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rwasp_multipleregression.wasp

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Multiple Regression

Date of computation

Mon, 08 Dec 2014 13:00:21 +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/08/t1418043659atd7yg4sz0lp3tj.htm/, Retrieved Sun, 19 May 2024 12:17:18 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=263971, Retrieved Sun, 19 May 2024 12:17:18 +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] [mutiple regressio...] [2014-12-08 13:00:21] [624214a256768d6065ce8a528542dcc5] [Current]

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26 50 4 13 12 13 12.9
57 62 4 8 8 13 12.2
37 54 5 14 11 11 12.8
67 71 4 16 13 14 7.4
43 54 4 14 11 15 6.7
52 65 9 13 10 14 12.6
52 73 8 15 7 11 14.8
43 52 11 13 10 13 13.3
84 84 4 20 15 16 11.1
67 42 4 17 12 14 8.2
49 66 6 15 12 14 11.4
70 65 4 16 10 15 6.4
52 78 8 12 10 15 10.6
58 73 4 17 14 13 12.0
68 75 4 11 6 14 6.3
62 72 11 16 12 11 11.3
43 66 4 16 14 12 11.9
56 70 4 15 11 14 9.3
56 61 6 13 8 13 9.6
74 81 6 14 12 12 10.0
65 71 4 19 15 15 6.4
63 69 8 16 13 15 13.8
58 71 5 17 11 14 10.8
57 72 4 10 12 14 13.8
63 68 9 15 7 12 11.7
53 70 4 14 11 12 10.9
57 68 7 14 7 12 16.1
51 61 10 16 12 15 13.4
64 67 4 15 12 14 9.9
53 76 4 17 13 16 11.5
29 70 7 14 9 12 8.3
54 60 12 16 11 12 11.7
58 72 7 15 12 14 9.0
43 69 5 16 15 16 9.7
51 71 8 16 12 15 10.8
53 62 5 10 6 12 10.3
54 70 4 8 5 14 10.4
56 64 9 17 13 13 12.7
61 58 7 14 11 14 9.3
47 76 4 10 6 16 11.8
39 52 4 14 12 12 5.9
48 59 4 12 10 14 11.4
50 68 4 16 6 15 13.0
35 76 4 16 12 13 10.8
30 65 7 16 11 16 12.3
68 67 4 8 6 16 11.3
49 59 7 16 12 12 11.8
61 69 4 15 12 12 7.9
67 76 4 8 8 16 12.7
47 63 4 13 10 12 12.3
56 75 4 14 11 15 11.6
50 63 8 13 7 12 6.7
43 60 4 16 12 13 10.9
67 73 4 19 13 12 12.1
62 63 4 19 14 14 13.3
57 70 4 14 12 14 10.1
41 75 7 15 6 11 5.7
54 66 12 13 14 10 14.3
45 63 4 10 10 12 8.0
48 63 4 16 12 11 13.3
61 64 4 15 11 16 9.3
56 70 5 11 10 14 12.5
41 75 15 9 7 14 7.6
43 61 5 16 12 15 15.9
53 60 10 12 7 15 9.2
44 62 9 12 12 14 9.1
66 73 8 14 12 13 11.1
58 61 4 14 10 11 13.0
46 66 5 13 10 16 14.5
37 64 4 15 12 12 12.2
51 59 9 17 12 15 12.3
51 64 4 14 12 14 11.4
56 60 10 11 8 15 8.8
66 56 4 9 10 14 14.6
37 78 4 7 5 13 12.6
59 53 6 13 10 6 NA
42 67 7 15 10 12 13.0
38 59 5 12 12 12 12.6
66 66 4 15 11 14 13.2
34 68 4 14 9 14 9.9
53 71 4 16 12 15 7.7
49 66 4 14 11 11 10.5
55 73 4 13 10 13 13.4
49 72 4 16 12 14 10.9
59 71 6 13 10 16 4.3
40 59 10 16 9 13 10.3
58 64 7 16 11 14 11.8
60 66 4 16 12 16 11.2
63 78 4 10 7 11 11.4
56 68 7 12 11 13 8.6
54 73 4 12 12 13 13.2
52 62 8 12 6 15 12.6
34 65 11 12 9 12 5.6
69 68 6 19 15 13 9.9
32 65 14 14 10 12 8.8
48 60 5 13 11 14 7.7
67 71 4 16 12 14 9.0
58 65 8 15 12 16 7.3
57 68 9 12 12 15 11.4
42 64 4 8 11 14 13.6
64 74 4 10 9 13 7.9
58 69 5 16 11 14 10.7
66 76 4 16 12 15 10.3
26 68 5 10 12 14 8.3
61 72 4 18 14 12 9.6
52 67 4 12 8 7 14.2
51 63 7 16 10 12 8.5
55 59 10 10 9 15 13.5
50 73 4 14 10 12 4.9
60 66 5 12 9 13 6.4
56 62 4 11 10 11 9.6
63 69 4 15 12 14 11.6
61 66 4 7 11 13 11.1

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	7 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 & 7 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263971&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]7 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=263971&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263971&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	7 seconds
R Server	'George Udny Yule' @ yule.wessa.net

Multiple Linear Regression - Estimated Regression Equation
TOT[t] = + 6.30158 + 0.429207AMS.I[t] -0.594643AMS.E[t] + 0.527528AMS.A[t] + 0.128008CONFSTATTOT[t] + 0.943736CONFSOFTTOT[t] + 0.0270845STRESSTOT[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TOT[t] =  +  6.30158 +  0.429207AMS.I[t] -0.594643AMS.E[t] +  0.527528AMS.A[t] +  0.128008CONFSTATTOT[t] +  0.943736CONFSOFTTOT[t] +  0.0270845STRESSTOT[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263971&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TOT[t] =  +  6.30158 +  0.429207AMS.I[t] -0.594643AMS.E[t] +  0.527528AMS.A[t] +  0.128008CONFSTATTOT[t] +  0.943736CONFSOFTTOT[t] +  0.0270845STRESSTOT[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263971&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263971&T=1

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Estimated Regression Equation
TOT[t] = + 6.30158 + 0.429207AMS.I[t] -0.594643AMS.E[t] + 0.527528AMS.A[t] + 0.128008CONFSTATTOT[t] + 0.943736CONFSOFTTOT[t] + 0.0270845STRESSTOT[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)	6.30158	14.4677	0.4356	0.664048	0.332024
AMS.I	0.429207	0.144054	2.979	0.00358731	0.00179366
AMS.E	-0.594643	0.389142	-1.528	0.129498	0.0647489
AMS.A	0.527528	0.442473	1.192	0.235861	0.11793
CONFSTATTOT	0.128008	0.542483	0.236	0.813918	0.406959
CONFSOFTTOT	0.943736	0.601454	1.569	0.119635	0.0598177
STRESSTOT	0.0270845	0.39117	0.06924	0.94493	0.472465

\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) & 6.30158 & 14.4677 & 0.4356 & 0.664048 & 0.332024 \tabularnewline
AMS.I & 0.429207 & 0.144054 & 2.979 & 0.00358731 & 0.00179366 \tabularnewline
AMS.E & -0.594643 & 0.389142 & -1.528 & 0.129498 & 0.0647489 \tabularnewline
AMS.A & 0.527528 & 0.442473 & 1.192 & 0.235861 & 0.11793 \tabularnewline
CONFSTATTOT & 0.128008 & 0.542483 & 0.236 & 0.813918 & 0.406959 \tabularnewline
CONFSOFTTOT & 0.943736 & 0.601454 & 1.569 & 0.119635 & 0.0598177 \tabularnewline
STRESSTOT & 0.0270845 & 0.39117 & 0.06924 & 0.94493 & 0.472465 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263971&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]6.30158[/C][C]14.4677[/C][C]0.4356[/C][C]0.664048[/C][C]0.332024[/C][/ROW]
[ROW][C]AMS.I[/C][C]0.429207[/C][C]0.144054[/C][C]2.979[/C][C]0.00358731[/C][C]0.00179366[/C][/ROW]
[ROW][C]AMS.E[/C][C]-0.594643[/C][C]0.389142[/C][C]-1.528[/C][C]0.129498[/C][C]0.0647489[/C][/ROW]
[ROW][C]AMS.A[/C][C]0.527528[/C][C]0.442473[/C][C]1.192[/C][C]0.235861[/C][C]0.11793[/C][/ROW]
[ROW][C]CONFSTATTOT[/C][C]0.128008[/C][C]0.542483[/C][C]0.236[/C][C]0.813918[/C][C]0.406959[/C][/ROW]
[ROW][C]CONFSOFTTOT[/C][C]0.943736[/C][C]0.601454[/C][C]1.569[/C][C]0.119635[/C][C]0.0598177[/C][/ROW]
[ROW][C]STRESSTOT[/C][C]0.0270845[/C][C]0.39117[/C][C]0.06924[/C][C]0.94493[/C][C]0.472465[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263971&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263971&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)	6.30158	14.4677	0.4356	0.664048	0.332024
AMS.I	0.429207	0.144054	2.979	0.00358731	0.00179366
AMS.E	-0.594643	0.389142	-1.528	0.129498	0.0647489
AMS.A	0.527528	0.442473	1.192	0.235861	0.11793
CONFSTATTOT	0.128008	0.542483	0.236	0.813918	0.406959
CONFSOFTTOT	0.943736	0.601454	1.569	0.119635	0.0598177
STRESSTOT	0.0270845	0.39117	0.06924	0.94493	0.472465

Multiple Linear Regression - Regression Statistics
Multiple R	0.402804
R-squared	0.162251
Adjusted R-squared	0.11438
F-TEST (value)	3.38932
F-TEST (DF numerator)	6
F-TEST (DF denominator)	105
p-value	0.00427392
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	10.1009
Sum Squared Residuals	10713.1

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.402804 \tabularnewline
R-squared & 0.162251 \tabularnewline
Adjusted R-squared & 0.11438 \tabularnewline
F-TEST (value) & 3.38932 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 105 \tabularnewline
p-value & 0.00427392 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 10.1009 \tabularnewline
Sum Squared Residuals & 10713.1 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263971&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.402804[/C][/ROW]
[ROW][C]R-squared[/C][C]0.162251[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.11438[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]3.38932[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]105[/C][/ROW]
[ROW][C]p-value[/C][C]0.00427392[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]10.1009[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]10713.1[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263971&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263971&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	0.402804
R-squared	0.162251
Adjusted R-squared	0.11438
F-TEST (value)	3.38932
F-TEST (DF numerator)	6
F-TEST (DF denominator)	105
p-value	0.00427392
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	10.1009
Sum Squared Residuals	10713.1

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	26	46.3953	-20.3953
2	57	48.3771	8.6229
3	37	46.0268	-9.02677
4	67	57.914	9.08605
5	43	50.2311	-7.23115
6	52	50.5397	1.46027
7	52	52.4674	-0.467435
8	43	42.846	0.154019
9	84	67.8475	16.1525
10	67	45.8881	21.1119
11	49	54.0314	-5.03144
12	70	55.8713	14.1287
13	52	57.0761	-5.0761
14	58	58.6088	-0.608756
15	68	56.0673	11.9327
16	62	51.3271	10.6729
17	43	54.1303	-11.1303
18	56	56.7527	-0.752664
19	56	49.3258	6.67417
20	74	58.0166	15.9834
21	65	60.6692	4.33079
22	63	55.794	7.20595
23	58	57.6829	0.31709
24	57	55.2233	1.77667
25	63	50.5865	12.4135
26	53	54.381	-1.381
27	57	51.3675	5.63254
28	51	51.0323	-0.0322642
29	64	55.6093	8.3907
30	53	62.586	-9.58603
31	29	52.2706	-23.2706
32	54	46.4085	7.59149
33	58	55.947	2.05297
34	43	58.6667	-15.6667
35	51	56.4432	-5.4432
36	53	47.5863	5.4137
37	54	52.3217	1.67829
38	56	51.6636	4.33637
39	61	49.2907	11.7093
40	47	58.0054	-11.0054
41	39	46.6479	-7.64787
42	48	50.3777	-2.37768
43	50	56.8257	-6.82569
44	35	59.0803	-24.0803
45	30	55.319	-25.319
46	68	53.074	14.926
47	49	49.0832	-0.0832364
48	61	54.5261	6.47393
49	67	57.2307	9.76925
50	47	50.7589	-3.75894
51	56	59.3772	-3.3772
52	50	47.8447	2.15533
53	43	52.2157	-9.21573
54	67	58.5948	8.40522
55	62	56.3507	5.64931
56	57	56.3748	0.625188
57	41	53.546	-12.546
58	54	45.9681	8.03186
59	45	49.0599	-4.05989
60	48	51.6809	-3.68088
61	61	56.0649	4.9351
62	56	54.0066	1.99343
63	41	48.6344	-7.63438
64	43	54.0732	-11.0732
65	53	47.7392	5.26085
66	44	48.8858	-4.8858
67	66	54.3672	11.6328
68	58	49.5033	8.49673
69	46	55.2864	-9.28644
70	37	52.4965	-15.4965
71	51	51.2662	-0.266229
72	51	53.8348	-2.83478
73	56	47.3288	8.6712
74	66	47.5941	18.4059
75	37	54.3437	-17.3437
76	59	68.7658	-9.76585
77	42	52.1841	-10.1841
78	38	27.1415	10.8585
79	66	87.127	-21.127
80	34	39.7378	-5.73781
81	53	55.7096	-2.7096
82	49	50.0245	-1.02453
83	55	64.3099	-9.30995
84	49	46.5616	2.43843
85	59	66.8184	-7.81839
86	40	34.9887	5.01127
87	58	55.6303	2.36969
88	60	51.2623	8.73769
89	63	58.565	4.43496
90	56	57.7476	-1.7476
91	54	51.7509	2.24907
92	52	64.6178	-12.6178
93	34	21.3996	12.6004
94	69	83.1036	-14.1036
95	32	34.7676	-2.76756
96	48	38.8293	9.17072
97	67	63.1894	3.81063
98	58	53.4671	4.53293
99	57	65.6012	-8.60119
100	42	32.5942	9.40582
101	64	62.2943	1.70574
102	58	52.9543	5.04574
103	66	92.7629	-26.7629
104	26	22.6983	3.30166
105	61	56.025	4.975
106	52	51.4547	0.545332
107	51	42.6274	8.37263
108	55	60.3781	-5.3781
109	50	41.5803	8.41969
110	60	52.2578	7.74219
111	56	49.5138	6.48624
112	63	51.9206	11.0794
113	61	NA	NA

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 26 & 46.3953 & -20.3953 \tabularnewline
2 & 57 & 48.3771 & 8.6229 \tabularnewline
3 & 37 & 46.0268 & -9.02677 \tabularnewline
4 & 67 & 57.914 & 9.08605 \tabularnewline
5 & 43 & 50.2311 & -7.23115 \tabularnewline
6 & 52 & 50.5397 & 1.46027 \tabularnewline
7 & 52 & 52.4674 & -0.467435 \tabularnewline
8 & 43 & 42.846 & 0.154019 \tabularnewline
9 & 84 & 67.8475 & 16.1525 \tabularnewline
10 & 67 & 45.8881 & 21.1119 \tabularnewline
11 & 49 & 54.0314 & -5.03144 \tabularnewline
12 & 70 & 55.8713 & 14.1287 \tabularnewline
13 & 52 & 57.0761 & -5.0761 \tabularnewline
14 & 58 & 58.6088 & -0.608756 \tabularnewline
15 & 68 & 56.0673 & 11.9327 \tabularnewline
16 & 62 & 51.3271 & 10.6729 \tabularnewline
17 & 43 & 54.1303 & -11.1303 \tabularnewline
18 & 56 & 56.7527 & -0.752664 \tabularnewline
19 & 56 & 49.3258 & 6.67417 \tabularnewline
20 & 74 & 58.0166 & 15.9834 \tabularnewline
21 & 65 & 60.6692 & 4.33079 \tabularnewline
22 & 63 & 55.794 & 7.20595 \tabularnewline
23 & 58 & 57.6829 & 0.31709 \tabularnewline
24 & 57 & 55.2233 & 1.77667 \tabularnewline
25 & 63 & 50.5865 & 12.4135 \tabularnewline
26 & 53 & 54.381 & -1.381 \tabularnewline
27 & 57 & 51.3675 & 5.63254 \tabularnewline
28 & 51 & 51.0323 & -0.0322642 \tabularnewline
29 & 64 & 55.6093 & 8.3907 \tabularnewline
30 & 53 & 62.586 & -9.58603 \tabularnewline
31 & 29 & 52.2706 & -23.2706 \tabularnewline
32 & 54 & 46.4085 & 7.59149 \tabularnewline
33 & 58 & 55.947 & 2.05297 \tabularnewline
34 & 43 & 58.6667 & -15.6667 \tabularnewline
35 & 51 & 56.4432 & -5.4432 \tabularnewline
36 & 53 & 47.5863 & 5.4137 \tabularnewline
37 & 54 & 52.3217 & 1.67829 \tabularnewline
38 & 56 & 51.6636 & 4.33637 \tabularnewline
39 & 61 & 49.2907 & 11.7093 \tabularnewline
40 & 47 & 58.0054 & -11.0054 \tabularnewline
41 & 39 & 46.6479 & -7.64787 \tabularnewline
42 & 48 & 50.3777 & -2.37768 \tabularnewline
43 & 50 & 56.8257 & -6.82569 \tabularnewline
44 & 35 & 59.0803 & -24.0803 \tabularnewline
45 & 30 & 55.319 & -25.319 \tabularnewline
46 & 68 & 53.074 & 14.926 \tabularnewline
47 & 49 & 49.0832 & -0.0832364 \tabularnewline
48 & 61 & 54.5261 & 6.47393 \tabularnewline
49 & 67 & 57.2307 & 9.76925 \tabularnewline
50 & 47 & 50.7589 & -3.75894 \tabularnewline
51 & 56 & 59.3772 & -3.3772 \tabularnewline
52 & 50 & 47.8447 & 2.15533 \tabularnewline
53 & 43 & 52.2157 & -9.21573 \tabularnewline
54 & 67 & 58.5948 & 8.40522 \tabularnewline
55 & 62 & 56.3507 & 5.64931 \tabularnewline
56 & 57 & 56.3748 & 0.625188 \tabularnewline
57 & 41 & 53.546 & -12.546 \tabularnewline
58 & 54 & 45.9681 & 8.03186 \tabularnewline
59 & 45 & 49.0599 & -4.05989 \tabularnewline
60 & 48 & 51.6809 & -3.68088 \tabularnewline
61 & 61 & 56.0649 & 4.9351 \tabularnewline
62 & 56 & 54.0066 & 1.99343 \tabularnewline
63 & 41 & 48.6344 & -7.63438 \tabularnewline
64 & 43 & 54.0732 & -11.0732 \tabularnewline
65 & 53 & 47.7392 & 5.26085 \tabularnewline
66 & 44 & 48.8858 & -4.8858 \tabularnewline
67 & 66 & 54.3672 & 11.6328 \tabularnewline
68 & 58 & 49.5033 & 8.49673 \tabularnewline
69 & 46 & 55.2864 & -9.28644 \tabularnewline
70 & 37 & 52.4965 & -15.4965 \tabularnewline
71 & 51 & 51.2662 & -0.266229 \tabularnewline
72 & 51 & 53.8348 & -2.83478 \tabularnewline
73 & 56 & 47.3288 & 8.6712 \tabularnewline
74 & 66 & 47.5941 & 18.4059 \tabularnewline
75 & 37 & 54.3437 & -17.3437 \tabularnewline
76 & 59 & 68.7658 & -9.76585 \tabularnewline
77 & 42 & 52.1841 & -10.1841 \tabularnewline
78 & 38 & 27.1415 & 10.8585 \tabularnewline
79 & 66 & 87.127 & -21.127 \tabularnewline
80 & 34 & 39.7378 & -5.73781 \tabularnewline
81 & 53 & 55.7096 & -2.7096 \tabularnewline
82 & 49 & 50.0245 & -1.02453 \tabularnewline
83 & 55 & 64.3099 & -9.30995 \tabularnewline
84 & 49 & 46.5616 & 2.43843 \tabularnewline
85 & 59 & 66.8184 & -7.81839 \tabularnewline
86 & 40 & 34.9887 & 5.01127 \tabularnewline
87 & 58 & 55.6303 & 2.36969 \tabularnewline
88 & 60 & 51.2623 & 8.73769 \tabularnewline
89 & 63 & 58.565 & 4.43496 \tabularnewline
90 & 56 & 57.7476 & -1.7476 \tabularnewline
91 & 54 & 51.7509 & 2.24907 \tabularnewline
92 & 52 & 64.6178 & -12.6178 \tabularnewline
93 & 34 & 21.3996 & 12.6004 \tabularnewline
94 & 69 & 83.1036 & -14.1036 \tabularnewline
95 & 32 & 34.7676 & -2.76756 \tabularnewline
96 & 48 & 38.8293 & 9.17072 \tabularnewline
97 & 67 & 63.1894 & 3.81063 \tabularnewline
98 & 58 & 53.4671 & 4.53293 \tabularnewline
99 & 57 & 65.6012 & -8.60119 \tabularnewline
100 & 42 & 32.5942 & 9.40582 \tabularnewline
101 & 64 & 62.2943 & 1.70574 \tabularnewline
102 & 58 & 52.9543 & 5.04574 \tabularnewline
103 & 66 & 92.7629 & -26.7629 \tabularnewline
104 & 26 & 22.6983 & 3.30166 \tabularnewline
105 & 61 & 56.025 & 4.975 \tabularnewline
106 & 52 & 51.4547 & 0.545332 \tabularnewline
107 & 51 & 42.6274 & 8.37263 \tabularnewline
108 & 55 & 60.3781 & -5.3781 \tabularnewline
109 & 50 & 41.5803 & 8.41969 \tabularnewline
110 & 60 & 52.2578 & 7.74219 \tabularnewline
111 & 56 & 49.5138 & 6.48624 \tabularnewline
112 & 63 & 51.9206 & 11.0794 \tabularnewline
113 & 61 & NA & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263971&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]26[/C][C]46.3953[/C][C]-20.3953[/C][/ROW]
[ROW][C]2[/C][C]57[/C][C]48.3771[/C][C]8.6229[/C][/ROW]
[ROW][C]3[/C][C]37[/C][C]46.0268[/C][C]-9.02677[/C][/ROW]
[ROW][C]4[/C][C]67[/C][C]57.914[/C][C]9.08605[/C][/ROW]
[ROW][C]5[/C][C]43[/C][C]50.2311[/C][C]-7.23115[/C][/ROW]
[ROW][C]6[/C][C]52[/C][C]50.5397[/C][C]1.46027[/C][/ROW]
[ROW][C]7[/C][C]52[/C][C]52.4674[/C][C]-0.467435[/C][/ROW]
[ROW][C]8[/C][C]43[/C][C]42.846[/C][C]0.154019[/C][/ROW]
[ROW][C]9[/C][C]84[/C][C]67.8475[/C][C]16.1525[/C][/ROW]
[ROW][C]10[/C][C]67[/C][C]45.8881[/C][C]21.1119[/C][/ROW]
[ROW][C]11[/C][C]49[/C][C]54.0314[/C][C]-5.03144[/C][/ROW]
[ROW][C]12[/C][C]70[/C][C]55.8713[/C][C]14.1287[/C][/ROW]
[ROW][C]13[/C][C]52[/C][C]57.0761[/C][C]-5.0761[/C][/ROW]
[ROW][C]14[/C][C]58[/C][C]58.6088[/C][C]-0.608756[/C][/ROW]
[ROW][C]15[/C][C]68[/C][C]56.0673[/C][C]11.9327[/C][/ROW]
[ROW][C]16[/C][C]62[/C][C]51.3271[/C][C]10.6729[/C][/ROW]
[ROW][C]17[/C][C]43[/C][C]54.1303[/C][C]-11.1303[/C][/ROW]
[ROW][C]18[/C][C]56[/C][C]56.7527[/C][C]-0.752664[/C][/ROW]
[ROW][C]19[/C][C]56[/C][C]49.3258[/C][C]6.67417[/C][/ROW]
[ROW][C]20[/C][C]74[/C][C]58.0166[/C][C]15.9834[/C][/ROW]
[ROW][C]21[/C][C]65[/C][C]60.6692[/C][C]4.33079[/C][/ROW]
[ROW][C]22[/C][C]63[/C][C]55.794[/C][C]7.20595[/C][/ROW]
[ROW][C]23[/C][C]58[/C][C]57.6829[/C][C]0.31709[/C][/ROW]
[ROW][C]24[/C][C]57[/C][C]55.2233[/C][C]1.77667[/C][/ROW]
[ROW][C]25[/C][C]63[/C][C]50.5865[/C][C]12.4135[/C][/ROW]
[ROW][C]26[/C][C]53[/C][C]54.381[/C][C]-1.381[/C][/ROW]
[ROW][C]27[/C][C]57[/C][C]51.3675[/C][C]5.63254[/C][/ROW]
[ROW][C]28[/C][C]51[/C][C]51.0323[/C][C]-0.0322642[/C][/ROW]
[ROW][C]29[/C][C]64[/C][C]55.6093[/C][C]8.3907[/C][/ROW]
[ROW][C]30[/C][C]53[/C][C]62.586[/C][C]-9.58603[/C][/ROW]
[ROW][C]31[/C][C]29[/C][C]52.2706[/C][C]-23.2706[/C][/ROW]
[ROW][C]32[/C][C]54[/C][C]46.4085[/C][C]7.59149[/C][/ROW]
[ROW][C]33[/C][C]58[/C][C]55.947[/C][C]2.05297[/C][/ROW]
[ROW][C]34[/C][C]43[/C][C]58.6667[/C][C]-15.6667[/C][/ROW]
[ROW][C]35[/C][C]51[/C][C]56.4432[/C][C]-5.4432[/C][/ROW]
[ROW][C]36[/C][C]53[/C][C]47.5863[/C][C]5.4137[/C][/ROW]
[ROW][C]37[/C][C]54[/C][C]52.3217[/C][C]1.67829[/C][/ROW]
[ROW][C]38[/C][C]56[/C][C]51.6636[/C][C]4.33637[/C][/ROW]
[ROW][C]39[/C][C]61[/C][C]49.2907[/C][C]11.7093[/C][/ROW]
[ROW][C]40[/C][C]47[/C][C]58.0054[/C][C]-11.0054[/C][/ROW]
[ROW][C]41[/C][C]39[/C][C]46.6479[/C][C]-7.64787[/C][/ROW]
[ROW][C]42[/C][C]48[/C][C]50.3777[/C][C]-2.37768[/C][/ROW]
[ROW][C]43[/C][C]50[/C][C]56.8257[/C][C]-6.82569[/C][/ROW]
[ROW][C]44[/C][C]35[/C][C]59.0803[/C][C]-24.0803[/C][/ROW]
[ROW][C]45[/C][C]30[/C][C]55.319[/C][C]-25.319[/C][/ROW]
[ROW][C]46[/C][C]68[/C][C]53.074[/C][C]14.926[/C][/ROW]
[ROW][C]47[/C][C]49[/C][C]49.0832[/C][C]-0.0832364[/C][/ROW]
[ROW][C]48[/C][C]61[/C][C]54.5261[/C][C]6.47393[/C][/ROW]
[ROW][C]49[/C][C]67[/C][C]57.2307[/C][C]9.76925[/C][/ROW]
[ROW][C]50[/C][C]47[/C][C]50.7589[/C][C]-3.75894[/C][/ROW]
[ROW][C]51[/C][C]56[/C][C]59.3772[/C][C]-3.3772[/C][/ROW]
[ROW][C]52[/C][C]50[/C][C]47.8447[/C][C]2.15533[/C][/ROW]
[ROW][C]53[/C][C]43[/C][C]52.2157[/C][C]-9.21573[/C][/ROW]
[ROW][C]54[/C][C]67[/C][C]58.5948[/C][C]8.40522[/C][/ROW]
[ROW][C]55[/C][C]62[/C][C]56.3507[/C][C]5.64931[/C][/ROW]
[ROW][C]56[/C][C]57[/C][C]56.3748[/C][C]0.625188[/C][/ROW]
[ROW][C]57[/C][C]41[/C][C]53.546[/C][C]-12.546[/C][/ROW]
[ROW][C]58[/C][C]54[/C][C]45.9681[/C][C]8.03186[/C][/ROW]
[ROW][C]59[/C][C]45[/C][C]49.0599[/C][C]-4.05989[/C][/ROW]
[ROW][C]60[/C][C]48[/C][C]51.6809[/C][C]-3.68088[/C][/ROW]
[ROW][C]61[/C][C]61[/C][C]56.0649[/C][C]4.9351[/C][/ROW]
[ROW][C]62[/C][C]56[/C][C]54.0066[/C][C]1.99343[/C][/ROW]
[ROW][C]63[/C][C]41[/C][C]48.6344[/C][C]-7.63438[/C][/ROW]
[ROW][C]64[/C][C]43[/C][C]54.0732[/C][C]-11.0732[/C][/ROW]
[ROW][C]65[/C][C]53[/C][C]47.7392[/C][C]5.26085[/C][/ROW]
[ROW][C]66[/C][C]44[/C][C]48.8858[/C][C]-4.8858[/C][/ROW]
[ROW][C]67[/C][C]66[/C][C]54.3672[/C][C]11.6328[/C][/ROW]
[ROW][C]68[/C][C]58[/C][C]49.5033[/C][C]8.49673[/C][/ROW]
[ROW][C]69[/C][C]46[/C][C]55.2864[/C][C]-9.28644[/C][/ROW]
[ROW][C]70[/C][C]37[/C][C]52.4965[/C][C]-15.4965[/C][/ROW]
[ROW][C]71[/C][C]51[/C][C]51.2662[/C][C]-0.266229[/C][/ROW]
[ROW][C]72[/C][C]51[/C][C]53.8348[/C][C]-2.83478[/C][/ROW]
[ROW][C]73[/C][C]56[/C][C]47.3288[/C][C]8.6712[/C][/ROW]
[ROW][C]74[/C][C]66[/C][C]47.5941[/C][C]18.4059[/C][/ROW]
[ROW][C]75[/C][C]37[/C][C]54.3437[/C][C]-17.3437[/C][/ROW]
[ROW][C]76[/C][C]59[/C][C]68.7658[/C][C]-9.76585[/C][/ROW]
[ROW][C]77[/C][C]42[/C][C]52.1841[/C][C]-10.1841[/C][/ROW]
[ROW][C]78[/C][C]38[/C][C]27.1415[/C][C]10.8585[/C][/ROW]
[ROW][C]79[/C][C]66[/C][C]87.127[/C][C]-21.127[/C][/ROW]
[ROW][C]80[/C][C]34[/C][C]39.7378[/C][C]-5.73781[/C][/ROW]
[ROW][C]81[/C][C]53[/C][C]55.7096[/C][C]-2.7096[/C][/ROW]
[ROW][C]82[/C][C]49[/C][C]50.0245[/C][C]-1.02453[/C][/ROW]
[ROW][C]83[/C][C]55[/C][C]64.3099[/C][C]-9.30995[/C][/ROW]
[ROW][C]84[/C][C]49[/C][C]46.5616[/C][C]2.43843[/C][/ROW]
[ROW][C]85[/C][C]59[/C][C]66.8184[/C][C]-7.81839[/C][/ROW]
[ROW][C]86[/C][C]40[/C][C]34.9887[/C][C]5.01127[/C][/ROW]
[ROW][C]87[/C][C]58[/C][C]55.6303[/C][C]2.36969[/C][/ROW]
[ROW][C]88[/C][C]60[/C][C]51.2623[/C][C]8.73769[/C][/ROW]
[ROW][C]89[/C][C]63[/C][C]58.565[/C][C]4.43496[/C][/ROW]
[ROW][C]90[/C][C]56[/C][C]57.7476[/C][C]-1.7476[/C][/ROW]
[ROW][C]91[/C][C]54[/C][C]51.7509[/C][C]2.24907[/C][/ROW]
[ROW][C]92[/C][C]52[/C][C]64.6178[/C][C]-12.6178[/C][/ROW]
[ROW][C]93[/C][C]34[/C][C]21.3996[/C][C]12.6004[/C][/ROW]
[ROW][C]94[/C][C]69[/C][C]83.1036[/C][C]-14.1036[/C][/ROW]
[ROW][C]95[/C][C]32[/C][C]34.7676[/C][C]-2.76756[/C][/ROW]
[ROW][C]96[/C][C]48[/C][C]38.8293[/C][C]9.17072[/C][/ROW]
[ROW][C]97[/C][C]67[/C][C]63.1894[/C][C]3.81063[/C][/ROW]
[ROW][C]98[/C][C]58[/C][C]53.4671[/C][C]4.53293[/C][/ROW]
[ROW][C]99[/C][C]57[/C][C]65.6012[/C][C]-8.60119[/C][/ROW]
[ROW][C]100[/C][C]42[/C][C]32.5942[/C][C]9.40582[/C][/ROW]
[ROW][C]101[/C][C]64[/C][C]62.2943[/C][C]1.70574[/C][/ROW]
[ROW][C]102[/C][C]58[/C][C]52.9543[/C][C]5.04574[/C][/ROW]
[ROW][C]103[/C][C]66[/C][C]92.7629[/C][C]-26.7629[/C][/ROW]
[ROW][C]104[/C][C]26[/C][C]22.6983[/C][C]3.30166[/C][/ROW]
[ROW][C]105[/C][C]61[/C][C]56.025[/C][C]4.975[/C][/ROW]
[ROW][C]106[/C][C]52[/C][C]51.4547[/C][C]0.545332[/C][/ROW]
[ROW][C]107[/C][C]51[/C][C]42.6274[/C][C]8.37263[/C][/ROW]
[ROW][C]108[/C][C]55[/C][C]60.3781[/C][C]-5.3781[/C][/ROW]
[ROW][C]109[/C][C]50[/C][C]41.5803[/C][C]8.41969[/C][/ROW]
[ROW][C]110[/C][C]60[/C][C]52.2578[/C][C]7.74219[/C][/ROW]
[ROW][C]111[/C][C]56[/C][C]49.5138[/C][C]6.48624[/C][/ROW]
[ROW][C]112[/C][C]63[/C][C]51.9206[/C][C]11.0794[/C][/ROW]
[ROW][C]113[/C][C]61[/C][C]NA[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263971&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263971&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	26	46.3953	-20.3953
2	57	48.3771	8.6229
3	37	46.0268	-9.02677
4	67	57.914	9.08605
5	43	50.2311	-7.23115
6	52	50.5397	1.46027
7	52	52.4674	-0.467435
8	43	42.846	0.154019
9	84	67.8475	16.1525
10	67	45.8881	21.1119
11	49	54.0314	-5.03144
12	70	55.8713	14.1287
13	52	57.0761	-5.0761
14	58	58.6088	-0.608756
15	68	56.0673	11.9327
16	62	51.3271	10.6729
17	43	54.1303	-11.1303
18	56	56.7527	-0.752664
19	56	49.3258	6.67417
20	74	58.0166	15.9834
21	65	60.6692	4.33079
22	63	55.794	7.20595
23	58	57.6829	0.31709
24	57	55.2233	1.77667
25	63	50.5865	12.4135
26	53	54.381	-1.381
27	57	51.3675	5.63254
28	51	51.0323	-0.0322642
29	64	55.6093	8.3907
30	53	62.586	-9.58603
31	29	52.2706	-23.2706
32	54	46.4085	7.59149
33	58	55.947	2.05297
34	43	58.6667	-15.6667
35	51	56.4432	-5.4432
36	53	47.5863	5.4137
37	54	52.3217	1.67829
38	56	51.6636	4.33637
39	61	49.2907	11.7093
40	47	58.0054	-11.0054
41	39	46.6479	-7.64787
42	48	50.3777	-2.37768
43	50	56.8257	-6.82569
44	35	59.0803	-24.0803
45	30	55.319	-25.319
46	68	53.074	14.926
47	49	49.0832	-0.0832364
48	61	54.5261	6.47393
49	67	57.2307	9.76925
50	47	50.7589	-3.75894
51	56	59.3772	-3.3772
52	50	47.8447	2.15533
53	43	52.2157	-9.21573
54	67	58.5948	8.40522
55	62	56.3507	5.64931
56	57	56.3748	0.625188
57	41	53.546	-12.546
58	54	45.9681	8.03186
59	45	49.0599	-4.05989
60	48	51.6809	-3.68088
61	61	56.0649	4.9351
62	56	54.0066	1.99343
63	41	48.6344	-7.63438
64	43	54.0732	-11.0732
65	53	47.7392	5.26085
66	44	48.8858	-4.8858
67	66	54.3672	11.6328
68	58	49.5033	8.49673
69	46	55.2864	-9.28644
70	37	52.4965	-15.4965
71	51	51.2662	-0.266229
72	51	53.8348	-2.83478
73	56	47.3288	8.6712
74	66	47.5941	18.4059
75	37	54.3437	-17.3437
76	59	68.7658	-9.76585
77	42	52.1841	-10.1841
78	38	27.1415	10.8585
79	66	87.127	-21.127
80	34	39.7378	-5.73781
81	53	55.7096	-2.7096
82	49	50.0245	-1.02453
83	55	64.3099	-9.30995
84	49	46.5616	2.43843
85	59	66.8184	-7.81839
86	40	34.9887	5.01127
87	58	55.6303	2.36969
88	60	51.2623	8.73769
89	63	58.565	4.43496
90	56	57.7476	-1.7476
91	54	51.7509	2.24907
92	52	64.6178	-12.6178
93	34	21.3996	12.6004
94	69	83.1036	-14.1036
95	32	34.7676	-2.76756
96	48	38.8293	9.17072
97	67	63.1894	3.81063
98	58	53.4671	4.53293
99	57	65.6012	-8.60119
100	42	32.5942	9.40582
101	64	62.2943	1.70574
102	58	52.9543	5.04574
103	66	92.7629	-26.7629
104	26	22.6983	3.30166
105	61	56.025	4.975
106	52	51.4547	0.545332
107	51	42.6274	8.37263
108	55	60.3781	-5.3781
109	50	41.5803	8.41969
110	60	52.2578	7.74219
111	56	49.5138	6.48624
112	63	51.9206	11.0794
113	61	NA	NA

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
10	0.96533	0.069339	0.0346695
11	0.940498	0.119003	0.0595017
12	0.904438	0.191123	0.0955617
13	0.872342	0.255316	0.127658
14	0.8057	0.3886	0.1943
15	0.7399	0.5202	0.2601
16	0.734155	0.531689	0.265845
17	0.698933	0.602135	0.301067
18	0.63844	0.723121	0.36156
19	0.557249	0.885502	0.442751
20	0.626695	0.746609	0.373305
21	0.590598	0.818803	0.409402
22	0.564913	0.870174	0.435087
23	0.513107	0.973786	0.486893
24	0.491444	0.982888	0.508556
25	0.456221	0.912441	0.543779
26	0.386782	0.773563	0.613218
27	0.336288	0.672577	0.663712
28	0.27654	0.553081	0.72346
29	0.246248	0.492495	0.753752
30	0.279087	0.558175	0.720913
31	0.737571	0.524857	0.262429
32	0.700073	0.599855	0.299927
33	0.646975	0.70605	0.353025
34	0.72125	0.5575	0.27875
35	0.694369	0.611262	0.305631
36	0.645795	0.70841	0.354205
37	0.588927	0.822146	0.411073
38	0.538956	0.922087	0.461044
39	0.546364	0.907271	0.453636
40	0.559912	0.880175	0.440088
41	0.550002	0.899995	0.449998
42	0.493147	0.986293	0.506853
43	0.477307	0.954614	0.522693
44	0.71926	0.561481	0.28074
45	0.900019	0.199962	0.0999811
46	0.930756	0.138487	0.0692437
47	0.909268	0.181463	0.0907315
48	0.892107	0.215786	0.107893
49	0.893508	0.212983	0.106492
50	0.867142	0.265716	0.132858
51	0.835964	0.328071	0.164036
52	0.812149	0.375703	0.187851
53	0.801178	0.397643	0.198822
54	0.796637	0.406726	0.203363
55	0.777075	0.445851	0.222925
56	0.731975	0.53605	0.268025
57	0.766386	0.467228	0.233614
58	0.746166	0.507669	0.253834
59	0.708859	0.582281	0.291141
60	0.663342	0.673315	0.336658
61	0.61879	0.76242	0.38121
62	0.565139	0.869721	0.434861
63	0.547762	0.904477	0.452238
64	0.563265	0.873471	0.436735
65	0.523428	0.953144	0.476572
66	0.478635	0.95727	0.521365
67	0.509157	0.981685	0.490843
68	0.481906	0.963812	0.518094
69	0.478204	0.956407	0.521796
70	0.573454	0.853092	0.426546
71	0.516196	0.967609	0.483804
72	0.471835	0.943671	0.528165
73	0.467705	0.935411	0.532295
74	0.562584	0.874833	0.437416
75	0.661971	0.676057	0.338029
76	0.664672	0.670656	0.335328
77	0.668274	0.663453	0.331726
78	0.652836	0.694328	0.347164
79	0.867117	0.265766	0.132883
80	0.852667	0.294667	0.147333
81	0.819989	0.360023	0.180011
82	0.789168	0.421663	0.210832
83	0.83957	0.32086	0.16043
84	0.797145	0.40571	0.202855
85	0.786463	0.427074	0.213537
86	0.731154	0.537692	0.268846
87	0.686152	0.627696	0.313848
88	0.642389	0.715223	0.357611
89	0.603971	0.792059	0.396029
90	0.546138	0.907725	0.453862
91	0.501152	0.997696	0.498848
92	0.445253	0.890506	0.554747
93	0.515775	0.968449	0.484225
94	0.499284	0.998569	0.500716
95	0.410988	0.821975	0.589012
96	0.355393	0.710787	0.644607
97	0.279356	0.558713	0.720644
98	0.233535	0.467071	0.766465
99	0.29342	0.58684	0.70658
100	0.259628	0.519255	0.740372
101	0.209004	0.418007	0.790996
102	0.117136	0.234273	0.882864
103	0.824155	0.351691	0.175845

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.96533 & 0.069339 & 0.0346695 \tabularnewline
11 & 0.940498 & 0.119003 & 0.0595017 \tabularnewline
12 & 0.904438 & 0.191123 & 0.0955617 \tabularnewline
13 & 0.872342 & 0.255316 & 0.127658 \tabularnewline
14 & 0.8057 & 0.3886 & 0.1943 \tabularnewline
15 & 0.7399 & 0.5202 & 0.2601 \tabularnewline
16 & 0.734155 & 0.531689 & 0.265845 \tabularnewline
17 & 0.698933 & 0.602135 & 0.301067 \tabularnewline
18 & 0.63844 & 0.723121 & 0.36156 \tabularnewline
19 & 0.557249 & 0.885502 & 0.442751 \tabularnewline
20 & 0.626695 & 0.746609 & 0.373305 \tabularnewline
21 & 0.590598 & 0.818803 & 0.409402 \tabularnewline
22 & 0.564913 & 0.870174 & 0.435087 \tabularnewline
23 & 0.513107 & 0.973786 & 0.486893 \tabularnewline
24 & 0.491444 & 0.982888 & 0.508556 \tabularnewline
25 & 0.456221 & 0.912441 & 0.543779 \tabularnewline
26 & 0.386782 & 0.773563 & 0.613218 \tabularnewline
27 & 0.336288 & 0.672577 & 0.663712 \tabularnewline
28 & 0.27654 & 0.553081 & 0.72346 \tabularnewline
29 & 0.246248 & 0.492495 & 0.753752 \tabularnewline
30 & 0.279087 & 0.558175 & 0.720913 \tabularnewline
31 & 0.737571 & 0.524857 & 0.262429 \tabularnewline
32 & 0.700073 & 0.599855 & 0.299927 \tabularnewline
33 & 0.646975 & 0.70605 & 0.353025 \tabularnewline
34 & 0.72125 & 0.5575 & 0.27875 \tabularnewline
35 & 0.694369 & 0.611262 & 0.305631 \tabularnewline
36 & 0.645795 & 0.70841 & 0.354205 \tabularnewline
37 & 0.588927 & 0.822146 & 0.411073 \tabularnewline
38 & 0.538956 & 0.922087 & 0.461044 \tabularnewline
39 & 0.546364 & 0.907271 & 0.453636 \tabularnewline
40 & 0.559912 & 0.880175 & 0.440088 \tabularnewline
41 & 0.550002 & 0.899995 & 0.449998 \tabularnewline
42 & 0.493147 & 0.986293 & 0.506853 \tabularnewline
43 & 0.477307 & 0.954614 & 0.522693 \tabularnewline
44 & 0.71926 & 0.561481 & 0.28074 \tabularnewline
45 & 0.900019 & 0.199962 & 0.0999811 \tabularnewline
46 & 0.930756 & 0.138487 & 0.0692437 \tabularnewline
47 & 0.909268 & 0.181463 & 0.0907315 \tabularnewline
48 & 0.892107 & 0.215786 & 0.107893 \tabularnewline
49 & 0.893508 & 0.212983 & 0.106492 \tabularnewline
50 & 0.867142 & 0.265716 & 0.132858 \tabularnewline
51 & 0.835964 & 0.328071 & 0.164036 \tabularnewline
52 & 0.812149 & 0.375703 & 0.187851 \tabularnewline
53 & 0.801178 & 0.397643 & 0.198822 \tabularnewline
54 & 0.796637 & 0.406726 & 0.203363 \tabularnewline
55 & 0.777075 & 0.445851 & 0.222925 \tabularnewline
56 & 0.731975 & 0.53605 & 0.268025 \tabularnewline
57 & 0.766386 & 0.467228 & 0.233614 \tabularnewline
58 & 0.746166 & 0.507669 & 0.253834 \tabularnewline
59 & 0.708859 & 0.582281 & 0.291141 \tabularnewline
60 & 0.663342 & 0.673315 & 0.336658 \tabularnewline
61 & 0.61879 & 0.76242 & 0.38121 \tabularnewline
62 & 0.565139 & 0.869721 & 0.434861 \tabularnewline
63 & 0.547762 & 0.904477 & 0.452238 \tabularnewline
64 & 0.563265 & 0.873471 & 0.436735 \tabularnewline
65 & 0.523428 & 0.953144 & 0.476572 \tabularnewline
66 & 0.478635 & 0.95727 & 0.521365 \tabularnewline
67 & 0.509157 & 0.981685 & 0.490843 \tabularnewline
68 & 0.481906 & 0.963812 & 0.518094 \tabularnewline
69 & 0.478204 & 0.956407 & 0.521796 \tabularnewline
70 & 0.573454 & 0.853092 & 0.426546 \tabularnewline
71 & 0.516196 & 0.967609 & 0.483804 \tabularnewline
72 & 0.471835 & 0.943671 & 0.528165 \tabularnewline
73 & 0.467705 & 0.935411 & 0.532295 \tabularnewline
74 & 0.562584 & 0.874833 & 0.437416 \tabularnewline
75 & 0.661971 & 0.676057 & 0.338029 \tabularnewline
76 & 0.664672 & 0.670656 & 0.335328 \tabularnewline
77 & 0.668274 & 0.663453 & 0.331726 \tabularnewline
78 & 0.652836 & 0.694328 & 0.347164 \tabularnewline
79 & 0.867117 & 0.265766 & 0.132883 \tabularnewline
80 & 0.852667 & 0.294667 & 0.147333 \tabularnewline
81 & 0.819989 & 0.360023 & 0.180011 \tabularnewline
82 & 0.789168 & 0.421663 & 0.210832 \tabularnewline
83 & 0.83957 & 0.32086 & 0.16043 \tabularnewline
84 & 0.797145 & 0.40571 & 0.202855 \tabularnewline
85 & 0.786463 & 0.427074 & 0.213537 \tabularnewline
86 & 0.731154 & 0.537692 & 0.268846 \tabularnewline
87 & 0.686152 & 0.627696 & 0.313848 \tabularnewline
88 & 0.642389 & 0.715223 & 0.357611 \tabularnewline
89 & 0.603971 & 0.792059 & 0.396029 \tabularnewline
90 & 0.546138 & 0.907725 & 0.453862 \tabularnewline
91 & 0.501152 & 0.997696 & 0.498848 \tabularnewline
92 & 0.445253 & 0.890506 & 0.554747 \tabularnewline
93 & 0.515775 & 0.968449 & 0.484225 \tabularnewline
94 & 0.499284 & 0.998569 & 0.500716 \tabularnewline
95 & 0.410988 & 0.821975 & 0.589012 \tabularnewline
96 & 0.355393 & 0.710787 & 0.644607 \tabularnewline
97 & 0.279356 & 0.558713 & 0.720644 \tabularnewline
98 & 0.233535 & 0.467071 & 0.766465 \tabularnewline
99 & 0.29342 & 0.58684 & 0.70658 \tabularnewline
100 & 0.259628 & 0.519255 & 0.740372 \tabularnewline
101 & 0.209004 & 0.418007 & 0.790996 \tabularnewline
102 & 0.117136 & 0.234273 & 0.882864 \tabularnewline
103 & 0.824155 & 0.351691 & 0.175845 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263971&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]10[/C][C]0.96533[/C][C]0.069339[/C][C]0.0346695[/C][/ROW]
[ROW][C]11[/C][C]0.940498[/C][C]0.119003[/C][C]0.0595017[/C][/ROW]
[ROW][C]12[/C][C]0.904438[/C][C]0.191123[/C][C]0.0955617[/C][/ROW]
[ROW][C]13[/C][C]0.872342[/C][C]0.255316[/C][C]0.127658[/C][/ROW]
[ROW][C]14[/C][C]0.8057[/C][C]0.3886[/C][C]0.1943[/C][/ROW]
[ROW][C]15[/C][C]0.7399[/C][C]0.5202[/C][C]0.2601[/C][/ROW]
[ROW][C]16[/C][C]0.734155[/C][C]0.531689[/C][C]0.265845[/C][/ROW]
[ROW][C]17[/C][C]0.698933[/C][C]0.602135[/C][C]0.301067[/C][/ROW]
[ROW][C]18[/C][C]0.63844[/C][C]0.723121[/C][C]0.36156[/C][/ROW]
[ROW][C]19[/C][C]0.557249[/C][C]0.885502[/C][C]0.442751[/C][/ROW]
[ROW][C]20[/C][C]0.626695[/C][C]0.746609[/C][C]0.373305[/C][/ROW]
[ROW][C]21[/C][C]0.590598[/C][C]0.818803[/C][C]0.409402[/C][/ROW]
[ROW][C]22[/C][C]0.564913[/C][C]0.870174[/C][C]0.435087[/C][/ROW]
[ROW][C]23[/C][C]0.513107[/C][C]0.973786[/C][C]0.486893[/C][/ROW]
[ROW][C]24[/C][C]0.491444[/C][C]0.982888[/C][C]0.508556[/C][/ROW]
[ROW][C]25[/C][C]0.456221[/C][C]0.912441[/C][C]0.543779[/C][/ROW]
[ROW][C]26[/C][C]0.386782[/C][C]0.773563[/C][C]0.613218[/C][/ROW]
[ROW][C]27[/C][C]0.336288[/C][C]0.672577[/C][C]0.663712[/C][/ROW]
[ROW][C]28[/C][C]0.27654[/C][C]0.553081[/C][C]0.72346[/C][/ROW]
[ROW][C]29[/C][C]0.246248[/C][C]0.492495[/C][C]0.753752[/C][/ROW]
[ROW][C]30[/C][C]0.279087[/C][C]0.558175[/C][C]0.720913[/C][/ROW]
[ROW][C]31[/C][C]0.737571[/C][C]0.524857[/C][C]0.262429[/C][/ROW]
[ROW][C]32[/C][C]0.700073[/C][C]0.599855[/C][C]0.299927[/C][/ROW]
[ROW][C]33[/C][C]0.646975[/C][C]0.70605[/C][C]0.353025[/C][/ROW]
[ROW][C]34[/C][C]0.72125[/C][C]0.5575[/C][C]0.27875[/C][/ROW]
[ROW][C]35[/C][C]0.694369[/C][C]0.611262[/C][C]0.305631[/C][/ROW]
[ROW][C]36[/C][C]0.645795[/C][C]0.70841[/C][C]0.354205[/C][/ROW]
[ROW][C]37[/C][C]0.588927[/C][C]0.822146[/C][C]0.411073[/C][/ROW]
[ROW][C]38[/C][C]0.538956[/C][C]0.922087[/C][C]0.461044[/C][/ROW]
[ROW][C]39[/C][C]0.546364[/C][C]0.907271[/C][C]0.453636[/C][/ROW]
[ROW][C]40[/C][C]0.559912[/C][C]0.880175[/C][C]0.440088[/C][/ROW]
[ROW][C]41[/C][C]0.550002[/C][C]0.899995[/C][C]0.449998[/C][/ROW]
[ROW][C]42[/C][C]0.493147[/C][C]0.986293[/C][C]0.506853[/C][/ROW]
[ROW][C]43[/C][C]0.477307[/C][C]0.954614[/C][C]0.522693[/C][/ROW]
[ROW][C]44[/C][C]0.71926[/C][C]0.561481[/C][C]0.28074[/C][/ROW]
[ROW][C]45[/C][C]0.900019[/C][C]0.199962[/C][C]0.0999811[/C][/ROW]
[ROW][C]46[/C][C]0.930756[/C][C]0.138487[/C][C]0.0692437[/C][/ROW]
[ROW][C]47[/C][C]0.909268[/C][C]0.181463[/C][C]0.0907315[/C][/ROW]
[ROW][C]48[/C][C]0.892107[/C][C]0.215786[/C][C]0.107893[/C][/ROW]
[ROW][C]49[/C][C]0.893508[/C][C]0.212983[/C][C]0.106492[/C][/ROW]
[ROW][C]50[/C][C]0.867142[/C][C]0.265716[/C][C]0.132858[/C][/ROW]
[ROW][C]51[/C][C]0.835964[/C][C]0.328071[/C][C]0.164036[/C][/ROW]
[ROW][C]52[/C][C]0.812149[/C][C]0.375703[/C][C]0.187851[/C][/ROW]
[ROW][C]53[/C][C]0.801178[/C][C]0.397643[/C][C]0.198822[/C][/ROW]
[ROW][C]54[/C][C]0.796637[/C][C]0.406726[/C][C]0.203363[/C][/ROW]
[ROW][C]55[/C][C]0.777075[/C][C]0.445851[/C][C]0.222925[/C][/ROW]
[ROW][C]56[/C][C]0.731975[/C][C]0.53605[/C][C]0.268025[/C][/ROW]
[ROW][C]57[/C][C]0.766386[/C][C]0.467228[/C][C]0.233614[/C][/ROW]
[ROW][C]58[/C][C]0.746166[/C][C]0.507669[/C][C]0.253834[/C][/ROW]
[ROW][C]59[/C][C]0.708859[/C][C]0.582281[/C][C]0.291141[/C][/ROW]
[ROW][C]60[/C][C]0.663342[/C][C]0.673315[/C][C]0.336658[/C][/ROW]
[ROW][C]61[/C][C]0.61879[/C][C]0.76242[/C][C]0.38121[/C][/ROW]
[ROW][C]62[/C][C]0.565139[/C][C]0.869721[/C][C]0.434861[/C][/ROW]
[ROW][C]63[/C][C]0.547762[/C][C]0.904477[/C][C]0.452238[/C][/ROW]
[ROW][C]64[/C][C]0.563265[/C][C]0.873471[/C][C]0.436735[/C][/ROW]
[ROW][C]65[/C][C]0.523428[/C][C]0.953144[/C][C]0.476572[/C][/ROW]
[ROW][C]66[/C][C]0.478635[/C][C]0.95727[/C][C]0.521365[/C][/ROW]
[ROW][C]67[/C][C]0.509157[/C][C]0.981685[/C][C]0.490843[/C][/ROW]
[ROW][C]68[/C][C]0.481906[/C][C]0.963812[/C][C]0.518094[/C][/ROW]
[ROW][C]69[/C][C]0.478204[/C][C]0.956407[/C][C]0.521796[/C][/ROW]
[ROW][C]70[/C][C]0.573454[/C][C]0.853092[/C][C]0.426546[/C][/ROW]
[ROW][C]71[/C][C]0.516196[/C][C]0.967609[/C][C]0.483804[/C][/ROW]
[ROW][C]72[/C][C]0.471835[/C][C]0.943671[/C][C]0.528165[/C][/ROW]
[ROW][C]73[/C][C]0.467705[/C][C]0.935411[/C][C]0.532295[/C][/ROW]
[ROW][C]74[/C][C]0.562584[/C][C]0.874833[/C][C]0.437416[/C][/ROW]
[ROW][C]75[/C][C]0.661971[/C][C]0.676057[/C][C]0.338029[/C][/ROW]
[ROW][C]76[/C][C]0.664672[/C][C]0.670656[/C][C]0.335328[/C][/ROW]
[ROW][C]77[/C][C]0.668274[/C][C]0.663453[/C][C]0.331726[/C][/ROW]
[ROW][C]78[/C][C]0.652836[/C][C]0.694328[/C][C]0.347164[/C][/ROW]
[ROW][C]79[/C][C]0.867117[/C][C]0.265766[/C][C]0.132883[/C][/ROW]
[ROW][C]80[/C][C]0.852667[/C][C]0.294667[/C][C]0.147333[/C][/ROW]
[ROW][C]81[/C][C]0.819989[/C][C]0.360023[/C][C]0.180011[/C][/ROW]
[ROW][C]82[/C][C]0.789168[/C][C]0.421663[/C][C]0.210832[/C][/ROW]
[ROW][C]83[/C][C]0.83957[/C][C]0.32086[/C][C]0.16043[/C][/ROW]
[ROW][C]84[/C][C]0.797145[/C][C]0.40571[/C][C]0.202855[/C][/ROW]
[ROW][C]85[/C][C]0.786463[/C][C]0.427074[/C][C]0.213537[/C][/ROW]
[ROW][C]86[/C][C]0.731154[/C][C]0.537692[/C][C]0.268846[/C][/ROW]
[ROW][C]87[/C][C]0.686152[/C][C]0.627696[/C][C]0.313848[/C][/ROW]
[ROW][C]88[/C][C]0.642389[/C][C]0.715223[/C][C]0.357611[/C][/ROW]
[ROW][C]89[/C][C]0.603971[/C][C]0.792059[/C][C]0.396029[/C][/ROW]
[ROW][C]90[/C][C]0.546138[/C][C]0.907725[/C][C]0.453862[/C][/ROW]
[ROW][C]91[/C][C]0.501152[/C][C]0.997696[/C][C]0.498848[/C][/ROW]
[ROW][C]92[/C][C]0.445253[/C][C]0.890506[/C][C]0.554747[/C][/ROW]
[ROW][C]93[/C][C]0.515775[/C][C]0.968449[/C][C]0.484225[/C][/ROW]
[ROW][C]94[/C][C]0.499284[/C][C]0.998569[/C][C]0.500716[/C][/ROW]
[ROW][C]95[/C][C]0.410988[/C][C]0.821975[/C][C]0.589012[/C][/ROW]
[ROW][C]96[/C][C]0.355393[/C][C]0.710787[/C][C]0.644607[/C][/ROW]
[ROW][C]97[/C][C]0.279356[/C][C]0.558713[/C][C]0.720644[/C][/ROW]
[ROW][C]98[/C][C]0.233535[/C][C]0.467071[/C][C]0.766465[/C][/ROW]
[ROW][C]99[/C][C]0.29342[/C][C]0.58684[/C][C]0.70658[/C][/ROW]
[ROW][C]100[/C][C]0.259628[/C][C]0.519255[/C][C]0.740372[/C][/ROW]
[ROW][C]101[/C][C]0.209004[/C][C]0.418007[/C][C]0.790996[/C][/ROW]
[ROW][C]102[/C][C]0.117136[/C][C]0.234273[/C][C]0.882864[/C][/ROW]
[ROW][C]103[/C][C]0.824155[/C][C]0.351691[/C][C]0.175845[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263971&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263971&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
10	0.96533	0.069339	0.0346695
11	0.940498	0.119003	0.0595017
12	0.904438	0.191123	0.0955617
13	0.872342	0.255316	0.127658
14	0.8057	0.3886	0.1943
15	0.7399	0.5202	0.2601
16	0.734155	0.531689	0.265845
17	0.698933	0.602135	0.301067
18	0.63844	0.723121	0.36156
19	0.557249	0.885502	0.442751
20	0.626695	0.746609	0.373305
21	0.590598	0.818803	0.409402
22	0.564913	0.870174	0.435087
23	0.513107	0.973786	0.486893
24	0.491444	0.982888	0.508556
25	0.456221	0.912441	0.543779
26	0.386782	0.773563	0.613218
27	0.336288	0.672577	0.663712
28	0.27654	0.553081	0.72346
29	0.246248	0.492495	0.753752
30	0.279087	0.558175	0.720913
31	0.737571	0.524857	0.262429
32	0.700073	0.599855	0.299927
33	0.646975	0.70605	0.353025
34	0.72125	0.5575	0.27875
35	0.694369	0.611262	0.305631
36	0.645795	0.70841	0.354205
37	0.588927	0.822146	0.411073
38	0.538956	0.922087	0.461044
39	0.546364	0.907271	0.453636
40	0.559912	0.880175	0.440088
41	0.550002	0.899995	0.449998
42	0.493147	0.986293	0.506853
43	0.477307	0.954614	0.522693
44	0.71926	0.561481	0.28074
45	0.900019	0.199962	0.0999811
46	0.930756	0.138487	0.0692437
47	0.909268	0.181463	0.0907315
48	0.892107	0.215786	0.107893
49	0.893508	0.212983	0.106492
50	0.867142	0.265716	0.132858
51	0.835964	0.328071	0.164036
52	0.812149	0.375703	0.187851
53	0.801178	0.397643	0.198822
54	0.796637	0.406726	0.203363
55	0.777075	0.445851	0.222925
56	0.731975	0.53605	0.268025
57	0.766386	0.467228	0.233614
58	0.746166	0.507669	0.253834
59	0.708859	0.582281	0.291141
60	0.663342	0.673315	0.336658
61	0.61879	0.76242	0.38121
62	0.565139	0.869721	0.434861
63	0.547762	0.904477	0.452238
64	0.563265	0.873471	0.436735
65	0.523428	0.953144	0.476572
66	0.478635	0.95727	0.521365
67	0.509157	0.981685	0.490843
68	0.481906	0.963812	0.518094
69	0.478204	0.956407	0.521796
70	0.573454	0.853092	0.426546
71	0.516196	0.967609	0.483804
72	0.471835	0.943671	0.528165
73	0.467705	0.935411	0.532295
74	0.562584	0.874833	0.437416
75	0.661971	0.676057	0.338029
76	0.664672	0.670656	0.335328
77	0.668274	0.663453	0.331726
78	0.652836	0.694328	0.347164
79	0.867117	0.265766	0.132883
80	0.852667	0.294667	0.147333
81	0.819989	0.360023	0.180011
82	0.789168	0.421663	0.210832
83	0.83957	0.32086	0.16043
84	0.797145	0.40571	0.202855
85	0.786463	0.427074	0.213537
86	0.731154	0.537692	0.268846
87	0.686152	0.627696	0.313848
88	0.642389	0.715223	0.357611
89	0.603971	0.792059	0.396029
90	0.546138	0.907725	0.453862
91	0.501152	0.997696	0.498848
92	0.445253	0.890506	0.554747
93	0.515775	0.968449	0.484225
94	0.499284	0.998569	0.500716
95	0.410988	0.821975	0.589012
96	0.355393	0.710787	0.644607
97	0.279356	0.558713	0.720644
98	0.233535	0.467071	0.766465
99	0.29342	0.58684	0.70658
100	0.259628	0.519255	0.740372
101	0.209004	0.418007	0.790996
102	0.117136	0.234273	0.882864
103	0.824155	0.351691	0.175845

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	0	0	OK
10% type I error level	1	0.0106383	OK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 1 & 0.0106383 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263971&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]1[/C][C]0.0106383[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263971&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263971&T=6

As an alternative you can also use a QR Code:

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	0	0	OK
10% type I error level	1	0.0106383	OK

Figure 1

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Figure 2

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Figure 3

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Figure 4

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Figure 5

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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Figure 10

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Parameters (Session):

par1 = 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