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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Fri, 16 Dec 2016 13:28:41 +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/16/t1481891339yh94xbeqq4jzs8f.htm/, Retrieved Thu, 02 May 2024 18:00:10 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=300211, Retrieved Thu, 02 May 2024 18:00:10 +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] [multipe regressio...] [2016-12-16 12:28:41] [2d1dd91c3b5ba64567b1d6b2c9fe9017] [Current]

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Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	9 seconds
R Server	Big Analytics Cloud Computing Center

\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=300211&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=300211&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=300211&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
TVDCSUM[t] = + 12.89 -0.157635EP2[t] -0.267706EP3[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TVDCSUM[t] =  +  12.89 -0.157635EP2[t] -0.267706EP3[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=300211&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TVDCSUM[t] =  +  12.89 -0.157635EP2[t] -0.267706EP3[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=300211&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=300211&T=1

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Estimated Regression Equation
TVDCSUM[t] = + 12.89 -0.157635EP2[t] -0.267706EP3[t] + e[t]

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	+12.89	0.5373	+2.3990e+01	1.134e-55	5.67e-56
EP2	-0.1576	0.1128	-1.3970e+00	0.1642	0.08211
EP3	-0.2677	0.09309	-2.8760e+00	0.004561	0.00228

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & +12.89 &  0.5373 & +2.3990e+01 &  1.134e-55 &  5.67e-56 \tabularnewline
EP2 & -0.1576 &  0.1128 & -1.3970e+00 &  0.1642 &  0.08211 \tabularnewline
EP3 & -0.2677 &  0.09309 & -2.8760e+00 &  0.004561 &  0.00228 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=300211&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]+12.89[/C][C] 0.5373[/C][C]+2.3990e+01[/C][C] 1.134e-55[/C][C] 5.67e-56[/C][/ROW]
[ROW][C]EP2[/C][C]-0.1576[/C][C] 0.1128[/C][C]-1.3970e+00[/C][C] 0.1642[/C][C] 0.08211[/C][/ROW]
[ROW][C]EP3[/C][C]-0.2677[/C][C] 0.09309[/C][C]-2.8760e+00[/C][C] 0.004561[/C][C] 0.00228[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=300211&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=300211&T=2

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	+12.89	0.5373	+2.3990e+01	1.134e-55	5.67e-56
EP2	-0.1576	0.1128	-1.3970e+00	0.1642	0.08211
EP3	-0.2677	0.09309	-2.8760e+00	0.004561	0.00228

Multiple Linear Regression - Regression Statistics
Multiple R	0.2538
R-squared	0.0644
Adjusted R-squared	0.05306
F-TEST (value)	5.679
F-TEST (DF numerator)	2
F-TEST (DF denominator)	165
p-value	0.004121
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.19
Sum Squared Residuals	233.8

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.2538 \tabularnewline
R-squared &  0.0644 \tabularnewline
Adjusted R-squared &  0.05306 \tabularnewline
F-TEST (value) &  5.679 \tabularnewline
F-TEST (DF numerator) & 2 \tabularnewline
F-TEST (DF denominator) & 165 \tabularnewline
p-value &  0.004121 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  1.19 \tabularnewline
Sum Squared Residuals &  233.8 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=300211&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.2538[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.0644[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.05306[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 5.679[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]2[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]165[/C][/ROW]
[ROW][C]p-value[/C][C] 0.004121[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 1.19[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 233.8[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=300211&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=300211&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	0.2538
R-squared	0.0644
Adjusted R-squared	0.05306
F-TEST (value)	5.679
F-TEST (DF numerator)	2
F-TEST (DF denominator)	165
p-value	0.004121
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.19
Sum Squared Residuals	233.8

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	9	11.03	-2.031
2	11	11.88	-0.8817
3	13	11.3	1.701
4	11	11.72	-0.724
5	12	11.72	0.276
6	11	11.3	-0.2987
7	12	11.61	0.386
8	12	11.57	0.4336
9	13	11.57	1.434
10	12	11.03	0.969
11	12	11.57	0.4336
12	11	11.72	-0.724
13	12	11.46	0.5437
14	10	11.57	-1.566
15	12	11.3	0.7013
16	12	11.57	0.4336
17	12	11.72	0.276
18	12	10.76	1.237
19	13	11.3	1.701
20	11	11.57	-0.5664
21	11	11.57	-0.5664
22	11	11.46	-0.4563
23	11	11.83	-0.8341
24	13	11.72	1.276
25	11	11.3	-0.2987
26	12	11.72	0.276
27	11	11.57	-0.5664
28	12	11.46	0.5437
29	12	10.76	1.237
30	10	11.57	-1.566
31	11	10.76	0.2367
32	12	11.57	0.4336
33	11	11.57	-0.5664
34	9	11.19	-2.189
35	12	11.99	0.008248
36	11	11.72	-0.724
37	11	11.72	-0.724
38	12	11.3	0.7013
39	13	11.57	1.434
40	11	11.3	-0.2987
41	12	11.57	0.4336
42	9	11.3	-2.299
43	12	11.03	0.969
44	11	11.03	-0.031
45	12	11.3	0.7013
46	12	11.57	0.4336
47	11	11.72	-0.724
48	10	11.03	-1.031
49	9	11.03	-2.031
50	12	11.3	0.7013
51	13	11.72	1.276
52	13	11.57	1.434
53	9	11.72	-2.724
54	11	11.88	-0.8817
55	11	11.61	-0.614
56	11	11.72	-0.724
57	12	11.57	0.4336
58	12	11.3	0.7013
59	11	11.46	-0.4563
60	12	11.57	0.4336
61	11	11.57	-0.5664
62	12	11.03	0.969
63	11	11.03	-0.031
64	11	11.46	-0.4563
65	8	11.03	-3.031
66	12	11.19	0.8114
67	11	11.03	-0.031
68	12	11.5	0.4961
69	11	11.08	-0.07856
70	11	11.3	-0.2987
71	11	11.03	-0.031
72	10	11.35	-1.346
73	10	11.57	-1.566
74	13	11.88	1.118
75	11	11.46	-0.4563
76	11	11.35	-0.3463
77	11	11.57	-0.5664
78	13	10.92	2.079
79	12	11.83	0.1659
80	12	11.3	0.7013
81	9	11.46	-2.456
82	12	11.72	0.276
83	12	11.57	0.4336
84	13	11.83	1.166
85	15	11.72	3.276
86	13	11.83	1.166
87	13	11.57	1.434
88	11	11.57	-0.5664
89	12	11.72	0.276
90	9	11.72	-2.724
91	11	11.46	-0.4563
92	13	11.88	1.118
93	12	11.99	0.008248
94	13	11.83	1.166
95	11	11.3	-0.2987
96	12	11.72	0.276
97	14	11.3	2.701
98	13	12.31	0.693
99	11	11.57	-0.5664
100	12	11.57	0.4336
101	13	11.46	1.544
102	11	11.57	-0.5664
103	11	11.57	-0.5664
104	11	11.46	-0.4563
105	13	11.83	1.166
106	12	11.03	0.969
107	12	11.3	0.7013
108	11	11.57	-0.5664
109	12	11.3	0.7013
110	12	11.3	0.7013
111	10	11.46	-1.456
112	11	11.03	-0.031
113	9	11.61	-2.614
114	14	11.72	2.276
115	12	11.3	0.7013
116	11	11.57	-0.5664
117	13	12.46	0.5353
118	11	11.83	-0.8341
119	11	11.57	-0.5664
120	11	11.19	-0.1886
121	11	11.46	-0.4563
122	12	11.57	0.4336
123	11	11.57	-0.5664
124	13	11.3	1.701
125	11	11.3	-0.2987
126	11	11.3	-0.2987
127	12	11.88	0.1183
128	11	11.72	-0.724
129	11	11.57	-0.5664
130	9	11.3	-2.299
131	12	11.03	0.969
132	14	11.72	2.276
133	10	11.57	-1.566
134	9	11.99	-2.992
135	12	11.46	0.5437
136	14	11.46	2.544
137	9	11.19	-2.189
138	11	11.46	-0.4563
139	14	11.83	2.166
140	13	12.31	0.693
141	10	11.57	-1.566
142	11	11.99	-0.9918
143	12	10.76	1.237
144	10	11.3	-1.299
145	13	11.72	1.276
146	12	11.72	0.276
147	14	11.5	2.496
148	10	11.57	-1.566
149	12	11.03	0.969
150	9	11.03	-2.031
151	12	11.46	0.5437
152	11	11.03	-0.031
153	11	11.3	-0.2987
154	10	11.19	-1.189
155	11	11.3	-0.2987
156	12	11.03	0.969
157	10	12.04	-2.039
158	11	11.03	-0.031
159	13	12.15	0.8506
160	11	11.03	-0.031
161	13	11.46	1.544
162	12	11.57	0.4336
163	11	11.72	-0.724
164	12	11.57	0.4336
165	10	11.03	-1.031
166	12	11.3	0.7013
167	10	11.46	-1.456
168	13	12.04	0.9607

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  9 &  11.03 & -2.031 \tabularnewline
2 &  11 &  11.88 & -0.8817 \tabularnewline
3 &  13 &  11.3 &  1.701 \tabularnewline
4 &  11 &  11.72 & -0.724 \tabularnewline
5 &  12 &  11.72 &  0.276 \tabularnewline
6 &  11 &  11.3 & -0.2987 \tabularnewline
7 &  12 &  11.61 &  0.386 \tabularnewline
8 &  12 &  11.57 &  0.4336 \tabularnewline
9 &  13 &  11.57 &  1.434 \tabularnewline
10 &  12 &  11.03 &  0.969 \tabularnewline
11 &  12 &  11.57 &  0.4336 \tabularnewline
12 &  11 &  11.72 & -0.724 \tabularnewline
13 &  12 &  11.46 &  0.5437 \tabularnewline
14 &  10 &  11.57 & -1.566 \tabularnewline
15 &  12 &  11.3 &  0.7013 \tabularnewline
16 &  12 &  11.57 &  0.4336 \tabularnewline
17 &  12 &  11.72 &  0.276 \tabularnewline
18 &  12 &  10.76 &  1.237 \tabularnewline
19 &  13 &  11.3 &  1.701 \tabularnewline
20 &  11 &  11.57 & -0.5664 \tabularnewline
21 &  11 &  11.57 & -0.5664 \tabularnewline
22 &  11 &  11.46 & -0.4563 \tabularnewline
23 &  11 &  11.83 & -0.8341 \tabularnewline
24 &  13 &  11.72 &  1.276 \tabularnewline
25 &  11 &  11.3 & -0.2987 \tabularnewline
26 &  12 &  11.72 &  0.276 \tabularnewline
27 &  11 &  11.57 & -0.5664 \tabularnewline
28 &  12 &  11.46 &  0.5437 \tabularnewline
29 &  12 &  10.76 &  1.237 \tabularnewline
30 &  10 &  11.57 & -1.566 \tabularnewline
31 &  11 &  10.76 &  0.2367 \tabularnewline
32 &  12 &  11.57 &  0.4336 \tabularnewline
33 &  11 &  11.57 & -0.5664 \tabularnewline
34 &  9 &  11.19 & -2.189 \tabularnewline
35 &  12 &  11.99 &  0.008248 \tabularnewline
36 &  11 &  11.72 & -0.724 \tabularnewline
37 &  11 &  11.72 & -0.724 \tabularnewline
38 &  12 &  11.3 &  0.7013 \tabularnewline
39 &  13 &  11.57 &  1.434 \tabularnewline
40 &  11 &  11.3 & -0.2987 \tabularnewline
41 &  12 &  11.57 &  0.4336 \tabularnewline
42 &  9 &  11.3 & -2.299 \tabularnewline
43 &  12 &  11.03 &  0.969 \tabularnewline
44 &  11 &  11.03 & -0.031 \tabularnewline
45 &  12 &  11.3 &  0.7013 \tabularnewline
46 &  12 &  11.57 &  0.4336 \tabularnewline
47 &  11 &  11.72 & -0.724 \tabularnewline
48 &  10 &  11.03 & -1.031 \tabularnewline
49 &  9 &  11.03 & -2.031 \tabularnewline
50 &  12 &  11.3 &  0.7013 \tabularnewline
51 &  13 &  11.72 &  1.276 \tabularnewline
52 &  13 &  11.57 &  1.434 \tabularnewline
53 &  9 &  11.72 & -2.724 \tabularnewline
54 &  11 &  11.88 & -0.8817 \tabularnewline
55 &  11 &  11.61 & -0.614 \tabularnewline
56 &  11 &  11.72 & -0.724 \tabularnewline
57 &  12 &  11.57 &  0.4336 \tabularnewline
58 &  12 &  11.3 &  0.7013 \tabularnewline
59 &  11 &  11.46 & -0.4563 \tabularnewline
60 &  12 &  11.57 &  0.4336 \tabularnewline
61 &  11 &  11.57 & -0.5664 \tabularnewline
62 &  12 &  11.03 &  0.969 \tabularnewline
63 &  11 &  11.03 & -0.031 \tabularnewline
64 &  11 &  11.46 & -0.4563 \tabularnewline
65 &  8 &  11.03 & -3.031 \tabularnewline
66 &  12 &  11.19 &  0.8114 \tabularnewline
67 &  11 &  11.03 & -0.031 \tabularnewline
68 &  12 &  11.5 &  0.4961 \tabularnewline
69 &  11 &  11.08 & -0.07856 \tabularnewline
70 &  11 &  11.3 & -0.2987 \tabularnewline
71 &  11 &  11.03 & -0.031 \tabularnewline
72 &  10 &  11.35 & -1.346 \tabularnewline
73 &  10 &  11.57 & -1.566 \tabularnewline
74 &  13 &  11.88 &  1.118 \tabularnewline
75 &  11 &  11.46 & -0.4563 \tabularnewline
76 &  11 &  11.35 & -0.3463 \tabularnewline
77 &  11 &  11.57 & -0.5664 \tabularnewline
78 &  13 &  10.92 &  2.079 \tabularnewline
79 &  12 &  11.83 &  0.1659 \tabularnewline
80 &  12 &  11.3 &  0.7013 \tabularnewline
81 &  9 &  11.46 & -2.456 \tabularnewline
82 &  12 &  11.72 &  0.276 \tabularnewline
83 &  12 &  11.57 &  0.4336 \tabularnewline
84 &  13 &  11.83 &  1.166 \tabularnewline
85 &  15 &  11.72 &  3.276 \tabularnewline
86 &  13 &  11.83 &  1.166 \tabularnewline
87 &  13 &  11.57 &  1.434 \tabularnewline
88 &  11 &  11.57 & -0.5664 \tabularnewline
89 &  12 &  11.72 &  0.276 \tabularnewline
90 &  9 &  11.72 & -2.724 \tabularnewline
91 &  11 &  11.46 & -0.4563 \tabularnewline
92 &  13 &  11.88 &  1.118 \tabularnewline
93 &  12 &  11.99 &  0.008248 \tabularnewline
94 &  13 &  11.83 &  1.166 \tabularnewline
95 &  11 &  11.3 & -0.2987 \tabularnewline
96 &  12 &  11.72 &  0.276 \tabularnewline
97 &  14 &  11.3 &  2.701 \tabularnewline
98 &  13 &  12.31 &  0.693 \tabularnewline
99 &  11 &  11.57 & -0.5664 \tabularnewline
100 &  12 &  11.57 &  0.4336 \tabularnewline
101 &  13 &  11.46 &  1.544 \tabularnewline
102 &  11 &  11.57 & -0.5664 \tabularnewline
103 &  11 &  11.57 & -0.5664 \tabularnewline
104 &  11 &  11.46 & -0.4563 \tabularnewline
105 &  13 &  11.83 &  1.166 \tabularnewline
106 &  12 &  11.03 &  0.969 \tabularnewline
107 &  12 &  11.3 &  0.7013 \tabularnewline
108 &  11 &  11.57 & -0.5664 \tabularnewline
109 &  12 &  11.3 &  0.7013 \tabularnewline
110 &  12 &  11.3 &  0.7013 \tabularnewline
111 &  10 &  11.46 & -1.456 \tabularnewline
112 &  11 &  11.03 & -0.031 \tabularnewline
113 &  9 &  11.61 & -2.614 \tabularnewline
114 &  14 &  11.72 &  2.276 \tabularnewline
115 &  12 &  11.3 &  0.7013 \tabularnewline
116 &  11 &  11.57 & -0.5664 \tabularnewline
117 &  13 &  12.46 &  0.5353 \tabularnewline
118 &  11 &  11.83 & -0.8341 \tabularnewline
119 &  11 &  11.57 & -0.5664 \tabularnewline
120 &  11 &  11.19 & -0.1886 \tabularnewline
121 &  11 &  11.46 & -0.4563 \tabularnewline
122 &  12 &  11.57 &  0.4336 \tabularnewline
123 &  11 &  11.57 & -0.5664 \tabularnewline
124 &  13 &  11.3 &  1.701 \tabularnewline
125 &  11 &  11.3 & -0.2987 \tabularnewline
126 &  11 &  11.3 & -0.2987 \tabularnewline
127 &  12 &  11.88 &  0.1183 \tabularnewline
128 &  11 &  11.72 & -0.724 \tabularnewline
129 &  11 &  11.57 & -0.5664 \tabularnewline
130 &  9 &  11.3 & -2.299 \tabularnewline
131 &  12 &  11.03 &  0.969 \tabularnewline
132 &  14 &  11.72 &  2.276 \tabularnewline
133 &  10 &  11.57 & -1.566 \tabularnewline
134 &  9 &  11.99 & -2.992 \tabularnewline
135 &  12 &  11.46 &  0.5437 \tabularnewline
136 &  14 &  11.46 &  2.544 \tabularnewline
137 &  9 &  11.19 & -2.189 \tabularnewline
138 &  11 &  11.46 & -0.4563 \tabularnewline
139 &  14 &  11.83 &  2.166 \tabularnewline
140 &  13 &  12.31 &  0.693 \tabularnewline
141 &  10 &  11.57 & -1.566 \tabularnewline
142 &  11 &  11.99 & -0.9918 \tabularnewline
143 &  12 &  10.76 &  1.237 \tabularnewline
144 &  10 &  11.3 & -1.299 \tabularnewline
145 &  13 &  11.72 &  1.276 \tabularnewline
146 &  12 &  11.72 &  0.276 \tabularnewline
147 &  14 &  11.5 &  2.496 \tabularnewline
148 &  10 &  11.57 & -1.566 \tabularnewline
149 &  12 &  11.03 &  0.969 \tabularnewline
150 &  9 &  11.03 & -2.031 \tabularnewline
151 &  12 &  11.46 &  0.5437 \tabularnewline
152 &  11 &  11.03 & -0.031 \tabularnewline
153 &  11 &  11.3 & -0.2987 \tabularnewline
154 &  10 &  11.19 & -1.189 \tabularnewline
155 &  11 &  11.3 & -0.2987 \tabularnewline
156 &  12 &  11.03 &  0.969 \tabularnewline
157 &  10 &  12.04 & -2.039 \tabularnewline
158 &  11 &  11.03 & -0.031 \tabularnewline
159 &  13 &  12.15 &  0.8506 \tabularnewline
160 &  11 &  11.03 & -0.031 \tabularnewline
161 &  13 &  11.46 &  1.544 \tabularnewline
162 &  12 &  11.57 &  0.4336 \tabularnewline
163 &  11 &  11.72 & -0.724 \tabularnewline
164 &  12 &  11.57 &  0.4336 \tabularnewline
165 &  10 &  11.03 & -1.031 \tabularnewline
166 &  12 &  11.3 &  0.7013 \tabularnewline
167 &  10 &  11.46 & -1.456 \tabularnewline
168 &  13 &  12.04 &  0.9607 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=300211&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C] 9[/C][C] 11.03[/C][C]-2.031[/C][/ROW]
[ROW][C]2[/C][C] 11[/C][C] 11.88[/C][C]-0.8817[/C][/ROW]
[ROW][C]3[/C][C] 13[/C][C] 11.3[/C][C] 1.701[/C][/ROW]
[ROW][C]4[/C][C] 11[/C][C] 11.72[/C][C]-0.724[/C][/ROW]
[ROW][C]5[/C][C] 12[/C][C] 11.72[/C][C] 0.276[/C][/ROW]
[ROW][C]6[/C][C] 11[/C][C] 11.3[/C][C]-0.2987[/C][/ROW]
[ROW][C]7[/C][C] 12[/C][C] 11.61[/C][C] 0.386[/C][/ROW]
[ROW][C]8[/C][C] 12[/C][C] 11.57[/C][C] 0.4336[/C][/ROW]
[ROW][C]9[/C][C] 13[/C][C] 11.57[/C][C] 1.434[/C][/ROW]
[ROW][C]10[/C][C] 12[/C][C] 11.03[/C][C] 0.969[/C][/ROW]
[ROW][C]11[/C][C] 12[/C][C] 11.57[/C][C] 0.4336[/C][/ROW]
[ROW][C]12[/C][C] 11[/C][C] 11.72[/C][C]-0.724[/C][/ROW]
[ROW][C]13[/C][C] 12[/C][C] 11.46[/C][C] 0.5437[/C][/ROW]
[ROW][C]14[/C][C] 10[/C][C] 11.57[/C][C]-1.566[/C][/ROW]
[ROW][C]15[/C][C] 12[/C][C] 11.3[/C][C] 0.7013[/C][/ROW]
[ROW][C]16[/C][C] 12[/C][C] 11.57[/C][C] 0.4336[/C][/ROW]
[ROW][C]17[/C][C] 12[/C][C] 11.72[/C][C] 0.276[/C][/ROW]
[ROW][C]18[/C][C] 12[/C][C] 10.76[/C][C] 1.237[/C][/ROW]
[ROW][C]19[/C][C] 13[/C][C] 11.3[/C][C] 1.701[/C][/ROW]
[ROW][C]20[/C][C] 11[/C][C] 11.57[/C][C]-0.5664[/C][/ROW]
[ROW][C]21[/C][C] 11[/C][C] 11.57[/C][C]-0.5664[/C][/ROW]
[ROW][C]22[/C][C] 11[/C][C] 11.46[/C][C]-0.4563[/C][/ROW]
[ROW][C]23[/C][C] 11[/C][C] 11.83[/C][C]-0.8341[/C][/ROW]
[ROW][C]24[/C][C] 13[/C][C] 11.72[/C][C] 1.276[/C][/ROW]
[ROW][C]25[/C][C] 11[/C][C] 11.3[/C][C]-0.2987[/C][/ROW]
[ROW][C]26[/C][C] 12[/C][C] 11.72[/C][C] 0.276[/C][/ROW]
[ROW][C]27[/C][C] 11[/C][C] 11.57[/C][C]-0.5664[/C][/ROW]
[ROW][C]28[/C][C] 12[/C][C] 11.46[/C][C] 0.5437[/C][/ROW]
[ROW][C]29[/C][C] 12[/C][C] 10.76[/C][C] 1.237[/C][/ROW]
[ROW][C]30[/C][C] 10[/C][C] 11.57[/C][C]-1.566[/C][/ROW]
[ROW][C]31[/C][C] 11[/C][C] 10.76[/C][C] 0.2367[/C][/ROW]
[ROW][C]32[/C][C] 12[/C][C] 11.57[/C][C] 0.4336[/C][/ROW]
[ROW][C]33[/C][C] 11[/C][C] 11.57[/C][C]-0.5664[/C][/ROW]
[ROW][C]34[/C][C] 9[/C][C] 11.19[/C][C]-2.189[/C][/ROW]
[ROW][C]35[/C][C] 12[/C][C] 11.99[/C][C] 0.008248[/C][/ROW]
[ROW][C]36[/C][C] 11[/C][C] 11.72[/C][C]-0.724[/C][/ROW]
[ROW][C]37[/C][C] 11[/C][C] 11.72[/C][C]-0.724[/C][/ROW]
[ROW][C]38[/C][C] 12[/C][C] 11.3[/C][C] 0.7013[/C][/ROW]
[ROW][C]39[/C][C] 13[/C][C] 11.57[/C][C] 1.434[/C][/ROW]
[ROW][C]40[/C][C] 11[/C][C] 11.3[/C][C]-0.2987[/C][/ROW]
[ROW][C]41[/C][C] 12[/C][C] 11.57[/C][C] 0.4336[/C][/ROW]
[ROW][C]42[/C][C] 9[/C][C] 11.3[/C][C]-2.299[/C][/ROW]
[ROW][C]43[/C][C] 12[/C][C] 11.03[/C][C] 0.969[/C][/ROW]
[ROW][C]44[/C][C] 11[/C][C] 11.03[/C][C]-0.031[/C][/ROW]
[ROW][C]45[/C][C] 12[/C][C] 11.3[/C][C] 0.7013[/C][/ROW]
[ROW][C]46[/C][C] 12[/C][C] 11.57[/C][C] 0.4336[/C][/ROW]
[ROW][C]47[/C][C] 11[/C][C] 11.72[/C][C]-0.724[/C][/ROW]
[ROW][C]48[/C][C] 10[/C][C] 11.03[/C][C]-1.031[/C][/ROW]
[ROW][C]49[/C][C] 9[/C][C] 11.03[/C][C]-2.031[/C][/ROW]
[ROW][C]50[/C][C] 12[/C][C] 11.3[/C][C] 0.7013[/C][/ROW]
[ROW][C]51[/C][C] 13[/C][C] 11.72[/C][C] 1.276[/C][/ROW]
[ROW][C]52[/C][C] 13[/C][C] 11.57[/C][C] 1.434[/C][/ROW]
[ROW][C]53[/C][C] 9[/C][C] 11.72[/C][C]-2.724[/C][/ROW]
[ROW][C]54[/C][C] 11[/C][C] 11.88[/C][C]-0.8817[/C][/ROW]
[ROW][C]55[/C][C] 11[/C][C] 11.61[/C][C]-0.614[/C][/ROW]
[ROW][C]56[/C][C] 11[/C][C] 11.72[/C][C]-0.724[/C][/ROW]
[ROW][C]57[/C][C] 12[/C][C] 11.57[/C][C] 0.4336[/C][/ROW]
[ROW][C]58[/C][C] 12[/C][C] 11.3[/C][C] 0.7013[/C][/ROW]
[ROW][C]59[/C][C] 11[/C][C] 11.46[/C][C]-0.4563[/C][/ROW]
[ROW][C]60[/C][C] 12[/C][C] 11.57[/C][C] 0.4336[/C][/ROW]
[ROW][C]61[/C][C] 11[/C][C] 11.57[/C][C]-0.5664[/C][/ROW]
[ROW][C]62[/C][C] 12[/C][C] 11.03[/C][C] 0.969[/C][/ROW]
[ROW][C]63[/C][C] 11[/C][C] 11.03[/C][C]-0.031[/C][/ROW]
[ROW][C]64[/C][C] 11[/C][C] 11.46[/C][C]-0.4563[/C][/ROW]
[ROW][C]65[/C][C] 8[/C][C] 11.03[/C][C]-3.031[/C][/ROW]
[ROW][C]66[/C][C] 12[/C][C] 11.19[/C][C] 0.8114[/C][/ROW]
[ROW][C]67[/C][C] 11[/C][C] 11.03[/C][C]-0.031[/C][/ROW]
[ROW][C]68[/C][C] 12[/C][C] 11.5[/C][C] 0.4961[/C][/ROW]
[ROW][C]69[/C][C] 11[/C][C] 11.08[/C][C]-0.07856[/C][/ROW]
[ROW][C]70[/C][C] 11[/C][C] 11.3[/C][C]-0.2987[/C][/ROW]
[ROW][C]71[/C][C] 11[/C][C] 11.03[/C][C]-0.031[/C][/ROW]
[ROW][C]72[/C][C] 10[/C][C] 11.35[/C][C]-1.346[/C][/ROW]
[ROW][C]73[/C][C] 10[/C][C] 11.57[/C][C]-1.566[/C][/ROW]
[ROW][C]74[/C][C] 13[/C][C] 11.88[/C][C] 1.118[/C][/ROW]
[ROW][C]75[/C][C] 11[/C][C] 11.46[/C][C]-0.4563[/C][/ROW]
[ROW][C]76[/C][C] 11[/C][C] 11.35[/C][C]-0.3463[/C][/ROW]
[ROW][C]77[/C][C] 11[/C][C] 11.57[/C][C]-0.5664[/C][/ROW]
[ROW][C]78[/C][C] 13[/C][C] 10.92[/C][C] 2.079[/C][/ROW]
[ROW][C]79[/C][C] 12[/C][C] 11.83[/C][C] 0.1659[/C][/ROW]
[ROW][C]80[/C][C] 12[/C][C] 11.3[/C][C] 0.7013[/C][/ROW]
[ROW][C]81[/C][C] 9[/C][C] 11.46[/C][C]-2.456[/C][/ROW]
[ROW][C]82[/C][C] 12[/C][C] 11.72[/C][C] 0.276[/C][/ROW]
[ROW][C]83[/C][C] 12[/C][C] 11.57[/C][C] 0.4336[/C][/ROW]
[ROW][C]84[/C][C] 13[/C][C] 11.83[/C][C] 1.166[/C][/ROW]
[ROW][C]85[/C][C] 15[/C][C] 11.72[/C][C] 3.276[/C][/ROW]
[ROW][C]86[/C][C] 13[/C][C] 11.83[/C][C] 1.166[/C][/ROW]
[ROW][C]87[/C][C] 13[/C][C] 11.57[/C][C] 1.434[/C][/ROW]
[ROW][C]88[/C][C] 11[/C][C] 11.57[/C][C]-0.5664[/C][/ROW]
[ROW][C]89[/C][C] 12[/C][C] 11.72[/C][C] 0.276[/C][/ROW]
[ROW][C]90[/C][C] 9[/C][C] 11.72[/C][C]-2.724[/C][/ROW]
[ROW][C]91[/C][C] 11[/C][C] 11.46[/C][C]-0.4563[/C][/ROW]
[ROW][C]92[/C][C] 13[/C][C] 11.88[/C][C] 1.118[/C][/ROW]
[ROW][C]93[/C][C] 12[/C][C] 11.99[/C][C] 0.008248[/C][/ROW]
[ROW][C]94[/C][C] 13[/C][C] 11.83[/C][C] 1.166[/C][/ROW]
[ROW][C]95[/C][C] 11[/C][C] 11.3[/C][C]-0.2987[/C][/ROW]
[ROW][C]96[/C][C] 12[/C][C] 11.72[/C][C] 0.276[/C][/ROW]
[ROW][C]97[/C][C] 14[/C][C] 11.3[/C][C] 2.701[/C][/ROW]
[ROW][C]98[/C][C] 13[/C][C] 12.31[/C][C] 0.693[/C][/ROW]
[ROW][C]99[/C][C] 11[/C][C] 11.57[/C][C]-0.5664[/C][/ROW]
[ROW][C]100[/C][C] 12[/C][C] 11.57[/C][C] 0.4336[/C][/ROW]
[ROW][C]101[/C][C] 13[/C][C] 11.46[/C][C] 1.544[/C][/ROW]
[ROW][C]102[/C][C] 11[/C][C] 11.57[/C][C]-0.5664[/C][/ROW]
[ROW][C]103[/C][C] 11[/C][C] 11.57[/C][C]-0.5664[/C][/ROW]
[ROW][C]104[/C][C] 11[/C][C] 11.46[/C][C]-0.4563[/C][/ROW]
[ROW][C]105[/C][C] 13[/C][C] 11.83[/C][C] 1.166[/C][/ROW]
[ROW][C]106[/C][C] 12[/C][C] 11.03[/C][C] 0.969[/C][/ROW]
[ROW][C]107[/C][C] 12[/C][C] 11.3[/C][C] 0.7013[/C][/ROW]
[ROW][C]108[/C][C] 11[/C][C] 11.57[/C][C]-0.5664[/C][/ROW]
[ROW][C]109[/C][C] 12[/C][C] 11.3[/C][C] 0.7013[/C][/ROW]
[ROW][C]110[/C][C] 12[/C][C] 11.3[/C][C] 0.7013[/C][/ROW]
[ROW][C]111[/C][C] 10[/C][C] 11.46[/C][C]-1.456[/C][/ROW]
[ROW][C]112[/C][C] 11[/C][C] 11.03[/C][C]-0.031[/C][/ROW]
[ROW][C]113[/C][C] 9[/C][C] 11.61[/C][C]-2.614[/C][/ROW]
[ROW][C]114[/C][C] 14[/C][C] 11.72[/C][C] 2.276[/C][/ROW]
[ROW][C]115[/C][C] 12[/C][C] 11.3[/C][C] 0.7013[/C][/ROW]
[ROW][C]116[/C][C] 11[/C][C] 11.57[/C][C]-0.5664[/C][/ROW]
[ROW][C]117[/C][C] 13[/C][C] 12.46[/C][C] 0.5353[/C][/ROW]
[ROW][C]118[/C][C] 11[/C][C] 11.83[/C][C]-0.8341[/C][/ROW]
[ROW][C]119[/C][C] 11[/C][C] 11.57[/C][C]-0.5664[/C][/ROW]
[ROW][C]120[/C][C] 11[/C][C] 11.19[/C][C]-0.1886[/C][/ROW]
[ROW][C]121[/C][C] 11[/C][C] 11.46[/C][C]-0.4563[/C][/ROW]
[ROW][C]122[/C][C] 12[/C][C] 11.57[/C][C] 0.4336[/C][/ROW]
[ROW][C]123[/C][C] 11[/C][C] 11.57[/C][C]-0.5664[/C][/ROW]
[ROW][C]124[/C][C] 13[/C][C] 11.3[/C][C] 1.701[/C][/ROW]
[ROW][C]125[/C][C] 11[/C][C] 11.3[/C][C]-0.2987[/C][/ROW]
[ROW][C]126[/C][C] 11[/C][C] 11.3[/C][C]-0.2987[/C][/ROW]
[ROW][C]127[/C][C] 12[/C][C] 11.88[/C][C] 0.1183[/C][/ROW]
[ROW][C]128[/C][C] 11[/C][C] 11.72[/C][C]-0.724[/C][/ROW]
[ROW][C]129[/C][C] 11[/C][C] 11.57[/C][C]-0.5664[/C][/ROW]
[ROW][C]130[/C][C] 9[/C][C] 11.3[/C][C]-2.299[/C][/ROW]
[ROW][C]131[/C][C] 12[/C][C] 11.03[/C][C] 0.969[/C][/ROW]
[ROW][C]132[/C][C] 14[/C][C] 11.72[/C][C] 2.276[/C][/ROW]
[ROW][C]133[/C][C] 10[/C][C] 11.57[/C][C]-1.566[/C][/ROW]
[ROW][C]134[/C][C] 9[/C][C] 11.99[/C][C]-2.992[/C][/ROW]
[ROW][C]135[/C][C] 12[/C][C] 11.46[/C][C] 0.5437[/C][/ROW]
[ROW][C]136[/C][C] 14[/C][C] 11.46[/C][C] 2.544[/C][/ROW]
[ROW][C]137[/C][C] 9[/C][C] 11.19[/C][C]-2.189[/C][/ROW]
[ROW][C]138[/C][C] 11[/C][C] 11.46[/C][C]-0.4563[/C][/ROW]
[ROW][C]139[/C][C] 14[/C][C] 11.83[/C][C] 2.166[/C][/ROW]
[ROW][C]140[/C][C] 13[/C][C] 12.31[/C][C] 0.693[/C][/ROW]
[ROW][C]141[/C][C] 10[/C][C] 11.57[/C][C]-1.566[/C][/ROW]
[ROW][C]142[/C][C] 11[/C][C] 11.99[/C][C]-0.9918[/C][/ROW]
[ROW][C]143[/C][C] 12[/C][C] 10.76[/C][C] 1.237[/C][/ROW]
[ROW][C]144[/C][C] 10[/C][C] 11.3[/C][C]-1.299[/C][/ROW]
[ROW][C]145[/C][C] 13[/C][C] 11.72[/C][C] 1.276[/C][/ROW]
[ROW][C]146[/C][C] 12[/C][C] 11.72[/C][C] 0.276[/C][/ROW]
[ROW][C]147[/C][C] 14[/C][C] 11.5[/C][C] 2.496[/C][/ROW]
[ROW][C]148[/C][C] 10[/C][C] 11.57[/C][C]-1.566[/C][/ROW]
[ROW][C]149[/C][C] 12[/C][C] 11.03[/C][C] 0.969[/C][/ROW]
[ROW][C]150[/C][C] 9[/C][C] 11.03[/C][C]-2.031[/C][/ROW]
[ROW][C]151[/C][C] 12[/C][C] 11.46[/C][C] 0.5437[/C][/ROW]
[ROW][C]152[/C][C] 11[/C][C] 11.03[/C][C]-0.031[/C][/ROW]
[ROW][C]153[/C][C] 11[/C][C] 11.3[/C][C]-0.2987[/C][/ROW]
[ROW][C]154[/C][C] 10[/C][C] 11.19[/C][C]-1.189[/C][/ROW]
[ROW][C]155[/C][C] 11[/C][C] 11.3[/C][C]-0.2987[/C][/ROW]
[ROW][C]156[/C][C] 12[/C][C] 11.03[/C][C] 0.969[/C][/ROW]
[ROW][C]157[/C][C] 10[/C][C] 12.04[/C][C]-2.039[/C][/ROW]
[ROW][C]158[/C][C] 11[/C][C] 11.03[/C][C]-0.031[/C][/ROW]
[ROW][C]159[/C][C] 13[/C][C] 12.15[/C][C] 0.8506[/C][/ROW]
[ROW][C]160[/C][C] 11[/C][C] 11.03[/C][C]-0.031[/C][/ROW]
[ROW][C]161[/C][C] 13[/C][C] 11.46[/C][C] 1.544[/C][/ROW]
[ROW][C]162[/C][C] 12[/C][C] 11.57[/C][C] 0.4336[/C][/ROW]
[ROW][C]163[/C][C] 11[/C][C] 11.72[/C][C]-0.724[/C][/ROW]
[ROW][C]164[/C][C] 12[/C][C] 11.57[/C][C] 0.4336[/C][/ROW]
[ROW][C]165[/C][C] 10[/C][C] 11.03[/C][C]-1.031[/C][/ROW]
[ROW][C]166[/C][C] 12[/C][C] 11.3[/C][C] 0.7013[/C][/ROW]
[ROW][C]167[/C][C] 10[/C][C] 11.46[/C][C]-1.456[/C][/ROW]
[ROW][C]168[/C][C] 13[/C][C] 12.04[/C][C] 0.9607[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=300211&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=300211&T=4

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	9	11.03	-2.031
2	11	11.88	-0.8817
3	13	11.3	1.701
4	11	11.72	-0.724
5	12	11.72	0.276
6	11	11.3	-0.2987
7	12	11.61	0.386
8	12	11.57	0.4336
9	13	11.57	1.434
10	12	11.03	0.969
11	12	11.57	0.4336
12	11	11.72	-0.724
13	12	11.46	0.5437
14	10	11.57	-1.566
15	12	11.3	0.7013
16	12	11.57	0.4336
17	12	11.72	0.276
18	12	10.76	1.237
19	13	11.3	1.701
20	11	11.57	-0.5664
21	11	11.57	-0.5664
22	11	11.46	-0.4563
23	11	11.83	-0.8341
24	13	11.72	1.276
25	11	11.3	-0.2987
26	12	11.72	0.276
27	11	11.57	-0.5664
28	12	11.46	0.5437
29	12	10.76	1.237
30	10	11.57	-1.566
31	11	10.76	0.2367
32	12	11.57	0.4336
33	11	11.57	-0.5664
34	9	11.19	-2.189
35	12	11.99	0.008248
36	11	11.72	-0.724
37	11	11.72	-0.724
38	12	11.3	0.7013
39	13	11.57	1.434
40	11	11.3	-0.2987
41	12	11.57	0.4336
42	9	11.3	-2.299
43	12	11.03	0.969
44	11	11.03	-0.031
45	12	11.3	0.7013
46	12	11.57	0.4336
47	11	11.72	-0.724
48	10	11.03	-1.031
49	9	11.03	-2.031
50	12	11.3	0.7013
51	13	11.72	1.276
52	13	11.57	1.434
53	9	11.72	-2.724
54	11	11.88	-0.8817
55	11	11.61	-0.614
56	11	11.72	-0.724
57	12	11.57	0.4336
58	12	11.3	0.7013
59	11	11.46	-0.4563
60	12	11.57	0.4336
61	11	11.57	-0.5664
62	12	11.03	0.969
63	11	11.03	-0.031
64	11	11.46	-0.4563
65	8	11.03	-3.031
66	12	11.19	0.8114
67	11	11.03	-0.031
68	12	11.5	0.4961
69	11	11.08	-0.07856
70	11	11.3	-0.2987
71	11	11.03	-0.031
72	10	11.35	-1.346
73	10	11.57	-1.566
74	13	11.88	1.118
75	11	11.46	-0.4563
76	11	11.35	-0.3463
77	11	11.57	-0.5664
78	13	10.92	2.079
79	12	11.83	0.1659
80	12	11.3	0.7013
81	9	11.46	-2.456
82	12	11.72	0.276
83	12	11.57	0.4336
84	13	11.83	1.166
85	15	11.72	3.276
86	13	11.83	1.166
87	13	11.57	1.434
88	11	11.57	-0.5664
89	12	11.72	0.276
90	9	11.72	-2.724
91	11	11.46	-0.4563
92	13	11.88	1.118
93	12	11.99	0.008248
94	13	11.83	1.166
95	11	11.3	-0.2987
96	12	11.72	0.276
97	14	11.3	2.701
98	13	12.31	0.693
99	11	11.57	-0.5664
100	12	11.57	0.4336
101	13	11.46	1.544
102	11	11.57	-0.5664
103	11	11.57	-0.5664
104	11	11.46	-0.4563
105	13	11.83	1.166
106	12	11.03	0.969
107	12	11.3	0.7013
108	11	11.57	-0.5664
109	12	11.3	0.7013
110	12	11.3	0.7013
111	10	11.46	-1.456
112	11	11.03	-0.031
113	9	11.61	-2.614
114	14	11.72	2.276
115	12	11.3	0.7013
116	11	11.57	-0.5664
117	13	12.46	0.5353
118	11	11.83	-0.8341
119	11	11.57	-0.5664
120	11	11.19	-0.1886
121	11	11.46	-0.4563
122	12	11.57	0.4336
123	11	11.57	-0.5664
124	13	11.3	1.701
125	11	11.3	-0.2987
126	11	11.3	-0.2987
127	12	11.88	0.1183
128	11	11.72	-0.724
129	11	11.57	-0.5664
130	9	11.3	-2.299
131	12	11.03	0.969
132	14	11.72	2.276
133	10	11.57	-1.566
134	9	11.99	-2.992
135	12	11.46	0.5437
136	14	11.46	2.544
137	9	11.19	-2.189
138	11	11.46	-0.4563
139	14	11.83	2.166
140	13	12.31	0.693
141	10	11.57	-1.566
142	11	11.99	-0.9918
143	12	10.76	1.237
144	10	11.3	-1.299
145	13	11.72	1.276
146	12	11.72	0.276
147	14	11.5	2.496
148	10	11.57	-1.566
149	12	11.03	0.969
150	9	11.03	-2.031
151	12	11.46	0.5437
152	11	11.03	-0.031
153	11	11.3	-0.2987
154	10	11.19	-1.189
155	11	11.3	-0.2987
156	12	11.03	0.969
157	10	12.04	-2.039
158	11	11.03	-0.031
159	13	12.15	0.8506
160	11	11.03	-0.031
161	13	11.46	1.544
162	12	11.57	0.4336
163	11	11.72	-0.724
164	12	11.57	0.4336
165	10	11.03	-1.031
166	12	11.3	0.7013
167	10	11.46	-1.456
168	13	12.04	0.9607

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
6	0.5776	0.8448	0.4224
7	0.7448	0.5104	0.2552
8	0.6217	0.7566	0.3783
9	0.5611	0.8779	0.4389
10	0.577	0.846	0.423
11	0.4708	0.9416	0.5292
12	0.4173	0.8345	0.5827
13	0.3574	0.7149	0.6426
14	0.4943	0.9886	0.5057
15	0.4251	0.8501	0.5749
16	0.3458	0.6917	0.6542
17	0.2765	0.553	0.7235
18	0.2477	0.4954	0.7523
19	0.2755	0.5509	0.7245
20	0.2412	0.4825	0.7588
21	0.2054	0.4108	0.7946
22	0.1672	0.3345	0.8328
23	0.1385	0.2769	0.8615
24	0.1638	0.3277	0.8362
25	0.1342	0.2683	0.8658
26	0.1036	0.2072	0.8964
27	0.08331	0.1666	0.9167
28	0.06304	0.1261	0.937
29	0.04937	0.09873	0.9506
30	0.06657	0.1331	0.9334
31	0.05368	0.1074	0.9463
32	0.04177	0.08355	0.9582
33	0.03188	0.06376	0.9681
34	0.1077	0.2154	0.8923
35	0.08476	0.1695	0.9152
36	0.06941	0.1388	0.9306
37	0.05611	0.1122	0.9439
38	0.04535	0.0907	0.9546
39	0.05484	0.1097	0.9452
40	0.04315	0.0863	0.9568
41	0.03315	0.06629	0.9669
42	0.09094	0.1819	0.9091
43	0.07987	0.1597	0.9201
44	0.06278	0.1256	0.9372
45	0.05191	0.1038	0.9481
46	0.04095	0.08191	0.959
47	0.03311	0.06622	0.9669
48	0.035	0.07	0.965
49	0.06885	0.1377	0.9311
50	0.05846	0.1169	0.9415
51	0.06605	0.1321	0.934
52	0.07403	0.1481	0.926
53	0.172	0.344	0.828
54	0.1495	0.2991	0.8505
55	0.1254	0.2509	0.8746
56	0.1074	0.2147	0.8926
57	0.08878	0.1776	0.9112
58	0.07603	0.1521	0.924
59	0.06136	0.1227	0.9386
60	0.0494	0.09879	0.9506
61	0.04142	0.08285	0.9586
62	0.0373	0.0746	0.9627
63	0.0288	0.0576	0.9712
64	0.02234	0.04468	0.9777
65	0.09857	0.1971	0.9014
66	0.09318	0.1864	0.9068
67	0.07535	0.1507	0.9246
68	0.06998	0.14	0.93
69	0.05601	0.112	0.944
70	0.04493	0.08987	0.9551
71	0.0351	0.07021	0.9649
72	0.03565	0.07131	0.9643
73	0.04316	0.08631	0.9568
74	0.04624	0.09249	0.9538
75	0.03735	0.0747	0.9627
76	0.02957	0.05913	0.9704
77	0.02407	0.04815	0.9759
78	0.04118	0.08235	0.9588
79	0.03261	0.06521	0.9674
80	0.02761	0.05521	0.9724
81	0.05924	0.1185	0.9408
82	0.04841	0.09681	0.9516
83	0.03956	0.07912	0.9604
84	0.03998	0.07996	0.96
85	0.1603	0.3206	0.8397
86	0.1595	0.319	0.8405
87	0.1716	0.3433	0.8284
88	0.1505	0.301	0.8495
89	0.1274	0.2548	0.8726
90	0.2456	0.4913	0.7544
91	0.2166	0.4333	0.7834
92	0.2152	0.4304	0.7848
93	0.184	0.3679	0.816
94	0.1842	0.3683	0.8158
95	0.1576	0.3151	0.8424
96	0.1335	0.267	0.8665
97	0.2553	0.5106	0.7447
98	0.2336	0.4672	0.7664
99	0.2068	0.4137	0.7932
100	0.181	0.362	0.819
101	0.2001	0.4001	0.7999
102	0.1753	0.3507	0.8247
103	0.1526	0.3051	0.8474
104	0.1307	0.2613	0.8693
105	0.1337	0.2675	0.8663
106	0.1246	0.2491	0.8754
107	0.1106	0.2212	0.8894
108	0.09361	0.1872	0.9064
109	0.08238	0.1648	0.9176
110	0.07237	0.1447	0.9276
111	0.07905	0.1581	0.9209
112	0.06326	0.1265	0.9367
113	0.1439	0.2878	0.8561
114	0.2295	0.4589	0.7705
115	0.21	0.42	0.79
116	0.1815	0.3629	0.8185
117	0.157	0.3141	0.843
118	0.1369	0.2738	0.8631
119	0.115	0.2299	0.885
120	0.09454	0.1891	0.9055
121	0.07842	0.1568	0.9216
122	0.06663	0.1333	0.9334
123	0.05357	0.1071	0.9464
124	0.07208	0.1442	0.9279
125	0.0568	0.1136	0.9432
126	0.04413	0.08826	0.9559
127	0.03363	0.06727	0.9664
128	0.02719	0.05438	0.9728
129	0.02069	0.04138	0.9793
130	0.03715	0.07431	0.9628
131	0.03319	0.06638	0.9668
132	0.06615	0.1323	0.9339
133	0.06764	0.1353	0.9324
134	0.1871	0.3742	0.8129
135	0.157	0.3141	0.843
136	0.2872	0.5744	0.7128
137	0.4135	0.827	0.5865
138	0.3675	0.735	0.6325
139	0.5929	0.8141	0.4071
140	0.5434	0.9132	0.4566
141	0.5391	0.9217	0.4609
142	0.4983	0.9965	0.5017
143	0.4956	0.9912	0.5044
144	0.4886	0.9772	0.5114
145	0.5008	0.9984	0.4992
146	0.4386	0.8772	0.5614
147	0.6452	0.7096	0.3548
148	0.742	0.516	0.258
149	0.7559	0.4881	0.2441
150	0.8423	0.3154	0.1577
151	0.8102	0.3797	0.1898
152	0.7481	0.5038	0.2519
153	0.6842	0.6315	0.3158
154	0.6365	0.7271	0.3635
155	0.562	0.8761	0.438
156	0.5503	0.8995	0.4497
157	0.7705	0.4589	0.2295
158	0.6798	0.6403	0.3202
159	0.5654	0.8692	0.4346
160	0.4491	0.8982	0.5509
161	0.6169	0.7662	0.3831
162	0.4512	0.9024	0.5488

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
6 &  0.5776 &  0.8448 &  0.4224 \tabularnewline
7 &  0.7448 &  0.5104 &  0.2552 \tabularnewline
8 &  0.6217 &  0.7566 &  0.3783 \tabularnewline
9 &  0.5611 &  0.8779 &  0.4389 \tabularnewline
10 &  0.577 &  0.846 &  0.423 \tabularnewline
11 &  0.4708 &  0.9416 &  0.5292 \tabularnewline
12 &  0.4173 &  0.8345 &  0.5827 \tabularnewline
13 &  0.3574 &  0.7149 &  0.6426 \tabularnewline
14 &  0.4943 &  0.9886 &  0.5057 \tabularnewline
15 &  0.4251 &  0.8501 &  0.5749 \tabularnewline
16 &  0.3458 &  0.6917 &  0.6542 \tabularnewline
17 &  0.2765 &  0.553 &  0.7235 \tabularnewline
18 &  0.2477 &  0.4954 &  0.7523 \tabularnewline
19 &  0.2755 &  0.5509 &  0.7245 \tabularnewline
20 &  0.2412 &  0.4825 &  0.7588 \tabularnewline
21 &  0.2054 &  0.4108 &  0.7946 \tabularnewline
22 &  0.1672 &  0.3345 &  0.8328 \tabularnewline
23 &  0.1385 &  0.2769 &  0.8615 \tabularnewline
24 &  0.1638 &  0.3277 &  0.8362 \tabularnewline
25 &  0.1342 &  0.2683 &  0.8658 \tabularnewline
26 &  0.1036 &  0.2072 &  0.8964 \tabularnewline
27 &  0.08331 &  0.1666 &  0.9167 \tabularnewline
28 &  0.06304 &  0.1261 &  0.937 \tabularnewline
29 &  0.04937 &  0.09873 &  0.9506 \tabularnewline
30 &  0.06657 &  0.1331 &  0.9334 \tabularnewline
31 &  0.05368 &  0.1074 &  0.9463 \tabularnewline
32 &  0.04177 &  0.08355 &  0.9582 \tabularnewline
33 &  0.03188 &  0.06376 &  0.9681 \tabularnewline
34 &  0.1077 &  0.2154 &  0.8923 \tabularnewline
35 &  0.08476 &  0.1695 &  0.9152 \tabularnewline
36 &  0.06941 &  0.1388 &  0.9306 \tabularnewline
37 &  0.05611 &  0.1122 &  0.9439 \tabularnewline
38 &  0.04535 &  0.0907 &  0.9546 \tabularnewline
39 &  0.05484 &  0.1097 &  0.9452 \tabularnewline
40 &  0.04315 &  0.0863 &  0.9568 \tabularnewline
41 &  0.03315 &  0.06629 &  0.9669 \tabularnewline
42 &  0.09094 &  0.1819 &  0.9091 \tabularnewline
43 &  0.07987 &  0.1597 &  0.9201 \tabularnewline
44 &  0.06278 &  0.1256 &  0.9372 \tabularnewline
45 &  0.05191 &  0.1038 &  0.9481 \tabularnewline
46 &  0.04095 &  0.08191 &  0.959 \tabularnewline
47 &  0.03311 &  0.06622 &  0.9669 \tabularnewline
48 &  0.035 &  0.07 &  0.965 \tabularnewline
49 &  0.06885 &  0.1377 &  0.9311 \tabularnewline
50 &  0.05846 &  0.1169 &  0.9415 \tabularnewline
51 &  0.06605 &  0.1321 &  0.934 \tabularnewline
52 &  0.07403 &  0.1481 &  0.926 \tabularnewline
53 &  0.172 &  0.344 &  0.828 \tabularnewline
54 &  0.1495 &  0.2991 &  0.8505 \tabularnewline
55 &  0.1254 &  0.2509 &  0.8746 \tabularnewline
56 &  0.1074 &  0.2147 &  0.8926 \tabularnewline
57 &  0.08878 &  0.1776 &  0.9112 \tabularnewline
58 &  0.07603 &  0.1521 &  0.924 \tabularnewline
59 &  0.06136 &  0.1227 &  0.9386 \tabularnewline
60 &  0.0494 &  0.09879 &  0.9506 \tabularnewline
61 &  0.04142 &  0.08285 &  0.9586 \tabularnewline
62 &  0.0373 &  0.0746 &  0.9627 \tabularnewline
63 &  0.0288 &  0.0576 &  0.9712 \tabularnewline
64 &  0.02234 &  0.04468 &  0.9777 \tabularnewline
65 &  0.09857 &  0.1971 &  0.9014 \tabularnewline
66 &  0.09318 &  0.1864 &  0.9068 \tabularnewline
67 &  0.07535 &  0.1507 &  0.9246 \tabularnewline
68 &  0.06998 &  0.14 &  0.93 \tabularnewline
69 &  0.05601 &  0.112 &  0.944 \tabularnewline
70 &  0.04493 &  0.08987 &  0.9551 \tabularnewline
71 &  0.0351 &  0.07021 &  0.9649 \tabularnewline
72 &  0.03565 &  0.07131 &  0.9643 \tabularnewline
73 &  0.04316 &  0.08631 &  0.9568 \tabularnewline
74 &  0.04624 &  0.09249 &  0.9538 \tabularnewline
75 &  0.03735 &  0.0747 &  0.9627 \tabularnewline
76 &  0.02957 &  0.05913 &  0.9704 \tabularnewline
77 &  0.02407 &  0.04815 &  0.9759 \tabularnewline
78 &  0.04118 &  0.08235 &  0.9588 \tabularnewline
79 &  0.03261 &  0.06521 &  0.9674 \tabularnewline
80 &  0.02761 &  0.05521 &  0.9724 \tabularnewline
81 &  0.05924 &  0.1185 &  0.9408 \tabularnewline
82 &  0.04841 &  0.09681 &  0.9516 \tabularnewline
83 &  0.03956 &  0.07912 &  0.9604 \tabularnewline
84 &  0.03998 &  0.07996 &  0.96 \tabularnewline
85 &  0.1603 &  0.3206 &  0.8397 \tabularnewline
86 &  0.1595 &  0.319 &  0.8405 \tabularnewline
87 &  0.1716 &  0.3433 &  0.8284 \tabularnewline
88 &  0.1505 &  0.301 &  0.8495 \tabularnewline
89 &  0.1274 &  0.2548 &  0.8726 \tabularnewline
90 &  0.2456 &  0.4913 &  0.7544 \tabularnewline
91 &  0.2166 &  0.4333 &  0.7834 \tabularnewline
92 &  0.2152 &  0.4304 &  0.7848 \tabularnewline
93 &  0.184 &  0.3679 &  0.816 \tabularnewline
94 &  0.1842 &  0.3683 &  0.8158 \tabularnewline
95 &  0.1576 &  0.3151 &  0.8424 \tabularnewline
96 &  0.1335 &  0.267 &  0.8665 \tabularnewline
97 &  0.2553 &  0.5106 &  0.7447 \tabularnewline
98 &  0.2336 &  0.4672 &  0.7664 \tabularnewline
99 &  0.2068 &  0.4137 &  0.7932 \tabularnewline
100 &  0.181 &  0.362 &  0.819 \tabularnewline
101 &  0.2001 &  0.4001 &  0.7999 \tabularnewline
102 &  0.1753 &  0.3507 &  0.8247 \tabularnewline
103 &  0.1526 &  0.3051 &  0.8474 \tabularnewline
104 &  0.1307 &  0.2613 &  0.8693 \tabularnewline
105 &  0.1337 &  0.2675 &  0.8663 \tabularnewline
106 &  0.1246 &  0.2491 &  0.8754 \tabularnewline
107 &  0.1106 &  0.2212 &  0.8894 \tabularnewline
108 &  0.09361 &  0.1872 &  0.9064 \tabularnewline
109 &  0.08238 &  0.1648 &  0.9176 \tabularnewline
110 &  0.07237 &  0.1447 &  0.9276 \tabularnewline
111 &  0.07905 &  0.1581 &  0.9209 \tabularnewline
112 &  0.06326 &  0.1265 &  0.9367 \tabularnewline
113 &  0.1439 &  0.2878 &  0.8561 \tabularnewline
114 &  0.2295 &  0.4589 &  0.7705 \tabularnewline
115 &  0.21 &  0.42 &  0.79 \tabularnewline
116 &  0.1815 &  0.3629 &  0.8185 \tabularnewline
117 &  0.157 &  0.3141 &  0.843 \tabularnewline
118 &  0.1369 &  0.2738 &  0.8631 \tabularnewline
119 &  0.115 &  0.2299 &  0.885 \tabularnewline
120 &  0.09454 &  0.1891 &  0.9055 \tabularnewline
121 &  0.07842 &  0.1568 &  0.9216 \tabularnewline
122 &  0.06663 &  0.1333 &  0.9334 \tabularnewline
123 &  0.05357 &  0.1071 &  0.9464 \tabularnewline
124 &  0.07208 &  0.1442 &  0.9279 \tabularnewline
125 &  0.0568 &  0.1136 &  0.9432 \tabularnewline
126 &  0.04413 &  0.08826 &  0.9559 \tabularnewline
127 &  0.03363 &  0.06727 &  0.9664 \tabularnewline
128 &  0.02719 &  0.05438 &  0.9728 \tabularnewline
129 &  0.02069 &  0.04138 &  0.9793 \tabularnewline
130 &  0.03715 &  0.07431 &  0.9628 \tabularnewline
131 &  0.03319 &  0.06638 &  0.9668 \tabularnewline
132 &  0.06615 &  0.1323 &  0.9339 \tabularnewline
133 &  0.06764 &  0.1353 &  0.9324 \tabularnewline
134 &  0.1871 &  0.3742 &  0.8129 \tabularnewline
135 &  0.157 &  0.3141 &  0.843 \tabularnewline
136 &  0.2872 &  0.5744 &  0.7128 \tabularnewline
137 &  0.4135 &  0.827 &  0.5865 \tabularnewline
138 &  0.3675 &  0.735 &  0.6325 \tabularnewline
139 &  0.5929 &  0.8141 &  0.4071 \tabularnewline
140 &  0.5434 &  0.9132 &  0.4566 \tabularnewline
141 &  0.5391 &  0.9217 &  0.4609 \tabularnewline
142 &  0.4983 &  0.9965 &  0.5017 \tabularnewline
143 &  0.4956 &  0.9912 &  0.5044 \tabularnewline
144 &  0.4886 &  0.9772 &  0.5114 \tabularnewline
145 &  0.5008 &  0.9984 &  0.4992 \tabularnewline
146 &  0.4386 &  0.8772 &  0.5614 \tabularnewline
147 &  0.6452 &  0.7096 &  0.3548 \tabularnewline
148 &  0.742 &  0.516 &  0.258 \tabularnewline
149 &  0.7559 &  0.4881 &  0.2441 \tabularnewline
150 &  0.8423 &  0.3154 &  0.1577 \tabularnewline
151 &  0.8102 &  0.3797 &  0.1898 \tabularnewline
152 &  0.7481 &  0.5038 &  0.2519 \tabularnewline
153 &  0.6842 &  0.6315 &  0.3158 \tabularnewline
154 &  0.6365 &  0.7271 &  0.3635 \tabularnewline
155 &  0.562 &  0.8761 &  0.438 \tabularnewline
156 &  0.5503 &  0.8995 &  0.4497 \tabularnewline
157 &  0.7705 &  0.4589 &  0.2295 \tabularnewline
158 &  0.6798 &  0.6403 &  0.3202 \tabularnewline
159 &  0.5654 &  0.8692 &  0.4346 \tabularnewline
160 &  0.4491 &  0.8982 &  0.5509 \tabularnewline
161 &  0.6169 &  0.7662 &  0.3831 \tabularnewline
162 &  0.4512 &  0.9024 &  0.5488 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=300211&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]6[/C][C] 0.5776[/C][C] 0.8448[/C][C] 0.4224[/C][/ROW]
[ROW][C]7[/C][C] 0.7448[/C][C] 0.5104[/C][C] 0.2552[/C][/ROW]
[ROW][C]8[/C][C] 0.6217[/C][C] 0.7566[/C][C] 0.3783[/C][/ROW]
[ROW][C]9[/C][C] 0.5611[/C][C] 0.8779[/C][C] 0.4389[/C][/ROW]
[ROW][C]10[/C][C] 0.577[/C][C] 0.846[/C][C] 0.423[/C][/ROW]
[ROW][C]11[/C][C] 0.4708[/C][C] 0.9416[/C][C] 0.5292[/C][/ROW]
[ROW][C]12[/C][C] 0.4173[/C][C] 0.8345[/C][C] 0.5827[/C][/ROW]
[ROW][C]13[/C][C] 0.3574[/C][C] 0.7149[/C][C] 0.6426[/C][/ROW]
[ROW][C]14[/C][C] 0.4943[/C][C] 0.9886[/C][C] 0.5057[/C][/ROW]
[ROW][C]15[/C][C] 0.4251[/C][C] 0.8501[/C][C] 0.5749[/C][/ROW]
[ROW][C]16[/C][C] 0.3458[/C][C] 0.6917[/C][C] 0.6542[/C][/ROW]
[ROW][C]17[/C][C] 0.2765[/C][C] 0.553[/C][C] 0.7235[/C][/ROW]
[ROW][C]18[/C][C] 0.2477[/C][C] 0.4954[/C][C] 0.7523[/C][/ROW]
[ROW][C]19[/C][C] 0.2755[/C][C] 0.5509[/C][C] 0.7245[/C][/ROW]
[ROW][C]20[/C][C] 0.2412[/C][C] 0.4825[/C][C] 0.7588[/C][/ROW]
[ROW][C]21[/C][C] 0.2054[/C][C] 0.4108[/C][C] 0.7946[/C][/ROW]
[ROW][C]22[/C][C] 0.1672[/C][C] 0.3345[/C][C] 0.8328[/C][/ROW]
[ROW][C]23[/C][C] 0.1385[/C][C] 0.2769[/C][C] 0.8615[/C][/ROW]
[ROW][C]24[/C][C] 0.1638[/C][C] 0.3277[/C][C] 0.8362[/C][/ROW]
[ROW][C]25[/C][C] 0.1342[/C][C] 0.2683[/C][C] 0.8658[/C][/ROW]
[ROW][C]26[/C][C] 0.1036[/C][C] 0.2072[/C][C] 0.8964[/C][/ROW]
[ROW][C]27[/C][C] 0.08331[/C][C] 0.1666[/C][C] 0.9167[/C][/ROW]
[ROW][C]28[/C][C] 0.06304[/C][C] 0.1261[/C][C] 0.937[/C][/ROW]
[ROW][C]29[/C][C] 0.04937[/C][C] 0.09873[/C][C] 0.9506[/C][/ROW]
[ROW][C]30[/C][C] 0.06657[/C][C] 0.1331[/C][C] 0.9334[/C][/ROW]
[ROW][C]31[/C][C] 0.05368[/C][C] 0.1074[/C][C] 0.9463[/C][/ROW]
[ROW][C]32[/C][C] 0.04177[/C][C] 0.08355[/C][C] 0.9582[/C][/ROW]
[ROW][C]33[/C][C] 0.03188[/C][C] 0.06376[/C][C] 0.9681[/C][/ROW]
[ROW][C]34[/C][C] 0.1077[/C][C] 0.2154[/C][C] 0.8923[/C][/ROW]
[ROW][C]35[/C][C] 0.08476[/C][C] 0.1695[/C][C] 0.9152[/C][/ROW]
[ROW][C]36[/C][C] 0.06941[/C][C] 0.1388[/C][C] 0.9306[/C][/ROW]
[ROW][C]37[/C][C] 0.05611[/C][C] 0.1122[/C][C] 0.9439[/C][/ROW]
[ROW][C]38[/C][C] 0.04535[/C][C] 0.0907[/C][C] 0.9546[/C][/ROW]
[ROW][C]39[/C][C] 0.05484[/C][C] 0.1097[/C][C] 0.9452[/C][/ROW]
[ROW][C]40[/C][C] 0.04315[/C][C] 0.0863[/C][C] 0.9568[/C][/ROW]
[ROW][C]41[/C][C] 0.03315[/C][C] 0.06629[/C][C] 0.9669[/C][/ROW]
[ROW][C]42[/C][C] 0.09094[/C][C] 0.1819[/C][C] 0.9091[/C][/ROW]
[ROW][C]43[/C][C] 0.07987[/C][C] 0.1597[/C][C] 0.9201[/C][/ROW]
[ROW][C]44[/C][C] 0.06278[/C][C] 0.1256[/C][C] 0.9372[/C][/ROW]
[ROW][C]45[/C][C] 0.05191[/C][C] 0.1038[/C][C] 0.9481[/C][/ROW]
[ROW][C]46[/C][C] 0.04095[/C][C] 0.08191[/C][C] 0.959[/C][/ROW]
[ROW][C]47[/C][C] 0.03311[/C][C] 0.06622[/C][C] 0.9669[/C][/ROW]
[ROW][C]48[/C][C] 0.035[/C][C] 0.07[/C][C] 0.965[/C][/ROW]
[ROW][C]49[/C][C] 0.06885[/C][C] 0.1377[/C][C] 0.9311[/C][/ROW]
[ROW][C]50[/C][C] 0.05846[/C][C] 0.1169[/C][C] 0.9415[/C][/ROW]
[ROW][C]51[/C][C] 0.06605[/C][C] 0.1321[/C][C] 0.934[/C][/ROW]
[ROW][C]52[/C][C] 0.07403[/C][C] 0.1481[/C][C] 0.926[/C][/ROW]
[ROW][C]53[/C][C] 0.172[/C][C] 0.344[/C][C] 0.828[/C][/ROW]
[ROW][C]54[/C][C] 0.1495[/C][C] 0.2991[/C][C] 0.8505[/C][/ROW]
[ROW][C]55[/C][C] 0.1254[/C][C] 0.2509[/C][C] 0.8746[/C][/ROW]
[ROW][C]56[/C][C] 0.1074[/C][C] 0.2147[/C][C] 0.8926[/C][/ROW]
[ROW][C]57[/C][C] 0.08878[/C][C] 0.1776[/C][C] 0.9112[/C][/ROW]
[ROW][C]58[/C][C] 0.07603[/C][C] 0.1521[/C][C] 0.924[/C][/ROW]
[ROW][C]59[/C][C] 0.06136[/C][C] 0.1227[/C][C] 0.9386[/C][/ROW]
[ROW][C]60[/C][C] 0.0494[/C][C] 0.09879[/C][C] 0.9506[/C][/ROW]
[ROW][C]61[/C][C] 0.04142[/C][C] 0.08285[/C][C] 0.9586[/C][/ROW]
[ROW][C]62[/C][C] 0.0373[/C][C] 0.0746[/C][C] 0.9627[/C][/ROW]
[ROW][C]63[/C][C] 0.0288[/C][C] 0.0576[/C][C] 0.9712[/C][/ROW]
[ROW][C]64[/C][C] 0.02234[/C][C] 0.04468[/C][C] 0.9777[/C][/ROW]
[ROW][C]65[/C][C] 0.09857[/C][C] 0.1971[/C][C] 0.9014[/C][/ROW]
[ROW][C]66[/C][C] 0.09318[/C][C] 0.1864[/C][C] 0.9068[/C][/ROW]
[ROW][C]67[/C][C] 0.07535[/C][C] 0.1507[/C][C] 0.9246[/C][/ROW]
[ROW][C]68[/C][C] 0.06998[/C][C] 0.14[/C][C] 0.93[/C][/ROW]
[ROW][C]69[/C][C] 0.05601[/C][C] 0.112[/C][C] 0.944[/C][/ROW]
[ROW][C]70[/C][C] 0.04493[/C][C] 0.08987[/C][C] 0.9551[/C][/ROW]
[ROW][C]71[/C][C] 0.0351[/C][C] 0.07021[/C][C] 0.9649[/C][/ROW]
[ROW][C]72[/C][C] 0.03565[/C][C] 0.07131[/C][C] 0.9643[/C][/ROW]
[ROW][C]73[/C][C] 0.04316[/C][C] 0.08631[/C][C] 0.9568[/C][/ROW]
[ROW][C]74[/C][C] 0.04624[/C][C] 0.09249[/C][C] 0.9538[/C][/ROW]
[ROW][C]75[/C][C] 0.03735[/C][C] 0.0747[/C][C] 0.9627[/C][/ROW]
[ROW][C]76[/C][C] 0.02957[/C][C] 0.05913[/C][C] 0.9704[/C][/ROW]
[ROW][C]77[/C][C] 0.02407[/C][C] 0.04815[/C][C] 0.9759[/C][/ROW]
[ROW][C]78[/C][C] 0.04118[/C][C] 0.08235[/C][C] 0.9588[/C][/ROW]
[ROW][C]79[/C][C] 0.03261[/C][C] 0.06521[/C][C] 0.9674[/C][/ROW]
[ROW][C]80[/C][C] 0.02761[/C][C] 0.05521[/C][C] 0.9724[/C][/ROW]
[ROW][C]81[/C][C] 0.05924[/C][C] 0.1185[/C][C] 0.9408[/C][/ROW]
[ROW][C]82[/C][C] 0.04841[/C][C] 0.09681[/C][C] 0.9516[/C][/ROW]
[ROW][C]83[/C][C] 0.03956[/C][C] 0.07912[/C][C] 0.9604[/C][/ROW]
[ROW][C]84[/C][C] 0.03998[/C][C] 0.07996[/C][C] 0.96[/C][/ROW]
[ROW][C]85[/C][C] 0.1603[/C][C] 0.3206[/C][C] 0.8397[/C][/ROW]
[ROW][C]86[/C][C] 0.1595[/C][C] 0.319[/C][C] 0.8405[/C][/ROW]
[ROW][C]87[/C][C] 0.1716[/C][C] 0.3433[/C][C] 0.8284[/C][/ROW]
[ROW][C]88[/C][C] 0.1505[/C][C] 0.301[/C][C] 0.8495[/C][/ROW]
[ROW][C]89[/C][C] 0.1274[/C][C] 0.2548[/C][C] 0.8726[/C][/ROW]
[ROW][C]90[/C][C] 0.2456[/C][C] 0.4913[/C][C] 0.7544[/C][/ROW]
[ROW][C]91[/C][C] 0.2166[/C][C] 0.4333[/C][C] 0.7834[/C][/ROW]
[ROW][C]92[/C][C] 0.2152[/C][C] 0.4304[/C][C] 0.7848[/C][/ROW]
[ROW][C]93[/C][C] 0.184[/C][C] 0.3679[/C][C] 0.816[/C][/ROW]
[ROW][C]94[/C][C] 0.1842[/C][C] 0.3683[/C][C] 0.8158[/C][/ROW]
[ROW][C]95[/C][C] 0.1576[/C][C] 0.3151[/C][C] 0.8424[/C][/ROW]
[ROW][C]96[/C][C] 0.1335[/C][C] 0.267[/C][C] 0.8665[/C][/ROW]
[ROW][C]97[/C][C] 0.2553[/C][C] 0.5106[/C][C] 0.7447[/C][/ROW]
[ROW][C]98[/C][C] 0.2336[/C][C] 0.4672[/C][C] 0.7664[/C][/ROW]
[ROW][C]99[/C][C] 0.2068[/C][C] 0.4137[/C][C] 0.7932[/C][/ROW]
[ROW][C]100[/C][C] 0.181[/C][C] 0.362[/C][C] 0.819[/C][/ROW]
[ROW][C]101[/C][C] 0.2001[/C][C] 0.4001[/C][C] 0.7999[/C][/ROW]
[ROW][C]102[/C][C] 0.1753[/C][C] 0.3507[/C][C] 0.8247[/C][/ROW]
[ROW][C]103[/C][C] 0.1526[/C][C] 0.3051[/C][C] 0.8474[/C][/ROW]
[ROW][C]104[/C][C] 0.1307[/C][C] 0.2613[/C][C] 0.8693[/C][/ROW]
[ROW][C]105[/C][C] 0.1337[/C][C] 0.2675[/C][C] 0.8663[/C][/ROW]
[ROW][C]106[/C][C] 0.1246[/C][C] 0.2491[/C][C] 0.8754[/C][/ROW]
[ROW][C]107[/C][C] 0.1106[/C][C] 0.2212[/C][C] 0.8894[/C][/ROW]
[ROW][C]108[/C][C] 0.09361[/C][C] 0.1872[/C][C] 0.9064[/C][/ROW]
[ROW][C]109[/C][C] 0.08238[/C][C] 0.1648[/C][C] 0.9176[/C][/ROW]
[ROW][C]110[/C][C] 0.07237[/C][C] 0.1447[/C][C] 0.9276[/C][/ROW]
[ROW][C]111[/C][C] 0.07905[/C][C] 0.1581[/C][C] 0.9209[/C][/ROW]
[ROW][C]112[/C][C] 0.06326[/C][C] 0.1265[/C][C] 0.9367[/C][/ROW]
[ROW][C]113[/C][C] 0.1439[/C][C] 0.2878[/C][C] 0.8561[/C][/ROW]
[ROW][C]114[/C][C] 0.2295[/C][C] 0.4589[/C][C] 0.7705[/C][/ROW]
[ROW][C]115[/C][C] 0.21[/C][C] 0.42[/C][C] 0.79[/C][/ROW]
[ROW][C]116[/C][C] 0.1815[/C][C] 0.3629[/C][C] 0.8185[/C][/ROW]
[ROW][C]117[/C][C] 0.157[/C][C] 0.3141[/C][C] 0.843[/C][/ROW]
[ROW][C]118[/C][C] 0.1369[/C][C] 0.2738[/C][C] 0.8631[/C][/ROW]
[ROW][C]119[/C][C] 0.115[/C][C] 0.2299[/C][C] 0.885[/C][/ROW]
[ROW][C]120[/C][C] 0.09454[/C][C] 0.1891[/C][C] 0.9055[/C][/ROW]
[ROW][C]121[/C][C] 0.07842[/C][C] 0.1568[/C][C] 0.9216[/C][/ROW]
[ROW][C]122[/C][C] 0.06663[/C][C] 0.1333[/C][C] 0.9334[/C][/ROW]
[ROW][C]123[/C][C] 0.05357[/C][C] 0.1071[/C][C] 0.9464[/C][/ROW]
[ROW][C]124[/C][C] 0.07208[/C][C] 0.1442[/C][C] 0.9279[/C][/ROW]
[ROW][C]125[/C][C] 0.0568[/C][C] 0.1136[/C][C] 0.9432[/C][/ROW]
[ROW][C]126[/C][C] 0.04413[/C][C] 0.08826[/C][C] 0.9559[/C][/ROW]
[ROW][C]127[/C][C] 0.03363[/C][C] 0.06727[/C][C] 0.9664[/C][/ROW]
[ROW][C]128[/C][C] 0.02719[/C][C] 0.05438[/C][C] 0.9728[/C][/ROW]
[ROW][C]129[/C][C] 0.02069[/C][C] 0.04138[/C][C] 0.9793[/C][/ROW]
[ROW][C]130[/C][C] 0.03715[/C][C] 0.07431[/C][C] 0.9628[/C][/ROW]
[ROW][C]131[/C][C] 0.03319[/C][C] 0.06638[/C][C] 0.9668[/C][/ROW]
[ROW][C]132[/C][C] 0.06615[/C][C] 0.1323[/C][C] 0.9339[/C][/ROW]
[ROW][C]133[/C][C] 0.06764[/C][C] 0.1353[/C][C] 0.9324[/C][/ROW]
[ROW][C]134[/C][C] 0.1871[/C][C] 0.3742[/C][C] 0.8129[/C][/ROW]
[ROW][C]135[/C][C] 0.157[/C][C] 0.3141[/C][C] 0.843[/C][/ROW]
[ROW][C]136[/C][C] 0.2872[/C][C] 0.5744[/C][C] 0.7128[/C][/ROW]
[ROW][C]137[/C][C] 0.4135[/C][C] 0.827[/C][C] 0.5865[/C][/ROW]
[ROW][C]138[/C][C] 0.3675[/C][C] 0.735[/C][C] 0.6325[/C][/ROW]
[ROW][C]139[/C][C] 0.5929[/C][C] 0.8141[/C][C] 0.4071[/C][/ROW]
[ROW][C]140[/C][C] 0.5434[/C][C] 0.9132[/C][C] 0.4566[/C][/ROW]
[ROW][C]141[/C][C] 0.5391[/C][C] 0.9217[/C][C] 0.4609[/C][/ROW]
[ROW][C]142[/C][C] 0.4983[/C][C] 0.9965[/C][C] 0.5017[/C][/ROW]
[ROW][C]143[/C][C] 0.4956[/C][C] 0.9912[/C][C] 0.5044[/C][/ROW]
[ROW][C]144[/C][C] 0.4886[/C][C] 0.9772[/C][C] 0.5114[/C][/ROW]
[ROW][C]145[/C][C] 0.5008[/C][C] 0.9984[/C][C] 0.4992[/C][/ROW]
[ROW][C]146[/C][C] 0.4386[/C][C] 0.8772[/C][C] 0.5614[/C][/ROW]
[ROW][C]147[/C][C] 0.6452[/C][C] 0.7096[/C][C] 0.3548[/C][/ROW]
[ROW][C]148[/C][C] 0.742[/C][C] 0.516[/C][C] 0.258[/C][/ROW]
[ROW][C]149[/C][C] 0.7559[/C][C] 0.4881[/C][C] 0.2441[/C][/ROW]
[ROW][C]150[/C][C] 0.8423[/C][C] 0.3154[/C][C] 0.1577[/C][/ROW]
[ROW][C]151[/C][C] 0.8102[/C][C] 0.3797[/C][C] 0.1898[/C][/ROW]
[ROW][C]152[/C][C] 0.7481[/C][C] 0.5038[/C][C] 0.2519[/C][/ROW]
[ROW][C]153[/C][C] 0.6842[/C][C] 0.6315[/C][C] 0.3158[/C][/ROW]
[ROW][C]154[/C][C] 0.6365[/C][C] 0.7271[/C][C] 0.3635[/C][/ROW]
[ROW][C]155[/C][C] 0.562[/C][C] 0.8761[/C][C] 0.438[/C][/ROW]
[ROW][C]156[/C][C] 0.5503[/C][C] 0.8995[/C][C] 0.4497[/C][/ROW]
[ROW][C]157[/C][C] 0.7705[/C][C] 0.4589[/C][C] 0.2295[/C][/ROW]
[ROW][C]158[/C][C] 0.6798[/C][C] 0.6403[/C][C] 0.3202[/C][/ROW]
[ROW][C]159[/C][C] 0.5654[/C][C] 0.8692[/C][C] 0.4346[/C][/ROW]
[ROW][C]160[/C][C] 0.4491[/C][C] 0.8982[/C][C] 0.5509[/C][/ROW]
[ROW][C]161[/C][C] 0.6169[/C][C] 0.7662[/C][C] 0.3831[/C][/ROW]
[ROW][C]162[/C][C] 0.4512[/C][C] 0.9024[/C][C] 0.5488[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=300211&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=300211&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
6	0.5776	0.8448	0.4224
7	0.7448	0.5104	0.2552
8	0.6217	0.7566	0.3783
9	0.5611	0.8779	0.4389
10	0.577	0.846	0.423
11	0.4708	0.9416	0.5292
12	0.4173	0.8345	0.5827
13	0.3574	0.7149	0.6426
14	0.4943	0.9886	0.5057
15	0.4251	0.8501	0.5749
16	0.3458	0.6917	0.6542
17	0.2765	0.553	0.7235
18	0.2477	0.4954	0.7523
19	0.2755	0.5509	0.7245
20	0.2412	0.4825	0.7588
21	0.2054	0.4108	0.7946
22	0.1672	0.3345	0.8328
23	0.1385	0.2769	0.8615
24	0.1638	0.3277	0.8362
25	0.1342	0.2683	0.8658
26	0.1036	0.2072	0.8964
27	0.08331	0.1666	0.9167
28	0.06304	0.1261	0.937
29	0.04937	0.09873	0.9506
30	0.06657	0.1331	0.9334
31	0.05368	0.1074	0.9463
32	0.04177	0.08355	0.9582
33	0.03188	0.06376	0.9681
34	0.1077	0.2154	0.8923
35	0.08476	0.1695	0.9152
36	0.06941	0.1388	0.9306
37	0.05611	0.1122	0.9439
38	0.04535	0.0907	0.9546
39	0.05484	0.1097	0.9452
40	0.04315	0.0863	0.9568
41	0.03315	0.06629	0.9669
42	0.09094	0.1819	0.9091
43	0.07987	0.1597	0.9201
44	0.06278	0.1256	0.9372
45	0.05191	0.1038	0.9481
46	0.04095	0.08191	0.959
47	0.03311	0.06622	0.9669
48	0.035	0.07	0.965
49	0.06885	0.1377	0.9311
50	0.05846	0.1169	0.9415
51	0.06605	0.1321	0.934
52	0.07403	0.1481	0.926
53	0.172	0.344	0.828
54	0.1495	0.2991	0.8505
55	0.1254	0.2509	0.8746
56	0.1074	0.2147	0.8926
57	0.08878	0.1776	0.9112
58	0.07603	0.1521	0.924
59	0.06136	0.1227	0.9386
60	0.0494	0.09879	0.9506
61	0.04142	0.08285	0.9586
62	0.0373	0.0746	0.9627
63	0.0288	0.0576	0.9712
64	0.02234	0.04468	0.9777
65	0.09857	0.1971	0.9014
66	0.09318	0.1864	0.9068
67	0.07535	0.1507	0.9246
68	0.06998	0.14	0.93
69	0.05601	0.112	0.944
70	0.04493	0.08987	0.9551
71	0.0351	0.07021	0.9649
72	0.03565	0.07131	0.9643
73	0.04316	0.08631	0.9568
74	0.04624	0.09249	0.9538
75	0.03735	0.0747	0.9627
76	0.02957	0.05913	0.9704
77	0.02407	0.04815	0.9759
78	0.04118	0.08235	0.9588
79	0.03261	0.06521	0.9674
80	0.02761	0.05521	0.9724
81	0.05924	0.1185	0.9408
82	0.04841	0.09681	0.9516
83	0.03956	0.07912	0.9604
84	0.03998	0.07996	0.96
85	0.1603	0.3206	0.8397
86	0.1595	0.319	0.8405
87	0.1716	0.3433	0.8284
88	0.1505	0.301	0.8495
89	0.1274	0.2548	0.8726
90	0.2456	0.4913	0.7544
91	0.2166	0.4333	0.7834
92	0.2152	0.4304	0.7848
93	0.184	0.3679	0.816
94	0.1842	0.3683	0.8158
95	0.1576	0.3151	0.8424
96	0.1335	0.267	0.8665
97	0.2553	0.5106	0.7447
98	0.2336	0.4672	0.7664
99	0.2068	0.4137	0.7932
100	0.181	0.362	0.819
101	0.2001	0.4001	0.7999
102	0.1753	0.3507	0.8247
103	0.1526	0.3051	0.8474
104	0.1307	0.2613	0.8693
105	0.1337	0.2675	0.8663
106	0.1246	0.2491	0.8754
107	0.1106	0.2212	0.8894
108	0.09361	0.1872	0.9064
109	0.08238	0.1648	0.9176
110	0.07237	0.1447	0.9276
111	0.07905	0.1581	0.9209
112	0.06326	0.1265	0.9367
113	0.1439	0.2878	0.8561
114	0.2295	0.4589	0.7705
115	0.21	0.42	0.79
116	0.1815	0.3629	0.8185
117	0.157	0.3141	0.843
118	0.1369	0.2738	0.8631
119	0.115	0.2299	0.885
120	0.09454	0.1891	0.9055
121	0.07842	0.1568	0.9216
122	0.06663	0.1333	0.9334
123	0.05357	0.1071	0.9464
124	0.07208	0.1442	0.9279
125	0.0568	0.1136	0.9432
126	0.04413	0.08826	0.9559
127	0.03363	0.06727	0.9664
128	0.02719	0.05438	0.9728
129	0.02069	0.04138	0.9793
130	0.03715	0.07431	0.9628
131	0.03319	0.06638	0.9668
132	0.06615	0.1323	0.9339
133	0.06764	0.1353	0.9324
134	0.1871	0.3742	0.8129
135	0.157	0.3141	0.843
136	0.2872	0.5744	0.7128
137	0.4135	0.827	0.5865
138	0.3675	0.735	0.6325
139	0.5929	0.8141	0.4071
140	0.5434	0.9132	0.4566
141	0.5391	0.9217	0.4609
142	0.4983	0.9965	0.5017
143	0.4956	0.9912	0.5044
144	0.4886	0.9772	0.5114
145	0.5008	0.9984	0.4992
146	0.4386	0.8772	0.5614
147	0.6452	0.7096	0.3548
148	0.742	0.516	0.258
149	0.7559	0.4881	0.2441
150	0.8423	0.3154	0.1577
151	0.8102	0.3797	0.1898
152	0.7481	0.5038	0.2519
153	0.6842	0.6315	0.3158
154	0.6365	0.7271	0.3635
155	0.562	0.8761	0.438
156	0.5503	0.8995	0.4497
157	0.7705	0.4589	0.2295
158	0.6798	0.6403	0.3202
159	0.5654	0.8692	0.4346
160	0.4491	0.8982	0.5509
161	0.6169	0.7662	0.3831
162	0.4512	0.9024	0.5488

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	3	0.0191083	OK
10% type I error level	34	0.216561	NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 &  0 & OK \tabularnewline
5% type I error level & 3 & 0.0191083 & OK \tabularnewline
10% type I error level & 34 & 0.216561 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=300211&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]3[/C][C]0.0191083[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]34[/C][C]0.216561[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=300211&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=300211&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	3	0.0191083	OK
10% type I error level	34	0.216561	NOK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 1.1694, df1 = 2, df2 = 163, p-value = 0.3131
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.2272, df1 = 4, df2 = 161, p-value = 0.3014
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 1.3918, df1 = 2, df2 = 163, p-value = 0.2516

\begin{tabular}{lllllllll}
\hline
Ramsey RESET F-Test for powers (2 and 3) of fitted values \tabularnewline
> reset_test_fitted
	RESET test
data:  mylm
RESET = 1.1694, df1 = 2, df2 = 163, p-value = 0.3131
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.2272, df1 = 4, df2 = 161, p-value = 0.3014
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 1.3918, df1 = 2, df2 = 163, p-value = 0.2516
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=300211&T=7

[TABLE]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of fitted values[/C][/ROW]
[ROW][C]> reset_test_fitted
	RESET test
data:  mylm
RESET = 1.1694, df1 = 2, df2 = 163, p-value = 0.3131
[/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 = 1.2272, df1 = 4, df2 = 161, p-value = 0.3014
[/C][/ROW]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of principal components[/C][/ROW]
[ROW][C]> reset_test_principal_components
	RESET test
data:  mylm
RESET = 1.3918, df1 = 2, df2 = 163, p-value = 0.2516
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=300211&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=300211&T=7

As an alternative you can also use a QR Code:

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

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 1.1694, df1 = 2, df2 = 163, p-value = 0.3131
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.2272, df1 = 4, df2 = 161, p-value = 0.3014
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 1.3918, df1 = 2, df2 = 163, p-value = 0.2516

Variance Inflation Factors (Multicollinearity)
> vif EP2 EP3 1.014752 1.014752

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
     EP2      EP3 
1.014752 1.014752 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=300211&T=8

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
     EP2      EP3 
1.014752 1.014752 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=300211&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=300211&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 EP2 EP3 1.014752 1.014752

Figure 1

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Figure 2

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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):

par1 = 3 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = 0 ; par5 = 0 ;

Parameters (R input):

par1 = 3 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = 0 ; par5 = 0 ;

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

par5 <- '0'
par4 <- '0'
par3 <- 'No Linear Trend'
par2 <- 'Do not include Seasonal Dummies'
par1 <- '3'
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