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rwasp_multipleregression.wasp

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Multiple Regression

Date of computation

Thu, 15 Nov 2012 09:52:23 -0500

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Nov/15/t1352991245wy3vr4ybrur41h0.htm/, Retrieved Thu, 02 May 2024 00:29:09 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=189692, Retrieved Thu, 02 May 2024 00:29:09 +0000

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

-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
- R PD    [Multiple Regression] [Workshop 7 - Mult...] [2012-11-15 14:52:23] [e3d79eec5d0d9e3c05706137ffeca8bc] [Current]

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104,29	103,65	104,12	106,67	105,03
104,56	103,87	104,76	106,86	105,32
104,79	103,94	105,37	107,22	105,52
105,08	105,32	104,97	107,5	105,67
105,21	105,54	105,63	107,35	105,71
105,43	106,08	106,17	107,45	105,81
105,69	106,21	106,05	108,23	105,99
105,74	105,53	106,21	108,39	106,02
106,2	105,56	108,06	108	106,19
106,04	105,14	107,95	107,59	106,22
106,45	105,97	108,22	107,74	106,34
106,4	105,45	107,56	108,17	106,42
106,48	106,22	106,7	108,44	106,84
106,83	106,31	107,38	108,85	107,23
107,14	107,37	107,42	108,8	107,42
107,94	109,31	108,17	109,46	107,63
108,46	110,82	108,89	109,56	107,69
108,81	111,22	108,87	109,94	107,81
108,92	110,66	108,24	111,06	107,92
108,99	110,76	108,23	110,9	108,06
109,16	110,69	109,03	110,79	108,21
109,22	111,08	108,24	111,08	108,44
109,43	110,97	108,01	111,91	108,55
109,23	110,24	107,72	112,09	108,66
109,93	112,51	107,81	112,43	109,23
110,09	111,52	107,98	113,44	109,7
110,33	112,13	108,34	113,4	109,94
110,11	112,23	108,91	112,5	110,13
110,35	112,92	108,78	112,73	110,39
110,09	111,89	108,34	113,12	110,46
110,44	111,99	108,64	113,77	110,67
110,39	111,51	108,68	113,93	110,89
110,62	112,33	109,31	113,41	110,98
110,43	112,04	109,65	112,62	111,12
110,46	112,09	109,07	113,12	111,33
110,55	111,41	109,18	113,65	111,43
110,94	112,61	109,71	113,55	111,87
111,56	113,14	110,68	114,28	112,22
111,82	113,65	111,09	114,31	112,47
111,73	114,26	109,64	115,09	112,64
111,57	114,4	109,08	114,73	112,84
111,85	114,93	109,27	115,13	113,03
112,06	114,86	109,41	115,74	113,09
112,2	114,95	109,99	115,78	113,27
112,47	116,17	110,35	115,42	113,44
112,15	114,6	110,25	115,44	113,51
112,36	114,62	110,33	116	113,66
112,32	113,82	110,29	116,44	113,62
112,67	115,02	110,45	116,38	114,01
113,02	115,18	110,75	117,17	114,55
113,05	115,59	111,15	116,75	114,77
113,5	116,6	111,56	117,5	114,87
113,67	117,07	112,33	117,43	115,11
113,65	116,96	112,13	117,65	115,09
114	116,66	112,49	118,65	115,24
114,03	116,07	113,14	118,58	115,27
114,08	116,04	113,42	118,42	115,41
114,49	115,81	114,67	118,55	115,59
114,48	116,22	114,03	118,77	115,6
114,25	115,85	113,37	118,71	115,68
114,68	116,43	113,2	119,58	116,19
115,28	117,39	114,2	119,97	116,55
115,9	119,17	114,97	119,99	116,73
115,87	119,24	115,72	119,67	117,04
116,09	120,03	115,47	120,04	117,12
116,29	119,34	116,3	120,51	117,28
116,76	118,49	117,66	121,47	117,48
116,78	118,59	118,01	121,2	117,66
116,65	117,5	119,07	120,81	117,92
116,46	117,56	118,29	121,19	118,12
116,82	118,25	117,57	121,67	118,17
116,91	118,01	117,61	121,67	118,39

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	8 seconds
R Server	'Sir Ronald Aylmer Fisher' @ fisher.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 8 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ fisher.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=189692&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]8 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ fisher.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=189692&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=189692&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	8 seconds
R Server	'Sir Ronald Aylmer Fisher' @ fisher.wessa.net

Multiple Linear Regression - Estimated Regression Equation
index[t] = + 14.7636057396207 + 0.306859054576138voeding[t] + 0.190742850008367nietvoeding[t] + 0.334604117049442diensten[t] + 0.0220578495423758huur[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
index[t] =  +  14.7636057396207 +  0.306859054576138voeding[t] +  0.190742850008367nietvoeding[t] +  0.334604117049442diensten[t] +  0.0220578495423758huur[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=189692&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]index[t] =  +  14.7636057396207 +  0.306859054576138voeding[t] +  0.190742850008367nietvoeding[t] +  0.334604117049442diensten[t] +  0.0220578495423758huur[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=189692&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=189692&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
index[t] = + 14.7636057396207 + 0.306859054576138voeding[t] + 0.190742850008367nietvoeding[t] + 0.334604117049442diensten[t] + 0.0220578495423758huur[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)	14.7636057396207	0.762241	19.3687	0	0
voeding	0.306859054576138	0.018508	16.58	0	0
nietvoeding	0.190742850008367	0.016097	11.8497	0	0
diensten	0.334604117049442	0.040944	8.1722	0	0
huur	0.0220578495423758	0.043416	0.5081	0.613078	0.306539

\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) & 14.7636057396207 & 0.762241 & 19.3687 & 0 & 0 \tabularnewline
voeding & 0.306859054576138 & 0.018508 & 16.58 & 0 & 0 \tabularnewline
nietvoeding & 0.190742850008367 & 0.016097 & 11.8497 & 0 & 0 \tabularnewline
diensten & 0.334604117049442 & 0.040944 & 8.1722 & 0 & 0 \tabularnewline
huur & 0.0220578495423758 & 0.043416 & 0.5081 & 0.613078 & 0.306539 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=189692&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]14.7636057396207[/C][C]0.762241[/C][C]19.3687[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]voeding[/C][C]0.306859054576138[/C][C]0.018508[/C][C]16.58[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]nietvoeding[/C][C]0.190742850008367[/C][C]0.016097[/C][C]11.8497[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]diensten[/C][C]0.334604117049442[/C][C]0.040944[/C][C]8.1722[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]huur[/C][C]0.0220578495423758[/C][C]0.043416[/C][C]0.5081[/C][C]0.613078[/C][C]0.306539[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=189692&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=189692&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)	14.7636057396207	0.762241	19.3687	0	0
voeding	0.306859054576138	0.018508	16.58	0	0
nietvoeding	0.190742850008367	0.016097	11.8497	0	0
diensten	0.334604117049442	0.040944	8.1722	0	0
huur	0.0220578495423758	0.043416	0.5081	0.613078	0.306539

Multiple Linear Regression - Regression Statistics
Multiple R	0.998870447041813
R-squared	0.997742169973512
Adjusted R-squared	0.997607374151035
F-TEST (value)	7401.87753329347
F-TEST (DF numerator)	4
F-TEST (DF denominator)	67
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.174807484111826
Sum Squared Residuals	2.04736298560093

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.998870447041813 \tabularnewline
R-squared & 0.997742169973512 \tabularnewline
Adjusted R-squared & 0.997607374151035 \tabularnewline
F-TEST (value) & 7401.87753329347 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 67 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.174807484111826 \tabularnewline
Sum Squared Residuals & 2.04736298560093 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=189692&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.998870447041813[/C][/ROW]
[ROW][C]R-squared[/C][C]0.997742169973512[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.997607374151035[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]7401.87753329347[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]67[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.174807484111826[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]2.04736298560093[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=189692&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=189692&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	0.998870447041813
R-squared	0.997742169973512
Adjusted R-squared	0.997607374151035
F-TEST (value)	7401.87753329347
F-TEST (DF numerator)	4
F-TEST (DF denominator)	67
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.174807484111826
Sum Squared Residuals	2.04736298560093

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	104.29	104.438649392408	-0.148649392408322
2	104.56	104.698205367027	-0.138205367027115
3	104.79	104.960907691399	-0.170907691398819
4	105.08	105.405073876916	-0.325073876915748
5	105.21	105.549164846352	-0.339164846352305
6	105.43	105.853536071487	-0.423536071487109
7	105.69	106.135500230797	-0.445500230797202
8	105.74	106.011553323901	-0.271553323900952
9	106.2	106.246887596827	-0.0468875968266316
10	106.04	105.9604991279	0.0795008721002696
11	106.45	106.319530272203	0.130469727797314
12	106.4	106.179717681112	0.220282318887777
13	106.48	106.35156771054	0.1284322894602
14	106.83	106.654680412769	0.175319587230856
15	107.14	106.975041510181	0.164958489819235
16	107.94	107.938876079221	0.00112392077871975
17	108.46	108.574351986315	-0.114351986314764
18	108.81	108.823077257569	-0.0130772575689173
19	108.92	108.908251166046	0.0117488339539551
20	108.99	108.886581083212	0.103418916788391
21	109.16	108.984197453954	0.175802546046113
22	109.22	109.055294133071	0.164705866928951
23	109.43	109.257816562166	0.172183437833558
24	109.23	109.041149130342	0.188850869658002
25	109.93	109.881224414767	0.0487755852334487
26	110.09	109.958177582742	0.131822417257552
27	110.33	110.205938751245	0.124061248754895
28	110.11	110.048395367276	0.0616046327239605
29	110.35	110.318025532235	0.0319744677651169
30	110.09	110.050073507135	0.039926492864981
31	110.44	110.360107092081	0.0798929079188199
32	110.39	110.278833845512	0.111166154487793
33	110.62	110.478617331363	0.141382668636995
34	110.43	110.193231621006	0.236768378994353
35	110.46	110.269877927658	0.19012207234177
36	110.55	110.261741451038	0.288258548962183
37	110.94	110.707311069127	0.232688930872687
38	111.56	111.306948185347	0.253051814653293
39	111.82	111.557203457581	0.262796542418945
40	111.73	111.732551394081	-0.00255139408112445
41	111.57	111.552649753488	0.0173502465122153
42	111.85	111.889558832148	-0.0395588321475521
43	112.06	112.100214679701	-0.0402146797010859
44	112.2	112.255817425217	-0.0558174252173989
45	112.47	112.582145250088	-0.112145250087708
46	112.15	112.089538381211	0.0604616187887197
47	112.36	112.301621973283	0.0583780267174736
48	112.32	112.194848513141	0.125151486858655
49	112.67	112.582124548933	0.0878754510674
50	113.02	112.964693343889	0.0553066561107549
51	113.05	113.031121694007	0.0188783059926361
52	113.5	113.672412780374	-0.172412780374007
53	113.67	113.945380126228	-0.275380126227944
54	113.65	113.946648808983	-0.29664880898292
55	114	114.261171313094	-0.261171313093896
56	114.03	114.181346770692	-0.15134677069222
57	114.08	114.175100437265	-0.0951004372653088
58	114.49	114.390420365357	0.0995796346426882
59	114.48	114.467990637974	0.0120093620255363
60	114.25	114.210250887716	0.0397491122838033
61	114.68	114.658157939969	0.0218420600314417
62	115.28	115.281921913855	-0.00192191385456
63	115.9	115.985665520765	-0.0856655207651393
64	115.87	116.049967407994	-0.179967407994061
65	116.09	116.370268499879	-0.280268499878806
66	116.29	116.477645508668	-0.187645508668231
67	116.76	116.801857110566	-0.041857110565829
68	116.78	116.812930314841	-0.0329303148406599
69	116.65	116.555880801593	0.094119198406734
70	116.46	116.557074056249	-0.0970740562485846
71	116.82	116.793184820561	0.0268151794390554
72	116.91	116.732021088362	0.177978911637672

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 104.29 & 104.438649392408 & -0.148649392408322 \tabularnewline
2 & 104.56 & 104.698205367027 & -0.138205367027115 \tabularnewline
3 & 104.79 & 104.960907691399 & -0.170907691398819 \tabularnewline
4 & 105.08 & 105.405073876916 & -0.325073876915748 \tabularnewline
5 & 105.21 & 105.549164846352 & -0.339164846352305 \tabularnewline
6 & 105.43 & 105.853536071487 & -0.423536071487109 \tabularnewline
7 & 105.69 & 106.135500230797 & -0.445500230797202 \tabularnewline
8 & 105.74 & 106.011553323901 & -0.271553323900952 \tabularnewline
9 & 106.2 & 106.246887596827 & -0.0468875968266316 \tabularnewline
10 & 106.04 & 105.9604991279 & 0.0795008721002696 \tabularnewline
11 & 106.45 & 106.319530272203 & 0.130469727797314 \tabularnewline
12 & 106.4 & 106.179717681112 & 0.220282318887777 \tabularnewline
13 & 106.48 & 106.35156771054 & 0.1284322894602 \tabularnewline
14 & 106.83 & 106.654680412769 & 0.175319587230856 \tabularnewline
15 & 107.14 & 106.975041510181 & 0.164958489819235 \tabularnewline
16 & 107.94 & 107.938876079221 & 0.00112392077871975 \tabularnewline
17 & 108.46 & 108.574351986315 & -0.114351986314764 \tabularnewline
18 & 108.81 & 108.823077257569 & -0.0130772575689173 \tabularnewline
19 & 108.92 & 108.908251166046 & 0.0117488339539551 \tabularnewline
20 & 108.99 & 108.886581083212 & 0.103418916788391 \tabularnewline
21 & 109.16 & 108.984197453954 & 0.175802546046113 \tabularnewline
22 & 109.22 & 109.055294133071 & 0.164705866928951 \tabularnewline
23 & 109.43 & 109.257816562166 & 0.172183437833558 \tabularnewline
24 & 109.23 & 109.041149130342 & 0.188850869658002 \tabularnewline
25 & 109.93 & 109.881224414767 & 0.0487755852334487 \tabularnewline
26 & 110.09 & 109.958177582742 & 0.131822417257552 \tabularnewline
27 & 110.33 & 110.205938751245 & 0.124061248754895 \tabularnewline
28 & 110.11 & 110.048395367276 & 0.0616046327239605 \tabularnewline
29 & 110.35 & 110.318025532235 & 0.0319744677651169 \tabularnewline
30 & 110.09 & 110.050073507135 & 0.039926492864981 \tabularnewline
31 & 110.44 & 110.360107092081 & 0.0798929079188199 \tabularnewline
32 & 110.39 & 110.278833845512 & 0.111166154487793 \tabularnewline
33 & 110.62 & 110.478617331363 & 0.141382668636995 \tabularnewline
34 & 110.43 & 110.193231621006 & 0.236768378994353 \tabularnewline
35 & 110.46 & 110.269877927658 & 0.19012207234177 \tabularnewline
36 & 110.55 & 110.261741451038 & 0.288258548962183 \tabularnewline
37 & 110.94 & 110.707311069127 & 0.232688930872687 \tabularnewline
38 & 111.56 & 111.306948185347 & 0.253051814653293 \tabularnewline
39 & 111.82 & 111.557203457581 & 0.262796542418945 \tabularnewline
40 & 111.73 & 111.732551394081 & -0.00255139408112445 \tabularnewline
41 & 111.57 & 111.552649753488 & 0.0173502465122153 \tabularnewline
42 & 111.85 & 111.889558832148 & -0.0395588321475521 \tabularnewline
43 & 112.06 & 112.100214679701 & -0.0402146797010859 \tabularnewline
44 & 112.2 & 112.255817425217 & -0.0558174252173989 \tabularnewline
45 & 112.47 & 112.582145250088 & -0.112145250087708 \tabularnewline
46 & 112.15 & 112.089538381211 & 0.0604616187887197 \tabularnewline
47 & 112.36 & 112.301621973283 & 0.0583780267174736 \tabularnewline
48 & 112.32 & 112.194848513141 & 0.125151486858655 \tabularnewline
49 & 112.67 & 112.582124548933 & 0.0878754510674 \tabularnewline
50 & 113.02 & 112.964693343889 & 0.0553066561107549 \tabularnewline
51 & 113.05 & 113.031121694007 & 0.0188783059926361 \tabularnewline
52 & 113.5 & 113.672412780374 & -0.172412780374007 \tabularnewline
53 & 113.67 & 113.945380126228 & -0.275380126227944 \tabularnewline
54 & 113.65 & 113.946648808983 & -0.29664880898292 \tabularnewline
55 & 114 & 114.261171313094 & -0.261171313093896 \tabularnewline
56 & 114.03 & 114.181346770692 & -0.15134677069222 \tabularnewline
57 & 114.08 & 114.175100437265 & -0.0951004372653088 \tabularnewline
58 & 114.49 & 114.390420365357 & 0.0995796346426882 \tabularnewline
59 & 114.48 & 114.467990637974 & 0.0120093620255363 \tabularnewline
60 & 114.25 & 114.210250887716 & 0.0397491122838033 \tabularnewline
61 & 114.68 & 114.658157939969 & 0.0218420600314417 \tabularnewline
62 & 115.28 & 115.281921913855 & -0.00192191385456 \tabularnewline
63 & 115.9 & 115.985665520765 & -0.0856655207651393 \tabularnewline
64 & 115.87 & 116.049967407994 & -0.179967407994061 \tabularnewline
65 & 116.09 & 116.370268499879 & -0.280268499878806 \tabularnewline
66 & 116.29 & 116.477645508668 & -0.187645508668231 \tabularnewline
67 & 116.76 & 116.801857110566 & -0.041857110565829 \tabularnewline
68 & 116.78 & 116.812930314841 & -0.0329303148406599 \tabularnewline
69 & 116.65 & 116.555880801593 & 0.094119198406734 \tabularnewline
70 & 116.46 & 116.557074056249 & -0.0970740562485846 \tabularnewline
71 & 116.82 & 116.793184820561 & 0.0268151794390554 \tabularnewline
72 & 116.91 & 116.732021088362 & 0.177978911637672 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=189692&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]104.29[/C][C]104.438649392408[/C][C]-0.148649392408322[/C][/ROW]
[ROW][C]2[/C][C]104.56[/C][C]104.698205367027[/C][C]-0.138205367027115[/C][/ROW]
[ROW][C]3[/C][C]104.79[/C][C]104.960907691399[/C][C]-0.170907691398819[/C][/ROW]
[ROW][C]4[/C][C]105.08[/C][C]105.405073876916[/C][C]-0.325073876915748[/C][/ROW]
[ROW][C]5[/C][C]105.21[/C][C]105.549164846352[/C][C]-0.339164846352305[/C][/ROW]
[ROW][C]6[/C][C]105.43[/C][C]105.853536071487[/C][C]-0.423536071487109[/C][/ROW]
[ROW][C]7[/C][C]105.69[/C][C]106.135500230797[/C][C]-0.445500230797202[/C][/ROW]
[ROW][C]8[/C][C]105.74[/C][C]106.011553323901[/C][C]-0.271553323900952[/C][/ROW]
[ROW][C]9[/C][C]106.2[/C][C]106.246887596827[/C][C]-0.0468875968266316[/C][/ROW]
[ROW][C]10[/C][C]106.04[/C][C]105.9604991279[/C][C]0.0795008721002696[/C][/ROW]
[ROW][C]11[/C][C]106.45[/C][C]106.319530272203[/C][C]0.130469727797314[/C][/ROW]
[ROW][C]12[/C][C]106.4[/C][C]106.179717681112[/C][C]0.220282318887777[/C][/ROW]
[ROW][C]13[/C][C]106.48[/C][C]106.35156771054[/C][C]0.1284322894602[/C][/ROW]
[ROW][C]14[/C][C]106.83[/C][C]106.654680412769[/C][C]0.175319587230856[/C][/ROW]
[ROW][C]15[/C][C]107.14[/C][C]106.975041510181[/C][C]0.164958489819235[/C][/ROW]
[ROW][C]16[/C][C]107.94[/C][C]107.938876079221[/C][C]0.00112392077871975[/C][/ROW]
[ROW][C]17[/C][C]108.46[/C][C]108.574351986315[/C][C]-0.114351986314764[/C][/ROW]
[ROW][C]18[/C][C]108.81[/C][C]108.823077257569[/C][C]-0.0130772575689173[/C][/ROW]
[ROW][C]19[/C][C]108.92[/C][C]108.908251166046[/C][C]0.0117488339539551[/C][/ROW]
[ROW][C]20[/C][C]108.99[/C][C]108.886581083212[/C][C]0.103418916788391[/C][/ROW]
[ROW][C]21[/C][C]109.16[/C][C]108.984197453954[/C][C]0.175802546046113[/C][/ROW]
[ROW][C]22[/C][C]109.22[/C][C]109.055294133071[/C][C]0.164705866928951[/C][/ROW]
[ROW][C]23[/C][C]109.43[/C][C]109.257816562166[/C][C]0.172183437833558[/C][/ROW]
[ROW][C]24[/C][C]109.23[/C][C]109.041149130342[/C][C]0.188850869658002[/C][/ROW]
[ROW][C]25[/C][C]109.93[/C][C]109.881224414767[/C][C]0.0487755852334487[/C][/ROW]
[ROW][C]26[/C][C]110.09[/C][C]109.958177582742[/C][C]0.131822417257552[/C][/ROW]
[ROW][C]27[/C][C]110.33[/C][C]110.205938751245[/C][C]0.124061248754895[/C][/ROW]
[ROW][C]28[/C][C]110.11[/C][C]110.048395367276[/C][C]0.0616046327239605[/C][/ROW]
[ROW][C]29[/C][C]110.35[/C][C]110.318025532235[/C][C]0.0319744677651169[/C][/ROW]
[ROW][C]30[/C][C]110.09[/C][C]110.050073507135[/C][C]0.039926492864981[/C][/ROW]
[ROW][C]31[/C][C]110.44[/C][C]110.360107092081[/C][C]0.0798929079188199[/C][/ROW]
[ROW][C]32[/C][C]110.39[/C][C]110.278833845512[/C][C]0.111166154487793[/C][/ROW]
[ROW][C]33[/C][C]110.62[/C][C]110.478617331363[/C][C]0.141382668636995[/C][/ROW]
[ROW][C]34[/C][C]110.43[/C][C]110.193231621006[/C][C]0.236768378994353[/C][/ROW]
[ROW][C]35[/C][C]110.46[/C][C]110.269877927658[/C][C]0.19012207234177[/C][/ROW]
[ROW][C]36[/C][C]110.55[/C][C]110.261741451038[/C][C]0.288258548962183[/C][/ROW]
[ROW][C]37[/C][C]110.94[/C][C]110.707311069127[/C][C]0.232688930872687[/C][/ROW]
[ROW][C]38[/C][C]111.56[/C][C]111.306948185347[/C][C]0.253051814653293[/C][/ROW]
[ROW][C]39[/C][C]111.82[/C][C]111.557203457581[/C][C]0.262796542418945[/C][/ROW]
[ROW][C]40[/C][C]111.73[/C][C]111.732551394081[/C][C]-0.00255139408112445[/C][/ROW]
[ROW][C]41[/C][C]111.57[/C][C]111.552649753488[/C][C]0.0173502465122153[/C][/ROW]
[ROW][C]42[/C][C]111.85[/C][C]111.889558832148[/C][C]-0.0395588321475521[/C][/ROW]
[ROW][C]43[/C][C]112.06[/C][C]112.100214679701[/C][C]-0.0402146797010859[/C][/ROW]
[ROW][C]44[/C][C]112.2[/C][C]112.255817425217[/C][C]-0.0558174252173989[/C][/ROW]
[ROW][C]45[/C][C]112.47[/C][C]112.582145250088[/C][C]-0.112145250087708[/C][/ROW]
[ROW][C]46[/C][C]112.15[/C][C]112.089538381211[/C][C]0.0604616187887197[/C][/ROW]
[ROW][C]47[/C][C]112.36[/C][C]112.301621973283[/C][C]0.0583780267174736[/C][/ROW]
[ROW][C]48[/C][C]112.32[/C][C]112.194848513141[/C][C]0.125151486858655[/C][/ROW]
[ROW][C]49[/C][C]112.67[/C][C]112.582124548933[/C][C]0.0878754510674[/C][/ROW]
[ROW][C]50[/C][C]113.02[/C][C]112.964693343889[/C][C]0.0553066561107549[/C][/ROW]
[ROW][C]51[/C][C]113.05[/C][C]113.031121694007[/C][C]0.0188783059926361[/C][/ROW]
[ROW][C]52[/C][C]113.5[/C][C]113.672412780374[/C][C]-0.172412780374007[/C][/ROW]
[ROW][C]53[/C][C]113.67[/C][C]113.945380126228[/C][C]-0.275380126227944[/C][/ROW]
[ROW][C]54[/C][C]113.65[/C][C]113.946648808983[/C][C]-0.29664880898292[/C][/ROW]
[ROW][C]55[/C][C]114[/C][C]114.261171313094[/C][C]-0.261171313093896[/C][/ROW]
[ROW][C]56[/C][C]114.03[/C][C]114.181346770692[/C][C]-0.15134677069222[/C][/ROW]
[ROW][C]57[/C][C]114.08[/C][C]114.175100437265[/C][C]-0.0951004372653088[/C][/ROW]
[ROW][C]58[/C][C]114.49[/C][C]114.390420365357[/C][C]0.0995796346426882[/C][/ROW]
[ROW][C]59[/C][C]114.48[/C][C]114.467990637974[/C][C]0.0120093620255363[/C][/ROW]
[ROW][C]60[/C][C]114.25[/C][C]114.210250887716[/C][C]0.0397491122838033[/C][/ROW]
[ROW][C]61[/C][C]114.68[/C][C]114.658157939969[/C][C]0.0218420600314417[/C][/ROW]
[ROW][C]62[/C][C]115.28[/C][C]115.281921913855[/C][C]-0.00192191385456[/C][/ROW]
[ROW][C]63[/C][C]115.9[/C][C]115.985665520765[/C][C]-0.0856655207651393[/C][/ROW]
[ROW][C]64[/C][C]115.87[/C][C]116.049967407994[/C][C]-0.179967407994061[/C][/ROW]
[ROW][C]65[/C][C]116.09[/C][C]116.370268499879[/C][C]-0.280268499878806[/C][/ROW]
[ROW][C]66[/C][C]116.29[/C][C]116.477645508668[/C][C]-0.187645508668231[/C][/ROW]
[ROW][C]67[/C][C]116.76[/C][C]116.801857110566[/C][C]-0.041857110565829[/C][/ROW]
[ROW][C]68[/C][C]116.78[/C][C]116.812930314841[/C][C]-0.0329303148406599[/C][/ROW]
[ROW][C]69[/C][C]116.65[/C][C]116.555880801593[/C][C]0.094119198406734[/C][/ROW]
[ROW][C]70[/C][C]116.46[/C][C]116.557074056249[/C][C]-0.0970740562485846[/C][/ROW]
[ROW][C]71[/C][C]116.82[/C][C]116.793184820561[/C][C]0.0268151794390554[/C][/ROW]
[ROW][C]72[/C][C]116.91[/C][C]116.732021088362[/C][C]0.177978911637672[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=189692&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=189692&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	104.29	104.438649392408	-0.148649392408322
2	104.56	104.698205367027	-0.138205367027115
3	104.79	104.960907691399	-0.170907691398819
4	105.08	105.405073876916	-0.325073876915748
5	105.21	105.549164846352	-0.339164846352305
6	105.43	105.853536071487	-0.423536071487109
7	105.69	106.135500230797	-0.445500230797202
8	105.74	106.011553323901	-0.271553323900952
9	106.2	106.246887596827	-0.0468875968266316
10	106.04	105.9604991279	0.0795008721002696
11	106.45	106.319530272203	0.130469727797314
12	106.4	106.179717681112	0.220282318887777
13	106.48	106.35156771054	0.1284322894602
14	106.83	106.654680412769	0.175319587230856
15	107.14	106.975041510181	0.164958489819235
16	107.94	107.938876079221	0.00112392077871975
17	108.46	108.574351986315	-0.114351986314764
18	108.81	108.823077257569	-0.0130772575689173
19	108.92	108.908251166046	0.0117488339539551
20	108.99	108.886581083212	0.103418916788391
21	109.16	108.984197453954	0.175802546046113
22	109.22	109.055294133071	0.164705866928951
23	109.43	109.257816562166	0.172183437833558
24	109.23	109.041149130342	0.188850869658002
25	109.93	109.881224414767	0.0487755852334487
26	110.09	109.958177582742	0.131822417257552
27	110.33	110.205938751245	0.124061248754895
28	110.11	110.048395367276	0.0616046327239605
29	110.35	110.318025532235	0.0319744677651169
30	110.09	110.050073507135	0.039926492864981
31	110.44	110.360107092081	0.0798929079188199
32	110.39	110.278833845512	0.111166154487793
33	110.62	110.478617331363	0.141382668636995
34	110.43	110.193231621006	0.236768378994353
35	110.46	110.269877927658	0.19012207234177
36	110.55	110.261741451038	0.288258548962183
37	110.94	110.707311069127	0.232688930872687
38	111.56	111.306948185347	0.253051814653293
39	111.82	111.557203457581	0.262796542418945
40	111.73	111.732551394081	-0.00255139408112445
41	111.57	111.552649753488	0.0173502465122153
42	111.85	111.889558832148	-0.0395588321475521
43	112.06	112.100214679701	-0.0402146797010859
44	112.2	112.255817425217	-0.0558174252173989
45	112.47	112.582145250088	-0.112145250087708
46	112.15	112.089538381211	0.0604616187887197
47	112.36	112.301621973283	0.0583780267174736
48	112.32	112.194848513141	0.125151486858655
49	112.67	112.582124548933	0.0878754510674
50	113.02	112.964693343889	0.0553066561107549
51	113.05	113.031121694007	0.0188783059926361
52	113.5	113.672412780374	-0.172412780374007
53	113.67	113.945380126228	-0.275380126227944
54	113.65	113.946648808983	-0.29664880898292
55	114	114.261171313094	-0.261171313093896
56	114.03	114.181346770692	-0.15134677069222
57	114.08	114.175100437265	-0.0951004372653088
58	114.49	114.390420365357	0.0995796346426882
59	114.48	114.467990637974	0.0120093620255363
60	114.25	114.210250887716	0.0397491122838033
61	114.68	114.658157939969	0.0218420600314417
62	115.28	115.281921913855	-0.00192191385456
63	115.9	115.985665520765	-0.0856655207651393
64	115.87	116.049967407994	-0.179967407994061
65	116.09	116.370268499879	-0.280268499878806
66	116.29	116.477645508668	-0.187645508668231
67	116.76	116.801857110566	-0.041857110565829
68	116.78	116.812930314841	-0.0329303148406599
69	116.65	116.555880801593	0.094119198406734
70	116.46	116.557074056249	-0.0970740562485846
71	116.82	116.793184820561	0.0268151794390554
72	116.91	116.732021088362	0.177978911637672

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
8	0.039858317024633	0.079716634049266	0.960141682975367
9	0.122014137174446	0.244028274348893	0.877985862825553
10	0.0986200980937401	0.19724019618748	0.90137990190626
11	0.304580764316889	0.609161528633778	0.695419235683111
12	0.384923044647639	0.769846089295277	0.615076955352361
13	0.348707198677177	0.697414397354354	0.651292801322823
14	0.445413891732022	0.890827783464044	0.554586108267978
15	0.432830986002731	0.865661972005462	0.567169013997269
16	0.684790060228158	0.630419879543684	0.315209939771842
17	0.831544772103714	0.336910455792573	0.168455227896286
18	0.939789109754061	0.120421780491877	0.0602108902459386
19	0.97480192788604	0.0503961442279196	0.0251980721139598
20	0.975862357252509	0.0482752854949824	0.0241376427474912
21	0.966691103248551	0.0666177935028989	0.0333088967514494
22	0.950535002261798	0.0989299954764044	0.0494649977382022
23	0.928727770911913	0.142544458176173	0.0712722290880865
24	0.914311663598909	0.171376672802182	0.0856883364010909
25	0.919897925781504	0.160204148436992	0.0801020742184962
26	0.96710997084182	0.0657800583163597	0.0328900291581799
27	0.983627976612584	0.0327440467748325	0.0163720233874163
28	0.995175551932977	0.00964889613404568	0.00482444806702284
29	0.995602819589573	0.00879436082085375	0.00439718041042687
30	0.996456599755985	0.00708680048803017	0.00354340024401509
31	0.996352329569062	0.00729534086187603	0.00364767043093802
32	0.996368429738288	0.00726314052342496	0.00363157026171248
33	0.994510229078417	0.0109795418431669	0.00548977092158346
34	0.991065768501535	0.0178684629969299	0.00893423149846493
35	0.986262601205472	0.0274747975890553	0.0137373987945276
36	0.978490116626886	0.0430197667462274	0.0215098833731137
37	0.967396504851478	0.065206990297045	0.0326034951485225
38	0.962793704678228	0.0744125906435433	0.0372062953217717
39	0.972071887207503	0.0558562255849931	0.0279281127924965
40	0.973566477231478	0.052867045537043	0.0264335227685215
41	0.962691455666171	0.0746170886676573	0.0373085443338287
42	0.952311492395971	0.0953770152080586	0.0476885076040293
43	0.947268774748977	0.105462450502046	0.0527312252510231
44	0.948669378684647	0.102661242630705	0.0513306213153525
45	0.948863731665604	0.102272536668792	0.0511362683343961
46	0.938847762592836	0.122304474814329	0.0611522374071645
47	0.934416738784112	0.131166522431776	0.0655832612158879
48	0.930433552884417	0.139132894231167	0.0695664471155834
49	0.953138571965909	0.0937228560681817	0.0468614280340909
50	0.956249858147837	0.0875002837043266	0.0437501418521633
51	0.966823753773814	0.0663524924523713	0.0331762462261857
52	0.97544016815346	0.0491196636930803	0.0245598318465402
53	0.984975993941315	0.0300480121173699	0.0150240060586849
54	0.99103003808002	0.0179399238399599	0.00896996191997996
55	0.996826641916664	0.00634671616667199	0.003173358083336
56	0.997826647087691	0.00434670582461855	0.00217335291230928
57	0.997459988861639	0.0050800222767227	0.00254001113836135
58	0.995212195406331	0.00957560918733763	0.00478780459366881
59	0.988545821093366	0.0229083578132688	0.0114541789066344
60	0.973771050201648	0.0524578995967042	0.0262289497983521
61	0.94597651307587	0.10804697384826	0.0540234869241299
62	0.899529580486873	0.200940839026254	0.100470419513127
63	0.850607232940014	0.298785534119972	0.149392767059986
64	0.816975270733781	0.366049458532438	0.183024729266219

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 0.039858317024633 & 0.079716634049266 & 0.960141682975367 \tabularnewline
9 & 0.122014137174446 & 0.244028274348893 & 0.877985862825553 \tabularnewline
10 & 0.0986200980937401 & 0.19724019618748 & 0.90137990190626 \tabularnewline
11 & 0.304580764316889 & 0.609161528633778 & 0.695419235683111 \tabularnewline
12 & 0.384923044647639 & 0.769846089295277 & 0.615076955352361 \tabularnewline
13 & 0.348707198677177 & 0.697414397354354 & 0.651292801322823 \tabularnewline
14 & 0.445413891732022 & 0.890827783464044 & 0.554586108267978 \tabularnewline
15 & 0.432830986002731 & 0.865661972005462 & 0.567169013997269 \tabularnewline
16 & 0.684790060228158 & 0.630419879543684 & 0.315209939771842 \tabularnewline
17 & 0.831544772103714 & 0.336910455792573 & 0.168455227896286 \tabularnewline
18 & 0.939789109754061 & 0.120421780491877 & 0.0602108902459386 \tabularnewline
19 & 0.97480192788604 & 0.0503961442279196 & 0.0251980721139598 \tabularnewline
20 & 0.975862357252509 & 0.0482752854949824 & 0.0241376427474912 \tabularnewline
21 & 0.966691103248551 & 0.0666177935028989 & 0.0333088967514494 \tabularnewline
22 & 0.950535002261798 & 0.0989299954764044 & 0.0494649977382022 \tabularnewline
23 & 0.928727770911913 & 0.142544458176173 & 0.0712722290880865 \tabularnewline
24 & 0.914311663598909 & 0.171376672802182 & 0.0856883364010909 \tabularnewline
25 & 0.919897925781504 & 0.160204148436992 & 0.0801020742184962 \tabularnewline
26 & 0.96710997084182 & 0.0657800583163597 & 0.0328900291581799 \tabularnewline
27 & 0.983627976612584 & 0.0327440467748325 & 0.0163720233874163 \tabularnewline
28 & 0.995175551932977 & 0.00964889613404568 & 0.00482444806702284 \tabularnewline
29 & 0.995602819589573 & 0.00879436082085375 & 0.00439718041042687 \tabularnewline
30 & 0.996456599755985 & 0.00708680048803017 & 0.00354340024401509 \tabularnewline
31 & 0.996352329569062 & 0.00729534086187603 & 0.00364767043093802 \tabularnewline
32 & 0.996368429738288 & 0.00726314052342496 & 0.00363157026171248 \tabularnewline
33 & 0.994510229078417 & 0.0109795418431669 & 0.00548977092158346 \tabularnewline
34 & 0.991065768501535 & 0.0178684629969299 & 0.00893423149846493 \tabularnewline
35 & 0.986262601205472 & 0.0274747975890553 & 0.0137373987945276 \tabularnewline
36 & 0.978490116626886 & 0.0430197667462274 & 0.0215098833731137 \tabularnewline
37 & 0.967396504851478 & 0.065206990297045 & 0.0326034951485225 \tabularnewline
38 & 0.962793704678228 & 0.0744125906435433 & 0.0372062953217717 \tabularnewline
39 & 0.972071887207503 & 0.0558562255849931 & 0.0279281127924965 \tabularnewline
40 & 0.973566477231478 & 0.052867045537043 & 0.0264335227685215 \tabularnewline
41 & 0.962691455666171 & 0.0746170886676573 & 0.0373085443338287 \tabularnewline
42 & 0.952311492395971 & 0.0953770152080586 & 0.0476885076040293 \tabularnewline
43 & 0.947268774748977 & 0.105462450502046 & 0.0527312252510231 \tabularnewline
44 & 0.948669378684647 & 0.102661242630705 & 0.0513306213153525 \tabularnewline
45 & 0.948863731665604 & 0.102272536668792 & 0.0511362683343961 \tabularnewline
46 & 0.938847762592836 & 0.122304474814329 & 0.0611522374071645 \tabularnewline
47 & 0.934416738784112 & 0.131166522431776 & 0.0655832612158879 \tabularnewline
48 & 0.930433552884417 & 0.139132894231167 & 0.0695664471155834 \tabularnewline
49 & 0.953138571965909 & 0.0937228560681817 & 0.0468614280340909 \tabularnewline
50 & 0.956249858147837 & 0.0875002837043266 & 0.0437501418521633 \tabularnewline
51 & 0.966823753773814 & 0.0663524924523713 & 0.0331762462261857 \tabularnewline
52 & 0.97544016815346 & 0.0491196636930803 & 0.0245598318465402 \tabularnewline
53 & 0.984975993941315 & 0.0300480121173699 & 0.0150240060586849 \tabularnewline
54 & 0.99103003808002 & 0.0179399238399599 & 0.00896996191997996 \tabularnewline
55 & 0.996826641916664 & 0.00634671616667199 & 0.003173358083336 \tabularnewline
56 & 0.997826647087691 & 0.00434670582461855 & 0.00217335291230928 \tabularnewline
57 & 0.997459988861639 & 0.0050800222767227 & 0.00254001113836135 \tabularnewline
58 & 0.995212195406331 & 0.00957560918733763 & 0.00478780459366881 \tabularnewline
59 & 0.988545821093366 & 0.0229083578132688 & 0.0114541789066344 \tabularnewline
60 & 0.973771050201648 & 0.0524578995967042 & 0.0262289497983521 \tabularnewline
61 & 0.94597651307587 & 0.10804697384826 & 0.0540234869241299 \tabularnewline
62 & 0.899529580486873 & 0.200940839026254 & 0.100470419513127 \tabularnewline
63 & 0.850607232940014 & 0.298785534119972 & 0.149392767059986 \tabularnewline
64 & 0.816975270733781 & 0.366049458532438 & 0.183024729266219 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=189692&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]8[/C][C]0.039858317024633[/C][C]0.079716634049266[/C][C]0.960141682975367[/C][/ROW]
[ROW][C]9[/C][C]0.122014137174446[/C][C]0.244028274348893[/C][C]0.877985862825553[/C][/ROW]
[ROW][C]10[/C][C]0.0986200980937401[/C][C]0.19724019618748[/C][C]0.90137990190626[/C][/ROW]
[ROW][C]11[/C][C]0.304580764316889[/C][C]0.609161528633778[/C][C]0.695419235683111[/C][/ROW]
[ROW][C]12[/C][C]0.384923044647639[/C][C]0.769846089295277[/C][C]0.615076955352361[/C][/ROW]
[ROW][C]13[/C][C]0.348707198677177[/C][C]0.697414397354354[/C][C]0.651292801322823[/C][/ROW]
[ROW][C]14[/C][C]0.445413891732022[/C][C]0.890827783464044[/C][C]0.554586108267978[/C][/ROW]
[ROW][C]15[/C][C]0.432830986002731[/C][C]0.865661972005462[/C][C]0.567169013997269[/C][/ROW]
[ROW][C]16[/C][C]0.684790060228158[/C][C]0.630419879543684[/C][C]0.315209939771842[/C][/ROW]
[ROW][C]17[/C][C]0.831544772103714[/C][C]0.336910455792573[/C][C]0.168455227896286[/C][/ROW]
[ROW][C]18[/C][C]0.939789109754061[/C][C]0.120421780491877[/C][C]0.0602108902459386[/C][/ROW]
[ROW][C]19[/C][C]0.97480192788604[/C][C]0.0503961442279196[/C][C]0.0251980721139598[/C][/ROW]
[ROW][C]20[/C][C]0.975862357252509[/C][C]0.0482752854949824[/C][C]0.0241376427474912[/C][/ROW]
[ROW][C]21[/C][C]0.966691103248551[/C][C]0.0666177935028989[/C][C]0.0333088967514494[/C][/ROW]
[ROW][C]22[/C][C]0.950535002261798[/C][C]0.0989299954764044[/C][C]0.0494649977382022[/C][/ROW]
[ROW][C]23[/C][C]0.928727770911913[/C][C]0.142544458176173[/C][C]0.0712722290880865[/C][/ROW]
[ROW][C]24[/C][C]0.914311663598909[/C][C]0.171376672802182[/C][C]0.0856883364010909[/C][/ROW]
[ROW][C]25[/C][C]0.919897925781504[/C][C]0.160204148436992[/C][C]0.0801020742184962[/C][/ROW]
[ROW][C]26[/C][C]0.96710997084182[/C][C]0.0657800583163597[/C][C]0.0328900291581799[/C][/ROW]
[ROW][C]27[/C][C]0.983627976612584[/C][C]0.0327440467748325[/C][C]0.0163720233874163[/C][/ROW]
[ROW][C]28[/C][C]0.995175551932977[/C][C]0.00964889613404568[/C][C]0.00482444806702284[/C][/ROW]
[ROW][C]29[/C][C]0.995602819589573[/C][C]0.00879436082085375[/C][C]0.00439718041042687[/C][/ROW]
[ROW][C]30[/C][C]0.996456599755985[/C][C]0.00708680048803017[/C][C]0.00354340024401509[/C][/ROW]
[ROW][C]31[/C][C]0.996352329569062[/C][C]0.00729534086187603[/C][C]0.00364767043093802[/C][/ROW]
[ROW][C]32[/C][C]0.996368429738288[/C][C]0.00726314052342496[/C][C]0.00363157026171248[/C][/ROW]
[ROW][C]33[/C][C]0.994510229078417[/C][C]0.0109795418431669[/C][C]0.00548977092158346[/C][/ROW]
[ROW][C]34[/C][C]0.991065768501535[/C][C]0.0178684629969299[/C][C]0.00893423149846493[/C][/ROW]
[ROW][C]35[/C][C]0.986262601205472[/C][C]0.0274747975890553[/C][C]0.0137373987945276[/C][/ROW]
[ROW][C]36[/C][C]0.978490116626886[/C][C]0.0430197667462274[/C][C]0.0215098833731137[/C][/ROW]
[ROW][C]37[/C][C]0.967396504851478[/C][C]0.065206990297045[/C][C]0.0326034951485225[/C][/ROW]
[ROW][C]38[/C][C]0.962793704678228[/C][C]0.0744125906435433[/C][C]0.0372062953217717[/C][/ROW]
[ROW][C]39[/C][C]0.972071887207503[/C][C]0.0558562255849931[/C][C]0.0279281127924965[/C][/ROW]
[ROW][C]40[/C][C]0.973566477231478[/C][C]0.052867045537043[/C][C]0.0264335227685215[/C][/ROW]
[ROW][C]41[/C][C]0.962691455666171[/C][C]0.0746170886676573[/C][C]0.0373085443338287[/C][/ROW]
[ROW][C]42[/C][C]0.952311492395971[/C][C]0.0953770152080586[/C][C]0.0476885076040293[/C][/ROW]
[ROW][C]43[/C][C]0.947268774748977[/C][C]0.105462450502046[/C][C]0.0527312252510231[/C][/ROW]
[ROW][C]44[/C][C]0.948669378684647[/C][C]0.102661242630705[/C][C]0.0513306213153525[/C][/ROW]
[ROW][C]45[/C][C]0.948863731665604[/C][C]0.102272536668792[/C][C]0.0511362683343961[/C][/ROW]
[ROW][C]46[/C][C]0.938847762592836[/C][C]0.122304474814329[/C][C]0.0611522374071645[/C][/ROW]
[ROW][C]47[/C][C]0.934416738784112[/C][C]0.131166522431776[/C][C]0.0655832612158879[/C][/ROW]
[ROW][C]48[/C][C]0.930433552884417[/C][C]0.139132894231167[/C][C]0.0695664471155834[/C][/ROW]
[ROW][C]49[/C][C]0.953138571965909[/C][C]0.0937228560681817[/C][C]0.0468614280340909[/C][/ROW]
[ROW][C]50[/C][C]0.956249858147837[/C][C]0.0875002837043266[/C][C]0.0437501418521633[/C][/ROW]
[ROW][C]51[/C][C]0.966823753773814[/C][C]0.0663524924523713[/C][C]0.0331762462261857[/C][/ROW]
[ROW][C]52[/C][C]0.97544016815346[/C][C]0.0491196636930803[/C][C]0.0245598318465402[/C][/ROW]
[ROW][C]53[/C][C]0.984975993941315[/C][C]0.0300480121173699[/C][C]0.0150240060586849[/C][/ROW]
[ROW][C]54[/C][C]0.99103003808002[/C][C]0.0179399238399599[/C][C]0.00896996191997996[/C][/ROW]
[ROW][C]55[/C][C]0.996826641916664[/C][C]0.00634671616667199[/C][C]0.003173358083336[/C][/ROW]
[ROW][C]56[/C][C]0.997826647087691[/C][C]0.00434670582461855[/C][C]0.00217335291230928[/C][/ROW]
[ROW][C]57[/C][C]0.997459988861639[/C][C]0.0050800222767227[/C][C]0.00254001113836135[/C][/ROW]
[ROW][C]58[/C][C]0.995212195406331[/C][C]0.00957560918733763[/C][C]0.00478780459366881[/C][/ROW]
[ROW][C]59[/C][C]0.988545821093366[/C][C]0.0229083578132688[/C][C]0.0114541789066344[/C][/ROW]
[ROW][C]60[/C][C]0.973771050201648[/C][C]0.0524578995967042[/C][C]0.0262289497983521[/C][/ROW]
[ROW][C]61[/C][C]0.94597651307587[/C][C]0.10804697384826[/C][C]0.0540234869241299[/C][/ROW]
[ROW][C]62[/C][C]0.899529580486873[/C][C]0.200940839026254[/C][C]0.100470419513127[/C][/ROW]
[ROW][C]63[/C][C]0.850607232940014[/C][C]0.298785534119972[/C][C]0.149392767059986[/C][/ROW]
[ROW][C]64[/C][C]0.816975270733781[/C][C]0.366049458532438[/C][C]0.183024729266219[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=189692&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=189692&T=5

As an alternative you can also use a QR Code:

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

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
8	0.039858317024633	0.079716634049266	0.960141682975367
9	0.122014137174446	0.244028274348893	0.877985862825553
10	0.0986200980937401	0.19724019618748	0.90137990190626
11	0.304580764316889	0.609161528633778	0.695419235683111
12	0.384923044647639	0.769846089295277	0.615076955352361
13	0.348707198677177	0.697414397354354	0.651292801322823
14	0.445413891732022	0.890827783464044	0.554586108267978
15	0.432830986002731	0.865661972005462	0.567169013997269
16	0.684790060228158	0.630419879543684	0.315209939771842
17	0.831544772103714	0.336910455792573	0.168455227896286
18	0.939789109754061	0.120421780491877	0.0602108902459386
19	0.97480192788604	0.0503961442279196	0.0251980721139598
20	0.975862357252509	0.0482752854949824	0.0241376427474912
21	0.966691103248551	0.0666177935028989	0.0333088967514494
22	0.950535002261798	0.0989299954764044	0.0494649977382022
23	0.928727770911913	0.142544458176173	0.0712722290880865
24	0.914311663598909	0.171376672802182	0.0856883364010909
25	0.919897925781504	0.160204148436992	0.0801020742184962
26	0.96710997084182	0.0657800583163597	0.0328900291581799
27	0.983627976612584	0.0327440467748325	0.0163720233874163
28	0.995175551932977	0.00964889613404568	0.00482444806702284
29	0.995602819589573	0.00879436082085375	0.00439718041042687
30	0.996456599755985	0.00708680048803017	0.00354340024401509
31	0.996352329569062	0.00729534086187603	0.00364767043093802
32	0.996368429738288	0.00726314052342496	0.00363157026171248
33	0.994510229078417	0.0109795418431669	0.00548977092158346
34	0.991065768501535	0.0178684629969299	0.00893423149846493
35	0.986262601205472	0.0274747975890553	0.0137373987945276
36	0.978490116626886	0.0430197667462274	0.0215098833731137
37	0.967396504851478	0.065206990297045	0.0326034951485225
38	0.962793704678228	0.0744125906435433	0.0372062953217717
39	0.972071887207503	0.0558562255849931	0.0279281127924965
40	0.973566477231478	0.052867045537043	0.0264335227685215
41	0.962691455666171	0.0746170886676573	0.0373085443338287
42	0.952311492395971	0.0953770152080586	0.0476885076040293
43	0.947268774748977	0.105462450502046	0.0527312252510231
44	0.948669378684647	0.102661242630705	0.0513306213153525
45	0.948863731665604	0.102272536668792	0.0511362683343961
46	0.938847762592836	0.122304474814329	0.0611522374071645
47	0.934416738784112	0.131166522431776	0.0655832612158879
48	0.930433552884417	0.139132894231167	0.0695664471155834
49	0.953138571965909	0.0937228560681817	0.0468614280340909
50	0.956249858147837	0.0875002837043266	0.0437501418521633
51	0.966823753773814	0.0663524924523713	0.0331762462261857
52	0.97544016815346	0.0491196636930803	0.0245598318465402
53	0.984975993941315	0.0300480121173699	0.0150240060586849
54	0.99103003808002	0.0179399238399599	0.00896996191997996
55	0.996826641916664	0.00634671616667199	0.003173358083336
56	0.997826647087691	0.00434670582461855	0.00217335291230928
57	0.997459988861639	0.0050800222767227	0.00254001113836135
58	0.995212195406331	0.00957560918733763	0.00478780459366881
59	0.988545821093366	0.0229083578132688	0.0114541789066344
60	0.973771050201648	0.0524578995967042	0.0262289497983521
61	0.94597651307587	0.10804697384826	0.0540234869241299
62	0.899529580486873	0.200940839026254	0.100470419513127
63	0.850607232940014	0.298785534119972	0.149392767059986
64	0.816975270733781	0.366049458532438	0.183024729266219

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	9	0.157894736842105	NOK
5% type I error level	19	0.333333333333333	NOK
10% type I error level	34	0.596491228070175	NOK

\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 & 9 & 0.157894736842105 & NOK \tabularnewline
5% type I error level & 19 & 0.333333333333333 & NOK \tabularnewline
10% type I error level & 34 & 0.596491228070175 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=189692&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]9[/C][C]0.157894736842105[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]19[/C][C]0.333333333333333[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]34[/C][C]0.596491228070175[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=189692&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=189692&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	9	0.157894736842105	NOK
5% type I error level	19	0.333333333333333	NOK
10% type I error level	34	0.596491228070175	NOK

Figure 1

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Figure 9

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Parameters (Session):

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

Parameters (R input):

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

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

library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], 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,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
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, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
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,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
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,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
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,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
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,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
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