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

Fri, 16 Dec 2016 13:25:39 +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/16/t1481891161npq92ol1oq6hvw5.htm/, Retrieved Thu, 02 May 2024 23:13:32 +0200

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=, Retrieved Thu, 02 May 2024 23:13:32 +0200

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

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	6 seconds
R Server	Big Analytics Cloud Computing Center
R Engine error message	Error in vif.default(mylm) : model contains fewer than 2 terms Calls: vif -> vif.default Execution halted

\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 time6 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
R Engine error message & Error in vif.default(mylm) : model contains fewer than 2 terms
Calls: vif -> vif.default
Execution halted
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&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]

6 seconds[/C][/ROW] [ROW]

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [ROW]

R Engine error message[/C][C]

Error in vif.default(mylm) : model contains fewer than 2 terms
Calls: vif -> vif.default
Execution halted

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

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&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	6 seconds
R Server	Big Analytics Cloud Computing Center
R Engine error message	Error in vif.default(mylm) : model contains fewer than 2 terms Calls: vif -> vif.default Execution halted

Multiple Linear Regression - Estimated Regression Equation
TVDCSUM[t] = + 12.2345 -0.283388EP3[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TVDCSUM[t] =  +  12.2345 -0.283388EP3[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TVDCSUM[t] =  +  12.2345 -0.283388EP3[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&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
TVDCSUM[t] = + 12.2345 -0.283388EP3[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)	+12.23	0.2627	+4.6570e+01	3.226e-97	1.613e-97
EP3	-0.2834	0.09267	-3.0580e+00	0.002598	0.001299

\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) & +12.23 &  0.2627 & +4.6570e+01 &  3.226e-97 &  1.613e-97 \tabularnewline
EP3 & -0.2834 &  0.09267 & -3.0580e+00 &  0.002598 &  0.001299 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&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]+12.23[/C][C] 0.2627[/C][C]+4.6570e+01[/C][C] 3.226e-97[/C][C] 1.613e-97[/C][/ROW]
[ROW][C]EP3[/C][C]-0.2834[/C][C] 0.09267[/C][C]-3.0580e+00[/C][C] 0.002598[/C][C] 0.001299[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&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)	+12.23	0.2627	+4.6570e+01	3.226e-97	1.613e-97
EP3	-0.2834	0.09267	-3.0580e+00	0.002598	0.001299

Multiple Linear Regression - Regression Statistics
Multiple R	0.2309
R-squared	0.05333
Adjusted R-squared	0.04763
F-TEST (value)	9.351
F-TEST (DF numerator)	1
F-TEST (DF denominator)	166
p-value	0.002598
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.194
Sum Squared Residuals	236.6

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.2309 \tabularnewline
R-squared &  0.05333 \tabularnewline
Adjusted R-squared &  0.04763 \tabularnewline
F-TEST (value) &  9.351 \tabularnewline
F-TEST (DF numerator) & 1 \tabularnewline
F-TEST (DF denominator) & 166 \tabularnewline
p-value &  0.002598 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  1.194 \tabularnewline
Sum Squared Residuals &  236.6 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.2309[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.05333[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.04763[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 9.351[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]1[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]166[/C][/ROW]
[ROW][C]p-value[/C][C] 0.002598[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 1.194[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 236.6[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&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	0.2309
R-squared	0.05333
Adjusted R-squared	0.04763
F-TEST (value)	9.351
F-TEST (DF numerator)	1
F-TEST (DF denominator)	166
p-value	0.002598
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.194
Sum Squared Residuals	236.6

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	9	11.1	-2.101
2	11	11.67	-0.6677
3	13	11.38	1.616
4	11	11.67	-0.6677
5	12	11.67	0.3323
6	11	11.38	-0.3843
7	12	11.38	0.6157
8	12	11.67	0.3323
9	13	11.67	1.332
10	12	11.1	0.8991
11	12	11.67	0.3323
12	11	11.67	-0.6677
13	12	11.38	0.6157
14	10	11.67	-1.668
15	12	11.38	0.6157
16	12	11.67	0.3323
17	12	11.67	0.3323
18	12	10.82	1.182
19	13	11.38	1.616
20	11	11.67	-0.6677
21	11	11.67	-0.6677
22	11	11.38	-0.3843
23	11	11.95	-0.9511
24	13	11.67	1.332
25	11	11.38	-0.3843
26	12	11.67	0.3323
27	11	11.67	-0.6677
28	12	11.38	0.6157
29	12	10.82	1.182
30	10	11.67	-1.668
31	11	10.82	0.1825
32	12	11.67	0.3323
33	11	11.67	-0.6677
34	9	11.1	-2.101
35	12	11.95	0.04892
36	11	11.67	-0.6677
37	11	11.67	-0.6677
38	12	11.38	0.6157
39	13	11.67	1.332
40	11	11.38	-0.3843
41	12	11.67	0.3323
42	9	11.38	-2.384
43	12	11.1	0.8991
44	11	11.1	-0.1009
45	12	11.38	0.6157
46	12	11.67	0.3323
47	11	11.67	-0.6677
48	10	11.1	-1.101
49	9	11.1	-2.101
50	12	11.38	0.6157
51	13	11.67	1.332
52	13	11.67	1.332
53	9	11.67	-2.668
54	11	11.67	-0.6677
55	11	11.38	-0.3843
56	11	11.67	-0.6677
57	12	11.67	0.3323
58	12	11.38	0.6157
59	11	11.38	-0.3843
60	12	11.67	0.3323
61	11	11.67	-0.6677
62	12	11.1	0.8991
63	11	11.1	-0.1009
64	11	11.38	-0.3843
65	8	11.1	-3.101
66	12	11.1	0.8991
67	11	11.1	-0.1009
68	12	11.1	0.8991
69	11	10.82	0.1825
70	11	11.38	-0.3843
71	11	11.1	-0.1009
72	10	11.1	-1.101
73	10	11.67	-1.668
74	13	11.67	1.332
75	11	11.38	-0.3843
76	11	11.1	-0.1009
77	11	11.67	-0.6677
78	13	10.82	2.182
79	12	11.95	0.04892
80	12	11.38	0.6157
81	9	11.38	-2.384
82	12	11.67	0.3323
83	12	11.67	0.3323
84	13	11.95	1.049
85	15	11.67	3.332
86	13	11.95	1.049
87	13	11.67	1.332
88	11	11.67	-0.6677
89	12	11.67	0.3323
90	9	11.67	-2.668
91	11	11.38	-0.3843
92	13	11.67	1.332
93	12	11.95	0.04892
94	13	11.95	1.049
95	11	11.38	-0.3843
96	12	11.67	0.3323
97	14	11.38	2.616
98	13	11.95	1.049
99	11	11.67	-0.6677
100	12	11.67	0.3323
101	13	11.38	1.616
102	11	11.67	-0.6677
103	11	11.67	-0.6677
104	11	11.38	-0.3843
105	13	11.95	1.049
106	12	11.1	0.8991
107	12	11.38	0.6157
108	11	11.67	-0.6677
109	12	11.38	0.6157
110	12	11.38	0.6157
111	10	11.38	-1.384
112	11	11.1	-0.1009
113	9	11.38	-2.384
114	14	11.67	2.332
115	12	11.38	0.6157
116	11	11.67	-0.6677
117	13	11.95	1.049
118	11	11.95	-0.9511
119	11	11.67	-0.6677
120	11	11.1	-0.1009
121	11	11.38	-0.3843
122	12	11.67	0.3323
123	11	11.67	-0.6677
124	13	11.38	1.616
125	11	11.38	-0.3843
126	11	11.38	-0.3843
127	12	11.67	0.3323
128	11	11.67	-0.6677
129	11	11.67	-0.6677
130	9	11.38	-2.384
131	12	11.1	0.8991
132	14	11.67	2.332
133	10	11.67	-1.668
134	9	11.95	-2.951
135	12	11.38	0.6157
136	14	11.38	2.616
137	9	11.1	-2.101
138	11	11.38	-0.3843
139	14	11.95	2.049
140	13	11.95	1.049
141	10	11.67	-1.668
142	11	11.95	-0.9511
143	12	10.82	1.182
144	10	11.38	-1.384
145	13	11.67	1.332
146	12	11.67	0.3323
147	14	11.1	2.899
148	10	11.67	-1.668
149	12	11.1	0.8991
150	9	11.1	-2.101
151	12	11.38	0.6157
152	11	11.1	-0.1009
153	11	11.38	-0.3843
154	10	11.1	-1.101
155	11	11.38	-0.3843
156	12	11.1	0.8991
157	10	11.67	-1.668
158	11	11.1	-0.1009
159	13	11.95	1.049
160	11	11.1	-0.1009
161	13	11.38	1.616
162	12	11.67	0.3323
163	11	11.67	-0.6677
164	12	11.67	0.3323
165	10	11.1	-1.101
166	12	11.38	0.6157
167	10	11.38	-1.384
168	13	11.67	1.332

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  9 &  11.1 & -2.101 \tabularnewline
2 &  11 &  11.67 & -0.6677 \tabularnewline
3 &  13 &  11.38 &  1.616 \tabularnewline
4 &  11 &  11.67 & -0.6677 \tabularnewline
5 &  12 &  11.67 &  0.3323 \tabularnewline
6 &  11 &  11.38 & -0.3843 \tabularnewline
7 &  12 &  11.38 &  0.6157 \tabularnewline
8 &  12 &  11.67 &  0.3323 \tabularnewline
9 &  13 &  11.67 &  1.332 \tabularnewline
10 &  12 &  11.1 &  0.8991 \tabularnewline
11 &  12 &  11.67 &  0.3323 \tabularnewline
12 &  11 &  11.67 & -0.6677 \tabularnewline
13 &  12 &  11.38 &  0.6157 \tabularnewline
14 &  10 &  11.67 & -1.668 \tabularnewline
15 &  12 &  11.38 &  0.6157 \tabularnewline
16 &  12 &  11.67 &  0.3323 \tabularnewline
17 &  12 &  11.67 &  0.3323 \tabularnewline
18 &  12 &  10.82 &  1.182 \tabularnewline
19 &  13 &  11.38 &  1.616 \tabularnewline
20 &  11 &  11.67 & -0.6677 \tabularnewline
21 &  11 &  11.67 & -0.6677 \tabularnewline
22 &  11 &  11.38 & -0.3843 \tabularnewline
23 &  11 &  11.95 & -0.9511 \tabularnewline
24 &  13 &  11.67 &  1.332 \tabularnewline
25 &  11 &  11.38 & -0.3843 \tabularnewline
26 &  12 &  11.67 &  0.3323 \tabularnewline
27 &  11 &  11.67 & -0.6677 \tabularnewline
28 &  12 &  11.38 &  0.6157 \tabularnewline
29 &  12 &  10.82 &  1.182 \tabularnewline
30 &  10 &  11.67 & -1.668 \tabularnewline
31 &  11 &  10.82 &  0.1825 \tabularnewline
32 &  12 &  11.67 &  0.3323 \tabularnewline
33 &  11 &  11.67 & -0.6677 \tabularnewline
34 &  9 &  11.1 & -2.101 \tabularnewline
35 &  12 &  11.95 &  0.04892 \tabularnewline
36 &  11 &  11.67 & -0.6677 \tabularnewline
37 &  11 &  11.67 & -0.6677 \tabularnewline
38 &  12 &  11.38 &  0.6157 \tabularnewline
39 &  13 &  11.67 &  1.332 \tabularnewline
40 &  11 &  11.38 & -0.3843 \tabularnewline
41 &  12 &  11.67 &  0.3323 \tabularnewline
42 &  9 &  11.38 & -2.384 \tabularnewline
43 &  12 &  11.1 &  0.8991 \tabularnewline
44 &  11 &  11.1 & -0.1009 \tabularnewline
45 &  12 &  11.38 &  0.6157 \tabularnewline
46 &  12 &  11.67 &  0.3323 \tabularnewline
47 &  11 &  11.67 & -0.6677 \tabularnewline
48 &  10 &  11.1 & -1.101 \tabularnewline
49 &  9 &  11.1 & -2.101 \tabularnewline
50 &  12 &  11.38 &  0.6157 \tabularnewline
51 &  13 &  11.67 &  1.332 \tabularnewline
52 &  13 &  11.67 &  1.332 \tabularnewline
53 &  9 &  11.67 & -2.668 \tabularnewline
54 &  11 &  11.67 & -0.6677 \tabularnewline
55 &  11 &  11.38 & -0.3843 \tabularnewline
56 &  11 &  11.67 & -0.6677 \tabularnewline
57 &  12 &  11.67 &  0.3323 \tabularnewline
58 &  12 &  11.38 &  0.6157 \tabularnewline
59 &  11 &  11.38 & -0.3843 \tabularnewline
60 &  12 &  11.67 &  0.3323 \tabularnewline
61 &  11 &  11.67 & -0.6677 \tabularnewline
62 &  12 &  11.1 &  0.8991 \tabularnewline
63 &  11 &  11.1 & -0.1009 \tabularnewline
64 &  11 &  11.38 & -0.3843 \tabularnewline
65 &  8 &  11.1 & -3.101 \tabularnewline
66 &  12 &  11.1 &  0.8991 \tabularnewline
67 &  11 &  11.1 & -0.1009 \tabularnewline
68 &  12 &  11.1 &  0.8991 \tabularnewline
69 &  11 &  10.82 &  0.1825 \tabularnewline
70 &  11 &  11.38 & -0.3843 \tabularnewline
71 &  11 &  11.1 & -0.1009 \tabularnewline
72 &  10 &  11.1 & -1.101 \tabularnewline
73 &  10 &  11.67 & -1.668 \tabularnewline
74 &  13 &  11.67 &  1.332 \tabularnewline
75 &  11 &  11.38 & -0.3843 \tabularnewline
76 &  11 &  11.1 & -0.1009 \tabularnewline
77 &  11 &  11.67 & -0.6677 \tabularnewline
78 &  13 &  10.82 &  2.182 \tabularnewline
79 &  12 &  11.95 &  0.04892 \tabularnewline
80 &  12 &  11.38 &  0.6157 \tabularnewline
81 &  9 &  11.38 & -2.384 \tabularnewline
82 &  12 &  11.67 &  0.3323 \tabularnewline
83 &  12 &  11.67 &  0.3323 \tabularnewline
84 &  13 &  11.95 &  1.049 \tabularnewline
85 &  15 &  11.67 &  3.332 \tabularnewline
86 &  13 &  11.95 &  1.049 \tabularnewline
87 &  13 &  11.67 &  1.332 \tabularnewline
88 &  11 &  11.67 & -0.6677 \tabularnewline
89 &  12 &  11.67 &  0.3323 \tabularnewline
90 &  9 &  11.67 & -2.668 \tabularnewline
91 &  11 &  11.38 & -0.3843 \tabularnewline
92 &  13 &  11.67 &  1.332 \tabularnewline
93 &  12 &  11.95 &  0.04892 \tabularnewline
94 &  13 &  11.95 &  1.049 \tabularnewline
95 &  11 &  11.38 & -0.3843 \tabularnewline
96 &  12 &  11.67 &  0.3323 \tabularnewline
97 &  14 &  11.38 &  2.616 \tabularnewline
98 &  13 &  11.95 &  1.049 \tabularnewline
99 &  11 &  11.67 & -0.6677 \tabularnewline
100 &  12 &  11.67 &  0.3323 \tabularnewline
101 &  13 &  11.38 &  1.616 \tabularnewline
102 &  11 &  11.67 & -0.6677 \tabularnewline
103 &  11 &  11.67 & -0.6677 \tabularnewline
104 &  11 &  11.38 & -0.3843 \tabularnewline
105 &  13 &  11.95 &  1.049 \tabularnewline
106 &  12 &  11.1 &  0.8991 \tabularnewline
107 &  12 &  11.38 &  0.6157 \tabularnewline
108 &  11 &  11.67 & -0.6677 \tabularnewline
109 &  12 &  11.38 &  0.6157 \tabularnewline
110 &  12 &  11.38 &  0.6157 \tabularnewline
111 &  10 &  11.38 & -1.384 \tabularnewline
112 &  11 &  11.1 & -0.1009 \tabularnewline
113 &  9 &  11.38 & -2.384 \tabularnewline
114 &  14 &  11.67 &  2.332 \tabularnewline
115 &  12 &  11.38 &  0.6157 \tabularnewline
116 &  11 &  11.67 & -0.6677 \tabularnewline
117 &  13 &  11.95 &  1.049 \tabularnewline
118 &  11 &  11.95 & -0.9511 \tabularnewline
119 &  11 &  11.67 & -0.6677 \tabularnewline
120 &  11 &  11.1 & -0.1009 \tabularnewline
121 &  11 &  11.38 & -0.3843 \tabularnewline
122 &  12 &  11.67 &  0.3323 \tabularnewline
123 &  11 &  11.67 & -0.6677 \tabularnewline
124 &  13 &  11.38 &  1.616 \tabularnewline
125 &  11 &  11.38 & -0.3843 \tabularnewline
126 &  11 &  11.38 & -0.3843 \tabularnewline
127 &  12 &  11.67 &  0.3323 \tabularnewline
128 &  11 &  11.67 & -0.6677 \tabularnewline
129 &  11 &  11.67 & -0.6677 \tabularnewline
130 &  9 &  11.38 & -2.384 \tabularnewline
131 &  12 &  11.1 &  0.8991 \tabularnewline
132 &  14 &  11.67 &  2.332 \tabularnewline
133 &  10 &  11.67 & -1.668 \tabularnewline
134 &  9 &  11.95 & -2.951 \tabularnewline
135 &  12 &  11.38 &  0.6157 \tabularnewline
136 &  14 &  11.38 &  2.616 \tabularnewline
137 &  9 &  11.1 & -2.101 \tabularnewline
138 &  11 &  11.38 & -0.3843 \tabularnewline
139 &  14 &  11.95 &  2.049 \tabularnewline
140 &  13 &  11.95 &  1.049 \tabularnewline
141 &  10 &  11.67 & -1.668 \tabularnewline
142 &  11 &  11.95 & -0.9511 \tabularnewline
143 &  12 &  10.82 &  1.182 \tabularnewline
144 &  10 &  11.38 & -1.384 \tabularnewline
145 &  13 &  11.67 &  1.332 \tabularnewline
146 &  12 &  11.67 &  0.3323 \tabularnewline
147 &  14 &  11.1 &  2.899 \tabularnewline
148 &  10 &  11.67 & -1.668 \tabularnewline
149 &  12 &  11.1 &  0.8991 \tabularnewline
150 &  9 &  11.1 & -2.101 \tabularnewline
151 &  12 &  11.38 &  0.6157 \tabularnewline
152 &  11 &  11.1 & -0.1009 \tabularnewline
153 &  11 &  11.38 & -0.3843 \tabularnewline
154 &  10 &  11.1 & -1.101 \tabularnewline
155 &  11 &  11.38 & -0.3843 \tabularnewline
156 &  12 &  11.1 &  0.8991 \tabularnewline
157 &  10 &  11.67 & -1.668 \tabularnewline
158 &  11 &  11.1 & -0.1009 \tabularnewline
159 &  13 &  11.95 &  1.049 \tabularnewline
160 &  11 &  11.1 & -0.1009 \tabularnewline
161 &  13 &  11.38 &  1.616 \tabularnewline
162 &  12 &  11.67 &  0.3323 \tabularnewline
163 &  11 &  11.67 & -0.6677 \tabularnewline
164 &  12 &  11.67 &  0.3323 \tabularnewline
165 &  10 &  11.1 & -1.101 \tabularnewline
166 &  12 &  11.38 &  0.6157 \tabularnewline
167 &  10 &  11.38 & -1.384 \tabularnewline
168 &  13 &  11.67 &  1.332 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&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] 9[/C][C] 11.1[/C][C]-2.101[/C][/ROW]
[ROW][C]2[/C][C] 11[/C][C] 11.67[/C][C]-0.6677[/C][/ROW]
[ROW][C]3[/C][C] 13[/C][C] 11.38[/C][C] 1.616[/C][/ROW]
[ROW][C]4[/C][C] 11[/C][C] 11.67[/C][C]-0.6677[/C][/ROW]
[ROW][C]5[/C][C] 12[/C][C] 11.67[/C][C] 0.3323[/C][/ROW]
[ROW][C]6[/C][C] 11[/C][C] 11.38[/C][C]-0.3843[/C][/ROW]
[ROW][C]7[/C][C] 12[/C][C] 11.38[/C][C] 0.6157[/C][/ROW]
[ROW][C]8[/C][C] 12[/C][C] 11.67[/C][C] 0.3323[/C][/ROW]
[ROW][C]9[/C][C] 13[/C][C] 11.67[/C][C] 1.332[/C][/ROW]
[ROW][C]10[/C][C] 12[/C][C] 11.1[/C][C] 0.8991[/C][/ROW]
[ROW][C]11[/C][C] 12[/C][C] 11.67[/C][C] 0.3323[/C][/ROW]
[ROW][C]12[/C][C] 11[/C][C] 11.67[/C][C]-0.6677[/C][/ROW]
[ROW][C]13[/C][C] 12[/C][C] 11.38[/C][C] 0.6157[/C][/ROW]
[ROW][C]14[/C][C] 10[/C][C] 11.67[/C][C]-1.668[/C][/ROW]
[ROW][C]15[/C][C] 12[/C][C] 11.38[/C][C] 0.6157[/C][/ROW]
[ROW][C]16[/C][C] 12[/C][C] 11.67[/C][C] 0.3323[/C][/ROW]
[ROW][C]17[/C][C] 12[/C][C] 11.67[/C][C] 0.3323[/C][/ROW]
[ROW][C]18[/C][C] 12[/C][C] 10.82[/C][C] 1.182[/C][/ROW]
[ROW][C]19[/C][C] 13[/C][C] 11.38[/C][C] 1.616[/C][/ROW]
[ROW][C]20[/C][C] 11[/C][C] 11.67[/C][C]-0.6677[/C][/ROW]
[ROW][C]21[/C][C] 11[/C][C] 11.67[/C][C]-0.6677[/C][/ROW]
[ROW][C]22[/C][C] 11[/C][C] 11.38[/C][C]-0.3843[/C][/ROW]
[ROW][C]23[/C][C] 11[/C][C] 11.95[/C][C]-0.9511[/C][/ROW]
[ROW][C]24[/C][C] 13[/C][C] 11.67[/C][C] 1.332[/C][/ROW]
[ROW][C]25[/C][C] 11[/C][C] 11.38[/C][C]-0.3843[/C][/ROW]
[ROW][C]26[/C][C] 12[/C][C] 11.67[/C][C] 0.3323[/C][/ROW]
[ROW][C]27[/C][C] 11[/C][C] 11.67[/C][C]-0.6677[/C][/ROW]
[ROW][C]28[/C][C] 12[/C][C] 11.38[/C][C] 0.6157[/C][/ROW]
[ROW][C]29[/C][C] 12[/C][C] 10.82[/C][C] 1.182[/C][/ROW]
[ROW][C]30[/C][C] 10[/C][C] 11.67[/C][C]-1.668[/C][/ROW]
[ROW][C]31[/C][C] 11[/C][C] 10.82[/C][C] 0.1825[/C][/ROW]
[ROW][C]32[/C][C] 12[/C][C] 11.67[/C][C] 0.3323[/C][/ROW]
[ROW][C]33[/C][C] 11[/C][C] 11.67[/C][C]-0.6677[/C][/ROW]
[ROW][C]34[/C][C] 9[/C][C] 11.1[/C][C]-2.101[/C][/ROW]
[ROW][C]35[/C][C] 12[/C][C] 11.95[/C][C] 0.04892[/C][/ROW]
[ROW][C]36[/C][C] 11[/C][C] 11.67[/C][C]-0.6677[/C][/ROW]
[ROW][C]37[/C][C] 11[/C][C] 11.67[/C][C]-0.6677[/C][/ROW]
[ROW][C]38[/C][C] 12[/C][C] 11.38[/C][C] 0.6157[/C][/ROW]
[ROW][C]39[/C][C] 13[/C][C] 11.67[/C][C] 1.332[/C][/ROW]
[ROW][C]40[/C][C] 11[/C][C] 11.38[/C][C]-0.3843[/C][/ROW]
[ROW][C]41[/C][C] 12[/C][C] 11.67[/C][C] 0.3323[/C][/ROW]
[ROW][C]42[/C][C] 9[/C][C] 11.38[/C][C]-2.384[/C][/ROW]
[ROW][C]43[/C][C] 12[/C][C] 11.1[/C][C] 0.8991[/C][/ROW]
[ROW][C]44[/C][C] 11[/C][C] 11.1[/C][C]-0.1009[/C][/ROW]
[ROW][C]45[/C][C] 12[/C][C] 11.38[/C][C] 0.6157[/C][/ROW]
[ROW][C]46[/C][C] 12[/C][C] 11.67[/C][C] 0.3323[/C][/ROW]
[ROW][C]47[/C][C] 11[/C][C] 11.67[/C][C]-0.6677[/C][/ROW]
[ROW][C]48[/C][C] 10[/C][C] 11.1[/C][C]-1.101[/C][/ROW]
[ROW][C]49[/C][C] 9[/C][C] 11.1[/C][C]-2.101[/C][/ROW]
[ROW][C]50[/C][C] 12[/C][C] 11.38[/C][C] 0.6157[/C][/ROW]
[ROW][C]51[/C][C] 13[/C][C] 11.67[/C][C] 1.332[/C][/ROW]
[ROW][C]52[/C][C] 13[/C][C] 11.67[/C][C] 1.332[/C][/ROW]
[ROW][C]53[/C][C] 9[/C][C] 11.67[/C][C]-2.668[/C][/ROW]
[ROW][C]54[/C][C] 11[/C][C] 11.67[/C][C]-0.6677[/C][/ROW]
[ROW][C]55[/C][C] 11[/C][C] 11.38[/C][C]-0.3843[/C][/ROW]
[ROW][C]56[/C][C] 11[/C][C] 11.67[/C][C]-0.6677[/C][/ROW]
[ROW][C]57[/C][C] 12[/C][C] 11.67[/C][C] 0.3323[/C][/ROW]
[ROW][C]58[/C][C] 12[/C][C] 11.38[/C][C] 0.6157[/C][/ROW]
[ROW][C]59[/C][C] 11[/C][C] 11.38[/C][C]-0.3843[/C][/ROW]
[ROW][C]60[/C][C] 12[/C][C] 11.67[/C][C] 0.3323[/C][/ROW]
[ROW][C]61[/C][C] 11[/C][C] 11.67[/C][C]-0.6677[/C][/ROW]
[ROW][C]62[/C][C] 12[/C][C] 11.1[/C][C] 0.8991[/C][/ROW]
[ROW][C]63[/C][C] 11[/C][C] 11.1[/C][C]-0.1009[/C][/ROW]
[ROW][C]64[/C][C] 11[/C][C] 11.38[/C][C]-0.3843[/C][/ROW]
[ROW][C]65[/C][C] 8[/C][C] 11.1[/C][C]-3.101[/C][/ROW]
[ROW][C]66[/C][C] 12[/C][C] 11.1[/C][C] 0.8991[/C][/ROW]
[ROW][C]67[/C][C] 11[/C][C] 11.1[/C][C]-0.1009[/C][/ROW]
[ROW][C]68[/C][C] 12[/C][C] 11.1[/C][C] 0.8991[/C][/ROW]
[ROW][C]69[/C][C] 11[/C][C] 10.82[/C][C] 0.1825[/C][/ROW]
[ROW][C]70[/C][C] 11[/C][C] 11.38[/C][C]-0.3843[/C][/ROW]
[ROW][C]71[/C][C] 11[/C][C] 11.1[/C][C]-0.1009[/C][/ROW]
[ROW][C]72[/C][C] 10[/C][C] 11.1[/C][C]-1.101[/C][/ROW]
[ROW][C]73[/C][C] 10[/C][C] 11.67[/C][C]-1.668[/C][/ROW]
[ROW][C]74[/C][C] 13[/C][C] 11.67[/C][C] 1.332[/C][/ROW]
[ROW][C]75[/C][C] 11[/C][C] 11.38[/C][C]-0.3843[/C][/ROW]
[ROW][C]76[/C][C] 11[/C][C] 11.1[/C][C]-0.1009[/C][/ROW]
[ROW][C]77[/C][C] 11[/C][C] 11.67[/C][C]-0.6677[/C][/ROW]
[ROW][C]78[/C][C] 13[/C][C] 10.82[/C][C] 2.182[/C][/ROW]
[ROW][C]79[/C][C] 12[/C][C] 11.95[/C][C] 0.04892[/C][/ROW]
[ROW][C]80[/C][C] 12[/C][C] 11.38[/C][C] 0.6157[/C][/ROW]
[ROW][C]81[/C][C] 9[/C][C] 11.38[/C][C]-2.384[/C][/ROW]
[ROW][C]82[/C][C] 12[/C][C] 11.67[/C][C] 0.3323[/C][/ROW]
[ROW][C]83[/C][C] 12[/C][C] 11.67[/C][C] 0.3323[/C][/ROW]
[ROW][C]84[/C][C] 13[/C][C] 11.95[/C][C] 1.049[/C][/ROW]
[ROW][C]85[/C][C] 15[/C][C] 11.67[/C][C] 3.332[/C][/ROW]
[ROW][C]86[/C][C] 13[/C][C] 11.95[/C][C] 1.049[/C][/ROW]
[ROW][C]87[/C][C] 13[/C][C] 11.67[/C][C] 1.332[/C][/ROW]
[ROW][C]88[/C][C] 11[/C][C] 11.67[/C][C]-0.6677[/C][/ROW]
[ROW][C]89[/C][C] 12[/C][C] 11.67[/C][C] 0.3323[/C][/ROW]
[ROW][C]90[/C][C] 9[/C][C] 11.67[/C][C]-2.668[/C][/ROW]
[ROW][C]91[/C][C] 11[/C][C] 11.38[/C][C]-0.3843[/C][/ROW]
[ROW][C]92[/C][C] 13[/C][C] 11.67[/C][C] 1.332[/C][/ROW]
[ROW][C]93[/C][C] 12[/C][C] 11.95[/C][C] 0.04892[/C][/ROW]
[ROW][C]94[/C][C] 13[/C][C] 11.95[/C][C] 1.049[/C][/ROW]
[ROW][C]95[/C][C] 11[/C][C] 11.38[/C][C]-0.3843[/C][/ROW]
[ROW][C]96[/C][C] 12[/C][C] 11.67[/C][C] 0.3323[/C][/ROW]
[ROW][C]97[/C][C] 14[/C][C] 11.38[/C][C] 2.616[/C][/ROW]
[ROW][C]98[/C][C] 13[/C][C] 11.95[/C][C] 1.049[/C][/ROW]
[ROW][C]99[/C][C] 11[/C][C] 11.67[/C][C]-0.6677[/C][/ROW]
[ROW][C]100[/C][C] 12[/C][C] 11.67[/C][C] 0.3323[/C][/ROW]
[ROW][C]101[/C][C] 13[/C][C] 11.38[/C][C] 1.616[/C][/ROW]
[ROW][C]102[/C][C] 11[/C][C] 11.67[/C][C]-0.6677[/C][/ROW]
[ROW][C]103[/C][C] 11[/C][C] 11.67[/C][C]-0.6677[/C][/ROW]
[ROW][C]104[/C][C] 11[/C][C] 11.38[/C][C]-0.3843[/C][/ROW]
[ROW][C]105[/C][C] 13[/C][C] 11.95[/C][C] 1.049[/C][/ROW]
[ROW][C]106[/C][C] 12[/C][C] 11.1[/C][C] 0.8991[/C][/ROW]
[ROW][C]107[/C][C] 12[/C][C] 11.38[/C][C] 0.6157[/C][/ROW]
[ROW][C]108[/C][C] 11[/C][C] 11.67[/C][C]-0.6677[/C][/ROW]
[ROW][C]109[/C][C] 12[/C][C] 11.38[/C][C] 0.6157[/C][/ROW]
[ROW][C]110[/C][C] 12[/C][C] 11.38[/C][C] 0.6157[/C][/ROW]
[ROW][C]111[/C][C] 10[/C][C] 11.38[/C][C]-1.384[/C][/ROW]
[ROW][C]112[/C][C] 11[/C][C] 11.1[/C][C]-0.1009[/C][/ROW]
[ROW][C]113[/C][C] 9[/C][C] 11.38[/C][C]-2.384[/C][/ROW]
[ROW][C]114[/C][C] 14[/C][C] 11.67[/C][C] 2.332[/C][/ROW]
[ROW][C]115[/C][C] 12[/C][C] 11.38[/C][C] 0.6157[/C][/ROW]
[ROW][C]116[/C][C] 11[/C][C] 11.67[/C][C]-0.6677[/C][/ROW]
[ROW][C]117[/C][C] 13[/C][C] 11.95[/C][C] 1.049[/C][/ROW]
[ROW][C]118[/C][C] 11[/C][C] 11.95[/C][C]-0.9511[/C][/ROW]
[ROW][C]119[/C][C] 11[/C][C] 11.67[/C][C]-0.6677[/C][/ROW]
[ROW][C]120[/C][C] 11[/C][C] 11.1[/C][C]-0.1009[/C][/ROW]
[ROW][C]121[/C][C] 11[/C][C] 11.38[/C][C]-0.3843[/C][/ROW]
[ROW][C]122[/C][C] 12[/C][C] 11.67[/C][C] 0.3323[/C][/ROW]
[ROW][C]123[/C][C] 11[/C][C] 11.67[/C][C]-0.6677[/C][/ROW]
[ROW][C]124[/C][C] 13[/C][C] 11.38[/C][C] 1.616[/C][/ROW]
[ROW][C]125[/C][C] 11[/C][C] 11.38[/C][C]-0.3843[/C][/ROW]
[ROW][C]126[/C][C] 11[/C][C] 11.38[/C][C]-0.3843[/C][/ROW]
[ROW][C]127[/C][C] 12[/C][C] 11.67[/C][C] 0.3323[/C][/ROW]
[ROW][C]128[/C][C] 11[/C][C] 11.67[/C][C]-0.6677[/C][/ROW]
[ROW][C]129[/C][C] 11[/C][C] 11.67[/C][C]-0.6677[/C][/ROW]
[ROW][C]130[/C][C] 9[/C][C] 11.38[/C][C]-2.384[/C][/ROW]
[ROW][C]131[/C][C] 12[/C][C] 11.1[/C][C] 0.8991[/C][/ROW]
[ROW][C]132[/C][C] 14[/C][C] 11.67[/C][C] 2.332[/C][/ROW]
[ROW][C]133[/C][C] 10[/C][C] 11.67[/C][C]-1.668[/C][/ROW]
[ROW][C]134[/C][C] 9[/C][C] 11.95[/C][C]-2.951[/C][/ROW]
[ROW][C]135[/C][C] 12[/C][C] 11.38[/C][C] 0.6157[/C][/ROW]
[ROW][C]136[/C][C] 14[/C][C] 11.38[/C][C] 2.616[/C][/ROW]
[ROW][C]137[/C][C] 9[/C][C] 11.1[/C][C]-2.101[/C][/ROW]
[ROW][C]138[/C][C] 11[/C][C] 11.38[/C][C]-0.3843[/C][/ROW]
[ROW][C]139[/C][C] 14[/C][C] 11.95[/C][C] 2.049[/C][/ROW]
[ROW][C]140[/C][C] 13[/C][C] 11.95[/C][C] 1.049[/C][/ROW]
[ROW][C]141[/C][C] 10[/C][C] 11.67[/C][C]-1.668[/C][/ROW]
[ROW][C]142[/C][C] 11[/C][C] 11.95[/C][C]-0.9511[/C][/ROW]
[ROW][C]143[/C][C] 12[/C][C] 10.82[/C][C] 1.182[/C][/ROW]
[ROW][C]144[/C][C] 10[/C][C] 11.38[/C][C]-1.384[/C][/ROW]
[ROW][C]145[/C][C] 13[/C][C] 11.67[/C][C] 1.332[/C][/ROW]
[ROW][C]146[/C][C] 12[/C][C] 11.67[/C][C] 0.3323[/C][/ROW]
[ROW][C]147[/C][C] 14[/C][C] 11.1[/C][C] 2.899[/C][/ROW]
[ROW][C]148[/C][C] 10[/C][C] 11.67[/C][C]-1.668[/C][/ROW]
[ROW][C]149[/C][C] 12[/C][C] 11.1[/C][C] 0.8991[/C][/ROW]
[ROW][C]150[/C][C] 9[/C][C] 11.1[/C][C]-2.101[/C][/ROW]
[ROW][C]151[/C][C] 12[/C][C] 11.38[/C][C] 0.6157[/C][/ROW]
[ROW][C]152[/C][C] 11[/C][C] 11.1[/C][C]-0.1009[/C][/ROW]
[ROW][C]153[/C][C] 11[/C][C] 11.38[/C][C]-0.3843[/C][/ROW]
[ROW][C]154[/C][C] 10[/C][C] 11.1[/C][C]-1.101[/C][/ROW]
[ROW][C]155[/C][C] 11[/C][C] 11.38[/C][C]-0.3843[/C][/ROW]
[ROW][C]156[/C][C] 12[/C][C] 11.1[/C][C] 0.8991[/C][/ROW]
[ROW][C]157[/C][C] 10[/C][C] 11.67[/C][C]-1.668[/C][/ROW]
[ROW][C]158[/C][C] 11[/C][C] 11.1[/C][C]-0.1009[/C][/ROW]
[ROW][C]159[/C][C] 13[/C][C] 11.95[/C][C] 1.049[/C][/ROW]
[ROW][C]160[/C][C] 11[/C][C] 11.1[/C][C]-0.1009[/C][/ROW]
[ROW][C]161[/C][C] 13[/C][C] 11.38[/C][C] 1.616[/C][/ROW]
[ROW][C]162[/C][C] 12[/C][C] 11.67[/C][C] 0.3323[/C][/ROW]
[ROW][C]163[/C][C] 11[/C][C] 11.67[/C][C]-0.6677[/C][/ROW]
[ROW][C]164[/C][C] 12[/C][C] 11.67[/C][C] 0.3323[/C][/ROW]
[ROW][C]165[/C][C] 10[/C][C] 11.1[/C][C]-1.101[/C][/ROW]
[ROW][C]166[/C][C] 12[/C][C] 11.38[/C][C] 0.6157[/C][/ROW]
[ROW][C]167[/C][C] 10[/C][C] 11.38[/C][C]-1.384[/C][/ROW]
[ROW][C]168[/C][C] 13[/C][C] 11.67[/C][C] 1.332[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&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	9	11.1	-2.101
2	11	11.67	-0.6677
3	13	11.38	1.616
4	11	11.67	-0.6677
5	12	11.67	0.3323
6	11	11.38	-0.3843
7	12	11.38	0.6157
8	12	11.67	0.3323
9	13	11.67	1.332
10	12	11.1	0.8991
11	12	11.67	0.3323
12	11	11.67	-0.6677
13	12	11.38	0.6157
14	10	11.67	-1.668
15	12	11.38	0.6157
16	12	11.67	0.3323
17	12	11.67	0.3323
18	12	10.82	1.182
19	13	11.38	1.616
20	11	11.67	-0.6677
21	11	11.67	-0.6677
22	11	11.38	-0.3843
23	11	11.95	-0.9511
24	13	11.67	1.332
25	11	11.38	-0.3843
26	12	11.67	0.3323
27	11	11.67	-0.6677
28	12	11.38	0.6157
29	12	10.82	1.182
30	10	11.67	-1.668
31	11	10.82	0.1825
32	12	11.67	0.3323
33	11	11.67	-0.6677
34	9	11.1	-2.101
35	12	11.95	0.04892
36	11	11.67	-0.6677
37	11	11.67	-0.6677
38	12	11.38	0.6157
39	13	11.67	1.332
40	11	11.38	-0.3843
41	12	11.67	0.3323
42	9	11.38	-2.384
43	12	11.1	0.8991
44	11	11.1	-0.1009
45	12	11.38	0.6157
46	12	11.67	0.3323
47	11	11.67	-0.6677
48	10	11.1	-1.101
49	9	11.1	-2.101
50	12	11.38	0.6157
51	13	11.67	1.332
52	13	11.67	1.332
53	9	11.67	-2.668
54	11	11.67	-0.6677
55	11	11.38	-0.3843
56	11	11.67	-0.6677
57	12	11.67	0.3323
58	12	11.38	0.6157
59	11	11.38	-0.3843
60	12	11.67	0.3323
61	11	11.67	-0.6677
62	12	11.1	0.8991
63	11	11.1	-0.1009
64	11	11.38	-0.3843
65	8	11.1	-3.101
66	12	11.1	0.8991
67	11	11.1	-0.1009
68	12	11.1	0.8991
69	11	10.82	0.1825
70	11	11.38	-0.3843
71	11	11.1	-0.1009
72	10	11.1	-1.101
73	10	11.67	-1.668
74	13	11.67	1.332
75	11	11.38	-0.3843
76	11	11.1	-0.1009
77	11	11.67	-0.6677
78	13	10.82	2.182
79	12	11.95	0.04892
80	12	11.38	0.6157
81	9	11.38	-2.384
82	12	11.67	0.3323
83	12	11.67	0.3323
84	13	11.95	1.049
85	15	11.67	3.332
86	13	11.95	1.049
87	13	11.67	1.332
88	11	11.67	-0.6677
89	12	11.67	0.3323
90	9	11.67	-2.668
91	11	11.38	-0.3843
92	13	11.67	1.332
93	12	11.95	0.04892
94	13	11.95	1.049
95	11	11.38	-0.3843
96	12	11.67	0.3323
97	14	11.38	2.616
98	13	11.95	1.049
99	11	11.67	-0.6677
100	12	11.67	0.3323
101	13	11.38	1.616
102	11	11.67	-0.6677
103	11	11.67	-0.6677
104	11	11.38	-0.3843
105	13	11.95	1.049
106	12	11.1	0.8991
107	12	11.38	0.6157
108	11	11.67	-0.6677
109	12	11.38	0.6157
110	12	11.38	0.6157
111	10	11.38	-1.384
112	11	11.1	-0.1009
113	9	11.38	-2.384
114	14	11.67	2.332
115	12	11.38	0.6157
116	11	11.67	-0.6677
117	13	11.95	1.049
118	11	11.95	-0.9511
119	11	11.67	-0.6677
120	11	11.1	-0.1009
121	11	11.38	-0.3843
122	12	11.67	0.3323
123	11	11.67	-0.6677
124	13	11.38	1.616
125	11	11.38	-0.3843
126	11	11.38	-0.3843
127	12	11.67	0.3323
128	11	11.67	-0.6677
129	11	11.67	-0.6677
130	9	11.38	-2.384
131	12	11.1	0.8991
132	14	11.67	2.332
133	10	11.67	-1.668
134	9	11.95	-2.951
135	12	11.38	0.6157
136	14	11.38	2.616
137	9	11.1	-2.101
138	11	11.38	-0.3843
139	14	11.95	2.049
140	13	11.95	1.049
141	10	11.67	-1.668
142	11	11.95	-0.9511
143	12	10.82	1.182
144	10	11.38	-1.384
145	13	11.67	1.332
146	12	11.67	0.3323
147	14	11.1	2.899
148	10	11.67	-1.668
149	12	11.1	0.8991
150	9	11.1	-2.101
151	12	11.38	0.6157
152	11	11.1	-0.1009
153	11	11.38	-0.3843
154	10	11.1	-1.101
155	11	11.38	-0.3843
156	12	11.1	0.8991
157	10	11.67	-1.668
158	11	11.1	-0.1009
159	13	11.95	1.049
160	11	11.1	-0.1009
161	13	11.38	1.616
162	12	11.67	0.3323
163	11	11.67	-0.6677
164	12	11.67	0.3323
165	10	11.1	-1.101
166	12	11.38	0.6157
167	10	11.38	-1.384
168	13	11.67	1.332

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
5	0.8013	0.3973	0.1987
6	0.6754	0.6492	0.3246
7	0.6237	0.7527	0.3763
8	0.4995	0.999	0.5005
9	0.4738	0.9477	0.5262
10	0.5047	0.9906	0.4953
11	0.4013	0.8027	0.5987
12	0.3623	0.7245	0.6377
13	0.297	0.5941	0.703
14	0.406	0.8121	0.594
15	0.3443	0.6886	0.6557
16	0.2749	0.5499	0.7251
17	0.214	0.4281	0.786
18	0.1901	0.3802	0.8099
19	0.2209	0.4418	0.7791
20	0.1877	0.3753	0.8123
21	0.1562	0.3124	0.8438
22	0.1268	0.2535	0.8732
23	0.1027	0.2055	0.8973
24	0.1239	0.2478	0.8761
25	0.1005	0.201	0.8995
26	0.07664	0.1533	0.9234
27	0.06177	0.1235	0.9382
28	0.04668	0.09336	0.9533
29	0.03585	0.07169	0.9642
30	0.0527	0.1054	0.9473
31	0.0425	0.08499	0.9575
32	0.03206	0.06411	0.9679
33	0.0249	0.0498	0.9751
34	0.08282	0.1656	0.9172
35	0.06439	0.1288	0.9356
36	0.05195	0.1039	0.948
37	0.04145	0.08289	0.9586
38	0.03306	0.06612	0.9669
39	0.0408	0.08159	0.9592
40	0.03162	0.06323	0.9684
41	0.02406	0.04812	0.9759
42	0.0705	0.141	0.9295
43	0.0612	0.1224	0.9388
44	0.04746	0.09492	0.9525
45	0.03873	0.07745	0.9613
46	0.03018	0.06036	0.9698
47	0.02426	0.04851	0.9757
48	0.02585	0.0517	0.9741
49	0.05356	0.1071	0.9464
50	0.04496	0.08993	0.955
51	0.05135	0.1027	0.9487
52	0.05724	0.1145	0.9428
53	0.1421	0.2842	0.8579
54	0.1229	0.2457	0.8771
55	0.1019	0.2037	0.8981
56	0.0868	0.1736	0.9132
57	0.07146	0.1429	0.9285
58	0.06088	0.1218	0.9391
59	0.04891	0.09781	0.9511
60	0.03925	0.07849	0.9608
61	0.03246	0.06493	0.9675
62	0.02897	0.05794	0.971
63	0.0221	0.0442	0.9779
64	0.01709	0.03419	0.9829
65	0.0802	0.1604	0.9198
66	0.07413	0.1483	0.9259
67	0.05934	0.1187	0.9407
68	0.05421	0.1084	0.9458
69	0.04289	0.08578	0.9571
70	0.03423	0.06846	0.9658
71	0.02647	0.05294	0.9735
72	0.02565	0.05129	0.9744
73	0.03225	0.0645	0.9678
74	0.03609	0.07218	0.9639
75	0.02868	0.05737	0.9713
76	0.02209	0.04418	0.9779
77	0.01814	0.03628	0.9819
78	0.03366	0.06732	0.9663
79	0.02645	0.0529	0.9736
80	0.02199	0.04399	0.978
81	0.04673	0.09346	0.9533
82	0.03794	0.07588	0.9621
83	0.03052	0.06105	0.9695
84	0.02986	0.05972	0.9701
85	0.1325	0.265	0.8675
86	0.1274	0.2548	0.8726
87	0.1329	0.2658	0.8671
88	0.1171	0.2341	0.8829
89	0.09812	0.1962	0.9019
90	0.1952	0.3904	0.8048
91	0.1689	0.3379	0.8311
92	0.1751	0.3502	0.8249
93	0.148	0.296	0.852
94	0.1417	0.2835	0.8583
95	0.1205	0.241	0.8795
96	0.101	0.2021	0.899
97	0.1929	0.3858	0.8071
98	0.1859	0.3718	0.8141
99	0.1655	0.331	0.8345
100	0.1411	0.2822	0.8589
101	0.1617	0.3234	0.8383
102	0.1428	0.2857	0.8572
103	0.1255	0.2511	0.8745
104	0.1056	0.2113	0.8944
105	0.1012	0.2023	0.8988
106	0.09218	0.1844	0.9078
107	0.07912	0.1582	0.9209
108	0.06765	0.1353	0.9323
109	0.05735	0.1147	0.9427
110	0.04834	0.09669	0.9517
111	0.05109	0.1022	0.9489
112	0.04011	0.08022	0.9599
113	0.0759	0.1518	0.9241
114	0.1316	0.2631	0.8684
115	0.114	0.228	0.886
116	0.09799	0.196	0.902
117	0.09512	0.1902	0.9049
118	0.0852	0.1704	0.9148
119	0.07206	0.1441	0.9279
120	0.05705	0.1141	0.9429
121	0.0455	0.091	0.9545
122	0.03592	0.07183	0.9641
123	0.02926	0.05851	0.9707
124	0.03546	0.07092	0.9645
125	0.02745	0.05491	0.9725
126	0.02099	0.04198	0.979
127	0.01591	0.03183	0.9841
128	0.01246	0.02492	0.9875
129	0.009678	0.01936	0.9903
130	0.02174	0.04348	0.9783
131	0.01834	0.03667	0.9817
132	0.03852	0.07703	0.9615
133	0.04579	0.09157	0.9542
134	0.1446	0.2893	0.8554
135	0.1214	0.2428	0.8786
136	0.2428	0.4856	0.7572
137	0.3267	0.6534	0.6733
138	0.2818	0.5636	0.7182
139	0.3748	0.7497	0.6252
140	0.3751	0.7503	0.6249
141	0.413	0.8259	0.587
142	0.3841	0.7681	0.6159
143	0.3816	0.7633	0.6184
144	0.4018	0.8035	0.5982
145	0.4061	0.8122	0.5939
146	0.347	0.6939	0.653
147	0.7086	0.5828	0.2914
148	0.7896	0.4208	0.2104
149	0.8035	0.393	0.1965
150	0.8787	0.2425	0.1213
151	0.8503	0.2994	0.1497
152	0.7979	0.4041	0.2021
153	0.7379	0.5243	0.2621
154	0.7097	0.5805	0.2903
155	0.638	0.724	0.362
156	0.63	0.7401	0.37
157	0.814	0.372	0.186
158	0.7373	0.5253	0.2627
159	0.6403	0.7195	0.3597
160	0.5337	0.9325	0.4663
161	0.737	0.5261	0.263
162	0.5975	0.8049	0.4025
163	0.6179	0.7642	0.3821

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
5 &  0.8013 &  0.3973 &  0.1987 \tabularnewline
6 &  0.6754 &  0.6492 &  0.3246 \tabularnewline
7 &  0.6237 &  0.7527 &  0.3763 \tabularnewline
8 &  0.4995 &  0.999 &  0.5005 \tabularnewline
9 &  0.4738 &  0.9477 &  0.5262 \tabularnewline
10 &  0.5047 &  0.9906 &  0.4953 \tabularnewline
11 &  0.4013 &  0.8027 &  0.5987 \tabularnewline
12 &  0.3623 &  0.7245 &  0.6377 \tabularnewline
13 &  0.297 &  0.5941 &  0.703 \tabularnewline
14 &  0.406 &  0.8121 &  0.594 \tabularnewline
15 &  0.3443 &  0.6886 &  0.6557 \tabularnewline
16 &  0.2749 &  0.5499 &  0.7251 \tabularnewline
17 &  0.214 &  0.4281 &  0.786 \tabularnewline
18 &  0.1901 &  0.3802 &  0.8099 \tabularnewline
19 &  0.2209 &  0.4418 &  0.7791 \tabularnewline
20 &  0.1877 &  0.3753 &  0.8123 \tabularnewline
21 &  0.1562 &  0.3124 &  0.8438 \tabularnewline
22 &  0.1268 &  0.2535 &  0.8732 \tabularnewline
23 &  0.1027 &  0.2055 &  0.8973 \tabularnewline
24 &  0.1239 &  0.2478 &  0.8761 \tabularnewline
25 &  0.1005 &  0.201 &  0.8995 \tabularnewline
26 &  0.07664 &  0.1533 &  0.9234 \tabularnewline
27 &  0.06177 &  0.1235 &  0.9382 \tabularnewline
28 &  0.04668 &  0.09336 &  0.9533 \tabularnewline
29 &  0.03585 &  0.07169 &  0.9642 \tabularnewline
30 &  0.0527 &  0.1054 &  0.9473 \tabularnewline
31 &  0.0425 &  0.08499 &  0.9575 \tabularnewline
32 &  0.03206 &  0.06411 &  0.9679 \tabularnewline
33 &  0.0249 &  0.0498 &  0.9751 \tabularnewline
34 &  0.08282 &  0.1656 &  0.9172 \tabularnewline
35 &  0.06439 &  0.1288 &  0.9356 \tabularnewline
36 &  0.05195 &  0.1039 &  0.948 \tabularnewline
37 &  0.04145 &  0.08289 &  0.9586 \tabularnewline
38 &  0.03306 &  0.06612 &  0.9669 \tabularnewline
39 &  0.0408 &  0.08159 &  0.9592 \tabularnewline
40 &  0.03162 &  0.06323 &  0.9684 \tabularnewline
41 &  0.02406 &  0.04812 &  0.9759 \tabularnewline
42 &  0.0705 &  0.141 &  0.9295 \tabularnewline
43 &  0.0612 &  0.1224 &  0.9388 \tabularnewline
44 &  0.04746 &  0.09492 &  0.9525 \tabularnewline
45 &  0.03873 &  0.07745 &  0.9613 \tabularnewline
46 &  0.03018 &  0.06036 &  0.9698 \tabularnewline
47 &  0.02426 &  0.04851 &  0.9757 \tabularnewline
48 &  0.02585 &  0.0517 &  0.9741 \tabularnewline
49 &  0.05356 &  0.1071 &  0.9464 \tabularnewline
50 &  0.04496 &  0.08993 &  0.955 \tabularnewline
51 &  0.05135 &  0.1027 &  0.9487 \tabularnewline
52 &  0.05724 &  0.1145 &  0.9428 \tabularnewline
53 &  0.1421 &  0.2842 &  0.8579 \tabularnewline
54 &  0.1229 &  0.2457 &  0.8771 \tabularnewline
55 &  0.1019 &  0.2037 &  0.8981 \tabularnewline
56 &  0.0868 &  0.1736 &  0.9132 \tabularnewline
57 &  0.07146 &  0.1429 &  0.9285 \tabularnewline
58 &  0.06088 &  0.1218 &  0.9391 \tabularnewline
59 &  0.04891 &  0.09781 &  0.9511 \tabularnewline
60 &  0.03925 &  0.07849 &  0.9608 \tabularnewline
61 &  0.03246 &  0.06493 &  0.9675 \tabularnewline
62 &  0.02897 &  0.05794 &  0.971 \tabularnewline
63 &  0.0221 &  0.0442 &  0.9779 \tabularnewline
64 &  0.01709 &  0.03419 &  0.9829 \tabularnewline
65 &  0.0802 &  0.1604 &  0.9198 \tabularnewline
66 &  0.07413 &  0.1483 &  0.9259 \tabularnewline
67 &  0.05934 &  0.1187 &  0.9407 \tabularnewline
68 &  0.05421 &  0.1084 &  0.9458 \tabularnewline
69 &  0.04289 &  0.08578 &  0.9571 \tabularnewline
70 &  0.03423 &  0.06846 &  0.9658 \tabularnewline
71 &  0.02647 &  0.05294 &  0.9735 \tabularnewline
72 &  0.02565 &  0.05129 &  0.9744 \tabularnewline
73 &  0.03225 &  0.0645 &  0.9678 \tabularnewline
74 &  0.03609 &  0.07218 &  0.9639 \tabularnewline
75 &  0.02868 &  0.05737 &  0.9713 \tabularnewline
76 &  0.02209 &  0.04418 &  0.9779 \tabularnewline
77 &  0.01814 &  0.03628 &  0.9819 \tabularnewline
78 &  0.03366 &  0.06732 &  0.9663 \tabularnewline
79 &  0.02645 &  0.0529 &  0.9736 \tabularnewline
80 &  0.02199 &  0.04399 &  0.978 \tabularnewline
81 &  0.04673 &  0.09346 &  0.9533 \tabularnewline
82 &  0.03794 &  0.07588 &  0.9621 \tabularnewline
83 &  0.03052 &  0.06105 &  0.9695 \tabularnewline
84 &  0.02986 &  0.05972 &  0.9701 \tabularnewline
85 &  0.1325 &  0.265 &  0.8675 \tabularnewline
86 &  0.1274 &  0.2548 &  0.8726 \tabularnewline
87 &  0.1329 &  0.2658 &  0.8671 \tabularnewline
88 &  0.1171 &  0.2341 &  0.8829 \tabularnewline
89 &  0.09812 &  0.1962 &  0.9019 \tabularnewline
90 &  0.1952 &  0.3904 &  0.8048 \tabularnewline
91 &  0.1689 &  0.3379 &  0.8311 \tabularnewline
92 &  0.1751 &  0.3502 &  0.8249 \tabularnewline
93 &  0.148 &  0.296 &  0.852 \tabularnewline
94 &  0.1417 &  0.2835 &  0.8583 \tabularnewline
95 &  0.1205 &  0.241 &  0.8795 \tabularnewline
96 &  0.101 &  0.2021 &  0.899 \tabularnewline
97 &  0.1929 &  0.3858 &  0.8071 \tabularnewline
98 &  0.1859 &  0.3718 &  0.8141 \tabularnewline
99 &  0.1655 &  0.331 &  0.8345 \tabularnewline
100 &  0.1411 &  0.2822 &  0.8589 \tabularnewline
101 &  0.1617 &  0.3234 &  0.8383 \tabularnewline
102 &  0.1428 &  0.2857 &  0.8572 \tabularnewline
103 &  0.1255 &  0.2511 &  0.8745 \tabularnewline
104 &  0.1056 &  0.2113 &  0.8944 \tabularnewline
105 &  0.1012 &  0.2023 &  0.8988 \tabularnewline
106 &  0.09218 &  0.1844 &  0.9078 \tabularnewline
107 &  0.07912 &  0.1582 &  0.9209 \tabularnewline
108 &  0.06765 &  0.1353 &  0.9323 \tabularnewline
109 &  0.05735 &  0.1147 &  0.9427 \tabularnewline
110 &  0.04834 &  0.09669 &  0.9517 \tabularnewline
111 &  0.05109 &  0.1022 &  0.9489 \tabularnewline
112 &  0.04011 &  0.08022 &  0.9599 \tabularnewline
113 &  0.0759 &  0.1518 &  0.9241 \tabularnewline
114 &  0.1316 &  0.2631 &  0.8684 \tabularnewline
115 &  0.114 &  0.228 &  0.886 \tabularnewline
116 &  0.09799 &  0.196 &  0.902 \tabularnewline
117 &  0.09512 &  0.1902 &  0.9049 \tabularnewline
118 &  0.0852 &  0.1704 &  0.9148 \tabularnewline
119 &  0.07206 &  0.1441 &  0.9279 \tabularnewline
120 &  0.05705 &  0.1141 &  0.9429 \tabularnewline
121 &  0.0455 &  0.091 &  0.9545 \tabularnewline
122 &  0.03592 &  0.07183 &  0.9641 \tabularnewline
123 &  0.02926 &  0.05851 &  0.9707 \tabularnewline
124 &  0.03546 &  0.07092 &  0.9645 \tabularnewline
125 &  0.02745 &  0.05491 &  0.9725 \tabularnewline
126 &  0.02099 &  0.04198 &  0.979 \tabularnewline
127 &  0.01591 &  0.03183 &  0.9841 \tabularnewline
128 &  0.01246 &  0.02492 &  0.9875 \tabularnewline
129 &  0.009678 &  0.01936 &  0.9903 \tabularnewline
130 &  0.02174 &  0.04348 &  0.9783 \tabularnewline
131 &  0.01834 &  0.03667 &  0.9817 \tabularnewline
132 &  0.03852 &  0.07703 &  0.9615 \tabularnewline
133 &  0.04579 &  0.09157 &  0.9542 \tabularnewline
134 &  0.1446 &  0.2893 &  0.8554 \tabularnewline
135 &  0.1214 &  0.2428 &  0.8786 \tabularnewline
136 &  0.2428 &  0.4856 &  0.7572 \tabularnewline
137 &  0.3267 &  0.6534 &  0.6733 \tabularnewline
138 &  0.2818 &  0.5636 &  0.7182 \tabularnewline
139 &  0.3748 &  0.7497 &  0.6252 \tabularnewline
140 &  0.3751 &  0.7503 &  0.6249 \tabularnewline
141 &  0.413 &  0.8259 &  0.587 \tabularnewline
142 &  0.3841 &  0.7681 &  0.6159 \tabularnewline
143 &  0.3816 &  0.7633 &  0.6184 \tabularnewline
144 &  0.4018 &  0.8035 &  0.5982 \tabularnewline
145 &  0.4061 &  0.8122 &  0.5939 \tabularnewline
146 &  0.347 &  0.6939 &  0.653 \tabularnewline
147 &  0.7086 &  0.5828 &  0.2914 \tabularnewline
148 &  0.7896 &  0.4208 &  0.2104 \tabularnewline
149 &  0.8035 &  0.393 &  0.1965 \tabularnewline
150 &  0.8787 &  0.2425 &  0.1213 \tabularnewline
151 &  0.8503 &  0.2994 &  0.1497 \tabularnewline
152 &  0.7979 &  0.4041 &  0.2021 \tabularnewline
153 &  0.7379 &  0.5243 &  0.2621 \tabularnewline
154 &  0.7097 &  0.5805 &  0.2903 \tabularnewline
155 &  0.638 &  0.724 &  0.362 \tabularnewline
156 &  0.63 &  0.7401 &  0.37 \tabularnewline
157 &  0.814 &  0.372 &  0.186 \tabularnewline
158 &  0.7373 &  0.5253 &  0.2627 \tabularnewline
159 &  0.6403 &  0.7195 &  0.3597 \tabularnewline
160 &  0.5337 &  0.9325 &  0.4663 \tabularnewline
161 &  0.737 &  0.5261 &  0.263 \tabularnewline
162 &  0.5975 &  0.8049 &  0.4025 \tabularnewline
163 &  0.6179 &  0.7642 &  0.3821 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&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]5[/C][C] 0.8013[/C][C] 0.3973[/C][C] 0.1987[/C][/ROW]
[ROW][C]6[/C][C] 0.6754[/C][C] 0.6492[/C][C] 0.3246[/C][/ROW]
[ROW][C]7[/C][C] 0.6237[/C][C] 0.7527[/C][C] 0.3763[/C][/ROW]
[ROW][C]8[/C][C] 0.4995[/C][C] 0.999[/C][C] 0.5005[/C][/ROW]
[ROW][C]9[/C][C] 0.4738[/C][C] 0.9477[/C][C] 0.5262[/C][/ROW]
[ROW][C]10[/C][C] 0.5047[/C][C] 0.9906[/C][C] 0.4953[/C][/ROW]
[ROW][C]11[/C][C] 0.4013[/C][C] 0.8027[/C][C] 0.5987[/C][/ROW]
[ROW][C]12[/C][C] 0.3623[/C][C] 0.7245[/C][C] 0.6377[/C][/ROW]
[ROW][C]13[/C][C] 0.297[/C][C] 0.5941[/C][C] 0.703[/C][/ROW]
[ROW][C]14[/C][C] 0.406[/C][C] 0.8121[/C][C] 0.594[/C][/ROW]
[ROW][C]15[/C][C] 0.3443[/C][C] 0.6886[/C][C] 0.6557[/C][/ROW]
[ROW][C]16[/C][C] 0.2749[/C][C] 0.5499[/C][C] 0.7251[/C][/ROW]
[ROW][C]17[/C][C] 0.214[/C][C] 0.4281[/C][C] 0.786[/C][/ROW]
[ROW][C]18[/C][C] 0.1901[/C][C] 0.3802[/C][C] 0.8099[/C][/ROW]
[ROW][C]19[/C][C] 0.2209[/C][C] 0.4418[/C][C] 0.7791[/C][/ROW]
[ROW][C]20[/C][C] 0.1877[/C][C] 0.3753[/C][C] 0.8123[/C][/ROW]
[ROW][C]21[/C][C] 0.1562[/C][C] 0.3124[/C][C] 0.8438[/C][/ROW]
[ROW][C]22[/C][C] 0.1268[/C][C] 0.2535[/C][C] 0.8732[/C][/ROW]
[ROW][C]23[/C][C] 0.1027[/C][C] 0.2055[/C][C] 0.8973[/C][/ROW]
[ROW][C]24[/C][C] 0.1239[/C][C] 0.2478[/C][C] 0.8761[/C][/ROW]
[ROW][C]25[/C][C] 0.1005[/C][C] 0.201[/C][C] 0.8995[/C][/ROW]
[ROW][C]26[/C][C] 0.07664[/C][C] 0.1533[/C][C] 0.9234[/C][/ROW]
[ROW][C]27[/C][C] 0.06177[/C][C] 0.1235[/C][C] 0.9382[/C][/ROW]
[ROW][C]28[/C][C] 0.04668[/C][C] 0.09336[/C][C] 0.9533[/C][/ROW]
[ROW][C]29[/C][C] 0.03585[/C][C] 0.07169[/C][C] 0.9642[/C][/ROW]
[ROW][C]30[/C][C] 0.0527[/C][C] 0.1054[/C][C] 0.9473[/C][/ROW]
[ROW][C]31[/C][C] 0.0425[/C][C] 0.08499[/C][C] 0.9575[/C][/ROW]
[ROW][C]32[/C][C] 0.03206[/C][C] 0.06411[/C][C] 0.9679[/C][/ROW]
[ROW][C]33[/C][C] 0.0249[/C][C] 0.0498[/C][C] 0.9751[/C][/ROW]
[ROW][C]34[/C][C] 0.08282[/C][C] 0.1656[/C][C] 0.9172[/C][/ROW]
[ROW][C]35[/C][C] 0.06439[/C][C] 0.1288[/C][C] 0.9356[/C][/ROW]
[ROW][C]36[/C][C] 0.05195[/C][C] 0.1039[/C][C] 0.948[/C][/ROW]
[ROW][C]37[/C][C] 0.04145[/C][C] 0.08289[/C][C] 0.9586[/C][/ROW]
[ROW][C]38[/C][C] 0.03306[/C][C] 0.06612[/C][C] 0.9669[/C][/ROW]
[ROW][C]39[/C][C] 0.0408[/C][C] 0.08159[/C][C] 0.9592[/C][/ROW]
[ROW][C]40[/C][C] 0.03162[/C][C] 0.06323[/C][C] 0.9684[/C][/ROW]
[ROW][C]41[/C][C] 0.02406[/C][C] 0.04812[/C][C] 0.9759[/C][/ROW]
[ROW][C]42[/C][C] 0.0705[/C][C] 0.141[/C][C] 0.9295[/C][/ROW]
[ROW][C]43[/C][C] 0.0612[/C][C] 0.1224[/C][C] 0.9388[/C][/ROW]
[ROW][C]44[/C][C] 0.04746[/C][C] 0.09492[/C][C] 0.9525[/C][/ROW]
[ROW][C]45[/C][C] 0.03873[/C][C] 0.07745[/C][C] 0.9613[/C][/ROW]
[ROW][C]46[/C][C] 0.03018[/C][C] 0.06036[/C][C] 0.9698[/C][/ROW]
[ROW][C]47[/C][C] 0.02426[/C][C] 0.04851[/C][C] 0.9757[/C][/ROW]
[ROW][C]48[/C][C] 0.02585[/C][C] 0.0517[/C][C] 0.9741[/C][/ROW]
[ROW][C]49[/C][C] 0.05356[/C][C] 0.1071[/C][C] 0.9464[/C][/ROW]
[ROW][C]50[/C][C] 0.04496[/C][C] 0.08993[/C][C] 0.955[/C][/ROW]
[ROW][C]51[/C][C] 0.05135[/C][C] 0.1027[/C][C] 0.9487[/C][/ROW]
[ROW][C]52[/C][C] 0.05724[/C][C] 0.1145[/C][C] 0.9428[/C][/ROW]
[ROW][C]53[/C][C] 0.1421[/C][C] 0.2842[/C][C] 0.8579[/C][/ROW]
[ROW][C]54[/C][C] 0.1229[/C][C] 0.2457[/C][C] 0.8771[/C][/ROW]
[ROW][C]55[/C][C] 0.1019[/C][C] 0.2037[/C][C] 0.8981[/C][/ROW]
[ROW][C]56[/C][C] 0.0868[/C][C] 0.1736[/C][C] 0.9132[/C][/ROW]
[ROW][C]57[/C][C] 0.07146[/C][C] 0.1429[/C][C] 0.9285[/C][/ROW]
[ROW][C]58[/C][C] 0.06088[/C][C] 0.1218[/C][C] 0.9391[/C][/ROW]
[ROW][C]59[/C][C] 0.04891[/C][C] 0.09781[/C][C] 0.9511[/C][/ROW]
[ROW][C]60[/C][C] 0.03925[/C][C] 0.07849[/C][C] 0.9608[/C][/ROW]
[ROW][C]61[/C][C] 0.03246[/C][C] 0.06493[/C][C] 0.9675[/C][/ROW]
[ROW][C]62[/C][C] 0.02897[/C][C] 0.05794[/C][C] 0.971[/C][/ROW]
[ROW][C]63[/C][C] 0.0221[/C][C] 0.0442[/C][C] 0.9779[/C][/ROW]
[ROW][C]64[/C][C] 0.01709[/C][C] 0.03419[/C][C] 0.9829[/C][/ROW]
[ROW][C]65[/C][C] 0.0802[/C][C] 0.1604[/C][C] 0.9198[/C][/ROW]
[ROW][C]66[/C][C] 0.07413[/C][C] 0.1483[/C][C] 0.9259[/C][/ROW]
[ROW][C]67[/C][C] 0.05934[/C][C] 0.1187[/C][C] 0.9407[/C][/ROW]
[ROW][C]68[/C][C] 0.05421[/C][C] 0.1084[/C][C] 0.9458[/C][/ROW]
[ROW][C]69[/C][C] 0.04289[/C][C] 0.08578[/C][C] 0.9571[/C][/ROW]
[ROW][C]70[/C][C] 0.03423[/C][C] 0.06846[/C][C] 0.9658[/C][/ROW]
[ROW][C]71[/C][C] 0.02647[/C][C] 0.05294[/C][C] 0.9735[/C][/ROW]
[ROW][C]72[/C][C] 0.02565[/C][C] 0.05129[/C][C] 0.9744[/C][/ROW]
[ROW][C]73[/C][C] 0.03225[/C][C] 0.0645[/C][C] 0.9678[/C][/ROW]
[ROW][C]74[/C][C] 0.03609[/C][C] 0.07218[/C][C] 0.9639[/C][/ROW]
[ROW][C]75[/C][C] 0.02868[/C][C] 0.05737[/C][C] 0.9713[/C][/ROW]
[ROW][C]76[/C][C] 0.02209[/C][C] 0.04418[/C][C] 0.9779[/C][/ROW]
[ROW][C]77[/C][C] 0.01814[/C][C] 0.03628[/C][C] 0.9819[/C][/ROW]
[ROW][C]78[/C][C] 0.03366[/C][C] 0.06732[/C][C] 0.9663[/C][/ROW]
[ROW][C]79[/C][C] 0.02645[/C][C] 0.0529[/C][C] 0.9736[/C][/ROW]
[ROW][C]80[/C][C] 0.02199[/C][C] 0.04399[/C][C] 0.978[/C][/ROW]
[ROW][C]81[/C][C] 0.04673[/C][C] 0.09346[/C][C] 0.9533[/C][/ROW]
[ROW][C]82[/C][C] 0.03794[/C][C] 0.07588[/C][C] 0.9621[/C][/ROW]
[ROW][C]83[/C][C] 0.03052[/C][C] 0.06105[/C][C] 0.9695[/C][/ROW]
[ROW][C]84[/C][C] 0.02986[/C][C] 0.05972[/C][C] 0.9701[/C][/ROW]
[ROW][C]85[/C][C] 0.1325[/C][C] 0.265[/C][C] 0.8675[/C][/ROW]
[ROW][C]86[/C][C] 0.1274[/C][C] 0.2548[/C][C] 0.8726[/C][/ROW]
[ROW][C]87[/C][C] 0.1329[/C][C] 0.2658[/C][C] 0.8671[/C][/ROW]
[ROW][C]88[/C][C] 0.1171[/C][C] 0.2341[/C][C] 0.8829[/C][/ROW]
[ROW][C]89[/C][C] 0.09812[/C][C] 0.1962[/C][C] 0.9019[/C][/ROW]
[ROW][C]90[/C][C] 0.1952[/C][C] 0.3904[/C][C] 0.8048[/C][/ROW]
[ROW][C]91[/C][C] 0.1689[/C][C] 0.3379[/C][C] 0.8311[/C][/ROW]
[ROW][C]92[/C][C] 0.1751[/C][C] 0.3502[/C][C] 0.8249[/C][/ROW]
[ROW][C]93[/C][C] 0.148[/C][C] 0.296[/C][C] 0.852[/C][/ROW]
[ROW][C]94[/C][C] 0.1417[/C][C] 0.2835[/C][C] 0.8583[/C][/ROW]
[ROW][C]95[/C][C] 0.1205[/C][C] 0.241[/C][C] 0.8795[/C][/ROW]
[ROW][C]96[/C][C] 0.101[/C][C] 0.2021[/C][C] 0.899[/C][/ROW]
[ROW][C]97[/C][C] 0.1929[/C][C] 0.3858[/C][C] 0.8071[/C][/ROW]
[ROW][C]98[/C][C] 0.1859[/C][C] 0.3718[/C][C] 0.8141[/C][/ROW]
[ROW][C]99[/C][C] 0.1655[/C][C] 0.331[/C][C] 0.8345[/C][/ROW]
[ROW][C]100[/C][C] 0.1411[/C][C] 0.2822[/C][C] 0.8589[/C][/ROW]
[ROW][C]101[/C][C] 0.1617[/C][C] 0.3234[/C][C] 0.8383[/C][/ROW]
[ROW][C]102[/C][C] 0.1428[/C][C] 0.2857[/C][C] 0.8572[/C][/ROW]
[ROW][C]103[/C][C] 0.1255[/C][C] 0.2511[/C][C] 0.8745[/C][/ROW]
[ROW][C]104[/C][C] 0.1056[/C][C] 0.2113[/C][C] 0.8944[/C][/ROW]
[ROW][C]105[/C][C] 0.1012[/C][C] 0.2023[/C][C] 0.8988[/C][/ROW]
[ROW][C]106[/C][C] 0.09218[/C][C] 0.1844[/C][C] 0.9078[/C][/ROW]
[ROW][C]107[/C][C] 0.07912[/C][C] 0.1582[/C][C] 0.9209[/C][/ROW]
[ROW][C]108[/C][C] 0.06765[/C][C] 0.1353[/C][C] 0.9323[/C][/ROW]
[ROW][C]109[/C][C] 0.05735[/C][C] 0.1147[/C][C] 0.9427[/C][/ROW]
[ROW][C]110[/C][C] 0.04834[/C][C] 0.09669[/C][C] 0.9517[/C][/ROW]
[ROW][C]111[/C][C] 0.05109[/C][C] 0.1022[/C][C] 0.9489[/C][/ROW]
[ROW][C]112[/C][C] 0.04011[/C][C] 0.08022[/C][C] 0.9599[/C][/ROW]
[ROW][C]113[/C][C] 0.0759[/C][C] 0.1518[/C][C] 0.9241[/C][/ROW]
[ROW][C]114[/C][C] 0.1316[/C][C] 0.2631[/C][C] 0.8684[/C][/ROW]
[ROW][C]115[/C][C] 0.114[/C][C] 0.228[/C][C] 0.886[/C][/ROW]
[ROW][C]116[/C][C] 0.09799[/C][C] 0.196[/C][C] 0.902[/C][/ROW]
[ROW][C]117[/C][C] 0.09512[/C][C] 0.1902[/C][C] 0.9049[/C][/ROW]
[ROW][C]118[/C][C] 0.0852[/C][C] 0.1704[/C][C] 0.9148[/C][/ROW]
[ROW][C]119[/C][C] 0.07206[/C][C] 0.1441[/C][C] 0.9279[/C][/ROW]
[ROW][C]120[/C][C] 0.05705[/C][C] 0.1141[/C][C] 0.9429[/C][/ROW]
[ROW][C]121[/C][C] 0.0455[/C][C] 0.091[/C][C] 0.9545[/C][/ROW]
[ROW][C]122[/C][C] 0.03592[/C][C] 0.07183[/C][C] 0.9641[/C][/ROW]
[ROW][C]123[/C][C] 0.02926[/C][C] 0.05851[/C][C] 0.9707[/C][/ROW]
[ROW][C]124[/C][C] 0.03546[/C][C] 0.07092[/C][C] 0.9645[/C][/ROW]
[ROW][C]125[/C][C] 0.02745[/C][C] 0.05491[/C][C] 0.9725[/C][/ROW]
[ROW][C]126[/C][C] 0.02099[/C][C] 0.04198[/C][C] 0.979[/C][/ROW]
[ROW][C]127[/C][C] 0.01591[/C][C] 0.03183[/C][C] 0.9841[/C][/ROW]
[ROW][C]128[/C][C] 0.01246[/C][C] 0.02492[/C][C] 0.9875[/C][/ROW]
[ROW][C]129[/C][C] 0.009678[/C][C] 0.01936[/C][C] 0.9903[/C][/ROW]
[ROW][C]130[/C][C] 0.02174[/C][C] 0.04348[/C][C] 0.9783[/C][/ROW]
[ROW][C]131[/C][C] 0.01834[/C][C] 0.03667[/C][C] 0.9817[/C][/ROW]
[ROW][C]132[/C][C] 0.03852[/C][C] 0.07703[/C][C] 0.9615[/C][/ROW]
[ROW][C]133[/C][C] 0.04579[/C][C] 0.09157[/C][C] 0.9542[/C][/ROW]
[ROW][C]134[/C][C] 0.1446[/C][C] 0.2893[/C][C] 0.8554[/C][/ROW]
[ROW][C]135[/C][C] 0.1214[/C][C] 0.2428[/C][C] 0.8786[/C][/ROW]
[ROW][C]136[/C][C] 0.2428[/C][C] 0.4856[/C][C] 0.7572[/C][/ROW]
[ROW][C]137[/C][C] 0.3267[/C][C] 0.6534[/C][C] 0.6733[/C][/ROW]
[ROW][C]138[/C][C] 0.2818[/C][C] 0.5636[/C][C] 0.7182[/C][/ROW]
[ROW][C]139[/C][C] 0.3748[/C][C] 0.7497[/C][C] 0.6252[/C][/ROW]
[ROW][C]140[/C][C] 0.3751[/C][C] 0.7503[/C][C] 0.6249[/C][/ROW]
[ROW][C]141[/C][C] 0.413[/C][C] 0.8259[/C][C] 0.587[/C][/ROW]
[ROW][C]142[/C][C] 0.3841[/C][C] 0.7681[/C][C] 0.6159[/C][/ROW]
[ROW][C]143[/C][C] 0.3816[/C][C] 0.7633[/C][C] 0.6184[/C][/ROW]
[ROW][C]144[/C][C] 0.4018[/C][C] 0.8035[/C][C] 0.5982[/C][/ROW]
[ROW][C]145[/C][C] 0.4061[/C][C] 0.8122[/C][C] 0.5939[/C][/ROW]
[ROW][C]146[/C][C] 0.347[/C][C] 0.6939[/C][C] 0.653[/C][/ROW]
[ROW][C]147[/C][C] 0.7086[/C][C] 0.5828[/C][C] 0.2914[/C][/ROW]
[ROW][C]148[/C][C] 0.7896[/C][C] 0.4208[/C][C] 0.2104[/C][/ROW]
[ROW][C]149[/C][C] 0.8035[/C][C] 0.393[/C][C] 0.1965[/C][/ROW]
[ROW][C]150[/C][C] 0.8787[/C][C] 0.2425[/C][C] 0.1213[/C][/ROW]
[ROW][C]151[/C][C] 0.8503[/C][C] 0.2994[/C][C] 0.1497[/C][/ROW]
[ROW][C]152[/C][C] 0.7979[/C][C] 0.4041[/C][C] 0.2021[/C][/ROW]
[ROW][C]153[/C][C] 0.7379[/C][C] 0.5243[/C][C] 0.2621[/C][/ROW]
[ROW][C]154[/C][C] 0.7097[/C][C] 0.5805[/C][C] 0.2903[/C][/ROW]
[ROW][C]155[/C][C] 0.638[/C][C] 0.724[/C][C] 0.362[/C][/ROW]
[ROW][C]156[/C][C] 0.63[/C][C] 0.7401[/C][C] 0.37[/C][/ROW]
[ROW][C]157[/C][C] 0.814[/C][C] 0.372[/C][C] 0.186[/C][/ROW]
[ROW][C]158[/C][C] 0.7373[/C][C] 0.5253[/C][C] 0.2627[/C][/ROW]
[ROW][C]159[/C][C] 0.6403[/C][C] 0.7195[/C][C] 0.3597[/C][/ROW]
[ROW][C]160[/C][C] 0.5337[/C][C] 0.9325[/C][C] 0.4663[/C][/ROW]
[ROW][C]161[/C][C] 0.737[/C][C] 0.5261[/C][C] 0.263[/C][/ROW]
[ROW][C]162[/C][C] 0.5975[/C][C] 0.8049[/C][C] 0.4025[/C][/ROW]
[ROW][C]163[/C][C] 0.6179[/C][C] 0.7642[/C][C] 0.3821[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&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
5	0.8013	0.3973	0.1987
6	0.6754	0.6492	0.3246
7	0.6237	0.7527	0.3763
8	0.4995	0.999	0.5005
9	0.4738	0.9477	0.5262
10	0.5047	0.9906	0.4953
11	0.4013	0.8027	0.5987
12	0.3623	0.7245	0.6377
13	0.297	0.5941	0.703
14	0.406	0.8121	0.594
15	0.3443	0.6886	0.6557
16	0.2749	0.5499	0.7251
17	0.214	0.4281	0.786
18	0.1901	0.3802	0.8099
19	0.2209	0.4418	0.7791
20	0.1877	0.3753	0.8123
21	0.1562	0.3124	0.8438
22	0.1268	0.2535	0.8732
23	0.1027	0.2055	0.8973
24	0.1239	0.2478	0.8761
25	0.1005	0.201	0.8995
26	0.07664	0.1533	0.9234
27	0.06177	0.1235	0.9382
28	0.04668	0.09336	0.9533
29	0.03585	0.07169	0.9642
30	0.0527	0.1054	0.9473
31	0.0425	0.08499	0.9575
32	0.03206	0.06411	0.9679
33	0.0249	0.0498	0.9751
34	0.08282	0.1656	0.9172
35	0.06439	0.1288	0.9356
36	0.05195	0.1039	0.948
37	0.04145	0.08289	0.9586
38	0.03306	0.06612	0.9669
39	0.0408	0.08159	0.9592
40	0.03162	0.06323	0.9684
41	0.02406	0.04812	0.9759
42	0.0705	0.141	0.9295
43	0.0612	0.1224	0.9388
44	0.04746	0.09492	0.9525
45	0.03873	0.07745	0.9613
46	0.03018	0.06036	0.9698
47	0.02426	0.04851	0.9757
48	0.02585	0.0517	0.9741
49	0.05356	0.1071	0.9464
50	0.04496	0.08993	0.955
51	0.05135	0.1027	0.9487
52	0.05724	0.1145	0.9428
53	0.1421	0.2842	0.8579
54	0.1229	0.2457	0.8771
55	0.1019	0.2037	0.8981
56	0.0868	0.1736	0.9132
57	0.07146	0.1429	0.9285
58	0.06088	0.1218	0.9391
59	0.04891	0.09781	0.9511
60	0.03925	0.07849	0.9608
61	0.03246	0.06493	0.9675
62	0.02897	0.05794	0.971
63	0.0221	0.0442	0.9779
64	0.01709	0.03419	0.9829
65	0.0802	0.1604	0.9198
66	0.07413	0.1483	0.9259
67	0.05934	0.1187	0.9407
68	0.05421	0.1084	0.9458
69	0.04289	0.08578	0.9571
70	0.03423	0.06846	0.9658
71	0.02647	0.05294	0.9735
72	0.02565	0.05129	0.9744
73	0.03225	0.0645	0.9678
74	0.03609	0.07218	0.9639
75	0.02868	0.05737	0.9713
76	0.02209	0.04418	0.9779
77	0.01814	0.03628	0.9819
78	0.03366	0.06732	0.9663
79	0.02645	0.0529	0.9736
80	0.02199	0.04399	0.978
81	0.04673	0.09346	0.9533
82	0.03794	0.07588	0.9621
83	0.03052	0.06105	0.9695
84	0.02986	0.05972	0.9701
85	0.1325	0.265	0.8675
86	0.1274	0.2548	0.8726
87	0.1329	0.2658	0.8671
88	0.1171	0.2341	0.8829
89	0.09812	0.1962	0.9019
90	0.1952	0.3904	0.8048
91	0.1689	0.3379	0.8311
92	0.1751	0.3502	0.8249
93	0.148	0.296	0.852
94	0.1417	0.2835	0.8583
95	0.1205	0.241	0.8795
96	0.101	0.2021	0.899
97	0.1929	0.3858	0.8071
98	0.1859	0.3718	0.8141
99	0.1655	0.331	0.8345
100	0.1411	0.2822	0.8589
101	0.1617	0.3234	0.8383
102	0.1428	0.2857	0.8572
103	0.1255	0.2511	0.8745
104	0.1056	0.2113	0.8944
105	0.1012	0.2023	0.8988
106	0.09218	0.1844	0.9078
107	0.07912	0.1582	0.9209
108	0.06765	0.1353	0.9323
109	0.05735	0.1147	0.9427
110	0.04834	0.09669	0.9517
111	0.05109	0.1022	0.9489
112	0.04011	0.08022	0.9599
113	0.0759	0.1518	0.9241
114	0.1316	0.2631	0.8684
115	0.114	0.228	0.886
116	0.09799	0.196	0.902
117	0.09512	0.1902	0.9049
118	0.0852	0.1704	0.9148
119	0.07206	0.1441	0.9279
120	0.05705	0.1141	0.9429
121	0.0455	0.091	0.9545
122	0.03592	0.07183	0.9641
123	0.02926	0.05851	0.9707
124	0.03546	0.07092	0.9645
125	0.02745	0.05491	0.9725
126	0.02099	0.04198	0.979
127	0.01591	0.03183	0.9841
128	0.01246	0.02492	0.9875
129	0.009678	0.01936	0.9903
130	0.02174	0.04348	0.9783
131	0.01834	0.03667	0.9817
132	0.03852	0.07703	0.9615
133	0.04579	0.09157	0.9542
134	0.1446	0.2893	0.8554
135	0.1214	0.2428	0.8786
136	0.2428	0.4856	0.7572
137	0.3267	0.6534	0.6733
138	0.2818	0.5636	0.7182
139	0.3748	0.7497	0.6252
140	0.3751	0.7503	0.6249
141	0.413	0.8259	0.587
142	0.3841	0.7681	0.6159
143	0.3816	0.7633	0.6184
144	0.4018	0.8035	0.5982
145	0.4061	0.8122	0.5939
146	0.347	0.6939	0.653
147	0.7086	0.5828	0.2914
148	0.7896	0.4208	0.2104
149	0.8035	0.393	0.1965
150	0.8787	0.2425	0.1213
151	0.8503	0.2994	0.1497
152	0.7979	0.4041	0.2021
153	0.7379	0.5243	0.2621
154	0.7097	0.5805	0.2903
155	0.638	0.724	0.362
156	0.63	0.7401	0.37
157	0.814	0.372	0.186
158	0.7373	0.5253	0.2627
159	0.6403	0.7195	0.3597
160	0.5337	0.9325	0.4663
161	0.737	0.5261	0.263
162	0.5975	0.8049	0.4025
163	0.6179	0.7642	0.3821

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	14	0.0880503	NOK
10% type I error level	53	0.333333	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 & 14 & 0.0880503 & NOK \tabularnewline
10% type I error level & 53 & 0.333333 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&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]14[/C][C]0.0880503[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]53[/C][C]0.333333[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&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	14	0.0880503	NOK
10% type I error level	53	0.333333	NOK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 1.8095, df1 = 2, df2 = 164, p-value = 0.167
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.8095, df1 = 2, df2 = 164, p-value = 0.167
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 1.8095, df1 = 2, df2 = 164, p-value = 0.167

\begin{tabular}{lllllllll}
\hline
Ramsey RESET F-Test for powers (2 and 3) of fitted values \tabularnewline
> reset_test_fitted
	RESET test
data:  mylm
RESET = 1.8095, df1 = 2, df2 = 164, p-value = 0.167
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.8095, df1 = 2, df2 = 164, p-value = 0.167
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 1.8095, df1 = 2, df2 = 164, p-value = 0.167
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&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 = 1.8095, df1 = 2, df2 = 164, p-value = 0.167
[/C][/ROW]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of regressors[/C][/ROW]
[ROW][C]> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.8095, df1 = 2, df2 = 164, p-value = 0.167
[/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 = 1.8095, df1 = 2, df2 = 164, p-value = 0.167
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&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 = 1.8095, df1 = 2, df2 = 164, p-value = 0.167
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.8095, df1 = 2, df2 = 164, p-value = 0.167
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 1.8095, df1 = 2, df2 = 164, p-value = 0.167

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 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = 0 ; par5 = 0 ;

Parameters (R input):

par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = 0 ; par5 = 0 ;

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

par5 <- '0'
par4 <- '0'
par3 <- 'No Linear Trend'
par2 <- 'Do not include Seasonal Dummies'
par1 <- '2'
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