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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationMon, 13 Dec 2010 13:48:19 +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/13/t1292248416gfp0q1gc8bd8rya.htm/, Retrieved Thu, 31 Oct 2024 23:48:56 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=108929, Retrieved Thu, 31 Oct 2024 23:48:56 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact270
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] [afe9379cca749d06b3d6872e02cc47ed]
-    D            [Multiple Regression] [WS10 Multiple Reg...] [2010-12-13 13:48:19] [aa6b599ccd367bc74fed0d8f67004a46] [Current]
-    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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Dataseries X:
10.81	24563400	-0,2643	24.45	2772.73	0,0373	 115.7	5,98
9.12	14163200	-0,2643	23.62	2151.83	0,0353	 109.2	5,49
11.03	18184800	-0,2643	21.90	1840.26	0,0292	 116.9	5,31
12.74	20810300	-0,1918	27.12	2116.24	0,0327	 109.9	4,8
9.98	12843000	-0,1918	27.70	2110.49	0,0362	 116.1	4,21
11.62	13866700	-0,1918	29.23	2160.54	0,0325	 118.9	3,97
9.40	15119200	-0,2246	26.50	2027.13	0,0272	 116.3	3,77
9.27	8301600	-0,2246	22.84	1805.43	0,0272	 114.0	3,65
7.76	14039600	-0,2246	20.49	1498.80	0,0265	 97.0	3,07
8.78	12139700	0,3654	23.28	1690.20	0,0213	 85.3	2,49
10.65	9649000	0,3654	25.71	1930.58	0,019	 84.9	2,09
10.95	8513600	0,3654	26.52	1950.40	0,0155	 94.6	1,82
12.36	15278600	0,0447	25.51	1934.03	0,0114	 97.8	1,73
10.85	15590900	0,0447	23.36	1731.49	0,0114	 95.0	1,74
11.84	9691100	0,0447	24.15	1845.35	0,0148	 110.7	1,73
12.14	10882700	-0,0312	20.92	1688.23	0,0164	 108.5	1,75
11.65	10294800	-0,0312	20.38	1615.73	0,0118	 110.3	1,75
8.86	16031900	-0,0312	21.90	1463.21	0,0107	 106.3	1,75
7.63	13683600	-0,0048	19.21	1328.26	0,0146	 97.4	1,73
7.38	8677200	-0,0048	19.65	1314.85	0,018	 94.5	1,74
7.25	9874100	-0,0048	17.51	1172.06	0,0151	 93.7	1,75
8.03	10725500	0,0705	21.41	1329.75	0,0203	 79.6	1,75
7.75	8348400	0,0705	23.09	1478.78	0,022	 84.9	1,34
7.16	8046200	0,0705	20.70	1335.51	0,0238	 80.7	1,24
7.18	10862300	-0,0134	19.00	1320.91	0,026	 78.8	1,24
7.51	8100300	-0,0134	19.04	1337.52	0,0298	 64.8	1,26
7.07	7287500	-0,0134	19.45	1341.17	0,0302	 61.4	1,25
7.11	14002500	0,0812	20.54	1464.31	0,0222	 81.0	1,26
8.98	19037900	0,0812	19.77	1595.91	0,0206	 83.6	1,26
9.53	10774600	0,0812	20.60	1622.80	0,0211	 83.5	1,22
10.54	8960600	0,1885	21.21	1735.02	0,0211	 77.0	1,01
11.31	7773300	0,1885	21.30	1810.45	0,0216	 81.7	1,03
10.36	9579700	0,1885	22.33	1786.94	0,0232	 77.0	1,01
11.44	11270700	0,3628	21.12	1932.21	0,0204	 81.7	1,01
10.45	9492800	0,3628	20.77	1960.26	0,0177	 92.5	1
10.69	9136800	0,3628	22.11	2003.37	0,0188	 91.7	0,98
11.28	14487600	0,2942	22.34	2066.15	0,0193	 96.4	1
11.96	10133200	0,2942	21.43	2029.82	0,0169	 88.5	1,01
13.52	18659700	0,2942	20.14	1994.22	0,0174	 88.5	1
12.89	15980700	0,3036	21.11	1920.15	0,0229	 93.0	1
14.03	9732100	0,3036	21.19	1986.74	0,0305	 93.1	1
16.27	14626300	0,3036	23.07	2047.79	0,0327	 102.8	1,03
16.17	16904000	0,3703	23.01	1887.36	0,0299	 105.7	1,26
17.25	13616700	0,3703	22.12	1838.10	0,0265	 98.7	1,43
19.38	13772900	0,3703	22.40	1896.84	0,0254	 96.7	1,61
26.20	28749200	0,7398	22.66	1974.99	0,0319	 92.9	1,76
33.53	31408300	0,7398	24.21	2096.81	0,0352	 92.6	1,93
32.20	26342800	0,7398	24.13	2175.44	0,0326	 102.7	2,16
38.45	48909500	0,6988	23.73	2062.41	0,0297	 105.1	2,28
44.86	41542400	0,6988	22.79	2051.72	0,0301	 104.4	2,5
41.67	24857200	0,6988	21.89	1999.23	0,0315	 103.0	2,63
36.06	34093700	0,7478	22.92	1921.65	0,0351	 97.5	2,79
39.76	22555200	0,7478	23.44	2068.22	0,028	 103.1	3
36.81	19067500	0,7478	22.57	2056.96	0,0253	 106.2	3,04
42.65	19029100	0,5651	23.27	2184.83	0,0317	 103.6	3,26
46.89	15223200	0,5651	24.95	2152.09	0,0364	 105.5	3,5
53.61	21903700	0,5651	23.45	2151.69	0,0469	 87.5	3,62
57.59	33306600	0,6473	23.42	2120.30	0,0435	 85.2	3,78
67.82	23898100	0,6473	25.30	2232.82	0,0346	 98.3	4
71.89	23279600	0,6473	23.90	2205.32	0,0342	 103.8	4,16
75.51	40699800	0,3441	25.73	2305.82	0,0399	 106.8	4,29
68.49	37646000	0,3441	24.64	2281.39	0,036	 102.7	4,49
62.72	37277000	0,3441	24.95	2339.79	0,0336	 107.5	4,59
70.39	39246800	0,2415	22.15	2322.57	0,0355	 109.8	4,79
59.77	27418400	0,2415	20.85	2178.88	0,0417	 104.7	4,94
57.27	30318700	0,2415	21.45	2172.09	0,0432	 105.7	4,99
67.96	32808100	0,3151	22.15	2091.47	0,0415	 107.0	5,24
67.85	28668200	0,3151	23.75	2183.75	0,0382	 100.2	5,25
76.98	32370300	0,3151	25.27	2258.43	0,0206	 105.9	5,25
81.08	24171100	0,239	26.53	2366.71	0,0131	 105.1	5,25
91.66	25009100	0,239	27.22	2431.77	0,0197	 105.3	5,25
84.84	32084300	0,239	27.69	2415.29	0,0254	 110.0	5,24
85.73	50117500	0,2127	28.61	2463.93	0,0208	 110.2	5,25
84.61	27522200	0,2127	26.21	2416.15	0,0242	 111.2	5,26
92.91	26816800	0,2127	25.93	2421.64	0,0278	 108.2	5,26
99.80	25136100	0,273	27.86	2525.09	0,0257	 106.3	5,25
121.19	30295600	0,273	28.65	2604.52	0,0269	 108.5	5,25
122.04	41526100	0,273	27.51	2603.23	0,0269	 105.3	5,25
131.76	43845100	0,3657	27.06	2546.27	0,0236	 111.9	5,26
138.48	39188900	0,3657	26.91	2596.36	0,0197	 105.6	5,02
153.47	40496400	0,3657	27.60	2701.50	0,0276	 99.5	4,94
189.95	37438400	0,4643	34.48	2859.12	0,0354	 95.2	4,76
182.22	46553700	0,4643	31.58	2660.96	0,0431	 87.8	4,49
198.08	31771400	0,4643	33.46	2652.28	0,0408	 90.6	4,24
135.36	62108100	0,5096	30.64	2389.86	0,0428	 87.9	3,94
125.02	46645400	0,5096	25.66	2271.48	0,0403	 76.4	2,98
143.50	42313100	0,5096	26.78	2279.10	0,0398	 65.9	2,61
173.95	38841700	0,3592	26.91	2412.80	0,0394	 62.3	2,28
188.75	32650300	0,3592	26.82	2522.66	0,0418	 57.2	1,98
167.44	34281100	0,3592	26.05	2292.98	0,0502	 50.4	2
158.95	33096200	0,7439	24.36	2325.55	0,056	 51.9	2,01
169.53	23273800	0,7439	25.94	2367.52	0,0537	 58.5	2
113.66	43697600	0,7439	25.37	2091.88	0,0494	 61.4	1,81
107.59	66902300	0,139	21.23	1720.95	0,0366	 38.8	0,97
92.67	44957200	0,139	19.35	1535.57	0,0107	 44.9	0,39
85.35	33800900	0,139	18.61	1577.03	0,0009	 38.6	0,16
90.13	33487900	0,1383	16.37	1476.42	0,0003	 4.0	0,15
89.31	27394900	0,1383	15.56	1377.84	0,0024	 25.3	0,22
105.12	25963400	0,1383	17.70	1528.59	-0,0038	 26.9	0,18
125.83	20952600	0,2874	19.52	1717.30	-0,0074	 40.8	0,15
135.81	17702900	0,2874	20.26	1774.33	-0,0128	 54.8	0,18
142.43	21282100	0,2874	23.05	1835.04	-0,0143	 49.3	0,21
163.39	18449100	0,0596	22.81	1978.50	-0,021	 47.4	0,16
168.21	14415700	0,0596	24.04	2009.06	-0,0148	 54.5	0,16
185.35	17906300	0,0596	25.08	2122.42	-0,0129	 53.4	0,15
188.50	22197500	0,3201	27.04	2045.11	-0,0018	 48.7	0,12
199.91	15856500	0,3201	28.81	2144.60	0,0184	 50.6	0,12
210.73	19068700	0,3201	29.86	2269.15	0,0272	 53.6	0,12
192.06	30855100	0,486	27.61	2147.35	0,0263	 56.5	0,11
204.62	21209000	0,486	28.22	2238.26	0,0214	 46.4	0,13
235.00	19541600	0,486	28.83	2397.96	0,0231	 52.3	0,16
261.09	21955000	0,6129	30.06	2461.19	0,0224	 57.7	0,2
256.88	33725900	0,6129	25.51	2257.04	0,0202	 62.7	0,2
251.53	28192800	0,6129	22.75	2109.24	0,0105	 54.3	0,18
257.25	27377000	0,6665	25.52	2254.70	0,0124	 51.0	0,18
243.10	16228100	0,6665	23.33	2114.03	0,0115	 53.2	0,19
283.75	21278900	0,6665	24.34	2368.62	0,0114	 48.6	0,19




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R Server'George Udny Yule' @ 72.249.76.132

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 8 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108929&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]8 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108929&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108929&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R Server'George Udny Yule' @ 72.249.76.132







Multiple Linear Regression - Estimated Regression Equation
APPLE[t] = -85.2045348232553 + 4.38917615134893e-07VOLUME[t] + 28.1570131846652REV.GROWTH[t] + 6.5065975563462MICROSOFT[t] + 0.0881512189265601NASDAQ[t] -961.803925838046INFLATION[t] -1.98103449491010CONS.CONF[t] + 1.03003237831381FED.FUNDS.RATE[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
APPLE[t] =  -85.2045348232553 +  4.38917615134893e-07VOLUME[t] +  28.1570131846652REV.GROWTH[t] +  6.5065975563462MICROSOFT[t] +  0.0881512189265601NASDAQ[t] -961.803925838046INFLATION[t] -1.98103449491010CONS.CONF[t] +  1.03003237831381FED.FUNDS.RATE[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108929&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]APPLE[t] =  -85.2045348232553 +  4.38917615134893e-07VOLUME[t] +  28.1570131846652REV.GROWTH[t] +  6.5065975563462MICROSOFT[t] +  0.0881512189265601NASDAQ[t] -961.803925838046INFLATION[t] -1.98103449491010CONS.CONF[t] +  1.03003237831381FED.FUNDS.RATE[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108929&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108929&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
APPLE[t] = -85.2045348232553 + 4.38917615134893e-07VOLUME[t] + 28.1570131846652REV.GROWTH[t] + 6.5065975563462MICROSOFT[t] + 0.0881512189265601NASDAQ[t] -961.803925838046INFLATION[t] -1.98103449491010CONS.CONF[t] + 1.03003237831381FED.FUNDS.RATE[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-85.204534823255328.425357-2.99750.0033720.001686
VOLUME4.38917615134893e-0701.27030.2066710.103335
REV.GROWTH28.157013184665215.8862811.77240.079120.03956
MICROSOFT6.50659755634621.5438884.21445.2e-052.6e-05
NASDAQ0.08815121892656010.0184214.78545e-063e-06
INFLATION-961.803925838046268.539097-3.58160.0005120.000256
CONS.CONF-1.981034494910100.216208-9.162600
FED.FUNDS.RATE1.030032378313813.8098390.27040.7873940.393697

\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) & -85.2045348232553 & 28.425357 & -2.9975 & 0.003372 & 0.001686 \tabularnewline
VOLUME & 4.38917615134893e-07 & 0 & 1.2703 & 0.206671 & 0.103335 \tabularnewline
REV.GROWTH & 28.1570131846652 & 15.886281 & 1.7724 & 0.07912 & 0.03956 \tabularnewline
MICROSOFT & 6.5065975563462 & 1.543888 & 4.2144 & 5.2e-05 & 2.6e-05 \tabularnewline
NASDAQ & 0.0881512189265601 & 0.018421 & 4.7854 & 5e-06 & 3e-06 \tabularnewline
INFLATION & -961.803925838046 & 268.539097 & -3.5816 & 0.000512 & 0.000256 \tabularnewline
CONS.CONF & -1.98103449491010 & 0.216208 & -9.1626 & 0 & 0 \tabularnewline
FED.FUNDS.RATE & 1.03003237831381 & 3.809839 & 0.2704 & 0.787394 & 0.393697 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108929&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]-85.2045348232553[/C][C]28.425357[/C][C]-2.9975[/C][C]0.003372[/C][C]0.001686[/C][/ROW]
[ROW][C]VOLUME[/C][C]4.38917615134893e-07[/C][C]0[/C][C]1.2703[/C][C]0.206671[/C][C]0.103335[/C][/ROW]
[ROW][C]REV.GROWTH[/C][C]28.1570131846652[/C][C]15.886281[/C][C]1.7724[/C][C]0.07912[/C][C]0.03956[/C][/ROW]
[ROW][C]MICROSOFT[/C][C]6.5065975563462[/C][C]1.543888[/C][C]4.2144[/C][C]5.2e-05[/C][C]2.6e-05[/C][/ROW]
[ROW][C]NASDAQ[/C][C]0.0881512189265601[/C][C]0.018421[/C][C]4.7854[/C][C]5e-06[/C][C]3e-06[/C][/ROW]
[ROW][C]INFLATION[/C][C]-961.803925838046[/C][C]268.539097[/C][C]-3.5816[/C][C]0.000512[/C][C]0.000256[/C][/ROW]
[ROW][C]CONS.CONF[/C][C]-1.98103449491010[/C][C]0.216208[/C][C]-9.1626[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]FED.FUNDS.RATE[/C][C]1.03003237831381[/C][C]3.809839[/C][C]0.2704[/C][C]0.787394[/C][C]0.393697[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108929&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108929&T=2

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-85.204534823255328.425357-2.99750.0033720.001686
VOLUME4.38917615134893e-0701.27030.2066710.103335
REV.GROWTH28.157013184665215.8862811.77240.079120.03956
MICROSOFT6.50659755634621.5438884.21445.2e-052.6e-05
NASDAQ0.08815121892656010.0184214.78545e-063e-06
INFLATION-961.803925838046268.539097-3.58160.0005120.000256
CONS.CONF-1.981034494910100.216208-9.162600
FED.FUNDS.RATE1.030032378313813.8098390.27040.7873940.393697







Multiple Linear Regression - Regression Statistics
Multiple R0.913298070864655
R-squared0.8341133662451
Adjusted R-squared0.823460096187447
F-TEST (value)78.2964631264382
F-TEST (DF numerator)7
F-TEST (DF denominator)109
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation31.9226167034062
Sum Squared Residuals111076.826833992

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.913298070864655 \tabularnewline
R-squared & 0.8341133662451 \tabularnewline
Adjusted R-squared & 0.823460096187447 \tabularnewline
F-TEST (value) & 78.2964631264382 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 109 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 31.9226167034062 \tabularnewline
Sum Squared Residuals & 111076.826833992 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108929&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.913298070864655[/C][/ROW]
[ROW][C]R-squared[/C][C]0.8341133662451[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.823460096187447[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]78.2964631264382[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]109[/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]31.9226167034062[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]111076.826833992[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108929&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108929&T=3

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Regression Statistics
Multiple R0.913298070864655
R-squared0.8341133662451
Adjusted R-squared0.823460096187447
F-TEST (value)78.2964631264382
F-TEST (DF numerator)7
F-TEST (DF denominator)109
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation31.9226167034062
Sum Squared Residuals111076.826833992







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
110.8162.719331174007-51.909331174007
29.1212.3165485930306-3.19654859303057
311.03-34.14729089509945.177290895099
412.7437.3144946138030-24.5744946138030
59.9820.8280166605108-10.8480166605108
611.6233.4089695606654-21.7889695606654
79.413.5541424133923-4.15414241339227
89.27-28.362719158901137.6327191589011
97.76-34.411092017985542.1710920179855
108.7843.9752618941057-35.1952618941057
1110.6582.475421733643-71.825421733643
1210.9572.8667462508443-61.9167462508443
1312.3656.3027536013476-43.9427536013476
1410.8530.1536918545558-19.3036918545558
1511.848.358600323553563.48139967644644
1612.14-25.282142116679537.4221421166795
1711.65-34.586271867203146.2362718672031
188.86-26.640830944383235.5008309443832
197.63-42.467379406274650.0973794062746
207.38-40.498814464525747.8788144645257
217.25-62.100345987433669.3503459874336
228.032.601073709051575.42892629094843
237.7513.0691199253444-5.31911992534443
247.16-8.757619698942515.9176196989425
257.18-20.584183947785427.7641839477854
267.514.028209905959113.48179009404089
277.0713.0014100041374-5.93141000413738
287.115.435983302321041.67401669767897
298.9810.6249259484946-1.64492594849464
309.5314.4448804594925-4.91488045949246
3110.5443.1917031351319-32.6517031351319
3211.3140.1342530329536-28.8242530329536
3310.3653.3078493361767-42.9478493361767
3411.4457.2727598180085-45.8327598180085
3510.4537.8791384671795-27.4291384671795
3610.6950.7481661995594-40.0581661995594
3711.2848.424642990894-37.144642990894
3811.9655.3586848232579-43.3986848232579
3913.5247.0782193405312-33.5582193405312
4012.8931.7444969980815-18.8544969980815
4114.0327.8845805751168-13.8545805751168
4216.2726.3456642223851-10.0756642223851
4316.1711.87592215214574.29407784785435
4417.2517.6123477229756-0.362347722975628
4519.3829.8862157063213-10.5062157063213
4626.256.8750375008186-30.6750375008186
4733.5376.4614339307148-42.9314339307148
4832.263.3780485465359-31.1780485465359
4938.4557.7205143361653-19.2705143361653
5044.8648.6570358397455-3.79703583974554
5141.6732.41143937141189.25856062858818
5236.0645.3062202578867-9.24622025788673
5339.7652.4968457444182-12.7368457444182
5436.8140.8095751396519-3.99957513965192
5542.6550.6967007326058-8.04670073260581
5646.8949.0340009471973-2.14400094719728
5753.6167.8543168254959-14.2443168254959
5857.5980.2028101607748-22.6128101607748
5967.8281.059542518191-13.2395425181909
6071.8958.908513902724812.9814860972752
6175.5167.49212952171378.01787047828636
6268.4968.985320509384-0.495320509383987
6362.7268.8908034214519-6.17080342145188
6470.3940.952236317590429.4377636824096
6559.7718.930114168514140.8398858314859
6657.2720.136279920234737.1337200797653
6767.9620.066374546445947.8936254535541
6867.8553.449737928683214.4002620713167
6976.9877.1836686205179-0.203668620517895
7081.0897.9838305532285-16.9038305532285
7191.66101.832201322439-10.1722013224393
7284.8491.7395551694772-6.89955516947724
7385.73113.226251384054-27.4962513840536
7484.6178.2402093002786.3697906997221
7592.9179.07330904240513.8366909575951
7699.8107.483918444190-7.68391844418985
77121.19116.3781366685214.81186333147939
78122.04120.1154750423551.92452495764459
79131.76105.90384339630125.8561566036990
80138.48123.28400497719615.1959950228038
81153.47142.01931804536811.4506819546316
82189.95202.944147671640-12.9941476716403
83182.22177.5834912442254.63650875577469
84198.08178.97027455676619.1097254432341
85135.36155.196026860577-19.8360268605767
86125.02129.76855374878-4.7485537487801
87143.5156.726784695558-13.2267846955583
88173.95170.7765220233803.17347797662046
89188.75184.643643420774.10635657922989
90167.44165.5152604220091.92473957799144
91158.95159.162411053746-0.21241105374623
92169.53157.95833850745811.5716614925423
93113.66137.1109921974-23.4509921974000
94107.59126.845763624126-19.2557636241260
9592.67100.868788778525-8.19878877852451
9685.35116.481247877928-31.1312478779284
9790.13161.991059648354-71.8610596483543
9889.31101.212722717554-11.9027227175536
99105.12130.549655028455-25.4296550284550
100125.83136.920775066969-11.0907750669691
101135.81122.82672994227712.9832700577233
102142.43160.272068135717-17.8420681357169
103163.39173.855587606783-10.4655876067827
104168.21162.7537442885415.45625571145851
105185.35181.3869239135723.96307608642802
106188.5195.147207171396-6.64720717139605
107199.91189.45846817730410.4515318226958
108210.73194.27254306000216.4574569399980
109192.06173.85071033356218.2092896664383
110204.62206.341607230959-1.72160723095854
111235210.36426094888124.6357390511195
112261.09218.59046403211842.499535967882
113256.88168.3666253251788.51337467483
114251.53160.90067804631490.6293219536864
115257.25197.60756287283259.6424371271679
116243.1162.58208162689280.5179183731076
117283.75203.02198814500980.7280118549914

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 10.81 & 62.719331174007 & -51.909331174007 \tabularnewline
2 & 9.12 & 12.3165485930306 & -3.19654859303057 \tabularnewline
3 & 11.03 & -34.147290895099 & 45.177290895099 \tabularnewline
4 & 12.74 & 37.3144946138030 & -24.5744946138030 \tabularnewline
5 & 9.98 & 20.8280166605108 & -10.8480166605108 \tabularnewline
6 & 11.62 & 33.4089695606654 & -21.7889695606654 \tabularnewline
7 & 9.4 & 13.5541424133923 & -4.15414241339227 \tabularnewline
8 & 9.27 & -28.3627191589011 & 37.6327191589011 \tabularnewline
9 & 7.76 & -34.4110920179855 & 42.1710920179855 \tabularnewline
10 & 8.78 & 43.9752618941057 & -35.1952618941057 \tabularnewline
11 & 10.65 & 82.475421733643 & -71.825421733643 \tabularnewline
12 & 10.95 & 72.8667462508443 & -61.9167462508443 \tabularnewline
13 & 12.36 & 56.3027536013476 & -43.9427536013476 \tabularnewline
14 & 10.85 & 30.1536918545558 & -19.3036918545558 \tabularnewline
15 & 11.84 & 8.35860032355356 & 3.48139967644644 \tabularnewline
16 & 12.14 & -25.2821421166795 & 37.4221421166795 \tabularnewline
17 & 11.65 & -34.5862718672031 & 46.2362718672031 \tabularnewline
18 & 8.86 & -26.6408309443832 & 35.5008309443832 \tabularnewline
19 & 7.63 & -42.4673794062746 & 50.0973794062746 \tabularnewline
20 & 7.38 & -40.4988144645257 & 47.8788144645257 \tabularnewline
21 & 7.25 & -62.1003459874336 & 69.3503459874336 \tabularnewline
22 & 8.03 & 2.60107370905157 & 5.42892629094843 \tabularnewline
23 & 7.75 & 13.0691199253444 & -5.31911992534443 \tabularnewline
24 & 7.16 & -8.7576196989425 & 15.9176196989425 \tabularnewline
25 & 7.18 & -20.5841839477854 & 27.7641839477854 \tabularnewline
26 & 7.51 & 4.02820990595911 & 3.48179009404089 \tabularnewline
27 & 7.07 & 13.0014100041374 & -5.93141000413738 \tabularnewline
28 & 7.11 & 5.43598330232104 & 1.67401669767897 \tabularnewline
29 & 8.98 & 10.6249259484946 & -1.64492594849464 \tabularnewline
30 & 9.53 & 14.4448804594925 & -4.91488045949246 \tabularnewline
31 & 10.54 & 43.1917031351319 & -32.6517031351319 \tabularnewline
32 & 11.31 & 40.1342530329536 & -28.8242530329536 \tabularnewline
33 & 10.36 & 53.3078493361767 & -42.9478493361767 \tabularnewline
34 & 11.44 & 57.2727598180085 & -45.8327598180085 \tabularnewline
35 & 10.45 & 37.8791384671795 & -27.4291384671795 \tabularnewline
36 & 10.69 & 50.7481661995594 & -40.0581661995594 \tabularnewline
37 & 11.28 & 48.424642990894 & -37.144642990894 \tabularnewline
38 & 11.96 & 55.3586848232579 & -43.3986848232579 \tabularnewline
39 & 13.52 & 47.0782193405312 & -33.5582193405312 \tabularnewline
40 & 12.89 & 31.7444969980815 & -18.8544969980815 \tabularnewline
41 & 14.03 & 27.8845805751168 & -13.8545805751168 \tabularnewline
42 & 16.27 & 26.3456642223851 & -10.0756642223851 \tabularnewline
43 & 16.17 & 11.8759221521457 & 4.29407784785435 \tabularnewline
44 & 17.25 & 17.6123477229756 & -0.362347722975628 \tabularnewline
45 & 19.38 & 29.8862157063213 & -10.5062157063213 \tabularnewline
46 & 26.2 & 56.8750375008186 & -30.6750375008186 \tabularnewline
47 & 33.53 & 76.4614339307148 & -42.9314339307148 \tabularnewline
48 & 32.2 & 63.3780485465359 & -31.1780485465359 \tabularnewline
49 & 38.45 & 57.7205143361653 & -19.2705143361653 \tabularnewline
50 & 44.86 & 48.6570358397455 & -3.79703583974554 \tabularnewline
51 & 41.67 & 32.4114393714118 & 9.25856062858818 \tabularnewline
52 & 36.06 & 45.3062202578867 & -9.24622025788673 \tabularnewline
53 & 39.76 & 52.4968457444182 & -12.7368457444182 \tabularnewline
54 & 36.81 & 40.8095751396519 & -3.99957513965192 \tabularnewline
55 & 42.65 & 50.6967007326058 & -8.04670073260581 \tabularnewline
56 & 46.89 & 49.0340009471973 & -2.14400094719728 \tabularnewline
57 & 53.61 & 67.8543168254959 & -14.2443168254959 \tabularnewline
58 & 57.59 & 80.2028101607748 & -22.6128101607748 \tabularnewline
59 & 67.82 & 81.059542518191 & -13.2395425181909 \tabularnewline
60 & 71.89 & 58.9085139027248 & 12.9814860972752 \tabularnewline
61 & 75.51 & 67.4921295217137 & 8.01787047828636 \tabularnewline
62 & 68.49 & 68.985320509384 & -0.495320509383987 \tabularnewline
63 & 62.72 & 68.8908034214519 & -6.17080342145188 \tabularnewline
64 & 70.39 & 40.9522363175904 & 29.4377636824096 \tabularnewline
65 & 59.77 & 18.9301141685141 & 40.8398858314859 \tabularnewline
66 & 57.27 & 20.1362799202347 & 37.1337200797653 \tabularnewline
67 & 67.96 & 20.0663745464459 & 47.8936254535541 \tabularnewline
68 & 67.85 & 53.4497379286832 & 14.4002620713167 \tabularnewline
69 & 76.98 & 77.1836686205179 & -0.203668620517895 \tabularnewline
70 & 81.08 & 97.9838305532285 & -16.9038305532285 \tabularnewline
71 & 91.66 & 101.832201322439 & -10.1722013224393 \tabularnewline
72 & 84.84 & 91.7395551694772 & -6.89955516947724 \tabularnewline
73 & 85.73 & 113.226251384054 & -27.4962513840536 \tabularnewline
74 & 84.61 & 78.240209300278 & 6.3697906997221 \tabularnewline
75 & 92.91 & 79.073309042405 & 13.8366909575951 \tabularnewline
76 & 99.8 & 107.483918444190 & -7.68391844418985 \tabularnewline
77 & 121.19 & 116.378136668521 & 4.81186333147939 \tabularnewline
78 & 122.04 & 120.115475042355 & 1.92452495764459 \tabularnewline
79 & 131.76 & 105.903843396301 & 25.8561566036990 \tabularnewline
80 & 138.48 & 123.284004977196 & 15.1959950228038 \tabularnewline
81 & 153.47 & 142.019318045368 & 11.4506819546316 \tabularnewline
82 & 189.95 & 202.944147671640 & -12.9941476716403 \tabularnewline
83 & 182.22 & 177.583491244225 & 4.63650875577469 \tabularnewline
84 & 198.08 & 178.970274556766 & 19.1097254432341 \tabularnewline
85 & 135.36 & 155.196026860577 & -19.8360268605767 \tabularnewline
86 & 125.02 & 129.76855374878 & -4.7485537487801 \tabularnewline
87 & 143.5 & 156.726784695558 & -13.2267846955583 \tabularnewline
88 & 173.95 & 170.776522023380 & 3.17347797662046 \tabularnewline
89 & 188.75 & 184.64364342077 & 4.10635657922989 \tabularnewline
90 & 167.44 & 165.515260422009 & 1.92473957799144 \tabularnewline
91 & 158.95 & 159.162411053746 & -0.21241105374623 \tabularnewline
92 & 169.53 & 157.958338507458 & 11.5716614925423 \tabularnewline
93 & 113.66 & 137.1109921974 & -23.4509921974000 \tabularnewline
94 & 107.59 & 126.845763624126 & -19.2557636241260 \tabularnewline
95 & 92.67 & 100.868788778525 & -8.19878877852451 \tabularnewline
96 & 85.35 & 116.481247877928 & -31.1312478779284 \tabularnewline
97 & 90.13 & 161.991059648354 & -71.8610596483543 \tabularnewline
98 & 89.31 & 101.212722717554 & -11.9027227175536 \tabularnewline
99 & 105.12 & 130.549655028455 & -25.4296550284550 \tabularnewline
100 & 125.83 & 136.920775066969 & -11.0907750669691 \tabularnewline
101 & 135.81 & 122.826729942277 & 12.9832700577233 \tabularnewline
102 & 142.43 & 160.272068135717 & -17.8420681357169 \tabularnewline
103 & 163.39 & 173.855587606783 & -10.4655876067827 \tabularnewline
104 & 168.21 & 162.753744288541 & 5.45625571145851 \tabularnewline
105 & 185.35 & 181.386923913572 & 3.96307608642802 \tabularnewline
106 & 188.5 & 195.147207171396 & -6.64720717139605 \tabularnewline
107 & 199.91 & 189.458468177304 & 10.4515318226958 \tabularnewline
108 & 210.73 & 194.272543060002 & 16.4574569399980 \tabularnewline
109 & 192.06 & 173.850710333562 & 18.2092896664383 \tabularnewline
110 & 204.62 & 206.341607230959 & -1.72160723095854 \tabularnewline
111 & 235 & 210.364260948881 & 24.6357390511195 \tabularnewline
112 & 261.09 & 218.590464032118 & 42.499535967882 \tabularnewline
113 & 256.88 & 168.36662532517 & 88.51337467483 \tabularnewline
114 & 251.53 & 160.900678046314 & 90.6293219536864 \tabularnewline
115 & 257.25 & 197.607562872832 & 59.6424371271679 \tabularnewline
116 & 243.1 & 162.582081626892 & 80.5179183731076 \tabularnewline
117 & 283.75 & 203.021988145009 & 80.7280118549914 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108929&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]10.81[/C][C]62.719331174007[/C][C]-51.909331174007[/C][/ROW]
[ROW][C]2[/C][C]9.12[/C][C]12.3165485930306[/C][C]-3.19654859303057[/C][/ROW]
[ROW][C]3[/C][C]11.03[/C][C]-34.147290895099[/C][C]45.177290895099[/C][/ROW]
[ROW][C]4[/C][C]12.74[/C][C]37.3144946138030[/C][C]-24.5744946138030[/C][/ROW]
[ROW][C]5[/C][C]9.98[/C][C]20.8280166605108[/C][C]-10.8480166605108[/C][/ROW]
[ROW][C]6[/C][C]11.62[/C][C]33.4089695606654[/C][C]-21.7889695606654[/C][/ROW]
[ROW][C]7[/C][C]9.4[/C][C]13.5541424133923[/C][C]-4.15414241339227[/C][/ROW]
[ROW][C]8[/C][C]9.27[/C][C]-28.3627191589011[/C][C]37.6327191589011[/C][/ROW]
[ROW][C]9[/C][C]7.76[/C][C]-34.4110920179855[/C][C]42.1710920179855[/C][/ROW]
[ROW][C]10[/C][C]8.78[/C][C]43.9752618941057[/C][C]-35.1952618941057[/C][/ROW]
[ROW][C]11[/C][C]10.65[/C][C]82.475421733643[/C][C]-71.825421733643[/C][/ROW]
[ROW][C]12[/C][C]10.95[/C][C]72.8667462508443[/C][C]-61.9167462508443[/C][/ROW]
[ROW][C]13[/C][C]12.36[/C][C]56.3027536013476[/C][C]-43.9427536013476[/C][/ROW]
[ROW][C]14[/C][C]10.85[/C][C]30.1536918545558[/C][C]-19.3036918545558[/C][/ROW]
[ROW][C]15[/C][C]11.84[/C][C]8.35860032355356[/C][C]3.48139967644644[/C][/ROW]
[ROW][C]16[/C][C]12.14[/C][C]-25.2821421166795[/C][C]37.4221421166795[/C][/ROW]
[ROW][C]17[/C][C]11.65[/C][C]-34.5862718672031[/C][C]46.2362718672031[/C][/ROW]
[ROW][C]18[/C][C]8.86[/C][C]-26.6408309443832[/C][C]35.5008309443832[/C][/ROW]
[ROW][C]19[/C][C]7.63[/C][C]-42.4673794062746[/C][C]50.0973794062746[/C][/ROW]
[ROW][C]20[/C][C]7.38[/C][C]-40.4988144645257[/C][C]47.8788144645257[/C][/ROW]
[ROW][C]21[/C][C]7.25[/C][C]-62.1003459874336[/C][C]69.3503459874336[/C][/ROW]
[ROW][C]22[/C][C]8.03[/C][C]2.60107370905157[/C][C]5.42892629094843[/C][/ROW]
[ROW][C]23[/C][C]7.75[/C][C]13.0691199253444[/C][C]-5.31911992534443[/C][/ROW]
[ROW][C]24[/C][C]7.16[/C][C]-8.7576196989425[/C][C]15.9176196989425[/C][/ROW]
[ROW][C]25[/C][C]7.18[/C][C]-20.5841839477854[/C][C]27.7641839477854[/C][/ROW]
[ROW][C]26[/C][C]7.51[/C][C]4.02820990595911[/C][C]3.48179009404089[/C][/ROW]
[ROW][C]27[/C][C]7.07[/C][C]13.0014100041374[/C][C]-5.93141000413738[/C][/ROW]
[ROW][C]28[/C][C]7.11[/C][C]5.43598330232104[/C][C]1.67401669767897[/C][/ROW]
[ROW][C]29[/C][C]8.98[/C][C]10.6249259484946[/C][C]-1.64492594849464[/C][/ROW]
[ROW][C]30[/C][C]9.53[/C][C]14.4448804594925[/C][C]-4.91488045949246[/C][/ROW]
[ROW][C]31[/C][C]10.54[/C][C]43.1917031351319[/C][C]-32.6517031351319[/C][/ROW]
[ROW][C]32[/C][C]11.31[/C][C]40.1342530329536[/C][C]-28.8242530329536[/C][/ROW]
[ROW][C]33[/C][C]10.36[/C][C]53.3078493361767[/C][C]-42.9478493361767[/C][/ROW]
[ROW][C]34[/C][C]11.44[/C][C]57.2727598180085[/C][C]-45.8327598180085[/C][/ROW]
[ROW][C]35[/C][C]10.45[/C][C]37.8791384671795[/C][C]-27.4291384671795[/C][/ROW]
[ROW][C]36[/C][C]10.69[/C][C]50.7481661995594[/C][C]-40.0581661995594[/C][/ROW]
[ROW][C]37[/C][C]11.28[/C][C]48.424642990894[/C][C]-37.144642990894[/C][/ROW]
[ROW][C]38[/C][C]11.96[/C][C]55.3586848232579[/C][C]-43.3986848232579[/C][/ROW]
[ROW][C]39[/C][C]13.52[/C][C]47.0782193405312[/C][C]-33.5582193405312[/C][/ROW]
[ROW][C]40[/C][C]12.89[/C][C]31.7444969980815[/C][C]-18.8544969980815[/C][/ROW]
[ROW][C]41[/C][C]14.03[/C][C]27.8845805751168[/C][C]-13.8545805751168[/C][/ROW]
[ROW][C]42[/C][C]16.27[/C][C]26.3456642223851[/C][C]-10.0756642223851[/C][/ROW]
[ROW][C]43[/C][C]16.17[/C][C]11.8759221521457[/C][C]4.29407784785435[/C][/ROW]
[ROW][C]44[/C][C]17.25[/C][C]17.6123477229756[/C][C]-0.362347722975628[/C][/ROW]
[ROW][C]45[/C][C]19.38[/C][C]29.8862157063213[/C][C]-10.5062157063213[/C][/ROW]
[ROW][C]46[/C][C]26.2[/C][C]56.8750375008186[/C][C]-30.6750375008186[/C][/ROW]
[ROW][C]47[/C][C]33.53[/C][C]76.4614339307148[/C][C]-42.9314339307148[/C][/ROW]
[ROW][C]48[/C][C]32.2[/C][C]63.3780485465359[/C][C]-31.1780485465359[/C][/ROW]
[ROW][C]49[/C][C]38.45[/C][C]57.7205143361653[/C][C]-19.2705143361653[/C][/ROW]
[ROW][C]50[/C][C]44.86[/C][C]48.6570358397455[/C][C]-3.79703583974554[/C][/ROW]
[ROW][C]51[/C][C]41.67[/C][C]32.4114393714118[/C][C]9.25856062858818[/C][/ROW]
[ROW][C]52[/C][C]36.06[/C][C]45.3062202578867[/C][C]-9.24622025788673[/C][/ROW]
[ROW][C]53[/C][C]39.76[/C][C]52.4968457444182[/C][C]-12.7368457444182[/C][/ROW]
[ROW][C]54[/C][C]36.81[/C][C]40.8095751396519[/C][C]-3.99957513965192[/C][/ROW]
[ROW][C]55[/C][C]42.65[/C][C]50.6967007326058[/C][C]-8.04670073260581[/C][/ROW]
[ROW][C]56[/C][C]46.89[/C][C]49.0340009471973[/C][C]-2.14400094719728[/C][/ROW]
[ROW][C]57[/C][C]53.61[/C][C]67.8543168254959[/C][C]-14.2443168254959[/C][/ROW]
[ROW][C]58[/C][C]57.59[/C][C]80.2028101607748[/C][C]-22.6128101607748[/C][/ROW]
[ROW][C]59[/C][C]67.82[/C][C]81.059542518191[/C][C]-13.2395425181909[/C][/ROW]
[ROW][C]60[/C][C]71.89[/C][C]58.9085139027248[/C][C]12.9814860972752[/C][/ROW]
[ROW][C]61[/C][C]75.51[/C][C]67.4921295217137[/C][C]8.01787047828636[/C][/ROW]
[ROW][C]62[/C][C]68.49[/C][C]68.985320509384[/C][C]-0.495320509383987[/C][/ROW]
[ROW][C]63[/C][C]62.72[/C][C]68.8908034214519[/C][C]-6.17080342145188[/C][/ROW]
[ROW][C]64[/C][C]70.39[/C][C]40.9522363175904[/C][C]29.4377636824096[/C][/ROW]
[ROW][C]65[/C][C]59.77[/C][C]18.9301141685141[/C][C]40.8398858314859[/C][/ROW]
[ROW][C]66[/C][C]57.27[/C][C]20.1362799202347[/C][C]37.1337200797653[/C][/ROW]
[ROW][C]67[/C][C]67.96[/C][C]20.0663745464459[/C][C]47.8936254535541[/C][/ROW]
[ROW][C]68[/C][C]67.85[/C][C]53.4497379286832[/C][C]14.4002620713167[/C][/ROW]
[ROW][C]69[/C][C]76.98[/C][C]77.1836686205179[/C][C]-0.203668620517895[/C][/ROW]
[ROW][C]70[/C][C]81.08[/C][C]97.9838305532285[/C][C]-16.9038305532285[/C][/ROW]
[ROW][C]71[/C][C]91.66[/C][C]101.832201322439[/C][C]-10.1722013224393[/C][/ROW]
[ROW][C]72[/C][C]84.84[/C][C]91.7395551694772[/C][C]-6.89955516947724[/C][/ROW]
[ROW][C]73[/C][C]85.73[/C][C]113.226251384054[/C][C]-27.4962513840536[/C][/ROW]
[ROW][C]74[/C][C]84.61[/C][C]78.240209300278[/C][C]6.3697906997221[/C][/ROW]
[ROW][C]75[/C][C]92.91[/C][C]79.073309042405[/C][C]13.8366909575951[/C][/ROW]
[ROW][C]76[/C][C]99.8[/C][C]107.483918444190[/C][C]-7.68391844418985[/C][/ROW]
[ROW][C]77[/C][C]121.19[/C][C]116.378136668521[/C][C]4.81186333147939[/C][/ROW]
[ROW][C]78[/C][C]122.04[/C][C]120.115475042355[/C][C]1.92452495764459[/C][/ROW]
[ROW][C]79[/C][C]131.76[/C][C]105.903843396301[/C][C]25.8561566036990[/C][/ROW]
[ROW][C]80[/C][C]138.48[/C][C]123.284004977196[/C][C]15.1959950228038[/C][/ROW]
[ROW][C]81[/C][C]153.47[/C][C]142.019318045368[/C][C]11.4506819546316[/C][/ROW]
[ROW][C]82[/C][C]189.95[/C][C]202.944147671640[/C][C]-12.9941476716403[/C][/ROW]
[ROW][C]83[/C][C]182.22[/C][C]177.583491244225[/C][C]4.63650875577469[/C][/ROW]
[ROW][C]84[/C][C]198.08[/C][C]178.970274556766[/C][C]19.1097254432341[/C][/ROW]
[ROW][C]85[/C][C]135.36[/C][C]155.196026860577[/C][C]-19.8360268605767[/C][/ROW]
[ROW][C]86[/C][C]125.02[/C][C]129.76855374878[/C][C]-4.7485537487801[/C][/ROW]
[ROW][C]87[/C][C]143.5[/C][C]156.726784695558[/C][C]-13.2267846955583[/C][/ROW]
[ROW][C]88[/C][C]173.95[/C][C]170.776522023380[/C][C]3.17347797662046[/C][/ROW]
[ROW][C]89[/C][C]188.75[/C][C]184.64364342077[/C][C]4.10635657922989[/C][/ROW]
[ROW][C]90[/C][C]167.44[/C][C]165.515260422009[/C][C]1.92473957799144[/C][/ROW]
[ROW][C]91[/C][C]158.95[/C][C]159.162411053746[/C][C]-0.21241105374623[/C][/ROW]
[ROW][C]92[/C][C]169.53[/C][C]157.958338507458[/C][C]11.5716614925423[/C][/ROW]
[ROW][C]93[/C][C]113.66[/C][C]137.1109921974[/C][C]-23.4509921974000[/C][/ROW]
[ROW][C]94[/C][C]107.59[/C][C]126.845763624126[/C][C]-19.2557636241260[/C][/ROW]
[ROW][C]95[/C][C]92.67[/C][C]100.868788778525[/C][C]-8.19878877852451[/C][/ROW]
[ROW][C]96[/C][C]85.35[/C][C]116.481247877928[/C][C]-31.1312478779284[/C][/ROW]
[ROW][C]97[/C][C]90.13[/C][C]161.991059648354[/C][C]-71.8610596483543[/C][/ROW]
[ROW][C]98[/C][C]89.31[/C][C]101.212722717554[/C][C]-11.9027227175536[/C][/ROW]
[ROW][C]99[/C][C]105.12[/C][C]130.549655028455[/C][C]-25.4296550284550[/C][/ROW]
[ROW][C]100[/C][C]125.83[/C][C]136.920775066969[/C][C]-11.0907750669691[/C][/ROW]
[ROW][C]101[/C][C]135.81[/C][C]122.826729942277[/C][C]12.9832700577233[/C][/ROW]
[ROW][C]102[/C][C]142.43[/C][C]160.272068135717[/C][C]-17.8420681357169[/C][/ROW]
[ROW][C]103[/C][C]163.39[/C][C]173.855587606783[/C][C]-10.4655876067827[/C][/ROW]
[ROW][C]104[/C][C]168.21[/C][C]162.753744288541[/C][C]5.45625571145851[/C][/ROW]
[ROW][C]105[/C][C]185.35[/C][C]181.386923913572[/C][C]3.96307608642802[/C][/ROW]
[ROW][C]106[/C][C]188.5[/C][C]195.147207171396[/C][C]-6.64720717139605[/C][/ROW]
[ROW][C]107[/C][C]199.91[/C][C]189.458468177304[/C][C]10.4515318226958[/C][/ROW]
[ROW][C]108[/C][C]210.73[/C][C]194.272543060002[/C][C]16.4574569399980[/C][/ROW]
[ROW][C]109[/C][C]192.06[/C][C]173.850710333562[/C][C]18.2092896664383[/C][/ROW]
[ROW][C]110[/C][C]204.62[/C][C]206.341607230959[/C][C]-1.72160723095854[/C][/ROW]
[ROW][C]111[/C][C]235[/C][C]210.364260948881[/C][C]24.6357390511195[/C][/ROW]
[ROW][C]112[/C][C]261.09[/C][C]218.590464032118[/C][C]42.499535967882[/C][/ROW]
[ROW][C]113[/C][C]256.88[/C][C]168.36662532517[/C][C]88.51337467483[/C][/ROW]
[ROW][C]114[/C][C]251.53[/C][C]160.900678046314[/C][C]90.6293219536864[/C][/ROW]
[ROW][C]115[/C][C]257.25[/C][C]197.607562872832[/C][C]59.6424371271679[/C][/ROW]
[ROW][C]116[/C][C]243.1[/C][C]162.582081626892[/C][C]80.5179183731076[/C][/ROW]
[ROW][C]117[/C][C]283.75[/C][C]203.021988145009[/C][C]80.7280118549914[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108929&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108929&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
110.8162.719331174007-51.909331174007
29.1212.3165485930306-3.19654859303057
311.03-34.14729089509945.177290895099
412.7437.3144946138030-24.5744946138030
59.9820.8280166605108-10.8480166605108
611.6233.4089695606654-21.7889695606654
79.413.5541424133923-4.15414241339227
89.27-28.362719158901137.6327191589011
97.76-34.411092017985542.1710920179855
108.7843.9752618941057-35.1952618941057
1110.6582.475421733643-71.825421733643
1210.9572.8667462508443-61.9167462508443
1312.3656.3027536013476-43.9427536013476
1410.8530.1536918545558-19.3036918545558
1511.848.358600323553563.48139967644644
1612.14-25.282142116679537.4221421166795
1711.65-34.586271867203146.2362718672031
188.86-26.640830944383235.5008309443832
197.63-42.467379406274650.0973794062746
207.38-40.498814464525747.8788144645257
217.25-62.100345987433669.3503459874336
228.032.601073709051575.42892629094843
237.7513.0691199253444-5.31911992534443
247.16-8.757619698942515.9176196989425
257.18-20.584183947785427.7641839477854
267.514.028209905959113.48179009404089
277.0713.0014100041374-5.93141000413738
287.115.435983302321041.67401669767897
298.9810.6249259484946-1.64492594849464
309.5314.4448804594925-4.91488045949246
3110.5443.1917031351319-32.6517031351319
3211.3140.1342530329536-28.8242530329536
3310.3653.3078493361767-42.9478493361767
3411.4457.2727598180085-45.8327598180085
3510.4537.8791384671795-27.4291384671795
3610.6950.7481661995594-40.0581661995594
3711.2848.424642990894-37.144642990894
3811.9655.3586848232579-43.3986848232579
3913.5247.0782193405312-33.5582193405312
4012.8931.7444969980815-18.8544969980815
4114.0327.8845805751168-13.8545805751168
4216.2726.3456642223851-10.0756642223851
4316.1711.87592215214574.29407784785435
4417.2517.6123477229756-0.362347722975628
4519.3829.8862157063213-10.5062157063213
4626.256.8750375008186-30.6750375008186
4733.5376.4614339307148-42.9314339307148
4832.263.3780485465359-31.1780485465359
4938.4557.7205143361653-19.2705143361653
5044.8648.6570358397455-3.79703583974554
5141.6732.41143937141189.25856062858818
5236.0645.3062202578867-9.24622025788673
5339.7652.4968457444182-12.7368457444182
5436.8140.8095751396519-3.99957513965192
5542.6550.6967007326058-8.04670073260581
5646.8949.0340009471973-2.14400094719728
5753.6167.8543168254959-14.2443168254959
5857.5980.2028101607748-22.6128101607748
5967.8281.059542518191-13.2395425181909
6071.8958.908513902724812.9814860972752
6175.5167.49212952171378.01787047828636
6268.4968.985320509384-0.495320509383987
6362.7268.8908034214519-6.17080342145188
6470.3940.952236317590429.4377636824096
6559.7718.930114168514140.8398858314859
6657.2720.136279920234737.1337200797653
6767.9620.066374546445947.8936254535541
6867.8553.449737928683214.4002620713167
6976.9877.1836686205179-0.203668620517895
7081.0897.9838305532285-16.9038305532285
7191.66101.832201322439-10.1722013224393
7284.8491.7395551694772-6.89955516947724
7385.73113.226251384054-27.4962513840536
7484.6178.2402093002786.3697906997221
7592.9179.07330904240513.8366909575951
7699.8107.483918444190-7.68391844418985
77121.19116.3781366685214.81186333147939
78122.04120.1154750423551.92452495764459
79131.76105.90384339630125.8561566036990
80138.48123.28400497719615.1959950228038
81153.47142.01931804536811.4506819546316
82189.95202.944147671640-12.9941476716403
83182.22177.5834912442254.63650875577469
84198.08178.97027455676619.1097254432341
85135.36155.196026860577-19.8360268605767
86125.02129.76855374878-4.7485537487801
87143.5156.726784695558-13.2267846955583
88173.95170.7765220233803.17347797662046
89188.75184.643643420774.10635657922989
90167.44165.5152604220091.92473957799144
91158.95159.162411053746-0.21241105374623
92169.53157.95833850745811.5716614925423
93113.66137.1109921974-23.4509921974000
94107.59126.845763624126-19.2557636241260
9592.67100.868788778525-8.19878877852451
9685.35116.481247877928-31.1312478779284
9790.13161.991059648354-71.8610596483543
9889.31101.212722717554-11.9027227175536
99105.12130.549655028455-25.4296550284550
100125.83136.920775066969-11.0907750669691
101135.81122.82672994227712.9832700577233
102142.43160.272068135717-17.8420681357169
103163.39173.855587606783-10.4655876067827
104168.21162.7537442885415.45625571145851
105185.35181.3869239135723.96307608642802
106188.5195.147207171396-6.64720717139605
107199.91189.45846817730410.4515318226958
108210.73194.27254306000216.4574569399980
109192.06173.85071033356218.2092896664383
110204.62206.341607230959-1.72160723095854
111235210.36426094888124.6357390511195
112261.09218.59046403211842.499535967882
113256.88168.3666253251788.51337467483
114251.53160.90067804631490.6293219536864
115257.25197.60756287283259.6424371271679
116243.1162.58208162689280.5179183731076
117283.75203.02198814500980.7280118549914







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
119.93984868386276e-050.0001987969736772550.999900601513161
123.75528495496602e-067.51056990993203e-060.999996244715045
131.40415187592706e-072.80830375185412e-070.999999859584812
144.43679524443318e-098.87359048886636e-090.999999995563205
153.2787499481599e-106.5574998963198e-100.999999999672125
164.83938169169561e-119.67876338339122e-110.999999999951606
171.87273445471078e-123.74546890942155e-120.999999999998127
181.01078373073410e-122.02156746146819e-120.99999999999899
198.59501249251238e-141.71900249850248e-130.999999999999914
205.04829534913243e-151.00965906982649e-140.999999999999995
214.49634627660944e-168.99269255321887e-161
222.78293446701977e-175.56586893403954e-171
231.1955696367963e-182.3911392735926e-181
246.60852797051415e-201.32170559410283e-191
255.80911855173071e-211.16182371034614e-201
267.9873602976131e-221.59747205952262e-211
274.97782091111176e-239.95564182222351e-231
284.83034863825841e-249.66069727651681e-241
292.84125116913218e-255.68250233826435e-251
301.88903654283953e-263.77807308567906e-261
311.29292177584207e-272.58584355168413e-271
329.73198006053913e-291.94639601210783e-281
334.39000219102545e-308.7800043820509e-301
343.06414982941582e-316.12829965883165e-311
352.20033502101293e-324.40067004202586e-321
361.64829608112117e-333.29659216224234e-331
371.15290506131888e-342.30581012263776e-341
381.35529818143040e-352.71059636286080e-351
391.3428213006449e-352.6856426012898e-351
403.04563572334256e-366.09127144668512e-361
412.48714847361289e-364.97429694722578e-361
421.54250888690184e-363.08501777380367e-361
431.9737339055093e-373.9474678110186e-371
446.80275674278552e-371.36055134855710e-361
454.48035287166176e-358.96070574332352e-351
461.75925932315777e-343.51851864631553e-341
475.4181966750562e-321.08363933501124e-311
485.78731021919091e-311.15746204383818e-301
492.11494834863372e-314.22989669726743e-311
501.0919007730059e-282.1838015460118e-281
516.05918482025972e-261.21183696405194e-251
529.56614976926253e-271.91322995385251e-261
537.7178701517261e-261.54357403034522e-251
541.03656652612921e-252.07313305225843e-251
551.92252220674638e-223.84504441349275e-221
565.97032392482011e-201.19406478496402e-191
572.8140491823456e-175.6280983646912e-171
588.70441440649926e-161.74088288129985e-151
593.28081589555314e-126.56163179110628e-120.99999999999672
605.44923997479065e-101.08984799495813e-090.999999999455076
612.46102583235247e-074.92205166470493e-070.999999753897417
621.54566562354627e-063.09133124709254e-060.999998454334377
638.0877778880804e-061.61755557761608e-050.999991912222112
642.32825904036498e-054.65651808072995e-050.999976717409596
651.76684941687336e-053.53369883374671e-050.999982331505831
661.04032478035949e-052.08064956071899e-050.999989596752196
673.17259754812911e-056.34519509625822e-050.99996827402452
686.14290417222864e-050.0001228580834445730.999938570958278
690.0001069245161536870.0002138490323073740.999893075483846
700.0002308855981196470.0004617711962392940.99976911440188
710.0007472453768071430.001494490753614290.999252754623193
720.0009074060560187060.001814812112037410.999092593943981
730.0008690996710562750.001738199342112550.999130900328944
740.001011960140339760.002023920280679510.99898803985966
750.002389938266738310.004779876533476610.997610061733262
760.004059595840944090.008119191681888190.995940404159056
770.01642238915945410.03284477831890830.983577610840546
780.02411436982886020.04822873965772040.97588563017114
790.03691196516251770.07382393032503530.963088034837482
800.05169094917802590.1033818983560520.948309050821974
810.1018447530157180.2036895060314370.898155246984282
820.2751693855386660.5503387710773320.724830614461334
830.369991471587950.73998294317590.63000852841205
840.8839451634970990.2321096730058020.116054836502901
850.9279235093253360.1441529813493280.0720764906746642
860.9068328686529030.1863342626941930.0931671313470965
870.921977607047760.1560447859044800.0780223929522399
880.9441659343681960.1116681312636080.0558340656318039
890.9444775739355320.1110448521289360.055522426064468
900.978718739657370.0425625206852590.0212812603426295
910.9713196577815230.05736068443695390.0286803422184769
920.966549439500990.06690112099801770.0334505604990089
930.985215049979190.02956990004161790.0147849500208090
940.9816591028732060.03668179425358820.0183408971267941
950.96886462283560.06227075432880090.0311353771644004
960.9715699584213060.05686008315738770.0284300415786938
970.959293917700410.08141216459917920.0407060822995896
980.940516143218880.1189677135622400.0594838567811202
990.9548085453601690.09038290927966230.0451914546398312
1000.9304403430052740.1391193139894520.0695596569947262
1010.9731571017788290.05368579644234270.0268428982211714
1020.9628075130840470.07438497383190610.0371924869159530
1030.9460931388273150.1078137223453690.0539068611726846
1040.9336822209419740.1326355581160510.0663177790580257
1050.962572059548520.07485588090296040.0374279404514802
1060.9739777856826370.0520444286347270.0260222143173635

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 & 9.93984868386276e-05 & 0.000198796973677255 & 0.999900601513161 \tabularnewline
12 & 3.75528495496602e-06 & 7.51056990993203e-06 & 0.999996244715045 \tabularnewline
13 & 1.40415187592706e-07 & 2.80830375185412e-07 & 0.999999859584812 \tabularnewline
14 & 4.43679524443318e-09 & 8.87359048886636e-09 & 0.999999995563205 \tabularnewline
15 & 3.2787499481599e-10 & 6.5574998963198e-10 & 0.999999999672125 \tabularnewline
16 & 4.83938169169561e-11 & 9.67876338339122e-11 & 0.999999999951606 \tabularnewline
17 & 1.87273445471078e-12 & 3.74546890942155e-12 & 0.999999999998127 \tabularnewline
18 & 1.01078373073410e-12 & 2.02156746146819e-12 & 0.99999999999899 \tabularnewline
19 & 8.59501249251238e-14 & 1.71900249850248e-13 & 0.999999999999914 \tabularnewline
20 & 5.04829534913243e-15 & 1.00965906982649e-14 & 0.999999999999995 \tabularnewline
21 & 4.49634627660944e-16 & 8.99269255321887e-16 & 1 \tabularnewline
22 & 2.78293446701977e-17 & 5.56586893403954e-17 & 1 \tabularnewline
23 & 1.1955696367963e-18 & 2.3911392735926e-18 & 1 \tabularnewline
24 & 6.60852797051415e-20 & 1.32170559410283e-19 & 1 \tabularnewline
25 & 5.80911855173071e-21 & 1.16182371034614e-20 & 1 \tabularnewline
26 & 7.9873602976131e-22 & 1.59747205952262e-21 & 1 \tabularnewline
27 & 4.97782091111176e-23 & 9.95564182222351e-23 & 1 \tabularnewline
28 & 4.83034863825841e-24 & 9.66069727651681e-24 & 1 \tabularnewline
29 & 2.84125116913218e-25 & 5.68250233826435e-25 & 1 \tabularnewline
30 & 1.88903654283953e-26 & 3.77807308567906e-26 & 1 \tabularnewline
31 & 1.29292177584207e-27 & 2.58584355168413e-27 & 1 \tabularnewline
32 & 9.73198006053913e-29 & 1.94639601210783e-28 & 1 \tabularnewline
33 & 4.39000219102545e-30 & 8.7800043820509e-30 & 1 \tabularnewline
34 & 3.06414982941582e-31 & 6.12829965883165e-31 & 1 \tabularnewline
35 & 2.20033502101293e-32 & 4.40067004202586e-32 & 1 \tabularnewline
36 & 1.64829608112117e-33 & 3.29659216224234e-33 & 1 \tabularnewline
37 & 1.15290506131888e-34 & 2.30581012263776e-34 & 1 \tabularnewline
38 & 1.35529818143040e-35 & 2.71059636286080e-35 & 1 \tabularnewline
39 & 1.3428213006449e-35 & 2.6856426012898e-35 & 1 \tabularnewline
40 & 3.04563572334256e-36 & 6.09127144668512e-36 & 1 \tabularnewline
41 & 2.48714847361289e-36 & 4.97429694722578e-36 & 1 \tabularnewline
42 & 1.54250888690184e-36 & 3.08501777380367e-36 & 1 \tabularnewline
43 & 1.9737339055093e-37 & 3.9474678110186e-37 & 1 \tabularnewline
44 & 6.80275674278552e-37 & 1.36055134855710e-36 & 1 \tabularnewline
45 & 4.48035287166176e-35 & 8.96070574332352e-35 & 1 \tabularnewline
46 & 1.75925932315777e-34 & 3.51851864631553e-34 & 1 \tabularnewline
47 & 5.4181966750562e-32 & 1.08363933501124e-31 & 1 \tabularnewline
48 & 5.78731021919091e-31 & 1.15746204383818e-30 & 1 \tabularnewline
49 & 2.11494834863372e-31 & 4.22989669726743e-31 & 1 \tabularnewline
50 & 1.0919007730059e-28 & 2.1838015460118e-28 & 1 \tabularnewline
51 & 6.05918482025972e-26 & 1.21183696405194e-25 & 1 \tabularnewline
52 & 9.56614976926253e-27 & 1.91322995385251e-26 & 1 \tabularnewline
53 & 7.7178701517261e-26 & 1.54357403034522e-25 & 1 \tabularnewline
54 & 1.03656652612921e-25 & 2.07313305225843e-25 & 1 \tabularnewline
55 & 1.92252220674638e-22 & 3.84504441349275e-22 & 1 \tabularnewline
56 & 5.97032392482011e-20 & 1.19406478496402e-19 & 1 \tabularnewline
57 & 2.8140491823456e-17 & 5.6280983646912e-17 & 1 \tabularnewline
58 & 8.70441440649926e-16 & 1.74088288129985e-15 & 1 \tabularnewline
59 & 3.28081589555314e-12 & 6.56163179110628e-12 & 0.99999999999672 \tabularnewline
60 & 5.44923997479065e-10 & 1.08984799495813e-09 & 0.999999999455076 \tabularnewline
61 & 2.46102583235247e-07 & 4.92205166470493e-07 & 0.999999753897417 \tabularnewline
62 & 1.54566562354627e-06 & 3.09133124709254e-06 & 0.999998454334377 \tabularnewline
63 & 8.0877778880804e-06 & 1.61755557761608e-05 & 0.999991912222112 \tabularnewline
64 & 2.32825904036498e-05 & 4.65651808072995e-05 & 0.999976717409596 \tabularnewline
65 & 1.76684941687336e-05 & 3.53369883374671e-05 & 0.999982331505831 \tabularnewline
66 & 1.04032478035949e-05 & 2.08064956071899e-05 & 0.999989596752196 \tabularnewline
67 & 3.17259754812911e-05 & 6.34519509625822e-05 & 0.99996827402452 \tabularnewline
68 & 6.14290417222864e-05 & 0.000122858083444573 & 0.999938570958278 \tabularnewline
69 & 0.000106924516153687 & 0.000213849032307374 & 0.999893075483846 \tabularnewline
70 & 0.000230885598119647 & 0.000461771196239294 & 0.99976911440188 \tabularnewline
71 & 0.000747245376807143 & 0.00149449075361429 & 0.999252754623193 \tabularnewline
72 & 0.000907406056018706 & 0.00181481211203741 & 0.999092593943981 \tabularnewline
73 & 0.000869099671056275 & 0.00173819934211255 & 0.999130900328944 \tabularnewline
74 & 0.00101196014033976 & 0.00202392028067951 & 0.99898803985966 \tabularnewline
75 & 0.00238993826673831 & 0.00477987653347661 & 0.997610061733262 \tabularnewline
76 & 0.00405959584094409 & 0.00811919168188819 & 0.995940404159056 \tabularnewline
77 & 0.0164223891594541 & 0.0328447783189083 & 0.983577610840546 \tabularnewline
78 & 0.0241143698288602 & 0.0482287396577204 & 0.97588563017114 \tabularnewline
79 & 0.0369119651625177 & 0.0738239303250353 & 0.963088034837482 \tabularnewline
80 & 0.0516909491780259 & 0.103381898356052 & 0.948309050821974 \tabularnewline
81 & 0.101844753015718 & 0.203689506031437 & 0.898155246984282 \tabularnewline
82 & 0.275169385538666 & 0.550338771077332 & 0.724830614461334 \tabularnewline
83 & 0.36999147158795 & 0.7399829431759 & 0.63000852841205 \tabularnewline
84 & 0.883945163497099 & 0.232109673005802 & 0.116054836502901 \tabularnewline
85 & 0.927923509325336 & 0.144152981349328 & 0.0720764906746642 \tabularnewline
86 & 0.906832868652903 & 0.186334262694193 & 0.0931671313470965 \tabularnewline
87 & 0.92197760704776 & 0.156044785904480 & 0.0780223929522399 \tabularnewline
88 & 0.944165934368196 & 0.111668131263608 & 0.0558340656318039 \tabularnewline
89 & 0.944477573935532 & 0.111044852128936 & 0.055522426064468 \tabularnewline
90 & 0.97871873965737 & 0.042562520685259 & 0.0212812603426295 \tabularnewline
91 & 0.971319657781523 & 0.0573606844369539 & 0.0286803422184769 \tabularnewline
92 & 0.96654943950099 & 0.0669011209980177 & 0.0334505604990089 \tabularnewline
93 & 0.98521504997919 & 0.0295699000416179 & 0.0147849500208090 \tabularnewline
94 & 0.981659102873206 & 0.0366817942535882 & 0.0183408971267941 \tabularnewline
95 & 0.9688646228356 & 0.0622707543288009 & 0.0311353771644004 \tabularnewline
96 & 0.971569958421306 & 0.0568600831573877 & 0.0284300415786938 \tabularnewline
97 & 0.95929391770041 & 0.0814121645991792 & 0.0407060822995896 \tabularnewline
98 & 0.94051614321888 & 0.118967713562240 & 0.0594838567811202 \tabularnewline
99 & 0.954808545360169 & 0.0903829092796623 & 0.0451914546398312 \tabularnewline
100 & 0.930440343005274 & 0.139119313989452 & 0.0695596569947262 \tabularnewline
101 & 0.973157101778829 & 0.0536857964423427 & 0.0268428982211714 \tabularnewline
102 & 0.962807513084047 & 0.0743849738319061 & 0.0371924869159530 \tabularnewline
103 & 0.946093138827315 & 0.107813722345369 & 0.0539068611726846 \tabularnewline
104 & 0.933682220941974 & 0.132635558116051 & 0.0663177790580257 \tabularnewline
105 & 0.96257205954852 & 0.0748558809029604 & 0.0374279404514802 \tabularnewline
106 & 0.973977785682637 & 0.052044428634727 & 0.0260222143173635 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108929&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]11[/C][C]9.93984868386276e-05[/C][C]0.000198796973677255[/C][C]0.999900601513161[/C][/ROW]
[ROW][C]12[/C][C]3.75528495496602e-06[/C][C]7.51056990993203e-06[/C][C]0.999996244715045[/C][/ROW]
[ROW][C]13[/C][C]1.40415187592706e-07[/C][C]2.80830375185412e-07[/C][C]0.999999859584812[/C][/ROW]
[ROW][C]14[/C][C]4.43679524443318e-09[/C][C]8.87359048886636e-09[/C][C]0.999999995563205[/C][/ROW]
[ROW][C]15[/C][C]3.2787499481599e-10[/C][C]6.5574998963198e-10[/C][C]0.999999999672125[/C][/ROW]
[ROW][C]16[/C][C]4.83938169169561e-11[/C][C]9.67876338339122e-11[/C][C]0.999999999951606[/C][/ROW]
[ROW][C]17[/C][C]1.87273445471078e-12[/C][C]3.74546890942155e-12[/C][C]0.999999999998127[/C][/ROW]
[ROW][C]18[/C][C]1.01078373073410e-12[/C][C]2.02156746146819e-12[/C][C]0.99999999999899[/C][/ROW]
[ROW][C]19[/C][C]8.59501249251238e-14[/C][C]1.71900249850248e-13[/C][C]0.999999999999914[/C][/ROW]
[ROW][C]20[/C][C]5.04829534913243e-15[/C][C]1.00965906982649e-14[/C][C]0.999999999999995[/C][/ROW]
[ROW][C]21[/C][C]4.49634627660944e-16[/C][C]8.99269255321887e-16[/C][C]1[/C][/ROW]
[ROW][C]22[/C][C]2.78293446701977e-17[/C][C]5.56586893403954e-17[/C][C]1[/C][/ROW]
[ROW][C]23[/C][C]1.1955696367963e-18[/C][C]2.3911392735926e-18[/C][C]1[/C][/ROW]
[ROW][C]24[/C][C]6.60852797051415e-20[/C][C]1.32170559410283e-19[/C][C]1[/C][/ROW]
[ROW][C]25[/C][C]5.80911855173071e-21[/C][C]1.16182371034614e-20[/C][C]1[/C][/ROW]
[ROW][C]26[/C][C]7.9873602976131e-22[/C][C]1.59747205952262e-21[/C][C]1[/C][/ROW]
[ROW][C]27[/C][C]4.97782091111176e-23[/C][C]9.95564182222351e-23[/C][C]1[/C][/ROW]
[ROW][C]28[/C][C]4.83034863825841e-24[/C][C]9.66069727651681e-24[/C][C]1[/C][/ROW]
[ROW][C]29[/C][C]2.84125116913218e-25[/C][C]5.68250233826435e-25[/C][C]1[/C][/ROW]
[ROW][C]30[/C][C]1.88903654283953e-26[/C][C]3.77807308567906e-26[/C][C]1[/C][/ROW]
[ROW][C]31[/C][C]1.29292177584207e-27[/C][C]2.58584355168413e-27[/C][C]1[/C][/ROW]
[ROW][C]32[/C][C]9.73198006053913e-29[/C][C]1.94639601210783e-28[/C][C]1[/C][/ROW]
[ROW][C]33[/C][C]4.39000219102545e-30[/C][C]8.7800043820509e-30[/C][C]1[/C][/ROW]
[ROW][C]34[/C][C]3.06414982941582e-31[/C][C]6.12829965883165e-31[/C][C]1[/C][/ROW]
[ROW][C]35[/C][C]2.20033502101293e-32[/C][C]4.40067004202586e-32[/C][C]1[/C][/ROW]
[ROW][C]36[/C][C]1.64829608112117e-33[/C][C]3.29659216224234e-33[/C][C]1[/C][/ROW]
[ROW][C]37[/C][C]1.15290506131888e-34[/C][C]2.30581012263776e-34[/C][C]1[/C][/ROW]
[ROW][C]38[/C][C]1.35529818143040e-35[/C][C]2.71059636286080e-35[/C][C]1[/C][/ROW]
[ROW][C]39[/C][C]1.3428213006449e-35[/C][C]2.6856426012898e-35[/C][C]1[/C][/ROW]
[ROW][C]40[/C][C]3.04563572334256e-36[/C][C]6.09127144668512e-36[/C][C]1[/C][/ROW]
[ROW][C]41[/C][C]2.48714847361289e-36[/C][C]4.97429694722578e-36[/C][C]1[/C][/ROW]
[ROW][C]42[/C][C]1.54250888690184e-36[/C][C]3.08501777380367e-36[/C][C]1[/C][/ROW]
[ROW][C]43[/C][C]1.9737339055093e-37[/C][C]3.9474678110186e-37[/C][C]1[/C][/ROW]
[ROW][C]44[/C][C]6.80275674278552e-37[/C][C]1.36055134855710e-36[/C][C]1[/C][/ROW]
[ROW][C]45[/C][C]4.48035287166176e-35[/C][C]8.96070574332352e-35[/C][C]1[/C][/ROW]
[ROW][C]46[/C][C]1.75925932315777e-34[/C][C]3.51851864631553e-34[/C][C]1[/C][/ROW]
[ROW][C]47[/C][C]5.4181966750562e-32[/C][C]1.08363933501124e-31[/C][C]1[/C][/ROW]
[ROW][C]48[/C][C]5.78731021919091e-31[/C][C]1.15746204383818e-30[/C][C]1[/C][/ROW]
[ROW][C]49[/C][C]2.11494834863372e-31[/C][C]4.22989669726743e-31[/C][C]1[/C][/ROW]
[ROW][C]50[/C][C]1.0919007730059e-28[/C][C]2.1838015460118e-28[/C][C]1[/C][/ROW]
[ROW][C]51[/C][C]6.05918482025972e-26[/C][C]1.21183696405194e-25[/C][C]1[/C][/ROW]
[ROW][C]52[/C][C]9.56614976926253e-27[/C][C]1.91322995385251e-26[/C][C]1[/C][/ROW]
[ROW][C]53[/C][C]7.7178701517261e-26[/C][C]1.54357403034522e-25[/C][C]1[/C][/ROW]
[ROW][C]54[/C][C]1.03656652612921e-25[/C][C]2.07313305225843e-25[/C][C]1[/C][/ROW]
[ROW][C]55[/C][C]1.92252220674638e-22[/C][C]3.84504441349275e-22[/C][C]1[/C][/ROW]
[ROW][C]56[/C][C]5.97032392482011e-20[/C][C]1.19406478496402e-19[/C][C]1[/C][/ROW]
[ROW][C]57[/C][C]2.8140491823456e-17[/C][C]5.6280983646912e-17[/C][C]1[/C][/ROW]
[ROW][C]58[/C][C]8.70441440649926e-16[/C][C]1.74088288129985e-15[/C][C]1[/C][/ROW]
[ROW][C]59[/C][C]3.28081589555314e-12[/C][C]6.56163179110628e-12[/C][C]0.99999999999672[/C][/ROW]
[ROW][C]60[/C][C]5.44923997479065e-10[/C][C]1.08984799495813e-09[/C][C]0.999999999455076[/C][/ROW]
[ROW][C]61[/C][C]2.46102583235247e-07[/C][C]4.92205166470493e-07[/C][C]0.999999753897417[/C][/ROW]
[ROW][C]62[/C][C]1.54566562354627e-06[/C][C]3.09133124709254e-06[/C][C]0.999998454334377[/C][/ROW]
[ROW][C]63[/C][C]8.0877778880804e-06[/C][C]1.61755557761608e-05[/C][C]0.999991912222112[/C][/ROW]
[ROW][C]64[/C][C]2.32825904036498e-05[/C][C]4.65651808072995e-05[/C][C]0.999976717409596[/C][/ROW]
[ROW][C]65[/C][C]1.76684941687336e-05[/C][C]3.53369883374671e-05[/C][C]0.999982331505831[/C][/ROW]
[ROW][C]66[/C][C]1.04032478035949e-05[/C][C]2.08064956071899e-05[/C][C]0.999989596752196[/C][/ROW]
[ROW][C]67[/C][C]3.17259754812911e-05[/C][C]6.34519509625822e-05[/C][C]0.99996827402452[/C][/ROW]
[ROW][C]68[/C][C]6.14290417222864e-05[/C][C]0.000122858083444573[/C][C]0.999938570958278[/C][/ROW]
[ROW][C]69[/C][C]0.000106924516153687[/C][C]0.000213849032307374[/C][C]0.999893075483846[/C][/ROW]
[ROW][C]70[/C][C]0.000230885598119647[/C][C]0.000461771196239294[/C][C]0.99976911440188[/C][/ROW]
[ROW][C]71[/C][C]0.000747245376807143[/C][C]0.00149449075361429[/C][C]0.999252754623193[/C][/ROW]
[ROW][C]72[/C][C]0.000907406056018706[/C][C]0.00181481211203741[/C][C]0.999092593943981[/C][/ROW]
[ROW][C]73[/C][C]0.000869099671056275[/C][C]0.00173819934211255[/C][C]0.999130900328944[/C][/ROW]
[ROW][C]74[/C][C]0.00101196014033976[/C][C]0.00202392028067951[/C][C]0.99898803985966[/C][/ROW]
[ROW][C]75[/C][C]0.00238993826673831[/C][C]0.00477987653347661[/C][C]0.997610061733262[/C][/ROW]
[ROW][C]76[/C][C]0.00405959584094409[/C][C]0.00811919168188819[/C][C]0.995940404159056[/C][/ROW]
[ROW][C]77[/C][C]0.0164223891594541[/C][C]0.0328447783189083[/C][C]0.983577610840546[/C][/ROW]
[ROW][C]78[/C][C]0.0241143698288602[/C][C]0.0482287396577204[/C][C]0.97588563017114[/C][/ROW]
[ROW][C]79[/C][C]0.0369119651625177[/C][C]0.0738239303250353[/C][C]0.963088034837482[/C][/ROW]
[ROW][C]80[/C][C]0.0516909491780259[/C][C]0.103381898356052[/C][C]0.948309050821974[/C][/ROW]
[ROW][C]81[/C][C]0.101844753015718[/C][C]0.203689506031437[/C][C]0.898155246984282[/C][/ROW]
[ROW][C]82[/C][C]0.275169385538666[/C][C]0.550338771077332[/C][C]0.724830614461334[/C][/ROW]
[ROW][C]83[/C][C]0.36999147158795[/C][C]0.7399829431759[/C][C]0.63000852841205[/C][/ROW]
[ROW][C]84[/C][C]0.883945163497099[/C][C]0.232109673005802[/C][C]0.116054836502901[/C][/ROW]
[ROW][C]85[/C][C]0.927923509325336[/C][C]0.144152981349328[/C][C]0.0720764906746642[/C][/ROW]
[ROW][C]86[/C][C]0.906832868652903[/C][C]0.186334262694193[/C][C]0.0931671313470965[/C][/ROW]
[ROW][C]87[/C][C]0.92197760704776[/C][C]0.156044785904480[/C][C]0.0780223929522399[/C][/ROW]
[ROW][C]88[/C][C]0.944165934368196[/C][C]0.111668131263608[/C][C]0.0558340656318039[/C][/ROW]
[ROW][C]89[/C][C]0.944477573935532[/C][C]0.111044852128936[/C][C]0.055522426064468[/C][/ROW]
[ROW][C]90[/C][C]0.97871873965737[/C][C]0.042562520685259[/C][C]0.0212812603426295[/C][/ROW]
[ROW][C]91[/C][C]0.971319657781523[/C][C]0.0573606844369539[/C][C]0.0286803422184769[/C][/ROW]
[ROW][C]92[/C][C]0.96654943950099[/C][C]0.0669011209980177[/C][C]0.0334505604990089[/C][/ROW]
[ROW][C]93[/C][C]0.98521504997919[/C][C]0.0295699000416179[/C][C]0.0147849500208090[/C][/ROW]
[ROW][C]94[/C][C]0.981659102873206[/C][C]0.0366817942535882[/C][C]0.0183408971267941[/C][/ROW]
[ROW][C]95[/C][C]0.9688646228356[/C][C]0.0622707543288009[/C][C]0.0311353771644004[/C][/ROW]
[ROW][C]96[/C][C]0.971569958421306[/C][C]0.0568600831573877[/C][C]0.0284300415786938[/C][/ROW]
[ROW][C]97[/C][C]0.95929391770041[/C][C]0.0814121645991792[/C][C]0.0407060822995896[/C][/ROW]
[ROW][C]98[/C][C]0.94051614321888[/C][C]0.118967713562240[/C][C]0.0594838567811202[/C][/ROW]
[ROW][C]99[/C][C]0.954808545360169[/C][C]0.0903829092796623[/C][C]0.0451914546398312[/C][/ROW]
[ROW][C]100[/C][C]0.930440343005274[/C][C]0.139119313989452[/C][C]0.0695596569947262[/C][/ROW]
[ROW][C]101[/C][C]0.973157101778829[/C][C]0.0536857964423427[/C][C]0.0268428982211714[/C][/ROW]
[ROW][C]102[/C][C]0.962807513084047[/C][C]0.0743849738319061[/C][C]0.0371924869159530[/C][/ROW]
[ROW][C]103[/C][C]0.946093138827315[/C][C]0.107813722345369[/C][C]0.0539068611726846[/C][/ROW]
[ROW][C]104[/C][C]0.933682220941974[/C][C]0.132635558116051[/C][C]0.0663177790580257[/C][/ROW]
[ROW][C]105[/C][C]0.96257205954852[/C][C]0.0748558809029604[/C][C]0.0374279404514802[/C][/ROW]
[ROW][C]106[/C][C]0.973977785682637[/C][C]0.052044428634727[/C][C]0.0260222143173635[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108929&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108929&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
119.93984868386276e-050.0001987969736772550.999900601513161
123.75528495496602e-067.51056990993203e-060.999996244715045
131.40415187592706e-072.80830375185412e-070.999999859584812
144.43679524443318e-098.87359048886636e-090.999999995563205
153.2787499481599e-106.5574998963198e-100.999999999672125
164.83938169169561e-119.67876338339122e-110.999999999951606
171.87273445471078e-123.74546890942155e-120.999999999998127
181.01078373073410e-122.02156746146819e-120.99999999999899
198.59501249251238e-141.71900249850248e-130.999999999999914
205.04829534913243e-151.00965906982649e-140.999999999999995
214.49634627660944e-168.99269255321887e-161
222.78293446701977e-175.56586893403954e-171
231.1955696367963e-182.3911392735926e-181
246.60852797051415e-201.32170559410283e-191
255.80911855173071e-211.16182371034614e-201
267.9873602976131e-221.59747205952262e-211
274.97782091111176e-239.95564182222351e-231
284.83034863825841e-249.66069727651681e-241
292.84125116913218e-255.68250233826435e-251
301.88903654283953e-263.77807308567906e-261
311.29292177584207e-272.58584355168413e-271
329.73198006053913e-291.94639601210783e-281
334.39000219102545e-308.7800043820509e-301
343.06414982941582e-316.12829965883165e-311
352.20033502101293e-324.40067004202586e-321
361.64829608112117e-333.29659216224234e-331
371.15290506131888e-342.30581012263776e-341
381.35529818143040e-352.71059636286080e-351
391.3428213006449e-352.6856426012898e-351
403.04563572334256e-366.09127144668512e-361
412.48714847361289e-364.97429694722578e-361
421.54250888690184e-363.08501777380367e-361
431.9737339055093e-373.9474678110186e-371
446.80275674278552e-371.36055134855710e-361
454.48035287166176e-358.96070574332352e-351
461.75925932315777e-343.51851864631553e-341
475.4181966750562e-321.08363933501124e-311
485.78731021919091e-311.15746204383818e-301
492.11494834863372e-314.22989669726743e-311
501.0919007730059e-282.1838015460118e-281
516.05918482025972e-261.21183696405194e-251
529.56614976926253e-271.91322995385251e-261
537.7178701517261e-261.54357403034522e-251
541.03656652612921e-252.07313305225843e-251
551.92252220674638e-223.84504441349275e-221
565.97032392482011e-201.19406478496402e-191
572.8140491823456e-175.6280983646912e-171
588.70441440649926e-161.74088288129985e-151
593.28081589555314e-126.56163179110628e-120.99999999999672
605.44923997479065e-101.08984799495813e-090.999999999455076
612.46102583235247e-074.92205166470493e-070.999999753897417
621.54566562354627e-063.09133124709254e-060.999998454334377
638.0877778880804e-061.61755557761608e-050.999991912222112
642.32825904036498e-054.65651808072995e-050.999976717409596
651.76684941687336e-053.53369883374671e-050.999982331505831
661.04032478035949e-052.08064956071899e-050.999989596752196
673.17259754812911e-056.34519509625822e-050.99996827402452
686.14290417222864e-050.0001228580834445730.999938570958278
690.0001069245161536870.0002138490323073740.999893075483846
700.0002308855981196470.0004617711962392940.99976911440188
710.0007472453768071430.001494490753614290.999252754623193
720.0009074060560187060.001814812112037410.999092593943981
730.0008690996710562750.001738199342112550.999130900328944
740.001011960140339760.002023920280679510.99898803985966
750.002389938266738310.004779876533476610.997610061733262
760.004059595840944090.008119191681888190.995940404159056
770.01642238915945410.03284477831890830.983577610840546
780.02411436982886020.04822873965772040.97588563017114
790.03691196516251770.07382393032503530.963088034837482
800.05169094917802590.1033818983560520.948309050821974
810.1018447530157180.2036895060314370.898155246984282
820.2751693855386660.5503387710773320.724830614461334
830.369991471587950.73998294317590.63000852841205
840.8839451634970990.2321096730058020.116054836502901
850.9279235093253360.1441529813493280.0720764906746642
860.9068328686529030.1863342626941930.0931671313470965
870.921977607047760.1560447859044800.0780223929522399
880.9441659343681960.1116681312636080.0558340656318039
890.9444775739355320.1110448521289360.055522426064468
900.978718739657370.0425625206852590.0212812603426295
910.9713196577815230.05736068443695390.0286803422184769
920.966549439500990.06690112099801770.0334505604990089
930.985215049979190.02956990004161790.0147849500208090
940.9816591028732060.03668179425358820.0183408971267941
950.96886462283560.06227075432880090.0311353771644004
960.9715699584213060.05686008315738770.0284300415786938
970.959293917700410.08141216459917920.0407060822995896
980.940516143218880.1189677135622400.0594838567811202
990.9548085453601690.09038290927966230.0451914546398312
1000.9304403430052740.1391193139894520.0695596569947262
1010.9731571017788290.05368579644234270.0268428982211714
1020.9628075130840470.07438497383190610.0371924869159530
1030.9460931388273150.1078137223453690.0539068611726846
1040.9336822209419740.1326355581160510.0663177790580257
1050.962572059548520.07485588090296040.0374279404514802
1060.9739777856826370.0520444286347270.0260222143173635







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level660.6875NOK
5% type I error level710.739583333333333NOK
10% type I error level820.854166666666667NOK

\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 & 66 & 0.6875 & NOK \tabularnewline
5% type I error level & 71 & 0.739583333333333 & NOK \tabularnewline
10% type I error level & 82 & 0.854166666666667 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108929&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]66[/C][C]0.6875[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]71[/C][C]0.739583333333333[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]82[/C][C]0.854166666666667[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108929&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108929&T=6

As an alternative you can also use a QR Code:  

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level660.6875NOK
5% type I error level710.739583333333333NOK
10% type I error level820.854166666666667NOK



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')
}