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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Thu, 15 Dec 2016 17:49:06 +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/15/t1481820727p3u3qhcsxffdz4s.htm/, Retrieved Fri, 03 May 2024 14:37:34 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=299934, Retrieved Fri, 03 May 2024 14:37:34 +0000

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

-       [Multiple Regression] [] [2016-12-15 16:49:06] [ab21f94b493a02d0f1353f0a7f852860] [Current]

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

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=299934&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=299934&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=299934&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] = + 21.8136 -0.0973757b[t] -0.0573509c[t] + 0.121752d[t] -0.144988e[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
a[t] =  +  21.8136 -0.0973757b[t] -0.0573509c[t] +  0.121752d[t] -0.144988e[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=299934&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]a[t] =  +  21.8136 -0.0973757b[t] -0.0573509c[t] +  0.121752d[t] -0.144988e[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=299934&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=299934&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] = + 21.8136 -0.0973757b[t] -0.0573509c[t] + 0.121752d[t] -0.144988e[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)	+21.81	1.072	+2.0340e+01	2.34e-45	1.17e-45
b	-0.09738	0.1418	-6.8670e-01	0.4933	0.2466
c	-0.05735	0.1283	-4.4690e-01	0.6556	0.3278
d	+0.1217	0.1507	+8.0810e-01	0.4203	0.2102
e	-0.145	0.1854	-7.8200e-01	0.4354	0.2177

\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) & +21.81 &  1.072 & +2.0340e+01 &  2.34e-45 &  1.17e-45 \tabularnewline
b & -0.09738 &  0.1418 & -6.8670e-01 &  0.4933 &  0.2466 \tabularnewline
c & -0.05735 &  0.1283 & -4.4690e-01 &  0.6556 &  0.3278 \tabularnewline
d & +0.1217 &  0.1507 & +8.0810e-01 &  0.4203 &  0.2102 \tabularnewline
e & -0.145 &  0.1854 & -7.8200e-01 &  0.4354 &  0.2177 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=299934&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]+21.81[/C][C] 1.072[/C][C]+2.0340e+01[/C][C] 2.34e-45[/C][C] 1.17e-45[/C][/ROW]
[ROW][C]b[/C][C]-0.09738[/C][C] 0.1418[/C][C]-6.8670e-01[/C][C] 0.4933[/C][C] 0.2466[/C][/ROW]
[ROW][C]c[/C][C]-0.05735[/C][C] 0.1283[/C][C]-4.4690e-01[/C][C] 0.6556[/C][C] 0.3278[/C][/ROW]
[ROW][C]d[/C][C]+0.1217[/C][C] 0.1507[/C][C]+8.0810e-01[/C][C] 0.4203[/C][C] 0.2102[/C][/ROW]
[ROW][C]e[/C][C]-0.145[/C][C] 0.1854[/C][C]-7.8200e-01[/C][C] 0.4354[/C][C] 0.2177[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=299934&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=299934&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)	+21.81	1.072	+2.0340e+01	2.34e-45	1.17e-45
b	-0.09738	0.1418	-6.8670e-01	0.4933	0.2466
c	-0.05735	0.1283	-4.4690e-01	0.6556	0.3278
d	+0.1217	0.1507	+8.0810e-01	0.4203	0.2102
e	-0.145	0.1854	-7.8200e-01	0.4354	0.2177

Multiple Linear Regression - Regression Statistics
Multiple R	0.1217
R-squared	0.01481
Adjusted R-squared	-0.01094
F-TEST (value)	0.5752
F-TEST (DF numerator)	4
F-TEST (DF denominator)	153
p-value	0.6811
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.771
Sum Squared Residuals	479.8

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.1217 \tabularnewline
R-squared &  0.01481 \tabularnewline
Adjusted R-squared & -0.01094 \tabularnewline
F-TEST (value) &  0.5752 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 153 \tabularnewline
p-value &  0.6811 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  1.771 \tabularnewline
Sum Squared Residuals &  479.8 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=299934&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.1217[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.01481[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]-0.01094[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 0.5752[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]153[/C][/ROW]
[ROW][C]p-value[/C][C] 0.6811[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 1.771[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 479.8[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=299934&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=299934&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	0.1217
R-squared	0.01481
Adjusted R-squared	-0.01094
F-TEST (value)	0.5752
F-TEST (DF numerator)	4
F-TEST (DF denominator)	153
p-value	0.6811
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.771
Sum Squared Residuals	479.8

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	22	21.29	0.7105
2	24	20.85	3.149
3	21	21.34	-0.3382
4	21	21.16	-0.1591
5	24	21.22	2.778
6	20	20.77	-0.7706
7	22	21.39	0.6061
8	20	21.34	-1.338
9	23	20.98	2.02
10	21	21.17	-0.1677
11	19	21.12	-2.118
12	19	21.19	-2.189
13	21	21	-0.004388
14	21	21.22	-0.2165
15	22	21.57	0.427
16	22	21.08	0.9199
17	21	20.92	0.08439
18	21	21	-0.004388
19	21	20.95	0.05296
20	20	21.04	-1.044
21	22	21.69	0.3123
22	22	20.98	1.02
23	24	21.41	2.589
24	21	20.98	0.01999
25	19	21.03	-2.028
26	19	21.28	-2.28
27	23	20.98	2.019
28	21	21.31	-0.3138
29	21	21.12	-0.1191
30	19	21.17	-2.175
31	21	21.39	-0.3868
32	21	21.24	-0.2397
33	21	20.98	0.01999
34	23	21.13	1.865
35	19	21.04	-2.038
36	19	20.98	-1.98
37	18	21.04	-3.037
38	22	21.56	0.4438
39	18	21.08	-3.077
40	22	21	1.002
41	18	21.31	-3.314
42	22	21.4	0.5985
43	22	21.28	0.7203
44	22	21.1	0.8982
45	25	21.6	3.399
46	19	21.1	-2.102
47	19	21.1	-2.102
48	19	21.17	-2.175
49	19	20.88	-1.876
50	21	21.46	-0.4588
51	21	21.25	-0.2494
52	20	21.18	-1.182
53	19	20.98	-1.98
54	19	21.45	-2.449
55	22	21.26	0.7435
56	26	21.09	4.905
57	19	21.16	-2.159
58	21	21.48	-0.4832
59	21	20.86	0.1417
60	20	21.18	-1.182
61	23	21.29	1.711
62	22	21.12	0.875
63	22	21.04	0.9556
64	22	20.98	1.02
65	21	21.16	-0.1591
66	21	21.09	-0.09471
67	22	20.84	1.159
68	23	21.34	1.656
69	18	21.46	-3.465
70	24	20.86	3.142
71	22	20.68	1.32
72	21	20.98	0.01999
73	21	20.86	0.1406
74	21	21.42	-0.4161
75	23	21.23	1.768
76	21	21.22	-0.2165
77	23	20.86	2.142
78	21	20.86	0.1417
79	19	20.81	-1.811
80	21	21.09	-0.09471
81	21	21.41	-0.4112
82	21	21.05	-0.05469
83	23	21.54	1.463
84	20	21.3	-1.297
85	20	20.98	-0.98
86	19	21.4	-2.401
87	23	21.08	1.923
88	22	21.22	0.7765
89	19	21.25	-2.247
90	23	21.36	1.639
91	22	21.08	0.9226
92	22	20.96	1.043
93	21	21.15	-0.1509
94	21	21.2	-0.1991
95	21	21.65	-0.6536
96	21	21.16	-0.1591
97	22	21.11	0.8885
98	25	21.34	3.656
99	21	20.94	0.06001
100	23	21.13	1.874
101	22	20.98	1.019
102	20	21.1	-1.102
103	21	21.04	-0.04441
104	25	20.9	4.101
105	21	21.36	-0.3615
106	19	21.39	-2.394
107	23	21.3	1.696
108	22	21.13	0.8653
109	21	21.15	-0.1521
110	24	21.17	2.825
111	21	21.27	-0.2721
112	19	20.88	-1.876
113	18	21.1	-3.102
114	19	21.28	-2.28
115	20	20.86	-0.8583
116	19	20.96	-1.957
117	22	21.22	0.7835
118	21	21.24	-0.2397
119	22	21.3	0.7035
120	24	21.22	2.776
121	28	21.36	6.639
122	18	21.22	-3.216
123	23	21.6	1.396
124	19	21.04	-2.044
125	23	21.39	1.614
126	22	21.12	0.8809
127	21	21.1	-0.1018
128	19	21.26	-2.264
129	21	21.59	-0.5865
130	23	21.33	1.669
131	22	21.26	0.7447
132	19	21.27	-2.274
133	19	21.22	-2.224
134	21	20.83	0.1747
135	22	21.05	0.9458
136	21	21.2	-0.1991
137	20	20.92	-0.9227
138	23	21.37	1.631
139	22	21.6	0.3989
140	23	20.92	2.077
141	22	20.77	1.229
142	21	21.04	-0.04441
143	20	21.4	-1.396
144	18	21.23	-3.232
145	18	20.85	-2.852
146	20	21.22	-1.216
147	21	20.86	0.1417
148	24	21.09	2.905
149	19	20.92	-1.923
150	20	21.44	-1.442
151	19	20.63	-1.632
152	23	21.4	1.599
153	22	21.09	0.9053
154	21	20.98	0.01999
155	24	20.83	3.172
156	21	21.04	-0.03736
157	21	21.13	-0.1347
158	22	21.73	0.2744

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  22 &  21.29 &  0.7105 \tabularnewline
2 &  24 &  20.85 &  3.149 \tabularnewline
3 &  21 &  21.34 & -0.3382 \tabularnewline
4 &  21 &  21.16 & -0.1591 \tabularnewline
5 &  24 &  21.22 &  2.778 \tabularnewline
6 &  20 &  20.77 & -0.7706 \tabularnewline
7 &  22 &  21.39 &  0.6061 \tabularnewline
8 &  20 &  21.34 & -1.338 \tabularnewline
9 &  23 &  20.98 &  2.02 \tabularnewline
10 &  21 &  21.17 & -0.1677 \tabularnewline
11 &  19 &  21.12 & -2.118 \tabularnewline
12 &  19 &  21.19 & -2.189 \tabularnewline
13 &  21 &  21 & -0.004388 \tabularnewline
14 &  21 &  21.22 & -0.2165 \tabularnewline
15 &  22 &  21.57 &  0.427 \tabularnewline
16 &  22 &  21.08 &  0.9199 \tabularnewline
17 &  21 &  20.92 &  0.08439 \tabularnewline
18 &  21 &  21 & -0.004388 \tabularnewline
19 &  21 &  20.95 &  0.05296 \tabularnewline
20 &  20 &  21.04 & -1.044 \tabularnewline
21 &  22 &  21.69 &  0.3123 \tabularnewline
22 &  22 &  20.98 &  1.02 \tabularnewline
23 &  24 &  21.41 &  2.589 \tabularnewline
24 &  21 &  20.98 &  0.01999 \tabularnewline
25 &  19 &  21.03 & -2.028 \tabularnewline
26 &  19 &  21.28 & -2.28 \tabularnewline
27 &  23 &  20.98 &  2.019 \tabularnewline
28 &  21 &  21.31 & -0.3138 \tabularnewline
29 &  21 &  21.12 & -0.1191 \tabularnewline
30 &  19 &  21.17 & -2.175 \tabularnewline
31 &  21 &  21.39 & -0.3868 \tabularnewline
32 &  21 &  21.24 & -0.2397 \tabularnewline
33 &  21 &  20.98 &  0.01999 \tabularnewline
34 &  23 &  21.13 &  1.865 \tabularnewline
35 &  19 &  21.04 & -2.038 \tabularnewline
36 &  19 &  20.98 & -1.98 \tabularnewline
37 &  18 &  21.04 & -3.037 \tabularnewline
38 &  22 &  21.56 &  0.4438 \tabularnewline
39 &  18 &  21.08 & -3.077 \tabularnewline
40 &  22 &  21 &  1.002 \tabularnewline
41 &  18 &  21.31 & -3.314 \tabularnewline
42 &  22 &  21.4 &  0.5985 \tabularnewline
43 &  22 &  21.28 &  0.7203 \tabularnewline
44 &  22 &  21.1 &  0.8982 \tabularnewline
45 &  25 &  21.6 &  3.399 \tabularnewline
46 &  19 &  21.1 & -2.102 \tabularnewline
47 &  19 &  21.1 & -2.102 \tabularnewline
48 &  19 &  21.17 & -2.175 \tabularnewline
49 &  19 &  20.88 & -1.876 \tabularnewline
50 &  21 &  21.46 & -0.4588 \tabularnewline
51 &  21 &  21.25 & -0.2494 \tabularnewline
52 &  20 &  21.18 & -1.182 \tabularnewline
53 &  19 &  20.98 & -1.98 \tabularnewline
54 &  19 &  21.45 & -2.449 \tabularnewline
55 &  22 &  21.26 &  0.7435 \tabularnewline
56 &  26 &  21.09 &  4.905 \tabularnewline
57 &  19 &  21.16 & -2.159 \tabularnewline
58 &  21 &  21.48 & -0.4832 \tabularnewline
59 &  21 &  20.86 &  0.1417 \tabularnewline
60 &  20 &  21.18 & -1.182 \tabularnewline
61 &  23 &  21.29 &  1.711 \tabularnewline
62 &  22 &  21.12 &  0.875 \tabularnewline
63 &  22 &  21.04 &  0.9556 \tabularnewline
64 &  22 &  20.98 &  1.02 \tabularnewline
65 &  21 &  21.16 & -0.1591 \tabularnewline
66 &  21 &  21.09 & -0.09471 \tabularnewline
67 &  22 &  20.84 &  1.159 \tabularnewline
68 &  23 &  21.34 &  1.656 \tabularnewline
69 &  18 &  21.46 & -3.465 \tabularnewline
70 &  24 &  20.86 &  3.142 \tabularnewline
71 &  22 &  20.68 &  1.32 \tabularnewline
72 &  21 &  20.98 &  0.01999 \tabularnewline
73 &  21 &  20.86 &  0.1406 \tabularnewline
74 &  21 &  21.42 & -0.4161 \tabularnewline
75 &  23 &  21.23 &  1.768 \tabularnewline
76 &  21 &  21.22 & -0.2165 \tabularnewline
77 &  23 &  20.86 &  2.142 \tabularnewline
78 &  21 &  20.86 &  0.1417 \tabularnewline
79 &  19 &  20.81 & -1.811 \tabularnewline
80 &  21 &  21.09 & -0.09471 \tabularnewline
81 &  21 &  21.41 & -0.4112 \tabularnewline
82 &  21 &  21.05 & -0.05469 \tabularnewline
83 &  23 &  21.54 &  1.463 \tabularnewline
84 &  20 &  21.3 & -1.297 \tabularnewline
85 &  20 &  20.98 & -0.98 \tabularnewline
86 &  19 &  21.4 & -2.401 \tabularnewline
87 &  23 &  21.08 &  1.923 \tabularnewline
88 &  22 &  21.22 &  0.7765 \tabularnewline
89 &  19 &  21.25 & -2.247 \tabularnewline
90 &  23 &  21.36 &  1.639 \tabularnewline
91 &  22 &  21.08 &  0.9226 \tabularnewline
92 &  22 &  20.96 &  1.043 \tabularnewline
93 &  21 &  21.15 & -0.1509 \tabularnewline
94 &  21 &  21.2 & -0.1991 \tabularnewline
95 &  21 &  21.65 & -0.6536 \tabularnewline
96 &  21 &  21.16 & -0.1591 \tabularnewline
97 &  22 &  21.11 &  0.8885 \tabularnewline
98 &  25 &  21.34 &  3.656 \tabularnewline
99 &  21 &  20.94 &  0.06001 \tabularnewline
100 &  23 &  21.13 &  1.874 \tabularnewline
101 &  22 &  20.98 &  1.019 \tabularnewline
102 &  20 &  21.1 & -1.102 \tabularnewline
103 &  21 &  21.04 & -0.04441 \tabularnewline
104 &  25 &  20.9 &  4.101 \tabularnewline
105 &  21 &  21.36 & -0.3615 \tabularnewline
106 &  19 &  21.39 & -2.394 \tabularnewline
107 &  23 &  21.3 &  1.696 \tabularnewline
108 &  22 &  21.13 &  0.8653 \tabularnewline
109 &  21 &  21.15 & -0.1521 \tabularnewline
110 &  24 &  21.17 &  2.825 \tabularnewline
111 &  21 &  21.27 & -0.2721 \tabularnewline
112 &  19 &  20.88 & -1.876 \tabularnewline
113 &  18 &  21.1 & -3.102 \tabularnewline
114 &  19 &  21.28 & -2.28 \tabularnewline
115 &  20 &  20.86 & -0.8583 \tabularnewline
116 &  19 &  20.96 & -1.957 \tabularnewline
117 &  22 &  21.22 &  0.7835 \tabularnewline
118 &  21 &  21.24 & -0.2397 \tabularnewline
119 &  22 &  21.3 &  0.7035 \tabularnewline
120 &  24 &  21.22 &  2.776 \tabularnewline
121 &  28 &  21.36 &  6.639 \tabularnewline
122 &  18 &  21.22 & -3.216 \tabularnewline
123 &  23 &  21.6 &  1.396 \tabularnewline
124 &  19 &  21.04 & -2.044 \tabularnewline
125 &  23 &  21.39 &  1.614 \tabularnewline
126 &  22 &  21.12 &  0.8809 \tabularnewline
127 &  21 &  21.1 & -0.1018 \tabularnewline
128 &  19 &  21.26 & -2.264 \tabularnewline
129 &  21 &  21.59 & -0.5865 \tabularnewline
130 &  23 &  21.33 &  1.669 \tabularnewline
131 &  22 &  21.26 &  0.7447 \tabularnewline
132 &  19 &  21.27 & -2.274 \tabularnewline
133 &  19 &  21.22 & -2.224 \tabularnewline
134 &  21 &  20.83 &  0.1747 \tabularnewline
135 &  22 &  21.05 &  0.9458 \tabularnewline
136 &  21 &  21.2 & -0.1991 \tabularnewline
137 &  20 &  20.92 & -0.9227 \tabularnewline
138 &  23 &  21.37 &  1.631 \tabularnewline
139 &  22 &  21.6 &  0.3989 \tabularnewline
140 &  23 &  20.92 &  2.077 \tabularnewline
141 &  22 &  20.77 &  1.229 \tabularnewline
142 &  21 &  21.04 & -0.04441 \tabularnewline
143 &  20 &  21.4 & -1.396 \tabularnewline
144 &  18 &  21.23 & -3.232 \tabularnewline
145 &  18 &  20.85 & -2.852 \tabularnewline
146 &  20 &  21.22 & -1.216 \tabularnewline
147 &  21 &  20.86 &  0.1417 \tabularnewline
148 &  24 &  21.09 &  2.905 \tabularnewline
149 &  19 &  20.92 & -1.923 \tabularnewline
150 &  20 &  21.44 & -1.442 \tabularnewline
151 &  19 &  20.63 & -1.632 \tabularnewline
152 &  23 &  21.4 &  1.599 \tabularnewline
153 &  22 &  21.09 &  0.9053 \tabularnewline
154 &  21 &  20.98 &  0.01999 \tabularnewline
155 &  24 &  20.83 &  3.172 \tabularnewline
156 &  21 &  21.04 & -0.03736 \tabularnewline
157 &  21 &  21.13 & -0.1347 \tabularnewline
158 &  22 &  21.73 &  0.2744 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=299934&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] 21.29[/C][C] 0.7105[/C][/ROW]
[ROW][C]2[/C][C] 24[/C][C] 20.85[/C][C] 3.149[/C][/ROW]
[ROW][C]3[/C][C] 21[/C][C] 21.34[/C][C]-0.3382[/C][/ROW]
[ROW][C]4[/C][C] 21[/C][C] 21.16[/C][C]-0.1591[/C][/ROW]
[ROW][C]5[/C][C] 24[/C][C] 21.22[/C][C] 2.778[/C][/ROW]
[ROW][C]6[/C][C] 20[/C][C] 20.77[/C][C]-0.7706[/C][/ROW]
[ROW][C]7[/C][C] 22[/C][C] 21.39[/C][C] 0.6061[/C][/ROW]
[ROW][C]8[/C][C] 20[/C][C] 21.34[/C][C]-1.338[/C][/ROW]
[ROW][C]9[/C][C] 23[/C][C] 20.98[/C][C] 2.02[/C][/ROW]
[ROW][C]10[/C][C] 21[/C][C] 21.17[/C][C]-0.1677[/C][/ROW]
[ROW][C]11[/C][C] 19[/C][C] 21.12[/C][C]-2.118[/C][/ROW]
[ROW][C]12[/C][C] 19[/C][C] 21.19[/C][C]-2.189[/C][/ROW]
[ROW][C]13[/C][C] 21[/C][C] 21[/C][C]-0.004388[/C][/ROW]
[ROW][C]14[/C][C] 21[/C][C] 21.22[/C][C]-0.2165[/C][/ROW]
[ROW][C]15[/C][C] 22[/C][C] 21.57[/C][C] 0.427[/C][/ROW]
[ROW][C]16[/C][C] 22[/C][C] 21.08[/C][C] 0.9199[/C][/ROW]
[ROW][C]17[/C][C] 21[/C][C] 20.92[/C][C] 0.08439[/C][/ROW]
[ROW][C]18[/C][C] 21[/C][C] 21[/C][C]-0.004388[/C][/ROW]
[ROW][C]19[/C][C] 21[/C][C] 20.95[/C][C] 0.05296[/C][/ROW]
[ROW][C]20[/C][C] 20[/C][C] 21.04[/C][C]-1.044[/C][/ROW]
[ROW][C]21[/C][C] 22[/C][C] 21.69[/C][C] 0.3123[/C][/ROW]
[ROW][C]22[/C][C] 22[/C][C] 20.98[/C][C] 1.02[/C][/ROW]
[ROW][C]23[/C][C] 24[/C][C] 21.41[/C][C] 2.589[/C][/ROW]
[ROW][C]24[/C][C] 21[/C][C] 20.98[/C][C] 0.01999[/C][/ROW]
[ROW][C]25[/C][C] 19[/C][C] 21.03[/C][C]-2.028[/C][/ROW]
[ROW][C]26[/C][C] 19[/C][C] 21.28[/C][C]-2.28[/C][/ROW]
[ROW][C]27[/C][C] 23[/C][C] 20.98[/C][C] 2.019[/C][/ROW]
[ROW][C]28[/C][C] 21[/C][C] 21.31[/C][C]-0.3138[/C][/ROW]
[ROW][C]29[/C][C] 21[/C][C] 21.12[/C][C]-0.1191[/C][/ROW]
[ROW][C]30[/C][C] 19[/C][C] 21.17[/C][C]-2.175[/C][/ROW]
[ROW][C]31[/C][C] 21[/C][C] 21.39[/C][C]-0.3868[/C][/ROW]
[ROW][C]32[/C][C] 21[/C][C] 21.24[/C][C]-0.2397[/C][/ROW]
[ROW][C]33[/C][C] 21[/C][C] 20.98[/C][C] 0.01999[/C][/ROW]
[ROW][C]34[/C][C] 23[/C][C] 21.13[/C][C] 1.865[/C][/ROW]
[ROW][C]35[/C][C] 19[/C][C] 21.04[/C][C]-2.038[/C][/ROW]
[ROW][C]36[/C][C] 19[/C][C] 20.98[/C][C]-1.98[/C][/ROW]
[ROW][C]37[/C][C] 18[/C][C] 21.04[/C][C]-3.037[/C][/ROW]
[ROW][C]38[/C][C] 22[/C][C] 21.56[/C][C] 0.4438[/C][/ROW]
[ROW][C]39[/C][C] 18[/C][C] 21.08[/C][C]-3.077[/C][/ROW]
[ROW][C]40[/C][C] 22[/C][C] 21[/C][C] 1.002[/C][/ROW]
[ROW][C]41[/C][C] 18[/C][C] 21.31[/C][C]-3.314[/C][/ROW]
[ROW][C]42[/C][C] 22[/C][C] 21.4[/C][C] 0.5985[/C][/ROW]
[ROW][C]43[/C][C] 22[/C][C] 21.28[/C][C] 0.7203[/C][/ROW]
[ROW][C]44[/C][C] 22[/C][C] 21.1[/C][C] 0.8982[/C][/ROW]
[ROW][C]45[/C][C] 25[/C][C] 21.6[/C][C] 3.399[/C][/ROW]
[ROW][C]46[/C][C] 19[/C][C] 21.1[/C][C]-2.102[/C][/ROW]
[ROW][C]47[/C][C] 19[/C][C] 21.1[/C][C]-2.102[/C][/ROW]
[ROW][C]48[/C][C] 19[/C][C] 21.17[/C][C]-2.175[/C][/ROW]
[ROW][C]49[/C][C] 19[/C][C] 20.88[/C][C]-1.876[/C][/ROW]
[ROW][C]50[/C][C] 21[/C][C] 21.46[/C][C]-0.4588[/C][/ROW]
[ROW][C]51[/C][C] 21[/C][C] 21.25[/C][C]-0.2494[/C][/ROW]
[ROW][C]52[/C][C] 20[/C][C] 21.18[/C][C]-1.182[/C][/ROW]
[ROW][C]53[/C][C] 19[/C][C] 20.98[/C][C]-1.98[/C][/ROW]
[ROW][C]54[/C][C] 19[/C][C] 21.45[/C][C]-2.449[/C][/ROW]
[ROW][C]55[/C][C] 22[/C][C] 21.26[/C][C] 0.7435[/C][/ROW]
[ROW][C]56[/C][C] 26[/C][C] 21.09[/C][C] 4.905[/C][/ROW]
[ROW][C]57[/C][C] 19[/C][C] 21.16[/C][C]-2.159[/C][/ROW]
[ROW][C]58[/C][C] 21[/C][C] 21.48[/C][C]-0.4832[/C][/ROW]
[ROW][C]59[/C][C] 21[/C][C] 20.86[/C][C] 0.1417[/C][/ROW]
[ROW][C]60[/C][C] 20[/C][C] 21.18[/C][C]-1.182[/C][/ROW]
[ROW][C]61[/C][C] 23[/C][C] 21.29[/C][C] 1.711[/C][/ROW]
[ROW][C]62[/C][C] 22[/C][C] 21.12[/C][C] 0.875[/C][/ROW]
[ROW][C]63[/C][C] 22[/C][C] 21.04[/C][C] 0.9556[/C][/ROW]
[ROW][C]64[/C][C] 22[/C][C] 20.98[/C][C] 1.02[/C][/ROW]
[ROW][C]65[/C][C] 21[/C][C] 21.16[/C][C]-0.1591[/C][/ROW]
[ROW][C]66[/C][C] 21[/C][C] 21.09[/C][C]-0.09471[/C][/ROW]
[ROW][C]67[/C][C] 22[/C][C] 20.84[/C][C] 1.159[/C][/ROW]
[ROW][C]68[/C][C] 23[/C][C] 21.34[/C][C] 1.656[/C][/ROW]
[ROW][C]69[/C][C] 18[/C][C] 21.46[/C][C]-3.465[/C][/ROW]
[ROW][C]70[/C][C] 24[/C][C] 20.86[/C][C] 3.142[/C][/ROW]
[ROW][C]71[/C][C] 22[/C][C] 20.68[/C][C] 1.32[/C][/ROW]
[ROW][C]72[/C][C] 21[/C][C] 20.98[/C][C] 0.01999[/C][/ROW]
[ROW][C]73[/C][C] 21[/C][C] 20.86[/C][C] 0.1406[/C][/ROW]
[ROW][C]74[/C][C] 21[/C][C] 21.42[/C][C]-0.4161[/C][/ROW]
[ROW][C]75[/C][C] 23[/C][C] 21.23[/C][C] 1.768[/C][/ROW]
[ROW][C]76[/C][C] 21[/C][C] 21.22[/C][C]-0.2165[/C][/ROW]
[ROW][C]77[/C][C] 23[/C][C] 20.86[/C][C] 2.142[/C][/ROW]
[ROW][C]78[/C][C] 21[/C][C] 20.86[/C][C] 0.1417[/C][/ROW]
[ROW][C]79[/C][C] 19[/C][C] 20.81[/C][C]-1.811[/C][/ROW]
[ROW][C]80[/C][C] 21[/C][C] 21.09[/C][C]-0.09471[/C][/ROW]
[ROW][C]81[/C][C] 21[/C][C] 21.41[/C][C]-0.4112[/C][/ROW]
[ROW][C]82[/C][C] 21[/C][C] 21.05[/C][C]-0.05469[/C][/ROW]
[ROW][C]83[/C][C] 23[/C][C] 21.54[/C][C] 1.463[/C][/ROW]
[ROW][C]84[/C][C] 20[/C][C] 21.3[/C][C]-1.297[/C][/ROW]
[ROW][C]85[/C][C] 20[/C][C] 20.98[/C][C]-0.98[/C][/ROW]
[ROW][C]86[/C][C] 19[/C][C] 21.4[/C][C]-2.401[/C][/ROW]
[ROW][C]87[/C][C] 23[/C][C] 21.08[/C][C] 1.923[/C][/ROW]
[ROW][C]88[/C][C] 22[/C][C] 21.22[/C][C] 0.7765[/C][/ROW]
[ROW][C]89[/C][C] 19[/C][C] 21.25[/C][C]-2.247[/C][/ROW]
[ROW][C]90[/C][C] 23[/C][C] 21.36[/C][C] 1.639[/C][/ROW]
[ROW][C]91[/C][C] 22[/C][C] 21.08[/C][C] 0.9226[/C][/ROW]
[ROW][C]92[/C][C] 22[/C][C] 20.96[/C][C] 1.043[/C][/ROW]
[ROW][C]93[/C][C] 21[/C][C] 21.15[/C][C]-0.1509[/C][/ROW]
[ROW][C]94[/C][C] 21[/C][C] 21.2[/C][C]-0.1991[/C][/ROW]
[ROW][C]95[/C][C] 21[/C][C] 21.65[/C][C]-0.6536[/C][/ROW]
[ROW][C]96[/C][C] 21[/C][C] 21.16[/C][C]-0.1591[/C][/ROW]
[ROW][C]97[/C][C] 22[/C][C] 21.11[/C][C] 0.8885[/C][/ROW]
[ROW][C]98[/C][C] 25[/C][C] 21.34[/C][C] 3.656[/C][/ROW]
[ROW][C]99[/C][C] 21[/C][C] 20.94[/C][C] 0.06001[/C][/ROW]
[ROW][C]100[/C][C] 23[/C][C] 21.13[/C][C] 1.874[/C][/ROW]
[ROW][C]101[/C][C] 22[/C][C] 20.98[/C][C] 1.019[/C][/ROW]
[ROW][C]102[/C][C] 20[/C][C] 21.1[/C][C]-1.102[/C][/ROW]
[ROW][C]103[/C][C] 21[/C][C] 21.04[/C][C]-0.04441[/C][/ROW]
[ROW][C]104[/C][C] 25[/C][C] 20.9[/C][C] 4.101[/C][/ROW]
[ROW][C]105[/C][C] 21[/C][C] 21.36[/C][C]-0.3615[/C][/ROW]
[ROW][C]106[/C][C] 19[/C][C] 21.39[/C][C]-2.394[/C][/ROW]
[ROW][C]107[/C][C] 23[/C][C] 21.3[/C][C] 1.696[/C][/ROW]
[ROW][C]108[/C][C] 22[/C][C] 21.13[/C][C] 0.8653[/C][/ROW]
[ROW][C]109[/C][C] 21[/C][C] 21.15[/C][C]-0.1521[/C][/ROW]
[ROW][C]110[/C][C] 24[/C][C] 21.17[/C][C] 2.825[/C][/ROW]
[ROW][C]111[/C][C] 21[/C][C] 21.27[/C][C]-0.2721[/C][/ROW]
[ROW][C]112[/C][C] 19[/C][C] 20.88[/C][C]-1.876[/C][/ROW]
[ROW][C]113[/C][C] 18[/C][C] 21.1[/C][C]-3.102[/C][/ROW]
[ROW][C]114[/C][C] 19[/C][C] 21.28[/C][C]-2.28[/C][/ROW]
[ROW][C]115[/C][C] 20[/C][C] 20.86[/C][C]-0.8583[/C][/ROW]
[ROW][C]116[/C][C] 19[/C][C] 20.96[/C][C]-1.957[/C][/ROW]
[ROW][C]117[/C][C] 22[/C][C] 21.22[/C][C] 0.7835[/C][/ROW]
[ROW][C]118[/C][C] 21[/C][C] 21.24[/C][C]-0.2397[/C][/ROW]
[ROW][C]119[/C][C] 22[/C][C] 21.3[/C][C] 0.7035[/C][/ROW]
[ROW][C]120[/C][C] 24[/C][C] 21.22[/C][C] 2.776[/C][/ROW]
[ROW][C]121[/C][C] 28[/C][C] 21.36[/C][C] 6.639[/C][/ROW]
[ROW][C]122[/C][C] 18[/C][C] 21.22[/C][C]-3.216[/C][/ROW]
[ROW][C]123[/C][C] 23[/C][C] 21.6[/C][C] 1.396[/C][/ROW]
[ROW][C]124[/C][C] 19[/C][C] 21.04[/C][C]-2.044[/C][/ROW]
[ROW][C]125[/C][C] 23[/C][C] 21.39[/C][C] 1.614[/C][/ROW]
[ROW][C]126[/C][C] 22[/C][C] 21.12[/C][C] 0.8809[/C][/ROW]
[ROW][C]127[/C][C] 21[/C][C] 21.1[/C][C]-0.1018[/C][/ROW]
[ROW][C]128[/C][C] 19[/C][C] 21.26[/C][C]-2.264[/C][/ROW]
[ROW][C]129[/C][C] 21[/C][C] 21.59[/C][C]-0.5865[/C][/ROW]
[ROW][C]130[/C][C] 23[/C][C] 21.33[/C][C] 1.669[/C][/ROW]
[ROW][C]131[/C][C] 22[/C][C] 21.26[/C][C] 0.7447[/C][/ROW]
[ROW][C]132[/C][C] 19[/C][C] 21.27[/C][C]-2.274[/C][/ROW]
[ROW][C]133[/C][C] 19[/C][C] 21.22[/C][C]-2.224[/C][/ROW]
[ROW][C]134[/C][C] 21[/C][C] 20.83[/C][C] 0.1747[/C][/ROW]
[ROW][C]135[/C][C] 22[/C][C] 21.05[/C][C] 0.9458[/C][/ROW]
[ROW][C]136[/C][C] 21[/C][C] 21.2[/C][C]-0.1991[/C][/ROW]
[ROW][C]137[/C][C] 20[/C][C] 20.92[/C][C]-0.9227[/C][/ROW]
[ROW][C]138[/C][C] 23[/C][C] 21.37[/C][C] 1.631[/C][/ROW]
[ROW][C]139[/C][C] 22[/C][C] 21.6[/C][C] 0.3989[/C][/ROW]
[ROW][C]140[/C][C] 23[/C][C] 20.92[/C][C] 2.077[/C][/ROW]
[ROW][C]141[/C][C] 22[/C][C] 20.77[/C][C] 1.229[/C][/ROW]
[ROW][C]142[/C][C] 21[/C][C] 21.04[/C][C]-0.04441[/C][/ROW]
[ROW][C]143[/C][C] 20[/C][C] 21.4[/C][C]-1.396[/C][/ROW]
[ROW][C]144[/C][C] 18[/C][C] 21.23[/C][C]-3.232[/C][/ROW]
[ROW][C]145[/C][C] 18[/C][C] 20.85[/C][C]-2.852[/C][/ROW]
[ROW][C]146[/C][C] 20[/C][C] 21.22[/C][C]-1.216[/C][/ROW]
[ROW][C]147[/C][C] 21[/C][C] 20.86[/C][C] 0.1417[/C][/ROW]
[ROW][C]148[/C][C] 24[/C][C] 21.09[/C][C] 2.905[/C][/ROW]
[ROW][C]149[/C][C] 19[/C][C] 20.92[/C][C]-1.923[/C][/ROW]
[ROW][C]150[/C][C] 20[/C][C] 21.44[/C][C]-1.442[/C][/ROW]
[ROW][C]151[/C][C] 19[/C][C] 20.63[/C][C]-1.632[/C][/ROW]
[ROW][C]152[/C][C] 23[/C][C] 21.4[/C][C] 1.599[/C][/ROW]
[ROW][C]153[/C][C] 22[/C][C] 21.09[/C][C] 0.9053[/C][/ROW]
[ROW][C]154[/C][C] 21[/C][C] 20.98[/C][C] 0.01999[/C][/ROW]
[ROW][C]155[/C][C] 24[/C][C] 20.83[/C][C] 3.172[/C][/ROW]
[ROW][C]156[/C][C] 21[/C][C] 21.04[/C][C]-0.03736[/C][/ROW]
[ROW][C]157[/C][C] 21[/C][C] 21.13[/C][C]-0.1347[/C][/ROW]
[ROW][C]158[/C][C] 22[/C][C] 21.73[/C][C] 0.2744[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=299934&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=299934&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	21.29	0.7105
2	24	20.85	3.149
3	21	21.34	-0.3382
4	21	21.16	-0.1591
5	24	21.22	2.778
6	20	20.77	-0.7706
7	22	21.39	0.6061
8	20	21.34	-1.338
9	23	20.98	2.02
10	21	21.17	-0.1677
11	19	21.12	-2.118
12	19	21.19	-2.189
13	21	21	-0.004388
14	21	21.22	-0.2165
15	22	21.57	0.427
16	22	21.08	0.9199
17	21	20.92	0.08439
18	21	21	-0.004388
19	21	20.95	0.05296
20	20	21.04	-1.044
21	22	21.69	0.3123
22	22	20.98	1.02
23	24	21.41	2.589
24	21	20.98	0.01999
25	19	21.03	-2.028
26	19	21.28	-2.28
27	23	20.98	2.019
28	21	21.31	-0.3138
29	21	21.12	-0.1191
30	19	21.17	-2.175
31	21	21.39	-0.3868
32	21	21.24	-0.2397
33	21	20.98	0.01999
34	23	21.13	1.865
35	19	21.04	-2.038
36	19	20.98	-1.98
37	18	21.04	-3.037
38	22	21.56	0.4438
39	18	21.08	-3.077
40	22	21	1.002
41	18	21.31	-3.314
42	22	21.4	0.5985
43	22	21.28	0.7203
44	22	21.1	0.8982
45	25	21.6	3.399
46	19	21.1	-2.102
47	19	21.1	-2.102
48	19	21.17	-2.175
49	19	20.88	-1.876
50	21	21.46	-0.4588
51	21	21.25	-0.2494
52	20	21.18	-1.182
53	19	20.98	-1.98
54	19	21.45	-2.449
55	22	21.26	0.7435
56	26	21.09	4.905
57	19	21.16	-2.159
58	21	21.48	-0.4832
59	21	20.86	0.1417
60	20	21.18	-1.182
61	23	21.29	1.711
62	22	21.12	0.875
63	22	21.04	0.9556
64	22	20.98	1.02
65	21	21.16	-0.1591
66	21	21.09	-0.09471
67	22	20.84	1.159
68	23	21.34	1.656
69	18	21.46	-3.465
70	24	20.86	3.142
71	22	20.68	1.32
72	21	20.98	0.01999
73	21	20.86	0.1406
74	21	21.42	-0.4161
75	23	21.23	1.768
76	21	21.22	-0.2165
77	23	20.86	2.142
78	21	20.86	0.1417
79	19	20.81	-1.811
80	21	21.09	-0.09471
81	21	21.41	-0.4112
82	21	21.05	-0.05469
83	23	21.54	1.463
84	20	21.3	-1.297
85	20	20.98	-0.98
86	19	21.4	-2.401
87	23	21.08	1.923
88	22	21.22	0.7765
89	19	21.25	-2.247
90	23	21.36	1.639
91	22	21.08	0.9226
92	22	20.96	1.043
93	21	21.15	-0.1509
94	21	21.2	-0.1991
95	21	21.65	-0.6536
96	21	21.16	-0.1591
97	22	21.11	0.8885
98	25	21.34	3.656
99	21	20.94	0.06001
100	23	21.13	1.874
101	22	20.98	1.019
102	20	21.1	-1.102
103	21	21.04	-0.04441
104	25	20.9	4.101
105	21	21.36	-0.3615
106	19	21.39	-2.394
107	23	21.3	1.696
108	22	21.13	0.8653
109	21	21.15	-0.1521
110	24	21.17	2.825
111	21	21.27	-0.2721
112	19	20.88	-1.876
113	18	21.1	-3.102
114	19	21.28	-2.28
115	20	20.86	-0.8583
116	19	20.96	-1.957
117	22	21.22	0.7835
118	21	21.24	-0.2397
119	22	21.3	0.7035
120	24	21.22	2.776
121	28	21.36	6.639
122	18	21.22	-3.216
123	23	21.6	1.396
124	19	21.04	-2.044
125	23	21.39	1.614
126	22	21.12	0.8809
127	21	21.1	-0.1018
128	19	21.26	-2.264
129	21	21.59	-0.5865
130	23	21.33	1.669
131	22	21.26	0.7447
132	19	21.27	-2.274
133	19	21.22	-2.224
134	21	20.83	0.1747
135	22	21.05	0.9458
136	21	21.2	-0.1991
137	20	20.92	-0.9227
138	23	21.37	1.631
139	22	21.6	0.3989
140	23	20.92	2.077
141	22	20.77	1.229
142	21	21.04	-0.04441
143	20	21.4	-1.396
144	18	21.23	-3.232
145	18	20.85	-2.852
146	20	21.22	-1.216
147	21	20.86	0.1417
148	24	21.09	2.905
149	19	20.92	-1.923
150	20	21.44	-1.442
151	19	20.63	-1.632
152	23	21.4	1.599
153	22	21.09	0.9053
154	21	20.98	0.01999
155	24	20.83	3.172
156	21	21.04	-0.03736
157	21	21.13	-0.1347
158	22	21.73	0.2744

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
8	0.05734	0.1147	0.9427
9	0.05103	0.1021	0.949
10	0.0748	0.1496	0.9252
11	0.5633	0.8734	0.4367
12	0.7176	0.5647	0.2824
13	0.6233	0.7534	0.3767
14	0.5263	0.9473	0.4737
15	0.4316	0.8631	0.5684
16	0.3432	0.6863	0.6568
17	0.2699	0.5397	0.7301
18	0.2025	0.4051	0.7975
19	0.1465	0.2929	0.8535
20	0.1262	0.2523	0.8738
21	0.09059	0.1812	0.9094
22	0.0675	0.135	0.9325
23	0.1003	0.2006	0.8997
24	0.07143	0.1429	0.9286
25	0.07104	0.1421	0.929
26	0.08951	0.179	0.9105
27	0.08944	0.1789	0.9106
28	0.06553	0.1311	0.9345
29	0.04731	0.09463	0.9527
30	0.08691	0.1738	0.9131
31	0.06848	0.137	0.9315
32	0.05186	0.1037	0.9481
33	0.03695	0.0739	0.963
34	0.03801	0.07603	0.962
35	0.05581	0.1116	0.9442
36	0.06481	0.1296	0.9352
37	0.1177	0.2355	0.8823
38	0.09707	0.1941	0.9029
39	0.1643	0.3286	0.8357
40	0.136	0.272	0.864
41	0.2288	0.4576	0.7712
42	0.2059	0.4119	0.7941
43	0.1822	0.3645	0.8178
44	0.164	0.3279	0.836
45	0.2984	0.5967	0.7016
46	0.3076	0.6152	0.6924
47	0.3145	0.629	0.6855
48	0.3364	0.6728	0.6636
49	0.3303	0.6605	0.6697
50	0.2893	0.5786	0.7107
51	0.2475	0.495	0.7525
52	0.2242	0.4484	0.7758
53	0.223	0.4459	0.777
54	0.2587	0.5175	0.7413
55	0.23	0.4599	0.77
56	0.5625	0.8751	0.4375
57	0.5745	0.851	0.4255
58	0.5293	0.9415	0.4707
59	0.4836	0.9671	0.5164
60	0.4539	0.9078	0.5461
61	0.4507	0.9015	0.5493
62	0.4195	0.839	0.5805
63	0.3949	0.7899	0.6051
64	0.369	0.7379	0.631
65	0.3257	0.6515	0.6743
66	0.2843	0.5686	0.7157
67	0.262	0.524	0.738
68	0.2577	0.5154	0.7423
69	0.379	0.7581	0.621
70	0.4752	0.9505	0.5248
71	0.453	0.906	0.547
72	0.4069	0.8138	0.5931
73	0.3626	0.7252	0.6374
74	0.3261	0.6523	0.6739
75	0.3269	0.6538	0.6731
76	0.2864	0.5728	0.7136
77	0.3012	0.6024	0.6988
78	0.2624	0.5247	0.7376
79	0.2666	0.5331	0.7334
80	0.2296	0.4593	0.7704
81	0.1968	0.3937	0.8032
82	0.1662	0.3323	0.8338
83	0.1576	0.3152	0.8424
84	0.1436	0.2872	0.8564
85	0.1255	0.2511	0.8745
86	0.1452	0.2904	0.8548
87	0.1498	0.2996	0.8502
88	0.1299	0.2598	0.8701
89	0.1459	0.2918	0.8541
90	0.1413	0.2825	0.8587
91	0.123	0.2461	0.877
92	0.1081	0.2162	0.8919
93	0.08838	0.1768	0.9116
94	0.07118	0.1424	0.9288
95	0.05802	0.116	0.942
96	0.0457	0.0914	0.9543
97	0.0378	0.07559	0.9622
98	0.0775	0.155	0.9225
99	0.06159	0.1232	0.9384
100	0.06116	0.1223	0.9388
101	0.0514	0.1028	0.9486
102	0.04389	0.08778	0.9561
103	0.03377	0.06754	0.9662
104	0.09393	0.1879	0.9061
105	0.07642	0.1528	0.9236
106	0.09769	0.1954	0.9023
107	0.09325	0.1865	0.9068
108	0.07799	0.156	0.922
109	0.06203	0.1241	0.938
110	0.08988	0.1798	0.9101
111	0.07225	0.1445	0.9277
112	0.078	0.156	0.922
113	0.1142	0.2284	0.8858
114	0.1365	0.273	0.8635
115	0.117	0.2339	0.883
116	0.1128	0.2255	0.8872
117	0.09226	0.1845	0.9077
118	0.07726	0.1545	0.9227
119	0.06637	0.1327	0.9336
120	0.1053	0.2106	0.8947
121	0.6074	0.7851	0.3926
122	0.7334	0.5332	0.2666
123	0.6987	0.6025	0.3013
124	0.6966	0.6068	0.3034
125	0.6765	0.6471	0.3235
126	0.6316	0.7368	0.3684
127	0.5727	0.8545	0.4273
128	0.5715	0.857	0.4285
129	0.5161	0.9677	0.4839
130	0.6096	0.7808	0.3904
131	0.5481	0.9037	0.4519
132	0.5935	0.8129	0.4065
133	0.5907	0.8187	0.4093
134	0.5237	0.9526	0.4763
135	0.5797	0.8405	0.4203
136	0.5398	0.9205	0.4602
137	0.4783	0.9566	0.5217
138	0.5197	0.9606	0.4803
139	0.4955	0.9911	0.5045
140	0.575	0.8501	0.425
141	0.5438	0.9125	0.4562
142	0.7212	0.5575	0.2788
143	0.656	0.6881	0.344
144	0.7711	0.4579	0.2289
145	0.8955	0.2089	0.1045
146	0.9498	0.1005	0.05025
147	0.9132	0.1736	0.08678
148	0.8882	0.2236	0.1118
149	0.8005	0.3991	0.1995
150	0.6806	0.6387	0.3194

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 &  0.05734 &  0.1147 &  0.9427 \tabularnewline
9 &  0.05103 &  0.1021 &  0.949 \tabularnewline
10 &  0.0748 &  0.1496 &  0.9252 \tabularnewline
11 &  0.5633 &  0.8734 &  0.4367 \tabularnewline
12 &  0.7176 &  0.5647 &  0.2824 \tabularnewline
13 &  0.6233 &  0.7534 &  0.3767 \tabularnewline
14 &  0.5263 &  0.9473 &  0.4737 \tabularnewline
15 &  0.4316 &  0.8631 &  0.5684 \tabularnewline
16 &  0.3432 &  0.6863 &  0.6568 \tabularnewline
17 &  0.2699 &  0.5397 &  0.7301 \tabularnewline
18 &  0.2025 &  0.4051 &  0.7975 \tabularnewline
19 &  0.1465 &  0.2929 &  0.8535 \tabularnewline
20 &  0.1262 &  0.2523 &  0.8738 \tabularnewline
21 &  0.09059 &  0.1812 &  0.9094 \tabularnewline
22 &  0.0675 &  0.135 &  0.9325 \tabularnewline
23 &  0.1003 &  0.2006 &  0.8997 \tabularnewline
24 &  0.07143 &  0.1429 &  0.9286 \tabularnewline
25 &  0.07104 &  0.1421 &  0.929 \tabularnewline
26 &  0.08951 &  0.179 &  0.9105 \tabularnewline
27 &  0.08944 &  0.1789 &  0.9106 \tabularnewline
28 &  0.06553 &  0.1311 &  0.9345 \tabularnewline
29 &  0.04731 &  0.09463 &  0.9527 \tabularnewline
30 &  0.08691 &  0.1738 &  0.9131 \tabularnewline
31 &  0.06848 &  0.137 &  0.9315 \tabularnewline
32 &  0.05186 &  0.1037 &  0.9481 \tabularnewline
33 &  0.03695 &  0.0739 &  0.963 \tabularnewline
34 &  0.03801 &  0.07603 &  0.962 \tabularnewline
35 &  0.05581 &  0.1116 &  0.9442 \tabularnewline
36 &  0.06481 &  0.1296 &  0.9352 \tabularnewline
37 &  0.1177 &  0.2355 &  0.8823 \tabularnewline
38 &  0.09707 &  0.1941 &  0.9029 \tabularnewline
39 &  0.1643 &  0.3286 &  0.8357 \tabularnewline
40 &  0.136 &  0.272 &  0.864 \tabularnewline
41 &  0.2288 &  0.4576 &  0.7712 \tabularnewline
42 &  0.2059 &  0.4119 &  0.7941 \tabularnewline
43 &  0.1822 &  0.3645 &  0.8178 \tabularnewline
44 &  0.164 &  0.3279 &  0.836 \tabularnewline
45 &  0.2984 &  0.5967 &  0.7016 \tabularnewline
46 &  0.3076 &  0.6152 &  0.6924 \tabularnewline
47 &  0.3145 &  0.629 &  0.6855 \tabularnewline
48 &  0.3364 &  0.6728 &  0.6636 \tabularnewline
49 &  0.3303 &  0.6605 &  0.6697 \tabularnewline
50 &  0.2893 &  0.5786 &  0.7107 \tabularnewline
51 &  0.2475 &  0.495 &  0.7525 \tabularnewline
52 &  0.2242 &  0.4484 &  0.7758 \tabularnewline
53 &  0.223 &  0.4459 &  0.777 \tabularnewline
54 &  0.2587 &  0.5175 &  0.7413 \tabularnewline
55 &  0.23 &  0.4599 &  0.77 \tabularnewline
56 &  0.5625 &  0.8751 &  0.4375 \tabularnewline
57 &  0.5745 &  0.851 &  0.4255 \tabularnewline
58 &  0.5293 &  0.9415 &  0.4707 \tabularnewline
59 &  0.4836 &  0.9671 &  0.5164 \tabularnewline
60 &  0.4539 &  0.9078 &  0.5461 \tabularnewline
61 &  0.4507 &  0.9015 &  0.5493 \tabularnewline
62 &  0.4195 &  0.839 &  0.5805 \tabularnewline
63 &  0.3949 &  0.7899 &  0.6051 \tabularnewline
64 &  0.369 &  0.7379 &  0.631 \tabularnewline
65 &  0.3257 &  0.6515 &  0.6743 \tabularnewline
66 &  0.2843 &  0.5686 &  0.7157 \tabularnewline
67 &  0.262 &  0.524 &  0.738 \tabularnewline
68 &  0.2577 &  0.5154 &  0.7423 \tabularnewline
69 &  0.379 &  0.7581 &  0.621 \tabularnewline
70 &  0.4752 &  0.9505 &  0.5248 \tabularnewline
71 &  0.453 &  0.906 &  0.547 \tabularnewline
72 &  0.4069 &  0.8138 &  0.5931 \tabularnewline
73 &  0.3626 &  0.7252 &  0.6374 \tabularnewline
74 &  0.3261 &  0.6523 &  0.6739 \tabularnewline
75 &  0.3269 &  0.6538 &  0.6731 \tabularnewline
76 &  0.2864 &  0.5728 &  0.7136 \tabularnewline
77 &  0.3012 &  0.6024 &  0.6988 \tabularnewline
78 &  0.2624 &  0.5247 &  0.7376 \tabularnewline
79 &  0.2666 &  0.5331 &  0.7334 \tabularnewline
80 &  0.2296 &  0.4593 &  0.7704 \tabularnewline
81 &  0.1968 &  0.3937 &  0.8032 \tabularnewline
82 &  0.1662 &  0.3323 &  0.8338 \tabularnewline
83 &  0.1576 &  0.3152 &  0.8424 \tabularnewline
84 &  0.1436 &  0.2872 &  0.8564 \tabularnewline
85 &  0.1255 &  0.2511 &  0.8745 \tabularnewline
86 &  0.1452 &  0.2904 &  0.8548 \tabularnewline
87 &  0.1498 &  0.2996 &  0.8502 \tabularnewline
88 &  0.1299 &  0.2598 &  0.8701 \tabularnewline
89 &  0.1459 &  0.2918 &  0.8541 \tabularnewline
90 &  0.1413 &  0.2825 &  0.8587 \tabularnewline
91 &  0.123 &  0.2461 &  0.877 \tabularnewline
92 &  0.1081 &  0.2162 &  0.8919 \tabularnewline
93 &  0.08838 &  0.1768 &  0.9116 \tabularnewline
94 &  0.07118 &  0.1424 &  0.9288 \tabularnewline
95 &  0.05802 &  0.116 &  0.942 \tabularnewline
96 &  0.0457 &  0.0914 &  0.9543 \tabularnewline
97 &  0.0378 &  0.07559 &  0.9622 \tabularnewline
98 &  0.0775 &  0.155 &  0.9225 \tabularnewline
99 &  0.06159 &  0.1232 &  0.9384 \tabularnewline
100 &  0.06116 &  0.1223 &  0.9388 \tabularnewline
101 &  0.0514 &  0.1028 &  0.9486 \tabularnewline
102 &  0.04389 &  0.08778 &  0.9561 \tabularnewline
103 &  0.03377 &  0.06754 &  0.9662 \tabularnewline
104 &  0.09393 &  0.1879 &  0.9061 \tabularnewline
105 &  0.07642 &  0.1528 &  0.9236 \tabularnewline
106 &  0.09769 &  0.1954 &  0.9023 \tabularnewline
107 &  0.09325 &  0.1865 &  0.9068 \tabularnewline
108 &  0.07799 &  0.156 &  0.922 \tabularnewline
109 &  0.06203 &  0.1241 &  0.938 \tabularnewline
110 &  0.08988 &  0.1798 &  0.9101 \tabularnewline
111 &  0.07225 &  0.1445 &  0.9277 \tabularnewline
112 &  0.078 &  0.156 &  0.922 \tabularnewline
113 &  0.1142 &  0.2284 &  0.8858 \tabularnewline
114 &  0.1365 &  0.273 &  0.8635 \tabularnewline
115 &  0.117 &  0.2339 &  0.883 \tabularnewline
116 &  0.1128 &  0.2255 &  0.8872 \tabularnewline
117 &  0.09226 &  0.1845 &  0.9077 \tabularnewline
118 &  0.07726 &  0.1545 &  0.9227 \tabularnewline
119 &  0.06637 &  0.1327 &  0.9336 \tabularnewline
120 &  0.1053 &  0.2106 &  0.8947 \tabularnewline
121 &  0.6074 &  0.7851 &  0.3926 \tabularnewline
122 &  0.7334 &  0.5332 &  0.2666 \tabularnewline
123 &  0.6987 &  0.6025 &  0.3013 \tabularnewline
124 &  0.6966 &  0.6068 &  0.3034 \tabularnewline
125 &  0.6765 &  0.6471 &  0.3235 \tabularnewline
126 &  0.6316 &  0.7368 &  0.3684 \tabularnewline
127 &  0.5727 &  0.8545 &  0.4273 \tabularnewline
128 &  0.5715 &  0.857 &  0.4285 \tabularnewline
129 &  0.5161 &  0.9677 &  0.4839 \tabularnewline
130 &  0.6096 &  0.7808 &  0.3904 \tabularnewline
131 &  0.5481 &  0.9037 &  0.4519 \tabularnewline
132 &  0.5935 &  0.8129 &  0.4065 \tabularnewline
133 &  0.5907 &  0.8187 &  0.4093 \tabularnewline
134 &  0.5237 &  0.9526 &  0.4763 \tabularnewline
135 &  0.5797 &  0.8405 &  0.4203 \tabularnewline
136 &  0.5398 &  0.9205 &  0.4602 \tabularnewline
137 &  0.4783 &  0.9566 &  0.5217 \tabularnewline
138 &  0.5197 &  0.9606 &  0.4803 \tabularnewline
139 &  0.4955 &  0.9911 &  0.5045 \tabularnewline
140 &  0.575 &  0.8501 &  0.425 \tabularnewline
141 &  0.5438 &  0.9125 &  0.4562 \tabularnewline
142 &  0.7212 &  0.5575 &  0.2788 \tabularnewline
143 &  0.656 &  0.6881 &  0.344 \tabularnewline
144 &  0.7711 &  0.4579 &  0.2289 \tabularnewline
145 &  0.8955 &  0.2089 &  0.1045 \tabularnewline
146 &  0.9498 &  0.1005 &  0.05025 \tabularnewline
147 &  0.9132 &  0.1736 &  0.08678 \tabularnewline
148 &  0.8882 &  0.2236 &  0.1118 \tabularnewline
149 &  0.8005 &  0.3991 &  0.1995 \tabularnewline
150 &  0.6806 &  0.6387 &  0.3194 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=299934&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] 0.05734[/C][C] 0.1147[/C][C] 0.9427[/C][/ROW]
[ROW][C]9[/C][C] 0.05103[/C][C] 0.1021[/C][C] 0.949[/C][/ROW]
[ROW][C]10[/C][C] 0.0748[/C][C] 0.1496[/C][C] 0.9252[/C][/ROW]
[ROW][C]11[/C][C] 0.5633[/C][C] 0.8734[/C][C] 0.4367[/C][/ROW]
[ROW][C]12[/C][C] 0.7176[/C][C] 0.5647[/C][C] 0.2824[/C][/ROW]
[ROW][C]13[/C][C] 0.6233[/C][C] 0.7534[/C][C] 0.3767[/C][/ROW]
[ROW][C]14[/C][C] 0.5263[/C][C] 0.9473[/C][C] 0.4737[/C][/ROW]
[ROW][C]15[/C][C] 0.4316[/C][C] 0.8631[/C][C] 0.5684[/C][/ROW]
[ROW][C]16[/C][C] 0.3432[/C][C] 0.6863[/C][C] 0.6568[/C][/ROW]
[ROW][C]17[/C][C] 0.2699[/C][C] 0.5397[/C][C] 0.7301[/C][/ROW]
[ROW][C]18[/C][C] 0.2025[/C][C] 0.4051[/C][C] 0.7975[/C][/ROW]
[ROW][C]19[/C][C] 0.1465[/C][C] 0.2929[/C][C] 0.8535[/C][/ROW]
[ROW][C]20[/C][C] 0.1262[/C][C] 0.2523[/C][C] 0.8738[/C][/ROW]
[ROW][C]21[/C][C] 0.09059[/C][C] 0.1812[/C][C] 0.9094[/C][/ROW]
[ROW][C]22[/C][C] 0.0675[/C][C] 0.135[/C][C] 0.9325[/C][/ROW]
[ROW][C]23[/C][C] 0.1003[/C][C] 0.2006[/C][C] 0.8997[/C][/ROW]
[ROW][C]24[/C][C] 0.07143[/C][C] 0.1429[/C][C] 0.9286[/C][/ROW]
[ROW][C]25[/C][C] 0.07104[/C][C] 0.1421[/C][C] 0.929[/C][/ROW]
[ROW][C]26[/C][C] 0.08951[/C][C] 0.179[/C][C] 0.9105[/C][/ROW]
[ROW][C]27[/C][C] 0.08944[/C][C] 0.1789[/C][C] 0.9106[/C][/ROW]
[ROW][C]28[/C][C] 0.06553[/C][C] 0.1311[/C][C] 0.9345[/C][/ROW]
[ROW][C]29[/C][C] 0.04731[/C][C] 0.09463[/C][C] 0.9527[/C][/ROW]
[ROW][C]30[/C][C] 0.08691[/C][C] 0.1738[/C][C] 0.9131[/C][/ROW]
[ROW][C]31[/C][C] 0.06848[/C][C] 0.137[/C][C] 0.9315[/C][/ROW]
[ROW][C]32[/C][C] 0.05186[/C][C] 0.1037[/C][C] 0.9481[/C][/ROW]
[ROW][C]33[/C][C] 0.03695[/C][C] 0.0739[/C][C] 0.963[/C][/ROW]
[ROW][C]34[/C][C] 0.03801[/C][C] 0.07603[/C][C] 0.962[/C][/ROW]
[ROW][C]35[/C][C] 0.05581[/C][C] 0.1116[/C][C] 0.9442[/C][/ROW]
[ROW][C]36[/C][C] 0.06481[/C][C] 0.1296[/C][C] 0.9352[/C][/ROW]
[ROW][C]37[/C][C] 0.1177[/C][C] 0.2355[/C][C] 0.8823[/C][/ROW]
[ROW][C]38[/C][C] 0.09707[/C][C] 0.1941[/C][C] 0.9029[/C][/ROW]
[ROW][C]39[/C][C] 0.1643[/C][C] 0.3286[/C][C] 0.8357[/C][/ROW]
[ROW][C]40[/C][C] 0.136[/C][C] 0.272[/C][C] 0.864[/C][/ROW]
[ROW][C]41[/C][C] 0.2288[/C][C] 0.4576[/C][C] 0.7712[/C][/ROW]
[ROW][C]42[/C][C] 0.2059[/C][C] 0.4119[/C][C] 0.7941[/C][/ROW]
[ROW][C]43[/C][C] 0.1822[/C][C] 0.3645[/C][C] 0.8178[/C][/ROW]
[ROW][C]44[/C][C] 0.164[/C][C] 0.3279[/C][C] 0.836[/C][/ROW]
[ROW][C]45[/C][C] 0.2984[/C][C] 0.5967[/C][C] 0.7016[/C][/ROW]
[ROW][C]46[/C][C] 0.3076[/C][C] 0.6152[/C][C] 0.6924[/C][/ROW]
[ROW][C]47[/C][C] 0.3145[/C][C] 0.629[/C][C] 0.6855[/C][/ROW]
[ROW][C]48[/C][C] 0.3364[/C][C] 0.6728[/C][C] 0.6636[/C][/ROW]
[ROW][C]49[/C][C] 0.3303[/C][C] 0.6605[/C][C] 0.6697[/C][/ROW]
[ROW][C]50[/C][C] 0.2893[/C][C] 0.5786[/C][C] 0.7107[/C][/ROW]
[ROW][C]51[/C][C] 0.2475[/C][C] 0.495[/C][C] 0.7525[/C][/ROW]
[ROW][C]52[/C][C] 0.2242[/C][C] 0.4484[/C][C] 0.7758[/C][/ROW]
[ROW][C]53[/C][C] 0.223[/C][C] 0.4459[/C][C] 0.777[/C][/ROW]
[ROW][C]54[/C][C] 0.2587[/C][C] 0.5175[/C][C] 0.7413[/C][/ROW]
[ROW][C]55[/C][C] 0.23[/C][C] 0.4599[/C][C] 0.77[/C][/ROW]
[ROW][C]56[/C][C] 0.5625[/C][C] 0.8751[/C][C] 0.4375[/C][/ROW]
[ROW][C]57[/C][C] 0.5745[/C][C] 0.851[/C][C] 0.4255[/C][/ROW]
[ROW][C]58[/C][C] 0.5293[/C][C] 0.9415[/C][C] 0.4707[/C][/ROW]
[ROW][C]59[/C][C] 0.4836[/C][C] 0.9671[/C][C] 0.5164[/C][/ROW]
[ROW][C]60[/C][C] 0.4539[/C][C] 0.9078[/C][C] 0.5461[/C][/ROW]
[ROW][C]61[/C][C] 0.4507[/C][C] 0.9015[/C][C] 0.5493[/C][/ROW]
[ROW][C]62[/C][C] 0.4195[/C][C] 0.839[/C][C] 0.5805[/C][/ROW]
[ROW][C]63[/C][C] 0.3949[/C][C] 0.7899[/C][C] 0.6051[/C][/ROW]
[ROW][C]64[/C][C] 0.369[/C][C] 0.7379[/C][C] 0.631[/C][/ROW]
[ROW][C]65[/C][C] 0.3257[/C][C] 0.6515[/C][C] 0.6743[/C][/ROW]
[ROW][C]66[/C][C] 0.2843[/C][C] 0.5686[/C][C] 0.7157[/C][/ROW]
[ROW][C]67[/C][C] 0.262[/C][C] 0.524[/C][C] 0.738[/C][/ROW]
[ROW][C]68[/C][C] 0.2577[/C][C] 0.5154[/C][C] 0.7423[/C][/ROW]
[ROW][C]69[/C][C] 0.379[/C][C] 0.7581[/C][C] 0.621[/C][/ROW]
[ROW][C]70[/C][C] 0.4752[/C][C] 0.9505[/C][C] 0.5248[/C][/ROW]
[ROW][C]71[/C][C] 0.453[/C][C] 0.906[/C][C] 0.547[/C][/ROW]
[ROW][C]72[/C][C] 0.4069[/C][C] 0.8138[/C][C] 0.5931[/C][/ROW]
[ROW][C]73[/C][C] 0.3626[/C][C] 0.7252[/C][C] 0.6374[/C][/ROW]
[ROW][C]74[/C][C] 0.3261[/C][C] 0.6523[/C][C] 0.6739[/C][/ROW]
[ROW][C]75[/C][C] 0.3269[/C][C] 0.6538[/C][C] 0.6731[/C][/ROW]
[ROW][C]76[/C][C] 0.2864[/C][C] 0.5728[/C][C] 0.7136[/C][/ROW]
[ROW][C]77[/C][C] 0.3012[/C][C] 0.6024[/C][C] 0.6988[/C][/ROW]
[ROW][C]78[/C][C] 0.2624[/C][C] 0.5247[/C][C] 0.7376[/C][/ROW]
[ROW][C]79[/C][C] 0.2666[/C][C] 0.5331[/C][C] 0.7334[/C][/ROW]
[ROW][C]80[/C][C] 0.2296[/C][C] 0.4593[/C][C] 0.7704[/C][/ROW]
[ROW][C]81[/C][C] 0.1968[/C][C] 0.3937[/C][C] 0.8032[/C][/ROW]
[ROW][C]82[/C][C] 0.1662[/C][C] 0.3323[/C][C] 0.8338[/C][/ROW]
[ROW][C]83[/C][C] 0.1576[/C][C] 0.3152[/C][C] 0.8424[/C][/ROW]
[ROW][C]84[/C][C] 0.1436[/C][C] 0.2872[/C][C] 0.8564[/C][/ROW]
[ROW][C]85[/C][C] 0.1255[/C][C] 0.2511[/C][C] 0.8745[/C][/ROW]
[ROW][C]86[/C][C] 0.1452[/C][C] 0.2904[/C][C] 0.8548[/C][/ROW]
[ROW][C]87[/C][C] 0.1498[/C][C] 0.2996[/C][C] 0.8502[/C][/ROW]
[ROW][C]88[/C][C] 0.1299[/C][C] 0.2598[/C][C] 0.8701[/C][/ROW]
[ROW][C]89[/C][C] 0.1459[/C][C] 0.2918[/C][C] 0.8541[/C][/ROW]
[ROW][C]90[/C][C] 0.1413[/C][C] 0.2825[/C][C] 0.8587[/C][/ROW]
[ROW][C]91[/C][C] 0.123[/C][C] 0.2461[/C][C] 0.877[/C][/ROW]
[ROW][C]92[/C][C] 0.1081[/C][C] 0.2162[/C][C] 0.8919[/C][/ROW]
[ROW][C]93[/C][C] 0.08838[/C][C] 0.1768[/C][C] 0.9116[/C][/ROW]
[ROW][C]94[/C][C] 0.07118[/C][C] 0.1424[/C][C] 0.9288[/C][/ROW]
[ROW][C]95[/C][C] 0.05802[/C][C] 0.116[/C][C] 0.942[/C][/ROW]
[ROW][C]96[/C][C] 0.0457[/C][C] 0.0914[/C][C] 0.9543[/C][/ROW]
[ROW][C]97[/C][C] 0.0378[/C][C] 0.07559[/C][C] 0.9622[/C][/ROW]
[ROW][C]98[/C][C] 0.0775[/C][C] 0.155[/C][C] 0.9225[/C][/ROW]
[ROW][C]99[/C][C] 0.06159[/C][C] 0.1232[/C][C] 0.9384[/C][/ROW]
[ROW][C]100[/C][C] 0.06116[/C][C] 0.1223[/C][C] 0.9388[/C][/ROW]
[ROW][C]101[/C][C] 0.0514[/C][C] 0.1028[/C][C] 0.9486[/C][/ROW]
[ROW][C]102[/C][C] 0.04389[/C][C] 0.08778[/C][C] 0.9561[/C][/ROW]
[ROW][C]103[/C][C] 0.03377[/C][C] 0.06754[/C][C] 0.9662[/C][/ROW]
[ROW][C]104[/C][C] 0.09393[/C][C] 0.1879[/C][C] 0.9061[/C][/ROW]
[ROW][C]105[/C][C] 0.07642[/C][C] 0.1528[/C][C] 0.9236[/C][/ROW]
[ROW][C]106[/C][C] 0.09769[/C][C] 0.1954[/C][C] 0.9023[/C][/ROW]
[ROW][C]107[/C][C] 0.09325[/C][C] 0.1865[/C][C] 0.9068[/C][/ROW]
[ROW][C]108[/C][C] 0.07799[/C][C] 0.156[/C][C] 0.922[/C][/ROW]
[ROW][C]109[/C][C] 0.06203[/C][C] 0.1241[/C][C] 0.938[/C][/ROW]
[ROW][C]110[/C][C] 0.08988[/C][C] 0.1798[/C][C] 0.9101[/C][/ROW]
[ROW][C]111[/C][C] 0.07225[/C][C] 0.1445[/C][C] 0.9277[/C][/ROW]
[ROW][C]112[/C][C] 0.078[/C][C] 0.156[/C][C] 0.922[/C][/ROW]
[ROW][C]113[/C][C] 0.1142[/C][C] 0.2284[/C][C] 0.8858[/C][/ROW]
[ROW][C]114[/C][C] 0.1365[/C][C] 0.273[/C][C] 0.8635[/C][/ROW]
[ROW][C]115[/C][C] 0.117[/C][C] 0.2339[/C][C] 0.883[/C][/ROW]
[ROW][C]116[/C][C] 0.1128[/C][C] 0.2255[/C][C] 0.8872[/C][/ROW]
[ROW][C]117[/C][C] 0.09226[/C][C] 0.1845[/C][C] 0.9077[/C][/ROW]
[ROW][C]118[/C][C] 0.07726[/C][C] 0.1545[/C][C] 0.9227[/C][/ROW]
[ROW][C]119[/C][C] 0.06637[/C][C] 0.1327[/C][C] 0.9336[/C][/ROW]
[ROW][C]120[/C][C] 0.1053[/C][C] 0.2106[/C][C] 0.8947[/C][/ROW]
[ROW][C]121[/C][C] 0.6074[/C][C] 0.7851[/C][C] 0.3926[/C][/ROW]
[ROW][C]122[/C][C] 0.7334[/C][C] 0.5332[/C][C] 0.2666[/C][/ROW]
[ROW][C]123[/C][C] 0.6987[/C][C] 0.6025[/C][C] 0.3013[/C][/ROW]
[ROW][C]124[/C][C] 0.6966[/C][C] 0.6068[/C][C] 0.3034[/C][/ROW]
[ROW][C]125[/C][C] 0.6765[/C][C] 0.6471[/C][C] 0.3235[/C][/ROW]
[ROW][C]126[/C][C] 0.6316[/C][C] 0.7368[/C][C] 0.3684[/C][/ROW]
[ROW][C]127[/C][C] 0.5727[/C][C] 0.8545[/C][C] 0.4273[/C][/ROW]
[ROW][C]128[/C][C] 0.5715[/C][C] 0.857[/C][C] 0.4285[/C][/ROW]
[ROW][C]129[/C][C] 0.5161[/C][C] 0.9677[/C][C] 0.4839[/C][/ROW]
[ROW][C]130[/C][C] 0.6096[/C][C] 0.7808[/C][C] 0.3904[/C][/ROW]
[ROW][C]131[/C][C] 0.5481[/C][C] 0.9037[/C][C] 0.4519[/C][/ROW]
[ROW][C]132[/C][C] 0.5935[/C][C] 0.8129[/C][C] 0.4065[/C][/ROW]
[ROW][C]133[/C][C] 0.5907[/C][C] 0.8187[/C][C] 0.4093[/C][/ROW]
[ROW][C]134[/C][C] 0.5237[/C][C] 0.9526[/C][C] 0.4763[/C][/ROW]
[ROW][C]135[/C][C] 0.5797[/C][C] 0.8405[/C][C] 0.4203[/C][/ROW]
[ROW][C]136[/C][C] 0.5398[/C][C] 0.9205[/C][C] 0.4602[/C][/ROW]
[ROW][C]137[/C][C] 0.4783[/C][C] 0.9566[/C][C] 0.5217[/C][/ROW]
[ROW][C]138[/C][C] 0.5197[/C][C] 0.9606[/C][C] 0.4803[/C][/ROW]
[ROW][C]139[/C][C] 0.4955[/C][C] 0.9911[/C][C] 0.5045[/C][/ROW]
[ROW][C]140[/C][C] 0.575[/C][C] 0.8501[/C][C] 0.425[/C][/ROW]
[ROW][C]141[/C][C] 0.5438[/C][C] 0.9125[/C][C] 0.4562[/C][/ROW]
[ROW][C]142[/C][C] 0.7212[/C][C] 0.5575[/C][C] 0.2788[/C][/ROW]
[ROW][C]143[/C][C] 0.656[/C][C] 0.6881[/C][C] 0.344[/C][/ROW]
[ROW][C]144[/C][C] 0.7711[/C][C] 0.4579[/C][C] 0.2289[/C][/ROW]
[ROW][C]145[/C][C] 0.8955[/C][C] 0.2089[/C][C] 0.1045[/C][/ROW]
[ROW][C]146[/C][C] 0.9498[/C][C] 0.1005[/C][C] 0.05025[/C][/ROW]
[ROW][C]147[/C][C] 0.9132[/C][C] 0.1736[/C][C] 0.08678[/C][/ROW]
[ROW][C]148[/C][C] 0.8882[/C][C] 0.2236[/C][C] 0.1118[/C][/ROW]
[ROW][C]149[/C][C] 0.8005[/C][C] 0.3991[/C][C] 0.1995[/C][/ROW]
[ROW][C]150[/C][C] 0.6806[/C][C] 0.6387[/C][C] 0.3194[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=299934&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=299934&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	0.05734	0.1147	0.9427
9	0.05103	0.1021	0.949
10	0.0748	0.1496	0.9252
11	0.5633	0.8734	0.4367
12	0.7176	0.5647	0.2824
13	0.6233	0.7534	0.3767
14	0.5263	0.9473	0.4737
15	0.4316	0.8631	0.5684
16	0.3432	0.6863	0.6568
17	0.2699	0.5397	0.7301
18	0.2025	0.4051	0.7975
19	0.1465	0.2929	0.8535
20	0.1262	0.2523	0.8738
21	0.09059	0.1812	0.9094
22	0.0675	0.135	0.9325
23	0.1003	0.2006	0.8997
24	0.07143	0.1429	0.9286
25	0.07104	0.1421	0.929
26	0.08951	0.179	0.9105
27	0.08944	0.1789	0.9106
28	0.06553	0.1311	0.9345
29	0.04731	0.09463	0.9527
30	0.08691	0.1738	0.9131
31	0.06848	0.137	0.9315
32	0.05186	0.1037	0.9481
33	0.03695	0.0739	0.963
34	0.03801	0.07603	0.962
35	0.05581	0.1116	0.9442
36	0.06481	0.1296	0.9352
37	0.1177	0.2355	0.8823
38	0.09707	0.1941	0.9029
39	0.1643	0.3286	0.8357
40	0.136	0.272	0.864
41	0.2288	0.4576	0.7712
42	0.2059	0.4119	0.7941
43	0.1822	0.3645	0.8178
44	0.164	0.3279	0.836
45	0.2984	0.5967	0.7016
46	0.3076	0.6152	0.6924
47	0.3145	0.629	0.6855
48	0.3364	0.6728	0.6636
49	0.3303	0.6605	0.6697
50	0.2893	0.5786	0.7107
51	0.2475	0.495	0.7525
52	0.2242	0.4484	0.7758
53	0.223	0.4459	0.777
54	0.2587	0.5175	0.7413
55	0.23	0.4599	0.77
56	0.5625	0.8751	0.4375
57	0.5745	0.851	0.4255
58	0.5293	0.9415	0.4707
59	0.4836	0.9671	0.5164
60	0.4539	0.9078	0.5461
61	0.4507	0.9015	0.5493
62	0.4195	0.839	0.5805
63	0.3949	0.7899	0.6051
64	0.369	0.7379	0.631
65	0.3257	0.6515	0.6743
66	0.2843	0.5686	0.7157
67	0.262	0.524	0.738
68	0.2577	0.5154	0.7423
69	0.379	0.7581	0.621
70	0.4752	0.9505	0.5248
71	0.453	0.906	0.547
72	0.4069	0.8138	0.5931
73	0.3626	0.7252	0.6374
74	0.3261	0.6523	0.6739
75	0.3269	0.6538	0.6731
76	0.2864	0.5728	0.7136
77	0.3012	0.6024	0.6988
78	0.2624	0.5247	0.7376
79	0.2666	0.5331	0.7334
80	0.2296	0.4593	0.7704
81	0.1968	0.3937	0.8032
82	0.1662	0.3323	0.8338
83	0.1576	0.3152	0.8424
84	0.1436	0.2872	0.8564
85	0.1255	0.2511	0.8745
86	0.1452	0.2904	0.8548
87	0.1498	0.2996	0.8502
88	0.1299	0.2598	0.8701
89	0.1459	0.2918	0.8541
90	0.1413	0.2825	0.8587
91	0.123	0.2461	0.877
92	0.1081	0.2162	0.8919
93	0.08838	0.1768	0.9116
94	0.07118	0.1424	0.9288
95	0.05802	0.116	0.942
96	0.0457	0.0914	0.9543
97	0.0378	0.07559	0.9622
98	0.0775	0.155	0.9225
99	0.06159	0.1232	0.9384
100	0.06116	0.1223	0.9388
101	0.0514	0.1028	0.9486
102	0.04389	0.08778	0.9561
103	0.03377	0.06754	0.9662
104	0.09393	0.1879	0.9061
105	0.07642	0.1528	0.9236
106	0.09769	0.1954	0.9023
107	0.09325	0.1865	0.9068
108	0.07799	0.156	0.922
109	0.06203	0.1241	0.938
110	0.08988	0.1798	0.9101
111	0.07225	0.1445	0.9277
112	0.078	0.156	0.922
113	0.1142	0.2284	0.8858
114	0.1365	0.273	0.8635
115	0.117	0.2339	0.883
116	0.1128	0.2255	0.8872
117	0.09226	0.1845	0.9077
118	0.07726	0.1545	0.9227
119	0.06637	0.1327	0.9336
120	0.1053	0.2106	0.8947
121	0.6074	0.7851	0.3926
122	0.7334	0.5332	0.2666
123	0.6987	0.6025	0.3013
124	0.6966	0.6068	0.3034
125	0.6765	0.6471	0.3235
126	0.6316	0.7368	0.3684
127	0.5727	0.8545	0.4273
128	0.5715	0.857	0.4285
129	0.5161	0.9677	0.4839
130	0.6096	0.7808	0.3904
131	0.5481	0.9037	0.4519
132	0.5935	0.8129	0.4065
133	0.5907	0.8187	0.4093
134	0.5237	0.9526	0.4763
135	0.5797	0.8405	0.4203
136	0.5398	0.9205	0.4602
137	0.4783	0.9566	0.5217
138	0.5197	0.9606	0.4803
139	0.4955	0.9911	0.5045
140	0.575	0.8501	0.425
141	0.5438	0.9125	0.4562
142	0.7212	0.5575	0.2788
143	0.656	0.6881	0.344
144	0.7711	0.4579	0.2289
145	0.8955	0.2089	0.1045
146	0.9498	0.1005	0.05025
147	0.9132	0.1736	0.08678
148	0.8882	0.2236	0.1118
149	0.8005	0.3991	0.1995
150	0.6806	0.6387	0.3194

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	7	0.048951	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 & 7 & 0.048951 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=299934&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]7[/C][C]0.048951[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=299934&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=299934&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	7	0.048951	OK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 0.73432, df1 = 2, df2 = 151, p-value = 0.4815
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 0.69241, df1 = 8, df2 = 145, p-value = 0.6978
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.88501, df1 = 2, df2 = 151, p-value = 0.4148

\begin{tabular}{lllllllll}
\hline
Ramsey RESET F-Test for powers (2 and 3) of fitted values \tabularnewline
> reset_test_fitted
	RESET test
data:  mylm
RESET = 0.73432, df1 = 2, df2 = 151, p-value = 0.4815
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 0.69241, df1 = 8, df2 = 145, p-value = 0.6978
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.88501, df1 = 2, df2 = 151, p-value = 0.4148
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=299934&T=7

[TABLE]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of fitted values[/C][/ROW]
[ROW][C]> reset_test_fitted
	RESET test
data:  mylm
RESET = 0.73432, df1 = 2, df2 = 151, p-value = 0.4815
[/C][/ROW]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of regressors[/C][/ROW]
[ROW][C]> reset_test_regressors
	RESET test
data:  mylm
RESET = 0.69241, df1 = 8, df2 = 145, p-value = 0.6978
[/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 = 0.88501, df1 = 2, df2 = 151, p-value = 0.4148
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=299934&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=299934&T=7

As an alternative you can also use a QR Code:

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

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 0.73432, df1 = 2, df2 = 151, p-value = 0.4815
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 0.69241, df1 = 8, df2 = 145, p-value = 0.6978
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.88501, df1 = 2, df2 = 151, p-value = 0.4148

Variance Inflation Factors (Multicollinearity)
> vif b c d e 1.031300 1.032837 1.052855 1.044114

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
       b        c        d        e 
1.031300 1.032837 1.052855 1.044114 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=299934&T=8

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
       b        c        d        e 
1.031300 1.032837 1.052855 1.044114 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=299934&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=299934&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 1.031300 1.032837 1.052855 1.044114

Figure 1

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Figure 3

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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 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = 0 ; par5 = 0 ;

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