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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Tue, 22 Nov 2011 14:17:05 -0500

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Nov/22/t1321989525eks1ozdddbqm83p.htm/, Retrieved Thu, 25 Apr 2024 11:24:45 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=146379, Retrieved Thu, 25 Apr 2024 11:24:45 +0000

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IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

137

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] [25b6caf3839c2bdc14961e5bff2d6373]
- R PD      [Multiple Regression] [month season] [2011-11-22 19:17:05] [2adf2d2c11e011c12275478b9efd18e5] [Current]
- RMPD        [Structural Time Series Models] [WS8 - Structural ...] [2011-11-27 11:25:53] [16760482ab7535714acc81f7eb88a6ca] 

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Dataseries X:

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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 Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	7 seconds
R Server	'Herman Ole Andreas Wold' @ wold.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 & 7 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.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=146379&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]7 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.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=146379&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146379&T=0

As an alternative you can also use a QR Code:

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

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	7 seconds
R Server	'Herman Ole Andreas Wold' @ wold.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] = + 0.470311002831475 + 0.00171388893656082`Zink-prijs`[t] + 0.00141340764409491`Lood-prijs`[t] + 0.128734027006698`NYSE-eindkoers-vorige-dag`[t] -2.50551896362825`Rente-op-LT-leningen-in-%`[t] -0.0458395501776176`Conjunctuurenquete `[t] + 0.0981553451240848M1[t] + 0.160386902535726M2[t] + 0.0669640934359867M3[t] + 0.1296844837058M4[t] -0.031283661505102M5[t] -0.000687812078578053M6[t] -0.088556222969061M7[t] -0.170235352832043M8[t] -0.103138880917442M9[t] -0.0433015164244993M10[t] + 0.0406707117805078M11[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
koers-nyrstar[t] =  +  0.470311002831475 +  0.00171388893656082`Zink-prijs`[t] +  0.00141340764409491`Lood-prijs`[t] +  0.128734027006698`NYSE-eindkoers-vorige-dag`[t] -2.50551896362825`Rente-op-LT-leningen-in-%`[t] -0.0458395501776176`Conjunctuurenquete
`[t] +  0.0981553451240848M1[t] +  0.160386902535726M2[t] +  0.0669640934359867M3[t] +  0.1296844837058M4[t] -0.031283661505102M5[t] -0.000687812078578053M6[t] -0.088556222969061M7[t] -0.170235352832043M8[t] -0.103138880917442M9[t] -0.0433015164244993M10[t] +  0.0406707117805078M11[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146379&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]koers-nyrstar[t] =  +  0.470311002831475 +  0.00171388893656082`Zink-prijs`[t] +  0.00141340764409491`Lood-prijs`[t] +  0.128734027006698`NYSE-eindkoers-vorige-dag`[t] -2.50551896362825`Rente-op-LT-leningen-in-%`[t] -0.0458395501776176`Conjunctuurenquete
`[t] +  0.0981553451240848M1[t] +  0.160386902535726M2[t] +  0.0669640934359867M3[t] +  0.1296844837058M4[t] -0.031283661505102M5[t] -0.000687812078578053M6[t] -0.088556222969061M7[t] -0.170235352832043M8[t] -0.103138880917442M9[t] -0.0433015164244993M10[t] +  0.0406707117805078M11[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146379&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146379&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] = + 0.470311002831475 + 0.00171388893656082`Zink-prijs`[t] + 0.00141340764409491`Lood-prijs`[t] + 0.128734027006698`NYSE-eindkoers-vorige-dag`[t] -2.50551896362825`Rente-op-LT-leningen-in-%`[t] -0.0458395501776176`Conjunctuurenquete `[t] + 0.0981553451240848M1[t] + 0.160386902535726M2[t] + 0.0669640934359867M3[t] + 0.1296844837058M4[t] -0.031283661505102M5[t] -0.000687812078578053M6[t] -0.088556222969061M7[t] -0.170235352832043M8[t] -0.103138880917442M9[t] -0.0433015164244993M10[t] + 0.0406707117805078M11[t] + e[t]

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	0.470311002831475	0.80986	0.5807	0.562173	0.281087
`Zink-prijs`	0.00171388893656082	0.000546	3.1377	0.002	0.001
`Lood-prijs`	0.00141340764409491	0.000415	3.4097	0.000808	0.000404
`NYSE-eindkoers-vorige-dag`	0.128734027006698	0.013144	9.7942	0	0
`Rente-op-LT-leningen-in-%`	-2.50551896362825	0.325576	-7.6956	0	0
`Conjunctuurenquete `	-0.0458395501776176	0.017066	-2.686	0.007932	0.003966
M1	0.0981553451240848	0.160938	0.6099	0.542726	0.271363
M2	0.160386902535726	0.160853	0.9971	0.320099	0.160049
M3	0.0669640934359867	0.160863	0.4163	0.677719	0.338859
M4	0.1296844837058	0.160928	0.8059	0.421427	0.210713
M5	-0.031283661505102	0.160868	-0.1945	0.846036	0.423018
M6	-0.000687812078578053	0.160892	-0.0043	0.996594	0.498297
M7	-0.088556222969061	0.160916	-0.5503	0.582802	0.291401
M8	-0.170235352832043	0.160927	-1.0578	0.291594	0.145797
M9	-0.103138880917442	0.160933	-0.6409	0.522444	0.261222
M10	-0.0433015164244993	0.160861	-0.2692	0.788106	0.394053
M11	0.0406707117805078	0.160921	0.2527	0.80077	0.400385

\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) & 0.470311002831475 & 0.80986 & 0.5807 & 0.562173 & 0.281087 \tabularnewline
`Zink-prijs` & 0.00171388893656082 & 0.000546 & 3.1377 & 0.002 & 0.001 \tabularnewline
`Lood-prijs` & 0.00141340764409491 & 0.000415 & 3.4097 & 0.000808 & 0.000404 \tabularnewline
`NYSE-eindkoers-vorige-dag` & 0.128734027006698 & 0.013144 & 9.7942 & 0 & 0 \tabularnewline
`Rente-op-LT-leningen-in-%` & -2.50551896362825 & 0.325576 & -7.6956 & 0 & 0 \tabularnewline
`Conjunctuurenquete
` & -0.0458395501776176 & 0.017066 & -2.686 & 0.007932 & 0.003966 \tabularnewline
M1 & 0.0981553451240848 & 0.160938 & 0.6099 & 0.542726 & 0.271363 \tabularnewline
M2 & 0.160386902535726 & 0.160853 & 0.9971 & 0.320099 & 0.160049 \tabularnewline
M3 & 0.0669640934359867 & 0.160863 & 0.4163 & 0.677719 & 0.338859 \tabularnewline
M4 & 0.1296844837058 & 0.160928 & 0.8059 & 0.421427 & 0.210713 \tabularnewline
M5 & -0.031283661505102 & 0.160868 & -0.1945 & 0.846036 & 0.423018 \tabularnewline
M6 & -0.000687812078578053 & 0.160892 & -0.0043 & 0.996594 & 0.498297 \tabularnewline
M7 & -0.088556222969061 & 0.160916 & -0.5503 & 0.582802 & 0.291401 \tabularnewline
M8 & -0.170235352832043 & 0.160927 & -1.0578 & 0.291594 & 0.145797 \tabularnewline
M9 & -0.103138880917442 & 0.160933 & -0.6409 & 0.522444 & 0.261222 \tabularnewline
M10 & -0.0433015164244993 & 0.160861 & -0.2692 & 0.788106 & 0.394053 \tabularnewline
M11 & 0.0406707117805078 & 0.160921 & 0.2527 & 0.80077 & 0.400385 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146379&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]0.470311002831475[/C][C]0.80986[/C][C]0.5807[/C][C]0.562173[/C][C]0.281087[/C][/ROW]
[ROW][C]`Zink-prijs`[/C][C]0.00171388893656082[/C][C]0.000546[/C][C]3.1377[/C][C]0.002[/C][C]0.001[/C][/ROW]
[ROW][C]`Lood-prijs`[/C][C]0.00141340764409491[/C][C]0.000415[/C][C]3.4097[/C][C]0.000808[/C][C]0.000404[/C][/ROW]
[ROW][C]`NYSE-eindkoers-vorige-dag`[/C][C]0.128734027006698[/C][C]0.013144[/C][C]9.7942[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]`Rente-op-LT-leningen-in-%`[/C][C]-2.50551896362825[/C][C]0.325576[/C][C]-7.6956[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]`Conjunctuurenquete
`[/C][C]-0.0458395501776176[/C][C]0.017066[/C][C]-2.686[/C][C]0.007932[/C][C]0.003966[/C][/ROW]
[ROW][C]M1[/C][C]0.0981553451240848[/C][C]0.160938[/C][C]0.6099[/C][C]0.542726[/C][C]0.271363[/C][/ROW]
[ROW][C]M2[/C][C]0.160386902535726[/C][C]0.160853[/C][C]0.9971[/C][C]0.320099[/C][C]0.160049[/C][/ROW]
[ROW][C]M3[/C][C]0.0669640934359867[/C][C]0.160863[/C][C]0.4163[/C][C]0.677719[/C][C]0.338859[/C][/ROW]
[ROW][C]M4[/C][C]0.1296844837058[/C][C]0.160928[/C][C]0.8059[/C][C]0.421427[/C][C]0.210713[/C][/ROW]
[ROW][C]M5[/C][C]-0.031283661505102[/C][C]0.160868[/C][C]-0.1945[/C][C]0.846036[/C][C]0.423018[/C][/ROW]
[ROW][C]M6[/C][C]-0.000687812078578053[/C][C]0.160892[/C][C]-0.0043[/C][C]0.996594[/C][C]0.498297[/C][/ROW]
[ROW][C]M7[/C][C]-0.088556222969061[/C][C]0.160916[/C][C]-0.5503[/C][C]0.582802[/C][C]0.291401[/C][/ROW]
[ROW][C]M8[/C][C]-0.170235352832043[/C][C]0.160927[/C][C]-1.0578[/C][C]0.291594[/C][C]0.145797[/C][/ROW]
[ROW][C]M9[/C][C]-0.103138880917442[/C][C]0.160933[/C][C]-0.6409[/C][C]0.522444[/C][C]0.261222[/C][/ROW]
[ROW][C]M10[/C][C]-0.0433015164244993[/C][C]0.160861[/C][C]-0.2692[/C][C]0.788106[/C][C]0.394053[/C][/ROW]
[ROW][C]M11[/C][C]0.0406707117805078[/C][C]0.160921[/C][C]0.2527[/C][C]0.80077[/C][C]0.400385[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146379&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146379&T=2

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	0.470311002831475	0.80986	0.5807	0.562173	0.281087
`Zink-prijs`	0.00171388893656082	0.000546	3.1377	0.002	0.001
`Lood-prijs`	0.00141340764409491	0.000415	3.4097	0.000808	0.000404
`NYSE-eindkoers-vorige-dag`	0.128734027006698	0.013144	9.7942	0	0
`Rente-op-LT-leningen-in-%`	-2.50551896362825	0.325576	-7.6956	0	0
`Conjunctuurenquete `	-0.0458395501776176	0.017066	-2.686	0.007932	0.003966
M1	0.0981553451240848	0.160938	0.6099	0.542726	0.271363
M2	0.160386902535726	0.160853	0.9971	0.320099	0.160049
M3	0.0669640934359867	0.160863	0.4163	0.677719	0.338859
M4	0.1296844837058	0.160928	0.8059	0.421427	0.210713
M5	-0.031283661505102	0.160868	-0.1945	0.846036	0.423018
M6	-0.000687812078578053	0.160892	-0.0043	0.996594	0.498297
M7	-0.088556222969061	0.160916	-0.5503	0.582802	0.291401
M8	-0.170235352832043	0.160927	-1.0578	0.291594	0.145797
M9	-0.103138880917442	0.160933	-0.6409	0.522444	0.261222
M10	-0.0433015164244993	0.160861	-0.2692	0.788106	0.394053
M11	0.0406707117805078	0.160921	0.2527	0.80077	0.400385

Multiple Linear Regression - Regression Statistics
Multiple R	0.955195833150033
R-squared	0.912399079667185
Adjusted R-squared	0.904343822625087
F-TEST (value)	113.267531364779
F-TEST (DF numerator)	16
F-TEST (DF denominator)	174
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.44752475235394
Sum Squared Residuals	34.8484422906852

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.955195833150033 \tabularnewline
R-squared & 0.912399079667185 \tabularnewline
Adjusted R-squared & 0.904343822625087 \tabularnewline
F-TEST (value) & 113.267531364779 \tabularnewline
F-TEST (DF numerator) & 16 \tabularnewline
F-TEST (DF denominator) & 174 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.44752475235394 \tabularnewline
Sum Squared Residuals & 34.8484422906852 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146379&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.955195833150033[/C][/ROW]
[ROW][C]R-squared[/C][C]0.912399079667185[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.904343822625087[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]113.267531364779[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]16[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]174[/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]0.44752475235394[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]34.8484422906852[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146379&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146379&T=3

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Regression Statistics
Multiple R	0.955195833150033
R-squared	0.912399079667185
Adjusted R-squared	0.904343822625087
F-TEST (value)	113.267531364779
F-TEST (DF numerator)	16
F-TEST (DF denominator)	174
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.44752475235394
Sum Squared Residuals	34.8484422906852

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	11.73	10.8115166850611	0.918483314938921
2	11.75	10.7250122626075	1.02498773739251
3	11.39	10.4019667341861	0.988033265813942
4	11.54	10.3289101647613	1.21108983523866
5	9.62	10.2908781055519	-0.670878105551865
6	9.82	10.4130353955693	-0.593035395569273
7	9.94	10.2461385709797	-0.306138570979669
8	9.9	10.0836602420698	-0.183660242069775
9	9.8	10.2759495953498	-0.475949595349817
10	9.86	10.3189553700776	-0.458955370077594
11	10.5	10.1759505230701	0.324049476929866
12	10.33	10.0056853539355	0.324314646064535
13	10.16	9.88970126295025	0.270298737049754
14	9.91	9.69225053406982	0.217749465930182
15	9.96	9.5403524421277	0.419647557872297
16	10.03	9.8397954990235	0.190204500976504
17	9.55	9.80688077990111	-0.256880779901105
18	9.51	9.93283402796397	-0.422834027963967
19	9.8	9.98329724350792	-0.183297243507923
20	10.08	9.86447001283802	0.215529987161982
21	10.2	9.69343810475313	0.506561895246867
22	10.23	9.88690241635473	0.343097583645266
23	10.2	10.2111821848045	-0.0111821848044533
24	10.07	10.1150268028644	-0.0450268028643546
25	10.01	10.1283329386444	-0.118332938644387
26	10.05	10.01800277597	0.0319972240299933
27	9.92	10.1271924503755	-0.207192450375495
28	10.03	10.1258491140287	-0.0958491140286924
29	10.18	10.0225316037304	0.15746839626965
30	10.1	9.97493485906157	0.125065140938427
31	10.16	10.7043701182734	-0.544370118273447
32	10.15	10.6890709204263	-0.539070920426282
33	10.13	10.6992672991947	-0.569267299194714
34	10.09	10.9548536337588	-0.864853633758794
35	10.18	11.1068751345876	-0.926875134587639
36	10.06	11.0660356756888	-1.00603567568877
37	9.65	10.1179670370563	-0.467967037056333
38	9.74	10.0868134620703	-0.346813462070282
39	9.53	9.83388536865625	-0.303885368656252
40	9.5	10.0321791260946	-0.532179126094647
41	9	9.72345900548243	-0.723459005482431
42	9.15	9.63417790751336	-0.484177907513363
43	9.32	9.70273640665487	-0.382736406654873
44	9.62	9.68101074390496	-0.0610107439049664
45	9.59	9.5036064942287	0.0863935057713036
46	9.37	9.38799081013808	-0.0179908101380854
47	9.35	9.5371480470467	-0.187148047046695
48	9.32	9.50935073796686	-0.189350737966857
49	9.49	9.80970480341725	-0.319704803417253
50	9.52	9.90519240507563	-0.385192405075634
51	9.59	9.69060130772455	-0.100601307724551
52	9.35	9.4505905320861	-0.100590532086101
53	9.2	9.24502533322068	-0.0450253332206761
54	9.57	9.2731165286661	0.296883471333902
55	9.78	9.28955720602983	0.490442793970172
56	9.79	9.12667285026805	0.66332714973195
57	9.57	9.39484233066975	0.175157669330247
58	9.53	9.42659512100652	0.103404878993478
59	9.65	9.37607820770216	0.273921792297839
60	9.36	9.32518545304971	0.0348145469502888
61	9.4	9.04274474921015	0.357255250789849
62	9.32	9.05012943513383	0.26987056486617
63	9.31	8.98004333854167	0.329956661458325
64	9.19	9.03117147274628	0.158828527253717
65	9.39	9.04082418983059	0.349175810169411
66	9.28	9.14019852276081	0.139801477239191
67	9.28	9.10497178794877	0.17502821205123
68	9.31	9.08503882542549	0.224961174574511
69	9.28	9.14183657517956	0.138163424820444
70	9.31	9.20213126542133	0.107868734578667
71	9.35	9.49527661329468	-0.145276613294681
72	9.19	9.16401331623819	0.0259866837618126
73	9.07	9.3095796718128	-0.239579671812798
74	8.96	9.5273611688599	-0.567361168859904
75	8.69	9.48225946418887	-0.792259464188875
76	8.58	9.55520242416515	-0.975202424165153
77	8.56	9.35663519279137	-0.796635192791367
78	8.47	9.32168117364273	-0.85168117364273
79	8.46	9.08077621290949	-0.620776212909493
80	8.75	9.16686368539998	-0.41686368539998
81	8.95	9.22177811881421	-0.271778118814212
82	9.33	9.00646521663304	0.323534783366956
83	9.51	9.02587022684909	0.484129773150912
84	9.561	8.79644354394518	0.764556456054819
85	9.94	9.07104805927655	0.868951940723446
86	9.9	9.27463210605324	0.625367893946762
87	9.275	9.17413639625525	0.100863603744752
88	9.56	9.16081461393403	0.399185386065974
89	9.779	8.97384341802118	0.805156581978824
90	9.746	9.10108009147924	0.644919908520757
91	9.991	9.21891980762969	0.77208019237031
92	9.98	9.34770134063471	0.632298659365295
93	10.195	9.54244786539016	0.652552134609839
94	10.31	9.70500487927981	0.604995120720189
95	10.25	9.82343450114748	0.426565498852524
96	9.871	9.7015114240432	0.1694885759568
97	10.06	10.0055114634471	0.0544885365529056
98	9.894	9.96895713396508	-0.0749571339650809
99	9.59	9.49795980573298	0.0920401942670233
100	9.64	9.00146200616894	0.638537993831059
101	9.89	9.09728163308206	0.792718366917944
102	9.53	8.92377733758868	0.606222662411316
103	9.388	8.96171663973362	0.426283360266379
104	9.16	8.95962397154615	0.200376028453855
105	9.418	9.22474460540115	0.193255394598849
106	9.57	9.23080027193856	0.339199728061443
107	9.857	9.39161709584401	0.465382904155992
108	9.877	9.29807189462429	0.578928105375711
109	9.76	9.40889300711011	0.351106992889895
110	9.76	9.61080371938521	0.149196280614794
111	9.695	9.36743807434676	0.32756192565324
112	9.475	9.41817636718456	0.0568236328154434
113	9.262	9.03440403876184	0.22759596123816
114	9.097	9.01992086072346	0.077079139276544
115	8.55	8.59179899370941	-0.0417989937094088
116	8.16	8.33741455584373	-0.177414555843734
117	7.532	8.01872712084923	-0.486727120849235
118	7.325	7.66559747825358	-0.340597478253582
119	6.749	6.88292794479699	-0.133927944796993
120	7.13	7.33987581723981	-0.209875817239808
121	6.995	7.25176452441999	-0.256764524419989
122	7.346	7.65768583714209	-0.311685837142088
123	7.73	7.68737260308018	0.0426273969198215
124	7.837	7.77958625205236	0.0574137479476419
125	7.514	7.30089090340952	0.213109096590484
126	7.58	7.49698528183688	0.0830147181631209
127	6.83	6.97026649121758	-0.140266491217578
128	6.617	6.94770498826007	-0.330704988260066
129	6.715	6.85793751373935	-0.142937513739348
130	6.63	7.16202418758732	-0.532024187587325
131	6.891	7.4477753010583	-0.556775301058296
132	7.002	7.41060640642365	-0.408606406423648
133	7.09	7.7354364279641	-0.645436427964097
134	7.36	7.89808052644096	-0.538080526440962
135	7.477	7.88049991876462	-0.403499918764622
136	7.826	8.0289770024401	-0.202977002440097
137	7.79	7.92858587033793	-0.138585870337928
138	7.578	7.6556195655335	-0.0776195655334987
139	7.204	7.36543233412228	-0.161432334122282
140	7.198	7.50356466075812	-0.305564660758117
141	7.685	7.6437674351298	0.0412325648702015
142	7.795	7.6140614296379	0.180938570362096
143	7.46	7.6011898358595	-0.141189835859505
144	7.274	7.59561880619321	-0.321618806193206
145	7.33	7.92478016266551	-0.594780162665506
146	7.655	7.8467355476029	-0.191735547602895
147	7.767	7.84818982641775	-0.0811898264177478
148	7.84	7.69897152299925	0.141028477000746
149	7.424	7.31611447074758	0.10788552925242
150	7.54	7.2562479368498	0.283752063150199
151	7.351	6.81767527523618	0.533324724763823
152	6.735	6.57665107473547	0.158348925264534
153	6.777	6.44403464284677	0.332965357153234
154	6.679	6.21285359590945	0.466146404090553
155	7.34	6.4692375247442	0.870762475255799
156	6.978	6.41023265115778	0.567767348842218
157	6.92	6.3714594553331	0.548540544666898
158	6.628	6.08501362948671	0.542986370513295
159	6.385	6.19131577860489	0.193684221395108
160	5.984	6.31280673199654	-0.328806731996542
161	6.268	6.04392408427795	0.224075915722053
162	6.596	6.2030131716913	0.392986828308705
163	6.395	6.38793062228958	0.00706937771041785
164	6.715	6.5315913579911	0.183408642008902
165	6.804	6.6659089499004	0.138091050099604
166	6.929	6.8189817208513	0.110018279148701
167	6.846	6.82911536670784	0.0168846332921595
168	6.992	6.76663120185351	0.225368798146487
169	6.774	6.80967685647817	-0.0356768564781696
170	6.75	6.65415060719599	0.0958493928040078
171	6.485	6.55098563695038	-0.0659856369503793
172	6.27	6.29556962339387	-0.0255696233938693
173	6.47	6.38107654240241	0.0889234575975886
174	6.78	6.57251950767288	0.207480492327117
175	6.71	6.4335727566576	0.276427243342401
176	6.141	6.45323795982391	-0.312237959823906
177	6.72	6.74243876622765	-0.0224387662276512
178	6.68	6.71705803751214	-0.0370580375121356
179	6.371	6.67091096813284	-0.299910968132837
180	6.097	6.60771091477603	-0.510710914776027
181	6.27	6.96088289515313	-0.690882895153134
182	6.447	6.98917884894087	-0.542178848940874
183	6.37	6.90980085404659	-0.539800854046588
184	6.446	7.03793754692465	-0.591937546924646
185	6.54	6.87464482845116	-0.334644828451161
186	6.374	6.80185783144645	-0.427857831446447
187	6.33	6.62983953310006	-0.299839533100059
188	6.63	6.5817228100742	0.0482771899257987
189	6.498	6.79327458232561	-0.295274582325611
190	6.485	6.81272456563983	-0.327724565639833
191	6.36	6.81941052435399	-0.459410524353993

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 11.73 & 10.8115166850611 & 0.918483314938921 \tabularnewline
2 & 11.75 & 10.7250122626075 & 1.02498773739251 \tabularnewline
3 & 11.39 & 10.4019667341861 & 0.988033265813942 \tabularnewline
4 & 11.54 & 10.3289101647613 & 1.21108983523866 \tabularnewline
5 & 9.62 & 10.2908781055519 & -0.670878105551865 \tabularnewline
6 & 9.82 & 10.4130353955693 & -0.593035395569273 \tabularnewline
7 & 9.94 & 10.2461385709797 & -0.306138570979669 \tabularnewline
8 & 9.9 & 10.0836602420698 & -0.183660242069775 \tabularnewline
9 & 9.8 & 10.2759495953498 & -0.475949595349817 \tabularnewline
10 & 9.86 & 10.3189553700776 & -0.458955370077594 \tabularnewline
11 & 10.5 & 10.1759505230701 & 0.324049476929866 \tabularnewline
12 & 10.33 & 10.0056853539355 & 0.324314646064535 \tabularnewline
13 & 10.16 & 9.88970126295025 & 0.270298737049754 \tabularnewline
14 & 9.91 & 9.69225053406982 & 0.217749465930182 \tabularnewline
15 & 9.96 & 9.5403524421277 & 0.419647557872297 \tabularnewline
16 & 10.03 & 9.8397954990235 & 0.190204500976504 \tabularnewline
17 & 9.55 & 9.80688077990111 & -0.256880779901105 \tabularnewline
18 & 9.51 & 9.93283402796397 & -0.422834027963967 \tabularnewline
19 & 9.8 & 9.98329724350792 & -0.183297243507923 \tabularnewline
20 & 10.08 & 9.86447001283802 & 0.215529987161982 \tabularnewline
21 & 10.2 & 9.69343810475313 & 0.506561895246867 \tabularnewline
22 & 10.23 & 9.88690241635473 & 0.343097583645266 \tabularnewline
23 & 10.2 & 10.2111821848045 & -0.0111821848044533 \tabularnewline
24 & 10.07 & 10.1150268028644 & -0.0450268028643546 \tabularnewline
25 & 10.01 & 10.1283329386444 & -0.118332938644387 \tabularnewline
26 & 10.05 & 10.01800277597 & 0.0319972240299933 \tabularnewline
27 & 9.92 & 10.1271924503755 & -0.207192450375495 \tabularnewline
28 & 10.03 & 10.1258491140287 & -0.0958491140286924 \tabularnewline
29 & 10.18 & 10.0225316037304 & 0.15746839626965 \tabularnewline
30 & 10.1 & 9.97493485906157 & 0.125065140938427 \tabularnewline
31 & 10.16 & 10.7043701182734 & -0.544370118273447 \tabularnewline
32 & 10.15 & 10.6890709204263 & -0.539070920426282 \tabularnewline
33 & 10.13 & 10.6992672991947 & -0.569267299194714 \tabularnewline
34 & 10.09 & 10.9548536337588 & -0.864853633758794 \tabularnewline
35 & 10.18 & 11.1068751345876 & -0.926875134587639 \tabularnewline
36 & 10.06 & 11.0660356756888 & -1.00603567568877 \tabularnewline
37 & 9.65 & 10.1179670370563 & -0.467967037056333 \tabularnewline
38 & 9.74 & 10.0868134620703 & -0.346813462070282 \tabularnewline
39 & 9.53 & 9.83388536865625 & -0.303885368656252 \tabularnewline
40 & 9.5 & 10.0321791260946 & -0.532179126094647 \tabularnewline
41 & 9 & 9.72345900548243 & -0.723459005482431 \tabularnewline
42 & 9.15 & 9.63417790751336 & -0.484177907513363 \tabularnewline
43 & 9.32 & 9.70273640665487 & -0.382736406654873 \tabularnewline
44 & 9.62 & 9.68101074390496 & -0.0610107439049664 \tabularnewline
45 & 9.59 & 9.5036064942287 & 0.0863935057713036 \tabularnewline
46 & 9.37 & 9.38799081013808 & -0.0179908101380854 \tabularnewline
47 & 9.35 & 9.5371480470467 & -0.187148047046695 \tabularnewline
48 & 9.32 & 9.50935073796686 & -0.189350737966857 \tabularnewline
49 & 9.49 & 9.80970480341725 & -0.319704803417253 \tabularnewline
50 & 9.52 & 9.90519240507563 & -0.385192405075634 \tabularnewline
51 & 9.59 & 9.69060130772455 & -0.100601307724551 \tabularnewline
52 & 9.35 & 9.4505905320861 & -0.100590532086101 \tabularnewline
53 & 9.2 & 9.24502533322068 & -0.0450253332206761 \tabularnewline
54 & 9.57 & 9.2731165286661 & 0.296883471333902 \tabularnewline
55 & 9.78 & 9.28955720602983 & 0.490442793970172 \tabularnewline
56 & 9.79 & 9.12667285026805 & 0.66332714973195 \tabularnewline
57 & 9.57 & 9.39484233066975 & 0.175157669330247 \tabularnewline
58 & 9.53 & 9.42659512100652 & 0.103404878993478 \tabularnewline
59 & 9.65 & 9.37607820770216 & 0.273921792297839 \tabularnewline
60 & 9.36 & 9.32518545304971 & 0.0348145469502888 \tabularnewline
61 & 9.4 & 9.04274474921015 & 0.357255250789849 \tabularnewline
62 & 9.32 & 9.05012943513383 & 0.26987056486617 \tabularnewline
63 & 9.31 & 8.98004333854167 & 0.329956661458325 \tabularnewline
64 & 9.19 & 9.03117147274628 & 0.158828527253717 \tabularnewline
65 & 9.39 & 9.04082418983059 & 0.349175810169411 \tabularnewline
66 & 9.28 & 9.14019852276081 & 0.139801477239191 \tabularnewline
67 & 9.28 & 9.10497178794877 & 0.17502821205123 \tabularnewline
68 & 9.31 & 9.08503882542549 & 0.224961174574511 \tabularnewline
69 & 9.28 & 9.14183657517956 & 0.138163424820444 \tabularnewline
70 & 9.31 & 9.20213126542133 & 0.107868734578667 \tabularnewline
71 & 9.35 & 9.49527661329468 & -0.145276613294681 \tabularnewline
72 & 9.19 & 9.16401331623819 & 0.0259866837618126 \tabularnewline
73 & 9.07 & 9.3095796718128 & -0.239579671812798 \tabularnewline
74 & 8.96 & 9.5273611688599 & -0.567361168859904 \tabularnewline
75 & 8.69 & 9.48225946418887 & -0.792259464188875 \tabularnewline
76 & 8.58 & 9.55520242416515 & -0.975202424165153 \tabularnewline
77 & 8.56 & 9.35663519279137 & -0.796635192791367 \tabularnewline
78 & 8.47 & 9.32168117364273 & -0.85168117364273 \tabularnewline
79 & 8.46 & 9.08077621290949 & -0.620776212909493 \tabularnewline
80 & 8.75 & 9.16686368539998 & -0.41686368539998 \tabularnewline
81 & 8.95 & 9.22177811881421 & -0.271778118814212 \tabularnewline
82 & 9.33 & 9.00646521663304 & 0.323534783366956 \tabularnewline
83 & 9.51 & 9.02587022684909 & 0.484129773150912 \tabularnewline
84 & 9.561 & 8.79644354394518 & 0.764556456054819 \tabularnewline
85 & 9.94 & 9.07104805927655 & 0.868951940723446 \tabularnewline
86 & 9.9 & 9.27463210605324 & 0.625367893946762 \tabularnewline
87 & 9.275 & 9.17413639625525 & 0.100863603744752 \tabularnewline
88 & 9.56 & 9.16081461393403 & 0.399185386065974 \tabularnewline
89 & 9.779 & 8.97384341802118 & 0.805156581978824 \tabularnewline
90 & 9.746 & 9.10108009147924 & 0.644919908520757 \tabularnewline
91 & 9.991 & 9.21891980762969 & 0.77208019237031 \tabularnewline
92 & 9.98 & 9.34770134063471 & 0.632298659365295 \tabularnewline
93 & 10.195 & 9.54244786539016 & 0.652552134609839 \tabularnewline
94 & 10.31 & 9.70500487927981 & 0.604995120720189 \tabularnewline
95 & 10.25 & 9.82343450114748 & 0.426565498852524 \tabularnewline
96 & 9.871 & 9.7015114240432 & 0.1694885759568 \tabularnewline
97 & 10.06 & 10.0055114634471 & 0.0544885365529056 \tabularnewline
98 & 9.894 & 9.96895713396508 & -0.0749571339650809 \tabularnewline
99 & 9.59 & 9.49795980573298 & 0.0920401942670233 \tabularnewline
100 & 9.64 & 9.00146200616894 & 0.638537993831059 \tabularnewline
101 & 9.89 & 9.09728163308206 & 0.792718366917944 \tabularnewline
102 & 9.53 & 8.92377733758868 & 0.606222662411316 \tabularnewline
103 & 9.388 & 8.96171663973362 & 0.426283360266379 \tabularnewline
104 & 9.16 & 8.95962397154615 & 0.200376028453855 \tabularnewline
105 & 9.418 & 9.22474460540115 & 0.193255394598849 \tabularnewline
106 & 9.57 & 9.23080027193856 & 0.339199728061443 \tabularnewline
107 & 9.857 & 9.39161709584401 & 0.465382904155992 \tabularnewline
108 & 9.877 & 9.29807189462429 & 0.578928105375711 \tabularnewline
109 & 9.76 & 9.40889300711011 & 0.351106992889895 \tabularnewline
110 & 9.76 & 9.61080371938521 & 0.149196280614794 \tabularnewline
111 & 9.695 & 9.36743807434676 & 0.32756192565324 \tabularnewline
112 & 9.475 & 9.41817636718456 & 0.0568236328154434 \tabularnewline
113 & 9.262 & 9.03440403876184 & 0.22759596123816 \tabularnewline
114 & 9.097 & 9.01992086072346 & 0.077079139276544 \tabularnewline
115 & 8.55 & 8.59179899370941 & -0.0417989937094088 \tabularnewline
116 & 8.16 & 8.33741455584373 & -0.177414555843734 \tabularnewline
117 & 7.532 & 8.01872712084923 & -0.486727120849235 \tabularnewline
118 & 7.325 & 7.66559747825358 & -0.340597478253582 \tabularnewline
119 & 6.749 & 6.88292794479699 & -0.133927944796993 \tabularnewline
120 & 7.13 & 7.33987581723981 & -0.209875817239808 \tabularnewline
121 & 6.995 & 7.25176452441999 & -0.256764524419989 \tabularnewline
122 & 7.346 & 7.65768583714209 & -0.311685837142088 \tabularnewline
123 & 7.73 & 7.68737260308018 & 0.0426273969198215 \tabularnewline
124 & 7.837 & 7.77958625205236 & 0.0574137479476419 \tabularnewline
125 & 7.514 & 7.30089090340952 & 0.213109096590484 \tabularnewline
126 & 7.58 & 7.49698528183688 & 0.0830147181631209 \tabularnewline
127 & 6.83 & 6.97026649121758 & -0.140266491217578 \tabularnewline
128 & 6.617 & 6.94770498826007 & -0.330704988260066 \tabularnewline
129 & 6.715 & 6.85793751373935 & -0.142937513739348 \tabularnewline
130 & 6.63 & 7.16202418758732 & -0.532024187587325 \tabularnewline
131 & 6.891 & 7.4477753010583 & -0.556775301058296 \tabularnewline
132 & 7.002 & 7.41060640642365 & -0.408606406423648 \tabularnewline
133 & 7.09 & 7.7354364279641 & -0.645436427964097 \tabularnewline
134 & 7.36 & 7.89808052644096 & -0.538080526440962 \tabularnewline
135 & 7.477 & 7.88049991876462 & -0.403499918764622 \tabularnewline
136 & 7.826 & 8.0289770024401 & -0.202977002440097 \tabularnewline
137 & 7.79 & 7.92858587033793 & -0.138585870337928 \tabularnewline
138 & 7.578 & 7.6556195655335 & -0.0776195655334987 \tabularnewline
139 & 7.204 & 7.36543233412228 & -0.161432334122282 \tabularnewline
140 & 7.198 & 7.50356466075812 & -0.305564660758117 \tabularnewline
141 & 7.685 & 7.6437674351298 & 0.0412325648702015 \tabularnewline
142 & 7.795 & 7.6140614296379 & 0.180938570362096 \tabularnewline
143 & 7.46 & 7.6011898358595 & -0.141189835859505 \tabularnewline
144 & 7.274 & 7.59561880619321 & -0.321618806193206 \tabularnewline
145 & 7.33 & 7.92478016266551 & -0.594780162665506 \tabularnewline
146 & 7.655 & 7.8467355476029 & -0.191735547602895 \tabularnewline
147 & 7.767 & 7.84818982641775 & -0.0811898264177478 \tabularnewline
148 & 7.84 & 7.69897152299925 & 0.141028477000746 \tabularnewline
149 & 7.424 & 7.31611447074758 & 0.10788552925242 \tabularnewline
150 & 7.54 & 7.2562479368498 & 0.283752063150199 \tabularnewline
151 & 7.351 & 6.81767527523618 & 0.533324724763823 \tabularnewline
152 & 6.735 & 6.57665107473547 & 0.158348925264534 \tabularnewline
153 & 6.777 & 6.44403464284677 & 0.332965357153234 \tabularnewline
154 & 6.679 & 6.21285359590945 & 0.466146404090553 \tabularnewline
155 & 7.34 & 6.4692375247442 & 0.870762475255799 \tabularnewline
156 & 6.978 & 6.41023265115778 & 0.567767348842218 \tabularnewline
157 & 6.92 & 6.3714594553331 & 0.548540544666898 \tabularnewline
158 & 6.628 & 6.08501362948671 & 0.542986370513295 \tabularnewline
159 & 6.385 & 6.19131577860489 & 0.193684221395108 \tabularnewline
160 & 5.984 & 6.31280673199654 & -0.328806731996542 \tabularnewline
161 & 6.268 & 6.04392408427795 & 0.224075915722053 \tabularnewline
162 & 6.596 & 6.2030131716913 & 0.392986828308705 \tabularnewline
163 & 6.395 & 6.38793062228958 & 0.00706937771041785 \tabularnewline
164 & 6.715 & 6.5315913579911 & 0.183408642008902 \tabularnewline
165 & 6.804 & 6.6659089499004 & 0.138091050099604 \tabularnewline
166 & 6.929 & 6.8189817208513 & 0.110018279148701 \tabularnewline
167 & 6.846 & 6.82911536670784 & 0.0168846332921595 \tabularnewline
168 & 6.992 & 6.76663120185351 & 0.225368798146487 \tabularnewline
169 & 6.774 & 6.80967685647817 & -0.0356768564781696 \tabularnewline
170 & 6.75 & 6.65415060719599 & 0.0958493928040078 \tabularnewline
171 & 6.485 & 6.55098563695038 & -0.0659856369503793 \tabularnewline
172 & 6.27 & 6.29556962339387 & -0.0255696233938693 \tabularnewline
173 & 6.47 & 6.38107654240241 & 0.0889234575975886 \tabularnewline
174 & 6.78 & 6.57251950767288 & 0.207480492327117 \tabularnewline
175 & 6.71 & 6.4335727566576 & 0.276427243342401 \tabularnewline
176 & 6.141 & 6.45323795982391 & -0.312237959823906 \tabularnewline
177 & 6.72 & 6.74243876622765 & -0.0224387662276512 \tabularnewline
178 & 6.68 & 6.71705803751214 & -0.0370580375121356 \tabularnewline
179 & 6.371 & 6.67091096813284 & -0.299910968132837 \tabularnewline
180 & 6.097 & 6.60771091477603 & -0.510710914776027 \tabularnewline
181 & 6.27 & 6.96088289515313 & -0.690882895153134 \tabularnewline
182 & 6.447 & 6.98917884894087 & -0.542178848940874 \tabularnewline
183 & 6.37 & 6.90980085404659 & -0.539800854046588 \tabularnewline
184 & 6.446 & 7.03793754692465 & -0.591937546924646 \tabularnewline
185 & 6.54 & 6.87464482845116 & -0.334644828451161 \tabularnewline
186 & 6.374 & 6.80185783144645 & -0.427857831446447 \tabularnewline
187 & 6.33 & 6.62983953310006 & -0.299839533100059 \tabularnewline
188 & 6.63 & 6.5817228100742 & 0.0482771899257987 \tabularnewline
189 & 6.498 & 6.79327458232561 & -0.295274582325611 \tabularnewline
190 & 6.485 & 6.81272456563983 & -0.327724565639833 \tabularnewline
191 & 6.36 & 6.81941052435399 & -0.459410524353993 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146379&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]10.8115166850611[/C][C]0.918483314938921[/C][/ROW]
[ROW][C]2[/C][C]11.75[/C][C]10.7250122626075[/C][C]1.02498773739251[/C][/ROW]
[ROW][C]3[/C][C]11.39[/C][C]10.4019667341861[/C][C]0.988033265813942[/C][/ROW]
[ROW][C]4[/C][C]11.54[/C][C]10.3289101647613[/C][C]1.21108983523866[/C][/ROW]
[ROW][C]5[/C][C]9.62[/C][C]10.2908781055519[/C][C]-0.670878105551865[/C][/ROW]
[ROW][C]6[/C][C]9.82[/C][C]10.4130353955693[/C][C]-0.593035395569273[/C][/ROW]
[ROW][C]7[/C][C]9.94[/C][C]10.2461385709797[/C][C]-0.306138570979669[/C][/ROW]
[ROW][C]8[/C][C]9.9[/C][C]10.0836602420698[/C][C]-0.183660242069775[/C][/ROW]
[ROW][C]9[/C][C]9.8[/C][C]10.2759495953498[/C][C]-0.475949595349817[/C][/ROW]
[ROW][C]10[/C][C]9.86[/C][C]10.3189553700776[/C][C]-0.458955370077594[/C][/ROW]
[ROW][C]11[/C][C]10.5[/C][C]10.1759505230701[/C][C]0.324049476929866[/C][/ROW]
[ROW][C]12[/C][C]10.33[/C][C]10.0056853539355[/C][C]0.324314646064535[/C][/ROW]
[ROW][C]13[/C][C]10.16[/C][C]9.88970126295025[/C][C]0.270298737049754[/C][/ROW]
[ROW][C]14[/C][C]9.91[/C][C]9.69225053406982[/C][C]0.217749465930182[/C][/ROW]
[ROW][C]15[/C][C]9.96[/C][C]9.5403524421277[/C][C]0.419647557872297[/C][/ROW]
[ROW][C]16[/C][C]10.03[/C][C]9.8397954990235[/C][C]0.190204500976504[/C][/ROW]
[ROW][C]17[/C][C]9.55[/C][C]9.80688077990111[/C][C]-0.256880779901105[/C][/ROW]
[ROW][C]18[/C][C]9.51[/C][C]9.93283402796397[/C][C]-0.422834027963967[/C][/ROW]
[ROW][C]19[/C][C]9.8[/C][C]9.98329724350792[/C][C]-0.183297243507923[/C][/ROW]
[ROW][C]20[/C][C]10.08[/C][C]9.86447001283802[/C][C]0.215529987161982[/C][/ROW]
[ROW][C]21[/C][C]10.2[/C][C]9.69343810475313[/C][C]0.506561895246867[/C][/ROW]
[ROW][C]22[/C][C]10.23[/C][C]9.88690241635473[/C][C]0.343097583645266[/C][/ROW]
[ROW][C]23[/C][C]10.2[/C][C]10.2111821848045[/C][C]-0.0111821848044533[/C][/ROW]
[ROW][C]24[/C][C]10.07[/C][C]10.1150268028644[/C][C]-0.0450268028643546[/C][/ROW]
[ROW][C]25[/C][C]10.01[/C][C]10.1283329386444[/C][C]-0.118332938644387[/C][/ROW]
[ROW][C]26[/C][C]10.05[/C][C]10.01800277597[/C][C]0.0319972240299933[/C][/ROW]
[ROW][C]27[/C][C]9.92[/C][C]10.1271924503755[/C][C]-0.207192450375495[/C][/ROW]
[ROW][C]28[/C][C]10.03[/C][C]10.1258491140287[/C][C]-0.0958491140286924[/C][/ROW]
[ROW][C]29[/C][C]10.18[/C][C]10.0225316037304[/C][C]0.15746839626965[/C][/ROW]
[ROW][C]30[/C][C]10.1[/C][C]9.97493485906157[/C][C]0.125065140938427[/C][/ROW]
[ROW][C]31[/C][C]10.16[/C][C]10.7043701182734[/C][C]-0.544370118273447[/C][/ROW]
[ROW][C]32[/C][C]10.15[/C][C]10.6890709204263[/C][C]-0.539070920426282[/C][/ROW]
[ROW][C]33[/C][C]10.13[/C][C]10.6992672991947[/C][C]-0.569267299194714[/C][/ROW]
[ROW][C]34[/C][C]10.09[/C][C]10.9548536337588[/C][C]-0.864853633758794[/C][/ROW]
[ROW][C]35[/C][C]10.18[/C][C]11.1068751345876[/C][C]-0.926875134587639[/C][/ROW]
[ROW][C]36[/C][C]10.06[/C][C]11.0660356756888[/C][C]-1.00603567568877[/C][/ROW]
[ROW][C]37[/C][C]9.65[/C][C]10.1179670370563[/C][C]-0.467967037056333[/C][/ROW]
[ROW][C]38[/C][C]9.74[/C][C]10.0868134620703[/C][C]-0.346813462070282[/C][/ROW]
[ROW][C]39[/C][C]9.53[/C][C]9.83388536865625[/C][C]-0.303885368656252[/C][/ROW]
[ROW][C]40[/C][C]9.5[/C][C]10.0321791260946[/C][C]-0.532179126094647[/C][/ROW]
[ROW][C]41[/C][C]9[/C][C]9.72345900548243[/C][C]-0.723459005482431[/C][/ROW]
[ROW][C]42[/C][C]9.15[/C][C]9.63417790751336[/C][C]-0.484177907513363[/C][/ROW]
[ROW][C]43[/C][C]9.32[/C][C]9.70273640665487[/C][C]-0.382736406654873[/C][/ROW]
[ROW][C]44[/C][C]9.62[/C][C]9.68101074390496[/C][C]-0.0610107439049664[/C][/ROW]
[ROW][C]45[/C][C]9.59[/C][C]9.5036064942287[/C][C]0.0863935057713036[/C][/ROW]
[ROW][C]46[/C][C]9.37[/C][C]9.38799081013808[/C][C]-0.0179908101380854[/C][/ROW]
[ROW][C]47[/C][C]9.35[/C][C]9.5371480470467[/C][C]-0.187148047046695[/C][/ROW]
[ROW][C]48[/C][C]9.32[/C][C]9.50935073796686[/C][C]-0.189350737966857[/C][/ROW]
[ROW][C]49[/C][C]9.49[/C][C]9.80970480341725[/C][C]-0.319704803417253[/C][/ROW]
[ROW][C]50[/C][C]9.52[/C][C]9.90519240507563[/C][C]-0.385192405075634[/C][/ROW]
[ROW][C]51[/C][C]9.59[/C][C]9.69060130772455[/C][C]-0.100601307724551[/C][/ROW]
[ROW][C]52[/C][C]9.35[/C][C]9.4505905320861[/C][C]-0.100590532086101[/C][/ROW]
[ROW][C]53[/C][C]9.2[/C][C]9.24502533322068[/C][C]-0.0450253332206761[/C][/ROW]
[ROW][C]54[/C][C]9.57[/C][C]9.2731165286661[/C][C]0.296883471333902[/C][/ROW]
[ROW][C]55[/C][C]9.78[/C][C]9.28955720602983[/C][C]0.490442793970172[/C][/ROW]
[ROW][C]56[/C][C]9.79[/C][C]9.12667285026805[/C][C]0.66332714973195[/C][/ROW]
[ROW][C]57[/C][C]9.57[/C][C]9.39484233066975[/C][C]0.175157669330247[/C][/ROW]
[ROW][C]58[/C][C]9.53[/C][C]9.42659512100652[/C][C]0.103404878993478[/C][/ROW]
[ROW][C]59[/C][C]9.65[/C][C]9.37607820770216[/C][C]0.273921792297839[/C][/ROW]
[ROW][C]60[/C][C]9.36[/C][C]9.32518545304971[/C][C]0.0348145469502888[/C][/ROW]
[ROW][C]61[/C][C]9.4[/C][C]9.04274474921015[/C][C]0.357255250789849[/C][/ROW]
[ROW][C]62[/C][C]9.32[/C][C]9.05012943513383[/C][C]0.26987056486617[/C][/ROW]
[ROW][C]63[/C][C]9.31[/C][C]8.98004333854167[/C][C]0.329956661458325[/C][/ROW]
[ROW][C]64[/C][C]9.19[/C][C]9.03117147274628[/C][C]0.158828527253717[/C][/ROW]
[ROW][C]65[/C][C]9.39[/C][C]9.04082418983059[/C][C]0.349175810169411[/C][/ROW]
[ROW][C]66[/C][C]9.28[/C][C]9.14019852276081[/C][C]0.139801477239191[/C][/ROW]
[ROW][C]67[/C][C]9.28[/C][C]9.10497178794877[/C][C]0.17502821205123[/C][/ROW]
[ROW][C]68[/C][C]9.31[/C][C]9.08503882542549[/C][C]0.224961174574511[/C][/ROW]
[ROW][C]69[/C][C]9.28[/C][C]9.14183657517956[/C][C]0.138163424820444[/C][/ROW]
[ROW][C]70[/C][C]9.31[/C][C]9.20213126542133[/C][C]0.107868734578667[/C][/ROW]
[ROW][C]71[/C][C]9.35[/C][C]9.49527661329468[/C][C]-0.145276613294681[/C][/ROW]
[ROW][C]72[/C][C]9.19[/C][C]9.16401331623819[/C][C]0.0259866837618126[/C][/ROW]
[ROW][C]73[/C][C]9.07[/C][C]9.3095796718128[/C][C]-0.239579671812798[/C][/ROW]
[ROW][C]74[/C][C]8.96[/C][C]9.5273611688599[/C][C]-0.567361168859904[/C][/ROW]
[ROW][C]75[/C][C]8.69[/C][C]9.48225946418887[/C][C]-0.792259464188875[/C][/ROW]
[ROW][C]76[/C][C]8.58[/C][C]9.55520242416515[/C][C]-0.975202424165153[/C][/ROW]
[ROW][C]77[/C][C]8.56[/C][C]9.35663519279137[/C][C]-0.796635192791367[/C][/ROW]
[ROW][C]78[/C][C]8.47[/C][C]9.32168117364273[/C][C]-0.85168117364273[/C][/ROW]
[ROW][C]79[/C][C]8.46[/C][C]9.08077621290949[/C][C]-0.620776212909493[/C][/ROW]
[ROW][C]80[/C][C]8.75[/C][C]9.16686368539998[/C][C]-0.41686368539998[/C][/ROW]
[ROW][C]81[/C][C]8.95[/C][C]9.22177811881421[/C][C]-0.271778118814212[/C][/ROW]
[ROW][C]82[/C][C]9.33[/C][C]9.00646521663304[/C][C]0.323534783366956[/C][/ROW]
[ROW][C]83[/C][C]9.51[/C][C]9.02587022684909[/C][C]0.484129773150912[/C][/ROW]
[ROW][C]84[/C][C]9.561[/C][C]8.79644354394518[/C][C]0.764556456054819[/C][/ROW]
[ROW][C]85[/C][C]9.94[/C][C]9.07104805927655[/C][C]0.868951940723446[/C][/ROW]
[ROW][C]86[/C][C]9.9[/C][C]9.27463210605324[/C][C]0.625367893946762[/C][/ROW]
[ROW][C]87[/C][C]9.275[/C][C]9.17413639625525[/C][C]0.100863603744752[/C][/ROW]
[ROW][C]88[/C][C]9.56[/C][C]9.16081461393403[/C][C]0.399185386065974[/C][/ROW]
[ROW][C]89[/C][C]9.779[/C][C]8.97384341802118[/C][C]0.805156581978824[/C][/ROW]
[ROW][C]90[/C][C]9.746[/C][C]9.10108009147924[/C][C]0.644919908520757[/C][/ROW]
[ROW][C]91[/C][C]9.991[/C][C]9.21891980762969[/C][C]0.77208019237031[/C][/ROW]
[ROW][C]92[/C][C]9.98[/C][C]9.34770134063471[/C][C]0.632298659365295[/C][/ROW]
[ROW][C]93[/C][C]10.195[/C][C]9.54244786539016[/C][C]0.652552134609839[/C][/ROW]
[ROW][C]94[/C][C]10.31[/C][C]9.70500487927981[/C][C]0.604995120720189[/C][/ROW]
[ROW][C]95[/C][C]10.25[/C][C]9.82343450114748[/C][C]0.426565498852524[/C][/ROW]
[ROW][C]96[/C][C]9.871[/C][C]9.7015114240432[/C][C]0.1694885759568[/C][/ROW]
[ROW][C]97[/C][C]10.06[/C][C]10.0055114634471[/C][C]0.0544885365529056[/C][/ROW]
[ROW][C]98[/C][C]9.894[/C][C]9.96895713396508[/C][C]-0.0749571339650809[/C][/ROW]
[ROW][C]99[/C][C]9.59[/C][C]9.49795980573298[/C][C]0.0920401942670233[/C][/ROW]
[ROW][C]100[/C][C]9.64[/C][C]9.00146200616894[/C][C]0.638537993831059[/C][/ROW]
[ROW][C]101[/C][C]9.89[/C][C]9.09728163308206[/C][C]0.792718366917944[/C][/ROW]
[ROW][C]102[/C][C]9.53[/C][C]8.92377733758868[/C][C]0.606222662411316[/C][/ROW]
[ROW][C]103[/C][C]9.388[/C][C]8.96171663973362[/C][C]0.426283360266379[/C][/ROW]
[ROW][C]104[/C][C]9.16[/C][C]8.95962397154615[/C][C]0.200376028453855[/C][/ROW]
[ROW][C]105[/C][C]9.418[/C][C]9.22474460540115[/C][C]0.193255394598849[/C][/ROW]
[ROW][C]106[/C][C]9.57[/C][C]9.23080027193856[/C][C]0.339199728061443[/C][/ROW]
[ROW][C]107[/C][C]9.857[/C][C]9.39161709584401[/C][C]0.465382904155992[/C][/ROW]
[ROW][C]108[/C][C]9.877[/C][C]9.29807189462429[/C][C]0.578928105375711[/C][/ROW]
[ROW][C]109[/C][C]9.76[/C][C]9.40889300711011[/C][C]0.351106992889895[/C][/ROW]
[ROW][C]110[/C][C]9.76[/C][C]9.61080371938521[/C][C]0.149196280614794[/C][/ROW]
[ROW][C]111[/C][C]9.695[/C][C]9.36743807434676[/C][C]0.32756192565324[/C][/ROW]
[ROW][C]112[/C][C]9.475[/C][C]9.41817636718456[/C][C]0.0568236328154434[/C][/ROW]
[ROW][C]113[/C][C]9.262[/C][C]9.03440403876184[/C][C]0.22759596123816[/C][/ROW]
[ROW][C]114[/C][C]9.097[/C][C]9.01992086072346[/C][C]0.077079139276544[/C][/ROW]
[ROW][C]115[/C][C]8.55[/C][C]8.59179899370941[/C][C]-0.0417989937094088[/C][/ROW]
[ROW][C]116[/C][C]8.16[/C][C]8.33741455584373[/C][C]-0.177414555843734[/C][/ROW]
[ROW][C]117[/C][C]7.532[/C][C]8.01872712084923[/C][C]-0.486727120849235[/C][/ROW]
[ROW][C]118[/C][C]7.325[/C][C]7.66559747825358[/C][C]-0.340597478253582[/C][/ROW]
[ROW][C]119[/C][C]6.749[/C][C]6.88292794479699[/C][C]-0.133927944796993[/C][/ROW]
[ROW][C]120[/C][C]7.13[/C][C]7.33987581723981[/C][C]-0.209875817239808[/C][/ROW]
[ROW][C]121[/C][C]6.995[/C][C]7.25176452441999[/C][C]-0.256764524419989[/C][/ROW]
[ROW][C]122[/C][C]7.346[/C][C]7.65768583714209[/C][C]-0.311685837142088[/C][/ROW]
[ROW][C]123[/C][C]7.73[/C][C]7.68737260308018[/C][C]0.0426273969198215[/C][/ROW]
[ROW][C]124[/C][C]7.837[/C][C]7.77958625205236[/C][C]0.0574137479476419[/C][/ROW]
[ROW][C]125[/C][C]7.514[/C][C]7.30089090340952[/C][C]0.213109096590484[/C][/ROW]
[ROW][C]126[/C][C]7.58[/C][C]7.49698528183688[/C][C]0.0830147181631209[/C][/ROW]
[ROW][C]127[/C][C]6.83[/C][C]6.97026649121758[/C][C]-0.140266491217578[/C][/ROW]
[ROW][C]128[/C][C]6.617[/C][C]6.94770498826007[/C][C]-0.330704988260066[/C][/ROW]
[ROW][C]129[/C][C]6.715[/C][C]6.85793751373935[/C][C]-0.142937513739348[/C][/ROW]
[ROW][C]130[/C][C]6.63[/C][C]7.16202418758732[/C][C]-0.532024187587325[/C][/ROW]
[ROW][C]131[/C][C]6.891[/C][C]7.4477753010583[/C][C]-0.556775301058296[/C][/ROW]
[ROW][C]132[/C][C]7.002[/C][C]7.41060640642365[/C][C]-0.408606406423648[/C][/ROW]
[ROW][C]133[/C][C]7.09[/C][C]7.7354364279641[/C][C]-0.645436427964097[/C][/ROW]
[ROW][C]134[/C][C]7.36[/C][C]7.89808052644096[/C][C]-0.538080526440962[/C][/ROW]
[ROW][C]135[/C][C]7.477[/C][C]7.88049991876462[/C][C]-0.403499918764622[/C][/ROW]
[ROW][C]136[/C][C]7.826[/C][C]8.0289770024401[/C][C]-0.202977002440097[/C][/ROW]
[ROW][C]137[/C][C]7.79[/C][C]7.92858587033793[/C][C]-0.138585870337928[/C][/ROW]
[ROW][C]138[/C][C]7.578[/C][C]7.6556195655335[/C][C]-0.0776195655334987[/C][/ROW]
[ROW][C]139[/C][C]7.204[/C][C]7.36543233412228[/C][C]-0.161432334122282[/C][/ROW]
[ROW][C]140[/C][C]7.198[/C][C]7.50356466075812[/C][C]-0.305564660758117[/C][/ROW]
[ROW][C]141[/C][C]7.685[/C][C]7.6437674351298[/C][C]0.0412325648702015[/C][/ROW]
[ROW][C]142[/C][C]7.795[/C][C]7.6140614296379[/C][C]0.180938570362096[/C][/ROW]
[ROW][C]143[/C][C]7.46[/C][C]7.6011898358595[/C][C]-0.141189835859505[/C][/ROW]
[ROW][C]144[/C][C]7.274[/C][C]7.59561880619321[/C][C]-0.321618806193206[/C][/ROW]
[ROW][C]145[/C][C]7.33[/C][C]7.92478016266551[/C][C]-0.594780162665506[/C][/ROW]
[ROW][C]146[/C][C]7.655[/C][C]7.8467355476029[/C][C]-0.191735547602895[/C][/ROW]
[ROW][C]147[/C][C]7.767[/C][C]7.84818982641775[/C][C]-0.0811898264177478[/C][/ROW]
[ROW][C]148[/C][C]7.84[/C][C]7.69897152299925[/C][C]0.141028477000746[/C][/ROW]
[ROW][C]149[/C][C]7.424[/C][C]7.31611447074758[/C][C]0.10788552925242[/C][/ROW]
[ROW][C]150[/C][C]7.54[/C][C]7.2562479368498[/C][C]0.283752063150199[/C][/ROW]
[ROW][C]151[/C][C]7.351[/C][C]6.81767527523618[/C][C]0.533324724763823[/C][/ROW]
[ROW][C]152[/C][C]6.735[/C][C]6.57665107473547[/C][C]0.158348925264534[/C][/ROW]
[ROW][C]153[/C][C]6.777[/C][C]6.44403464284677[/C][C]0.332965357153234[/C][/ROW]
[ROW][C]154[/C][C]6.679[/C][C]6.21285359590945[/C][C]0.466146404090553[/C][/ROW]
[ROW][C]155[/C][C]7.34[/C][C]6.4692375247442[/C][C]0.870762475255799[/C][/ROW]
[ROW][C]156[/C][C]6.978[/C][C]6.41023265115778[/C][C]0.567767348842218[/C][/ROW]
[ROW][C]157[/C][C]6.92[/C][C]6.3714594553331[/C][C]0.548540544666898[/C][/ROW]
[ROW][C]158[/C][C]6.628[/C][C]6.08501362948671[/C][C]0.542986370513295[/C][/ROW]
[ROW][C]159[/C][C]6.385[/C][C]6.19131577860489[/C][C]0.193684221395108[/C][/ROW]
[ROW][C]160[/C][C]5.984[/C][C]6.31280673199654[/C][C]-0.328806731996542[/C][/ROW]
[ROW][C]161[/C][C]6.268[/C][C]6.04392408427795[/C][C]0.224075915722053[/C][/ROW]
[ROW][C]162[/C][C]6.596[/C][C]6.2030131716913[/C][C]0.392986828308705[/C][/ROW]
[ROW][C]163[/C][C]6.395[/C][C]6.38793062228958[/C][C]0.00706937771041785[/C][/ROW]
[ROW][C]164[/C][C]6.715[/C][C]6.5315913579911[/C][C]0.183408642008902[/C][/ROW]
[ROW][C]165[/C][C]6.804[/C][C]6.6659089499004[/C][C]0.138091050099604[/C][/ROW]
[ROW][C]166[/C][C]6.929[/C][C]6.8189817208513[/C][C]0.110018279148701[/C][/ROW]
[ROW][C]167[/C][C]6.846[/C][C]6.82911536670784[/C][C]0.0168846332921595[/C][/ROW]
[ROW][C]168[/C][C]6.992[/C][C]6.76663120185351[/C][C]0.225368798146487[/C][/ROW]
[ROW][C]169[/C][C]6.774[/C][C]6.80967685647817[/C][C]-0.0356768564781696[/C][/ROW]
[ROW][C]170[/C][C]6.75[/C][C]6.65415060719599[/C][C]0.0958493928040078[/C][/ROW]
[ROW][C]171[/C][C]6.485[/C][C]6.55098563695038[/C][C]-0.0659856369503793[/C][/ROW]
[ROW][C]172[/C][C]6.27[/C][C]6.29556962339387[/C][C]-0.0255696233938693[/C][/ROW]
[ROW][C]173[/C][C]6.47[/C][C]6.38107654240241[/C][C]0.0889234575975886[/C][/ROW]
[ROW][C]174[/C][C]6.78[/C][C]6.57251950767288[/C][C]0.207480492327117[/C][/ROW]
[ROW][C]175[/C][C]6.71[/C][C]6.4335727566576[/C][C]0.276427243342401[/C][/ROW]
[ROW][C]176[/C][C]6.141[/C][C]6.45323795982391[/C][C]-0.312237959823906[/C][/ROW]
[ROW][C]177[/C][C]6.72[/C][C]6.74243876622765[/C][C]-0.0224387662276512[/C][/ROW]
[ROW][C]178[/C][C]6.68[/C][C]6.71705803751214[/C][C]-0.0370580375121356[/C][/ROW]
[ROW][C]179[/C][C]6.371[/C][C]6.67091096813284[/C][C]-0.299910968132837[/C][/ROW]
[ROW][C]180[/C][C]6.097[/C][C]6.60771091477603[/C][C]-0.510710914776027[/C][/ROW]
[ROW][C]181[/C][C]6.27[/C][C]6.96088289515313[/C][C]-0.690882895153134[/C][/ROW]
[ROW][C]182[/C][C]6.447[/C][C]6.98917884894087[/C][C]-0.542178848940874[/C][/ROW]
[ROW][C]183[/C][C]6.37[/C][C]6.90980085404659[/C][C]-0.539800854046588[/C][/ROW]
[ROW][C]184[/C][C]6.446[/C][C]7.03793754692465[/C][C]-0.591937546924646[/C][/ROW]
[ROW][C]185[/C][C]6.54[/C][C]6.87464482845116[/C][C]-0.334644828451161[/C][/ROW]
[ROW][C]186[/C][C]6.374[/C][C]6.80185783144645[/C][C]-0.427857831446447[/C][/ROW]
[ROW][C]187[/C][C]6.33[/C][C]6.62983953310006[/C][C]-0.299839533100059[/C][/ROW]
[ROW][C]188[/C][C]6.63[/C][C]6.5817228100742[/C][C]0.0482771899257987[/C][/ROW]
[ROW][C]189[/C][C]6.498[/C][C]6.79327458232561[/C][C]-0.295274582325611[/C][/ROW]
[ROW][C]190[/C][C]6.485[/C][C]6.81272456563983[/C][C]-0.327724565639833[/C][/ROW]
[ROW][C]191[/C][C]6.36[/C][C]6.81941052435399[/C][C]-0.459410524353993[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146379&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146379&T=4

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	11.73	10.8115166850611	0.918483314938921
2	11.75	10.7250122626075	1.02498773739251
3	11.39	10.4019667341861	0.988033265813942
4	11.54	10.3289101647613	1.21108983523866
5	9.62	10.2908781055519	-0.670878105551865
6	9.82	10.4130353955693	-0.593035395569273
7	9.94	10.2461385709797	-0.306138570979669
8	9.9	10.0836602420698	-0.183660242069775
9	9.8	10.2759495953498	-0.475949595349817
10	9.86	10.3189553700776	-0.458955370077594
11	10.5	10.1759505230701	0.324049476929866
12	10.33	10.0056853539355	0.324314646064535
13	10.16	9.88970126295025	0.270298737049754
14	9.91	9.69225053406982	0.217749465930182
15	9.96	9.5403524421277	0.419647557872297
16	10.03	9.8397954990235	0.190204500976504
17	9.55	9.80688077990111	-0.256880779901105
18	9.51	9.93283402796397	-0.422834027963967
19	9.8	9.98329724350792	-0.183297243507923
20	10.08	9.86447001283802	0.215529987161982
21	10.2	9.69343810475313	0.506561895246867
22	10.23	9.88690241635473	0.343097583645266
23	10.2	10.2111821848045	-0.0111821848044533
24	10.07	10.1150268028644	-0.0450268028643546
25	10.01	10.1283329386444	-0.118332938644387
26	10.05	10.01800277597	0.0319972240299933
27	9.92	10.1271924503755	-0.207192450375495
28	10.03	10.1258491140287	-0.0958491140286924
29	10.18	10.0225316037304	0.15746839626965
30	10.1	9.97493485906157	0.125065140938427
31	10.16	10.7043701182734	-0.544370118273447
32	10.15	10.6890709204263	-0.539070920426282
33	10.13	10.6992672991947	-0.569267299194714
34	10.09	10.9548536337588	-0.864853633758794
35	10.18	11.1068751345876	-0.926875134587639
36	10.06	11.0660356756888	-1.00603567568877
37	9.65	10.1179670370563	-0.467967037056333
38	9.74	10.0868134620703	-0.346813462070282
39	9.53	9.83388536865625	-0.303885368656252
40	9.5	10.0321791260946	-0.532179126094647
41	9	9.72345900548243	-0.723459005482431
42	9.15	9.63417790751336	-0.484177907513363
43	9.32	9.70273640665487	-0.382736406654873
44	9.62	9.68101074390496	-0.0610107439049664
45	9.59	9.5036064942287	0.0863935057713036
46	9.37	9.38799081013808	-0.0179908101380854
47	9.35	9.5371480470467	-0.187148047046695
48	9.32	9.50935073796686	-0.189350737966857
49	9.49	9.80970480341725	-0.319704803417253
50	9.52	9.90519240507563	-0.385192405075634
51	9.59	9.69060130772455	-0.100601307724551
52	9.35	9.4505905320861	-0.100590532086101
53	9.2	9.24502533322068	-0.0450253332206761
54	9.57	9.2731165286661	0.296883471333902
55	9.78	9.28955720602983	0.490442793970172
56	9.79	9.12667285026805	0.66332714973195
57	9.57	9.39484233066975	0.175157669330247
58	9.53	9.42659512100652	0.103404878993478
59	9.65	9.37607820770216	0.273921792297839
60	9.36	9.32518545304971	0.0348145469502888
61	9.4	9.04274474921015	0.357255250789849
62	9.32	9.05012943513383	0.26987056486617
63	9.31	8.98004333854167	0.329956661458325
64	9.19	9.03117147274628	0.158828527253717
65	9.39	9.04082418983059	0.349175810169411
66	9.28	9.14019852276081	0.139801477239191
67	9.28	9.10497178794877	0.17502821205123
68	9.31	9.08503882542549	0.224961174574511
69	9.28	9.14183657517956	0.138163424820444
70	9.31	9.20213126542133	0.107868734578667
71	9.35	9.49527661329468	-0.145276613294681
72	9.19	9.16401331623819	0.0259866837618126
73	9.07	9.3095796718128	-0.239579671812798
74	8.96	9.5273611688599	-0.567361168859904
75	8.69	9.48225946418887	-0.792259464188875
76	8.58	9.55520242416515	-0.975202424165153
77	8.56	9.35663519279137	-0.796635192791367
78	8.47	9.32168117364273	-0.85168117364273
79	8.46	9.08077621290949	-0.620776212909493
80	8.75	9.16686368539998	-0.41686368539998
81	8.95	9.22177811881421	-0.271778118814212
82	9.33	9.00646521663304	0.323534783366956
83	9.51	9.02587022684909	0.484129773150912
84	9.561	8.79644354394518	0.764556456054819
85	9.94	9.07104805927655	0.868951940723446
86	9.9	9.27463210605324	0.625367893946762
87	9.275	9.17413639625525	0.100863603744752
88	9.56	9.16081461393403	0.399185386065974
89	9.779	8.97384341802118	0.805156581978824
90	9.746	9.10108009147924	0.644919908520757
91	9.991	9.21891980762969	0.77208019237031
92	9.98	9.34770134063471	0.632298659365295
93	10.195	9.54244786539016	0.652552134609839
94	10.31	9.70500487927981	0.604995120720189
95	10.25	9.82343450114748	0.426565498852524
96	9.871	9.7015114240432	0.1694885759568
97	10.06	10.0055114634471	0.0544885365529056
98	9.894	9.96895713396508	-0.0749571339650809
99	9.59	9.49795980573298	0.0920401942670233
100	9.64	9.00146200616894	0.638537993831059
101	9.89	9.09728163308206	0.792718366917944
102	9.53	8.92377733758868	0.606222662411316
103	9.388	8.96171663973362	0.426283360266379
104	9.16	8.95962397154615	0.200376028453855
105	9.418	9.22474460540115	0.193255394598849
106	9.57	9.23080027193856	0.339199728061443
107	9.857	9.39161709584401	0.465382904155992
108	9.877	9.29807189462429	0.578928105375711
109	9.76	9.40889300711011	0.351106992889895
110	9.76	9.61080371938521	0.149196280614794
111	9.695	9.36743807434676	0.32756192565324
112	9.475	9.41817636718456	0.0568236328154434
113	9.262	9.03440403876184	0.22759596123816
114	9.097	9.01992086072346	0.077079139276544
115	8.55	8.59179899370941	-0.0417989937094088
116	8.16	8.33741455584373	-0.177414555843734
117	7.532	8.01872712084923	-0.486727120849235
118	7.325	7.66559747825358	-0.340597478253582
119	6.749	6.88292794479699	-0.133927944796993
120	7.13	7.33987581723981	-0.209875817239808
121	6.995	7.25176452441999	-0.256764524419989
122	7.346	7.65768583714209	-0.311685837142088
123	7.73	7.68737260308018	0.0426273969198215
124	7.837	7.77958625205236	0.0574137479476419
125	7.514	7.30089090340952	0.213109096590484
126	7.58	7.49698528183688	0.0830147181631209
127	6.83	6.97026649121758	-0.140266491217578
128	6.617	6.94770498826007	-0.330704988260066
129	6.715	6.85793751373935	-0.142937513739348
130	6.63	7.16202418758732	-0.532024187587325
131	6.891	7.4477753010583	-0.556775301058296
132	7.002	7.41060640642365	-0.408606406423648
133	7.09	7.7354364279641	-0.645436427964097
134	7.36	7.89808052644096	-0.538080526440962
135	7.477	7.88049991876462	-0.403499918764622
136	7.826	8.0289770024401	-0.202977002440097
137	7.79	7.92858587033793	-0.138585870337928
138	7.578	7.6556195655335	-0.0776195655334987
139	7.204	7.36543233412228	-0.161432334122282
140	7.198	7.50356466075812	-0.305564660758117
141	7.685	7.6437674351298	0.0412325648702015
142	7.795	7.6140614296379	0.180938570362096
143	7.46	7.6011898358595	-0.141189835859505
144	7.274	7.59561880619321	-0.321618806193206
145	7.33	7.92478016266551	-0.594780162665506
146	7.655	7.8467355476029	-0.191735547602895
147	7.767	7.84818982641775	-0.0811898264177478
148	7.84	7.69897152299925	0.141028477000746
149	7.424	7.31611447074758	0.10788552925242
150	7.54	7.2562479368498	0.283752063150199
151	7.351	6.81767527523618	0.533324724763823
152	6.735	6.57665107473547	0.158348925264534
153	6.777	6.44403464284677	0.332965357153234
154	6.679	6.21285359590945	0.466146404090553
155	7.34	6.4692375247442	0.870762475255799
156	6.978	6.41023265115778	0.567767348842218
157	6.92	6.3714594553331	0.548540544666898
158	6.628	6.08501362948671	0.542986370513295
159	6.385	6.19131577860489	0.193684221395108
160	5.984	6.31280673199654	-0.328806731996542
161	6.268	6.04392408427795	0.224075915722053
162	6.596	6.2030131716913	0.392986828308705
163	6.395	6.38793062228958	0.00706937771041785
164	6.715	6.5315913579911	0.183408642008902
165	6.804	6.6659089499004	0.138091050099604
166	6.929	6.8189817208513	0.110018279148701
167	6.846	6.82911536670784	0.0168846332921595
168	6.992	6.76663120185351	0.225368798146487
169	6.774	6.80967685647817	-0.0356768564781696
170	6.75	6.65415060719599	0.0958493928040078
171	6.485	6.55098563695038	-0.0659856369503793
172	6.27	6.29556962339387	-0.0255696233938693
173	6.47	6.38107654240241	0.0889234575975886
174	6.78	6.57251950767288	0.207480492327117
175	6.71	6.4335727566576	0.276427243342401
176	6.141	6.45323795982391	-0.312237959823906
177	6.72	6.74243876622765	-0.0224387662276512
178	6.68	6.71705803751214	-0.0370580375121356
179	6.371	6.67091096813284	-0.299910968132837
180	6.097	6.60771091477603	-0.510710914776027
181	6.27	6.96088289515313	-0.690882895153134
182	6.447	6.98917884894087	-0.542178848940874
183	6.37	6.90980085404659	-0.539800854046588
184	6.446	7.03793754692465	-0.591937546924646
185	6.54	6.87464482845116	-0.334644828451161
186	6.374	6.80185783144645	-0.427857831446447
187	6.33	6.62983953310006	-0.299839533100059
188	6.63	6.5817228100742	0.0482771899257987
189	6.498	6.79327458232561	-0.295274582325611
190	6.485	6.81272456563983	-0.327724565639833
191	6.36	6.81941052435399	-0.459410524353993

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
20	0.31542661815014	0.630853236300281	0.68457338184986
21	0.374782090015821	0.749564180031642	0.625217909984179
22	0.429973655937269	0.859947311874537	0.570026344062731
23	0.303839952025256	0.607679904050513	0.696160047974744
24	0.210314124448672	0.420628248897343	0.789685875551328
25	0.182312915669312	0.364625831338624	0.817687084330688
26	0.140485928834177	0.280971857668354	0.859514071165823
27	0.13782886467734	0.27565772935468	0.86217113532266
28	0.0988450462686875	0.197690092537375	0.901154953731312
29	0.464540811580436	0.929081623160873	0.535459188419564
30	0.738838232074571	0.522323535850858	0.261161767925429
31	0.731124792504985	0.537750414990031	0.268875207495015
32	0.695916357119277	0.608167285761447	0.304083642880723
33	0.645388391840302	0.709223216319396	0.354611608159698
34	0.611280507193621	0.777438985612757	0.388719492806379
35	0.603431006565121	0.793137986869759	0.396568993434879
36	0.588466455910872	0.823067088178256	0.411533544089128
37	0.528773871233898	0.942452257532205	0.471226128766102
38	0.467607851392948	0.935215702785896	0.532392148607052
39	0.403014919555115	0.806029839110229	0.596985080444886
40	0.356925983274998	0.713851966549995	0.643074016725003
41	0.337862056669644	0.675724113339289	0.662137943330356
42	0.309547108948808	0.619094217897616	0.690452891051192
43	0.27610031884788	0.552200637695759	0.723899681152121
44	0.230095515130043	0.460191030260086	0.769904484869957
45	0.186452656983332	0.372905313966663	0.813547343016668
46	0.158872691454369	0.317745382908737	0.841127308545631
47	0.145732468257251	0.291464936514502	0.854267531742749
48	0.125850804309319	0.251701608618638	0.874149195690681
49	0.660406801493471	0.679186397013059	0.33959319850653
50	0.716409609087055	0.56718078182589	0.283590390912945
51	0.678094524697384	0.643810950605233	0.321905475302616
52	0.649332641434899	0.701334717130201	0.350667358565101
53	0.608637889029522	0.782724221940955	0.391362110970478
54	0.588295970823572	0.823408058352857	0.411704029176428
55	0.565089450832861	0.869821098334278	0.434910549167139
56	0.543541881630758	0.912916236738484	0.456458118369242
57	0.492208623844936	0.984417247689873	0.507791376155064
58	0.443164405635233	0.886328811270466	0.556835594364767
59	0.39280590302712	0.785611806054239	0.60719409697288
60	0.352752593678151	0.705505187356302	0.647247406321849
61	0.332362185740743	0.664724371481486	0.667637814259257
62	0.306737099919738	0.613474199839475	0.693262900080262
63	0.270448629979961	0.540897259959921	0.729551370020039
64	0.240047267610495	0.480094535220991	0.759952732389505
65	0.227830619128606	0.455661238257211	0.772169380871394
66	0.193246036247987	0.386492072495973	0.806753963752013
67	0.163294223606622	0.326588447213243	0.836705776393378
68	0.141282122614892	0.282564245229783	0.858717877385109
69	0.119563581877623	0.239127163755246	0.880436418122377
70	0.0983325567430524	0.196665113486105	0.901667443256948
71	0.0996840683745272	0.199368136749054	0.900315931625473
72	0.088509709185209	0.177019418370418	0.911490290814791
73	0.110505077715517	0.221010155431034	0.889494922284483
74	0.160107624215727	0.320215248431455	0.839892375784272
75	0.24511702034816	0.49023404069632	0.75488297965184
76	0.381740815479972	0.763481630959943	0.618259184520028
77	0.494022842090949	0.988045684181898	0.505977157909051
78	0.666405048384646	0.667189903230709	0.333594951615354
79	0.785700013194308	0.428599973611383	0.214299986805692
80	0.827369629446084	0.345260741107832	0.172630370553916
81	0.858689568266514	0.282620863466973	0.141310431733486
82	0.860643996962425	0.27871200607515	0.139356003037575
83	0.855394386073555	0.289211227852891	0.144605613926445
84	0.869622876791192	0.260754246417615	0.130377123208808
85	0.905571532331688	0.188856935336625	0.0944284676683125
86	0.914942582224192	0.170114835551615	0.0850574177758076
87	0.904206844709178	0.191586310581643	0.0957931552908215
88	0.902599504077055	0.19480099184589	0.097400495922945
89	0.943897213363892	0.112205573272216	0.0561027866361081
90	0.958376474167086	0.0832470516658271	0.0416235258329136
91	0.972438829007469	0.0551223419850621	0.027561170992531
92	0.976323229643032	0.0473535407139368	0.0236767703569684
93	0.982653528042953	0.0346929439140947	0.0173464719570473
94	0.986775487016218	0.0264490259675649	0.0132245129837824
95	0.986796181430368	0.0264076371392647	0.0132038185696323
96	0.983450524164261	0.0330989516714774	0.0165494758357387
97	0.979298006880909	0.0414039862381823	0.0207019931190912
98	0.973338255611596	0.0533234887768076	0.0266617443884038
99	0.965696760952213	0.0686064780955738	0.0343032390477869
100	0.972322099026253	0.0553558019474935	0.0276779009737468
101	0.984362368829498	0.0312752623410048	0.0156376311705024
102	0.984511298474666	0.030977403050668	0.015488701525334
103	0.981019612971298	0.037960774057403	0.0189803870287015
104	0.976317139493417	0.0473657210131667	0.0236828605065834
105	0.970204877960317	0.0595902440793667	0.0297951220396833
106	0.965690790527947	0.0686184189441049	0.0343092094720525
107	0.968242674335749	0.0635146513285011	0.0317573256642506
108	0.977306127372044	0.0453877452559109	0.0226938726279555
109	0.984879903132029	0.0302401937359413	0.0151200968679707
110	0.986293012373608	0.0274139752527841	0.013706987626392
111	0.991312953877547	0.0173740922449069	0.00868704612245345
112	0.993252186393979	0.0134956272120418	0.0067478136060209
113	0.995085227688585	0.00982954462282987	0.00491477231141494
114	0.996465112476307	0.00706977504738594	0.00353488752369297
115	0.998288010626671	0.00342397874665823	0.00171198937332912
116	0.999415907102072	0.00116818579585635	0.000584092897928174
117	0.999702091538871	0.000595816922257503	0.000297908461128751
118	0.999788883717072	0.000422232565856281	0.00021111628292814
119	0.999850796029128	0.000298407941744896	0.000149203970872448
120	0.999822856411893	0.000354287176213653	0.000177143588106826
121	0.999794277498385	0.000411445003230767	0.000205722501615383
122	0.999737258104898	0.000525483790203196	0.000262741895101598
123	0.999729901094316	0.000540197811368857	0.000270098905684428
124	0.99981391080614	0.000372178387720261	0.00018608919386013
125	0.999751596898171	0.000496806203658612	0.000248403101829306
126	0.999699922806626	0.000600154386747976	0.000300077193373988
127	0.999550942242643	0.000898115514713517	0.000449057757356758
128	0.999481925249883	0.00103614950023306	0.00051807475011653
129	0.999494336737831	0.00101132652433801	0.000505663262169007
130	0.999766944064783	0.000466111870434802	0.000233055935217401
131	0.999802047934524	0.000395904130951217	0.000197952065475608
132	0.999828003861582	0.000343992276835984	0.000171996138417992
133	0.999924343299072	0.000151313401855558	7.56567009277788e-05
134	0.999969209616986	6.15807660287947e-05	3.07903830143973e-05
135	0.999996111806734	7.77638653234047e-06	3.88819326617024e-06
136	0.999999947161171	1.056776571567e-07	5.28388285783501e-08
137	0.999999894860818	2.10278364478801e-07	1.05139182239401e-07
138	0.999999769678566	4.60642867825941e-07	2.30321433912971e-07
139	0.999999682697058	6.34605884284664e-07	3.17302942142332e-07
140	0.999999612240628	7.75518743753209e-07	3.87759371876605e-07
141	0.999999133092502	1.73381499525289e-06	8.66907497626443e-07
142	0.99999851555922	2.96888155995675e-06	1.48444077997838e-06
143	0.999997019053443	5.96189311401489e-06	2.98094655700744e-06
144	0.999997178153707	5.64369258508935e-06	2.82184629254467e-06
145	0.999998791208024	2.41758395103517e-06	1.20879197551759e-06
146	0.999998077257431	3.84548513766787e-06	1.92274256883393e-06
147	0.999996008260856	7.98347828737386e-06	3.99173914368693e-06
148	0.999995246731744	9.50653651130403e-06	4.75326825565201e-06
149	0.999989478089654	2.10438206915513e-05	1.05219103457757e-05
150	0.999977175685122	4.56486297563678e-05	2.28243148781839e-05
151	0.999960314458537	7.93710829257772e-05	3.96855414628886e-05
152	0.999960783166367	7.84336672664157e-05	3.92168336332079e-05
153	0.999963080255819	7.3839488361271e-05	3.69197441806355e-05
154	0.999956865652528	8.62686949431953e-05	4.31343474715976e-05
155	0.999971243628491	5.75127430174894e-05	2.87563715087447e-05
156	0.999931739937308	0.000136520125384534	6.82600626922671e-05
157	0.999848129930545	0.000303740138910456	0.000151870069455228
158	0.999717158774542	0.000565682450915196	0.000282841225457598
159	0.99954478472469	0.000910430550620946	0.000455215275310473
160	0.999251585469394	0.00149682906121254	0.000748414530606269
161	0.998300744022892	0.00339851195421551	0.00169925597710775
162	0.99768616037434	0.00462767925132067	0.00231383962566033
163	0.995172823145726	0.00965435370854833	0.00482717685427417
164	0.992217686454129	0.0155646270917429	0.00778231354587144
165	0.984011254356872	0.0319774912862567	0.0159887456431283
166	0.971146473720545	0.0577070525589094	0.0288535262794547
167	0.947716012200546	0.104567975598908	0.0522839877994539
168	0.947512069907534	0.104975860184932	0.0524879300924658
169	0.930389732520387	0.139220534959227	0.0696102674796134
170	0.900902381615416	0.198195236769167	0.0990976183845837
171	0.840224127919028	0.319551744161944	0.159775872080972

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
20 & 0.31542661815014 & 0.630853236300281 & 0.68457338184986 \tabularnewline
21 & 0.374782090015821 & 0.749564180031642 & 0.625217909984179 \tabularnewline
22 & 0.429973655937269 & 0.859947311874537 & 0.570026344062731 \tabularnewline
23 & 0.303839952025256 & 0.607679904050513 & 0.696160047974744 \tabularnewline
24 & 0.210314124448672 & 0.420628248897343 & 0.789685875551328 \tabularnewline
25 & 0.182312915669312 & 0.364625831338624 & 0.817687084330688 \tabularnewline
26 & 0.140485928834177 & 0.280971857668354 & 0.859514071165823 \tabularnewline
27 & 0.13782886467734 & 0.27565772935468 & 0.86217113532266 \tabularnewline
28 & 0.0988450462686875 & 0.197690092537375 & 0.901154953731312 \tabularnewline
29 & 0.464540811580436 & 0.929081623160873 & 0.535459188419564 \tabularnewline
30 & 0.738838232074571 & 0.522323535850858 & 0.261161767925429 \tabularnewline
31 & 0.731124792504985 & 0.537750414990031 & 0.268875207495015 \tabularnewline
32 & 0.695916357119277 & 0.608167285761447 & 0.304083642880723 \tabularnewline
33 & 0.645388391840302 & 0.709223216319396 & 0.354611608159698 \tabularnewline
34 & 0.611280507193621 & 0.777438985612757 & 0.388719492806379 \tabularnewline
35 & 0.603431006565121 & 0.793137986869759 & 0.396568993434879 \tabularnewline
36 & 0.588466455910872 & 0.823067088178256 & 0.411533544089128 \tabularnewline
37 & 0.528773871233898 & 0.942452257532205 & 0.471226128766102 \tabularnewline
38 & 0.467607851392948 & 0.935215702785896 & 0.532392148607052 \tabularnewline
39 & 0.403014919555115 & 0.806029839110229 & 0.596985080444886 \tabularnewline
40 & 0.356925983274998 & 0.713851966549995 & 0.643074016725003 \tabularnewline
41 & 0.337862056669644 & 0.675724113339289 & 0.662137943330356 \tabularnewline
42 & 0.309547108948808 & 0.619094217897616 & 0.690452891051192 \tabularnewline
43 & 0.27610031884788 & 0.552200637695759 & 0.723899681152121 \tabularnewline
44 & 0.230095515130043 & 0.460191030260086 & 0.769904484869957 \tabularnewline
45 & 0.186452656983332 & 0.372905313966663 & 0.813547343016668 \tabularnewline
46 & 0.158872691454369 & 0.317745382908737 & 0.841127308545631 \tabularnewline
47 & 0.145732468257251 & 0.291464936514502 & 0.854267531742749 \tabularnewline
48 & 0.125850804309319 & 0.251701608618638 & 0.874149195690681 \tabularnewline
49 & 0.660406801493471 & 0.679186397013059 & 0.33959319850653 \tabularnewline
50 & 0.716409609087055 & 0.56718078182589 & 0.283590390912945 \tabularnewline
51 & 0.678094524697384 & 0.643810950605233 & 0.321905475302616 \tabularnewline
52 & 0.649332641434899 & 0.701334717130201 & 0.350667358565101 \tabularnewline
53 & 0.608637889029522 & 0.782724221940955 & 0.391362110970478 \tabularnewline
54 & 0.588295970823572 & 0.823408058352857 & 0.411704029176428 \tabularnewline
55 & 0.565089450832861 & 0.869821098334278 & 0.434910549167139 \tabularnewline
56 & 0.543541881630758 & 0.912916236738484 & 0.456458118369242 \tabularnewline
57 & 0.492208623844936 & 0.984417247689873 & 0.507791376155064 \tabularnewline
58 & 0.443164405635233 & 0.886328811270466 & 0.556835594364767 \tabularnewline
59 & 0.39280590302712 & 0.785611806054239 & 0.60719409697288 \tabularnewline
60 & 0.352752593678151 & 0.705505187356302 & 0.647247406321849 \tabularnewline
61 & 0.332362185740743 & 0.664724371481486 & 0.667637814259257 \tabularnewline
62 & 0.306737099919738 & 0.613474199839475 & 0.693262900080262 \tabularnewline
63 & 0.270448629979961 & 0.540897259959921 & 0.729551370020039 \tabularnewline
64 & 0.240047267610495 & 0.480094535220991 & 0.759952732389505 \tabularnewline
65 & 0.227830619128606 & 0.455661238257211 & 0.772169380871394 \tabularnewline
66 & 0.193246036247987 & 0.386492072495973 & 0.806753963752013 \tabularnewline
67 & 0.163294223606622 & 0.326588447213243 & 0.836705776393378 \tabularnewline
68 & 0.141282122614892 & 0.282564245229783 & 0.858717877385109 \tabularnewline
69 & 0.119563581877623 & 0.239127163755246 & 0.880436418122377 \tabularnewline
70 & 0.0983325567430524 & 0.196665113486105 & 0.901667443256948 \tabularnewline
71 & 0.0996840683745272 & 0.199368136749054 & 0.900315931625473 \tabularnewline
72 & 0.088509709185209 & 0.177019418370418 & 0.911490290814791 \tabularnewline
73 & 0.110505077715517 & 0.221010155431034 & 0.889494922284483 \tabularnewline
74 & 0.160107624215727 & 0.320215248431455 & 0.839892375784272 \tabularnewline
75 & 0.24511702034816 & 0.49023404069632 & 0.75488297965184 \tabularnewline
76 & 0.381740815479972 & 0.763481630959943 & 0.618259184520028 \tabularnewline
77 & 0.494022842090949 & 0.988045684181898 & 0.505977157909051 \tabularnewline
78 & 0.666405048384646 & 0.667189903230709 & 0.333594951615354 \tabularnewline
79 & 0.785700013194308 & 0.428599973611383 & 0.214299986805692 \tabularnewline
80 & 0.827369629446084 & 0.345260741107832 & 0.172630370553916 \tabularnewline
81 & 0.858689568266514 & 0.282620863466973 & 0.141310431733486 \tabularnewline
82 & 0.860643996962425 & 0.27871200607515 & 0.139356003037575 \tabularnewline
83 & 0.855394386073555 & 0.289211227852891 & 0.144605613926445 \tabularnewline
84 & 0.869622876791192 & 0.260754246417615 & 0.130377123208808 \tabularnewline
85 & 0.905571532331688 & 0.188856935336625 & 0.0944284676683125 \tabularnewline
86 & 0.914942582224192 & 0.170114835551615 & 0.0850574177758076 \tabularnewline
87 & 0.904206844709178 & 0.191586310581643 & 0.0957931552908215 \tabularnewline
88 & 0.902599504077055 & 0.19480099184589 & 0.097400495922945 \tabularnewline
89 & 0.943897213363892 & 0.112205573272216 & 0.0561027866361081 \tabularnewline
90 & 0.958376474167086 & 0.0832470516658271 & 0.0416235258329136 \tabularnewline
91 & 0.972438829007469 & 0.0551223419850621 & 0.027561170992531 \tabularnewline
92 & 0.976323229643032 & 0.0473535407139368 & 0.0236767703569684 \tabularnewline
93 & 0.982653528042953 & 0.0346929439140947 & 0.0173464719570473 \tabularnewline
94 & 0.986775487016218 & 0.0264490259675649 & 0.0132245129837824 \tabularnewline
95 & 0.986796181430368 & 0.0264076371392647 & 0.0132038185696323 \tabularnewline
96 & 0.983450524164261 & 0.0330989516714774 & 0.0165494758357387 \tabularnewline
97 & 0.979298006880909 & 0.0414039862381823 & 0.0207019931190912 \tabularnewline
98 & 0.973338255611596 & 0.0533234887768076 & 0.0266617443884038 \tabularnewline
99 & 0.965696760952213 & 0.0686064780955738 & 0.0343032390477869 \tabularnewline
100 & 0.972322099026253 & 0.0553558019474935 & 0.0276779009737468 \tabularnewline
101 & 0.984362368829498 & 0.0312752623410048 & 0.0156376311705024 \tabularnewline
102 & 0.984511298474666 & 0.030977403050668 & 0.015488701525334 \tabularnewline
103 & 0.981019612971298 & 0.037960774057403 & 0.0189803870287015 \tabularnewline
104 & 0.976317139493417 & 0.0473657210131667 & 0.0236828605065834 \tabularnewline
105 & 0.970204877960317 & 0.0595902440793667 & 0.0297951220396833 \tabularnewline
106 & 0.965690790527947 & 0.0686184189441049 & 0.0343092094720525 \tabularnewline
107 & 0.968242674335749 & 0.0635146513285011 & 0.0317573256642506 \tabularnewline
108 & 0.977306127372044 & 0.0453877452559109 & 0.0226938726279555 \tabularnewline
109 & 0.984879903132029 & 0.0302401937359413 & 0.0151200968679707 \tabularnewline
110 & 0.986293012373608 & 0.0274139752527841 & 0.013706987626392 \tabularnewline
111 & 0.991312953877547 & 0.0173740922449069 & 0.00868704612245345 \tabularnewline
112 & 0.993252186393979 & 0.0134956272120418 & 0.0067478136060209 \tabularnewline
113 & 0.995085227688585 & 0.00982954462282987 & 0.00491477231141494 \tabularnewline
114 & 0.996465112476307 & 0.00706977504738594 & 0.00353488752369297 \tabularnewline
115 & 0.998288010626671 & 0.00342397874665823 & 0.00171198937332912 \tabularnewline
116 & 0.999415907102072 & 0.00116818579585635 & 0.000584092897928174 \tabularnewline
117 & 0.999702091538871 & 0.000595816922257503 & 0.000297908461128751 \tabularnewline
118 & 0.999788883717072 & 0.000422232565856281 & 0.00021111628292814 \tabularnewline
119 & 0.999850796029128 & 0.000298407941744896 & 0.000149203970872448 \tabularnewline
120 & 0.999822856411893 & 0.000354287176213653 & 0.000177143588106826 \tabularnewline
121 & 0.999794277498385 & 0.000411445003230767 & 0.000205722501615383 \tabularnewline
122 & 0.999737258104898 & 0.000525483790203196 & 0.000262741895101598 \tabularnewline
123 & 0.999729901094316 & 0.000540197811368857 & 0.000270098905684428 \tabularnewline
124 & 0.99981391080614 & 0.000372178387720261 & 0.00018608919386013 \tabularnewline
125 & 0.999751596898171 & 0.000496806203658612 & 0.000248403101829306 \tabularnewline
126 & 0.999699922806626 & 0.000600154386747976 & 0.000300077193373988 \tabularnewline
127 & 0.999550942242643 & 0.000898115514713517 & 0.000449057757356758 \tabularnewline
128 & 0.999481925249883 & 0.00103614950023306 & 0.00051807475011653 \tabularnewline
129 & 0.999494336737831 & 0.00101132652433801 & 0.000505663262169007 \tabularnewline
130 & 0.999766944064783 & 0.000466111870434802 & 0.000233055935217401 \tabularnewline
131 & 0.999802047934524 & 0.000395904130951217 & 0.000197952065475608 \tabularnewline
132 & 0.999828003861582 & 0.000343992276835984 & 0.000171996138417992 \tabularnewline
133 & 0.999924343299072 & 0.000151313401855558 & 7.56567009277788e-05 \tabularnewline
134 & 0.999969209616986 & 6.15807660287947e-05 & 3.07903830143973e-05 \tabularnewline
135 & 0.999996111806734 & 7.77638653234047e-06 & 3.88819326617024e-06 \tabularnewline
136 & 0.999999947161171 & 1.056776571567e-07 & 5.28388285783501e-08 \tabularnewline
137 & 0.999999894860818 & 2.10278364478801e-07 & 1.05139182239401e-07 \tabularnewline
138 & 0.999999769678566 & 4.60642867825941e-07 & 2.30321433912971e-07 \tabularnewline
139 & 0.999999682697058 & 6.34605884284664e-07 & 3.17302942142332e-07 \tabularnewline
140 & 0.999999612240628 & 7.75518743753209e-07 & 3.87759371876605e-07 \tabularnewline
141 & 0.999999133092502 & 1.73381499525289e-06 & 8.66907497626443e-07 \tabularnewline
142 & 0.99999851555922 & 2.96888155995675e-06 & 1.48444077997838e-06 \tabularnewline
143 & 0.999997019053443 & 5.96189311401489e-06 & 2.98094655700744e-06 \tabularnewline
144 & 0.999997178153707 & 5.64369258508935e-06 & 2.82184629254467e-06 \tabularnewline
145 & 0.999998791208024 & 2.41758395103517e-06 & 1.20879197551759e-06 \tabularnewline
146 & 0.999998077257431 & 3.84548513766787e-06 & 1.92274256883393e-06 \tabularnewline
147 & 0.999996008260856 & 7.98347828737386e-06 & 3.99173914368693e-06 \tabularnewline
148 & 0.999995246731744 & 9.50653651130403e-06 & 4.75326825565201e-06 \tabularnewline
149 & 0.999989478089654 & 2.10438206915513e-05 & 1.05219103457757e-05 \tabularnewline
150 & 0.999977175685122 & 4.56486297563678e-05 & 2.28243148781839e-05 \tabularnewline
151 & 0.999960314458537 & 7.93710829257772e-05 & 3.96855414628886e-05 \tabularnewline
152 & 0.999960783166367 & 7.84336672664157e-05 & 3.92168336332079e-05 \tabularnewline
153 & 0.999963080255819 & 7.3839488361271e-05 & 3.69197441806355e-05 \tabularnewline
154 & 0.999956865652528 & 8.62686949431953e-05 & 4.31343474715976e-05 \tabularnewline
155 & 0.999971243628491 & 5.75127430174894e-05 & 2.87563715087447e-05 \tabularnewline
156 & 0.999931739937308 & 0.000136520125384534 & 6.82600626922671e-05 \tabularnewline
157 & 0.999848129930545 & 0.000303740138910456 & 0.000151870069455228 \tabularnewline
158 & 0.999717158774542 & 0.000565682450915196 & 0.000282841225457598 \tabularnewline
159 & 0.99954478472469 & 0.000910430550620946 & 0.000455215275310473 \tabularnewline
160 & 0.999251585469394 & 0.00149682906121254 & 0.000748414530606269 \tabularnewline
161 & 0.998300744022892 & 0.00339851195421551 & 0.00169925597710775 \tabularnewline
162 & 0.99768616037434 & 0.00462767925132067 & 0.00231383962566033 \tabularnewline
163 & 0.995172823145726 & 0.00965435370854833 & 0.00482717685427417 \tabularnewline
164 & 0.992217686454129 & 0.0155646270917429 & 0.00778231354587144 \tabularnewline
165 & 0.984011254356872 & 0.0319774912862567 & 0.0159887456431283 \tabularnewline
166 & 0.971146473720545 & 0.0577070525589094 & 0.0288535262794547 \tabularnewline
167 & 0.947716012200546 & 0.104567975598908 & 0.0522839877994539 \tabularnewline
168 & 0.947512069907534 & 0.104975860184932 & 0.0524879300924658 \tabularnewline
169 & 0.930389732520387 & 0.139220534959227 & 0.0696102674796134 \tabularnewline
170 & 0.900902381615416 & 0.198195236769167 & 0.0990976183845837 \tabularnewline
171 & 0.840224127919028 & 0.319551744161944 & 0.159775872080972 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146379&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]20[/C][C]0.31542661815014[/C][C]0.630853236300281[/C][C]0.68457338184986[/C][/ROW]
[ROW][C]21[/C][C]0.374782090015821[/C][C]0.749564180031642[/C][C]0.625217909984179[/C][/ROW]
[ROW][C]22[/C][C]0.429973655937269[/C][C]0.859947311874537[/C][C]0.570026344062731[/C][/ROW]
[ROW][C]23[/C][C]0.303839952025256[/C][C]0.607679904050513[/C][C]0.696160047974744[/C][/ROW]
[ROW][C]24[/C][C]0.210314124448672[/C][C]0.420628248897343[/C][C]0.789685875551328[/C][/ROW]
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[ROW][C]167[/C][C]0.947716012200546[/C][C]0.104567975598908[/C][C]0.0522839877994539[/C][/ROW]
[ROW][C]168[/C][C]0.947512069907534[/C][C]0.104975860184932[/C][C]0.0524879300924658[/C][/ROW]
[ROW][C]169[/C][C]0.930389732520387[/C][C]0.139220534959227[/C][C]0.0696102674796134[/C][/ROW]
[ROW][C]170[/C][C]0.900902381615416[/C][C]0.198195236769167[/C][C]0.0990976183845837[/C][/ROW]
[ROW][C]171[/C][C]0.840224127919028[/C][C]0.319551744161944[/C][C]0.159775872080972[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146379&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146379&T=5

As an alternative you can also use a QR Code:

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

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
20	0.31542661815014	0.630853236300281	0.68457338184986
21	0.374782090015821	0.749564180031642	0.625217909984179
22	0.429973655937269	0.859947311874537	0.570026344062731
23	0.303839952025256	0.607679904050513	0.696160047974744
24	0.210314124448672	0.420628248897343	0.789685875551328
25	0.182312915669312	0.364625831338624	0.817687084330688
26	0.140485928834177	0.280971857668354	0.859514071165823
27	0.13782886467734	0.27565772935468	0.86217113532266
28	0.0988450462686875	0.197690092537375	0.901154953731312
29	0.464540811580436	0.929081623160873	0.535459188419564
30	0.738838232074571	0.522323535850858	0.261161767925429
31	0.731124792504985	0.537750414990031	0.268875207495015
32	0.695916357119277	0.608167285761447	0.304083642880723
33	0.645388391840302	0.709223216319396	0.354611608159698
34	0.611280507193621	0.777438985612757	0.388719492806379
35	0.603431006565121	0.793137986869759	0.396568993434879
36	0.588466455910872	0.823067088178256	0.411533544089128
37	0.528773871233898	0.942452257532205	0.471226128766102
38	0.467607851392948	0.935215702785896	0.532392148607052
39	0.403014919555115	0.806029839110229	0.596985080444886
40	0.356925983274998	0.713851966549995	0.643074016725003
41	0.337862056669644	0.675724113339289	0.662137943330356
42	0.309547108948808	0.619094217897616	0.690452891051192
43	0.27610031884788	0.552200637695759	0.723899681152121
44	0.230095515130043	0.460191030260086	0.769904484869957
45	0.186452656983332	0.372905313966663	0.813547343016668
46	0.158872691454369	0.317745382908737	0.841127308545631
47	0.145732468257251	0.291464936514502	0.854267531742749
48	0.125850804309319	0.251701608618638	0.874149195690681
49	0.660406801493471	0.679186397013059	0.33959319850653
50	0.716409609087055	0.56718078182589	0.283590390912945
51	0.678094524697384	0.643810950605233	0.321905475302616
52	0.649332641434899	0.701334717130201	0.350667358565101
53	0.608637889029522	0.782724221940955	0.391362110970478
54	0.588295970823572	0.823408058352857	0.411704029176428
55	0.565089450832861	0.869821098334278	0.434910549167139
56	0.543541881630758	0.912916236738484	0.456458118369242
57	0.492208623844936	0.984417247689873	0.507791376155064
58	0.443164405635233	0.886328811270466	0.556835594364767
59	0.39280590302712	0.785611806054239	0.60719409697288
60	0.352752593678151	0.705505187356302	0.647247406321849
61	0.332362185740743	0.664724371481486	0.667637814259257
62	0.306737099919738	0.613474199839475	0.693262900080262
63	0.270448629979961	0.540897259959921	0.729551370020039
64	0.240047267610495	0.480094535220991	0.759952732389505
65	0.227830619128606	0.455661238257211	0.772169380871394
66	0.193246036247987	0.386492072495973	0.806753963752013
67	0.163294223606622	0.326588447213243	0.836705776393378
68	0.141282122614892	0.282564245229783	0.858717877385109
69	0.119563581877623	0.239127163755246	0.880436418122377
70	0.0983325567430524	0.196665113486105	0.901667443256948
71	0.0996840683745272	0.199368136749054	0.900315931625473
72	0.088509709185209	0.177019418370418	0.911490290814791
73	0.110505077715517	0.221010155431034	0.889494922284483
74	0.160107624215727	0.320215248431455	0.839892375784272
75	0.24511702034816	0.49023404069632	0.75488297965184
76	0.381740815479972	0.763481630959943	0.618259184520028
77	0.494022842090949	0.988045684181898	0.505977157909051
78	0.666405048384646	0.667189903230709	0.333594951615354
79	0.785700013194308	0.428599973611383	0.214299986805692
80	0.827369629446084	0.345260741107832	0.172630370553916
81	0.858689568266514	0.282620863466973	0.141310431733486
82	0.860643996962425	0.27871200607515	0.139356003037575
83	0.855394386073555	0.289211227852891	0.144605613926445
84	0.869622876791192	0.260754246417615	0.130377123208808
85	0.905571532331688	0.188856935336625	0.0944284676683125
86	0.914942582224192	0.170114835551615	0.0850574177758076
87	0.904206844709178	0.191586310581643	0.0957931552908215
88	0.902599504077055	0.19480099184589	0.097400495922945
89	0.943897213363892	0.112205573272216	0.0561027866361081
90	0.958376474167086	0.0832470516658271	0.0416235258329136
91	0.972438829007469	0.0551223419850621	0.027561170992531
92	0.976323229643032	0.0473535407139368	0.0236767703569684
93	0.982653528042953	0.0346929439140947	0.0173464719570473
94	0.986775487016218	0.0264490259675649	0.0132245129837824
95	0.986796181430368	0.0264076371392647	0.0132038185696323
96	0.983450524164261	0.0330989516714774	0.0165494758357387
97	0.979298006880909	0.0414039862381823	0.0207019931190912
98	0.973338255611596	0.0533234887768076	0.0266617443884038
99	0.965696760952213	0.0686064780955738	0.0343032390477869
100	0.972322099026253	0.0553558019474935	0.0276779009737468
101	0.984362368829498	0.0312752623410048	0.0156376311705024
102	0.984511298474666	0.030977403050668	0.015488701525334
103	0.981019612971298	0.037960774057403	0.0189803870287015
104	0.976317139493417	0.0473657210131667	0.0236828605065834
105	0.970204877960317	0.0595902440793667	0.0297951220396833
106	0.965690790527947	0.0686184189441049	0.0343092094720525
107	0.968242674335749	0.0635146513285011	0.0317573256642506
108	0.977306127372044	0.0453877452559109	0.0226938726279555
109	0.984879903132029	0.0302401937359413	0.0151200968679707
110	0.986293012373608	0.0274139752527841	0.013706987626392
111	0.991312953877547	0.0173740922449069	0.00868704612245345
112	0.993252186393979	0.0134956272120418	0.0067478136060209
113	0.995085227688585	0.00982954462282987	0.00491477231141494
114	0.996465112476307	0.00706977504738594	0.00353488752369297
115	0.998288010626671	0.00342397874665823	0.00171198937332912
116	0.999415907102072	0.00116818579585635	0.000584092897928174
117	0.999702091538871	0.000595816922257503	0.000297908461128751
118	0.999788883717072	0.000422232565856281	0.00021111628292814
119	0.999850796029128	0.000298407941744896	0.000149203970872448
120	0.999822856411893	0.000354287176213653	0.000177143588106826
121	0.999794277498385	0.000411445003230767	0.000205722501615383
122	0.999737258104898	0.000525483790203196	0.000262741895101598
123	0.999729901094316	0.000540197811368857	0.000270098905684428
124	0.99981391080614	0.000372178387720261	0.00018608919386013
125	0.999751596898171	0.000496806203658612	0.000248403101829306
126	0.999699922806626	0.000600154386747976	0.000300077193373988
127	0.999550942242643	0.000898115514713517	0.000449057757356758
128	0.999481925249883	0.00103614950023306	0.00051807475011653
129	0.999494336737831	0.00101132652433801	0.000505663262169007
130	0.999766944064783	0.000466111870434802	0.000233055935217401
131	0.999802047934524	0.000395904130951217	0.000197952065475608
132	0.999828003861582	0.000343992276835984	0.000171996138417992
133	0.999924343299072	0.000151313401855558	7.56567009277788e-05
134	0.999969209616986	6.15807660287947e-05	3.07903830143973e-05
135	0.999996111806734	7.77638653234047e-06	3.88819326617024e-06
136	0.999999947161171	1.056776571567e-07	5.28388285783501e-08
137	0.999999894860818	2.10278364478801e-07	1.05139182239401e-07
138	0.999999769678566	4.60642867825941e-07	2.30321433912971e-07
139	0.999999682697058	6.34605884284664e-07	3.17302942142332e-07
140	0.999999612240628	7.75518743753209e-07	3.87759371876605e-07
141	0.999999133092502	1.73381499525289e-06	8.66907497626443e-07
142	0.99999851555922	2.96888155995675e-06	1.48444077997838e-06
143	0.999997019053443	5.96189311401489e-06	2.98094655700744e-06
144	0.999997178153707	5.64369258508935e-06	2.82184629254467e-06
145	0.999998791208024	2.41758395103517e-06	1.20879197551759e-06
146	0.999998077257431	3.84548513766787e-06	1.92274256883393e-06
147	0.999996008260856	7.98347828737386e-06	3.99173914368693e-06
148	0.999995246731744	9.50653651130403e-06	4.75326825565201e-06
149	0.999989478089654	2.10438206915513e-05	1.05219103457757e-05
150	0.999977175685122	4.56486297563678e-05	2.28243148781839e-05
151	0.999960314458537	7.93710829257772e-05	3.96855414628886e-05
152	0.999960783166367	7.84336672664157e-05	3.92168336332079e-05
153	0.999963080255819	7.3839488361271e-05	3.69197441806355e-05
154	0.999956865652528	8.62686949431953e-05	4.31343474715976e-05
155	0.999971243628491	5.75127430174894e-05	2.87563715087447e-05
156	0.999931739937308	0.000136520125384534	6.82600626922671e-05
157	0.999848129930545	0.000303740138910456	0.000151870069455228
158	0.999717158774542	0.000565682450915196	0.000282841225457598
159	0.99954478472469	0.000910430550620946	0.000455215275310473
160	0.999251585469394	0.00149682906121254	0.000748414530606269
161	0.998300744022892	0.00339851195421551	0.00169925597710775
162	0.99768616037434	0.00462767925132067	0.00231383962566033
163	0.995172823145726	0.00965435370854833	0.00482717685427417
164	0.992217686454129	0.0155646270917429	0.00778231354587144
165	0.984011254356872	0.0319774912862567	0.0159887456431283
166	0.971146473720545	0.0577070525589094	0.0288535262794547
167	0.947716012200546	0.104567975598908	0.0522839877994539
168	0.947512069907534	0.104975860184932	0.0524879300924658
169	0.930389732520387	0.139220534959227	0.0696102674796134
170	0.900902381615416	0.198195236769167	0.0990976183845837
171	0.840224127919028	0.319551744161944	0.159775872080972

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	51	0.335526315789474	NOK
5% type I error level	68	0.447368421052632	NOK
10% type I error level	77	0.506578947368421	NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 51 & 0.335526315789474 & NOK \tabularnewline
5% type I error level & 68 & 0.447368421052632 & NOK \tabularnewline
10% type I error level & 77 & 0.506578947368421 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146379&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]51[/C][C]0.335526315789474[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]68[/C][C]0.447368421052632[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]77[/C][C]0.506578947368421[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146379&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146379&T=6

As an alternative you can also use a QR Code:

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	51	0.335526315789474	NOK
5% type I error level	68	0.447368421052632	NOK
10% type I error level	77	0.506578947368421	NOK

Figure 1

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Figure 3

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Figure 4

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Figure 5

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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Figure 10

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Parameters (Session):

Parameters (R input):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;

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

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

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code