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Author's title

Author

*The author of this computation has been verified*

R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Thu, 20 Dec 2012 11:20:46 -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/2012/Dec/20/t13560204874cy4qqz1o7s7cmf.htm/, Retrieved Fri, 26 Apr 2024 22:09:56 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=202852, Retrieved Fri, 26 Apr 2024 22:09:56 +0000

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

178

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

-     [Bivariate Data Series] [Bivariate dataset] [2008-01-05 23:51:08] [74be16979710d4c4e7c6647856088456]
F RMPD  [Univariate Explorative Data Analysis] [Colombia Coffee] [2008-01-07 14:21:11] [74be16979710d4c4e7c6647856088456]
- RMPD    [Univariate Explorative Data Analysis] [Workshop 6, Tutor...] [2010-11-07 12:24:29] [8ffb4cfa64b4677df0d2c448735a40bb]
- R P       [Univariate Explorative Data Analysis] [WS6 2. Technique 2] [2010-11-11 18:06:41] [afe9379cca749d06b3d6872e02cc47ed]
- RMPD        [Multiple Regression] [Apple Inc - Multi...] [2010-12-11 10:33:09] [afe9379cca749d06b3d6872e02cc47ed]
-    D          [Multiple Regression] [WS10 Multiple Reg...] [2010-12-13 13:48:19] [afe9379cca749d06b3d6872e02cc47ed]
- R PD            [Multiple Regression] [] [2012-12-20 16:07:49] [d1865ed705b6ad9ba3d459a02c528b22]
-    D                [Multiple Regression] [] [2012-12-20 16:20:46] [14d0a7ecb926325afa0eb6a607fbc7a0] [Current]
-   PD                  [Multiple Regression] [] [2012-12-21 08:01:57] [74be16979710d4c4e7c6647856088456] 
- R PD                    [Multiple Regression] [] [2012-12-21 08:23:32] [d1865ed705b6ad9ba3d459a02c528b22] 
- R  D                      [Multiple Regression] [] [2012-12-21 08:31:34] [d1865ed705b6ad9ba3d459a02c528b22] 
- R  D                      [Multiple Regression] [] [2012-12-21 08:31:34] [d1865ed705b6ad9ba3d459a02c528b22] 
- R  D                      [Multiple Regression] [] [2012-12-21 08:31:34] [d1865ed705b6ad9ba3d459a02c528b22] 
- R  D                      [Multiple Regression] [] [2012-12-21 08:31:34] [d1865ed705b6ad9ba3d459a02c528b22] 
- R  D                      [Multiple Regression] [] [2012-12-21 08:31:34] [d1865ed705b6ad9ba3d459a02c528b22] 
- R  D                      [Multiple Regression] [] [2012-12-21 08:31:34] [d1865ed705b6ad9ba3d459a02c528b22] 
- R PD                      [Multiple Regression] [] [2012-12-21 08:35:48] [d1865ed705b6ad9ba3d459a02c528b22] 
- R PD                      [Multiple Regression] [] [2012-12-21 08:42:03] [d1865ed705b6ad9ba3d459a02c528b22] 
- R PD                      [Multiple Regression] [] [2012-12-21 08:45:23] [d1865ed705b6ad9ba3d459a02c528b22] 
- RMPD                      [Testing Mean with unknown Variance - Critical Value] [] [2012-12-21 09:21:24] [d1865ed705b6ad9ba3d459a02c528b22] 
- RMPD                      [Testing Mean with unknown Variance - Critical Value] [] [2012-12-21 09:34:12] [d1865ed705b6ad9ba3d459a02c528b22] 

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27.72	91.51	2747.48	0,016	62,7	0,16
26.90	91.09	2760.01	0,016	62,7	0,17
25.86	93.00	2778.11	0,016	62,7	0,17
26.81	93.08	2844.72	0,016	62,7	0,16
26.31	94.13	2831.02	0,016	62,7	0,16
27.10	96.26	2858.42	0,016	62,7	0,17
27.00	94.29	2809.73	0,016	62,7	0,17
27.40	94.46	2843.07	0,016	62,7	0,16
27.27	95.53	2818.61	0,016	62,7	0,17
28.29	98.29	2836.33	0,016	62,7	0,17
30.01	102.01	2872.80	0,016	62,7	0,18
31.41	105.16	2895.33	0,016	62,7	0,17
31.91	105.34	2929.76	0,016	62,7	0,17
31.60	105.27	2930.45	0,016	62,7	0,16
31.84	102.19	2859.09	0,016	62,7	0,17
33.05	106.85	2892.42	0,016	62,7	0,17
32.06	103.05	2836.16	0,016	62,7	0,17
33.10	106.42	2854.06	0,016	62,7	0,16
32.23	105.17	2875.32	0,016	62,7	0,15
31.36	102.74	2849.49	0,016	62,7	0,15
31.09	106.27	2935.05	0,016	62,7	0,09
30.77	107.63	2951.23	0,0141	65,4	0,18
31.20	108.54	2976.08	0,0141	65,4	0,17
31.47	108.24	2976.12	0,0141	65,4	0,17
31.73	108.86	2937.33	0,0141	65,4	0,17
32.17	102.98	2931.77	0,0141	65,4	0,17
31.47	99.53	2902.33	0,0141	65,4	0,17
30.97	101.08	2887.98	0,0141	65,4	0,17
30.81	104.64	2866.19	0,0141	65,4	0,18
30.72	105.59	2908.47	0,0141	65,4	0,19
28.24	103.21	2896.94	0,0141	65,4	0,18
28.09	103.84	2910.04	0,0141	65,4	0,17
29.11	104.61	2942.60	0,0141	65,4	0,16
29.00	108.65	2965.90	0,0141	65,4	0,13
28.76	106.26	2925.30	0,0141	65,4	0,13
28.75	104.20	2890.15	0,0141	65,4	0,14
28.45	102.99	2862.99	0,0141	65,4	0,15
29.34	102.19	2854.24	0,0141	65,4	0,15
26.84	100.82	2893.25	0,0141	65,4	0,14
23.70	103.42	2958.09	0,0141	65,4	0,14
23.15	104.18	2945.84	0,0141	65,4	0,14
21.71	102.65	2939.52	0,0141	65,4	0,13
20.88	95.64	2920.21	0,0169	61,3	0,14
20.04	93.51	2909.77	0,0169	61,3	0,14
21.09	108.51	2967.90	0,0169	61,3	0,14
21.92	111.55	2989.91	0,0169	61,3	0,14
20.72	106.70	3015.86	0,0169	61,3	0,13
20.72	104.93	3011.25	0,0169	61,3	0,13
21.01	105.23	3018.64	0,0169	61,3	0,13
21.80	104.92	3020.86	0,0169	61,3	0,13
21.60	104.60	3022.52	0,0169	61,3	0,13
20.38	101.76	3016.98	0,0169	61,3	0,13
21.20	102.23	3030.93	0,0169	61,3	0,13
19.87	103.99	3062.39	0,0169	61,3	0,13
19.05	101.36	3076.59	0,0169	61,3	0,13
20.01	102.92	3076.21	0,0169	61,3	0,13
19.15	105.25	3067.26	0,0169	61,3	0,13
19.43	105.71	3073.67	0,0169	61,3	0,13
19.44	105.42	3053.40	0,0169	61,3	0,13
19.40	105.11	3069.79	0,0169	61,3	0,13
19.15	104.67	3073.19	0,0169	61,3	0,13
19.34	107.51	3077.14	0,0169	61,3	0,13
19.10	109.00	3081.19	0,0169	61,3	0,13
19.08	107.37	3048.71	0,0169	61,3	0,14
18.05	107.30	3066.96	0,0169	61,3	0,13
17.72	107.37	3075.06	0,0199	70,3	0,14
18.58	113.28	3069.27	0,0199	70,3	0,16
18.96	119.10	3135.81	0,0199	70,3	0,16
18.98	119.04	3136.42	0,0199	70,3	0,15
18.81	117.80	3104.02	0,0199	70,3	0,15
19.43	117.90	3104.53	0,0199	70,3	0,15
20.93	119.55	3114.31	0,0199	70,3	0,15
20.71	119.47	3155.83	0,0199	70,3	0,15
22.00	123.23	3183.95	0,0199	70,3	0,16
21.52	121.40	3178.67	0,0199	70,3	0,16
21.87	121.43	3177.80	0,0199	70,3	0,16
23.29	122.51	3182.62	0,0199	70,3	0,15
22.59	122.78	3175.96	0,0199	70,3	0,16
22.86	122.84	3179.96	0,0199	70,3	0,15
20.79	122.70	3160.78	0,0199	70,3	0,16
20.28	119.89	3117.73	0,0199	70,3	0,15
20.62	118.00	3093.70	0,0199	70,3	0,16
20.32	119.61	3136.60	0,0199	70,3	0,14
21.66	120.40	3116.23	0,0199	70,3	0,09
21.99	117.94	3113.53	0,0216	73,1	0,15
22.27	118.77	3120.04	0,0216	73,1	0,16
21.83	121.68	3135.23	0,0216	73,1	0,16
21.94	121.98	3149.46	0,0216	73,1	0,15
20.91	118.83	3136.19	0,0216	73,1	0,15
20.40	117.97	3112.35	0,0216	73,1	0,15
20.22	113.07	3065.02	0,0216	73,1	0,16
19.64	111.98	3051.78	0,0216	73,1	0,16
19.75	113.77	3049.41	0,0216	73,1	0,16
19.51	110.41	3044.11	0,0216	73,1	0,16
19.52	110.85	3064.18	0,0216	73,1	0,16
19.48	111.18	3101.17	0,0216	73,1	0,16
19.88	109.42	3104.12	0,0216	73,1	0,15
18.97	108.87	3072.87	0,0216	73,1	0,15
19.00	106.72	3005.62	0,0216	73,1	0,16
19.32	107.28	3016.96	0,0216	73,1	0,15
19.50	104.13	2990.46	0,0216	73,1	0,15
23.22	107.55	2981.70	0,0216	73,1	0,17
22.56	105.72	2986.12	0,0216	73,1	0,16
21.94	104.55	2987.95	0,0216	73,1	0,16
21.11	106.93	2977.23	0,0216	73,1	0,18
21.21	106.85	3020.06	0,0176	73,1	0,17
21.18	106.78	2982.13	0,0176	73,1	0,16
21.25	107.29	2999.66	0,0176	73,1	0,17
21.17	104.14	3011.93	0,0176	73,1	0,16
20.47	101.21	2937.29	0,0176	73,1	0,16
19.99	96.35	2895.58	0,0176	73,1	0,16
19.21	95.62	2904.87	0,0176	73,1	0,16
20.07	99.00	2904.26	0,0176	73,1	0,16
19.86	99.26	2883.89	0,0176	73,1	0,16
22.36	98.77	2846.81	0,0176	73,1	0,16
22.17	100.65	2836.94	0,0176	73,1	0,16
23.56	103.13	2853.13	0,0176	73,1	0,16
22.92	105.53	2916.07	0,0176	73,1	0,16
23.10	106.76	2916.68	0,0176	73,1	0,16
24.32	107.59	2926.55	0,0176	73,1	0,16
23.99	107.62	2966.85	0,0176	73,1	0,16
25.94	108.82	2976.78	0,0176	73,1	0,16
26.15	107.59	2967.79	0,0176	73,1	0,16
26.36	107.85	2991.78	0,0176	73,1	0,16
27.32	107.11	3012.03	0,0176	73,1	0,16
28.00	108.14	3010.24	0,0176	73,1	0,16

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	10 seconds
R Server	'Gertrude Mary Cox' @ cox.wessa.net

\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 & 10 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202852&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]10 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202852&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202852&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	10 seconds
R Server	'Gertrude Mary Cox' @ cox.wessa.net

Multiple Linear Regression - Estimated Regression Equation
FACEBOOK[t] = + 110.838361984403 + 0.535322028869228LINKEDIN[t] -0.042428875298038NASDAQ[t] -717.351597184499INFLATION[t] -0.205419350395299CONS.CONF[t] + 59.045863714208FED.FUNDS.RATE[t] + 0.564451898134325M1[t] + 0.634429972449484M2[t] + 0.0914513267850999M3[t] + 0.392307210111445M4[t] + 0.137855005342375M5[t] + 0.134247654018534M6[t] -0.136634019141963M7[t] -0.449345608683153M8[t] -0.974464299508926M9[t] -1.43833710203499M10[t] -0.881095681795309M11[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
FACEBOOK[t] =  +  110.838361984403 +  0.535322028869228LINKEDIN[t] -0.042428875298038NASDAQ[t] -717.351597184499INFLATION[t] -0.205419350395299CONS.CONF[t] +  59.045863714208FED.FUNDS.RATE[t] +  0.564451898134325M1[t] +  0.634429972449484M2[t] +  0.0914513267850999M3[t] +  0.392307210111445M4[t] +  0.137855005342375M5[t] +  0.134247654018534M6[t] -0.136634019141963M7[t] -0.449345608683153M8[t] -0.974464299508926M9[t] -1.43833710203499M10[t] -0.881095681795309M11[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202852&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]FACEBOOK[t] =  +  110.838361984403 +  0.535322028869228LINKEDIN[t] -0.042428875298038NASDAQ[t] -717.351597184499INFLATION[t] -0.205419350395299CONS.CONF[t] +  59.045863714208FED.FUNDS.RATE[t] +  0.564451898134325M1[t] +  0.634429972449484M2[t] +  0.0914513267850999M3[t] +  0.392307210111445M4[t] +  0.137855005342375M5[t] +  0.134247654018534M6[t] -0.136634019141963M7[t] -0.449345608683153M8[t] -0.974464299508926M9[t] -1.43833710203499M10[t] -0.881095681795309M11[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202852&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202852&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
FACEBOOK[t] = + 110.838361984403 + 0.535322028869228LINKEDIN[t] -0.042428875298038NASDAQ[t] -717.351597184499INFLATION[t] -0.205419350395299CONS.CONF[t] + 59.045863714208FED.FUNDS.RATE[t] + 0.564451898134325M1[t] + 0.634429972449484M2[t] + 0.0914513267850999M3[t] + 0.392307210111445M4[t] + 0.137855005342375M5[t] + 0.134247654018534M6[t] -0.136634019141963M7[t] -0.449345608683153M8[t] -0.974464299508926M9[t] -1.43833710203499M10[t] -0.881095681795309M11[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)	110.838361984403	10.974792	10.0994	0	0
LINKEDIN	0.535322028869228	0.058186	9.2001	0	0
NASDAQ	-0.042428875298038	0.004839	-8.7689	0	0
INFLATION	-717.351597184499	147.45621	-4.8648	4e-06	2e-06
CONS.CONF	-0.205419350395299	0.065745	-3.1245	0.002282	0.001141
FED.FUNDS.RATE	59.045863714208	15.623095	3.7794	0.000257	0.000128
M1	0.564451898134325	0.98886	0.5708	0.569303	0.284651
M2	0.634429972449484	0.991475	0.6399	0.52359	0.261795
M3	0.0914513267850999	0.995768	0.0918	0.926994	0.463497
M4	0.392307210111445	0.994963	0.3943	0.694135	0.347067
M5	0.137855005342375	0.990979	0.1391	0.88962	0.44481
M6	0.134247654018534	0.993337	0.1351	0.892744	0.446372
M7	-0.136634019141963	1.030189	-0.1326	0.894731	0.447365
M8	-0.449345608683153	1.026853	-0.4376	0.662546	0.331273
M9	-0.974464299508926	1.014664	-0.9604	0.338989	0.169494
M10	-1.43833710203499	1.009988	-1.4241	0.15727	0.078635
M11	-0.881095681795309	1.005353	-0.8764	0.382737	0.191369

\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) & 110.838361984403 & 10.974792 & 10.0994 & 0 & 0 \tabularnewline
LINKEDIN & 0.535322028869228 & 0.058186 & 9.2001 & 0 & 0 \tabularnewline
NASDAQ & -0.042428875298038 & 0.004839 & -8.7689 & 0 & 0 \tabularnewline
INFLATION & -717.351597184499 & 147.45621 & -4.8648 & 4e-06 & 2e-06 \tabularnewline
CONS.CONF & -0.205419350395299 & 0.065745 & -3.1245 & 0.002282 & 0.001141 \tabularnewline
FED.FUNDS.RATE & 59.045863714208 & 15.623095 & 3.7794 & 0.000257 & 0.000128 \tabularnewline
M1 & 0.564451898134325 & 0.98886 & 0.5708 & 0.569303 & 0.284651 \tabularnewline
M2 & 0.634429972449484 & 0.991475 & 0.6399 & 0.52359 & 0.261795 \tabularnewline
M3 & 0.0914513267850999 & 0.995768 & 0.0918 & 0.926994 & 0.463497 \tabularnewline
M4 & 0.392307210111445 & 0.994963 & 0.3943 & 0.694135 & 0.347067 \tabularnewline
M5 & 0.137855005342375 & 0.990979 & 0.1391 & 0.88962 & 0.44481 \tabularnewline
M6 & 0.134247654018534 & 0.993337 & 0.1351 & 0.892744 & 0.446372 \tabularnewline
M7 & -0.136634019141963 & 1.030189 & -0.1326 & 0.894731 & 0.447365 \tabularnewline
M8 & -0.449345608683153 & 1.026853 & -0.4376 & 0.662546 & 0.331273 \tabularnewline
M9 & -0.974464299508926 & 1.014664 & -0.9604 & 0.338989 & 0.169494 \tabularnewline
M10 & -1.43833710203499 & 1.009988 & -1.4241 & 0.15727 & 0.078635 \tabularnewline
M11 & -0.881095681795309 & 1.005353 & -0.8764 & 0.382737 & 0.191369 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202852&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]110.838361984403[/C][C]10.974792[/C][C]10.0994[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]LINKEDIN[/C][C]0.535322028869228[/C][C]0.058186[/C][C]9.2001[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]NASDAQ[/C][C]-0.042428875298038[/C][C]0.004839[/C][C]-8.7689[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]INFLATION[/C][C]-717.351597184499[/C][C]147.45621[/C][C]-4.8648[/C][C]4e-06[/C][C]2e-06[/C][/ROW]
[ROW][C]CONS.CONF[/C][C]-0.205419350395299[/C][C]0.065745[/C][C]-3.1245[/C][C]0.002282[/C][C]0.001141[/C][/ROW]
[ROW][C]FED.FUNDS.RATE[/C][C]59.045863714208[/C][C]15.623095[/C][C]3.7794[/C][C]0.000257[/C][C]0.000128[/C][/ROW]
[ROW][C]M1[/C][C]0.564451898134325[/C][C]0.98886[/C][C]0.5708[/C][C]0.569303[/C][C]0.284651[/C][/ROW]
[ROW][C]M2[/C][C]0.634429972449484[/C][C]0.991475[/C][C]0.6399[/C][C]0.52359[/C][C]0.261795[/C][/ROW]
[ROW][C]M3[/C][C]0.0914513267850999[/C][C]0.995768[/C][C]0.0918[/C][C]0.926994[/C][C]0.463497[/C][/ROW]
[ROW][C]M4[/C][C]0.392307210111445[/C][C]0.994963[/C][C]0.3943[/C][C]0.694135[/C][C]0.347067[/C][/ROW]
[ROW][C]M5[/C][C]0.137855005342375[/C][C]0.990979[/C][C]0.1391[/C][C]0.88962[/C][C]0.44481[/C][/ROW]
[ROW][C]M6[/C][C]0.134247654018534[/C][C]0.993337[/C][C]0.1351[/C][C]0.892744[/C][C]0.446372[/C][/ROW]
[ROW][C]M7[/C][C]-0.136634019141963[/C][C]1.030189[/C][C]-0.1326[/C][C]0.894731[/C][C]0.447365[/C][/ROW]
[ROW][C]M8[/C][C]-0.449345608683153[/C][C]1.026853[/C][C]-0.4376[/C][C]0.662546[/C][C]0.331273[/C][/ROW]
[ROW][C]M9[/C][C]-0.974464299508926[/C][C]1.014664[/C][C]-0.9604[/C][C]0.338989[/C][C]0.169494[/C][/ROW]
[ROW][C]M10[/C][C]-1.43833710203499[/C][C]1.009988[/C][C]-1.4241[/C][C]0.15727[/C][C]0.078635[/C][/ROW]
[ROW][C]M11[/C][C]-0.881095681795309[/C][C]1.005353[/C][C]-0.8764[/C][C]0.382737[/C][C]0.191369[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202852&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202852&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)	110.838361984403	10.974792	10.0994	0	0
LINKEDIN	0.535322028869228	0.058186	9.2001	0	0
NASDAQ	-0.042428875298038	0.004839	-8.7689	0	0
INFLATION	-717.351597184499	147.45621	-4.8648	4e-06	2e-06
CONS.CONF	-0.205419350395299	0.065745	-3.1245	0.002282	0.001141
FED.FUNDS.RATE	59.045863714208	15.623095	3.7794	0.000257	0.000128
M1	0.564451898134325	0.98886	0.5708	0.569303	0.284651
M2	0.634429972449484	0.991475	0.6399	0.52359	0.261795
M3	0.0914513267850999	0.995768	0.0918	0.926994	0.463497
M4	0.392307210111445	0.994963	0.3943	0.694135	0.347067
M5	0.137855005342375	0.990979	0.1391	0.88962	0.44481
M6	0.134247654018534	0.993337	0.1351	0.892744	0.446372
M7	-0.136634019141963	1.030189	-0.1326	0.894731	0.447365
M8	-0.449345608683153	1.026853	-0.4376	0.662546	0.331273
M9	-0.974464299508926	1.014664	-0.9604	0.338989	0.169494
M10	-1.43833710203499	1.009988	-1.4241	0.15727	0.078635
M11	-0.881095681795309	1.005353	-0.8764	0.382737	0.191369

Multiple Linear Regression - Regression Statistics
Multiple R	0.883825369911781
R-squared	0.781147284499697
Adjusted R-squared	0.749022115251946
F-TEST (value)	24.3157406728491
F-TEST (DF numerator)	16
F-TEST (DF denominator)	109
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.24436335530696
Sum Squared Residuals	549.051188900272

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.883825369911781 \tabularnewline
R-squared & 0.781147284499697 \tabularnewline
Adjusted R-squared & 0.749022115251946 \tabularnewline
F-TEST (value) & 24.3157406728491 \tabularnewline
F-TEST (DF numerator) & 16 \tabularnewline
F-TEST (DF denominator) & 109 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.24436335530696 \tabularnewline
Sum Squared Residuals & 549.051188900272 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202852&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.883825369911781[/C][/ROW]
[ROW][C]R-squared[/C][C]0.781147284499697[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.749022115251946[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]24.3157406728491[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]16[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]109[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.24436335530696[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]549.051188900272[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202852&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202852&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	0.883825369911781
R-squared	0.781147284499697
Adjusted R-squared	0.749022115251946
F-TEST (value)	24.3157406728491
F-TEST (DF numerator)	16
F-TEST (DF denominator)	109
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.24436335530696
Sum Squared Residuals	549.051188900272

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	27.72	28.9075658100432	-1.18756581004317
2	26.9	28.8115334618907	-1.91153346189067
3	25.86	28.523057248472	-2.66305724847202
4	26.81	25.4500928733635	1.35990712663648
5	26.31	26.3390043904902	-0.0290043904902507
6	27.1	26.9035404146337	0.196459585366294
7	27	27.6439362828623	-0.643936282862303
8	27.4	25.4171920986502	1.9828079013498
9	27.27	27.0931369056466	0.176863094353402
10	28.29	27.3549132325184	0.935086767481617
11	30.01	28.9466301551742	1.06336984482579
12	31.41	29.9676090303007	1.44239096969928
13	31.91	29.16759271712	2.74240728287995
14	31.6	28.5803636883166	3.01963631168335
15	31.84	30.0067763721451	1.8332236278549
16	33.05	31.3880784963184	1.66192150368155
17	32.06	31.4864511061139	0.573548893886067
18	33.1	31.9369434871024	1.16305651289757
19	32.23	29.504412751877	2.72558724812298
20	31.36	28.9868064811319	2.37319351886806
21	31.09	23.1784081588619	7.91159184113808
22	30.77	28.8785376361377	1.89146236386229
23	31.2	28.2781059143501	2.92189408564992
24	31.47	28.9969078324727	2.47309216752731
25	31.73	31.5390754613168	0.190924538683168
26	32.17	28.697264552538	3.47273544746198
27	31.47	27.556530996049	3.91346900395096
28	30.97	29.2959903846495	1.67400961535047
29	30.81	32.4622684325412	-1.65226843254124
30	30.72	31.7637827981842	-1.04378279818421
31	28.24	30.1175809913592	-1.87758099135923
32	28.09	28.9958453764593	-0.90584537645929
33	29.11	26.9109818310166	2.19901816898338
34	29	25.8498413192517	3.15015868074829
35	28.76	26.8502754275943	1.90972457240572
36	28.75	28.7104413337871	0.0395586662129045
37	28.45	30.3699804672265	-1.91998046722645
38	29.34	30.3829535773041	-1.04295357730407
39	26.84	26.8609746895703	-0.0209746895702863
40	23.7	25.8025795736318	-2.10257957363184
41	23.15	26.4747258332043	-3.32472583320435
42	21.71	25.3297676324521	-3.61976763245212
43	20.88	21.5496736205697	-0.669673620569653
44	20.04	20.5396835676485	-0.49968356764853
45	21.09	25.5780047887862	-4.48800478878622
46	21.92	25.8076514087128	-3.8876514087128
47	20.72	22.0770930378105	-1.35709303781055
48	20.72	22.2062658436313	-1.48626584363129
49	21.01	22.6177649619739	-1.60776496197388
50	21.8	22.4276011041779	-0.627601104177924
51	21.6	21.6428874762806	-0.0428874762806458
52	20.38	20.6584847667695	-0.27848476676952
53	21.2	20.0637511051614	1.13624889483864
54	19.87	19.6674981077711	0.202501892228919
55	19.05	17.3862294694524	1.66377053054763
56	20.01	17.9247432175604	2.08525678243957
57	19.15	19.0266632879174	0.123336712082606
58	19.43	18.5370695280108	0.892930471989246
59	19.44	19.7991008621696	-0.35910086216959
60	19.4	19.8188374488806	-0.418837448880602
61	19.15	20.0034894782991	-0.853489478299135
62	19.34	21.4261880571757	-2.08618805717566
63	19.1	21.5090022895694	-2.40900228956936
64	19.08	22.9058317726612	-3.82583177266122
65	18.05	21.24912141454	-3.19912141454003
66	17.72	17.5289424073538	0.191057592646174
67	18.58	21.8483943870703	-3.26839438707026
68	18.96	21.8280396432165	-2.86803964321652
69	18.98	20.6544613795847	-1.67446137958472
70	18.81	20.9014848209172	-2.09148482091724
71	19.43	21.4906197176418	-2.06061971764184
72	20.93	22.8400423466566	-1.91004234665657
73	20.71	21.6000215801068	-0.890021580106819
74	22	23.0801691467315	-1.08016914673154
75	21.52	21.7815756498101	-0.261575649810096
76	21.87	22.1354043155118	-0.265404315511806
77	23.29	21.6641340858429	1.62586591415711
78	22.59	22.6780986289407	-0.0880986289407461
79	22.86	21.6791621391782	1.18083786082183
80	20.79	22.6957499309537	-1.90574993095373
81	20.28	21.9024807834439	-1.62248078344389
82	20.62	22.0368738569089	-1.41687385690893
83	20.32	20.4548677190581	-0.134867719058072
84	21.66	19.6708508077707	1.9891491922293
85	21.99	20.7810484047234	1.20895159527659
86	22.27	21.6095904219519	0.660409578048107
87	21.83	21.9799042645198	-0.14990426451977
88	21.94	21.2471352238737	0.692864776126283
89	20.91	19.8694498033715	1.04055019662846
90	20.4	20.4169698943254	-0.0169698943253979
91	20.22	20.1216275847039	0.0983724152961021
92	19.64	19.7871732926413	-0.147173292641267
93	19.75	20.3208374679478	-0.570837467947773
94	19.51	18.2831556875007	1.22684431249931
95	19.52	18.2243912732112	1.29560872678878
96	19.48	17.7126991272589	1.76730087274105
97	19.88	16.6193604353121	3.26063956468786
98	18.97	17.7208137468129	1.24918625318708
99	19	19.4706932400148	-0.470693240014826
100	19.32	18.9997273764861	0.320272623513898
101	19.5	18.183375976177	1.31662402382303
102	23.22	21.5631641854809	1.65683581451913
103	22.56	19.5346489335303	3.02535106646972
104	21.94	18.5179657284167	3.42203427158332
105	21.11	20.9026682837788	0.207331716221205
106	21.21	20.8576887415241	0.352311258475863
107	21.18	22.3963262226555	-1.21632622265547
108	21.25	23.3971165923416	-2.14711659234157
109	21.17	21.1642431624888	0.0057568375111814
110	20.47	22.8326189444627	-2.36261894446269
111	19.99	21.457683627175	-1.46768362717503
112	19.21	20.9735901779081	-1.76359017790807
113	20.07	22.5544080446488	-2.48440804464878
114	19.86	23.554260610652	-3.69426061065198
115	22.36	24.5943338393968	-2.23433383939681
116	22.17	25.7068006633214	-3.5368006633214
117	23.56	25.8223571130161	-2.26235711301607
118	22.92	23.9727837685176	-1.05278376851764
119	23.1	25.1625896703347	-2.06258967033469
120	24.32	26.0692296368998	-1.7492296368998
121	23.99	24.9398575213893	-0.949857521389288
122	25.94	25.230903298638	0.709096701362018
123	26.15	24.4109141463938	1.73908585360617
124	26.36	23.8330850388262	2.52691496117378
125	27.32	22.3233098079087	4.99669019209134
126	28	22.9470318331036	5.05296816689637

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 27.72 & 28.9075658100432 & -1.18756581004317 \tabularnewline
2 & 26.9 & 28.8115334618907 & -1.91153346189067 \tabularnewline
3 & 25.86 & 28.523057248472 & -2.66305724847202 \tabularnewline
4 & 26.81 & 25.4500928733635 & 1.35990712663648 \tabularnewline
5 & 26.31 & 26.3390043904902 & -0.0290043904902507 \tabularnewline
6 & 27.1 & 26.9035404146337 & 0.196459585366294 \tabularnewline
7 & 27 & 27.6439362828623 & -0.643936282862303 \tabularnewline
8 & 27.4 & 25.4171920986502 & 1.9828079013498 \tabularnewline
9 & 27.27 & 27.0931369056466 & 0.176863094353402 \tabularnewline
10 & 28.29 & 27.3549132325184 & 0.935086767481617 \tabularnewline
11 & 30.01 & 28.9466301551742 & 1.06336984482579 \tabularnewline
12 & 31.41 & 29.9676090303007 & 1.44239096969928 \tabularnewline
13 & 31.91 & 29.16759271712 & 2.74240728287995 \tabularnewline
14 & 31.6 & 28.5803636883166 & 3.01963631168335 \tabularnewline
15 & 31.84 & 30.0067763721451 & 1.8332236278549 \tabularnewline
16 & 33.05 & 31.3880784963184 & 1.66192150368155 \tabularnewline
17 & 32.06 & 31.4864511061139 & 0.573548893886067 \tabularnewline
18 & 33.1 & 31.9369434871024 & 1.16305651289757 \tabularnewline
19 & 32.23 & 29.504412751877 & 2.72558724812298 \tabularnewline
20 & 31.36 & 28.9868064811319 & 2.37319351886806 \tabularnewline
21 & 31.09 & 23.1784081588619 & 7.91159184113808 \tabularnewline
22 & 30.77 & 28.8785376361377 & 1.89146236386229 \tabularnewline
23 & 31.2 & 28.2781059143501 & 2.92189408564992 \tabularnewline
24 & 31.47 & 28.9969078324727 & 2.47309216752731 \tabularnewline
25 & 31.73 & 31.5390754613168 & 0.190924538683168 \tabularnewline
26 & 32.17 & 28.697264552538 & 3.47273544746198 \tabularnewline
27 & 31.47 & 27.556530996049 & 3.91346900395096 \tabularnewline
28 & 30.97 & 29.2959903846495 & 1.67400961535047 \tabularnewline
29 & 30.81 & 32.4622684325412 & -1.65226843254124 \tabularnewline
30 & 30.72 & 31.7637827981842 & -1.04378279818421 \tabularnewline
31 & 28.24 & 30.1175809913592 & -1.87758099135923 \tabularnewline
32 & 28.09 & 28.9958453764593 & -0.90584537645929 \tabularnewline
33 & 29.11 & 26.9109818310166 & 2.19901816898338 \tabularnewline
34 & 29 & 25.8498413192517 & 3.15015868074829 \tabularnewline
35 & 28.76 & 26.8502754275943 & 1.90972457240572 \tabularnewline
36 & 28.75 & 28.7104413337871 & 0.0395586662129045 \tabularnewline
37 & 28.45 & 30.3699804672265 & -1.91998046722645 \tabularnewline
38 & 29.34 & 30.3829535773041 & -1.04295357730407 \tabularnewline
39 & 26.84 & 26.8609746895703 & -0.0209746895702863 \tabularnewline
40 & 23.7 & 25.8025795736318 & -2.10257957363184 \tabularnewline
41 & 23.15 & 26.4747258332043 & -3.32472583320435 \tabularnewline
42 & 21.71 & 25.3297676324521 & -3.61976763245212 \tabularnewline
43 & 20.88 & 21.5496736205697 & -0.669673620569653 \tabularnewline
44 & 20.04 & 20.5396835676485 & -0.49968356764853 \tabularnewline
45 & 21.09 & 25.5780047887862 & -4.48800478878622 \tabularnewline
46 & 21.92 & 25.8076514087128 & -3.8876514087128 \tabularnewline
47 & 20.72 & 22.0770930378105 & -1.35709303781055 \tabularnewline
48 & 20.72 & 22.2062658436313 & -1.48626584363129 \tabularnewline
49 & 21.01 & 22.6177649619739 & -1.60776496197388 \tabularnewline
50 & 21.8 & 22.4276011041779 & -0.627601104177924 \tabularnewline
51 & 21.6 & 21.6428874762806 & -0.0428874762806458 \tabularnewline
52 & 20.38 & 20.6584847667695 & -0.27848476676952 \tabularnewline
53 & 21.2 & 20.0637511051614 & 1.13624889483864 \tabularnewline
54 & 19.87 & 19.6674981077711 & 0.202501892228919 \tabularnewline
55 & 19.05 & 17.3862294694524 & 1.66377053054763 \tabularnewline
56 & 20.01 & 17.9247432175604 & 2.08525678243957 \tabularnewline
57 & 19.15 & 19.0266632879174 & 0.123336712082606 \tabularnewline
58 & 19.43 & 18.5370695280108 & 0.892930471989246 \tabularnewline
59 & 19.44 & 19.7991008621696 & -0.35910086216959 \tabularnewline
60 & 19.4 & 19.8188374488806 & -0.418837448880602 \tabularnewline
61 & 19.15 & 20.0034894782991 & -0.853489478299135 \tabularnewline
62 & 19.34 & 21.4261880571757 & -2.08618805717566 \tabularnewline
63 & 19.1 & 21.5090022895694 & -2.40900228956936 \tabularnewline
64 & 19.08 & 22.9058317726612 & -3.82583177266122 \tabularnewline
65 & 18.05 & 21.24912141454 & -3.19912141454003 \tabularnewline
66 & 17.72 & 17.5289424073538 & 0.191057592646174 \tabularnewline
67 & 18.58 & 21.8483943870703 & -3.26839438707026 \tabularnewline
68 & 18.96 & 21.8280396432165 & -2.86803964321652 \tabularnewline
69 & 18.98 & 20.6544613795847 & -1.67446137958472 \tabularnewline
70 & 18.81 & 20.9014848209172 & -2.09148482091724 \tabularnewline
71 & 19.43 & 21.4906197176418 & -2.06061971764184 \tabularnewline
72 & 20.93 & 22.8400423466566 & -1.91004234665657 \tabularnewline
73 & 20.71 & 21.6000215801068 & -0.890021580106819 \tabularnewline
74 & 22 & 23.0801691467315 & -1.08016914673154 \tabularnewline
75 & 21.52 & 21.7815756498101 & -0.261575649810096 \tabularnewline
76 & 21.87 & 22.1354043155118 & -0.265404315511806 \tabularnewline
77 & 23.29 & 21.6641340858429 & 1.62586591415711 \tabularnewline
78 & 22.59 & 22.6780986289407 & -0.0880986289407461 \tabularnewline
79 & 22.86 & 21.6791621391782 & 1.18083786082183 \tabularnewline
80 & 20.79 & 22.6957499309537 & -1.90574993095373 \tabularnewline
81 & 20.28 & 21.9024807834439 & -1.62248078344389 \tabularnewline
82 & 20.62 & 22.0368738569089 & -1.41687385690893 \tabularnewline
83 & 20.32 & 20.4548677190581 & -0.134867719058072 \tabularnewline
84 & 21.66 & 19.6708508077707 & 1.9891491922293 \tabularnewline
85 & 21.99 & 20.7810484047234 & 1.20895159527659 \tabularnewline
86 & 22.27 & 21.6095904219519 & 0.660409578048107 \tabularnewline
87 & 21.83 & 21.9799042645198 & -0.14990426451977 \tabularnewline
88 & 21.94 & 21.2471352238737 & 0.692864776126283 \tabularnewline
89 & 20.91 & 19.8694498033715 & 1.04055019662846 \tabularnewline
90 & 20.4 & 20.4169698943254 & -0.0169698943253979 \tabularnewline
91 & 20.22 & 20.1216275847039 & 0.0983724152961021 \tabularnewline
92 & 19.64 & 19.7871732926413 & -0.147173292641267 \tabularnewline
93 & 19.75 & 20.3208374679478 & -0.570837467947773 \tabularnewline
94 & 19.51 & 18.2831556875007 & 1.22684431249931 \tabularnewline
95 & 19.52 & 18.2243912732112 & 1.29560872678878 \tabularnewline
96 & 19.48 & 17.7126991272589 & 1.76730087274105 \tabularnewline
97 & 19.88 & 16.6193604353121 & 3.26063956468786 \tabularnewline
98 & 18.97 & 17.7208137468129 & 1.24918625318708 \tabularnewline
99 & 19 & 19.4706932400148 & -0.470693240014826 \tabularnewline
100 & 19.32 & 18.9997273764861 & 0.320272623513898 \tabularnewline
101 & 19.5 & 18.183375976177 & 1.31662402382303 \tabularnewline
102 & 23.22 & 21.5631641854809 & 1.65683581451913 \tabularnewline
103 & 22.56 & 19.5346489335303 & 3.02535106646972 \tabularnewline
104 & 21.94 & 18.5179657284167 & 3.42203427158332 \tabularnewline
105 & 21.11 & 20.9026682837788 & 0.207331716221205 \tabularnewline
106 & 21.21 & 20.8576887415241 & 0.352311258475863 \tabularnewline
107 & 21.18 & 22.3963262226555 & -1.21632622265547 \tabularnewline
108 & 21.25 & 23.3971165923416 & -2.14711659234157 \tabularnewline
109 & 21.17 & 21.1642431624888 & 0.0057568375111814 \tabularnewline
110 & 20.47 & 22.8326189444627 & -2.36261894446269 \tabularnewline
111 & 19.99 & 21.457683627175 & -1.46768362717503 \tabularnewline
112 & 19.21 & 20.9735901779081 & -1.76359017790807 \tabularnewline
113 & 20.07 & 22.5544080446488 & -2.48440804464878 \tabularnewline
114 & 19.86 & 23.554260610652 & -3.69426061065198 \tabularnewline
115 & 22.36 & 24.5943338393968 & -2.23433383939681 \tabularnewline
116 & 22.17 & 25.7068006633214 & -3.5368006633214 \tabularnewline
117 & 23.56 & 25.8223571130161 & -2.26235711301607 \tabularnewline
118 & 22.92 & 23.9727837685176 & -1.05278376851764 \tabularnewline
119 & 23.1 & 25.1625896703347 & -2.06258967033469 \tabularnewline
120 & 24.32 & 26.0692296368998 & -1.7492296368998 \tabularnewline
121 & 23.99 & 24.9398575213893 & -0.949857521389288 \tabularnewline
122 & 25.94 & 25.230903298638 & 0.709096701362018 \tabularnewline
123 & 26.15 & 24.4109141463938 & 1.73908585360617 \tabularnewline
124 & 26.36 & 23.8330850388262 & 2.52691496117378 \tabularnewline
125 & 27.32 & 22.3233098079087 & 4.99669019209134 \tabularnewline
126 & 28 & 22.9470318331036 & 5.05296816689637 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202852&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]27.72[/C][C]28.9075658100432[/C][C]-1.18756581004317[/C][/ROW]
[ROW][C]2[/C][C]26.9[/C][C]28.8115334618907[/C][C]-1.91153346189067[/C][/ROW]
[ROW][C]3[/C][C]25.86[/C][C]28.523057248472[/C][C]-2.66305724847202[/C][/ROW]
[ROW][C]4[/C][C]26.81[/C][C]25.4500928733635[/C][C]1.35990712663648[/C][/ROW]
[ROW][C]5[/C][C]26.31[/C][C]26.3390043904902[/C][C]-0.0290043904902507[/C][/ROW]
[ROW][C]6[/C][C]27.1[/C][C]26.9035404146337[/C][C]0.196459585366294[/C][/ROW]
[ROW][C]7[/C][C]27[/C][C]27.6439362828623[/C][C]-0.643936282862303[/C][/ROW]
[ROW][C]8[/C][C]27.4[/C][C]25.4171920986502[/C][C]1.9828079013498[/C][/ROW]
[ROW][C]9[/C][C]27.27[/C][C]27.0931369056466[/C][C]0.176863094353402[/C][/ROW]
[ROW][C]10[/C][C]28.29[/C][C]27.3549132325184[/C][C]0.935086767481617[/C][/ROW]
[ROW][C]11[/C][C]30.01[/C][C]28.9466301551742[/C][C]1.06336984482579[/C][/ROW]
[ROW][C]12[/C][C]31.41[/C][C]29.9676090303007[/C][C]1.44239096969928[/C][/ROW]
[ROW][C]13[/C][C]31.91[/C][C]29.16759271712[/C][C]2.74240728287995[/C][/ROW]
[ROW][C]14[/C][C]31.6[/C][C]28.5803636883166[/C][C]3.01963631168335[/C][/ROW]
[ROW][C]15[/C][C]31.84[/C][C]30.0067763721451[/C][C]1.8332236278549[/C][/ROW]
[ROW][C]16[/C][C]33.05[/C][C]31.3880784963184[/C][C]1.66192150368155[/C][/ROW]
[ROW][C]17[/C][C]32.06[/C][C]31.4864511061139[/C][C]0.573548893886067[/C][/ROW]
[ROW][C]18[/C][C]33.1[/C][C]31.9369434871024[/C][C]1.16305651289757[/C][/ROW]
[ROW][C]19[/C][C]32.23[/C][C]29.504412751877[/C][C]2.72558724812298[/C][/ROW]
[ROW][C]20[/C][C]31.36[/C][C]28.9868064811319[/C][C]2.37319351886806[/C][/ROW]
[ROW][C]21[/C][C]31.09[/C][C]23.1784081588619[/C][C]7.91159184113808[/C][/ROW]
[ROW][C]22[/C][C]30.77[/C][C]28.8785376361377[/C][C]1.89146236386229[/C][/ROW]
[ROW][C]23[/C][C]31.2[/C][C]28.2781059143501[/C][C]2.92189408564992[/C][/ROW]
[ROW][C]24[/C][C]31.47[/C][C]28.9969078324727[/C][C]2.47309216752731[/C][/ROW]
[ROW][C]25[/C][C]31.73[/C][C]31.5390754613168[/C][C]0.190924538683168[/C][/ROW]
[ROW][C]26[/C][C]32.17[/C][C]28.697264552538[/C][C]3.47273544746198[/C][/ROW]
[ROW][C]27[/C][C]31.47[/C][C]27.556530996049[/C][C]3.91346900395096[/C][/ROW]
[ROW][C]28[/C][C]30.97[/C][C]29.2959903846495[/C][C]1.67400961535047[/C][/ROW]
[ROW][C]29[/C][C]30.81[/C][C]32.4622684325412[/C][C]-1.65226843254124[/C][/ROW]
[ROW][C]30[/C][C]30.72[/C][C]31.7637827981842[/C][C]-1.04378279818421[/C][/ROW]
[ROW][C]31[/C][C]28.24[/C][C]30.1175809913592[/C][C]-1.87758099135923[/C][/ROW]
[ROW][C]32[/C][C]28.09[/C][C]28.9958453764593[/C][C]-0.90584537645929[/C][/ROW]
[ROW][C]33[/C][C]29.11[/C][C]26.9109818310166[/C][C]2.19901816898338[/C][/ROW]
[ROW][C]34[/C][C]29[/C][C]25.8498413192517[/C][C]3.15015868074829[/C][/ROW]
[ROW][C]35[/C][C]28.76[/C][C]26.8502754275943[/C][C]1.90972457240572[/C][/ROW]
[ROW][C]36[/C][C]28.75[/C][C]28.7104413337871[/C][C]0.0395586662129045[/C][/ROW]
[ROW][C]37[/C][C]28.45[/C][C]30.3699804672265[/C][C]-1.91998046722645[/C][/ROW]
[ROW][C]38[/C][C]29.34[/C][C]30.3829535773041[/C][C]-1.04295357730407[/C][/ROW]
[ROW][C]39[/C][C]26.84[/C][C]26.8609746895703[/C][C]-0.0209746895702863[/C][/ROW]
[ROW][C]40[/C][C]23.7[/C][C]25.8025795736318[/C][C]-2.10257957363184[/C][/ROW]
[ROW][C]41[/C][C]23.15[/C][C]26.4747258332043[/C][C]-3.32472583320435[/C][/ROW]
[ROW][C]42[/C][C]21.71[/C][C]25.3297676324521[/C][C]-3.61976763245212[/C][/ROW]
[ROW][C]43[/C][C]20.88[/C][C]21.5496736205697[/C][C]-0.669673620569653[/C][/ROW]
[ROW][C]44[/C][C]20.04[/C][C]20.5396835676485[/C][C]-0.49968356764853[/C][/ROW]
[ROW][C]45[/C][C]21.09[/C][C]25.5780047887862[/C][C]-4.48800478878622[/C][/ROW]
[ROW][C]46[/C][C]21.92[/C][C]25.8076514087128[/C][C]-3.8876514087128[/C][/ROW]
[ROW][C]47[/C][C]20.72[/C][C]22.0770930378105[/C][C]-1.35709303781055[/C][/ROW]
[ROW][C]48[/C][C]20.72[/C][C]22.2062658436313[/C][C]-1.48626584363129[/C][/ROW]
[ROW][C]49[/C][C]21.01[/C][C]22.6177649619739[/C][C]-1.60776496197388[/C][/ROW]
[ROW][C]50[/C][C]21.8[/C][C]22.4276011041779[/C][C]-0.627601104177924[/C][/ROW]
[ROW][C]51[/C][C]21.6[/C][C]21.6428874762806[/C][C]-0.0428874762806458[/C][/ROW]
[ROW][C]52[/C][C]20.38[/C][C]20.6584847667695[/C][C]-0.27848476676952[/C][/ROW]
[ROW][C]53[/C][C]21.2[/C][C]20.0637511051614[/C][C]1.13624889483864[/C][/ROW]
[ROW][C]54[/C][C]19.87[/C][C]19.6674981077711[/C][C]0.202501892228919[/C][/ROW]
[ROW][C]55[/C][C]19.05[/C][C]17.3862294694524[/C][C]1.66377053054763[/C][/ROW]
[ROW][C]56[/C][C]20.01[/C][C]17.9247432175604[/C][C]2.08525678243957[/C][/ROW]
[ROW][C]57[/C][C]19.15[/C][C]19.0266632879174[/C][C]0.123336712082606[/C][/ROW]
[ROW][C]58[/C][C]19.43[/C][C]18.5370695280108[/C][C]0.892930471989246[/C][/ROW]
[ROW][C]59[/C][C]19.44[/C][C]19.7991008621696[/C][C]-0.35910086216959[/C][/ROW]
[ROW][C]60[/C][C]19.4[/C][C]19.8188374488806[/C][C]-0.418837448880602[/C][/ROW]
[ROW][C]61[/C][C]19.15[/C][C]20.0034894782991[/C][C]-0.853489478299135[/C][/ROW]
[ROW][C]62[/C][C]19.34[/C][C]21.4261880571757[/C][C]-2.08618805717566[/C][/ROW]
[ROW][C]63[/C][C]19.1[/C][C]21.5090022895694[/C][C]-2.40900228956936[/C][/ROW]
[ROW][C]64[/C][C]19.08[/C][C]22.9058317726612[/C][C]-3.82583177266122[/C][/ROW]
[ROW][C]65[/C][C]18.05[/C][C]21.24912141454[/C][C]-3.19912141454003[/C][/ROW]
[ROW][C]66[/C][C]17.72[/C][C]17.5289424073538[/C][C]0.191057592646174[/C][/ROW]
[ROW][C]67[/C][C]18.58[/C][C]21.8483943870703[/C][C]-3.26839438707026[/C][/ROW]
[ROW][C]68[/C][C]18.96[/C][C]21.8280396432165[/C][C]-2.86803964321652[/C][/ROW]
[ROW][C]69[/C][C]18.98[/C][C]20.6544613795847[/C][C]-1.67446137958472[/C][/ROW]
[ROW][C]70[/C][C]18.81[/C][C]20.9014848209172[/C][C]-2.09148482091724[/C][/ROW]
[ROW][C]71[/C][C]19.43[/C][C]21.4906197176418[/C][C]-2.06061971764184[/C][/ROW]
[ROW][C]72[/C][C]20.93[/C][C]22.8400423466566[/C][C]-1.91004234665657[/C][/ROW]
[ROW][C]73[/C][C]20.71[/C][C]21.6000215801068[/C][C]-0.890021580106819[/C][/ROW]
[ROW][C]74[/C][C]22[/C][C]23.0801691467315[/C][C]-1.08016914673154[/C][/ROW]
[ROW][C]75[/C][C]21.52[/C][C]21.7815756498101[/C][C]-0.261575649810096[/C][/ROW]
[ROW][C]76[/C][C]21.87[/C][C]22.1354043155118[/C][C]-0.265404315511806[/C][/ROW]
[ROW][C]77[/C][C]23.29[/C][C]21.6641340858429[/C][C]1.62586591415711[/C][/ROW]
[ROW][C]78[/C][C]22.59[/C][C]22.6780986289407[/C][C]-0.0880986289407461[/C][/ROW]
[ROW][C]79[/C][C]22.86[/C][C]21.6791621391782[/C][C]1.18083786082183[/C][/ROW]
[ROW][C]80[/C][C]20.79[/C][C]22.6957499309537[/C][C]-1.90574993095373[/C][/ROW]
[ROW][C]81[/C][C]20.28[/C][C]21.9024807834439[/C][C]-1.62248078344389[/C][/ROW]
[ROW][C]82[/C][C]20.62[/C][C]22.0368738569089[/C][C]-1.41687385690893[/C][/ROW]
[ROW][C]83[/C][C]20.32[/C][C]20.4548677190581[/C][C]-0.134867719058072[/C][/ROW]
[ROW][C]84[/C][C]21.66[/C][C]19.6708508077707[/C][C]1.9891491922293[/C][/ROW]
[ROW][C]85[/C][C]21.99[/C][C]20.7810484047234[/C][C]1.20895159527659[/C][/ROW]
[ROW][C]86[/C][C]22.27[/C][C]21.6095904219519[/C][C]0.660409578048107[/C][/ROW]
[ROW][C]87[/C][C]21.83[/C][C]21.9799042645198[/C][C]-0.14990426451977[/C][/ROW]
[ROW][C]88[/C][C]21.94[/C][C]21.2471352238737[/C][C]0.692864776126283[/C][/ROW]
[ROW][C]89[/C][C]20.91[/C][C]19.8694498033715[/C][C]1.04055019662846[/C][/ROW]
[ROW][C]90[/C][C]20.4[/C][C]20.4169698943254[/C][C]-0.0169698943253979[/C][/ROW]
[ROW][C]91[/C][C]20.22[/C][C]20.1216275847039[/C][C]0.0983724152961021[/C][/ROW]
[ROW][C]92[/C][C]19.64[/C][C]19.7871732926413[/C][C]-0.147173292641267[/C][/ROW]
[ROW][C]93[/C][C]19.75[/C][C]20.3208374679478[/C][C]-0.570837467947773[/C][/ROW]
[ROW][C]94[/C][C]19.51[/C][C]18.2831556875007[/C][C]1.22684431249931[/C][/ROW]
[ROW][C]95[/C][C]19.52[/C][C]18.2243912732112[/C][C]1.29560872678878[/C][/ROW]
[ROW][C]96[/C][C]19.48[/C][C]17.7126991272589[/C][C]1.76730087274105[/C][/ROW]
[ROW][C]97[/C][C]19.88[/C][C]16.6193604353121[/C][C]3.26063956468786[/C][/ROW]
[ROW][C]98[/C][C]18.97[/C][C]17.7208137468129[/C][C]1.24918625318708[/C][/ROW]
[ROW][C]99[/C][C]19[/C][C]19.4706932400148[/C][C]-0.470693240014826[/C][/ROW]
[ROW][C]100[/C][C]19.32[/C][C]18.9997273764861[/C][C]0.320272623513898[/C][/ROW]
[ROW][C]101[/C][C]19.5[/C][C]18.183375976177[/C][C]1.31662402382303[/C][/ROW]
[ROW][C]102[/C][C]23.22[/C][C]21.5631641854809[/C][C]1.65683581451913[/C][/ROW]
[ROW][C]103[/C][C]22.56[/C][C]19.5346489335303[/C][C]3.02535106646972[/C][/ROW]
[ROW][C]104[/C][C]21.94[/C][C]18.5179657284167[/C][C]3.42203427158332[/C][/ROW]
[ROW][C]105[/C][C]21.11[/C][C]20.9026682837788[/C][C]0.207331716221205[/C][/ROW]
[ROW][C]106[/C][C]21.21[/C][C]20.8576887415241[/C][C]0.352311258475863[/C][/ROW]
[ROW][C]107[/C][C]21.18[/C][C]22.3963262226555[/C][C]-1.21632622265547[/C][/ROW]
[ROW][C]108[/C][C]21.25[/C][C]23.3971165923416[/C][C]-2.14711659234157[/C][/ROW]
[ROW][C]109[/C][C]21.17[/C][C]21.1642431624888[/C][C]0.0057568375111814[/C][/ROW]
[ROW][C]110[/C][C]20.47[/C][C]22.8326189444627[/C][C]-2.36261894446269[/C][/ROW]
[ROW][C]111[/C][C]19.99[/C][C]21.457683627175[/C][C]-1.46768362717503[/C][/ROW]
[ROW][C]112[/C][C]19.21[/C][C]20.9735901779081[/C][C]-1.76359017790807[/C][/ROW]
[ROW][C]113[/C][C]20.07[/C][C]22.5544080446488[/C][C]-2.48440804464878[/C][/ROW]
[ROW][C]114[/C][C]19.86[/C][C]23.554260610652[/C][C]-3.69426061065198[/C][/ROW]
[ROW][C]115[/C][C]22.36[/C][C]24.5943338393968[/C][C]-2.23433383939681[/C][/ROW]
[ROW][C]116[/C][C]22.17[/C][C]25.7068006633214[/C][C]-3.5368006633214[/C][/ROW]
[ROW][C]117[/C][C]23.56[/C][C]25.8223571130161[/C][C]-2.26235711301607[/C][/ROW]
[ROW][C]118[/C][C]22.92[/C][C]23.9727837685176[/C][C]-1.05278376851764[/C][/ROW]
[ROW][C]119[/C][C]23.1[/C][C]25.1625896703347[/C][C]-2.06258967033469[/C][/ROW]
[ROW][C]120[/C][C]24.32[/C][C]26.0692296368998[/C][C]-1.7492296368998[/C][/ROW]
[ROW][C]121[/C][C]23.99[/C][C]24.9398575213893[/C][C]-0.949857521389288[/C][/ROW]
[ROW][C]122[/C][C]25.94[/C][C]25.230903298638[/C][C]0.709096701362018[/C][/ROW]
[ROW][C]123[/C][C]26.15[/C][C]24.4109141463938[/C][C]1.73908585360617[/C][/ROW]
[ROW][C]124[/C][C]26.36[/C][C]23.8330850388262[/C][C]2.52691496117378[/C][/ROW]
[ROW][C]125[/C][C]27.32[/C][C]22.3233098079087[/C][C]4.99669019209134[/C][/ROW]
[ROW][C]126[/C][C]28[/C][C]22.9470318331036[/C][C]5.05296816689637[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202852&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202852&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	27.72	28.9075658100432	-1.18756581004317
2	26.9	28.8115334618907	-1.91153346189067
3	25.86	28.523057248472	-2.66305724847202
4	26.81	25.4500928733635	1.35990712663648
5	26.31	26.3390043904902	-0.0290043904902507
6	27.1	26.9035404146337	0.196459585366294
7	27	27.6439362828623	-0.643936282862303
8	27.4	25.4171920986502	1.9828079013498
9	27.27	27.0931369056466	0.176863094353402
10	28.29	27.3549132325184	0.935086767481617
11	30.01	28.9466301551742	1.06336984482579
12	31.41	29.9676090303007	1.44239096969928
13	31.91	29.16759271712	2.74240728287995
14	31.6	28.5803636883166	3.01963631168335
15	31.84	30.0067763721451	1.8332236278549
16	33.05	31.3880784963184	1.66192150368155
17	32.06	31.4864511061139	0.573548893886067
18	33.1	31.9369434871024	1.16305651289757
19	32.23	29.504412751877	2.72558724812298
20	31.36	28.9868064811319	2.37319351886806
21	31.09	23.1784081588619	7.91159184113808
22	30.77	28.8785376361377	1.89146236386229
23	31.2	28.2781059143501	2.92189408564992
24	31.47	28.9969078324727	2.47309216752731
25	31.73	31.5390754613168	0.190924538683168
26	32.17	28.697264552538	3.47273544746198
27	31.47	27.556530996049	3.91346900395096
28	30.97	29.2959903846495	1.67400961535047
29	30.81	32.4622684325412	-1.65226843254124
30	30.72	31.7637827981842	-1.04378279818421
31	28.24	30.1175809913592	-1.87758099135923
32	28.09	28.9958453764593	-0.90584537645929
33	29.11	26.9109818310166	2.19901816898338
34	29	25.8498413192517	3.15015868074829
35	28.76	26.8502754275943	1.90972457240572
36	28.75	28.7104413337871	0.0395586662129045
37	28.45	30.3699804672265	-1.91998046722645
38	29.34	30.3829535773041	-1.04295357730407
39	26.84	26.8609746895703	-0.0209746895702863
40	23.7	25.8025795736318	-2.10257957363184
41	23.15	26.4747258332043	-3.32472583320435
42	21.71	25.3297676324521	-3.61976763245212
43	20.88	21.5496736205697	-0.669673620569653
44	20.04	20.5396835676485	-0.49968356764853
45	21.09	25.5780047887862	-4.48800478878622
46	21.92	25.8076514087128	-3.8876514087128
47	20.72	22.0770930378105	-1.35709303781055
48	20.72	22.2062658436313	-1.48626584363129
49	21.01	22.6177649619739	-1.60776496197388
50	21.8	22.4276011041779	-0.627601104177924
51	21.6	21.6428874762806	-0.0428874762806458
52	20.38	20.6584847667695	-0.27848476676952
53	21.2	20.0637511051614	1.13624889483864
54	19.87	19.6674981077711	0.202501892228919
55	19.05	17.3862294694524	1.66377053054763
56	20.01	17.9247432175604	2.08525678243957
57	19.15	19.0266632879174	0.123336712082606
58	19.43	18.5370695280108	0.892930471989246
59	19.44	19.7991008621696	-0.35910086216959
60	19.4	19.8188374488806	-0.418837448880602
61	19.15	20.0034894782991	-0.853489478299135
62	19.34	21.4261880571757	-2.08618805717566
63	19.1	21.5090022895694	-2.40900228956936
64	19.08	22.9058317726612	-3.82583177266122
65	18.05	21.24912141454	-3.19912141454003
66	17.72	17.5289424073538	0.191057592646174
67	18.58	21.8483943870703	-3.26839438707026
68	18.96	21.8280396432165	-2.86803964321652
69	18.98	20.6544613795847	-1.67446137958472
70	18.81	20.9014848209172	-2.09148482091724
71	19.43	21.4906197176418	-2.06061971764184
72	20.93	22.8400423466566	-1.91004234665657
73	20.71	21.6000215801068	-0.890021580106819
74	22	23.0801691467315	-1.08016914673154
75	21.52	21.7815756498101	-0.261575649810096
76	21.87	22.1354043155118	-0.265404315511806
77	23.29	21.6641340858429	1.62586591415711
78	22.59	22.6780986289407	-0.0880986289407461
79	22.86	21.6791621391782	1.18083786082183
80	20.79	22.6957499309537	-1.90574993095373
81	20.28	21.9024807834439	-1.62248078344389
82	20.62	22.0368738569089	-1.41687385690893
83	20.32	20.4548677190581	-0.134867719058072
84	21.66	19.6708508077707	1.9891491922293
85	21.99	20.7810484047234	1.20895159527659
86	22.27	21.6095904219519	0.660409578048107
87	21.83	21.9799042645198	-0.14990426451977
88	21.94	21.2471352238737	0.692864776126283
89	20.91	19.8694498033715	1.04055019662846
90	20.4	20.4169698943254	-0.0169698943253979
91	20.22	20.1216275847039	0.0983724152961021
92	19.64	19.7871732926413	-0.147173292641267
93	19.75	20.3208374679478	-0.570837467947773
94	19.51	18.2831556875007	1.22684431249931
95	19.52	18.2243912732112	1.29560872678878
96	19.48	17.7126991272589	1.76730087274105
97	19.88	16.6193604353121	3.26063956468786
98	18.97	17.7208137468129	1.24918625318708
99	19	19.4706932400148	-0.470693240014826
100	19.32	18.9997273764861	0.320272623513898
101	19.5	18.183375976177	1.31662402382303
102	23.22	21.5631641854809	1.65683581451913
103	22.56	19.5346489335303	3.02535106646972
104	21.94	18.5179657284167	3.42203427158332
105	21.11	20.9026682837788	0.207331716221205
106	21.21	20.8576887415241	0.352311258475863
107	21.18	22.3963262226555	-1.21632622265547
108	21.25	23.3971165923416	-2.14711659234157
109	21.17	21.1642431624888	0.0057568375111814
110	20.47	22.8326189444627	-2.36261894446269
111	19.99	21.457683627175	-1.46768362717503
112	19.21	20.9735901779081	-1.76359017790807
113	20.07	22.5544080446488	-2.48440804464878
114	19.86	23.554260610652	-3.69426061065198
115	22.36	24.5943338393968	-2.23433383939681
116	22.17	25.7068006633214	-3.5368006633214
117	23.56	25.8223571130161	-2.26235711301607
118	22.92	23.9727837685176	-1.05278376851764
119	23.1	25.1625896703347	-2.06258967033469
120	24.32	26.0692296368998	-1.7492296368998
121	23.99	24.9398575213893	-0.949857521389288
122	25.94	25.230903298638	0.709096701362018
123	26.15	24.4109141463938	1.73908585360617
124	26.36	23.8330850388262	2.52691496117378
125	27.32	22.3233098079087	4.99669019209134
126	28	22.9470318331036	5.05296816689637

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
20	0.116711676385035	0.233423352770071	0.883288323614965
21	0.0631511213805686	0.126302242761137	0.936848878619431
22	0.0239710949796031	0.0479421899592062	0.976028905020397
23	0.00888070674109352	0.017761413482187	0.991119293258906
24	0.00334125652348075	0.00668251304696151	0.996658743476519
25	0.00337701096663207	0.00675402193326413	0.996622989033368
26	0.0142910100413093	0.0285820200826187	0.985708989958691
27	0.0552501690388806	0.110500338077761	0.944749830961119
28	0.0387094193405462	0.0774188386810923	0.961290580659454
29	0.027914623198807	0.0558292463976139	0.972085376801193
30	0.0184262805728305	0.036852561145661	0.98157371942717
31	0.0232774691345601	0.0465549382691203	0.97672253086544
32	0.037633461108096	0.075266922216192	0.962366538891904
33	0.0337678090038147	0.0675356180076293	0.966232190996185
34	0.0608779642464338	0.121755928492868	0.939122035753566
35	0.0725888070661383	0.145177614132277	0.927411192933862
36	0.0665990439863181	0.133198087972636	0.933400956013682
37	0.0640806195137605	0.128161239027521	0.93591938048624
38	0.0630315597824151	0.12606311956483	0.936968440217585
39	0.0924101636515062	0.184820327303012	0.907589836348494
40	0.448265122557915	0.89653024511583	0.551734877442085
41	0.668026488399942	0.663947023200116	0.331973511600058
42	0.770676859951	0.458646280098	0.229323140049
43	0.721999414064507	0.556001171870986	0.278000585935493
44	0.6722683337113	0.6554633325774	0.3277316662887
45	0.884113987497394	0.231772025005213	0.115886012502606
46	0.890959001212596	0.218081997574808	0.109040998787404
47	0.865783841986206	0.268432316027589	0.134216158013794
48	0.831652362898516	0.336695274202967	0.168347637101484
49	0.791045148109002	0.417909703781995	0.208954851890998
50	0.756367660102822	0.487264679794355	0.243632339897178
51	0.726120756216581	0.547758487566837	0.273879243783419
52	0.691760622227819	0.616478755544362	0.308239377772181
53	0.71565176835905	0.568696463281899	0.28434823164095
54	0.669834650284465	0.66033069943107	0.330165349715535
55	0.620253484010956	0.759493031978089	0.379746515989044
56	0.608919185260368	0.782161629479264	0.391080814739632
57	0.588101399064721	0.823797201870558	0.411898600935279
58	0.574820103904948	0.850359792190104	0.425179896095052
59	0.570811383485803	0.858377233028394	0.429188616514197
60	0.565524795383498	0.868950409233004	0.434475204616502
61	0.550474131473353	0.899051737053295	0.449525868526647
62	0.596577301691714	0.806845396616572	0.403422698308286
63	0.651117939152077	0.697764121695845	0.348882060847923
64	0.684394284846759	0.631211430306483	0.315605715153241
65	0.749882954743528	0.500234090512944	0.250117045256472
66	0.989252530479708	0.0214949390405833	0.0107474695202917
67	0.990595253509725	0.0188094929805493	0.00940474649027463
68	0.989790793822228	0.0204184123555447	0.0102092061777724
69	0.986533497520025	0.0269330049599496	0.0134665024799748
70	0.981764637963547	0.0364707240729068	0.0182353620364534
71	0.974109860137505	0.0517802797249906	0.0258901398624953
72	0.963329850933545	0.0733402981329102	0.0366701490664551
73	0.953265034197969	0.0934699316040609	0.0467349658020305
74	0.937475275628952	0.125049448742095	0.0625247243710477
75	0.920166631979833	0.159666736040335	0.0798333680201675
76	0.899845849339076	0.200308301321848	0.100154150660924
77	0.90372587688228	0.192548246235441	0.0962741231177205
78	0.877627734367179	0.244744531265641	0.122372265632821
79	0.852826852075826	0.294346295848349	0.147173147924174
80	0.834154166210836	0.331691667578327	0.165845833789164
81	0.811989031861501	0.376021936276998	0.188010968138499
82	0.772490594592225	0.45501881081555	0.227509405407775
83	0.764209129552023	0.471581740895954	0.235790870447977
84	0.724640428097784	0.550719143804432	0.275359571902216
85	0.688360335040787	0.623279329918426	0.311639664959213
86	0.634523723159932	0.730952553680137	0.365476276840068
87	0.609183457843254	0.781633084313492	0.390816542156746
88	0.57982239070021	0.840355218599579	0.42017760929979
89	0.612006714100458	0.775986571799084	0.387993285899542
90	0.718818093355442	0.562363813289117	0.281181906644558
91	0.831187270059272	0.337625459881456	0.168812729940728
92	0.931665524279163	0.136668951441674	0.0683344757208372
93	0.987232509154667	0.0255349816906655	0.0127674908453328
94	0.978354887112173	0.043290225775653	0.0216451128878265
95	0.963944299628987	0.072111400742027	0.0360557003710135
96	0.942641228302472	0.114717543395057	0.0573587716975285
97	0.921086357544005	0.157827284911989	0.0789136424559945
98	0.890043220383406	0.219913559233189	0.109956779616594
99	0.898963069058589	0.202073861882823	0.101036930941411
100	0.948176606022016	0.103646787955968	0.0518233939779839
101	0.962498869840994	0.0750022603180126	0.0375011301590063
102	0.932289781157813	0.135420437684373	0.0677102188421867
103	0.917575222284967	0.164849555430066	0.0824247777150329
104	0.888588885117571	0.222822229764858	0.111411114882429
105	0.788954857572687	0.422090284854626	0.211045142427313
106	0.633763518441823	0.732472963116353	0.366236481558177

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
20 & 0.116711676385035 & 0.233423352770071 & 0.883288323614965 \tabularnewline
21 & 0.0631511213805686 & 0.126302242761137 & 0.936848878619431 \tabularnewline
22 & 0.0239710949796031 & 0.0479421899592062 & 0.976028905020397 \tabularnewline
23 & 0.00888070674109352 & 0.017761413482187 & 0.991119293258906 \tabularnewline
24 & 0.00334125652348075 & 0.00668251304696151 & 0.996658743476519 \tabularnewline
25 & 0.00337701096663207 & 0.00675402193326413 & 0.996622989033368 \tabularnewline
26 & 0.0142910100413093 & 0.0285820200826187 & 0.985708989958691 \tabularnewline
27 & 0.0552501690388806 & 0.110500338077761 & 0.944749830961119 \tabularnewline
28 & 0.0387094193405462 & 0.0774188386810923 & 0.961290580659454 \tabularnewline
29 & 0.027914623198807 & 0.0558292463976139 & 0.972085376801193 \tabularnewline
30 & 0.0184262805728305 & 0.036852561145661 & 0.98157371942717 \tabularnewline
31 & 0.0232774691345601 & 0.0465549382691203 & 0.97672253086544 \tabularnewline
32 & 0.037633461108096 & 0.075266922216192 & 0.962366538891904 \tabularnewline
33 & 0.0337678090038147 & 0.0675356180076293 & 0.966232190996185 \tabularnewline
34 & 0.0608779642464338 & 0.121755928492868 & 0.939122035753566 \tabularnewline
35 & 0.0725888070661383 & 0.145177614132277 & 0.927411192933862 \tabularnewline
36 & 0.0665990439863181 & 0.133198087972636 & 0.933400956013682 \tabularnewline
37 & 0.0640806195137605 & 0.128161239027521 & 0.93591938048624 \tabularnewline
38 & 0.0630315597824151 & 0.12606311956483 & 0.936968440217585 \tabularnewline
39 & 0.0924101636515062 & 0.184820327303012 & 0.907589836348494 \tabularnewline
40 & 0.448265122557915 & 0.89653024511583 & 0.551734877442085 \tabularnewline
41 & 0.668026488399942 & 0.663947023200116 & 0.331973511600058 \tabularnewline
42 & 0.770676859951 & 0.458646280098 & 0.229323140049 \tabularnewline
43 & 0.721999414064507 & 0.556001171870986 & 0.278000585935493 \tabularnewline
44 & 0.6722683337113 & 0.6554633325774 & 0.3277316662887 \tabularnewline
45 & 0.884113987497394 & 0.231772025005213 & 0.115886012502606 \tabularnewline
46 & 0.890959001212596 & 0.218081997574808 & 0.109040998787404 \tabularnewline
47 & 0.865783841986206 & 0.268432316027589 & 0.134216158013794 \tabularnewline
48 & 0.831652362898516 & 0.336695274202967 & 0.168347637101484 \tabularnewline
49 & 0.791045148109002 & 0.417909703781995 & 0.208954851890998 \tabularnewline
50 & 0.756367660102822 & 0.487264679794355 & 0.243632339897178 \tabularnewline
51 & 0.726120756216581 & 0.547758487566837 & 0.273879243783419 \tabularnewline
52 & 0.691760622227819 & 0.616478755544362 & 0.308239377772181 \tabularnewline
53 & 0.71565176835905 & 0.568696463281899 & 0.28434823164095 \tabularnewline
54 & 0.669834650284465 & 0.66033069943107 & 0.330165349715535 \tabularnewline
55 & 0.620253484010956 & 0.759493031978089 & 0.379746515989044 \tabularnewline
56 & 0.608919185260368 & 0.782161629479264 & 0.391080814739632 \tabularnewline
57 & 0.588101399064721 & 0.823797201870558 & 0.411898600935279 \tabularnewline
58 & 0.574820103904948 & 0.850359792190104 & 0.425179896095052 \tabularnewline
59 & 0.570811383485803 & 0.858377233028394 & 0.429188616514197 \tabularnewline
60 & 0.565524795383498 & 0.868950409233004 & 0.434475204616502 \tabularnewline
61 & 0.550474131473353 & 0.899051737053295 & 0.449525868526647 \tabularnewline
62 & 0.596577301691714 & 0.806845396616572 & 0.403422698308286 \tabularnewline
63 & 0.651117939152077 & 0.697764121695845 & 0.348882060847923 \tabularnewline
64 & 0.684394284846759 & 0.631211430306483 & 0.315605715153241 \tabularnewline
65 & 0.749882954743528 & 0.500234090512944 & 0.250117045256472 \tabularnewline
66 & 0.989252530479708 & 0.0214949390405833 & 0.0107474695202917 \tabularnewline
67 & 0.990595253509725 & 0.0188094929805493 & 0.00940474649027463 \tabularnewline
68 & 0.989790793822228 & 0.0204184123555447 & 0.0102092061777724 \tabularnewline
69 & 0.986533497520025 & 0.0269330049599496 & 0.0134665024799748 \tabularnewline
70 & 0.981764637963547 & 0.0364707240729068 & 0.0182353620364534 \tabularnewline
71 & 0.974109860137505 & 0.0517802797249906 & 0.0258901398624953 \tabularnewline
72 & 0.963329850933545 & 0.0733402981329102 & 0.0366701490664551 \tabularnewline
73 & 0.953265034197969 & 0.0934699316040609 & 0.0467349658020305 \tabularnewline
74 & 0.937475275628952 & 0.125049448742095 & 0.0625247243710477 \tabularnewline
75 & 0.920166631979833 & 0.159666736040335 & 0.0798333680201675 \tabularnewline
76 & 0.899845849339076 & 0.200308301321848 & 0.100154150660924 \tabularnewline
77 & 0.90372587688228 & 0.192548246235441 & 0.0962741231177205 \tabularnewline
78 & 0.877627734367179 & 0.244744531265641 & 0.122372265632821 \tabularnewline
79 & 0.852826852075826 & 0.294346295848349 & 0.147173147924174 \tabularnewline
80 & 0.834154166210836 & 0.331691667578327 & 0.165845833789164 \tabularnewline
81 & 0.811989031861501 & 0.376021936276998 & 0.188010968138499 \tabularnewline
82 & 0.772490594592225 & 0.45501881081555 & 0.227509405407775 \tabularnewline
83 & 0.764209129552023 & 0.471581740895954 & 0.235790870447977 \tabularnewline
84 & 0.724640428097784 & 0.550719143804432 & 0.275359571902216 \tabularnewline
85 & 0.688360335040787 & 0.623279329918426 & 0.311639664959213 \tabularnewline
86 & 0.634523723159932 & 0.730952553680137 & 0.365476276840068 \tabularnewline
87 & 0.609183457843254 & 0.781633084313492 & 0.390816542156746 \tabularnewline
88 & 0.57982239070021 & 0.840355218599579 & 0.42017760929979 \tabularnewline
89 & 0.612006714100458 & 0.775986571799084 & 0.387993285899542 \tabularnewline
90 & 0.718818093355442 & 0.562363813289117 & 0.281181906644558 \tabularnewline
91 & 0.831187270059272 & 0.337625459881456 & 0.168812729940728 \tabularnewline
92 & 0.931665524279163 & 0.136668951441674 & 0.0683344757208372 \tabularnewline
93 & 0.987232509154667 & 0.0255349816906655 & 0.0127674908453328 \tabularnewline
94 & 0.978354887112173 & 0.043290225775653 & 0.0216451128878265 \tabularnewline
95 & 0.963944299628987 & 0.072111400742027 & 0.0360557003710135 \tabularnewline
96 & 0.942641228302472 & 0.114717543395057 & 0.0573587716975285 \tabularnewline
97 & 0.921086357544005 & 0.157827284911989 & 0.0789136424559945 \tabularnewline
98 & 0.890043220383406 & 0.219913559233189 & 0.109956779616594 \tabularnewline
99 & 0.898963069058589 & 0.202073861882823 & 0.101036930941411 \tabularnewline
100 & 0.948176606022016 & 0.103646787955968 & 0.0518233939779839 \tabularnewline
101 & 0.962498869840994 & 0.0750022603180126 & 0.0375011301590063 \tabularnewline
102 & 0.932289781157813 & 0.135420437684373 & 0.0677102188421867 \tabularnewline
103 & 0.917575222284967 & 0.164849555430066 & 0.0824247777150329 \tabularnewline
104 & 0.888588885117571 & 0.222822229764858 & 0.111411114882429 \tabularnewline
105 & 0.788954857572687 & 0.422090284854626 & 0.211045142427313 \tabularnewline
106 & 0.633763518441823 & 0.732472963116353 & 0.366236481558177 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202852&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]0.116711676385035[/C][C]0.233423352770071[/C][C]0.883288323614965[/C][/ROW]
[ROW][C]21[/C][C]0.0631511213805686[/C][C]0.126302242761137[/C][C]0.936848878619431[/C][/ROW]
[ROW][C]22[/C][C]0.0239710949796031[/C][C]0.0479421899592062[/C][C]0.976028905020397[/C][/ROW]
[ROW][C]23[/C][C]0.00888070674109352[/C][C]0.017761413482187[/C][C]0.991119293258906[/C][/ROW]
[ROW][C]24[/C][C]0.00334125652348075[/C][C]0.00668251304696151[/C][C]0.996658743476519[/C][/ROW]
[ROW][C]25[/C][C]0.00337701096663207[/C][C]0.00675402193326413[/C][C]0.996622989033368[/C][/ROW]
[ROW][C]26[/C][C]0.0142910100413093[/C][C]0.0285820200826187[/C][C]0.985708989958691[/C][/ROW]
[ROW][C]27[/C][C]0.0552501690388806[/C][C]0.110500338077761[/C][C]0.944749830961119[/C][/ROW]
[ROW][C]28[/C][C]0.0387094193405462[/C][C]0.0774188386810923[/C][C]0.961290580659454[/C][/ROW]
[ROW][C]29[/C][C]0.027914623198807[/C][C]0.0558292463976139[/C][C]0.972085376801193[/C][/ROW]
[ROW][C]30[/C][C]0.0184262805728305[/C][C]0.036852561145661[/C][C]0.98157371942717[/C][/ROW]
[ROW][C]31[/C][C]0.0232774691345601[/C][C]0.0465549382691203[/C][C]0.97672253086544[/C][/ROW]
[ROW][C]32[/C][C]0.037633461108096[/C][C]0.075266922216192[/C][C]0.962366538891904[/C][/ROW]
[ROW][C]33[/C][C]0.0337678090038147[/C][C]0.0675356180076293[/C][C]0.966232190996185[/C][/ROW]
[ROW][C]34[/C][C]0.0608779642464338[/C][C]0.121755928492868[/C][C]0.939122035753566[/C][/ROW]
[ROW][C]35[/C][C]0.0725888070661383[/C][C]0.145177614132277[/C][C]0.927411192933862[/C][/ROW]
[ROW][C]36[/C][C]0.0665990439863181[/C][C]0.133198087972636[/C][C]0.933400956013682[/C][/ROW]
[ROW][C]37[/C][C]0.0640806195137605[/C][C]0.128161239027521[/C][C]0.93591938048624[/C][/ROW]
[ROW][C]38[/C][C]0.0630315597824151[/C][C]0.12606311956483[/C][C]0.936968440217585[/C][/ROW]
[ROW][C]39[/C][C]0.0924101636515062[/C][C]0.184820327303012[/C][C]0.907589836348494[/C][/ROW]
[ROW][C]40[/C][C]0.448265122557915[/C][C]0.89653024511583[/C][C]0.551734877442085[/C][/ROW]
[ROW][C]41[/C][C]0.668026488399942[/C][C]0.663947023200116[/C][C]0.331973511600058[/C][/ROW]
[ROW][C]42[/C][C]0.770676859951[/C][C]0.458646280098[/C][C]0.229323140049[/C][/ROW]
[ROW][C]43[/C][C]0.721999414064507[/C][C]0.556001171870986[/C][C]0.278000585935493[/C][/ROW]
[ROW][C]44[/C][C]0.6722683337113[/C][C]0.6554633325774[/C][C]0.3277316662887[/C][/ROW]
[ROW][C]45[/C][C]0.884113987497394[/C][C]0.231772025005213[/C][C]0.115886012502606[/C][/ROW]
[ROW][C]46[/C][C]0.890959001212596[/C][C]0.218081997574808[/C][C]0.109040998787404[/C][/ROW]
[ROW][C]47[/C][C]0.865783841986206[/C][C]0.268432316027589[/C][C]0.134216158013794[/C][/ROW]
[ROW][C]48[/C][C]0.831652362898516[/C][C]0.336695274202967[/C][C]0.168347637101484[/C][/ROW]
[ROW][C]49[/C][C]0.791045148109002[/C][C]0.417909703781995[/C][C]0.208954851890998[/C][/ROW]
[ROW][C]50[/C][C]0.756367660102822[/C][C]0.487264679794355[/C][C]0.243632339897178[/C][/ROW]
[ROW][C]51[/C][C]0.726120756216581[/C][C]0.547758487566837[/C][C]0.273879243783419[/C][/ROW]
[ROW][C]52[/C][C]0.691760622227819[/C][C]0.616478755544362[/C][C]0.308239377772181[/C][/ROW]
[ROW][C]53[/C][C]0.71565176835905[/C][C]0.568696463281899[/C][C]0.28434823164095[/C][/ROW]
[ROW][C]54[/C][C]0.669834650284465[/C][C]0.66033069943107[/C][C]0.330165349715535[/C][/ROW]
[ROW][C]55[/C][C]0.620253484010956[/C][C]0.759493031978089[/C][C]0.379746515989044[/C][/ROW]
[ROW][C]56[/C][C]0.608919185260368[/C][C]0.782161629479264[/C][C]0.391080814739632[/C][/ROW]
[ROW][C]57[/C][C]0.588101399064721[/C][C]0.823797201870558[/C][C]0.411898600935279[/C][/ROW]
[ROW][C]58[/C][C]0.574820103904948[/C][C]0.850359792190104[/C][C]0.425179896095052[/C][/ROW]
[ROW][C]59[/C][C]0.570811383485803[/C][C]0.858377233028394[/C][C]0.429188616514197[/C][/ROW]
[ROW][C]60[/C][C]0.565524795383498[/C][C]0.868950409233004[/C][C]0.434475204616502[/C][/ROW]
[ROW][C]61[/C][C]0.550474131473353[/C][C]0.899051737053295[/C][C]0.449525868526647[/C][/ROW]
[ROW][C]62[/C][C]0.596577301691714[/C][C]0.806845396616572[/C][C]0.403422698308286[/C][/ROW]
[ROW][C]63[/C][C]0.651117939152077[/C][C]0.697764121695845[/C][C]0.348882060847923[/C][/ROW]
[ROW][C]64[/C][C]0.684394284846759[/C][C]0.631211430306483[/C][C]0.315605715153241[/C][/ROW]
[ROW][C]65[/C][C]0.749882954743528[/C][C]0.500234090512944[/C][C]0.250117045256472[/C][/ROW]
[ROW][C]66[/C][C]0.989252530479708[/C][C]0.0214949390405833[/C][C]0.0107474695202917[/C][/ROW]
[ROW][C]67[/C][C]0.990595253509725[/C][C]0.0188094929805493[/C][C]0.00940474649027463[/C][/ROW]
[ROW][C]68[/C][C]0.989790793822228[/C][C]0.0204184123555447[/C][C]0.0102092061777724[/C][/ROW]
[ROW][C]69[/C][C]0.986533497520025[/C][C]0.0269330049599496[/C][C]0.0134665024799748[/C][/ROW]
[ROW][C]70[/C][C]0.981764637963547[/C][C]0.0364707240729068[/C][C]0.0182353620364534[/C][/ROW]
[ROW][C]71[/C][C]0.974109860137505[/C][C]0.0517802797249906[/C][C]0.0258901398624953[/C][/ROW]
[ROW][C]72[/C][C]0.963329850933545[/C][C]0.0733402981329102[/C][C]0.0366701490664551[/C][/ROW]
[ROW][C]73[/C][C]0.953265034197969[/C][C]0.0934699316040609[/C][C]0.0467349658020305[/C][/ROW]
[ROW][C]74[/C][C]0.937475275628952[/C][C]0.125049448742095[/C][C]0.0625247243710477[/C][/ROW]
[ROW][C]75[/C][C]0.920166631979833[/C][C]0.159666736040335[/C][C]0.0798333680201675[/C][/ROW]
[ROW][C]76[/C][C]0.899845849339076[/C][C]0.200308301321848[/C][C]0.100154150660924[/C][/ROW]
[ROW][C]77[/C][C]0.90372587688228[/C][C]0.192548246235441[/C][C]0.0962741231177205[/C][/ROW]
[ROW][C]78[/C][C]0.877627734367179[/C][C]0.244744531265641[/C][C]0.122372265632821[/C][/ROW]
[ROW][C]79[/C][C]0.852826852075826[/C][C]0.294346295848349[/C][C]0.147173147924174[/C][/ROW]
[ROW][C]80[/C][C]0.834154166210836[/C][C]0.331691667578327[/C][C]0.165845833789164[/C][/ROW]
[ROW][C]81[/C][C]0.811989031861501[/C][C]0.376021936276998[/C][C]0.188010968138499[/C][/ROW]
[ROW][C]82[/C][C]0.772490594592225[/C][C]0.45501881081555[/C][C]0.227509405407775[/C][/ROW]
[ROW][C]83[/C][C]0.764209129552023[/C][C]0.471581740895954[/C][C]0.235790870447977[/C][/ROW]
[ROW][C]84[/C][C]0.724640428097784[/C][C]0.550719143804432[/C][C]0.275359571902216[/C][/ROW]
[ROW][C]85[/C][C]0.688360335040787[/C][C]0.623279329918426[/C][C]0.311639664959213[/C][/ROW]
[ROW][C]86[/C][C]0.634523723159932[/C][C]0.730952553680137[/C][C]0.365476276840068[/C][/ROW]
[ROW][C]87[/C][C]0.609183457843254[/C][C]0.781633084313492[/C][C]0.390816542156746[/C][/ROW]
[ROW][C]88[/C][C]0.57982239070021[/C][C]0.840355218599579[/C][C]0.42017760929979[/C][/ROW]
[ROW][C]89[/C][C]0.612006714100458[/C][C]0.775986571799084[/C][C]0.387993285899542[/C][/ROW]
[ROW][C]90[/C][C]0.718818093355442[/C][C]0.562363813289117[/C][C]0.281181906644558[/C][/ROW]
[ROW][C]91[/C][C]0.831187270059272[/C][C]0.337625459881456[/C][C]0.168812729940728[/C][/ROW]
[ROW][C]92[/C][C]0.931665524279163[/C][C]0.136668951441674[/C][C]0.0683344757208372[/C][/ROW]
[ROW][C]93[/C][C]0.987232509154667[/C][C]0.0255349816906655[/C][C]0.0127674908453328[/C][/ROW]
[ROW][C]94[/C][C]0.978354887112173[/C][C]0.043290225775653[/C][C]0.0216451128878265[/C][/ROW]
[ROW][C]95[/C][C]0.963944299628987[/C][C]0.072111400742027[/C][C]0.0360557003710135[/C][/ROW]
[ROW][C]96[/C][C]0.942641228302472[/C][C]0.114717543395057[/C][C]0.0573587716975285[/C][/ROW]
[ROW][C]97[/C][C]0.921086357544005[/C][C]0.157827284911989[/C][C]0.0789136424559945[/C][/ROW]
[ROW][C]98[/C][C]0.890043220383406[/C][C]0.219913559233189[/C][C]0.109956779616594[/C][/ROW]
[ROW][C]99[/C][C]0.898963069058589[/C][C]0.202073861882823[/C][C]0.101036930941411[/C][/ROW]
[ROW][C]100[/C][C]0.948176606022016[/C][C]0.103646787955968[/C][C]0.0518233939779839[/C][/ROW]
[ROW][C]101[/C][C]0.962498869840994[/C][C]0.0750022603180126[/C][C]0.0375011301590063[/C][/ROW]
[ROW][C]102[/C][C]0.932289781157813[/C][C]0.135420437684373[/C][C]0.0677102188421867[/C][/ROW]
[ROW][C]103[/C][C]0.917575222284967[/C][C]0.164849555430066[/C][C]0.0824247777150329[/C][/ROW]
[ROW][C]104[/C][C]0.888588885117571[/C][C]0.222822229764858[/C][C]0.111411114882429[/C][/ROW]
[ROW][C]105[/C][C]0.788954857572687[/C][C]0.422090284854626[/C][C]0.211045142427313[/C][/ROW]
[ROW][C]106[/C][C]0.633763518441823[/C][C]0.732472963116353[/C][C]0.366236481558177[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202852&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202852&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	0.116711676385035	0.233423352770071	0.883288323614965
21	0.0631511213805686	0.126302242761137	0.936848878619431
22	0.0239710949796031	0.0479421899592062	0.976028905020397
23	0.00888070674109352	0.017761413482187	0.991119293258906
24	0.00334125652348075	0.00668251304696151	0.996658743476519
25	0.00337701096663207	0.00675402193326413	0.996622989033368
26	0.0142910100413093	0.0285820200826187	0.985708989958691
27	0.0552501690388806	0.110500338077761	0.944749830961119
28	0.0387094193405462	0.0774188386810923	0.961290580659454
29	0.027914623198807	0.0558292463976139	0.972085376801193
30	0.0184262805728305	0.036852561145661	0.98157371942717
31	0.0232774691345601	0.0465549382691203	0.97672253086544
32	0.037633461108096	0.075266922216192	0.962366538891904
33	0.0337678090038147	0.0675356180076293	0.966232190996185
34	0.0608779642464338	0.121755928492868	0.939122035753566
35	0.0725888070661383	0.145177614132277	0.927411192933862
36	0.0665990439863181	0.133198087972636	0.933400956013682
37	0.0640806195137605	0.128161239027521	0.93591938048624
38	0.0630315597824151	0.12606311956483	0.936968440217585
39	0.0924101636515062	0.184820327303012	0.907589836348494
40	0.448265122557915	0.89653024511583	0.551734877442085
41	0.668026488399942	0.663947023200116	0.331973511600058
42	0.770676859951	0.458646280098	0.229323140049
43	0.721999414064507	0.556001171870986	0.278000585935493
44	0.6722683337113	0.6554633325774	0.3277316662887
45	0.884113987497394	0.231772025005213	0.115886012502606
46	0.890959001212596	0.218081997574808	0.109040998787404
47	0.865783841986206	0.268432316027589	0.134216158013794
48	0.831652362898516	0.336695274202967	0.168347637101484
49	0.791045148109002	0.417909703781995	0.208954851890998
50	0.756367660102822	0.487264679794355	0.243632339897178
51	0.726120756216581	0.547758487566837	0.273879243783419
52	0.691760622227819	0.616478755544362	0.308239377772181
53	0.71565176835905	0.568696463281899	0.28434823164095
54	0.669834650284465	0.66033069943107	0.330165349715535
55	0.620253484010956	0.759493031978089	0.379746515989044
56	0.608919185260368	0.782161629479264	0.391080814739632
57	0.588101399064721	0.823797201870558	0.411898600935279
58	0.574820103904948	0.850359792190104	0.425179896095052
59	0.570811383485803	0.858377233028394	0.429188616514197
60	0.565524795383498	0.868950409233004	0.434475204616502
61	0.550474131473353	0.899051737053295	0.449525868526647
62	0.596577301691714	0.806845396616572	0.403422698308286
63	0.651117939152077	0.697764121695845	0.348882060847923
64	0.684394284846759	0.631211430306483	0.315605715153241
65	0.749882954743528	0.500234090512944	0.250117045256472
66	0.989252530479708	0.0214949390405833	0.0107474695202917
67	0.990595253509725	0.0188094929805493	0.00940474649027463
68	0.989790793822228	0.0204184123555447	0.0102092061777724
69	0.986533497520025	0.0269330049599496	0.0134665024799748
70	0.981764637963547	0.0364707240729068	0.0182353620364534
71	0.974109860137505	0.0517802797249906	0.0258901398624953
72	0.963329850933545	0.0733402981329102	0.0366701490664551
73	0.953265034197969	0.0934699316040609	0.0467349658020305
74	0.937475275628952	0.125049448742095	0.0625247243710477
75	0.920166631979833	0.159666736040335	0.0798333680201675
76	0.899845849339076	0.200308301321848	0.100154150660924
77	0.90372587688228	0.192548246235441	0.0962741231177205
78	0.877627734367179	0.244744531265641	0.122372265632821
79	0.852826852075826	0.294346295848349	0.147173147924174
80	0.834154166210836	0.331691667578327	0.165845833789164
81	0.811989031861501	0.376021936276998	0.188010968138499
82	0.772490594592225	0.45501881081555	0.227509405407775
83	0.764209129552023	0.471581740895954	0.235790870447977
84	0.724640428097784	0.550719143804432	0.275359571902216
85	0.688360335040787	0.623279329918426	0.311639664959213
86	0.634523723159932	0.730952553680137	0.365476276840068
87	0.609183457843254	0.781633084313492	0.390816542156746
88	0.57982239070021	0.840355218599579	0.42017760929979
89	0.612006714100458	0.775986571799084	0.387993285899542
90	0.718818093355442	0.562363813289117	0.281181906644558
91	0.831187270059272	0.337625459881456	0.168812729940728
92	0.931665524279163	0.136668951441674	0.0683344757208372
93	0.987232509154667	0.0255349816906655	0.0127674908453328
94	0.978354887112173	0.043290225775653	0.0216451128878265
95	0.963944299628987	0.072111400742027	0.0360557003710135
96	0.942641228302472	0.114717543395057	0.0573587716975285
97	0.921086357544005	0.157827284911989	0.0789136424559945
98	0.890043220383406	0.219913559233189	0.109956779616594
99	0.898963069058589	0.202073861882823	0.101036930941411
100	0.948176606022016	0.103646787955968	0.0518233939779839
101	0.962498869840994	0.0750022603180126	0.0375011301590063
102	0.932289781157813	0.135420437684373	0.0677102188421867
103	0.917575222284967	0.164849555430066	0.0824247777150329
104	0.888588885117571	0.222822229764858	0.111411114882429
105	0.788954857572687	0.422090284854626	0.211045142427313
106	0.633763518441823	0.732472963116353	0.366236481558177

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	2	0.0229885057471264	NOK
5% type I error level	14	0.160919540229885	NOK
10% type I error level	23	0.264367816091954	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 & 2 & 0.0229885057471264 & NOK \tabularnewline
5% type I error level & 14 & 0.160919540229885 & NOK \tabularnewline
10% type I error level & 23 & 0.264367816091954 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202852&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]2[/C][C]0.0229885057471264[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]14[/C][C]0.160919540229885[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]23[/C][C]0.264367816091954[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202852&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202852&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	2	0.0229885057471264	NOK
5% type I error level	14	0.160919540229885	NOK
10% type I error level	23	0.264367816091954	NOK

Figure 1

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Figure 8

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Figure 9

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Figure 10

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Parameters (Session):

par1 = grey ;

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