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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Wed, 14 Dec 2016 11:20:34 +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/14/t14817108746ueu90j2yy413b1.htm/, Retrieved Fri, 03 May 2024 17:16:45 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=299281, Retrieved Fri, 03 May 2024 17:16:45 +0000

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No (this computation is public)

User-defined keywords

Estimated Impact

108

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

-       [Multiple Regression] [] [2016-12-14 10:20:34] [6c55ad42faec53ff18247bf53b5ba716] [Current]

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Dataseries X:

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4	2	3	5	4	10
5	3	4	5	4	13
4	4	4	5	4	14
3	4	3	4	4	12
4	4	4	5	4	12
3	4	4	5	5	13
3	4	3	3	4	13
3	4	4	4	4	13
4	5	4	5	5	13
4	5	4	5	5	14
4	4	4	5	4	14
4	4	3	5	4	12
4	4	3	4	5	12
3	3	4	4	5	11
4	4	4	2	5	12
3	4	4	4	5	14
3	4	4	4	5	12
5	5	3	4	4	11
4	4	4	5	4	13
3	4	3	4	5	13
4	4	4	5	5	12
4	4	4	4	5	13
4	4	4	4	4	12
4	4	4	4	5	13
3	4	4	4	4	12
3	4	3	5	5	12
4	4	4	4	4	12
2	4	4	5	5	13
5	4	4	4	4	13
4	3	4	4	4	10
4	5	4	5	5	12
5	4	4	4	5	13
4	3	4	4	5	13
2	3	4	5	4	10
4	5	4	4	4	14
3	4	4	4	4	12
4	3	3	4	5	10
4	3	4	4	4	10
4	4	4	4	4	14
5	4	4	4	4	12
4	5	4	5	5	14
3	3	4	4	4	10
5	5	3	5	5	13
5	4	3	4	4	12
4	4	3	4	5	12
4	4	4	4	4	13
3	5	3	3	4	12
4	4	4	5	4	10
4	5	4	4	4	14
5	5	4	5	4	14
5	5	4	4	4	13
4	3	4	5	5	8
4	3	3	4	5	11
4	4	4	4	4	10
3	4	3	3	4	12
3	4	4	4	3	14
4	4	3	5	4	12
4	4	4	5	4	12
5	5	4	5	5	14
2	4	4	5	5	13
4	4	4	5	5	13
3	4	4	2	4	13
4	4	4	5	5	12
4	2	4	4	4	10
4	4	3	5	3	14
4	4	3	5	4	11
5	4	3	3	5	10
3	4	3	5	5	13
3	4	3	4	5	12
4	5	5	5	4	12
4	4	4	4	4	10
4	4	4	4	4	10
4	4	5	5	4	13
3	4	4	4	4	12
4	4	4	5	4	13
3	4	3	5	5	11
3	3	4	4	5	10
4	3	4	4	4	14
4	4	4	4	5	13
3	3	4	4	4	7
4	4	4	5	4	13
4	4	4	5	5	13
4	4	4	5	5	13
5	4	4	4	4	15
5	4	5	4	5	13
4	4	4	5	5	14
3	4	4	4	5	12
3	4	4	4	4	13
4	2	3	4	4	11
4	4	4	4	3	12
4	4	4	4	5	14
4	4	4	5	4	13
4	5	4	5	3	14
3	4	3	5	5	12
4	4	4	4	5	12
5	4	4	4	5	13
5	4	5	4	5	14
4	5	4	5	5	13
5	3	4	5	5	13
4	4	4	4	5	12
5	4	4	4	5	10
3	4	3	4	4	12
5	4	5	5	5	13
4	4	3	4	5	12
4	4	3	4	3	13
4	4	4	4	4	12
4	4	4	4	4	12
3	4	4	5	3	12
4	4	4	4	4	10
4	4	3	4	5	12
3	3	3	5	5	9
4	4	3	4	4	14
3	4	4	4	4	12
4	4	4	3	4	13
5	4	1	5	5	13
5	4	4	5	5	13
4	4	4	4	3	11
4	4	3	4	4	12
3	4	3	4	5	11
4	4	4	4	4	12
4	4	4	5	4	12
4	5	4	4	4	13
3	4	4	4	4	12
4	4	3	4	4	13
4	4	4	4	5	13
3	4	3	4	4	12
4	4	3	4	3	12
3	2	2	4	4	8
4	4	3	5	4	12
5	4	3	5	4	13
2	4	3	3	5	10
3	3	4	4	4	8
5	5	4	5	4	13
4	5	4	4	4	12
5	5	5	5	4	15
4	5	4	5	5	14
4	4	3	4	5	10
3	4	4	5	4	11
4	4	4	4	4	12
4	4	4	4	4	10
4	4	4	5	5	14
4	4	4	5	5	10
5	4	3	5	4	15
4	3	4	4	4	11
4	4	4	4	4	12
3	3	3	4	4	9
4	5	4	4	3	12
4	4	3	4	4	13
4	4	4	4	5	12
3	4	3	5	5	9
4	4	4	4	5	12
5	4	4	5	4	14
4	4	4	3	4	10
2	3	4	4	4	12
4	4	4	4	5	14
4	3	3	5	5	12
4	4	4	4	3	15
4	5	5	4	4	11
5	4	4	4	4	12
5	4	3	4	4	12
3	3	4	5	5	10
4	4	4	4	5	12
4	4	4	5	4	10
2	3	5	5	4	11

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	10 seconds
R Server	Big Analytics Cloud Computing Center

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time10 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=299281&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]

10 seconds[/C][/ROW] [ROW]

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=299281&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=299281&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	10 seconds
R Server	Big Analytics Cloud Computing Center

Multiple Linear Regression - Estimated Regression Equation
TVDCSUM[t] = + 4.84183 + 0.361358SK1[t] + 1.07158SK2[t] + 0.206801SK4[t] + 0.228626SK5[t] -0.0294875SK6[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TVDCSUM[t] =  +  4.84183 +  0.361358SK1[t] +  1.07158SK2[t] +  0.206801SK4[t] +  0.228626SK5[t] -0.0294875SK6[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=299281&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TVDCSUM[t] =  +  4.84183 +  0.361358SK1[t] +  1.07158SK2[t] +  0.206801SK4[t] +  0.228626SK5[t] -0.0294875SK6[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=299281&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=299281&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] = + 4.84183 + 0.361358SK1[t] + 1.07158SK2[t] + 0.206801SK4[t] + 0.228626SK5[t] -0.0294875SK6[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)	+4.842	1.296	+3.7360e+00	0.000261	0.0001305
SK1	+0.3614	0.1433	+2.5220e+00	0.01267	0.006335
SK2	+1.072	0.1732	+6.1870e+00	5.058e-09	2.529e-09
SK4	+0.2068	0.1746	+1.1850e+00	0.2379	0.119
SK5	+0.2286	0.165	+1.3850e+00	0.1679	0.08396
SK6	-0.02949	0.1698	-1.7370e-01	0.8623	0.4312

\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) & +4.842 &  1.296 & +3.7360e+00 &  0.000261 &  0.0001305 \tabularnewline
SK1 & +0.3614 &  0.1433 & +2.5220e+00 &  0.01267 &  0.006335 \tabularnewline
SK2 & +1.072 &  0.1732 & +6.1870e+00 &  5.058e-09 &  2.529e-09 \tabularnewline
SK4 & +0.2068 &  0.1746 & +1.1850e+00 &  0.2379 &  0.119 \tabularnewline
SK5 & +0.2286 &  0.165 & +1.3850e+00 &  0.1679 &  0.08396 \tabularnewline
SK6 & -0.02949 &  0.1698 & -1.7370e-01 &  0.8623 &  0.4312 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=299281&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]+4.842[/C][C] 1.296[/C][C]+3.7360e+00[/C][C] 0.000261[/C][C] 0.0001305[/C][/ROW]
[ROW][C]SK1[/C][C]+0.3614[/C][C] 0.1433[/C][C]+2.5220e+00[/C][C] 0.01267[/C][C] 0.006335[/C][/ROW]
[ROW][C]SK2[/C][C]+1.072[/C][C] 0.1732[/C][C]+6.1870e+00[/C][C] 5.058e-09[/C][C] 2.529e-09[/C][/ROW]
[ROW][C]SK4[/C][C]+0.2068[/C][C] 0.1746[/C][C]+1.1850e+00[/C][C] 0.2379[/C][C] 0.119[/C][/ROW]
[ROW][C]SK5[/C][C]+0.2286[/C][C] 0.165[/C][C]+1.3850e+00[/C][C] 0.1679[/C][C] 0.08396[/C][/ROW]
[ROW][C]SK6[/C][C]-0.02949[/C][C] 0.1698[/C][C]-1.7370e-01[/C][C] 0.8623[/C][C] 0.4312[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=299281&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=299281&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)	+4.842	1.296	+3.7360e+00	0.000261	0.0001305
SK1	+0.3614	0.1433	+2.5220e+00	0.01267	0.006335
SK2	+1.072	0.1732	+6.1870e+00	5.058e-09	2.529e-09
SK4	+0.2068	0.1746	+1.1850e+00	0.2379	0.119
SK5	+0.2286	0.165	+1.3850e+00	0.1679	0.08396
SK6	-0.02949	0.1698	-1.7370e-01	0.8623	0.4312

Multiple Linear Regression - Regression Statistics
Multiple R	0.5469
R-squared	0.2991
Adjusted R-squared	0.2769
F-TEST (value)	13.48
F-TEST (DF numerator)	5
F-TEST (DF denominator)	158
p-value	5.937e-11
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.273
Sum Squared Residuals	256

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.5469 \tabularnewline
R-squared &  0.2991 \tabularnewline
Adjusted R-squared &  0.2769 \tabularnewline
F-TEST (value) &  13.48 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 158 \tabularnewline
p-value &  5.937e-11 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  1.273 \tabularnewline
Sum Squared Residuals &  256 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=299281&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.5469[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.2991[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.2769[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 13.48[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]158[/C][/ROW]
[ROW][C]p-value[/C][C] 5.937e-11[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 1.273[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 256[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=299281&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=299281&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	0.5469
R-squared	0.2991
Adjusted R-squared	0.2769
F-TEST (value)	13.48
F-TEST (DF numerator)	5
F-TEST (DF denominator)	158
p-value	5.937e-11
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.273
Sum Squared Residuals	256

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	10	10.08	-0.076
2	13	11.72	1.284
3	14	12.43	1.574
4	12	11.63	0.3708
5	12	12.43	-0.426
6	13	12.04	0.9649
7	13	11.4	1.599
8	13	11.84	1.164
9	13	13.47	-0.468
10	14	13.47	0.532
11	14	12.43	1.574
12	12	12.22	-0.2192
13	12	11.96	0.03896
14	11	10.73	0.2651
15	12	11.71	0.2894
16	14	11.81	2.194
17	12	11.81	0.1935
18	11	13.42	-2.423
19	13	12.43	0.574
20	13	11.6	1.4
21	12	12.4	-0.3965
22	13	12.17	0.8322
23	12	12.2	-0.1973
24	13	12.17	0.8322
25	12	11.84	0.164
26	12	11.83	0.1717
27	12	12.2	-0.1973
28	13	11.67	1.326
29	13	12.56	0.4413
30	10	11.13	-1.126
31	12	13.47	-1.468
32	13	12.53	0.4708
33	13	11.1	1.904
34	10	10.63	-0.6317
35	14	13.27	0.7311
36	12	11.84	0.164
37	10	10.89	-0.8895
38	10	11.13	-1.126
39	14	12.2	1.803
40	12	12.56	-0.5587
41	14	13.47	0.532
42	10	10.76	-0.7644
43	13	13.62	-0.6226
44	12	12.35	-0.3519
45	12	11.96	0.03896
46	13	12.2	0.8027
47	12	12.47	-0.4721
48	10	12.43	-2.426
49	14	13.27	0.7311
50	14	13.86	0.1411
51	13	13.63	-0.6303
52	8	11.32	-3.325
53	11	10.89	0.1105
54	10	12.2	-2.197
55	12	11.4	0.5995
56	14	11.87	2.135
57	12	12.22	-0.2192
58	12	12.43	-0.426
59	14	13.83	0.1706
60	13	11.67	1.326
61	13	12.4	0.6035
62	13	11.38	1.621
63	12	12.4	-0.3965
64	10	10.05	-0.05417
65	14	12.25	1.751
66	11	12.22	-1.219
67	10	12.09	-2.094
68	13	11.83	1.172
69	12	11.6	0.4003
70	12	13.7	-1.704
71	10	12.2	-2.197
72	10	12.2	-2.197
73	13	12.63	0.3672
74	12	11.84	0.164
75	13	12.43	0.574
76	11	11.83	-0.8283
77	10	10.73	-0.7349
78	14	11.13	2.874
79	13	12.17	0.8322
80	7	10.76	-3.764
81	13	12.43	0.574
82	13	12.4	0.6035
83	13	12.4	0.6035
84	15	12.56	2.441
85	13	12.74	0.264
86	14	12.4	1.604
87	12	11.81	0.1935
88	13	11.84	1.164
89	11	9.847	1.153
90	12	12.23	-0.2268
91	14	12.17	1.832
92	13	12.43	0.574
93	14	13.53	0.473
94	12	11.83	0.1717
95	12	12.17	-0.1678
96	13	12.53	0.4708
97	14	12.74	1.264
98	13	13.47	-0.468
99	13	11.69	1.314
100	12	12.17	-0.1678
101	10	12.53	-2.529
102	12	11.63	0.3708
103	13	12.96	0.03537
104	12	11.96	0.03896
105	13	12.02	0.98
106	12	12.2	-0.1973
107	12	12.2	-0.1973
108	12	12.09	-0.09408
109	10	12.2	-2.197
110	12	11.96	0.03896
111	9	10.76	-1.757
112	14	11.99	2.009
113	12	11.84	0.164
114	13	11.97	1.031
115	13	12.14	0.8626
116	13	12.76	0.2422
117	11	12.23	-1.227
118	12	11.99	0.009474
119	11	11.6	-0.5997
120	12	12.2	-0.1973
121	12	12.43	-0.426
122	13	13.27	-0.2689
123	12	11.84	0.164
124	13	11.99	1.009
125	13	12.17	0.8322
126	12	11.63	0.3708
127	12	12.02	-0.02001
128	8	9.279	-1.279
129	12	12.22	-0.2192
130	13	12.58	0.4195
131	10	11.01	-1.01
132	8	10.76	-2.764
133	13	13.86	-0.8589
134	12	13.27	-1.269
135	15	14.07	0.9343
136	14	13.47	0.532
137	10	11.96	-1.961
138	11	12.06	-1.065
139	12	12.2	-0.1973
140	10	12.2	-2.197
141	14	12.4	1.604
142	10	12.4	-2.396
143	15	12.58	2.419
144	11	11.13	-0.1258
145	12	12.2	-0.1973
146	9	10.56	-1.558
147	12	13.3	-1.298
148	13	11.99	1.009
149	12	12.17	-0.1678
150	9	11.83	-2.828
151	12	12.17	-0.1678
152	14	12.79	1.213
153	10	11.97	-1.969
154	12	10.4	1.597
155	14	12.17	1.832
156	12	11.12	0.8819
157	15	12.23	2.773
158	11	13.48	-2.476
159	12	12.56	-0.5587
160	12	12.35	-0.3519
161	10	10.96	-0.9635
162	12	12.17	-0.1678
163	10	12.43	-2.426
164	11	10.84	0.1615

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  10 &  10.08 & -0.076 \tabularnewline
2 &  13 &  11.72 &  1.284 \tabularnewline
3 &  14 &  12.43 &  1.574 \tabularnewline
4 &  12 &  11.63 &  0.3708 \tabularnewline
5 &  12 &  12.43 & -0.426 \tabularnewline
6 &  13 &  12.04 &  0.9649 \tabularnewline
7 &  13 &  11.4 &  1.599 \tabularnewline
8 &  13 &  11.84 &  1.164 \tabularnewline
9 &  13 &  13.47 & -0.468 \tabularnewline
10 &  14 &  13.47 &  0.532 \tabularnewline
11 &  14 &  12.43 &  1.574 \tabularnewline
12 &  12 &  12.22 & -0.2192 \tabularnewline
13 &  12 &  11.96 &  0.03896 \tabularnewline
14 &  11 &  10.73 &  0.2651 \tabularnewline
15 &  12 &  11.71 &  0.2894 \tabularnewline
16 &  14 &  11.81 &  2.194 \tabularnewline
17 &  12 &  11.81 &  0.1935 \tabularnewline
18 &  11 &  13.42 & -2.423 \tabularnewline
19 &  13 &  12.43 &  0.574 \tabularnewline
20 &  13 &  11.6 &  1.4 \tabularnewline
21 &  12 &  12.4 & -0.3965 \tabularnewline
22 &  13 &  12.17 &  0.8322 \tabularnewline
23 &  12 &  12.2 & -0.1973 \tabularnewline
24 &  13 &  12.17 &  0.8322 \tabularnewline
25 &  12 &  11.84 &  0.164 \tabularnewline
26 &  12 &  11.83 &  0.1717 \tabularnewline
27 &  12 &  12.2 & -0.1973 \tabularnewline
28 &  13 &  11.67 &  1.326 \tabularnewline
29 &  13 &  12.56 &  0.4413 \tabularnewline
30 &  10 &  11.13 & -1.126 \tabularnewline
31 &  12 &  13.47 & -1.468 \tabularnewline
32 &  13 &  12.53 &  0.4708 \tabularnewline
33 &  13 &  11.1 &  1.904 \tabularnewline
34 &  10 &  10.63 & -0.6317 \tabularnewline
35 &  14 &  13.27 &  0.7311 \tabularnewline
36 &  12 &  11.84 &  0.164 \tabularnewline
37 &  10 &  10.89 & -0.8895 \tabularnewline
38 &  10 &  11.13 & -1.126 \tabularnewline
39 &  14 &  12.2 &  1.803 \tabularnewline
40 &  12 &  12.56 & -0.5587 \tabularnewline
41 &  14 &  13.47 &  0.532 \tabularnewline
42 &  10 &  10.76 & -0.7644 \tabularnewline
43 &  13 &  13.62 & -0.6226 \tabularnewline
44 &  12 &  12.35 & -0.3519 \tabularnewline
45 &  12 &  11.96 &  0.03896 \tabularnewline
46 &  13 &  12.2 &  0.8027 \tabularnewline
47 &  12 &  12.47 & -0.4721 \tabularnewline
48 &  10 &  12.43 & -2.426 \tabularnewline
49 &  14 &  13.27 &  0.7311 \tabularnewline
50 &  14 &  13.86 &  0.1411 \tabularnewline
51 &  13 &  13.63 & -0.6303 \tabularnewline
52 &  8 &  11.32 & -3.325 \tabularnewline
53 &  11 &  10.89 &  0.1105 \tabularnewline
54 &  10 &  12.2 & -2.197 \tabularnewline
55 &  12 &  11.4 &  0.5995 \tabularnewline
56 &  14 &  11.87 &  2.135 \tabularnewline
57 &  12 &  12.22 & -0.2192 \tabularnewline
58 &  12 &  12.43 & -0.426 \tabularnewline
59 &  14 &  13.83 &  0.1706 \tabularnewline
60 &  13 &  11.67 &  1.326 \tabularnewline
61 &  13 &  12.4 &  0.6035 \tabularnewline
62 &  13 &  11.38 &  1.621 \tabularnewline
63 &  12 &  12.4 & -0.3965 \tabularnewline
64 &  10 &  10.05 & -0.05417 \tabularnewline
65 &  14 &  12.25 &  1.751 \tabularnewline
66 &  11 &  12.22 & -1.219 \tabularnewline
67 &  10 &  12.09 & -2.094 \tabularnewline
68 &  13 &  11.83 &  1.172 \tabularnewline
69 &  12 &  11.6 &  0.4003 \tabularnewline
70 &  12 &  13.7 & -1.704 \tabularnewline
71 &  10 &  12.2 & -2.197 \tabularnewline
72 &  10 &  12.2 & -2.197 \tabularnewline
73 &  13 &  12.63 &  0.3672 \tabularnewline
74 &  12 &  11.84 &  0.164 \tabularnewline
75 &  13 &  12.43 &  0.574 \tabularnewline
76 &  11 &  11.83 & -0.8283 \tabularnewline
77 &  10 &  10.73 & -0.7349 \tabularnewline
78 &  14 &  11.13 &  2.874 \tabularnewline
79 &  13 &  12.17 &  0.8322 \tabularnewline
80 &  7 &  10.76 & -3.764 \tabularnewline
81 &  13 &  12.43 &  0.574 \tabularnewline
82 &  13 &  12.4 &  0.6035 \tabularnewline
83 &  13 &  12.4 &  0.6035 \tabularnewline
84 &  15 &  12.56 &  2.441 \tabularnewline
85 &  13 &  12.74 &  0.264 \tabularnewline
86 &  14 &  12.4 &  1.604 \tabularnewline
87 &  12 &  11.81 &  0.1935 \tabularnewline
88 &  13 &  11.84 &  1.164 \tabularnewline
89 &  11 &  9.847 &  1.153 \tabularnewline
90 &  12 &  12.23 & -0.2268 \tabularnewline
91 &  14 &  12.17 &  1.832 \tabularnewline
92 &  13 &  12.43 &  0.574 \tabularnewline
93 &  14 &  13.53 &  0.473 \tabularnewline
94 &  12 &  11.83 &  0.1717 \tabularnewline
95 &  12 &  12.17 & -0.1678 \tabularnewline
96 &  13 &  12.53 &  0.4708 \tabularnewline
97 &  14 &  12.74 &  1.264 \tabularnewline
98 &  13 &  13.47 & -0.468 \tabularnewline
99 &  13 &  11.69 &  1.314 \tabularnewline
100 &  12 &  12.17 & -0.1678 \tabularnewline
101 &  10 &  12.53 & -2.529 \tabularnewline
102 &  12 &  11.63 &  0.3708 \tabularnewline
103 &  13 &  12.96 &  0.03537 \tabularnewline
104 &  12 &  11.96 &  0.03896 \tabularnewline
105 &  13 &  12.02 &  0.98 \tabularnewline
106 &  12 &  12.2 & -0.1973 \tabularnewline
107 &  12 &  12.2 & -0.1973 \tabularnewline
108 &  12 &  12.09 & -0.09408 \tabularnewline
109 &  10 &  12.2 & -2.197 \tabularnewline
110 &  12 &  11.96 &  0.03896 \tabularnewline
111 &  9 &  10.76 & -1.757 \tabularnewline
112 &  14 &  11.99 &  2.009 \tabularnewline
113 &  12 &  11.84 &  0.164 \tabularnewline
114 &  13 &  11.97 &  1.031 \tabularnewline
115 &  13 &  12.14 &  0.8626 \tabularnewline
116 &  13 &  12.76 &  0.2422 \tabularnewline
117 &  11 &  12.23 & -1.227 \tabularnewline
118 &  12 &  11.99 &  0.009474 \tabularnewline
119 &  11 &  11.6 & -0.5997 \tabularnewline
120 &  12 &  12.2 & -0.1973 \tabularnewline
121 &  12 &  12.43 & -0.426 \tabularnewline
122 &  13 &  13.27 & -0.2689 \tabularnewline
123 &  12 &  11.84 &  0.164 \tabularnewline
124 &  13 &  11.99 &  1.009 \tabularnewline
125 &  13 &  12.17 &  0.8322 \tabularnewline
126 &  12 &  11.63 &  0.3708 \tabularnewline
127 &  12 &  12.02 & -0.02001 \tabularnewline
128 &  8 &  9.279 & -1.279 \tabularnewline
129 &  12 &  12.22 & -0.2192 \tabularnewline
130 &  13 &  12.58 &  0.4195 \tabularnewline
131 &  10 &  11.01 & -1.01 \tabularnewline
132 &  8 &  10.76 & -2.764 \tabularnewline
133 &  13 &  13.86 & -0.8589 \tabularnewline
134 &  12 &  13.27 & -1.269 \tabularnewline
135 &  15 &  14.07 &  0.9343 \tabularnewline
136 &  14 &  13.47 &  0.532 \tabularnewline
137 &  10 &  11.96 & -1.961 \tabularnewline
138 &  11 &  12.06 & -1.065 \tabularnewline
139 &  12 &  12.2 & -0.1973 \tabularnewline
140 &  10 &  12.2 & -2.197 \tabularnewline
141 &  14 &  12.4 &  1.604 \tabularnewline
142 &  10 &  12.4 & -2.396 \tabularnewline
143 &  15 &  12.58 &  2.419 \tabularnewline
144 &  11 &  11.13 & -0.1258 \tabularnewline
145 &  12 &  12.2 & -0.1973 \tabularnewline
146 &  9 &  10.56 & -1.558 \tabularnewline
147 &  12 &  13.3 & -1.298 \tabularnewline
148 &  13 &  11.99 &  1.009 \tabularnewline
149 &  12 &  12.17 & -0.1678 \tabularnewline
150 &  9 &  11.83 & -2.828 \tabularnewline
151 &  12 &  12.17 & -0.1678 \tabularnewline
152 &  14 &  12.79 &  1.213 \tabularnewline
153 &  10 &  11.97 & -1.969 \tabularnewline
154 &  12 &  10.4 &  1.597 \tabularnewline
155 &  14 &  12.17 &  1.832 \tabularnewline
156 &  12 &  11.12 &  0.8819 \tabularnewline
157 &  15 &  12.23 &  2.773 \tabularnewline
158 &  11 &  13.48 & -2.476 \tabularnewline
159 &  12 &  12.56 & -0.5587 \tabularnewline
160 &  12 &  12.35 & -0.3519 \tabularnewline
161 &  10 &  10.96 & -0.9635 \tabularnewline
162 &  12 &  12.17 & -0.1678 \tabularnewline
163 &  10 &  12.43 & -2.426 \tabularnewline
164 &  11 &  10.84 &  0.1615 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=299281&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] 10[/C][C] 10.08[/C][C]-0.076[/C][/ROW]
[ROW][C]2[/C][C] 13[/C][C] 11.72[/C][C] 1.284[/C][/ROW]
[ROW][C]3[/C][C] 14[/C][C] 12.43[/C][C] 1.574[/C][/ROW]
[ROW][C]4[/C][C] 12[/C][C] 11.63[/C][C] 0.3708[/C][/ROW]
[ROW][C]5[/C][C] 12[/C][C] 12.43[/C][C]-0.426[/C][/ROW]
[ROW][C]6[/C][C] 13[/C][C] 12.04[/C][C] 0.9649[/C][/ROW]
[ROW][C]7[/C][C] 13[/C][C] 11.4[/C][C] 1.599[/C][/ROW]
[ROW][C]8[/C][C] 13[/C][C] 11.84[/C][C] 1.164[/C][/ROW]
[ROW][C]9[/C][C] 13[/C][C] 13.47[/C][C]-0.468[/C][/ROW]
[ROW][C]10[/C][C] 14[/C][C] 13.47[/C][C] 0.532[/C][/ROW]
[ROW][C]11[/C][C] 14[/C][C] 12.43[/C][C] 1.574[/C][/ROW]
[ROW][C]12[/C][C] 12[/C][C] 12.22[/C][C]-0.2192[/C][/ROW]
[ROW][C]13[/C][C] 12[/C][C] 11.96[/C][C] 0.03896[/C][/ROW]
[ROW][C]14[/C][C] 11[/C][C] 10.73[/C][C] 0.2651[/C][/ROW]
[ROW][C]15[/C][C] 12[/C][C] 11.71[/C][C] 0.2894[/C][/ROW]
[ROW][C]16[/C][C] 14[/C][C] 11.81[/C][C] 2.194[/C][/ROW]
[ROW][C]17[/C][C] 12[/C][C] 11.81[/C][C] 0.1935[/C][/ROW]
[ROW][C]18[/C][C] 11[/C][C] 13.42[/C][C]-2.423[/C][/ROW]
[ROW][C]19[/C][C] 13[/C][C] 12.43[/C][C] 0.574[/C][/ROW]
[ROW][C]20[/C][C] 13[/C][C] 11.6[/C][C] 1.4[/C][/ROW]
[ROW][C]21[/C][C] 12[/C][C] 12.4[/C][C]-0.3965[/C][/ROW]
[ROW][C]22[/C][C] 13[/C][C] 12.17[/C][C] 0.8322[/C][/ROW]
[ROW][C]23[/C][C] 12[/C][C] 12.2[/C][C]-0.1973[/C][/ROW]
[ROW][C]24[/C][C] 13[/C][C] 12.17[/C][C] 0.8322[/C][/ROW]
[ROW][C]25[/C][C] 12[/C][C] 11.84[/C][C] 0.164[/C][/ROW]
[ROW][C]26[/C][C] 12[/C][C] 11.83[/C][C] 0.1717[/C][/ROW]
[ROW][C]27[/C][C] 12[/C][C] 12.2[/C][C]-0.1973[/C][/ROW]
[ROW][C]28[/C][C] 13[/C][C] 11.67[/C][C] 1.326[/C][/ROW]
[ROW][C]29[/C][C] 13[/C][C] 12.56[/C][C] 0.4413[/C][/ROW]
[ROW][C]30[/C][C] 10[/C][C] 11.13[/C][C]-1.126[/C][/ROW]
[ROW][C]31[/C][C] 12[/C][C] 13.47[/C][C]-1.468[/C][/ROW]
[ROW][C]32[/C][C] 13[/C][C] 12.53[/C][C] 0.4708[/C][/ROW]
[ROW][C]33[/C][C] 13[/C][C] 11.1[/C][C] 1.904[/C][/ROW]
[ROW][C]34[/C][C] 10[/C][C] 10.63[/C][C]-0.6317[/C][/ROW]
[ROW][C]35[/C][C] 14[/C][C] 13.27[/C][C] 0.7311[/C][/ROW]
[ROW][C]36[/C][C] 12[/C][C] 11.84[/C][C] 0.164[/C][/ROW]
[ROW][C]37[/C][C] 10[/C][C] 10.89[/C][C]-0.8895[/C][/ROW]
[ROW][C]38[/C][C] 10[/C][C] 11.13[/C][C]-1.126[/C][/ROW]
[ROW][C]39[/C][C] 14[/C][C] 12.2[/C][C] 1.803[/C][/ROW]
[ROW][C]40[/C][C] 12[/C][C] 12.56[/C][C]-0.5587[/C][/ROW]
[ROW][C]41[/C][C] 14[/C][C] 13.47[/C][C] 0.532[/C][/ROW]
[ROW][C]42[/C][C] 10[/C][C] 10.76[/C][C]-0.7644[/C][/ROW]
[ROW][C]43[/C][C] 13[/C][C] 13.62[/C][C]-0.6226[/C][/ROW]
[ROW][C]44[/C][C] 12[/C][C] 12.35[/C][C]-0.3519[/C][/ROW]
[ROW][C]45[/C][C] 12[/C][C] 11.96[/C][C] 0.03896[/C][/ROW]
[ROW][C]46[/C][C] 13[/C][C] 12.2[/C][C] 0.8027[/C][/ROW]
[ROW][C]47[/C][C] 12[/C][C] 12.47[/C][C]-0.4721[/C][/ROW]
[ROW][C]48[/C][C] 10[/C][C] 12.43[/C][C]-2.426[/C][/ROW]
[ROW][C]49[/C][C] 14[/C][C] 13.27[/C][C] 0.7311[/C][/ROW]
[ROW][C]50[/C][C] 14[/C][C] 13.86[/C][C] 0.1411[/C][/ROW]
[ROW][C]51[/C][C] 13[/C][C] 13.63[/C][C]-0.6303[/C][/ROW]
[ROW][C]52[/C][C] 8[/C][C] 11.32[/C][C]-3.325[/C][/ROW]
[ROW][C]53[/C][C] 11[/C][C] 10.89[/C][C] 0.1105[/C][/ROW]
[ROW][C]54[/C][C] 10[/C][C] 12.2[/C][C]-2.197[/C][/ROW]
[ROW][C]55[/C][C] 12[/C][C] 11.4[/C][C] 0.5995[/C][/ROW]
[ROW][C]56[/C][C] 14[/C][C] 11.87[/C][C] 2.135[/C][/ROW]
[ROW][C]57[/C][C] 12[/C][C] 12.22[/C][C]-0.2192[/C][/ROW]
[ROW][C]58[/C][C] 12[/C][C] 12.43[/C][C]-0.426[/C][/ROW]
[ROW][C]59[/C][C] 14[/C][C] 13.83[/C][C] 0.1706[/C][/ROW]
[ROW][C]60[/C][C] 13[/C][C] 11.67[/C][C] 1.326[/C][/ROW]
[ROW][C]61[/C][C] 13[/C][C] 12.4[/C][C] 0.6035[/C][/ROW]
[ROW][C]62[/C][C] 13[/C][C] 11.38[/C][C] 1.621[/C][/ROW]
[ROW][C]63[/C][C] 12[/C][C] 12.4[/C][C]-0.3965[/C][/ROW]
[ROW][C]64[/C][C] 10[/C][C] 10.05[/C][C]-0.05417[/C][/ROW]
[ROW][C]65[/C][C] 14[/C][C] 12.25[/C][C] 1.751[/C][/ROW]
[ROW][C]66[/C][C] 11[/C][C] 12.22[/C][C]-1.219[/C][/ROW]
[ROW][C]67[/C][C] 10[/C][C] 12.09[/C][C]-2.094[/C][/ROW]
[ROW][C]68[/C][C] 13[/C][C] 11.83[/C][C] 1.172[/C][/ROW]
[ROW][C]69[/C][C] 12[/C][C] 11.6[/C][C] 0.4003[/C][/ROW]
[ROW][C]70[/C][C] 12[/C][C] 13.7[/C][C]-1.704[/C][/ROW]
[ROW][C]71[/C][C] 10[/C][C] 12.2[/C][C]-2.197[/C][/ROW]
[ROW][C]72[/C][C] 10[/C][C] 12.2[/C][C]-2.197[/C][/ROW]
[ROW][C]73[/C][C] 13[/C][C] 12.63[/C][C] 0.3672[/C][/ROW]
[ROW][C]74[/C][C] 12[/C][C] 11.84[/C][C] 0.164[/C][/ROW]
[ROW][C]75[/C][C] 13[/C][C] 12.43[/C][C] 0.574[/C][/ROW]
[ROW][C]76[/C][C] 11[/C][C] 11.83[/C][C]-0.8283[/C][/ROW]
[ROW][C]77[/C][C] 10[/C][C] 10.73[/C][C]-0.7349[/C][/ROW]
[ROW][C]78[/C][C] 14[/C][C] 11.13[/C][C] 2.874[/C][/ROW]
[ROW][C]79[/C][C] 13[/C][C] 12.17[/C][C] 0.8322[/C][/ROW]
[ROW][C]80[/C][C] 7[/C][C] 10.76[/C][C]-3.764[/C][/ROW]
[ROW][C]81[/C][C] 13[/C][C] 12.43[/C][C] 0.574[/C][/ROW]
[ROW][C]82[/C][C] 13[/C][C] 12.4[/C][C] 0.6035[/C][/ROW]
[ROW][C]83[/C][C] 13[/C][C] 12.4[/C][C] 0.6035[/C][/ROW]
[ROW][C]84[/C][C] 15[/C][C] 12.56[/C][C] 2.441[/C][/ROW]
[ROW][C]85[/C][C] 13[/C][C] 12.74[/C][C] 0.264[/C][/ROW]
[ROW][C]86[/C][C] 14[/C][C] 12.4[/C][C] 1.604[/C][/ROW]
[ROW][C]87[/C][C] 12[/C][C] 11.81[/C][C] 0.1935[/C][/ROW]
[ROW][C]88[/C][C] 13[/C][C] 11.84[/C][C] 1.164[/C][/ROW]
[ROW][C]89[/C][C] 11[/C][C] 9.847[/C][C] 1.153[/C][/ROW]
[ROW][C]90[/C][C] 12[/C][C] 12.23[/C][C]-0.2268[/C][/ROW]
[ROW][C]91[/C][C] 14[/C][C] 12.17[/C][C] 1.832[/C][/ROW]
[ROW][C]92[/C][C] 13[/C][C] 12.43[/C][C] 0.574[/C][/ROW]
[ROW][C]93[/C][C] 14[/C][C] 13.53[/C][C] 0.473[/C][/ROW]
[ROW][C]94[/C][C] 12[/C][C] 11.83[/C][C] 0.1717[/C][/ROW]
[ROW][C]95[/C][C] 12[/C][C] 12.17[/C][C]-0.1678[/C][/ROW]
[ROW][C]96[/C][C] 13[/C][C] 12.53[/C][C] 0.4708[/C][/ROW]
[ROW][C]97[/C][C] 14[/C][C] 12.74[/C][C] 1.264[/C][/ROW]
[ROW][C]98[/C][C] 13[/C][C] 13.47[/C][C]-0.468[/C][/ROW]
[ROW][C]99[/C][C] 13[/C][C] 11.69[/C][C] 1.314[/C][/ROW]
[ROW][C]100[/C][C] 12[/C][C] 12.17[/C][C]-0.1678[/C][/ROW]
[ROW][C]101[/C][C] 10[/C][C] 12.53[/C][C]-2.529[/C][/ROW]
[ROW][C]102[/C][C] 12[/C][C] 11.63[/C][C] 0.3708[/C][/ROW]
[ROW][C]103[/C][C] 13[/C][C] 12.96[/C][C] 0.03537[/C][/ROW]
[ROW][C]104[/C][C] 12[/C][C] 11.96[/C][C] 0.03896[/C][/ROW]
[ROW][C]105[/C][C] 13[/C][C] 12.02[/C][C] 0.98[/C][/ROW]
[ROW][C]106[/C][C] 12[/C][C] 12.2[/C][C]-0.1973[/C][/ROW]
[ROW][C]107[/C][C] 12[/C][C] 12.2[/C][C]-0.1973[/C][/ROW]
[ROW][C]108[/C][C] 12[/C][C] 12.09[/C][C]-0.09408[/C][/ROW]
[ROW][C]109[/C][C] 10[/C][C] 12.2[/C][C]-2.197[/C][/ROW]
[ROW][C]110[/C][C] 12[/C][C] 11.96[/C][C] 0.03896[/C][/ROW]
[ROW][C]111[/C][C] 9[/C][C] 10.76[/C][C]-1.757[/C][/ROW]
[ROW][C]112[/C][C] 14[/C][C] 11.99[/C][C] 2.009[/C][/ROW]
[ROW][C]113[/C][C] 12[/C][C] 11.84[/C][C] 0.164[/C][/ROW]
[ROW][C]114[/C][C] 13[/C][C] 11.97[/C][C] 1.031[/C][/ROW]
[ROW][C]115[/C][C] 13[/C][C] 12.14[/C][C] 0.8626[/C][/ROW]
[ROW][C]116[/C][C] 13[/C][C] 12.76[/C][C] 0.2422[/C][/ROW]
[ROW][C]117[/C][C] 11[/C][C] 12.23[/C][C]-1.227[/C][/ROW]
[ROW][C]118[/C][C] 12[/C][C] 11.99[/C][C] 0.009474[/C][/ROW]
[ROW][C]119[/C][C] 11[/C][C] 11.6[/C][C]-0.5997[/C][/ROW]
[ROW][C]120[/C][C] 12[/C][C] 12.2[/C][C]-0.1973[/C][/ROW]
[ROW][C]121[/C][C] 12[/C][C] 12.43[/C][C]-0.426[/C][/ROW]
[ROW][C]122[/C][C] 13[/C][C] 13.27[/C][C]-0.2689[/C][/ROW]
[ROW][C]123[/C][C] 12[/C][C] 11.84[/C][C] 0.164[/C][/ROW]
[ROW][C]124[/C][C] 13[/C][C] 11.99[/C][C] 1.009[/C][/ROW]
[ROW][C]125[/C][C] 13[/C][C] 12.17[/C][C] 0.8322[/C][/ROW]
[ROW][C]126[/C][C] 12[/C][C] 11.63[/C][C] 0.3708[/C][/ROW]
[ROW][C]127[/C][C] 12[/C][C] 12.02[/C][C]-0.02001[/C][/ROW]
[ROW][C]128[/C][C] 8[/C][C] 9.279[/C][C]-1.279[/C][/ROW]
[ROW][C]129[/C][C] 12[/C][C] 12.22[/C][C]-0.2192[/C][/ROW]
[ROW][C]130[/C][C] 13[/C][C] 12.58[/C][C] 0.4195[/C][/ROW]
[ROW][C]131[/C][C] 10[/C][C] 11.01[/C][C]-1.01[/C][/ROW]
[ROW][C]132[/C][C] 8[/C][C] 10.76[/C][C]-2.764[/C][/ROW]
[ROW][C]133[/C][C] 13[/C][C] 13.86[/C][C]-0.8589[/C][/ROW]
[ROW][C]134[/C][C] 12[/C][C] 13.27[/C][C]-1.269[/C][/ROW]
[ROW][C]135[/C][C] 15[/C][C] 14.07[/C][C] 0.9343[/C][/ROW]
[ROW][C]136[/C][C] 14[/C][C] 13.47[/C][C] 0.532[/C][/ROW]
[ROW][C]137[/C][C] 10[/C][C] 11.96[/C][C]-1.961[/C][/ROW]
[ROW][C]138[/C][C] 11[/C][C] 12.06[/C][C]-1.065[/C][/ROW]
[ROW][C]139[/C][C] 12[/C][C] 12.2[/C][C]-0.1973[/C][/ROW]
[ROW][C]140[/C][C] 10[/C][C] 12.2[/C][C]-2.197[/C][/ROW]
[ROW][C]141[/C][C] 14[/C][C] 12.4[/C][C] 1.604[/C][/ROW]
[ROW][C]142[/C][C] 10[/C][C] 12.4[/C][C]-2.396[/C][/ROW]
[ROW][C]143[/C][C] 15[/C][C] 12.58[/C][C] 2.419[/C][/ROW]
[ROW][C]144[/C][C] 11[/C][C] 11.13[/C][C]-0.1258[/C][/ROW]
[ROW][C]145[/C][C] 12[/C][C] 12.2[/C][C]-0.1973[/C][/ROW]
[ROW][C]146[/C][C] 9[/C][C] 10.56[/C][C]-1.558[/C][/ROW]
[ROW][C]147[/C][C] 12[/C][C] 13.3[/C][C]-1.298[/C][/ROW]
[ROW][C]148[/C][C] 13[/C][C] 11.99[/C][C] 1.009[/C][/ROW]
[ROW][C]149[/C][C] 12[/C][C] 12.17[/C][C]-0.1678[/C][/ROW]
[ROW][C]150[/C][C] 9[/C][C] 11.83[/C][C]-2.828[/C][/ROW]
[ROW][C]151[/C][C] 12[/C][C] 12.17[/C][C]-0.1678[/C][/ROW]
[ROW][C]152[/C][C] 14[/C][C] 12.79[/C][C] 1.213[/C][/ROW]
[ROW][C]153[/C][C] 10[/C][C] 11.97[/C][C]-1.969[/C][/ROW]
[ROW][C]154[/C][C] 12[/C][C] 10.4[/C][C] 1.597[/C][/ROW]
[ROW][C]155[/C][C] 14[/C][C] 12.17[/C][C] 1.832[/C][/ROW]
[ROW][C]156[/C][C] 12[/C][C] 11.12[/C][C] 0.8819[/C][/ROW]
[ROW][C]157[/C][C] 15[/C][C] 12.23[/C][C] 2.773[/C][/ROW]
[ROW][C]158[/C][C] 11[/C][C] 13.48[/C][C]-2.476[/C][/ROW]
[ROW][C]159[/C][C] 12[/C][C] 12.56[/C][C]-0.5587[/C][/ROW]
[ROW][C]160[/C][C] 12[/C][C] 12.35[/C][C]-0.3519[/C][/ROW]
[ROW][C]161[/C][C] 10[/C][C] 10.96[/C][C]-0.9635[/C][/ROW]
[ROW][C]162[/C][C] 12[/C][C] 12.17[/C][C]-0.1678[/C][/ROW]
[ROW][C]163[/C][C] 10[/C][C] 12.43[/C][C]-2.426[/C][/ROW]
[ROW][C]164[/C][C] 11[/C][C] 10.84[/C][C] 0.1615[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=299281&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=299281&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	10	10.08	-0.076
2	13	11.72	1.284
3	14	12.43	1.574
4	12	11.63	0.3708
5	12	12.43	-0.426
6	13	12.04	0.9649
7	13	11.4	1.599
8	13	11.84	1.164
9	13	13.47	-0.468
10	14	13.47	0.532
11	14	12.43	1.574
12	12	12.22	-0.2192
13	12	11.96	0.03896
14	11	10.73	0.2651
15	12	11.71	0.2894
16	14	11.81	2.194
17	12	11.81	0.1935
18	11	13.42	-2.423
19	13	12.43	0.574
20	13	11.6	1.4
21	12	12.4	-0.3965
22	13	12.17	0.8322
23	12	12.2	-0.1973
24	13	12.17	0.8322
25	12	11.84	0.164
26	12	11.83	0.1717
27	12	12.2	-0.1973
28	13	11.67	1.326
29	13	12.56	0.4413
30	10	11.13	-1.126
31	12	13.47	-1.468
32	13	12.53	0.4708
33	13	11.1	1.904
34	10	10.63	-0.6317
35	14	13.27	0.7311
36	12	11.84	0.164
37	10	10.89	-0.8895
38	10	11.13	-1.126
39	14	12.2	1.803
40	12	12.56	-0.5587
41	14	13.47	0.532
42	10	10.76	-0.7644
43	13	13.62	-0.6226
44	12	12.35	-0.3519
45	12	11.96	0.03896
46	13	12.2	0.8027
47	12	12.47	-0.4721
48	10	12.43	-2.426
49	14	13.27	0.7311
50	14	13.86	0.1411
51	13	13.63	-0.6303
52	8	11.32	-3.325
53	11	10.89	0.1105
54	10	12.2	-2.197
55	12	11.4	0.5995
56	14	11.87	2.135
57	12	12.22	-0.2192
58	12	12.43	-0.426
59	14	13.83	0.1706
60	13	11.67	1.326
61	13	12.4	0.6035
62	13	11.38	1.621
63	12	12.4	-0.3965
64	10	10.05	-0.05417
65	14	12.25	1.751
66	11	12.22	-1.219
67	10	12.09	-2.094
68	13	11.83	1.172
69	12	11.6	0.4003
70	12	13.7	-1.704
71	10	12.2	-2.197
72	10	12.2	-2.197
73	13	12.63	0.3672
74	12	11.84	0.164
75	13	12.43	0.574
76	11	11.83	-0.8283
77	10	10.73	-0.7349
78	14	11.13	2.874
79	13	12.17	0.8322
80	7	10.76	-3.764
81	13	12.43	0.574
82	13	12.4	0.6035
83	13	12.4	0.6035
84	15	12.56	2.441
85	13	12.74	0.264
86	14	12.4	1.604
87	12	11.81	0.1935
88	13	11.84	1.164
89	11	9.847	1.153
90	12	12.23	-0.2268
91	14	12.17	1.832
92	13	12.43	0.574
93	14	13.53	0.473
94	12	11.83	0.1717
95	12	12.17	-0.1678
96	13	12.53	0.4708
97	14	12.74	1.264
98	13	13.47	-0.468
99	13	11.69	1.314
100	12	12.17	-0.1678
101	10	12.53	-2.529
102	12	11.63	0.3708
103	13	12.96	0.03537
104	12	11.96	0.03896
105	13	12.02	0.98
106	12	12.2	-0.1973
107	12	12.2	-0.1973
108	12	12.09	-0.09408
109	10	12.2	-2.197
110	12	11.96	0.03896
111	9	10.76	-1.757
112	14	11.99	2.009
113	12	11.84	0.164
114	13	11.97	1.031
115	13	12.14	0.8626
116	13	12.76	0.2422
117	11	12.23	-1.227
118	12	11.99	0.009474
119	11	11.6	-0.5997
120	12	12.2	-0.1973
121	12	12.43	-0.426
122	13	13.27	-0.2689
123	12	11.84	0.164
124	13	11.99	1.009
125	13	12.17	0.8322
126	12	11.63	0.3708
127	12	12.02	-0.02001
128	8	9.279	-1.279
129	12	12.22	-0.2192
130	13	12.58	0.4195
131	10	11.01	-1.01
132	8	10.76	-2.764
133	13	13.86	-0.8589
134	12	13.27	-1.269
135	15	14.07	0.9343
136	14	13.47	0.532
137	10	11.96	-1.961
138	11	12.06	-1.065
139	12	12.2	-0.1973
140	10	12.2	-2.197
141	14	12.4	1.604
142	10	12.4	-2.396
143	15	12.58	2.419
144	11	11.13	-0.1258
145	12	12.2	-0.1973
146	9	10.56	-1.558
147	12	13.3	-1.298
148	13	11.99	1.009
149	12	12.17	-0.1678
150	9	11.83	-2.828
151	12	12.17	-0.1678
152	14	12.79	1.213
153	10	11.97	-1.969
154	12	10.4	1.597
155	14	12.17	1.832
156	12	11.12	0.8819
157	15	12.23	2.773
158	11	13.48	-2.476
159	12	12.56	-0.5587
160	12	12.35	-0.3519
161	10	10.96	-0.9635
162	12	12.17	-0.1678
163	10	12.43	-2.426
164	11	10.84	0.1615

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
9	0.3625	0.7249	0.6375
10	0.2296	0.4592	0.7704
11	0.2026	0.4052	0.7974
12	0.118	0.236	0.882
13	0.06395	0.1279	0.9361
14	0.05177	0.1035	0.9482
15	0.04219	0.08437	0.9578
16	0.07535	0.1507	0.9247
17	0.05519	0.1104	0.9448
18	0.1148	0.2297	0.8852
19	0.07744	0.1549	0.9226
20	0.08372	0.1674	0.9163
21	0.06471	0.1294	0.9353
22	0.04724	0.09448	0.9528
23	0.03893	0.07785	0.9611
24	0.02771	0.05542	0.9723
25	0.02699	0.05399	0.973
26	0.01765	0.0353	0.9824
27	0.01286	0.02571	0.9871
28	0.008977	0.01795	0.991
29	0.006305	0.01261	0.9937
30	0.01444	0.02888	0.9856
31	0.02102	0.04204	0.979
32	0.01655	0.0331	0.9835
33	0.0182	0.03639	0.9818
34	0.03498	0.06995	0.965
35	0.02886	0.05772	0.9711
36	0.02098	0.04197	0.979
37	0.02143	0.04285	0.9786
38	0.02766	0.05532	0.9723
39	0.03706	0.07413	0.9629
40	0.02867	0.05734	0.9713
41	0.0211	0.0422	0.9789
42	0.02322	0.04643	0.9768
43	0.01691	0.03381	0.9831
44	0.012	0.024	0.988
45	0.008245	0.01649	0.9918
46	0.00626	0.01252	0.9937
47	0.004628	0.009255	0.9954
48	0.01768	0.03535	0.9823
49	0.01408	0.02817	0.9859
50	0.01034	0.02068	0.9897
51	0.007817	0.01563	0.9922
52	0.07311	0.1462	0.9269
53	0.05745	0.1149	0.9425
54	0.1043	0.2087	0.8957
55	0.08641	0.1728	0.9136
56	0.1175	0.235	0.8825
57	0.0953	0.1906	0.9047
58	0.07788	0.1558	0.9221
59	0.06294	0.1259	0.9371
60	0.05994	0.1199	0.9401
61	0.04963	0.09925	0.9504
62	0.05391	0.1078	0.9461
63	0.04317	0.08635	0.9568
64	0.03336	0.06671	0.9666
65	0.04793	0.09585	0.9521
66	0.04702	0.09404	0.953
67	0.0642	0.1284	0.9358
68	0.06365	0.1273	0.9364
69	0.05267	0.1053	0.9473
70	0.07398	0.148	0.926
71	0.1213	0.2426	0.8787
72	0.1819	0.3638	0.8181
73	0.1553	0.3106	0.8447
74	0.1338	0.2675	0.8662
75	0.1156	0.2312	0.8844
76	0.1066	0.2131	0.8934
77	0.09642	0.1928	0.9036
78	0.2115	0.4231	0.7885
79	0.1959	0.3917	0.8041
80	0.5426	0.9149	0.4574
81	0.5062	0.9876	0.4938
82	0.4726	0.9452	0.5274
83	0.4391	0.8783	0.5609
84	0.562	0.8761	0.438
85	0.518	0.964	0.482
86	0.5493	0.9014	0.4507
87	0.5142	0.9717	0.4858
88	0.5201	0.9598	0.4799
89	0.5082	0.9835	0.4918
90	0.4643	0.9287	0.5357
91	0.5286	0.9427	0.4714
92	0.4922	0.9845	0.5078
93	0.4528	0.9056	0.5472
94	0.4157	0.8313	0.5843
95	0.3728	0.7457	0.6272
96	0.3358	0.6716	0.6642
97	0.3401	0.6802	0.6599
98	0.3025	0.605	0.6975
99	0.3062	0.6123	0.6938
100	0.2699	0.5398	0.7301
101	0.3856	0.7713	0.6144
102	0.3529	0.7058	0.6471
103	0.3098	0.6196	0.6902
104	0.2701	0.5402	0.7299
105	0.2521	0.5042	0.7479
106	0.2165	0.4329	0.7835
107	0.1838	0.3676	0.8162
108	0.1569	0.3138	0.8431
109	0.2133	0.4266	0.7867
110	0.1801	0.3603	0.8199
111	0.1953	0.3906	0.8047
112	0.2566	0.5132	0.7434
113	0.2297	0.4595	0.7703
114	0.2298	0.4596	0.7702
115	0.2068	0.4135	0.7932
116	0.1733	0.3467	0.8267
117	0.1664	0.3328	0.8336
118	0.1373	0.2746	0.8627
119	0.1153	0.2306	0.8847
120	0.09247	0.1849	0.9075
121	0.07436	0.1487	0.9256
122	0.05861	0.1172	0.9414
123	0.04896	0.09793	0.951
124	0.04755	0.09509	0.9525
125	0.04507	0.09013	0.9549
126	0.04164	0.08327	0.9584
127	0.0314	0.0628	0.9686
128	0.02949	0.05898	0.9705
129	0.02137	0.04274	0.9786
130	0.01545	0.0309	0.9845
131	0.01593	0.03186	0.9841
132	0.04018	0.08036	0.9598
133	0.03285	0.0657	0.9672
134	0.02564	0.05127	0.9744
135	0.02021	0.04043	0.9798
136	0.02692	0.05384	0.9731
137	0.02598	0.05196	0.974
138	0.01905	0.03811	0.9809
139	0.01279	0.02558	0.9872
140	0.01952	0.03903	0.9805
141	0.03415	0.0683	0.9659
142	0.04334	0.08668	0.9567
143	0.07531	0.1506	0.9247
144	0.07282	0.1456	0.9272
145	0.05	0.1	0.95
146	0.09642	0.1928	0.9036
147	0.06873	0.1375	0.9313
148	0.06178	0.1236	0.9382
149	0.04129	0.08259	0.9587
150	0.03703	0.07406	0.963
151	0.0228	0.0456	0.9772
152	0.02587	0.05174	0.9741
153	0.1839	0.3678	0.8161
154	0.3141	0.6281	0.6859
155	0.4525	0.9051	0.5475

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 &  0.3625 &  0.7249 &  0.6375 \tabularnewline
10 &  0.2296 &  0.4592 &  0.7704 \tabularnewline
11 &  0.2026 &  0.4052 &  0.7974 \tabularnewline
12 &  0.118 &  0.236 &  0.882 \tabularnewline
13 &  0.06395 &  0.1279 &  0.9361 \tabularnewline
14 &  0.05177 &  0.1035 &  0.9482 \tabularnewline
15 &  0.04219 &  0.08437 &  0.9578 \tabularnewline
16 &  0.07535 &  0.1507 &  0.9247 \tabularnewline
17 &  0.05519 &  0.1104 &  0.9448 \tabularnewline
18 &  0.1148 &  0.2297 &  0.8852 \tabularnewline
19 &  0.07744 &  0.1549 &  0.9226 \tabularnewline
20 &  0.08372 &  0.1674 &  0.9163 \tabularnewline
21 &  0.06471 &  0.1294 &  0.9353 \tabularnewline
22 &  0.04724 &  0.09448 &  0.9528 \tabularnewline
23 &  0.03893 &  0.07785 &  0.9611 \tabularnewline
24 &  0.02771 &  0.05542 &  0.9723 \tabularnewline
25 &  0.02699 &  0.05399 &  0.973 \tabularnewline
26 &  0.01765 &  0.0353 &  0.9824 \tabularnewline
27 &  0.01286 &  0.02571 &  0.9871 \tabularnewline
28 &  0.008977 &  0.01795 &  0.991 \tabularnewline
29 &  0.006305 &  0.01261 &  0.9937 \tabularnewline
30 &  0.01444 &  0.02888 &  0.9856 \tabularnewline
31 &  0.02102 &  0.04204 &  0.979 \tabularnewline
32 &  0.01655 &  0.0331 &  0.9835 \tabularnewline
33 &  0.0182 &  0.03639 &  0.9818 \tabularnewline
34 &  0.03498 &  0.06995 &  0.965 \tabularnewline
35 &  0.02886 &  0.05772 &  0.9711 \tabularnewline
36 &  0.02098 &  0.04197 &  0.979 \tabularnewline
37 &  0.02143 &  0.04285 &  0.9786 \tabularnewline
38 &  0.02766 &  0.05532 &  0.9723 \tabularnewline
39 &  0.03706 &  0.07413 &  0.9629 \tabularnewline
40 &  0.02867 &  0.05734 &  0.9713 \tabularnewline
41 &  0.0211 &  0.0422 &  0.9789 \tabularnewline
42 &  0.02322 &  0.04643 &  0.9768 \tabularnewline
43 &  0.01691 &  0.03381 &  0.9831 \tabularnewline
44 &  0.012 &  0.024 &  0.988 \tabularnewline
45 &  0.008245 &  0.01649 &  0.9918 \tabularnewline
46 &  0.00626 &  0.01252 &  0.9937 \tabularnewline
47 &  0.004628 &  0.009255 &  0.9954 \tabularnewline
48 &  0.01768 &  0.03535 &  0.9823 \tabularnewline
49 &  0.01408 &  0.02817 &  0.9859 \tabularnewline
50 &  0.01034 &  0.02068 &  0.9897 \tabularnewline
51 &  0.007817 &  0.01563 &  0.9922 \tabularnewline
52 &  0.07311 &  0.1462 &  0.9269 \tabularnewline
53 &  0.05745 &  0.1149 &  0.9425 \tabularnewline
54 &  0.1043 &  0.2087 &  0.8957 \tabularnewline
55 &  0.08641 &  0.1728 &  0.9136 \tabularnewline
56 &  0.1175 &  0.235 &  0.8825 \tabularnewline
57 &  0.0953 &  0.1906 &  0.9047 \tabularnewline
58 &  0.07788 &  0.1558 &  0.9221 \tabularnewline
59 &  0.06294 &  0.1259 &  0.9371 \tabularnewline
60 &  0.05994 &  0.1199 &  0.9401 \tabularnewline
61 &  0.04963 &  0.09925 &  0.9504 \tabularnewline
62 &  0.05391 &  0.1078 &  0.9461 \tabularnewline
63 &  0.04317 &  0.08635 &  0.9568 \tabularnewline
64 &  0.03336 &  0.06671 &  0.9666 \tabularnewline
65 &  0.04793 &  0.09585 &  0.9521 \tabularnewline
66 &  0.04702 &  0.09404 &  0.953 \tabularnewline
67 &  0.0642 &  0.1284 &  0.9358 \tabularnewline
68 &  0.06365 &  0.1273 &  0.9364 \tabularnewline
69 &  0.05267 &  0.1053 &  0.9473 \tabularnewline
70 &  0.07398 &  0.148 &  0.926 \tabularnewline
71 &  0.1213 &  0.2426 &  0.8787 \tabularnewline
72 &  0.1819 &  0.3638 &  0.8181 \tabularnewline
73 &  0.1553 &  0.3106 &  0.8447 \tabularnewline
74 &  0.1338 &  0.2675 &  0.8662 \tabularnewline
75 &  0.1156 &  0.2312 &  0.8844 \tabularnewline
76 &  0.1066 &  0.2131 &  0.8934 \tabularnewline
77 &  0.09642 &  0.1928 &  0.9036 \tabularnewline
78 &  0.2115 &  0.4231 &  0.7885 \tabularnewline
79 &  0.1959 &  0.3917 &  0.8041 \tabularnewline
80 &  0.5426 &  0.9149 &  0.4574 \tabularnewline
81 &  0.5062 &  0.9876 &  0.4938 \tabularnewline
82 &  0.4726 &  0.9452 &  0.5274 \tabularnewline
83 &  0.4391 &  0.8783 &  0.5609 \tabularnewline
84 &  0.562 &  0.8761 &  0.438 \tabularnewline
85 &  0.518 &  0.964 &  0.482 \tabularnewline
86 &  0.5493 &  0.9014 &  0.4507 \tabularnewline
87 &  0.5142 &  0.9717 &  0.4858 \tabularnewline
88 &  0.5201 &  0.9598 &  0.4799 \tabularnewline
89 &  0.5082 &  0.9835 &  0.4918 \tabularnewline
90 &  0.4643 &  0.9287 &  0.5357 \tabularnewline
91 &  0.5286 &  0.9427 &  0.4714 \tabularnewline
92 &  0.4922 &  0.9845 &  0.5078 \tabularnewline
93 &  0.4528 &  0.9056 &  0.5472 \tabularnewline
94 &  0.4157 &  0.8313 &  0.5843 \tabularnewline
95 &  0.3728 &  0.7457 &  0.6272 \tabularnewline
96 &  0.3358 &  0.6716 &  0.6642 \tabularnewline
97 &  0.3401 &  0.6802 &  0.6599 \tabularnewline
98 &  0.3025 &  0.605 &  0.6975 \tabularnewline
99 &  0.3062 &  0.6123 &  0.6938 \tabularnewline
100 &  0.2699 &  0.5398 &  0.7301 \tabularnewline
101 &  0.3856 &  0.7713 &  0.6144 \tabularnewline
102 &  0.3529 &  0.7058 &  0.6471 \tabularnewline
103 &  0.3098 &  0.6196 &  0.6902 \tabularnewline
104 &  0.2701 &  0.5402 &  0.7299 \tabularnewline
105 &  0.2521 &  0.5042 &  0.7479 \tabularnewline
106 &  0.2165 &  0.4329 &  0.7835 \tabularnewline
107 &  0.1838 &  0.3676 &  0.8162 \tabularnewline
108 &  0.1569 &  0.3138 &  0.8431 \tabularnewline
109 &  0.2133 &  0.4266 &  0.7867 \tabularnewline
110 &  0.1801 &  0.3603 &  0.8199 \tabularnewline
111 &  0.1953 &  0.3906 &  0.8047 \tabularnewline
112 &  0.2566 &  0.5132 &  0.7434 \tabularnewline
113 &  0.2297 &  0.4595 &  0.7703 \tabularnewline
114 &  0.2298 &  0.4596 &  0.7702 \tabularnewline
115 &  0.2068 &  0.4135 &  0.7932 \tabularnewline
116 &  0.1733 &  0.3467 &  0.8267 \tabularnewline
117 &  0.1664 &  0.3328 &  0.8336 \tabularnewline
118 &  0.1373 &  0.2746 &  0.8627 \tabularnewline
119 &  0.1153 &  0.2306 &  0.8847 \tabularnewline
120 &  0.09247 &  0.1849 &  0.9075 \tabularnewline
121 &  0.07436 &  0.1487 &  0.9256 \tabularnewline
122 &  0.05861 &  0.1172 &  0.9414 \tabularnewline
123 &  0.04896 &  0.09793 &  0.951 \tabularnewline
124 &  0.04755 &  0.09509 &  0.9525 \tabularnewline
125 &  0.04507 &  0.09013 &  0.9549 \tabularnewline
126 &  0.04164 &  0.08327 &  0.9584 \tabularnewline
127 &  0.0314 &  0.0628 &  0.9686 \tabularnewline
128 &  0.02949 &  0.05898 &  0.9705 \tabularnewline
129 &  0.02137 &  0.04274 &  0.9786 \tabularnewline
130 &  0.01545 &  0.0309 &  0.9845 \tabularnewline
131 &  0.01593 &  0.03186 &  0.9841 \tabularnewline
132 &  0.04018 &  0.08036 &  0.9598 \tabularnewline
133 &  0.03285 &  0.0657 &  0.9672 \tabularnewline
134 &  0.02564 &  0.05127 &  0.9744 \tabularnewline
135 &  0.02021 &  0.04043 &  0.9798 \tabularnewline
136 &  0.02692 &  0.05384 &  0.9731 \tabularnewline
137 &  0.02598 &  0.05196 &  0.974 \tabularnewline
138 &  0.01905 &  0.03811 &  0.9809 \tabularnewline
139 &  0.01279 &  0.02558 &  0.9872 \tabularnewline
140 &  0.01952 &  0.03903 &  0.9805 \tabularnewline
141 &  0.03415 &  0.0683 &  0.9659 \tabularnewline
142 &  0.04334 &  0.08668 &  0.9567 \tabularnewline
143 &  0.07531 &  0.1506 &  0.9247 \tabularnewline
144 &  0.07282 &  0.1456 &  0.9272 \tabularnewline
145 &  0.05 &  0.1 &  0.95 \tabularnewline
146 &  0.09642 &  0.1928 &  0.9036 \tabularnewline
147 &  0.06873 &  0.1375 &  0.9313 \tabularnewline
148 &  0.06178 &  0.1236 &  0.9382 \tabularnewline
149 &  0.04129 &  0.08259 &  0.9587 \tabularnewline
150 &  0.03703 &  0.07406 &  0.963 \tabularnewline
151 &  0.0228 &  0.0456 &  0.9772 \tabularnewline
152 &  0.02587 &  0.05174 &  0.9741 \tabularnewline
153 &  0.1839 &  0.3678 &  0.8161 \tabularnewline
154 &  0.3141 &  0.6281 &  0.6859 \tabularnewline
155 &  0.4525 &  0.9051 &  0.5475 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=299281&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]9[/C][C] 0.3625[/C][C] 0.7249[/C][C] 0.6375[/C][/ROW]
[ROW][C]10[/C][C] 0.2296[/C][C] 0.4592[/C][C] 0.7704[/C][/ROW]
[ROW][C]11[/C][C] 0.2026[/C][C] 0.4052[/C][C] 0.7974[/C][/ROW]
[ROW][C]12[/C][C] 0.118[/C][C] 0.236[/C][C] 0.882[/C][/ROW]
[ROW][C]13[/C][C] 0.06395[/C][C] 0.1279[/C][C] 0.9361[/C][/ROW]
[ROW][C]14[/C][C] 0.05177[/C][C] 0.1035[/C][C] 0.9482[/C][/ROW]
[ROW][C]15[/C][C] 0.04219[/C][C] 0.08437[/C][C] 0.9578[/C][/ROW]
[ROW][C]16[/C][C] 0.07535[/C][C] 0.1507[/C][C] 0.9247[/C][/ROW]
[ROW][C]17[/C][C] 0.05519[/C][C] 0.1104[/C][C] 0.9448[/C][/ROW]
[ROW][C]18[/C][C] 0.1148[/C][C] 0.2297[/C][C] 0.8852[/C][/ROW]
[ROW][C]19[/C][C] 0.07744[/C][C] 0.1549[/C][C] 0.9226[/C][/ROW]
[ROW][C]20[/C][C] 0.08372[/C][C] 0.1674[/C][C] 0.9163[/C][/ROW]
[ROW][C]21[/C][C] 0.06471[/C][C] 0.1294[/C][C] 0.9353[/C][/ROW]
[ROW][C]22[/C][C] 0.04724[/C][C] 0.09448[/C][C] 0.9528[/C][/ROW]
[ROW][C]23[/C][C] 0.03893[/C][C] 0.07785[/C][C] 0.9611[/C][/ROW]
[ROW][C]24[/C][C] 0.02771[/C][C] 0.05542[/C][C] 0.9723[/C][/ROW]
[ROW][C]25[/C][C] 0.02699[/C][C] 0.05399[/C][C] 0.973[/C][/ROW]
[ROW][C]26[/C][C] 0.01765[/C][C] 0.0353[/C][C] 0.9824[/C][/ROW]
[ROW][C]27[/C][C] 0.01286[/C][C] 0.02571[/C][C] 0.9871[/C][/ROW]
[ROW][C]28[/C][C] 0.008977[/C][C] 0.01795[/C][C] 0.991[/C][/ROW]
[ROW][C]29[/C][C] 0.006305[/C][C] 0.01261[/C][C] 0.9937[/C][/ROW]
[ROW][C]30[/C][C] 0.01444[/C][C] 0.02888[/C][C] 0.9856[/C][/ROW]
[ROW][C]31[/C][C] 0.02102[/C][C] 0.04204[/C][C] 0.979[/C][/ROW]
[ROW][C]32[/C][C] 0.01655[/C][C] 0.0331[/C][C] 0.9835[/C][/ROW]
[ROW][C]33[/C][C] 0.0182[/C][C] 0.03639[/C][C] 0.9818[/C][/ROW]
[ROW][C]34[/C][C] 0.03498[/C][C] 0.06995[/C][C] 0.965[/C][/ROW]
[ROW][C]35[/C][C] 0.02886[/C][C] 0.05772[/C][C] 0.9711[/C][/ROW]
[ROW][C]36[/C][C] 0.02098[/C][C] 0.04197[/C][C] 0.979[/C][/ROW]
[ROW][C]37[/C][C] 0.02143[/C][C] 0.04285[/C][C] 0.9786[/C][/ROW]
[ROW][C]38[/C][C] 0.02766[/C][C] 0.05532[/C][C] 0.9723[/C][/ROW]
[ROW][C]39[/C][C] 0.03706[/C][C] 0.07413[/C][C] 0.9629[/C][/ROW]
[ROW][C]40[/C][C] 0.02867[/C][C] 0.05734[/C][C] 0.9713[/C][/ROW]
[ROW][C]41[/C][C] 0.0211[/C][C] 0.0422[/C][C] 0.9789[/C][/ROW]
[ROW][C]42[/C][C] 0.02322[/C][C] 0.04643[/C][C] 0.9768[/C][/ROW]
[ROW][C]43[/C][C] 0.01691[/C][C] 0.03381[/C][C] 0.9831[/C][/ROW]
[ROW][C]44[/C][C] 0.012[/C][C] 0.024[/C][C] 0.988[/C][/ROW]
[ROW][C]45[/C][C] 0.008245[/C][C] 0.01649[/C][C] 0.9918[/C][/ROW]
[ROW][C]46[/C][C] 0.00626[/C][C] 0.01252[/C][C] 0.9937[/C][/ROW]
[ROW][C]47[/C][C] 0.004628[/C][C] 0.009255[/C][C] 0.9954[/C][/ROW]
[ROW][C]48[/C][C] 0.01768[/C][C] 0.03535[/C][C] 0.9823[/C][/ROW]
[ROW][C]49[/C][C] 0.01408[/C][C] 0.02817[/C][C] 0.9859[/C][/ROW]
[ROW][C]50[/C][C] 0.01034[/C][C] 0.02068[/C][C] 0.9897[/C][/ROW]
[ROW][C]51[/C][C] 0.007817[/C][C] 0.01563[/C][C] 0.9922[/C][/ROW]
[ROW][C]52[/C][C] 0.07311[/C][C] 0.1462[/C][C] 0.9269[/C][/ROW]
[ROW][C]53[/C][C] 0.05745[/C][C] 0.1149[/C][C] 0.9425[/C][/ROW]
[ROW][C]54[/C][C] 0.1043[/C][C] 0.2087[/C][C] 0.8957[/C][/ROW]
[ROW][C]55[/C][C] 0.08641[/C][C] 0.1728[/C][C] 0.9136[/C][/ROW]
[ROW][C]56[/C][C] 0.1175[/C][C] 0.235[/C][C] 0.8825[/C][/ROW]
[ROW][C]57[/C][C] 0.0953[/C][C] 0.1906[/C][C] 0.9047[/C][/ROW]
[ROW][C]58[/C][C] 0.07788[/C][C] 0.1558[/C][C] 0.9221[/C][/ROW]
[ROW][C]59[/C][C] 0.06294[/C][C] 0.1259[/C][C] 0.9371[/C][/ROW]
[ROW][C]60[/C][C] 0.05994[/C][C] 0.1199[/C][C] 0.9401[/C][/ROW]
[ROW][C]61[/C][C] 0.04963[/C][C] 0.09925[/C][C] 0.9504[/C][/ROW]
[ROW][C]62[/C][C] 0.05391[/C][C] 0.1078[/C][C] 0.9461[/C][/ROW]
[ROW][C]63[/C][C] 0.04317[/C][C] 0.08635[/C][C] 0.9568[/C][/ROW]
[ROW][C]64[/C][C] 0.03336[/C][C] 0.06671[/C][C] 0.9666[/C][/ROW]
[ROW][C]65[/C][C] 0.04793[/C][C] 0.09585[/C][C] 0.9521[/C][/ROW]
[ROW][C]66[/C][C] 0.04702[/C][C] 0.09404[/C][C] 0.953[/C][/ROW]
[ROW][C]67[/C][C] 0.0642[/C][C] 0.1284[/C][C] 0.9358[/C][/ROW]
[ROW][C]68[/C][C] 0.06365[/C][C] 0.1273[/C][C] 0.9364[/C][/ROW]
[ROW][C]69[/C][C] 0.05267[/C][C] 0.1053[/C][C] 0.9473[/C][/ROW]
[ROW][C]70[/C][C] 0.07398[/C][C] 0.148[/C][C] 0.926[/C][/ROW]
[ROW][C]71[/C][C] 0.1213[/C][C] 0.2426[/C][C] 0.8787[/C][/ROW]
[ROW][C]72[/C][C] 0.1819[/C][C] 0.3638[/C][C] 0.8181[/C][/ROW]
[ROW][C]73[/C][C] 0.1553[/C][C] 0.3106[/C][C] 0.8447[/C][/ROW]
[ROW][C]74[/C][C] 0.1338[/C][C] 0.2675[/C][C] 0.8662[/C][/ROW]
[ROW][C]75[/C][C] 0.1156[/C][C] 0.2312[/C][C] 0.8844[/C][/ROW]
[ROW][C]76[/C][C] 0.1066[/C][C] 0.2131[/C][C] 0.8934[/C][/ROW]
[ROW][C]77[/C][C] 0.09642[/C][C] 0.1928[/C][C] 0.9036[/C][/ROW]
[ROW][C]78[/C][C] 0.2115[/C][C] 0.4231[/C][C] 0.7885[/C][/ROW]
[ROW][C]79[/C][C] 0.1959[/C][C] 0.3917[/C][C] 0.8041[/C][/ROW]
[ROW][C]80[/C][C] 0.5426[/C][C] 0.9149[/C][C] 0.4574[/C][/ROW]
[ROW][C]81[/C][C] 0.5062[/C][C] 0.9876[/C][C] 0.4938[/C][/ROW]
[ROW][C]82[/C][C] 0.4726[/C][C] 0.9452[/C][C] 0.5274[/C][/ROW]
[ROW][C]83[/C][C] 0.4391[/C][C] 0.8783[/C][C] 0.5609[/C][/ROW]
[ROW][C]84[/C][C] 0.562[/C][C] 0.8761[/C][C] 0.438[/C][/ROW]
[ROW][C]85[/C][C] 0.518[/C][C] 0.964[/C][C] 0.482[/C][/ROW]
[ROW][C]86[/C][C] 0.5493[/C][C] 0.9014[/C][C] 0.4507[/C][/ROW]
[ROW][C]87[/C][C] 0.5142[/C][C] 0.9717[/C][C] 0.4858[/C][/ROW]
[ROW][C]88[/C][C] 0.5201[/C][C] 0.9598[/C][C] 0.4799[/C][/ROW]
[ROW][C]89[/C][C] 0.5082[/C][C] 0.9835[/C][C] 0.4918[/C][/ROW]
[ROW][C]90[/C][C] 0.4643[/C][C] 0.9287[/C][C] 0.5357[/C][/ROW]
[ROW][C]91[/C][C] 0.5286[/C][C] 0.9427[/C][C] 0.4714[/C][/ROW]
[ROW][C]92[/C][C] 0.4922[/C][C] 0.9845[/C][C] 0.5078[/C][/ROW]
[ROW][C]93[/C][C] 0.4528[/C][C] 0.9056[/C][C] 0.5472[/C][/ROW]
[ROW][C]94[/C][C] 0.4157[/C][C] 0.8313[/C][C] 0.5843[/C][/ROW]
[ROW][C]95[/C][C] 0.3728[/C][C] 0.7457[/C][C] 0.6272[/C][/ROW]
[ROW][C]96[/C][C] 0.3358[/C][C] 0.6716[/C][C] 0.6642[/C][/ROW]
[ROW][C]97[/C][C] 0.3401[/C][C] 0.6802[/C][C] 0.6599[/C][/ROW]
[ROW][C]98[/C][C] 0.3025[/C][C] 0.605[/C][C] 0.6975[/C][/ROW]
[ROW][C]99[/C][C] 0.3062[/C][C] 0.6123[/C][C] 0.6938[/C][/ROW]
[ROW][C]100[/C][C] 0.2699[/C][C] 0.5398[/C][C] 0.7301[/C][/ROW]
[ROW][C]101[/C][C] 0.3856[/C][C] 0.7713[/C][C] 0.6144[/C][/ROW]
[ROW][C]102[/C][C] 0.3529[/C][C] 0.7058[/C][C] 0.6471[/C][/ROW]
[ROW][C]103[/C][C] 0.3098[/C][C] 0.6196[/C][C] 0.6902[/C][/ROW]
[ROW][C]104[/C][C] 0.2701[/C][C] 0.5402[/C][C] 0.7299[/C][/ROW]
[ROW][C]105[/C][C] 0.2521[/C][C] 0.5042[/C][C] 0.7479[/C][/ROW]
[ROW][C]106[/C][C] 0.2165[/C][C] 0.4329[/C][C] 0.7835[/C][/ROW]
[ROW][C]107[/C][C] 0.1838[/C][C] 0.3676[/C][C] 0.8162[/C][/ROW]
[ROW][C]108[/C][C] 0.1569[/C][C] 0.3138[/C][C] 0.8431[/C][/ROW]
[ROW][C]109[/C][C] 0.2133[/C][C] 0.4266[/C][C] 0.7867[/C][/ROW]
[ROW][C]110[/C][C] 0.1801[/C][C] 0.3603[/C][C] 0.8199[/C][/ROW]
[ROW][C]111[/C][C] 0.1953[/C][C] 0.3906[/C][C] 0.8047[/C][/ROW]
[ROW][C]112[/C][C] 0.2566[/C][C] 0.5132[/C][C] 0.7434[/C][/ROW]
[ROW][C]113[/C][C] 0.2297[/C][C] 0.4595[/C][C] 0.7703[/C][/ROW]
[ROW][C]114[/C][C] 0.2298[/C][C] 0.4596[/C][C] 0.7702[/C][/ROW]
[ROW][C]115[/C][C] 0.2068[/C][C] 0.4135[/C][C] 0.7932[/C][/ROW]
[ROW][C]116[/C][C] 0.1733[/C][C] 0.3467[/C][C] 0.8267[/C][/ROW]
[ROW][C]117[/C][C] 0.1664[/C][C] 0.3328[/C][C] 0.8336[/C][/ROW]
[ROW][C]118[/C][C] 0.1373[/C][C] 0.2746[/C][C] 0.8627[/C][/ROW]
[ROW][C]119[/C][C] 0.1153[/C][C] 0.2306[/C][C] 0.8847[/C][/ROW]
[ROW][C]120[/C][C] 0.09247[/C][C] 0.1849[/C][C] 0.9075[/C][/ROW]
[ROW][C]121[/C][C] 0.07436[/C][C] 0.1487[/C][C] 0.9256[/C][/ROW]
[ROW][C]122[/C][C] 0.05861[/C][C] 0.1172[/C][C] 0.9414[/C][/ROW]
[ROW][C]123[/C][C] 0.04896[/C][C] 0.09793[/C][C] 0.951[/C][/ROW]
[ROW][C]124[/C][C] 0.04755[/C][C] 0.09509[/C][C] 0.9525[/C][/ROW]
[ROW][C]125[/C][C] 0.04507[/C][C] 0.09013[/C][C] 0.9549[/C][/ROW]
[ROW][C]126[/C][C] 0.04164[/C][C] 0.08327[/C][C] 0.9584[/C][/ROW]
[ROW][C]127[/C][C] 0.0314[/C][C] 0.0628[/C][C] 0.9686[/C][/ROW]
[ROW][C]128[/C][C] 0.02949[/C][C] 0.05898[/C][C] 0.9705[/C][/ROW]
[ROW][C]129[/C][C] 0.02137[/C][C] 0.04274[/C][C] 0.9786[/C][/ROW]
[ROW][C]130[/C][C] 0.01545[/C][C] 0.0309[/C][C] 0.9845[/C][/ROW]
[ROW][C]131[/C][C] 0.01593[/C][C] 0.03186[/C][C] 0.9841[/C][/ROW]
[ROW][C]132[/C][C] 0.04018[/C][C] 0.08036[/C][C] 0.9598[/C][/ROW]
[ROW][C]133[/C][C] 0.03285[/C][C] 0.0657[/C][C] 0.9672[/C][/ROW]
[ROW][C]134[/C][C] 0.02564[/C][C] 0.05127[/C][C] 0.9744[/C][/ROW]
[ROW][C]135[/C][C] 0.02021[/C][C] 0.04043[/C][C] 0.9798[/C][/ROW]
[ROW][C]136[/C][C] 0.02692[/C][C] 0.05384[/C][C] 0.9731[/C][/ROW]
[ROW][C]137[/C][C] 0.02598[/C][C] 0.05196[/C][C] 0.974[/C][/ROW]
[ROW][C]138[/C][C] 0.01905[/C][C] 0.03811[/C][C] 0.9809[/C][/ROW]
[ROW][C]139[/C][C] 0.01279[/C][C] 0.02558[/C][C] 0.9872[/C][/ROW]
[ROW][C]140[/C][C] 0.01952[/C][C] 0.03903[/C][C] 0.9805[/C][/ROW]
[ROW][C]141[/C][C] 0.03415[/C][C] 0.0683[/C][C] 0.9659[/C][/ROW]
[ROW][C]142[/C][C] 0.04334[/C][C] 0.08668[/C][C] 0.9567[/C][/ROW]
[ROW][C]143[/C][C] 0.07531[/C][C] 0.1506[/C][C] 0.9247[/C][/ROW]
[ROW][C]144[/C][C] 0.07282[/C][C] 0.1456[/C][C] 0.9272[/C][/ROW]
[ROW][C]145[/C][C] 0.05[/C][C] 0.1[/C][C] 0.95[/C][/ROW]
[ROW][C]146[/C][C] 0.09642[/C][C] 0.1928[/C][C] 0.9036[/C][/ROW]
[ROW][C]147[/C][C] 0.06873[/C][C] 0.1375[/C][C] 0.9313[/C][/ROW]
[ROW][C]148[/C][C] 0.06178[/C][C] 0.1236[/C][C] 0.9382[/C][/ROW]
[ROW][C]149[/C][C] 0.04129[/C][C] 0.08259[/C][C] 0.9587[/C][/ROW]
[ROW][C]150[/C][C] 0.03703[/C][C] 0.07406[/C][C] 0.963[/C][/ROW]
[ROW][C]151[/C][C] 0.0228[/C][C] 0.0456[/C][C] 0.9772[/C][/ROW]
[ROW][C]152[/C][C] 0.02587[/C][C] 0.05174[/C][C] 0.9741[/C][/ROW]
[ROW][C]153[/C][C] 0.1839[/C][C] 0.3678[/C][C] 0.8161[/C][/ROW]
[ROW][C]154[/C][C] 0.3141[/C][C] 0.6281[/C][C] 0.6859[/C][/ROW]
[ROW][C]155[/C][C] 0.4525[/C][C] 0.9051[/C][C] 0.5475[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=299281&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=299281&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
9	0.3625	0.7249	0.6375
10	0.2296	0.4592	0.7704
11	0.2026	0.4052	0.7974
12	0.118	0.236	0.882
13	0.06395	0.1279	0.9361
14	0.05177	0.1035	0.9482
15	0.04219	0.08437	0.9578
16	0.07535	0.1507	0.9247
17	0.05519	0.1104	0.9448
18	0.1148	0.2297	0.8852
19	0.07744	0.1549	0.9226
20	0.08372	0.1674	0.9163
21	0.06471	0.1294	0.9353
22	0.04724	0.09448	0.9528
23	0.03893	0.07785	0.9611
24	0.02771	0.05542	0.9723
25	0.02699	0.05399	0.973
26	0.01765	0.0353	0.9824
27	0.01286	0.02571	0.9871
28	0.008977	0.01795	0.991
29	0.006305	0.01261	0.9937
30	0.01444	0.02888	0.9856
31	0.02102	0.04204	0.979
32	0.01655	0.0331	0.9835
33	0.0182	0.03639	0.9818
34	0.03498	0.06995	0.965
35	0.02886	0.05772	0.9711
36	0.02098	0.04197	0.979
37	0.02143	0.04285	0.9786
38	0.02766	0.05532	0.9723
39	0.03706	0.07413	0.9629
40	0.02867	0.05734	0.9713
41	0.0211	0.0422	0.9789
42	0.02322	0.04643	0.9768
43	0.01691	0.03381	0.9831
44	0.012	0.024	0.988
45	0.008245	0.01649	0.9918
46	0.00626	0.01252	0.9937
47	0.004628	0.009255	0.9954
48	0.01768	0.03535	0.9823
49	0.01408	0.02817	0.9859
50	0.01034	0.02068	0.9897
51	0.007817	0.01563	0.9922
52	0.07311	0.1462	0.9269
53	0.05745	0.1149	0.9425
54	0.1043	0.2087	0.8957
55	0.08641	0.1728	0.9136
56	0.1175	0.235	0.8825
57	0.0953	0.1906	0.9047
58	0.07788	0.1558	0.9221
59	0.06294	0.1259	0.9371
60	0.05994	0.1199	0.9401
61	0.04963	0.09925	0.9504
62	0.05391	0.1078	0.9461
63	0.04317	0.08635	0.9568
64	0.03336	0.06671	0.9666
65	0.04793	0.09585	0.9521
66	0.04702	0.09404	0.953
67	0.0642	0.1284	0.9358
68	0.06365	0.1273	0.9364
69	0.05267	0.1053	0.9473
70	0.07398	0.148	0.926
71	0.1213	0.2426	0.8787
72	0.1819	0.3638	0.8181
73	0.1553	0.3106	0.8447
74	0.1338	0.2675	0.8662
75	0.1156	0.2312	0.8844
76	0.1066	0.2131	0.8934
77	0.09642	0.1928	0.9036
78	0.2115	0.4231	0.7885
79	0.1959	0.3917	0.8041
80	0.5426	0.9149	0.4574
81	0.5062	0.9876	0.4938
82	0.4726	0.9452	0.5274
83	0.4391	0.8783	0.5609
84	0.562	0.8761	0.438
85	0.518	0.964	0.482
86	0.5493	0.9014	0.4507
87	0.5142	0.9717	0.4858
88	0.5201	0.9598	0.4799
89	0.5082	0.9835	0.4918
90	0.4643	0.9287	0.5357
91	0.5286	0.9427	0.4714
92	0.4922	0.9845	0.5078
93	0.4528	0.9056	0.5472
94	0.4157	0.8313	0.5843
95	0.3728	0.7457	0.6272
96	0.3358	0.6716	0.6642
97	0.3401	0.6802	0.6599
98	0.3025	0.605	0.6975
99	0.3062	0.6123	0.6938
100	0.2699	0.5398	0.7301
101	0.3856	0.7713	0.6144
102	0.3529	0.7058	0.6471
103	0.3098	0.6196	0.6902
104	0.2701	0.5402	0.7299
105	0.2521	0.5042	0.7479
106	0.2165	0.4329	0.7835
107	0.1838	0.3676	0.8162
108	0.1569	0.3138	0.8431
109	0.2133	0.4266	0.7867
110	0.1801	0.3603	0.8199
111	0.1953	0.3906	0.8047
112	0.2566	0.5132	0.7434
113	0.2297	0.4595	0.7703
114	0.2298	0.4596	0.7702
115	0.2068	0.4135	0.7932
116	0.1733	0.3467	0.8267
117	0.1664	0.3328	0.8336
118	0.1373	0.2746	0.8627
119	0.1153	0.2306	0.8847
120	0.09247	0.1849	0.9075
121	0.07436	0.1487	0.9256
122	0.05861	0.1172	0.9414
123	0.04896	0.09793	0.951
124	0.04755	0.09509	0.9525
125	0.04507	0.09013	0.9549
126	0.04164	0.08327	0.9584
127	0.0314	0.0628	0.9686
128	0.02949	0.05898	0.9705
129	0.02137	0.04274	0.9786
130	0.01545	0.0309	0.9845
131	0.01593	0.03186	0.9841
132	0.04018	0.08036	0.9598
133	0.03285	0.0657	0.9672
134	0.02564	0.05127	0.9744
135	0.02021	0.04043	0.9798
136	0.02692	0.05384	0.9731
137	0.02598	0.05196	0.974
138	0.01905	0.03811	0.9809
139	0.01279	0.02558	0.9872
140	0.01952	0.03903	0.9805
141	0.03415	0.0683	0.9659
142	0.04334	0.08668	0.9567
143	0.07531	0.1506	0.9247
144	0.07282	0.1456	0.9272
145	0.05	0.1	0.95
146	0.09642	0.1928	0.9036
147	0.06873	0.1375	0.9313
148	0.06178	0.1236	0.9382
149	0.04129	0.08259	0.9587
150	0.03703	0.07406	0.963
151	0.0228	0.0456	0.9772
152	0.02587	0.05174	0.9741
153	0.1839	0.3678	0.8161
154	0.3141	0.6281	0.6859
155	0.4525	0.9051	0.5475

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	1	0.006803	OK
5% type I error level	29	0.197279	NOK
10% type I error level	61	0.414966	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 & 1 &  0.006803 & OK \tabularnewline
5% type I error level & 29 & 0.197279 & NOK \tabularnewline
10% type I error level & 61 & 0.414966 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=299281&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]1[/C][C] 0.006803[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]29[/C][C]0.197279[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]61[/C][C]0.414966[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=299281&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=299281&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	1	0.006803	OK
5% type I error level	29	0.197279	NOK
10% type I error level	61	0.414966	NOK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 2.0166, df1 = 2, df2 = 156, p-value = 0.1366
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.4251, df1 = 10, df2 = 148, p-value = 0.1744
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.43409, df1 = 2, df2 = 156, p-value = 0.6486

\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 = 2.0166, df1 = 2, df2 = 156, p-value = 0.1366
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.4251, df1 = 10, df2 = 148, p-value = 0.1744
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.43409, df1 = 2, df2 = 156, p-value = 0.6486
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=299281&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 = 2.0166, df1 = 2, df2 = 156, p-value = 0.1366
[/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 = 1.4251, df1 = 10, df2 = 148, p-value = 0.1744
[/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 = 0.43409, df1 = 2, df2 = 156, p-value = 0.6486
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=299281&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=299281&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 = 2.0166, df1 = 2, df2 = 156, p-value = 0.1366
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.4251, df1 = 10, df2 = 148, p-value = 0.1744
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.43409, df1 = 2, df2 = 156, p-value = 0.6486

Variance Inflation Factors (Multicollinearity)
> vif SK1 SK2 SK4 SK5 SK6 1.087083 1.103731 1.038943 1.034091 1.017053

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
     SK1      SK2      SK4      SK5      SK6 
1.087083 1.103731 1.038943 1.034091 1.017053 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=299281&T=8

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
     SK1      SK2      SK4      SK5      SK6 
1.087083 1.103731 1.038943 1.034091 1.017053 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=299281&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=299281&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 SK1 SK2 SK4 SK5 SK6 1.087083 1.103731 1.038943 1.034091 1.017053

Figure 1

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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Figure 10

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Parameters (Session):

par1 = 6 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = 0 ; par5 = 0 ;

Parameters (R input):

par1 = 6 ; 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