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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Thu, 03 Sep 2015 01:15:51 +0100

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2015/Sep/03/t1441239420y2kwj0g9iymxwvg.htm/, Retrieved Thu, 16 May 2024 07:36:02 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=280425, Retrieved Thu, 16 May 2024 07:36:02 +0000

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onderzoeksvraag2

Estimated Impact

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

-       [Multiple Regression] [Multiple Lin Regr...] [2015-09-03 00:15:51] [71b0783e9a96780cfc39e1fa76ab729e] [Current]

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Dataseries X:

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0 26 13 13 13 96 13.125 12.9
1 57 8 13 16 70 13.75 12.2
0 37 14 11 11 88 13.75 12.8
1 67 16 14 10 114 11.25 7.4
1 43 14 15 9 69 14.375 6.7
1 52 13 14 8 176 7.5 12.6
0 52 15 11 26 114 12.5 14.8
1 43 13 13 10 121 13.75 13.3
1 84 20 16 10 110 13.125 11.1
1 67 17 14 8 158 11.875 8.2
1 49 15 14 13 116 13.75 11.4
1 70 16 15 11 181 9.375 6.4
1 52 12 15 8 77 12.5 10.6
0 58 17 13 12 141 11.875 12.0
0 68 11 14 24 35 11.25 6.3
0 62 16 11 21 80 9.375 11.3
1 43 16 12 5 152 12.5 11.9
0 56 15 14 14 97 13.125 9.3
1 56 13 13 11 99 13.125 9.6
0 74 14 12 9 84 9.375 10.0
1 63 16 15 17 101 14.375 13.8
0 58 17 14 18 107 13.125 10.8
1 63 15 12 23 112 15.625 11.7
1 53 14 12 9 171 5.625 10.9
1 57 14 12 14 137 18.75 16.1
0 51 16 15 13 77 12.5 13.4
1 64 15 14 10 66 14.375 9.9
0 53 17 16 8 93 10 11.5
0 29 14 12 10 105 10 8.3
0 54 16 12 19 131 11.875 11.7
1 58 15 14 11 102 15.625 9.0
1 43 16 16 16 161 11.25 9.7
1 51 16 15 12 120 14.375 10.8
1 53 10 12 11 127 13.125 10.3
0 54 8 14 11 77 6.25 10.4
1 56 17 13 10 108 8.75 12.7
1 61 14 14 13 85 13.75 9.3
0 47 10 16 14 168 16.25 11.8
1 39 14 12 8 48 14.375 5.9
1 48 12 14 11 152 14.375 11.4
1 50 16 15 11 75 15 13.0
1 35 16 13 13 107 15 10.8
1 30 16 16 15 62 11.25 12.3
0 68 8 16 15 121 14.375 11.3
1 49 16 12 16 124 9.375 11.8
1 61 15 12 12 72 11.875 7.9
0 67 8 16 12 40 10 12.7
1 47 13 12 17 58 15.625 12.3
1 56 14 15 14 97 14.375 11.6
1 50 13 12 15 88 10.625 6.7
1 43 16 13 12 126 11.875 10.9
1 67 19 12 13 104 13.125 12.1
1 62 19 14 7 148 11.25 13.3
1 57 14 14 8 146 16.875 10.1
0 41 15 11 16 80 13.125 5.7
1 54 13 10 20 97 8.125 14.3
0 45 10 12 14 25 5 8.0
1 48 16 11 10 99 18.125 13.3
1 61 15 16 16 118 17.5 9.3
0 56 11 14 11 58 14.375 12.5
0 41 9 14 26 63 13.125 7.6
1 43 16 15 9 139 11.875 15.9
0 53 12 15 15 50 11.875 9.2
1 44 12 14 12 60 12.5 9.1
0 66 14 13 21 152 11.25 11.1
1 58 14 11 20 142 11.875 13.0
1 46 13 16 20 94 10.625 14.5
0 37 15 12 10 66 11.875 12.2
0 51 17 15 15 127 15.625 12.3
0 51 14 14 10 67 11.875 11.4
1 66 9 14 9 75 14.375 14.6
0 37 7 13 17 128 8.75 12.6
1 59 13 6 10 41 17.5 NA
0 42 15 12 19 146 10 13.0
1 38 12 12 13 69 15 12.6
0 66 15 14 8 186 12.5 13.2
0 34 14 14 11 81 7.5 9.9
1 53 16 15 9 85 15 7.7
0 49 14 11 12 54 13.75 10.5
0 55 13 13 10 46 7.5 13.4
0 49 16 14 9 106 13.75 10.9
1 59 13 16 14 34 12.5 4.3
0 40 16 13 14 60 6.25 10.3
1 58 16 14 10 95 14.375 11.8
1 60 16 16 8 57 10.625 11.2
0 63 10 11 13 62 13.75 11.4
0 56 12 13 9 36 15 8.6
0 54 12 13 14 56 11.25 13.2
1 52 12 15 8 54 13.125 12.6
1 34 12 12 16 64 12.5 5.6
1 69 19 13 14 76 12.5 9.9
0 32 14 12 14 98 13.75 8.8
1 48 13 14 8 88 11.875 7.7
0 67 16 14 11 35 12.5 9.0
1 58 15 16 11 102 16.25 7.3
1 57 12 15 13 61 14.375 11.4
1 42 8 14 12 80 15 13.6
1 64 10 13 13 49 13.125 7.9
1 58 16 14 9 78 13.125 10.7
0 66 16 15 10 90 11.875 10.3
1 61 18 12 11 55 10.625 9.6
1 52 12 7 13 96 12.5 14.2
0 51 16 12 17 43 6.875 8.5
0 55 10 15 15 52 5 13.5
0 60 12 13 14 54 11.25 6.4
0 56 11 11 10 51 11.25 9.6
0 63 15 14 15 51 11.875 11.6
1 61 7 13 14 38 11.875 11.1

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	5 seconds
R Server	'Herman Ole Andreas Wold' @ wold.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 5 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=280425&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=280425&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=280425&T=0

As an alternative you can also use a QR Code:

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

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	5 seconds
R Server	'Herman Ole Andreas Wold' @ wold.wessa.net

Multiple Linear Regression - Estimated Regression Equation
TOT[t] = + 10.8308 -0.225854Gender[t] -0.00421814AMS.I[t] -0.0706575CONFSTATTOT[t] -0.116856STRESSTOT[t] + 0.0170441CESDTOT[t] + 0.0206485BEREK[t] + 0.042758NUMTOT[t] + 0.0038034t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TOT[t] =  +  10.8308 -0.225854Gender[t] -0.00421814AMS.I[t] -0.0706575CONFSTATTOT[t] -0.116856STRESSTOT[t] +  0.0170441CESDTOT[t] +  0.0206485BEREK[t] +  0.042758NUMTOT[t] +  0.0038034t  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=280425&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TOT[t] =  +  10.8308 -0.225854Gender[t] -0.00421814AMS.I[t] -0.0706575CONFSTATTOT[t] -0.116856STRESSTOT[t] +  0.0170441CESDTOT[t] +  0.0206485BEREK[t] +  0.042758NUMTOT[t] +  0.0038034t  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=280425&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=280425&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] = + 10.8308 -0.225854Gender[t] -0.00421814AMS.I[t] -0.0706575CONFSTATTOT[t] -0.116856STRESSTOT[t] + 0.0170441CESDTOT[t] + 0.0206485BEREK[t] + 0.042758NUMTOT[t] + 0.0038034t + 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)	+10.83	2.915	+3.7150e+00	0.0003377	0.0001688
Gender	-0.2258	0.5104	-4.4250e-01	0.6591	0.3296
AMS.I	-0.004218	0.02233	-1.8890e-01	0.8506	0.4253
CONFSTATTOT	-0.07066	0.08977	-7.8710e-01	0.4331	0.2166
STRESSTOT	-0.1169	0.147	-7.9480e-01	0.4286	0.2143
CESDTOT	+0.01704	0.05974	+2.8530e-01	0.776	0.388
BEREK	+0.02065	0.007092	+2.9120e+00	0.004453	0.002226
NUMTOT	+0.04276	0.09024	+4.7380e-01	0.6367	0.3183
t	+0.003803	0.008203	+4.6370e-01	0.6439	0.322

\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) & +10.83 &  2.915 & +3.7150e+00 &  0.0003377 &  0.0001688 \tabularnewline
Gender & -0.2258 &  0.5104 & -4.4250e-01 &  0.6591 &  0.3296 \tabularnewline
AMS.I & -0.004218 &  0.02233 & -1.8890e-01 &  0.8506 &  0.4253 \tabularnewline
CONFSTATTOT & -0.07066 &  0.08977 & -7.8710e-01 &  0.4331 &  0.2166 \tabularnewline
STRESSTOT & -0.1169 &  0.147 & -7.9480e-01 &  0.4286 &  0.2143 \tabularnewline
CESDTOT & +0.01704 &  0.05974 & +2.8530e-01 &  0.776 &  0.388 \tabularnewline
BEREK & +0.02065 &  0.007092 & +2.9120e+00 &  0.004453 &  0.002226 \tabularnewline
NUMTOT & +0.04276 &  0.09024 & +4.7380e-01 &  0.6367 &  0.3183 \tabularnewline
t & +0.003803 &  0.008203 & +4.6370e-01 &  0.6439 &  0.322 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=280425&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]+10.83[/C][C] 2.915[/C][C]+3.7150e+00[/C][C] 0.0003377[/C][C] 0.0001688[/C][/ROW]
[ROW][C]Gender[/C][C]-0.2258[/C][C] 0.5104[/C][C]-4.4250e-01[/C][C] 0.6591[/C][C] 0.3296[/C][/ROW]
[ROW][C]AMS.I[/C][C]-0.004218[/C][C] 0.02233[/C][C]-1.8890e-01[/C][C] 0.8506[/C][C] 0.4253[/C][/ROW]
[ROW][C]CONFSTATTOT[/C][C]-0.07066[/C][C] 0.08977[/C][C]-7.8710e-01[/C][C] 0.4331[/C][C] 0.2166[/C][/ROW]
[ROW][C]STRESSTOT[/C][C]-0.1169[/C][C] 0.147[/C][C]-7.9480e-01[/C][C] 0.4286[/C][C] 0.2143[/C][/ROW]
[ROW][C]CESDTOT[/C][C]+0.01704[/C][C] 0.05974[/C][C]+2.8530e-01[/C][C] 0.776[/C][C] 0.388[/C][/ROW]
[ROW][C]BEREK[/C][C]+0.02065[/C][C] 0.007092[/C][C]+2.9120e+00[/C][C] 0.004453[/C][C] 0.002226[/C][/ROW]
[ROW][C]NUMTOT[/C][C]+0.04276[/C][C] 0.09024[/C][C]+4.7380e-01[/C][C] 0.6367[/C][C] 0.3183[/C][/ROW]
[ROW][C]t[/C][C]+0.003803[/C][C] 0.008203[/C][C]+4.6370e-01[/C][C] 0.6439[/C][C] 0.322[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=280425&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=280425&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)	+10.83	2.915	+3.7150e+00	0.0003377	0.0001688
Gender	-0.2258	0.5104	-4.4250e-01	0.6591	0.3296
AMS.I	-0.004218	0.02233	-1.8890e-01	0.8506	0.4253
CONFSTATTOT	-0.07066	0.08977	-7.8710e-01	0.4331	0.2166
STRESSTOT	-0.1169	0.147	-7.9480e-01	0.4286	0.2143
CESDTOT	+0.01704	0.05974	+2.8530e-01	0.776	0.388
BEREK	+0.02065	0.007092	+2.9120e+00	0.004453	0.002226
NUMTOT	+0.04276	0.09024	+4.7380e-01	0.6367	0.3183
t	+0.003803	0.008203	+4.6370e-01	0.6439	0.322

Multiple Linear Regression - Regression Statistics
Multiple R	0.3078
R-squared	0.09472
Adjusted R-squared	0.02082
F-TEST (value)	1.282
F-TEST (DF numerator)	8
F-TEST (DF denominator)	98
p-value	0.2618
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.366
Sum Squared Residuals	548.5

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.3078 \tabularnewline
R-squared &  0.09472 \tabularnewline
Adjusted R-squared &  0.02082 \tabularnewline
F-TEST (value) &  1.282 \tabularnewline
F-TEST (DF numerator) & 8 \tabularnewline
F-TEST (DF denominator) & 98 \tabularnewline
p-value &  0.2618 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  2.366 \tabularnewline
Sum Squared Residuals &  548.5 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=280425&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.3078[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.09472[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.02082[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 1.282[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]8[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]98[/C][/ROW]
[ROW][C]p-value[/C][C] 0.2618[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 2.366[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 548.5[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=280425&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=280425&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	0.3078
R-squared	0.09472
Adjusted R-squared	0.02082
F-TEST (value)	1.282
F-TEST (DF numerator)	8
F-TEST (DF denominator)	98
p-value	0.2618
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.366
Sum Squared Residuals	548.5

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	12.9	11.05	1.848
2	12.2	10.59	1.606
3	12.8	11	1.796
4	7.4	10.58	-3.176
5	6.7	9.893	-3.193
6	12.6	11.95	0.655
7	14.8	11.62	3.176
8	13.3	11.27	2.027
9	11.1	10.01	1.095
10	8.2	11.43	-3.23
11	11.4	10.95	0.4511
12	6.4	11.8	-5.398
13	10.6	10.1	0.5049
14	12	11.54	0.4572
15	6.3	9.801	-3.501
16	11.3	10.62	0.6752
17	11.9	11.71	0.1863
18	9.3	10.77	-1.47
19	9.6	10.8	-1.196
20	10	10.49	-0.492
21	13.8	10.53	3.274
22	10.8	10.91	-0.11
23	11.7	11.34	0.3627
24	10.9	12.01	-1.106
25	16.1	11.94	4.163
26	13.4	10.18	3.223
27	9.9	9.89	0.0103
28	11.5	10.13	1.373
29	8.3	11.19	-2.893
30	11.7	11.72	-0.02085
31	9	10.74	-1.744
32	9.7	11.62	-1.923
33	10.8	10.93	-0.1289
34	10.3	11.77	-1.473
35	10.4	10.58	-0.1795
36	12.7	10.56	2.14
37	9.3	10.43	-1.128
38	11.8	12.6	-0.8032
39	5.9	9.939	-4.039
40	11.4	12.01	-0.6114
41	13	10.04	2.956
42	10.8	11.04	-0.2397
43	12.3	9.659	2.641
44	11.3	11.65	-0.3451
45	11.8	11.27	0.5294
46	7.9	10.26	-2.359
47	12.7	9.75	2.95
48	12.3	10.42	1.876
49	11.6	10.67	0.9308
50	6.7	10.79	-4.09
51	10.9	11.28	-0.3819
52	12.1	10.71	1.394
53	13.3	11.22	2.077
54	10.1	11.82	-1.717
55	5.7	11.01	-5.308
56	14.3	11.19	3.106
57	8	9.718	-1.718
58	13.3	11.2	2.103
59	9.3	11.1	-1.8
60	12.5	10.41	2.091
61	7.6	10.92	-3.323
62	15.9	11.31	4.593
63	9.2	10.04	-0.8419
64	9.1	10.16	-1.057
65	11.1	12.27	-1.169
66	13	12.12	0.8826
67	14.5	10.61	3.886
68	12.2	10.51	1.688
69	12.3	11.47	0.8298
70	11.4	10.32	1.082
71	14.6	10.64	3.959
72	12.6	12.24	0.3583
73	NA	NA	0.761
74	13	11.17	1.833
75	12.6	12.06	0.543
76	13.2	13.84	-0.6357
77	9.9	12.54	-2.645
78	7.7	7.757	-0.05741
79	10.5	7.006	3.494
80	13.4	13.6	-0.1957
81	10.9	15.95	-5.055
82	4.3	4.073	0.2271
83	10.3	9.16	1.14
84	11.8	10.04	1.757
85	11.2	10.79	0.4102
86	11.4	12.9	-1.497
87	8.6	5.847	2.753
88	13.2	10.54	2.664
89	12.6	17.68	-5.082
90	5.6	5.841	-0.2407
91	9.9	12.6	-2.704
92	8.8	11.76	-2.963
93	7.7	8.284	-0.5838
94	9	12.48	-3.48
95	7.3	6.125	1.175
96	11.4	8.893	2.507
97	13.6	15.98	-2.377
98	7.9	7.495	0.4045
99	10.7	10.99	-0.2859
100	10.3	10.54	-0.2351
101	9.6	7.246	2.354
102	14.2	15.65	-1.446
103	8.5	5.078	3.422
104	13.5	17.54	-4.045
105	6.4	7.44	-1.04
106	9.6	8.093	1.507
107	11.6	10.78	0.8243
108	11.1	NA	NA

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  12.9 &  11.05 &  1.848 \tabularnewline
2 &  12.2 &  10.59 &  1.606 \tabularnewline
3 &  12.8 &  11 &  1.796 \tabularnewline
4 &  7.4 &  10.58 & -3.176 \tabularnewline
5 &  6.7 &  9.893 & -3.193 \tabularnewline
6 &  12.6 &  11.95 &  0.655 \tabularnewline
7 &  14.8 &  11.62 &  3.176 \tabularnewline
8 &  13.3 &  11.27 &  2.027 \tabularnewline
9 &  11.1 &  10.01 &  1.095 \tabularnewline
10 &  8.2 &  11.43 & -3.23 \tabularnewline
11 &  11.4 &  10.95 &  0.4511 \tabularnewline
12 &  6.4 &  11.8 & -5.398 \tabularnewline
13 &  10.6 &  10.1 &  0.5049 \tabularnewline
14 &  12 &  11.54 &  0.4572 \tabularnewline
15 &  6.3 &  9.801 & -3.501 \tabularnewline
16 &  11.3 &  10.62 &  0.6752 \tabularnewline
17 &  11.9 &  11.71 &  0.1863 \tabularnewline
18 &  9.3 &  10.77 & -1.47 \tabularnewline
19 &  9.6 &  10.8 & -1.196 \tabularnewline
20 &  10 &  10.49 & -0.492 \tabularnewline
21 &  13.8 &  10.53 &  3.274 \tabularnewline
22 &  10.8 &  10.91 & -0.11 \tabularnewline
23 &  11.7 &  11.34 &  0.3627 \tabularnewline
24 &  10.9 &  12.01 & -1.106 \tabularnewline
25 &  16.1 &  11.94 &  4.163 \tabularnewline
26 &  13.4 &  10.18 &  3.223 \tabularnewline
27 &  9.9 &  9.89 &  0.0103 \tabularnewline
28 &  11.5 &  10.13 &  1.373 \tabularnewline
29 &  8.3 &  11.19 & -2.893 \tabularnewline
30 &  11.7 &  11.72 & -0.02085 \tabularnewline
31 &  9 &  10.74 & -1.744 \tabularnewline
32 &  9.7 &  11.62 & -1.923 \tabularnewline
33 &  10.8 &  10.93 & -0.1289 \tabularnewline
34 &  10.3 &  11.77 & -1.473 \tabularnewline
35 &  10.4 &  10.58 & -0.1795 \tabularnewline
36 &  12.7 &  10.56 &  2.14 \tabularnewline
37 &  9.3 &  10.43 & -1.128 \tabularnewline
38 &  11.8 &  12.6 & -0.8032 \tabularnewline
39 &  5.9 &  9.939 & -4.039 \tabularnewline
40 &  11.4 &  12.01 & -0.6114 \tabularnewline
41 &  13 &  10.04 &  2.956 \tabularnewline
42 &  10.8 &  11.04 & -0.2397 \tabularnewline
43 &  12.3 &  9.659 &  2.641 \tabularnewline
44 &  11.3 &  11.65 & -0.3451 \tabularnewline
45 &  11.8 &  11.27 &  0.5294 \tabularnewline
46 &  7.9 &  10.26 & -2.359 \tabularnewline
47 &  12.7 &  9.75 &  2.95 \tabularnewline
48 &  12.3 &  10.42 &  1.876 \tabularnewline
49 &  11.6 &  10.67 &  0.9308 \tabularnewline
50 &  6.7 &  10.79 & -4.09 \tabularnewline
51 &  10.9 &  11.28 & -0.3819 \tabularnewline
52 &  12.1 &  10.71 &  1.394 \tabularnewline
53 &  13.3 &  11.22 &  2.077 \tabularnewline
54 &  10.1 &  11.82 & -1.717 \tabularnewline
55 &  5.7 &  11.01 & -5.308 \tabularnewline
56 &  14.3 &  11.19 &  3.106 \tabularnewline
57 &  8 &  9.718 & -1.718 \tabularnewline
58 &  13.3 &  11.2 &  2.103 \tabularnewline
59 &  9.3 &  11.1 & -1.8 \tabularnewline
60 &  12.5 &  10.41 &  2.091 \tabularnewline
61 &  7.6 &  10.92 & -3.323 \tabularnewline
62 &  15.9 &  11.31 &  4.593 \tabularnewline
63 &  9.2 &  10.04 & -0.8419 \tabularnewline
64 &  9.1 &  10.16 & -1.057 \tabularnewline
65 &  11.1 &  12.27 & -1.169 \tabularnewline
66 &  13 &  12.12 &  0.8826 \tabularnewline
67 &  14.5 &  10.61 &  3.886 \tabularnewline
68 &  12.2 &  10.51 &  1.688 \tabularnewline
69 &  12.3 &  11.47 &  0.8298 \tabularnewline
70 &  11.4 &  10.32 &  1.082 \tabularnewline
71 &  14.6 &  10.64 &  3.959 \tabularnewline
72 &  12.6 &  12.24 &  0.3583 \tabularnewline
73 & NA & NA &  0.761 \tabularnewline
74 &  13 &  11.17 &  1.833 \tabularnewline
75 &  12.6 &  12.06 &  0.543 \tabularnewline
76 &  13.2 &  13.84 & -0.6357 \tabularnewline
77 &  9.9 &  12.54 & -2.645 \tabularnewline
78 &  7.7 &  7.757 & -0.05741 \tabularnewline
79 &  10.5 &  7.006 &  3.494 \tabularnewline
80 &  13.4 &  13.6 & -0.1957 \tabularnewline
81 &  10.9 &  15.95 & -5.055 \tabularnewline
82 &  4.3 &  4.073 &  0.2271 \tabularnewline
83 &  10.3 &  9.16 &  1.14 \tabularnewline
84 &  11.8 &  10.04 &  1.757 \tabularnewline
85 &  11.2 &  10.79 &  0.4102 \tabularnewline
86 &  11.4 &  12.9 & -1.497 \tabularnewline
87 &  8.6 &  5.847 &  2.753 \tabularnewline
88 &  13.2 &  10.54 &  2.664 \tabularnewline
89 &  12.6 &  17.68 & -5.082 \tabularnewline
90 &  5.6 &  5.841 & -0.2407 \tabularnewline
91 &  9.9 &  12.6 & -2.704 \tabularnewline
92 &  8.8 &  11.76 & -2.963 \tabularnewline
93 &  7.7 &  8.284 & -0.5838 \tabularnewline
94 &  9 &  12.48 & -3.48 \tabularnewline
95 &  7.3 &  6.125 &  1.175 \tabularnewline
96 &  11.4 &  8.893 &  2.507 \tabularnewline
97 &  13.6 &  15.98 & -2.377 \tabularnewline
98 &  7.9 &  7.495 &  0.4045 \tabularnewline
99 &  10.7 &  10.99 & -0.2859 \tabularnewline
100 &  10.3 &  10.54 & -0.2351 \tabularnewline
101 &  9.6 &  7.246 &  2.354 \tabularnewline
102 &  14.2 &  15.65 & -1.446 \tabularnewline
103 &  8.5 &  5.078 &  3.422 \tabularnewline
104 &  13.5 &  17.54 & -4.045 \tabularnewline
105 &  6.4 &  7.44 & -1.04 \tabularnewline
106 &  9.6 &  8.093 &  1.507 \tabularnewline
107 &  11.6 &  10.78 &  0.8243 \tabularnewline
108 &  11.1 & NA & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=280425&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] 11.05[/C][C] 1.848[/C][/ROW]
[ROW][C]2[/C][C] 12.2[/C][C] 10.59[/C][C] 1.606[/C][/ROW]
[ROW][C]3[/C][C] 12.8[/C][C] 11[/C][C] 1.796[/C][/ROW]
[ROW][C]4[/C][C] 7.4[/C][C] 10.58[/C][C]-3.176[/C][/ROW]
[ROW][C]5[/C][C] 6.7[/C][C] 9.893[/C][C]-3.193[/C][/ROW]
[ROW][C]6[/C][C] 12.6[/C][C] 11.95[/C][C] 0.655[/C][/ROW]
[ROW][C]7[/C][C] 14.8[/C][C] 11.62[/C][C] 3.176[/C][/ROW]
[ROW][C]8[/C][C] 13.3[/C][C] 11.27[/C][C] 2.027[/C][/ROW]
[ROW][C]9[/C][C] 11.1[/C][C] 10.01[/C][C] 1.095[/C][/ROW]
[ROW][C]10[/C][C] 8.2[/C][C] 11.43[/C][C]-3.23[/C][/ROW]
[ROW][C]11[/C][C] 11.4[/C][C] 10.95[/C][C] 0.4511[/C][/ROW]
[ROW][C]12[/C][C] 6.4[/C][C] 11.8[/C][C]-5.398[/C][/ROW]
[ROW][C]13[/C][C] 10.6[/C][C] 10.1[/C][C] 0.5049[/C][/ROW]
[ROW][C]14[/C][C] 12[/C][C] 11.54[/C][C] 0.4572[/C][/ROW]
[ROW][C]15[/C][C] 6.3[/C][C] 9.801[/C][C]-3.501[/C][/ROW]
[ROW][C]16[/C][C] 11.3[/C][C] 10.62[/C][C] 0.6752[/C][/ROW]
[ROW][C]17[/C][C] 11.9[/C][C] 11.71[/C][C] 0.1863[/C][/ROW]
[ROW][C]18[/C][C] 9.3[/C][C] 10.77[/C][C]-1.47[/C][/ROW]
[ROW][C]19[/C][C] 9.6[/C][C] 10.8[/C][C]-1.196[/C][/ROW]
[ROW][C]20[/C][C] 10[/C][C] 10.49[/C][C]-0.492[/C][/ROW]
[ROW][C]21[/C][C] 13.8[/C][C] 10.53[/C][C] 3.274[/C][/ROW]
[ROW][C]22[/C][C] 10.8[/C][C] 10.91[/C][C]-0.11[/C][/ROW]
[ROW][C]23[/C][C] 11.7[/C][C] 11.34[/C][C] 0.3627[/C][/ROW]
[ROW][C]24[/C][C] 10.9[/C][C] 12.01[/C][C]-1.106[/C][/ROW]
[ROW][C]25[/C][C] 16.1[/C][C] 11.94[/C][C] 4.163[/C][/ROW]
[ROW][C]26[/C][C] 13.4[/C][C] 10.18[/C][C] 3.223[/C][/ROW]
[ROW][C]27[/C][C] 9.9[/C][C] 9.89[/C][C] 0.0103[/C][/ROW]
[ROW][C]28[/C][C] 11.5[/C][C] 10.13[/C][C] 1.373[/C][/ROW]
[ROW][C]29[/C][C] 8.3[/C][C] 11.19[/C][C]-2.893[/C][/ROW]
[ROW][C]30[/C][C] 11.7[/C][C] 11.72[/C][C]-0.02085[/C][/ROW]
[ROW][C]31[/C][C] 9[/C][C] 10.74[/C][C]-1.744[/C][/ROW]
[ROW][C]32[/C][C] 9.7[/C][C] 11.62[/C][C]-1.923[/C][/ROW]
[ROW][C]33[/C][C] 10.8[/C][C] 10.93[/C][C]-0.1289[/C][/ROW]
[ROW][C]34[/C][C] 10.3[/C][C] 11.77[/C][C]-1.473[/C][/ROW]
[ROW][C]35[/C][C] 10.4[/C][C] 10.58[/C][C]-0.1795[/C][/ROW]
[ROW][C]36[/C][C] 12.7[/C][C] 10.56[/C][C] 2.14[/C][/ROW]
[ROW][C]37[/C][C] 9.3[/C][C] 10.43[/C][C]-1.128[/C][/ROW]
[ROW][C]38[/C][C] 11.8[/C][C] 12.6[/C][C]-0.8032[/C][/ROW]
[ROW][C]39[/C][C] 5.9[/C][C] 9.939[/C][C]-4.039[/C][/ROW]
[ROW][C]40[/C][C] 11.4[/C][C] 12.01[/C][C]-0.6114[/C][/ROW]
[ROW][C]41[/C][C] 13[/C][C] 10.04[/C][C] 2.956[/C][/ROW]
[ROW][C]42[/C][C] 10.8[/C][C] 11.04[/C][C]-0.2397[/C][/ROW]
[ROW][C]43[/C][C] 12.3[/C][C] 9.659[/C][C] 2.641[/C][/ROW]
[ROW][C]44[/C][C] 11.3[/C][C] 11.65[/C][C]-0.3451[/C][/ROW]
[ROW][C]45[/C][C] 11.8[/C][C] 11.27[/C][C] 0.5294[/C][/ROW]
[ROW][C]46[/C][C] 7.9[/C][C] 10.26[/C][C]-2.359[/C][/ROW]
[ROW][C]47[/C][C] 12.7[/C][C] 9.75[/C][C] 2.95[/C][/ROW]
[ROW][C]48[/C][C] 12.3[/C][C] 10.42[/C][C] 1.876[/C][/ROW]
[ROW][C]49[/C][C] 11.6[/C][C] 10.67[/C][C] 0.9308[/C][/ROW]
[ROW][C]50[/C][C] 6.7[/C][C] 10.79[/C][C]-4.09[/C][/ROW]
[ROW][C]51[/C][C] 10.9[/C][C] 11.28[/C][C]-0.3819[/C][/ROW]
[ROW][C]52[/C][C] 12.1[/C][C] 10.71[/C][C] 1.394[/C][/ROW]
[ROW][C]53[/C][C] 13.3[/C][C] 11.22[/C][C] 2.077[/C][/ROW]
[ROW][C]54[/C][C] 10.1[/C][C] 11.82[/C][C]-1.717[/C][/ROW]
[ROW][C]55[/C][C] 5.7[/C][C] 11.01[/C][C]-5.308[/C][/ROW]
[ROW][C]56[/C][C] 14.3[/C][C] 11.19[/C][C] 3.106[/C][/ROW]
[ROW][C]57[/C][C] 8[/C][C] 9.718[/C][C]-1.718[/C][/ROW]
[ROW][C]58[/C][C] 13.3[/C][C] 11.2[/C][C] 2.103[/C][/ROW]
[ROW][C]59[/C][C] 9.3[/C][C] 11.1[/C][C]-1.8[/C][/ROW]
[ROW][C]60[/C][C] 12.5[/C][C] 10.41[/C][C] 2.091[/C][/ROW]
[ROW][C]61[/C][C] 7.6[/C][C] 10.92[/C][C]-3.323[/C][/ROW]
[ROW][C]62[/C][C] 15.9[/C][C] 11.31[/C][C] 4.593[/C][/ROW]
[ROW][C]63[/C][C] 9.2[/C][C] 10.04[/C][C]-0.8419[/C][/ROW]
[ROW][C]64[/C][C] 9.1[/C][C] 10.16[/C][C]-1.057[/C][/ROW]
[ROW][C]65[/C][C] 11.1[/C][C] 12.27[/C][C]-1.169[/C][/ROW]
[ROW][C]66[/C][C] 13[/C][C] 12.12[/C][C] 0.8826[/C][/ROW]
[ROW][C]67[/C][C] 14.5[/C][C] 10.61[/C][C] 3.886[/C][/ROW]
[ROW][C]68[/C][C] 12.2[/C][C] 10.51[/C][C] 1.688[/C][/ROW]
[ROW][C]69[/C][C] 12.3[/C][C] 11.47[/C][C] 0.8298[/C][/ROW]
[ROW][C]70[/C][C] 11.4[/C][C] 10.32[/C][C] 1.082[/C][/ROW]
[ROW][C]71[/C][C] 14.6[/C][C] 10.64[/C][C] 3.959[/C][/ROW]
[ROW][C]72[/C][C] 12.6[/C][C] 12.24[/C][C] 0.3583[/C][/ROW]
[ROW][C]73[/C][C]NA[/C][C]NA[/C][C] 0.761[/C][/ROW]
[ROW][C]74[/C][C] 13[/C][C] 11.17[/C][C] 1.833[/C][/ROW]
[ROW][C]75[/C][C] 12.6[/C][C] 12.06[/C][C] 0.543[/C][/ROW]
[ROW][C]76[/C][C] 13.2[/C][C] 13.84[/C][C]-0.6357[/C][/ROW]
[ROW][C]77[/C][C] 9.9[/C][C] 12.54[/C][C]-2.645[/C][/ROW]
[ROW][C]78[/C][C] 7.7[/C][C] 7.757[/C][C]-0.05741[/C][/ROW]
[ROW][C]79[/C][C] 10.5[/C][C] 7.006[/C][C] 3.494[/C][/ROW]
[ROW][C]80[/C][C] 13.4[/C][C] 13.6[/C][C]-0.1957[/C][/ROW]
[ROW][C]81[/C][C] 10.9[/C][C] 15.95[/C][C]-5.055[/C][/ROW]
[ROW][C]82[/C][C] 4.3[/C][C] 4.073[/C][C] 0.2271[/C][/ROW]
[ROW][C]83[/C][C] 10.3[/C][C] 9.16[/C][C] 1.14[/C][/ROW]
[ROW][C]84[/C][C] 11.8[/C][C] 10.04[/C][C] 1.757[/C][/ROW]
[ROW][C]85[/C][C] 11.2[/C][C] 10.79[/C][C] 0.4102[/C][/ROW]
[ROW][C]86[/C][C] 11.4[/C][C] 12.9[/C][C]-1.497[/C][/ROW]
[ROW][C]87[/C][C] 8.6[/C][C] 5.847[/C][C] 2.753[/C][/ROW]
[ROW][C]88[/C][C] 13.2[/C][C] 10.54[/C][C] 2.664[/C][/ROW]
[ROW][C]89[/C][C] 12.6[/C][C] 17.68[/C][C]-5.082[/C][/ROW]
[ROW][C]90[/C][C] 5.6[/C][C] 5.841[/C][C]-0.2407[/C][/ROW]
[ROW][C]91[/C][C] 9.9[/C][C] 12.6[/C][C]-2.704[/C][/ROW]
[ROW][C]92[/C][C] 8.8[/C][C] 11.76[/C][C]-2.963[/C][/ROW]
[ROW][C]93[/C][C] 7.7[/C][C] 8.284[/C][C]-0.5838[/C][/ROW]
[ROW][C]94[/C][C] 9[/C][C] 12.48[/C][C]-3.48[/C][/ROW]
[ROW][C]95[/C][C] 7.3[/C][C] 6.125[/C][C] 1.175[/C][/ROW]
[ROW][C]96[/C][C] 11.4[/C][C] 8.893[/C][C] 2.507[/C][/ROW]
[ROW][C]97[/C][C] 13.6[/C][C] 15.98[/C][C]-2.377[/C][/ROW]
[ROW][C]98[/C][C] 7.9[/C][C] 7.495[/C][C] 0.4045[/C][/ROW]
[ROW][C]99[/C][C] 10.7[/C][C] 10.99[/C][C]-0.2859[/C][/ROW]
[ROW][C]100[/C][C] 10.3[/C][C] 10.54[/C][C]-0.2351[/C][/ROW]
[ROW][C]101[/C][C] 9.6[/C][C] 7.246[/C][C] 2.354[/C][/ROW]
[ROW][C]102[/C][C] 14.2[/C][C] 15.65[/C][C]-1.446[/C][/ROW]
[ROW][C]103[/C][C] 8.5[/C][C] 5.078[/C][C] 3.422[/C][/ROW]
[ROW][C]104[/C][C] 13.5[/C][C] 17.54[/C][C]-4.045[/C][/ROW]
[ROW][C]105[/C][C] 6.4[/C][C] 7.44[/C][C]-1.04[/C][/ROW]
[ROW][C]106[/C][C] 9.6[/C][C] 8.093[/C][C] 1.507[/C][/ROW]
[ROW][C]107[/C][C] 11.6[/C][C] 10.78[/C][C] 0.8243[/C][/ROW]
[ROW][C]108[/C][C] 11.1[/C][C]NA[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=280425&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=280425&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	11.05	1.848
2	12.2	10.59	1.606
3	12.8	11	1.796
4	7.4	10.58	-3.176
5	6.7	9.893	-3.193
6	12.6	11.95	0.655
7	14.8	11.62	3.176
8	13.3	11.27	2.027
9	11.1	10.01	1.095
10	8.2	11.43	-3.23
11	11.4	10.95	0.4511
12	6.4	11.8	-5.398
13	10.6	10.1	0.5049
14	12	11.54	0.4572
15	6.3	9.801	-3.501
16	11.3	10.62	0.6752
17	11.9	11.71	0.1863
18	9.3	10.77	-1.47
19	9.6	10.8	-1.196
20	10	10.49	-0.492
21	13.8	10.53	3.274
22	10.8	10.91	-0.11
23	11.7	11.34	0.3627
24	10.9	12.01	-1.106
25	16.1	11.94	4.163
26	13.4	10.18	3.223
27	9.9	9.89	0.0103
28	11.5	10.13	1.373
29	8.3	11.19	-2.893
30	11.7	11.72	-0.02085
31	9	10.74	-1.744
32	9.7	11.62	-1.923
33	10.8	10.93	-0.1289
34	10.3	11.77	-1.473
35	10.4	10.58	-0.1795
36	12.7	10.56	2.14
37	9.3	10.43	-1.128
38	11.8	12.6	-0.8032
39	5.9	9.939	-4.039
40	11.4	12.01	-0.6114
41	13	10.04	2.956
42	10.8	11.04	-0.2397
43	12.3	9.659	2.641
44	11.3	11.65	-0.3451
45	11.8	11.27	0.5294
46	7.9	10.26	-2.359
47	12.7	9.75	2.95
48	12.3	10.42	1.876
49	11.6	10.67	0.9308
50	6.7	10.79	-4.09
51	10.9	11.28	-0.3819
52	12.1	10.71	1.394
53	13.3	11.22	2.077
54	10.1	11.82	-1.717
55	5.7	11.01	-5.308
56	14.3	11.19	3.106
57	8	9.718	-1.718
58	13.3	11.2	2.103
59	9.3	11.1	-1.8
60	12.5	10.41	2.091
61	7.6	10.92	-3.323
62	15.9	11.31	4.593
63	9.2	10.04	-0.8419
64	9.1	10.16	-1.057
65	11.1	12.27	-1.169
66	13	12.12	0.8826
67	14.5	10.61	3.886
68	12.2	10.51	1.688
69	12.3	11.47	0.8298
70	11.4	10.32	1.082
71	14.6	10.64	3.959
72	12.6	12.24	0.3583
73	NA	NA	0.761
74	13	11.17	1.833
75	12.6	12.06	0.543
76	13.2	13.84	-0.6357
77	9.9	12.54	-2.645
78	7.7	7.757	-0.05741
79	10.5	7.006	3.494
80	13.4	13.6	-0.1957
81	10.9	15.95	-5.055
82	4.3	4.073	0.2271
83	10.3	9.16	1.14
84	11.8	10.04	1.757
85	11.2	10.79	0.4102
86	11.4	12.9	-1.497
87	8.6	5.847	2.753
88	13.2	10.54	2.664
89	12.6	17.68	-5.082
90	5.6	5.841	-0.2407
91	9.9	12.6	-2.704
92	8.8	11.76	-2.963
93	7.7	8.284	-0.5838
94	9	12.48	-3.48
95	7.3	6.125	1.175
96	11.4	8.893	2.507
97	13.6	15.98	-2.377
98	7.9	7.495	0.4045
99	10.7	10.99	-0.2859
100	10.3	10.54	-0.2351
101	9.6	7.246	2.354
102	14.2	15.65	-1.446
103	8.5	5.078	3.422
104	13.5	17.54	-4.045
105	6.4	7.44	-1.04
106	9.6	8.093	1.507
107	11.6	10.78	0.8243
108	11.1	NA	NA

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
12	0.9603	0.07947	0.03974
13	0.9272	0.1456	0.07281
14	0.8709	0.2582	0.1291
15	0.8939	0.2122	0.1061
16	0.838	0.3239	0.162
17	0.7716	0.4567	0.2284
18	0.6929	0.6142	0.3071
19	0.6122	0.7757	0.3878
20	0.5347	0.9307	0.4653
21	0.6708	0.6584	0.3292
22	0.5893	0.8215	0.4107
23	0.5502	0.8995	0.4498
24	0.4859	0.9718	0.5141
25	0.4853	0.9706	0.5147
26	0.569	0.862	0.431
27	0.4949	0.9898	0.5051
28	0.4521	0.9041	0.5479
29	0.5876	0.8248	0.4124
30	0.5287	0.9426	0.4713
31	0.527	0.946	0.473
32	0.4884	0.9768	0.5116
33	0.4204	0.8408	0.5796
34	0.3785	0.757	0.6215
35	0.3702	0.7403	0.6298
36	0.3894	0.7788	0.6106
37	0.3458	0.6917	0.6542
38	0.2905	0.5809	0.7095
39	0.4487	0.8974	0.5513
40	0.4009	0.8018	0.5991
41	0.4436	0.8873	0.5564
42	0.3865	0.7731	0.6135
43	0.4023	0.8045	0.5977
44	0.3556	0.7113	0.6444
45	0.3026	0.6051	0.6974
46	0.3128	0.6256	0.6872
47	0.3525	0.7049	0.6475
48	0.3413	0.6825	0.6587
49	0.2931	0.5862	0.7069
50	0.4343	0.8685	0.5657
51	0.3865	0.7731	0.6135
52	0.3479	0.6958	0.6521
53	0.3326	0.6652	0.6674
54	0.3538	0.7076	0.6462
55	0.5937	0.8126	0.4063
56	0.6174	0.7652	0.3826
57	0.6741	0.6518	0.3259
58	0.6551	0.6898	0.3449
59	0.6295	0.7411	0.3705
60	0.6024	0.7952	0.3976
61	0.6099	0.7801	0.3901
62	0.7003	0.5995	0.2997
63	0.662	0.676	0.338
64	0.6431	0.7138	0.3569
65	0.6367	0.7266	0.3633
66	0.5969	0.8063	0.4031
67	0.6636	0.6729	0.3364
68	0.6246	0.7508	0.3754
69	0.6123	0.7754	0.3877
70	0.5532	0.8937	0.4468
71	0.5706	0.8587	0.4294
72	0.5189	0.9621	0.4811
73	0.466	0.932	0.534
74	0.4993	0.9986	0.5007
75	0.4794	0.9589	0.5206
76	0.4338	0.8676	0.5662
77	0.4298	0.8595	0.5702
78	0.3738	0.7476	0.6262
79	0.3471	0.6942	0.6529
80	0.2835	0.5671	0.7165
81	0.4655	0.931	0.5345
82	0.3891	0.7782	0.6109
83	0.328	0.6561	0.672
84	0.2676	0.5352	0.7324
85	0.2076	0.4151	0.7924
86	0.163	0.326	0.837
87	0.1734	0.3468	0.8266
88	0.2396	0.4792	0.7604
89	0.3111	0.6222	0.6889
90	0.2329	0.4659	0.7671
91	0.1807	0.3614	0.8193
92	0.2281	0.4562	0.7719
93	0.2481	0.4962	0.7519
94	0.4794	0.9588	0.5206
95	0.4111	0.8222	0.5889
96	0.2734	0.5468	0.7266

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
12 &  0.9603 &  0.07947 &  0.03974 \tabularnewline
13 &  0.9272 &  0.1456 &  0.07281 \tabularnewline
14 &  0.8709 &  0.2582 &  0.1291 \tabularnewline
15 &  0.8939 &  0.2122 &  0.1061 \tabularnewline
16 &  0.838 &  0.3239 &  0.162 \tabularnewline
17 &  0.7716 &  0.4567 &  0.2284 \tabularnewline
18 &  0.6929 &  0.6142 &  0.3071 \tabularnewline
19 &  0.6122 &  0.7757 &  0.3878 \tabularnewline
20 &  0.5347 &  0.9307 &  0.4653 \tabularnewline
21 &  0.6708 &  0.6584 &  0.3292 \tabularnewline
22 &  0.5893 &  0.8215 &  0.4107 \tabularnewline
23 &  0.5502 &  0.8995 &  0.4498 \tabularnewline
24 &  0.4859 &  0.9718 &  0.5141 \tabularnewline
25 &  0.4853 &  0.9706 &  0.5147 \tabularnewline
26 &  0.569 &  0.862 &  0.431 \tabularnewline
27 &  0.4949 &  0.9898 &  0.5051 \tabularnewline
28 &  0.4521 &  0.9041 &  0.5479 \tabularnewline
29 &  0.5876 &  0.8248 &  0.4124 \tabularnewline
30 &  0.5287 &  0.9426 &  0.4713 \tabularnewline
31 &  0.527 &  0.946 &  0.473 \tabularnewline
32 &  0.4884 &  0.9768 &  0.5116 \tabularnewline
33 &  0.4204 &  0.8408 &  0.5796 \tabularnewline
34 &  0.3785 &  0.757 &  0.6215 \tabularnewline
35 &  0.3702 &  0.7403 &  0.6298 \tabularnewline
36 &  0.3894 &  0.7788 &  0.6106 \tabularnewline
37 &  0.3458 &  0.6917 &  0.6542 \tabularnewline
38 &  0.2905 &  0.5809 &  0.7095 \tabularnewline
39 &  0.4487 &  0.8974 &  0.5513 \tabularnewline
40 &  0.4009 &  0.8018 &  0.5991 \tabularnewline
41 &  0.4436 &  0.8873 &  0.5564 \tabularnewline
42 &  0.3865 &  0.7731 &  0.6135 \tabularnewline
43 &  0.4023 &  0.8045 &  0.5977 \tabularnewline
44 &  0.3556 &  0.7113 &  0.6444 \tabularnewline
45 &  0.3026 &  0.6051 &  0.6974 \tabularnewline
46 &  0.3128 &  0.6256 &  0.6872 \tabularnewline
47 &  0.3525 &  0.7049 &  0.6475 \tabularnewline
48 &  0.3413 &  0.6825 &  0.6587 \tabularnewline
49 &  0.2931 &  0.5862 &  0.7069 \tabularnewline
50 &  0.4343 &  0.8685 &  0.5657 \tabularnewline
51 &  0.3865 &  0.7731 &  0.6135 \tabularnewline
52 &  0.3479 &  0.6958 &  0.6521 \tabularnewline
53 &  0.3326 &  0.6652 &  0.6674 \tabularnewline
54 &  0.3538 &  0.7076 &  0.6462 \tabularnewline
55 &  0.5937 &  0.8126 &  0.4063 \tabularnewline
56 &  0.6174 &  0.7652 &  0.3826 \tabularnewline
57 &  0.6741 &  0.6518 &  0.3259 \tabularnewline
58 &  0.6551 &  0.6898 &  0.3449 \tabularnewline
59 &  0.6295 &  0.7411 &  0.3705 \tabularnewline
60 &  0.6024 &  0.7952 &  0.3976 \tabularnewline
61 &  0.6099 &  0.7801 &  0.3901 \tabularnewline
62 &  0.7003 &  0.5995 &  0.2997 \tabularnewline
63 &  0.662 &  0.676 &  0.338 \tabularnewline
64 &  0.6431 &  0.7138 &  0.3569 \tabularnewline
65 &  0.6367 &  0.7266 &  0.3633 \tabularnewline
66 &  0.5969 &  0.8063 &  0.4031 \tabularnewline
67 &  0.6636 &  0.6729 &  0.3364 \tabularnewline
68 &  0.6246 &  0.7508 &  0.3754 \tabularnewline
69 &  0.6123 &  0.7754 &  0.3877 \tabularnewline
70 &  0.5532 &  0.8937 &  0.4468 \tabularnewline
71 &  0.5706 &  0.8587 &  0.4294 \tabularnewline
72 &  0.5189 &  0.9621 &  0.4811 \tabularnewline
73 &  0.466 &  0.932 &  0.534 \tabularnewline
74 &  0.4993 &  0.9986 &  0.5007 \tabularnewline
75 &  0.4794 &  0.9589 &  0.5206 \tabularnewline
76 &  0.4338 &  0.8676 &  0.5662 \tabularnewline
77 &  0.4298 &  0.8595 &  0.5702 \tabularnewline
78 &  0.3738 &  0.7476 &  0.6262 \tabularnewline
79 &  0.3471 &  0.6942 &  0.6529 \tabularnewline
80 &  0.2835 &  0.5671 &  0.7165 \tabularnewline
81 &  0.4655 &  0.931 &  0.5345 \tabularnewline
82 &  0.3891 &  0.7782 &  0.6109 \tabularnewline
83 &  0.328 &  0.6561 &  0.672 \tabularnewline
84 &  0.2676 &  0.5352 &  0.7324 \tabularnewline
85 &  0.2076 &  0.4151 &  0.7924 \tabularnewline
86 &  0.163 &  0.326 &  0.837 \tabularnewline
87 &  0.1734 &  0.3468 &  0.8266 \tabularnewline
88 &  0.2396 &  0.4792 &  0.7604 \tabularnewline
89 &  0.3111 &  0.6222 &  0.6889 \tabularnewline
90 &  0.2329 &  0.4659 &  0.7671 \tabularnewline
91 &  0.1807 &  0.3614 &  0.8193 \tabularnewline
92 &  0.2281 &  0.4562 &  0.7719 \tabularnewline
93 &  0.2481 &  0.4962 &  0.7519 \tabularnewline
94 &  0.4794 &  0.9588 &  0.5206 \tabularnewline
95 &  0.4111 &  0.8222 &  0.5889 \tabularnewline
96 &  0.2734 &  0.5468 &  0.7266 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=280425&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]12[/C][C] 0.9603[/C][C] 0.07947[/C][C] 0.03974[/C][/ROW]
[ROW][C]13[/C][C] 0.9272[/C][C] 0.1456[/C][C] 0.07281[/C][/ROW]
[ROW][C]14[/C][C] 0.8709[/C][C] 0.2582[/C][C] 0.1291[/C][/ROW]
[ROW][C]15[/C][C] 0.8939[/C][C] 0.2122[/C][C] 0.1061[/C][/ROW]
[ROW][C]16[/C][C] 0.838[/C][C] 0.3239[/C][C] 0.162[/C][/ROW]
[ROW][C]17[/C][C] 0.7716[/C][C] 0.4567[/C][C] 0.2284[/C][/ROW]
[ROW][C]18[/C][C] 0.6929[/C][C] 0.6142[/C][C] 0.3071[/C][/ROW]
[ROW][C]19[/C][C] 0.6122[/C][C] 0.7757[/C][C] 0.3878[/C][/ROW]
[ROW][C]20[/C][C] 0.5347[/C][C] 0.9307[/C][C] 0.4653[/C][/ROW]
[ROW][C]21[/C][C] 0.6708[/C][C] 0.6584[/C][C] 0.3292[/C][/ROW]
[ROW][C]22[/C][C] 0.5893[/C][C] 0.8215[/C][C] 0.4107[/C][/ROW]
[ROW][C]23[/C][C] 0.5502[/C][C] 0.8995[/C][C] 0.4498[/C][/ROW]
[ROW][C]24[/C][C] 0.4859[/C][C] 0.9718[/C][C] 0.5141[/C][/ROW]
[ROW][C]25[/C][C] 0.4853[/C][C] 0.9706[/C][C] 0.5147[/C][/ROW]
[ROW][C]26[/C][C] 0.569[/C][C] 0.862[/C][C] 0.431[/C][/ROW]
[ROW][C]27[/C][C] 0.4949[/C][C] 0.9898[/C][C] 0.5051[/C][/ROW]
[ROW][C]28[/C][C] 0.4521[/C][C] 0.9041[/C][C] 0.5479[/C][/ROW]
[ROW][C]29[/C][C] 0.5876[/C][C] 0.8248[/C][C] 0.4124[/C][/ROW]
[ROW][C]30[/C][C] 0.5287[/C][C] 0.9426[/C][C] 0.4713[/C][/ROW]
[ROW][C]31[/C][C] 0.527[/C][C] 0.946[/C][C] 0.473[/C][/ROW]
[ROW][C]32[/C][C] 0.4884[/C][C] 0.9768[/C][C] 0.5116[/C][/ROW]
[ROW][C]33[/C][C] 0.4204[/C][C] 0.8408[/C][C] 0.5796[/C][/ROW]
[ROW][C]34[/C][C] 0.3785[/C][C] 0.757[/C][C] 0.6215[/C][/ROW]
[ROW][C]35[/C][C] 0.3702[/C][C] 0.7403[/C][C] 0.6298[/C][/ROW]
[ROW][C]36[/C][C] 0.3894[/C][C] 0.7788[/C][C] 0.6106[/C][/ROW]
[ROW][C]37[/C][C] 0.3458[/C][C] 0.6917[/C][C] 0.6542[/C][/ROW]
[ROW][C]38[/C][C] 0.2905[/C][C] 0.5809[/C][C] 0.7095[/C][/ROW]
[ROW][C]39[/C][C] 0.4487[/C][C] 0.8974[/C][C] 0.5513[/C][/ROW]
[ROW][C]40[/C][C] 0.4009[/C][C] 0.8018[/C][C] 0.5991[/C][/ROW]
[ROW][C]41[/C][C] 0.4436[/C][C] 0.8873[/C][C] 0.5564[/C][/ROW]
[ROW][C]42[/C][C] 0.3865[/C][C] 0.7731[/C][C] 0.6135[/C][/ROW]
[ROW][C]43[/C][C] 0.4023[/C][C] 0.8045[/C][C] 0.5977[/C][/ROW]
[ROW][C]44[/C][C] 0.3556[/C][C] 0.7113[/C][C] 0.6444[/C][/ROW]
[ROW][C]45[/C][C] 0.3026[/C][C] 0.6051[/C][C] 0.6974[/C][/ROW]
[ROW][C]46[/C][C] 0.3128[/C][C] 0.6256[/C][C] 0.6872[/C][/ROW]
[ROW][C]47[/C][C] 0.3525[/C][C] 0.7049[/C][C] 0.6475[/C][/ROW]
[ROW][C]48[/C][C] 0.3413[/C][C] 0.6825[/C][C] 0.6587[/C][/ROW]
[ROW][C]49[/C][C] 0.2931[/C][C] 0.5862[/C][C] 0.7069[/C][/ROW]
[ROW][C]50[/C][C] 0.4343[/C][C] 0.8685[/C][C] 0.5657[/C][/ROW]
[ROW][C]51[/C][C] 0.3865[/C][C] 0.7731[/C][C] 0.6135[/C][/ROW]
[ROW][C]52[/C][C] 0.3479[/C][C] 0.6958[/C][C] 0.6521[/C][/ROW]
[ROW][C]53[/C][C] 0.3326[/C][C] 0.6652[/C][C] 0.6674[/C][/ROW]
[ROW][C]54[/C][C] 0.3538[/C][C] 0.7076[/C][C] 0.6462[/C][/ROW]
[ROW][C]55[/C][C] 0.5937[/C][C] 0.8126[/C][C] 0.4063[/C][/ROW]
[ROW][C]56[/C][C] 0.6174[/C][C] 0.7652[/C][C] 0.3826[/C][/ROW]
[ROW][C]57[/C][C] 0.6741[/C][C] 0.6518[/C][C] 0.3259[/C][/ROW]
[ROW][C]58[/C][C] 0.6551[/C][C] 0.6898[/C][C] 0.3449[/C][/ROW]
[ROW][C]59[/C][C] 0.6295[/C][C] 0.7411[/C][C] 0.3705[/C][/ROW]
[ROW][C]60[/C][C] 0.6024[/C][C] 0.7952[/C][C] 0.3976[/C][/ROW]
[ROW][C]61[/C][C] 0.6099[/C][C] 0.7801[/C][C] 0.3901[/C][/ROW]
[ROW][C]62[/C][C] 0.7003[/C][C] 0.5995[/C][C] 0.2997[/C][/ROW]
[ROW][C]63[/C][C] 0.662[/C][C] 0.676[/C][C] 0.338[/C][/ROW]
[ROW][C]64[/C][C] 0.6431[/C][C] 0.7138[/C][C] 0.3569[/C][/ROW]
[ROW][C]65[/C][C] 0.6367[/C][C] 0.7266[/C][C] 0.3633[/C][/ROW]
[ROW][C]66[/C][C] 0.5969[/C][C] 0.8063[/C][C] 0.4031[/C][/ROW]
[ROW][C]67[/C][C] 0.6636[/C][C] 0.6729[/C][C] 0.3364[/C][/ROW]
[ROW][C]68[/C][C] 0.6246[/C][C] 0.7508[/C][C] 0.3754[/C][/ROW]
[ROW][C]69[/C][C] 0.6123[/C][C] 0.7754[/C][C] 0.3877[/C][/ROW]
[ROW][C]70[/C][C] 0.5532[/C][C] 0.8937[/C][C] 0.4468[/C][/ROW]
[ROW][C]71[/C][C] 0.5706[/C][C] 0.8587[/C][C] 0.4294[/C][/ROW]
[ROW][C]72[/C][C] 0.5189[/C][C] 0.9621[/C][C] 0.4811[/C][/ROW]
[ROW][C]73[/C][C] 0.466[/C][C] 0.932[/C][C] 0.534[/C][/ROW]
[ROW][C]74[/C][C] 0.4993[/C][C] 0.9986[/C][C] 0.5007[/C][/ROW]
[ROW][C]75[/C][C] 0.4794[/C][C] 0.9589[/C][C] 0.5206[/C][/ROW]
[ROW][C]76[/C][C] 0.4338[/C][C] 0.8676[/C][C] 0.5662[/C][/ROW]
[ROW][C]77[/C][C] 0.4298[/C][C] 0.8595[/C][C] 0.5702[/C][/ROW]
[ROW][C]78[/C][C] 0.3738[/C][C] 0.7476[/C][C] 0.6262[/C][/ROW]
[ROW][C]79[/C][C] 0.3471[/C][C] 0.6942[/C][C] 0.6529[/C][/ROW]
[ROW][C]80[/C][C] 0.2835[/C][C] 0.5671[/C][C] 0.7165[/C][/ROW]
[ROW][C]81[/C][C] 0.4655[/C][C] 0.931[/C][C] 0.5345[/C][/ROW]
[ROW][C]82[/C][C] 0.3891[/C][C] 0.7782[/C][C] 0.6109[/C][/ROW]
[ROW][C]83[/C][C] 0.328[/C][C] 0.6561[/C][C] 0.672[/C][/ROW]
[ROW][C]84[/C][C] 0.2676[/C][C] 0.5352[/C][C] 0.7324[/C][/ROW]
[ROW][C]85[/C][C] 0.2076[/C][C] 0.4151[/C][C] 0.7924[/C][/ROW]
[ROW][C]86[/C][C] 0.163[/C][C] 0.326[/C][C] 0.837[/C][/ROW]
[ROW][C]87[/C][C] 0.1734[/C][C] 0.3468[/C][C] 0.8266[/C][/ROW]
[ROW][C]88[/C][C] 0.2396[/C][C] 0.4792[/C][C] 0.7604[/C][/ROW]
[ROW][C]89[/C][C] 0.3111[/C][C] 0.6222[/C][C] 0.6889[/C][/ROW]
[ROW][C]90[/C][C] 0.2329[/C][C] 0.4659[/C][C] 0.7671[/C][/ROW]
[ROW][C]91[/C][C] 0.1807[/C][C] 0.3614[/C][C] 0.8193[/C][/ROW]
[ROW][C]92[/C][C] 0.2281[/C][C] 0.4562[/C][C] 0.7719[/C][/ROW]
[ROW][C]93[/C][C] 0.2481[/C][C] 0.4962[/C][C] 0.7519[/C][/ROW]
[ROW][C]94[/C][C] 0.4794[/C][C] 0.9588[/C][C] 0.5206[/C][/ROW]
[ROW][C]95[/C][C] 0.4111[/C][C] 0.8222[/C][C] 0.5889[/C][/ROW]
[ROW][C]96[/C][C] 0.2734[/C][C] 0.5468[/C][C] 0.7266[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=280425&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=280425&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
12	0.9603	0.07947	0.03974
13	0.9272	0.1456	0.07281
14	0.8709	0.2582	0.1291
15	0.8939	0.2122	0.1061
16	0.838	0.3239	0.162
17	0.7716	0.4567	0.2284
18	0.6929	0.6142	0.3071
19	0.6122	0.7757	0.3878
20	0.5347	0.9307	0.4653
21	0.6708	0.6584	0.3292
22	0.5893	0.8215	0.4107
23	0.5502	0.8995	0.4498
24	0.4859	0.9718	0.5141
25	0.4853	0.9706	0.5147
26	0.569	0.862	0.431
27	0.4949	0.9898	0.5051
28	0.4521	0.9041	0.5479
29	0.5876	0.8248	0.4124
30	0.5287	0.9426	0.4713
31	0.527	0.946	0.473
32	0.4884	0.9768	0.5116
33	0.4204	0.8408	0.5796
34	0.3785	0.757	0.6215
35	0.3702	0.7403	0.6298
36	0.3894	0.7788	0.6106
37	0.3458	0.6917	0.6542
38	0.2905	0.5809	0.7095
39	0.4487	0.8974	0.5513
40	0.4009	0.8018	0.5991
41	0.4436	0.8873	0.5564
42	0.3865	0.7731	0.6135
43	0.4023	0.8045	0.5977
44	0.3556	0.7113	0.6444
45	0.3026	0.6051	0.6974
46	0.3128	0.6256	0.6872
47	0.3525	0.7049	0.6475
48	0.3413	0.6825	0.6587
49	0.2931	0.5862	0.7069
50	0.4343	0.8685	0.5657
51	0.3865	0.7731	0.6135
52	0.3479	0.6958	0.6521
53	0.3326	0.6652	0.6674
54	0.3538	0.7076	0.6462
55	0.5937	0.8126	0.4063
56	0.6174	0.7652	0.3826
57	0.6741	0.6518	0.3259
58	0.6551	0.6898	0.3449
59	0.6295	0.7411	0.3705
60	0.6024	0.7952	0.3976
61	0.6099	0.7801	0.3901
62	0.7003	0.5995	0.2997
63	0.662	0.676	0.338
64	0.6431	0.7138	0.3569
65	0.6367	0.7266	0.3633
66	0.5969	0.8063	0.4031
67	0.6636	0.6729	0.3364
68	0.6246	0.7508	0.3754
69	0.6123	0.7754	0.3877
70	0.5532	0.8937	0.4468
71	0.5706	0.8587	0.4294
72	0.5189	0.9621	0.4811
73	0.466	0.932	0.534
74	0.4993	0.9986	0.5007
75	0.4794	0.9589	0.5206
76	0.4338	0.8676	0.5662
77	0.4298	0.8595	0.5702
78	0.3738	0.7476	0.6262
79	0.3471	0.6942	0.6529
80	0.2835	0.5671	0.7165
81	0.4655	0.931	0.5345
82	0.3891	0.7782	0.6109
83	0.328	0.6561	0.672
84	0.2676	0.5352	0.7324
85	0.2076	0.4151	0.7924
86	0.163	0.326	0.837
87	0.1734	0.3468	0.8266
88	0.2396	0.4792	0.7604
89	0.3111	0.6222	0.6889
90	0.2329	0.4659	0.7671
91	0.1807	0.3614	0.8193
92	0.2281	0.4562	0.7719
93	0.2481	0.4962	0.7519
94	0.4794	0.9588	0.5206
95	0.4111	0.8222	0.5889
96	0.2734	0.5468	0.7266

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	1	0.0117647	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 & 1 & 0.0117647 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=280425&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]1[/C][C]0.0117647[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=280425&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=280425&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	1	0.0117647	OK

Figure 1

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Figure 2

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Figure 3

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Figure 4

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Figure 5

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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Figure 10

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Parameters (Session):

par1 = 8 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 8 ; par2 = Do not include Seasonal Dummies ; par3 = 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
mywarning <- ''
par1 <- as.numeric(par1)
if(is.na(par1)) {
par1 <- 1
mywarning = 'Warning: you did not specify the column number of the endogenous series! The first column was selected by default.'
}
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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, mywarning)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,formatC(signif(mysum$coefficients[i,1],5),format='g',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,2],5),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,3],4),format='e',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4],4),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4]/2,4),format='g',flag=' '))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a,formatC(signif(sqrt(mysum$r.squared),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$adj.r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a,formatC(signif(mysum$fstatistic[1],6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, signif(mysum$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$fstatistic[3],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a,formatC(signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6),format='g',flag=' '))
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,formatC(signif(mysum$sigma,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a,formatC(signif(sum(myerror*myerror),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,formatC(signif(x[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(x[i]-mysum$resid[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$resid[i],6),format='g',flag=' '))
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,formatC(signif(gqarr[mypoint-kp3+1,1],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,2],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,3],6),format='g',flag=' '))
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,formatC(signif(numsignificant1/numgqtests,6),format='g',flag=' '))
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