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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Thu, 18 Dec 2014 13:08:04 +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/18/t14189081180whmhcmpg1fgis3.htm/, Retrieved Fri, 17 May 2024 18:16:05 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=270891, Retrieved Fri, 17 May 2024 18:16:05 +0000

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-     [Multiple Regression] [Verklaring examen...] [2014-12-18 10:27:57] [94e0b03eaaae24ea322c1a0c8a3c30a1]
- R  D    [Multiple Regression] [totaal score en r...] [2014-12-18 13:08:04] [0adf43ccf8dfa476608a94fd7836e72e] [Current]

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13	20	39	12
12	20	32	9
13	31	25	12
7	16	36	8
7	24	28	9
13	24	34	9
15	27	36	12
13	21	26	9
11	24	32	10
8	16	36	13
11	19	26	8
6	24	29	11
11	24	34	7
12	11	35	11
6	27	42	13
11	40	33	12
12	21	32	9
9	27	34	9
10	24	33	9
10	19	36	12
14	27	32	12
11	29	34	9
14	0	0	0
12	19	32	14
11	29	35	13
16	31	42	12
13	21	28	9
10	22	33	10
12	19	34	8
8	18	30	4
12	32	30	7
9	22	30	8
10	39	34	13
11	20	30	11
10	17	30	5
10	23	38	9
13	22	31	9
9	22	35	10
12	14	36	12
6	15	30	5
11	20	32	8
13	31	43	15
11	16	31	4
12	23	32	9
11	25	37	9
12	13	33	10
8	19	32	11
13	37	44	14
12	22	33	12
12	13	33	12
7	22	29	7
11	14	22	7
12	25	39	10
13	15	28	15
10	17	37	10
6	27	39	10
14	16	34	10
8	17	32	11
13	22	33	9
9	15	32	11
13	18	35	10
8	28	39	10
16	14	29	9
9	26	33	9
9	16	27	8
11	20	40	8
13	14	41	12
15	25	24	9
12	10	32	3
12	22	38	13
11	26	36	10
9	0	0	0
15	21	33	12
13	18	24	7
13	18	32	9
13	22	36	12
13	22	35	11
10	12	32	9
8	11	36	10
11	13	33	11
13	13	32	6
11	19	42	10
4	26	24	10
10	26	29	9
12	22	33	9
11	19	32	10
11	34	42	12
9	22	30	7
13	18	30	10
13	20	31	8
6	29	24	12
10	22	35	11
9	22	26	7
8	16	34	9
9	26	31	6
7	18	32	9
11	16	26	10
14	18	33	8
8	28	42	11
11	29	35	10
10	21	28	6
10	23	30	9
14	14	33	12
9	28	34	10
14	21	31	10
6	24	32	10
10	10	36	6
12	18	36	12
11	12	39	11

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

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 5 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270891&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270891&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270891&T=0

As an alternative you can also use a QR Code:

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

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

Multiple Linear Regression - Estimated Regression Equation
TOT[t] = + 10.532 -0.054965PO[t] -0.0156928PS[t] + 0.205519PV[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TOT[t] =  +  10.532 -0.054965PO[t] -0.0156928PS[t] +  0.205519PV[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270891&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TOT[t] =  +  10.532 -0.054965PO[t] -0.0156928PS[t] +  0.205519PV[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270891&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270891&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] = + 10.532 -0.054965PO[t] -0.0156928PS[t] + 0.205519PV[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)	10.532	1.24331	8.471	1.60519e-13	8.02596e-14
PO	-0.054965	0.039834	-1.38	0.170564	0.085282
PS	-0.0156928	0.046927	-0.3344	0.738738	0.369369
PV	0.205519	0.114923	1.788	0.0766098	0.0383049

\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) & 10.532 & 1.24331 & 8.471 & 1.60519e-13 & 8.02596e-14 \tabularnewline
PO & -0.054965 & 0.039834 & -1.38 & 0.170564 & 0.085282 \tabularnewline
PS & -0.0156928 & 0.046927 & -0.3344 & 0.738738 & 0.369369 \tabularnewline
PV & 0.205519 & 0.114923 & 1.788 & 0.0766098 & 0.0383049 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270891&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]10.532[/C][C]1.24331[/C][C]8.471[/C][C]1.60519e-13[/C][C]8.02596e-14[/C][/ROW]
[ROW][C]PO[/C][C]-0.054965[/C][C]0.039834[/C][C]-1.38[/C][C]0.170564[/C][C]0.085282[/C][/ROW]
[ROW][C]PS[/C][C]-0.0156928[/C][C]0.046927[/C][C]-0.3344[/C][C]0.738738[/C][C]0.369369[/C][/ROW]
[ROW][C]PV[/C][C]0.205519[/C][C]0.114923[/C][C]1.788[/C][C]0.0766098[/C][C]0.0383049[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270891&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270891&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)	10.532	1.24331	8.471	1.60519e-13	8.02596e-14
PO	-0.054965	0.039834	-1.38	0.170564	0.085282
PS	-0.0156928	0.046927	-0.3344	0.738738	0.369369
PV	0.205519	0.114923	1.788	0.0766098	0.0383049

Multiple Linear Regression - Regression Statistics
Multiple R	0.194766
R-squared	0.0379338
Adjusted R-squared	0.0104462
F-TEST (value)	1.38003
F-TEST (DF numerator)	3
F-TEST (DF denominator)	105
p-value	0.25298
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.40835
Sum Squared Residuals	609.014

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.194766 \tabularnewline
R-squared & 0.0379338 \tabularnewline
Adjusted R-squared & 0.0104462 \tabularnewline
F-TEST (value) & 1.38003 \tabularnewline
F-TEST (DF numerator) & 3 \tabularnewline
F-TEST (DF denominator) & 105 \tabularnewline
p-value & 0.25298 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.40835 \tabularnewline
Sum Squared Residuals & 609.014 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270891&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.194766[/C][/ROW]
[ROW][C]R-squared[/C][C]0.0379338[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.0104462[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]1.38003[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]3[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]105[/C][/ROW]
[ROW][C]p-value[/C][C]0.25298[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.40835[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]609.014[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270891&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270891&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	0.194766
R-squared	0.0379338
Adjusted R-squared	0.0104462
F-TEST (value)	1.38003
F-TEST (DF numerator)	3
F-TEST (DF denominator)	105
p-value	0.25298
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.40835
Sum Squared Residuals	609.014

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	13	11.2869	1.71313
2	12	10.7802	1.21984
3	13	10.902	2.09805
4	7	10.7317	-3.73173
5	7	10.6231	-3.62307
6	13	10.5289	2.47108
7	15	10.9492	4.05081
8	13	10.8194	2.18064
9	11	10.7658	0.234178
10	8	11.7593	-3.75933
11	11	10.7238	0.276233
12	6	11.0184	-5.01842
13	11	10.1179	0.88212
14	12	11.6388	0.361193
15	6	11.0606	-5.06055
16	11	10.2817	0.718273
17	12	10.7252	1.2748
18	9	10.364	-1.36402
19	10	10.5446	-0.544611
20	10	11.3889	-1.38891
21	14	11.012	2.98804
22	11	10.2541	0.745907
23	14	10.532	3.46803
24	12	11.8627	0.137278
25	11	11.0605	-0.0604748
26	16	10.6352	5.36482
27	13	10.788	2.21203
28	10	10.8601	-0.860059
29	12	10.5982	1.40178
30	8	9.89389	-1.89389
31	12	9.74093	2.25907
32	9	10.4961	-1.4961
33	10	10.5265	-0.526518
34	11	11.2226	-0.222586
35	10	10.1544	-0.154369
36	10	10.5211	-0.521111
37	13	10.6859	2.31407
38	9	10.8287	-1.82867
39	12	11.6637	0.336262
40	6	10.2643	-4.2643
41	11	10.5746	0.425355
42	13	11.236	1.76396
43	11	9.98812	1.01188
44	12	10.6153	1.38473
45	11	10.4269	0.573126
46	12	11.3547	0.645256
47	8	11.2462	-3.24617
48	13	10.685	2.31496
49	12	11.2711	0.728903
50	12	11.7658	0.234219
51	7	10.3063	-3.30627
52	11	10.8558	0.144156
53	12	10.601	1.39899
54	13	12.3509	0.649129
55	10	11.0721	-1.07211
56	6	10.4911	-4.49108
57	14	11.1742	2.82584
58	8	11.3561	-3.3561
59	13	10.6545	2.34546
60	9	11.466	-2.46603
61	13	11.0485	1.95147
62	8	10.4361	-2.43611
63	16	11.157	4.84297
64	9	10.4347	-1.43468
65	9	10.873	-1.87297
66	11	10.4491	0.550898
67	13	11.5853	1.41473
68	15	10.6309	4.36912
69	12	10.0967	1.9033
70	12	11.3982	0.601849
71	11	10.5931	0.406879
72	9	10.532	-1.53197
73	15	11.3261	3.67394
74	13	10.6046	2.3954
75	13	10.8901	2.10991
76	13	11.224	1.77598
77	13	11.0342	1.96581
78	10	11.2199	-1.21988
79	8	11.4176	-3.4176
80	11	11.5603	-0.560262
81	13	10.5484	2.45164
82	11	10.8837	0.116281
83	4	10.7814	-6.78143
84	10	10.4975	-0.497452
85	12	10.6545	1.34546
86	11	11.0406	-0.0406469
87	11	10.4703	0.529719
88	9	10.2906	-1.29058
89	13	11.127	1.873
90	13	10.5903	2.40966
91	6	11.0276	-5.02758
92	10	11.0342	-1.03419
93	9	10.3534	-1.35335
94	8	10.9686	-2.96864
95	9	9.84951	-0.84951
96	7	10.8901	-3.89009
97	11	11.2997	-0.299699
98	14	10.6689	3.33112
99	8	10.5946	-2.59455
100	11	10.4439	0.556081
101	10	10.1714	-0.171414
102	10	10.6467	-0.646654
103	14	11.7108	2.28918
104	9	10.5146	-1.51458
105	14	10.9464	3.05359
106	6	10.7658	-4.76582
107	10	10.6505	-0.650486
108	12	11.4439	0.556122
109	11	11.5211	-0.52107

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 13 & 11.2869 & 1.71313 \tabularnewline
2 & 12 & 10.7802 & 1.21984 \tabularnewline
3 & 13 & 10.902 & 2.09805 \tabularnewline
4 & 7 & 10.7317 & -3.73173 \tabularnewline
5 & 7 & 10.6231 & -3.62307 \tabularnewline
6 & 13 & 10.5289 & 2.47108 \tabularnewline
7 & 15 & 10.9492 & 4.05081 \tabularnewline
8 & 13 & 10.8194 & 2.18064 \tabularnewline
9 & 11 & 10.7658 & 0.234178 \tabularnewline
10 & 8 & 11.7593 & -3.75933 \tabularnewline
11 & 11 & 10.7238 & 0.276233 \tabularnewline
12 & 6 & 11.0184 & -5.01842 \tabularnewline
13 & 11 & 10.1179 & 0.88212 \tabularnewline
14 & 12 & 11.6388 & 0.361193 \tabularnewline
15 & 6 & 11.0606 & -5.06055 \tabularnewline
16 & 11 & 10.2817 & 0.718273 \tabularnewline
17 & 12 & 10.7252 & 1.2748 \tabularnewline
18 & 9 & 10.364 & -1.36402 \tabularnewline
19 & 10 & 10.5446 & -0.544611 \tabularnewline
20 & 10 & 11.3889 & -1.38891 \tabularnewline
21 & 14 & 11.012 & 2.98804 \tabularnewline
22 & 11 & 10.2541 & 0.745907 \tabularnewline
23 & 14 & 10.532 & 3.46803 \tabularnewline
24 & 12 & 11.8627 & 0.137278 \tabularnewline
25 & 11 & 11.0605 & -0.0604748 \tabularnewline
26 & 16 & 10.6352 & 5.36482 \tabularnewline
27 & 13 & 10.788 & 2.21203 \tabularnewline
28 & 10 & 10.8601 & -0.860059 \tabularnewline
29 & 12 & 10.5982 & 1.40178 \tabularnewline
30 & 8 & 9.89389 & -1.89389 \tabularnewline
31 & 12 & 9.74093 & 2.25907 \tabularnewline
32 & 9 & 10.4961 & -1.4961 \tabularnewline
33 & 10 & 10.5265 & -0.526518 \tabularnewline
34 & 11 & 11.2226 & -0.222586 \tabularnewline
35 & 10 & 10.1544 & -0.154369 \tabularnewline
36 & 10 & 10.5211 & -0.521111 \tabularnewline
37 & 13 & 10.6859 & 2.31407 \tabularnewline
38 & 9 & 10.8287 & -1.82867 \tabularnewline
39 & 12 & 11.6637 & 0.336262 \tabularnewline
40 & 6 & 10.2643 & -4.2643 \tabularnewline
41 & 11 & 10.5746 & 0.425355 \tabularnewline
42 & 13 & 11.236 & 1.76396 \tabularnewline
43 & 11 & 9.98812 & 1.01188 \tabularnewline
44 & 12 & 10.6153 & 1.38473 \tabularnewline
45 & 11 & 10.4269 & 0.573126 \tabularnewline
46 & 12 & 11.3547 & 0.645256 \tabularnewline
47 & 8 & 11.2462 & -3.24617 \tabularnewline
48 & 13 & 10.685 & 2.31496 \tabularnewline
49 & 12 & 11.2711 & 0.728903 \tabularnewline
50 & 12 & 11.7658 & 0.234219 \tabularnewline
51 & 7 & 10.3063 & -3.30627 \tabularnewline
52 & 11 & 10.8558 & 0.144156 \tabularnewline
53 & 12 & 10.601 & 1.39899 \tabularnewline
54 & 13 & 12.3509 & 0.649129 \tabularnewline
55 & 10 & 11.0721 & -1.07211 \tabularnewline
56 & 6 & 10.4911 & -4.49108 \tabularnewline
57 & 14 & 11.1742 & 2.82584 \tabularnewline
58 & 8 & 11.3561 & -3.3561 \tabularnewline
59 & 13 & 10.6545 & 2.34546 \tabularnewline
60 & 9 & 11.466 & -2.46603 \tabularnewline
61 & 13 & 11.0485 & 1.95147 \tabularnewline
62 & 8 & 10.4361 & -2.43611 \tabularnewline
63 & 16 & 11.157 & 4.84297 \tabularnewline
64 & 9 & 10.4347 & -1.43468 \tabularnewline
65 & 9 & 10.873 & -1.87297 \tabularnewline
66 & 11 & 10.4491 & 0.550898 \tabularnewline
67 & 13 & 11.5853 & 1.41473 \tabularnewline
68 & 15 & 10.6309 & 4.36912 \tabularnewline
69 & 12 & 10.0967 & 1.9033 \tabularnewline
70 & 12 & 11.3982 & 0.601849 \tabularnewline
71 & 11 & 10.5931 & 0.406879 \tabularnewline
72 & 9 & 10.532 & -1.53197 \tabularnewline
73 & 15 & 11.3261 & 3.67394 \tabularnewline
74 & 13 & 10.6046 & 2.3954 \tabularnewline
75 & 13 & 10.8901 & 2.10991 \tabularnewline
76 & 13 & 11.224 & 1.77598 \tabularnewline
77 & 13 & 11.0342 & 1.96581 \tabularnewline
78 & 10 & 11.2199 & -1.21988 \tabularnewline
79 & 8 & 11.4176 & -3.4176 \tabularnewline
80 & 11 & 11.5603 & -0.560262 \tabularnewline
81 & 13 & 10.5484 & 2.45164 \tabularnewline
82 & 11 & 10.8837 & 0.116281 \tabularnewline
83 & 4 & 10.7814 & -6.78143 \tabularnewline
84 & 10 & 10.4975 & -0.497452 \tabularnewline
85 & 12 & 10.6545 & 1.34546 \tabularnewline
86 & 11 & 11.0406 & -0.0406469 \tabularnewline
87 & 11 & 10.4703 & 0.529719 \tabularnewline
88 & 9 & 10.2906 & -1.29058 \tabularnewline
89 & 13 & 11.127 & 1.873 \tabularnewline
90 & 13 & 10.5903 & 2.40966 \tabularnewline
91 & 6 & 11.0276 & -5.02758 \tabularnewline
92 & 10 & 11.0342 & -1.03419 \tabularnewline
93 & 9 & 10.3534 & -1.35335 \tabularnewline
94 & 8 & 10.9686 & -2.96864 \tabularnewline
95 & 9 & 9.84951 & -0.84951 \tabularnewline
96 & 7 & 10.8901 & -3.89009 \tabularnewline
97 & 11 & 11.2997 & -0.299699 \tabularnewline
98 & 14 & 10.6689 & 3.33112 \tabularnewline
99 & 8 & 10.5946 & -2.59455 \tabularnewline
100 & 11 & 10.4439 & 0.556081 \tabularnewline
101 & 10 & 10.1714 & -0.171414 \tabularnewline
102 & 10 & 10.6467 & -0.646654 \tabularnewline
103 & 14 & 11.7108 & 2.28918 \tabularnewline
104 & 9 & 10.5146 & -1.51458 \tabularnewline
105 & 14 & 10.9464 & 3.05359 \tabularnewline
106 & 6 & 10.7658 & -4.76582 \tabularnewline
107 & 10 & 10.6505 & -0.650486 \tabularnewline
108 & 12 & 11.4439 & 0.556122 \tabularnewline
109 & 11 & 11.5211 & -0.52107 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270891&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]13[/C][C]11.2869[/C][C]1.71313[/C][/ROW]
[ROW][C]2[/C][C]12[/C][C]10.7802[/C][C]1.21984[/C][/ROW]
[ROW][C]3[/C][C]13[/C][C]10.902[/C][C]2.09805[/C][/ROW]
[ROW][C]4[/C][C]7[/C][C]10.7317[/C][C]-3.73173[/C][/ROW]
[ROW][C]5[/C][C]7[/C][C]10.6231[/C][C]-3.62307[/C][/ROW]
[ROW][C]6[/C][C]13[/C][C]10.5289[/C][C]2.47108[/C][/ROW]
[ROW][C]7[/C][C]15[/C][C]10.9492[/C][C]4.05081[/C][/ROW]
[ROW][C]8[/C][C]13[/C][C]10.8194[/C][C]2.18064[/C][/ROW]
[ROW][C]9[/C][C]11[/C][C]10.7658[/C][C]0.234178[/C][/ROW]
[ROW][C]10[/C][C]8[/C][C]11.7593[/C][C]-3.75933[/C][/ROW]
[ROW][C]11[/C][C]11[/C][C]10.7238[/C][C]0.276233[/C][/ROW]
[ROW][C]12[/C][C]6[/C][C]11.0184[/C][C]-5.01842[/C][/ROW]
[ROW][C]13[/C][C]11[/C][C]10.1179[/C][C]0.88212[/C][/ROW]
[ROW][C]14[/C][C]12[/C][C]11.6388[/C][C]0.361193[/C][/ROW]
[ROW][C]15[/C][C]6[/C][C]11.0606[/C][C]-5.06055[/C][/ROW]
[ROW][C]16[/C][C]11[/C][C]10.2817[/C][C]0.718273[/C][/ROW]
[ROW][C]17[/C][C]12[/C][C]10.7252[/C][C]1.2748[/C][/ROW]
[ROW][C]18[/C][C]9[/C][C]10.364[/C][C]-1.36402[/C][/ROW]
[ROW][C]19[/C][C]10[/C][C]10.5446[/C][C]-0.544611[/C][/ROW]
[ROW][C]20[/C][C]10[/C][C]11.3889[/C][C]-1.38891[/C][/ROW]
[ROW][C]21[/C][C]14[/C][C]11.012[/C][C]2.98804[/C][/ROW]
[ROW][C]22[/C][C]11[/C][C]10.2541[/C][C]0.745907[/C][/ROW]
[ROW][C]23[/C][C]14[/C][C]10.532[/C][C]3.46803[/C][/ROW]
[ROW][C]24[/C][C]12[/C][C]11.8627[/C][C]0.137278[/C][/ROW]
[ROW][C]25[/C][C]11[/C][C]11.0605[/C][C]-0.0604748[/C][/ROW]
[ROW][C]26[/C][C]16[/C][C]10.6352[/C][C]5.36482[/C][/ROW]
[ROW][C]27[/C][C]13[/C][C]10.788[/C][C]2.21203[/C][/ROW]
[ROW][C]28[/C][C]10[/C][C]10.8601[/C][C]-0.860059[/C][/ROW]
[ROW][C]29[/C][C]12[/C][C]10.5982[/C][C]1.40178[/C][/ROW]
[ROW][C]30[/C][C]8[/C][C]9.89389[/C][C]-1.89389[/C][/ROW]
[ROW][C]31[/C][C]12[/C][C]9.74093[/C][C]2.25907[/C][/ROW]
[ROW][C]32[/C][C]9[/C][C]10.4961[/C][C]-1.4961[/C][/ROW]
[ROW][C]33[/C][C]10[/C][C]10.5265[/C][C]-0.526518[/C][/ROW]
[ROW][C]34[/C][C]11[/C][C]11.2226[/C][C]-0.222586[/C][/ROW]
[ROW][C]35[/C][C]10[/C][C]10.1544[/C][C]-0.154369[/C][/ROW]
[ROW][C]36[/C][C]10[/C][C]10.5211[/C][C]-0.521111[/C][/ROW]
[ROW][C]37[/C][C]13[/C][C]10.6859[/C][C]2.31407[/C][/ROW]
[ROW][C]38[/C][C]9[/C][C]10.8287[/C][C]-1.82867[/C][/ROW]
[ROW][C]39[/C][C]12[/C][C]11.6637[/C][C]0.336262[/C][/ROW]
[ROW][C]40[/C][C]6[/C][C]10.2643[/C][C]-4.2643[/C][/ROW]
[ROW][C]41[/C][C]11[/C][C]10.5746[/C][C]0.425355[/C][/ROW]
[ROW][C]42[/C][C]13[/C][C]11.236[/C][C]1.76396[/C][/ROW]
[ROW][C]43[/C][C]11[/C][C]9.98812[/C][C]1.01188[/C][/ROW]
[ROW][C]44[/C][C]12[/C][C]10.6153[/C][C]1.38473[/C][/ROW]
[ROW][C]45[/C][C]11[/C][C]10.4269[/C][C]0.573126[/C][/ROW]
[ROW][C]46[/C][C]12[/C][C]11.3547[/C][C]0.645256[/C][/ROW]
[ROW][C]47[/C][C]8[/C][C]11.2462[/C][C]-3.24617[/C][/ROW]
[ROW][C]48[/C][C]13[/C][C]10.685[/C][C]2.31496[/C][/ROW]
[ROW][C]49[/C][C]12[/C][C]11.2711[/C][C]0.728903[/C][/ROW]
[ROW][C]50[/C][C]12[/C][C]11.7658[/C][C]0.234219[/C][/ROW]
[ROW][C]51[/C][C]7[/C][C]10.3063[/C][C]-3.30627[/C][/ROW]
[ROW][C]52[/C][C]11[/C][C]10.8558[/C][C]0.144156[/C][/ROW]
[ROW][C]53[/C][C]12[/C][C]10.601[/C][C]1.39899[/C][/ROW]
[ROW][C]54[/C][C]13[/C][C]12.3509[/C][C]0.649129[/C][/ROW]
[ROW][C]55[/C][C]10[/C][C]11.0721[/C][C]-1.07211[/C][/ROW]
[ROW][C]56[/C][C]6[/C][C]10.4911[/C][C]-4.49108[/C][/ROW]
[ROW][C]57[/C][C]14[/C][C]11.1742[/C][C]2.82584[/C][/ROW]
[ROW][C]58[/C][C]8[/C][C]11.3561[/C][C]-3.3561[/C][/ROW]
[ROW][C]59[/C][C]13[/C][C]10.6545[/C][C]2.34546[/C][/ROW]
[ROW][C]60[/C][C]9[/C][C]11.466[/C][C]-2.46603[/C][/ROW]
[ROW][C]61[/C][C]13[/C][C]11.0485[/C][C]1.95147[/C][/ROW]
[ROW][C]62[/C][C]8[/C][C]10.4361[/C][C]-2.43611[/C][/ROW]
[ROW][C]63[/C][C]16[/C][C]11.157[/C][C]4.84297[/C][/ROW]
[ROW][C]64[/C][C]9[/C][C]10.4347[/C][C]-1.43468[/C][/ROW]
[ROW][C]65[/C][C]9[/C][C]10.873[/C][C]-1.87297[/C][/ROW]
[ROW][C]66[/C][C]11[/C][C]10.4491[/C][C]0.550898[/C][/ROW]
[ROW][C]67[/C][C]13[/C][C]11.5853[/C][C]1.41473[/C][/ROW]
[ROW][C]68[/C][C]15[/C][C]10.6309[/C][C]4.36912[/C][/ROW]
[ROW][C]69[/C][C]12[/C][C]10.0967[/C][C]1.9033[/C][/ROW]
[ROW][C]70[/C][C]12[/C][C]11.3982[/C][C]0.601849[/C][/ROW]
[ROW][C]71[/C][C]11[/C][C]10.5931[/C][C]0.406879[/C][/ROW]
[ROW][C]72[/C][C]9[/C][C]10.532[/C][C]-1.53197[/C][/ROW]
[ROW][C]73[/C][C]15[/C][C]11.3261[/C][C]3.67394[/C][/ROW]
[ROW][C]74[/C][C]13[/C][C]10.6046[/C][C]2.3954[/C][/ROW]
[ROW][C]75[/C][C]13[/C][C]10.8901[/C][C]2.10991[/C][/ROW]
[ROW][C]76[/C][C]13[/C][C]11.224[/C][C]1.77598[/C][/ROW]
[ROW][C]77[/C][C]13[/C][C]11.0342[/C][C]1.96581[/C][/ROW]
[ROW][C]78[/C][C]10[/C][C]11.2199[/C][C]-1.21988[/C][/ROW]
[ROW][C]79[/C][C]8[/C][C]11.4176[/C][C]-3.4176[/C][/ROW]
[ROW][C]80[/C][C]11[/C][C]11.5603[/C][C]-0.560262[/C][/ROW]
[ROW][C]81[/C][C]13[/C][C]10.5484[/C][C]2.45164[/C][/ROW]
[ROW][C]82[/C][C]11[/C][C]10.8837[/C][C]0.116281[/C][/ROW]
[ROW][C]83[/C][C]4[/C][C]10.7814[/C][C]-6.78143[/C][/ROW]
[ROW][C]84[/C][C]10[/C][C]10.4975[/C][C]-0.497452[/C][/ROW]
[ROW][C]85[/C][C]12[/C][C]10.6545[/C][C]1.34546[/C][/ROW]
[ROW][C]86[/C][C]11[/C][C]11.0406[/C][C]-0.0406469[/C][/ROW]
[ROW][C]87[/C][C]11[/C][C]10.4703[/C][C]0.529719[/C][/ROW]
[ROW][C]88[/C][C]9[/C][C]10.2906[/C][C]-1.29058[/C][/ROW]
[ROW][C]89[/C][C]13[/C][C]11.127[/C][C]1.873[/C][/ROW]
[ROW][C]90[/C][C]13[/C][C]10.5903[/C][C]2.40966[/C][/ROW]
[ROW][C]91[/C][C]6[/C][C]11.0276[/C][C]-5.02758[/C][/ROW]
[ROW][C]92[/C][C]10[/C][C]11.0342[/C][C]-1.03419[/C][/ROW]
[ROW][C]93[/C][C]9[/C][C]10.3534[/C][C]-1.35335[/C][/ROW]
[ROW][C]94[/C][C]8[/C][C]10.9686[/C][C]-2.96864[/C][/ROW]
[ROW][C]95[/C][C]9[/C][C]9.84951[/C][C]-0.84951[/C][/ROW]
[ROW][C]96[/C][C]7[/C][C]10.8901[/C][C]-3.89009[/C][/ROW]
[ROW][C]97[/C][C]11[/C][C]11.2997[/C][C]-0.299699[/C][/ROW]
[ROW][C]98[/C][C]14[/C][C]10.6689[/C][C]3.33112[/C][/ROW]
[ROW][C]99[/C][C]8[/C][C]10.5946[/C][C]-2.59455[/C][/ROW]
[ROW][C]100[/C][C]11[/C][C]10.4439[/C][C]0.556081[/C][/ROW]
[ROW][C]101[/C][C]10[/C][C]10.1714[/C][C]-0.171414[/C][/ROW]
[ROW][C]102[/C][C]10[/C][C]10.6467[/C][C]-0.646654[/C][/ROW]
[ROW][C]103[/C][C]14[/C][C]11.7108[/C][C]2.28918[/C][/ROW]
[ROW][C]104[/C][C]9[/C][C]10.5146[/C][C]-1.51458[/C][/ROW]
[ROW][C]105[/C][C]14[/C][C]10.9464[/C][C]3.05359[/C][/ROW]
[ROW][C]106[/C][C]6[/C][C]10.7658[/C][C]-4.76582[/C][/ROW]
[ROW][C]107[/C][C]10[/C][C]10.6505[/C][C]-0.650486[/C][/ROW]
[ROW][C]108[/C][C]12[/C][C]11.4439[/C][C]0.556122[/C][/ROW]
[ROW][C]109[/C][C]11[/C][C]11.5211[/C][C]-0.52107[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270891&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270891&T=4

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	13	11.2869	1.71313
2	12	10.7802	1.21984
3	13	10.902	2.09805
4	7	10.7317	-3.73173
5	7	10.6231	-3.62307
6	13	10.5289	2.47108
7	15	10.9492	4.05081
8	13	10.8194	2.18064
9	11	10.7658	0.234178
10	8	11.7593	-3.75933
11	11	10.7238	0.276233
12	6	11.0184	-5.01842
13	11	10.1179	0.88212
14	12	11.6388	0.361193
15	6	11.0606	-5.06055
16	11	10.2817	0.718273
17	12	10.7252	1.2748
18	9	10.364	-1.36402
19	10	10.5446	-0.544611
20	10	11.3889	-1.38891
21	14	11.012	2.98804
22	11	10.2541	0.745907
23	14	10.532	3.46803
24	12	11.8627	0.137278
25	11	11.0605	-0.0604748
26	16	10.6352	5.36482
27	13	10.788	2.21203
28	10	10.8601	-0.860059
29	12	10.5982	1.40178
30	8	9.89389	-1.89389
31	12	9.74093	2.25907
32	9	10.4961	-1.4961
33	10	10.5265	-0.526518
34	11	11.2226	-0.222586
35	10	10.1544	-0.154369
36	10	10.5211	-0.521111
37	13	10.6859	2.31407
38	9	10.8287	-1.82867
39	12	11.6637	0.336262
40	6	10.2643	-4.2643
41	11	10.5746	0.425355
42	13	11.236	1.76396
43	11	9.98812	1.01188
44	12	10.6153	1.38473
45	11	10.4269	0.573126
46	12	11.3547	0.645256
47	8	11.2462	-3.24617
48	13	10.685	2.31496
49	12	11.2711	0.728903
50	12	11.7658	0.234219
51	7	10.3063	-3.30627
52	11	10.8558	0.144156
53	12	10.601	1.39899
54	13	12.3509	0.649129
55	10	11.0721	-1.07211
56	6	10.4911	-4.49108
57	14	11.1742	2.82584
58	8	11.3561	-3.3561
59	13	10.6545	2.34546
60	9	11.466	-2.46603
61	13	11.0485	1.95147
62	8	10.4361	-2.43611
63	16	11.157	4.84297
64	9	10.4347	-1.43468
65	9	10.873	-1.87297
66	11	10.4491	0.550898
67	13	11.5853	1.41473
68	15	10.6309	4.36912
69	12	10.0967	1.9033
70	12	11.3982	0.601849
71	11	10.5931	0.406879
72	9	10.532	-1.53197
73	15	11.3261	3.67394
74	13	10.6046	2.3954
75	13	10.8901	2.10991
76	13	11.224	1.77598
77	13	11.0342	1.96581
78	10	11.2199	-1.21988
79	8	11.4176	-3.4176
80	11	11.5603	-0.560262
81	13	10.5484	2.45164
82	11	10.8837	0.116281
83	4	10.7814	-6.78143
84	10	10.4975	-0.497452
85	12	10.6545	1.34546
86	11	11.0406	-0.0406469
87	11	10.4703	0.529719
88	9	10.2906	-1.29058
89	13	11.127	1.873
90	13	10.5903	2.40966
91	6	11.0276	-5.02758
92	10	11.0342	-1.03419
93	9	10.3534	-1.35335
94	8	10.9686	-2.96864
95	9	9.84951	-0.84951
96	7	10.8901	-3.89009
97	11	11.2997	-0.299699
98	14	10.6689	3.33112
99	8	10.5946	-2.59455
100	11	10.4439	0.556081
101	10	10.1714	-0.171414
102	10	10.6467	-0.646654
103	14	11.7108	2.28918
104	9	10.5146	-1.51458
105	14	10.9464	3.05359
106	6	10.7658	-4.76582
107	10	10.6505	-0.650486
108	12	11.4439	0.556122
109	11	11.5211	-0.52107

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
7	0.622946	0.754107	0.377054
8	0.807997	0.384006	0.192003
9	0.70796	0.58408	0.29204
10	0.742207	0.515585	0.257793
11	0.70516	0.58968	0.29484
12	0.900043	0.199915	0.0999574
13	0.859368	0.281263	0.140632
14	0.879743	0.240514	0.120257
15	0.965197	0.0696059	0.0348029
16	0.946493	0.107014	0.0535068
17	0.927804	0.144392	0.0721958
18	0.907772	0.184457	0.0922283
19	0.8728	0.254399	0.1272
20	0.832698	0.334605	0.167302
21	0.851394	0.297211	0.148606
22	0.807616	0.384768	0.192384
23	0.794502	0.410997	0.205498
24	0.744748	0.510503	0.255252
25	0.685731	0.628537	0.314269
26	0.882247	0.235506	0.117753
27	0.86856	0.26288	0.13144
28	0.835782	0.328436	0.164218
29	0.805994	0.388011	0.194006
30	0.79914	0.401719	0.20086
31	0.778261	0.443479	0.221739
32	0.754123	0.491754	0.245877
33	0.721316	0.557369	0.278684
34	0.667538	0.664923	0.332462
35	0.610705	0.778589	0.389295
36	0.55224	0.89552	0.44776
37	0.545361	0.909279	0.454639
38	0.514256	0.971489	0.485744
39	0.467106	0.934212	0.532894
40	0.579576	0.840848	0.420424
41	0.524559	0.950882	0.475441
42	0.504591	0.990818	0.495409
43	0.46342	0.92684	0.53658
44	0.425686	0.851373	0.574314
45	0.374538	0.749076	0.625462
46	0.330042	0.660084	0.669958
47	0.370755	0.74151	0.629245
48	0.374648	0.749297	0.625352
49	0.328207	0.656413	0.671793
50	0.282511	0.565021	0.717489
51	0.329216	0.658432	0.670784
52	0.279794	0.559588	0.720206
53	0.253778	0.507557	0.746222
54	0.214	0.428001	0.786
55	0.181543	0.363085	0.818457
56	0.277732	0.555465	0.722268
57	0.302972	0.605944	0.697028
58	0.345793	0.691586	0.654207
59	0.346668	0.693336	0.653332
60	0.348528	0.697056	0.651472
61	0.333708	0.667417	0.666292
62	0.327605	0.655209	0.672395
63	0.494862	0.989724	0.505138
64	0.45541	0.91082	0.54459
65	0.430988	0.861975	0.569012
66	0.38192	0.763841	0.61808
67	0.347877	0.695754	0.652123
68	0.522801	0.954399	0.477199
69	0.49163	0.983259	0.50837
70	0.43768	0.875359	0.56232
71	0.38359	0.76718	0.61641
72	0.348435	0.696869	0.651565
73	0.45631	0.91262	0.54369
74	0.486642	0.973283	0.513358
75	0.484418	0.968835	0.515582
76	0.476424	0.952847	0.523576
77	0.483552	0.967104	0.516448
78	0.43485	0.8697	0.56515
79	0.537906	0.924189	0.462094
80	0.477113	0.954226	0.522887
81	0.456289	0.912578	0.543711
82	0.397578	0.795156	0.602422
83	0.677966	0.644069	0.322034
84	0.617809	0.764383	0.382191
85	0.586345	0.827309	0.413655
86	0.515721	0.968559	0.484279
87	0.49786	0.99572	0.50214
88	0.432185	0.86437	0.567815
89	0.418189	0.836378	0.581811
90	0.451767	0.903533	0.548233
91	0.576204	0.847593	0.423796
92	0.495716	0.991432	0.504284
93	0.423648	0.847295	0.576352
94	0.445579	0.891158	0.554421
95	0.35893	0.71786	0.64107
96	0.503211	0.993578	0.496789
97	0.466203	0.932407	0.533797
98	0.583159	0.833682	0.416841
99	0.470538	0.941077	0.529462
100	0.527913	0.944174	0.472087
101	0.383919	0.767838	0.616081
102	0.245221	0.490443	0.754779

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
7 & 0.622946 & 0.754107 & 0.377054 \tabularnewline
8 & 0.807997 & 0.384006 & 0.192003 \tabularnewline
9 & 0.70796 & 0.58408 & 0.29204 \tabularnewline
10 & 0.742207 & 0.515585 & 0.257793 \tabularnewline
11 & 0.70516 & 0.58968 & 0.29484 \tabularnewline
12 & 0.900043 & 0.199915 & 0.0999574 \tabularnewline
13 & 0.859368 & 0.281263 & 0.140632 \tabularnewline
14 & 0.879743 & 0.240514 & 0.120257 \tabularnewline
15 & 0.965197 & 0.0696059 & 0.0348029 \tabularnewline
16 & 0.946493 & 0.107014 & 0.0535068 \tabularnewline
17 & 0.927804 & 0.144392 & 0.0721958 \tabularnewline
18 & 0.907772 & 0.184457 & 0.0922283 \tabularnewline
19 & 0.8728 & 0.254399 & 0.1272 \tabularnewline
20 & 0.832698 & 0.334605 & 0.167302 \tabularnewline
21 & 0.851394 & 0.297211 & 0.148606 \tabularnewline
22 & 0.807616 & 0.384768 & 0.192384 \tabularnewline
23 & 0.794502 & 0.410997 & 0.205498 \tabularnewline
24 & 0.744748 & 0.510503 & 0.255252 \tabularnewline
25 & 0.685731 & 0.628537 & 0.314269 \tabularnewline
26 & 0.882247 & 0.235506 & 0.117753 \tabularnewline
27 & 0.86856 & 0.26288 & 0.13144 \tabularnewline
28 & 0.835782 & 0.328436 & 0.164218 \tabularnewline
29 & 0.805994 & 0.388011 & 0.194006 \tabularnewline
30 & 0.79914 & 0.401719 & 0.20086 \tabularnewline
31 & 0.778261 & 0.443479 & 0.221739 \tabularnewline
32 & 0.754123 & 0.491754 & 0.245877 \tabularnewline
33 & 0.721316 & 0.557369 & 0.278684 \tabularnewline
34 & 0.667538 & 0.664923 & 0.332462 \tabularnewline
35 & 0.610705 & 0.778589 & 0.389295 \tabularnewline
36 & 0.55224 & 0.89552 & 0.44776 \tabularnewline
37 & 0.545361 & 0.909279 & 0.454639 \tabularnewline
38 & 0.514256 & 0.971489 & 0.485744 \tabularnewline
39 & 0.467106 & 0.934212 & 0.532894 \tabularnewline
40 & 0.579576 & 0.840848 & 0.420424 \tabularnewline
41 & 0.524559 & 0.950882 & 0.475441 \tabularnewline
42 & 0.504591 & 0.990818 & 0.495409 \tabularnewline
43 & 0.46342 & 0.92684 & 0.53658 \tabularnewline
44 & 0.425686 & 0.851373 & 0.574314 \tabularnewline
45 & 0.374538 & 0.749076 & 0.625462 \tabularnewline
46 & 0.330042 & 0.660084 & 0.669958 \tabularnewline
47 & 0.370755 & 0.74151 & 0.629245 \tabularnewline
48 & 0.374648 & 0.749297 & 0.625352 \tabularnewline
49 & 0.328207 & 0.656413 & 0.671793 \tabularnewline
50 & 0.282511 & 0.565021 & 0.717489 \tabularnewline
51 & 0.329216 & 0.658432 & 0.670784 \tabularnewline
52 & 0.279794 & 0.559588 & 0.720206 \tabularnewline
53 & 0.253778 & 0.507557 & 0.746222 \tabularnewline
54 & 0.214 & 0.428001 & 0.786 \tabularnewline
55 & 0.181543 & 0.363085 & 0.818457 \tabularnewline
56 & 0.277732 & 0.555465 & 0.722268 \tabularnewline
57 & 0.302972 & 0.605944 & 0.697028 \tabularnewline
58 & 0.345793 & 0.691586 & 0.654207 \tabularnewline
59 & 0.346668 & 0.693336 & 0.653332 \tabularnewline
60 & 0.348528 & 0.697056 & 0.651472 \tabularnewline
61 & 0.333708 & 0.667417 & 0.666292 \tabularnewline
62 & 0.327605 & 0.655209 & 0.672395 \tabularnewline
63 & 0.494862 & 0.989724 & 0.505138 \tabularnewline
64 & 0.45541 & 0.91082 & 0.54459 \tabularnewline
65 & 0.430988 & 0.861975 & 0.569012 \tabularnewline
66 & 0.38192 & 0.763841 & 0.61808 \tabularnewline
67 & 0.347877 & 0.695754 & 0.652123 \tabularnewline
68 & 0.522801 & 0.954399 & 0.477199 \tabularnewline
69 & 0.49163 & 0.983259 & 0.50837 \tabularnewline
70 & 0.43768 & 0.875359 & 0.56232 \tabularnewline
71 & 0.38359 & 0.76718 & 0.61641 \tabularnewline
72 & 0.348435 & 0.696869 & 0.651565 \tabularnewline
73 & 0.45631 & 0.91262 & 0.54369 \tabularnewline
74 & 0.486642 & 0.973283 & 0.513358 \tabularnewline
75 & 0.484418 & 0.968835 & 0.515582 \tabularnewline
76 & 0.476424 & 0.952847 & 0.523576 \tabularnewline
77 & 0.483552 & 0.967104 & 0.516448 \tabularnewline
78 & 0.43485 & 0.8697 & 0.56515 \tabularnewline
79 & 0.537906 & 0.924189 & 0.462094 \tabularnewline
80 & 0.477113 & 0.954226 & 0.522887 \tabularnewline
81 & 0.456289 & 0.912578 & 0.543711 \tabularnewline
82 & 0.397578 & 0.795156 & 0.602422 \tabularnewline
83 & 0.677966 & 0.644069 & 0.322034 \tabularnewline
84 & 0.617809 & 0.764383 & 0.382191 \tabularnewline
85 & 0.586345 & 0.827309 & 0.413655 \tabularnewline
86 & 0.515721 & 0.968559 & 0.484279 \tabularnewline
87 & 0.49786 & 0.99572 & 0.50214 \tabularnewline
88 & 0.432185 & 0.86437 & 0.567815 \tabularnewline
89 & 0.418189 & 0.836378 & 0.581811 \tabularnewline
90 & 0.451767 & 0.903533 & 0.548233 \tabularnewline
91 & 0.576204 & 0.847593 & 0.423796 \tabularnewline
92 & 0.495716 & 0.991432 & 0.504284 \tabularnewline
93 & 0.423648 & 0.847295 & 0.576352 \tabularnewline
94 & 0.445579 & 0.891158 & 0.554421 \tabularnewline
95 & 0.35893 & 0.71786 & 0.64107 \tabularnewline
96 & 0.503211 & 0.993578 & 0.496789 \tabularnewline
97 & 0.466203 & 0.932407 & 0.533797 \tabularnewline
98 & 0.583159 & 0.833682 & 0.416841 \tabularnewline
99 & 0.470538 & 0.941077 & 0.529462 \tabularnewline
100 & 0.527913 & 0.944174 & 0.472087 \tabularnewline
101 & 0.383919 & 0.767838 & 0.616081 \tabularnewline
102 & 0.245221 & 0.490443 & 0.754779 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270891&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]7[/C][C]0.622946[/C][C]0.754107[/C][C]0.377054[/C][/ROW]
[ROW][C]8[/C][C]0.807997[/C][C]0.384006[/C][C]0.192003[/C][/ROW]
[ROW][C]9[/C][C]0.70796[/C][C]0.58408[/C][C]0.29204[/C][/ROW]
[ROW][C]10[/C][C]0.742207[/C][C]0.515585[/C][C]0.257793[/C][/ROW]
[ROW][C]11[/C][C]0.70516[/C][C]0.58968[/C][C]0.29484[/C][/ROW]
[ROW][C]12[/C][C]0.900043[/C][C]0.199915[/C][C]0.0999574[/C][/ROW]
[ROW][C]13[/C][C]0.859368[/C][C]0.281263[/C][C]0.140632[/C][/ROW]
[ROW][C]14[/C][C]0.879743[/C][C]0.240514[/C][C]0.120257[/C][/ROW]
[ROW][C]15[/C][C]0.965197[/C][C]0.0696059[/C][C]0.0348029[/C][/ROW]
[ROW][C]16[/C][C]0.946493[/C][C]0.107014[/C][C]0.0535068[/C][/ROW]
[ROW][C]17[/C][C]0.927804[/C][C]0.144392[/C][C]0.0721958[/C][/ROW]
[ROW][C]18[/C][C]0.907772[/C][C]0.184457[/C][C]0.0922283[/C][/ROW]
[ROW][C]19[/C][C]0.8728[/C][C]0.254399[/C][C]0.1272[/C][/ROW]
[ROW][C]20[/C][C]0.832698[/C][C]0.334605[/C][C]0.167302[/C][/ROW]
[ROW][C]21[/C][C]0.851394[/C][C]0.297211[/C][C]0.148606[/C][/ROW]
[ROW][C]22[/C][C]0.807616[/C][C]0.384768[/C][C]0.192384[/C][/ROW]
[ROW][C]23[/C][C]0.794502[/C][C]0.410997[/C][C]0.205498[/C][/ROW]
[ROW][C]24[/C][C]0.744748[/C][C]0.510503[/C][C]0.255252[/C][/ROW]
[ROW][C]25[/C][C]0.685731[/C][C]0.628537[/C][C]0.314269[/C][/ROW]
[ROW][C]26[/C][C]0.882247[/C][C]0.235506[/C][C]0.117753[/C][/ROW]
[ROW][C]27[/C][C]0.86856[/C][C]0.26288[/C][C]0.13144[/C][/ROW]
[ROW][C]28[/C][C]0.835782[/C][C]0.328436[/C][C]0.164218[/C][/ROW]
[ROW][C]29[/C][C]0.805994[/C][C]0.388011[/C][C]0.194006[/C][/ROW]
[ROW][C]30[/C][C]0.79914[/C][C]0.401719[/C][C]0.20086[/C][/ROW]
[ROW][C]31[/C][C]0.778261[/C][C]0.443479[/C][C]0.221739[/C][/ROW]
[ROW][C]32[/C][C]0.754123[/C][C]0.491754[/C][C]0.245877[/C][/ROW]
[ROW][C]33[/C][C]0.721316[/C][C]0.557369[/C][C]0.278684[/C][/ROW]
[ROW][C]34[/C][C]0.667538[/C][C]0.664923[/C][C]0.332462[/C][/ROW]
[ROW][C]35[/C][C]0.610705[/C][C]0.778589[/C][C]0.389295[/C][/ROW]
[ROW][C]36[/C][C]0.55224[/C][C]0.89552[/C][C]0.44776[/C][/ROW]
[ROW][C]37[/C][C]0.545361[/C][C]0.909279[/C][C]0.454639[/C][/ROW]
[ROW][C]38[/C][C]0.514256[/C][C]0.971489[/C][C]0.485744[/C][/ROW]
[ROW][C]39[/C][C]0.467106[/C][C]0.934212[/C][C]0.532894[/C][/ROW]
[ROW][C]40[/C][C]0.579576[/C][C]0.840848[/C][C]0.420424[/C][/ROW]
[ROW][C]41[/C][C]0.524559[/C][C]0.950882[/C][C]0.475441[/C][/ROW]
[ROW][C]42[/C][C]0.504591[/C][C]0.990818[/C][C]0.495409[/C][/ROW]
[ROW][C]43[/C][C]0.46342[/C][C]0.92684[/C][C]0.53658[/C][/ROW]
[ROW][C]44[/C][C]0.425686[/C][C]0.851373[/C][C]0.574314[/C][/ROW]
[ROW][C]45[/C][C]0.374538[/C][C]0.749076[/C][C]0.625462[/C][/ROW]
[ROW][C]46[/C][C]0.330042[/C][C]0.660084[/C][C]0.669958[/C][/ROW]
[ROW][C]47[/C][C]0.370755[/C][C]0.74151[/C][C]0.629245[/C][/ROW]
[ROW][C]48[/C][C]0.374648[/C][C]0.749297[/C][C]0.625352[/C][/ROW]
[ROW][C]49[/C][C]0.328207[/C][C]0.656413[/C][C]0.671793[/C][/ROW]
[ROW][C]50[/C][C]0.282511[/C][C]0.565021[/C][C]0.717489[/C][/ROW]
[ROW][C]51[/C][C]0.329216[/C][C]0.658432[/C][C]0.670784[/C][/ROW]
[ROW][C]52[/C][C]0.279794[/C][C]0.559588[/C][C]0.720206[/C][/ROW]
[ROW][C]53[/C][C]0.253778[/C][C]0.507557[/C][C]0.746222[/C][/ROW]
[ROW][C]54[/C][C]0.214[/C][C]0.428001[/C][C]0.786[/C][/ROW]
[ROW][C]55[/C][C]0.181543[/C][C]0.363085[/C][C]0.818457[/C][/ROW]
[ROW][C]56[/C][C]0.277732[/C][C]0.555465[/C][C]0.722268[/C][/ROW]
[ROW][C]57[/C][C]0.302972[/C][C]0.605944[/C][C]0.697028[/C][/ROW]
[ROW][C]58[/C][C]0.345793[/C][C]0.691586[/C][C]0.654207[/C][/ROW]
[ROW][C]59[/C][C]0.346668[/C][C]0.693336[/C][C]0.653332[/C][/ROW]
[ROW][C]60[/C][C]0.348528[/C][C]0.697056[/C][C]0.651472[/C][/ROW]
[ROW][C]61[/C][C]0.333708[/C][C]0.667417[/C][C]0.666292[/C][/ROW]
[ROW][C]62[/C][C]0.327605[/C][C]0.655209[/C][C]0.672395[/C][/ROW]
[ROW][C]63[/C][C]0.494862[/C][C]0.989724[/C][C]0.505138[/C][/ROW]
[ROW][C]64[/C][C]0.45541[/C][C]0.91082[/C][C]0.54459[/C][/ROW]
[ROW][C]65[/C][C]0.430988[/C][C]0.861975[/C][C]0.569012[/C][/ROW]
[ROW][C]66[/C][C]0.38192[/C][C]0.763841[/C][C]0.61808[/C][/ROW]
[ROW][C]67[/C][C]0.347877[/C][C]0.695754[/C][C]0.652123[/C][/ROW]
[ROW][C]68[/C][C]0.522801[/C][C]0.954399[/C][C]0.477199[/C][/ROW]
[ROW][C]69[/C][C]0.49163[/C][C]0.983259[/C][C]0.50837[/C][/ROW]
[ROW][C]70[/C][C]0.43768[/C][C]0.875359[/C][C]0.56232[/C][/ROW]
[ROW][C]71[/C][C]0.38359[/C][C]0.76718[/C][C]0.61641[/C][/ROW]
[ROW][C]72[/C][C]0.348435[/C][C]0.696869[/C][C]0.651565[/C][/ROW]
[ROW][C]73[/C][C]0.45631[/C][C]0.91262[/C][C]0.54369[/C][/ROW]
[ROW][C]74[/C][C]0.486642[/C][C]0.973283[/C][C]0.513358[/C][/ROW]
[ROW][C]75[/C][C]0.484418[/C][C]0.968835[/C][C]0.515582[/C][/ROW]
[ROW][C]76[/C][C]0.476424[/C][C]0.952847[/C][C]0.523576[/C][/ROW]
[ROW][C]77[/C][C]0.483552[/C][C]0.967104[/C][C]0.516448[/C][/ROW]
[ROW][C]78[/C][C]0.43485[/C][C]0.8697[/C][C]0.56515[/C][/ROW]
[ROW][C]79[/C][C]0.537906[/C][C]0.924189[/C][C]0.462094[/C][/ROW]
[ROW][C]80[/C][C]0.477113[/C][C]0.954226[/C][C]0.522887[/C][/ROW]
[ROW][C]81[/C][C]0.456289[/C][C]0.912578[/C][C]0.543711[/C][/ROW]
[ROW][C]82[/C][C]0.397578[/C][C]0.795156[/C][C]0.602422[/C][/ROW]
[ROW][C]83[/C][C]0.677966[/C][C]0.644069[/C][C]0.322034[/C][/ROW]
[ROW][C]84[/C][C]0.617809[/C][C]0.764383[/C][C]0.382191[/C][/ROW]
[ROW][C]85[/C][C]0.586345[/C][C]0.827309[/C][C]0.413655[/C][/ROW]
[ROW][C]86[/C][C]0.515721[/C][C]0.968559[/C][C]0.484279[/C][/ROW]
[ROW][C]87[/C][C]0.49786[/C][C]0.99572[/C][C]0.50214[/C][/ROW]
[ROW][C]88[/C][C]0.432185[/C][C]0.86437[/C][C]0.567815[/C][/ROW]
[ROW][C]89[/C][C]0.418189[/C][C]0.836378[/C][C]0.581811[/C][/ROW]
[ROW][C]90[/C][C]0.451767[/C][C]0.903533[/C][C]0.548233[/C][/ROW]
[ROW][C]91[/C][C]0.576204[/C][C]0.847593[/C][C]0.423796[/C][/ROW]
[ROW][C]92[/C][C]0.495716[/C][C]0.991432[/C][C]0.504284[/C][/ROW]
[ROW][C]93[/C][C]0.423648[/C][C]0.847295[/C][C]0.576352[/C][/ROW]
[ROW][C]94[/C][C]0.445579[/C][C]0.891158[/C][C]0.554421[/C][/ROW]
[ROW][C]95[/C][C]0.35893[/C][C]0.71786[/C][C]0.64107[/C][/ROW]
[ROW][C]96[/C][C]0.503211[/C][C]0.993578[/C][C]0.496789[/C][/ROW]
[ROW][C]97[/C][C]0.466203[/C][C]0.932407[/C][C]0.533797[/C][/ROW]
[ROW][C]98[/C][C]0.583159[/C][C]0.833682[/C][C]0.416841[/C][/ROW]
[ROW][C]99[/C][C]0.470538[/C][C]0.941077[/C][C]0.529462[/C][/ROW]
[ROW][C]100[/C][C]0.527913[/C][C]0.944174[/C][C]0.472087[/C][/ROW]
[ROW][C]101[/C][C]0.383919[/C][C]0.767838[/C][C]0.616081[/C][/ROW]
[ROW][C]102[/C][C]0.245221[/C][C]0.490443[/C][C]0.754779[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270891&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270891&T=5

As an alternative you can also use a QR Code:

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

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
7	0.622946	0.754107	0.377054
8	0.807997	0.384006	0.192003
9	0.70796	0.58408	0.29204
10	0.742207	0.515585	0.257793
11	0.70516	0.58968	0.29484
12	0.900043	0.199915	0.0999574
13	0.859368	0.281263	0.140632
14	0.879743	0.240514	0.120257
15	0.965197	0.0696059	0.0348029
16	0.946493	0.107014	0.0535068
17	0.927804	0.144392	0.0721958
18	0.907772	0.184457	0.0922283
19	0.8728	0.254399	0.1272
20	0.832698	0.334605	0.167302
21	0.851394	0.297211	0.148606
22	0.807616	0.384768	0.192384
23	0.794502	0.410997	0.205498
24	0.744748	0.510503	0.255252
25	0.685731	0.628537	0.314269
26	0.882247	0.235506	0.117753
27	0.86856	0.26288	0.13144
28	0.835782	0.328436	0.164218
29	0.805994	0.388011	0.194006
30	0.79914	0.401719	0.20086
31	0.778261	0.443479	0.221739
32	0.754123	0.491754	0.245877
33	0.721316	0.557369	0.278684
34	0.667538	0.664923	0.332462
35	0.610705	0.778589	0.389295
36	0.55224	0.89552	0.44776
37	0.545361	0.909279	0.454639
38	0.514256	0.971489	0.485744
39	0.467106	0.934212	0.532894
40	0.579576	0.840848	0.420424
41	0.524559	0.950882	0.475441
42	0.504591	0.990818	0.495409
43	0.46342	0.92684	0.53658
44	0.425686	0.851373	0.574314
45	0.374538	0.749076	0.625462
46	0.330042	0.660084	0.669958
47	0.370755	0.74151	0.629245
48	0.374648	0.749297	0.625352
49	0.328207	0.656413	0.671793
50	0.282511	0.565021	0.717489
51	0.329216	0.658432	0.670784
52	0.279794	0.559588	0.720206
53	0.253778	0.507557	0.746222
54	0.214	0.428001	0.786
55	0.181543	0.363085	0.818457
56	0.277732	0.555465	0.722268
57	0.302972	0.605944	0.697028
58	0.345793	0.691586	0.654207
59	0.346668	0.693336	0.653332
60	0.348528	0.697056	0.651472
61	0.333708	0.667417	0.666292
62	0.327605	0.655209	0.672395
63	0.494862	0.989724	0.505138
64	0.45541	0.91082	0.54459
65	0.430988	0.861975	0.569012
66	0.38192	0.763841	0.61808
67	0.347877	0.695754	0.652123
68	0.522801	0.954399	0.477199
69	0.49163	0.983259	0.50837
70	0.43768	0.875359	0.56232
71	0.38359	0.76718	0.61641
72	0.348435	0.696869	0.651565
73	0.45631	0.91262	0.54369
74	0.486642	0.973283	0.513358
75	0.484418	0.968835	0.515582
76	0.476424	0.952847	0.523576
77	0.483552	0.967104	0.516448
78	0.43485	0.8697	0.56515
79	0.537906	0.924189	0.462094
80	0.477113	0.954226	0.522887
81	0.456289	0.912578	0.543711
82	0.397578	0.795156	0.602422
83	0.677966	0.644069	0.322034
84	0.617809	0.764383	0.382191
85	0.586345	0.827309	0.413655
86	0.515721	0.968559	0.484279
87	0.49786	0.99572	0.50214
88	0.432185	0.86437	0.567815
89	0.418189	0.836378	0.581811
90	0.451767	0.903533	0.548233
91	0.576204	0.847593	0.423796
92	0.495716	0.991432	0.504284
93	0.423648	0.847295	0.576352
94	0.445579	0.891158	0.554421
95	0.35893	0.71786	0.64107
96	0.503211	0.993578	0.496789
97	0.466203	0.932407	0.533797
98	0.583159	0.833682	0.416841
99	0.470538	0.941077	0.529462
100	0.527913	0.944174	0.472087
101	0.383919	0.767838	0.616081
102	0.245221	0.490443	0.754779

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	0.0104167	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 & 0.0104167 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270891&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]0.0104167[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270891&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270891&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	0.0104167	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