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Author

*The author of this computation has been verified*

R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Fri, 23 Dec 2016 20:17:21 +0100

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2016/Dec/23/t1482520652wva8e9q16qj2xb1.htm/, Retrieved Fri, 01 Nov 2024 03:40:08 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=303031, Retrieved Fri, 01 Nov 2024 03:40:08 +0000

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Original text written by user:

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No (this computation is public)

User-defined keywords

Estimated Impact

103

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

-       [Multiple Regression] [] [2016-12-23 19:17:21] [f297cab75242d404ff18382737066a42] [Current]

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Dataseries X:

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22	4	5	5	4
24	5	5	5	4
21	5	5	4	4
21	3	4	4	4
24	5	5	5	4
20	5	5	5	4
22	5	4	5	5
20	4	NA	4	4
19	5	5	4	4
23	5	5	5	5
21	4	3	4	3
19	3	5	4	3
19	4	5	5	4
21	5	5	5	4
21	4	4	4	4
22	5	4	5	4
22	4	5	5	4
19	NA	NA	NA	NA
21	5	4	4	4
21	5	4	5	5
21	5	5	5	4
20	3	5	5	4
22	4	5	5	4
22	4	4	4	4
24	5	5	5	5
21	3	4	3	3
19	5	5	4	5
19	4	4	4	3
23	4	5	4	4
21	4	5	4	4
21	4	3	5	4
19	5	4	5	3
21	5	5	5	4
19	4	4	5	5
21	5	5	5	4
21	5	5	5	5
23	4	4	4	4
19	5	4	4	4
19	4	4	4	4
19	4	5	4	3
18	4	4	4	4
22	4	4	4	4
18	4	3	4	3
22	5	5	4	3
18	5	4	5	4
22	4	4	4	4
22	4	4	4	4
19	4	NA	4	1
22	4	4	4	4
25	4	4	4	3
19	5	5	5	4
19	4	4	4	4
19	4	5	4	4
19	5	5	5	4
21	4	5	4	4
21	4	5	4	4
20	4	4	4	3
19	5	4	3	4
19	4	4	4	4
22	5	4	4	3
26	4	5	4	4
19	4	5	5	4
21	4	5	5	4
21	5	5	5	3
20	5	5	5	4
23	4	4	3	3
22	4	2	4	3
22	4	5	5	4
22	4	4	4	4
21	4	4	4	3
21	4	5	5	4
22	4	5	5	4
23	2	5	4	5
18	5	5	5	4
24	4	5	4	4
22	5	5	4	3
21	5	5	5	4
21	4	5	5	5
21	5	5	5	5
23	5	5	5	4
21	4	5	5	4
23	4	4	4	4
21	4	4	4	4
19	4	3	4	4
21	5	5	5	5
21	4	5	4	3
21	4	4	4	4
23	5	5	5	5
23	5	5	5	5
20	4	5	5	4
20	5	4	2	4
19	4	3	4	3
23	4	4	4	4
22	3	4	3	4
19	4	5	5	4
23	5	5	5	5
22	5	5	5	5
22	4	5	5	4
21	5	5	5	5
21	3	4	4	3
21	5	5	5	5
21	4	5	4	4
22	5	5	5	5
25	3	4	4	3
21	4	4	4	4
23	5	5	5	5
19	5	5	5	4
22	4	5	4	5
20	4	5	4	4
21	4	5	4	4
25	5	4	5	5
21	4	4	4	3
19	5	4	5	4
23	4	3	4	4
22	4	4	4	4
21	4	4	4	4
24	5	5	5	5
21	5	5	4	4
19	5	5	5	5
18	5	5	5	3
19	4	5	4	4
20	5	4	5	5
19	4	5	5	4
22	5	5	5	4
21	5	4	3	5
22	5	5	4	4
24	4	5	4	4
28	4	4	4	4
19	5	5	5	4
18	5	5	4	4
23	4	5	4	4
19	5	5	4	4
23	4	4	4	4
19	5	5	5	5
22	4	3	4	3
21	4	5	4	4
19	3	3	2	5
22	2	3	4	4
21	4	5	4	4
23	4	5	5	4
22	4	4	4	4
19	4	5	NA	4
19	5	5	5	4
21	5	5	4	NA
22	3	5	5	4
21	4	5	4	3
20	4	5	4	4
23	5	5	4	3
22	4	5	4	4
23	5	5	5	5
22	3	4	4	3
21	5	5	5	5
20	5	5	5	4
18	3	5	5	3
18	5	5	5	4
20	4	5	4	4
19	5	5	5	4
21	5	5	5	5
24	5	4	5	5
19	5	5	5	4
20	4	5	4	3
19	5	4	5	4
23	5	4	2	5
22	4	5	4	4
21	4	5	5	4
24	4	4	5	3
21	4	5	4	4
21	4	4	4	3
22	5	5	5	3

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

\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
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=303031&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] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=303031&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=303031&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

Multiple Linear Regression - Estimated Regression Equation
a[t] = + 21.364 -0.345897b[t] -0.0589782c[t] -0.106646d[t] + 0.493261e[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
a[t] =  +  21.364 -0.345897b[t] -0.0589782c[t] -0.106646d[t] +  0.493261e[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=303031&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]a[t] =  +  21.364 -0.345897b[t] -0.0589782c[t] -0.106646d[t] +  0.493261e[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=303031&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=303031&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
a[t] = + 21.364 -0.345897b[t] -0.0589782c[t] -0.106646d[t] + 0.493261e[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)	+21.36	1.351	+1.5810e+01	1.974e-34	9.87e-35
b	-0.3459	0.2378	-1.4540e+00	0.1478	0.07392
c	-0.05898	0.2496	-2.3630e-01	0.8135	0.4068
d	-0.1066	0.2523	-4.2280e-01	0.673	0.3365
e	+0.4933	0.2404	+2.0520e+00	0.04185	0.02092

\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) & +21.36 &  1.351 & +1.5810e+01 &  1.974e-34 &  9.87e-35 \tabularnewline
b & -0.3459 &  0.2378 & -1.4540e+00 &  0.1478 &  0.07392 \tabularnewline
c & -0.05898 &  0.2496 & -2.3630e-01 &  0.8135 &  0.4068 \tabularnewline
d & -0.1066 &  0.2523 & -4.2280e-01 &  0.673 &  0.3365 \tabularnewline
e & +0.4933 &  0.2404 & +2.0520e+00 &  0.04185 &  0.02092 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=303031&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]+21.36[/C][C] 1.351[/C][C]+1.5810e+01[/C][C] 1.974e-34[/C][C] 9.87e-35[/C][/ROW]
[ROW][C]b[/C][C]-0.3459[/C][C] 0.2378[/C][C]-1.4540e+00[/C][C] 0.1478[/C][C] 0.07392[/C][/ROW]
[ROW][C]c[/C][C]-0.05898[/C][C] 0.2496[/C][C]-2.3630e-01[/C][C] 0.8135[/C][C] 0.4068[/C][/ROW]
[ROW][C]d[/C][C]-0.1066[/C][C] 0.2523[/C][C]-4.2280e-01[/C][C] 0.673[/C][C] 0.3365[/C][/ROW]
[ROW][C]e[/C][C]+0.4933[/C][C] 0.2404[/C][C]+2.0520e+00[/C][C] 0.04185[/C][C] 0.02092[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=303031&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=303031&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)	+21.36	1.351	+1.5810e+01	1.974e-34	9.87e-35
b	-0.3459	0.2378	-1.4540e+00	0.1478	0.07392
c	-0.05898	0.2496	-2.3630e-01	0.8135	0.4068
d	-0.1066	0.2523	-4.2280e-01	0.673	0.3365
e	+0.4933	0.2404	+2.0520e+00	0.04185	0.02092

Multiple Linear Regression - Regression Statistics
Multiple R	0.1857
R-squared	0.03448
Adjusted R-squared	0.01019
F-TEST (value)	1.42
F-TEST (DF numerator)	4
F-TEST (DF denominator)	159
p-value	0.2299
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.772
Sum Squared Residuals	499.4

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.1857 \tabularnewline
R-squared &  0.03448 \tabularnewline
Adjusted R-squared &  0.01019 \tabularnewline
F-TEST (value) &  1.42 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 159 \tabularnewline
p-value &  0.2299 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  1.772 \tabularnewline
Sum Squared Residuals &  499.4 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=303031&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.1857[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.03448[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.01019[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 1.42[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]159[/C][/ROW]
[ROW][C]p-value[/C][C] 0.2299[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 1.772[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 499.4[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=303031&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=303031&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	0.1857
R-squared	0.03448
Adjusted R-squared	0.01019
F-TEST (value)	1.42
F-TEST (DF numerator)	4
F-TEST (DF denominator)	159
p-value	0.2299
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.772
Sum Squared Residuals	499.4

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	22	21.13	0.8747
2	24	20.78	3.221
3	21	20.89	0.1139
4	21	21.64	-0.6368
5	24	20.78	3.221
6	20	20.78	-0.7794
7	22	21.33	0.6684
8	19	20.89	-1.886
9	23	21.27	1.727
10	21	20.86	0.1434
11	19	21.08	-2.085
12	19	21.13	-2.125
13	21	20.78	0.2206
14	21	21.29	-0.2909
15	22	20.84	1.162
16	22	21.13	0.8747
17	21	20.95	0.05497
18	21	21.33	-0.3316
19	21	20.78	0.2206
20	20	21.47	-1.471
21	22	21.13	0.8747
22	22	21.29	0.7091
23	24	21.27	2.727
24	21	21.25	-0.2502
25	19	21.38	-2.379
26	19	20.8	-1.798
27	23	21.23	1.768
28	21	21.23	-0.2319
29	21	21.24	-0.2433
30	19	20.35	-1.345
31	21	20.78	0.2206
32	19	21.68	-2.678
33	21	20.78	0.2206
34	21	21.27	-0.2727
35	23	21.29	1.709
36	19	20.95	-1.945
37	19	21.29	-2.291
38	19	20.74	-1.739
39	18	21.29	-3.291
40	22	21.29	0.7091
41	18	20.86	-2.857
42	22	20.39	1.607
43	18	20.84	-2.838
44	22	21.29	0.7091
45	22	21.29	0.7091
46	22	21.29	0.7091
47	25	20.8	4.202
48	19	20.78	-1.779
49	19	21.29	-2.291
50	19	21.23	-2.232
51	19	20.78	-1.779
52	21	21.23	-0.2319
53	21	21.23	-0.2319
54	20	20.8	-0.7977
55	19	21.05	-2.052
56	19	21.29	-2.291
57	22	20.45	1.548
58	26	21.23	4.768
59	19	21.13	-2.125
60	21	21.13	-0.1253
61	21	20.29	0.7139
62	20	20.78	-0.7794
63	23	20.9	2.096
64	22	20.92	1.084
65	22	21.13	0.8747
66	22	21.29	0.7091
67	21	20.8	0.2023
68	21	21.13	-0.1253
69	22	21.13	0.8747
70	23	22.42	0.583
71	18	20.78	-2.779
72	24	21.23	2.768
73	22	20.39	1.607
74	21	20.78	0.2206
75	21	21.62	-0.6186
76	21	21.27	-0.2727
77	23	20.78	2.221
78	21	21.13	-0.1253
79	23	21.29	1.709
80	21	21.29	-0.2909
81	19	21.35	-2.35
82	21	21.27	-0.2727
83	21	20.74	0.2613
84	21	21.29	-0.2909
85	23	21.27	1.727
86	23	21.27	1.727
87	20	21.13	-1.125
88	20	21.16	-1.158
89	19	20.86	-1.857
90	23	21.29	1.709
91	22	21.74	0.2565
92	19	21.13	-2.125
93	23	21.27	1.727
94	22	21.27	0.7273
95	22	21.13	0.8747
96	21	21.27	-0.2727
97	21	21.14	-0.1436
98	21	21.27	-0.2727
99	21	21.23	-0.2319
100	22	21.27	0.7273
101	25	21.14	3.856
102	21	21.29	-0.2909
103	23	21.27	1.727
104	19	20.78	-1.779
105	22	21.73	0.2748
106	20	21.23	-1.232
107	21	21.23	-0.2319
108	25	21.33	3.668
109	21	20.8	0.2023
110	19	20.84	-1.838
111	23	21.35	1.65
112	22	21.29	0.7091
113	21	21.29	-0.2909
114	24	21.27	2.727
115	21	20.89	0.1139
116	19	21.27	-2.273
117	18	20.29	-2.286
118	19	21.23	-2.232
119	20	21.33	-1.332
120	19	21.13	-2.125
121	22	20.78	1.221
122	21	21.54	-0.5449
123	22	20.89	1.114
124	24	21.23	2.768
125	28	21.29	6.709
126	19	20.78	-1.779
127	18	20.89	-2.886
128	23	21.23	1.768
129	19	20.89	-1.886
130	23	21.29	1.709
131	19	21.27	-2.273
132	22	20.86	1.143
133	21	21.23	-0.2319
134	19	22.4	-3.402
135	22	22.04	-0.0417
136	21	21.23	-0.2319
137	23	21.13	1.875
138	22	21.29	0.7091
139	19	20.78	-1.779
140	22	21.47	0.5288
141	21	20.74	0.2613
142	20	21.23	-1.232
143	23	20.39	2.607
144	22	21.23	0.7681
145	23	21.27	1.727
146	22	21.14	0.8564
147	21	21.27	-0.2727
148	20	20.78	-0.7794
149	18	20.98	-2.978
150	18	20.78	-2.779
151	20	21.23	-1.232
152	19	20.78	-1.779
153	21	21.27	-0.2727
154	24	21.33	2.668
155	19	20.78	-1.779
156	20	20.74	-0.7387
157	19	20.84	-1.838
158	23	21.65	1.348
159	22	21.23	0.7681
160	21	21.13	-0.1253
161	24	20.69	3.309
162	21	21.23	-0.2319
163	21	20.8	0.2023
164	22	20.29	1.714

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  22 &  21.13 &  0.8747 \tabularnewline
2 &  24 &  20.78 &  3.221 \tabularnewline
3 &  21 &  20.89 &  0.1139 \tabularnewline
4 &  21 &  21.64 & -0.6368 \tabularnewline
5 &  24 &  20.78 &  3.221 \tabularnewline
6 &  20 &  20.78 & -0.7794 \tabularnewline
7 &  22 &  21.33 &  0.6684 \tabularnewline
8 &  19 &  20.89 & -1.886 \tabularnewline
9 &  23 &  21.27 &  1.727 \tabularnewline
10 &  21 &  20.86 &  0.1434 \tabularnewline
11 &  19 &  21.08 & -2.085 \tabularnewline
12 &  19 &  21.13 & -2.125 \tabularnewline
13 &  21 &  20.78 &  0.2206 \tabularnewline
14 &  21 &  21.29 & -0.2909 \tabularnewline
15 &  22 &  20.84 &  1.162 \tabularnewline
16 &  22 &  21.13 &  0.8747 \tabularnewline
17 &  21 &  20.95 &  0.05497 \tabularnewline
18 &  21 &  21.33 & -0.3316 \tabularnewline
19 &  21 &  20.78 &  0.2206 \tabularnewline
20 &  20 &  21.47 & -1.471 \tabularnewline
21 &  22 &  21.13 &  0.8747 \tabularnewline
22 &  22 &  21.29 &  0.7091 \tabularnewline
23 &  24 &  21.27 &  2.727 \tabularnewline
24 &  21 &  21.25 & -0.2502 \tabularnewline
25 &  19 &  21.38 & -2.379 \tabularnewline
26 &  19 &  20.8 & -1.798 \tabularnewline
27 &  23 &  21.23 &  1.768 \tabularnewline
28 &  21 &  21.23 & -0.2319 \tabularnewline
29 &  21 &  21.24 & -0.2433 \tabularnewline
30 &  19 &  20.35 & -1.345 \tabularnewline
31 &  21 &  20.78 &  0.2206 \tabularnewline
32 &  19 &  21.68 & -2.678 \tabularnewline
33 &  21 &  20.78 &  0.2206 \tabularnewline
34 &  21 &  21.27 & -0.2727 \tabularnewline
35 &  23 &  21.29 &  1.709 \tabularnewline
36 &  19 &  20.95 & -1.945 \tabularnewline
37 &  19 &  21.29 & -2.291 \tabularnewline
38 &  19 &  20.74 & -1.739 \tabularnewline
39 &  18 &  21.29 & -3.291 \tabularnewline
40 &  22 &  21.29 &  0.7091 \tabularnewline
41 &  18 &  20.86 & -2.857 \tabularnewline
42 &  22 &  20.39 &  1.607 \tabularnewline
43 &  18 &  20.84 & -2.838 \tabularnewline
44 &  22 &  21.29 &  0.7091 \tabularnewline
45 &  22 &  21.29 &  0.7091 \tabularnewline
46 &  22 &  21.29 &  0.7091 \tabularnewline
47 &  25 &  20.8 &  4.202 \tabularnewline
48 &  19 &  20.78 & -1.779 \tabularnewline
49 &  19 &  21.29 & -2.291 \tabularnewline
50 &  19 &  21.23 & -2.232 \tabularnewline
51 &  19 &  20.78 & -1.779 \tabularnewline
52 &  21 &  21.23 & -0.2319 \tabularnewline
53 &  21 &  21.23 & -0.2319 \tabularnewline
54 &  20 &  20.8 & -0.7977 \tabularnewline
55 &  19 &  21.05 & -2.052 \tabularnewline
56 &  19 &  21.29 & -2.291 \tabularnewline
57 &  22 &  20.45 &  1.548 \tabularnewline
58 &  26 &  21.23 &  4.768 \tabularnewline
59 &  19 &  21.13 & -2.125 \tabularnewline
60 &  21 &  21.13 & -0.1253 \tabularnewline
61 &  21 &  20.29 &  0.7139 \tabularnewline
62 &  20 &  20.78 & -0.7794 \tabularnewline
63 &  23 &  20.9 &  2.096 \tabularnewline
64 &  22 &  20.92 &  1.084 \tabularnewline
65 &  22 &  21.13 &  0.8747 \tabularnewline
66 &  22 &  21.29 &  0.7091 \tabularnewline
67 &  21 &  20.8 &  0.2023 \tabularnewline
68 &  21 &  21.13 & -0.1253 \tabularnewline
69 &  22 &  21.13 &  0.8747 \tabularnewline
70 &  23 &  22.42 &  0.583 \tabularnewline
71 &  18 &  20.78 & -2.779 \tabularnewline
72 &  24 &  21.23 &  2.768 \tabularnewline
73 &  22 &  20.39 &  1.607 \tabularnewline
74 &  21 &  20.78 &  0.2206 \tabularnewline
75 &  21 &  21.62 & -0.6186 \tabularnewline
76 &  21 &  21.27 & -0.2727 \tabularnewline
77 &  23 &  20.78 &  2.221 \tabularnewline
78 &  21 &  21.13 & -0.1253 \tabularnewline
79 &  23 &  21.29 &  1.709 \tabularnewline
80 &  21 &  21.29 & -0.2909 \tabularnewline
81 &  19 &  21.35 & -2.35 \tabularnewline
82 &  21 &  21.27 & -0.2727 \tabularnewline
83 &  21 &  20.74 &  0.2613 \tabularnewline
84 &  21 &  21.29 & -0.2909 \tabularnewline
85 &  23 &  21.27 &  1.727 \tabularnewline
86 &  23 &  21.27 &  1.727 \tabularnewline
87 &  20 &  21.13 & -1.125 \tabularnewline
88 &  20 &  21.16 & -1.158 \tabularnewline
89 &  19 &  20.86 & -1.857 \tabularnewline
90 &  23 &  21.29 &  1.709 \tabularnewline
91 &  22 &  21.74 &  0.2565 \tabularnewline
92 &  19 &  21.13 & -2.125 \tabularnewline
93 &  23 &  21.27 &  1.727 \tabularnewline
94 &  22 &  21.27 &  0.7273 \tabularnewline
95 &  22 &  21.13 &  0.8747 \tabularnewline
96 &  21 &  21.27 & -0.2727 \tabularnewline
97 &  21 &  21.14 & -0.1436 \tabularnewline
98 &  21 &  21.27 & -0.2727 \tabularnewline
99 &  21 &  21.23 & -0.2319 \tabularnewline
100 &  22 &  21.27 &  0.7273 \tabularnewline
101 &  25 &  21.14 &  3.856 \tabularnewline
102 &  21 &  21.29 & -0.2909 \tabularnewline
103 &  23 &  21.27 &  1.727 \tabularnewline
104 &  19 &  20.78 & -1.779 \tabularnewline
105 &  22 &  21.73 &  0.2748 \tabularnewline
106 &  20 &  21.23 & -1.232 \tabularnewline
107 &  21 &  21.23 & -0.2319 \tabularnewline
108 &  25 &  21.33 &  3.668 \tabularnewline
109 &  21 &  20.8 &  0.2023 \tabularnewline
110 &  19 &  20.84 & -1.838 \tabularnewline
111 &  23 &  21.35 &  1.65 \tabularnewline
112 &  22 &  21.29 &  0.7091 \tabularnewline
113 &  21 &  21.29 & -0.2909 \tabularnewline
114 &  24 &  21.27 &  2.727 \tabularnewline
115 &  21 &  20.89 &  0.1139 \tabularnewline
116 &  19 &  21.27 & -2.273 \tabularnewline
117 &  18 &  20.29 & -2.286 \tabularnewline
118 &  19 &  21.23 & -2.232 \tabularnewline
119 &  20 &  21.33 & -1.332 \tabularnewline
120 &  19 &  21.13 & -2.125 \tabularnewline
121 &  22 &  20.78 &  1.221 \tabularnewline
122 &  21 &  21.54 & -0.5449 \tabularnewline
123 &  22 &  20.89 &  1.114 \tabularnewline
124 &  24 &  21.23 &  2.768 \tabularnewline
125 &  28 &  21.29 &  6.709 \tabularnewline
126 &  19 &  20.78 & -1.779 \tabularnewline
127 &  18 &  20.89 & -2.886 \tabularnewline
128 &  23 &  21.23 &  1.768 \tabularnewline
129 &  19 &  20.89 & -1.886 \tabularnewline
130 &  23 &  21.29 &  1.709 \tabularnewline
131 &  19 &  21.27 & -2.273 \tabularnewline
132 &  22 &  20.86 &  1.143 \tabularnewline
133 &  21 &  21.23 & -0.2319 \tabularnewline
134 &  19 &  22.4 & -3.402 \tabularnewline
135 &  22 &  22.04 & -0.0417 \tabularnewline
136 &  21 &  21.23 & -0.2319 \tabularnewline
137 &  23 &  21.13 &  1.875 \tabularnewline
138 &  22 &  21.29 &  0.7091 \tabularnewline
139 &  19 &  20.78 & -1.779 \tabularnewline
140 &  22 &  21.47 &  0.5288 \tabularnewline
141 &  21 &  20.74 &  0.2613 \tabularnewline
142 &  20 &  21.23 & -1.232 \tabularnewline
143 &  23 &  20.39 &  2.607 \tabularnewline
144 &  22 &  21.23 &  0.7681 \tabularnewline
145 &  23 &  21.27 &  1.727 \tabularnewline
146 &  22 &  21.14 &  0.8564 \tabularnewline
147 &  21 &  21.27 & -0.2727 \tabularnewline
148 &  20 &  20.78 & -0.7794 \tabularnewline
149 &  18 &  20.98 & -2.978 \tabularnewline
150 &  18 &  20.78 & -2.779 \tabularnewline
151 &  20 &  21.23 & -1.232 \tabularnewline
152 &  19 &  20.78 & -1.779 \tabularnewline
153 &  21 &  21.27 & -0.2727 \tabularnewline
154 &  24 &  21.33 &  2.668 \tabularnewline
155 &  19 &  20.78 & -1.779 \tabularnewline
156 &  20 &  20.74 & -0.7387 \tabularnewline
157 &  19 &  20.84 & -1.838 \tabularnewline
158 &  23 &  21.65 &  1.348 \tabularnewline
159 &  22 &  21.23 &  0.7681 \tabularnewline
160 &  21 &  21.13 & -0.1253 \tabularnewline
161 &  24 &  20.69 &  3.309 \tabularnewline
162 &  21 &  21.23 & -0.2319 \tabularnewline
163 &  21 &  20.8 &  0.2023 \tabularnewline
164 &  22 &  20.29 &  1.714 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=303031&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] 22[/C][C] 21.13[/C][C] 0.8747[/C][/ROW]
[ROW][C]2[/C][C] 24[/C][C] 20.78[/C][C] 3.221[/C][/ROW]
[ROW][C]3[/C][C] 21[/C][C] 20.89[/C][C] 0.1139[/C][/ROW]
[ROW][C]4[/C][C] 21[/C][C] 21.64[/C][C]-0.6368[/C][/ROW]
[ROW][C]5[/C][C] 24[/C][C] 20.78[/C][C] 3.221[/C][/ROW]
[ROW][C]6[/C][C] 20[/C][C] 20.78[/C][C]-0.7794[/C][/ROW]
[ROW][C]7[/C][C] 22[/C][C] 21.33[/C][C] 0.6684[/C][/ROW]
[ROW][C]8[/C][C] 19[/C][C] 20.89[/C][C]-1.886[/C][/ROW]
[ROW][C]9[/C][C] 23[/C][C] 21.27[/C][C] 1.727[/C][/ROW]
[ROW][C]10[/C][C] 21[/C][C] 20.86[/C][C] 0.1434[/C][/ROW]
[ROW][C]11[/C][C] 19[/C][C] 21.08[/C][C]-2.085[/C][/ROW]
[ROW][C]12[/C][C] 19[/C][C] 21.13[/C][C]-2.125[/C][/ROW]
[ROW][C]13[/C][C] 21[/C][C] 20.78[/C][C] 0.2206[/C][/ROW]
[ROW][C]14[/C][C] 21[/C][C] 21.29[/C][C]-0.2909[/C][/ROW]
[ROW][C]15[/C][C] 22[/C][C] 20.84[/C][C] 1.162[/C][/ROW]
[ROW][C]16[/C][C] 22[/C][C] 21.13[/C][C] 0.8747[/C][/ROW]
[ROW][C]17[/C][C] 21[/C][C] 20.95[/C][C] 0.05497[/C][/ROW]
[ROW][C]18[/C][C] 21[/C][C] 21.33[/C][C]-0.3316[/C][/ROW]
[ROW][C]19[/C][C] 21[/C][C] 20.78[/C][C] 0.2206[/C][/ROW]
[ROW][C]20[/C][C] 20[/C][C] 21.47[/C][C]-1.471[/C][/ROW]
[ROW][C]21[/C][C] 22[/C][C] 21.13[/C][C] 0.8747[/C][/ROW]
[ROW][C]22[/C][C] 22[/C][C] 21.29[/C][C] 0.7091[/C][/ROW]
[ROW][C]23[/C][C] 24[/C][C] 21.27[/C][C] 2.727[/C][/ROW]
[ROW][C]24[/C][C] 21[/C][C] 21.25[/C][C]-0.2502[/C][/ROW]
[ROW][C]25[/C][C] 19[/C][C] 21.38[/C][C]-2.379[/C][/ROW]
[ROW][C]26[/C][C] 19[/C][C] 20.8[/C][C]-1.798[/C][/ROW]
[ROW][C]27[/C][C] 23[/C][C] 21.23[/C][C] 1.768[/C][/ROW]
[ROW][C]28[/C][C] 21[/C][C] 21.23[/C][C]-0.2319[/C][/ROW]
[ROW][C]29[/C][C] 21[/C][C] 21.24[/C][C]-0.2433[/C][/ROW]
[ROW][C]30[/C][C] 19[/C][C] 20.35[/C][C]-1.345[/C][/ROW]
[ROW][C]31[/C][C] 21[/C][C] 20.78[/C][C] 0.2206[/C][/ROW]
[ROW][C]32[/C][C] 19[/C][C] 21.68[/C][C]-2.678[/C][/ROW]
[ROW][C]33[/C][C] 21[/C][C] 20.78[/C][C] 0.2206[/C][/ROW]
[ROW][C]34[/C][C] 21[/C][C] 21.27[/C][C]-0.2727[/C][/ROW]
[ROW][C]35[/C][C] 23[/C][C] 21.29[/C][C] 1.709[/C][/ROW]
[ROW][C]36[/C][C] 19[/C][C] 20.95[/C][C]-1.945[/C][/ROW]
[ROW][C]37[/C][C] 19[/C][C] 21.29[/C][C]-2.291[/C][/ROW]
[ROW][C]38[/C][C] 19[/C][C] 20.74[/C][C]-1.739[/C][/ROW]
[ROW][C]39[/C][C] 18[/C][C] 21.29[/C][C]-3.291[/C][/ROW]
[ROW][C]40[/C][C] 22[/C][C] 21.29[/C][C] 0.7091[/C][/ROW]
[ROW][C]41[/C][C] 18[/C][C] 20.86[/C][C]-2.857[/C][/ROW]
[ROW][C]42[/C][C] 22[/C][C] 20.39[/C][C] 1.607[/C][/ROW]
[ROW][C]43[/C][C] 18[/C][C] 20.84[/C][C]-2.838[/C][/ROW]
[ROW][C]44[/C][C] 22[/C][C] 21.29[/C][C] 0.7091[/C][/ROW]
[ROW][C]45[/C][C] 22[/C][C] 21.29[/C][C] 0.7091[/C][/ROW]
[ROW][C]46[/C][C] 22[/C][C] 21.29[/C][C] 0.7091[/C][/ROW]
[ROW][C]47[/C][C] 25[/C][C] 20.8[/C][C] 4.202[/C][/ROW]
[ROW][C]48[/C][C] 19[/C][C] 20.78[/C][C]-1.779[/C][/ROW]
[ROW][C]49[/C][C] 19[/C][C] 21.29[/C][C]-2.291[/C][/ROW]
[ROW][C]50[/C][C] 19[/C][C] 21.23[/C][C]-2.232[/C][/ROW]
[ROW][C]51[/C][C] 19[/C][C] 20.78[/C][C]-1.779[/C][/ROW]
[ROW][C]52[/C][C] 21[/C][C] 21.23[/C][C]-0.2319[/C][/ROW]
[ROW][C]53[/C][C] 21[/C][C] 21.23[/C][C]-0.2319[/C][/ROW]
[ROW][C]54[/C][C] 20[/C][C] 20.8[/C][C]-0.7977[/C][/ROW]
[ROW][C]55[/C][C] 19[/C][C] 21.05[/C][C]-2.052[/C][/ROW]
[ROW][C]56[/C][C] 19[/C][C] 21.29[/C][C]-2.291[/C][/ROW]
[ROW][C]57[/C][C] 22[/C][C] 20.45[/C][C] 1.548[/C][/ROW]
[ROW][C]58[/C][C] 26[/C][C] 21.23[/C][C] 4.768[/C][/ROW]
[ROW][C]59[/C][C] 19[/C][C] 21.13[/C][C]-2.125[/C][/ROW]
[ROW][C]60[/C][C] 21[/C][C] 21.13[/C][C]-0.1253[/C][/ROW]
[ROW][C]61[/C][C] 21[/C][C] 20.29[/C][C] 0.7139[/C][/ROW]
[ROW][C]62[/C][C] 20[/C][C] 20.78[/C][C]-0.7794[/C][/ROW]
[ROW][C]63[/C][C] 23[/C][C] 20.9[/C][C] 2.096[/C][/ROW]
[ROW][C]64[/C][C] 22[/C][C] 20.92[/C][C] 1.084[/C][/ROW]
[ROW][C]65[/C][C] 22[/C][C] 21.13[/C][C] 0.8747[/C][/ROW]
[ROW][C]66[/C][C] 22[/C][C] 21.29[/C][C] 0.7091[/C][/ROW]
[ROW][C]67[/C][C] 21[/C][C] 20.8[/C][C] 0.2023[/C][/ROW]
[ROW][C]68[/C][C] 21[/C][C] 21.13[/C][C]-0.1253[/C][/ROW]
[ROW][C]69[/C][C] 22[/C][C] 21.13[/C][C] 0.8747[/C][/ROW]
[ROW][C]70[/C][C] 23[/C][C] 22.42[/C][C] 0.583[/C][/ROW]
[ROW][C]71[/C][C] 18[/C][C] 20.78[/C][C]-2.779[/C][/ROW]
[ROW][C]72[/C][C] 24[/C][C] 21.23[/C][C] 2.768[/C][/ROW]
[ROW][C]73[/C][C] 22[/C][C] 20.39[/C][C] 1.607[/C][/ROW]
[ROW][C]74[/C][C] 21[/C][C] 20.78[/C][C] 0.2206[/C][/ROW]
[ROW][C]75[/C][C] 21[/C][C] 21.62[/C][C]-0.6186[/C][/ROW]
[ROW][C]76[/C][C] 21[/C][C] 21.27[/C][C]-0.2727[/C][/ROW]
[ROW][C]77[/C][C] 23[/C][C] 20.78[/C][C] 2.221[/C][/ROW]
[ROW][C]78[/C][C] 21[/C][C] 21.13[/C][C]-0.1253[/C][/ROW]
[ROW][C]79[/C][C] 23[/C][C] 21.29[/C][C] 1.709[/C][/ROW]
[ROW][C]80[/C][C] 21[/C][C] 21.29[/C][C]-0.2909[/C][/ROW]
[ROW][C]81[/C][C] 19[/C][C] 21.35[/C][C]-2.35[/C][/ROW]
[ROW][C]82[/C][C] 21[/C][C] 21.27[/C][C]-0.2727[/C][/ROW]
[ROW][C]83[/C][C] 21[/C][C] 20.74[/C][C] 0.2613[/C][/ROW]
[ROW][C]84[/C][C] 21[/C][C] 21.29[/C][C]-0.2909[/C][/ROW]
[ROW][C]85[/C][C] 23[/C][C] 21.27[/C][C] 1.727[/C][/ROW]
[ROW][C]86[/C][C] 23[/C][C] 21.27[/C][C] 1.727[/C][/ROW]
[ROW][C]87[/C][C] 20[/C][C] 21.13[/C][C]-1.125[/C][/ROW]
[ROW][C]88[/C][C] 20[/C][C] 21.16[/C][C]-1.158[/C][/ROW]
[ROW][C]89[/C][C] 19[/C][C] 20.86[/C][C]-1.857[/C][/ROW]
[ROW][C]90[/C][C] 23[/C][C] 21.29[/C][C] 1.709[/C][/ROW]
[ROW][C]91[/C][C] 22[/C][C] 21.74[/C][C] 0.2565[/C][/ROW]
[ROW][C]92[/C][C] 19[/C][C] 21.13[/C][C]-2.125[/C][/ROW]
[ROW][C]93[/C][C] 23[/C][C] 21.27[/C][C] 1.727[/C][/ROW]
[ROW][C]94[/C][C] 22[/C][C] 21.27[/C][C] 0.7273[/C][/ROW]
[ROW][C]95[/C][C] 22[/C][C] 21.13[/C][C] 0.8747[/C][/ROW]
[ROW][C]96[/C][C] 21[/C][C] 21.27[/C][C]-0.2727[/C][/ROW]
[ROW][C]97[/C][C] 21[/C][C] 21.14[/C][C]-0.1436[/C][/ROW]
[ROW][C]98[/C][C] 21[/C][C] 21.27[/C][C]-0.2727[/C][/ROW]
[ROW][C]99[/C][C] 21[/C][C] 21.23[/C][C]-0.2319[/C][/ROW]
[ROW][C]100[/C][C] 22[/C][C] 21.27[/C][C] 0.7273[/C][/ROW]
[ROW][C]101[/C][C] 25[/C][C] 21.14[/C][C] 3.856[/C][/ROW]
[ROW][C]102[/C][C] 21[/C][C] 21.29[/C][C]-0.2909[/C][/ROW]
[ROW][C]103[/C][C] 23[/C][C] 21.27[/C][C] 1.727[/C][/ROW]
[ROW][C]104[/C][C] 19[/C][C] 20.78[/C][C]-1.779[/C][/ROW]
[ROW][C]105[/C][C] 22[/C][C] 21.73[/C][C] 0.2748[/C][/ROW]
[ROW][C]106[/C][C] 20[/C][C] 21.23[/C][C]-1.232[/C][/ROW]
[ROW][C]107[/C][C] 21[/C][C] 21.23[/C][C]-0.2319[/C][/ROW]
[ROW][C]108[/C][C] 25[/C][C] 21.33[/C][C] 3.668[/C][/ROW]
[ROW][C]109[/C][C] 21[/C][C] 20.8[/C][C] 0.2023[/C][/ROW]
[ROW][C]110[/C][C] 19[/C][C] 20.84[/C][C]-1.838[/C][/ROW]
[ROW][C]111[/C][C] 23[/C][C] 21.35[/C][C] 1.65[/C][/ROW]
[ROW][C]112[/C][C] 22[/C][C] 21.29[/C][C] 0.7091[/C][/ROW]
[ROW][C]113[/C][C] 21[/C][C] 21.29[/C][C]-0.2909[/C][/ROW]
[ROW][C]114[/C][C] 24[/C][C] 21.27[/C][C] 2.727[/C][/ROW]
[ROW][C]115[/C][C] 21[/C][C] 20.89[/C][C] 0.1139[/C][/ROW]
[ROW][C]116[/C][C] 19[/C][C] 21.27[/C][C]-2.273[/C][/ROW]
[ROW][C]117[/C][C] 18[/C][C] 20.29[/C][C]-2.286[/C][/ROW]
[ROW][C]118[/C][C] 19[/C][C] 21.23[/C][C]-2.232[/C][/ROW]
[ROW][C]119[/C][C] 20[/C][C] 21.33[/C][C]-1.332[/C][/ROW]
[ROW][C]120[/C][C] 19[/C][C] 21.13[/C][C]-2.125[/C][/ROW]
[ROW][C]121[/C][C] 22[/C][C] 20.78[/C][C] 1.221[/C][/ROW]
[ROW][C]122[/C][C] 21[/C][C] 21.54[/C][C]-0.5449[/C][/ROW]
[ROW][C]123[/C][C] 22[/C][C] 20.89[/C][C] 1.114[/C][/ROW]
[ROW][C]124[/C][C] 24[/C][C] 21.23[/C][C] 2.768[/C][/ROW]
[ROW][C]125[/C][C] 28[/C][C] 21.29[/C][C] 6.709[/C][/ROW]
[ROW][C]126[/C][C] 19[/C][C] 20.78[/C][C]-1.779[/C][/ROW]
[ROW][C]127[/C][C] 18[/C][C] 20.89[/C][C]-2.886[/C][/ROW]
[ROW][C]128[/C][C] 23[/C][C] 21.23[/C][C] 1.768[/C][/ROW]
[ROW][C]129[/C][C] 19[/C][C] 20.89[/C][C]-1.886[/C][/ROW]
[ROW][C]130[/C][C] 23[/C][C] 21.29[/C][C] 1.709[/C][/ROW]
[ROW][C]131[/C][C] 19[/C][C] 21.27[/C][C]-2.273[/C][/ROW]
[ROW][C]132[/C][C] 22[/C][C] 20.86[/C][C] 1.143[/C][/ROW]
[ROW][C]133[/C][C] 21[/C][C] 21.23[/C][C]-0.2319[/C][/ROW]
[ROW][C]134[/C][C] 19[/C][C] 22.4[/C][C]-3.402[/C][/ROW]
[ROW][C]135[/C][C] 22[/C][C] 22.04[/C][C]-0.0417[/C][/ROW]
[ROW][C]136[/C][C] 21[/C][C] 21.23[/C][C]-0.2319[/C][/ROW]
[ROW][C]137[/C][C] 23[/C][C] 21.13[/C][C] 1.875[/C][/ROW]
[ROW][C]138[/C][C] 22[/C][C] 21.29[/C][C] 0.7091[/C][/ROW]
[ROW][C]139[/C][C] 19[/C][C] 20.78[/C][C]-1.779[/C][/ROW]
[ROW][C]140[/C][C] 22[/C][C] 21.47[/C][C] 0.5288[/C][/ROW]
[ROW][C]141[/C][C] 21[/C][C] 20.74[/C][C] 0.2613[/C][/ROW]
[ROW][C]142[/C][C] 20[/C][C] 21.23[/C][C]-1.232[/C][/ROW]
[ROW][C]143[/C][C] 23[/C][C] 20.39[/C][C] 2.607[/C][/ROW]
[ROW][C]144[/C][C] 22[/C][C] 21.23[/C][C] 0.7681[/C][/ROW]
[ROW][C]145[/C][C] 23[/C][C] 21.27[/C][C] 1.727[/C][/ROW]
[ROW][C]146[/C][C] 22[/C][C] 21.14[/C][C] 0.8564[/C][/ROW]
[ROW][C]147[/C][C] 21[/C][C] 21.27[/C][C]-0.2727[/C][/ROW]
[ROW][C]148[/C][C] 20[/C][C] 20.78[/C][C]-0.7794[/C][/ROW]
[ROW][C]149[/C][C] 18[/C][C] 20.98[/C][C]-2.978[/C][/ROW]
[ROW][C]150[/C][C] 18[/C][C] 20.78[/C][C]-2.779[/C][/ROW]
[ROW][C]151[/C][C] 20[/C][C] 21.23[/C][C]-1.232[/C][/ROW]
[ROW][C]152[/C][C] 19[/C][C] 20.78[/C][C]-1.779[/C][/ROW]
[ROW][C]153[/C][C] 21[/C][C] 21.27[/C][C]-0.2727[/C][/ROW]
[ROW][C]154[/C][C] 24[/C][C] 21.33[/C][C] 2.668[/C][/ROW]
[ROW][C]155[/C][C] 19[/C][C] 20.78[/C][C]-1.779[/C][/ROW]
[ROW][C]156[/C][C] 20[/C][C] 20.74[/C][C]-0.7387[/C][/ROW]
[ROW][C]157[/C][C] 19[/C][C] 20.84[/C][C]-1.838[/C][/ROW]
[ROW][C]158[/C][C] 23[/C][C] 21.65[/C][C] 1.348[/C][/ROW]
[ROW][C]159[/C][C] 22[/C][C] 21.23[/C][C] 0.7681[/C][/ROW]
[ROW][C]160[/C][C] 21[/C][C] 21.13[/C][C]-0.1253[/C][/ROW]
[ROW][C]161[/C][C] 24[/C][C] 20.69[/C][C] 3.309[/C][/ROW]
[ROW][C]162[/C][C] 21[/C][C] 21.23[/C][C]-0.2319[/C][/ROW]
[ROW][C]163[/C][C] 21[/C][C] 20.8[/C][C] 0.2023[/C][/ROW]
[ROW][C]164[/C][C] 22[/C][C] 20.29[/C][C] 1.714[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=303031&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=303031&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	22	21.13	0.8747
2	24	20.78	3.221
3	21	20.89	0.1139
4	21	21.64	-0.6368
5	24	20.78	3.221
6	20	20.78	-0.7794
7	22	21.33	0.6684
8	19	20.89	-1.886
9	23	21.27	1.727
10	21	20.86	0.1434
11	19	21.08	-2.085
12	19	21.13	-2.125
13	21	20.78	0.2206
14	21	21.29	-0.2909
15	22	20.84	1.162
16	22	21.13	0.8747
17	21	20.95	0.05497
18	21	21.33	-0.3316
19	21	20.78	0.2206
20	20	21.47	-1.471
21	22	21.13	0.8747
22	22	21.29	0.7091
23	24	21.27	2.727
24	21	21.25	-0.2502
25	19	21.38	-2.379
26	19	20.8	-1.798
27	23	21.23	1.768
28	21	21.23	-0.2319
29	21	21.24	-0.2433
30	19	20.35	-1.345
31	21	20.78	0.2206
32	19	21.68	-2.678
33	21	20.78	0.2206
34	21	21.27	-0.2727
35	23	21.29	1.709
36	19	20.95	-1.945
37	19	21.29	-2.291
38	19	20.74	-1.739
39	18	21.29	-3.291
40	22	21.29	0.7091
41	18	20.86	-2.857
42	22	20.39	1.607
43	18	20.84	-2.838
44	22	21.29	0.7091
45	22	21.29	0.7091
46	22	21.29	0.7091
47	25	20.8	4.202
48	19	20.78	-1.779
49	19	21.29	-2.291
50	19	21.23	-2.232
51	19	20.78	-1.779
52	21	21.23	-0.2319
53	21	21.23	-0.2319
54	20	20.8	-0.7977
55	19	21.05	-2.052
56	19	21.29	-2.291
57	22	20.45	1.548
58	26	21.23	4.768
59	19	21.13	-2.125
60	21	21.13	-0.1253
61	21	20.29	0.7139
62	20	20.78	-0.7794
63	23	20.9	2.096
64	22	20.92	1.084
65	22	21.13	0.8747
66	22	21.29	0.7091
67	21	20.8	0.2023
68	21	21.13	-0.1253
69	22	21.13	0.8747
70	23	22.42	0.583
71	18	20.78	-2.779
72	24	21.23	2.768
73	22	20.39	1.607
74	21	20.78	0.2206
75	21	21.62	-0.6186
76	21	21.27	-0.2727
77	23	20.78	2.221
78	21	21.13	-0.1253
79	23	21.29	1.709
80	21	21.29	-0.2909
81	19	21.35	-2.35
82	21	21.27	-0.2727
83	21	20.74	0.2613
84	21	21.29	-0.2909
85	23	21.27	1.727
86	23	21.27	1.727
87	20	21.13	-1.125
88	20	21.16	-1.158
89	19	20.86	-1.857
90	23	21.29	1.709
91	22	21.74	0.2565
92	19	21.13	-2.125
93	23	21.27	1.727
94	22	21.27	0.7273
95	22	21.13	0.8747
96	21	21.27	-0.2727
97	21	21.14	-0.1436
98	21	21.27	-0.2727
99	21	21.23	-0.2319
100	22	21.27	0.7273
101	25	21.14	3.856
102	21	21.29	-0.2909
103	23	21.27	1.727
104	19	20.78	-1.779
105	22	21.73	0.2748
106	20	21.23	-1.232
107	21	21.23	-0.2319
108	25	21.33	3.668
109	21	20.8	0.2023
110	19	20.84	-1.838
111	23	21.35	1.65
112	22	21.29	0.7091
113	21	21.29	-0.2909
114	24	21.27	2.727
115	21	20.89	0.1139
116	19	21.27	-2.273
117	18	20.29	-2.286
118	19	21.23	-2.232
119	20	21.33	-1.332
120	19	21.13	-2.125
121	22	20.78	1.221
122	21	21.54	-0.5449
123	22	20.89	1.114
124	24	21.23	2.768
125	28	21.29	6.709
126	19	20.78	-1.779
127	18	20.89	-2.886
128	23	21.23	1.768
129	19	20.89	-1.886
130	23	21.29	1.709
131	19	21.27	-2.273
132	22	20.86	1.143
133	21	21.23	-0.2319
134	19	22.4	-3.402
135	22	22.04	-0.0417
136	21	21.23	-0.2319
137	23	21.13	1.875
138	22	21.29	0.7091
139	19	20.78	-1.779
140	22	21.47	0.5288
141	21	20.74	0.2613
142	20	21.23	-1.232
143	23	20.39	2.607
144	22	21.23	0.7681
145	23	21.27	1.727
146	22	21.14	0.8564
147	21	21.27	-0.2727
148	20	20.78	-0.7794
149	18	20.98	-2.978
150	18	20.78	-2.779
151	20	21.23	-1.232
152	19	20.78	-1.779
153	21	21.27	-0.2727
154	24	21.33	2.668
155	19	20.78	-1.779
156	20	20.74	-0.7387
157	19	20.84	-1.838
158	23	21.65	1.348
159	22	21.23	0.7681
160	21	21.13	-0.1253
161	24	20.69	3.309
162	21	21.23	-0.2319
163	21	20.8	0.2023
164	22	20.29	1.714

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
8	0.7396	0.5207	0.2604
9	0.6478	0.7045	0.3522
10	0.5136	0.9729	0.4864
11	0.4353	0.8707	0.5647
12	0.6041	0.7918	0.3959
13	0.5222	0.9555	0.4778
14	0.4217	0.8434	0.5783
15	0.3358	0.6716	0.6642
16	0.2642	0.5284	0.7358
17	0.1935	0.3869	0.8065
18	0.1849	0.3698	0.8151
19	0.1417	0.2834	0.8583
20	0.1106	0.2213	0.8894
21	0.08408	0.1682	0.9159
22	0.07577	0.1515	0.9242
23	0.09298	0.186	0.907
24	0.0923	0.1846	0.9077
25	0.1191	0.2382	0.8809
26	0.122	0.2439	0.878
27	0.1685	0.337	0.8315
28	0.1303	0.2605	0.8697
29	0.1035	0.2071	0.8965
30	0.1101	0.2203	0.8899
31	0.08353	0.1671	0.9165
32	0.139	0.2779	0.861
33	0.1088	0.2176	0.8912
34	0.08759	0.1752	0.9124
35	0.1028	0.2055	0.8972
36	0.1125	0.225	0.8875
37	0.1183	0.2367	0.8817
38	0.1104	0.2208	0.8896
39	0.1606	0.3212	0.8394
40	0.1483	0.2966	0.8517
41	0.1648	0.3295	0.8352
42	0.1619	0.3239	0.8381
43	0.2313	0.4625	0.7687
44	0.2158	0.4317	0.7842
45	0.1987	0.3974	0.8013
46	0.1806	0.3613	0.8194
47	0.4432	0.8863	0.5568
48	0.4607	0.9214	0.5393
49	0.4743	0.9485	0.5257
50	0.4936	0.9872	0.5064
51	0.5045	0.9909	0.4955
52	0.4552	0.9104	0.5448
53	0.4068	0.8135	0.5932
54	0.3654	0.7309	0.6346
55	0.3629	0.7259	0.6371
56	0.3745	0.749	0.6255
57	0.3677	0.7355	0.6323
58	0.68	0.6399	0.32
59	0.6988	0.6023	0.3012
60	0.6563	0.6874	0.3437
61	0.6153	0.7694	0.3847
62	0.5823	0.8354	0.4177
63	0.6099	0.7802	0.3901
64	0.5982	0.8036	0.4018
65	0.5653	0.8695	0.4347
66	0.5311	0.9378	0.4689
67	0.4852	0.9704	0.5148
68	0.4391	0.8782	0.5609
69	0.4055	0.811	0.5945
70	0.3768	0.7535	0.6232
71	0.444	0.888	0.556
72	0.5091	0.9818	0.4909
73	0.4961	0.9922	0.5039
74	0.4508	0.9016	0.5492
75	0.4104	0.8209	0.5896
76	0.3673	0.7346	0.6327
77	0.3891	0.7781	0.6109
78	0.3461	0.6922	0.6539
79	0.3452	0.6904	0.6548
80	0.3054	0.6108	0.6946
81	0.3394	0.6789	0.6606
82	0.2995	0.5989	0.7005
83	0.2634	0.5269	0.7366
84	0.2295	0.459	0.7705
85	0.2273	0.4547	0.7727
86	0.2246	0.4491	0.7754
87	0.2044	0.4088	0.7956
88	0.1863	0.3726	0.8137
89	0.2033	0.4066	0.7967
90	0.2	0.4001	0.8
91	0.1713	0.3426	0.8287
92	0.1835	0.3669	0.8165
93	0.1821	0.3642	0.8179
94	0.1584	0.3168	0.8416
95	0.1388	0.2777	0.8612
96	0.1155	0.2311	0.8845
97	0.09657	0.1931	0.9034
98	0.07867	0.1573	0.9213
99	0.0633	0.1266	0.9367
100	0.05283	0.1057	0.9472
101	0.1085	0.2171	0.8915
102	0.08973	0.1795	0.9103
103	0.09097	0.1819	0.909
104	0.09004	0.1801	0.91
105	0.07403	0.1481	0.926
106	0.06463	0.1293	0.9354
107	0.05121	0.1024	0.9488
108	0.1011	0.2022	0.8989
109	0.08229	0.1646	0.9177
110	0.08726	0.1745	0.9127
111	0.07894	0.1579	0.9211
112	0.06403	0.1281	0.936
113	0.05105	0.1021	0.949
114	0.0797	0.1594	0.9203
115	0.06329	0.1266	0.9367
116	0.06491	0.1298	0.9351
117	0.07919	0.1584	0.9208
118	0.08449	0.169	0.9155
119	0.07559	0.1512	0.9244
120	0.07837	0.1567	0.9216
121	0.06839	0.1368	0.9316
122	0.05457	0.1091	0.9454
123	0.04625	0.0925	0.9537
124	0.06983	0.1397	0.9302
125	0.5479	0.9041	0.4521
126	0.5408	0.9184	0.4592
127	0.62	0.7599	0.38
128	0.6444	0.7113	0.3556
129	0.6492	0.7016	0.3508
130	0.6404	0.7191	0.3596
131	0.6522	0.6955	0.3478
132	0.5997	0.8005	0.4003
133	0.5403	0.9194	0.4597
134	0.7483	0.5034	0.2517
135	0.728	0.544	0.272
136	0.6713	0.6575	0.3287
137	0.7161	0.5677	0.2839
138	0.6594	0.6813	0.3406
139	0.642	0.716	0.358
140	0.6267	0.7466	0.3733
141	0.561	0.8781	0.439
142	0.5062	0.9876	0.4938
143	0.6161	0.7679	0.3839
144	0.5782	0.8436	0.4218
145	0.6235	0.7529	0.3765
146	0.5434	0.9133	0.4566
147	0.4651	0.9301	0.5349
148	0.3804	0.7609	0.6196
149	0.4893	0.9786	0.5107
150	0.51	0.98	0.49
151	0.45	0.9	0.55
152	0.3959	0.7917	0.6041
153	0.2898	0.5796	0.7102
154	0.3619	0.7239	0.6381
155	0.2844	0.5689	0.7156
156	0.3142	0.6284	0.6858

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 &  0.7396 &  0.5207 &  0.2604 \tabularnewline
9 &  0.6478 &  0.7045 &  0.3522 \tabularnewline
10 &  0.5136 &  0.9729 &  0.4864 \tabularnewline
11 &  0.4353 &  0.8707 &  0.5647 \tabularnewline
12 &  0.6041 &  0.7918 &  0.3959 \tabularnewline
13 &  0.5222 &  0.9555 &  0.4778 \tabularnewline
14 &  0.4217 &  0.8434 &  0.5783 \tabularnewline
15 &  0.3358 &  0.6716 &  0.6642 \tabularnewline
16 &  0.2642 &  0.5284 &  0.7358 \tabularnewline
17 &  0.1935 &  0.3869 &  0.8065 \tabularnewline
18 &  0.1849 &  0.3698 &  0.8151 \tabularnewline
19 &  0.1417 &  0.2834 &  0.8583 \tabularnewline
20 &  0.1106 &  0.2213 &  0.8894 \tabularnewline
21 &  0.08408 &  0.1682 &  0.9159 \tabularnewline
22 &  0.07577 &  0.1515 &  0.9242 \tabularnewline
23 &  0.09298 &  0.186 &  0.907 \tabularnewline
24 &  0.0923 &  0.1846 &  0.9077 \tabularnewline
25 &  0.1191 &  0.2382 &  0.8809 \tabularnewline
26 &  0.122 &  0.2439 &  0.878 \tabularnewline
27 &  0.1685 &  0.337 &  0.8315 \tabularnewline
28 &  0.1303 &  0.2605 &  0.8697 \tabularnewline
29 &  0.1035 &  0.2071 &  0.8965 \tabularnewline
30 &  0.1101 &  0.2203 &  0.8899 \tabularnewline
31 &  0.08353 &  0.1671 &  0.9165 \tabularnewline
32 &  0.139 &  0.2779 &  0.861 \tabularnewline
33 &  0.1088 &  0.2176 &  0.8912 \tabularnewline
34 &  0.08759 &  0.1752 &  0.9124 \tabularnewline
35 &  0.1028 &  0.2055 &  0.8972 \tabularnewline
36 &  0.1125 &  0.225 &  0.8875 \tabularnewline
37 &  0.1183 &  0.2367 &  0.8817 \tabularnewline
38 &  0.1104 &  0.2208 &  0.8896 \tabularnewline
39 &  0.1606 &  0.3212 &  0.8394 \tabularnewline
40 &  0.1483 &  0.2966 &  0.8517 \tabularnewline
41 &  0.1648 &  0.3295 &  0.8352 \tabularnewline
42 &  0.1619 &  0.3239 &  0.8381 \tabularnewline
43 &  0.2313 &  0.4625 &  0.7687 \tabularnewline
44 &  0.2158 &  0.4317 &  0.7842 \tabularnewline
45 &  0.1987 &  0.3974 &  0.8013 \tabularnewline
46 &  0.1806 &  0.3613 &  0.8194 \tabularnewline
47 &  0.4432 &  0.8863 &  0.5568 \tabularnewline
48 &  0.4607 &  0.9214 &  0.5393 \tabularnewline
49 &  0.4743 &  0.9485 &  0.5257 \tabularnewline
50 &  0.4936 &  0.9872 &  0.5064 \tabularnewline
51 &  0.5045 &  0.9909 &  0.4955 \tabularnewline
52 &  0.4552 &  0.9104 &  0.5448 \tabularnewline
53 &  0.4068 &  0.8135 &  0.5932 \tabularnewline
54 &  0.3654 &  0.7309 &  0.6346 \tabularnewline
55 &  0.3629 &  0.7259 &  0.6371 \tabularnewline
56 &  0.3745 &  0.749 &  0.6255 \tabularnewline
57 &  0.3677 &  0.7355 &  0.6323 \tabularnewline
58 &  0.68 &  0.6399 &  0.32 \tabularnewline
59 &  0.6988 &  0.6023 &  0.3012 \tabularnewline
60 &  0.6563 &  0.6874 &  0.3437 \tabularnewline
61 &  0.6153 &  0.7694 &  0.3847 \tabularnewline
62 &  0.5823 &  0.8354 &  0.4177 \tabularnewline
63 &  0.6099 &  0.7802 &  0.3901 \tabularnewline
64 &  0.5982 &  0.8036 &  0.4018 \tabularnewline
65 &  0.5653 &  0.8695 &  0.4347 \tabularnewline
66 &  0.5311 &  0.9378 &  0.4689 \tabularnewline
67 &  0.4852 &  0.9704 &  0.5148 \tabularnewline
68 &  0.4391 &  0.8782 &  0.5609 \tabularnewline
69 &  0.4055 &  0.811 &  0.5945 \tabularnewline
70 &  0.3768 &  0.7535 &  0.6232 \tabularnewline
71 &  0.444 &  0.888 &  0.556 \tabularnewline
72 &  0.5091 &  0.9818 &  0.4909 \tabularnewline
73 &  0.4961 &  0.9922 &  0.5039 \tabularnewline
74 &  0.4508 &  0.9016 &  0.5492 \tabularnewline
75 &  0.4104 &  0.8209 &  0.5896 \tabularnewline
76 &  0.3673 &  0.7346 &  0.6327 \tabularnewline
77 &  0.3891 &  0.7781 &  0.6109 \tabularnewline
78 &  0.3461 &  0.6922 &  0.6539 \tabularnewline
79 &  0.3452 &  0.6904 &  0.6548 \tabularnewline
80 &  0.3054 &  0.6108 &  0.6946 \tabularnewline
81 &  0.3394 &  0.6789 &  0.6606 \tabularnewline
82 &  0.2995 &  0.5989 &  0.7005 \tabularnewline
83 &  0.2634 &  0.5269 &  0.7366 \tabularnewline
84 &  0.2295 &  0.459 &  0.7705 \tabularnewline
85 &  0.2273 &  0.4547 &  0.7727 \tabularnewline
86 &  0.2246 &  0.4491 &  0.7754 \tabularnewline
87 &  0.2044 &  0.4088 &  0.7956 \tabularnewline
88 &  0.1863 &  0.3726 &  0.8137 \tabularnewline
89 &  0.2033 &  0.4066 &  0.7967 \tabularnewline
90 &  0.2 &  0.4001 &  0.8 \tabularnewline
91 &  0.1713 &  0.3426 &  0.8287 \tabularnewline
92 &  0.1835 &  0.3669 &  0.8165 \tabularnewline
93 &  0.1821 &  0.3642 &  0.8179 \tabularnewline
94 &  0.1584 &  0.3168 &  0.8416 \tabularnewline
95 &  0.1388 &  0.2777 &  0.8612 \tabularnewline
96 &  0.1155 &  0.2311 &  0.8845 \tabularnewline
97 &  0.09657 &  0.1931 &  0.9034 \tabularnewline
98 &  0.07867 &  0.1573 &  0.9213 \tabularnewline
99 &  0.0633 &  0.1266 &  0.9367 \tabularnewline
100 &  0.05283 &  0.1057 &  0.9472 \tabularnewline
101 &  0.1085 &  0.2171 &  0.8915 \tabularnewline
102 &  0.08973 &  0.1795 &  0.9103 \tabularnewline
103 &  0.09097 &  0.1819 &  0.909 \tabularnewline
104 &  0.09004 &  0.1801 &  0.91 \tabularnewline
105 &  0.07403 &  0.1481 &  0.926 \tabularnewline
106 &  0.06463 &  0.1293 &  0.9354 \tabularnewline
107 &  0.05121 &  0.1024 &  0.9488 \tabularnewline
108 &  0.1011 &  0.2022 &  0.8989 \tabularnewline
109 &  0.08229 &  0.1646 &  0.9177 \tabularnewline
110 &  0.08726 &  0.1745 &  0.9127 \tabularnewline
111 &  0.07894 &  0.1579 &  0.9211 \tabularnewline
112 &  0.06403 &  0.1281 &  0.936 \tabularnewline
113 &  0.05105 &  0.1021 &  0.949 \tabularnewline
114 &  0.0797 &  0.1594 &  0.9203 \tabularnewline
115 &  0.06329 &  0.1266 &  0.9367 \tabularnewline
116 &  0.06491 &  0.1298 &  0.9351 \tabularnewline
117 &  0.07919 &  0.1584 &  0.9208 \tabularnewline
118 &  0.08449 &  0.169 &  0.9155 \tabularnewline
119 &  0.07559 &  0.1512 &  0.9244 \tabularnewline
120 &  0.07837 &  0.1567 &  0.9216 \tabularnewline
121 &  0.06839 &  0.1368 &  0.9316 \tabularnewline
122 &  0.05457 &  0.1091 &  0.9454 \tabularnewline
123 &  0.04625 &  0.0925 &  0.9537 \tabularnewline
124 &  0.06983 &  0.1397 &  0.9302 \tabularnewline
125 &  0.5479 &  0.9041 &  0.4521 \tabularnewline
126 &  0.5408 &  0.9184 &  0.4592 \tabularnewline
127 &  0.62 &  0.7599 &  0.38 \tabularnewline
128 &  0.6444 &  0.7113 &  0.3556 \tabularnewline
129 &  0.6492 &  0.7016 &  0.3508 \tabularnewline
130 &  0.6404 &  0.7191 &  0.3596 \tabularnewline
131 &  0.6522 &  0.6955 &  0.3478 \tabularnewline
132 &  0.5997 &  0.8005 &  0.4003 \tabularnewline
133 &  0.5403 &  0.9194 &  0.4597 \tabularnewline
134 &  0.7483 &  0.5034 &  0.2517 \tabularnewline
135 &  0.728 &  0.544 &  0.272 \tabularnewline
136 &  0.6713 &  0.6575 &  0.3287 \tabularnewline
137 &  0.7161 &  0.5677 &  0.2839 \tabularnewline
138 &  0.6594 &  0.6813 &  0.3406 \tabularnewline
139 &  0.642 &  0.716 &  0.358 \tabularnewline
140 &  0.6267 &  0.7466 &  0.3733 \tabularnewline
141 &  0.561 &  0.8781 &  0.439 \tabularnewline
142 &  0.5062 &  0.9876 &  0.4938 \tabularnewline
143 &  0.6161 &  0.7679 &  0.3839 \tabularnewline
144 &  0.5782 &  0.8436 &  0.4218 \tabularnewline
145 &  0.6235 &  0.7529 &  0.3765 \tabularnewline
146 &  0.5434 &  0.9133 &  0.4566 \tabularnewline
147 &  0.4651 &  0.9301 &  0.5349 \tabularnewline
148 &  0.3804 &  0.7609 &  0.6196 \tabularnewline
149 &  0.4893 &  0.9786 &  0.5107 \tabularnewline
150 &  0.51 &  0.98 &  0.49 \tabularnewline
151 &  0.45 &  0.9 &  0.55 \tabularnewline
152 &  0.3959 &  0.7917 &  0.6041 \tabularnewline
153 &  0.2898 &  0.5796 &  0.7102 \tabularnewline
154 &  0.3619 &  0.7239 &  0.6381 \tabularnewline
155 &  0.2844 &  0.5689 &  0.7156 \tabularnewline
156 &  0.3142 &  0.6284 &  0.6858 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=303031&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] 0.7396[/C][C] 0.5207[/C][C] 0.2604[/C][/ROW]
[ROW][C]9[/C][C] 0.6478[/C][C] 0.7045[/C][C] 0.3522[/C][/ROW]
[ROW][C]10[/C][C] 0.5136[/C][C] 0.9729[/C][C] 0.4864[/C][/ROW]
[ROW][C]11[/C][C] 0.4353[/C][C] 0.8707[/C][C] 0.5647[/C][/ROW]
[ROW][C]12[/C][C] 0.6041[/C][C] 0.7918[/C][C] 0.3959[/C][/ROW]
[ROW][C]13[/C][C] 0.5222[/C][C] 0.9555[/C][C] 0.4778[/C][/ROW]
[ROW][C]14[/C][C] 0.4217[/C][C] 0.8434[/C][C] 0.5783[/C][/ROW]
[ROW][C]15[/C][C] 0.3358[/C][C] 0.6716[/C][C] 0.6642[/C][/ROW]
[ROW][C]16[/C][C] 0.2642[/C][C] 0.5284[/C][C] 0.7358[/C][/ROW]
[ROW][C]17[/C][C] 0.1935[/C][C] 0.3869[/C][C] 0.8065[/C][/ROW]
[ROW][C]18[/C][C] 0.1849[/C][C] 0.3698[/C][C] 0.8151[/C][/ROW]
[ROW][C]19[/C][C] 0.1417[/C][C] 0.2834[/C][C] 0.8583[/C][/ROW]
[ROW][C]20[/C][C] 0.1106[/C][C] 0.2213[/C][C] 0.8894[/C][/ROW]
[ROW][C]21[/C][C] 0.08408[/C][C] 0.1682[/C][C] 0.9159[/C][/ROW]
[ROW][C]22[/C][C] 0.07577[/C][C] 0.1515[/C][C] 0.9242[/C][/ROW]
[ROW][C]23[/C][C] 0.09298[/C][C] 0.186[/C][C] 0.907[/C][/ROW]
[ROW][C]24[/C][C] 0.0923[/C][C] 0.1846[/C][C] 0.9077[/C][/ROW]
[ROW][C]25[/C][C] 0.1191[/C][C] 0.2382[/C][C] 0.8809[/C][/ROW]
[ROW][C]26[/C][C] 0.122[/C][C] 0.2439[/C][C] 0.878[/C][/ROW]
[ROW][C]27[/C][C] 0.1685[/C][C] 0.337[/C][C] 0.8315[/C][/ROW]
[ROW][C]28[/C][C] 0.1303[/C][C] 0.2605[/C][C] 0.8697[/C][/ROW]
[ROW][C]29[/C][C] 0.1035[/C][C] 0.2071[/C][C] 0.8965[/C][/ROW]
[ROW][C]30[/C][C] 0.1101[/C][C] 0.2203[/C][C] 0.8899[/C][/ROW]
[ROW][C]31[/C][C] 0.08353[/C][C] 0.1671[/C][C] 0.9165[/C][/ROW]
[ROW][C]32[/C][C] 0.139[/C][C] 0.2779[/C][C] 0.861[/C][/ROW]
[ROW][C]33[/C][C] 0.1088[/C][C] 0.2176[/C][C] 0.8912[/C][/ROW]
[ROW][C]34[/C][C] 0.08759[/C][C] 0.1752[/C][C] 0.9124[/C][/ROW]
[ROW][C]35[/C][C] 0.1028[/C][C] 0.2055[/C][C] 0.8972[/C][/ROW]
[ROW][C]36[/C][C] 0.1125[/C][C] 0.225[/C][C] 0.8875[/C][/ROW]
[ROW][C]37[/C][C] 0.1183[/C][C] 0.2367[/C][C] 0.8817[/C][/ROW]
[ROW][C]38[/C][C] 0.1104[/C][C] 0.2208[/C][C] 0.8896[/C][/ROW]
[ROW][C]39[/C][C] 0.1606[/C][C] 0.3212[/C][C] 0.8394[/C][/ROW]
[ROW][C]40[/C][C] 0.1483[/C][C] 0.2966[/C][C] 0.8517[/C][/ROW]
[ROW][C]41[/C][C] 0.1648[/C][C] 0.3295[/C][C] 0.8352[/C][/ROW]
[ROW][C]42[/C][C] 0.1619[/C][C] 0.3239[/C][C] 0.8381[/C][/ROW]
[ROW][C]43[/C][C] 0.2313[/C][C] 0.4625[/C][C] 0.7687[/C][/ROW]
[ROW][C]44[/C][C] 0.2158[/C][C] 0.4317[/C][C] 0.7842[/C][/ROW]
[ROW][C]45[/C][C] 0.1987[/C][C] 0.3974[/C][C] 0.8013[/C][/ROW]
[ROW][C]46[/C][C] 0.1806[/C][C] 0.3613[/C][C] 0.8194[/C][/ROW]
[ROW][C]47[/C][C] 0.4432[/C][C] 0.8863[/C][C] 0.5568[/C][/ROW]
[ROW][C]48[/C][C] 0.4607[/C][C] 0.9214[/C][C] 0.5393[/C][/ROW]
[ROW][C]49[/C][C] 0.4743[/C][C] 0.9485[/C][C] 0.5257[/C][/ROW]
[ROW][C]50[/C][C] 0.4936[/C][C] 0.9872[/C][C] 0.5064[/C][/ROW]
[ROW][C]51[/C][C] 0.5045[/C][C] 0.9909[/C][C] 0.4955[/C][/ROW]
[ROW][C]52[/C][C] 0.4552[/C][C] 0.9104[/C][C] 0.5448[/C][/ROW]
[ROW][C]53[/C][C] 0.4068[/C][C] 0.8135[/C][C] 0.5932[/C][/ROW]
[ROW][C]54[/C][C] 0.3654[/C][C] 0.7309[/C][C] 0.6346[/C][/ROW]
[ROW][C]55[/C][C] 0.3629[/C][C] 0.7259[/C][C] 0.6371[/C][/ROW]
[ROW][C]56[/C][C] 0.3745[/C][C] 0.749[/C][C] 0.6255[/C][/ROW]
[ROW][C]57[/C][C] 0.3677[/C][C] 0.7355[/C][C] 0.6323[/C][/ROW]
[ROW][C]58[/C][C] 0.68[/C][C] 0.6399[/C][C] 0.32[/C][/ROW]
[ROW][C]59[/C][C] 0.6988[/C][C] 0.6023[/C][C] 0.3012[/C][/ROW]
[ROW][C]60[/C][C] 0.6563[/C][C] 0.6874[/C][C] 0.3437[/C][/ROW]
[ROW][C]61[/C][C] 0.6153[/C][C] 0.7694[/C][C] 0.3847[/C][/ROW]
[ROW][C]62[/C][C] 0.5823[/C][C] 0.8354[/C][C] 0.4177[/C][/ROW]
[ROW][C]63[/C][C] 0.6099[/C][C] 0.7802[/C][C] 0.3901[/C][/ROW]
[ROW][C]64[/C][C] 0.5982[/C][C] 0.8036[/C][C] 0.4018[/C][/ROW]
[ROW][C]65[/C][C] 0.5653[/C][C] 0.8695[/C][C] 0.4347[/C][/ROW]
[ROW][C]66[/C][C] 0.5311[/C][C] 0.9378[/C][C] 0.4689[/C][/ROW]
[ROW][C]67[/C][C] 0.4852[/C][C] 0.9704[/C][C] 0.5148[/C][/ROW]
[ROW][C]68[/C][C] 0.4391[/C][C] 0.8782[/C][C] 0.5609[/C][/ROW]
[ROW][C]69[/C][C] 0.4055[/C][C] 0.811[/C][C] 0.5945[/C][/ROW]
[ROW][C]70[/C][C] 0.3768[/C][C] 0.7535[/C][C] 0.6232[/C][/ROW]
[ROW][C]71[/C][C] 0.444[/C][C] 0.888[/C][C] 0.556[/C][/ROW]
[ROW][C]72[/C][C] 0.5091[/C][C] 0.9818[/C][C] 0.4909[/C][/ROW]
[ROW][C]73[/C][C] 0.4961[/C][C] 0.9922[/C][C] 0.5039[/C][/ROW]
[ROW][C]74[/C][C] 0.4508[/C][C] 0.9016[/C][C] 0.5492[/C][/ROW]
[ROW][C]75[/C][C] 0.4104[/C][C] 0.8209[/C][C] 0.5896[/C][/ROW]
[ROW][C]76[/C][C] 0.3673[/C][C] 0.7346[/C][C] 0.6327[/C][/ROW]
[ROW][C]77[/C][C] 0.3891[/C][C] 0.7781[/C][C] 0.6109[/C][/ROW]
[ROW][C]78[/C][C] 0.3461[/C][C] 0.6922[/C][C] 0.6539[/C][/ROW]
[ROW][C]79[/C][C] 0.3452[/C][C] 0.6904[/C][C] 0.6548[/C][/ROW]
[ROW][C]80[/C][C] 0.3054[/C][C] 0.6108[/C][C] 0.6946[/C][/ROW]
[ROW][C]81[/C][C] 0.3394[/C][C] 0.6789[/C][C] 0.6606[/C][/ROW]
[ROW][C]82[/C][C] 0.2995[/C][C] 0.5989[/C][C] 0.7005[/C][/ROW]
[ROW][C]83[/C][C] 0.2634[/C][C] 0.5269[/C][C] 0.7366[/C][/ROW]
[ROW][C]84[/C][C] 0.2295[/C][C] 0.459[/C][C] 0.7705[/C][/ROW]
[ROW][C]85[/C][C] 0.2273[/C][C] 0.4547[/C][C] 0.7727[/C][/ROW]
[ROW][C]86[/C][C] 0.2246[/C][C] 0.4491[/C][C] 0.7754[/C][/ROW]
[ROW][C]87[/C][C] 0.2044[/C][C] 0.4088[/C][C] 0.7956[/C][/ROW]
[ROW][C]88[/C][C] 0.1863[/C][C] 0.3726[/C][C] 0.8137[/C][/ROW]
[ROW][C]89[/C][C] 0.2033[/C][C] 0.4066[/C][C] 0.7967[/C][/ROW]
[ROW][C]90[/C][C] 0.2[/C][C] 0.4001[/C][C] 0.8[/C][/ROW]
[ROW][C]91[/C][C] 0.1713[/C][C] 0.3426[/C][C] 0.8287[/C][/ROW]
[ROW][C]92[/C][C] 0.1835[/C][C] 0.3669[/C][C] 0.8165[/C][/ROW]
[ROW][C]93[/C][C] 0.1821[/C][C] 0.3642[/C][C] 0.8179[/C][/ROW]
[ROW][C]94[/C][C] 0.1584[/C][C] 0.3168[/C][C] 0.8416[/C][/ROW]
[ROW][C]95[/C][C] 0.1388[/C][C] 0.2777[/C][C] 0.8612[/C][/ROW]
[ROW][C]96[/C][C] 0.1155[/C][C] 0.2311[/C][C] 0.8845[/C][/ROW]
[ROW][C]97[/C][C] 0.09657[/C][C] 0.1931[/C][C] 0.9034[/C][/ROW]
[ROW][C]98[/C][C] 0.07867[/C][C] 0.1573[/C][C] 0.9213[/C][/ROW]
[ROW][C]99[/C][C] 0.0633[/C][C] 0.1266[/C][C] 0.9367[/C][/ROW]
[ROW][C]100[/C][C] 0.05283[/C][C] 0.1057[/C][C] 0.9472[/C][/ROW]
[ROW][C]101[/C][C] 0.1085[/C][C] 0.2171[/C][C] 0.8915[/C][/ROW]
[ROW][C]102[/C][C] 0.08973[/C][C] 0.1795[/C][C] 0.9103[/C][/ROW]
[ROW][C]103[/C][C] 0.09097[/C][C] 0.1819[/C][C] 0.909[/C][/ROW]
[ROW][C]104[/C][C] 0.09004[/C][C] 0.1801[/C][C] 0.91[/C][/ROW]
[ROW][C]105[/C][C] 0.07403[/C][C] 0.1481[/C][C] 0.926[/C][/ROW]
[ROW][C]106[/C][C] 0.06463[/C][C] 0.1293[/C][C] 0.9354[/C][/ROW]
[ROW][C]107[/C][C] 0.05121[/C][C] 0.1024[/C][C] 0.9488[/C][/ROW]
[ROW][C]108[/C][C] 0.1011[/C][C] 0.2022[/C][C] 0.8989[/C][/ROW]
[ROW][C]109[/C][C] 0.08229[/C][C] 0.1646[/C][C] 0.9177[/C][/ROW]
[ROW][C]110[/C][C] 0.08726[/C][C] 0.1745[/C][C] 0.9127[/C][/ROW]
[ROW][C]111[/C][C] 0.07894[/C][C] 0.1579[/C][C] 0.9211[/C][/ROW]
[ROW][C]112[/C][C] 0.06403[/C][C] 0.1281[/C][C] 0.936[/C][/ROW]
[ROW][C]113[/C][C] 0.05105[/C][C] 0.1021[/C][C] 0.949[/C][/ROW]
[ROW][C]114[/C][C] 0.0797[/C][C] 0.1594[/C][C] 0.9203[/C][/ROW]
[ROW][C]115[/C][C] 0.06329[/C][C] 0.1266[/C][C] 0.9367[/C][/ROW]
[ROW][C]116[/C][C] 0.06491[/C][C] 0.1298[/C][C] 0.9351[/C][/ROW]
[ROW][C]117[/C][C] 0.07919[/C][C] 0.1584[/C][C] 0.9208[/C][/ROW]
[ROW][C]118[/C][C] 0.08449[/C][C] 0.169[/C][C] 0.9155[/C][/ROW]
[ROW][C]119[/C][C] 0.07559[/C][C] 0.1512[/C][C] 0.9244[/C][/ROW]
[ROW][C]120[/C][C] 0.07837[/C][C] 0.1567[/C][C] 0.9216[/C][/ROW]
[ROW][C]121[/C][C] 0.06839[/C][C] 0.1368[/C][C] 0.9316[/C][/ROW]
[ROW][C]122[/C][C] 0.05457[/C][C] 0.1091[/C][C] 0.9454[/C][/ROW]
[ROW][C]123[/C][C] 0.04625[/C][C] 0.0925[/C][C] 0.9537[/C][/ROW]
[ROW][C]124[/C][C] 0.06983[/C][C] 0.1397[/C][C] 0.9302[/C][/ROW]
[ROW][C]125[/C][C] 0.5479[/C][C] 0.9041[/C][C] 0.4521[/C][/ROW]
[ROW][C]126[/C][C] 0.5408[/C][C] 0.9184[/C][C] 0.4592[/C][/ROW]
[ROW][C]127[/C][C] 0.62[/C][C] 0.7599[/C][C] 0.38[/C][/ROW]
[ROW][C]128[/C][C] 0.6444[/C][C] 0.7113[/C][C] 0.3556[/C][/ROW]
[ROW][C]129[/C][C] 0.6492[/C][C] 0.7016[/C][C] 0.3508[/C][/ROW]
[ROW][C]130[/C][C] 0.6404[/C][C] 0.7191[/C][C] 0.3596[/C][/ROW]
[ROW][C]131[/C][C] 0.6522[/C][C] 0.6955[/C][C] 0.3478[/C][/ROW]
[ROW][C]132[/C][C] 0.5997[/C][C] 0.8005[/C][C] 0.4003[/C][/ROW]
[ROW][C]133[/C][C] 0.5403[/C][C] 0.9194[/C][C] 0.4597[/C][/ROW]
[ROW][C]134[/C][C] 0.7483[/C][C] 0.5034[/C][C] 0.2517[/C][/ROW]
[ROW][C]135[/C][C] 0.728[/C][C] 0.544[/C][C] 0.272[/C][/ROW]
[ROW][C]136[/C][C] 0.6713[/C][C] 0.6575[/C][C] 0.3287[/C][/ROW]
[ROW][C]137[/C][C] 0.7161[/C][C] 0.5677[/C][C] 0.2839[/C][/ROW]
[ROW][C]138[/C][C] 0.6594[/C][C] 0.6813[/C][C] 0.3406[/C][/ROW]
[ROW][C]139[/C][C] 0.642[/C][C] 0.716[/C][C] 0.358[/C][/ROW]
[ROW][C]140[/C][C] 0.6267[/C][C] 0.7466[/C][C] 0.3733[/C][/ROW]
[ROW][C]141[/C][C] 0.561[/C][C] 0.8781[/C][C] 0.439[/C][/ROW]
[ROW][C]142[/C][C] 0.5062[/C][C] 0.9876[/C][C] 0.4938[/C][/ROW]
[ROW][C]143[/C][C] 0.6161[/C][C] 0.7679[/C][C] 0.3839[/C][/ROW]
[ROW][C]144[/C][C] 0.5782[/C][C] 0.8436[/C][C] 0.4218[/C][/ROW]
[ROW][C]145[/C][C] 0.6235[/C][C] 0.7529[/C][C] 0.3765[/C][/ROW]
[ROW][C]146[/C][C] 0.5434[/C][C] 0.9133[/C][C] 0.4566[/C][/ROW]
[ROW][C]147[/C][C] 0.4651[/C][C] 0.9301[/C][C] 0.5349[/C][/ROW]
[ROW][C]148[/C][C] 0.3804[/C][C] 0.7609[/C][C] 0.6196[/C][/ROW]
[ROW][C]149[/C][C] 0.4893[/C][C] 0.9786[/C][C] 0.5107[/C][/ROW]
[ROW][C]150[/C][C] 0.51[/C][C] 0.98[/C][C] 0.49[/C][/ROW]
[ROW][C]151[/C][C] 0.45[/C][C] 0.9[/C][C] 0.55[/C][/ROW]
[ROW][C]152[/C][C] 0.3959[/C][C] 0.7917[/C][C] 0.6041[/C][/ROW]
[ROW][C]153[/C][C] 0.2898[/C][C] 0.5796[/C][C] 0.7102[/C][/ROW]
[ROW][C]154[/C][C] 0.3619[/C][C] 0.7239[/C][C] 0.6381[/C][/ROW]
[ROW][C]155[/C][C] 0.2844[/C][C] 0.5689[/C][C] 0.7156[/C][/ROW]
[ROW][C]156[/C][C] 0.3142[/C][C] 0.6284[/C][C] 0.6858[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=303031&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=303031&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	0.7396	0.5207	0.2604
9	0.6478	0.7045	0.3522
10	0.5136	0.9729	0.4864
11	0.4353	0.8707	0.5647
12	0.6041	0.7918	0.3959
13	0.5222	0.9555	0.4778
14	0.4217	0.8434	0.5783
15	0.3358	0.6716	0.6642
16	0.2642	0.5284	0.7358
17	0.1935	0.3869	0.8065
18	0.1849	0.3698	0.8151
19	0.1417	0.2834	0.8583
20	0.1106	0.2213	0.8894
21	0.08408	0.1682	0.9159
22	0.07577	0.1515	0.9242
23	0.09298	0.186	0.907
24	0.0923	0.1846	0.9077
25	0.1191	0.2382	0.8809
26	0.122	0.2439	0.878
27	0.1685	0.337	0.8315
28	0.1303	0.2605	0.8697
29	0.1035	0.2071	0.8965
30	0.1101	0.2203	0.8899
31	0.08353	0.1671	0.9165
32	0.139	0.2779	0.861
33	0.1088	0.2176	0.8912
34	0.08759	0.1752	0.9124
35	0.1028	0.2055	0.8972
36	0.1125	0.225	0.8875
37	0.1183	0.2367	0.8817
38	0.1104	0.2208	0.8896
39	0.1606	0.3212	0.8394
40	0.1483	0.2966	0.8517
41	0.1648	0.3295	0.8352
42	0.1619	0.3239	0.8381
43	0.2313	0.4625	0.7687
44	0.2158	0.4317	0.7842
45	0.1987	0.3974	0.8013
46	0.1806	0.3613	0.8194
47	0.4432	0.8863	0.5568
48	0.4607	0.9214	0.5393
49	0.4743	0.9485	0.5257
50	0.4936	0.9872	0.5064
51	0.5045	0.9909	0.4955
52	0.4552	0.9104	0.5448
53	0.4068	0.8135	0.5932
54	0.3654	0.7309	0.6346
55	0.3629	0.7259	0.6371
56	0.3745	0.749	0.6255
57	0.3677	0.7355	0.6323
58	0.68	0.6399	0.32
59	0.6988	0.6023	0.3012
60	0.6563	0.6874	0.3437
61	0.6153	0.7694	0.3847
62	0.5823	0.8354	0.4177
63	0.6099	0.7802	0.3901
64	0.5982	0.8036	0.4018
65	0.5653	0.8695	0.4347
66	0.5311	0.9378	0.4689
67	0.4852	0.9704	0.5148
68	0.4391	0.8782	0.5609
69	0.4055	0.811	0.5945
70	0.3768	0.7535	0.6232
71	0.444	0.888	0.556
72	0.5091	0.9818	0.4909
73	0.4961	0.9922	0.5039
74	0.4508	0.9016	0.5492
75	0.4104	0.8209	0.5896
76	0.3673	0.7346	0.6327
77	0.3891	0.7781	0.6109
78	0.3461	0.6922	0.6539
79	0.3452	0.6904	0.6548
80	0.3054	0.6108	0.6946
81	0.3394	0.6789	0.6606
82	0.2995	0.5989	0.7005
83	0.2634	0.5269	0.7366
84	0.2295	0.459	0.7705
85	0.2273	0.4547	0.7727
86	0.2246	0.4491	0.7754
87	0.2044	0.4088	0.7956
88	0.1863	0.3726	0.8137
89	0.2033	0.4066	0.7967
90	0.2	0.4001	0.8
91	0.1713	0.3426	0.8287
92	0.1835	0.3669	0.8165
93	0.1821	0.3642	0.8179
94	0.1584	0.3168	0.8416
95	0.1388	0.2777	0.8612
96	0.1155	0.2311	0.8845
97	0.09657	0.1931	0.9034
98	0.07867	0.1573	0.9213
99	0.0633	0.1266	0.9367
100	0.05283	0.1057	0.9472
101	0.1085	0.2171	0.8915
102	0.08973	0.1795	0.9103
103	0.09097	0.1819	0.909
104	0.09004	0.1801	0.91
105	0.07403	0.1481	0.926
106	0.06463	0.1293	0.9354
107	0.05121	0.1024	0.9488
108	0.1011	0.2022	0.8989
109	0.08229	0.1646	0.9177
110	0.08726	0.1745	0.9127
111	0.07894	0.1579	0.9211
112	0.06403	0.1281	0.936
113	0.05105	0.1021	0.949
114	0.0797	0.1594	0.9203
115	0.06329	0.1266	0.9367
116	0.06491	0.1298	0.9351
117	0.07919	0.1584	0.9208
118	0.08449	0.169	0.9155
119	0.07559	0.1512	0.9244
120	0.07837	0.1567	0.9216
121	0.06839	0.1368	0.9316
122	0.05457	0.1091	0.9454
123	0.04625	0.0925	0.9537
124	0.06983	0.1397	0.9302
125	0.5479	0.9041	0.4521
126	0.5408	0.9184	0.4592
127	0.62	0.7599	0.38
128	0.6444	0.7113	0.3556
129	0.6492	0.7016	0.3508
130	0.6404	0.7191	0.3596
131	0.6522	0.6955	0.3478
132	0.5997	0.8005	0.4003
133	0.5403	0.9194	0.4597
134	0.7483	0.5034	0.2517
135	0.728	0.544	0.272
136	0.6713	0.6575	0.3287
137	0.7161	0.5677	0.2839
138	0.6594	0.6813	0.3406
139	0.642	0.716	0.358
140	0.6267	0.7466	0.3733
141	0.561	0.8781	0.439
142	0.5062	0.9876	0.4938
143	0.6161	0.7679	0.3839
144	0.5782	0.8436	0.4218
145	0.6235	0.7529	0.3765
146	0.5434	0.9133	0.4566
147	0.4651	0.9301	0.5349
148	0.3804	0.7609	0.6196
149	0.4893	0.9786	0.5107
150	0.51	0.98	0.49
151	0.45	0.9	0.55
152	0.3959	0.7917	0.6041
153	0.2898	0.5796	0.7102
154	0.3619	0.7239	0.6381
155	0.2844	0.5689	0.7156
156	0.3142	0.6284	0.6858

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	0	0	OK
10% type I error level	1	0.00671141	OK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 &  0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 1 & 0.00671141 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=303031&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C] 0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]1[/C][C]0.00671141[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=303031&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=303031&T=6

As an alternative you can also use a QR Code:

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	0	0	OK
10% type I error level	1	0.00671141	OK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 2.1976, df1 = 2, df2 = 157, p-value = 0.1145
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.3192, df1 = 8, df2 = 151, p-value = 0.2379
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 2.7209, df1 = 2, df2 = 157, p-value = 0.06892

\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 = 2.1976, df1 = 2, df2 = 157, p-value = 0.1145
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.3192, df1 = 8, df2 = 151, p-value = 0.2379
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 2.7209, df1 = 2, df2 = 157, p-value = 0.06892
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=303031&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 = 2.1976, df1 = 2, df2 = 157, p-value = 0.1145
[/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 = 1.3192, df1 = 8, df2 = 151, p-value = 0.2379
[/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 = 2.7209, df1 = 2, df2 = 157, p-value = 0.06892
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=303031&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=303031&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 = 2.1976, df1 = 2, df2 = 157, p-value = 0.1145
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.3192, df1 = 8, df2 = 151, p-value = 0.2379
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 2.7209, df1 = 2, df2 = 157, p-value = 0.06892

Variance Inflation Factors (Multicollinearity)
> vif b c d e 1.270755 1.283208 1.362280 1.159302

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
       b        c        d        e 
1.270755 1.283208 1.362280 1.159302 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=303031&T=8

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
       b        c        d        e 
1.270755 1.283208 1.362280 1.159302 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=303031&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=303031&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 b c d e 1.270755 1.283208 1.362280 1.159302

Figure 1

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Figure 3

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Figure 4

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Figure 5

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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):

Parameters (R input):

par1 = 1 ; 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