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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Sun, 14 Dec 2014 14:45:41 +0000

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2014/Dec/14/t1418568358dy1xokmm9xpl2if.htm/, Retrieved Thu, 16 May 2024 20:40:51 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=267644, Retrieved Thu, 16 May 2024 20:40:51 +0000

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Estimated Impact

103

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

-       [Multiple Regression] [paper28] [2014-12-14 14:45:41] [0015a2406d94cac8c1a56a29b9122359] [Current]

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149	96	18	68	86	13
152	75	7	55	62	7
139	70	31	39	70	12
148	88	39	32	71	13
158	114	46	62	108	7
128	69	31	33	64	7
224	176	67	52	119	13
159	114	35	62	97	15
105	121	52	77	129	13
159	110	77	76	153	11
167	158	37	41	78	8
165	116	32	48	80	11
159	181	36	63	99	6
119	77	38	30	68	11
176	141	69	78	147	12
54	35	21	19	40	6
163	152	54	66	120	12
124	97	36	35	71	9
121	84	23	45	68	10
153	68	34	21	55	6
148	101	112	25	137	14
221	107	35	44	79	11
188	88	47	69	116	14
149	112	47	54	101	12
244	171	37	74	111	11
150	66	20	61	81	10
153	93	22	41	63	12
94	105	23	46	69	8
156	131	32	39	71	12
146	89	7	63	70	6
132	102	30	34	64	9
161	161	92	51	143	10
105	120	43	42	85	11
97	127	55	31	86	10
151	77	16	39	55	10
166	85	71	49	120	9
157	168	43	53	96	12
111	48	29	31	60	6
145	152	56	39	95	11
162	75	46	54	100	13
163	107	19	49	68	11
187	121	59	46	105	11
109	124	30	55	85	12
105	40	7	50	57	13
148	126	19	30	49	11
125	148	48	45	93	13
116	146	23	35	58	10
138	97	33	41	74	14
164	118	34	73	107	9
162	58	48	17	65	13
99	63	18	40	58	8
202	139	43	64	107	16
186	50	33	37	70	9
183	152	71	65	136	11
214	142	26	100	126	13
188	94	67	28	95	15
177	127	80	56	136	12
126	67	29	29	58	11
157	96	58	52	110	7
139	128	32	50	82	13
162	146	43	59	102	13
159	186	29	61	90	13
110	85	32	51	83	8
48	41	23	12	34	4
50	146	16	45	61	13
150	182	33	37	70	18
154	192	32	37	69	18
194	439	52	68	120	17
158	214	75	72	147	19
159	341	72	143	215	16
67	58	15	9	24	13
147	292	29	55	84	18
39	85	13	17	30	15
100	200	40	37	77	11
111	158	19	27	46	13
138	199	24	37	61	16
101	297	121	58	178	14
101	108	36	21	57	14
114	86	23	19	42	15
165	302	85	78	163	16
114	148	41	35	75	15
111	178	46	48	94	15
75	120	18	27	45	12
82	207	35	43	78	13
121	157	17	30	47	17
32	128	4	25	29	9
150	296	28	69	97	18
117	323	44	72	116	16
165	70	38	13	50	18
154	146	57	61	118	15
126	246	23	43	66	18
138	145	26	22	48	16
149	196	36	51	86	16
145	199	22	67	89	18
120	127	40	36	76	14
138	91	18	21	39	12
109	153	31	44	75	14
132	299	11	45	57	15
172	228	38	34	72	18
169	190	24	36	60	10
114	180	37	72	109	16
156	212	37	39	76	18
172	269	22	43	65	17
167	243	43	80	123	19
113	190	31	40	71	16
173	157	31	61	93	17
2	96	-4	23	19	16
165	222	21	29	49	15
165	222	21	29	49	15
118	165	32	54	86	13
158	249	26	43	69	19
49	122	32	20	52	8
155	274	33	61	94	19
151	268	30	57	87	15
220	255	67	54	121	19
141	132	22	36	58	14
122	92	33	16	50	13
44	171	24	40	64	10
152	117	28	27	56	15
107	219	41	61	102	12
154	279	31	69	100	16
103	148	33	34	67	12
154	130	41	21	62	14
175	181	21	34	55	18
143	234	52	34	86	18
110	85	29	13	43	12
131	66	11	12	23	16
167	236	26	51	77	19
137	135	7	19	26	17
121	218	13	81	94	18
149	199	20	42	62	19
168	112	52	22	74	19
140	278	28	85	114	19
168	113	39	25	64	13
94	84	9	22	31	10
51	86	19	19	38	5
145	222	60	45	105	14
66	167	19	45	64	12
109	207	14	51	65	13
128	85	24	24	48	18
164	237	-2	73	71	18
119	102	51	24	76	14
126	221	2	61	63	16
132	128	24	23	46	13
142	91	40	14	53	12
83	198	20	54	74	12
166	138	20	36	56	13
93	196	25	26	52	8
117	139	38	30	68	15

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	6 seconds
R Server	'Gertrude Mary Cox' @ cox.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 & 6 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267644&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]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267644&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267644&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	6 seconds
R Server	'Gertrude Mary Cox' @ cox.wessa.net

Multiple Linear Regression - Estimated Regression Equation
Ex[t] = + 7.63121 + 0.0214299LFM[t] + 0.0321816B[t] -0.224334PRH[t] -0.244749CH[t] + 0.207175H[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Ex[t] =  +  7.63121 +  0.0214299LFM[t] +  0.0321816B[t] -0.224334PRH[t] -0.244749CH[t] +  0.207175H[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267644&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Ex[t] =  +  7.63121 +  0.0214299LFM[t] +  0.0321816B[t] -0.224334PRH[t] -0.244749CH[t] +  0.207175H[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267644&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267644&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
Ex[t] = + 7.63121 + 0.0214299LFM[t] + 0.0321816B[t] -0.224334PRH[t] -0.244749CH[t] + 0.207175H[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)	7.63121	0.91003	8.386	4.35971e-14	2.17985e-14
LFM	0.0214299	0.00669649	3.2	0.00169219	0.000846093
B	0.0321816	0.00377811	8.518	2.04849e-14	1.02424e-14
PRH	-0.224334	0.690742	-0.3248	0.745828	0.372914
CH	-0.244749	0.693649	-0.3528	0.724726	0.362363
H	0.207175	0.692077	0.2994	0.765105	0.382553

\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) & 7.63121 & 0.91003 & 8.386 & 4.35971e-14 & 2.17985e-14 \tabularnewline
LFM & 0.0214299 & 0.00669649 & 3.2 & 0.00169219 & 0.000846093 \tabularnewline
B & 0.0321816 & 0.00377811 & 8.518 & 2.04849e-14 & 1.02424e-14 \tabularnewline
PRH & -0.224334 & 0.690742 & -0.3248 & 0.745828 & 0.372914 \tabularnewline
CH & -0.244749 & 0.693649 & -0.3528 & 0.724726 & 0.362363 \tabularnewline
H & 0.207175 & 0.692077 & 0.2994 & 0.765105 & 0.382553 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267644&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]7.63121[/C][C]0.91003[/C][C]8.386[/C][C]4.35971e-14[/C][C]2.17985e-14[/C][/ROW]
[ROW][C]LFM[/C][C]0.0214299[/C][C]0.00669649[/C][C]3.2[/C][C]0.00169219[/C][C]0.000846093[/C][/ROW]
[ROW][C]B[/C][C]0.0321816[/C][C]0.00377811[/C][C]8.518[/C][C]2.04849e-14[/C][C]1.02424e-14[/C][/ROW]
[ROW][C]PRH[/C][C]-0.224334[/C][C]0.690742[/C][C]-0.3248[/C][C]0.745828[/C][C]0.372914[/C][/ROW]
[ROW][C]CH[/C][C]-0.244749[/C][C]0.693649[/C][C]-0.3528[/C][C]0.724726[/C][C]0.362363[/C][/ROW]
[ROW][C]H[/C][C]0.207175[/C][C]0.692077[/C][C]0.2994[/C][C]0.765105[/C][C]0.382553[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267644&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267644&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)	7.63121	0.91003	8.386	4.35971e-14	2.17985e-14
LFM	0.0214299	0.00669649	3.2	0.00169219	0.000846093
B	0.0321816	0.00377811	8.518	2.04849e-14	1.02424e-14
PRH	-0.224334	0.690742	-0.3248	0.745828	0.372914
CH	-0.244749	0.693649	-0.3528	0.724726	0.362363
H	0.207175	0.692077	0.2994	0.765105	0.382553

Multiple Linear Regression - Regression Statistics
Multiple R	0.622547
R-squared	0.387564
Adjusted R-squared	0.366151
F-TEST (value)	18.0988
F-TEST (DF numerator)	5
F-TEST (DF denominator)	143
p-value	6.92779e-14
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.8234
Sum Squared Residuals	1139.94

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.622547 \tabularnewline
R-squared & 0.387564 \tabularnewline
Adjusted R-squared & 0.366151 \tabularnewline
F-TEST (value) & 18.0988 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 143 \tabularnewline
p-value & 6.92779e-14 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.8234 \tabularnewline
Sum Squared Residuals & 1139.94 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267644&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.622547[/C][/ROW]
[ROW][C]R-squared[/C][C]0.387564[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.366151[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]18.0988[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]143[/C][/ROW]
[ROW][C]p-value[/C][C]6.92779e-14[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.8234[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1139.94[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267644&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267644&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	0.622547
R-squared	0.387564
Adjusted R-squared	0.366151
F-TEST (value)	18.0988
F-TEST (DF numerator)	5
F-TEST (DF denominator)	143
p-value	6.92779e-14
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.8234
Sum Squared Residuals	1139.94

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	13	11.0498	1.95019
2	7	11.1155	-4.1155
3	12	10.8654	1.13463
4	13	11.7632	1.23675
5	7	11.5669	-4.56694
6	7	10.8229	-3.8229
7	13	14.992	-1.99197
8	15	11.7771	3.22288
9	13	9.98987	3.01013
10	11	10.4017	0.598317
11	8	14.1193	-6.11928
12	11	12.5476	-1.54757
13	6	13.8786	-7.87855
14	11	10.8801	0.11991
15	12	11.8257	0.174256
16	6	8.84053	-2.84053
17	12	12.6094	-0.609424
18	9	11.4773	-2.47732
19	10	10.842	-0.841999
20	6	11.7259	-5.72587
21	14	11.192	2.80798
22	11	13.5568	-2.55683
23	14	11.0929	2.90706
24	12	11.5931	0.40686
25	11	14.9478	-3.94781
26	10	10.3345	-0.334493
27	12	11.9848	0.0151559
28	8	10.9016	-2.90163
29	12	13.1756	-1.17559
30	6	11.1369	-5.13687
31	9	11.9502	-2.9502
32	10	12.7678	-2.76776
33	11	11.4272	-0.427199
34	10	11.6884	-1.68843
35	10	11.6052	-1.60518
36	9	10.8646	-1.8646
37	12	13.673	-1.67296
38	6	9.89224	-3.89224
39	11	13.2039	-2.20386
40	13	10.6982	2.30183
41	11	12.4006	-1.40057
42	11	12.7918	-1.79179
43	12	11.3763	0.623748
44	13	9.16981	3.83019
45	11	13.4045	-2.40448
46	13	12.5584	0.441635
47	10	13.1058	-3.10585
48	14	11.6034	2.39663
49	9	11.6168	-2.61684
50	13	11.507	1.493
51	8	9.96839	-1.96839
52	16	13.2907	2.70927
53	9	11.2698	-2.26977
54	11	12.7839	-1.78389
55	13	12.5835	0.416525
56	15	12.4834	2.51662
57	12	12.0345	-0.0345071
58	11	10.9003	0.0997117
59	7	11.1361	-4.13607
60	13	12.3014	0.698573
61	13	12.8467	0.15333
62	13	14.2347	-1.23472
63	8	10.2586	-2.25858
64	4	8.92657	-4.92657
65	13	11.4358	1.56415
66	18	14.7463	3.25374
67	18	15.171	2.82904
68	17	22.469	-5.46903
69	19	13.9118	5.08825
70	16	15.404	0.596022
71	13	10.338	2.66201
72	18	17.6143	0.385747
73	15	10.3406	4.65942
74	11	14.1339	-3.13392
75	13	13.7541	-0.754101
76	16	15.1906	0.80938
77	14	14.8909	-0.890862
78	14	11.8645	2.13554
79	15	11.7333	3.26673
80	16	16.4967	-0.496706
81	15	12.6113	2.38869
82	15	13.1454	1.85461
83	12	11.7769	0.223117
84	13	13.8338	-0.833807
85	17	13.8578	3.14218
86	9	11.4282	-2.42822
87	18	17.2984	0.701605
88	16	17.0728	-1.07285
89	18	12.0722	5.92783
90	15	12.3599	2.64014
91	18	16.2377	1.76229
92	16	13.9821	2.0179
93	16	14.3907	1.60931
94	18	14.2477	3.75227
95	14	12.2508	1.74916
96	12	12.4191	-0.419144
97	14	12.7057	1.29433
98	15	18.4099	-3.40985
99	18	16.725	1.275
100	10	15.6029	-5.60289
101	16	12.5267	3.4733
102	18	15.6965	2.3035
103	17	17.9808	-0.980823
104	19	15.2864	3.71362
105	16	14.1324	1.8676
106	17	13.7743	3.22567
107	16	9.96794	6.03206
108	15	16.6543	-1.6543
109	15	16.6543	-1.6543
110	13	12.8918	0.108178
111	19	16.9685	2.03146
112	8	11.3069	-3.30686
113	19	16.9123	2.08765
114	15	16.8353	-1.83531
115	19	17.3734	1.62655
116	14	13.1706	0.829364
117	13	12.2461	0.753893
118	10	12.1624	-2.1624
119	15	13.366	1.63397
120	12	13.9765	-1.97645
121	16	16.7856	-0.785551
122	12	12.7576	-0.757604
123	14	13.6224	0.377552
124	18	15.5685	2.43154
125	18	16.0564	1.9436
126	12	11.945	0.0549656
127	16	11.9229	4.07713
128	19	16.4425	2.55755
129	17	14.0776	2.9224
130	18	13.9733	4.02674
131	19	15.3071	3.69288
132	19	13.1169	5.88312
133	19	16.1108	2.88918
134	13	13.2594	-0.259402
135	10	11.3678	-1.36781
136	5	10.4518	-5.45182
137	14	15.1625	-1.16249
138	12	12.4031	-0.403061
139	13	14.4722	-1.47216
140	18	11.7961	6.20392
141	18	16.0642	1.93583
142	14	11.8942	2.10582
143	16	15.1172	0.882817
144	13	13.096	-0.0960067
145	12	12.1832	-0.183208
146	12	13.4097	-1.40968
147	13	13.9338	-0.933791
148	8	14.7331	-6.73306
149	15	12.8325	2.16751

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 13 & 11.0498 & 1.95019 \tabularnewline
2 & 7 & 11.1155 & -4.1155 \tabularnewline
3 & 12 & 10.8654 & 1.13463 \tabularnewline
4 & 13 & 11.7632 & 1.23675 \tabularnewline
5 & 7 & 11.5669 & -4.56694 \tabularnewline
6 & 7 & 10.8229 & -3.8229 \tabularnewline
7 & 13 & 14.992 & -1.99197 \tabularnewline
8 & 15 & 11.7771 & 3.22288 \tabularnewline
9 & 13 & 9.98987 & 3.01013 \tabularnewline
10 & 11 & 10.4017 & 0.598317 \tabularnewline
11 & 8 & 14.1193 & -6.11928 \tabularnewline
12 & 11 & 12.5476 & -1.54757 \tabularnewline
13 & 6 & 13.8786 & -7.87855 \tabularnewline
14 & 11 & 10.8801 & 0.11991 \tabularnewline
15 & 12 & 11.8257 & 0.174256 \tabularnewline
16 & 6 & 8.84053 & -2.84053 \tabularnewline
17 & 12 & 12.6094 & -0.609424 \tabularnewline
18 & 9 & 11.4773 & -2.47732 \tabularnewline
19 & 10 & 10.842 & -0.841999 \tabularnewline
20 & 6 & 11.7259 & -5.72587 \tabularnewline
21 & 14 & 11.192 & 2.80798 \tabularnewline
22 & 11 & 13.5568 & -2.55683 \tabularnewline
23 & 14 & 11.0929 & 2.90706 \tabularnewline
24 & 12 & 11.5931 & 0.40686 \tabularnewline
25 & 11 & 14.9478 & -3.94781 \tabularnewline
26 & 10 & 10.3345 & -0.334493 \tabularnewline
27 & 12 & 11.9848 & 0.0151559 \tabularnewline
28 & 8 & 10.9016 & -2.90163 \tabularnewline
29 & 12 & 13.1756 & -1.17559 \tabularnewline
30 & 6 & 11.1369 & -5.13687 \tabularnewline
31 & 9 & 11.9502 & -2.9502 \tabularnewline
32 & 10 & 12.7678 & -2.76776 \tabularnewline
33 & 11 & 11.4272 & -0.427199 \tabularnewline
34 & 10 & 11.6884 & -1.68843 \tabularnewline
35 & 10 & 11.6052 & -1.60518 \tabularnewline
36 & 9 & 10.8646 & -1.8646 \tabularnewline
37 & 12 & 13.673 & -1.67296 \tabularnewline
38 & 6 & 9.89224 & -3.89224 \tabularnewline
39 & 11 & 13.2039 & -2.20386 \tabularnewline
40 & 13 & 10.6982 & 2.30183 \tabularnewline
41 & 11 & 12.4006 & -1.40057 \tabularnewline
42 & 11 & 12.7918 & -1.79179 \tabularnewline
43 & 12 & 11.3763 & 0.623748 \tabularnewline
44 & 13 & 9.16981 & 3.83019 \tabularnewline
45 & 11 & 13.4045 & -2.40448 \tabularnewline
46 & 13 & 12.5584 & 0.441635 \tabularnewline
47 & 10 & 13.1058 & -3.10585 \tabularnewline
48 & 14 & 11.6034 & 2.39663 \tabularnewline
49 & 9 & 11.6168 & -2.61684 \tabularnewline
50 & 13 & 11.507 & 1.493 \tabularnewline
51 & 8 & 9.96839 & -1.96839 \tabularnewline
52 & 16 & 13.2907 & 2.70927 \tabularnewline
53 & 9 & 11.2698 & -2.26977 \tabularnewline
54 & 11 & 12.7839 & -1.78389 \tabularnewline
55 & 13 & 12.5835 & 0.416525 \tabularnewline
56 & 15 & 12.4834 & 2.51662 \tabularnewline
57 & 12 & 12.0345 & -0.0345071 \tabularnewline
58 & 11 & 10.9003 & 0.0997117 \tabularnewline
59 & 7 & 11.1361 & -4.13607 \tabularnewline
60 & 13 & 12.3014 & 0.698573 \tabularnewline
61 & 13 & 12.8467 & 0.15333 \tabularnewline
62 & 13 & 14.2347 & -1.23472 \tabularnewline
63 & 8 & 10.2586 & -2.25858 \tabularnewline
64 & 4 & 8.92657 & -4.92657 \tabularnewline
65 & 13 & 11.4358 & 1.56415 \tabularnewline
66 & 18 & 14.7463 & 3.25374 \tabularnewline
67 & 18 & 15.171 & 2.82904 \tabularnewline
68 & 17 & 22.469 & -5.46903 \tabularnewline
69 & 19 & 13.9118 & 5.08825 \tabularnewline
70 & 16 & 15.404 & 0.596022 \tabularnewline
71 & 13 & 10.338 & 2.66201 \tabularnewline
72 & 18 & 17.6143 & 0.385747 \tabularnewline
73 & 15 & 10.3406 & 4.65942 \tabularnewline
74 & 11 & 14.1339 & -3.13392 \tabularnewline
75 & 13 & 13.7541 & -0.754101 \tabularnewline
76 & 16 & 15.1906 & 0.80938 \tabularnewline
77 & 14 & 14.8909 & -0.890862 \tabularnewline
78 & 14 & 11.8645 & 2.13554 \tabularnewline
79 & 15 & 11.7333 & 3.26673 \tabularnewline
80 & 16 & 16.4967 & -0.496706 \tabularnewline
81 & 15 & 12.6113 & 2.38869 \tabularnewline
82 & 15 & 13.1454 & 1.85461 \tabularnewline
83 & 12 & 11.7769 & 0.223117 \tabularnewline
84 & 13 & 13.8338 & -0.833807 \tabularnewline
85 & 17 & 13.8578 & 3.14218 \tabularnewline
86 & 9 & 11.4282 & -2.42822 \tabularnewline
87 & 18 & 17.2984 & 0.701605 \tabularnewline
88 & 16 & 17.0728 & -1.07285 \tabularnewline
89 & 18 & 12.0722 & 5.92783 \tabularnewline
90 & 15 & 12.3599 & 2.64014 \tabularnewline
91 & 18 & 16.2377 & 1.76229 \tabularnewline
92 & 16 & 13.9821 & 2.0179 \tabularnewline
93 & 16 & 14.3907 & 1.60931 \tabularnewline
94 & 18 & 14.2477 & 3.75227 \tabularnewline
95 & 14 & 12.2508 & 1.74916 \tabularnewline
96 & 12 & 12.4191 & -0.419144 \tabularnewline
97 & 14 & 12.7057 & 1.29433 \tabularnewline
98 & 15 & 18.4099 & -3.40985 \tabularnewline
99 & 18 & 16.725 & 1.275 \tabularnewline
100 & 10 & 15.6029 & -5.60289 \tabularnewline
101 & 16 & 12.5267 & 3.4733 \tabularnewline
102 & 18 & 15.6965 & 2.3035 \tabularnewline
103 & 17 & 17.9808 & -0.980823 \tabularnewline
104 & 19 & 15.2864 & 3.71362 \tabularnewline
105 & 16 & 14.1324 & 1.8676 \tabularnewline
106 & 17 & 13.7743 & 3.22567 \tabularnewline
107 & 16 & 9.96794 & 6.03206 \tabularnewline
108 & 15 & 16.6543 & -1.6543 \tabularnewline
109 & 15 & 16.6543 & -1.6543 \tabularnewline
110 & 13 & 12.8918 & 0.108178 \tabularnewline
111 & 19 & 16.9685 & 2.03146 \tabularnewline
112 & 8 & 11.3069 & -3.30686 \tabularnewline
113 & 19 & 16.9123 & 2.08765 \tabularnewline
114 & 15 & 16.8353 & -1.83531 \tabularnewline
115 & 19 & 17.3734 & 1.62655 \tabularnewline
116 & 14 & 13.1706 & 0.829364 \tabularnewline
117 & 13 & 12.2461 & 0.753893 \tabularnewline
118 & 10 & 12.1624 & -2.1624 \tabularnewline
119 & 15 & 13.366 & 1.63397 \tabularnewline
120 & 12 & 13.9765 & -1.97645 \tabularnewline
121 & 16 & 16.7856 & -0.785551 \tabularnewline
122 & 12 & 12.7576 & -0.757604 \tabularnewline
123 & 14 & 13.6224 & 0.377552 \tabularnewline
124 & 18 & 15.5685 & 2.43154 \tabularnewline
125 & 18 & 16.0564 & 1.9436 \tabularnewline
126 & 12 & 11.945 & 0.0549656 \tabularnewline
127 & 16 & 11.9229 & 4.07713 \tabularnewline
128 & 19 & 16.4425 & 2.55755 \tabularnewline
129 & 17 & 14.0776 & 2.9224 \tabularnewline
130 & 18 & 13.9733 & 4.02674 \tabularnewline
131 & 19 & 15.3071 & 3.69288 \tabularnewline
132 & 19 & 13.1169 & 5.88312 \tabularnewline
133 & 19 & 16.1108 & 2.88918 \tabularnewline
134 & 13 & 13.2594 & -0.259402 \tabularnewline
135 & 10 & 11.3678 & -1.36781 \tabularnewline
136 & 5 & 10.4518 & -5.45182 \tabularnewline
137 & 14 & 15.1625 & -1.16249 \tabularnewline
138 & 12 & 12.4031 & -0.403061 \tabularnewline
139 & 13 & 14.4722 & -1.47216 \tabularnewline
140 & 18 & 11.7961 & 6.20392 \tabularnewline
141 & 18 & 16.0642 & 1.93583 \tabularnewline
142 & 14 & 11.8942 & 2.10582 \tabularnewline
143 & 16 & 15.1172 & 0.882817 \tabularnewline
144 & 13 & 13.096 & -0.0960067 \tabularnewline
145 & 12 & 12.1832 & -0.183208 \tabularnewline
146 & 12 & 13.4097 & -1.40968 \tabularnewline
147 & 13 & 13.9338 & -0.933791 \tabularnewline
148 & 8 & 14.7331 & -6.73306 \tabularnewline
149 & 15 & 12.8325 & 2.16751 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267644&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]13[/C][C]11.0498[/C][C]1.95019[/C][/ROW]
[ROW][C]2[/C][C]7[/C][C]11.1155[/C][C]-4.1155[/C][/ROW]
[ROW][C]3[/C][C]12[/C][C]10.8654[/C][C]1.13463[/C][/ROW]
[ROW][C]4[/C][C]13[/C][C]11.7632[/C][C]1.23675[/C][/ROW]
[ROW][C]5[/C][C]7[/C][C]11.5669[/C][C]-4.56694[/C][/ROW]
[ROW][C]6[/C][C]7[/C][C]10.8229[/C][C]-3.8229[/C][/ROW]
[ROW][C]7[/C][C]13[/C][C]14.992[/C][C]-1.99197[/C][/ROW]
[ROW][C]8[/C][C]15[/C][C]11.7771[/C][C]3.22288[/C][/ROW]
[ROW][C]9[/C][C]13[/C][C]9.98987[/C][C]3.01013[/C][/ROW]
[ROW][C]10[/C][C]11[/C][C]10.4017[/C][C]0.598317[/C][/ROW]
[ROW][C]11[/C][C]8[/C][C]14.1193[/C][C]-6.11928[/C][/ROW]
[ROW][C]12[/C][C]11[/C][C]12.5476[/C][C]-1.54757[/C][/ROW]
[ROW][C]13[/C][C]6[/C][C]13.8786[/C][C]-7.87855[/C][/ROW]
[ROW][C]14[/C][C]11[/C][C]10.8801[/C][C]0.11991[/C][/ROW]
[ROW][C]15[/C][C]12[/C][C]11.8257[/C][C]0.174256[/C][/ROW]
[ROW][C]16[/C][C]6[/C][C]8.84053[/C][C]-2.84053[/C][/ROW]
[ROW][C]17[/C][C]12[/C][C]12.6094[/C][C]-0.609424[/C][/ROW]
[ROW][C]18[/C][C]9[/C][C]11.4773[/C][C]-2.47732[/C][/ROW]
[ROW][C]19[/C][C]10[/C][C]10.842[/C][C]-0.841999[/C][/ROW]
[ROW][C]20[/C][C]6[/C][C]11.7259[/C][C]-5.72587[/C][/ROW]
[ROW][C]21[/C][C]14[/C][C]11.192[/C][C]2.80798[/C][/ROW]
[ROW][C]22[/C][C]11[/C][C]13.5568[/C][C]-2.55683[/C][/ROW]
[ROW][C]23[/C][C]14[/C][C]11.0929[/C][C]2.90706[/C][/ROW]
[ROW][C]24[/C][C]12[/C][C]11.5931[/C][C]0.40686[/C][/ROW]
[ROW][C]25[/C][C]11[/C][C]14.9478[/C][C]-3.94781[/C][/ROW]
[ROW][C]26[/C][C]10[/C][C]10.3345[/C][C]-0.334493[/C][/ROW]
[ROW][C]27[/C][C]12[/C][C]11.9848[/C][C]0.0151559[/C][/ROW]
[ROW][C]28[/C][C]8[/C][C]10.9016[/C][C]-2.90163[/C][/ROW]
[ROW][C]29[/C][C]12[/C][C]13.1756[/C][C]-1.17559[/C][/ROW]
[ROW][C]30[/C][C]6[/C][C]11.1369[/C][C]-5.13687[/C][/ROW]
[ROW][C]31[/C][C]9[/C][C]11.9502[/C][C]-2.9502[/C][/ROW]
[ROW][C]32[/C][C]10[/C][C]12.7678[/C][C]-2.76776[/C][/ROW]
[ROW][C]33[/C][C]11[/C][C]11.4272[/C][C]-0.427199[/C][/ROW]
[ROW][C]34[/C][C]10[/C][C]11.6884[/C][C]-1.68843[/C][/ROW]
[ROW][C]35[/C][C]10[/C][C]11.6052[/C][C]-1.60518[/C][/ROW]
[ROW][C]36[/C][C]9[/C][C]10.8646[/C][C]-1.8646[/C][/ROW]
[ROW][C]37[/C][C]12[/C][C]13.673[/C][C]-1.67296[/C][/ROW]
[ROW][C]38[/C][C]6[/C][C]9.89224[/C][C]-3.89224[/C][/ROW]
[ROW][C]39[/C][C]11[/C][C]13.2039[/C][C]-2.20386[/C][/ROW]
[ROW][C]40[/C][C]13[/C][C]10.6982[/C][C]2.30183[/C][/ROW]
[ROW][C]41[/C][C]11[/C][C]12.4006[/C][C]-1.40057[/C][/ROW]
[ROW][C]42[/C][C]11[/C][C]12.7918[/C][C]-1.79179[/C][/ROW]
[ROW][C]43[/C][C]12[/C][C]11.3763[/C][C]0.623748[/C][/ROW]
[ROW][C]44[/C][C]13[/C][C]9.16981[/C][C]3.83019[/C][/ROW]
[ROW][C]45[/C][C]11[/C][C]13.4045[/C][C]-2.40448[/C][/ROW]
[ROW][C]46[/C][C]13[/C][C]12.5584[/C][C]0.441635[/C][/ROW]
[ROW][C]47[/C][C]10[/C][C]13.1058[/C][C]-3.10585[/C][/ROW]
[ROW][C]48[/C][C]14[/C][C]11.6034[/C][C]2.39663[/C][/ROW]
[ROW][C]49[/C][C]9[/C][C]11.6168[/C][C]-2.61684[/C][/ROW]
[ROW][C]50[/C][C]13[/C][C]11.507[/C][C]1.493[/C][/ROW]
[ROW][C]51[/C][C]8[/C][C]9.96839[/C][C]-1.96839[/C][/ROW]
[ROW][C]52[/C][C]16[/C][C]13.2907[/C][C]2.70927[/C][/ROW]
[ROW][C]53[/C][C]9[/C][C]11.2698[/C][C]-2.26977[/C][/ROW]
[ROW][C]54[/C][C]11[/C][C]12.7839[/C][C]-1.78389[/C][/ROW]
[ROW][C]55[/C][C]13[/C][C]12.5835[/C][C]0.416525[/C][/ROW]
[ROW][C]56[/C][C]15[/C][C]12.4834[/C][C]2.51662[/C][/ROW]
[ROW][C]57[/C][C]12[/C][C]12.0345[/C][C]-0.0345071[/C][/ROW]
[ROW][C]58[/C][C]11[/C][C]10.9003[/C][C]0.0997117[/C][/ROW]
[ROW][C]59[/C][C]7[/C][C]11.1361[/C][C]-4.13607[/C][/ROW]
[ROW][C]60[/C][C]13[/C][C]12.3014[/C][C]0.698573[/C][/ROW]
[ROW][C]61[/C][C]13[/C][C]12.8467[/C][C]0.15333[/C][/ROW]
[ROW][C]62[/C][C]13[/C][C]14.2347[/C][C]-1.23472[/C][/ROW]
[ROW][C]63[/C][C]8[/C][C]10.2586[/C][C]-2.25858[/C][/ROW]
[ROW][C]64[/C][C]4[/C][C]8.92657[/C][C]-4.92657[/C][/ROW]
[ROW][C]65[/C][C]13[/C][C]11.4358[/C][C]1.56415[/C][/ROW]
[ROW][C]66[/C][C]18[/C][C]14.7463[/C][C]3.25374[/C][/ROW]
[ROW][C]67[/C][C]18[/C][C]15.171[/C][C]2.82904[/C][/ROW]
[ROW][C]68[/C][C]17[/C][C]22.469[/C][C]-5.46903[/C][/ROW]
[ROW][C]69[/C][C]19[/C][C]13.9118[/C][C]5.08825[/C][/ROW]
[ROW][C]70[/C][C]16[/C][C]15.404[/C][C]0.596022[/C][/ROW]
[ROW][C]71[/C][C]13[/C][C]10.338[/C][C]2.66201[/C][/ROW]
[ROW][C]72[/C][C]18[/C][C]17.6143[/C][C]0.385747[/C][/ROW]
[ROW][C]73[/C][C]15[/C][C]10.3406[/C][C]4.65942[/C][/ROW]
[ROW][C]74[/C][C]11[/C][C]14.1339[/C][C]-3.13392[/C][/ROW]
[ROW][C]75[/C][C]13[/C][C]13.7541[/C][C]-0.754101[/C][/ROW]
[ROW][C]76[/C][C]16[/C][C]15.1906[/C][C]0.80938[/C][/ROW]
[ROW][C]77[/C][C]14[/C][C]14.8909[/C][C]-0.890862[/C][/ROW]
[ROW][C]78[/C][C]14[/C][C]11.8645[/C][C]2.13554[/C][/ROW]
[ROW][C]79[/C][C]15[/C][C]11.7333[/C][C]3.26673[/C][/ROW]
[ROW][C]80[/C][C]16[/C][C]16.4967[/C][C]-0.496706[/C][/ROW]
[ROW][C]81[/C][C]15[/C][C]12.6113[/C][C]2.38869[/C][/ROW]
[ROW][C]82[/C][C]15[/C][C]13.1454[/C][C]1.85461[/C][/ROW]
[ROW][C]83[/C][C]12[/C][C]11.7769[/C][C]0.223117[/C][/ROW]
[ROW][C]84[/C][C]13[/C][C]13.8338[/C][C]-0.833807[/C][/ROW]
[ROW][C]85[/C][C]17[/C][C]13.8578[/C][C]3.14218[/C][/ROW]
[ROW][C]86[/C][C]9[/C][C]11.4282[/C][C]-2.42822[/C][/ROW]
[ROW][C]87[/C][C]18[/C][C]17.2984[/C][C]0.701605[/C][/ROW]
[ROW][C]88[/C][C]16[/C][C]17.0728[/C][C]-1.07285[/C][/ROW]
[ROW][C]89[/C][C]18[/C][C]12.0722[/C][C]5.92783[/C][/ROW]
[ROW][C]90[/C][C]15[/C][C]12.3599[/C][C]2.64014[/C][/ROW]
[ROW][C]91[/C][C]18[/C][C]16.2377[/C][C]1.76229[/C][/ROW]
[ROW][C]92[/C][C]16[/C][C]13.9821[/C][C]2.0179[/C][/ROW]
[ROW][C]93[/C][C]16[/C][C]14.3907[/C][C]1.60931[/C][/ROW]
[ROW][C]94[/C][C]18[/C][C]14.2477[/C][C]3.75227[/C][/ROW]
[ROW][C]95[/C][C]14[/C][C]12.2508[/C][C]1.74916[/C][/ROW]
[ROW][C]96[/C][C]12[/C][C]12.4191[/C][C]-0.419144[/C][/ROW]
[ROW][C]97[/C][C]14[/C][C]12.7057[/C][C]1.29433[/C][/ROW]
[ROW][C]98[/C][C]15[/C][C]18.4099[/C][C]-3.40985[/C][/ROW]
[ROW][C]99[/C][C]18[/C][C]16.725[/C][C]1.275[/C][/ROW]
[ROW][C]100[/C][C]10[/C][C]15.6029[/C][C]-5.60289[/C][/ROW]
[ROW][C]101[/C][C]16[/C][C]12.5267[/C][C]3.4733[/C][/ROW]
[ROW][C]102[/C][C]18[/C][C]15.6965[/C][C]2.3035[/C][/ROW]
[ROW][C]103[/C][C]17[/C][C]17.9808[/C][C]-0.980823[/C][/ROW]
[ROW][C]104[/C][C]19[/C][C]15.2864[/C][C]3.71362[/C][/ROW]
[ROW][C]105[/C][C]16[/C][C]14.1324[/C][C]1.8676[/C][/ROW]
[ROW][C]106[/C][C]17[/C][C]13.7743[/C][C]3.22567[/C][/ROW]
[ROW][C]107[/C][C]16[/C][C]9.96794[/C][C]6.03206[/C][/ROW]
[ROW][C]108[/C][C]15[/C][C]16.6543[/C][C]-1.6543[/C][/ROW]
[ROW][C]109[/C][C]15[/C][C]16.6543[/C][C]-1.6543[/C][/ROW]
[ROW][C]110[/C][C]13[/C][C]12.8918[/C][C]0.108178[/C][/ROW]
[ROW][C]111[/C][C]19[/C][C]16.9685[/C][C]2.03146[/C][/ROW]
[ROW][C]112[/C][C]8[/C][C]11.3069[/C][C]-3.30686[/C][/ROW]
[ROW][C]113[/C][C]19[/C][C]16.9123[/C][C]2.08765[/C][/ROW]
[ROW][C]114[/C][C]15[/C][C]16.8353[/C][C]-1.83531[/C][/ROW]
[ROW][C]115[/C][C]19[/C][C]17.3734[/C][C]1.62655[/C][/ROW]
[ROW][C]116[/C][C]14[/C][C]13.1706[/C][C]0.829364[/C][/ROW]
[ROW][C]117[/C][C]13[/C][C]12.2461[/C][C]0.753893[/C][/ROW]
[ROW][C]118[/C][C]10[/C][C]12.1624[/C][C]-2.1624[/C][/ROW]
[ROW][C]119[/C][C]15[/C][C]13.366[/C][C]1.63397[/C][/ROW]
[ROW][C]120[/C][C]12[/C][C]13.9765[/C][C]-1.97645[/C][/ROW]
[ROW][C]121[/C][C]16[/C][C]16.7856[/C][C]-0.785551[/C][/ROW]
[ROW][C]122[/C][C]12[/C][C]12.7576[/C][C]-0.757604[/C][/ROW]
[ROW][C]123[/C][C]14[/C][C]13.6224[/C][C]0.377552[/C][/ROW]
[ROW][C]124[/C][C]18[/C][C]15.5685[/C][C]2.43154[/C][/ROW]
[ROW][C]125[/C][C]18[/C][C]16.0564[/C][C]1.9436[/C][/ROW]
[ROW][C]126[/C][C]12[/C][C]11.945[/C][C]0.0549656[/C][/ROW]
[ROW][C]127[/C][C]16[/C][C]11.9229[/C][C]4.07713[/C][/ROW]
[ROW][C]128[/C][C]19[/C][C]16.4425[/C][C]2.55755[/C][/ROW]
[ROW][C]129[/C][C]17[/C][C]14.0776[/C][C]2.9224[/C][/ROW]
[ROW][C]130[/C][C]18[/C][C]13.9733[/C][C]4.02674[/C][/ROW]
[ROW][C]131[/C][C]19[/C][C]15.3071[/C][C]3.69288[/C][/ROW]
[ROW][C]132[/C][C]19[/C][C]13.1169[/C][C]5.88312[/C][/ROW]
[ROW][C]133[/C][C]19[/C][C]16.1108[/C][C]2.88918[/C][/ROW]
[ROW][C]134[/C][C]13[/C][C]13.2594[/C][C]-0.259402[/C][/ROW]
[ROW][C]135[/C][C]10[/C][C]11.3678[/C][C]-1.36781[/C][/ROW]
[ROW][C]136[/C][C]5[/C][C]10.4518[/C][C]-5.45182[/C][/ROW]
[ROW][C]137[/C][C]14[/C][C]15.1625[/C][C]-1.16249[/C][/ROW]
[ROW][C]138[/C][C]12[/C][C]12.4031[/C][C]-0.403061[/C][/ROW]
[ROW][C]139[/C][C]13[/C][C]14.4722[/C][C]-1.47216[/C][/ROW]
[ROW][C]140[/C][C]18[/C][C]11.7961[/C][C]6.20392[/C][/ROW]
[ROW][C]141[/C][C]18[/C][C]16.0642[/C][C]1.93583[/C][/ROW]
[ROW][C]142[/C][C]14[/C][C]11.8942[/C][C]2.10582[/C][/ROW]
[ROW][C]143[/C][C]16[/C][C]15.1172[/C][C]0.882817[/C][/ROW]
[ROW][C]144[/C][C]13[/C][C]13.096[/C][C]-0.0960067[/C][/ROW]
[ROW][C]145[/C][C]12[/C][C]12.1832[/C][C]-0.183208[/C][/ROW]
[ROW][C]146[/C][C]12[/C][C]13.4097[/C][C]-1.40968[/C][/ROW]
[ROW][C]147[/C][C]13[/C][C]13.9338[/C][C]-0.933791[/C][/ROW]
[ROW][C]148[/C][C]8[/C][C]14.7331[/C][C]-6.73306[/C][/ROW]
[ROW][C]149[/C][C]15[/C][C]12.8325[/C][C]2.16751[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267644&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267644&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	13	11.0498	1.95019
2	7	11.1155	-4.1155
3	12	10.8654	1.13463
4	13	11.7632	1.23675
5	7	11.5669	-4.56694
6	7	10.8229	-3.8229
7	13	14.992	-1.99197
8	15	11.7771	3.22288
9	13	9.98987	3.01013
10	11	10.4017	0.598317
11	8	14.1193	-6.11928
12	11	12.5476	-1.54757
13	6	13.8786	-7.87855
14	11	10.8801	0.11991
15	12	11.8257	0.174256
16	6	8.84053	-2.84053
17	12	12.6094	-0.609424
18	9	11.4773	-2.47732
19	10	10.842	-0.841999
20	6	11.7259	-5.72587
21	14	11.192	2.80798
22	11	13.5568	-2.55683
23	14	11.0929	2.90706
24	12	11.5931	0.40686
25	11	14.9478	-3.94781
26	10	10.3345	-0.334493
27	12	11.9848	0.0151559
28	8	10.9016	-2.90163
29	12	13.1756	-1.17559
30	6	11.1369	-5.13687
31	9	11.9502	-2.9502
32	10	12.7678	-2.76776
33	11	11.4272	-0.427199
34	10	11.6884	-1.68843
35	10	11.6052	-1.60518
36	9	10.8646	-1.8646
37	12	13.673	-1.67296
38	6	9.89224	-3.89224
39	11	13.2039	-2.20386
40	13	10.6982	2.30183
41	11	12.4006	-1.40057
42	11	12.7918	-1.79179
43	12	11.3763	0.623748
44	13	9.16981	3.83019
45	11	13.4045	-2.40448
46	13	12.5584	0.441635
47	10	13.1058	-3.10585
48	14	11.6034	2.39663
49	9	11.6168	-2.61684
50	13	11.507	1.493
51	8	9.96839	-1.96839
52	16	13.2907	2.70927
53	9	11.2698	-2.26977
54	11	12.7839	-1.78389
55	13	12.5835	0.416525
56	15	12.4834	2.51662
57	12	12.0345	-0.0345071
58	11	10.9003	0.0997117
59	7	11.1361	-4.13607
60	13	12.3014	0.698573
61	13	12.8467	0.15333
62	13	14.2347	-1.23472
63	8	10.2586	-2.25858
64	4	8.92657	-4.92657
65	13	11.4358	1.56415
66	18	14.7463	3.25374
67	18	15.171	2.82904
68	17	22.469	-5.46903
69	19	13.9118	5.08825
70	16	15.404	0.596022
71	13	10.338	2.66201
72	18	17.6143	0.385747
73	15	10.3406	4.65942
74	11	14.1339	-3.13392
75	13	13.7541	-0.754101
76	16	15.1906	0.80938
77	14	14.8909	-0.890862
78	14	11.8645	2.13554
79	15	11.7333	3.26673
80	16	16.4967	-0.496706
81	15	12.6113	2.38869
82	15	13.1454	1.85461
83	12	11.7769	0.223117
84	13	13.8338	-0.833807
85	17	13.8578	3.14218
86	9	11.4282	-2.42822
87	18	17.2984	0.701605
88	16	17.0728	-1.07285
89	18	12.0722	5.92783
90	15	12.3599	2.64014
91	18	16.2377	1.76229
92	16	13.9821	2.0179
93	16	14.3907	1.60931
94	18	14.2477	3.75227
95	14	12.2508	1.74916
96	12	12.4191	-0.419144
97	14	12.7057	1.29433
98	15	18.4099	-3.40985
99	18	16.725	1.275
100	10	15.6029	-5.60289
101	16	12.5267	3.4733
102	18	15.6965	2.3035
103	17	17.9808	-0.980823
104	19	15.2864	3.71362
105	16	14.1324	1.8676
106	17	13.7743	3.22567
107	16	9.96794	6.03206
108	15	16.6543	-1.6543
109	15	16.6543	-1.6543
110	13	12.8918	0.108178
111	19	16.9685	2.03146
112	8	11.3069	-3.30686
113	19	16.9123	2.08765
114	15	16.8353	-1.83531
115	19	17.3734	1.62655
116	14	13.1706	0.829364
117	13	12.2461	0.753893
118	10	12.1624	-2.1624
119	15	13.366	1.63397
120	12	13.9765	-1.97645
121	16	16.7856	-0.785551
122	12	12.7576	-0.757604
123	14	13.6224	0.377552
124	18	15.5685	2.43154
125	18	16.0564	1.9436
126	12	11.945	0.0549656
127	16	11.9229	4.07713
128	19	16.4425	2.55755
129	17	14.0776	2.9224
130	18	13.9733	4.02674
131	19	15.3071	3.69288
132	19	13.1169	5.88312
133	19	16.1108	2.88918
134	13	13.2594	-0.259402
135	10	11.3678	-1.36781
136	5	10.4518	-5.45182
137	14	15.1625	-1.16249
138	12	12.4031	-0.403061
139	13	14.4722	-1.47216
140	18	11.7961	6.20392
141	18	16.0642	1.93583
142	14	11.8942	2.10582
143	16	15.1172	0.882817
144	13	13.096	-0.0960067
145	12	12.1832	-0.183208
146	12	13.4097	-1.40968
147	13	13.9338	-0.933791
148	8	14.7331	-6.73306
149	15	12.8325	2.16751

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
9	0.943017	0.113967	0.0569834
10	0.90598	0.18804	0.0940198
11	0.928005	0.14399	0.0719951
12	0.881073	0.237854	0.118927
13	0.914833	0.170335	0.0851674
14	0.872513	0.254974	0.127487
15	0.815139	0.369723	0.184861
16	0.783178	0.433644	0.216822
17	0.725773	0.548453	0.274227
18	0.655449	0.689102	0.344551
19	0.581159	0.837681	0.418841
20	0.661602	0.676796	0.338398
21	0.614822	0.770357	0.385178
22	0.552533	0.894935	0.447467
23	0.492466	0.984932	0.507534
24	0.431725	0.86345	0.568275
25	0.395353	0.790706	0.604647
26	0.335799	0.671597	0.664201
27	0.334906	0.669812	0.665094
28	0.285934	0.571867	0.714066
29	0.296324	0.592648	0.703676
30	0.37712	0.754239	0.62288
31	0.335259	0.670519	0.664741
32	0.331168	0.662337	0.668832
33	0.302901	0.605801	0.697099
34	0.257915	0.51583	0.742085
35	0.223487	0.446974	0.776513
36	0.25464	0.509279	0.74536
37	0.237048	0.474096	0.762952
38	0.281719	0.563437	0.718281
39	0.247375	0.49475	0.752625
40	0.226404	0.452809	0.773596
41	0.205926	0.411852	0.794074
42	0.18127	0.362541	0.81873
43	0.172485	0.34497	0.827515
44	0.211452	0.422903	0.788548
45	0.210321	0.420643	0.789679
46	0.205362	0.410724	0.794638
47	0.19031	0.38062	0.80969
48	0.223782	0.447564	0.776218
49	0.240395	0.48079	0.759605
50	0.231173	0.462345	0.768827
51	0.220235	0.440469	0.779765
52	0.26506	0.53012	0.73494
53	0.287009	0.574017	0.712991
54	0.28024	0.560481	0.71976
55	0.273854	0.547708	0.726146
56	0.282023	0.564046	0.717977
57	0.253367	0.506733	0.746633
58	0.2288	0.4576	0.7712
59	0.411326	0.822653	0.588674
60	0.407054	0.814109	0.592946
61	0.400798	0.801596	0.599202
62	0.411169	0.822337	0.588831
63	0.490017	0.980033	0.509983
64	0.59017	0.819659	0.40983
65	0.612726	0.774549	0.387274
66	0.746381	0.507238	0.253619
67	0.807956	0.384089	0.192044
68	0.791532	0.416937	0.208468
69	0.853026	0.293947	0.146974
70	0.84529	0.30942	0.15471
71	0.857442	0.285116	0.142558
72	0.850668	0.298664	0.149332
73	0.916937	0.166126	0.083063
74	0.915503	0.168995	0.0844973
75	0.89681	0.20638	0.10319
76	0.885313	0.229374	0.114687
77	0.869268	0.261464	0.130732
78	0.860163	0.279675	0.139837
79	0.871082	0.257836	0.128918
80	0.848109	0.303781	0.151891
81	0.875683	0.248633	0.124317
82	0.858254	0.283492	0.141746
83	0.829191	0.341619	0.170809
84	0.797813	0.404374	0.202187
85	0.823346	0.353307	0.176654
86	0.805864	0.388271	0.194136
87	0.779045	0.441911	0.220955
88	0.741758	0.516484	0.258242
89	0.838949	0.322102	0.161051
90	0.823894	0.352212	0.176106
91	0.830943	0.338113	0.169057
92	0.820462	0.359075	0.179538
93	0.788967	0.422066	0.211033
94	0.789595	0.42081	0.210405
95	0.758257	0.483486	0.241743
96	0.736587	0.526825	0.263413
97	0.697405	0.605191	0.302595
98	0.658454	0.683092	0.341546
99	0.632903	0.734194	0.367097
100	0.820129	0.359742	0.179871
101	0.805867	0.388267	0.194133
102	0.799645	0.400709	0.200355
103	0.761119	0.477761	0.238881
104	0.751881	0.496238	0.248119
105	0.737822	0.524356	0.262178
106	0.727821	0.544357	0.272179
107	0.974212	0.0515758	0.0257879
108	0.966368	0.0672643	0.0336322
109	0.958274	0.0834515	0.0417257
110	0.946041	0.107919	0.0539593
111	0.943797	0.112406	0.0562032
112	0.932755	0.13449	0.0672451
113	0.925948	0.148104	0.0740522
114	0.911824	0.176352	0.088176
115	0.901689	0.196622	0.0983111
116	0.880531	0.238938	0.119469
117	0.846475	0.307051	0.153525
118	0.830866	0.338268	0.169134
119	0.799765	0.400469	0.200235
120	0.774351	0.451298	0.225649
121	0.751884	0.496232	0.248116
122	0.695569	0.608861	0.304431
123	0.651417	0.697165	0.348583
124	0.59526	0.809479	0.40474
125	0.632795	0.734411	0.367205
126	0.561685	0.876631	0.438315
127	0.558484	0.883032	0.441516
128	0.508835	0.982331	0.491165
129	0.623124	0.753753	0.376876
130	0.55863	0.882739	0.44137
131	0.726201	0.547598	0.273799
132	0.794394	0.411213	0.205606
133	0.723226	0.553549	0.276774
134	0.715844	0.568311	0.284156
135	0.632683	0.734634	0.367317
136	0.841841	0.316318	0.158159
137	0.812048	0.375904	0.187952
138	0.76644	0.46712	0.23356
139	0.632883	0.734235	0.367117
140	0.673301	0.653398	0.326699

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.943017 & 0.113967 & 0.0569834 \tabularnewline
10 & 0.90598 & 0.18804 & 0.0940198 \tabularnewline
11 & 0.928005 & 0.14399 & 0.0719951 \tabularnewline
12 & 0.881073 & 0.237854 & 0.118927 \tabularnewline
13 & 0.914833 & 0.170335 & 0.0851674 \tabularnewline
14 & 0.872513 & 0.254974 & 0.127487 \tabularnewline
15 & 0.815139 & 0.369723 & 0.184861 \tabularnewline
16 & 0.783178 & 0.433644 & 0.216822 \tabularnewline
17 & 0.725773 & 0.548453 & 0.274227 \tabularnewline
18 & 0.655449 & 0.689102 & 0.344551 \tabularnewline
19 & 0.581159 & 0.837681 & 0.418841 \tabularnewline
20 & 0.661602 & 0.676796 & 0.338398 \tabularnewline
21 & 0.614822 & 0.770357 & 0.385178 \tabularnewline
22 & 0.552533 & 0.894935 & 0.447467 \tabularnewline
23 & 0.492466 & 0.984932 & 0.507534 \tabularnewline
24 & 0.431725 & 0.86345 & 0.568275 \tabularnewline
25 & 0.395353 & 0.790706 & 0.604647 \tabularnewline
26 & 0.335799 & 0.671597 & 0.664201 \tabularnewline
27 & 0.334906 & 0.669812 & 0.665094 \tabularnewline
28 & 0.285934 & 0.571867 & 0.714066 \tabularnewline
29 & 0.296324 & 0.592648 & 0.703676 \tabularnewline
30 & 0.37712 & 0.754239 & 0.62288 \tabularnewline
31 & 0.335259 & 0.670519 & 0.664741 \tabularnewline
32 & 0.331168 & 0.662337 & 0.668832 \tabularnewline
33 & 0.302901 & 0.605801 & 0.697099 \tabularnewline
34 & 0.257915 & 0.51583 & 0.742085 \tabularnewline
35 & 0.223487 & 0.446974 & 0.776513 \tabularnewline
36 & 0.25464 & 0.509279 & 0.74536 \tabularnewline
37 & 0.237048 & 0.474096 & 0.762952 \tabularnewline
38 & 0.281719 & 0.563437 & 0.718281 \tabularnewline
39 & 0.247375 & 0.49475 & 0.752625 \tabularnewline
40 & 0.226404 & 0.452809 & 0.773596 \tabularnewline
41 & 0.205926 & 0.411852 & 0.794074 \tabularnewline
42 & 0.18127 & 0.362541 & 0.81873 \tabularnewline
43 & 0.172485 & 0.34497 & 0.827515 \tabularnewline
44 & 0.211452 & 0.422903 & 0.788548 \tabularnewline
45 & 0.210321 & 0.420643 & 0.789679 \tabularnewline
46 & 0.205362 & 0.410724 & 0.794638 \tabularnewline
47 & 0.19031 & 0.38062 & 0.80969 \tabularnewline
48 & 0.223782 & 0.447564 & 0.776218 \tabularnewline
49 & 0.240395 & 0.48079 & 0.759605 \tabularnewline
50 & 0.231173 & 0.462345 & 0.768827 \tabularnewline
51 & 0.220235 & 0.440469 & 0.779765 \tabularnewline
52 & 0.26506 & 0.53012 & 0.73494 \tabularnewline
53 & 0.287009 & 0.574017 & 0.712991 \tabularnewline
54 & 0.28024 & 0.560481 & 0.71976 \tabularnewline
55 & 0.273854 & 0.547708 & 0.726146 \tabularnewline
56 & 0.282023 & 0.564046 & 0.717977 \tabularnewline
57 & 0.253367 & 0.506733 & 0.746633 \tabularnewline
58 & 0.2288 & 0.4576 & 0.7712 \tabularnewline
59 & 0.411326 & 0.822653 & 0.588674 \tabularnewline
60 & 0.407054 & 0.814109 & 0.592946 \tabularnewline
61 & 0.400798 & 0.801596 & 0.599202 \tabularnewline
62 & 0.411169 & 0.822337 & 0.588831 \tabularnewline
63 & 0.490017 & 0.980033 & 0.509983 \tabularnewline
64 & 0.59017 & 0.819659 & 0.40983 \tabularnewline
65 & 0.612726 & 0.774549 & 0.387274 \tabularnewline
66 & 0.746381 & 0.507238 & 0.253619 \tabularnewline
67 & 0.807956 & 0.384089 & 0.192044 \tabularnewline
68 & 0.791532 & 0.416937 & 0.208468 \tabularnewline
69 & 0.853026 & 0.293947 & 0.146974 \tabularnewline
70 & 0.84529 & 0.30942 & 0.15471 \tabularnewline
71 & 0.857442 & 0.285116 & 0.142558 \tabularnewline
72 & 0.850668 & 0.298664 & 0.149332 \tabularnewline
73 & 0.916937 & 0.166126 & 0.083063 \tabularnewline
74 & 0.915503 & 0.168995 & 0.0844973 \tabularnewline
75 & 0.89681 & 0.20638 & 0.10319 \tabularnewline
76 & 0.885313 & 0.229374 & 0.114687 \tabularnewline
77 & 0.869268 & 0.261464 & 0.130732 \tabularnewline
78 & 0.860163 & 0.279675 & 0.139837 \tabularnewline
79 & 0.871082 & 0.257836 & 0.128918 \tabularnewline
80 & 0.848109 & 0.303781 & 0.151891 \tabularnewline
81 & 0.875683 & 0.248633 & 0.124317 \tabularnewline
82 & 0.858254 & 0.283492 & 0.141746 \tabularnewline
83 & 0.829191 & 0.341619 & 0.170809 \tabularnewline
84 & 0.797813 & 0.404374 & 0.202187 \tabularnewline
85 & 0.823346 & 0.353307 & 0.176654 \tabularnewline
86 & 0.805864 & 0.388271 & 0.194136 \tabularnewline
87 & 0.779045 & 0.441911 & 0.220955 \tabularnewline
88 & 0.741758 & 0.516484 & 0.258242 \tabularnewline
89 & 0.838949 & 0.322102 & 0.161051 \tabularnewline
90 & 0.823894 & 0.352212 & 0.176106 \tabularnewline
91 & 0.830943 & 0.338113 & 0.169057 \tabularnewline
92 & 0.820462 & 0.359075 & 0.179538 \tabularnewline
93 & 0.788967 & 0.422066 & 0.211033 \tabularnewline
94 & 0.789595 & 0.42081 & 0.210405 \tabularnewline
95 & 0.758257 & 0.483486 & 0.241743 \tabularnewline
96 & 0.736587 & 0.526825 & 0.263413 \tabularnewline
97 & 0.697405 & 0.605191 & 0.302595 \tabularnewline
98 & 0.658454 & 0.683092 & 0.341546 \tabularnewline
99 & 0.632903 & 0.734194 & 0.367097 \tabularnewline
100 & 0.820129 & 0.359742 & 0.179871 \tabularnewline
101 & 0.805867 & 0.388267 & 0.194133 \tabularnewline
102 & 0.799645 & 0.400709 & 0.200355 \tabularnewline
103 & 0.761119 & 0.477761 & 0.238881 \tabularnewline
104 & 0.751881 & 0.496238 & 0.248119 \tabularnewline
105 & 0.737822 & 0.524356 & 0.262178 \tabularnewline
106 & 0.727821 & 0.544357 & 0.272179 \tabularnewline
107 & 0.974212 & 0.0515758 & 0.0257879 \tabularnewline
108 & 0.966368 & 0.0672643 & 0.0336322 \tabularnewline
109 & 0.958274 & 0.0834515 & 0.0417257 \tabularnewline
110 & 0.946041 & 0.107919 & 0.0539593 \tabularnewline
111 & 0.943797 & 0.112406 & 0.0562032 \tabularnewline
112 & 0.932755 & 0.13449 & 0.0672451 \tabularnewline
113 & 0.925948 & 0.148104 & 0.0740522 \tabularnewline
114 & 0.911824 & 0.176352 & 0.088176 \tabularnewline
115 & 0.901689 & 0.196622 & 0.0983111 \tabularnewline
116 & 0.880531 & 0.238938 & 0.119469 \tabularnewline
117 & 0.846475 & 0.307051 & 0.153525 \tabularnewline
118 & 0.830866 & 0.338268 & 0.169134 \tabularnewline
119 & 0.799765 & 0.400469 & 0.200235 \tabularnewline
120 & 0.774351 & 0.451298 & 0.225649 \tabularnewline
121 & 0.751884 & 0.496232 & 0.248116 \tabularnewline
122 & 0.695569 & 0.608861 & 0.304431 \tabularnewline
123 & 0.651417 & 0.697165 & 0.348583 \tabularnewline
124 & 0.59526 & 0.809479 & 0.40474 \tabularnewline
125 & 0.632795 & 0.734411 & 0.367205 \tabularnewline
126 & 0.561685 & 0.876631 & 0.438315 \tabularnewline
127 & 0.558484 & 0.883032 & 0.441516 \tabularnewline
128 & 0.508835 & 0.982331 & 0.491165 \tabularnewline
129 & 0.623124 & 0.753753 & 0.376876 \tabularnewline
130 & 0.55863 & 0.882739 & 0.44137 \tabularnewline
131 & 0.726201 & 0.547598 & 0.273799 \tabularnewline
132 & 0.794394 & 0.411213 & 0.205606 \tabularnewline
133 & 0.723226 & 0.553549 & 0.276774 \tabularnewline
134 & 0.715844 & 0.568311 & 0.284156 \tabularnewline
135 & 0.632683 & 0.734634 & 0.367317 \tabularnewline
136 & 0.841841 & 0.316318 & 0.158159 \tabularnewline
137 & 0.812048 & 0.375904 & 0.187952 \tabularnewline
138 & 0.76644 & 0.46712 & 0.23356 \tabularnewline
139 & 0.632883 & 0.734235 & 0.367117 \tabularnewline
140 & 0.673301 & 0.653398 & 0.326699 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267644&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]9[/C][C]0.943017[/C][C]0.113967[/C][C]0.0569834[/C][/ROW]
[ROW][C]10[/C][C]0.90598[/C][C]0.18804[/C][C]0.0940198[/C][/ROW]
[ROW][C]11[/C][C]0.928005[/C][C]0.14399[/C][C]0.0719951[/C][/ROW]
[ROW][C]12[/C][C]0.881073[/C][C]0.237854[/C][C]0.118927[/C][/ROW]
[ROW][C]13[/C][C]0.914833[/C][C]0.170335[/C][C]0.0851674[/C][/ROW]
[ROW][C]14[/C][C]0.872513[/C][C]0.254974[/C][C]0.127487[/C][/ROW]
[ROW][C]15[/C][C]0.815139[/C][C]0.369723[/C][C]0.184861[/C][/ROW]
[ROW][C]16[/C][C]0.783178[/C][C]0.433644[/C][C]0.216822[/C][/ROW]
[ROW][C]17[/C][C]0.725773[/C][C]0.548453[/C][C]0.274227[/C][/ROW]
[ROW][C]18[/C][C]0.655449[/C][C]0.689102[/C][C]0.344551[/C][/ROW]
[ROW][C]19[/C][C]0.581159[/C][C]0.837681[/C][C]0.418841[/C][/ROW]
[ROW][C]20[/C][C]0.661602[/C][C]0.676796[/C][C]0.338398[/C][/ROW]
[ROW][C]21[/C][C]0.614822[/C][C]0.770357[/C][C]0.385178[/C][/ROW]
[ROW][C]22[/C][C]0.552533[/C][C]0.894935[/C][C]0.447467[/C][/ROW]
[ROW][C]23[/C][C]0.492466[/C][C]0.984932[/C][C]0.507534[/C][/ROW]
[ROW][C]24[/C][C]0.431725[/C][C]0.86345[/C][C]0.568275[/C][/ROW]
[ROW][C]25[/C][C]0.395353[/C][C]0.790706[/C][C]0.604647[/C][/ROW]
[ROW][C]26[/C][C]0.335799[/C][C]0.671597[/C][C]0.664201[/C][/ROW]
[ROW][C]27[/C][C]0.334906[/C][C]0.669812[/C][C]0.665094[/C][/ROW]
[ROW][C]28[/C][C]0.285934[/C][C]0.571867[/C][C]0.714066[/C][/ROW]
[ROW][C]29[/C][C]0.296324[/C][C]0.592648[/C][C]0.703676[/C][/ROW]
[ROW][C]30[/C][C]0.37712[/C][C]0.754239[/C][C]0.62288[/C][/ROW]
[ROW][C]31[/C][C]0.335259[/C][C]0.670519[/C][C]0.664741[/C][/ROW]
[ROW][C]32[/C][C]0.331168[/C][C]0.662337[/C][C]0.668832[/C][/ROW]
[ROW][C]33[/C][C]0.302901[/C][C]0.605801[/C][C]0.697099[/C][/ROW]
[ROW][C]34[/C][C]0.257915[/C][C]0.51583[/C][C]0.742085[/C][/ROW]
[ROW][C]35[/C][C]0.223487[/C][C]0.446974[/C][C]0.776513[/C][/ROW]
[ROW][C]36[/C][C]0.25464[/C][C]0.509279[/C][C]0.74536[/C][/ROW]
[ROW][C]37[/C][C]0.237048[/C][C]0.474096[/C][C]0.762952[/C][/ROW]
[ROW][C]38[/C][C]0.281719[/C][C]0.563437[/C][C]0.718281[/C][/ROW]
[ROW][C]39[/C][C]0.247375[/C][C]0.49475[/C][C]0.752625[/C][/ROW]
[ROW][C]40[/C][C]0.226404[/C][C]0.452809[/C][C]0.773596[/C][/ROW]
[ROW][C]41[/C][C]0.205926[/C][C]0.411852[/C][C]0.794074[/C][/ROW]
[ROW][C]42[/C][C]0.18127[/C][C]0.362541[/C][C]0.81873[/C][/ROW]
[ROW][C]43[/C][C]0.172485[/C][C]0.34497[/C][C]0.827515[/C][/ROW]
[ROW][C]44[/C][C]0.211452[/C][C]0.422903[/C][C]0.788548[/C][/ROW]
[ROW][C]45[/C][C]0.210321[/C][C]0.420643[/C][C]0.789679[/C][/ROW]
[ROW][C]46[/C][C]0.205362[/C][C]0.410724[/C][C]0.794638[/C][/ROW]
[ROW][C]47[/C][C]0.19031[/C][C]0.38062[/C][C]0.80969[/C][/ROW]
[ROW][C]48[/C][C]0.223782[/C][C]0.447564[/C][C]0.776218[/C][/ROW]
[ROW][C]49[/C][C]0.240395[/C][C]0.48079[/C][C]0.759605[/C][/ROW]
[ROW][C]50[/C][C]0.231173[/C][C]0.462345[/C][C]0.768827[/C][/ROW]
[ROW][C]51[/C][C]0.220235[/C][C]0.440469[/C][C]0.779765[/C][/ROW]
[ROW][C]52[/C][C]0.26506[/C][C]0.53012[/C][C]0.73494[/C][/ROW]
[ROW][C]53[/C][C]0.287009[/C][C]0.574017[/C][C]0.712991[/C][/ROW]
[ROW][C]54[/C][C]0.28024[/C][C]0.560481[/C][C]0.71976[/C][/ROW]
[ROW][C]55[/C][C]0.273854[/C][C]0.547708[/C][C]0.726146[/C][/ROW]
[ROW][C]56[/C][C]0.282023[/C][C]0.564046[/C][C]0.717977[/C][/ROW]
[ROW][C]57[/C][C]0.253367[/C][C]0.506733[/C][C]0.746633[/C][/ROW]
[ROW][C]58[/C][C]0.2288[/C][C]0.4576[/C][C]0.7712[/C][/ROW]
[ROW][C]59[/C][C]0.411326[/C][C]0.822653[/C][C]0.588674[/C][/ROW]
[ROW][C]60[/C][C]0.407054[/C][C]0.814109[/C][C]0.592946[/C][/ROW]
[ROW][C]61[/C][C]0.400798[/C][C]0.801596[/C][C]0.599202[/C][/ROW]
[ROW][C]62[/C][C]0.411169[/C][C]0.822337[/C][C]0.588831[/C][/ROW]
[ROW][C]63[/C][C]0.490017[/C][C]0.980033[/C][C]0.509983[/C][/ROW]
[ROW][C]64[/C][C]0.59017[/C][C]0.819659[/C][C]0.40983[/C][/ROW]
[ROW][C]65[/C][C]0.612726[/C][C]0.774549[/C][C]0.387274[/C][/ROW]
[ROW][C]66[/C][C]0.746381[/C][C]0.507238[/C][C]0.253619[/C][/ROW]
[ROW][C]67[/C][C]0.807956[/C][C]0.384089[/C][C]0.192044[/C][/ROW]
[ROW][C]68[/C][C]0.791532[/C][C]0.416937[/C][C]0.208468[/C][/ROW]
[ROW][C]69[/C][C]0.853026[/C][C]0.293947[/C][C]0.146974[/C][/ROW]
[ROW][C]70[/C][C]0.84529[/C][C]0.30942[/C][C]0.15471[/C][/ROW]
[ROW][C]71[/C][C]0.857442[/C][C]0.285116[/C][C]0.142558[/C][/ROW]
[ROW][C]72[/C][C]0.850668[/C][C]0.298664[/C][C]0.149332[/C][/ROW]
[ROW][C]73[/C][C]0.916937[/C][C]0.166126[/C][C]0.083063[/C][/ROW]
[ROW][C]74[/C][C]0.915503[/C][C]0.168995[/C][C]0.0844973[/C][/ROW]
[ROW][C]75[/C][C]0.89681[/C][C]0.20638[/C][C]0.10319[/C][/ROW]
[ROW][C]76[/C][C]0.885313[/C][C]0.229374[/C][C]0.114687[/C][/ROW]
[ROW][C]77[/C][C]0.869268[/C][C]0.261464[/C][C]0.130732[/C][/ROW]
[ROW][C]78[/C][C]0.860163[/C][C]0.279675[/C][C]0.139837[/C][/ROW]
[ROW][C]79[/C][C]0.871082[/C][C]0.257836[/C][C]0.128918[/C][/ROW]
[ROW][C]80[/C][C]0.848109[/C][C]0.303781[/C][C]0.151891[/C][/ROW]
[ROW][C]81[/C][C]0.875683[/C][C]0.248633[/C][C]0.124317[/C][/ROW]
[ROW][C]82[/C][C]0.858254[/C][C]0.283492[/C][C]0.141746[/C][/ROW]
[ROW][C]83[/C][C]0.829191[/C][C]0.341619[/C][C]0.170809[/C][/ROW]
[ROW][C]84[/C][C]0.797813[/C][C]0.404374[/C][C]0.202187[/C][/ROW]
[ROW][C]85[/C][C]0.823346[/C][C]0.353307[/C][C]0.176654[/C][/ROW]
[ROW][C]86[/C][C]0.805864[/C][C]0.388271[/C][C]0.194136[/C][/ROW]
[ROW][C]87[/C][C]0.779045[/C][C]0.441911[/C][C]0.220955[/C][/ROW]
[ROW][C]88[/C][C]0.741758[/C][C]0.516484[/C][C]0.258242[/C][/ROW]
[ROW][C]89[/C][C]0.838949[/C][C]0.322102[/C][C]0.161051[/C][/ROW]
[ROW][C]90[/C][C]0.823894[/C][C]0.352212[/C][C]0.176106[/C][/ROW]
[ROW][C]91[/C][C]0.830943[/C][C]0.338113[/C][C]0.169057[/C][/ROW]
[ROW][C]92[/C][C]0.820462[/C][C]0.359075[/C][C]0.179538[/C][/ROW]
[ROW][C]93[/C][C]0.788967[/C][C]0.422066[/C][C]0.211033[/C][/ROW]
[ROW][C]94[/C][C]0.789595[/C][C]0.42081[/C][C]0.210405[/C][/ROW]
[ROW][C]95[/C][C]0.758257[/C][C]0.483486[/C][C]0.241743[/C][/ROW]
[ROW][C]96[/C][C]0.736587[/C][C]0.526825[/C][C]0.263413[/C][/ROW]
[ROW][C]97[/C][C]0.697405[/C][C]0.605191[/C][C]0.302595[/C][/ROW]
[ROW][C]98[/C][C]0.658454[/C][C]0.683092[/C][C]0.341546[/C][/ROW]
[ROW][C]99[/C][C]0.632903[/C][C]0.734194[/C][C]0.367097[/C][/ROW]
[ROW][C]100[/C][C]0.820129[/C][C]0.359742[/C][C]0.179871[/C][/ROW]
[ROW][C]101[/C][C]0.805867[/C][C]0.388267[/C][C]0.194133[/C][/ROW]
[ROW][C]102[/C][C]0.799645[/C][C]0.400709[/C][C]0.200355[/C][/ROW]
[ROW][C]103[/C][C]0.761119[/C][C]0.477761[/C][C]0.238881[/C][/ROW]
[ROW][C]104[/C][C]0.751881[/C][C]0.496238[/C][C]0.248119[/C][/ROW]
[ROW][C]105[/C][C]0.737822[/C][C]0.524356[/C][C]0.262178[/C][/ROW]
[ROW][C]106[/C][C]0.727821[/C][C]0.544357[/C][C]0.272179[/C][/ROW]
[ROW][C]107[/C][C]0.974212[/C][C]0.0515758[/C][C]0.0257879[/C][/ROW]
[ROW][C]108[/C][C]0.966368[/C][C]0.0672643[/C][C]0.0336322[/C][/ROW]
[ROW][C]109[/C][C]0.958274[/C][C]0.0834515[/C][C]0.0417257[/C][/ROW]
[ROW][C]110[/C][C]0.946041[/C][C]0.107919[/C][C]0.0539593[/C][/ROW]
[ROW][C]111[/C][C]0.943797[/C][C]0.112406[/C][C]0.0562032[/C][/ROW]
[ROW][C]112[/C][C]0.932755[/C][C]0.13449[/C][C]0.0672451[/C][/ROW]
[ROW][C]113[/C][C]0.925948[/C][C]0.148104[/C][C]0.0740522[/C][/ROW]
[ROW][C]114[/C][C]0.911824[/C][C]0.176352[/C][C]0.088176[/C][/ROW]
[ROW][C]115[/C][C]0.901689[/C][C]0.196622[/C][C]0.0983111[/C][/ROW]
[ROW][C]116[/C][C]0.880531[/C][C]0.238938[/C][C]0.119469[/C][/ROW]
[ROW][C]117[/C][C]0.846475[/C][C]0.307051[/C][C]0.153525[/C][/ROW]
[ROW][C]118[/C][C]0.830866[/C][C]0.338268[/C][C]0.169134[/C][/ROW]
[ROW][C]119[/C][C]0.799765[/C][C]0.400469[/C][C]0.200235[/C][/ROW]
[ROW][C]120[/C][C]0.774351[/C][C]0.451298[/C][C]0.225649[/C][/ROW]
[ROW][C]121[/C][C]0.751884[/C][C]0.496232[/C][C]0.248116[/C][/ROW]
[ROW][C]122[/C][C]0.695569[/C][C]0.608861[/C][C]0.304431[/C][/ROW]
[ROW][C]123[/C][C]0.651417[/C][C]0.697165[/C][C]0.348583[/C][/ROW]
[ROW][C]124[/C][C]0.59526[/C][C]0.809479[/C][C]0.40474[/C][/ROW]
[ROW][C]125[/C][C]0.632795[/C][C]0.734411[/C][C]0.367205[/C][/ROW]
[ROW][C]126[/C][C]0.561685[/C][C]0.876631[/C][C]0.438315[/C][/ROW]
[ROW][C]127[/C][C]0.558484[/C][C]0.883032[/C][C]0.441516[/C][/ROW]
[ROW][C]128[/C][C]0.508835[/C][C]0.982331[/C][C]0.491165[/C][/ROW]
[ROW][C]129[/C][C]0.623124[/C][C]0.753753[/C][C]0.376876[/C][/ROW]
[ROW][C]130[/C][C]0.55863[/C][C]0.882739[/C][C]0.44137[/C][/ROW]
[ROW][C]131[/C][C]0.726201[/C][C]0.547598[/C][C]0.273799[/C][/ROW]
[ROW][C]132[/C][C]0.794394[/C][C]0.411213[/C][C]0.205606[/C][/ROW]
[ROW][C]133[/C][C]0.723226[/C][C]0.553549[/C][C]0.276774[/C][/ROW]
[ROW][C]134[/C][C]0.715844[/C][C]0.568311[/C][C]0.284156[/C][/ROW]
[ROW][C]135[/C][C]0.632683[/C][C]0.734634[/C][C]0.367317[/C][/ROW]
[ROW][C]136[/C][C]0.841841[/C][C]0.316318[/C][C]0.158159[/C][/ROW]
[ROW][C]137[/C][C]0.812048[/C][C]0.375904[/C][C]0.187952[/C][/ROW]
[ROW][C]138[/C][C]0.76644[/C][C]0.46712[/C][C]0.23356[/C][/ROW]
[ROW][C]139[/C][C]0.632883[/C][C]0.734235[/C][C]0.367117[/C][/ROW]
[ROW][C]140[/C][C]0.673301[/C][C]0.653398[/C][C]0.326699[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267644&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267644&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
9	0.943017	0.113967	0.0569834
10	0.90598	0.18804	0.0940198
11	0.928005	0.14399	0.0719951
12	0.881073	0.237854	0.118927
13	0.914833	0.170335	0.0851674
14	0.872513	0.254974	0.127487
15	0.815139	0.369723	0.184861
16	0.783178	0.433644	0.216822
17	0.725773	0.548453	0.274227
18	0.655449	0.689102	0.344551
19	0.581159	0.837681	0.418841
20	0.661602	0.676796	0.338398
21	0.614822	0.770357	0.385178
22	0.552533	0.894935	0.447467
23	0.492466	0.984932	0.507534
24	0.431725	0.86345	0.568275
25	0.395353	0.790706	0.604647
26	0.335799	0.671597	0.664201
27	0.334906	0.669812	0.665094
28	0.285934	0.571867	0.714066
29	0.296324	0.592648	0.703676
30	0.37712	0.754239	0.62288
31	0.335259	0.670519	0.664741
32	0.331168	0.662337	0.668832
33	0.302901	0.605801	0.697099
34	0.257915	0.51583	0.742085
35	0.223487	0.446974	0.776513
36	0.25464	0.509279	0.74536
37	0.237048	0.474096	0.762952
38	0.281719	0.563437	0.718281
39	0.247375	0.49475	0.752625
40	0.226404	0.452809	0.773596
41	0.205926	0.411852	0.794074
42	0.18127	0.362541	0.81873
43	0.172485	0.34497	0.827515
44	0.211452	0.422903	0.788548
45	0.210321	0.420643	0.789679
46	0.205362	0.410724	0.794638
47	0.19031	0.38062	0.80969
48	0.223782	0.447564	0.776218
49	0.240395	0.48079	0.759605
50	0.231173	0.462345	0.768827
51	0.220235	0.440469	0.779765
52	0.26506	0.53012	0.73494
53	0.287009	0.574017	0.712991
54	0.28024	0.560481	0.71976
55	0.273854	0.547708	0.726146
56	0.282023	0.564046	0.717977
57	0.253367	0.506733	0.746633
58	0.2288	0.4576	0.7712
59	0.411326	0.822653	0.588674
60	0.407054	0.814109	0.592946
61	0.400798	0.801596	0.599202
62	0.411169	0.822337	0.588831
63	0.490017	0.980033	0.509983
64	0.59017	0.819659	0.40983
65	0.612726	0.774549	0.387274
66	0.746381	0.507238	0.253619
67	0.807956	0.384089	0.192044
68	0.791532	0.416937	0.208468
69	0.853026	0.293947	0.146974
70	0.84529	0.30942	0.15471
71	0.857442	0.285116	0.142558
72	0.850668	0.298664	0.149332
73	0.916937	0.166126	0.083063
74	0.915503	0.168995	0.0844973
75	0.89681	0.20638	0.10319
76	0.885313	0.229374	0.114687
77	0.869268	0.261464	0.130732
78	0.860163	0.279675	0.139837
79	0.871082	0.257836	0.128918
80	0.848109	0.303781	0.151891
81	0.875683	0.248633	0.124317
82	0.858254	0.283492	0.141746
83	0.829191	0.341619	0.170809
84	0.797813	0.404374	0.202187
85	0.823346	0.353307	0.176654
86	0.805864	0.388271	0.194136
87	0.779045	0.441911	0.220955
88	0.741758	0.516484	0.258242
89	0.838949	0.322102	0.161051
90	0.823894	0.352212	0.176106
91	0.830943	0.338113	0.169057
92	0.820462	0.359075	0.179538
93	0.788967	0.422066	0.211033
94	0.789595	0.42081	0.210405
95	0.758257	0.483486	0.241743
96	0.736587	0.526825	0.263413
97	0.697405	0.605191	0.302595
98	0.658454	0.683092	0.341546
99	0.632903	0.734194	0.367097
100	0.820129	0.359742	0.179871
101	0.805867	0.388267	0.194133
102	0.799645	0.400709	0.200355
103	0.761119	0.477761	0.238881
104	0.751881	0.496238	0.248119
105	0.737822	0.524356	0.262178
106	0.727821	0.544357	0.272179
107	0.974212	0.0515758	0.0257879
108	0.966368	0.0672643	0.0336322
109	0.958274	0.0834515	0.0417257
110	0.946041	0.107919	0.0539593
111	0.943797	0.112406	0.0562032
112	0.932755	0.13449	0.0672451
113	0.925948	0.148104	0.0740522
114	0.911824	0.176352	0.088176
115	0.901689	0.196622	0.0983111
116	0.880531	0.238938	0.119469
117	0.846475	0.307051	0.153525
118	0.830866	0.338268	0.169134
119	0.799765	0.400469	0.200235
120	0.774351	0.451298	0.225649
121	0.751884	0.496232	0.248116
122	0.695569	0.608861	0.304431
123	0.651417	0.697165	0.348583
124	0.59526	0.809479	0.40474
125	0.632795	0.734411	0.367205
126	0.561685	0.876631	0.438315
127	0.558484	0.883032	0.441516
128	0.508835	0.982331	0.491165
129	0.623124	0.753753	0.376876
130	0.55863	0.882739	0.44137
131	0.726201	0.547598	0.273799
132	0.794394	0.411213	0.205606
133	0.723226	0.553549	0.276774
134	0.715844	0.568311	0.284156
135	0.632683	0.734634	0.367317
136	0.841841	0.316318	0.158159
137	0.812048	0.375904	0.187952
138	0.76644	0.46712	0.23356
139	0.632883	0.734235	0.367117
140	0.673301	0.653398	0.326699

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	0	0	OK
10% type I error level	3	0.0227273	OK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 3 & 0.0227273 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267644&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]3[/C][C]0.0227273[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267644&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267644&T=6

As an alternative you can also use a QR Code:

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	0	0	OK
10% type I error level	3	0.0227273	OK

Figure 1

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Figure 2

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Figure 3

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Figure 4

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Figure 5

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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Figure 10

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Parameters (Session):

par1 = 6 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 6 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

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

par3 <- 'No Linear Trend'
par2 <- 'Do not include Seasonal Dummies'
par1 <- '6'
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- 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'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
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[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,signif(mysum$coefficients[i,1],6))
a<-table.element(a, signif(mysum$coefficients[i,2],6))
a<-table.element(a, signif(mysum$coefficients[i,3],4))
a<-table.element(a, signif(mysum$coefficients[i,4],6))
a<-table.element(a, signif(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, signif(sqrt(mysum$r.squared),6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, signif(mysum$r.squared,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, signif(mysum$adj.r.squared,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[1],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, signif(mysum$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$fstatistic[3],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6))
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, signif(mysum$sigma,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, signif(sum(myerror*myerror),6))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,signif(x[i],6))
a<-table.element(a,signif(x[i]-mysum$resid[i],6))
a<-table.element(a,signif(mysum$resid[i],6))
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,signif(gqarr[mypoint-kp3+1,1],6))
a<-table.element(a,signif(gqarr[mypoint-kp3+1,2],6))
a<-table.element(a,signif(gqarr[mypoint-kp3+1,3],6))
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,signif(numsignificant1/numgqtests,6))
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