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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Thu, 18 Dec 2014 11:03:39 +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/t1418907563u8n05e5o684sqfn.htm/, Retrieved Sun, 19 May 2024 19:50:28 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=270886, Retrieved Sun, 19 May 2024 19:50:28 +0000

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-     [Multiple Regression] [Verklaring examen...] [2014-12-18 10:27:57] [94e0b03eaaae24ea322c1a0c8a3c30a1]
-    D    [Multiple Regression] [verklaring examen...] [2014-12-18 11:03:39] [0adf43ccf8dfa476608a94fd7836e72e] [Current]

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13	26	12	12	18
12	57	8	9	31
13	37	11	12	39
7	67	13	8	46
7	43	11	9	31
13	52	10	9	67
15	52	7	12	35
13	43	10	9	52
11	84	15	10	77
8	67	12	13	37
11	49	12	8	32
6	70	10	11	36
11	52	10	7	38
12	58	14	11	69
6	68	6	13	21
11	62	12	12	26
12	43	14	9	54
9	56	11	9	36
10	56	8	9	42
10	74	12	12	23
14	63	13	12	112
11	58	11	9	35
14	57	12	0	47
12	63	7	14	47
11	53	11	13	37
16	57	7	12	109
13	51	12	9	24
10	64	12	10	20
12	53	13	8	22
8	29	9	4	23
12	54	11	7	32
9	58	12	8	30
10	43	15	13	92
11	51	12	11	43
10	53	6	5	55
10	54	5	9	16
13	56	13	9	49
9	61	11	10	71
12	47	6	12	43
6	39	12	5	29
11	48	10	8	56
13	50	6	15	46
11	35	12	4	19
12	30	11	9	23
11	68	6	9	59
12	49	12	10	30
8	61	12	11	61
13	67	8	14	7
12	47	10	12	38
12	56	11	12	32
7	50	7	7	16
11	43	12	7	19
12	67	13	10	22
13	62	14	15	48
10	57	12	10	23
6	41	6	10	26
14	54	14	10	33
8	45	10	11	9
13	48	12	9	24
9	61	11	11	34
13	56	10	10	48
8	41	7	10	18
16	43	12	9	43
9	53	7	9	33
9	44	12	8	28
11	66	12	8	71
13	58	10	12	26
15	46	10	9	67
12	37	12	3	34
12	51	12	13	80
11	51	12	10	29
9	56	8	0	16
15	66	10	12	59
13	37	5	7	32
13	42	10	9	43
13	38	12	12	38
13	66	11	11	29
10	34	9	9	36
8	53	12	10	32
11	49	11	11	35
13	55	10	6	21
11	49	12	10	29
4	59	10	10	12
10	40	9	9	37
12	58	11	9	37
11	60	12	10	47
11	63	7	12	51
9	56	11	7	32
13	54	12	10	21
13	52	6	8	13
6	34	9	12	14
10	69	15	11	-2
9	32	10	7	20
8	48	11	9	24
9	67	12	6	11
7	58	12	9	23
11	57	12	10	24
14	42	11	8	14
8	64	9	11	52
11	58	11	10	15
10	66	12	6	23
10	61	14	9	35
14	52	8	12	24
9	51	10	10	39
14	55	9	10	29
6	60	9	10	8
10	56	10	6	18
12	63	12	12	24
11	61	11	11	19

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 Ronald Aylmer Fisher' @ fisher.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 Ronald Aylmer Fisher' @ fisher.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270886&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 Ronald Aylmer Fisher' @ fisher.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270886&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270886&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 Ronald Aylmer Fisher' @ fisher.wessa.net

Multiple Linear Regression - Estimated Regression Equation
TOT[t] = + 9.67888 -0.0318243IMS[t] + 0.0595582CONFSOFT[t] + 0.092643PV[t] + 0.037623PRH[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TOT[t] =  +  9.67888 -0.0318243IMS[t] +  0.0595582CONFSOFT[t] +  0.092643PV[t] +  0.037623PRH[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270886&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TOT[t] =  +  9.67888 -0.0318243IMS[t] +  0.0595582CONFSOFT[t] +  0.092643PV[t] +  0.037623PRH[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270886&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270886&T=1

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Estimated Regression Equation
TOT[t] = + 9.67888 -0.0318243IMS[t] + 0.0595582CONFSOFT[t] + 0.092643PV[t] + 0.037623PRH[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)	9.67888	1.56192	6.197	1.17397e-08	5.86985e-09
IMS	-0.0318243	0.0221226	-1.439	0.153282	0.076641
CONFSOFT	0.0595582	0.0982172	0.6064	0.545575	0.272788
PV	0.092643	0.0888634	1.043	0.299582	0.149791
PRH	0.037623	0.0115794	3.249	0.00155951	0.000779756

\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) & 9.67888 & 1.56192 & 6.197 & 1.17397e-08 & 5.86985e-09 \tabularnewline
IMS & -0.0318243 & 0.0221226 & -1.439 & 0.153282 & 0.076641 \tabularnewline
CONFSOFT & 0.0595582 & 0.0982172 & 0.6064 & 0.545575 & 0.272788 \tabularnewline
PV & 0.092643 & 0.0888634 & 1.043 & 0.299582 & 0.149791 \tabularnewline
PRH & 0.037623 & 0.0115794 & 3.249 & 0.00155951 & 0.000779756 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270886&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]9.67888[/C][C]1.56192[/C][C]6.197[/C][C]1.17397e-08[/C][C]5.86985e-09[/C][/ROW]
[ROW][C]IMS[/C][C]-0.0318243[/C][C]0.0221226[/C][C]-1.439[/C][C]0.153282[/C][C]0.076641[/C][/ROW]
[ROW][C]CONFSOFT[/C][C]0.0595582[/C][C]0.0982172[/C][C]0.6064[/C][C]0.545575[/C][C]0.272788[/C][/ROW]
[ROW][C]PV[/C][C]0.092643[/C][C]0.0888634[/C][C]1.043[/C][C]0.299582[/C][C]0.149791[/C][/ROW]
[ROW][C]PRH[/C][C]0.037623[/C][C]0.0115794[/C][C]3.249[/C][C]0.00155951[/C][C]0.000779756[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270886&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270886&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)	9.67888	1.56192	6.197	1.17397e-08	5.86985e-09
IMS	-0.0318243	0.0221226	-1.439	0.153282	0.076641
CONFSOFT	0.0595582	0.0982172	0.6064	0.545575	0.272788
PV	0.092643	0.0888634	1.043	0.299582	0.149791
PRH	0.037623	0.0115794	3.249	0.00155951	0.000779756

Multiple Linear Regression - Regression Statistics
Multiple R	0.349355
R-squared	0.122049
Adjusted R-squared	0.0882815
F-TEST (value)	3.6144
F-TEST (DF numerator)	4
F-TEST (DF denominator)	104
p-value	0.00844388
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.31169
Sum Squared Residuals	555.767

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.349355 \tabularnewline
R-squared & 0.122049 \tabularnewline
Adjusted R-squared & 0.0882815 \tabularnewline
F-TEST (value) & 3.6144 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 104 \tabularnewline
p-value & 0.00844388 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.31169 \tabularnewline
Sum Squared Residuals & 555.767 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270886&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.349355[/C][/ROW]
[ROW][C]R-squared[/C][C]0.122049[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.0882815[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]3.6144[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]104[/C][/ROW]
[ROW][C]p-value[/C][C]0.00844388[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.31169[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]555.767[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270886&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270886&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	0.349355
R-squared	0.122049
Adjusted R-squared	0.0882815
F-TEST (value)	3.6144
F-TEST (DF numerator)	4
F-TEST (DF denominator)	104
p-value	0.00844388
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.31169
Sum Squared Residuals	555.767

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	13	11.3551	1.64492
2	12	10.3415	1.65854
3	13	11.7355	1.26447
4	7	10.7927	-3.79271
5	7	10.9657	-3.96567
6	13	11.9741	1.02587
7	15	10.8694	4.13056
8	13	11.6962	1.3038
9	11	11.7224	-0.722414
10	8	10.8578	-2.85776
11	11	10.7793	0.220733
12	6	10.4203	-4.42026
13	11	10.6978	0.302227
14	12	12.2819	-0.281947
15	6	9.86662	-3.86662
16	11	10.5104	0.489615
17	12	12.0097	-0.00967982
18	9	10.7401	-1.74007
19	10	10.7871	-0.787137
20	10	10.0156	-0.0156234
21	14	13.7737	0.226299
22	11	10.6388	0.361198
23	14	10.3479	3.65213
24	12	11.1561	0.843861
25	11	11.2437	-0.243742
26	16	13.4944	2.50557
27	13	10.5073	2.49272
28	10	10.0357	-0.0357117
29	12	10.3353	1.6647
30	8	10.5279	-2.5279
31	12	10.4679	1.53206
32	9	10.4176	-1.4176
33	10	13.8695	-3.86949
34	11	11.4074	-0.407401
35	10	10.882	-0.882022
36	10	9.69391	0.306088
37	13	11.3483	1.65171
38	9	11.9904	-2.9904
39	12	11.27	0.730008
40	6	10.7067	-4.70671
41	11	11.5949	-0.594928
42	13	11.5653	1.43468
43	11	10.3651	0.634864
44	12	11.0784	0.921593
45	11	10.9257	0.0742792
46	12	10.8893	1.11069
47	8	11.7664	-3.76637
48	13	9.58348	3.41652
49	12	11.3201	0.67989
50	12	10.8675	1.13249
51	7	9.75504	-2.75504
52	11	10.3885	0.611529
53	12	10.075	1.92496
54	13	11.7351	1.26486
55	10	10.3714	-0.371351
56	6	10.6361	-4.63606
57	14	10.9622	3.03783
58	8	10.2	-2.20005
59	13	10.6028	2.39725
60	9	10.691	-1.69099
61	13	11.2246	1.77536
62	8	10.3946	-2.39463
63	16	11.4767	4.52329
64	9	10.4844	-1.48444
65	9	10.7879	-1.7879
66	11	11.7056	-0.705553
67	13	10.5186	2.48143
68	15	12.1651	2.83493
69	12	10.7732	1.22681
70	12	12.9847	-0.98474
71	11	10.788	0.211965
72	9	8.97515	0.0248485
73	15	11.5055	3.49447
74	13	10.6516	2.34839
75	13	11.3894	1.61058
76	13	11.7256	1.27435
77	13	10.3438	2.65624
78	10	11.3211	-1.32109
79	8	10.8373	-2.83726
80	11	11.1105	-0.110507
81	13	9.87007	3.12993
82	11	10.8517	0.148316
83	4	9.77473	-5.77473
84	10	11.1678	-1.16777
85	12	10.714	1.28595
86	11	11.1788	-0.178831
87	11	11.1213	-0.121345
88	9	10.4043	-1.4043
89	13	10.3916	2.60842
90	13	9.61161	3.38839
91	6	10.7713	-4.77131
92	10	9.3202	0.679799
93	9	10.657	-1.65704
94	8	10.5432	-2.54319
95	9	9.23106	-0.231059
96	7	10.2469	-3.24688
97	11	10.409	0.591026
98	14	10.2653	3.73474
99	8	11.1536	-3.15362
100	11	9.97898	1.02102
101	10	9.71436	0.28564
102	10	10.722	-0.722004
103	14	10.5151	3.48485
104	9	11.0451	-2.04515
105	14	10.4821	3.51794
106	6	9.53286	-3.53286
107	10	9.72537	0.274628
108	12	10.4033	1.59669
109	11	10.1266	0.873354

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 13 & 11.3551 & 1.64492 \tabularnewline
2 & 12 & 10.3415 & 1.65854 \tabularnewline
3 & 13 & 11.7355 & 1.26447 \tabularnewline
4 & 7 & 10.7927 & -3.79271 \tabularnewline
5 & 7 & 10.9657 & -3.96567 \tabularnewline
6 & 13 & 11.9741 & 1.02587 \tabularnewline
7 & 15 & 10.8694 & 4.13056 \tabularnewline
8 & 13 & 11.6962 & 1.3038 \tabularnewline
9 & 11 & 11.7224 & -0.722414 \tabularnewline
10 & 8 & 10.8578 & -2.85776 \tabularnewline
11 & 11 & 10.7793 & 0.220733 \tabularnewline
12 & 6 & 10.4203 & -4.42026 \tabularnewline
13 & 11 & 10.6978 & 0.302227 \tabularnewline
14 & 12 & 12.2819 & -0.281947 \tabularnewline
15 & 6 & 9.86662 & -3.86662 \tabularnewline
16 & 11 & 10.5104 & 0.489615 \tabularnewline
17 & 12 & 12.0097 & -0.00967982 \tabularnewline
18 & 9 & 10.7401 & -1.74007 \tabularnewline
19 & 10 & 10.7871 & -0.787137 \tabularnewline
20 & 10 & 10.0156 & -0.0156234 \tabularnewline
21 & 14 & 13.7737 & 0.226299 \tabularnewline
22 & 11 & 10.6388 & 0.361198 \tabularnewline
23 & 14 & 10.3479 & 3.65213 \tabularnewline
24 & 12 & 11.1561 & 0.843861 \tabularnewline
25 & 11 & 11.2437 & -0.243742 \tabularnewline
26 & 16 & 13.4944 & 2.50557 \tabularnewline
27 & 13 & 10.5073 & 2.49272 \tabularnewline
28 & 10 & 10.0357 & -0.0357117 \tabularnewline
29 & 12 & 10.3353 & 1.6647 \tabularnewline
30 & 8 & 10.5279 & -2.5279 \tabularnewline
31 & 12 & 10.4679 & 1.53206 \tabularnewline
32 & 9 & 10.4176 & -1.4176 \tabularnewline
33 & 10 & 13.8695 & -3.86949 \tabularnewline
34 & 11 & 11.4074 & -0.407401 \tabularnewline
35 & 10 & 10.882 & -0.882022 \tabularnewline
36 & 10 & 9.69391 & 0.306088 \tabularnewline
37 & 13 & 11.3483 & 1.65171 \tabularnewline
38 & 9 & 11.9904 & -2.9904 \tabularnewline
39 & 12 & 11.27 & 0.730008 \tabularnewline
40 & 6 & 10.7067 & -4.70671 \tabularnewline
41 & 11 & 11.5949 & -0.594928 \tabularnewline
42 & 13 & 11.5653 & 1.43468 \tabularnewline
43 & 11 & 10.3651 & 0.634864 \tabularnewline
44 & 12 & 11.0784 & 0.921593 \tabularnewline
45 & 11 & 10.9257 & 0.0742792 \tabularnewline
46 & 12 & 10.8893 & 1.11069 \tabularnewline
47 & 8 & 11.7664 & -3.76637 \tabularnewline
48 & 13 & 9.58348 & 3.41652 \tabularnewline
49 & 12 & 11.3201 & 0.67989 \tabularnewline
50 & 12 & 10.8675 & 1.13249 \tabularnewline
51 & 7 & 9.75504 & -2.75504 \tabularnewline
52 & 11 & 10.3885 & 0.611529 \tabularnewline
53 & 12 & 10.075 & 1.92496 \tabularnewline
54 & 13 & 11.7351 & 1.26486 \tabularnewline
55 & 10 & 10.3714 & -0.371351 \tabularnewline
56 & 6 & 10.6361 & -4.63606 \tabularnewline
57 & 14 & 10.9622 & 3.03783 \tabularnewline
58 & 8 & 10.2 & -2.20005 \tabularnewline
59 & 13 & 10.6028 & 2.39725 \tabularnewline
60 & 9 & 10.691 & -1.69099 \tabularnewline
61 & 13 & 11.2246 & 1.77536 \tabularnewline
62 & 8 & 10.3946 & -2.39463 \tabularnewline
63 & 16 & 11.4767 & 4.52329 \tabularnewline
64 & 9 & 10.4844 & -1.48444 \tabularnewline
65 & 9 & 10.7879 & -1.7879 \tabularnewline
66 & 11 & 11.7056 & -0.705553 \tabularnewline
67 & 13 & 10.5186 & 2.48143 \tabularnewline
68 & 15 & 12.1651 & 2.83493 \tabularnewline
69 & 12 & 10.7732 & 1.22681 \tabularnewline
70 & 12 & 12.9847 & -0.98474 \tabularnewline
71 & 11 & 10.788 & 0.211965 \tabularnewline
72 & 9 & 8.97515 & 0.0248485 \tabularnewline
73 & 15 & 11.5055 & 3.49447 \tabularnewline
74 & 13 & 10.6516 & 2.34839 \tabularnewline
75 & 13 & 11.3894 & 1.61058 \tabularnewline
76 & 13 & 11.7256 & 1.27435 \tabularnewline
77 & 13 & 10.3438 & 2.65624 \tabularnewline
78 & 10 & 11.3211 & -1.32109 \tabularnewline
79 & 8 & 10.8373 & -2.83726 \tabularnewline
80 & 11 & 11.1105 & -0.110507 \tabularnewline
81 & 13 & 9.87007 & 3.12993 \tabularnewline
82 & 11 & 10.8517 & 0.148316 \tabularnewline
83 & 4 & 9.77473 & -5.77473 \tabularnewline
84 & 10 & 11.1678 & -1.16777 \tabularnewline
85 & 12 & 10.714 & 1.28595 \tabularnewline
86 & 11 & 11.1788 & -0.178831 \tabularnewline
87 & 11 & 11.1213 & -0.121345 \tabularnewline
88 & 9 & 10.4043 & -1.4043 \tabularnewline
89 & 13 & 10.3916 & 2.60842 \tabularnewline
90 & 13 & 9.61161 & 3.38839 \tabularnewline
91 & 6 & 10.7713 & -4.77131 \tabularnewline
92 & 10 & 9.3202 & 0.679799 \tabularnewline
93 & 9 & 10.657 & -1.65704 \tabularnewline
94 & 8 & 10.5432 & -2.54319 \tabularnewline
95 & 9 & 9.23106 & -0.231059 \tabularnewline
96 & 7 & 10.2469 & -3.24688 \tabularnewline
97 & 11 & 10.409 & 0.591026 \tabularnewline
98 & 14 & 10.2653 & 3.73474 \tabularnewline
99 & 8 & 11.1536 & -3.15362 \tabularnewline
100 & 11 & 9.97898 & 1.02102 \tabularnewline
101 & 10 & 9.71436 & 0.28564 \tabularnewline
102 & 10 & 10.722 & -0.722004 \tabularnewline
103 & 14 & 10.5151 & 3.48485 \tabularnewline
104 & 9 & 11.0451 & -2.04515 \tabularnewline
105 & 14 & 10.4821 & 3.51794 \tabularnewline
106 & 6 & 9.53286 & -3.53286 \tabularnewline
107 & 10 & 9.72537 & 0.274628 \tabularnewline
108 & 12 & 10.4033 & 1.59669 \tabularnewline
109 & 11 & 10.1266 & 0.873354 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270886&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]13[/C][C]11.3551[/C][C]1.64492[/C][/ROW]
[ROW][C]2[/C][C]12[/C][C]10.3415[/C][C]1.65854[/C][/ROW]
[ROW][C]3[/C][C]13[/C][C]11.7355[/C][C]1.26447[/C][/ROW]
[ROW][C]4[/C][C]7[/C][C]10.7927[/C][C]-3.79271[/C][/ROW]
[ROW][C]5[/C][C]7[/C][C]10.9657[/C][C]-3.96567[/C][/ROW]
[ROW][C]6[/C][C]13[/C][C]11.9741[/C][C]1.02587[/C][/ROW]
[ROW][C]7[/C][C]15[/C][C]10.8694[/C][C]4.13056[/C][/ROW]
[ROW][C]8[/C][C]13[/C][C]11.6962[/C][C]1.3038[/C][/ROW]
[ROW][C]9[/C][C]11[/C][C]11.7224[/C][C]-0.722414[/C][/ROW]
[ROW][C]10[/C][C]8[/C][C]10.8578[/C][C]-2.85776[/C][/ROW]
[ROW][C]11[/C][C]11[/C][C]10.7793[/C][C]0.220733[/C][/ROW]
[ROW][C]12[/C][C]6[/C][C]10.4203[/C][C]-4.42026[/C][/ROW]
[ROW][C]13[/C][C]11[/C][C]10.6978[/C][C]0.302227[/C][/ROW]
[ROW][C]14[/C][C]12[/C][C]12.2819[/C][C]-0.281947[/C][/ROW]
[ROW][C]15[/C][C]6[/C][C]9.86662[/C][C]-3.86662[/C][/ROW]
[ROW][C]16[/C][C]11[/C][C]10.5104[/C][C]0.489615[/C][/ROW]
[ROW][C]17[/C][C]12[/C][C]12.0097[/C][C]-0.00967982[/C][/ROW]
[ROW][C]18[/C][C]9[/C][C]10.7401[/C][C]-1.74007[/C][/ROW]
[ROW][C]19[/C][C]10[/C][C]10.7871[/C][C]-0.787137[/C][/ROW]
[ROW][C]20[/C][C]10[/C][C]10.0156[/C][C]-0.0156234[/C][/ROW]
[ROW][C]21[/C][C]14[/C][C]13.7737[/C][C]0.226299[/C][/ROW]
[ROW][C]22[/C][C]11[/C][C]10.6388[/C][C]0.361198[/C][/ROW]
[ROW][C]23[/C][C]14[/C][C]10.3479[/C][C]3.65213[/C][/ROW]
[ROW][C]24[/C][C]12[/C][C]11.1561[/C][C]0.843861[/C][/ROW]
[ROW][C]25[/C][C]11[/C][C]11.2437[/C][C]-0.243742[/C][/ROW]
[ROW][C]26[/C][C]16[/C][C]13.4944[/C][C]2.50557[/C][/ROW]
[ROW][C]27[/C][C]13[/C][C]10.5073[/C][C]2.49272[/C][/ROW]
[ROW][C]28[/C][C]10[/C][C]10.0357[/C][C]-0.0357117[/C][/ROW]
[ROW][C]29[/C][C]12[/C][C]10.3353[/C][C]1.6647[/C][/ROW]
[ROW][C]30[/C][C]8[/C][C]10.5279[/C][C]-2.5279[/C][/ROW]
[ROW][C]31[/C][C]12[/C][C]10.4679[/C][C]1.53206[/C][/ROW]
[ROW][C]32[/C][C]9[/C][C]10.4176[/C][C]-1.4176[/C][/ROW]
[ROW][C]33[/C][C]10[/C][C]13.8695[/C][C]-3.86949[/C][/ROW]
[ROW][C]34[/C][C]11[/C][C]11.4074[/C][C]-0.407401[/C][/ROW]
[ROW][C]35[/C][C]10[/C][C]10.882[/C][C]-0.882022[/C][/ROW]
[ROW][C]36[/C][C]10[/C][C]9.69391[/C][C]0.306088[/C][/ROW]
[ROW][C]37[/C][C]13[/C][C]11.3483[/C][C]1.65171[/C][/ROW]
[ROW][C]38[/C][C]9[/C][C]11.9904[/C][C]-2.9904[/C][/ROW]
[ROW][C]39[/C][C]12[/C][C]11.27[/C][C]0.730008[/C][/ROW]
[ROW][C]40[/C][C]6[/C][C]10.7067[/C][C]-4.70671[/C][/ROW]
[ROW][C]41[/C][C]11[/C][C]11.5949[/C][C]-0.594928[/C][/ROW]
[ROW][C]42[/C][C]13[/C][C]11.5653[/C][C]1.43468[/C][/ROW]
[ROW][C]43[/C][C]11[/C][C]10.3651[/C][C]0.634864[/C][/ROW]
[ROW][C]44[/C][C]12[/C][C]11.0784[/C][C]0.921593[/C][/ROW]
[ROW][C]45[/C][C]11[/C][C]10.9257[/C][C]0.0742792[/C][/ROW]
[ROW][C]46[/C][C]12[/C][C]10.8893[/C][C]1.11069[/C][/ROW]
[ROW][C]47[/C][C]8[/C][C]11.7664[/C][C]-3.76637[/C][/ROW]
[ROW][C]48[/C][C]13[/C][C]9.58348[/C][C]3.41652[/C][/ROW]
[ROW][C]49[/C][C]12[/C][C]11.3201[/C][C]0.67989[/C][/ROW]
[ROW][C]50[/C][C]12[/C][C]10.8675[/C][C]1.13249[/C][/ROW]
[ROW][C]51[/C][C]7[/C][C]9.75504[/C][C]-2.75504[/C][/ROW]
[ROW][C]52[/C][C]11[/C][C]10.3885[/C][C]0.611529[/C][/ROW]
[ROW][C]53[/C][C]12[/C][C]10.075[/C][C]1.92496[/C][/ROW]
[ROW][C]54[/C][C]13[/C][C]11.7351[/C][C]1.26486[/C][/ROW]
[ROW][C]55[/C][C]10[/C][C]10.3714[/C][C]-0.371351[/C][/ROW]
[ROW][C]56[/C][C]6[/C][C]10.6361[/C][C]-4.63606[/C][/ROW]
[ROW][C]57[/C][C]14[/C][C]10.9622[/C][C]3.03783[/C][/ROW]
[ROW][C]58[/C][C]8[/C][C]10.2[/C][C]-2.20005[/C][/ROW]
[ROW][C]59[/C][C]13[/C][C]10.6028[/C][C]2.39725[/C][/ROW]
[ROW][C]60[/C][C]9[/C][C]10.691[/C][C]-1.69099[/C][/ROW]
[ROW][C]61[/C][C]13[/C][C]11.2246[/C][C]1.77536[/C][/ROW]
[ROW][C]62[/C][C]8[/C][C]10.3946[/C][C]-2.39463[/C][/ROW]
[ROW][C]63[/C][C]16[/C][C]11.4767[/C][C]4.52329[/C][/ROW]
[ROW][C]64[/C][C]9[/C][C]10.4844[/C][C]-1.48444[/C][/ROW]
[ROW][C]65[/C][C]9[/C][C]10.7879[/C][C]-1.7879[/C][/ROW]
[ROW][C]66[/C][C]11[/C][C]11.7056[/C][C]-0.705553[/C][/ROW]
[ROW][C]67[/C][C]13[/C][C]10.5186[/C][C]2.48143[/C][/ROW]
[ROW][C]68[/C][C]15[/C][C]12.1651[/C][C]2.83493[/C][/ROW]
[ROW][C]69[/C][C]12[/C][C]10.7732[/C][C]1.22681[/C][/ROW]
[ROW][C]70[/C][C]12[/C][C]12.9847[/C][C]-0.98474[/C][/ROW]
[ROW][C]71[/C][C]11[/C][C]10.788[/C][C]0.211965[/C][/ROW]
[ROW][C]72[/C][C]9[/C][C]8.97515[/C][C]0.0248485[/C][/ROW]
[ROW][C]73[/C][C]15[/C][C]11.5055[/C][C]3.49447[/C][/ROW]
[ROW][C]74[/C][C]13[/C][C]10.6516[/C][C]2.34839[/C][/ROW]
[ROW][C]75[/C][C]13[/C][C]11.3894[/C][C]1.61058[/C][/ROW]
[ROW][C]76[/C][C]13[/C][C]11.7256[/C][C]1.27435[/C][/ROW]
[ROW][C]77[/C][C]13[/C][C]10.3438[/C][C]2.65624[/C][/ROW]
[ROW][C]78[/C][C]10[/C][C]11.3211[/C][C]-1.32109[/C][/ROW]
[ROW][C]79[/C][C]8[/C][C]10.8373[/C][C]-2.83726[/C][/ROW]
[ROW][C]80[/C][C]11[/C][C]11.1105[/C][C]-0.110507[/C][/ROW]
[ROW][C]81[/C][C]13[/C][C]9.87007[/C][C]3.12993[/C][/ROW]
[ROW][C]82[/C][C]11[/C][C]10.8517[/C][C]0.148316[/C][/ROW]
[ROW][C]83[/C][C]4[/C][C]9.77473[/C][C]-5.77473[/C][/ROW]
[ROW][C]84[/C][C]10[/C][C]11.1678[/C][C]-1.16777[/C][/ROW]
[ROW][C]85[/C][C]12[/C][C]10.714[/C][C]1.28595[/C][/ROW]
[ROW][C]86[/C][C]11[/C][C]11.1788[/C][C]-0.178831[/C][/ROW]
[ROW][C]87[/C][C]11[/C][C]11.1213[/C][C]-0.121345[/C][/ROW]
[ROW][C]88[/C][C]9[/C][C]10.4043[/C][C]-1.4043[/C][/ROW]
[ROW][C]89[/C][C]13[/C][C]10.3916[/C][C]2.60842[/C][/ROW]
[ROW][C]90[/C][C]13[/C][C]9.61161[/C][C]3.38839[/C][/ROW]
[ROW][C]91[/C][C]6[/C][C]10.7713[/C][C]-4.77131[/C][/ROW]
[ROW][C]92[/C][C]10[/C][C]9.3202[/C][C]0.679799[/C][/ROW]
[ROW][C]93[/C][C]9[/C][C]10.657[/C][C]-1.65704[/C][/ROW]
[ROW][C]94[/C][C]8[/C][C]10.5432[/C][C]-2.54319[/C][/ROW]
[ROW][C]95[/C][C]9[/C][C]9.23106[/C][C]-0.231059[/C][/ROW]
[ROW][C]96[/C][C]7[/C][C]10.2469[/C][C]-3.24688[/C][/ROW]
[ROW][C]97[/C][C]11[/C][C]10.409[/C][C]0.591026[/C][/ROW]
[ROW][C]98[/C][C]14[/C][C]10.2653[/C][C]3.73474[/C][/ROW]
[ROW][C]99[/C][C]8[/C][C]11.1536[/C][C]-3.15362[/C][/ROW]
[ROW][C]100[/C][C]11[/C][C]9.97898[/C][C]1.02102[/C][/ROW]
[ROW][C]101[/C][C]10[/C][C]9.71436[/C][C]0.28564[/C][/ROW]
[ROW][C]102[/C][C]10[/C][C]10.722[/C][C]-0.722004[/C][/ROW]
[ROW][C]103[/C][C]14[/C][C]10.5151[/C][C]3.48485[/C][/ROW]
[ROW][C]104[/C][C]9[/C][C]11.0451[/C][C]-2.04515[/C][/ROW]
[ROW][C]105[/C][C]14[/C][C]10.4821[/C][C]3.51794[/C][/ROW]
[ROW][C]106[/C][C]6[/C][C]9.53286[/C][C]-3.53286[/C][/ROW]
[ROW][C]107[/C][C]10[/C][C]9.72537[/C][C]0.274628[/C][/ROW]
[ROW][C]108[/C][C]12[/C][C]10.4033[/C][C]1.59669[/C][/ROW]
[ROW][C]109[/C][C]11[/C][C]10.1266[/C][C]0.873354[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270886&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270886&T=4

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	13	11.3551	1.64492
2	12	10.3415	1.65854
3	13	11.7355	1.26447
4	7	10.7927	-3.79271
5	7	10.9657	-3.96567
6	13	11.9741	1.02587
7	15	10.8694	4.13056
8	13	11.6962	1.3038
9	11	11.7224	-0.722414
10	8	10.8578	-2.85776
11	11	10.7793	0.220733
12	6	10.4203	-4.42026
13	11	10.6978	0.302227
14	12	12.2819	-0.281947
15	6	9.86662	-3.86662
16	11	10.5104	0.489615
17	12	12.0097	-0.00967982
18	9	10.7401	-1.74007
19	10	10.7871	-0.787137
20	10	10.0156	-0.0156234
21	14	13.7737	0.226299
22	11	10.6388	0.361198
23	14	10.3479	3.65213
24	12	11.1561	0.843861
25	11	11.2437	-0.243742
26	16	13.4944	2.50557
27	13	10.5073	2.49272
28	10	10.0357	-0.0357117
29	12	10.3353	1.6647
30	8	10.5279	-2.5279
31	12	10.4679	1.53206
32	9	10.4176	-1.4176
33	10	13.8695	-3.86949
34	11	11.4074	-0.407401
35	10	10.882	-0.882022
36	10	9.69391	0.306088
37	13	11.3483	1.65171
38	9	11.9904	-2.9904
39	12	11.27	0.730008
40	6	10.7067	-4.70671
41	11	11.5949	-0.594928
42	13	11.5653	1.43468
43	11	10.3651	0.634864
44	12	11.0784	0.921593
45	11	10.9257	0.0742792
46	12	10.8893	1.11069
47	8	11.7664	-3.76637
48	13	9.58348	3.41652
49	12	11.3201	0.67989
50	12	10.8675	1.13249
51	7	9.75504	-2.75504
52	11	10.3885	0.611529
53	12	10.075	1.92496
54	13	11.7351	1.26486
55	10	10.3714	-0.371351
56	6	10.6361	-4.63606
57	14	10.9622	3.03783
58	8	10.2	-2.20005
59	13	10.6028	2.39725
60	9	10.691	-1.69099
61	13	11.2246	1.77536
62	8	10.3946	-2.39463
63	16	11.4767	4.52329
64	9	10.4844	-1.48444
65	9	10.7879	-1.7879
66	11	11.7056	-0.705553
67	13	10.5186	2.48143
68	15	12.1651	2.83493
69	12	10.7732	1.22681
70	12	12.9847	-0.98474
71	11	10.788	0.211965
72	9	8.97515	0.0248485
73	15	11.5055	3.49447
74	13	10.6516	2.34839
75	13	11.3894	1.61058
76	13	11.7256	1.27435
77	13	10.3438	2.65624
78	10	11.3211	-1.32109
79	8	10.8373	-2.83726
80	11	11.1105	-0.110507
81	13	9.87007	3.12993
82	11	10.8517	0.148316
83	4	9.77473	-5.77473
84	10	11.1678	-1.16777
85	12	10.714	1.28595
86	11	11.1788	-0.178831
87	11	11.1213	-0.121345
88	9	10.4043	-1.4043
89	13	10.3916	2.60842
90	13	9.61161	3.38839
91	6	10.7713	-4.77131
92	10	9.3202	0.679799
93	9	10.657	-1.65704
94	8	10.5432	-2.54319
95	9	9.23106	-0.231059
96	7	10.2469	-3.24688
97	11	10.409	0.591026
98	14	10.2653	3.73474
99	8	11.1536	-3.15362
100	11	9.97898	1.02102
101	10	9.71436	0.28564
102	10	10.722	-0.722004
103	14	10.5151	3.48485
104	9	11.0451	-2.04515
105	14	10.4821	3.51794
106	6	9.53286	-3.53286
107	10	9.72537	0.274628
108	12	10.4033	1.59669
109	11	10.1266	0.873354

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
8	0.431047	0.862094	0.568953
9	0.286811	0.573621	0.713189
10	0.497496	0.994992	0.502504
11	0.540302	0.919396	0.459698
12	0.663447	0.673107	0.336553
13	0.578627	0.842745	0.421373
14	0.475181	0.950362	0.524819
15	0.544291	0.911418	0.455709
16	0.620564	0.758872	0.379436
17	0.537529	0.924943	0.462471
18	0.461728	0.923455	0.538272
19	0.38947	0.778941	0.61053
20	0.452563	0.905127	0.547437
21	0.39213	0.78426	0.60787
22	0.342715	0.68543	0.657285
23	0.43702	0.874041	0.56298
24	0.39236	0.78472	0.60764
25	0.326344	0.652687	0.673656
26	0.282583	0.565166	0.717417
27	0.317622	0.635243	0.682378
28	0.275858	0.551716	0.724142
29	0.25695	0.513899	0.74305
30	0.394352	0.788704	0.605648
31	0.359637	0.719273	0.640363
32	0.316054	0.632109	0.683946
33	0.439487	0.878973	0.560513
34	0.37873	0.75746	0.62127
35	0.358262	0.716523	0.641738
36	0.302252	0.604504	0.697748
37	0.282081	0.564161	0.717919
38	0.316304	0.632608	0.683696
39	0.267287	0.534575	0.732713
40	0.453722	0.907444	0.546278
41	0.400304	0.800608	0.599696
42	0.362918	0.725837	0.637082
43	0.313108	0.626216	0.686892
44	0.270274	0.540547	0.729726
45	0.223799	0.447597	0.776201
46	0.194672	0.389345	0.805328
47	0.269637	0.539274	0.730363
48	0.352429	0.704859	0.647571
49	0.304275	0.608551	0.695725
50	0.268576	0.537151	0.731424
51	0.290657	0.581315	0.709343
52	0.247102	0.494205	0.752898
53	0.238963	0.477927	0.761037
54	0.20836	0.41672	0.79164
55	0.170586	0.341172	0.829414
56	0.292074	0.584148	0.707926
57	0.321953	0.643906	0.678047
58	0.309716	0.619432	0.690284
59	0.312497	0.624994	0.687503
60	0.289848	0.579695	0.710152
61	0.265009	0.530018	0.734991
62	0.263142	0.526285	0.736858
63	0.400641	0.801281	0.599359
64	0.377404	0.754807	0.622596
65	0.349009	0.698017	0.650991
66	0.310865	0.62173	0.689135
67	0.314395	0.62879	0.685605
68	0.322913	0.645826	0.677087
69	0.288221	0.576443	0.711779
70	0.249505	0.49901	0.750495
71	0.205372	0.410744	0.794628
72	0.169706	0.339412	0.830294
73	0.204966	0.409933	0.795034
74	0.194247	0.388494	0.805753
75	0.177905	0.355809	0.822095
76	0.172112	0.344224	0.827888
77	0.181235	0.36247	0.818765
78	0.147824	0.295648	0.852176
79	0.14855	0.2971	0.85145
80	0.115911	0.231822	0.884089
81	0.129303	0.258607	0.870697
82	0.0999281	0.199856	0.900072
83	0.340869	0.681739	0.659131
84	0.283085	0.56617	0.716915
85	0.252575	0.505151	0.747425
86	0.205978	0.411957	0.794022
87	0.15782	0.31564	0.84218
88	0.123135	0.246271	0.876865
89	0.141019	0.282038	0.858981
90	0.14589	0.29178	0.85411
91	0.327043	0.654086	0.672957
92	0.260449	0.520898	0.739551
93	0.257044	0.514088	0.742956
94	0.352771	0.705542	0.647229
95	0.277237	0.554475	0.722763
96	0.390489	0.780978	0.609511
97	0.294153	0.588306	0.705847
98	0.220182	0.440365	0.779818
99	0.235361	0.470722	0.764639
100	0.191866	0.383732	0.808134
101	0.11062	0.221239	0.88938

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 0.431047 & 0.862094 & 0.568953 \tabularnewline
9 & 0.286811 & 0.573621 & 0.713189 \tabularnewline
10 & 0.497496 & 0.994992 & 0.502504 \tabularnewline
11 & 0.540302 & 0.919396 & 0.459698 \tabularnewline
12 & 0.663447 & 0.673107 & 0.336553 \tabularnewline
13 & 0.578627 & 0.842745 & 0.421373 \tabularnewline
14 & 0.475181 & 0.950362 & 0.524819 \tabularnewline
15 & 0.544291 & 0.911418 & 0.455709 \tabularnewline
16 & 0.620564 & 0.758872 & 0.379436 \tabularnewline
17 & 0.537529 & 0.924943 & 0.462471 \tabularnewline
18 & 0.461728 & 0.923455 & 0.538272 \tabularnewline
19 & 0.38947 & 0.778941 & 0.61053 \tabularnewline
20 & 0.452563 & 0.905127 & 0.547437 \tabularnewline
21 & 0.39213 & 0.78426 & 0.60787 \tabularnewline
22 & 0.342715 & 0.68543 & 0.657285 \tabularnewline
23 & 0.43702 & 0.874041 & 0.56298 \tabularnewline
24 & 0.39236 & 0.78472 & 0.60764 \tabularnewline
25 & 0.326344 & 0.652687 & 0.673656 \tabularnewline
26 & 0.282583 & 0.565166 & 0.717417 \tabularnewline
27 & 0.317622 & 0.635243 & 0.682378 \tabularnewline
28 & 0.275858 & 0.551716 & 0.724142 \tabularnewline
29 & 0.25695 & 0.513899 & 0.74305 \tabularnewline
30 & 0.394352 & 0.788704 & 0.605648 \tabularnewline
31 & 0.359637 & 0.719273 & 0.640363 \tabularnewline
32 & 0.316054 & 0.632109 & 0.683946 \tabularnewline
33 & 0.439487 & 0.878973 & 0.560513 \tabularnewline
34 & 0.37873 & 0.75746 & 0.62127 \tabularnewline
35 & 0.358262 & 0.716523 & 0.641738 \tabularnewline
36 & 0.302252 & 0.604504 & 0.697748 \tabularnewline
37 & 0.282081 & 0.564161 & 0.717919 \tabularnewline
38 & 0.316304 & 0.632608 & 0.683696 \tabularnewline
39 & 0.267287 & 0.534575 & 0.732713 \tabularnewline
40 & 0.453722 & 0.907444 & 0.546278 \tabularnewline
41 & 0.400304 & 0.800608 & 0.599696 \tabularnewline
42 & 0.362918 & 0.725837 & 0.637082 \tabularnewline
43 & 0.313108 & 0.626216 & 0.686892 \tabularnewline
44 & 0.270274 & 0.540547 & 0.729726 \tabularnewline
45 & 0.223799 & 0.447597 & 0.776201 \tabularnewline
46 & 0.194672 & 0.389345 & 0.805328 \tabularnewline
47 & 0.269637 & 0.539274 & 0.730363 \tabularnewline
48 & 0.352429 & 0.704859 & 0.647571 \tabularnewline
49 & 0.304275 & 0.608551 & 0.695725 \tabularnewline
50 & 0.268576 & 0.537151 & 0.731424 \tabularnewline
51 & 0.290657 & 0.581315 & 0.709343 \tabularnewline
52 & 0.247102 & 0.494205 & 0.752898 \tabularnewline
53 & 0.238963 & 0.477927 & 0.761037 \tabularnewline
54 & 0.20836 & 0.41672 & 0.79164 \tabularnewline
55 & 0.170586 & 0.341172 & 0.829414 \tabularnewline
56 & 0.292074 & 0.584148 & 0.707926 \tabularnewline
57 & 0.321953 & 0.643906 & 0.678047 \tabularnewline
58 & 0.309716 & 0.619432 & 0.690284 \tabularnewline
59 & 0.312497 & 0.624994 & 0.687503 \tabularnewline
60 & 0.289848 & 0.579695 & 0.710152 \tabularnewline
61 & 0.265009 & 0.530018 & 0.734991 \tabularnewline
62 & 0.263142 & 0.526285 & 0.736858 \tabularnewline
63 & 0.400641 & 0.801281 & 0.599359 \tabularnewline
64 & 0.377404 & 0.754807 & 0.622596 \tabularnewline
65 & 0.349009 & 0.698017 & 0.650991 \tabularnewline
66 & 0.310865 & 0.62173 & 0.689135 \tabularnewline
67 & 0.314395 & 0.62879 & 0.685605 \tabularnewline
68 & 0.322913 & 0.645826 & 0.677087 \tabularnewline
69 & 0.288221 & 0.576443 & 0.711779 \tabularnewline
70 & 0.249505 & 0.49901 & 0.750495 \tabularnewline
71 & 0.205372 & 0.410744 & 0.794628 \tabularnewline
72 & 0.169706 & 0.339412 & 0.830294 \tabularnewline
73 & 0.204966 & 0.409933 & 0.795034 \tabularnewline
74 & 0.194247 & 0.388494 & 0.805753 \tabularnewline
75 & 0.177905 & 0.355809 & 0.822095 \tabularnewline
76 & 0.172112 & 0.344224 & 0.827888 \tabularnewline
77 & 0.181235 & 0.36247 & 0.818765 \tabularnewline
78 & 0.147824 & 0.295648 & 0.852176 \tabularnewline
79 & 0.14855 & 0.2971 & 0.85145 \tabularnewline
80 & 0.115911 & 0.231822 & 0.884089 \tabularnewline
81 & 0.129303 & 0.258607 & 0.870697 \tabularnewline
82 & 0.0999281 & 0.199856 & 0.900072 \tabularnewline
83 & 0.340869 & 0.681739 & 0.659131 \tabularnewline
84 & 0.283085 & 0.56617 & 0.716915 \tabularnewline
85 & 0.252575 & 0.505151 & 0.747425 \tabularnewline
86 & 0.205978 & 0.411957 & 0.794022 \tabularnewline
87 & 0.15782 & 0.31564 & 0.84218 \tabularnewline
88 & 0.123135 & 0.246271 & 0.876865 \tabularnewline
89 & 0.141019 & 0.282038 & 0.858981 \tabularnewline
90 & 0.14589 & 0.29178 & 0.85411 \tabularnewline
91 & 0.327043 & 0.654086 & 0.672957 \tabularnewline
92 & 0.260449 & 0.520898 & 0.739551 \tabularnewline
93 & 0.257044 & 0.514088 & 0.742956 \tabularnewline
94 & 0.352771 & 0.705542 & 0.647229 \tabularnewline
95 & 0.277237 & 0.554475 & 0.722763 \tabularnewline
96 & 0.390489 & 0.780978 & 0.609511 \tabularnewline
97 & 0.294153 & 0.588306 & 0.705847 \tabularnewline
98 & 0.220182 & 0.440365 & 0.779818 \tabularnewline
99 & 0.235361 & 0.470722 & 0.764639 \tabularnewline
100 & 0.191866 & 0.383732 & 0.808134 \tabularnewline
101 & 0.11062 & 0.221239 & 0.88938 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270886&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]0.431047[/C][C]0.862094[/C][C]0.568953[/C][/ROW]
[ROW][C]9[/C][C]0.286811[/C][C]0.573621[/C][C]0.713189[/C][/ROW]
[ROW][C]10[/C][C]0.497496[/C][C]0.994992[/C][C]0.502504[/C][/ROW]
[ROW][C]11[/C][C]0.540302[/C][C]0.919396[/C][C]0.459698[/C][/ROW]
[ROW][C]12[/C][C]0.663447[/C][C]0.673107[/C][C]0.336553[/C][/ROW]
[ROW][C]13[/C][C]0.578627[/C][C]0.842745[/C][C]0.421373[/C][/ROW]
[ROW][C]14[/C][C]0.475181[/C][C]0.950362[/C][C]0.524819[/C][/ROW]
[ROW][C]15[/C][C]0.544291[/C][C]0.911418[/C][C]0.455709[/C][/ROW]
[ROW][C]16[/C][C]0.620564[/C][C]0.758872[/C][C]0.379436[/C][/ROW]
[ROW][C]17[/C][C]0.537529[/C][C]0.924943[/C][C]0.462471[/C][/ROW]
[ROW][C]18[/C][C]0.461728[/C][C]0.923455[/C][C]0.538272[/C][/ROW]
[ROW][C]19[/C][C]0.38947[/C][C]0.778941[/C][C]0.61053[/C][/ROW]
[ROW][C]20[/C][C]0.452563[/C][C]0.905127[/C][C]0.547437[/C][/ROW]
[ROW][C]21[/C][C]0.39213[/C][C]0.78426[/C][C]0.60787[/C][/ROW]
[ROW][C]22[/C][C]0.342715[/C][C]0.68543[/C][C]0.657285[/C][/ROW]
[ROW][C]23[/C][C]0.43702[/C][C]0.874041[/C][C]0.56298[/C][/ROW]
[ROW][C]24[/C][C]0.39236[/C][C]0.78472[/C][C]0.60764[/C][/ROW]
[ROW][C]25[/C][C]0.326344[/C][C]0.652687[/C][C]0.673656[/C][/ROW]
[ROW][C]26[/C][C]0.282583[/C][C]0.565166[/C][C]0.717417[/C][/ROW]
[ROW][C]27[/C][C]0.317622[/C][C]0.635243[/C][C]0.682378[/C][/ROW]
[ROW][C]28[/C][C]0.275858[/C][C]0.551716[/C][C]0.724142[/C][/ROW]
[ROW][C]29[/C][C]0.25695[/C][C]0.513899[/C][C]0.74305[/C][/ROW]
[ROW][C]30[/C][C]0.394352[/C][C]0.788704[/C][C]0.605648[/C][/ROW]
[ROW][C]31[/C][C]0.359637[/C][C]0.719273[/C][C]0.640363[/C][/ROW]
[ROW][C]32[/C][C]0.316054[/C][C]0.632109[/C][C]0.683946[/C][/ROW]
[ROW][C]33[/C][C]0.439487[/C][C]0.878973[/C][C]0.560513[/C][/ROW]
[ROW][C]34[/C][C]0.37873[/C][C]0.75746[/C][C]0.62127[/C][/ROW]
[ROW][C]35[/C][C]0.358262[/C][C]0.716523[/C][C]0.641738[/C][/ROW]
[ROW][C]36[/C][C]0.302252[/C][C]0.604504[/C][C]0.697748[/C][/ROW]
[ROW][C]37[/C][C]0.282081[/C][C]0.564161[/C][C]0.717919[/C][/ROW]
[ROW][C]38[/C][C]0.316304[/C][C]0.632608[/C][C]0.683696[/C][/ROW]
[ROW][C]39[/C][C]0.267287[/C][C]0.534575[/C][C]0.732713[/C][/ROW]
[ROW][C]40[/C][C]0.453722[/C][C]0.907444[/C][C]0.546278[/C][/ROW]
[ROW][C]41[/C][C]0.400304[/C][C]0.800608[/C][C]0.599696[/C][/ROW]
[ROW][C]42[/C][C]0.362918[/C][C]0.725837[/C][C]0.637082[/C][/ROW]
[ROW][C]43[/C][C]0.313108[/C][C]0.626216[/C][C]0.686892[/C][/ROW]
[ROW][C]44[/C][C]0.270274[/C][C]0.540547[/C][C]0.729726[/C][/ROW]
[ROW][C]45[/C][C]0.223799[/C][C]0.447597[/C][C]0.776201[/C][/ROW]
[ROW][C]46[/C][C]0.194672[/C][C]0.389345[/C][C]0.805328[/C][/ROW]
[ROW][C]47[/C][C]0.269637[/C][C]0.539274[/C][C]0.730363[/C][/ROW]
[ROW][C]48[/C][C]0.352429[/C][C]0.704859[/C][C]0.647571[/C][/ROW]
[ROW][C]49[/C][C]0.304275[/C][C]0.608551[/C][C]0.695725[/C][/ROW]
[ROW][C]50[/C][C]0.268576[/C][C]0.537151[/C][C]0.731424[/C][/ROW]
[ROW][C]51[/C][C]0.290657[/C][C]0.581315[/C][C]0.709343[/C][/ROW]
[ROW][C]52[/C][C]0.247102[/C][C]0.494205[/C][C]0.752898[/C][/ROW]
[ROW][C]53[/C][C]0.238963[/C][C]0.477927[/C][C]0.761037[/C][/ROW]
[ROW][C]54[/C][C]0.20836[/C][C]0.41672[/C][C]0.79164[/C][/ROW]
[ROW][C]55[/C][C]0.170586[/C][C]0.341172[/C][C]0.829414[/C][/ROW]
[ROW][C]56[/C][C]0.292074[/C][C]0.584148[/C][C]0.707926[/C][/ROW]
[ROW][C]57[/C][C]0.321953[/C][C]0.643906[/C][C]0.678047[/C][/ROW]
[ROW][C]58[/C][C]0.309716[/C][C]0.619432[/C][C]0.690284[/C][/ROW]
[ROW][C]59[/C][C]0.312497[/C][C]0.624994[/C][C]0.687503[/C][/ROW]
[ROW][C]60[/C][C]0.289848[/C][C]0.579695[/C][C]0.710152[/C][/ROW]
[ROW][C]61[/C][C]0.265009[/C][C]0.530018[/C][C]0.734991[/C][/ROW]
[ROW][C]62[/C][C]0.263142[/C][C]0.526285[/C][C]0.736858[/C][/ROW]
[ROW][C]63[/C][C]0.400641[/C][C]0.801281[/C][C]0.599359[/C][/ROW]
[ROW][C]64[/C][C]0.377404[/C][C]0.754807[/C][C]0.622596[/C][/ROW]
[ROW][C]65[/C][C]0.349009[/C][C]0.698017[/C][C]0.650991[/C][/ROW]
[ROW][C]66[/C][C]0.310865[/C][C]0.62173[/C][C]0.689135[/C][/ROW]
[ROW][C]67[/C][C]0.314395[/C][C]0.62879[/C][C]0.685605[/C][/ROW]
[ROW][C]68[/C][C]0.322913[/C][C]0.645826[/C][C]0.677087[/C][/ROW]
[ROW][C]69[/C][C]0.288221[/C][C]0.576443[/C][C]0.711779[/C][/ROW]
[ROW][C]70[/C][C]0.249505[/C][C]0.49901[/C][C]0.750495[/C][/ROW]
[ROW][C]71[/C][C]0.205372[/C][C]0.410744[/C][C]0.794628[/C][/ROW]
[ROW][C]72[/C][C]0.169706[/C][C]0.339412[/C][C]0.830294[/C][/ROW]
[ROW][C]73[/C][C]0.204966[/C][C]0.409933[/C][C]0.795034[/C][/ROW]
[ROW][C]74[/C][C]0.194247[/C][C]0.388494[/C][C]0.805753[/C][/ROW]
[ROW][C]75[/C][C]0.177905[/C][C]0.355809[/C][C]0.822095[/C][/ROW]
[ROW][C]76[/C][C]0.172112[/C][C]0.344224[/C][C]0.827888[/C][/ROW]
[ROW][C]77[/C][C]0.181235[/C][C]0.36247[/C][C]0.818765[/C][/ROW]
[ROW][C]78[/C][C]0.147824[/C][C]0.295648[/C][C]0.852176[/C][/ROW]
[ROW][C]79[/C][C]0.14855[/C][C]0.2971[/C][C]0.85145[/C][/ROW]
[ROW][C]80[/C][C]0.115911[/C][C]0.231822[/C][C]0.884089[/C][/ROW]
[ROW][C]81[/C][C]0.129303[/C][C]0.258607[/C][C]0.870697[/C][/ROW]
[ROW][C]82[/C][C]0.0999281[/C][C]0.199856[/C][C]0.900072[/C][/ROW]
[ROW][C]83[/C][C]0.340869[/C][C]0.681739[/C][C]0.659131[/C][/ROW]
[ROW][C]84[/C][C]0.283085[/C][C]0.56617[/C][C]0.716915[/C][/ROW]
[ROW][C]85[/C][C]0.252575[/C][C]0.505151[/C][C]0.747425[/C][/ROW]
[ROW][C]86[/C][C]0.205978[/C][C]0.411957[/C][C]0.794022[/C][/ROW]
[ROW][C]87[/C][C]0.15782[/C][C]0.31564[/C][C]0.84218[/C][/ROW]
[ROW][C]88[/C][C]0.123135[/C][C]0.246271[/C][C]0.876865[/C][/ROW]
[ROW][C]89[/C][C]0.141019[/C][C]0.282038[/C][C]0.858981[/C][/ROW]
[ROW][C]90[/C][C]0.14589[/C][C]0.29178[/C][C]0.85411[/C][/ROW]
[ROW][C]91[/C][C]0.327043[/C][C]0.654086[/C][C]0.672957[/C][/ROW]
[ROW][C]92[/C][C]0.260449[/C][C]0.520898[/C][C]0.739551[/C][/ROW]
[ROW][C]93[/C][C]0.257044[/C][C]0.514088[/C][C]0.742956[/C][/ROW]
[ROW][C]94[/C][C]0.352771[/C][C]0.705542[/C][C]0.647229[/C][/ROW]
[ROW][C]95[/C][C]0.277237[/C][C]0.554475[/C][C]0.722763[/C][/ROW]
[ROW][C]96[/C][C]0.390489[/C][C]0.780978[/C][C]0.609511[/C][/ROW]
[ROW][C]97[/C][C]0.294153[/C][C]0.588306[/C][C]0.705847[/C][/ROW]
[ROW][C]98[/C][C]0.220182[/C][C]0.440365[/C][C]0.779818[/C][/ROW]
[ROW][C]99[/C][C]0.235361[/C][C]0.470722[/C][C]0.764639[/C][/ROW]
[ROW][C]100[/C][C]0.191866[/C][C]0.383732[/C][C]0.808134[/C][/ROW]
[ROW][C]101[/C][C]0.11062[/C][C]0.221239[/C][C]0.88938[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270886&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270886&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	0.431047	0.862094	0.568953
9	0.286811	0.573621	0.713189
10	0.497496	0.994992	0.502504
11	0.540302	0.919396	0.459698
12	0.663447	0.673107	0.336553
13	0.578627	0.842745	0.421373
14	0.475181	0.950362	0.524819
15	0.544291	0.911418	0.455709
16	0.620564	0.758872	0.379436
17	0.537529	0.924943	0.462471
18	0.461728	0.923455	0.538272
19	0.38947	0.778941	0.61053
20	0.452563	0.905127	0.547437
21	0.39213	0.78426	0.60787
22	0.342715	0.68543	0.657285
23	0.43702	0.874041	0.56298
24	0.39236	0.78472	0.60764
25	0.326344	0.652687	0.673656
26	0.282583	0.565166	0.717417
27	0.317622	0.635243	0.682378
28	0.275858	0.551716	0.724142
29	0.25695	0.513899	0.74305
30	0.394352	0.788704	0.605648
31	0.359637	0.719273	0.640363
32	0.316054	0.632109	0.683946
33	0.439487	0.878973	0.560513
34	0.37873	0.75746	0.62127
35	0.358262	0.716523	0.641738
36	0.302252	0.604504	0.697748
37	0.282081	0.564161	0.717919
38	0.316304	0.632608	0.683696
39	0.267287	0.534575	0.732713
40	0.453722	0.907444	0.546278
41	0.400304	0.800608	0.599696
42	0.362918	0.725837	0.637082
43	0.313108	0.626216	0.686892
44	0.270274	0.540547	0.729726
45	0.223799	0.447597	0.776201
46	0.194672	0.389345	0.805328
47	0.269637	0.539274	0.730363
48	0.352429	0.704859	0.647571
49	0.304275	0.608551	0.695725
50	0.268576	0.537151	0.731424
51	0.290657	0.581315	0.709343
52	0.247102	0.494205	0.752898
53	0.238963	0.477927	0.761037
54	0.20836	0.41672	0.79164
55	0.170586	0.341172	0.829414
56	0.292074	0.584148	0.707926
57	0.321953	0.643906	0.678047
58	0.309716	0.619432	0.690284
59	0.312497	0.624994	0.687503
60	0.289848	0.579695	0.710152
61	0.265009	0.530018	0.734991
62	0.263142	0.526285	0.736858
63	0.400641	0.801281	0.599359
64	0.377404	0.754807	0.622596
65	0.349009	0.698017	0.650991
66	0.310865	0.62173	0.689135
67	0.314395	0.62879	0.685605
68	0.322913	0.645826	0.677087
69	0.288221	0.576443	0.711779
70	0.249505	0.49901	0.750495
71	0.205372	0.410744	0.794628
72	0.169706	0.339412	0.830294
73	0.204966	0.409933	0.795034
74	0.194247	0.388494	0.805753
75	0.177905	0.355809	0.822095
76	0.172112	0.344224	0.827888
77	0.181235	0.36247	0.818765
78	0.147824	0.295648	0.852176
79	0.14855	0.2971	0.85145
80	0.115911	0.231822	0.884089
81	0.129303	0.258607	0.870697
82	0.0999281	0.199856	0.900072
83	0.340869	0.681739	0.659131
84	0.283085	0.56617	0.716915
85	0.252575	0.505151	0.747425
86	0.205978	0.411957	0.794022
87	0.15782	0.31564	0.84218
88	0.123135	0.246271	0.876865
89	0.141019	0.282038	0.858981
90	0.14589	0.29178	0.85411
91	0.327043	0.654086	0.672957
92	0.260449	0.520898	0.739551
93	0.257044	0.514088	0.742956
94	0.352771	0.705542	0.647229
95	0.277237	0.554475	0.722763
96	0.390489	0.780978	0.609511
97	0.294153	0.588306	0.705847
98	0.220182	0.440365	0.779818
99	0.235361	0.470722	0.764639
100	0.191866	0.383732	0.808134
101	0.11062	0.221239	0.88938

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	0	0	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 & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270886&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]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270886&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270886&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	0	0	OK

Figure 1

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Figure 2

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Figure 3

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Figure 4

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Figure 5

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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Figure 10

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Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

R code (references can be found in the software module):

par3 <- 'No Linear Trend'
par2 <- 'Do not include Seasonal Dummies'
par1 <- '1'
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