Repository of Reproducible Computations

Free Statistics

of Irreproducible Research!

Author's title

Author

*The author of this computation has been verified*

R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Mon, 13 Dec 2010 13:48:19 +0000

Cite this page as follows

Statistical 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 Sat, 27 Apr 2024 09:54:12 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=108929, Retrieved Sat, 27 Apr 2024 09:54:12 +0000

QR Codes:

Paste this QR Code to cite your computation.

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

194

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] 

Feedback Forum

Post a new message

Dataseries X:

Download CSV

Histogram

Boxplots

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 Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	8 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 Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	8 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
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	-85.2045348232553	28.425357	-2.9975	0.003372	0.001686
VOLUME	4.38917615134893e-07	0	1.2703	0.206671	0.103335
REV.GROWTH	28.1570131846652	15.886281	1.7724	0.07912	0.03956
MICROSOFT	6.5065975563462	1.543888	4.2144	5.2e-05	2.6e-05
NASDAQ	0.0881512189265601	0.018421	4.7854	5e-06	3e-06
INFLATION	-961.803925838046	268.539097	-3.5816	0.000512	0.000256
CONS.CONF	-1.98103449491010	0.216208	-9.1626	0	0
FED.FUNDS.RATE	1.03003237831381	3.809839	0.2704	0.787394	0.393697

\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
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	-85.2045348232553	28.425357	-2.9975	0.003372	0.001686
VOLUME	4.38917615134893e-07	0	1.2703	0.206671	0.103335
REV.GROWTH	28.1570131846652	15.886281	1.7724	0.07912	0.03956
MICROSOFT	6.5065975563462	1.543888	4.2144	5.2e-05	2.6e-05
NASDAQ	0.0881512189265601	0.018421	4.7854	5e-06	3e-06
INFLATION	-961.803925838046	268.539097	-3.5816	0.000512	0.000256
CONS.CONF	-1.98103449491010	0.216208	-9.1626	0	0
FED.FUNDS.RATE	1.03003237831381	3.809839	0.2704	0.787394	0.393697

Multiple Linear Regression - Regression Statistics
Multiple R	0.913298070864655
R-squared	0.8341133662451
Adjusted R-squared	0.823460096187447
F-TEST (value)	78.2964631264382
F-TEST (DF numerator)	7
F-TEST (DF denominator)	109
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	31.9226167034062
Sum Squared Residuals	111076.826833992

\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 R	0.913298070864655
R-squared	0.8341133662451
Adjusted R-squared	0.823460096187447
F-TEST (value)	78.2964631264382
F-TEST (DF numerator)	7
F-TEST (DF denominator)	109
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	31.9226167034062
Sum Squared Residuals	111076.826833992

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

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

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

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	66	0.6875	NOK
5% type I error level	71	0.739583333333333	NOK
10% type I error level	82	0.854166666666667	NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 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 tests	OK/NOK
1% type I error level	66	0.6875	NOK
5% type I error level	71	0.739583333333333	NOK
10% type I error level	82	0.854166666666667	NOK

Figure 1

PNG link

Postscript link

PDF link

Figure 2

PNG link

Postscript link

PDF link

Figure 3

PNG link

Postscript link

PDF link

Figure 4

PNG link

Postscript link

PDF link

Figure 5

PNG link

Postscript link

PDF link

Figure 6

PNG link

Postscript link

PDF link

Figure 7

PNG link

Postscript link

PDF link

Figure 8

PNG link

Postscript link

PDF link

Figure 9

PNG link

Postscript link

PDF link

Figure 10

PNG link

Postscript link

PDF link

Parameters (Session):

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

Parameters (R input):

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

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

library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code