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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationSat, 11 Dec 2010 10:33:09 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/11/t12920636540n6d81osj9z0r91.htm/, Retrieved Thu, 31 Oct 2024 22:59:43 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=108050, Retrieved Thu, 31 Oct 2024 22:59:43 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact276

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Bivariate Data Series] [Bivariate dataset] [2008-01-05 23:51:08] [74be16979710d4c4e7c6647856088456]
F RMPD  [Univariate Explorative Data Analysis] [Colombia Coffee] [2008-01-07 14:21:11] [74be16979710d4c4e7c6647856088456]
- RMPD    [Univariate Explorative Data Analysis] [Workshop 6, Tutor...] [2010-11-07 12:24:29] [8ffb4cfa64b4677df0d2c448735a40bb]
- R P       [Univariate Explorative Data Analysis] [WS6 2. Technique 2] [2010-11-11 18:06:41] [afe9379cca749d06b3d6872e02cc47ed]
- RMPD          [Multiple Regression] [Apple Inc - Multi...] [2010-12-11 10:33:09] [aa6b599ccd367bc74fed0d8f67004a46] [Current]
-    D            [Multiple Regression] [WS10 Multiple Reg...] [2010-12-13 13:48:19] [afe9379cca749d06b3d6872e02cc47ed]
-    D            [Multiple Regression] [WS10 Multiple Reg...] [2010-12-13 13:48:19] [afe9379cca749d06b3d6872e02cc47ed]
-    D            [Multiple Regression] [WS10 Multiple Reg...] [2010-12-13 13:48:19] [afe9379cca749d06b3d6872e02cc47ed]
-    D            [Multiple Regression] [WS10 Multiple Reg...] [2010-12-13 13:48:19] [afe9379cca749d06b3d6872e02cc47ed]
-    D            [Multiple Regression] [WS10 Multiple Reg...] [2010-12-13 13:48:19] [afe9379cca749d06b3d6872e02cc47ed]
-    D            [Multiple Regression] [WS10 Multiple Reg...] [2010-12-13 13:48:19] [afe9379cca749d06b3d6872e02cc47ed]
-    D              [Multiple Regression] [Apple Inc - Multi...] [2010-12-14 10:39:25] [afe9379cca749d06b3d6872e02cc47ed]
-    D              [Multiple Regression] [Apple Inc - Multi...] [2010-12-14 10:39:25] [afe9379cca749d06b3d6872e02cc47ed]
-   PD              [Multiple Regression] [Apple Inc - Multi...] [2010-12-14 10:47:59] [afe9379cca749d06b3d6872e02cc47ed]
-    D              [Multiple Regression] [Apple Inc - Multi...] [2010-12-14 10:51:38] [afe9379cca749d06b3d6872e02cc47ed]
-   PD              [Multiple Regression] [Apple Inc - Multi...] [2010-12-14 11:00:31] [afe9379cca749d06b3d6872e02cc47ed]
-   PD              [Multiple Regression] [apple Inc - Multi...] [2010-12-14 11:13:03] [afe9379cca749d06b3d6872e02cc47ed]
-   PD              [Multiple Regression] [Apple Inc - Multi...] [2010-12-14 11:31:30] [afe9379cca749d06b3d6872e02cc47ed]
-    D                [Multiple Regression] [Apple Inc - Multi...] [2010-12-22 08:38:21] [afe9379cca749d06b3d6872e02cc47ed]
-    D                  [Multiple Regression] [Apple Inc - Multi...] [2010-12-22 08:46:34] [afe9379cca749d06b3d6872e02cc47ed]
-    D              [Multiple Regression] [Paper - Multiple ...] [2010-12-21 11:10:31] [18fa53e8b37a5effc0c5f8a5122cdd2d]
-    D                [Multiple Regression] [Paper - Multiple ...] [2010-12-21 11:18:37] [18fa53e8b37a5effc0c5f8a5122cdd2d]
-   P                 [Multiple Regression] [Paper - Multiple ...] [2010-12-21 11:31:18] [18fa53e8b37a5effc0c5f8a5122cdd2d]
-   PD                [Multiple Regression] [Paper - Multiple ...] [2010-12-21 11:34:09] [18fa53e8b37a5effc0c5f8a5122cdd2d]
-   P                   [Multiple Regression] [Paper - Multiple ...] [2010-12-21 11:39:48] [18fa53e8b37a5effc0c5f8a5122cdd2d]
-    D              [Multiple Regression] [] [2010-12-24 15:43:44] [69c775ce4d55db2aa75a88e773e8d700]
- R  D              [Multiple Regression] [WS10 Multiple Reg...] [2012-12-06 14:14:23] [74be16979710d4c4e7c6647856088456]
- RM                [Multiple Regression] [WS 10 Multiple re...] [2012-12-10 19:14:44] [74be16979710d4c4e7c6647856088456]
- RM                [Multiple Regression] [] [2012-12-11 23:33:43] [74be16979710d4c4e7c6647856088456]
- R PD              [Multiple Regression] [Workshop 10: Meer...] [2012-12-12 01:00:49] [081ff4808467d7c84e980fa7f896f721]
- R  D              [Multiple Regression] [] [2012-12-15 20:40:16] [74be16979710d4c4e7c6647856088456]
- R PD              [Multiple Regression] [] [2012-12-20 16:07:49] [d1865ed705b6ad9ba3d459a02c528b22]
-    D                [Multiple Regression] [] [2012-12-20 16:20:46] [d1865ed705b6ad9ba3d459a02c528b22]
-   PD                  [Multiple Regression] [] [2012-12-21 08:01:57] [74be16979710d4c4e7c6647856088456]
- R PD                    [Multiple Regression] [] [2012-12-21 08:23:32] [d1865ed705b6ad9ba3d459a02c528b22]
- R  D                      [Multiple Regression] [] [2012-12-21 08:31:34] [d1865ed705b6ad9ba3d459a02c528b22]
- R  D                      [Multiple Regression] [] [2012-12-21 08:31:34] [d1865ed705b6ad9ba3d459a02c528b22]
- R  D                      [Multiple Regression] [] [2012-12-21 08:31:34] [d1865ed705b6ad9ba3d459a02c528b22]
- R  D                      [Multiple Regression] [] [2012-12-21 08:31:34] [d1865ed705b6ad9ba3d459a02c528b22]
- R  D                      [Multiple Regression] [] [2012-12-21 08:31:34] [d1865ed705b6ad9ba3d459a02c528b22]
- R  D                      [Multiple Regression] [] [2012-12-21 08:31:34] [d1865ed705b6ad9ba3d459a02c528b22]
- R PD                      [Multiple Regression] [] [2012-12-21 08:35:48] [d1865ed705b6ad9ba3d459a02c528b22]
- R PD                      [Multiple Regression] [] [2012-12-21 08:42:03] [d1865ed705b6ad9ba3d459a02c528b22]
- R PD                      [Multiple Regression] [] [2012-12-21 08:45:23] [d1865ed705b6ad9ba3d459a02c528b22]
- RMPD                      [Testing Mean with unknown Variance - Critical Value] [] [2012-12-21 09:21:24] [d1865ed705b6ad9ba3d459a02c528b22]
- RMPD                      [Testing Mean with unknown Variance - Critical Value] [] [2012-12-21 09:34:12] [d1865ed705b6ad9ba3d459a02c528b22]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
25.94	23688100	39.18	3940.35	0.02740	 144.7	5.45
28.66	13741000	35.78	4696.69	0.03220	 140.8	5.73
33.95	14143500	42.54	4572.83	0.03760	 137.1	5.85
31.01	16763800	27.92	3860.66	0.03070	 137.7	6.02
21.00	16634600	25.05	3400.91	0.03190	 144.7	6.27
26.19	13693300	32.03	3966.11	0.03730	 139.2	6.53
25.41	10545800	27.95	3766.99	0.03660	 143.0	6.54
30.47	9409900	27.95	4206.35	0.03410	 140.8	6.5
12.88	39182200	24.15	3672.82	0.03450	 142.5	6.52
9.78	37005800	27.57	3369.63	0.03450	 135.8	6.51
8.25	15818500	22.97	2597.93	0.03450	 132.6	6.51
7.44	16952000	17.37	2470.52	0.03390	 128.6	6.4
10.81	24563400	24.45	2772.73	0.03730	 115.7	5.98
9.12	14163200	23.62	2151.83	0.03530	 109.2	5.49
11.03	18184800	21.90	1840.26	0.02920	 116.9	5.31
12.74	20810300	27.12	2116.24	0.03270	 109.9	4.8
9.98	12843000	27.70	2110.49	0.03620	 116.1	4.21
11.62	13866700	29.23	2160.54	0.03250	 118.9	3.97
9.40	15119200	26.50	2027.13	0.02720	 116.3	3.77
9.27	8301600	22.84	1805.43	0.02720	 114.0	3.65
7.76	14039600	20.49	1498.80	0.02650	 97.0	3.07
8.78	12139700	23.28	1690.20	0.02130	 85.3	2.49
10.65	9649000	25.71	1930.58	0.01900	 84.9	2.09
10.95	8513600	26.52	1950.40	0.01550	 94.6	1.82
12.36	15278600	25.51	1934.03	0.01140	 97.8	1.73
10.85	15590900	23.36	1731.49	0.01140	 95.0	1.74
11.84	9691100	24.15	1845.35	0.01480	 110.7	1.73
12.14	10882700	20.92	1688.23	0.01640	 108.5	1.75
11.65	10294800	20.38	1615.73	0.01180	 110.3	1.75
8.86	16031900	21.90	1463.21	0.01070	 106.3	1.75
7.63	13683600	19.21	1328.26	0.01460	 97.4	1.73
7.38	8677200	19.65	1314.85	0.01800	 94.5	1.74
7.25	9874100	17.51	1172.06	0.01510	 93.7	1.75
8.03	10725500	21.41	1329.75	0.02030	 79.6	1.75
7.75	8348400	23.09	1478.78	0.02200	 84.9	1.34
7.16	8046200	20.70	1335.51	0.02380	 80.7	1.24
7.18	10862300	19.00	1320.91	0.02600	 78.8	1.24
7.51	8100300	19.04	1337.52	0.02980	 64.8	1.26
7.07	7287500	19.45	1341.17	0.03020	 61.4	1.25
7.11	14002500	20.54	1464.31	0.02220	 81.0	1.26
8.98	19037900	19.77	1595.91	0.02060	 83.6	1.26
9.53	10774600	20.60	1622.80	0.02110	 83.5	1.22
10.54	8960600	21.21	1735.02	0.02110	 77.0	1.01
11.31	7773300	21.30	1810.45	0.02160	 81.7	1.03
10.36	9579700	22.33	1786.94	0.02320	 77.0	1.01
11.44	11270700	21.12	1932.21	0.02040	 81.7	1.01
10.45	9492800	20.77	1960.26	0.01770	 92.5	1
10.69	9136800	22.11	2003.37	0.01880	 91.7	0.98
11.28	14487600	22.34	2066.15	0.01930	 96.4	1
11.96	10133200	21.43	2029.82	0.01690	 88.5	1.01
13.52	18659700	20.14	1994.22	0.01740	 88.5	1
12.89	15980700	21.11	1920.15	0.02290	 93.0	1
14.03	9732100	21.19	1986.74	0.03050	 93.1	1
16.27	14626300	23.07	2047.79	0.03270	 102.8	1.03
16.17	16904000	23.01	1887.36	0.02990	 105.7	1.26
17.25	13616700	22.12	1838.10	0.02650	 98.7	1.43
19.38	13772900	22.40	1896.84	0.02540	 96.7	1.61
26.20	28749200	22.66	1974.99	0.03190	 92.9	1.76
33.53	31408300	24.21	2096.81	0.03520	 92.6	1.93
32.20	26342800	24.13	2175.44	0.03260	 102.7	2.16
38.45	48909500	23.73	2062.41	0.02970	 105.1	2.28
44.86	41542400	22.79	2051.72	0.03010	 104.4	2.5
41.67	24857200	21.89	1999.23	0.03150	 103.0	2.63
36.06	34093700	22.92	1921.65	0.03510	 97.5	2.79
39.76	22555200	23.44	2068.22	0.02800	 103.1	3
36.81	19067500	22.57	2056.96	0.02530	 106.2	3.04
42.65	19029100	23.27	2184.83	0.03170	 103.6	3.26
46.89	15223200	24.95	2152.09	0.03640	 105.5	3.5
53.61	21903700	23.45	2151.69	0.04690	 87.5	3.62
57.59	33306600	23.42	2120.30	0.04350	 85.2	3.78
67.82	23898100	25.30	2232.82	0.03460	 98.3	4
71.89	23279600	23.90	2205.32	0.03420	 103.8	4.16
75.51	40699800	25.73	2305.82	0.03990	 106.8	4.29
68.49	37646000	24.64	2281.39	0.03600	 102.7	4.49
62.72	37277000	24.95	2339.79	0.03360	 107.5	4.59
70.39	39246800	22.15	2322.57	0.03550	 109.8	4.79
59.77	27418400	20.85	2178.88	0.04170	 104.7	4.94
57.27	30318700	21.45	2172.09	0.04320	 105.7	4.99
67.96	32808100	22.15	2091.47	0.04150	 107.0	5.24
67.85	28668200	23.75	2183.75	0.03820	 100.2	5.25
76.98	32370300	25.27	2258.43	0.02060	 105.9	5.25
81.08	24171100	26.53	2366.71	0.01310	 105.1	5.25
91.66	25009100	27.22	2431.77	0.01970	 105.3	5.25
84.84	32084300	27.69	2415.29	0.02540	 110.0	5.24
85.73	50117500	28.61	2463.93	0.02080	 110.2	5.25
84.61	27522200	26.21	2416.15	0.02420	 111.2	5.26
92.91	26816800	25.93	2421.64	0.02780	 108.2	5.26
99.80	25136100	27.86	2525.09	0.02570	 106.3	5.25
121.19	30295600	28.65	2604.52	0.02690	 108.5	5.25
122.04	41526100	27.51	2603.23	0.02690	 105.3	5.25
131.76	43845100	27.06	2546.27	0.02360	 111.9	5.26
138.48	39188900	26.91	2596.36	0.01970	 105.6	5.02
153.47	40496400	27.60	2701.50	0.02760	 99.5	4.94
189.95	37438400	34.48	2859.12	0.03540	 95.2	4.76
182.22	46553700	31.58	2660.96	0.04310	 87.8	4.49
198.08	31771400	33.46	2652.28	0.04080	 90.6	4.24
135.36	62108100	30.64	2389.86	0.04280	 87.9	3.94
125.02	46645400	25.66	2271.48	0.04030	 76.4	2.98
143.50	42313100	26.78	2279.10	0.03980	 65.9	2.61
173.95	38841700	26.91	2412.80	0.03940	 62.3	2.28
188.75	32650300	26.82	2522.66	0.04180	 57.2	1.98
167.44	34281100	26.05	2292.98	0.05020	 50.4	2
158.95	33096200	24.36	2325.55	0.05600	 51.9	2.01
169.53	23273800	25.94	2367.52	0.05370	 58.5	2
113.66	43697600	25.37	2091.88	0.04940	 61.4	1.81
107.59	66902300	21.23	1720.95	0.03660	 38.8	0.97
92.67	44957200	19.35	1535.57	0.01070	 44.9	0.39
85.35	33800900	18.61	1577.03	0.00090	 38.6	0.16
90.13	33487900	16.37	1476.42	0.00030	 4.0	0.15
89.31	27394900	15.56	1377.84	0.00240	 25.3	0.22
105.12	25963400	17.70	1528.59	-0.00380	 26.9	0.18
125.83	20952600	19.52	1717.30	-0.00740	 40.8	0.15
135.81	17702900	20.26	1774.33	-0.01280	 54.8	0.18
142.43	21282100	23.05	1835.04	-0.01430	 49.3	0.21
163.39	18449100	22.81	1978.50	-0.02100	 47.4	0.16
168.21	14415700	24.04	2009.06	-0.01480	 54.5	0.16
185.35	17906300	25.08	2122.42	-0.01290	 53.4	0.15
188.50	22197500	27.04	2045.11	-0.00180	 48.7	0.12
199.91	15856500	28.81	2144.60	0.01840	 50.6	0.12
210.73	19068700	29.86	2269.15	0.02720	 53.6	0.12
192.06	30855100	27.61	2147.35	0.02630	 56.5	0.11
204.62	21209000	28.22	2238.26	0.02140	 46.4	0.13
235.00	19541600	28.83	2397.96	0.02310	 52.3	0.16
261.09	21955000	30.06	2461.19	0.02240	 57.7	0.2
256.88	33725900	25.51	2257.04	0.02020	 62.7	0.2
251.53	28192800	22.75	2109.24	0.01050	 54.3	0.18
257.25	27377000	25.52	2254.70	0.01240	 51.0	0.18
243.10	16228100	23.33	2114.03	0.01150	 53.2	0.19
283.75	21278900	24.34	2368.62	0.01140	 48.6	0.19
300.98	21457400	26.51	2507.41	0.01170	 49.9	0.19

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 6 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108050&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108050&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108050&T=0

As an alternative you can also use a QR Code:  
QR Code


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24






Multiple Linear Regression - Estimated Regression Equation
APPLE[t] = + 23.0147069811760 + 1.24781152025288e-06VOLUME[t] + 6.92603621715406MICROSOFT[t] + 0.0288863890196801NASDAQ[t] -605.881541364843INFLATION[t] -2.26245296320921CONS.CONF[t] + 3.36290179952853FED.FUNDS.RATE[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
APPLE[t] =  +  23.0147069811760 +  1.24781152025288e-06VOLUME[t] +  6.92603621715406MICROSOFT[t] +  0.0288863890196801NASDAQ[t] -605.881541364843INFLATION[t] -2.26245296320921CONS.CONF[t] +  3.36290179952853FED.FUNDS.RATE[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108050&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]APPLE[t] =  +  23.0147069811760 +  1.24781152025288e-06VOLUME[t] +  6.92603621715406MICROSOFT[t] +  0.0288863890196801NASDAQ[t] -605.881541364843INFLATION[t] -2.26245296320921CONS.CONF[t] +  3.36290179952853FED.FUNDS.RATE[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108050&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108050&T=1

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Estimated Regression Equation
APPLE[t] = + 23.0147069811760 + 1.24781152025288e-06VOLUME[t] + 6.92603621715406MICROSOFT[t] + 0.0288863890196801NASDAQ[t] -605.881541364843INFLATION[t] -2.26245296320921CONS.CONF[t] + 3.36290179952853FED.FUNDS.RATE[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)23.014706981176029.4011980.78280.435260.21763
VOLUME1.24781152025288e-0603.2880.0013160.000658
MICROSOFT6.926036217154061.3982254.95352e-061e-06
NASDAQ0.02888638901968010.0107812.67930.0083880.004194
INFLATION-605.881541364843320.051667-1.89310.0606980.030349
CONS.CONF-2.262452963209210.264611-8.550100
FED.FUNDS.RATE3.362901799528534.0164630.83730.4040590.20203

\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) & 23.0147069811760 & 29.401198 & 0.7828 & 0.43526 & 0.21763 \tabularnewline
VOLUME & 1.24781152025288e-06 & 0 & 3.288 & 0.001316 & 0.000658 \tabularnewline
MICROSOFT & 6.92603621715406 & 1.398225 & 4.9535 & 2e-06 & 1e-06 \tabularnewline
NASDAQ & 0.0288863890196801 & 0.010781 & 2.6793 & 0.008388 & 0.004194 \tabularnewline
INFLATION & -605.881541364843 & 320.051667 & -1.8931 & 0.060698 & 0.030349 \tabularnewline
CONS.CONF & -2.26245296320921 & 0.264611 & -8.5501 & 0 & 0 \tabularnewline
FED.FUNDS.RATE & 3.36290179952853 & 4.016463 & 0.8373 & 0.404059 & 0.20203 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108050&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]23.0147069811760[/C][C]29.401198[/C][C]0.7828[/C][C]0.43526[/C][C]0.21763[/C][/ROW]
[ROW][C]VOLUME[/C][C]1.24781152025288e-06[/C][C]0[/C][C]3.288[/C][C]0.001316[/C][C]0.000658[/C][/ROW]
[ROW][C]MICROSOFT[/C][C]6.92603621715406[/C][C]1.398225[/C][C]4.9535[/C][C]2e-06[/C][C]1e-06[/C][/ROW]
[ROW][C]NASDAQ[/C][C]0.0288863890196801[/C][C]0.010781[/C][C]2.6793[/C][C]0.008388[/C][C]0.004194[/C][/ROW]
[ROW][C]INFLATION[/C][C]-605.881541364843[/C][C]320.051667[/C][C]-1.8931[/C][C]0.060698[/C][C]0.030349[/C][/ROW]
[ROW][C]CONS.CONF[/C][C]-2.26245296320921[/C][C]0.264611[/C][C]-8.5501[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]FED.FUNDS.RATE[/C][C]3.36290179952853[/C][C]4.016463[/C][C]0.8373[/C][C]0.404059[/C][C]0.20203[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108050&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108050&T=2

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)23.014706981176029.4011980.78280.435260.21763
VOLUME1.24781152025288e-0603.2880.0013160.000658
MICROSOFT6.926036217154061.3982254.95352e-061e-06
NASDAQ0.02888638901968010.0107812.67930.0083880.004194
INFLATION-605.881541364843320.051667-1.89310.0606980.030349
CONS.CONF-2.262452963209210.264611-8.550100
FED.FUNDS.RATE3.362901799528534.0164630.83730.4040590.20203






Multiple Linear Regression - Regression Statistics
Multiple R0.85090925788459
R-squared0.724046565153704
Adjusted R-squared0.71058542199047
F-TEST (value)53.7878957510289
F-TEST (DF numerator)6
F-TEST (DF denominator)123
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation41.230775839512
Sum Squared Residuals209097.155788354

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.85090925788459 \tabularnewline
R-squared & 0.724046565153704 \tabularnewline
Adjusted R-squared & 0.71058542199047 \tabularnewline
F-TEST (value) & 53.7878957510289 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 123 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 41.230775839512 \tabularnewline
Sum Squared Residuals & 209097.155788354 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108050&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.85090925788459[/C][/ROW]
[ROW][C]R-squared[/C][C]0.724046565153704[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.71058542199047[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]53.7878957510289[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]123[/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]41.230775839512[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]209097.155788354[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108050&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108050&T=3

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Regression Statistics
Multiple R0.85090925788459
R-squared0.724046565153704
Adjusted R-squared0.71058542199047
F-TEST (value)53.7878957510289
F-TEST (DF numerator)6
F-TEST (DF denominator)123
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation41.230775839512
Sum Squared Residuals209097.155788354






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
125.94112.107289813532-86.167289813532
228.66104.851539835078-76.191539835078
333.95154.098784512411-120.148784512411
431.0138.9325600394035-7.9225600394035
521-10.110401646263431.1104016462634
626.1960.9358156410332-34.7458156410332
725.4114.858668170205710.5513318297943
830.4732.4903872445283-2.0203872445283
912.8823.8886487920867-11.0086487920867
109.7851.2266972107049-41.4466972107049
118.25-22.122603335475330.3726033354753
127.44-54.131005038621261.5710050386212
1310.8138.8459068386762-28.0359068386762
149.1216.4541339250056-7.33413392500557
1511.03-13.770914523812724.8009145238127
1612.7445.6326947477347-32.8926947477347
179.9821.4101044631143-11.4301044631143
1811.6229.8298852732551-18.2098852732551
199.417.0519266840977-7.65192668409769
209.27-18.408464537387527.6784645373875
217.760.553193800192357.20680619980765
228.7850.705773238009-41.9257732380090
2310.6572.3251752953616-61.6751752953616
2410.9556.3578358273062-45.4078358273062
2512.3652.272717669608-39.912717669608
2610.8538.2892794234368-27.4392794234368
2711.842.073876100560949.76612389943906
2812.14-19.273714027218431.4137140272184
2911.65-27.126985424663338.7769854246633
308.86-4.1300614066897912.9900614066898
317.63-11.883917496801419.5139174968014
327.38-10.9361262623418.31612626234
337.25-24.788377788058532.0383777880585
348.0336.5906476378518-28.5606476378518
357.7535.1653652103446-27.4153652103446
367.1622.1719225461455-15.0119225461455
377.1816.4556029585753-9.2756029585753
387.5143.205241573673-35.695241573673
397.0752.5524889793366-45.4824889793366
407.1132.5745960284295-25.4645960284295
418.9832.4132598271319-23.4332598271319
429.5328.4163724064607-18.8863724064607
4310.5447.6190898599333-37.0790898599333
4411.3138.0705951634603-26.7605951634603
4510.3656.3762006163701-46.0162006163701
4611.4445.3650111959885-33.9250111959885
4710.4518.7004367711593-8.25043677115928
4810.6929.8586312706496-19.1686312706496
4911.2829.0726853240445-17.7926853240445
5011.9635.6482024961839-23.6882024961839
5113.5235.988155465713-22.468155465713
5212.8923.7105218869592-10.8205218869592
5314.0313.56012935300610.469870646993858
5416.2711.27378455318274.99621544681726
5516.174.974941425812611.1950585741874
5617.2511.75475614793355.49524385206646
5719.3821.3824388850511-2.00243888505105
5826.249.2918057854151-23.0918057854151
5933.5366.1151775542645-32.5851775542645
6032.241.0096266626957-8.8096266626957
6138.4559.8638894332297-21.4138894332297
6244.8645.9330704932263-1.07307049322626
6341.6720.119783784942121.5502162150579
6436.0647.3383881719415-11.2783881719415
6539.7633.11416354465816.64583645534192
6636.8117.168051103904119.6419488960959
6742.6528.40663729298614.2433627070140
6846.8928.008384453744618.8816155462554
6953.6160.7097258028334-7.09972580283339
7057.5981.6255742932288-24.0355742932288
7167.8262.65083448167635.16916551832365
7271.8939.725162261162932.1648377388371
7375.5167.23631043867348.27368956132664
7468.4967.48224517806271.00775482193725
7562.7261.78647072898080.933529271019227
7670.3938.671848450555931.7181515494441
7759.7719.044182169692940.7258178303071
7857.2723.619562885451133.6504371145489
7967.9628.174804771240239.7851952287598
8067.8554.174002039049813.6759979609502
8176.9869.24586888797067.7341311120294
8281.0877.2235104385823.85648956141799
8391.6680.476181186360811.1838188136392
8484.8477.96320385461396.87679614538613
8585.73110.610419358969-24.8804193589688
8684.6160.124243941014424.4857560589856
8792.9162.069519170257130.8404808297429
8899.881.865252040329817.9347479596702
89121.1990.364895701761330.8251042982387
90122.04103.68534773284018.3546522671602
91131.7688.917786179344342.8422138206557
92138.4899.325035219819639.1549647801804
93153.47117.51809546874735.9519045312526
94189.95170.30383904635519.6461609536453
95182.22167.03726409239515.1827359076046
96198.08155.57988788639142.5001121136088
97135.36170.210572672005-34.8505726720045
98125.02137.309333586984-12.2893335869840
99143.5162.695137803889-19.1951378038885
100173.95170.4034055028993.54659449710135
101188.75174.30334456779614.4466554322041
102167.44174.733135116124-7.29313511612409
103158.95155.6162683624223.33373163757757
104169.53141.94297242611327.587027573887
105113.66150.923166132943-37.263166132943
106107.59196.551523085261-88.9615230852615
10792.67150.733153409489-58.0631534094888
10885.35152.302181993855-66.9521819938554
10990.13212.101808696183-121.971808696183
11089.31146.813987310572-57.5039873105725
111105.12164.206110512103-59.0861105121031
112125.83146.642303239864-20.8123032398637
113135.81121.05825360141114.7517463985886
114142.43160.054954981629-17.6249549816289
115163.39167.191619488664-3.80161948866429
116168.21151.74060750317016.4693924968302
117185.35167.87775143381717.4722485661827
118188.5188.3815412439850.118458756015371
119199.91179.06469157632420.8453084236755
120210.73181.82393306845528.9060669315445
121192.06171.37974587549720.6802541245033
122204.62192.08102740532012.5389725946802
123235184.56088084608850.4391191539117
124261.09186.25924724348774.8307527565127
125256.88153.55716533577103.322834664230
126251.53148.082028962810103.447971037190
127257.25176.76591864290580.4840813570952
128243.1139.224251011955103.875748988045
129283.75170.344051583193113.405948416807
130300.98186.482473148243114.497526851757

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 25.94 & 112.107289813532 & -86.167289813532 \tabularnewline
2 & 28.66 & 104.851539835078 & -76.191539835078 \tabularnewline
3 & 33.95 & 154.098784512411 & -120.148784512411 \tabularnewline
4 & 31.01 & 38.9325600394035 & -7.9225600394035 \tabularnewline
5 & 21 & -10.1104016462634 & 31.1104016462634 \tabularnewline
6 & 26.19 & 60.9358156410332 & -34.7458156410332 \tabularnewline
7 & 25.41 & 14.8586681702057 & 10.5513318297943 \tabularnewline
8 & 30.47 & 32.4903872445283 & -2.0203872445283 \tabularnewline
9 & 12.88 & 23.8886487920867 & -11.0086487920867 \tabularnewline
10 & 9.78 & 51.2266972107049 & -41.4466972107049 \tabularnewline
11 & 8.25 & -22.1226033354753 & 30.3726033354753 \tabularnewline
12 & 7.44 & -54.1310050386212 & 61.5710050386212 \tabularnewline
13 & 10.81 & 38.8459068386762 & -28.0359068386762 \tabularnewline
14 & 9.12 & 16.4541339250056 & -7.33413392500557 \tabularnewline
15 & 11.03 & -13.7709145238127 & 24.8009145238127 \tabularnewline
16 & 12.74 & 45.6326947477347 & -32.8926947477347 \tabularnewline
17 & 9.98 & 21.4101044631143 & -11.4301044631143 \tabularnewline
18 & 11.62 & 29.8298852732551 & -18.2098852732551 \tabularnewline
19 & 9.4 & 17.0519266840977 & -7.65192668409769 \tabularnewline
20 & 9.27 & -18.4084645373875 & 27.6784645373875 \tabularnewline
21 & 7.76 & 0.55319380019235 & 7.20680619980765 \tabularnewline
22 & 8.78 & 50.705773238009 & -41.9257732380090 \tabularnewline
23 & 10.65 & 72.3251752953616 & -61.6751752953616 \tabularnewline
24 & 10.95 & 56.3578358273062 & -45.4078358273062 \tabularnewline
25 & 12.36 & 52.272717669608 & -39.912717669608 \tabularnewline
26 & 10.85 & 38.2892794234368 & -27.4392794234368 \tabularnewline
27 & 11.84 & 2.07387610056094 & 9.76612389943906 \tabularnewline
28 & 12.14 & -19.2737140272184 & 31.4137140272184 \tabularnewline
29 & 11.65 & -27.1269854246633 & 38.7769854246633 \tabularnewline
30 & 8.86 & -4.13006140668979 & 12.9900614066898 \tabularnewline
31 & 7.63 & -11.8839174968014 & 19.5139174968014 \tabularnewline
32 & 7.38 & -10.93612626234 & 18.31612626234 \tabularnewline
33 & 7.25 & -24.7883777880585 & 32.0383777880585 \tabularnewline
34 & 8.03 & 36.5906476378518 & -28.5606476378518 \tabularnewline
35 & 7.75 & 35.1653652103446 & -27.4153652103446 \tabularnewline
36 & 7.16 & 22.1719225461455 & -15.0119225461455 \tabularnewline
37 & 7.18 & 16.4556029585753 & -9.2756029585753 \tabularnewline
38 & 7.51 & 43.205241573673 & -35.695241573673 \tabularnewline
39 & 7.07 & 52.5524889793366 & -45.4824889793366 \tabularnewline
40 & 7.11 & 32.5745960284295 & -25.4645960284295 \tabularnewline
41 & 8.98 & 32.4132598271319 & -23.4332598271319 \tabularnewline
42 & 9.53 & 28.4163724064607 & -18.8863724064607 \tabularnewline
43 & 10.54 & 47.6190898599333 & -37.0790898599333 \tabularnewline
44 & 11.31 & 38.0705951634603 & -26.7605951634603 \tabularnewline
45 & 10.36 & 56.3762006163701 & -46.0162006163701 \tabularnewline
46 & 11.44 & 45.3650111959885 & -33.9250111959885 \tabularnewline
47 & 10.45 & 18.7004367711593 & -8.25043677115928 \tabularnewline
48 & 10.69 & 29.8586312706496 & -19.1686312706496 \tabularnewline
49 & 11.28 & 29.0726853240445 & -17.7926853240445 \tabularnewline
50 & 11.96 & 35.6482024961839 & -23.6882024961839 \tabularnewline
51 & 13.52 & 35.988155465713 & -22.468155465713 \tabularnewline
52 & 12.89 & 23.7105218869592 & -10.8205218869592 \tabularnewline
53 & 14.03 & 13.5601293530061 & 0.469870646993858 \tabularnewline
54 & 16.27 & 11.2737845531827 & 4.99621544681726 \tabularnewline
55 & 16.17 & 4.9749414258126 & 11.1950585741874 \tabularnewline
56 & 17.25 & 11.7547561479335 & 5.49524385206646 \tabularnewline
57 & 19.38 & 21.3824388850511 & -2.00243888505105 \tabularnewline
58 & 26.2 & 49.2918057854151 & -23.0918057854151 \tabularnewline
59 & 33.53 & 66.1151775542645 & -32.5851775542645 \tabularnewline
60 & 32.2 & 41.0096266626957 & -8.8096266626957 \tabularnewline
61 & 38.45 & 59.8638894332297 & -21.4138894332297 \tabularnewline
62 & 44.86 & 45.9330704932263 & -1.07307049322626 \tabularnewline
63 & 41.67 & 20.1197837849421 & 21.5502162150579 \tabularnewline
64 & 36.06 & 47.3383881719415 & -11.2783881719415 \tabularnewline
65 & 39.76 & 33.1141635446581 & 6.64583645534192 \tabularnewline
66 & 36.81 & 17.1680511039041 & 19.6419488960959 \tabularnewline
67 & 42.65 & 28.406637292986 & 14.2433627070140 \tabularnewline
68 & 46.89 & 28.0083844537446 & 18.8816155462554 \tabularnewline
69 & 53.61 & 60.7097258028334 & -7.09972580283339 \tabularnewline
70 & 57.59 & 81.6255742932288 & -24.0355742932288 \tabularnewline
71 & 67.82 & 62.6508344816763 & 5.16916551832365 \tabularnewline
72 & 71.89 & 39.7251622611629 & 32.1648377388371 \tabularnewline
73 & 75.51 & 67.2363104386734 & 8.27368956132664 \tabularnewline
74 & 68.49 & 67.4822451780627 & 1.00775482193725 \tabularnewline
75 & 62.72 & 61.7864707289808 & 0.933529271019227 \tabularnewline
76 & 70.39 & 38.6718484505559 & 31.7181515494441 \tabularnewline
77 & 59.77 & 19.0441821696929 & 40.7258178303071 \tabularnewline
78 & 57.27 & 23.6195628854511 & 33.6504371145489 \tabularnewline
79 & 67.96 & 28.1748047712402 & 39.7851952287598 \tabularnewline
80 & 67.85 & 54.1740020390498 & 13.6759979609502 \tabularnewline
81 & 76.98 & 69.2458688879706 & 7.7341311120294 \tabularnewline
82 & 81.08 & 77.223510438582 & 3.85648956141799 \tabularnewline
83 & 91.66 & 80.4761811863608 & 11.1838188136392 \tabularnewline
84 & 84.84 & 77.9632038546139 & 6.87679614538613 \tabularnewline
85 & 85.73 & 110.610419358969 & -24.8804193589688 \tabularnewline
86 & 84.61 & 60.1242439410144 & 24.4857560589856 \tabularnewline
87 & 92.91 & 62.0695191702571 & 30.8404808297429 \tabularnewline
88 & 99.8 & 81.8652520403298 & 17.9347479596702 \tabularnewline
89 & 121.19 & 90.3648957017613 & 30.8251042982387 \tabularnewline
90 & 122.04 & 103.685347732840 & 18.3546522671602 \tabularnewline
91 & 131.76 & 88.9177861793443 & 42.8422138206557 \tabularnewline
92 & 138.48 & 99.3250352198196 & 39.1549647801804 \tabularnewline
93 & 153.47 & 117.518095468747 & 35.9519045312526 \tabularnewline
94 & 189.95 & 170.303839046355 & 19.6461609536453 \tabularnewline
95 & 182.22 & 167.037264092395 & 15.1827359076046 \tabularnewline
96 & 198.08 & 155.579887886391 & 42.5001121136088 \tabularnewline
97 & 135.36 & 170.210572672005 & -34.8505726720045 \tabularnewline
98 & 125.02 & 137.309333586984 & -12.2893335869840 \tabularnewline
99 & 143.5 & 162.695137803889 & -19.1951378038885 \tabularnewline
100 & 173.95 & 170.403405502899 & 3.54659449710135 \tabularnewline
101 & 188.75 & 174.303344567796 & 14.4466554322041 \tabularnewline
102 & 167.44 & 174.733135116124 & -7.29313511612409 \tabularnewline
103 & 158.95 & 155.616268362422 & 3.33373163757757 \tabularnewline
104 & 169.53 & 141.942972426113 & 27.587027573887 \tabularnewline
105 & 113.66 & 150.923166132943 & -37.263166132943 \tabularnewline
106 & 107.59 & 196.551523085261 & -88.9615230852615 \tabularnewline
107 & 92.67 & 150.733153409489 & -58.0631534094888 \tabularnewline
108 & 85.35 & 152.302181993855 & -66.9521819938554 \tabularnewline
109 & 90.13 & 212.101808696183 & -121.971808696183 \tabularnewline
110 & 89.31 & 146.813987310572 & -57.5039873105725 \tabularnewline
111 & 105.12 & 164.206110512103 & -59.0861105121031 \tabularnewline
112 & 125.83 & 146.642303239864 & -20.8123032398637 \tabularnewline
113 & 135.81 & 121.058253601411 & 14.7517463985886 \tabularnewline
114 & 142.43 & 160.054954981629 & -17.6249549816289 \tabularnewline
115 & 163.39 & 167.191619488664 & -3.80161948866429 \tabularnewline
116 & 168.21 & 151.740607503170 & 16.4693924968302 \tabularnewline
117 & 185.35 & 167.877751433817 & 17.4722485661827 \tabularnewline
118 & 188.5 & 188.381541243985 & 0.118458756015371 \tabularnewline
119 & 199.91 & 179.064691576324 & 20.8453084236755 \tabularnewline
120 & 210.73 & 181.823933068455 & 28.9060669315445 \tabularnewline
121 & 192.06 & 171.379745875497 & 20.6802541245033 \tabularnewline
122 & 204.62 & 192.081027405320 & 12.5389725946802 \tabularnewline
123 & 235 & 184.560880846088 & 50.4391191539117 \tabularnewline
124 & 261.09 & 186.259247243487 & 74.8307527565127 \tabularnewline
125 & 256.88 & 153.55716533577 & 103.322834664230 \tabularnewline
126 & 251.53 & 148.082028962810 & 103.447971037190 \tabularnewline
127 & 257.25 & 176.765918642905 & 80.4840813570952 \tabularnewline
128 & 243.1 & 139.224251011955 & 103.875748988045 \tabularnewline
129 & 283.75 & 170.344051583193 & 113.405948416807 \tabularnewline
130 & 300.98 & 186.482473148243 & 114.497526851757 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108050&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]25.94[/C][C]112.107289813532[/C][C]-86.167289813532[/C][/ROW]
[ROW][C]2[/C][C]28.66[/C][C]104.851539835078[/C][C]-76.191539835078[/C][/ROW]
[ROW][C]3[/C][C]33.95[/C][C]154.098784512411[/C][C]-120.148784512411[/C][/ROW]
[ROW][C]4[/C][C]31.01[/C][C]38.9325600394035[/C][C]-7.9225600394035[/C][/ROW]
[ROW][C]5[/C][C]21[/C][C]-10.1104016462634[/C][C]31.1104016462634[/C][/ROW]
[ROW][C]6[/C][C]26.19[/C][C]60.9358156410332[/C][C]-34.7458156410332[/C][/ROW]
[ROW][C]7[/C][C]25.41[/C][C]14.8586681702057[/C][C]10.5513318297943[/C][/ROW]
[ROW][C]8[/C][C]30.47[/C][C]32.4903872445283[/C][C]-2.0203872445283[/C][/ROW]
[ROW][C]9[/C][C]12.88[/C][C]23.8886487920867[/C][C]-11.0086487920867[/C][/ROW]
[ROW][C]10[/C][C]9.78[/C][C]51.2266972107049[/C][C]-41.4466972107049[/C][/ROW]
[ROW][C]11[/C][C]8.25[/C][C]-22.1226033354753[/C][C]30.3726033354753[/C][/ROW]
[ROW][C]12[/C][C]7.44[/C][C]-54.1310050386212[/C][C]61.5710050386212[/C][/ROW]
[ROW][C]13[/C][C]10.81[/C][C]38.8459068386762[/C][C]-28.0359068386762[/C][/ROW]
[ROW][C]14[/C][C]9.12[/C][C]16.4541339250056[/C][C]-7.33413392500557[/C][/ROW]
[ROW][C]15[/C][C]11.03[/C][C]-13.7709145238127[/C][C]24.8009145238127[/C][/ROW]
[ROW][C]16[/C][C]12.74[/C][C]45.6326947477347[/C][C]-32.8926947477347[/C][/ROW]
[ROW][C]17[/C][C]9.98[/C][C]21.4101044631143[/C][C]-11.4301044631143[/C][/ROW]
[ROW][C]18[/C][C]11.62[/C][C]29.8298852732551[/C][C]-18.2098852732551[/C][/ROW]
[ROW][C]19[/C][C]9.4[/C][C]17.0519266840977[/C][C]-7.65192668409769[/C][/ROW]
[ROW][C]20[/C][C]9.27[/C][C]-18.4084645373875[/C][C]27.6784645373875[/C][/ROW]
[ROW][C]21[/C][C]7.76[/C][C]0.55319380019235[/C][C]7.20680619980765[/C][/ROW]
[ROW][C]22[/C][C]8.78[/C][C]50.705773238009[/C][C]-41.9257732380090[/C][/ROW]
[ROW][C]23[/C][C]10.65[/C][C]72.3251752953616[/C][C]-61.6751752953616[/C][/ROW]
[ROW][C]24[/C][C]10.95[/C][C]56.3578358273062[/C][C]-45.4078358273062[/C][/ROW]
[ROW][C]25[/C][C]12.36[/C][C]52.272717669608[/C][C]-39.912717669608[/C][/ROW]
[ROW][C]26[/C][C]10.85[/C][C]38.2892794234368[/C][C]-27.4392794234368[/C][/ROW]
[ROW][C]27[/C][C]11.84[/C][C]2.07387610056094[/C][C]9.76612389943906[/C][/ROW]
[ROW][C]28[/C][C]12.14[/C][C]-19.2737140272184[/C][C]31.4137140272184[/C][/ROW]
[ROW][C]29[/C][C]11.65[/C][C]-27.1269854246633[/C][C]38.7769854246633[/C][/ROW]
[ROW][C]30[/C][C]8.86[/C][C]-4.13006140668979[/C][C]12.9900614066898[/C][/ROW]
[ROW][C]31[/C][C]7.63[/C][C]-11.8839174968014[/C][C]19.5139174968014[/C][/ROW]
[ROW][C]32[/C][C]7.38[/C][C]-10.93612626234[/C][C]18.31612626234[/C][/ROW]
[ROW][C]33[/C][C]7.25[/C][C]-24.7883777880585[/C][C]32.0383777880585[/C][/ROW]
[ROW][C]34[/C][C]8.03[/C][C]36.5906476378518[/C][C]-28.5606476378518[/C][/ROW]
[ROW][C]35[/C][C]7.75[/C][C]35.1653652103446[/C][C]-27.4153652103446[/C][/ROW]
[ROW][C]36[/C][C]7.16[/C][C]22.1719225461455[/C][C]-15.0119225461455[/C][/ROW]
[ROW][C]37[/C][C]7.18[/C][C]16.4556029585753[/C][C]-9.2756029585753[/C][/ROW]
[ROW][C]38[/C][C]7.51[/C][C]43.205241573673[/C][C]-35.695241573673[/C][/ROW]
[ROW][C]39[/C][C]7.07[/C][C]52.5524889793366[/C][C]-45.4824889793366[/C][/ROW]
[ROW][C]40[/C][C]7.11[/C][C]32.5745960284295[/C][C]-25.4645960284295[/C][/ROW]
[ROW][C]41[/C][C]8.98[/C][C]32.4132598271319[/C][C]-23.4332598271319[/C][/ROW]
[ROW][C]42[/C][C]9.53[/C][C]28.4163724064607[/C][C]-18.8863724064607[/C][/ROW]
[ROW][C]43[/C][C]10.54[/C][C]47.6190898599333[/C][C]-37.0790898599333[/C][/ROW]
[ROW][C]44[/C][C]11.31[/C][C]38.0705951634603[/C][C]-26.7605951634603[/C][/ROW]
[ROW][C]45[/C][C]10.36[/C][C]56.3762006163701[/C][C]-46.0162006163701[/C][/ROW]
[ROW][C]46[/C][C]11.44[/C][C]45.3650111959885[/C][C]-33.9250111959885[/C][/ROW]
[ROW][C]47[/C][C]10.45[/C][C]18.7004367711593[/C][C]-8.25043677115928[/C][/ROW]
[ROW][C]48[/C][C]10.69[/C][C]29.8586312706496[/C][C]-19.1686312706496[/C][/ROW]
[ROW][C]49[/C][C]11.28[/C][C]29.0726853240445[/C][C]-17.7926853240445[/C][/ROW]
[ROW][C]50[/C][C]11.96[/C][C]35.6482024961839[/C][C]-23.6882024961839[/C][/ROW]
[ROW][C]51[/C][C]13.52[/C][C]35.988155465713[/C][C]-22.468155465713[/C][/ROW]
[ROW][C]52[/C][C]12.89[/C][C]23.7105218869592[/C][C]-10.8205218869592[/C][/ROW]
[ROW][C]53[/C][C]14.03[/C][C]13.5601293530061[/C][C]0.469870646993858[/C][/ROW]
[ROW][C]54[/C][C]16.27[/C][C]11.2737845531827[/C][C]4.99621544681726[/C][/ROW]
[ROW][C]55[/C][C]16.17[/C][C]4.9749414258126[/C][C]11.1950585741874[/C][/ROW]
[ROW][C]56[/C][C]17.25[/C][C]11.7547561479335[/C][C]5.49524385206646[/C][/ROW]
[ROW][C]57[/C][C]19.38[/C][C]21.3824388850511[/C][C]-2.00243888505105[/C][/ROW]
[ROW][C]58[/C][C]26.2[/C][C]49.2918057854151[/C][C]-23.0918057854151[/C][/ROW]
[ROW][C]59[/C][C]33.53[/C][C]66.1151775542645[/C][C]-32.5851775542645[/C][/ROW]
[ROW][C]60[/C][C]32.2[/C][C]41.0096266626957[/C][C]-8.8096266626957[/C][/ROW]
[ROW][C]61[/C][C]38.45[/C][C]59.8638894332297[/C][C]-21.4138894332297[/C][/ROW]
[ROW][C]62[/C][C]44.86[/C][C]45.9330704932263[/C][C]-1.07307049322626[/C][/ROW]
[ROW][C]63[/C][C]41.67[/C][C]20.1197837849421[/C][C]21.5502162150579[/C][/ROW]
[ROW][C]64[/C][C]36.06[/C][C]47.3383881719415[/C][C]-11.2783881719415[/C][/ROW]
[ROW][C]65[/C][C]39.76[/C][C]33.1141635446581[/C][C]6.64583645534192[/C][/ROW]
[ROW][C]66[/C][C]36.81[/C][C]17.1680511039041[/C][C]19.6419488960959[/C][/ROW]
[ROW][C]67[/C][C]42.65[/C][C]28.406637292986[/C][C]14.2433627070140[/C][/ROW]
[ROW][C]68[/C][C]46.89[/C][C]28.0083844537446[/C][C]18.8816155462554[/C][/ROW]
[ROW][C]69[/C][C]53.61[/C][C]60.7097258028334[/C][C]-7.09972580283339[/C][/ROW]
[ROW][C]70[/C][C]57.59[/C][C]81.6255742932288[/C][C]-24.0355742932288[/C][/ROW]
[ROW][C]71[/C][C]67.82[/C][C]62.6508344816763[/C][C]5.16916551832365[/C][/ROW]
[ROW][C]72[/C][C]71.89[/C][C]39.7251622611629[/C][C]32.1648377388371[/C][/ROW]
[ROW][C]73[/C][C]75.51[/C][C]67.2363104386734[/C][C]8.27368956132664[/C][/ROW]
[ROW][C]74[/C][C]68.49[/C][C]67.4822451780627[/C][C]1.00775482193725[/C][/ROW]
[ROW][C]75[/C][C]62.72[/C][C]61.7864707289808[/C][C]0.933529271019227[/C][/ROW]
[ROW][C]76[/C][C]70.39[/C][C]38.6718484505559[/C][C]31.7181515494441[/C][/ROW]
[ROW][C]77[/C][C]59.77[/C][C]19.0441821696929[/C][C]40.7258178303071[/C][/ROW]
[ROW][C]78[/C][C]57.27[/C][C]23.6195628854511[/C][C]33.6504371145489[/C][/ROW]
[ROW][C]79[/C][C]67.96[/C][C]28.1748047712402[/C][C]39.7851952287598[/C][/ROW]
[ROW][C]80[/C][C]67.85[/C][C]54.1740020390498[/C][C]13.6759979609502[/C][/ROW]
[ROW][C]81[/C][C]76.98[/C][C]69.2458688879706[/C][C]7.7341311120294[/C][/ROW]
[ROW][C]82[/C][C]81.08[/C][C]77.223510438582[/C][C]3.85648956141799[/C][/ROW]
[ROW][C]83[/C][C]91.66[/C][C]80.4761811863608[/C][C]11.1838188136392[/C][/ROW]
[ROW][C]84[/C][C]84.84[/C][C]77.9632038546139[/C][C]6.87679614538613[/C][/ROW]
[ROW][C]85[/C][C]85.73[/C][C]110.610419358969[/C][C]-24.8804193589688[/C][/ROW]
[ROW][C]86[/C][C]84.61[/C][C]60.1242439410144[/C][C]24.4857560589856[/C][/ROW]
[ROW][C]87[/C][C]92.91[/C][C]62.0695191702571[/C][C]30.8404808297429[/C][/ROW]
[ROW][C]88[/C][C]99.8[/C][C]81.8652520403298[/C][C]17.9347479596702[/C][/ROW]
[ROW][C]89[/C][C]121.19[/C][C]90.3648957017613[/C][C]30.8251042982387[/C][/ROW]
[ROW][C]90[/C][C]122.04[/C][C]103.685347732840[/C][C]18.3546522671602[/C][/ROW]
[ROW][C]91[/C][C]131.76[/C][C]88.9177861793443[/C][C]42.8422138206557[/C][/ROW]
[ROW][C]92[/C][C]138.48[/C][C]99.3250352198196[/C][C]39.1549647801804[/C][/ROW]
[ROW][C]93[/C][C]153.47[/C][C]117.518095468747[/C][C]35.9519045312526[/C][/ROW]
[ROW][C]94[/C][C]189.95[/C][C]170.303839046355[/C][C]19.6461609536453[/C][/ROW]
[ROW][C]95[/C][C]182.22[/C][C]167.037264092395[/C][C]15.1827359076046[/C][/ROW]
[ROW][C]96[/C][C]198.08[/C][C]155.579887886391[/C][C]42.5001121136088[/C][/ROW]
[ROW][C]97[/C][C]135.36[/C][C]170.210572672005[/C][C]-34.8505726720045[/C][/ROW]
[ROW][C]98[/C][C]125.02[/C][C]137.309333586984[/C][C]-12.2893335869840[/C][/ROW]
[ROW][C]99[/C][C]143.5[/C][C]162.695137803889[/C][C]-19.1951378038885[/C][/ROW]
[ROW][C]100[/C][C]173.95[/C][C]170.403405502899[/C][C]3.54659449710135[/C][/ROW]
[ROW][C]101[/C][C]188.75[/C][C]174.303344567796[/C][C]14.4466554322041[/C][/ROW]
[ROW][C]102[/C][C]167.44[/C][C]174.733135116124[/C][C]-7.29313511612409[/C][/ROW]
[ROW][C]103[/C][C]158.95[/C][C]155.616268362422[/C][C]3.33373163757757[/C][/ROW]
[ROW][C]104[/C][C]169.53[/C][C]141.942972426113[/C][C]27.587027573887[/C][/ROW]
[ROW][C]105[/C][C]113.66[/C][C]150.923166132943[/C][C]-37.263166132943[/C][/ROW]
[ROW][C]106[/C][C]107.59[/C][C]196.551523085261[/C][C]-88.9615230852615[/C][/ROW]
[ROW][C]107[/C][C]92.67[/C][C]150.733153409489[/C][C]-58.0631534094888[/C][/ROW]
[ROW][C]108[/C][C]85.35[/C][C]152.302181993855[/C][C]-66.9521819938554[/C][/ROW]
[ROW][C]109[/C][C]90.13[/C][C]212.101808696183[/C][C]-121.971808696183[/C][/ROW]
[ROW][C]110[/C][C]89.31[/C][C]146.813987310572[/C][C]-57.5039873105725[/C][/ROW]
[ROW][C]111[/C][C]105.12[/C][C]164.206110512103[/C][C]-59.0861105121031[/C][/ROW]
[ROW][C]112[/C][C]125.83[/C][C]146.642303239864[/C][C]-20.8123032398637[/C][/ROW]
[ROW][C]113[/C][C]135.81[/C][C]121.058253601411[/C][C]14.7517463985886[/C][/ROW]
[ROW][C]114[/C][C]142.43[/C][C]160.054954981629[/C][C]-17.6249549816289[/C][/ROW]
[ROW][C]115[/C][C]163.39[/C][C]167.191619488664[/C][C]-3.80161948866429[/C][/ROW]
[ROW][C]116[/C][C]168.21[/C][C]151.740607503170[/C][C]16.4693924968302[/C][/ROW]
[ROW][C]117[/C][C]185.35[/C][C]167.877751433817[/C][C]17.4722485661827[/C][/ROW]
[ROW][C]118[/C][C]188.5[/C][C]188.381541243985[/C][C]0.118458756015371[/C][/ROW]
[ROW][C]119[/C][C]199.91[/C][C]179.064691576324[/C][C]20.8453084236755[/C][/ROW]
[ROW][C]120[/C][C]210.73[/C][C]181.823933068455[/C][C]28.9060669315445[/C][/ROW]
[ROW][C]121[/C][C]192.06[/C][C]171.379745875497[/C][C]20.6802541245033[/C][/ROW]
[ROW][C]122[/C][C]204.62[/C][C]192.081027405320[/C][C]12.5389725946802[/C][/ROW]
[ROW][C]123[/C][C]235[/C][C]184.560880846088[/C][C]50.4391191539117[/C][/ROW]
[ROW][C]124[/C][C]261.09[/C][C]186.259247243487[/C][C]74.8307527565127[/C][/ROW]
[ROW][C]125[/C][C]256.88[/C][C]153.55716533577[/C][C]103.322834664230[/C][/ROW]
[ROW][C]126[/C][C]251.53[/C][C]148.082028962810[/C][C]103.447971037190[/C][/ROW]
[ROW][C]127[/C][C]257.25[/C][C]176.765918642905[/C][C]80.4840813570952[/C][/ROW]
[ROW][C]128[/C][C]243.1[/C][C]139.224251011955[/C][C]103.875748988045[/C][/ROW]
[ROW][C]129[/C][C]283.75[/C][C]170.344051583193[/C][C]113.405948416807[/C][/ROW]
[ROW][C]130[/C][C]300.98[/C][C]186.482473148243[/C][C]114.497526851757[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108050&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108050&T=4

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
125.94112.107289813532-86.167289813532
228.66104.851539835078-76.191539835078
333.95154.098784512411-120.148784512411
431.0138.9325600394035-7.9225600394035
521-10.110401646263431.1104016462634
626.1960.9358156410332-34.7458156410332
725.4114.858668170205710.5513318297943
830.4732.4903872445283-2.0203872445283
912.8823.8886487920867-11.0086487920867
109.7851.2266972107049-41.4466972107049
118.25-22.122603335475330.3726033354753
127.44-54.131005038621261.5710050386212
1310.8138.8459068386762-28.0359068386762
149.1216.4541339250056-7.33413392500557
1511.03-13.770914523812724.8009145238127
1612.7445.6326947477347-32.8926947477347
179.9821.4101044631143-11.4301044631143
1811.6229.8298852732551-18.2098852732551
199.417.0519266840977-7.65192668409769
209.27-18.408464537387527.6784645373875
217.760.553193800192357.20680619980765
228.7850.705773238009-41.9257732380090
2310.6572.3251752953616-61.6751752953616
2410.9556.3578358273062-45.4078358273062
2512.3652.272717669608-39.912717669608
2610.8538.2892794234368-27.4392794234368
2711.842.073876100560949.76612389943906
2812.14-19.273714027218431.4137140272184
2911.65-27.126985424663338.7769854246633
308.86-4.1300614066897912.9900614066898
317.63-11.883917496801419.5139174968014
327.38-10.9361262623418.31612626234
337.25-24.788377788058532.0383777880585
348.0336.5906476378518-28.5606476378518
357.7535.1653652103446-27.4153652103446
367.1622.1719225461455-15.0119225461455
377.1816.4556029585753-9.2756029585753
387.5143.205241573673-35.695241573673
397.0752.5524889793366-45.4824889793366
407.1132.5745960284295-25.4645960284295
418.9832.4132598271319-23.4332598271319
429.5328.4163724064607-18.8863724064607
4310.5447.6190898599333-37.0790898599333
4411.3138.0705951634603-26.7605951634603
4510.3656.3762006163701-46.0162006163701
4611.4445.3650111959885-33.9250111959885
4710.4518.7004367711593-8.25043677115928
4810.6929.8586312706496-19.1686312706496
4911.2829.0726853240445-17.7926853240445
5011.9635.6482024961839-23.6882024961839
5113.5235.988155465713-22.468155465713
5212.8923.7105218869592-10.8205218869592
5314.0313.56012935300610.469870646993858
5416.2711.27378455318274.99621544681726
5516.174.974941425812611.1950585741874
5617.2511.75475614793355.49524385206646
5719.3821.3824388850511-2.00243888505105
5826.249.2918057854151-23.0918057854151
5933.5366.1151775542645-32.5851775542645
6032.241.0096266626957-8.8096266626957
6138.4559.8638894332297-21.4138894332297
6244.8645.9330704932263-1.07307049322626
6341.6720.119783784942121.5502162150579
6436.0647.3383881719415-11.2783881719415
6539.7633.11416354465816.64583645534192
6636.8117.168051103904119.6419488960959
6742.6528.40663729298614.2433627070140
6846.8928.008384453744618.8816155462554
6953.6160.7097258028334-7.09972580283339
7057.5981.6255742932288-24.0355742932288
7167.8262.65083448167635.16916551832365
7271.8939.725162261162932.1648377388371
7375.5167.23631043867348.27368956132664
7468.4967.48224517806271.00775482193725
7562.7261.78647072898080.933529271019227
7670.3938.671848450555931.7181515494441
7759.7719.044182169692940.7258178303071
7857.2723.619562885451133.6504371145489
7967.9628.174804771240239.7851952287598
8067.8554.174002039049813.6759979609502
8176.9869.24586888797067.7341311120294
8281.0877.2235104385823.85648956141799
8391.6680.476181186360811.1838188136392
8484.8477.96320385461396.87679614538613
8585.73110.610419358969-24.8804193589688
8684.6160.124243941014424.4857560589856
8792.9162.069519170257130.8404808297429
8899.881.865252040329817.9347479596702
89121.1990.364895701761330.8251042982387
90122.04103.68534773284018.3546522671602
91131.7688.917786179344342.8422138206557
92138.4899.325035219819639.1549647801804
93153.47117.51809546874735.9519045312526
94189.95170.30383904635519.6461609536453
95182.22167.03726409239515.1827359076046
96198.08155.57988788639142.5001121136088
97135.36170.210572672005-34.8505726720045
98125.02137.309333586984-12.2893335869840
99143.5162.695137803889-19.1951378038885
100173.95170.4034055028993.54659449710135
101188.75174.30334456779614.4466554322041
102167.44174.733135116124-7.29313511612409
103158.95155.6162683624223.33373163757757
104169.53141.94297242611327.587027573887
105113.66150.923166132943-37.263166132943
106107.59196.551523085261-88.9615230852615
10792.67150.733153409489-58.0631534094888
10885.35152.302181993855-66.9521819938554
10990.13212.101808696183-121.971808696183
11089.31146.813987310572-57.5039873105725
111105.12164.206110512103-59.0861105121031
112125.83146.642303239864-20.8123032398637
113135.81121.05825360141114.7517463985886
114142.43160.054954981629-17.6249549816289
115163.39167.191619488664-3.80161948866429
116168.21151.74060750317016.4693924968302
117185.35167.87775143381717.4722485661827
118188.5188.3815412439850.118458756015371
119199.91179.06469157632420.8453084236755
120210.73181.82393306845528.9060669315445
121192.06171.37974587549720.6802541245033
122204.62192.08102740532012.5389725946802
123235184.56088084608850.4391191539117
124261.09186.25924724348774.8307527565127
125256.88153.55716533577103.322834664230
126251.53148.082028962810103.447971037190
127257.25176.76591864290580.4840813570952
128243.1139.224251011955103.875748988045
129283.75170.344051583193113.405948416807
130300.98186.482473148243114.497526851757






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.00265787514148280.00531575028296560.997342124858517
110.0007859504652557280.001571900930511460.999214049534744
129.45105180145496e-050.0001890210360290990.999905489481985
131.65499756536042e-053.30999513072083e-050.999983450024346
141.86935219100852e-063.73870438201703e-060.999998130647809
153.60583871968644e-077.21167743937288e-070.999999639416128
164.51245599911661e-089.02491199823323e-080.99999995487544
176.01429667309514e-091.20285933461903e-080.999999993985703
186.65214944748363e-101.33042988949673e-090.999999999334785
191.11340835863572e-102.22681671727144e-100.99999999988866
201.38933705726807e-112.77867411453614e-110.999999999986107
211.46353595036418e-122.92707190072836e-120.999999999998537
222.55232757362905e-135.1046551472581e-130.999999999999745
235.67539247561571e-141.13507849512314e-130.999999999999943
241.12154246086375e-142.2430849217275e-140.999999999999989
251.49975328372773e-152.99950656745547e-150.999999999999998
261.66673847794097e-163.33347695588195e-161
271.54821804800824e-173.09643609601648e-171
282.36151854034225e-184.7230370806845e-181
292.72597295534781e-195.45194591069561e-191
302.90652269543326e-205.81304539086653e-201
314.43332188901879e-218.86664377803758e-211
326.41793414546564e-221.28358682909313e-211
332.61909393137559e-225.23818786275119e-221
343.3459127697811e-236.6918255395622e-231
353.26179967193770e-246.52359934387539e-241
364.48720590291235e-258.9744118058247e-251
371.18226771299606e-252.36453542599211e-251
381.69337869303772e-263.38675738607544e-261
392.01653258482603e-274.03306516965207e-271
403.05942245766981e-286.11884491533963e-281
415.34545332063709e-291.06909066412742e-281
425.83822448945647e-301.16764489789129e-291
435.32259377760811e-311.06451875552162e-301
445.05011635935353e-321.01002327187071e-311
455.14853638251029e-331.02970727650206e-321
468.34542704857767e-341.66908540971553e-331
471.92184844952545e-343.84369689905091e-341
486.70367926551214e-351.34073585310243e-341
491.87011578771344e-353.74023157542687e-351
501.17086367664636e-352.34172735329272e-351
512.42784048076956e-354.85568096153912e-351
521.00715687718135e-352.0143137543627e-351
534.62677177348736e-369.25354354697472e-361
545.2902296786556e-361.05804593573112e-351
552.62044385831136e-365.24088771662272e-361
562.5121289635039e-365.0242579270078e-361
571.40648584224924e-352.81297168449848e-351
587.93659113980082e-311.58731822796016e-301
592.26827449967387e-264.53654899934774e-261
605.94742554854418e-241.18948510970884e-231
613.24116233171372e-226.48232466342743e-221
629.25530905943884e-201.85106181188777e-191
631.29065878887416e-172.58131757774831e-171
641.30666398916882e-172.61332797833763e-171
657.02459226681011e-161.40491845336202e-151
661.93533748642887e-143.87067497285775e-140.99999999999998
673.45197915025763e-126.90395830051526e-120.999999999996548
681.54218957709820e-103.08437915419640e-100.999999999845781
691.88504878956781e-093.77009757913561e-090.999999998114951
707.532373679655e-091.506474735931e-080.999999992467626
718.85397858636576e-071.77079571727315e-060.999999114602141
723.43206090658258e-056.86412181316516e-050.999965679390934
730.0001048718345372430.0002097436690744860.999895128165463
740.0002103437443258360.0004206874886516720.999789656255674
750.0006321732608462890.001264346521692580.999367826739154
760.001709531474521450.003419062949042900.998290468525479
770.00175429191833310.00350858383666620.998245708081667
780.001386375286355910.002772750572711820.998613624713644
790.002949281412683990.005898562825367980.997050718587316
800.005190320020526060.01038064004105210.994809679979474
810.01288188659842620.02576377319685230.987118113401574
820.02729541547244970.05459083094489950.97270458452755
830.04751136450007750.0950227290001550.952488635499922
840.04684056942232270.09368113884464540.953159430577677
850.04278531358583230.08557062717166470.957214686414168
860.04837484807494620.09674969614989230.951625151925054
870.06382269053728610.1276453810745720.936177309462714
880.08232921017130320.1646584203426060.917670789828697
890.1336010971953630.2672021943907260.866398902804637
900.1455352903153970.2910705806307950.854464709684603
910.1650179667430530.3300359334861070.834982033256947
920.1949021075425450.389804215085090.805097892457455
930.2806722255734130.5613444511468250.719327774426587
940.4253612438155820.8507224876311640.574638756184418
950.4792452823016410.9584905646032810.520754717698359
960.9139367172063990.1721265655872020.0860632827936011
970.9774610769132310.04507784617353770.0225389230867689
980.97080420522440.05839158955120020.0291957947756001
990.9849020997103350.03019580057932970.0150979002896649
1000.987810916790960.02437816641807980.0121890832090399
1010.9890667687924050.02186646241519070.0109332312075953
1020.9912652879034310.01746942419313720.00873471209656859
1030.9890085639000150.02198287219996970.0109914360999849
1040.9862042919384160.02759141612316840.0137957080615842
1050.9843856038369560.03122879232608760.0156143961630438
1060.9888594467059870.02228110658802670.0111405532940133
1070.9865827636822810.02683447263543780.0134172363177189
1080.9919899463096480.01602010738070350.00801005369035177
1090.9884001817758450.02319963644830950.0115998182241548
1100.9792738543483040.04145229130339220.0207261456516961
1110.9647858094482210.07042838110355740.0352141905517787
1120.9690313559891450.06193728802171080.0309686440108554
1130.9769854491996520.04602910160069660.0230145508003483
1140.9790086634600440.04198267307991120.0209913365399556
1150.988008017334570.02398396533086170.0119919826654309
1160.98568487152580.02863025694840090.0143151284742004
1170.997777066120660.004445867758678620.00222293387933931
1180.99941332315240.001173353695198480.00058667684759924
1190.9975078917169750.004984216566049410.00249210828302471
1200.99951715744550.0009656851090013760.000482842554500688

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.0026578751414828 & 0.0053157502829656 & 0.997342124858517 \tabularnewline
11 & 0.000785950465255728 & 0.00157190093051146 & 0.999214049534744 \tabularnewline
12 & 9.45105180145496e-05 & 0.000189021036029099 & 0.999905489481985 \tabularnewline
13 & 1.65499756536042e-05 & 3.30999513072083e-05 & 0.999983450024346 \tabularnewline
14 & 1.86935219100852e-06 & 3.73870438201703e-06 & 0.999998130647809 \tabularnewline
15 & 3.60583871968644e-07 & 7.21167743937288e-07 & 0.999999639416128 \tabularnewline
16 & 4.51245599911661e-08 & 9.02491199823323e-08 & 0.99999995487544 \tabularnewline
17 & 6.01429667309514e-09 & 1.20285933461903e-08 & 0.999999993985703 \tabularnewline
18 & 6.65214944748363e-10 & 1.33042988949673e-09 & 0.999999999334785 \tabularnewline
19 & 1.11340835863572e-10 & 2.22681671727144e-10 & 0.99999999988866 \tabularnewline
20 & 1.38933705726807e-11 & 2.77867411453614e-11 & 0.999999999986107 \tabularnewline
21 & 1.46353595036418e-12 & 2.92707190072836e-12 & 0.999999999998537 \tabularnewline
22 & 2.55232757362905e-13 & 5.1046551472581e-13 & 0.999999999999745 \tabularnewline
23 & 5.67539247561571e-14 & 1.13507849512314e-13 & 0.999999999999943 \tabularnewline
24 & 1.12154246086375e-14 & 2.2430849217275e-14 & 0.999999999999989 \tabularnewline
25 & 1.49975328372773e-15 & 2.99950656745547e-15 & 0.999999999999998 \tabularnewline
26 & 1.66673847794097e-16 & 3.33347695588195e-16 & 1 \tabularnewline
27 & 1.54821804800824e-17 & 3.09643609601648e-17 & 1 \tabularnewline
28 & 2.36151854034225e-18 & 4.7230370806845e-18 & 1 \tabularnewline
29 & 2.72597295534781e-19 & 5.45194591069561e-19 & 1 \tabularnewline
30 & 2.90652269543326e-20 & 5.81304539086653e-20 & 1 \tabularnewline
31 & 4.43332188901879e-21 & 8.86664377803758e-21 & 1 \tabularnewline
32 & 6.41793414546564e-22 & 1.28358682909313e-21 & 1 \tabularnewline
33 & 2.61909393137559e-22 & 5.23818786275119e-22 & 1 \tabularnewline
34 & 3.3459127697811e-23 & 6.6918255395622e-23 & 1 \tabularnewline
35 & 3.26179967193770e-24 & 6.52359934387539e-24 & 1 \tabularnewline
36 & 4.48720590291235e-25 & 8.9744118058247e-25 & 1 \tabularnewline
37 & 1.18226771299606e-25 & 2.36453542599211e-25 & 1 \tabularnewline
38 & 1.69337869303772e-26 & 3.38675738607544e-26 & 1 \tabularnewline
39 & 2.01653258482603e-27 & 4.03306516965207e-27 & 1 \tabularnewline
40 & 3.05942245766981e-28 & 6.11884491533963e-28 & 1 \tabularnewline
41 & 5.34545332063709e-29 & 1.06909066412742e-28 & 1 \tabularnewline
42 & 5.83822448945647e-30 & 1.16764489789129e-29 & 1 \tabularnewline
43 & 5.32259377760811e-31 & 1.06451875552162e-30 & 1 \tabularnewline
44 & 5.05011635935353e-32 & 1.01002327187071e-31 & 1 \tabularnewline
45 & 5.14853638251029e-33 & 1.02970727650206e-32 & 1 \tabularnewline
46 & 8.34542704857767e-34 & 1.66908540971553e-33 & 1 \tabularnewline
47 & 1.92184844952545e-34 & 3.84369689905091e-34 & 1 \tabularnewline
48 & 6.70367926551214e-35 & 1.34073585310243e-34 & 1 \tabularnewline
49 & 1.87011578771344e-35 & 3.74023157542687e-35 & 1 \tabularnewline
50 & 1.17086367664636e-35 & 2.34172735329272e-35 & 1 \tabularnewline
51 & 2.42784048076956e-35 & 4.85568096153912e-35 & 1 \tabularnewline
52 & 1.00715687718135e-35 & 2.0143137543627e-35 & 1 \tabularnewline
53 & 4.62677177348736e-36 & 9.25354354697472e-36 & 1 \tabularnewline
54 & 5.2902296786556e-36 & 1.05804593573112e-35 & 1 \tabularnewline
55 & 2.62044385831136e-36 & 5.24088771662272e-36 & 1 \tabularnewline
56 & 2.5121289635039e-36 & 5.0242579270078e-36 & 1 \tabularnewline
57 & 1.40648584224924e-35 & 2.81297168449848e-35 & 1 \tabularnewline
58 & 7.93659113980082e-31 & 1.58731822796016e-30 & 1 \tabularnewline
59 & 2.26827449967387e-26 & 4.53654899934774e-26 & 1 \tabularnewline
60 & 5.94742554854418e-24 & 1.18948510970884e-23 & 1 \tabularnewline
61 & 3.24116233171372e-22 & 6.48232466342743e-22 & 1 \tabularnewline
62 & 9.25530905943884e-20 & 1.85106181188777e-19 & 1 \tabularnewline
63 & 1.29065878887416e-17 & 2.58131757774831e-17 & 1 \tabularnewline
64 & 1.30666398916882e-17 & 2.61332797833763e-17 & 1 \tabularnewline
65 & 7.02459226681011e-16 & 1.40491845336202e-15 & 1 \tabularnewline
66 & 1.93533748642887e-14 & 3.87067497285775e-14 & 0.99999999999998 \tabularnewline
67 & 3.45197915025763e-12 & 6.90395830051526e-12 & 0.999999999996548 \tabularnewline
68 & 1.54218957709820e-10 & 3.08437915419640e-10 & 0.999999999845781 \tabularnewline
69 & 1.88504878956781e-09 & 3.77009757913561e-09 & 0.999999998114951 \tabularnewline
70 & 7.532373679655e-09 & 1.506474735931e-08 & 0.999999992467626 \tabularnewline
71 & 8.85397858636576e-07 & 1.77079571727315e-06 & 0.999999114602141 \tabularnewline
72 & 3.43206090658258e-05 & 6.86412181316516e-05 & 0.999965679390934 \tabularnewline
73 & 0.000104871834537243 & 0.000209743669074486 & 0.999895128165463 \tabularnewline
74 & 0.000210343744325836 & 0.000420687488651672 & 0.999789656255674 \tabularnewline
75 & 0.000632173260846289 & 0.00126434652169258 & 0.999367826739154 \tabularnewline
76 & 0.00170953147452145 & 0.00341906294904290 & 0.998290468525479 \tabularnewline
77 & 0.0017542919183331 & 0.0035085838366662 & 0.998245708081667 \tabularnewline
78 & 0.00138637528635591 & 0.00277275057271182 & 0.998613624713644 \tabularnewline
79 & 0.00294928141268399 & 0.00589856282536798 & 0.997050718587316 \tabularnewline
80 & 0.00519032002052606 & 0.0103806400410521 & 0.994809679979474 \tabularnewline
81 & 0.0128818865984262 & 0.0257637731968523 & 0.987118113401574 \tabularnewline
82 & 0.0272954154724497 & 0.0545908309448995 & 0.97270458452755 \tabularnewline
83 & 0.0475113645000775 & 0.095022729000155 & 0.952488635499922 \tabularnewline
84 & 0.0468405694223227 & 0.0936811388446454 & 0.953159430577677 \tabularnewline
85 & 0.0427853135858323 & 0.0855706271716647 & 0.957214686414168 \tabularnewline
86 & 0.0483748480749462 & 0.0967496961498923 & 0.951625151925054 \tabularnewline
87 & 0.0638226905372861 & 0.127645381074572 & 0.936177309462714 \tabularnewline
88 & 0.0823292101713032 & 0.164658420342606 & 0.917670789828697 \tabularnewline
89 & 0.133601097195363 & 0.267202194390726 & 0.866398902804637 \tabularnewline
90 & 0.145535290315397 & 0.291070580630795 & 0.854464709684603 \tabularnewline
91 & 0.165017966743053 & 0.330035933486107 & 0.834982033256947 \tabularnewline
92 & 0.194902107542545 & 0.38980421508509 & 0.805097892457455 \tabularnewline
93 & 0.280672225573413 & 0.561344451146825 & 0.719327774426587 \tabularnewline
94 & 0.425361243815582 & 0.850722487631164 & 0.574638756184418 \tabularnewline
95 & 0.479245282301641 & 0.958490564603281 & 0.520754717698359 \tabularnewline
96 & 0.913936717206399 & 0.172126565587202 & 0.0860632827936011 \tabularnewline
97 & 0.977461076913231 & 0.0450778461735377 & 0.0225389230867689 \tabularnewline
98 & 0.9708042052244 & 0.0583915895512002 & 0.0291957947756001 \tabularnewline
99 & 0.984902099710335 & 0.0301958005793297 & 0.0150979002896649 \tabularnewline
100 & 0.98781091679096 & 0.0243781664180798 & 0.0121890832090399 \tabularnewline
101 & 0.989066768792405 & 0.0218664624151907 & 0.0109332312075953 \tabularnewline
102 & 0.991265287903431 & 0.0174694241931372 & 0.00873471209656859 \tabularnewline
103 & 0.989008563900015 & 0.0219828721999697 & 0.0109914360999849 \tabularnewline
104 & 0.986204291938416 & 0.0275914161231684 & 0.0137957080615842 \tabularnewline
105 & 0.984385603836956 & 0.0312287923260876 & 0.0156143961630438 \tabularnewline
106 & 0.988859446705987 & 0.0222811065880267 & 0.0111405532940133 \tabularnewline
107 & 0.986582763682281 & 0.0268344726354378 & 0.0134172363177189 \tabularnewline
108 & 0.991989946309648 & 0.0160201073807035 & 0.00801005369035177 \tabularnewline
109 & 0.988400181775845 & 0.0231996364483095 & 0.0115998182241548 \tabularnewline
110 & 0.979273854348304 & 0.0414522913033922 & 0.0207261456516961 \tabularnewline
111 & 0.964785809448221 & 0.0704283811035574 & 0.0352141905517787 \tabularnewline
112 & 0.969031355989145 & 0.0619372880217108 & 0.0309686440108554 \tabularnewline
113 & 0.976985449199652 & 0.0460291016006966 & 0.0230145508003483 \tabularnewline
114 & 0.979008663460044 & 0.0419826730799112 & 0.0209913365399556 \tabularnewline
115 & 0.98800801733457 & 0.0239839653308617 & 0.0119919826654309 \tabularnewline
116 & 0.9856848715258 & 0.0286302569484009 & 0.0143151284742004 \tabularnewline
117 & 0.99777706612066 & 0.00444586775867862 & 0.00222293387933931 \tabularnewline
118 & 0.9994133231524 & 0.00117335369519848 & 0.00058667684759924 \tabularnewline
119 & 0.997507891716975 & 0.00498421656604941 & 0.00249210828302471 \tabularnewline
120 & 0.9995171574455 & 0.000965685109001376 & 0.000482842554500688 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108050&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]10[/C][C]0.0026578751414828[/C][C]0.0053157502829656[/C][C]0.997342124858517[/C][/ROW]
[ROW][C]11[/C][C]0.000785950465255728[/C][C]0.00157190093051146[/C][C]0.999214049534744[/C][/ROW]
[ROW][C]12[/C][C]9.45105180145496e-05[/C][C]0.000189021036029099[/C][C]0.999905489481985[/C][/ROW]
[ROW][C]13[/C][C]1.65499756536042e-05[/C][C]3.30999513072083e-05[/C][C]0.999983450024346[/C][/ROW]
[ROW][C]14[/C][C]1.86935219100852e-06[/C][C]3.73870438201703e-06[/C][C]0.999998130647809[/C][/ROW]
[ROW][C]15[/C][C]3.60583871968644e-07[/C][C]7.21167743937288e-07[/C][C]0.999999639416128[/C][/ROW]
[ROW][C]16[/C][C]4.51245599911661e-08[/C][C]9.02491199823323e-08[/C][C]0.99999995487544[/C][/ROW]
[ROW][C]17[/C][C]6.01429667309514e-09[/C][C]1.20285933461903e-08[/C][C]0.999999993985703[/C][/ROW]
[ROW][C]18[/C][C]6.65214944748363e-10[/C][C]1.33042988949673e-09[/C][C]0.999999999334785[/C][/ROW]
[ROW][C]19[/C][C]1.11340835863572e-10[/C][C]2.22681671727144e-10[/C][C]0.99999999988866[/C][/ROW]
[ROW][C]20[/C][C]1.38933705726807e-11[/C][C]2.77867411453614e-11[/C][C]0.999999999986107[/C][/ROW]
[ROW][C]21[/C][C]1.46353595036418e-12[/C][C]2.92707190072836e-12[/C][C]0.999999999998537[/C][/ROW]
[ROW][C]22[/C][C]2.55232757362905e-13[/C][C]5.1046551472581e-13[/C][C]0.999999999999745[/C][/ROW]
[ROW][C]23[/C][C]5.67539247561571e-14[/C][C]1.13507849512314e-13[/C][C]0.999999999999943[/C][/ROW]
[ROW][C]24[/C][C]1.12154246086375e-14[/C][C]2.2430849217275e-14[/C][C]0.999999999999989[/C][/ROW]
[ROW][C]25[/C][C]1.49975328372773e-15[/C][C]2.99950656745547e-15[/C][C]0.999999999999998[/C][/ROW]
[ROW][C]26[/C][C]1.66673847794097e-16[/C][C]3.33347695588195e-16[/C][C]1[/C][/ROW]
[ROW][C]27[/C][C]1.54821804800824e-17[/C][C]3.09643609601648e-17[/C][C]1[/C][/ROW]
[ROW][C]28[/C][C]2.36151854034225e-18[/C][C]4.7230370806845e-18[/C][C]1[/C][/ROW]
[ROW][C]29[/C][C]2.72597295534781e-19[/C][C]5.45194591069561e-19[/C][C]1[/C][/ROW]
[ROW][C]30[/C][C]2.90652269543326e-20[/C][C]5.81304539086653e-20[/C][C]1[/C][/ROW]
[ROW][C]31[/C][C]4.43332188901879e-21[/C][C]8.86664377803758e-21[/C][C]1[/C][/ROW]
[ROW][C]32[/C][C]6.41793414546564e-22[/C][C]1.28358682909313e-21[/C][C]1[/C][/ROW]
[ROW][C]33[/C][C]2.61909393137559e-22[/C][C]5.23818786275119e-22[/C][C]1[/C][/ROW]
[ROW][C]34[/C][C]3.3459127697811e-23[/C][C]6.6918255395622e-23[/C][C]1[/C][/ROW]
[ROW][C]35[/C][C]3.26179967193770e-24[/C][C]6.52359934387539e-24[/C][C]1[/C][/ROW]
[ROW][C]36[/C][C]4.48720590291235e-25[/C][C]8.9744118058247e-25[/C][C]1[/C][/ROW]
[ROW][C]37[/C][C]1.18226771299606e-25[/C][C]2.36453542599211e-25[/C][C]1[/C][/ROW]
[ROW][C]38[/C][C]1.69337869303772e-26[/C][C]3.38675738607544e-26[/C][C]1[/C][/ROW]
[ROW][C]39[/C][C]2.01653258482603e-27[/C][C]4.03306516965207e-27[/C][C]1[/C][/ROW]
[ROW][C]40[/C][C]3.05942245766981e-28[/C][C]6.11884491533963e-28[/C][C]1[/C][/ROW]
[ROW][C]41[/C][C]5.34545332063709e-29[/C][C]1.06909066412742e-28[/C][C]1[/C][/ROW]
[ROW][C]42[/C][C]5.83822448945647e-30[/C][C]1.16764489789129e-29[/C][C]1[/C][/ROW]
[ROW][C]43[/C][C]5.32259377760811e-31[/C][C]1.06451875552162e-30[/C][C]1[/C][/ROW]
[ROW][C]44[/C][C]5.05011635935353e-32[/C][C]1.01002327187071e-31[/C][C]1[/C][/ROW]
[ROW][C]45[/C][C]5.14853638251029e-33[/C][C]1.02970727650206e-32[/C][C]1[/C][/ROW]
[ROW][C]46[/C][C]8.34542704857767e-34[/C][C]1.66908540971553e-33[/C][C]1[/C][/ROW]
[ROW][C]47[/C][C]1.92184844952545e-34[/C][C]3.84369689905091e-34[/C][C]1[/C][/ROW]
[ROW][C]48[/C][C]6.70367926551214e-35[/C][C]1.34073585310243e-34[/C][C]1[/C][/ROW]
[ROW][C]49[/C][C]1.87011578771344e-35[/C][C]3.74023157542687e-35[/C][C]1[/C][/ROW]
[ROW][C]50[/C][C]1.17086367664636e-35[/C][C]2.34172735329272e-35[/C][C]1[/C][/ROW]
[ROW][C]51[/C][C]2.42784048076956e-35[/C][C]4.85568096153912e-35[/C][C]1[/C][/ROW]
[ROW][C]52[/C][C]1.00715687718135e-35[/C][C]2.0143137543627e-35[/C][C]1[/C][/ROW]
[ROW][C]53[/C][C]4.62677177348736e-36[/C][C]9.25354354697472e-36[/C][C]1[/C][/ROW]
[ROW][C]54[/C][C]5.2902296786556e-36[/C][C]1.05804593573112e-35[/C][C]1[/C][/ROW]
[ROW][C]55[/C][C]2.62044385831136e-36[/C][C]5.24088771662272e-36[/C][C]1[/C][/ROW]
[ROW][C]56[/C][C]2.5121289635039e-36[/C][C]5.0242579270078e-36[/C][C]1[/C][/ROW]
[ROW][C]57[/C][C]1.40648584224924e-35[/C][C]2.81297168449848e-35[/C][C]1[/C][/ROW]
[ROW][C]58[/C][C]7.93659113980082e-31[/C][C]1.58731822796016e-30[/C][C]1[/C][/ROW]
[ROW][C]59[/C][C]2.26827449967387e-26[/C][C]4.53654899934774e-26[/C][C]1[/C][/ROW]
[ROW][C]60[/C][C]5.94742554854418e-24[/C][C]1.18948510970884e-23[/C][C]1[/C][/ROW]
[ROW][C]61[/C][C]3.24116233171372e-22[/C][C]6.48232466342743e-22[/C][C]1[/C][/ROW]
[ROW][C]62[/C][C]9.25530905943884e-20[/C][C]1.85106181188777e-19[/C][C]1[/C][/ROW]
[ROW][C]63[/C][C]1.29065878887416e-17[/C][C]2.58131757774831e-17[/C][C]1[/C][/ROW]
[ROW][C]64[/C][C]1.30666398916882e-17[/C][C]2.61332797833763e-17[/C][C]1[/C][/ROW]
[ROW][C]65[/C][C]7.02459226681011e-16[/C][C]1.40491845336202e-15[/C][C]1[/C][/ROW]
[ROW][C]66[/C][C]1.93533748642887e-14[/C][C]3.87067497285775e-14[/C][C]0.99999999999998[/C][/ROW]
[ROW][C]67[/C][C]3.45197915025763e-12[/C][C]6.90395830051526e-12[/C][C]0.999999999996548[/C][/ROW]
[ROW][C]68[/C][C]1.54218957709820e-10[/C][C]3.08437915419640e-10[/C][C]0.999999999845781[/C][/ROW]
[ROW][C]69[/C][C]1.88504878956781e-09[/C][C]3.77009757913561e-09[/C][C]0.999999998114951[/C][/ROW]
[ROW][C]70[/C][C]7.532373679655e-09[/C][C]1.506474735931e-08[/C][C]0.999999992467626[/C][/ROW]
[ROW][C]71[/C][C]8.85397858636576e-07[/C][C]1.77079571727315e-06[/C][C]0.999999114602141[/C][/ROW]
[ROW][C]72[/C][C]3.43206090658258e-05[/C][C]6.86412181316516e-05[/C][C]0.999965679390934[/C][/ROW]
[ROW][C]73[/C][C]0.000104871834537243[/C][C]0.000209743669074486[/C][C]0.999895128165463[/C][/ROW]
[ROW][C]74[/C][C]0.000210343744325836[/C][C]0.000420687488651672[/C][C]0.999789656255674[/C][/ROW]
[ROW][C]75[/C][C]0.000632173260846289[/C][C]0.00126434652169258[/C][C]0.999367826739154[/C][/ROW]
[ROW][C]76[/C][C]0.00170953147452145[/C][C]0.00341906294904290[/C][C]0.998290468525479[/C][/ROW]
[ROW][C]77[/C][C]0.0017542919183331[/C][C]0.0035085838366662[/C][C]0.998245708081667[/C][/ROW]
[ROW][C]78[/C][C]0.00138637528635591[/C][C]0.00277275057271182[/C][C]0.998613624713644[/C][/ROW]
[ROW][C]79[/C][C]0.00294928141268399[/C][C]0.00589856282536798[/C][C]0.997050718587316[/C][/ROW]
[ROW][C]80[/C][C]0.00519032002052606[/C][C]0.0103806400410521[/C][C]0.994809679979474[/C][/ROW]
[ROW][C]81[/C][C]0.0128818865984262[/C][C]0.0257637731968523[/C][C]0.987118113401574[/C][/ROW]
[ROW][C]82[/C][C]0.0272954154724497[/C][C]0.0545908309448995[/C][C]0.97270458452755[/C][/ROW]
[ROW][C]83[/C][C]0.0475113645000775[/C][C]0.095022729000155[/C][C]0.952488635499922[/C][/ROW]
[ROW][C]84[/C][C]0.0468405694223227[/C][C]0.0936811388446454[/C][C]0.953159430577677[/C][/ROW]
[ROW][C]85[/C][C]0.0427853135858323[/C][C]0.0855706271716647[/C][C]0.957214686414168[/C][/ROW]
[ROW][C]86[/C][C]0.0483748480749462[/C][C]0.0967496961498923[/C][C]0.951625151925054[/C][/ROW]
[ROW][C]87[/C][C]0.0638226905372861[/C][C]0.127645381074572[/C][C]0.936177309462714[/C][/ROW]
[ROW][C]88[/C][C]0.0823292101713032[/C][C]0.164658420342606[/C][C]0.917670789828697[/C][/ROW]
[ROW][C]89[/C][C]0.133601097195363[/C][C]0.267202194390726[/C][C]0.866398902804637[/C][/ROW]
[ROW][C]90[/C][C]0.145535290315397[/C][C]0.291070580630795[/C][C]0.854464709684603[/C][/ROW]
[ROW][C]91[/C][C]0.165017966743053[/C][C]0.330035933486107[/C][C]0.834982033256947[/C][/ROW]
[ROW][C]92[/C][C]0.194902107542545[/C][C]0.38980421508509[/C][C]0.805097892457455[/C][/ROW]
[ROW][C]93[/C][C]0.280672225573413[/C][C]0.561344451146825[/C][C]0.719327774426587[/C][/ROW]
[ROW][C]94[/C][C]0.425361243815582[/C][C]0.850722487631164[/C][C]0.574638756184418[/C][/ROW]
[ROW][C]95[/C][C]0.479245282301641[/C][C]0.958490564603281[/C][C]0.520754717698359[/C][/ROW]
[ROW][C]96[/C][C]0.913936717206399[/C][C]0.172126565587202[/C][C]0.0860632827936011[/C][/ROW]
[ROW][C]97[/C][C]0.977461076913231[/C][C]0.0450778461735377[/C][C]0.0225389230867689[/C][/ROW]
[ROW][C]98[/C][C]0.9708042052244[/C][C]0.0583915895512002[/C][C]0.0291957947756001[/C][/ROW]
[ROW][C]99[/C][C]0.984902099710335[/C][C]0.0301958005793297[/C][C]0.0150979002896649[/C][/ROW]
[ROW][C]100[/C][C]0.98781091679096[/C][C]0.0243781664180798[/C][C]0.0121890832090399[/C][/ROW]
[ROW][C]101[/C][C]0.989066768792405[/C][C]0.0218664624151907[/C][C]0.0109332312075953[/C][/ROW]
[ROW][C]102[/C][C]0.991265287903431[/C][C]0.0174694241931372[/C][C]0.00873471209656859[/C][/ROW]
[ROW][C]103[/C][C]0.989008563900015[/C][C]0.0219828721999697[/C][C]0.0109914360999849[/C][/ROW]
[ROW][C]104[/C][C]0.986204291938416[/C][C]0.0275914161231684[/C][C]0.0137957080615842[/C][/ROW]
[ROW][C]105[/C][C]0.984385603836956[/C][C]0.0312287923260876[/C][C]0.0156143961630438[/C][/ROW]
[ROW][C]106[/C][C]0.988859446705987[/C][C]0.0222811065880267[/C][C]0.0111405532940133[/C][/ROW]
[ROW][C]107[/C][C]0.986582763682281[/C][C]0.0268344726354378[/C][C]0.0134172363177189[/C][/ROW]
[ROW][C]108[/C][C]0.991989946309648[/C][C]0.0160201073807035[/C][C]0.00801005369035177[/C][/ROW]
[ROW][C]109[/C][C]0.988400181775845[/C][C]0.0231996364483095[/C][C]0.0115998182241548[/C][/ROW]
[ROW][C]110[/C][C]0.979273854348304[/C][C]0.0414522913033922[/C][C]0.0207261456516961[/C][/ROW]
[ROW][C]111[/C][C]0.964785809448221[/C][C]0.0704283811035574[/C][C]0.0352141905517787[/C][/ROW]
[ROW][C]112[/C][C]0.969031355989145[/C][C]0.0619372880217108[/C][C]0.0309686440108554[/C][/ROW]
[ROW][C]113[/C][C]0.976985449199652[/C][C]0.0460291016006966[/C][C]0.0230145508003483[/C][/ROW]
[ROW][C]114[/C][C]0.979008663460044[/C][C]0.0419826730799112[/C][C]0.0209913365399556[/C][/ROW]
[ROW][C]115[/C][C]0.98800801733457[/C][C]0.0239839653308617[/C][C]0.0119919826654309[/C][/ROW]
[ROW][C]116[/C][C]0.9856848715258[/C][C]0.0286302569484009[/C][C]0.0143151284742004[/C][/ROW]
[ROW][C]117[/C][C]0.99777706612066[/C][C]0.00444586775867862[/C][C]0.00222293387933931[/C][/ROW]
[ROW][C]118[/C][C]0.9994133231524[/C][C]0.00117335369519848[/C][C]0.00058667684759924[/C][/ROW]
[ROW][C]119[/C][C]0.997507891716975[/C][C]0.00498421656604941[/C][C]0.00249210828302471[/C][/ROW]
[ROW][C]120[/C][C]0.9995171574455[/C][C]0.000965685109001376[/C][C]0.000482842554500688[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108050&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108050&T=5

As an alternative you can also use a QR Code:  
QR Code


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.00265787514148280.00531575028296560.997342124858517
110.0007859504652557280.001571900930511460.999214049534744
129.45105180145496e-050.0001890210360290990.999905489481985
131.65499756536042e-053.30999513072083e-050.999983450024346
141.86935219100852e-063.73870438201703e-060.999998130647809
153.60583871968644e-077.21167743937288e-070.999999639416128
164.51245599911661e-089.02491199823323e-080.99999995487544
176.01429667309514e-091.20285933461903e-080.999999993985703
186.65214944748363e-101.33042988949673e-090.999999999334785
191.11340835863572e-102.22681671727144e-100.99999999988866
201.38933705726807e-112.77867411453614e-110.999999999986107
211.46353595036418e-122.92707190072836e-120.999999999998537
222.55232757362905e-135.1046551472581e-130.999999999999745
235.67539247561571e-141.13507849512314e-130.999999999999943
241.12154246086375e-142.2430849217275e-140.999999999999989
251.49975328372773e-152.99950656745547e-150.999999999999998
261.66673847794097e-163.33347695588195e-161
271.54821804800824e-173.09643609601648e-171
282.36151854034225e-184.7230370806845e-181
292.72597295534781e-195.45194591069561e-191
302.90652269543326e-205.81304539086653e-201
314.43332188901879e-218.86664377803758e-211
326.41793414546564e-221.28358682909313e-211
332.61909393137559e-225.23818786275119e-221
343.3459127697811e-236.6918255395622e-231
353.26179967193770e-246.52359934387539e-241
364.48720590291235e-258.9744118058247e-251
371.18226771299606e-252.36453542599211e-251
381.69337869303772e-263.38675738607544e-261
392.01653258482603e-274.03306516965207e-271
403.05942245766981e-286.11884491533963e-281
415.34545332063709e-291.06909066412742e-281
425.83822448945647e-301.16764489789129e-291
435.32259377760811e-311.06451875552162e-301
445.05011635935353e-321.01002327187071e-311
455.14853638251029e-331.02970727650206e-321
468.34542704857767e-341.66908540971553e-331
471.92184844952545e-343.84369689905091e-341
486.70367926551214e-351.34073585310243e-341
491.87011578771344e-353.74023157542687e-351
501.17086367664636e-352.34172735329272e-351
512.42784048076956e-354.85568096153912e-351
521.00715687718135e-352.0143137543627e-351
534.62677177348736e-369.25354354697472e-361
545.2902296786556e-361.05804593573112e-351
552.62044385831136e-365.24088771662272e-361
562.5121289635039e-365.0242579270078e-361
571.40648584224924e-352.81297168449848e-351
587.93659113980082e-311.58731822796016e-301
592.26827449967387e-264.53654899934774e-261
605.94742554854418e-241.18948510970884e-231
613.24116233171372e-226.48232466342743e-221
629.25530905943884e-201.85106181188777e-191
631.29065878887416e-172.58131757774831e-171
641.30666398916882e-172.61332797833763e-171
657.02459226681011e-161.40491845336202e-151
661.93533748642887e-143.87067497285775e-140.99999999999998
673.45197915025763e-126.90395830051526e-120.999999999996548
681.54218957709820e-103.08437915419640e-100.999999999845781
691.88504878956781e-093.77009757913561e-090.999999998114951
707.532373679655e-091.506474735931e-080.999999992467626
718.85397858636576e-071.77079571727315e-060.999999114602141
723.43206090658258e-056.86412181316516e-050.999965679390934
730.0001048718345372430.0002097436690744860.999895128165463
740.0002103437443258360.0004206874886516720.999789656255674
750.0006321732608462890.001264346521692580.999367826739154
760.001709531474521450.003419062949042900.998290468525479
770.00175429191833310.00350858383666620.998245708081667
780.001386375286355910.002772750572711820.998613624713644
790.002949281412683990.005898562825367980.997050718587316
800.005190320020526060.01038064004105210.994809679979474
810.01288188659842620.02576377319685230.987118113401574
820.02729541547244970.05459083094489950.97270458452755
830.04751136450007750.0950227290001550.952488635499922
840.04684056942232270.09368113884464540.953159430577677
850.04278531358583230.08557062717166470.957214686414168
860.04837484807494620.09674969614989230.951625151925054
870.06382269053728610.1276453810745720.936177309462714
880.08232921017130320.1646584203426060.917670789828697
890.1336010971953630.2672021943907260.866398902804637
900.1455352903153970.2910705806307950.854464709684603
910.1650179667430530.3300359334861070.834982033256947
920.1949021075425450.389804215085090.805097892457455
930.2806722255734130.5613444511468250.719327774426587
940.4253612438155820.8507224876311640.574638756184418
950.4792452823016410.9584905646032810.520754717698359
960.9139367172063990.1721265655872020.0860632827936011
970.9774610769132310.04507784617353770.0225389230867689
980.97080420522440.05839158955120020.0291957947756001
990.9849020997103350.03019580057932970.0150979002896649
1000.987810916790960.02437816641807980.0121890832090399
1010.9890667687924050.02186646241519070.0109332312075953
1020.9912652879034310.01746942419313720.00873471209656859
1030.9890085639000150.02198287219996970.0109914360999849
1040.9862042919384160.02759141612316840.0137957080615842
1050.9843856038369560.03122879232608760.0156143961630438
1060.9888594467059870.02228110658802670.0111405532940133
1070.9865827636822810.02683447263543780.0134172363177189
1080.9919899463096480.01602010738070350.00801005369035177
1090.9884001817758450.02319963644830950.0115998182241548
1100.9792738543483040.04145229130339220.0207261456516961
1110.9647858094482210.07042838110355740.0352141905517787
1120.9690313559891450.06193728802171080.0309686440108554
1130.9769854491996520.04602910160069660.0230145508003483
1140.9790086634600440.04198267307991120.0209913365399556
1150.988008017334570.02398396533086170.0119919826654309
1160.98568487152580.02863025694840090.0143151284742004
1170.997777066120660.004445867758678620.00222293387933931
1180.99941332315240.001173353695198480.00058667684759924
1190.9975078917169750.004984216566049410.00249210828302471
1200.99951715744550.0009656851090013760.000482842554500688






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level740.666666666666667NOK
5% type I error level930.837837837837838NOK
10% type I error level1010.90990990990991NOK

\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 & 74 & 0.666666666666667 & NOK \tabularnewline
5% type I error level & 93 & 0.837837837837838 & NOK \tabularnewline
10% type I error level & 101 & 0.90990990990991 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108050&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]74[/C][C]0.666666666666667[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]93[/C][C]0.837837837837838[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]101[/C][C]0.90990990990991[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108050&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108050&T=6

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level740.666666666666667NOK
5% type I error level930.837837837837838NOK
10% type I error level1010.90990990990991NOK


Figures (Output of Computation)

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Figure 10
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Input Parameters & R Code

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')
}