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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Tue, 20 Dec 2016 12:32:48 +0100

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2016/Dec/20/t1482233642wf694oxmzwccqmw.htm/, Retrieved Sun, 28 Apr 2024 16:09:00 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=301614, Retrieved Sun, 28 Apr 2024 16:09:00 +0000

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User-defined keywords

Stap 3.

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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 11:32:48] [16e0888ced5f28ae20ce1ff74f042113] [Current]

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Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	9 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 time9 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=301614&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]

9 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=301614&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=301614&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	9 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] = + 12.4174 -0.273014TVDC1[t] + 0.578048TVDC2[t] -0.128915TVDC3[t] + 0.280259TVDC4[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
SOMIVBH
[t] =  +  12.4174 -0.273014TVDC1[t] +  0.578048TVDC2[t] -0.128915TVDC3[t] +  0.280259TVDC4[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=301614&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]SOMIVBH
[t] =  +  12.4174 -0.273014TVDC1[t] +  0.578048TVDC2[t] -0.128915TVDC3[t] +  0.280259TVDC4[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=301614&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=301614&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] = + 12.4174 -0.273014TVDC1[t] + 0.578048TVDC2[t] -0.128915TVDC3[t] + 0.280259TVDC4[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)	+12.42	1.543	+8.0490e+00	1.649e-13	8.243e-14
TVDC1	-0.273	0.2241	-1.2180e+00	0.2249	0.1125
TVDC2	+0.578	0.3414	+1.6930e+00	0.09232	0.04616
TVDC3	-0.1289	0.2875	-4.4840e-01	0.6544	0.3272
TVDC4	+0.2803	0.2826	+9.9170e-01	0.3228	0.1614

\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) & +12.42 &  1.543 & +8.0490e+00 &  1.649e-13 &  8.243e-14 \tabularnewline
TVDC1 & -0.273 &  0.2241 & -1.2180e+00 &  0.2249 &  0.1125 \tabularnewline
TVDC2 & +0.578 &  0.3414 & +1.6930e+00 &  0.09232 &  0.04616 \tabularnewline
TVDC3 & -0.1289 &  0.2875 & -4.4840e-01 &  0.6544 &  0.3272 \tabularnewline
TVDC4 & +0.2803 &  0.2826 & +9.9170e-01 &  0.3228 &  0.1614 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=301614&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]+12.42[/C][C] 1.543[/C][C]+8.0490e+00[/C][C] 1.649e-13[/C][C] 8.243e-14[/C][/ROW]
[ROW][C]TVDC1[/C][C]-0.273[/C][C] 0.2241[/C][C]-1.2180e+00[/C][C] 0.2249[/C][C] 0.1125[/C][/ROW]
[ROW][C]TVDC2[/C][C]+0.578[/C][C] 0.3414[/C][C]+1.6930e+00[/C][C] 0.09232[/C][C] 0.04616[/C][/ROW]
[ROW][C]TVDC3[/C][C]-0.1289[/C][C] 0.2875[/C][C]-4.4840e-01[/C][C] 0.6544[/C][C] 0.3272[/C][/ROW]
[ROW][C]TVDC4[/C][C]+0.2803[/C][C] 0.2826[/C][C]+9.9170e-01[/C][C] 0.3228[/C][C] 0.1614[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=301614&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=301614&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)	+12.42	1.543	+8.0490e+00	1.649e-13	8.243e-14
TVDC1	-0.273	0.2241	-1.2180e+00	0.2249	0.1125
TVDC2	+0.578	0.3414	+1.6930e+00	0.09232	0.04616
TVDC3	-0.1289	0.2875	-4.4840e-01	0.6544	0.3272
TVDC4	+0.2803	0.2826	+9.9170e-01	0.3228	0.1614

Multiple Linear Regression - Regression Statistics
Multiple R	0.184
R-squared	0.03385
Adjusted R-squared	0.01015
F-TEST (value)	1.428
F-TEST (DF numerator)	4
F-TEST (DF denominator)	163
p-value	0.227
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.08
Sum Squared Residuals	705.3

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.184 \tabularnewline
R-squared &  0.03385 \tabularnewline
Adjusted R-squared &  0.01015 \tabularnewline
F-TEST (value) &  1.428 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 163 \tabularnewline
p-value &  0.227 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  2.08 \tabularnewline
Sum Squared Residuals &  705.3 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=301614&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.184[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.03385[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.01015[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 1.428[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]163[/C][/ROW]
[ROW][C]p-value[/C][C] 0.227[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 2.08[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 705.3[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=301614&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=301614&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	0.184
R-squared	0.03385
Adjusted R-squared	0.01015
F-TEST (value)	1.428
F-TEST (DF numerator)	4
F-TEST (DF denominator)	163
p-value	0.227
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.08
Sum Squared Residuals	705.3

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	11	13.51	-2.514
2	11	13.69	-2.69
3	15	14.41	0.5882
4	15	13.96	1.037
5	13	14.24	-1.243
6	14	12.98	1.017
7	13	13.54	-0.5432
8	15	13.83	1.166
9	14	14.11	-0.114
10	15	13.56	1.439
11	10	13.56	-3.561
12	11	13.96	-2.963
13	16	14.24	1.757
14	17	13.38	3.615
15	14	14.24	-0.2429
16	13	13.56	-0.5607
17	10	14.24	-4.243
18	13	14.52	-1.516
19	17	14.11	2.886
20	18	13.69	4.31
21	17	13.96	3.037
22	11	13.69	-2.69
23	15	13.96	1.037
24	12	14.11	-2.114
25	15	13.53	1.471
26	15	14.24	0.7571
27	12	13.96	-1.963
28	19	13.83	5.166
29	13	13.83	-0.8337
30	15	14.36	0.6354
31	13	13.26	-0.2557
32	10	13.97	-3.97
33	14	13.55	0.4465
34	12	12.81	-0.8066
35	15	13.56	1.439
36	13	13.96	-0.9627
37	18	13.94	4.062
38	15	14.79	0.2111
39	11	13.84	-2.841
40	14	13.96	0.03735
41	11	13.56	-2.561
42	14	13.51	0.4865
43	9	13.83	-4.834
44	13	13.96	-0.9627
45	13	14.11	-1.107
46	12	13.83	-1.834
47	17	13.96	3.037
48	16	14.36	1.635
49	15	13.79	1.213
50	16	13.56	2.439
51	16	14.14	1.861
52	13	14.55	-1.548
53	13	14.06	-1.06
54	12	14.24	-2.236
55	11	14.51	-3.509
56	13	13.96	-0.9627
57	15	14.27	0.7323
58	13	14.24	-1.243
59	14	13.96	0.03735
60	13	13.56	-0.5607
61	15	13.69	1.31
62	14	14.54	-0.5407
63	14	13.69	0.3104
64	13	13.96	-0.9627
65	11	12.53	-1.526
66	14	13.56	0.4393
67	17	14.24	2.764
68	15	14.79	0.2111
69	15	13.69	1.31
70	13	13.96	-0.9627
71	12	13.96	-1.963
72	14	14.09	-0.09156
73	11	13.66	-2.658
74	14	14.55	-0.5479
75	18	13.96	4.037
76	15	12.98	2.017
77	18	14.24	3.764
78	16	15.07	0.9308
79	12	13.56	-1.561
80	14	13.83	0.1663
81	14	14.33	-0.3326
82	14	13.83	0.1663
83	14	13.97	0.03011
84	13	14.11	-1.114
85	12	14.7	-2.699
86	13	14.11	-1.114
87	15	13.84	1.159
88	13	13.96	-0.9627
89	14	13.97	0.03011
90	15	13.95	1.053
91	13	13.96	-0.9627
92	14	14.41	-0.4118
93	17	13.83	3.166
94	15	14.41	0.5882
95	13	13.96	-0.9627
96	14	14.24	-0.2429
97	17	15.1	1.899
98	8	13.84	-5.841
99	15	13.69	1.31
100	10	14.24	-4.243
101	15	14.11	0.886
102	15	13.96	1.037
103	14	14.51	-0.5087
104	15	13.96	1.037
105	18	14.11	3.886
106	14	14.24	-0.2429
107	19	13.83	5.166
108	16	13.96	2.037
109	17	14.24	2.757
110	18	14.24	3.757
111	13	14.09	-1.092
112	10	13.96	-3.963
113	14	13.79	0.2135
114	13	14.12	-1.121
115	12	14.24	-2.243
116	13	13.69	-0.6896
117	12	14.11	-2.114
118	13	13.69	-0.6896
119	16	14.24	1.764
120	12	13.96	-1.963
121	14	14.24	-0.2357
122	17	14.24	2.757
123	14	13.96	0.03735
124	12	14.11	-2.114
125	14	13.96	0.03735
126	17	13.69	3.31
127	13	13.83	-0.8337
128	11	13.96	-2.963
129	14	13.96	0.03735
130	11	14.06	-3.06
131	17	14.24	2.757
132	15	15.1	-0.1012
133	10	13.66	-3.658
134	15	14.06	0.9405
135	16	14.24	1.757
136	17	14.39	2.606
137	15	13.79	1.213
138	12	13.96	-1.963
139	15	14.42	0.581
140	10	14.41	-4.412
141	13	13.66	-0.6576
142	17	14.24	2.764
143	17	14.24	2.757
144	16	14.36	1.635
145	15	14.41	0.5882
146	16	14.79	1.211
147	16	14.42	1.581
148	15	13.38	1.615
149	16	14.24	1.757
150	14	13.79	0.2135
151	17	14.24	2.757
152	14	13.69	0.3104
153	12	13.96	-1.963
154	15	14.64	0.3624
155	14	13.96	0.03735
156	15	13.56	1.439
157	14	14.09	-0.09156
158	13	13.96	-0.9627
159	16	13.84	2.159
160	13	13.96	-0.9627
161	14	14.14	-0.1388
162	13	14.52	-1.516
163	13	13.96	-0.9627
164	15	14.24	0.7571
165	13	13.66	-0.6576
166	14	14.24	-0.2429
167	13	14.09	-1.092
168	12	14.8	-2.796

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  11 &  13.51 & -2.514 \tabularnewline
2 &  11 &  13.69 & -2.69 \tabularnewline
3 &  15 &  14.41 &  0.5882 \tabularnewline
4 &  15 &  13.96 &  1.037 \tabularnewline
5 &  13 &  14.24 & -1.243 \tabularnewline
6 &  14 &  12.98 &  1.017 \tabularnewline
7 &  13 &  13.54 & -0.5432 \tabularnewline
8 &  15 &  13.83 &  1.166 \tabularnewline
9 &  14 &  14.11 & -0.114 \tabularnewline
10 &  15 &  13.56 &  1.439 \tabularnewline
11 &  10 &  13.56 & -3.561 \tabularnewline
12 &  11 &  13.96 & -2.963 \tabularnewline
13 &  16 &  14.24 &  1.757 \tabularnewline
14 &  17 &  13.38 &  3.615 \tabularnewline
15 &  14 &  14.24 & -0.2429 \tabularnewline
16 &  13 &  13.56 & -0.5607 \tabularnewline
17 &  10 &  14.24 & -4.243 \tabularnewline
18 &  13 &  14.52 & -1.516 \tabularnewline
19 &  17 &  14.11 &  2.886 \tabularnewline
20 &  18 &  13.69 &  4.31 \tabularnewline
21 &  17 &  13.96 &  3.037 \tabularnewline
22 &  11 &  13.69 & -2.69 \tabularnewline
23 &  15 &  13.96 &  1.037 \tabularnewline
24 &  12 &  14.11 & -2.114 \tabularnewline
25 &  15 &  13.53 &  1.471 \tabularnewline
26 &  15 &  14.24 &  0.7571 \tabularnewline
27 &  12 &  13.96 & -1.963 \tabularnewline
28 &  19 &  13.83 &  5.166 \tabularnewline
29 &  13 &  13.83 & -0.8337 \tabularnewline
30 &  15 &  14.36 &  0.6354 \tabularnewline
31 &  13 &  13.26 & -0.2557 \tabularnewline
32 &  10 &  13.97 & -3.97 \tabularnewline
33 &  14 &  13.55 &  0.4465 \tabularnewline
34 &  12 &  12.81 & -0.8066 \tabularnewline
35 &  15 &  13.56 &  1.439 \tabularnewline
36 &  13 &  13.96 & -0.9627 \tabularnewline
37 &  18 &  13.94 &  4.062 \tabularnewline
38 &  15 &  14.79 &  0.2111 \tabularnewline
39 &  11 &  13.84 & -2.841 \tabularnewline
40 &  14 &  13.96 &  0.03735 \tabularnewline
41 &  11 &  13.56 & -2.561 \tabularnewline
42 &  14 &  13.51 &  0.4865 \tabularnewline
43 &  9 &  13.83 & -4.834 \tabularnewline
44 &  13 &  13.96 & -0.9627 \tabularnewline
45 &  13 &  14.11 & -1.107 \tabularnewline
46 &  12 &  13.83 & -1.834 \tabularnewline
47 &  17 &  13.96 &  3.037 \tabularnewline
48 &  16 &  14.36 &  1.635 \tabularnewline
49 &  15 &  13.79 &  1.213 \tabularnewline
50 &  16 &  13.56 &  2.439 \tabularnewline
51 &  16 &  14.14 &  1.861 \tabularnewline
52 &  13 &  14.55 & -1.548 \tabularnewline
53 &  13 &  14.06 & -1.06 \tabularnewline
54 &  12 &  14.24 & -2.236 \tabularnewline
55 &  11 &  14.51 & -3.509 \tabularnewline
56 &  13 &  13.96 & -0.9627 \tabularnewline
57 &  15 &  14.27 &  0.7323 \tabularnewline
58 &  13 &  14.24 & -1.243 \tabularnewline
59 &  14 &  13.96 &  0.03735 \tabularnewline
60 &  13 &  13.56 & -0.5607 \tabularnewline
61 &  15 &  13.69 &  1.31 \tabularnewline
62 &  14 &  14.54 & -0.5407 \tabularnewline
63 &  14 &  13.69 &  0.3104 \tabularnewline
64 &  13 &  13.96 & -0.9627 \tabularnewline
65 &  11 &  12.53 & -1.526 \tabularnewline
66 &  14 &  13.56 &  0.4393 \tabularnewline
67 &  17 &  14.24 &  2.764 \tabularnewline
68 &  15 &  14.79 &  0.2111 \tabularnewline
69 &  15 &  13.69 &  1.31 \tabularnewline
70 &  13 &  13.96 & -0.9627 \tabularnewline
71 &  12 &  13.96 & -1.963 \tabularnewline
72 &  14 &  14.09 & -0.09156 \tabularnewline
73 &  11 &  13.66 & -2.658 \tabularnewline
74 &  14 &  14.55 & -0.5479 \tabularnewline
75 &  18 &  13.96 &  4.037 \tabularnewline
76 &  15 &  12.98 &  2.017 \tabularnewline
77 &  18 &  14.24 &  3.764 \tabularnewline
78 &  16 &  15.07 &  0.9308 \tabularnewline
79 &  12 &  13.56 & -1.561 \tabularnewline
80 &  14 &  13.83 &  0.1663 \tabularnewline
81 &  14 &  14.33 & -0.3326 \tabularnewline
82 &  14 &  13.83 &  0.1663 \tabularnewline
83 &  14 &  13.97 &  0.03011 \tabularnewline
84 &  13 &  14.11 & -1.114 \tabularnewline
85 &  12 &  14.7 & -2.699 \tabularnewline
86 &  13 &  14.11 & -1.114 \tabularnewline
87 &  15 &  13.84 &  1.159 \tabularnewline
88 &  13 &  13.96 & -0.9627 \tabularnewline
89 &  14 &  13.97 &  0.03011 \tabularnewline
90 &  15 &  13.95 &  1.053 \tabularnewline
91 &  13 &  13.96 & -0.9627 \tabularnewline
92 &  14 &  14.41 & -0.4118 \tabularnewline
93 &  17 &  13.83 &  3.166 \tabularnewline
94 &  15 &  14.41 &  0.5882 \tabularnewline
95 &  13 &  13.96 & -0.9627 \tabularnewline
96 &  14 &  14.24 & -0.2429 \tabularnewline
97 &  17 &  15.1 &  1.899 \tabularnewline
98 &  8 &  13.84 & -5.841 \tabularnewline
99 &  15 &  13.69 &  1.31 \tabularnewline
100 &  10 &  14.24 & -4.243 \tabularnewline
101 &  15 &  14.11 &  0.886 \tabularnewline
102 &  15 &  13.96 &  1.037 \tabularnewline
103 &  14 &  14.51 & -0.5087 \tabularnewline
104 &  15 &  13.96 &  1.037 \tabularnewline
105 &  18 &  14.11 &  3.886 \tabularnewline
106 &  14 &  14.24 & -0.2429 \tabularnewline
107 &  19 &  13.83 &  5.166 \tabularnewline
108 &  16 &  13.96 &  2.037 \tabularnewline
109 &  17 &  14.24 &  2.757 \tabularnewline
110 &  18 &  14.24 &  3.757 \tabularnewline
111 &  13 &  14.09 & -1.092 \tabularnewline
112 &  10 &  13.96 & -3.963 \tabularnewline
113 &  14 &  13.79 &  0.2135 \tabularnewline
114 &  13 &  14.12 & -1.121 \tabularnewline
115 &  12 &  14.24 & -2.243 \tabularnewline
116 &  13 &  13.69 & -0.6896 \tabularnewline
117 &  12 &  14.11 & -2.114 \tabularnewline
118 &  13 &  13.69 & -0.6896 \tabularnewline
119 &  16 &  14.24 &  1.764 \tabularnewline
120 &  12 &  13.96 & -1.963 \tabularnewline
121 &  14 &  14.24 & -0.2357 \tabularnewline
122 &  17 &  14.24 &  2.757 \tabularnewline
123 &  14 &  13.96 &  0.03735 \tabularnewline
124 &  12 &  14.11 & -2.114 \tabularnewline
125 &  14 &  13.96 &  0.03735 \tabularnewline
126 &  17 &  13.69 &  3.31 \tabularnewline
127 &  13 &  13.83 & -0.8337 \tabularnewline
128 &  11 &  13.96 & -2.963 \tabularnewline
129 &  14 &  13.96 &  0.03735 \tabularnewline
130 &  11 &  14.06 & -3.06 \tabularnewline
131 &  17 &  14.24 &  2.757 \tabularnewline
132 &  15 &  15.1 & -0.1012 \tabularnewline
133 &  10 &  13.66 & -3.658 \tabularnewline
134 &  15 &  14.06 &  0.9405 \tabularnewline
135 &  16 &  14.24 &  1.757 \tabularnewline
136 &  17 &  14.39 &  2.606 \tabularnewline
137 &  15 &  13.79 &  1.213 \tabularnewline
138 &  12 &  13.96 & -1.963 \tabularnewline
139 &  15 &  14.42 &  0.581 \tabularnewline
140 &  10 &  14.41 & -4.412 \tabularnewline
141 &  13 &  13.66 & -0.6576 \tabularnewline
142 &  17 &  14.24 &  2.764 \tabularnewline
143 &  17 &  14.24 &  2.757 \tabularnewline
144 &  16 &  14.36 &  1.635 \tabularnewline
145 &  15 &  14.41 &  0.5882 \tabularnewline
146 &  16 &  14.79 &  1.211 \tabularnewline
147 &  16 &  14.42 &  1.581 \tabularnewline
148 &  15 &  13.38 &  1.615 \tabularnewline
149 &  16 &  14.24 &  1.757 \tabularnewline
150 &  14 &  13.79 &  0.2135 \tabularnewline
151 &  17 &  14.24 &  2.757 \tabularnewline
152 &  14 &  13.69 &  0.3104 \tabularnewline
153 &  12 &  13.96 & -1.963 \tabularnewline
154 &  15 &  14.64 &  0.3624 \tabularnewline
155 &  14 &  13.96 &  0.03735 \tabularnewline
156 &  15 &  13.56 &  1.439 \tabularnewline
157 &  14 &  14.09 & -0.09156 \tabularnewline
158 &  13 &  13.96 & -0.9627 \tabularnewline
159 &  16 &  13.84 &  2.159 \tabularnewline
160 &  13 &  13.96 & -0.9627 \tabularnewline
161 &  14 &  14.14 & -0.1388 \tabularnewline
162 &  13 &  14.52 & -1.516 \tabularnewline
163 &  13 &  13.96 & -0.9627 \tabularnewline
164 &  15 &  14.24 &  0.7571 \tabularnewline
165 &  13 &  13.66 & -0.6576 \tabularnewline
166 &  14 &  14.24 & -0.2429 \tabularnewline
167 &  13 &  14.09 & -1.092 \tabularnewline
168 &  12 &  14.8 & -2.796 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=301614&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] 13.51[/C][C]-2.514[/C][/ROW]
[ROW][C]2[/C][C] 11[/C][C] 13.69[/C][C]-2.69[/C][/ROW]
[ROW][C]3[/C][C] 15[/C][C] 14.41[/C][C] 0.5882[/C][/ROW]
[ROW][C]4[/C][C] 15[/C][C] 13.96[/C][C] 1.037[/C][/ROW]
[ROW][C]5[/C][C] 13[/C][C] 14.24[/C][C]-1.243[/C][/ROW]
[ROW][C]6[/C][C] 14[/C][C] 12.98[/C][C] 1.017[/C][/ROW]
[ROW][C]7[/C][C] 13[/C][C] 13.54[/C][C]-0.5432[/C][/ROW]
[ROW][C]8[/C][C] 15[/C][C] 13.83[/C][C] 1.166[/C][/ROW]
[ROW][C]9[/C][C] 14[/C][C] 14.11[/C][C]-0.114[/C][/ROW]
[ROW][C]10[/C][C] 15[/C][C] 13.56[/C][C] 1.439[/C][/ROW]
[ROW][C]11[/C][C] 10[/C][C] 13.56[/C][C]-3.561[/C][/ROW]
[ROW][C]12[/C][C] 11[/C][C] 13.96[/C][C]-2.963[/C][/ROW]
[ROW][C]13[/C][C] 16[/C][C] 14.24[/C][C] 1.757[/C][/ROW]
[ROW][C]14[/C][C] 17[/C][C] 13.38[/C][C] 3.615[/C][/ROW]
[ROW][C]15[/C][C] 14[/C][C] 14.24[/C][C]-0.2429[/C][/ROW]
[ROW][C]16[/C][C] 13[/C][C] 13.56[/C][C]-0.5607[/C][/ROW]
[ROW][C]17[/C][C] 10[/C][C] 14.24[/C][C]-4.243[/C][/ROW]
[ROW][C]18[/C][C] 13[/C][C] 14.52[/C][C]-1.516[/C][/ROW]
[ROW][C]19[/C][C] 17[/C][C] 14.11[/C][C] 2.886[/C][/ROW]
[ROW][C]20[/C][C] 18[/C][C] 13.69[/C][C] 4.31[/C][/ROW]
[ROW][C]21[/C][C] 17[/C][C] 13.96[/C][C] 3.037[/C][/ROW]
[ROW][C]22[/C][C] 11[/C][C] 13.69[/C][C]-2.69[/C][/ROW]
[ROW][C]23[/C][C] 15[/C][C] 13.96[/C][C] 1.037[/C][/ROW]
[ROW][C]24[/C][C] 12[/C][C] 14.11[/C][C]-2.114[/C][/ROW]
[ROW][C]25[/C][C] 15[/C][C] 13.53[/C][C] 1.471[/C][/ROW]
[ROW][C]26[/C][C] 15[/C][C] 14.24[/C][C] 0.7571[/C][/ROW]
[ROW][C]27[/C][C] 12[/C][C] 13.96[/C][C]-1.963[/C][/ROW]
[ROW][C]28[/C][C] 19[/C][C] 13.83[/C][C] 5.166[/C][/ROW]
[ROW][C]29[/C][C] 13[/C][C] 13.83[/C][C]-0.8337[/C][/ROW]
[ROW][C]30[/C][C] 15[/C][C] 14.36[/C][C] 0.6354[/C][/ROW]
[ROW][C]31[/C][C] 13[/C][C] 13.26[/C][C]-0.2557[/C][/ROW]
[ROW][C]32[/C][C] 10[/C][C] 13.97[/C][C]-3.97[/C][/ROW]
[ROW][C]33[/C][C] 14[/C][C] 13.55[/C][C] 0.4465[/C][/ROW]
[ROW][C]34[/C][C] 12[/C][C] 12.81[/C][C]-0.8066[/C][/ROW]
[ROW][C]35[/C][C] 15[/C][C] 13.56[/C][C] 1.439[/C][/ROW]
[ROW][C]36[/C][C] 13[/C][C] 13.96[/C][C]-0.9627[/C][/ROW]
[ROW][C]37[/C][C] 18[/C][C] 13.94[/C][C] 4.062[/C][/ROW]
[ROW][C]38[/C][C] 15[/C][C] 14.79[/C][C] 0.2111[/C][/ROW]
[ROW][C]39[/C][C] 11[/C][C] 13.84[/C][C]-2.841[/C][/ROW]
[ROW][C]40[/C][C] 14[/C][C] 13.96[/C][C] 0.03735[/C][/ROW]
[ROW][C]41[/C][C] 11[/C][C] 13.56[/C][C]-2.561[/C][/ROW]
[ROW][C]42[/C][C] 14[/C][C] 13.51[/C][C] 0.4865[/C][/ROW]
[ROW][C]43[/C][C] 9[/C][C] 13.83[/C][C]-4.834[/C][/ROW]
[ROW][C]44[/C][C] 13[/C][C] 13.96[/C][C]-0.9627[/C][/ROW]
[ROW][C]45[/C][C] 13[/C][C] 14.11[/C][C]-1.107[/C][/ROW]
[ROW][C]46[/C][C] 12[/C][C] 13.83[/C][C]-1.834[/C][/ROW]
[ROW][C]47[/C][C] 17[/C][C] 13.96[/C][C] 3.037[/C][/ROW]
[ROW][C]48[/C][C] 16[/C][C] 14.36[/C][C] 1.635[/C][/ROW]
[ROW][C]49[/C][C] 15[/C][C] 13.79[/C][C] 1.213[/C][/ROW]
[ROW][C]50[/C][C] 16[/C][C] 13.56[/C][C] 2.439[/C][/ROW]
[ROW][C]51[/C][C] 16[/C][C] 14.14[/C][C] 1.861[/C][/ROW]
[ROW][C]52[/C][C] 13[/C][C] 14.55[/C][C]-1.548[/C][/ROW]
[ROW][C]53[/C][C] 13[/C][C] 14.06[/C][C]-1.06[/C][/ROW]
[ROW][C]54[/C][C] 12[/C][C] 14.24[/C][C]-2.236[/C][/ROW]
[ROW][C]55[/C][C] 11[/C][C] 14.51[/C][C]-3.509[/C][/ROW]
[ROW][C]56[/C][C] 13[/C][C] 13.96[/C][C]-0.9627[/C][/ROW]
[ROW][C]57[/C][C] 15[/C][C] 14.27[/C][C] 0.7323[/C][/ROW]
[ROW][C]58[/C][C] 13[/C][C] 14.24[/C][C]-1.243[/C][/ROW]
[ROW][C]59[/C][C] 14[/C][C] 13.96[/C][C] 0.03735[/C][/ROW]
[ROW][C]60[/C][C] 13[/C][C] 13.56[/C][C]-0.5607[/C][/ROW]
[ROW][C]61[/C][C] 15[/C][C] 13.69[/C][C] 1.31[/C][/ROW]
[ROW][C]62[/C][C] 14[/C][C] 14.54[/C][C]-0.5407[/C][/ROW]
[ROW][C]63[/C][C] 14[/C][C] 13.69[/C][C] 0.3104[/C][/ROW]
[ROW][C]64[/C][C] 13[/C][C] 13.96[/C][C]-0.9627[/C][/ROW]
[ROW][C]65[/C][C] 11[/C][C] 12.53[/C][C]-1.526[/C][/ROW]
[ROW][C]66[/C][C] 14[/C][C] 13.56[/C][C] 0.4393[/C][/ROW]
[ROW][C]67[/C][C] 17[/C][C] 14.24[/C][C] 2.764[/C][/ROW]
[ROW][C]68[/C][C] 15[/C][C] 14.79[/C][C] 0.2111[/C][/ROW]
[ROW][C]69[/C][C] 15[/C][C] 13.69[/C][C] 1.31[/C][/ROW]
[ROW][C]70[/C][C] 13[/C][C] 13.96[/C][C]-0.9627[/C][/ROW]
[ROW][C]71[/C][C] 12[/C][C] 13.96[/C][C]-1.963[/C][/ROW]
[ROW][C]72[/C][C] 14[/C][C] 14.09[/C][C]-0.09156[/C][/ROW]
[ROW][C]73[/C][C] 11[/C][C] 13.66[/C][C]-2.658[/C][/ROW]
[ROW][C]74[/C][C] 14[/C][C] 14.55[/C][C]-0.5479[/C][/ROW]
[ROW][C]75[/C][C] 18[/C][C] 13.96[/C][C] 4.037[/C][/ROW]
[ROW][C]76[/C][C] 15[/C][C] 12.98[/C][C] 2.017[/C][/ROW]
[ROW][C]77[/C][C] 18[/C][C] 14.24[/C][C] 3.764[/C][/ROW]
[ROW][C]78[/C][C] 16[/C][C] 15.07[/C][C] 0.9308[/C][/ROW]
[ROW][C]79[/C][C] 12[/C][C] 13.56[/C][C]-1.561[/C][/ROW]
[ROW][C]80[/C][C] 14[/C][C] 13.83[/C][C] 0.1663[/C][/ROW]
[ROW][C]81[/C][C] 14[/C][C] 14.33[/C][C]-0.3326[/C][/ROW]
[ROW][C]82[/C][C] 14[/C][C] 13.83[/C][C] 0.1663[/C][/ROW]
[ROW][C]83[/C][C] 14[/C][C] 13.97[/C][C] 0.03011[/C][/ROW]
[ROW][C]84[/C][C] 13[/C][C] 14.11[/C][C]-1.114[/C][/ROW]
[ROW][C]85[/C][C] 12[/C][C] 14.7[/C][C]-2.699[/C][/ROW]
[ROW][C]86[/C][C] 13[/C][C] 14.11[/C][C]-1.114[/C][/ROW]
[ROW][C]87[/C][C] 15[/C][C] 13.84[/C][C] 1.159[/C][/ROW]
[ROW][C]88[/C][C] 13[/C][C] 13.96[/C][C]-0.9627[/C][/ROW]
[ROW][C]89[/C][C] 14[/C][C] 13.97[/C][C] 0.03011[/C][/ROW]
[ROW][C]90[/C][C] 15[/C][C] 13.95[/C][C] 1.053[/C][/ROW]
[ROW][C]91[/C][C] 13[/C][C] 13.96[/C][C]-0.9627[/C][/ROW]
[ROW][C]92[/C][C] 14[/C][C] 14.41[/C][C]-0.4118[/C][/ROW]
[ROW][C]93[/C][C] 17[/C][C] 13.83[/C][C] 3.166[/C][/ROW]
[ROW][C]94[/C][C] 15[/C][C] 14.41[/C][C] 0.5882[/C][/ROW]
[ROW][C]95[/C][C] 13[/C][C] 13.96[/C][C]-0.9627[/C][/ROW]
[ROW][C]96[/C][C] 14[/C][C] 14.24[/C][C]-0.2429[/C][/ROW]
[ROW][C]97[/C][C] 17[/C][C] 15.1[/C][C] 1.899[/C][/ROW]
[ROW][C]98[/C][C] 8[/C][C] 13.84[/C][C]-5.841[/C][/ROW]
[ROW][C]99[/C][C] 15[/C][C] 13.69[/C][C] 1.31[/C][/ROW]
[ROW][C]100[/C][C] 10[/C][C] 14.24[/C][C]-4.243[/C][/ROW]
[ROW][C]101[/C][C] 15[/C][C] 14.11[/C][C] 0.886[/C][/ROW]
[ROW][C]102[/C][C] 15[/C][C] 13.96[/C][C] 1.037[/C][/ROW]
[ROW][C]103[/C][C] 14[/C][C] 14.51[/C][C]-0.5087[/C][/ROW]
[ROW][C]104[/C][C] 15[/C][C] 13.96[/C][C] 1.037[/C][/ROW]
[ROW][C]105[/C][C] 18[/C][C] 14.11[/C][C] 3.886[/C][/ROW]
[ROW][C]106[/C][C] 14[/C][C] 14.24[/C][C]-0.2429[/C][/ROW]
[ROW][C]107[/C][C] 19[/C][C] 13.83[/C][C] 5.166[/C][/ROW]
[ROW][C]108[/C][C] 16[/C][C] 13.96[/C][C] 2.037[/C][/ROW]
[ROW][C]109[/C][C] 17[/C][C] 14.24[/C][C] 2.757[/C][/ROW]
[ROW][C]110[/C][C] 18[/C][C] 14.24[/C][C] 3.757[/C][/ROW]
[ROW][C]111[/C][C] 13[/C][C] 14.09[/C][C]-1.092[/C][/ROW]
[ROW][C]112[/C][C] 10[/C][C] 13.96[/C][C]-3.963[/C][/ROW]
[ROW][C]113[/C][C] 14[/C][C] 13.79[/C][C] 0.2135[/C][/ROW]
[ROW][C]114[/C][C] 13[/C][C] 14.12[/C][C]-1.121[/C][/ROW]
[ROW][C]115[/C][C] 12[/C][C] 14.24[/C][C]-2.243[/C][/ROW]
[ROW][C]116[/C][C] 13[/C][C] 13.69[/C][C]-0.6896[/C][/ROW]
[ROW][C]117[/C][C] 12[/C][C] 14.11[/C][C]-2.114[/C][/ROW]
[ROW][C]118[/C][C] 13[/C][C] 13.69[/C][C]-0.6896[/C][/ROW]
[ROW][C]119[/C][C] 16[/C][C] 14.24[/C][C] 1.764[/C][/ROW]
[ROW][C]120[/C][C] 12[/C][C] 13.96[/C][C]-1.963[/C][/ROW]
[ROW][C]121[/C][C] 14[/C][C] 14.24[/C][C]-0.2357[/C][/ROW]
[ROW][C]122[/C][C] 17[/C][C] 14.24[/C][C] 2.757[/C][/ROW]
[ROW][C]123[/C][C] 14[/C][C] 13.96[/C][C] 0.03735[/C][/ROW]
[ROW][C]124[/C][C] 12[/C][C] 14.11[/C][C]-2.114[/C][/ROW]
[ROW][C]125[/C][C] 14[/C][C] 13.96[/C][C] 0.03735[/C][/ROW]
[ROW][C]126[/C][C] 17[/C][C] 13.69[/C][C] 3.31[/C][/ROW]
[ROW][C]127[/C][C] 13[/C][C] 13.83[/C][C]-0.8337[/C][/ROW]
[ROW][C]128[/C][C] 11[/C][C] 13.96[/C][C]-2.963[/C][/ROW]
[ROW][C]129[/C][C] 14[/C][C] 13.96[/C][C] 0.03735[/C][/ROW]
[ROW][C]130[/C][C] 11[/C][C] 14.06[/C][C]-3.06[/C][/ROW]
[ROW][C]131[/C][C] 17[/C][C] 14.24[/C][C] 2.757[/C][/ROW]
[ROW][C]132[/C][C] 15[/C][C] 15.1[/C][C]-0.1012[/C][/ROW]
[ROW][C]133[/C][C] 10[/C][C] 13.66[/C][C]-3.658[/C][/ROW]
[ROW][C]134[/C][C] 15[/C][C] 14.06[/C][C] 0.9405[/C][/ROW]
[ROW][C]135[/C][C] 16[/C][C] 14.24[/C][C] 1.757[/C][/ROW]
[ROW][C]136[/C][C] 17[/C][C] 14.39[/C][C] 2.606[/C][/ROW]
[ROW][C]137[/C][C] 15[/C][C] 13.79[/C][C] 1.213[/C][/ROW]
[ROW][C]138[/C][C] 12[/C][C] 13.96[/C][C]-1.963[/C][/ROW]
[ROW][C]139[/C][C] 15[/C][C] 14.42[/C][C] 0.581[/C][/ROW]
[ROW][C]140[/C][C] 10[/C][C] 14.41[/C][C]-4.412[/C][/ROW]
[ROW][C]141[/C][C] 13[/C][C] 13.66[/C][C]-0.6576[/C][/ROW]
[ROW][C]142[/C][C] 17[/C][C] 14.24[/C][C] 2.764[/C][/ROW]
[ROW][C]143[/C][C] 17[/C][C] 14.24[/C][C] 2.757[/C][/ROW]
[ROW][C]144[/C][C] 16[/C][C] 14.36[/C][C] 1.635[/C][/ROW]
[ROW][C]145[/C][C] 15[/C][C] 14.41[/C][C] 0.5882[/C][/ROW]
[ROW][C]146[/C][C] 16[/C][C] 14.79[/C][C] 1.211[/C][/ROW]
[ROW][C]147[/C][C] 16[/C][C] 14.42[/C][C] 1.581[/C][/ROW]
[ROW][C]148[/C][C] 15[/C][C] 13.38[/C][C] 1.615[/C][/ROW]
[ROW][C]149[/C][C] 16[/C][C] 14.24[/C][C] 1.757[/C][/ROW]
[ROW][C]150[/C][C] 14[/C][C] 13.79[/C][C] 0.2135[/C][/ROW]
[ROW][C]151[/C][C] 17[/C][C] 14.24[/C][C] 2.757[/C][/ROW]
[ROW][C]152[/C][C] 14[/C][C] 13.69[/C][C] 0.3104[/C][/ROW]
[ROW][C]153[/C][C] 12[/C][C] 13.96[/C][C]-1.963[/C][/ROW]
[ROW][C]154[/C][C] 15[/C][C] 14.64[/C][C] 0.3624[/C][/ROW]
[ROW][C]155[/C][C] 14[/C][C] 13.96[/C][C] 0.03735[/C][/ROW]
[ROW][C]156[/C][C] 15[/C][C] 13.56[/C][C] 1.439[/C][/ROW]
[ROW][C]157[/C][C] 14[/C][C] 14.09[/C][C]-0.09156[/C][/ROW]
[ROW][C]158[/C][C] 13[/C][C] 13.96[/C][C]-0.9627[/C][/ROW]
[ROW][C]159[/C][C] 16[/C][C] 13.84[/C][C] 2.159[/C][/ROW]
[ROW][C]160[/C][C] 13[/C][C] 13.96[/C][C]-0.9627[/C][/ROW]
[ROW][C]161[/C][C] 14[/C][C] 14.14[/C][C]-0.1388[/C][/ROW]
[ROW][C]162[/C][C] 13[/C][C] 14.52[/C][C]-1.516[/C][/ROW]
[ROW][C]163[/C][C] 13[/C][C] 13.96[/C][C]-0.9627[/C][/ROW]
[ROW][C]164[/C][C] 15[/C][C] 14.24[/C][C] 0.7571[/C][/ROW]
[ROW][C]165[/C][C] 13[/C][C] 13.66[/C][C]-0.6576[/C][/ROW]
[ROW][C]166[/C][C] 14[/C][C] 14.24[/C][C]-0.2429[/C][/ROW]
[ROW][C]167[/C][C] 13[/C][C] 14.09[/C][C]-1.092[/C][/ROW]
[ROW][C]168[/C][C] 12[/C][C] 14.8[/C][C]-2.796[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=301614&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=301614&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	13.51	-2.514
2	11	13.69	-2.69
3	15	14.41	0.5882
4	15	13.96	1.037
5	13	14.24	-1.243
6	14	12.98	1.017
7	13	13.54	-0.5432
8	15	13.83	1.166
9	14	14.11	-0.114
10	15	13.56	1.439
11	10	13.56	-3.561
12	11	13.96	-2.963
13	16	14.24	1.757
14	17	13.38	3.615
15	14	14.24	-0.2429
16	13	13.56	-0.5607
17	10	14.24	-4.243
18	13	14.52	-1.516
19	17	14.11	2.886
20	18	13.69	4.31
21	17	13.96	3.037
22	11	13.69	-2.69
23	15	13.96	1.037
24	12	14.11	-2.114
25	15	13.53	1.471
26	15	14.24	0.7571
27	12	13.96	-1.963
28	19	13.83	5.166
29	13	13.83	-0.8337
30	15	14.36	0.6354
31	13	13.26	-0.2557
32	10	13.97	-3.97
33	14	13.55	0.4465
34	12	12.81	-0.8066
35	15	13.56	1.439
36	13	13.96	-0.9627
37	18	13.94	4.062
38	15	14.79	0.2111
39	11	13.84	-2.841
40	14	13.96	0.03735
41	11	13.56	-2.561
42	14	13.51	0.4865
43	9	13.83	-4.834
44	13	13.96	-0.9627
45	13	14.11	-1.107
46	12	13.83	-1.834
47	17	13.96	3.037
48	16	14.36	1.635
49	15	13.79	1.213
50	16	13.56	2.439
51	16	14.14	1.861
52	13	14.55	-1.548
53	13	14.06	-1.06
54	12	14.24	-2.236
55	11	14.51	-3.509
56	13	13.96	-0.9627
57	15	14.27	0.7323
58	13	14.24	-1.243
59	14	13.96	0.03735
60	13	13.56	-0.5607
61	15	13.69	1.31
62	14	14.54	-0.5407
63	14	13.69	0.3104
64	13	13.96	-0.9627
65	11	12.53	-1.526
66	14	13.56	0.4393
67	17	14.24	2.764
68	15	14.79	0.2111
69	15	13.69	1.31
70	13	13.96	-0.9627
71	12	13.96	-1.963
72	14	14.09	-0.09156
73	11	13.66	-2.658
74	14	14.55	-0.5479
75	18	13.96	4.037
76	15	12.98	2.017
77	18	14.24	3.764
78	16	15.07	0.9308
79	12	13.56	-1.561
80	14	13.83	0.1663
81	14	14.33	-0.3326
82	14	13.83	0.1663
83	14	13.97	0.03011
84	13	14.11	-1.114
85	12	14.7	-2.699
86	13	14.11	-1.114
87	15	13.84	1.159
88	13	13.96	-0.9627
89	14	13.97	0.03011
90	15	13.95	1.053
91	13	13.96	-0.9627
92	14	14.41	-0.4118
93	17	13.83	3.166
94	15	14.41	0.5882
95	13	13.96	-0.9627
96	14	14.24	-0.2429
97	17	15.1	1.899
98	8	13.84	-5.841
99	15	13.69	1.31
100	10	14.24	-4.243
101	15	14.11	0.886
102	15	13.96	1.037
103	14	14.51	-0.5087
104	15	13.96	1.037
105	18	14.11	3.886
106	14	14.24	-0.2429
107	19	13.83	5.166
108	16	13.96	2.037
109	17	14.24	2.757
110	18	14.24	3.757
111	13	14.09	-1.092
112	10	13.96	-3.963
113	14	13.79	0.2135
114	13	14.12	-1.121
115	12	14.24	-2.243
116	13	13.69	-0.6896
117	12	14.11	-2.114
118	13	13.69	-0.6896
119	16	14.24	1.764
120	12	13.96	-1.963
121	14	14.24	-0.2357
122	17	14.24	2.757
123	14	13.96	0.03735
124	12	14.11	-2.114
125	14	13.96	0.03735
126	17	13.69	3.31
127	13	13.83	-0.8337
128	11	13.96	-2.963
129	14	13.96	0.03735
130	11	14.06	-3.06
131	17	14.24	2.757
132	15	15.1	-0.1012
133	10	13.66	-3.658
134	15	14.06	0.9405
135	16	14.24	1.757
136	17	14.39	2.606
137	15	13.79	1.213
138	12	13.96	-1.963
139	15	14.42	0.581
140	10	14.41	-4.412
141	13	13.66	-0.6576
142	17	14.24	2.764
143	17	14.24	2.757
144	16	14.36	1.635
145	15	14.41	0.5882
146	16	14.79	1.211
147	16	14.42	1.581
148	15	13.38	1.615
149	16	14.24	1.757
150	14	13.79	0.2135
151	17	14.24	2.757
152	14	13.69	0.3104
153	12	13.96	-1.963
154	15	14.64	0.3624
155	14	13.96	0.03735
156	15	13.56	1.439
157	14	14.09	-0.09156
158	13	13.96	-0.9627
159	16	13.84	2.159
160	13	13.96	-0.9627
161	14	14.14	-0.1388
162	13	14.52	-1.516
163	13	13.96	-0.9627
164	15	14.24	0.7571
165	13	13.66	-0.6576
166	14	14.24	-0.2429
167	13	14.09	-1.092
168	12	14.8	-2.796

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
8	0.1417	0.2834	0.8583
9	0.08644	0.1729	0.9136
10	0.06396	0.1279	0.936
11	0.3277	0.6554	0.6723
12	0.35	0.6999	0.65
13	0.4566	0.9132	0.5434
14	0.562	0.876	0.438
15	0.4695	0.939	0.5305
16	0.3798	0.7595	0.6202
17	0.5213	0.9573	0.4787
18	0.5264	0.9472	0.4736
19	0.5385	0.923	0.4615
20	0.9019	0.1961	0.09805
21	0.9254	0.1492	0.07461
22	0.9184	0.1633	0.08163
23	0.8954	0.2093	0.1046
24	0.8977	0.2045	0.1023
25	0.8757	0.2486	0.1243
26	0.8558	0.2884	0.1442
27	0.8469	0.3063	0.1531
28	0.9297	0.1406	0.07028
29	0.9224	0.1552	0.07758
30	0.9015	0.197	0.0985
31	0.8873	0.2254	0.1127
32	0.9052	0.1897	0.09483
33	0.8882	0.2236	0.1118
34	0.8681	0.2637	0.1319
35	0.8504	0.2992	0.1496
36	0.8219	0.3562	0.1781
37	0.8825	0.2349	0.1175
38	0.8607	0.2786	0.1393
39	0.865	0.27	0.135
40	0.8338	0.3324	0.1662
41	0.8406	0.3187	0.1594
42	0.8111	0.3779	0.1889
43	0.9339	0.1321	0.06607
44	0.9189	0.1622	0.08111
45	0.9144	0.1712	0.08561
46	0.9096	0.1807	0.09037
47	0.931	0.138	0.06899
48	0.9207	0.1586	0.07928
49	0.9037	0.1925	0.09625
50	0.918	0.1639	0.08195
51	0.9226	0.1547	0.07735
52	0.9094	0.1812	0.0906
53	0.9041	0.1917	0.09586
54	0.91	0.1801	0.09003
55	0.9416	0.1168	0.05839
56	0.9291	0.1418	0.07089
57	0.9166	0.1668	0.08341
58	0.9017	0.1966	0.09832
59	0.8797	0.2405	0.1203
60	0.8562	0.2875	0.1438
61	0.8403	0.3195	0.1597
62	0.8117	0.3766	0.1883
63	0.7796	0.4407	0.2204
64	0.7508	0.4985	0.2492
65	0.7449	0.5103	0.2551
66	0.7082	0.5837	0.2918
67	0.7363	0.5274	0.2637
68	0.6985	0.6031	0.3015
69	0.6749	0.6502	0.3251
70	0.6411	0.7178	0.3589
71	0.6341	0.7318	0.3659
72	0.5907	0.8186	0.4093
73	0.618	0.7639	0.382
74	0.5764	0.8473	0.4236
75	0.6904	0.6193	0.3096
76	0.6862	0.6276	0.3138
77	0.7678	0.4643	0.2322
78	0.7428	0.5144	0.2572
79	0.7253	0.5495	0.2747
80	0.6866	0.6268	0.3134
81	0.6477	0.7046	0.3523
82	0.6053	0.7894	0.3947
83	0.5629	0.8743	0.4371
84	0.5291	0.9417	0.4709
85	0.5559	0.8883	0.4441
86	0.5232	0.9537	0.4768
87	0.4948	0.9896	0.5052
88	0.459	0.918	0.541
89	0.417	0.8341	0.583
90	0.3832	0.7664	0.6168
91	0.3495	0.699	0.6505
92	0.31	0.62	0.69
93	0.3643	0.7285	0.6358
94	0.3265	0.653	0.6735
95	0.2945	0.5889	0.7055
96	0.2582	0.5163	0.7418
97	0.2526	0.5052	0.7474
98	0.5622	0.8757	0.4378
99	0.5308	0.9384	0.4692
100	0.6865	0.627	0.3135
101	0.6517	0.6965	0.3483
102	0.6187	0.7626	0.3813
103	0.5766	0.8469	0.4234
104	0.542	0.9159	0.458
105	0.6451	0.7099	0.3549
106	0.6046	0.7909	0.3954
107	0.8416	0.3168	0.1584
108	0.8457	0.3087	0.1543
109	0.859	0.2819	0.1409
110	0.9057	0.1885	0.09426
111	0.8945	0.2109	0.1055
112	0.9428	0.1143	0.05716
113	0.9272	0.1457	0.07283
114	0.9269	0.1461	0.07306
115	0.9418	0.1165	0.05825
116	0.9314	0.1372	0.06859
117	0.9349	0.1301	0.06506
118	0.9247	0.1506	0.07532
119	0.9357	0.1286	0.06431
120	0.9351	0.1298	0.06489
121	0.9196	0.1608	0.08041
122	0.9232	0.1536	0.07678
123	0.9021	0.1959	0.09794
124	0.9101	0.1799	0.08995
125	0.8859	0.2282	0.1141
126	0.9102	0.1796	0.0898
127	0.8869	0.2262	0.1131
128	0.9143	0.1714	0.0857
129	0.8898	0.2203	0.1102
130	0.9116	0.1767	0.08837
131	0.916	0.168	0.084
132	0.9019	0.1963	0.09813
133	0.9554	0.08916	0.04458
134	0.9434	0.1132	0.05661
135	0.9305	0.1389	0.06945
136	0.9228	0.1543	0.07715
137	0.9033	0.1934	0.09668
138	0.905	0.19	0.09501
139	0.8747	0.2506	0.1253
140	0.9683	0.06348	0.03174
141	0.9582	0.08363	0.04182
142	0.974	0.05206	0.02603
143	0.9813	0.03742	0.01871
144	0.986	0.02792	0.01396
145	0.9782	0.04352	0.02176
146	0.9833	0.0335	0.01675
147	0.9793	0.04134	0.02067
148	0.9695	0.06098	0.03049
149	0.969	0.06193	0.03097
150	0.9517	0.09658	0.04829
151	0.9867	0.02656	0.01328
152	0.975	0.05	0.025
153	0.979	0.0419	0.02095
154	0.9998	0.0004979	0.0002489
155	0.9994	0.001202	0.0006011
156	0.9981	0.003877	0.001938
157	0.9962	0.007656	0.003828
158	0.9885	0.02296	0.01148
159	0.9651	0.06975	0.03487
160	0.909	0.182	0.091

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 &  0.1417 &  0.2834 &  0.8583 \tabularnewline
9 &  0.08644 &  0.1729 &  0.9136 \tabularnewline
10 &  0.06396 &  0.1279 &  0.936 \tabularnewline
11 &  0.3277 &  0.6554 &  0.6723 \tabularnewline
12 &  0.35 &  0.6999 &  0.65 \tabularnewline
13 &  0.4566 &  0.9132 &  0.5434 \tabularnewline
14 &  0.562 &  0.876 &  0.438 \tabularnewline
15 &  0.4695 &  0.939 &  0.5305 \tabularnewline
16 &  0.3798 &  0.7595 &  0.6202 \tabularnewline
17 &  0.5213 &  0.9573 &  0.4787 \tabularnewline
18 &  0.5264 &  0.9472 &  0.4736 \tabularnewline
19 &  0.5385 &  0.923 &  0.4615 \tabularnewline
20 &  0.9019 &  0.1961 &  0.09805 \tabularnewline
21 &  0.9254 &  0.1492 &  0.07461 \tabularnewline
22 &  0.9184 &  0.1633 &  0.08163 \tabularnewline
23 &  0.8954 &  0.2093 &  0.1046 \tabularnewline
24 &  0.8977 &  0.2045 &  0.1023 \tabularnewline
25 &  0.8757 &  0.2486 &  0.1243 \tabularnewline
26 &  0.8558 &  0.2884 &  0.1442 \tabularnewline
27 &  0.8469 &  0.3063 &  0.1531 \tabularnewline
28 &  0.9297 &  0.1406 &  0.07028 \tabularnewline
29 &  0.9224 &  0.1552 &  0.07758 \tabularnewline
30 &  0.9015 &  0.197 &  0.0985 \tabularnewline
31 &  0.8873 &  0.2254 &  0.1127 \tabularnewline
32 &  0.9052 &  0.1897 &  0.09483 \tabularnewline
33 &  0.8882 &  0.2236 &  0.1118 \tabularnewline
34 &  0.8681 &  0.2637 &  0.1319 \tabularnewline
35 &  0.8504 &  0.2992 &  0.1496 \tabularnewline
36 &  0.8219 &  0.3562 &  0.1781 \tabularnewline
37 &  0.8825 &  0.2349 &  0.1175 \tabularnewline
38 &  0.8607 &  0.2786 &  0.1393 \tabularnewline
39 &  0.865 &  0.27 &  0.135 \tabularnewline
40 &  0.8338 &  0.3324 &  0.1662 \tabularnewline
41 &  0.8406 &  0.3187 &  0.1594 \tabularnewline
42 &  0.8111 &  0.3779 &  0.1889 \tabularnewline
43 &  0.9339 &  0.1321 &  0.06607 \tabularnewline
44 &  0.9189 &  0.1622 &  0.08111 \tabularnewline
45 &  0.9144 &  0.1712 &  0.08561 \tabularnewline
46 &  0.9096 &  0.1807 &  0.09037 \tabularnewline
47 &  0.931 &  0.138 &  0.06899 \tabularnewline
48 &  0.9207 &  0.1586 &  0.07928 \tabularnewline
49 &  0.9037 &  0.1925 &  0.09625 \tabularnewline
50 &  0.918 &  0.1639 &  0.08195 \tabularnewline
51 &  0.9226 &  0.1547 &  0.07735 \tabularnewline
52 &  0.9094 &  0.1812 &  0.0906 \tabularnewline
53 &  0.9041 &  0.1917 &  0.09586 \tabularnewline
54 &  0.91 &  0.1801 &  0.09003 \tabularnewline
55 &  0.9416 &  0.1168 &  0.05839 \tabularnewline
56 &  0.9291 &  0.1418 &  0.07089 \tabularnewline
57 &  0.9166 &  0.1668 &  0.08341 \tabularnewline
58 &  0.9017 &  0.1966 &  0.09832 \tabularnewline
59 &  0.8797 &  0.2405 &  0.1203 \tabularnewline
60 &  0.8562 &  0.2875 &  0.1438 \tabularnewline
61 &  0.8403 &  0.3195 &  0.1597 \tabularnewline
62 &  0.8117 &  0.3766 &  0.1883 \tabularnewline
63 &  0.7796 &  0.4407 &  0.2204 \tabularnewline
64 &  0.7508 &  0.4985 &  0.2492 \tabularnewline
65 &  0.7449 &  0.5103 &  0.2551 \tabularnewline
66 &  0.7082 &  0.5837 &  0.2918 \tabularnewline
67 &  0.7363 &  0.5274 &  0.2637 \tabularnewline
68 &  0.6985 &  0.6031 &  0.3015 \tabularnewline
69 &  0.6749 &  0.6502 &  0.3251 \tabularnewline
70 &  0.6411 &  0.7178 &  0.3589 \tabularnewline
71 &  0.6341 &  0.7318 &  0.3659 \tabularnewline
72 &  0.5907 &  0.8186 &  0.4093 \tabularnewline
73 &  0.618 &  0.7639 &  0.382 \tabularnewline
74 &  0.5764 &  0.8473 &  0.4236 \tabularnewline
75 &  0.6904 &  0.6193 &  0.3096 \tabularnewline
76 &  0.6862 &  0.6276 &  0.3138 \tabularnewline
77 &  0.7678 &  0.4643 &  0.2322 \tabularnewline
78 &  0.7428 &  0.5144 &  0.2572 \tabularnewline
79 &  0.7253 &  0.5495 &  0.2747 \tabularnewline
80 &  0.6866 &  0.6268 &  0.3134 \tabularnewline
81 &  0.6477 &  0.7046 &  0.3523 \tabularnewline
82 &  0.6053 &  0.7894 &  0.3947 \tabularnewline
83 &  0.5629 &  0.8743 &  0.4371 \tabularnewline
84 &  0.5291 &  0.9417 &  0.4709 \tabularnewline
85 &  0.5559 &  0.8883 &  0.4441 \tabularnewline
86 &  0.5232 &  0.9537 &  0.4768 \tabularnewline
87 &  0.4948 &  0.9896 &  0.5052 \tabularnewline
88 &  0.459 &  0.918 &  0.541 \tabularnewline
89 &  0.417 &  0.8341 &  0.583 \tabularnewline
90 &  0.3832 &  0.7664 &  0.6168 \tabularnewline
91 &  0.3495 &  0.699 &  0.6505 \tabularnewline
92 &  0.31 &  0.62 &  0.69 \tabularnewline
93 &  0.3643 &  0.7285 &  0.6358 \tabularnewline
94 &  0.3265 &  0.653 &  0.6735 \tabularnewline
95 &  0.2945 &  0.5889 &  0.7055 \tabularnewline
96 &  0.2582 &  0.5163 &  0.7418 \tabularnewline
97 &  0.2526 &  0.5052 &  0.7474 \tabularnewline
98 &  0.5622 &  0.8757 &  0.4378 \tabularnewline
99 &  0.5308 &  0.9384 &  0.4692 \tabularnewline
100 &  0.6865 &  0.627 &  0.3135 \tabularnewline
101 &  0.6517 &  0.6965 &  0.3483 \tabularnewline
102 &  0.6187 &  0.7626 &  0.3813 \tabularnewline
103 &  0.5766 &  0.8469 &  0.4234 \tabularnewline
104 &  0.542 &  0.9159 &  0.458 \tabularnewline
105 &  0.6451 &  0.7099 &  0.3549 \tabularnewline
106 &  0.6046 &  0.7909 &  0.3954 \tabularnewline
107 &  0.8416 &  0.3168 &  0.1584 \tabularnewline
108 &  0.8457 &  0.3087 &  0.1543 \tabularnewline
109 &  0.859 &  0.2819 &  0.1409 \tabularnewline
110 &  0.9057 &  0.1885 &  0.09426 \tabularnewline
111 &  0.8945 &  0.2109 &  0.1055 \tabularnewline
112 &  0.9428 &  0.1143 &  0.05716 \tabularnewline
113 &  0.9272 &  0.1457 &  0.07283 \tabularnewline
114 &  0.9269 &  0.1461 &  0.07306 \tabularnewline
115 &  0.9418 &  0.1165 &  0.05825 \tabularnewline
116 &  0.9314 &  0.1372 &  0.06859 \tabularnewline
117 &  0.9349 &  0.1301 &  0.06506 \tabularnewline
118 &  0.9247 &  0.1506 &  0.07532 \tabularnewline
119 &  0.9357 &  0.1286 &  0.06431 \tabularnewline
120 &  0.9351 &  0.1298 &  0.06489 \tabularnewline
121 &  0.9196 &  0.1608 &  0.08041 \tabularnewline
122 &  0.9232 &  0.1536 &  0.07678 \tabularnewline
123 &  0.9021 &  0.1959 &  0.09794 \tabularnewline
124 &  0.9101 &  0.1799 &  0.08995 \tabularnewline
125 &  0.8859 &  0.2282 &  0.1141 \tabularnewline
126 &  0.9102 &  0.1796 &  0.0898 \tabularnewline
127 &  0.8869 &  0.2262 &  0.1131 \tabularnewline
128 &  0.9143 &  0.1714 &  0.0857 \tabularnewline
129 &  0.8898 &  0.2203 &  0.1102 \tabularnewline
130 &  0.9116 &  0.1767 &  0.08837 \tabularnewline
131 &  0.916 &  0.168 &  0.084 \tabularnewline
132 &  0.9019 &  0.1963 &  0.09813 \tabularnewline
133 &  0.9554 &  0.08916 &  0.04458 \tabularnewline
134 &  0.9434 &  0.1132 &  0.05661 \tabularnewline
135 &  0.9305 &  0.1389 &  0.06945 \tabularnewline
136 &  0.9228 &  0.1543 &  0.07715 \tabularnewline
137 &  0.9033 &  0.1934 &  0.09668 \tabularnewline
138 &  0.905 &  0.19 &  0.09501 \tabularnewline
139 &  0.8747 &  0.2506 &  0.1253 \tabularnewline
140 &  0.9683 &  0.06348 &  0.03174 \tabularnewline
141 &  0.9582 &  0.08363 &  0.04182 \tabularnewline
142 &  0.974 &  0.05206 &  0.02603 \tabularnewline
143 &  0.9813 &  0.03742 &  0.01871 \tabularnewline
144 &  0.986 &  0.02792 &  0.01396 \tabularnewline
145 &  0.9782 &  0.04352 &  0.02176 \tabularnewline
146 &  0.9833 &  0.0335 &  0.01675 \tabularnewline
147 &  0.9793 &  0.04134 &  0.02067 \tabularnewline
148 &  0.9695 &  0.06098 &  0.03049 \tabularnewline
149 &  0.969 &  0.06193 &  0.03097 \tabularnewline
150 &  0.9517 &  0.09658 &  0.04829 \tabularnewline
151 &  0.9867 &  0.02656 &  0.01328 \tabularnewline
152 &  0.975 &  0.05 &  0.025 \tabularnewline
153 &  0.979 &  0.0419 &  0.02095 \tabularnewline
154 &  0.9998 &  0.0004979 &  0.0002489 \tabularnewline
155 &  0.9994 &  0.001202 &  0.0006011 \tabularnewline
156 &  0.9981 &  0.003877 &  0.001938 \tabularnewline
157 &  0.9962 &  0.007656 &  0.003828 \tabularnewline
158 &  0.9885 &  0.02296 &  0.01148 \tabularnewline
159 &  0.9651 &  0.06975 &  0.03487 \tabularnewline
160 &  0.909 &  0.182 &  0.091 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=301614&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]8[/C][C] 0.1417[/C][C] 0.2834[/C][C] 0.8583[/C][/ROW]
[ROW][C]9[/C][C] 0.08644[/C][C] 0.1729[/C][C] 0.9136[/C][/ROW]
[ROW][C]10[/C][C] 0.06396[/C][C] 0.1279[/C][C] 0.936[/C][/ROW]
[ROW][C]11[/C][C] 0.3277[/C][C] 0.6554[/C][C] 0.6723[/C][/ROW]
[ROW][C]12[/C][C] 0.35[/C][C] 0.6999[/C][C] 0.65[/C][/ROW]
[ROW][C]13[/C][C] 0.4566[/C][C] 0.9132[/C][C] 0.5434[/C][/ROW]
[ROW][C]14[/C][C] 0.562[/C][C] 0.876[/C][C] 0.438[/C][/ROW]
[ROW][C]15[/C][C] 0.4695[/C][C] 0.939[/C][C] 0.5305[/C][/ROW]
[ROW][C]16[/C][C] 0.3798[/C][C] 0.7595[/C][C] 0.6202[/C][/ROW]
[ROW][C]17[/C][C] 0.5213[/C][C] 0.9573[/C][C] 0.4787[/C][/ROW]
[ROW][C]18[/C][C] 0.5264[/C][C] 0.9472[/C][C] 0.4736[/C][/ROW]
[ROW][C]19[/C][C] 0.5385[/C][C] 0.923[/C][C] 0.4615[/C][/ROW]
[ROW][C]20[/C][C] 0.9019[/C][C] 0.1961[/C][C] 0.09805[/C][/ROW]
[ROW][C]21[/C][C] 0.9254[/C][C] 0.1492[/C][C] 0.07461[/C][/ROW]
[ROW][C]22[/C][C] 0.9184[/C][C] 0.1633[/C][C] 0.08163[/C][/ROW]
[ROW][C]23[/C][C] 0.8954[/C][C] 0.2093[/C][C] 0.1046[/C][/ROW]
[ROW][C]24[/C][C] 0.8977[/C][C] 0.2045[/C][C] 0.1023[/C][/ROW]
[ROW][C]25[/C][C] 0.8757[/C][C] 0.2486[/C][C] 0.1243[/C][/ROW]
[ROW][C]26[/C][C] 0.8558[/C][C] 0.2884[/C][C] 0.1442[/C][/ROW]
[ROW][C]27[/C][C] 0.8469[/C][C] 0.3063[/C][C] 0.1531[/C][/ROW]
[ROW][C]28[/C][C] 0.9297[/C][C] 0.1406[/C][C] 0.07028[/C][/ROW]
[ROW][C]29[/C][C] 0.9224[/C][C] 0.1552[/C][C] 0.07758[/C][/ROW]
[ROW][C]30[/C][C] 0.9015[/C][C] 0.197[/C][C] 0.0985[/C][/ROW]
[ROW][C]31[/C][C] 0.8873[/C][C] 0.2254[/C][C] 0.1127[/C][/ROW]
[ROW][C]32[/C][C] 0.9052[/C][C] 0.1897[/C][C] 0.09483[/C][/ROW]
[ROW][C]33[/C][C] 0.8882[/C][C] 0.2236[/C][C] 0.1118[/C][/ROW]
[ROW][C]34[/C][C] 0.8681[/C][C] 0.2637[/C][C] 0.1319[/C][/ROW]
[ROW][C]35[/C][C] 0.8504[/C][C] 0.2992[/C][C] 0.1496[/C][/ROW]
[ROW][C]36[/C][C] 0.8219[/C][C] 0.3562[/C][C] 0.1781[/C][/ROW]
[ROW][C]37[/C][C] 0.8825[/C][C] 0.2349[/C][C] 0.1175[/C][/ROW]
[ROW][C]38[/C][C] 0.8607[/C][C] 0.2786[/C][C] 0.1393[/C][/ROW]
[ROW][C]39[/C][C] 0.865[/C][C] 0.27[/C][C] 0.135[/C][/ROW]
[ROW][C]40[/C][C] 0.8338[/C][C] 0.3324[/C][C] 0.1662[/C][/ROW]
[ROW][C]41[/C][C] 0.8406[/C][C] 0.3187[/C][C] 0.1594[/C][/ROW]
[ROW][C]42[/C][C] 0.8111[/C][C] 0.3779[/C][C] 0.1889[/C][/ROW]
[ROW][C]43[/C][C] 0.9339[/C][C] 0.1321[/C][C] 0.06607[/C][/ROW]
[ROW][C]44[/C][C] 0.9189[/C][C] 0.1622[/C][C] 0.08111[/C][/ROW]
[ROW][C]45[/C][C] 0.9144[/C][C] 0.1712[/C][C] 0.08561[/C][/ROW]
[ROW][C]46[/C][C] 0.9096[/C][C] 0.1807[/C][C] 0.09037[/C][/ROW]
[ROW][C]47[/C][C] 0.931[/C][C] 0.138[/C][C] 0.06899[/C][/ROW]
[ROW][C]48[/C][C] 0.9207[/C][C] 0.1586[/C][C] 0.07928[/C][/ROW]
[ROW][C]49[/C][C] 0.9037[/C][C] 0.1925[/C][C] 0.09625[/C][/ROW]
[ROW][C]50[/C][C] 0.918[/C][C] 0.1639[/C][C] 0.08195[/C][/ROW]
[ROW][C]51[/C][C] 0.9226[/C][C] 0.1547[/C][C] 0.07735[/C][/ROW]
[ROW][C]52[/C][C] 0.9094[/C][C] 0.1812[/C][C] 0.0906[/C][/ROW]
[ROW][C]53[/C][C] 0.9041[/C][C] 0.1917[/C][C] 0.09586[/C][/ROW]
[ROW][C]54[/C][C] 0.91[/C][C] 0.1801[/C][C] 0.09003[/C][/ROW]
[ROW][C]55[/C][C] 0.9416[/C][C] 0.1168[/C][C] 0.05839[/C][/ROW]
[ROW][C]56[/C][C] 0.9291[/C][C] 0.1418[/C][C] 0.07089[/C][/ROW]
[ROW][C]57[/C][C] 0.9166[/C][C] 0.1668[/C][C] 0.08341[/C][/ROW]
[ROW][C]58[/C][C] 0.9017[/C][C] 0.1966[/C][C] 0.09832[/C][/ROW]
[ROW][C]59[/C][C] 0.8797[/C][C] 0.2405[/C][C] 0.1203[/C][/ROW]
[ROW][C]60[/C][C] 0.8562[/C][C] 0.2875[/C][C] 0.1438[/C][/ROW]
[ROW][C]61[/C][C] 0.8403[/C][C] 0.3195[/C][C] 0.1597[/C][/ROW]
[ROW][C]62[/C][C] 0.8117[/C][C] 0.3766[/C][C] 0.1883[/C][/ROW]
[ROW][C]63[/C][C] 0.7796[/C][C] 0.4407[/C][C] 0.2204[/C][/ROW]
[ROW][C]64[/C][C] 0.7508[/C][C] 0.4985[/C][C] 0.2492[/C][/ROW]
[ROW][C]65[/C][C] 0.7449[/C][C] 0.5103[/C][C] 0.2551[/C][/ROW]
[ROW][C]66[/C][C] 0.7082[/C][C] 0.5837[/C][C] 0.2918[/C][/ROW]
[ROW][C]67[/C][C] 0.7363[/C][C] 0.5274[/C][C] 0.2637[/C][/ROW]
[ROW][C]68[/C][C] 0.6985[/C][C] 0.6031[/C][C] 0.3015[/C][/ROW]
[ROW][C]69[/C][C] 0.6749[/C][C] 0.6502[/C][C] 0.3251[/C][/ROW]
[ROW][C]70[/C][C] 0.6411[/C][C] 0.7178[/C][C] 0.3589[/C][/ROW]
[ROW][C]71[/C][C] 0.6341[/C][C] 0.7318[/C][C] 0.3659[/C][/ROW]
[ROW][C]72[/C][C] 0.5907[/C][C] 0.8186[/C][C] 0.4093[/C][/ROW]
[ROW][C]73[/C][C] 0.618[/C][C] 0.7639[/C][C] 0.382[/C][/ROW]
[ROW][C]74[/C][C] 0.5764[/C][C] 0.8473[/C][C] 0.4236[/C][/ROW]
[ROW][C]75[/C][C] 0.6904[/C][C] 0.6193[/C][C] 0.3096[/C][/ROW]
[ROW][C]76[/C][C] 0.6862[/C][C] 0.6276[/C][C] 0.3138[/C][/ROW]
[ROW][C]77[/C][C] 0.7678[/C][C] 0.4643[/C][C] 0.2322[/C][/ROW]
[ROW][C]78[/C][C] 0.7428[/C][C] 0.5144[/C][C] 0.2572[/C][/ROW]
[ROW][C]79[/C][C] 0.7253[/C][C] 0.5495[/C][C] 0.2747[/C][/ROW]
[ROW][C]80[/C][C] 0.6866[/C][C] 0.6268[/C][C] 0.3134[/C][/ROW]
[ROW][C]81[/C][C] 0.6477[/C][C] 0.7046[/C][C] 0.3523[/C][/ROW]
[ROW][C]82[/C][C] 0.6053[/C][C] 0.7894[/C][C] 0.3947[/C][/ROW]
[ROW][C]83[/C][C] 0.5629[/C][C] 0.8743[/C][C] 0.4371[/C][/ROW]
[ROW][C]84[/C][C] 0.5291[/C][C] 0.9417[/C][C] 0.4709[/C][/ROW]
[ROW][C]85[/C][C] 0.5559[/C][C] 0.8883[/C][C] 0.4441[/C][/ROW]
[ROW][C]86[/C][C] 0.5232[/C][C] 0.9537[/C][C] 0.4768[/C][/ROW]
[ROW][C]87[/C][C] 0.4948[/C][C] 0.9896[/C][C] 0.5052[/C][/ROW]
[ROW][C]88[/C][C] 0.459[/C][C] 0.918[/C][C] 0.541[/C][/ROW]
[ROW][C]89[/C][C] 0.417[/C][C] 0.8341[/C][C] 0.583[/C][/ROW]
[ROW][C]90[/C][C] 0.3832[/C][C] 0.7664[/C][C] 0.6168[/C][/ROW]
[ROW][C]91[/C][C] 0.3495[/C][C] 0.699[/C][C] 0.6505[/C][/ROW]
[ROW][C]92[/C][C] 0.31[/C][C] 0.62[/C][C] 0.69[/C][/ROW]
[ROW][C]93[/C][C] 0.3643[/C][C] 0.7285[/C][C] 0.6358[/C][/ROW]
[ROW][C]94[/C][C] 0.3265[/C][C] 0.653[/C][C] 0.6735[/C][/ROW]
[ROW][C]95[/C][C] 0.2945[/C][C] 0.5889[/C][C] 0.7055[/C][/ROW]
[ROW][C]96[/C][C] 0.2582[/C][C] 0.5163[/C][C] 0.7418[/C][/ROW]
[ROW][C]97[/C][C] 0.2526[/C][C] 0.5052[/C][C] 0.7474[/C][/ROW]
[ROW][C]98[/C][C] 0.5622[/C][C] 0.8757[/C][C] 0.4378[/C][/ROW]
[ROW][C]99[/C][C] 0.5308[/C][C] 0.9384[/C][C] 0.4692[/C][/ROW]
[ROW][C]100[/C][C] 0.6865[/C][C] 0.627[/C][C] 0.3135[/C][/ROW]
[ROW][C]101[/C][C] 0.6517[/C][C] 0.6965[/C][C] 0.3483[/C][/ROW]
[ROW][C]102[/C][C] 0.6187[/C][C] 0.7626[/C][C] 0.3813[/C][/ROW]
[ROW][C]103[/C][C] 0.5766[/C][C] 0.8469[/C][C] 0.4234[/C][/ROW]
[ROW][C]104[/C][C] 0.542[/C][C] 0.9159[/C][C] 0.458[/C][/ROW]
[ROW][C]105[/C][C] 0.6451[/C][C] 0.7099[/C][C] 0.3549[/C][/ROW]
[ROW][C]106[/C][C] 0.6046[/C][C] 0.7909[/C][C] 0.3954[/C][/ROW]
[ROW][C]107[/C][C] 0.8416[/C][C] 0.3168[/C][C] 0.1584[/C][/ROW]
[ROW][C]108[/C][C] 0.8457[/C][C] 0.3087[/C][C] 0.1543[/C][/ROW]
[ROW][C]109[/C][C] 0.859[/C][C] 0.2819[/C][C] 0.1409[/C][/ROW]
[ROW][C]110[/C][C] 0.9057[/C][C] 0.1885[/C][C] 0.09426[/C][/ROW]
[ROW][C]111[/C][C] 0.8945[/C][C] 0.2109[/C][C] 0.1055[/C][/ROW]
[ROW][C]112[/C][C] 0.9428[/C][C] 0.1143[/C][C] 0.05716[/C][/ROW]
[ROW][C]113[/C][C] 0.9272[/C][C] 0.1457[/C][C] 0.07283[/C][/ROW]
[ROW][C]114[/C][C] 0.9269[/C][C] 0.1461[/C][C] 0.07306[/C][/ROW]
[ROW][C]115[/C][C] 0.9418[/C][C] 0.1165[/C][C] 0.05825[/C][/ROW]
[ROW][C]116[/C][C] 0.9314[/C][C] 0.1372[/C][C] 0.06859[/C][/ROW]
[ROW][C]117[/C][C] 0.9349[/C][C] 0.1301[/C][C] 0.06506[/C][/ROW]
[ROW][C]118[/C][C] 0.9247[/C][C] 0.1506[/C][C] 0.07532[/C][/ROW]
[ROW][C]119[/C][C] 0.9357[/C][C] 0.1286[/C][C] 0.06431[/C][/ROW]
[ROW][C]120[/C][C] 0.9351[/C][C] 0.1298[/C][C] 0.06489[/C][/ROW]
[ROW][C]121[/C][C] 0.9196[/C][C] 0.1608[/C][C] 0.08041[/C][/ROW]
[ROW][C]122[/C][C] 0.9232[/C][C] 0.1536[/C][C] 0.07678[/C][/ROW]
[ROW][C]123[/C][C] 0.9021[/C][C] 0.1959[/C][C] 0.09794[/C][/ROW]
[ROW][C]124[/C][C] 0.9101[/C][C] 0.1799[/C][C] 0.08995[/C][/ROW]
[ROW][C]125[/C][C] 0.8859[/C][C] 0.2282[/C][C] 0.1141[/C][/ROW]
[ROW][C]126[/C][C] 0.9102[/C][C] 0.1796[/C][C] 0.0898[/C][/ROW]
[ROW][C]127[/C][C] 0.8869[/C][C] 0.2262[/C][C] 0.1131[/C][/ROW]
[ROW][C]128[/C][C] 0.9143[/C][C] 0.1714[/C][C] 0.0857[/C][/ROW]
[ROW][C]129[/C][C] 0.8898[/C][C] 0.2203[/C][C] 0.1102[/C][/ROW]
[ROW][C]130[/C][C] 0.9116[/C][C] 0.1767[/C][C] 0.08837[/C][/ROW]
[ROW][C]131[/C][C] 0.916[/C][C] 0.168[/C][C] 0.084[/C][/ROW]
[ROW][C]132[/C][C] 0.9019[/C][C] 0.1963[/C][C] 0.09813[/C][/ROW]
[ROW][C]133[/C][C] 0.9554[/C][C] 0.08916[/C][C] 0.04458[/C][/ROW]
[ROW][C]134[/C][C] 0.9434[/C][C] 0.1132[/C][C] 0.05661[/C][/ROW]
[ROW][C]135[/C][C] 0.9305[/C][C] 0.1389[/C][C] 0.06945[/C][/ROW]
[ROW][C]136[/C][C] 0.9228[/C][C] 0.1543[/C][C] 0.07715[/C][/ROW]
[ROW][C]137[/C][C] 0.9033[/C][C] 0.1934[/C][C] 0.09668[/C][/ROW]
[ROW][C]138[/C][C] 0.905[/C][C] 0.19[/C][C] 0.09501[/C][/ROW]
[ROW][C]139[/C][C] 0.8747[/C][C] 0.2506[/C][C] 0.1253[/C][/ROW]
[ROW][C]140[/C][C] 0.9683[/C][C] 0.06348[/C][C] 0.03174[/C][/ROW]
[ROW][C]141[/C][C] 0.9582[/C][C] 0.08363[/C][C] 0.04182[/C][/ROW]
[ROW][C]142[/C][C] 0.974[/C][C] 0.05206[/C][C] 0.02603[/C][/ROW]
[ROW][C]143[/C][C] 0.9813[/C][C] 0.03742[/C][C] 0.01871[/C][/ROW]
[ROW][C]144[/C][C] 0.986[/C][C] 0.02792[/C][C] 0.01396[/C][/ROW]
[ROW][C]145[/C][C] 0.9782[/C][C] 0.04352[/C][C] 0.02176[/C][/ROW]
[ROW][C]146[/C][C] 0.9833[/C][C] 0.0335[/C][C] 0.01675[/C][/ROW]
[ROW][C]147[/C][C] 0.9793[/C][C] 0.04134[/C][C] 0.02067[/C][/ROW]
[ROW][C]148[/C][C] 0.9695[/C][C] 0.06098[/C][C] 0.03049[/C][/ROW]
[ROW][C]149[/C][C] 0.969[/C][C] 0.06193[/C][C] 0.03097[/C][/ROW]
[ROW][C]150[/C][C] 0.9517[/C][C] 0.09658[/C][C] 0.04829[/C][/ROW]
[ROW][C]151[/C][C] 0.9867[/C][C] 0.02656[/C][C] 0.01328[/C][/ROW]
[ROW][C]152[/C][C] 0.975[/C][C] 0.05[/C][C] 0.025[/C][/ROW]
[ROW][C]153[/C][C] 0.979[/C][C] 0.0419[/C][C] 0.02095[/C][/ROW]
[ROW][C]154[/C][C] 0.9998[/C][C] 0.0004979[/C][C] 0.0002489[/C][/ROW]
[ROW][C]155[/C][C] 0.9994[/C][C] 0.001202[/C][C] 0.0006011[/C][/ROW]
[ROW][C]156[/C][C] 0.9981[/C][C] 0.003877[/C][C] 0.001938[/C][/ROW]
[ROW][C]157[/C][C] 0.9962[/C][C] 0.007656[/C][C] 0.003828[/C][/ROW]
[ROW][C]158[/C][C] 0.9885[/C][C] 0.02296[/C][C] 0.01148[/C][/ROW]
[ROW][C]159[/C][C] 0.9651[/C][C] 0.06975[/C][C] 0.03487[/C][/ROW]
[ROW][C]160[/C][C] 0.909[/C][C] 0.182[/C][C] 0.091[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=301614&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=301614&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
8	0.1417	0.2834	0.8583
9	0.08644	0.1729	0.9136
10	0.06396	0.1279	0.936
11	0.3277	0.6554	0.6723
12	0.35	0.6999	0.65
13	0.4566	0.9132	0.5434
14	0.562	0.876	0.438
15	0.4695	0.939	0.5305
16	0.3798	0.7595	0.6202
17	0.5213	0.9573	0.4787
18	0.5264	0.9472	0.4736
19	0.5385	0.923	0.4615
20	0.9019	0.1961	0.09805
21	0.9254	0.1492	0.07461
22	0.9184	0.1633	0.08163
23	0.8954	0.2093	0.1046
24	0.8977	0.2045	0.1023
25	0.8757	0.2486	0.1243
26	0.8558	0.2884	0.1442
27	0.8469	0.3063	0.1531
28	0.9297	0.1406	0.07028
29	0.9224	0.1552	0.07758
30	0.9015	0.197	0.0985
31	0.8873	0.2254	0.1127
32	0.9052	0.1897	0.09483
33	0.8882	0.2236	0.1118
34	0.8681	0.2637	0.1319
35	0.8504	0.2992	0.1496
36	0.8219	0.3562	0.1781
37	0.8825	0.2349	0.1175
38	0.8607	0.2786	0.1393
39	0.865	0.27	0.135
40	0.8338	0.3324	0.1662
41	0.8406	0.3187	0.1594
42	0.8111	0.3779	0.1889
43	0.9339	0.1321	0.06607
44	0.9189	0.1622	0.08111
45	0.9144	0.1712	0.08561
46	0.9096	0.1807	0.09037
47	0.931	0.138	0.06899
48	0.9207	0.1586	0.07928
49	0.9037	0.1925	0.09625
50	0.918	0.1639	0.08195
51	0.9226	0.1547	0.07735
52	0.9094	0.1812	0.0906
53	0.9041	0.1917	0.09586
54	0.91	0.1801	0.09003
55	0.9416	0.1168	0.05839
56	0.9291	0.1418	0.07089
57	0.9166	0.1668	0.08341
58	0.9017	0.1966	0.09832
59	0.8797	0.2405	0.1203
60	0.8562	0.2875	0.1438
61	0.8403	0.3195	0.1597
62	0.8117	0.3766	0.1883
63	0.7796	0.4407	0.2204
64	0.7508	0.4985	0.2492
65	0.7449	0.5103	0.2551
66	0.7082	0.5837	0.2918
67	0.7363	0.5274	0.2637
68	0.6985	0.6031	0.3015
69	0.6749	0.6502	0.3251
70	0.6411	0.7178	0.3589
71	0.6341	0.7318	0.3659
72	0.5907	0.8186	0.4093
73	0.618	0.7639	0.382
74	0.5764	0.8473	0.4236
75	0.6904	0.6193	0.3096
76	0.6862	0.6276	0.3138
77	0.7678	0.4643	0.2322
78	0.7428	0.5144	0.2572
79	0.7253	0.5495	0.2747
80	0.6866	0.6268	0.3134
81	0.6477	0.7046	0.3523
82	0.6053	0.7894	0.3947
83	0.5629	0.8743	0.4371
84	0.5291	0.9417	0.4709
85	0.5559	0.8883	0.4441
86	0.5232	0.9537	0.4768
87	0.4948	0.9896	0.5052
88	0.459	0.918	0.541
89	0.417	0.8341	0.583
90	0.3832	0.7664	0.6168
91	0.3495	0.699	0.6505
92	0.31	0.62	0.69
93	0.3643	0.7285	0.6358
94	0.3265	0.653	0.6735
95	0.2945	0.5889	0.7055
96	0.2582	0.5163	0.7418
97	0.2526	0.5052	0.7474
98	0.5622	0.8757	0.4378
99	0.5308	0.9384	0.4692
100	0.6865	0.627	0.3135
101	0.6517	0.6965	0.3483
102	0.6187	0.7626	0.3813
103	0.5766	0.8469	0.4234
104	0.542	0.9159	0.458
105	0.6451	0.7099	0.3549
106	0.6046	0.7909	0.3954
107	0.8416	0.3168	0.1584
108	0.8457	0.3087	0.1543
109	0.859	0.2819	0.1409
110	0.9057	0.1885	0.09426
111	0.8945	0.2109	0.1055
112	0.9428	0.1143	0.05716
113	0.9272	0.1457	0.07283
114	0.9269	0.1461	0.07306
115	0.9418	0.1165	0.05825
116	0.9314	0.1372	0.06859
117	0.9349	0.1301	0.06506
118	0.9247	0.1506	0.07532
119	0.9357	0.1286	0.06431
120	0.9351	0.1298	0.06489
121	0.9196	0.1608	0.08041
122	0.9232	0.1536	0.07678
123	0.9021	0.1959	0.09794
124	0.9101	0.1799	0.08995
125	0.8859	0.2282	0.1141
126	0.9102	0.1796	0.0898
127	0.8869	0.2262	0.1131
128	0.9143	0.1714	0.0857
129	0.8898	0.2203	0.1102
130	0.9116	0.1767	0.08837
131	0.916	0.168	0.084
132	0.9019	0.1963	0.09813
133	0.9554	0.08916	0.04458
134	0.9434	0.1132	0.05661
135	0.9305	0.1389	0.06945
136	0.9228	0.1543	0.07715
137	0.9033	0.1934	0.09668
138	0.905	0.19	0.09501
139	0.8747	0.2506	0.1253
140	0.9683	0.06348	0.03174
141	0.9582	0.08363	0.04182
142	0.974	0.05206	0.02603
143	0.9813	0.03742	0.01871
144	0.986	0.02792	0.01396
145	0.9782	0.04352	0.02176
146	0.9833	0.0335	0.01675
147	0.9793	0.04134	0.02067
148	0.9695	0.06098	0.03049
149	0.969	0.06193	0.03097
150	0.9517	0.09658	0.04829
151	0.9867	0.02656	0.01328
152	0.975	0.05	0.025
153	0.979	0.0419	0.02095
154	0.9998	0.0004979	0.0002489
155	0.9994	0.001202	0.0006011
156	0.9981	0.003877	0.001938
157	0.9962	0.007656	0.003828
158	0.9885	0.02296	0.01148
159	0.9651	0.06975	0.03487
160	0.909	0.182	0.091

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	4	0.02614	NOK
5% type I error level	13	0.0849673	NOK
10% type I error level	21	0.137255	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 & 4 &  0.02614 & NOK \tabularnewline
5% type I error level & 13 & 0.0849673 & NOK \tabularnewline
10% type I error level & 21 & 0.137255 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=301614&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]4[/C][C] 0.02614[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]13[/C][C]0.0849673[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]21[/C][C]0.137255[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=301614&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=301614&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	4	0.02614	NOK
5% type I error level	13	0.0849673	NOK
10% type I error level	21	0.137255	NOK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 0.1889, df1 = 2, df2 = 161, p-value = 0.8281
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 0.21683, df1 = 8, df2 = 155, p-value = 0.9875
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.43373, df1 = 2, df2 = 161, p-value = 0.6488

\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 = 0.1889, df1 = 2, df2 = 161, p-value = 0.8281
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 0.21683, df1 = 8, df2 = 155, p-value = 0.9875
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.43373, df1 = 2, df2 = 161, p-value = 0.6488
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=301614&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 = 0.1889, df1 = 2, df2 = 161, p-value = 0.8281
[/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 = 0.21683, df1 = 8, df2 = 155, p-value = 0.9875
[/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 = 0.43373, df1 = 2, df2 = 161, p-value = 0.6488
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=301614&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=301614&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 = 0.1889, df1 = 2, df2 = 161, p-value = 0.8281
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 0.21683, df1 = 8, df2 = 155, p-value = 0.9875
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.43373, df1 = 2, df2 = 161, p-value = 0.6488

Variance Inflation Factors (Multicollinearity)
> vif TVDC1 TVDC2 TVDC3 TVDC4 1.310090 1.223031 1.297079 1.081239

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
   TVDC1    TVDC2    TVDC3    TVDC4 
1.310090 1.223031 1.297079 1.081239 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=301614&T=8

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
   TVDC1    TVDC2    TVDC3    TVDC4 
1.310090 1.223031 1.297079 1.081239 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=301614&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=301614&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 TVDC1 TVDC2 TVDC3 TVDC4 1.310090 1.223031 1.297079 1.081239

Figure 1

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Parameters (Session):

par1 = 12 ;

Parameters (R input):

par1 = 5 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = 0 ; par5 = 0 ;

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