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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Wed, 10 Dec 2014 13:19:14 +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/10/t1418217587qfjaa0o8bp43cgj.htm/, Retrieved Sun, 19 May 2024 15:26:22 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=265122, Retrieved Sun, 19 May 2024 15:26:22 +0000

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Estimated Impact

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

-       [Multiple Regression] [MR impact mot] [2014-12-10 13:19:14] [ec1b40d1a9751af99658fe8fca4f9eca] [Current]

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12.9 59 71 6
12.2 50 73 4
12.8 34 65 11
7.4 41 75 7
6.7 39 52 4
12.6 68 75 4
14.8 70 65 4
13.3 65 71 4
11.1 60 66 5
8.2 43 54 4
11.4 50 63 8
6.4 58 65 8
10.6 67 71 4
12 41 75 15
6.3 53 71 4
11.3 48 60 5
11.9 61 69 4
9.3 64 74 4
9.6 45 63 4
10 67 42 4
6.4 29 70 7
13.8 26 68 5
10.8 51 63 7
13.8 56 68 7
11.7 56 60 10
10.9 32 65 14
16.1 58 72 7
13.4 67 71 4
9.9 44 62 9
11.5 53 60 10
8.3 56 70 4
11.7 61 58 7
9 61 64 4
9.7 56 61 6
10.8 61 72 4
10.3 56 62 4
10.4 43 69 5
12.7 64 67 4
9.3 34 68 4
11.8 69 68 6
5.9 74 81 6
11.4 57 70 4
13 53 62 5
10.8 40 59 10
12.3 66 76 4
11.3 54 70 4
11.8 49 66 4
7.9 52 78 8
12.7 58 69 5
12.3 58 71 5
11.6 51 71 8
6.7 35 76 4
10.9 53 70 4
12.1 43 60 4
13.3 49 72 4
10.1 84 84 4
5.7 66 73 8
14.3 61 66 4
8 60 66 4
13.3 62 72 11
9.3 68 67 4
12.5 49 66 6
7.6 48 59 4
15.9 51 64 4
9.2 63 78 4
9.1 57 68 9
11.1 53 76 4
13 56 75 4
14.5 63 69 4
12.2 63 68 9
12.3 54 60 12
11.4 47 76 4
8.8 49 59 7
14.6 58 64 7
12.6 43 66 4
13 58 73 4
12.6 67 73 4
13.2 57 62 4
9.9 37 64 4
7.7 30 65 7
10.5 47 63 4
13.4 51 59 9
10.9 56 70 5
4.3 52 65 9
10.3 37 78 4
11.8 38 59 5
11.2 52 62 8
11.4 56 64 9
8.6 67 76 4
13.2 37 54 5
12.6 26 50 4
5.6 50 68 4
9.9 58 61 4
8.8 42 67 7
7.7 66 66 4
9 54 73 4
7.3 43 52 11
11.4 62 63 4
13.6 48 63 4
7.9 51 61 10
10.7 55 73 4
10.3 55 59 10
8.3 42 64 4
9.6 63 69 8
14.2 57 72 4
8.5 52 67 4
13.5 54 66 12
4.9 46 66 5
6.4 66 56 4
9.6 52 73 8
11.6 43 61 5
11.1 57 68 7

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	6 seconds
R Server	'Gwilym Jenkins' @ jenkins.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 6 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=265122&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=265122&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=265122&T=0

As an alternative you can also use a QR Code:

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

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	6 seconds
R Server	'Gwilym Jenkins' @ jenkins.wessa.net

Multiple Linear Regression - Estimated Regression Equation
TOT[t] = + 9.74987 + 0.0266956AMS.I[t] -0.00791095AMS.E[t] + 0.00834692AMS.A[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TOT[t] =  +  9.74987 +  0.0266956AMS.I[t] -0.00791095AMS.E[t] +  0.00834692AMS.A[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=265122&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TOT[t] =  +  9.74987 +  0.0266956AMS.I[t] -0.00791095AMS.E[t] +  0.00834692AMS.A[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=265122&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=265122&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] = + 9.74987 + 0.0266956AMS.I[t] -0.00791095AMS.E[t] + 0.00834692AMS.A[t] + e[t]

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	9.74987	2.57691	3.784	0.000254139	0.00012707
AMS.I	0.0266956	0.0234198	1.14	0.256861	0.128431
AMS.E	-0.00791095	0.0367516	-0.2153	0.829975	0.414987
AMS.A	0.00834692	0.0956352	0.08728	0.930612	0.465306

\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) & 9.74987 & 2.57691 & 3.784 & 0.000254139 & 0.00012707 \tabularnewline
AMS.I & 0.0266956 & 0.0234198 & 1.14 & 0.256861 & 0.128431 \tabularnewline
AMS.E & -0.00791095 & 0.0367516 & -0.2153 & 0.829975 & 0.414987 \tabularnewline
AMS.A & 0.00834692 & 0.0956352 & 0.08728 & 0.930612 & 0.465306 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=265122&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]9.74987[/C][C]2.57691[/C][C]3.784[/C][C]0.000254139[/C][C]0.00012707[/C][/ROW]
[ROW][C]AMS.I[/C][C]0.0266956[/C][C]0.0234198[/C][C]1.14[/C][C]0.256861[/C][C]0.128431[/C][/ROW]
[ROW][C]AMS.E[/C][C]-0.00791095[/C][C]0.0367516[/C][C]-0.2153[/C][C]0.829975[/C][C]0.414987[/C][/ROW]
[ROW][C]AMS.A[/C][C]0.00834692[/C][C]0.0956352[/C][C]0.08728[/C][C]0.930612[/C][C]0.465306[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=265122&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=265122&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)	9.74987	2.57691	3.784	0.000254139	0.00012707
AMS.I	0.0266956	0.0234198	1.14	0.256861	0.128431
AMS.E	-0.00791095	0.0367516	-0.2153	0.829975	0.414987
AMS.A	0.00834692	0.0956352	0.08728	0.930612	0.465306

Multiple Linear Regression - Regression Statistics
Multiple R	0.110099
R-squared	0.0121217
Adjusted R-squared	-0.0153194
F-TEST (value)	0.441736
F-TEST (DF numerator)	3
F-TEST (DF denominator)	108
p-value	0.723629
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.4902
Sum Squared Residuals	669.717

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.110099 \tabularnewline
R-squared & 0.0121217 \tabularnewline
Adjusted R-squared & -0.0153194 \tabularnewline
F-TEST (value) & 0.441736 \tabularnewline
F-TEST (DF numerator) & 3 \tabularnewline
F-TEST (DF denominator) & 108 \tabularnewline
p-value & 0.723629 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.4902 \tabularnewline
Sum Squared Residuals & 669.717 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=265122&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.110099[/C][/ROW]
[ROW][C]R-squared[/C][C]0.0121217[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]-0.0153194[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]0.441736[/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]0.723629[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.4902[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]669.717[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=265122&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=265122&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	0.110099
R-squared	0.0121217
Adjusted R-squared	-0.0153194
F-TEST (value)	0.441736
F-TEST (DF numerator)	3
F-TEST (DF denominator)	108
p-value	0.723629
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.4902
Sum Squared Residuals	669.717

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	12.9	10.8133	2.08668
2	12.2	10.5405	1.65946
3	12.8	10.2351	2.56487
4	7.4	10.3095	-2.9095
5	6.7	10.413	-3.71302
6	12.6	11.0052	1.59476
7	14.8	11.1377	3.66226
8	13.3	10.9568	2.34321
9	11.1	10.8712	0.228781
10	8.2	10.504	-2.30398
11	11.4	10.653	0.746964
12	6.4	10.8508	-4.45078
13	10.6	11.0102	-0.410186
14	12	10.3763	1.62373
15	6.3	10.6364	-4.33645
16	11.3	10.5983	0.701663
17	11.9	10.8658	1.03417
18	9.3	10.9064	-1.60637
19	9.6	10.4862	-0.886171
20	10	11.2396	-1.2396
21	6.4	10.0287	-3.62871
22	13.8	9.94775	3.85225
23	10.8	10.6714	0.128615
24	13.8	10.7653	3.03469
25	11.7	10.8536	0.846363
26	10.9	10.2068	0.693225
27	16.1	10.7871	5.31294
28	13.4	11.0102	2.38981
29	9.9	10.5091	-0.609121
30	11.5	10.7735	0.72645
31	8.3	10.7244	-2.42445
32	11.7	10.9779	0.722104
33	9	10.9054	-1.90539
34	9.7	10.8123	-1.11234
35	10.8	10.8421	-0.0421016
36	10.3	10.7877	-0.487733
37	10.4	10.3937	0.00633934
38	12.7	10.9617	1.73826
39	9.3	10.153	-0.852964
40	11.8	11.104	0.695996
41	5.9	11.1346	-5.23464
42	11.4	10.7511	0.648859
43	13	10.716	2.28401
44	10.8	10.4344	0.365582
45	12.3	10.9439	1.35606
46	11.3	10.6711	0.628946
47	11.8	10.5692	1.23078
48	7.9	10.5878	-2.68776
49	12.7	10.7941	1.90591
50	12.3	10.7783	1.52173
51	11.6	10.6164	0.983556
52	6.7	10.1164	-3.41637
53	10.9	10.6444	0.255641
54	12.1	10.4565	1.64349
55	13.3	10.5218	2.77825
56	10.1	11.3612	-1.26117
57	5.7	11.0011	-5.30106
58	14.3	10.8896	3.41043
59	8	10.8629	-2.86287
60	13.3	10.9272	2.37277
61	9.3	11.0685	-1.76853
62	12.5	10.5859	1.91409
63	7.6	10.5979	-2.9979
64	15.9	10.6384	5.26157
65	9.2	10.848	-1.64803
66	9.1	10.8087	-1.7087
67	11.1	10.5969	0.503107
68	13	10.6849	2.31511
69	14.5	10.9192	3.58077
70	12.2	10.9689	1.23113
71	12.3	10.8169	1.48306
72	11.4	10.4367	0.963281
73	8.8	10.6496	-1.84964
74	14.6	10.8503	3.74966
75	12.6	10.409	2.19095
76	13	10.7541	2.2459
77	12.6	10.9944	1.60564
78	13.2	10.8144	2.38557
79	9.9	10.2647	-0.364695
80	7.7	10.095	-2.39496
81	10.5	10.5396	-0.0395618
82	13.4	10.7197	2.68028
83	10.9	10.7328	0.167208
84	4.3	10.699	-6.39895
85	10.3	10.1539	0.146058
86	11.8	10.3393	1.46071
87	11.2	10.7143	0.485662
88	11.4	10.8136	0.586354
89	8.6	10.9706	-2.37063
90	13.2	10.3522	2.84785
91	12.6	10.0818	2.5182
92	5.6	10.5801	-4.98009
93	9.9	10.849	-0.949035
94	8.8	10.3995	-1.59948
95	7.7	11.023	-3.32305
96	9	10.6473	-1.64732
97	7.3	10.5782	-3.27823
98	11.4	10.94	0.460004
99	13.6	10.5663	3.03374
100	7.9	10.7122	-2.81225
101	10.7	10.674	0.0259829
102	10.3	10.8349	-0.534852
103	8.3	10.3982	-2.09817
104	9.6	10.9526	-1.35261
105	14.2	10.7353	3.46468
106	8.5	10.6414	-2.1414
107	13.5	10.7695	2.73053
108	4.9	10.4975	-5.59748
109	6.4	11.1022	-4.70215
110	9.6	10.6273	-1.02732
111	11.6	10.4569	1.14305
112	11.1	10.792	0.307996

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 12.9 & 10.8133 & 2.08668 \tabularnewline
2 & 12.2 & 10.5405 & 1.65946 \tabularnewline
3 & 12.8 & 10.2351 & 2.56487 \tabularnewline
4 & 7.4 & 10.3095 & -2.9095 \tabularnewline
5 & 6.7 & 10.413 & -3.71302 \tabularnewline
6 & 12.6 & 11.0052 & 1.59476 \tabularnewline
7 & 14.8 & 11.1377 & 3.66226 \tabularnewline
8 & 13.3 & 10.9568 & 2.34321 \tabularnewline
9 & 11.1 & 10.8712 & 0.228781 \tabularnewline
10 & 8.2 & 10.504 & -2.30398 \tabularnewline
11 & 11.4 & 10.653 & 0.746964 \tabularnewline
12 & 6.4 & 10.8508 & -4.45078 \tabularnewline
13 & 10.6 & 11.0102 & -0.410186 \tabularnewline
14 & 12 & 10.3763 & 1.62373 \tabularnewline
15 & 6.3 & 10.6364 & -4.33645 \tabularnewline
16 & 11.3 & 10.5983 & 0.701663 \tabularnewline
17 & 11.9 & 10.8658 & 1.03417 \tabularnewline
18 & 9.3 & 10.9064 & -1.60637 \tabularnewline
19 & 9.6 & 10.4862 & -0.886171 \tabularnewline
20 & 10 & 11.2396 & -1.2396 \tabularnewline
21 & 6.4 & 10.0287 & -3.62871 \tabularnewline
22 & 13.8 & 9.94775 & 3.85225 \tabularnewline
23 & 10.8 & 10.6714 & 0.128615 \tabularnewline
24 & 13.8 & 10.7653 & 3.03469 \tabularnewline
25 & 11.7 & 10.8536 & 0.846363 \tabularnewline
26 & 10.9 & 10.2068 & 0.693225 \tabularnewline
27 & 16.1 & 10.7871 & 5.31294 \tabularnewline
28 & 13.4 & 11.0102 & 2.38981 \tabularnewline
29 & 9.9 & 10.5091 & -0.609121 \tabularnewline
30 & 11.5 & 10.7735 & 0.72645 \tabularnewline
31 & 8.3 & 10.7244 & -2.42445 \tabularnewline
32 & 11.7 & 10.9779 & 0.722104 \tabularnewline
33 & 9 & 10.9054 & -1.90539 \tabularnewline
34 & 9.7 & 10.8123 & -1.11234 \tabularnewline
35 & 10.8 & 10.8421 & -0.0421016 \tabularnewline
36 & 10.3 & 10.7877 & -0.487733 \tabularnewline
37 & 10.4 & 10.3937 & 0.00633934 \tabularnewline
38 & 12.7 & 10.9617 & 1.73826 \tabularnewline
39 & 9.3 & 10.153 & -0.852964 \tabularnewline
40 & 11.8 & 11.104 & 0.695996 \tabularnewline
41 & 5.9 & 11.1346 & -5.23464 \tabularnewline
42 & 11.4 & 10.7511 & 0.648859 \tabularnewline
43 & 13 & 10.716 & 2.28401 \tabularnewline
44 & 10.8 & 10.4344 & 0.365582 \tabularnewline
45 & 12.3 & 10.9439 & 1.35606 \tabularnewline
46 & 11.3 & 10.6711 & 0.628946 \tabularnewline
47 & 11.8 & 10.5692 & 1.23078 \tabularnewline
48 & 7.9 & 10.5878 & -2.68776 \tabularnewline
49 & 12.7 & 10.7941 & 1.90591 \tabularnewline
50 & 12.3 & 10.7783 & 1.52173 \tabularnewline
51 & 11.6 & 10.6164 & 0.983556 \tabularnewline
52 & 6.7 & 10.1164 & -3.41637 \tabularnewline
53 & 10.9 & 10.6444 & 0.255641 \tabularnewline
54 & 12.1 & 10.4565 & 1.64349 \tabularnewline
55 & 13.3 & 10.5218 & 2.77825 \tabularnewline
56 & 10.1 & 11.3612 & -1.26117 \tabularnewline
57 & 5.7 & 11.0011 & -5.30106 \tabularnewline
58 & 14.3 & 10.8896 & 3.41043 \tabularnewline
59 & 8 & 10.8629 & -2.86287 \tabularnewline
60 & 13.3 & 10.9272 & 2.37277 \tabularnewline
61 & 9.3 & 11.0685 & -1.76853 \tabularnewline
62 & 12.5 & 10.5859 & 1.91409 \tabularnewline
63 & 7.6 & 10.5979 & -2.9979 \tabularnewline
64 & 15.9 & 10.6384 & 5.26157 \tabularnewline
65 & 9.2 & 10.848 & -1.64803 \tabularnewline
66 & 9.1 & 10.8087 & -1.7087 \tabularnewline
67 & 11.1 & 10.5969 & 0.503107 \tabularnewline
68 & 13 & 10.6849 & 2.31511 \tabularnewline
69 & 14.5 & 10.9192 & 3.58077 \tabularnewline
70 & 12.2 & 10.9689 & 1.23113 \tabularnewline
71 & 12.3 & 10.8169 & 1.48306 \tabularnewline
72 & 11.4 & 10.4367 & 0.963281 \tabularnewline
73 & 8.8 & 10.6496 & -1.84964 \tabularnewline
74 & 14.6 & 10.8503 & 3.74966 \tabularnewline
75 & 12.6 & 10.409 & 2.19095 \tabularnewline
76 & 13 & 10.7541 & 2.2459 \tabularnewline
77 & 12.6 & 10.9944 & 1.60564 \tabularnewline
78 & 13.2 & 10.8144 & 2.38557 \tabularnewline
79 & 9.9 & 10.2647 & -0.364695 \tabularnewline
80 & 7.7 & 10.095 & -2.39496 \tabularnewline
81 & 10.5 & 10.5396 & -0.0395618 \tabularnewline
82 & 13.4 & 10.7197 & 2.68028 \tabularnewline
83 & 10.9 & 10.7328 & 0.167208 \tabularnewline
84 & 4.3 & 10.699 & -6.39895 \tabularnewline
85 & 10.3 & 10.1539 & 0.146058 \tabularnewline
86 & 11.8 & 10.3393 & 1.46071 \tabularnewline
87 & 11.2 & 10.7143 & 0.485662 \tabularnewline
88 & 11.4 & 10.8136 & 0.586354 \tabularnewline
89 & 8.6 & 10.9706 & -2.37063 \tabularnewline
90 & 13.2 & 10.3522 & 2.84785 \tabularnewline
91 & 12.6 & 10.0818 & 2.5182 \tabularnewline
92 & 5.6 & 10.5801 & -4.98009 \tabularnewline
93 & 9.9 & 10.849 & -0.949035 \tabularnewline
94 & 8.8 & 10.3995 & -1.59948 \tabularnewline
95 & 7.7 & 11.023 & -3.32305 \tabularnewline
96 & 9 & 10.6473 & -1.64732 \tabularnewline
97 & 7.3 & 10.5782 & -3.27823 \tabularnewline
98 & 11.4 & 10.94 & 0.460004 \tabularnewline
99 & 13.6 & 10.5663 & 3.03374 \tabularnewline
100 & 7.9 & 10.7122 & -2.81225 \tabularnewline
101 & 10.7 & 10.674 & 0.0259829 \tabularnewline
102 & 10.3 & 10.8349 & -0.534852 \tabularnewline
103 & 8.3 & 10.3982 & -2.09817 \tabularnewline
104 & 9.6 & 10.9526 & -1.35261 \tabularnewline
105 & 14.2 & 10.7353 & 3.46468 \tabularnewline
106 & 8.5 & 10.6414 & -2.1414 \tabularnewline
107 & 13.5 & 10.7695 & 2.73053 \tabularnewline
108 & 4.9 & 10.4975 & -5.59748 \tabularnewline
109 & 6.4 & 11.1022 & -4.70215 \tabularnewline
110 & 9.6 & 10.6273 & -1.02732 \tabularnewline
111 & 11.6 & 10.4569 & 1.14305 \tabularnewline
112 & 11.1 & 10.792 & 0.307996 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=265122&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]10.8133[/C][C]2.08668[/C][/ROW]
[ROW][C]2[/C][C]12.2[/C][C]10.5405[/C][C]1.65946[/C][/ROW]
[ROW][C]3[/C][C]12.8[/C][C]10.2351[/C][C]2.56487[/C][/ROW]
[ROW][C]4[/C][C]7.4[/C][C]10.3095[/C][C]-2.9095[/C][/ROW]
[ROW][C]5[/C][C]6.7[/C][C]10.413[/C][C]-3.71302[/C][/ROW]
[ROW][C]6[/C][C]12.6[/C][C]11.0052[/C][C]1.59476[/C][/ROW]
[ROW][C]7[/C][C]14.8[/C][C]11.1377[/C][C]3.66226[/C][/ROW]
[ROW][C]8[/C][C]13.3[/C][C]10.9568[/C][C]2.34321[/C][/ROW]
[ROW][C]9[/C][C]11.1[/C][C]10.8712[/C][C]0.228781[/C][/ROW]
[ROW][C]10[/C][C]8.2[/C][C]10.504[/C][C]-2.30398[/C][/ROW]
[ROW][C]11[/C][C]11.4[/C][C]10.653[/C][C]0.746964[/C][/ROW]
[ROW][C]12[/C][C]6.4[/C][C]10.8508[/C][C]-4.45078[/C][/ROW]
[ROW][C]13[/C][C]10.6[/C][C]11.0102[/C][C]-0.410186[/C][/ROW]
[ROW][C]14[/C][C]12[/C][C]10.3763[/C][C]1.62373[/C][/ROW]
[ROW][C]15[/C][C]6.3[/C][C]10.6364[/C][C]-4.33645[/C][/ROW]
[ROW][C]16[/C][C]11.3[/C][C]10.5983[/C][C]0.701663[/C][/ROW]
[ROW][C]17[/C][C]11.9[/C][C]10.8658[/C][C]1.03417[/C][/ROW]
[ROW][C]18[/C][C]9.3[/C][C]10.9064[/C][C]-1.60637[/C][/ROW]
[ROW][C]19[/C][C]9.6[/C][C]10.4862[/C][C]-0.886171[/C][/ROW]
[ROW][C]20[/C][C]10[/C][C]11.2396[/C][C]-1.2396[/C][/ROW]
[ROW][C]21[/C][C]6.4[/C][C]10.0287[/C][C]-3.62871[/C][/ROW]
[ROW][C]22[/C][C]13.8[/C][C]9.94775[/C][C]3.85225[/C][/ROW]
[ROW][C]23[/C][C]10.8[/C][C]10.6714[/C][C]0.128615[/C][/ROW]
[ROW][C]24[/C][C]13.8[/C][C]10.7653[/C][C]3.03469[/C][/ROW]
[ROW][C]25[/C][C]11.7[/C][C]10.8536[/C][C]0.846363[/C][/ROW]
[ROW][C]26[/C][C]10.9[/C][C]10.2068[/C][C]0.693225[/C][/ROW]
[ROW][C]27[/C][C]16.1[/C][C]10.7871[/C][C]5.31294[/C][/ROW]
[ROW][C]28[/C][C]13.4[/C][C]11.0102[/C][C]2.38981[/C][/ROW]
[ROW][C]29[/C][C]9.9[/C][C]10.5091[/C][C]-0.609121[/C][/ROW]
[ROW][C]30[/C][C]11.5[/C][C]10.7735[/C][C]0.72645[/C][/ROW]
[ROW][C]31[/C][C]8.3[/C][C]10.7244[/C][C]-2.42445[/C][/ROW]
[ROW][C]32[/C][C]11.7[/C][C]10.9779[/C][C]0.722104[/C][/ROW]
[ROW][C]33[/C][C]9[/C][C]10.9054[/C][C]-1.90539[/C][/ROW]
[ROW][C]34[/C][C]9.7[/C][C]10.8123[/C][C]-1.11234[/C][/ROW]
[ROW][C]35[/C][C]10.8[/C][C]10.8421[/C][C]-0.0421016[/C][/ROW]
[ROW][C]36[/C][C]10.3[/C][C]10.7877[/C][C]-0.487733[/C][/ROW]
[ROW][C]37[/C][C]10.4[/C][C]10.3937[/C][C]0.00633934[/C][/ROW]
[ROW][C]38[/C][C]12.7[/C][C]10.9617[/C][C]1.73826[/C][/ROW]
[ROW][C]39[/C][C]9.3[/C][C]10.153[/C][C]-0.852964[/C][/ROW]
[ROW][C]40[/C][C]11.8[/C][C]11.104[/C][C]0.695996[/C][/ROW]
[ROW][C]41[/C][C]5.9[/C][C]11.1346[/C][C]-5.23464[/C][/ROW]
[ROW][C]42[/C][C]11.4[/C][C]10.7511[/C][C]0.648859[/C][/ROW]
[ROW][C]43[/C][C]13[/C][C]10.716[/C][C]2.28401[/C][/ROW]
[ROW][C]44[/C][C]10.8[/C][C]10.4344[/C][C]0.365582[/C][/ROW]
[ROW][C]45[/C][C]12.3[/C][C]10.9439[/C][C]1.35606[/C][/ROW]
[ROW][C]46[/C][C]11.3[/C][C]10.6711[/C][C]0.628946[/C][/ROW]
[ROW][C]47[/C][C]11.8[/C][C]10.5692[/C][C]1.23078[/C][/ROW]
[ROW][C]48[/C][C]7.9[/C][C]10.5878[/C][C]-2.68776[/C][/ROW]
[ROW][C]49[/C][C]12.7[/C][C]10.7941[/C][C]1.90591[/C][/ROW]
[ROW][C]50[/C][C]12.3[/C][C]10.7783[/C][C]1.52173[/C][/ROW]
[ROW][C]51[/C][C]11.6[/C][C]10.6164[/C][C]0.983556[/C][/ROW]
[ROW][C]52[/C][C]6.7[/C][C]10.1164[/C][C]-3.41637[/C][/ROW]
[ROW][C]53[/C][C]10.9[/C][C]10.6444[/C][C]0.255641[/C][/ROW]
[ROW][C]54[/C][C]12.1[/C][C]10.4565[/C][C]1.64349[/C][/ROW]
[ROW][C]55[/C][C]13.3[/C][C]10.5218[/C][C]2.77825[/C][/ROW]
[ROW][C]56[/C][C]10.1[/C][C]11.3612[/C][C]-1.26117[/C][/ROW]
[ROW][C]57[/C][C]5.7[/C][C]11.0011[/C][C]-5.30106[/C][/ROW]
[ROW][C]58[/C][C]14.3[/C][C]10.8896[/C][C]3.41043[/C][/ROW]
[ROW][C]59[/C][C]8[/C][C]10.8629[/C][C]-2.86287[/C][/ROW]
[ROW][C]60[/C][C]13.3[/C][C]10.9272[/C][C]2.37277[/C][/ROW]
[ROW][C]61[/C][C]9.3[/C][C]11.0685[/C][C]-1.76853[/C][/ROW]
[ROW][C]62[/C][C]12.5[/C][C]10.5859[/C][C]1.91409[/C][/ROW]
[ROW][C]63[/C][C]7.6[/C][C]10.5979[/C][C]-2.9979[/C][/ROW]
[ROW][C]64[/C][C]15.9[/C][C]10.6384[/C][C]5.26157[/C][/ROW]
[ROW][C]65[/C][C]9.2[/C][C]10.848[/C][C]-1.64803[/C][/ROW]
[ROW][C]66[/C][C]9.1[/C][C]10.8087[/C][C]-1.7087[/C][/ROW]
[ROW][C]67[/C][C]11.1[/C][C]10.5969[/C][C]0.503107[/C][/ROW]
[ROW][C]68[/C][C]13[/C][C]10.6849[/C][C]2.31511[/C][/ROW]
[ROW][C]69[/C][C]14.5[/C][C]10.9192[/C][C]3.58077[/C][/ROW]
[ROW][C]70[/C][C]12.2[/C][C]10.9689[/C][C]1.23113[/C][/ROW]
[ROW][C]71[/C][C]12.3[/C][C]10.8169[/C][C]1.48306[/C][/ROW]
[ROW][C]72[/C][C]11.4[/C][C]10.4367[/C][C]0.963281[/C][/ROW]
[ROW][C]73[/C][C]8.8[/C][C]10.6496[/C][C]-1.84964[/C][/ROW]
[ROW][C]74[/C][C]14.6[/C][C]10.8503[/C][C]3.74966[/C][/ROW]
[ROW][C]75[/C][C]12.6[/C][C]10.409[/C][C]2.19095[/C][/ROW]
[ROW][C]76[/C][C]13[/C][C]10.7541[/C][C]2.2459[/C][/ROW]
[ROW][C]77[/C][C]12.6[/C][C]10.9944[/C][C]1.60564[/C][/ROW]
[ROW][C]78[/C][C]13.2[/C][C]10.8144[/C][C]2.38557[/C][/ROW]
[ROW][C]79[/C][C]9.9[/C][C]10.2647[/C][C]-0.364695[/C][/ROW]
[ROW][C]80[/C][C]7.7[/C][C]10.095[/C][C]-2.39496[/C][/ROW]
[ROW][C]81[/C][C]10.5[/C][C]10.5396[/C][C]-0.0395618[/C][/ROW]
[ROW][C]82[/C][C]13.4[/C][C]10.7197[/C][C]2.68028[/C][/ROW]
[ROW][C]83[/C][C]10.9[/C][C]10.7328[/C][C]0.167208[/C][/ROW]
[ROW][C]84[/C][C]4.3[/C][C]10.699[/C][C]-6.39895[/C][/ROW]
[ROW][C]85[/C][C]10.3[/C][C]10.1539[/C][C]0.146058[/C][/ROW]
[ROW][C]86[/C][C]11.8[/C][C]10.3393[/C][C]1.46071[/C][/ROW]
[ROW][C]87[/C][C]11.2[/C][C]10.7143[/C][C]0.485662[/C][/ROW]
[ROW][C]88[/C][C]11.4[/C][C]10.8136[/C][C]0.586354[/C][/ROW]
[ROW][C]89[/C][C]8.6[/C][C]10.9706[/C][C]-2.37063[/C][/ROW]
[ROW][C]90[/C][C]13.2[/C][C]10.3522[/C][C]2.84785[/C][/ROW]
[ROW][C]91[/C][C]12.6[/C][C]10.0818[/C][C]2.5182[/C][/ROW]
[ROW][C]92[/C][C]5.6[/C][C]10.5801[/C][C]-4.98009[/C][/ROW]
[ROW][C]93[/C][C]9.9[/C][C]10.849[/C][C]-0.949035[/C][/ROW]
[ROW][C]94[/C][C]8.8[/C][C]10.3995[/C][C]-1.59948[/C][/ROW]
[ROW][C]95[/C][C]7.7[/C][C]11.023[/C][C]-3.32305[/C][/ROW]
[ROW][C]96[/C][C]9[/C][C]10.6473[/C][C]-1.64732[/C][/ROW]
[ROW][C]97[/C][C]7.3[/C][C]10.5782[/C][C]-3.27823[/C][/ROW]
[ROW][C]98[/C][C]11.4[/C][C]10.94[/C][C]0.460004[/C][/ROW]
[ROW][C]99[/C][C]13.6[/C][C]10.5663[/C][C]3.03374[/C][/ROW]
[ROW][C]100[/C][C]7.9[/C][C]10.7122[/C][C]-2.81225[/C][/ROW]
[ROW][C]101[/C][C]10.7[/C][C]10.674[/C][C]0.0259829[/C][/ROW]
[ROW][C]102[/C][C]10.3[/C][C]10.8349[/C][C]-0.534852[/C][/ROW]
[ROW][C]103[/C][C]8.3[/C][C]10.3982[/C][C]-2.09817[/C][/ROW]
[ROW][C]104[/C][C]9.6[/C][C]10.9526[/C][C]-1.35261[/C][/ROW]
[ROW][C]105[/C][C]14.2[/C][C]10.7353[/C][C]3.46468[/C][/ROW]
[ROW][C]106[/C][C]8.5[/C][C]10.6414[/C][C]-2.1414[/C][/ROW]
[ROW][C]107[/C][C]13.5[/C][C]10.7695[/C][C]2.73053[/C][/ROW]
[ROW][C]108[/C][C]4.9[/C][C]10.4975[/C][C]-5.59748[/C][/ROW]
[ROW][C]109[/C][C]6.4[/C][C]11.1022[/C][C]-4.70215[/C][/ROW]
[ROW][C]110[/C][C]9.6[/C][C]10.6273[/C][C]-1.02732[/C][/ROW]
[ROW][C]111[/C][C]11.6[/C][C]10.4569[/C][C]1.14305[/C][/ROW]
[ROW][C]112[/C][C]11.1[/C][C]10.792[/C][C]0.307996[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=265122&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=265122&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	10.8133	2.08668
2	12.2	10.5405	1.65946
3	12.8	10.2351	2.56487
4	7.4	10.3095	-2.9095
5	6.7	10.413	-3.71302
6	12.6	11.0052	1.59476
7	14.8	11.1377	3.66226
8	13.3	10.9568	2.34321
9	11.1	10.8712	0.228781
10	8.2	10.504	-2.30398
11	11.4	10.653	0.746964
12	6.4	10.8508	-4.45078
13	10.6	11.0102	-0.410186
14	12	10.3763	1.62373
15	6.3	10.6364	-4.33645
16	11.3	10.5983	0.701663
17	11.9	10.8658	1.03417
18	9.3	10.9064	-1.60637
19	9.6	10.4862	-0.886171
20	10	11.2396	-1.2396
21	6.4	10.0287	-3.62871
22	13.8	9.94775	3.85225
23	10.8	10.6714	0.128615
24	13.8	10.7653	3.03469
25	11.7	10.8536	0.846363
26	10.9	10.2068	0.693225
27	16.1	10.7871	5.31294
28	13.4	11.0102	2.38981
29	9.9	10.5091	-0.609121
30	11.5	10.7735	0.72645
31	8.3	10.7244	-2.42445
32	11.7	10.9779	0.722104
33	9	10.9054	-1.90539
34	9.7	10.8123	-1.11234
35	10.8	10.8421	-0.0421016
36	10.3	10.7877	-0.487733
37	10.4	10.3937	0.00633934
38	12.7	10.9617	1.73826
39	9.3	10.153	-0.852964
40	11.8	11.104	0.695996
41	5.9	11.1346	-5.23464
42	11.4	10.7511	0.648859
43	13	10.716	2.28401
44	10.8	10.4344	0.365582
45	12.3	10.9439	1.35606
46	11.3	10.6711	0.628946
47	11.8	10.5692	1.23078
48	7.9	10.5878	-2.68776
49	12.7	10.7941	1.90591
50	12.3	10.7783	1.52173
51	11.6	10.6164	0.983556
52	6.7	10.1164	-3.41637
53	10.9	10.6444	0.255641
54	12.1	10.4565	1.64349
55	13.3	10.5218	2.77825
56	10.1	11.3612	-1.26117
57	5.7	11.0011	-5.30106
58	14.3	10.8896	3.41043
59	8	10.8629	-2.86287
60	13.3	10.9272	2.37277
61	9.3	11.0685	-1.76853
62	12.5	10.5859	1.91409
63	7.6	10.5979	-2.9979
64	15.9	10.6384	5.26157
65	9.2	10.848	-1.64803
66	9.1	10.8087	-1.7087
67	11.1	10.5969	0.503107
68	13	10.6849	2.31511
69	14.5	10.9192	3.58077
70	12.2	10.9689	1.23113
71	12.3	10.8169	1.48306
72	11.4	10.4367	0.963281
73	8.8	10.6496	-1.84964
74	14.6	10.8503	3.74966
75	12.6	10.409	2.19095
76	13	10.7541	2.2459
77	12.6	10.9944	1.60564
78	13.2	10.8144	2.38557
79	9.9	10.2647	-0.364695
80	7.7	10.095	-2.39496
81	10.5	10.5396	-0.0395618
82	13.4	10.7197	2.68028
83	10.9	10.7328	0.167208
84	4.3	10.699	-6.39895
85	10.3	10.1539	0.146058
86	11.8	10.3393	1.46071
87	11.2	10.7143	0.485662
88	11.4	10.8136	0.586354
89	8.6	10.9706	-2.37063
90	13.2	10.3522	2.84785
91	12.6	10.0818	2.5182
92	5.6	10.5801	-4.98009
93	9.9	10.849	-0.949035
94	8.8	10.3995	-1.59948
95	7.7	11.023	-3.32305
96	9	10.6473	-1.64732
97	7.3	10.5782	-3.27823
98	11.4	10.94	0.460004
99	13.6	10.5663	3.03374
100	7.9	10.7122	-2.81225
101	10.7	10.674	0.0259829
102	10.3	10.8349	-0.534852
103	8.3	10.3982	-2.09817
104	9.6	10.9526	-1.35261
105	14.2	10.7353	3.46468
106	8.5	10.6414	-2.1414
107	13.5	10.7695	2.73053
108	4.9	10.4975	-5.59748
109	6.4	11.1022	-4.70215
110	9.6	10.6273	-1.02732
111	11.6	10.4569	1.14305
112	11.1	10.792	0.307996

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
7	0.549346	0.901308	0.450654
8	0.384937	0.769873	0.615063
9	0.311586	0.623172	0.688414
10	0.202835	0.40567	0.797165
11	0.143424	0.286847	0.856576
12	0.712554	0.574891	0.287446
13	0.660076	0.679848	0.339924
14	0.572043	0.855914	0.427957
15	0.700702	0.598597	0.299298
16	0.658921	0.682159	0.341079
17	0.585573	0.828853	0.414427
18	0.559014	0.881972	0.440986
19	0.485766	0.971532	0.514234
20	0.425573	0.851145	0.574427
21	0.40378	0.807561	0.59622
22	0.686676	0.626649	0.313324
23	0.620469	0.759062	0.379531
24	0.631241	0.737518	0.368759
25	0.566038	0.867923	0.433962
26	0.50064	0.99872	0.49936
27	0.661306	0.677389	0.338694
28	0.629038	0.741924	0.370962
29	0.56957	0.860861	0.43043
30	0.507625	0.98475	0.492375
31	0.515417	0.969166	0.484583
32	0.454257	0.908513	0.545743
33	0.432398	0.864797	0.567602
34	0.384167	0.768334	0.615833
35	0.328404	0.656808	0.671596
36	0.276636	0.553273	0.723364
37	0.229108	0.458217	0.770892
38	0.200971	0.401942	0.799029
39	0.163954	0.327908	0.836046
40	0.131852	0.263704	0.868148
41	0.375691	0.751382	0.624309
42	0.32601	0.652019	0.67399
43	0.320979	0.641957	0.679021
44	0.272107	0.544215	0.727893
45	0.237004	0.474008	0.762996
46	0.197909	0.395818	0.802091
47	0.17141	0.34282	0.82859
48	0.184641	0.369283	0.815359
49	0.168378	0.336756	0.831622
50	0.146205	0.292411	0.853795
51	0.120221	0.240443	0.879779
52	0.138662	0.277324	0.861338
53	0.110438	0.220875	0.889562
54	0.0994001	0.1988	0.9006
55	0.107209	0.214418	0.892791
56	0.0920865	0.184173	0.907913
57	0.20392	0.407839	0.79608
58	0.238487	0.476975	0.761513
59	0.248909	0.497817	0.751091
60	0.243197	0.486394	0.756803
61	0.221037	0.442075	0.778963
62	0.203784	0.407569	0.796216
63	0.221344	0.442687	0.778656
64	0.377555	0.755109	0.622445
65	0.345219	0.690438	0.654781
66	0.316056	0.632112	0.683944
67	0.269339	0.538677	0.730661
68	0.262104	0.524208	0.737896
69	0.314599	0.629198	0.685401
70	0.282841	0.565683	0.717159
71	0.261382	0.522764	0.738618
72	0.224382	0.448765	0.775618
73	0.20204	0.404079	0.79796
74	0.271874	0.543748	0.728126
75	0.260567	0.521133	0.739433
76	0.26645	0.5329	0.73355
77	0.263478	0.526957	0.736522
78	0.278639	0.557278	0.721361
79	0.230557	0.461114	0.769443
80	0.238788	0.477576	0.761212
81	0.193787	0.387574	0.806213
82	0.219553	0.439105	0.780447
83	0.183575	0.367151	0.816425
84	0.431607	0.863213	0.568393
85	0.368484	0.736968	0.631516
86	0.322942	0.645884	0.677058
87	0.274521	0.549043	0.725479
88	0.237461	0.474923	0.762539
89	0.199875	0.399749	0.800125
90	0.220348	0.440695	0.779652
91	0.2938	0.587601	0.7062
92	0.422695	0.84539	0.577305
93	0.359745	0.71949	0.640255
94	0.30628	0.61256	0.69372
95	0.297133	0.594266	0.702867
96	0.264804	0.529608	0.735196
97	0.220581	0.441162	0.779419
98	0.187235	0.374469	0.812765
99	0.341096	0.682193	0.658904
100	0.312348	0.624695	0.687652
101	0.226511	0.453023	0.773489
102	0.151832	0.303665	0.848168
103	0.0955163	0.191033	0.904484
104	0.0624622	0.124924	0.937538
105	0.246709	0.493418	0.753291

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
7 & 0.549346 & 0.901308 & 0.450654 \tabularnewline
8 & 0.384937 & 0.769873 & 0.615063 \tabularnewline
9 & 0.311586 & 0.623172 & 0.688414 \tabularnewline
10 & 0.202835 & 0.40567 & 0.797165 \tabularnewline
11 & 0.143424 & 0.286847 & 0.856576 \tabularnewline
12 & 0.712554 & 0.574891 & 0.287446 \tabularnewline
13 & 0.660076 & 0.679848 & 0.339924 \tabularnewline
14 & 0.572043 & 0.855914 & 0.427957 \tabularnewline
15 & 0.700702 & 0.598597 & 0.299298 \tabularnewline
16 & 0.658921 & 0.682159 & 0.341079 \tabularnewline
17 & 0.585573 & 0.828853 & 0.414427 \tabularnewline
18 & 0.559014 & 0.881972 & 0.440986 \tabularnewline
19 & 0.485766 & 0.971532 & 0.514234 \tabularnewline
20 & 0.425573 & 0.851145 & 0.574427 \tabularnewline
21 & 0.40378 & 0.807561 & 0.59622 \tabularnewline
22 & 0.686676 & 0.626649 & 0.313324 \tabularnewline
23 & 0.620469 & 0.759062 & 0.379531 \tabularnewline
24 & 0.631241 & 0.737518 & 0.368759 \tabularnewline
25 & 0.566038 & 0.867923 & 0.433962 \tabularnewline
26 & 0.50064 & 0.99872 & 0.49936 \tabularnewline
27 & 0.661306 & 0.677389 & 0.338694 \tabularnewline
28 & 0.629038 & 0.741924 & 0.370962 \tabularnewline
29 & 0.56957 & 0.860861 & 0.43043 \tabularnewline
30 & 0.507625 & 0.98475 & 0.492375 \tabularnewline
31 & 0.515417 & 0.969166 & 0.484583 \tabularnewline
32 & 0.454257 & 0.908513 & 0.545743 \tabularnewline
33 & 0.432398 & 0.864797 & 0.567602 \tabularnewline
34 & 0.384167 & 0.768334 & 0.615833 \tabularnewline
35 & 0.328404 & 0.656808 & 0.671596 \tabularnewline
36 & 0.276636 & 0.553273 & 0.723364 \tabularnewline
37 & 0.229108 & 0.458217 & 0.770892 \tabularnewline
38 & 0.200971 & 0.401942 & 0.799029 \tabularnewline
39 & 0.163954 & 0.327908 & 0.836046 \tabularnewline
40 & 0.131852 & 0.263704 & 0.868148 \tabularnewline
41 & 0.375691 & 0.751382 & 0.624309 \tabularnewline
42 & 0.32601 & 0.652019 & 0.67399 \tabularnewline
43 & 0.320979 & 0.641957 & 0.679021 \tabularnewline
44 & 0.272107 & 0.544215 & 0.727893 \tabularnewline
45 & 0.237004 & 0.474008 & 0.762996 \tabularnewline
46 & 0.197909 & 0.395818 & 0.802091 \tabularnewline
47 & 0.17141 & 0.34282 & 0.82859 \tabularnewline
48 & 0.184641 & 0.369283 & 0.815359 \tabularnewline
49 & 0.168378 & 0.336756 & 0.831622 \tabularnewline
50 & 0.146205 & 0.292411 & 0.853795 \tabularnewline
51 & 0.120221 & 0.240443 & 0.879779 \tabularnewline
52 & 0.138662 & 0.277324 & 0.861338 \tabularnewline
53 & 0.110438 & 0.220875 & 0.889562 \tabularnewline
54 & 0.0994001 & 0.1988 & 0.9006 \tabularnewline
55 & 0.107209 & 0.214418 & 0.892791 \tabularnewline
56 & 0.0920865 & 0.184173 & 0.907913 \tabularnewline
57 & 0.20392 & 0.407839 & 0.79608 \tabularnewline
58 & 0.238487 & 0.476975 & 0.761513 \tabularnewline
59 & 0.248909 & 0.497817 & 0.751091 \tabularnewline
60 & 0.243197 & 0.486394 & 0.756803 \tabularnewline
61 & 0.221037 & 0.442075 & 0.778963 \tabularnewline
62 & 0.203784 & 0.407569 & 0.796216 \tabularnewline
63 & 0.221344 & 0.442687 & 0.778656 \tabularnewline
64 & 0.377555 & 0.755109 & 0.622445 \tabularnewline
65 & 0.345219 & 0.690438 & 0.654781 \tabularnewline
66 & 0.316056 & 0.632112 & 0.683944 \tabularnewline
67 & 0.269339 & 0.538677 & 0.730661 \tabularnewline
68 & 0.262104 & 0.524208 & 0.737896 \tabularnewline
69 & 0.314599 & 0.629198 & 0.685401 \tabularnewline
70 & 0.282841 & 0.565683 & 0.717159 \tabularnewline
71 & 0.261382 & 0.522764 & 0.738618 \tabularnewline
72 & 0.224382 & 0.448765 & 0.775618 \tabularnewline
73 & 0.20204 & 0.404079 & 0.79796 \tabularnewline
74 & 0.271874 & 0.543748 & 0.728126 \tabularnewline
75 & 0.260567 & 0.521133 & 0.739433 \tabularnewline
76 & 0.26645 & 0.5329 & 0.73355 \tabularnewline
77 & 0.263478 & 0.526957 & 0.736522 \tabularnewline
78 & 0.278639 & 0.557278 & 0.721361 \tabularnewline
79 & 0.230557 & 0.461114 & 0.769443 \tabularnewline
80 & 0.238788 & 0.477576 & 0.761212 \tabularnewline
81 & 0.193787 & 0.387574 & 0.806213 \tabularnewline
82 & 0.219553 & 0.439105 & 0.780447 \tabularnewline
83 & 0.183575 & 0.367151 & 0.816425 \tabularnewline
84 & 0.431607 & 0.863213 & 0.568393 \tabularnewline
85 & 0.368484 & 0.736968 & 0.631516 \tabularnewline
86 & 0.322942 & 0.645884 & 0.677058 \tabularnewline
87 & 0.274521 & 0.549043 & 0.725479 \tabularnewline
88 & 0.237461 & 0.474923 & 0.762539 \tabularnewline
89 & 0.199875 & 0.399749 & 0.800125 \tabularnewline
90 & 0.220348 & 0.440695 & 0.779652 \tabularnewline
91 & 0.2938 & 0.587601 & 0.7062 \tabularnewline
92 & 0.422695 & 0.84539 & 0.577305 \tabularnewline
93 & 0.359745 & 0.71949 & 0.640255 \tabularnewline
94 & 0.30628 & 0.61256 & 0.69372 \tabularnewline
95 & 0.297133 & 0.594266 & 0.702867 \tabularnewline
96 & 0.264804 & 0.529608 & 0.735196 \tabularnewline
97 & 0.220581 & 0.441162 & 0.779419 \tabularnewline
98 & 0.187235 & 0.374469 & 0.812765 \tabularnewline
99 & 0.341096 & 0.682193 & 0.658904 \tabularnewline
100 & 0.312348 & 0.624695 & 0.687652 \tabularnewline
101 & 0.226511 & 0.453023 & 0.773489 \tabularnewline
102 & 0.151832 & 0.303665 & 0.848168 \tabularnewline
103 & 0.0955163 & 0.191033 & 0.904484 \tabularnewline
104 & 0.0624622 & 0.124924 & 0.937538 \tabularnewline
105 & 0.246709 & 0.493418 & 0.753291 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=265122&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]0.549346[/C][C]0.901308[/C][C]0.450654[/C][/ROW]
[ROW][C]8[/C][C]0.384937[/C][C]0.769873[/C][C]0.615063[/C][/ROW]
[ROW][C]9[/C][C]0.311586[/C][C]0.623172[/C][C]0.688414[/C][/ROW]
[ROW][C]10[/C][C]0.202835[/C][C]0.40567[/C][C]0.797165[/C][/ROW]
[ROW][C]11[/C][C]0.143424[/C][C]0.286847[/C][C]0.856576[/C][/ROW]
[ROW][C]12[/C][C]0.712554[/C][C]0.574891[/C][C]0.287446[/C][/ROW]
[ROW][C]13[/C][C]0.660076[/C][C]0.679848[/C][C]0.339924[/C][/ROW]
[ROW][C]14[/C][C]0.572043[/C][C]0.855914[/C][C]0.427957[/C][/ROW]
[ROW][C]15[/C][C]0.700702[/C][C]0.598597[/C][C]0.299298[/C][/ROW]
[ROW][C]16[/C][C]0.658921[/C][C]0.682159[/C][C]0.341079[/C][/ROW]
[ROW][C]17[/C][C]0.585573[/C][C]0.828853[/C][C]0.414427[/C][/ROW]
[ROW][C]18[/C][C]0.559014[/C][C]0.881972[/C][C]0.440986[/C][/ROW]
[ROW][C]19[/C][C]0.485766[/C][C]0.971532[/C][C]0.514234[/C][/ROW]
[ROW][C]20[/C][C]0.425573[/C][C]0.851145[/C][C]0.574427[/C][/ROW]
[ROW][C]21[/C][C]0.40378[/C][C]0.807561[/C][C]0.59622[/C][/ROW]
[ROW][C]22[/C][C]0.686676[/C][C]0.626649[/C][C]0.313324[/C][/ROW]
[ROW][C]23[/C][C]0.620469[/C][C]0.759062[/C][C]0.379531[/C][/ROW]
[ROW][C]24[/C][C]0.631241[/C][C]0.737518[/C][C]0.368759[/C][/ROW]
[ROW][C]25[/C][C]0.566038[/C][C]0.867923[/C][C]0.433962[/C][/ROW]
[ROW][C]26[/C][C]0.50064[/C][C]0.99872[/C][C]0.49936[/C][/ROW]
[ROW][C]27[/C][C]0.661306[/C][C]0.677389[/C][C]0.338694[/C][/ROW]
[ROW][C]28[/C][C]0.629038[/C][C]0.741924[/C][C]0.370962[/C][/ROW]
[ROW][C]29[/C][C]0.56957[/C][C]0.860861[/C][C]0.43043[/C][/ROW]
[ROW][C]30[/C][C]0.507625[/C][C]0.98475[/C][C]0.492375[/C][/ROW]
[ROW][C]31[/C][C]0.515417[/C][C]0.969166[/C][C]0.484583[/C][/ROW]
[ROW][C]32[/C][C]0.454257[/C][C]0.908513[/C][C]0.545743[/C][/ROW]
[ROW][C]33[/C][C]0.432398[/C][C]0.864797[/C][C]0.567602[/C][/ROW]
[ROW][C]34[/C][C]0.384167[/C][C]0.768334[/C][C]0.615833[/C][/ROW]
[ROW][C]35[/C][C]0.328404[/C][C]0.656808[/C][C]0.671596[/C][/ROW]
[ROW][C]36[/C][C]0.276636[/C][C]0.553273[/C][C]0.723364[/C][/ROW]
[ROW][C]37[/C][C]0.229108[/C][C]0.458217[/C][C]0.770892[/C][/ROW]
[ROW][C]38[/C][C]0.200971[/C][C]0.401942[/C][C]0.799029[/C][/ROW]
[ROW][C]39[/C][C]0.163954[/C][C]0.327908[/C][C]0.836046[/C][/ROW]
[ROW][C]40[/C][C]0.131852[/C][C]0.263704[/C][C]0.868148[/C][/ROW]
[ROW][C]41[/C][C]0.375691[/C][C]0.751382[/C][C]0.624309[/C][/ROW]
[ROW][C]42[/C][C]0.32601[/C][C]0.652019[/C][C]0.67399[/C][/ROW]
[ROW][C]43[/C][C]0.320979[/C][C]0.641957[/C][C]0.679021[/C][/ROW]
[ROW][C]44[/C][C]0.272107[/C][C]0.544215[/C][C]0.727893[/C][/ROW]
[ROW][C]45[/C][C]0.237004[/C][C]0.474008[/C][C]0.762996[/C][/ROW]
[ROW][C]46[/C][C]0.197909[/C][C]0.395818[/C][C]0.802091[/C][/ROW]
[ROW][C]47[/C][C]0.17141[/C][C]0.34282[/C][C]0.82859[/C][/ROW]
[ROW][C]48[/C][C]0.184641[/C][C]0.369283[/C][C]0.815359[/C][/ROW]
[ROW][C]49[/C][C]0.168378[/C][C]0.336756[/C][C]0.831622[/C][/ROW]
[ROW][C]50[/C][C]0.146205[/C][C]0.292411[/C][C]0.853795[/C][/ROW]
[ROW][C]51[/C][C]0.120221[/C][C]0.240443[/C][C]0.879779[/C][/ROW]
[ROW][C]52[/C][C]0.138662[/C][C]0.277324[/C][C]0.861338[/C][/ROW]
[ROW][C]53[/C][C]0.110438[/C][C]0.220875[/C][C]0.889562[/C][/ROW]
[ROW][C]54[/C][C]0.0994001[/C][C]0.1988[/C][C]0.9006[/C][/ROW]
[ROW][C]55[/C][C]0.107209[/C][C]0.214418[/C][C]0.892791[/C][/ROW]
[ROW][C]56[/C][C]0.0920865[/C][C]0.184173[/C][C]0.907913[/C][/ROW]
[ROW][C]57[/C][C]0.20392[/C][C]0.407839[/C][C]0.79608[/C][/ROW]
[ROW][C]58[/C][C]0.238487[/C][C]0.476975[/C][C]0.761513[/C][/ROW]
[ROW][C]59[/C][C]0.248909[/C][C]0.497817[/C][C]0.751091[/C][/ROW]
[ROW][C]60[/C][C]0.243197[/C][C]0.486394[/C][C]0.756803[/C][/ROW]
[ROW][C]61[/C][C]0.221037[/C][C]0.442075[/C][C]0.778963[/C][/ROW]
[ROW][C]62[/C][C]0.203784[/C][C]0.407569[/C][C]0.796216[/C][/ROW]
[ROW][C]63[/C][C]0.221344[/C][C]0.442687[/C][C]0.778656[/C][/ROW]
[ROW][C]64[/C][C]0.377555[/C][C]0.755109[/C][C]0.622445[/C][/ROW]
[ROW][C]65[/C][C]0.345219[/C][C]0.690438[/C][C]0.654781[/C][/ROW]
[ROW][C]66[/C][C]0.316056[/C][C]0.632112[/C][C]0.683944[/C][/ROW]
[ROW][C]67[/C][C]0.269339[/C][C]0.538677[/C][C]0.730661[/C][/ROW]
[ROW][C]68[/C][C]0.262104[/C][C]0.524208[/C][C]0.737896[/C][/ROW]
[ROW][C]69[/C][C]0.314599[/C][C]0.629198[/C][C]0.685401[/C][/ROW]
[ROW][C]70[/C][C]0.282841[/C][C]0.565683[/C][C]0.717159[/C][/ROW]
[ROW][C]71[/C][C]0.261382[/C][C]0.522764[/C][C]0.738618[/C][/ROW]
[ROW][C]72[/C][C]0.224382[/C][C]0.448765[/C][C]0.775618[/C][/ROW]
[ROW][C]73[/C][C]0.20204[/C][C]0.404079[/C][C]0.79796[/C][/ROW]
[ROW][C]74[/C][C]0.271874[/C][C]0.543748[/C][C]0.728126[/C][/ROW]
[ROW][C]75[/C][C]0.260567[/C][C]0.521133[/C][C]0.739433[/C][/ROW]
[ROW][C]76[/C][C]0.26645[/C][C]0.5329[/C][C]0.73355[/C][/ROW]
[ROW][C]77[/C][C]0.263478[/C][C]0.526957[/C][C]0.736522[/C][/ROW]
[ROW][C]78[/C][C]0.278639[/C][C]0.557278[/C][C]0.721361[/C][/ROW]
[ROW][C]79[/C][C]0.230557[/C][C]0.461114[/C][C]0.769443[/C][/ROW]
[ROW][C]80[/C][C]0.238788[/C][C]0.477576[/C][C]0.761212[/C][/ROW]
[ROW][C]81[/C][C]0.193787[/C][C]0.387574[/C][C]0.806213[/C][/ROW]
[ROW][C]82[/C][C]0.219553[/C][C]0.439105[/C][C]0.780447[/C][/ROW]
[ROW][C]83[/C][C]0.183575[/C][C]0.367151[/C][C]0.816425[/C][/ROW]
[ROW][C]84[/C][C]0.431607[/C][C]0.863213[/C][C]0.568393[/C][/ROW]
[ROW][C]85[/C][C]0.368484[/C][C]0.736968[/C][C]0.631516[/C][/ROW]
[ROW][C]86[/C][C]0.322942[/C][C]0.645884[/C][C]0.677058[/C][/ROW]
[ROW][C]87[/C][C]0.274521[/C][C]0.549043[/C][C]0.725479[/C][/ROW]
[ROW][C]88[/C][C]0.237461[/C][C]0.474923[/C][C]0.762539[/C][/ROW]
[ROW][C]89[/C][C]0.199875[/C][C]0.399749[/C][C]0.800125[/C][/ROW]
[ROW][C]90[/C][C]0.220348[/C][C]0.440695[/C][C]0.779652[/C][/ROW]
[ROW][C]91[/C][C]0.2938[/C][C]0.587601[/C][C]0.7062[/C][/ROW]
[ROW][C]92[/C][C]0.422695[/C][C]0.84539[/C][C]0.577305[/C][/ROW]
[ROW][C]93[/C][C]0.359745[/C][C]0.71949[/C][C]0.640255[/C][/ROW]
[ROW][C]94[/C][C]0.30628[/C][C]0.61256[/C][C]0.69372[/C][/ROW]
[ROW][C]95[/C][C]0.297133[/C][C]0.594266[/C][C]0.702867[/C][/ROW]
[ROW][C]96[/C][C]0.264804[/C][C]0.529608[/C][C]0.735196[/C][/ROW]
[ROW][C]97[/C][C]0.220581[/C][C]0.441162[/C][C]0.779419[/C][/ROW]
[ROW][C]98[/C][C]0.187235[/C][C]0.374469[/C][C]0.812765[/C][/ROW]
[ROW][C]99[/C][C]0.341096[/C][C]0.682193[/C][C]0.658904[/C][/ROW]
[ROW][C]100[/C][C]0.312348[/C][C]0.624695[/C][C]0.687652[/C][/ROW]
[ROW][C]101[/C][C]0.226511[/C][C]0.453023[/C][C]0.773489[/C][/ROW]
[ROW][C]102[/C][C]0.151832[/C][C]0.303665[/C][C]0.848168[/C][/ROW]
[ROW][C]103[/C][C]0.0955163[/C][C]0.191033[/C][C]0.904484[/C][/ROW]
[ROW][C]104[/C][C]0.0624622[/C][C]0.124924[/C][C]0.937538[/C][/ROW]
[ROW][C]105[/C][C]0.246709[/C][C]0.493418[/C][C]0.753291[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=265122&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=265122&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	0.549346	0.901308	0.450654
8	0.384937	0.769873	0.615063
9	0.311586	0.623172	0.688414
10	0.202835	0.40567	0.797165
11	0.143424	0.286847	0.856576
12	0.712554	0.574891	0.287446
13	0.660076	0.679848	0.339924
14	0.572043	0.855914	0.427957
15	0.700702	0.598597	0.299298
16	0.658921	0.682159	0.341079
17	0.585573	0.828853	0.414427
18	0.559014	0.881972	0.440986
19	0.485766	0.971532	0.514234
20	0.425573	0.851145	0.574427
21	0.40378	0.807561	0.59622
22	0.686676	0.626649	0.313324
23	0.620469	0.759062	0.379531
24	0.631241	0.737518	0.368759
25	0.566038	0.867923	0.433962
26	0.50064	0.99872	0.49936
27	0.661306	0.677389	0.338694
28	0.629038	0.741924	0.370962
29	0.56957	0.860861	0.43043
30	0.507625	0.98475	0.492375
31	0.515417	0.969166	0.484583
32	0.454257	0.908513	0.545743
33	0.432398	0.864797	0.567602
34	0.384167	0.768334	0.615833
35	0.328404	0.656808	0.671596
36	0.276636	0.553273	0.723364
37	0.229108	0.458217	0.770892
38	0.200971	0.401942	0.799029
39	0.163954	0.327908	0.836046
40	0.131852	0.263704	0.868148
41	0.375691	0.751382	0.624309
42	0.32601	0.652019	0.67399
43	0.320979	0.641957	0.679021
44	0.272107	0.544215	0.727893
45	0.237004	0.474008	0.762996
46	0.197909	0.395818	0.802091
47	0.17141	0.34282	0.82859
48	0.184641	0.369283	0.815359
49	0.168378	0.336756	0.831622
50	0.146205	0.292411	0.853795
51	0.120221	0.240443	0.879779
52	0.138662	0.277324	0.861338
53	0.110438	0.220875	0.889562
54	0.0994001	0.1988	0.9006
55	0.107209	0.214418	0.892791
56	0.0920865	0.184173	0.907913
57	0.20392	0.407839	0.79608
58	0.238487	0.476975	0.761513
59	0.248909	0.497817	0.751091
60	0.243197	0.486394	0.756803
61	0.221037	0.442075	0.778963
62	0.203784	0.407569	0.796216
63	0.221344	0.442687	0.778656
64	0.377555	0.755109	0.622445
65	0.345219	0.690438	0.654781
66	0.316056	0.632112	0.683944
67	0.269339	0.538677	0.730661
68	0.262104	0.524208	0.737896
69	0.314599	0.629198	0.685401
70	0.282841	0.565683	0.717159
71	0.261382	0.522764	0.738618
72	0.224382	0.448765	0.775618
73	0.20204	0.404079	0.79796
74	0.271874	0.543748	0.728126
75	0.260567	0.521133	0.739433
76	0.26645	0.5329	0.73355
77	0.263478	0.526957	0.736522
78	0.278639	0.557278	0.721361
79	0.230557	0.461114	0.769443
80	0.238788	0.477576	0.761212
81	0.193787	0.387574	0.806213
82	0.219553	0.439105	0.780447
83	0.183575	0.367151	0.816425
84	0.431607	0.863213	0.568393
85	0.368484	0.736968	0.631516
86	0.322942	0.645884	0.677058
87	0.274521	0.549043	0.725479
88	0.237461	0.474923	0.762539
89	0.199875	0.399749	0.800125
90	0.220348	0.440695	0.779652
91	0.2938	0.587601	0.7062
92	0.422695	0.84539	0.577305
93	0.359745	0.71949	0.640255
94	0.30628	0.61256	0.69372
95	0.297133	0.594266	0.702867
96	0.264804	0.529608	0.735196
97	0.220581	0.441162	0.779419
98	0.187235	0.374469	0.812765
99	0.341096	0.682193	0.658904
100	0.312348	0.624695	0.687652
101	0.226511	0.453023	0.773489
102	0.151832	0.303665	0.848168
103	0.0955163	0.191033	0.904484
104	0.0624622	0.124924	0.937538
105	0.246709	0.493418	0.753291

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=265122&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=265122&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=265122&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 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