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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Sun, 14 Dec 2014 09:37:20 +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/14/t14185499216mcjs1xqax7gv5x.htm/, Retrieved Thu, 16 May 2024 05:38:55 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=267357, Retrieved Thu, 16 May 2024 05:38:55 +0000

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No (this computation is public)

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Estimated Impact

123

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

-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
- RMPD    [Multiple Regression] [multplereg] [2014-12-14 09:37:20] [ba449e08135e498de67ac1fe8477f1b8] [Current]

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Dataseries X:

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26 50 7.5 12.9
57 62 6 12.2
37 54 6.5 12.8
67 71 1 7.4
43 54 1 6.7
52 65 5.5 12.6
52 73 8.5 14.8
43 52 6.5 13.3
84 84 4.5 11.1
67 42 2 8.2
49 66 5 11.4
70 65 0.5 6.4
52 78 5 10.6
58 73 5 12
68 75 2.5 6.3
62 72 5 11.3
43 66 5.5 11.9
56 70 3.5 9.3
56 61 3 9.6
74 81 4 10
65 71 0.5 6.4
63 69 6.5 13.8
58 71 4.5 10.8
57 72 7.5 13.8
63 68 5.5 11.7
53 70 4 10.9
57 68 7.5 16.1
51 61 7 13.4
64 67 4 9.9
53 76 5.5 11.5
29 70 2.5 8.3
54 60 5.5 11.7
58 72 3.5 9
43 69 2.5 9.7
51 71 4.5 10.8
53 62 4.5 10.3
54 70 4.5 10.4
56 64 6 12.7
61 58 2.5 9.3
47 76 5 11.8
39 52 0 5.9
48 59 5 11.4
50 68 6.5 13
35 76 5 10.8
30 65 6 12.3
68 67 4.5 11.3
49 59 5.5 11.8
61 69 1 7.9
67 76 7.5 12.7
47 63 6 12.3
56 75 5 11.6
50 63 1 6.7
43 60 5 10.9
67 73 6.5 12.1
62 63 7 13.3
57 70 4.5 10.1
41 75 0 5.7
54 66 8.5 14.3
45 63 3.5 8
48 63 7.5 13.3
61 64 3.5 9.3
56 70 6 12.5
41 75 1.5 7.6
43 61 9 15.9
53 60 3.5 9.2
44 62 3.5 9.1
66 73 4 11.1
58 61 6.5 13
46 66 7.5 14.5
37 64 6 12.2
51 59 5 12.3
51 64 5.5 11.4
56 60 3.5 8.8
66 56 7.5 14.6
37 78 6.5 12.6
42 67 6.5 13
38 59 6.5 12.6
66 66 7 13.2
34 68 3.5 9.9
53 71 1.5 7.7
49 66 4 10.5
55 73 7.5 13.4
49 72 4.5 10.9
59 71 0 4.3
40 59 3.5 10.3
58 64 5.5 11.8
60 66 5 11.2
63 78 4.5 11.4
56 68 2.5 8.6
54 73 7.5 13.2
52 62 7 12.6
34 65 0 5.6
69 68 4.5 9.9
32 65 3 8.8
48 60 1.5 7.7
67 71 3.5 9
58 65 2.5 7.3
57 68 5.5 11.4
42 64 8 13.6
64 74 1 7.9
58 69 5 10.7
66 76 4.5 10.3
26 68 3 8.3
61 72 3 9.6
52 67 8 14.2
51 63 2.5 8.5
55 59 7 13.5
50 73 0 4.9
60 66 1 6.4
56 62 3.5 9.6
63 69 5.5 11.6
61 66 5.5 11.1

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

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 6 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267357&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267357&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267357&T=0

As an alternative you can also use a QR Code:

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

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

Multiple Linear Regression - Estimated Regression Equation
TOT[t] = + 5.95063 + 0.00500678AMS.I[t] -0.00696006AMS.E[t] + 1.08295Ex[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TOT[t] =  +  5.95063 +  0.00500678AMS.I[t] -0.00696006AMS.E[t] +  1.08295Ex[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267357&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TOT[t] =  +  5.95063 +  0.00500678AMS.I[t] -0.00696006AMS.E[t] +  1.08295Ex[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267357&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267357&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] = + 5.95063 + 0.00500678AMS.I[t] -0.00696006AMS.E[t] + 1.08295Ex[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)	5.95063	0.621342	9.577	4.27332e-16	2.13666e-16
AMS.I	0.00500678	0.00582953	0.8589	0.392317	0.196158
AMS.E	-0.00696006	0.00923188	-0.7539	0.45254	0.22627
Ex	1.08295	0.0270157	40.09	1.13328e-66	5.66639e-67

\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) & 5.95063 & 0.621342 & 9.577 & 4.27332e-16 & 2.13666e-16 \tabularnewline
AMS.I & 0.00500678 & 0.00582953 & 0.8589 & 0.392317 & 0.196158 \tabularnewline
AMS.E & -0.00696006 & 0.00923188 & -0.7539 & 0.45254 & 0.22627 \tabularnewline
Ex & 1.08295 & 0.0270157 & 40.09 & 1.13328e-66 & 5.66639e-67 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267357&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]5.95063[/C][C]0.621342[/C][C]9.577[/C][C]4.27332e-16[/C][C]2.13666e-16[/C][/ROW]
[ROW][C]AMS.I[/C][C]0.00500678[/C][C]0.00582953[/C][C]0.8589[/C][C]0.392317[/C][C]0.196158[/C][/ROW]
[ROW][C]AMS.E[/C][C]-0.00696006[/C][C]0.00923188[/C][C]-0.7539[/C][C]0.45254[/C][C]0.22627[/C][/ROW]
[ROW][C]Ex[/C][C]1.08295[/C][C]0.0270157[/C][C]40.09[/C][C]1.13328e-66[/C][C]5.66639e-67[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267357&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267357&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)	5.95063	0.621342	9.577	4.27332e-16	2.13666e-16
AMS.I	0.00500678	0.00582953	0.8589	0.392317	0.196158
AMS.E	-0.00696006	0.00923188	-0.7539	0.45254	0.22627
Ex	1.08295	0.0270157	40.09	1.13328e-66	5.66639e-67

Multiple Linear Regression - Regression Statistics
Multiple R	0.968126
R-squared	0.937268
Adjusted R-squared	0.935525
F-TEST (value)	537.869
F-TEST (DF numerator)	3
F-TEST (DF denominator)	108
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.627519
Sum Squared Residuals	42.5283

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.968126 \tabularnewline
R-squared & 0.937268 \tabularnewline
Adjusted R-squared & 0.935525 \tabularnewline
F-TEST (value) & 537.869 \tabularnewline
F-TEST (DF numerator) & 3 \tabularnewline
F-TEST (DF denominator) & 108 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.627519 \tabularnewline
Sum Squared Residuals & 42.5283 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267357&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.968126[/C][/ROW]
[ROW][C]R-squared[/C][C]0.937268[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.935525[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]537.869[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]3[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]108[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.627519[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]42.5283[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267357&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267357&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	0.968126
R-squared	0.937268
Adjusted R-squared	0.935525
F-TEST (value)	537.869
F-TEST (DF numerator)	3
F-TEST (DF denominator)	108
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.627519
Sum Squared Residuals	42.5283

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	12.9	13.8549	-0.954923
2	12.2	12.3022	-0.10219
3	12.8	12.7992	0.000791155
4	7.4	6.87487	0.525128
5	6.7	6.87303	-0.17303
6	12.6	11.7148	0.885199
7	14.8	14.908	-0.107967
8	13.3	12.8432	0.45683
9	11.1	10.6598	0.440172
10	8.2	8.15966	0.0403375
11	11.4	11.1513	0.248654
12	6.4	6.39018	0.00982213
13	10.6	11.0828	-0.482846
14	12	11.1477	0.852313
15	6.3	8.47646	-2.17646
16	11.3	11.1747	0.125326
17	11.9	11.6628	0.23722
18	9.3	9.53413	-0.23413
19	9.6	9.0553	0.544704
20	10	10.0892	-0.0891656
21	6.4	6.32338	0.0766164
22	13.8	12.825	0.975016
23	10.8	10.6201	0.179868
24	13.8	13.857	-0.0570124
25	11.7	11.749	-0.0489953
26	10.9	10.0606	0.839416
27	16.1	13.8849	2.21515
28	13.4	13.3621	0.0379421
29	9.9	10.1365	-0.236539
30	11.5	11.6432	-0.143247
31	8.3	8.316	-0.0159975
32	11.7	11.7596	-0.0596148
33	9	9.53022	-0.530223
34	9.7	8.39305	1.30695
35	10.8	10.5851	0.214915
36	10.3	10.6577	-0.357739
37	10.4	10.6071	-0.207065
38	12.7	12.2833	0.416737
39	9.3	8.55974	0.740265
40	11.8	11.0717	0.728268
41	5.9	5.78397	0.116026
42	11.4	11.1951	0.20494
43	13	12.7669	0.233144
44	10.8	11.0117	-0.21165
45	12.3	12.1461	0.153874
46	11.3	10.698	0.60196
47	11.8	11.7415	0.0584591
48	7.9	6.85875	1.04125
49	12.7	13.8792	-1.17924
50	12.3	12.2452	0.0548384
51	11.6	11.1238	0.476247
52	6.7	6.84544	-0.145437
53	10.9	11.1631	-0.263066
54	12.1	12.8172	-0.717171
55	13.3	13.4032	-0.103212
56	10.1	10.6221	-0.522085
57	5.7	5.63391	0.066094
58	14.3	14.9667	-0.666701
59	8	9.52778	-1.52778
60	13.3	13.8746	-0.574592
61	9.3	9.60092	-0.300924
62	12.5	12.2415	0.258498
63	7.6	7.25833	0.34167
64	15.9	15.4879	0.412098
65	9.2	9.58871	-0.38871
66	9.1	9.52973	-0.429729
67	11.1	10.1048	0.995208
68	13	12.8556	0.144369
69	14.5	13.8437	0.656302
70	12.2	12.1881	0.0118663
71	12.3	11.2101	1.08992
72	11.4	11.7168	-0.316754
73	8.8	9.60373	-0.80373
74	14.6	14.0134	0.586566
75	12.6	12.6322	-0.0321674
76	13	12.7338	0.266238
77	12.6	12.7694	-0.169415
78	13.2	13.4024	-0.202359
79	9.9	9.4379	0.462099
80	7.7	7.34625	0.353749
81	10.5	10.0684	0.431603
82	13.4	13.84	-0.440039
83	10.9	10.5681	0.331889
84	4.3	5.75187	-1.45187
85	10.3	9.53058	0.769418
86	11.8	11.7518	0.0481983
87	11.2	11.2064	-0.00642058
88	11.4	10.5964	0.803554
89	8.6	8.4651	0.134899
90	13.2	13.835	-0.635032
91	12.6	13.3601	-0.760105
92	5.6	5.66846	-0.0684592
93	9.9	10.6961	-0.796087
94	8.8	8.90729	-0.107293
95	7.7	7.39778	0.302222
96	9	9.58224	-0.582244
97	7.3	8.49599	-1.19599
98	11.4	11.719	-0.318955
99	13.6	14.3791	-0.779066
100	7.9	6.83897	1.06103
101	10.7	11.1755	-0.475527
102	10.3	10.6254	-0.325386
103	8.3	8.85637	-0.556372
104	9.6	9.00377	0.596231
105	14.2	14.4083	-0.208253
106	8.5	8.47487	0.0251328
107	13.5	13.396	0.103995
108	4.9	5.69289	-0.792887
109	6.4	6.87462	-0.474624
110	9.6	9.58981	0.0101898
111	11.6	11.742	-0.142035
112	11.1	11.7529	-0.652902

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 12.9 & 13.8549 & -0.954923 \tabularnewline
2 & 12.2 & 12.3022 & -0.10219 \tabularnewline
3 & 12.8 & 12.7992 & 0.000791155 \tabularnewline
4 & 7.4 & 6.87487 & 0.525128 \tabularnewline
5 & 6.7 & 6.87303 & -0.17303 \tabularnewline
6 & 12.6 & 11.7148 & 0.885199 \tabularnewline
7 & 14.8 & 14.908 & -0.107967 \tabularnewline
8 & 13.3 & 12.8432 & 0.45683 \tabularnewline
9 & 11.1 & 10.6598 & 0.440172 \tabularnewline
10 & 8.2 & 8.15966 & 0.0403375 \tabularnewline
11 & 11.4 & 11.1513 & 0.248654 \tabularnewline
12 & 6.4 & 6.39018 & 0.00982213 \tabularnewline
13 & 10.6 & 11.0828 & -0.482846 \tabularnewline
14 & 12 & 11.1477 & 0.852313 \tabularnewline
15 & 6.3 & 8.47646 & -2.17646 \tabularnewline
16 & 11.3 & 11.1747 & 0.125326 \tabularnewline
17 & 11.9 & 11.6628 & 0.23722 \tabularnewline
18 & 9.3 & 9.53413 & -0.23413 \tabularnewline
19 & 9.6 & 9.0553 & 0.544704 \tabularnewline
20 & 10 & 10.0892 & -0.0891656 \tabularnewline
21 & 6.4 & 6.32338 & 0.0766164 \tabularnewline
22 & 13.8 & 12.825 & 0.975016 \tabularnewline
23 & 10.8 & 10.6201 & 0.179868 \tabularnewline
24 & 13.8 & 13.857 & -0.0570124 \tabularnewline
25 & 11.7 & 11.749 & -0.0489953 \tabularnewline
26 & 10.9 & 10.0606 & 0.839416 \tabularnewline
27 & 16.1 & 13.8849 & 2.21515 \tabularnewline
28 & 13.4 & 13.3621 & 0.0379421 \tabularnewline
29 & 9.9 & 10.1365 & -0.236539 \tabularnewline
30 & 11.5 & 11.6432 & -0.143247 \tabularnewline
31 & 8.3 & 8.316 & -0.0159975 \tabularnewline
32 & 11.7 & 11.7596 & -0.0596148 \tabularnewline
33 & 9 & 9.53022 & -0.530223 \tabularnewline
34 & 9.7 & 8.39305 & 1.30695 \tabularnewline
35 & 10.8 & 10.5851 & 0.214915 \tabularnewline
36 & 10.3 & 10.6577 & -0.357739 \tabularnewline
37 & 10.4 & 10.6071 & -0.207065 \tabularnewline
38 & 12.7 & 12.2833 & 0.416737 \tabularnewline
39 & 9.3 & 8.55974 & 0.740265 \tabularnewline
40 & 11.8 & 11.0717 & 0.728268 \tabularnewline
41 & 5.9 & 5.78397 & 0.116026 \tabularnewline
42 & 11.4 & 11.1951 & 0.20494 \tabularnewline
43 & 13 & 12.7669 & 0.233144 \tabularnewline
44 & 10.8 & 11.0117 & -0.21165 \tabularnewline
45 & 12.3 & 12.1461 & 0.153874 \tabularnewline
46 & 11.3 & 10.698 & 0.60196 \tabularnewline
47 & 11.8 & 11.7415 & 0.0584591 \tabularnewline
48 & 7.9 & 6.85875 & 1.04125 \tabularnewline
49 & 12.7 & 13.8792 & -1.17924 \tabularnewline
50 & 12.3 & 12.2452 & 0.0548384 \tabularnewline
51 & 11.6 & 11.1238 & 0.476247 \tabularnewline
52 & 6.7 & 6.84544 & -0.145437 \tabularnewline
53 & 10.9 & 11.1631 & -0.263066 \tabularnewline
54 & 12.1 & 12.8172 & -0.717171 \tabularnewline
55 & 13.3 & 13.4032 & -0.103212 \tabularnewline
56 & 10.1 & 10.6221 & -0.522085 \tabularnewline
57 & 5.7 & 5.63391 & 0.066094 \tabularnewline
58 & 14.3 & 14.9667 & -0.666701 \tabularnewline
59 & 8 & 9.52778 & -1.52778 \tabularnewline
60 & 13.3 & 13.8746 & -0.574592 \tabularnewline
61 & 9.3 & 9.60092 & -0.300924 \tabularnewline
62 & 12.5 & 12.2415 & 0.258498 \tabularnewline
63 & 7.6 & 7.25833 & 0.34167 \tabularnewline
64 & 15.9 & 15.4879 & 0.412098 \tabularnewline
65 & 9.2 & 9.58871 & -0.38871 \tabularnewline
66 & 9.1 & 9.52973 & -0.429729 \tabularnewline
67 & 11.1 & 10.1048 & 0.995208 \tabularnewline
68 & 13 & 12.8556 & 0.144369 \tabularnewline
69 & 14.5 & 13.8437 & 0.656302 \tabularnewline
70 & 12.2 & 12.1881 & 0.0118663 \tabularnewline
71 & 12.3 & 11.2101 & 1.08992 \tabularnewline
72 & 11.4 & 11.7168 & -0.316754 \tabularnewline
73 & 8.8 & 9.60373 & -0.80373 \tabularnewline
74 & 14.6 & 14.0134 & 0.586566 \tabularnewline
75 & 12.6 & 12.6322 & -0.0321674 \tabularnewline
76 & 13 & 12.7338 & 0.266238 \tabularnewline
77 & 12.6 & 12.7694 & -0.169415 \tabularnewline
78 & 13.2 & 13.4024 & -0.202359 \tabularnewline
79 & 9.9 & 9.4379 & 0.462099 \tabularnewline
80 & 7.7 & 7.34625 & 0.353749 \tabularnewline
81 & 10.5 & 10.0684 & 0.431603 \tabularnewline
82 & 13.4 & 13.84 & -0.440039 \tabularnewline
83 & 10.9 & 10.5681 & 0.331889 \tabularnewline
84 & 4.3 & 5.75187 & -1.45187 \tabularnewline
85 & 10.3 & 9.53058 & 0.769418 \tabularnewline
86 & 11.8 & 11.7518 & 0.0481983 \tabularnewline
87 & 11.2 & 11.2064 & -0.00642058 \tabularnewline
88 & 11.4 & 10.5964 & 0.803554 \tabularnewline
89 & 8.6 & 8.4651 & 0.134899 \tabularnewline
90 & 13.2 & 13.835 & -0.635032 \tabularnewline
91 & 12.6 & 13.3601 & -0.760105 \tabularnewline
92 & 5.6 & 5.66846 & -0.0684592 \tabularnewline
93 & 9.9 & 10.6961 & -0.796087 \tabularnewline
94 & 8.8 & 8.90729 & -0.107293 \tabularnewline
95 & 7.7 & 7.39778 & 0.302222 \tabularnewline
96 & 9 & 9.58224 & -0.582244 \tabularnewline
97 & 7.3 & 8.49599 & -1.19599 \tabularnewline
98 & 11.4 & 11.719 & -0.318955 \tabularnewline
99 & 13.6 & 14.3791 & -0.779066 \tabularnewline
100 & 7.9 & 6.83897 & 1.06103 \tabularnewline
101 & 10.7 & 11.1755 & -0.475527 \tabularnewline
102 & 10.3 & 10.6254 & -0.325386 \tabularnewline
103 & 8.3 & 8.85637 & -0.556372 \tabularnewline
104 & 9.6 & 9.00377 & 0.596231 \tabularnewline
105 & 14.2 & 14.4083 & -0.208253 \tabularnewline
106 & 8.5 & 8.47487 & 0.0251328 \tabularnewline
107 & 13.5 & 13.396 & 0.103995 \tabularnewline
108 & 4.9 & 5.69289 & -0.792887 \tabularnewline
109 & 6.4 & 6.87462 & -0.474624 \tabularnewline
110 & 9.6 & 9.58981 & 0.0101898 \tabularnewline
111 & 11.6 & 11.742 & -0.142035 \tabularnewline
112 & 11.1 & 11.7529 & -0.652902 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267357&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]12.9[/C][C]13.8549[/C][C]-0.954923[/C][/ROW]
[ROW][C]2[/C][C]12.2[/C][C]12.3022[/C][C]-0.10219[/C][/ROW]
[ROW][C]3[/C][C]12.8[/C][C]12.7992[/C][C]0.000791155[/C][/ROW]
[ROW][C]4[/C][C]7.4[/C][C]6.87487[/C][C]0.525128[/C][/ROW]
[ROW][C]5[/C][C]6.7[/C][C]6.87303[/C][C]-0.17303[/C][/ROW]
[ROW][C]6[/C][C]12.6[/C][C]11.7148[/C][C]0.885199[/C][/ROW]
[ROW][C]7[/C][C]14.8[/C][C]14.908[/C][C]-0.107967[/C][/ROW]
[ROW][C]8[/C][C]13.3[/C][C]12.8432[/C][C]0.45683[/C][/ROW]
[ROW][C]9[/C][C]11.1[/C][C]10.6598[/C][C]0.440172[/C][/ROW]
[ROW][C]10[/C][C]8.2[/C][C]8.15966[/C][C]0.0403375[/C][/ROW]
[ROW][C]11[/C][C]11.4[/C][C]11.1513[/C][C]0.248654[/C][/ROW]
[ROW][C]12[/C][C]6.4[/C][C]6.39018[/C][C]0.00982213[/C][/ROW]
[ROW][C]13[/C][C]10.6[/C][C]11.0828[/C][C]-0.482846[/C][/ROW]
[ROW][C]14[/C][C]12[/C][C]11.1477[/C][C]0.852313[/C][/ROW]
[ROW][C]15[/C][C]6.3[/C][C]8.47646[/C][C]-2.17646[/C][/ROW]
[ROW][C]16[/C][C]11.3[/C][C]11.1747[/C][C]0.125326[/C][/ROW]
[ROW][C]17[/C][C]11.9[/C][C]11.6628[/C][C]0.23722[/C][/ROW]
[ROW][C]18[/C][C]9.3[/C][C]9.53413[/C][C]-0.23413[/C][/ROW]
[ROW][C]19[/C][C]9.6[/C][C]9.0553[/C][C]0.544704[/C][/ROW]
[ROW][C]20[/C][C]10[/C][C]10.0892[/C][C]-0.0891656[/C][/ROW]
[ROW][C]21[/C][C]6.4[/C][C]6.32338[/C][C]0.0766164[/C][/ROW]
[ROW][C]22[/C][C]13.8[/C][C]12.825[/C][C]0.975016[/C][/ROW]
[ROW][C]23[/C][C]10.8[/C][C]10.6201[/C][C]0.179868[/C][/ROW]
[ROW][C]24[/C][C]13.8[/C][C]13.857[/C][C]-0.0570124[/C][/ROW]
[ROW][C]25[/C][C]11.7[/C][C]11.749[/C][C]-0.0489953[/C][/ROW]
[ROW][C]26[/C][C]10.9[/C][C]10.0606[/C][C]0.839416[/C][/ROW]
[ROW][C]27[/C][C]16.1[/C][C]13.8849[/C][C]2.21515[/C][/ROW]
[ROW][C]28[/C][C]13.4[/C][C]13.3621[/C][C]0.0379421[/C][/ROW]
[ROW][C]29[/C][C]9.9[/C][C]10.1365[/C][C]-0.236539[/C][/ROW]
[ROW][C]30[/C][C]11.5[/C][C]11.6432[/C][C]-0.143247[/C][/ROW]
[ROW][C]31[/C][C]8.3[/C][C]8.316[/C][C]-0.0159975[/C][/ROW]
[ROW][C]32[/C][C]11.7[/C][C]11.7596[/C][C]-0.0596148[/C][/ROW]
[ROW][C]33[/C][C]9[/C][C]9.53022[/C][C]-0.530223[/C][/ROW]
[ROW][C]34[/C][C]9.7[/C][C]8.39305[/C][C]1.30695[/C][/ROW]
[ROW][C]35[/C][C]10.8[/C][C]10.5851[/C][C]0.214915[/C][/ROW]
[ROW][C]36[/C][C]10.3[/C][C]10.6577[/C][C]-0.357739[/C][/ROW]
[ROW][C]37[/C][C]10.4[/C][C]10.6071[/C][C]-0.207065[/C][/ROW]
[ROW][C]38[/C][C]12.7[/C][C]12.2833[/C][C]0.416737[/C][/ROW]
[ROW][C]39[/C][C]9.3[/C][C]8.55974[/C][C]0.740265[/C][/ROW]
[ROW][C]40[/C][C]11.8[/C][C]11.0717[/C][C]0.728268[/C][/ROW]
[ROW][C]41[/C][C]5.9[/C][C]5.78397[/C][C]0.116026[/C][/ROW]
[ROW][C]42[/C][C]11.4[/C][C]11.1951[/C][C]0.20494[/C][/ROW]
[ROW][C]43[/C][C]13[/C][C]12.7669[/C][C]0.233144[/C][/ROW]
[ROW][C]44[/C][C]10.8[/C][C]11.0117[/C][C]-0.21165[/C][/ROW]
[ROW][C]45[/C][C]12.3[/C][C]12.1461[/C][C]0.153874[/C][/ROW]
[ROW][C]46[/C][C]11.3[/C][C]10.698[/C][C]0.60196[/C][/ROW]
[ROW][C]47[/C][C]11.8[/C][C]11.7415[/C][C]0.0584591[/C][/ROW]
[ROW][C]48[/C][C]7.9[/C][C]6.85875[/C][C]1.04125[/C][/ROW]
[ROW][C]49[/C][C]12.7[/C][C]13.8792[/C][C]-1.17924[/C][/ROW]
[ROW][C]50[/C][C]12.3[/C][C]12.2452[/C][C]0.0548384[/C][/ROW]
[ROW][C]51[/C][C]11.6[/C][C]11.1238[/C][C]0.476247[/C][/ROW]
[ROW][C]52[/C][C]6.7[/C][C]6.84544[/C][C]-0.145437[/C][/ROW]
[ROW][C]53[/C][C]10.9[/C][C]11.1631[/C][C]-0.263066[/C][/ROW]
[ROW][C]54[/C][C]12.1[/C][C]12.8172[/C][C]-0.717171[/C][/ROW]
[ROW][C]55[/C][C]13.3[/C][C]13.4032[/C][C]-0.103212[/C][/ROW]
[ROW][C]56[/C][C]10.1[/C][C]10.6221[/C][C]-0.522085[/C][/ROW]
[ROW][C]57[/C][C]5.7[/C][C]5.63391[/C][C]0.066094[/C][/ROW]
[ROW][C]58[/C][C]14.3[/C][C]14.9667[/C][C]-0.666701[/C][/ROW]
[ROW][C]59[/C][C]8[/C][C]9.52778[/C][C]-1.52778[/C][/ROW]
[ROW][C]60[/C][C]13.3[/C][C]13.8746[/C][C]-0.574592[/C][/ROW]
[ROW][C]61[/C][C]9.3[/C][C]9.60092[/C][C]-0.300924[/C][/ROW]
[ROW][C]62[/C][C]12.5[/C][C]12.2415[/C][C]0.258498[/C][/ROW]
[ROW][C]63[/C][C]7.6[/C][C]7.25833[/C][C]0.34167[/C][/ROW]
[ROW][C]64[/C][C]15.9[/C][C]15.4879[/C][C]0.412098[/C][/ROW]
[ROW][C]65[/C][C]9.2[/C][C]9.58871[/C][C]-0.38871[/C][/ROW]
[ROW][C]66[/C][C]9.1[/C][C]9.52973[/C][C]-0.429729[/C][/ROW]
[ROW][C]67[/C][C]11.1[/C][C]10.1048[/C][C]0.995208[/C][/ROW]
[ROW][C]68[/C][C]13[/C][C]12.8556[/C][C]0.144369[/C][/ROW]
[ROW][C]69[/C][C]14.5[/C][C]13.8437[/C][C]0.656302[/C][/ROW]
[ROW][C]70[/C][C]12.2[/C][C]12.1881[/C][C]0.0118663[/C][/ROW]
[ROW][C]71[/C][C]12.3[/C][C]11.2101[/C][C]1.08992[/C][/ROW]
[ROW][C]72[/C][C]11.4[/C][C]11.7168[/C][C]-0.316754[/C][/ROW]
[ROW][C]73[/C][C]8.8[/C][C]9.60373[/C][C]-0.80373[/C][/ROW]
[ROW][C]74[/C][C]14.6[/C][C]14.0134[/C][C]0.586566[/C][/ROW]
[ROW][C]75[/C][C]12.6[/C][C]12.6322[/C][C]-0.0321674[/C][/ROW]
[ROW][C]76[/C][C]13[/C][C]12.7338[/C][C]0.266238[/C][/ROW]
[ROW][C]77[/C][C]12.6[/C][C]12.7694[/C][C]-0.169415[/C][/ROW]
[ROW][C]78[/C][C]13.2[/C][C]13.4024[/C][C]-0.202359[/C][/ROW]
[ROW][C]79[/C][C]9.9[/C][C]9.4379[/C][C]0.462099[/C][/ROW]
[ROW][C]80[/C][C]7.7[/C][C]7.34625[/C][C]0.353749[/C][/ROW]
[ROW][C]81[/C][C]10.5[/C][C]10.0684[/C][C]0.431603[/C][/ROW]
[ROW][C]82[/C][C]13.4[/C][C]13.84[/C][C]-0.440039[/C][/ROW]
[ROW][C]83[/C][C]10.9[/C][C]10.5681[/C][C]0.331889[/C][/ROW]
[ROW][C]84[/C][C]4.3[/C][C]5.75187[/C][C]-1.45187[/C][/ROW]
[ROW][C]85[/C][C]10.3[/C][C]9.53058[/C][C]0.769418[/C][/ROW]
[ROW][C]86[/C][C]11.8[/C][C]11.7518[/C][C]0.0481983[/C][/ROW]
[ROW][C]87[/C][C]11.2[/C][C]11.2064[/C][C]-0.00642058[/C][/ROW]
[ROW][C]88[/C][C]11.4[/C][C]10.5964[/C][C]0.803554[/C][/ROW]
[ROW][C]89[/C][C]8.6[/C][C]8.4651[/C][C]0.134899[/C][/ROW]
[ROW][C]90[/C][C]13.2[/C][C]13.835[/C][C]-0.635032[/C][/ROW]
[ROW][C]91[/C][C]12.6[/C][C]13.3601[/C][C]-0.760105[/C][/ROW]
[ROW][C]92[/C][C]5.6[/C][C]5.66846[/C][C]-0.0684592[/C][/ROW]
[ROW][C]93[/C][C]9.9[/C][C]10.6961[/C][C]-0.796087[/C][/ROW]
[ROW][C]94[/C][C]8.8[/C][C]8.90729[/C][C]-0.107293[/C][/ROW]
[ROW][C]95[/C][C]7.7[/C][C]7.39778[/C][C]0.302222[/C][/ROW]
[ROW][C]96[/C][C]9[/C][C]9.58224[/C][C]-0.582244[/C][/ROW]
[ROW][C]97[/C][C]7.3[/C][C]8.49599[/C][C]-1.19599[/C][/ROW]
[ROW][C]98[/C][C]11.4[/C][C]11.719[/C][C]-0.318955[/C][/ROW]
[ROW][C]99[/C][C]13.6[/C][C]14.3791[/C][C]-0.779066[/C][/ROW]
[ROW][C]100[/C][C]7.9[/C][C]6.83897[/C][C]1.06103[/C][/ROW]
[ROW][C]101[/C][C]10.7[/C][C]11.1755[/C][C]-0.475527[/C][/ROW]
[ROW][C]102[/C][C]10.3[/C][C]10.6254[/C][C]-0.325386[/C][/ROW]
[ROW][C]103[/C][C]8.3[/C][C]8.85637[/C][C]-0.556372[/C][/ROW]
[ROW][C]104[/C][C]9.6[/C][C]9.00377[/C][C]0.596231[/C][/ROW]
[ROW][C]105[/C][C]14.2[/C][C]14.4083[/C][C]-0.208253[/C][/ROW]
[ROW][C]106[/C][C]8.5[/C][C]8.47487[/C][C]0.0251328[/C][/ROW]
[ROW][C]107[/C][C]13.5[/C][C]13.396[/C][C]0.103995[/C][/ROW]
[ROW][C]108[/C][C]4.9[/C][C]5.69289[/C][C]-0.792887[/C][/ROW]
[ROW][C]109[/C][C]6.4[/C][C]6.87462[/C][C]-0.474624[/C][/ROW]
[ROW][C]110[/C][C]9.6[/C][C]9.58981[/C][C]0.0101898[/C][/ROW]
[ROW][C]111[/C][C]11.6[/C][C]11.742[/C][C]-0.142035[/C][/ROW]
[ROW][C]112[/C][C]11.1[/C][C]11.7529[/C][C]-0.652902[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267357&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267357&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	12.9	13.8549	-0.954923
2	12.2	12.3022	-0.10219
3	12.8	12.7992	0.000791155
4	7.4	6.87487	0.525128
5	6.7	6.87303	-0.17303
6	12.6	11.7148	0.885199
7	14.8	14.908	-0.107967
8	13.3	12.8432	0.45683
9	11.1	10.6598	0.440172
10	8.2	8.15966	0.0403375
11	11.4	11.1513	0.248654
12	6.4	6.39018	0.00982213
13	10.6	11.0828	-0.482846
14	12	11.1477	0.852313
15	6.3	8.47646	-2.17646
16	11.3	11.1747	0.125326
17	11.9	11.6628	0.23722
18	9.3	9.53413	-0.23413
19	9.6	9.0553	0.544704
20	10	10.0892	-0.0891656
21	6.4	6.32338	0.0766164
22	13.8	12.825	0.975016
23	10.8	10.6201	0.179868
24	13.8	13.857	-0.0570124
25	11.7	11.749	-0.0489953
26	10.9	10.0606	0.839416
27	16.1	13.8849	2.21515
28	13.4	13.3621	0.0379421
29	9.9	10.1365	-0.236539
30	11.5	11.6432	-0.143247
31	8.3	8.316	-0.0159975
32	11.7	11.7596	-0.0596148
33	9	9.53022	-0.530223
34	9.7	8.39305	1.30695
35	10.8	10.5851	0.214915
36	10.3	10.6577	-0.357739
37	10.4	10.6071	-0.207065
38	12.7	12.2833	0.416737
39	9.3	8.55974	0.740265
40	11.8	11.0717	0.728268
41	5.9	5.78397	0.116026
42	11.4	11.1951	0.20494
43	13	12.7669	0.233144
44	10.8	11.0117	-0.21165
45	12.3	12.1461	0.153874
46	11.3	10.698	0.60196
47	11.8	11.7415	0.0584591
48	7.9	6.85875	1.04125
49	12.7	13.8792	-1.17924
50	12.3	12.2452	0.0548384
51	11.6	11.1238	0.476247
52	6.7	6.84544	-0.145437
53	10.9	11.1631	-0.263066
54	12.1	12.8172	-0.717171
55	13.3	13.4032	-0.103212
56	10.1	10.6221	-0.522085
57	5.7	5.63391	0.066094
58	14.3	14.9667	-0.666701
59	8	9.52778	-1.52778
60	13.3	13.8746	-0.574592
61	9.3	9.60092	-0.300924
62	12.5	12.2415	0.258498
63	7.6	7.25833	0.34167
64	15.9	15.4879	0.412098
65	9.2	9.58871	-0.38871
66	9.1	9.52973	-0.429729
67	11.1	10.1048	0.995208
68	13	12.8556	0.144369
69	14.5	13.8437	0.656302
70	12.2	12.1881	0.0118663
71	12.3	11.2101	1.08992
72	11.4	11.7168	-0.316754
73	8.8	9.60373	-0.80373
74	14.6	14.0134	0.586566
75	12.6	12.6322	-0.0321674
76	13	12.7338	0.266238
77	12.6	12.7694	-0.169415
78	13.2	13.4024	-0.202359
79	9.9	9.4379	0.462099
80	7.7	7.34625	0.353749
81	10.5	10.0684	0.431603
82	13.4	13.84	-0.440039
83	10.9	10.5681	0.331889
84	4.3	5.75187	-1.45187
85	10.3	9.53058	0.769418
86	11.8	11.7518	0.0481983
87	11.2	11.2064	-0.00642058
88	11.4	10.5964	0.803554
89	8.6	8.4651	0.134899
90	13.2	13.835	-0.635032
91	12.6	13.3601	-0.760105
92	5.6	5.66846	-0.0684592
93	9.9	10.6961	-0.796087
94	8.8	8.90729	-0.107293
95	7.7	7.39778	0.302222
96	9	9.58224	-0.582244
97	7.3	8.49599	-1.19599
98	11.4	11.719	-0.318955
99	13.6	14.3791	-0.779066
100	7.9	6.83897	1.06103
101	10.7	11.1755	-0.475527
102	10.3	10.6254	-0.325386
103	8.3	8.85637	-0.556372
104	9.6	9.00377	0.596231
105	14.2	14.4083	-0.208253
106	8.5	8.47487	0.0251328
107	13.5	13.396	0.103995
108	4.9	5.69289	-0.792887
109	6.4	6.87462	-0.474624
110	9.6	9.58981	0.0101898
111	11.6	11.742	-0.142035
112	11.1	11.7529	-0.652902

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
7	0.500144	0.999713	0.499856
8	0.434282	0.868565	0.565718
9	0.364271	0.728542	0.635729
10	0.293628	0.587255	0.706372
11	0.198988	0.397976	0.801012
12	0.154604	0.309208	0.845396
13	0.160586	0.321172	0.839414
14	0.197637	0.395274	0.802363
15	0.952726	0.0945473	0.0472737
16	0.928735	0.14253	0.0712651
17	0.90618	0.18764	0.0938198
18	0.870042	0.259916	0.129958
19	0.857997	0.284005	0.142003
20	0.812933	0.374133	0.187067
21	0.76351	0.47298	0.23649
22	0.787372	0.425256	0.212628
23	0.735182	0.529635	0.264818
24	0.682786	0.634427	0.317214
25	0.625985	0.74803	0.374015
26	0.67982	0.640361	0.32018
27	0.965383	0.0692348	0.0346174
28	0.954054	0.0918911	0.0459455
29	0.942537	0.114927	0.0574634
30	0.923449	0.153102	0.0765509
31	0.90769	0.18462	0.0923102
32	0.883014	0.233971	0.116986
33	0.871873	0.256254	0.128127
34	0.951883	0.0962335	0.0481167
35	0.936605	0.12679	0.063395
36	0.925056	0.149887	0.0749437
37	0.905925	0.188151	0.0940754
38	0.887799	0.224402	0.112201
39	0.894854	0.210292	0.105146
40	0.899894	0.200212	0.100106
41	0.873774	0.252451	0.126226
42	0.844501	0.310998	0.155499
43	0.812929	0.374142	0.187071
44	0.778346	0.443309	0.221654
45	0.736353	0.527293	0.263647
46	0.728307	0.543386	0.271693
47	0.682661	0.634679	0.317339
48	0.770406	0.459187	0.229594
49	0.87312	0.253761	0.12688
50	0.843122	0.313757	0.156878
51	0.827732	0.344535	0.172268
52	0.793728	0.412544	0.206272
53	0.761327	0.477345	0.238673
54	0.776091	0.447818	0.223909
55	0.734848	0.530305	0.265152
56	0.720895	0.558209	0.279105
57	0.673339	0.653322	0.326661
58	0.678892	0.642216	0.321108
59	0.870127	0.259746	0.129873
60	0.864729	0.270542	0.135271
61	0.838431	0.323137	0.161569
62	0.80874	0.382521	0.19126
63	0.781985	0.436031	0.218015
64	0.756534	0.486933	0.243466
65	0.72595	0.548099	0.27405
66	0.698386	0.603227	0.301614
67	0.787358	0.425283	0.212642
68	0.746796	0.506407	0.253204
69	0.755971	0.488057	0.244029
70	0.708071	0.583858	0.291929
71	0.815194	0.369613	0.184806
72	0.780698	0.438604	0.219302
73	0.798936	0.402127	0.201064
74	0.819842	0.360316	0.180158
75	0.777335	0.445331	0.222665
76	0.745501	0.508998	0.254499
77	0.695188	0.609624	0.304812
78	0.643949	0.712102	0.356051
79	0.623375	0.75325	0.376625
80	0.591869	0.816261	0.408131
81	0.581726	0.836547	0.418274
82	0.532635	0.934731	0.467365
83	0.507998	0.984004	0.492002
84	0.77219	0.455621	0.22781
85	0.855431	0.289139	0.144569
86	0.827312	0.345375	0.172688
87	0.787519	0.424962	0.212481
88	0.862317	0.275365	0.137683
89	0.829686	0.340629	0.170314
90	0.790162	0.419676	0.209838
91	0.762335	0.475331	0.237665
92	0.698259	0.603482	0.301741
93	0.706924	0.586151	0.293076
94	0.651035	0.697931	0.348965
95	0.639163	0.721674	0.360837
96	0.621047	0.757907	0.378953
97	0.802832	0.394337	0.197168
98	0.731282	0.537435	0.268718
99	0.663794	0.672411	0.336206
100	0.90818	0.183639	0.0918197
101	0.865304	0.269392	0.134696
102	0.785804	0.428392	0.214196
103	0.675307	0.649385	0.324693
104	0.951419	0.0971629	0.0485814
105	0.87275	0.254499	0.12725

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
7 & 0.500144 & 0.999713 & 0.499856 \tabularnewline
8 & 0.434282 & 0.868565 & 0.565718 \tabularnewline
9 & 0.364271 & 0.728542 & 0.635729 \tabularnewline
10 & 0.293628 & 0.587255 & 0.706372 \tabularnewline
11 & 0.198988 & 0.397976 & 0.801012 \tabularnewline
12 & 0.154604 & 0.309208 & 0.845396 \tabularnewline
13 & 0.160586 & 0.321172 & 0.839414 \tabularnewline
14 & 0.197637 & 0.395274 & 0.802363 \tabularnewline
15 & 0.952726 & 0.0945473 & 0.0472737 \tabularnewline
16 & 0.928735 & 0.14253 & 0.0712651 \tabularnewline
17 & 0.90618 & 0.18764 & 0.0938198 \tabularnewline
18 & 0.870042 & 0.259916 & 0.129958 \tabularnewline
19 & 0.857997 & 0.284005 & 0.142003 \tabularnewline
20 & 0.812933 & 0.374133 & 0.187067 \tabularnewline
21 & 0.76351 & 0.47298 & 0.23649 \tabularnewline
22 & 0.787372 & 0.425256 & 0.212628 \tabularnewline
23 & 0.735182 & 0.529635 & 0.264818 \tabularnewline
24 & 0.682786 & 0.634427 & 0.317214 \tabularnewline
25 & 0.625985 & 0.74803 & 0.374015 \tabularnewline
26 & 0.67982 & 0.640361 & 0.32018 \tabularnewline
27 & 0.965383 & 0.0692348 & 0.0346174 \tabularnewline
28 & 0.954054 & 0.0918911 & 0.0459455 \tabularnewline
29 & 0.942537 & 0.114927 & 0.0574634 \tabularnewline
30 & 0.923449 & 0.153102 & 0.0765509 \tabularnewline
31 & 0.90769 & 0.18462 & 0.0923102 \tabularnewline
32 & 0.883014 & 0.233971 & 0.116986 \tabularnewline
33 & 0.871873 & 0.256254 & 0.128127 \tabularnewline
34 & 0.951883 & 0.0962335 & 0.0481167 \tabularnewline
35 & 0.936605 & 0.12679 & 0.063395 \tabularnewline
36 & 0.925056 & 0.149887 & 0.0749437 \tabularnewline
37 & 0.905925 & 0.188151 & 0.0940754 \tabularnewline
38 & 0.887799 & 0.224402 & 0.112201 \tabularnewline
39 & 0.894854 & 0.210292 & 0.105146 \tabularnewline
40 & 0.899894 & 0.200212 & 0.100106 \tabularnewline
41 & 0.873774 & 0.252451 & 0.126226 \tabularnewline
42 & 0.844501 & 0.310998 & 0.155499 \tabularnewline
43 & 0.812929 & 0.374142 & 0.187071 \tabularnewline
44 & 0.778346 & 0.443309 & 0.221654 \tabularnewline
45 & 0.736353 & 0.527293 & 0.263647 \tabularnewline
46 & 0.728307 & 0.543386 & 0.271693 \tabularnewline
47 & 0.682661 & 0.634679 & 0.317339 \tabularnewline
48 & 0.770406 & 0.459187 & 0.229594 \tabularnewline
49 & 0.87312 & 0.253761 & 0.12688 \tabularnewline
50 & 0.843122 & 0.313757 & 0.156878 \tabularnewline
51 & 0.827732 & 0.344535 & 0.172268 \tabularnewline
52 & 0.793728 & 0.412544 & 0.206272 \tabularnewline
53 & 0.761327 & 0.477345 & 0.238673 \tabularnewline
54 & 0.776091 & 0.447818 & 0.223909 \tabularnewline
55 & 0.734848 & 0.530305 & 0.265152 \tabularnewline
56 & 0.720895 & 0.558209 & 0.279105 \tabularnewline
57 & 0.673339 & 0.653322 & 0.326661 \tabularnewline
58 & 0.678892 & 0.642216 & 0.321108 \tabularnewline
59 & 0.870127 & 0.259746 & 0.129873 \tabularnewline
60 & 0.864729 & 0.270542 & 0.135271 \tabularnewline
61 & 0.838431 & 0.323137 & 0.161569 \tabularnewline
62 & 0.80874 & 0.382521 & 0.19126 \tabularnewline
63 & 0.781985 & 0.436031 & 0.218015 \tabularnewline
64 & 0.756534 & 0.486933 & 0.243466 \tabularnewline
65 & 0.72595 & 0.548099 & 0.27405 \tabularnewline
66 & 0.698386 & 0.603227 & 0.301614 \tabularnewline
67 & 0.787358 & 0.425283 & 0.212642 \tabularnewline
68 & 0.746796 & 0.506407 & 0.253204 \tabularnewline
69 & 0.755971 & 0.488057 & 0.244029 \tabularnewline
70 & 0.708071 & 0.583858 & 0.291929 \tabularnewline
71 & 0.815194 & 0.369613 & 0.184806 \tabularnewline
72 & 0.780698 & 0.438604 & 0.219302 \tabularnewline
73 & 0.798936 & 0.402127 & 0.201064 \tabularnewline
74 & 0.819842 & 0.360316 & 0.180158 \tabularnewline
75 & 0.777335 & 0.445331 & 0.222665 \tabularnewline
76 & 0.745501 & 0.508998 & 0.254499 \tabularnewline
77 & 0.695188 & 0.609624 & 0.304812 \tabularnewline
78 & 0.643949 & 0.712102 & 0.356051 \tabularnewline
79 & 0.623375 & 0.75325 & 0.376625 \tabularnewline
80 & 0.591869 & 0.816261 & 0.408131 \tabularnewline
81 & 0.581726 & 0.836547 & 0.418274 \tabularnewline
82 & 0.532635 & 0.934731 & 0.467365 \tabularnewline
83 & 0.507998 & 0.984004 & 0.492002 \tabularnewline
84 & 0.77219 & 0.455621 & 0.22781 \tabularnewline
85 & 0.855431 & 0.289139 & 0.144569 \tabularnewline
86 & 0.827312 & 0.345375 & 0.172688 \tabularnewline
87 & 0.787519 & 0.424962 & 0.212481 \tabularnewline
88 & 0.862317 & 0.275365 & 0.137683 \tabularnewline
89 & 0.829686 & 0.340629 & 0.170314 \tabularnewline
90 & 0.790162 & 0.419676 & 0.209838 \tabularnewline
91 & 0.762335 & 0.475331 & 0.237665 \tabularnewline
92 & 0.698259 & 0.603482 & 0.301741 \tabularnewline
93 & 0.706924 & 0.586151 & 0.293076 \tabularnewline
94 & 0.651035 & 0.697931 & 0.348965 \tabularnewline
95 & 0.639163 & 0.721674 & 0.360837 \tabularnewline
96 & 0.621047 & 0.757907 & 0.378953 \tabularnewline
97 & 0.802832 & 0.394337 & 0.197168 \tabularnewline
98 & 0.731282 & 0.537435 & 0.268718 \tabularnewline
99 & 0.663794 & 0.672411 & 0.336206 \tabularnewline
100 & 0.90818 & 0.183639 & 0.0918197 \tabularnewline
101 & 0.865304 & 0.269392 & 0.134696 \tabularnewline
102 & 0.785804 & 0.428392 & 0.214196 \tabularnewline
103 & 0.675307 & 0.649385 & 0.324693 \tabularnewline
104 & 0.951419 & 0.0971629 & 0.0485814 \tabularnewline
105 & 0.87275 & 0.254499 & 0.12725 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267357&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]7[/C][C]0.500144[/C][C]0.999713[/C][C]0.499856[/C][/ROW]
[ROW][C]8[/C][C]0.434282[/C][C]0.868565[/C][C]0.565718[/C][/ROW]
[ROW][C]9[/C][C]0.364271[/C][C]0.728542[/C][C]0.635729[/C][/ROW]
[ROW][C]10[/C][C]0.293628[/C][C]0.587255[/C][C]0.706372[/C][/ROW]
[ROW][C]11[/C][C]0.198988[/C][C]0.397976[/C][C]0.801012[/C][/ROW]
[ROW][C]12[/C][C]0.154604[/C][C]0.309208[/C][C]0.845396[/C][/ROW]
[ROW][C]13[/C][C]0.160586[/C][C]0.321172[/C][C]0.839414[/C][/ROW]
[ROW][C]14[/C][C]0.197637[/C][C]0.395274[/C][C]0.802363[/C][/ROW]
[ROW][C]15[/C][C]0.952726[/C][C]0.0945473[/C][C]0.0472737[/C][/ROW]
[ROW][C]16[/C][C]0.928735[/C][C]0.14253[/C][C]0.0712651[/C][/ROW]
[ROW][C]17[/C][C]0.90618[/C][C]0.18764[/C][C]0.0938198[/C][/ROW]
[ROW][C]18[/C][C]0.870042[/C][C]0.259916[/C][C]0.129958[/C][/ROW]
[ROW][C]19[/C][C]0.857997[/C][C]0.284005[/C][C]0.142003[/C][/ROW]
[ROW][C]20[/C][C]0.812933[/C][C]0.374133[/C][C]0.187067[/C][/ROW]
[ROW][C]21[/C][C]0.76351[/C][C]0.47298[/C][C]0.23649[/C][/ROW]
[ROW][C]22[/C][C]0.787372[/C][C]0.425256[/C][C]0.212628[/C][/ROW]
[ROW][C]23[/C][C]0.735182[/C][C]0.529635[/C][C]0.264818[/C][/ROW]
[ROW][C]24[/C][C]0.682786[/C][C]0.634427[/C][C]0.317214[/C][/ROW]
[ROW][C]25[/C][C]0.625985[/C][C]0.74803[/C][C]0.374015[/C][/ROW]
[ROW][C]26[/C][C]0.67982[/C][C]0.640361[/C][C]0.32018[/C][/ROW]
[ROW][C]27[/C][C]0.965383[/C][C]0.0692348[/C][C]0.0346174[/C][/ROW]
[ROW][C]28[/C][C]0.954054[/C][C]0.0918911[/C][C]0.0459455[/C][/ROW]
[ROW][C]29[/C][C]0.942537[/C][C]0.114927[/C][C]0.0574634[/C][/ROW]
[ROW][C]30[/C][C]0.923449[/C][C]0.153102[/C][C]0.0765509[/C][/ROW]
[ROW][C]31[/C][C]0.90769[/C][C]0.18462[/C][C]0.0923102[/C][/ROW]
[ROW][C]32[/C][C]0.883014[/C][C]0.233971[/C][C]0.116986[/C][/ROW]
[ROW][C]33[/C][C]0.871873[/C][C]0.256254[/C][C]0.128127[/C][/ROW]
[ROW][C]34[/C][C]0.951883[/C][C]0.0962335[/C][C]0.0481167[/C][/ROW]
[ROW][C]35[/C][C]0.936605[/C][C]0.12679[/C][C]0.063395[/C][/ROW]
[ROW][C]36[/C][C]0.925056[/C][C]0.149887[/C][C]0.0749437[/C][/ROW]
[ROW][C]37[/C][C]0.905925[/C][C]0.188151[/C][C]0.0940754[/C][/ROW]
[ROW][C]38[/C][C]0.887799[/C][C]0.224402[/C][C]0.112201[/C][/ROW]
[ROW][C]39[/C][C]0.894854[/C][C]0.210292[/C][C]0.105146[/C][/ROW]
[ROW][C]40[/C][C]0.899894[/C][C]0.200212[/C][C]0.100106[/C][/ROW]
[ROW][C]41[/C][C]0.873774[/C][C]0.252451[/C][C]0.126226[/C][/ROW]
[ROW][C]42[/C][C]0.844501[/C][C]0.310998[/C][C]0.155499[/C][/ROW]
[ROW][C]43[/C][C]0.812929[/C][C]0.374142[/C][C]0.187071[/C][/ROW]
[ROW][C]44[/C][C]0.778346[/C][C]0.443309[/C][C]0.221654[/C][/ROW]
[ROW][C]45[/C][C]0.736353[/C][C]0.527293[/C][C]0.263647[/C][/ROW]
[ROW][C]46[/C][C]0.728307[/C][C]0.543386[/C][C]0.271693[/C][/ROW]
[ROW][C]47[/C][C]0.682661[/C][C]0.634679[/C][C]0.317339[/C][/ROW]
[ROW][C]48[/C][C]0.770406[/C][C]0.459187[/C][C]0.229594[/C][/ROW]
[ROW][C]49[/C][C]0.87312[/C][C]0.253761[/C][C]0.12688[/C][/ROW]
[ROW][C]50[/C][C]0.843122[/C][C]0.313757[/C][C]0.156878[/C][/ROW]
[ROW][C]51[/C][C]0.827732[/C][C]0.344535[/C][C]0.172268[/C][/ROW]
[ROW][C]52[/C][C]0.793728[/C][C]0.412544[/C][C]0.206272[/C][/ROW]
[ROW][C]53[/C][C]0.761327[/C][C]0.477345[/C][C]0.238673[/C][/ROW]
[ROW][C]54[/C][C]0.776091[/C][C]0.447818[/C][C]0.223909[/C][/ROW]
[ROW][C]55[/C][C]0.734848[/C][C]0.530305[/C][C]0.265152[/C][/ROW]
[ROW][C]56[/C][C]0.720895[/C][C]0.558209[/C][C]0.279105[/C][/ROW]
[ROW][C]57[/C][C]0.673339[/C][C]0.653322[/C][C]0.326661[/C][/ROW]
[ROW][C]58[/C][C]0.678892[/C][C]0.642216[/C][C]0.321108[/C][/ROW]
[ROW][C]59[/C][C]0.870127[/C][C]0.259746[/C][C]0.129873[/C][/ROW]
[ROW][C]60[/C][C]0.864729[/C][C]0.270542[/C][C]0.135271[/C][/ROW]
[ROW][C]61[/C][C]0.838431[/C][C]0.323137[/C][C]0.161569[/C][/ROW]
[ROW][C]62[/C][C]0.80874[/C][C]0.382521[/C][C]0.19126[/C][/ROW]
[ROW][C]63[/C][C]0.781985[/C][C]0.436031[/C][C]0.218015[/C][/ROW]
[ROW][C]64[/C][C]0.756534[/C][C]0.486933[/C][C]0.243466[/C][/ROW]
[ROW][C]65[/C][C]0.72595[/C][C]0.548099[/C][C]0.27405[/C][/ROW]
[ROW][C]66[/C][C]0.698386[/C][C]0.603227[/C][C]0.301614[/C][/ROW]
[ROW][C]67[/C][C]0.787358[/C][C]0.425283[/C][C]0.212642[/C][/ROW]
[ROW][C]68[/C][C]0.746796[/C][C]0.506407[/C][C]0.253204[/C][/ROW]
[ROW][C]69[/C][C]0.755971[/C][C]0.488057[/C][C]0.244029[/C][/ROW]
[ROW][C]70[/C][C]0.708071[/C][C]0.583858[/C][C]0.291929[/C][/ROW]
[ROW][C]71[/C][C]0.815194[/C][C]0.369613[/C][C]0.184806[/C][/ROW]
[ROW][C]72[/C][C]0.780698[/C][C]0.438604[/C][C]0.219302[/C][/ROW]
[ROW][C]73[/C][C]0.798936[/C][C]0.402127[/C][C]0.201064[/C][/ROW]
[ROW][C]74[/C][C]0.819842[/C][C]0.360316[/C][C]0.180158[/C][/ROW]
[ROW][C]75[/C][C]0.777335[/C][C]0.445331[/C][C]0.222665[/C][/ROW]
[ROW][C]76[/C][C]0.745501[/C][C]0.508998[/C][C]0.254499[/C][/ROW]
[ROW][C]77[/C][C]0.695188[/C][C]0.609624[/C][C]0.304812[/C][/ROW]
[ROW][C]78[/C][C]0.643949[/C][C]0.712102[/C][C]0.356051[/C][/ROW]
[ROW][C]79[/C][C]0.623375[/C][C]0.75325[/C][C]0.376625[/C][/ROW]
[ROW][C]80[/C][C]0.591869[/C][C]0.816261[/C][C]0.408131[/C][/ROW]
[ROW][C]81[/C][C]0.581726[/C][C]0.836547[/C][C]0.418274[/C][/ROW]
[ROW][C]82[/C][C]0.532635[/C][C]0.934731[/C][C]0.467365[/C][/ROW]
[ROW][C]83[/C][C]0.507998[/C][C]0.984004[/C][C]0.492002[/C][/ROW]
[ROW][C]84[/C][C]0.77219[/C][C]0.455621[/C][C]0.22781[/C][/ROW]
[ROW][C]85[/C][C]0.855431[/C][C]0.289139[/C][C]0.144569[/C][/ROW]
[ROW][C]86[/C][C]0.827312[/C][C]0.345375[/C][C]0.172688[/C][/ROW]
[ROW][C]87[/C][C]0.787519[/C][C]0.424962[/C][C]0.212481[/C][/ROW]
[ROW][C]88[/C][C]0.862317[/C][C]0.275365[/C][C]0.137683[/C][/ROW]
[ROW][C]89[/C][C]0.829686[/C][C]0.340629[/C][C]0.170314[/C][/ROW]
[ROW][C]90[/C][C]0.790162[/C][C]0.419676[/C][C]0.209838[/C][/ROW]
[ROW][C]91[/C][C]0.762335[/C][C]0.475331[/C][C]0.237665[/C][/ROW]
[ROW][C]92[/C][C]0.698259[/C][C]0.603482[/C][C]0.301741[/C][/ROW]
[ROW][C]93[/C][C]0.706924[/C][C]0.586151[/C][C]0.293076[/C][/ROW]
[ROW][C]94[/C][C]0.651035[/C][C]0.697931[/C][C]0.348965[/C][/ROW]
[ROW][C]95[/C][C]0.639163[/C][C]0.721674[/C][C]0.360837[/C][/ROW]
[ROW][C]96[/C][C]0.621047[/C][C]0.757907[/C][C]0.378953[/C][/ROW]
[ROW][C]97[/C][C]0.802832[/C][C]0.394337[/C][C]0.197168[/C][/ROW]
[ROW][C]98[/C][C]0.731282[/C][C]0.537435[/C][C]0.268718[/C][/ROW]
[ROW][C]99[/C][C]0.663794[/C][C]0.672411[/C][C]0.336206[/C][/ROW]
[ROW][C]100[/C][C]0.90818[/C][C]0.183639[/C][C]0.0918197[/C][/ROW]
[ROW][C]101[/C][C]0.865304[/C][C]0.269392[/C][C]0.134696[/C][/ROW]
[ROW][C]102[/C][C]0.785804[/C][C]0.428392[/C][C]0.214196[/C][/ROW]
[ROW][C]103[/C][C]0.675307[/C][C]0.649385[/C][C]0.324693[/C][/ROW]
[ROW][C]104[/C][C]0.951419[/C][C]0.0971629[/C][C]0.0485814[/C][/ROW]
[ROW][C]105[/C][C]0.87275[/C][C]0.254499[/C][C]0.12725[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267357&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267357&T=5

As an alternative you can also use a QR Code:

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

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
7	0.500144	0.999713	0.499856
8	0.434282	0.868565	0.565718
9	0.364271	0.728542	0.635729
10	0.293628	0.587255	0.706372
11	0.198988	0.397976	0.801012
12	0.154604	0.309208	0.845396
13	0.160586	0.321172	0.839414
14	0.197637	0.395274	0.802363
15	0.952726	0.0945473	0.0472737
16	0.928735	0.14253	0.0712651
17	0.90618	0.18764	0.0938198
18	0.870042	0.259916	0.129958
19	0.857997	0.284005	0.142003
20	0.812933	0.374133	0.187067
21	0.76351	0.47298	0.23649
22	0.787372	0.425256	0.212628
23	0.735182	0.529635	0.264818
24	0.682786	0.634427	0.317214
25	0.625985	0.74803	0.374015
26	0.67982	0.640361	0.32018
27	0.965383	0.0692348	0.0346174
28	0.954054	0.0918911	0.0459455
29	0.942537	0.114927	0.0574634
30	0.923449	0.153102	0.0765509
31	0.90769	0.18462	0.0923102
32	0.883014	0.233971	0.116986
33	0.871873	0.256254	0.128127
34	0.951883	0.0962335	0.0481167
35	0.936605	0.12679	0.063395
36	0.925056	0.149887	0.0749437
37	0.905925	0.188151	0.0940754
38	0.887799	0.224402	0.112201
39	0.894854	0.210292	0.105146
40	0.899894	0.200212	0.100106
41	0.873774	0.252451	0.126226
42	0.844501	0.310998	0.155499
43	0.812929	0.374142	0.187071
44	0.778346	0.443309	0.221654
45	0.736353	0.527293	0.263647
46	0.728307	0.543386	0.271693
47	0.682661	0.634679	0.317339
48	0.770406	0.459187	0.229594
49	0.87312	0.253761	0.12688
50	0.843122	0.313757	0.156878
51	0.827732	0.344535	0.172268
52	0.793728	0.412544	0.206272
53	0.761327	0.477345	0.238673
54	0.776091	0.447818	0.223909
55	0.734848	0.530305	0.265152
56	0.720895	0.558209	0.279105
57	0.673339	0.653322	0.326661
58	0.678892	0.642216	0.321108
59	0.870127	0.259746	0.129873
60	0.864729	0.270542	0.135271
61	0.838431	0.323137	0.161569
62	0.80874	0.382521	0.19126
63	0.781985	0.436031	0.218015
64	0.756534	0.486933	0.243466
65	0.72595	0.548099	0.27405
66	0.698386	0.603227	0.301614
67	0.787358	0.425283	0.212642
68	0.746796	0.506407	0.253204
69	0.755971	0.488057	0.244029
70	0.708071	0.583858	0.291929
71	0.815194	0.369613	0.184806
72	0.780698	0.438604	0.219302
73	0.798936	0.402127	0.201064
74	0.819842	0.360316	0.180158
75	0.777335	0.445331	0.222665
76	0.745501	0.508998	0.254499
77	0.695188	0.609624	0.304812
78	0.643949	0.712102	0.356051
79	0.623375	0.75325	0.376625
80	0.591869	0.816261	0.408131
81	0.581726	0.836547	0.418274
82	0.532635	0.934731	0.467365
83	0.507998	0.984004	0.492002
84	0.77219	0.455621	0.22781
85	0.855431	0.289139	0.144569
86	0.827312	0.345375	0.172688
87	0.787519	0.424962	0.212481
88	0.862317	0.275365	0.137683
89	0.829686	0.340629	0.170314
90	0.790162	0.419676	0.209838
91	0.762335	0.475331	0.237665
92	0.698259	0.603482	0.301741
93	0.706924	0.586151	0.293076
94	0.651035	0.697931	0.348965
95	0.639163	0.721674	0.360837
96	0.621047	0.757907	0.378953
97	0.802832	0.394337	0.197168
98	0.731282	0.537435	0.268718
99	0.663794	0.672411	0.336206
100	0.90818	0.183639	0.0918197
101	0.865304	0.269392	0.134696
102	0.785804	0.428392	0.214196
103	0.675307	0.649385	0.324693
104	0.951419	0.0971629	0.0485814
105	0.87275	0.254499	0.12725

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	5	0.0505051	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 & 5 & 0.0505051 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267357&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]5[/C][C]0.0505051[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267357&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267357&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	5	0.0505051	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

PNG link

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Parameters (Session):

par1 = 6 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 4 ; 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