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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Thu, 21 Dec 2017 19:05:58 +0100

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2017/Dec/21/t1513879589xd7pymtvzn8ewws.htm/, Retrieved Tue, 14 May 2024 08:54:36 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=310685, Retrieved Tue, 14 May 2024 08:54:36 +0000

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Estimated Impact

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

-       [Multiple Regression] [Multiple regressi...] [2017-12-21 18:05:58] [e148a3deb2f75bb475cf14ca194361cd] [Current]

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Dataseries X:

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Histogram

Boxplots

57.7	63.2
60.1	68.6
66.5	77.7
63.4	68.1
71.4	75.1
68.5	73.3
61.6	60.5
68.3	65.9
69.3	77.7
76.1	77.1
73.3	77.7
69.7	71.3
67.4	76
63.7	75.3
73	81.7
67.5	72.5
74.4	77.4
72.9	81.1
71.7	65.1
75.6	68.7
72.5	75.6
80	79.7
75.4	75.3
71	67.7
70.6	73.2
67.5	72.2
74.1	79.3
73.2	77.5
74	75.6
73	77.4
74	69.2
73	67.1
76	77.9
81.7	82.7
73.5	75.7
77	70.1
73.6	76.4
70.4	74.3
74.7	80.5
76.8	78
72.7	73.5
76	78.8
77.5	71.2
73.6	66.2
78.5	82.7
84.3	83.8
74.4	75
78.5	80.4
72.7	74.6
71.3	77.7
84.4	89.8
79.1	82.4
76.2	77
84.9	89.6
77.1	75.7
78.7	75.1
84.7	89.9
83.7	88.8
82.5	86.5
85.2	90
76	84
72.2	82.7
83.2	91.7
80.2	87.5
81.1	82
86	92.2
76	73.1
83.9	75.6
87.9	91.6
85	87.5
88.1	90.1
87.4	91.3
79.5	87.6
75.2	88.4
87.3	100.7
79.5	85.3
87.6	92
89.1	96.8
83	77.9
88.3	80.9
88.9	95.3
93.9	99.3
91.7	96.1
87.2	92.5
87.8	93.7
81	92.1
93.7	103.6
87.5	92.5
91.4	95.7
93.8	103.4
89.5	89
93.3	89.1
92.8	98.7
104.1	109.4
99.9	101.1
93.4	95.4
99	101.4
93.2	102.1
95.7	103.6
102.6	106
98.8	98.4
98	106.6
101.5	95.8
94.9	87.2
104.7	108.5
108.4	107
97	92
102.3	94.9
90.8	84.4
89.6	85
99.9	94
99.2	84.5
94	88.2
103	92.1
99.8	81.1
94.9	81.2
102	96.1
103.2	95.3
98	92.1
101.1	91.7
88.2	90.3
90.3	96.1
105.5	108.7
99.4	95.9
94.3	95.1
105.9	109.4
98	91.2
99	91.4
103.9	107.4
104.3	105.6
105.7	105.3
105.5	103.7
97.4	99.5
95.4	103.2
110.5	123.1
102.8	102.2
110	110
104.3	106.2
96.5	91.3
105.6	99.3
111.3	111.8
108.5	104.4
109.1	102.4
107.7	101
102.3	100.6
102.4	104.5
110.8	117.4
101.7	97.4
108.9	99.5
111.5	106.4
104	95.2
109.9	94
106.8	104.1
118.4	105.8
111.8	101.1
105	93.5
104.9	97.9
96.5	96.8
106.3	108.4
105.6	103.5
109.3	101.3
105.1	107.4
111.5	100.7
103.1	91.1
106.5	105
114.4	112.8
104.7	105.6
105.5	101
100.5	101.9
96.4	103.5
105.1	109.5
108.4	105
105.7	102.9
109	108.5
107.2	96.9
101.6	88.4
112.7	112.4
115.9	111.3
105	101.6
110.4	101.2
100.9	101.8
98.5	98.8
111.3	114.4
109.6	104.5
103.4	97.6
115.7	109.1
110.4	94.5
105.2	90.4
113.2	111.8
117.4	110.5
112.3	106.8
113.9	101.8
102.2	103.7
106.9	107.4
118	117.5
113.8	109.6
114.9	102.8
118.8	115.5
106.3	97.8
114.2	100.2
117.3	112.9
114.7	108.7
117	109
116.6	113.9
106.5	106.9
105.7	109.6
121	124.5
107.8	104.2
119.7	110.8
121	118.7
108.8	102.1
115	105.1

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	11 seconds
R Server	Big Analytics Cloud Computing Center

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time11 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310685&T=0

[TABLE]
[ROW]

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

Raw Input[/C]

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

Raw Output[/C]

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

Computing time[/C]

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

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=310685&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310685&T=0

As an alternative you can also use a QR Code:

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

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	11 seconds
R Server	Big Analytics Cloud Computing Center

Multiple Linear Regression - Estimated Regression Equation
(1-Bs)(1-B)FBT[t] = -0.0238126 + 0.451149`(1-Bs)(1-B)TotIBMIN`[t] -0.617187`(1-Bs)(1-B)FBT(t-1)`[t] -0.476499`(1-Bs)(1-B)FBT(t-2)`[t] -0.103476`(1-Bs)(1-B)FBT(t-3)`[t] -0.0420682`(1-Bs)(1-B)FBT(t-1s)`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
(1-Bs)(1-B)FBT[t] =  -0.0238126 +  0.451149`(1-Bs)(1-B)TotIBMIN`[t] -0.617187`(1-Bs)(1-B)FBT(t-1)`[t] -0.476499`(1-Bs)(1-B)FBT(t-2)`[t] -0.103476`(1-Bs)(1-B)FBT(t-3)`[t] -0.0420682`(1-Bs)(1-B)FBT(t-1s)`[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310685&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C](1-Bs)(1-B)FBT[t] =  -0.0238126 +  0.451149`(1-Bs)(1-B)TotIBMIN`[t] -0.617187`(1-Bs)(1-B)FBT(t-1)`[t] -0.476499`(1-Bs)(1-B)FBT(t-2)`[t] -0.103476`(1-Bs)(1-B)FBT(t-3)`[t] -0.0420682`(1-Bs)(1-B)FBT(t-1s)`[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310685&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310685&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)FBT[t] = -0.0238126 + 0.451149`(1-Bs)(1-B)TotIBMIN`[t] -0.617187`(1-Bs)(1-B)FBT(t-1)`[t] -0.476499`(1-Bs)(1-B)FBT(t-2)`[t] -0.103476`(1-Bs)(1-B)FBT(t-3)`[t] -0.0420682`(1-Bs)(1-B)FBT(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)	-0.02381	0.2335	-1.0200e-01	0.9189	0.4594
`(1-Bs)(1-B)TotIBMIN`	+0.4511	0.04836	+9.3300e+00	4.222e-17	2.111e-17
`(1-Bs)(1-B)FBT(t-1)`	-0.6172	0.07132	-8.6540e+00	2.89e-15	1.445e-15
`(1-Bs)(1-B)FBT(t-2)`	-0.4765	0.07713	-6.1780e+00	4.304e-09	2.152e-09
`(1-Bs)(1-B)FBT(t-3)`	-0.1035	0.06173	-1.6760e+00	0.09545	0.04772
`(1-Bs)(1-B)FBT(t-1s)`	-0.04207	0.04397	-9.5670e-01	0.34	0.17

\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) & -0.02381 &  0.2335 & -1.0200e-01 &  0.9189 &  0.4594 \tabularnewline
`(1-Bs)(1-B)TotIBMIN` & +0.4511 &  0.04836 & +9.3300e+00 &  4.222e-17 &  2.111e-17 \tabularnewline
`(1-Bs)(1-B)FBT(t-1)` & -0.6172 &  0.07132 & -8.6540e+00 &  2.89e-15 &  1.445e-15 \tabularnewline
`(1-Bs)(1-B)FBT(t-2)` & -0.4765 &  0.07713 & -6.1780e+00 &  4.304e-09 &  2.152e-09 \tabularnewline
`(1-Bs)(1-B)FBT(t-3)` & -0.1035 &  0.06173 & -1.6760e+00 &  0.09545 &  0.04772 \tabularnewline
`(1-Bs)(1-B)FBT(t-1s)` & -0.04207 &  0.04397 & -9.5670e-01 &  0.34 &  0.17 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310685&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]-0.02381[/C][C] 0.2335[/C][C]-1.0200e-01[/C][C] 0.9189[/C][C] 0.4594[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)TotIBMIN`[/C][C]+0.4511[/C][C] 0.04836[/C][C]+9.3300e+00[/C][C] 4.222e-17[/C][C] 2.111e-17[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)FBT(t-1)`[/C][C]-0.6172[/C][C] 0.07132[/C][C]-8.6540e+00[/C][C] 2.89e-15[/C][C] 1.445e-15[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)FBT(t-2)`[/C][C]-0.4765[/C][C] 0.07713[/C][C]-6.1780e+00[/C][C] 4.304e-09[/C][C] 2.152e-09[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)FBT(t-3)`[/C][C]-0.1035[/C][C] 0.06173[/C][C]-1.6760e+00[/C][C] 0.09545[/C][C] 0.04772[/C][/ROW]
[ROW][C]`(1-Bs)(1-B)FBT(t-1s)`[/C][C]-0.04207[/C][C] 0.04397[/C][C]-9.5670e-01[/C][C] 0.34[/C][C] 0.17[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310685&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310685&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)	-0.02381	0.2335	-1.0200e-01	0.9189	0.4594
`(1-Bs)(1-B)TotIBMIN`	+0.4511	0.04836	+9.3300e+00	4.222e-17	2.111e-17
`(1-Bs)(1-B)FBT(t-1)`	-0.6172	0.07132	-8.6540e+00	2.89e-15	1.445e-15
`(1-Bs)(1-B)FBT(t-2)`	-0.4765	0.07713	-6.1780e+00	4.304e-09	2.152e-09
`(1-Bs)(1-B)FBT(t-3)`	-0.1035	0.06173	-1.6760e+00	0.09545	0.04772
`(1-Bs)(1-B)FBT(t-1s)`	-0.04207	0.04397	-9.5670e-01	0.34	0.17

Multiple Linear Regression - Regression Statistics
Multiple R	0.8412
R-squared	0.7077
Adjusted R-squared	0.6994
F-TEST (value)	86.17
F-TEST (DF numerator)	5
F-TEST (DF denominator)	178
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	3.167
Sum Squared Residuals	1786

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.8412 \tabularnewline
R-squared &  0.7077 \tabularnewline
Adjusted R-squared &  0.6994 \tabularnewline
F-TEST (value) &  86.17 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 178 \tabularnewline
p-value &  0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  3.167 \tabularnewline
Sum Squared Residuals &  1786 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310685&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.8412[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.7077[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.6994[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 86.17[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]178[/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] 3.167[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 1786[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310685&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310685&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	0.8412
R-squared	0.7077
Adjusted R-squared	0.6994
F-TEST (value)	86.17
F-TEST (DF numerator)	5
F-TEST (DF denominator)	178
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	3.167
Sum Squared Residuals	1786

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=310685&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=310685&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310685&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	-6.1	-4.66	-1.44
2	0.5	0.9124	-0.4124
3	2.2	5.377	-3.177
4	-4.9	-3.442	-1.458
5	6.1	3.832	2.268
6	-1.8	-1.395	-0.4049
7	-3.6	-2.41	-1.19
8	7.9	3.361	4.539
9	-3	-2.717	-0.2831
10	-0.1	-2.086	1.986
11	-2.3	0.3575	-2.657
12	3	1.244	1.756
13	-4.9	-1.685	-3.215
14	4.3	3.367	0.9331
15	0.5	-0.4752	0.9752
16	-2.9	-2.977	0.07652
17	1.9	3.398	-1.498
18	0.1	-1.46	1.56
19	-1.7	-1.351	-0.3486
20	0.6	5.411	-4.811
21	-2.4	-4.927	2.527
22	1.8	3.698	-1.898
23	8.8	2.705	6.095
24	-7.4	-8.401	1.001
25	1.2	-0.03598	1.236
26	5.4	4.964	0.4364
27	-9.3	-6.026	-3.274
28	5.5	5.126	0.3742
29	1.1	-0.3926	1.493
30	-6.8	-3.358	-3.442
31	8.7	6.084	2.616
32	-1.4	-3.149	1.749
33	-3.4	-2.591	-0.8091
34	-2.4	-0.2193	-2.181
35	-2.1	1.454	-3.554
36	2.3	4.523	-2.223
37	3.8	-0.2899	4.09
38	-3.8	-4.558	0.7577
39	-2.2	-1.682	-0.5181
40	6.3	3.919	2.381
41	-2	-1.975	-0.02452
42	-1.9	-2.631	0.7311
43	4.3	3.295	1.005
44	-3.4	-2.544	-0.8558
45	1.3	1.403	-0.103
46	-0.5	1.397	-1.897
47	1.1	1.594	-0.4943
48	-4.8	-5.749	0.9486
49	7.2	7.81	-0.6104
50	-3.4	-4.571	1.171
51	3.9	-0.6767	4.577
52	-2.6	-1.595	-1.005
53	-3.4	-0.5634	-2.837
54	7.9	6.644	1.256
55	-5.3	-5.808	0.508
56	-3.8	-2.188	-1.612
57	8.5	6.185	2.315
58	-2.5	-3.973	1.472
59	0.6	-2.545	3.145
60	1.6	2.059	-0.4594
61	-4.2	-2.92	-1.28
62	0.9	3.195	-2.295
63	1.8	3.123	-1.323
64	-1.5	-2.328	0.828
65	-1.1	-2.071	0.9713
66	6.3	3.874	2.426
67	-2	-5.311	3.311
68	-2	-2.465	0.4651
69	5	3.32	1.68
70	1	-0.807	1.807
71	-10.2	-7.353	-2.847
72	13.1	11.3	1.799
73	-7.7	-8.048	0.3479
74	-3.2	-0.2704	-2.93
75	7.8	5.813	1.987
76	-10.4	-6.378	-4.022
77	10.3	8.334	1.966
78	-7.6	-8.001	0.4014
79	-7.2	-2.104	-5.096
80	11.8	10.94	0.8605
81	-17.1	-10.74	-6.356
82	4.6	5.565	-0.9652
83	7.8	7.877	-0.07696
84	-7.6	-11.18	3.58
85	-1.4	5.896	-7.296
86	9.8	1.849	7.951
87	-6.7	-5.037	-1.663
88	1.7	3.949	-2.249
89	-2.7	-2.215	-0.4848
90	-2.5	2.161	-4.661
91	6.2	8.256	-2.056
92	-2.2	-4.365	2.165
93	-1.4	3.463	-4.863
94	3.3	3.399	-0.09946
95	4.9	0.1302	4.77
96	-5.4	-5.645	0.2447
97	0.1	-1.339	1.439
98	2.6	6.26	-3.66
99	-4.7	-4.084	-0.6162
100	5.9	1.601	4.299
101	-2.2	-1.085	-1.115
102	-0.8	-1.337	0.537
103	6.6	1.955	4.645
104	-3.3	-3.937	0.6372
105	4.8	-2.254	7.054
106	-4.1	-3.183	-0.917
107	-0.1	3.648	-3.748
108	-1.6	-1.932	0.3323
109	12.3	5.311	6.989
110	-17.3	-15.12	-2.182
111	0.1	6.645	-6.545
112	8.1	10.16	-2.056
113	0.8	-4.767	5.567
114	-3.2	-6.88	3.68
115	-0.8	-0.3128	-0.4872
116	-1.2	2.141	-3.341
117	2.7	2.942	-0.2416
118	2.1	-0.7729	2.873
119	-6.7	-5.636	-1.064
120	-1.4	3.305	-4.705
121	0	0.7265	-0.7265
122	8.3	6.892	1.408
123	0.3	-3.337	3.637
124	-3.2	-8.655	5.455
125	-8.8	-0.167	-8.633
126	14.4	11.14	3.259
127	-7.2	-5.571	-1.629
128	-5.4	-4.278	-1.122
129	5.3	7.302	-2.002
130	-8.5	-2.321	-6.179
131	1.4	2.951	-1.551
132	8.4	9.485	-1.085
133	-3.5	-6.936	3.436
134	-6.8	-2.721	-4.079
135	13.9	6.989	6.911
136	-14.3	-8.655	-5.645
137	6.5	4.967	1.533
138	-3.7	3.486	-7.186
139	-3.1	-0.1827	-2.917
140	7.6	4.561	3.039
141	-4.9	-4.656	-0.2436
142	4.3	1.275	3.025
143	-1.1	-3.715	2.615
144	4	-1.06	5.06
145	-6.4	-2.221	-4.179
146	7.5	2.195	5.306
147	-8.2	-4.812	-3.388
148	2.8	3.223	-0.4235
149	7.7	5.662	2.038
150	-4.7	-9.121	4.421
151	-1.2	-2.079	0.8793
152	4.6	3.735	0.8653
153	-4.5	-1.734	-2.766
154	1.7	-1.57	3.27
155	4.1	4.973	-0.8725
156	-5	-5.503	0.5032
157	-3.5	-0.9637	-2.536
158	9	6.441	2.559
159	-3.5	-4.402	0.9019
160	0.4	0.07728	0.3227
161	-3.1	-1.031	-2.069
162	1	2.169	-1.169
163	5.8	3.552	2.248
164	-3.8	-6.028	2.228
165	-2.2	0.2301	-2.43
166	7.1	5.496	1.604
167	-1.7	-5.618	3.918
168	-2.5	-1.017	-1.483
169	7.3	1.787	5.513
170	-8.4	-2.999	-5.401
171	-7.2	0.6895	-7.889
172	13.1	10.58	2.517
173	-4.9	-7.604	2.704
174	-6.8	-3.847	-2.953
175	7.4	6.713	0.687
176	-2	3.782	-5.782
177	1.6	-5.535	7.135
178	-5.5	-1.574	-3.926
179	4.2	5.052	-0.8523
180	-9	-5.65	-3.35
181	10.8	9.837	0.963
182	-2.6	-4.648	2.048
183	0.3	-1.835	2.135
184	-1.7	-0.368	-1.332

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & -6.1 & -4.66 & -1.44 \tabularnewline
2 &  0.5 &  0.9124 & -0.4124 \tabularnewline
3 &  2.2 &  5.377 & -3.177 \tabularnewline
4 & -4.9 & -3.442 & -1.458 \tabularnewline
5 &  6.1 &  3.832 &  2.268 \tabularnewline
6 & -1.8 & -1.395 & -0.4049 \tabularnewline
7 & -3.6 & -2.41 & -1.19 \tabularnewline
8 &  7.9 &  3.361 &  4.539 \tabularnewline
9 & -3 & -2.717 & -0.2831 \tabularnewline
10 & -0.1 & -2.086 &  1.986 \tabularnewline
11 & -2.3 &  0.3575 & -2.657 \tabularnewline
12 &  3 &  1.244 &  1.756 \tabularnewline
13 & -4.9 & -1.685 & -3.215 \tabularnewline
14 &  4.3 &  3.367 &  0.9331 \tabularnewline
15 &  0.5 & -0.4752 &  0.9752 \tabularnewline
16 & -2.9 & -2.977 &  0.07652 \tabularnewline
17 &  1.9 &  3.398 & -1.498 \tabularnewline
18 &  0.1 & -1.46 &  1.56 \tabularnewline
19 & -1.7 & -1.351 & -0.3486 \tabularnewline
20 &  0.6 &  5.411 & -4.811 \tabularnewline
21 & -2.4 & -4.927 &  2.527 \tabularnewline
22 &  1.8 &  3.698 & -1.898 \tabularnewline
23 &  8.8 &  2.705 &  6.095 \tabularnewline
24 & -7.4 & -8.401 &  1.001 \tabularnewline
25 &  1.2 & -0.03598 &  1.236 \tabularnewline
26 &  5.4 &  4.964 &  0.4364 \tabularnewline
27 & -9.3 & -6.026 & -3.274 \tabularnewline
28 &  5.5 &  5.126 &  0.3742 \tabularnewline
29 &  1.1 & -0.3926 &  1.493 \tabularnewline
30 & -6.8 & -3.358 & -3.442 \tabularnewline
31 &  8.7 &  6.084 &  2.616 \tabularnewline
32 & -1.4 & -3.149 &  1.749 \tabularnewline
33 & -3.4 & -2.591 & -0.8091 \tabularnewline
34 & -2.4 & -0.2193 & -2.181 \tabularnewline
35 & -2.1 &  1.454 & -3.554 \tabularnewline
36 &  2.3 &  4.523 & -2.223 \tabularnewline
37 &  3.8 & -0.2899 &  4.09 \tabularnewline
38 & -3.8 & -4.558 &  0.7577 \tabularnewline
39 & -2.2 & -1.682 & -0.5181 \tabularnewline
40 &  6.3 &  3.919 &  2.381 \tabularnewline
41 & -2 & -1.975 & -0.02452 \tabularnewline
42 & -1.9 & -2.631 &  0.7311 \tabularnewline
43 &  4.3 &  3.295 &  1.005 \tabularnewline
44 & -3.4 & -2.544 & -0.8558 \tabularnewline
45 &  1.3 &  1.403 & -0.103 \tabularnewline
46 & -0.5 &  1.397 & -1.897 \tabularnewline
47 &  1.1 &  1.594 & -0.4943 \tabularnewline
48 & -4.8 & -5.749 &  0.9486 \tabularnewline
49 &  7.2 &  7.81 & -0.6104 \tabularnewline
50 & -3.4 & -4.571 &  1.171 \tabularnewline
51 &  3.9 & -0.6767 &  4.577 \tabularnewline
52 & -2.6 & -1.595 & -1.005 \tabularnewline
53 & -3.4 & -0.5634 & -2.837 \tabularnewline
54 &  7.9 &  6.644 &  1.256 \tabularnewline
55 & -5.3 & -5.808 &  0.508 \tabularnewline
56 & -3.8 & -2.188 & -1.612 \tabularnewline
57 &  8.5 &  6.185 &  2.315 \tabularnewline
58 & -2.5 & -3.973 &  1.472 \tabularnewline
59 &  0.6 & -2.545 &  3.145 \tabularnewline
60 &  1.6 &  2.059 & -0.4594 \tabularnewline
61 & -4.2 & -2.92 & -1.28 \tabularnewline
62 &  0.9 &  3.195 & -2.295 \tabularnewline
63 &  1.8 &  3.123 & -1.323 \tabularnewline
64 & -1.5 & -2.328 &  0.828 \tabularnewline
65 & -1.1 & -2.071 &  0.9713 \tabularnewline
66 &  6.3 &  3.874 &  2.426 \tabularnewline
67 & -2 & -5.311 &  3.311 \tabularnewline
68 & -2 & -2.465 &  0.4651 \tabularnewline
69 &  5 &  3.32 &  1.68 \tabularnewline
70 &  1 & -0.807 &  1.807 \tabularnewline
71 & -10.2 & -7.353 & -2.847 \tabularnewline
72 &  13.1 &  11.3 &  1.799 \tabularnewline
73 & -7.7 & -8.048 &  0.3479 \tabularnewline
74 & -3.2 & -0.2704 & -2.93 \tabularnewline
75 &  7.8 &  5.813 &  1.987 \tabularnewline
76 & -10.4 & -6.378 & -4.022 \tabularnewline
77 &  10.3 &  8.334 &  1.966 \tabularnewline
78 & -7.6 & -8.001 &  0.4014 \tabularnewline
79 & -7.2 & -2.104 & -5.096 \tabularnewline
80 &  11.8 &  10.94 &  0.8605 \tabularnewline
81 & -17.1 & -10.74 & -6.356 \tabularnewline
82 &  4.6 &  5.565 & -0.9652 \tabularnewline
83 &  7.8 &  7.877 & -0.07696 \tabularnewline
84 & -7.6 & -11.18 &  3.58 \tabularnewline
85 & -1.4 &  5.896 & -7.296 \tabularnewline
86 &  9.8 &  1.849 &  7.951 \tabularnewline
87 & -6.7 & -5.037 & -1.663 \tabularnewline
88 &  1.7 &  3.949 & -2.249 \tabularnewline
89 & -2.7 & -2.215 & -0.4848 \tabularnewline
90 & -2.5 &  2.161 & -4.661 \tabularnewline
91 &  6.2 &  8.256 & -2.056 \tabularnewline
92 & -2.2 & -4.365 &  2.165 \tabularnewline
93 & -1.4 &  3.463 & -4.863 \tabularnewline
94 &  3.3 &  3.399 & -0.09946 \tabularnewline
95 &  4.9 &  0.1302 &  4.77 \tabularnewline
96 & -5.4 & -5.645 &  0.2447 \tabularnewline
97 &  0.1 & -1.339 &  1.439 \tabularnewline
98 &  2.6 &  6.26 & -3.66 \tabularnewline
99 & -4.7 & -4.084 & -0.6162 \tabularnewline
100 &  5.9 &  1.601 &  4.299 \tabularnewline
101 & -2.2 & -1.085 & -1.115 \tabularnewline
102 & -0.8 & -1.337 &  0.537 \tabularnewline
103 &  6.6 &  1.955 &  4.645 \tabularnewline
104 & -3.3 & -3.937 &  0.6372 \tabularnewline
105 &  4.8 & -2.254 &  7.054 \tabularnewline
106 & -4.1 & -3.183 & -0.917 \tabularnewline
107 & -0.1 &  3.648 & -3.748 \tabularnewline
108 & -1.6 & -1.932 &  0.3323 \tabularnewline
109 &  12.3 &  5.311 &  6.989 \tabularnewline
110 & -17.3 & -15.12 & -2.182 \tabularnewline
111 &  0.1 &  6.645 & -6.545 \tabularnewline
112 &  8.1 &  10.16 & -2.056 \tabularnewline
113 &  0.8 & -4.767 &  5.567 \tabularnewline
114 & -3.2 & -6.88 &  3.68 \tabularnewline
115 & -0.8 & -0.3128 & -0.4872 \tabularnewline
116 & -1.2 &  2.141 & -3.341 \tabularnewline
117 &  2.7 &  2.942 & -0.2416 \tabularnewline
118 &  2.1 & -0.7729 &  2.873 \tabularnewline
119 & -6.7 & -5.636 & -1.064 \tabularnewline
120 & -1.4 &  3.305 & -4.705 \tabularnewline
121 &  0 &  0.7265 & -0.7265 \tabularnewline
122 &  8.3 &  6.892 &  1.408 \tabularnewline
123 &  0.3 & -3.337 &  3.637 \tabularnewline
124 & -3.2 & -8.655 &  5.455 \tabularnewline
125 & -8.8 & -0.167 & -8.633 \tabularnewline
126 &  14.4 &  11.14 &  3.259 \tabularnewline
127 & -7.2 & -5.571 & -1.629 \tabularnewline
128 & -5.4 & -4.278 & -1.122 \tabularnewline
129 &  5.3 &  7.302 & -2.002 \tabularnewline
130 & -8.5 & -2.321 & -6.179 \tabularnewline
131 &  1.4 &  2.951 & -1.551 \tabularnewline
132 &  8.4 &  9.485 & -1.085 \tabularnewline
133 & -3.5 & -6.936 &  3.436 \tabularnewline
134 & -6.8 & -2.721 & -4.079 \tabularnewline
135 &  13.9 &  6.989 &  6.911 \tabularnewline
136 & -14.3 & -8.655 & -5.645 \tabularnewline
137 &  6.5 &  4.967 &  1.533 \tabularnewline
138 & -3.7 &  3.486 & -7.186 \tabularnewline
139 & -3.1 & -0.1827 & -2.917 \tabularnewline
140 &  7.6 &  4.561 &  3.039 \tabularnewline
141 & -4.9 & -4.656 & -0.2436 \tabularnewline
142 &  4.3 &  1.275 &  3.025 \tabularnewline
143 & -1.1 & -3.715 &  2.615 \tabularnewline
144 &  4 & -1.06 &  5.06 \tabularnewline
145 & -6.4 & -2.221 & -4.179 \tabularnewline
146 &  7.5 &  2.195 &  5.306 \tabularnewline
147 & -8.2 & -4.812 & -3.388 \tabularnewline
148 &  2.8 &  3.223 & -0.4235 \tabularnewline
149 &  7.7 &  5.662 &  2.038 \tabularnewline
150 & -4.7 & -9.121 &  4.421 \tabularnewline
151 & -1.2 & -2.079 &  0.8793 \tabularnewline
152 &  4.6 &  3.735 &  0.8653 \tabularnewline
153 & -4.5 & -1.734 & -2.766 \tabularnewline
154 &  1.7 & -1.57 &  3.27 \tabularnewline
155 &  4.1 &  4.973 & -0.8725 \tabularnewline
156 & -5 & -5.503 &  0.5032 \tabularnewline
157 & -3.5 & -0.9637 & -2.536 \tabularnewline
158 &  9 &  6.441 &  2.559 \tabularnewline
159 & -3.5 & -4.402 &  0.9019 \tabularnewline
160 &  0.4 &  0.07728 &  0.3227 \tabularnewline
161 & -3.1 & -1.031 & -2.069 \tabularnewline
162 &  1 &  2.169 & -1.169 \tabularnewline
163 &  5.8 &  3.552 &  2.248 \tabularnewline
164 & -3.8 & -6.028 &  2.228 \tabularnewline
165 & -2.2 &  0.2301 & -2.43 \tabularnewline
166 &  7.1 &  5.496 &  1.604 \tabularnewline
167 & -1.7 & -5.618 &  3.918 \tabularnewline
168 & -2.5 & -1.017 & -1.483 \tabularnewline
169 &  7.3 &  1.787 &  5.513 \tabularnewline
170 & -8.4 & -2.999 & -5.401 \tabularnewline
171 & -7.2 &  0.6895 & -7.889 \tabularnewline
172 &  13.1 &  10.58 &  2.517 \tabularnewline
173 & -4.9 & -7.604 &  2.704 \tabularnewline
174 & -6.8 & -3.847 & -2.953 \tabularnewline
175 &  7.4 &  6.713 &  0.687 \tabularnewline
176 & -2 &  3.782 & -5.782 \tabularnewline
177 &  1.6 & -5.535 &  7.135 \tabularnewline
178 & -5.5 & -1.574 & -3.926 \tabularnewline
179 &  4.2 &  5.052 & -0.8523 \tabularnewline
180 & -9 & -5.65 & -3.35 \tabularnewline
181 &  10.8 &  9.837 &  0.963 \tabularnewline
182 & -2.6 & -4.648 &  2.048 \tabularnewline
183 &  0.3 & -1.835 &  2.135 \tabularnewline
184 & -1.7 & -0.368 & -1.332 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310685&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]-6.1[/C][C]-4.66[/C][C]-1.44[/C][/ROW]
[ROW][C]2[/C][C] 0.5[/C][C] 0.9124[/C][C]-0.4124[/C][/ROW]
[ROW][C]3[/C][C] 2.2[/C][C] 5.377[/C][C]-3.177[/C][/ROW]
[ROW][C]4[/C][C]-4.9[/C][C]-3.442[/C][C]-1.458[/C][/ROW]
[ROW][C]5[/C][C] 6.1[/C][C] 3.832[/C][C] 2.268[/C][/ROW]
[ROW][C]6[/C][C]-1.8[/C][C]-1.395[/C][C]-0.4049[/C][/ROW]
[ROW][C]7[/C][C]-3.6[/C][C]-2.41[/C][C]-1.19[/C][/ROW]
[ROW][C]8[/C][C] 7.9[/C][C] 3.361[/C][C] 4.539[/C][/ROW]
[ROW][C]9[/C][C]-3[/C][C]-2.717[/C][C]-0.2831[/C][/ROW]
[ROW][C]10[/C][C]-0.1[/C][C]-2.086[/C][C] 1.986[/C][/ROW]
[ROW][C]11[/C][C]-2.3[/C][C] 0.3575[/C][C]-2.657[/C][/ROW]
[ROW][C]12[/C][C] 3[/C][C] 1.244[/C][C] 1.756[/C][/ROW]
[ROW][C]13[/C][C]-4.9[/C][C]-1.685[/C][C]-3.215[/C][/ROW]
[ROW][C]14[/C][C] 4.3[/C][C] 3.367[/C][C] 0.9331[/C][/ROW]
[ROW][C]15[/C][C] 0.5[/C][C]-0.4752[/C][C] 0.9752[/C][/ROW]
[ROW][C]16[/C][C]-2.9[/C][C]-2.977[/C][C] 0.07652[/C][/ROW]
[ROW][C]17[/C][C] 1.9[/C][C] 3.398[/C][C]-1.498[/C][/ROW]
[ROW][C]18[/C][C] 0.1[/C][C]-1.46[/C][C] 1.56[/C][/ROW]
[ROW][C]19[/C][C]-1.7[/C][C]-1.351[/C][C]-0.3486[/C][/ROW]
[ROW][C]20[/C][C] 0.6[/C][C] 5.411[/C][C]-4.811[/C][/ROW]
[ROW][C]21[/C][C]-2.4[/C][C]-4.927[/C][C] 2.527[/C][/ROW]
[ROW][C]22[/C][C] 1.8[/C][C] 3.698[/C][C]-1.898[/C][/ROW]
[ROW][C]23[/C][C] 8.8[/C][C] 2.705[/C][C] 6.095[/C][/ROW]
[ROW][C]24[/C][C]-7.4[/C][C]-8.401[/C][C] 1.001[/C][/ROW]
[ROW][C]25[/C][C] 1.2[/C][C]-0.03598[/C][C] 1.236[/C][/ROW]
[ROW][C]26[/C][C] 5.4[/C][C] 4.964[/C][C] 0.4364[/C][/ROW]
[ROW][C]27[/C][C]-9.3[/C][C]-6.026[/C][C]-3.274[/C][/ROW]
[ROW][C]28[/C][C] 5.5[/C][C] 5.126[/C][C] 0.3742[/C][/ROW]
[ROW][C]29[/C][C] 1.1[/C][C]-0.3926[/C][C] 1.493[/C][/ROW]
[ROW][C]30[/C][C]-6.8[/C][C]-3.358[/C][C]-3.442[/C][/ROW]
[ROW][C]31[/C][C] 8.7[/C][C] 6.084[/C][C] 2.616[/C][/ROW]
[ROW][C]32[/C][C]-1.4[/C][C]-3.149[/C][C] 1.749[/C][/ROW]
[ROW][C]33[/C][C]-3.4[/C][C]-2.591[/C][C]-0.8091[/C][/ROW]
[ROW][C]34[/C][C]-2.4[/C][C]-0.2193[/C][C]-2.181[/C][/ROW]
[ROW][C]35[/C][C]-2.1[/C][C] 1.454[/C][C]-3.554[/C][/ROW]
[ROW][C]36[/C][C] 2.3[/C][C] 4.523[/C][C]-2.223[/C][/ROW]
[ROW][C]37[/C][C] 3.8[/C][C]-0.2899[/C][C] 4.09[/C][/ROW]
[ROW][C]38[/C][C]-3.8[/C][C]-4.558[/C][C] 0.7577[/C][/ROW]
[ROW][C]39[/C][C]-2.2[/C][C]-1.682[/C][C]-0.5181[/C][/ROW]
[ROW][C]40[/C][C] 6.3[/C][C] 3.919[/C][C] 2.381[/C][/ROW]
[ROW][C]41[/C][C]-2[/C][C]-1.975[/C][C]-0.02452[/C][/ROW]
[ROW][C]42[/C][C]-1.9[/C][C]-2.631[/C][C] 0.7311[/C][/ROW]
[ROW][C]43[/C][C] 4.3[/C][C] 3.295[/C][C] 1.005[/C][/ROW]
[ROW][C]44[/C][C]-3.4[/C][C]-2.544[/C][C]-0.8558[/C][/ROW]
[ROW][C]45[/C][C] 1.3[/C][C] 1.403[/C][C]-0.103[/C][/ROW]
[ROW][C]46[/C][C]-0.5[/C][C] 1.397[/C][C]-1.897[/C][/ROW]
[ROW][C]47[/C][C] 1.1[/C][C] 1.594[/C][C]-0.4943[/C][/ROW]
[ROW][C]48[/C][C]-4.8[/C][C]-5.749[/C][C] 0.9486[/C][/ROW]
[ROW][C]49[/C][C] 7.2[/C][C] 7.81[/C][C]-0.6104[/C][/ROW]
[ROW][C]50[/C][C]-3.4[/C][C]-4.571[/C][C] 1.171[/C][/ROW]
[ROW][C]51[/C][C] 3.9[/C][C]-0.6767[/C][C] 4.577[/C][/ROW]
[ROW][C]52[/C][C]-2.6[/C][C]-1.595[/C][C]-1.005[/C][/ROW]
[ROW][C]53[/C][C]-3.4[/C][C]-0.5634[/C][C]-2.837[/C][/ROW]
[ROW][C]54[/C][C] 7.9[/C][C] 6.644[/C][C] 1.256[/C][/ROW]
[ROW][C]55[/C][C]-5.3[/C][C]-5.808[/C][C] 0.508[/C][/ROW]
[ROW][C]56[/C][C]-3.8[/C][C]-2.188[/C][C]-1.612[/C][/ROW]
[ROW][C]57[/C][C] 8.5[/C][C] 6.185[/C][C] 2.315[/C][/ROW]
[ROW][C]58[/C][C]-2.5[/C][C]-3.973[/C][C] 1.472[/C][/ROW]
[ROW][C]59[/C][C] 0.6[/C][C]-2.545[/C][C] 3.145[/C][/ROW]
[ROW][C]60[/C][C] 1.6[/C][C] 2.059[/C][C]-0.4594[/C][/ROW]
[ROW][C]61[/C][C]-4.2[/C][C]-2.92[/C][C]-1.28[/C][/ROW]
[ROW][C]62[/C][C] 0.9[/C][C] 3.195[/C][C]-2.295[/C][/ROW]
[ROW][C]63[/C][C] 1.8[/C][C] 3.123[/C][C]-1.323[/C][/ROW]
[ROW][C]64[/C][C]-1.5[/C][C]-2.328[/C][C] 0.828[/C][/ROW]
[ROW][C]65[/C][C]-1.1[/C][C]-2.071[/C][C] 0.9713[/C][/ROW]
[ROW][C]66[/C][C] 6.3[/C][C] 3.874[/C][C] 2.426[/C][/ROW]
[ROW][C]67[/C][C]-2[/C][C]-5.311[/C][C] 3.311[/C][/ROW]
[ROW][C]68[/C][C]-2[/C][C]-2.465[/C][C] 0.4651[/C][/ROW]
[ROW][C]69[/C][C] 5[/C][C] 3.32[/C][C] 1.68[/C][/ROW]
[ROW][C]70[/C][C] 1[/C][C]-0.807[/C][C] 1.807[/C][/ROW]
[ROW][C]71[/C][C]-10.2[/C][C]-7.353[/C][C]-2.847[/C][/ROW]
[ROW][C]72[/C][C] 13.1[/C][C] 11.3[/C][C] 1.799[/C][/ROW]
[ROW][C]73[/C][C]-7.7[/C][C]-8.048[/C][C] 0.3479[/C][/ROW]
[ROW][C]74[/C][C]-3.2[/C][C]-0.2704[/C][C]-2.93[/C][/ROW]
[ROW][C]75[/C][C] 7.8[/C][C] 5.813[/C][C] 1.987[/C][/ROW]
[ROW][C]76[/C][C]-10.4[/C][C]-6.378[/C][C]-4.022[/C][/ROW]
[ROW][C]77[/C][C] 10.3[/C][C] 8.334[/C][C] 1.966[/C][/ROW]
[ROW][C]78[/C][C]-7.6[/C][C]-8.001[/C][C] 0.4014[/C][/ROW]
[ROW][C]79[/C][C]-7.2[/C][C]-2.104[/C][C]-5.096[/C][/ROW]
[ROW][C]80[/C][C] 11.8[/C][C] 10.94[/C][C] 0.8605[/C][/ROW]
[ROW][C]81[/C][C]-17.1[/C][C]-10.74[/C][C]-6.356[/C][/ROW]
[ROW][C]82[/C][C] 4.6[/C][C] 5.565[/C][C]-0.9652[/C][/ROW]
[ROW][C]83[/C][C] 7.8[/C][C] 7.877[/C][C]-0.07696[/C][/ROW]
[ROW][C]84[/C][C]-7.6[/C][C]-11.18[/C][C] 3.58[/C][/ROW]
[ROW][C]85[/C][C]-1.4[/C][C] 5.896[/C][C]-7.296[/C][/ROW]
[ROW][C]86[/C][C] 9.8[/C][C] 1.849[/C][C] 7.951[/C][/ROW]
[ROW][C]87[/C][C]-6.7[/C][C]-5.037[/C][C]-1.663[/C][/ROW]
[ROW][C]88[/C][C] 1.7[/C][C] 3.949[/C][C]-2.249[/C][/ROW]
[ROW][C]89[/C][C]-2.7[/C][C]-2.215[/C][C]-0.4848[/C][/ROW]
[ROW][C]90[/C][C]-2.5[/C][C] 2.161[/C][C]-4.661[/C][/ROW]
[ROW][C]91[/C][C] 6.2[/C][C] 8.256[/C][C]-2.056[/C][/ROW]
[ROW][C]92[/C][C]-2.2[/C][C]-4.365[/C][C] 2.165[/C][/ROW]
[ROW][C]93[/C][C]-1.4[/C][C] 3.463[/C][C]-4.863[/C][/ROW]
[ROW][C]94[/C][C] 3.3[/C][C] 3.399[/C][C]-0.09946[/C][/ROW]
[ROW][C]95[/C][C] 4.9[/C][C] 0.1302[/C][C] 4.77[/C][/ROW]
[ROW][C]96[/C][C]-5.4[/C][C]-5.645[/C][C] 0.2447[/C][/ROW]
[ROW][C]97[/C][C] 0.1[/C][C]-1.339[/C][C] 1.439[/C][/ROW]
[ROW][C]98[/C][C] 2.6[/C][C] 6.26[/C][C]-3.66[/C][/ROW]
[ROW][C]99[/C][C]-4.7[/C][C]-4.084[/C][C]-0.6162[/C][/ROW]
[ROW][C]100[/C][C] 5.9[/C][C] 1.601[/C][C] 4.299[/C][/ROW]
[ROW][C]101[/C][C]-2.2[/C][C]-1.085[/C][C]-1.115[/C][/ROW]
[ROW][C]102[/C][C]-0.8[/C][C]-1.337[/C][C] 0.537[/C][/ROW]
[ROW][C]103[/C][C] 6.6[/C][C] 1.955[/C][C] 4.645[/C][/ROW]
[ROW][C]104[/C][C]-3.3[/C][C]-3.937[/C][C] 0.6372[/C][/ROW]
[ROW][C]105[/C][C] 4.8[/C][C]-2.254[/C][C] 7.054[/C][/ROW]
[ROW][C]106[/C][C]-4.1[/C][C]-3.183[/C][C]-0.917[/C][/ROW]
[ROW][C]107[/C][C]-0.1[/C][C] 3.648[/C][C]-3.748[/C][/ROW]
[ROW][C]108[/C][C]-1.6[/C][C]-1.932[/C][C] 0.3323[/C][/ROW]
[ROW][C]109[/C][C] 12.3[/C][C] 5.311[/C][C] 6.989[/C][/ROW]
[ROW][C]110[/C][C]-17.3[/C][C]-15.12[/C][C]-2.182[/C][/ROW]
[ROW][C]111[/C][C] 0.1[/C][C] 6.645[/C][C]-6.545[/C][/ROW]
[ROW][C]112[/C][C] 8.1[/C][C] 10.16[/C][C]-2.056[/C][/ROW]
[ROW][C]113[/C][C] 0.8[/C][C]-4.767[/C][C] 5.567[/C][/ROW]
[ROW][C]114[/C][C]-3.2[/C][C]-6.88[/C][C] 3.68[/C][/ROW]
[ROW][C]115[/C][C]-0.8[/C][C]-0.3128[/C][C]-0.4872[/C][/ROW]
[ROW][C]116[/C][C]-1.2[/C][C] 2.141[/C][C]-3.341[/C][/ROW]
[ROW][C]117[/C][C] 2.7[/C][C] 2.942[/C][C]-0.2416[/C][/ROW]
[ROW][C]118[/C][C] 2.1[/C][C]-0.7729[/C][C] 2.873[/C][/ROW]
[ROW][C]119[/C][C]-6.7[/C][C]-5.636[/C][C]-1.064[/C][/ROW]
[ROW][C]120[/C][C]-1.4[/C][C] 3.305[/C][C]-4.705[/C][/ROW]
[ROW][C]121[/C][C] 0[/C][C] 0.7265[/C][C]-0.7265[/C][/ROW]
[ROW][C]122[/C][C] 8.3[/C][C] 6.892[/C][C] 1.408[/C][/ROW]
[ROW][C]123[/C][C] 0.3[/C][C]-3.337[/C][C] 3.637[/C][/ROW]
[ROW][C]124[/C][C]-3.2[/C][C]-8.655[/C][C] 5.455[/C][/ROW]
[ROW][C]125[/C][C]-8.8[/C][C]-0.167[/C][C]-8.633[/C][/ROW]
[ROW][C]126[/C][C] 14.4[/C][C] 11.14[/C][C] 3.259[/C][/ROW]
[ROW][C]127[/C][C]-7.2[/C][C]-5.571[/C][C]-1.629[/C][/ROW]
[ROW][C]128[/C][C]-5.4[/C][C]-4.278[/C][C]-1.122[/C][/ROW]
[ROW][C]129[/C][C] 5.3[/C][C] 7.302[/C][C]-2.002[/C][/ROW]
[ROW][C]130[/C][C]-8.5[/C][C]-2.321[/C][C]-6.179[/C][/ROW]
[ROW][C]131[/C][C] 1.4[/C][C] 2.951[/C][C]-1.551[/C][/ROW]
[ROW][C]132[/C][C] 8.4[/C][C] 9.485[/C][C]-1.085[/C][/ROW]
[ROW][C]133[/C][C]-3.5[/C][C]-6.936[/C][C] 3.436[/C][/ROW]
[ROW][C]134[/C][C]-6.8[/C][C]-2.721[/C][C]-4.079[/C][/ROW]
[ROW][C]135[/C][C] 13.9[/C][C] 6.989[/C][C] 6.911[/C][/ROW]
[ROW][C]136[/C][C]-14.3[/C][C]-8.655[/C][C]-5.645[/C][/ROW]
[ROW][C]137[/C][C] 6.5[/C][C] 4.967[/C][C] 1.533[/C][/ROW]
[ROW][C]138[/C][C]-3.7[/C][C] 3.486[/C][C]-7.186[/C][/ROW]
[ROW][C]139[/C][C]-3.1[/C][C]-0.1827[/C][C]-2.917[/C][/ROW]
[ROW][C]140[/C][C] 7.6[/C][C] 4.561[/C][C] 3.039[/C][/ROW]
[ROW][C]141[/C][C]-4.9[/C][C]-4.656[/C][C]-0.2436[/C][/ROW]
[ROW][C]142[/C][C] 4.3[/C][C] 1.275[/C][C] 3.025[/C][/ROW]
[ROW][C]143[/C][C]-1.1[/C][C]-3.715[/C][C] 2.615[/C][/ROW]
[ROW][C]144[/C][C] 4[/C][C]-1.06[/C][C] 5.06[/C][/ROW]
[ROW][C]145[/C][C]-6.4[/C][C]-2.221[/C][C]-4.179[/C][/ROW]
[ROW][C]146[/C][C] 7.5[/C][C] 2.195[/C][C] 5.306[/C][/ROW]
[ROW][C]147[/C][C]-8.2[/C][C]-4.812[/C][C]-3.388[/C][/ROW]
[ROW][C]148[/C][C] 2.8[/C][C] 3.223[/C][C]-0.4235[/C][/ROW]
[ROW][C]149[/C][C] 7.7[/C][C] 5.662[/C][C] 2.038[/C][/ROW]
[ROW][C]150[/C][C]-4.7[/C][C]-9.121[/C][C] 4.421[/C][/ROW]
[ROW][C]151[/C][C]-1.2[/C][C]-2.079[/C][C] 0.8793[/C][/ROW]
[ROW][C]152[/C][C] 4.6[/C][C] 3.735[/C][C] 0.8653[/C][/ROW]
[ROW][C]153[/C][C]-4.5[/C][C]-1.734[/C][C]-2.766[/C][/ROW]
[ROW][C]154[/C][C] 1.7[/C][C]-1.57[/C][C] 3.27[/C][/ROW]
[ROW][C]155[/C][C] 4.1[/C][C] 4.973[/C][C]-0.8725[/C][/ROW]
[ROW][C]156[/C][C]-5[/C][C]-5.503[/C][C] 0.5032[/C][/ROW]
[ROW][C]157[/C][C]-3.5[/C][C]-0.9637[/C][C]-2.536[/C][/ROW]
[ROW][C]158[/C][C] 9[/C][C] 6.441[/C][C] 2.559[/C][/ROW]
[ROW][C]159[/C][C]-3.5[/C][C]-4.402[/C][C] 0.9019[/C][/ROW]
[ROW][C]160[/C][C] 0.4[/C][C] 0.07728[/C][C] 0.3227[/C][/ROW]
[ROW][C]161[/C][C]-3.1[/C][C]-1.031[/C][C]-2.069[/C][/ROW]
[ROW][C]162[/C][C] 1[/C][C] 2.169[/C][C]-1.169[/C][/ROW]
[ROW][C]163[/C][C] 5.8[/C][C] 3.552[/C][C] 2.248[/C][/ROW]
[ROW][C]164[/C][C]-3.8[/C][C]-6.028[/C][C] 2.228[/C][/ROW]
[ROW][C]165[/C][C]-2.2[/C][C] 0.2301[/C][C]-2.43[/C][/ROW]
[ROW][C]166[/C][C] 7.1[/C][C] 5.496[/C][C] 1.604[/C][/ROW]
[ROW][C]167[/C][C]-1.7[/C][C]-5.618[/C][C] 3.918[/C][/ROW]
[ROW][C]168[/C][C]-2.5[/C][C]-1.017[/C][C]-1.483[/C][/ROW]
[ROW][C]169[/C][C] 7.3[/C][C] 1.787[/C][C] 5.513[/C][/ROW]
[ROW][C]170[/C][C]-8.4[/C][C]-2.999[/C][C]-5.401[/C][/ROW]
[ROW][C]171[/C][C]-7.2[/C][C] 0.6895[/C][C]-7.889[/C][/ROW]
[ROW][C]172[/C][C] 13.1[/C][C] 10.58[/C][C] 2.517[/C][/ROW]
[ROW][C]173[/C][C]-4.9[/C][C]-7.604[/C][C] 2.704[/C][/ROW]
[ROW][C]174[/C][C]-6.8[/C][C]-3.847[/C][C]-2.953[/C][/ROW]
[ROW][C]175[/C][C] 7.4[/C][C] 6.713[/C][C] 0.687[/C][/ROW]
[ROW][C]176[/C][C]-2[/C][C] 3.782[/C][C]-5.782[/C][/ROW]
[ROW][C]177[/C][C] 1.6[/C][C]-5.535[/C][C] 7.135[/C][/ROW]
[ROW][C]178[/C][C]-5.5[/C][C]-1.574[/C][C]-3.926[/C][/ROW]
[ROW][C]179[/C][C] 4.2[/C][C] 5.052[/C][C]-0.8523[/C][/ROW]
[ROW][C]180[/C][C]-9[/C][C]-5.65[/C][C]-3.35[/C][/ROW]
[ROW][C]181[/C][C] 10.8[/C][C] 9.837[/C][C] 0.963[/C][/ROW]
[ROW][C]182[/C][C]-2.6[/C][C]-4.648[/C][C] 2.048[/C][/ROW]
[ROW][C]183[/C][C] 0.3[/C][C]-1.835[/C][C] 2.135[/C][/ROW]
[ROW][C]184[/C][C]-1.7[/C][C]-0.368[/C][C]-1.332[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310685&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310685&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	-6.1	-4.66	-1.44
2	0.5	0.9124	-0.4124
3	2.2	5.377	-3.177
4	-4.9	-3.442	-1.458
5	6.1	3.832	2.268
6	-1.8	-1.395	-0.4049
7	-3.6	-2.41	-1.19
8	7.9	3.361	4.539
9	-3	-2.717	-0.2831
10	-0.1	-2.086	1.986
11	-2.3	0.3575	-2.657
12	3	1.244	1.756
13	-4.9	-1.685	-3.215
14	4.3	3.367	0.9331
15	0.5	-0.4752	0.9752
16	-2.9	-2.977	0.07652
17	1.9	3.398	-1.498
18	0.1	-1.46	1.56
19	-1.7	-1.351	-0.3486
20	0.6	5.411	-4.811
21	-2.4	-4.927	2.527
22	1.8	3.698	-1.898
23	8.8	2.705	6.095
24	-7.4	-8.401	1.001
25	1.2	-0.03598	1.236
26	5.4	4.964	0.4364
27	-9.3	-6.026	-3.274
28	5.5	5.126	0.3742
29	1.1	-0.3926	1.493
30	-6.8	-3.358	-3.442
31	8.7	6.084	2.616
32	-1.4	-3.149	1.749
33	-3.4	-2.591	-0.8091
34	-2.4	-0.2193	-2.181
35	-2.1	1.454	-3.554
36	2.3	4.523	-2.223
37	3.8	-0.2899	4.09
38	-3.8	-4.558	0.7577
39	-2.2	-1.682	-0.5181
40	6.3	3.919	2.381
41	-2	-1.975	-0.02452
42	-1.9	-2.631	0.7311
43	4.3	3.295	1.005
44	-3.4	-2.544	-0.8558
45	1.3	1.403	-0.103
46	-0.5	1.397	-1.897
47	1.1	1.594	-0.4943
48	-4.8	-5.749	0.9486
49	7.2	7.81	-0.6104
50	-3.4	-4.571	1.171
51	3.9	-0.6767	4.577
52	-2.6	-1.595	-1.005
53	-3.4	-0.5634	-2.837
54	7.9	6.644	1.256
55	-5.3	-5.808	0.508
56	-3.8	-2.188	-1.612
57	8.5	6.185	2.315
58	-2.5	-3.973	1.472
59	0.6	-2.545	3.145
60	1.6	2.059	-0.4594
61	-4.2	-2.92	-1.28
62	0.9	3.195	-2.295
63	1.8	3.123	-1.323
64	-1.5	-2.328	0.828
65	-1.1	-2.071	0.9713
66	6.3	3.874	2.426
67	-2	-5.311	3.311
68	-2	-2.465	0.4651
69	5	3.32	1.68
70	1	-0.807	1.807
71	-10.2	-7.353	-2.847
72	13.1	11.3	1.799
73	-7.7	-8.048	0.3479
74	-3.2	-0.2704	-2.93
75	7.8	5.813	1.987
76	-10.4	-6.378	-4.022
77	10.3	8.334	1.966
78	-7.6	-8.001	0.4014
79	-7.2	-2.104	-5.096
80	11.8	10.94	0.8605
81	-17.1	-10.74	-6.356
82	4.6	5.565	-0.9652
83	7.8	7.877	-0.07696
84	-7.6	-11.18	3.58
85	-1.4	5.896	-7.296
86	9.8	1.849	7.951
87	-6.7	-5.037	-1.663
88	1.7	3.949	-2.249
89	-2.7	-2.215	-0.4848
90	-2.5	2.161	-4.661
91	6.2	8.256	-2.056
92	-2.2	-4.365	2.165
93	-1.4	3.463	-4.863
94	3.3	3.399	-0.09946
95	4.9	0.1302	4.77
96	-5.4	-5.645	0.2447
97	0.1	-1.339	1.439
98	2.6	6.26	-3.66
99	-4.7	-4.084	-0.6162
100	5.9	1.601	4.299
101	-2.2	-1.085	-1.115
102	-0.8	-1.337	0.537
103	6.6	1.955	4.645
104	-3.3	-3.937	0.6372
105	4.8	-2.254	7.054
106	-4.1	-3.183	-0.917
107	-0.1	3.648	-3.748
108	-1.6	-1.932	0.3323
109	12.3	5.311	6.989
110	-17.3	-15.12	-2.182
111	0.1	6.645	-6.545
112	8.1	10.16	-2.056
113	0.8	-4.767	5.567
114	-3.2	-6.88	3.68
115	-0.8	-0.3128	-0.4872
116	-1.2	2.141	-3.341
117	2.7	2.942	-0.2416
118	2.1	-0.7729	2.873
119	-6.7	-5.636	-1.064
120	-1.4	3.305	-4.705
121	0	0.7265	-0.7265
122	8.3	6.892	1.408
123	0.3	-3.337	3.637
124	-3.2	-8.655	5.455
125	-8.8	-0.167	-8.633
126	14.4	11.14	3.259
127	-7.2	-5.571	-1.629
128	-5.4	-4.278	-1.122
129	5.3	7.302	-2.002
130	-8.5	-2.321	-6.179
131	1.4	2.951	-1.551
132	8.4	9.485	-1.085
133	-3.5	-6.936	3.436
134	-6.8	-2.721	-4.079
135	13.9	6.989	6.911
136	-14.3	-8.655	-5.645
137	6.5	4.967	1.533
138	-3.7	3.486	-7.186
139	-3.1	-0.1827	-2.917
140	7.6	4.561	3.039
141	-4.9	-4.656	-0.2436
142	4.3	1.275	3.025
143	-1.1	-3.715	2.615
144	4	-1.06	5.06
145	-6.4	-2.221	-4.179
146	7.5	2.195	5.306
147	-8.2	-4.812	-3.388
148	2.8	3.223	-0.4235
149	7.7	5.662	2.038
150	-4.7	-9.121	4.421
151	-1.2	-2.079	0.8793
152	4.6	3.735	0.8653
153	-4.5	-1.734	-2.766
154	1.7	-1.57	3.27
155	4.1	4.973	-0.8725
156	-5	-5.503	0.5032
157	-3.5	-0.9637	-2.536
158	9	6.441	2.559
159	-3.5	-4.402	0.9019
160	0.4	0.07728	0.3227
161	-3.1	-1.031	-2.069
162	1	2.169	-1.169
163	5.8	3.552	2.248
164	-3.8	-6.028	2.228
165	-2.2	0.2301	-2.43
166	7.1	5.496	1.604
167	-1.7	-5.618	3.918
168	-2.5	-1.017	-1.483
169	7.3	1.787	5.513
170	-8.4	-2.999	-5.401
171	-7.2	0.6895	-7.889
172	13.1	10.58	2.517
173	-4.9	-7.604	2.704
174	-6.8	-3.847	-2.953
175	7.4	6.713	0.687
176	-2	3.782	-5.782
177	1.6	-5.535	7.135
178	-5.5	-1.574	-3.926
179	4.2	5.052	-0.8523
180	-9	-5.65	-3.35
181	10.8	9.837	0.963
182	-2.6	-4.648	2.048
183	0.3	-1.835	2.135
184	-1.7	-0.368	-1.332

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
9	0.2681	0.5362	0.7319
10	0.1567	0.3135	0.8433
11	0.3773	0.7546	0.6227
12	0.3473	0.6946	0.6527
13	0.293	0.5859	0.707
14	0.2013	0.4026	0.7987
15	0.1603	0.3205	0.8397
16	0.1051	0.2101	0.8949
17	0.09864	0.1973	0.9014
18	0.08267	0.1653	0.9173
19	0.0526	0.1052	0.9474
20	0.07744	0.1549	0.9226
21	0.05213	0.1043	0.9479
22	0.03635	0.07269	0.9637
23	0.1985	0.3971	0.8015
24	0.185	0.37	0.815
25	0.1402	0.2804	0.8598
26	0.1078	0.2156	0.8922
27	0.1017	0.2034	0.8983
28	0.07548	0.151	0.9245
29	0.05889	0.1178	0.9411
30	0.05211	0.1042	0.9479
31	0.04045	0.08089	0.9596
32	0.03392	0.06784	0.9661
33	0.02345	0.0469	0.9766
34	0.02453	0.04905	0.9755
35	0.01926	0.03852	0.9807
36	0.02111	0.04222	0.9789
37	0.03863	0.07725	0.9614
38	0.03086	0.06173	0.9691
39	0.02547	0.05095	0.9745
40	0.02446	0.04892	0.9755
41	0.01735	0.03469	0.9827
42	0.01213	0.02425	0.9879
43	0.008777	0.01755	0.9912
44	0.006133	0.01227	0.9939
45	0.004105	0.008209	0.9959
46	0.003754	0.007508	0.9962
47	0.002482	0.004965	0.9975
48	0.001696	0.003392	0.9983
49	0.001092	0.002184	0.9989
50	0.0007186	0.001437	0.9993
51	0.001593	0.003186	0.9984
52	0.001123	0.002246	0.9989
53	0.001125	0.00225	0.9989
54	0.0007594	0.001519	0.9992
55	0.0005138	0.001028	0.9995
56	0.0003819	0.0007639	0.9996
57	0.0002946	0.0005892	0.9997
58	0.0002211	0.0004422	0.9998
59	0.0002621	0.0005242	0.9997
60	0.0001876	0.0003751	0.9998
61	0.0001219	0.0002437	0.9999
62	0.0001105	0.000221	0.9999
63	7.362e-05	0.0001472	0.9999
64	4.699e-05	9.398e-05	1
65	2.913e-05	5.826e-05	1
66	2.743e-05	5.486e-05	1
67	2.925e-05	5.849e-05	1
68	1.753e-05	3.507e-05	1
69	1.223e-05	2.447e-05	1
70	8.519e-06	1.704e-05	1
71	8.721e-06	1.744e-05	1
72	5.914e-06	1.183e-05	1
73	3.431e-06	6.861e-06	1
74	2.903e-06	5.805e-06	1
75	1.88e-06	3.76e-06	1
76	2.499e-06	4.998e-06	1
77	1.818e-06	3.635e-06	1
78	1.047e-06	2.094e-06	1
79	1.844e-06	3.689e-06	1
80	1.088e-06	2.177e-06	1
81	3.09e-06	6.179e-06	1
82	2.069e-06	4.138e-06	1
83	1.539e-06	3.079e-06	1
84	7.236e-06	1.447e-05	1
85	0.0001432	0.0002863	0.9999
86	0.001326	0.002653	0.9987
87	0.001014	0.002029	0.999
88	0.0008073	0.001615	0.9992
89	0.0005943	0.001189	0.9994
90	0.0008671	0.001734	0.9991
91	0.0006811	0.001362	0.9993
92	0.0005423	0.001085	0.9995
93	0.0007638	0.001528	0.9992
94	0.0005321	0.001064	0.9995
95	0.0008711	0.001742	0.9991
96	0.0006145	0.001229	0.9994
97	0.0004472	0.0008944	0.9996
98	0.0006041	0.001208	0.9994
99	0.0004161	0.0008321	0.9996
100	0.0005837	0.001167	0.9994
101	0.0004158	0.0008315	0.9996
102	0.0002894	0.0005789	0.9997
103	0.0004191	0.0008382	0.9996
104	0.0002906	0.0005812	0.9997
105	0.001293	0.002587	0.9987
106	0.0009887	0.001977	0.999
107	0.001164	0.002328	0.9988
108	0.0008275	0.001655	0.9992
109	0.003602	0.007204	0.9964
110	0.003062	0.006123	0.9969
111	0.008649	0.0173	0.9914
112	0.007639	0.01528	0.9924
113	0.01488	0.02975	0.9851
114	0.01578	0.03157	0.9842
115	0.01212	0.02423	0.9879
116	0.01213	0.02426	0.9879
117	0.009053	0.01811	0.9909
118	0.008546	0.01709	0.9915
119	0.00648	0.01296	0.9935
120	0.009141	0.01828	0.9909
121	0.006827	0.01365	0.9932
122	0.005333	0.01067	0.9947
123	0.00596	0.01192	0.994
124	0.0111	0.02219	0.9889
125	0.05468	0.1094	0.9453
126	0.05307	0.1061	0.9469
127	0.04398	0.08797	0.956
128	0.03599	0.07198	0.964
129	0.03139	0.06277	0.9686
130	0.05952	0.119	0.9405
131	0.05611	0.1122	0.9439
132	0.04477	0.08954	0.9552
133	0.04963	0.09925	0.9504
134	0.05415	0.1083	0.9459
135	0.1028	0.2056	0.8972
136	0.1465	0.293	0.8535
137	0.1236	0.2472	0.8764
138	0.2215	0.4429	0.7785
139	0.2407	0.4815	0.7593
140	0.2296	0.4593	0.7704
141	0.1935	0.387	0.8065
142	0.1895	0.3791	0.8105
143	0.1785	0.3571	0.8215
144	0.2189	0.4379	0.7811
145	0.2287	0.4574	0.7713
146	0.2806	0.5612	0.7194
147	0.2944	0.5888	0.7056
148	0.2485	0.497	0.7515
149	0.2306	0.4611	0.7694
150	0.2539	0.5079	0.7461
151	0.219	0.4381	0.781
152	0.1817	0.3633	0.8183
153	0.1701	0.3402	0.8299
154	0.1639	0.3279	0.8361
155	0.1294	0.2588	0.8706
156	0.1001	0.2003	0.8999
157	0.08953	0.1791	0.9105
158	0.07932	0.1586	0.9207
159	0.05832	0.1166	0.9417
160	0.04471	0.08941	0.9553
161	0.03386	0.06773	0.9661
162	0.02548	0.05096	0.9745
163	0.02083	0.04166	0.9792
164	0.01768	0.03537	0.9823
165	0.0128	0.02561	0.9872
166	0.00919	0.01838	0.9908
167	0.01267	0.02534	0.9873
168	0.007737	0.01547	0.9923
169	0.01346	0.02692	0.9865
170	0.01332	0.02663	0.9867
171	0.146	0.292	0.854
172	0.1012	0.2023	0.8988
173	0.1067	0.2134	0.8933
174	0.197	0.3939	0.803
175	0.1223	0.2446	0.8777

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 &  0.2681 &  0.5362 &  0.7319 \tabularnewline
10 &  0.1567 &  0.3135 &  0.8433 \tabularnewline
11 &  0.3773 &  0.7546 &  0.6227 \tabularnewline
12 &  0.3473 &  0.6946 &  0.6527 \tabularnewline
13 &  0.293 &  0.5859 &  0.707 \tabularnewline
14 &  0.2013 &  0.4026 &  0.7987 \tabularnewline
15 &  0.1603 &  0.3205 &  0.8397 \tabularnewline
16 &  0.1051 &  0.2101 &  0.8949 \tabularnewline
17 &  0.09864 &  0.1973 &  0.9014 \tabularnewline
18 &  0.08267 &  0.1653 &  0.9173 \tabularnewline
19 &  0.0526 &  0.1052 &  0.9474 \tabularnewline
20 &  0.07744 &  0.1549 &  0.9226 \tabularnewline
21 &  0.05213 &  0.1043 &  0.9479 \tabularnewline
22 &  0.03635 &  0.07269 &  0.9637 \tabularnewline
23 &  0.1985 &  0.3971 &  0.8015 \tabularnewline
24 &  0.185 &  0.37 &  0.815 \tabularnewline
25 &  0.1402 &  0.2804 &  0.8598 \tabularnewline
26 &  0.1078 &  0.2156 &  0.8922 \tabularnewline
27 &  0.1017 &  0.2034 &  0.8983 \tabularnewline
28 &  0.07548 &  0.151 &  0.9245 \tabularnewline
29 &  0.05889 &  0.1178 &  0.9411 \tabularnewline
30 &  0.05211 &  0.1042 &  0.9479 \tabularnewline
31 &  0.04045 &  0.08089 &  0.9596 \tabularnewline
32 &  0.03392 &  0.06784 &  0.9661 \tabularnewline
33 &  0.02345 &  0.0469 &  0.9766 \tabularnewline
34 &  0.02453 &  0.04905 &  0.9755 \tabularnewline
35 &  0.01926 &  0.03852 &  0.9807 \tabularnewline
36 &  0.02111 &  0.04222 &  0.9789 \tabularnewline
37 &  0.03863 &  0.07725 &  0.9614 \tabularnewline
38 &  0.03086 &  0.06173 &  0.9691 \tabularnewline
39 &  0.02547 &  0.05095 &  0.9745 \tabularnewline
40 &  0.02446 &  0.04892 &  0.9755 \tabularnewline
41 &  0.01735 &  0.03469 &  0.9827 \tabularnewline
42 &  0.01213 &  0.02425 &  0.9879 \tabularnewline
43 &  0.008777 &  0.01755 &  0.9912 \tabularnewline
44 &  0.006133 &  0.01227 &  0.9939 \tabularnewline
45 &  0.004105 &  0.008209 &  0.9959 \tabularnewline
46 &  0.003754 &  0.007508 &  0.9962 \tabularnewline
47 &  0.002482 &  0.004965 &  0.9975 \tabularnewline
48 &  0.001696 &  0.003392 &  0.9983 \tabularnewline
49 &  0.001092 &  0.002184 &  0.9989 \tabularnewline
50 &  0.0007186 &  0.001437 &  0.9993 \tabularnewline
51 &  0.001593 &  0.003186 &  0.9984 \tabularnewline
52 &  0.001123 &  0.002246 &  0.9989 \tabularnewline
53 &  0.001125 &  0.00225 &  0.9989 \tabularnewline
54 &  0.0007594 &  0.001519 &  0.9992 \tabularnewline
55 &  0.0005138 &  0.001028 &  0.9995 \tabularnewline
56 &  0.0003819 &  0.0007639 &  0.9996 \tabularnewline
57 &  0.0002946 &  0.0005892 &  0.9997 \tabularnewline
58 &  0.0002211 &  0.0004422 &  0.9998 \tabularnewline
59 &  0.0002621 &  0.0005242 &  0.9997 \tabularnewline
60 &  0.0001876 &  0.0003751 &  0.9998 \tabularnewline
61 &  0.0001219 &  0.0002437 &  0.9999 \tabularnewline
62 &  0.0001105 &  0.000221 &  0.9999 \tabularnewline
63 &  7.362e-05 &  0.0001472 &  0.9999 \tabularnewline
64 &  4.699e-05 &  9.398e-05 &  1 \tabularnewline
65 &  2.913e-05 &  5.826e-05 &  1 \tabularnewline
66 &  2.743e-05 &  5.486e-05 &  1 \tabularnewline
67 &  2.925e-05 &  5.849e-05 &  1 \tabularnewline
68 &  1.753e-05 &  3.507e-05 &  1 \tabularnewline
69 &  1.223e-05 &  2.447e-05 &  1 \tabularnewline
70 &  8.519e-06 &  1.704e-05 &  1 \tabularnewline
71 &  8.721e-06 &  1.744e-05 &  1 \tabularnewline
72 &  5.914e-06 &  1.183e-05 &  1 \tabularnewline
73 &  3.431e-06 &  6.861e-06 &  1 \tabularnewline
74 &  2.903e-06 &  5.805e-06 &  1 \tabularnewline
75 &  1.88e-06 &  3.76e-06 &  1 \tabularnewline
76 &  2.499e-06 &  4.998e-06 &  1 \tabularnewline
77 &  1.818e-06 &  3.635e-06 &  1 \tabularnewline
78 &  1.047e-06 &  2.094e-06 &  1 \tabularnewline
79 &  1.844e-06 &  3.689e-06 &  1 \tabularnewline
80 &  1.088e-06 &  2.177e-06 &  1 \tabularnewline
81 &  3.09e-06 &  6.179e-06 &  1 \tabularnewline
82 &  2.069e-06 &  4.138e-06 &  1 \tabularnewline
83 &  1.539e-06 &  3.079e-06 &  1 \tabularnewline
84 &  7.236e-06 &  1.447e-05 &  1 \tabularnewline
85 &  0.0001432 &  0.0002863 &  0.9999 \tabularnewline
86 &  0.001326 &  0.002653 &  0.9987 \tabularnewline
87 &  0.001014 &  0.002029 &  0.999 \tabularnewline
88 &  0.0008073 &  0.001615 &  0.9992 \tabularnewline
89 &  0.0005943 &  0.001189 &  0.9994 \tabularnewline
90 &  0.0008671 &  0.001734 &  0.9991 \tabularnewline
91 &  0.0006811 &  0.001362 &  0.9993 \tabularnewline
92 &  0.0005423 &  0.001085 &  0.9995 \tabularnewline
93 &  0.0007638 &  0.001528 &  0.9992 \tabularnewline
94 &  0.0005321 &  0.001064 &  0.9995 \tabularnewline
95 &  0.0008711 &  0.001742 &  0.9991 \tabularnewline
96 &  0.0006145 &  0.001229 &  0.9994 \tabularnewline
97 &  0.0004472 &  0.0008944 &  0.9996 \tabularnewline
98 &  0.0006041 &  0.001208 &  0.9994 \tabularnewline
99 &  0.0004161 &  0.0008321 &  0.9996 \tabularnewline
100 &  0.0005837 &  0.001167 &  0.9994 \tabularnewline
101 &  0.0004158 &  0.0008315 &  0.9996 \tabularnewline
102 &  0.0002894 &  0.0005789 &  0.9997 \tabularnewline
103 &  0.0004191 &  0.0008382 &  0.9996 \tabularnewline
104 &  0.0002906 &  0.0005812 &  0.9997 \tabularnewline
105 &  0.001293 &  0.002587 &  0.9987 \tabularnewline
106 &  0.0009887 &  0.001977 &  0.999 \tabularnewline
107 &  0.001164 &  0.002328 &  0.9988 \tabularnewline
108 &  0.0008275 &  0.001655 &  0.9992 \tabularnewline
109 &  0.003602 &  0.007204 &  0.9964 \tabularnewline
110 &  0.003062 &  0.006123 &  0.9969 \tabularnewline
111 &  0.008649 &  0.0173 &  0.9914 \tabularnewline
112 &  0.007639 &  0.01528 &  0.9924 \tabularnewline
113 &  0.01488 &  0.02975 &  0.9851 \tabularnewline
114 &  0.01578 &  0.03157 &  0.9842 \tabularnewline
115 &  0.01212 &  0.02423 &  0.9879 \tabularnewline
116 &  0.01213 &  0.02426 &  0.9879 \tabularnewline
117 &  0.009053 &  0.01811 &  0.9909 \tabularnewline
118 &  0.008546 &  0.01709 &  0.9915 \tabularnewline
119 &  0.00648 &  0.01296 &  0.9935 \tabularnewline
120 &  0.009141 &  0.01828 &  0.9909 \tabularnewline
121 &  0.006827 &  0.01365 &  0.9932 \tabularnewline
122 &  0.005333 &  0.01067 &  0.9947 \tabularnewline
123 &  0.00596 &  0.01192 &  0.994 \tabularnewline
124 &  0.0111 &  0.02219 &  0.9889 \tabularnewline
125 &  0.05468 &  0.1094 &  0.9453 \tabularnewline
126 &  0.05307 &  0.1061 &  0.9469 \tabularnewline
127 &  0.04398 &  0.08797 &  0.956 \tabularnewline
128 &  0.03599 &  0.07198 &  0.964 \tabularnewline
129 &  0.03139 &  0.06277 &  0.9686 \tabularnewline
130 &  0.05952 &  0.119 &  0.9405 \tabularnewline
131 &  0.05611 &  0.1122 &  0.9439 \tabularnewline
132 &  0.04477 &  0.08954 &  0.9552 \tabularnewline
133 &  0.04963 &  0.09925 &  0.9504 \tabularnewline
134 &  0.05415 &  0.1083 &  0.9459 \tabularnewline
135 &  0.1028 &  0.2056 &  0.8972 \tabularnewline
136 &  0.1465 &  0.293 &  0.8535 \tabularnewline
137 &  0.1236 &  0.2472 &  0.8764 \tabularnewline
138 &  0.2215 &  0.4429 &  0.7785 \tabularnewline
139 &  0.2407 &  0.4815 &  0.7593 \tabularnewline
140 &  0.2296 &  0.4593 &  0.7704 \tabularnewline
141 &  0.1935 &  0.387 &  0.8065 \tabularnewline
142 &  0.1895 &  0.3791 &  0.8105 \tabularnewline
143 &  0.1785 &  0.3571 &  0.8215 \tabularnewline
144 &  0.2189 &  0.4379 &  0.7811 \tabularnewline
145 &  0.2287 &  0.4574 &  0.7713 \tabularnewline
146 &  0.2806 &  0.5612 &  0.7194 \tabularnewline
147 &  0.2944 &  0.5888 &  0.7056 \tabularnewline
148 &  0.2485 &  0.497 &  0.7515 \tabularnewline
149 &  0.2306 &  0.4611 &  0.7694 \tabularnewline
150 &  0.2539 &  0.5079 &  0.7461 \tabularnewline
151 &  0.219 &  0.4381 &  0.781 \tabularnewline
152 &  0.1817 &  0.3633 &  0.8183 \tabularnewline
153 &  0.1701 &  0.3402 &  0.8299 \tabularnewline
154 &  0.1639 &  0.3279 &  0.8361 \tabularnewline
155 &  0.1294 &  0.2588 &  0.8706 \tabularnewline
156 &  0.1001 &  0.2003 &  0.8999 \tabularnewline
157 &  0.08953 &  0.1791 &  0.9105 \tabularnewline
158 &  0.07932 &  0.1586 &  0.9207 \tabularnewline
159 &  0.05832 &  0.1166 &  0.9417 \tabularnewline
160 &  0.04471 &  0.08941 &  0.9553 \tabularnewline
161 &  0.03386 &  0.06773 &  0.9661 \tabularnewline
162 &  0.02548 &  0.05096 &  0.9745 \tabularnewline
163 &  0.02083 &  0.04166 &  0.9792 \tabularnewline
164 &  0.01768 &  0.03537 &  0.9823 \tabularnewline
165 &  0.0128 &  0.02561 &  0.9872 \tabularnewline
166 &  0.00919 &  0.01838 &  0.9908 \tabularnewline
167 &  0.01267 &  0.02534 &  0.9873 \tabularnewline
168 &  0.007737 &  0.01547 &  0.9923 \tabularnewline
169 &  0.01346 &  0.02692 &  0.9865 \tabularnewline
170 &  0.01332 &  0.02663 &  0.9867 \tabularnewline
171 &  0.146 &  0.292 &  0.854 \tabularnewline
172 &  0.1012 &  0.2023 &  0.8988 \tabularnewline
173 &  0.1067 &  0.2134 &  0.8933 \tabularnewline
174 &  0.197 &  0.3939 &  0.803 \tabularnewline
175 &  0.1223 &  0.2446 &  0.8777 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310685&T=6

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]9[/C][C] 0.2681[/C][C] 0.5362[/C][C] 0.7319[/C][/ROW]
[ROW][C]10[/C][C] 0.1567[/C][C] 0.3135[/C][C] 0.8433[/C][/ROW]
[ROW][C]11[/C][C] 0.3773[/C][C] 0.7546[/C][C] 0.6227[/C][/ROW]
[ROW][C]12[/C][C] 0.3473[/C][C] 0.6946[/C][C] 0.6527[/C][/ROW]
[ROW][C]13[/C][C] 0.293[/C][C] 0.5859[/C][C] 0.707[/C][/ROW]
[ROW][C]14[/C][C] 0.2013[/C][C] 0.4026[/C][C] 0.7987[/C][/ROW]
[ROW][C]15[/C][C] 0.1603[/C][C] 0.3205[/C][C] 0.8397[/C][/ROW]
[ROW][C]16[/C][C] 0.1051[/C][C] 0.2101[/C][C] 0.8949[/C][/ROW]
[ROW][C]17[/C][C] 0.09864[/C][C] 0.1973[/C][C] 0.9014[/C][/ROW]
[ROW][C]18[/C][C] 0.08267[/C][C] 0.1653[/C][C] 0.9173[/C][/ROW]
[ROW][C]19[/C][C] 0.0526[/C][C] 0.1052[/C][C] 0.9474[/C][/ROW]
[ROW][C]20[/C][C] 0.07744[/C][C] 0.1549[/C][C] 0.9226[/C][/ROW]
[ROW][C]21[/C][C] 0.05213[/C][C] 0.1043[/C][C] 0.9479[/C][/ROW]
[ROW][C]22[/C][C] 0.03635[/C][C] 0.07269[/C][C] 0.9637[/C][/ROW]
[ROW][C]23[/C][C] 0.1985[/C][C] 0.3971[/C][C] 0.8015[/C][/ROW]
[ROW][C]24[/C][C] 0.185[/C][C] 0.37[/C][C] 0.815[/C][/ROW]
[ROW][C]25[/C][C] 0.1402[/C][C] 0.2804[/C][C] 0.8598[/C][/ROW]
[ROW][C]26[/C][C] 0.1078[/C][C] 0.2156[/C][C] 0.8922[/C][/ROW]
[ROW][C]27[/C][C] 0.1017[/C][C] 0.2034[/C][C] 0.8983[/C][/ROW]
[ROW][C]28[/C][C] 0.07548[/C][C] 0.151[/C][C] 0.9245[/C][/ROW]
[ROW][C]29[/C][C] 0.05889[/C][C] 0.1178[/C][C] 0.9411[/C][/ROW]
[ROW][C]30[/C][C] 0.05211[/C][C] 0.1042[/C][C] 0.9479[/C][/ROW]
[ROW][C]31[/C][C] 0.04045[/C][C] 0.08089[/C][C] 0.9596[/C][/ROW]
[ROW][C]32[/C][C] 0.03392[/C][C] 0.06784[/C][C] 0.9661[/C][/ROW]
[ROW][C]33[/C][C] 0.02345[/C][C] 0.0469[/C][C] 0.9766[/C][/ROW]
[ROW][C]34[/C][C] 0.02453[/C][C] 0.04905[/C][C] 0.9755[/C][/ROW]
[ROW][C]35[/C][C] 0.01926[/C][C] 0.03852[/C][C] 0.9807[/C][/ROW]
[ROW][C]36[/C][C] 0.02111[/C][C] 0.04222[/C][C] 0.9789[/C][/ROW]
[ROW][C]37[/C][C] 0.03863[/C][C] 0.07725[/C][C] 0.9614[/C][/ROW]
[ROW][C]38[/C][C] 0.03086[/C][C] 0.06173[/C][C] 0.9691[/C][/ROW]
[ROW][C]39[/C][C] 0.02547[/C][C] 0.05095[/C][C] 0.9745[/C][/ROW]
[ROW][C]40[/C][C] 0.02446[/C][C] 0.04892[/C][C] 0.9755[/C][/ROW]
[ROW][C]41[/C][C] 0.01735[/C][C] 0.03469[/C][C] 0.9827[/C][/ROW]
[ROW][C]42[/C][C] 0.01213[/C][C] 0.02425[/C][C] 0.9879[/C][/ROW]
[ROW][C]43[/C][C] 0.008777[/C][C] 0.01755[/C][C] 0.9912[/C][/ROW]
[ROW][C]44[/C][C] 0.006133[/C][C] 0.01227[/C][C] 0.9939[/C][/ROW]
[ROW][C]45[/C][C] 0.004105[/C][C] 0.008209[/C][C] 0.9959[/C][/ROW]
[ROW][C]46[/C][C] 0.003754[/C][C] 0.007508[/C][C] 0.9962[/C][/ROW]
[ROW][C]47[/C][C] 0.002482[/C][C] 0.004965[/C][C] 0.9975[/C][/ROW]
[ROW][C]48[/C][C] 0.001696[/C][C] 0.003392[/C][C] 0.9983[/C][/ROW]
[ROW][C]49[/C][C] 0.001092[/C][C] 0.002184[/C][C] 0.9989[/C][/ROW]
[ROW][C]50[/C][C] 0.0007186[/C][C] 0.001437[/C][C] 0.9993[/C][/ROW]
[ROW][C]51[/C][C] 0.001593[/C][C] 0.003186[/C][C] 0.9984[/C][/ROW]
[ROW][C]52[/C][C] 0.001123[/C][C] 0.002246[/C][C] 0.9989[/C][/ROW]
[ROW][C]53[/C][C] 0.001125[/C][C] 0.00225[/C][C] 0.9989[/C][/ROW]
[ROW][C]54[/C][C] 0.0007594[/C][C] 0.001519[/C][C] 0.9992[/C][/ROW]
[ROW][C]55[/C][C] 0.0005138[/C][C] 0.001028[/C][C] 0.9995[/C][/ROW]
[ROW][C]56[/C][C] 0.0003819[/C][C] 0.0007639[/C][C] 0.9996[/C][/ROW]
[ROW][C]57[/C][C] 0.0002946[/C][C] 0.0005892[/C][C] 0.9997[/C][/ROW]
[ROW][C]58[/C][C] 0.0002211[/C][C] 0.0004422[/C][C] 0.9998[/C][/ROW]
[ROW][C]59[/C][C] 0.0002621[/C][C] 0.0005242[/C][C] 0.9997[/C][/ROW]
[ROW][C]60[/C][C] 0.0001876[/C][C] 0.0003751[/C][C] 0.9998[/C][/ROW]
[ROW][C]61[/C][C] 0.0001219[/C][C] 0.0002437[/C][C] 0.9999[/C][/ROW]
[ROW][C]62[/C][C] 0.0001105[/C][C] 0.000221[/C][C] 0.9999[/C][/ROW]
[ROW][C]63[/C][C] 7.362e-05[/C][C] 0.0001472[/C][C] 0.9999[/C][/ROW]
[ROW][C]64[/C][C] 4.699e-05[/C][C] 9.398e-05[/C][C] 1[/C][/ROW]
[ROW][C]65[/C][C] 2.913e-05[/C][C] 5.826e-05[/C][C] 1[/C][/ROW]
[ROW][C]66[/C][C] 2.743e-05[/C][C] 5.486e-05[/C][C] 1[/C][/ROW]
[ROW][C]67[/C][C] 2.925e-05[/C][C] 5.849e-05[/C][C] 1[/C][/ROW]
[ROW][C]68[/C][C] 1.753e-05[/C][C] 3.507e-05[/C][C] 1[/C][/ROW]
[ROW][C]69[/C][C] 1.223e-05[/C][C] 2.447e-05[/C][C] 1[/C][/ROW]
[ROW][C]70[/C][C] 8.519e-06[/C][C] 1.704e-05[/C][C] 1[/C][/ROW]
[ROW][C]71[/C][C] 8.721e-06[/C][C] 1.744e-05[/C][C] 1[/C][/ROW]
[ROW][C]72[/C][C] 5.914e-06[/C][C] 1.183e-05[/C][C] 1[/C][/ROW]
[ROW][C]73[/C][C] 3.431e-06[/C][C] 6.861e-06[/C][C] 1[/C][/ROW]
[ROW][C]74[/C][C] 2.903e-06[/C][C] 5.805e-06[/C][C] 1[/C][/ROW]
[ROW][C]75[/C][C] 1.88e-06[/C][C] 3.76e-06[/C][C] 1[/C][/ROW]
[ROW][C]76[/C][C] 2.499e-06[/C][C] 4.998e-06[/C][C] 1[/C][/ROW]
[ROW][C]77[/C][C] 1.818e-06[/C][C] 3.635e-06[/C][C] 1[/C][/ROW]
[ROW][C]78[/C][C] 1.047e-06[/C][C] 2.094e-06[/C][C] 1[/C][/ROW]
[ROW][C]79[/C][C] 1.844e-06[/C][C] 3.689e-06[/C][C] 1[/C][/ROW]
[ROW][C]80[/C][C] 1.088e-06[/C][C] 2.177e-06[/C][C] 1[/C][/ROW]
[ROW][C]81[/C][C] 3.09e-06[/C][C] 6.179e-06[/C][C] 1[/C][/ROW]
[ROW][C]82[/C][C] 2.069e-06[/C][C] 4.138e-06[/C][C] 1[/C][/ROW]
[ROW][C]83[/C][C] 1.539e-06[/C][C] 3.079e-06[/C][C] 1[/C][/ROW]
[ROW][C]84[/C][C] 7.236e-06[/C][C] 1.447e-05[/C][C] 1[/C][/ROW]
[ROW][C]85[/C][C] 0.0001432[/C][C] 0.0002863[/C][C] 0.9999[/C][/ROW]
[ROW][C]86[/C][C] 0.001326[/C][C] 0.002653[/C][C] 0.9987[/C][/ROW]
[ROW][C]87[/C][C] 0.001014[/C][C] 0.002029[/C][C] 0.999[/C][/ROW]
[ROW][C]88[/C][C] 0.0008073[/C][C] 0.001615[/C][C] 0.9992[/C][/ROW]
[ROW][C]89[/C][C] 0.0005943[/C][C] 0.001189[/C][C] 0.9994[/C][/ROW]
[ROW][C]90[/C][C] 0.0008671[/C][C] 0.001734[/C][C] 0.9991[/C][/ROW]
[ROW][C]91[/C][C] 0.0006811[/C][C] 0.001362[/C][C] 0.9993[/C][/ROW]
[ROW][C]92[/C][C] 0.0005423[/C][C] 0.001085[/C][C] 0.9995[/C][/ROW]
[ROW][C]93[/C][C] 0.0007638[/C][C] 0.001528[/C][C] 0.9992[/C][/ROW]
[ROW][C]94[/C][C] 0.0005321[/C][C] 0.001064[/C][C] 0.9995[/C][/ROW]
[ROW][C]95[/C][C] 0.0008711[/C][C] 0.001742[/C][C] 0.9991[/C][/ROW]
[ROW][C]96[/C][C] 0.0006145[/C][C] 0.001229[/C][C] 0.9994[/C][/ROW]
[ROW][C]97[/C][C] 0.0004472[/C][C] 0.0008944[/C][C] 0.9996[/C][/ROW]
[ROW][C]98[/C][C] 0.0006041[/C][C] 0.001208[/C][C] 0.9994[/C][/ROW]
[ROW][C]99[/C][C] 0.0004161[/C][C] 0.0008321[/C][C] 0.9996[/C][/ROW]
[ROW][C]100[/C][C] 0.0005837[/C][C] 0.001167[/C][C] 0.9994[/C][/ROW]
[ROW][C]101[/C][C] 0.0004158[/C][C] 0.0008315[/C][C] 0.9996[/C][/ROW]
[ROW][C]102[/C][C] 0.0002894[/C][C] 0.0005789[/C][C] 0.9997[/C][/ROW]
[ROW][C]103[/C][C] 0.0004191[/C][C] 0.0008382[/C][C] 0.9996[/C][/ROW]
[ROW][C]104[/C][C] 0.0002906[/C][C] 0.0005812[/C][C] 0.9997[/C][/ROW]
[ROW][C]105[/C][C] 0.001293[/C][C] 0.002587[/C][C] 0.9987[/C][/ROW]
[ROW][C]106[/C][C] 0.0009887[/C][C] 0.001977[/C][C] 0.999[/C][/ROW]
[ROW][C]107[/C][C] 0.001164[/C][C] 0.002328[/C][C] 0.9988[/C][/ROW]
[ROW][C]108[/C][C] 0.0008275[/C][C] 0.001655[/C][C] 0.9992[/C][/ROW]
[ROW][C]109[/C][C] 0.003602[/C][C] 0.007204[/C][C] 0.9964[/C][/ROW]
[ROW][C]110[/C][C] 0.003062[/C][C] 0.006123[/C][C] 0.9969[/C][/ROW]
[ROW][C]111[/C][C] 0.008649[/C][C] 0.0173[/C][C] 0.9914[/C][/ROW]
[ROW][C]112[/C][C] 0.007639[/C][C] 0.01528[/C][C] 0.9924[/C][/ROW]
[ROW][C]113[/C][C] 0.01488[/C][C] 0.02975[/C][C] 0.9851[/C][/ROW]
[ROW][C]114[/C][C] 0.01578[/C][C] 0.03157[/C][C] 0.9842[/C][/ROW]
[ROW][C]115[/C][C] 0.01212[/C][C] 0.02423[/C][C] 0.9879[/C][/ROW]
[ROW][C]116[/C][C] 0.01213[/C][C] 0.02426[/C][C] 0.9879[/C][/ROW]
[ROW][C]117[/C][C] 0.009053[/C][C] 0.01811[/C][C] 0.9909[/C][/ROW]
[ROW][C]118[/C][C] 0.008546[/C][C] 0.01709[/C][C] 0.9915[/C][/ROW]
[ROW][C]119[/C][C] 0.00648[/C][C] 0.01296[/C][C] 0.9935[/C][/ROW]
[ROW][C]120[/C][C] 0.009141[/C][C] 0.01828[/C][C] 0.9909[/C][/ROW]
[ROW][C]121[/C][C] 0.006827[/C][C] 0.01365[/C][C] 0.9932[/C][/ROW]
[ROW][C]122[/C][C] 0.005333[/C][C] 0.01067[/C][C] 0.9947[/C][/ROW]
[ROW][C]123[/C][C] 0.00596[/C][C] 0.01192[/C][C] 0.994[/C][/ROW]
[ROW][C]124[/C][C] 0.0111[/C][C] 0.02219[/C][C] 0.9889[/C][/ROW]
[ROW][C]125[/C][C] 0.05468[/C][C] 0.1094[/C][C] 0.9453[/C][/ROW]
[ROW][C]126[/C][C] 0.05307[/C][C] 0.1061[/C][C] 0.9469[/C][/ROW]
[ROW][C]127[/C][C] 0.04398[/C][C] 0.08797[/C][C] 0.956[/C][/ROW]
[ROW][C]128[/C][C] 0.03599[/C][C] 0.07198[/C][C] 0.964[/C][/ROW]
[ROW][C]129[/C][C] 0.03139[/C][C] 0.06277[/C][C] 0.9686[/C][/ROW]
[ROW][C]130[/C][C] 0.05952[/C][C] 0.119[/C][C] 0.9405[/C][/ROW]
[ROW][C]131[/C][C] 0.05611[/C][C] 0.1122[/C][C] 0.9439[/C][/ROW]
[ROW][C]132[/C][C] 0.04477[/C][C] 0.08954[/C][C] 0.9552[/C][/ROW]
[ROW][C]133[/C][C] 0.04963[/C][C] 0.09925[/C][C] 0.9504[/C][/ROW]
[ROW][C]134[/C][C] 0.05415[/C][C] 0.1083[/C][C] 0.9459[/C][/ROW]
[ROW][C]135[/C][C] 0.1028[/C][C] 0.2056[/C][C] 0.8972[/C][/ROW]
[ROW][C]136[/C][C] 0.1465[/C][C] 0.293[/C][C] 0.8535[/C][/ROW]
[ROW][C]137[/C][C] 0.1236[/C][C] 0.2472[/C][C] 0.8764[/C][/ROW]
[ROW][C]138[/C][C] 0.2215[/C][C] 0.4429[/C][C] 0.7785[/C][/ROW]
[ROW][C]139[/C][C] 0.2407[/C][C] 0.4815[/C][C] 0.7593[/C][/ROW]
[ROW][C]140[/C][C] 0.2296[/C][C] 0.4593[/C][C] 0.7704[/C][/ROW]
[ROW][C]141[/C][C] 0.1935[/C][C] 0.387[/C][C] 0.8065[/C][/ROW]
[ROW][C]142[/C][C] 0.1895[/C][C] 0.3791[/C][C] 0.8105[/C][/ROW]
[ROW][C]143[/C][C] 0.1785[/C][C] 0.3571[/C][C] 0.8215[/C][/ROW]
[ROW][C]144[/C][C] 0.2189[/C][C] 0.4379[/C][C] 0.7811[/C][/ROW]
[ROW][C]145[/C][C] 0.2287[/C][C] 0.4574[/C][C] 0.7713[/C][/ROW]
[ROW][C]146[/C][C] 0.2806[/C][C] 0.5612[/C][C] 0.7194[/C][/ROW]
[ROW][C]147[/C][C] 0.2944[/C][C] 0.5888[/C][C] 0.7056[/C][/ROW]
[ROW][C]148[/C][C] 0.2485[/C][C] 0.497[/C][C] 0.7515[/C][/ROW]
[ROW][C]149[/C][C] 0.2306[/C][C] 0.4611[/C][C] 0.7694[/C][/ROW]
[ROW][C]150[/C][C] 0.2539[/C][C] 0.5079[/C][C] 0.7461[/C][/ROW]
[ROW][C]151[/C][C] 0.219[/C][C] 0.4381[/C][C] 0.781[/C][/ROW]
[ROW][C]152[/C][C] 0.1817[/C][C] 0.3633[/C][C] 0.8183[/C][/ROW]
[ROW][C]153[/C][C] 0.1701[/C][C] 0.3402[/C][C] 0.8299[/C][/ROW]
[ROW][C]154[/C][C] 0.1639[/C][C] 0.3279[/C][C] 0.8361[/C][/ROW]
[ROW][C]155[/C][C] 0.1294[/C][C] 0.2588[/C][C] 0.8706[/C][/ROW]
[ROW][C]156[/C][C] 0.1001[/C][C] 0.2003[/C][C] 0.8999[/C][/ROW]
[ROW][C]157[/C][C] 0.08953[/C][C] 0.1791[/C][C] 0.9105[/C][/ROW]
[ROW][C]158[/C][C] 0.07932[/C][C] 0.1586[/C][C] 0.9207[/C][/ROW]
[ROW][C]159[/C][C] 0.05832[/C][C] 0.1166[/C][C] 0.9417[/C][/ROW]
[ROW][C]160[/C][C] 0.04471[/C][C] 0.08941[/C][C] 0.9553[/C][/ROW]
[ROW][C]161[/C][C] 0.03386[/C][C] 0.06773[/C][C] 0.9661[/C][/ROW]
[ROW][C]162[/C][C] 0.02548[/C][C] 0.05096[/C][C] 0.9745[/C][/ROW]
[ROW][C]163[/C][C] 0.02083[/C][C] 0.04166[/C][C] 0.9792[/C][/ROW]
[ROW][C]164[/C][C] 0.01768[/C][C] 0.03537[/C][C] 0.9823[/C][/ROW]
[ROW][C]165[/C][C] 0.0128[/C][C] 0.02561[/C][C] 0.9872[/C][/ROW]
[ROW][C]166[/C][C] 0.00919[/C][C] 0.01838[/C][C] 0.9908[/C][/ROW]
[ROW][C]167[/C][C] 0.01267[/C][C] 0.02534[/C][C] 0.9873[/C][/ROW]
[ROW][C]168[/C][C] 0.007737[/C][C] 0.01547[/C][C] 0.9923[/C][/ROW]
[ROW][C]169[/C][C] 0.01346[/C][C] 0.02692[/C][C] 0.9865[/C][/ROW]
[ROW][C]170[/C][C] 0.01332[/C][C] 0.02663[/C][C] 0.9867[/C][/ROW]
[ROW][C]171[/C][C] 0.146[/C][C] 0.292[/C][C] 0.854[/C][/ROW]
[ROW][C]172[/C][C] 0.1012[/C][C] 0.2023[/C][C] 0.8988[/C][/ROW]
[ROW][C]173[/C][C] 0.1067[/C][C] 0.2134[/C][C] 0.8933[/C][/ROW]
[ROW][C]174[/C][C] 0.197[/C][C] 0.3939[/C][C] 0.803[/C][/ROW]
[ROW][C]175[/C][C] 0.1223[/C][C] 0.2446[/C][C] 0.8777[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310685&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310685&T=6

As an alternative you can also use a QR Code:

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

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
9	0.2681	0.5362	0.7319
10	0.1567	0.3135	0.8433
11	0.3773	0.7546	0.6227
12	0.3473	0.6946	0.6527
13	0.293	0.5859	0.707
14	0.2013	0.4026	0.7987
15	0.1603	0.3205	0.8397
16	0.1051	0.2101	0.8949
17	0.09864	0.1973	0.9014
18	0.08267	0.1653	0.9173
19	0.0526	0.1052	0.9474
20	0.07744	0.1549	0.9226
21	0.05213	0.1043	0.9479
22	0.03635	0.07269	0.9637
23	0.1985	0.3971	0.8015
24	0.185	0.37	0.815
25	0.1402	0.2804	0.8598
26	0.1078	0.2156	0.8922
27	0.1017	0.2034	0.8983
28	0.07548	0.151	0.9245
29	0.05889	0.1178	0.9411
30	0.05211	0.1042	0.9479
31	0.04045	0.08089	0.9596
32	0.03392	0.06784	0.9661
33	0.02345	0.0469	0.9766
34	0.02453	0.04905	0.9755
35	0.01926	0.03852	0.9807
36	0.02111	0.04222	0.9789
37	0.03863	0.07725	0.9614
38	0.03086	0.06173	0.9691
39	0.02547	0.05095	0.9745
40	0.02446	0.04892	0.9755
41	0.01735	0.03469	0.9827
42	0.01213	0.02425	0.9879
43	0.008777	0.01755	0.9912
44	0.006133	0.01227	0.9939
45	0.004105	0.008209	0.9959
46	0.003754	0.007508	0.9962
47	0.002482	0.004965	0.9975
48	0.001696	0.003392	0.9983
49	0.001092	0.002184	0.9989
50	0.0007186	0.001437	0.9993
51	0.001593	0.003186	0.9984
52	0.001123	0.002246	0.9989
53	0.001125	0.00225	0.9989
54	0.0007594	0.001519	0.9992
55	0.0005138	0.001028	0.9995
56	0.0003819	0.0007639	0.9996
57	0.0002946	0.0005892	0.9997
58	0.0002211	0.0004422	0.9998
59	0.0002621	0.0005242	0.9997
60	0.0001876	0.0003751	0.9998
61	0.0001219	0.0002437	0.9999
62	0.0001105	0.000221	0.9999
63	7.362e-05	0.0001472	0.9999
64	4.699e-05	9.398e-05	1
65	2.913e-05	5.826e-05	1
66	2.743e-05	5.486e-05	1
67	2.925e-05	5.849e-05	1
68	1.753e-05	3.507e-05	1
69	1.223e-05	2.447e-05	1
70	8.519e-06	1.704e-05	1
71	8.721e-06	1.744e-05	1
72	5.914e-06	1.183e-05	1
73	3.431e-06	6.861e-06	1
74	2.903e-06	5.805e-06	1
75	1.88e-06	3.76e-06	1
76	2.499e-06	4.998e-06	1
77	1.818e-06	3.635e-06	1
78	1.047e-06	2.094e-06	1
79	1.844e-06	3.689e-06	1
80	1.088e-06	2.177e-06	1
81	3.09e-06	6.179e-06	1
82	2.069e-06	4.138e-06	1
83	1.539e-06	3.079e-06	1
84	7.236e-06	1.447e-05	1
85	0.0001432	0.0002863	0.9999
86	0.001326	0.002653	0.9987
87	0.001014	0.002029	0.999
88	0.0008073	0.001615	0.9992
89	0.0005943	0.001189	0.9994
90	0.0008671	0.001734	0.9991
91	0.0006811	0.001362	0.9993
92	0.0005423	0.001085	0.9995
93	0.0007638	0.001528	0.9992
94	0.0005321	0.001064	0.9995
95	0.0008711	0.001742	0.9991
96	0.0006145	0.001229	0.9994
97	0.0004472	0.0008944	0.9996
98	0.0006041	0.001208	0.9994
99	0.0004161	0.0008321	0.9996
100	0.0005837	0.001167	0.9994
101	0.0004158	0.0008315	0.9996
102	0.0002894	0.0005789	0.9997
103	0.0004191	0.0008382	0.9996
104	0.0002906	0.0005812	0.9997
105	0.001293	0.002587	0.9987
106	0.0009887	0.001977	0.999
107	0.001164	0.002328	0.9988
108	0.0008275	0.001655	0.9992
109	0.003602	0.007204	0.9964
110	0.003062	0.006123	0.9969
111	0.008649	0.0173	0.9914
112	0.007639	0.01528	0.9924
113	0.01488	0.02975	0.9851
114	0.01578	0.03157	0.9842
115	0.01212	0.02423	0.9879
116	0.01213	0.02426	0.9879
117	0.009053	0.01811	0.9909
118	0.008546	0.01709	0.9915
119	0.00648	0.01296	0.9935
120	0.009141	0.01828	0.9909
121	0.006827	0.01365	0.9932
122	0.005333	0.01067	0.9947
123	0.00596	0.01192	0.994
124	0.0111	0.02219	0.9889
125	0.05468	0.1094	0.9453
126	0.05307	0.1061	0.9469
127	0.04398	0.08797	0.956
128	0.03599	0.07198	0.964
129	0.03139	0.06277	0.9686
130	0.05952	0.119	0.9405
131	0.05611	0.1122	0.9439
132	0.04477	0.08954	0.9552
133	0.04963	0.09925	0.9504
134	0.05415	0.1083	0.9459
135	0.1028	0.2056	0.8972
136	0.1465	0.293	0.8535
137	0.1236	0.2472	0.8764
138	0.2215	0.4429	0.7785
139	0.2407	0.4815	0.7593
140	0.2296	0.4593	0.7704
141	0.1935	0.387	0.8065
142	0.1895	0.3791	0.8105
143	0.1785	0.3571	0.8215
144	0.2189	0.4379	0.7811
145	0.2287	0.4574	0.7713
146	0.2806	0.5612	0.7194
147	0.2944	0.5888	0.7056
148	0.2485	0.497	0.7515
149	0.2306	0.4611	0.7694
150	0.2539	0.5079	0.7461
151	0.219	0.4381	0.781
152	0.1817	0.3633	0.8183
153	0.1701	0.3402	0.8299
154	0.1639	0.3279	0.8361
155	0.1294	0.2588	0.8706
156	0.1001	0.2003	0.8999
157	0.08953	0.1791	0.9105
158	0.07932	0.1586	0.9207
159	0.05832	0.1166	0.9417
160	0.04471	0.08941	0.9553
161	0.03386	0.06773	0.9661
162	0.02548	0.05096	0.9745
163	0.02083	0.04166	0.9792
164	0.01768	0.03537	0.9823
165	0.0128	0.02561	0.9872
166	0.00919	0.01838	0.9908
167	0.01267	0.02534	0.9873
168	0.007737	0.01547	0.9923
169	0.01346	0.02692	0.9865
170	0.01332	0.02663	0.9867
171	0.146	0.292	0.854
172	0.1012	0.2023	0.8988
173	0.1067	0.2134	0.8933
174	0.197	0.3939	0.803
175	0.1223	0.2446	0.8777

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	66	0.3952	NOK
5% type I error level	97	0.580838	NOK
10% type I error level	111	0.664671	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 & 66 &  0.3952 & NOK \tabularnewline
5% type I error level & 97 & 0.580838 & NOK \tabularnewline
10% type I error level & 111 & 0.664671 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310685&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]66[/C][C] 0.3952[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]97[/C][C]0.580838[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]111[/C][C]0.664671[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310685&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310685&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	66	0.3952	NOK
5% type I error level	97	0.580838	NOK
10% type I error level	111	0.664671	NOK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 1.13, df1 = 2, df2 = 176, p-value = 0.3254
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.507, df1 = 10, df2 = 168, p-value = 0.1406
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 5.718, df1 = 2, df2 = 176, p-value = 0.003927

\begin{tabular}{lllllllll}
\hline
Ramsey RESET F-Test for powers (2 and 3) of fitted values \tabularnewline
> reset_test_fitted
	RESET test
data:  mylm
RESET = 1.13, df1 = 2, df2 = 176, p-value = 0.3254
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.507, df1 = 10, df2 = 168, p-value = 0.1406
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 5.718, df1 = 2, df2 = 176, p-value = 0.003927
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310685&T=8

[TABLE]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of fitted values[/C][/ROW]
[ROW][C]> reset_test_fitted
	RESET test
data:  mylm
RESET = 1.13, df1 = 2, df2 = 176, p-value = 0.3254
[/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 = 1.507, df1 = 10, df2 = 168, p-value = 0.1406
[/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 = 5.718, df1 = 2, df2 = 176, p-value = 0.003927
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310685&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310685&T=8

As an alternative you can also use a QR Code:

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

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 1.13, df1 = 2, df2 = 176, p-value = 0.3254
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.507, df1 = 10, df2 = 168, p-value = 0.1406
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 5.718, df1 = 2, df2 = 176, p-value = 0.003927

Variance Inflation Factors (Multicollinearity)
> vif `(1-Bs)(1-B)TotIBMIN` `(1-Bs)(1-B)FBT(t-1)` `(1-Bs)(1-B)FBT(t-2)` 1.534819 3.106109 3.637609 `(1-Bs)(1-B)FBT(t-3)` `(1-Bs)(1-B)FBT(t-1s)` 2.327487 1.121487

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
 `(1-Bs)(1-B)TotIBMIN`  `(1-Bs)(1-B)FBT(t-1)`  `(1-Bs)(1-B)FBT(t-2)` 
              1.534819               3.106109               3.637609 
 `(1-Bs)(1-B)FBT(t-3)` `(1-Bs)(1-B)FBT(t-1s)` 
              2.327487               1.121487 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310685&T=9

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
 `(1-Bs)(1-B)TotIBMIN`  `(1-Bs)(1-B)FBT(t-1)`  `(1-Bs)(1-B)FBT(t-2)` 
              1.534819               3.106109               3.637609 
 `(1-Bs)(1-B)FBT(t-3)` `(1-Bs)(1-B)FBT(t-1s)` 
              2.327487               1.121487 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310685&T=9

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310685&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)TotIBMIN` `(1-Bs)(1-B)FBT(t-1)` `(1-Bs)(1-B)FBT(t-2)` 1.534819 3.106109 3.637609 `(1-Bs)(1-B)FBT(t-3)` `(1-Bs)(1-B)FBT(t-1s)` 2.327487 1.121487

Figure 1

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Figure 7

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Figure 8

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Figure 9

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Figure 10

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Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = 6 ; par5 = 3 ; par6 = 12 ;

Parameters (R input):

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

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

par6 <- '12'
par5 <- '1'
par4 <- '3'
par3 <- 'First and Seasonal Differences (s)'
par2 <- 'Do not include Seasonal Dummies'
par1 <- '1'
library(lattice)
library(lmtest)
library(car)
library(MASS)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
mywarning <- ''
par6 <- as.numeric(par6)
if(is.na(par6)) {
par6 <- 12
mywarning = 'Warning: you did not specify the seasonality. The seasonal period was set to s = 12.'
}
par1 <- as.numeric(par1)
if(is.na(par1)) {
par1 <- 1
mywarning = 'Warning: you did not specify the column number of the endogenous series! The first column was selected by default.'
}
if (par4=='') par4 <- 0
par4 <- as.numeric(par4)
if (!is.numeric(par4)) par4 <- 0
if (par5=='') par5 <- 0
par5 <- as.numeric(par5)
if (!is.numeric(par5)) par5 <- 0
x <- na.omit(t(y))
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'Seasonal Differences (s)'){
(n <- n - par6)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-Bs)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+par6,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'First and Seasonal Differences (s)'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
(n <- n - par6)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-Bs)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+par6,j] - x[i,j]
}
}
x <- x2
}
if(par4 > 0) {
x2 <- array(0, dim=c(n-par4,par4), dimnames=list(1:(n-par4), paste(colnames(x)[par1],'(t-',1:par4,')',sep='')))
for (i in 1:(n-par4)) {
for (j in 1:par4) {
x2[i,j] <- x[i+par4-j,par1]
}
}
x <- cbind(x[(par4+1):n,], x2)
n <- n - par4
}
if(par5 > 0) {
x2 <- array(0, dim=c(n-par5*par6,par5), dimnames=list(1:(n-par5*par6), paste(colnames(x)[par1],'(t-',1:par5,'s)',sep='')))
for (i in 1:(n-par5*par6)) {
for (j in 1:par5) {
x2[i,j] <- x[i+par5*par6-j*par6,par1]
}
}
x <- cbind(x[(par5*par6+1):n,], x2)
n <- n - par5*par6
}
if (par2 == 'Include Seasonal Dummies'){
x2 <- array(0, dim=c(n,par6-1), dimnames=list(1:n, paste('M', seq(1:(par6-1)), sep ='')))
for (i in 1:(par6-1)){
x2[seq(i,n,par6),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
(k <- length(x[n,]))
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
print(x)
(k <- length(x[n,]))
head(x)
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
sresid <- studres(mylm)
hist(sresid, freq=FALSE, main='Distribution of Studentized Residuals')
xfit<-seq(min(sresid),max(sresid),length=40)
yfit<-dnorm(xfit)
lines(xfit, yfit)
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqPlot(mylm, main='QQ Plot')
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
print(z)
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, mywarning)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Multiple Linear Regression - Ordinary Least Squares', 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,formatC(signif(mysum$coefficients[i,1],5),format='g',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,2],5),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,3],4),format='e',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4],4),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4]/2,4),format='g',flag=' '))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a,formatC(signif(sqrt(mysum$r.squared),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$adj.r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a,formatC(signif(mysum$fstatistic[1],6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[2],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[3],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a,formatC(signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a,formatC(signif(mysum$sigma,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a,formatC(signif(sum(myerror*myerror),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
myr <- as.numeric(mysum$resid)
myr
a <-table.start()
a <- table.row.start(a)
a <- table.element(a,'Menu of Residual Diagnostics',2,TRUE)
a <- table.row.end(a)
a <- table.row.start(a)
a <- table.element(a,'Description',1,TRUE)
a <- table.element(a,'Link',1,TRUE)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Histogram',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_histogram.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Central Tendency',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_centraltendency.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'QQ Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_fitdistrnorm.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Kernel Density Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_density.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Skewness/Kurtosis Test',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_skewness_kurtosis.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Skewness-Kurtosis Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_skewness_kurtosis_plot.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Harrell-Davis Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_harrell_davis.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Bootstrap Plot -- Central Tendency',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_bootstrapplot1.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Blocked Bootstrap Plot -- Central Tendency',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_bootstrapplot.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'(Partial) Autocorrelation Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_autocorrelation.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Spectral Analysis',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_spectrum.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Tukey lambda PPCC Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_tukeylambda.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Box-Cox Normality Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_boxcoxnorm.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <- table.element(a,'Summary Statistics',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_summary1.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable7.tab')
if(n < 200) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,formatC(signif(x[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(x[i]-mysum$resid[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$resid[i],6),format='g',flag=' '))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,1],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,2],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,3],6),format='g',flag=' '))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant1,6))
a<-table.element(a,formatC(signif(numsignificant1/numgqtests,6),format='g',flag=' '))
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant5,6))
a<-table.element(a,signif(numsignificant5/numgqtests,6))
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant10,6))
a<-table.element(a,signif(numsignificant10/numgqtests,6))
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}
}
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of fitted values',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_fitted <- resettest(mylm,power=2:3,type='fitted')
a<-table.element(a,paste('',RC.texteval('reset_test_fitted'),'
',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of regressors',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_regressors <- resettest(mylm,power=2:3,type='regressor')
a<-table.element(a,paste('',RC.texteval('reset_test_regressors'),'
',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of principal components',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_principal_components <- resettest(mylm,power=2:3,type='princomp')
a<-table.element(a,paste('',RC.texteval('reset_test_principal_components'),'
',sep=''))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable8.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Variance Inflation Factors (Multicollinearity)',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
vif <- vif(mylm)
a<-table.element(a,paste('',RC.texteval('vif'),'
',sep=''))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable9.tab')

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code