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R Software Module

rwasp_multipleregression.wasp

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Multiple Regression

Date of computation

Wed, 14 Dec 2016 15:17:43 +0100

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2016/Dec/14/t1481725514f26mj0msqtkk3v2.htm/, Retrieved Fri, 17 May 2024 16:45:45 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=299490, Retrieved Fri, 17 May 2024 16:45:45 +0000

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

-       [Multiple Regression] [] [2016-12-14 14:17:43] [6e17bb30248b72d8119c893128a7a697] [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=299490&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=299490&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=299490&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
ECSUM[t] = + 9.46512 + 0.599337EP1[t] + 0.115934EP2[t] + 0.208572EP4[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
ECSUM[t] =  +  9.46512 +  0.599337EP1[t] +  0.115934EP2[t] +  0.208572EP4[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=299490&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]ECSUM[t] =  +  9.46512 +  0.599337EP1[t] +  0.115934EP2[t] +  0.208572EP4[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=299490&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=299490&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
ECSUM[t] = + 9.46512 + 0.599337EP1[t] + 0.115934EP2[t] + 0.208572EP4[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)	+9.465	1.653	+5.7250e+00	4.808e-08	2.404e-08
EP1	+0.5993	0.4085	+1.4670e+00	0.1443	0.07213
EP2	+0.1159	0.3819	+3.0360e-01	0.7618	0.3809
EP4	+0.2086	0.1851	+1.1270e+00	0.2615	0.1307

\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) & +9.465 &  1.653 & +5.7250e+00 &  4.808e-08 &  2.404e-08 \tabularnewline
EP1 & +0.5993 &  0.4085 & +1.4670e+00 &  0.1443 &  0.07213 \tabularnewline
EP2 & +0.1159 &  0.3819 & +3.0360e-01 &  0.7618 &  0.3809 \tabularnewline
EP4 & +0.2086 &  0.1851 & +1.1270e+00 &  0.2615 &  0.1307 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=299490&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]+9.465[/C][C] 1.653[/C][C]+5.7250e+00[/C][C] 4.808e-08[/C][C] 2.404e-08[/C][/ROW]
[ROW][C]EP1[/C][C]+0.5993[/C][C] 0.4085[/C][C]+1.4670e+00[/C][C] 0.1443[/C][C] 0.07213[/C][/ROW]
[ROW][C]EP2[/C][C]+0.1159[/C][C] 0.3819[/C][C]+3.0360e-01[/C][C] 0.7618[/C][C] 0.3809[/C][/ROW]
[ROW][C]EP4[/C][C]+0.2086[/C][C] 0.1851[/C][C]+1.1270e+00[/C][C] 0.2615[/C][C] 0.1307[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=299490&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=299490&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)	+9.465	1.653	+5.7250e+00	4.808e-08	2.404e-08
EP1	+0.5993	0.4085	+1.4670e+00	0.1443	0.07213
EP2	+0.1159	0.3819	+3.0360e-01	0.7618	0.3809
EP4	+0.2086	0.1851	+1.1270e+00	0.2615	0.1307

Multiple Linear Regression - Regression Statistics
Multiple R	0.176
R-squared	0.03099
Adjusted R-squared	0.01326
F-TEST (value)	1.748
F-TEST (DF numerator)	3
F-TEST (DF denominator)	164
p-value	0.1592
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.908
Sum Squared Residuals	1387

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.176 \tabularnewline
R-squared &  0.03099 \tabularnewline
Adjusted R-squared &  0.01326 \tabularnewline
F-TEST (value) &  1.748 \tabularnewline
F-TEST (DF numerator) & 3 \tabularnewline
F-TEST (DF denominator) & 164 \tabularnewline
p-value &  0.1592 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  2.908 \tabularnewline
Sum Squared Residuals &  1387 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=299490&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.176[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.03099[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.01326[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 1.748[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]3[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]164[/C][/ROW]
[ROW][C]p-value[/C][C] 0.1592[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 2.908[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 1387[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=299490&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=299490&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	0.176
R-squared	0.03099
Adjusted R-squared	0.01326
F-TEST (value)	1.748
F-TEST (DF numerator)	3
F-TEST (DF denominator)	164
p-value	0.1592
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.908
Sum Squared Residuals	1387

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	17	13.25	3.75
2	11	12.65	-1.654
3	12	13.25	-1.25
4	12	13.34	-1.343
5	13	13.13	-0.1341
6	17	13.88	3.124
7	17	13.02	3.982
8	12	13.25	-1.25
9	16	13.25	2.75
10	15	13.46	1.541
11	11	12.65	-1.651
12	16	11.96	4.038
13	15	13.13	1.866
14	16	13.48	2.515
15	15	13.46	1.541
16	11	12.65	-1.651
17	8	13.34	-5.343
18	10	14.08	-4.084
19	14	13.46	0.5414
20	16	12.65	3.349
21	15	13.28	1.724
22	15	11.94	3.065
23	12	13.46	-1.459
24	18	12.95	5.048
25	10	13.25	-3.25
26	17	13.16	3.84
27	12	13.46	-1.459
28	13	13.55	-0.5513
29	9	13.25	-4.25
30	11	13.88	-2.876
31	10	13.25	-3.25
32	15	13.25	1.75
33	15	13.25	1.75
34	13	13.13	-0.1341
35	13	13.55	-0.5513
36	9	13.16	-4.16
37	14	12.74	1.257
38	14	13.88	0.1242
39	11	13.46	-2.459
40	15	13.46	1.541
41	12	13.25	-1.25
42	11	13.25	-2.25
43	12	13.25	-1.25
44	15	14.08	0.9157
45	13	13.25	-0.25
46	11	13.25	-2.25
47	10	13.13	-3.134
48	16	12.65	3.349
49	13	13.25	-0.25
50	15	13.46	1.541
51	14	12.74	1.257
52	12	13.46	-1.459
53	10	12.14	-2.144
54	12	12.84	-0.836
55	9	11.82	-2.82
56	15	13.34	1.657
57	16	13.46	2.541
58	12	13.25	-1.25
59	11	13.55	-2.551
60	11	13.67	-2.667
61	9	13.25	-4.25
62	13	13.25	-0.25
63	17	13.46	3.541
64	18	12.53	5.465
65	15	13.67	1.333
66	12	12.95	-0.9519
67	18	13.88	4.124
68	11	11.73	-0.73
69	6	13.04	-7.045
70	10	13.46	-3.459
71	19	13.25	5.75
72	16	12.42	3.581
73	12	13.25	-1.25
74	10	11.64	-1.637
75	14	13.34	0.6573
76	12	11.82	0.1805
77	13	12.65	0.3493
78	16	12.53	3.465
79	18	13.25	4.75
80	13	13.25	-0.25
81	15	12.53	2.465
82	16	12.95	3.048
83	9	13.25	-4.25
84	9	13.28	-4.276
85	8	12.74	-4.743
86	18	13.88	4.124
87	18	13.25	4.75
88	14	13.25	0.75
89	8	12.53	-4.535
90	14	12.74	1.257
91	13	13.37	-0.3691
92	14	12.24	1.763
93	7	13.16	-6.16
94	18	13.25	4.75
95	16	13.88	2.124
96	9	13.16	-4.16
97	11	13.46	-2.459
98	10	11.52	-1.521
99	13	13.25	-0.25
100	10	13.25	-3.25
101	12	13.16	-1.16
102	11	12.68	-1.677
103	12	13.25	-1.25
104	12	12.95	-0.9519
105	10	13.25	-3.25
106	20	14.08	5.916
107	12	13.46	-1.459
108	12	13.46	-1.459
109	16	13.25	2.75
110	11	13.07	-2.068
111	12	13.13	-1.134
112	12	13.25	-1.25
113	13	13.44	-0.4353
114	10	12.53	-2.535
115	14	13.88	0.1242
116	13	13.25	-0.25
117	15	11.82	3.177
118	13	13.25	-0.25
119	13	13.25	-0.25
120	17	13.76	3.24
121	12	13.34	-1.343
122	17	13.25	3.75
123	9	13.88	-4.876
124	12	13.25	-1.25
125	14	13.25	0.75
126	14	12.86	1.141
127	14	12.03	1.972
128	12	13.13	-1.134
129	14	13.25	0.75
130	13	13.25	-0.25
131	15	13.88	1.124
132	16	13.16	2.84
133	13	13.07	-0.06786
134	14	13.16	0.8395
135	14	13.13	0.8659
136	17	13.37	3.631
137	13	12.95	0.04808
138	15	12.74	2.257
139	8	13.67	-5.667
140	11	11.52	-0.5214
141	11	13.25	-2.25
142	9	13.16	-4.16
143	15	13.25	1.75
144	16	13.25	2.75
145	16	12.95	3.048
146	10	13.55	-3.551
147	15	12.51	2.489
148	10	13.88	-3.876
149	12	13.88	-1.876
150	14	13.46	0.5414
151	18	13.16	4.84
152	15	13.88	1.124
153	19	13.46	5.541
154	13	13.13	-0.1341
155	10	13.25	-3.25
156	15	13.25	1.75
157	7	11.52	-4.521
158	14	13.67	0.3328
159	13	12.45	0.5548
160	14	13.25	0.75
161	11	13.55	-2.551
162	18	13.67	4.333
163	8	12.95	-4.952
164	19	13.46	5.541
165	5	13.25	-8.25
166	17	13.46	3.541
167	14	13.34	0.6573
168	17	13.53	3.472

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  17 &  13.25 &  3.75 \tabularnewline
2 &  11 &  12.65 & -1.654 \tabularnewline
3 &  12 &  13.25 & -1.25 \tabularnewline
4 &  12 &  13.34 & -1.343 \tabularnewline
5 &  13 &  13.13 & -0.1341 \tabularnewline
6 &  17 &  13.88 &  3.124 \tabularnewline
7 &  17 &  13.02 &  3.982 \tabularnewline
8 &  12 &  13.25 & -1.25 \tabularnewline
9 &  16 &  13.25 &  2.75 \tabularnewline
10 &  15 &  13.46 &  1.541 \tabularnewline
11 &  11 &  12.65 & -1.651 \tabularnewline
12 &  16 &  11.96 &  4.038 \tabularnewline
13 &  15 &  13.13 &  1.866 \tabularnewline
14 &  16 &  13.48 &  2.515 \tabularnewline
15 &  15 &  13.46 &  1.541 \tabularnewline
16 &  11 &  12.65 & -1.651 \tabularnewline
17 &  8 &  13.34 & -5.343 \tabularnewline
18 &  10 &  14.08 & -4.084 \tabularnewline
19 &  14 &  13.46 &  0.5414 \tabularnewline
20 &  16 &  12.65 &  3.349 \tabularnewline
21 &  15 &  13.28 &  1.724 \tabularnewline
22 &  15 &  11.94 &  3.065 \tabularnewline
23 &  12 &  13.46 & -1.459 \tabularnewline
24 &  18 &  12.95 &  5.048 \tabularnewline
25 &  10 &  13.25 & -3.25 \tabularnewline
26 &  17 &  13.16 &  3.84 \tabularnewline
27 &  12 &  13.46 & -1.459 \tabularnewline
28 &  13 &  13.55 & -0.5513 \tabularnewline
29 &  9 &  13.25 & -4.25 \tabularnewline
30 &  11 &  13.88 & -2.876 \tabularnewline
31 &  10 &  13.25 & -3.25 \tabularnewline
32 &  15 &  13.25 &  1.75 \tabularnewline
33 &  15 &  13.25 &  1.75 \tabularnewline
34 &  13 &  13.13 & -0.1341 \tabularnewline
35 &  13 &  13.55 & -0.5513 \tabularnewline
36 &  9 &  13.16 & -4.16 \tabularnewline
37 &  14 &  12.74 &  1.257 \tabularnewline
38 &  14 &  13.88 &  0.1242 \tabularnewline
39 &  11 &  13.46 & -2.459 \tabularnewline
40 &  15 &  13.46 &  1.541 \tabularnewline
41 &  12 &  13.25 & -1.25 \tabularnewline
42 &  11 &  13.25 & -2.25 \tabularnewline
43 &  12 &  13.25 & -1.25 \tabularnewline
44 &  15 &  14.08 &  0.9157 \tabularnewline
45 &  13 &  13.25 & -0.25 \tabularnewline
46 &  11 &  13.25 & -2.25 \tabularnewline
47 &  10 &  13.13 & -3.134 \tabularnewline
48 &  16 &  12.65 &  3.349 \tabularnewline
49 &  13 &  13.25 & -0.25 \tabularnewline
50 &  15 &  13.46 &  1.541 \tabularnewline
51 &  14 &  12.74 &  1.257 \tabularnewline
52 &  12 &  13.46 & -1.459 \tabularnewline
53 &  10 &  12.14 & -2.144 \tabularnewline
54 &  12 &  12.84 & -0.836 \tabularnewline
55 &  9 &  11.82 & -2.82 \tabularnewline
56 &  15 &  13.34 &  1.657 \tabularnewline
57 &  16 &  13.46 &  2.541 \tabularnewline
58 &  12 &  13.25 & -1.25 \tabularnewline
59 &  11 &  13.55 & -2.551 \tabularnewline
60 &  11 &  13.67 & -2.667 \tabularnewline
61 &  9 &  13.25 & -4.25 \tabularnewline
62 &  13 &  13.25 & -0.25 \tabularnewline
63 &  17 &  13.46 &  3.541 \tabularnewline
64 &  18 &  12.53 &  5.465 \tabularnewline
65 &  15 &  13.67 &  1.333 \tabularnewline
66 &  12 &  12.95 & -0.9519 \tabularnewline
67 &  18 &  13.88 &  4.124 \tabularnewline
68 &  11 &  11.73 & -0.73 \tabularnewline
69 &  6 &  13.04 & -7.045 \tabularnewline
70 &  10 &  13.46 & -3.459 \tabularnewline
71 &  19 &  13.25 &  5.75 \tabularnewline
72 &  16 &  12.42 &  3.581 \tabularnewline
73 &  12 &  13.25 & -1.25 \tabularnewline
74 &  10 &  11.64 & -1.637 \tabularnewline
75 &  14 &  13.34 &  0.6573 \tabularnewline
76 &  12 &  11.82 &  0.1805 \tabularnewline
77 &  13 &  12.65 &  0.3493 \tabularnewline
78 &  16 &  12.53 &  3.465 \tabularnewline
79 &  18 &  13.25 &  4.75 \tabularnewline
80 &  13 &  13.25 & -0.25 \tabularnewline
81 &  15 &  12.53 &  2.465 \tabularnewline
82 &  16 &  12.95 &  3.048 \tabularnewline
83 &  9 &  13.25 & -4.25 \tabularnewline
84 &  9 &  13.28 & -4.276 \tabularnewline
85 &  8 &  12.74 & -4.743 \tabularnewline
86 &  18 &  13.88 &  4.124 \tabularnewline
87 &  18 &  13.25 &  4.75 \tabularnewline
88 &  14 &  13.25 &  0.75 \tabularnewline
89 &  8 &  12.53 & -4.535 \tabularnewline
90 &  14 &  12.74 &  1.257 \tabularnewline
91 &  13 &  13.37 & -0.3691 \tabularnewline
92 &  14 &  12.24 &  1.763 \tabularnewline
93 &  7 &  13.16 & -6.16 \tabularnewline
94 &  18 &  13.25 &  4.75 \tabularnewline
95 &  16 &  13.88 &  2.124 \tabularnewline
96 &  9 &  13.16 & -4.16 \tabularnewline
97 &  11 &  13.46 & -2.459 \tabularnewline
98 &  10 &  11.52 & -1.521 \tabularnewline
99 &  13 &  13.25 & -0.25 \tabularnewline
100 &  10 &  13.25 & -3.25 \tabularnewline
101 &  12 &  13.16 & -1.16 \tabularnewline
102 &  11 &  12.68 & -1.677 \tabularnewline
103 &  12 &  13.25 & -1.25 \tabularnewline
104 &  12 &  12.95 & -0.9519 \tabularnewline
105 &  10 &  13.25 & -3.25 \tabularnewline
106 &  20 &  14.08 &  5.916 \tabularnewline
107 &  12 &  13.46 & -1.459 \tabularnewline
108 &  12 &  13.46 & -1.459 \tabularnewline
109 &  16 &  13.25 &  2.75 \tabularnewline
110 &  11 &  13.07 & -2.068 \tabularnewline
111 &  12 &  13.13 & -1.134 \tabularnewline
112 &  12 &  13.25 & -1.25 \tabularnewline
113 &  13 &  13.44 & -0.4353 \tabularnewline
114 &  10 &  12.53 & -2.535 \tabularnewline
115 &  14 &  13.88 &  0.1242 \tabularnewline
116 &  13 &  13.25 & -0.25 \tabularnewline
117 &  15 &  11.82 &  3.177 \tabularnewline
118 &  13 &  13.25 & -0.25 \tabularnewline
119 &  13 &  13.25 & -0.25 \tabularnewline
120 &  17 &  13.76 &  3.24 \tabularnewline
121 &  12 &  13.34 & -1.343 \tabularnewline
122 &  17 &  13.25 &  3.75 \tabularnewline
123 &  9 &  13.88 & -4.876 \tabularnewline
124 &  12 &  13.25 & -1.25 \tabularnewline
125 &  14 &  13.25 &  0.75 \tabularnewline
126 &  14 &  12.86 &  1.141 \tabularnewline
127 &  14 &  12.03 &  1.972 \tabularnewline
128 &  12 &  13.13 & -1.134 \tabularnewline
129 &  14 &  13.25 &  0.75 \tabularnewline
130 &  13 &  13.25 & -0.25 \tabularnewline
131 &  15 &  13.88 &  1.124 \tabularnewline
132 &  16 &  13.16 &  2.84 \tabularnewline
133 &  13 &  13.07 & -0.06786 \tabularnewline
134 &  14 &  13.16 &  0.8395 \tabularnewline
135 &  14 &  13.13 &  0.8659 \tabularnewline
136 &  17 &  13.37 &  3.631 \tabularnewline
137 &  13 &  12.95 &  0.04808 \tabularnewline
138 &  15 &  12.74 &  2.257 \tabularnewline
139 &  8 &  13.67 & -5.667 \tabularnewline
140 &  11 &  11.52 & -0.5214 \tabularnewline
141 &  11 &  13.25 & -2.25 \tabularnewline
142 &  9 &  13.16 & -4.16 \tabularnewline
143 &  15 &  13.25 &  1.75 \tabularnewline
144 &  16 &  13.25 &  2.75 \tabularnewline
145 &  16 &  12.95 &  3.048 \tabularnewline
146 &  10 &  13.55 & -3.551 \tabularnewline
147 &  15 &  12.51 &  2.489 \tabularnewline
148 &  10 &  13.88 & -3.876 \tabularnewline
149 &  12 &  13.88 & -1.876 \tabularnewline
150 &  14 &  13.46 &  0.5414 \tabularnewline
151 &  18 &  13.16 &  4.84 \tabularnewline
152 &  15 &  13.88 &  1.124 \tabularnewline
153 &  19 &  13.46 &  5.541 \tabularnewline
154 &  13 &  13.13 & -0.1341 \tabularnewline
155 &  10 &  13.25 & -3.25 \tabularnewline
156 &  15 &  13.25 &  1.75 \tabularnewline
157 &  7 &  11.52 & -4.521 \tabularnewline
158 &  14 &  13.67 &  0.3328 \tabularnewline
159 &  13 &  12.45 &  0.5548 \tabularnewline
160 &  14 &  13.25 &  0.75 \tabularnewline
161 &  11 &  13.55 & -2.551 \tabularnewline
162 &  18 &  13.67 &  4.333 \tabularnewline
163 &  8 &  12.95 & -4.952 \tabularnewline
164 &  19 &  13.46 &  5.541 \tabularnewline
165 &  5 &  13.25 & -8.25 \tabularnewline
166 &  17 &  13.46 &  3.541 \tabularnewline
167 &  14 &  13.34 &  0.6573 \tabularnewline
168 &  17 &  13.53 &  3.472 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=299490&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] 17[/C][C] 13.25[/C][C] 3.75[/C][/ROW]
[ROW][C]2[/C][C] 11[/C][C] 12.65[/C][C]-1.654[/C][/ROW]
[ROW][C]3[/C][C] 12[/C][C] 13.25[/C][C]-1.25[/C][/ROW]
[ROW][C]4[/C][C] 12[/C][C] 13.34[/C][C]-1.343[/C][/ROW]
[ROW][C]5[/C][C] 13[/C][C] 13.13[/C][C]-0.1341[/C][/ROW]
[ROW][C]6[/C][C] 17[/C][C] 13.88[/C][C] 3.124[/C][/ROW]
[ROW][C]7[/C][C] 17[/C][C] 13.02[/C][C] 3.982[/C][/ROW]
[ROW][C]8[/C][C] 12[/C][C] 13.25[/C][C]-1.25[/C][/ROW]
[ROW][C]9[/C][C] 16[/C][C] 13.25[/C][C] 2.75[/C][/ROW]
[ROW][C]10[/C][C] 15[/C][C] 13.46[/C][C] 1.541[/C][/ROW]
[ROW][C]11[/C][C] 11[/C][C] 12.65[/C][C]-1.651[/C][/ROW]
[ROW][C]12[/C][C] 16[/C][C] 11.96[/C][C] 4.038[/C][/ROW]
[ROW][C]13[/C][C] 15[/C][C] 13.13[/C][C] 1.866[/C][/ROW]
[ROW][C]14[/C][C] 16[/C][C] 13.48[/C][C] 2.515[/C][/ROW]
[ROW][C]15[/C][C] 15[/C][C] 13.46[/C][C] 1.541[/C][/ROW]
[ROW][C]16[/C][C] 11[/C][C] 12.65[/C][C]-1.651[/C][/ROW]
[ROW][C]17[/C][C] 8[/C][C] 13.34[/C][C]-5.343[/C][/ROW]
[ROW][C]18[/C][C] 10[/C][C] 14.08[/C][C]-4.084[/C][/ROW]
[ROW][C]19[/C][C] 14[/C][C] 13.46[/C][C] 0.5414[/C][/ROW]
[ROW][C]20[/C][C] 16[/C][C] 12.65[/C][C] 3.349[/C][/ROW]
[ROW][C]21[/C][C] 15[/C][C] 13.28[/C][C] 1.724[/C][/ROW]
[ROW][C]22[/C][C] 15[/C][C] 11.94[/C][C] 3.065[/C][/ROW]
[ROW][C]23[/C][C] 12[/C][C] 13.46[/C][C]-1.459[/C][/ROW]
[ROW][C]24[/C][C] 18[/C][C] 12.95[/C][C] 5.048[/C][/ROW]
[ROW][C]25[/C][C] 10[/C][C] 13.25[/C][C]-3.25[/C][/ROW]
[ROW][C]26[/C][C] 17[/C][C] 13.16[/C][C] 3.84[/C][/ROW]
[ROW][C]27[/C][C] 12[/C][C] 13.46[/C][C]-1.459[/C][/ROW]
[ROW][C]28[/C][C] 13[/C][C] 13.55[/C][C]-0.5513[/C][/ROW]
[ROW][C]29[/C][C] 9[/C][C] 13.25[/C][C]-4.25[/C][/ROW]
[ROW][C]30[/C][C] 11[/C][C] 13.88[/C][C]-2.876[/C][/ROW]
[ROW][C]31[/C][C] 10[/C][C] 13.25[/C][C]-3.25[/C][/ROW]
[ROW][C]32[/C][C] 15[/C][C] 13.25[/C][C] 1.75[/C][/ROW]
[ROW][C]33[/C][C] 15[/C][C] 13.25[/C][C] 1.75[/C][/ROW]
[ROW][C]34[/C][C] 13[/C][C] 13.13[/C][C]-0.1341[/C][/ROW]
[ROW][C]35[/C][C] 13[/C][C] 13.55[/C][C]-0.5513[/C][/ROW]
[ROW][C]36[/C][C] 9[/C][C] 13.16[/C][C]-4.16[/C][/ROW]
[ROW][C]37[/C][C] 14[/C][C] 12.74[/C][C] 1.257[/C][/ROW]
[ROW][C]38[/C][C] 14[/C][C] 13.88[/C][C] 0.1242[/C][/ROW]
[ROW][C]39[/C][C] 11[/C][C] 13.46[/C][C]-2.459[/C][/ROW]
[ROW][C]40[/C][C] 15[/C][C] 13.46[/C][C] 1.541[/C][/ROW]
[ROW][C]41[/C][C] 12[/C][C] 13.25[/C][C]-1.25[/C][/ROW]
[ROW][C]42[/C][C] 11[/C][C] 13.25[/C][C]-2.25[/C][/ROW]
[ROW][C]43[/C][C] 12[/C][C] 13.25[/C][C]-1.25[/C][/ROW]
[ROW][C]44[/C][C] 15[/C][C] 14.08[/C][C] 0.9157[/C][/ROW]
[ROW][C]45[/C][C] 13[/C][C] 13.25[/C][C]-0.25[/C][/ROW]
[ROW][C]46[/C][C] 11[/C][C] 13.25[/C][C]-2.25[/C][/ROW]
[ROW][C]47[/C][C] 10[/C][C] 13.13[/C][C]-3.134[/C][/ROW]
[ROW][C]48[/C][C] 16[/C][C] 12.65[/C][C] 3.349[/C][/ROW]
[ROW][C]49[/C][C] 13[/C][C] 13.25[/C][C]-0.25[/C][/ROW]
[ROW][C]50[/C][C] 15[/C][C] 13.46[/C][C] 1.541[/C][/ROW]
[ROW][C]51[/C][C] 14[/C][C] 12.74[/C][C] 1.257[/C][/ROW]
[ROW][C]52[/C][C] 12[/C][C] 13.46[/C][C]-1.459[/C][/ROW]
[ROW][C]53[/C][C] 10[/C][C] 12.14[/C][C]-2.144[/C][/ROW]
[ROW][C]54[/C][C] 12[/C][C] 12.84[/C][C]-0.836[/C][/ROW]
[ROW][C]55[/C][C] 9[/C][C] 11.82[/C][C]-2.82[/C][/ROW]
[ROW][C]56[/C][C] 15[/C][C] 13.34[/C][C] 1.657[/C][/ROW]
[ROW][C]57[/C][C] 16[/C][C] 13.46[/C][C] 2.541[/C][/ROW]
[ROW][C]58[/C][C] 12[/C][C] 13.25[/C][C]-1.25[/C][/ROW]
[ROW][C]59[/C][C] 11[/C][C] 13.55[/C][C]-2.551[/C][/ROW]
[ROW][C]60[/C][C] 11[/C][C] 13.67[/C][C]-2.667[/C][/ROW]
[ROW][C]61[/C][C] 9[/C][C] 13.25[/C][C]-4.25[/C][/ROW]
[ROW][C]62[/C][C] 13[/C][C] 13.25[/C][C]-0.25[/C][/ROW]
[ROW][C]63[/C][C] 17[/C][C] 13.46[/C][C] 3.541[/C][/ROW]
[ROW][C]64[/C][C] 18[/C][C] 12.53[/C][C] 5.465[/C][/ROW]
[ROW][C]65[/C][C] 15[/C][C] 13.67[/C][C] 1.333[/C][/ROW]
[ROW][C]66[/C][C] 12[/C][C] 12.95[/C][C]-0.9519[/C][/ROW]
[ROW][C]67[/C][C] 18[/C][C] 13.88[/C][C] 4.124[/C][/ROW]
[ROW][C]68[/C][C] 11[/C][C] 11.73[/C][C]-0.73[/C][/ROW]
[ROW][C]69[/C][C] 6[/C][C] 13.04[/C][C]-7.045[/C][/ROW]
[ROW][C]70[/C][C] 10[/C][C] 13.46[/C][C]-3.459[/C][/ROW]
[ROW][C]71[/C][C] 19[/C][C] 13.25[/C][C] 5.75[/C][/ROW]
[ROW][C]72[/C][C] 16[/C][C] 12.42[/C][C] 3.581[/C][/ROW]
[ROW][C]73[/C][C] 12[/C][C] 13.25[/C][C]-1.25[/C][/ROW]
[ROW][C]74[/C][C] 10[/C][C] 11.64[/C][C]-1.637[/C][/ROW]
[ROW][C]75[/C][C] 14[/C][C] 13.34[/C][C] 0.6573[/C][/ROW]
[ROW][C]76[/C][C] 12[/C][C] 11.82[/C][C] 0.1805[/C][/ROW]
[ROW][C]77[/C][C] 13[/C][C] 12.65[/C][C] 0.3493[/C][/ROW]
[ROW][C]78[/C][C] 16[/C][C] 12.53[/C][C] 3.465[/C][/ROW]
[ROW][C]79[/C][C] 18[/C][C] 13.25[/C][C] 4.75[/C][/ROW]
[ROW][C]80[/C][C] 13[/C][C] 13.25[/C][C]-0.25[/C][/ROW]
[ROW][C]81[/C][C] 15[/C][C] 12.53[/C][C] 2.465[/C][/ROW]
[ROW][C]82[/C][C] 16[/C][C] 12.95[/C][C] 3.048[/C][/ROW]
[ROW][C]83[/C][C] 9[/C][C] 13.25[/C][C]-4.25[/C][/ROW]
[ROW][C]84[/C][C] 9[/C][C] 13.28[/C][C]-4.276[/C][/ROW]
[ROW][C]85[/C][C] 8[/C][C] 12.74[/C][C]-4.743[/C][/ROW]
[ROW][C]86[/C][C] 18[/C][C] 13.88[/C][C] 4.124[/C][/ROW]
[ROW][C]87[/C][C] 18[/C][C] 13.25[/C][C] 4.75[/C][/ROW]
[ROW][C]88[/C][C] 14[/C][C] 13.25[/C][C] 0.75[/C][/ROW]
[ROW][C]89[/C][C] 8[/C][C] 12.53[/C][C]-4.535[/C][/ROW]
[ROW][C]90[/C][C] 14[/C][C] 12.74[/C][C] 1.257[/C][/ROW]
[ROW][C]91[/C][C] 13[/C][C] 13.37[/C][C]-0.3691[/C][/ROW]
[ROW][C]92[/C][C] 14[/C][C] 12.24[/C][C] 1.763[/C][/ROW]
[ROW][C]93[/C][C] 7[/C][C] 13.16[/C][C]-6.16[/C][/ROW]
[ROW][C]94[/C][C] 18[/C][C] 13.25[/C][C] 4.75[/C][/ROW]
[ROW][C]95[/C][C] 16[/C][C] 13.88[/C][C] 2.124[/C][/ROW]
[ROW][C]96[/C][C] 9[/C][C] 13.16[/C][C]-4.16[/C][/ROW]
[ROW][C]97[/C][C] 11[/C][C] 13.46[/C][C]-2.459[/C][/ROW]
[ROW][C]98[/C][C] 10[/C][C] 11.52[/C][C]-1.521[/C][/ROW]
[ROW][C]99[/C][C] 13[/C][C] 13.25[/C][C]-0.25[/C][/ROW]
[ROW][C]100[/C][C] 10[/C][C] 13.25[/C][C]-3.25[/C][/ROW]
[ROW][C]101[/C][C] 12[/C][C] 13.16[/C][C]-1.16[/C][/ROW]
[ROW][C]102[/C][C] 11[/C][C] 12.68[/C][C]-1.677[/C][/ROW]
[ROW][C]103[/C][C] 12[/C][C] 13.25[/C][C]-1.25[/C][/ROW]
[ROW][C]104[/C][C] 12[/C][C] 12.95[/C][C]-0.9519[/C][/ROW]
[ROW][C]105[/C][C] 10[/C][C] 13.25[/C][C]-3.25[/C][/ROW]
[ROW][C]106[/C][C] 20[/C][C] 14.08[/C][C] 5.916[/C][/ROW]
[ROW][C]107[/C][C] 12[/C][C] 13.46[/C][C]-1.459[/C][/ROW]
[ROW][C]108[/C][C] 12[/C][C] 13.46[/C][C]-1.459[/C][/ROW]
[ROW][C]109[/C][C] 16[/C][C] 13.25[/C][C] 2.75[/C][/ROW]
[ROW][C]110[/C][C] 11[/C][C] 13.07[/C][C]-2.068[/C][/ROW]
[ROW][C]111[/C][C] 12[/C][C] 13.13[/C][C]-1.134[/C][/ROW]
[ROW][C]112[/C][C] 12[/C][C] 13.25[/C][C]-1.25[/C][/ROW]
[ROW][C]113[/C][C] 13[/C][C] 13.44[/C][C]-0.4353[/C][/ROW]
[ROW][C]114[/C][C] 10[/C][C] 12.53[/C][C]-2.535[/C][/ROW]
[ROW][C]115[/C][C] 14[/C][C] 13.88[/C][C] 0.1242[/C][/ROW]
[ROW][C]116[/C][C] 13[/C][C] 13.25[/C][C]-0.25[/C][/ROW]
[ROW][C]117[/C][C] 15[/C][C] 11.82[/C][C] 3.177[/C][/ROW]
[ROW][C]118[/C][C] 13[/C][C] 13.25[/C][C]-0.25[/C][/ROW]
[ROW][C]119[/C][C] 13[/C][C] 13.25[/C][C]-0.25[/C][/ROW]
[ROW][C]120[/C][C] 17[/C][C] 13.76[/C][C] 3.24[/C][/ROW]
[ROW][C]121[/C][C] 12[/C][C] 13.34[/C][C]-1.343[/C][/ROW]
[ROW][C]122[/C][C] 17[/C][C] 13.25[/C][C] 3.75[/C][/ROW]
[ROW][C]123[/C][C] 9[/C][C] 13.88[/C][C]-4.876[/C][/ROW]
[ROW][C]124[/C][C] 12[/C][C] 13.25[/C][C]-1.25[/C][/ROW]
[ROW][C]125[/C][C] 14[/C][C] 13.25[/C][C] 0.75[/C][/ROW]
[ROW][C]126[/C][C] 14[/C][C] 12.86[/C][C] 1.141[/C][/ROW]
[ROW][C]127[/C][C] 14[/C][C] 12.03[/C][C] 1.972[/C][/ROW]
[ROW][C]128[/C][C] 12[/C][C] 13.13[/C][C]-1.134[/C][/ROW]
[ROW][C]129[/C][C] 14[/C][C] 13.25[/C][C] 0.75[/C][/ROW]
[ROW][C]130[/C][C] 13[/C][C] 13.25[/C][C]-0.25[/C][/ROW]
[ROW][C]131[/C][C] 15[/C][C] 13.88[/C][C] 1.124[/C][/ROW]
[ROW][C]132[/C][C] 16[/C][C] 13.16[/C][C] 2.84[/C][/ROW]
[ROW][C]133[/C][C] 13[/C][C] 13.07[/C][C]-0.06786[/C][/ROW]
[ROW][C]134[/C][C] 14[/C][C] 13.16[/C][C] 0.8395[/C][/ROW]
[ROW][C]135[/C][C] 14[/C][C] 13.13[/C][C] 0.8659[/C][/ROW]
[ROW][C]136[/C][C] 17[/C][C] 13.37[/C][C] 3.631[/C][/ROW]
[ROW][C]137[/C][C] 13[/C][C] 12.95[/C][C] 0.04808[/C][/ROW]
[ROW][C]138[/C][C] 15[/C][C] 12.74[/C][C] 2.257[/C][/ROW]
[ROW][C]139[/C][C] 8[/C][C] 13.67[/C][C]-5.667[/C][/ROW]
[ROW][C]140[/C][C] 11[/C][C] 11.52[/C][C]-0.5214[/C][/ROW]
[ROW][C]141[/C][C] 11[/C][C] 13.25[/C][C]-2.25[/C][/ROW]
[ROW][C]142[/C][C] 9[/C][C] 13.16[/C][C]-4.16[/C][/ROW]
[ROW][C]143[/C][C] 15[/C][C] 13.25[/C][C] 1.75[/C][/ROW]
[ROW][C]144[/C][C] 16[/C][C] 13.25[/C][C] 2.75[/C][/ROW]
[ROW][C]145[/C][C] 16[/C][C] 12.95[/C][C] 3.048[/C][/ROW]
[ROW][C]146[/C][C] 10[/C][C] 13.55[/C][C]-3.551[/C][/ROW]
[ROW][C]147[/C][C] 15[/C][C] 12.51[/C][C] 2.489[/C][/ROW]
[ROW][C]148[/C][C] 10[/C][C] 13.88[/C][C]-3.876[/C][/ROW]
[ROW][C]149[/C][C] 12[/C][C] 13.88[/C][C]-1.876[/C][/ROW]
[ROW][C]150[/C][C] 14[/C][C] 13.46[/C][C] 0.5414[/C][/ROW]
[ROW][C]151[/C][C] 18[/C][C] 13.16[/C][C] 4.84[/C][/ROW]
[ROW][C]152[/C][C] 15[/C][C] 13.88[/C][C] 1.124[/C][/ROW]
[ROW][C]153[/C][C] 19[/C][C] 13.46[/C][C] 5.541[/C][/ROW]
[ROW][C]154[/C][C] 13[/C][C] 13.13[/C][C]-0.1341[/C][/ROW]
[ROW][C]155[/C][C] 10[/C][C] 13.25[/C][C]-3.25[/C][/ROW]
[ROW][C]156[/C][C] 15[/C][C] 13.25[/C][C] 1.75[/C][/ROW]
[ROW][C]157[/C][C] 7[/C][C] 11.52[/C][C]-4.521[/C][/ROW]
[ROW][C]158[/C][C] 14[/C][C] 13.67[/C][C] 0.3328[/C][/ROW]
[ROW][C]159[/C][C] 13[/C][C] 12.45[/C][C] 0.5548[/C][/ROW]
[ROW][C]160[/C][C] 14[/C][C] 13.25[/C][C] 0.75[/C][/ROW]
[ROW][C]161[/C][C] 11[/C][C] 13.55[/C][C]-2.551[/C][/ROW]
[ROW][C]162[/C][C] 18[/C][C] 13.67[/C][C] 4.333[/C][/ROW]
[ROW][C]163[/C][C] 8[/C][C] 12.95[/C][C]-4.952[/C][/ROW]
[ROW][C]164[/C][C] 19[/C][C] 13.46[/C][C] 5.541[/C][/ROW]
[ROW][C]165[/C][C] 5[/C][C] 13.25[/C][C]-8.25[/C][/ROW]
[ROW][C]166[/C][C] 17[/C][C] 13.46[/C][C] 3.541[/C][/ROW]
[ROW][C]167[/C][C] 14[/C][C] 13.34[/C][C] 0.6573[/C][/ROW]
[ROW][C]168[/C][C] 17[/C][C] 13.53[/C][C] 3.472[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=299490&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=299490&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	17	13.25	3.75
2	11	12.65	-1.654
3	12	13.25	-1.25
4	12	13.34	-1.343
5	13	13.13	-0.1341
6	17	13.88	3.124
7	17	13.02	3.982
8	12	13.25	-1.25
9	16	13.25	2.75
10	15	13.46	1.541
11	11	12.65	-1.651
12	16	11.96	4.038
13	15	13.13	1.866
14	16	13.48	2.515
15	15	13.46	1.541
16	11	12.65	-1.651
17	8	13.34	-5.343
18	10	14.08	-4.084
19	14	13.46	0.5414
20	16	12.65	3.349
21	15	13.28	1.724
22	15	11.94	3.065
23	12	13.46	-1.459
24	18	12.95	5.048
25	10	13.25	-3.25
26	17	13.16	3.84
27	12	13.46	-1.459
28	13	13.55	-0.5513
29	9	13.25	-4.25
30	11	13.88	-2.876
31	10	13.25	-3.25
32	15	13.25	1.75
33	15	13.25	1.75
34	13	13.13	-0.1341
35	13	13.55	-0.5513
36	9	13.16	-4.16
37	14	12.74	1.257
38	14	13.88	0.1242
39	11	13.46	-2.459
40	15	13.46	1.541
41	12	13.25	-1.25
42	11	13.25	-2.25
43	12	13.25	-1.25
44	15	14.08	0.9157
45	13	13.25	-0.25
46	11	13.25	-2.25
47	10	13.13	-3.134
48	16	12.65	3.349
49	13	13.25	-0.25
50	15	13.46	1.541
51	14	12.74	1.257
52	12	13.46	-1.459
53	10	12.14	-2.144
54	12	12.84	-0.836
55	9	11.82	-2.82
56	15	13.34	1.657
57	16	13.46	2.541
58	12	13.25	-1.25
59	11	13.55	-2.551
60	11	13.67	-2.667
61	9	13.25	-4.25
62	13	13.25	-0.25
63	17	13.46	3.541
64	18	12.53	5.465
65	15	13.67	1.333
66	12	12.95	-0.9519
67	18	13.88	4.124
68	11	11.73	-0.73
69	6	13.04	-7.045
70	10	13.46	-3.459
71	19	13.25	5.75
72	16	12.42	3.581
73	12	13.25	-1.25
74	10	11.64	-1.637
75	14	13.34	0.6573
76	12	11.82	0.1805
77	13	12.65	0.3493
78	16	12.53	3.465
79	18	13.25	4.75
80	13	13.25	-0.25
81	15	12.53	2.465
82	16	12.95	3.048
83	9	13.25	-4.25
84	9	13.28	-4.276
85	8	12.74	-4.743
86	18	13.88	4.124
87	18	13.25	4.75
88	14	13.25	0.75
89	8	12.53	-4.535
90	14	12.74	1.257
91	13	13.37	-0.3691
92	14	12.24	1.763
93	7	13.16	-6.16
94	18	13.25	4.75
95	16	13.88	2.124
96	9	13.16	-4.16
97	11	13.46	-2.459
98	10	11.52	-1.521
99	13	13.25	-0.25
100	10	13.25	-3.25
101	12	13.16	-1.16
102	11	12.68	-1.677
103	12	13.25	-1.25
104	12	12.95	-0.9519
105	10	13.25	-3.25
106	20	14.08	5.916
107	12	13.46	-1.459
108	12	13.46	-1.459
109	16	13.25	2.75
110	11	13.07	-2.068
111	12	13.13	-1.134
112	12	13.25	-1.25
113	13	13.44	-0.4353
114	10	12.53	-2.535
115	14	13.88	0.1242
116	13	13.25	-0.25
117	15	11.82	3.177
118	13	13.25	-0.25
119	13	13.25	-0.25
120	17	13.76	3.24
121	12	13.34	-1.343
122	17	13.25	3.75
123	9	13.88	-4.876
124	12	13.25	-1.25
125	14	13.25	0.75
126	14	12.86	1.141
127	14	12.03	1.972
128	12	13.13	-1.134
129	14	13.25	0.75
130	13	13.25	-0.25
131	15	13.88	1.124
132	16	13.16	2.84
133	13	13.07	-0.06786
134	14	13.16	0.8395
135	14	13.13	0.8659
136	17	13.37	3.631
137	13	12.95	0.04808
138	15	12.74	2.257
139	8	13.67	-5.667
140	11	11.52	-0.5214
141	11	13.25	-2.25
142	9	13.16	-4.16
143	15	13.25	1.75
144	16	13.25	2.75
145	16	12.95	3.048
146	10	13.55	-3.551
147	15	12.51	2.489
148	10	13.88	-3.876
149	12	13.88	-1.876
150	14	13.46	0.5414
151	18	13.16	4.84
152	15	13.88	1.124
153	19	13.46	5.541
154	13	13.13	-0.1341
155	10	13.25	-3.25
156	15	13.25	1.75
157	7	11.52	-4.521
158	14	13.67	0.3328
159	13	12.45	0.5548
160	14	13.25	0.75
161	11	13.55	-2.551
162	18	13.67	4.333
163	8	12.95	-4.952
164	19	13.46	5.541
165	5	13.25	-8.25
166	17	13.46	3.541
167	14	13.34	0.6573
168	17	13.53	3.472

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
7	0.6803	0.6394	0.3197
8	0.5645	0.871	0.4355
9	0.5105	0.979	0.4895
10	0.3821	0.7642	0.6179
11	0.2714	0.5428	0.7286
12	0.5061	0.9879	0.4939
13	0.4149	0.8298	0.5851
14	0.3324	0.6647	0.6676
15	0.2516	0.5031	0.7484
16	0.2249	0.4499	0.7751
17	0.52	0.9599	0.48
18	0.6238	0.7525	0.3762
19	0.548	0.9039	0.452
20	0.5146	0.9708	0.4854
21	0.4529	0.9057	0.5471
22	0.3971	0.7943	0.6029
23	0.3539	0.7077	0.6461
24	0.4408	0.8815	0.5592
25	0.4788	0.9577	0.5212
26	0.4854	0.9707	0.5147
27	0.4376	0.8752	0.5624
28	0.378	0.756	0.622
29	0.4491	0.8982	0.5509
30	0.4302	0.8603	0.5698
31	0.4341	0.8681	0.5659
32	0.4053	0.8107	0.5947
33	0.3746	0.7492	0.6254
34	0.3209	0.6417	0.6791
35	0.2717	0.5435	0.7283
36	0.3627	0.7254	0.6373
37	0.3134	0.6268	0.6866
38	0.2706	0.5412	0.7294
39	0.2513	0.5025	0.7487
40	0.2271	0.4543	0.7729
41	0.1941	0.3881	0.8059
42	0.1777	0.3554	0.8223
43	0.149	0.298	0.851
44	0.1305	0.261	0.8695
45	0.1044	0.2088	0.8956
46	0.09328	0.1866	0.9067
47	0.09722	0.1944	0.9028
48	0.09694	0.1939	0.9031
49	0.07668	0.1534	0.9233
50	0.06731	0.1346	0.9327
51	0.05316	0.1063	0.9468
52	0.04279	0.08558	0.9572
53	0.05373	0.1075	0.9463
54	0.04347	0.08694	0.9565
55	0.05236	0.1047	0.9476
56	0.04684	0.09368	0.9532
57	0.04601	0.09202	0.954
58	0.03689	0.07378	0.9631
59	0.03376	0.06753	0.9662
60	0.03211	0.06422	0.9679
61	0.04294	0.08589	0.9571
62	0.03317	0.06635	0.9668
63	0.04084	0.08168	0.9592
64	0.07425	0.1485	0.9257
65	0.06314	0.1263	0.9369
66	0.05206	0.1041	0.9479
67	0.06908	0.1382	0.9309
68	0.05911	0.1182	0.9409
69	0.1539	0.3077	0.8461
70	0.1661	0.3322	0.8339
71	0.2653	0.5307	0.7347
72	0.2933	0.5866	0.7067
73	0.2626	0.5252	0.7374
74	0.2459	0.4919	0.7541
75	0.2158	0.4317	0.7842
76	0.1848	0.3697	0.8152
77	0.1585	0.3171	0.8415
78	0.1714	0.3429	0.8286
79	0.2236	0.4473	0.7764
80	0.1919	0.3839	0.8081
81	0.1855	0.371	0.8145
82	0.1906	0.3811	0.8094
83	0.2275	0.4551	0.7725
84	0.2679	0.5359	0.7321
85	0.3284	0.6569	0.6716
86	0.3709	0.7418	0.6291
87	0.4443	0.8886	0.5557
88	0.4042	0.8083	0.5958
89	0.4597	0.9193	0.5403
90	0.4253	0.8506	0.5747
91	0.3826	0.7652	0.6174
92	0.3594	0.7188	0.6406
93	0.5078	0.9845	0.4922
94	0.5886	0.8228	0.4114
95	0.5659	0.8681	0.4341
96	0.6127	0.7747	0.3873
97	0.599	0.802	0.401
98	0.5618	0.8764	0.4382
99	0.5173	0.9654	0.4827
100	0.5239	0.9522	0.4761
101	0.4872	0.9744	0.5128
102	0.4559	0.9118	0.5441
103	0.4175	0.8349	0.5825
104	0.3779	0.7557	0.6221
105	0.3844	0.7687	0.6156
106	0.5125	0.9749	0.4875
107	0.477	0.954	0.523
108	0.4418	0.8835	0.5582
109	0.4378	0.8757	0.5622
110	0.413	0.8259	0.587
111	0.3746	0.7491	0.6254
112	0.3376	0.6751	0.6624
113	0.3021	0.6042	0.6979
114	0.2906	0.5813	0.7094
115	0.2509	0.5017	0.7491
116	0.2142	0.4284	0.7858
117	0.2145	0.4291	0.7855
118	0.181	0.362	0.819
119	0.1508	0.3016	0.8492
120	0.1515	0.3029	0.8485
121	0.1315	0.2631	0.8685
122	0.1444	0.2889	0.8556
123	0.2021	0.4042	0.7979
124	0.174	0.348	0.826
125	0.1447	0.2893	0.8553
126	0.1224	0.2448	0.8776
127	0.1112	0.2223	0.8888
128	0.0925	0.185	0.9075
129	0.07374	0.1475	0.9263
130	0.05702	0.114	0.943
131	0.04422	0.08844	0.9558
132	0.04178	0.08356	0.9582
133	0.03127	0.06253	0.9687
134	0.0234	0.0468	0.9766
135	0.01699	0.03397	0.983
136	0.02007	0.04015	0.9799
137	0.01424	0.02848	0.9858
138	0.01248	0.02496	0.9875
139	0.02705	0.0541	0.9729
140	0.02025	0.0405	0.9798
141	0.01737	0.03473	0.9826
142	0.02088	0.04176	0.9791
143	0.01566	0.03133	0.9843
144	0.01408	0.02817	0.9859
145	0.01446	0.02892	0.9855
146	0.02013	0.04027	0.9799
147	0.01683	0.03367	0.9832
148	0.03458	0.06916	0.9654
149	0.05321	0.1064	0.9468
150	0.0363	0.07261	0.9637
151	0.04144	0.08288	0.9586
152	0.03459	0.06918	0.9654
153	0.05861	0.1172	0.9414
154	0.04145	0.0829	0.9585
155	0.03147	0.06295	0.9685
156	0.02814	0.05629	0.9719
157	0.01964	0.03928	0.9804
158	0.01611	0.03222	0.9839
159	0.02898	0.05796	0.971
160	0.0276	0.05521	0.9724
161	0.09702	0.194	0.903

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
7 &  0.6803 &  0.6394 &  0.3197 \tabularnewline
8 &  0.5645 &  0.871 &  0.4355 \tabularnewline
9 &  0.5105 &  0.979 &  0.4895 \tabularnewline
10 &  0.3821 &  0.7642 &  0.6179 \tabularnewline
11 &  0.2714 &  0.5428 &  0.7286 \tabularnewline
12 &  0.5061 &  0.9879 &  0.4939 \tabularnewline
13 &  0.4149 &  0.8298 &  0.5851 \tabularnewline
14 &  0.3324 &  0.6647 &  0.6676 \tabularnewline
15 &  0.2516 &  0.5031 &  0.7484 \tabularnewline
16 &  0.2249 &  0.4499 &  0.7751 \tabularnewline
17 &  0.52 &  0.9599 &  0.48 \tabularnewline
18 &  0.6238 &  0.7525 &  0.3762 \tabularnewline
19 &  0.548 &  0.9039 &  0.452 \tabularnewline
20 &  0.5146 &  0.9708 &  0.4854 \tabularnewline
21 &  0.4529 &  0.9057 &  0.5471 \tabularnewline
22 &  0.3971 &  0.7943 &  0.6029 \tabularnewline
23 &  0.3539 &  0.7077 &  0.6461 \tabularnewline
24 &  0.4408 &  0.8815 &  0.5592 \tabularnewline
25 &  0.4788 &  0.9577 &  0.5212 \tabularnewline
26 &  0.4854 &  0.9707 &  0.5147 \tabularnewline
27 &  0.4376 &  0.8752 &  0.5624 \tabularnewline
28 &  0.378 &  0.756 &  0.622 \tabularnewline
29 &  0.4491 &  0.8982 &  0.5509 \tabularnewline
30 &  0.4302 &  0.8603 &  0.5698 \tabularnewline
31 &  0.4341 &  0.8681 &  0.5659 \tabularnewline
32 &  0.4053 &  0.8107 &  0.5947 \tabularnewline
33 &  0.3746 &  0.7492 &  0.6254 \tabularnewline
34 &  0.3209 &  0.6417 &  0.6791 \tabularnewline
35 &  0.2717 &  0.5435 &  0.7283 \tabularnewline
36 &  0.3627 &  0.7254 &  0.6373 \tabularnewline
37 &  0.3134 &  0.6268 &  0.6866 \tabularnewline
38 &  0.2706 &  0.5412 &  0.7294 \tabularnewline
39 &  0.2513 &  0.5025 &  0.7487 \tabularnewline
40 &  0.2271 &  0.4543 &  0.7729 \tabularnewline
41 &  0.1941 &  0.3881 &  0.8059 \tabularnewline
42 &  0.1777 &  0.3554 &  0.8223 \tabularnewline
43 &  0.149 &  0.298 &  0.851 \tabularnewline
44 &  0.1305 &  0.261 &  0.8695 \tabularnewline
45 &  0.1044 &  0.2088 &  0.8956 \tabularnewline
46 &  0.09328 &  0.1866 &  0.9067 \tabularnewline
47 &  0.09722 &  0.1944 &  0.9028 \tabularnewline
48 &  0.09694 &  0.1939 &  0.9031 \tabularnewline
49 &  0.07668 &  0.1534 &  0.9233 \tabularnewline
50 &  0.06731 &  0.1346 &  0.9327 \tabularnewline
51 &  0.05316 &  0.1063 &  0.9468 \tabularnewline
52 &  0.04279 &  0.08558 &  0.9572 \tabularnewline
53 &  0.05373 &  0.1075 &  0.9463 \tabularnewline
54 &  0.04347 &  0.08694 &  0.9565 \tabularnewline
55 &  0.05236 &  0.1047 &  0.9476 \tabularnewline
56 &  0.04684 &  0.09368 &  0.9532 \tabularnewline
57 &  0.04601 &  0.09202 &  0.954 \tabularnewline
58 &  0.03689 &  0.07378 &  0.9631 \tabularnewline
59 &  0.03376 &  0.06753 &  0.9662 \tabularnewline
60 &  0.03211 &  0.06422 &  0.9679 \tabularnewline
61 &  0.04294 &  0.08589 &  0.9571 \tabularnewline
62 &  0.03317 &  0.06635 &  0.9668 \tabularnewline
63 &  0.04084 &  0.08168 &  0.9592 \tabularnewline
64 &  0.07425 &  0.1485 &  0.9257 \tabularnewline
65 &  0.06314 &  0.1263 &  0.9369 \tabularnewline
66 &  0.05206 &  0.1041 &  0.9479 \tabularnewline
67 &  0.06908 &  0.1382 &  0.9309 \tabularnewline
68 &  0.05911 &  0.1182 &  0.9409 \tabularnewline
69 &  0.1539 &  0.3077 &  0.8461 \tabularnewline
70 &  0.1661 &  0.3322 &  0.8339 \tabularnewline
71 &  0.2653 &  0.5307 &  0.7347 \tabularnewline
72 &  0.2933 &  0.5866 &  0.7067 \tabularnewline
73 &  0.2626 &  0.5252 &  0.7374 \tabularnewline
74 &  0.2459 &  0.4919 &  0.7541 \tabularnewline
75 &  0.2158 &  0.4317 &  0.7842 \tabularnewline
76 &  0.1848 &  0.3697 &  0.8152 \tabularnewline
77 &  0.1585 &  0.3171 &  0.8415 \tabularnewline
78 &  0.1714 &  0.3429 &  0.8286 \tabularnewline
79 &  0.2236 &  0.4473 &  0.7764 \tabularnewline
80 &  0.1919 &  0.3839 &  0.8081 \tabularnewline
81 &  0.1855 &  0.371 &  0.8145 \tabularnewline
82 &  0.1906 &  0.3811 &  0.8094 \tabularnewline
83 &  0.2275 &  0.4551 &  0.7725 \tabularnewline
84 &  0.2679 &  0.5359 &  0.7321 \tabularnewline
85 &  0.3284 &  0.6569 &  0.6716 \tabularnewline
86 &  0.3709 &  0.7418 &  0.6291 \tabularnewline
87 &  0.4443 &  0.8886 &  0.5557 \tabularnewline
88 &  0.4042 &  0.8083 &  0.5958 \tabularnewline
89 &  0.4597 &  0.9193 &  0.5403 \tabularnewline
90 &  0.4253 &  0.8506 &  0.5747 \tabularnewline
91 &  0.3826 &  0.7652 &  0.6174 \tabularnewline
92 &  0.3594 &  0.7188 &  0.6406 \tabularnewline
93 &  0.5078 &  0.9845 &  0.4922 \tabularnewline
94 &  0.5886 &  0.8228 &  0.4114 \tabularnewline
95 &  0.5659 &  0.8681 &  0.4341 \tabularnewline
96 &  0.6127 &  0.7747 &  0.3873 \tabularnewline
97 &  0.599 &  0.802 &  0.401 \tabularnewline
98 &  0.5618 &  0.8764 &  0.4382 \tabularnewline
99 &  0.5173 &  0.9654 &  0.4827 \tabularnewline
100 &  0.5239 &  0.9522 &  0.4761 \tabularnewline
101 &  0.4872 &  0.9744 &  0.5128 \tabularnewline
102 &  0.4559 &  0.9118 &  0.5441 \tabularnewline
103 &  0.4175 &  0.8349 &  0.5825 \tabularnewline
104 &  0.3779 &  0.7557 &  0.6221 \tabularnewline
105 &  0.3844 &  0.7687 &  0.6156 \tabularnewline
106 &  0.5125 &  0.9749 &  0.4875 \tabularnewline
107 &  0.477 &  0.954 &  0.523 \tabularnewline
108 &  0.4418 &  0.8835 &  0.5582 \tabularnewline
109 &  0.4378 &  0.8757 &  0.5622 \tabularnewline
110 &  0.413 &  0.8259 &  0.587 \tabularnewline
111 &  0.3746 &  0.7491 &  0.6254 \tabularnewline
112 &  0.3376 &  0.6751 &  0.6624 \tabularnewline
113 &  0.3021 &  0.6042 &  0.6979 \tabularnewline
114 &  0.2906 &  0.5813 &  0.7094 \tabularnewline
115 &  0.2509 &  0.5017 &  0.7491 \tabularnewline
116 &  0.2142 &  0.4284 &  0.7858 \tabularnewline
117 &  0.2145 &  0.4291 &  0.7855 \tabularnewline
118 &  0.181 &  0.362 &  0.819 \tabularnewline
119 &  0.1508 &  0.3016 &  0.8492 \tabularnewline
120 &  0.1515 &  0.3029 &  0.8485 \tabularnewline
121 &  0.1315 &  0.2631 &  0.8685 \tabularnewline
122 &  0.1444 &  0.2889 &  0.8556 \tabularnewline
123 &  0.2021 &  0.4042 &  0.7979 \tabularnewline
124 &  0.174 &  0.348 &  0.826 \tabularnewline
125 &  0.1447 &  0.2893 &  0.8553 \tabularnewline
126 &  0.1224 &  0.2448 &  0.8776 \tabularnewline
127 &  0.1112 &  0.2223 &  0.8888 \tabularnewline
128 &  0.0925 &  0.185 &  0.9075 \tabularnewline
129 &  0.07374 &  0.1475 &  0.9263 \tabularnewline
130 &  0.05702 &  0.114 &  0.943 \tabularnewline
131 &  0.04422 &  0.08844 &  0.9558 \tabularnewline
132 &  0.04178 &  0.08356 &  0.9582 \tabularnewline
133 &  0.03127 &  0.06253 &  0.9687 \tabularnewline
134 &  0.0234 &  0.0468 &  0.9766 \tabularnewline
135 &  0.01699 &  0.03397 &  0.983 \tabularnewline
136 &  0.02007 &  0.04015 &  0.9799 \tabularnewline
137 &  0.01424 &  0.02848 &  0.9858 \tabularnewline
138 &  0.01248 &  0.02496 &  0.9875 \tabularnewline
139 &  0.02705 &  0.0541 &  0.9729 \tabularnewline
140 &  0.02025 &  0.0405 &  0.9798 \tabularnewline
141 &  0.01737 &  0.03473 &  0.9826 \tabularnewline
142 &  0.02088 &  0.04176 &  0.9791 \tabularnewline
143 &  0.01566 &  0.03133 &  0.9843 \tabularnewline
144 &  0.01408 &  0.02817 &  0.9859 \tabularnewline
145 &  0.01446 &  0.02892 &  0.9855 \tabularnewline
146 &  0.02013 &  0.04027 &  0.9799 \tabularnewline
147 &  0.01683 &  0.03367 &  0.9832 \tabularnewline
148 &  0.03458 &  0.06916 &  0.9654 \tabularnewline
149 &  0.05321 &  0.1064 &  0.9468 \tabularnewline
150 &  0.0363 &  0.07261 &  0.9637 \tabularnewline
151 &  0.04144 &  0.08288 &  0.9586 \tabularnewline
152 &  0.03459 &  0.06918 &  0.9654 \tabularnewline
153 &  0.05861 &  0.1172 &  0.9414 \tabularnewline
154 &  0.04145 &  0.0829 &  0.9585 \tabularnewline
155 &  0.03147 &  0.06295 &  0.9685 \tabularnewline
156 &  0.02814 &  0.05629 &  0.9719 \tabularnewline
157 &  0.01964 &  0.03928 &  0.9804 \tabularnewline
158 &  0.01611 &  0.03222 &  0.9839 \tabularnewline
159 &  0.02898 &  0.05796 &  0.971 \tabularnewline
160 &  0.0276 &  0.05521 &  0.9724 \tabularnewline
161 &  0.09702 &  0.194 &  0.903 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=299490&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]7[/C][C] 0.6803[/C][C] 0.6394[/C][C] 0.3197[/C][/ROW]
[ROW][C]8[/C][C] 0.5645[/C][C] 0.871[/C][C] 0.4355[/C][/ROW]
[ROW][C]9[/C][C] 0.5105[/C][C] 0.979[/C][C] 0.4895[/C][/ROW]
[ROW][C]10[/C][C] 0.3821[/C][C] 0.7642[/C][C] 0.6179[/C][/ROW]
[ROW][C]11[/C][C] 0.2714[/C][C] 0.5428[/C][C] 0.7286[/C][/ROW]
[ROW][C]12[/C][C] 0.5061[/C][C] 0.9879[/C][C] 0.4939[/C][/ROW]
[ROW][C]13[/C][C] 0.4149[/C][C] 0.8298[/C][C] 0.5851[/C][/ROW]
[ROW][C]14[/C][C] 0.3324[/C][C] 0.6647[/C][C] 0.6676[/C][/ROW]
[ROW][C]15[/C][C] 0.2516[/C][C] 0.5031[/C][C] 0.7484[/C][/ROW]
[ROW][C]16[/C][C] 0.2249[/C][C] 0.4499[/C][C] 0.7751[/C][/ROW]
[ROW][C]17[/C][C] 0.52[/C][C] 0.9599[/C][C] 0.48[/C][/ROW]
[ROW][C]18[/C][C] 0.6238[/C][C] 0.7525[/C][C] 0.3762[/C][/ROW]
[ROW][C]19[/C][C] 0.548[/C][C] 0.9039[/C][C] 0.452[/C][/ROW]
[ROW][C]20[/C][C] 0.5146[/C][C] 0.9708[/C][C] 0.4854[/C][/ROW]
[ROW][C]21[/C][C] 0.4529[/C][C] 0.9057[/C][C] 0.5471[/C][/ROW]
[ROW][C]22[/C][C] 0.3971[/C][C] 0.7943[/C][C] 0.6029[/C][/ROW]
[ROW][C]23[/C][C] 0.3539[/C][C] 0.7077[/C][C] 0.6461[/C][/ROW]
[ROW][C]24[/C][C] 0.4408[/C][C] 0.8815[/C][C] 0.5592[/C][/ROW]
[ROW][C]25[/C][C] 0.4788[/C][C] 0.9577[/C][C] 0.5212[/C][/ROW]
[ROW][C]26[/C][C] 0.4854[/C][C] 0.9707[/C][C] 0.5147[/C][/ROW]
[ROW][C]27[/C][C] 0.4376[/C][C] 0.8752[/C][C] 0.5624[/C][/ROW]
[ROW][C]28[/C][C] 0.378[/C][C] 0.756[/C][C] 0.622[/C][/ROW]
[ROW][C]29[/C][C] 0.4491[/C][C] 0.8982[/C][C] 0.5509[/C][/ROW]
[ROW][C]30[/C][C] 0.4302[/C][C] 0.8603[/C][C] 0.5698[/C][/ROW]
[ROW][C]31[/C][C] 0.4341[/C][C] 0.8681[/C][C] 0.5659[/C][/ROW]
[ROW][C]32[/C][C] 0.4053[/C][C] 0.8107[/C][C] 0.5947[/C][/ROW]
[ROW][C]33[/C][C] 0.3746[/C][C] 0.7492[/C][C] 0.6254[/C][/ROW]
[ROW][C]34[/C][C] 0.3209[/C][C] 0.6417[/C][C] 0.6791[/C][/ROW]
[ROW][C]35[/C][C] 0.2717[/C][C] 0.5435[/C][C] 0.7283[/C][/ROW]
[ROW][C]36[/C][C] 0.3627[/C][C] 0.7254[/C][C] 0.6373[/C][/ROW]
[ROW][C]37[/C][C] 0.3134[/C][C] 0.6268[/C][C] 0.6866[/C][/ROW]
[ROW][C]38[/C][C] 0.2706[/C][C] 0.5412[/C][C] 0.7294[/C][/ROW]
[ROW][C]39[/C][C] 0.2513[/C][C] 0.5025[/C][C] 0.7487[/C][/ROW]
[ROW][C]40[/C][C] 0.2271[/C][C] 0.4543[/C][C] 0.7729[/C][/ROW]
[ROW][C]41[/C][C] 0.1941[/C][C] 0.3881[/C][C] 0.8059[/C][/ROW]
[ROW][C]42[/C][C] 0.1777[/C][C] 0.3554[/C][C] 0.8223[/C][/ROW]
[ROW][C]43[/C][C] 0.149[/C][C] 0.298[/C][C] 0.851[/C][/ROW]
[ROW][C]44[/C][C] 0.1305[/C][C] 0.261[/C][C] 0.8695[/C][/ROW]
[ROW][C]45[/C][C] 0.1044[/C][C] 0.2088[/C][C] 0.8956[/C][/ROW]
[ROW][C]46[/C][C] 0.09328[/C][C] 0.1866[/C][C] 0.9067[/C][/ROW]
[ROW][C]47[/C][C] 0.09722[/C][C] 0.1944[/C][C] 0.9028[/C][/ROW]
[ROW][C]48[/C][C] 0.09694[/C][C] 0.1939[/C][C] 0.9031[/C][/ROW]
[ROW][C]49[/C][C] 0.07668[/C][C] 0.1534[/C][C] 0.9233[/C][/ROW]
[ROW][C]50[/C][C] 0.06731[/C][C] 0.1346[/C][C] 0.9327[/C][/ROW]
[ROW][C]51[/C][C] 0.05316[/C][C] 0.1063[/C][C] 0.9468[/C][/ROW]
[ROW][C]52[/C][C] 0.04279[/C][C] 0.08558[/C][C] 0.9572[/C][/ROW]
[ROW][C]53[/C][C] 0.05373[/C][C] 0.1075[/C][C] 0.9463[/C][/ROW]
[ROW][C]54[/C][C] 0.04347[/C][C] 0.08694[/C][C] 0.9565[/C][/ROW]
[ROW][C]55[/C][C] 0.05236[/C][C] 0.1047[/C][C] 0.9476[/C][/ROW]
[ROW][C]56[/C][C] 0.04684[/C][C] 0.09368[/C][C] 0.9532[/C][/ROW]
[ROW][C]57[/C][C] 0.04601[/C][C] 0.09202[/C][C] 0.954[/C][/ROW]
[ROW][C]58[/C][C] 0.03689[/C][C] 0.07378[/C][C] 0.9631[/C][/ROW]
[ROW][C]59[/C][C] 0.03376[/C][C] 0.06753[/C][C] 0.9662[/C][/ROW]
[ROW][C]60[/C][C] 0.03211[/C][C] 0.06422[/C][C] 0.9679[/C][/ROW]
[ROW][C]61[/C][C] 0.04294[/C][C] 0.08589[/C][C] 0.9571[/C][/ROW]
[ROW][C]62[/C][C] 0.03317[/C][C] 0.06635[/C][C] 0.9668[/C][/ROW]
[ROW][C]63[/C][C] 0.04084[/C][C] 0.08168[/C][C] 0.9592[/C][/ROW]
[ROW][C]64[/C][C] 0.07425[/C][C] 0.1485[/C][C] 0.9257[/C][/ROW]
[ROW][C]65[/C][C] 0.06314[/C][C] 0.1263[/C][C] 0.9369[/C][/ROW]
[ROW][C]66[/C][C] 0.05206[/C][C] 0.1041[/C][C] 0.9479[/C][/ROW]
[ROW][C]67[/C][C] 0.06908[/C][C] 0.1382[/C][C] 0.9309[/C][/ROW]
[ROW][C]68[/C][C] 0.05911[/C][C] 0.1182[/C][C] 0.9409[/C][/ROW]
[ROW][C]69[/C][C] 0.1539[/C][C] 0.3077[/C][C] 0.8461[/C][/ROW]
[ROW][C]70[/C][C] 0.1661[/C][C] 0.3322[/C][C] 0.8339[/C][/ROW]
[ROW][C]71[/C][C] 0.2653[/C][C] 0.5307[/C][C] 0.7347[/C][/ROW]
[ROW][C]72[/C][C] 0.2933[/C][C] 0.5866[/C][C] 0.7067[/C][/ROW]
[ROW][C]73[/C][C] 0.2626[/C][C] 0.5252[/C][C] 0.7374[/C][/ROW]
[ROW][C]74[/C][C] 0.2459[/C][C] 0.4919[/C][C] 0.7541[/C][/ROW]
[ROW][C]75[/C][C] 0.2158[/C][C] 0.4317[/C][C] 0.7842[/C][/ROW]
[ROW][C]76[/C][C] 0.1848[/C][C] 0.3697[/C][C] 0.8152[/C][/ROW]
[ROW][C]77[/C][C] 0.1585[/C][C] 0.3171[/C][C] 0.8415[/C][/ROW]
[ROW][C]78[/C][C] 0.1714[/C][C] 0.3429[/C][C] 0.8286[/C][/ROW]
[ROW][C]79[/C][C] 0.2236[/C][C] 0.4473[/C][C] 0.7764[/C][/ROW]
[ROW][C]80[/C][C] 0.1919[/C][C] 0.3839[/C][C] 0.8081[/C][/ROW]
[ROW][C]81[/C][C] 0.1855[/C][C] 0.371[/C][C] 0.8145[/C][/ROW]
[ROW][C]82[/C][C] 0.1906[/C][C] 0.3811[/C][C] 0.8094[/C][/ROW]
[ROW][C]83[/C][C] 0.2275[/C][C] 0.4551[/C][C] 0.7725[/C][/ROW]
[ROW][C]84[/C][C] 0.2679[/C][C] 0.5359[/C][C] 0.7321[/C][/ROW]
[ROW][C]85[/C][C] 0.3284[/C][C] 0.6569[/C][C] 0.6716[/C][/ROW]
[ROW][C]86[/C][C] 0.3709[/C][C] 0.7418[/C][C] 0.6291[/C][/ROW]
[ROW][C]87[/C][C] 0.4443[/C][C] 0.8886[/C][C] 0.5557[/C][/ROW]
[ROW][C]88[/C][C] 0.4042[/C][C] 0.8083[/C][C] 0.5958[/C][/ROW]
[ROW][C]89[/C][C] 0.4597[/C][C] 0.9193[/C][C] 0.5403[/C][/ROW]
[ROW][C]90[/C][C] 0.4253[/C][C] 0.8506[/C][C] 0.5747[/C][/ROW]
[ROW][C]91[/C][C] 0.3826[/C][C] 0.7652[/C][C] 0.6174[/C][/ROW]
[ROW][C]92[/C][C] 0.3594[/C][C] 0.7188[/C][C] 0.6406[/C][/ROW]
[ROW][C]93[/C][C] 0.5078[/C][C] 0.9845[/C][C] 0.4922[/C][/ROW]
[ROW][C]94[/C][C] 0.5886[/C][C] 0.8228[/C][C] 0.4114[/C][/ROW]
[ROW][C]95[/C][C] 0.5659[/C][C] 0.8681[/C][C] 0.4341[/C][/ROW]
[ROW][C]96[/C][C] 0.6127[/C][C] 0.7747[/C][C] 0.3873[/C][/ROW]
[ROW][C]97[/C][C] 0.599[/C][C] 0.802[/C][C] 0.401[/C][/ROW]
[ROW][C]98[/C][C] 0.5618[/C][C] 0.8764[/C][C] 0.4382[/C][/ROW]
[ROW][C]99[/C][C] 0.5173[/C][C] 0.9654[/C][C] 0.4827[/C][/ROW]
[ROW][C]100[/C][C] 0.5239[/C][C] 0.9522[/C][C] 0.4761[/C][/ROW]
[ROW][C]101[/C][C] 0.4872[/C][C] 0.9744[/C][C] 0.5128[/C][/ROW]
[ROW][C]102[/C][C] 0.4559[/C][C] 0.9118[/C][C] 0.5441[/C][/ROW]
[ROW][C]103[/C][C] 0.4175[/C][C] 0.8349[/C][C] 0.5825[/C][/ROW]
[ROW][C]104[/C][C] 0.3779[/C][C] 0.7557[/C][C] 0.6221[/C][/ROW]
[ROW][C]105[/C][C] 0.3844[/C][C] 0.7687[/C][C] 0.6156[/C][/ROW]
[ROW][C]106[/C][C] 0.5125[/C][C] 0.9749[/C][C] 0.4875[/C][/ROW]
[ROW][C]107[/C][C] 0.477[/C][C] 0.954[/C][C] 0.523[/C][/ROW]
[ROW][C]108[/C][C] 0.4418[/C][C] 0.8835[/C][C] 0.5582[/C][/ROW]
[ROW][C]109[/C][C] 0.4378[/C][C] 0.8757[/C][C] 0.5622[/C][/ROW]
[ROW][C]110[/C][C] 0.413[/C][C] 0.8259[/C][C] 0.587[/C][/ROW]
[ROW][C]111[/C][C] 0.3746[/C][C] 0.7491[/C][C] 0.6254[/C][/ROW]
[ROW][C]112[/C][C] 0.3376[/C][C] 0.6751[/C][C] 0.6624[/C][/ROW]
[ROW][C]113[/C][C] 0.3021[/C][C] 0.6042[/C][C] 0.6979[/C][/ROW]
[ROW][C]114[/C][C] 0.2906[/C][C] 0.5813[/C][C] 0.7094[/C][/ROW]
[ROW][C]115[/C][C] 0.2509[/C][C] 0.5017[/C][C] 0.7491[/C][/ROW]
[ROW][C]116[/C][C] 0.2142[/C][C] 0.4284[/C][C] 0.7858[/C][/ROW]
[ROW][C]117[/C][C] 0.2145[/C][C] 0.4291[/C][C] 0.7855[/C][/ROW]
[ROW][C]118[/C][C] 0.181[/C][C] 0.362[/C][C] 0.819[/C][/ROW]
[ROW][C]119[/C][C] 0.1508[/C][C] 0.3016[/C][C] 0.8492[/C][/ROW]
[ROW][C]120[/C][C] 0.1515[/C][C] 0.3029[/C][C] 0.8485[/C][/ROW]
[ROW][C]121[/C][C] 0.1315[/C][C] 0.2631[/C][C] 0.8685[/C][/ROW]
[ROW][C]122[/C][C] 0.1444[/C][C] 0.2889[/C][C] 0.8556[/C][/ROW]
[ROW][C]123[/C][C] 0.2021[/C][C] 0.4042[/C][C] 0.7979[/C][/ROW]
[ROW][C]124[/C][C] 0.174[/C][C] 0.348[/C][C] 0.826[/C][/ROW]
[ROW][C]125[/C][C] 0.1447[/C][C] 0.2893[/C][C] 0.8553[/C][/ROW]
[ROW][C]126[/C][C] 0.1224[/C][C] 0.2448[/C][C] 0.8776[/C][/ROW]
[ROW][C]127[/C][C] 0.1112[/C][C] 0.2223[/C][C] 0.8888[/C][/ROW]
[ROW][C]128[/C][C] 0.0925[/C][C] 0.185[/C][C] 0.9075[/C][/ROW]
[ROW][C]129[/C][C] 0.07374[/C][C] 0.1475[/C][C] 0.9263[/C][/ROW]
[ROW][C]130[/C][C] 0.05702[/C][C] 0.114[/C][C] 0.943[/C][/ROW]
[ROW][C]131[/C][C] 0.04422[/C][C] 0.08844[/C][C] 0.9558[/C][/ROW]
[ROW][C]132[/C][C] 0.04178[/C][C] 0.08356[/C][C] 0.9582[/C][/ROW]
[ROW][C]133[/C][C] 0.03127[/C][C] 0.06253[/C][C] 0.9687[/C][/ROW]
[ROW][C]134[/C][C] 0.0234[/C][C] 0.0468[/C][C] 0.9766[/C][/ROW]
[ROW][C]135[/C][C] 0.01699[/C][C] 0.03397[/C][C] 0.983[/C][/ROW]
[ROW][C]136[/C][C] 0.02007[/C][C] 0.04015[/C][C] 0.9799[/C][/ROW]
[ROW][C]137[/C][C] 0.01424[/C][C] 0.02848[/C][C] 0.9858[/C][/ROW]
[ROW][C]138[/C][C] 0.01248[/C][C] 0.02496[/C][C] 0.9875[/C][/ROW]
[ROW][C]139[/C][C] 0.02705[/C][C] 0.0541[/C][C] 0.9729[/C][/ROW]
[ROW][C]140[/C][C] 0.02025[/C][C] 0.0405[/C][C] 0.9798[/C][/ROW]
[ROW][C]141[/C][C] 0.01737[/C][C] 0.03473[/C][C] 0.9826[/C][/ROW]
[ROW][C]142[/C][C] 0.02088[/C][C] 0.04176[/C][C] 0.9791[/C][/ROW]
[ROW][C]143[/C][C] 0.01566[/C][C] 0.03133[/C][C] 0.9843[/C][/ROW]
[ROW][C]144[/C][C] 0.01408[/C][C] 0.02817[/C][C] 0.9859[/C][/ROW]
[ROW][C]145[/C][C] 0.01446[/C][C] 0.02892[/C][C] 0.9855[/C][/ROW]
[ROW][C]146[/C][C] 0.02013[/C][C] 0.04027[/C][C] 0.9799[/C][/ROW]
[ROW][C]147[/C][C] 0.01683[/C][C] 0.03367[/C][C] 0.9832[/C][/ROW]
[ROW][C]148[/C][C] 0.03458[/C][C] 0.06916[/C][C] 0.9654[/C][/ROW]
[ROW][C]149[/C][C] 0.05321[/C][C] 0.1064[/C][C] 0.9468[/C][/ROW]
[ROW][C]150[/C][C] 0.0363[/C][C] 0.07261[/C][C] 0.9637[/C][/ROW]
[ROW][C]151[/C][C] 0.04144[/C][C] 0.08288[/C][C] 0.9586[/C][/ROW]
[ROW][C]152[/C][C] 0.03459[/C][C] 0.06918[/C][C] 0.9654[/C][/ROW]
[ROW][C]153[/C][C] 0.05861[/C][C] 0.1172[/C][C] 0.9414[/C][/ROW]
[ROW][C]154[/C][C] 0.04145[/C][C] 0.0829[/C][C] 0.9585[/C][/ROW]
[ROW][C]155[/C][C] 0.03147[/C][C] 0.06295[/C][C] 0.9685[/C][/ROW]
[ROW][C]156[/C][C] 0.02814[/C][C] 0.05629[/C][C] 0.9719[/C][/ROW]
[ROW][C]157[/C][C] 0.01964[/C][C] 0.03928[/C][C] 0.9804[/C][/ROW]
[ROW][C]158[/C][C] 0.01611[/C][C] 0.03222[/C][C] 0.9839[/C][/ROW]
[ROW][C]159[/C][C] 0.02898[/C][C] 0.05796[/C][C] 0.971[/C][/ROW]
[ROW][C]160[/C][C] 0.0276[/C][C] 0.05521[/C][C] 0.9724[/C][/ROW]
[ROW][C]161[/C][C] 0.09702[/C][C] 0.194[/C][C] 0.903[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=299490&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=299490&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
7	0.6803	0.6394	0.3197
8	0.5645	0.871	0.4355
9	0.5105	0.979	0.4895
10	0.3821	0.7642	0.6179
11	0.2714	0.5428	0.7286
12	0.5061	0.9879	0.4939
13	0.4149	0.8298	0.5851
14	0.3324	0.6647	0.6676
15	0.2516	0.5031	0.7484
16	0.2249	0.4499	0.7751
17	0.52	0.9599	0.48
18	0.6238	0.7525	0.3762
19	0.548	0.9039	0.452
20	0.5146	0.9708	0.4854
21	0.4529	0.9057	0.5471
22	0.3971	0.7943	0.6029
23	0.3539	0.7077	0.6461
24	0.4408	0.8815	0.5592
25	0.4788	0.9577	0.5212
26	0.4854	0.9707	0.5147
27	0.4376	0.8752	0.5624
28	0.378	0.756	0.622
29	0.4491	0.8982	0.5509
30	0.4302	0.8603	0.5698
31	0.4341	0.8681	0.5659
32	0.4053	0.8107	0.5947
33	0.3746	0.7492	0.6254
34	0.3209	0.6417	0.6791
35	0.2717	0.5435	0.7283
36	0.3627	0.7254	0.6373
37	0.3134	0.6268	0.6866
38	0.2706	0.5412	0.7294
39	0.2513	0.5025	0.7487
40	0.2271	0.4543	0.7729
41	0.1941	0.3881	0.8059
42	0.1777	0.3554	0.8223
43	0.149	0.298	0.851
44	0.1305	0.261	0.8695
45	0.1044	0.2088	0.8956
46	0.09328	0.1866	0.9067
47	0.09722	0.1944	0.9028
48	0.09694	0.1939	0.9031
49	0.07668	0.1534	0.9233
50	0.06731	0.1346	0.9327
51	0.05316	0.1063	0.9468
52	0.04279	0.08558	0.9572
53	0.05373	0.1075	0.9463
54	0.04347	0.08694	0.9565
55	0.05236	0.1047	0.9476
56	0.04684	0.09368	0.9532
57	0.04601	0.09202	0.954
58	0.03689	0.07378	0.9631
59	0.03376	0.06753	0.9662
60	0.03211	0.06422	0.9679
61	0.04294	0.08589	0.9571
62	0.03317	0.06635	0.9668
63	0.04084	0.08168	0.9592
64	0.07425	0.1485	0.9257
65	0.06314	0.1263	0.9369
66	0.05206	0.1041	0.9479
67	0.06908	0.1382	0.9309
68	0.05911	0.1182	0.9409
69	0.1539	0.3077	0.8461
70	0.1661	0.3322	0.8339
71	0.2653	0.5307	0.7347
72	0.2933	0.5866	0.7067
73	0.2626	0.5252	0.7374
74	0.2459	0.4919	0.7541
75	0.2158	0.4317	0.7842
76	0.1848	0.3697	0.8152
77	0.1585	0.3171	0.8415
78	0.1714	0.3429	0.8286
79	0.2236	0.4473	0.7764
80	0.1919	0.3839	0.8081
81	0.1855	0.371	0.8145
82	0.1906	0.3811	0.8094
83	0.2275	0.4551	0.7725
84	0.2679	0.5359	0.7321
85	0.3284	0.6569	0.6716
86	0.3709	0.7418	0.6291
87	0.4443	0.8886	0.5557
88	0.4042	0.8083	0.5958
89	0.4597	0.9193	0.5403
90	0.4253	0.8506	0.5747
91	0.3826	0.7652	0.6174
92	0.3594	0.7188	0.6406
93	0.5078	0.9845	0.4922
94	0.5886	0.8228	0.4114
95	0.5659	0.8681	0.4341
96	0.6127	0.7747	0.3873
97	0.599	0.802	0.401
98	0.5618	0.8764	0.4382
99	0.5173	0.9654	0.4827
100	0.5239	0.9522	0.4761
101	0.4872	0.9744	0.5128
102	0.4559	0.9118	0.5441
103	0.4175	0.8349	0.5825
104	0.3779	0.7557	0.6221
105	0.3844	0.7687	0.6156
106	0.5125	0.9749	0.4875
107	0.477	0.954	0.523
108	0.4418	0.8835	0.5582
109	0.4378	0.8757	0.5622
110	0.413	0.8259	0.587
111	0.3746	0.7491	0.6254
112	0.3376	0.6751	0.6624
113	0.3021	0.6042	0.6979
114	0.2906	0.5813	0.7094
115	0.2509	0.5017	0.7491
116	0.2142	0.4284	0.7858
117	0.2145	0.4291	0.7855
118	0.181	0.362	0.819
119	0.1508	0.3016	0.8492
120	0.1515	0.3029	0.8485
121	0.1315	0.2631	0.8685
122	0.1444	0.2889	0.8556
123	0.2021	0.4042	0.7979
124	0.174	0.348	0.826
125	0.1447	0.2893	0.8553
126	0.1224	0.2448	0.8776
127	0.1112	0.2223	0.8888
128	0.0925	0.185	0.9075
129	0.07374	0.1475	0.9263
130	0.05702	0.114	0.943
131	0.04422	0.08844	0.9558
132	0.04178	0.08356	0.9582
133	0.03127	0.06253	0.9687
134	0.0234	0.0468	0.9766
135	0.01699	0.03397	0.983
136	0.02007	0.04015	0.9799
137	0.01424	0.02848	0.9858
138	0.01248	0.02496	0.9875
139	0.02705	0.0541	0.9729
140	0.02025	0.0405	0.9798
141	0.01737	0.03473	0.9826
142	0.02088	0.04176	0.9791
143	0.01566	0.03133	0.9843
144	0.01408	0.02817	0.9859
145	0.01446	0.02892	0.9855
146	0.02013	0.04027	0.9799
147	0.01683	0.03367	0.9832
148	0.03458	0.06916	0.9654
149	0.05321	0.1064	0.9468
150	0.0363	0.07261	0.9637
151	0.04144	0.08288	0.9586
152	0.03459	0.06918	0.9654
153	0.05861	0.1172	0.9414
154	0.04145	0.0829	0.9585
155	0.03147	0.06295	0.9685
156	0.02814	0.05629	0.9719
157	0.01964	0.03928	0.9804
158	0.01611	0.03222	0.9839
159	0.02898	0.05796	0.971
160	0.0276	0.05521	0.9724
161	0.09702	0.194	0.903

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	15	0.0967742	NOK
10% type I error level	38	0.245161	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 & 15 & 0.0967742 & NOK \tabularnewline
10% type I error level & 38 & 0.245161 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=299490&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]15[/C][C]0.0967742[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]38[/C][C]0.245161[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=299490&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=299490&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	15	0.0967742	NOK
10% type I error level	38	0.245161	NOK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 2.3765, df1 = 2, df2 = 162, p-value = 0.0961
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 0.67063, df1 = 6, df2 = 158, p-value = 0.6735
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 1.1718, df1 = 2, df2 = 162, p-value = 0.3124

\begin{tabular}{lllllllll}
\hline
Ramsey RESET F-Test for powers (2 and 3) of fitted values \tabularnewline
> reset_test_fitted
	RESET test
data:  mylm
RESET = 2.3765, df1 = 2, df2 = 162, p-value = 0.0961
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 0.67063, df1 = 6, df2 = 158, p-value = 0.6735
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 1.1718, df1 = 2, df2 = 162, p-value = 0.3124
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=299490&T=7

[TABLE]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of fitted values[/C][/ROW]
[ROW][C]> reset_test_fitted
	RESET test
data:  mylm
RESET = 2.3765, df1 = 2, df2 = 162, p-value = 0.0961
[/C][/ROW]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of regressors[/C][/ROW]
[ROW][C]> reset_test_regressors
	RESET test
data:  mylm
RESET = 0.67063, df1 = 6, df2 = 158, p-value = 0.6735
[/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 = 1.1718, df1 = 2, df2 = 162, p-value = 0.3124
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=299490&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=299490&T=7

As an alternative you can also use a QR Code:

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

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 2.3765, df1 = 2, df2 = 162, p-value = 0.0961
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 0.67063, df1 = 6, df2 = 158, p-value = 0.6735
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 1.1718, df1 = 2, df2 = 162, p-value = 0.3124

Variance Inflation Factors (Multicollinearity)
> vif EP1 EP2 EP4 2.012273 1.948407 1.128271

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
     EP1      EP2      EP4 
2.012273 1.948407 1.128271 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=299490&T=8

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
     EP1      EP2      EP4 
2.012273 1.948407 1.128271 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=299490&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=299490&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 EP1 EP2 EP4 2.012273 1.948407 1.128271

Figure 1

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Figure 3

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Figure 4

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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Figure 10

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Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = 0 ; par5 = 0 ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = 0 ; par5 = 0 ;

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

par5 <- '0'
par4 <- '0'
par3 <- 'No Linear Trend'
par2 <- 'Do not include Seasonal Dummies'
par1 <- '1'
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