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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Mon, 15 Dec 2014 16:31:53 +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/2014/Dec/15/t1418661343apwyzkemm31r716.htm/, Retrieved Thu, 16 May 2024 17:18:13 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=268728, Retrieved Thu, 16 May 2024 17:18:13 +0000

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

-       [Multiple Regression] [Multiple Regressi...] [2014-12-15 16:31:53] [3bdd9332f2b4587684e97c4d9c2e2a73] [Current]

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149	96	68	86	13
139	70	39	70	13
148	88	32	71	11
158	114	62	108	14
128	69	33	64	15
224	176	52	119	14
159	114	62	97	11
105	121	77	129	13
159	110	76	153	16
167	158	41	78	14
165	116	48	80	14
159	181	63	99	15
119	77	30	68	15
176	141	78	147	13
54	35	19	40	14
91	80	31	57	11
163	152	66	120	12
124	97	35	71	14
137	99	42	84	13
121	84	45	68	12
153	68	21	55	15
148	101	25	137	15
221	107	44	79	14
188	88	69	116	14
149	112	54	101	12
244	171	74	111	12
148	137	80	189	12
92	77	42	66	15
150	66	61	81	14
153	93	41	63	16
94	105	46	69	12
156	131	39	71	12
132	102	34	64	14
161	161	51	143	16
105	120	42	85	15
97	127	31	86	12
151	77	39	55	14
131	108	20	69	13
166	85	49	120	14
157	168	53	96	16
111	48	31	60	12
145	152	39	95	14
162	75	54	100	15
163	107	49	68	13
59	62	34	57	16
187	121	46	105	16
109	124	55	85	12
90	72	42	103	12
105	40	50	57	16
83	58	13	51	12
116	97	37	69	15
42	88	25	41	12
148	126	30	49	13
155	104	28	50	12
125	148	45	93	14
116	146	35	58	14
128	80	28	54	11
138	97	41	74	10
49	25	6	15	12
96	99	45	69	11
164	118	73	107	16
162	58	17	65	14
99	63	40	58	14
202	139	64	107	15
186	50	37	70	15
66	60	25	53	14
183	152	65	136	13
214	142	100	126	11
188	94	28	95	16
104	66	35	69	12
177	127	56	136	15
126	67	29	58	14
76	90	43	59	15
99	75	59	118	14
139	128	50	82	13
78	41	3	50	6
162	146	59	102	12
108	69	27	65	12
159	186	61	90	14
74	81	28	64	14
110	85	51	83	15
96	54	35	70	11
116	46	29	50	13
87	106	48	77	14
97	34	25	37	16
127	60	44	81	13
106	95	64	101	14
80	57	32	79	16
74	62	20	71	11
91	36	28	60	13
133	56	34	55	13
74	54	31	44	15
114	64	26	40	12
140	76	58	56	13
95	98	23	43	12
98	88	21	45	14
121	35	21	32	14
126	102	33	56	16
98	61	16	40	15
95	80	20	34	14
110	49	37	89	13
70	78	35	50	14
102	90	33	56	15
86	45	27	46	14
130	55	41	76	12
96	96	40	64	7
102	43	35	74	12
100	52	28	57	15
94	60	32	45	12
52	54	22	30	13
98	51	44	62	11
118	51	27	51	14
99	38	17	36	13

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	'George Udny Yule' @ yule.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 & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268728&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]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268728&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268728&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	'George Udny Yule' @ yule.wessa.net

Multiple Linear Regression - Estimated Regression Equation
S[t] = + 12.5152 + 0.00700745L[t] -0.00159914B[t] -0.00328322C[t] + 0.00365168H[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
S[t] =  +  12.5152 +  0.00700745L[t] -0.00159914B[t] -0.00328322C[t] +  0.00365168H[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268728&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]S[t] =  +  12.5152 +  0.00700745L[t] -0.00159914B[t] -0.00328322C[t] +  0.00365168H[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268728&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268728&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
S[t] = + 12.5152 + 0.00700745L[t] -0.00159914B[t] -0.00328322C[t] + 0.00365168H[t] + e[t]

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	12.5152	0.573181	21.83	2.07145e-41	1.03573e-41
L	0.00700745	0.00569459	1.231	0.221166	0.110583
B	-0.00159914	0.0062777	-0.2547	0.799414	0.399707
C	-0.00328322	0.0163372	-0.201	0.841103	0.420552
H	0.00365168	0.00958416	0.381	0.703943	0.351971

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & 12.5152 & 0.573181 & 21.83 & 2.07145e-41 & 1.03573e-41 \tabularnewline
L & 0.00700745 & 0.00569459 & 1.231 & 0.221166 & 0.110583 \tabularnewline
B & -0.00159914 & 0.0062777 & -0.2547 & 0.799414 & 0.399707 \tabularnewline
C & -0.00328322 & 0.0163372 & -0.201 & 0.841103 & 0.420552 \tabularnewline
H & 0.00365168 & 0.00958416 & 0.381 & 0.703943 & 0.351971 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268728&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]12.5152[/C][C]0.573181[/C][C]21.83[/C][C]2.07145e-41[/C][C]1.03573e-41[/C][/ROW]
[ROW][C]L[/C][C]0.00700745[/C][C]0.00569459[/C][C]1.231[/C][C]0.221166[/C][C]0.110583[/C][/ROW]
[ROW][C]B[/C][C]-0.00159914[/C][C]0.0062777[/C][C]-0.2547[/C][C]0.799414[/C][C]0.399707[/C][/ROW]
[ROW][C]C[/C][C]-0.00328322[/C][C]0.0163372[/C][C]-0.201[/C][C]0.841103[/C][C]0.420552[/C][/ROW]
[ROW][C]H[/C][C]0.00365168[/C][C]0.00958416[/C][C]0.381[/C][C]0.703943[/C][C]0.351971[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268728&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268728&T=2

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	12.5152	0.573181	21.83	2.07145e-41	1.03573e-41
L	0.00700745	0.00569459	1.231	0.221166	0.110583
B	-0.00159914	0.0062777	-0.2547	0.799414	0.399707
C	-0.00328322	0.0163372	-0.201	0.841103	0.420552
H	0.00365168	0.00958416	0.381	0.703943	0.351971

Multiple Linear Regression - Regression Statistics
Multiple R	0.162003
R-squared	0.0262451
Adjusted R-squared	-0.00981991
F-TEST (value)	0.727716
F-TEST (DF numerator)	4
F-TEST (DF denominator)	108
p-value	0.574911
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.76389
Sum Squared Residuals	336.023

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.162003 \tabularnewline
R-squared & 0.0262451 \tabularnewline
Adjusted R-squared & -0.00981991 \tabularnewline
F-TEST (value) & 0.727716 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 108 \tabularnewline
p-value & 0.574911 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.76389 \tabularnewline
Sum Squared Residuals & 336.023 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268728&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.162003[/C][/ROW]
[ROW][C]R-squared[/C][C]0.0262451[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]-0.00981991[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]0.727716[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]108[/C][/ROW]
[ROW][C]p-value[/C][C]0.574911[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.76389[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]336.023[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268728&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268728&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	0.162003
R-squared	0.0262451
Adjusted R-squared	-0.00981991
F-TEST (value)	0.727716
F-TEST (DF numerator)	4
F-TEST (DF denominator)	108
p-value	0.574911
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.76389
Sum Squared Residuals	336.023

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	13	13.4965	-0.496532
2	13	13.5048	-0.504822
3	11	13.5657	-2.56574
4	14	13.6309	0.369149
5	15	13.4271	1.57287
6	14	14.0672	-0.0671968
7	11	13.5977	-2.59769
8	13	13.2757	-0.275699
9	16	13.7626	2.23738
10	14	13.583	0.417047
11	14	13.6204	0.379577
12	15	13.4946	1.50543
13	15	13.3757	1.62428
14	13	13.8037	-0.803692
15	14	12.9213	1.07873
16	11	13.1313	-2.13127
17	12	13.6358	-1.63581
18	14	13.3733	0.626682
19	13	13.4857	-0.485705
20	12	13.3293	-1.3293
21	15	13.6104	1.38955
22	15	13.8089	1.19106
23	14	14.0367	-0.0367136
24	14	13.8889	0.111117
25	12	13.5717	-1.57169
26	12	14.1139	-2.1139
27	12	13.7607	-1.76068
28	15	13.1398	1.86018
29	14	13.5562	0.443762
30	16	13.534	2.46598
31	12	13.1069	-1.10688
32	12	13.5301	-1.53005
33	14	13.3991	0.600897
34	16	13.7406	2.25936
35	15	13.2315	1.76846
36	12	13.2041	-1.20405
37	14	13.5229	0.477058
38	13	13.4467	-0.446724
39	14	13.8198	0.180213
40	16	13.5232	2.47678
41	12	13.3335	-1.33354
42	14	13.507	0.492971
43	15	13.7183	1.2817
44	13	13.5737	-0.573697
45	16	12.926	3.07404
46	16	13.8644	2.13555
47	12	13.2105	-1.21049
48	12	13.2689	-1.26891
49	16	13.231	2.76904
50	12	13.1476	-1.14758
51	15	13.3034	1.69661
52	12	12.7364	-0.73638
53	13	13.4312	-0.431201
54	12	13.5257	-1.52565
55	14	13.3463	0.653726
56	14	13.1914	0.808572
57	11	13.3894	-2.38944
58	10	13.4627	-3.46268
59	12	12.8536	-0.853616
60	11	13.1338	-2.13378
61	16	13.6267	2.37327
62	14	13.7392	0.260845
63	14	13.1886	0.811386
64	15	13.889	1.11102
65	15	13.8727	1.12728
66	14	12.9932	1.00684
67	13	13.8377	-0.837667
68	11	13.9195	-2.91946
69	16	13.9372	2.06279
70	12	13.2754	-1.27544
71	15	13.8651	1.13485
72	14	13.4075	0.592466
73	15	12.9781	2.02193
74	14	13.3261	0.673856
75	13	13.4198	-0.419776
76	6	13.1689	-7.1689
77	12	13.5956	-1.59565
78	12	13.3103	-1.31033
79	14	13.4603	0.539727
80	14	13.046	0.954048
81	15	13.2857	1.71431
82	11	13.2422	-2.24222
83	13	13.3418	-0.341828
84	14	13.0789	0.921122
85	16	13.1935	2.80646
86	13	13.4605	-0.460476
87	14	13.2647	0.735281
88	16	13.168	2.83198
89	11	13.1282	-2.12816
90	13	13.2224	-0.222433
91	13	13.4468	-0.446806
92	15	13.0062	1.99375
93	12	13.2724	-1.27236
94	13	13.3887	-0.388729
95	12	13.1057	-1.10565
96	14	13.1565	0.843463
97	14	13.355	0.645009
98	16	13.3311	2.66887
99	15	13.1979	1.80213
100	14	13.1114	0.888577
101	13	13.4111	-0.411136
102	14	12.9486	1.05139
103	15	13.1821	1.81786
104	14	13.1252	0.874837
105	12	13.4811	-1.48109
106	7	13.1367	-6.13673
107	12	13.3165	-1.31646
108	15	13.249	1.75104
109	12	13.1372	-1.13717
110	13	12.8305	0.169493
111	11	13.2023	-2.20227
112	14	13.3581	0.641935
113	13	13.2238	-0.22377

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 13 & 13.4965 & -0.496532 \tabularnewline
2 & 13 & 13.5048 & -0.504822 \tabularnewline
3 & 11 & 13.5657 & -2.56574 \tabularnewline
4 & 14 & 13.6309 & 0.369149 \tabularnewline
5 & 15 & 13.4271 & 1.57287 \tabularnewline
6 & 14 & 14.0672 & -0.0671968 \tabularnewline
7 & 11 & 13.5977 & -2.59769 \tabularnewline
8 & 13 & 13.2757 & -0.275699 \tabularnewline
9 & 16 & 13.7626 & 2.23738 \tabularnewline
10 & 14 & 13.583 & 0.417047 \tabularnewline
11 & 14 & 13.6204 & 0.379577 \tabularnewline
12 & 15 & 13.4946 & 1.50543 \tabularnewline
13 & 15 & 13.3757 & 1.62428 \tabularnewline
14 & 13 & 13.8037 & -0.803692 \tabularnewline
15 & 14 & 12.9213 & 1.07873 \tabularnewline
16 & 11 & 13.1313 & -2.13127 \tabularnewline
17 & 12 & 13.6358 & -1.63581 \tabularnewline
18 & 14 & 13.3733 & 0.626682 \tabularnewline
19 & 13 & 13.4857 & -0.485705 \tabularnewline
20 & 12 & 13.3293 & -1.3293 \tabularnewline
21 & 15 & 13.6104 & 1.38955 \tabularnewline
22 & 15 & 13.8089 & 1.19106 \tabularnewline
23 & 14 & 14.0367 & -0.0367136 \tabularnewline
24 & 14 & 13.8889 & 0.111117 \tabularnewline
25 & 12 & 13.5717 & -1.57169 \tabularnewline
26 & 12 & 14.1139 & -2.1139 \tabularnewline
27 & 12 & 13.7607 & -1.76068 \tabularnewline
28 & 15 & 13.1398 & 1.86018 \tabularnewline
29 & 14 & 13.5562 & 0.443762 \tabularnewline
30 & 16 & 13.534 & 2.46598 \tabularnewline
31 & 12 & 13.1069 & -1.10688 \tabularnewline
32 & 12 & 13.5301 & -1.53005 \tabularnewline
33 & 14 & 13.3991 & 0.600897 \tabularnewline
34 & 16 & 13.7406 & 2.25936 \tabularnewline
35 & 15 & 13.2315 & 1.76846 \tabularnewline
36 & 12 & 13.2041 & -1.20405 \tabularnewline
37 & 14 & 13.5229 & 0.477058 \tabularnewline
38 & 13 & 13.4467 & -0.446724 \tabularnewline
39 & 14 & 13.8198 & 0.180213 \tabularnewline
40 & 16 & 13.5232 & 2.47678 \tabularnewline
41 & 12 & 13.3335 & -1.33354 \tabularnewline
42 & 14 & 13.507 & 0.492971 \tabularnewline
43 & 15 & 13.7183 & 1.2817 \tabularnewline
44 & 13 & 13.5737 & -0.573697 \tabularnewline
45 & 16 & 12.926 & 3.07404 \tabularnewline
46 & 16 & 13.8644 & 2.13555 \tabularnewline
47 & 12 & 13.2105 & -1.21049 \tabularnewline
48 & 12 & 13.2689 & -1.26891 \tabularnewline
49 & 16 & 13.231 & 2.76904 \tabularnewline
50 & 12 & 13.1476 & -1.14758 \tabularnewline
51 & 15 & 13.3034 & 1.69661 \tabularnewline
52 & 12 & 12.7364 & -0.73638 \tabularnewline
53 & 13 & 13.4312 & -0.431201 \tabularnewline
54 & 12 & 13.5257 & -1.52565 \tabularnewline
55 & 14 & 13.3463 & 0.653726 \tabularnewline
56 & 14 & 13.1914 & 0.808572 \tabularnewline
57 & 11 & 13.3894 & -2.38944 \tabularnewline
58 & 10 & 13.4627 & -3.46268 \tabularnewline
59 & 12 & 12.8536 & -0.853616 \tabularnewline
60 & 11 & 13.1338 & -2.13378 \tabularnewline
61 & 16 & 13.6267 & 2.37327 \tabularnewline
62 & 14 & 13.7392 & 0.260845 \tabularnewline
63 & 14 & 13.1886 & 0.811386 \tabularnewline
64 & 15 & 13.889 & 1.11102 \tabularnewline
65 & 15 & 13.8727 & 1.12728 \tabularnewline
66 & 14 & 12.9932 & 1.00684 \tabularnewline
67 & 13 & 13.8377 & -0.837667 \tabularnewline
68 & 11 & 13.9195 & -2.91946 \tabularnewline
69 & 16 & 13.9372 & 2.06279 \tabularnewline
70 & 12 & 13.2754 & -1.27544 \tabularnewline
71 & 15 & 13.8651 & 1.13485 \tabularnewline
72 & 14 & 13.4075 & 0.592466 \tabularnewline
73 & 15 & 12.9781 & 2.02193 \tabularnewline
74 & 14 & 13.3261 & 0.673856 \tabularnewline
75 & 13 & 13.4198 & -0.419776 \tabularnewline
76 & 6 & 13.1689 & -7.1689 \tabularnewline
77 & 12 & 13.5956 & -1.59565 \tabularnewline
78 & 12 & 13.3103 & -1.31033 \tabularnewline
79 & 14 & 13.4603 & 0.539727 \tabularnewline
80 & 14 & 13.046 & 0.954048 \tabularnewline
81 & 15 & 13.2857 & 1.71431 \tabularnewline
82 & 11 & 13.2422 & -2.24222 \tabularnewline
83 & 13 & 13.3418 & -0.341828 \tabularnewline
84 & 14 & 13.0789 & 0.921122 \tabularnewline
85 & 16 & 13.1935 & 2.80646 \tabularnewline
86 & 13 & 13.4605 & -0.460476 \tabularnewline
87 & 14 & 13.2647 & 0.735281 \tabularnewline
88 & 16 & 13.168 & 2.83198 \tabularnewline
89 & 11 & 13.1282 & -2.12816 \tabularnewline
90 & 13 & 13.2224 & -0.222433 \tabularnewline
91 & 13 & 13.4468 & -0.446806 \tabularnewline
92 & 15 & 13.0062 & 1.99375 \tabularnewline
93 & 12 & 13.2724 & -1.27236 \tabularnewline
94 & 13 & 13.3887 & -0.388729 \tabularnewline
95 & 12 & 13.1057 & -1.10565 \tabularnewline
96 & 14 & 13.1565 & 0.843463 \tabularnewline
97 & 14 & 13.355 & 0.645009 \tabularnewline
98 & 16 & 13.3311 & 2.66887 \tabularnewline
99 & 15 & 13.1979 & 1.80213 \tabularnewline
100 & 14 & 13.1114 & 0.888577 \tabularnewline
101 & 13 & 13.4111 & -0.411136 \tabularnewline
102 & 14 & 12.9486 & 1.05139 \tabularnewline
103 & 15 & 13.1821 & 1.81786 \tabularnewline
104 & 14 & 13.1252 & 0.874837 \tabularnewline
105 & 12 & 13.4811 & -1.48109 \tabularnewline
106 & 7 & 13.1367 & -6.13673 \tabularnewline
107 & 12 & 13.3165 & -1.31646 \tabularnewline
108 & 15 & 13.249 & 1.75104 \tabularnewline
109 & 12 & 13.1372 & -1.13717 \tabularnewline
110 & 13 & 12.8305 & 0.169493 \tabularnewline
111 & 11 & 13.2023 & -2.20227 \tabularnewline
112 & 14 & 13.3581 & 0.641935 \tabularnewline
113 & 13 & 13.2238 & -0.22377 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268728&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]13.4965[/C][C]-0.496532[/C][/ROW]
[ROW][C]2[/C][C]13[/C][C]13.5048[/C][C]-0.504822[/C][/ROW]
[ROW][C]3[/C][C]11[/C][C]13.5657[/C][C]-2.56574[/C][/ROW]
[ROW][C]4[/C][C]14[/C][C]13.6309[/C][C]0.369149[/C][/ROW]
[ROW][C]5[/C][C]15[/C][C]13.4271[/C][C]1.57287[/C][/ROW]
[ROW][C]6[/C][C]14[/C][C]14.0672[/C][C]-0.0671968[/C][/ROW]
[ROW][C]7[/C][C]11[/C][C]13.5977[/C][C]-2.59769[/C][/ROW]
[ROW][C]8[/C][C]13[/C][C]13.2757[/C][C]-0.275699[/C][/ROW]
[ROW][C]9[/C][C]16[/C][C]13.7626[/C][C]2.23738[/C][/ROW]
[ROW][C]10[/C][C]14[/C][C]13.583[/C][C]0.417047[/C][/ROW]
[ROW][C]11[/C][C]14[/C][C]13.6204[/C][C]0.379577[/C][/ROW]
[ROW][C]12[/C][C]15[/C][C]13.4946[/C][C]1.50543[/C][/ROW]
[ROW][C]13[/C][C]15[/C][C]13.3757[/C][C]1.62428[/C][/ROW]
[ROW][C]14[/C][C]13[/C][C]13.8037[/C][C]-0.803692[/C][/ROW]
[ROW][C]15[/C][C]14[/C][C]12.9213[/C][C]1.07873[/C][/ROW]
[ROW][C]16[/C][C]11[/C][C]13.1313[/C][C]-2.13127[/C][/ROW]
[ROW][C]17[/C][C]12[/C][C]13.6358[/C][C]-1.63581[/C][/ROW]
[ROW][C]18[/C][C]14[/C][C]13.3733[/C][C]0.626682[/C][/ROW]
[ROW][C]19[/C][C]13[/C][C]13.4857[/C][C]-0.485705[/C][/ROW]
[ROW][C]20[/C][C]12[/C][C]13.3293[/C][C]-1.3293[/C][/ROW]
[ROW][C]21[/C][C]15[/C][C]13.6104[/C][C]1.38955[/C][/ROW]
[ROW][C]22[/C][C]15[/C][C]13.8089[/C][C]1.19106[/C][/ROW]
[ROW][C]23[/C][C]14[/C][C]14.0367[/C][C]-0.0367136[/C][/ROW]
[ROW][C]24[/C][C]14[/C][C]13.8889[/C][C]0.111117[/C][/ROW]
[ROW][C]25[/C][C]12[/C][C]13.5717[/C][C]-1.57169[/C][/ROW]
[ROW][C]26[/C][C]12[/C][C]14.1139[/C][C]-2.1139[/C][/ROW]
[ROW][C]27[/C][C]12[/C][C]13.7607[/C][C]-1.76068[/C][/ROW]
[ROW][C]28[/C][C]15[/C][C]13.1398[/C][C]1.86018[/C][/ROW]
[ROW][C]29[/C][C]14[/C][C]13.5562[/C][C]0.443762[/C][/ROW]
[ROW][C]30[/C][C]16[/C][C]13.534[/C][C]2.46598[/C][/ROW]
[ROW][C]31[/C][C]12[/C][C]13.1069[/C][C]-1.10688[/C][/ROW]
[ROW][C]32[/C][C]12[/C][C]13.5301[/C][C]-1.53005[/C][/ROW]
[ROW][C]33[/C][C]14[/C][C]13.3991[/C][C]0.600897[/C][/ROW]
[ROW][C]34[/C][C]16[/C][C]13.7406[/C][C]2.25936[/C][/ROW]
[ROW][C]35[/C][C]15[/C][C]13.2315[/C][C]1.76846[/C][/ROW]
[ROW][C]36[/C][C]12[/C][C]13.2041[/C][C]-1.20405[/C][/ROW]
[ROW][C]37[/C][C]14[/C][C]13.5229[/C][C]0.477058[/C][/ROW]
[ROW][C]38[/C][C]13[/C][C]13.4467[/C][C]-0.446724[/C][/ROW]
[ROW][C]39[/C][C]14[/C][C]13.8198[/C][C]0.180213[/C][/ROW]
[ROW][C]40[/C][C]16[/C][C]13.5232[/C][C]2.47678[/C][/ROW]
[ROW][C]41[/C][C]12[/C][C]13.3335[/C][C]-1.33354[/C][/ROW]
[ROW][C]42[/C][C]14[/C][C]13.507[/C][C]0.492971[/C][/ROW]
[ROW][C]43[/C][C]15[/C][C]13.7183[/C][C]1.2817[/C][/ROW]
[ROW][C]44[/C][C]13[/C][C]13.5737[/C][C]-0.573697[/C][/ROW]
[ROW][C]45[/C][C]16[/C][C]12.926[/C][C]3.07404[/C][/ROW]
[ROW][C]46[/C][C]16[/C][C]13.8644[/C][C]2.13555[/C][/ROW]
[ROW][C]47[/C][C]12[/C][C]13.2105[/C][C]-1.21049[/C][/ROW]
[ROW][C]48[/C][C]12[/C][C]13.2689[/C][C]-1.26891[/C][/ROW]
[ROW][C]49[/C][C]16[/C][C]13.231[/C][C]2.76904[/C][/ROW]
[ROW][C]50[/C][C]12[/C][C]13.1476[/C][C]-1.14758[/C][/ROW]
[ROW][C]51[/C][C]15[/C][C]13.3034[/C][C]1.69661[/C][/ROW]
[ROW][C]52[/C][C]12[/C][C]12.7364[/C][C]-0.73638[/C][/ROW]
[ROW][C]53[/C][C]13[/C][C]13.4312[/C][C]-0.431201[/C][/ROW]
[ROW][C]54[/C][C]12[/C][C]13.5257[/C][C]-1.52565[/C][/ROW]
[ROW][C]55[/C][C]14[/C][C]13.3463[/C][C]0.653726[/C][/ROW]
[ROW][C]56[/C][C]14[/C][C]13.1914[/C][C]0.808572[/C][/ROW]
[ROW][C]57[/C][C]11[/C][C]13.3894[/C][C]-2.38944[/C][/ROW]
[ROW][C]58[/C][C]10[/C][C]13.4627[/C][C]-3.46268[/C][/ROW]
[ROW][C]59[/C][C]12[/C][C]12.8536[/C][C]-0.853616[/C][/ROW]
[ROW][C]60[/C][C]11[/C][C]13.1338[/C][C]-2.13378[/C][/ROW]
[ROW][C]61[/C][C]16[/C][C]13.6267[/C][C]2.37327[/C][/ROW]
[ROW][C]62[/C][C]14[/C][C]13.7392[/C][C]0.260845[/C][/ROW]
[ROW][C]63[/C][C]14[/C][C]13.1886[/C][C]0.811386[/C][/ROW]
[ROW][C]64[/C][C]15[/C][C]13.889[/C][C]1.11102[/C][/ROW]
[ROW][C]65[/C][C]15[/C][C]13.8727[/C][C]1.12728[/C][/ROW]
[ROW][C]66[/C][C]14[/C][C]12.9932[/C][C]1.00684[/C][/ROW]
[ROW][C]67[/C][C]13[/C][C]13.8377[/C][C]-0.837667[/C][/ROW]
[ROW][C]68[/C][C]11[/C][C]13.9195[/C][C]-2.91946[/C][/ROW]
[ROW][C]69[/C][C]16[/C][C]13.9372[/C][C]2.06279[/C][/ROW]
[ROW][C]70[/C][C]12[/C][C]13.2754[/C][C]-1.27544[/C][/ROW]
[ROW][C]71[/C][C]15[/C][C]13.8651[/C][C]1.13485[/C][/ROW]
[ROW][C]72[/C][C]14[/C][C]13.4075[/C][C]0.592466[/C][/ROW]
[ROW][C]73[/C][C]15[/C][C]12.9781[/C][C]2.02193[/C][/ROW]
[ROW][C]74[/C][C]14[/C][C]13.3261[/C][C]0.673856[/C][/ROW]
[ROW][C]75[/C][C]13[/C][C]13.4198[/C][C]-0.419776[/C][/ROW]
[ROW][C]76[/C][C]6[/C][C]13.1689[/C][C]-7.1689[/C][/ROW]
[ROW][C]77[/C][C]12[/C][C]13.5956[/C][C]-1.59565[/C][/ROW]
[ROW][C]78[/C][C]12[/C][C]13.3103[/C][C]-1.31033[/C][/ROW]
[ROW][C]79[/C][C]14[/C][C]13.4603[/C][C]0.539727[/C][/ROW]
[ROW][C]80[/C][C]14[/C][C]13.046[/C][C]0.954048[/C][/ROW]
[ROW][C]81[/C][C]15[/C][C]13.2857[/C][C]1.71431[/C][/ROW]
[ROW][C]82[/C][C]11[/C][C]13.2422[/C][C]-2.24222[/C][/ROW]
[ROW][C]83[/C][C]13[/C][C]13.3418[/C][C]-0.341828[/C][/ROW]
[ROW][C]84[/C][C]14[/C][C]13.0789[/C][C]0.921122[/C][/ROW]
[ROW][C]85[/C][C]16[/C][C]13.1935[/C][C]2.80646[/C][/ROW]
[ROW][C]86[/C][C]13[/C][C]13.4605[/C][C]-0.460476[/C][/ROW]
[ROW][C]87[/C][C]14[/C][C]13.2647[/C][C]0.735281[/C][/ROW]
[ROW][C]88[/C][C]16[/C][C]13.168[/C][C]2.83198[/C][/ROW]
[ROW][C]89[/C][C]11[/C][C]13.1282[/C][C]-2.12816[/C][/ROW]
[ROW][C]90[/C][C]13[/C][C]13.2224[/C][C]-0.222433[/C][/ROW]
[ROW][C]91[/C][C]13[/C][C]13.4468[/C][C]-0.446806[/C][/ROW]
[ROW][C]92[/C][C]15[/C][C]13.0062[/C][C]1.99375[/C][/ROW]
[ROW][C]93[/C][C]12[/C][C]13.2724[/C][C]-1.27236[/C][/ROW]
[ROW][C]94[/C][C]13[/C][C]13.3887[/C][C]-0.388729[/C][/ROW]
[ROW][C]95[/C][C]12[/C][C]13.1057[/C][C]-1.10565[/C][/ROW]
[ROW][C]96[/C][C]14[/C][C]13.1565[/C][C]0.843463[/C][/ROW]
[ROW][C]97[/C][C]14[/C][C]13.355[/C][C]0.645009[/C][/ROW]
[ROW][C]98[/C][C]16[/C][C]13.3311[/C][C]2.66887[/C][/ROW]
[ROW][C]99[/C][C]15[/C][C]13.1979[/C][C]1.80213[/C][/ROW]
[ROW][C]100[/C][C]14[/C][C]13.1114[/C][C]0.888577[/C][/ROW]
[ROW][C]101[/C][C]13[/C][C]13.4111[/C][C]-0.411136[/C][/ROW]
[ROW][C]102[/C][C]14[/C][C]12.9486[/C][C]1.05139[/C][/ROW]
[ROW][C]103[/C][C]15[/C][C]13.1821[/C][C]1.81786[/C][/ROW]
[ROW][C]104[/C][C]14[/C][C]13.1252[/C][C]0.874837[/C][/ROW]
[ROW][C]105[/C][C]12[/C][C]13.4811[/C][C]-1.48109[/C][/ROW]
[ROW][C]106[/C][C]7[/C][C]13.1367[/C][C]-6.13673[/C][/ROW]
[ROW][C]107[/C][C]12[/C][C]13.3165[/C][C]-1.31646[/C][/ROW]
[ROW][C]108[/C][C]15[/C][C]13.249[/C][C]1.75104[/C][/ROW]
[ROW][C]109[/C][C]12[/C][C]13.1372[/C][C]-1.13717[/C][/ROW]
[ROW][C]110[/C][C]13[/C][C]12.8305[/C][C]0.169493[/C][/ROW]
[ROW][C]111[/C][C]11[/C][C]13.2023[/C][C]-2.20227[/C][/ROW]
[ROW][C]112[/C][C]14[/C][C]13.3581[/C][C]0.641935[/C][/ROW]
[ROW][C]113[/C][C]13[/C][C]13.2238[/C][C]-0.22377[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268728&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268728&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	13.4965	-0.496532
2	13	13.5048	-0.504822
3	11	13.5657	-2.56574
4	14	13.6309	0.369149
5	15	13.4271	1.57287
6	14	14.0672	-0.0671968
7	11	13.5977	-2.59769
8	13	13.2757	-0.275699
9	16	13.7626	2.23738
10	14	13.583	0.417047
11	14	13.6204	0.379577
12	15	13.4946	1.50543
13	15	13.3757	1.62428
14	13	13.8037	-0.803692
15	14	12.9213	1.07873
16	11	13.1313	-2.13127
17	12	13.6358	-1.63581
18	14	13.3733	0.626682
19	13	13.4857	-0.485705
20	12	13.3293	-1.3293
21	15	13.6104	1.38955
22	15	13.8089	1.19106
23	14	14.0367	-0.0367136
24	14	13.8889	0.111117
25	12	13.5717	-1.57169
26	12	14.1139	-2.1139
27	12	13.7607	-1.76068
28	15	13.1398	1.86018
29	14	13.5562	0.443762
30	16	13.534	2.46598
31	12	13.1069	-1.10688
32	12	13.5301	-1.53005
33	14	13.3991	0.600897
34	16	13.7406	2.25936
35	15	13.2315	1.76846
36	12	13.2041	-1.20405
37	14	13.5229	0.477058
38	13	13.4467	-0.446724
39	14	13.8198	0.180213
40	16	13.5232	2.47678
41	12	13.3335	-1.33354
42	14	13.507	0.492971
43	15	13.7183	1.2817
44	13	13.5737	-0.573697
45	16	12.926	3.07404
46	16	13.8644	2.13555
47	12	13.2105	-1.21049
48	12	13.2689	-1.26891
49	16	13.231	2.76904
50	12	13.1476	-1.14758
51	15	13.3034	1.69661
52	12	12.7364	-0.73638
53	13	13.4312	-0.431201
54	12	13.5257	-1.52565
55	14	13.3463	0.653726
56	14	13.1914	0.808572
57	11	13.3894	-2.38944
58	10	13.4627	-3.46268
59	12	12.8536	-0.853616
60	11	13.1338	-2.13378
61	16	13.6267	2.37327
62	14	13.7392	0.260845
63	14	13.1886	0.811386
64	15	13.889	1.11102
65	15	13.8727	1.12728
66	14	12.9932	1.00684
67	13	13.8377	-0.837667
68	11	13.9195	-2.91946
69	16	13.9372	2.06279
70	12	13.2754	-1.27544
71	15	13.8651	1.13485
72	14	13.4075	0.592466
73	15	12.9781	2.02193
74	14	13.3261	0.673856
75	13	13.4198	-0.419776
76	6	13.1689	-7.1689
77	12	13.5956	-1.59565
78	12	13.3103	-1.31033
79	14	13.4603	0.539727
80	14	13.046	0.954048
81	15	13.2857	1.71431
82	11	13.2422	-2.24222
83	13	13.3418	-0.341828
84	14	13.0789	0.921122
85	16	13.1935	2.80646
86	13	13.4605	-0.460476
87	14	13.2647	0.735281
88	16	13.168	2.83198
89	11	13.1282	-2.12816
90	13	13.2224	-0.222433
91	13	13.4468	-0.446806
92	15	13.0062	1.99375
93	12	13.2724	-1.27236
94	13	13.3887	-0.388729
95	12	13.1057	-1.10565
96	14	13.1565	0.843463
97	14	13.355	0.645009
98	16	13.3311	2.66887
99	15	13.1979	1.80213
100	14	13.1114	0.888577
101	13	13.4111	-0.411136
102	14	12.9486	1.05139
103	15	13.1821	1.81786
104	14	13.1252	0.874837
105	12	13.4811	-1.48109
106	7	13.1367	-6.13673
107	12	13.3165	-1.31646
108	15	13.249	1.75104
109	12	13.1372	-1.13717
110	13	12.8305	0.169493
111	11	13.2023	-2.20227
112	14	13.3581	0.641935
113	13	13.2238	-0.22377

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
8	0.730536	0.538928	0.269464
9	0.593149	0.813702	0.406851
10	0.607897	0.784206	0.392103
11	0.521226	0.957548	0.478774
12	0.488827	0.977655	0.511173
13	0.438165	0.876331	0.561835
14	0.375595	0.75119	0.624405
15	0.28618	0.57236	0.71382
16	0.399516	0.799033	0.600484
17	0.397675	0.79535	0.602325
18	0.321515	0.64303	0.678485
19	0.254731	0.509462	0.745269
20	0.209853	0.419707	0.790147
21	0.189344	0.378688	0.810656
22	0.153105	0.30621	0.846895
23	0.111749	0.223498	0.888251
24	0.080074	0.160148	0.919926
25	0.07628	0.15256	0.92372
26	0.0698981	0.139796	0.930102
27	0.0810102	0.16202	0.91899
28	0.088852	0.177704	0.911148
29	0.0685212	0.137042	0.931479
30	0.0989701	0.19794	0.90103
31	0.0829626	0.165925	0.917037
32	0.0763011	0.152602	0.923699
33	0.0570438	0.114088	0.942956
34	0.0733166	0.146633	0.926683
35	0.0718735	0.143747	0.928127
36	0.0696821	0.139364	0.930318
37	0.0518283	0.103657	0.948172
38	0.0407337	0.0814674	0.959266
39	0.0290299	0.0580598	0.97097
40	0.0484327	0.0968654	0.951567
41	0.0454467	0.0908933	0.954553
42	0.0334334	0.0668667	0.966567
43	0.0293334	0.0586669	0.970667
44	0.0213413	0.0426827	0.978659
45	0.0385259	0.0770517	0.961474
46	0.0459389	0.0918779	0.954061
47	0.0391401	0.0782802	0.96086
48	0.0368009	0.0736018	0.963199
49	0.0552268	0.110454	0.944773
50	0.052607	0.105214	0.947393
51	0.0498991	0.0997982	0.950101
52	0.0402845	0.0805691	0.959715
53	0.0301193	0.0602385	0.969881
54	0.0287959	0.0575918	0.971204
55	0.0218566	0.0437131	0.978143
56	0.0167467	0.0334933	0.983253
57	0.0238026	0.0476052	0.976197
58	0.0577269	0.115454	0.942273
59	0.0470939	0.0941879	0.952906
60	0.0530134	0.106027	0.946987
61	0.0662351	0.13247	0.933765
62	0.0502057	0.100411	0.949794
63	0.039534	0.0790679	0.960466
64	0.0327813	0.0655625	0.967219
65	0.0273478	0.0546956	0.972652
66	0.0215348	0.0430695	0.978465
67	0.0161539	0.0323078	0.983846
68	0.0302324	0.0604648	0.969768
69	0.0382979	0.0765957	0.961702
70	0.0327784	0.0655568	0.967222
71	0.0356516	0.0713031	0.964348
72	0.0281343	0.0562686	0.971866
73	0.0269462	0.0538923	0.973054
74	0.0214633	0.0429265	0.978537
75	0.0150988	0.0301976	0.984901
76	0.40557	0.81114	0.59443
77	0.374973	0.749947	0.625027
78	0.347337	0.694673	0.652663
79	0.297176	0.594351	0.702824
80	0.25648	0.51296	0.74352
81	0.268209	0.536419	0.731791
82	0.286242	0.572483	0.713758
83	0.23693	0.47386	0.76307
84	0.211965	0.423931	0.788035
85	0.256916	0.513832	0.743084
86	0.206974	0.413948	0.793026
87	0.215613	0.431227	0.784387
88	0.382457	0.764914	0.617543
89	0.379802	0.759603	0.620198
90	0.313067	0.626135	0.686933
91	0.254085	0.50817	0.745915
92	0.290365	0.58073	0.709635
93	0.294291	0.588583	0.705709
94	0.267909	0.535817	0.732091
95	0.278665	0.557329	0.721335
96	0.225554	0.451107	0.774446
97	0.168973	0.337945	0.831027
98	0.248258	0.496517	0.751742
99	0.186431	0.372862	0.813569
100	0.128825	0.257651	0.871175
101	0.0831998	0.1664	0.9168
102	0.109353	0.218706	0.890647
103	0.525104	0.949793	0.474896
104	0.401542	0.803084	0.598458
105	0.263685	0.52737	0.736315

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 0.730536 & 0.538928 & 0.269464 \tabularnewline
9 & 0.593149 & 0.813702 & 0.406851 \tabularnewline
10 & 0.607897 & 0.784206 & 0.392103 \tabularnewline
11 & 0.521226 & 0.957548 & 0.478774 \tabularnewline
12 & 0.488827 & 0.977655 & 0.511173 \tabularnewline
13 & 0.438165 & 0.876331 & 0.561835 \tabularnewline
14 & 0.375595 & 0.75119 & 0.624405 \tabularnewline
15 & 0.28618 & 0.57236 & 0.71382 \tabularnewline
16 & 0.399516 & 0.799033 & 0.600484 \tabularnewline
17 & 0.397675 & 0.79535 & 0.602325 \tabularnewline
18 & 0.321515 & 0.64303 & 0.678485 \tabularnewline
19 & 0.254731 & 0.509462 & 0.745269 \tabularnewline
20 & 0.209853 & 0.419707 & 0.790147 \tabularnewline
21 & 0.189344 & 0.378688 & 0.810656 \tabularnewline
22 & 0.153105 & 0.30621 & 0.846895 \tabularnewline
23 & 0.111749 & 0.223498 & 0.888251 \tabularnewline
24 & 0.080074 & 0.160148 & 0.919926 \tabularnewline
25 & 0.07628 & 0.15256 & 0.92372 \tabularnewline
26 & 0.0698981 & 0.139796 & 0.930102 \tabularnewline
27 & 0.0810102 & 0.16202 & 0.91899 \tabularnewline
28 & 0.088852 & 0.177704 & 0.911148 \tabularnewline
29 & 0.0685212 & 0.137042 & 0.931479 \tabularnewline
30 & 0.0989701 & 0.19794 & 0.90103 \tabularnewline
31 & 0.0829626 & 0.165925 & 0.917037 \tabularnewline
32 & 0.0763011 & 0.152602 & 0.923699 \tabularnewline
33 & 0.0570438 & 0.114088 & 0.942956 \tabularnewline
34 & 0.0733166 & 0.146633 & 0.926683 \tabularnewline
35 & 0.0718735 & 0.143747 & 0.928127 \tabularnewline
36 & 0.0696821 & 0.139364 & 0.930318 \tabularnewline
37 & 0.0518283 & 0.103657 & 0.948172 \tabularnewline
38 & 0.0407337 & 0.0814674 & 0.959266 \tabularnewline
39 & 0.0290299 & 0.0580598 & 0.97097 \tabularnewline
40 & 0.0484327 & 0.0968654 & 0.951567 \tabularnewline
41 & 0.0454467 & 0.0908933 & 0.954553 \tabularnewline
42 & 0.0334334 & 0.0668667 & 0.966567 \tabularnewline
43 & 0.0293334 & 0.0586669 & 0.970667 \tabularnewline
44 & 0.0213413 & 0.0426827 & 0.978659 \tabularnewline
45 & 0.0385259 & 0.0770517 & 0.961474 \tabularnewline
46 & 0.0459389 & 0.0918779 & 0.954061 \tabularnewline
47 & 0.0391401 & 0.0782802 & 0.96086 \tabularnewline
48 & 0.0368009 & 0.0736018 & 0.963199 \tabularnewline
49 & 0.0552268 & 0.110454 & 0.944773 \tabularnewline
50 & 0.052607 & 0.105214 & 0.947393 \tabularnewline
51 & 0.0498991 & 0.0997982 & 0.950101 \tabularnewline
52 & 0.0402845 & 0.0805691 & 0.959715 \tabularnewline
53 & 0.0301193 & 0.0602385 & 0.969881 \tabularnewline
54 & 0.0287959 & 0.0575918 & 0.971204 \tabularnewline
55 & 0.0218566 & 0.0437131 & 0.978143 \tabularnewline
56 & 0.0167467 & 0.0334933 & 0.983253 \tabularnewline
57 & 0.0238026 & 0.0476052 & 0.976197 \tabularnewline
58 & 0.0577269 & 0.115454 & 0.942273 \tabularnewline
59 & 0.0470939 & 0.0941879 & 0.952906 \tabularnewline
60 & 0.0530134 & 0.106027 & 0.946987 \tabularnewline
61 & 0.0662351 & 0.13247 & 0.933765 \tabularnewline
62 & 0.0502057 & 0.100411 & 0.949794 \tabularnewline
63 & 0.039534 & 0.0790679 & 0.960466 \tabularnewline
64 & 0.0327813 & 0.0655625 & 0.967219 \tabularnewline
65 & 0.0273478 & 0.0546956 & 0.972652 \tabularnewline
66 & 0.0215348 & 0.0430695 & 0.978465 \tabularnewline
67 & 0.0161539 & 0.0323078 & 0.983846 \tabularnewline
68 & 0.0302324 & 0.0604648 & 0.969768 \tabularnewline
69 & 0.0382979 & 0.0765957 & 0.961702 \tabularnewline
70 & 0.0327784 & 0.0655568 & 0.967222 \tabularnewline
71 & 0.0356516 & 0.0713031 & 0.964348 \tabularnewline
72 & 0.0281343 & 0.0562686 & 0.971866 \tabularnewline
73 & 0.0269462 & 0.0538923 & 0.973054 \tabularnewline
74 & 0.0214633 & 0.0429265 & 0.978537 \tabularnewline
75 & 0.0150988 & 0.0301976 & 0.984901 \tabularnewline
76 & 0.40557 & 0.81114 & 0.59443 \tabularnewline
77 & 0.374973 & 0.749947 & 0.625027 \tabularnewline
78 & 0.347337 & 0.694673 & 0.652663 \tabularnewline
79 & 0.297176 & 0.594351 & 0.702824 \tabularnewline
80 & 0.25648 & 0.51296 & 0.74352 \tabularnewline
81 & 0.268209 & 0.536419 & 0.731791 \tabularnewline
82 & 0.286242 & 0.572483 & 0.713758 \tabularnewline
83 & 0.23693 & 0.47386 & 0.76307 \tabularnewline
84 & 0.211965 & 0.423931 & 0.788035 \tabularnewline
85 & 0.256916 & 0.513832 & 0.743084 \tabularnewline
86 & 0.206974 & 0.413948 & 0.793026 \tabularnewline
87 & 0.215613 & 0.431227 & 0.784387 \tabularnewline
88 & 0.382457 & 0.764914 & 0.617543 \tabularnewline
89 & 0.379802 & 0.759603 & 0.620198 \tabularnewline
90 & 0.313067 & 0.626135 & 0.686933 \tabularnewline
91 & 0.254085 & 0.50817 & 0.745915 \tabularnewline
92 & 0.290365 & 0.58073 & 0.709635 \tabularnewline
93 & 0.294291 & 0.588583 & 0.705709 \tabularnewline
94 & 0.267909 & 0.535817 & 0.732091 \tabularnewline
95 & 0.278665 & 0.557329 & 0.721335 \tabularnewline
96 & 0.225554 & 0.451107 & 0.774446 \tabularnewline
97 & 0.168973 & 0.337945 & 0.831027 \tabularnewline
98 & 0.248258 & 0.496517 & 0.751742 \tabularnewline
99 & 0.186431 & 0.372862 & 0.813569 \tabularnewline
100 & 0.128825 & 0.257651 & 0.871175 \tabularnewline
101 & 0.0831998 & 0.1664 & 0.9168 \tabularnewline
102 & 0.109353 & 0.218706 & 0.890647 \tabularnewline
103 & 0.525104 & 0.949793 & 0.474896 \tabularnewline
104 & 0.401542 & 0.803084 & 0.598458 \tabularnewline
105 & 0.263685 & 0.52737 & 0.736315 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268728&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]8[/C][C]0.730536[/C][C]0.538928[/C][C]0.269464[/C][/ROW]
[ROW][C]9[/C][C]0.593149[/C][C]0.813702[/C][C]0.406851[/C][/ROW]
[ROW][C]10[/C][C]0.607897[/C][C]0.784206[/C][C]0.392103[/C][/ROW]
[ROW][C]11[/C][C]0.521226[/C][C]0.957548[/C][C]0.478774[/C][/ROW]
[ROW][C]12[/C][C]0.488827[/C][C]0.977655[/C][C]0.511173[/C][/ROW]
[ROW][C]13[/C][C]0.438165[/C][C]0.876331[/C][C]0.561835[/C][/ROW]
[ROW][C]14[/C][C]0.375595[/C][C]0.75119[/C][C]0.624405[/C][/ROW]
[ROW][C]15[/C][C]0.28618[/C][C]0.57236[/C][C]0.71382[/C][/ROW]
[ROW][C]16[/C][C]0.399516[/C][C]0.799033[/C][C]0.600484[/C][/ROW]
[ROW][C]17[/C][C]0.397675[/C][C]0.79535[/C][C]0.602325[/C][/ROW]
[ROW][C]18[/C][C]0.321515[/C][C]0.64303[/C][C]0.678485[/C][/ROW]
[ROW][C]19[/C][C]0.254731[/C][C]0.509462[/C][C]0.745269[/C][/ROW]
[ROW][C]20[/C][C]0.209853[/C][C]0.419707[/C][C]0.790147[/C][/ROW]
[ROW][C]21[/C][C]0.189344[/C][C]0.378688[/C][C]0.810656[/C][/ROW]
[ROW][C]22[/C][C]0.153105[/C][C]0.30621[/C][C]0.846895[/C][/ROW]
[ROW][C]23[/C][C]0.111749[/C][C]0.223498[/C][C]0.888251[/C][/ROW]
[ROW][C]24[/C][C]0.080074[/C][C]0.160148[/C][C]0.919926[/C][/ROW]
[ROW][C]25[/C][C]0.07628[/C][C]0.15256[/C][C]0.92372[/C][/ROW]
[ROW][C]26[/C][C]0.0698981[/C][C]0.139796[/C][C]0.930102[/C][/ROW]
[ROW][C]27[/C][C]0.0810102[/C][C]0.16202[/C][C]0.91899[/C][/ROW]
[ROW][C]28[/C][C]0.088852[/C][C]0.177704[/C][C]0.911148[/C][/ROW]
[ROW][C]29[/C][C]0.0685212[/C][C]0.137042[/C][C]0.931479[/C][/ROW]
[ROW][C]30[/C][C]0.0989701[/C][C]0.19794[/C][C]0.90103[/C][/ROW]
[ROW][C]31[/C][C]0.0829626[/C][C]0.165925[/C][C]0.917037[/C][/ROW]
[ROW][C]32[/C][C]0.0763011[/C][C]0.152602[/C][C]0.923699[/C][/ROW]
[ROW][C]33[/C][C]0.0570438[/C][C]0.114088[/C][C]0.942956[/C][/ROW]
[ROW][C]34[/C][C]0.0733166[/C][C]0.146633[/C][C]0.926683[/C][/ROW]
[ROW][C]35[/C][C]0.0718735[/C][C]0.143747[/C][C]0.928127[/C][/ROW]
[ROW][C]36[/C][C]0.0696821[/C][C]0.139364[/C][C]0.930318[/C][/ROW]
[ROW][C]37[/C][C]0.0518283[/C][C]0.103657[/C][C]0.948172[/C][/ROW]
[ROW][C]38[/C][C]0.0407337[/C][C]0.0814674[/C][C]0.959266[/C][/ROW]
[ROW][C]39[/C][C]0.0290299[/C][C]0.0580598[/C][C]0.97097[/C][/ROW]
[ROW][C]40[/C][C]0.0484327[/C][C]0.0968654[/C][C]0.951567[/C][/ROW]
[ROW][C]41[/C][C]0.0454467[/C][C]0.0908933[/C][C]0.954553[/C][/ROW]
[ROW][C]42[/C][C]0.0334334[/C][C]0.0668667[/C][C]0.966567[/C][/ROW]
[ROW][C]43[/C][C]0.0293334[/C][C]0.0586669[/C][C]0.970667[/C][/ROW]
[ROW][C]44[/C][C]0.0213413[/C][C]0.0426827[/C][C]0.978659[/C][/ROW]
[ROW][C]45[/C][C]0.0385259[/C][C]0.0770517[/C][C]0.961474[/C][/ROW]
[ROW][C]46[/C][C]0.0459389[/C][C]0.0918779[/C][C]0.954061[/C][/ROW]
[ROW][C]47[/C][C]0.0391401[/C][C]0.0782802[/C][C]0.96086[/C][/ROW]
[ROW][C]48[/C][C]0.0368009[/C][C]0.0736018[/C][C]0.963199[/C][/ROW]
[ROW][C]49[/C][C]0.0552268[/C][C]0.110454[/C][C]0.944773[/C][/ROW]
[ROW][C]50[/C][C]0.052607[/C][C]0.105214[/C][C]0.947393[/C][/ROW]
[ROW][C]51[/C][C]0.0498991[/C][C]0.0997982[/C][C]0.950101[/C][/ROW]
[ROW][C]52[/C][C]0.0402845[/C][C]0.0805691[/C][C]0.959715[/C][/ROW]
[ROW][C]53[/C][C]0.0301193[/C][C]0.0602385[/C][C]0.969881[/C][/ROW]
[ROW][C]54[/C][C]0.0287959[/C][C]0.0575918[/C][C]0.971204[/C][/ROW]
[ROW][C]55[/C][C]0.0218566[/C][C]0.0437131[/C][C]0.978143[/C][/ROW]
[ROW][C]56[/C][C]0.0167467[/C][C]0.0334933[/C][C]0.983253[/C][/ROW]
[ROW][C]57[/C][C]0.0238026[/C][C]0.0476052[/C][C]0.976197[/C][/ROW]
[ROW][C]58[/C][C]0.0577269[/C][C]0.115454[/C][C]0.942273[/C][/ROW]
[ROW][C]59[/C][C]0.0470939[/C][C]0.0941879[/C][C]0.952906[/C][/ROW]
[ROW][C]60[/C][C]0.0530134[/C][C]0.106027[/C][C]0.946987[/C][/ROW]
[ROW][C]61[/C][C]0.0662351[/C][C]0.13247[/C][C]0.933765[/C][/ROW]
[ROW][C]62[/C][C]0.0502057[/C][C]0.100411[/C][C]0.949794[/C][/ROW]
[ROW][C]63[/C][C]0.039534[/C][C]0.0790679[/C][C]0.960466[/C][/ROW]
[ROW][C]64[/C][C]0.0327813[/C][C]0.0655625[/C][C]0.967219[/C][/ROW]
[ROW][C]65[/C][C]0.0273478[/C][C]0.0546956[/C][C]0.972652[/C][/ROW]
[ROW][C]66[/C][C]0.0215348[/C][C]0.0430695[/C][C]0.978465[/C][/ROW]
[ROW][C]67[/C][C]0.0161539[/C][C]0.0323078[/C][C]0.983846[/C][/ROW]
[ROW][C]68[/C][C]0.0302324[/C][C]0.0604648[/C][C]0.969768[/C][/ROW]
[ROW][C]69[/C][C]0.0382979[/C][C]0.0765957[/C][C]0.961702[/C][/ROW]
[ROW][C]70[/C][C]0.0327784[/C][C]0.0655568[/C][C]0.967222[/C][/ROW]
[ROW][C]71[/C][C]0.0356516[/C][C]0.0713031[/C][C]0.964348[/C][/ROW]
[ROW][C]72[/C][C]0.0281343[/C][C]0.0562686[/C][C]0.971866[/C][/ROW]
[ROW][C]73[/C][C]0.0269462[/C][C]0.0538923[/C][C]0.973054[/C][/ROW]
[ROW][C]74[/C][C]0.0214633[/C][C]0.0429265[/C][C]0.978537[/C][/ROW]
[ROW][C]75[/C][C]0.0150988[/C][C]0.0301976[/C][C]0.984901[/C][/ROW]
[ROW][C]76[/C][C]0.40557[/C][C]0.81114[/C][C]0.59443[/C][/ROW]
[ROW][C]77[/C][C]0.374973[/C][C]0.749947[/C][C]0.625027[/C][/ROW]
[ROW][C]78[/C][C]0.347337[/C][C]0.694673[/C][C]0.652663[/C][/ROW]
[ROW][C]79[/C][C]0.297176[/C][C]0.594351[/C][C]0.702824[/C][/ROW]
[ROW][C]80[/C][C]0.25648[/C][C]0.51296[/C][C]0.74352[/C][/ROW]
[ROW][C]81[/C][C]0.268209[/C][C]0.536419[/C][C]0.731791[/C][/ROW]
[ROW][C]82[/C][C]0.286242[/C][C]0.572483[/C][C]0.713758[/C][/ROW]
[ROW][C]83[/C][C]0.23693[/C][C]0.47386[/C][C]0.76307[/C][/ROW]
[ROW][C]84[/C][C]0.211965[/C][C]0.423931[/C][C]0.788035[/C][/ROW]
[ROW][C]85[/C][C]0.256916[/C][C]0.513832[/C][C]0.743084[/C][/ROW]
[ROW][C]86[/C][C]0.206974[/C][C]0.413948[/C][C]0.793026[/C][/ROW]
[ROW][C]87[/C][C]0.215613[/C][C]0.431227[/C][C]0.784387[/C][/ROW]
[ROW][C]88[/C][C]0.382457[/C][C]0.764914[/C][C]0.617543[/C][/ROW]
[ROW][C]89[/C][C]0.379802[/C][C]0.759603[/C][C]0.620198[/C][/ROW]
[ROW][C]90[/C][C]0.313067[/C][C]0.626135[/C][C]0.686933[/C][/ROW]
[ROW][C]91[/C][C]0.254085[/C][C]0.50817[/C][C]0.745915[/C][/ROW]
[ROW][C]92[/C][C]0.290365[/C][C]0.58073[/C][C]0.709635[/C][/ROW]
[ROW][C]93[/C][C]0.294291[/C][C]0.588583[/C][C]0.705709[/C][/ROW]
[ROW][C]94[/C][C]0.267909[/C][C]0.535817[/C][C]0.732091[/C][/ROW]
[ROW][C]95[/C][C]0.278665[/C][C]0.557329[/C][C]0.721335[/C][/ROW]
[ROW][C]96[/C][C]0.225554[/C][C]0.451107[/C][C]0.774446[/C][/ROW]
[ROW][C]97[/C][C]0.168973[/C][C]0.337945[/C][C]0.831027[/C][/ROW]
[ROW][C]98[/C][C]0.248258[/C][C]0.496517[/C][C]0.751742[/C][/ROW]
[ROW][C]99[/C][C]0.186431[/C][C]0.372862[/C][C]0.813569[/C][/ROW]
[ROW][C]100[/C][C]0.128825[/C][C]0.257651[/C][C]0.871175[/C][/ROW]
[ROW][C]101[/C][C]0.0831998[/C][C]0.1664[/C][C]0.9168[/C][/ROW]
[ROW][C]102[/C][C]0.109353[/C][C]0.218706[/C][C]0.890647[/C][/ROW]
[ROW][C]103[/C][C]0.525104[/C][C]0.949793[/C][C]0.474896[/C][/ROW]
[ROW][C]104[/C][C]0.401542[/C][C]0.803084[/C][C]0.598458[/C][/ROW]
[ROW][C]105[/C][C]0.263685[/C][C]0.52737[/C][C]0.736315[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268728&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268728&T=5

As an alternative you can also use a QR Code:

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

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
8	0.730536	0.538928	0.269464
9	0.593149	0.813702	0.406851
10	0.607897	0.784206	0.392103
11	0.521226	0.957548	0.478774
12	0.488827	0.977655	0.511173
13	0.438165	0.876331	0.561835
14	0.375595	0.75119	0.624405
15	0.28618	0.57236	0.71382
16	0.399516	0.799033	0.600484
17	0.397675	0.79535	0.602325
18	0.321515	0.64303	0.678485
19	0.254731	0.509462	0.745269
20	0.209853	0.419707	0.790147
21	0.189344	0.378688	0.810656
22	0.153105	0.30621	0.846895
23	0.111749	0.223498	0.888251
24	0.080074	0.160148	0.919926
25	0.07628	0.15256	0.92372
26	0.0698981	0.139796	0.930102
27	0.0810102	0.16202	0.91899
28	0.088852	0.177704	0.911148
29	0.0685212	0.137042	0.931479
30	0.0989701	0.19794	0.90103
31	0.0829626	0.165925	0.917037
32	0.0763011	0.152602	0.923699
33	0.0570438	0.114088	0.942956
34	0.0733166	0.146633	0.926683
35	0.0718735	0.143747	0.928127
36	0.0696821	0.139364	0.930318
37	0.0518283	0.103657	0.948172
38	0.0407337	0.0814674	0.959266
39	0.0290299	0.0580598	0.97097
40	0.0484327	0.0968654	0.951567
41	0.0454467	0.0908933	0.954553
42	0.0334334	0.0668667	0.966567
43	0.0293334	0.0586669	0.970667
44	0.0213413	0.0426827	0.978659
45	0.0385259	0.0770517	0.961474
46	0.0459389	0.0918779	0.954061
47	0.0391401	0.0782802	0.96086
48	0.0368009	0.0736018	0.963199
49	0.0552268	0.110454	0.944773
50	0.052607	0.105214	0.947393
51	0.0498991	0.0997982	0.950101
52	0.0402845	0.0805691	0.959715
53	0.0301193	0.0602385	0.969881
54	0.0287959	0.0575918	0.971204
55	0.0218566	0.0437131	0.978143
56	0.0167467	0.0334933	0.983253
57	0.0238026	0.0476052	0.976197
58	0.0577269	0.115454	0.942273
59	0.0470939	0.0941879	0.952906
60	0.0530134	0.106027	0.946987
61	0.0662351	0.13247	0.933765
62	0.0502057	0.100411	0.949794
63	0.039534	0.0790679	0.960466
64	0.0327813	0.0655625	0.967219
65	0.0273478	0.0546956	0.972652
66	0.0215348	0.0430695	0.978465
67	0.0161539	0.0323078	0.983846
68	0.0302324	0.0604648	0.969768
69	0.0382979	0.0765957	0.961702
70	0.0327784	0.0655568	0.967222
71	0.0356516	0.0713031	0.964348
72	0.0281343	0.0562686	0.971866
73	0.0269462	0.0538923	0.973054
74	0.0214633	0.0429265	0.978537
75	0.0150988	0.0301976	0.984901
76	0.40557	0.81114	0.59443
77	0.374973	0.749947	0.625027
78	0.347337	0.694673	0.652663
79	0.297176	0.594351	0.702824
80	0.25648	0.51296	0.74352
81	0.268209	0.536419	0.731791
82	0.286242	0.572483	0.713758
83	0.23693	0.47386	0.76307
84	0.211965	0.423931	0.788035
85	0.256916	0.513832	0.743084
86	0.206974	0.413948	0.793026
87	0.215613	0.431227	0.784387
88	0.382457	0.764914	0.617543
89	0.379802	0.759603	0.620198
90	0.313067	0.626135	0.686933
91	0.254085	0.50817	0.745915
92	0.290365	0.58073	0.709635
93	0.294291	0.588583	0.705709
94	0.267909	0.535817	0.732091
95	0.278665	0.557329	0.721335
96	0.225554	0.451107	0.774446
97	0.168973	0.337945	0.831027
98	0.248258	0.496517	0.751742
99	0.186431	0.372862	0.813569
100	0.128825	0.257651	0.871175
101	0.0831998	0.1664	0.9168
102	0.109353	0.218706	0.890647
103	0.525104	0.949793	0.474896
104	0.401542	0.803084	0.598458
105	0.263685	0.52737	0.736315

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	8	0.0816327	NOK
10% type I error level	32	0.326531	NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 8 & 0.0816327 & NOK \tabularnewline
10% type I error level & 32 & 0.326531 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268728&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]8[/C][C]0.0816327[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]32[/C][C]0.326531[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268728&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268728&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	8	0.0816327	NOK
10% type I error level	32	0.326531	NOK

Figure 1

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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 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

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

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

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