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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 computationSat, 19 Nov 2011 05:08:04 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Nov/19/t1321697372mxbu9s12y370e94.htm/, Retrieved Thu, 28 Mar 2024 11:02:39 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=145492, Retrieved Thu, 28 Mar 2024 11:02:39 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact246
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
-   PD    [Multiple Regression] [Nyrstar] [2011-11-19 10:08:04] [2adf2d2c11e011c12275478b9efd18e5] [Current]
-   PD      [Multiple Regression] [test] [2011-11-22 18:44:21] [25b6caf3839c2bdc14961e5bff2d6373]
-   PD        [Multiple Regression] [jill] [2011-11-22 19:01:15] [25b6caf3839c2bdc14961e5bff2d6373]
- R PD      [Multiple Regression] [month season] [2011-11-22 19:17:05] [25b6caf3839c2bdc14961e5bff2d6373]
- RMPD        [Structural Time Series Models] [WS8 - Structural ...] [2011-11-27 11:25:53] [16760482ab7535714acc81f7eb88a6ca]
- R  D      [Multiple Regression] [Unemployment work...] [2012-11-05 17:38:43] [46762b18b00d15214a19b2ee3ead9dc9]
- R           [Multiple Regression] [unemployment usa] [2012-12-19 20:07:59] [46762b18b00d15214a19b2ee3ead9dc9]
- R  D      [Multiple Regression] [] [2012-11-12 11:51:45] [4f34f32e808f328a79844d0e6ce51f0b]
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Dataseries X:
11,73	2582,5	2666	36,98	1	4,5
11,75	2502,5	2584	37,79	1	4,5
11,39	2483,5	2547,5	36,66	1	4,5
11,54	2458,5	2469	36,8	1	4,5
9,62	2493,5	2493,5	37,02	1	4,5
9,82	2517,5	2531	37	1	4,5
9,94	2497,5	2541,5	37	1	5,8
9,9	2487,5	2542	36,5	1	5,8
9,8	2516	2611,5	36,33	1	5,8
9,86	2493	2637,5	36,22	1	5,8
10,5	2417,5	2588,5	36	1	5,8
10,33	2390	2567,5	35,59	1	5,8
10,16	2327,5	2535,5	35,11	1	5,8
9,91	2272,5	2413	35,17	1	5,8
9,96	2277,5	2427,5	34,49	1	5,8
10,03	2312,5	2481,5	35,27	1	5,8
9,55	2282	2492,5	36,55	1	5,8
9,51	2319	2582,5	35,81	1	5,8
9,8	2322,5	2657	36,02	1	5,8
10,08	2327,5	2687,5	35,33	1	5,8
10,2	2289,5	2632,5	34,59	1	5,8
10,23	2337,5	2682,5	34,44	1	5,8
10,2	2412,5	2721,5	34,88	1	5,8
10,07	2425	2693,5	34,59	1	5,8
10,01	2372,5	2652,5	35,08	1	5,8
10,05	2337,5	2627,5	34,48	1	5,8
9,92	2367,5	2652,5	35,38	1	5,8
10,03	2348,5	2637,5	35,3	1	5,8
10,18	2337,5	2672,5	35,51	1	5,8
10,1	2331	2669	35,17	1	6,2
10,16	2407,5	2751	39,6	1	6,2
10,15	2428,5	2772,5	39,6	1	6,2
10,13	2429,5	2787,5	38,98	1	6,2
10,09	2469	2802,5	39,81	1	6,2
10,18	2497,5	2842,5	39,52	1	6,2
10,06	2542,5	2862,5	38,7	1	6,2
9,65	2467,5	2757,5	37,59	1,25	6,2
9,74	2447,5	2692	37,85	1,25	6,2
9,53	2412,5	2622,5	37,84	1,25	6,2
9,5	2402,5	2634	38,9	1,25	6,2
9	2350,5	2582,5	39,01	1,25	6,2
9,15	2353	2557,5	38,32	1,25	6,2
9,32	2358	2627,5	38,7	1,25	6,2
9,62	2366,5	2625	39,08	1,25	6,2
9,59	2284,5	2556	39,03	1,25	6,2
9,37	2225,5	2528	38,76	1,25	6,2
9,35	2253	2492,5	39,29	1,25	6,2
9,32	2253	2492,5	39,39	1,25	6,2
9,49	2253	2492,5	39,75	1,25	2,8
9,52	2237,5	2507,5	40,05	1,25	2,8
9,59	2173,5	2467,5	40,4	1,25	2,8
9,35	2122,5	2352,5	39,99	1,25	2,8
9,2	2162,5	2302,5	39,66	1,25	2,8
9,57	2164,5	2306,5	39,57	1,25	2,8
9,78	2169	2337,5	39,98	1,25	2,8
9,79	2124	2274,5	40,64	1,25	2,8
9,57	2154	2297,5	41,55	1,25	2,8
9,53	2167,5	2340,5	40,68	1,25	2,8
9,65	2130,5	2297,5	40,6	1,25	2,8
9,36	2111	2287,5	40,89	1,25	2,8
9,4	2182,5	2401,5	35,73	1,25	2,8
9,32	2176,5	2482	34,5	1,25	2,8
9,31	2152,5	2467,5	35,16	1,25	2,8
9,19	2127,5	2429,5	35,82	1,25	2,8
9,39	2189,5	2480,5	35,76	1,25	2,8
9,28	2239	2516,5	35,24	1,25	2,8
9,28	2264	2512,5	35,36	1,25	2,8
9,31	2282	2512,5	35,6	1,25	2,8
9,28	2282	2512,5	35,52	1,25	2,8
9,31	2271	2512,5	35,67	1,25	2,8
9,35	2252,5	2508,5	36,41	1,25	-0,5
9,19	2220,5	2425,5	35,49	1,25	-0,5
9,07	2237,5	2427,5	35,61	1,25	-0,5
8,96	2281	2467,5	35,8	1,25	-0,5
8,69	2274	2537,5	35,5	1,25	-0,5
8,58	2272,5	2567,5	35,27	1,25	-0,5
8,56	2286,5	2589,5	34,55	1,25	-0,5
8,47	2267,5	2552,5	34,7	1,25	-0,5
8,46	2237,5	2522,5	34,24	1,25	-0,5
8,75	2270,5	2577,5	34,5	1,25	-0,5
8,95	2267,5	2562,5	34,61	1,25	-0,5
9,33	2207,5	2492,5	34,04	1,25	-0,5
9,51	2217,5	2477,5	33,57	1,25	-0,5
9,561	2170	2422,5	33,34	1,25	-0,5
9,94	2222,5	2485,5	33,32	1,25	-0,5
9,9	2242,5	2497,5	34,02	1,25	-0,5
9,275	2232,5	2536,5	33,67	1,25	-0,5
9,56	2245,5	2573,5	32,5	1,25	-0,5
9,779	2257,5	2561,5	32,27	1,25	-0,5
9,746	2270,5	2579,5	32,65	1,25	-0,5
9,991	2302,5	2632,5	33,24	1,25	-0,5
9,98	2363,5	2645,5	33,92	1,25	-0,5
10,195	2377,5	2667,5	34,27	1,25	-1,1
10,31	2392,5	2681	34,72	1,25	-1,1
10,25	2411	2687,5	34,67	1,25	-1,1
9,871	2391	2682,5	34,36	1,25	-1,1
10,06	2400	2722,5	35,4	1,25	-1,1
9,894	2357,5	2700,5	35,44	1,25	-1,1
9,59	2302,5	2664	33,64	1,25	-1,1
9,64	2342,5	2704	33,19	1,5	-1,1
9,89	2387,5	2771	33,85	1,5	-1,1
9,53	2351	2674,5	33,81	1,5	-1,1
9,388	2369	2692,5	34,35	1,5	-1,1
9,16	2417,5	2715,5	34,07	1,5	-1,1
9,418	2483,5	2761	34,23	1,5	-1,1
9,57	2462,5	2722	34,52	1,5	-1,1
9,857	2458,5	2737,5	35	1,5	-1,1
9,877	2468,5	2682,5	35,06	1,5	-1,1
9,76	2471	2677,5	35,18	1,5	-1,1
9,76	2512	2717,5	35,28	1,5	-1,1
9,695	2515	2702,5	34,24	1,5	-1,1
9,475	2512,5	2692,5	34,29	1,5	-1,1
9,262	2493,5	2633,5	33,46	1,5	-1,1
9,097	2477,5	2617,5	33	1,5	-2,5
8,55	2437,5	2571	31,4	1,5	-2,5
8,16	2380,5	2532,5	31,24	1,5	-2,5
7,532	2337,5	2497,5	29,2	1,5	-2,5
7,325	2267,5	2399,5	28	1,5	-2,5
6,749	2051	2287,5	25,38	1,5	-2,5
7,13	2130,5	2284,5	28,22	1,5	-2,5
6,995	2112,5	2297,5	26,87	1,5	-2,5
7,346	2171,5	2332,5	28,37	1,5	-2,5
7,73	2205,5	2397,5	28,16	1,5	-2,5
7,837	2182,5	2382,5	28,86	1,5	-2,5
7,514	2175,5	2377,5	26,54	1,5	-2,5
7,58	2209	2397,5	27,16	1,5	-2,5
6,83	2162	2312,5	25,31	1,5	-2,5
6,617	2201	2312,5	25,25	1,5	-2,5
6,715	2157,5	2282,5	24,94	1,5	-2,5
6,63	2182,5	2307,5	26,23	1,5	-2,5
6,891	2192,5	2342,5	27,28	1,5	-2,5
7,002	2202,5	2387,5	26,68	1,5	-2,5
7,09	2247,5	2450,5	27,15	1,5	-2,5
7,36	2247,5	2450,5	27,93	1,5	-2,5
7,477	2277,5	2511,5	27,45	1,5	-2,5
7,826	2290	2572,5	27,28	1,5	-2,5
7,79	2242,5	2537,5	26,88	1,5	-7,8
7,578	2187,5	2480,5	25,88	1,5	-7,8
7,204	2172,5	2427,5	25,09	1,5	-7,8
7,198	2173	2387,5	27,23	1,5	-7,8
7,685	2237,5	2427,5	26,5	1,5	-7,8
7,795	2241	2435,5	25,67	1,5	-7,8
7,46	2197,5	2442,5	25,42	1,5	-7,8
7,274	2174	2417,5	26,28	1,5	-7,8
7,33	2217,5	2402,5	27,66	1,5	-7,8
7,655	2155	2333,5	28,16	1,5	-7,8
7,767	2194	2392,5	27,73	1,5	-7,8
7,84	2187,5	2382,5	26,28	1,5	-7,8
7,424	2107,5	2312,5	26,39	1,5	-7,8
7,54	2095	2326,5	25,7	1,5	-7,8
7,351	2067,5	2236,5	24,33	1,5	-7,8
6,735	2031	2132,5	24,72	1,5	-7,8
6,777	1957,5	2027,5	25,3	1,5	-7,8
6,679	1867,5	1902,5	25,61	1,5	-7,8
7,34	1995,5	2015	24,01	1,5	-7,8
6,978	1960	2005	24,45	1,5	-7,8
6,92	1926,5	2012,5	23,75	1,5	-7,8
6,628	1872,5	1992,5	21,98	1,5	-7,8
6,385	1877,5	1936,5	23,51	1,5	-9,4
5,984	1876	1912,5	24,25	1,5	-9,4
6,268	1831	1872,5	24,45	1,5	-9,4
6,596	1862,5	1932,5	24,37	1,5	-9,4
6,395	1892,5	1952,5	25,87	1,5	-9,4
6,715	1947,5	1991,5	26,46	1,5	-9,4
6,804	1908,5	1972,5	27,71	1,5	-9,4
6,929	1967,5	2032,5	26,99	1,5	-9,4
6,846	1912,5	2000,5	27,5	1,5	-9,4
6,992	1922,5	2028,5	26,89	1,5	-9,4
6,774	1897,5	1992,5	27,19	1,5	-9,4
6,75	1869,5	1932,5	26,53	1,5	-9,4
6,485	1851	1902,5	27,03	1,5	-9,4
6,27	1762,5	1807,5	26,78	1,5	-9,4
6,47	1803,5	1867,5	27,49	1,5	-9,4
6,78	1873,5	1987,5	26,49	1,5	-9,4
6,71	1868,5	1997,5	26,05	1,5	-9,4
6,141	1862,5	1943,5	27,51	1,5	-9,4
6,72	1919	1987,5	28	1,5	-9,4
6,68	1947	2023,5	26,57	1,5	-9,4
6,371	1962,5	2077,5	24,76	1,5	-9,4
6,097	1922,5	2007,5	25,53	1,5	-10,4
6,27	1942,5	2022,5	27,08	1,5	-10,4
6,447	1946	2032,5	26,66	1,5	-10,4
6,37	1951,5	2038,5	26,63	1,5	-10,4
6,446	1937,5	2012,5	27,61	1,5	-10,4
6,54	1986,5	2032,5	26,72	1,5	-10,4
6,374	1946,5	1986	26,96	1,5	-10,4
6,33	1877,5	1940	27,73	1,5	-10,4
6,63	1907,5	1997,5	26,96	1,5	-10,4
6,498	1947,5	2017,5	27,33	1,5	-10,4
6,485	1937,5	2022	27,1	1,5	-10,4
6,36	1936	2016,5	26,58	1,5	-10,4




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 6 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
R Framework error message & 
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
\tabularnewline \hline \end{tabular} %Source: https://freestatistics.org/blog/index.php?pk=145492&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=145492&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145492&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 time6 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.







Multiple Linear Regression - Estimated Regression Equation
koers-nyrstar[t] = + 0.54042756691463 + 0.00169284007905437`Zink-prijs`[t] + 0.00142257765147403`Lood-prijs`[t] + 0.12761506022474`NYSE-eindkoers-vorige-dag`[t] -2.50753204802146`Rente-op-LT-leningen-in-%`[t] -0.0443777798111209`Conjunctuurenquete `[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
koers-nyrstar[t] =  +  0.54042756691463 +  0.00169284007905437`Zink-prijs`[t] +  0.00142257765147403`Lood-prijs`[t] +  0.12761506022474`NYSE-eindkoers-vorige-dag`[t] -2.50753204802146`Rente-op-LT-leningen-in-%`[t] -0.0443777798111209`Conjunctuurenquete
`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145492&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]koers-nyrstar[t] =  +  0.54042756691463 +  0.00169284007905437`Zink-prijs`[t] +  0.00142257765147403`Lood-prijs`[t] +  0.12761506022474`NYSE-eindkoers-vorige-dag`[t] -2.50753204802146`Rente-op-LT-leningen-in-%`[t] -0.0443777798111209`Conjunctuurenquete
`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145492&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145492&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
koers-nyrstar[t] = + 0.54042756691463 + 0.00169284007905437`Zink-prijs`[t] + 0.00142257765147403`Lood-prijs`[t] + 0.12761506022474`NYSE-eindkoers-vorige-dag`[t] -2.50753204802146`Rente-op-LT-leningen-in-%`[t] -0.0443777798111209`Conjunctuurenquete `[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.540427566914630.7969160.67810.4985250.249262
`Zink-prijs`0.001692840079054370.0005413.13070.0020270.001014
`Lood-prijs`0.001422577651474030.0004113.46390.0006620.000331
`NYSE-eindkoers-vorige-dag`0.127615060224740.0130459.782800
`Rente-op-LT-leningen-in-%`-2.507532048021460.323161-7.759400
`Conjunctuurenquete `-0.04437777981112090.016936-2.62030.0095150.004758

\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) & 0.54042756691463 & 0.796916 & 0.6781 & 0.498525 & 0.249262 \tabularnewline
`Zink-prijs` & 0.00169284007905437 & 0.000541 & 3.1307 & 0.002027 & 0.001014 \tabularnewline
`Lood-prijs` & 0.00142257765147403 & 0.000411 & 3.4639 & 0.000662 & 0.000331 \tabularnewline
`NYSE-eindkoers-vorige-dag` & 0.12761506022474 & 0.013045 & 9.7828 & 0 & 0 \tabularnewline
`Rente-op-LT-leningen-in-%` & -2.50753204802146 & 0.323161 & -7.7594 & 0 & 0 \tabularnewline
`Conjunctuurenquete
` & -0.0443777798111209 & 0.016936 & -2.6203 & 0.009515 & 0.004758 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145492&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]0.54042756691463[/C][C]0.796916[/C][C]0.6781[/C][C]0.498525[/C][C]0.249262[/C][/ROW]
[ROW][C]`Zink-prijs`[/C][C]0.00169284007905437[/C][C]0.000541[/C][C]3.1307[/C][C]0.002027[/C][C]0.001014[/C][/ROW]
[ROW][C]`Lood-prijs`[/C][C]0.00142257765147403[/C][C]0.000411[/C][C]3.4639[/C][C]0.000662[/C][C]0.000331[/C][/ROW]
[ROW][C]`NYSE-eindkoers-vorige-dag`[/C][C]0.12761506022474[/C][C]0.013045[/C][C]9.7828[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]`Rente-op-LT-leningen-in-%`[/C][C]-2.50753204802146[/C][C]0.323161[/C][C]-7.7594[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]`Conjunctuurenquete
`[/C][C]-0.0443777798111209[/C][C]0.016936[/C][C]-2.6203[/C][C]0.009515[/C][C]0.004758[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145492&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145492&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)0.540427566914630.7969160.67810.4985250.249262
`Zink-prijs`0.001692840079054370.0005413.13070.0020270.001014
`Lood-prijs`0.001422577651474030.0004113.46390.0006620.000331
`NYSE-eindkoers-vorige-dag`0.127615060224740.0130459.782800
`Rente-op-LT-leningen-in-%`-2.507532048021460.323161-7.759400
`Conjunctuurenquete `-0.04437777981112090.016936-2.62030.0095150.004758







Multiple Linear Regression - Regression Statistics
Multiple R0.952916010593209
R-squared0.908048923244876
Adjusted R-squared0.905563759008251
F-TEST (value)365.387892623979
F-TEST (DF numerator)5
F-TEST (DF denominator)185
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.444661876114785
Sum Squared Residuals36.5789740529353

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.952916010593209 \tabularnewline
R-squared & 0.908048923244876 \tabularnewline
Adjusted R-squared & 0.905563759008251 \tabularnewline
F-TEST (value) & 365.387892623979 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 185 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.444661876114785 \tabularnewline
Sum Squared Residuals & 36.5789740529353 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145492&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.952916010593209[/C][/ROW]
[ROW][C]R-squared[/C][C]0.908048923244876[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.905563759008251[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]365.387892623979[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]185[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.444661876114785[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]36.5789740529353[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145492&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145492&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 R0.952916010593209
R-squared0.908048923244876
Adjusted R-squared0.905563759008251
F-TEST (value)365.387892623979
F-TEST (DF numerator)5
F-TEST (DF denominator)185
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.444661876114785
Sum Squared Residuals36.5789740529353







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
111.7310.71675195984171.0132480401583
211.7510.56804158487851.1819584151215
311.3910.33974852104371.05025147895629
411.5410.20362128185811.33637871814189
59.6210.3257991503356-0.705799150335569
69.8210.4172216729587-0.597221672958654
79.9410.3406108229636-0.400610822963587
89.910.2605861808864-0.36058618088641
99.810.3860067096787-0.586006709678698
109.8610.3700207501741-0.510020750174053
1110.510.14442970603380.355570293966223
1210.3310.01568029848670.314319701513316
1310.169.803100079790740.356899920209259
149.919.543385016750670.366614983249333
159.969.485698352139490.474301647860512
1610.039.721306695061290.308693304938712
179.559.84867070390401-0.298670703904009
189.519.94490263089538-0.434902630895377
199.810.0836087688541-0.283608768854077
2010.0810.04740719606420.0325928039357634
2110.29.810402357662790.389597642337207
2210.239.943645304997390.286354695002609
2310.210.18223946583280.0177605341671566
2410.0710.1265594251146-0.0565594251145745
2510.0110.0418910167639-0.0318910167639074
2610.059.870508136575310.179491863424691
279.9210.0717113344361-0.151711334436058
2810.0310.00799950334390.0220004966560648
2910.1810.06596764292310.114032357076877
3010.19.988844928228250.111155071771749
3110.1610.8003332784924-0.640333278492379
3210.1510.8664683396592-0.716468339659212
3310.1310.810378507171-0.680378507171037
3410.0911.0045048550523-0.914504855052331
3510.1811.0726455358992-0.892645535899168
3610.0611.0726305431018-1.01263054310181
379.6510.0277611549131-0.37776115491313
389.749.93390543281892-0.193905432818926
399.539.77451073267233-0.244510732672331
409.59.90921393871196-0.409213938711962
4199.76196116217494-0.761961162174943
429.159.64257442953066-0.492574429530658
439.329.79911278841451-0.479112788414513
449.629.85843920784319-0.238439207843192
459.599.61508771039779-0.0250877103977885
469.379.44092190523163-0.0709219052316282
479.359.50460948269741-0.154609482697407
489.329.51737098871988-0.197370988719881
499.499.7141968617586-0.224196861758599
509.529.74758102337279-0.227581023372788
519.599.62700142333301-0.0370014233330065
529.359.324747974689580.0252520253104227
539.29.27921972540389-0.079219725403886
549.579.276810360747660.293189639252336
559.789.380850222991250.399149777008753
569.799.299275967139260.490724032860734
579.579.498910160299310.0710898397006886
589.539.471909237984410.058090762015594
599.659.337894111228030.312105888771969
609.369.327666320636910.032333679363094
619.48.952384527797670.447615472202327
629.328.899778464190580.420221535809423
639.318.922748866095230.387251133904774
649.198.910595853111180.279404146888816
659.399.080446494624250.309553505375756
669.289.149095042673640.130904957326362
679.289.201039541271070.0789604587289306
689.319.262138277147980.0478617228520153
699.289.251929072330010.0280709276699931
709.319.252450090494120.0575499095058815
719.359.45632405636872-0.106324056368723
729.199.166673373359880.0233266266401209
739.079.21361061723372-0.143610617233719
748.969.36839912817425-0.408399128174245
758.699.41784516515663-0.727845165156626
768.589.42863177073058-0.848631770730576
778.569.39174539680795-0.831745396807951
788.479.32608832123509-0.856088321235091
798.469.17392286161586-0.713922861615858
808.759.34120827071416-0.591208270714157
818.959.32882874232961-0.378828742329605
829.339.054937317655060.275062682344942
839.518.990547975367860.519452024632136
849.5618.802544836930020.75845516306998
859.948.978489031918740.961510968081256
869.99.118747307474840.781252692525163
879.2759.112634164013120.162365835986879
889.569.037966837682420.522033162317579
899.7799.01185852296170.767141477038304
909.7469.107965564601340.638034435398665
919.9919.31282594819180.678174051808203
929.989.52136094343610.458639056563902
9310.1959.647649351840620.54735064815938
9410.319.749673528422470.560326471577533
9510.259.783857071608320.466142928391682
969.8719.703326713100190.16767328689981
9710.069.908185042504370.15181495749563
989.8949.810047233221120.0839527667788793
999.599.43530983618980.154690163810203
1009.648.875616756304440.764383243695566
1019.899.131333202258970.758666797741031
1029.538.927161193597250.602838806402748
1039.3889.052150845268120.335849154731878
1049.169.131240658223230.0287593417767655
1059.4189.328113796218850.08988620378115
1069.579.27409199361640.295908006383604
1079.8579.35062581580590.506374184194099
1089.8779.296969349378860.580030650621143
1099.769.309402368546090.450597631453908
1109.769.448473423868760.311526576131244
1119.6959.299493616700080.395506383299921
1129.4759.287416492998940.18758350700106
1139.2629.06539995007340.196600049926596
1149.0979.018979230417140.0780207695828604
1158.558.68093167010184-0.130931670101837
1168.168.50925213637803-0.34925213637803
1177.5328.12633507231863-0.594335072318632
1187.3257.71528558467068-0.390285584670683
1196.7496.8551055528015-0.106105552801502
1207.137.34784537717016-0.217845377170164
1216.9957.16358743391295-0.168587433912948
1227.3467.50467780671586-0.158677806715857
1237.737.627902754102320.102097245897678
1247.8377.656959309669280.180040690330721
1257.5147.341929601137130.172070398862868
1267.587.506212634154270.0737873658457273
1276.837.06964218864766-0.239642188647655
1286.6177.12800604811729-0.511006048117291
1296.7156.97212950646454-0.257129506464536
1306.637.21463837741766-0.584638377417661
1316.8917.41535280924577-0.524352809245773
1327.0027.4197281682178-0.417728168217804
1337.097.64550744212374-0.555507442123742
1347.367.74504718909904-0.385047189099039
1357.4777.82135439930271-0.344354399302711
1367.8267.9075975767926-0.0815975767926013
1377.797.96155366414497-0.171553664144972
1387.5787.65974547343822-0.0817454734382217
1397.2047.45814035914674-0.254140359146739
1407.1987.67517990200825-0.477179902008247
1417.6857.74811219920216-0.0631121992021561
1427.7957.65949726070410.135502739295896
1437.467.56391299576937-0.103912995769372
1447.2747.59831576441802-0.32431576441802
1457.337.82672442619492-0.496724426194916
1467.6557.68657159341468-0.0315715934146794
1477.7677.78164996203813-0.0146499620381294
1487.847.571378887683660.268621112316337
1497.4247.350408902380850.0735910976191476
1507.547.261110096958240.278889903041761
1517.3516.911692373643690.439307626356313
1526.7356.75172550849255-0.0167255084925514
1536.7776.551947844207630.225052155792368
1546.6796.261330699328150.417669300671846
1557.346.433870118878360.906129881121641
1566.9786.415699146056070.562300853943927
1576.926.280327793636490.639672206363511
1586.6285.934584219740280.693415780259718
1596.3856.129639561494650.255360438505345
1605.9846.187393582307-0.203393582307004
1616.2686.079835684735540.188164315264456
1626.5966.208305601496220.387694398503781
1636.3956.47896494723444-0.0839649472344411
1646.7156.702844565522510.0121554344774852
1656.8046.769313652342310.0346863476576876
1666.9296.862663032733150.0663369672668507
1676.8466.789118024252610.0568819757473924
1686.9926.768033412547330.223966587452667
1696.7746.712784133185330.0612158668146695
1706.756.495804012135040.254195987864962
1716.4856.48561667124068-0.000616671240680839
1726.276.168751682298150.101248317701848
1736.476.414119477387390.0558805226126121
1746.786.575712540873340.204287459126663
1756.716.525323490493920.18467650950608
1766.1416.62466524476812-0.483665244768117
1776.726.84543550540967-0.125435505409669
1786.686.76155828695488-0.0815582869548782
1796.3716.63363324235304-0.262633242353039
1806.0976.60898057977185-0.511980579771852
1816.276.8619793894734-0.591979389473398
1826.4476.82853178097044-0.381531780970437
1836.376.84254941550734-0.472549415507338
1846.4466.9069253944825-0.460925394482498
1856.546.90474870778562-0.364748707785624
1866.3746.80151285828384-0.427512858283844
1876.336.71753191723434-0.387531917234337
1886.636.75185173819268-0.121851738192675
1896.4986.89523446666748-0.397234466667484
1906.4856.85535620145688-0.370356201456883
1916.366.77863293293833-0.418632932938329

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 11.73 & 10.7167519598417 & 1.0132480401583 \tabularnewline
2 & 11.75 & 10.5680415848785 & 1.1819584151215 \tabularnewline
3 & 11.39 & 10.3397485210437 & 1.05025147895629 \tabularnewline
4 & 11.54 & 10.2036212818581 & 1.33637871814189 \tabularnewline
5 & 9.62 & 10.3257991503356 & -0.705799150335569 \tabularnewline
6 & 9.82 & 10.4172216729587 & -0.597221672958654 \tabularnewline
7 & 9.94 & 10.3406108229636 & -0.400610822963587 \tabularnewline
8 & 9.9 & 10.2605861808864 & -0.36058618088641 \tabularnewline
9 & 9.8 & 10.3860067096787 & -0.586006709678698 \tabularnewline
10 & 9.86 & 10.3700207501741 & -0.510020750174053 \tabularnewline
11 & 10.5 & 10.1444297060338 & 0.355570293966223 \tabularnewline
12 & 10.33 & 10.0156802984867 & 0.314319701513316 \tabularnewline
13 & 10.16 & 9.80310007979074 & 0.356899920209259 \tabularnewline
14 & 9.91 & 9.54338501675067 & 0.366614983249333 \tabularnewline
15 & 9.96 & 9.48569835213949 & 0.474301647860512 \tabularnewline
16 & 10.03 & 9.72130669506129 & 0.308693304938712 \tabularnewline
17 & 9.55 & 9.84867070390401 & -0.298670703904009 \tabularnewline
18 & 9.51 & 9.94490263089538 & -0.434902630895377 \tabularnewline
19 & 9.8 & 10.0836087688541 & -0.283608768854077 \tabularnewline
20 & 10.08 & 10.0474071960642 & 0.0325928039357634 \tabularnewline
21 & 10.2 & 9.81040235766279 & 0.389597642337207 \tabularnewline
22 & 10.23 & 9.94364530499739 & 0.286354695002609 \tabularnewline
23 & 10.2 & 10.1822394658328 & 0.0177605341671566 \tabularnewline
24 & 10.07 & 10.1265594251146 & -0.0565594251145745 \tabularnewline
25 & 10.01 & 10.0418910167639 & -0.0318910167639074 \tabularnewline
26 & 10.05 & 9.87050813657531 & 0.179491863424691 \tabularnewline
27 & 9.92 & 10.0717113344361 & -0.151711334436058 \tabularnewline
28 & 10.03 & 10.0079995033439 & 0.0220004966560648 \tabularnewline
29 & 10.18 & 10.0659676429231 & 0.114032357076877 \tabularnewline
30 & 10.1 & 9.98884492822825 & 0.111155071771749 \tabularnewline
31 & 10.16 & 10.8003332784924 & -0.640333278492379 \tabularnewline
32 & 10.15 & 10.8664683396592 & -0.716468339659212 \tabularnewline
33 & 10.13 & 10.810378507171 & -0.680378507171037 \tabularnewline
34 & 10.09 & 11.0045048550523 & -0.914504855052331 \tabularnewline
35 & 10.18 & 11.0726455358992 & -0.892645535899168 \tabularnewline
36 & 10.06 & 11.0726305431018 & -1.01263054310181 \tabularnewline
37 & 9.65 & 10.0277611549131 & -0.37776115491313 \tabularnewline
38 & 9.74 & 9.93390543281892 & -0.193905432818926 \tabularnewline
39 & 9.53 & 9.77451073267233 & -0.244510732672331 \tabularnewline
40 & 9.5 & 9.90921393871196 & -0.409213938711962 \tabularnewline
41 & 9 & 9.76196116217494 & -0.761961162174943 \tabularnewline
42 & 9.15 & 9.64257442953066 & -0.492574429530658 \tabularnewline
43 & 9.32 & 9.79911278841451 & -0.479112788414513 \tabularnewline
44 & 9.62 & 9.85843920784319 & -0.238439207843192 \tabularnewline
45 & 9.59 & 9.61508771039779 & -0.0250877103977885 \tabularnewline
46 & 9.37 & 9.44092190523163 & -0.0709219052316282 \tabularnewline
47 & 9.35 & 9.50460948269741 & -0.154609482697407 \tabularnewline
48 & 9.32 & 9.51737098871988 & -0.197370988719881 \tabularnewline
49 & 9.49 & 9.7141968617586 & -0.224196861758599 \tabularnewline
50 & 9.52 & 9.74758102337279 & -0.227581023372788 \tabularnewline
51 & 9.59 & 9.62700142333301 & -0.0370014233330065 \tabularnewline
52 & 9.35 & 9.32474797468958 & 0.0252520253104227 \tabularnewline
53 & 9.2 & 9.27921972540389 & -0.079219725403886 \tabularnewline
54 & 9.57 & 9.27681036074766 & 0.293189639252336 \tabularnewline
55 & 9.78 & 9.38085022299125 & 0.399149777008753 \tabularnewline
56 & 9.79 & 9.29927596713926 & 0.490724032860734 \tabularnewline
57 & 9.57 & 9.49891016029931 & 0.0710898397006886 \tabularnewline
58 & 9.53 & 9.47190923798441 & 0.058090762015594 \tabularnewline
59 & 9.65 & 9.33789411122803 & 0.312105888771969 \tabularnewline
60 & 9.36 & 9.32766632063691 & 0.032333679363094 \tabularnewline
61 & 9.4 & 8.95238452779767 & 0.447615472202327 \tabularnewline
62 & 9.32 & 8.89977846419058 & 0.420221535809423 \tabularnewline
63 & 9.31 & 8.92274886609523 & 0.387251133904774 \tabularnewline
64 & 9.19 & 8.91059585311118 & 0.279404146888816 \tabularnewline
65 & 9.39 & 9.08044649462425 & 0.309553505375756 \tabularnewline
66 & 9.28 & 9.14909504267364 & 0.130904957326362 \tabularnewline
67 & 9.28 & 9.20103954127107 & 0.0789604587289306 \tabularnewline
68 & 9.31 & 9.26213827714798 & 0.0478617228520153 \tabularnewline
69 & 9.28 & 9.25192907233001 & 0.0280709276699931 \tabularnewline
70 & 9.31 & 9.25245009049412 & 0.0575499095058815 \tabularnewline
71 & 9.35 & 9.45632405636872 & -0.106324056368723 \tabularnewline
72 & 9.19 & 9.16667337335988 & 0.0233266266401209 \tabularnewline
73 & 9.07 & 9.21361061723372 & -0.143610617233719 \tabularnewline
74 & 8.96 & 9.36839912817425 & -0.408399128174245 \tabularnewline
75 & 8.69 & 9.41784516515663 & -0.727845165156626 \tabularnewline
76 & 8.58 & 9.42863177073058 & -0.848631770730576 \tabularnewline
77 & 8.56 & 9.39174539680795 & -0.831745396807951 \tabularnewline
78 & 8.47 & 9.32608832123509 & -0.856088321235091 \tabularnewline
79 & 8.46 & 9.17392286161586 & -0.713922861615858 \tabularnewline
80 & 8.75 & 9.34120827071416 & -0.591208270714157 \tabularnewline
81 & 8.95 & 9.32882874232961 & -0.378828742329605 \tabularnewline
82 & 9.33 & 9.05493731765506 & 0.275062682344942 \tabularnewline
83 & 9.51 & 8.99054797536786 & 0.519452024632136 \tabularnewline
84 & 9.561 & 8.80254483693002 & 0.75845516306998 \tabularnewline
85 & 9.94 & 8.97848903191874 & 0.961510968081256 \tabularnewline
86 & 9.9 & 9.11874730747484 & 0.781252692525163 \tabularnewline
87 & 9.275 & 9.11263416401312 & 0.162365835986879 \tabularnewline
88 & 9.56 & 9.03796683768242 & 0.522033162317579 \tabularnewline
89 & 9.779 & 9.0118585229617 & 0.767141477038304 \tabularnewline
90 & 9.746 & 9.10796556460134 & 0.638034435398665 \tabularnewline
91 & 9.991 & 9.3128259481918 & 0.678174051808203 \tabularnewline
92 & 9.98 & 9.5213609434361 & 0.458639056563902 \tabularnewline
93 & 10.195 & 9.64764935184062 & 0.54735064815938 \tabularnewline
94 & 10.31 & 9.74967352842247 & 0.560326471577533 \tabularnewline
95 & 10.25 & 9.78385707160832 & 0.466142928391682 \tabularnewline
96 & 9.871 & 9.70332671310019 & 0.16767328689981 \tabularnewline
97 & 10.06 & 9.90818504250437 & 0.15181495749563 \tabularnewline
98 & 9.894 & 9.81004723322112 & 0.0839527667788793 \tabularnewline
99 & 9.59 & 9.4353098361898 & 0.154690163810203 \tabularnewline
100 & 9.64 & 8.87561675630444 & 0.764383243695566 \tabularnewline
101 & 9.89 & 9.13133320225897 & 0.758666797741031 \tabularnewline
102 & 9.53 & 8.92716119359725 & 0.602838806402748 \tabularnewline
103 & 9.388 & 9.05215084526812 & 0.335849154731878 \tabularnewline
104 & 9.16 & 9.13124065822323 & 0.0287593417767655 \tabularnewline
105 & 9.418 & 9.32811379621885 & 0.08988620378115 \tabularnewline
106 & 9.57 & 9.2740919936164 & 0.295908006383604 \tabularnewline
107 & 9.857 & 9.3506258158059 & 0.506374184194099 \tabularnewline
108 & 9.877 & 9.29696934937886 & 0.580030650621143 \tabularnewline
109 & 9.76 & 9.30940236854609 & 0.450597631453908 \tabularnewline
110 & 9.76 & 9.44847342386876 & 0.311526576131244 \tabularnewline
111 & 9.695 & 9.29949361670008 & 0.395506383299921 \tabularnewline
112 & 9.475 & 9.28741649299894 & 0.18758350700106 \tabularnewline
113 & 9.262 & 9.0653999500734 & 0.196600049926596 \tabularnewline
114 & 9.097 & 9.01897923041714 & 0.0780207695828604 \tabularnewline
115 & 8.55 & 8.68093167010184 & -0.130931670101837 \tabularnewline
116 & 8.16 & 8.50925213637803 & -0.34925213637803 \tabularnewline
117 & 7.532 & 8.12633507231863 & -0.594335072318632 \tabularnewline
118 & 7.325 & 7.71528558467068 & -0.390285584670683 \tabularnewline
119 & 6.749 & 6.8551055528015 & -0.106105552801502 \tabularnewline
120 & 7.13 & 7.34784537717016 & -0.217845377170164 \tabularnewline
121 & 6.995 & 7.16358743391295 & -0.168587433912948 \tabularnewline
122 & 7.346 & 7.50467780671586 & -0.158677806715857 \tabularnewline
123 & 7.73 & 7.62790275410232 & 0.102097245897678 \tabularnewline
124 & 7.837 & 7.65695930966928 & 0.180040690330721 \tabularnewline
125 & 7.514 & 7.34192960113713 & 0.172070398862868 \tabularnewline
126 & 7.58 & 7.50621263415427 & 0.0737873658457273 \tabularnewline
127 & 6.83 & 7.06964218864766 & -0.239642188647655 \tabularnewline
128 & 6.617 & 7.12800604811729 & -0.511006048117291 \tabularnewline
129 & 6.715 & 6.97212950646454 & -0.257129506464536 \tabularnewline
130 & 6.63 & 7.21463837741766 & -0.584638377417661 \tabularnewline
131 & 6.891 & 7.41535280924577 & -0.524352809245773 \tabularnewline
132 & 7.002 & 7.4197281682178 & -0.417728168217804 \tabularnewline
133 & 7.09 & 7.64550744212374 & -0.555507442123742 \tabularnewline
134 & 7.36 & 7.74504718909904 & -0.385047189099039 \tabularnewline
135 & 7.477 & 7.82135439930271 & -0.344354399302711 \tabularnewline
136 & 7.826 & 7.9075975767926 & -0.0815975767926013 \tabularnewline
137 & 7.79 & 7.96155366414497 & -0.171553664144972 \tabularnewline
138 & 7.578 & 7.65974547343822 & -0.0817454734382217 \tabularnewline
139 & 7.204 & 7.45814035914674 & -0.254140359146739 \tabularnewline
140 & 7.198 & 7.67517990200825 & -0.477179902008247 \tabularnewline
141 & 7.685 & 7.74811219920216 & -0.0631121992021561 \tabularnewline
142 & 7.795 & 7.6594972607041 & 0.135502739295896 \tabularnewline
143 & 7.46 & 7.56391299576937 & -0.103912995769372 \tabularnewline
144 & 7.274 & 7.59831576441802 & -0.32431576441802 \tabularnewline
145 & 7.33 & 7.82672442619492 & -0.496724426194916 \tabularnewline
146 & 7.655 & 7.68657159341468 & -0.0315715934146794 \tabularnewline
147 & 7.767 & 7.78164996203813 & -0.0146499620381294 \tabularnewline
148 & 7.84 & 7.57137888768366 & 0.268621112316337 \tabularnewline
149 & 7.424 & 7.35040890238085 & 0.0735910976191476 \tabularnewline
150 & 7.54 & 7.26111009695824 & 0.278889903041761 \tabularnewline
151 & 7.351 & 6.91169237364369 & 0.439307626356313 \tabularnewline
152 & 6.735 & 6.75172550849255 & -0.0167255084925514 \tabularnewline
153 & 6.777 & 6.55194784420763 & 0.225052155792368 \tabularnewline
154 & 6.679 & 6.26133069932815 & 0.417669300671846 \tabularnewline
155 & 7.34 & 6.43387011887836 & 0.906129881121641 \tabularnewline
156 & 6.978 & 6.41569914605607 & 0.562300853943927 \tabularnewline
157 & 6.92 & 6.28032779363649 & 0.639672206363511 \tabularnewline
158 & 6.628 & 5.93458421974028 & 0.693415780259718 \tabularnewline
159 & 6.385 & 6.12963956149465 & 0.255360438505345 \tabularnewline
160 & 5.984 & 6.187393582307 & -0.203393582307004 \tabularnewline
161 & 6.268 & 6.07983568473554 & 0.188164315264456 \tabularnewline
162 & 6.596 & 6.20830560149622 & 0.387694398503781 \tabularnewline
163 & 6.395 & 6.47896494723444 & -0.0839649472344411 \tabularnewline
164 & 6.715 & 6.70284456552251 & 0.0121554344774852 \tabularnewline
165 & 6.804 & 6.76931365234231 & 0.0346863476576876 \tabularnewline
166 & 6.929 & 6.86266303273315 & 0.0663369672668507 \tabularnewline
167 & 6.846 & 6.78911802425261 & 0.0568819757473924 \tabularnewline
168 & 6.992 & 6.76803341254733 & 0.223966587452667 \tabularnewline
169 & 6.774 & 6.71278413318533 & 0.0612158668146695 \tabularnewline
170 & 6.75 & 6.49580401213504 & 0.254195987864962 \tabularnewline
171 & 6.485 & 6.48561667124068 & -0.000616671240680839 \tabularnewline
172 & 6.27 & 6.16875168229815 & 0.101248317701848 \tabularnewline
173 & 6.47 & 6.41411947738739 & 0.0558805226126121 \tabularnewline
174 & 6.78 & 6.57571254087334 & 0.204287459126663 \tabularnewline
175 & 6.71 & 6.52532349049392 & 0.18467650950608 \tabularnewline
176 & 6.141 & 6.62466524476812 & -0.483665244768117 \tabularnewline
177 & 6.72 & 6.84543550540967 & -0.125435505409669 \tabularnewline
178 & 6.68 & 6.76155828695488 & -0.0815582869548782 \tabularnewline
179 & 6.371 & 6.63363324235304 & -0.262633242353039 \tabularnewline
180 & 6.097 & 6.60898057977185 & -0.511980579771852 \tabularnewline
181 & 6.27 & 6.8619793894734 & -0.591979389473398 \tabularnewline
182 & 6.447 & 6.82853178097044 & -0.381531780970437 \tabularnewline
183 & 6.37 & 6.84254941550734 & -0.472549415507338 \tabularnewline
184 & 6.446 & 6.9069253944825 & -0.460925394482498 \tabularnewline
185 & 6.54 & 6.90474870778562 & -0.364748707785624 \tabularnewline
186 & 6.374 & 6.80151285828384 & -0.427512858283844 \tabularnewline
187 & 6.33 & 6.71753191723434 & -0.387531917234337 \tabularnewline
188 & 6.63 & 6.75185173819268 & -0.121851738192675 \tabularnewline
189 & 6.498 & 6.89523446666748 & -0.397234466667484 \tabularnewline
190 & 6.485 & 6.85535620145688 & -0.370356201456883 \tabularnewline
191 & 6.36 & 6.77863293293833 & -0.418632932938329 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145492&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]11.73[/C][C]10.7167519598417[/C][C]1.0132480401583[/C][/ROW]
[ROW][C]2[/C][C]11.75[/C][C]10.5680415848785[/C][C]1.1819584151215[/C][/ROW]
[ROW][C]3[/C][C]11.39[/C][C]10.3397485210437[/C][C]1.05025147895629[/C][/ROW]
[ROW][C]4[/C][C]11.54[/C][C]10.2036212818581[/C][C]1.33637871814189[/C][/ROW]
[ROW][C]5[/C][C]9.62[/C][C]10.3257991503356[/C][C]-0.705799150335569[/C][/ROW]
[ROW][C]6[/C][C]9.82[/C][C]10.4172216729587[/C][C]-0.597221672958654[/C][/ROW]
[ROW][C]7[/C][C]9.94[/C][C]10.3406108229636[/C][C]-0.400610822963587[/C][/ROW]
[ROW][C]8[/C][C]9.9[/C][C]10.2605861808864[/C][C]-0.36058618088641[/C][/ROW]
[ROW][C]9[/C][C]9.8[/C][C]10.3860067096787[/C][C]-0.586006709678698[/C][/ROW]
[ROW][C]10[/C][C]9.86[/C][C]10.3700207501741[/C][C]-0.510020750174053[/C][/ROW]
[ROW][C]11[/C][C]10.5[/C][C]10.1444297060338[/C][C]0.355570293966223[/C][/ROW]
[ROW][C]12[/C][C]10.33[/C][C]10.0156802984867[/C][C]0.314319701513316[/C][/ROW]
[ROW][C]13[/C][C]10.16[/C][C]9.80310007979074[/C][C]0.356899920209259[/C][/ROW]
[ROW][C]14[/C][C]9.91[/C][C]9.54338501675067[/C][C]0.366614983249333[/C][/ROW]
[ROW][C]15[/C][C]9.96[/C][C]9.48569835213949[/C][C]0.474301647860512[/C][/ROW]
[ROW][C]16[/C][C]10.03[/C][C]9.72130669506129[/C][C]0.308693304938712[/C][/ROW]
[ROW][C]17[/C][C]9.55[/C][C]9.84867070390401[/C][C]-0.298670703904009[/C][/ROW]
[ROW][C]18[/C][C]9.51[/C][C]9.94490263089538[/C][C]-0.434902630895377[/C][/ROW]
[ROW][C]19[/C][C]9.8[/C][C]10.0836087688541[/C][C]-0.283608768854077[/C][/ROW]
[ROW][C]20[/C][C]10.08[/C][C]10.0474071960642[/C][C]0.0325928039357634[/C][/ROW]
[ROW][C]21[/C][C]10.2[/C][C]9.81040235766279[/C][C]0.389597642337207[/C][/ROW]
[ROW][C]22[/C][C]10.23[/C][C]9.94364530499739[/C][C]0.286354695002609[/C][/ROW]
[ROW][C]23[/C][C]10.2[/C][C]10.1822394658328[/C][C]0.0177605341671566[/C][/ROW]
[ROW][C]24[/C][C]10.07[/C][C]10.1265594251146[/C][C]-0.0565594251145745[/C][/ROW]
[ROW][C]25[/C][C]10.01[/C][C]10.0418910167639[/C][C]-0.0318910167639074[/C][/ROW]
[ROW][C]26[/C][C]10.05[/C][C]9.87050813657531[/C][C]0.179491863424691[/C][/ROW]
[ROW][C]27[/C][C]9.92[/C][C]10.0717113344361[/C][C]-0.151711334436058[/C][/ROW]
[ROW][C]28[/C][C]10.03[/C][C]10.0079995033439[/C][C]0.0220004966560648[/C][/ROW]
[ROW][C]29[/C][C]10.18[/C][C]10.0659676429231[/C][C]0.114032357076877[/C][/ROW]
[ROW][C]30[/C][C]10.1[/C][C]9.98884492822825[/C][C]0.111155071771749[/C][/ROW]
[ROW][C]31[/C][C]10.16[/C][C]10.8003332784924[/C][C]-0.640333278492379[/C][/ROW]
[ROW][C]32[/C][C]10.15[/C][C]10.8664683396592[/C][C]-0.716468339659212[/C][/ROW]
[ROW][C]33[/C][C]10.13[/C][C]10.810378507171[/C][C]-0.680378507171037[/C][/ROW]
[ROW][C]34[/C][C]10.09[/C][C]11.0045048550523[/C][C]-0.914504855052331[/C][/ROW]
[ROW][C]35[/C][C]10.18[/C][C]11.0726455358992[/C][C]-0.892645535899168[/C][/ROW]
[ROW][C]36[/C][C]10.06[/C][C]11.0726305431018[/C][C]-1.01263054310181[/C][/ROW]
[ROW][C]37[/C][C]9.65[/C][C]10.0277611549131[/C][C]-0.37776115491313[/C][/ROW]
[ROW][C]38[/C][C]9.74[/C][C]9.93390543281892[/C][C]-0.193905432818926[/C][/ROW]
[ROW][C]39[/C][C]9.53[/C][C]9.77451073267233[/C][C]-0.244510732672331[/C][/ROW]
[ROW][C]40[/C][C]9.5[/C][C]9.90921393871196[/C][C]-0.409213938711962[/C][/ROW]
[ROW][C]41[/C][C]9[/C][C]9.76196116217494[/C][C]-0.761961162174943[/C][/ROW]
[ROW][C]42[/C][C]9.15[/C][C]9.64257442953066[/C][C]-0.492574429530658[/C][/ROW]
[ROW][C]43[/C][C]9.32[/C][C]9.79911278841451[/C][C]-0.479112788414513[/C][/ROW]
[ROW][C]44[/C][C]9.62[/C][C]9.85843920784319[/C][C]-0.238439207843192[/C][/ROW]
[ROW][C]45[/C][C]9.59[/C][C]9.61508771039779[/C][C]-0.0250877103977885[/C][/ROW]
[ROW][C]46[/C][C]9.37[/C][C]9.44092190523163[/C][C]-0.0709219052316282[/C][/ROW]
[ROW][C]47[/C][C]9.35[/C][C]9.50460948269741[/C][C]-0.154609482697407[/C][/ROW]
[ROW][C]48[/C][C]9.32[/C][C]9.51737098871988[/C][C]-0.197370988719881[/C][/ROW]
[ROW][C]49[/C][C]9.49[/C][C]9.7141968617586[/C][C]-0.224196861758599[/C][/ROW]
[ROW][C]50[/C][C]9.52[/C][C]9.74758102337279[/C][C]-0.227581023372788[/C][/ROW]
[ROW][C]51[/C][C]9.59[/C][C]9.62700142333301[/C][C]-0.0370014233330065[/C][/ROW]
[ROW][C]52[/C][C]9.35[/C][C]9.32474797468958[/C][C]0.0252520253104227[/C][/ROW]
[ROW][C]53[/C][C]9.2[/C][C]9.27921972540389[/C][C]-0.079219725403886[/C][/ROW]
[ROW][C]54[/C][C]9.57[/C][C]9.27681036074766[/C][C]0.293189639252336[/C][/ROW]
[ROW][C]55[/C][C]9.78[/C][C]9.38085022299125[/C][C]0.399149777008753[/C][/ROW]
[ROW][C]56[/C][C]9.79[/C][C]9.29927596713926[/C][C]0.490724032860734[/C][/ROW]
[ROW][C]57[/C][C]9.57[/C][C]9.49891016029931[/C][C]0.0710898397006886[/C][/ROW]
[ROW][C]58[/C][C]9.53[/C][C]9.47190923798441[/C][C]0.058090762015594[/C][/ROW]
[ROW][C]59[/C][C]9.65[/C][C]9.33789411122803[/C][C]0.312105888771969[/C][/ROW]
[ROW][C]60[/C][C]9.36[/C][C]9.32766632063691[/C][C]0.032333679363094[/C][/ROW]
[ROW][C]61[/C][C]9.4[/C][C]8.95238452779767[/C][C]0.447615472202327[/C][/ROW]
[ROW][C]62[/C][C]9.32[/C][C]8.89977846419058[/C][C]0.420221535809423[/C][/ROW]
[ROW][C]63[/C][C]9.31[/C][C]8.92274886609523[/C][C]0.387251133904774[/C][/ROW]
[ROW][C]64[/C][C]9.19[/C][C]8.91059585311118[/C][C]0.279404146888816[/C][/ROW]
[ROW][C]65[/C][C]9.39[/C][C]9.08044649462425[/C][C]0.309553505375756[/C][/ROW]
[ROW][C]66[/C][C]9.28[/C][C]9.14909504267364[/C][C]0.130904957326362[/C][/ROW]
[ROW][C]67[/C][C]9.28[/C][C]9.20103954127107[/C][C]0.0789604587289306[/C][/ROW]
[ROW][C]68[/C][C]9.31[/C][C]9.26213827714798[/C][C]0.0478617228520153[/C][/ROW]
[ROW][C]69[/C][C]9.28[/C][C]9.25192907233001[/C][C]0.0280709276699931[/C][/ROW]
[ROW][C]70[/C][C]9.31[/C][C]9.25245009049412[/C][C]0.0575499095058815[/C][/ROW]
[ROW][C]71[/C][C]9.35[/C][C]9.45632405636872[/C][C]-0.106324056368723[/C][/ROW]
[ROW][C]72[/C][C]9.19[/C][C]9.16667337335988[/C][C]0.0233266266401209[/C][/ROW]
[ROW][C]73[/C][C]9.07[/C][C]9.21361061723372[/C][C]-0.143610617233719[/C][/ROW]
[ROW][C]74[/C][C]8.96[/C][C]9.36839912817425[/C][C]-0.408399128174245[/C][/ROW]
[ROW][C]75[/C][C]8.69[/C][C]9.41784516515663[/C][C]-0.727845165156626[/C][/ROW]
[ROW][C]76[/C][C]8.58[/C][C]9.42863177073058[/C][C]-0.848631770730576[/C][/ROW]
[ROW][C]77[/C][C]8.56[/C][C]9.39174539680795[/C][C]-0.831745396807951[/C][/ROW]
[ROW][C]78[/C][C]8.47[/C][C]9.32608832123509[/C][C]-0.856088321235091[/C][/ROW]
[ROW][C]79[/C][C]8.46[/C][C]9.17392286161586[/C][C]-0.713922861615858[/C][/ROW]
[ROW][C]80[/C][C]8.75[/C][C]9.34120827071416[/C][C]-0.591208270714157[/C][/ROW]
[ROW][C]81[/C][C]8.95[/C][C]9.32882874232961[/C][C]-0.378828742329605[/C][/ROW]
[ROW][C]82[/C][C]9.33[/C][C]9.05493731765506[/C][C]0.275062682344942[/C][/ROW]
[ROW][C]83[/C][C]9.51[/C][C]8.99054797536786[/C][C]0.519452024632136[/C][/ROW]
[ROW][C]84[/C][C]9.561[/C][C]8.80254483693002[/C][C]0.75845516306998[/C][/ROW]
[ROW][C]85[/C][C]9.94[/C][C]8.97848903191874[/C][C]0.961510968081256[/C][/ROW]
[ROW][C]86[/C][C]9.9[/C][C]9.11874730747484[/C][C]0.781252692525163[/C][/ROW]
[ROW][C]87[/C][C]9.275[/C][C]9.11263416401312[/C][C]0.162365835986879[/C][/ROW]
[ROW][C]88[/C][C]9.56[/C][C]9.03796683768242[/C][C]0.522033162317579[/C][/ROW]
[ROW][C]89[/C][C]9.779[/C][C]9.0118585229617[/C][C]0.767141477038304[/C][/ROW]
[ROW][C]90[/C][C]9.746[/C][C]9.10796556460134[/C][C]0.638034435398665[/C][/ROW]
[ROW][C]91[/C][C]9.991[/C][C]9.3128259481918[/C][C]0.678174051808203[/C][/ROW]
[ROW][C]92[/C][C]9.98[/C][C]9.5213609434361[/C][C]0.458639056563902[/C][/ROW]
[ROW][C]93[/C][C]10.195[/C][C]9.64764935184062[/C][C]0.54735064815938[/C][/ROW]
[ROW][C]94[/C][C]10.31[/C][C]9.74967352842247[/C][C]0.560326471577533[/C][/ROW]
[ROW][C]95[/C][C]10.25[/C][C]9.78385707160832[/C][C]0.466142928391682[/C][/ROW]
[ROW][C]96[/C][C]9.871[/C][C]9.70332671310019[/C][C]0.16767328689981[/C][/ROW]
[ROW][C]97[/C][C]10.06[/C][C]9.90818504250437[/C][C]0.15181495749563[/C][/ROW]
[ROW][C]98[/C][C]9.894[/C][C]9.81004723322112[/C][C]0.0839527667788793[/C][/ROW]
[ROW][C]99[/C][C]9.59[/C][C]9.4353098361898[/C][C]0.154690163810203[/C][/ROW]
[ROW][C]100[/C][C]9.64[/C][C]8.87561675630444[/C][C]0.764383243695566[/C][/ROW]
[ROW][C]101[/C][C]9.89[/C][C]9.13133320225897[/C][C]0.758666797741031[/C][/ROW]
[ROW][C]102[/C][C]9.53[/C][C]8.92716119359725[/C][C]0.602838806402748[/C][/ROW]
[ROW][C]103[/C][C]9.388[/C][C]9.05215084526812[/C][C]0.335849154731878[/C][/ROW]
[ROW][C]104[/C][C]9.16[/C][C]9.13124065822323[/C][C]0.0287593417767655[/C][/ROW]
[ROW][C]105[/C][C]9.418[/C][C]9.32811379621885[/C][C]0.08988620378115[/C][/ROW]
[ROW][C]106[/C][C]9.57[/C][C]9.2740919936164[/C][C]0.295908006383604[/C][/ROW]
[ROW][C]107[/C][C]9.857[/C][C]9.3506258158059[/C][C]0.506374184194099[/C][/ROW]
[ROW][C]108[/C][C]9.877[/C][C]9.29696934937886[/C][C]0.580030650621143[/C][/ROW]
[ROW][C]109[/C][C]9.76[/C][C]9.30940236854609[/C][C]0.450597631453908[/C][/ROW]
[ROW][C]110[/C][C]9.76[/C][C]9.44847342386876[/C][C]0.311526576131244[/C][/ROW]
[ROW][C]111[/C][C]9.695[/C][C]9.29949361670008[/C][C]0.395506383299921[/C][/ROW]
[ROW][C]112[/C][C]9.475[/C][C]9.28741649299894[/C][C]0.18758350700106[/C][/ROW]
[ROW][C]113[/C][C]9.262[/C][C]9.0653999500734[/C][C]0.196600049926596[/C][/ROW]
[ROW][C]114[/C][C]9.097[/C][C]9.01897923041714[/C][C]0.0780207695828604[/C][/ROW]
[ROW][C]115[/C][C]8.55[/C][C]8.68093167010184[/C][C]-0.130931670101837[/C][/ROW]
[ROW][C]116[/C][C]8.16[/C][C]8.50925213637803[/C][C]-0.34925213637803[/C][/ROW]
[ROW][C]117[/C][C]7.532[/C][C]8.12633507231863[/C][C]-0.594335072318632[/C][/ROW]
[ROW][C]118[/C][C]7.325[/C][C]7.71528558467068[/C][C]-0.390285584670683[/C][/ROW]
[ROW][C]119[/C][C]6.749[/C][C]6.8551055528015[/C][C]-0.106105552801502[/C][/ROW]
[ROW][C]120[/C][C]7.13[/C][C]7.34784537717016[/C][C]-0.217845377170164[/C][/ROW]
[ROW][C]121[/C][C]6.995[/C][C]7.16358743391295[/C][C]-0.168587433912948[/C][/ROW]
[ROW][C]122[/C][C]7.346[/C][C]7.50467780671586[/C][C]-0.158677806715857[/C][/ROW]
[ROW][C]123[/C][C]7.73[/C][C]7.62790275410232[/C][C]0.102097245897678[/C][/ROW]
[ROW][C]124[/C][C]7.837[/C][C]7.65695930966928[/C][C]0.180040690330721[/C][/ROW]
[ROW][C]125[/C][C]7.514[/C][C]7.34192960113713[/C][C]0.172070398862868[/C][/ROW]
[ROW][C]126[/C][C]7.58[/C][C]7.50621263415427[/C][C]0.0737873658457273[/C][/ROW]
[ROW][C]127[/C][C]6.83[/C][C]7.06964218864766[/C][C]-0.239642188647655[/C][/ROW]
[ROW][C]128[/C][C]6.617[/C][C]7.12800604811729[/C][C]-0.511006048117291[/C][/ROW]
[ROW][C]129[/C][C]6.715[/C][C]6.97212950646454[/C][C]-0.257129506464536[/C][/ROW]
[ROW][C]130[/C][C]6.63[/C][C]7.21463837741766[/C][C]-0.584638377417661[/C][/ROW]
[ROW][C]131[/C][C]6.891[/C][C]7.41535280924577[/C][C]-0.524352809245773[/C][/ROW]
[ROW][C]132[/C][C]7.002[/C][C]7.4197281682178[/C][C]-0.417728168217804[/C][/ROW]
[ROW][C]133[/C][C]7.09[/C][C]7.64550744212374[/C][C]-0.555507442123742[/C][/ROW]
[ROW][C]134[/C][C]7.36[/C][C]7.74504718909904[/C][C]-0.385047189099039[/C][/ROW]
[ROW][C]135[/C][C]7.477[/C][C]7.82135439930271[/C][C]-0.344354399302711[/C][/ROW]
[ROW][C]136[/C][C]7.826[/C][C]7.9075975767926[/C][C]-0.0815975767926013[/C][/ROW]
[ROW][C]137[/C][C]7.79[/C][C]7.96155366414497[/C][C]-0.171553664144972[/C][/ROW]
[ROW][C]138[/C][C]7.578[/C][C]7.65974547343822[/C][C]-0.0817454734382217[/C][/ROW]
[ROW][C]139[/C][C]7.204[/C][C]7.45814035914674[/C][C]-0.254140359146739[/C][/ROW]
[ROW][C]140[/C][C]7.198[/C][C]7.67517990200825[/C][C]-0.477179902008247[/C][/ROW]
[ROW][C]141[/C][C]7.685[/C][C]7.74811219920216[/C][C]-0.0631121992021561[/C][/ROW]
[ROW][C]142[/C][C]7.795[/C][C]7.6594972607041[/C][C]0.135502739295896[/C][/ROW]
[ROW][C]143[/C][C]7.46[/C][C]7.56391299576937[/C][C]-0.103912995769372[/C][/ROW]
[ROW][C]144[/C][C]7.274[/C][C]7.59831576441802[/C][C]-0.32431576441802[/C][/ROW]
[ROW][C]145[/C][C]7.33[/C][C]7.82672442619492[/C][C]-0.496724426194916[/C][/ROW]
[ROW][C]146[/C][C]7.655[/C][C]7.68657159341468[/C][C]-0.0315715934146794[/C][/ROW]
[ROW][C]147[/C][C]7.767[/C][C]7.78164996203813[/C][C]-0.0146499620381294[/C][/ROW]
[ROW][C]148[/C][C]7.84[/C][C]7.57137888768366[/C][C]0.268621112316337[/C][/ROW]
[ROW][C]149[/C][C]7.424[/C][C]7.35040890238085[/C][C]0.0735910976191476[/C][/ROW]
[ROW][C]150[/C][C]7.54[/C][C]7.26111009695824[/C][C]0.278889903041761[/C][/ROW]
[ROW][C]151[/C][C]7.351[/C][C]6.91169237364369[/C][C]0.439307626356313[/C][/ROW]
[ROW][C]152[/C][C]6.735[/C][C]6.75172550849255[/C][C]-0.0167255084925514[/C][/ROW]
[ROW][C]153[/C][C]6.777[/C][C]6.55194784420763[/C][C]0.225052155792368[/C][/ROW]
[ROW][C]154[/C][C]6.679[/C][C]6.26133069932815[/C][C]0.417669300671846[/C][/ROW]
[ROW][C]155[/C][C]7.34[/C][C]6.43387011887836[/C][C]0.906129881121641[/C][/ROW]
[ROW][C]156[/C][C]6.978[/C][C]6.41569914605607[/C][C]0.562300853943927[/C][/ROW]
[ROW][C]157[/C][C]6.92[/C][C]6.28032779363649[/C][C]0.639672206363511[/C][/ROW]
[ROW][C]158[/C][C]6.628[/C][C]5.93458421974028[/C][C]0.693415780259718[/C][/ROW]
[ROW][C]159[/C][C]6.385[/C][C]6.12963956149465[/C][C]0.255360438505345[/C][/ROW]
[ROW][C]160[/C][C]5.984[/C][C]6.187393582307[/C][C]-0.203393582307004[/C][/ROW]
[ROW][C]161[/C][C]6.268[/C][C]6.07983568473554[/C][C]0.188164315264456[/C][/ROW]
[ROW][C]162[/C][C]6.596[/C][C]6.20830560149622[/C][C]0.387694398503781[/C][/ROW]
[ROW][C]163[/C][C]6.395[/C][C]6.47896494723444[/C][C]-0.0839649472344411[/C][/ROW]
[ROW][C]164[/C][C]6.715[/C][C]6.70284456552251[/C][C]0.0121554344774852[/C][/ROW]
[ROW][C]165[/C][C]6.804[/C][C]6.76931365234231[/C][C]0.0346863476576876[/C][/ROW]
[ROW][C]166[/C][C]6.929[/C][C]6.86266303273315[/C][C]0.0663369672668507[/C][/ROW]
[ROW][C]167[/C][C]6.846[/C][C]6.78911802425261[/C][C]0.0568819757473924[/C][/ROW]
[ROW][C]168[/C][C]6.992[/C][C]6.76803341254733[/C][C]0.223966587452667[/C][/ROW]
[ROW][C]169[/C][C]6.774[/C][C]6.71278413318533[/C][C]0.0612158668146695[/C][/ROW]
[ROW][C]170[/C][C]6.75[/C][C]6.49580401213504[/C][C]0.254195987864962[/C][/ROW]
[ROW][C]171[/C][C]6.485[/C][C]6.48561667124068[/C][C]-0.000616671240680839[/C][/ROW]
[ROW][C]172[/C][C]6.27[/C][C]6.16875168229815[/C][C]0.101248317701848[/C][/ROW]
[ROW][C]173[/C][C]6.47[/C][C]6.41411947738739[/C][C]0.0558805226126121[/C][/ROW]
[ROW][C]174[/C][C]6.78[/C][C]6.57571254087334[/C][C]0.204287459126663[/C][/ROW]
[ROW][C]175[/C][C]6.71[/C][C]6.52532349049392[/C][C]0.18467650950608[/C][/ROW]
[ROW][C]176[/C][C]6.141[/C][C]6.62466524476812[/C][C]-0.483665244768117[/C][/ROW]
[ROW][C]177[/C][C]6.72[/C][C]6.84543550540967[/C][C]-0.125435505409669[/C][/ROW]
[ROW][C]178[/C][C]6.68[/C][C]6.76155828695488[/C][C]-0.0815582869548782[/C][/ROW]
[ROW][C]179[/C][C]6.371[/C][C]6.63363324235304[/C][C]-0.262633242353039[/C][/ROW]
[ROW][C]180[/C][C]6.097[/C][C]6.60898057977185[/C][C]-0.511980579771852[/C][/ROW]
[ROW][C]181[/C][C]6.27[/C][C]6.8619793894734[/C][C]-0.591979389473398[/C][/ROW]
[ROW][C]182[/C][C]6.447[/C][C]6.82853178097044[/C][C]-0.381531780970437[/C][/ROW]
[ROW][C]183[/C][C]6.37[/C][C]6.84254941550734[/C][C]-0.472549415507338[/C][/ROW]
[ROW][C]184[/C][C]6.446[/C][C]6.9069253944825[/C][C]-0.460925394482498[/C][/ROW]
[ROW][C]185[/C][C]6.54[/C][C]6.90474870778562[/C][C]-0.364748707785624[/C][/ROW]
[ROW][C]186[/C][C]6.374[/C][C]6.80151285828384[/C][C]-0.427512858283844[/C][/ROW]
[ROW][C]187[/C][C]6.33[/C][C]6.71753191723434[/C][C]-0.387531917234337[/C][/ROW]
[ROW][C]188[/C][C]6.63[/C][C]6.75185173819268[/C][C]-0.121851738192675[/C][/ROW]
[ROW][C]189[/C][C]6.498[/C][C]6.89523446666748[/C][C]-0.397234466667484[/C][/ROW]
[ROW][C]190[/C][C]6.485[/C][C]6.85535620145688[/C][C]-0.370356201456883[/C][/ROW]
[ROW][C]191[/C][C]6.36[/C][C]6.77863293293833[/C][C]-0.418632932938329[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145492&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145492&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
111.7310.71675195984171.0132480401583
211.7510.56804158487851.1819584151215
311.3910.33974852104371.05025147895629
411.5410.20362128185811.33637871814189
59.6210.3257991503356-0.705799150335569
69.8210.4172216729587-0.597221672958654
79.9410.3406108229636-0.400610822963587
89.910.2605861808864-0.36058618088641
99.810.3860067096787-0.586006709678698
109.8610.3700207501741-0.510020750174053
1110.510.14442970603380.355570293966223
1210.3310.01568029848670.314319701513316
1310.169.803100079790740.356899920209259
149.919.543385016750670.366614983249333
159.969.485698352139490.474301647860512
1610.039.721306695061290.308693304938712
179.559.84867070390401-0.298670703904009
189.519.94490263089538-0.434902630895377
199.810.0836087688541-0.283608768854077
2010.0810.04740719606420.0325928039357634
2110.29.810402357662790.389597642337207
2210.239.943645304997390.286354695002609
2310.210.18223946583280.0177605341671566
2410.0710.1265594251146-0.0565594251145745
2510.0110.0418910167639-0.0318910167639074
2610.059.870508136575310.179491863424691
279.9210.0717113344361-0.151711334436058
2810.0310.00799950334390.0220004966560648
2910.1810.06596764292310.114032357076877
3010.19.988844928228250.111155071771749
3110.1610.8003332784924-0.640333278492379
3210.1510.8664683396592-0.716468339659212
3310.1310.810378507171-0.680378507171037
3410.0911.0045048550523-0.914504855052331
3510.1811.0726455358992-0.892645535899168
3610.0611.0726305431018-1.01263054310181
379.6510.0277611549131-0.37776115491313
389.749.93390543281892-0.193905432818926
399.539.77451073267233-0.244510732672331
409.59.90921393871196-0.409213938711962
4199.76196116217494-0.761961162174943
429.159.64257442953066-0.492574429530658
439.329.79911278841451-0.479112788414513
449.629.85843920784319-0.238439207843192
459.599.61508771039779-0.0250877103977885
469.379.44092190523163-0.0709219052316282
479.359.50460948269741-0.154609482697407
489.329.51737098871988-0.197370988719881
499.499.7141968617586-0.224196861758599
509.529.74758102337279-0.227581023372788
519.599.62700142333301-0.0370014233330065
529.359.324747974689580.0252520253104227
539.29.27921972540389-0.079219725403886
549.579.276810360747660.293189639252336
559.789.380850222991250.399149777008753
569.799.299275967139260.490724032860734
579.579.498910160299310.0710898397006886
589.539.471909237984410.058090762015594
599.659.337894111228030.312105888771969
609.369.327666320636910.032333679363094
619.48.952384527797670.447615472202327
629.328.899778464190580.420221535809423
639.318.922748866095230.387251133904774
649.198.910595853111180.279404146888816
659.399.080446494624250.309553505375756
669.289.149095042673640.130904957326362
679.289.201039541271070.0789604587289306
689.319.262138277147980.0478617228520153
699.289.251929072330010.0280709276699931
709.319.252450090494120.0575499095058815
719.359.45632405636872-0.106324056368723
729.199.166673373359880.0233266266401209
739.079.21361061723372-0.143610617233719
748.969.36839912817425-0.408399128174245
758.699.41784516515663-0.727845165156626
768.589.42863177073058-0.848631770730576
778.569.39174539680795-0.831745396807951
788.479.32608832123509-0.856088321235091
798.469.17392286161586-0.713922861615858
808.759.34120827071416-0.591208270714157
818.959.32882874232961-0.378828742329605
829.339.054937317655060.275062682344942
839.518.990547975367860.519452024632136
849.5618.802544836930020.75845516306998
859.948.978489031918740.961510968081256
869.99.118747307474840.781252692525163
879.2759.112634164013120.162365835986879
889.569.037966837682420.522033162317579
899.7799.01185852296170.767141477038304
909.7469.107965564601340.638034435398665
919.9919.31282594819180.678174051808203
929.989.52136094343610.458639056563902
9310.1959.647649351840620.54735064815938
9410.319.749673528422470.560326471577533
9510.259.783857071608320.466142928391682
969.8719.703326713100190.16767328689981
9710.069.908185042504370.15181495749563
989.8949.810047233221120.0839527667788793
999.599.43530983618980.154690163810203
1009.648.875616756304440.764383243695566
1019.899.131333202258970.758666797741031
1029.538.927161193597250.602838806402748
1039.3889.052150845268120.335849154731878
1049.169.131240658223230.0287593417767655
1059.4189.328113796218850.08988620378115
1069.579.27409199361640.295908006383604
1079.8579.35062581580590.506374184194099
1089.8779.296969349378860.580030650621143
1099.769.309402368546090.450597631453908
1109.769.448473423868760.311526576131244
1119.6959.299493616700080.395506383299921
1129.4759.287416492998940.18758350700106
1139.2629.06539995007340.196600049926596
1149.0979.018979230417140.0780207695828604
1158.558.68093167010184-0.130931670101837
1168.168.50925213637803-0.34925213637803
1177.5328.12633507231863-0.594335072318632
1187.3257.71528558467068-0.390285584670683
1196.7496.8551055528015-0.106105552801502
1207.137.34784537717016-0.217845377170164
1216.9957.16358743391295-0.168587433912948
1227.3467.50467780671586-0.158677806715857
1237.737.627902754102320.102097245897678
1247.8377.656959309669280.180040690330721
1257.5147.341929601137130.172070398862868
1267.587.506212634154270.0737873658457273
1276.837.06964218864766-0.239642188647655
1286.6177.12800604811729-0.511006048117291
1296.7156.97212950646454-0.257129506464536
1306.637.21463837741766-0.584638377417661
1316.8917.41535280924577-0.524352809245773
1327.0027.4197281682178-0.417728168217804
1337.097.64550744212374-0.555507442123742
1347.367.74504718909904-0.385047189099039
1357.4777.82135439930271-0.344354399302711
1367.8267.9075975767926-0.0815975767926013
1377.797.96155366414497-0.171553664144972
1387.5787.65974547343822-0.0817454734382217
1397.2047.45814035914674-0.254140359146739
1407.1987.67517990200825-0.477179902008247
1417.6857.74811219920216-0.0631121992021561
1427.7957.65949726070410.135502739295896
1437.467.56391299576937-0.103912995769372
1447.2747.59831576441802-0.32431576441802
1457.337.82672442619492-0.496724426194916
1467.6557.68657159341468-0.0315715934146794
1477.7677.78164996203813-0.0146499620381294
1487.847.571378887683660.268621112316337
1497.4247.350408902380850.0735910976191476
1507.547.261110096958240.278889903041761
1517.3516.911692373643690.439307626356313
1526.7356.75172550849255-0.0167255084925514
1536.7776.551947844207630.225052155792368
1546.6796.261330699328150.417669300671846
1557.346.433870118878360.906129881121641
1566.9786.415699146056070.562300853943927
1576.926.280327793636490.639672206363511
1586.6285.934584219740280.693415780259718
1596.3856.129639561494650.255360438505345
1605.9846.187393582307-0.203393582307004
1616.2686.079835684735540.188164315264456
1626.5966.208305601496220.387694398503781
1636.3956.47896494723444-0.0839649472344411
1646.7156.702844565522510.0121554344774852
1656.8046.769313652342310.0346863476576876
1666.9296.862663032733150.0663369672668507
1676.8466.789118024252610.0568819757473924
1686.9926.768033412547330.223966587452667
1696.7746.712784133185330.0612158668146695
1706.756.495804012135040.254195987864962
1716.4856.48561667124068-0.000616671240680839
1726.276.168751682298150.101248317701848
1736.476.414119477387390.0558805226126121
1746.786.575712540873340.204287459126663
1756.716.525323490493920.18467650950608
1766.1416.62466524476812-0.483665244768117
1776.726.84543550540967-0.125435505409669
1786.686.76155828695488-0.0815582869548782
1796.3716.63363324235304-0.262633242353039
1806.0976.60898057977185-0.511980579771852
1816.276.8619793894734-0.591979389473398
1826.4476.82853178097044-0.381531780970437
1836.376.84254941550734-0.472549415507338
1846.4466.9069253944825-0.460925394482498
1856.546.90474870778562-0.364748707785624
1866.3746.80151285828384-0.427512858283844
1876.336.71753191723434-0.387531917234337
1886.636.75185173819268-0.121851738192675
1896.4986.89523446666748-0.397234466667484
1906.4856.85535620145688-0.370356201456883
1916.366.77863293293833-0.418632932938329







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.9939753000063880.01204939998722390.00602469999361193
100.9987364634613050.00252707307739020.0012635365386951
110.9975389300128460.004922139974308770.00246106998715439
120.9952158385924320.009568322815135540.00478416140756777
130.9924036794295990.01519264114080270.00759632057040135
140.9873447263265730.02531054734685370.0126552736734269
150.9842232044668330.03155359106633450.0157767955331673
160.9773172171349320.04536556573013560.0226827828650678
170.9916902387293130.01661952254137370.00830976127068683
180.9965609186502310.00687816269953730.00343908134976865
190.9962124768761840.007575046247632290.00378752312381614
200.9942139214611680.01157215707766350.00578607853883174
210.9912947087434860.01741058251302840.0087052912565142
220.9874233316040030.02515333679199390.012576668395997
230.981898458446420.03620308310715990.0181015415535799
240.975734433292260.04853113341547950.0242655667077398
250.9664706754647760.06705864907044850.0335293245352242
260.9562764957359560.0874470085280890.0437235042640445
270.9425100198818080.1149799602363830.0574899801181915
280.925447423223120.1491051535537610.0745525767768803
290.9063158063570820.1873683872858360.0936841936429178
300.8954921673026070.2090156653947860.104507832697393
310.8794217951144930.2411564097710130.120578204885507
320.854340598225040.2913188035499210.14565940177496
330.825819253992860.348361492014280.17418074600714
340.8054840739986030.3890318520027940.194515926001397
350.7873060094908740.4253879810182530.212693990509126
360.7836023107053150.4327953785893690.216397689294685
370.7482605111625460.5034789776749080.251739488837454
380.7080163860986490.5839672278027010.29198361390135
390.6614444627514070.6771110744971850.338555537248593
400.6219044031464580.7561911937070850.378095596853542
410.6544752158173480.6910495683653030.345524784182652
420.6273267969875830.7453464060248350.372673203012417
430.6032778203993040.7934443592013930.396722179600696
440.5738147522192850.852370495561430.426185247780715
450.5425183110101210.9149633779797590.457481688989879
460.4985693256720550.997138651344110.501430674327945
470.4580167189047450.916033437809490.541983281095255
480.4206777289235890.8413554578471780.579322271076411
490.7771442467680770.4457115064638460.222855753231923
500.7889916910881230.4220166178237550.211008308911878
510.7620383950709120.4759232098581770.237961604929089
520.7332459608731390.5335080782537230.266754039126861
530.7184500263735410.5630999472529180.281549973626459
540.6782729321996270.6434541356007470.321727067800373
550.6478709093145910.7042581813708180.352129090685409
560.6316137289097470.7367725421805070.368386271090253
570.5873818496264820.8252363007470360.412618150373518
580.5437632442139450.912473511572110.456236755786055
590.5019769011079050.996046197784190.498023098892095
600.4618008348259860.9236016696519720.538199165174014
610.4415752968281130.8831505936562260.558424703171887
620.4162671045851470.8325342091702930.583732895414853
630.3798640975710640.7597281951421290.620135902428936
640.3464281748883230.6928563497766460.653571825111677
650.3088869427484830.6177738854969660.691113057251517
660.2853894149721460.5707788299442910.714610585027854
670.2642185826777980.5284371653555970.735781417322201
680.2417764835271440.4835529670542890.758223516472856
690.220227446513290.440454893026580.77977255348671
700.1949400089684930.3898800179369850.805059991031507
710.1962909839228170.3925819678456340.803709016077183
720.1923188125463220.3846376250926440.807681187453678
730.1984013406089350.396802681217870.801598659391065
740.2278305403396610.4556610806793230.772169459660339
750.323013502437520.646027004875040.67698649756248
760.4645922169007650.929184433801530.535407783099235
770.6027259273123650.794548145375270.397274072687635
780.7622709006504880.4754581986990250.237729099349512
790.8676191614096710.2647616771806570.132380838590329
800.922892648644710.1542147027105810.0771073513552903
810.9489932457514920.1020135084970150.0510067542485076
820.9496465778060260.1007068443879470.0503534221939736
830.9513808212794730.09723835744105370.0486191787205269
840.9553258073092660.08934838538146810.044674192690734
850.9762182745599480.04756345088010430.0237817254400522
860.9829748773382520.03405024532349690.0170251226617484
870.9811638920968360.03767221580632730.0188361079031636
880.9804463433059810.03910731338803730.0195536566940186
890.9842110720578640.03157785588427190.015788927942136
900.9850863597729660.02982728045406890.0149136402270344
910.9887796342563620.02244073148727650.0112203657436383
920.9891612025221490.0216775949557030.0108387974778515
930.9914935130242880.01701297395142410.00850648697571207
940.993765613771510.01246877245698040.00623438622849019
950.9946962882781920.01060742344361630.00530371172180817
960.9932658759181880.01346824816362410.00673412408181207
970.991921976719050.01615604656189910.00807802328094955
980.9897863897025170.02042722059496630.0102136102974831
990.986598476789230.02680304642154080.0134015232107704
1000.9898325942622020.02033481147559590.010167405737798
1010.9930663187692030.01386736246159470.00693368123079735
1020.993156099579230.01368780084154020.00684390042077008
1030.9913604807106440.01727903857871120.00863951928935562
1040.988826898254590.02234620349082080.0111731017454104
1050.9853125144699040.0293749710601920.014687485530096
1060.982128174903090.035743650193820.01787182509691
1070.9838069656205780.03238606875884330.0161930343794216
1080.9878689209250210.02426215814995720.0121310790749786
1090.9901349696074220.01973006078515670.00986503039257833
1100.991564813120930.01687037375814090.00843518687907044
1110.9948770717539030.0102458564921930.00512292824609649
1120.9965868106408240.006826378718351680.00341318935917584
1130.9984397365756450.003120526848710160.00156026342435508
1140.9995389584894830.0009220830210347780.000461041510517389
1150.9998587814090.0002824371819997010.000141218590999851
1160.9999484347581150.0001031304837704225.15652418852111e-05
1170.9999787572130754.24855738491865e-052.12427869245932e-05
1180.9999856968153792.86063692412873e-051.43031846206437e-05
1190.9999905884832421.88230335153392e-059.41151675766961e-06
1200.9999885196258522.29607482965414e-051.14803741482707e-05
1210.9999857450792432.8509841514977e-051.42549207574885e-05
1220.9999804060964743.91878070514993e-051.95939035257497e-05
1230.9999804178878063.91642243874337e-051.95821121937169e-05
1240.9999889262588792.21474822424017e-051.10737411212009e-05
1250.9999863422492372.73155015254315e-051.36577507627157e-05
1260.9999863597324312.7280535137431e-051.36402675687155e-05
1270.9999820220709633.5955858074905e-051.79779290374525e-05
1280.9999872573789032.54852421942854e-051.27426210971427e-05
1290.9999861112323632.77775352747091e-051.38887676373545e-05
1300.9999929933443751.40133112501497e-057.00665562507485e-06
1310.9999942420446771.1515910646643e-055.7579553233215e-06
1320.9999959129003568.1741992872467e-064.08709964362335e-06
1330.9999987246270122.55074597550203e-061.27537298775101e-06
1340.9999992660997271.46780054500726e-067.33900272503631e-07
1350.9999999129080371.74183925907249e-078.70919629536244e-08
1360.9999999994971561.00568783646856e-095.02843918234282e-10
1370.9999999990169171.96616591357262e-099.83082956786311e-10
1380.9999999978674474.26510574520356e-092.13255287260178e-09
1390.999999997328345.34331932334644e-092.67165966167322e-09
1400.9999999989814612.03707879675603e-091.01853939837802e-09
1410.9999999976080574.78388573773706e-092.39194286886853e-09
1420.9999999961482687.70346384248096e-093.85173192124048e-09
1430.9999999915389231.6922153210568e-088.46107660528398e-09
1440.9999999935937591.28124821554985e-086.40624107774925e-09
1450.9999999984584283.08314335765712e-091.54157167882856e-09
1460.999999996511426.97716075910934e-093.48858037955467e-09
1470.9999999917633181.6473363602159e-088.23668180107948e-09
1480.9999999848244093.03511819674654e-081.51755909837327e-08
1490.9999999685490496.29019012785129e-083.14509506392564e-08
1500.9999999262898961.47420208962963e-077.37101044814813e-08
1510.9999998556828452.88634310956999e-071.44317155478499e-07
1520.9999999595595118.0880977970274e-084.0440488985137e-08
1530.999999976818324.63633603458418e-082.31816801729209e-08
1540.9999999753048854.93902297851023e-082.46951148925511e-08
1550.9999999862544942.7491012730475e-081.37455063652375e-08
1560.9999999642552667.14894680164446e-083.57447340082223e-08
1570.99999991047421.79051600431495e-078.95258002157474e-08
1580.9999998109561113.7808777819987e-071.89043889099935e-07
1590.9999996348269977.3034600615272e-073.6517300307636e-07
1600.9999997244177415.51164517421127e-072.75582258710563e-07
1610.9999992590175881.48196482479697e-067.40982412398486e-07
1620.9999994494076371.10118472548822e-065.50592362744109e-07
1630.9999986727150452.65456990922808e-061.32728495461404e-06
1640.999996703030416.59393918005965e-063.29696959002983e-06
1650.999991650969451.66980610999499e-058.34903054997494e-06
1660.9999842939475263.14121049488306e-051.57060524744153e-05
1670.9999630057425727.398851485519e-053.6994257427595e-05
1680.9999608529232337.82941535344209e-053.91470767672105e-05
1690.999914861735680.0001702765286398688.51382643199338e-05
1700.9999307004595320.0001385990809367476.92995404683734e-05
1710.9998264248471180.0003471503057638810.00017357515288194
1720.9995746028447080.0008507943105848230.000425397155292412
1730.9991048954829130.001790209034173690.000895104517086843
1740.9990551953517740.001889609296451050.000944804648225527
1750.9996452792807460.0007094414385082310.000354720719254115
1760.9998616090411630.0002767819176731230.000138390958836562
1770.9995360003261060.0009279993477874340.000463999673893717
1780.9985618139103890.002876372179222710.00143818608961136
1790.995156681563040.009686636873920030.00484331843696001
1800.9909178524280730.01816429514385320.00908214757192662
1810.9865251974823440.02694960503531230.0134748025176562
1820.9516984306204920.09660313875901530.0483015693795077

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.993975300006388 & 0.0120493999872239 & 0.00602469999361193 \tabularnewline
10 & 0.998736463461305 & 0.0025270730773902 & 0.0012635365386951 \tabularnewline
11 & 0.997538930012846 & 0.00492213997430877 & 0.00246106998715439 \tabularnewline
12 & 0.995215838592432 & 0.00956832281513554 & 0.00478416140756777 \tabularnewline
13 & 0.992403679429599 & 0.0151926411408027 & 0.00759632057040135 \tabularnewline
14 & 0.987344726326573 & 0.0253105473468537 & 0.0126552736734269 \tabularnewline
15 & 0.984223204466833 & 0.0315535910663345 & 0.0157767955331673 \tabularnewline
16 & 0.977317217134932 & 0.0453655657301356 & 0.0226827828650678 \tabularnewline
17 & 0.991690238729313 & 0.0166195225413737 & 0.00830976127068683 \tabularnewline
18 & 0.996560918650231 & 0.0068781626995373 & 0.00343908134976865 \tabularnewline
19 & 0.996212476876184 & 0.00757504624763229 & 0.00378752312381614 \tabularnewline
20 & 0.994213921461168 & 0.0115721570776635 & 0.00578607853883174 \tabularnewline
21 & 0.991294708743486 & 0.0174105825130284 & 0.0087052912565142 \tabularnewline
22 & 0.987423331604003 & 0.0251533367919939 & 0.012576668395997 \tabularnewline
23 & 0.98189845844642 & 0.0362030831071599 & 0.0181015415535799 \tabularnewline
24 & 0.97573443329226 & 0.0485311334154795 & 0.0242655667077398 \tabularnewline
25 & 0.966470675464776 & 0.0670586490704485 & 0.0335293245352242 \tabularnewline
26 & 0.956276495735956 & 0.087447008528089 & 0.0437235042640445 \tabularnewline
27 & 0.942510019881808 & 0.114979960236383 & 0.0574899801181915 \tabularnewline
28 & 0.92544742322312 & 0.149105153553761 & 0.0745525767768803 \tabularnewline
29 & 0.906315806357082 & 0.187368387285836 & 0.0936841936429178 \tabularnewline
30 & 0.895492167302607 & 0.209015665394786 & 0.104507832697393 \tabularnewline
31 & 0.879421795114493 & 0.241156409771013 & 0.120578204885507 \tabularnewline
32 & 0.85434059822504 & 0.291318803549921 & 0.14565940177496 \tabularnewline
33 & 0.82581925399286 & 0.34836149201428 & 0.17418074600714 \tabularnewline
34 & 0.805484073998603 & 0.389031852002794 & 0.194515926001397 \tabularnewline
35 & 0.787306009490874 & 0.425387981018253 & 0.212693990509126 \tabularnewline
36 & 0.783602310705315 & 0.432795378589369 & 0.216397689294685 \tabularnewline
37 & 0.748260511162546 & 0.503478977674908 & 0.251739488837454 \tabularnewline
38 & 0.708016386098649 & 0.583967227802701 & 0.29198361390135 \tabularnewline
39 & 0.661444462751407 & 0.677111074497185 & 0.338555537248593 \tabularnewline
40 & 0.621904403146458 & 0.756191193707085 & 0.378095596853542 \tabularnewline
41 & 0.654475215817348 & 0.691049568365303 & 0.345524784182652 \tabularnewline
42 & 0.627326796987583 & 0.745346406024835 & 0.372673203012417 \tabularnewline
43 & 0.603277820399304 & 0.793444359201393 & 0.396722179600696 \tabularnewline
44 & 0.573814752219285 & 0.85237049556143 & 0.426185247780715 \tabularnewline
45 & 0.542518311010121 & 0.914963377979759 & 0.457481688989879 \tabularnewline
46 & 0.498569325672055 & 0.99713865134411 & 0.501430674327945 \tabularnewline
47 & 0.458016718904745 & 0.91603343780949 & 0.541983281095255 \tabularnewline
48 & 0.420677728923589 & 0.841355457847178 & 0.579322271076411 \tabularnewline
49 & 0.777144246768077 & 0.445711506463846 & 0.222855753231923 \tabularnewline
50 & 0.788991691088123 & 0.422016617823755 & 0.211008308911878 \tabularnewline
51 & 0.762038395070912 & 0.475923209858177 & 0.237961604929089 \tabularnewline
52 & 0.733245960873139 & 0.533508078253723 & 0.266754039126861 \tabularnewline
53 & 0.718450026373541 & 0.563099947252918 & 0.281549973626459 \tabularnewline
54 & 0.678272932199627 & 0.643454135600747 & 0.321727067800373 \tabularnewline
55 & 0.647870909314591 & 0.704258181370818 & 0.352129090685409 \tabularnewline
56 & 0.631613728909747 & 0.736772542180507 & 0.368386271090253 \tabularnewline
57 & 0.587381849626482 & 0.825236300747036 & 0.412618150373518 \tabularnewline
58 & 0.543763244213945 & 0.91247351157211 & 0.456236755786055 \tabularnewline
59 & 0.501976901107905 & 0.99604619778419 & 0.498023098892095 \tabularnewline
60 & 0.461800834825986 & 0.923601669651972 & 0.538199165174014 \tabularnewline
61 & 0.441575296828113 & 0.883150593656226 & 0.558424703171887 \tabularnewline
62 & 0.416267104585147 & 0.832534209170293 & 0.583732895414853 \tabularnewline
63 & 0.379864097571064 & 0.759728195142129 & 0.620135902428936 \tabularnewline
64 & 0.346428174888323 & 0.692856349776646 & 0.653571825111677 \tabularnewline
65 & 0.308886942748483 & 0.617773885496966 & 0.691113057251517 \tabularnewline
66 & 0.285389414972146 & 0.570778829944291 & 0.714610585027854 \tabularnewline
67 & 0.264218582677798 & 0.528437165355597 & 0.735781417322201 \tabularnewline
68 & 0.241776483527144 & 0.483552967054289 & 0.758223516472856 \tabularnewline
69 & 0.22022744651329 & 0.44045489302658 & 0.77977255348671 \tabularnewline
70 & 0.194940008968493 & 0.389880017936985 & 0.805059991031507 \tabularnewline
71 & 0.196290983922817 & 0.392581967845634 & 0.803709016077183 \tabularnewline
72 & 0.192318812546322 & 0.384637625092644 & 0.807681187453678 \tabularnewline
73 & 0.198401340608935 & 0.39680268121787 & 0.801598659391065 \tabularnewline
74 & 0.227830540339661 & 0.455661080679323 & 0.772169459660339 \tabularnewline
75 & 0.32301350243752 & 0.64602700487504 & 0.67698649756248 \tabularnewline
76 & 0.464592216900765 & 0.92918443380153 & 0.535407783099235 \tabularnewline
77 & 0.602725927312365 & 0.79454814537527 & 0.397274072687635 \tabularnewline
78 & 0.762270900650488 & 0.475458198699025 & 0.237729099349512 \tabularnewline
79 & 0.867619161409671 & 0.264761677180657 & 0.132380838590329 \tabularnewline
80 & 0.92289264864471 & 0.154214702710581 & 0.0771073513552903 \tabularnewline
81 & 0.948993245751492 & 0.102013508497015 & 0.0510067542485076 \tabularnewline
82 & 0.949646577806026 & 0.100706844387947 & 0.0503534221939736 \tabularnewline
83 & 0.951380821279473 & 0.0972383574410537 & 0.0486191787205269 \tabularnewline
84 & 0.955325807309266 & 0.0893483853814681 & 0.044674192690734 \tabularnewline
85 & 0.976218274559948 & 0.0475634508801043 & 0.0237817254400522 \tabularnewline
86 & 0.982974877338252 & 0.0340502453234969 & 0.0170251226617484 \tabularnewline
87 & 0.981163892096836 & 0.0376722158063273 & 0.0188361079031636 \tabularnewline
88 & 0.980446343305981 & 0.0391073133880373 & 0.0195536566940186 \tabularnewline
89 & 0.984211072057864 & 0.0315778558842719 & 0.015788927942136 \tabularnewline
90 & 0.985086359772966 & 0.0298272804540689 & 0.0149136402270344 \tabularnewline
91 & 0.988779634256362 & 0.0224407314872765 & 0.0112203657436383 \tabularnewline
92 & 0.989161202522149 & 0.021677594955703 & 0.0108387974778515 \tabularnewline
93 & 0.991493513024288 & 0.0170129739514241 & 0.00850648697571207 \tabularnewline
94 & 0.99376561377151 & 0.0124687724569804 & 0.00623438622849019 \tabularnewline
95 & 0.994696288278192 & 0.0106074234436163 & 0.00530371172180817 \tabularnewline
96 & 0.993265875918188 & 0.0134682481636241 & 0.00673412408181207 \tabularnewline
97 & 0.99192197671905 & 0.0161560465618991 & 0.00807802328094955 \tabularnewline
98 & 0.989786389702517 & 0.0204272205949663 & 0.0102136102974831 \tabularnewline
99 & 0.98659847678923 & 0.0268030464215408 & 0.0134015232107704 \tabularnewline
100 & 0.989832594262202 & 0.0203348114755959 & 0.010167405737798 \tabularnewline
101 & 0.993066318769203 & 0.0138673624615947 & 0.00693368123079735 \tabularnewline
102 & 0.99315609957923 & 0.0136878008415402 & 0.00684390042077008 \tabularnewline
103 & 0.991360480710644 & 0.0172790385787112 & 0.00863951928935562 \tabularnewline
104 & 0.98882689825459 & 0.0223462034908208 & 0.0111731017454104 \tabularnewline
105 & 0.985312514469904 & 0.029374971060192 & 0.014687485530096 \tabularnewline
106 & 0.98212817490309 & 0.03574365019382 & 0.01787182509691 \tabularnewline
107 & 0.983806965620578 & 0.0323860687588433 & 0.0161930343794216 \tabularnewline
108 & 0.987868920925021 & 0.0242621581499572 & 0.0121310790749786 \tabularnewline
109 & 0.990134969607422 & 0.0197300607851567 & 0.00986503039257833 \tabularnewline
110 & 0.99156481312093 & 0.0168703737581409 & 0.00843518687907044 \tabularnewline
111 & 0.994877071753903 & 0.010245856492193 & 0.00512292824609649 \tabularnewline
112 & 0.996586810640824 & 0.00682637871835168 & 0.00341318935917584 \tabularnewline
113 & 0.998439736575645 & 0.00312052684871016 & 0.00156026342435508 \tabularnewline
114 & 0.999538958489483 & 0.000922083021034778 & 0.000461041510517389 \tabularnewline
115 & 0.999858781409 & 0.000282437181999701 & 0.000141218590999851 \tabularnewline
116 & 0.999948434758115 & 0.000103130483770422 & 5.15652418852111e-05 \tabularnewline
117 & 0.999978757213075 & 4.24855738491865e-05 & 2.12427869245932e-05 \tabularnewline
118 & 0.999985696815379 & 2.86063692412873e-05 & 1.43031846206437e-05 \tabularnewline
119 & 0.999990588483242 & 1.88230335153392e-05 & 9.41151675766961e-06 \tabularnewline
120 & 0.999988519625852 & 2.29607482965414e-05 & 1.14803741482707e-05 \tabularnewline
121 & 0.999985745079243 & 2.8509841514977e-05 & 1.42549207574885e-05 \tabularnewline
122 & 0.999980406096474 & 3.91878070514993e-05 & 1.95939035257497e-05 \tabularnewline
123 & 0.999980417887806 & 3.91642243874337e-05 & 1.95821121937169e-05 \tabularnewline
124 & 0.999988926258879 & 2.21474822424017e-05 & 1.10737411212009e-05 \tabularnewline
125 & 0.999986342249237 & 2.73155015254315e-05 & 1.36577507627157e-05 \tabularnewline
126 & 0.999986359732431 & 2.7280535137431e-05 & 1.36402675687155e-05 \tabularnewline
127 & 0.999982022070963 & 3.5955858074905e-05 & 1.79779290374525e-05 \tabularnewline
128 & 0.999987257378903 & 2.54852421942854e-05 & 1.27426210971427e-05 \tabularnewline
129 & 0.999986111232363 & 2.77775352747091e-05 & 1.38887676373545e-05 \tabularnewline
130 & 0.999992993344375 & 1.40133112501497e-05 & 7.00665562507485e-06 \tabularnewline
131 & 0.999994242044677 & 1.1515910646643e-05 & 5.7579553233215e-06 \tabularnewline
132 & 0.999995912900356 & 8.1741992872467e-06 & 4.08709964362335e-06 \tabularnewline
133 & 0.999998724627012 & 2.55074597550203e-06 & 1.27537298775101e-06 \tabularnewline
134 & 0.999999266099727 & 1.46780054500726e-06 & 7.33900272503631e-07 \tabularnewline
135 & 0.999999912908037 & 1.74183925907249e-07 & 8.70919629536244e-08 \tabularnewline
136 & 0.999999999497156 & 1.00568783646856e-09 & 5.02843918234282e-10 \tabularnewline
137 & 0.999999999016917 & 1.96616591357262e-09 & 9.83082956786311e-10 \tabularnewline
138 & 0.999999997867447 & 4.26510574520356e-09 & 2.13255287260178e-09 \tabularnewline
139 & 0.99999999732834 & 5.34331932334644e-09 & 2.67165966167322e-09 \tabularnewline
140 & 0.999999998981461 & 2.03707879675603e-09 & 1.01853939837802e-09 \tabularnewline
141 & 0.999999997608057 & 4.78388573773706e-09 & 2.39194286886853e-09 \tabularnewline
142 & 0.999999996148268 & 7.70346384248096e-09 & 3.85173192124048e-09 \tabularnewline
143 & 0.999999991538923 & 1.6922153210568e-08 & 8.46107660528398e-09 \tabularnewline
144 & 0.999999993593759 & 1.28124821554985e-08 & 6.40624107774925e-09 \tabularnewline
145 & 0.999999998458428 & 3.08314335765712e-09 & 1.54157167882856e-09 \tabularnewline
146 & 0.99999999651142 & 6.97716075910934e-09 & 3.48858037955467e-09 \tabularnewline
147 & 0.999999991763318 & 1.6473363602159e-08 & 8.23668180107948e-09 \tabularnewline
148 & 0.999999984824409 & 3.03511819674654e-08 & 1.51755909837327e-08 \tabularnewline
149 & 0.999999968549049 & 6.29019012785129e-08 & 3.14509506392564e-08 \tabularnewline
150 & 0.999999926289896 & 1.47420208962963e-07 & 7.37101044814813e-08 \tabularnewline
151 & 0.999999855682845 & 2.88634310956999e-07 & 1.44317155478499e-07 \tabularnewline
152 & 0.999999959559511 & 8.0880977970274e-08 & 4.0440488985137e-08 \tabularnewline
153 & 0.99999997681832 & 4.63633603458418e-08 & 2.31816801729209e-08 \tabularnewline
154 & 0.999999975304885 & 4.93902297851023e-08 & 2.46951148925511e-08 \tabularnewline
155 & 0.999999986254494 & 2.7491012730475e-08 & 1.37455063652375e-08 \tabularnewline
156 & 0.999999964255266 & 7.14894680164446e-08 & 3.57447340082223e-08 \tabularnewline
157 & 0.9999999104742 & 1.79051600431495e-07 & 8.95258002157474e-08 \tabularnewline
158 & 0.999999810956111 & 3.7808777819987e-07 & 1.89043889099935e-07 \tabularnewline
159 & 0.999999634826997 & 7.3034600615272e-07 & 3.6517300307636e-07 \tabularnewline
160 & 0.999999724417741 & 5.51164517421127e-07 & 2.75582258710563e-07 \tabularnewline
161 & 0.999999259017588 & 1.48196482479697e-06 & 7.40982412398486e-07 \tabularnewline
162 & 0.999999449407637 & 1.10118472548822e-06 & 5.50592362744109e-07 \tabularnewline
163 & 0.999998672715045 & 2.65456990922808e-06 & 1.32728495461404e-06 \tabularnewline
164 & 0.99999670303041 & 6.59393918005965e-06 & 3.29696959002983e-06 \tabularnewline
165 & 0.99999165096945 & 1.66980610999499e-05 & 8.34903054997494e-06 \tabularnewline
166 & 0.999984293947526 & 3.14121049488306e-05 & 1.57060524744153e-05 \tabularnewline
167 & 0.999963005742572 & 7.398851485519e-05 & 3.6994257427595e-05 \tabularnewline
168 & 0.999960852923233 & 7.82941535344209e-05 & 3.91470767672105e-05 \tabularnewline
169 & 0.99991486173568 & 0.000170276528639868 & 8.51382643199338e-05 \tabularnewline
170 & 0.999930700459532 & 0.000138599080936747 & 6.92995404683734e-05 \tabularnewline
171 & 0.999826424847118 & 0.000347150305763881 & 0.00017357515288194 \tabularnewline
172 & 0.999574602844708 & 0.000850794310584823 & 0.000425397155292412 \tabularnewline
173 & 0.999104895482913 & 0.00179020903417369 & 0.000895104517086843 \tabularnewline
174 & 0.999055195351774 & 0.00188960929645105 & 0.000944804648225527 \tabularnewline
175 & 0.999645279280746 & 0.000709441438508231 & 0.000354720719254115 \tabularnewline
176 & 0.999861609041163 & 0.000276781917673123 & 0.000138390958836562 \tabularnewline
177 & 0.999536000326106 & 0.000927999347787434 & 0.000463999673893717 \tabularnewline
178 & 0.998561813910389 & 0.00287637217922271 & 0.00143818608961136 \tabularnewline
179 & 0.99515668156304 & 0.00968663687392003 & 0.00484331843696001 \tabularnewline
180 & 0.990917852428073 & 0.0181642951438532 & 0.00908214757192662 \tabularnewline
181 & 0.986525197482344 & 0.0269496050353123 & 0.0134748025176562 \tabularnewline
182 & 0.951698430620492 & 0.0966031387590153 & 0.0483015693795077 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145492&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]9[/C][C]0.993975300006388[/C][C]0.0120493999872239[/C][C]0.00602469999361193[/C][/ROW]
[ROW][C]10[/C][C]0.998736463461305[/C][C]0.0025270730773902[/C][C]0.0012635365386951[/C][/ROW]
[ROW][C]11[/C][C]0.997538930012846[/C][C]0.00492213997430877[/C][C]0.00246106998715439[/C][/ROW]
[ROW][C]12[/C][C]0.995215838592432[/C][C]0.00956832281513554[/C][C]0.00478416140756777[/C][/ROW]
[ROW][C]13[/C][C]0.992403679429599[/C][C]0.0151926411408027[/C][C]0.00759632057040135[/C][/ROW]
[ROW][C]14[/C][C]0.987344726326573[/C][C]0.0253105473468537[/C][C]0.0126552736734269[/C][/ROW]
[ROW][C]15[/C][C]0.984223204466833[/C][C]0.0315535910663345[/C][C]0.0157767955331673[/C][/ROW]
[ROW][C]16[/C][C]0.977317217134932[/C][C]0.0453655657301356[/C][C]0.0226827828650678[/C][/ROW]
[ROW][C]17[/C][C]0.991690238729313[/C][C]0.0166195225413737[/C][C]0.00830976127068683[/C][/ROW]
[ROW][C]18[/C][C]0.996560918650231[/C][C]0.0068781626995373[/C][C]0.00343908134976865[/C][/ROW]
[ROW][C]19[/C][C]0.996212476876184[/C][C]0.00757504624763229[/C][C]0.00378752312381614[/C][/ROW]
[ROW][C]20[/C][C]0.994213921461168[/C][C]0.0115721570776635[/C][C]0.00578607853883174[/C][/ROW]
[ROW][C]21[/C][C]0.991294708743486[/C][C]0.0174105825130284[/C][C]0.0087052912565142[/C][/ROW]
[ROW][C]22[/C][C]0.987423331604003[/C][C]0.0251533367919939[/C][C]0.012576668395997[/C][/ROW]
[ROW][C]23[/C][C]0.98189845844642[/C][C]0.0362030831071599[/C][C]0.0181015415535799[/C][/ROW]
[ROW][C]24[/C][C]0.97573443329226[/C][C]0.0485311334154795[/C][C]0.0242655667077398[/C][/ROW]
[ROW][C]25[/C][C]0.966470675464776[/C][C]0.0670586490704485[/C][C]0.0335293245352242[/C][/ROW]
[ROW][C]26[/C][C]0.956276495735956[/C][C]0.087447008528089[/C][C]0.0437235042640445[/C][/ROW]
[ROW][C]27[/C][C]0.942510019881808[/C][C]0.114979960236383[/C][C]0.0574899801181915[/C][/ROW]
[ROW][C]28[/C][C]0.92544742322312[/C][C]0.149105153553761[/C][C]0.0745525767768803[/C][/ROW]
[ROW][C]29[/C][C]0.906315806357082[/C][C]0.187368387285836[/C][C]0.0936841936429178[/C][/ROW]
[ROW][C]30[/C][C]0.895492167302607[/C][C]0.209015665394786[/C][C]0.104507832697393[/C][/ROW]
[ROW][C]31[/C][C]0.879421795114493[/C][C]0.241156409771013[/C][C]0.120578204885507[/C][/ROW]
[ROW][C]32[/C][C]0.85434059822504[/C][C]0.291318803549921[/C][C]0.14565940177496[/C][/ROW]
[ROW][C]33[/C][C]0.82581925399286[/C][C]0.34836149201428[/C][C]0.17418074600714[/C][/ROW]
[ROW][C]34[/C][C]0.805484073998603[/C][C]0.389031852002794[/C][C]0.194515926001397[/C][/ROW]
[ROW][C]35[/C][C]0.787306009490874[/C][C]0.425387981018253[/C][C]0.212693990509126[/C][/ROW]
[ROW][C]36[/C][C]0.783602310705315[/C][C]0.432795378589369[/C][C]0.216397689294685[/C][/ROW]
[ROW][C]37[/C][C]0.748260511162546[/C][C]0.503478977674908[/C][C]0.251739488837454[/C][/ROW]
[ROW][C]38[/C][C]0.708016386098649[/C][C]0.583967227802701[/C][C]0.29198361390135[/C][/ROW]
[ROW][C]39[/C][C]0.661444462751407[/C][C]0.677111074497185[/C][C]0.338555537248593[/C][/ROW]
[ROW][C]40[/C][C]0.621904403146458[/C][C]0.756191193707085[/C][C]0.378095596853542[/C][/ROW]
[ROW][C]41[/C][C]0.654475215817348[/C][C]0.691049568365303[/C][C]0.345524784182652[/C][/ROW]
[ROW][C]42[/C][C]0.627326796987583[/C][C]0.745346406024835[/C][C]0.372673203012417[/C][/ROW]
[ROW][C]43[/C][C]0.603277820399304[/C][C]0.793444359201393[/C][C]0.396722179600696[/C][/ROW]
[ROW][C]44[/C][C]0.573814752219285[/C][C]0.85237049556143[/C][C]0.426185247780715[/C][/ROW]
[ROW][C]45[/C][C]0.542518311010121[/C][C]0.914963377979759[/C][C]0.457481688989879[/C][/ROW]
[ROW][C]46[/C][C]0.498569325672055[/C][C]0.99713865134411[/C][C]0.501430674327945[/C][/ROW]
[ROW][C]47[/C][C]0.458016718904745[/C][C]0.91603343780949[/C][C]0.541983281095255[/C][/ROW]
[ROW][C]48[/C][C]0.420677728923589[/C][C]0.841355457847178[/C][C]0.579322271076411[/C][/ROW]
[ROW][C]49[/C][C]0.777144246768077[/C][C]0.445711506463846[/C][C]0.222855753231923[/C][/ROW]
[ROW][C]50[/C][C]0.788991691088123[/C][C]0.422016617823755[/C][C]0.211008308911878[/C][/ROW]
[ROW][C]51[/C][C]0.762038395070912[/C][C]0.475923209858177[/C][C]0.237961604929089[/C][/ROW]
[ROW][C]52[/C][C]0.733245960873139[/C][C]0.533508078253723[/C][C]0.266754039126861[/C][/ROW]
[ROW][C]53[/C][C]0.718450026373541[/C][C]0.563099947252918[/C][C]0.281549973626459[/C][/ROW]
[ROW][C]54[/C][C]0.678272932199627[/C][C]0.643454135600747[/C][C]0.321727067800373[/C][/ROW]
[ROW][C]55[/C][C]0.647870909314591[/C][C]0.704258181370818[/C][C]0.352129090685409[/C][/ROW]
[ROW][C]56[/C][C]0.631613728909747[/C][C]0.736772542180507[/C][C]0.368386271090253[/C][/ROW]
[ROW][C]57[/C][C]0.587381849626482[/C][C]0.825236300747036[/C][C]0.412618150373518[/C][/ROW]
[ROW][C]58[/C][C]0.543763244213945[/C][C]0.91247351157211[/C][C]0.456236755786055[/C][/ROW]
[ROW][C]59[/C][C]0.501976901107905[/C][C]0.99604619778419[/C][C]0.498023098892095[/C][/ROW]
[ROW][C]60[/C][C]0.461800834825986[/C][C]0.923601669651972[/C][C]0.538199165174014[/C][/ROW]
[ROW][C]61[/C][C]0.441575296828113[/C][C]0.883150593656226[/C][C]0.558424703171887[/C][/ROW]
[ROW][C]62[/C][C]0.416267104585147[/C][C]0.832534209170293[/C][C]0.583732895414853[/C][/ROW]
[ROW][C]63[/C][C]0.379864097571064[/C][C]0.759728195142129[/C][C]0.620135902428936[/C][/ROW]
[ROW][C]64[/C][C]0.346428174888323[/C][C]0.692856349776646[/C][C]0.653571825111677[/C][/ROW]
[ROW][C]65[/C][C]0.308886942748483[/C][C]0.617773885496966[/C][C]0.691113057251517[/C][/ROW]
[ROW][C]66[/C][C]0.285389414972146[/C][C]0.570778829944291[/C][C]0.714610585027854[/C][/ROW]
[ROW][C]67[/C][C]0.264218582677798[/C][C]0.528437165355597[/C][C]0.735781417322201[/C][/ROW]
[ROW][C]68[/C][C]0.241776483527144[/C][C]0.483552967054289[/C][C]0.758223516472856[/C][/ROW]
[ROW][C]69[/C][C]0.22022744651329[/C][C]0.44045489302658[/C][C]0.77977255348671[/C][/ROW]
[ROW][C]70[/C][C]0.194940008968493[/C][C]0.389880017936985[/C][C]0.805059991031507[/C][/ROW]
[ROW][C]71[/C][C]0.196290983922817[/C][C]0.392581967845634[/C][C]0.803709016077183[/C][/ROW]
[ROW][C]72[/C][C]0.192318812546322[/C][C]0.384637625092644[/C][C]0.807681187453678[/C][/ROW]
[ROW][C]73[/C][C]0.198401340608935[/C][C]0.39680268121787[/C][C]0.801598659391065[/C][/ROW]
[ROW][C]74[/C][C]0.227830540339661[/C][C]0.455661080679323[/C][C]0.772169459660339[/C][/ROW]
[ROW][C]75[/C][C]0.32301350243752[/C][C]0.64602700487504[/C][C]0.67698649756248[/C][/ROW]
[ROW][C]76[/C][C]0.464592216900765[/C][C]0.92918443380153[/C][C]0.535407783099235[/C][/ROW]
[ROW][C]77[/C][C]0.602725927312365[/C][C]0.79454814537527[/C][C]0.397274072687635[/C][/ROW]
[ROW][C]78[/C][C]0.762270900650488[/C][C]0.475458198699025[/C][C]0.237729099349512[/C][/ROW]
[ROW][C]79[/C][C]0.867619161409671[/C][C]0.264761677180657[/C][C]0.132380838590329[/C][/ROW]
[ROW][C]80[/C][C]0.92289264864471[/C][C]0.154214702710581[/C][C]0.0771073513552903[/C][/ROW]
[ROW][C]81[/C][C]0.948993245751492[/C][C]0.102013508497015[/C][C]0.0510067542485076[/C][/ROW]
[ROW][C]82[/C][C]0.949646577806026[/C][C]0.100706844387947[/C][C]0.0503534221939736[/C][/ROW]
[ROW][C]83[/C][C]0.951380821279473[/C][C]0.0972383574410537[/C][C]0.0486191787205269[/C][/ROW]
[ROW][C]84[/C][C]0.955325807309266[/C][C]0.0893483853814681[/C][C]0.044674192690734[/C][/ROW]
[ROW][C]85[/C][C]0.976218274559948[/C][C]0.0475634508801043[/C][C]0.0237817254400522[/C][/ROW]
[ROW][C]86[/C][C]0.982974877338252[/C][C]0.0340502453234969[/C][C]0.0170251226617484[/C][/ROW]
[ROW][C]87[/C][C]0.981163892096836[/C][C]0.0376722158063273[/C][C]0.0188361079031636[/C][/ROW]
[ROW][C]88[/C][C]0.980446343305981[/C][C]0.0391073133880373[/C][C]0.0195536566940186[/C][/ROW]
[ROW][C]89[/C][C]0.984211072057864[/C][C]0.0315778558842719[/C][C]0.015788927942136[/C][/ROW]
[ROW][C]90[/C][C]0.985086359772966[/C][C]0.0298272804540689[/C][C]0.0149136402270344[/C][/ROW]
[ROW][C]91[/C][C]0.988779634256362[/C][C]0.0224407314872765[/C][C]0.0112203657436383[/C][/ROW]
[ROW][C]92[/C][C]0.989161202522149[/C][C]0.021677594955703[/C][C]0.0108387974778515[/C][/ROW]
[ROW][C]93[/C][C]0.991493513024288[/C][C]0.0170129739514241[/C][C]0.00850648697571207[/C][/ROW]
[ROW][C]94[/C][C]0.99376561377151[/C][C]0.0124687724569804[/C][C]0.00623438622849019[/C][/ROW]
[ROW][C]95[/C][C]0.994696288278192[/C][C]0.0106074234436163[/C][C]0.00530371172180817[/C][/ROW]
[ROW][C]96[/C][C]0.993265875918188[/C][C]0.0134682481636241[/C][C]0.00673412408181207[/C][/ROW]
[ROW][C]97[/C][C]0.99192197671905[/C][C]0.0161560465618991[/C][C]0.00807802328094955[/C][/ROW]
[ROW][C]98[/C][C]0.989786389702517[/C][C]0.0204272205949663[/C][C]0.0102136102974831[/C][/ROW]
[ROW][C]99[/C][C]0.98659847678923[/C][C]0.0268030464215408[/C][C]0.0134015232107704[/C][/ROW]
[ROW][C]100[/C][C]0.989832594262202[/C][C]0.0203348114755959[/C][C]0.010167405737798[/C][/ROW]
[ROW][C]101[/C][C]0.993066318769203[/C][C]0.0138673624615947[/C][C]0.00693368123079735[/C][/ROW]
[ROW][C]102[/C][C]0.99315609957923[/C][C]0.0136878008415402[/C][C]0.00684390042077008[/C][/ROW]
[ROW][C]103[/C][C]0.991360480710644[/C][C]0.0172790385787112[/C][C]0.00863951928935562[/C][/ROW]
[ROW][C]104[/C][C]0.98882689825459[/C][C]0.0223462034908208[/C][C]0.0111731017454104[/C][/ROW]
[ROW][C]105[/C][C]0.985312514469904[/C][C]0.029374971060192[/C][C]0.014687485530096[/C][/ROW]
[ROW][C]106[/C][C]0.98212817490309[/C][C]0.03574365019382[/C][C]0.01787182509691[/C][/ROW]
[ROW][C]107[/C][C]0.983806965620578[/C][C]0.0323860687588433[/C][C]0.0161930343794216[/C][/ROW]
[ROW][C]108[/C][C]0.987868920925021[/C][C]0.0242621581499572[/C][C]0.0121310790749786[/C][/ROW]
[ROW][C]109[/C][C]0.990134969607422[/C][C]0.0197300607851567[/C][C]0.00986503039257833[/C][/ROW]
[ROW][C]110[/C][C]0.99156481312093[/C][C]0.0168703737581409[/C][C]0.00843518687907044[/C][/ROW]
[ROW][C]111[/C][C]0.994877071753903[/C][C]0.010245856492193[/C][C]0.00512292824609649[/C][/ROW]
[ROW][C]112[/C][C]0.996586810640824[/C][C]0.00682637871835168[/C][C]0.00341318935917584[/C][/ROW]
[ROW][C]113[/C][C]0.998439736575645[/C][C]0.00312052684871016[/C][C]0.00156026342435508[/C][/ROW]
[ROW][C]114[/C][C]0.999538958489483[/C][C]0.000922083021034778[/C][C]0.000461041510517389[/C][/ROW]
[ROW][C]115[/C][C]0.999858781409[/C][C]0.000282437181999701[/C][C]0.000141218590999851[/C][/ROW]
[ROW][C]116[/C][C]0.999948434758115[/C][C]0.000103130483770422[/C][C]5.15652418852111e-05[/C][/ROW]
[ROW][C]117[/C][C]0.999978757213075[/C][C]4.24855738491865e-05[/C][C]2.12427869245932e-05[/C][/ROW]
[ROW][C]118[/C][C]0.999985696815379[/C][C]2.86063692412873e-05[/C][C]1.43031846206437e-05[/C][/ROW]
[ROW][C]119[/C][C]0.999990588483242[/C][C]1.88230335153392e-05[/C][C]9.41151675766961e-06[/C][/ROW]
[ROW][C]120[/C][C]0.999988519625852[/C][C]2.29607482965414e-05[/C][C]1.14803741482707e-05[/C][/ROW]
[ROW][C]121[/C][C]0.999985745079243[/C][C]2.8509841514977e-05[/C][C]1.42549207574885e-05[/C][/ROW]
[ROW][C]122[/C][C]0.999980406096474[/C][C]3.91878070514993e-05[/C][C]1.95939035257497e-05[/C][/ROW]
[ROW][C]123[/C][C]0.999980417887806[/C][C]3.91642243874337e-05[/C][C]1.95821121937169e-05[/C][/ROW]
[ROW][C]124[/C][C]0.999988926258879[/C][C]2.21474822424017e-05[/C][C]1.10737411212009e-05[/C][/ROW]
[ROW][C]125[/C][C]0.999986342249237[/C][C]2.73155015254315e-05[/C][C]1.36577507627157e-05[/C][/ROW]
[ROW][C]126[/C][C]0.999986359732431[/C][C]2.7280535137431e-05[/C][C]1.36402675687155e-05[/C][/ROW]
[ROW][C]127[/C][C]0.999982022070963[/C][C]3.5955858074905e-05[/C][C]1.79779290374525e-05[/C][/ROW]
[ROW][C]128[/C][C]0.999987257378903[/C][C]2.54852421942854e-05[/C][C]1.27426210971427e-05[/C][/ROW]
[ROW][C]129[/C][C]0.999986111232363[/C][C]2.77775352747091e-05[/C][C]1.38887676373545e-05[/C][/ROW]
[ROW][C]130[/C][C]0.999992993344375[/C][C]1.40133112501497e-05[/C][C]7.00665562507485e-06[/C][/ROW]
[ROW][C]131[/C][C]0.999994242044677[/C][C]1.1515910646643e-05[/C][C]5.7579553233215e-06[/C][/ROW]
[ROW][C]132[/C][C]0.999995912900356[/C][C]8.1741992872467e-06[/C][C]4.08709964362335e-06[/C][/ROW]
[ROW][C]133[/C][C]0.999998724627012[/C][C]2.55074597550203e-06[/C][C]1.27537298775101e-06[/C][/ROW]
[ROW][C]134[/C][C]0.999999266099727[/C][C]1.46780054500726e-06[/C][C]7.33900272503631e-07[/C][/ROW]
[ROW][C]135[/C][C]0.999999912908037[/C][C]1.74183925907249e-07[/C][C]8.70919629536244e-08[/C][/ROW]
[ROW][C]136[/C][C]0.999999999497156[/C][C]1.00568783646856e-09[/C][C]5.02843918234282e-10[/C][/ROW]
[ROW][C]137[/C][C]0.999999999016917[/C][C]1.96616591357262e-09[/C][C]9.83082956786311e-10[/C][/ROW]
[ROW][C]138[/C][C]0.999999997867447[/C][C]4.26510574520356e-09[/C][C]2.13255287260178e-09[/C][/ROW]
[ROW][C]139[/C][C]0.99999999732834[/C][C]5.34331932334644e-09[/C][C]2.67165966167322e-09[/C][/ROW]
[ROW][C]140[/C][C]0.999999998981461[/C][C]2.03707879675603e-09[/C][C]1.01853939837802e-09[/C][/ROW]
[ROW][C]141[/C][C]0.999999997608057[/C][C]4.78388573773706e-09[/C][C]2.39194286886853e-09[/C][/ROW]
[ROW][C]142[/C][C]0.999999996148268[/C][C]7.70346384248096e-09[/C][C]3.85173192124048e-09[/C][/ROW]
[ROW][C]143[/C][C]0.999999991538923[/C][C]1.6922153210568e-08[/C][C]8.46107660528398e-09[/C][/ROW]
[ROW][C]144[/C][C]0.999999993593759[/C][C]1.28124821554985e-08[/C][C]6.40624107774925e-09[/C][/ROW]
[ROW][C]145[/C][C]0.999999998458428[/C][C]3.08314335765712e-09[/C][C]1.54157167882856e-09[/C][/ROW]
[ROW][C]146[/C][C]0.99999999651142[/C][C]6.97716075910934e-09[/C][C]3.48858037955467e-09[/C][/ROW]
[ROW][C]147[/C][C]0.999999991763318[/C][C]1.6473363602159e-08[/C][C]8.23668180107948e-09[/C][/ROW]
[ROW][C]148[/C][C]0.999999984824409[/C][C]3.03511819674654e-08[/C][C]1.51755909837327e-08[/C][/ROW]
[ROW][C]149[/C][C]0.999999968549049[/C][C]6.29019012785129e-08[/C][C]3.14509506392564e-08[/C][/ROW]
[ROW][C]150[/C][C]0.999999926289896[/C][C]1.47420208962963e-07[/C][C]7.37101044814813e-08[/C][/ROW]
[ROW][C]151[/C][C]0.999999855682845[/C][C]2.88634310956999e-07[/C][C]1.44317155478499e-07[/C][/ROW]
[ROW][C]152[/C][C]0.999999959559511[/C][C]8.0880977970274e-08[/C][C]4.0440488985137e-08[/C][/ROW]
[ROW][C]153[/C][C]0.99999997681832[/C][C]4.63633603458418e-08[/C][C]2.31816801729209e-08[/C][/ROW]
[ROW][C]154[/C][C]0.999999975304885[/C][C]4.93902297851023e-08[/C][C]2.46951148925511e-08[/C][/ROW]
[ROW][C]155[/C][C]0.999999986254494[/C][C]2.7491012730475e-08[/C][C]1.37455063652375e-08[/C][/ROW]
[ROW][C]156[/C][C]0.999999964255266[/C][C]7.14894680164446e-08[/C][C]3.57447340082223e-08[/C][/ROW]
[ROW][C]157[/C][C]0.9999999104742[/C][C]1.79051600431495e-07[/C][C]8.95258002157474e-08[/C][/ROW]
[ROW][C]158[/C][C]0.999999810956111[/C][C]3.7808777819987e-07[/C][C]1.89043889099935e-07[/C][/ROW]
[ROW][C]159[/C][C]0.999999634826997[/C][C]7.3034600615272e-07[/C][C]3.6517300307636e-07[/C][/ROW]
[ROW][C]160[/C][C]0.999999724417741[/C][C]5.51164517421127e-07[/C][C]2.75582258710563e-07[/C][/ROW]
[ROW][C]161[/C][C]0.999999259017588[/C][C]1.48196482479697e-06[/C][C]7.40982412398486e-07[/C][/ROW]
[ROW][C]162[/C][C]0.999999449407637[/C][C]1.10118472548822e-06[/C][C]5.50592362744109e-07[/C][/ROW]
[ROW][C]163[/C][C]0.999998672715045[/C][C]2.65456990922808e-06[/C][C]1.32728495461404e-06[/C][/ROW]
[ROW][C]164[/C][C]0.99999670303041[/C][C]6.59393918005965e-06[/C][C]3.29696959002983e-06[/C][/ROW]
[ROW][C]165[/C][C]0.99999165096945[/C][C]1.66980610999499e-05[/C][C]8.34903054997494e-06[/C][/ROW]
[ROW][C]166[/C][C]0.999984293947526[/C][C]3.14121049488306e-05[/C][C]1.57060524744153e-05[/C][/ROW]
[ROW][C]167[/C][C]0.999963005742572[/C][C]7.398851485519e-05[/C][C]3.6994257427595e-05[/C][/ROW]
[ROW][C]168[/C][C]0.999960852923233[/C][C]7.82941535344209e-05[/C][C]3.91470767672105e-05[/C][/ROW]
[ROW][C]169[/C][C]0.99991486173568[/C][C]0.000170276528639868[/C][C]8.51382643199338e-05[/C][/ROW]
[ROW][C]170[/C][C]0.999930700459532[/C][C]0.000138599080936747[/C][C]6.92995404683734e-05[/C][/ROW]
[ROW][C]171[/C][C]0.999826424847118[/C][C]0.000347150305763881[/C][C]0.00017357515288194[/C][/ROW]
[ROW][C]172[/C][C]0.999574602844708[/C][C]0.000850794310584823[/C][C]0.000425397155292412[/C][/ROW]
[ROW][C]173[/C][C]0.999104895482913[/C][C]0.00179020903417369[/C][C]0.000895104517086843[/C][/ROW]
[ROW][C]174[/C][C]0.999055195351774[/C][C]0.00188960929645105[/C][C]0.000944804648225527[/C][/ROW]
[ROW][C]175[/C][C]0.999645279280746[/C][C]0.000709441438508231[/C][C]0.000354720719254115[/C][/ROW]
[ROW][C]176[/C][C]0.999861609041163[/C][C]0.000276781917673123[/C][C]0.000138390958836562[/C][/ROW]
[ROW][C]177[/C][C]0.999536000326106[/C][C]0.000927999347787434[/C][C]0.000463999673893717[/C][/ROW]
[ROW][C]178[/C][C]0.998561813910389[/C][C]0.00287637217922271[/C][C]0.00143818608961136[/C][/ROW]
[ROW][C]179[/C][C]0.99515668156304[/C][C]0.00968663687392003[/C][C]0.00484331843696001[/C][/ROW]
[ROW][C]180[/C][C]0.990917852428073[/C][C]0.0181642951438532[/C][C]0.00908214757192662[/C][/ROW]
[ROW][C]181[/C][C]0.986525197482344[/C][C]0.0269496050353123[/C][C]0.0134748025176562[/C][/ROW]
[ROW][C]182[/C][C]0.951698430620492[/C][C]0.0966031387590153[/C][C]0.0483015693795077[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145492&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145492&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
90.9939753000063880.01204939998722390.00602469999361193
100.9987364634613050.00252707307739020.0012635365386951
110.9975389300128460.004922139974308770.00246106998715439
120.9952158385924320.009568322815135540.00478416140756777
130.9924036794295990.01519264114080270.00759632057040135
140.9873447263265730.02531054734685370.0126552736734269
150.9842232044668330.03155359106633450.0157767955331673
160.9773172171349320.04536556573013560.0226827828650678
170.9916902387293130.01661952254137370.00830976127068683
180.9965609186502310.00687816269953730.00343908134976865
190.9962124768761840.007575046247632290.00378752312381614
200.9942139214611680.01157215707766350.00578607853883174
210.9912947087434860.01741058251302840.0087052912565142
220.9874233316040030.02515333679199390.012576668395997
230.981898458446420.03620308310715990.0181015415535799
240.975734433292260.04853113341547950.0242655667077398
250.9664706754647760.06705864907044850.0335293245352242
260.9562764957359560.0874470085280890.0437235042640445
270.9425100198818080.1149799602363830.0574899801181915
280.925447423223120.1491051535537610.0745525767768803
290.9063158063570820.1873683872858360.0936841936429178
300.8954921673026070.2090156653947860.104507832697393
310.8794217951144930.2411564097710130.120578204885507
320.854340598225040.2913188035499210.14565940177496
330.825819253992860.348361492014280.17418074600714
340.8054840739986030.3890318520027940.194515926001397
350.7873060094908740.4253879810182530.212693990509126
360.7836023107053150.4327953785893690.216397689294685
370.7482605111625460.5034789776749080.251739488837454
380.7080163860986490.5839672278027010.29198361390135
390.6614444627514070.6771110744971850.338555537248593
400.6219044031464580.7561911937070850.378095596853542
410.6544752158173480.6910495683653030.345524784182652
420.6273267969875830.7453464060248350.372673203012417
430.6032778203993040.7934443592013930.396722179600696
440.5738147522192850.852370495561430.426185247780715
450.5425183110101210.9149633779797590.457481688989879
460.4985693256720550.997138651344110.501430674327945
470.4580167189047450.916033437809490.541983281095255
480.4206777289235890.8413554578471780.579322271076411
490.7771442467680770.4457115064638460.222855753231923
500.7889916910881230.4220166178237550.211008308911878
510.7620383950709120.4759232098581770.237961604929089
520.7332459608731390.5335080782537230.266754039126861
530.7184500263735410.5630999472529180.281549973626459
540.6782729321996270.6434541356007470.321727067800373
550.6478709093145910.7042581813708180.352129090685409
560.6316137289097470.7367725421805070.368386271090253
570.5873818496264820.8252363007470360.412618150373518
580.5437632442139450.912473511572110.456236755786055
590.5019769011079050.996046197784190.498023098892095
600.4618008348259860.9236016696519720.538199165174014
610.4415752968281130.8831505936562260.558424703171887
620.4162671045851470.8325342091702930.583732895414853
630.3798640975710640.7597281951421290.620135902428936
640.3464281748883230.6928563497766460.653571825111677
650.3088869427484830.6177738854969660.691113057251517
660.2853894149721460.5707788299442910.714610585027854
670.2642185826777980.5284371653555970.735781417322201
680.2417764835271440.4835529670542890.758223516472856
690.220227446513290.440454893026580.77977255348671
700.1949400089684930.3898800179369850.805059991031507
710.1962909839228170.3925819678456340.803709016077183
720.1923188125463220.3846376250926440.807681187453678
730.1984013406089350.396802681217870.801598659391065
740.2278305403396610.4556610806793230.772169459660339
750.323013502437520.646027004875040.67698649756248
760.4645922169007650.929184433801530.535407783099235
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780.7622709006504880.4754581986990250.237729099349512
790.8676191614096710.2647616771806570.132380838590329
800.922892648644710.1542147027105810.0771073513552903
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870.9811638920968360.03767221580632730.0188361079031636
880.9804463433059810.03910731338803730.0195536566940186
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900.9850863597729660.02982728045406890.0149136402270344
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960.9932658759181880.01346824816362410.00673412408181207
970.991921976719050.01615604656189910.00807802328094955
980.9897863897025170.02042722059496630.0102136102974831
990.986598476789230.02680304642154080.0134015232107704
1000.9898325942622020.02033481147559590.010167405737798
1010.9930663187692030.01386736246159470.00693368123079735
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1030.9913604807106440.01727903857871120.00863951928935562
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1080.9878689209250210.02426215814995720.0121310790749786
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1100.991564813120930.01687037375814090.00843518687907044
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1120.9965868106408240.006826378718351680.00341318935917584
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1340.9999992660997271.46780054500726e-067.33900272503631e-07
1350.9999999129080371.74183925907249e-078.70919629536244e-08
1360.9999999994971561.00568783646856e-095.02843918234282e-10
1370.9999999990169171.96616591357262e-099.83082956786311e-10
1380.9999999978674474.26510574520356e-092.13255287260178e-09
1390.999999997328345.34331932334644e-092.67165966167322e-09
1400.9999999989814612.03707879675603e-091.01853939837802e-09
1410.9999999976080574.78388573773706e-092.39194286886853e-09
1420.9999999961482687.70346384248096e-093.85173192124048e-09
1430.9999999915389231.6922153210568e-088.46107660528398e-09
1440.9999999935937591.28124821554985e-086.40624107774925e-09
1450.9999999984584283.08314335765712e-091.54157167882856e-09
1460.999999996511426.97716075910934e-093.48858037955467e-09
1470.9999999917633181.6473363602159e-088.23668180107948e-09
1480.9999999848244093.03511819674654e-081.51755909837327e-08
1490.9999999685490496.29019012785129e-083.14509506392564e-08
1500.9999999262898961.47420208962963e-077.37101044814813e-08
1510.9999998556828452.88634310956999e-071.44317155478499e-07
1520.9999999595595118.0880977970274e-084.0440488985137e-08
1530.999999976818324.63633603458418e-082.31816801729209e-08
1540.9999999753048854.93902297851023e-082.46951148925511e-08
1550.9999999862544942.7491012730475e-081.37455063652375e-08
1560.9999999642552667.14894680164446e-083.57447340082223e-08
1570.99999991047421.79051600431495e-078.95258002157474e-08
1580.9999998109561113.7808777819987e-071.89043889099935e-07
1590.9999996348269977.3034600615272e-073.6517300307636e-07
1600.9999997244177415.51164517421127e-072.75582258710563e-07
1610.9999992590175881.48196482479697e-067.40982412398486e-07
1620.9999994494076371.10118472548822e-065.50592362744109e-07
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1640.999996703030416.59393918005965e-063.29696959002983e-06
1650.999991650969451.66980610999499e-058.34903054997494e-06
1660.9999842939475263.14121049488306e-051.57060524744153e-05
1670.9999630057425727.398851485519e-053.6994257427595e-05
1680.9999608529232337.82941535344209e-053.91470767672105e-05
1690.999914861735680.0001702765286398688.51382643199338e-05
1700.9999307004595320.0001385990809367476.92995404683734e-05
1710.9998264248471180.0003471503057638810.00017357515288194
1720.9995746028447080.0008507943105848230.000425397155292412
1730.9991048954829130.001790209034173690.000895104517086843
1740.9990551953517740.001889609296451050.000944804648225527
1750.9996452792807460.0007094414385082310.000354720719254115
1760.9998616090411630.0002767819176731230.000138390958836562
1770.9995360003261060.0009279993477874340.000463999673893717
1780.9985618139103890.002876372179222710.00143818608961136
1790.995156681563040.009686636873920030.00484331843696001
1800.9909178524280730.01816429514385320.00908214757192662
1810.9865251974823440.02694960503531230.0134748025176562
1820.9516984306204920.09660313875901530.0483015693795077







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level730.419540229885057NOK
5% type I error level1130.649425287356322NOK
10% type I error level1180.67816091954023NOK

\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 & 73 & 0.419540229885057 & NOK \tabularnewline
5% type I error level & 113 & 0.649425287356322 & NOK \tabularnewline
10% type I error level & 118 & 0.67816091954023 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145492&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]73[/C][C]0.419540229885057[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]113[/C][C]0.649425287356322[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]118[/C][C]0.67816091954023[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145492&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145492&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 level730.419540229885057NOK
5% type I error level1130.649425287356322NOK
10% type I error level1180.67816091954023NOK



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')
}