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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Sat, 13 Dec 2014 18:59:02 +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/13/t1418497197hi4emvofl620uao.htm/, Retrieved Thu, 16 May 2024 21:10:16 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=267266, Retrieved Thu, 16 May 2024 21:10:16 +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] [hjg] [2014-12-13 18:59:02] [cf34f1111566f5ca061ad80c95189d56] [Current]

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68	26	50	4
55	51	68	9
39	57	62	4
32	37	54	5
62	67	71	4
33	43	54	4
52	52	65	9
62	52	73	8
77	43	52	11
76	84	84	4
41	67	42	4
48	49	66	6
63	70	65	4
30	52	78	8
78	58	73	4
19	68	75	4
31	62	72	11
66	43	66	4
35	56	70	4
42	56	61	6
45	74	81	6
21	65	71	4
25	63	69	8
44	58	71	5
69	57	72	4
54	63	68	9
74	53	70	4
80	57	68	7
42	51	61	10
61	64	67	4
41	53	76	4
46	29	70	7
39	54	60	12
63	51	77	4
34	58	72	7
51	43	69	5
42	51	71	8
31	53	62	5
39	54	70	4
20	56	64	9
49	61	58	7
53	47	76	4
31	39	52	4
39	48	59	4
54	50	68	4
49	35	76	4
34	30	65	7
46	68	67	4
55	49	59	7
42	61	69	4
50	67	76	4
13	47	63	4
37	56	75	4
25	50	63	8
30	43	60	4
28	67	73	4
45	62	63	4
35	57	70	4
28	41	75	7
41	54	66	12
6	45	63	4
45	48	63	4
73	61	64	4
17	56	70	5
40	41	75	15
64	43	61	5
37	53	60	10
25	44	62	9
65	66	73	8
100	58	61	4
28	46	66	5
35	37	64	4
56	51	59	9
29	51	64	4
43	56	60	10
59	66	56	4
52	45	66	7
50	37	78	4
3	59	53	6
59	42	67	7
27	38	59	5
61	66	66	4
28	34	68	4
51	53	71	4
35	49	66	4
29	55	73	4
48	49	72	4
25	59	71	6
44	40	59	10
64	58	64	7
32	60	66	4
20	63	78	4
28	56	68	7
34	54	73	4
31	52	62	8
26	34	65	11
58	69	68	6
23	32	65	14
21	48	60	5
21	67	71	4
33	58	65	8
16	57	68	9
20	42	64	4
37	64	74	4
35	58	69	5
33	66	76	4
27	26	68	5
41	61	72	4
40	52	67	4
35	51	63	7
28	55	59	10
32	50	73	4
22	60	66	5
44	56	62	4
27	63	69	4
17	61	66	4

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

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 5 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267266&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267266&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267266&T=0

As an alternative you can also use a QR Code:

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

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

Multiple Linear Regression - Estimated Regression Equation
CH[t] = + 32.1071 + 0.181104AMS.I[t] + 0.01647AMS.E[t] -0.254992AMS.A[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
CH[t] =  +  32.1071 +  0.181104AMS.I[t] +  0.01647AMS.E[t] -0.254992AMS.A[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267266&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]CH[t] =  +  32.1071 +  0.181104AMS.I[t] +  0.01647AMS.E[t] -0.254992AMS.A[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267266&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267266&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
CH[t] = + 32.1071 + 0.181104AMS.I[t] + 0.01647AMS.E[t] -0.254992AMS.A[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)	32.1071	17.5767	1.827	0.0704106	0.0352053
AMS.I	0.181104	0.160743	1.127	0.262292	0.131146
AMS.E	0.01647	0.246369	0.06685	0.94682	0.47341
AMS.A	-0.254992	0.657379	-0.3879	0.698832	0.349416

\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) & 32.1071 & 17.5767 & 1.827 & 0.0704106 & 0.0352053 \tabularnewline
AMS.I & 0.181104 & 0.160743 & 1.127 & 0.262292 & 0.131146 \tabularnewline
AMS.E & 0.01647 & 0.246369 & 0.06685 & 0.94682 & 0.47341 \tabularnewline
AMS.A & -0.254992 & 0.657379 & -0.3879 & 0.698832 & 0.349416 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267266&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]32.1071[/C][C]17.5767[/C][C]1.827[/C][C]0.0704106[/C][C]0.0352053[/C][/ROW]
[ROW][C]AMS.I[/C][C]0.181104[/C][C]0.160743[/C][C]1.127[/C][C]0.262292[/C][C]0.131146[/C][/ROW]
[ROW][C]AMS.E[/C][C]0.01647[/C][C]0.246369[/C][C]0.06685[/C][C]0.94682[/C][C]0.47341[/C][/ROW]
[ROW][C]AMS.A[/C][C]-0.254992[/C][C]0.657379[/C][C]-0.3879[/C][C]0.698832[/C][C]0.349416[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267266&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267266&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)	32.1071	17.5767	1.827	0.0704106	0.0352053
AMS.I	0.181104	0.160743	1.127	0.262292	0.131146
AMS.E	0.01647	0.246369	0.06685	0.94682	0.47341
AMS.A	-0.254992	0.657379	-0.3879	0.698832	0.349416

Multiple Linear Regression - Regression Statistics
Multiple R	0.126377
R-squared	0.0159712
Adjusted R-squared	-0.0103867
F-TEST (value)	0.605937
F-TEST (DF numerator)	3
F-TEST (DF denominator)	112
p-value	0.612495
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	17.2788
Sum Squared Residuals	33438.5

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.126377 \tabularnewline
R-squared & 0.0159712 \tabularnewline
Adjusted R-squared & -0.0103867 \tabularnewline
F-TEST (value) & 0.605937 \tabularnewline
F-TEST (DF numerator) & 3 \tabularnewline
F-TEST (DF denominator) & 112 \tabularnewline
p-value & 0.612495 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 17.2788 \tabularnewline
Sum Squared Residuals & 33438.5 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267266&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.126377[/C][/ROW]
[ROW][C]R-squared[/C][C]0.0159712[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]-0.0103867[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]0.605937[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]3[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]112[/C][/ROW]
[ROW][C]p-value[/C][C]0.612495[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]17.2788[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]33438.5[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267266&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267266&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	0.126377
R-squared	0.0159712
Adjusted R-squared	-0.0103867
F-TEST (value)	0.605937
F-TEST (DF numerator)	3
F-TEST (DF denominator)	112
p-value	0.612495
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	17.2788
Sum Squared Residuals	33438.5

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	68	36.6194	31.3806
2	55	40.1685	14.8315
3	39	42.4312	-3.43124
4	32	38.4224	-6.42241
5	62	44.3905	17.6095
6	33	39.764	-6.76402
7	52	40.3002	11.6998
8	62	40.6869	21.3131
9	77	37.9461	39.0539
10	76	47.6834	28.3166
11	41	43.9129	-2.91288
12	48	40.5383	7.4617
13	63	44.835	18.165
14	30	40.7693	-10.7693
15	78	42.7935	35.2065
16	19	44.6375	-25.6375
17	31	41.7165	-10.7165
18	66	39.9617	26.0383
19	35	42.3819	-7.38189
20	42	41.7237	0.276323
21	45	45.3129	-0.312947
22	21	44.0283	-23.0283
23	25	42.6132	-17.6132
24	44	42.5056	1.49442
25	69	42.5959	26.4041
26	54	42.3417	11.6583
27	74	41.8386	32.1614
28	80	41.7651	38.2349
29	42	39.7982	2.20181
30	61	43.7813	17.2187
31	41	41.9374	-0.937399
32	46	36.7271	9.27289
33	39	39.815	-0.815046
34	63	41.5917	21.4083
35	34	42.0121	-8.01206
36	51	39.7561	11.2439
37	42	40.4729	1.52713
38	31	41.4518	-10.4518
39	39	42.0197	-3.01968
40	20	41.0081	-21.0081
41	49	42.3248	6.67521
42	53	40.8508	12.1492
43	31	39.0067	-8.00667
44	39	40.7519	-1.75189
45	54	41.2623	12.7377
46	49	38.6775	10.3225
47	34	36.8259	-2.82586
48	46	44.5057	1.49427
49	55	40.168	14.832
50	42	43.2709	-1.27094
51	50	44.4729	5.52715
52	13	40.6367	-27.6367
53	37	42.4642	-5.46424
54	25	40.16	-15.16
55	30	39.8628	-9.86284
56	28	44.4234	-16.4234
57	45	43.3532	1.64678
58	35	42.563	-7.563
59	28	38.9827	-10.9827
60	41	39.9139	1.08613
61	6	40.2745	-34.2745
62	45	40.8178	4.18223
63	73	43.1886	29.8114
64	17	42.1269	-25.1269
65	40	36.9428	3.05723
66	64	39.6243	24.3757
67	37	40.1439	-3.14393
68	25	38.8019	-13.8019
69	65	43.2224	21.7776
70	100	42.5959	57.4041
71	28	40.25	-12.25
72	35	38.8421	-3.8421
73	56	40.0202	15.9798
74	29	41.3776	-12.3776
75	43	40.6872	2.31276
76	59	43.9624	15.0376
77	52	39.5589	12.4411
78	50	39.0727	10.9273
79	3	42.1352	-39.1352
80	59	39.032	19.968
81	27	38.6859	-11.6859
82	61	44.1271	16.8729
83	28	38.3647	-10.3647
84	51	41.855	9.14495
85	35	41.0483	-6.04828
86	29	42.2502	-13.2502
87	48	41.1471	6.8529
88	25	42.4317	-17.4317
89	44	37.7731	6.22689
90	64	41.8803	22.1197
91	32	43.0404	-11.0404
92	20	43.7814	-23.7814
93	28	41.584	-13.584
94	34	42.0691	-8.06909
95	31	40.5057	-9.50575
96	26	36.5303	-10.5303
97	58	44.1933	13.8067
98	23	35.4031	-12.4031
99	21	40.5134	-19.5134
100	21	44.3905	-23.3905
101	33	41.6418	-8.64178
102	16	41.2551	-25.2551
103	20	39.7476	-19.7476
104	37	43.8966	-6.8966
105	35	42.4726	-7.47264
106	33	44.2918	-11.2918
107	27	36.6608	-9.66084
108	41	43.3204	-2.32035
109	40	41.6081	-1.60807
110	35	40.5961	-5.59611
111	28	40.4897	-12.4897
112	32	41.3447	-9.34468
113	22	42.7854	-20.7854
114	44	42.2501	1.74987
115	27	43.6331	-16.6331
116	17	43.2215	-26.2215

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 68 & 36.6194 & 31.3806 \tabularnewline
2 & 55 & 40.1685 & 14.8315 \tabularnewline
3 & 39 & 42.4312 & -3.43124 \tabularnewline
4 & 32 & 38.4224 & -6.42241 \tabularnewline
5 & 62 & 44.3905 & 17.6095 \tabularnewline
6 & 33 & 39.764 & -6.76402 \tabularnewline
7 & 52 & 40.3002 & 11.6998 \tabularnewline
8 & 62 & 40.6869 & 21.3131 \tabularnewline
9 & 77 & 37.9461 & 39.0539 \tabularnewline
10 & 76 & 47.6834 & 28.3166 \tabularnewline
11 & 41 & 43.9129 & -2.91288 \tabularnewline
12 & 48 & 40.5383 & 7.4617 \tabularnewline
13 & 63 & 44.835 & 18.165 \tabularnewline
14 & 30 & 40.7693 & -10.7693 \tabularnewline
15 & 78 & 42.7935 & 35.2065 \tabularnewline
16 & 19 & 44.6375 & -25.6375 \tabularnewline
17 & 31 & 41.7165 & -10.7165 \tabularnewline
18 & 66 & 39.9617 & 26.0383 \tabularnewline
19 & 35 & 42.3819 & -7.38189 \tabularnewline
20 & 42 & 41.7237 & 0.276323 \tabularnewline
21 & 45 & 45.3129 & -0.312947 \tabularnewline
22 & 21 & 44.0283 & -23.0283 \tabularnewline
23 & 25 & 42.6132 & -17.6132 \tabularnewline
24 & 44 & 42.5056 & 1.49442 \tabularnewline
25 & 69 & 42.5959 & 26.4041 \tabularnewline
26 & 54 & 42.3417 & 11.6583 \tabularnewline
27 & 74 & 41.8386 & 32.1614 \tabularnewline
28 & 80 & 41.7651 & 38.2349 \tabularnewline
29 & 42 & 39.7982 & 2.20181 \tabularnewline
30 & 61 & 43.7813 & 17.2187 \tabularnewline
31 & 41 & 41.9374 & -0.937399 \tabularnewline
32 & 46 & 36.7271 & 9.27289 \tabularnewline
33 & 39 & 39.815 & -0.815046 \tabularnewline
34 & 63 & 41.5917 & 21.4083 \tabularnewline
35 & 34 & 42.0121 & -8.01206 \tabularnewline
36 & 51 & 39.7561 & 11.2439 \tabularnewline
37 & 42 & 40.4729 & 1.52713 \tabularnewline
38 & 31 & 41.4518 & -10.4518 \tabularnewline
39 & 39 & 42.0197 & -3.01968 \tabularnewline
40 & 20 & 41.0081 & -21.0081 \tabularnewline
41 & 49 & 42.3248 & 6.67521 \tabularnewline
42 & 53 & 40.8508 & 12.1492 \tabularnewline
43 & 31 & 39.0067 & -8.00667 \tabularnewline
44 & 39 & 40.7519 & -1.75189 \tabularnewline
45 & 54 & 41.2623 & 12.7377 \tabularnewline
46 & 49 & 38.6775 & 10.3225 \tabularnewline
47 & 34 & 36.8259 & -2.82586 \tabularnewline
48 & 46 & 44.5057 & 1.49427 \tabularnewline
49 & 55 & 40.168 & 14.832 \tabularnewline
50 & 42 & 43.2709 & -1.27094 \tabularnewline
51 & 50 & 44.4729 & 5.52715 \tabularnewline
52 & 13 & 40.6367 & -27.6367 \tabularnewline
53 & 37 & 42.4642 & -5.46424 \tabularnewline
54 & 25 & 40.16 & -15.16 \tabularnewline
55 & 30 & 39.8628 & -9.86284 \tabularnewline
56 & 28 & 44.4234 & -16.4234 \tabularnewline
57 & 45 & 43.3532 & 1.64678 \tabularnewline
58 & 35 & 42.563 & -7.563 \tabularnewline
59 & 28 & 38.9827 & -10.9827 \tabularnewline
60 & 41 & 39.9139 & 1.08613 \tabularnewline
61 & 6 & 40.2745 & -34.2745 \tabularnewline
62 & 45 & 40.8178 & 4.18223 \tabularnewline
63 & 73 & 43.1886 & 29.8114 \tabularnewline
64 & 17 & 42.1269 & -25.1269 \tabularnewline
65 & 40 & 36.9428 & 3.05723 \tabularnewline
66 & 64 & 39.6243 & 24.3757 \tabularnewline
67 & 37 & 40.1439 & -3.14393 \tabularnewline
68 & 25 & 38.8019 & -13.8019 \tabularnewline
69 & 65 & 43.2224 & 21.7776 \tabularnewline
70 & 100 & 42.5959 & 57.4041 \tabularnewline
71 & 28 & 40.25 & -12.25 \tabularnewline
72 & 35 & 38.8421 & -3.8421 \tabularnewline
73 & 56 & 40.0202 & 15.9798 \tabularnewline
74 & 29 & 41.3776 & -12.3776 \tabularnewline
75 & 43 & 40.6872 & 2.31276 \tabularnewline
76 & 59 & 43.9624 & 15.0376 \tabularnewline
77 & 52 & 39.5589 & 12.4411 \tabularnewline
78 & 50 & 39.0727 & 10.9273 \tabularnewline
79 & 3 & 42.1352 & -39.1352 \tabularnewline
80 & 59 & 39.032 & 19.968 \tabularnewline
81 & 27 & 38.6859 & -11.6859 \tabularnewline
82 & 61 & 44.1271 & 16.8729 \tabularnewline
83 & 28 & 38.3647 & -10.3647 \tabularnewline
84 & 51 & 41.855 & 9.14495 \tabularnewline
85 & 35 & 41.0483 & -6.04828 \tabularnewline
86 & 29 & 42.2502 & -13.2502 \tabularnewline
87 & 48 & 41.1471 & 6.8529 \tabularnewline
88 & 25 & 42.4317 & -17.4317 \tabularnewline
89 & 44 & 37.7731 & 6.22689 \tabularnewline
90 & 64 & 41.8803 & 22.1197 \tabularnewline
91 & 32 & 43.0404 & -11.0404 \tabularnewline
92 & 20 & 43.7814 & -23.7814 \tabularnewline
93 & 28 & 41.584 & -13.584 \tabularnewline
94 & 34 & 42.0691 & -8.06909 \tabularnewline
95 & 31 & 40.5057 & -9.50575 \tabularnewline
96 & 26 & 36.5303 & -10.5303 \tabularnewline
97 & 58 & 44.1933 & 13.8067 \tabularnewline
98 & 23 & 35.4031 & -12.4031 \tabularnewline
99 & 21 & 40.5134 & -19.5134 \tabularnewline
100 & 21 & 44.3905 & -23.3905 \tabularnewline
101 & 33 & 41.6418 & -8.64178 \tabularnewline
102 & 16 & 41.2551 & -25.2551 \tabularnewline
103 & 20 & 39.7476 & -19.7476 \tabularnewline
104 & 37 & 43.8966 & -6.8966 \tabularnewline
105 & 35 & 42.4726 & -7.47264 \tabularnewline
106 & 33 & 44.2918 & -11.2918 \tabularnewline
107 & 27 & 36.6608 & -9.66084 \tabularnewline
108 & 41 & 43.3204 & -2.32035 \tabularnewline
109 & 40 & 41.6081 & -1.60807 \tabularnewline
110 & 35 & 40.5961 & -5.59611 \tabularnewline
111 & 28 & 40.4897 & -12.4897 \tabularnewline
112 & 32 & 41.3447 & -9.34468 \tabularnewline
113 & 22 & 42.7854 & -20.7854 \tabularnewline
114 & 44 & 42.2501 & 1.74987 \tabularnewline
115 & 27 & 43.6331 & -16.6331 \tabularnewline
116 & 17 & 43.2215 & -26.2215 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267266&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]68[/C][C]36.6194[/C][C]31.3806[/C][/ROW]
[ROW][C]2[/C][C]55[/C][C]40.1685[/C][C]14.8315[/C][/ROW]
[ROW][C]3[/C][C]39[/C][C]42.4312[/C][C]-3.43124[/C][/ROW]
[ROW][C]4[/C][C]32[/C][C]38.4224[/C][C]-6.42241[/C][/ROW]
[ROW][C]5[/C][C]62[/C][C]44.3905[/C][C]17.6095[/C][/ROW]
[ROW][C]6[/C][C]33[/C][C]39.764[/C][C]-6.76402[/C][/ROW]
[ROW][C]7[/C][C]52[/C][C]40.3002[/C][C]11.6998[/C][/ROW]
[ROW][C]8[/C][C]62[/C][C]40.6869[/C][C]21.3131[/C][/ROW]
[ROW][C]9[/C][C]77[/C][C]37.9461[/C][C]39.0539[/C][/ROW]
[ROW][C]10[/C][C]76[/C][C]47.6834[/C][C]28.3166[/C][/ROW]
[ROW][C]11[/C][C]41[/C][C]43.9129[/C][C]-2.91288[/C][/ROW]
[ROW][C]12[/C][C]48[/C][C]40.5383[/C][C]7.4617[/C][/ROW]
[ROW][C]13[/C][C]63[/C][C]44.835[/C][C]18.165[/C][/ROW]
[ROW][C]14[/C][C]30[/C][C]40.7693[/C][C]-10.7693[/C][/ROW]
[ROW][C]15[/C][C]78[/C][C]42.7935[/C][C]35.2065[/C][/ROW]
[ROW][C]16[/C][C]19[/C][C]44.6375[/C][C]-25.6375[/C][/ROW]
[ROW][C]17[/C][C]31[/C][C]41.7165[/C][C]-10.7165[/C][/ROW]
[ROW][C]18[/C][C]66[/C][C]39.9617[/C][C]26.0383[/C][/ROW]
[ROW][C]19[/C][C]35[/C][C]42.3819[/C][C]-7.38189[/C][/ROW]
[ROW][C]20[/C][C]42[/C][C]41.7237[/C][C]0.276323[/C][/ROW]
[ROW][C]21[/C][C]45[/C][C]45.3129[/C][C]-0.312947[/C][/ROW]
[ROW][C]22[/C][C]21[/C][C]44.0283[/C][C]-23.0283[/C][/ROW]
[ROW][C]23[/C][C]25[/C][C]42.6132[/C][C]-17.6132[/C][/ROW]
[ROW][C]24[/C][C]44[/C][C]42.5056[/C][C]1.49442[/C][/ROW]
[ROW][C]25[/C][C]69[/C][C]42.5959[/C][C]26.4041[/C][/ROW]
[ROW][C]26[/C][C]54[/C][C]42.3417[/C][C]11.6583[/C][/ROW]
[ROW][C]27[/C][C]74[/C][C]41.8386[/C][C]32.1614[/C][/ROW]
[ROW][C]28[/C][C]80[/C][C]41.7651[/C][C]38.2349[/C][/ROW]
[ROW][C]29[/C][C]42[/C][C]39.7982[/C][C]2.20181[/C][/ROW]
[ROW][C]30[/C][C]61[/C][C]43.7813[/C][C]17.2187[/C][/ROW]
[ROW][C]31[/C][C]41[/C][C]41.9374[/C][C]-0.937399[/C][/ROW]
[ROW][C]32[/C][C]46[/C][C]36.7271[/C][C]9.27289[/C][/ROW]
[ROW][C]33[/C][C]39[/C][C]39.815[/C][C]-0.815046[/C][/ROW]
[ROW][C]34[/C][C]63[/C][C]41.5917[/C][C]21.4083[/C][/ROW]
[ROW][C]35[/C][C]34[/C][C]42.0121[/C][C]-8.01206[/C][/ROW]
[ROW][C]36[/C][C]51[/C][C]39.7561[/C][C]11.2439[/C][/ROW]
[ROW][C]37[/C][C]42[/C][C]40.4729[/C][C]1.52713[/C][/ROW]
[ROW][C]38[/C][C]31[/C][C]41.4518[/C][C]-10.4518[/C][/ROW]
[ROW][C]39[/C][C]39[/C][C]42.0197[/C][C]-3.01968[/C][/ROW]
[ROW][C]40[/C][C]20[/C][C]41.0081[/C][C]-21.0081[/C][/ROW]
[ROW][C]41[/C][C]49[/C][C]42.3248[/C][C]6.67521[/C][/ROW]
[ROW][C]42[/C][C]53[/C][C]40.8508[/C][C]12.1492[/C][/ROW]
[ROW][C]43[/C][C]31[/C][C]39.0067[/C][C]-8.00667[/C][/ROW]
[ROW][C]44[/C][C]39[/C][C]40.7519[/C][C]-1.75189[/C][/ROW]
[ROW][C]45[/C][C]54[/C][C]41.2623[/C][C]12.7377[/C][/ROW]
[ROW][C]46[/C][C]49[/C][C]38.6775[/C][C]10.3225[/C][/ROW]
[ROW][C]47[/C][C]34[/C][C]36.8259[/C][C]-2.82586[/C][/ROW]
[ROW][C]48[/C][C]46[/C][C]44.5057[/C][C]1.49427[/C][/ROW]
[ROW][C]49[/C][C]55[/C][C]40.168[/C][C]14.832[/C][/ROW]
[ROW][C]50[/C][C]42[/C][C]43.2709[/C][C]-1.27094[/C][/ROW]
[ROW][C]51[/C][C]50[/C][C]44.4729[/C][C]5.52715[/C][/ROW]
[ROW][C]52[/C][C]13[/C][C]40.6367[/C][C]-27.6367[/C][/ROW]
[ROW][C]53[/C][C]37[/C][C]42.4642[/C][C]-5.46424[/C][/ROW]
[ROW][C]54[/C][C]25[/C][C]40.16[/C][C]-15.16[/C][/ROW]
[ROW][C]55[/C][C]30[/C][C]39.8628[/C][C]-9.86284[/C][/ROW]
[ROW][C]56[/C][C]28[/C][C]44.4234[/C][C]-16.4234[/C][/ROW]
[ROW][C]57[/C][C]45[/C][C]43.3532[/C][C]1.64678[/C][/ROW]
[ROW][C]58[/C][C]35[/C][C]42.563[/C][C]-7.563[/C][/ROW]
[ROW][C]59[/C][C]28[/C][C]38.9827[/C][C]-10.9827[/C][/ROW]
[ROW][C]60[/C][C]41[/C][C]39.9139[/C][C]1.08613[/C][/ROW]
[ROW][C]61[/C][C]6[/C][C]40.2745[/C][C]-34.2745[/C][/ROW]
[ROW][C]62[/C][C]45[/C][C]40.8178[/C][C]4.18223[/C][/ROW]
[ROW][C]63[/C][C]73[/C][C]43.1886[/C][C]29.8114[/C][/ROW]
[ROW][C]64[/C][C]17[/C][C]42.1269[/C][C]-25.1269[/C][/ROW]
[ROW][C]65[/C][C]40[/C][C]36.9428[/C][C]3.05723[/C][/ROW]
[ROW][C]66[/C][C]64[/C][C]39.6243[/C][C]24.3757[/C][/ROW]
[ROW][C]67[/C][C]37[/C][C]40.1439[/C][C]-3.14393[/C][/ROW]
[ROW][C]68[/C][C]25[/C][C]38.8019[/C][C]-13.8019[/C][/ROW]
[ROW][C]69[/C][C]65[/C][C]43.2224[/C][C]21.7776[/C][/ROW]
[ROW][C]70[/C][C]100[/C][C]42.5959[/C][C]57.4041[/C][/ROW]
[ROW][C]71[/C][C]28[/C][C]40.25[/C][C]-12.25[/C][/ROW]
[ROW][C]72[/C][C]35[/C][C]38.8421[/C][C]-3.8421[/C][/ROW]
[ROW][C]73[/C][C]56[/C][C]40.0202[/C][C]15.9798[/C][/ROW]
[ROW][C]74[/C][C]29[/C][C]41.3776[/C][C]-12.3776[/C][/ROW]
[ROW][C]75[/C][C]43[/C][C]40.6872[/C][C]2.31276[/C][/ROW]
[ROW][C]76[/C][C]59[/C][C]43.9624[/C][C]15.0376[/C][/ROW]
[ROW][C]77[/C][C]52[/C][C]39.5589[/C][C]12.4411[/C][/ROW]
[ROW][C]78[/C][C]50[/C][C]39.0727[/C][C]10.9273[/C][/ROW]
[ROW][C]79[/C][C]3[/C][C]42.1352[/C][C]-39.1352[/C][/ROW]
[ROW][C]80[/C][C]59[/C][C]39.032[/C][C]19.968[/C][/ROW]
[ROW][C]81[/C][C]27[/C][C]38.6859[/C][C]-11.6859[/C][/ROW]
[ROW][C]82[/C][C]61[/C][C]44.1271[/C][C]16.8729[/C][/ROW]
[ROW][C]83[/C][C]28[/C][C]38.3647[/C][C]-10.3647[/C][/ROW]
[ROW][C]84[/C][C]51[/C][C]41.855[/C][C]9.14495[/C][/ROW]
[ROW][C]85[/C][C]35[/C][C]41.0483[/C][C]-6.04828[/C][/ROW]
[ROW][C]86[/C][C]29[/C][C]42.2502[/C][C]-13.2502[/C][/ROW]
[ROW][C]87[/C][C]48[/C][C]41.1471[/C][C]6.8529[/C][/ROW]
[ROW][C]88[/C][C]25[/C][C]42.4317[/C][C]-17.4317[/C][/ROW]
[ROW][C]89[/C][C]44[/C][C]37.7731[/C][C]6.22689[/C][/ROW]
[ROW][C]90[/C][C]64[/C][C]41.8803[/C][C]22.1197[/C][/ROW]
[ROW][C]91[/C][C]32[/C][C]43.0404[/C][C]-11.0404[/C][/ROW]
[ROW][C]92[/C][C]20[/C][C]43.7814[/C][C]-23.7814[/C][/ROW]
[ROW][C]93[/C][C]28[/C][C]41.584[/C][C]-13.584[/C][/ROW]
[ROW][C]94[/C][C]34[/C][C]42.0691[/C][C]-8.06909[/C][/ROW]
[ROW][C]95[/C][C]31[/C][C]40.5057[/C][C]-9.50575[/C][/ROW]
[ROW][C]96[/C][C]26[/C][C]36.5303[/C][C]-10.5303[/C][/ROW]
[ROW][C]97[/C][C]58[/C][C]44.1933[/C][C]13.8067[/C][/ROW]
[ROW][C]98[/C][C]23[/C][C]35.4031[/C][C]-12.4031[/C][/ROW]
[ROW][C]99[/C][C]21[/C][C]40.5134[/C][C]-19.5134[/C][/ROW]
[ROW][C]100[/C][C]21[/C][C]44.3905[/C][C]-23.3905[/C][/ROW]
[ROW][C]101[/C][C]33[/C][C]41.6418[/C][C]-8.64178[/C][/ROW]
[ROW][C]102[/C][C]16[/C][C]41.2551[/C][C]-25.2551[/C][/ROW]
[ROW][C]103[/C][C]20[/C][C]39.7476[/C][C]-19.7476[/C][/ROW]
[ROW][C]104[/C][C]37[/C][C]43.8966[/C][C]-6.8966[/C][/ROW]
[ROW][C]105[/C][C]35[/C][C]42.4726[/C][C]-7.47264[/C][/ROW]
[ROW][C]106[/C][C]33[/C][C]44.2918[/C][C]-11.2918[/C][/ROW]
[ROW][C]107[/C][C]27[/C][C]36.6608[/C][C]-9.66084[/C][/ROW]
[ROW][C]108[/C][C]41[/C][C]43.3204[/C][C]-2.32035[/C][/ROW]
[ROW][C]109[/C][C]40[/C][C]41.6081[/C][C]-1.60807[/C][/ROW]
[ROW][C]110[/C][C]35[/C][C]40.5961[/C][C]-5.59611[/C][/ROW]
[ROW][C]111[/C][C]28[/C][C]40.4897[/C][C]-12.4897[/C][/ROW]
[ROW][C]112[/C][C]32[/C][C]41.3447[/C][C]-9.34468[/C][/ROW]
[ROW][C]113[/C][C]22[/C][C]42.7854[/C][C]-20.7854[/C][/ROW]
[ROW][C]114[/C][C]44[/C][C]42.2501[/C][C]1.74987[/C][/ROW]
[ROW][C]115[/C][C]27[/C][C]43.6331[/C][C]-16.6331[/C][/ROW]
[ROW][C]116[/C][C]17[/C][C]43.2215[/C][C]-26.2215[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267266&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267266&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	68	36.6194	31.3806
2	55	40.1685	14.8315
3	39	42.4312	-3.43124
4	32	38.4224	-6.42241
5	62	44.3905	17.6095
6	33	39.764	-6.76402
7	52	40.3002	11.6998
8	62	40.6869	21.3131
9	77	37.9461	39.0539
10	76	47.6834	28.3166
11	41	43.9129	-2.91288
12	48	40.5383	7.4617
13	63	44.835	18.165
14	30	40.7693	-10.7693
15	78	42.7935	35.2065
16	19	44.6375	-25.6375
17	31	41.7165	-10.7165
18	66	39.9617	26.0383
19	35	42.3819	-7.38189
20	42	41.7237	0.276323
21	45	45.3129	-0.312947
22	21	44.0283	-23.0283
23	25	42.6132	-17.6132
24	44	42.5056	1.49442
25	69	42.5959	26.4041
26	54	42.3417	11.6583
27	74	41.8386	32.1614
28	80	41.7651	38.2349
29	42	39.7982	2.20181
30	61	43.7813	17.2187
31	41	41.9374	-0.937399
32	46	36.7271	9.27289
33	39	39.815	-0.815046
34	63	41.5917	21.4083
35	34	42.0121	-8.01206
36	51	39.7561	11.2439
37	42	40.4729	1.52713
38	31	41.4518	-10.4518
39	39	42.0197	-3.01968
40	20	41.0081	-21.0081
41	49	42.3248	6.67521
42	53	40.8508	12.1492
43	31	39.0067	-8.00667
44	39	40.7519	-1.75189
45	54	41.2623	12.7377
46	49	38.6775	10.3225
47	34	36.8259	-2.82586
48	46	44.5057	1.49427
49	55	40.168	14.832
50	42	43.2709	-1.27094
51	50	44.4729	5.52715
52	13	40.6367	-27.6367
53	37	42.4642	-5.46424
54	25	40.16	-15.16
55	30	39.8628	-9.86284
56	28	44.4234	-16.4234
57	45	43.3532	1.64678
58	35	42.563	-7.563
59	28	38.9827	-10.9827
60	41	39.9139	1.08613
61	6	40.2745	-34.2745
62	45	40.8178	4.18223
63	73	43.1886	29.8114
64	17	42.1269	-25.1269
65	40	36.9428	3.05723
66	64	39.6243	24.3757
67	37	40.1439	-3.14393
68	25	38.8019	-13.8019
69	65	43.2224	21.7776
70	100	42.5959	57.4041
71	28	40.25	-12.25
72	35	38.8421	-3.8421
73	56	40.0202	15.9798
74	29	41.3776	-12.3776
75	43	40.6872	2.31276
76	59	43.9624	15.0376
77	52	39.5589	12.4411
78	50	39.0727	10.9273
79	3	42.1352	-39.1352
80	59	39.032	19.968
81	27	38.6859	-11.6859
82	61	44.1271	16.8729
83	28	38.3647	-10.3647
84	51	41.855	9.14495
85	35	41.0483	-6.04828
86	29	42.2502	-13.2502
87	48	41.1471	6.8529
88	25	42.4317	-17.4317
89	44	37.7731	6.22689
90	64	41.8803	22.1197
91	32	43.0404	-11.0404
92	20	43.7814	-23.7814
93	28	41.584	-13.584
94	34	42.0691	-8.06909
95	31	40.5057	-9.50575
96	26	36.5303	-10.5303
97	58	44.1933	13.8067
98	23	35.4031	-12.4031
99	21	40.5134	-19.5134
100	21	44.3905	-23.3905
101	33	41.6418	-8.64178
102	16	41.2551	-25.2551
103	20	39.7476	-19.7476
104	37	43.8966	-6.8966
105	35	42.4726	-7.47264
106	33	44.2918	-11.2918
107	27	36.6608	-9.66084
108	41	43.3204	-2.32035
109	40	41.6081	-1.60807
110	35	40.5961	-5.59611
111	28	40.4897	-12.4897
112	32	41.3447	-9.34468
113	22	42.7854	-20.7854
114	44	42.2501	1.74987
115	27	43.6331	-16.6331
116	17	43.2215	-26.2215

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
7	0.269588	0.539176	0.730412
8	0.301967	0.603933	0.698033
9	0.59336	0.813281	0.40664
10	0.639413	0.721173	0.360587
11	0.523968	0.952064	0.476032
12	0.436252	0.872504	0.563748
13	0.374504	0.749007	0.625496
14	0.572018	0.855963	0.427982
15	0.679328	0.641344	0.320672
16	0.856655	0.286689	0.143345
17	0.883331	0.233338	0.116669
18	0.878104	0.243793	0.121896
19	0.867369	0.265261	0.132631
20	0.830436	0.339127	0.169564
21	0.783753	0.432493	0.216247
22	0.8419	0.316199	0.1581
23	0.858495	0.28301	0.141505
24	0.817687	0.364627	0.182313
25	0.838017	0.323966	0.161983
26	0.805678	0.388643	0.194322
27	0.853539	0.292922	0.146461
28	0.92899	0.142019	0.0710097
29	0.908417	0.183165	0.0915827
30	0.90013	0.199741	0.0998704
31	0.882635	0.234729	0.117365
32	0.860457	0.279086	0.139543
33	0.828628	0.342744	0.171372
34	0.824197	0.351605	0.175803
35	0.80767	0.384659	0.19233
36	0.777486	0.445027	0.222514
37	0.738364	0.523273	0.261636
38	0.732683	0.534634	0.267317
39	0.699827	0.600347	0.300173
40	0.740507	0.518987	0.259493
41	0.698694	0.602613	0.301306
42	0.667926	0.664147	0.332074
43	0.654257	0.691486	0.345743
44	0.610741	0.778518	0.389259
45	0.580585	0.83883	0.419415
46	0.555412	0.889176	0.444588
47	0.523724	0.952552	0.476276
48	0.470244	0.940488	0.529756
49	0.45186	0.903721	0.54814
50	0.404247	0.808494	0.595753
51	0.360483	0.720966	0.639517
52	0.483124	0.966247	0.516876
53	0.444908	0.889815	0.555092
54	0.444232	0.888465	0.555768
55	0.417941	0.835882	0.582059
56	0.41593	0.831861	0.58407
57	0.364689	0.729379	0.635311
58	0.327579	0.655157	0.672421
59	0.309388	0.618776	0.690612
60	0.264025	0.528049	0.735975
61	0.416861	0.833723	0.583139
62	0.368422	0.736845	0.631578
63	0.482244	0.964489	0.517756
64	0.536961	0.926078	0.463039
65	0.491599	0.983197	0.508401
66	0.553265	0.893469	0.446735
67	0.500498	0.999005	0.499502
68	0.477278	0.954556	0.522722
69	0.538505	0.92299	0.461495
70	0.961593	0.0768147	0.0384074
71	0.953774	0.0924529	0.0462265
72	0.939144	0.121712	0.0608561
73	0.948211	0.103579	0.0517893
74	0.937437	0.125127	0.0625634
75	0.923878	0.152245	0.0761224
76	0.948133	0.103734	0.0518672
77	0.951387	0.0972263	0.0486132
78	0.944859	0.110282	0.055141
79	0.985196	0.0296081	0.0148041
80	0.994334	0.0113312	0.00566561
81	0.992275	0.0154497	0.00772487
82	0.995959	0.00808113	0.00404056
83	0.993913	0.012174	0.00608702
84	0.995448	0.00910359	0.0045518
85	0.992913	0.0141735	0.00708674
86	0.989659	0.0206812	0.0103406
87	0.992259	0.0154822	0.0077411
88	0.98973	0.0205394	0.0102697
89	0.988851	0.022298	0.011149
90	0.998972	0.0020568	0.0010284
91	0.998153	0.00369337	0.00184668
92	0.998395	0.00321008	0.00160504
93	0.99722	0.00556019	0.0027801
94	0.995038	0.00992308	0.00496154
95	0.991407	0.0171869	0.00859345
96	0.985465	0.0290708	0.0145354
97	0.997735	0.00452943	0.00226471
98	0.995485	0.00903025	0.00451512
99	0.993611	0.0127772	0.0063886
100	0.993399	0.0132018	0.00660088
101	0.988488	0.0230237	0.0115118
102	0.988714	0.0225725	0.0112862
103	0.986397	0.0272051	0.0136025
104	0.973629	0.0527428	0.0263714
105	0.949138	0.101723	0.0508615
106	0.904577	0.190845	0.0954227
107	0.899815	0.20037	0.100185
108	0.962144	0.0757114	0.0378557
109	0.896267	0.207466	0.103733

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
7 & 0.269588 & 0.539176 & 0.730412 \tabularnewline
8 & 0.301967 & 0.603933 & 0.698033 \tabularnewline
9 & 0.59336 & 0.813281 & 0.40664 \tabularnewline
10 & 0.639413 & 0.721173 & 0.360587 \tabularnewline
11 & 0.523968 & 0.952064 & 0.476032 \tabularnewline
12 & 0.436252 & 0.872504 & 0.563748 \tabularnewline
13 & 0.374504 & 0.749007 & 0.625496 \tabularnewline
14 & 0.572018 & 0.855963 & 0.427982 \tabularnewline
15 & 0.679328 & 0.641344 & 0.320672 \tabularnewline
16 & 0.856655 & 0.286689 & 0.143345 \tabularnewline
17 & 0.883331 & 0.233338 & 0.116669 \tabularnewline
18 & 0.878104 & 0.243793 & 0.121896 \tabularnewline
19 & 0.867369 & 0.265261 & 0.132631 \tabularnewline
20 & 0.830436 & 0.339127 & 0.169564 \tabularnewline
21 & 0.783753 & 0.432493 & 0.216247 \tabularnewline
22 & 0.8419 & 0.316199 & 0.1581 \tabularnewline
23 & 0.858495 & 0.28301 & 0.141505 \tabularnewline
24 & 0.817687 & 0.364627 & 0.182313 \tabularnewline
25 & 0.838017 & 0.323966 & 0.161983 \tabularnewline
26 & 0.805678 & 0.388643 & 0.194322 \tabularnewline
27 & 0.853539 & 0.292922 & 0.146461 \tabularnewline
28 & 0.92899 & 0.142019 & 0.0710097 \tabularnewline
29 & 0.908417 & 0.183165 & 0.0915827 \tabularnewline
30 & 0.90013 & 0.199741 & 0.0998704 \tabularnewline
31 & 0.882635 & 0.234729 & 0.117365 \tabularnewline
32 & 0.860457 & 0.279086 & 0.139543 \tabularnewline
33 & 0.828628 & 0.342744 & 0.171372 \tabularnewline
34 & 0.824197 & 0.351605 & 0.175803 \tabularnewline
35 & 0.80767 & 0.384659 & 0.19233 \tabularnewline
36 & 0.777486 & 0.445027 & 0.222514 \tabularnewline
37 & 0.738364 & 0.523273 & 0.261636 \tabularnewline
38 & 0.732683 & 0.534634 & 0.267317 \tabularnewline
39 & 0.699827 & 0.600347 & 0.300173 \tabularnewline
40 & 0.740507 & 0.518987 & 0.259493 \tabularnewline
41 & 0.698694 & 0.602613 & 0.301306 \tabularnewline
42 & 0.667926 & 0.664147 & 0.332074 \tabularnewline
43 & 0.654257 & 0.691486 & 0.345743 \tabularnewline
44 & 0.610741 & 0.778518 & 0.389259 \tabularnewline
45 & 0.580585 & 0.83883 & 0.419415 \tabularnewline
46 & 0.555412 & 0.889176 & 0.444588 \tabularnewline
47 & 0.523724 & 0.952552 & 0.476276 \tabularnewline
48 & 0.470244 & 0.940488 & 0.529756 \tabularnewline
49 & 0.45186 & 0.903721 & 0.54814 \tabularnewline
50 & 0.404247 & 0.808494 & 0.595753 \tabularnewline
51 & 0.360483 & 0.720966 & 0.639517 \tabularnewline
52 & 0.483124 & 0.966247 & 0.516876 \tabularnewline
53 & 0.444908 & 0.889815 & 0.555092 \tabularnewline
54 & 0.444232 & 0.888465 & 0.555768 \tabularnewline
55 & 0.417941 & 0.835882 & 0.582059 \tabularnewline
56 & 0.41593 & 0.831861 & 0.58407 \tabularnewline
57 & 0.364689 & 0.729379 & 0.635311 \tabularnewline
58 & 0.327579 & 0.655157 & 0.672421 \tabularnewline
59 & 0.309388 & 0.618776 & 0.690612 \tabularnewline
60 & 0.264025 & 0.528049 & 0.735975 \tabularnewline
61 & 0.416861 & 0.833723 & 0.583139 \tabularnewline
62 & 0.368422 & 0.736845 & 0.631578 \tabularnewline
63 & 0.482244 & 0.964489 & 0.517756 \tabularnewline
64 & 0.536961 & 0.926078 & 0.463039 \tabularnewline
65 & 0.491599 & 0.983197 & 0.508401 \tabularnewline
66 & 0.553265 & 0.893469 & 0.446735 \tabularnewline
67 & 0.500498 & 0.999005 & 0.499502 \tabularnewline
68 & 0.477278 & 0.954556 & 0.522722 \tabularnewline
69 & 0.538505 & 0.92299 & 0.461495 \tabularnewline
70 & 0.961593 & 0.0768147 & 0.0384074 \tabularnewline
71 & 0.953774 & 0.0924529 & 0.0462265 \tabularnewline
72 & 0.939144 & 0.121712 & 0.0608561 \tabularnewline
73 & 0.948211 & 0.103579 & 0.0517893 \tabularnewline
74 & 0.937437 & 0.125127 & 0.0625634 \tabularnewline
75 & 0.923878 & 0.152245 & 0.0761224 \tabularnewline
76 & 0.948133 & 0.103734 & 0.0518672 \tabularnewline
77 & 0.951387 & 0.0972263 & 0.0486132 \tabularnewline
78 & 0.944859 & 0.110282 & 0.055141 \tabularnewline
79 & 0.985196 & 0.0296081 & 0.0148041 \tabularnewline
80 & 0.994334 & 0.0113312 & 0.00566561 \tabularnewline
81 & 0.992275 & 0.0154497 & 0.00772487 \tabularnewline
82 & 0.995959 & 0.00808113 & 0.00404056 \tabularnewline
83 & 0.993913 & 0.012174 & 0.00608702 \tabularnewline
84 & 0.995448 & 0.00910359 & 0.0045518 \tabularnewline
85 & 0.992913 & 0.0141735 & 0.00708674 \tabularnewline
86 & 0.989659 & 0.0206812 & 0.0103406 \tabularnewline
87 & 0.992259 & 0.0154822 & 0.0077411 \tabularnewline
88 & 0.98973 & 0.0205394 & 0.0102697 \tabularnewline
89 & 0.988851 & 0.022298 & 0.011149 \tabularnewline
90 & 0.998972 & 0.0020568 & 0.0010284 \tabularnewline
91 & 0.998153 & 0.00369337 & 0.00184668 \tabularnewline
92 & 0.998395 & 0.00321008 & 0.00160504 \tabularnewline
93 & 0.99722 & 0.00556019 & 0.0027801 \tabularnewline
94 & 0.995038 & 0.00992308 & 0.00496154 \tabularnewline
95 & 0.991407 & 0.0171869 & 0.00859345 \tabularnewline
96 & 0.985465 & 0.0290708 & 0.0145354 \tabularnewline
97 & 0.997735 & 0.00452943 & 0.00226471 \tabularnewline
98 & 0.995485 & 0.00903025 & 0.00451512 \tabularnewline
99 & 0.993611 & 0.0127772 & 0.0063886 \tabularnewline
100 & 0.993399 & 0.0132018 & 0.00660088 \tabularnewline
101 & 0.988488 & 0.0230237 & 0.0115118 \tabularnewline
102 & 0.988714 & 0.0225725 & 0.0112862 \tabularnewline
103 & 0.986397 & 0.0272051 & 0.0136025 \tabularnewline
104 & 0.973629 & 0.0527428 & 0.0263714 \tabularnewline
105 & 0.949138 & 0.101723 & 0.0508615 \tabularnewline
106 & 0.904577 & 0.190845 & 0.0954227 \tabularnewline
107 & 0.899815 & 0.20037 & 0.100185 \tabularnewline
108 & 0.962144 & 0.0757114 & 0.0378557 \tabularnewline
109 & 0.896267 & 0.207466 & 0.103733 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267266&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]7[/C][C]0.269588[/C][C]0.539176[/C][C]0.730412[/C][/ROW]
[ROW][C]8[/C][C]0.301967[/C][C]0.603933[/C][C]0.698033[/C][/ROW]
[ROW][C]9[/C][C]0.59336[/C][C]0.813281[/C][C]0.40664[/C][/ROW]
[ROW][C]10[/C][C]0.639413[/C][C]0.721173[/C][C]0.360587[/C][/ROW]
[ROW][C]11[/C][C]0.523968[/C][C]0.952064[/C][C]0.476032[/C][/ROW]
[ROW][C]12[/C][C]0.436252[/C][C]0.872504[/C][C]0.563748[/C][/ROW]
[ROW][C]13[/C][C]0.374504[/C][C]0.749007[/C][C]0.625496[/C][/ROW]
[ROW][C]14[/C][C]0.572018[/C][C]0.855963[/C][C]0.427982[/C][/ROW]
[ROW][C]15[/C][C]0.679328[/C][C]0.641344[/C][C]0.320672[/C][/ROW]
[ROW][C]16[/C][C]0.856655[/C][C]0.286689[/C][C]0.143345[/C][/ROW]
[ROW][C]17[/C][C]0.883331[/C][C]0.233338[/C][C]0.116669[/C][/ROW]
[ROW][C]18[/C][C]0.878104[/C][C]0.243793[/C][C]0.121896[/C][/ROW]
[ROW][C]19[/C][C]0.867369[/C][C]0.265261[/C][C]0.132631[/C][/ROW]
[ROW][C]20[/C][C]0.830436[/C][C]0.339127[/C][C]0.169564[/C][/ROW]
[ROW][C]21[/C][C]0.783753[/C][C]0.432493[/C][C]0.216247[/C][/ROW]
[ROW][C]22[/C][C]0.8419[/C][C]0.316199[/C][C]0.1581[/C][/ROW]
[ROW][C]23[/C][C]0.858495[/C][C]0.28301[/C][C]0.141505[/C][/ROW]
[ROW][C]24[/C][C]0.817687[/C][C]0.364627[/C][C]0.182313[/C][/ROW]
[ROW][C]25[/C][C]0.838017[/C][C]0.323966[/C][C]0.161983[/C][/ROW]
[ROW][C]26[/C][C]0.805678[/C][C]0.388643[/C][C]0.194322[/C][/ROW]
[ROW][C]27[/C][C]0.853539[/C][C]0.292922[/C][C]0.146461[/C][/ROW]
[ROW][C]28[/C][C]0.92899[/C][C]0.142019[/C][C]0.0710097[/C][/ROW]
[ROW][C]29[/C][C]0.908417[/C][C]0.183165[/C][C]0.0915827[/C][/ROW]
[ROW][C]30[/C][C]0.90013[/C][C]0.199741[/C][C]0.0998704[/C][/ROW]
[ROW][C]31[/C][C]0.882635[/C][C]0.234729[/C][C]0.117365[/C][/ROW]
[ROW][C]32[/C][C]0.860457[/C][C]0.279086[/C][C]0.139543[/C][/ROW]
[ROW][C]33[/C][C]0.828628[/C][C]0.342744[/C][C]0.171372[/C][/ROW]
[ROW][C]34[/C][C]0.824197[/C][C]0.351605[/C][C]0.175803[/C][/ROW]
[ROW][C]35[/C][C]0.80767[/C][C]0.384659[/C][C]0.19233[/C][/ROW]
[ROW][C]36[/C][C]0.777486[/C][C]0.445027[/C][C]0.222514[/C][/ROW]
[ROW][C]37[/C][C]0.738364[/C][C]0.523273[/C][C]0.261636[/C][/ROW]
[ROW][C]38[/C][C]0.732683[/C][C]0.534634[/C][C]0.267317[/C][/ROW]
[ROW][C]39[/C][C]0.699827[/C][C]0.600347[/C][C]0.300173[/C][/ROW]
[ROW][C]40[/C][C]0.740507[/C][C]0.518987[/C][C]0.259493[/C][/ROW]
[ROW][C]41[/C][C]0.698694[/C][C]0.602613[/C][C]0.301306[/C][/ROW]
[ROW][C]42[/C][C]0.667926[/C][C]0.664147[/C][C]0.332074[/C][/ROW]
[ROW][C]43[/C][C]0.654257[/C][C]0.691486[/C][C]0.345743[/C][/ROW]
[ROW][C]44[/C][C]0.610741[/C][C]0.778518[/C][C]0.389259[/C][/ROW]
[ROW][C]45[/C][C]0.580585[/C][C]0.83883[/C][C]0.419415[/C][/ROW]
[ROW][C]46[/C][C]0.555412[/C][C]0.889176[/C][C]0.444588[/C][/ROW]
[ROW][C]47[/C][C]0.523724[/C][C]0.952552[/C][C]0.476276[/C][/ROW]
[ROW][C]48[/C][C]0.470244[/C][C]0.940488[/C][C]0.529756[/C][/ROW]
[ROW][C]49[/C][C]0.45186[/C][C]0.903721[/C][C]0.54814[/C][/ROW]
[ROW][C]50[/C][C]0.404247[/C][C]0.808494[/C][C]0.595753[/C][/ROW]
[ROW][C]51[/C][C]0.360483[/C][C]0.720966[/C][C]0.639517[/C][/ROW]
[ROW][C]52[/C][C]0.483124[/C][C]0.966247[/C][C]0.516876[/C][/ROW]
[ROW][C]53[/C][C]0.444908[/C][C]0.889815[/C][C]0.555092[/C][/ROW]
[ROW][C]54[/C][C]0.444232[/C][C]0.888465[/C][C]0.555768[/C][/ROW]
[ROW][C]55[/C][C]0.417941[/C][C]0.835882[/C][C]0.582059[/C][/ROW]
[ROW][C]56[/C][C]0.41593[/C][C]0.831861[/C][C]0.58407[/C][/ROW]
[ROW][C]57[/C][C]0.364689[/C][C]0.729379[/C][C]0.635311[/C][/ROW]
[ROW][C]58[/C][C]0.327579[/C][C]0.655157[/C][C]0.672421[/C][/ROW]
[ROW][C]59[/C][C]0.309388[/C][C]0.618776[/C][C]0.690612[/C][/ROW]
[ROW][C]60[/C][C]0.264025[/C][C]0.528049[/C][C]0.735975[/C][/ROW]
[ROW][C]61[/C][C]0.416861[/C][C]0.833723[/C][C]0.583139[/C][/ROW]
[ROW][C]62[/C][C]0.368422[/C][C]0.736845[/C][C]0.631578[/C][/ROW]
[ROW][C]63[/C][C]0.482244[/C][C]0.964489[/C][C]0.517756[/C][/ROW]
[ROW][C]64[/C][C]0.536961[/C][C]0.926078[/C][C]0.463039[/C][/ROW]
[ROW][C]65[/C][C]0.491599[/C][C]0.983197[/C][C]0.508401[/C][/ROW]
[ROW][C]66[/C][C]0.553265[/C][C]0.893469[/C][C]0.446735[/C][/ROW]
[ROW][C]67[/C][C]0.500498[/C][C]0.999005[/C][C]0.499502[/C][/ROW]
[ROW][C]68[/C][C]0.477278[/C][C]0.954556[/C][C]0.522722[/C][/ROW]
[ROW][C]69[/C][C]0.538505[/C][C]0.92299[/C][C]0.461495[/C][/ROW]
[ROW][C]70[/C][C]0.961593[/C][C]0.0768147[/C][C]0.0384074[/C][/ROW]
[ROW][C]71[/C][C]0.953774[/C][C]0.0924529[/C][C]0.0462265[/C][/ROW]
[ROW][C]72[/C][C]0.939144[/C][C]0.121712[/C][C]0.0608561[/C][/ROW]
[ROW][C]73[/C][C]0.948211[/C][C]0.103579[/C][C]0.0517893[/C][/ROW]
[ROW][C]74[/C][C]0.937437[/C][C]0.125127[/C][C]0.0625634[/C][/ROW]
[ROW][C]75[/C][C]0.923878[/C][C]0.152245[/C][C]0.0761224[/C][/ROW]
[ROW][C]76[/C][C]0.948133[/C][C]0.103734[/C][C]0.0518672[/C][/ROW]
[ROW][C]77[/C][C]0.951387[/C][C]0.0972263[/C][C]0.0486132[/C][/ROW]
[ROW][C]78[/C][C]0.944859[/C][C]0.110282[/C][C]0.055141[/C][/ROW]
[ROW][C]79[/C][C]0.985196[/C][C]0.0296081[/C][C]0.0148041[/C][/ROW]
[ROW][C]80[/C][C]0.994334[/C][C]0.0113312[/C][C]0.00566561[/C][/ROW]
[ROW][C]81[/C][C]0.992275[/C][C]0.0154497[/C][C]0.00772487[/C][/ROW]
[ROW][C]82[/C][C]0.995959[/C][C]0.00808113[/C][C]0.00404056[/C][/ROW]
[ROW][C]83[/C][C]0.993913[/C][C]0.012174[/C][C]0.00608702[/C][/ROW]
[ROW][C]84[/C][C]0.995448[/C][C]0.00910359[/C][C]0.0045518[/C][/ROW]
[ROW][C]85[/C][C]0.992913[/C][C]0.0141735[/C][C]0.00708674[/C][/ROW]
[ROW][C]86[/C][C]0.989659[/C][C]0.0206812[/C][C]0.0103406[/C][/ROW]
[ROW][C]87[/C][C]0.992259[/C][C]0.0154822[/C][C]0.0077411[/C][/ROW]
[ROW][C]88[/C][C]0.98973[/C][C]0.0205394[/C][C]0.0102697[/C][/ROW]
[ROW][C]89[/C][C]0.988851[/C][C]0.022298[/C][C]0.011149[/C][/ROW]
[ROW][C]90[/C][C]0.998972[/C][C]0.0020568[/C][C]0.0010284[/C][/ROW]
[ROW][C]91[/C][C]0.998153[/C][C]0.00369337[/C][C]0.00184668[/C][/ROW]
[ROW][C]92[/C][C]0.998395[/C][C]0.00321008[/C][C]0.00160504[/C][/ROW]
[ROW][C]93[/C][C]0.99722[/C][C]0.00556019[/C][C]0.0027801[/C][/ROW]
[ROW][C]94[/C][C]0.995038[/C][C]0.00992308[/C][C]0.00496154[/C][/ROW]
[ROW][C]95[/C][C]0.991407[/C][C]0.0171869[/C][C]0.00859345[/C][/ROW]
[ROW][C]96[/C][C]0.985465[/C][C]0.0290708[/C][C]0.0145354[/C][/ROW]
[ROW][C]97[/C][C]0.997735[/C][C]0.00452943[/C][C]0.00226471[/C][/ROW]
[ROW][C]98[/C][C]0.995485[/C][C]0.00903025[/C][C]0.00451512[/C][/ROW]
[ROW][C]99[/C][C]0.993611[/C][C]0.0127772[/C][C]0.0063886[/C][/ROW]
[ROW][C]100[/C][C]0.993399[/C][C]0.0132018[/C][C]0.00660088[/C][/ROW]
[ROW][C]101[/C][C]0.988488[/C][C]0.0230237[/C][C]0.0115118[/C][/ROW]
[ROW][C]102[/C][C]0.988714[/C][C]0.0225725[/C][C]0.0112862[/C][/ROW]
[ROW][C]103[/C][C]0.986397[/C][C]0.0272051[/C][C]0.0136025[/C][/ROW]
[ROW][C]104[/C][C]0.973629[/C][C]0.0527428[/C][C]0.0263714[/C][/ROW]
[ROW][C]105[/C][C]0.949138[/C][C]0.101723[/C][C]0.0508615[/C][/ROW]
[ROW][C]106[/C][C]0.904577[/C][C]0.190845[/C][C]0.0954227[/C][/ROW]
[ROW][C]107[/C][C]0.899815[/C][C]0.20037[/C][C]0.100185[/C][/ROW]
[ROW][C]108[/C][C]0.962144[/C][C]0.0757114[/C][C]0.0378557[/C][/ROW]
[ROW][C]109[/C][C]0.896267[/C][C]0.207466[/C][C]0.103733[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267266&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267266&T=5

As an alternative you can also use a QR Code:

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

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
7	0.269588	0.539176	0.730412
8	0.301967	0.603933	0.698033
9	0.59336	0.813281	0.40664
10	0.639413	0.721173	0.360587
11	0.523968	0.952064	0.476032
12	0.436252	0.872504	0.563748
13	0.374504	0.749007	0.625496
14	0.572018	0.855963	0.427982
15	0.679328	0.641344	0.320672
16	0.856655	0.286689	0.143345
17	0.883331	0.233338	0.116669
18	0.878104	0.243793	0.121896
19	0.867369	0.265261	0.132631
20	0.830436	0.339127	0.169564
21	0.783753	0.432493	0.216247
22	0.8419	0.316199	0.1581
23	0.858495	0.28301	0.141505
24	0.817687	0.364627	0.182313
25	0.838017	0.323966	0.161983
26	0.805678	0.388643	0.194322
27	0.853539	0.292922	0.146461
28	0.92899	0.142019	0.0710097
29	0.908417	0.183165	0.0915827
30	0.90013	0.199741	0.0998704
31	0.882635	0.234729	0.117365
32	0.860457	0.279086	0.139543
33	0.828628	0.342744	0.171372
34	0.824197	0.351605	0.175803
35	0.80767	0.384659	0.19233
36	0.777486	0.445027	0.222514
37	0.738364	0.523273	0.261636
38	0.732683	0.534634	0.267317
39	0.699827	0.600347	0.300173
40	0.740507	0.518987	0.259493
41	0.698694	0.602613	0.301306
42	0.667926	0.664147	0.332074
43	0.654257	0.691486	0.345743
44	0.610741	0.778518	0.389259
45	0.580585	0.83883	0.419415
46	0.555412	0.889176	0.444588
47	0.523724	0.952552	0.476276
48	0.470244	0.940488	0.529756
49	0.45186	0.903721	0.54814
50	0.404247	0.808494	0.595753
51	0.360483	0.720966	0.639517
52	0.483124	0.966247	0.516876
53	0.444908	0.889815	0.555092
54	0.444232	0.888465	0.555768
55	0.417941	0.835882	0.582059
56	0.41593	0.831861	0.58407
57	0.364689	0.729379	0.635311
58	0.327579	0.655157	0.672421
59	0.309388	0.618776	0.690612
60	0.264025	0.528049	0.735975
61	0.416861	0.833723	0.583139
62	0.368422	0.736845	0.631578
63	0.482244	0.964489	0.517756
64	0.536961	0.926078	0.463039
65	0.491599	0.983197	0.508401
66	0.553265	0.893469	0.446735
67	0.500498	0.999005	0.499502
68	0.477278	0.954556	0.522722
69	0.538505	0.92299	0.461495
70	0.961593	0.0768147	0.0384074
71	0.953774	0.0924529	0.0462265
72	0.939144	0.121712	0.0608561
73	0.948211	0.103579	0.0517893
74	0.937437	0.125127	0.0625634
75	0.923878	0.152245	0.0761224
76	0.948133	0.103734	0.0518672
77	0.951387	0.0972263	0.0486132
78	0.944859	0.110282	0.055141
79	0.985196	0.0296081	0.0148041
80	0.994334	0.0113312	0.00566561
81	0.992275	0.0154497	0.00772487
82	0.995959	0.00808113	0.00404056
83	0.993913	0.012174	0.00608702
84	0.995448	0.00910359	0.0045518
85	0.992913	0.0141735	0.00708674
86	0.989659	0.0206812	0.0103406
87	0.992259	0.0154822	0.0077411
88	0.98973	0.0205394	0.0102697
89	0.988851	0.022298	0.011149
90	0.998972	0.0020568	0.0010284
91	0.998153	0.00369337	0.00184668
92	0.998395	0.00321008	0.00160504
93	0.99722	0.00556019	0.0027801
94	0.995038	0.00992308	0.00496154
95	0.991407	0.0171869	0.00859345
96	0.985465	0.0290708	0.0145354
97	0.997735	0.00452943	0.00226471
98	0.995485	0.00903025	0.00451512
99	0.993611	0.0127772	0.0063886
100	0.993399	0.0132018	0.00660088
101	0.988488	0.0230237	0.0115118
102	0.988714	0.0225725	0.0112862
103	0.986397	0.0272051	0.0136025
104	0.973629	0.0527428	0.0263714
105	0.949138	0.101723	0.0508615
106	0.904577	0.190845	0.0954227
107	0.899815	0.20037	0.100185
108	0.962144	0.0757114	0.0378557
109	0.896267	0.207466	0.103733

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	9	0.0873786	NOK
5% type I error level	25	0.242718	NOK
10% type I error level	30	0.291262	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 & 9 & 0.0873786 & NOK \tabularnewline
5% type I error level & 25 & 0.242718 & NOK \tabularnewline
10% type I error level & 30 & 0.291262 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267266&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]9[/C][C]0.0873786[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]25[/C][C]0.242718[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]30[/C][C]0.291262[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267266&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267266&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	9	0.0873786	NOK
5% type I error level	25	0.242718	NOK
10% type I error level	30	0.291262	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 = 1 ; 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