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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Sat, 16 Dec 2017 15:41:42 +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/16/t1513435391ho8ezru3sut1495.htm/, Retrieved Wed, 15 May 2024 09:41:40 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=309897, Retrieved Wed, 15 May 2024 09:41:40 +0000

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

-       [Multiple Regression] [] [2017-12-16 14:41:42] [deec28e763260dad9f228be262d61467] [Current]

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62,4	58,1	46,8
67,4	60,3	52,8
76,1	66,7	58,3
67,4	63,7	54,5
74,5	71,7	64,7
72,6	68,8	58,3
60,5	61,8	57,5
66,1	68,7	56,7
76,5	69,7	56
76,8	76,4	66,2
77	73,8	58,2
71	70,2	53,9
74,8	67,8	53,1
73,7	64	54,4
80,5	73,4	59,2
71,8	67,8	57,8
76,9	74,8	61,5
79,9	73,3	60,1
65,9	72	60,1
69,5	76,1	58,4
75,1	73	56,8
79,6	80,5	63,8
75,2	76,1	53,9
68	71,3	63,1
72,8	71	55,7
71,5	67,9	54,9
78,5	74,4	64,6
76,8	73,6	60,2
75,3	74,3	63,9
76,7	73,1	69,9
69,7	74,5	58,5
67,8	73,7	52
77,5	76,3	66,7
82,5	82	72
75,3	73,7	68,4
70,9	77,2	70,8
76	74,1	56,5
73,7	70,7	62,6
79,7	74,9	66,5
77,8	77	69,2
73,3	73	63,7
78,3	76,1	73,6
71,9	77,9	64,1
67	74,2	53,8
82	78,7	72,2
83,7	84,4	80,2
74,8	74,5	69,1
80	78,7	72
74,3	72,9	66,3
76,8	71,3	72,5
89	84,3	88,9
81,9	78,8	88,6
76,8	76,3	73,7
88,9	84,9	86
75,8	77,3	70
75,5	78,9	71,6
89,1	84,6	90,5
88	83,6	85,7
85,9	82,5	84,8
89,3	85,4	81,1
82,9	76,2	70,8
81,2	72,4	65,7
90,5	83,2	86,2
86,4	80,3	76,1
81,8	81,1	79,8
91,3	86,1	85,2
73,4	76,1	75,8
76,6	84,3	69,4
91	88	85
87	85,3	75
89,7	88,4	77,7
90,7	87,9	68,5
86,5	79,8	68,4
86,6	75,5	65
98,8	87,7	73,2
84,4	79,8	67,9
91,4	88	76,5
95,7	89,2	85,5
78,5	83,3	71,7
81,7	89,1	57,9
94,3	89,3	75,5
98,5	94,4	78,2
95,4	92,2	75,7
91,7	87,8	67,1
92,8	88,2	74,6
90,5	81,5	66,2
102,2	94,3	74,9
91,8	88	69,5
95	91,9	76,1
102	94,1	82,3
88,9	89,8	82,1
89,6	94,3	60,5
97,9	93,5	71,2
108,6	104,8	81,4
100,8	100,7	74,5
95,1	94,3	61,4
101	99,4	83,8
100,9	93,4	85,4
102,5	95,8	91,6
105,4	102,9	91,9
98,4	99,2	86,3
105,3	98	96,8
96,5	102,1	81
88,1	95,6	70,8
107,9	104,9	98,8
107	108,8	94,5
92,5	97,3	84,5
95,7	102,5	92,8
85,2	91	81,2
85,5	90	75,7
94,7	100,2	86,7
86,2	99,5	87,5
88,8	94,2	87,8
93,4	103	103,1
83,4	99,9	96,4
82,9	95,4	77,1
96,7	101,8	106,5
96,2	103,4	95,7
92,8	98	95,3
92,8	101,5	86,6
90	88,1	89,6
95,4	90,6	81,9
108,3	105,7	98,4
96,3	99,5	92,9
95	94,5	83,9
109	105,5	121,8
92	97,8	103,9
92,3	99,3	87,5
107	103,5	118,9
105,5	104,1	109
105,4	105,5	112,2
103,9	105,7	100,1
99,2	97	111,3
102,2	95,3	102,7
121,5	110,3	122,6
102,3	102,3	124,8
110	109,8	120,3
105,9	103,9	118,3
91,9	96,2	108,7
100	105,7	100,7
111,7	111	124
104,9	108,6	103,1
103,3	109	115
101,8	107,6	112,7
100,8	102,3	101,7
104,2	102,1	111,5
116,5	110,7	114,4
97,9	101,5	112,5
100,7	108,9	107,2
107	110,9	136,7
96,3	103,9	107,8
96	110,2	94,6
104,5	106,7	110,7
107,4	118,2	126,6
102,4	111,4	127,9
94,9	104,9	109,2
98,8	105,3	87,1
96,8	96,7	90,8
108,2	106,6	94,5
103,8	105,7	103,3
102,3	109,4	103,2
107,2	105,1	105,4
102	111,6	103,9
92,6	103,6	79,8
105,2	106,5	105,6
113	114,4	113
105,6	105,1	87,7
101,6	105,4	110
101,7	100,8	90,3
102,7	96	108,9
109	105	105,1
105,5	108,2	113
103,3	105,8	100,4
108,6	108,9	110,1
98,2	107	114,7
90	101,9	88,6
112,4	112,6	117,2
111,9	115,6	127,7
102,1	105	107,8
102,4	110,6	102,8
101,7	100,8	100,2
98,7	98,2	108,4
114	111,2	114,2
105,1	109,9	94,4
98,3	103,6	92,2
110	115,7	115,3
96,5	110,6	102
92,2	105,6	86,3
112	113,1	112
111,4	117,5	112,5
107,5	112,4	109,5
103,4	114,1	105,9
103,5	101,9	115,3
107,4	106,3	126,2
117,6	118,1	112,2
110,2	113,7	112,5
104,3	115	106,9
115,9	119,4	90,6
98,9	107,1	75,6
101,9	115,1	78,8
113,5	117,6	101,8
109,5	115,2	93,9
110	117,4	100
114,2	117,3	89,2
106,9	106,6	97,7
109,2	105,2	121,1
124,2	121,3	108,8
104,7	108,1	92,9
111,9	119,8	113,6
119	121,2	112,6
102,9	109	98,8
106,3	115,9	78

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

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time10 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309897&T=0

[TABLE]
[ROW]

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

Raw Input[/C]

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

Raw Output[/C]

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

Computing time[/C]

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

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=309897&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309897&T=0

As an alternative you can also use a QR Code:

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

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

Multiple Linear Regression - Estimated Regression Equation
(1-Bs)(1-B)All[t] = + 0.0562832 + 0.703019`(1-Bs)(1-B)Foo`[t] + 0.0116218`(1-Bs)(1-B)Tob`[t] -0.202593`(1-Bs)(1-B)All(t-1)`[t] -0.134897`(1-Bs)(1-B)All(t-1s)`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
(1-Bs)(1-B)All[t] =  +  0.0562832 +  0.703019`(1-Bs)(1-B)Foo`[t] +  0.0116218`(1-Bs)(1-B)Tob`[t] -0.202593`(1-Bs)(1-B)All(t-1)`[t] -0.134897`(1-Bs)(1-B)All(t-1s)`[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309897&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C](1-Bs)(1-B)All[t] =  +  0.0562832 +  0.703019`(1-Bs)(1-B)Foo`[t] +  0.0116218`(1-Bs)(1-B)Tob`[t] -0.202593`(1-Bs)(1-B)All(t-1)`[t] -0.134897`(1-Bs)(1-B)All(t-1s)`[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309897&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309897&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)All[t] = + 0.0562832 + 0.703019`(1-Bs)(1-B)Foo`[t] + 0.0116218`(1-Bs)(1-B)Tob`[t] -0.202593`(1-Bs)(1-B)All(t-1)`[t] -0.134897`(1-Bs)(1-B)All(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)	+0.05628	0.2347	+2.3980e-01	0.8107	0.4054
`(1-Bs)(1-B)Foo`	+0.703	0.04976	+1.4130e+01	5.01e-31	2.505e-31
`(1-Bs)(1-B)Tob`	+0.01162	0.02256	+5.1510e-01	0.6071	0.3036
`(1-Bs)(1-B)All(t-1)`	-0.2026	0.04512	-4.4900e+00	1.264e-05	6.321e-06
`(1-Bs)(1-B)All(t-1s)`	-0.1349	0.04391	-3.0720e+00	0.002455	0.001228

\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) & +0.05628 &  0.2347 & +2.3980e-01 &  0.8107 &  0.4054 \tabularnewline
`(1-Bs)(1-B)Foo` & +0.703 &  0.04976 & +1.4130e+01 &  5.01e-31 &  2.505e-31 \tabularnewline
`(1-Bs)(1-B)Tob` & +0.01162 &  0.02256 & +5.1510e-01 &  0.6071 &  0.3036 \tabularnewline
`(1-Bs)(1-B)All(t-1)` & -0.2026 &  0.04512 & -4.4900e+00 &  1.264e-05 &  6.321e-06 \tabularnewline
`(1-Bs)(1-B)All(t-1s)` & -0.1349 &  0.04391 & -3.0720e+00 &  0.002455 &  0.001228 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309897&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]+0.05628[/C][C] 0.2347[/C][C]+2.3980e-01[/C][C] 0.8107[/C][C] 0.4054[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)Foo`[/C][C]+0.703[/C][C] 0.04976[/C][C]+1.4130e+01[/C][C] 5.01e-31[/C][C] 2.505e-31[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)Tob`[/C][C]+0.01162[/C][C] 0.02256[/C][C]+5.1510e-01[/C][C] 0.6071[/C][C] 0.3036[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)All(t-1)`[/C][C]-0.2026[/C][C] 0.04512[/C][C]-4.4900e+00[/C][C] 1.264e-05[/C][C] 6.321e-06[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)All(t-1s)`[/C][C]-0.1349[/C][C] 0.04391[/C][C]-3.0720e+00[/C][C] 0.002455[/C][C] 0.001228[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309897&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309897&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)	+0.05628	0.2347	+2.3980e-01	0.8107	0.4054
`(1-Bs)(1-B)Foo`	+0.703	0.04976	+1.4130e+01	5.01e-31	2.505e-31
`(1-Bs)(1-B)Tob`	+0.01162	0.02256	+5.1510e-01	0.6071	0.3036
`(1-Bs)(1-B)All(t-1)`	-0.2026	0.04512	-4.4900e+00	1.264e-05	6.321e-06
`(1-Bs)(1-B)All(t-1s)`	-0.1349	0.04391	-3.0720e+00	0.002455	0.001228

Multiple Linear Regression - Regression Statistics
Multiple R	0.8373
R-squared	0.7011
Adjusted R-squared	0.6945
F-TEST (value)	106.1
F-TEST (DF numerator)	4
F-TEST (DF denominator)	181
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	3.201
Sum Squared Residuals	1854

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.8373 \tabularnewline
R-squared &  0.7011 \tabularnewline
Adjusted R-squared &  0.6945 \tabularnewline
F-TEST (value) &  106.1 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 181 \tabularnewline
p-value &  0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  3.201 \tabularnewline
Sum Squared Residuals &  1854 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309897&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.8373[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.7011[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.6945[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 106.1[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]181[/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] 3.201[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 1854[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309897&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309897&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	0.8373
R-squared	0.7011
Adjusted R-squared	0.6945
F-TEST (value)	106.1
F-TEST (DF numerator)	4
F-TEST (DF denominator)	181
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	3.201
Sum Squared Residuals	1854

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=309897&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=309897&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309897&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	0.2	-1.629	1.829
2	7	3.355	3.645
3	-6.6	-5.521	-1.079
4	-1.6	1.029	-2.629
5	7	2.402	4.598
6	-5.5	-4.593	-0.9073
7	4.1	6.015	-1.915
8	0.5	-2.626	3.126
9	-2.8	-2.093	-0.707
10	2.8	6.541	-3.741
11	0.3	-2.695	2.995
12	-1	-0.1082	-0.8918
13	-1	-1.452	0.4525
14	-0.2	1.436	-1.636
15	-3	-2.424	-0.576
16	3.6	3.948	-0.3482
17	0.6	-1.314	1.914
18	-3	-1.406	-1.594
19	5.3	1.49	3.81
20	-3.3	-1.054	-2.246
21	-1.7	-0.1094	-1.591
22	9.6	0.5209	9.079
23	-10.8	-3.727	-7.073
24	4.8	3.646	1.154
25	6.2	5.551	0.6494
26	-5.2	-6.551	1.351
27	-0.6	2.46	-3.06
28	7.1	3.587	3.513
29	-6.7	-8.147	1.447
30	4.6	5.683	-1.083
31	-1.4	-0.7412	-0.6588
32	-2.8	-4.074	1.274
33	6.8	7.158	-0.358
34	-1.8	-3.607	1.807
35	-0.7	-0.5659	-0.1341
36	-4.2	-2.127	-2.073
37	-2.9	-1.428	-1.472
38	3	3.059	-0.05923
39	0.5	2.066	-1.566
40	-2.6	-3.614	1.014
41	-4.8	-0.1237	-4.676
42	3.5	4.955	-1.455
43	0.8	-1.908	2.708
44	-2.9	-0.9836	-1.916
45	4.8	2.721	2.079
46	-2.4	-3.128	0.7275
47	2.2	1.529	0.6712
48	1.8	-0.1546	1.955
49	2.9	0.9241	1.976
50	-10.3	-4.395	-5.905
51	11.6	7.335	4.265
52	-5.2	-4.573	-0.6273
53	0.7	4.589	-3.889
54	1.421e-14	-2.331	2.331
55	-1.8	-2.489	0.689
56	8.2	6.443	1.757
57	-5.8	-6.039	0.2389
58	-4.7	-1.18	-3.52
59	5.3	6.776	-1.476
60	-2.4	-3.006	0.6056
61	-0.5	0.5789	-1.079
62	4	2.671	1.329
63	-3.8	-5.365	1.565
64	2.7	2.198	0.5019
65	4.1	0.6977	3.402
66	-2.5	-1.779	-0.7211
67	-4.3	0.02237	-4.322
68	6.5	4.267	2.233
69	-4.7	-1.865	-2.835
70	-2	0.1842	-2.184
71	4.8	3.224	1.576
72	2.2	0.01592	2.184
73	-10.1	-7.662	-2.438
74	13.3	11.05	2.25
75	-10.2	-7.61	-2.59
76	-0.1	-0.5818	0.4818
77	4.3	5.248	-0.9475
78	-9.1	-8.078	-1.022
79	11.5	9.781	1.719
80	-11.6	-8.521	-3.079
81	-6.7	-2.198	-4.502
82	8.9	10.09	-1.187
83	-16.4	-14.46	-1.94
84	0.4	6.515	-6.115
85	7.6	6.877	0.723
86	-11.4	-8.755	-2.645
87	9.6	2.686	6.914
88	-2.3	5.211	-7.511
89	-1.2	-5.014	3.814
90	7.9	2.827	5.073
91	-6	-5.118	-0.882
92	0.4	1.144	-0.7442
93	11.1	5.279	5.821
94	-3.2	-4.786	1.586
95	7.7	1.751	5.949
96	5.1	0.8774	4.223
97	3.7	1.507	2.193
98	-3.5	-3.095	-0.4047
99	-3.9	-0.4268	-3.473
100	9.4	2.966	6.434
101	-7	-5.05	-1.95
102	0.8	4.661	-3.861
103	0.9	-0.8198	1.72
104	-1	-0.8726	-0.1274
105	3.3	3.584	-0.2839
106	-1.5	-2.54	1.04
107	-1.9	2.721	-4.621
108	-2.4	-3.21	0.8099
109	6.4	0.0126	6.387
110	-7.2	-1.944	-5.256
111	9	10.88	-1.881
112	-18.1	-15.38	-2.72
113	3	4.764	-1.764
114	7.8	5.062	2.738
115	-3	-0.9662	-2.034
116	-5.3	-1.438	-3.862
117	-1.5	0.08295	-1.583
118	0	-0.4484	0.4484
119	3.7	2.445	1.255
120	0.4	0.8988	-0.4988
121	-7	-5.585	-1.415
122	0.6	1.554	-0.9544
123	-4.9	-1.359	-3.541
124	10.4	9.411	0.9894
125	3.3	-2.188	5.488
126	-8.4	-3.975	-4.425
127	-3.2	-4.107	0.9075
128	9.7	11.62	-1.919
129	-3.4	-6.891	3.491
130	-6	-3.031	-2.969
131	4.9	4.651	0.2491
132	-5.4	-6.967	1.567
133	-0.9	3.018	-3.918
134	14.2	6.117	8.083
135	-4.3	-4.7	0.4003
136	-1.4	-5.222	3.822
137	5.5	9.704	-4.204
138	-9.1	-10.1	1.005
139	4.1	6.944	-2.844
140	4.9	-4.713	9.613
141	-2.4	-2.544	0.1445
142	3.5	6.609	-3.109
143	-3.8	-4.801	1.001
144	3	4.399	-1.399
145	-5.1	-1.15	-3.95
146	0.9	2.046	-1.146
147	-0.7	-3.98	3.28
148	0.4	5.676	-5.276
149	-5.2	-6.601	1.401
150	1.2	4.353	-3.153
151	9.8	4.776	5.024
152	-8.3	-5.999	-2.301
153	-2.4	1.21	-3.61
154	4.3	3.479	0.8209
155	-0.8	-3.759	2.959
156	-4	1.239	-5.239
157	9	4.478	4.522
158	-5.4	-5.374	-0.02603
159	-4.6	-1.376	-3.224
160	6.4	7.417	-1.017
161	-3.1	-2.997	-0.1035
162	3.9	0.7136	3.186
163	-2.6	-4.339	1.739
164	-0.1	2.571	-2.671
165	5.9	4.463	1.437
166	-4.4	-4.445	0.04458
167	0.8	-0.4922	1.292
168	6.9	5.386	1.514
169	-5.1	-3.629	-1.471
170	1.5	-0.1278	1.628
171	0.9	5.676	-4.776
172	-0.1	-6.861	6.761
173	-3.5	-4.587	1.087
174	7.3	9.598	-2.298
175	-8.2	-4.618	-3.582
176	-3.4	-3.147	-0.2529
177	4.4	5.187	-0.787
178	8.3	-1.591	9.891
179	-7.4	-0.6891	-6.711
180	-1.6	-3.308	1.708
181	4.8	4.111	0.6889
182	-12.1	-7.493	-4.607
183	13.1	10	3.097
184	-4.5	-4.515	0.01544
185	0.9	1.524	-0.6243
186	0.4	-2.163	2.563

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  0.2 & -1.629 &  1.829 \tabularnewline
2 &  7 &  3.355 &  3.645 \tabularnewline
3 & -6.6 & -5.521 & -1.079 \tabularnewline
4 & -1.6 &  1.029 & -2.629 \tabularnewline
5 &  7 &  2.402 &  4.598 \tabularnewline
6 & -5.5 & -4.593 & -0.9073 \tabularnewline
7 &  4.1 &  6.015 & -1.915 \tabularnewline
8 &  0.5 & -2.626 &  3.126 \tabularnewline
9 & -2.8 & -2.093 & -0.707 \tabularnewline
10 &  2.8 &  6.541 & -3.741 \tabularnewline
11 &  0.3 & -2.695 &  2.995 \tabularnewline
12 & -1 & -0.1082 & -0.8918 \tabularnewline
13 & -1 & -1.452 &  0.4525 \tabularnewline
14 & -0.2 &  1.436 & -1.636 \tabularnewline
15 & -3 & -2.424 & -0.576 \tabularnewline
16 &  3.6 &  3.948 & -0.3482 \tabularnewline
17 &  0.6 & -1.314 &  1.914 \tabularnewline
18 & -3 & -1.406 & -1.594 \tabularnewline
19 &  5.3 &  1.49 &  3.81 \tabularnewline
20 & -3.3 & -1.054 & -2.246 \tabularnewline
21 & -1.7 & -0.1094 & -1.591 \tabularnewline
22 &  9.6 &  0.5209 &  9.079 \tabularnewline
23 & -10.8 & -3.727 & -7.073 \tabularnewline
24 &  4.8 &  3.646 &  1.154 \tabularnewline
25 &  6.2 &  5.551 &  0.6494 \tabularnewline
26 & -5.2 & -6.551 &  1.351 \tabularnewline
27 & -0.6 &  2.46 & -3.06 \tabularnewline
28 &  7.1 &  3.587 &  3.513 \tabularnewline
29 & -6.7 & -8.147 &  1.447 \tabularnewline
30 &  4.6 &  5.683 & -1.083 \tabularnewline
31 & -1.4 & -0.7412 & -0.6588 \tabularnewline
32 & -2.8 & -4.074 &  1.274 \tabularnewline
33 &  6.8 &  7.158 & -0.358 \tabularnewline
34 & -1.8 & -3.607 &  1.807 \tabularnewline
35 & -0.7 & -0.5659 & -0.1341 \tabularnewline
36 & -4.2 & -2.127 & -2.073 \tabularnewline
37 & -2.9 & -1.428 & -1.472 \tabularnewline
38 &  3 &  3.059 & -0.05923 \tabularnewline
39 &  0.5 &  2.066 & -1.566 \tabularnewline
40 & -2.6 & -3.614 &  1.014 \tabularnewline
41 & -4.8 & -0.1237 & -4.676 \tabularnewline
42 &  3.5 &  4.955 & -1.455 \tabularnewline
43 &  0.8 & -1.908 &  2.708 \tabularnewline
44 & -2.9 & -0.9836 & -1.916 \tabularnewline
45 &  4.8 &  2.721 &  2.079 \tabularnewline
46 & -2.4 & -3.128 &  0.7275 \tabularnewline
47 &  2.2 &  1.529 &  0.6712 \tabularnewline
48 &  1.8 & -0.1546 &  1.955 \tabularnewline
49 &  2.9 &  0.9241 &  1.976 \tabularnewline
50 & -10.3 & -4.395 & -5.905 \tabularnewline
51 &  11.6 &  7.335 &  4.265 \tabularnewline
52 & -5.2 & -4.573 & -0.6273 \tabularnewline
53 &  0.7 &  4.589 & -3.889 \tabularnewline
54 &  1.421e-14 & -2.331 &  2.331 \tabularnewline
55 & -1.8 & -2.489 &  0.689 \tabularnewline
56 &  8.2 &  6.443 &  1.757 \tabularnewline
57 & -5.8 & -6.039 &  0.2389 \tabularnewline
58 & -4.7 & -1.18 & -3.52 \tabularnewline
59 &  5.3 &  6.776 & -1.476 \tabularnewline
60 & -2.4 & -3.006 &  0.6056 \tabularnewline
61 & -0.5 &  0.5789 & -1.079 \tabularnewline
62 &  4 &  2.671 &  1.329 \tabularnewline
63 & -3.8 & -5.365 &  1.565 \tabularnewline
64 &  2.7 &  2.198 &  0.5019 \tabularnewline
65 &  4.1 &  0.6977 &  3.402 \tabularnewline
66 & -2.5 & -1.779 & -0.7211 \tabularnewline
67 & -4.3 &  0.02237 & -4.322 \tabularnewline
68 &  6.5 &  4.267 &  2.233 \tabularnewline
69 & -4.7 & -1.865 & -2.835 \tabularnewline
70 & -2 &  0.1842 & -2.184 \tabularnewline
71 &  4.8 &  3.224 &  1.576 \tabularnewline
72 &  2.2 &  0.01592 &  2.184 \tabularnewline
73 & -10.1 & -7.662 & -2.438 \tabularnewline
74 &  13.3 &  11.05 &  2.25 \tabularnewline
75 & -10.2 & -7.61 & -2.59 \tabularnewline
76 & -0.1 & -0.5818 &  0.4818 \tabularnewline
77 &  4.3 &  5.248 & -0.9475 \tabularnewline
78 & -9.1 & -8.078 & -1.022 \tabularnewline
79 &  11.5 &  9.781 &  1.719 \tabularnewline
80 & -11.6 & -8.521 & -3.079 \tabularnewline
81 & -6.7 & -2.198 & -4.502 \tabularnewline
82 &  8.9 &  10.09 & -1.187 \tabularnewline
83 & -16.4 & -14.46 & -1.94 \tabularnewline
84 &  0.4 &  6.515 & -6.115 \tabularnewline
85 &  7.6 &  6.877 &  0.723 \tabularnewline
86 & -11.4 & -8.755 & -2.645 \tabularnewline
87 &  9.6 &  2.686 &  6.914 \tabularnewline
88 & -2.3 &  5.211 & -7.511 \tabularnewline
89 & -1.2 & -5.014 &  3.814 \tabularnewline
90 &  7.9 &  2.827 &  5.073 \tabularnewline
91 & -6 & -5.118 & -0.882 \tabularnewline
92 &  0.4 &  1.144 & -0.7442 \tabularnewline
93 &  11.1 &  5.279 &  5.821 \tabularnewline
94 & -3.2 & -4.786 &  1.586 \tabularnewline
95 &  7.7 &  1.751 &  5.949 \tabularnewline
96 &  5.1 &  0.8774 &  4.223 \tabularnewline
97 &  3.7 &  1.507 &  2.193 \tabularnewline
98 & -3.5 & -3.095 & -0.4047 \tabularnewline
99 & -3.9 & -0.4268 & -3.473 \tabularnewline
100 &  9.4 &  2.966 &  6.434 \tabularnewline
101 & -7 & -5.05 & -1.95 \tabularnewline
102 &  0.8 &  4.661 & -3.861 \tabularnewline
103 &  0.9 & -0.8198 &  1.72 \tabularnewline
104 & -1 & -0.8726 & -0.1274 \tabularnewline
105 &  3.3 &  3.584 & -0.2839 \tabularnewline
106 & -1.5 & -2.54 &  1.04 \tabularnewline
107 & -1.9 &  2.721 & -4.621 \tabularnewline
108 & -2.4 & -3.21 &  0.8099 \tabularnewline
109 &  6.4 &  0.0126 &  6.387 \tabularnewline
110 & -7.2 & -1.944 & -5.256 \tabularnewline
111 &  9 &  10.88 & -1.881 \tabularnewline
112 & -18.1 & -15.38 & -2.72 \tabularnewline
113 &  3 &  4.764 & -1.764 \tabularnewline
114 &  7.8 &  5.062 &  2.738 \tabularnewline
115 & -3 & -0.9662 & -2.034 \tabularnewline
116 & -5.3 & -1.438 & -3.862 \tabularnewline
117 & -1.5 &  0.08295 & -1.583 \tabularnewline
118 &  0 & -0.4484 &  0.4484 \tabularnewline
119 &  3.7 &  2.445 &  1.255 \tabularnewline
120 &  0.4 &  0.8988 & -0.4988 \tabularnewline
121 & -7 & -5.585 & -1.415 \tabularnewline
122 &  0.6 &  1.554 & -0.9544 \tabularnewline
123 & -4.9 & -1.359 & -3.541 \tabularnewline
124 &  10.4 &  9.411 &  0.9894 \tabularnewline
125 &  3.3 & -2.188 &  5.488 \tabularnewline
126 & -8.4 & -3.975 & -4.425 \tabularnewline
127 & -3.2 & -4.107 &  0.9075 \tabularnewline
128 &  9.7 &  11.62 & -1.919 \tabularnewline
129 & -3.4 & -6.891 &  3.491 \tabularnewline
130 & -6 & -3.031 & -2.969 \tabularnewline
131 &  4.9 &  4.651 &  0.2491 \tabularnewline
132 & -5.4 & -6.967 &  1.567 \tabularnewline
133 & -0.9 &  3.018 & -3.918 \tabularnewline
134 &  14.2 &  6.117 &  8.083 \tabularnewline
135 & -4.3 & -4.7 &  0.4003 \tabularnewline
136 & -1.4 & -5.222 &  3.822 \tabularnewline
137 &  5.5 &  9.704 & -4.204 \tabularnewline
138 & -9.1 & -10.1 &  1.005 \tabularnewline
139 &  4.1 &  6.944 & -2.844 \tabularnewline
140 &  4.9 & -4.713 &  9.613 \tabularnewline
141 & -2.4 & -2.544 &  0.1445 \tabularnewline
142 &  3.5 &  6.609 & -3.109 \tabularnewline
143 & -3.8 & -4.801 &  1.001 \tabularnewline
144 &  3 &  4.399 & -1.399 \tabularnewline
145 & -5.1 & -1.15 & -3.95 \tabularnewline
146 &  0.9 &  2.046 & -1.146 \tabularnewline
147 & -0.7 & -3.98 &  3.28 \tabularnewline
148 &  0.4 &  5.676 & -5.276 \tabularnewline
149 & -5.2 & -6.601 &  1.401 \tabularnewline
150 &  1.2 &  4.353 & -3.153 \tabularnewline
151 &  9.8 &  4.776 &  5.024 \tabularnewline
152 & -8.3 & -5.999 & -2.301 \tabularnewline
153 & -2.4 &  1.21 & -3.61 \tabularnewline
154 &  4.3 &  3.479 &  0.8209 \tabularnewline
155 & -0.8 & -3.759 &  2.959 \tabularnewline
156 & -4 &  1.239 & -5.239 \tabularnewline
157 &  9 &  4.478 &  4.522 \tabularnewline
158 & -5.4 & -5.374 & -0.02603 \tabularnewline
159 & -4.6 & -1.376 & -3.224 \tabularnewline
160 &  6.4 &  7.417 & -1.017 \tabularnewline
161 & -3.1 & -2.997 & -0.1035 \tabularnewline
162 &  3.9 &  0.7136 &  3.186 \tabularnewline
163 & -2.6 & -4.339 &  1.739 \tabularnewline
164 & -0.1 &  2.571 & -2.671 \tabularnewline
165 &  5.9 &  4.463 &  1.437 \tabularnewline
166 & -4.4 & -4.445 &  0.04458 \tabularnewline
167 &  0.8 & -0.4922 &  1.292 \tabularnewline
168 &  6.9 &  5.386 &  1.514 \tabularnewline
169 & -5.1 & -3.629 & -1.471 \tabularnewline
170 &  1.5 & -0.1278 &  1.628 \tabularnewline
171 &  0.9 &  5.676 & -4.776 \tabularnewline
172 & -0.1 & -6.861 &  6.761 \tabularnewline
173 & -3.5 & -4.587 &  1.087 \tabularnewline
174 &  7.3 &  9.598 & -2.298 \tabularnewline
175 & -8.2 & -4.618 & -3.582 \tabularnewline
176 & -3.4 & -3.147 & -0.2529 \tabularnewline
177 &  4.4 &  5.187 & -0.787 \tabularnewline
178 &  8.3 & -1.591 &  9.891 \tabularnewline
179 & -7.4 & -0.6891 & -6.711 \tabularnewline
180 & -1.6 & -3.308 &  1.708 \tabularnewline
181 &  4.8 &  4.111 &  0.6889 \tabularnewline
182 & -12.1 & -7.493 & -4.607 \tabularnewline
183 &  13.1 &  10 &  3.097 \tabularnewline
184 & -4.5 & -4.515 &  0.01544 \tabularnewline
185 &  0.9 &  1.524 & -0.6243 \tabularnewline
186 &  0.4 & -2.163 &  2.563 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309897&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] 0.2[/C][C]-1.629[/C][C] 1.829[/C][/ROW]
[ROW][C]2[/C][C] 7[/C][C] 3.355[/C][C] 3.645[/C][/ROW]
[ROW][C]3[/C][C]-6.6[/C][C]-5.521[/C][C]-1.079[/C][/ROW]
[ROW][C]4[/C][C]-1.6[/C][C] 1.029[/C][C]-2.629[/C][/ROW]
[ROW][C]5[/C][C] 7[/C][C] 2.402[/C][C] 4.598[/C][/ROW]
[ROW][C]6[/C][C]-5.5[/C][C]-4.593[/C][C]-0.9073[/C][/ROW]
[ROW][C]7[/C][C] 4.1[/C][C] 6.015[/C][C]-1.915[/C][/ROW]
[ROW][C]8[/C][C] 0.5[/C][C]-2.626[/C][C] 3.126[/C][/ROW]
[ROW][C]9[/C][C]-2.8[/C][C]-2.093[/C][C]-0.707[/C][/ROW]
[ROW][C]10[/C][C] 2.8[/C][C] 6.541[/C][C]-3.741[/C][/ROW]
[ROW][C]11[/C][C] 0.3[/C][C]-2.695[/C][C] 2.995[/C][/ROW]
[ROW][C]12[/C][C]-1[/C][C]-0.1082[/C][C]-0.8918[/C][/ROW]
[ROW][C]13[/C][C]-1[/C][C]-1.452[/C][C] 0.4525[/C][/ROW]
[ROW][C]14[/C][C]-0.2[/C][C] 1.436[/C][C]-1.636[/C][/ROW]
[ROW][C]15[/C][C]-3[/C][C]-2.424[/C][C]-0.576[/C][/ROW]
[ROW][C]16[/C][C] 3.6[/C][C] 3.948[/C][C]-0.3482[/C][/ROW]
[ROW][C]17[/C][C] 0.6[/C][C]-1.314[/C][C] 1.914[/C][/ROW]
[ROW][C]18[/C][C]-3[/C][C]-1.406[/C][C]-1.594[/C][/ROW]
[ROW][C]19[/C][C] 5.3[/C][C] 1.49[/C][C] 3.81[/C][/ROW]
[ROW][C]20[/C][C]-3.3[/C][C]-1.054[/C][C]-2.246[/C][/ROW]
[ROW][C]21[/C][C]-1.7[/C][C]-0.1094[/C][C]-1.591[/C][/ROW]
[ROW][C]22[/C][C] 9.6[/C][C] 0.5209[/C][C] 9.079[/C][/ROW]
[ROW][C]23[/C][C]-10.8[/C][C]-3.727[/C][C]-7.073[/C][/ROW]
[ROW][C]24[/C][C] 4.8[/C][C] 3.646[/C][C] 1.154[/C][/ROW]
[ROW][C]25[/C][C] 6.2[/C][C] 5.551[/C][C] 0.6494[/C][/ROW]
[ROW][C]26[/C][C]-5.2[/C][C]-6.551[/C][C] 1.351[/C][/ROW]
[ROW][C]27[/C][C]-0.6[/C][C] 2.46[/C][C]-3.06[/C][/ROW]
[ROW][C]28[/C][C] 7.1[/C][C] 3.587[/C][C] 3.513[/C][/ROW]
[ROW][C]29[/C][C]-6.7[/C][C]-8.147[/C][C] 1.447[/C][/ROW]
[ROW][C]30[/C][C] 4.6[/C][C] 5.683[/C][C]-1.083[/C][/ROW]
[ROW][C]31[/C][C]-1.4[/C][C]-0.7412[/C][C]-0.6588[/C][/ROW]
[ROW][C]32[/C][C]-2.8[/C][C]-4.074[/C][C] 1.274[/C][/ROW]
[ROW][C]33[/C][C] 6.8[/C][C] 7.158[/C][C]-0.358[/C][/ROW]
[ROW][C]34[/C][C]-1.8[/C][C]-3.607[/C][C] 1.807[/C][/ROW]
[ROW][C]35[/C][C]-0.7[/C][C]-0.5659[/C][C]-0.1341[/C][/ROW]
[ROW][C]36[/C][C]-4.2[/C][C]-2.127[/C][C]-2.073[/C][/ROW]
[ROW][C]37[/C][C]-2.9[/C][C]-1.428[/C][C]-1.472[/C][/ROW]
[ROW][C]38[/C][C] 3[/C][C] 3.059[/C][C]-0.05923[/C][/ROW]
[ROW][C]39[/C][C] 0.5[/C][C] 2.066[/C][C]-1.566[/C][/ROW]
[ROW][C]40[/C][C]-2.6[/C][C]-3.614[/C][C] 1.014[/C][/ROW]
[ROW][C]41[/C][C]-4.8[/C][C]-0.1237[/C][C]-4.676[/C][/ROW]
[ROW][C]42[/C][C] 3.5[/C][C] 4.955[/C][C]-1.455[/C][/ROW]
[ROW][C]43[/C][C] 0.8[/C][C]-1.908[/C][C] 2.708[/C][/ROW]
[ROW][C]44[/C][C]-2.9[/C][C]-0.9836[/C][C]-1.916[/C][/ROW]
[ROW][C]45[/C][C] 4.8[/C][C] 2.721[/C][C] 2.079[/C][/ROW]
[ROW][C]46[/C][C]-2.4[/C][C]-3.128[/C][C] 0.7275[/C][/ROW]
[ROW][C]47[/C][C] 2.2[/C][C] 1.529[/C][C] 0.6712[/C][/ROW]
[ROW][C]48[/C][C] 1.8[/C][C]-0.1546[/C][C] 1.955[/C][/ROW]
[ROW][C]49[/C][C] 2.9[/C][C] 0.9241[/C][C] 1.976[/C][/ROW]
[ROW][C]50[/C][C]-10.3[/C][C]-4.395[/C][C]-5.905[/C][/ROW]
[ROW][C]51[/C][C] 11.6[/C][C] 7.335[/C][C] 4.265[/C][/ROW]
[ROW][C]52[/C][C]-5.2[/C][C]-4.573[/C][C]-0.6273[/C][/ROW]
[ROW][C]53[/C][C] 0.7[/C][C] 4.589[/C][C]-3.889[/C][/ROW]
[ROW][C]54[/C][C] 1.421e-14[/C][C]-2.331[/C][C] 2.331[/C][/ROW]
[ROW][C]55[/C][C]-1.8[/C][C]-2.489[/C][C] 0.689[/C][/ROW]
[ROW][C]56[/C][C] 8.2[/C][C] 6.443[/C][C] 1.757[/C][/ROW]
[ROW][C]57[/C][C]-5.8[/C][C]-6.039[/C][C] 0.2389[/C][/ROW]
[ROW][C]58[/C][C]-4.7[/C][C]-1.18[/C][C]-3.52[/C][/ROW]
[ROW][C]59[/C][C] 5.3[/C][C] 6.776[/C][C]-1.476[/C][/ROW]
[ROW][C]60[/C][C]-2.4[/C][C]-3.006[/C][C] 0.6056[/C][/ROW]
[ROW][C]61[/C][C]-0.5[/C][C] 0.5789[/C][C]-1.079[/C][/ROW]
[ROW][C]62[/C][C] 4[/C][C] 2.671[/C][C] 1.329[/C][/ROW]
[ROW][C]63[/C][C]-3.8[/C][C]-5.365[/C][C] 1.565[/C][/ROW]
[ROW][C]64[/C][C] 2.7[/C][C] 2.198[/C][C] 0.5019[/C][/ROW]
[ROW][C]65[/C][C] 4.1[/C][C] 0.6977[/C][C] 3.402[/C][/ROW]
[ROW][C]66[/C][C]-2.5[/C][C]-1.779[/C][C]-0.7211[/C][/ROW]
[ROW][C]67[/C][C]-4.3[/C][C] 0.02237[/C][C]-4.322[/C][/ROW]
[ROW][C]68[/C][C] 6.5[/C][C] 4.267[/C][C] 2.233[/C][/ROW]
[ROW][C]69[/C][C]-4.7[/C][C]-1.865[/C][C]-2.835[/C][/ROW]
[ROW][C]70[/C][C]-2[/C][C] 0.1842[/C][C]-2.184[/C][/ROW]
[ROW][C]71[/C][C] 4.8[/C][C] 3.224[/C][C] 1.576[/C][/ROW]
[ROW][C]72[/C][C] 2.2[/C][C] 0.01592[/C][C] 2.184[/C][/ROW]
[ROW][C]73[/C][C]-10.1[/C][C]-7.662[/C][C]-2.438[/C][/ROW]
[ROW][C]74[/C][C] 13.3[/C][C] 11.05[/C][C] 2.25[/C][/ROW]
[ROW][C]75[/C][C]-10.2[/C][C]-7.61[/C][C]-2.59[/C][/ROW]
[ROW][C]76[/C][C]-0.1[/C][C]-0.5818[/C][C] 0.4818[/C][/ROW]
[ROW][C]77[/C][C] 4.3[/C][C] 5.248[/C][C]-0.9475[/C][/ROW]
[ROW][C]78[/C][C]-9.1[/C][C]-8.078[/C][C]-1.022[/C][/ROW]
[ROW][C]79[/C][C] 11.5[/C][C] 9.781[/C][C] 1.719[/C][/ROW]
[ROW][C]80[/C][C]-11.6[/C][C]-8.521[/C][C]-3.079[/C][/ROW]
[ROW][C]81[/C][C]-6.7[/C][C]-2.198[/C][C]-4.502[/C][/ROW]
[ROW][C]82[/C][C] 8.9[/C][C] 10.09[/C][C]-1.187[/C][/ROW]
[ROW][C]83[/C][C]-16.4[/C][C]-14.46[/C][C]-1.94[/C][/ROW]
[ROW][C]84[/C][C] 0.4[/C][C] 6.515[/C][C]-6.115[/C][/ROW]
[ROW][C]85[/C][C] 7.6[/C][C] 6.877[/C][C] 0.723[/C][/ROW]
[ROW][C]86[/C][C]-11.4[/C][C]-8.755[/C][C]-2.645[/C][/ROW]
[ROW][C]87[/C][C] 9.6[/C][C] 2.686[/C][C] 6.914[/C][/ROW]
[ROW][C]88[/C][C]-2.3[/C][C] 5.211[/C][C]-7.511[/C][/ROW]
[ROW][C]89[/C][C]-1.2[/C][C]-5.014[/C][C] 3.814[/C][/ROW]
[ROW][C]90[/C][C] 7.9[/C][C] 2.827[/C][C] 5.073[/C][/ROW]
[ROW][C]91[/C][C]-6[/C][C]-5.118[/C][C]-0.882[/C][/ROW]
[ROW][C]92[/C][C] 0.4[/C][C] 1.144[/C][C]-0.7442[/C][/ROW]
[ROW][C]93[/C][C] 11.1[/C][C] 5.279[/C][C] 5.821[/C][/ROW]
[ROW][C]94[/C][C]-3.2[/C][C]-4.786[/C][C] 1.586[/C][/ROW]
[ROW][C]95[/C][C] 7.7[/C][C] 1.751[/C][C] 5.949[/C][/ROW]
[ROW][C]96[/C][C] 5.1[/C][C] 0.8774[/C][C] 4.223[/C][/ROW]
[ROW][C]97[/C][C] 3.7[/C][C] 1.507[/C][C] 2.193[/C][/ROW]
[ROW][C]98[/C][C]-3.5[/C][C]-3.095[/C][C]-0.4047[/C][/ROW]
[ROW][C]99[/C][C]-3.9[/C][C]-0.4268[/C][C]-3.473[/C][/ROW]
[ROW][C]100[/C][C] 9.4[/C][C] 2.966[/C][C] 6.434[/C][/ROW]
[ROW][C]101[/C][C]-7[/C][C]-5.05[/C][C]-1.95[/C][/ROW]
[ROW][C]102[/C][C] 0.8[/C][C] 4.661[/C][C]-3.861[/C][/ROW]
[ROW][C]103[/C][C] 0.9[/C][C]-0.8198[/C][C] 1.72[/C][/ROW]
[ROW][C]104[/C][C]-1[/C][C]-0.8726[/C][C]-0.1274[/C][/ROW]
[ROW][C]105[/C][C] 3.3[/C][C] 3.584[/C][C]-0.2839[/C][/ROW]
[ROW][C]106[/C][C]-1.5[/C][C]-2.54[/C][C] 1.04[/C][/ROW]
[ROW][C]107[/C][C]-1.9[/C][C] 2.721[/C][C]-4.621[/C][/ROW]
[ROW][C]108[/C][C]-2.4[/C][C]-3.21[/C][C] 0.8099[/C][/ROW]
[ROW][C]109[/C][C] 6.4[/C][C] 0.0126[/C][C] 6.387[/C][/ROW]
[ROW][C]110[/C][C]-7.2[/C][C]-1.944[/C][C]-5.256[/C][/ROW]
[ROW][C]111[/C][C] 9[/C][C] 10.88[/C][C]-1.881[/C][/ROW]
[ROW][C]112[/C][C]-18.1[/C][C]-15.38[/C][C]-2.72[/C][/ROW]
[ROW][C]113[/C][C] 3[/C][C] 4.764[/C][C]-1.764[/C][/ROW]
[ROW][C]114[/C][C] 7.8[/C][C] 5.062[/C][C] 2.738[/C][/ROW]
[ROW][C]115[/C][C]-3[/C][C]-0.9662[/C][C]-2.034[/C][/ROW]
[ROW][C]116[/C][C]-5.3[/C][C]-1.438[/C][C]-3.862[/C][/ROW]
[ROW][C]117[/C][C]-1.5[/C][C] 0.08295[/C][C]-1.583[/C][/ROW]
[ROW][C]118[/C][C] 0[/C][C]-0.4484[/C][C] 0.4484[/C][/ROW]
[ROW][C]119[/C][C] 3.7[/C][C] 2.445[/C][C] 1.255[/C][/ROW]
[ROW][C]120[/C][C] 0.4[/C][C] 0.8988[/C][C]-0.4988[/C][/ROW]
[ROW][C]121[/C][C]-7[/C][C]-5.585[/C][C]-1.415[/C][/ROW]
[ROW][C]122[/C][C] 0.6[/C][C] 1.554[/C][C]-0.9544[/C][/ROW]
[ROW][C]123[/C][C]-4.9[/C][C]-1.359[/C][C]-3.541[/C][/ROW]
[ROW][C]124[/C][C] 10.4[/C][C] 9.411[/C][C] 0.9894[/C][/ROW]
[ROW][C]125[/C][C] 3.3[/C][C]-2.188[/C][C] 5.488[/C][/ROW]
[ROW][C]126[/C][C]-8.4[/C][C]-3.975[/C][C]-4.425[/C][/ROW]
[ROW][C]127[/C][C]-3.2[/C][C]-4.107[/C][C] 0.9075[/C][/ROW]
[ROW][C]128[/C][C] 9.7[/C][C] 11.62[/C][C]-1.919[/C][/ROW]
[ROW][C]129[/C][C]-3.4[/C][C]-6.891[/C][C] 3.491[/C][/ROW]
[ROW][C]130[/C][C]-6[/C][C]-3.031[/C][C]-2.969[/C][/ROW]
[ROW][C]131[/C][C] 4.9[/C][C] 4.651[/C][C] 0.2491[/C][/ROW]
[ROW][C]132[/C][C]-5.4[/C][C]-6.967[/C][C] 1.567[/C][/ROW]
[ROW][C]133[/C][C]-0.9[/C][C] 3.018[/C][C]-3.918[/C][/ROW]
[ROW][C]134[/C][C] 14.2[/C][C] 6.117[/C][C] 8.083[/C][/ROW]
[ROW][C]135[/C][C]-4.3[/C][C]-4.7[/C][C] 0.4003[/C][/ROW]
[ROW][C]136[/C][C]-1.4[/C][C]-5.222[/C][C] 3.822[/C][/ROW]
[ROW][C]137[/C][C] 5.5[/C][C] 9.704[/C][C]-4.204[/C][/ROW]
[ROW][C]138[/C][C]-9.1[/C][C]-10.1[/C][C] 1.005[/C][/ROW]
[ROW][C]139[/C][C] 4.1[/C][C] 6.944[/C][C]-2.844[/C][/ROW]
[ROW][C]140[/C][C] 4.9[/C][C]-4.713[/C][C] 9.613[/C][/ROW]
[ROW][C]141[/C][C]-2.4[/C][C]-2.544[/C][C] 0.1445[/C][/ROW]
[ROW][C]142[/C][C] 3.5[/C][C] 6.609[/C][C]-3.109[/C][/ROW]
[ROW][C]143[/C][C]-3.8[/C][C]-4.801[/C][C] 1.001[/C][/ROW]
[ROW][C]144[/C][C] 3[/C][C] 4.399[/C][C]-1.399[/C][/ROW]
[ROW][C]145[/C][C]-5.1[/C][C]-1.15[/C][C]-3.95[/C][/ROW]
[ROW][C]146[/C][C] 0.9[/C][C] 2.046[/C][C]-1.146[/C][/ROW]
[ROW][C]147[/C][C]-0.7[/C][C]-3.98[/C][C] 3.28[/C][/ROW]
[ROW][C]148[/C][C] 0.4[/C][C] 5.676[/C][C]-5.276[/C][/ROW]
[ROW][C]149[/C][C]-5.2[/C][C]-6.601[/C][C] 1.401[/C][/ROW]
[ROW][C]150[/C][C] 1.2[/C][C] 4.353[/C][C]-3.153[/C][/ROW]
[ROW][C]151[/C][C] 9.8[/C][C] 4.776[/C][C] 5.024[/C][/ROW]
[ROW][C]152[/C][C]-8.3[/C][C]-5.999[/C][C]-2.301[/C][/ROW]
[ROW][C]153[/C][C]-2.4[/C][C] 1.21[/C][C]-3.61[/C][/ROW]
[ROW][C]154[/C][C] 4.3[/C][C] 3.479[/C][C] 0.8209[/C][/ROW]
[ROW][C]155[/C][C]-0.8[/C][C]-3.759[/C][C] 2.959[/C][/ROW]
[ROW][C]156[/C][C]-4[/C][C] 1.239[/C][C]-5.239[/C][/ROW]
[ROW][C]157[/C][C] 9[/C][C] 4.478[/C][C] 4.522[/C][/ROW]
[ROW][C]158[/C][C]-5.4[/C][C]-5.374[/C][C]-0.02603[/C][/ROW]
[ROW][C]159[/C][C]-4.6[/C][C]-1.376[/C][C]-3.224[/C][/ROW]
[ROW][C]160[/C][C] 6.4[/C][C] 7.417[/C][C]-1.017[/C][/ROW]
[ROW][C]161[/C][C]-3.1[/C][C]-2.997[/C][C]-0.1035[/C][/ROW]
[ROW][C]162[/C][C] 3.9[/C][C] 0.7136[/C][C] 3.186[/C][/ROW]
[ROW][C]163[/C][C]-2.6[/C][C]-4.339[/C][C] 1.739[/C][/ROW]
[ROW][C]164[/C][C]-0.1[/C][C] 2.571[/C][C]-2.671[/C][/ROW]
[ROW][C]165[/C][C] 5.9[/C][C] 4.463[/C][C] 1.437[/C][/ROW]
[ROW][C]166[/C][C]-4.4[/C][C]-4.445[/C][C] 0.04458[/C][/ROW]
[ROW][C]167[/C][C] 0.8[/C][C]-0.4922[/C][C] 1.292[/C][/ROW]
[ROW][C]168[/C][C] 6.9[/C][C] 5.386[/C][C] 1.514[/C][/ROW]
[ROW][C]169[/C][C]-5.1[/C][C]-3.629[/C][C]-1.471[/C][/ROW]
[ROW][C]170[/C][C] 1.5[/C][C]-0.1278[/C][C] 1.628[/C][/ROW]
[ROW][C]171[/C][C] 0.9[/C][C] 5.676[/C][C]-4.776[/C][/ROW]
[ROW][C]172[/C][C]-0.1[/C][C]-6.861[/C][C] 6.761[/C][/ROW]
[ROW][C]173[/C][C]-3.5[/C][C]-4.587[/C][C] 1.087[/C][/ROW]
[ROW][C]174[/C][C] 7.3[/C][C] 9.598[/C][C]-2.298[/C][/ROW]
[ROW][C]175[/C][C]-8.2[/C][C]-4.618[/C][C]-3.582[/C][/ROW]
[ROW][C]176[/C][C]-3.4[/C][C]-3.147[/C][C]-0.2529[/C][/ROW]
[ROW][C]177[/C][C] 4.4[/C][C] 5.187[/C][C]-0.787[/C][/ROW]
[ROW][C]178[/C][C] 8.3[/C][C]-1.591[/C][C] 9.891[/C][/ROW]
[ROW][C]179[/C][C]-7.4[/C][C]-0.6891[/C][C]-6.711[/C][/ROW]
[ROW][C]180[/C][C]-1.6[/C][C]-3.308[/C][C] 1.708[/C][/ROW]
[ROW][C]181[/C][C] 4.8[/C][C] 4.111[/C][C] 0.6889[/C][/ROW]
[ROW][C]182[/C][C]-12.1[/C][C]-7.493[/C][C]-4.607[/C][/ROW]
[ROW][C]183[/C][C] 13.1[/C][C] 10[/C][C] 3.097[/C][/ROW]
[ROW][C]184[/C][C]-4.5[/C][C]-4.515[/C][C] 0.01544[/C][/ROW]
[ROW][C]185[/C][C] 0.9[/C][C] 1.524[/C][C]-0.6243[/C][/ROW]
[ROW][C]186[/C][C] 0.4[/C][C]-2.163[/C][C] 2.563[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309897&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309897&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	0.2	-1.629	1.829
2	7	3.355	3.645
3	-6.6	-5.521	-1.079
4	-1.6	1.029	-2.629
5	7	2.402	4.598
6	-5.5	-4.593	-0.9073
7	4.1	6.015	-1.915
8	0.5	-2.626	3.126
9	-2.8	-2.093	-0.707
10	2.8	6.541	-3.741
11	0.3	-2.695	2.995
12	-1	-0.1082	-0.8918
13	-1	-1.452	0.4525
14	-0.2	1.436	-1.636
15	-3	-2.424	-0.576
16	3.6	3.948	-0.3482
17	0.6	-1.314	1.914
18	-3	-1.406	-1.594
19	5.3	1.49	3.81
20	-3.3	-1.054	-2.246
21	-1.7	-0.1094	-1.591
22	9.6	0.5209	9.079
23	-10.8	-3.727	-7.073
24	4.8	3.646	1.154
25	6.2	5.551	0.6494
26	-5.2	-6.551	1.351
27	-0.6	2.46	-3.06
28	7.1	3.587	3.513
29	-6.7	-8.147	1.447
30	4.6	5.683	-1.083
31	-1.4	-0.7412	-0.6588
32	-2.8	-4.074	1.274
33	6.8	7.158	-0.358
34	-1.8	-3.607	1.807
35	-0.7	-0.5659	-0.1341
36	-4.2	-2.127	-2.073
37	-2.9	-1.428	-1.472
38	3	3.059	-0.05923
39	0.5	2.066	-1.566
40	-2.6	-3.614	1.014
41	-4.8	-0.1237	-4.676
42	3.5	4.955	-1.455
43	0.8	-1.908	2.708
44	-2.9	-0.9836	-1.916
45	4.8	2.721	2.079
46	-2.4	-3.128	0.7275
47	2.2	1.529	0.6712
48	1.8	-0.1546	1.955
49	2.9	0.9241	1.976
50	-10.3	-4.395	-5.905
51	11.6	7.335	4.265
52	-5.2	-4.573	-0.6273
53	0.7	4.589	-3.889
54	1.421e-14	-2.331	2.331
55	-1.8	-2.489	0.689
56	8.2	6.443	1.757
57	-5.8	-6.039	0.2389
58	-4.7	-1.18	-3.52
59	5.3	6.776	-1.476
60	-2.4	-3.006	0.6056
61	-0.5	0.5789	-1.079
62	4	2.671	1.329
63	-3.8	-5.365	1.565
64	2.7	2.198	0.5019
65	4.1	0.6977	3.402
66	-2.5	-1.779	-0.7211
67	-4.3	0.02237	-4.322
68	6.5	4.267	2.233
69	-4.7	-1.865	-2.835
70	-2	0.1842	-2.184
71	4.8	3.224	1.576
72	2.2	0.01592	2.184
73	-10.1	-7.662	-2.438
74	13.3	11.05	2.25
75	-10.2	-7.61	-2.59
76	-0.1	-0.5818	0.4818
77	4.3	5.248	-0.9475
78	-9.1	-8.078	-1.022
79	11.5	9.781	1.719
80	-11.6	-8.521	-3.079
81	-6.7	-2.198	-4.502
82	8.9	10.09	-1.187
83	-16.4	-14.46	-1.94
84	0.4	6.515	-6.115
85	7.6	6.877	0.723
86	-11.4	-8.755	-2.645
87	9.6	2.686	6.914
88	-2.3	5.211	-7.511
89	-1.2	-5.014	3.814
90	7.9	2.827	5.073
91	-6	-5.118	-0.882
92	0.4	1.144	-0.7442
93	11.1	5.279	5.821
94	-3.2	-4.786	1.586
95	7.7	1.751	5.949
96	5.1	0.8774	4.223
97	3.7	1.507	2.193
98	-3.5	-3.095	-0.4047
99	-3.9	-0.4268	-3.473
100	9.4	2.966	6.434
101	-7	-5.05	-1.95
102	0.8	4.661	-3.861
103	0.9	-0.8198	1.72
104	-1	-0.8726	-0.1274
105	3.3	3.584	-0.2839
106	-1.5	-2.54	1.04
107	-1.9	2.721	-4.621
108	-2.4	-3.21	0.8099
109	6.4	0.0126	6.387
110	-7.2	-1.944	-5.256
111	9	10.88	-1.881
112	-18.1	-15.38	-2.72
113	3	4.764	-1.764
114	7.8	5.062	2.738
115	-3	-0.9662	-2.034
116	-5.3	-1.438	-3.862
117	-1.5	0.08295	-1.583
118	0	-0.4484	0.4484
119	3.7	2.445	1.255
120	0.4	0.8988	-0.4988
121	-7	-5.585	-1.415
122	0.6	1.554	-0.9544
123	-4.9	-1.359	-3.541
124	10.4	9.411	0.9894
125	3.3	-2.188	5.488
126	-8.4	-3.975	-4.425
127	-3.2	-4.107	0.9075
128	9.7	11.62	-1.919
129	-3.4	-6.891	3.491
130	-6	-3.031	-2.969
131	4.9	4.651	0.2491
132	-5.4	-6.967	1.567
133	-0.9	3.018	-3.918
134	14.2	6.117	8.083
135	-4.3	-4.7	0.4003
136	-1.4	-5.222	3.822
137	5.5	9.704	-4.204
138	-9.1	-10.1	1.005
139	4.1	6.944	-2.844
140	4.9	-4.713	9.613
141	-2.4	-2.544	0.1445
142	3.5	6.609	-3.109
143	-3.8	-4.801	1.001
144	3	4.399	-1.399
145	-5.1	-1.15	-3.95
146	0.9	2.046	-1.146
147	-0.7	-3.98	3.28
148	0.4	5.676	-5.276
149	-5.2	-6.601	1.401
150	1.2	4.353	-3.153
151	9.8	4.776	5.024
152	-8.3	-5.999	-2.301
153	-2.4	1.21	-3.61
154	4.3	3.479	0.8209
155	-0.8	-3.759	2.959
156	-4	1.239	-5.239
157	9	4.478	4.522
158	-5.4	-5.374	-0.02603
159	-4.6	-1.376	-3.224
160	6.4	7.417	-1.017
161	-3.1	-2.997	-0.1035
162	3.9	0.7136	3.186
163	-2.6	-4.339	1.739
164	-0.1	2.571	-2.671
165	5.9	4.463	1.437
166	-4.4	-4.445	0.04458
167	0.8	-0.4922	1.292
168	6.9	5.386	1.514
169	-5.1	-3.629	-1.471
170	1.5	-0.1278	1.628
171	0.9	5.676	-4.776
172	-0.1	-6.861	6.761
173	-3.5	-4.587	1.087
174	7.3	9.598	-2.298
175	-8.2	-4.618	-3.582
176	-3.4	-3.147	-0.2529
177	4.4	5.187	-0.787
178	8.3	-1.591	9.891
179	-7.4	-0.6891	-6.711
180	-1.6	-3.308	1.708
181	4.8	4.111	0.6889
182	-12.1	-7.493	-4.607
183	13.1	10	3.097
184	-4.5	-4.515	0.01544
185	0.9	1.524	-0.6243
186	0.4	-2.163	2.563

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
8	0.3939	0.7878	0.6061
9	0.2498	0.4997	0.7502
10	0.669	0.6619	0.331
11	0.5666	0.8668	0.4334
12	0.4488	0.8976	0.5512
13	0.3508	0.7016	0.6492
14	0.2658	0.5317	0.7341
15	0.2182	0.4365	0.7818
16	0.153	0.306	0.847
17	0.1082	0.2164	0.8918
18	0.08251	0.165	0.9175
19	0.105	0.21	0.895
20	0.09392	0.1878	0.9061
21	0.08083	0.1617	0.9192
22	0.4279	0.8557	0.5721
23	0.5428	0.9144	0.4572
24	0.4822	0.9644	0.5178
25	0.4878	0.9756	0.5122
26	0.4339	0.8679	0.5661
27	0.4869	0.9738	0.5131
28	0.458	0.916	0.542
29	0.3998	0.7995	0.6002
30	0.3417	0.6834	0.6583
31	0.3122	0.6244	0.6878
32	0.2613	0.5226	0.7387
33	0.216	0.432	0.784
34	0.183	0.366	0.817
35	0.1533	0.3065	0.8467
36	0.1886	0.3771	0.8114
37	0.1721	0.3441	0.8279
38	0.1392	0.2783	0.8608
39	0.1112	0.2225	0.8888
40	0.0881	0.1762	0.9119
41	0.09295	0.1859	0.9071
42	0.09518	0.1904	0.9048
43	0.09017	0.1803	0.9098
44	0.07787	0.1557	0.9221
45	0.0637	0.1274	0.9363
46	0.04935	0.0987	0.9507
47	0.03972	0.07945	0.9603
48	0.03647	0.07293	0.9635
49	0.0289	0.05781	0.9711
50	0.05773	0.1155	0.9423
51	0.06881	0.1376	0.9312
52	0.05421	0.1084	0.9458
53	0.06721	0.1344	0.9328
54	0.05642	0.1128	0.9436
55	0.04481	0.08962	0.9552
56	0.04013	0.08026	0.9599
57	0.03081	0.06161	0.9692
58	0.03034	0.06067	0.9697
59	0.02573	0.05145	0.9743
60	0.01939	0.03878	0.9806
61	0.01531	0.03062	0.9847
62	0.01304	0.02609	0.987
63	0.009862	0.01972	0.9901
64	0.00727	0.01454	0.9927
65	0.009147	0.01829	0.9909
66	0.007046	0.01409	0.993
67	0.01011	0.02021	0.9899
68	0.008109	0.01622	0.9919
69	0.007479	0.01496	0.9925
70	0.00603	0.01206	0.994
71	0.004658	0.009316	0.9953
72	0.004239	0.008477	0.9958
73	0.003387	0.006775	0.9966
74	0.002637	0.005274	0.9974
75	0.002344	0.004687	0.9977
76	0.001697	0.003394	0.9983
77	0.001547	0.003094	0.9985
78	0.001121	0.002242	0.9989
79	0.0008809	0.001762	0.9991
80	0.0009527	0.001905	0.999
81	0.001156	0.002312	0.9988
82	0.0008918	0.001784	0.9991
83	0.0006954	0.001391	0.9993
84	0.002063	0.004127	0.9979
85	0.001485	0.00297	0.9985
86	0.001458	0.002915	0.9985
87	0.00818	0.01636	0.9918
88	0.0327	0.0654	0.9673
89	0.0357	0.07141	0.9643
90	0.05397	0.1079	0.946
91	0.04404	0.08808	0.956
92	0.03523	0.07046	0.9648
93	0.05872	0.1174	0.9413
94	0.05045	0.1009	0.9495
95	0.08198	0.164	0.918
96	0.09415	0.1883	0.9059
97	0.08416	0.1683	0.9158
98	0.06898	0.138	0.931
99	0.06982	0.1396	0.9302
100	0.116	0.232	0.884
101	0.1027	0.2053	0.8973
102	0.1109	0.2218	0.8891
103	0.09697	0.1939	0.903
104	0.07983	0.1597	0.9202
105	0.06517	0.1303	0.9348
106	0.05391	0.1078	0.9461
107	0.06919	0.1384	0.9308
108	0.05697	0.1139	0.943
109	0.102	0.204	0.898
110	0.1411	0.2822	0.8589
111	0.1257	0.2514	0.8743
112	0.1256	0.2513	0.8744
113	0.1102	0.2203	0.8898
114	0.1049	0.2098	0.8951
115	0.09342	0.1868	0.9066
116	0.1	0.2	0.9
117	0.08667	0.1733	0.9133
118	0.07075	0.1415	0.9292
119	0.06019	0.1204	0.9398
120	0.0489	0.09781	0.9511
121	0.04182	0.08364	0.9582
122	0.0333	0.06661	0.9667
123	0.03641	0.07281	0.9636
124	0.03374	0.06748	0.9663
125	0.05172	0.1034	0.9483
126	0.07082	0.1416	0.9292
127	0.0581	0.1162	0.9419
128	0.05099	0.102	0.949
129	0.05112	0.1022	0.9489
130	0.05166	0.1033	0.9483
131	0.04063	0.08127	0.9594
132	0.03279	0.06557	0.9672
133	0.03457	0.06914	0.9654
134	0.13	0.2599	0.87
135	0.1096	0.2192	0.8904
136	0.106	0.212	0.894
137	0.108	0.2161	0.892
138	0.08818	0.1764	0.9118
139	0.08028	0.1606	0.9197
140	0.2745	0.549	0.7255
141	0.2345	0.469	0.7655
142	0.2165	0.433	0.7835
143	0.1831	0.3661	0.8169
144	0.1547	0.3093	0.8453
145	0.1643	0.3286	0.8357
146	0.1411	0.2821	0.8589
147	0.1367	0.2735	0.8633
148	0.1732	0.3463	0.8268
149	0.1431	0.2861	0.8569
150	0.1393	0.2786	0.8607
151	0.1918	0.3837	0.8082
152	0.1674	0.3349	0.8326
153	0.2001	0.4001	0.7999
154	0.1645	0.3291	0.8355
155	0.1551	0.3102	0.8449
156	0.2245	0.4489	0.7755
157	0.2596	0.5192	0.7404
158	0.212	0.4241	0.788
159	0.2426	0.4852	0.7574
160	0.1991	0.3982	0.8009
161	0.1574	0.3148	0.8426
162	0.1409	0.2817	0.8591
163	0.1127	0.2253	0.8873
164	0.1161	0.2321	0.8839
165	0.09573	0.1915	0.9043
166	0.07065	0.1413	0.9293
167	0.04969	0.09938	0.9503
168	0.03788	0.07575	0.9621
169	0.02532	0.05065	0.9747
170	0.01662	0.03325	0.9834
171	0.02361	0.04723	0.9764
172	0.03899	0.07798	0.961
173	0.02343	0.04686	0.9766
174	0.01651	0.03303	0.9835
175	0.01296	0.02592	0.987
176	0.008018	0.01604	0.992
177	0.003553	0.007105	0.9964
178	0.2154	0.4309	0.7846

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 &  0.3939 &  0.7878 &  0.6061 \tabularnewline
9 &  0.2498 &  0.4997 &  0.7502 \tabularnewline
10 &  0.669 &  0.6619 &  0.331 \tabularnewline
11 &  0.5666 &  0.8668 &  0.4334 \tabularnewline
12 &  0.4488 &  0.8976 &  0.5512 \tabularnewline
13 &  0.3508 &  0.7016 &  0.6492 \tabularnewline
14 &  0.2658 &  0.5317 &  0.7341 \tabularnewline
15 &  0.2182 &  0.4365 &  0.7818 \tabularnewline
16 &  0.153 &  0.306 &  0.847 \tabularnewline
17 &  0.1082 &  0.2164 &  0.8918 \tabularnewline
18 &  0.08251 &  0.165 &  0.9175 \tabularnewline
19 &  0.105 &  0.21 &  0.895 \tabularnewline
20 &  0.09392 &  0.1878 &  0.9061 \tabularnewline
21 &  0.08083 &  0.1617 &  0.9192 \tabularnewline
22 &  0.4279 &  0.8557 &  0.5721 \tabularnewline
23 &  0.5428 &  0.9144 &  0.4572 \tabularnewline
24 &  0.4822 &  0.9644 &  0.5178 \tabularnewline
25 &  0.4878 &  0.9756 &  0.5122 \tabularnewline
26 &  0.4339 &  0.8679 &  0.5661 \tabularnewline
27 &  0.4869 &  0.9738 &  0.5131 \tabularnewline
28 &  0.458 &  0.916 &  0.542 \tabularnewline
29 &  0.3998 &  0.7995 &  0.6002 \tabularnewline
30 &  0.3417 &  0.6834 &  0.6583 \tabularnewline
31 &  0.3122 &  0.6244 &  0.6878 \tabularnewline
32 &  0.2613 &  0.5226 &  0.7387 \tabularnewline
33 &  0.216 &  0.432 &  0.784 \tabularnewline
34 &  0.183 &  0.366 &  0.817 \tabularnewline
35 &  0.1533 &  0.3065 &  0.8467 \tabularnewline
36 &  0.1886 &  0.3771 &  0.8114 \tabularnewline
37 &  0.1721 &  0.3441 &  0.8279 \tabularnewline
38 &  0.1392 &  0.2783 &  0.8608 \tabularnewline
39 &  0.1112 &  0.2225 &  0.8888 \tabularnewline
40 &  0.0881 &  0.1762 &  0.9119 \tabularnewline
41 &  0.09295 &  0.1859 &  0.9071 \tabularnewline
42 &  0.09518 &  0.1904 &  0.9048 \tabularnewline
43 &  0.09017 &  0.1803 &  0.9098 \tabularnewline
44 &  0.07787 &  0.1557 &  0.9221 \tabularnewline
45 &  0.0637 &  0.1274 &  0.9363 \tabularnewline
46 &  0.04935 &  0.0987 &  0.9507 \tabularnewline
47 &  0.03972 &  0.07945 &  0.9603 \tabularnewline
48 &  0.03647 &  0.07293 &  0.9635 \tabularnewline
49 &  0.0289 &  0.05781 &  0.9711 \tabularnewline
50 &  0.05773 &  0.1155 &  0.9423 \tabularnewline
51 &  0.06881 &  0.1376 &  0.9312 \tabularnewline
52 &  0.05421 &  0.1084 &  0.9458 \tabularnewline
53 &  0.06721 &  0.1344 &  0.9328 \tabularnewline
54 &  0.05642 &  0.1128 &  0.9436 \tabularnewline
55 &  0.04481 &  0.08962 &  0.9552 \tabularnewline
56 &  0.04013 &  0.08026 &  0.9599 \tabularnewline
57 &  0.03081 &  0.06161 &  0.9692 \tabularnewline
58 &  0.03034 &  0.06067 &  0.9697 \tabularnewline
59 &  0.02573 &  0.05145 &  0.9743 \tabularnewline
60 &  0.01939 &  0.03878 &  0.9806 \tabularnewline
61 &  0.01531 &  0.03062 &  0.9847 \tabularnewline
62 &  0.01304 &  0.02609 &  0.987 \tabularnewline
63 &  0.009862 &  0.01972 &  0.9901 \tabularnewline
64 &  0.00727 &  0.01454 &  0.9927 \tabularnewline
65 &  0.009147 &  0.01829 &  0.9909 \tabularnewline
66 &  0.007046 &  0.01409 &  0.993 \tabularnewline
67 &  0.01011 &  0.02021 &  0.9899 \tabularnewline
68 &  0.008109 &  0.01622 &  0.9919 \tabularnewline
69 &  0.007479 &  0.01496 &  0.9925 \tabularnewline
70 &  0.00603 &  0.01206 &  0.994 \tabularnewline
71 &  0.004658 &  0.009316 &  0.9953 \tabularnewline
72 &  0.004239 &  0.008477 &  0.9958 \tabularnewline
73 &  0.003387 &  0.006775 &  0.9966 \tabularnewline
74 &  0.002637 &  0.005274 &  0.9974 \tabularnewline
75 &  0.002344 &  0.004687 &  0.9977 \tabularnewline
76 &  0.001697 &  0.003394 &  0.9983 \tabularnewline
77 &  0.001547 &  0.003094 &  0.9985 \tabularnewline
78 &  0.001121 &  0.002242 &  0.9989 \tabularnewline
79 &  0.0008809 &  0.001762 &  0.9991 \tabularnewline
80 &  0.0009527 &  0.001905 &  0.999 \tabularnewline
81 &  0.001156 &  0.002312 &  0.9988 \tabularnewline
82 &  0.0008918 &  0.001784 &  0.9991 \tabularnewline
83 &  0.0006954 &  0.001391 &  0.9993 \tabularnewline
84 &  0.002063 &  0.004127 &  0.9979 \tabularnewline
85 &  0.001485 &  0.00297 &  0.9985 \tabularnewline
86 &  0.001458 &  0.002915 &  0.9985 \tabularnewline
87 &  0.00818 &  0.01636 &  0.9918 \tabularnewline
88 &  0.0327 &  0.0654 &  0.9673 \tabularnewline
89 &  0.0357 &  0.07141 &  0.9643 \tabularnewline
90 &  0.05397 &  0.1079 &  0.946 \tabularnewline
91 &  0.04404 &  0.08808 &  0.956 \tabularnewline
92 &  0.03523 &  0.07046 &  0.9648 \tabularnewline
93 &  0.05872 &  0.1174 &  0.9413 \tabularnewline
94 &  0.05045 &  0.1009 &  0.9495 \tabularnewline
95 &  0.08198 &  0.164 &  0.918 \tabularnewline
96 &  0.09415 &  0.1883 &  0.9059 \tabularnewline
97 &  0.08416 &  0.1683 &  0.9158 \tabularnewline
98 &  0.06898 &  0.138 &  0.931 \tabularnewline
99 &  0.06982 &  0.1396 &  0.9302 \tabularnewline
100 &  0.116 &  0.232 &  0.884 \tabularnewline
101 &  0.1027 &  0.2053 &  0.8973 \tabularnewline
102 &  0.1109 &  0.2218 &  0.8891 \tabularnewline
103 &  0.09697 &  0.1939 &  0.903 \tabularnewline
104 &  0.07983 &  0.1597 &  0.9202 \tabularnewline
105 &  0.06517 &  0.1303 &  0.9348 \tabularnewline
106 &  0.05391 &  0.1078 &  0.9461 \tabularnewline
107 &  0.06919 &  0.1384 &  0.9308 \tabularnewline
108 &  0.05697 &  0.1139 &  0.943 \tabularnewline
109 &  0.102 &  0.204 &  0.898 \tabularnewline
110 &  0.1411 &  0.2822 &  0.8589 \tabularnewline
111 &  0.1257 &  0.2514 &  0.8743 \tabularnewline
112 &  0.1256 &  0.2513 &  0.8744 \tabularnewline
113 &  0.1102 &  0.2203 &  0.8898 \tabularnewline
114 &  0.1049 &  0.2098 &  0.8951 \tabularnewline
115 &  0.09342 &  0.1868 &  0.9066 \tabularnewline
116 &  0.1 &  0.2 &  0.9 \tabularnewline
117 &  0.08667 &  0.1733 &  0.9133 \tabularnewline
118 &  0.07075 &  0.1415 &  0.9292 \tabularnewline
119 &  0.06019 &  0.1204 &  0.9398 \tabularnewline
120 &  0.0489 &  0.09781 &  0.9511 \tabularnewline
121 &  0.04182 &  0.08364 &  0.9582 \tabularnewline
122 &  0.0333 &  0.06661 &  0.9667 \tabularnewline
123 &  0.03641 &  0.07281 &  0.9636 \tabularnewline
124 &  0.03374 &  0.06748 &  0.9663 \tabularnewline
125 &  0.05172 &  0.1034 &  0.9483 \tabularnewline
126 &  0.07082 &  0.1416 &  0.9292 \tabularnewline
127 &  0.0581 &  0.1162 &  0.9419 \tabularnewline
128 &  0.05099 &  0.102 &  0.949 \tabularnewline
129 &  0.05112 &  0.1022 &  0.9489 \tabularnewline
130 &  0.05166 &  0.1033 &  0.9483 \tabularnewline
131 &  0.04063 &  0.08127 &  0.9594 \tabularnewline
132 &  0.03279 &  0.06557 &  0.9672 \tabularnewline
133 &  0.03457 &  0.06914 &  0.9654 \tabularnewline
134 &  0.13 &  0.2599 &  0.87 \tabularnewline
135 &  0.1096 &  0.2192 &  0.8904 \tabularnewline
136 &  0.106 &  0.212 &  0.894 \tabularnewline
137 &  0.108 &  0.2161 &  0.892 \tabularnewline
138 &  0.08818 &  0.1764 &  0.9118 \tabularnewline
139 &  0.08028 &  0.1606 &  0.9197 \tabularnewline
140 &  0.2745 &  0.549 &  0.7255 \tabularnewline
141 &  0.2345 &  0.469 &  0.7655 \tabularnewline
142 &  0.2165 &  0.433 &  0.7835 \tabularnewline
143 &  0.1831 &  0.3661 &  0.8169 \tabularnewline
144 &  0.1547 &  0.3093 &  0.8453 \tabularnewline
145 &  0.1643 &  0.3286 &  0.8357 \tabularnewline
146 &  0.1411 &  0.2821 &  0.8589 \tabularnewline
147 &  0.1367 &  0.2735 &  0.8633 \tabularnewline
148 &  0.1732 &  0.3463 &  0.8268 \tabularnewline
149 &  0.1431 &  0.2861 &  0.8569 \tabularnewline
150 &  0.1393 &  0.2786 &  0.8607 \tabularnewline
151 &  0.1918 &  0.3837 &  0.8082 \tabularnewline
152 &  0.1674 &  0.3349 &  0.8326 \tabularnewline
153 &  0.2001 &  0.4001 &  0.7999 \tabularnewline
154 &  0.1645 &  0.3291 &  0.8355 \tabularnewline
155 &  0.1551 &  0.3102 &  0.8449 \tabularnewline
156 &  0.2245 &  0.4489 &  0.7755 \tabularnewline
157 &  0.2596 &  0.5192 &  0.7404 \tabularnewline
158 &  0.212 &  0.4241 &  0.788 \tabularnewline
159 &  0.2426 &  0.4852 &  0.7574 \tabularnewline
160 &  0.1991 &  0.3982 &  0.8009 \tabularnewline
161 &  0.1574 &  0.3148 &  0.8426 \tabularnewline
162 &  0.1409 &  0.2817 &  0.8591 \tabularnewline
163 &  0.1127 &  0.2253 &  0.8873 \tabularnewline
164 &  0.1161 &  0.2321 &  0.8839 \tabularnewline
165 &  0.09573 &  0.1915 &  0.9043 \tabularnewline
166 &  0.07065 &  0.1413 &  0.9293 \tabularnewline
167 &  0.04969 &  0.09938 &  0.9503 \tabularnewline
168 &  0.03788 &  0.07575 &  0.9621 \tabularnewline
169 &  0.02532 &  0.05065 &  0.9747 \tabularnewline
170 &  0.01662 &  0.03325 &  0.9834 \tabularnewline
171 &  0.02361 &  0.04723 &  0.9764 \tabularnewline
172 &  0.03899 &  0.07798 &  0.961 \tabularnewline
173 &  0.02343 &  0.04686 &  0.9766 \tabularnewline
174 &  0.01651 &  0.03303 &  0.9835 \tabularnewline
175 &  0.01296 &  0.02592 &  0.987 \tabularnewline
176 &  0.008018 &  0.01604 &  0.992 \tabularnewline
177 &  0.003553 &  0.007105 &  0.9964 \tabularnewline
178 &  0.2154 &  0.4309 &  0.7846 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309897&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]8[/C][C] 0.3939[/C][C] 0.7878[/C][C] 0.6061[/C][/ROW]
[ROW][C]9[/C][C] 0.2498[/C][C] 0.4997[/C][C] 0.7502[/C][/ROW]
[ROW][C]10[/C][C] 0.669[/C][C] 0.6619[/C][C] 0.331[/C][/ROW]
[ROW][C]11[/C][C] 0.5666[/C][C] 0.8668[/C][C] 0.4334[/C][/ROW]
[ROW][C]12[/C][C] 0.4488[/C][C] 0.8976[/C][C] 0.5512[/C][/ROW]
[ROW][C]13[/C][C] 0.3508[/C][C] 0.7016[/C][C] 0.6492[/C][/ROW]
[ROW][C]14[/C][C] 0.2658[/C][C] 0.5317[/C][C] 0.7341[/C][/ROW]
[ROW][C]15[/C][C] 0.2182[/C][C] 0.4365[/C][C] 0.7818[/C][/ROW]
[ROW][C]16[/C][C] 0.153[/C][C] 0.306[/C][C] 0.847[/C][/ROW]
[ROW][C]17[/C][C] 0.1082[/C][C] 0.2164[/C][C] 0.8918[/C][/ROW]
[ROW][C]18[/C][C] 0.08251[/C][C] 0.165[/C][C] 0.9175[/C][/ROW]
[ROW][C]19[/C][C] 0.105[/C][C] 0.21[/C][C] 0.895[/C][/ROW]
[ROW][C]20[/C][C] 0.09392[/C][C] 0.1878[/C][C] 0.9061[/C][/ROW]
[ROW][C]21[/C][C] 0.08083[/C][C] 0.1617[/C][C] 0.9192[/C][/ROW]
[ROW][C]22[/C][C] 0.4279[/C][C] 0.8557[/C][C] 0.5721[/C][/ROW]
[ROW][C]23[/C][C] 0.5428[/C][C] 0.9144[/C][C] 0.4572[/C][/ROW]
[ROW][C]24[/C][C] 0.4822[/C][C] 0.9644[/C][C] 0.5178[/C][/ROW]
[ROW][C]25[/C][C] 0.4878[/C][C] 0.9756[/C][C] 0.5122[/C][/ROW]
[ROW][C]26[/C][C] 0.4339[/C][C] 0.8679[/C][C] 0.5661[/C][/ROW]
[ROW][C]27[/C][C] 0.4869[/C][C] 0.9738[/C][C] 0.5131[/C][/ROW]
[ROW][C]28[/C][C] 0.458[/C][C] 0.916[/C][C] 0.542[/C][/ROW]
[ROW][C]29[/C][C] 0.3998[/C][C] 0.7995[/C][C] 0.6002[/C][/ROW]
[ROW][C]30[/C][C] 0.3417[/C][C] 0.6834[/C][C] 0.6583[/C][/ROW]
[ROW][C]31[/C][C] 0.3122[/C][C] 0.6244[/C][C] 0.6878[/C][/ROW]
[ROW][C]32[/C][C] 0.2613[/C][C] 0.5226[/C][C] 0.7387[/C][/ROW]
[ROW][C]33[/C][C] 0.216[/C][C] 0.432[/C][C] 0.784[/C][/ROW]
[ROW][C]34[/C][C] 0.183[/C][C] 0.366[/C][C] 0.817[/C][/ROW]
[ROW][C]35[/C][C] 0.1533[/C][C] 0.3065[/C][C] 0.8467[/C][/ROW]
[ROW][C]36[/C][C] 0.1886[/C][C] 0.3771[/C][C] 0.8114[/C][/ROW]
[ROW][C]37[/C][C] 0.1721[/C][C] 0.3441[/C][C] 0.8279[/C][/ROW]
[ROW][C]38[/C][C] 0.1392[/C][C] 0.2783[/C][C] 0.8608[/C][/ROW]
[ROW][C]39[/C][C] 0.1112[/C][C] 0.2225[/C][C] 0.8888[/C][/ROW]
[ROW][C]40[/C][C] 0.0881[/C][C] 0.1762[/C][C] 0.9119[/C][/ROW]
[ROW][C]41[/C][C] 0.09295[/C][C] 0.1859[/C][C] 0.9071[/C][/ROW]
[ROW][C]42[/C][C] 0.09518[/C][C] 0.1904[/C][C] 0.9048[/C][/ROW]
[ROW][C]43[/C][C] 0.09017[/C][C] 0.1803[/C][C] 0.9098[/C][/ROW]
[ROW][C]44[/C][C] 0.07787[/C][C] 0.1557[/C][C] 0.9221[/C][/ROW]
[ROW][C]45[/C][C] 0.0637[/C][C] 0.1274[/C][C] 0.9363[/C][/ROW]
[ROW][C]46[/C][C] 0.04935[/C][C] 0.0987[/C][C] 0.9507[/C][/ROW]
[ROW][C]47[/C][C] 0.03972[/C][C] 0.07945[/C][C] 0.9603[/C][/ROW]
[ROW][C]48[/C][C] 0.03647[/C][C] 0.07293[/C][C] 0.9635[/C][/ROW]
[ROW][C]49[/C][C] 0.0289[/C][C] 0.05781[/C][C] 0.9711[/C][/ROW]
[ROW][C]50[/C][C] 0.05773[/C][C] 0.1155[/C][C] 0.9423[/C][/ROW]
[ROW][C]51[/C][C] 0.06881[/C][C] 0.1376[/C][C] 0.9312[/C][/ROW]
[ROW][C]52[/C][C] 0.05421[/C][C] 0.1084[/C][C] 0.9458[/C][/ROW]
[ROW][C]53[/C][C] 0.06721[/C][C] 0.1344[/C][C] 0.9328[/C][/ROW]
[ROW][C]54[/C][C] 0.05642[/C][C] 0.1128[/C][C] 0.9436[/C][/ROW]
[ROW][C]55[/C][C] 0.04481[/C][C] 0.08962[/C][C] 0.9552[/C][/ROW]
[ROW][C]56[/C][C] 0.04013[/C][C] 0.08026[/C][C] 0.9599[/C][/ROW]
[ROW][C]57[/C][C] 0.03081[/C][C] 0.06161[/C][C] 0.9692[/C][/ROW]
[ROW][C]58[/C][C] 0.03034[/C][C] 0.06067[/C][C] 0.9697[/C][/ROW]
[ROW][C]59[/C][C] 0.02573[/C][C] 0.05145[/C][C] 0.9743[/C][/ROW]
[ROW][C]60[/C][C] 0.01939[/C][C] 0.03878[/C][C] 0.9806[/C][/ROW]
[ROW][C]61[/C][C] 0.01531[/C][C] 0.03062[/C][C] 0.9847[/C][/ROW]
[ROW][C]62[/C][C] 0.01304[/C][C] 0.02609[/C][C] 0.987[/C][/ROW]
[ROW][C]63[/C][C] 0.009862[/C][C] 0.01972[/C][C] 0.9901[/C][/ROW]
[ROW][C]64[/C][C] 0.00727[/C][C] 0.01454[/C][C] 0.9927[/C][/ROW]
[ROW][C]65[/C][C] 0.009147[/C][C] 0.01829[/C][C] 0.9909[/C][/ROW]
[ROW][C]66[/C][C] 0.007046[/C][C] 0.01409[/C][C] 0.993[/C][/ROW]
[ROW][C]67[/C][C] 0.01011[/C][C] 0.02021[/C][C] 0.9899[/C][/ROW]
[ROW][C]68[/C][C] 0.008109[/C][C] 0.01622[/C][C] 0.9919[/C][/ROW]
[ROW][C]69[/C][C] 0.007479[/C][C] 0.01496[/C][C] 0.9925[/C][/ROW]
[ROW][C]70[/C][C] 0.00603[/C][C] 0.01206[/C][C] 0.994[/C][/ROW]
[ROW][C]71[/C][C] 0.004658[/C][C] 0.009316[/C][C] 0.9953[/C][/ROW]
[ROW][C]72[/C][C] 0.004239[/C][C] 0.008477[/C][C] 0.9958[/C][/ROW]
[ROW][C]73[/C][C] 0.003387[/C][C] 0.006775[/C][C] 0.9966[/C][/ROW]
[ROW][C]74[/C][C] 0.002637[/C][C] 0.005274[/C][C] 0.9974[/C][/ROW]
[ROW][C]75[/C][C] 0.002344[/C][C] 0.004687[/C][C] 0.9977[/C][/ROW]
[ROW][C]76[/C][C] 0.001697[/C][C] 0.003394[/C][C] 0.9983[/C][/ROW]
[ROW][C]77[/C][C] 0.001547[/C][C] 0.003094[/C][C] 0.9985[/C][/ROW]
[ROW][C]78[/C][C] 0.001121[/C][C] 0.002242[/C][C] 0.9989[/C][/ROW]
[ROW][C]79[/C][C] 0.0008809[/C][C] 0.001762[/C][C] 0.9991[/C][/ROW]
[ROW][C]80[/C][C] 0.0009527[/C][C] 0.001905[/C][C] 0.999[/C][/ROW]
[ROW][C]81[/C][C] 0.001156[/C][C] 0.002312[/C][C] 0.9988[/C][/ROW]
[ROW][C]82[/C][C] 0.0008918[/C][C] 0.001784[/C][C] 0.9991[/C][/ROW]
[ROW][C]83[/C][C] 0.0006954[/C][C] 0.001391[/C][C] 0.9993[/C][/ROW]
[ROW][C]84[/C][C] 0.002063[/C][C] 0.004127[/C][C] 0.9979[/C][/ROW]
[ROW][C]85[/C][C] 0.001485[/C][C] 0.00297[/C][C] 0.9985[/C][/ROW]
[ROW][C]86[/C][C] 0.001458[/C][C] 0.002915[/C][C] 0.9985[/C][/ROW]
[ROW][C]87[/C][C] 0.00818[/C][C] 0.01636[/C][C] 0.9918[/C][/ROW]
[ROW][C]88[/C][C] 0.0327[/C][C] 0.0654[/C][C] 0.9673[/C][/ROW]
[ROW][C]89[/C][C] 0.0357[/C][C] 0.07141[/C][C] 0.9643[/C][/ROW]
[ROW][C]90[/C][C] 0.05397[/C][C] 0.1079[/C][C] 0.946[/C][/ROW]
[ROW][C]91[/C][C] 0.04404[/C][C] 0.08808[/C][C] 0.956[/C][/ROW]
[ROW][C]92[/C][C] 0.03523[/C][C] 0.07046[/C][C] 0.9648[/C][/ROW]
[ROW][C]93[/C][C] 0.05872[/C][C] 0.1174[/C][C] 0.9413[/C][/ROW]
[ROW][C]94[/C][C] 0.05045[/C][C] 0.1009[/C][C] 0.9495[/C][/ROW]
[ROW][C]95[/C][C] 0.08198[/C][C] 0.164[/C][C] 0.918[/C][/ROW]
[ROW][C]96[/C][C] 0.09415[/C][C] 0.1883[/C][C] 0.9059[/C][/ROW]
[ROW][C]97[/C][C] 0.08416[/C][C] 0.1683[/C][C] 0.9158[/C][/ROW]
[ROW][C]98[/C][C] 0.06898[/C][C] 0.138[/C][C] 0.931[/C][/ROW]
[ROW][C]99[/C][C] 0.06982[/C][C] 0.1396[/C][C] 0.9302[/C][/ROW]
[ROW][C]100[/C][C] 0.116[/C][C] 0.232[/C][C] 0.884[/C][/ROW]
[ROW][C]101[/C][C] 0.1027[/C][C] 0.2053[/C][C] 0.8973[/C][/ROW]
[ROW][C]102[/C][C] 0.1109[/C][C] 0.2218[/C][C] 0.8891[/C][/ROW]
[ROW][C]103[/C][C] 0.09697[/C][C] 0.1939[/C][C] 0.903[/C][/ROW]
[ROW][C]104[/C][C] 0.07983[/C][C] 0.1597[/C][C] 0.9202[/C][/ROW]
[ROW][C]105[/C][C] 0.06517[/C][C] 0.1303[/C][C] 0.9348[/C][/ROW]
[ROW][C]106[/C][C] 0.05391[/C][C] 0.1078[/C][C] 0.9461[/C][/ROW]
[ROW][C]107[/C][C] 0.06919[/C][C] 0.1384[/C][C] 0.9308[/C][/ROW]
[ROW][C]108[/C][C] 0.05697[/C][C] 0.1139[/C][C] 0.943[/C][/ROW]
[ROW][C]109[/C][C] 0.102[/C][C] 0.204[/C][C] 0.898[/C][/ROW]
[ROW][C]110[/C][C] 0.1411[/C][C] 0.2822[/C][C] 0.8589[/C][/ROW]
[ROW][C]111[/C][C] 0.1257[/C][C] 0.2514[/C][C] 0.8743[/C][/ROW]
[ROW][C]112[/C][C] 0.1256[/C][C] 0.2513[/C][C] 0.8744[/C][/ROW]
[ROW][C]113[/C][C] 0.1102[/C][C] 0.2203[/C][C] 0.8898[/C][/ROW]
[ROW][C]114[/C][C] 0.1049[/C][C] 0.2098[/C][C] 0.8951[/C][/ROW]
[ROW][C]115[/C][C] 0.09342[/C][C] 0.1868[/C][C] 0.9066[/C][/ROW]
[ROW][C]116[/C][C] 0.1[/C][C] 0.2[/C][C] 0.9[/C][/ROW]
[ROW][C]117[/C][C] 0.08667[/C][C] 0.1733[/C][C] 0.9133[/C][/ROW]
[ROW][C]118[/C][C] 0.07075[/C][C] 0.1415[/C][C] 0.9292[/C][/ROW]
[ROW][C]119[/C][C] 0.06019[/C][C] 0.1204[/C][C] 0.9398[/C][/ROW]
[ROW][C]120[/C][C] 0.0489[/C][C] 0.09781[/C][C] 0.9511[/C][/ROW]
[ROW][C]121[/C][C] 0.04182[/C][C] 0.08364[/C][C] 0.9582[/C][/ROW]
[ROW][C]122[/C][C] 0.0333[/C][C] 0.06661[/C][C] 0.9667[/C][/ROW]
[ROW][C]123[/C][C] 0.03641[/C][C] 0.07281[/C][C] 0.9636[/C][/ROW]
[ROW][C]124[/C][C] 0.03374[/C][C] 0.06748[/C][C] 0.9663[/C][/ROW]
[ROW][C]125[/C][C] 0.05172[/C][C] 0.1034[/C][C] 0.9483[/C][/ROW]
[ROW][C]126[/C][C] 0.07082[/C][C] 0.1416[/C][C] 0.9292[/C][/ROW]
[ROW][C]127[/C][C] 0.0581[/C][C] 0.1162[/C][C] 0.9419[/C][/ROW]
[ROW][C]128[/C][C] 0.05099[/C][C] 0.102[/C][C] 0.949[/C][/ROW]
[ROW][C]129[/C][C] 0.05112[/C][C] 0.1022[/C][C] 0.9489[/C][/ROW]
[ROW][C]130[/C][C] 0.05166[/C][C] 0.1033[/C][C] 0.9483[/C][/ROW]
[ROW][C]131[/C][C] 0.04063[/C][C] 0.08127[/C][C] 0.9594[/C][/ROW]
[ROW][C]132[/C][C] 0.03279[/C][C] 0.06557[/C][C] 0.9672[/C][/ROW]
[ROW][C]133[/C][C] 0.03457[/C][C] 0.06914[/C][C] 0.9654[/C][/ROW]
[ROW][C]134[/C][C] 0.13[/C][C] 0.2599[/C][C] 0.87[/C][/ROW]
[ROW][C]135[/C][C] 0.1096[/C][C] 0.2192[/C][C] 0.8904[/C][/ROW]
[ROW][C]136[/C][C] 0.106[/C][C] 0.212[/C][C] 0.894[/C][/ROW]
[ROW][C]137[/C][C] 0.108[/C][C] 0.2161[/C][C] 0.892[/C][/ROW]
[ROW][C]138[/C][C] 0.08818[/C][C] 0.1764[/C][C] 0.9118[/C][/ROW]
[ROW][C]139[/C][C] 0.08028[/C][C] 0.1606[/C][C] 0.9197[/C][/ROW]
[ROW][C]140[/C][C] 0.2745[/C][C] 0.549[/C][C] 0.7255[/C][/ROW]
[ROW][C]141[/C][C] 0.2345[/C][C] 0.469[/C][C] 0.7655[/C][/ROW]
[ROW][C]142[/C][C] 0.2165[/C][C] 0.433[/C][C] 0.7835[/C][/ROW]
[ROW][C]143[/C][C] 0.1831[/C][C] 0.3661[/C][C] 0.8169[/C][/ROW]
[ROW][C]144[/C][C] 0.1547[/C][C] 0.3093[/C][C] 0.8453[/C][/ROW]
[ROW][C]145[/C][C] 0.1643[/C][C] 0.3286[/C][C] 0.8357[/C][/ROW]
[ROW][C]146[/C][C] 0.1411[/C][C] 0.2821[/C][C] 0.8589[/C][/ROW]
[ROW][C]147[/C][C] 0.1367[/C][C] 0.2735[/C][C] 0.8633[/C][/ROW]
[ROW][C]148[/C][C] 0.1732[/C][C] 0.3463[/C][C] 0.8268[/C][/ROW]
[ROW][C]149[/C][C] 0.1431[/C][C] 0.2861[/C][C] 0.8569[/C][/ROW]
[ROW][C]150[/C][C] 0.1393[/C][C] 0.2786[/C][C] 0.8607[/C][/ROW]
[ROW][C]151[/C][C] 0.1918[/C][C] 0.3837[/C][C] 0.8082[/C][/ROW]
[ROW][C]152[/C][C] 0.1674[/C][C] 0.3349[/C][C] 0.8326[/C][/ROW]
[ROW][C]153[/C][C] 0.2001[/C][C] 0.4001[/C][C] 0.7999[/C][/ROW]
[ROW][C]154[/C][C] 0.1645[/C][C] 0.3291[/C][C] 0.8355[/C][/ROW]
[ROW][C]155[/C][C] 0.1551[/C][C] 0.3102[/C][C] 0.8449[/C][/ROW]
[ROW][C]156[/C][C] 0.2245[/C][C] 0.4489[/C][C] 0.7755[/C][/ROW]
[ROW][C]157[/C][C] 0.2596[/C][C] 0.5192[/C][C] 0.7404[/C][/ROW]
[ROW][C]158[/C][C] 0.212[/C][C] 0.4241[/C][C] 0.788[/C][/ROW]
[ROW][C]159[/C][C] 0.2426[/C][C] 0.4852[/C][C] 0.7574[/C][/ROW]
[ROW][C]160[/C][C] 0.1991[/C][C] 0.3982[/C][C] 0.8009[/C][/ROW]
[ROW][C]161[/C][C] 0.1574[/C][C] 0.3148[/C][C] 0.8426[/C][/ROW]
[ROW][C]162[/C][C] 0.1409[/C][C] 0.2817[/C][C] 0.8591[/C][/ROW]
[ROW][C]163[/C][C] 0.1127[/C][C] 0.2253[/C][C] 0.8873[/C][/ROW]
[ROW][C]164[/C][C] 0.1161[/C][C] 0.2321[/C][C] 0.8839[/C][/ROW]
[ROW][C]165[/C][C] 0.09573[/C][C] 0.1915[/C][C] 0.9043[/C][/ROW]
[ROW][C]166[/C][C] 0.07065[/C][C] 0.1413[/C][C] 0.9293[/C][/ROW]
[ROW][C]167[/C][C] 0.04969[/C][C] 0.09938[/C][C] 0.9503[/C][/ROW]
[ROW][C]168[/C][C] 0.03788[/C][C] 0.07575[/C][C] 0.9621[/C][/ROW]
[ROW][C]169[/C][C] 0.02532[/C][C] 0.05065[/C][C] 0.9747[/C][/ROW]
[ROW][C]170[/C][C] 0.01662[/C][C] 0.03325[/C][C] 0.9834[/C][/ROW]
[ROW][C]171[/C][C] 0.02361[/C][C] 0.04723[/C][C] 0.9764[/C][/ROW]
[ROW][C]172[/C][C] 0.03899[/C][C] 0.07798[/C][C] 0.961[/C][/ROW]
[ROW][C]173[/C][C] 0.02343[/C][C] 0.04686[/C][C] 0.9766[/C][/ROW]
[ROW][C]174[/C][C] 0.01651[/C][C] 0.03303[/C][C] 0.9835[/C][/ROW]
[ROW][C]175[/C][C] 0.01296[/C][C] 0.02592[/C][C] 0.987[/C][/ROW]
[ROW][C]176[/C][C] 0.008018[/C][C] 0.01604[/C][C] 0.992[/C][/ROW]
[ROW][C]177[/C][C] 0.003553[/C][C] 0.007105[/C][C] 0.9964[/C][/ROW]
[ROW][C]178[/C][C] 0.2154[/C][C] 0.4309[/C][C] 0.7846[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309897&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309897&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
8	0.3939	0.7878	0.6061
9	0.2498	0.4997	0.7502
10	0.669	0.6619	0.331
11	0.5666	0.8668	0.4334
12	0.4488	0.8976	0.5512
13	0.3508	0.7016	0.6492
14	0.2658	0.5317	0.7341
15	0.2182	0.4365	0.7818
16	0.153	0.306	0.847
17	0.1082	0.2164	0.8918
18	0.08251	0.165	0.9175
19	0.105	0.21	0.895
20	0.09392	0.1878	0.9061
21	0.08083	0.1617	0.9192
22	0.4279	0.8557	0.5721
23	0.5428	0.9144	0.4572
24	0.4822	0.9644	0.5178
25	0.4878	0.9756	0.5122
26	0.4339	0.8679	0.5661
27	0.4869	0.9738	0.5131
28	0.458	0.916	0.542
29	0.3998	0.7995	0.6002
30	0.3417	0.6834	0.6583
31	0.3122	0.6244	0.6878
32	0.2613	0.5226	0.7387
33	0.216	0.432	0.784
34	0.183	0.366	0.817
35	0.1533	0.3065	0.8467
36	0.1886	0.3771	0.8114
37	0.1721	0.3441	0.8279
38	0.1392	0.2783	0.8608
39	0.1112	0.2225	0.8888
40	0.0881	0.1762	0.9119
41	0.09295	0.1859	0.9071
42	0.09518	0.1904	0.9048
43	0.09017	0.1803	0.9098
44	0.07787	0.1557	0.9221
45	0.0637	0.1274	0.9363
46	0.04935	0.0987	0.9507
47	0.03972	0.07945	0.9603
48	0.03647	0.07293	0.9635
49	0.0289	0.05781	0.9711
50	0.05773	0.1155	0.9423
51	0.06881	0.1376	0.9312
52	0.05421	0.1084	0.9458
53	0.06721	0.1344	0.9328
54	0.05642	0.1128	0.9436
55	0.04481	0.08962	0.9552
56	0.04013	0.08026	0.9599
57	0.03081	0.06161	0.9692
58	0.03034	0.06067	0.9697
59	0.02573	0.05145	0.9743
60	0.01939	0.03878	0.9806
61	0.01531	0.03062	0.9847
62	0.01304	0.02609	0.987
63	0.009862	0.01972	0.9901
64	0.00727	0.01454	0.9927
65	0.009147	0.01829	0.9909
66	0.007046	0.01409	0.993
67	0.01011	0.02021	0.9899
68	0.008109	0.01622	0.9919
69	0.007479	0.01496	0.9925
70	0.00603	0.01206	0.994
71	0.004658	0.009316	0.9953
72	0.004239	0.008477	0.9958
73	0.003387	0.006775	0.9966
74	0.002637	0.005274	0.9974
75	0.002344	0.004687	0.9977
76	0.001697	0.003394	0.9983
77	0.001547	0.003094	0.9985
78	0.001121	0.002242	0.9989
79	0.0008809	0.001762	0.9991
80	0.0009527	0.001905	0.999
81	0.001156	0.002312	0.9988
82	0.0008918	0.001784	0.9991
83	0.0006954	0.001391	0.9993
84	0.002063	0.004127	0.9979
85	0.001485	0.00297	0.9985
86	0.001458	0.002915	0.9985
87	0.00818	0.01636	0.9918
88	0.0327	0.0654	0.9673
89	0.0357	0.07141	0.9643
90	0.05397	0.1079	0.946
91	0.04404	0.08808	0.956
92	0.03523	0.07046	0.9648
93	0.05872	0.1174	0.9413
94	0.05045	0.1009	0.9495
95	0.08198	0.164	0.918
96	0.09415	0.1883	0.9059
97	0.08416	0.1683	0.9158
98	0.06898	0.138	0.931
99	0.06982	0.1396	0.9302
100	0.116	0.232	0.884
101	0.1027	0.2053	0.8973
102	0.1109	0.2218	0.8891
103	0.09697	0.1939	0.903
104	0.07983	0.1597	0.9202
105	0.06517	0.1303	0.9348
106	0.05391	0.1078	0.9461
107	0.06919	0.1384	0.9308
108	0.05697	0.1139	0.943
109	0.102	0.204	0.898
110	0.1411	0.2822	0.8589
111	0.1257	0.2514	0.8743
112	0.1256	0.2513	0.8744
113	0.1102	0.2203	0.8898
114	0.1049	0.2098	0.8951
115	0.09342	0.1868	0.9066
116	0.1	0.2	0.9
117	0.08667	0.1733	0.9133
118	0.07075	0.1415	0.9292
119	0.06019	0.1204	0.9398
120	0.0489	0.09781	0.9511
121	0.04182	0.08364	0.9582
122	0.0333	0.06661	0.9667
123	0.03641	0.07281	0.9636
124	0.03374	0.06748	0.9663
125	0.05172	0.1034	0.9483
126	0.07082	0.1416	0.9292
127	0.0581	0.1162	0.9419
128	0.05099	0.102	0.949
129	0.05112	0.1022	0.9489
130	0.05166	0.1033	0.9483
131	0.04063	0.08127	0.9594
132	0.03279	0.06557	0.9672
133	0.03457	0.06914	0.9654
134	0.13	0.2599	0.87
135	0.1096	0.2192	0.8904
136	0.106	0.212	0.894
137	0.108	0.2161	0.892
138	0.08818	0.1764	0.9118
139	0.08028	0.1606	0.9197
140	0.2745	0.549	0.7255
141	0.2345	0.469	0.7655
142	0.2165	0.433	0.7835
143	0.1831	0.3661	0.8169
144	0.1547	0.3093	0.8453
145	0.1643	0.3286	0.8357
146	0.1411	0.2821	0.8589
147	0.1367	0.2735	0.8633
148	0.1732	0.3463	0.8268
149	0.1431	0.2861	0.8569
150	0.1393	0.2786	0.8607
151	0.1918	0.3837	0.8082
152	0.1674	0.3349	0.8326
153	0.2001	0.4001	0.7999
154	0.1645	0.3291	0.8355
155	0.1551	0.3102	0.8449
156	0.2245	0.4489	0.7755
157	0.2596	0.5192	0.7404
158	0.212	0.4241	0.788
159	0.2426	0.4852	0.7574
160	0.1991	0.3982	0.8009
161	0.1574	0.3148	0.8426
162	0.1409	0.2817	0.8591
163	0.1127	0.2253	0.8873
164	0.1161	0.2321	0.8839
165	0.09573	0.1915	0.9043
166	0.07065	0.1413	0.9293
167	0.04969	0.09938	0.9503
168	0.03788	0.07575	0.9621
169	0.02532	0.05065	0.9747
170	0.01662	0.03325	0.9834
171	0.02361	0.04723	0.9764
172	0.03899	0.07798	0.961
173	0.02343	0.04686	0.9766
174	0.01651	0.03303	0.9835
175	0.01296	0.02592	0.987
176	0.008018	0.01604	0.992
177	0.003553	0.007105	0.9964
178	0.2154	0.4309	0.7846

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	17	0.09942	NOK
5% type I error level	35	0.204678	NOK
10% type I error level	60	0.350877	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 & 17 &  0.09942 & NOK \tabularnewline
5% type I error level & 35 & 0.204678 & NOK \tabularnewline
10% type I error level & 60 & 0.350877 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309897&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]17[/C][C] 0.09942[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]35[/C][C]0.204678[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]60[/C][C]0.350877[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309897&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309897&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	17	0.09942	NOK
5% type I error level	35	0.204678	NOK
10% type I error level	60	0.350877	NOK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 0.83435, df1 = 2, df2 = 179, p-value = 0.4358
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.9484, df1 = 8, df2 = 173, p-value = 0.05574
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.72057, df1 = 2, df2 = 179, p-value = 0.4879

\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 = 0.83435, df1 = 2, df2 = 179, p-value = 0.4358
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.9484, df1 = 8, df2 = 173, p-value = 0.05574
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.72057, df1 = 2, df2 = 179, p-value = 0.4879
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309897&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 = 0.83435, df1 = 2, df2 = 179, p-value = 0.4358
[/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 = 1.9484, df1 = 8, df2 = 173, p-value = 0.05574
[/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 = 0.72057, df1 = 2, df2 = 179, p-value = 0.4879
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309897&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309897&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 = 0.83435, df1 = 2, df2 = 179, p-value = 0.4358
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.9484, df1 = 8, df2 = 173, p-value = 0.05574
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.72057, df1 = 2, df2 = 179, p-value = 0.4879

Variance Inflation Factors (Multicollinearity)
> vif `(1-Bs)(1-B)Foo` `(1-Bs)(1-B)Tob` `(1-Bs)(1-B)All(t-1)` 1.476828 1.404577 1.232700 `(1-Bs)(1-B)All(t-1s)` 1.076823

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
      `(1-Bs)(1-B)Foo`       `(1-Bs)(1-B)Tob`  `(1-Bs)(1-B)All(t-1)` 
              1.476828               1.404577               1.232700 
`(1-Bs)(1-B)All(t-1s)` 
              1.076823 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309897&T=9

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
      `(1-Bs)(1-B)Foo`       `(1-Bs)(1-B)Tob`  `(1-Bs)(1-B)All(t-1)` 
              1.476828               1.404577               1.232700 
`(1-Bs)(1-B)All(t-1s)` 
              1.076823 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309897&T=9

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309897&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)Foo` `(1-Bs)(1-B)Tob` `(1-Bs)(1-B)All(t-1)` 1.476828 1.404577 1.232700 `(1-Bs)(1-B)All(t-1s)` 1.076823

Figure 1

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Figure 4

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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Figure 10

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Parameters (Session):

par1 = 1 ; par2 = 2 ; par3 = TRUE ;

Parameters (R input):

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