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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Sat, 10 Dec 2016 10:55:51 +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/10/t1481365573i6rwdyrta0aai7u.htm/, Retrieved Fri, 01 Nov 2024 03:44:52 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=298625, Retrieved Fri, 01 Nov 2024 03:44:52 +0000

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152

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-       [Multiple Regression] [Multiple regressi...] [2016-12-10 09:55:51] [ee2f08b6fcfe19fae25bd9410e008f6d] [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	8 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 time8 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298625&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]

8 seconds[/C][/ROW] [ROW]

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=298625&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298625&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	8 seconds
R Server	Big Analytics Cloud Computing Center

Multiple Linear Regression - Estimated Regression Equation
TVDCsum[t] = + 11.8177 -0.349609IVHB1[t] + 0.120691IVHB2[t] + 0.148376IVHB3[t] + 0.176208IVHB4[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TVDCsum[t] =  +  11.8177 -0.349609IVHB1[t] +  0.120691IVHB2[t] +  0.148376IVHB3[t] +  0.176208IVHB4[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298625&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TVDCsum[t] =  +  11.8177 -0.349609IVHB1[t] +  0.120691IVHB2[t] +  0.148376IVHB3[t] +  0.176208IVHB4[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298625&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298625&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] = + 11.8177 -0.349609IVHB1[t] + 0.120691IVHB2[t] + 0.148376IVHB3[t] + 0.176208IVHB4[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)	+11.82	0.856	+1.3810e+01	4.73e-29	2.365e-29
IVHB1	-0.3496	0.1167	-2.9960e+00	0.003173	0.001587
IVHB2	+0.1207	0.1052	+1.1470e+00	0.2529	0.1265
IVHB3	+0.1484	0.12	+1.2370e+00	0.218	0.109
IVHB4	+0.1762	0.148	+1.1900e+00	0.2357	0.1179

\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) & +11.82 &  0.856 & +1.3810e+01 &  4.73e-29 &  2.365e-29 \tabularnewline
IVHB1 & -0.3496 &  0.1167 & -2.9960e+00 &  0.003173 &  0.001587 \tabularnewline
IVHB2 & +0.1207 &  0.1052 & +1.1470e+00 &  0.2529 &  0.1265 \tabularnewline
IVHB3 & +0.1484 &  0.12 & +1.2370e+00 &  0.218 &  0.109 \tabularnewline
IVHB4 & +0.1762 &  0.148 & +1.1900e+00 &  0.2357 &  0.1179 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298625&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]+11.82[/C][C] 0.856[/C][C]+1.3810e+01[/C][C] 4.73e-29[/C][C] 2.365e-29[/C][/ROW]
[ROW][C]IVHB1[/C][C]-0.3496[/C][C] 0.1167[/C][C]-2.9960e+00[/C][C] 0.003173[/C][C] 0.001587[/C][/ROW]
[ROW][C]IVHB2[/C][C]+0.1207[/C][C] 0.1052[/C][C]+1.1470e+00[/C][C] 0.2529[/C][C] 0.1265[/C][/ROW]
[ROW][C]IVHB3[/C][C]+0.1484[/C][C] 0.12[/C][C]+1.2370e+00[/C][C] 0.218[/C][C] 0.109[/C][/ROW]
[ROW][C]IVHB4[/C][C]+0.1762[/C][C] 0.148[/C][C]+1.1900e+00[/C][C] 0.2357[/C][C] 0.1179[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298625&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298625&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)	+11.82	0.856	+1.3810e+01	4.73e-29	2.365e-29
IVHB1	-0.3496	0.1167	-2.9960e+00	0.003173	0.001587
IVHB2	+0.1207	0.1052	+1.1470e+00	0.2529	0.1265
IVHB3	+0.1484	0.12	+1.2370e+00	0.218	0.109
IVHB4	+0.1762	0.148	+1.1900e+00	0.2357	0.1179

Multiple Linear Regression - Regression Statistics
Multiple R	0.2597
R-squared	0.06743
Adjusted R-squared	0.04412
F-TEST (value)	2.892
F-TEST (DF numerator)	4
F-TEST (DF denominator)	160
p-value	0.024
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.456
Sum Squared Residuals	339.3

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.2597 \tabularnewline
R-squared &  0.06743 \tabularnewline
Adjusted R-squared &  0.04412 \tabularnewline
F-TEST (value) &  2.892 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 160 \tabularnewline
p-value &  0.024 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  1.456 \tabularnewline
Sum Squared Residuals &  339.3 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298625&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.2597[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.06743[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.04412[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 2.892[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]160[/C][/ROW]
[ROW][C]p-value[/C][C] 0.024[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 1.456[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 339.3[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298625&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298625&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	0.2597
R-squared	0.06743
Adjusted R-squared	0.04412
F-TEST (value)	2.892
F-TEST (DF numerator)	4
F-TEST (DF denominator)	160
p-value	0.024
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.456
Sum Squared Residuals	339.3

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	10	12.51	-2.51
2	13	11.51	1.486
3	14	12.11	1.893
4	12	12.08	-0.07972
5	12	12.23	-0.2254
6	13	11.96	1.041
7	13	13.25	-0.2492
8	13	12.11	0.8926
9	13	12.23	0.7747
10	14	12.05	1.948
11	14	12.36	1.639
12	12	11.49	0.5139
13	12	12.14	-0.1449
14	11	11.85	-0.8508
15	12	11.96	0.04098
16	14	13.28	0.7231
17	12	12.42	-0.417
18	11	11.78	-0.783
19	13	11.85	1.149
20	13	11.97	1.029
21	12	12.32	-0.3211
22	13	13.04	-0.03554
23	12	12.05	-0.05203
24	13	12.66	0.3418
25	11	12.05	-1.052
26	12	11.53	0.4738
27	12	12.1	-0.1047
28	13	12.18	0.8246
29	13	12.31	0.6914
30	10	11.61	-1.609
31	12	12.75	-0.7512
32	13	12.86	0.1405
33	13	11.82	1.18
34	10	11.63	-1.634
35	14	12.05	1.948
36	12	12.28	-0.2809
37	10	12.05	-2.055
38	10	12.05	-2.052
39	14	12.51	1.49
40	12	11.93	0.06866
41	14	12.48	1.518
42	10	12.4	-2.402
43	13	12.74	0.2611
44	12	12.31	-0.3086
45	12	12.25	-0.2531
46	13	12.1	0.8953
47	12	12.38	-0.3766
48	10	12.2	-2.2
49	14	12.2	1.8
50	15	12.2	2.8
51	14	12.75	1.249
52	8	11.31	-3.313
53	11	12.13	-1.132
54	10	12.04	-2.04
55	12	11.76	0.2449
56	14	12.05	1.948
57	12	11.73	0.2727
58	12	12.43	-0.4293
59	14	11.81	2.189
60	13	12.08	0.9203
61	13	11.93	1.069
62	13	11.9	1.096
63	12	11.76	0.2449
64	10	12.51	-2.51
65	14	11.88	2.124
66	11	12.32	-1.321
67	10	12.05	-2.052
68	13	12.08	0.9203
69	12	11.81	0.1894
70	12	11.11	0.8912
71	11	12.37	-1.374
72	10	12.4	-2.399
73	14	11.9	2.096
74	12	12	0.0006782
75	13	12.05	0.948
76	11	12.03	-1.027
77	10	11.65	-1.647
78	14	12.63	1.369
79	13	11.96	1.041
80	7	11.9	-4.904
81	13	11.9	1.096
82	13	12.43	0.5705
83	13	11.81	1.189
84	15	12.66	2.342
85	13	11.34	1.66
86	14	12.58	1.422
87	12	11.67	0.3282
88	13	12.9	0.1004
89	11	12.05	-1.052
90	12	12.25	-0.2531
91	14	12.4	1.598
92	13	12.35	0.6512
93	14	12.02	1.976
94	12	11.78	0.2172
95	12	12.4	-0.4016
96	13	12.38	0.6234
97	14	11.57	2.433
98	13	12.55	0.45
99	13	12.08	0.9203
100	12	12.61	-0.6055
101	10	12.37	-2.374
102	12	11.58	0.4183
103	13	12	1.001
104	12	11.88	0.1242
105	13	12.18	0.8246
106	12	12.2	-0.2004
107	12	12.32	-0.3211
108	12	12.5	-0.4973
109	11	11.78	-0.7828
110	12	11.86	0.1366
111	9	11.9	-2.904
112	14	12.28	1.719
113	12	11.69	0.31
114	13	12.75	0.2488
115	13	13.1	-0.1009
116	13	11.31	1.687
117	11	12.2	-1.2
118	12	12.1	-0.1047
119	11	11.9	-0.9037
120	12	12.38	-0.3766
121	12	11.96	0.04098
122	13	11.63	1.366
123	12	12.9	-0.8996
124	13	12.35	0.6512
125	13	11.78	1.217
126	12	11.49	0.5139
127	12	11.96	0.04098
128	8	11.96	-3.956
129	12	12.32	-0.3211
130	13	11.58	1.418
131	10	12.36	-2.361
132	8	11.61	-3.609
133	12	12.2	-0.2004
134	13	12.82	0.1809
135	12	12.55	-0.5472
136	15	13.1	1.896
137	14	12.31	1.694
138	10	11.84	-1.838
139	11	12.35	-1.349
140	12	11.82	0.1769
141	10	12.73	-2.726
142	14	12.55	1.45
143	10	12.17	-2.173
144	15	12.17	2.827
145	11	11.94	-0.9408
146	12	12.17	-0.1727
147	9	11.96	-2.959
148	12	12.32	-0.3211
149	13	11.99	1.013
150	12	12.63	-0.6306
151	9	11.64	-2.637
152	12	11.96	0.04098
153	14	12.05	1.948
154	11	11.9	-0.9037
155	12	11.81	0.1894
156	14	12.17	1.827
157	12	12.72	-0.7234
158	15	12.4	2.598
159	11	12.25	-1.253
160	12	11.81	0.1894
161	12	12.05	-0.05203
162	10	11.84	-1.838
163	12	11.93	0.06866
164	11	12.28	-1.281
165	11	12.1	-1.105

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  10 &  12.51 & -2.51 \tabularnewline
2 &  13 &  11.51 &  1.486 \tabularnewline
3 &  14 &  12.11 &  1.893 \tabularnewline
4 &  12 &  12.08 & -0.07972 \tabularnewline
5 &  12 &  12.23 & -0.2254 \tabularnewline
6 &  13 &  11.96 &  1.041 \tabularnewline
7 &  13 &  13.25 & -0.2492 \tabularnewline
8 &  13 &  12.11 &  0.8926 \tabularnewline
9 &  13 &  12.23 &  0.7747 \tabularnewline
10 &  14 &  12.05 &  1.948 \tabularnewline
11 &  14 &  12.36 &  1.639 \tabularnewline
12 &  12 &  11.49 &  0.5139 \tabularnewline
13 &  12 &  12.14 & -0.1449 \tabularnewline
14 &  11 &  11.85 & -0.8508 \tabularnewline
15 &  12 &  11.96 &  0.04098 \tabularnewline
16 &  14 &  13.28 &  0.7231 \tabularnewline
17 &  12 &  12.42 & -0.417 \tabularnewline
18 &  11 &  11.78 & -0.783 \tabularnewline
19 &  13 &  11.85 &  1.149 \tabularnewline
20 &  13 &  11.97 &  1.029 \tabularnewline
21 &  12 &  12.32 & -0.3211 \tabularnewline
22 &  13 &  13.04 & -0.03554 \tabularnewline
23 &  12 &  12.05 & -0.05203 \tabularnewline
24 &  13 &  12.66 &  0.3418 \tabularnewline
25 &  11 &  12.05 & -1.052 \tabularnewline
26 &  12 &  11.53 &  0.4738 \tabularnewline
27 &  12 &  12.1 & -0.1047 \tabularnewline
28 &  13 &  12.18 &  0.8246 \tabularnewline
29 &  13 &  12.31 &  0.6914 \tabularnewline
30 &  10 &  11.61 & -1.609 \tabularnewline
31 &  12 &  12.75 & -0.7512 \tabularnewline
32 &  13 &  12.86 &  0.1405 \tabularnewline
33 &  13 &  11.82 &  1.18 \tabularnewline
34 &  10 &  11.63 & -1.634 \tabularnewline
35 &  14 &  12.05 &  1.948 \tabularnewline
36 &  12 &  12.28 & -0.2809 \tabularnewline
37 &  10 &  12.05 & -2.055 \tabularnewline
38 &  10 &  12.05 & -2.052 \tabularnewline
39 &  14 &  12.51 &  1.49 \tabularnewline
40 &  12 &  11.93 &  0.06866 \tabularnewline
41 &  14 &  12.48 &  1.518 \tabularnewline
42 &  10 &  12.4 & -2.402 \tabularnewline
43 &  13 &  12.74 &  0.2611 \tabularnewline
44 &  12 &  12.31 & -0.3086 \tabularnewline
45 &  12 &  12.25 & -0.2531 \tabularnewline
46 &  13 &  12.1 &  0.8953 \tabularnewline
47 &  12 &  12.38 & -0.3766 \tabularnewline
48 &  10 &  12.2 & -2.2 \tabularnewline
49 &  14 &  12.2 &  1.8 \tabularnewline
50 &  15 &  12.2 &  2.8 \tabularnewline
51 &  14 &  12.75 &  1.249 \tabularnewline
52 &  8 &  11.31 & -3.313 \tabularnewline
53 &  11 &  12.13 & -1.132 \tabularnewline
54 &  10 &  12.04 & -2.04 \tabularnewline
55 &  12 &  11.76 &  0.2449 \tabularnewline
56 &  14 &  12.05 &  1.948 \tabularnewline
57 &  12 &  11.73 &  0.2727 \tabularnewline
58 &  12 &  12.43 & -0.4293 \tabularnewline
59 &  14 &  11.81 &  2.189 \tabularnewline
60 &  13 &  12.08 &  0.9203 \tabularnewline
61 &  13 &  11.93 &  1.069 \tabularnewline
62 &  13 &  11.9 &  1.096 \tabularnewline
63 &  12 &  11.76 &  0.2449 \tabularnewline
64 &  10 &  12.51 & -2.51 \tabularnewline
65 &  14 &  11.88 &  2.124 \tabularnewline
66 &  11 &  12.32 & -1.321 \tabularnewline
67 &  10 &  12.05 & -2.052 \tabularnewline
68 &  13 &  12.08 &  0.9203 \tabularnewline
69 &  12 &  11.81 &  0.1894 \tabularnewline
70 &  12 &  11.11 &  0.8912 \tabularnewline
71 &  11 &  12.37 & -1.374 \tabularnewline
72 &  10 &  12.4 & -2.399 \tabularnewline
73 &  14 &  11.9 &  2.096 \tabularnewline
74 &  12 &  12 &  0.0006782 \tabularnewline
75 &  13 &  12.05 &  0.948 \tabularnewline
76 &  11 &  12.03 & -1.027 \tabularnewline
77 &  10 &  11.65 & -1.647 \tabularnewline
78 &  14 &  12.63 &  1.369 \tabularnewline
79 &  13 &  11.96 &  1.041 \tabularnewline
80 &  7 &  11.9 & -4.904 \tabularnewline
81 &  13 &  11.9 &  1.096 \tabularnewline
82 &  13 &  12.43 &  0.5705 \tabularnewline
83 &  13 &  11.81 &  1.189 \tabularnewline
84 &  15 &  12.66 &  2.342 \tabularnewline
85 &  13 &  11.34 &  1.66 \tabularnewline
86 &  14 &  12.58 &  1.422 \tabularnewline
87 &  12 &  11.67 &  0.3282 \tabularnewline
88 &  13 &  12.9 &  0.1004 \tabularnewline
89 &  11 &  12.05 & -1.052 \tabularnewline
90 &  12 &  12.25 & -0.2531 \tabularnewline
91 &  14 &  12.4 &  1.598 \tabularnewline
92 &  13 &  12.35 &  0.6512 \tabularnewline
93 &  14 &  12.02 &  1.976 \tabularnewline
94 &  12 &  11.78 &  0.2172 \tabularnewline
95 &  12 &  12.4 & -0.4016 \tabularnewline
96 &  13 &  12.38 &  0.6234 \tabularnewline
97 &  14 &  11.57 &  2.433 \tabularnewline
98 &  13 &  12.55 &  0.45 \tabularnewline
99 &  13 &  12.08 &  0.9203 \tabularnewline
100 &  12 &  12.61 & -0.6055 \tabularnewline
101 &  10 &  12.37 & -2.374 \tabularnewline
102 &  12 &  11.58 &  0.4183 \tabularnewline
103 &  13 &  12 &  1.001 \tabularnewline
104 &  12 &  11.88 &  0.1242 \tabularnewline
105 &  13 &  12.18 &  0.8246 \tabularnewline
106 &  12 &  12.2 & -0.2004 \tabularnewline
107 &  12 &  12.32 & -0.3211 \tabularnewline
108 &  12 &  12.5 & -0.4973 \tabularnewline
109 &  11 &  11.78 & -0.7828 \tabularnewline
110 &  12 &  11.86 &  0.1366 \tabularnewline
111 &  9 &  11.9 & -2.904 \tabularnewline
112 &  14 &  12.28 &  1.719 \tabularnewline
113 &  12 &  11.69 &  0.31 \tabularnewline
114 &  13 &  12.75 &  0.2488 \tabularnewline
115 &  13 &  13.1 & -0.1009 \tabularnewline
116 &  13 &  11.31 &  1.687 \tabularnewline
117 &  11 &  12.2 & -1.2 \tabularnewline
118 &  12 &  12.1 & -0.1047 \tabularnewline
119 &  11 &  11.9 & -0.9037 \tabularnewline
120 &  12 &  12.38 & -0.3766 \tabularnewline
121 &  12 &  11.96 &  0.04098 \tabularnewline
122 &  13 &  11.63 &  1.366 \tabularnewline
123 &  12 &  12.9 & -0.8996 \tabularnewline
124 &  13 &  12.35 &  0.6512 \tabularnewline
125 &  13 &  11.78 &  1.217 \tabularnewline
126 &  12 &  11.49 &  0.5139 \tabularnewline
127 &  12 &  11.96 &  0.04098 \tabularnewline
128 &  8 &  11.96 & -3.956 \tabularnewline
129 &  12 &  12.32 & -0.3211 \tabularnewline
130 &  13 &  11.58 &  1.418 \tabularnewline
131 &  10 &  12.36 & -2.361 \tabularnewline
132 &  8 &  11.61 & -3.609 \tabularnewline
133 &  12 &  12.2 & -0.2004 \tabularnewline
134 &  13 &  12.82 &  0.1809 \tabularnewline
135 &  12 &  12.55 & -0.5472 \tabularnewline
136 &  15 &  13.1 &  1.896 \tabularnewline
137 &  14 &  12.31 &  1.694 \tabularnewline
138 &  10 &  11.84 & -1.838 \tabularnewline
139 &  11 &  12.35 & -1.349 \tabularnewline
140 &  12 &  11.82 &  0.1769 \tabularnewline
141 &  10 &  12.73 & -2.726 \tabularnewline
142 &  14 &  12.55 &  1.45 \tabularnewline
143 &  10 &  12.17 & -2.173 \tabularnewline
144 &  15 &  12.17 &  2.827 \tabularnewline
145 &  11 &  11.94 & -0.9408 \tabularnewline
146 &  12 &  12.17 & -0.1727 \tabularnewline
147 &  9 &  11.96 & -2.959 \tabularnewline
148 &  12 &  12.32 & -0.3211 \tabularnewline
149 &  13 &  11.99 &  1.013 \tabularnewline
150 &  12 &  12.63 & -0.6306 \tabularnewline
151 &  9 &  11.64 & -2.637 \tabularnewline
152 &  12 &  11.96 &  0.04098 \tabularnewline
153 &  14 &  12.05 &  1.948 \tabularnewline
154 &  11 &  11.9 & -0.9037 \tabularnewline
155 &  12 &  11.81 &  0.1894 \tabularnewline
156 &  14 &  12.17 &  1.827 \tabularnewline
157 &  12 &  12.72 & -0.7234 \tabularnewline
158 &  15 &  12.4 &  2.598 \tabularnewline
159 &  11 &  12.25 & -1.253 \tabularnewline
160 &  12 &  11.81 &  0.1894 \tabularnewline
161 &  12 &  12.05 & -0.05203 \tabularnewline
162 &  10 &  11.84 & -1.838 \tabularnewline
163 &  12 &  11.93 &  0.06866 \tabularnewline
164 &  11 &  12.28 & -1.281 \tabularnewline
165 &  11 &  12.1 & -1.105 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298625&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C] 10[/C][C] 12.51[/C][C]-2.51[/C][/ROW]
[ROW][C]2[/C][C] 13[/C][C] 11.51[/C][C] 1.486[/C][/ROW]
[ROW][C]3[/C][C] 14[/C][C] 12.11[/C][C] 1.893[/C][/ROW]
[ROW][C]4[/C][C] 12[/C][C] 12.08[/C][C]-0.07972[/C][/ROW]
[ROW][C]5[/C][C] 12[/C][C] 12.23[/C][C]-0.2254[/C][/ROW]
[ROW][C]6[/C][C] 13[/C][C] 11.96[/C][C] 1.041[/C][/ROW]
[ROW][C]7[/C][C] 13[/C][C] 13.25[/C][C]-0.2492[/C][/ROW]
[ROW][C]8[/C][C] 13[/C][C] 12.11[/C][C] 0.8926[/C][/ROW]
[ROW][C]9[/C][C] 13[/C][C] 12.23[/C][C] 0.7747[/C][/ROW]
[ROW][C]10[/C][C] 14[/C][C] 12.05[/C][C] 1.948[/C][/ROW]
[ROW][C]11[/C][C] 14[/C][C] 12.36[/C][C] 1.639[/C][/ROW]
[ROW][C]12[/C][C] 12[/C][C] 11.49[/C][C] 0.5139[/C][/ROW]
[ROW][C]13[/C][C] 12[/C][C] 12.14[/C][C]-0.1449[/C][/ROW]
[ROW][C]14[/C][C] 11[/C][C] 11.85[/C][C]-0.8508[/C][/ROW]
[ROW][C]15[/C][C] 12[/C][C] 11.96[/C][C] 0.04098[/C][/ROW]
[ROW][C]16[/C][C] 14[/C][C] 13.28[/C][C] 0.7231[/C][/ROW]
[ROW][C]17[/C][C] 12[/C][C] 12.42[/C][C]-0.417[/C][/ROW]
[ROW][C]18[/C][C] 11[/C][C] 11.78[/C][C]-0.783[/C][/ROW]
[ROW][C]19[/C][C] 13[/C][C] 11.85[/C][C] 1.149[/C][/ROW]
[ROW][C]20[/C][C] 13[/C][C] 11.97[/C][C] 1.029[/C][/ROW]
[ROW][C]21[/C][C] 12[/C][C] 12.32[/C][C]-0.3211[/C][/ROW]
[ROW][C]22[/C][C] 13[/C][C] 13.04[/C][C]-0.03554[/C][/ROW]
[ROW][C]23[/C][C] 12[/C][C] 12.05[/C][C]-0.05203[/C][/ROW]
[ROW][C]24[/C][C] 13[/C][C] 12.66[/C][C] 0.3418[/C][/ROW]
[ROW][C]25[/C][C] 11[/C][C] 12.05[/C][C]-1.052[/C][/ROW]
[ROW][C]26[/C][C] 12[/C][C] 11.53[/C][C] 0.4738[/C][/ROW]
[ROW][C]27[/C][C] 12[/C][C] 12.1[/C][C]-0.1047[/C][/ROW]
[ROW][C]28[/C][C] 13[/C][C] 12.18[/C][C] 0.8246[/C][/ROW]
[ROW][C]29[/C][C] 13[/C][C] 12.31[/C][C] 0.6914[/C][/ROW]
[ROW][C]30[/C][C] 10[/C][C] 11.61[/C][C]-1.609[/C][/ROW]
[ROW][C]31[/C][C] 12[/C][C] 12.75[/C][C]-0.7512[/C][/ROW]
[ROW][C]32[/C][C] 13[/C][C] 12.86[/C][C] 0.1405[/C][/ROW]
[ROW][C]33[/C][C] 13[/C][C] 11.82[/C][C] 1.18[/C][/ROW]
[ROW][C]34[/C][C] 10[/C][C] 11.63[/C][C]-1.634[/C][/ROW]
[ROW][C]35[/C][C] 14[/C][C] 12.05[/C][C] 1.948[/C][/ROW]
[ROW][C]36[/C][C] 12[/C][C] 12.28[/C][C]-0.2809[/C][/ROW]
[ROW][C]37[/C][C] 10[/C][C] 12.05[/C][C]-2.055[/C][/ROW]
[ROW][C]38[/C][C] 10[/C][C] 12.05[/C][C]-2.052[/C][/ROW]
[ROW][C]39[/C][C] 14[/C][C] 12.51[/C][C] 1.49[/C][/ROW]
[ROW][C]40[/C][C] 12[/C][C] 11.93[/C][C] 0.06866[/C][/ROW]
[ROW][C]41[/C][C] 14[/C][C] 12.48[/C][C] 1.518[/C][/ROW]
[ROW][C]42[/C][C] 10[/C][C] 12.4[/C][C]-2.402[/C][/ROW]
[ROW][C]43[/C][C] 13[/C][C] 12.74[/C][C] 0.2611[/C][/ROW]
[ROW][C]44[/C][C] 12[/C][C] 12.31[/C][C]-0.3086[/C][/ROW]
[ROW][C]45[/C][C] 12[/C][C] 12.25[/C][C]-0.2531[/C][/ROW]
[ROW][C]46[/C][C] 13[/C][C] 12.1[/C][C] 0.8953[/C][/ROW]
[ROW][C]47[/C][C] 12[/C][C] 12.38[/C][C]-0.3766[/C][/ROW]
[ROW][C]48[/C][C] 10[/C][C] 12.2[/C][C]-2.2[/C][/ROW]
[ROW][C]49[/C][C] 14[/C][C] 12.2[/C][C] 1.8[/C][/ROW]
[ROW][C]50[/C][C] 15[/C][C] 12.2[/C][C] 2.8[/C][/ROW]
[ROW][C]51[/C][C] 14[/C][C] 12.75[/C][C] 1.249[/C][/ROW]
[ROW][C]52[/C][C] 8[/C][C] 11.31[/C][C]-3.313[/C][/ROW]
[ROW][C]53[/C][C] 11[/C][C] 12.13[/C][C]-1.132[/C][/ROW]
[ROW][C]54[/C][C] 10[/C][C] 12.04[/C][C]-2.04[/C][/ROW]
[ROW][C]55[/C][C] 12[/C][C] 11.76[/C][C] 0.2449[/C][/ROW]
[ROW][C]56[/C][C] 14[/C][C] 12.05[/C][C] 1.948[/C][/ROW]
[ROW][C]57[/C][C] 12[/C][C] 11.73[/C][C] 0.2727[/C][/ROW]
[ROW][C]58[/C][C] 12[/C][C] 12.43[/C][C]-0.4293[/C][/ROW]
[ROW][C]59[/C][C] 14[/C][C] 11.81[/C][C] 2.189[/C][/ROW]
[ROW][C]60[/C][C] 13[/C][C] 12.08[/C][C] 0.9203[/C][/ROW]
[ROW][C]61[/C][C] 13[/C][C] 11.93[/C][C] 1.069[/C][/ROW]
[ROW][C]62[/C][C] 13[/C][C] 11.9[/C][C] 1.096[/C][/ROW]
[ROW][C]63[/C][C] 12[/C][C] 11.76[/C][C] 0.2449[/C][/ROW]
[ROW][C]64[/C][C] 10[/C][C] 12.51[/C][C]-2.51[/C][/ROW]
[ROW][C]65[/C][C] 14[/C][C] 11.88[/C][C] 2.124[/C][/ROW]
[ROW][C]66[/C][C] 11[/C][C] 12.32[/C][C]-1.321[/C][/ROW]
[ROW][C]67[/C][C] 10[/C][C] 12.05[/C][C]-2.052[/C][/ROW]
[ROW][C]68[/C][C] 13[/C][C] 12.08[/C][C] 0.9203[/C][/ROW]
[ROW][C]69[/C][C] 12[/C][C] 11.81[/C][C] 0.1894[/C][/ROW]
[ROW][C]70[/C][C] 12[/C][C] 11.11[/C][C] 0.8912[/C][/ROW]
[ROW][C]71[/C][C] 11[/C][C] 12.37[/C][C]-1.374[/C][/ROW]
[ROW][C]72[/C][C] 10[/C][C] 12.4[/C][C]-2.399[/C][/ROW]
[ROW][C]73[/C][C] 14[/C][C] 11.9[/C][C] 2.096[/C][/ROW]
[ROW][C]74[/C][C] 12[/C][C] 12[/C][C] 0.0006782[/C][/ROW]
[ROW][C]75[/C][C] 13[/C][C] 12.05[/C][C] 0.948[/C][/ROW]
[ROW][C]76[/C][C] 11[/C][C] 12.03[/C][C]-1.027[/C][/ROW]
[ROW][C]77[/C][C] 10[/C][C] 11.65[/C][C]-1.647[/C][/ROW]
[ROW][C]78[/C][C] 14[/C][C] 12.63[/C][C] 1.369[/C][/ROW]
[ROW][C]79[/C][C] 13[/C][C] 11.96[/C][C] 1.041[/C][/ROW]
[ROW][C]80[/C][C] 7[/C][C] 11.9[/C][C]-4.904[/C][/ROW]
[ROW][C]81[/C][C] 13[/C][C] 11.9[/C][C] 1.096[/C][/ROW]
[ROW][C]82[/C][C] 13[/C][C] 12.43[/C][C] 0.5705[/C][/ROW]
[ROW][C]83[/C][C] 13[/C][C] 11.81[/C][C] 1.189[/C][/ROW]
[ROW][C]84[/C][C] 15[/C][C] 12.66[/C][C] 2.342[/C][/ROW]
[ROW][C]85[/C][C] 13[/C][C] 11.34[/C][C] 1.66[/C][/ROW]
[ROW][C]86[/C][C] 14[/C][C] 12.58[/C][C] 1.422[/C][/ROW]
[ROW][C]87[/C][C] 12[/C][C] 11.67[/C][C] 0.3282[/C][/ROW]
[ROW][C]88[/C][C] 13[/C][C] 12.9[/C][C] 0.1004[/C][/ROW]
[ROW][C]89[/C][C] 11[/C][C] 12.05[/C][C]-1.052[/C][/ROW]
[ROW][C]90[/C][C] 12[/C][C] 12.25[/C][C]-0.2531[/C][/ROW]
[ROW][C]91[/C][C] 14[/C][C] 12.4[/C][C] 1.598[/C][/ROW]
[ROW][C]92[/C][C] 13[/C][C] 12.35[/C][C] 0.6512[/C][/ROW]
[ROW][C]93[/C][C] 14[/C][C] 12.02[/C][C] 1.976[/C][/ROW]
[ROW][C]94[/C][C] 12[/C][C] 11.78[/C][C] 0.2172[/C][/ROW]
[ROW][C]95[/C][C] 12[/C][C] 12.4[/C][C]-0.4016[/C][/ROW]
[ROW][C]96[/C][C] 13[/C][C] 12.38[/C][C] 0.6234[/C][/ROW]
[ROW][C]97[/C][C] 14[/C][C] 11.57[/C][C] 2.433[/C][/ROW]
[ROW][C]98[/C][C] 13[/C][C] 12.55[/C][C] 0.45[/C][/ROW]
[ROW][C]99[/C][C] 13[/C][C] 12.08[/C][C] 0.9203[/C][/ROW]
[ROW][C]100[/C][C] 12[/C][C] 12.61[/C][C]-0.6055[/C][/ROW]
[ROW][C]101[/C][C] 10[/C][C] 12.37[/C][C]-2.374[/C][/ROW]
[ROW][C]102[/C][C] 12[/C][C] 11.58[/C][C] 0.4183[/C][/ROW]
[ROW][C]103[/C][C] 13[/C][C] 12[/C][C] 1.001[/C][/ROW]
[ROW][C]104[/C][C] 12[/C][C] 11.88[/C][C] 0.1242[/C][/ROW]
[ROW][C]105[/C][C] 13[/C][C] 12.18[/C][C] 0.8246[/C][/ROW]
[ROW][C]106[/C][C] 12[/C][C] 12.2[/C][C]-0.2004[/C][/ROW]
[ROW][C]107[/C][C] 12[/C][C] 12.32[/C][C]-0.3211[/C][/ROW]
[ROW][C]108[/C][C] 12[/C][C] 12.5[/C][C]-0.4973[/C][/ROW]
[ROW][C]109[/C][C] 11[/C][C] 11.78[/C][C]-0.7828[/C][/ROW]
[ROW][C]110[/C][C] 12[/C][C] 11.86[/C][C] 0.1366[/C][/ROW]
[ROW][C]111[/C][C] 9[/C][C] 11.9[/C][C]-2.904[/C][/ROW]
[ROW][C]112[/C][C] 14[/C][C] 12.28[/C][C] 1.719[/C][/ROW]
[ROW][C]113[/C][C] 12[/C][C] 11.69[/C][C] 0.31[/C][/ROW]
[ROW][C]114[/C][C] 13[/C][C] 12.75[/C][C] 0.2488[/C][/ROW]
[ROW][C]115[/C][C] 13[/C][C] 13.1[/C][C]-0.1009[/C][/ROW]
[ROW][C]116[/C][C] 13[/C][C] 11.31[/C][C] 1.687[/C][/ROW]
[ROW][C]117[/C][C] 11[/C][C] 12.2[/C][C]-1.2[/C][/ROW]
[ROW][C]118[/C][C] 12[/C][C] 12.1[/C][C]-0.1047[/C][/ROW]
[ROW][C]119[/C][C] 11[/C][C] 11.9[/C][C]-0.9037[/C][/ROW]
[ROW][C]120[/C][C] 12[/C][C] 12.38[/C][C]-0.3766[/C][/ROW]
[ROW][C]121[/C][C] 12[/C][C] 11.96[/C][C] 0.04098[/C][/ROW]
[ROW][C]122[/C][C] 13[/C][C] 11.63[/C][C] 1.366[/C][/ROW]
[ROW][C]123[/C][C] 12[/C][C] 12.9[/C][C]-0.8996[/C][/ROW]
[ROW][C]124[/C][C] 13[/C][C] 12.35[/C][C] 0.6512[/C][/ROW]
[ROW][C]125[/C][C] 13[/C][C] 11.78[/C][C] 1.217[/C][/ROW]
[ROW][C]126[/C][C] 12[/C][C] 11.49[/C][C] 0.5139[/C][/ROW]
[ROW][C]127[/C][C] 12[/C][C] 11.96[/C][C] 0.04098[/C][/ROW]
[ROW][C]128[/C][C] 8[/C][C] 11.96[/C][C]-3.956[/C][/ROW]
[ROW][C]129[/C][C] 12[/C][C] 12.32[/C][C]-0.3211[/C][/ROW]
[ROW][C]130[/C][C] 13[/C][C] 11.58[/C][C] 1.418[/C][/ROW]
[ROW][C]131[/C][C] 10[/C][C] 12.36[/C][C]-2.361[/C][/ROW]
[ROW][C]132[/C][C] 8[/C][C] 11.61[/C][C]-3.609[/C][/ROW]
[ROW][C]133[/C][C] 12[/C][C] 12.2[/C][C]-0.2004[/C][/ROW]
[ROW][C]134[/C][C] 13[/C][C] 12.82[/C][C] 0.1809[/C][/ROW]
[ROW][C]135[/C][C] 12[/C][C] 12.55[/C][C]-0.5472[/C][/ROW]
[ROW][C]136[/C][C] 15[/C][C] 13.1[/C][C] 1.896[/C][/ROW]
[ROW][C]137[/C][C] 14[/C][C] 12.31[/C][C] 1.694[/C][/ROW]
[ROW][C]138[/C][C] 10[/C][C] 11.84[/C][C]-1.838[/C][/ROW]
[ROW][C]139[/C][C] 11[/C][C] 12.35[/C][C]-1.349[/C][/ROW]
[ROW][C]140[/C][C] 12[/C][C] 11.82[/C][C] 0.1769[/C][/ROW]
[ROW][C]141[/C][C] 10[/C][C] 12.73[/C][C]-2.726[/C][/ROW]
[ROW][C]142[/C][C] 14[/C][C] 12.55[/C][C] 1.45[/C][/ROW]
[ROW][C]143[/C][C] 10[/C][C] 12.17[/C][C]-2.173[/C][/ROW]
[ROW][C]144[/C][C] 15[/C][C] 12.17[/C][C] 2.827[/C][/ROW]
[ROW][C]145[/C][C] 11[/C][C] 11.94[/C][C]-0.9408[/C][/ROW]
[ROW][C]146[/C][C] 12[/C][C] 12.17[/C][C]-0.1727[/C][/ROW]
[ROW][C]147[/C][C] 9[/C][C] 11.96[/C][C]-2.959[/C][/ROW]
[ROW][C]148[/C][C] 12[/C][C] 12.32[/C][C]-0.3211[/C][/ROW]
[ROW][C]149[/C][C] 13[/C][C] 11.99[/C][C] 1.013[/C][/ROW]
[ROW][C]150[/C][C] 12[/C][C] 12.63[/C][C]-0.6306[/C][/ROW]
[ROW][C]151[/C][C] 9[/C][C] 11.64[/C][C]-2.637[/C][/ROW]
[ROW][C]152[/C][C] 12[/C][C] 11.96[/C][C] 0.04098[/C][/ROW]
[ROW][C]153[/C][C] 14[/C][C] 12.05[/C][C] 1.948[/C][/ROW]
[ROW][C]154[/C][C] 11[/C][C] 11.9[/C][C]-0.9037[/C][/ROW]
[ROW][C]155[/C][C] 12[/C][C] 11.81[/C][C] 0.1894[/C][/ROW]
[ROW][C]156[/C][C] 14[/C][C] 12.17[/C][C] 1.827[/C][/ROW]
[ROW][C]157[/C][C] 12[/C][C] 12.72[/C][C]-0.7234[/C][/ROW]
[ROW][C]158[/C][C] 15[/C][C] 12.4[/C][C] 2.598[/C][/ROW]
[ROW][C]159[/C][C] 11[/C][C] 12.25[/C][C]-1.253[/C][/ROW]
[ROW][C]160[/C][C] 12[/C][C] 11.81[/C][C] 0.1894[/C][/ROW]
[ROW][C]161[/C][C] 12[/C][C] 12.05[/C][C]-0.05203[/C][/ROW]
[ROW][C]162[/C][C] 10[/C][C] 11.84[/C][C]-1.838[/C][/ROW]
[ROW][C]163[/C][C] 12[/C][C] 11.93[/C][C] 0.06866[/C][/ROW]
[ROW][C]164[/C][C] 11[/C][C] 12.28[/C][C]-1.281[/C][/ROW]
[ROW][C]165[/C][C] 11[/C][C] 12.1[/C][C]-1.105[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298625&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298625&T=4

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	10	12.51	-2.51
2	13	11.51	1.486
3	14	12.11	1.893
4	12	12.08	-0.07972
5	12	12.23	-0.2254
6	13	11.96	1.041
7	13	13.25	-0.2492
8	13	12.11	0.8926
9	13	12.23	0.7747
10	14	12.05	1.948
11	14	12.36	1.639
12	12	11.49	0.5139
13	12	12.14	-0.1449
14	11	11.85	-0.8508
15	12	11.96	0.04098
16	14	13.28	0.7231
17	12	12.42	-0.417
18	11	11.78	-0.783
19	13	11.85	1.149
20	13	11.97	1.029
21	12	12.32	-0.3211
22	13	13.04	-0.03554
23	12	12.05	-0.05203
24	13	12.66	0.3418
25	11	12.05	-1.052
26	12	11.53	0.4738
27	12	12.1	-0.1047
28	13	12.18	0.8246
29	13	12.31	0.6914
30	10	11.61	-1.609
31	12	12.75	-0.7512
32	13	12.86	0.1405
33	13	11.82	1.18
34	10	11.63	-1.634
35	14	12.05	1.948
36	12	12.28	-0.2809
37	10	12.05	-2.055
38	10	12.05	-2.052
39	14	12.51	1.49
40	12	11.93	0.06866
41	14	12.48	1.518
42	10	12.4	-2.402
43	13	12.74	0.2611
44	12	12.31	-0.3086
45	12	12.25	-0.2531
46	13	12.1	0.8953
47	12	12.38	-0.3766
48	10	12.2	-2.2
49	14	12.2	1.8
50	15	12.2	2.8
51	14	12.75	1.249
52	8	11.31	-3.313
53	11	12.13	-1.132
54	10	12.04	-2.04
55	12	11.76	0.2449
56	14	12.05	1.948
57	12	11.73	0.2727
58	12	12.43	-0.4293
59	14	11.81	2.189
60	13	12.08	0.9203
61	13	11.93	1.069
62	13	11.9	1.096
63	12	11.76	0.2449
64	10	12.51	-2.51
65	14	11.88	2.124
66	11	12.32	-1.321
67	10	12.05	-2.052
68	13	12.08	0.9203
69	12	11.81	0.1894
70	12	11.11	0.8912
71	11	12.37	-1.374
72	10	12.4	-2.399
73	14	11.9	2.096
74	12	12	0.0006782
75	13	12.05	0.948
76	11	12.03	-1.027
77	10	11.65	-1.647
78	14	12.63	1.369
79	13	11.96	1.041
80	7	11.9	-4.904
81	13	11.9	1.096
82	13	12.43	0.5705
83	13	11.81	1.189
84	15	12.66	2.342
85	13	11.34	1.66
86	14	12.58	1.422
87	12	11.67	0.3282
88	13	12.9	0.1004
89	11	12.05	-1.052
90	12	12.25	-0.2531
91	14	12.4	1.598
92	13	12.35	0.6512
93	14	12.02	1.976
94	12	11.78	0.2172
95	12	12.4	-0.4016
96	13	12.38	0.6234
97	14	11.57	2.433
98	13	12.55	0.45
99	13	12.08	0.9203
100	12	12.61	-0.6055
101	10	12.37	-2.374
102	12	11.58	0.4183
103	13	12	1.001
104	12	11.88	0.1242
105	13	12.18	0.8246
106	12	12.2	-0.2004
107	12	12.32	-0.3211
108	12	12.5	-0.4973
109	11	11.78	-0.7828
110	12	11.86	0.1366
111	9	11.9	-2.904
112	14	12.28	1.719
113	12	11.69	0.31
114	13	12.75	0.2488
115	13	13.1	-0.1009
116	13	11.31	1.687
117	11	12.2	-1.2
118	12	12.1	-0.1047
119	11	11.9	-0.9037
120	12	12.38	-0.3766
121	12	11.96	0.04098
122	13	11.63	1.366
123	12	12.9	-0.8996
124	13	12.35	0.6512
125	13	11.78	1.217
126	12	11.49	0.5139
127	12	11.96	0.04098
128	8	11.96	-3.956
129	12	12.32	-0.3211
130	13	11.58	1.418
131	10	12.36	-2.361
132	8	11.61	-3.609
133	12	12.2	-0.2004
134	13	12.82	0.1809
135	12	12.55	-0.5472
136	15	13.1	1.896
137	14	12.31	1.694
138	10	11.84	-1.838
139	11	12.35	-1.349
140	12	11.82	0.1769
141	10	12.73	-2.726
142	14	12.55	1.45
143	10	12.17	-2.173
144	15	12.17	2.827
145	11	11.94	-0.9408
146	12	12.17	-0.1727
147	9	11.96	-2.959
148	12	12.32	-0.3211
149	13	11.99	1.013
150	12	12.63	-0.6306
151	9	11.64	-2.637
152	12	11.96	0.04098
153	14	12.05	1.948
154	11	11.9	-0.9037
155	12	11.81	0.1894
156	14	12.17	1.827
157	12	12.72	-0.7234
158	15	12.4	2.598
159	11	12.25	-1.253
160	12	11.81	0.1894
161	12	12.05	-0.05203
162	10	11.84	-1.838
163	12	11.93	0.06866
164	11	12.28	-1.281
165	11	12.1	-1.105

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
8	0.615	0.7699	0.385
9	0.4858	0.9717	0.5142
10	0.3598	0.7195	0.6402
11	0.553	0.8939	0.447
12	0.4518	0.9036	0.5482
13	0.3896	0.7791	0.6104
14	0.4432	0.8864	0.5568
15	0.3685	0.7371	0.6315
16	0.3198	0.6396	0.6802
17	0.2731	0.5462	0.7269
18	0.2546	0.5091	0.7454
19	0.2036	0.4073	0.7964
20	0.1564	0.3128	0.8436
21	0.1214	0.2428	0.8786
22	0.0859	0.1718	0.9141
23	0.06082	0.1216	0.9392
24	0.0412	0.0824	0.9588
25	0.04141	0.08281	0.9586
26	0.02759	0.05518	0.9724
27	0.01821	0.03642	0.9818
28	0.01187	0.02374	0.9881
29	0.007624	0.01525	0.9924
30	0.02051	0.04101	0.9795
31	0.01532	0.03064	0.9847
32	0.01008	0.02017	0.9899
33	0.007548	0.0151	0.9925
34	0.013	0.02601	0.987
35	0.01813	0.03625	0.9819
36	0.01273	0.02545	0.9873
37	0.02627	0.05254	0.9737
38	0.04453	0.08905	0.9555
39	0.04621	0.09241	0.9538
40	0.03381	0.06762	0.9662
41	0.03227	0.06454	0.9677
42	0.06022	0.1204	0.9398
43	0.04655	0.0931	0.9534
44	0.0357	0.07139	0.9643
45	0.02728	0.05456	0.9727
46	0.02154	0.04309	0.9785
47	0.01557	0.03114	0.9844
48	0.0251	0.0502	0.9749
49	0.03241	0.06483	0.9676
50	0.07316	0.1463	0.9268
51	0.06834	0.1367	0.9317
52	0.1657	0.3314	0.8343
53	0.1589	0.3179	0.8411
54	0.1799	0.3599	0.8201
55	0.1499	0.2999	0.8501
56	0.1722	0.3443	0.8278
57	0.1432	0.2864	0.8568
58	0.1204	0.2408	0.8796
59	0.1666	0.3331	0.8335
60	0.1487	0.2975	0.8513
61	0.1351	0.2701	0.8649
62	0.123	0.2459	0.877
63	0.1006	0.2012	0.8994
64	0.1507	0.3015	0.8493
65	0.1748	0.3497	0.8252
66	0.1795	0.3589	0.8205
67	0.2157	0.4314	0.7843
68	0.1955	0.3909	0.8045
69	0.1657	0.3315	0.8343
70	0.1478	0.2955	0.8522
71	0.1522	0.3044	0.8478
72	0.2137	0.4274	0.7863
73	0.2501	0.5001	0.7499
74	0.2154	0.4309	0.7846
75	0.1959	0.3918	0.8041
76	0.1806	0.3612	0.8194
77	0.1911	0.3823	0.8089
78	0.1857	0.3714	0.8143
79	0.1708	0.3416	0.8292
80	0.5751	0.8499	0.4249
81	0.5548	0.8905	0.4452
82	0.5162	0.9676	0.4838
83	0.4998	0.9996	0.5002
84	0.5709	0.8581	0.4291
85	0.5825	0.8349	0.4175
86	0.5833	0.8333	0.4167
87	0.5398	0.9204	0.4602
88	0.4952	0.9904	0.5048
89	0.4733	0.9466	0.5267
90	0.4295	0.8591	0.5705
91	0.4395	0.879	0.5605
92	0.4046	0.8092	0.5954
93	0.4408	0.8816	0.5592
94	0.399	0.7979	0.601
95	0.3579	0.7158	0.6421
96	0.3244	0.6487	0.6756
97	0.4124	0.8249	0.5876
98	0.3739	0.7478	0.6261
99	0.3511	0.7022	0.6489
100	0.3143	0.6287	0.6857
101	0.3757	0.7514	0.6243
102	0.3372	0.6745	0.6628
103	0.3157	0.6314	0.6843
104	0.2758	0.5517	0.7242
105	0.2527	0.5054	0.7473
106	0.2169	0.4338	0.7831
107	0.1845	0.3691	0.8155
108	0.1567	0.3134	0.8433
109	0.1362	0.2724	0.8638
110	0.1149	0.2298	0.8851
111	0.1851	0.3701	0.8149
112	0.205	0.41	0.795
113	0.1808	0.3616	0.8192
114	0.1525	0.3051	0.8475
115	0.1257	0.2514	0.8743
116	0.1479	0.2958	0.8521
117	0.136	0.2721	0.864
118	0.1112	0.2224	0.8888
119	0.09432	0.1886	0.9057
120	0.07553	0.151	0.9245
121	0.06092	0.1218	0.9391
122	0.06831	0.1366	0.9317
123	0.05775	0.1155	0.9422
124	0.04629	0.09258	0.9537
125	0.05079	0.1016	0.9492
126	0.05058	0.1012	0.9494
127	0.04127	0.08255	0.9587
128	0.1206	0.2412	0.8794
129	0.09696	0.1939	0.903
130	0.126	0.252	0.874
131	0.1587	0.3175	0.8413
132	0.2589	0.5178	0.7411
133	0.2138	0.4277	0.7862
134	0.1741	0.3482	0.8259
135	0.1519	0.3038	0.8481
136	0.1853	0.3705	0.8147
137	0.1915	0.3829	0.8085
138	0.1728	0.3457	0.8272
139	0.1601	0.3203	0.8399
140	0.1236	0.2473	0.8764
141	0.2787	0.5575	0.7212
142	0.2401	0.4801	0.7599
143	0.3588	0.7175	0.6412
144	0.4757	0.9513	0.5243
145	0.4307	0.8615	0.5693
146	0.3752	0.7505	0.6248
147	0.5609	0.8782	0.4391
148	0.6034	0.7933	0.3966
149	0.7173	0.5653	0.2827
150	0.6321	0.7359	0.3679
151	0.7973	0.4055	0.2027
152	0.7193	0.5613	0.2807
153	0.7052	0.5897	0.2948
154	0.9567	0.08666	0.04333
155	0.9485	0.103	0.05151
156	0.9154	0.1692	0.08458
157	0.8839	0.2323	0.1161

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 &  0.615 &  0.7699 &  0.385 \tabularnewline
9 &  0.4858 &  0.9717 &  0.5142 \tabularnewline
10 &  0.3598 &  0.7195 &  0.6402 \tabularnewline
11 &  0.553 &  0.8939 &  0.447 \tabularnewline
12 &  0.4518 &  0.9036 &  0.5482 \tabularnewline
13 &  0.3896 &  0.7791 &  0.6104 \tabularnewline
14 &  0.4432 &  0.8864 &  0.5568 \tabularnewline
15 &  0.3685 &  0.7371 &  0.6315 \tabularnewline
16 &  0.3198 &  0.6396 &  0.6802 \tabularnewline
17 &  0.2731 &  0.5462 &  0.7269 \tabularnewline
18 &  0.2546 &  0.5091 &  0.7454 \tabularnewline
19 &  0.2036 &  0.4073 &  0.7964 \tabularnewline
20 &  0.1564 &  0.3128 &  0.8436 \tabularnewline
21 &  0.1214 &  0.2428 &  0.8786 \tabularnewline
22 &  0.0859 &  0.1718 &  0.9141 \tabularnewline
23 &  0.06082 &  0.1216 &  0.9392 \tabularnewline
24 &  0.0412 &  0.0824 &  0.9588 \tabularnewline
25 &  0.04141 &  0.08281 &  0.9586 \tabularnewline
26 &  0.02759 &  0.05518 &  0.9724 \tabularnewline
27 &  0.01821 &  0.03642 &  0.9818 \tabularnewline
28 &  0.01187 &  0.02374 &  0.9881 \tabularnewline
29 &  0.007624 &  0.01525 &  0.9924 \tabularnewline
30 &  0.02051 &  0.04101 &  0.9795 \tabularnewline
31 &  0.01532 &  0.03064 &  0.9847 \tabularnewline
32 &  0.01008 &  0.02017 &  0.9899 \tabularnewline
33 &  0.007548 &  0.0151 &  0.9925 \tabularnewline
34 &  0.013 &  0.02601 &  0.987 \tabularnewline
35 &  0.01813 &  0.03625 &  0.9819 \tabularnewline
36 &  0.01273 &  0.02545 &  0.9873 \tabularnewline
37 &  0.02627 &  0.05254 &  0.9737 \tabularnewline
38 &  0.04453 &  0.08905 &  0.9555 \tabularnewline
39 &  0.04621 &  0.09241 &  0.9538 \tabularnewline
40 &  0.03381 &  0.06762 &  0.9662 \tabularnewline
41 &  0.03227 &  0.06454 &  0.9677 \tabularnewline
42 &  0.06022 &  0.1204 &  0.9398 \tabularnewline
43 &  0.04655 &  0.0931 &  0.9534 \tabularnewline
44 &  0.0357 &  0.07139 &  0.9643 \tabularnewline
45 &  0.02728 &  0.05456 &  0.9727 \tabularnewline
46 &  0.02154 &  0.04309 &  0.9785 \tabularnewline
47 &  0.01557 &  0.03114 &  0.9844 \tabularnewline
48 &  0.0251 &  0.0502 &  0.9749 \tabularnewline
49 &  0.03241 &  0.06483 &  0.9676 \tabularnewline
50 &  0.07316 &  0.1463 &  0.9268 \tabularnewline
51 &  0.06834 &  0.1367 &  0.9317 \tabularnewline
52 &  0.1657 &  0.3314 &  0.8343 \tabularnewline
53 &  0.1589 &  0.3179 &  0.8411 \tabularnewline
54 &  0.1799 &  0.3599 &  0.8201 \tabularnewline
55 &  0.1499 &  0.2999 &  0.8501 \tabularnewline
56 &  0.1722 &  0.3443 &  0.8278 \tabularnewline
57 &  0.1432 &  0.2864 &  0.8568 \tabularnewline
58 &  0.1204 &  0.2408 &  0.8796 \tabularnewline
59 &  0.1666 &  0.3331 &  0.8335 \tabularnewline
60 &  0.1487 &  0.2975 &  0.8513 \tabularnewline
61 &  0.1351 &  0.2701 &  0.8649 \tabularnewline
62 &  0.123 &  0.2459 &  0.877 \tabularnewline
63 &  0.1006 &  0.2012 &  0.8994 \tabularnewline
64 &  0.1507 &  0.3015 &  0.8493 \tabularnewline
65 &  0.1748 &  0.3497 &  0.8252 \tabularnewline
66 &  0.1795 &  0.3589 &  0.8205 \tabularnewline
67 &  0.2157 &  0.4314 &  0.7843 \tabularnewline
68 &  0.1955 &  0.3909 &  0.8045 \tabularnewline
69 &  0.1657 &  0.3315 &  0.8343 \tabularnewline
70 &  0.1478 &  0.2955 &  0.8522 \tabularnewline
71 &  0.1522 &  0.3044 &  0.8478 \tabularnewline
72 &  0.2137 &  0.4274 &  0.7863 \tabularnewline
73 &  0.2501 &  0.5001 &  0.7499 \tabularnewline
74 &  0.2154 &  0.4309 &  0.7846 \tabularnewline
75 &  0.1959 &  0.3918 &  0.8041 \tabularnewline
76 &  0.1806 &  0.3612 &  0.8194 \tabularnewline
77 &  0.1911 &  0.3823 &  0.8089 \tabularnewline
78 &  0.1857 &  0.3714 &  0.8143 \tabularnewline
79 &  0.1708 &  0.3416 &  0.8292 \tabularnewline
80 &  0.5751 &  0.8499 &  0.4249 \tabularnewline
81 &  0.5548 &  0.8905 &  0.4452 \tabularnewline
82 &  0.5162 &  0.9676 &  0.4838 \tabularnewline
83 &  0.4998 &  0.9996 &  0.5002 \tabularnewline
84 &  0.5709 &  0.8581 &  0.4291 \tabularnewline
85 &  0.5825 &  0.8349 &  0.4175 \tabularnewline
86 &  0.5833 &  0.8333 &  0.4167 \tabularnewline
87 &  0.5398 &  0.9204 &  0.4602 \tabularnewline
88 &  0.4952 &  0.9904 &  0.5048 \tabularnewline
89 &  0.4733 &  0.9466 &  0.5267 \tabularnewline
90 &  0.4295 &  0.8591 &  0.5705 \tabularnewline
91 &  0.4395 &  0.879 &  0.5605 \tabularnewline
92 &  0.4046 &  0.8092 &  0.5954 \tabularnewline
93 &  0.4408 &  0.8816 &  0.5592 \tabularnewline
94 &  0.399 &  0.7979 &  0.601 \tabularnewline
95 &  0.3579 &  0.7158 &  0.6421 \tabularnewline
96 &  0.3244 &  0.6487 &  0.6756 \tabularnewline
97 &  0.4124 &  0.8249 &  0.5876 \tabularnewline
98 &  0.3739 &  0.7478 &  0.6261 \tabularnewline
99 &  0.3511 &  0.7022 &  0.6489 \tabularnewline
100 &  0.3143 &  0.6287 &  0.6857 \tabularnewline
101 &  0.3757 &  0.7514 &  0.6243 \tabularnewline
102 &  0.3372 &  0.6745 &  0.6628 \tabularnewline
103 &  0.3157 &  0.6314 &  0.6843 \tabularnewline
104 &  0.2758 &  0.5517 &  0.7242 \tabularnewline
105 &  0.2527 &  0.5054 &  0.7473 \tabularnewline
106 &  0.2169 &  0.4338 &  0.7831 \tabularnewline
107 &  0.1845 &  0.3691 &  0.8155 \tabularnewline
108 &  0.1567 &  0.3134 &  0.8433 \tabularnewline
109 &  0.1362 &  0.2724 &  0.8638 \tabularnewline
110 &  0.1149 &  0.2298 &  0.8851 \tabularnewline
111 &  0.1851 &  0.3701 &  0.8149 \tabularnewline
112 &  0.205 &  0.41 &  0.795 \tabularnewline
113 &  0.1808 &  0.3616 &  0.8192 \tabularnewline
114 &  0.1525 &  0.3051 &  0.8475 \tabularnewline
115 &  0.1257 &  0.2514 &  0.8743 \tabularnewline
116 &  0.1479 &  0.2958 &  0.8521 \tabularnewline
117 &  0.136 &  0.2721 &  0.864 \tabularnewline
118 &  0.1112 &  0.2224 &  0.8888 \tabularnewline
119 &  0.09432 &  0.1886 &  0.9057 \tabularnewline
120 &  0.07553 &  0.151 &  0.9245 \tabularnewline
121 &  0.06092 &  0.1218 &  0.9391 \tabularnewline
122 &  0.06831 &  0.1366 &  0.9317 \tabularnewline
123 &  0.05775 &  0.1155 &  0.9422 \tabularnewline
124 &  0.04629 &  0.09258 &  0.9537 \tabularnewline
125 &  0.05079 &  0.1016 &  0.9492 \tabularnewline
126 &  0.05058 &  0.1012 &  0.9494 \tabularnewline
127 &  0.04127 &  0.08255 &  0.9587 \tabularnewline
128 &  0.1206 &  0.2412 &  0.8794 \tabularnewline
129 &  0.09696 &  0.1939 &  0.903 \tabularnewline
130 &  0.126 &  0.252 &  0.874 \tabularnewline
131 &  0.1587 &  0.3175 &  0.8413 \tabularnewline
132 &  0.2589 &  0.5178 &  0.7411 \tabularnewline
133 &  0.2138 &  0.4277 &  0.7862 \tabularnewline
134 &  0.1741 &  0.3482 &  0.8259 \tabularnewline
135 &  0.1519 &  0.3038 &  0.8481 \tabularnewline
136 &  0.1853 &  0.3705 &  0.8147 \tabularnewline
137 &  0.1915 &  0.3829 &  0.8085 \tabularnewline
138 &  0.1728 &  0.3457 &  0.8272 \tabularnewline
139 &  0.1601 &  0.3203 &  0.8399 \tabularnewline
140 &  0.1236 &  0.2473 &  0.8764 \tabularnewline
141 &  0.2787 &  0.5575 &  0.7212 \tabularnewline
142 &  0.2401 &  0.4801 &  0.7599 \tabularnewline
143 &  0.3588 &  0.7175 &  0.6412 \tabularnewline
144 &  0.4757 &  0.9513 &  0.5243 \tabularnewline
145 &  0.4307 &  0.8615 &  0.5693 \tabularnewline
146 &  0.3752 &  0.7505 &  0.6248 \tabularnewline
147 &  0.5609 &  0.8782 &  0.4391 \tabularnewline
148 &  0.6034 &  0.7933 &  0.3966 \tabularnewline
149 &  0.7173 &  0.5653 &  0.2827 \tabularnewline
150 &  0.6321 &  0.7359 &  0.3679 \tabularnewline
151 &  0.7973 &  0.4055 &  0.2027 \tabularnewline
152 &  0.7193 &  0.5613 &  0.2807 \tabularnewline
153 &  0.7052 &  0.5897 &  0.2948 \tabularnewline
154 &  0.9567 &  0.08666 &  0.04333 \tabularnewline
155 &  0.9485 &  0.103 &  0.05151 \tabularnewline
156 &  0.9154 &  0.1692 &  0.08458 \tabularnewline
157 &  0.8839 &  0.2323 &  0.1161 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298625&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] 0.615[/C][C] 0.7699[/C][C] 0.385[/C][/ROW]
[ROW][C]9[/C][C] 0.4858[/C][C] 0.9717[/C][C] 0.5142[/C][/ROW]
[ROW][C]10[/C][C] 0.3598[/C][C] 0.7195[/C][C] 0.6402[/C][/ROW]
[ROW][C]11[/C][C] 0.553[/C][C] 0.8939[/C][C] 0.447[/C][/ROW]
[ROW][C]12[/C][C] 0.4518[/C][C] 0.9036[/C][C] 0.5482[/C][/ROW]
[ROW][C]13[/C][C] 0.3896[/C][C] 0.7791[/C][C] 0.6104[/C][/ROW]
[ROW][C]14[/C][C] 0.4432[/C][C] 0.8864[/C][C] 0.5568[/C][/ROW]
[ROW][C]15[/C][C] 0.3685[/C][C] 0.7371[/C][C] 0.6315[/C][/ROW]
[ROW][C]16[/C][C] 0.3198[/C][C] 0.6396[/C][C] 0.6802[/C][/ROW]
[ROW][C]17[/C][C] 0.2731[/C][C] 0.5462[/C][C] 0.7269[/C][/ROW]
[ROW][C]18[/C][C] 0.2546[/C][C] 0.5091[/C][C] 0.7454[/C][/ROW]
[ROW][C]19[/C][C] 0.2036[/C][C] 0.4073[/C][C] 0.7964[/C][/ROW]
[ROW][C]20[/C][C] 0.1564[/C][C] 0.3128[/C][C] 0.8436[/C][/ROW]
[ROW][C]21[/C][C] 0.1214[/C][C] 0.2428[/C][C] 0.8786[/C][/ROW]
[ROW][C]22[/C][C] 0.0859[/C][C] 0.1718[/C][C] 0.9141[/C][/ROW]
[ROW][C]23[/C][C] 0.06082[/C][C] 0.1216[/C][C] 0.9392[/C][/ROW]
[ROW][C]24[/C][C] 0.0412[/C][C] 0.0824[/C][C] 0.9588[/C][/ROW]
[ROW][C]25[/C][C] 0.04141[/C][C] 0.08281[/C][C] 0.9586[/C][/ROW]
[ROW][C]26[/C][C] 0.02759[/C][C] 0.05518[/C][C] 0.9724[/C][/ROW]
[ROW][C]27[/C][C] 0.01821[/C][C] 0.03642[/C][C] 0.9818[/C][/ROW]
[ROW][C]28[/C][C] 0.01187[/C][C] 0.02374[/C][C] 0.9881[/C][/ROW]
[ROW][C]29[/C][C] 0.007624[/C][C] 0.01525[/C][C] 0.9924[/C][/ROW]
[ROW][C]30[/C][C] 0.02051[/C][C] 0.04101[/C][C] 0.9795[/C][/ROW]
[ROW][C]31[/C][C] 0.01532[/C][C] 0.03064[/C][C] 0.9847[/C][/ROW]
[ROW][C]32[/C][C] 0.01008[/C][C] 0.02017[/C][C] 0.9899[/C][/ROW]
[ROW][C]33[/C][C] 0.007548[/C][C] 0.0151[/C][C] 0.9925[/C][/ROW]
[ROW][C]34[/C][C] 0.013[/C][C] 0.02601[/C][C] 0.987[/C][/ROW]
[ROW][C]35[/C][C] 0.01813[/C][C] 0.03625[/C][C] 0.9819[/C][/ROW]
[ROW][C]36[/C][C] 0.01273[/C][C] 0.02545[/C][C] 0.9873[/C][/ROW]
[ROW][C]37[/C][C] 0.02627[/C][C] 0.05254[/C][C] 0.9737[/C][/ROW]
[ROW][C]38[/C][C] 0.04453[/C][C] 0.08905[/C][C] 0.9555[/C][/ROW]
[ROW][C]39[/C][C] 0.04621[/C][C] 0.09241[/C][C] 0.9538[/C][/ROW]
[ROW][C]40[/C][C] 0.03381[/C][C] 0.06762[/C][C] 0.9662[/C][/ROW]
[ROW][C]41[/C][C] 0.03227[/C][C] 0.06454[/C][C] 0.9677[/C][/ROW]
[ROW][C]42[/C][C] 0.06022[/C][C] 0.1204[/C][C] 0.9398[/C][/ROW]
[ROW][C]43[/C][C] 0.04655[/C][C] 0.0931[/C][C] 0.9534[/C][/ROW]
[ROW][C]44[/C][C] 0.0357[/C][C] 0.07139[/C][C] 0.9643[/C][/ROW]
[ROW][C]45[/C][C] 0.02728[/C][C] 0.05456[/C][C] 0.9727[/C][/ROW]
[ROW][C]46[/C][C] 0.02154[/C][C] 0.04309[/C][C] 0.9785[/C][/ROW]
[ROW][C]47[/C][C] 0.01557[/C][C] 0.03114[/C][C] 0.9844[/C][/ROW]
[ROW][C]48[/C][C] 0.0251[/C][C] 0.0502[/C][C] 0.9749[/C][/ROW]
[ROW][C]49[/C][C] 0.03241[/C][C] 0.06483[/C][C] 0.9676[/C][/ROW]
[ROW][C]50[/C][C] 0.07316[/C][C] 0.1463[/C][C] 0.9268[/C][/ROW]
[ROW][C]51[/C][C] 0.06834[/C][C] 0.1367[/C][C] 0.9317[/C][/ROW]
[ROW][C]52[/C][C] 0.1657[/C][C] 0.3314[/C][C] 0.8343[/C][/ROW]
[ROW][C]53[/C][C] 0.1589[/C][C] 0.3179[/C][C] 0.8411[/C][/ROW]
[ROW][C]54[/C][C] 0.1799[/C][C] 0.3599[/C][C] 0.8201[/C][/ROW]
[ROW][C]55[/C][C] 0.1499[/C][C] 0.2999[/C][C] 0.8501[/C][/ROW]
[ROW][C]56[/C][C] 0.1722[/C][C] 0.3443[/C][C] 0.8278[/C][/ROW]
[ROW][C]57[/C][C] 0.1432[/C][C] 0.2864[/C][C] 0.8568[/C][/ROW]
[ROW][C]58[/C][C] 0.1204[/C][C] 0.2408[/C][C] 0.8796[/C][/ROW]
[ROW][C]59[/C][C] 0.1666[/C][C] 0.3331[/C][C] 0.8335[/C][/ROW]
[ROW][C]60[/C][C] 0.1487[/C][C] 0.2975[/C][C] 0.8513[/C][/ROW]
[ROW][C]61[/C][C] 0.1351[/C][C] 0.2701[/C][C] 0.8649[/C][/ROW]
[ROW][C]62[/C][C] 0.123[/C][C] 0.2459[/C][C] 0.877[/C][/ROW]
[ROW][C]63[/C][C] 0.1006[/C][C] 0.2012[/C][C] 0.8994[/C][/ROW]
[ROW][C]64[/C][C] 0.1507[/C][C] 0.3015[/C][C] 0.8493[/C][/ROW]
[ROW][C]65[/C][C] 0.1748[/C][C] 0.3497[/C][C] 0.8252[/C][/ROW]
[ROW][C]66[/C][C] 0.1795[/C][C] 0.3589[/C][C] 0.8205[/C][/ROW]
[ROW][C]67[/C][C] 0.2157[/C][C] 0.4314[/C][C] 0.7843[/C][/ROW]
[ROW][C]68[/C][C] 0.1955[/C][C] 0.3909[/C][C] 0.8045[/C][/ROW]
[ROW][C]69[/C][C] 0.1657[/C][C] 0.3315[/C][C] 0.8343[/C][/ROW]
[ROW][C]70[/C][C] 0.1478[/C][C] 0.2955[/C][C] 0.8522[/C][/ROW]
[ROW][C]71[/C][C] 0.1522[/C][C] 0.3044[/C][C] 0.8478[/C][/ROW]
[ROW][C]72[/C][C] 0.2137[/C][C] 0.4274[/C][C] 0.7863[/C][/ROW]
[ROW][C]73[/C][C] 0.2501[/C][C] 0.5001[/C][C] 0.7499[/C][/ROW]
[ROW][C]74[/C][C] 0.2154[/C][C] 0.4309[/C][C] 0.7846[/C][/ROW]
[ROW][C]75[/C][C] 0.1959[/C][C] 0.3918[/C][C] 0.8041[/C][/ROW]
[ROW][C]76[/C][C] 0.1806[/C][C] 0.3612[/C][C] 0.8194[/C][/ROW]
[ROW][C]77[/C][C] 0.1911[/C][C] 0.3823[/C][C] 0.8089[/C][/ROW]
[ROW][C]78[/C][C] 0.1857[/C][C] 0.3714[/C][C] 0.8143[/C][/ROW]
[ROW][C]79[/C][C] 0.1708[/C][C] 0.3416[/C][C] 0.8292[/C][/ROW]
[ROW][C]80[/C][C] 0.5751[/C][C] 0.8499[/C][C] 0.4249[/C][/ROW]
[ROW][C]81[/C][C] 0.5548[/C][C] 0.8905[/C][C] 0.4452[/C][/ROW]
[ROW][C]82[/C][C] 0.5162[/C][C] 0.9676[/C][C] 0.4838[/C][/ROW]
[ROW][C]83[/C][C] 0.4998[/C][C] 0.9996[/C][C] 0.5002[/C][/ROW]
[ROW][C]84[/C][C] 0.5709[/C][C] 0.8581[/C][C] 0.4291[/C][/ROW]
[ROW][C]85[/C][C] 0.5825[/C][C] 0.8349[/C][C] 0.4175[/C][/ROW]
[ROW][C]86[/C][C] 0.5833[/C][C] 0.8333[/C][C] 0.4167[/C][/ROW]
[ROW][C]87[/C][C] 0.5398[/C][C] 0.9204[/C][C] 0.4602[/C][/ROW]
[ROW][C]88[/C][C] 0.4952[/C][C] 0.9904[/C][C] 0.5048[/C][/ROW]
[ROW][C]89[/C][C] 0.4733[/C][C] 0.9466[/C][C] 0.5267[/C][/ROW]
[ROW][C]90[/C][C] 0.4295[/C][C] 0.8591[/C][C] 0.5705[/C][/ROW]
[ROW][C]91[/C][C] 0.4395[/C][C] 0.879[/C][C] 0.5605[/C][/ROW]
[ROW][C]92[/C][C] 0.4046[/C][C] 0.8092[/C][C] 0.5954[/C][/ROW]
[ROW][C]93[/C][C] 0.4408[/C][C] 0.8816[/C][C] 0.5592[/C][/ROW]
[ROW][C]94[/C][C] 0.399[/C][C] 0.7979[/C][C] 0.601[/C][/ROW]
[ROW][C]95[/C][C] 0.3579[/C][C] 0.7158[/C][C] 0.6421[/C][/ROW]
[ROW][C]96[/C][C] 0.3244[/C][C] 0.6487[/C][C] 0.6756[/C][/ROW]
[ROW][C]97[/C][C] 0.4124[/C][C] 0.8249[/C][C] 0.5876[/C][/ROW]
[ROW][C]98[/C][C] 0.3739[/C][C] 0.7478[/C][C] 0.6261[/C][/ROW]
[ROW][C]99[/C][C] 0.3511[/C][C] 0.7022[/C][C] 0.6489[/C][/ROW]
[ROW][C]100[/C][C] 0.3143[/C][C] 0.6287[/C][C] 0.6857[/C][/ROW]
[ROW][C]101[/C][C] 0.3757[/C][C] 0.7514[/C][C] 0.6243[/C][/ROW]
[ROW][C]102[/C][C] 0.3372[/C][C] 0.6745[/C][C] 0.6628[/C][/ROW]
[ROW][C]103[/C][C] 0.3157[/C][C] 0.6314[/C][C] 0.6843[/C][/ROW]
[ROW][C]104[/C][C] 0.2758[/C][C] 0.5517[/C][C] 0.7242[/C][/ROW]
[ROW][C]105[/C][C] 0.2527[/C][C] 0.5054[/C][C] 0.7473[/C][/ROW]
[ROW][C]106[/C][C] 0.2169[/C][C] 0.4338[/C][C] 0.7831[/C][/ROW]
[ROW][C]107[/C][C] 0.1845[/C][C] 0.3691[/C][C] 0.8155[/C][/ROW]
[ROW][C]108[/C][C] 0.1567[/C][C] 0.3134[/C][C] 0.8433[/C][/ROW]
[ROW][C]109[/C][C] 0.1362[/C][C] 0.2724[/C][C] 0.8638[/C][/ROW]
[ROW][C]110[/C][C] 0.1149[/C][C] 0.2298[/C][C] 0.8851[/C][/ROW]
[ROW][C]111[/C][C] 0.1851[/C][C] 0.3701[/C][C] 0.8149[/C][/ROW]
[ROW][C]112[/C][C] 0.205[/C][C] 0.41[/C][C] 0.795[/C][/ROW]
[ROW][C]113[/C][C] 0.1808[/C][C] 0.3616[/C][C] 0.8192[/C][/ROW]
[ROW][C]114[/C][C] 0.1525[/C][C] 0.3051[/C][C] 0.8475[/C][/ROW]
[ROW][C]115[/C][C] 0.1257[/C][C] 0.2514[/C][C] 0.8743[/C][/ROW]
[ROW][C]116[/C][C] 0.1479[/C][C] 0.2958[/C][C] 0.8521[/C][/ROW]
[ROW][C]117[/C][C] 0.136[/C][C] 0.2721[/C][C] 0.864[/C][/ROW]
[ROW][C]118[/C][C] 0.1112[/C][C] 0.2224[/C][C] 0.8888[/C][/ROW]
[ROW][C]119[/C][C] 0.09432[/C][C] 0.1886[/C][C] 0.9057[/C][/ROW]
[ROW][C]120[/C][C] 0.07553[/C][C] 0.151[/C][C] 0.9245[/C][/ROW]
[ROW][C]121[/C][C] 0.06092[/C][C] 0.1218[/C][C] 0.9391[/C][/ROW]
[ROW][C]122[/C][C] 0.06831[/C][C] 0.1366[/C][C] 0.9317[/C][/ROW]
[ROW][C]123[/C][C] 0.05775[/C][C] 0.1155[/C][C] 0.9422[/C][/ROW]
[ROW][C]124[/C][C] 0.04629[/C][C] 0.09258[/C][C] 0.9537[/C][/ROW]
[ROW][C]125[/C][C] 0.05079[/C][C] 0.1016[/C][C] 0.9492[/C][/ROW]
[ROW][C]126[/C][C] 0.05058[/C][C] 0.1012[/C][C] 0.9494[/C][/ROW]
[ROW][C]127[/C][C] 0.04127[/C][C] 0.08255[/C][C] 0.9587[/C][/ROW]
[ROW][C]128[/C][C] 0.1206[/C][C] 0.2412[/C][C] 0.8794[/C][/ROW]
[ROW][C]129[/C][C] 0.09696[/C][C] 0.1939[/C][C] 0.903[/C][/ROW]
[ROW][C]130[/C][C] 0.126[/C][C] 0.252[/C][C] 0.874[/C][/ROW]
[ROW][C]131[/C][C] 0.1587[/C][C] 0.3175[/C][C] 0.8413[/C][/ROW]
[ROW][C]132[/C][C] 0.2589[/C][C] 0.5178[/C][C] 0.7411[/C][/ROW]
[ROW][C]133[/C][C] 0.2138[/C][C] 0.4277[/C][C] 0.7862[/C][/ROW]
[ROW][C]134[/C][C] 0.1741[/C][C] 0.3482[/C][C] 0.8259[/C][/ROW]
[ROW][C]135[/C][C] 0.1519[/C][C] 0.3038[/C][C] 0.8481[/C][/ROW]
[ROW][C]136[/C][C] 0.1853[/C][C] 0.3705[/C][C] 0.8147[/C][/ROW]
[ROW][C]137[/C][C] 0.1915[/C][C] 0.3829[/C][C] 0.8085[/C][/ROW]
[ROW][C]138[/C][C] 0.1728[/C][C] 0.3457[/C][C] 0.8272[/C][/ROW]
[ROW][C]139[/C][C] 0.1601[/C][C] 0.3203[/C][C] 0.8399[/C][/ROW]
[ROW][C]140[/C][C] 0.1236[/C][C] 0.2473[/C][C] 0.8764[/C][/ROW]
[ROW][C]141[/C][C] 0.2787[/C][C] 0.5575[/C][C] 0.7212[/C][/ROW]
[ROW][C]142[/C][C] 0.2401[/C][C] 0.4801[/C][C] 0.7599[/C][/ROW]
[ROW][C]143[/C][C] 0.3588[/C][C] 0.7175[/C][C] 0.6412[/C][/ROW]
[ROW][C]144[/C][C] 0.4757[/C][C] 0.9513[/C][C] 0.5243[/C][/ROW]
[ROW][C]145[/C][C] 0.4307[/C][C] 0.8615[/C][C] 0.5693[/C][/ROW]
[ROW][C]146[/C][C] 0.3752[/C][C] 0.7505[/C][C] 0.6248[/C][/ROW]
[ROW][C]147[/C][C] 0.5609[/C][C] 0.8782[/C][C] 0.4391[/C][/ROW]
[ROW][C]148[/C][C] 0.6034[/C][C] 0.7933[/C][C] 0.3966[/C][/ROW]
[ROW][C]149[/C][C] 0.7173[/C][C] 0.5653[/C][C] 0.2827[/C][/ROW]
[ROW][C]150[/C][C] 0.6321[/C][C] 0.7359[/C][C] 0.3679[/C][/ROW]
[ROW][C]151[/C][C] 0.7973[/C][C] 0.4055[/C][C] 0.2027[/C][/ROW]
[ROW][C]152[/C][C] 0.7193[/C][C] 0.5613[/C][C] 0.2807[/C][/ROW]
[ROW][C]153[/C][C] 0.7052[/C][C] 0.5897[/C][C] 0.2948[/C][/ROW]
[ROW][C]154[/C][C] 0.9567[/C][C] 0.08666[/C][C] 0.04333[/C][/ROW]
[ROW][C]155[/C][C] 0.9485[/C][C] 0.103[/C][C] 0.05151[/C][/ROW]
[ROW][C]156[/C][C] 0.9154[/C][C] 0.1692[/C][C] 0.08458[/C][/ROW]
[ROW][C]157[/C][C] 0.8839[/C][C] 0.2323[/C][C] 0.1161[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298625&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298625&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	0.615	0.7699	0.385
9	0.4858	0.9717	0.5142
10	0.3598	0.7195	0.6402
11	0.553	0.8939	0.447
12	0.4518	0.9036	0.5482
13	0.3896	0.7791	0.6104
14	0.4432	0.8864	0.5568
15	0.3685	0.7371	0.6315
16	0.3198	0.6396	0.6802
17	0.2731	0.5462	0.7269
18	0.2546	0.5091	0.7454
19	0.2036	0.4073	0.7964
20	0.1564	0.3128	0.8436
21	0.1214	0.2428	0.8786
22	0.0859	0.1718	0.9141
23	0.06082	0.1216	0.9392
24	0.0412	0.0824	0.9588
25	0.04141	0.08281	0.9586
26	0.02759	0.05518	0.9724
27	0.01821	0.03642	0.9818
28	0.01187	0.02374	0.9881
29	0.007624	0.01525	0.9924
30	0.02051	0.04101	0.9795
31	0.01532	0.03064	0.9847
32	0.01008	0.02017	0.9899
33	0.007548	0.0151	0.9925
34	0.013	0.02601	0.987
35	0.01813	0.03625	0.9819
36	0.01273	0.02545	0.9873
37	0.02627	0.05254	0.9737
38	0.04453	0.08905	0.9555
39	0.04621	0.09241	0.9538
40	0.03381	0.06762	0.9662
41	0.03227	0.06454	0.9677
42	0.06022	0.1204	0.9398
43	0.04655	0.0931	0.9534
44	0.0357	0.07139	0.9643
45	0.02728	0.05456	0.9727
46	0.02154	0.04309	0.9785
47	0.01557	0.03114	0.9844
48	0.0251	0.0502	0.9749
49	0.03241	0.06483	0.9676
50	0.07316	0.1463	0.9268
51	0.06834	0.1367	0.9317
52	0.1657	0.3314	0.8343
53	0.1589	0.3179	0.8411
54	0.1799	0.3599	0.8201
55	0.1499	0.2999	0.8501
56	0.1722	0.3443	0.8278
57	0.1432	0.2864	0.8568
58	0.1204	0.2408	0.8796
59	0.1666	0.3331	0.8335
60	0.1487	0.2975	0.8513
61	0.1351	0.2701	0.8649
62	0.123	0.2459	0.877
63	0.1006	0.2012	0.8994
64	0.1507	0.3015	0.8493
65	0.1748	0.3497	0.8252
66	0.1795	0.3589	0.8205
67	0.2157	0.4314	0.7843
68	0.1955	0.3909	0.8045
69	0.1657	0.3315	0.8343
70	0.1478	0.2955	0.8522
71	0.1522	0.3044	0.8478
72	0.2137	0.4274	0.7863
73	0.2501	0.5001	0.7499
74	0.2154	0.4309	0.7846
75	0.1959	0.3918	0.8041
76	0.1806	0.3612	0.8194
77	0.1911	0.3823	0.8089
78	0.1857	0.3714	0.8143
79	0.1708	0.3416	0.8292
80	0.5751	0.8499	0.4249
81	0.5548	0.8905	0.4452
82	0.5162	0.9676	0.4838
83	0.4998	0.9996	0.5002
84	0.5709	0.8581	0.4291
85	0.5825	0.8349	0.4175
86	0.5833	0.8333	0.4167
87	0.5398	0.9204	0.4602
88	0.4952	0.9904	0.5048
89	0.4733	0.9466	0.5267
90	0.4295	0.8591	0.5705
91	0.4395	0.879	0.5605
92	0.4046	0.8092	0.5954
93	0.4408	0.8816	0.5592
94	0.399	0.7979	0.601
95	0.3579	0.7158	0.6421
96	0.3244	0.6487	0.6756
97	0.4124	0.8249	0.5876
98	0.3739	0.7478	0.6261
99	0.3511	0.7022	0.6489
100	0.3143	0.6287	0.6857
101	0.3757	0.7514	0.6243
102	0.3372	0.6745	0.6628
103	0.3157	0.6314	0.6843
104	0.2758	0.5517	0.7242
105	0.2527	0.5054	0.7473
106	0.2169	0.4338	0.7831
107	0.1845	0.3691	0.8155
108	0.1567	0.3134	0.8433
109	0.1362	0.2724	0.8638
110	0.1149	0.2298	0.8851
111	0.1851	0.3701	0.8149
112	0.205	0.41	0.795
113	0.1808	0.3616	0.8192
114	0.1525	0.3051	0.8475
115	0.1257	0.2514	0.8743
116	0.1479	0.2958	0.8521
117	0.136	0.2721	0.864
118	0.1112	0.2224	0.8888
119	0.09432	0.1886	0.9057
120	0.07553	0.151	0.9245
121	0.06092	0.1218	0.9391
122	0.06831	0.1366	0.9317
123	0.05775	0.1155	0.9422
124	0.04629	0.09258	0.9537
125	0.05079	0.1016	0.9492
126	0.05058	0.1012	0.9494
127	0.04127	0.08255	0.9587
128	0.1206	0.2412	0.8794
129	0.09696	0.1939	0.903
130	0.126	0.252	0.874
131	0.1587	0.3175	0.8413
132	0.2589	0.5178	0.7411
133	0.2138	0.4277	0.7862
134	0.1741	0.3482	0.8259
135	0.1519	0.3038	0.8481
136	0.1853	0.3705	0.8147
137	0.1915	0.3829	0.8085
138	0.1728	0.3457	0.8272
139	0.1601	0.3203	0.8399
140	0.1236	0.2473	0.8764
141	0.2787	0.5575	0.7212
142	0.2401	0.4801	0.7599
143	0.3588	0.7175	0.6412
144	0.4757	0.9513	0.5243
145	0.4307	0.8615	0.5693
146	0.3752	0.7505	0.6248
147	0.5609	0.8782	0.4391
148	0.6034	0.7933	0.3966
149	0.7173	0.5653	0.2827
150	0.6321	0.7359	0.3679
151	0.7973	0.4055	0.2027
152	0.7193	0.5613	0.2807
153	0.7052	0.5897	0.2948
154	0.9567	0.08666	0.04333
155	0.9485	0.103	0.05151
156	0.9154	0.1692	0.08458
157	0.8839	0.2323	0.1161

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	12	0.08	NOK
10% type I error level	28	0.186667	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 & 12 & 0.08 & NOK \tabularnewline
10% type I error level & 28 & 0.186667 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298625&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]12[/C][C]0.08[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]28[/C][C]0.186667[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298625&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298625&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	12	0.08	NOK
10% type I error level	28	0.186667	NOK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 0.25369, df1 = 2, df2 = 158, p-value = 0.7762
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.8619, df1 = 8, df2 = 152, p-value = 0.06993
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.14892, df1 = 2, df2 = 158, p-value = 0.8618

\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 = 0.25369, df1 = 2, df2 = 158, p-value = 0.7762
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.8619, df1 = 8, df2 = 152, p-value = 0.06993
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.14892, df1 = 2, df2 = 158, p-value = 0.8618
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298625&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 = 0.25369, df1 = 2, df2 = 158, p-value = 0.7762
[/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 = 1.8619, df1 = 8, df2 = 152, p-value = 0.06993
[/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 = 0.14892, df1 = 2, df2 = 158, p-value = 0.8618
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298625&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298625&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 = 0.25369, df1 = 2, df2 = 158, p-value = 0.7762
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.8619, df1 = 8, df2 = 152, p-value = 0.06993
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.14892, df1 = 2, df2 = 158, p-value = 0.8618

Variance Inflation Factors (Multicollinearity)
> vif IVHB1 IVHB2 IVHB3 IVHB4 1.031368 1.038313 1.053205 1.041045

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
   IVHB1    IVHB2    IVHB3    IVHB4 
1.031368 1.038313 1.053205 1.041045 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298625&T=8

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
   IVHB1    IVHB2    IVHB3    IVHB4 
1.031368 1.038313 1.053205 1.041045 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298625&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298625&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 IVHB1 IVHB2 IVHB3 IVHB4 1.031368 1.038313 1.053205 1.041045

Figure 1

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Parameters (Session):

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

Parameters (R input):

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

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

library(lattice)
library(lmtest)
library(car)
library(MASS)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
mywarning <- ''
par1 <- as.numeric(par1)
if(is.na(par1)) {
par1 <- 1
mywarning = 'Warning: you did not specify the column number of the endogenous series! The first column was selected by default.'
}
if (par4=='') par4 <- 0
par4 <- as.numeric(par4)
if (par5=='') par5 <- 0
par5 <- as.numeric(par5)
x <- na.omit(t(y))
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'Seasonal Differences (s=12)'){
(n <- n - 12)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B12)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+12,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'First and Seasonal Differences (s=12)'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
(n <- n - 12)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B12)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+12,j] - x[i,j]
}
}
x <- x2
}
if(par4 > 0) {
x2 <- array(0, dim=c(n-par4,par4), dimnames=list(1:(n-par4), paste(colnames(x)[par1],'(t-',1:par4,')',sep='')))
for (i in 1:(n-par4)) {
for (j in 1:par4) {
x2[i,j] <- x[i+par4-j,par1]
}
}
x <- cbind(x[(par4+1):n,], x2)
n <- n - par4
}
if(par5 > 0) {
x2 <- array(0, dim=c(n-par5*12,par5), dimnames=list(1:(n-par5*12), paste(colnames(x)[par1],'(t-',1:par5,'s)',sep='')))
for (i in 1:(n-par5*12)) {
for (j in 1:par5) {
x2[i,j] <- x[i+par5*12-j*12,par1]
}
}
x <- cbind(x[(par5*12+1):n,], x2)
n <- n - par5*12
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
(k <- length(x[n,]))
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
print(x)
(k <- length(x[n,]))
head(x)
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
sresid <- studres(mylm)
hist(sresid, freq=FALSE, main='Distribution of Studentized Residuals')
xfit<-seq(min(sresid),max(sresid),length=40)
yfit<-dnorm(xfit)
lines(xfit, yfit)
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqPlot(mylm, main='QQ Plot')
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
print(z)
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, mywarning)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Multiple Linear Regression - Ordinary Least Squares', 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,formatC(signif(mysum$coefficients[i,1],5),format='g',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,2],5),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,3],4),format='e',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4],4),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4]/2,4),format='g',flag=' '))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a,formatC(signif(sqrt(mysum$r.squared),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$adj.r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a,formatC(signif(mysum$fstatistic[1],6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[2],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[3],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a,formatC(signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a,formatC(signif(mysum$sigma,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a,formatC(signif(sum(myerror*myerror),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
myr <- as.numeric(mysum$resid)
myr
if(n < 200) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,formatC(signif(x[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(x[i]-mysum$resid[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$resid[i],6),format='g',flag=' '))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,1],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,2],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,3],6),format='g',flag=' '))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant1,6))
a<-table.element(a,formatC(signif(numsignificant1/numgqtests,6),format='g',flag=' '))
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant5,6))
a<-table.element(a,signif(numsignificant5/numgqtests,6))
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant10,6))
a<-table.element(a,signif(numsignificant10/numgqtests,6))
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}
}
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of fitted values',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_fitted <- resettest(mylm,power=2:3,type='fitted')
a<-table.element(a,paste('',RC.texteval('reset_test_fitted'),'
',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of regressors',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_regressors <- resettest(mylm,power=2:3,type='regressor')
a<-table.element(a,paste('',RC.texteval('reset_test_regressors'),'
',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of principal components',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_principal_components <- resettest(mylm,power=2:3,type='princomp')
a<-table.element(a,paste('',RC.texteval('reset_test_principal_components'),'
',sep=''))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable8.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Variance Inflation Factors (Multicollinearity)',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
vif <- vif(mylm)
a<-table.element(a,paste('',RC.texteval('vif'),'
',sep=''))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable9.tab')

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code