Repository of Reproducible Computations

Free Statistics

of Irreproducible Research!

Author's title

Author

*The author of this computation has been verified*

R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Wed, 07 Dec 2016 15:29:50 +0100

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2016/Dec/07/t148112111018lzdc24k7hw7j1.htm/, Retrieved Tue, 07 May 2024 21:08:36 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=298147, Retrieved Tue, 07 May 2024 21:08:36 +0000

QR Codes:

Paste this QR Code to cite your computation.

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

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

-       [Multiple Regression] [regressie analyse...] [2016-12-07 14:29:50] [111362aa4cdbe055231fbc5cb9e916c4] [Current]

Feedback Forum

Post a new message

Dataseries X:

Download CSV

Histogram

Boxplots

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	8 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 time8 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298147&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]

8 seconds[/C][/ROW] [ROW]

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=298147&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298147&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	8 seconds
R Server	Big Analytics Cloud Computing Center

Multiple Linear Regression - Estimated Regression Equation
SPriv[t] = + 13.5385 -0.224736Gesl[t] + 0.57265Opl[t] + 0.201344GeslxOpl[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
SPriv[t] =  +  13.5385 -0.224736Gesl[t] +  0.57265Opl[t] +  0.201344GeslxOpl[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298147&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]SPriv[t] =  +  13.5385 -0.224736Gesl[t] +  0.57265Opl[t] +  0.201344GeslxOpl[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298147&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298147&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
SPriv[t] = + 13.5385 -0.224736Gesl[t] + 0.57265Opl[t] + 0.201344GeslxOpl[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)	+13.54	0.3739	+3.6210e+01	4.13e-78	2.065e-78
Gesl	-0.2247	0.4594	-4.8920e-01	0.6254	0.3127
Opl	+0.5726	0.5239	+1.0930e+00	0.276	0.138
GeslxOpl	+0.2013	0.6399	+3.1470e-01	0.7534	0.3767

\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) & +13.54 &  0.3739 & +3.6210e+01 &  4.13e-78 &  2.065e-78 \tabularnewline
Gesl & -0.2247 &  0.4594 & -4.8920e-01 &  0.6254 &  0.3127 \tabularnewline
Opl & +0.5726 &  0.5239 & +1.0930e+00 &  0.276 &  0.138 \tabularnewline
GeslxOpl & +0.2013 &  0.6399 & +3.1470e-01 &  0.7534 &  0.3767 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298147&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]+13.54[/C][C] 0.3739[/C][C]+3.6210e+01[/C][C] 4.13e-78[/C][C] 2.065e-78[/C][/ROW]
[ROW][C]Gesl[/C][C]-0.2247[/C][C] 0.4594[/C][C]-4.8920e-01[/C][C] 0.6254[/C][C] 0.3127[/C][/ROW]
[ROW][C]Opl[/C][C]+0.5726[/C][C] 0.5239[/C][C]+1.0930e+00[/C][C] 0.276[/C][C] 0.138[/C][/ROW]
[ROW][C]GeslxOpl[/C][C]+0.2013[/C][C] 0.6399[/C][C]+3.1470e-01[/C][C] 0.7534[/C][C] 0.3767[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298147&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298147&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)	+13.54	0.3739	+3.6210e+01	4.13e-78	2.065e-78
Gesl	-0.2247	0.4594	-4.8920e-01	0.6254	0.3127
Opl	+0.5726	0.5239	+1.0930e+00	0.276	0.138
GeslxOpl	+0.2013	0.6399	+3.1470e-01	0.7534	0.3767

Multiple Linear Regression - Regression Statistics
Multiple R	0.1879
R-squared	0.0353
Adjusted R-squared	0.01687
F-TEST (value)	1.915
F-TEST (DF numerator)	3
F-TEST (DF denominator)	157
p-value	0.1294
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.907
Sum Squared Residuals	570.7

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.1879 \tabularnewline
R-squared &  0.0353 \tabularnewline
Adjusted R-squared &  0.01687 \tabularnewline
F-TEST (value) &  1.915 \tabularnewline
F-TEST (DF numerator) & 3 \tabularnewline
F-TEST (DF denominator) & 157 \tabularnewline
p-value &  0.1294 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  1.907 \tabularnewline
Sum Squared Residuals &  570.7 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298147&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.1879[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.0353[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.01687[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 1.915[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]3[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]157[/C][/ROW]
[ROW][C]p-value[/C][C] 0.1294[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 1.907[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 570.7[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298147&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298147&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	0.1879
R-squared	0.0353
Adjusted R-squared	0.01687
F-TEST (value)	1.915
F-TEST (DF numerator)	3
F-TEST (DF denominator)	157
p-value	0.1294
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.907
Sum Squared Residuals	570.7

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	15	13.31	1.686
2	13	14.09	-1.088
3	14	14.09	-0.08772
4	13	14.09	-1.088
5	12	13.31	-1.314
6	17	14.09	2.912
7	12	14.09	-2.088
8	13	14.11	-1.111
9	13	13.54	-0.5385
10	16	13.31	2.686
11	12	13.31	-1.314
12	12	13.54	-1.538
13	13	13.54	-0.5385
14	16	13.31	2.686
15	15	13.31	1.686
16	12	14.09	-2.088
17	15	14.09	0.9123
18	12	14.09	-2.088
19	15	14.09	0.9123
20	11	13.31	-2.314
21	13	14.09	-1.088
22	13	14.09	-1.088
23	14	13.31	0.6863
24	14	13.31	0.6863
25	14	13.54	0.4615
26	15	13.54	1.462
27	16	13.31	2.686
28	16	13.31	2.686
29	16	13.31	2.686
30	13	14.11	-1.111
31	13	13.31	-0.3137
32	14	13.54	0.4615
33	13	14.09	-1.088
34	14	13.31	0.6863
35	12	13.31	-1.314
36	17	14.11	2.889
37	14	13.54	0.4615
38	15	13.54	1.462
39	13	14.11	-1.111
40	14	14.11	-0.1111
41	15	14.11	0.8889
42	19	14.09	4.912
43	14	14.11	-0.1111
44	13	14.11	-1.111
45	12	14.09	-2.088
46	14	14.09	-0.08772
47	15	13.31	1.686
48	15	14.11	0.8889
49	12	13.54	-1.538
50	14	14.11	-0.1111
51	11	14.11	-3.111
52	12	13.31	-1.314
53	10	14.09	-4.088
54	14	13.54	0.4615
55	14	14.11	-0.1111
56	15	13.31	1.686
57	15	14.09	0.9123
58	13	14.11	-1.111
59	15	14.09	0.9123
60	16	14.09	1.912
61	12	13.31	-1.314
62	17	14.09	2.912
63	15	13.31	1.686
64	12	13.31	-1.314
65	16	14.09	1.912
66	15	14.09	0.9123
67	15	14.09	0.9123
68	12	13.31	-1.314
69	13	14.11	-1.111
70	10	13.31	-3.314
71	14	14.09	-0.08772
72	11	13.31	-2.314
73	12	14.09	-2.088
74	14	13.31	0.6863
75	12	14.09	-2.088
76	14	14.09	-0.08772
77	12	13.31	-1.314
78	13	13.31	-0.3137
79	13	13.54	-0.5385
80	14	14.09	-0.08772
81	12	14.09	-2.088
82	15	14.09	0.9123
83	13	14.09	-1.088
84	13	14.09	-1.088
85	11	13.54	-2.538
86	12	14.11	-2.111
87	16	13.54	2.462
88	11	13.31	-2.314
89	13	13.31	-0.3137
90	12	13.54	-1.538
91	17	14.09	2.912
92	14	14.09	-0.08772
93	15	14.09	0.9123
94	8	13.31	-5.314
95	13	14.09	-1.088
96	13	14.09	-1.088
97	15	13.31	1.686
98	14	13.31	0.6863
99	13	14.09	-1.088
100	14	14.09	-0.08772
101	12	14.09	-2.088
102	19	14.11	4.889
103	15	13.31	1.686
104	14	13.54	0.4615
105	14	13.31	0.6863
106	15	14.09	0.9123
107	13	14.09	-1.088
108	15	14.11	0.8889
109	14	13.31	0.6863
110	11	13.31	-2.314
111	17	14.09	2.912
112	13	13.31	-0.3137
113	9	13.31	-4.314
114	12	14.11	-2.111
115	13	13.54	-0.5385
116	17	13.54	3.462
117	14	13.54	0.4615
118	13	14.11	-1.111
119	16	14.09	1.912
120	14	14.09	-0.08772
121	14	14.09	-0.08772
122	14	14.09	-0.08772
123	10	13.31	-3.314
124	12	13.54	-1.538
125	13	14.11	-1.111
126	14	14.09	-0.08772
127	18	14.11	3.889
128	14	13.31	0.6863
129	14	14.11	-0.1111
130	13	13.31	-0.3137
131	13	13.31	-0.3137
132	16	13.54	2.462
133	13	14.09	-1.088
134	14	13.54	0.4615
135	8	13.31	-5.314
136	13	13.54	-0.5385
137	13	13.54	-0.5385
138	16	14.09	1.912
139	14	13.31	0.6863
140	13	14.09	-1.088
141	14	14.09	-0.08772
142	12	14.09	-2.088
143	16	14.09	1.912
144	18	13.31	4.686
145	16	13.31	2.686
146	15	13.31	1.686
147	18	13.54	4.462
148	15	14.11	0.8889
149	14	14.11	-0.1111
150	14	14.11	-0.1111
151	15	14.11	0.8889
152	9	13.54	-4.538
153	17	14.09	2.912
154	11	13.54	-2.538
155	15	14.11	0.8889
156	15	13.31	1.686
157	13	13.31	-0.3137
158	15	13.31	1.686
159	15	14.09	0.9123
160	14	14.09	-0.08772
161	13	13.31	-0.3137

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  15 &  13.31 &  1.686 \tabularnewline
2 &  13 &  14.09 & -1.088 \tabularnewline
3 &  14 &  14.09 & -0.08772 \tabularnewline
4 &  13 &  14.09 & -1.088 \tabularnewline
5 &  12 &  13.31 & -1.314 \tabularnewline
6 &  17 &  14.09 &  2.912 \tabularnewline
7 &  12 &  14.09 & -2.088 \tabularnewline
8 &  13 &  14.11 & -1.111 \tabularnewline
9 &  13 &  13.54 & -0.5385 \tabularnewline
10 &  16 &  13.31 &  2.686 \tabularnewline
11 &  12 &  13.31 & -1.314 \tabularnewline
12 &  12 &  13.54 & -1.538 \tabularnewline
13 &  13 &  13.54 & -0.5385 \tabularnewline
14 &  16 &  13.31 &  2.686 \tabularnewline
15 &  15 &  13.31 &  1.686 \tabularnewline
16 &  12 &  14.09 & -2.088 \tabularnewline
17 &  15 &  14.09 &  0.9123 \tabularnewline
18 &  12 &  14.09 & -2.088 \tabularnewline
19 &  15 &  14.09 &  0.9123 \tabularnewline
20 &  11 &  13.31 & -2.314 \tabularnewline
21 &  13 &  14.09 & -1.088 \tabularnewline
22 &  13 &  14.09 & -1.088 \tabularnewline
23 &  14 &  13.31 &  0.6863 \tabularnewline
24 &  14 &  13.31 &  0.6863 \tabularnewline
25 &  14 &  13.54 &  0.4615 \tabularnewline
26 &  15 &  13.54 &  1.462 \tabularnewline
27 &  16 &  13.31 &  2.686 \tabularnewline
28 &  16 &  13.31 &  2.686 \tabularnewline
29 &  16 &  13.31 &  2.686 \tabularnewline
30 &  13 &  14.11 & -1.111 \tabularnewline
31 &  13 &  13.31 & -0.3137 \tabularnewline
32 &  14 &  13.54 &  0.4615 \tabularnewline
33 &  13 &  14.09 & -1.088 \tabularnewline
34 &  14 &  13.31 &  0.6863 \tabularnewline
35 &  12 &  13.31 & -1.314 \tabularnewline
36 &  17 &  14.11 &  2.889 \tabularnewline
37 &  14 &  13.54 &  0.4615 \tabularnewline
38 &  15 &  13.54 &  1.462 \tabularnewline
39 &  13 &  14.11 & -1.111 \tabularnewline
40 &  14 &  14.11 & -0.1111 \tabularnewline
41 &  15 &  14.11 &  0.8889 \tabularnewline
42 &  19 &  14.09 &  4.912 \tabularnewline
43 &  14 &  14.11 & -0.1111 \tabularnewline
44 &  13 &  14.11 & -1.111 \tabularnewline
45 &  12 &  14.09 & -2.088 \tabularnewline
46 &  14 &  14.09 & -0.08772 \tabularnewline
47 &  15 &  13.31 &  1.686 \tabularnewline
48 &  15 &  14.11 &  0.8889 \tabularnewline
49 &  12 &  13.54 & -1.538 \tabularnewline
50 &  14 &  14.11 & -0.1111 \tabularnewline
51 &  11 &  14.11 & -3.111 \tabularnewline
52 &  12 &  13.31 & -1.314 \tabularnewline
53 &  10 &  14.09 & -4.088 \tabularnewline
54 &  14 &  13.54 &  0.4615 \tabularnewline
55 &  14 &  14.11 & -0.1111 \tabularnewline
56 &  15 &  13.31 &  1.686 \tabularnewline
57 &  15 &  14.09 &  0.9123 \tabularnewline
58 &  13 &  14.11 & -1.111 \tabularnewline
59 &  15 &  14.09 &  0.9123 \tabularnewline
60 &  16 &  14.09 &  1.912 \tabularnewline
61 &  12 &  13.31 & -1.314 \tabularnewline
62 &  17 &  14.09 &  2.912 \tabularnewline
63 &  15 &  13.31 &  1.686 \tabularnewline
64 &  12 &  13.31 & -1.314 \tabularnewline
65 &  16 &  14.09 &  1.912 \tabularnewline
66 &  15 &  14.09 &  0.9123 \tabularnewline
67 &  15 &  14.09 &  0.9123 \tabularnewline
68 &  12 &  13.31 & -1.314 \tabularnewline
69 &  13 &  14.11 & -1.111 \tabularnewline
70 &  10 &  13.31 & -3.314 \tabularnewline
71 &  14 &  14.09 & -0.08772 \tabularnewline
72 &  11 &  13.31 & -2.314 \tabularnewline
73 &  12 &  14.09 & -2.088 \tabularnewline
74 &  14 &  13.31 &  0.6863 \tabularnewline
75 &  12 &  14.09 & -2.088 \tabularnewline
76 &  14 &  14.09 & -0.08772 \tabularnewline
77 &  12 &  13.31 & -1.314 \tabularnewline
78 &  13 &  13.31 & -0.3137 \tabularnewline
79 &  13 &  13.54 & -0.5385 \tabularnewline
80 &  14 &  14.09 & -0.08772 \tabularnewline
81 &  12 &  14.09 & -2.088 \tabularnewline
82 &  15 &  14.09 &  0.9123 \tabularnewline
83 &  13 &  14.09 & -1.088 \tabularnewline
84 &  13 &  14.09 & -1.088 \tabularnewline
85 &  11 &  13.54 & -2.538 \tabularnewline
86 &  12 &  14.11 & -2.111 \tabularnewline
87 &  16 &  13.54 &  2.462 \tabularnewline
88 &  11 &  13.31 & -2.314 \tabularnewline
89 &  13 &  13.31 & -0.3137 \tabularnewline
90 &  12 &  13.54 & -1.538 \tabularnewline
91 &  17 &  14.09 &  2.912 \tabularnewline
92 &  14 &  14.09 & -0.08772 \tabularnewline
93 &  15 &  14.09 &  0.9123 \tabularnewline
94 &  8 &  13.31 & -5.314 \tabularnewline
95 &  13 &  14.09 & -1.088 \tabularnewline
96 &  13 &  14.09 & -1.088 \tabularnewline
97 &  15 &  13.31 &  1.686 \tabularnewline
98 &  14 &  13.31 &  0.6863 \tabularnewline
99 &  13 &  14.09 & -1.088 \tabularnewline
100 &  14 &  14.09 & -0.08772 \tabularnewline
101 &  12 &  14.09 & -2.088 \tabularnewline
102 &  19 &  14.11 &  4.889 \tabularnewline
103 &  15 &  13.31 &  1.686 \tabularnewline
104 &  14 &  13.54 &  0.4615 \tabularnewline
105 &  14 &  13.31 &  0.6863 \tabularnewline
106 &  15 &  14.09 &  0.9123 \tabularnewline
107 &  13 &  14.09 & -1.088 \tabularnewline
108 &  15 &  14.11 &  0.8889 \tabularnewline
109 &  14 &  13.31 &  0.6863 \tabularnewline
110 &  11 &  13.31 & -2.314 \tabularnewline
111 &  17 &  14.09 &  2.912 \tabularnewline
112 &  13 &  13.31 & -0.3137 \tabularnewline
113 &  9 &  13.31 & -4.314 \tabularnewline
114 &  12 &  14.11 & -2.111 \tabularnewline
115 &  13 &  13.54 & -0.5385 \tabularnewline
116 &  17 &  13.54 &  3.462 \tabularnewline
117 &  14 &  13.54 &  0.4615 \tabularnewline
118 &  13 &  14.11 & -1.111 \tabularnewline
119 &  16 &  14.09 &  1.912 \tabularnewline
120 &  14 &  14.09 & -0.08772 \tabularnewline
121 &  14 &  14.09 & -0.08772 \tabularnewline
122 &  14 &  14.09 & -0.08772 \tabularnewline
123 &  10 &  13.31 & -3.314 \tabularnewline
124 &  12 &  13.54 & -1.538 \tabularnewline
125 &  13 &  14.11 & -1.111 \tabularnewline
126 &  14 &  14.09 & -0.08772 \tabularnewline
127 &  18 &  14.11 &  3.889 \tabularnewline
128 &  14 &  13.31 &  0.6863 \tabularnewline
129 &  14 &  14.11 & -0.1111 \tabularnewline
130 &  13 &  13.31 & -0.3137 \tabularnewline
131 &  13 &  13.31 & -0.3137 \tabularnewline
132 &  16 &  13.54 &  2.462 \tabularnewline
133 &  13 &  14.09 & -1.088 \tabularnewline
134 &  14 &  13.54 &  0.4615 \tabularnewline
135 &  8 &  13.31 & -5.314 \tabularnewline
136 &  13 &  13.54 & -0.5385 \tabularnewline
137 &  13 &  13.54 & -0.5385 \tabularnewline
138 &  16 &  14.09 &  1.912 \tabularnewline
139 &  14 &  13.31 &  0.6863 \tabularnewline
140 &  13 &  14.09 & -1.088 \tabularnewline
141 &  14 &  14.09 & -0.08772 \tabularnewline
142 &  12 &  14.09 & -2.088 \tabularnewline
143 &  16 &  14.09 &  1.912 \tabularnewline
144 &  18 &  13.31 &  4.686 \tabularnewline
145 &  16 &  13.31 &  2.686 \tabularnewline
146 &  15 &  13.31 &  1.686 \tabularnewline
147 &  18 &  13.54 &  4.462 \tabularnewline
148 &  15 &  14.11 &  0.8889 \tabularnewline
149 &  14 &  14.11 & -0.1111 \tabularnewline
150 &  14 &  14.11 & -0.1111 \tabularnewline
151 &  15 &  14.11 &  0.8889 \tabularnewline
152 &  9 &  13.54 & -4.538 \tabularnewline
153 &  17 &  14.09 &  2.912 \tabularnewline
154 &  11 &  13.54 & -2.538 \tabularnewline
155 &  15 &  14.11 &  0.8889 \tabularnewline
156 &  15 &  13.31 &  1.686 \tabularnewline
157 &  13 &  13.31 & -0.3137 \tabularnewline
158 &  15 &  13.31 &  1.686 \tabularnewline
159 &  15 &  14.09 &  0.9123 \tabularnewline
160 &  14 &  14.09 & -0.08772 \tabularnewline
161 &  13 &  13.31 & -0.3137 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298147&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C] 15[/C][C] 13.31[/C][C] 1.686[/C][/ROW]
[ROW][C]2[/C][C] 13[/C][C] 14.09[/C][C]-1.088[/C][/ROW]
[ROW][C]3[/C][C] 14[/C][C] 14.09[/C][C]-0.08772[/C][/ROW]
[ROW][C]4[/C][C] 13[/C][C] 14.09[/C][C]-1.088[/C][/ROW]
[ROW][C]5[/C][C] 12[/C][C] 13.31[/C][C]-1.314[/C][/ROW]
[ROW][C]6[/C][C] 17[/C][C] 14.09[/C][C] 2.912[/C][/ROW]
[ROW][C]7[/C][C] 12[/C][C] 14.09[/C][C]-2.088[/C][/ROW]
[ROW][C]8[/C][C] 13[/C][C] 14.11[/C][C]-1.111[/C][/ROW]
[ROW][C]9[/C][C] 13[/C][C] 13.54[/C][C]-0.5385[/C][/ROW]
[ROW][C]10[/C][C] 16[/C][C] 13.31[/C][C] 2.686[/C][/ROW]
[ROW][C]11[/C][C] 12[/C][C] 13.31[/C][C]-1.314[/C][/ROW]
[ROW][C]12[/C][C] 12[/C][C] 13.54[/C][C]-1.538[/C][/ROW]
[ROW][C]13[/C][C] 13[/C][C] 13.54[/C][C]-0.5385[/C][/ROW]
[ROW][C]14[/C][C] 16[/C][C] 13.31[/C][C] 2.686[/C][/ROW]
[ROW][C]15[/C][C] 15[/C][C] 13.31[/C][C] 1.686[/C][/ROW]
[ROW][C]16[/C][C] 12[/C][C] 14.09[/C][C]-2.088[/C][/ROW]
[ROW][C]17[/C][C] 15[/C][C] 14.09[/C][C] 0.9123[/C][/ROW]
[ROW][C]18[/C][C] 12[/C][C] 14.09[/C][C]-2.088[/C][/ROW]
[ROW][C]19[/C][C] 15[/C][C] 14.09[/C][C] 0.9123[/C][/ROW]
[ROW][C]20[/C][C] 11[/C][C] 13.31[/C][C]-2.314[/C][/ROW]
[ROW][C]21[/C][C] 13[/C][C] 14.09[/C][C]-1.088[/C][/ROW]
[ROW][C]22[/C][C] 13[/C][C] 14.09[/C][C]-1.088[/C][/ROW]
[ROW][C]23[/C][C] 14[/C][C] 13.31[/C][C] 0.6863[/C][/ROW]
[ROW][C]24[/C][C] 14[/C][C] 13.31[/C][C] 0.6863[/C][/ROW]
[ROW][C]25[/C][C] 14[/C][C] 13.54[/C][C] 0.4615[/C][/ROW]
[ROW][C]26[/C][C] 15[/C][C] 13.54[/C][C] 1.462[/C][/ROW]
[ROW][C]27[/C][C] 16[/C][C] 13.31[/C][C] 2.686[/C][/ROW]
[ROW][C]28[/C][C] 16[/C][C] 13.31[/C][C] 2.686[/C][/ROW]
[ROW][C]29[/C][C] 16[/C][C] 13.31[/C][C] 2.686[/C][/ROW]
[ROW][C]30[/C][C] 13[/C][C] 14.11[/C][C]-1.111[/C][/ROW]
[ROW][C]31[/C][C] 13[/C][C] 13.31[/C][C]-0.3137[/C][/ROW]
[ROW][C]32[/C][C] 14[/C][C] 13.54[/C][C] 0.4615[/C][/ROW]
[ROW][C]33[/C][C] 13[/C][C] 14.09[/C][C]-1.088[/C][/ROW]
[ROW][C]34[/C][C] 14[/C][C] 13.31[/C][C] 0.6863[/C][/ROW]
[ROW][C]35[/C][C] 12[/C][C] 13.31[/C][C]-1.314[/C][/ROW]
[ROW][C]36[/C][C] 17[/C][C] 14.11[/C][C] 2.889[/C][/ROW]
[ROW][C]37[/C][C] 14[/C][C] 13.54[/C][C] 0.4615[/C][/ROW]
[ROW][C]38[/C][C] 15[/C][C] 13.54[/C][C] 1.462[/C][/ROW]
[ROW][C]39[/C][C] 13[/C][C] 14.11[/C][C]-1.111[/C][/ROW]
[ROW][C]40[/C][C] 14[/C][C] 14.11[/C][C]-0.1111[/C][/ROW]
[ROW][C]41[/C][C] 15[/C][C] 14.11[/C][C] 0.8889[/C][/ROW]
[ROW][C]42[/C][C] 19[/C][C] 14.09[/C][C] 4.912[/C][/ROW]
[ROW][C]43[/C][C] 14[/C][C] 14.11[/C][C]-0.1111[/C][/ROW]
[ROW][C]44[/C][C] 13[/C][C] 14.11[/C][C]-1.111[/C][/ROW]
[ROW][C]45[/C][C] 12[/C][C] 14.09[/C][C]-2.088[/C][/ROW]
[ROW][C]46[/C][C] 14[/C][C] 14.09[/C][C]-0.08772[/C][/ROW]
[ROW][C]47[/C][C] 15[/C][C] 13.31[/C][C] 1.686[/C][/ROW]
[ROW][C]48[/C][C] 15[/C][C] 14.11[/C][C] 0.8889[/C][/ROW]
[ROW][C]49[/C][C] 12[/C][C] 13.54[/C][C]-1.538[/C][/ROW]
[ROW][C]50[/C][C] 14[/C][C] 14.11[/C][C]-0.1111[/C][/ROW]
[ROW][C]51[/C][C] 11[/C][C] 14.11[/C][C]-3.111[/C][/ROW]
[ROW][C]52[/C][C] 12[/C][C] 13.31[/C][C]-1.314[/C][/ROW]
[ROW][C]53[/C][C] 10[/C][C] 14.09[/C][C]-4.088[/C][/ROW]
[ROW][C]54[/C][C] 14[/C][C] 13.54[/C][C] 0.4615[/C][/ROW]
[ROW][C]55[/C][C] 14[/C][C] 14.11[/C][C]-0.1111[/C][/ROW]
[ROW][C]56[/C][C] 15[/C][C] 13.31[/C][C] 1.686[/C][/ROW]
[ROW][C]57[/C][C] 15[/C][C] 14.09[/C][C] 0.9123[/C][/ROW]
[ROW][C]58[/C][C] 13[/C][C] 14.11[/C][C]-1.111[/C][/ROW]
[ROW][C]59[/C][C] 15[/C][C] 14.09[/C][C] 0.9123[/C][/ROW]
[ROW][C]60[/C][C] 16[/C][C] 14.09[/C][C] 1.912[/C][/ROW]
[ROW][C]61[/C][C] 12[/C][C] 13.31[/C][C]-1.314[/C][/ROW]
[ROW][C]62[/C][C] 17[/C][C] 14.09[/C][C] 2.912[/C][/ROW]
[ROW][C]63[/C][C] 15[/C][C] 13.31[/C][C] 1.686[/C][/ROW]
[ROW][C]64[/C][C] 12[/C][C] 13.31[/C][C]-1.314[/C][/ROW]
[ROW][C]65[/C][C] 16[/C][C] 14.09[/C][C] 1.912[/C][/ROW]
[ROW][C]66[/C][C] 15[/C][C] 14.09[/C][C] 0.9123[/C][/ROW]
[ROW][C]67[/C][C] 15[/C][C] 14.09[/C][C] 0.9123[/C][/ROW]
[ROW][C]68[/C][C] 12[/C][C] 13.31[/C][C]-1.314[/C][/ROW]
[ROW][C]69[/C][C] 13[/C][C] 14.11[/C][C]-1.111[/C][/ROW]
[ROW][C]70[/C][C] 10[/C][C] 13.31[/C][C]-3.314[/C][/ROW]
[ROW][C]71[/C][C] 14[/C][C] 14.09[/C][C]-0.08772[/C][/ROW]
[ROW][C]72[/C][C] 11[/C][C] 13.31[/C][C]-2.314[/C][/ROW]
[ROW][C]73[/C][C] 12[/C][C] 14.09[/C][C]-2.088[/C][/ROW]
[ROW][C]74[/C][C] 14[/C][C] 13.31[/C][C] 0.6863[/C][/ROW]
[ROW][C]75[/C][C] 12[/C][C] 14.09[/C][C]-2.088[/C][/ROW]
[ROW][C]76[/C][C] 14[/C][C] 14.09[/C][C]-0.08772[/C][/ROW]
[ROW][C]77[/C][C] 12[/C][C] 13.31[/C][C]-1.314[/C][/ROW]
[ROW][C]78[/C][C] 13[/C][C] 13.31[/C][C]-0.3137[/C][/ROW]
[ROW][C]79[/C][C] 13[/C][C] 13.54[/C][C]-0.5385[/C][/ROW]
[ROW][C]80[/C][C] 14[/C][C] 14.09[/C][C]-0.08772[/C][/ROW]
[ROW][C]81[/C][C] 12[/C][C] 14.09[/C][C]-2.088[/C][/ROW]
[ROW][C]82[/C][C] 15[/C][C] 14.09[/C][C] 0.9123[/C][/ROW]
[ROW][C]83[/C][C] 13[/C][C] 14.09[/C][C]-1.088[/C][/ROW]
[ROW][C]84[/C][C] 13[/C][C] 14.09[/C][C]-1.088[/C][/ROW]
[ROW][C]85[/C][C] 11[/C][C] 13.54[/C][C]-2.538[/C][/ROW]
[ROW][C]86[/C][C] 12[/C][C] 14.11[/C][C]-2.111[/C][/ROW]
[ROW][C]87[/C][C] 16[/C][C] 13.54[/C][C] 2.462[/C][/ROW]
[ROW][C]88[/C][C] 11[/C][C] 13.31[/C][C]-2.314[/C][/ROW]
[ROW][C]89[/C][C] 13[/C][C] 13.31[/C][C]-0.3137[/C][/ROW]
[ROW][C]90[/C][C] 12[/C][C] 13.54[/C][C]-1.538[/C][/ROW]
[ROW][C]91[/C][C] 17[/C][C] 14.09[/C][C] 2.912[/C][/ROW]
[ROW][C]92[/C][C] 14[/C][C] 14.09[/C][C]-0.08772[/C][/ROW]
[ROW][C]93[/C][C] 15[/C][C] 14.09[/C][C] 0.9123[/C][/ROW]
[ROW][C]94[/C][C] 8[/C][C] 13.31[/C][C]-5.314[/C][/ROW]
[ROW][C]95[/C][C] 13[/C][C] 14.09[/C][C]-1.088[/C][/ROW]
[ROW][C]96[/C][C] 13[/C][C] 14.09[/C][C]-1.088[/C][/ROW]
[ROW][C]97[/C][C] 15[/C][C] 13.31[/C][C] 1.686[/C][/ROW]
[ROW][C]98[/C][C] 14[/C][C] 13.31[/C][C] 0.6863[/C][/ROW]
[ROW][C]99[/C][C] 13[/C][C] 14.09[/C][C]-1.088[/C][/ROW]
[ROW][C]100[/C][C] 14[/C][C] 14.09[/C][C]-0.08772[/C][/ROW]
[ROW][C]101[/C][C] 12[/C][C] 14.09[/C][C]-2.088[/C][/ROW]
[ROW][C]102[/C][C] 19[/C][C] 14.11[/C][C] 4.889[/C][/ROW]
[ROW][C]103[/C][C] 15[/C][C] 13.31[/C][C] 1.686[/C][/ROW]
[ROW][C]104[/C][C] 14[/C][C] 13.54[/C][C] 0.4615[/C][/ROW]
[ROW][C]105[/C][C] 14[/C][C] 13.31[/C][C] 0.6863[/C][/ROW]
[ROW][C]106[/C][C] 15[/C][C] 14.09[/C][C] 0.9123[/C][/ROW]
[ROW][C]107[/C][C] 13[/C][C] 14.09[/C][C]-1.088[/C][/ROW]
[ROW][C]108[/C][C] 15[/C][C] 14.11[/C][C] 0.8889[/C][/ROW]
[ROW][C]109[/C][C] 14[/C][C] 13.31[/C][C] 0.6863[/C][/ROW]
[ROW][C]110[/C][C] 11[/C][C] 13.31[/C][C]-2.314[/C][/ROW]
[ROW][C]111[/C][C] 17[/C][C] 14.09[/C][C] 2.912[/C][/ROW]
[ROW][C]112[/C][C] 13[/C][C] 13.31[/C][C]-0.3137[/C][/ROW]
[ROW][C]113[/C][C] 9[/C][C] 13.31[/C][C]-4.314[/C][/ROW]
[ROW][C]114[/C][C] 12[/C][C] 14.11[/C][C]-2.111[/C][/ROW]
[ROW][C]115[/C][C] 13[/C][C] 13.54[/C][C]-0.5385[/C][/ROW]
[ROW][C]116[/C][C] 17[/C][C] 13.54[/C][C] 3.462[/C][/ROW]
[ROW][C]117[/C][C] 14[/C][C] 13.54[/C][C] 0.4615[/C][/ROW]
[ROW][C]118[/C][C] 13[/C][C] 14.11[/C][C]-1.111[/C][/ROW]
[ROW][C]119[/C][C] 16[/C][C] 14.09[/C][C] 1.912[/C][/ROW]
[ROW][C]120[/C][C] 14[/C][C] 14.09[/C][C]-0.08772[/C][/ROW]
[ROW][C]121[/C][C] 14[/C][C] 14.09[/C][C]-0.08772[/C][/ROW]
[ROW][C]122[/C][C] 14[/C][C] 14.09[/C][C]-0.08772[/C][/ROW]
[ROW][C]123[/C][C] 10[/C][C] 13.31[/C][C]-3.314[/C][/ROW]
[ROW][C]124[/C][C] 12[/C][C] 13.54[/C][C]-1.538[/C][/ROW]
[ROW][C]125[/C][C] 13[/C][C] 14.11[/C][C]-1.111[/C][/ROW]
[ROW][C]126[/C][C] 14[/C][C] 14.09[/C][C]-0.08772[/C][/ROW]
[ROW][C]127[/C][C] 18[/C][C] 14.11[/C][C] 3.889[/C][/ROW]
[ROW][C]128[/C][C] 14[/C][C] 13.31[/C][C] 0.6863[/C][/ROW]
[ROW][C]129[/C][C] 14[/C][C] 14.11[/C][C]-0.1111[/C][/ROW]
[ROW][C]130[/C][C] 13[/C][C] 13.31[/C][C]-0.3137[/C][/ROW]
[ROW][C]131[/C][C] 13[/C][C] 13.31[/C][C]-0.3137[/C][/ROW]
[ROW][C]132[/C][C] 16[/C][C] 13.54[/C][C] 2.462[/C][/ROW]
[ROW][C]133[/C][C] 13[/C][C] 14.09[/C][C]-1.088[/C][/ROW]
[ROW][C]134[/C][C] 14[/C][C] 13.54[/C][C] 0.4615[/C][/ROW]
[ROW][C]135[/C][C] 8[/C][C] 13.31[/C][C]-5.314[/C][/ROW]
[ROW][C]136[/C][C] 13[/C][C] 13.54[/C][C]-0.5385[/C][/ROW]
[ROW][C]137[/C][C] 13[/C][C] 13.54[/C][C]-0.5385[/C][/ROW]
[ROW][C]138[/C][C] 16[/C][C] 14.09[/C][C] 1.912[/C][/ROW]
[ROW][C]139[/C][C] 14[/C][C] 13.31[/C][C] 0.6863[/C][/ROW]
[ROW][C]140[/C][C] 13[/C][C] 14.09[/C][C]-1.088[/C][/ROW]
[ROW][C]141[/C][C] 14[/C][C] 14.09[/C][C]-0.08772[/C][/ROW]
[ROW][C]142[/C][C] 12[/C][C] 14.09[/C][C]-2.088[/C][/ROW]
[ROW][C]143[/C][C] 16[/C][C] 14.09[/C][C] 1.912[/C][/ROW]
[ROW][C]144[/C][C] 18[/C][C] 13.31[/C][C] 4.686[/C][/ROW]
[ROW][C]145[/C][C] 16[/C][C] 13.31[/C][C] 2.686[/C][/ROW]
[ROW][C]146[/C][C] 15[/C][C] 13.31[/C][C] 1.686[/C][/ROW]
[ROW][C]147[/C][C] 18[/C][C] 13.54[/C][C] 4.462[/C][/ROW]
[ROW][C]148[/C][C] 15[/C][C] 14.11[/C][C] 0.8889[/C][/ROW]
[ROW][C]149[/C][C] 14[/C][C] 14.11[/C][C]-0.1111[/C][/ROW]
[ROW][C]150[/C][C] 14[/C][C] 14.11[/C][C]-0.1111[/C][/ROW]
[ROW][C]151[/C][C] 15[/C][C] 14.11[/C][C] 0.8889[/C][/ROW]
[ROW][C]152[/C][C] 9[/C][C] 13.54[/C][C]-4.538[/C][/ROW]
[ROW][C]153[/C][C] 17[/C][C] 14.09[/C][C] 2.912[/C][/ROW]
[ROW][C]154[/C][C] 11[/C][C] 13.54[/C][C]-2.538[/C][/ROW]
[ROW][C]155[/C][C] 15[/C][C] 14.11[/C][C] 0.8889[/C][/ROW]
[ROW][C]156[/C][C] 15[/C][C] 13.31[/C][C] 1.686[/C][/ROW]
[ROW][C]157[/C][C] 13[/C][C] 13.31[/C][C]-0.3137[/C][/ROW]
[ROW][C]158[/C][C] 15[/C][C] 13.31[/C][C] 1.686[/C][/ROW]
[ROW][C]159[/C][C] 15[/C][C] 14.09[/C][C] 0.9123[/C][/ROW]
[ROW][C]160[/C][C] 14[/C][C] 14.09[/C][C]-0.08772[/C][/ROW]
[ROW][C]161[/C][C] 13[/C][C] 13.31[/C][C]-0.3137[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298147&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298147&T=4

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	15	13.31	1.686
2	13	14.09	-1.088
3	14	14.09	-0.08772
4	13	14.09	-1.088
5	12	13.31	-1.314
6	17	14.09	2.912
7	12	14.09	-2.088
8	13	14.11	-1.111
9	13	13.54	-0.5385
10	16	13.31	2.686
11	12	13.31	-1.314
12	12	13.54	-1.538
13	13	13.54	-0.5385
14	16	13.31	2.686
15	15	13.31	1.686
16	12	14.09	-2.088
17	15	14.09	0.9123
18	12	14.09	-2.088
19	15	14.09	0.9123
20	11	13.31	-2.314
21	13	14.09	-1.088
22	13	14.09	-1.088
23	14	13.31	0.6863
24	14	13.31	0.6863
25	14	13.54	0.4615
26	15	13.54	1.462
27	16	13.31	2.686
28	16	13.31	2.686
29	16	13.31	2.686
30	13	14.11	-1.111
31	13	13.31	-0.3137
32	14	13.54	0.4615
33	13	14.09	-1.088
34	14	13.31	0.6863
35	12	13.31	-1.314
36	17	14.11	2.889
37	14	13.54	0.4615
38	15	13.54	1.462
39	13	14.11	-1.111
40	14	14.11	-0.1111
41	15	14.11	0.8889
42	19	14.09	4.912
43	14	14.11	-0.1111
44	13	14.11	-1.111
45	12	14.09	-2.088
46	14	14.09	-0.08772
47	15	13.31	1.686
48	15	14.11	0.8889
49	12	13.54	-1.538
50	14	14.11	-0.1111
51	11	14.11	-3.111
52	12	13.31	-1.314
53	10	14.09	-4.088
54	14	13.54	0.4615
55	14	14.11	-0.1111
56	15	13.31	1.686
57	15	14.09	0.9123
58	13	14.11	-1.111
59	15	14.09	0.9123
60	16	14.09	1.912
61	12	13.31	-1.314
62	17	14.09	2.912
63	15	13.31	1.686
64	12	13.31	-1.314
65	16	14.09	1.912
66	15	14.09	0.9123
67	15	14.09	0.9123
68	12	13.31	-1.314
69	13	14.11	-1.111
70	10	13.31	-3.314
71	14	14.09	-0.08772
72	11	13.31	-2.314
73	12	14.09	-2.088
74	14	13.31	0.6863
75	12	14.09	-2.088
76	14	14.09	-0.08772
77	12	13.31	-1.314
78	13	13.31	-0.3137
79	13	13.54	-0.5385
80	14	14.09	-0.08772
81	12	14.09	-2.088
82	15	14.09	0.9123
83	13	14.09	-1.088
84	13	14.09	-1.088
85	11	13.54	-2.538
86	12	14.11	-2.111
87	16	13.54	2.462
88	11	13.31	-2.314
89	13	13.31	-0.3137
90	12	13.54	-1.538
91	17	14.09	2.912
92	14	14.09	-0.08772
93	15	14.09	0.9123
94	8	13.31	-5.314
95	13	14.09	-1.088
96	13	14.09	-1.088
97	15	13.31	1.686
98	14	13.31	0.6863
99	13	14.09	-1.088
100	14	14.09	-0.08772
101	12	14.09	-2.088
102	19	14.11	4.889
103	15	13.31	1.686
104	14	13.54	0.4615
105	14	13.31	0.6863
106	15	14.09	0.9123
107	13	14.09	-1.088
108	15	14.11	0.8889
109	14	13.31	0.6863
110	11	13.31	-2.314
111	17	14.09	2.912
112	13	13.31	-0.3137
113	9	13.31	-4.314
114	12	14.11	-2.111
115	13	13.54	-0.5385
116	17	13.54	3.462
117	14	13.54	0.4615
118	13	14.11	-1.111
119	16	14.09	1.912
120	14	14.09	-0.08772
121	14	14.09	-0.08772
122	14	14.09	-0.08772
123	10	13.31	-3.314
124	12	13.54	-1.538
125	13	14.11	-1.111
126	14	14.09	-0.08772
127	18	14.11	3.889
128	14	13.31	0.6863
129	14	14.11	-0.1111
130	13	13.31	-0.3137
131	13	13.31	-0.3137
132	16	13.54	2.462
133	13	14.09	-1.088
134	14	13.54	0.4615
135	8	13.31	-5.314
136	13	13.54	-0.5385
137	13	13.54	-0.5385
138	16	14.09	1.912
139	14	13.31	0.6863
140	13	14.09	-1.088
141	14	14.09	-0.08772
142	12	14.09	-2.088
143	16	14.09	1.912
144	18	13.31	4.686
145	16	13.31	2.686
146	15	13.31	1.686
147	18	13.54	4.462
148	15	14.11	0.8889
149	14	14.11	-0.1111
150	14	14.11	-0.1111
151	15	14.11	0.8889
152	9	13.54	-4.538
153	17	14.09	2.912
154	11	13.54	-2.538
155	15	14.11	0.8889
156	15	13.31	1.686
157	13	13.31	-0.3137
158	15	13.31	1.686
159	15	14.09	0.9123
160	14	14.09	-0.08772
161	13	13.31	-0.3137

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
7	0.8411	0.3178	0.1589
8	0.7304	0.5392	0.2696
9	0.6036	0.7927	0.3964
10	0.6113	0.7774	0.3887
11	0.6101	0.7799	0.3899
12	0.5187	0.9625	0.4813
13	0.4197	0.8394	0.5803
14	0.4382	0.8763	0.5618
15	0.3643	0.7286	0.6357
16	0.3493	0.6985	0.6507
17	0.313	0.6261	0.687
18	0.299	0.598	0.701
19	0.2691	0.5382	0.7309
20	0.3879	0.7759	0.6121
21	0.3265	0.653	0.6735
22	0.2696	0.5393	0.7304
23	0.2134	0.4269	0.7866
24	0.1655	0.331	0.8345
25	0.1396	0.2791	0.8604
26	0.1383	0.2766	0.8617
27	0.1534	0.3069	0.8466
28	0.1616	0.3232	0.8384
29	0.165	0.3299	0.835
30	0.1298	0.2597	0.8702
31	0.1143	0.2285	0.8857
32	0.08896	0.1779	0.911
33	0.06929	0.1386	0.9307
34	0.05215	0.1043	0.9479
35	0.05965	0.1193	0.9404
36	0.1116	0.2232	0.8884
37	0.08774	0.1755	0.9123
38	0.07857	0.1571	0.9214
39	0.06697	0.1339	0.933
40	0.05038	0.1008	0.9496
41	0.04048	0.08096	0.9595
42	0.228	0.456	0.772
43	0.1894	0.3788	0.8106
44	0.1664	0.3328	0.8336
45	0.1693	0.3385	0.8307
46	0.1383	0.2766	0.8617
47	0.1227	0.2454	0.8773
48	0.104	0.2081	0.896
49	0.09899	0.198	0.901
50	0.07837	0.1567	0.9216
51	0.1161	0.2322	0.8839
52	0.1176	0.2353	0.8824
53	0.2193	0.4385	0.7807
54	0.1861	0.3722	0.8139
55	0.1551	0.3103	0.8449
56	0.1418	0.2836	0.8582
57	0.1272	0.2544	0.8728
58	0.11	0.2201	0.89
59	0.09705	0.1941	0.9029
60	0.1032	0.2064	0.8968
61	0.1017	0.2034	0.8983
62	0.1401	0.2802	0.8599
63	0.1298	0.2596	0.8702
64	0.1258	0.2517	0.8742
65	0.1275	0.2549	0.8725
66	0.1094	0.2188	0.8906
67	0.09318	0.1864	0.9068
68	0.08857	0.1771	0.9114
69	0.07639	0.1528	0.9236
70	0.1272	0.2543	0.8729
71	0.1045	0.209	0.8955
72	0.1189	0.2377	0.8811
73	0.124	0.2481	0.876
74	0.1044	0.2088	0.8956
75	0.1085	0.2169	0.8915
76	0.08849	0.177	0.9115
77	0.08016	0.1603	0.9198
78	0.065	0.13	0.935
79	0.05262	0.1052	0.9474
80	0.04137	0.08274	0.9586
81	0.04327	0.08654	0.9567
82	0.03587	0.07174	0.9641
83	0.03014	0.06027	0.9699
84	0.02522	0.05045	0.9748
85	0.03074	0.06147	0.9693
86	0.03285	0.0657	0.9672
87	0.03919	0.07838	0.9608
88	0.04412	0.08825	0.9559
89	0.03466	0.06931	0.9653
90	0.03163	0.06327	0.9684
91	0.0438	0.08759	0.9562
92	0.03415	0.0683	0.9658
93	0.02786	0.05573	0.9721
94	0.1246	0.2491	0.8754
95	0.1094	0.2188	0.8906
96	0.09588	0.1918	0.9041
97	0.0909	0.1818	0.9091
98	0.07528	0.1506	0.9247
99	0.0653	0.1306	0.9347
100	0.05191	0.1038	0.9481
101	0.05596	0.1119	0.944
102	0.1608	0.3215	0.8392
103	0.1539	0.3078	0.8461
104	0.1286	0.2572	0.8714
105	0.1083	0.2165	0.8917
106	0.09069	0.1814	0.9093
107	0.08047	0.1609	0.9195
108	0.06666	0.1333	0.9333
109	0.05433	0.1087	0.9457
110	0.05861	0.1172	0.9414
111	0.07232	0.1446	0.9277
112	0.05716	0.1143	0.9428
113	0.136	0.272	0.864
114	0.145	0.2899	0.855
115	0.1205	0.2409	0.8795
116	0.1819	0.3639	0.8181
117	0.153	0.3059	0.847
118	0.1393	0.2786	0.8607
119	0.1331	0.2661	0.8669
120	0.1074	0.2148	0.8926
121	0.08547	0.1709	0.9145
122	0.06703	0.1341	0.933
123	0.1151	0.2303	0.8849
124	0.1027	0.2053	0.8973
125	0.09505	0.1901	0.905
126	0.07441	0.1488	0.9256
127	0.1221	0.2442	0.8779
128	0.09688	0.1938	0.9031
129	0.0757	0.1514	0.9243
130	0.05998	0.12	0.94
131	0.04732	0.09463	0.9527
132	0.05854	0.1171	0.9415
133	0.05002	0.1	0.95
134	0.03896	0.07792	0.961
135	0.346	0.6921	0.654
136	0.2894	0.5789	0.7106
137	0.2372	0.4744	0.7628
138	0.218	0.4359	0.782
139	0.1806	0.3612	0.8194
140	0.1603	0.3205	0.8397
141	0.1245	0.249	0.8755
142	0.1761	0.3521	0.8239
143	0.1394	0.2787	0.8606
144	0.2413	0.4826	0.7587
145	0.2277	0.4554	0.7723
146	0.182	0.3639	0.818
147	0.9736	0.05283	0.02642
148	0.9537	0.09253	0.04627
149	0.9236	0.1529	0.07643
150	0.8858	0.2283	0.1142
151	0.8093	0.3813	0.1907
152	0.7992	0.4015	0.2008
153	0.8767	0.2466	0.1233
154	0.7534	0.4931	0.2466

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
7 &  0.8411 &  0.3178 &  0.1589 \tabularnewline
8 &  0.7304 &  0.5392 &  0.2696 \tabularnewline
9 &  0.6036 &  0.7927 &  0.3964 \tabularnewline
10 &  0.6113 &  0.7774 &  0.3887 \tabularnewline
11 &  0.6101 &  0.7799 &  0.3899 \tabularnewline
12 &  0.5187 &  0.9625 &  0.4813 \tabularnewline
13 &  0.4197 &  0.8394 &  0.5803 \tabularnewline
14 &  0.4382 &  0.8763 &  0.5618 \tabularnewline
15 &  0.3643 &  0.7286 &  0.6357 \tabularnewline
16 &  0.3493 &  0.6985 &  0.6507 \tabularnewline
17 &  0.313 &  0.6261 &  0.687 \tabularnewline
18 &  0.299 &  0.598 &  0.701 \tabularnewline
19 &  0.2691 &  0.5382 &  0.7309 \tabularnewline
20 &  0.3879 &  0.7759 &  0.6121 \tabularnewline
21 &  0.3265 &  0.653 &  0.6735 \tabularnewline
22 &  0.2696 &  0.5393 &  0.7304 \tabularnewline
23 &  0.2134 &  0.4269 &  0.7866 \tabularnewline
24 &  0.1655 &  0.331 &  0.8345 \tabularnewline
25 &  0.1396 &  0.2791 &  0.8604 \tabularnewline
26 &  0.1383 &  0.2766 &  0.8617 \tabularnewline
27 &  0.1534 &  0.3069 &  0.8466 \tabularnewline
28 &  0.1616 &  0.3232 &  0.8384 \tabularnewline
29 &  0.165 &  0.3299 &  0.835 \tabularnewline
30 &  0.1298 &  0.2597 &  0.8702 \tabularnewline
31 &  0.1143 &  0.2285 &  0.8857 \tabularnewline
32 &  0.08896 &  0.1779 &  0.911 \tabularnewline
33 &  0.06929 &  0.1386 &  0.9307 \tabularnewline
34 &  0.05215 &  0.1043 &  0.9479 \tabularnewline
35 &  0.05965 &  0.1193 &  0.9404 \tabularnewline
36 &  0.1116 &  0.2232 &  0.8884 \tabularnewline
37 &  0.08774 &  0.1755 &  0.9123 \tabularnewline
38 &  0.07857 &  0.1571 &  0.9214 \tabularnewline
39 &  0.06697 &  0.1339 &  0.933 \tabularnewline
40 &  0.05038 &  0.1008 &  0.9496 \tabularnewline
41 &  0.04048 &  0.08096 &  0.9595 \tabularnewline
42 &  0.228 &  0.456 &  0.772 \tabularnewline
43 &  0.1894 &  0.3788 &  0.8106 \tabularnewline
44 &  0.1664 &  0.3328 &  0.8336 \tabularnewline
45 &  0.1693 &  0.3385 &  0.8307 \tabularnewline
46 &  0.1383 &  0.2766 &  0.8617 \tabularnewline
47 &  0.1227 &  0.2454 &  0.8773 \tabularnewline
48 &  0.104 &  0.2081 &  0.896 \tabularnewline
49 &  0.09899 &  0.198 &  0.901 \tabularnewline
50 &  0.07837 &  0.1567 &  0.9216 \tabularnewline
51 &  0.1161 &  0.2322 &  0.8839 \tabularnewline
52 &  0.1176 &  0.2353 &  0.8824 \tabularnewline
53 &  0.2193 &  0.4385 &  0.7807 \tabularnewline
54 &  0.1861 &  0.3722 &  0.8139 \tabularnewline
55 &  0.1551 &  0.3103 &  0.8449 \tabularnewline
56 &  0.1418 &  0.2836 &  0.8582 \tabularnewline
57 &  0.1272 &  0.2544 &  0.8728 \tabularnewline
58 &  0.11 &  0.2201 &  0.89 \tabularnewline
59 &  0.09705 &  0.1941 &  0.9029 \tabularnewline
60 &  0.1032 &  0.2064 &  0.8968 \tabularnewline
61 &  0.1017 &  0.2034 &  0.8983 \tabularnewline
62 &  0.1401 &  0.2802 &  0.8599 \tabularnewline
63 &  0.1298 &  0.2596 &  0.8702 \tabularnewline
64 &  0.1258 &  0.2517 &  0.8742 \tabularnewline
65 &  0.1275 &  0.2549 &  0.8725 \tabularnewline
66 &  0.1094 &  0.2188 &  0.8906 \tabularnewline
67 &  0.09318 &  0.1864 &  0.9068 \tabularnewline
68 &  0.08857 &  0.1771 &  0.9114 \tabularnewline
69 &  0.07639 &  0.1528 &  0.9236 \tabularnewline
70 &  0.1272 &  0.2543 &  0.8729 \tabularnewline
71 &  0.1045 &  0.209 &  0.8955 \tabularnewline
72 &  0.1189 &  0.2377 &  0.8811 \tabularnewline
73 &  0.124 &  0.2481 &  0.876 \tabularnewline
74 &  0.1044 &  0.2088 &  0.8956 \tabularnewline
75 &  0.1085 &  0.2169 &  0.8915 \tabularnewline
76 &  0.08849 &  0.177 &  0.9115 \tabularnewline
77 &  0.08016 &  0.1603 &  0.9198 \tabularnewline
78 &  0.065 &  0.13 &  0.935 \tabularnewline
79 &  0.05262 &  0.1052 &  0.9474 \tabularnewline
80 &  0.04137 &  0.08274 &  0.9586 \tabularnewline
81 &  0.04327 &  0.08654 &  0.9567 \tabularnewline
82 &  0.03587 &  0.07174 &  0.9641 \tabularnewline
83 &  0.03014 &  0.06027 &  0.9699 \tabularnewline
84 &  0.02522 &  0.05045 &  0.9748 \tabularnewline
85 &  0.03074 &  0.06147 &  0.9693 \tabularnewline
86 &  0.03285 &  0.0657 &  0.9672 \tabularnewline
87 &  0.03919 &  0.07838 &  0.9608 \tabularnewline
88 &  0.04412 &  0.08825 &  0.9559 \tabularnewline
89 &  0.03466 &  0.06931 &  0.9653 \tabularnewline
90 &  0.03163 &  0.06327 &  0.9684 \tabularnewline
91 &  0.0438 &  0.08759 &  0.9562 \tabularnewline
92 &  0.03415 &  0.0683 &  0.9658 \tabularnewline
93 &  0.02786 &  0.05573 &  0.9721 \tabularnewline
94 &  0.1246 &  0.2491 &  0.8754 \tabularnewline
95 &  0.1094 &  0.2188 &  0.8906 \tabularnewline
96 &  0.09588 &  0.1918 &  0.9041 \tabularnewline
97 &  0.0909 &  0.1818 &  0.9091 \tabularnewline
98 &  0.07528 &  0.1506 &  0.9247 \tabularnewline
99 &  0.0653 &  0.1306 &  0.9347 \tabularnewline
100 &  0.05191 &  0.1038 &  0.9481 \tabularnewline
101 &  0.05596 &  0.1119 &  0.944 \tabularnewline
102 &  0.1608 &  0.3215 &  0.8392 \tabularnewline
103 &  0.1539 &  0.3078 &  0.8461 \tabularnewline
104 &  0.1286 &  0.2572 &  0.8714 \tabularnewline
105 &  0.1083 &  0.2165 &  0.8917 \tabularnewline
106 &  0.09069 &  0.1814 &  0.9093 \tabularnewline
107 &  0.08047 &  0.1609 &  0.9195 \tabularnewline
108 &  0.06666 &  0.1333 &  0.9333 \tabularnewline
109 &  0.05433 &  0.1087 &  0.9457 \tabularnewline
110 &  0.05861 &  0.1172 &  0.9414 \tabularnewline
111 &  0.07232 &  0.1446 &  0.9277 \tabularnewline
112 &  0.05716 &  0.1143 &  0.9428 \tabularnewline
113 &  0.136 &  0.272 &  0.864 \tabularnewline
114 &  0.145 &  0.2899 &  0.855 \tabularnewline
115 &  0.1205 &  0.2409 &  0.8795 \tabularnewline
116 &  0.1819 &  0.3639 &  0.8181 \tabularnewline
117 &  0.153 &  0.3059 &  0.847 \tabularnewline
118 &  0.1393 &  0.2786 &  0.8607 \tabularnewline
119 &  0.1331 &  0.2661 &  0.8669 \tabularnewline
120 &  0.1074 &  0.2148 &  0.8926 \tabularnewline
121 &  0.08547 &  0.1709 &  0.9145 \tabularnewline
122 &  0.06703 &  0.1341 &  0.933 \tabularnewline
123 &  0.1151 &  0.2303 &  0.8849 \tabularnewline
124 &  0.1027 &  0.2053 &  0.8973 \tabularnewline
125 &  0.09505 &  0.1901 &  0.905 \tabularnewline
126 &  0.07441 &  0.1488 &  0.9256 \tabularnewline
127 &  0.1221 &  0.2442 &  0.8779 \tabularnewline
128 &  0.09688 &  0.1938 &  0.9031 \tabularnewline
129 &  0.0757 &  0.1514 &  0.9243 \tabularnewline
130 &  0.05998 &  0.12 &  0.94 \tabularnewline
131 &  0.04732 &  0.09463 &  0.9527 \tabularnewline
132 &  0.05854 &  0.1171 &  0.9415 \tabularnewline
133 &  0.05002 &  0.1 &  0.95 \tabularnewline
134 &  0.03896 &  0.07792 &  0.961 \tabularnewline
135 &  0.346 &  0.6921 &  0.654 \tabularnewline
136 &  0.2894 &  0.5789 &  0.7106 \tabularnewline
137 &  0.2372 &  0.4744 &  0.7628 \tabularnewline
138 &  0.218 &  0.4359 &  0.782 \tabularnewline
139 &  0.1806 &  0.3612 &  0.8194 \tabularnewline
140 &  0.1603 &  0.3205 &  0.8397 \tabularnewline
141 &  0.1245 &  0.249 &  0.8755 \tabularnewline
142 &  0.1761 &  0.3521 &  0.8239 \tabularnewline
143 &  0.1394 &  0.2787 &  0.8606 \tabularnewline
144 &  0.2413 &  0.4826 &  0.7587 \tabularnewline
145 &  0.2277 &  0.4554 &  0.7723 \tabularnewline
146 &  0.182 &  0.3639 &  0.818 \tabularnewline
147 &  0.9736 &  0.05283 &  0.02642 \tabularnewline
148 &  0.9537 &  0.09253 &  0.04627 \tabularnewline
149 &  0.9236 &  0.1529 &  0.07643 \tabularnewline
150 &  0.8858 &  0.2283 &  0.1142 \tabularnewline
151 &  0.8093 &  0.3813 &  0.1907 \tabularnewline
152 &  0.7992 &  0.4015 &  0.2008 \tabularnewline
153 &  0.8767 &  0.2466 &  0.1233 \tabularnewline
154 &  0.7534 &  0.4931 &  0.2466 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298147&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]7[/C][C] 0.8411[/C][C] 0.3178[/C][C] 0.1589[/C][/ROW]
[ROW][C]8[/C][C] 0.7304[/C][C] 0.5392[/C][C] 0.2696[/C][/ROW]
[ROW][C]9[/C][C] 0.6036[/C][C] 0.7927[/C][C] 0.3964[/C][/ROW]
[ROW][C]10[/C][C] 0.6113[/C][C] 0.7774[/C][C] 0.3887[/C][/ROW]
[ROW][C]11[/C][C] 0.6101[/C][C] 0.7799[/C][C] 0.3899[/C][/ROW]
[ROW][C]12[/C][C] 0.5187[/C][C] 0.9625[/C][C] 0.4813[/C][/ROW]
[ROW][C]13[/C][C] 0.4197[/C][C] 0.8394[/C][C] 0.5803[/C][/ROW]
[ROW][C]14[/C][C] 0.4382[/C][C] 0.8763[/C][C] 0.5618[/C][/ROW]
[ROW][C]15[/C][C] 0.3643[/C][C] 0.7286[/C][C] 0.6357[/C][/ROW]
[ROW][C]16[/C][C] 0.3493[/C][C] 0.6985[/C][C] 0.6507[/C][/ROW]
[ROW][C]17[/C][C] 0.313[/C][C] 0.6261[/C][C] 0.687[/C][/ROW]
[ROW][C]18[/C][C] 0.299[/C][C] 0.598[/C][C] 0.701[/C][/ROW]
[ROW][C]19[/C][C] 0.2691[/C][C] 0.5382[/C][C] 0.7309[/C][/ROW]
[ROW][C]20[/C][C] 0.3879[/C][C] 0.7759[/C][C] 0.6121[/C][/ROW]
[ROW][C]21[/C][C] 0.3265[/C][C] 0.653[/C][C] 0.6735[/C][/ROW]
[ROW][C]22[/C][C] 0.2696[/C][C] 0.5393[/C][C] 0.7304[/C][/ROW]
[ROW][C]23[/C][C] 0.2134[/C][C] 0.4269[/C][C] 0.7866[/C][/ROW]
[ROW][C]24[/C][C] 0.1655[/C][C] 0.331[/C][C] 0.8345[/C][/ROW]
[ROW][C]25[/C][C] 0.1396[/C][C] 0.2791[/C][C] 0.8604[/C][/ROW]
[ROW][C]26[/C][C] 0.1383[/C][C] 0.2766[/C][C] 0.8617[/C][/ROW]
[ROW][C]27[/C][C] 0.1534[/C][C] 0.3069[/C][C] 0.8466[/C][/ROW]
[ROW][C]28[/C][C] 0.1616[/C][C] 0.3232[/C][C] 0.8384[/C][/ROW]
[ROW][C]29[/C][C] 0.165[/C][C] 0.3299[/C][C] 0.835[/C][/ROW]
[ROW][C]30[/C][C] 0.1298[/C][C] 0.2597[/C][C] 0.8702[/C][/ROW]
[ROW][C]31[/C][C] 0.1143[/C][C] 0.2285[/C][C] 0.8857[/C][/ROW]
[ROW][C]32[/C][C] 0.08896[/C][C] 0.1779[/C][C] 0.911[/C][/ROW]
[ROW][C]33[/C][C] 0.06929[/C][C] 0.1386[/C][C] 0.9307[/C][/ROW]
[ROW][C]34[/C][C] 0.05215[/C][C] 0.1043[/C][C] 0.9479[/C][/ROW]
[ROW][C]35[/C][C] 0.05965[/C][C] 0.1193[/C][C] 0.9404[/C][/ROW]
[ROW][C]36[/C][C] 0.1116[/C][C] 0.2232[/C][C] 0.8884[/C][/ROW]
[ROW][C]37[/C][C] 0.08774[/C][C] 0.1755[/C][C] 0.9123[/C][/ROW]
[ROW][C]38[/C][C] 0.07857[/C][C] 0.1571[/C][C] 0.9214[/C][/ROW]
[ROW][C]39[/C][C] 0.06697[/C][C] 0.1339[/C][C] 0.933[/C][/ROW]
[ROW][C]40[/C][C] 0.05038[/C][C] 0.1008[/C][C] 0.9496[/C][/ROW]
[ROW][C]41[/C][C] 0.04048[/C][C] 0.08096[/C][C] 0.9595[/C][/ROW]
[ROW][C]42[/C][C] 0.228[/C][C] 0.456[/C][C] 0.772[/C][/ROW]
[ROW][C]43[/C][C] 0.1894[/C][C] 0.3788[/C][C] 0.8106[/C][/ROW]
[ROW][C]44[/C][C] 0.1664[/C][C] 0.3328[/C][C] 0.8336[/C][/ROW]
[ROW][C]45[/C][C] 0.1693[/C][C] 0.3385[/C][C] 0.8307[/C][/ROW]
[ROW][C]46[/C][C] 0.1383[/C][C] 0.2766[/C][C] 0.8617[/C][/ROW]
[ROW][C]47[/C][C] 0.1227[/C][C] 0.2454[/C][C] 0.8773[/C][/ROW]
[ROW][C]48[/C][C] 0.104[/C][C] 0.2081[/C][C] 0.896[/C][/ROW]
[ROW][C]49[/C][C] 0.09899[/C][C] 0.198[/C][C] 0.901[/C][/ROW]
[ROW][C]50[/C][C] 0.07837[/C][C] 0.1567[/C][C] 0.9216[/C][/ROW]
[ROW][C]51[/C][C] 0.1161[/C][C] 0.2322[/C][C] 0.8839[/C][/ROW]
[ROW][C]52[/C][C] 0.1176[/C][C] 0.2353[/C][C] 0.8824[/C][/ROW]
[ROW][C]53[/C][C] 0.2193[/C][C] 0.4385[/C][C] 0.7807[/C][/ROW]
[ROW][C]54[/C][C] 0.1861[/C][C] 0.3722[/C][C] 0.8139[/C][/ROW]
[ROW][C]55[/C][C] 0.1551[/C][C] 0.3103[/C][C] 0.8449[/C][/ROW]
[ROW][C]56[/C][C] 0.1418[/C][C] 0.2836[/C][C] 0.8582[/C][/ROW]
[ROW][C]57[/C][C] 0.1272[/C][C] 0.2544[/C][C] 0.8728[/C][/ROW]
[ROW][C]58[/C][C] 0.11[/C][C] 0.2201[/C][C] 0.89[/C][/ROW]
[ROW][C]59[/C][C] 0.09705[/C][C] 0.1941[/C][C] 0.9029[/C][/ROW]
[ROW][C]60[/C][C] 0.1032[/C][C] 0.2064[/C][C] 0.8968[/C][/ROW]
[ROW][C]61[/C][C] 0.1017[/C][C] 0.2034[/C][C] 0.8983[/C][/ROW]
[ROW][C]62[/C][C] 0.1401[/C][C] 0.2802[/C][C] 0.8599[/C][/ROW]
[ROW][C]63[/C][C] 0.1298[/C][C] 0.2596[/C][C] 0.8702[/C][/ROW]
[ROW][C]64[/C][C] 0.1258[/C][C] 0.2517[/C][C] 0.8742[/C][/ROW]
[ROW][C]65[/C][C] 0.1275[/C][C] 0.2549[/C][C] 0.8725[/C][/ROW]
[ROW][C]66[/C][C] 0.1094[/C][C] 0.2188[/C][C] 0.8906[/C][/ROW]
[ROW][C]67[/C][C] 0.09318[/C][C] 0.1864[/C][C] 0.9068[/C][/ROW]
[ROW][C]68[/C][C] 0.08857[/C][C] 0.1771[/C][C] 0.9114[/C][/ROW]
[ROW][C]69[/C][C] 0.07639[/C][C] 0.1528[/C][C] 0.9236[/C][/ROW]
[ROW][C]70[/C][C] 0.1272[/C][C] 0.2543[/C][C] 0.8729[/C][/ROW]
[ROW][C]71[/C][C] 0.1045[/C][C] 0.209[/C][C] 0.8955[/C][/ROW]
[ROW][C]72[/C][C] 0.1189[/C][C] 0.2377[/C][C] 0.8811[/C][/ROW]
[ROW][C]73[/C][C] 0.124[/C][C] 0.2481[/C][C] 0.876[/C][/ROW]
[ROW][C]74[/C][C] 0.1044[/C][C] 0.2088[/C][C] 0.8956[/C][/ROW]
[ROW][C]75[/C][C] 0.1085[/C][C] 0.2169[/C][C] 0.8915[/C][/ROW]
[ROW][C]76[/C][C] 0.08849[/C][C] 0.177[/C][C] 0.9115[/C][/ROW]
[ROW][C]77[/C][C] 0.08016[/C][C] 0.1603[/C][C] 0.9198[/C][/ROW]
[ROW][C]78[/C][C] 0.065[/C][C] 0.13[/C][C] 0.935[/C][/ROW]
[ROW][C]79[/C][C] 0.05262[/C][C] 0.1052[/C][C] 0.9474[/C][/ROW]
[ROW][C]80[/C][C] 0.04137[/C][C] 0.08274[/C][C] 0.9586[/C][/ROW]
[ROW][C]81[/C][C] 0.04327[/C][C] 0.08654[/C][C] 0.9567[/C][/ROW]
[ROW][C]82[/C][C] 0.03587[/C][C] 0.07174[/C][C] 0.9641[/C][/ROW]
[ROW][C]83[/C][C] 0.03014[/C][C] 0.06027[/C][C] 0.9699[/C][/ROW]
[ROW][C]84[/C][C] 0.02522[/C][C] 0.05045[/C][C] 0.9748[/C][/ROW]
[ROW][C]85[/C][C] 0.03074[/C][C] 0.06147[/C][C] 0.9693[/C][/ROW]
[ROW][C]86[/C][C] 0.03285[/C][C] 0.0657[/C][C] 0.9672[/C][/ROW]
[ROW][C]87[/C][C] 0.03919[/C][C] 0.07838[/C][C] 0.9608[/C][/ROW]
[ROW][C]88[/C][C] 0.04412[/C][C] 0.08825[/C][C] 0.9559[/C][/ROW]
[ROW][C]89[/C][C] 0.03466[/C][C] 0.06931[/C][C] 0.9653[/C][/ROW]
[ROW][C]90[/C][C] 0.03163[/C][C] 0.06327[/C][C] 0.9684[/C][/ROW]
[ROW][C]91[/C][C] 0.0438[/C][C] 0.08759[/C][C] 0.9562[/C][/ROW]
[ROW][C]92[/C][C] 0.03415[/C][C] 0.0683[/C][C] 0.9658[/C][/ROW]
[ROW][C]93[/C][C] 0.02786[/C][C] 0.05573[/C][C] 0.9721[/C][/ROW]
[ROW][C]94[/C][C] 0.1246[/C][C] 0.2491[/C][C] 0.8754[/C][/ROW]
[ROW][C]95[/C][C] 0.1094[/C][C] 0.2188[/C][C] 0.8906[/C][/ROW]
[ROW][C]96[/C][C] 0.09588[/C][C] 0.1918[/C][C] 0.9041[/C][/ROW]
[ROW][C]97[/C][C] 0.0909[/C][C] 0.1818[/C][C] 0.9091[/C][/ROW]
[ROW][C]98[/C][C] 0.07528[/C][C] 0.1506[/C][C] 0.9247[/C][/ROW]
[ROW][C]99[/C][C] 0.0653[/C][C] 0.1306[/C][C] 0.9347[/C][/ROW]
[ROW][C]100[/C][C] 0.05191[/C][C] 0.1038[/C][C] 0.9481[/C][/ROW]
[ROW][C]101[/C][C] 0.05596[/C][C] 0.1119[/C][C] 0.944[/C][/ROW]
[ROW][C]102[/C][C] 0.1608[/C][C] 0.3215[/C][C] 0.8392[/C][/ROW]
[ROW][C]103[/C][C] 0.1539[/C][C] 0.3078[/C][C] 0.8461[/C][/ROW]
[ROW][C]104[/C][C] 0.1286[/C][C] 0.2572[/C][C] 0.8714[/C][/ROW]
[ROW][C]105[/C][C] 0.1083[/C][C] 0.2165[/C][C] 0.8917[/C][/ROW]
[ROW][C]106[/C][C] 0.09069[/C][C] 0.1814[/C][C] 0.9093[/C][/ROW]
[ROW][C]107[/C][C] 0.08047[/C][C] 0.1609[/C][C] 0.9195[/C][/ROW]
[ROW][C]108[/C][C] 0.06666[/C][C] 0.1333[/C][C] 0.9333[/C][/ROW]
[ROW][C]109[/C][C] 0.05433[/C][C] 0.1087[/C][C] 0.9457[/C][/ROW]
[ROW][C]110[/C][C] 0.05861[/C][C] 0.1172[/C][C] 0.9414[/C][/ROW]
[ROW][C]111[/C][C] 0.07232[/C][C] 0.1446[/C][C] 0.9277[/C][/ROW]
[ROW][C]112[/C][C] 0.05716[/C][C] 0.1143[/C][C] 0.9428[/C][/ROW]
[ROW][C]113[/C][C] 0.136[/C][C] 0.272[/C][C] 0.864[/C][/ROW]
[ROW][C]114[/C][C] 0.145[/C][C] 0.2899[/C][C] 0.855[/C][/ROW]
[ROW][C]115[/C][C] 0.1205[/C][C] 0.2409[/C][C] 0.8795[/C][/ROW]
[ROW][C]116[/C][C] 0.1819[/C][C] 0.3639[/C][C] 0.8181[/C][/ROW]
[ROW][C]117[/C][C] 0.153[/C][C] 0.3059[/C][C] 0.847[/C][/ROW]
[ROW][C]118[/C][C] 0.1393[/C][C] 0.2786[/C][C] 0.8607[/C][/ROW]
[ROW][C]119[/C][C] 0.1331[/C][C] 0.2661[/C][C] 0.8669[/C][/ROW]
[ROW][C]120[/C][C] 0.1074[/C][C] 0.2148[/C][C] 0.8926[/C][/ROW]
[ROW][C]121[/C][C] 0.08547[/C][C] 0.1709[/C][C] 0.9145[/C][/ROW]
[ROW][C]122[/C][C] 0.06703[/C][C] 0.1341[/C][C] 0.933[/C][/ROW]
[ROW][C]123[/C][C] 0.1151[/C][C] 0.2303[/C][C] 0.8849[/C][/ROW]
[ROW][C]124[/C][C] 0.1027[/C][C] 0.2053[/C][C] 0.8973[/C][/ROW]
[ROW][C]125[/C][C] 0.09505[/C][C] 0.1901[/C][C] 0.905[/C][/ROW]
[ROW][C]126[/C][C] 0.07441[/C][C] 0.1488[/C][C] 0.9256[/C][/ROW]
[ROW][C]127[/C][C] 0.1221[/C][C] 0.2442[/C][C] 0.8779[/C][/ROW]
[ROW][C]128[/C][C] 0.09688[/C][C] 0.1938[/C][C] 0.9031[/C][/ROW]
[ROW][C]129[/C][C] 0.0757[/C][C] 0.1514[/C][C] 0.9243[/C][/ROW]
[ROW][C]130[/C][C] 0.05998[/C][C] 0.12[/C][C] 0.94[/C][/ROW]
[ROW][C]131[/C][C] 0.04732[/C][C] 0.09463[/C][C] 0.9527[/C][/ROW]
[ROW][C]132[/C][C] 0.05854[/C][C] 0.1171[/C][C] 0.9415[/C][/ROW]
[ROW][C]133[/C][C] 0.05002[/C][C] 0.1[/C][C] 0.95[/C][/ROW]
[ROW][C]134[/C][C] 0.03896[/C][C] 0.07792[/C][C] 0.961[/C][/ROW]
[ROW][C]135[/C][C] 0.346[/C][C] 0.6921[/C][C] 0.654[/C][/ROW]
[ROW][C]136[/C][C] 0.2894[/C][C] 0.5789[/C][C] 0.7106[/C][/ROW]
[ROW][C]137[/C][C] 0.2372[/C][C] 0.4744[/C][C] 0.7628[/C][/ROW]
[ROW][C]138[/C][C] 0.218[/C][C] 0.4359[/C][C] 0.782[/C][/ROW]
[ROW][C]139[/C][C] 0.1806[/C][C] 0.3612[/C][C] 0.8194[/C][/ROW]
[ROW][C]140[/C][C] 0.1603[/C][C] 0.3205[/C][C] 0.8397[/C][/ROW]
[ROW][C]141[/C][C] 0.1245[/C][C] 0.249[/C][C] 0.8755[/C][/ROW]
[ROW][C]142[/C][C] 0.1761[/C][C] 0.3521[/C][C] 0.8239[/C][/ROW]
[ROW][C]143[/C][C] 0.1394[/C][C] 0.2787[/C][C] 0.8606[/C][/ROW]
[ROW][C]144[/C][C] 0.2413[/C][C] 0.4826[/C][C] 0.7587[/C][/ROW]
[ROW][C]145[/C][C] 0.2277[/C][C] 0.4554[/C][C] 0.7723[/C][/ROW]
[ROW][C]146[/C][C] 0.182[/C][C] 0.3639[/C][C] 0.818[/C][/ROW]
[ROW][C]147[/C][C] 0.9736[/C][C] 0.05283[/C][C] 0.02642[/C][/ROW]
[ROW][C]148[/C][C] 0.9537[/C][C] 0.09253[/C][C] 0.04627[/C][/ROW]
[ROW][C]149[/C][C] 0.9236[/C][C] 0.1529[/C][C] 0.07643[/C][/ROW]
[ROW][C]150[/C][C] 0.8858[/C][C] 0.2283[/C][C] 0.1142[/C][/ROW]
[ROW][C]151[/C][C] 0.8093[/C][C] 0.3813[/C][C] 0.1907[/C][/ROW]
[ROW][C]152[/C][C] 0.7992[/C][C] 0.4015[/C][C] 0.2008[/C][/ROW]
[ROW][C]153[/C][C] 0.8767[/C][C] 0.2466[/C][C] 0.1233[/C][/ROW]
[ROW][C]154[/C][C] 0.7534[/C][C] 0.4931[/C][C] 0.2466[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298147&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298147&T=5

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
7	0.8411	0.3178	0.1589
8	0.7304	0.5392	0.2696
9	0.6036	0.7927	0.3964
10	0.6113	0.7774	0.3887
11	0.6101	0.7799	0.3899
12	0.5187	0.9625	0.4813
13	0.4197	0.8394	0.5803
14	0.4382	0.8763	0.5618
15	0.3643	0.7286	0.6357
16	0.3493	0.6985	0.6507
17	0.313	0.6261	0.687
18	0.299	0.598	0.701
19	0.2691	0.5382	0.7309
20	0.3879	0.7759	0.6121
21	0.3265	0.653	0.6735
22	0.2696	0.5393	0.7304
23	0.2134	0.4269	0.7866
24	0.1655	0.331	0.8345
25	0.1396	0.2791	0.8604
26	0.1383	0.2766	0.8617
27	0.1534	0.3069	0.8466
28	0.1616	0.3232	0.8384
29	0.165	0.3299	0.835
30	0.1298	0.2597	0.8702
31	0.1143	0.2285	0.8857
32	0.08896	0.1779	0.911
33	0.06929	0.1386	0.9307
34	0.05215	0.1043	0.9479
35	0.05965	0.1193	0.9404
36	0.1116	0.2232	0.8884
37	0.08774	0.1755	0.9123
38	0.07857	0.1571	0.9214
39	0.06697	0.1339	0.933
40	0.05038	0.1008	0.9496
41	0.04048	0.08096	0.9595
42	0.228	0.456	0.772
43	0.1894	0.3788	0.8106
44	0.1664	0.3328	0.8336
45	0.1693	0.3385	0.8307
46	0.1383	0.2766	0.8617
47	0.1227	0.2454	0.8773
48	0.104	0.2081	0.896
49	0.09899	0.198	0.901
50	0.07837	0.1567	0.9216
51	0.1161	0.2322	0.8839
52	0.1176	0.2353	0.8824
53	0.2193	0.4385	0.7807
54	0.1861	0.3722	0.8139
55	0.1551	0.3103	0.8449
56	0.1418	0.2836	0.8582
57	0.1272	0.2544	0.8728
58	0.11	0.2201	0.89
59	0.09705	0.1941	0.9029
60	0.1032	0.2064	0.8968
61	0.1017	0.2034	0.8983
62	0.1401	0.2802	0.8599
63	0.1298	0.2596	0.8702
64	0.1258	0.2517	0.8742
65	0.1275	0.2549	0.8725
66	0.1094	0.2188	0.8906
67	0.09318	0.1864	0.9068
68	0.08857	0.1771	0.9114
69	0.07639	0.1528	0.9236
70	0.1272	0.2543	0.8729
71	0.1045	0.209	0.8955
72	0.1189	0.2377	0.8811
73	0.124	0.2481	0.876
74	0.1044	0.2088	0.8956
75	0.1085	0.2169	0.8915
76	0.08849	0.177	0.9115
77	0.08016	0.1603	0.9198
78	0.065	0.13	0.935
79	0.05262	0.1052	0.9474
80	0.04137	0.08274	0.9586
81	0.04327	0.08654	0.9567
82	0.03587	0.07174	0.9641
83	0.03014	0.06027	0.9699
84	0.02522	0.05045	0.9748
85	0.03074	0.06147	0.9693
86	0.03285	0.0657	0.9672
87	0.03919	0.07838	0.9608
88	0.04412	0.08825	0.9559
89	0.03466	0.06931	0.9653
90	0.03163	0.06327	0.9684
91	0.0438	0.08759	0.9562
92	0.03415	0.0683	0.9658
93	0.02786	0.05573	0.9721
94	0.1246	0.2491	0.8754
95	0.1094	0.2188	0.8906
96	0.09588	0.1918	0.9041
97	0.0909	0.1818	0.9091
98	0.07528	0.1506	0.9247
99	0.0653	0.1306	0.9347
100	0.05191	0.1038	0.9481
101	0.05596	0.1119	0.944
102	0.1608	0.3215	0.8392
103	0.1539	0.3078	0.8461
104	0.1286	0.2572	0.8714
105	0.1083	0.2165	0.8917
106	0.09069	0.1814	0.9093
107	0.08047	0.1609	0.9195
108	0.06666	0.1333	0.9333
109	0.05433	0.1087	0.9457
110	0.05861	0.1172	0.9414
111	0.07232	0.1446	0.9277
112	0.05716	0.1143	0.9428
113	0.136	0.272	0.864
114	0.145	0.2899	0.855
115	0.1205	0.2409	0.8795
116	0.1819	0.3639	0.8181
117	0.153	0.3059	0.847
118	0.1393	0.2786	0.8607
119	0.1331	0.2661	0.8669
120	0.1074	0.2148	0.8926
121	0.08547	0.1709	0.9145
122	0.06703	0.1341	0.933
123	0.1151	0.2303	0.8849
124	0.1027	0.2053	0.8973
125	0.09505	0.1901	0.905
126	0.07441	0.1488	0.9256
127	0.1221	0.2442	0.8779
128	0.09688	0.1938	0.9031
129	0.0757	0.1514	0.9243
130	0.05998	0.12	0.94
131	0.04732	0.09463	0.9527
132	0.05854	0.1171	0.9415
133	0.05002	0.1	0.95
134	0.03896	0.07792	0.961
135	0.346	0.6921	0.654
136	0.2894	0.5789	0.7106
137	0.2372	0.4744	0.7628
138	0.218	0.4359	0.782
139	0.1806	0.3612	0.8194
140	0.1603	0.3205	0.8397
141	0.1245	0.249	0.8755
142	0.1761	0.3521	0.8239
143	0.1394	0.2787	0.8606
144	0.2413	0.4826	0.7587
145	0.2277	0.4554	0.7723
146	0.182	0.3639	0.818
147	0.9736	0.05283	0.02642
148	0.9537	0.09253	0.04627
149	0.9236	0.1529	0.07643
150	0.8858	0.2283	0.1142
151	0.8093	0.3813	0.1907
152	0.7992	0.4015	0.2008
153	0.8767	0.2466	0.1233
154	0.7534	0.4931	0.2466

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	0	0	OK
10% type I error level	19	0.128378	NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 &  0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 19 & 0.128378 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298147&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C] 0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]19[/C][C]0.128378[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298147&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298147&T=6

As an alternative you can also use a QR Code:

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	0	0	OK
10% type I error level	19	0.128378	NOK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 0, df1 = 2, df2 = 155, p-value = 1
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 0, df1 = 6, df2 = 151, p-value = 1
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0, df1 = 2, df2 = 155, p-value = 1

\begin{tabular}{lllllllll}
\hline
Ramsey RESET F-Test for powers (2 and 3) of fitted values \tabularnewline
> reset_test_fitted
	RESET test
data:  mylm
RESET = 0, df1 = 2, df2 = 155, p-value = 1
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 0, df1 = 6, df2 = 151, p-value = 1
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0, df1 = 2, df2 = 155, p-value = 1
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298147&T=7

[TABLE]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of fitted values[/C][/ROW]
[ROW][C]> reset_test_fitted
	RESET test
data:  mylm
RESET = 0, df1 = 2, df2 = 155, p-value = 1
[/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 = 0, df1 = 6, df2 = 151, p-value = 1
[/C][/ROW]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of principal components[/C][/ROW]
[ROW][C]> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0, df1 = 2, df2 = 155, p-value = 1
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298147&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298147&T=7

As an alternative you can also use a QR Code:

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

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 0, df1 = 2, df2 = 155, p-value = 1
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 0, df1 = 6, df2 = 151, p-value = 1
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0, df1 = 2, df2 = 155, p-value = 1

Variance Inflation Factors (Multicollinearity)
> vif Gesl Opl GeslxOpl 2.064529 3.033073 4.147769

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
    Gesl      Opl GeslxOpl 
2.064529 3.033073 4.147769 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298147&T=8

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
    Gesl      Opl GeslxOpl 
2.064529 3.033073 4.147769 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298147&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298147&T=8

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 Gesl Opl GeslxOpl 2.064529 3.033073 4.147769

Figure 1

PNG link

Postscript link

PDF link

Figure 2

PNG link

Postscript link

PDF link

Figure 3

PNG link

Postscript link

PDF link

Figure 4

PNG link

Postscript link

PDF link

Figure 5

PNG link

Postscript link

PDF link

Figure 6

PNG link

Postscript link

PDF link

Figure 7

PNG link

Postscript link

PDF link

Figure 8

PNG link

Postscript link

PDF link

Figure 9

PNG link

Postscript link

PDF link

Figure 10

PNG link

Postscript link

PDF link

Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

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

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

library(lattice)
library(lmtest)
library(car)
library(MASS)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
mywarning <- ''
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 (par5=='') par5 <- 0
par5 <- as.numeric(par5)
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=12)'){
(n <- n - 12)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B12)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+12,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'First and Seasonal Differences (s=12)'){
(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 - 12)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B12)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+12,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*12,par5), dimnames=list(1:(n-par5*12), paste(colnames(x)[par1],'(t-',1:par5,'s)',sep='')))
for (i in 1:(n-par5*12)) {
for (j in 1:par5) {
x2[i,j] <- x[i+par5*12-j*12,par1]
}
}
x <- cbind(x[(par5*12+1):n,], x2)
n <- n - par5*12
}
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$resid)
myr
if(n < 200) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,formatC(signif(x[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(x[i]-mysum$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