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rwasp_multipleregression.wasp

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Multiple Regression

Date of computation

Thu, 11 Dec 2014 16:55:21 +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/11/t14183170031grqcycllg5961a.htm/, Retrieved Thu, 16 May 2024 15:08:48 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=266219, Retrieved Thu, 16 May 2024 15:08:48 +0000

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-       [Multiple Regression] [] [2014-12-11 16:55:21] [b4b65124834fa3a3e625dd03af063494] [Current]

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26 21 86 12.9 13 13 13
51 23 70 12.2 8 13 16
57 22 71 12.8 14 11 11
37 21 108 7.4 16 14 10
67 21 64 6.7 14 15 9
43 21 119 12.6 13 14 8
52 21 97 14.8 15 11 26
52 21 129 13.3 13 13 10
43 23 153 11.1 20 16 10
84 22 78 8.2 17 14 8
67 25 80 11.4 15 14 13
49 21 99 6.4 16 15 11
70 23 68 10.6 12 15 8
52 22 147 12 17 13 12
58 21 40 6.3 11 14 24
68 21 57 11.3 16 11 21
62 25 120 11.9 16 12 5
43 21 71 9.3 15 14 14
56 21 84 9.6 13 13 11
56 20 68 10 14 12 9
74 24 55 6.4 19 15 8
65 23 137 13.8 16 15 17
63 21 79 10.8 17 14 18
58 24 116 13.8 10 14 16
57 23 101 11.7 15 12 23
63 21 111 10.9 14 12 9
53 22 189 16.1 14 12 14
57 20 66 13.4 16 15 13
51 18 81 9.9 15 14 10
64 21 63 11.5 17 16 8
53 22 69 8.3 14 12 10
29 22 71 11.7 16 12 19
54 21 64 9 15 14 11
51 23 143 9.7 16 16 16
58 21 85 10.8 16 15 12
43 25 86 10.3 10 12 11
51 22 55 10.4 8 14 11
53 22 69 12.7 17 13 10
54 20 120 9.3 14 14 13
56 21 96 11.8 10 16 14
61 21 60 5.9 14 12 8
47 21 95 11.4 12 14 11
39 22 100 13 16 15 11
48 21 68 10.8 16 13 13
50 24 57 12.3 16 16 15
35 22 105 11.3 8 16 15
30 22 85 11.8 16 12 16
68 21 103 7.9 15 12 12
49 22 57 12.7 8 16 12
61 19 51 12.3 13 12 17
67 22 69 11.6 14 15 14
47 23 41 6.7 13 12 15
56 20 49 10.9 16 13 12
50 20 50 12.1 19 12 13
43 23 93 13.3 19 14 7
67 20 58 10.1 14 14 8
62 23 54 5.7 15 11 16
57 21 74 14.3 13 10 20
41 22 15 8 10 12 14
54 21 69 13.3 16 11 10
45 21 107 9.3 15 16 16
48 19 65 12.5 11 14 11
61 22 58 7.6 9 14 26
56 21 107 15.9 16 15 9
41 21 70 9.2 12 15 15
43 21 53 9.1 12 14 12
53 21 136 11.1 14 13 21
44 21 126 13 14 11 20
66 21 95 14.5 13 16 20
58 22 69 12.2 15 12 10
46 22 136 12.3 17 15 15
37 18 58 11.4 14 14 10
51 21 59 8.8 11 15 16
51 23 118 14.6 9 14 9
56 19 82 12.6 7 13 17
45 23 102 13 15 12 19
37 21 65 12.6 12 12 13
59 21 90 13.2 15 14 8
42 21 64 9.9 14 14 11
38 21 83 7.7 16 15 9
66 20 70 10.5 14 11 12
34 19 50 13.4 13 13 10
53 21 77 10.9 16 14 9
49 19 37 4.3 13 16 14
55 19 81 10.3 16 13 14
49 19 101 11.8 16 14 10
59 20 79 11.2 16 16 8
40 19 71 11.4 10 11 13
58 19 60 8.6 12 13 9
60 19 55 13.2 12 13 14
63 20 44 12.6 12 15 8
56 19 40 5.6 12 12 16
54 18 56 9.9 19 13 14
52 19 43 8.8 14 12 14
34 21 45 7.7 13 14 8
69 18 32 9 16 14 11
32 18 56 7.3 15 16 11
48 19 40 11.4 12 15 13
67 21 34 13.6 8 14 12
58 20 89 7.9 10 13 13
57 24 50 10.7 16 14 9
42 22 56 10.3 16 15 10
64 21 46 8.3 10 14 12
58 21 76 9.6 18 12 11
66 19 64 14.2 12 7 13
26 19 74 8.5 16 12 17
61 20 57 13.5 10 15 15
52 18 45 4.9 14 12 15
51 19 30 6.4 12 13 14
55 19 62 9.6 11 11 10
50 20 51 11.6 15 14 15
60 21 36 11.1 7 13 14

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	6 seconds
R Server	'Gertrude Mary Cox' @ cox.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 6 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266219&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266219&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266219&T=0

As an alternative you can also use a QR Code:

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

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	6 seconds
R Server	'Gertrude Mary Cox' @ cox.wessa.net

Multiple Linear Regression - Estimated Regression Equation
IM[t] = + 41.1716 + 0.939664age[t] -0.0528542RFC[t] + 0.0997299EX[t] + 0.106487Zelfvert[t] -0.353424Stress[t] -0.147033Depress[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
IM[t] =  +  41.1716 +  0.939664age[t] -0.0528542RFC[t] +  0.0997299EX[t] +  0.106487Zelfvert[t] -0.353424Stress[t] -0.147033Depress[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266219&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]IM[t] =  +  41.1716 +  0.939664age[t] -0.0528542RFC[t] +  0.0997299EX[t] +  0.106487Zelfvert[t] -0.353424Stress[t] -0.147033Depress[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266219&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266219&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
IM[t] = + 41.1716 + 0.939664age[t] -0.0528542RFC[t] + 0.0997299EX[t] + 0.106487Zelfvert[t] -0.353424Stress[t] -0.147033Depress[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)	41.1716	17.8694	2.304	0.0231904	0.0115952
age	0.939664	0.68864	1.365	0.175321	0.0876603
RFC	-0.0528542	0.0408121	-1.295	0.198139	0.0990696
EX	0.0997299	0.459931	0.2168	0.828756	0.414378
Zelfvert	0.106487	0.388053	0.2744	0.784306	0.392153
Stress	-0.353424	0.654493	-0.54	0.590343	0.295172
Depress	-0.147033	0.263474	-0.5581	0.577996	0.288998

\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) & 41.1716 & 17.8694 & 2.304 & 0.0231904 & 0.0115952 \tabularnewline
age & 0.939664 & 0.68864 & 1.365 & 0.175321 & 0.0876603 \tabularnewline
RFC & -0.0528542 & 0.0408121 & -1.295 & 0.198139 & 0.0990696 \tabularnewline
EX & 0.0997299 & 0.459931 & 0.2168 & 0.828756 & 0.414378 \tabularnewline
Zelfvert & 0.106487 & 0.388053 & 0.2744 & 0.784306 & 0.392153 \tabularnewline
Stress & -0.353424 & 0.654493 & -0.54 & 0.590343 & 0.295172 \tabularnewline
Depress & -0.147033 & 0.263474 & -0.5581 & 0.577996 & 0.288998 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266219&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]41.1716[/C][C]17.8694[/C][C]2.304[/C][C]0.0231904[/C][C]0.0115952[/C][/ROW]
[ROW][C]age[/C][C]0.939664[/C][C]0.68864[/C][C]1.365[/C][C]0.175321[/C][C]0.0876603[/C][/ROW]
[ROW][C]RFC[/C][C]-0.0528542[/C][C]0.0408121[/C][C]-1.295[/C][C]0.198139[/C][C]0.0990696[/C][/ROW]
[ROW][C]EX[/C][C]0.0997299[/C][C]0.459931[/C][C]0.2168[/C][C]0.828756[/C][C]0.414378[/C][/ROW]
[ROW][C]Zelfvert[/C][C]0.106487[/C][C]0.388053[/C][C]0.2744[/C][C]0.784306[/C][C]0.392153[/C][/ROW]
[ROW][C]Stress[/C][C]-0.353424[/C][C]0.654493[/C][C]-0.54[/C][C]0.590343[/C][C]0.295172[/C][/ROW]
[ROW][C]Depress[/C][C]-0.147033[/C][C]0.263474[/C][C]-0.5581[/C][C]0.577996[/C][C]0.288998[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266219&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266219&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)	41.1716	17.8694	2.304	0.0231904	0.0115952
age	0.939664	0.68864	1.365	0.175321	0.0876603
RFC	-0.0528542	0.0408121	-1.295	0.198139	0.0990696
EX	0.0997299	0.459931	0.2168	0.828756	0.414378
Zelfvert	0.106487	0.388053	0.2744	0.784306	0.392153
Stress	-0.353424	0.654493	-0.54	0.590343	0.295172
Depress	-0.147033	0.263474	-0.5581	0.577996	0.288998

Multiple Linear Regression - Regression Statistics
Multiple R	0.17676
R-squared	0.031244
Adjusted R-squared	-0.0241135
F-TEST (value)	0.564404
F-TEST (DF numerator)	6
F-TEST (DF denominator)	105
p-value	0.757767
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	10.7595
Sum Squared Residuals	12155.5

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.17676 \tabularnewline
R-squared & 0.031244 \tabularnewline
Adjusted R-squared & -0.0241135 \tabularnewline
F-TEST (value) & 0.564404 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 105 \tabularnewline
p-value & 0.757767 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 10.7595 \tabularnewline
Sum Squared Residuals & 12155.5 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266219&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.17676[/C][/ROW]
[ROW][C]R-squared[/C][C]0.031244[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]-0.0241135[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]0.564404[/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]0.757767[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]10.7595[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]12155.5[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266219&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266219&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	0.17676
R-squared	0.031244
Adjusted R-squared	-0.0241135
F-TEST (value)	0.564404
F-TEST (DF numerator)	6
F-TEST (DF denominator)	105
p-value	0.757767
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	10.7595
Sum Squared Residuals	12155.5

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	26	52.524	-26.524
2	51	54.2056	-3.20561
3	57	55.3539	1.64614
4	37	51.2198	-14.2198
5	67	53.0562	13.9438
6	43	51.1316	-8.13159
7	52	51.1404	0.859552
8	52	50.7322	1.26778
9	43	50.8088	-7.80878
10	84	54.2254	29.7746
11	67	56.3097	10.6903
12	49	51.0953	-2.09529
13	70	55.0471	14.9529
14	52	50.7227	1.27726
15	58	52.1133	5.88672
16	68	53.7472	14.2528
17	62	56.235	5.76501
18	43	52.6703	-9.67026
19	56	52.5946	3.40537
20	56	53.2945	2.7055
21	74	57.0004	16.9996
22	65	50.822	14.178
23	63	52.0219	10.9781
24	58	52.7331	5.2669
25	57	52.5869	4.41313
26	63	52.0512	10.9488
27	53	48.6517	4.34835
28	57	52.3039	4.69614
29	51	49.9707	1.0293
30	64	53.7008	10.2992
31	53	54.8044	-1.8044
32	29	53.9275	-24.9275
33	54	53.4514	0.548577
34	51	49.8896	1.11045
35	58	52.127	5.87297
36	43	56.3513	-13.3513
37	51	54.261	-3.26099
38	53	55.2092	-2.20925
39	54	49.1813	4.81871
40	56	50.3589	5.64105
41	61	54.3951	6.60486
42	47	51.7328	-4.73283
43	39	52.6403	-13.6403
44	48	53.5854	-5.58537
45	50	55.781	-5.78101
46	35	50.4131	-15.4131
47	30	53.6386	-23.6386
48	68	51.8402	16.1598
49	49	53.5308	-4.53078
50	61	52.2	8.80001
51	67	53.4851	13.5149
52	47	56.2228	-9.22276
53	56	53.8069	2.19306
54	50	54.3996	-4.39961
55	43	55.2409	-12.2409
56	67	53.2732	13.7268
57	62	55.8553	6.14471
58	57	53.3289	3.67112
59	41	56.6145	-15.6145
60	54	54.9298	-0.929781
61	45	49.7666	-4.7666
62	48	51.4423	-3.44235
63	61	51.7242	9.27582
64	56	51.914	4.08604
65	41	51.8932	-10.8932
66	43	53.5763	-10.5763
67	53	48.632	4.36804
68	44	50.2039	-6.20387
69	66	50.1183	15.8817
70	58	55.2998	2.70017
71	46	50.1861	-4.18611
72	37	51.2295	-14.2295
73	51	52.1812	-1.18121
74	51	52.6903	-1.69025
75	56	49.5991	6.40092
76	45	53.2518	-8.2518
77	37	53.8509	-16.8509
78	59	52.9372	6.06282
79	42	53.4347	-11.4347
80	38	52.3647	-14.3647
81	66	53.151	12.849
82	34	53.0383	-19.0383
83	53	53.3544	-0.354357
84	49	51.1695	-2.16951
85	55	50.822	4.17797
86	49	50.1493	-1.14925
87	59	51.7791	7.22091
88	40	51.6752	-11.6752
89	58	52.0716	5.92835
90	60	52.0595	7.94049
91	63	53.6961	9.30392
92	56	52.1537	3.84626
93	54	51.4833	2.5167
94	52	52.8213	-0.82135
95	34	54.5541	-20.5541
96	69	52.4303	16.5697
97	32	50.1789	-18.1789
98	48	52.113	-4.113
99	67	54.6034	12.3966
100	58	50.6076	7.39238
101	57	57.5805	-0.580468
102	42	54.8437	-12.8437
103	64	53.6535	10.3465
104	58	53.9033	4.09668
105	66	53.9511	12.0489
106	26	50.9248	-24.9248
107	61	51.8565	9.14347
108	52	51.24	0.760002
109	51	52.7027	-1.70271
110	55	52.519	2.481
111	50	52.87	-2.87003
112	60	54.2012	5.7988

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 26 & 52.524 & -26.524 \tabularnewline
2 & 51 & 54.2056 & -3.20561 \tabularnewline
3 & 57 & 55.3539 & 1.64614 \tabularnewline
4 & 37 & 51.2198 & -14.2198 \tabularnewline
5 & 67 & 53.0562 & 13.9438 \tabularnewline
6 & 43 & 51.1316 & -8.13159 \tabularnewline
7 & 52 & 51.1404 & 0.859552 \tabularnewline
8 & 52 & 50.7322 & 1.26778 \tabularnewline
9 & 43 & 50.8088 & -7.80878 \tabularnewline
10 & 84 & 54.2254 & 29.7746 \tabularnewline
11 & 67 & 56.3097 & 10.6903 \tabularnewline
12 & 49 & 51.0953 & -2.09529 \tabularnewline
13 & 70 & 55.0471 & 14.9529 \tabularnewline
14 & 52 & 50.7227 & 1.27726 \tabularnewline
15 & 58 & 52.1133 & 5.88672 \tabularnewline
16 & 68 & 53.7472 & 14.2528 \tabularnewline
17 & 62 & 56.235 & 5.76501 \tabularnewline
18 & 43 & 52.6703 & -9.67026 \tabularnewline
19 & 56 & 52.5946 & 3.40537 \tabularnewline
20 & 56 & 53.2945 & 2.7055 \tabularnewline
21 & 74 & 57.0004 & 16.9996 \tabularnewline
22 & 65 & 50.822 & 14.178 \tabularnewline
23 & 63 & 52.0219 & 10.9781 \tabularnewline
24 & 58 & 52.7331 & 5.2669 \tabularnewline
25 & 57 & 52.5869 & 4.41313 \tabularnewline
26 & 63 & 52.0512 & 10.9488 \tabularnewline
27 & 53 & 48.6517 & 4.34835 \tabularnewline
28 & 57 & 52.3039 & 4.69614 \tabularnewline
29 & 51 & 49.9707 & 1.0293 \tabularnewline
30 & 64 & 53.7008 & 10.2992 \tabularnewline
31 & 53 & 54.8044 & -1.8044 \tabularnewline
32 & 29 & 53.9275 & -24.9275 \tabularnewline
33 & 54 & 53.4514 & 0.548577 \tabularnewline
34 & 51 & 49.8896 & 1.11045 \tabularnewline
35 & 58 & 52.127 & 5.87297 \tabularnewline
36 & 43 & 56.3513 & -13.3513 \tabularnewline
37 & 51 & 54.261 & -3.26099 \tabularnewline
38 & 53 & 55.2092 & -2.20925 \tabularnewline
39 & 54 & 49.1813 & 4.81871 \tabularnewline
40 & 56 & 50.3589 & 5.64105 \tabularnewline
41 & 61 & 54.3951 & 6.60486 \tabularnewline
42 & 47 & 51.7328 & -4.73283 \tabularnewline
43 & 39 & 52.6403 & -13.6403 \tabularnewline
44 & 48 & 53.5854 & -5.58537 \tabularnewline
45 & 50 & 55.781 & -5.78101 \tabularnewline
46 & 35 & 50.4131 & -15.4131 \tabularnewline
47 & 30 & 53.6386 & -23.6386 \tabularnewline
48 & 68 & 51.8402 & 16.1598 \tabularnewline
49 & 49 & 53.5308 & -4.53078 \tabularnewline
50 & 61 & 52.2 & 8.80001 \tabularnewline
51 & 67 & 53.4851 & 13.5149 \tabularnewline
52 & 47 & 56.2228 & -9.22276 \tabularnewline
53 & 56 & 53.8069 & 2.19306 \tabularnewline
54 & 50 & 54.3996 & -4.39961 \tabularnewline
55 & 43 & 55.2409 & -12.2409 \tabularnewline
56 & 67 & 53.2732 & 13.7268 \tabularnewline
57 & 62 & 55.8553 & 6.14471 \tabularnewline
58 & 57 & 53.3289 & 3.67112 \tabularnewline
59 & 41 & 56.6145 & -15.6145 \tabularnewline
60 & 54 & 54.9298 & -0.929781 \tabularnewline
61 & 45 & 49.7666 & -4.7666 \tabularnewline
62 & 48 & 51.4423 & -3.44235 \tabularnewline
63 & 61 & 51.7242 & 9.27582 \tabularnewline
64 & 56 & 51.914 & 4.08604 \tabularnewline
65 & 41 & 51.8932 & -10.8932 \tabularnewline
66 & 43 & 53.5763 & -10.5763 \tabularnewline
67 & 53 & 48.632 & 4.36804 \tabularnewline
68 & 44 & 50.2039 & -6.20387 \tabularnewline
69 & 66 & 50.1183 & 15.8817 \tabularnewline
70 & 58 & 55.2998 & 2.70017 \tabularnewline
71 & 46 & 50.1861 & -4.18611 \tabularnewline
72 & 37 & 51.2295 & -14.2295 \tabularnewline
73 & 51 & 52.1812 & -1.18121 \tabularnewline
74 & 51 & 52.6903 & -1.69025 \tabularnewline
75 & 56 & 49.5991 & 6.40092 \tabularnewline
76 & 45 & 53.2518 & -8.2518 \tabularnewline
77 & 37 & 53.8509 & -16.8509 \tabularnewline
78 & 59 & 52.9372 & 6.06282 \tabularnewline
79 & 42 & 53.4347 & -11.4347 \tabularnewline
80 & 38 & 52.3647 & -14.3647 \tabularnewline
81 & 66 & 53.151 & 12.849 \tabularnewline
82 & 34 & 53.0383 & -19.0383 \tabularnewline
83 & 53 & 53.3544 & -0.354357 \tabularnewline
84 & 49 & 51.1695 & -2.16951 \tabularnewline
85 & 55 & 50.822 & 4.17797 \tabularnewline
86 & 49 & 50.1493 & -1.14925 \tabularnewline
87 & 59 & 51.7791 & 7.22091 \tabularnewline
88 & 40 & 51.6752 & -11.6752 \tabularnewline
89 & 58 & 52.0716 & 5.92835 \tabularnewline
90 & 60 & 52.0595 & 7.94049 \tabularnewline
91 & 63 & 53.6961 & 9.30392 \tabularnewline
92 & 56 & 52.1537 & 3.84626 \tabularnewline
93 & 54 & 51.4833 & 2.5167 \tabularnewline
94 & 52 & 52.8213 & -0.82135 \tabularnewline
95 & 34 & 54.5541 & -20.5541 \tabularnewline
96 & 69 & 52.4303 & 16.5697 \tabularnewline
97 & 32 & 50.1789 & -18.1789 \tabularnewline
98 & 48 & 52.113 & -4.113 \tabularnewline
99 & 67 & 54.6034 & 12.3966 \tabularnewline
100 & 58 & 50.6076 & 7.39238 \tabularnewline
101 & 57 & 57.5805 & -0.580468 \tabularnewline
102 & 42 & 54.8437 & -12.8437 \tabularnewline
103 & 64 & 53.6535 & 10.3465 \tabularnewline
104 & 58 & 53.9033 & 4.09668 \tabularnewline
105 & 66 & 53.9511 & 12.0489 \tabularnewline
106 & 26 & 50.9248 & -24.9248 \tabularnewline
107 & 61 & 51.8565 & 9.14347 \tabularnewline
108 & 52 & 51.24 & 0.760002 \tabularnewline
109 & 51 & 52.7027 & -1.70271 \tabularnewline
110 & 55 & 52.519 & 2.481 \tabularnewline
111 & 50 & 52.87 & -2.87003 \tabularnewline
112 & 60 & 54.2012 & 5.7988 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266219&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]52.524[/C][C]-26.524[/C][/ROW]
[ROW][C]2[/C][C]51[/C][C]54.2056[/C][C]-3.20561[/C][/ROW]
[ROW][C]3[/C][C]57[/C][C]55.3539[/C][C]1.64614[/C][/ROW]
[ROW][C]4[/C][C]37[/C][C]51.2198[/C][C]-14.2198[/C][/ROW]
[ROW][C]5[/C][C]67[/C][C]53.0562[/C][C]13.9438[/C][/ROW]
[ROW][C]6[/C][C]43[/C][C]51.1316[/C][C]-8.13159[/C][/ROW]
[ROW][C]7[/C][C]52[/C][C]51.1404[/C][C]0.859552[/C][/ROW]
[ROW][C]8[/C][C]52[/C][C]50.7322[/C][C]1.26778[/C][/ROW]
[ROW][C]9[/C][C]43[/C][C]50.8088[/C][C]-7.80878[/C][/ROW]
[ROW][C]10[/C][C]84[/C][C]54.2254[/C][C]29.7746[/C][/ROW]
[ROW][C]11[/C][C]67[/C][C]56.3097[/C][C]10.6903[/C][/ROW]
[ROW][C]12[/C][C]49[/C][C]51.0953[/C][C]-2.09529[/C][/ROW]
[ROW][C]13[/C][C]70[/C][C]55.0471[/C][C]14.9529[/C][/ROW]
[ROW][C]14[/C][C]52[/C][C]50.7227[/C][C]1.27726[/C][/ROW]
[ROW][C]15[/C][C]58[/C][C]52.1133[/C][C]5.88672[/C][/ROW]
[ROW][C]16[/C][C]68[/C][C]53.7472[/C][C]14.2528[/C][/ROW]
[ROW][C]17[/C][C]62[/C][C]56.235[/C][C]5.76501[/C][/ROW]
[ROW][C]18[/C][C]43[/C][C]52.6703[/C][C]-9.67026[/C][/ROW]
[ROW][C]19[/C][C]56[/C][C]52.5946[/C][C]3.40537[/C][/ROW]
[ROW][C]20[/C][C]56[/C][C]53.2945[/C][C]2.7055[/C][/ROW]
[ROW][C]21[/C][C]74[/C][C]57.0004[/C][C]16.9996[/C][/ROW]
[ROW][C]22[/C][C]65[/C][C]50.822[/C][C]14.178[/C][/ROW]
[ROW][C]23[/C][C]63[/C][C]52.0219[/C][C]10.9781[/C][/ROW]
[ROW][C]24[/C][C]58[/C][C]52.7331[/C][C]5.2669[/C][/ROW]
[ROW][C]25[/C][C]57[/C][C]52.5869[/C][C]4.41313[/C][/ROW]
[ROW][C]26[/C][C]63[/C][C]52.0512[/C][C]10.9488[/C][/ROW]
[ROW][C]27[/C][C]53[/C][C]48.6517[/C][C]4.34835[/C][/ROW]
[ROW][C]28[/C][C]57[/C][C]52.3039[/C][C]4.69614[/C][/ROW]
[ROW][C]29[/C][C]51[/C][C]49.9707[/C][C]1.0293[/C][/ROW]
[ROW][C]30[/C][C]64[/C][C]53.7008[/C][C]10.2992[/C][/ROW]
[ROW][C]31[/C][C]53[/C][C]54.8044[/C][C]-1.8044[/C][/ROW]
[ROW][C]32[/C][C]29[/C][C]53.9275[/C][C]-24.9275[/C][/ROW]
[ROW][C]33[/C][C]54[/C][C]53.4514[/C][C]0.548577[/C][/ROW]
[ROW][C]34[/C][C]51[/C][C]49.8896[/C][C]1.11045[/C][/ROW]
[ROW][C]35[/C][C]58[/C][C]52.127[/C][C]5.87297[/C][/ROW]
[ROW][C]36[/C][C]43[/C][C]56.3513[/C][C]-13.3513[/C][/ROW]
[ROW][C]37[/C][C]51[/C][C]54.261[/C][C]-3.26099[/C][/ROW]
[ROW][C]38[/C][C]53[/C][C]55.2092[/C][C]-2.20925[/C][/ROW]
[ROW][C]39[/C][C]54[/C][C]49.1813[/C][C]4.81871[/C][/ROW]
[ROW][C]40[/C][C]56[/C][C]50.3589[/C][C]5.64105[/C][/ROW]
[ROW][C]41[/C][C]61[/C][C]54.3951[/C][C]6.60486[/C][/ROW]
[ROW][C]42[/C][C]47[/C][C]51.7328[/C][C]-4.73283[/C][/ROW]
[ROW][C]43[/C][C]39[/C][C]52.6403[/C][C]-13.6403[/C][/ROW]
[ROW][C]44[/C][C]48[/C][C]53.5854[/C][C]-5.58537[/C][/ROW]
[ROW][C]45[/C][C]50[/C][C]55.781[/C][C]-5.78101[/C][/ROW]
[ROW][C]46[/C][C]35[/C][C]50.4131[/C][C]-15.4131[/C][/ROW]
[ROW][C]47[/C][C]30[/C][C]53.6386[/C][C]-23.6386[/C][/ROW]
[ROW][C]48[/C][C]68[/C][C]51.8402[/C][C]16.1598[/C][/ROW]
[ROW][C]49[/C][C]49[/C][C]53.5308[/C][C]-4.53078[/C][/ROW]
[ROW][C]50[/C][C]61[/C][C]52.2[/C][C]8.80001[/C][/ROW]
[ROW][C]51[/C][C]67[/C][C]53.4851[/C][C]13.5149[/C][/ROW]
[ROW][C]52[/C][C]47[/C][C]56.2228[/C][C]-9.22276[/C][/ROW]
[ROW][C]53[/C][C]56[/C][C]53.8069[/C][C]2.19306[/C][/ROW]
[ROW][C]54[/C][C]50[/C][C]54.3996[/C][C]-4.39961[/C][/ROW]
[ROW][C]55[/C][C]43[/C][C]55.2409[/C][C]-12.2409[/C][/ROW]
[ROW][C]56[/C][C]67[/C][C]53.2732[/C][C]13.7268[/C][/ROW]
[ROW][C]57[/C][C]62[/C][C]55.8553[/C][C]6.14471[/C][/ROW]
[ROW][C]58[/C][C]57[/C][C]53.3289[/C][C]3.67112[/C][/ROW]
[ROW][C]59[/C][C]41[/C][C]56.6145[/C][C]-15.6145[/C][/ROW]
[ROW][C]60[/C][C]54[/C][C]54.9298[/C][C]-0.929781[/C][/ROW]
[ROW][C]61[/C][C]45[/C][C]49.7666[/C][C]-4.7666[/C][/ROW]
[ROW][C]62[/C][C]48[/C][C]51.4423[/C][C]-3.44235[/C][/ROW]
[ROW][C]63[/C][C]61[/C][C]51.7242[/C][C]9.27582[/C][/ROW]
[ROW][C]64[/C][C]56[/C][C]51.914[/C][C]4.08604[/C][/ROW]
[ROW][C]65[/C][C]41[/C][C]51.8932[/C][C]-10.8932[/C][/ROW]
[ROW][C]66[/C][C]43[/C][C]53.5763[/C][C]-10.5763[/C][/ROW]
[ROW][C]67[/C][C]53[/C][C]48.632[/C][C]4.36804[/C][/ROW]
[ROW][C]68[/C][C]44[/C][C]50.2039[/C][C]-6.20387[/C][/ROW]
[ROW][C]69[/C][C]66[/C][C]50.1183[/C][C]15.8817[/C][/ROW]
[ROW][C]70[/C][C]58[/C][C]55.2998[/C][C]2.70017[/C][/ROW]
[ROW][C]71[/C][C]46[/C][C]50.1861[/C][C]-4.18611[/C][/ROW]
[ROW][C]72[/C][C]37[/C][C]51.2295[/C][C]-14.2295[/C][/ROW]
[ROW][C]73[/C][C]51[/C][C]52.1812[/C][C]-1.18121[/C][/ROW]
[ROW][C]74[/C][C]51[/C][C]52.6903[/C][C]-1.69025[/C][/ROW]
[ROW][C]75[/C][C]56[/C][C]49.5991[/C][C]6.40092[/C][/ROW]
[ROW][C]76[/C][C]45[/C][C]53.2518[/C][C]-8.2518[/C][/ROW]
[ROW][C]77[/C][C]37[/C][C]53.8509[/C][C]-16.8509[/C][/ROW]
[ROW][C]78[/C][C]59[/C][C]52.9372[/C][C]6.06282[/C][/ROW]
[ROW][C]79[/C][C]42[/C][C]53.4347[/C][C]-11.4347[/C][/ROW]
[ROW][C]80[/C][C]38[/C][C]52.3647[/C][C]-14.3647[/C][/ROW]
[ROW][C]81[/C][C]66[/C][C]53.151[/C][C]12.849[/C][/ROW]
[ROW][C]82[/C][C]34[/C][C]53.0383[/C][C]-19.0383[/C][/ROW]
[ROW][C]83[/C][C]53[/C][C]53.3544[/C][C]-0.354357[/C][/ROW]
[ROW][C]84[/C][C]49[/C][C]51.1695[/C][C]-2.16951[/C][/ROW]
[ROW][C]85[/C][C]55[/C][C]50.822[/C][C]4.17797[/C][/ROW]
[ROW][C]86[/C][C]49[/C][C]50.1493[/C][C]-1.14925[/C][/ROW]
[ROW][C]87[/C][C]59[/C][C]51.7791[/C][C]7.22091[/C][/ROW]
[ROW][C]88[/C][C]40[/C][C]51.6752[/C][C]-11.6752[/C][/ROW]
[ROW][C]89[/C][C]58[/C][C]52.0716[/C][C]5.92835[/C][/ROW]
[ROW][C]90[/C][C]60[/C][C]52.0595[/C][C]7.94049[/C][/ROW]
[ROW][C]91[/C][C]63[/C][C]53.6961[/C][C]9.30392[/C][/ROW]
[ROW][C]92[/C][C]56[/C][C]52.1537[/C][C]3.84626[/C][/ROW]
[ROW][C]93[/C][C]54[/C][C]51.4833[/C][C]2.5167[/C][/ROW]
[ROW][C]94[/C][C]52[/C][C]52.8213[/C][C]-0.82135[/C][/ROW]
[ROW][C]95[/C][C]34[/C][C]54.5541[/C][C]-20.5541[/C][/ROW]
[ROW][C]96[/C][C]69[/C][C]52.4303[/C][C]16.5697[/C][/ROW]
[ROW][C]97[/C][C]32[/C][C]50.1789[/C][C]-18.1789[/C][/ROW]
[ROW][C]98[/C][C]48[/C][C]52.113[/C][C]-4.113[/C][/ROW]
[ROW][C]99[/C][C]67[/C][C]54.6034[/C][C]12.3966[/C][/ROW]
[ROW][C]100[/C][C]58[/C][C]50.6076[/C][C]7.39238[/C][/ROW]
[ROW][C]101[/C][C]57[/C][C]57.5805[/C][C]-0.580468[/C][/ROW]
[ROW][C]102[/C][C]42[/C][C]54.8437[/C][C]-12.8437[/C][/ROW]
[ROW][C]103[/C][C]64[/C][C]53.6535[/C][C]10.3465[/C][/ROW]
[ROW][C]104[/C][C]58[/C][C]53.9033[/C][C]4.09668[/C][/ROW]
[ROW][C]105[/C][C]66[/C][C]53.9511[/C][C]12.0489[/C][/ROW]
[ROW][C]106[/C][C]26[/C][C]50.9248[/C][C]-24.9248[/C][/ROW]
[ROW][C]107[/C][C]61[/C][C]51.8565[/C][C]9.14347[/C][/ROW]
[ROW][C]108[/C][C]52[/C][C]51.24[/C][C]0.760002[/C][/ROW]
[ROW][C]109[/C][C]51[/C][C]52.7027[/C][C]-1.70271[/C][/ROW]
[ROW][C]110[/C][C]55[/C][C]52.519[/C][C]2.481[/C][/ROW]
[ROW][C]111[/C][C]50[/C][C]52.87[/C][C]-2.87003[/C][/ROW]
[ROW][C]112[/C][C]60[/C][C]54.2012[/C][C]5.7988[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266219&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266219&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	52.524	-26.524
2	51	54.2056	-3.20561
3	57	55.3539	1.64614
4	37	51.2198	-14.2198
5	67	53.0562	13.9438
6	43	51.1316	-8.13159
7	52	51.1404	0.859552
8	52	50.7322	1.26778
9	43	50.8088	-7.80878
10	84	54.2254	29.7746
11	67	56.3097	10.6903
12	49	51.0953	-2.09529
13	70	55.0471	14.9529
14	52	50.7227	1.27726
15	58	52.1133	5.88672
16	68	53.7472	14.2528
17	62	56.235	5.76501
18	43	52.6703	-9.67026
19	56	52.5946	3.40537
20	56	53.2945	2.7055
21	74	57.0004	16.9996
22	65	50.822	14.178
23	63	52.0219	10.9781
24	58	52.7331	5.2669
25	57	52.5869	4.41313
26	63	52.0512	10.9488
27	53	48.6517	4.34835
28	57	52.3039	4.69614
29	51	49.9707	1.0293
30	64	53.7008	10.2992
31	53	54.8044	-1.8044
32	29	53.9275	-24.9275
33	54	53.4514	0.548577
34	51	49.8896	1.11045
35	58	52.127	5.87297
36	43	56.3513	-13.3513
37	51	54.261	-3.26099
38	53	55.2092	-2.20925
39	54	49.1813	4.81871
40	56	50.3589	5.64105
41	61	54.3951	6.60486
42	47	51.7328	-4.73283
43	39	52.6403	-13.6403
44	48	53.5854	-5.58537
45	50	55.781	-5.78101
46	35	50.4131	-15.4131
47	30	53.6386	-23.6386
48	68	51.8402	16.1598
49	49	53.5308	-4.53078
50	61	52.2	8.80001
51	67	53.4851	13.5149
52	47	56.2228	-9.22276
53	56	53.8069	2.19306
54	50	54.3996	-4.39961
55	43	55.2409	-12.2409
56	67	53.2732	13.7268
57	62	55.8553	6.14471
58	57	53.3289	3.67112
59	41	56.6145	-15.6145
60	54	54.9298	-0.929781
61	45	49.7666	-4.7666
62	48	51.4423	-3.44235
63	61	51.7242	9.27582
64	56	51.914	4.08604
65	41	51.8932	-10.8932
66	43	53.5763	-10.5763
67	53	48.632	4.36804
68	44	50.2039	-6.20387
69	66	50.1183	15.8817
70	58	55.2998	2.70017
71	46	50.1861	-4.18611
72	37	51.2295	-14.2295
73	51	52.1812	-1.18121
74	51	52.6903	-1.69025
75	56	49.5991	6.40092
76	45	53.2518	-8.2518
77	37	53.8509	-16.8509
78	59	52.9372	6.06282
79	42	53.4347	-11.4347
80	38	52.3647	-14.3647
81	66	53.151	12.849
82	34	53.0383	-19.0383
83	53	53.3544	-0.354357
84	49	51.1695	-2.16951
85	55	50.822	4.17797
86	49	50.1493	-1.14925
87	59	51.7791	7.22091
88	40	51.6752	-11.6752
89	58	52.0716	5.92835
90	60	52.0595	7.94049
91	63	53.6961	9.30392
92	56	52.1537	3.84626
93	54	51.4833	2.5167
94	52	52.8213	-0.82135
95	34	54.5541	-20.5541
96	69	52.4303	16.5697
97	32	50.1789	-18.1789
98	48	52.113	-4.113
99	67	54.6034	12.3966
100	58	50.6076	7.39238
101	57	57.5805	-0.580468
102	42	54.8437	-12.8437
103	64	53.6535	10.3465
104	58	53.9033	4.09668
105	66	53.9511	12.0489
106	26	50.9248	-24.9248
107	61	51.8565	9.14347
108	52	51.24	0.760002
109	51	52.7027	-1.70271
110	55	52.519	2.481
111	50	52.87	-2.87003
112	60	54.2012	5.7988

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
10	0.994616	0.0107682	0.00538408
11	0.987709	0.0245811	0.0122906
12	0.975521	0.0489589	0.0244794
13	0.96601	0.0679792	0.0339896
14	0.952859	0.0942814	0.0471407
15	0.925851	0.148298	0.074149
16	0.900625	0.19875	0.0993752
17	0.862441	0.275117	0.137559
18	0.883997	0.232006	0.116003
19	0.840347	0.319305	0.159653
20	0.785195	0.42961	0.214805
21	0.786635	0.426729	0.213365
22	0.861416	0.277168	0.138584
23	0.836453	0.327094	0.163547
24	0.802435	0.395129	0.197565
25	0.755326	0.489348	0.244674
26	0.788434	0.423131	0.211566
27	0.78902	0.42196	0.21098
28	0.743734	0.512532	0.256266
29	0.694701	0.610597	0.305299
30	0.66316	0.673681	0.33684
31	0.626139	0.747723	0.373861
32	0.898922	0.202156	0.101078
33	0.871461	0.257077	0.128539
34	0.840267	0.319466	0.159733
35	0.810262	0.379477	0.189738
36	0.83692	0.326159	0.16308
37	0.798477	0.403046	0.201523
38	0.770212	0.459575	0.229788
39	0.737411	0.525179	0.262589
40	0.699797	0.600406	0.300203
41	0.67435	0.6513	0.32565
42	0.627538	0.744925	0.372462
43	0.67686	0.64628	0.32314
44	0.64207	0.71586	0.35793
45	0.620558	0.758885	0.379442
46	0.656721	0.686557	0.343279
47	0.826682	0.346636	0.173318
48	0.875558	0.248885	0.124442
49	0.850241	0.299518	0.149759
50	0.838542	0.322916	0.161458
51	0.860121	0.279758	0.139879
52	0.85181	0.29638	0.14819
53	0.818645	0.362709	0.181355
54	0.787906	0.424187	0.212094
55	0.788051	0.423898	0.211949
56	0.820984	0.358032	0.179016
57	0.814669	0.370662	0.185331
58	0.783108	0.433784	0.216892
59	0.822956	0.354088	0.177044
60	0.782786	0.434427	0.217214
61	0.748586	0.502828	0.251414
62	0.709255	0.58149	0.290745
63	0.689973	0.620054	0.310027
64	0.646805	0.70639	0.353195
65	0.643482	0.713035	0.356518
66	0.635055	0.729891	0.364945
67	0.594308	0.811385	0.405692
68	0.552127	0.895747	0.447873
69	0.619045	0.76191	0.380955
70	0.567617	0.864765	0.432383
71	0.52292	0.954161	0.47708
72	0.582074	0.835853	0.417926
73	0.523972	0.952056	0.476028
74	0.46408	0.928159	0.53592
75	0.422361	0.844723	0.577639
76	0.371788	0.743577	0.628212
77	0.474201	0.948403	0.525799
78	0.435672	0.871343	0.564328
79	0.428305	0.85661	0.571695
80	0.433834	0.867669	0.566166
81	0.452183	0.904367	0.547817
82	0.691866	0.616268	0.308134
83	0.628924	0.742151	0.371076
84	0.573437	0.853126	0.426563
85	0.533742	0.932515	0.466258
86	0.462718	0.925435	0.537282
87	0.466385	0.93277	0.533615
88	0.564891	0.870218	0.435109
89	0.503886	0.992228	0.496114
90	0.43829	0.876581	0.56171
91	0.384636	0.769273	0.615364
92	0.3227	0.645399	0.6773
93	0.283305	0.566611	0.716695
94	0.214064	0.428127	0.785936
95	0.45263	0.90526	0.54737
96	0.747157	0.505687	0.252843
97	0.722046	0.555908	0.277954
98	0.638658	0.722685	0.361342
99	0.532561	0.934878	0.467439
100	0.440478	0.880957	0.559522
101	0.305448	0.610896	0.694552
102	0.459519	0.919038	0.540481

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.994616 & 0.0107682 & 0.00538408 \tabularnewline
11 & 0.987709 & 0.0245811 & 0.0122906 \tabularnewline
12 & 0.975521 & 0.0489589 & 0.0244794 \tabularnewline
13 & 0.96601 & 0.0679792 & 0.0339896 \tabularnewline
14 & 0.952859 & 0.0942814 & 0.0471407 \tabularnewline
15 & 0.925851 & 0.148298 & 0.074149 \tabularnewline
16 & 0.900625 & 0.19875 & 0.0993752 \tabularnewline
17 & 0.862441 & 0.275117 & 0.137559 \tabularnewline
18 & 0.883997 & 0.232006 & 0.116003 \tabularnewline
19 & 0.840347 & 0.319305 & 0.159653 \tabularnewline
20 & 0.785195 & 0.42961 & 0.214805 \tabularnewline
21 & 0.786635 & 0.426729 & 0.213365 \tabularnewline
22 & 0.861416 & 0.277168 & 0.138584 \tabularnewline
23 & 0.836453 & 0.327094 & 0.163547 \tabularnewline
24 & 0.802435 & 0.395129 & 0.197565 \tabularnewline
25 & 0.755326 & 0.489348 & 0.244674 \tabularnewline
26 & 0.788434 & 0.423131 & 0.211566 \tabularnewline
27 & 0.78902 & 0.42196 & 0.21098 \tabularnewline
28 & 0.743734 & 0.512532 & 0.256266 \tabularnewline
29 & 0.694701 & 0.610597 & 0.305299 \tabularnewline
30 & 0.66316 & 0.673681 & 0.33684 \tabularnewline
31 & 0.626139 & 0.747723 & 0.373861 \tabularnewline
32 & 0.898922 & 0.202156 & 0.101078 \tabularnewline
33 & 0.871461 & 0.257077 & 0.128539 \tabularnewline
34 & 0.840267 & 0.319466 & 0.159733 \tabularnewline
35 & 0.810262 & 0.379477 & 0.189738 \tabularnewline
36 & 0.83692 & 0.326159 & 0.16308 \tabularnewline
37 & 0.798477 & 0.403046 & 0.201523 \tabularnewline
38 & 0.770212 & 0.459575 & 0.229788 \tabularnewline
39 & 0.737411 & 0.525179 & 0.262589 \tabularnewline
40 & 0.699797 & 0.600406 & 0.300203 \tabularnewline
41 & 0.67435 & 0.6513 & 0.32565 \tabularnewline
42 & 0.627538 & 0.744925 & 0.372462 \tabularnewline
43 & 0.67686 & 0.64628 & 0.32314 \tabularnewline
44 & 0.64207 & 0.71586 & 0.35793 \tabularnewline
45 & 0.620558 & 0.758885 & 0.379442 \tabularnewline
46 & 0.656721 & 0.686557 & 0.343279 \tabularnewline
47 & 0.826682 & 0.346636 & 0.173318 \tabularnewline
48 & 0.875558 & 0.248885 & 0.124442 \tabularnewline
49 & 0.850241 & 0.299518 & 0.149759 \tabularnewline
50 & 0.838542 & 0.322916 & 0.161458 \tabularnewline
51 & 0.860121 & 0.279758 & 0.139879 \tabularnewline
52 & 0.85181 & 0.29638 & 0.14819 \tabularnewline
53 & 0.818645 & 0.362709 & 0.181355 \tabularnewline
54 & 0.787906 & 0.424187 & 0.212094 \tabularnewline
55 & 0.788051 & 0.423898 & 0.211949 \tabularnewline
56 & 0.820984 & 0.358032 & 0.179016 \tabularnewline
57 & 0.814669 & 0.370662 & 0.185331 \tabularnewline
58 & 0.783108 & 0.433784 & 0.216892 \tabularnewline
59 & 0.822956 & 0.354088 & 0.177044 \tabularnewline
60 & 0.782786 & 0.434427 & 0.217214 \tabularnewline
61 & 0.748586 & 0.502828 & 0.251414 \tabularnewline
62 & 0.709255 & 0.58149 & 0.290745 \tabularnewline
63 & 0.689973 & 0.620054 & 0.310027 \tabularnewline
64 & 0.646805 & 0.70639 & 0.353195 \tabularnewline
65 & 0.643482 & 0.713035 & 0.356518 \tabularnewline
66 & 0.635055 & 0.729891 & 0.364945 \tabularnewline
67 & 0.594308 & 0.811385 & 0.405692 \tabularnewline
68 & 0.552127 & 0.895747 & 0.447873 \tabularnewline
69 & 0.619045 & 0.76191 & 0.380955 \tabularnewline
70 & 0.567617 & 0.864765 & 0.432383 \tabularnewline
71 & 0.52292 & 0.954161 & 0.47708 \tabularnewline
72 & 0.582074 & 0.835853 & 0.417926 \tabularnewline
73 & 0.523972 & 0.952056 & 0.476028 \tabularnewline
74 & 0.46408 & 0.928159 & 0.53592 \tabularnewline
75 & 0.422361 & 0.844723 & 0.577639 \tabularnewline
76 & 0.371788 & 0.743577 & 0.628212 \tabularnewline
77 & 0.474201 & 0.948403 & 0.525799 \tabularnewline
78 & 0.435672 & 0.871343 & 0.564328 \tabularnewline
79 & 0.428305 & 0.85661 & 0.571695 \tabularnewline
80 & 0.433834 & 0.867669 & 0.566166 \tabularnewline
81 & 0.452183 & 0.904367 & 0.547817 \tabularnewline
82 & 0.691866 & 0.616268 & 0.308134 \tabularnewline
83 & 0.628924 & 0.742151 & 0.371076 \tabularnewline
84 & 0.573437 & 0.853126 & 0.426563 \tabularnewline
85 & 0.533742 & 0.932515 & 0.466258 \tabularnewline
86 & 0.462718 & 0.925435 & 0.537282 \tabularnewline
87 & 0.466385 & 0.93277 & 0.533615 \tabularnewline
88 & 0.564891 & 0.870218 & 0.435109 \tabularnewline
89 & 0.503886 & 0.992228 & 0.496114 \tabularnewline
90 & 0.43829 & 0.876581 & 0.56171 \tabularnewline
91 & 0.384636 & 0.769273 & 0.615364 \tabularnewline
92 & 0.3227 & 0.645399 & 0.6773 \tabularnewline
93 & 0.283305 & 0.566611 & 0.716695 \tabularnewline
94 & 0.214064 & 0.428127 & 0.785936 \tabularnewline
95 & 0.45263 & 0.90526 & 0.54737 \tabularnewline
96 & 0.747157 & 0.505687 & 0.252843 \tabularnewline
97 & 0.722046 & 0.555908 & 0.277954 \tabularnewline
98 & 0.638658 & 0.722685 & 0.361342 \tabularnewline
99 & 0.532561 & 0.934878 & 0.467439 \tabularnewline
100 & 0.440478 & 0.880957 & 0.559522 \tabularnewline
101 & 0.305448 & 0.610896 & 0.694552 \tabularnewline
102 & 0.459519 & 0.919038 & 0.540481 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266219&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]0.994616[/C][C]0.0107682[/C][C]0.00538408[/C][/ROW]
[ROW][C]11[/C][C]0.987709[/C][C]0.0245811[/C][C]0.0122906[/C][/ROW]
[ROW][C]12[/C][C]0.975521[/C][C]0.0489589[/C][C]0.0244794[/C][/ROW]
[ROW][C]13[/C][C]0.96601[/C][C]0.0679792[/C][C]0.0339896[/C][/ROW]
[ROW][C]14[/C][C]0.952859[/C][C]0.0942814[/C][C]0.0471407[/C][/ROW]
[ROW][C]15[/C][C]0.925851[/C][C]0.148298[/C][C]0.074149[/C][/ROW]
[ROW][C]16[/C][C]0.900625[/C][C]0.19875[/C][C]0.0993752[/C][/ROW]
[ROW][C]17[/C][C]0.862441[/C][C]0.275117[/C][C]0.137559[/C][/ROW]
[ROW][C]18[/C][C]0.883997[/C][C]0.232006[/C][C]0.116003[/C][/ROW]
[ROW][C]19[/C][C]0.840347[/C][C]0.319305[/C][C]0.159653[/C][/ROW]
[ROW][C]20[/C][C]0.785195[/C][C]0.42961[/C][C]0.214805[/C][/ROW]
[ROW][C]21[/C][C]0.786635[/C][C]0.426729[/C][C]0.213365[/C][/ROW]
[ROW][C]22[/C][C]0.861416[/C][C]0.277168[/C][C]0.138584[/C][/ROW]
[ROW][C]23[/C][C]0.836453[/C][C]0.327094[/C][C]0.163547[/C][/ROW]
[ROW][C]24[/C][C]0.802435[/C][C]0.395129[/C][C]0.197565[/C][/ROW]
[ROW][C]25[/C][C]0.755326[/C][C]0.489348[/C][C]0.244674[/C][/ROW]
[ROW][C]26[/C][C]0.788434[/C][C]0.423131[/C][C]0.211566[/C][/ROW]
[ROW][C]27[/C][C]0.78902[/C][C]0.42196[/C][C]0.21098[/C][/ROW]
[ROW][C]28[/C][C]0.743734[/C][C]0.512532[/C][C]0.256266[/C][/ROW]
[ROW][C]29[/C][C]0.694701[/C][C]0.610597[/C][C]0.305299[/C][/ROW]
[ROW][C]30[/C][C]0.66316[/C][C]0.673681[/C][C]0.33684[/C][/ROW]
[ROW][C]31[/C][C]0.626139[/C][C]0.747723[/C][C]0.373861[/C][/ROW]
[ROW][C]32[/C][C]0.898922[/C][C]0.202156[/C][C]0.101078[/C][/ROW]
[ROW][C]33[/C][C]0.871461[/C][C]0.257077[/C][C]0.128539[/C][/ROW]
[ROW][C]34[/C][C]0.840267[/C][C]0.319466[/C][C]0.159733[/C][/ROW]
[ROW][C]35[/C][C]0.810262[/C][C]0.379477[/C][C]0.189738[/C][/ROW]
[ROW][C]36[/C][C]0.83692[/C][C]0.326159[/C][C]0.16308[/C][/ROW]
[ROW][C]37[/C][C]0.798477[/C][C]0.403046[/C][C]0.201523[/C][/ROW]
[ROW][C]38[/C][C]0.770212[/C][C]0.459575[/C][C]0.229788[/C][/ROW]
[ROW][C]39[/C][C]0.737411[/C][C]0.525179[/C][C]0.262589[/C][/ROW]
[ROW][C]40[/C][C]0.699797[/C][C]0.600406[/C][C]0.300203[/C][/ROW]
[ROW][C]41[/C][C]0.67435[/C][C]0.6513[/C][C]0.32565[/C][/ROW]
[ROW][C]42[/C][C]0.627538[/C][C]0.744925[/C][C]0.372462[/C][/ROW]
[ROW][C]43[/C][C]0.67686[/C][C]0.64628[/C][C]0.32314[/C][/ROW]
[ROW][C]44[/C][C]0.64207[/C][C]0.71586[/C][C]0.35793[/C][/ROW]
[ROW][C]45[/C][C]0.620558[/C][C]0.758885[/C][C]0.379442[/C][/ROW]
[ROW][C]46[/C][C]0.656721[/C][C]0.686557[/C][C]0.343279[/C][/ROW]
[ROW][C]47[/C][C]0.826682[/C][C]0.346636[/C][C]0.173318[/C][/ROW]
[ROW][C]48[/C][C]0.875558[/C][C]0.248885[/C][C]0.124442[/C][/ROW]
[ROW][C]49[/C][C]0.850241[/C][C]0.299518[/C][C]0.149759[/C][/ROW]
[ROW][C]50[/C][C]0.838542[/C][C]0.322916[/C][C]0.161458[/C][/ROW]
[ROW][C]51[/C][C]0.860121[/C][C]0.279758[/C][C]0.139879[/C][/ROW]
[ROW][C]52[/C][C]0.85181[/C][C]0.29638[/C][C]0.14819[/C][/ROW]
[ROW][C]53[/C][C]0.818645[/C][C]0.362709[/C][C]0.181355[/C][/ROW]
[ROW][C]54[/C][C]0.787906[/C][C]0.424187[/C][C]0.212094[/C][/ROW]
[ROW][C]55[/C][C]0.788051[/C][C]0.423898[/C][C]0.211949[/C][/ROW]
[ROW][C]56[/C][C]0.820984[/C][C]0.358032[/C][C]0.179016[/C][/ROW]
[ROW][C]57[/C][C]0.814669[/C][C]0.370662[/C][C]0.185331[/C][/ROW]
[ROW][C]58[/C][C]0.783108[/C][C]0.433784[/C][C]0.216892[/C][/ROW]
[ROW][C]59[/C][C]0.822956[/C][C]0.354088[/C][C]0.177044[/C][/ROW]
[ROW][C]60[/C][C]0.782786[/C][C]0.434427[/C][C]0.217214[/C][/ROW]
[ROW][C]61[/C][C]0.748586[/C][C]0.502828[/C][C]0.251414[/C][/ROW]
[ROW][C]62[/C][C]0.709255[/C][C]0.58149[/C][C]0.290745[/C][/ROW]
[ROW][C]63[/C][C]0.689973[/C][C]0.620054[/C][C]0.310027[/C][/ROW]
[ROW][C]64[/C][C]0.646805[/C][C]0.70639[/C][C]0.353195[/C][/ROW]
[ROW][C]65[/C][C]0.643482[/C][C]0.713035[/C][C]0.356518[/C][/ROW]
[ROW][C]66[/C][C]0.635055[/C][C]0.729891[/C][C]0.364945[/C][/ROW]
[ROW][C]67[/C][C]0.594308[/C][C]0.811385[/C][C]0.405692[/C][/ROW]
[ROW][C]68[/C][C]0.552127[/C][C]0.895747[/C][C]0.447873[/C][/ROW]
[ROW][C]69[/C][C]0.619045[/C][C]0.76191[/C][C]0.380955[/C][/ROW]
[ROW][C]70[/C][C]0.567617[/C][C]0.864765[/C][C]0.432383[/C][/ROW]
[ROW][C]71[/C][C]0.52292[/C][C]0.954161[/C][C]0.47708[/C][/ROW]
[ROW][C]72[/C][C]0.582074[/C][C]0.835853[/C][C]0.417926[/C][/ROW]
[ROW][C]73[/C][C]0.523972[/C][C]0.952056[/C][C]0.476028[/C][/ROW]
[ROW][C]74[/C][C]0.46408[/C][C]0.928159[/C][C]0.53592[/C][/ROW]
[ROW][C]75[/C][C]0.422361[/C][C]0.844723[/C][C]0.577639[/C][/ROW]
[ROW][C]76[/C][C]0.371788[/C][C]0.743577[/C][C]0.628212[/C][/ROW]
[ROW][C]77[/C][C]0.474201[/C][C]0.948403[/C][C]0.525799[/C][/ROW]
[ROW][C]78[/C][C]0.435672[/C][C]0.871343[/C][C]0.564328[/C][/ROW]
[ROW][C]79[/C][C]0.428305[/C][C]0.85661[/C][C]0.571695[/C][/ROW]
[ROW][C]80[/C][C]0.433834[/C][C]0.867669[/C][C]0.566166[/C][/ROW]
[ROW][C]81[/C][C]0.452183[/C][C]0.904367[/C][C]0.547817[/C][/ROW]
[ROW][C]82[/C][C]0.691866[/C][C]0.616268[/C][C]0.308134[/C][/ROW]
[ROW][C]83[/C][C]0.628924[/C][C]0.742151[/C][C]0.371076[/C][/ROW]
[ROW][C]84[/C][C]0.573437[/C][C]0.853126[/C][C]0.426563[/C][/ROW]
[ROW][C]85[/C][C]0.533742[/C][C]0.932515[/C][C]0.466258[/C][/ROW]
[ROW][C]86[/C][C]0.462718[/C][C]0.925435[/C][C]0.537282[/C][/ROW]
[ROW][C]87[/C][C]0.466385[/C][C]0.93277[/C][C]0.533615[/C][/ROW]
[ROW][C]88[/C][C]0.564891[/C][C]0.870218[/C][C]0.435109[/C][/ROW]
[ROW][C]89[/C][C]0.503886[/C][C]0.992228[/C][C]0.496114[/C][/ROW]
[ROW][C]90[/C][C]0.43829[/C][C]0.876581[/C][C]0.56171[/C][/ROW]
[ROW][C]91[/C][C]0.384636[/C][C]0.769273[/C][C]0.615364[/C][/ROW]
[ROW][C]92[/C][C]0.3227[/C][C]0.645399[/C][C]0.6773[/C][/ROW]
[ROW][C]93[/C][C]0.283305[/C][C]0.566611[/C][C]0.716695[/C][/ROW]
[ROW][C]94[/C][C]0.214064[/C][C]0.428127[/C][C]0.785936[/C][/ROW]
[ROW][C]95[/C][C]0.45263[/C][C]0.90526[/C][C]0.54737[/C][/ROW]
[ROW][C]96[/C][C]0.747157[/C][C]0.505687[/C][C]0.252843[/C][/ROW]
[ROW][C]97[/C][C]0.722046[/C][C]0.555908[/C][C]0.277954[/C][/ROW]
[ROW][C]98[/C][C]0.638658[/C][C]0.722685[/C][C]0.361342[/C][/ROW]
[ROW][C]99[/C][C]0.532561[/C][C]0.934878[/C][C]0.467439[/C][/ROW]
[ROW][C]100[/C][C]0.440478[/C][C]0.880957[/C][C]0.559522[/C][/ROW]
[ROW][C]101[/C][C]0.305448[/C][C]0.610896[/C][C]0.694552[/C][/ROW]
[ROW][C]102[/C][C]0.459519[/C][C]0.919038[/C][C]0.540481[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266219&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266219&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	0.994616	0.0107682	0.00538408
11	0.987709	0.0245811	0.0122906
12	0.975521	0.0489589	0.0244794
13	0.96601	0.0679792	0.0339896
14	0.952859	0.0942814	0.0471407
15	0.925851	0.148298	0.074149
16	0.900625	0.19875	0.0993752
17	0.862441	0.275117	0.137559
18	0.883997	0.232006	0.116003
19	0.840347	0.319305	0.159653
20	0.785195	0.42961	0.214805
21	0.786635	0.426729	0.213365
22	0.861416	0.277168	0.138584
23	0.836453	0.327094	0.163547
24	0.802435	0.395129	0.197565
25	0.755326	0.489348	0.244674
26	0.788434	0.423131	0.211566
27	0.78902	0.42196	0.21098
28	0.743734	0.512532	0.256266
29	0.694701	0.610597	0.305299
30	0.66316	0.673681	0.33684
31	0.626139	0.747723	0.373861
32	0.898922	0.202156	0.101078
33	0.871461	0.257077	0.128539
34	0.840267	0.319466	0.159733
35	0.810262	0.379477	0.189738
36	0.83692	0.326159	0.16308
37	0.798477	0.403046	0.201523
38	0.770212	0.459575	0.229788
39	0.737411	0.525179	0.262589
40	0.699797	0.600406	0.300203
41	0.67435	0.6513	0.32565
42	0.627538	0.744925	0.372462
43	0.67686	0.64628	0.32314
44	0.64207	0.71586	0.35793
45	0.620558	0.758885	0.379442
46	0.656721	0.686557	0.343279
47	0.826682	0.346636	0.173318
48	0.875558	0.248885	0.124442
49	0.850241	0.299518	0.149759
50	0.838542	0.322916	0.161458
51	0.860121	0.279758	0.139879
52	0.85181	0.29638	0.14819
53	0.818645	0.362709	0.181355
54	0.787906	0.424187	0.212094
55	0.788051	0.423898	0.211949
56	0.820984	0.358032	0.179016
57	0.814669	0.370662	0.185331
58	0.783108	0.433784	0.216892
59	0.822956	0.354088	0.177044
60	0.782786	0.434427	0.217214
61	0.748586	0.502828	0.251414
62	0.709255	0.58149	0.290745
63	0.689973	0.620054	0.310027
64	0.646805	0.70639	0.353195
65	0.643482	0.713035	0.356518
66	0.635055	0.729891	0.364945
67	0.594308	0.811385	0.405692
68	0.552127	0.895747	0.447873
69	0.619045	0.76191	0.380955
70	0.567617	0.864765	0.432383
71	0.52292	0.954161	0.47708
72	0.582074	0.835853	0.417926
73	0.523972	0.952056	0.476028
74	0.46408	0.928159	0.53592
75	0.422361	0.844723	0.577639
76	0.371788	0.743577	0.628212
77	0.474201	0.948403	0.525799
78	0.435672	0.871343	0.564328
79	0.428305	0.85661	0.571695
80	0.433834	0.867669	0.566166
81	0.452183	0.904367	0.547817
82	0.691866	0.616268	0.308134
83	0.628924	0.742151	0.371076
84	0.573437	0.853126	0.426563
85	0.533742	0.932515	0.466258
86	0.462718	0.925435	0.537282
87	0.466385	0.93277	0.533615
88	0.564891	0.870218	0.435109
89	0.503886	0.992228	0.496114
90	0.43829	0.876581	0.56171
91	0.384636	0.769273	0.615364
92	0.3227	0.645399	0.6773
93	0.283305	0.566611	0.716695
94	0.214064	0.428127	0.785936
95	0.45263	0.90526	0.54737
96	0.747157	0.505687	0.252843
97	0.722046	0.555908	0.277954
98	0.638658	0.722685	0.361342
99	0.532561	0.934878	0.467439
100	0.440478	0.880957	0.559522
101	0.305448	0.610896	0.694552
102	0.459519	0.919038	0.540481

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	3	0.0322581	OK
10% type I error level	5	0.0537634	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 & 3 & 0.0322581 & OK \tabularnewline
10% type I error level & 5 & 0.0537634 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266219&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]3[/C][C]0.0322581[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]5[/C][C]0.0537634[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266219&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266219&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	3	0.0322581	OK
10% type I error level	5	0.0537634	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):

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