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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Thu, 18 Dec 2014 12:43:12 +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/t1418906633rusxtnoffcgr93p.htm/, Retrieved Sun, 19 May 2024 18:20:02 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=270865, Retrieved Sun, 19 May 2024 18:20:02 +0000

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

-     [Multiple Regression] [] [2013-11-04 07:18:26] [0307e7a6407eb638caabc417e3a6b260]
- RMPD  [Multiple Regression] [] [2014-11-13 19:21:10] [5efa6717cfe6505454df834acc87b53b]
- R PD      [Multiple Regression] [] [2014-12-18 12:43:12] [4621f922aed0297f88122271e88ec2ef] [Current]

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Dataseries X:

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49 40 26 50 4
41 58 57 62 4
38 75 37 54 5
44 49 67 71 4
37 59 43 54 4
41 59 52 65 9
48 72 52 73 8
34 51 43 52 11
39 61 84 84 4
48 53 67 42 4
32 41 49 66 6
40 57 70 65 4
41 50 52 78 8
45 50 58 73 4
54 61 68 75 4
45 83 62 72 11
41 51 43 66 4
42 60 56 70 4
43 61 56 61 6
48 59 74 81 6
41 61 63 69 8
43 63 58 71 5
46 67 63 68 9
49 72 53 70 4
51 77 57 68 7
36 52 51 61 10
43 56 64 67 4
40 46 53 76 4
32 34 29 70 7
37 64 54 60 12
36 52 58 72 7
49 69 43 69 5
43 58 51 71 8
35 53 53 62 5
47 56 54 70 4
41 57 56 64 9
44 61 61 58 7
48 57 47 76 4
33 33 39 52 4
38 54 48 59 4
57 66 50 68 4
32 33 35 76 4
42 60 30 65 7
45 58 68 67 4
41 43 49 59 7
42 49 61 69 4
59 93 67 76 4
45 69 47 63 4
45 43 56 75 4
36 63 50 63 8
27 34 43 60 4
48 57 67 73 4
39 42 62 63 4
49 64 57 70 4
47 60 41 75 7
42 47 54 66 12
41 44 45 63 4
43 56 48 63 4
41 46 61 64 4
44 57 56 70 5
50 79 41 75 15
36 37 43 61 5
42 65 53 60 10
35 38 44 62 9
46 58 66 73 8
52 59 58 61 4
34 49 46 66 5
36 25 37 64 4
49 63 51 59 9
44 59 51 64 4
43 49 66 56 4
30 50 37 78 4
43 52 59 53 6
42 56 42 67 7
48 59 38 59 5
45 48 66 66 4
41 39 34 68 4
44 30 53 71 4
44 46 49 66 4
38 38 55 73 4
49 51 49 72 4
35 51 59 71 6
37 70 40 59 10
41 50 58 64 7
40 41 60 66 4
55 74 63 78 4
36 50 56 68 7
40 59 54 73 4
39 48 52 62 8
38 77 34 65 11
45 51 69 68 6
32 46 32 65 14
41 45 48 60 5
36 53 67 71 4
41 51 58 65 8
36 47 57 68 9
40 57 42 64 4
52 71 64 74 4
44 65 58 69 5
34 50 66 76 4
38 67 61 72 4
47 42 52 67 4
43 64 51 63 7
42 59 55 59 10
42 61 60 66 5
40 37 56 62 4
47 55 63 69 4
49 51 61 66 4

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	'Gwilym Jenkins' @ jenkins.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 & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270865&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]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270865&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270865&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	'Gwilym Jenkins' @ jenkins.wessa.net

Multiple Linear Regression - Estimated Regression Equation
POS_PERF[t] = + 24.9069 + 0.27853NEG_PERF[t] + 0.0759037AMS.I[t] + 0.0251038AMS.E[t] -0.64415AMS.A[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
POS_PERF[t] =  +  24.9069 +  0.27853NEG_PERF[t] +  0.0759037AMS.I[t] +  0.0251038AMS.E[t] -0.64415AMS.A[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270865&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]POS_PERF[t] =  +  24.9069 +  0.27853NEG_PERF[t] +  0.0759037AMS.I[t] +  0.0251038AMS.E[t] -0.64415AMS.A[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270865&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270865&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
POS_PERF[t] = + 24.9069 + 0.27853NEG_PERF[t] + 0.0759037AMS.I[t] + 0.0251038AMS.E[t] -0.64415AMS.A[t] + e[t]

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	24.9069	4.8134	5.174	1.13303e-06	5.66514e-07
NEG_PERF	0.27853	0.0415258	6.707	1.09218e-09	5.46089e-10
AMS.I	0.0759037	0.0468545	1.62	0.108293	0.0541464
AMS.E	0.0251038	0.0685698	0.3661	0.715037	0.357519
AMS.A	-0.64415	0.191915	-3.356	0.00110682	0.000553411

\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) & 24.9069 & 4.8134 & 5.174 & 1.13303e-06 & 5.66514e-07 \tabularnewline
NEG_PERF & 0.27853 & 0.0415258 & 6.707 & 1.09218e-09 & 5.46089e-10 \tabularnewline
AMS.I & 0.0759037 & 0.0468545 & 1.62 & 0.108293 & 0.0541464 \tabularnewline
AMS.E & 0.0251038 & 0.0685698 & 0.3661 & 0.715037 & 0.357519 \tabularnewline
AMS.A & -0.64415 & 0.191915 & -3.356 & 0.00110682 & 0.000553411 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270865&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]24.9069[/C][C]4.8134[/C][C]5.174[/C][C]1.13303e-06[/C][C]5.66514e-07[/C][/ROW]
[ROW][C]NEG_PERF[/C][C]0.27853[/C][C]0.0415258[/C][C]6.707[/C][C]1.09218e-09[/C][C]5.46089e-10[/C][/ROW]
[ROW][C]AMS.I[/C][C]0.0759037[/C][C]0.0468545[/C][C]1.62[/C][C]0.108293[/C][C]0.0541464[/C][/ROW]
[ROW][C]AMS.E[/C][C]0.0251038[/C][C]0.0685698[/C][C]0.3661[/C][C]0.715037[/C][C]0.357519[/C][/ROW]
[ROW][C]AMS.A[/C][C]-0.64415[/C][C]0.191915[/C][C]-3.356[/C][C]0.00110682[/C][C]0.000553411[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270865&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270865&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)	24.9069	4.8134	5.174	1.13303e-06	5.66514e-07
NEG_PERF	0.27853	0.0415258	6.707	1.09218e-09	5.46089e-10
AMS.I	0.0759037	0.0468545	1.62	0.108293	0.0541464
AMS.E	0.0251038	0.0685698	0.3661	0.715037	0.357519
AMS.A	-0.64415	0.191915	-3.356	0.00110682	0.000553411

Multiple Linear Regression - Regression Statistics
Multiple R	0.627938
R-squared	0.394306
Adjusted R-squared	0.370783
F-TEST (value)	16.7632
F-TEST (DF numerator)	4
F-TEST (DF denominator)	103
p-value	1.3019e-10
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	4.63715
Sum Squared Residuals	2214.82

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.627938 \tabularnewline
R-squared & 0.394306 \tabularnewline
Adjusted R-squared & 0.370783 \tabularnewline
F-TEST (value) & 16.7632 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 103 \tabularnewline
p-value & 1.3019e-10 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 4.63715 \tabularnewline
Sum Squared Residuals & 2214.82 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270865&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.627938[/C][/ROW]
[ROW][C]R-squared[/C][C]0.394306[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.370783[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]16.7632[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]103[/C][/ROW]
[ROW][C]p-value[/C][C]1.3019e-10[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]4.63715[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]2214.82[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270865&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270865&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	0.627938
R-squared	0.394306
Adjusted R-squared	0.370783
F-TEST (value)	16.7632
F-TEST (DF numerator)	4
F-TEST (DF denominator)	103
p-value	1.3019e-10
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	4.63715
Sum Squared Residuals	2214.82

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	49	36.7002	12.2998
2	41	44.368	-3.36798
3	38	46.7399	-8.73993
4	44	42.8462	1.15382
5	37	43.383	-6.38302
6	41	41.1215	-0.121549
7	48	45.5874	2.41258
8	34	36.5955	-2.59552
9	39	47.8053	-8.80525
10	48	43.2323	4.76771
11	32	37.8378	-5.83785
12	40	45.1515	-5.15151
13	41	39.5853	1.41472
14	45	42.4918	2.50822
15	54	46.3649	7.63514
16	45	47.4527	-2.45274
17	41	41.456	-0.456028
18	42	45.05	-3.04996
19	43	43.8143	-0.814259
20	48	45.1255	2.87446
21	41	43.2581	-2.25812
22	43	45.4183	-2.41832
23	46	44.26	1.73996
24	49	48.1646	0.835386
25	51	47.8782	3.12178
26	36	38.3514	-2.35137
27	43	44.4678	-1.46776
28	40	41.0735	-1.07345
29	32	33.8263	-1.82633
30	37	40.608	-3.60804
31	36	41.0913	-5.09129
32	49	45.9007	3.09927
33	43	41.5619	1.43811
34	35	42.0276	-7.02756
35	47	43.784	3.21596
36	41	40.843	0.157
37	44	43.4743	0.525684
38	48	43.6819	4.31814
39	33	35.7874	-2.78742
40	38	42.4954	-4.49541
41	57	46.2155	10.7845
42	32	36.0863	-4.08629
43	42	41.0185	0.981503
44	45	45.3284	-0.328436
45	41	37.575	3.42497
46	42	42.3405	-0.340546
47	59	55.227	3.77298
48	45	46.6979	-1.69787
49	45	40.4405	4.55953
50	36	42.6778	-6.6778
51	27	36.5704	-9.57039
52	48	45.1246	2.87537
53	39	40.3161	-1.31612
54	49	46.24	2.76001
55	47	42.1045	4.89552
56	42	36.0236	5.97635
57	41	39.5828	1.41719
58	43	43.1529	-0.152886
59	41	41.3794	-0.379436
60	44	43.5702	0.429777
61	50	42.2433	7.75665
62	36	36.7869	-0.786936
63	42	42.099	-0.0989647
64	35	34.5899	0.410126
65	46	42.7507	3.24935
66	52	44.6973	7.30269
67	34	40.4825	-6.48253
68	36	33.7086	2.29139
69	49	42.0091	6.99086
70	44	44.2413	-0.241292
71	43	42.3937	0.606285
72	30	41.0233	-11.0233
73	43	41.3344	1.66563
74	42	40.8654	1.13457
75	48	42.4849	5.51513
76	45	42.3662	2.63378
77	41	37.4807	3.51926
78	44	36.4915	7.50855
79	44	40.5188	3.4812
80	38	38.9217	-0.921707
81	49	42.0621	6.93793
82	35	41.5077	-6.50771
83	37	42.4798	-5.47976
84	41	40.3334	0.666604
85	40	39.9611	0.0389107
86	55	49.6815	5.31846
87	36	40.282	-4.282
88	40	44.6949	-4.69494
89	39	38.6266	0.373445
90	38	43.4805	-5.48053
91	45	42.1914	2.80857
92	32	32.7618	-0.761832
93	41	39.3696	1.63041
94	36	43.9603	-7.9603
95	41	39.9929	1.00712
96	36	38.234	-2.23402
97	40	43.0011	-3.0011
98	52	48.8214	3.17856
99	44	45.9252	-1.92517
100	34	43.1743	-9.17432
101	38	47.4294	-9.4294
102	47	39.6575	7.34251
103	43	43.6764	-0.676389
104	42	40.5545	1.44551
105	42	44.8875	-2.88754
106	40	38.4429	1.55706
107	47	44.1635	2.83647
108	49	42.8223	6.17771

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 49 & 36.7002 & 12.2998 \tabularnewline
2 & 41 & 44.368 & -3.36798 \tabularnewline
3 & 38 & 46.7399 & -8.73993 \tabularnewline
4 & 44 & 42.8462 & 1.15382 \tabularnewline
5 & 37 & 43.383 & -6.38302 \tabularnewline
6 & 41 & 41.1215 & -0.121549 \tabularnewline
7 & 48 & 45.5874 & 2.41258 \tabularnewline
8 & 34 & 36.5955 & -2.59552 \tabularnewline
9 & 39 & 47.8053 & -8.80525 \tabularnewline
10 & 48 & 43.2323 & 4.76771 \tabularnewline
11 & 32 & 37.8378 & -5.83785 \tabularnewline
12 & 40 & 45.1515 & -5.15151 \tabularnewline
13 & 41 & 39.5853 & 1.41472 \tabularnewline
14 & 45 & 42.4918 & 2.50822 \tabularnewline
15 & 54 & 46.3649 & 7.63514 \tabularnewline
16 & 45 & 47.4527 & -2.45274 \tabularnewline
17 & 41 & 41.456 & -0.456028 \tabularnewline
18 & 42 & 45.05 & -3.04996 \tabularnewline
19 & 43 & 43.8143 & -0.814259 \tabularnewline
20 & 48 & 45.1255 & 2.87446 \tabularnewline
21 & 41 & 43.2581 & -2.25812 \tabularnewline
22 & 43 & 45.4183 & -2.41832 \tabularnewline
23 & 46 & 44.26 & 1.73996 \tabularnewline
24 & 49 & 48.1646 & 0.835386 \tabularnewline
25 & 51 & 47.8782 & 3.12178 \tabularnewline
26 & 36 & 38.3514 & -2.35137 \tabularnewline
27 & 43 & 44.4678 & -1.46776 \tabularnewline
28 & 40 & 41.0735 & -1.07345 \tabularnewline
29 & 32 & 33.8263 & -1.82633 \tabularnewline
30 & 37 & 40.608 & -3.60804 \tabularnewline
31 & 36 & 41.0913 & -5.09129 \tabularnewline
32 & 49 & 45.9007 & 3.09927 \tabularnewline
33 & 43 & 41.5619 & 1.43811 \tabularnewline
34 & 35 & 42.0276 & -7.02756 \tabularnewline
35 & 47 & 43.784 & 3.21596 \tabularnewline
36 & 41 & 40.843 & 0.157 \tabularnewline
37 & 44 & 43.4743 & 0.525684 \tabularnewline
38 & 48 & 43.6819 & 4.31814 \tabularnewline
39 & 33 & 35.7874 & -2.78742 \tabularnewline
40 & 38 & 42.4954 & -4.49541 \tabularnewline
41 & 57 & 46.2155 & 10.7845 \tabularnewline
42 & 32 & 36.0863 & -4.08629 \tabularnewline
43 & 42 & 41.0185 & 0.981503 \tabularnewline
44 & 45 & 45.3284 & -0.328436 \tabularnewline
45 & 41 & 37.575 & 3.42497 \tabularnewline
46 & 42 & 42.3405 & -0.340546 \tabularnewline
47 & 59 & 55.227 & 3.77298 \tabularnewline
48 & 45 & 46.6979 & -1.69787 \tabularnewline
49 & 45 & 40.4405 & 4.55953 \tabularnewline
50 & 36 & 42.6778 & -6.6778 \tabularnewline
51 & 27 & 36.5704 & -9.57039 \tabularnewline
52 & 48 & 45.1246 & 2.87537 \tabularnewline
53 & 39 & 40.3161 & -1.31612 \tabularnewline
54 & 49 & 46.24 & 2.76001 \tabularnewline
55 & 47 & 42.1045 & 4.89552 \tabularnewline
56 & 42 & 36.0236 & 5.97635 \tabularnewline
57 & 41 & 39.5828 & 1.41719 \tabularnewline
58 & 43 & 43.1529 & -0.152886 \tabularnewline
59 & 41 & 41.3794 & -0.379436 \tabularnewline
60 & 44 & 43.5702 & 0.429777 \tabularnewline
61 & 50 & 42.2433 & 7.75665 \tabularnewline
62 & 36 & 36.7869 & -0.786936 \tabularnewline
63 & 42 & 42.099 & -0.0989647 \tabularnewline
64 & 35 & 34.5899 & 0.410126 \tabularnewline
65 & 46 & 42.7507 & 3.24935 \tabularnewline
66 & 52 & 44.6973 & 7.30269 \tabularnewline
67 & 34 & 40.4825 & -6.48253 \tabularnewline
68 & 36 & 33.7086 & 2.29139 \tabularnewline
69 & 49 & 42.0091 & 6.99086 \tabularnewline
70 & 44 & 44.2413 & -0.241292 \tabularnewline
71 & 43 & 42.3937 & 0.606285 \tabularnewline
72 & 30 & 41.0233 & -11.0233 \tabularnewline
73 & 43 & 41.3344 & 1.66563 \tabularnewline
74 & 42 & 40.8654 & 1.13457 \tabularnewline
75 & 48 & 42.4849 & 5.51513 \tabularnewline
76 & 45 & 42.3662 & 2.63378 \tabularnewline
77 & 41 & 37.4807 & 3.51926 \tabularnewline
78 & 44 & 36.4915 & 7.50855 \tabularnewline
79 & 44 & 40.5188 & 3.4812 \tabularnewline
80 & 38 & 38.9217 & -0.921707 \tabularnewline
81 & 49 & 42.0621 & 6.93793 \tabularnewline
82 & 35 & 41.5077 & -6.50771 \tabularnewline
83 & 37 & 42.4798 & -5.47976 \tabularnewline
84 & 41 & 40.3334 & 0.666604 \tabularnewline
85 & 40 & 39.9611 & 0.0389107 \tabularnewline
86 & 55 & 49.6815 & 5.31846 \tabularnewline
87 & 36 & 40.282 & -4.282 \tabularnewline
88 & 40 & 44.6949 & -4.69494 \tabularnewline
89 & 39 & 38.6266 & 0.373445 \tabularnewline
90 & 38 & 43.4805 & -5.48053 \tabularnewline
91 & 45 & 42.1914 & 2.80857 \tabularnewline
92 & 32 & 32.7618 & -0.761832 \tabularnewline
93 & 41 & 39.3696 & 1.63041 \tabularnewline
94 & 36 & 43.9603 & -7.9603 \tabularnewline
95 & 41 & 39.9929 & 1.00712 \tabularnewline
96 & 36 & 38.234 & -2.23402 \tabularnewline
97 & 40 & 43.0011 & -3.0011 \tabularnewline
98 & 52 & 48.8214 & 3.17856 \tabularnewline
99 & 44 & 45.9252 & -1.92517 \tabularnewline
100 & 34 & 43.1743 & -9.17432 \tabularnewline
101 & 38 & 47.4294 & -9.4294 \tabularnewline
102 & 47 & 39.6575 & 7.34251 \tabularnewline
103 & 43 & 43.6764 & -0.676389 \tabularnewline
104 & 42 & 40.5545 & 1.44551 \tabularnewline
105 & 42 & 44.8875 & -2.88754 \tabularnewline
106 & 40 & 38.4429 & 1.55706 \tabularnewline
107 & 47 & 44.1635 & 2.83647 \tabularnewline
108 & 49 & 42.8223 & 6.17771 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270865&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]49[/C][C]36.7002[/C][C]12.2998[/C][/ROW]
[ROW][C]2[/C][C]41[/C][C]44.368[/C][C]-3.36798[/C][/ROW]
[ROW][C]3[/C][C]38[/C][C]46.7399[/C][C]-8.73993[/C][/ROW]
[ROW][C]4[/C][C]44[/C][C]42.8462[/C][C]1.15382[/C][/ROW]
[ROW][C]5[/C][C]37[/C][C]43.383[/C][C]-6.38302[/C][/ROW]
[ROW][C]6[/C][C]41[/C][C]41.1215[/C][C]-0.121549[/C][/ROW]
[ROW][C]7[/C][C]48[/C][C]45.5874[/C][C]2.41258[/C][/ROW]
[ROW][C]8[/C][C]34[/C][C]36.5955[/C][C]-2.59552[/C][/ROW]
[ROW][C]9[/C][C]39[/C][C]47.8053[/C][C]-8.80525[/C][/ROW]
[ROW][C]10[/C][C]48[/C][C]43.2323[/C][C]4.76771[/C][/ROW]
[ROW][C]11[/C][C]32[/C][C]37.8378[/C][C]-5.83785[/C][/ROW]
[ROW][C]12[/C][C]40[/C][C]45.1515[/C][C]-5.15151[/C][/ROW]
[ROW][C]13[/C][C]41[/C][C]39.5853[/C][C]1.41472[/C][/ROW]
[ROW][C]14[/C][C]45[/C][C]42.4918[/C][C]2.50822[/C][/ROW]
[ROW][C]15[/C][C]54[/C][C]46.3649[/C][C]7.63514[/C][/ROW]
[ROW][C]16[/C][C]45[/C][C]47.4527[/C][C]-2.45274[/C][/ROW]
[ROW][C]17[/C][C]41[/C][C]41.456[/C][C]-0.456028[/C][/ROW]
[ROW][C]18[/C][C]42[/C][C]45.05[/C][C]-3.04996[/C][/ROW]
[ROW][C]19[/C][C]43[/C][C]43.8143[/C][C]-0.814259[/C][/ROW]
[ROW][C]20[/C][C]48[/C][C]45.1255[/C][C]2.87446[/C][/ROW]
[ROW][C]21[/C][C]41[/C][C]43.2581[/C][C]-2.25812[/C][/ROW]
[ROW][C]22[/C][C]43[/C][C]45.4183[/C][C]-2.41832[/C][/ROW]
[ROW][C]23[/C][C]46[/C][C]44.26[/C][C]1.73996[/C][/ROW]
[ROW][C]24[/C][C]49[/C][C]48.1646[/C][C]0.835386[/C][/ROW]
[ROW][C]25[/C][C]51[/C][C]47.8782[/C][C]3.12178[/C][/ROW]
[ROW][C]26[/C][C]36[/C][C]38.3514[/C][C]-2.35137[/C][/ROW]
[ROW][C]27[/C][C]43[/C][C]44.4678[/C][C]-1.46776[/C][/ROW]
[ROW][C]28[/C][C]40[/C][C]41.0735[/C][C]-1.07345[/C][/ROW]
[ROW][C]29[/C][C]32[/C][C]33.8263[/C][C]-1.82633[/C][/ROW]
[ROW][C]30[/C][C]37[/C][C]40.608[/C][C]-3.60804[/C][/ROW]
[ROW][C]31[/C][C]36[/C][C]41.0913[/C][C]-5.09129[/C][/ROW]
[ROW][C]32[/C][C]49[/C][C]45.9007[/C][C]3.09927[/C][/ROW]
[ROW][C]33[/C][C]43[/C][C]41.5619[/C][C]1.43811[/C][/ROW]
[ROW][C]34[/C][C]35[/C][C]42.0276[/C][C]-7.02756[/C][/ROW]
[ROW][C]35[/C][C]47[/C][C]43.784[/C][C]3.21596[/C][/ROW]
[ROW][C]36[/C][C]41[/C][C]40.843[/C][C]0.157[/C][/ROW]
[ROW][C]37[/C][C]44[/C][C]43.4743[/C][C]0.525684[/C][/ROW]
[ROW][C]38[/C][C]48[/C][C]43.6819[/C][C]4.31814[/C][/ROW]
[ROW][C]39[/C][C]33[/C][C]35.7874[/C][C]-2.78742[/C][/ROW]
[ROW][C]40[/C][C]38[/C][C]42.4954[/C][C]-4.49541[/C][/ROW]
[ROW][C]41[/C][C]57[/C][C]46.2155[/C][C]10.7845[/C][/ROW]
[ROW][C]42[/C][C]32[/C][C]36.0863[/C][C]-4.08629[/C][/ROW]
[ROW][C]43[/C][C]42[/C][C]41.0185[/C][C]0.981503[/C][/ROW]
[ROW][C]44[/C][C]45[/C][C]45.3284[/C][C]-0.328436[/C][/ROW]
[ROW][C]45[/C][C]41[/C][C]37.575[/C][C]3.42497[/C][/ROW]
[ROW][C]46[/C][C]42[/C][C]42.3405[/C][C]-0.340546[/C][/ROW]
[ROW][C]47[/C][C]59[/C][C]55.227[/C][C]3.77298[/C][/ROW]
[ROW][C]48[/C][C]45[/C][C]46.6979[/C][C]-1.69787[/C][/ROW]
[ROW][C]49[/C][C]45[/C][C]40.4405[/C][C]4.55953[/C][/ROW]
[ROW][C]50[/C][C]36[/C][C]42.6778[/C][C]-6.6778[/C][/ROW]
[ROW][C]51[/C][C]27[/C][C]36.5704[/C][C]-9.57039[/C][/ROW]
[ROW][C]52[/C][C]48[/C][C]45.1246[/C][C]2.87537[/C][/ROW]
[ROW][C]53[/C][C]39[/C][C]40.3161[/C][C]-1.31612[/C][/ROW]
[ROW][C]54[/C][C]49[/C][C]46.24[/C][C]2.76001[/C][/ROW]
[ROW][C]55[/C][C]47[/C][C]42.1045[/C][C]4.89552[/C][/ROW]
[ROW][C]56[/C][C]42[/C][C]36.0236[/C][C]5.97635[/C][/ROW]
[ROW][C]57[/C][C]41[/C][C]39.5828[/C][C]1.41719[/C][/ROW]
[ROW][C]58[/C][C]43[/C][C]43.1529[/C][C]-0.152886[/C][/ROW]
[ROW][C]59[/C][C]41[/C][C]41.3794[/C][C]-0.379436[/C][/ROW]
[ROW][C]60[/C][C]44[/C][C]43.5702[/C][C]0.429777[/C][/ROW]
[ROW][C]61[/C][C]50[/C][C]42.2433[/C][C]7.75665[/C][/ROW]
[ROW][C]62[/C][C]36[/C][C]36.7869[/C][C]-0.786936[/C][/ROW]
[ROW][C]63[/C][C]42[/C][C]42.099[/C][C]-0.0989647[/C][/ROW]
[ROW][C]64[/C][C]35[/C][C]34.5899[/C][C]0.410126[/C][/ROW]
[ROW][C]65[/C][C]46[/C][C]42.7507[/C][C]3.24935[/C][/ROW]
[ROW][C]66[/C][C]52[/C][C]44.6973[/C][C]7.30269[/C][/ROW]
[ROW][C]67[/C][C]34[/C][C]40.4825[/C][C]-6.48253[/C][/ROW]
[ROW][C]68[/C][C]36[/C][C]33.7086[/C][C]2.29139[/C][/ROW]
[ROW][C]69[/C][C]49[/C][C]42.0091[/C][C]6.99086[/C][/ROW]
[ROW][C]70[/C][C]44[/C][C]44.2413[/C][C]-0.241292[/C][/ROW]
[ROW][C]71[/C][C]43[/C][C]42.3937[/C][C]0.606285[/C][/ROW]
[ROW][C]72[/C][C]30[/C][C]41.0233[/C][C]-11.0233[/C][/ROW]
[ROW][C]73[/C][C]43[/C][C]41.3344[/C][C]1.66563[/C][/ROW]
[ROW][C]74[/C][C]42[/C][C]40.8654[/C][C]1.13457[/C][/ROW]
[ROW][C]75[/C][C]48[/C][C]42.4849[/C][C]5.51513[/C][/ROW]
[ROW][C]76[/C][C]45[/C][C]42.3662[/C][C]2.63378[/C][/ROW]
[ROW][C]77[/C][C]41[/C][C]37.4807[/C][C]3.51926[/C][/ROW]
[ROW][C]78[/C][C]44[/C][C]36.4915[/C][C]7.50855[/C][/ROW]
[ROW][C]79[/C][C]44[/C][C]40.5188[/C][C]3.4812[/C][/ROW]
[ROW][C]80[/C][C]38[/C][C]38.9217[/C][C]-0.921707[/C][/ROW]
[ROW][C]81[/C][C]49[/C][C]42.0621[/C][C]6.93793[/C][/ROW]
[ROW][C]82[/C][C]35[/C][C]41.5077[/C][C]-6.50771[/C][/ROW]
[ROW][C]83[/C][C]37[/C][C]42.4798[/C][C]-5.47976[/C][/ROW]
[ROW][C]84[/C][C]41[/C][C]40.3334[/C][C]0.666604[/C][/ROW]
[ROW][C]85[/C][C]40[/C][C]39.9611[/C][C]0.0389107[/C][/ROW]
[ROW][C]86[/C][C]55[/C][C]49.6815[/C][C]5.31846[/C][/ROW]
[ROW][C]87[/C][C]36[/C][C]40.282[/C][C]-4.282[/C][/ROW]
[ROW][C]88[/C][C]40[/C][C]44.6949[/C][C]-4.69494[/C][/ROW]
[ROW][C]89[/C][C]39[/C][C]38.6266[/C][C]0.373445[/C][/ROW]
[ROW][C]90[/C][C]38[/C][C]43.4805[/C][C]-5.48053[/C][/ROW]
[ROW][C]91[/C][C]45[/C][C]42.1914[/C][C]2.80857[/C][/ROW]
[ROW][C]92[/C][C]32[/C][C]32.7618[/C][C]-0.761832[/C][/ROW]
[ROW][C]93[/C][C]41[/C][C]39.3696[/C][C]1.63041[/C][/ROW]
[ROW][C]94[/C][C]36[/C][C]43.9603[/C][C]-7.9603[/C][/ROW]
[ROW][C]95[/C][C]41[/C][C]39.9929[/C][C]1.00712[/C][/ROW]
[ROW][C]96[/C][C]36[/C][C]38.234[/C][C]-2.23402[/C][/ROW]
[ROW][C]97[/C][C]40[/C][C]43.0011[/C][C]-3.0011[/C][/ROW]
[ROW][C]98[/C][C]52[/C][C]48.8214[/C][C]3.17856[/C][/ROW]
[ROW][C]99[/C][C]44[/C][C]45.9252[/C][C]-1.92517[/C][/ROW]
[ROW][C]100[/C][C]34[/C][C]43.1743[/C][C]-9.17432[/C][/ROW]
[ROW][C]101[/C][C]38[/C][C]47.4294[/C][C]-9.4294[/C][/ROW]
[ROW][C]102[/C][C]47[/C][C]39.6575[/C][C]7.34251[/C][/ROW]
[ROW][C]103[/C][C]43[/C][C]43.6764[/C][C]-0.676389[/C][/ROW]
[ROW][C]104[/C][C]42[/C][C]40.5545[/C][C]1.44551[/C][/ROW]
[ROW][C]105[/C][C]42[/C][C]44.8875[/C][C]-2.88754[/C][/ROW]
[ROW][C]106[/C][C]40[/C][C]38.4429[/C][C]1.55706[/C][/ROW]
[ROW][C]107[/C][C]47[/C][C]44.1635[/C][C]2.83647[/C][/ROW]
[ROW][C]108[/C][C]49[/C][C]42.8223[/C][C]6.17771[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270865&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270865&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	49	36.7002	12.2998
2	41	44.368	-3.36798
3	38	46.7399	-8.73993
4	44	42.8462	1.15382
5	37	43.383	-6.38302
6	41	41.1215	-0.121549
7	48	45.5874	2.41258
8	34	36.5955	-2.59552
9	39	47.8053	-8.80525
10	48	43.2323	4.76771
11	32	37.8378	-5.83785
12	40	45.1515	-5.15151
13	41	39.5853	1.41472
14	45	42.4918	2.50822
15	54	46.3649	7.63514
16	45	47.4527	-2.45274
17	41	41.456	-0.456028
18	42	45.05	-3.04996
19	43	43.8143	-0.814259
20	48	45.1255	2.87446
21	41	43.2581	-2.25812
22	43	45.4183	-2.41832
23	46	44.26	1.73996
24	49	48.1646	0.835386
25	51	47.8782	3.12178
26	36	38.3514	-2.35137
27	43	44.4678	-1.46776
28	40	41.0735	-1.07345
29	32	33.8263	-1.82633
30	37	40.608	-3.60804
31	36	41.0913	-5.09129
32	49	45.9007	3.09927
33	43	41.5619	1.43811
34	35	42.0276	-7.02756
35	47	43.784	3.21596
36	41	40.843	0.157
37	44	43.4743	0.525684
38	48	43.6819	4.31814
39	33	35.7874	-2.78742
40	38	42.4954	-4.49541
41	57	46.2155	10.7845
42	32	36.0863	-4.08629
43	42	41.0185	0.981503
44	45	45.3284	-0.328436
45	41	37.575	3.42497
46	42	42.3405	-0.340546
47	59	55.227	3.77298
48	45	46.6979	-1.69787
49	45	40.4405	4.55953
50	36	42.6778	-6.6778
51	27	36.5704	-9.57039
52	48	45.1246	2.87537
53	39	40.3161	-1.31612
54	49	46.24	2.76001
55	47	42.1045	4.89552
56	42	36.0236	5.97635
57	41	39.5828	1.41719
58	43	43.1529	-0.152886
59	41	41.3794	-0.379436
60	44	43.5702	0.429777
61	50	42.2433	7.75665
62	36	36.7869	-0.786936
63	42	42.099	-0.0989647
64	35	34.5899	0.410126
65	46	42.7507	3.24935
66	52	44.6973	7.30269
67	34	40.4825	-6.48253
68	36	33.7086	2.29139
69	49	42.0091	6.99086
70	44	44.2413	-0.241292
71	43	42.3937	0.606285
72	30	41.0233	-11.0233
73	43	41.3344	1.66563
74	42	40.8654	1.13457
75	48	42.4849	5.51513
76	45	42.3662	2.63378
77	41	37.4807	3.51926
78	44	36.4915	7.50855
79	44	40.5188	3.4812
80	38	38.9217	-0.921707
81	49	42.0621	6.93793
82	35	41.5077	-6.50771
83	37	42.4798	-5.47976
84	41	40.3334	0.666604
85	40	39.9611	0.0389107
86	55	49.6815	5.31846
87	36	40.282	-4.282
88	40	44.6949	-4.69494
89	39	38.6266	0.373445
90	38	43.4805	-5.48053
91	45	42.1914	2.80857
92	32	32.7618	-0.761832
93	41	39.3696	1.63041
94	36	43.9603	-7.9603
95	41	39.9929	1.00712
96	36	38.234	-2.23402
97	40	43.0011	-3.0011
98	52	48.8214	3.17856
99	44	45.9252	-1.92517
100	34	43.1743	-9.17432
101	38	47.4294	-9.4294
102	47	39.6575	7.34251
103	43	43.6764	-0.676389
104	42	40.5545	1.44551
105	42	44.8875	-2.88754
106	40	38.4429	1.55706
107	47	44.1635	2.83647
108	49	42.8223	6.17771

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
8	0.0548776	0.109755	0.945122
9	0.143338	0.286677	0.856662
10	0.931941	0.136118	0.0680588
11	0.974116	0.0517675	0.0258838
12	0.960441	0.079119	0.0395595
13	0.940983	0.118035	0.0590173
14	0.921319	0.157362	0.0786808
15	0.97888	0.0422397	0.0211198
16	0.973417	0.0531659	0.026583
17	0.958787	0.0824253	0.0412127
18	0.941654	0.116692	0.0583462
19	0.915334	0.169331	0.0846657
20	0.903829	0.192343	0.0961715
21	0.871608	0.256784	0.128392
22	0.832904	0.334191	0.167096
23	0.806446	0.387108	0.193554
24	0.777205	0.445589	0.222795
25	0.780834	0.438331	0.219166
26	0.742541	0.514917	0.257459
27	0.688237	0.623527	0.311763
28	0.63339	0.733219	0.36661
29	0.591899	0.816203	0.408101
30	0.551541	0.896918	0.448459
31	0.55206	0.89588	0.44794
32	0.523418	0.953163	0.476582
33	0.471384	0.942768	0.528616
34	0.53919	0.921619	0.46081
35	0.508459	0.983082	0.491541
36	0.451298	0.902596	0.548702
37	0.3968	0.7936	0.6032
38	0.384016	0.768032	0.615984
39	0.347638	0.695276	0.652362
40	0.340519	0.681038	0.659481
41	0.579409	0.841183	0.420591
42	0.565229	0.869541	0.434771
43	0.509533	0.980934	0.490467
44	0.452176	0.904351	0.547824
45	0.438486	0.876973	0.561514
46	0.382276	0.764553	0.617724
47	0.353292	0.706583	0.646708
48	0.311458	0.622916	0.688542
49	0.312498	0.624997	0.687502
50	0.362126	0.724251	0.637874
51	0.532666	0.934669	0.467334
52	0.500374	0.999252	0.499626
53	0.450494	0.900989	0.549506
54	0.415103	0.830206	0.584897
55	0.433393	0.866786	0.566607
56	0.483539	0.967078	0.516461
57	0.431906	0.863813	0.568094
58	0.376265	0.75253	0.623735
59	0.325472	0.650945	0.674528
60	0.27624	0.55248	0.72376
61	0.451827	0.903655	0.548173
62	0.409034	0.818068	0.590966
63	0.353442	0.706884	0.646558
64	0.302555	0.60511	0.697445
65	0.310627	0.621255	0.689373
66	0.368486	0.736973	0.631514
67	0.435628	0.871255	0.564372
68	0.392112	0.784223	0.607888
69	0.476642	0.953285	0.523358
70	0.418392	0.836783	0.581608
71	0.386269	0.772537	0.613731
72	0.643769	0.712462	0.356231
73	0.587563	0.824874	0.412437
74	0.529832	0.940337	0.470168
75	0.517633	0.964735	0.482367
76	0.469321	0.938643	0.530679
77	0.419437	0.838873	0.580563
78	0.470296	0.940591	0.529704
79	0.426639	0.853278	0.573361
80	0.365257	0.730514	0.634743
81	0.48625	0.972501	0.51375
82	0.510169	0.979662	0.489831
83	0.511921	0.976158	0.488079
84	0.440127	0.880254	0.559873
85	0.369942	0.739884	0.630058
86	0.643162	0.713675	0.356838
87	0.608115	0.783769	0.391885
88	0.544872	0.910257	0.455128
89	0.468825	0.93765	0.531175
90	0.408436	0.816872	0.591564
91	0.362096	0.724192	0.637904
92	0.289747	0.579494	0.710253
93	0.226838	0.453676	0.773162
94	0.313101	0.626203	0.686899
95	0.233073	0.466146	0.766927
96	0.16434	0.328679	0.83566
97	0.144677	0.289353	0.855323
98	0.325484	0.650967	0.674516
99	0.22457	0.449141	0.77543
100	0.255128	0.510256	0.744872

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 0.0548776 & 0.109755 & 0.945122 \tabularnewline
9 & 0.143338 & 0.286677 & 0.856662 \tabularnewline
10 & 0.931941 & 0.136118 & 0.0680588 \tabularnewline
11 & 0.974116 & 0.0517675 & 0.0258838 \tabularnewline
12 & 0.960441 & 0.079119 & 0.0395595 \tabularnewline
13 & 0.940983 & 0.118035 & 0.0590173 \tabularnewline
14 & 0.921319 & 0.157362 & 0.0786808 \tabularnewline
15 & 0.97888 & 0.0422397 & 0.0211198 \tabularnewline
16 & 0.973417 & 0.0531659 & 0.026583 \tabularnewline
17 & 0.958787 & 0.0824253 & 0.0412127 \tabularnewline
18 & 0.941654 & 0.116692 & 0.0583462 \tabularnewline
19 & 0.915334 & 0.169331 & 0.0846657 \tabularnewline
20 & 0.903829 & 0.192343 & 0.0961715 \tabularnewline
21 & 0.871608 & 0.256784 & 0.128392 \tabularnewline
22 & 0.832904 & 0.334191 & 0.167096 \tabularnewline
23 & 0.806446 & 0.387108 & 0.193554 \tabularnewline
24 & 0.777205 & 0.445589 & 0.222795 \tabularnewline
25 & 0.780834 & 0.438331 & 0.219166 \tabularnewline
26 & 0.742541 & 0.514917 & 0.257459 \tabularnewline
27 & 0.688237 & 0.623527 & 0.311763 \tabularnewline
28 & 0.63339 & 0.733219 & 0.36661 \tabularnewline
29 & 0.591899 & 0.816203 & 0.408101 \tabularnewline
30 & 0.551541 & 0.896918 & 0.448459 \tabularnewline
31 & 0.55206 & 0.89588 & 0.44794 \tabularnewline
32 & 0.523418 & 0.953163 & 0.476582 \tabularnewline
33 & 0.471384 & 0.942768 & 0.528616 \tabularnewline
34 & 0.53919 & 0.921619 & 0.46081 \tabularnewline
35 & 0.508459 & 0.983082 & 0.491541 \tabularnewline
36 & 0.451298 & 0.902596 & 0.548702 \tabularnewline
37 & 0.3968 & 0.7936 & 0.6032 \tabularnewline
38 & 0.384016 & 0.768032 & 0.615984 \tabularnewline
39 & 0.347638 & 0.695276 & 0.652362 \tabularnewline
40 & 0.340519 & 0.681038 & 0.659481 \tabularnewline
41 & 0.579409 & 0.841183 & 0.420591 \tabularnewline
42 & 0.565229 & 0.869541 & 0.434771 \tabularnewline
43 & 0.509533 & 0.980934 & 0.490467 \tabularnewline
44 & 0.452176 & 0.904351 & 0.547824 \tabularnewline
45 & 0.438486 & 0.876973 & 0.561514 \tabularnewline
46 & 0.382276 & 0.764553 & 0.617724 \tabularnewline
47 & 0.353292 & 0.706583 & 0.646708 \tabularnewline
48 & 0.311458 & 0.622916 & 0.688542 \tabularnewline
49 & 0.312498 & 0.624997 & 0.687502 \tabularnewline
50 & 0.362126 & 0.724251 & 0.637874 \tabularnewline
51 & 0.532666 & 0.934669 & 0.467334 \tabularnewline
52 & 0.500374 & 0.999252 & 0.499626 \tabularnewline
53 & 0.450494 & 0.900989 & 0.549506 \tabularnewline
54 & 0.415103 & 0.830206 & 0.584897 \tabularnewline
55 & 0.433393 & 0.866786 & 0.566607 \tabularnewline
56 & 0.483539 & 0.967078 & 0.516461 \tabularnewline
57 & 0.431906 & 0.863813 & 0.568094 \tabularnewline
58 & 0.376265 & 0.75253 & 0.623735 \tabularnewline
59 & 0.325472 & 0.650945 & 0.674528 \tabularnewline
60 & 0.27624 & 0.55248 & 0.72376 \tabularnewline
61 & 0.451827 & 0.903655 & 0.548173 \tabularnewline
62 & 0.409034 & 0.818068 & 0.590966 \tabularnewline
63 & 0.353442 & 0.706884 & 0.646558 \tabularnewline
64 & 0.302555 & 0.60511 & 0.697445 \tabularnewline
65 & 0.310627 & 0.621255 & 0.689373 \tabularnewline
66 & 0.368486 & 0.736973 & 0.631514 \tabularnewline
67 & 0.435628 & 0.871255 & 0.564372 \tabularnewline
68 & 0.392112 & 0.784223 & 0.607888 \tabularnewline
69 & 0.476642 & 0.953285 & 0.523358 \tabularnewline
70 & 0.418392 & 0.836783 & 0.581608 \tabularnewline
71 & 0.386269 & 0.772537 & 0.613731 \tabularnewline
72 & 0.643769 & 0.712462 & 0.356231 \tabularnewline
73 & 0.587563 & 0.824874 & 0.412437 \tabularnewline
74 & 0.529832 & 0.940337 & 0.470168 \tabularnewline
75 & 0.517633 & 0.964735 & 0.482367 \tabularnewline
76 & 0.469321 & 0.938643 & 0.530679 \tabularnewline
77 & 0.419437 & 0.838873 & 0.580563 \tabularnewline
78 & 0.470296 & 0.940591 & 0.529704 \tabularnewline
79 & 0.426639 & 0.853278 & 0.573361 \tabularnewline
80 & 0.365257 & 0.730514 & 0.634743 \tabularnewline
81 & 0.48625 & 0.972501 & 0.51375 \tabularnewline
82 & 0.510169 & 0.979662 & 0.489831 \tabularnewline
83 & 0.511921 & 0.976158 & 0.488079 \tabularnewline
84 & 0.440127 & 0.880254 & 0.559873 \tabularnewline
85 & 0.369942 & 0.739884 & 0.630058 \tabularnewline
86 & 0.643162 & 0.713675 & 0.356838 \tabularnewline
87 & 0.608115 & 0.783769 & 0.391885 \tabularnewline
88 & 0.544872 & 0.910257 & 0.455128 \tabularnewline
89 & 0.468825 & 0.93765 & 0.531175 \tabularnewline
90 & 0.408436 & 0.816872 & 0.591564 \tabularnewline
91 & 0.362096 & 0.724192 & 0.637904 \tabularnewline
92 & 0.289747 & 0.579494 & 0.710253 \tabularnewline
93 & 0.226838 & 0.453676 & 0.773162 \tabularnewline
94 & 0.313101 & 0.626203 & 0.686899 \tabularnewline
95 & 0.233073 & 0.466146 & 0.766927 \tabularnewline
96 & 0.16434 & 0.328679 & 0.83566 \tabularnewline
97 & 0.144677 & 0.289353 & 0.855323 \tabularnewline
98 & 0.325484 & 0.650967 & 0.674516 \tabularnewline
99 & 0.22457 & 0.449141 & 0.77543 \tabularnewline
100 & 0.255128 & 0.510256 & 0.744872 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270865&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]8[/C][C]0.0548776[/C][C]0.109755[/C][C]0.945122[/C][/ROW]
[ROW][C]9[/C][C]0.143338[/C][C]0.286677[/C][C]0.856662[/C][/ROW]
[ROW][C]10[/C][C]0.931941[/C][C]0.136118[/C][C]0.0680588[/C][/ROW]
[ROW][C]11[/C][C]0.974116[/C][C]0.0517675[/C][C]0.0258838[/C][/ROW]
[ROW][C]12[/C][C]0.960441[/C][C]0.079119[/C][C]0.0395595[/C][/ROW]
[ROW][C]13[/C][C]0.940983[/C][C]0.118035[/C][C]0.0590173[/C][/ROW]
[ROW][C]14[/C][C]0.921319[/C][C]0.157362[/C][C]0.0786808[/C][/ROW]
[ROW][C]15[/C][C]0.97888[/C][C]0.0422397[/C][C]0.0211198[/C][/ROW]
[ROW][C]16[/C][C]0.973417[/C][C]0.0531659[/C][C]0.026583[/C][/ROW]
[ROW][C]17[/C][C]0.958787[/C][C]0.0824253[/C][C]0.0412127[/C][/ROW]
[ROW][C]18[/C][C]0.941654[/C][C]0.116692[/C][C]0.0583462[/C][/ROW]
[ROW][C]19[/C][C]0.915334[/C][C]0.169331[/C][C]0.0846657[/C][/ROW]
[ROW][C]20[/C][C]0.903829[/C][C]0.192343[/C][C]0.0961715[/C][/ROW]
[ROW][C]21[/C][C]0.871608[/C][C]0.256784[/C][C]0.128392[/C][/ROW]
[ROW][C]22[/C][C]0.832904[/C][C]0.334191[/C][C]0.167096[/C][/ROW]
[ROW][C]23[/C][C]0.806446[/C][C]0.387108[/C][C]0.193554[/C][/ROW]
[ROW][C]24[/C][C]0.777205[/C][C]0.445589[/C][C]0.222795[/C][/ROW]
[ROW][C]25[/C][C]0.780834[/C][C]0.438331[/C][C]0.219166[/C][/ROW]
[ROW][C]26[/C][C]0.742541[/C][C]0.514917[/C][C]0.257459[/C][/ROW]
[ROW][C]27[/C][C]0.688237[/C][C]0.623527[/C][C]0.311763[/C][/ROW]
[ROW][C]28[/C][C]0.63339[/C][C]0.733219[/C][C]0.36661[/C][/ROW]
[ROW][C]29[/C][C]0.591899[/C][C]0.816203[/C][C]0.408101[/C][/ROW]
[ROW][C]30[/C][C]0.551541[/C][C]0.896918[/C][C]0.448459[/C][/ROW]
[ROW][C]31[/C][C]0.55206[/C][C]0.89588[/C][C]0.44794[/C][/ROW]
[ROW][C]32[/C][C]0.523418[/C][C]0.953163[/C][C]0.476582[/C][/ROW]
[ROW][C]33[/C][C]0.471384[/C][C]0.942768[/C][C]0.528616[/C][/ROW]
[ROW][C]34[/C][C]0.53919[/C][C]0.921619[/C][C]0.46081[/C][/ROW]
[ROW][C]35[/C][C]0.508459[/C][C]0.983082[/C][C]0.491541[/C][/ROW]
[ROW][C]36[/C][C]0.451298[/C][C]0.902596[/C][C]0.548702[/C][/ROW]
[ROW][C]37[/C][C]0.3968[/C][C]0.7936[/C][C]0.6032[/C][/ROW]
[ROW][C]38[/C][C]0.384016[/C][C]0.768032[/C][C]0.615984[/C][/ROW]
[ROW][C]39[/C][C]0.347638[/C][C]0.695276[/C][C]0.652362[/C][/ROW]
[ROW][C]40[/C][C]0.340519[/C][C]0.681038[/C][C]0.659481[/C][/ROW]
[ROW][C]41[/C][C]0.579409[/C][C]0.841183[/C][C]0.420591[/C][/ROW]
[ROW][C]42[/C][C]0.565229[/C][C]0.869541[/C][C]0.434771[/C][/ROW]
[ROW][C]43[/C][C]0.509533[/C][C]0.980934[/C][C]0.490467[/C][/ROW]
[ROW][C]44[/C][C]0.452176[/C][C]0.904351[/C][C]0.547824[/C][/ROW]
[ROW][C]45[/C][C]0.438486[/C][C]0.876973[/C][C]0.561514[/C][/ROW]
[ROW][C]46[/C][C]0.382276[/C][C]0.764553[/C][C]0.617724[/C][/ROW]
[ROW][C]47[/C][C]0.353292[/C][C]0.706583[/C][C]0.646708[/C][/ROW]
[ROW][C]48[/C][C]0.311458[/C][C]0.622916[/C][C]0.688542[/C][/ROW]
[ROW][C]49[/C][C]0.312498[/C][C]0.624997[/C][C]0.687502[/C][/ROW]
[ROW][C]50[/C][C]0.362126[/C][C]0.724251[/C][C]0.637874[/C][/ROW]
[ROW][C]51[/C][C]0.532666[/C][C]0.934669[/C][C]0.467334[/C][/ROW]
[ROW][C]52[/C][C]0.500374[/C][C]0.999252[/C][C]0.499626[/C][/ROW]
[ROW][C]53[/C][C]0.450494[/C][C]0.900989[/C][C]0.549506[/C][/ROW]
[ROW][C]54[/C][C]0.415103[/C][C]0.830206[/C][C]0.584897[/C][/ROW]
[ROW][C]55[/C][C]0.433393[/C][C]0.866786[/C][C]0.566607[/C][/ROW]
[ROW][C]56[/C][C]0.483539[/C][C]0.967078[/C][C]0.516461[/C][/ROW]
[ROW][C]57[/C][C]0.431906[/C][C]0.863813[/C][C]0.568094[/C][/ROW]
[ROW][C]58[/C][C]0.376265[/C][C]0.75253[/C][C]0.623735[/C][/ROW]
[ROW][C]59[/C][C]0.325472[/C][C]0.650945[/C][C]0.674528[/C][/ROW]
[ROW][C]60[/C][C]0.27624[/C][C]0.55248[/C][C]0.72376[/C][/ROW]
[ROW][C]61[/C][C]0.451827[/C][C]0.903655[/C][C]0.548173[/C][/ROW]
[ROW][C]62[/C][C]0.409034[/C][C]0.818068[/C][C]0.590966[/C][/ROW]
[ROW][C]63[/C][C]0.353442[/C][C]0.706884[/C][C]0.646558[/C][/ROW]
[ROW][C]64[/C][C]0.302555[/C][C]0.60511[/C][C]0.697445[/C][/ROW]
[ROW][C]65[/C][C]0.310627[/C][C]0.621255[/C][C]0.689373[/C][/ROW]
[ROW][C]66[/C][C]0.368486[/C][C]0.736973[/C][C]0.631514[/C][/ROW]
[ROW][C]67[/C][C]0.435628[/C][C]0.871255[/C][C]0.564372[/C][/ROW]
[ROW][C]68[/C][C]0.392112[/C][C]0.784223[/C][C]0.607888[/C][/ROW]
[ROW][C]69[/C][C]0.476642[/C][C]0.953285[/C][C]0.523358[/C][/ROW]
[ROW][C]70[/C][C]0.418392[/C][C]0.836783[/C][C]0.581608[/C][/ROW]
[ROW][C]71[/C][C]0.386269[/C][C]0.772537[/C][C]0.613731[/C][/ROW]
[ROW][C]72[/C][C]0.643769[/C][C]0.712462[/C][C]0.356231[/C][/ROW]
[ROW][C]73[/C][C]0.587563[/C][C]0.824874[/C][C]0.412437[/C][/ROW]
[ROW][C]74[/C][C]0.529832[/C][C]0.940337[/C][C]0.470168[/C][/ROW]
[ROW][C]75[/C][C]0.517633[/C][C]0.964735[/C][C]0.482367[/C][/ROW]
[ROW][C]76[/C][C]0.469321[/C][C]0.938643[/C][C]0.530679[/C][/ROW]
[ROW][C]77[/C][C]0.419437[/C][C]0.838873[/C][C]0.580563[/C][/ROW]
[ROW][C]78[/C][C]0.470296[/C][C]0.940591[/C][C]0.529704[/C][/ROW]
[ROW][C]79[/C][C]0.426639[/C][C]0.853278[/C][C]0.573361[/C][/ROW]
[ROW][C]80[/C][C]0.365257[/C][C]0.730514[/C][C]0.634743[/C][/ROW]
[ROW][C]81[/C][C]0.48625[/C][C]0.972501[/C][C]0.51375[/C][/ROW]
[ROW][C]82[/C][C]0.510169[/C][C]0.979662[/C][C]0.489831[/C][/ROW]
[ROW][C]83[/C][C]0.511921[/C][C]0.976158[/C][C]0.488079[/C][/ROW]
[ROW][C]84[/C][C]0.440127[/C][C]0.880254[/C][C]0.559873[/C][/ROW]
[ROW][C]85[/C][C]0.369942[/C][C]0.739884[/C][C]0.630058[/C][/ROW]
[ROW][C]86[/C][C]0.643162[/C][C]0.713675[/C][C]0.356838[/C][/ROW]
[ROW][C]87[/C][C]0.608115[/C][C]0.783769[/C][C]0.391885[/C][/ROW]
[ROW][C]88[/C][C]0.544872[/C][C]0.910257[/C][C]0.455128[/C][/ROW]
[ROW][C]89[/C][C]0.468825[/C][C]0.93765[/C][C]0.531175[/C][/ROW]
[ROW][C]90[/C][C]0.408436[/C][C]0.816872[/C][C]0.591564[/C][/ROW]
[ROW][C]91[/C][C]0.362096[/C][C]0.724192[/C][C]0.637904[/C][/ROW]
[ROW][C]92[/C][C]0.289747[/C][C]0.579494[/C][C]0.710253[/C][/ROW]
[ROW][C]93[/C][C]0.226838[/C][C]0.453676[/C][C]0.773162[/C][/ROW]
[ROW][C]94[/C][C]0.313101[/C][C]0.626203[/C][C]0.686899[/C][/ROW]
[ROW][C]95[/C][C]0.233073[/C][C]0.466146[/C][C]0.766927[/C][/ROW]
[ROW][C]96[/C][C]0.16434[/C][C]0.328679[/C][C]0.83566[/C][/ROW]
[ROW][C]97[/C][C]0.144677[/C][C]0.289353[/C][C]0.855323[/C][/ROW]
[ROW][C]98[/C][C]0.325484[/C][C]0.650967[/C][C]0.674516[/C][/ROW]
[ROW][C]99[/C][C]0.22457[/C][C]0.449141[/C][C]0.77543[/C][/ROW]
[ROW][C]100[/C][C]0.255128[/C][C]0.510256[/C][C]0.744872[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270865&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270865&T=5

As an alternative you can also use a QR Code:

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

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
8	0.0548776	0.109755	0.945122
9	0.143338	0.286677	0.856662
10	0.931941	0.136118	0.0680588
11	0.974116	0.0517675	0.0258838
12	0.960441	0.079119	0.0395595
13	0.940983	0.118035	0.0590173
14	0.921319	0.157362	0.0786808
15	0.97888	0.0422397	0.0211198
16	0.973417	0.0531659	0.026583
17	0.958787	0.0824253	0.0412127
18	0.941654	0.116692	0.0583462
19	0.915334	0.169331	0.0846657
20	0.903829	0.192343	0.0961715
21	0.871608	0.256784	0.128392
22	0.832904	0.334191	0.167096
23	0.806446	0.387108	0.193554
24	0.777205	0.445589	0.222795
25	0.780834	0.438331	0.219166
26	0.742541	0.514917	0.257459
27	0.688237	0.623527	0.311763
28	0.63339	0.733219	0.36661
29	0.591899	0.816203	0.408101
30	0.551541	0.896918	0.448459
31	0.55206	0.89588	0.44794
32	0.523418	0.953163	0.476582
33	0.471384	0.942768	0.528616
34	0.53919	0.921619	0.46081
35	0.508459	0.983082	0.491541
36	0.451298	0.902596	0.548702
37	0.3968	0.7936	0.6032
38	0.384016	0.768032	0.615984
39	0.347638	0.695276	0.652362
40	0.340519	0.681038	0.659481
41	0.579409	0.841183	0.420591
42	0.565229	0.869541	0.434771
43	0.509533	0.980934	0.490467
44	0.452176	0.904351	0.547824
45	0.438486	0.876973	0.561514
46	0.382276	0.764553	0.617724
47	0.353292	0.706583	0.646708
48	0.311458	0.622916	0.688542
49	0.312498	0.624997	0.687502
50	0.362126	0.724251	0.637874
51	0.532666	0.934669	0.467334
52	0.500374	0.999252	0.499626
53	0.450494	0.900989	0.549506
54	0.415103	0.830206	0.584897
55	0.433393	0.866786	0.566607
56	0.483539	0.967078	0.516461
57	0.431906	0.863813	0.568094
58	0.376265	0.75253	0.623735
59	0.325472	0.650945	0.674528
60	0.27624	0.55248	0.72376
61	0.451827	0.903655	0.548173
62	0.409034	0.818068	0.590966
63	0.353442	0.706884	0.646558
64	0.302555	0.60511	0.697445
65	0.310627	0.621255	0.689373
66	0.368486	0.736973	0.631514
67	0.435628	0.871255	0.564372
68	0.392112	0.784223	0.607888
69	0.476642	0.953285	0.523358
70	0.418392	0.836783	0.581608
71	0.386269	0.772537	0.613731
72	0.643769	0.712462	0.356231
73	0.587563	0.824874	0.412437
74	0.529832	0.940337	0.470168
75	0.517633	0.964735	0.482367
76	0.469321	0.938643	0.530679
77	0.419437	0.838873	0.580563
78	0.470296	0.940591	0.529704
79	0.426639	0.853278	0.573361
80	0.365257	0.730514	0.634743
81	0.48625	0.972501	0.51375
82	0.510169	0.979662	0.489831
83	0.511921	0.976158	0.488079
84	0.440127	0.880254	0.559873
85	0.369942	0.739884	0.630058
86	0.643162	0.713675	0.356838
87	0.608115	0.783769	0.391885
88	0.544872	0.910257	0.455128
89	0.468825	0.93765	0.531175
90	0.408436	0.816872	0.591564
91	0.362096	0.724192	0.637904
92	0.289747	0.579494	0.710253
93	0.226838	0.453676	0.773162
94	0.313101	0.626203	0.686899
95	0.233073	0.466146	0.766927
96	0.16434	0.328679	0.83566
97	0.144677	0.289353	0.855323
98	0.325484	0.650967	0.674516
99	0.22457	0.449141	0.77543
100	0.255128	0.510256	0.744872

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	1	0.0107527	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 & 1 & 0.0107527 & OK \tabularnewline
10% type I error level & 5 & 0.0537634 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270865&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]1[/C][C]0.0107527[/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=270865&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270865&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	1	0.0107527	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