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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Fri, 11 Dec 2015 23:59:16 +0000

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2015/Dec/12/t1449878463q0zipv94u1e8mta.htm/, Retrieved Thu, 16 May 2024 21:26:04 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=286047, Retrieved Thu, 16 May 2024 21:26:04 +0000

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101

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-       [Multiple Regression] [Multiple regression] [2015-12-11 23:59:16] [d7b41ff8615e11945ad30de5daa5ba50] [Current]

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86	149	12,2	1
71	148	12,6	1
108	158	10,6	1
64	128	12	1
97	159	11,9	1
129	105	9,6	1
153	159	13,8	1
78	167	9,9	1
80	165	11,5	1
99	159	8,3	1
57	91	10,3	1
68	121	9,3	1
55	153	12,3	1
79	221	7,9	1
116	188	9,3	1
101	149	12,5	1
66	92	15,9	1
71	156	9,1	1
64	132	12,2	1
143	161	12,3	1
85	105	14,6	1
69	131	12,6	1
96	157	12,6	1
60	111	17,1	1
95	145	16,1	1
100	162	13,35	1
105	187	14,5	1
41	42	8,6	1
50	155	17,65	1
93	125	16,35	1
54	128	13,6	1
69	96	14,35	1
58	99	18,25	1
136	183	18,25	1
126	214	18,95	1
64	74	15,9	1
36	99	13,35	1
35	48	15,35	1
61	50	14,85	1
70	150	13,6	1
24	68	15,25	1
147	158	13,2	1
84	147	15,65	1
30	39	15,6	1
77	100	15,2	1
46	111	18,4	1
61	138	19,05	1
159	131	18,55	1
57	101	12,4	1
163	165	14,6	1
76	114	14,05	1
94	111	11,85	1
45	75	7,85	1
78	82	15,2	1
47	121	12,9	1
97	150	12,8	1
33	71	7,4	1
51	165	6,7	1
118	154	14,8	1
89	145	13,3	1
56	132	11,1	1
60	169	8,2	1
109	114	11,4	1
58	89	6,4	1
92	173	11,3	1
95	141	10	1
50	165	6,4	1
80	110	10,8	1
68	121	13,8	1
79	110	11,7	1
57	117	13,4	1
69	63	11,7	1
49	42	9	1
100	154	9,7	1
78	96	10,8	1
38	49	12,7	1
42	110	11,8	1
90	86	5,9	1
52	88	11,4	1
64	168	13	1
31	94	11,3	1
27	48	6,7	1
105	145	12,1	1
71	164	13,3	1
63	126	5,7	1
47	132	13,3	1
78	81	7,6	1
70	139	11,1	0
119	224	13	0
68	119	9,9	0
147	176	11,1	0
120	163	4,35	0
84	137	12,7	0
137	148	18,1	0
81	150	12,6	0
63	153	19,1	0
69	94	18,4	0
86	97	14,7	0
120	166	10,6	0
57	59	12,6	0
103	90	16,2	0
107	164	18,9	0
65	162	14,1	0
107	202	16,15	0
53	66	14,75	0
69	104	14,8	0
136	177	12,45	0
118	99	12,65	0
82	139	17,35	0
65	108	18,4	0
120	194	11,6	0
215	159	17,75	0
24	67	15,25	0
42	114	17,65	0
29	32	14,75	0
66	126	9,9	0
87	149	16	0
76	120	13,85	0
75	109	17,1	0
72	172	14,6	0
76	156	15,4	0
123	167	17,6	0
46	87	13,9	0
86	118	16,25	0
79	146	15,65	0
75	73	14,6	0
43	65	11,2	0
55	152	16,35	0
39	77	15,85	0
95	112	7,65	0
23	131	12,35	0
48	56	15,6	0
94	121	13,1	0
62	149	12,85	0
74	168	9,5	0
62	85	11,85	0
80	114	13,6	0
75	119	17,6	0
54	142	16,1	0
51	64	13,35	0
76	105	15,15	0

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	5 seconds
R Server	'Herman Ole Andreas Wold' @ wold.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 5 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=286047&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=286047&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=286047&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	5 seconds
R Server	'Herman Ole Andreas Wold' @ wold.wessa.net

Multiple Linear Regression - Estimated Regression Equation
TOT[t] = + 13.8248 + 0.0123091H[t] -0.00473222LFM[t] -1.78005Geslacht[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TOT[t] =  +  13.8248 +  0.0123091H[t] -0.00473222LFM[t] -1.78005Geslacht[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=286047&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TOT[t] =  +  13.8248 +  0.0123091H[t] -0.00473222LFM[t] -1.78005Geslacht[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=286047&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=286047&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
TOT[t] = + 13.8248 + 0.0123091H[t] -0.00473222LFM[t] -1.78005Geslacht[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)	+13.82	0.9524	+1.4520e+01	1.707e-29	8.537e-30
H	+0.01231	0.01005	+1.2250e+00	0.2226	0.1113
LFM	-0.004732	0.007954	-5.9500e-01	0.5529	0.2764
Geslacht	-1.78	0.5434	-3.2760e+00	0.001335	0.0006674

\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) & +13.82 &  0.9524 & +1.4520e+01 &  1.707e-29 &  8.537e-30 \tabularnewline
H & +0.01231 &  0.01005 & +1.2250e+00 &  0.2226 &  0.1113 \tabularnewline
LFM & -0.004732 &  0.007954 & -5.9500e-01 &  0.5529 &  0.2764 \tabularnewline
Geslacht & -1.78 &  0.5434 & -3.2760e+00 &  0.001335 &  0.0006674 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=286047&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]+13.82[/C][C] 0.9524[/C][C]+1.4520e+01[/C][C] 1.707e-29[/C][C] 8.537e-30[/C][/ROW]
[ROW][C]H[/C][C]+0.01231[/C][C] 0.01005[/C][C]+1.2250e+00[/C][C] 0.2226[/C][C] 0.1113[/C][/ROW]
[ROW][C]LFM[/C][C]-0.004732[/C][C] 0.007954[/C][C]-5.9500e-01[/C][C] 0.5529[/C][C] 0.2764[/C][/ROW]
[ROW][C]Geslacht[/C][C]-1.78[/C][C] 0.5434[/C][C]-3.2760e+00[/C][C] 0.001335[/C][C] 0.0006674[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=286047&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=286047&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)	+13.82	0.9524	+1.4520e+01	1.707e-29	8.537e-30
H	+0.01231	0.01005	+1.2250e+00	0.2226	0.1113
LFM	-0.004732	0.007954	-5.9500e-01	0.5529	0.2764
Geslacht	-1.78	0.5434	-3.2760e+00	0.001335	0.0006674

Multiple Linear Regression - Regression Statistics
Multiple R	0.2925
R-squared	0.08557
Adjusted R-squared	0.06555
F-TEST (value)	4.273
F-TEST (DF numerator)	3
F-TEST (DF denominator)	137
p-value	0.006425
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	3.13
Sum Squared Residuals	1342

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.2925 \tabularnewline
R-squared &  0.08557 \tabularnewline
Adjusted R-squared &  0.06555 \tabularnewline
F-TEST (value) &  4.273 \tabularnewline
F-TEST (DF numerator) & 3 \tabularnewline
F-TEST (DF denominator) & 137 \tabularnewline
p-value &  0.006425 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  3.13 \tabularnewline
Sum Squared Residuals &  1342 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=286047&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.2925[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.08557[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.06555[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 4.273[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]3[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]137[/C][/ROW]
[ROW][C]p-value[/C][C] 0.006425[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 3.13[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 1342[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=286047&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=286047&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	0.2925
R-squared	0.08557
Adjusted R-squared	0.06555
F-TEST (value)	4.273
F-TEST (DF numerator)	3
F-TEST (DF denominator)	137
p-value	0.006425
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	3.13
Sum Squared Residuals	1342

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	12.2	12.4	-0.1982
2	12.6	12.22	0.3817
3	10.6	12.63	-2.026
4	12	12.23	-0.2268
5	11.9	12.49	-0.5863
6	9.6	13.14	-3.536
7	13.8	13.18	0.6244
8	9.9	12.21	-2.315
9	11.5	12.25	-0.7486
10	8.3	12.51	-4.211
11	10.3	12.32	-2.016
12	9.3	12.31	-3.009
13	12.3	12	0.3023
14	7.9	11.97	-4.071
15	9.3	12.58	-3.283
16	12.5	12.58	-0.08285
17	15.9	12.42	3.478
18	9.1	12.18	-3.08
19	12.2	12.21	-0.007864
20	12.3	13.04	-0.7431
21	14.6	12.59	2.006
22	12.6	12.27	0.3259
23	12.6	12.48	0.1165
24	17.1	12.26	4.842
25	16.1	12.53	3.572
26	13.35	12.51	0.841
27	14.5	12.45	2.048
28	8.6	12.35	-3.751
29	17.65	11.93	5.723
30	16.35	12.6	3.752
31	13.6	12.1	1.496
32	14.35	12.44	1.91
33	18.25	12.29	5.96
34	18.25	12.85	5.397
35	18.95	12.58	6.367
36	15.9	12.48	3.418
37	13.35	12.02	1.331
38	15.35	12.25	3.102
39	14.85	12.56	2.291
40	13.6	12.2	1.403
41	15.25	12.02	3.232
42	13.2	13.11	0.09352
43	15.65	12.38	3.267
44	15.6	12.23	3.371
45	15.2	12.52	2.681
46	18.4	12.09	6.314
47	19.05	12.14	6.907
48	18.55	13.38	5.168
49	12.4	12.27	0.1316
50	14.6	13.27	1.33
51	14.05	12.44	1.609
52	11.85	12.68	-0.8265
53	7.85	12.24	-4.394
54	15.2	12.62	2.583
55	12.9	12.05	0.8493
56	12.8	12.53	0.2711
57	7.4	12.11	-4.715
58	6.7	11.89	-5.192
59	14.8	12.77	2.032
60	13.3	12.45	0.8459
61	11.1	12.11	-1.009
62	8.2	11.98	-3.784
63	11.4	12.85	-1.447
64	6.4	12.34	-5.937
65	11.3	12.36	-1.058
66	10	12.55	-2.547
67	6.4	11.88	-5.479
68	10.8	12.51	-1.709
69	13.8	12.31	1.491
70	11.7	12.5	-0.7966
71	13.4	12.19	1.207
72	11.7	12.6	-0.8959
73	9	12.45	-3.449
74	9.7	12.55	-2.847
75	10.8	12.55	-1.751
76	12.7	12.28	0.4194
77	11.8	12.04	-0.2412
78	5.9	12.75	-6.846
79	11.4	12.27	-0.8684
80	13	12.04	0.9625
81	11.3	11.98	-0.6815
82	6.7	12.15	-5.45
83	12.1	12.65	-0.551
84	13.3	12.14	1.157
85	5.7	12.22	-6.524
86	13.3	12	1.301
87	7.6	12.62	-5.022
88	11.1	14.03	-2.929
89	13	14.23	-1.23
90	9.9	14.1	-4.199
91	11.1	14.8	-3.701
92	4.35	14.53	-10.18
93	12.7	14.21	-1.51
94	18.1	14.81	3.289
95	12.6	14.11	-1.512
96	19.1	13.88	5.224
97	18.4	14.23	4.171
98	14.7	14.42	0.2757
99	10.6	14.52	-3.916
100	12.6	14.25	-1.647
101	16.2	14.67	1.533
102	18.9	14.37	4.534
103	14.1	13.86	0.2417
104	16.15	14.19	1.964
105	14.75	14.16	0.5852
106	14.8	14.18	0.618
107	12.45	14.66	-2.211
108	12.65	14.81	-2.159
109	17.35	14.18	3.174
110	18.4	14.11	4.286
111	11.6	14.38	-2.784
112	17.75	15.72	2.031
113	15.25	13.8	1.447
114	17.65	13.8	3.848
115	14.75	14.03	0.7197
116	9.9	14.04	-4.141
117	16	14.19	1.809
118	13.85	14.19	-0.3424
119	17.1	14.23	2.868
120	14.6	13.9	0.7029
121	15.4	14.02	1.378
122	17.6	14.55	3.051
123	13.9	13.98	-0.07929
124	16.25	14.32	1.925
125	15.65	14.11	1.544
126	14.6	14.4	0.1975
127	11.2	14.05	-2.846
128	16.35	13.78	2.568
129	15.85	13.94	1.91
130	7.65	14.46	-6.814
131	12.35	13.49	-1.138
132	15.6	14.15	1.449
133	13.1	14.41	-1.309
134	12.85	13.88	-1.033
135	9.5	13.94	-4.441
136	11.85	14.19	-2.336
137	13.6	14.27	-0.67
138	17.6	14.18	3.415
139	16.1	13.82	2.283
140	13.35	14.15	-0.7997
141	15.15	14.26	0.8866

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  12.2 &  12.4 & -0.1982 \tabularnewline
2 &  12.6 &  12.22 &  0.3817 \tabularnewline
3 &  10.6 &  12.63 & -2.026 \tabularnewline
4 &  12 &  12.23 & -0.2268 \tabularnewline
5 &  11.9 &  12.49 & -0.5863 \tabularnewline
6 &  9.6 &  13.14 & -3.536 \tabularnewline
7 &  13.8 &  13.18 &  0.6244 \tabularnewline
8 &  9.9 &  12.21 & -2.315 \tabularnewline
9 &  11.5 &  12.25 & -0.7486 \tabularnewline
10 &  8.3 &  12.51 & -4.211 \tabularnewline
11 &  10.3 &  12.32 & -2.016 \tabularnewline
12 &  9.3 &  12.31 & -3.009 \tabularnewline
13 &  12.3 &  12 &  0.3023 \tabularnewline
14 &  7.9 &  11.97 & -4.071 \tabularnewline
15 &  9.3 &  12.58 & -3.283 \tabularnewline
16 &  12.5 &  12.58 & -0.08285 \tabularnewline
17 &  15.9 &  12.42 &  3.478 \tabularnewline
18 &  9.1 &  12.18 & -3.08 \tabularnewline
19 &  12.2 &  12.21 & -0.007864 \tabularnewline
20 &  12.3 &  13.04 & -0.7431 \tabularnewline
21 &  14.6 &  12.59 &  2.006 \tabularnewline
22 &  12.6 &  12.27 &  0.3259 \tabularnewline
23 &  12.6 &  12.48 &  0.1165 \tabularnewline
24 &  17.1 &  12.26 &  4.842 \tabularnewline
25 &  16.1 &  12.53 &  3.572 \tabularnewline
26 &  13.35 &  12.51 &  0.841 \tabularnewline
27 &  14.5 &  12.45 &  2.048 \tabularnewline
28 &  8.6 &  12.35 & -3.751 \tabularnewline
29 &  17.65 &  11.93 &  5.723 \tabularnewline
30 &  16.35 &  12.6 &  3.752 \tabularnewline
31 &  13.6 &  12.1 &  1.496 \tabularnewline
32 &  14.35 &  12.44 &  1.91 \tabularnewline
33 &  18.25 &  12.29 &  5.96 \tabularnewline
34 &  18.25 &  12.85 &  5.397 \tabularnewline
35 &  18.95 &  12.58 &  6.367 \tabularnewline
36 &  15.9 &  12.48 &  3.418 \tabularnewline
37 &  13.35 &  12.02 &  1.331 \tabularnewline
38 &  15.35 &  12.25 &  3.102 \tabularnewline
39 &  14.85 &  12.56 &  2.291 \tabularnewline
40 &  13.6 &  12.2 &  1.403 \tabularnewline
41 &  15.25 &  12.02 &  3.232 \tabularnewline
42 &  13.2 &  13.11 &  0.09352 \tabularnewline
43 &  15.65 &  12.38 &  3.267 \tabularnewline
44 &  15.6 &  12.23 &  3.371 \tabularnewline
45 &  15.2 &  12.52 &  2.681 \tabularnewline
46 &  18.4 &  12.09 &  6.314 \tabularnewline
47 &  19.05 &  12.14 &  6.907 \tabularnewline
48 &  18.55 &  13.38 &  5.168 \tabularnewline
49 &  12.4 &  12.27 &  0.1316 \tabularnewline
50 &  14.6 &  13.27 &  1.33 \tabularnewline
51 &  14.05 &  12.44 &  1.609 \tabularnewline
52 &  11.85 &  12.68 & -0.8265 \tabularnewline
53 &  7.85 &  12.24 & -4.394 \tabularnewline
54 &  15.2 &  12.62 &  2.583 \tabularnewline
55 &  12.9 &  12.05 &  0.8493 \tabularnewline
56 &  12.8 &  12.53 &  0.2711 \tabularnewline
57 &  7.4 &  12.11 & -4.715 \tabularnewline
58 &  6.7 &  11.89 & -5.192 \tabularnewline
59 &  14.8 &  12.77 &  2.032 \tabularnewline
60 &  13.3 &  12.45 &  0.8459 \tabularnewline
61 &  11.1 &  12.11 & -1.009 \tabularnewline
62 &  8.2 &  11.98 & -3.784 \tabularnewline
63 &  11.4 &  12.85 & -1.447 \tabularnewline
64 &  6.4 &  12.34 & -5.937 \tabularnewline
65 &  11.3 &  12.36 & -1.058 \tabularnewline
66 &  10 &  12.55 & -2.547 \tabularnewline
67 &  6.4 &  11.88 & -5.479 \tabularnewline
68 &  10.8 &  12.51 & -1.709 \tabularnewline
69 &  13.8 &  12.31 &  1.491 \tabularnewline
70 &  11.7 &  12.5 & -0.7966 \tabularnewline
71 &  13.4 &  12.19 &  1.207 \tabularnewline
72 &  11.7 &  12.6 & -0.8959 \tabularnewline
73 &  9 &  12.45 & -3.449 \tabularnewline
74 &  9.7 &  12.55 & -2.847 \tabularnewline
75 &  10.8 &  12.55 & -1.751 \tabularnewline
76 &  12.7 &  12.28 &  0.4194 \tabularnewline
77 &  11.8 &  12.04 & -0.2412 \tabularnewline
78 &  5.9 &  12.75 & -6.846 \tabularnewline
79 &  11.4 &  12.27 & -0.8684 \tabularnewline
80 &  13 &  12.04 &  0.9625 \tabularnewline
81 &  11.3 &  11.98 & -0.6815 \tabularnewline
82 &  6.7 &  12.15 & -5.45 \tabularnewline
83 &  12.1 &  12.65 & -0.551 \tabularnewline
84 &  13.3 &  12.14 &  1.157 \tabularnewline
85 &  5.7 &  12.22 & -6.524 \tabularnewline
86 &  13.3 &  12 &  1.301 \tabularnewline
87 &  7.6 &  12.62 & -5.022 \tabularnewline
88 &  11.1 &  14.03 & -2.929 \tabularnewline
89 &  13 &  14.23 & -1.23 \tabularnewline
90 &  9.9 &  14.1 & -4.199 \tabularnewline
91 &  11.1 &  14.8 & -3.701 \tabularnewline
92 &  4.35 &  14.53 & -10.18 \tabularnewline
93 &  12.7 &  14.21 & -1.51 \tabularnewline
94 &  18.1 &  14.81 &  3.289 \tabularnewline
95 &  12.6 &  14.11 & -1.512 \tabularnewline
96 &  19.1 &  13.88 &  5.224 \tabularnewline
97 &  18.4 &  14.23 &  4.171 \tabularnewline
98 &  14.7 &  14.42 &  0.2757 \tabularnewline
99 &  10.6 &  14.52 & -3.916 \tabularnewline
100 &  12.6 &  14.25 & -1.647 \tabularnewline
101 &  16.2 &  14.67 &  1.533 \tabularnewline
102 &  18.9 &  14.37 &  4.534 \tabularnewline
103 &  14.1 &  13.86 &  0.2417 \tabularnewline
104 &  16.15 &  14.19 &  1.964 \tabularnewline
105 &  14.75 &  14.16 &  0.5852 \tabularnewline
106 &  14.8 &  14.18 &  0.618 \tabularnewline
107 &  12.45 &  14.66 & -2.211 \tabularnewline
108 &  12.65 &  14.81 & -2.159 \tabularnewline
109 &  17.35 &  14.18 &  3.174 \tabularnewline
110 &  18.4 &  14.11 &  4.286 \tabularnewline
111 &  11.6 &  14.38 & -2.784 \tabularnewline
112 &  17.75 &  15.72 &  2.031 \tabularnewline
113 &  15.25 &  13.8 &  1.447 \tabularnewline
114 &  17.65 &  13.8 &  3.848 \tabularnewline
115 &  14.75 &  14.03 &  0.7197 \tabularnewline
116 &  9.9 &  14.04 & -4.141 \tabularnewline
117 &  16 &  14.19 &  1.809 \tabularnewline
118 &  13.85 &  14.19 & -0.3424 \tabularnewline
119 &  17.1 &  14.23 &  2.868 \tabularnewline
120 &  14.6 &  13.9 &  0.7029 \tabularnewline
121 &  15.4 &  14.02 &  1.378 \tabularnewline
122 &  17.6 &  14.55 &  3.051 \tabularnewline
123 &  13.9 &  13.98 & -0.07929 \tabularnewline
124 &  16.25 &  14.32 &  1.925 \tabularnewline
125 &  15.65 &  14.11 &  1.544 \tabularnewline
126 &  14.6 &  14.4 &  0.1975 \tabularnewline
127 &  11.2 &  14.05 & -2.846 \tabularnewline
128 &  16.35 &  13.78 &  2.568 \tabularnewline
129 &  15.85 &  13.94 &  1.91 \tabularnewline
130 &  7.65 &  14.46 & -6.814 \tabularnewline
131 &  12.35 &  13.49 & -1.138 \tabularnewline
132 &  15.6 &  14.15 &  1.449 \tabularnewline
133 &  13.1 &  14.41 & -1.309 \tabularnewline
134 &  12.85 &  13.88 & -1.033 \tabularnewline
135 &  9.5 &  13.94 & -4.441 \tabularnewline
136 &  11.85 &  14.19 & -2.336 \tabularnewline
137 &  13.6 &  14.27 & -0.67 \tabularnewline
138 &  17.6 &  14.18 &  3.415 \tabularnewline
139 &  16.1 &  13.82 &  2.283 \tabularnewline
140 &  13.35 &  14.15 & -0.7997 \tabularnewline
141 &  15.15 &  14.26 &  0.8866 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=286047&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] 12.2[/C][C] 12.4[/C][C]-0.1982[/C][/ROW]
[ROW][C]2[/C][C] 12.6[/C][C] 12.22[/C][C] 0.3817[/C][/ROW]
[ROW][C]3[/C][C] 10.6[/C][C] 12.63[/C][C]-2.026[/C][/ROW]
[ROW][C]4[/C][C] 12[/C][C] 12.23[/C][C]-0.2268[/C][/ROW]
[ROW][C]5[/C][C] 11.9[/C][C] 12.49[/C][C]-0.5863[/C][/ROW]
[ROW][C]6[/C][C] 9.6[/C][C] 13.14[/C][C]-3.536[/C][/ROW]
[ROW][C]7[/C][C] 13.8[/C][C] 13.18[/C][C] 0.6244[/C][/ROW]
[ROW][C]8[/C][C] 9.9[/C][C] 12.21[/C][C]-2.315[/C][/ROW]
[ROW][C]9[/C][C] 11.5[/C][C] 12.25[/C][C]-0.7486[/C][/ROW]
[ROW][C]10[/C][C] 8.3[/C][C] 12.51[/C][C]-4.211[/C][/ROW]
[ROW][C]11[/C][C] 10.3[/C][C] 12.32[/C][C]-2.016[/C][/ROW]
[ROW][C]12[/C][C] 9.3[/C][C] 12.31[/C][C]-3.009[/C][/ROW]
[ROW][C]13[/C][C] 12.3[/C][C] 12[/C][C] 0.3023[/C][/ROW]
[ROW][C]14[/C][C] 7.9[/C][C] 11.97[/C][C]-4.071[/C][/ROW]
[ROW][C]15[/C][C] 9.3[/C][C] 12.58[/C][C]-3.283[/C][/ROW]
[ROW][C]16[/C][C] 12.5[/C][C] 12.58[/C][C]-0.08285[/C][/ROW]
[ROW][C]17[/C][C] 15.9[/C][C] 12.42[/C][C] 3.478[/C][/ROW]
[ROW][C]18[/C][C] 9.1[/C][C] 12.18[/C][C]-3.08[/C][/ROW]
[ROW][C]19[/C][C] 12.2[/C][C] 12.21[/C][C]-0.007864[/C][/ROW]
[ROW][C]20[/C][C] 12.3[/C][C] 13.04[/C][C]-0.7431[/C][/ROW]
[ROW][C]21[/C][C] 14.6[/C][C] 12.59[/C][C] 2.006[/C][/ROW]
[ROW][C]22[/C][C] 12.6[/C][C] 12.27[/C][C] 0.3259[/C][/ROW]
[ROW][C]23[/C][C] 12.6[/C][C] 12.48[/C][C] 0.1165[/C][/ROW]
[ROW][C]24[/C][C] 17.1[/C][C] 12.26[/C][C] 4.842[/C][/ROW]
[ROW][C]25[/C][C] 16.1[/C][C] 12.53[/C][C] 3.572[/C][/ROW]
[ROW][C]26[/C][C] 13.35[/C][C] 12.51[/C][C] 0.841[/C][/ROW]
[ROW][C]27[/C][C] 14.5[/C][C] 12.45[/C][C] 2.048[/C][/ROW]
[ROW][C]28[/C][C] 8.6[/C][C] 12.35[/C][C]-3.751[/C][/ROW]
[ROW][C]29[/C][C] 17.65[/C][C] 11.93[/C][C] 5.723[/C][/ROW]
[ROW][C]30[/C][C] 16.35[/C][C] 12.6[/C][C] 3.752[/C][/ROW]
[ROW][C]31[/C][C] 13.6[/C][C] 12.1[/C][C] 1.496[/C][/ROW]
[ROW][C]32[/C][C] 14.35[/C][C] 12.44[/C][C] 1.91[/C][/ROW]
[ROW][C]33[/C][C] 18.25[/C][C] 12.29[/C][C] 5.96[/C][/ROW]
[ROW][C]34[/C][C] 18.25[/C][C] 12.85[/C][C] 5.397[/C][/ROW]
[ROW][C]35[/C][C] 18.95[/C][C] 12.58[/C][C] 6.367[/C][/ROW]
[ROW][C]36[/C][C] 15.9[/C][C] 12.48[/C][C] 3.418[/C][/ROW]
[ROW][C]37[/C][C] 13.35[/C][C] 12.02[/C][C] 1.331[/C][/ROW]
[ROW][C]38[/C][C] 15.35[/C][C] 12.25[/C][C] 3.102[/C][/ROW]
[ROW][C]39[/C][C] 14.85[/C][C] 12.56[/C][C] 2.291[/C][/ROW]
[ROW][C]40[/C][C] 13.6[/C][C] 12.2[/C][C] 1.403[/C][/ROW]
[ROW][C]41[/C][C] 15.25[/C][C] 12.02[/C][C] 3.232[/C][/ROW]
[ROW][C]42[/C][C] 13.2[/C][C] 13.11[/C][C] 0.09352[/C][/ROW]
[ROW][C]43[/C][C] 15.65[/C][C] 12.38[/C][C] 3.267[/C][/ROW]
[ROW][C]44[/C][C] 15.6[/C][C] 12.23[/C][C] 3.371[/C][/ROW]
[ROW][C]45[/C][C] 15.2[/C][C] 12.52[/C][C] 2.681[/C][/ROW]
[ROW][C]46[/C][C] 18.4[/C][C] 12.09[/C][C] 6.314[/C][/ROW]
[ROW][C]47[/C][C] 19.05[/C][C] 12.14[/C][C] 6.907[/C][/ROW]
[ROW][C]48[/C][C] 18.55[/C][C] 13.38[/C][C] 5.168[/C][/ROW]
[ROW][C]49[/C][C] 12.4[/C][C] 12.27[/C][C] 0.1316[/C][/ROW]
[ROW][C]50[/C][C] 14.6[/C][C] 13.27[/C][C] 1.33[/C][/ROW]
[ROW][C]51[/C][C] 14.05[/C][C] 12.44[/C][C] 1.609[/C][/ROW]
[ROW][C]52[/C][C] 11.85[/C][C] 12.68[/C][C]-0.8265[/C][/ROW]
[ROW][C]53[/C][C] 7.85[/C][C] 12.24[/C][C]-4.394[/C][/ROW]
[ROW][C]54[/C][C] 15.2[/C][C] 12.62[/C][C] 2.583[/C][/ROW]
[ROW][C]55[/C][C] 12.9[/C][C] 12.05[/C][C] 0.8493[/C][/ROW]
[ROW][C]56[/C][C] 12.8[/C][C] 12.53[/C][C] 0.2711[/C][/ROW]
[ROW][C]57[/C][C] 7.4[/C][C] 12.11[/C][C]-4.715[/C][/ROW]
[ROW][C]58[/C][C] 6.7[/C][C] 11.89[/C][C]-5.192[/C][/ROW]
[ROW][C]59[/C][C] 14.8[/C][C] 12.77[/C][C] 2.032[/C][/ROW]
[ROW][C]60[/C][C] 13.3[/C][C] 12.45[/C][C] 0.8459[/C][/ROW]
[ROW][C]61[/C][C] 11.1[/C][C] 12.11[/C][C]-1.009[/C][/ROW]
[ROW][C]62[/C][C] 8.2[/C][C] 11.98[/C][C]-3.784[/C][/ROW]
[ROW][C]63[/C][C] 11.4[/C][C] 12.85[/C][C]-1.447[/C][/ROW]
[ROW][C]64[/C][C] 6.4[/C][C] 12.34[/C][C]-5.937[/C][/ROW]
[ROW][C]65[/C][C] 11.3[/C][C] 12.36[/C][C]-1.058[/C][/ROW]
[ROW][C]66[/C][C] 10[/C][C] 12.55[/C][C]-2.547[/C][/ROW]
[ROW][C]67[/C][C] 6.4[/C][C] 11.88[/C][C]-5.479[/C][/ROW]
[ROW][C]68[/C][C] 10.8[/C][C] 12.51[/C][C]-1.709[/C][/ROW]
[ROW][C]69[/C][C] 13.8[/C][C] 12.31[/C][C] 1.491[/C][/ROW]
[ROW][C]70[/C][C] 11.7[/C][C] 12.5[/C][C]-0.7966[/C][/ROW]
[ROW][C]71[/C][C] 13.4[/C][C] 12.19[/C][C] 1.207[/C][/ROW]
[ROW][C]72[/C][C] 11.7[/C][C] 12.6[/C][C]-0.8959[/C][/ROW]
[ROW][C]73[/C][C] 9[/C][C] 12.45[/C][C]-3.449[/C][/ROW]
[ROW][C]74[/C][C] 9.7[/C][C] 12.55[/C][C]-2.847[/C][/ROW]
[ROW][C]75[/C][C] 10.8[/C][C] 12.55[/C][C]-1.751[/C][/ROW]
[ROW][C]76[/C][C] 12.7[/C][C] 12.28[/C][C] 0.4194[/C][/ROW]
[ROW][C]77[/C][C] 11.8[/C][C] 12.04[/C][C]-0.2412[/C][/ROW]
[ROW][C]78[/C][C] 5.9[/C][C] 12.75[/C][C]-6.846[/C][/ROW]
[ROW][C]79[/C][C] 11.4[/C][C] 12.27[/C][C]-0.8684[/C][/ROW]
[ROW][C]80[/C][C] 13[/C][C] 12.04[/C][C] 0.9625[/C][/ROW]
[ROW][C]81[/C][C] 11.3[/C][C] 11.98[/C][C]-0.6815[/C][/ROW]
[ROW][C]82[/C][C] 6.7[/C][C] 12.15[/C][C]-5.45[/C][/ROW]
[ROW][C]83[/C][C] 12.1[/C][C] 12.65[/C][C]-0.551[/C][/ROW]
[ROW][C]84[/C][C] 13.3[/C][C] 12.14[/C][C] 1.157[/C][/ROW]
[ROW][C]85[/C][C] 5.7[/C][C] 12.22[/C][C]-6.524[/C][/ROW]
[ROW][C]86[/C][C] 13.3[/C][C] 12[/C][C] 1.301[/C][/ROW]
[ROW][C]87[/C][C] 7.6[/C][C] 12.62[/C][C]-5.022[/C][/ROW]
[ROW][C]88[/C][C] 11.1[/C][C] 14.03[/C][C]-2.929[/C][/ROW]
[ROW][C]89[/C][C] 13[/C][C] 14.23[/C][C]-1.23[/C][/ROW]
[ROW][C]90[/C][C] 9.9[/C][C] 14.1[/C][C]-4.199[/C][/ROW]
[ROW][C]91[/C][C] 11.1[/C][C] 14.8[/C][C]-3.701[/C][/ROW]
[ROW][C]92[/C][C] 4.35[/C][C] 14.53[/C][C]-10.18[/C][/ROW]
[ROW][C]93[/C][C] 12.7[/C][C] 14.21[/C][C]-1.51[/C][/ROW]
[ROW][C]94[/C][C] 18.1[/C][C] 14.81[/C][C] 3.289[/C][/ROW]
[ROW][C]95[/C][C] 12.6[/C][C] 14.11[/C][C]-1.512[/C][/ROW]
[ROW][C]96[/C][C] 19.1[/C][C] 13.88[/C][C] 5.224[/C][/ROW]
[ROW][C]97[/C][C] 18.4[/C][C] 14.23[/C][C] 4.171[/C][/ROW]
[ROW][C]98[/C][C] 14.7[/C][C] 14.42[/C][C] 0.2757[/C][/ROW]
[ROW][C]99[/C][C] 10.6[/C][C] 14.52[/C][C]-3.916[/C][/ROW]
[ROW][C]100[/C][C] 12.6[/C][C] 14.25[/C][C]-1.647[/C][/ROW]
[ROW][C]101[/C][C] 16.2[/C][C] 14.67[/C][C] 1.533[/C][/ROW]
[ROW][C]102[/C][C] 18.9[/C][C] 14.37[/C][C] 4.534[/C][/ROW]
[ROW][C]103[/C][C] 14.1[/C][C] 13.86[/C][C] 0.2417[/C][/ROW]
[ROW][C]104[/C][C] 16.15[/C][C] 14.19[/C][C] 1.964[/C][/ROW]
[ROW][C]105[/C][C] 14.75[/C][C] 14.16[/C][C] 0.5852[/C][/ROW]
[ROW][C]106[/C][C] 14.8[/C][C] 14.18[/C][C] 0.618[/C][/ROW]
[ROW][C]107[/C][C] 12.45[/C][C] 14.66[/C][C]-2.211[/C][/ROW]
[ROW][C]108[/C][C] 12.65[/C][C] 14.81[/C][C]-2.159[/C][/ROW]
[ROW][C]109[/C][C] 17.35[/C][C] 14.18[/C][C] 3.174[/C][/ROW]
[ROW][C]110[/C][C] 18.4[/C][C] 14.11[/C][C] 4.286[/C][/ROW]
[ROW][C]111[/C][C] 11.6[/C][C] 14.38[/C][C]-2.784[/C][/ROW]
[ROW][C]112[/C][C] 17.75[/C][C] 15.72[/C][C] 2.031[/C][/ROW]
[ROW][C]113[/C][C] 15.25[/C][C] 13.8[/C][C] 1.447[/C][/ROW]
[ROW][C]114[/C][C] 17.65[/C][C] 13.8[/C][C] 3.848[/C][/ROW]
[ROW][C]115[/C][C] 14.75[/C][C] 14.03[/C][C] 0.7197[/C][/ROW]
[ROW][C]116[/C][C] 9.9[/C][C] 14.04[/C][C]-4.141[/C][/ROW]
[ROW][C]117[/C][C] 16[/C][C] 14.19[/C][C] 1.809[/C][/ROW]
[ROW][C]118[/C][C] 13.85[/C][C] 14.19[/C][C]-0.3424[/C][/ROW]
[ROW][C]119[/C][C] 17.1[/C][C] 14.23[/C][C] 2.868[/C][/ROW]
[ROW][C]120[/C][C] 14.6[/C][C] 13.9[/C][C] 0.7029[/C][/ROW]
[ROW][C]121[/C][C] 15.4[/C][C] 14.02[/C][C] 1.378[/C][/ROW]
[ROW][C]122[/C][C] 17.6[/C][C] 14.55[/C][C] 3.051[/C][/ROW]
[ROW][C]123[/C][C] 13.9[/C][C] 13.98[/C][C]-0.07929[/C][/ROW]
[ROW][C]124[/C][C] 16.25[/C][C] 14.32[/C][C] 1.925[/C][/ROW]
[ROW][C]125[/C][C] 15.65[/C][C] 14.11[/C][C] 1.544[/C][/ROW]
[ROW][C]126[/C][C] 14.6[/C][C] 14.4[/C][C] 0.1975[/C][/ROW]
[ROW][C]127[/C][C] 11.2[/C][C] 14.05[/C][C]-2.846[/C][/ROW]
[ROW][C]128[/C][C] 16.35[/C][C] 13.78[/C][C] 2.568[/C][/ROW]
[ROW][C]129[/C][C] 15.85[/C][C] 13.94[/C][C] 1.91[/C][/ROW]
[ROW][C]130[/C][C] 7.65[/C][C] 14.46[/C][C]-6.814[/C][/ROW]
[ROW][C]131[/C][C] 12.35[/C][C] 13.49[/C][C]-1.138[/C][/ROW]
[ROW][C]132[/C][C] 15.6[/C][C] 14.15[/C][C] 1.449[/C][/ROW]
[ROW][C]133[/C][C] 13.1[/C][C] 14.41[/C][C]-1.309[/C][/ROW]
[ROW][C]134[/C][C] 12.85[/C][C] 13.88[/C][C]-1.033[/C][/ROW]
[ROW][C]135[/C][C] 9.5[/C][C] 13.94[/C][C]-4.441[/C][/ROW]
[ROW][C]136[/C][C] 11.85[/C][C] 14.19[/C][C]-2.336[/C][/ROW]
[ROW][C]137[/C][C] 13.6[/C][C] 14.27[/C][C]-0.67[/C][/ROW]
[ROW][C]138[/C][C] 17.6[/C][C] 14.18[/C][C] 3.415[/C][/ROW]
[ROW][C]139[/C][C] 16.1[/C][C] 13.82[/C][C] 2.283[/C][/ROW]
[ROW][C]140[/C][C] 13.35[/C][C] 14.15[/C][C]-0.7997[/C][/ROW]
[ROW][C]141[/C][C] 15.15[/C][C] 14.26[/C][C] 0.8866[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=286047&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=286047&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	12.2	12.4	-0.1982
2	12.6	12.22	0.3817
3	10.6	12.63	-2.026
4	12	12.23	-0.2268
5	11.9	12.49	-0.5863
6	9.6	13.14	-3.536
7	13.8	13.18	0.6244
8	9.9	12.21	-2.315
9	11.5	12.25	-0.7486
10	8.3	12.51	-4.211
11	10.3	12.32	-2.016
12	9.3	12.31	-3.009
13	12.3	12	0.3023
14	7.9	11.97	-4.071
15	9.3	12.58	-3.283
16	12.5	12.58	-0.08285
17	15.9	12.42	3.478
18	9.1	12.18	-3.08
19	12.2	12.21	-0.007864
20	12.3	13.04	-0.7431
21	14.6	12.59	2.006
22	12.6	12.27	0.3259
23	12.6	12.48	0.1165
24	17.1	12.26	4.842
25	16.1	12.53	3.572
26	13.35	12.51	0.841
27	14.5	12.45	2.048
28	8.6	12.35	-3.751
29	17.65	11.93	5.723
30	16.35	12.6	3.752
31	13.6	12.1	1.496
32	14.35	12.44	1.91
33	18.25	12.29	5.96
34	18.25	12.85	5.397
35	18.95	12.58	6.367
36	15.9	12.48	3.418
37	13.35	12.02	1.331
38	15.35	12.25	3.102
39	14.85	12.56	2.291
40	13.6	12.2	1.403
41	15.25	12.02	3.232
42	13.2	13.11	0.09352
43	15.65	12.38	3.267
44	15.6	12.23	3.371
45	15.2	12.52	2.681
46	18.4	12.09	6.314
47	19.05	12.14	6.907
48	18.55	13.38	5.168
49	12.4	12.27	0.1316
50	14.6	13.27	1.33
51	14.05	12.44	1.609
52	11.85	12.68	-0.8265
53	7.85	12.24	-4.394
54	15.2	12.62	2.583
55	12.9	12.05	0.8493
56	12.8	12.53	0.2711
57	7.4	12.11	-4.715
58	6.7	11.89	-5.192
59	14.8	12.77	2.032
60	13.3	12.45	0.8459
61	11.1	12.11	-1.009
62	8.2	11.98	-3.784
63	11.4	12.85	-1.447
64	6.4	12.34	-5.937
65	11.3	12.36	-1.058
66	10	12.55	-2.547
67	6.4	11.88	-5.479
68	10.8	12.51	-1.709
69	13.8	12.31	1.491
70	11.7	12.5	-0.7966
71	13.4	12.19	1.207
72	11.7	12.6	-0.8959
73	9	12.45	-3.449
74	9.7	12.55	-2.847
75	10.8	12.55	-1.751
76	12.7	12.28	0.4194
77	11.8	12.04	-0.2412
78	5.9	12.75	-6.846
79	11.4	12.27	-0.8684
80	13	12.04	0.9625
81	11.3	11.98	-0.6815
82	6.7	12.15	-5.45
83	12.1	12.65	-0.551
84	13.3	12.14	1.157
85	5.7	12.22	-6.524
86	13.3	12	1.301
87	7.6	12.62	-5.022
88	11.1	14.03	-2.929
89	13	14.23	-1.23
90	9.9	14.1	-4.199
91	11.1	14.8	-3.701
92	4.35	14.53	-10.18
93	12.7	14.21	-1.51
94	18.1	14.81	3.289
95	12.6	14.11	-1.512
96	19.1	13.88	5.224
97	18.4	14.23	4.171
98	14.7	14.42	0.2757
99	10.6	14.52	-3.916
100	12.6	14.25	-1.647
101	16.2	14.67	1.533
102	18.9	14.37	4.534
103	14.1	13.86	0.2417
104	16.15	14.19	1.964
105	14.75	14.16	0.5852
106	14.8	14.18	0.618
107	12.45	14.66	-2.211
108	12.65	14.81	-2.159
109	17.35	14.18	3.174
110	18.4	14.11	4.286
111	11.6	14.38	-2.784
112	17.75	15.72	2.031
113	15.25	13.8	1.447
114	17.65	13.8	3.848
115	14.75	14.03	0.7197
116	9.9	14.04	-4.141
117	16	14.19	1.809
118	13.85	14.19	-0.3424
119	17.1	14.23	2.868
120	14.6	13.9	0.7029
121	15.4	14.02	1.378
122	17.6	14.55	3.051
123	13.9	13.98	-0.07929
124	16.25	14.32	1.925
125	15.65	14.11	1.544
126	14.6	14.4	0.1975
127	11.2	14.05	-2.846
128	16.35	13.78	2.568
129	15.85	13.94	1.91
130	7.65	14.46	-6.814
131	12.35	13.49	-1.138
132	15.6	14.15	1.449
133	13.1	14.41	-1.309
134	12.85	13.88	-1.033
135	9.5	13.94	-4.441
136	11.85	14.19	-2.336
137	13.6	14.27	-0.67
138	17.6	14.18	3.415
139	16.1	13.82	2.283
140	13.35	14.15	-0.7997
141	15.15	14.26	0.8866

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
7	0.1205	0.2411	0.8795
8	0.1293	0.2587	0.8707
9	0.0601	0.1202	0.9399
10	0.111	0.222	0.889
11	0.05934	0.1187	0.9407
12	0.03774	0.07548	0.9623
13	0.02301	0.04601	0.977
14	0.04003	0.08006	0.96
15	0.02741	0.05482	0.9726
16	0.01905	0.0381	0.981
17	0.04123	0.08246	0.9588
18	0.0322	0.06439	0.9678
19	0.02004	0.04008	0.98
20	0.01294	0.02588	0.9871
21	0.01096	0.02192	0.989
22	0.006712	0.01342	0.9933
23	0.00457	0.009139	0.9954
24	0.01465	0.0293	0.9853
25	0.02936	0.05873	0.9706
26	0.02393	0.04787	0.9761
27	0.03294	0.06587	0.9671
28	0.0684	0.1368	0.9316
29	0.1632	0.3264	0.8368
30	0.1944	0.3888	0.8056
31	0.1599	0.3197	0.8401
32	0.1338	0.2676	0.8662
33	0.2172	0.4344	0.7828
34	0.3608	0.7216	0.6392
35	0.5465	0.9069	0.4535
36	0.5411	0.9178	0.4589
37	0.4892	0.9783	0.5108
38	0.4656	0.9312	0.5344
39	0.4248	0.8496	0.5752
40	0.3786	0.7572	0.6214
41	0.3601	0.7203	0.6399
42	0.3102	0.6205	0.6898
43	0.3088	0.6175	0.6912
44	0.2943	0.5886	0.7057
45	0.2739	0.5477	0.7261
46	0.3999	0.7998	0.6001
47	0.5933	0.8134	0.4067
48	0.6953	0.6094	0.3047
49	0.661	0.678	0.339
50	0.633	0.734	0.367
51	0.6062	0.7877	0.3938
52	0.5763	0.8474	0.4237
53	0.6667	0.6666	0.3333
54	0.6655	0.6691	0.3345
55	0.6316	0.7369	0.3684
56	0.5955	0.8089	0.4045
57	0.6822	0.6356	0.3178
58	0.7497	0.5005	0.2503
59	0.7479	0.5042	0.2521
60	0.7243	0.5514	0.2757
61	0.6887	0.6226	0.3113
62	0.6933	0.6134	0.3067
63	0.6731	0.6538	0.3269
64	0.7766	0.4467	0.2234
65	0.7429	0.5143	0.2571
66	0.7247	0.5507	0.2753
67	0.7804	0.4391	0.2196
68	0.7546	0.4907	0.2454
69	0.7383	0.5234	0.2617
70	0.7053	0.5895	0.2947
71	0.6838	0.6325	0.3162
72	0.6559	0.6883	0.3441
73	0.6573	0.6855	0.3427
74	0.6352	0.7297	0.3648
75	0.6014	0.7972	0.3986
76	0.5712	0.8575	0.4288
77	0.5291	0.9419	0.4709
78	0.6501	0.6998	0.3499
79	0.608	0.7839	0.392
80	0.5833	0.8335	0.4167
81	0.5401	0.9198	0.4599
82	0.5851	0.8298	0.4149
83	0.55	0.9	0.45
84	0.5513	0.8975	0.4487
85	0.6269	0.7462	0.3731
86	0.6367	0.7265	0.3633
87	0.6342	0.7317	0.3658
88	0.6158	0.7684	0.3842
89	0.5694	0.8612	0.4306
90	0.5905	0.819	0.4095
91	0.5763	0.8474	0.4237
92	0.9056	0.1889	0.09445
93	0.8962	0.2076	0.1038
94	0.9192	0.1615	0.08077
95	0.9075	0.185	0.09251
96	0.9509	0.09826	0.04913
97	0.9657	0.06856	0.03428
98	0.9543	0.09135	0.04568
99	0.9648	0.07032	0.03516
100	0.9564	0.08716	0.04358
101	0.9468	0.1063	0.05315
102	0.9641	0.07176	0.03588
103	0.9514	0.09723	0.04862
104	0.9416	0.1169	0.05845
105	0.9227	0.1545	0.07726
106	0.8994	0.2012	0.1006
107	0.8871	0.2257	0.1129
108	0.8743	0.2514	0.1257
109	0.8742	0.2517	0.1258
110	0.9023	0.1953	0.09766
111	0.906	0.188	0.09401
112	0.8915	0.217	0.1085
113	0.8637	0.2726	0.1363
114	0.882	0.236	0.118
115	0.8518	0.2964	0.1482
116	0.8924	0.2152	0.1076
117	0.865	0.2701	0.135
118	0.8219	0.3563	0.1781
119	0.8202	0.3596	0.1798
120	0.7659	0.4683	0.2341
121	0.7106	0.5789	0.2894
122	0.7422	0.5156	0.2578
123	0.6713	0.6574	0.3287
124	0.6629	0.6742	0.3371
125	0.6351	0.7297	0.3649
126	0.5644	0.8712	0.4356
127	0.5787	0.8427	0.4213
128	0.571	0.858	0.429
129	0.4785	0.9571	0.5215
130	0.7297	0.5407	0.2703
131	0.6299	0.7402	0.3701
132	0.5071	0.9858	0.4929
133	0.3718	0.7437	0.6282
134	0.2352	0.4704	0.7648

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
7 &  0.1205 &  0.2411 &  0.8795 \tabularnewline
8 &  0.1293 &  0.2587 &  0.8707 \tabularnewline
9 &  0.0601 &  0.1202 &  0.9399 \tabularnewline
10 &  0.111 &  0.222 &  0.889 \tabularnewline
11 &  0.05934 &  0.1187 &  0.9407 \tabularnewline
12 &  0.03774 &  0.07548 &  0.9623 \tabularnewline
13 &  0.02301 &  0.04601 &  0.977 \tabularnewline
14 &  0.04003 &  0.08006 &  0.96 \tabularnewline
15 &  0.02741 &  0.05482 &  0.9726 \tabularnewline
16 &  0.01905 &  0.0381 &  0.981 \tabularnewline
17 &  0.04123 &  0.08246 &  0.9588 \tabularnewline
18 &  0.0322 &  0.06439 &  0.9678 \tabularnewline
19 &  0.02004 &  0.04008 &  0.98 \tabularnewline
20 &  0.01294 &  0.02588 &  0.9871 \tabularnewline
21 &  0.01096 &  0.02192 &  0.989 \tabularnewline
22 &  0.006712 &  0.01342 &  0.9933 \tabularnewline
23 &  0.00457 &  0.009139 &  0.9954 \tabularnewline
24 &  0.01465 &  0.0293 &  0.9853 \tabularnewline
25 &  0.02936 &  0.05873 &  0.9706 \tabularnewline
26 &  0.02393 &  0.04787 &  0.9761 \tabularnewline
27 &  0.03294 &  0.06587 &  0.9671 \tabularnewline
28 &  0.0684 &  0.1368 &  0.9316 \tabularnewline
29 &  0.1632 &  0.3264 &  0.8368 \tabularnewline
30 &  0.1944 &  0.3888 &  0.8056 \tabularnewline
31 &  0.1599 &  0.3197 &  0.8401 \tabularnewline
32 &  0.1338 &  0.2676 &  0.8662 \tabularnewline
33 &  0.2172 &  0.4344 &  0.7828 \tabularnewline
34 &  0.3608 &  0.7216 &  0.6392 \tabularnewline
35 &  0.5465 &  0.9069 &  0.4535 \tabularnewline
36 &  0.5411 &  0.9178 &  0.4589 \tabularnewline
37 &  0.4892 &  0.9783 &  0.5108 \tabularnewline
38 &  0.4656 &  0.9312 &  0.5344 \tabularnewline
39 &  0.4248 &  0.8496 &  0.5752 \tabularnewline
40 &  0.3786 &  0.7572 &  0.6214 \tabularnewline
41 &  0.3601 &  0.7203 &  0.6399 \tabularnewline
42 &  0.3102 &  0.6205 &  0.6898 \tabularnewline
43 &  0.3088 &  0.6175 &  0.6912 \tabularnewline
44 &  0.2943 &  0.5886 &  0.7057 \tabularnewline
45 &  0.2739 &  0.5477 &  0.7261 \tabularnewline
46 &  0.3999 &  0.7998 &  0.6001 \tabularnewline
47 &  0.5933 &  0.8134 &  0.4067 \tabularnewline
48 &  0.6953 &  0.6094 &  0.3047 \tabularnewline
49 &  0.661 &  0.678 &  0.339 \tabularnewline
50 &  0.633 &  0.734 &  0.367 \tabularnewline
51 &  0.6062 &  0.7877 &  0.3938 \tabularnewline
52 &  0.5763 &  0.8474 &  0.4237 \tabularnewline
53 &  0.6667 &  0.6666 &  0.3333 \tabularnewline
54 &  0.6655 &  0.6691 &  0.3345 \tabularnewline
55 &  0.6316 &  0.7369 &  0.3684 \tabularnewline
56 &  0.5955 &  0.8089 &  0.4045 \tabularnewline
57 &  0.6822 &  0.6356 &  0.3178 \tabularnewline
58 &  0.7497 &  0.5005 &  0.2503 \tabularnewline
59 &  0.7479 &  0.5042 &  0.2521 \tabularnewline
60 &  0.7243 &  0.5514 &  0.2757 \tabularnewline
61 &  0.6887 &  0.6226 &  0.3113 \tabularnewline
62 &  0.6933 &  0.6134 &  0.3067 \tabularnewline
63 &  0.6731 &  0.6538 &  0.3269 \tabularnewline
64 &  0.7766 &  0.4467 &  0.2234 \tabularnewline
65 &  0.7429 &  0.5143 &  0.2571 \tabularnewline
66 &  0.7247 &  0.5507 &  0.2753 \tabularnewline
67 &  0.7804 &  0.4391 &  0.2196 \tabularnewline
68 &  0.7546 &  0.4907 &  0.2454 \tabularnewline
69 &  0.7383 &  0.5234 &  0.2617 \tabularnewline
70 &  0.7053 &  0.5895 &  0.2947 \tabularnewline
71 &  0.6838 &  0.6325 &  0.3162 \tabularnewline
72 &  0.6559 &  0.6883 &  0.3441 \tabularnewline
73 &  0.6573 &  0.6855 &  0.3427 \tabularnewline
74 &  0.6352 &  0.7297 &  0.3648 \tabularnewline
75 &  0.6014 &  0.7972 &  0.3986 \tabularnewline
76 &  0.5712 &  0.8575 &  0.4288 \tabularnewline
77 &  0.5291 &  0.9419 &  0.4709 \tabularnewline
78 &  0.6501 &  0.6998 &  0.3499 \tabularnewline
79 &  0.608 &  0.7839 &  0.392 \tabularnewline
80 &  0.5833 &  0.8335 &  0.4167 \tabularnewline
81 &  0.5401 &  0.9198 &  0.4599 \tabularnewline
82 &  0.5851 &  0.8298 &  0.4149 \tabularnewline
83 &  0.55 &  0.9 &  0.45 \tabularnewline
84 &  0.5513 &  0.8975 &  0.4487 \tabularnewline
85 &  0.6269 &  0.7462 &  0.3731 \tabularnewline
86 &  0.6367 &  0.7265 &  0.3633 \tabularnewline
87 &  0.6342 &  0.7317 &  0.3658 \tabularnewline
88 &  0.6158 &  0.7684 &  0.3842 \tabularnewline
89 &  0.5694 &  0.8612 &  0.4306 \tabularnewline
90 &  0.5905 &  0.819 &  0.4095 \tabularnewline
91 &  0.5763 &  0.8474 &  0.4237 \tabularnewline
92 &  0.9056 &  0.1889 &  0.09445 \tabularnewline
93 &  0.8962 &  0.2076 &  0.1038 \tabularnewline
94 &  0.9192 &  0.1615 &  0.08077 \tabularnewline
95 &  0.9075 &  0.185 &  0.09251 \tabularnewline
96 &  0.9509 &  0.09826 &  0.04913 \tabularnewline
97 &  0.9657 &  0.06856 &  0.03428 \tabularnewline
98 &  0.9543 &  0.09135 &  0.04568 \tabularnewline
99 &  0.9648 &  0.07032 &  0.03516 \tabularnewline
100 &  0.9564 &  0.08716 &  0.04358 \tabularnewline
101 &  0.9468 &  0.1063 &  0.05315 \tabularnewline
102 &  0.9641 &  0.07176 &  0.03588 \tabularnewline
103 &  0.9514 &  0.09723 &  0.04862 \tabularnewline
104 &  0.9416 &  0.1169 &  0.05845 \tabularnewline
105 &  0.9227 &  0.1545 &  0.07726 \tabularnewline
106 &  0.8994 &  0.2012 &  0.1006 \tabularnewline
107 &  0.8871 &  0.2257 &  0.1129 \tabularnewline
108 &  0.8743 &  0.2514 &  0.1257 \tabularnewline
109 &  0.8742 &  0.2517 &  0.1258 \tabularnewline
110 &  0.9023 &  0.1953 &  0.09766 \tabularnewline
111 &  0.906 &  0.188 &  0.09401 \tabularnewline
112 &  0.8915 &  0.217 &  0.1085 \tabularnewline
113 &  0.8637 &  0.2726 &  0.1363 \tabularnewline
114 &  0.882 &  0.236 &  0.118 \tabularnewline
115 &  0.8518 &  0.2964 &  0.1482 \tabularnewline
116 &  0.8924 &  0.2152 &  0.1076 \tabularnewline
117 &  0.865 &  0.2701 &  0.135 \tabularnewline
118 &  0.8219 &  0.3563 &  0.1781 \tabularnewline
119 &  0.8202 &  0.3596 &  0.1798 \tabularnewline
120 &  0.7659 &  0.4683 &  0.2341 \tabularnewline
121 &  0.7106 &  0.5789 &  0.2894 \tabularnewline
122 &  0.7422 &  0.5156 &  0.2578 \tabularnewline
123 &  0.6713 &  0.6574 &  0.3287 \tabularnewline
124 &  0.6629 &  0.6742 &  0.3371 \tabularnewline
125 &  0.6351 &  0.7297 &  0.3649 \tabularnewline
126 &  0.5644 &  0.8712 &  0.4356 \tabularnewline
127 &  0.5787 &  0.8427 &  0.4213 \tabularnewline
128 &  0.571 &  0.858 &  0.429 \tabularnewline
129 &  0.4785 &  0.9571 &  0.5215 \tabularnewline
130 &  0.7297 &  0.5407 &  0.2703 \tabularnewline
131 &  0.6299 &  0.7402 &  0.3701 \tabularnewline
132 &  0.5071 &  0.9858 &  0.4929 \tabularnewline
133 &  0.3718 &  0.7437 &  0.6282 \tabularnewline
134 &  0.2352 &  0.4704 &  0.7648 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=286047&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] 0.1205[/C][C] 0.2411[/C][C] 0.8795[/C][/ROW]
[ROW][C]8[/C][C] 0.1293[/C][C] 0.2587[/C][C] 0.8707[/C][/ROW]
[ROW][C]9[/C][C] 0.0601[/C][C] 0.1202[/C][C] 0.9399[/C][/ROW]
[ROW][C]10[/C][C] 0.111[/C][C] 0.222[/C][C] 0.889[/C][/ROW]
[ROW][C]11[/C][C] 0.05934[/C][C] 0.1187[/C][C] 0.9407[/C][/ROW]
[ROW][C]12[/C][C] 0.03774[/C][C] 0.07548[/C][C] 0.9623[/C][/ROW]
[ROW][C]13[/C][C] 0.02301[/C][C] 0.04601[/C][C] 0.977[/C][/ROW]
[ROW][C]14[/C][C] 0.04003[/C][C] 0.08006[/C][C] 0.96[/C][/ROW]
[ROW][C]15[/C][C] 0.02741[/C][C] 0.05482[/C][C] 0.9726[/C][/ROW]
[ROW][C]16[/C][C] 0.01905[/C][C] 0.0381[/C][C] 0.981[/C][/ROW]
[ROW][C]17[/C][C] 0.04123[/C][C] 0.08246[/C][C] 0.9588[/C][/ROW]
[ROW][C]18[/C][C] 0.0322[/C][C] 0.06439[/C][C] 0.9678[/C][/ROW]
[ROW][C]19[/C][C] 0.02004[/C][C] 0.04008[/C][C] 0.98[/C][/ROW]
[ROW][C]20[/C][C] 0.01294[/C][C] 0.02588[/C][C] 0.9871[/C][/ROW]
[ROW][C]21[/C][C] 0.01096[/C][C] 0.02192[/C][C] 0.989[/C][/ROW]
[ROW][C]22[/C][C] 0.006712[/C][C] 0.01342[/C][C] 0.9933[/C][/ROW]
[ROW][C]23[/C][C] 0.00457[/C][C] 0.009139[/C][C] 0.9954[/C][/ROW]
[ROW][C]24[/C][C] 0.01465[/C][C] 0.0293[/C][C] 0.9853[/C][/ROW]
[ROW][C]25[/C][C] 0.02936[/C][C] 0.05873[/C][C] 0.9706[/C][/ROW]
[ROW][C]26[/C][C] 0.02393[/C][C] 0.04787[/C][C] 0.9761[/C][/ROW]
[ROW][C]27[/C][C] 0.03294[/C][C] 0.06587[/C][C] 0.9671[/C][/ROW]
[ROW][C]28[/C][C] 0.0684[/C][C] 0.1368[/C][C] 0.9316[/C][/ROW]
[ROW][C]29[/C][C] 0.1632[/C][C] 0.3264[/C][C] 0.8368[/C][/ROW]
[ROW][C]30[/C][C] 0.1944[/C][C] 0.3888[/C][C] 0.8056[/C][/ROW]
[ROW][C]31[/C][C] 0.1599[/C][C] 0.3197[/C][C] 0.8401[/C][/ROW]
[ROW][C]32[/C][C] 0.1338[/C][C] 0.2676[/C][C] 0.8662[/C][/ROW]
[ROW][C]33[/C][C] 0.2172[/C][C] 0.4344[/C][C] 0.7828[/C][/ROW]
[ROW][C]34[/C][C] 0.3608[/C][C] 0.7216[/C][C] 0.6392[/C][/ROW]
[ROW][C]35[/C][C] 0.5465[/C][C] 0.9069[/C][C] 0.4535[/C][/ROW]
[ROW][C]36[/C][C] 0.5411[/C][C] 0.9178[/C][C] 0.4589[/C][/ROW]
[ROW][C]37[/C][C] 0.4892[/C][C] 0.9783[/C][C] 0.5108[/C][/ROW]
[ROW][C]38[/C][C] 0.4656[/C][C] 0.9312[/C][C] 0.5344[/C][/ROW]
[ROW][C]39[/C][C] 0.4248[/C][C] 0.8496[/C][C] 0.5752[/C][/ROW]
[ROW][C]40[/C][C] 0.3786[/C][C] 0.7572[/C][C] 0.6214[/C][/ROW]
[ROW][C]41[/C][C] 0.3601[/C][C] 0.7203[/C][C] 0.6399[/C][/ROW]
[ROW][C]42[/C][C] 0.3102[/C][C] 0.6205[/C][C] 0.6898[/C][/ROW]
[ROW][C]43[/C][C] 0.3088[/C][C] 0.6175[/C][C] 0.6912[/C][/ROW]
[ROW][C]44[/C][C] 0.2943[/C][C] 0.5886[/C][C] 0.7057[/C][/ROW]
[ROW][C]45[/C][C] 0.2739[/C][C] 0.5477[/C][C] 0.7261[/C][/ROW]
[ROW][C]46[/C][C] 0.3999[/C][C] 0.7998[/C][C] 0.6001[/C][/ROW]
[ROW][C]47[/C][C] 0.5933[/C][C] 0.8134[/C][C] 0.4067[/C][/ROW]
[ROW][C]48[/C][C] 0.6953[/C][C] 0.6094[/C][C] 0.3047[/C][/ROW]
[ROW][C]49[/C][C] 0.661[/C][C] 0.678[/C][C] 0.339[/C][/ROW]
[ROW][C]50[/C][C] 0.633[/C][C] 0.734[/C][C] 0.367[/C][/ROW]
[ROW][C]51[/C][C] 0.6062[/C][C] 0.7877[/C][C] 0.3938[/C][/ROW]
[ROW][C]52[/C][C] 0.5763[/C][C] 0.8474[/C][C] 0.4237[/C][/ROW]
[ROW][C]53[/C][C] 0.6667[/C][C] 0.6666[/C][C] 0.3333[/C][/ROW]
[ROW][C]54[/C][C] 0.6655[/C][C] 0.6691[/C][C] 0.3345[/C][/ROW]
[ROW][C]55[/C][C] 0.6316[/C][C] 0.7369[/C][C] 0.3684[/C][/ROW]
[ROW][C]56[/C][C] 0.5955[/C][C] 0.8089[/C][C] 0.4045[/C][/ROW]
[ROW][C]57[/C][C] 0.6822[/C][C] 0.6356[/C][C] 0.3178[/C][/ROW]
[ROW][C]58[/C][C] 0.7497[/C][C] 0.5005[/C][C] 0.2503[/C][/ROW]
[ROW][C]59[/C][C] 0.7479[/C][C] 0.5042[/C][C] 0.2521[/C][/ROW]
[ROW][C]60[/C][C] 0.7243[/C][C] 0.5514[/C][C] 0.2757[/C][/ROW]
[ROW][C]61[/C][C] 0.6887[/C][C] 0.6226[/C][C] 0.3113[/C][/ROW]
[ROW][C]62[/C][C] 0.6933[/C][C] 0.6134[/C][C] 0.3067[/C][/ROW]
[ROW][C]63[/C][C] 0.6731[/C][C] 0.6538[/C][C] 0.3269[/C][/ROW]
[ROW][C]64[/C][C] 0.7766[/C][C] 0.4467[/C][C] 0.2234[/C][/ROW]
[ROW][C]65[/C][C] 0.7429[/C][C] 0.5143[/C][C] 0.2571[/C][/ROW]
[ROW][C]66[/C][C] 0.7247[/C][C] 0.5507[/C][C] 0.2753[/C][/ROW]
[ROW][C]67[/C][C] 0.7804[/C][C] 0.4391[/C][C] 0.2196[/C][/ROW]
[ROW][C]68[/C][C] 0.7546[/C][C] 0.4907[/C][C] 0.2454[/C][/ROW]
[ROW][C]69[/C][C] 0.7383[/C][C] 0.5234[/C][C] 0.2617[/C][/ROW]
[ROW][C]70[/C][C] 0.7053[/C][C] 0.5895[/C][C] 0.2947[/C][/ROW]
[ROW][C]71[/C][C] 0.6838[/C][C] 0.6325[/C][C] 0.3162[/C][/ROW]
[ROW][C]72[/C][C] 0.6559[/C][C] 0.6883[/C][C] 0.3441[/C][/ROW]
[ROW][C]73[/C][C] 0.6573[/C][C] 0.6855[/C][C] 0.3427[/C][/ROW]
[ROW][C]74[/C][C] 0.6352[/C][C] 0.7297[/C][C] 0.3648[/C][/ROW]
[ROW][C]75[/C][C] 0.6014[/C][C] 0.7972[/C][C] 0.3986[/C][/ROW]
[ROW][C]76[/C][C] 0.5712[/C][C] 0.8575[/C][C] 0.4288[/C][/ROW]
[ROW][C]77[/C][C] 0.5291[/C][C] 0.9419[/C][C] 0.4709[/C][/ROW]
[ROW][C]78[/C][C] 0.6501[/C][C] 0.6998[/C][C] 0.3499[/C][/ROW]
[ROW][C]79[/C][C] 0.608[/C][C] 0.7839[/C][C] 0.392[/C][/ROW]
[ROW][C]80[/C][C] 0.5833[/C][C] 0.8335[/C][C] 0.4167[/C][/ROW]
[ROW][C]81[/C][C] 0.5401[/C][C] 0.9198[/C][C] 0.4599[/C][/ROW]
[ROW][C]82[/C][C] 0.5851[/C][C] 0.8298[/C][C] 0.4149[/C][/ROW]
[ROW][C]83[/C][C] 0.55[/C][C] 0.9[/C][C] 0.45[/C][/ROW]
[ROW][C]84[/C][C] 0.5513[/C][C] 0.8975[/C][C] 0.4487[/C][/ROW]
[ROW][C]85[/C][C] 0.6269[/C][C] 0.7462[/C][C] 0.3731[/C][/ROW]
[ROW][C]86[/C][C] 0.6367[/C][C] 0.7265[/C][C] 0.3633[/C][/ROW]
[ROW][C]87[/C][C] 0.6342[/C][C] 0.7317[/C][C] 0.3658[/C][/ROW]
[ROW][C]88[/C][C] 0.6158[/C][C] 0.7684[/C][C] 0.3842[/C][/ROW]
[ROW][C]89[/C][C] 0.5694[/C][C] 0.8612[/C][C] 0.4306[/C][/ROW]
[ROW][C]90[/C][C] 0.5905[/C][C] 0.819[/C][C] 0.4095[/C][/ROW]
[ROW][C]91[/C][C] 0.5763[/C][C] 0.8474[/C][C] 0.4237[/C][/ROW]
[ROW][C]92[/C][C] 0.9056[/C][C] 0.1889[/C][C] 0.09445[/C][/ROW]
[ROW][C]93[/C][C] 0.8962[/C][C] 0.2076[/C][C] 0.1038[/C][/ROW]
[ROW][C]94[/C][C] 0.9192[/C][C] 0.1615[/C][C] 0.08077[/C][/ROW]
[ROW][C]95[/C][C] 0.9075[/C][C] 0.185[/C][C] 0.09251[/C][/ROW]
[ROW][C]96[/C][C] 0.9509[/C][C] 0.09826[/C][C] 0.04913[/C][/ROW]
[ROW][C]97[/C][C] 0.9657[/C][C] 0.06856[/C][C] 0.03428[/C][/ROW]
[ROW][C]98[/C][C] 0.9543[/C][C] 0.09135[/C][C] 0.04568[/C][/ROW]
[ROW][C]99[/C][C] 0.9648[/C][C] 0.07032[/C][C] 0.03516[/C][/ROW]
[ROW][C]100[/C][C] 0.9564[/C][C] 0.08716[/C][C] 0.04358[/C][/ROW]
[ROW][C]101[/C][C] 0.9468[/C][C] 0.1063[/C][C] 0.05315[/C][/ROW]
[ROW][C]102[/C][C] 0.9641[/C][C] 0.07176[/C][C] 0.03588[/C][/ROW]
[ROW][C]103[/C][C] 0.9514[/C][C] 0.09723[/C][C] 0.04862[/C][/ROW]
[ROW][C]104[/C][C] 0.9416[/C][C] 0.1169[/C][C] 0.05845[/C][/ROW]
[ROW][C]105[/C][C] 0.9227[/C][C] 0.1545[/C][C] 0.07726[/C][/ROW]
[ROW][C]106[/C][C] 0.8994[/C][C] 0.2012[/C][C] 0.1006[/C][/ROW]
[ROW][C]107[/C][C] 0.8871[/C][C] 0.2257[/C][C] 0.1129[/C][/ROW]
[ROW][C]108[/C][C] 0.8743[/C][C] 0.2514[/C][C] 0.1257[/C][/ROW]
[ROW][C]109[/C][C] 0.8742[/C][C] 0.2517[/C][C] 0.1258[/C][/ROW]
[ROW][C]110[/C][C] 0.9023[/C][C] 0.1953[/C][C] 0.09766[/C][/ROW]
[ROW][C]111[/C][C] 0.906[/C][C] 0.188[/C][C] 0.09401[/C][/ROW]
[ROW][C]112[/C][C] 0.8915[/C][C] 0.217[/C][C] 0.1085[/C][/ROW]
[ROW][C]113[/C][C] 0.8637[/C][C] 0.2726[/C][C] 0.1363[/C][/ROW]
[ROW][C]114[/C][C] 0.882[/C][C] 0.236[/C][C] 0.118[/C][/ROW]
[ROW][C]115[/C][C] 0.8518[/C][C] 0.2964[/C][C] 0.1482[/C][/ROW]
[ROW][C]116[/C][C] 0.8924[/C][C] 0.2152[/C][C] 0.1076[/C][/ROW]
[ROW][C]117[/C][C] 0.865[/C][C] 0.2701[/C][C] 0.135[/C][/ROW]
[ROW][C]118[/C][C] 0.8219[/C][C] 0.3563[/C][C] 0.1781[/C][/ROW]
[ROW][C]119[/C][C] 0.8202[/C][C] 0.3596[/C][C] 0.1798[/C][/ROW]
[ROW][C]120[/C][C] 0.7659[/C][C] 0.4683[/C][C] 0.2341[/C][/ROW]
[ROW][C]121[/C][C] 0.7106[/C][C] 0.5789[/C][C] 0.2894[/C][/ROW]
[ROW][C]122[/C][C] 0.7422[/C][C] 0.5156[/C][C] 0.2578[/C][/ROW]
[ROW][C]123[/C][C] 0.6713[/C][C] 0.6574[/C][C] 0.3287[/C][/ROW]
[ROW][C]124[/C][C] 0.6629[/C][C] 0.6742[/C][C] 0.3371[/C][/ROW]
[ROW][C]125[/C][C] 0.6351[/C][C] 0.7297[/C][C] 0.3649[/C][/ROW]
[ROW][C]126[/C][C] 0.5644[/C][C] 0.8712[/C][C] 0.4356[/C][/ROW]
[ROW][C]127[/C][C] 0.5787[/C][C] 0.8427[/C][C] 0.4213[/C][/ROW]
[ROW][C]128[/C][C] 0.571[/C][C] 0.858[/C][C] 0.429[/C][/ROW]
[ROW][C]129[/C][C] 0.4785[/C][C] 0.9571[/C][C] 0.5215[/C][/ROW]
[ROW][C]130[/C][C] 0.7297[/C][C] 0.5407[/C][C] 0.2703[/C][/ROW]
[ROW][C]131[/C][C] 0.6299[/C][C] 0.7402[/C][C] 0.3701[/C][/ROW]
[ROW][C]132[/C][C] 0.5071[/C][C] 0.9858[/C][C] 0.4929[/C][/ROW]
[ROW][C]133[/C][C] 0.3718[/C][C] 0.7437[/C][C] 0.6282[/C][/ROW]
[ROW][C]134[/C][C] 0.2352[/C][C] 0.4704[/C][C] 0.7648[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=286047&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=286047&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	0.1205	0.2411	0.8795
8	0.1293	0.2587	0.8707
9	0.0601	0.1202	0.9399
10	0.111	0.222	0.889
11	0.05934	0.1187	0.9407
12	0.03774	0.07548	0.9623
13	0.02301	0.04601	0.977
14	0.04003	0.08006	0.96
15	0.02741	0.05482	0.9726
16	0.01905	0.0381	0.981
17	0.04123	0.08246	0.9588
18	0.0322	0.06439	0.9678
19	0.02004	0.04008	0.98
20	0.01294	0.02588	0.9871
21	0.01096	0.02192	0.989
22	0.006712	0.01342	0.9933
23	0.00457	0.009139	0.9954
24	0.01465	0.0293	0.9853
25	0.02936	0.05873	0.9706
26	0.02393	0.04787	0.9761
27	0.03294	0.06587	0.9671
28	0.0684	0.1368	0.9316
29	0.1632	0.3264	0.8368
30	0.1944	0.3888	0.8056
31	0.1599	0.3197	0.8401
32	0.1338	0.2676	0.8662
33	0.2172	0.4344	0.7828
34	0.3608	0.7216	0.6392
35	0.5465	0.9069	0.4535
36	0.5411	0.9178	0.4589
37	0.4892	0.9783	0.5108
38	0.4656	0.9312	0.5344
39	0.4248	0.8496	0.5752
40	0.3786	0.7572	0.6214
41	0.3601	0.7203	0.6399
42	0.3102	0.6205	0.6898
43	0.3088	0.6175	0.6912
44	0.2943	0.5886	0.7057
45	0.2739	0.5477	0.7261
46	0.3999	0.7998	0.6001
47	0.5933	0.8134	0.4067
48	0.6953	0.6094	0.3047
49	0.661	0.678	0.339
50	0.633	0.734	0.367
51	0.6062	0.7877	0.3938
52	0.5763	0.8474	0.4237
53	0.6667	0.6666	0.3333
54	0.6655	0.6691	0.3345
55	0.6316	0.7369	0.3684
56	0.5955	0.8089	0.4045
57	0.6822	0.6356	0.3178
58	0.7497	0.5005	0.2503
59	0.7479	0.5042	0.2521
60	0.7243	0.5514	0.2757
61	0.6887	0.6226	0.3113
62	0.6933	0.6134	0.3067
63	0.6731	0.6538	0.3269
64	0.7766	0.4467	0.2234
65	0.7429	0.5143	0.2571
66	0.7247	0.5507	0.2753
67	0.7804	0.4391	0.2196
68	0.7546	0.4907	0.2454
69	0.7383	0.5234	0.2617
70	0.7053	0.5895	0.2947
71	0.6838	0.6325	0.3162
72	0.6559	0.6883	0.3441
73	0.6573	0.6855	0.3427
74	0.6352	0.7297	0.3648
75	0.6014	0.7972	0.3986
76	0.5712	0.8575	0.4288
77	0.5291	0.9419	0.4709
78	0.6501	0.6998	0.3499
79	0.608	0.7839	0.392
80	0.5833	0.8335	0.4167
81	0.5401	0.9198	0.4599
82	0.5851	0.8298	0.4149
83	0.55	0.9	0.45
84	0.5513	0.8975	0.4487
85	0.6269	0.7462	0.3731
86	0.6367	0.7265	0.3633
87	0.6342	0.7317	0.3658
88	0.6158	0.7684	0.3842
89	0.5694	0.8612	0.4306
90	0.5905	0.819	0.4095
91	0.5763	0.8474	0.4237
92	0.9056	0.1889	0.09445
93	0.8962	0.2076	0.1038
94	0.9192	0.1615	0.08077
95	0.9075	0.185	0.09251
96	0.9509	0.09826	0.04913
97	0.9657	0.06856	0.03428
98	0.9543	0.09135	0.04568
99	0.9648	0.07032	0.03516
100	0.9564	0.08716	0.04358
101	0.9468	0.1063	0.05315
102	0.9641	0.07176	0.03588
103	0.9514	0.09723	0.04862
104	0.9416	0.1169	0.05845
105	0.9227	0.1545	0.07726
106	0.8994	0.2012	0.1006
107	0.8871	0.2257	0.1129
108	0.8743	0.2514	0.1257
109	0.8742	0.2517	0.1258
110	0.9023	0.1953	0.09766
111	0.906	0.188	0.09401
112	0.8915	0.217	0.1085
113	0.8637	0.2726	0.1363
114	0.882	0.236	0.118
115	0.8518	0.2964	0.1482
116	0.8924	0.2152	0.1076
117	0.865	0.2701	0.135
118	0.8219	0.3563	0.1781
119	0.8202	0.3596	0.1798
120	0.7659	0.4683	0.2341
121	0.7106	0.5789	0.2894
122	0.7422	0.5156	0.2578
123	0.6713	0.6574	0.3287
124	0.6629	0.6742	0.3371
125	0.6351	0.7297	0.3649
126	0.5644	0.8712	0.4356
127	0.5787	0.8427	0.4213
128	0.571	0.858	0.429
129	0.4785	0.9571	0.5215
130	0.7297	0.5407	0.2703
131	0.6299	0.7402	0.3701
132	0.5071	0.9858	0.4929
133	0.3718	0.7437	0.6282
134	0.2352	0.4704	0.7648

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	1	0.007812	OK
5% type I error level	9	0.0703125	NOK
10% type I error level	23	0.179688	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 & 1 &  0.007812 & OK \tabularnewline
5% type I error level & 9 & 0.0703125 & NOK \tabularnewline
10% type I error level & 23 & 0.179688 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=286047&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]1[/C][C] 0.007812[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]9[/C][C]0.0703125[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]23[/C][C]0.179688[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=286047&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=286047&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	1	0.007812	OK
5% type I error level	9	0.0703125	NOK
10% type I error level	23	0.179688	NOK

Figure 1

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Figure 5

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Figure 6

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Figure 7

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Figure 8

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Figure 9

PNG link

Postscript link

PDF link

Figure 10

PNG link

Postscript link

PDF link

Parameters (Session):

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

Parameters (R input):

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

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

library(lattice)
library(lmtest)
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'
}
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$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, mywarning)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$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$sigma,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a,formatC(signif(sum(myerror*myerror),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
if(n < 200) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,formatC(signif(x[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(x[i]-mysum$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')
}
}

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code