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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Tue, 12 Dec 2017 14:37:58 +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/12/t1513085922qsu6min1mdszwra.htm/, Retrieved Fri, 01 Nov 2024 00:35:45 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=309084, Retrieved Fri, 01 Nov 2024 00:35:45 +0000

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Original text written by user:

Vraag: Als ik meer papier produceer, zal er dan meer geprint worden?

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No (this computation is public)

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Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

-       [Multiple Regression] [Multiple regressi...] [2017-12-12 13:37:58] [c594df30d4ca3ffb9387e41ef17d0596] [Current]

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Dataseries X:

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Histogram

Boxplots

81.6	79.3
86.1	81.3
96.5	92.4
85.4	83.3
94.5	89.2
90	86.1
69.1	57.3
82.4	80.8
96.5	95.5
100	98.4
94.7	94.2
88.8	93.4
95.4	92.6
88.4	84.8
101.3	98.9
88.7	86.1
92.8	90.4
93.3	90.2
77.6	69.2
84.6	81.4
96.9	98.3
101.3	100.2
92.2	90.5
87.6	94.5
89.8	83.6
84.8	80.4
94.2	89.5
91.3	85.5
88.9	84.4
89.7	84.2
75.8	63.3
80.5	77.2
98	97.3
101.4	98
90.4	87.3
86.5	90.8
89.9	82.6
83.4	77.2
93.2	90.4
90.3	84.3
86.8	84.1
87.5	82.5
75.7	63
77.4	73.4
98	95.8
100.8	99.2
86.5	85.3
92	96.5
87.8	83.7
84.7	78.9
99.9	94.8
92.2	86
85.3	81.9
96.3	89.9
72.9	60.7
82.8	76.4
98.5	93.8
95.2	91.7
89.5	85.1
94	97.3
85	79.2
86	82.6
96.8	93.8
92.1	87.9
87.3	83.7
94.3	86.9
71	58.6
81.5	74.5
101.5	99.7
92.8	90.1
92.1	87.9
97.9	102
88.9	83.8
89.7	86.5
102.2	97.6
88.4	83.5
94.3	90.1
97.6	92.6
73.9	62.7
87.9	82.8
102.7	102.3
104.5	101.2
100.5	97.4
99.5	103.8
94.2	89.2
95.1	90.7
108.1	103.9
94.6	89.9
98	92.1
101.7	94
83.1	66.6
92.9	86.4
104.1	99.8
111.1	103.3
104.1	100.6
99	103.1
110.7	109
107.7	106.7
113.2	117.4
114.3	114.3
107.1	108.8
109.6	108.2
89.2	78.2
96.1	95.9
118.7	122
120.8	124.9
105	110.2
109.8	120.8
96.1	99
97.8	103.4
108.3	114.6
99.1	100.6
93.6	96.4
100.1	100
80.9	71
87.5	88.2
107.4	111.9
107.1	111.9
99.5	103.4
101.8	112.9
92	92.6
95.8	95.3
110	113.2
99.4	97.9
94.4	95.8
105.4	103.1
84.1	73.3
92.3	89.8
109.8	111.6
106.8	108.1
103.8	106.2
106.2	113.2
91.2	85.8
94.7	91.7
109.9	109
93.7	92
101.4	99.6
93.6	87.9
78.3	69.5
91.8	87.4
107.8	107.9
98.8	98.9
98.6	96.8
99.9	106.7
89.7	81.6
94.4	90.2
103.2	99.3
89.9	85.3
92.6	86.3
97.8	92
81.1	67.1
91.2	84
101.4	100.2
105.3	101.5
95.8	91.3
91.3	96.6
91.1	81.7
88.8	83.6
95.3	91.7
89.4	82.4
88.3	82.7
87.4	81
78.9	65.3
82.2	76.6
94.5	91.6
98.8	94
88.1	86
86.5	88.6
88.1	82.1
85.8	82.2
94	88.5
90.4	84.4
86.4	81.1
90.3	83.5
82.1	69.6
82.1	75.4
97.7	91.8
99.1	95.6
85.9	82.8
89.1	91.3
85.7	77.6
85.9	80.5
99.9	94.9
91.6	86.3
82.9	77
96.6	89.4
81.5	70.3
84	79.5
100.8	96.6
102	99.2
92.9	90.4
93.2	94
86.7	82.7
91.6	88.4
97.6	93
92.8	86.6
93.5	87.5
95.5	89.7
75.1	65.3
90.9	88.1
98.9	94.5
95.5	91.8
94.3	90.3
90.3	89.2
87.9	80.5
88.7	83.4
100.3	93.9
85.5	79.5
93	86.5
96	87.6
77.6	66.3
87.5	81.3

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	11 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 time11 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309084&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]

11 seconds[/C][/ROW] [ROW]

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=309084&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309084&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	11 seconds
R Server	Big Analytics Cloud Computing Center

Multiple Linear Regression - Estimated Regression Equation
(1-Bs)(1-B)papier[t] = + 0.0128028 + 0.80185`(1-Bs)(1-B)print`[t] -0.180491`(1-Bs)(1-B)papier(t-1)`[t] -0.138108`(1-Bs)(1-B)papier(t-2)`[t] + 0.0191076`(1-Bs)(1-B)papier(t-3)`[t] -0.0372056`(1-Bs)(1-B)papier(t-4)`[t] -0.0339939`(1-Bs)(1-B)papier(t-1s)`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
(1-Bs)(1-B)papier[t] =  +  0.0128028 +  0.80185`(1-Bs)(1-B)print`[t] -0.180491`(1-Bs)(1-B)papier(t-1)`[t] -0.138108`(1-Bs)(1-B)papier(t-2)`[t] +  0.0191076`(1-Bs)(1-B)papier(t-3)`[t] -0.0372056`(1-Bs)(1-B)papier(t-4)`[t] -0.0339939`(1-Bs)(1-B)papier(t-1s)`[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309084&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C](1-Bs)(1-B)papier[t] =  +  0.0128028 +  0.80185`(1-Bs)(1-B)print`[t] -0.180491`(1-Bs)(1-B)papier(t-1)`[t] -0.138108`(1-Bs)(1-B)papier(t-2)`[t] +  0.0191076`(1-Bs)(1-B)papier(t-3)`[t] -0.0372056`(1-Bs)(1-B)papier(t-4)`[t] -0.0339939`(1-Bs)(1-B)papier(t-1s)`[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309084&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309084&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)papier[t] = + 0.0128028 + 0.80185`(1-Bs)(1-B)print`[t] -0.180491`(1-Bs)(1-B)papier(t-1)`[t] -0.138108`(1-Bs)(1-B)papier(t-2)`[t] + 0.0191076`(1-Bs)(1-B)papier(t-3)`[t] -0.0372056`(1-Bs)(1-B)papier(t-4)`[t] -0.0339939`(1-Bs)(1-B)papier(t-1s)`[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)	+0.0128	0.1443	+8.8730e-02	0.9294	0.4647
`(1-Bs)(1-B)print`	+0.8018	0.0309	+2.5950e+01	4.663e-62	2.332e-62
`(1-Bs)(1-B)papier(t-1)`	-0.1805	0.04034	-4.4740e+00	1.372e-05	6.862e-06
`(1-Bs)(1-B)papier(t-2)`	-0.1381	0.0445	-3.1030e+00	0.00223	0.001115
`(1-Bs)(1-B)papier(t-3)`	+0.01911	0.04302	+4.4420e-01	0.6575	0.3287
`(1-Bs)(1-B)papier(t-4)`	-0.03721	0.03401	-1.0940e+00	0.2755	0.1377
`(1-Bs)(1-B)papier(t-1s)`	-0.03399	0.02616	-1.2990e+00	0.1955	0.09776

\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) & +0.0128 &  0.1443 & +8.8730e-02 &  0.9294 &  0.4647 \tabularnewline
`(1-Bs)(1-B)print` & +0.8018 &  0.0309 & +2.5950e+01 &  4.663e-62 &  2.332e-62 \tabularnewline
`(1-Bs)(1-B)papier(t-1)` & -0.1805 &  0.04034 & -4.4740e+00 &  1.372e-05 &  6.862e-06 \tabularnewline
`(1-Bs)(1-B)papier(t-2)` & -0.1381 &  0.0445 & -3.1030e+00 &  0.00223 &  0.001115 \tabularnewline
`(1-Bs)(1-B)papier(t-3)` & +0.01911 &  0.04302 & +4.4420e-01 &  0.6575 &  0.3287 \tabularnewline
`(1-Bs)(1-B)papier(t-4)` & -0.03721 &  0.03401 & -1.0940e+00 &  0.2755 &  0.1377 \tabularnewline
`(1-Bs)(1-B)papier(t-1s)` & -0.03399 &  0.02616 & -1.2990e+00 &  0.1955 &  0.09776 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309084&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]+0.0128[/C][C] 0.1443[/C][C]+8.8730e-02[/C][C] 0.9294[/C][C] 0.4647[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)print`[/C][C]+0.8018[/C][C] 0.0309[/C][C]+2.5950e+01[/C][C] 4.663e-62[/C][C] 2.332e-62[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)papier(t-1)`[/C][C]-0.1805[/C][C] 0.04034[/C][C]-4.4740e+00[/C][C] 1.372e-05[/C][C] 6.862e-06[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)papier(t-2)`[/C][C]-0.1381[/C][C] 0.0445[/C][C]-3.1030e+00[/C][C] 0.00223[/C][C] 0.001115[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)papier(t-3)`[/C][C]+0.01911[/C][C] 0.04302[/C][C]+4.4420e-01[/C][C] 0.6575[/C][C] 0.3287[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)papier(t-4)`[/C][C]-0.03721[/C][C] 0.03401[/C][C]-1.0940e+00[/C][C] 0.2755[/C][C] 0.1377[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)papier(t-1s)`[/C][C]-0.03399[/C][C] 0.02616[/C][C]-1.2990e+00[/C][C] 0.1955[/C][C] 0.09776[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309084&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309084&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)	+0.0128	0.1443	+8.8730e-02	0.9294	0.4647
`(1-Bs)(1-B)print`	+0.8018	0.0309	+2.5950e+01	4.663e-62	2.332e-62
`(1-Bs)(1-B)papier(t-1)`	-0.1805	0.04034	-4.4740e+00	1.372e-05	6.862e-06
`(1-Bs)(1-B)papier(t-2)`	-0.1381	0.0445	-3.1030e+00	0.00223	0.001115
`(1-Bs)(1-B)papier(t-3)`	+0.01911	0.04302	+4.4420e-01	0.6575	0.3287
`(1-Bs)(1-B)papier(t-4)`	-0.03721	0.03401	-1.0940e+00	0.2755	0.1377
`(1-Bs)(1-B)papier(t-1s)`	-0.03399	0.02616	-1.2990e+00	0.1955	0.09776

Multiple Linear Regression - Regression Statistics
Multiple R	0.9472
R-squared	0.8972
Adjusted R-squared	0.8937
F-TEST (value)	256.1
F-TEST (DF numerator)	6
F-TEST (DF denominator)	176
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.952
Sum Squared Residuals	670.3

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.9472 \tabularnewline
R-squared &  0.8972 \tabularnewline
Adjusted R-squared &  0.8937 \tabularnewline
F-TEST (value) &  256.1 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 176 \tabularnewline
p-value &  0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  1.952 \tabularnewline
Sum Squared Residuals &  670.3 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309084&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.9472[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.8972[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.8937[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 256.1[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]176[/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] 1.952[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 670.3[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309084&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309084&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	0.9472
R-squared	0.8972
Adjusted R-squared	0.8937
F-TEST (value)	256.1
F-TEST (DF numerator)	6
F-TEST (DF denominator)	176
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.952
Sum Squared Residuals	670.3

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=309084&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=309084&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309084&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	0.3	-0.4649	0.7649
2	1.8	1.075	0.7247
3	-2.3	0.7387	-3.039
4	5.2	3.054	2.146
5	-1	-1.578	0.5777
6	-1.9	-1.308	-0.5915
7	0.7	0.2337	0.4663
8	1.2	2.251	-1.051
9	-1.5	-2.132	0.6316
10	0.4	3.608	-3.208
11	0	-1.869	1.869
12	-1.1	0.8269	-1.927
13	-0.1	-0.858	0.758
14	2.1	1.229	0.8707
15	-3	-3.102	0.1017
16	3.1	1.971	1.129
17	-0.6	2.11	-2.71
18	-3.3	-2.944	-0.3562
19	9.4	7.013	2.387
20	-7.6	-5.084	-2.516
21	3.4	0.5777	2.822
22	5.4	2.903	2.497
23	-4.8	-4.091	-0.7086
24	-3.4	-2.609	-0.7913
25	10.3	8.967	1.333
26	-11.6	-9.519	-2.081
27	8.2	5.149	3.051
28	-4.9	-3.656	-1.244
29	-6.1	-5.23	-0.8701
30	8.6	8.344	0.2555
31	-1	-0.6134	-0.3866
32	-4.8	-4.92	0.1201
33	4.1	7.868	-3.768
34	-4.4	-4.356	-0.04437
35	3	2.675	0.3253
36	2.1	0.3713	1.729
37	-4	-5.216	1.216
38	0.1	1.782	-1.682
39	0.6	0.3573	0.2427
40	4.3	6.157	-1.857
41	-5.4	-6.502	1.102
42	5	3.637	1.363
43	1.3	1.473	-0.1735
44	0	-1.093	1.093
45	-0.2	-0.571	0.371
46	1.7	-0.0429	1.743
47	-9.1	-6.992	-2.108
48	10.7	10.01	0.6947
49	-3.7	-1.047	-2.653
50	-0.4	-2.321	1.921
51	3.5	4.486	-0.9864
52	-5.2	-5.749	0.5492
53	10.5	7.597	2.903
54	-3.3	-2.535	-0.7646
55	-6.8	-7.29	0.4897
56	3.7	4.977	-1.277
57	0.1	-1.125	1.225
58	0.5	1.103	-0.6027
59	0.3	0.622	-0.322
60	-2.5	-4.138	1.638
61	0.4	0.0731	0.3269
62	5.1	2.291	2.809
63	-4.2	-1.381	-2.819
64	-3.6	-4.547	0.9473
65	5.2	4.657	0.5432
66	-3	0.2956	-3.296
67	-4.1	-2.972	-1.128
68	17	17.71	-0.7126
69	-3.9	-5.791	1.891
70	-7.5	-3.619	-3.881
71	14.6	11.11	3.488
72	-10.6	-8.383	-2.217
73	-1.2	-2.107	0.9068
74	-1.8	-0.006832	-1.793
75	-2.9	-1.783	-1.117
76	11.4	11.46	-0.06214
77	-4.9	-2.292	-2.608
78	-8.8	-10.19	1.386
79	9.9	9.238	0.6621
80	-25.4	-23.87	-1.534
81	4.7	8.749	-4.049
82	5	3.845	1.155
83	-10.3	-11.63	1.329
84	1.7	3.619	-1.919
85	4	4.458	-0.4577
86	1.2	-0.4637	1.664
87	-0.3	-0.6429	0.3429
88	-2.7	-2.398	-0.3024
89	-2.4	-1.743	-0.6569
90	8.2	6.039	2.161
91	-2.5	-2.395	-0.1052
92	3.9	1.452	2.448
93	2.1	-1.623	3.723
94	3.7	3.945	-0.2447
95	-1.4	-1.47	0.06977
96	0.5	1.276	-0.7756
97	4.5	2.939	1.561
98	-2.1	-1.715	-0.3849
99	1.6	-0.7191	2.319
100	-2.4	-1.35	-1.05
101	-2.7	-2.707	0.007429
102	4.6	5.954	-1.354
103	0.1	-2.47	2.57
104	-5.2	-6.429	1.229
105	-0.3	3.62	-3.92
106	1	0.008987	0.991
107	-5.6	-1.545	-4.055
108	12.7	8.834	3.866
109	-18.8	-16.86	-1.936
110	6	10.72	-4.72
111	5.3	3.045	2.255
112	-1.5	-3.565	2.065
113	-6	-3.953	-2.047
114	2.8	0.8642	1.936
115	-1.1	2.432	-3.532
116	4.8	1.787	3.013
117	1.2	1.75	-0.5503
118	-6.4	-7.601	1.201
119	2.9	3.731	-0.8308
120	-5	-5.506	0.5063
121	13	14.94	-1.939
122	-1.4	-6.766	5.365
123	-3.4	-2.715	-0.6846
124	-5.8	-2.143	-3.657
125	12.9	9.482	3.418
126	-9.3	-8.118	-1.182
127	-5.8	-3.726	-2.074
128	10	10.82	-0.822
129	-7	-7.062	0.06192
130	-2.3	-0.4539	-1.846
131	7.4	5.472	1.928
132	-3.8	-1.902	-1.898
133	-6.1	-6.482	0.3824
134	8.2	9.29	-1.09
135	-6.8	-5.347	-1.453
136	2.1	-0.6326	2.733
137	0.4	1.4	-1
138	-1.2	1.296	-2.496
139	2.9	-1.501	4.401
140	1.8	5.98	-4.18
141	0	-1.956	1.956
142	1.7	-1.501	3.201
143	2.3	3.551	-1.251
144	-2.9	-3.462	0.5616
145	4.8	3.746	1.054
146	0.3	0.6922	-0.3922
147	-3.3	-5.024	1.724
148	3.3	1.818	1.482
149	-2.9	0.8091	-3.709
150	-2.5	-3.802	1.302
151	4.8	5.683	-0.8827
152	-5	-6.521	1.521
153	2.5	2.558	-0.05765
154	5.8	6.874	-1.074
155	-4.7	-5.34	0.64
156	-4.7	-4.419	-0.2814
157	9.8	9.383	0.4166
158	-6.9	-5.592	-1.308
159	2.5	2.828	-0.3283
160	1.2	1.326	-0.1258
161	-0.2	-1.909	1.709
162	4.1	3.48	0.62
163	-2.9	-4.862	1.962
164	-3.1	2.016	-5.116
165	4.7	3.219	1.481
166	-8	-8.671	0.6706
167	3.5	2.78	0.7199
168	9.4	9.03	0.3703
169	-11.7	-11.01	-0.6931
170	-5.3	-2.824	-2.476
171	13.3	13.45	-0.1548
172	-8.8	-10.85	2.05
173	-4.6	-4.145	-0.4553
174	7.9	8.224	-0.3239
175	-4.3	-5.111	0.8109
176	4.1	2.128	1.972
177	-4.1	-2.216	-1.884
178	5.6	4.813	0.7866
179	-10	-6.727	-3.273
180	6.8	5.385	1.415
181	1	-0.05822	1.058
182	2	1.16	0.8403
183	-5.9	-6.691	0.7908

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  0.3 & -0.4649 &  0.7649 \tabularnewline
2 &  1.8 &  1.075 &  0.7247 \tabularnewline
3 & -2.3 &  0.7387 & -3.039 \tabularnewline
4 &  5.2 &  3.054 &  2.146 \tabularnewline
5 & -1 & -1.578 &  0.5777 \tabularnewline
6 & -1.9 & -1.308 & -0.5915 \tabularnewline
7 &  0.7 &  0.2337 &  0.4663 \tabularnewline
8 &  1.2 &  2.251 & -1.051 \tabularnewline
9 & -1.5 & -2.132 &  0.6316 \tabularnewline
10 &  0.4 &  3.608 & -3.208 \tabularnewline
11 &  0 & -1.869 &  1.869 \tabularnewline
12 & -1.1 &  0.8269 & -1.927 \tabularnewline
13 & -0.1 & -0.858 &  0.758 \tabularnewline
14 &  2.1 &  1.229 &  0.8707 \tabularnewline
15 & -3 & -3.102 &  0.1017 \tabularnewline
16 &  3.1 &  1.971 &  1.129 \tabularnewline
17 & -0.6 &  2.11 & -2.71 \tabularnewline
18 & -3.3 & -2.944 & -0.3562 \tabularnewline
19 &  9.4 &  7.013 &  2.387 \tabularnewline
20 & -7.6 & -5.084 & -2.516 \tabularnewline
21 &  3.4 &  0.5777 &  2.822 \tabularnewline
22 &  5.4 &  2.903 &  2.497 \tabularnewline
23 & -4.8 & -4.091 & -0.7086 \tabularnewline
24 & -3.4 & -2.609 & -0.7913 \tabularnewline
25 &  10.3 &  8.967 &  1.333 \tabularnewline
26 & -11.6 & -9.519 & -2.081 \tabularnewline
27 &  8.2 &  5.149 &  3.051 \tabularnewline
28 & -4.9 & -3.656 & -1.244 \tabularnewline
29 & -6.1 & -5.23 & -0.8701 \tabularnewline
30 &  8.6 &  8.344 &  0.2555 \tabularnewline
31 & -1 & -0.6134 & -0.3866 \tabularnewline
32 & -4.8 & -4.92 &  0.1201 \tabularnewline
33 &  4.1 &  7.868 & -3.768 \tabularnewline
34 & -4.4 & -4.356 & -0.04437 \tabularnewline
35 &  3 &  2.675 &  0.3253 \tabularnewline
36 &  2.1 &  0.3713 &  1.729 \tabularnewline
37 & -4 & -5.216 &  1.216 \tabularnewline
38 &  0.1 &  1.782 & -1.682 \tabularnewline
39 &  0.6 &  0.3573 &  0.2427 \tabularnewline
40 &  4.3 &  6.157 & -1.857 \tabularnewline
41 & -5.4 & -6.502 &  1.102 \tabularnewline
42 &  5 &  3.637 &  1.363 \tabularnewline
43 &  1.3 &  1.473 & -0.1735 \tabularnewline
44 &  0 & -1.093 &  1.093 \tabularnewline
45 & -0.2 & -0.571 &  0.371 \tabularnewline
46 &  1.7 & -0.0429 &  1.743 \tabularnewline
47 & -9.1 & -6.992 & -2.108 \tabularnewline
48 &  10.7 &  10.01 &  0.6947 \tabularnewline
49 & -3.7 & -1.047 & -2.653 \tabularnewline
50 & -0.4 & -2.321 &  1.921 \tabularnewline
51 &  3.5 &  4.486 & -0.9864 \tabularnewline
52 & -5.2 & -5.749 &  0.5492 \tabularnewline
53 &  10.5 &  7.597 &  2.903 \tabularnewline
54 & -3.3 & -2.535 & -0.7646 \tabularnewline
55 & -6.8 & -7.29 &  0.4897 \tabularnewline
56 &  3.7 &  4.977 & -1.277 \tabularnewline
57 &  0.1 & -1.125 &  1.225 \tabularnewline
58 &  0.5 &  1.103 & -0.6027 \tabularnewline
59 &  0.3 &  0.622 & -0.322 \tabularnewline
60 & -2.5 & -4.138 &  1.638 \tabularnewline
61 &  0.4 &  0.0731 &  0.3269 \tabularnewline
62 &  5.1 &  2.291 &  2.809 \tabularnewline
63 & -4.2 & -1.381 & -2.819 \tabularnewline
64 & -3.6 & -4.547 &  0.9473 \tabularnewline
65 &  5.2 &  4.657 &  0.5432 \tabularnewline
66 & -3 &  0.2956 & -3.296 \tabularnewline
67 & -4.1 & -2.972 & -1.128 \tabularnewline
68 &  17 &  17.71 & -0.7126 \tabularnewline
69 & -3.9 & -5.791 &  1.891 \tabularnewline
70 & -7.5 & -3.619 & -3.881 \tabularnewline
71 &  14.6 &  11.11 &  3.488 \tabularnewline
72 & -10.6 & -8.383 & -2.217 \tabularnewline
73 & -1.2 & -2.107 &  0.9068 \tabularnewline
74 & -1.8 & -0.006832 & -1.793 \tabularnewline
75 & -2.9 & -1.783 & -1.117 \tabularnewline
76 &  11.4 &  11.46 & -0.06214 \tabularnewline
77 & -4.9 & -2.292 & -2.608 \tabularnewline
78 & -8.8 & -10.19 &  1.386 \tabularnewline
79 &  9.9 &  9.238 &  0.6621 \tabularnewline
80 & -25.4 & -23.87 & -1.534 \tabularnewline
81 &  4.7 &  8.749 & -4.049 \tabularnewline
82 &  5 &  3.845 &  1.155 \tabularnewline
83 & -10.3 & -11.63 &  1.329 \tabularnewline
84 &  1.7 &  3.619 & -1.919 \tabularnewline
85 &  4 &  4.458 & -0.4577 \tabularnewline
86 &  1.2 & -0.4637 &  1.664 \tabularnewline
87 & -0.3 & -0.6429 &  0.3429 \tabularnewline
88 & -2.7 & -2.398 & -0.3024 \tabularnewline
89 & -2.4 & -1.743 & -0.6569 \tabularnewline
90 &  8.2 &  6.039 &  2.161 \tabularnewline
91 & -2.5 & -2.395 & -0.1052 \tabularnewline
92 &  3.9 &  1.452 &  2.448 \tabularnewline
93 &  2.1 & -1.623 &  3.723 \tabularnewline
94 &  3.7 &  3.945 & -0.2447 \tabularnewline
95 & -1.4 & -1.47 &  0.06977 \tabularnewline
96 &  0.5 &  1.276 & -0.7756 \tabularnewline
97 &  4.5 &  2.939 &  1.561 \tabularnewline
98 & -2.1 & -1.715 & -0.3849 \tabularnewline
99 &  1.6 & -0.7191 &  2.319 \tabularnewline
100 & -2.4 & -1.35 & -1.05 \tabularnewline
101 & -2.7 & -2.707 &  0.007429 \tabularnewline
102 &  4.6 &  5.954 & -1.354 \tabularnewline
103 &  0.1 & -2.47 &  2.57 \tabularnewline
104 & -5.2 & -6.429 &  1.229 \tabularnewline
105 & -0.3 &  3.62 & -3.92 \tabularnewline
106 &  1 &  0.008987 &  0.991 \tabularnewline
107 & -5.6 & -1.545 & -4.055 \tabularnewline
108 &  12.7 &  8.834 &  3.866 \tabularnewline
109 & -18.8 & -16.86 & -1.936 \tabularnewline
110 &  6 &  10.72 & -4.72 \tabularnewline
111 &  5.3 &  3.045 &  2.255 \tabularnewline
112 & -1.5 & -3.565 &  2.065 \tabularnewline
113 & -6 & -3.953 & -2.047 \tabularnewline
114 &  2.8 &  0.8642 &  1.936 \tabularnewline
115 & -1.1 &  2.432 & -3.532 \tabularnewline
116 &  4.8 &  1.787 &  3.013 \tabularnewline
117 &  1.2 &  1.75 & -0.5503 \tabularnewline
118 & -6.4 & -7.601 &  1.201 \tabularnewline
119 &  2.9 &  3.731 & -0.8308 \tabularnewline
120 & -5 & -5.506 &  0.5063 \tabularnewline
121 &  13 &  14.94 & -1.939 \tabularnewline
122 & -1.4 & -6.766 &  5.365 \tabularnewline
123 & -3.4 & -2.715 & -0.6846 \tabularnewline
124 & -5.8 & -2.143 & -3.657 \tabularnewline
125 &  12.9 &  9.482 &  3.418 \tabularnewline
126 & -9.3 & -8.118 & -1.182 \tabularnewline
127 & -5.8 & -3.726 & -2.074 \tabularnewline
128 &  10 &  10.82 & -0.822 \tabularnewline
129 & -7 & -7.062 &  0.06192 \tabularnewline
130 & -2.3 & -0.4539 & -1.846 \tabularnewline
131 &  7.4 &  5.472 &  1.928 \tabularnewline
132 & -3.8 & -1.902 & -1.898 \tabularnewline
133 & -6.1 & -6.482 &  0.3824 \tabularnewline
134 &  8.2 &  9.29 & -1.09 \tabularnewline
135 & -6.8 & -5.347 & -1.453 \tabularnewline
136 &  2.1 & -0.6326 &  2.733 \tabularnewline
137 &  0.4 &  1.4 & -1 \tabularnewline
138 & -1.2 &  1.296 & -2.496 \tabularnewline
139 &  2.9 & -1.501 &  4.401 \tabularnewline
140 &  1.8 &  5.98 & -4.18 \tabularnewline
141 &  0 & -1.956 &  1.956 \tabularnewline
142 &  1.7 & -1.501 &  3.201 \tabularnewline
143 &  2.3 &  3.551 & -1.251 \tabularnewline
144 & -2.9 & -3.462 &  0.5616 \tabularnewline
145 &  4.8 &  3.746 &  1.054 \tabularnewline
146 &  0.3 &  0.6922 & -0.3922 \tabularnewline
147 & -3.3 & -5.024 &  1.724 \tabularnewline
148 &  3.3 &  1.818 &  1.482 \tabularnewline
149 & -2.9 &  0.8091 & -3.709 \tabularnewline
150 & -2.5 & -3.802 &  1.302 \tabularnewline
151 &  4.8 &  5.683 & -0.8827 \tabularnewline
152 & -5 & -6.521 &  1.521 \tabularnewline
153 &  2.5 &  2.558 & -0.05765 \tabularnewline
154 &  5.8 &  6.874 & -1.074 \tabularnewline
155 & -4.7 & -5.34 &  0.64 \tabularnewline
156 & -4.7 & -4.419 & -0.2814 \tabularnewline
157 &  9.8 &  9.383 &  0.4166 \tabularnewline
158 & -6.9 & -5.592 & -1.308 \tabularnewline
159 &  2.5 &  2.828 & -0.3283 \tabularnewline
160 &  1.2 &  1.326 & -0.1258 \tabularnewline
161 & -0.2 & -1.909 &  1.709 \tabularnewline
162 &  4.1 &  3.48 &  0.62 \tabularnewline
163 & -2.9 & -4.862 &  1.962 \tabularnewline
164 & -3.1 &  2.016 & -5.116 \tabularnewline
165 &  4.7 &  3.219 &  1.481 \tabularnewline
166 & -8 & -8.671 &  0.6706 \tabularnewline
167 &  3.5 &  2.78 &  0.7199 \tabularnewline
168 &  9.4 &  9.03 &  0.3703 \tabularnewline
169 & -11.7 & -11.01 & -0.6931 \tabularnewline
170 & -5.3 & -2.824 & -2.476 \tabularnewline
171 &  13.3 &  13.45 & -0.1548 \tabularnewline
172 & -8.8 & -10.85 &  2.05 \tabularnewline
173 & -4.6 & -4.145 & -0.4553 \tabularnewline
174 &  7.9 &  8.224 & -0.3239 \tabularnewline
175 & -4.3 & -5.111 &  0.8109 \tabularnewline
176 &  4.1 &  2.128 &  1.972 \tabularnewline
177 & -4.1 & -2.216 & -1.884 \tabularnewline
178 &  5.6 &  4.813 &  0.7866 \tabularnewline
179 & -10 & -6.727 & -3.273 \tabularnewline
180 &  6.8 &  5.385 &  1.415 \tabularnewline
181 &  1 & -0.05822 &  1.058 \tabularnewline
182 &  2 &  1.16 &  0.8403 \tabularnewline
183 & -5.9 & -6.691 &  0.7908 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309084&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] 0.3[/C][C]-0.4649[/C][C] 0.7649[/C][/ROW]
[ROW][C]2[/C][C] 1.8[/C][C] 1.075[/C][C] 0.7247[/C][/ROW]
[ROW][C]3[/C][C]-2.3[/C][C] 0.7387[/C][C]-3.039[/C][/ROW]
[ROW][C]4[/C][C] 5.2[/C][C] 3.054[/C][C] 2.146[/C][/ROW]
[ROW][C]5[/C][C]-1[/C][C]-1.578[/C][C] 0.5777[/C][/ROW]
[ROW][C]6[/C][C]-1.9[/C][C]-1.308[/C][C]-0.5915[/C][/ROW]
[ROW][C]7[/C][C] 0.7[/C][C] 0.2337[/C][C] 0.4663[/C][/ROW]
[ROW][C]8[/C][C] 1.2[/C][C] 2.251[/C][C]-1.051[/C][/ROW]
[ROW][C]9[/C][C]-1.5[/C][C]-2.132[/C][C] 0.6316[/C][/ROW]
[ROW][C]10[/C][C] 0.4[/C][C] 3.608[/C][C]-3.208[/C][/ROW]
[ROW][C]11[/C][C] 0[/C][C]-1.869[/C][C] 1.869[/C][/ROW]
[ROW][C]12[/C][C]-1.1[/C][C] 0.8269[/C][C]-1.927[/C][/ROW]
[ROW][C]13[/C][C]-0.1[/C][C]-0.858[/C][C] 0.758[/C][/ROW]
[ROW][C]14[/C][C] 2.1[/C][C] 1.229[/C][C] 0.8707[/C][/ROW]
[ROW][C]15[/C][C]-3[/C][C]-3.102[/C][C] 0.1017[/C][/ROW]
[ROW][C]16[/C][C] 3.1[/C][C] 1.971[/C][C] 1.129[/C][/ROW]
[ROW][C]17[/C][C]-0.6[/C][C] 2.11[/C][C]-2.71[/C][/ROW]
[ROW][C]18[/C][C]-3.3[/C][C]-2.944[/C][C]-0.3562[/C][/ROW]
[ROW][C]19[/C][C] 9.4[/C][C] 7.013[/C][C] 2.387[/C][/ROW]
[ROW][C]20[/C][C]-7.6[/C][C]-5.084[/C][C]-2.516[/C][/ROW]
[ROW][C]21[/C][C] 3.4[/C][C] 0.5777[/C][C] 2.822[/C][/ROW]
[ROW][C]22[/C][C] 5.4[/C][C] 2.903[/C][C] 2.497[/C][/ROW]
[ROW][C]23[/C][C]-4.8[/C][C]-4.091[/C][C]-0.7086[/C][/ROW]
[ROW][C]24[/C][C]-3.4[/C][C]-2.609[/C][C]-0.7913[/C][/ROW]
[ROW][C]25[/C][C] 10.3[/C][C] 8.967[/C][C] 1.333[/C][/ROW]
[ROW][C]26[/C][C]-11.6[/C][C]-9.519[/C][C]-2.081[/C][/ROW]
[ROW][C]27[/C][C] 8.2[/C][C] 5.149[/C][C] 3.051[/C][/ROW]
[ROW][C]28[/C][C]-4.9[/C][C]-3.656[/C][C]-1.244[/C][/ROW]
[ROW][C]29[/C][C]-6.1[/C][C]-5.23[/C][C]-0.8701[/C][/ROW]
[ROW][C]30[/C][C] 8.6[/C][C] 8.344[/C][C] 0.2555[/C][/ROW]
[ROW][C]31[/C][C]-1[/C][C]-0.6134[/C][C]-0.3866[/C][/ROW]
[ROW][C]32[/C][C]-4.8[/C][C]-4.92[/C][C] 0.1201[/C][/ROW]
[ROW][C]33[/C][C] 4.1[/C][C] 7.868[/C][C]-3.768[/C][/ROW]
[ROW][C]34[/C][C]-4.4[/C][C]-4.356[/C][C]-0.04437[/C][/ROW]
[ROW][C]35[/C][C] 3[/C][C] 2.675[/C][C] 0.3253[/C][/ROW]
[ROW][C]36[/C][C] 2.1[/C][C] 0.3713[/C][C] 1.729[/C][/ROW]
[ROW][C]37[/C][C]-4[/C][C]-5.216[/C][C] 1.216[/C][/ROW]
[ROW][C]38[/C][C] 0.1[/C][C] 1.782[/C][C]-1.682[/C][/ROW]
[ROW][C]39[/C][C] 0.6[/C][C] 0.3573[/C][C] 0.2427[/C][/ROW]
[ROW][C]40[/C][C] 4.3[/C][C] 6.157[/C][C]-1.857[/C][/ROW]
[ROW][C]41[/C][C]-5.4[/C][C]-6.502[/C][C] 1.102[/C][/ROW]
[ROW][C]42[/C][C] 5[/C][C] 3.637[/C][C] 1.363[/C][/ROW]
[ROW][C]43[/C][C] 1.3[/C][C] 1.473[/C][C]-0.1735[/C][/ROW]
[ROW][C]44[/C][C] 0[/C][C]-1.093[/C][C] 1.093[/C][/ROW]
[ROW][C]45[/C][C]-0.2[/C][C]-0.571[/C][C] 0.371[/C][/ROW]
[ROW][C]46[/C][C] 1.7[/C][C]-0.0429[/C][C] 1.743[/C][/ROW]
[ROW][C]47[/C][C]-9.1[/C][C]-6.992[/C][C]-2.108[/C][/ROW]
[ROW][C]48[/C][C] 10.7[/C][C] 10.01[/C][C] 0.6947[/C][/ROW]
[ROW][C]49[/C][C]-3.7[/C][C]-1.047[/C][C]-2.653[/C][/ROW]
[ROW][C]50[/C][C]-0.4[/C][C]-2.321[/C][C] 1.921[/C][/ROW]
[ROW][C]51[/C][C] 3.5[/C][C] 4.486[/C][C]-0.9864[/C][/ROW]
[ROW][C]52[/C][C]-5.2[/C][C]-5.749[/C][C] 0.5492[/C][/ROW]
[ROW][C]53[/C][C] 10.5[/C][C] 7.597[/C][C] 2.903[/C][/ROW]
[ROW][C]54[/C][C]-3.3[/C][C]-2.535[/C][C]-0.7646[/C][/ROW]
[ROW][C]55[/C][C]-6.8[/C][C]-7.29[/C][C] 0.4897[/C][/ROW]
[ROW][C]56[/C][C] 3.7[/C][C] 4.977[/C][C]-1.277[/C][/ROW]
[ROW][C]57[/C][C] 0.1[/C][C]-1.125[/C][C] 1.225[/C][/ROW]
[ROW][C]58[/C][C] 0.5[/C][C] 1.103[/C][C]-0.6027[/C][/ROW]
[ROW][C]59[/C][C] 0.3[/C][C] 0.622[/C][C]-0.322[/C][/ROW]
[ROW][C]60[/C][C]-2.5[/C][C]-4.138[/C][C] 1.638[/C][/ROW]
[ROW][C]61[/C][C] 0.4[/C][C] 0.0731[/C][C] 0.3269[/C][/ROW]
[ROW][C]62[/C][C] 5.1[/C][C] 2.291[/C][C] 2.809[/C][/ROW]
[ROW][C]63[/C][C]-4.2[/C][C]-1.381[/C][C]-2.819[/C][/ROW]
[ROW][C]64[/C][C]-3.6[/C][C]-4.547[/C][C] 0.9473[/C][/ROW]
[ROW][C]65[/C][C] 5.2[/C][C] 4.657[/C][C] 0.5432[/C][/ROW]
[ROW][C]66[/C][C]-3[/C][C] 0.2956[/C][C]-3.296[/C][/ROW]
[ROW][C]67[/C][C]-4.1[/C][C]-2.972[/C][C]-1.128[/C][/ROW]
[ROW][C]68[/C][C] 17[/C][C] 17.71[/C][C]-0.7126[/C][/ROW]
[ROW][C]69[/C][C]-3.9[/C][C]-5.791[/C][C] 1.891[/C][/ROW]
[ROW][C]70[/C][C]-7.5[/C][C]-3.619[/C][C]-3.881[/C][/ROW]
[ROW][C]71[/C][C] 14.6[/C][C] 11.11[/C][C] 3.488[/C][/ROW]
[ROW][C]72[/C][C]-10.6[/C][C]-8.383[/C][C]-2.217[/C][/ROW]
[ROW][C]73[/C][C]-1.2[/C][C]-2.107[/C][C] 0.9068[/C][/ROW]
[ROW][C]74[/C][C]-1.8[/C][C]-0.006832[/C][C]-1.793[/C][/ROW]
[ROW][C]75[/C][C]-2.9[/C][C]-1.783[/C][C]-1.117[/C][/ROW]
[ROW][C]76[/C][C] 11.4[/C][C] 11.46[/C][C]-0.06214[/C][/ROW]
[ROW][C]77[/C][C]-4.9[/C][C]-2.292[/C][C]-2.608[/C][/ROW]
[ROW][C]78[/C][C]-8.8[/C][C]-10.19[/C][C] 1.386[/C][/ROW]
[ROW][C]79[/C][C] 9.9[/C][C] 9.238[/C][C] 0.6621[/C][/ROW]
[ROW][C]80[/C][C]-25.4[/C][C]-23.87[/C][C]-1.534[/C][/ROW]
[ROW][C]81[/C][C] 4.7[/C][C] 8.749[/C][C]-4.049[/C][/ROW]
[ROW][C]82[/C][C] 5[/C][C] 3.845[/C][C] 1.155[/C][/ROW]
[ROW][C]83[/C][C]-10.3[/C][C]-11.63[/C][C] 1.329[/C][/ROW]
[ROW][C]84[/C][C] 1.7[/C][C] 3.619[/C][C]-1.919[/C][/ROW]
[ROW][C]85[/C][C] 4[/C][C] 4.458[/C][C]-0.4577[/C][/ROW]
[ROW][C]86[/C][C] 1.2[/C][C]-0.4637[/C][C] 1.664[/C][/ROW]
[ROW][C]87[/C][C]-0.3[/C][C]-0.6429[/C][C] 0.3429[/C][/ROW]
[ROW][C]88[/C][C]-2.7[/C][C]-2.398[/C][C]-0.3024[/C][/ROW]
[ROW][C]89[/C][C]-2.4[/C][C]-1.743[/C][C]-0.6569[/C][/ROW]
[ROW][C]90[/C][C] 8.2[/C][C] 6.039[/C][C] 2.161[/C][/ROW]
[ROW][C]91[/C][C]-2.5[/C][C]-2.395[/C][C]-0.1052[/C][/ROW]
[ROW][C]92[/C][C] 3.9[/C][C] 1.452[/C][C] 2.448[/C][/ROW]
[ROW][C]93[/C][C] 2.1[/C][C]-1.623[/C][C] 3.723[/C][/ROW]
[ROW][C]94[/C][C] 3.7[/C][C] 3.945[/C][C]-0.2447[/C][/ROW]
[ROW][C]95[/C][C]-1.4[/C][C]-1.47[/C][C] 0.06977[/C][/ROW]
[ROW][C]96[/C][C] 0.5[/C][C] 1.276[/C][C]-0.7756[/C][/ROW]
[ROW][C]97[/C][C] 4.5[/C][C] 2.939[/C][C] 1.561[/C][/ROW]
[ROW][C]98[/C][C]-2.1[/C][C]-1.715[/C][C]-0.3849[/C][/ROW]
[ROW][C]99[/C][C] 1.6[/C][C]-0.7191[/C][C] 2.319[/C][/ROW]
[ROW][C]100[/C][C]-2.4[/C][C]-1.35[/C][C]-1.05[/C][/ROW]
[ROW][C]101[/C][C]-2.7[/C][C]-2.707[/C][C] 0.007429[/C][/ROW]
[ROW][C]102[/C][C] 4.6[/C][C] 5.954[/C][C]-1.354[/C][/ROW]
[ROW][C]103[/C][C] 0.1[/C][C]-2.47[/C][C] 2.57[/C][/ROW]
[ROW][C]104[/C][C]-5.2[/C][C]-6.429[/C][C] 1.229[/C][/ROW]
[ROW][C]105[/C][C]-0.3[/C][C] 3.62[/C][C]-3.92[/C][/ROW]
[ROW][C]106[/C][C] 1[/C][C] 0.008987[/C][C] 0.991[/C][/ROW]
[ROW][C]107[/C][C]-5.6[/C][C]-1.545[/C][C]-4.055[/C][/ROW]
[ROW][C]108[/C][C] 12.7[/C][C] 8.834[/C][C] 3.866[/C][/ROW]
[ROW][C]109[/C][C]-18.8[/C][C]-16.86[/C][C]-1.936[/C][/ROW]
[ROW][C]110[/C][C] 6[/C][C] 10.72[/C][C]-4.72[/C][/ROW]
[ROW][C]111[/C][C] 5.3[/C][C] 3.045[/C][C] 2.255[/C][/ROW]
[ROW][C]112[/C][C]-1.5[/C][C]-3.565[/C][C] 2.065[/C][/ROW]
[ROW][C]113[/C][C]-6[/C][C]-3.953[/C][C]-2.047[/C][/ROW]
[ROW][C]114[/C][C] 2.8[/C][C] 0.8642[/C][C] 1.936[/C][/ROW]
[ROW][C]115[/C][C]-1.1[/C][C] 2.432[/C][C]-3.532[/C][/ROW]
[ROW][C]116[/C][C] 4.8[/C][C] 1.787[/C][C] 3.013[/C][/ROW]
[ROW][C]117[/C][C] 1.2[/C][C] 1.75[/C][C]-0.5503[/C][/ROW]
[ROW][C]118[/C][C]-6.4[/C][C]-7.601[/C][C] 1.201[/C][/ROW]
[ROW][C]119[/C][C] 2.9[/C][C] 3.731[/C][C]-0.8308[/C][/ROW]
[ROW][C]120[/C][C]-5[/C][C]-5.506[/C][C] 0.5063[/C][/ROW]
[ROW][C]121[/C][C] 13[/C][C] 14.94[/C][C]-1.939[/C][/ROW]
[ROW][C]122[/C][C]-1.4[/C][C]-6.766[/C][C] 5.365[/C][/ROW]
[ROW][C]123[/C][C]-3.4[/C][C]-2.715[/C][C]-0.6846[/C][/ROW]
[ROW][C]124[/C][C]-5.8[/C][C]-2.143[/C][C]-3.657[/C][/ROW]
[ROW][C]125[/C][C] 12.9[/C][C] 9.482[/C][C] 3.418[/C][/ROW]
[ROW][C]126[/C][C]-9.3[/C][C]-8.118[/C][C]-1.182[/C][/ROW]
[ROW][C]127[/C][C]-5.8[/C][C]-3.726[/C][C]-2.074[/C][/ROW]
[ROW][C]128[/C][C] 10[/C][C] 10.82[/C][C]-0.822[/C][/ROW]
[ROW][C]129[/C][C]-7[/C][C]-7.062[/C][C] 0.06192[/C][/ROW]
[ROW][C]130[/C][C]-2.3[/C][C]-0.4539[/C][C]-1.846[/C][/ROW]
[ROW][C]131[/C][C] 7.4[/C][C] 5.472[/C][C] 1.928[/C][/ROW]
[ROW][C]132[/C][C]-3.8[/C][C]-1.902[/C][C]-1.898[/C][/ROW]
[ROW][C]133[/C][C]-6.1[/C][C]-6.482[/C][C] 0.3824[/C][/ROW]
[ROW][C]134[/C][C] 8.2[/C][C] 9.29[/C][C]-1.09[/C][/ROW]
[ROW][C]135[/C][C]-6.8[/C][C]-5.347[/C][C]-1.453[/C][/ROW]
[ROW][C]136[/C][C] 2.1[/C][C]-0.6326[/C][C] 2.733[/C][/ROW]
[ROW][C]137[/C][C] 0.4[/C][C] 1.4[/C][C]-1[/C][/ROW]
[ROW][C]138[/C][C]-1.2[/C][C] 1.296[/C][C]-2.496[/C][/ROW]
[ROW][C]139[/C][C] 2.9[/C][C]-1.501[/C][C] 4.401[/C][/ROW]
[ROW][C]140[/C][C] 1.8[/C][C] 5.98[/C][C]-4.18[/C][/ROW]
[ROW][C]141[/C][C] 0[/C][C]-1.956[/C][C] 1.956[/C][/ROW]
[ROW][C]142[/C][C] 1.7[/C][C]-1.501[/C][C] 3.201[/C][/ROW]
[ROW][C]143[/C][C] 2.3[/C][C] 3.551[/C][C]-1.251[/C][/ROW]
[ROW][C]144[/C][C]-2.9[/C][C]-3.462[/C][C] 0.5616[/C][/ROW]
[ROW][C]145[/C][C] 4.8[/C][C] 3.746[/C][C] 1.054[/C][/ROW]
[ROW][C]146[/C][C] 0.3[/C][C] 0.6922[/C][C]-0.3922[/C][/ROW]
[ROW][C]147[/C][C]-3.3[/C][C]-5.024[/C][C] 1.724[/C][/ROW]
[ROW][C]148[/C][C] 3.3[/C][C] 1.818[/C][C] 1.482[/C][/ROW]
[ROW][C]149[/C][C]-2.9[/C][C] 0.8091[/C][C]-3.709[/C][/ROW]
[ROW][C]150[/C][C]-2.5[/C][C]-3.802[/C][C] 1.302[/C][/ROW]
[ROW][C]151[/C][C] 4.8[/C][C] 5.683[/C][C]-0.8827[/C][/ROW]
[ROW][C]152[/C][C]-5[/C][C]-6.521[/C][C] 1.521[/C][/ROW]
[ROW][C]153[/C][C] 2.5[/C][C] 2.558[/C][C]-0.05765[/C][/ROW]
[ROW][C]154[/C][C] 5.8[/C][C] 6.874[/C][C]-1.074[/C][/ROW]
[ROW][C]155[/C][C]-4.7[/C][C]-5.34[/C][C] 0.64[/C][/ROW]
[ROW][C]156[/C][C]-4.7[/C][C]-4.419[/C][C]-0.2814[/C][/ROW]
[ROW][C]157[/C][C] 9.8[/C][C] 9.383[/C][C] 0.4166[/C][/ROW]
[ROW][C]158[/C][C]-6.9[/C][C]-5.592[/C][C]-1.308[/C][/ROW]
[ROW][C]159[/C][C] 2.5[/C][C] 2.828[/C][C]-0.3283[/C][/ROW]
[ROW][C]160[/C][C] 1.2[/C][C] 1.326[/C][C]-0.1258[/C][/ROW]
[ROW][C]161[/C][C]-0.2[/C][C]-1.909[/C][C] 1.709[/C][/ROW]
[ROW][C]162[/C][C] 4.1[/C][C] 3.48[/C][C] 0.62[/C][/ROW]
[ROW][C]163[/C][C]-2.9[/C][C]-4.862[/C][C] 1.962[/C][/ROW]
[ROW][C]164[/C][C]-3.1[/C][C] 2.016[/C][C]-5.116[/C][/ROW]
[ROW][C]165[/C][C] 4.7[/C][C] 3.219[/C][C] 1.481[/C][/ROW]
[ROW][C]166[/C][C]-8[/C][C]-8.671[/C][C] 0.6706[/C][/ROW]
[ROW][C]167[/C][C] 3.5[/C][C] 2.78[/C][C] 0.7199[/C][/ROW]
[ROW][C]168[/C][C] 9.4[/C][C] 9.03[/C][C] 0.3703[/C][/ROW]
[ROW][C]169[/C][C]-11.7[/C][C]-11.01[/C][C]-0.6931[/C][/ROW]
[ROW][C]170[/C][C]-5.3[/C][C]-2.824[/C][C]-2.476[/C][/ROW]
[ROW][C]171[/C][C] 13.3[/C][C] 13.45[/C][C]-0.1548[/C][/ROW]
[ROW][C]172[/C][C]-8.8[/C][C]-10.85[/C][C] 2.05[/C][/ROW]
[ROW][C]173[/C][C]-4.6[/C][C]-4.145[/C][C]-0.4553[/C][/ROW]
[ROW][C]174[/C][C] 7.9[/C][C] 8.224[/C][C]-0.3239[/C][/ROW]
[ROW][C]175[/C][C]-4.3[/C][C]-5.111[/C][C] 0.8109[/C][/ROW]
[ROW][C]176[/C][C] 4.1[/C][C] 2.128[/C][C] 1.972[/C][/ROW]
[ROW][C]177[/C][C]-4.1[/C][C]-2.216[/C][C]-1.884[/C][/ROW]
[ROW][C]178[/C][C] 5.6[/C][C] 4.813[/C][C] 0.7866[/C][/ROW]
[ROW][C]179[/C][C]-10[/C][C]-6.727[/C][C]-3.273[/C][/ROW]
[ROW][C]180[/C][C] 6.8[/C][C] 5.385[/C][C] 1.415[/C][/ROW]
[ROW][C]181[/C][C] 1[/C][C]-0.05822[/C][C] 1.058[/C][/ROW]
[ROW][C]182[/C][C] 2[/C][C] 1.16[/C][C] 0.8403[/C][/ROW]
[ROW][C]183[/C][C]-5.9[/C][C]-6.691[/C][C] 0.7908[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309084&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309084&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	0.3	-0.4649	0.7649
2	1.8	1.075	0.7247
3	-2.3	0.7387	-3.039
4	5.2	3.054	2.146
5	-1	-1.578	0.5777
6	-1.9	-1.308	-0.5915
7	0.7	0.2337	0.4663
8	1.2	2.251	-1.051
9	-1.5	-2.132	0.6316
10	0.4	3.608	-3.208
11	0	-1.869	1.869
12	-1.1	0.8269	-1.927
13	-0.1	-0.858	0.758
14	2.1	1.229	0.8707
15	-3	-3.102	0.1017
16	3.1	1.971	1.129
17	-0.6	2.11	-2.71
18	-3.3	-2.944	-0.3562
19	9.4	7.013	2.387
20	-7.6	-5.084	-2.516
21	3.4	0.5777	2.822
22	5.4	2.903	2.497
23	-4.8	-4.091	-0.7086
24	-3.4	-2.609	-0.7913
25	10.3	8.967	1.333
26	-11.6	-9.519	-2.081
27	8.2	5.149	3.051
28	-4.9	-3.656	-1.244
29	-6.1	-5.23	-0.8701
30	8.6	8.344	0.2555
31	-1	-0.6134	-0.3866
32	-4.8	-4.92	0.1201
33	4.1	7.868	-3.768
34	-4.4	-4.356	-0.04437
35	3	2.675	0.3253
36	2.1	0.3713	1.729
37	-4	-5.216	1.216
38	0.1	1.782	-1.682
39	0.6	0.3573	0.2427
40	4.3	6.157	-1.857
41	-5.4	-6.502	1.102
42	5	3.637	1.363
43	1.3	1.473	-0.1735
44	0	-1.093	1.093
45	-0.2	-0.571	0.371
46	1.7	-0.0429	1.743
47	-9.1	-6.992	-2.108
48	10.7	10.01	0.6947
49	-3.7	-1.047	-2.653
50	-0.4	-2.321	1.921
51	3.5	4.486	-0.9864
52	-5.2	-5.749	0.5492
53	10.5	7.597	2.903
54	-3.3	-2.535	-0.7646
55	-6.8	-7.29	0.4897
56	3.7	4.977	-1.277
57	0.1	-1.125	1.225
58	0.5	1.103	-0.6027
59	0.3	0.622	-0.322
60	-2.5	-4.138	1.638
61	0.4	0.0731	0.3269
62	5.1	2.291	2.809
63	-4.2	-1.381	-2.819
64	-3.6	-4.547	0.9473
65	5.2	4.657	0.5432
66	-3	0.2956	-3.296
67	-4.1	-2.972	-1.128
68	17	17.71	-0.7126
69	-3.9	-5.791	1.891
70	-7.5	-3.619	-3.881
71	14.6	11.11	3.488
72	-10.6	-8.383	-2.217
73	-1.2	-2.107	0.9068
74	-1.8	-0.006832	-1.793
75	-2.9	-1.783	-1.117
76	11.4	11.46	-0.06214
77	-4.9	-2.292	-2.608
78	-8.8	-10.19	1.386
79	9.9	9.238	0.6621
80	-25.4	-23.87	-1.534
81	4.7	8.749	-4.049
82	5	3.845	1.155
83	-10.3	-11.63	1.329
84	1.7	3.619	-1.919
85	4	4.458	-0.4577
86	1.2	-0.4637	1.664
87	-0.3	-0.6429	0.3429
88	-2.7	-2.398	-0.3024
89	-2.4	-1.743	-0.6569
90	8.2	6.039	2.161
91	-2.5	-2.395	-0.1052
92	3.9	1.452	2.448
93	2.1	-1.623	3.723
94	3.7	3.945	-0.2447
95	-1.4	-1.47	0.06977
96	0.5	1.276	-0.7756
97	4.5	2.939	1.561
98	-2.1	-1.715	-0.3849
99	1.6	-0.7191	2.319
100	-2.4	-1.35	-1.05
101	-2.7	-2.707	0.007429
102	4.6	5.954	-1.354
103	0.1	-2.47	2.57
104	-5.2	-6.429	1.229
105	-0.3	3.62	-3.92
106	1	0.008987	0.991
107	-5.6	-1.545	-4.055
108	12.7	8.834	3.866
109	-18.8	-16.86	-1.936
110	6	10.72	-4.72
111	5.3	3.045	2.255
112	-1.5	-3.565	2.065
113	-6	-3.953	-2.047
114	2.8	0.8642	1.936
115	-1.1	2.432	-3.532
116	4.8	1.787	3.013
117	1.2	1.75	-0.5503
118	-6.4	-7.601	1.201
119	2.9	3.731	-0.8308
120	-5	-5.506	0.5063
121	13	14.94	-1.939
122	-1.4	-6.766	5.365
123	-3.4	-2.715	-0.6846
124	-5.8	-2.143	-3.657
125	12.9	9.482	3.418
126	-9.3	-8.118	-1.182
127	-5.8	-3.726	-2.074
128	10	10.82	-0.822
129	-7	-7.062	0.06192
130	-2.3	-0.4539	-1.846
131	7.4	5.472	1.928
132	-3.8	-1.902	-1.898
133	-6.1	-6.482	0.3824
134	8.2	9.29	-1.09
135	-6.8	-5.347	-1.453
136	2.1	-0.6326	2.733
137	0.4	1.4	-1
138	-1.2	1.296	-2.496
139	2.9	-1.501	4.401
140	1.8	5.98	-4.18
141	0	-1.956	1.956
142	1.7	-1.501	3.201
143	2.3	3.551	-1.251
144	-2.9	-3.462	0.5616
145	4.8	3.746	1.054
146	0.3	0.6922	-0.3922
147	-3.3	-5.024	1.724
148	3.3	1.818	1.482
149	-2.9	0.8091	-3.709
150	-2.5	-3.802	1.302
151	4.8	5.683	-0.8827
152	-5	-6.521	1.521
153	2.5	2.558	-0.05765
154	5.8	6.874	-1.074
155	-4.7	-5.34	0.64
156	-4.7	-4.419	-0.2814
157	9.8	9.383	0.4166
158	-6.9	-5.592	-1.308
159	2.5	2.828	-0.3283
160	1.2	1.326	-0.1258
161	-0.2	-1.909	1.709
162	4.1	3.48	0.62
163	-2.9	-4.862	1.962
164	-3.1	2.016	-5.116
165	4.7	3.219	1.481
166	-8	-8.671	0.6706
167	3.5	2.78	0.7199
168	9.4	9.03	0.3703
169	-11.7	-11.01	-0.6931
170	-5.3	-2.824	-2.476
171	13.3	13.45	-0.1548
172	-8.8	-10.85	2.05
173	-4.6	-4.145	-0.4553
174	7.9	8.224	-0.3239
175	-4.3	-5.111	0.8109
176	4.1	2.128	1.972
177	-4.1	-2.216	-1.884
178	5.6	4.813	0.7866
179	-10	-6.727	-3.273
180	6.8	5.385	1.415
181	1	-0.05822	1.058
182	2	1.16	0.8403
183	-5.9	-6.691	0.7908

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
10	0.4995	0.9991	0.5004
11	0.333	0.6661	0.667
12	0.224	0.448	0.776
13	0.1313	0.2626	0.8687
14	0.08097	0.1619	0.919
15	0.04354	0.08707	0.9565
16	0.02213	0.04427	0.9779
17	0.02644	0.05288	0.9736
18	0.01506	0.03013	0.9849
19	0.04013	0.08025	0.9599
20	0.02775	0.05551	0.9722
21	0.03139	0.06277	0.9686
22	0.07668	0.1534	0.9233
23	0.07926	0.1585	0.9207
24	0.09114	0.1823	0.9089
25	0.06613	0.1323	0.9339
26	0.04705	0.09409	0.953
27	0.04015	0.08031	0.9598
28	0.02792	0.05583	0.9721
29	0.01866	0.03732	0.9813
30	0.01663	0.03326	0.9834
31	0.01154	0.02309	0.9885
32	0.009093	0.01819	0.9909
33	0.1623	0.3245	0.8377
34	0.1277	0.2553	0.8723
35	0.09808	0.1962	0.9019
36	0.1132	0.2263	0.8868
37	0.0887	0.1774	0.9113
38	0.07123	0.1424	0.9288
39	0.05535	0.1107	0.9447
40	0.04677	0.09353	0.9532
41	0.04739	0.09477	0.9526
42	0.03608	0.07216	0.9639
43	0.02883	0.05767	0.9712
44	0.03245	0.0649	0.9676
45	0.02403	0.04806	0.976
46	0.03385	0.06771	0.9661
47	0.04116	0.08232	0.9588
48	0.03142	0.06284	0.9686
49	0.02894	0.05788	0.9711
50	0.02548	0.05096	0.9745
51	0.0217	0.0434	0.9783
52	0.01706	0.03412	0.9829
53	0.02492	0.04983	0.9751
54	0.01868	0.03736	0.9813
55	0.01379	0.02759	0.9862
56	0.01306	0.02613	0.9869
57	0.01402	0.02804	0.986
58	0.012	0.024	0.988
59	0.008782	0.01756	0.9912
60	0.007155	0.01431	0.9928
61	0.005108	0.01022	0.9949
62	0.008442	0.01688	0.9916
63	0.01223	0.02446	0.9878
64	0.009327	0.01865	0.9907
65	0.007095	0.01419	0.9929
66	0.01057	0.02113	0.9894
67	0.009077	0.01815	0.9909
68	0.00709	0.01418	0.9929
69	0.01484	0.02968	0.9852
70	0.04287	0.08573	0.9571
71	0.06708	0.1342	0.9329
72	0.06501	0.13	0.935
73	0.05398	0.108	0.946
74	0.05985	0.1197	0.9402
75	0.05182	0.1036	0.9482
76	0.04089	0.08179	0.9591
77	0.04508	0.09016	0.9549
78	0.0396	0.07919	0.9604
79	0.03201	0.06402	0.968
80	0.02858	0.05716	0.9714
81	0.07685	0.1537	0.9232
82	0.08575	0.1715	0.9142
83	0.08502	0.17	0.915
84	0.08269	0.1654	0.9173
85	0.06805	0.1361	0.932
86	0.06788	0.1358	0.9321
87	0.05542	0.1108	0.9446
88	0.04569	0.09137	0.9543
89	0.03697	0.07394	0.963
90	0.04021	0.08041	0.9598
91	0.03162	0.06325	0.9684
92	0.03892	0.07783	0.9611
93	0.07069	0.1414	0.9293
94	0.05825	0.1165	0.9417
95	0.04675	0.0935	0.9533
96	0.039	0.07801	0.961
97	0.03625	0.07249	0.9638
98	0.02853	0.05706	0.9715
99	0.03223	0.06446	0.9678
100	0.02678	0.05357	0.9732
101	0.02068	0.04137	0.9793
102	0.01832	0.03663	0.9817
103	0.02243	0.04485	0.9776
104	0.01908	0.03816	0.9809
105	0.04035	0.08071	0.9596
106	0.03338	0.06676	0.9666
107	0.07435	0.1487	0.9257
108	0.1255	0.251	0.8745
109	0.1352	0.2703	0.8648
110	0.2777	0.5555	0.7223
111	0.279	0.5581	0.721
112	0.2779	0.5558	0.7221
113	0.2783	0.5566	0.7217
114	0.2785	0.557	0.7215
115	0.3783	0.7566	0.6217
116	0.4229	0.8459	0.5771
117	0.3807	0.7614	0.6193
118	0.3566	0.7131	0.6434
119	0.3203	0.6406	0.6797
120	0.2814	0.5628	0.7186
121	0.2841	0.5681	0.7159
122	0.6023	0.7954	0.3977
123	0.5637	0.8727	0.4363
124	0.66	0.68	0.34
125	0.7252	0.5496	0.2748
126	0.7032	0.5936	0.2968
127	0.7259	0.5483	0.2741
128	0.6917	0.6165	0.3083
129	0.6489	0.7021	0.3511
130	0.6887	0.6225	0.3113
131	0.6884	0.6233	0.3116
132	0.6884	0.6232	0.3116
133	0.6429	0.7141	0.3571
134	0.6078	0.7844	0.3922
135	0.6122	0.7756	0.3878
136	0.6147	0.7707	0.3853
137	0.5718	0.8564	0.4282
138	0.6441	0.7119	0.3559
139	0.7857	0.4285	0.2143
140	0.8776	0.2448	0.1224
141	0.8782	0.2435	0.1218
142	0.9406	0.1187	0.05937
143	0.9264	0.1472	0.07361
144	0.9104	0.1792	0.08959
145	0.903	0.194	0.097
146	0.8752	0.2497	0.1248
147	0.8919	0.2163	0.1081
148	0.8987	0.2027	0.1013
149	0.9591	0.0818	0.0409
150	0.949	0.1019	0.05096
151	0.9412	0.1176	0.05879
152	0.929	0.142	0.07101
153	0.9054	0.1893	0.09463
154	0.8913	0.2174	0.1087
155	0.8612	0.2777	0.1388
156	0.8396	0.3208	0.1604
157	0.792	0.4161	0.208
158	0.8022	0.3955	0.1978
159	0.7517	0.4965	0.2483
160	0.6935	0.6129	0.3065
161	0.6834	0.6331	0.3166
162	0.6182	0.7636	0.3818
163	0.7756	0.4487	0.2244
164	0.9606	0.07878	0.03939
165	0.9606	0.07877	0.03938
166	0.9413	0.1174	0.05869
167	0.9021	0.1959	0.09795
168	0.8722	0.2556	0.1278
169	0.8191	0.3618	0.1809
170	0.7327	0.5346	0.2673
171	0.6613	0.6775	0.3387
172	0.5339	0.9321	0.4661
173	0.3917	0.7834	0.6083

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 &  0.4995 &  0.9991 &  0.5004 \tabularnewline
11 &  0.333 &  0.6661 &  0.667 \tabularnewline
12 &  0.224 &  0.448 &  0.776 \tabularnewline
13 &  0.1313 &  0.2626 &  0.8687 \tabularnewline
14 &  0.08097 &  0.1619 &  0.919 \tabularnewline
15 &  0.04354 &  0.08707 &  0.9565 \tabularnewline
16 &  0.02213 &  0.04427 &  0.9779 \tabularnewline
17 &  0.02644 &  0.05288 &  0.9736 \tabularnewline
18 &  0.01506 &  0.03013 &  0.9849 \tabularnewline
19 &  0.04013 &  0.08025 &  0.9599 \tabularnewline
20 &  0.02775 &  0.05551 &  0.9722 \tabularnewline
21 &  0.03139 &  0.06277 &  0.9686 \tabularnewline
22 &  0.07668 &  0.1534 &  0.9233 \tabularnewline
23 &  0.07926 &  0.1585 &  0.9207 \tabularnewline
24 &  0.09114 &  0.1823 &  0.9089 \tabularnewline
25 &  0.06613 &  0.1323 &  0.9339 \tabularnewline
26 &  0.04705 &  0.09409 &  0.953 \tabularnewline
27 &  0.04015 &  0.08031 &  0.9598 \tabularnewline
28 &  0.02792 &  0.05583 &  0.9721 \tabularnewline
29 &  0.01866 &  0.03732 &  0.9813 \tabularnewline
30 &  0.01663 &  0.03326 &  0.9834 \tabularnewline
31 &  0.01154 &  0.02309 &  0.9885 \tabularnewline
32 &  0.009093 &  0.01819 &  0.9909 \tabularnewline
33 &  0.1623 &  0.3245 &  0.8377 \tabularnewline
34 &  0.1277 &  0.2553 &  0.8723 \tabularnewline
35 &  0.09808 &  0.1962 &  0.9019 \tabularnewline
36 &  0.1132 &  0.2263 &  0.8868 \tabularnewline
37 &  0.0887 &  0.1774 &  0.9113 \tabularnewline
38 &  0.07123 &  0.1424 &  0.9288 \tabularnewline
39 &  0.05535 &  0.1107 &  0.9447 \tabularnewline
40 &  0.04677 &  0.09353 &  0.9532 \tabularnewline
41 &  0.04739 &  0.09477 &  0.9526 \tabularnewline
42 &  0.03608 &  0.07216 &  0.9639 \tabularnewline
43 &  0.02883 &  0.05767 &  0.9712 \tabularnewline
44 &  0.03245 &  0.0649 &  0.9676 \tabularnewline
45 &  0.02403 &  0.04806 &  0.976 \tabularnewline
46 &  0.03385 &  0.06771 &  0.9661 \tabularnewline
47 &  0.04116 &  0.08232 &  0.9588 \tabularnewline
48 &  0.03142 &  0.06284 &  0.9686 \tabularnewline
49 &  0.02894 &  0.05788 &  0.9711 \tabularnewline
50 &  0.02548 &  0.05096 &  0.9745 \tabularnewline
51 &  0.0217 &  0.0434 &  0.9783 \tabularnewline
52 &  0.01706 &  0.03412 &  0.9829 \tabularnewline
53 &  0.02492 &  0.04983 &  0.9751 \tabularnewline
54 &  0.01868 &  0.03736 &  0.9813 \tabularnewline
55 &  0.01379 &  0.02759 &  0.9862 \tabularnewline
56 &  0.01306 &  0.02613 &  0.9869 \tabularnewline
57 &  0.01402 &  0.02804 &  0.986 \tabularnewline
58 &  0.012 &  0.024 &  0.988 \tabularnewline
59 &  0.008782 &  0.01756 &  0.9912 \tabularnewline
60 &  0.007155 &  0.01431 &  0.9928 \tabularnewline
61 &  0.005108 &  0.01022 &  0.9949 \tabularnewline
62 &  0.008442 &  0.01688 &  0.9916 \tabularnewline
63 &  0.01223 &  0.02446 &  0.9878 \tabularnewline
64 &  0.009327 &  0.01865 &  0.9907 \tabularnewline
65 &  0.007095 &  0.01419 &  0.9929 \tabularnewline
66 &  0.01057 &  0.02113 &  0.9894 \tabularnewline
67 &  0.009077 &  0.01815 &  0.9909 \tabularnewline
68 &  0.00709 &  0.01418 &  0.9929 \tabularnewline
69 &  0.01484 &  0.02968 &  0.9852 \tabularnewline
70 &  0.04287 &  0.08573 &  0.9571 \tabularnewline
71 &  0.06708 &  0.1342 &  0.9329 \tabularnewline
72 &  0.06501 &  0.13 &  0.935 \tabularnewline
73 &  0.05398 &  0.108 &  0.946 \tabularnewline
74 &  0.05985 &  0.1197 &  0.9402 \tabularnewline
75 &  0.05182 &  0.1036 &  0.9482 \tabularnewline
76 &  0.04089 &  0.08179 &  0.9591 \tabularnewline
77 &  0.04508 &  0.09016 &  0.9549 \tabularnewline
78 &  0.0396 &  0.07919 &  0.9604 \tabularnewline
79 &  0.03201 &  0.06402 &  0.968 \tabularnewline
80 &  0.02858 &  0.05716 &  0.9714 \tabularnewline
81 &  0.07685 &  0.1537 &  0.9232 \tabularnewline
82 &  0.08575 &  0.1715 &  0.9142 \tabularnewline
83 &  0.08502 &  0.17 &  0.915 \tabularnewline
84 &  0.08269 &  0.1654 &  0.9173 \tabularnewline
85 &  0.06805 &  0.1361 &  0.932 \tabularnewline
86 &  0.06788 &  0.1358 &  0.9321 \tabularnewline
87 &  0.05542 &  0.1108 &  0.9446 \tabularnewline
88 &  0.04569 &  0.09137 &  0.9543 \tabularnewline
89 &  0.03697 &  0.07394 &  0.963 \tabularnewline
90 &  0.04021 &  0.08041 &  0.9598 \tabularnewline
91 &  0.03162 &  0.06325 &  0.9684 \tabularnewline
92 &  0.03892 &  0.07783 &  0.9611 \tabularnewline
93 &  0.07069 &  0.1414 &  0.9293 \tabularnewline
94 &  0.05825 &  0.1165 &  0.9417 \tabularnewline
95 &  0.04675 &  0.0935 &  0.9533 \tabularnewline
96 &  0.039 &  0.07801 &  0.961 \tabularnewline
97 &  0.03625 &  0.07249 &  0.9638 \tabularnewline
98 &  0.02853 &  0.05706 &  0.9715 \tabularnewline
99 &  0.03223 &  0.06446 &  0.9678 \tabularnewline
100 &  0.02678 &  0.05357 &  0.9732 \tabularnewline
101 &  0.02068 &  0.04137 &  0.9793 \tabularnewline
102 &  0.01832 &  0.03663 &  0.9817 \tabularnewline
103 &  0.02243 &  0.04485 &  0.9776 \tabularnewline
104 &  0.01908 &  0.03816 &  0.9809 \tabularnewline
105 &  0.04035 &  0.08071 &  0.9596 \tabularnewline
106 &  0.03338 &  0.06676 &  0.9666 \tabularnewline
107 &  0.07435 &  0.1487 &  0.9257 \tabularnewline
108 &  0.1255 &  0.251 &  0.8745 \tabularnewline
109 &  0.1352 &  0.2703 &  0.8648 \tabularnewline
110 &  0.2777 &  0.5555 &  0.7223 \tabularnewline
111 &  0.279 &  0.5581 &  0.721 \tabularnewline
112 &  0.2779 &  0.5558 &  0.7221 \tabularnewline
113 &  0.2783 &  0.5566 &  0.7217 \tabularnewline
114 &  0.2785 &  0.557 &  0.7215 \tabularnewline
115 &  0.3783 &  0.7566 &  0.6217 \tabularnewline
116 &  0.4229 &  0.8459 &  0.5771 \tabularnewline
117 &  0.3807 &  0.7614 &  0.6193 \tabularnewline
118 &  0.3566 &  0.7131 &  0.6434 \tabularnewline
119 &  0.3203 &  0.6406 &  0.6797 \tabularnewline
120 &  0.2814 &  0.5628 &  0.7186 \tabularnewline
121 &  0.2841 &  0.5681 &  0.7159 \tabularnewline
122 &  0.6023 &  0.7954 &  0.3977 \tabularnewline
123 &  0.5637 &  0.8727 &  0.4363 \tabularnewline
124 &  0.66 &  0.68 &  0.34 \tabularnewline
125 &  0.7252 &  0.5496 &  0.2748 \tabularnewline
126 &  0.7032 &  0.5936 &  0.2968 \tabularnewline
127 &  0.7259 &  0.5483 &  0.2741 \tabularnewline
128 &  0.6917 &  0.6165 &  0.3083 \tabularnewline
129 &  0.6489 &  0.7021 &  0.3511 \tabularnewline
130 &  0.6887 &  0.6225 &  0.3113 \tabularnewline
131 &  0.6884 &  0.6233 &  0.3116 \tabularnewline
132 &  0.6884 &  0.6232 &  0.3116 \tabularnewline
133 &  0.6429 &  0.7141 &  0.3571 \tabularnewline
134 &  0.6078 &  0.7844 &  0.3922 \tabularnewline
135 &  0.6122 &  0.7756 &  0.3878 \tabularnewline
136 &  0.6147 &  0.7707 &  0.3853 \tabularnewline
137 &  0.5718 &  0.8564 &  0.4282 \tabularnewline
138 &  0.6441 &  0.7119 &  0.3559 \tabularnewline
139 &  0.7857 &  0.4285 &  0.2143 \tabularnewline
140 &  0.8776 &  0.2448 &  0.1224 \tabularnewline
141 &  0.8782 &  0.2435 &  0.1218 \tabularnewline
142 &  0.9406 &  0.1187 &  0.05937 \tabularnewline
143 &  0.9264 &  0.1472 &  0.07361 \tabularnewline
144 &  0.9104 &  0.1792 &  0.08959 \tabularnewline
145 &  0.903 &  0.194 &  0.097 \tabularnewline
146 &  0.8752 &  0.2497 &  0.1248 \tabularnewline
147 &  0.8919 &  0.2163 &  0.1081 \tabularnewline
148 &  0.8987 &  0.2027 &  0.1013 \tabularnewline
149 &  0.9591 &  0.0818 &  0.0409 \tabularnewline
150 &  0.949 &  0.1019 &  0.05096 \tabularnewline
151 &  0.9412 &  0.1176 &  0.05879 \tabularnewline
152 &  0.929 &  0.142 &  0.07101 \tabularnewline
153 &  0.9054 &  0.1893 &  0.09463 \tabularnewline
154 &  0.8913 &  0.2174 &  0.1087 \tabularnewline
155 &  0.8612 &  0.2777 &  0.1388 \tabularnewline
156 &  0.8396 &  0.3208 &  0.1604 \tabularnewline
157 &  0.792 &  0.4161 &  0.208 \tabularnewline
158 &  0.8022 &  0.3955 &  0.1978 \tabularnewline
159 &  0.7517 &  0.4965 &  0.2483 \tabularnewline
160 &  0.6935 &  0.6129 &  0.3065 \tabularnewline
161 &  0.6834 &  0.6331 &  0.3166 \tabularnewline
162 &  0.6182 &  0.7636 &  0.3818 \tabularnewline
163 &  0.7756 &  0.4487 &  0.2244 \tabularnewline
164 &  0.9606 &  0.07878 &  0.03939 \tabularnewline
165 &  0.9606 &  0.07877 &  0.03938 \tabularnewline
166 &  0.9413 &  0.1174 &  0.05869 \tabularnewline
167 &  0.9021 &  0.1959 &  0.09795 \tabularnewline
168 &  0.8722 &  0.2556 &  0.1278 \tabularnewline
169 &  0.8191 &  0.3618 &  0.1809 \tabularnewline
170 &  0.7327 &  0.5346 &  0.2673 \tabularnewline
171 &  0.6613 &  0.6775 &  0.3387 \tabularnewline
172 &  0.5339 &  0.9321 &  0.4661 \tabularnewline
173 &  0.3917 &  0.7834 &  0.6083 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309084&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]10[/C][C] 0.4995[/C][C] 0.9991[/C][C] 0.5004[/C][/ROW]
[ROW][C]11[/C][C] 0.333[/C][C] 0.6661[/C][C] 0.667[/C][/ROW]
[ROW][C]12[/C][C] 0.224[/C][C] 0.448[/C][C] 0.776[/C][/ROW]
[ROW][C]13[/C][C] 0.1313[/C][C] 0.2626[/C][C] 0.8687[/C][/ROW]
[ROW][C]14[/C][C] 0.08097[/C][C] 0.1619[/C][C] 0.919[/C][/ROW]
[ROW][C]15[/C][C] 0.04354[/C][C] 0.08707[/C][C] 0.9565[/C][/ROW]
[ROW][C]16[/C][C] 0.02213[/C][C] 0.04427[/C][C] 0.9779[/C][/ROW]
[ROW][C]17[/C][C] 0.02644[/C][C] 0.05288[/C][C] 0.9736[/C][/ROW]
[ROW][C]18[/C][C] 0.01506[/C][C] 0.03013[/C][C] 0.9849[/C][/ROW]
[ROW][C]19[/C][C] 0.04013[/C][C] 0.08025[/C][C] 0.9599[/C][/ROW]
[ROW][C]20[/C][C] 0.02775[/C][C] 0.05551[/C][C] 0.9722[/C][/ROW]
[ROW][C]21[/C][C] 0.03139[/C][C] 0.06277[/C][C] 0.9686[/C][/ROW]
[ROW][C]22[/C][C] 0.07668[/C][C] 0.1534[/C][C] 0.9233[/C][/ROW]
[ROW][C]23[/C][C] 0.07926[/C][C] 0.1585[/C][C] 0.9207[/C][/ROW]
[ROW][C]24[/C][C] 0.09114[/C][C] 0.1823[/C][C] 0.9089[/C][/ROW]
[ROW][C]25[/C][C] 0.06613[/C][C] 0.1323[/C][C] 0.9339[/C][/ROW]
[ROW][C]26[/C][C] 0.04705[/C][C] 0.09409[/C][C] 0.953[/C][/ROW]
[ROW][C]27[/C][C] 0.04015[/C][C] 0.08031[/C][C] 0.9598[/C][/ROW]
[ROW][C]28[/C][C] 0.02792[/C][C] 0.05583[/C][C] 0.9721[/C][/ROW]
[ROW][C]29[/C][C] 0.01866[/C][C] 0.03732[/C][C] 0.9813[/C][/ROW]
[ROW][C]30[/C][C] 0.01663[/C][C] 0.03326[/C][C] 0.9834[/C][/ROW]
[ROW][C]31[/C][C] 0.01154[/C][C] 0.02309[/C][C] 0.9885[/C][/ROW]
[ROW][C]32[/C][C] 0.009093[/C][C] 0.01819[/C][C] 0.9909[/C][/ROW]
[ROW][C]33[/C][C] 0.1623[/C][C] 0.3245[/C][C] 0.8377[/C][/ROW]
[ROW][C]34[/C][C] 0.1277[/C][C] 0.2553[/C][C] 0.8723[/C][/ROW]
[ROW][C]35[/C][C] 0.09808[/C][C] 0.1962[/C][C] 0.9019[/C][/ROW]
[ROW][C]36[/C][C] 0.1132[/C][C] 0.2263[/C][C] 0.8868[/C][/ROW]
[ROW][C]37[/C][C] 0.0887[/C][C] 0.1774[/C][C] 0.9113[/C][/ROW]
[ROW][C]38[/C][C] 0.07123[/C][C] 0.1424[/C][C] 0.9288[/C][/ROW]
[ROW][C]39[/C][C] 0.05535[/C][C] 0.1107[/C][C] 0.9447[/C][/ROW]
[ROW][C]40[/C][C] 0.04677[/C][C] 0.09353[/C][C] 0.9532[/C][/ROW]
[ROW][C]41[/C][C] 0.04739[/C][C] 0.09477[/C][C] 0.9526[/C][/ROW]
[ROW][C]42[/C][C] 0.03608[/C][C] 0.07216[/C][C] 0.9639[/C][/ROW]
[ROW][C]43[/C][C] 0.02883[/C][C] 0.05767[/C][C] 0.9712[/C][/ROW]
[ROW][C]44[/C][C] 0.03245[/C][C] 0.0649[/C][C] 0.9676[/C][/ROW]
[ROW][C]45[/C][C] 0.02403[/C][C] 0.04806[/C][C] 0.976[/C][/ROW]
[ROW][C]46[/C][C] 0.03385[/C][C] 0.06771[/C][C] 0.9661[/C][/ROW]
[ROW][C]47[/C][C] 0.04116[/C][C] 0.08232[/C][C] 0.9588[/C][/ROW]
[ROW][C]48[/C][C] 0.03142[/C][C] 0.06284[/C][C] 0.9686[/C][/ROW]
[ROW][C]49[/C][C] 0.02894[/C][C] 0.05788[/C][C] 0.9711[/C][/ROW]
[ROW][C]50[/C][C] 0.02548[/C][C] 0.05096[/C][C] 0.9745[/C][/ROW]
[ROW][C]51[/C][C] 0.0217[/C][C] 0.0434[/C][C] 0.9783[/C][/ROW]
[ROW][C]52[/C][C] 0.01706[/C][C] 0.03412[/C][C] 0.9829[/C][/ROW]
[ROW][C]53[/C][C] 0.02492[/C][C] 0.04983[/C][C] 0.9751[/C][/ROW]
[ROW][C]54[/C][C] 0.01868[/C][C] 0.03736[/C][C] 0.9813[/C][/ROW]
[ROW][C]55[/C][C] 0.01379[/C][C] 0.02759[/C][C] 0.9862[/C][/ROW]
[ROW][C]56[/C][C] 0.01306[/C][C] 0.02613[/C][C] 0.9869[/C][/ROW]
[ROW][C]57[/C][C] 0.01402[/C][C] 0.02804[/C][C] 0.986[/C][/ROW]
[ROW][C]58[/C][C] 0.012[/C][C] 0.024[/C][C] 0.988[/C][/ROW]
[ROW][C]59[/C][C] 0.008782[/C][C] 0.01756[/C][C] 0.9912[/C][/ROW]
[ROW][C]60[/C][C] 0.007155[/C][C] 0.01431[/C][C] 0.9928[/C][/ROW]
[ROW][C]61[/C][C] 0.005108[/C][C] 0.01022[/C][C] 0.9949[/C][/ROW]
[ROW][C]62[/C][C] 0.008442[/C][C] 0.01688[/C][C] 0.9916[/C][/ROW]
[ROW][C]63[/C][C] 0.01223[/C][C] 0.02446[/C][C] 0.9878[/C][/ROW]
[ROW][C]64[/C][C] 0.009327[/C][C] 0.01865[/C][C] 0.9907[/C][/ROW]
[ROW][C]65[/C][C] 0.007095[/C][C] 0.01419[/C][C] 0.9929[/C][/ROW]
[ROW][C]66[/C][C] 0.01057[/C][C] 0.02113[/C][C] 0.9894[/C][/ROW]
[ROW][C]67[/C][C] 0.009077[/C][C] 0.01815[/C][C] 0.9909[/C][/ROW]
[ROW][C]68[/C][C] 0.00709[/C][C] 0.01418[/C][C] 0.9929[/C][/ROW]
[ROW][C]69[/C][C] 0.01484[/C][C] 0.02968[/C][C] 0.9852[/C][/ROW]
[ROW][C]70[/C][C] 0.04287[/C][C] 0.08573[/C][C] 0.9571[/C][/ROW]
[ROW][C]71[/C][C] 0.06708[/C][C] 0.1342[/C][C] 0.9329[/C][/ROW]
[ROW][C]72[/C][C] 0.06501[/C][C] 0.13[/C][C] 0.935[/C][/ROW]
[ROW][C]73[/C][C] 0.05398[/C][C] 0.108[/C][C] 0.946[/C][/ROW]
[ROW][C]74[/C][C] 0.05985[/C][C] 0.1197[/C][C] 0.9402[/C][/ROW]
[ROW][C]75[/C][C] 0.05182[/C][C] 0.1036[/C][C] 0.9482[/C][/ROW]
[ROW][C]76[/C][C] 0.04089[/C][C] 0.08179[/C][C] 0.9591[/C][/ROW]
[ROW][C]77[/C][C] 0.04508[/C][C] 0.09016[/C][C] 0.9549[/C][/ROW]
[ROW][C]78[/C][C] 0.0396[/C][C] 0.07919[/C][C] 0.9604[/C][/ROW]
[ROW][C]79[/C][C] 0.03201[/C][C] 0.06402[/C][C] 0.968[/C][/ROW]
[ROW][C]80[/C][C] 0.02858[/C][C] 0.05716[/C][C] 0.9714[/C][/ROW]
[ROW][C]81[/C][C] 0.07685[/C][C] 0.1537[/C][C] 0.9232[/C][/ROW]
[ROW][C]82[/C][C] 0.08575[/C][C] 0.1715[/C][C] 0.9142[/C][/ROW]
[ROW][C]83[/C][C] 0.08502[/C][C] 0.17[/C][C] 0.915[/C][/ROW]
[ROW][C]84[/C][C] 0.08269[/C][C] 0.1654[/C][C] 0.9173[/C][/ROW]
[ROW][C]85[/C][C] 0.06805[/C][C] 0.1361[/C][C] 0.932[/C][/ROW]
[ROW][C]86[/C][C] 0.06788[/C][C] 0.1358[/C][C] 0.9321[/C][/ROW]
[ROW][C]87[/C][C] 0.05542[/C][C] 0.1108[/C][C] 0.9446[/C][/ROW]
[ROW][C]88[/C][C] 0.04569[/C][C] 0.09137[/C][C] 0.9543[/C][/ROW]
[ROW][C]89[/C][C] 0.03697[/C][C] 0.07394[/C][C] 0.963[/C][/ROW]
[ROW][C]90[/C][C] 0.04021[/C][C] 0.08041[/C][C] 0.9598[/C][/ROW]
[ROW][C]91[/C][C] 0.03162[/C][C] 0.06325[/C][C] 0.9684[/C][/ROW]
[ROW][C]92[/C][C] 0.03892[/C][C] 0.07783[/C][C] 0.9611[/C][/ROW]
[ROW][C]93[/C][C] 0.07069[/C][C] 0.1414[/C][C] 0.9293[/C][/ROW]
[ROW][C]94[/C][C] 0.05825[/C][C] 0.1165[/C][C] 0.9417[/C][/ROW]
[ROW][C]95[/C][C] 0.04675[/C][C] 0.0935[/C][C] 0.9533[/C][/ROW]
[ROW][C]96[/C][C] 0.039[/C][C] 0.07801[/C][C] 0.961[/C][/ROW]
[ROW][C]97[/C][C] 0.03625[/C][C] 0.07249[/C][C] 0.9638[/C][/ROW]
[ROW][C]98[/C][C] 0.02853[/C][C] 0.05706[/C][C] 0.9715[/C][/ROW]
[ROW][C]99[/C][C] 0.03223[/C][C] 0.06446[/C][C] 0.9678[/C][/ROW]
[ROW][C]100[/C][C] 0.02678[/C][C] 0.05357[/C][C] 0.9732[/C][/ROW]
[ROW][C]101[/C][C] 0.02068[/C][C] 0.04137[/C][C] 0.9793[/C][/ROW]
[ROW][C]102[/C][C] 0.01832[/C][C] 0.03663[/C][C] 0.9817[/C][/ROW]
[ROW][C]103[/C][C] 0.02243[/C][C] 0.04485[/C][C] 0.9776[/C][/ROW]
[ROW][C]104[/C][C] 0.01908[/C][C] 0.03816[/C][C] 0.9809[/C][/ROW]
[ROW][C]105[/C][C] 0.04035[/C][C] 0.08071[/C][C] 0.9596[/C][/ROW]
[ROW][C]106[/C][C] 0.03338[/C][C] 0.06676[/C][C] 0.9666[/C][/ROW]
[ROW][C]107[/C][C] 0.07435[/C][C] 0.1487[/C][C] 0.9257[/C][/ROW]
[ROW][C]108[/C][C] 0.1255[/C][C] 0.251[/C][C] 0.8745[/C][/ROW]
[ROW][C]109[/C][C] 0.1352[/C][C] 0.2703[/C][C] 0.8648[/C][/ROW]
[ROW][C]110[/C][C] 0.2777[/C][C] 0.5555[/C][C] 0.7223[/C][/ROW]
[ROW][C]111[/C][C] 0.279[/C][C] 0.5581[/C][C] 0.721[/C][/ROW]
[ROW][C]112[/C][C] 0.2779[/C][C] 0.5558[/C][C] 0.7221[/C][/ROW]
[ROW][C]113[/C][C] 0.2783[/C][C] 0.5566[/C][C] 0.7217[/C][/ROW]
[ROW][C]114[/C][C] 0.2785[/C][C] 0.557[/C][C] 0.7215[/C][/ROW]
[ROW][C]115[/C][C] 0.3783[/C][C] 0.7566[/C][C] 0.6217[/C][/ROW]
[ROW][C]116[/C][C] 0.4229[/C][C] 0.8459[/C][C] 0.5771[/C][/ROW]
[ROW][C]117[/C][C] 0.3807[/C][C] 0.7614[/C][C] 0.6193[/C][/ROW]
[ROW][C]118[/C][C] 0.3566[/C][C] 0.7131[/C][C] 0.6434[/C][/ROW]
[ROW][C]119[/C][C] 0.3203[/C][C] 0.6406[/C][C] 0.6797[/C][/ROW]
[ROW][C]120[/C][C] 0.2814[/C][C] 0.5628[/C][C] 0.7186[/C][/ROW]
[ROW][C]121[/C][C] 0.2841[/C][C] 0.5681[/C][C] 0.7159[/C][/ROW]
[ROW][C]122[/C][C] 0.6023[/C][C] 0.7954[/C][C] 0.3977[/C][/ROW]
[ROW][C]123[/C][C] 0.5637[/C][C] 0.8727[/C][C] 0.4363[/C][/ROW]
[ROW][C]124[/C][C] 0.66[/C][C] 0.68[/C][C] 0.34[/C][/ROW]
[ROW][C]125[/C][C] 0.7252[/C][C] 0.5496[/C][C] 0.2748[/C][/ROW]
[ROW][C]126[/C][C] 0.7032[/C][C] 0.5936[/C][C] 0.2968[/C][/ROW]
[ROW][C]127[/C][C] 0.7259[/C][C] 0.5483[/C][C] 0.2741[/C][/ROW]
[ROW][C]128[/C][C] 0.6917[/C][C] 0.6165[/C][C] 0.3083[/C][/ROW]
[ROW][C]129[/C][C] 0.6489[/C][C] 0.7021[/C][C] 0.3511[/C][/ROW]
[ROW][C]130[/C][C] 0.6887[/C][C] 0.6225[/C][C] 0.3113[/C][/ROW]
[ROW][C]131[/C][C] 0.6884[/C][C] 0.6233[/C][C] 0.3116[/C][/ROW]
[ROW][C]132[/C][C] 0.6884[/C][C] 0.6232[/C][C] 0.3116[/C][/ROW]
[ROW][C]133[/C][C] 0.6429[/C][C] 0.7141[/C][C] 0.3571[/C][/ROW]
[ROW][C]134[/C][C] 0.6078[/C][C] 0.7844[/C][C] 0.3922[/C][/ROW]
[ROW][C]135[/C][C] 0.6122[/C][C] 0.7756[/C][C] 0.3878[/C][/ROW]
[ROW][C]136[/C][C] 0.6147[/C][C] 0.7707[/C][C] 0.3853[/C][/ROW]
[ROW][C]137[/C][C] 0.5718[/C][C] 0.8564[/C][C] 0.4282[/C][/ROW]
[ROW][C]138[/C][C] 0.6441[/C][C] 0.7119[/C][C] 0.3559[/C][/ROW]
[ROW][C]139[/C][C] 0.7857[/C][C] 0.4285[/C][C] 0.2143[/C][/ROW]
[ROW][C]140[/C][C] 0.8776[/C][C] 0.2448[/C][C] 0.1224[/C][/ROW]
[ROW][C]141[/C][C] 0.8782[/C][C] 0.2435[/C][C] 0.1218[/C][/ROW]
[ROW][C]142[/C][C] 0.9406[/C][C] 0.1187[/C][C] 0.05937[/C][/ROW]
[ROW][C]143[/C][C] 0.9264[/C][C] 0.1472[/C][C] 0.07361[/C][/ROW]
[ROW][C]144[/C][C] 0.9104[/C][C] 0.1792[/C][C] 0.08959[/C][/ROW]
[ROW][C]145[/C][C] 0.903[/C][C] 0.194[/C][C] 0.097[/C][/ROW]
[ROW][C]146[/C][C] 0.8752[/C][C] 0.2497[/C][C] 0.1248[/C][/ROW]
[ROW][C]147[/C][C] 0.8919[/C][C] 0.2163[/C][C] 0.1081[/C][/ROW]
[ROW][C]148[/C][C] 0.8987[/C][C] 0.2027[/C][C] 0.1013[/C][/ROW]
[ROW][C]149[/C][C] 0.9591[/C][C] 0.0818[/C][C] 0.0409[/C][/ROW]
[ROW][C]150[/C][C] 0.949[/C][C] 0.1019[/C][C] 0.05096[/C][/ROW]
[ROW][C]151[/C][C] 0.9412[/C][C] 0.1176[/C][C] 0.05879[/C][/ROW]
[ROW][C]152[/C][C] 0.929[/C][C] 0.142[/C][C] 0.07101[/C][/ROW]
[ROW][C]153[/C][C] 0.9054[/C][C] 0.1893[/C][C] 0.09463[/C][/ROW]
[ROW][C]154[/C][C] 0.8913[/C][C] 0.2174[/C][C] 0.1087[/C][/ROW]
[ROW][C]155[/C][C] 0.8612[/C][C] 0.2777[/C][C] 0.1388[/C][/ROW]
[ROW][C]156[/C][C] 0.8396[/C][C] 0.3208[/C][C] 0.1604[/C][/ROW]
[ROW][C]157[/C][C] 0.792[/C][C] 0.4161[/C][C] 0.208[/C][/ROW]
[ROW][C]158[/C][C] 0.8022[/C][C] 0.3955[/C][C] 0.1978[/C][/ROW]
[ROW][C]159[/C][C] 0.7517[/C][C] 0.4965[/C][C] 0.2483[/C][/ROW]
[ROW][C]160[/C][C] 0.6935[/C][C] 0.6129[/C][C] 0.3065[/C][/ROW]
[ROW][C]161[/C][C] 0.6834[/C][C] 0.6331[/C][C] 0.3166[/C][/ROW]
[ROW][C]162[/C][C] 0.6182[/C][C] 0.7636[/C][C] 0.3818[/C][/ROW]
[ROW][C]163[/C][C] 0.7756[/C][C] 0.4487[/C][C] 0.2244[/C][/ROW]
[ROW][C]164[/C][C] 0.9606[/C][C] 0.07878[/C][C] 0.03939[/C][/ROW]
[ROW][C]165[/C][C] 0.9606[/C][C] 0.07877[/C][C] 0.03938[/C][/ROW]
[ROW][C]166[/C][C] 0.9413[/C][C] 0.1174[/C][C] 0.05869[/C][/ROW]
[ROW][C]167[/C][C] 0.9021[/C][C] 0.1959[/C][C] 0.09795[/C][/ROW]
[ROW][C]168[/C][C] 0.8722[/C][C] 0.2556[/C][C] 0.1278[/C][/ROW]
[ROW][C]169[/C][C] 0.8191[/C][C] 0.3618[/C][C] 0.1809[/C][/ROW]
[ROW][C]170[/C][C] 0.7327[/C][C] 0.5346[/C][C] 0.2673[/C][/ROW]
[ROW][C]171[/C][C] 0.6613[/C][C] 0.6775[/C][C] 0.3387[/C][/ROW]
[ROW][C]172[/C][C] 0.5339[/C][C] 0.9321[/C][C] 0.4661[/C][/ROW]
[ROW][C]173[/C][C] 0.3917[/C][C] 0.7834[/C][C] 0.6083[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309084&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309084&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
10	0.4995	0.9991	0.5004
11	0.333	0.6661	0.667
12	0.224	0.448	0.776
13	0.1313	0.2626	0.8687
14	0.08097	0.1619	0.919
15	0.04354	0.08707	0.9565
16	0.02213	0.04427	0.9779
17	0.02644	0.05288	0.9736
18	0.01506	0.03013	0.9849
19	0.04013	0.08025	0.9599
20	0.02775	0.05551	0.9722
21	0.03139	0.06277	0.9686
22	0.07668	0.1534	0.9233
23	0.07926	0.1585	0.9207
24	0.09114	0.1823	0.9089
25	0.06613	0.1323	0.9339
26	0.04705	0.09409	0.953
27	0.04015	0.08031	0.9598
28	0.02792	0.05583	0.9721
29	0.01866	0.03732	0.9813
30	0.01663	0.03326	0.9834
31	0.01154	0.02309	0.9885
32	0.009093	0.01819	0.9909
33	0.1623	0.3245	0.8377
34	0.1277	0.2553	0.8723
35	0.09808	0.1962	0.9019
36	0.1132	0.2263	0.8868
37	0.0887	0.1774	0.9113
38	0.07123	0.1424	0.9288
39	0.05535	0.1107	0.9447
40	0.04677	0.09353	0.9532
41	0.04739	0.09477	0.9526
42	0.03608	0.07216	0.9639
43	0.02883	0.05767	0.9712
44	0.03245	0.0649	0.9676
45	0.02403	0.04806	0.976
46	0.03385	0.06771	0.9661
47	0.04116	0.08232	0.9588
48	0.03142	0.06284	0.9686
49	0.02894	0.05788	0.9711
50	0.02548	0.05096	0.9745
51	0.0217	0.0434	0.9783
52	0.01706	0.03412	0.9829
53	0.02492	0.04983	0.9751
54	0.01868	0.03736	0.9813
55	0.01379	0.02759	0.9862
56	0.01306	0.02613	0.9869
57	0.01402	0.02804	0.986
58	0.012	0.024	0.988
59	0.008782	0.01756	0.9912
60	0.007155	0.01431	0.9928
61	0.005108	0.01022	0.9949
62	0.008442	0.01688	0.9916
63	0.01223	0.02446	0.9878
64	0.009327	0.01865	0.9907
65	0.007095	0.01419	0.9929
66	0.01057	0.02113	0.9894
67	0.009077	0.01815	0.9909
68	0.00709	0.01418	0.9929
69	0.01484	0.02968	0.9852
70	0.04287	0.08573	0.9571
71	0.06708	0.1342	0.9329
72	0.06501	0.13	0.935
73	0.05398	0.108	0.946
74	0.05985	0.1197	0.9402
75	0.05182	0.1036	0.9482
76	0.04089	0.08179	0.9591
77	0.04508	0.09016	0.9549
78	0.0396	0.07919	0.9604
79	0.03201	0.06402	0.968
80	0.02858	0.05716	0.9714
81	0.07685	0.1537	0.9232
82	0.08575	0.1715	0.9142
83	0.08502	0.17	0.915
84	0.08269	0.1654	0.9173
85	0.06805	0.1361	0.932
86	0.06788	0.1358	0.9321
87	0.05542	0.1108	0.9446
88	0.04569	0.09137	0.9543
89	0.03697	0.07394	0.963
90	0.04021	0.08041	0.9598
91	0.03162	0.06325	0.9684
92	0.03892	0.07783	0.9611
93	0.07069	0.1414	0.9293
94	0.05825	0.1165	0.9417
95	0.04675	0.0935	0.9533
96	0.039	0.07801	0.961
97	0.03625	0.07249	0.9638
98	0.02853	0.05706	0.9715
99	0.03223	0.06446	0.9678
100	0.02678	0.05357	0.9732
101	0.02068	0.04137	0.9793
102	0.01832	0.03663	0.9817
103	0.02243	0.04485	0.9776
104	0.01908	0.03816	0.9809
105	0.04035	0.08071	0.9596
106	0.03338	0.06676	0.9666
107	0.07435	0.1487	0.9257
108	0.1255	0.251	0.8745
109	0.1352	0.2703	0.8648
110	0.2777	0.5555	0.7223
111	0.279	0.5581	0.721
112	0.2779	0.5558	0.7221
113	0.2783	0.5566	0.7217
114	0.2785	0.557	0.7215
115	0.3783	0.7566	0.6217
116	0.4229	0.8459	0.5771
117	0.3807	0.7614	0.6193
118	0.3566	0.7131	0.6434
119	0.3203	0.6406	0.6797
120	0.2814	0.5628	0.7186
121	0.2841	0.5681	0.7159
122	0.6023	0.7954	0.3977
123	0.5637	0.8727	0.4363
124	0.66	0.68	0.34
125	0.7252	0.5496	0.2748
126	0.7032	0.5936	0.2968
127	0.7259	0.5483	0.2741
128	0.6917	0.6165	0.3083
129	0.6489	0.7021	0.3511
130	0.6887	0.6225	0.3113
131	0.6884	0.6233	0.3116
132	0.6884	0.6232	0.3116
133	0.6429	0.7141	0.3571
134	0.6078	0.7844	0.3922
135	0.6122	0.7756	0.3878
136	0.6147	0.7707	0.3853
137	0.5718	0.8564	0.4282
138	0.6441	0.7119	0.3559
139	0.7857	0.4285	0.2143
140	0.8776	0.2448	0.1224
141	0.8782	0.2435	0.1218
142	0.9406	0.1187	0.05937
143	0.9264	0.1472	0.07361
144	0.9104	0.1792	0.08959
145	0.903	0.194	0.097
146	0.8752	0.2497	0.1248
147	0.8919	0.2163	0.1081
148	0.8987	0.2027	0.1013
149	0.9591	0.0818	0.0409
150	0.949	0.1019	0.05096
151	0.9412	0.1176	0.05879
152	0.929	0.142	0.07101
153	0.9054	0.1893	0.09463
154	0.8913	0.2174	0.1087
155	0.8612	0.2777	0.1388
156	0.8396	0.3208	0.1604
157	0.792	0.4161	0.208
158	0.8022	0.3955	0.1978
159	0.7517	0.4965	0.2483
160	0.6935	0.6129	0.3065
161	0.6834	0.6331	0.3166
162	0.6182	0.7636	0.3818
163	0.7756	0.4487	0.2244
164	0.9606	0.07878	0.03939
165	0.9606	0.07877	0.03938
166	0.9413	0.1174	0.05869
167	0.9021	0.1959	0.09795
168	0.8722	0.2556	0.1278
169	0.8191	0.3618	0.1809
170	0.7327	0.5346	0.2673
171	0.6613	0.6775	0.3387
172	0.5339	0.9321	0.4661
173	0.3917	0.7834	0.6083

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	30	0.182927	NOK
10% type I error level	70	0.426829	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 & 0 &  0 & OK \tabularnewline
5% type I error level & 30 & 0.182927 & NOK \tabularnewline
10% type I error level & 70 & 0.426829 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309084&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]0[/C][C] 0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]30[/C][C]0.182927[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]70[/C][C]0.426829[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309084&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309084&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	0	0	OK
5% type I error level	30	0.182927	NOK
10% type I error level	70	0.426829	NOK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 0.54881, df1 = 2, df2 = 174, p-value = 0.5786
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.992, df1 = 12, df2 = 164, p-value = 0.02788
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 2.6597, df1 = 2, df2 = 174, p-value = 0.07281

\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 = 0.54881, df1 = 2, df2 = 174, p-value = 0.5786
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.992, df1 = 12, df2 = 164, p-value = 0.02788
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 2.6597, df1 = 2, df2 = 174, p-value = 0.07281
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309084&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 = 0.54881, df1 = 2, df2 = 174, p-value = 0.5786
[/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 = 1.992, df1 = 12, df2 = 164, p-value = 0.02788
[/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 = 2.6597, df1 = 2, df2 = 174, p-value = 0.07281
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309084&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309084&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 = 0.54881, df1 = 2, df2 = 174, p-value = 0.5786
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.992, df1 = 12, df2 = 164, p-value = 0.02788
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 2.6597, df1 = 2, df2 = 174, p-value = 0.07281

Variance Inflation Factors (Multicollinearity)

> vif
       `(1-Bs)(1-B)print`  `(1-Bs)(1-B)papier(t-1)`  `(1-Bs)(1-B)papier(t-2)` 
                 1.694786                  2.790326                  3.442644 
 `(1-Bs)(1-B)papier(t-3)`  `(1-Bs)(1-B)papier(t-4)` `(1-Bs)(1-B)papier(t-1s)` 
                 3.222477                  2.001308                  1.145830

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
       `(1-Bs)(1-B)print`  `(1-Bs)(1-B)papier(t-1)`  `(1-Bs)(1-B)papier(t-2)` 
                 1.694786                  2.790326                  3.442644 
 `(1-Bs)(1-B)papier(t-3)`  `(1-Bs)(1-B)papier(t-4)` `(1-Bs)(1-B)papier(t-1s)` 
                 3.222477                  2.001308                  1.145830 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309084&T=9

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
       `(1-Bs)(1-B)print`  `(1-Bs)(1-B)papier(t-1)`  `(1-Bs)(1-B)papier(t-2)` 
                 1.694786                  2.790326                  3.442644 
 `(1-Bs)(1-B)papier(t-3)`  `(1-Bs)(1-B)papier(t-4)` `(1-Bs)(1-B)papier(t-1s)` 
                 3.222477                  2.001308                  1.145830 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309084&T=9

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309084&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)print`  `(1-Bs)(1-B)papier(t-1)`  `(1-Bs)(1-B)papier(t-2)` 
                 1.694786                  2.790326                  3.442644 
 `(1-Bs)(1-B)papier(t-3)`  `(1-Bs)(1-B)papier(t-4)` `(1-Bs)(1-B)papier(t-1s)` 
                 3.222477                  2.001308                  1.145830

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):

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = First and Seasonal Differences (s) ; par4 = 4 ; par5 = 1 ; par6 = 12 ;

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

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