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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Wed, 17 Dec 2014 19:06:35 +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/17/t1418843206j2gi9oijvrmtxan.htm/, Retrieved Thu, 16 May 2024 22:31:49 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=270586, Retrieved Thu, 16 May 2024 22:31:49 +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] [] [2014-12-17 19:06:35] [98d5d60fbe7cdd1cc5f92fd2f560c762] [Current]

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21 26 50 4 13 0
22 57 62 4 13 1
22 37 54 5 11 0
18 67 71 4 14 1
23 43 54 4 15 1
12 52 65 9 14 1
20 52 73 8 11 0
22 43 52 11 13 1
21 84 84 4 16 1
19 67 42 4 14 1
22 49 66 6 14 1
15 70 65 4 15 1
20 52 78 8 15 1
19 58 73 4 13 0
18 68 75 4 14 0
15 62 72 11 11 0
20 43 66 4 12 1
21 56 70 4 14 0
21 56 61 6 13 1
15 74 81 6 12 0
23 63 69 8 15 1
21 58 71 5 14 0
25 63 68 9 12 1
9 53 70 4 12 1
30 57 68 7 12 1
20 51 61 10 15 0
23 64 67 4 14 1
16 53 76 4 16 0
16 29 70 7 12 0
19 54 60 12 12 0
25 58 72 7 14 1
18 43 69 5 16 1
23 51 71 8 15 1
21 53 62 5 12 1
10 54 70 4 14 0
14 56 64 9 13 1
22 61 58 7 14 1
26 47 76 4 16 0
23 39 52 4 12 1
23 48 59 4 14 1
24 50 68 4 15 1
24 35 76 4 13 1
18 30 65 7 16 1
23 68 67 4 16 0
15 49 59 7 12 1
19 61 69 4 12 1
16 67 76 4 16 0
25 47 63 4 12 1
23 56 75 4 15 1
17 50 63 8 12 1
19 43 60 4 13 1
21 67 73 4 12 1
18 62 63 4 14 1
27 57 70 4 14 1
21 41 75 7 11 0
13 54 66 12 10 1
8 45 63 4 12 0
29 48 63 4 11 1
28 61 64 4 16 1
23 56 70 5 14 0
21 41 75 15 14 0
19 43 61 5 15 1
19 53 60 10 15 0
20 44 62 9 14 1
18 66 73 8 13 0
19 58 61 4 11 1
17 46 66 5 16 1
19 37 64 4 12 0
25 51 59 9 15 0
19 51 64 4 14 0
23 66 56 4 14 1
14 37 78 4 13 0
28 59 53 6 6 1
16 42 67 7 12 0
24 38 59 5 12 1
20 66 66 4 14 0
12 34 68 4 14 0
24 53 71 4 15 1
22 49 66 4 11 0
12 55 73 4 13 0
22 49 72 4 14 0
20 59 71 6 16 1
10 40 59 10 13 0
23 58 64 7 14 1
17 60 66 4 16 1
22 63 78 4 11 0
24 56 68 7 13 0
18 54 73 4 13 0
21 52 62 8 15 1
20 34 65 11 12 1
20 69 68 6 13 1
22 32 65 14 12 0
19 48 60 5 14 1
20 67 71 4 14 0
26 58 65 8 16 1
23 57 68 9 15 1
24 42 64 4 14 1
21 64 74 4 13 1
21 58 69 5 14 1
19 66 76 4 15 0
17 61 72 4 12 1
20 52 67 4 7 1
11 51 63 7 12 0
8 55 59 10 15 0
18 60 66 5 13 0
18 56 62 4 11 0
19 63 69 4 14 0
19 61 66 4 13 1

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	'Sir Maurice George Kendall' @ kendall.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 & 'Sir Maurice George Kendall' @ kendall.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270586&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]'Sir Maurice George Kendall' @ kendall.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270586&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270586&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	'Sir Maurice George Kendall' @ kendall.wessa.net

Multiple Linear Regression - Estimated Regression Equation
NUMERACYTOT[t] = + 18.9014 + 0.00579586AMS.I[t] -0.0116968AMS.E[t] -0.108467AMS.A[t] + 0.0327078STRESSTOT[t] + 2.66033gender[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
NUMERACYTOT[t] =  +  18.9014 +  0.00579586AMS.I[t] -0.0116968AMS.E[t] -0.108467AMS.A[t] +  0.0327078STRESSTOT[t] +  2.66033gender[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270586&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]NUMERACYTOT[t] =  +  18.9014 +  0.00579586AMS.I[t] -0.0116968AMS.E[t] -0.108467AMS.A[t] +  0.0327078STRESSTOT[t] +  2.66033gender[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270586&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270586&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
NUMERACYTOT[t] = + 18.9014 + 0.00579586AMS.I[t] -0.0116968AMS.E[t] -0.108467AMS.A[t] + 0.0327078STRESSTOT[t] + 2.66033gender[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)	18.9014	5.14063	3.677	0.000378786	0.000189393
AMS.I	0.00579586	0.0419691	0.1381	0.890435	0.445217
AMS.E	-0.0116968	0.0650775	-0.1797	0.857717	0.428858
AMS.A	-0.108467	0.166341	-0.6521	0.515817	0.257908
STRESSTOT	0.0327078	0.235389	0.139	0.889762	0.444881
gender	2.66033	0.865391	3.074	0.00270877	0.00135439

\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) & 18.9014 & 5.14063 & 3.677 & 0.000378786 & 0.000189393 \tabularnewline
AMS.I & 0.00579586 & 0.0419691 & 0.1381 & 0.890435 & 0.445217 \tabularnewline
AMS.E & -0.0116968 & 0.0650775 & -0.1797 & 0.857717 & 0.428858 \tabularnewline
AMS.A & -0.108467 & 0.166341 & -0.6521 & 0.515817 & 0.257908 \tabularnewline
STRESSTOT & 0.0327078 & 0.235389 & 0.139 & 0.889762 & 0.444881 \tabularnewline
gender & 2.66033 & 0.865391 & 3.074 & 0.00270877 & 0.00135439 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270586&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]18.9014[/C][C]5.14063[/C][C]3.677[/C][C]0.000378786[/C][C]0.000189393[/C][/ROW]
[ROW][C]AMS.I[/C][C]0.00579586[/C][C]0.0419691[/C][C]0.1381[/C][C]0.890435[/C][C]0.445217[/C][/ROW]
[ROW][C]AMS.E[/C][C]-0.0116968[/C][C]0.0650775[/C][C]-0.1797[/C][C]0.857717[/C][C]0.428858[/C][/ROW]
[ROW][C]AMS.A[/C][C]-0.108467[/C][C]0.166341[/C][C]-0.6521[/C][C]0.515817[/C][C]0.257908[/C][/ROW]
[ROW][C]STRESSTOT[/C][C]0.0327078[/C][C]0.235389[/C][C]0.139[/C][C]0.889762[/C][C]0.444881[/C][/ROW]
[ROW][C]gender[/C][C]2.66033[/C][C]0.865391[/C][C]3.074[/C][C]0.00270877[/C][C]0.00135439[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270586&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270586&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)	18.9014	5.14063	3.677	0.000378786	0.000189393
AMS.I	0.00579586	0.0419691	0.1381	0.890435	0.445217
AMS.E	-0.0116968	0.0650775	-0.1797	0.857717	0.428858
AMS.A	-0.108467	0.166341	-0.6521	0.515817	0.257908
STRESSTOT	0.0327078	0.235389	0.139	0.889762	0.444881
gender	2.66033	0.865391	3.074	0.00270877	0.00135439

Multiple Linear Regression - Regression Statistics
Multiple R	0.320924
R-squared	0.102992
Adjusted R-squared	0.0590213
F-TEST (value)	2.34228
F-TEST (DF numerator)	5
F-TEST (DF denominator)	102
p-value	0.0466949
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	4.22529
Sum Squared Residuals	1821.02

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.320924 \tabularnewline
R-squared & 0.102992 \tabularnewline
Adjusted R-squared & 0.0590213 \tabularnewline
F-TEST (value) & 2.34228 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 102 \tabularnewline
p-value & 0.0466949 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 4.22529 \tabularnewline
Sum Squared Residuals & 1821.02 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270586&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.320924[/C][/ROW]
[ROW][C]R-squared[/C][C]0.102992[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.0590213[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]2.34228[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]102[/C][/ROW]
[ROW][C]p-value[/C][C]0.0466949[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]4.22529[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1821.02[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270586&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270586&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	0.320924
R-squared	0.102992
Adjusted R-squared	0.0590213
F-TEST (value)	2.34228
F-TEST (DF numerator)	5
F-TEST (DF denominator)	102
p-value	0.0466949
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	4.22529
Sum Squared Residuals	1821.02

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	21	18.4586	2.54139
2	22	21.1582	0.841751
3	22	18.3017	3.6983
4	18	21.1436	-3.14364
5	23	21.2361	1.7639
6	12	20.5846	-8.58455
7	20	17.841	2.15901
8	22	20.4348	1.5652
9	21	21.1555	-0.155532
10	19	21.4829	-2.48285
11	22	20.8809	1.11913
12	15	21.2639	-6.26392
13	20	20.5737	-0.573668
14	19	18.3751	0.624946
15	18	18.4423	-0.442327
16	15	17.5852	-2.58525
17	20	20.9976	-0.997612
18	21	18.4313	2.56874
19	21	20.9472	0.0527845
20	15	18.1246	-3.12457
21	23	20.7427	2.25731
22	21	18.3227	2.67731
23	25	20.5478	4.4522
24	9	21.0088	-12.0088
25	30	20.73	9.27004
26	20	17.8895	2.11054
27	23	21.173	1.82696
28	16	18.4091	-2.40911
29	16	17.884	-1.88395
30	19	17.6035	1.39652
31	25	20.7544	4.24562
32	18	20.9849	-2.98489
33	23	20.6497	2.35025
34	21	20.9939	0.00610934
35	10	18.4197	-8.41967
36	14	20.5867	-6.58672
37	22	20.9355	1.06447
38	26	18.3743	7.62567
39	23	21.1382	1.86182
40	23	21.1739	1.82612
41	24	21.1129	2.88709
42	24	20.867	3.13301
43	18	20.7394	-2.73939
44	23	18.6013	4.39868
45	15	20.7889	-5.78886
46	19	21.0668	-2.06685
47	16	18.4902	-2.49025
48	25	21.0559	3.94411
49	23	21.0658	1.93419
50	17	20.6394	-3.6394
51	19	21.1005	-2.1005
52	21	21.0548	-0.0548356
53	18	21.2082	-3.20824
54	27	21.0974	5.90262
55	21	17.8623	3.13769
56	13	20.1282	-7.12821
57	8	18.384	-10.384
58	29	21.029	7.97103
59	28	21.2562	6.74384
60	23	18.3228	4.67721
61	21	17.0927	3.9073
62	19	21.0458	-2.04575
63	19	17.9127	1.08726
64	20	20.5733	-0.573274
65	18	17.9876	0.0124482
66	19	21.1103	-2.11033
67	17	21.0374	-4.03736
68	19	18.3259	0.674096
69	25	18.0213	6.97868
70	19	18.4725	0.527539
71	23	21.3133	1.6867
72	14	18.1949	-4.19486
73	28	20.8292	7.17078
74	16	17.9944	-1.99439
75	24	20.942	3.05796
76	20	18.536	1.46399
77	12	18.3271	-6.32714
78	24	21.0952	2.90479
79	22	18.3394	3.66065
80	12	18.3577	-6.35767
81	22	18.3673	3.6327
82	20	20.9458	-0.945759
83	10	17.7837	-7.78368
84	23	20.848	2.15204
85	17	21.227	-4.22697
86	22	18.2801	3.71987
87	24	18.0965	5.90346
88	18	18.3519	-0.351871
89	21	20.7608	0.239184
90	20	20.1979	-0.197876
91	20	20.9407	-0.940684
92	22	17.2006	4.79944
93	19	21.0537	-2.05372
94	20	18.4833	1.51668
95	26	20.7932	5.20679
96	23	20.6111	2.38885
97	24	21.0806	2.91937
98	21	21.0585	-0.0584591
99	21	21.0064	-0.00640817
100	19	18.4517	0.548254
101	17	21.0318	-4.03176
102	20	20.8745	-0.87454
103	11	18.0933	-7.09334
104	8	17.936	-9.93603
105	18	18.3601	-0.360056
106	18	18.4267	-0.426711
107	19	18.4835	0.516472
108	19	21.1346	-2.13465

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 21 & 18.4586 & 2.54139 \tabularnewline
2 & 22 & 21.1582 & 0.841751 \tabularnewline
3 & 22 & 18.3017 & 3.6983 \tabularnewline
4 & 18 & 21.1436 & -3.14364 \tabularnewline
5 & 23 & 21.2361 & 1.7639 \tabularnewline
6 & 12 & 20.5846 & -8.58455 \tabularnewline
7 & 20 & 17.841 & 2.15901 \tabularnewline
8 & 22 & 20.4348 & 1.5652 \tabularnewline
9 & 21 & 21.1555 & -0.155532 \tabularnewline
10 & 19 & 21.4829 & -2.48285 \tabularnewline
11 & 22 & 20.8809 & 1.11913 \tabularnewline
12 & 15 & 21.2639 & -6.26392 \tabularnewline
13 & 20 & 20.5737 & -0.573668 \tabularnewline
14 & 19 & 18.3751 & 0.624946 \tabularnewline
15 & 18 & 18.4423 & -0.442327 \tabularnewline
16 & 15 & 17.5852 & -2.58525 \tabularnewline
17 & 20 & 20.9976 & -0.997612 \tabularnewline
18 & 21 & 18.4313 & 2.56874 \tabularnewline
19 & 21 & 20.9472 & 0.0527845 \tabularnewline
20 & 15 & 18.1246 & -3.12457 \tabularnewline
21 & 23 & 20.7427 & 2.25731 \tabularnewline
22 & 21 & 18.3227 & 2.67731 \tabularnewline
23 & 25 & 20.5478 & 4.4522 \tabularnewline
24 & 9 & 21.0088 & -12.0088 \tabularnewline
25 & 30 & 20.73 & 9.27004 \tabularnewline
26 & 20 & 17.8895 & 2.11054 \tabularnewline
27 & 23 & 21.173 & 1.82696 \tabularnewline
28 & 16 & 18.4091 & -2.40911 \tabularnewline
29 & 16 & 17.884 & -1.88395 \tabularnewline
30 & 19 & 17.6035 & 1.39652 \tabularnewline
31 & 25 & 20.7544 & 4.24562 \tabularnewline
32 & 18 & 20.9849 & -2.98489 \tabularnewline
33 & 23 & 20.6497 & 2.35025 \tabularnewline
34 & 21 & 20.9939 & 0.00610934 \tabularnewline
35 & 10 & 18.4197 & -8.41967 \tabularnewline
36 & 14 & 20.5867 & -6.58672 \tabularnewline
37 & 22 & 20.9355 & 1.06447 \tabularnewline
38 & 26 & 18.3743 & 7.62567 \tabularnewline
39 & 23 & 21.1382 & 1.86182 \tabularnewline
40 & 23 & 21.1739 & 1.82612 \tabularnewline
41 & 24 & 21.1129 & 2.88709 \tabularnewline
42 & 24 & 20.867 & 3.13301 \tabularnewline
43 & 18 & 20.7394 & -2.73939 \tabularnewline
44 & 23 & 18.6013 & 4.39868 \tabularnewline
45 & 15 & 20.7889 & -5.78886 \tabularnewline
46 & 19 & 21.0668 & -2.06685 \tabularnewline
47 & 16 & 18.4902 & -2.49025 \tabularnewline
48 & 25 & 21.0559 & 3.94411 \tabularnewline
49 & 23 & 21.0658 & 1.93419 \tabularnewline
50 & 17 & 20.6394 & -3.6394 \tabularnewline
51 & 19 & 21.1005 & -2.1005 \tabularnewline
52 & 21 & 21.0548 & -0.0548356 \tabularnewline
53 & 18 & 21.2082 & -3.20824 \tabularnewline
54 & 27 & 21.0974 & 5.90262 \tabularnewline
55 & 21 & 17.8623 & 3.13769 \tabularnewline
56 & 13 & 20.1282 & -7.12821 \tabularnewline
57 & 8 & 18.384 & -10.384 \tabularnewline
58 & 29 & 21.029 & 7.97103 \tabularnewline
59 & 28 & 21.2562 & 6.74384 \tabularnewline
60 & 23 & 18.3228 & 4.67721 \tabularnewline
61 & 21 & 17.0927 & 3.9073 \tabularnewline
62 & 19 & 21.0458 & -2.04575 \tabularnewline
63 & 19 & 17.9127 & 1.08726 \tabularnewline
64 & 20 & 20.5733 & -0.573274 \tabularnewline
65 & 18 & 17.9876 & 0.0124482 \tabularnewline
66 & 19 & 21.1103 & -2.11033 \tabularnewline
67 & 17 & 21.0374 & -4.03736 \tabularnewline
68 & 19 & 18.3259 & 0.674096 \tabularnewline
69 & 25 & 18.0213 & 6.97868 \tabularnewline
70 & 19 & 18.4725 & 0.527539 \tabularnewline
71 & 23 & 21.3133 & 1.6867 \tabularnewline
72 & 14 & 18.1949 & -4.19486 \tabularnewline
73 & 28 & 20.8292 & 7.17078 \tabularnewline
74 & 16 & 17.9944 & -1.99439 \tabularnewline
75 & 24 & 20.942 & 3.05796 \tabularnewline
76 & 20 & 18.536 & 1.46399 \tabularnewline
77 & 12 & 18.3271 & -6.32714 \tabularnewline
78 & 24 & 21.0952 & 2.90479 \tabularnewline
79 & 22 & 18.3394 & 3.66065 \tabularnewline
80 & 12 & 18.3577 & -6.35767 \tabularnewline
81 & 22 & 18.3673 & 3.6327 \tabularnewline
82 & 20 & 20.9458 & -0.945759 \tabularnewline
83 & 10 & 17.7837 & -7.78368 \tabularnewline
84 & 23 & 20.848 & 2.15204 \tabularnewline
85 & 17 & 21.227 & -4.22697 \tabularnewline
86 & 22 & 18.2801 & 3.71987 \tabularnewline
87 & 24 & 18.0965 & 5.90346 \tabularnewline
88 & 18 & 18.3519 & -0.351871 \tabularnewline
89 & 21 & 20.7608 & 0.239184 \tabularnewline
90 & 20 & 20.1979 & -0.197876 \tabularnewline
91 & 20 & 20.9407 & -0.940684 \tabularnewline
92 & 22 & 17.2006 & 4.79944 \tabularnewline
93 & 19 & 21.0537 & -2.05372 \tabularnewline
94 & 20 & 18.4833 & 1.51668 \tabularnewline
95 & 26 & 20.7932 & 5.20679 \tabularnewline
96 & 23 & 20.6111 & 2.38885 \tabularnewline
97 & 24 & 21.0806 & 2.91937 \tabularnewline
98 & 21 & 21.0585 & -0.0584591 \tabularnewline
99 & 21 & 21.0064 & -0.00640817 \tabularnewline
100 & 19 & 18.4517 & 0.548254 \tabularnewline
101 & 17 & 21.0318 & -4.03176 \tabularnewline
102 & 20 & 20.8745 & -0.87454 \tabularnewline
103 & 11 & 18.0933 & -7.09334 \tabularnewline
104 & 8 & 17.936 & -9.93603 \tabularnewline
105 & 18 & 18.3601 & -0.360056 \tabularnewline
106 & 18 & 18.4267 & -0.426711 \tabularnewline
107 & 19 & 18.4835 & 0.516472 \tabularnewline
108 & 19 & 21.1346 & -2.13465 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270586&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]21[/C][C]18.4586[/C][C]2.54139[/C][/ROW]
[ROW][C]2[/C][C]22[/C][C]21.1582[/C][C]0.841751[/C][/ROW]
[ROW][C]3[/C][C]22[/C][C]18.3017[/C][C]3.6983[/C][/ROW]
[ROW][C]4[/C][C]18[/C][C]21.1436[/C][C]-3.14364[/C][/ROW]
[ROW][C]5[/C][C]23[/C][C]21.2361[/C][C]1.7639[/C][/ROW]
[ROW][C]6[/C][C]12[/C][C]20.5846[/C][C]-8.58455[/C][/ROW]
[ROW][C]7[/C][C]20[/C][C]17.841[/C][C]2.15901[/C][/ROW]
[ROW][C]8[/C][C]22[/C][C]20.4348[/C][C]1.5652[/C][/ROW]
[ROW][C]9[/C][C]21[/C][C]21.1555[/C][C]-0.155532[/C][/ROW]
[ROW][C]10[/C][C]19[/C][C]21.4829[/C][C]-2.48285[/C][/ROW]
[ROW][C]11[/C][C]22[/C][C]20.8809[/C][C]1.11913[/C][/ROW]
[ROW][C]12[/C][C]15[/C][C]21.2639[/C][C]-6.26392[/C][/ROW]
[ROW][C]13[/C][C]20[/C][C]20.5737[/C][C]-0.573668[/C][/ROW]
[ROW][C]14[/C][C]19[/C][C]18.3751[/C][C]0.624946[/C][/ROW]
[ROW][C]15[/C][C]18[/C][C]18.4423[/C][C]-0.442327[/C][/ROW]
[ROW][C]16[/C][C]15[/C][C]17.5852[/C][C]-2.58525[/C][/ROW]
[ROW][C]17[/C][C]20[/C][C]20.9976[/C][C]-0.997612[/C][/ROW]
[ROW][C]18[/C][C]21[/C][C]18.4313[/C][C]2.56874[/C][/ROW]
[ROW][C]19[/C][C]21[/C][C]20.9472[/C][C]0.0527845[/C][/ROW]
[ROW][C]20[/C][C]15[/C][C]18.1246[/C][C]-3.12457[/C][/ROW]
[ROW][C]21[/C][C]23[/C][C]20.7427[/C][C]2.25731[/C][/ROW]
[ROW][C]22[/C][C]21[/C][C]18.3227[/C][C]2.67731[/C][/ROW]
[ROW][C]23[/C][C]25[/C][C]20.5478[/C][C]4.4522[/C][/ROW]
[ROW][C]24[/C][C]9[/C][C]21.0088[/C][C]-12.0088[/C][/ROW]
[ROW][C]25[/C][C]30[/C][C]20.73[/C][C]9.27004[/C][/ROW]
[ROW][C]26[/C][C]20[/C][C]17.8895[/C][C]2.11054[/C][/ROW]
[ROW][C]27[/C][C]23[/C][C]21.173[/C][C]1.82696[/C][/ROW]
[ROW][C]28[/C][C]16[/C][C]18.4091[/C][C]-2.40911[/C][/ROW]
[ROW][C]29[/C][C]16[/C][C]17.884[/C][C]-1.88395[/C][/ROW]
[ROW][C]30[/C][C]19[/C][C]17.6035[/C][C]1.39652[/C][/ROW]
[ROW][C]31[/C][C]25[/C][C]20.7544[/C][C]4.24562[/C][/ROW]
[ROW][C]32[/C][C]18[/C][C]20.9849[/C][C]-2.98489[/C][/ROW]
[ROW][C]33[/C][C]23[/C][C]20.6497[/C][C]2.35025[/C][/ROW]
[ROW][C]34[/C][C]21[/C][C]20.9939[/C][C]0.00610934[/C][/ROW]
[ROW][C]35[/C][C]10[/C][C]18.4197[/C][C]-8.41967[/C][/ROW]
[ROW][C]36[/C][C]14[/C][C]20.5867[/C][C]-6.58672[/C][/ROW]
[ROW][C]37[/C][C]22[/C][C]20.9355[/C][C]1.06447[/C][/ROW]
[ROW][C]38[/C][C]26[/C][C]18.3743[/C][C]7.62567[/C][/ROW]
[ROW][C]39[/C][C]23[/C][C]21.1382[/C][C]1.86182[/C][/ROW]
[ROW][C]40[/C][C]23[/C][C]21.1739[/C][C]1.82612[/C][/ROW]
[ROW][C]41[/C][C]24[/C][C]21.1129[/C][C]2.88709[/C][/ROW]
[ROW][C]42[/C][C]24[/C][C]20.867[/C][C]3.13301[/C][/ROW]
[ROW][C]43[/C][C]18[/C][C]20.7394[/C][C]-2.73939[/C][/ROW]
[ROW][C]44[/C][C]23[/C][C]18.6013[/C][C]4.39868[/C][/ROW]
[ROW][C]45[/C][C]15[/C][C]20.7889[/C][C]-5.78886[/C][/ROW]
[ROW][C]46[/C][C]19[/C][C]21.0668[/C][C]-2.06685[/C][/ROW]
[ROW][C]47[/C][C]16[/C][C]18.4902[/C][C]-2.49025[/C][/ROW]
[ROW][C]48[/C][C]25[/C][C]21.0559[/C][C]3.94411[/C][/ROW]
[ROW][C]49[/C][C]23[/C][C]21.0658[/C][C]1.93419[/C][/ROW]
[ROW][C]50[/C][C]17[/C][C]20.6394[/C][C]-3.6394[/C][/ROW]
[ROW][C]51[/C][C]19[/C][C]21.1005[/C][C]-2.1005[/C][/ROW]
[ROW][C]52[/C][C]21[/C][C]21.0548[/C][C]-0.0548356[/C][/ROW]
[ROW][C]53[/C][C]18[/C][C]21.2082[/C][C]-3.20824[/C][/ROW]
[ROW][C]54[/C][C]27[/C][C]21.0974[/C][C]5.90262[/C][/ROW]
[ROW][C]55[/C][C]21[/C][C]17.8623[/C][C]3.13769[/C][/ROW]
[ROW][C]56[/C][C]13[/C][C]20.1282[/C][C]-7.12821[/C][/ROW]
[ROW][C]57[/C][C]8[/C][C]18.384[/C][C]-10.384[/C][/ROW]
[ROW][C]58[/C][C]29[/C][C]21.029[/C][C]7.97103[/C][/ROW]
[ROW][C]59[/C][C]28[/C][C]21.2562[/C][C]6.74384[/C][/ROW]
[ROW][C]60[/C][C]23[/C][C]18.3228[/C][C]4.67721[/C][/ROW]
[ROW][C]61[/C][C]21[/C][C]17.0927[/C][C]3.9073[/C][/ROW]
[ROW][C]62[/C][C]19[/C][C]21.0458[/C][C]-2.04575[/C][/ROW]
[ROW][C]63[/C][C]19[/C][C]17.9127[/C][C]1.08726[/C][/ROW]
[ROW][C]64[/C][C]20[/C][C]20.5733[/C][C]-0.573274[/C][/ROW]
[ROW][C]65[/C][C]18[/C][C]17.9876[/C][C]0.0124482[/C][/ROW]
[ROW][C]66[/C][C]19[/C][C]21.1103[/C][C]-2.11033[/C][/ROW]
[ROW][C]67[/C][C]17[/C][C]21.0374[/C][C]-4.03736[/C][/ROW]
[ROW][C]68[/C][C]19[/C][C]18.3259[/C][C]0.674096[/C][/ROW]
[ROW][C]69[/C][C]25[/C][C]18.0213[/C][C]6.97868[/C][/ROW]
[ROW][C]70[/C][C]19[/C][C]18.4725[/C][C]0.527539[/C][/ROW]
[ROW][C]71[/C][C]23[/C][C]21.3133[/C][C]1.6867[/C][/ROW]
[ROW][C]72[/C][C]14[/C][C]18.1949[/C][C]-4.19486[/C][/ROW]
[ROW][C]73[/C][C]28[/C][C]20.8292[/C][C]7.17078[/C][/ROW]
[ROW][C]74[/C][C]16[/C][C]17.9944[/C][C]-1.99439[/C][/ROW]
[ROW][C]75[/C][C]24[/C][C]20.942[/C][C]3.05796[/C][/ROW]
[ROW][C]76[/C][C]20[/C][C]18.536[/C][C]1.46399[/C][/ROW]
[ROW][C]77[/C][C]12[/C][C]18.3271[/C][C]-6.32714[/C][/ROW]
[ROW][C]78[/C][C]24[/C][C]21.0952[/C][C]2.90479[/C][/ROW]
[ROW][C]79[/C][C]22[/C][C]18.3394[/C][C]3.66065[/C][/ROW]
[ROW][C]80[/C][C]12[/C][C]18.3577[/C][C]-6.35767[/C][/ROW]
[ROW][C]81[/C][C]22[/C][C]18.3673[/C][C]3.6327[/C][/ROW]
[ROW][C]82[/C][C]20[/C][C]20.9458[/C][C]-0.945759[/C][/ROW]
[ROW][C]83[/C][C]10[/C][C]17.7837[/C][C]-7.78368[/C][/ROW]
[ROW][C]84[/C][C]23[/C][C]20.848[/C][C]2.15204[/C][/ROW]
[ROW][C]85[/C][C]17[/C][C]21.227[/C][C]-4.22697[/C][/ROW]
[ROW][C]86[/C][C]22[/C][C]18.2801[/C][C]3.71987[/C][/ROW]
[ROW][C]87[/C][C]24[/C][C]18.0965[/C][C]5.90346[/C][/ROW]
[ROW][C]88[/C][C]18[/C][C]18.3519[/C][C]-0.351871[/C][/ROW]
[ROW][C]89[/C][C]21[/C][C]20.7608[/C][C]0.239184[/C][/ROW]
[ROW][C]90[/C][C]20[/C][C]20.1979[/C][C]-0.197876[/C][/ROW]
[ROW][C]91[/C][C]20[/C][C]20.9407[/C][C]-0.940684[/C][/ROW]
[ROW][C]92[/C][C]22[/C][C]17.2006[/C][C]4.79944[/C][/ROW]
[ROW][C]93[/C][C]19[/C][C]21.0537[/C][C]-2.05372[/C][/ROW]
[ROW][C]94[/C][C]20[/C][C]18.4833[/C][C]1.51668[/C][/ROW]
[ROW][C]95[/C][C]26[/C][C]20.7932[/C][C]5.20679[/C][/ROW]
[ROW][C]96[/C][C]23[/C][C]20.6111[/C][C]2.38885[/C][/ROW]
[ROW][C]97[/C][C]24[/C][C]21.0806[/C][C]2.91937[/C][/ROW]
[ROW][C]98[/C][C]21[/C][C]21.0585[/C][C]-0.0584591[/C][/ROW]
[ROW][C]99[/C][C]21[/C][C]21.0064[/C][C]-0.00640817[/C][/ROW]
[ROW][C]100[/C][C]19[/C][C]18.4517[/C][C]0.548254[/C][/ROW]
[ROW][C]101[/C][C]17[/C][C]21.0318[/C][C]-4.03176[/C][/ROW]
[ROW][C]102[/C][C]20[/C][C]20.8745[/C][C]-0.87454[/C][/ROW]
[ROW][C]103[/C][C]11[/C][C]18.0933[/C][C]-7.09334[/C][/ROW]
[ROW][C]104[/C][C]8[/C][C]17.936[/C][C]-9.93603[/C][/ROW]
[ROW][C]105[/C][C]18[/C][C]18.3601[/C][C]-0.360056[/C][/ROW]
[ROW][C]106[/C][C]18[/C][C]18.4267[/C][C]-0.426711[/C][/ROW]
[ROW][C]107[/C][C]19[/C][C]18.4835[/C][C]0.516472[/C][/ROW]
[ROW][C]108[/C][C]19[/C][C]21.1346[/C][C]-2.13465[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270586&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270586&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	21	18.4586	2.54139
2	22	21.1582	0.841751
3	22	18.3017	3.6983
4	18	21.1436	-3.14364
5	23	21.2361	1.7639
6	12	20.5846	-8.58455
7	20	17.841	2.15901
8	22	20.4348	1.5652
9	21	21.1555	-0.155532
10	19	21.4829	-2.48285
11	22	20.8809	1.11913
12	15	21.2639	-6.26392
13	20	20.5737	-0.573668
14	19	18.3751	0.624946
15	18	18.4423	-0.442327
16	15	17.5852	-2.58525
17	20	20.9976	-0.997612
18	21	18.4313	2.56874
19	21	20.9472	0.0527845
20	15	18.1246	-3.12457
21	23	20.7427	2.25731
22	21	18.3227	2.67731
23	25	20.5478	4.4522
24	9	21.0088	-12.0088
25	30	20.73	9.27004
26	20	17.8895	2.11054
27	23	21.173	1.82696
28	16	18.4091	-2.40911
29	16	17.884	-1.88395
30	19	17.6035	1.39652
31	25	20.7544	4.24562
32	18	20.9849	-2.98489
33	23	20.6497	2.35025
34	21	20.9939	0.00610934
35	10	18.4197	-8.41967
36	14	20.5867	-6.58672
37	22	20.9355	1.06447
38	26	18.3743	7.62567
39	23	21.1382	1.86182
40	23	21.1739	1.82612
41	24	21.1129	2.88709
42	24	20.867	3.13301
43	18	20.7394	-2.73939
44	23	18.6013	4.39868
45	15	20.7889	-5.78886
46	19	21.0668	-2.06685
47	16	18.4902	-2.49025
48	25	21.0559	3.94411
49	23	21.0658	1.93419
50	17	20.6394	-3.6394
51	19	21.1005	-2.1005
52	21	21.0548	-0.0548356
53	18	21.2082	-3.20824
54	27	21.0974	5.90262
55	21	17.8623	3.13769
56	13	20.1282	-7.12821
57	8	18.384	-10.384
58	29	21.029	7.97103
59	28	21.2562	6.74384
60	23	18.3228	4.67721
61	21	17.0927	3.9073
62	19	21.0458	-2.04575
63	19	17.9127	1.08726
64	20	20.5733	-0.573274
65	18	17.9876	0.0124482
66	19	21.1103	-2.11033
67	17	21.0374	-4.03736
68	19	18.3259	0.674096
69	25	18.0213	6.97868
70	19	18.4725	0.527539
71	23	21.3133	1.6867
72	14	18.1949	-4.19486
73	28	20.8292	7.17078
74	16	17.9944	-1.99439
75	24	20.942	3.05796
76	20	18.536	1.46399
77	12	18.3271	-6.32714
78	24	21.0952	2.90479
79	22	18.3394	3.66065
80	12	18.3577	-6.35767
81	22	18.3673	3.6327
82	20	20.9458	-0.945759
83	10	17.7837	-7.78368
84	23	20.848	2.15204
85	17	21.227	-4.22697
86	22	18.2801	3.71987
87	24	18.0965	5.90346
88	18	18.3519	-0.351871
89	21	20.7608	0.239184
90	20	20.1979	-0.197876
91	20	20.9407	-0.940684
92	22	17.2006	4.79944
93	19	21.0537	-2.05372
94	20	18.4833	1.51668
95	26	20.7932	5.20679
96	23	20.6111	2.38885
97	24	21.0806	2.91937
98	21	21.0585	-0.0584591
99	21	21.0064	-0.00640817
100	19	18.4517	0.548254
101	17	21.0318	-4.03176
102	20	20.8745	-0.87454
103	11	18.0933	-7.09334
104	8	17.936	-9.93603
105	18	18.3601	-0.360056
106	18	18.4267	-0.426711
107	19	18.4835	0.516472
108	19	21.1346	-2.13465

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
9	0.489435	0.978869	0.510565
10	0.589544	0.820912	0.410456
11	0.465877	0.931754	0.534123
12	0.468984	0.937968	0.531016
13	0.355513	0.711026	0.644487
14	0.254166	0.508332	0.745834
15	0.17351	0.347019	0.82649
16	0.118068	0.236136	0.881932
17	0.0948761	0.189752	0.905124
18	0.0643628	0.128726	0.935637
19	0.0429351	0.0858701	0.957065
20	0.0308501	0.0617002	0.96915
21	0.0396385	0.079277	0.960362
22	0.0270342	0.0540683	0.972966
23	0.0656826	0.131365	0.934317
24	0.366789	0.733578	0.633211
25	0.730248	0.539504	0.269752
26	0.675164	0.649673	0.324836
27	0.639696	0.720608	0.360304
28	0.60026	0.79948	0.39974
29	0.560954	0.878092	0.439046
30	0.499667	0.999333	0.500333
31	0.508642	0.982715	0.491358
32	0.466882	0.933765	0.533118
33	0.422324	0.844648	0.577676
34	0.360282	0.720564	0.639718
35	0.51831	0.963381	0.48169
36	0.614402	0.771195	0.385598
37	0.557596	0.884809	0.442404
38	0.679486	0.641029	0.320514
39	0.639139	0.721723	0.360861
40	0.59413	0.81174	0.40587
41	0.561957	0.876087	0.438043
42	0.530603	0.938793	0.469397
43	0.509914	0.980173	0.490086
44	0.507192	0.985616	0.492808
45	0.545401	0.909199	0.454599
46	0.498381	0.996761	0.501619
47	0.465872	0.931744	0.534128
48	0.464087	0.928174	0.535913
49	0.417996	0.835992	0.582004
50	0.400086	0.800172	0.599914
51	0.355312	0.710625	0.644688
52	0.307648	0.615295	0.692352
53	0.28522	0.570439	0.71478
54	0.32886	0.65772	0.67114
55	0.30398	0.60796	0.69602
56	0.428616	0.857233	0.571384
57	0.684153	0.631693	0.315847
58	0.804338	0.391325	0.195662
59	0.862794	0.274412	0.137206
60	0.873093	0.253813	0.126907
61	0.85863	0.28274	0.14137
62	0.829755	0.34049	0.170245
63	0.791535	0.41693	0.208465
64	0.747102	0.505796	0.252898
65	0.698532	0.602936	0.301468
66	0.665676	0.668649	0.334324
67	0.651765	0.696471	0.348235
68	0.603719	0.792563	0.396281
69	0.723689	0.552622	0.276311
70	0.683773	0.632454	0.316227
71	0.638084	0.723833	0.361916
72	0.625886	0.748227	0.374114
73	0.733893	0.532215	0.266107
74	0.688606	0.622787	0.311394
75	0.703558	0.592883	0.296442
76	0.677748	0.644505	0.322252
77	0.751516	0.496967	0.248484
78	0.705978	0.588044	0.294022
79	0.727698	0.544605	0.272302
80	0.843404	0.313192	0.156596
81	0.810546	0.378909	0.189454
82	0.782588	0.434824	0.217412
83	0.849822	0.300355	0.150178
84	0.836124	0.327752	0.163876
85	0.831533	0.336934	0.168467
86	0.789664	0.420671	0.210336
87	0.873484	0.253032	0.126516
88	0.836564	0.326873	0.163436
89	0.779777	0.440446	0.220223
90	0.744291	0.511418	0.255709
91	0.681289	0.637422	0.318711
92	0.702154	0.595693	0.297846
93	0.644684	0.710632	0.355316
94	0.574465	0.851069	0.425535
95	0.711024	0.577952	0.288976
96	0.971797	0.0564064	0.0282032
97	0.936755	0.12649	0.0632448
98	0.87396	0.25208	0.12604
99	0.968777	0.062446	0.031223

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.489435 & 0.978869 & 0.510565 \tabularnewline
10 & 0.589544 & 0.820912 & 0.410456 \tabularnewline
11 & 0.465877 & 0.931754 & 0.534123 \tabularnewline
12 & 0.468984 & 0.937968 & 0.531016 \tabularnewline
13 & 0.355513 & 0.711026 & 0.644487 \tabularnewline
14 & 0.254166 & 0.508332 & 0.745834 \tabularnewline
15 & 0.17351 & 0.347019 & 0.82649 \tabularnewline
16 & 0.118068 & 0.236136 & 0.881932 \tabularnewline
17 & 0.0948761 & 0.189752 & 0.905124 \tabularnewline
18 & 0.0643628 & 0.128726 & 0.935637 \tabularnewline
19 & 0.0429351 & 0.0858701 & 0.957065 \tabularnewline
20 & 0.0308501 & 0.0617002 & 0.96915 \tabularnewline
21 & 0.0396385 & 0.079277 & 0.960362 \tabularnewline
22 & 0.0270342 & 0.0540683 & 0.972966 \tabularnewline
23 & 0.0656826 & 0.131365 & 0.934317 \tabularnewline
24 & 0.366789 & 0.733578 & 0.633211 \tabularnewline
25 & 0.730248 & 0.539504 & 0.269752 \tabularnewline
26 & 0.675164 & 0.649673 & 0.324836 \tabularnewline
27 & 0.639696 & 0.720608 & 0.360304 \tabularnewline
28 & 0.60026 & 0.79948 & 0.39974 \tabularnewline
29 & 0.560954 & 0.878092 & 0.439046 \tabularnewline
30 & 0.499667 & 0.999333 & 0.500333 \tabularnewline
31 & 0.508642 & 0.982715 & 0.491358 \tabularnewline
32 & 0.466882 & 0.933765 & 0.533118 \tabularnewline
33 & 0.422324 & 0.844648 & 0.577676 \tabularnewline
34 & 0.360282 & 0.720564 & 0.639718 \tabularnewline
35 & 0.51831 & 0.963381 & 0.48169 \tabularnewline
36 & 0.614402 & 0.771195 & 0.385598 \tabularnewline
37 & 0.557596 & 0.884809 & 0.442404 \tabularnewline
38 & 0.679486 & 0.641029 & 0.320514 \tabularnewline
39 & 0.639139 & 0.721723 & 0.360861 \tabularnewline
40 & 0.59413 & 0.81174 & 0.40587 \tabularnewline
41 & 0.561957 & 0.876087 & 0.438043 \tabularnewline
42 & 0.530603 & 0.938793 & 0.469397 \tabularnewline
43 & 0.509914 & 0.980173 & 0.490086 \tabularnewline
44 & 0.507192 & 0.985616 & 0.492808 \tabularnewline
45 & 0.545401 & 0.909199 & 0.454599 \tabularnewline
46 & 0.498381 & 0.996761 & 0.501619 \tabularnewline
47 & 0.465872 & 0.931744 & 0.534128 \tabularnewline
48 & 0.464087 & 0.928174 & 0.535913 \tabularnewline
49 & 0.417996 & 0.835992 & 0.582004 \tabularnewline
50 & 0.400086 & 0.800172 & 0.599914 \tabularnewline
51 & 0.355312 & 0.710625 & 0.644688 \tabularnewline
52 & 0.307648 & 0.615295 & 0.692352 \tabularnewline
53 & 0.28522 & 0.570439 & 0.71478 \tabularnewline
54 & 0.32886 & 0.65772 & 0.67114 \tabularnewline
55 & 0.30398 & 0.60796 & 0.69602 \tabularnewline
56 & 0.428616 & 0.857233 & 0.571384 \tabularnewline
57 & 0.684153 & 0.631693 & 0.315847 \tabularnewline
58 & 0.804338 & 0.391325 & 0.195662 \tabularnewline
59 & 0.862794 & 0.274412 & 0.137206 \tabularnewline
60 & 0.873093 & 0.253813 & 0.126907 \tabularnewline
61 & 0.85863 & 0.28274 & 0.14137 \tabularnewline
62 & 0.829755 & 0.34049 & 0.170245 \tabularnewline
63 & 0.791535 & 0.41693 & 0.208465 \tabularnewline
64 & 0.747102 & 0.505796 & 0.252898 \tabularnewline
65 & 0.698532 & 0.602936 & 0.301468 \tabularnewline
66 & 0.665676 & 0.668649 & 0.334324 \tabularnewline
67 & 0.651765 & 0.696471 & 0.348235 \tabularnewline
68 & 0.603719 & 0.792563 & 0.396281 \tabularnewline
69 & 0.723689 & 0.552622 & 0.276311 \tabularnewline
70 & 0.683773 & 0.632454 & 0.316227 \tabularnewline
71 & 0.638084 & 0.723833 & 0.361916 \tabularnewline
72 & 0.625886 & 0.748227 & 0.374114 \tabularnewline
73 & 0.733893 & 0.532215 & 0.266107 \tabularnewline
74 & 0.688606 & 0.622787 & 0.311394 \tabularnewline
75 & 0.703558 & 0.592883 & 0.296442 \tabularnewline
76 & 0.677748 & 0.644505 & 0.322252 \tabularnewline
77 & 0.751516 & 0.496967 & 0.248484 \tabularnewline
78 & 0.705978 & 0.588044 & 0.294022 \tabularnewline
79 & 0.727698 & 0.544605 & 0.272302 \tabularnewline
80 & 0.843404 & 0.313192 & 0.156596 \tabularnewline
81 & 0.810546 & 0.378909 & 0.189454 \tabularnewline
82 & 0.782588 & 0.434824 & 0.217412 \tabularnewline
83 & 0.849822 & 0.300355 & 0.150178 \tabularnewline
84 & 0.836124 & 0.327752 & 0.163876 \tabularnewline
85 & 0.831533 & 0.336934 & 0.168467 \tabularnewline
86 & 0.789664 & 0.420671 & 0.210336 \tabularnewline
87 & 0.873484 & 0.253032 & 0.126516 \tabularnewline
88 & 0.836564 & 0.326873 & 0.163436 \tabularnewline
89 & 0.779777 & 0.440446 & 0.220223 \tabularnewline
90 & 0.744291 & 0.511418 & 0.255709 \tabularnewline
91 & 0.681289 & 0.637422 & 0.318711 \tabularnewline
92 & 0.702154 & 0.595693 & 0.297846 \tabularnewline
93 & 0.644684 & 0.710632 & 0.355316 \tabularnewline
94 & 0.574465 & 0.851069 & 0.425535 \tabularnewline
95 & 0.711024 & 0.577952 & 0.288976 \tabularnewline
96 & 0.971797 & 0.0564064 & 0.0282032 \tabularnewline
97 & 0.936755 & 0.12649 & 0.0632448 \tabularnewline
98 & 0.87396 & 0.25208 & 0.12604 \tabularnewline
99 & 0.968777 & 0.062446 & 0.031223 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270586&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]9[/C][C]0.489435[/C][C]0.978869[/C][C]0.510565[/C][/ROW]
[ROW][C]10[/C][C]0.589544[/C][C]0.820912[/C][C]0.410456[/C][/ROW]
[ROW][C]11[/C][C]0.465877[/C][C]0.931754[/C][C]0.534123[/C][/ROW]
[ROW][C]12[/C][C]0.468984[/C][C]0.937968[/C][C]0.531016[/C][/ROW]
[ROW][C]13[/C][C]0.355513[/C][C]0.711026[/C][C]0.644487[/C][/ROW]
[ROW][C]14[/C][C]0.254166[/C][C]0.508332[/C][C]0.745834[/C][/ROW]
[ROW][C]15[/C][C]0.17351[/C][C]0.347019[/C][C]0.82649[/C][/ROW]
[ROW][C]16[/C][C]0.118068[/C][C]0.236136[/C][C]0.881932[/C][/ROW]
[ROW][C]17[/C][C]0.0948761[/C][C]0.189752[/C][C]0.905124[/C][/ROW]
[ROW][C]18[/C][C]0.0643628[/C][C]0.128726[/C][C]0.935637[/C][/ROW]
[ROW][C]19[/C][C]0.0429351[/C][C]0.0858701[/C][C]0.957065[/C][/ROW]
[ROW][C]20[/C][C]0.0308501[/C][C]0.0617002[/C][C]0.96915[/C][/ROW]
[ROW][C]21[/C][C]0.0396385[/C][C]0.079277[/C][C]0.960362[/C][/ROW]
[ROW][C]22[/C][C]0.0270342[/C][C]0.0540683[/C][C]0.972966[/C][/ROW]
[ROW][C]23[/C][C]0.0656826[/C][C]0.131365[/C][C]0.934317[/C][/ROW]
[ROW][C]24[/C][C]0.366789[/C][C]0.733578[/C][C]0.633211[/C][/ROW]
[ROW][C]25[/C][C]0.730248[/C][C]0.539504[/C][C]0.269752[/C][/ROW]
[ROW][C]26[/C][C]0.675164[/C][C]0.649673[/C][C]0.324836[/C][/ROW]
[ROW][C]27[/C][C]0.639696[/C][C]0.720608[/C][C]0.360304[/C][/ROW]
[ROW][C]28[/C][C]0.60026[/C][C]0.79948[/C][C]0.39974[/C][/ROW]
[ROW][C]29[/C][C]0.560954[/C][C]0.878092[/C][C]0.439046[/C][/ROW]
[ROW][C]30[/C][C]0.499667[/C][C]0.999333[/C][C]0.500333[/C][/ROW]
[ROW][C]31[/C][C]0.508642[/C][C]0.982715[/C][C]0.491358[/C][/ROW]
[ROW][C]32[/C][C]0.466882[/C][C]0.933765[/C][C]0.533118[/C][/ROW]
[ROW][C]33[/C][C]0.422324[/C][C]0.844648[/C][C]0.577676[/C][/ROW]
[ROW][C]34[/C][C]0.360282[/C][C]0.720564[/C][C]0.639718[/C][/ROW]
[ROW][C]35[/C][C]0.51831[/C][C]0.963381[/C][C]0.48169[/C][/ROW]
[ROW][C]36[/C][C]0.614402[/C][C]0.771195[/C][C]0.385598[/C][/ROW]
[ROW][C]37[/C][C]0.557596[/C][C]0.884809[/C][C]0.442404[/C][/ROW]
[ROW][C]38[/C][C]0.679486[/C][C]0.641029[/C][C]0.320514[/C][/ROW]
[ROW][C]39[/C][C]0.639139[/C][C]0.721723[/C][C]0.360861[/C][/ROW]
[ROW][C]40[/C][C]0.59413[/C][C]0.81174[/C][C]0.40587[/C][/ROW]
[ROW][C]41[/C][C]0.561957[/C][C]0.876087[/C][C]0.438043[/C][/ROW]
[ROW][C]42[/C][C]0.530603[/C][C]0.938793[/C][C]0.469397[/C][/ROW]
[ROW][C]43[/C][C]0.509914[/C][C]0.980173[/C][C]0.490086[/C][/ROW]
[ROW][C]44[/C][C]0.507192[/C][C]0.985616[/C][C]0.492808[/C][/ROW]
[ROW][C]45[/C][C]0.545401[/C][C]0.909199[/C][C]0.454599[/C][/ROW]
[ROW][C]46[/C][C]0.498381[/C][C]0.996761[/C][C]0.501619[/C][/ROW]
[ROW][C]47[/C][C]0.465872[/C][C]0.931744[/C][C]0.534128[/C][/ROW]
[ROW][C]48[/C][C]0.464087[/C][C]0.928174[/C][C]0.535913[/C][/ROW]
[ROW][C]49[/C][C]0.417996[/C][C]0.835992[/C][C]0.582004[/C][/ROW]
[ROW][C]50[/C][C]0.400086[/C][C]0.800172[/C][C]0.599914[/C][/ROW]
[ROW][C]51[/C][C]0.355312[/C][C]0.710625[/C][C]0.644688[/C][/ROW]
[ROW][C]52[/C][C]0.307648[/C][C]0.615295[/C][C]0.692352[/C][/ROW]
[ROW][C]53[/C][C]0.28522[/C][C]0.570439[/C][C]0.71478[/C][/ROW]
[ROW][C]54[/C][C]0.32886[/C][C]0.65772[/C][C]0.67114[/C][/ROW]
[ROW][C]55[/C][C]0.30398[/C][C]0.60796[/C][C]0.69602[/C][/ROW]
[ROW][C]56[/C][C]0.428616[/C][C]0.857233[/C][C]0.571384[/C][/ROW]
[ROW][C]57[/C][C]0.684153[/C][C]0.631693[/C][C]0.315847[/C][/ROW]
[ROW][C]58[/C][C]0.804338[/C][C]0.391325[/C][C]0.195662[/C][/ROW]
[ROW][C]59[/C][C]0.862794[/C][C]0.274412[/C][C]0.137206[/C][/ROW]
[ROW][C]60[/C][C]0.873093[/C][C]0.253813[/C][C]0.126907[/C][/ROW]
[ROW][C]61[/C][C]0.85863[/C][C]0.28274[/C][C]0.14137[/C][/ROW]
[ROW][C]62[/C][C]0.829755[/C][C]0.34049[/C][C]0.170245[/C][/ROW]
[ROW][C]63[/C][C]0.791535[/C][C]0.41693[/C][C]0.208465[/C][/ROW]
[ROW][C]64[/C][C]0.747102[/C][C]0.505796[/C][C]0.252898[/C][/ROW]
[ROW][C]65[/C][C]0.698532[/C][C]0.602936[/C][C]0.301468[/C][/ROW]
[ROW][C]66[/C][C]0.665676[/C][C]0.668649[/C][C]0.334324[/C][/ROW]
[ROW][C]67[/C][C]0.651765[/C][C]0.696471[/C][C]0.348235[/C][/ROW]
[ROW][C]68[/C][C]0.603719[/C][C]0.792563[/C][C]0.396281[/C][/ROW]
[ROW][C]69[/C][C]0.723689[/C][C]0.552622[/C][C]0.276311[/C][/ROW]
[ROW][C]70[/C][C]0.683773[/C][C]0.632454[/C][C]0.316227[/C][/ROW]
[ROW][C]71[/C][C]0.638084[/C][C]0.723833[/C][C]0.361916[/C][/ROW]
[ROW][C]72[/C][C]0.625886[/C][C]0.748227[/C][C]0.374114[/C][/ROW]
[ROW][C]73[/C][C]0.733893[/C][C]0.532215[/C][C]0.266107[/C][/ROW]
[ROW][C]74[/C][C]0.688606[/C][C]0.622787[/C][C]0.311394[/C][/ROW]
[ROW][C]75[/C][C]0.703558[/C][C]0.592883[/C][C]0.296442[/C][/ROW]
[ROW][C]76[/C][C]0.677748[/C][C]0.644505[/C][C]0.322252[/C][/ROW]
[ROW][C]77[/C][C]0.751516[/C][C]0.496967[/C][C]0.248484[/C][/ROW]
[ROW][C]78[/C][C]0.705978[/C][C]0.588044[/C][C]0.294022[/C][/ROW]
[ROW][C]79[/C][C]0.727698[/C][C]0.544605[/C][C]0.272302[/C][/ROW]
[ROW][C]80[/C][C]0.843404[/C][C]0.313192[/C][C]0.156596[/C][/ROW]
[ROW][C]81[/C][C]0.810546[/C][C]0.378909[/C][C]0.189454[/C][/ROW]
[ROW][C]82[/C][C]0.782588[/C][C]0.434824[/C][C]0.217412[/C][/ROW]
[ROW][C]83[/C][C]0.849822[/C][C]0.300355[/C][C]0.150178[/C][/ROW]
[ROW][C]84[/C][C]0.836124[/C][C]0.327752[/C][C]0.163876[/C][/ROW]
[ROW][C]85[/C][C]0.831533[/C][C]0.336934[/C][C]0.168467[/C][/ROW]
[ROW][C]86[/C][C]0.789664[/C][C]0.420671[/C][C]0.210336[/C][/ROW]
[ROW][C]87[/C][C]0.873484[/C][C]0.253032[/C][C]0.126516[/C][/ROW]
[ROW][C]88[/C][C]0.836564[/C][C]0.326873[/C][C]0.163436[/C][/ROW]
[ROW][C]89[/C][C]0.779777[/C][C]0.440446[/C][C]0.220223[/C][/ROW]
[ROW][C]90[/C][C]0.744291[/C][C]0.511418[/C][C]0.255709[/C][/ROW]
[ROW][C]91[/C][C]0.681289[/C][C]0.637422[/C][C]0.318711[/C][/ROW]
[ROW][C]92[/C][C]0.702154[/C][C]0.595693[/C][C]0.297846[/C][/ROW]
[ROW][C]93[/C][C]0.644684[/C][C]0.710632[/C][C]0.355316[/C][/ROW]
[ROW][C]94[/C][C]0.574465[/C][C]0.851069[/C][C]0.425535[/C][/ROW]
[ROW][C]95[/C][C]0.711024[/C][C]0.577952[/C][C]0.288976[/C][/ROW]
[ROW][C]96[/C][C]0.971797[/C][C]0.0564064[/C][C]0.0282032[/C][/ROW]
[ROW][C]97[/C][C]0.936755[/C][C]0.12649[/C][C]0.0632448[/C][/ROW]
[ROW][C]98[/C][C]0.87396[/C][C]0.25208[/C][C]0.12604[/C][/ROW]
[ROW][C]99[/C][C]0.968777[/C][C]0.062446[/C][C]0.031223[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270586&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270586&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
9	0.489435	0.978869	0.510565
10	0.589544	0.820912	0.410456
11	0.465877	0.931754	0.534123
12	0.468984	0.937968	0.531016
13	0.355513	0.711026	0.644487
14	0.254166	0.508332	0.745834
15	0.17351	0.347019	0.82649
16	0.118068	0.236136	0.881932
17	0.0948761	0.189752	0.905124
18	0.0643628	0.128726	0.935637
19	0.0429351	0.0858701	0.957065
20	0.0308501	0.0617002	0.96915
21	0.0396385	0.079277	0.960362
22	0.0270342	0.0540683	0.972966
23	0.0656826	0.131365	0.934317
24	0.366789	0.733578	0.633211
25	0.730248	0.539504	0.269752
26	0.675164	0.649673	0.324836
27	0.639696	0.720608	0.360304
28	0.60026	0.79948	0.39974
29	0.560954	0.878092	0.439046
30	0.499667	0.999333	0.500333
31	0.508642	0.982715	0.491358
32	0.466882	0.933765	0.533118
33	0.422324	0.844648	0.577676
34	0.360282	0.720564	0.639718
35	0.51831	0.963381	0.48169
36	0.614402	0.771195	0.385598
37	0.557596	0.884809	0.442404
38	0.679486	0.641029	0.320514
39	0.639139	0.721723	0.360861
40	0.59413	0.81174	0.40587
41	0.561957	0.876087	0.438043
42	0.530603	0.938793	0.469397
43	0.509914	0.980173	0.490086
44	0.507192	0.985616	0.492808
45	0.545401	0.909199	0.454599
46	0.498381	0.996761	0.501619
47	0.465872	0.931744	0.534128
48	0.464087	0.928174	0.535913
49	0.417996	0.835992	0.582004
50	0.400086	0.800172	0.599914
51	0.355312	0.710625	0.644688
52	0.307648	0.615295	0.692352
53	0.28522	0.570439	0.71478
54	0.32886	0.65772	0.67114
55	0.30398	0.60796	0.69602
56	0.428616	0.857233	0.571384
57	0.684153	0.631693	0.315847
58	0.804338	0.391325	0.195662
59	0.862794	0.274412	0.137206
60	0.873093	0.253813	0.126907
61	0.85863	0.28274	0.14137
62	0.829755	0.34049	0.170245
63	0.791535	0.41693	0.208465
64	0.747102	0.505796	0.252898
65	0.698532	0.602936	0.301468
66	0.665676	0.668649	0.334324
67	0.651765	0.696471	0.348235
68	0.603719	0.792563	0.396281
69	0.723689	0.552622	0.276311
70	0.683773	0.632454	0.316227
71	0.638084	0.723833	0.361916
72	0.625886	0.748227	0.374114
73	0.733893	0.532215	0.266107
74	0.688606	0.622787	0.311394
75	0.703558	0.592883	0.296442
76	0.677748	0.644505	0.322252
77	0.751516	0.496967	0.248484
78	0.705978	0.588044	0.294022
79	0.727698	0.544605	0.272302
80	0.843404	0.313192	0.156596
81	0.810546	0.378909	0.189454
82	0.782588	0.434824	0.217412
83	0.849822	0.300355	0.150178
84	0.836124	0.327752	0.163876
85	0.831533	0.336934	0.168467
86	0.789664	0.420671	0.210336
87	0.873484	0.253032	0.126516
88	0.836564	0.326873	0.163436
89	0.779777	0.440446	0.220223
90	0.744291	0.511418	0.255709
91	0.681289	0.637422	0.318711
92	0.702154	0.595693	0.297846
93	0.644684	0.710632	0.355316
94	0.574465	0.851069	0.425535
95	0.711024	0.577952	0.288976
96	0.971797	0.0564064	0.0282032
97	0.936755	0.12649	0.0632448
98	0.87396	0.25208	0.12604
99	0.968777	0.062446	0.031223

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	6	0.0659341	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 & 6 & 0.0659341 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270586&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]6[/C][C]0.0659341[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270586&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270586&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	6	0.0659341	OK

Figure 1

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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