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, 21 Dec 2016 15:52:37 +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/21/t1482332017r338ll7u2bx0idb.htm/, Retrieved Tue, 07 May 2024 01:54:42 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=302352, Retrieved Tue, 07 May 2024 01:54:42 +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] [MR optimalisatie] [2016-12-20 11:35:23] [29aab2222b4b721088e78b64014cd237]
- R  D    [Multiple Regression] [MR optimalisatie ...] [2016-12-21 14:52:37] [16e0888ced5f28ae20ce1ff74f042113] [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	11 seconds
R Server	Big Analytics Cloud Computing Center
R Framework error message	The field 'Names of X columns' contains a hard return which cannot be interpreted. Please, resubmit your request without hard returns in the 'Names of X columns'.

\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
R Framework error message & The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'. \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=302352&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] [ROW]

R Framework error message[/C][C]

The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.

[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=302352&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=302352&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
R Framework error message	The field 'Names of X columns' contains a hard return which cannot be interpreted. Please, resubmit your request without hard returns in the 'Names of X columns'.

Multiple Linear Regression - Estimated Regression Equation
SOMIVBH [t] = + 11.544 + 0.387538TVDC2[t] + 0.280236TVDC4[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
SOMIVBH
[t] =  +  11.544 +  0.387538TVDC2[t] +  0.280236TVDC4[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=302352&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]SOMIVBH
[t] =  +  11.544 +  0.387538TVDC2[t] +  0.280236TVDC4[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=302352&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=302352&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
SOMIVBH [t] = + 11.544 + 0.387538TVDC2[t] + 0.280236TVDC4[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)	+11.54	1.383	+8.3450e+00	2.728e-14	1.364e-14
TVDC2	+0.3875	0.3187	+1.2160e+00	0.2258	0.1129
TVDC4	+0.2802	0.2806	+9.9860e-01	0.3194	0.1597

\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) & +11.54 &  1.383 & +8.3450e+00 &  2.728e-14 &  1.364e-14 \tabularnewline
TVDC2 & +0.3875 &  0.3187 & +1.2160e+00 &  0.2258 &  0.1129 \tabularnewline
TVDC4 & +0.2802 &  0.2806 & +9.9860e-01 &  0.3194 &  0.1597 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=302352&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]+11.54[/C][C] 1.383[/C][C]+8.3450e+00[/C][C] 2.728e-14[/C][C] 1.364e-14[/C][/ROW]
[ROW][C]TVDC2[/C][C]+0.3875[/C][C] 0.3187[/C][C]+1.2160e+00[/C][C] 0.2258[/C][C] 0.1129[/C][/ROW]
[ROW][C]TVDC4[/C][C]+0.2802[/C][C] 0.2806[/C][C]+9.9860e-01[/C][C] 0.3194[/C][C] 0.1597[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=302352&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=302352&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)	+11.54	1.383	+8.3450e+00	2.728e-14	1.364e-14
TVDC2	+0.3875	0.3187	+1.2160e+00	0.2258	0.1129
TVDC4	+0.2802	0.2806	+9.9860e-01	0.3194	0.1597

Multiple Linear Regression - Regression Statistics
Multiple R	0.1392
R-squared	0.01938
Adjusted R-squared	0.007497
F-TEST (value)	1.631
F-TEST (DF numerator)	2
F-TEST (DF denominator)	165
p-value	0.1989
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.083
Sum Squared Residuals	715.8

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.1392 \tabularnewline
R-squared &  0.01938 \tabularnewline
Adjusted R-squared &  0.007497 \tabularnewline
F-TEST (value) &  1.631 \tabularnewline
F-TEST (DF numerator) & 2 \tabularnewline
F-TEST (DF denominator) & 165 \tabularnewline
p-value &  0.1989 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  2.083 \tabularnewline
Sum Squared Residuals &  715.8 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=302352&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.1392[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.01938[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.007497[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 1.631[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]2[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]165[/C][/ROW]
[ROW][C]p-value[/C][C] 0.1989[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 2.083[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 715.8[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=302352&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=302352&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	0.1392
R-squared	0.01938
Adjusted R-squared	0.007497
F-TEST (value)	1.631
F-TEST (DF numerator)	2
F-TEST (DF denominator)	165
p-value	0.1989
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.083
Sum Squared Residuals	715.8

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	11	13.55	-2.547
2	11	13.93	-2.935
3	15	14.32	0.6776
4	15	13.93	1.065
5	13	14.22	-1.215
6	14	13.55	0.4526
7	13	14.11	-1.108
8	15	13.93	1.065
9	14	14.22	-0.2151
10	15	13.93	1.065
11	10	13.93	-3.935
12	11	13.93	-2.935
13	16	14.22	1.785
14	17	13.55	3.453
15	14	14.22	-0.2151
16	13	13.93	-0.9349
17	10	14.22	-4.215
18	13	14.22	-1.215
19	17	14.22	2.785
20	18	13.93	4.065
21	17	13.93	3.065
22	11	13.93	-2.935
23	15	13.93	1.065
24	12	14.22	-2.215
25	15	13.55	1.453
26	15	14.22	0.7849
27	12	13.93	-1.935
28	19	13.93	5.065
29	13	13.93	-0.9349
30	15	13.93	1.065
31	13	13.55	-0.5474
32	10	14.22	-4.215
33	14	13.65	0.3453
34	12	13.16	-1.16
35	15	13.93	1.065
36	13	13.93	-0.9349
37	18	13.83	4.172
38	15	14.22	0.7849
39	11	14.22	-3.215
40	14	13.93	0.06511
41	11	13.93	-2.935
42	14	13.55	0.4526
43	9	13.93	-4.935
44	13	13.93	-0.9349
45	13	13.93	-0.9349
46	12	13.93	-1.935
47	17	13.93	3.065
48	16	13.93	2.065
49	15	13.55	1.453
50	16	13.93	2.065
51	16	14.32	1.678
52	13	14.6	-1.603
53	13	13.55	-0.5474
54	12	13.93	-1.935
55	11	13.93	-2.935
56	13	13.93	-0.9349
57	15	14.32	0.6776
58	13	14.22	-1.215
59	14	13.93	0.06511
60	13	13.93	-0.9349
61	15	13.93	1.065
62	14	14.32	-0.3224
63	14	13.93	0.06511
64	13	13.93	-0.9349
65	11	12.88	-1.88
66	14	13.93	0.06511
67	17	13.93	3.065
68	15	14.22	0.7849
69	15	13.93	1.065
70	13	13.93	-0.9349
71	12	13.93	-1.935
72	14	13.93	0.06511
73	11	13.55	-2.547
74	14	14.6	-0.6027
75	18	13.93	4.065
76	15	13.55	1.453
77	18	13.93	4.065
78	16	14.5	1.505
79	12	13.93	-1.935
80	14	13.93	0.06511
81	14	13.55	0.4526
82	14	13.93	0.06511
83	14	14.22	-0.2151
84	13	14.22	-1.215
85	12	14.88	-2.883
86	13	14.22	-1.215
87	15	14.22	0.7849
88	13	13.93	-0.9349
89	14	14.22	-0.2151
90	15	13.93	1.065
91	13	13.93	-0.9349
92	14	14.32	-0.3224
93	17	13.93	3.065
94	15	14.32	0.6776
95	13	13.93	-0.9349
96	14	14.22	-0.2151
97	17	14.88	2.117
98	8	14.22	-6.215
99	15	13.93	1.065
100	10	14.22	-4.215
101	15	14.22	0.7849
102	15	13.93	1.065
103	14	13.93	0.06511
104	15	13.93	1.065
105	18	14.22	3.785
106	14	14.22	-0.2151
107	19	13.93	5.065
108	16	13.93	2.065
109	17	14.22	2.785
110	18	14.22	3.785
111	13	13.93	-0.9349
112	10	13.93	-3.935
113	14	13.55	0.4526
114	13	14.5	-1.495
115	12	14.22	-2.215
116	13	13.93	-0.9349
117	12	14.22	-2.215
118	13	13.93	-0.9349
119	16	13.93	2.065
120	12	13.93	-1.935
121	14	13.93	0.06511
122	17	14.22	2.785
123	14	13.93	0.06511
124	12	14.22	-2.215
125	14	13.93	0.06511
126	17	13.93	3.065
127	13	13.93	-0.9349
128	11	13.93	-2.935
129	14	13.93	0.06511
130	11	13.55	-2.547
131	17	14.22	2.785
132	15	14.88	0.1171
133	10	13.55	-3.547
134	15	13.55	1.453
135	16	14.22	1.785
136	17	14.5	2.505
137	15	13.55	1.453
138	12	13.93	-1.935
139	15	14.6	0.3973
140	10	14.32	-4.322
141	13	13.55	-0.5474
142	17	13.93	3.065
143	17	14.22	2.785
144	16	13.93	2.065
145	15	14.32	0.6776
146	16	14.22	1.785
147	16	14.6	1.397
148	15	13.55	1.453
149	16	14.22	1.785
150	14	13.55	0.4526
151	17	14.22	2.785
152	14	13.93	0.06511
153	12	13.93	-1.935
154	15	13.93	1.065
155	14	13.93	0.06511
156	15	13.93	1.065
157	14	13.93	0.06511
158	13	13.93	-0.9349
159	16	14.22	1.785
160	13	13.93	-0.9349
161	14	14.32	-0.3224
162	13	14.22	-1.215
163	13	13.93	-0.9349
164	15	14.22	0.7849
165	13	13.55	-0.5474
166	14	14.22	-0.2151
167	13	13.93	-0.9349
168	12	14.5	-2.495

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  11 &  13.55 & -2.547 \tabularnewline
2 &  11 &  13.93 & -2.935 \tabularnewline
3 &  15 &  14.32 &  0.6776 \tabularnewline
4 &  15 &  13.93 &  1.065 \tabularnewline
5 &  13 &  14.22 & -1.215 \tabularnewline
6 &  14 &  13.55 &  0.4526 \tabularnewline
7 &  13 &  14.11 & -1.108 \tabularnewline
8 &  15 &  13.93 &  1.065 \tabularnewline
9 &  14 &  14.22 & -0.2151 \tabularnewline
10 &  15 &  13.93 &  1.065 \tabularnewline
11 &  10 &  13.93 & -3.935 \tabularnewline
12 &  11 &  13.93 & -2.935 \tabularnewline
13 &  16 &  14.22 &  1.785 \tabularnewline
14 &  17 &  13.55 &  3.453 \tabularnewline
15 &  14 &  14.22 & -0.2151 \tabularnewline
16 &  13 &  13.93 & -0.9349 \tabularnewline
17 &  10 &  14.22 & -4.215 \tabularnewline
18 &  13 &  14.22 & -1.215 \tabularnewline
19 &  17 &  14.22 &  2.785 \tabularnewline
20 &  18 &  13.93 &  4.065 \tabularnewline
21 &  17 &  13.93 &  3.065 \tabularnewline
22 &  11 &  13.93 & -2.935 \tabularnewline
23 &  15 &  13.93 &  1.065 \tabularnewline
24 &  12 &  14.22 & -2.215 \tabularnewline
25 &  15 &  13.55 &  1.453 \tabularnewline
26 &  15 &  14.22 &  0.7849 \tabularnewline
27 &  12 &  13.93 & -1.935 \tabularnewline
28 &  19 &  13.93 &  5.065 \tabularnewline
29 &  13 &  13.93 & -0.9349 \tabularnewline
30 &  15 &  13.93 &  1.065 \tabularnewline
31 &  13 &  13.55 & -0.5474 \tabularnewline
32 &  10 &  14.22 & -4.215 \tabularnewline
33 &  14 &  13.65 &  0.3453 \tabularnewline
34 &  12 &  13.16 & -1.16 \tabularnewline
35 &  15 &  13.93 &  1.065 \tabularnewline
36 &  13 &  13.93 & -0.9349 \tabularnewline
37 &  18 &  13.83 &  4.172 \tabularnewline
38 &  15 &  14.22 &  0.7849 \tabularnewline
39 &  11 &  14.22 & -3.215 \tabularnewline
40 &  14 &  13.93 &  0.06511 \tabularnewline
41 &  11 &  13.93 & -2.935 \tabularnewline
42 &  14 &  13.55 &  0.4526 \tabularnewline
43 &  9 &  13.93 & -4.935 \tabularnewline
44 &  13 &  13.93 & -0.9349 \tabularnewline
45 &  13 &  13.93 & -0.9349 \tabularnewline
46 &  12 &  13.93 & -1.935 \tabularnewline
47 &  17 &  13.93 &  3.065 \tabularnewline
48 &  16 &  13.93 &  2.065 \tabularnewline
49 &  15 &  13.55 &  1.453 \tabularnewline
50 &  16 &  13.93 &  2.065 \tabularnewline
51 &  16 &  14.32 &  1.678 \tabularnewline
52 &  13 &  14.6 & -1.603 \tabularnewline
53 &  13 &  13.55 & -0.5474 \tabularnewline
54 &  12 &  13.93 & -1.935 \tabularnewline
55 &  11 &  13.93 & -2.935 \tabularnewline
56 &  13 &  13.93 & -0.9349 \tabularnewline
57 &  15 &  14.32 &  0.6776 \tabularnewline
58 &  13 &  14.22 & -1.215 \tabularnewline
59 &  14 &  13.93 &  0.06511 \tabularnewline
60 &  13 &  13.93 & -0.9349 \tabularnewline
61 &  15 &  13.93 &  1.065 \tabularnewline
62 &  14 &  14.32 & -0.3224 \tabularnewline
63 &  14 &  13.93 &  0.06511 \tabularnewline
64 &  13 &  13.93 & -0.9349 \tabularnewline
65 &  11 &  12.88 & -1.88 \tabularnewline
66 &  14 &  13.93 &  0.06511 \tabularnewline
67 &  17 &  13.93 &  3.065 \tabularnewline
68 &  15 &  14.22 &  0.7849 \tabularnewline
69 &  15 &  13.93 &  1.065 \tabularnewline
70 &  13 &  13.93 & -0.9349 \tabularnewline
71 &  12 &  13.93 & -1.935 \tabularnewline
72 &  14 &  13.93 &  0.06511 \tabularnewline
73 &  11 &  13.55 & -2.547 \tabularnewline
74 &  14 &  14.6 & -0.6027 \tabularnewline
75 &  18 &  13.93 &  4.065 \tabularnewline
76 &  15 &  13.55 &  1.453 \tabularnewline
77 &  18 &  13.93 &  4.065 \tabularnewline
78 &  16 &  14.5 &  1.505 \tabularnewline
79 &  12 &  13.93 & -1.935 \tabularnewline
80 &  14 &  13.93 &  0.06511 \tabularnewline
81 &  14 &  13.55 &  0.4526 \tabularnewline
82 &  14 &  13.93 &  0.06511 \tabularnewline
83 &  14 &  14.22 & -0.2151 \tabularnewline
84 &  13 &  14.22 & -1.215 \tabularnewline
85 &  12 &  14.88 & -2.883 \tabularnewline
86 &  13 &  14.22 & -1.215 \tabularnewline
87 &  15 &  14.22 &  0.7849 \tabularnewline
88 &  13 &  13.93 & -0.9349 \tabularnewline
89 &  14 &  14.22 & -0.2151 \tabularnewline
90 &  15 &  13.93 &  1.065 \tabularnewline
91 &  13 &  13.93 & -0.9349 \tabularnewline
92 &  14 &  14.32 & -0.3224 \tabularnewline
93 &  17 &  13.93 &  3.065 \tabularnewline
94 &  15 &  14.32 &  0.6776 \tabularnewline
95 &  13 &  13.93 & -0.9349 \tabularnewline
96 &  14 &  14.22 & -0.2151 \tabularnewline
97 &  17 &  14.88 &  2.117 \tabularnewline
98 &  8 &  14.22 & -6.215 \tabularnewline
99 &  15 &  13.93 &  1.065 \tabularnewline
100 &  10 &  14.22 & -4.215 \tabularnewline
101 &  15 &  14.22 &  0.7849 \tabularnewline
102 &  15 &  13.93 &  1.065 \tabularnewline
103 &  14 &  13.93 &  0.06511 \tabularnewline
104 &  15 &  13.93 &  1.065 \tabularnewline
105 &  18 &  14.22 &  3.785 \tabularnewline
106 &  14 &  14.22 & -0.2151 \tabularnewline
107 &  19 &  13.93 &  5.065 \tabularnewline
108 &  16 &  13.93 &  2.065 \tabularnewline
109 &  17 &  14.22 &  2.785 \tabularnewline
110 &  18 &  14.22 &  3.785 \tabularnewline
111 &  13 &  13.93 & -0.9349 \tabularnewline
112 &  10 &  13.93 & -3.935 \tabularnewline
113 &  14 &  13.55 &  0.4526 \tabularnewline
114 &  13 &  14.5 & -1.495 \tabularnewline
115 &  12 &  14.22 & -2.215 \tabularnewline
116 &  13 &  13.93 & -0.9349 \tabularnewline
117 &  12 &  14.22 & -2.215 \tabularnewline
118 &  13 &  13.93 & -0.9349 \tabularnewline
119 &  16 &  13.93 &  2.065 \tabularnewline
120 &  12 &  13.93 & -1.935 \tabularnewline
121 &  14 &  13.93 &  0.06511 \tabularnewline
122 &  17 &  14.22 &  2.785 \tabularnewline
123 &  14 &  13.93 &  0.06511 \tabularnewline
124 &  12 &  14.22 & -2.215 \tabularnewline
125 &  14 &  13.93 &  0.06511 \tabularnewline
126 &  17 &  13.93 &  3.065 \tabularnewline
127 &  13 &  13.93 & -0.9349 \tabularnewline
128 &  11 &  13.93 & -2.935 \tabularnewline
129 &  14 &  13.93 &  0.06511 \tabularnewline
130 &  11 &  13.55 & -2.547 \tabularnewline
131 &  17 &  14.22 &  2.785 \tabularnewline
132 &  15 &  14.88 &  0.1171 \tabularnewline
133 &  10 &  13.55 & -3.547 \tabularnewline
134 &  15 &  13.55 &  1.453 \tabularnewline
135 &  16 &  14.22 &  1.785 \tabularnewline
136 &  17 &  14.5 &  2.505 \tabularnewline
137 &  15 &  13.55 &  1.453 \tabularnewline
138 &  12 &  13.93 & -1.935 \tabularnewline
139 &  15 &  14.6 &  0.3973 \tabularnewline
140 &  10 &  14.32 & -4.322 \tabularnewline
141 &  13 &  13.55 & -0.5474 \tabularnewline
142 &  17 &  13.93 &  3.065 \tabularnewline
143 &  17 &  14.22 &  2.785 \tabularnewline
144 &  16 &  13.93 &  2.065 \tabularnewline
145 &  15 &  14.32 &  0.6776 \tabularnewline
146 &  16 &  14.22 &  1.785 \tabularnewline
147 &  16 &  14.6 &  1.397 \tabularnewline
148 &  15 &  13.55 &  1.453 \tabularnewline
149 &  16 &  14.22 &  1.785 \tabularnewline
150 &  14 &  13.55 &  0.4526 \tabularnewline
151 &  17 &  14.22 &  2.785 \tabularnewline
152 &  14 &  13.93 &  0.06511 \tabularnewline
153 &  12 &  13.93 & -1.935 \tabularnewline
154 &  15 &  13.93 &  1.065 \tabularnewline
155 &  14 &  13.93 &  0.06511 \tabularnewline
156 &  15 &  13.93 &  1.065 \tabularnewline
157 &  14 &  13.93 &  0.06511 \tabularnewline
158 &  13 &  13.93 & -0.9349 \tabularnewline
159 &  16 &  14.22 &  1.785 \tabularnewline
160 &  13 &  13.93 & -0.9349 \tabularnewline
161 &  14 &  14.32 & -0.3224 \tabularnewline
162 &  13 &  14.22 & -1.215 \tabularnewline
163 &  13 &  13.93 & -0.9349 \tabularnewline
164 &  15 &  14.22 &  0.7849 \tabularnewline
165 &  13 &  13.55 & -0.5474 \tabularnewline
166 &  14 &  14.22 & -0.2151 \tabularnewline
167 &  13 &  13.93 & -0.9349 \tabularnewline
168 &  12 &  14.5 & -2.495 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=302352&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] 11[/C][C] 13.55[/C][C]-2.547[/C][/ROW]
[ROW][C]2[/C][C] 11[/C][C] 13.93[/C][C]-2.935[/C][/ROW]
[ROW][C]3[/C][C] 15[/C][C] 14.32[/C][C] 0.6776[/C][/ROW]
[ROW][C]4[/C][C] 15[/C][C] 13.93[/C][C] 1.065[/C][/ROW]
[ROW][C]5[/C][C] 13[/C][C] 14.22[/C][C]-1.215[/C][/ROW]
[ROW][C]6[/C][C] 14[/C][C] 13.55[/C][C] 0.4526[/C][/ROW]
[ROW][C]7[/C][C] 13[/C][C] 14.11[/C][C]-1.108[/C][/ROW]
[ROW][C]8[/C][C] 15[/C][C] 13.93[/C][C] 1.065[/C][/ROW]
[ROW][C]9[/C][C] 14[/C][C] 14.22[/C][C]-0.2151[/C][/ROW]
[ROW][C]10[/C][C] 15[/C][C] 13.93[/C][C] 1.065[/C][/ROW]
[ROW][C]11[/C][C] 10[/C][C] 13.93[/C][C]-3.935[/C][/ROW]
[ROW][C]12[/C][C] 11[/C][C] 13.93[/C][C]-2.935[/C][/ROW]
[ROW][C]13[/C][C] 16[/C][C] 14.22[/C][C] 1.785[/C][/ROW]
[ROW][C]14[/C][C] 17[/C][C] 13.55[/C][C] 3.453[/C][/ROW]
[ROW][C]15[/C][C] 14[/C][C] 14.22[/C][C]-0.2151[/C][/ROW]
[ROW][C]16[/C][C] 13[/C][C] 13.93[/C][C]-0.9349[/C][/ROW]
[ROW][C]17[/C][C] 10[/C][C] 14.22[/C][C]-4.215[/C][/ROW]
[ROW][C]18[/C][C] 13[/C][C] 14.22[/C][C]-1.215[/C][/ROW]
[ROW][C]19[/C][C] 17[/C][C] 14.22[/C][C] 2.785[/C][/ROW]
[ROW][C]20[/C][C] 18[/C][C] 13.93[/C][C] 4.065[/C][/ROW]
[ROW][C]21[/C][C] 17[/C][C] 13.93[/C][C] 3.065[/C][/ROW]
[ROW][C]22[/C][C] 11[/C][C] 13.93[/C][C]-2.935[/C][/ROW]
[ROW][C]23[/C][C] 15[/C][C] 13.93[/C][C] 1.065[/C][/ROW]
[ROW][C]24[/C][C] 12[/C][C] 14.22[/C][C]-2.215[/C][/ROW]
[ROW][C]25[/C][C] 15[/C][C] 13.55[/C][C] 1.453[/C][/ROW]
[ROW][C]26[/C][C] 15[/C][C] 14.22[/C][C] 0.7849[/C][/ROW]
[ROW][C]27[/C][C] 12[/C][C] 13.93[/C][C]-1.935[/C][/ROW]
[ROW][C]28[/C][C] 19[/C][C] 13.93[/C][C] 5.065[/C][/ROW]
[ROW][C]29[/C][C] 13[/C][C] 13.93[/C][C]-0.9349[/C][/ROW]
[ROW][C]30[/C][C] 15[/C][C] 13.93[/C][C] 1.065[/C][/ROW]
[ROW][C]31[/C][C] 13[/C][C] 13.55[/C][C]-0.5474[/C][/ROW]
[ROW][C]32[/C][C] 10[/C][C] 14.22[/C][C]-4.215[/C][/ROW]
[ROW][C]33[/C][C] 14[/C][C] 13.65[/C][C] 0.3453[/C][/ROW]
[ROW][C]34[/C][C] 12[/C][C] 13.16[/C][C]-1.16[/C][/ROW]
[ROW][C]35[/C][C] 15[/C][C] 13.93[/C][C] 1.065[/C][/ROW]
[ROW][C]36[/C][C] 13[/C][C] 13.93[/C][C]-0.9349[/C][/ROW]
[ROW][C]37[/C][C] 18[/C][C] 13.83[/C][C] 4.172[/C][/ROW]
[ROW][C]38[/C][C] 15[/C][C] 14.22[/C][C] 0.7849[/C][/ROW]
[ROW][C]39[/C][C] 11[/C][C] 14.22[/C][C]-3.215[/C][/ROW]
[ROW][C]40[/C][C] 14[/C][C] 13.93[/C][C] 0.06511[/C][/ROW]
[ROW][C]41[/C][C] 11[/C][C] 13.93[/C][C]-2.935[/C][/ROW]
[ROW][C]42[/C][C] 14[/C][C] 13.55[/C][C] 0.4526[/C][/ROW]
[ROW][C]43[/C][C] 9[/C][C] 13.93[/C][C]-4.935[/C][/ROW]
[ROW][C]44[/C][C] 13[/C][C] 13.93[/C][C]-0.9349[/C][/ROW]
[ROW][C]45[/C][C] 13[/C][C] 13.93[/C][C]-0.9349[/C][/ROW]
[ROW][C]46[/C][C] 12[/C][C] 13.93[/C][C]-1.935[/C][/ROW]
[ROW][C]47[/C][C] 17[/C][C] 13.93[/C][C] 3.065[/C][/ROW]
[ROW][C]48[/C][C] 16[/C][C] 13.93[/C][C] 2.065[/C][/ROW]
[ROW][C]49[/C][C] 15[/C][C] 13.55[/C][C] 1.453[/C][/ROW]
[ROW][C]50[/C][C] 16[/C][C] 13.93[/C][C] 2.065[/C][/ROW]
[ROW][C]51[/C][C] 16[/C][C] 14.32[/C][C] 1.678[/C][/ROW]
[ROW][C]52[/C][C] 13[/C][C] 14.6[/C][C]-1.603[/C][/ROW]
[ROW][C]53[/C][C] 13[/C][C] 13.55[/C][C]-0.5474[/C][/ROW]
[ROW][C]54[/C][C] 12[/C][C] 13.93[/C][C]-1.935[/C][/ROW]
[ROW][C]55[/C][C] 11[/C][C] 13.93[/C][C]-2.935[/C][/ROW]
[ROW][C]56[/C][C] 13[/C][C] 13.93[/C][C]-0.9349[/C][/ROW]
[ROW][C]57[/C][C] 15[/C][C] 14.32[/C][C] 0.6776[/C][/ROW]
[ROW][C]58[/C][C] 13[/C][C] 14.22[/C][C]-1.215[/C][/ROW]
[ROW][C]59[/C][C] 14[/C][C] 13.93[/C][C] 0.06511[/C][/ROW]
[ROW][C]60[/C][C] 13[/C][C] 13.93[/C][C]-0.9349[/C][/ROW]
[ROW][C]61[/C][C] 15[/C][C] 13.93[/C][C] 1.065[/C][/ROW]
[ROW][C]62[/C][C] 14[/C][C] 14.32[/C][C]-0.3224[/C][/ROW]
[ROW][C]63[/C][C] 14[/C][C] 13.93[/C][C] 0.06511[/C][/ROW]
[ROW][C]64[/C][C] 13[/C][C] 13.93[/C][C]-0.9349[/C][/ROW]
[ROW][C]65[/C][C] 11[/C][C] 12.88[/C][C]-1.88[/C][/ROW]
[ROW][C]66[/C][C] 14[/C][C] 13.93[/C][C] 0.06511[/C][/ROW]
[ROW][C]67[/C][C] 17[/C][C] 13.93[/C][C] 3.065[/C][/ROW]
[ROW][C]68[/C][C] 15[/C][C] 14.22[/C][C] 0.7849[/C][/ROW]
[ROW][C]69[/C][C] 15[/C][C] 13.93[/C][C] 1.065[/C][/ROW]
[ROW][C]70[/C][C] 13[/C][C] 13.93[/C][C]-0.9349[/C][/ROW]
[ROW][C]71[/C][C] 12[/C][C] 13.93[/C][C]-1.935[/C][/ROW]
[ROW][C]72[/C][C] 14[/C][C] 13.93[/C][C] 0.06511[/C][/ROW]
[ROW][C]73[/C][C] 11[/C][C] 13.55[/C][C]-2.547[/C][/ROW]
[ROW][C]74[/C][C] 14[/C][C] 14.6[/C][C]-0.6027[/C][/ROW]
[ROW][C]75[/C][C] 18[/C][C] 13.93[/C][C] 4.065[/C][/ROW]
[ROW][C]76[/C][C] 15[/C][C] 13.55[/C][C] 1.453[/C][/ROW]
[ROW][C]77[/C][C] 18[/C][C] 13.93[/C][C] 4.065[/C][/ROW]
[ROW][C]78[/C][C] 16[/C][C] 14.5[/C][C] 1.505[/C][/ROW]
[ROW][C]79[/C][C] 12[/C][C] 13.93[/C][C]-1.935[/C][/ROW]
[ROW][C]80[/C][C] 14[/C][C] 13.93[/C][C] 0.06511[/C][/ROW]
[ROW][C]81[/C][C] 14[/C][C] 13.55[/C][C] 0.4526[/C][/ROW]
[ROW][C]82[/C][C] 14[/C][C] 13.93[/C][C] 0.06511[/C][/ROW]
[ROW][C]83[/C][C] 14[/C][C] 14.22[/C][C]-0.2151[/C][/ROW]
[ROW][C]84[/C][C] 13[/C][C] 14.22[/C][C]-1.215[/C][/ROW]
[ROW][C]85[/C][C] 12[/C][C] 14.88[/C][C]-2.883[/C][/ROW]
[ROW][C]86[/C][C] 13[/C][C] 14.22[/C][C]-1.215[/C][/ROW]
[ROW][C]87[/C][C] 15[/C][C] 14.22[/C][C] 0.7849[/C][/ROW]
[ROW][C]88[/C][C] 13[/C][C] 13.93[/C][C]-0.9349[/C][/ROW]
[ROW][C]89[/C][C] 14[/C][C] 14.22[/C][C]-0.2151[/C][/ROW]
[ROW][C]90[/C][C] 15[/C][C] 13.93[/C][C] 1.065[/C][/ROW]
[ROW][C]91[/C][C] 13[/C][C] 13.93[/C][C]-0.9349[/C][/ROW]
[ROW][C]92[/C][C] 14[/C][C] 14.32[/C][C]-0.3224[/C][/ROW]
[ROW][C]93[/C][C] 17[/C][C] 13.93[/C][C] 3.065[/C][/ROW]
[ROW][C]94[/C][C] 15[/C][C] 14.32[/C][C] 0.6776[/C][/ROW]
[ROW][C]95[/C][C] 13[/C][C] 13.93[/C][C]-0.9349[/C][/ROW]
[ROW][C]96[/C][C] 14[/C][C] 14.22[/C][C]-0.2151[/C][/ROW]
[ROW][C]97[/C][C] 17[/C][C] 14.88[/C][C] 2.117[/C][/ROW]
[ROW][C]98[/C][C] 8[/C][C] 14.22[/C][C]-6.215[/C][/ROW]
[ROW][C]99[/C][C] 15[/C][C] 13.93[/C][C] 1.065[/C][/ROW]
[ROW][C]100[/C][C] 10[/C][C] 14.22[/C][C]-4.215[/C][/ROW]
[ROW][C]101[/C][C] 15[/C][C] 14.22[/C][C] 0.7849[/C][/ROW]
[ROW][C]102[/C][C] 15[/C][C] 13.93[/C][C] 1.065[/C][/ROW]
[ROW][C]103[/C][C] 14[/C][C] 13.93[/C][C] 0.06511[/C][/ROW]
[ROW][C]104[/C][C] 15[/C][C] 13.93[/C][C] 1.065[/C][/ROW]
[ROW][C]105[/C][C] 18[/C][C] 14.22[/C][C] 3.785[/C][/ROW]
[ROW][C]106[/C][C] 14[/C][C] 14.22[/C][C]-0.2151[/C][/ROW]
[ROW][C]107[/C][C] 19[/C][C] 13.93[/C][C] 5.065[/C][/ROW]
[ROW][C]108[/C][C] 16[/C][C] 13.93[/C][C] 2.065[/C][/ROW]
[ROW][C]109[/C][C] 17[/C][C] 14.22[/C][C] 2.785[/C][/ROW]
[ROW][C]110[/C][C] 18[/C][C] 14.22[/C][C] 3.785[/C][/ROW]
[ROW][C]111[/C][C] 13[/C][C] 13.93[/C][C]-0.9349[/C][/ROW]
[ROW][C]112[/C][C] 10[/C][C] 13.93[/C][C]-3.935[/C][/ROW]
[ROW][C]113[/C][C] 14[/C][C] 13.55[/C][C] 0.4526[/C][/ROW]
[ROW][C]114[/C][C] 13[/C][C] 14.5[/C][C]-1.495[/C][/ROW]
[ROW][C]115[/C][C] 12[/C][C] 14.22[/C][C]-2.215[/C][/ROW]
[ROW][C]116[/C][C] 13[/C][C] 13.93[/C][C]-0.9349[/C][/ROW]
[ROW][C]117[/C][C] 12[/C][C] 14.22[/C][C]-2.215[/C][/ROW]
[ROW][C]118[/C][C] 13[/C][C] 13.93[/C][C]-0.9349[/C][/ROW]
[ROW][C]119[/C][C] 16[/C][C] 13.93[/C][C] 2.065[/C][/ROW]
[ROW][C]120[/C][C] 12[/C][C] 13.93[/C][C]-1.935[/C][/ROW]
[ROW][C]121[/C][C] 14[/C][C] 13.93[/C][C] 0.06511[/C][/ROW]
[ROW][C]122[/C][C] 17[/C][C] 14.22[/C][C] 2.785[/C][/ROW]
[ROW][C]123[/C][C] 14[/C][C] 13.93[/C][C] 0.06511[/C][/ROW]
[ROW][C]124[/C][C] 12[/C][C] 14.22[/C][C]-2.215[/C][/ROW]
[ROW][C]125[/C][C] 14[/C][C] 13.93[/C][C] 0.06511[/C][/ROW]
[ROW][C]126[/C][C] 17[/C][C] 13.93[/C][C] 3.065[/C][/ROW]
[ROW][C]127[/C][C] 13[/C][C] 13.93[/C][C]-0.9349[/C][/ROW]
[ROW][C]128[/C][C] 11[/C][C] 13.93[/C][C]-2.935[/C][/ROW]
[ROW][C]129[/C][C] 14[/C][C] 13.93[/C][C] 0.06511[/C][/ROW]
[ROW][C]130[/C][C] 11[/C][C] 13.55[/C][C]-2.547[/C][/ROW]
[ROW][C]131[/C][C] 17[/C][C] 14.22[/C][C] 2.785[/C][/ROW]
[ROW][C]132[/C][C] 15[/C][C] 14.88[/C][C] 0.1171[/C][/ROW]
[ROW][C]133[/C][C] 10[/C][C] 13.55[/C][C]-3.547[/C][/ROW]
[ROW][C]134[/C][C] 15[/C][C] 13.55[/C][C] 1.453[/C][/ROW]
[ROW][C]135[/C][C] 16[/C][C] 14.22[/C][C] 1.785[/C][/ROW]
[ROW][C]136[/C][C] 17[/C][C] 14.5[/C][C] 2.505[/C][/ROW]
[ROW][C]137[/C][C] 15[/C][C] 13.55[/C][C] 1.453[/C][/ROW]
[ROW][C]138[/C][C] 12[/C][C] 13.93[/C][C]-1.935[/C][/ROW]
[ROW][C]139[/C][C] 15[/C][C] 14.6[/C][C] 0.3973[/C][/ROW]
[ROW][C]140[/C][C] 10[/C][C] 14.32[/C][C]-4.322[/C][/ROW]
[ROW][C]141[/C][C] 13[/C][C] 13.55[/C][C]-0.5474[/C][/ROW]
[ROW][C]142[/C][C] 17[/C][C] 13.93[/C][C] 3.065[/C][/ROW]
[ROW][C]143[/C][C] 17[/C][C] 14.22[/C][C] 2.785[/C][/ROW]
[ROW][C]144[/C][C] 16[/C][C] 13.93[/C][C] 2.065[/C][/ROW]
[ROW][C]145[/C][C] 15[/C][C] 14.32[/C][C] 0.6776[/C][/ROW]
[ROW][C]146[/C][C] 16[/C][C] 14.22[/C][C] 1.785[/C][/ROW]
[ROW][C]147[/C][C] 16[/C][C] 14.6[/C][C] 1.397[/C][/ROW]
[ROW][C]148[/C][C] 15[/C][C] 13.55[/C][C] 1.453[/C][/ROW]
[ROW][C]149[/C][C] 16[/C][C] 14.22[/C][C] 1.785[/C][/ROW]
[ROW][C]150[/C][C] 14[/C][C] 13.55[/C][C] 0.4526[/C][/ROW]
[ROW][C]151[/C][C] 17[/C][C] 14.22[/C][C] 2.785[/C][/ROW]
[ROW][C]152[/C][C] 14[/C][C] 13.93[/C][C] 0.06511[/C][/ROW]
[ROW][C]153[/C][C] 12[/C][C] 13.93[/C][C]-1.935[/C][/ROW]
[ROW][C]154[/C][C] 15[/C][C] 13.93[/C][C] 1.065[/C][/ROW]
[ROW][C]155[/C][C] 14[/C][C] 13.93[/C][C] 0.06511[/C][/ROW]
[ROW][C]156[/C][C] 15[/C][C] 13.93[/C][C] 1.065[/C][/ROW]
[ROW][C]157[/C][C] 14[/C][C] 13.93[/C][C] 0.06511[/C][/ROW]
[ROW][C]158[/C][C] 13[/C][C] 13.93[/C][C]-0.9349[/C][/ROW]
[ROW][C]159[/C][C] 16[/C][C] 14.22[/C][C] 1.785[/C][/ROW]
[ROW][C]160[/C][C] 13[/C][C] 13.93[/C][C]-0.9349[/C][/ROW]
[ROW][C]161[/C][C] 14[/C][C] 14.32[/C][C]-0.3224[/C][/ROW]
[ROW][C]162[/C][C] 13[/C][C] 14.22[/C][C]-1.215[/C][/ROW]
[ROW][C]163[/C][C] 13[/C][C] 13.93[/C][C]-0.9349[/C][/ROW]
[ROW][C]164[/C][C] 15[/C][C] 14.22[/C][C] 0.7849[/C][/ROW]
[ROW][C]165[/C][C] 13[/C][C] 13.55[/C][C]-0.5474[/C][/ROW]
[ROW][C]166[/C][C] 14[/C][C] 14.22[/C][C]-0.2151[/C][/ROW]
[ROW][C]167[/C][C] 13[/C][C] 13.93[/C][C]-0.9349[/C][/ROW]
[ROW][C]168[/C][C] 12[/C][C] 14.5[/C][C]-2.495[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=302352&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=302352&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	11	13.55	-2.547
2	11	13.93	-2.935
3	15	14.32	0.6776
4	15	13.93	1.065
5	13	14.22	-1.215
6	14	13.55	0.4526
7	13	14.11	-1.108
8	15	13.93	1.065
9	14	14.22	-0.2151
10	15	13.93	1.065
11	10	13.93	-3.935
12	11	13.93	-2.935
13	16	14.22	1.785
14	17	13.55	3.453
15	14	14.22	-0.2151
16	13	13.93	-0.9349
17	10	14.22	-4.215
18	13	14.22	-1.215
19	17	14.22	2.785
20	18	13.93	4.065
21	17	13.93	3.065
22	11	13.93	-2.935
23	15	13.93	1.065
24	12	14.22	-2.215
25	15	13.55	1.453
26	15	14.22	0.7849
27	12	13.93	-1.935
28	19	13.93	5.065
29	13	13.93	-0.9349
30	15	13.93	1.065
31	13	13.55	-0.5474
32	10	14.22	-4.215
33	14	13.65	0.3453
34	12	13.16	-1.16
35	15	13.93	1.065
36	13	13.93	-0.9349
37	18	13.83	4.172
38	15	14.22	0.7849
39	11	14.22	-3.215
40	14	13.93	0.06511
41	11	13.93	-2.935
42	14	13.55	0.4526
43	9	13.93	-4.935
44	13	13.93	-0.9349
45	13	13.93	-0.9349
46	12	13.93	-1.935
47	17	13.93	3.065
48	16	13.93	2.065
49	15	13.55	1.453
50	16	13.93	2.065
51	16	14.32	1.678
52	13	14.6	-1.603
53	13	13.55	-0.5474
54	12	13.93	-1.935
55	11	13.93	-2.935
56	13	13.93	-0.9349
57	15	14.32	0.6776
58	13	14.22	-1.215
59	14	13.93	0.06511
60	13	13.93	-0.9349
61	15	13.93	1.065
62	14	14.32	-0.3224
63	14	13.93	0.06511
64	13	13.93	-0.9349
65	11	12.88	-1.88
66	14	13.93	0.06511
67	17	13.93	3.065
68	15	14.22	0.7849
69	15	13.93	1.065
70	13	13.93	-0.9349
71	12	13.93	-1.935
72	14	13.93	0.06511
73	11	13.55	-2.547
74	14	14.6	-0.6027
75	18	13.93	4.065
76	15	13.55	1.453
77	18	13.93	4.065
78	16	14.5	1.505
79	12	13.93	-1.935
80	14	13.93	0.06511
81	14	13.55	0.4526
82	14	13.93	0.06511
83	14	14.22	-0.2151
84	13	14.22	-1.215
85	12	14.88	-2.883
86	13	14.22	-1.215
87	15	14.22	0.7849
88	13	13.93	-0.9349
89	14	14.22	-0.2151
90	15	13.93	1.065
91	13	13.93	-0.9349
92	14	14.32	-0.3224
93	17	13.93	3.065
94	15	14.32	0.6776
95	13	13.93	-0.9349
96	14	14.22	-0.2151
97	17	14.88	2.117
98	8	14.22	-6.215
99	15	13.93	1.065
100	10	14.22	-4.215
101	15	14.22	0.7849
102	15	13.93	1.065
103	14	13.93	0.06511
104	15	13.93	1.065
105	18	14.22	3.785
106	14	14.22	-0.2151
107	19	13.93	5.065
108	16	13.93	2.065
109	17	14.22	2.785
110	18	14.22	3.785
111	13	13.93	-0.9349
112	10	13.93	-3.935
113	14	13.55	0.4526
114	13	14.5	-1.495
115	12	14.22	-2.215
116	13	13.93	-0.9349
117	12	14.22	-2.215
118	13	13.93	-0.9349
119	16	13.93	2.065
120	12	13.93	-1.935
121	14	13.93	0.06511
122	17	14.22	2.785
123	14	13.93	0.06511
124	12	14.22	-2.215
125	14	13.93	0.06511
126	17	13.93	3.065
127	13	13.93	-0.9349
128	11	13.93	-2.935
129	14	13.93	0.06511
130	11	13.55	-2.547
131	17	14.22	2.785
132	15	14.88	0.1171
133	10	13.55	-3.547
134	15	13.55	1.453
135	16	14.22	1.785
136	17	14.5	2.505
137	15	13.55	1.453
138	12	13.93	-1.935
139	15	14.6	0.3973
140	10	14.32	-4.322
141	13	13.55	-0.5474
142	17	13.93	3.065
143	17	14.22	2.785
144	16	13.93	2.065
145	15	14.32	0.6776
146	16	14.22	1.785
147	16	14.6	1.397
148	15	13.55	1.453
149	16	14.22	1.785
150	14	13.55	0.4526
151	17	14.22	2.785
152	14	13.93	0.06511
153	12	13.93	-1.935
154	15	13.93	1.065
155	14	13.93	0.06511
156	15	13.93	1.065
157	14	13.93	0.06511
158	13	13.93	-0.9349
159	16	14.22	1.785
160	13	13.93	-0.9349
161	14	14.32	-0.3224
162	13	14.22	-1.215
163	13	13.93	-0.9349
164	15	14.22	0.7849
165	13	13.55	-0.5474
166	14	14.22	-0.2151
167	13	13.93	-0.9349
168	12	14.5	-2.495

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
6	0.6051	0.7898	0.3949
7	0.4543	0.9087	0.5457
8	0.388	0.776	0.612
9	0.2669	0.5337	0.7331
10	0.2118	0.4235	0.7882
11	0.4513	0.9027	0.5487
12	0.4727	0.9454	0.5273
13	0.4844	0.9689	0.5156
14	0.7327	0.5347	0.2673
15	0.6577	0.6846	0.3423
16	0.5831	0.8338	0.4169
17	0.7158	0.5685	0.2842
18	0.6503	0.6993	0.3497
19	0.7479	0.5041	0.2521
20	0.8763	0.2473	0.1237
21	0.9026	0.1948	0.09742
22	0.9208	0.1584	0.0792
23	0.9005	0.1989	0.09945
24	0.8901	0.2198	0.1099
25	0.8678	0.2644	0.1322
26	0.8438	0.3124	0.1562
27	0.8344	0.3313	0.1656
28	0.9431	0.1139	0.05693
29	0.9289	0.1423	0.07115
30	0.9111	0.1777	0.08886
31	0.8897	0.2206	0.1103
32	0.9328	0.1344	0.06719
33	0.9142	0.1715	0.08576
34	0.8998	0.2003	0.1002
35	0.8794	0.2412	0.1206
36	0.8565	0.287	0.1435
37	0.9315	0.137	0.06848
38	0.9172	0.1656	0.08279
39	0.9317	0.1366	0.06829
40	0.9129	0.1742	0.0871
41	0.9278	0.1445	0.07224
42	0.9089	0.1823	0.09114
43	0.9662	0.06767	0.03384
44	0.9572	0.08561	0.0428
45	0.9464	0.1071	0.05356
46	0.9418	0.1164	0.05821
47	0.9571	0.08575	0.04287
48	0.9572	0.08568	0.04284
49	0.9494	0.1011	0.05055
50	0.9491	0.1018	0.0509
51	0.9465	0.107	0.0535
52	0.9373	0.1254	0.0627
53	0.9232	0.1536	0.07678
54	0.9196	0.1608	0.08042
55	0.9329	0.1341	0.06707
56	0.9193	0.1614	0.08071
57	0.9042	0.1915	0.09577
58	0.8879	0.2243	0.1121
59	0.8642	0.2716	0.1358
60	0.8423	0.3154	0.1577
61	0.8213	0.3574	0.1787
62	0.7903	0.4194	0.2097
63	0.7558	0.4884	0.2442
64	0.7254	0.5491	0.2746
65	0.7296	0.5407	0.2704
66	0.6909	0.6182	0.3091
67	0.7362	0.5276	0.2638
68	0.7071	0.5858	0.2929
69	0.678	0.6441	0.3221
70	0.6443	0.7114	0.3557
71	0.6369	0.7263	0.3631
72	0.594	0.812	0.406
73	0.6154	0.7692	0.3846
74	0.574	0.852	0.426
75	0.6906	0.6188	0.3094
76	0.6691	0.6617	0.3309
77	0.7716	0.4569	0.2284
78	0.7608	0.4784	0.2392
79	0.7552	0.4896	0.2448
80	0.7188	0.5624	0.2812
81	0.6816	0.6367	0.3184
82	0.6409	0.7183	0.3592
83	0.599	0.802	0.401
84	0.5693	0.8614	0.4307
85	0.6037	0.7926	0.3963
86	0.5752	0.8496	0.4248
87	0.5393	0.9213	0.4607
88	0.5036	0.9928	0.4964
89	0.4608	0.9217	0.5392
90	0.4281	0.8563	0.5719
91	0.3932	0.7865	0.6068
92	0.352	0.7039	0.648
93	0.4004	0.8007	0.5996
94	0.3623	0.7246	0.6377
95	0.3289	0.6578	0.6711
96	0.2908	0.5817	0.7092
97	0.2935	0.5869	0.7065
98	0.6421	0.7157	0.3579
99	0.6103	0.7793	0.3897
100	0.7519	0.4962	0.2481
101	0.7198	0.5603	0.2802
102	0.6908	0.6184	0.3092
103	0.6491	0.7017	0.3509
104	0.6175	0.765	0.3825
105	0.7045	0.5911	0.2955
106	0.6663	0.6674	0.3337
107	0.8508	0.2983	0.1492
108	0.8549	0.2902	0.1451
109	0.8712	0.2576	0.1288
110	0.9182	0.1636	0.08182
111	0.9016	0.1967	0.09837
112	0.9456	0.1089	0.05444
113	0.9315	0.137	0.0685
114	0.9338	0.1324	0.06619
115	0.9435	0.113	0.05651
116	0.9307	0.1385	0.06925
117	0.943	0.1141	0.05703
118	0.9301	0.1398	0.06988
119	0.9336	0.1328	0.06638
120	0.9311	0.1379	0.06893
121	0.9124	0.1752	0.0876
122	0.9209	0.1582	0.07908
123	0.9	0.2	0.1
124	0.9186	0.1627	0.08137
125	0.8968	0.2063	0.1032
126	0.9321	0.1358	0.0679
127	0.9154	0.1692	0.08458
128	0.9355	0.1291	0.06455
129	0.9161	0.1678	0.08388
130	0.9353	0.1294	0.06468
131	0.9414	0.1172	0.05861
132	0.9273	0.1454	0.0727
133	0.9739	0.05222	0.02611
134	0.9663	0.0675	0.03375
135	0.9587	0.08262	0.04131
136	0.9529	0.0941	0.04705
137	0.941	0.1181	0.05903
138	0.9422	0.1156	0.0578
139	0.9217	0.1565	0.07826
140	0.9845	0.03106	0.01553
141	0.9783	0.04347	0.02173
142	0.9882	0.02352	0.01176
143	0.9921	0.01571	0.007854
144	0.9931	0.01385	0.006927
145	0.9892	0.02158	0.01079
146	0.988	0.02406	0.01203
147	0.9864	0.02719	0.0136
148	0.9826	0.03477	0.01738
149	0.9839	0.03229	0.01615
150	0.9743	0.05145	0.02572
151	0.9944	0.01117	0.005587
152	0.9894	0.02119	0.01059
153	0.9913	0.01747	0.008735
154	0.9885	0.02305	0.01152
155	0.9776	0.04471	0.02235
156	0.9737	0.05256	0.02628
157	0.9521	0.09579	0.0479
158	0.916	0.1681	0.08405
159	0.9766	0.04684	0.02342
160	0.9487	0.1027	0.05134
161	0.8841	0.2318	0.1159
162	0.7673	0.4655	0.2327

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
6 &  0.6051 &  0.7898 &  0.3949 \tabularnewline
7 &  0.4543 &  0.9087 &  0.5457 \tabularnewline
8 &  0.388 &  0.776 &  0.612 \tabularnewline
9 &  0.2669 &  0.5337 &  0.7331 \tabularnewline
10 &  0.2118 &  0.4235 &  0.7882 \tabularnewline
11 &  0.4513 &  0.9027 &  0.5487 \tabularnewline
12 &  0.4727 &  0.9454 &  0.5273 \tabularnewline
13 &  0.4844 &  0.9689 &  0.5156 \tabularnewline
14 &  0.7327 &  0.5347 &  0.2673 \tabularnewline
15 &  0.6577 &  0.6846 &  0.3423 \tabularnewline
16 &  0.5831 &  0.8338 &  0.4169 \tabularnewline
17 &  0.7158 &  0.5685 &  0.2842 \tabularnewline
18 &  0.6503 &  0.6993 &  0.3497 \tabularnewline
19 &  0.7479 &  0.5041 &  0.2521 \tabularnewline
20 &  0.8763 &  0.2473 &  0.1237 \tabularnewline
21 &  0.9026 &  0.1948 &  0.09742 \tabularnewline
22 &  0.9208 &  0.1584 &  0.0792 \tabularnewline
23 &  0.9005 &  0.1989 &  0.09945 \tabularnewline
24 &  0.8901 &  0.2198 &  0.1099 \tabularnewline
25 &  0.8678 &  0.2644 &  0.1322 \tabularnewline
26 &  0.8438 &  0.3124 &  0.1562 \tabularnewline
27 &  0.8344 &  0.3313 &  0.1656 \tabularnewline
28 &  0.9431 &  0.1139 &  0.05693 \tabularnewline
29 &  0.9289 &  0.1423 &  0.07115 \tabularnewline
30 &  0.9111 &  0.1777 &  0.08886 \tabularnewline
31 &  0.8897 &  0.2206 &  0.1103 \tabularnewline
32 &  0.9328 &  0.1344 &  0.06719 \tabularnewline
33 &  0.9142 &  0.1715 &  0.08576 \tabularnewline
34 &  0.8998 &  0.2003 &  0.1002 \tabularnewline
35 &  0.8794 &  0.2412 &  0.1206 \tabularnewline
36 &  0.8565 &  0.287 &  0.1435 \tabularnewline
37 &  0.9315 &  0.137 &  0.06848 \tabularnewline
38 &  0.9172 &  0.1656 &  0.08279 \tabularnewline
39 &  0.9317 &  0.1366 &  0.06829 \tabularnewline
40 &  0.9129 &  0.1742 &  0.0871 \tabularnewline
41 &  0.9278 &  0.1445 &  0.07224 \tabularnewline
42 &  0.9089 &  0.1823 &  0.09114 \tabularnewline
43 &  0.9662 &  0.06767 &  0.03384 \tabularnewline
44 &  0.9572 &  0.08561 &  0.0428 \tabularnewline
45 &  0.9464 &  0.1071 &  0.05356 \tabularnewline
46 &  0.9418 &  0.1164 &  0.05821 \tabularnewline
47 &  0.9571 &  0.08575 &  0.04287 \tabularnewline
48 &  0.9572 &  0.08568 &  0.04284 \tabularnewline
49 &  0.9494 &  0.1011 &  0.05055 \tabularnewline
50 &  0.9491 &  0.1018 &  0.0509 \tabularnewline
51 &  0.9465 &  0.107 &  0.0535 \tabularnewline
52 &  0.9373 &  0.1254 &  0.0627 \tabularnewline
53 &  0.9232 &  0.1536 &  0.07678 \tabularnewline
54 &  0.9196 &  0.1608 &  0.08042 \tabularnewline
55 &  0.9329 &  0.1341 &  0.06707 \tabularnewline
56 &  0.9193 &  0.1614 &  0.08071 \tabularnewline
57 &  0.9042 &  0.1915 &  0.09577 \tabularnewline
58 &  0.8879 &  0.2243 &  0.1121 \tabularnewline
59 &  0.8642 &  0.2716 &  0.1358 \tabularnewline
60 &  0.8423 &  0.3154 &  0.1577 \tabularnewline
61 &  0.8213 &  0.3574 &  0.1787 \tabularnewline
62 &  0.7903 &  0.4194 &  0.2097 \tabularnewline
63 &  0.7558 &  0.4884 &  0.2442 \tabularnewline
64 &  0.7254 &  0.5491 &  0.2746 \tabularnewline
65 &  0.7296 &  0.5407 &  0.2704 \tabularnewline
66 &  0.6909 &  0.6182 &  0.3091 \tabularnewline
67 &  0.7362 &  0.5276 &  0.2638 \tabularnewline
68 &  0.7071 &  0.5858 &  0.2929 \tabularnewline
69 &  0.678 &  0.6441 &  0.3221 \tabularnewline
70 &  0.6443 &  0.7114 &  0.3557 \tabularnewline
71 &  0.6369 &  0.7263 &  0.3631 \tabularnewline
72 &  0.594 &  0.812 &  0.406 \tabularnewline
73 &  0.6154 &  0.7692 &  0.3846 \tabularnewline
74 &  0.574 &  0.852 &  0.426 \tabularnewline
75 &  0.6906 &  0.6188 &  0.3094 \tabularnewline
76 &  0.6691 &  0.6617 &  0.3309 \tabularnewline
77 &  0.7716 &  0.4569 &  0.2284 \tabularnewline
78 &  0.7608 &  0.4784 &  0.2392 \tabularnewline
79 &  0.7552 &  0.4896 &  0.2448 \tabularnewline
80 &  0.7188 &  0.5624 &  0.2812 \tabularnewline
81 &  0.6816 &  0.6367 &  0.3184 \tabularnewline
82 &  0.6409 &  0.7183 &  0.3592 \tabularnewline
83 &  0.599 &  0.802 &  0.401 \tabularnewline
84 &  0.5693 &  0.8614 &  0.4307 \tabularnewline
85 &  0.6037 &  0.7926 &  0.3963 \tabularnewline
86 &  0.5752 &  0.8496 &  0.4248 \tabularnewline
87 &  0.5393 &  0.9213 &  0.4607 \tabularnewline
88 &  0.5036 &  0.9928 &  0.4964 \tabularnewline
89 &  0.4608 &  0.9217 &  0.5392 \tabularnewline
90 &  0.4281 &  0.8563 &  0.5719 \tabularnewline
91 &  0.3932 &  0.7865 &  0.6068 \tabularnewline
92 &  0.352 &  0.7039 &  0.648 \tabularnewline
93 &  0.4004 &  0.8007 &  0.5996 \tabularnewline
94 &  0.3623 &  0.7246 &  0.6377 \tabularnewline
95 &  0.3289 &  0.6578 &  0.6711 \tabularnewline
96 &  0.2908 &  0.5817 &  0.7092 \tabularnewline
97 &  0.2935 &  0.5869 &  0.7065 \tabularnewline
98 &  0.6421 &  0.7157 &  0.3579 \tabularnewline
99 &  0.6103 &  0.7793 &  0.3897 \tabularnewline
100 &  0.7519 &  0.4962 &  0.2481 \tabularnewline
101 &  0.7198 &  0.5603 &  0.2802 \tabularnewline
102 &  0.6908 &  0.6184 &  0.3092 \tabularnewline
103 &  0.6491 &  0.7017 &  0.3509 \tabularnewline
104 &  0.6175 &  0.765 &  0.3825 \tabularnewline
105 &  0.7045 &  0.5911 &  0.2955 \tabularnewline
106 &  0.6663 &  0.6674 &  0.3337 \tabularnewline
107 &  0.8508 &  0.2983 &  0.1492 \tabularnewline
108 &  0.8549 &  0.2902 &  0.1451 \tabularnewline
109 &  0.8712 &  0.2576 &  0.1288 \tabularnewline
110 &  0.9182 &  0.1636 &  0.08182 \tabularnewline
111 &  0.9016 &  0.1967 &  0.09837 \tabularnewline
112 &  0.9456 &  0.1089 &  0.05444 \tabularnewline
113 &  0.9315 &  0.137 &  0.0685 \tabularnewline
114 &  0.9338 &  0.1324 &  0.06619 \tabularnewline
115 &  0.9435 &  0.113 &  0.05651 \tabularnewline
116 &  0.9307 &  0.1385 &  0.06925 \tabularnewline
117 &  0.943 &  0.1141 &  0.05703 \tabularnewline
118 &  0.9301 &  0.1398 &  0.06988 \tabularnewline
119 &  0.9336 &  0.1328 &  0.06638 \tabularnewline
120 &  0.9311 &  0.1379 &  0.06893 \tabularnewline
121 &  0.9124 &  0.1752 &  0.0876 \tabularnewline
122 &  0.9209 &  0.1582 &  0.07908 \tabularnewline
123 &  0.9 &  0.2 &  0.1 \tabularnewline
124 &  0.9186 &  0.1627 &  0.08137 \tabularnewline
125 &  0.8968 &  0.2063 &  0.1032 \tabularnewline
126 &  0.9321 &  0.1358 &  0.0679 \tabularnewline
127 &  0.9154 &  0.1692 &  0.08458 \tabularnewline
128 &  0.9355 &  0.1291 &  0.06455 \tabularnewline
129 &  0.9161 &  0.1678 &  0.08388 \tabularnewline
130 &  0.9353 &  0.1294 &  0.06468 \tabularnewline
131 &  0.9414 &  0.1172 &  0.05861 \tabularnewline
132 &  0.9273 &  0.1454 &  0.0727 \tabularnewline
133 &  0.9739 &  0.05222 &  0.02611 \tabularnewline
134 &  0.9663 &  0.0675 &  0.03375 \tabularnewline
135 &  0.9587 &  0.08262 &  0.04131 \tabularnewline
136 &  0.9529 &  0.0941 &  0.04705 \tabularnewline
137 &  0.941 &  0.1181 &  0.05903 \tabularnewline
138 &  0.9422 &  0.1156 &  0.0578 \tabularnewline
139 &  0.9217 &  0.1565 &  0.07826 \tabularnewline
140 &  0.9845 &  0.03106 &  0.01553 \tabularnewline
141 &  0.9783 &  0.04347 &  0.02173 \tabularnewline
142 &  0.9882 &  0.02352 &  0.01176 \tabularnewline
143 &  0.9921 &  0.01571 &  0.007854 \tabularnewline
144 &  0.9931 &  0.01385 &  0.006927 \tabularnewline
145 &  0.9892 &  0.02158 &  0.01079 \tabularnewline
146 &  0.988 &  0.02406 &  0.01203 \tabularnewline
147 &  0.9864 &  0.02719 &  0.0136 \tabularnewline
148 &  0.9826 &  0.03477 &  0.01738 \tabularnewline
149 &  0.9839 &  0.03229 &  0.01615 \tabularnewline
150 &  0.9743 &  0.05145 &  0.02572 \tabularnewline
151 &  0.9944 &  0.01117 &  0.005587 \tabularnewline
152 &  0.9894 &  0.02119 &  0.01059 \tabularnewline
153 &  0.9913 &  0.01747 &  0.008735 \tabularnewline
154 &  0.9885 &  0.02305 &  0.01152 \tabularnewline
155 &  0.9776 &  0.04471 &  0.02235 \tabularnewline
156 &  0.9737 &  0.05256 &  0.02628 \tabularnewline
157 &  0.9521 &  0.09579 &  0.0479 \tabularnewline
158 &  0.916 &  0.1681 &  0.08405 \tabularnewline
159 &  0.9766 &  0.04684 &  0.02342 \tabularnewline
160 &  0.9487 &  0.1027 &  0.05134 \tabularnewline
161 &  0.8841 &  0.2318 &  0.1159 \tabularnewline
162 &  0.7673 &  0.4655 &  0.2327 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=302352&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]6[/C][C] 0.6051[/C][C] 0.7898[/C][C] 0.3949[/C][/ROW]
[ROW][C]7[/C][C] 0.4543[/C][C] 0.9087[/C][C] 0.5457[/C][/ROW]
[ROW][C]8[/C][C] 0.388[/C][C] 0.776[/C][C] 0.612[/C][/ROW]
[ROW][C]9[/C][C] 0.2669[/C][C] 0.5337[/C][C] 0.7331[/C][/ROW]
[ROW][C]10[/C][C] 0.2118[/C][C] 0.4235[/C][C] 0.7882[/C][/ROW]
[ROW][C]11[/C][C] 0.4513[/C][C] 0.9027[/C][C] 0.5487[/C][/ROW]
[ROW][C]12[/C][C] 0.4727[/C][C] 0.9454[/C][C] 0.5273[/C][/ROW]
[ROW][C]13[/C][C] 0.4844[/C][C] 0.9689[/C][C] 0.5156[/C][/ROW]
[ROW][C]14[/C][C] 0.7327[/C][C] 0.5347[/C][C] 0.2673[/C][/ROW]
[ROW][C]15[/C][C] 0.6577[/C][C] 0.6846[/C][C] 0.3423[/C][/ROW]
[ROW][C]16[/C][C] 0.5831[/C][C] 0.8338[/C][C] 0.4169[/C][/ROW]
[ROW][C]17[/C][C] 0.7158[/C][C] 0.5685[/C][C] 0.2842[/C][/ROW]
[ROW][C]18[/C][C] 0.6503[/C][C] 0.6993[/C][C] 0.3497[/C][/ROW]
[ROW][C]19[/C][C] 0.7479[/C][C] 0.5041[/C][C] 0.2521[/C][/ROW]
[ROW][C]20[/C][C] 0.8763[/C][C] 0.2473[/C][C] 0.1237[/C][/ROW]
[ROW][C]21[/C][C] 0.9026[/C][C] 0.1948[/C][C] 0.09742[/C][/ROW]
[ROW][C]22[/C][C] 0.9208[/C][C] 0.1584[/C][C] 0.0792[/C][/ROW]
[ROW][C]23[/C][C] 0.9005[/C][C] 0.1989[/C][C] 0.09945[/C][/ROW]
[ROW][C]24[/C][C] 0.8901[/C][C] 0.2198[/C][C] 0.1099[/C][/ROW]
[ROW][C]25[/C][C] 0.8678[/C][C] 0.2644[/C][C] 0.1322[/C][/ROW]
[ROW][C]26[/C][C] 0.8438[/C][C] 0.3124[/C][C] 0.1562[/C][/ROW]
[ROW][C]27[/C][C] 0.8344[/C][C] 0.3313[/C][C] 0.1656[/C][/ROW]
[ROW][C]28[/C][C] 0.9431[/C][C] 0.1139[/C][C] 0.05693[/C][/ROW]
[ROW][C]29[/C][C] 0.9289[/C][C] 0.1423[/C][C] 0.07115[/C][/ROW]
[ROW][C]30[/C][C] 0.9111[/C][C] 0.1777[/C][C] 0.08886[/C][/ROW]
[ROW][C]31[/C][C] 0.8897[/C][C] 0.2206[/C][C] 0.1103[/C][/ROW]
[ROW][C]32[/C][C] 0.9328[/C][C] 0.1344[/C][C] 0.06719[/C][/ROW]
[ROW][C]33[/C][C] 0.9142[/C][C] 0.1715[/C][C] 0.08576[/C][/ROW]
[ROW][C]34[/C][C] 0.8998[/C][C] 0.2003[/C][C] 0.1002[/C][/ROW]
[ROW][C]35[/C][C] 0.8794[/C][C] 0.2412[/C][C] 0.1206[/C][/ROW]
[ROW][C]36[/C][C] 0.8565[/C][C] 0.287[/C][C] 0.1435[/C][/ROW]
[ROW][C]37[/C][C] 0.9315[/C][C] 0.137[/C][C] 0.06848[/C][/ROW]
[ROW][C]38[/C][C] 0.9172[/C][C] 0.1656[/C][C] 0.08279[/C][/ROW]
[ROW][C]39[/C][C] 0.9317[/C][C] 0.1366[/C][C] 0.06829[/C][/ROW]
[ROW][C]40[/C][C] 0.9129[/C][C] 0.1742[/C][C] 0.0871[/C][/ROW]
[ROW][C]41[/C][C] 0.9278[/C][C] 0.1445[/C][C] 0.07224[/C][/ROW]
[ROW][C]42[/C][C] 0.9089[/C][C] 0.1823[/C][C] 0.09114[/C][/ROW]
[ROW][C]43[/C][C] 0.9662[/C][C] 0.06767[/C][C] 0.03384[/C][/ROW]
[ROW][C]44[/C][C] 0.9572[/C][C] 0.08561[/C][C] 0.0428[/C][/ROW]
[ROW][C]45[/C][C] 0.9464[/C][C] 0.1071[/C][C] 0.05356[/C][/ROW]
[ROW][C]46[/C][C] 0.9418[/C][C] 0.1164[/C][C] 0.05821[/C][/ROW]
[ROW][C]47[/C][C] 0.9571[/C][C] 0.08575[/C][C] 0.04287[/C][/ROW]
[ROW][C]48[/C][C] 0.9572[/C][C] 0.08568[/C][C] 0.04284[/C][/ROW]
[ROW][C]49[/C][C] 0.9494[/C][C] 0.1011[/C][C] 0.05055[/C][/ROW]
[ROW][C]50[/C][C] 0.9491[/C][C] 0.1018[/C][C] 0.0509[/C][/ROW]
[ROW][C]51[/C][C] 0.9465[/C][C] 0.107[/C][C] 0.0535[/C][/ROW]
[ROW][C]52[/C][C] 0.9373[/C][C] 0.1254[/C][C] 0.0627[/C][/ROW]
[ROW][C]53[/C][C] 0.9232[/C][C] 0.1536[/C][C] 0.07678[/C][/ROW]
[ROW][C]54[/C][C] 0.9196[/C][C] 0.1608[/C][C] 0.08042[/C][/ROW]
[ROW][C]55[/C][C] 0.9329[/C][C] 0.1341[/C][C] 0.06707[/C][/ROW]
[ROW][C]56[/C][C] 0.9193[/C][C] 0.1614[/C][C] 0.08071[/C][/ROW]
[ROW][C]57[/C][C] 0.9042[/C][C] 0.1915[/C][C] 0.09577[/C][/ROW]
[ROW][C]58[/C][C] 0.8879[/C][C] 0.2243[/C][C] 0.1121[/C][/ROW]
[ROW][C]59[/C][C] 0.8642[/C][C] 0.2716[/C][C] 0.1358[/C][/ROW]
[ROW][C]60[/C][C] 0.8423[/C][C] 0.3154[/C][C] 0.1577[/C][/ROW]
[ROW][C]61[/C][C] 0.8213[/C][C] 0.3574[/C][C] 0.1787[/C][/ROW]
[ROW][C]62[/C][C] 0.7903[/C][C] 0.4194[/C][C] 0.2097[/C][/ROW]
[ROW][C]63[/C][C] 0.7558[/C][C] 0.4884[/C][C] 0.2442[/C][/ROW]
[ROW][C]64[/C][C] 0.7254[/C][C] 0.5491[/C][C] 0.2746[/C][/ROW]
[ROW][C]65[/C][C] 0.7296[/C][C] 0.5407[/C][C] 0.2704[/C][/ROW]
[ROW][C]66[/C][C] 0.6909[/C][C] 0.6182[/C][C] 0.3091[/C][/ROW]
[ROW][C]67[/C][C] 0.7362[/C][C] 0.5276[/C][C] 0.2638[/C][/ROW]
[ROW][C]68[/C][C] 0.7071[/C][C] 0.5858[/C][C] 0.2929[/C][/ROW]
[ROW][C]69[/C][C] 0.678[/C][C] 0.6441[/C][C] 0.3221[/C][/ROW]
[ROW][C]70[/C][C] 0.6443[/C][C] 0.7114[/C][C] 0.3557[/C][/ROW]
[ROW][C]71[/C][C] 0.6369[/C][C] 0.7263[/C][C] 0.3631[/C][/ROW]
[ROW][C]72[/C][C] 0.594[/C][C] 0.812[/C][C] 0.406[/C][/ROW]
[ROW][C]73[/C][C] 0.6154[/C][C] 0.7692[/C][C] 0.3846[/C][/ROW]
[ROW][C]74[/C][C] 0.574[/C][C] 0.852[/C][C] 0.426[/C][/ROW]
[ROW][C]75[/C][C] 0.6906[/C][C] 0.6188[/C][C] 0.3094[/C][/ROW]
[ROW][C]76[/C][C] 0.6691[/C][C] 0.6617[/C][C] 0.3309[/C][/ROW]
[ROW][C]77[/C][C] 0.7716[/C][C] 0.4569[/C][C] 0.2284[/C][/ROW]
[ROW][C]78[/C][C] 0.7608[/C][C] 0.4784[/C][C] 0.2392[/C][/ROW]
[ROW][C]79[/C][C] 0.7552[/C][C] 0.4896[/C][C] 0.2448[/C][/ROW]
[ROW][C]80[/C][C] 0.7188[/C][C] 0.5624[/C][C] 0.2812[/C][/ROW]
[ROW][C]81[/C][C] 0.6816[/C][C] 0.6367[/C][C] 0.3184[/C][/ROW]
[ROW][C]82[/C][C] 0.6409[/C][C] 0.7183[/C][C] 0.3592[/C][/ROW]
[ROW][C]83[/C][C] 0.599[/C][C] 0.802[/C][C] 0.401[/C][/ROW]
[ROW][C]84[/C][C] 0.5693[/C][C] 0.8614[/C][C] 0.4307[/C][/ROW]
[ROW][C]85[/C][C] 0.6037[/C][C] 0.7926[/C][C] 0.3963[/C][/ROW]
[ROW][C]86[/C][C] 0.5752[/C][C] 0.8496[/C][C] 0.4248[/C][/ROW]
[ROW][C]87[/C][C] 0.5393[/C][C] 0.9213[/C][C] 0.4607[/C][/ROW]
[ROW][C]88[/C][C] 0.5036[/C][C] 0.9928[/C][C] 0.4964[/C][/ROW]
[ROW][C]89[/C][C] 0.4608[/C][C] 0.9217[/C][C] 0.5392[/C][/ROW]
[ROW][C]90[/C][C] 0.4281[/C][C] 0.8563[/C][C] 0.5719[/C][/ROW]
[ROW][C]91[/C][C] 0.3932[/C][C] 0.7865[/C][C] 0.6068[/C][/ROW]
[ROW][C]92[/C][C] 0.352[/C][C] 0.7039[/C][C] 0.648[/C][/ROW]
[ROW][C]93[/C][C] 0.4004[/C][C] 0.8007[/C][C] 0.5996[/C][/ROW]
[ROW][C]94[/C][C] 0.3623[/C][C] 0.7246[/C][C] 0.6377[/C][/ROW]
[ROW][C]95[/C][C] 0.3289[/C][C] 0.6578[/C][C] 0.6711[/C][/ROW]
[ROW][C]96[/C][C] 0.2908[/C][C] 0.5817[/C][C] 0.7092[/C][/ROW]
[ROW][C]97[/C][C] 0.2935[/C][C] 0.5869[/C][C] 0.7065[/C][/ROW]
[ROW][C]98[/C][C] 0.6421[/C][C] 0.7157[/C][C] 0.3579[/C][/ROW]
[ROW][C]99[/C][C] 0.6103[/C][C] 0.7793[/C][C] 0.3897[/C][/ROW]
[ROW][C]100[/C][C] 0.7519[/C][C] 0.4962[/C][C] 0.2481[/C][/ROW]
[ROW][C]101[/C][C] 0.7198[/C][C] 0.5603[/C][C] 0.2802[/C][/ROW]
[ROW][C]102[/C][C] 0.6908[/C][C] 0.6184[/C][C] 0.3092[/C][/ROW]
[ROW][C]103[/C][C] 0.6491[/C][C] 0.7017[/C][C] 0.3509[/C][/ROW]
[ROW][C]104[/C][C] 0.6175[/C][C] 0.765[/C][C] 0.3825[/C][/ROW]
[ROW][C]105[/C][C] 0.7045[/C][C] 0.5911[/C][C] 0.2955[/C][/ROW]
[ROW][C]106[/C][C] 0.6663[/C][C] 0.6674[/C][C] 0.3337[/C][/ROW]
[ROW][C]107[/C][C] 0.8508[/C][C] 0.2983[/C][C] 0.1492[/C][/ROW]
[ROW][C]108[/C][C] 0.8549[/C][C] 0.2902[/C][C] 0.1451[/C][/ROW]
[ROW][C]109[/C][C] 0.8712[/C][C] 0.2576[/C][C] 0.1288[/C][/ROW]
[ROW][C]110[/C][C] 0.9182[/C][C] 0.1636[/C][C] 0.08182[/C][/ROW]
[ROW][C]111[/C][C] 0.9016[/C][C] 0.1967[/C][C] 0.09837[/C][/ROW]
[ROW][C]112[/C][C] 0.9456[/C][C] 0.1089[/C][C] 0.05444[/C][/ROW]
[ROW][C]113[/C][C] 0.9315[/C][C] 0.137[/C][C] 0.0685[/C][/ROW]
[ROW][C]114[/C][C] 0.9338[/C][C] 0.1324[/C][C] 0.06619[/C][/ROW]
[ROW][C]115[/C][C] 0.9435[/C][C] 0.113[/C][C] 0.05651[/C][/ROW]
[ROW][C]116[/C][C] 0.9307[/C][C] 0.1385[/C][C] 0.06925[/C][/ROW]
[ROW][C]117[/C][C] 0.943[/C][C] 0.1141[/C][C] 0.05703[/C][/ROW]
[ROW][C]118[/C][C] 0.9301[/C][C] 0.1398[/C][C] 0.06988[/C][/ROW]
[ROW][C]119[/C][C] 0.9336[/C][C] 0.1328[/C][C] 0.06638[/C][/ROW]
[ROW][C]120[/C][C] 0.9311[/C][C] 0.1379[/C][C] 0.06893[/C][/ROW]
[ROW][C]121[/C][C] 0.9124[/C][C] 0.1752[/C][C] 0.0876[/C][/ROW]
[ROW][C]122[/C][C] 0.9209[/C][C] 0.1582[/C][C] 0.07908[/C][/ROW]
[ROW][C]123[/C][C] 0.9[/C][C] 0.2[/C][C] 0.1[/C][/ROW]
[ROW][C]124[/C][C] 0.9186[/C][C] 0.1627[/C][C] 0.08137[/C][/ROW]
[ROW][C]125[/C][C] 0.8968[/C][C] 0.2063[/C][C] 0.1032[/C][/ROW]
[ROW][C]126[/C][C] 0.9321[/C][C] 0.1358[/C][C] 0.0679[/C][/ROW]
[ROW][C]127[/C][C] 0.9154[/C][C] 0.1692[/C][C] 0.08458[/C][/ROW]
[ROW][C]128[/C][C] 0.9355[/C][C] 0.1291[/C][C] 0.06455[/C][/ROW]
[ROW][C]129[/C][C] 0.9161[/C][C] 0.1678[/C][C] 0.08388[/C][/ROW]
[ROW][C]130[/C][C] 0.9353[/C][C] 0.1294[/C][C] 0.06468[/C][/ROW]
[ROW][C]131[/C][C] 0.9414[/C][C] 0.1172[/C][C] 0.05861[/C][/ROW]
[ROW][C]132[/C][C] 0.9273[/C][C] 0.1454[/C][C] 0.0727[/C][/ROW]
[ROW][C]133[/C][C] 0.9739[/C][C] 0.05222[/C][C] 0.02611[/C][/ROW]
[ROW][C]134[/C][C] 0.9663[/C][C] 0.0675[/C][C] 0.03375[/C][/ROW]
[ROW][C]135[/C][C] 0.9587[/C][C] 0.08262[/C][C] 0.04131[/C][/ROW]
[ROW][C]136[/C][C] 0.9529[/C][C] 0.0941[/C][C] 0.04705[/C][/ROW]
[ROW][C]137[/C][C] 0.941[/C][C] 0.1181[/C][C] 0.05903[/C][/ROW]
[ROW][C]138[/C][C] 0.9422[/C][C] 0.1156[/C][C] 0.0578[/C][/ROW]
[ROW][C]139[/C][C] 0.9217[/C][C] 0.1565[/C][C] 0.07826[/C][/ROW]
[ROW][C]140[/C][C] 0.9845[/C][C] 0.03106[/C][C] 0.01553[/C][/ROW]
[ROW][C]141[/C][C] 0.9783[/C][C] 0.04347[/C][C] 0.02173[/C][/ROW]
[ROW][C]142[/C][C] 0.9882[/C][C] 0.02352[/C][C] 0.01176[/C][/ROW]
[ROW][C]143[/C][C] 0.9921[/C][C] 0.01571[/C][C] 0.007854[/C][/ROW]
[ROW][C]144[/C][C] 0.9931[/C][C] 0.01385[/C][C] 0.006927[/C][/ROW]
[ROW][C]145[/C][C] 0.9892[/C][C] 0.02158[/C][C] 0.01079[/C][/ROW]
[ROW][C]146[/C][C] 0.988[/C][C] 0.02406[/C][C] 0.01203[/C][/ROW]
[ROW][C]147[/C][C] 0.9864[/C][C] 0.02719[/C][C] 0.0136[/C][/ROW]
[ROW][C]148[/C][C] 0.9826[/C][C] 0.03477[/C][C] 0.01738[/C][/ROW]
[ROW][C]149[/C][C] 0.9839[/C][C] 0.03229[/C][C] 0.01615[/C][/ROW]
[ROW][C]150[/C][C] 0.9743[/C][C] 0.05145[/C][C] 0.02572[/C][/ROW]
[ROW][C]151[/C][C] 0.9944[/C][C] 0.01117[/C][C] 0.005587[/C][/ROW]
[ROW][C]152[/C][C] 0.9894[/C][C] 0.02119[/C][C] 0.01059[/C][/ROW]
[ROW][C]153[/C][C] 0.9913[/C][C] 0.01747[/C][C] 0.008735[/C][/ROW]
[ROW][C]154[/C][C] 0.9885[/C][C] 0.02305[/C][C] 0.01152[/C][/ROW]
[ROW][C]155[/C][C] 0.9776[/C][C] 0.04471[/C][C] 0.02235[/C][/ROW]
[ROW][C]156[/C][C] 0.9737[/C][C] 0.05256[/C][C] 0.02628[/C][/ROW]
[ROW][C]157[/C][C] 0.9521[/C][C] 0.09579[/C][C] 0.0479[/C][/ROW]
[ROW][C]158[/C][C] 0.916[/C][C] 0.1681[/C][C] 0.08405[/C][/ROW]
[ROW][C]159[/C][C] 0.9766[/C][C] 0.04684[/C][C] 0.02342[/C][/ROW]
[ROW][C]160[/C][C] 0.9487[/C][C] 0.1027[/C][C] 0.05134[/C][/ROW]
[ROW][C]161[/C][C] 0.8841[/C][C] 0.2318[/C][C] 0.1159[/C][/ROW]
[ROW][C]162[/C][C] 0.7673[/C][C] 0.4655[/C][C] 0.2327[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=302352&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=302352&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
6	0.6051	0.7898	0.3949
7	0.4543	0.9087	0.5457
8	0.388	0.776	0.612
9	0.2669	0.5337	0.7331
10	0.2118	0.4235	0.7882
11	0.4513	0.9027	0.5487
12	0.4727	0.9454	0.5273
13	0.4844	0.9689	0.5156
14	0.7327	0.5347	0.2673
15	0.6577	0.6846	0.3423
16	0.5831	0.8338	0.4169
17	0.7158	0.5685	0.2842
18	0.6503	0.6993	0.3497
19	0.7479	0.5041	0.2521
20	0.8763	0.2473	0.1237
21	0.9026	0.1948	0.09742
22	0.9208	0.1584	0.0792
23	0.9005	0.1989	0.09945
24	0.8901	0.2198	0.1099
25	0.8678	0.2644	0.1322
26	0.8438	0.3124	0.1562
27	0.8344	0.3313	0.1656
28	0.9431	0.1139	0.05693
29	0.9289	0.1423	0.07115
30	0.9111	0.1777	0.08886
31	0.8897	0.2206	0.1103
32	0.9328	0.1344	0.06719
33	0.9142	0.1715	0.08576
34	0.8998	0.2003	0.1002
35	0.8794	0.2412	0.1206
36	0.8565	0.287	0.1435
37	0.9315	0.137	0.06848
38	0.9172	0.1656	0.08279
39	0.9317	0.1366	0.06829
40	0.9129	0.1742	0.0871
41	0.9278	0.1445	0.07224
42	0.9089	0.1823	0.09114
43	0.9662	0.06767	0.03384
44	0.9572	0.08561	0.0428
45	0.9464	0.1071	0.05356
46	0.9418	0.1164	0.05821
47	0.9571	0.08575	0.04287
48	0.9572	0.08568	0.04284
49	0.9494	0.1011	0.05055
50	0.9491	0.1018	0.0509
51	0.9465	0.107	0.0535
52	0.9373	0.1254	0.0627
53	0.9232	0.1536	0.07678
54	0.9196	0.1608	0.08042
55	0.9329	0.1341	0.06707
56	0.9193	0.1614	0.08071
57	0.9042	0.1915	0.09577
58	0.8879	0.2243	0.1121
59	0.8642	0.2716	0.1358
60	0.8423	0.3154	0.1577
61	0.8213	0.3574	0.1787
62	0.7903	0.4194	0.2097
63	0.7558	0.4884	0.2442
64	0.7254	0.5491	0.2746
65	0.7296	0.5407	0.2704
66	0.6909	0.6182	0.3091
67	0.7362	0.5276	0.2638
68	0.7071	0.5858	0.2929
69	0.678	0.6441	0.3221
70	0.6443	0.7114	0.3557
71	0.6369	0.7263	0.3631
72	0.594	0.812	0.406
73	0.6154	0.7692	0.3846
74	0.574	0.852	0.426
75	0.6906	0.6188	0.3094
76	0.6691	0.6617	0.3309
77	0.7716	0.4569	0.2284
78	0.7608	0.4784	0.2392
79	0.7552	0.4896	0.2448
80	0.7188	0.5624	0.2812
81	0.6816	0.6367	0.3184
82	0.6409	0.7183	0.3592
83	0.599	0.802	0.401
84	0.5693	0.8614	0.4307
85	0.6037	0.7926	0.3963
86	0.5752	0.8496	0.4248
87	0.5393	0.9213	0.4607
88	0.5036	0.9928	0.4964
89	0.4608	0.9217	0.5392
90	0.4281	0.8563	0.5719
91	0.3932	0.7865	0.6068
92	0.352	0.7039	0.648
93	0.4004	0.8007	0.5996
94	0.3623	0.7246	0.6377
95	0.3289	0.6578	0.6711
96	0.2908	0.5817	0.7092
97	0.2935	0.5869	0.7065
98	0.6421	0.7157	0.3579
99	0.6103	0.7793	0.3897
100	0.7519	0.4962	0.2481
101	0.7198	0.5603	0.2802
102	0.6908	0.6184	0.3092
103	0.6491	0.7017	0.3509
104	0.6175	0.765	0.3825
105	0.7045	0.5911	0.2955
106	0.6663	0.6674	0.3337
107	0.8508	0.2983	0.1492
108	0.8549	0.2902	0.1451
109	0.8712	0.2576	0.1288
110	0.9182	0.1636	0.08182
111	0.9016	0.1967	0.09837
112	0.9456	0.1089	0.05444
113	0.9315	0.137	0.0685
114	0.9338	0.1324	0.06619
115	0.9435	0.113	0.05651
116	0.9307	0.1385	0.06925
117	0.943	0.1141	0.05703
118	0.9301	0.1398	0.06988
119	0.9336	0.1328	0.06638
120	0.9311	0.1379	0.06893
121	0.9124	0.1752	0.0876
122	0.9209	0.1582	0.07908
123	0.9	0.2	0.1
124	0.9186	0.1627	0.08137
125	0.8968	0.2063	0.1032
126	0.9321	0.1358	0.0679
127	0.9154	0.1692	0.08458
128	0.9355	0.1291	0.06455
129	0.9161	0.1678	0.08388
130	0.9353	0.1294	0.06468
131	0.9414	0.1172	0.05861
132	0.9273	0.1454	0.0727
133	0.9739	0.05222	0.02611
134	0.9663	0.0675	0.03375
135	0.9587	0.08262	0.04131
136	0.9529	0.0941	0.04705
137	0.941	0.1181	0.05903
138	0.9422	0.1156	0.0578
139	0.9217	0.1565	0.07826
140	0.9845	0.03106	0.01553
141	0.9783	0.04347	0.02173
142	0.9882	0.02352	0.01176
143	0.9921	0.01571	0.007854
144	0.9931	0.01385	0.006927
145	0.9892	0.02158	0.01079
146	0.988	0.02406	0.01203
147	0.9864	0.02719	0.0136
148	0.9826	0.03477	0.01738
149	0.9839	0.03229	0.01615
150	0.9743	0.05145	0.02572
151	0.9944	0.01117	0.005587
152	0.9894	0.02119	0.01059
153	0.9913	0.01747	0.008735
154	0.9885	0.02305	0.01152
155	0.9776	0.04471	0.02235
156	0.9737	0.05256	0.02628
157	0.9521	0.09579	0.0479
158	0.916	0.1681	0.08405
159	0.9766	0.04684	0.02342
160	0.9487	0.1027	0.05134
161	0.8841	0.2318	0.1159
162	0.7673	0.4655	0.2327

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	16	0.101911	NOK
10% type I error level	27	0.171975	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 & 16 & 0.101911 & NOK \tabularnewline
10% type I error level & 27 & 0.171975 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=302352&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]16[/C][C]0.101911[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]27[/C][C]0.171975[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=302352&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=302352&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	16	0.101911	NOK
10% type I error level	27	0.171975	NOK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 0.69448, df1 = 2, df2 = 163, p-value = 0.5008
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 0.34336, df1 = 4, df2 = 161, p-value = 0.8483
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.63447, df1 = 2, df2 = 163, p-value = 0.5315

\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 = 0.69448, df1 = 2, df2 = 163, p-value = 0.5008
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 0.34336, df1 = 4, df2 = 161, p-value = 0.8483
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.63447, df1 = 2, df2 = 163, p-value = 0.5315
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=302352&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 = 0.69448, df1 = 2, df2 = 163, p-value = 0.5008
[/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 = 0.34336, df1 = 4, df2 = 161, p-value = 0.8483
[/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 = 0.63447, df1 = 2, df2 = 163, p-value = 0.5315
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=302352&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=302352&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 = 0.69448, df1 = 2, df2 = 163, p-value = 0.5008
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 0.34336, df1 = 4, df2 = 161, p-value = 0.8483
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.63447, df1 = 2, df2 = 163, p-value = 0.5315

Variance Inflation Factors (Multicollinearity)
> vif TVDC2 TVDC4 1.063181 1.063181

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
   TVDC2    TVDC4 
1.063181 1.063181 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=302352&T=8

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
   TVDC2    TVDC4 
1.063181 1.063181 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=302352&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=302352&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 TVDC2 TVDC4 1.063181 1.063181

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 = 12 ;

Parameters (R input):

par1 = 3 ; 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