4.7	0.4479	4.252
11	-8.9	-2.468	-6.432
12	3.2	5.45	-2.25
13	3.7	-6.677	10.38
14	-7.4	0.2859	-7.686
15	-2.9	-0.3797	-2.52
16	5	2.938	2.062
17	1.4	-1.075	2.475
18	-13.8	-7.137	-6.663
19	9.4	5.987	3.413
20	-4.5	-1.519	-2.981
21	1.2	-3.698	4.898
22	9.6	5.446	4.154
23	18.2	-0.7439	18.94
24	-14	-3.592	-10.41
25	-11.7	1.192	-12.89
26	7.7	0.9466	6.753
27	-4	-1.103	-2.897
28	14	7.513	6.487
29	-11.8	-11.93	0.1345
30	7.8	10.38	-2.582
31	-9.6	-5.695	-3.905
32	14	1.868	12.13
33	11.9	7.531	4.369
34	-21.4	-8.641	-12.76
35	-9.4	-11.23	1.826
36	11.1	21.58	-10.48
37	3.2	-2.002	5.202
38	1.8	1.395	0.4047
39	-0.6	1.829	-2.429
40	-5.8	-9.753	3.953
41	3.9	9.829	-5.929
42	-3.5	3.892	-7.392
43	16.9	-3.261	20.16
44	22	-6.104	28.1
45	12.7	4.287	8.413
46	-32.1	-1.087	-31.01
47	-3.1	-1.368	-1.732
48	-7.1	-5.14	-1.96
49	6.4	8.239	-1.839
50	-17.1	-10.95	-6.148
51	21.4	10.5	10.9
52	-11.1	-8.908	-2.192
53	-4.4	2.516	-6.916
54	9.8	-2.095	11.9
55	-30.5	-5.587	-24.91
56	-4.3	0.02007	-4.32
57	-28.4	-20.97	-7.426
58	28.9	8.237	20.66
59	-18.8	4.734	-23.53
60	13.4	-1.073	14.47
61	-1.9	-5.348	3.448
62	14.7	8.168	6.532
63	-21.7	-8.15	-13.55
64	-2.5	14.12	-16.62
65	13	-7.706	20.71
66	-10.7	-2.033	-8.667
67	5.7	14.31	-8.613
68	-13.6	-14.72	1.119
69	7	2.026	4.974
70	17.6	17.66	-0.06386
71	-2.5	10.05	-12.55
72	2	-5.462	7.462
73	5.6	0.02493	5.575
74	10.8	5.667	5.133
75	-0.7	-4.322	3.622
76	1.3	-4.566	5.866
77	4.2	9.584	-5.384
78	-1.1	-0.3319	-0.7681
79	10.9	0.6843	10.22
80	-8.7	4.12	-12.82
81	-3	9.293	-12.29
82	-9.9	-10.45	0.5506
83	19.5	5.106	14.39
84	-7.3	1.188	-8.488
85	-1.5	-4.559	3.059
86	-1.9	-4.99	3.09
87	-3.1	-3.904	0.8039
88	21.5	14.16	7.343
89	-31.5	-19.03	-12.47
90	13.2	4.165	9.035
91	-1	3.047	-4.047
92	6.8	11.31	-4.514
93	-12	-0.3461	-11.65
94	11.1	-9.521	20.62
95	9.1	1.065	8.035
96	-1.3	-4.355	3.055
97	-14.5	4.221	-18.72
98	17.5	-5.344	22.84
99	-21.9	7.828	-29.73
100	-6.4	-14.35	7.951
101	22.1	20.46	1.635
102	-14.8	-5.831	-8.969
103	-4	-8.515	4.515
104	-6.5	-4.098	-2.402
105	16.2	10.03	6.17
106	-4.7	-5.494	0.7936
107	-25.9	-15.38	-10.52
108	10.7	7.931	2.769
109	2.9	-0.1421	3.042
110	-18.5	-12.77	-5.731
111	31.3	18.29	13.01
112	2.9	-5.976	8.876
113	-13.4	-5.45	-7.95
114	20.4	9.042	11.36
115	-11.3	-3.698	-7.602
116	7.8	4.441	3.359
117	-3.7	0.2807	-3.981
118	-7.3	-0.343	-6.957
119	7.4	5.713	1.687
120	-1.1	-0.02553	-1.074
121	7.3	2.633	4.667
122	-9.8	-1.633	-8.167
123	9.6	-2.811	12.41
124	-14.1	-1.712	-12.39
125	-2.3	1.351	-3.651
126	1	-2.779	3.779
127	4.1	5.061	-0.961
128	6.3	1.469	4.831
129	-1.7	-6.472	4.772
130	-13	1.823	-14.82
131	10.8	7.601	3.199
132	-2.2	-9.827	7.627
133	2.7	2.242	0.4584
134	2.6	9.92	-7.32
135	-6.1	-8.494	2.394
136	-1.1	-0.916	-0.184
137	13.2	9	4.2
138	-6.9	-9.784	2.884
139	-6.1	4.736	-10.84
140	17.2	0.2375	16.96
141	-6.1	-2.778	-3.322
142	5.3	13.32	-8.017
143	-6.6	-5.983	-0.6165
144	-2.2	1.403	-3.603
145	-4	-0.4014	-3.599
146	7.6	3.009	4.591
147	-3.4	-7.466	4.066
148	0.9	4.03	-3.13
149	-3.4	0.0298	-3.43
150	-20.2	-3.829	-16.37
151	17.8	5.29	12.51
152	-16.8	-14.02	-2.775
153	-5.4	3.157	-8.557
154	23.9	5.46	18.44
155	-13.4	-2.123	-11.28
156	3.7	0.9555	2.745
157	8.1	-0.8488	8.949
158	9.2	6.644	2.556
159	-26.7	-9.492	-17.21
160	23.7	10.75	12.95
161	-9.7	-5.984	-3.716
162	4.4	7.994	-3.594
163	19.6	-2.491	22.09

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  2.9 &  0.4961 &  2.404 \tabularnewline
2 &  7.8 &  6.536 &  1.264 \tabularnewline
3 & -2.3 & -3.713 &  1.413 \tabularnewline
4 & -1.2 &  3.466 & -4.666 \tabularnewline
5 & -1.6 & -0.1911 & -1.409 \tabularnewline
6 &  0.8 & -1.557 &  2.357 \tabularnewline
7 &  12.8 &  5.269 &  7.531 \tabularnewline
8 & -14.9 & -7.88 & -7.02 \tabularnewline
9 &  3.3 &  5.456 & -2.156 \tabularnewline
10 &  4.7 &  0.4479 &  4.252 \tabularnewline
11 & -8.9 & -2.468 & -6.432 \tabularnewline
12 &  3.2 &  5.45 & -2.25 \tabularnewline
13 &  3.7 & -6.677 &  10.38 \tabularnewline
14 & -7.4 &  0.2859 & -7.686 \tabularnewline
15 & -2.9 & -0.3797 & -2.52 \tabularnewline
16 &  5 &  2.938 &  2.062 \tabularnewline
17 &  1.4 & -1.075 &  2.475 \tabularnewline
18 & -13.8 & -7.137 & -6.663 \tabularnewline
19 &  9.4 &  5.987 &  3.413 \tabularnewline
20 & -4.5 & -1.519 & -2.981 \tabularnewline
21 &  1.2 & -3.698 &  4.898 \tabularnewline
22 &  9.6 &  5.446 &  4.154 \tabularnewline
23 &  18.2 & -0.7439 &  18.94 \tabularnewline
24 & -14 & -3.592 & -10.41 \tabularnewline
25 & -11.7 &  1.192 & -12.89 \tabularnewline
26 &  7.7 &  0.9466 &  6.753 \tabularnewline
27 & -4 & -1.103 & -2.897 \tabularnewline
28 &  14 &  7.513 &  6.487 \tabularnewline
29 & -11.8 & -11.93 &  0.1345 \tabularnewline
30 &  7.8 &  10.38 & -2.582 \tabularnewline
31 & -9.6 & -5.695 & -3.905 \tabularnewline
32 &  14 &  1.868 &  12.13 \tabularnewline
33 &  11.9 &  7.531 &  4.369 \tabularnewline
34 & -21.4 & -8.641 & -12.76 \tabularnewline
35 & -9.4 & -11.23 &  1.826 \tabularnewline
36 &  11.1 &  21.58 & -10.48 \tabularnewline
37 &  3.2 & -2.002 &  5.202 \tabularnewline
38 &  1.8 &  1.395 &  0.4047 \tabularnewline
39 & -0.6 &  1.829 & -2.429 \tabularnewline
40 & -5.8 & -9.753 &  3.953 \tabularnewline
41 &  3.9 &  9.829 & -5.929 \tabularnewline
42 & -3.5 &  3.892 & -7.392 \tabularnewline
43 &  16.9 & -3.261 &  20.16 \tabularnewline
44 &  22 & -6.104 &  28.1 \tabularnewline
45 &  12.7 &  4.287 &  8.413 \tabularnewline
46 & -32.1 & -1.087 & -31.01 \tabularnewline
47 & -3.1 & -1.368 & -1.732 \tabularnewline
48 & -7.1 & -5.14 & -1.96 \tabularnewline
49 &  6.4 &  8.239 & -1.839 \tabularnewline
50 & -17.1 & -10.95 & -6.148 \tabularnewline
51 &  21.4 &  10.5 &  10.9 \tabularnewline
52 & -11.1 & -8.908 & -2.192 \tabularnewline
53 & -4.4 &  2.516 & -6.916 \tabularnewline
54 &  9.8 & -2.095 &  11.9 \tabularnewline
55 & -30.5 & -5.587 & -24.91 \tabularnewline
56 & -4.3 &  0.02007 & -4.32 \tabularnewline
57 & -28.4 & -20.97 & -7.426 \tabularnewline
58 &  28.9 &  8.237 &  20.66 \tabularnewline
59 & -18.8 &  4.734 & -23.53 \tabularnewline
60 &  13.4 & -1.073 &  14.47 \tabularnewline
61 & -1.9 & -5.348 &  3.448 \tabularnewline
62 &  14.7 &  8.168 &  6.532 \tabularnewline
63 & -21.7 & -8.15 & -13.55 \tabularnewline
64 & -2.5 &  14.12 & -16.62 \tabularnewline
65 &  13 & -7.706 &  20.71 \tabularnewline
66 & -10.7 & -2.033 & -8.667 \tabularnewline
67 &  5.7 &  14.31 & -8.613 \tabularnewline
68 & -13.6 & -14.72 &  1.119 \tabularnewline
69 &  7 &  2.026 &  4.974 \tabularnewline
70 &  17.6 &  17.66 & -0.06386 \tabularnewline
71 & -2.5 &  10.05 & -12.55 \tabularnewline
72 &  2 & -5.462 &  7.462 \tabularnewline
73 &  5.6 &  0.02493 &  5.575 \tabularnewline
74 &  10.8 &  5.667 &  5.133 \tabularnewline
75 & -0.7 & -4.322 &  3.622 \tabularnewline
76 &  1.3 & -4.566 &  5.866 \tabularnewline
77 &  4.2 &  9.584 & -5.384 \tabularnewline
78 & -1.1 & -0.3319 & -0.7681 \tabularnewline
79 &  10.9 &  0.6843 &  10.22 \tabularnewline
80 & -8.7 &  4.12 & -12.82 \tabularnewline
81 & -3 &  9.293 & -12.29 \tabularnewline
82 & -9.9 & -10.45 &  0.5506 \tabularnewline
83 &  19.5 &  5.106 &  14.39 \tabularnewline
84 & -7.3 &  1.188 & -8.488 \tabularnewline
85 & -1.5 & -4.559 &  3.059 \tabularnewline
86 & -1.9 & -4.99 &  3.09 \tabularnewline
87 & -3.1 & -3.904 &  0.8039 \tabularnewline
88 &  21.5 &  14.16 &  7.343 \tabularnewline
89 & -31.5 & -19.03 & -12.47 \tabularnewline
90 &  13.2 &  4.165 &  9.035 \tabularnewline
91 & -1 &  3.047 & -4.047 \tabularnewline
92 &  6.8 &  11.31 & -4.514 \tabularnewline
93 & -12 & -0.3461 & -11.65 \tabularnewline
94 &  11.1 & -9.521 &  20.62 \tabularnewline
95 &  9.1 &  1.065 &  8.035 \tabularnewline
96 & -1.3 & -4.355 &  3.055 \tabularnewline
97 & -14.5 &  4.221 & -18.72 \tabularnewline
98 &  17.5 & -5.344 &  22.84 \tabularnewline
99 & -21.9 &  7.828 & -29.73 \tabularnewline
100 & -6.4 & -14.35 &  7.951 \tabularnewline
101 &  22.1 &  20.46 &  1.635 \tabularnewline
102 & -14.8 & -5.831 & -8.969 \tabularnewline
103 & -4 & -8.515 &  4.515 \tabularnewline
104 & -6.5 & -4.098 & -2.402 \tabularnewline
105 &  16.2 &  10.03 &  6.17 \tabularnewline
106 & -4.7 & -5.494 &  0.7936 \tabularnewline
107 & -25.9 & -15.38 & -10.52 \tabularnewline
108 &  10.7 &  7.931 &  2.769 \tabularnewline
109 &  2.9 & -0.1421 &  3.042 \tabularnewline
110 & -18.5 & -12.77 & -5.731 \tabularnewline
111 &  31.3 &  18.29 &  13.01 \tabularnewline
112 &  2.9 & -5.976 &  8.876 \tabularnewline
113 & -13.4 & -5.45 & -7.95 \tabularnewline
114 &  20.4 &  9.042 &  11.36 \tabularnewline
115 & -11.3 & -3.698 & -7.602 \tabularnewline
116 &  7.8 &  4.441 &  3.359 \tabularnewline
117 & -3.7 &  0.2807 & -3.981 \tabularnewline
118 & -7.3 & -0.343 & -6.957 \tabularnewline
119 &  7.4 &  5.713 &  1.687 \tabularnewline
120 & -1.1 & -0.02553 & -1.074 \tabularnewline
121 &  7.3 &  2.633 &  4.667 \tabularnewline
122 & -9.8 & -1.633 & -8.167 \tabularnewline
123 &  9.6 & -2.811 &  12.41 \tabularnewline
124 & -14.1 & -1.712 & -12.39 \tabularnewline
125 & -2.3 &  1.351 & -3.651 \tabularnewline
126 &  1 & -2.779 &  3.779 \tabularnewline
127 &  4.1 &  5.061 & -0.961 \tabularnewline
128 &  6.3 &  1.469 &  4.831 \tabularnewline
129 & -1.7 & -6.472 &  4.772 \tabularnewline
130 & -13 &  1.823 & -14.82 \tabularnewline
131 &  10.8 &  7.601 &  3.199 \tabularnewline
132 & -2.2 & -9.827 &  7.627 \tabularnewline
133 &  2.7 &  2.242 &  0.4584 \tabularnewline
134 &  2.6 &  9.92 & -7.32 \tabularnewline
135 & -6.1 & -8.494 &  2.394 \tabularnewline
136 & -1.1 & -0.916 & -0.184 \tabularnewline
137 &  13.2 &  9 &  4.2 \tabularnewline
138 & -6.9 & -9.784 &  2.884 \tabularnewline
139 & -6.1 &  4.736 & -10.84 \tabularnewline
140 &  17.2 &  0.2375 &  16.96 \tabularnewline
141 & -6.1 & -2.778 & -3.322 \tabularnewline
142 &  5.3 &  13.32 & -8.017 \tabularnewline
143 & -6.6 & -5.983 & -0.6165 \tabularnewline
144 & -2.2 &  1.403 & -3.603 \tabularnewline
145 & -4 & -0.4014 & -3.599 \tabularnewline
146 &  7.6 &  3.009 &  4.591 \tabularnewline
147 & -3.4 & -7.466 &  4.066 \tabularnewline
148 &  0.9 &  4.03 & -3.13 \tabularnewline
149 & -3.4 &  0.0298 & -3.43 \tabularnewline
150 & -20.2 & -3.829 & -16.37 \tabularnewline
151 &  17.8 &  5.29 &  12.51 \tabularnewline
152 & -16.8 & -14.02 & -2.775 \tabularnewline
153 & -5.4 &  3.157 & -8.557 \tabularnewline
154 &  23.9 &  5.46 &  18.44 \tabularnewline
155 & -13.4 & -2.123 & -11.28 \tabularnewline
156 &  3.7 &  0.9555 &  2.745 \tabularnewline
157 &  8.1 & -0.8488 &  8.949 \tabularnewline
158 &  9.2 &  6.644 &  2.556 \tabularnewline
159 & -26.7 & -9.492 & -17.21 \tabularnewline
160 &  23.7 &  10.75 &  12.95 \tabularnewline
161 & -9.7 & -5.984 & -3.716 \tabularnewline
162 &  4.4 &  7.994 & -3.594 \tabularnewline
163 &  19.6 & -2.491 &  22.09 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309617&T=5

[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] 2.9[/C][C] 0.4961[/C][C] 2.404[/C][/ROW]
[ROW][C]2[/C][C] 7.8[/C][C] 6.536[/C][C] 1.264[/C][/ROW]
[ROW][C]3[/C][C]-2.3[/C][C]-3.713[/C][C] 1.413[/C][/ROW]
[ROW][C]4[/C][C]-1.2[/C][C] 3.466[/C][C]-4.666[/C][/ROW]
[ROW][C]5[/C][C]-1.6[/C][C]-0.1911[/C][C]-1.409[/C][/ROW]
[ROW][C]6[/C][C] 0.8[/C][C]-1.557[/C][C] 2.357[/C][/ROW]
[ROW][C]7[/C][C] 12.8[/C][C] 5.269[/C][C] 7.531[/C][/ROW]
[ROW][C]8[/C][C]-14.9[/C][C]-7.88[/C][C]-7.02[/C][/ROW]
[ROW][C]9[/C][C] 3.3[/C][C] 5.456[/C][C]-2.156[/C][/ROW]
[ROW][C]10[/C][C] 4.7[/C][C] 0.4479[/C][C] 4.252[/C][/ROW]
[ROW][C]11[/C][C]-8.9[/C][C]-2.468[/C][C]-6.432[/C][/ROW]
[ROW][C]12[/C][C] 3.2[/C][C] 5.45[/C][C]-2.25[/C][/ROW]
[ROW][C]13[/C][C] 3.7[/C][C]-6.677[/C][C] 10.38[/C][/ROW]
[ROW][C]14[/C][C]-7.4[/C][C] 0.2859[/C][C]-7.686[/C][/ROW]
[ROW][C]15[/C][C]-2.9[/C][C]-0.3797[/C][C]-2.52[/C][/ROW]
[ROW][C]16[/C][C] 5[/C][C] 2.938[/C][C] 2.062[/C][/ROW]
[ROW][C]17[/C][C] 1.4[/C][C]-1.075[/C][C] 2.475[/C][/ROW]
[ROW][C]18[/C][C]-13.8[/C][C]-7.137[/C][C]-6.663[/C][/ROW]
[ROW][C]19[/C][C] 9.4[/C][C] 5.987[/C][C] 3.413[/C][/ROW]
[ROW][C]20[/C][C]-4.5[/C][C]-1.519[/C][C]-2.981[/C][/ROW]
[ROW][C]21[/C][C] 1.2[/C][C]-3.698[/C][C] 4.898[/C][/ROW]
[ROW][C]22[/C][C] 9.6[/C][C] 5.446[/C][C] 4.154[/C][/ROW]
[ROW][C]23[/C][C] 18.2[/C][C]-0.7439[/C][C] 18.94[/C][/ROW]
[ROW][C]24[/C][C]-14[/C][C]-3.592[/C][C]-10.41[/C][/ROW]
[ROW][C]25[/C][C]-11.7[/C][C] 1.192[/C][C]-12.89[/C][/ROW]
[ROW][C]26[/C][C] 7.7[/C][C] 0.9466[/C][C] 6.753[/C][/ROW]
[ROW][C]27[/C][C]-4[/C][C]-1.103[/C][C]-2.897[/C][/ROW]
[ROW][C]28[/C][C] 14[/C][C] 7.513[/C][C] 6.487[/C][/ROW]
[ROW][C]29[/C][C]-11.8[/C][C]-11.93[/C][C] 0.1345[/C][/ROW]
[ROW][C]30[/C][C] 7.8[/C][C] 10.38[/C][C]-2.582[/C][/ROW]
[ROW][C]31[/C][C]-9.6[/C][C]-5.695[/C][C]-3.905[/C][/ROW]
[ROW][C]32[/C][C] 14[/C][C] 1.868[/C][C] 12.13[/C][/ROW]
[ROW][C]33[/C][C] 11.9[/C][C] 7.531[/C][C] 4.369[/C][/ROW]
[ROW][C]34[/C][C]-21.4[/C][C]-8.641[/C][C]-12.76[/C][/ROW]
[ROW][C]35[/C][C]-9.4[/C][C]-11.23[/C][C] 1.826[/C][/ROW]
[ROW][C]36[/C][C] 11.1[/C][C] 21.58[/C][C]-10.48[/C][/ROW]
[ROW][C]37[/C][C] 3.2[/C][C]-2.002[/C][C] 5.202[/C][/ROW]
[ROW][C]38[/C][C] 1.8[/C][C] 1.395[/C][C] 0.4047[/C][/ROW]
[ROW][C]39[/C][C]-0.6[/C][C] 1.829[/C][C]-2.429[/C][/ROW]
[ROW][C]40[/C][C]-5.8[/C][C]-9.753[/C][C] 3.953[/C][/ROW]
[ROW][C]41[/C][C] 3.9[/C][C] 9.829[/C][C]-5.929[/C][/ROW]
[ROW][C]42[/C][C]-3.5[/C][C] 3.892[/C][C]-7.392[/C][/ROW]
[ROW][C]43[/C][C] 16.9[/C][C]-3.261[/C][C] 20.16[/C][/ROW]
[ROW][C]44[/C][C] 22[/C][C]-6.104[/C][C] 28.1[/C][/ROW]
[ROW][C]45[/C][C] 12.7[/C][C] 4.287[/C][C] 8.413[/C][/ROW]
[ROW][C]46[/C][C]-32.1[/C][C]-1.087[/C][C]-31.01[/C][/ROW]
[ROW][C]47[/C][C]-3.1[/C][C]-1.368[/C][C]-1.732[/C][/ROW]
[ROW][C]48[/C][C]-7.1[/C][C]-5.14[/C][C]-1.96[/C][/ROW]
[ROW][C]49[/C][C] 6.4[/C][C] 8.239[/C][C]-1.839[/C][/ROW]
[ROW][C]50[/C][C]-17.1[/C][C]-10.95[/C][C]-6.148[/C][/ROW]
[ROW][C]51[/C][C] 21.4[/C][C] 10.5[/C][C] 10.9[/C][/ROW]
[ROW][C]52[/C][C]-11.1[/C][C]-8.908[/C][C]-2.192[/C][/ROW]
[ROW][C]53[/C][C]-4.4[/C][C] 2.516[/C][C]-6.916[/C][/ROW]
[ROW][C]54[/C][C] 9.8[/C][C]-2.095[/C][C] 11.9[/C][/ROW]
[ROW][C]55[/C][C]-30.5[/C][C]-5.587[/C][C]-24.91[/C][/ROW]
[ROW][C]56[/C][C]-4.3[/C][C] 0.02007[/C][C]-4.32[/C][/ROW]
[ROW][C]57[/C][C]-28.4[/C][C]-20.97[/C][C]-7.426[/C][/ROW]
[ROW][C]58[/C][C] 28.9[/C][C] 8.237[/C][C] 20.66[/C][/ROW]
[ROW][C]59[/C][C]-18.8[/C][C] 4.734[/C][C]-23.53[/C][/ROW]
[ROW][C]60[/C][C] 13.4[/C][C]-1.073[/C][C] 14.47[/C][/ROW]
[ROW][C]61[/C][C]-1.9[/C][C]-5.348[/C][C] 3.448[/C][/ROW]
[ROW][C]62[/C][C] 14.7[/C][C] 8.168[/C][C] 6.532[/C][/ROW]
[ROW][C]63[/C][C]-21.7[/C][C]-8.15[/C][C]-13.55[/C][/ROW]
[ROW][C]64[/C][C]-2.5[/C][C] 14.12[/C][C]-16.62[/C][/ROW]
[ROW][C]65[/C][C] 13[/C][C]-7.706[/C][C] 20.71[/C][/ROW]
[ROW][C]66[/C][C]-10.7[/C][C]-2.033[/C][C]-8.667[/C][/ROW]
[ROW][C]67[/C][C] 5.7[/C][C] 14.31[/C][C]-8.613[/C][/ROW]
[ROW][C]68[/C][C]-13.6[/C][C]-14.72[/C][C] 1.119[/C][/ROW]
[ROW][C]69[/C][C] 7[/C][C] 2.026[/C][C] 4.974[/C][/ROW]
[ROW][C]70[/C][C] 17.6[/C][C] 17.66[/C][C]-0.06386[/C][/ROW]
[ROW][C]71[/C][C]-2.5[/C][C] 10.05[/C][C]-12.55[/C][/ROW]
[ROW][C]72[/C][C] 2[/C][C]-5.462[/C][C] 7.462[/C][/ROW]
[ROW][C]73[/C][C] 5.6[/C][C] 0.02493[/C][C] 5.575[/C][/ROW]
[ROW][C]74[/C][C] 10.8[/C][C] 5.667[/C][C] 5.133[/C][/ROW]
[ROW][C]75[/C][C]-0.7[/C][C]-4.322[/C][C] 3.622[/C][/ROW]
[ROW][C]76[/C][C] 1.3[/C][C]-4.566[/C][C] 5.866[/C][/ROW]
[ROW][C]77[/C][C] 4.2[/C][C] 9.584[/C][C]-5.384[/C][/ROW]
[ROW][C]78[/C][C]-1.1[/C][C]-0.3319[/C][C]-0.7681[/C][/ROW]
[ROW][C]79[/C][C] 10.9[/C][C] 0.6843[/C][C] 10.22[/C][/ROW]
[ROW][C]80[/C][C]-8.7[/C][C] 4.12[/C][C]-12.82[/C][/ROW]
[ROW][C]81[/C][C]-3[/C][C] 9.293[/C][C]-12.29[/C][/ROW]
[ROW][C]82[/C][C]-9.9[/C][C]-10.45[/C][C] 0.5506[/C][/ROW]
[ROW][C]83[/C][C] 19.5[/C][C] 5.106[/C][C] 14.39[/C][/ROW]
[ROW][C]84[/C][C]-7.3[/C][C] 1.188[/C][C]-8.488[/C][/ROW]
[ROW][C]85[/C][C]-1.5[/C][C]-4.559[/C][C] 3.059[/C][/ROW]
[ROW][C]86[/C][C]-1.9[/C][C]-4.99[/C][C] 3.09[/C][/ROW]
[ROW][C]87[/C][C]-3.1[/C][C]-3.904[/C][C] 0.8039[/C][/ROW]
[ROW][C]88[/C][C] 21.5[/C][C] 14.16[/C][C] 7.343[/C][/ROW]
[ROW][C]89[/C][C]-31.5[/C][C]-19.03[/C][C]-12.47[/C][/ROW]
[ROW][C]90[/C][C] 13.2[/C][C] 4.165[/C][C] 9.035[/C][/ROW]
[ROW][C]91[/C][C]-1[/C][C] 3.047[/C][C]-4.047[/C][/ROW]
[ROW][C]92[/C][C] 6.8[/C][C] 11.31[/C][C]-4.514[/C][/ROW]
[ROW][C]93[/C][C]-12[/C][C]-0.3461[/C][C]-11.65[/C][/ROW]
[ROW][C]94[/C][C] 11.1[/C][C]-9.521[/C][C] 20.62[/C][/ROW]
[ROW][C]95[/C][C] 9.1[/C][C] 1.065[/C][C] 8.035[/C][/ROW]
[ROW][C]96[/C][C]-1.3[/C][C]-4.355[/C][C] 3.055[/C][/ROW]
[ROW][C]97[/C][C]-14.5[/C][C] 4.221[/C][C]-18.72[/C][/ROW]
[ROW][C]98[/C][C] 17.5[/C][C]-5.344[/C][C] 22.84[/C][/ROW]
[ROW][C]99[/C][C]-21.9[/C][C] 7.828[/C][C]-29.73[/C][/ROW]
[ROW][C]100[/C][C]-6.4[/C][C]-14.35[/C][C] 7.951[/C][/ROW]
[ROW][C]101[/C][C] 22.1[/C][C] 20.46[/C][C] 1.635[/C][/ROW]
[ROW][C]102[/C][C]-14.8[/C][C]-5.831[/C][C]-8.969[/C][/ROW]
[ROW][C]103[/C][C]-4[/C][C]-8.515[/C][C] 4.515[/C][/ROW]
[ROW][C]104[/C][C]-6.5[/C][C]-4.098[/C][C]-2.402[/C][/ROW]
[ROW][C]105[/C][C] 16.2[/C][C] 10.03[/C][C] 6.17[/C][/ROW]
[ROW][C]106[/C][C]-4.7[/C][C]-5.494[/C][C] 0.7936[/C][/ROW]
[ROW][C]107[/C][C]-25.9[/C][C]-15.38[/C][C]-10.52[/C][/ROW]
[ROW][C]108[/C][C] 10.7[/C][C] 7.931[/C][C] 2.769[/C][/ROW]
[ROW][C]109[/C][C] 2.9[/C][C]-0.1421[/C][C] 3.042[/C][/ROW]
[ROW][C]110[/C][C]-18.5[/C][C]-12.77[/C][C]-5.731[/C][/ROW]
[ROW][C]111[/C][C] 31.3[/C][C] 18.29[/C][C] 13.01[/C][/ROW]
[ROW][C]112[/C][C] 2.9[/C][C]-5.976[/C][C] 8.876[/C][/ROW]
[ROW][C]113[/C][C]-13.4[/C][C]-5.45[/C][C]-7.95[/C][/ROW]
[ROW][C]114[/C][C] 20.4[/C][C] 9.042[/C][C] 11.36[/C][/ROW]
[ROW][C]115[/C][C]-11.3[/C][C]-3.698[/C][C]-7.602[/C][/ROW]
[ROW][C]116[/C][C] 7.8[/C][C] 4.441[/C][C] 3.359[/C][/ROW]
[ROW][C]117[/C][C]-3.7[/C][C] 0.2807[/C][C]-3.981[/C][/ROW]
[ROW][C]118[/C][C]-7.3[/C][C]-0.343[/C][C]-6.957[/C][/ROW]
[ROW][C]119[/C][C] 7.4[/C][C] 5.713[/C][C] 1.687[/C][/ROW]
[ROW][C]120[/C][C]-1.1[/C][C]-0.02553[/C][C]-1.074[/C][/ROW]
[ROW][C]121[/C][C] 7.3[/C][C] 2.633[/C][C] 4.667[/C][/ROW]
[ROW][C]122[/C][C]-9.8[/C][C]-1.633[/C][C]-8.167[/C][/ROW]
[ROW][C]123[/C][C] 9.6[/C][C]-2.811[/C][C] 12.41[/C][/ROW]
[ROW][C]124[/C][C]-14.1[/C][C]-1.712[/C][C]-12.39[/C][/ROW]
[ROW][C]125[/C][C]-2.3[/C][C] 1.351[/C][C]-3.651[/C][/ROW]
[ROW][C]126[/C][C] 1[/C][C]-2.779[/C][C] 3.779[/C][/ROW]
[ROW][C]127[/C][C] 4.1[/C][C] 5.061[/C][C]-0.961[/C][/ROW]
[ROW][C]128[/C][C] 6.3[/C][C] 1.469[/C][C] 4.831[/C][/ROW]
[ROW][C]129[/C][C]-1.7[/C][C]-6.472[/C][C] 4.772[/C][/ROW]
[ROW][C]130[/C][C]-13[/C][C] 1.823[/C][C]-14.82[/C][/ROW]
[ROW][C]131[/C][C] 10.8[/C][C] 7.601[/C][C] 3.199[/C][/ROW]
[ROW][C]132[/C][C]-2.2[/C][C]-9.827[/C][C] 7.627[/C][/ROW]
[ROW][C]133[/C][C] 2.7[/C][C] 2.242[/C][C] 0.4584[/C][/ROW]
[ROW][C]134[/C][C] 2.6[/C][C] 9.92[/C][C]-7.32[/C][/ROW]
[ROW][C]135[/C][C]-6.1[/C][C]-8.494[/C][C] 2.394[/C][/ROW]
[ROW][C]136[/C][C]-1.1[/C][C]-0.916[/C][C]-0.184[/C][/ROW]
[ROW][C]137[/C][C] 13.2[/C][C] 9[/C][C] 4.2[/C][/ROW]
[ROW][C]138[/C][C]-6.9[/C][C]-9.784[/C][C] 2.884[/C][/ROW]
[ROW][C]139[/C][C]-6.1[/C][C] 4.736[/C][C]-10.84[/C][/ROW]
[ROW][C]140[/C][C] 17.2[/C][C] 0.2375[/C][C] 16.96[/C][/ROW]
[ROW][C]141[/C][C]-6.1[/C][C]-2.778[/C][C]-3.322[/C][/ROW]
[ROW][C]142[/C][C] 5.3[/C][C] 13.32[/C][C]-8.017[/C][/ROW]
[ROW][C]143[/C][C]-6.6[/C][C]-5.983[/C][C]-0.6165[/C][/ROW]
[ROW][C]144[/C][C]-2.2[/C][C] 1.403[/C][C]-3.603[/C][/ROW]
[ROW][C]145[/C][C]-4[/C][C]-0.4014[/C][C]-3.599[/C][/ROW]
[ROW][C]146[/C][C] 7.6[/C][C] 3.009[/C][C] 4.591[/C][/ROW]
[ROW][C]147[/C][C]-3.4[/C][C]-7.466[/C][C] 4.066[/C][/ROW]
[ROW][C]148[/C][C] 0.9[/C][C] 4.03[/C][C]-3.13[/C][/ROW]
[ROW][C]149[/C][C]-3.4[/C][C] 0.0298[/C][C]-3.43[/C][/ROW]
[ROW][C]150[/C][C]-20.2[/C][C]-3.829[/C][C]-16.37[/C][/ROW]
[ROW][C]151[/C][C] 17.8[/C][C] 5.29[/C][C] 12.51[/C][/ROW]
[ROW][C]152[/C][C]-16.8[/C][C]-14.02[/C][C]-2.775[/C][/ROW]
[ROW][C]153[/C][C]-5.4[/C][C] 3.157[/C][C]-8.557[/C][/ROW]
[ROW][C]154[/C][C] 23.9[/C][C] 5.46[/C][C] 18.44[/C][/ROW]
[ROW][C]155[/C][C]-13.4[/C][C]-2.123[/C][C]-11.28[/C][/ROW]
[ROW][C]156[/C][C] 3.7[/C][C] 0.9555[/C][C] 2.745[/C][/ROW]
[ROW][C]157[/C][C] 8.1[/C][C]-0.8488[/C][C] 8.949[/C][/ROW]
[ROW][C]158[/C][C] 9.2[/C][C] 6.644[/C][C] 2.556[/C][/ROW]
[ROW][C]159[/C][C]-26.7[/C][C]-9.492[/C][C]-17.21[/C][/ROW]
[ROW][C]160[/C][C] 23.7[/C][C] 10.75[/C][C] 12.95[/C][/ROW]
[ROW][C]161[/C][C]-9.7[/C][C]-5.984[/C][C]-3.716[/C][/ROW]
[ROW][C]162[/C][C] 4.4[/C][C] 7.994[/C][C]-3.594[/C][/ROW]
[ROW][C]163[/C][C] 19.6[/C][C]-2.491[/C][C] 22.09[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309617&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309617&T=5

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	2.9	0.4961	2.404
2	7.8	6.536	1.264
3	-2.3	-3.713	1.413
4	-1.2	3.466	-4.666
5	-1.6	-0.1911	-1.409
6	0.8	-1.557	2.357
7	12.8	5.269	7.531
8	-14.9	-7.88	-7.02
9	3.3	5.456	-2.156
10	4.7	0.4479	4.252
11	-8.9	-2.468	-6.432
12	3.2	5.45	-2.25
13	3.7	-6.677	10.38
14	-7.4	0.2859	-7.686
15	-2.9	-0.3797	-2.52
16	5	2.938	2.062
17	1.4	-1.075	2.475
18	-13.8	-7.137	-6.663
19	9.4	5.987	3.413
20	-4.5	-1.519	-2.981
21	1.2	-3.698	4.898
22	9.6	5.446	4.154
23	18.2	-0.7439	18.94
24	-14	-3.592	-10.41
25	-11.7	1.192	-12.89
26	7.7	0.9466	6.753
27	-4	-1.103	-2.897
28	14	7.513	6.487
29	-11.8	-11.93	0.1345
30	7.8	10.38	-2.582
31	-9.6	-5.695	-3.905
32	14	1.868	12.13
33	11.9	7.531	4.369
34	-21.4	-8.641	-12.76
35	-9.4	-11.23	1.826
36	11.1	21.58	-10.48
37	3.2	-2.002	5.202
38	1.8	1.395	0.4047
39	-0.6	1.829	-2.429
40	-5.8	-9.753	3.953
41	3.9	9.829	-5.929
42	-3.5	3.892	-7.392
43	16.9	-3.261	20.16
44	22	-6.104	28.1
45	12.7	4.287	8.413
46	-32.1	-1.087	-31.01
47	-3.1	-1.368	-1.732
48	-7.1	-5.14	-1.96
49	6.4	8.239	-1.839
50	-17.1	-10.95	-6.148
51	21.4	10.5	10.9
52	-11.1	-8.908	-2.192
53	-4.4	2.516	-6.916
54	9.8	-2.095	11.9
55	-30.5	-5.587	-24.91
56	-4.3	0.02007	-4.32
57	-28.4	-20.97	-7.426
58	28.9	8.237	20.66
59	-18.8	4.734	-23.53
60	13.4	-1.073	14.47
61	-1.9	-5.348	3.448
62	14.7	8.168	6.532
63	-21.7	-8.15	-13.55
64	-2.5	14.12	-16.62
65	13	-7.706	20.71
66	-10.7	-2.033	-8.667
67	5.7	14.31	-8.613
68	-13.6	-14.72	1.119
69	7	2.026	4.974
70	17.6	17.66	-0.06386
71	-2.5	10.05	-12.55
72	2	-5.462	7.462
73	5.6	0.02493	5.575
74	10.8	5.667	5.133
75	-0.7	-4.322	3.622
76	1.3	-4.566	5.866
77	4.2	9.584	-5.384
78	-1.1	-0.3319	-0.7681
79	10.9	0.6843	10.22
80	-8.7	4.12	-12.82
81	-3	9.293	-12.29
82	-9.9	-10.45	0.5506
83	19.5	5.106	14.39
84	-7.3	1.188	-8.488
85	-1.5	-4.559	3.059
86	-1.9	-4.99	3.09
87	-3.1	-3.904	0.8039
88	21.5	14.16	7.343
89	-31.5	-19.03	-12.47
90	13.2	4.165	9.035
91	-1	3.047	-4.047
92	6.8	11.31	-4.514
93	-12	-0.3461	-11.65
94	11.1	-9.521	20.62
95	9.1	1.065	8.035
96	-1.3	-4.355	3.055
97	-14.5	4.221	-18.72
98	17.5	-5.344	22.84
99	-21.9	7.828	-29.73
100	-6.4	-14.35	7.951
101	22.1	20.46	1.635
102	-14.8	-5.831	-8.969
103	-4	-8.515	4.515
104	-6.5	-4.098	-2.402
105	16.2	10.03	6.17
106	-4.7	-5.494	0.7936
107	-25.9	-15.38	-10.52
108	10.7	7.931	2.769
109	2.9	-0.1421	3.042
110	-18.5	-12.77	-5.731
111	31.3	18.29	13.01
112	2.9	-5.976	8.876
113	-13.4	-5.45	-7.95
114	20.4	9.042	11.36
115	-11.3	-3.698	-7.602
116	7.8	4.441	3.359
117	-3.7	0.2807	-3.981
118	-7.3	-0.343	-6.957
119	7.4	5.713	1.687
120	-1.1	-0.02553	-1.074
121	7.3	2.633	4.667
122	-9.8	-1.633	-8.167
123	9.6	-2.811	12.41
124	-14.1	-1.712	-12.39
125	-2.3	1.351	-3.651
126	1	-2.779	3.779
127	4.1	5.061	-0.961
128	6.3	1.469	4.831
129	-1.7	-6.472	4.772
130	-13	1.823	-14.82
131	10.8	7.601	3.199
132	-2.2	-9.827	7.627
133	2.7	2.242	0.4584
134	2.6	9.92	-7.32
135	-6.1	-8.494	2.394
136	-1.1	-0.916	-0.184
137	13.2	9	4.2
138	-6.9	-9.784	2.884
139	-6.1	4.736	-10.84
140	17.2	0.2375	16.96
141	-6.1	-2.778	-3.322
142	5.3	13.32	-8.017
143	-6.6	-5.983	-0.6165
144	-2.2	1.403	-3.603
145	-4	-0.4014	-3.599
146	7.6	3.009	4.591
147	-3.4	-7.466	4.066
148	0.9	4.03	-3.13
149	-3.4	0.0298	-3.43
150	-20.2	-3.829	-16.37
151	17.8	5.29	12.51
152	-16.8	-14.02	-2.775
153	-5.4	3.157	-8.557
154	23.9	5.46	18.44
155	-13.4	-2.123	-11.28
156	3.7	0.9555	2.745
157	8.1	-0.8488	8.949
158	9.2	6.644	2.556
159	-26.7	-9.492	-17.21
160	23.7	10.75	12.95
161	-9.7	-5.984	-3.716
162	4.4	7.994	-3.594
163	19.6	-2.491	22.09

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
9	0.1233	0.2466	0.8767
10	0.096	0.192	0.904
11	0.06005	0.1201	0.9399
12	0.04219	0.08438	0.9578
13	0.04501	0.09003	0.955
14	0.03707	0.07414	0.9629
15	0.02718	0.05436	0.9728
16	0.01507	0.03013	0.9849
17	0.007929	0.01586	0.9921
18	0.00667	0.01334	0.9933
19	0.00427	0.008539	0.9957
20	0.003105	0.006209	0.9969
21	0.001992	0.003983	0.998
22	0.001309	0.002618	0.9987
23	0.01156	0.02312	0.9884
24	0.01121	0.02243	0.9888
25	0.03054	0.06107	0.9695
26	0.02512	0.05024	0.9749
27	0.01658	0.03316	0.9834
28	0.01234	0.02467	0.9877
29	0.007604	0.01521	0.9924
30	0.005643	0.01129	0.9944
31	0.003401	0.006802	0.9966
32	0.002901	0.005802	0.9971
33	0.002095	0.004189	0.9979
34	0.002796	0.005592	0.9972
35	0.002139	0.004278	0.9979
36	0.003757	0.007514	0.9962
37	0.002594	0.005188	0.9974
38	0.001594	0.003189	0.9984
39	0.001021	0.002042	0.999
40	0.00078	0.00156	0.9992
41	0.0006237	0.001247	0.9994
42	0.0005672	0.001134	0.9994
43	0.00189	0.003779	0.9981
44	0.07056	0.1411	0.9294
45	0.06964	0.1393	0.9304
46	0.3583	0.7166	0.6417
47	0.3116	0.6231	0.6884
48	0.2884	0.5767	0.7116
49	0.247	0.4941	0.753
50	0.224	0.4481	0.776
51	0.2271	0.4542	0.7729
52	0.191	0.382	0.809
53	0.1771	0.3543	0.8229
54	0.1948	0.3896	0.8052
55	0.4461	0.8922	0.5539
56	0.404	0.808	0.596
57	0.3863	0.7726	0.6137
58	0.5109	0.9782	0.4891
59	0.7061	0.5878	0.2939
60	0.7449	0.5101	0.2551
61	0.7106	0.5787	0.2894
62	0.6885	0.6231	0.3115
63	0.7149	0.5701	0.2851
64	0.795	0.41	0.205
65	0.8769	0.2462	0.1231
66	0.869	0.262	0.131
67	0.8708	0.2584	0.1292
68	0.8472	0.3055	0.1528
69	0.827	0.3459	0.173
70	0.7979	0.4041	0.2021
71	0.8248	0.3505	0.1752
72	0.8146	0.3709	0.1854
73	0.794	0.4121	0.206
74	0.7706	0.4589	0.2294
75	0.7422	0.5156	0.2578
76	0.7281	0.5439	0.2719
77	0.7375	0.5251	0.2625
78	0.6999	0.6003	0.3001
79	0.717	0.5661	0.283
80	0.7665	0.4669	0.2334
81	0.7781	0.4438	0.2219
82	0.7426	0.5149	0.2574
83	0.7788	0.4424	0.2212
84	0.7767	0.4466	0.2233
85	0.7439	0.5122	0.2561
86	0.7063	0.5873	0.2937
87	0.6777	0.6446	0.3223
88	0.6545	0.691	0.3455
89	0.6706	0.6589	0.3294
90	0.6671	0.6658	0.3329
91	0.639	0.722	0.361
92	0.6041	0.7917	0.3959
93	0.6199	0.7603	0.3801
94	0.7566	0.4867	0.2434
95	0.7349	0.5301	0.2651
96	0.7059	0.5881	0.2941
97	0.8325	0.3351	0.1675
98	0.9307	0.1387	0.06933
99	0.9969	0.006211	0.003105
100	0.9969	0.006188	0.003094
101	0.9959	0.008255	0.004128
102	0.9959	0.008249	0.004125
103	0.9958	0.008498	0.004249
104	0.9939	0.01213	0.006064
105	0.992	0.01599	0.007995
106	0.989	0.02196	0.01098
107	0.989	0.02191	0.01096
108	0.9849	0.03015	0.01507
109	0.9853	0.02935	0.01468
110	0.9807	0.03861	0.0193
111	0.983	0.03403	0.01701
112	0.9833	0.03335	0.01667
113	0.9789	0.04224	0.02112
114	0.9805	0.03904	0.01952
115	0.9795	0.0409	0.02045
116	0.9763	0.04739	0.0237
117	0.9737	0.05262	0.02631
118	0.968	0.06402	0.03201
119	0.9687	0.06263	0.03132
120	0.9605	0.07905	0.03953
121	0.9492	0.1017	0.05085
122	0.9366	0.1267	0.06336
123	0.9256	0.1487	0.07437
124	0.932	0.1359	0.06797
125	0.9163	0.1675	0.08374
126	0.8964	0.2072	0.1036
127	0.874	0.252	0.126
128	0.8415	0.3171	0.1585
129	0.8142	0.3715	0.1858
130	0.8355	0.3289	0.1645
131	0.7959	0.4082	0.2041
132	0.8152	0.3697	0.1848
133	0.7776	0.4448	0.2224
134	0.7405	0.519	0.2595
135	0.6949	0.6102	0.3051
136	0.6806	0.6387	0.3194
137	0.6252	0.7496	0.3748
138	0.5748	0.8504	0.4252
139	0.6864	0.6271	0.3136
140	0.7117	0.5766	0.2883
141	0.6447	0.7106	0.3553
142	0.6525	0.695	0.3475
143	0.5776	0.8447	0.4224
144	0.5803	0.8393	0.4197
145	0.5107	0.9785	0.4893
146	0.4355	0.8709	0.5645
147	0.3977	0.7955	0.6023
148	0.3568	0.7136	0.6432
149	0.488	0.976	0.512
150	0.4484	0.8968	0.5516
151	0.5504	0.8991	0.4496
152	0.4288	0.8575	0.5712
153	0.3995	0.7991	0.6005
154	0.2746	0.5491	0.7254

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 &  0.1233 &  0.2466 &  0.8767 \tabularnewline
10 &  0.096 &  0.192 &  0.904 \tabularnewline
11 &  0.06005 &  0.1201 &  0.9399 \tabularnewline
12 &  0.04219 &  0.08438 &  0.9578 \tabularnewline
13 &  0.04501 &  0.09003 &  0.955 \tabularnewline
14 &  0.03707 &  0.07414 &  0.9629 \tabularnewline
15 &  0.02718 &  0.05436 &  0.9728 \tabularnewline
16 &  0.01507 &  0.03013 &  0.9849 \tabularnewline
17 &  0.007929 &  0.01586 &  0.9921 \tabularnewline
18 &  0.00667 &  0.01334 &  0.9933 \tabularnewline
19 &  0.00427 &  0.008539 &  0.9957 \tabularnewline
20 &  0.003105 &  0.006209 &  0.9969 \tabularnewline
21 &  0.001992 &  0.003983 &  0.998 \tabularnewline
22 &  0.001309 &  0.002618 &  0.9987 \tabularnewline
23 &  0.01156 &  0.02312 &  0.9884 \tabularnewline
24 &  0.01121 &  0.02243 &  0.9888 \tabularnewline
25 &  0.03054 &  0.06107 &  0.9695 \tabularnewline
26 &  0.02512 &  0.05024 &  0.9749 \tabularnewline
27 &  0.01658 &  0.03316 &  0.9834 \tabularnewline
28 &  0.01234 &  0.02467 &  0.9877 \tabularnewline
29 &  0.007604 &  0.01521 &  0.9924 \tabularnewline
30 &  0.005643 &  0.01129 &  0.9944 \tabularnewline
31 &  0.003401 &  0.006802 &  0.9966 \tabularnewline
32 &  0.002901 &  0.005802 &  0.9971 \tabularnewline
33 &  0.002095 &  0.004189 &  0.9979 \tabularnewline
34 &  0.002796 &  0.005592 &  0.9972 \tabularnewline
35 &  0.002139 &  0.004278 &  0.9979 \tabularnewline
36 &  0.003757 &  0.007514 &  0.9962 \tabularnewline
37 &  0.002594 &  0.005188 &  0.9974 \tabularnewline
38 &  0.001594 &  0.003189 &  0.9984 \tabularnewline
39 &  0.001021 &  0.002042 &  0.999 \tabularnewline
40 &  0.00078 &  0.00156 &  0.9992 \tabularnewline
41 &  0.0006237 &  0.001247 &  0.9994 \tabularnewline
42 &  0.0005672 &  0.001134 &  0.9994 \tabularnewline
43 &  0.00189 &  0.003779 &  0.9981 \tabularnewline
44 &  0.07056 &  0.1411 &  0.9294 \tabularnewline
45 &  0.06964 &  0.1393 &  0.9304 \tabularnewline
46 &  0.3583 &  0.7166 &  0.6417 \tabularnewline
47 &  0.3116 &  0.6231 &  0.6884 \tabularnewline
48 &  0.2884 &  0.5767 &  0.7116 \tabularnewline
49 &  0.247 &  0.4941 &  0.753 \tabularnewline
50 &  0.224 &  0.4481 &  0.776 \tabularnewline
51 &  0.2271 &  0.4542 &  0.7729 \tabularnewline
52 &  0.191 &  0.382 &  0.809 \tabularnewline
53 &  0.1771 &  0.3543 &  0.8229 \tabularnewline
54 &  0.1948 &  0.3896 &  0.8052 \tabularnewline
55 &  0.4461 &  0.8922 &  0.5539 \tabularnewline
56 &  0.404 &  0.808 &  0.596 \tabularnewline
57 &  0.3863 &  0.7726 &  0.6137 \tabularnewline
58 &  0.5109 &  0.9782 &  0.4891 \tabularnewline
59 &  0.7061 &  0.5878 &  0.2939 \tabularnewline
60 &  0.7449 &  0.5101 &  0.2551 \tabularnewline
61 &  0.7106 &  0.5787 &  0.2894 \tabularnewline
62 &  0.6885 &  0.6231 &  0.3115 \tabularnewline
63 &  0.7149 &  0.5701 &  0.2851 \tabularnewline
64 &  0.795 &  0.41 &  0.205 \tabularnewline
65 &  0.8769 &  0.2462 &  0.1231 \tabularnewline
66 &  0.869 &  0.262 &  0.131 \tabularnewline
67 &  0.8708 &  0.2584 &  0.1292 \tabularnewline
68 &  0.8472 &  0.3055 &  0.1528 \tabularnewline
69 &  0.827 &  0.3459 &  0.173 \tabularnewline
70 &  0.7979 &  0.4041 &  0.2021 \tabularnewline
71 &  0.8248 &  0.3505 &  0.1752 \tabularnewline
72 &  0.8146 &  0.3709 &  0.1854 \tabularnewline
73 &  0.794 &  0.4121 &  0.206 \tabularnewline
74 &  0.7706 &  0.4589 &  0.2294 \tabularnewline
75 &  0.7422 &  0.5156 &  0.2578 \tabularnewline
76 &  0.7281 &  0.5439 &  0.2719 \tabularnewline
77 &  0.7375 &  0.5251 &  0.2625 \tabularnewline
78 &  0.6999 &  0.6003 &  0.3001 \tabularnewline
79 &  0.717 &  0.5661 &  0.283 \tabularnewline
80 &  0.7665 &  0.4669 &  0.2334 \tabularnewline
81 &  0.7781 &  0.4438 &  0.2219 \tabularnewline
82 &  0.7426 &  0.5149 &  0.2574 \tabularnewline
83 &  0.7788 &  0.4424 &  0.2212 \tabularnewline
84 &  0.7767 &  0.4466 &  0.2233 \tabularnewline
85 &  0.7439 &  0.5122 &  0.2561 \tabularnewline
86 &  0.7063 &  0.5873 &  0.2937 \tabularnewline
87 &  0.6777 &  0.6446 &  0.3223 \tabularnewline
88 &  0.6545 &  0.691 &  0.3455 \tabularnewline
89 &  0.6706 &  0.6589 &  0.3294 \tabularnewline
90 &  0.6671 &  0.6658 &  0.3329 \tabularnewline
91 &  0.639 &  0.722 &  0.361 \tabularnewline
92 &  0.6041 &  0.7917 &  0.3959 \tabularnewline
93 &  0.6199 &  0.7603 &  0.3801 \tabularnewline
94 &  0.7566 &  0.4867 &  0.2434 \tabularnewline
95 &  0.7349 &  0.5301 &  0.2651 \tabularnewline
96 &  0.7059 &  0.5881 &  0.2941 \tabularnewline
97 &  0.8325 &  0.3351 &  0.1675 \tabularnewline
98 &  0.9307 &  0.1387 &  0.06933 \tabularnewline
99 &  0.9969 &  0.006211 &  0.003105 \tabularnewline
100 &  0.9969 &  0.006188 &  0.003094 \tabularnewline
101 &  0.9959 &  0.008255 &  0.004128 \tabularnewline
102 &  0.9959 &  0.008249 &  0.004125 \tabularnewline
103 &  0.9958 &  0.008498 &  0.004249 \tabularnewline
104 &  0.9939 &  0.01213 &  0.006064 \tabularnewline
105 &  0.992 &  0.01599 &  0.007995 \tabularnewline
106 &  0.989 &  0.02196 &  0.01098 \tabularnewline
107 &  0.989 &  0.02191 &  0.01096 \tabularnewline
108 &  0.9849 &  0.03015 &  0.01507 \tabularnewline
109 &  0.9853 &  0.02935 &  0.01468 \tabularnewline
110 &  0.9807 &  0.03861 &  0.0193 \tabularnewline
111 &  0.983 &  0.03403 &  0.01701 \tabularnewline
112 &  0.9833 &  0.03335 &  0.01667 \tabularnewline
113 &  0.9789 &  0.04224 &  0.02112 \tabularnewline
114 &  0.9805 &  0.03904 &  0.01952 \tabularnewline
115 &  0.9795 &  0.0409 &  0.02045 \tabularnewline
116 &  0.9763 &  0.04739 &  0.0237 \tabularnewline
117 &  0.9737 &  0.05262 &  0.02631 \tabularnewline
118 &  0.968 &  0.06402 &  0.03201 \tabularnewline
119 &  0.9687 &  0.06263 &  0.03132 \tabularnewline
120 &  0.9605 &  0.07905 &  0.03953 \tabularnewline
121 &  0.9492 &  0.1017 &  0.05085 \tabularnewline
122 &  0.9366 &  0.1267 &  0.06336 \tabularnewline
123 &  0.9256 &  0.1487 &  0.07437 \tabularnewline
124 &  0.932 &  0.1359 &  0.06797 \tabularnewline
125 &  0.9163 &  0.1675 &  0.08374 \tabularnewline
126 &  0.8964 &  0.2072 &  0.1036 \tabularnewline
127 &  0.874 &  0.252 &  0.126 \tabularnewline
128 &  0.8415 &  0.3171 &  0.1585 \tabularnewline
129 &  0.8142 &  0.3715 &  0.1858 \tabularnewline
130 &  0.8355 &  0.3289 &  0.1645 \tabularnewline
131 &  0.7959 &  0.4082 &  0.2041 \tabularnewline
132 &  0.8152 &  0.3697 &  0.1848 \tabularnewline
133 &  0.7776 &  0.4448 &  0.2224 \tabularnewline
134 &  0.7405 &  0.519 &  0.2595 \tabularnewline
135 &  0.6949 &  0.6102 &  0.3051 \tabularnewline
136 &  0.6806 &  0.6387 &  0.3194 \tabularnewline
137 &  0.6252 &  0.7496 &  0.3748 \tabularnewline
138 &  0.5748 &  0.8504 &  0.4252 \tabularnewline
139 &  0.6864 &  0.6271 &  0.3136 \tabularnewline
140 &  0.7117 &  0.5766 &  0.2883 \tabularnewline
141 &  0.6447 &  0.7106 &  0.3553 \tabularnewline
142 &  0.6525 &  0.695 &  0.3475 \tabularnewline
143 &  0.5776 &  0.8447 &  0.4224 \tabularnewline
144 &  0.5803 &  0.8393 &  0.4197 \tabularnewline
145 &  0.5107 &  0.9785 &  0.4893 \tabularnewline
146 &  0.4355 &  0.8709 &  0.5645 \tabularnewline
147 &  0.3977 &  0.7955 &  0.6023 \tabularnewline
148 &  0.3568 &  0.7136 &  0.6432 \tabularnewline
149 &  0.488 &  0.976 &  0.512 \tabularnewline
150 &  0.4484 &  0.8968 &  0.5516 \tabularnewline
151 &  0.5504 &  0.8991 &  0.4496 \tabularnewline
152 &  0.4288 &  0.8575 &  0.5712 \tabularnewline
153 &  0.3995 &  0.7991 &  0.6005 \tabularnewline
154 &  0.2746 &  0.5491 &  0.7254 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309617&T=6

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]9[/C][C] 0.1233[/C][C] 0.2466[/C][C] 0.8767[/C][/ROW]
[ROW][C]10[/C][C] 0.096[/C][C] 0.192[/C][C] 0.904[/C][/ROW]
[ROW][C]11[/C][C] 0.06005[/C][C] 0.1201[/C][C] 0.9399[/C][/ROW]
[ROW][C]12[/C][C] 0.04219[/C][C] 0.08438[/C][C] 0.9578[/C][/ROW]
[ROW][C]13[/C][C] 0.04501[/C][C] 0.09003[/C][C] 0.955[/C][/ROW]
[ROW][C]14[/C][C] 0.03707[/C][C] 0.07414[/C][C] 0.9629[/C][/ROW]
[ROW][C]15[/C][C] 0.02718[/C][C] 0.05436[/C][C] 0.9728[/C][/ROW]
[ROW][C]16[/C][C] 0.01507[/C][C] 0.03013[/C][C] 0.9849[/C][/ROW]
[ROW][C]17[/C][C] 0.007929[/C][C] 0.01586[/C][C] 0.9921[/C][/ROW]
[ROW][C]18[/C][C] 0.00667[/C][C] 0.01334[/C][C] 0.9933[/C][/ROW]
[ROW][C]19[/C][C] 0.00427[/C][C] 0.008539[/C][C] 0.9957[/C][/ROW]
[ROW][C]20[/C][C] 0.003105[/C][C] 0.006209[/C][C] 0.9969[/C][/ROW]
[ROW][C]21[/C][C] 0.001992[/C][C] 0.003983[/C][C] 0.998[/C][/ROW]
[ROW][C]22[/C][C] 0.001309[/C][C] 0.002618[/C][C] 0.9987[/C][/ROW]
[ROW][C]23[/C][C] 0.01156[/C][C] 0.02312[/C][C] 0.9884[/C][/ROW]
[ROW][C]24[/C][C] 0.01121[/C][C] 0.02243[/C][C] 0.9888[/C][/ROW]
[ROW][C]25[/C][C] 0.03054[/C][C] 0.06107[/C][C] 0.9695[/C][/ROW]
[ROW][C]26[/C][C] 0.02512[/C][C] 0.05024[/C][C] 0.9749[/C][/ROW]
[ROW][C]27[/C][C] 0.01658[/C][C] 0.03316[/C][C] 0.9834[/C][/ROW]
[ROW][C]28[/C][C] 0.01234[/C][C] 0.02467[/C][C] 0.9877[/C][/ROW]
[ROW][C]29[/C][C] 0.007604[/C][C] 0.01521[/C][C] 0.9924[/C][/ROW]
[ROW][C]30[/C][C] 0.005643[/C][C] 0.01129[/C][C] 0.9944[/C][/ROW]
[ROW][C]31[/C][C] 0.003401[/C][C] 0.006802[/C][C] 0.9966[/C][/ROW]
[ROW][C]32[/C][C] 0.002901[/C][C] 0.005802[/C][C] 0.9971[/C][/ROW]
[ROW][C]33[/C][C] 0.002095[/C][C] 0.004189[/C][C] 0.9979[/C][/ROW]
[ROW][C]34[/C][C] 0.002796[/C][C] 0.005592[/C][C] 0.9972[/C][/ROW]
[ROW][C]35[/C][C] 0.002139[/C][C] 0.004278[/C][C] 0.9979[/C][/ROW]
[ROW][C]36[/C][C] 0.003757[/C][C] 0.007514[/C][C] 0.9962[/C][/ROW]
[ROW][C]37[/C][C] 0.002594[/C][C] 0.005188[/C][C] 0.9974[/C][/ROW]
[ROW][C]38[/C][C] 0.001594[/C][C] 0.003189[/C][C] 0.9984[/C][/ROW]
[ROW][C]39[/C][C] 0.001021[/C][C] 0.002042[/C][C] 0.999[/C][/ROW]
[ROW][C]40[/C][C] 0.00078[/C][C] 0.00156[/C][C] 0.9992[/C][/ROW]
[ROW][C]41[/C][C] 0.0006237[/C][C] 0.001247[/C][C] 0.9994[/C][/ROW]
[ROW][C]42[/C][C] 0.0005672[/C][C] 0.001134[/C][C] 0.9994[/C][/ROW]
[ROW][C]43[/C][C] 0.00189[/C][C] 0.003779[/C][C] 0.9981[/C][/ROW]
[ROW][C]44[/C][C] 0.07056[/C][C] 0.1411[/C][C] 0.9294[/C][/ROW]
[ROW][C]45[/C][C] 0.06964[/C][C] 0.1393[/C][C] 0.9304[/C][/ROW]
[ROW][C]46[/C][C] 0.3583[/C][C] 0.7166[/C][C] 0.6417[/C][/ROW]
[ROW][C]47[/C][C] 0.3116[/C][C] 0.6231[/C][C] 0.6884[/C][/ROW]
[ROW][C]48[/C][C] 0.2884[/C][C] 0.5767[/C][C] 0.7116[/C][/ROW]
[ROW][C]49[/C][C] 0.247[/C][C] 0.4941[/C][C] 0.753[/C][/ROW]
[ROW][C]50[/C][C] 0.224[/C][C] 0.4481[/C][C] 0.776[/C][/ROW]
[ROW][C]51[/C][C] 0.2271[/C][C] 0.4542[/C][C] 0.7729[/C][/ROW]
[ROW][C]52[/C][C] 0.191[/C][C] 0.382[/C][C] 0.809[/C][/ROW]
[ROW][C]53[/C][C] 0.1771[/C][C] 0.3543[/C][C] 0.8229[/C][/ROW]
[ROW][C]54[/C][C] 0.1948[/C][C] 0.3896[/C][C] 0.8052[/C][/ROW]
[ROW][C]55[/C][C] 0.4461[/C][C] 0.8922[/C][C] 0.5539[/C][/ROW]
[ROW][C]56[/C][C] 0.404[/C][C] 0.808[/C][C] 0.596[/C][/ROW]
[ROW][C]57[/C][C] 0.3863[/C][C] 0.7726[/C][C] 0.6137[/C][/ROW]
[ROW][C]58[/C][C] 0.5109[/C][C] 0.9782[/C][C] 0.4891[/C][/ROW]
[ROW][C]59[/C][C] 0.7061[/C][C] 0.5878[/C][C] 0.2939[/C][/ROW]
[ROW][C]60[/C][C] 0.7449[/C][C] 0.5101[/C][C] 0.2551[/C][/ROW]
[ROW][C]61[/C][C] 0.7106[/C][C] 0.5787[/C][C] 0.2894[/C][/ROW]
[ROW][C]62[/C][C] 0.6885[/C][C] 0.6231[/C][C] 0.3115[/C][/ROW]
[ROW][C]63[/C][C] 0.7149[/C][C] 0.5701[/C][C] 0.2851[/C][/ROW]
[ROW][C]64[/C][C] 0.795[/C][C] 0.41[/C][C] 0.205[/C][/ROW]
[ROW][C]65[/C][C] 0.8769[/C][C] 0.2462[/C][C] 0.1231[/C][/ROW]
[ROW][C]66[/C][C] 0.869[/C][C] 0.262[/C][C] 0.131[/C][/ROW]
[ROW][C]67[/C][C] 0.8708[/C][C] 0.2584[/C][C] 0.1292[/C][/ROW]
[ROW][C]68[/C][C] 0.8472[/C][C] 0.3055[/C][C] 0.1528[/C][/ROW]
[ROW][C]69[/C][C] 0.827[/C][C] 0.3459[/C][C] 0.173[/C][/ROW]
[ROW][C]70[/C][C] 0.7979[/C][C] 0.4041[/C][C] 0.2021[/C][/ROW]
[ROW][C]71[/C][C] 0.8248[/C][C] 0.3505[/C][C] 0.1752[/C][/ROW]
[ROW][C]72[/C][C] 0.8146[/C][C] 0.3709[/C][C] 0.1854[/C][/ROW]
[ROW][C]73[/C][C] 0.794[/C][C] 0.4121[/C][C] 0.206[/C][/ROW]
[ROW][C]74[/C][C] 0.7706[/C][C] 0.4589[/C][C] 0.2294[/C][/ROW]
[ROW][C]75[/C][C] 0.7422[/C][C] 0.5156[/C][C] 0.2578[/C][/ROW]
[ROW][C]76[/C][C] 0.7281[/C][C] 0.5439[/C][C] 0.2719[/C][/ROW]
[ROW][C]77[/C][C] 0.7375[/C][C] 0.5251[/C][C] 0.2625[/C][/ROW]
[ROW][C]78[/C][C] 0.6999[/C][C] 0.6003[/C][C] 0.3001[/C][/ROW]
[ROW][C]79[/C][C] 0.717[/C][C] 0.5661[/C][C] 0.283[/C][/ROW]
[ROW][C]80[/C][C] 0.7665[/C][C] 0.4669[/C][C] 0.2334[/C][/ROW]
[ROW][C]81[/C][C] 0.7781[/C][C] 0.4438[/C][C] 0.2219[/C][/ROW]
[ROW][C]82[/C][C] 0.7426[/C][C] 0.5149[/C][C] 0.2574[/C][/ROW]
[ROW][C]83[/C][C] 0.7788[/C][C] 0.4424[/C][C] 0.2212[/C][/ROW]
[ROW][C]84[/C][C] 0.7767[/C][C] 0.4466[/C][C] 0.2233[/C][/ROW]
[ROW][C]85[/C][C] 0.7439[/C][C] 0.5122[/C][C] 0.2561[/C][/ROW]
[ROW][C]86[/C][C] 0.7063[/C][C] 0.5873[/C][C] 0.2937[/C][/ROW]
[ROW][C]87[/C][C] 0.6777[/C][C] 0.6446[/C][C] 0.3223[/C][/ROW]
[ROW][C]88[/C][C] 0.6545[/C][C] 0.691[/C][C] 0.3455[/C][/ROW]
[ROW][C]89[/C][C] 0.6706[/C][C] 0.6589[/C][C] 0.3294[/C][/ROW]
[ROW][C]90[/C][C] 0.6671[/C][C] 0.6658[/C][C] 0.3329[/C][/ROW]
[ROW][C]91[/C][C] 0.639[/C][C] 0.722[/C][C] 0.361[/C][/ROW]
[ROW][C]92[/C][C] 0.6041[/C][C] 0.7917[/C][C] 0.3959[/C][/ROW]
[ROW][C]93[/C][C] 0.6199[/C][C] 0.7603[/C][C] 0.3801[/C][/ROW]
[ROW][C]94[/C][C] 0.7566[/C][C] 0.4867[/C][C] 0.2434[/C][/ROW]
[ROW][C]95[/C][C] 0.7349[/C][C] 0.5301[/C][C] 0.2651[/C][/ROW]
[ROW][C]96[/C][C] 0.7059[/C][C] 0.5881[/C][C] 0.2941[/C][/ROW]
[ROW][C]97[/C][C] 0.8325[/C][C] 0.3351[/C][C] 0.1675[/C][/ROW]
[ROW][C]98[/C][C] 0.9307[/C][C] 0.1387[/C][C] 0.06933[/C][/ROW]
[ROW][C]99[/C][C] 0.9969[/C][C] 0.006211[/C][C] 0.003105[/C][/ROW]
[ROW][C]100[/C][C] 0.9969[/C][C] 0.006188[/C][C] 0.003094[/C][/ROW]
[ROW][C]101[/C][C] 0.9959[/C][C] 0.008255[/C][C] 0.004128[/C][/ROW]
[ROW][C]102[/C][C] 0.9959[/C][C] 0.008249[/C][C] 0.004125[/C][/ROW]
[ROW][C]103[/C][C] 0.9958[/C][C] 0.008498[/C][C] 0.004249[/C][/ROW]
[ROW][C]104[/C][C] 0.9939[/C][C] 0.01213[/C][C] 0.006064[/C][/ROW]
[ROW][C]105[/C][C] 0.992[/C][C] 0.01599[/C][C] 0.007995[/C][/ROW]
[ROW][C]106[/C][C] 0.989[/C][C] 0.02196[/C][C] 0.01098[/C][/ROW]
[ROW][C]107[/C][C] 0.989[/C][C] 0.02191[/C][C] 0.01096[/C][/ROW]
[ROW][C]108[/C][C] 0.9849[/C][C] 0.03015[/C][C] 0.01507[/C][/ROW]
[ROW][C]109[/C][C] 0.9853[/C][C] 0.02935[/C][C] 0.01468[/C][/ROW]
[ROW][C]110[/C][C] 0.9807[/C][C] 0.03861[/C][C] 0.0193[/C][/ROW]
[ROW][C]111[/C][C] 0.983[/C][C] 0.03403[/C][C] 0.01701[/C][/ROW]
[ROW][C]112[/C][C] 0.9833[/C][C] 0.03335[/C][C] 0.01667[/C][/ROW]
[ROW][C]113[/C][C] 0.9789[/C][C] 0.04224[/C][C] 0.02112[/C][/ROW]
[ROW][C]114[/C][C] 0.9805[/C][C] 0.03904[/C][C] 0.01952[/C][/ROW]
[ROW][C]115[/C][C] 0.9795[/C][C] 0.0409[/C][C] 0.02045[/C][/ROW]
[ROW][C]116[/C][C] 0.9763[/C][C] 0.04739[/C][C] 0.0237[/C][/ROW]
[ROW][C]117[/C][C] 0.9737[/C][C] 0.05262[/C][C] 0.02631[/C][/ROW]
[ROW][C]118[/C][C] 0.968[/C][C] 0.06402[/C][C] 0.03201[/C][/ROW]
[ROW][C]119[/C][C] 0.9687[/C][C] 0.06263[/C][C] 0.03132[/C][/ROW]
[ROW][C]120[/C][C] 0.9605[/C][C] 0.07905[/C][C] 0.03953[/C][/ROW]
[ROW][C]121[/C][C] 0.9492[/C][C] 0.1017[/C][C] 0.05085[/C][/ROW]
[ROW][C]122[/C][C] 0.9366[/C][C] 0.1267[/C][C] 0.06336[/C][/ROW]
[ROW][C]123[/C][C] 0.9256[/C][C] 0.1487[/C][C] 0.07437[/C][/ROW]
[ROW][C]124[/C][C] 0.932[/C][C] 0.1359[/C][C] 0.06797[/C][/ROW]
[ROW][C]125[/C][C] 0.9163[/C][C] 0.1675[/C][C] 0.08374[/C][/ROW]
[ROW][C]126[/C][C] 0.8964[/C][C] 0.2072[/C][C] 0.1036[/C][/ROW]
[ROW][C]127[/C][C] 0.874[/C][C] 0.252[/C][C] 0.126[/C][/ROW]
[ROW][C]128[/C][C] 0.8415[/C][C] 0.3171[/C][C] 0.1585[/C][/ROW]
[ROW][C]129[/C][C] 0.8142[/C][C] 0.3715[/C][C] 0.1858[/C][/ROW]
[ROW][C]130[/C][C] 0.8355[/C][C] 0.3289[/C][C] 0.1645[/C][/ROW]
[ROW][C]131[/C][C] 0.7959[/C][C] 0.4082[/C][C] 0.2041[/C][/ROW]
[ROW][C]132[/C][C] 0.8152[/C][C] 0.3697[/C][C] 0.1848[/C][/ROW]
[ROW][C]133[/C][C] 0.7776[/C][C] 0.4448[/C][C] 0.2224[/C][/ROW]
[ROW][C]134[/C][C] 0.7405[/C][C] 0.519[/C][C] 0.2595[/C][/ROW]
[ROW][C]135[/C][C] 0.6949[/C][C] 0.6102[/C][C] 0.3051[/C][/ROW]
[ROW][C]136[/C][C] 0.6806[/C][C] 0.6387[/C][C] 0.3194[/C][/ROW]
[ROW][C]137[/C][C] 0.6252[/C][C] 0.7496[/C][C] 0.3748[/C][/ROW]
[ROW][C]138[/C][C] 0.5748[/C][C] 0.8504[/C][C] 0.4252[/C][/ROW]
[ROW][C]139[/C][C] 0.6864[/C][C] 0.6271[/C][C] 0.3136[/C][/ROW]
[ROW][C]140[/C][C] 0.7117[/C][C] 0.5766[/C][C] 0.2883[/C][/ROW]
[ROW][C]141[/C][C] 0.6447[/C][C] 0.7106[/C][C] 0.3553[/C][/ROW]
[ROW][C]142[/C][C] 0.6525[/C][C] 0.695[/C][C] 0.3475[/C][/ROW]
[ROW][C]143[/C][C] 0.5776[/C][C] 0.8447[/C][C] 0.4224[/C][/ROW]
[ROW][C]144[/C][C] 0.5803[/C][C] 0.8393[/C][C] 0.4197[/C][/ROW]
[ROW][C]145[/C][C] 0.5107[/C][C] 0.9785[/C][C] 0.4893[/C][/ROW]
[ROW][C]146[/C][C] 0.4355[/C][C] 0.8709[/C][C] 0.5645[/C][/ROW]
[ROW][C]147[/C][C] 0.3977[/C][C] 0.7955[/C][C] 0.6023[/C][/ROW]
[ROW][C]148[/C][C] 0.3568[/C][C] 0.7136[/C][C] 0.6432[/C][/ROW]
[ROW][C]149[/C][C] 0.488[/C][C] 0.976[/C][C] 0.512[/C][/ROW]
[ROW][C]150[/C][C] 0.4484[/C][C] 0.8968[/C][C] 0.5516[/C][/ROW]
[ROW][C]151[/C][C] 0.5504[/C][C] 0.8991[/C][C] 0.4496[/C][/ROW]
[ROW][C]152[/C][C] 0.4288[/C][C] 0.8575[/C][C] 0.5712[/C][/ROW]
[ROW][C]153[/C][C] 0.3995[/C][C] 0.7991[/C][C] 0.6005[/C][/ROW]
[ROW][C]154[/C][C] 0.2746[/C][C] 0.5491[/C][C] 0.7254[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309617&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309617&T=6

As an alternative you can also use a QR Code:

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

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
9	0.1233	0.2466	0.8767
10	0.096	0.192	0.904
11	0.06005	0.1201	0.9399
12	0.04219	0.08438	0.9578
13	0.04501	0.09003	0.955
14	0.03707	0.07414	0.9629
15	0.02718	0.05436	0.9728
16	0.01507	0.03013	0.9849
17	0.007929	0.01586	0.9921
18	0.00667	0.01334	0.9933
19	0.00427	0.008539	0.9957
20	0.003105	0.006209	0.9969
21	0.001992	0.003983	0.998
22	0.001309	0.002618	0.9987
23	0.01156	0.02312	0.9884
24	0.01121	0.02243	0.9888
25	0.03054	0.06107	0.9695
26	0.02512	0.05024	0.9749
27	0.01658	0.03316	0.9834
28	0.01234	0.02467	0.9877
29	0.007604	0.01521	0.9924
30	0.005643	0.01129	0.9944
31	0.003401	0.006802	0.9966
32	0.002901	0.005802	0.9971
33	0.002095	0.004189	0.9979
34	0.002796	0.005592	0.9972
35	0.002139	0.004278	0.9979
36	0.003757	0.007514	0.9962
37	0.002594	0.005188	0.9974
38	0.001594	0.003189	0.9984
39	0.001021	0.002042	0.999
40	0.00078	0.00156	0.9992
41	0.0006237	0.001247	0.9994
42	0.0005672	0.001134	0.9994
43	0.00189	0.003779	0.9981
44	0.07056	0.1411	0.9294
45	0.06964	0.1393	0.9304
46	0.3583	0.7166	0.6417
47	0.3116	0.6231	0.6884
48	0.2884	0.5767	0.7116
49	0.247	0.4941	0.753
50	0.224	0.4481	0.776
51	0.2271	0.4542	0.7729
52	0.191	0.382	0.809
53	0.1771	0.3543	0.8229
54	0.1948	0.3896	0.8052
55	0.4461	0.8922	0.5539
56	0.404	0.808	0.596
57	0.3863	0.7726	0.6137
58	0.5109	0.9782	0.4891
59	0.7061	0.5878	0.2939
60	0.7449	0.5101	0.2551
61	0.7106	0.5787	0.2894
62	0.6885	0.6231	0.3115
63	0.7149	0.5701	0.2851
64	0.795	0.41	0.205
65	0.8769	0.2462	0.1231
66	0.869	0.262	0.131
67	0.8708	0.2584	0.1292
68	0.8472	0.3055	0.1528
69	0.827	0.3459	0.173
70	0.7979	0.4041	0.2021
71	0.8248	0.3505	0.1752
72	0.8146	0.3709	0.1854
73	0.794	0.4121	0.206
74	0.7706	0.4589	0.2294
75	0.7422	0.5156	0.2578
76	0.7281	0.5439	0.2719
77	0.7375	0.5251	0.2625
78	0.6999	0.6003	0.3001
79	0.717	0.5661	0.283
80	0.7665	0.4669	0.2334
81	0.7781	0.4438	0.2219
82	0.7426	0.5149	0.2574
83	0.7788	0.4424	0.2212
84	0.7767	0.4466	0.2233
85	0.7439	0.5122	0.2561
86	0.7063	0.5873	0.2937
87	0.6777	0.6446	0.3223
88	0.6545	0.691	0.3455
89	0.6706	0.6589	0.3294
90	0.6671	0.6658	0.3329
91	0.639	0.722	0.361
92	0.6041	0.7917	0.3959
93	0.6199	0.7603	0.3801
94	0.7566	0.4867	0.2434
95	0.7349	0.5301	0.2651
96	0.7059	0.5881	0.2941
97	0.8325	0.3351	0.1675
98	0.9307	0.1387	0.06933
99	0.9969	0.006211	0.003105
100	0.9969	0.006188	0.003094
101	0.9959	0.008255	0.004128
102	0.9959	0.008249	0.004125
103	0.9958	0.008498	0.004249
104	0.9939	0.01213	0.006064
105	0.992	0.01599	0.007995
106	0.989	0.02196	0.01098
107	0.989	0.02191	0.01096
108	0.9849	0.03015	0.01507
109	0.9853	0.02935	0.01468
110	0.9807	0.03861	0.0193
111	0.983	0.03403	0.01701
112	0.9833	0.03335	0.01667
113	0.9789	0.04224	0.02112
114	0.9805	0.03904	0.01952
115	0.9795	0.0409	0.02045
116	0.9763	0.04739	0.0237
117	0.9737	0.05262	0.02631
118	0.968	0.06402	0.03201
119	0.9687	0.06263	0.03132
120	0.9605	0.07905	0.03953
121	0.9492	0.1017	0.05085
122	0.9366	0.1267	0.06336
123	0.9256	0.1487	0.07437
124	0.932	0.1359	0.06797
125	0.9163	0.1675	0.08374
126	0.8964	0.2072	0.1036
127	0.874	0.252	0.126
128	0.8415	0.3171	0.1585
129	0.8142	0.3715	0.1858
130	0.8355	0.3289	0.1645
131	0.7959	0.4082	0.2041
132	0.8152	0.3697	0.1848
133	0.7776	0.4448	0.2224
134	0.7405	0.519	0.2595
135	0.6949	0.6102	0.3051
136	0.6806	0.6387	0.3194
137	0.6252	0.7496	0.3748
138	0.5748	0.8504	0.4252
139	0.6864	0.6271	0.3136
140	0.7117	0.5766	0.2883
141	0.6447	0.7106	0.3553
142	0.6525	0.695	0.3475
143	0.5776	0.8447	0.4224
144	0.5803	0.8393	0.4197
145	0.5107	0.9785	0.4893
146	0.4355	0.8709	0.5645
147	0.3977	0.7955	0.6023
148	0.3568	0.7136	0.6432
149	0.488	0.976	0.512
150	0.4484	0.8968	0.5516
151	0.5504	0.8991	0.4496
152	0.4288	0.8575	0.5712
153	0.3995	0.7991	0.6005
154	0.2746	0.5491	0.7254

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	22	0.1507	NOK
5% type I error level	44	0.30137	NOK
10% type I error level	54	0.369863	NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 22 &  0.1507 & NOK \tabularnewline
5% type I error level & 44 & 0.30137 & NOK \tabularnewline
10% type I error level & 54 & 0.369863 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309617&T=7

[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]22[/C][C] 0.1507[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]44[/C][C]0.30137[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]54[/C][C]0.369863[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309617&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309617&T=7

As an alternative you can also use a QR Code:

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	22	0.1507	NOK
5% type I error level	44	0.30137	NOK
10% type I error level	54	0.369863	NOK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 1.0343, df1 = 2, df2 = 155, p-value = 0.3579
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.5822, df1 = 10, df2 = 147, p-value = 0.1172
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 4.8328, df1 = 2, df2 = 155, p-value = 0.009204

\begin{tabular}{lllllllll}
\hline
Ramsey RESET F-Test for powers (2 and 3) of fitted values \tabularnewline
> reset_test_fitted
	RESET test
data:  mylm
RESET = 1.0343, df1 = 2, df2 = 155, p-value = 0.3579
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.5822, df1 = 10, df2 = 147, p-value = 0.1172
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 4.8328, df1 = 2, df2 = 155, p-value = 0.009204
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309617&T=8

[TABLE]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of fitted values[/C][/ROW]
[ROW][C]> reset_test_fitted
	RESET test
data:  mylm
RESET = 1.0343, df1 = 2, df2 = 155, p-value = 0.3579
[/C][/ROW]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of regressors[/C][/ROW]
[ROW][C]> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.5822, df1 = 10, df2 = 147, p-value = 0.1172
[/C][/ROW]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of principal components[/C][/ROW]
[ROW][C]> reset_test_principal_components
	RESET test
data:  mylm
RESET = 4.8328, df1 = 2, df2 = 155, p-value = 0.009204
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309617&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309617&T=8

As an alternative you can also use a QR Code:

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

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 1.0343, df1 = 2, df2 = 155, p-value = 0.3579
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.5822, df1 = 10, df2 = 147, p-value = 0.1172
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 4.8328, df1 = 2, df2 = 155, p-value = 0.009204

Variance Inflation Factors (Multicollinearity)
> vif `(1-Bs)(1-B)b` `(1-Bs)(1-B)c` `(1-Bs)(1-B)a(t-1s)` 1.191662 1.161718 1.211002 `(1-Bs)(1-B)a(t-2s)` `(1-Bs)(1-B)a(t-3s)` 1.234332 1.183846

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
      `(1-Bs)(1-B)b`       `(1-Bs)(1-B)c` `(1-Bs)(1-B)a(t-1s)` 
            1.191662             1.161718             1.211002 
`(1-Bs)(1-B)a(t-2s)` `(1-Bs)(1-B)a(t-3s)` 
            1.234332             1.183846 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309617&T=9

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
      `(1-Bs)(1-B)b`       `(1-Bs)(1-B)c` `(1-Bs)(1-B)a(t-1s)` 
            1.191662             1.161718             1.211002 
`(1-Bs)(1-B)a(t-2s)` `(1-Bs)(1-B)a(t-3s)` 
            1.234332             1.183846 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309617&T=9

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309617&T=9

As an alternative you can also use a QR Code:

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

Variance Inflation Factors (Multicollinearity)
> vif `(1-Bs)(1-B)b` `(1-Bs)(1-B)c` `(1-Bs)(1-B)a(t-1s)` 1.191662 1.161718 1.211002 `(1-Bs)(1-B)a(t-2s)` `(1-Bs)(1-B)a(t-3s)` 1.234332 1.183846

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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Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = First and Seasonal Differences (s) ; par4 = 0 ; par5 = 3 ; par6 = 12 ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = First and Seasonal Differences (s) ; par4 = 0 ; par5 = 3 ; par6 = 12 ;

R code (references can be found in the software module):

par6 <- '12'
par5 <- '2'
par4 <- '0'
par3 <- 'First and Seasonal Differences (s)'
par2 <- 'Do not include Seasonal Dummies'
par1 <- '1'
library(lattice)
library(lmtest)
library(car)
library(MASS)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
mywarning <- ''
par6 <- as.numeric(par6)
if(is.na(par6)) {
par6 <- 12
mywarning = 'Warning: you did not specify the seasonality. The seasonal period was set to s = 12.'
}
par1 <- as.numeric(par1)
if(is.na(par1)) {
par1 <- 1
mywarning = 'Warning: you did not specify the column number of the endogenous series! The first column was selected by default.'
}
if (par4=='') par4 <- 0
par4 <- as.numeric(par4)
if (!is.numeric(par4)) par4 <- 0
if (par5=='') par5 <- 0
par5 <- as.numeric(par5)
if (!is.numeric(par5)) par5 <- 0
x <- na.omit(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'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'Seasonal Differences (s)'){
(n <- n - par6)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-Bs)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+par6,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'First and Seasonal Differences (s)'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
(n <- n - par6)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-Bs)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+par6,j] - x[i,j]
}
}
x <- x2
}
if(par4 > 0) {
x2 <- array(0, dim=c(n-par4,par4), dimnames=list(1:(n-par4), paste(colnames(x)[par1],'(t-',1:par4,')',sep='')))
for (i in 1:(n-par4)) {
for (j in 1:par4) {
x2[i,j] <- x[i+par4-j,par1]
}
}
x <- cbind(x[(par4+1):n,], x2)
n <- n - par4
}
if(par5 > 0) {
x2 <- array(0, dim=c(n-par5*par6,par5), dimnames=list(1:(n-par5*par6), paste(colnames(x)[par1],'(t-',1:par5,'s)',sep='')))
for (i in 1:(n-par5*par6)) {
for (j in 1:par5) {
x2[i,j] <- x[i+par5*par6-j*par6,par1]
}
}
x <- cbind(x[(par5*par6+1):n,], x2)
n <- n - par5*par6
}
if (par2 == 'Include Seasonal Dummies'){
x2 <- array(0, dim=c(n,par6-1), dimnames=list(1:n, paste('M', seq(1:(par6-1)), sep ='')))
for (i in 1:(par6-1)){
x2[seq(i,n,par6),i] <- 1
}
x <- cbind(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[n,]))
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
print(x)
(k <- length(x[n,]))
head(x)
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$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
sresid <- studres(mylm)
hist(sresid, freq=FALSE, main='Distribution of Studentized Residuals')
xfit<-seq(min(sresid),max(sresid),length=40)
yfit<-dnorm(xfit)
lines(xfit, yfit)
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqPlot(mylm, main='QQ Plot')
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
print(z)
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$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, signif(mysum$coefficients[i,1],6), sep=' ')
if (rownames(mysum$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.row.start(a)
a<-table.element(a, mywarning)
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,'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$coefficients)[i],header=TRUE)
a<-table.element(a,formatC(signif(mysum$coefficients[i,1],5),format='g',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,2],5),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,3],4),format='e',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4],4),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4]/2,4),format='g',flag=' '))
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,formatC(signif(sqrt(mysum$r.squared),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$adj.r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a,formatC(signif(mysum$fstatistic[1],6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[2],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[3],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a,formatC(signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a,formatC(signif(mysum$sigma,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a,formatC(signif(sum(myerror*myerror),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
myr <- as.numeric(mysum$resid)
myr
a <-table.start()
a <- table.row.start(a)
a <- table.element(a,'Menu of Residual Diagnostics',2,TRUE)
a <- table.row.end(a)
a <- table.row.start(a)
a <- table.element(a,'Description',1,TRUE)
a <- table.element(a,'Link',1,TRUE)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Histogram',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_histogram.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Central Tendency',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_centraltendency.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'QQ Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_fitdistrnorm.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Kernel Density Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_density.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Skewness/Kurtosis Test',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_skewness_kurtosis.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Skewness-Kurtosis Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_skewness_kurtosis_plot.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Harrell-Davis Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_harrell_davis.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Bootstrap Plot -- Central Tendency',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_bootstrapplot1.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Blocked Bootstrap Plot -- Central Tendency',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_bootstrapplot.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'(Partial) Autocorrelation Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_autocorrelation.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Spectral Analysis',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_spectrum.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Tukey lambda PPCC Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_tukeylambda.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Box-Cox Normality Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_boxcoxnorm.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <- table.element(a,'Summary Statistics',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_summary1.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable7.tab')
if(n < 200) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,formatC(signif(x[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(x[i]-mysum$resid[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$resid[i],6),format='g',flag=' '))
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,formatC(signif(gqarr[mypoint-kp3+1,1],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,2],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,3],6),format='g',flag=' '))
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,signif(numsignificant1,6))
a<-table.element(a,formatC(signif(numsignificant1/numgqtests,6),format='g',flag=' '))
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,signif(numsignificant5,6))
a<-table.element(a,signif(numsignificant5/numgqtests,6))
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,signif(numsignificant10,6))
a<-table.element(a,signif(numsignificant10/numgqtests,6))
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')
}
}
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of fitted values',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_fitted <- resettest(mylm,power=2:3,type='fitted')
a<-table.element(a,paste('',RC.texteval('reset_test_fitted'),'
',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of regressors',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_regressors <- resettest(mylm,power=2:3,type='regressor')
a<-table.element(a,paste('',RC.texteval('reset_test_regressors'),'
',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of principal components',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_principal_components <- resettest(mylm,power=2:3,type='princomp')
a<-table.element(a,paste('',RC.texteval('reset_test_principal_components'),'
',sep=''))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable8.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Variance Inflation Factors (Multicollinearity)',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
vif <- vif(mylm)
a<-table.element(a,paste('',RC.texteval('vif'),'
',sep=''))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable9.tab')

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code