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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 11:07:49 -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/t1356019705g85c11dcn37tcw4.htm/, Retrieved Tue, 23 Apr 2024 13:05:03 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=202832, Retrieved Tue, 23 Apr 2024 13:05:03 +0000

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

134

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] [14d0a7ecb926325afa0eb6a607fbc7a0] [Current]
-    D                [Multiple Regression] [] [2012-12-20 16:20:46] [d1865ed705b6ad9ba3d459a02c528b22] 
-   PD                  [Multiple Regression] [] [2012-12-21 08:01:57] [74be16979710d4c4e7c6647856088456] 
- R PD                    [Multiple Regression] [] [2012-12-21 08:23:32] [d1865ed705b6ad9ba3d459a02c528b22] 
- R  D                      [Multiple Regression] [] [2012-12-21 08:31:34] [d1865ed705b6ad9ba3d459a02c528b22] 
- R  D                      [Multiple Regression] [] [2012-12-21 08:31:34] [d1865ed705b6ad9ba3d459a02c528b22] 
- R  D                      [Multiple Regression] [] [2012-12-21 08:31:34] [d1865ed705b6ad9ba3d459a02c528b22] 
- R  D                      [Multiple Regression] [] [2012-12-21 08:31:34] [d1865ed705b6ad9ba3d459a02c528b22] 
- R  D                      [Multiple Regression] [] [2012-12-21 08:31:34] [d1865ed705b6ad9ba3d459a02c528b22] 
- R  D                      [Multiple Regression] [] [2012-12-21 08:31:34] [d1865ed705b6ad9ba3d459a02c528b22] 
- R PD                      [Multiple Regression] [] [2012-12-21 08:35:48] [d1865ed705b6ad9ba3d459a02c528b22] 
- R PD                      [Multiple Regression] [] [2012-12-21 08:42:03] [d1865ed705b6ad9ba3d459a02c528b22] 
- R PD                      [Multiple Regression] [] [2012-12-21 08:45:23] [d1865ed705b6ad9ba3d459a02c528b22] 
- RMPD                      [Testing Mean with unknown Variance - Critical Value] [] [2012-12-21 09:21:24] [d1865ed705b6ad9ba3d459a02c528b22] 
- RMPD                      [Testing Mean with unknown Variance - Critical Value] [] [2012-12-21 09:34:12] [d1865ed705b6ad9ba3d459a02c528b22] 

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Dataseries X:

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Histogram

Boxplots

27.72	41837160	91.51	2747.48	0.016	62.7	0.16
26.90	35204750	91.09	2760.01	0.016	62.7	0.17
25.86	42367740	93.00	2778.11	0.016	62.7	0.17
26.81	61427940	93.08	2844.72	0.016	62.7	0.16
26.31	26132090	94.13	2831.02	0.016	62.7	0.16
27.10	3799718	96.26	2858.42	0.016	62.7	0.17
27.00	28202230	94.29	2809.73	0.016	62.7	0.17
27.40	15809640	94.46	2843.07	0.016	62.7	0.16
27.27	17110160	95.53	2818.61	0.016	62.7	0.17
28.29	16835510	98.29	2836.33	0.016	62.7	0.17
30.01	43517670	102.01	2872.80	0.016	62.7	0.18
31.41	42958450	105.16	2895.33	0.016	62.7	0.17
31.91	30826830	105.34	2929.76	0.016	62.7	0.17
31.60	15549740	105.27	2930.45	0.016	62.7	0.16
31.84	21843070	102.19	2859.09	0.016	62.7	0.17
33.05	73424890	106.85	2892.42	0.016	62.7	0.17
32.06	24330740	103.05	2836.16	0.016	62.7	0.17
33.10	24785970	106.42	2854.06	0.016	62.7	0.16
32.23	28553940	105.17	2875.32	0.016	62.7	0.15
31.36	17659080	102.74	2849.49	0.016	62.7	0.15
31.09	19508980	106.27	2935.05	0.016	62.7	0.09
30.77	14110230	107.63	2951.23	0.0141	65.4	0.18
31.20	8765498	108.54	2976.08	0.0141	65.4	0.17
31.47	10027250	108.24	2976.12	0.0141	65.4	0.17
31.73	10943350	108.86	2937.33	0.0141	65.4	0.17
32.17	17755740	102.98	2931.77	0.0141	65.4	0.17
31.47	14238190	99.53	2902.33	0.0141	65.4	0.17
30.97	12997760	101.08	2887.98	0.0141	65.4	0.17
30.81	11299240	104.64	2866.19	0.0141	65.4	0.18
30.72	8102653	105.59	2908.47	0.0141	65.4	0.19
28.24	24549800	103.21	2896.94	0.0141	65.4	0.18
28.09	30410530	103.84	2910.04	0.0141	65.4	0.17
29.11	16807730	104.61	2942.60	0.0141	65.4	0.16
29.00	13671200	108.65	2965.90	0.0141	65.4	0.13
28.76	11854290	106.26	2925.30	0.0141	65.4	0.13
28.75	12383610	104.20	2890.15	0.0141	65.4	0.14
28.45	11512350	102.99	2862.99	0.0141	65.4	0.15
29.34	16749990	102.19	2854.24	0.0141	65.4	0.15
26.84	61009290	100.82	2893.25	0.0141	65.4	0.14
23.70	123011300	103.42	2958.09	0.0141	65.4	0.14
23.15	29253590	104.18	2945.84	0.0141	65.4	0.14
21.71	55998620	102.65	2939.52	0.0141	65.4	0.13
20.88	44488370	95.64	2920.21	0.0169	61.3	0.14
20.04	56264460	93.51	2909.77	0.0169	61.3	0.14
21.09	80626220	108.51	2967.90	0.0169	61.3	0.14
21.92	27733830	111.55	2989.91	0.0169	61.3	0.14
20.72	36699380	106.70	3015.86	0.0169	61.3	0.13
20.72	29514550	104.93	3011.25	0.0169	61.3	0.13
21.01	15605960	105.23	3018.64	0.0169	61.3	0.13
21.80	25714310	104.92	3020.86	0.0169	61.3	0.13
21.60	24904700	104.60	3022.52	0.0169	61.3	0.13
20.38	38971320	101.76	3016.98	0.0169	61.3	0.13
21.20	47682050	102.23	3030.93	0.0169	61.3	0.13
19.87	157188200	103.99	3062.39	0.0169	61.3	0.13
19.05	129057400	101.36	3076.59	0.0169	61.3	0.13
20.01	100818300	102.92	3076.21	0.0169	61.3	0.13
19.15	70483330	105.25	3067.26	0.0169	61.3	0.13
19.43	49779450	105.71	3073.67	0.0169	61.3	0.13
19.44	32747000	105.42	3053.40	0.0169	61.3	0.13
19.40	29588690	105.11	3069.79	0.0169	61.3	0.13
19.15	20663220	104.67	3073.19	0.0169	61.3	0.13
19.34	25402980	107.51	3077.14	0.0169	61.3	0.13
19.10	16071190	109.00	3081.19	0.0169	61.3	0.13
19.08	30571430	107.37	3048.71	0.0169	61.3	0.14
18.05	58612440	107.30	3066.96	0.0169	61.3	0.13
17.72	46177000	107.37	3075.06	0.0199	70.3	0.14
18.58	60657900	113.28	3069.27	0.0199	70.3	0.16
18.96	46028860	119.10	3135.81	0.0199	70.3	0.16
18.98	36325880	119.04	3136.42	0.0199	70.3	0.15
18.81	24752340	117.80	3104.02	0.0199	70.3	0.15
19.43	47343020	117.90	3104.53	0.0199	70.3	0.15
20.93	121399400	119.55	3114.31	0.0199	70.3	0.15
20.71	64896660	119.47	3155.83	0.0199	70.3	0.15
22.00	72707430	123.23	3183.95	0.0199	70.3	0.16
21.52	50593510	121.40	3178.67	0.0199	70.3	0.16
21.87	36696330	121.43	3177.80	0.0199	70.3	0.16
23.29	78525460	122.51	3182.62	0.0199	70.3	0.15
22.59	57115160	122.78	3175.96	0.0199	70.3	0.16
22.86	51163120	122.84	3179.96	0.0199	70.3	0.15
20.79	78968380	122.70	3160.78	0.0199	70.3	0.16
20.28	46169460	119.89	3117.73	0.0199	70.3	0.15
20.62	38212360	118.00	3093.70	0.0199	70.3	0.16
20.32	30061050	119.61	3136.60	0.0199	70.3	0.14
21.66	65415370	120.40	3116.23	0.0199	70.3	0.09
21.99	51198150	117.94	3113.53	0.0216	73.1	0.15
22.27	29276680	118.77	3120.04	0.0216	73.1	0.16
21.83	31940720	121.68	3135.23	0.0216	73.1	0.16
21.94	46549400	121.98	3149.46	0.0216	73.1	0.15
20.91	40483780	118.83	3136.19	0.0216	73.1	0.15
20.40	32190200	117.97	3112.35	0.0216	73.1	0.15
20.22	27125670	113.07	3065.02	0.0216	73.1	0.16
19.64	39282420	111.98	3051.78	0.0216	73.1	0.16
19.75	21803710	113.77	3049.41	0.0216	73.1	0.16
19.51	18743920	110.41	3044.11	0.0216	73.1	0.16
19.52	20154860	110.85	3064.18	0.0216	73.1	0.16
19.48	21816100	111.18	3101.17	0.0216	73.1	0.16
19.88	44020450	109.42	3104.12	0.0216	73.1	0.15
18.97	52059860	108.87	3072.87	0.0216	73.1	0.15
19.00	34769600	106.72	3005.62	0.0216	73.1	0.16
19.32	32269470	107.28	3016.96	0.0216	73.1	0.15
19.50	72281000	104.13	2990.46	0.0216	73.1	0.15
23.22	228364700	107.55	2981.70	0.0216	73.1	0.17
22.56	76050080	105.72	2986.12	0.0216	73.1	0.16
21.94	9999999	104.55	2987.95	0.0216	73.1	0.16
21.11	99311480	106.93	2977.23	0.0216	73.1	0.18
21.21	37631000	106.85	3020.06	0.0176	73.1	0.17
21.18	38308550	106.78	2982.13	0.0176	73.1	0.16
21.25	31752420	107.29	2999.66	0.0176	73.1	0.17
21.17	29030780	104.14	3011.93	0.0176	73.1	0.16
20.47	33352920	101.21	2937.29	0.0176	73.1	0.16
19.99	34106840	96.35	2895.58	0.0176	73.1	0.16
19.21	42257790	95.62	2904.87	0.0176	73.1	0.16
20.07	67220540	99.00	2904.26	0.0176	73.1	0.16
19.86	71524510	99.26	2883.89	0.0176	73.1	0.16
22.36	229081600	98.77	2846.81	0.0176	73.1	0.16
22.17	78808770	100.65	2836.94	0.0176	73.1	0.16
23.56	107091400	103.13	2853.13	0.0176	73.1	0.16
22.92	84944370	105.53	2916.07	0.0176	73.1	0.16
23.10	46515660	106.76	2916.68	0.0176	73.1	0.16
24.32	89720920	107.59	2926.55	0.0176	73.1	0.16
23.99	29520310	107.62	2966.85	0.0176	73.1	0.16
25.94	123513900	108.82	2976.78	0.0176	73.1	0.16
26.15	85687430	107.59	2967.79	0.0176	73.1	0.16
26.36	49113040	107.85	2991.78	0.0176	73.1	0.16
27.32	88572990	107.11	3012.03	0.0176	73.1	0.16
28.00	126867400	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	9 seconds
R Server	'Sir Maurice George Kendall' @ kendall.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 & 9 seconds \tabularnewline
R Server & 'Sir Maurice George Kendall' @ kendall.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202832&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]9 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Maurice George Kendall' @ kendall.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202832&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202832&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	9 seconds
R Server	'Sir Maurice George Kendall' @ kendall.wessa.net

Multiple Linear Regression - Estimated Regression Equation
FACEBOOK[t] = + 110.317328010726 -5.29796186625743e-09VOLUME[t] + 0.529574074143094LINKEDIN[t] -0.0422051117111733NASDAQ[t] -715.090964814529INFLATION[t] -0.191049313170624CONS.CONF[t] + 57.0463126358806FED.FUNDS.RATE[t] + 0.496712339624463M1[t] + 0.617083335648218M2[t] + 0.0583013002444314M3[t] + 0.417080547551598M4[t] + 0.160868652950662M5[t] + 0.285573118946714M6[t] -0.0014167468113675M7[t] -0.434289053597319M8[t] -0.936679221719422M9[t] -1.4897352646202M10[t] -0.946967387637012M11[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
FACEBOOK[t] =  +  110.317328010726 -5.29796186625743e-09VOLUME[t] +  0.529574074143094LINKEDIN[t] -0.0422051117111733NASDAQ[t] -715.090964814529INFLATION[t] -0.191049313170624CONS.CONF[t] +  57.0463126358806FED.FUNDS.RATE[t] +  0.496712339624463M1[t] +  0.617083335648218M2[t] +  0.0583013002444314M3[t] +  0.417080547551598M4[t] +  0.160868652950662M5[t] +  0.285573118946714M6[t] -0.0014167468113675M7[t] -0.434289053597319M8[t] -0.936679221719422M9[t] -1.4897352646202M10[t] -0.946967387637012M11[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202832&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]FACEBOOK[t] =  +  110.317328010726 -5.29796186625743e-09VOLUME[t] +  0.529574074143094LINKEDIN[t] -0.0422051117111733NASDAQ[t] -715.090964814529INFLATION[t] -0.191049313170624CONS.CONF[t] +  57.0463126358806FED.FUNDS.RATE[t] +  0.496712339624463M1[t] +  0.617083335648218M2[t] +  0.0583013002444314M3[t] +  0.417080547551598M4[t] +  0.160868652950662M5[t] +  0.285573118946714M6[t] -0.0014167468113675M7[t] -0.434289053597319M8[t] -0.936679221719422M9[t] -1.4897352646202M10[t] -0.946967387637012M11[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202832&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202832&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] = + 110.317328010726 -5.29796186625743e-09VOLUME[t] + 0.529574074143094LINKEDIN[t] -0.0422051117111733NASDAQ[t] -715.090964814529INFLATION[t] -0.191049313170624CONS.CONF[t] + 57.0463126358806FED.FUNDS.RATE[t] + 0.496712339624463M1[t] + 0.617083335648218M2[t] + 0.0583013002444314M3[t] + 0.417080547551598M4[t] + 0.160868652950662M5[t] + 0.285573118946714M6[t] -0.0014167468113675M7[t] -0.434289053597319M8[t] -0.936679221719422M9[t] -1.4897352646202M10[t] -0.946967387637012M11[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)	110.317328010726	11.0029	10.0262	0	0
VOLUME	-5.29796186625743e-09	0	-0.8737	0.384228	0.192114
LINKEDIN	0.529574074143094	0.05862	9.034	0	0
NASDAQ	-0.0422051117111733	0.004851	-8.701	0	0
INFLATION	-715.090964814529	147.639234	-4.8435	4e-06	2e-06
CONS.CONF	-0.191049313170624	0.06784	-2.8162	0.005779	0.002889
FED.FUNDS.RATE	57.0463126358806	15.806647	3.609	0.000467	0.000234
M1	0.496712339624463	0.992967	0.5002	0.617931	0.308965
M2	0.617083335648218	0.992751	0.6216	0.535522	0.267761
M3	0.0583013002444314	0.997573	0.0584	0.953504	0.476752
M4	0.417080547551598	0.996448	0.4186	0.676364	0.338182
M5	0.160868652950662	0.992406	0.1621	0.87153	0.435765
M6	0.285573118946714	1.009388	0.2829	0.777782	0.388891
M7	-0.0014167468113675	1.042858	-0.0014	0.998919	0.499459
M8	-0.434289053597319	1.028114	-0.4224	0.673563	0.336782
M9	-0.936679221719422	1.016687	-0.9213	0.358945	0.179472
M10	-1.4897352646202	1.012796	-1.4709	0.144222	0.072111
M11	-0.946967387637012	1.009266	-0.9383	0.350197	0.175099

\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) & 110.317328010726 & 11.0029 & 10.0262 & 0 & 0 \tabularnewline
VOLUME & -5.29796186625743e-09 & 0 & -0.8737 & 0.384228 & 0.192114 \tabularnewline
LINKEDIN & 0.529574074143094 & 0.05862 & 9.034 & 0 & 0 \tabularnewline
NASDAQ & -0.0422051117111733 & 0.004851 & -8.701 & 0 & 0 \tabularnewline
INFLATION & -715.090964814529 & 147.639234 & -4.8435 & 4e-06 & 2e-06 \tabularnewline
CONS.CONF & -0.191049313170624 & 0.06784 & -2.8162 & 0.005779 & 0.002889 \tabularnewline
FED.FUNDS.RATE & 57.0463126358806 & 15.806647 & 3.609 & 0.000467 & 0.000234 \tabularnewline
M1 & 0.496712339624463 & 0.992967 & 0.5002 & 0.617931 & 0.308965 \tabularnewline
M2 & 0.617083335648218 & 0.992751 & 0.6216 & 0.535522 & 0.267761 \tabularnewline
M3 & 0.0583013002444314 & 0.997573 & 0.0584 & 0.953504 & 0.476752 \tabularnewline
M4 & 0.417080547551598 & 0.996448 & 0.4186 & 0.676364 & 0.338182 \tabularnewline
M5 & 0.160868652950662 & 0.992406 & 0.1621 & 0.87153 & 0.435765 \tabularnewline
M6 & 0.285573118946714 & 1.009388 & 0.2829 & 0.777782 & 0.388891 \tabularnewline
M7 & -0.0014167468113675 & 1.042858 & -0.0014 & 0.998919 & 0.499459 \tabularnewline
M8 & -0.434289053597319 & 1.028114 & -0.4224 & 0.673563 & 0.336782 \tabularnewline
M9 & -0.936679221719422 & 1.016687 & -0.9213 & 0.358945 & 0.179472 \tabularnewline
M10 & -1.4897352646202 & 1.012796 & -1.4709 & 0.144222 & 0.072111 \tabularnewline
M11 & -0.946967387637012 & 1.009266 & -0.9383 & 0.350197 & 0.175099 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202832&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]110.317328010726[/C][C]11.0029[/C][C]10.0262[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]VOLUME[/C][C]-5.29796186625743e-09[/C][C]0[/C][C]-0.8737[/C][C]0.384228[/C][C]0.192114[/C][/ROW]
[ROW][C]LINKEDIN[/C][C]0.529574074143094[/C][C]0.05862[/C][C]9.034[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]NASDAQ[/C][C]-0.0422051117111733[/C][C]0.004851[/C][C]-8.701[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]INFLATION[/C][C]-715.090964814529[/C][C]147.639234[/C][C]-4.8435[/C][C]4e-06[/C][C]2e-06[/C][/ROW]
[ROW][C]CONS.CONF[/C][C]-0.191049313170624[/C][C]0.06784[/C][C]-2.8162[/C][C]0.005779[/C][C]0.002889[/C][/ROW]
[ROW][C]FED.FUNDS.RATE[/C][C]57.0463126358806[/C][C]15.806647[/C][C]3.609[/C][C]0.000467[/C][C]0.000234[/C][/ROW]
[ROW][C]M1[/C][C]0.496712339624463[/C][C]0.992967[/C][C]0.5002[/C][C]0.617931[/C][C]0.308965[/C][/ROW]
[ROW][C]M2[/C][C]0.617083335648218[/C][C]0.992751[/C][C]0.6216[/C][C]0.535522[/C][C]0.267761[/C][/ROW]
[ROW][C]M3[/C][C]0.0583013002444314[/C][C]0.997573[/C][C]0.0584[/C][C]0.953504[/C][C]0.476752[/C][/ROW]
[ROW][C]M4[/C][C]0.417080547551598[/C][C]0.996448[/C][C]0.4186[/C][C]0.676364[/C][C]0.338182[/C][/ROW]
[ROW][C]M5[/C][C]0.160868652950662[/C][C]0.992406[/C][C]0.1621[/C][C]0.87153[/C][C]0.435765[/C][/ROW]
[ROW][C]M6[/C][C]0.285573118946714[/C][C]1.009388[/C][C]0.2829[/C][C]0.777782[/C][C]0.388891[/C][/ROW]
[ROW][C]M7[/C][C]-0.0014167468113675[/C][C]1.042858[/C][C]-0.0014[/C][C]0.998919[/C][C]0.499459[/C][/ROW]
[ROW][C]M8[/C][C]-0.434289053597319[/C][C]1.028114[/C][C]-0.4224[/C][C]0.673563[/C][C]0.336782[/C][/ROW]
[ROW][C]M9[/C][C]-0.936679221719422[/C][C]1.016687[/C][C]-0.9213[/C][C]0.358945[/C][C]0.179472[/C][/ROW]
[ROW][C]M10[/C][C]-1.4897352646202[/C][C]1.012796[/C][C]-1.4709[/C][C]0.144222[/C][C]0.072111[/C][/ROW]
[ROW][C]M11[/C][C]-0.946967387637012[/C][C]1.009266[/C][C]-0.9383[/C][C]0.350197[/C][C]0.175099[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202832&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202832&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)	110.317328010726	11.0029	10.0262	0	0
VOLUME	-5.29796186625743e-09	0	-0.8737	0.384228	0.192114
LINKEDIN	0.529574074143094	0.05862	9.034	0	0
NASDAQ	-0.0422051117111733	0.004851	-8.701	0	0
INFLATION	-715.090964814529	147.639234	-4.8435	4e-06	2e-06
CONS.CONF	-0.191049313170624	0.06784	-2.8162	0.005779	0.002889
FED.FUNDS.RATE	57.0463126358806	15.806647	3.609	0.000467	0.000234
M1	0.496712339624463	0.992967	0.5002	0.617931	0.308965
M2	0.617083335648218	0.992751	0.6216	0.535522	0.267761
M3	0.0583013002444314	0.997573	0.0584	0.953504	0.476752
M4	0.417080547551598	0.996448	0.4186	0.676364	0.338182
M5	0.160868652950662	0.992406	0.1621	0.87153	0.435765
M6	0.285573118946714	1.009388	0.2829	0.777782	0.388891
M7	-0.0014167468113675	1.042858	-0.0014	0.998919	0.499459
M8	-0.434289053597319	1.028114	-0.4224	0.673563	0.336782
M9	-0.936679221719422	1.016687	-0.9213	0.358945	0.179472
M10	-1.4897352646202	1.012796	-1.4709	0.144222	0.072111
M11	-0.946967387637012	1.009266	-0.9383	0.350197	0.175099

Multiple Linear Regression - Regression Statistics
Multiple R	0.884693869679452
R-squared	0.782683243048404
Adjusted R-squared	0.748475975750467
F-TEST (value)	22.8806129478697
F-TEST (DF numerator)	17
F-TEST (DF denominator)	108
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.24680394762341
Sum Squared Residuals	545.197821738064

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.884693869679452 \tabularnewline
R-squared & 0.782683243048404 \tabularnewline
Adjusted R-squared & 0.748475975750467 \tabularnewline
F-TEST (value) & 22.8806129478697 \tabularnewline
F-TEST (DF numerator) & 17 \tabularnewline
F-TEST (DF denominator) & 108 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.24680394762341 \tabularnewline
Sum Squared Residuals & 545.197821738064 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202832&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.884693869679452[/C][/ROW]
[ROW][C]R-squared[/C][C]0.782683243048404[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.748475975750467[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]22.8806129478697[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]17[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]108[/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]2.24680394762341[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]545.197821738064[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202832&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202832&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	0.884693869679452
R-squared	0.782683243048404
Adjusted R-squared	0.748475975750467
F-TEST (value)	22.8806129478697
F-TEST (DF numerator)	17
F-TEST (DF denominator)	108
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.24680394762341
Sum Squared Residuals	545.197821738064

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	27.72	28.8031745216087	-1.0831745216087
2	26.9	28.7778957383713	-1.8778957383713
3	25.86	28.4287384147402	-2.5687384147402
4	26.81	25.3471577577755	1.46284224222447
5	26.31	26.412204738805	-0.102204738805046
6	27.1	27.1972611034376	-0.0972611034376203
7	27	27.7926936228178	-0.79269362281779
8	27.4	25.537922827071	1.86207717292901
9	27.27	27.1980869717298	0.0719130282701972
10	28.29	27.3602358791685	0.929764120831449
11	30.01	28.762900948027	1.24709905197303
12	31.41	29.859645102238	1.55035489776196
13	31.91	29.0628316391285	2.84716836087152
14	31.6	28.6274852367701	2.9725147632299
15	31.84	29.9864931287219	1.85350687127807
16	33.05	31.1331126728504	1.91688732714964
17	32.06	31.4990778159326	0.560922184067391
18	33.1	32.0801004946217	1.0198995053783
19	32.23	29.6434366734732	2.58656332652681
20	31.36	29.0715779548374	2.28842204516265
21	31.09	23.3949354526231	7.69506454737686
22	30.77	28.8848316395118	1.88516836048818
23	31.2	28.3185679579051	2.88143204209486
24	31.47	29.0982902048501	2.3717097951499
25	31.73	31.555621290854	0.174378709145987
26	32.17	28.7606653695924	3.40933463040757
27	31.47	27.6360071129346	3.83399288706544
28	30.97	29.4278412790566	1.54215872094338
29	30.81	32.5560242931394	-1.74602429313945
30	30.72	31.98679052881	-1.26679052881002
31	28.24	30.2684398206477	-2.02843982064765
32	28.09	29.0147991667483	-0.924799166748252
33	29.11	27.047586587716	2.06241341228395
34	29	25.955858538739	3.04414146126102
35	28.76	26.9560978338882	1.80390216611177
36	28.75	28.863311114622	-0.113311114621973
37	28.45	30.4406086872232	-1.99060868722317
38	29.34	30.478866334416	-1.13886633441603
39	26.84	26.7431991995973	0.0968008004027293
40	23.7	25.4138073117127	-1.71380731171269
41	23.15	26.57380910417	-3.42380910417
42	21.71	25.442844267331	-3.73284426733102
43	20.88	21.6710123234214	-0.791012323421387
44	20.04	20.4883793292217	-0.448379329221684
45	21.09	25.3471494540006	-4.25714945400056
46	21.92	25.7552859529671	-3.83528595296709
47	20.72	22.0164346530825	-1.29643465308248
48	20.72	22.2586864498303	-1.53868644983028
49	21.01	22.6760624155855	-1.66606241558551
50	21.8	22.4850164477953	-0.685016447795306
51	21.6	21.6909995061317	-0.0909995061317235
52	20.38	20.7050802854053	-0.325080285405276
53	21.2	20.0628577819135	1.13714221808653
54	19.87	19.2116803971472	0.65831960285282
55	19.05	17.0816340357614	1.9683659642386
56	20.01	17.6405449020264	2.36945509797364
57	19.15	18.9105115907467	0.239488409253275
58	19.43	18.4402132226067	0.989786777393278
59	19.44	19.7751395030628	-0.335139503062833
60	19.4	19.8829297527112	-0.482929752711182
61	19.15	20.0504189195931	-0.900418919593117
62	19.34	21.4829590271889	-2.14295902718892
63	19.1	21.591751127392	-2.491751127392
64	19.08	22.9517880700121	-3.87178807001207
65	18.05	21.169239373462	-3.11923937346204
66	17.72	17.7607815200774	-0.0407815200773515
67	18.58	21.9121490260412	-3.33214902604117
68	18.96	21.8305737935665	-2.8705737935665
69	18.98	20.7516069545223	-1.77160695452225
70	18.81	20.9706408527037	-2.16064085270366
71	19.43	21.4251569689556	-1.99515696895562
72	20.93	22.4408077092004	-1.51080770920041
73	20.71	21.4421472465046	-0.732147246504564
74	22	22.8959909847409	-0.895990984740864
75	21.52	21.7080900883637	-0.188090088363669
76	21.87	22.1931017347724	-0.323101734772358
77	23.29	21.5133289398006	1.77667106019941
78	22.59	22.7459985291156	-0.155998529115623
79	22.86	21.7830332155491	1.07696678445093
80	20.79	22.5086665002008	-1.71866650020081
81	20.28	21.9384075439583	-1.65840754395825
82	20.62	22.0112648740713	-1.39126487407134
83	20.32	20.498306794838	-0.178306794837998
84	21.66	19.6837343556432	1.97626564435684
85	21.99	20.7391566049903	1.25084339500965
86	22.27	21.7109210437843	0.559078956215745
87	21.83	22.037989934914	-0.207989934913971
88	21.94	21.3072033088989	0.632796691101102
89	20.91	19.9750303366097	0.934969663390306
90	20.4	20.6944100326118	-0.29441003261182
91	20.22	20.4073699540117	-0.187369954011728
92	19.64	19.8916515875481	-0.251651587548112
93	19.75	20.529826665949	-0.779826665949009
94	19.51	18.4372994767354	1.0727005232646
95	19.52	18.3585482479827	1.16145175201727
96	19.48	17.91030681172	1.56969318828004
97	19.88	16.6623627753808	3.21763722461922
98	18.97	17.7677852839928	1.20221471600721
99	19	19.5707790162542	-0.570779016254242
100	19.32	19.1902962453187	0.129703754681291
101	19.5	18.1723819173625	1.3276180826375
102	23.22	20.7919472576911	2.42805274230892
103	22.56	19.5857841645624	2.97421583543757
104	21.94	18.8060056469988	3.13399435300118
105	21.11	20.6841980050417	0.425801994958305
106	21.21	20.8978126654517	0.312187334548324
107	21.18	22.4302974840284	-1.25029748402836
108	21.25	23.5126892942705	-2.26268929427053
109	21.17	21.267342598223	-0.0973425982230165
110	20.47	22.9633525622289	-2.49335256222885
111	19.99	21.5872214965525	-1.59722149655246
112	19.21	21.1241427596646	-1.9141427596646
113	20.07	22.5513846562342	-2.48138465623421
114	19.86	23.6506942381306	-3.79069423813057
115	22.36	23.8344471637142	-1.47444716371418
116	22.17	25.6098782917811	-3.43987829178111
117	23.56	25.5876907737125	-2.02769077371251
118	22.92	23.7665568980448	-0.846556898044766
119	23.1	25.1385496082296	-2.03854960822963
120	24.32	25.8795992049144	-1.55959920491437
121	23.99	25.0102733009083	-1.02027330090829
122	25.94	24.8490619711192	1.09093802888085
123	26.15	24.218730974398	1.93126902560204
124	26.36	23.8964685745329	2.46353142546711
125	27.32	22.1846610425704	5.13533895742962
126	28	22.727491631026	5.27250836897399

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 27.72 & 28.8031745216087 & -1.0831745216087 \tabularnewline
2 & 26.9 & 28.7778957383713 & -1.8778957383713 \tabularnewline
3 & 25.86 & 28.4287384147402 & -2.5687384147402 \tabularnewline
4 & 26.81 & 25.3471577577755 & 1.46284224222447 \tabularnewline
5 & 26.31 & 26.412204738805 & -0.102204738805046 \tabularnewline
6 & 27.1 & 27.1972611034376 & -0.0972611034376203 \tabularnewline
7 & 27 & 27.7926936228178 & -0.79269362281779 \tabularnewline
8 & 27.4 & 25.537922827071 & 1.86207717292901 \tabularnewline
9 & 27.27 & 27.1980869717298 & 0.0719130282701972 \tabularnewline
10 & 28.29 & 27.3602358791685 & 0.929764120831449 \tabularnewline
11 & 30.01 & 28.762900948027 & 1.24709905197303 \tabularnewline
12 & 31.41 & 29.859645102238 & 1.55035489776196 \tabularnewline
13 & 31.91 & 29.0628316391285 & 2.84716836087152 \tabularnewline
14 & 31.6 & 28.6274852367701 & 2.9725147632299 \tabularnewline
15 & 31.84 & 29.9864931287219 & 1.85350687127807 \tabularnewline
16 & 33.05 & 31.1331126728504 & 1.91688732714964 \tabularnewline
17 & 32.06 & 31.4990778159326 & 0.560922184067391 \tabularnewline
18 & 33.1 & 32.0801004946217 & 1.0198995053783 \tabularnewline
19 & 32.23 & 29.6434366734732 & 2.58656332652681 \tabularnewline
20 & 31.36 & 29.0715779548374 & 2.28842204516265 \tabularnewline
21 & 31.09 & 23.3949354526231 & 7.69506454737686 \tabularnewline
22 & 30.77 & 28.8848316395118 & 1.88516836048818 \tabularnewline
23 & 31.2 & 28.3185679579051 & 2.88143204209486 \tabularnewline
24 & 31.47 & 29.0982902048501 & 2.3717097951499 \tabularnewline
25 & 31.73 & 31.555621290854 & 0.174378709145987 \tabularnewline
26 & 32.17 & 28.7606653695924 & 3.40933463040757 \tabularnewline
27 & 31.47 & 27.6360071129346 & 3.83399288706544 \tabularnewline
28 & 30.97 & 29.4278412790566 & 1.54215872094338 \tabularnewline
29 & 30.81 & 32.5560242931394 & -1.74602429313945 \tabularnewline
30 & 30.72 & 31.98679052881 & -1.26679052881002 \tabularnewline
31 & 28.24 & 30.2684398206477 & -2.02843982064765 \tabularnewline
32 & 28.09 & 29.0147991667483 & -0.924799166748252 \tabularnewline
33 & 29.11 & 27.047586587716 & 2.06241341228395 \tabularnewline
34 & 29 & 25.955858538739 & 3.04414146126102 \tabularnewline
35 & 28.76 & 26.9560978338882 & 1.80390216611177 \tabularnewline
36 & 28.75 & 28.863311114622 & -0.113311114621973 \tabularnewline
37 & 28.45 & 30.4406086872232 & -1.99060868722317 \tabularnewline
38 & 29.34 & 30.478866334416 & -1.13886633441603 \tabularnewline
39 & 26.84 & 26.7431991995973 & 0.0968008004027293 \tabularnewline
40 & 23.7 & 25.4138073117127 & -1.71380731171269 \tabularnewline
41 & 23.15 & 26.57380910417 & -3.42380910417 \tabularnewline
42 & 21.71 & 25.442844267331 & -3.73284426733102 \tabularnewline
43 & 20.88 & 21.6710123234214 & -0.791012323421387 \tabularnewline
44 & 20.04 & 20.4883793292217 & -0.448379329221684 \tabularnewline
45 & 21.09 & 25.3471494540006 & -4.25714945400056 \tabularnewline
46 & 21.92 & 25.7552859529671 & -3.83528595296709 \tabularnewline
47 & 20.72 & 22.0164346530825 & -1.29643465308248 \tabularnewline
48 & 20.72 & 22.2586864498303 & -1.53868644983028 \tabularnewline
49 & 21.01 & 22.6760624155855 & -1.66606241558551 \tabularnewline
50 & 21.8 & 22.4850164477953 & -0.685016447795306 \tabularnewline
51 & 21.6 & 21.6909995061317 & -0.0909995061317235 \tabularnewline
52 & 20.38 & 20.7050802854053 & -0.325080285405276 \tabularnewline
53 & 21.2 & 20.0628577819135 & 1.13714221808653 \tabularnewline
54 & 19.87 & 19.2116803971472 & 0.65831960285282 \tabularnewline
55 & 19.05 & 17.0816340357614 & 1.9683659642386 \tabularnewline
56 & 20.01 & 17.6405449020264 & 2.36945509797364 \tabularnewline
57 & 19.15 & 18.9105115907467 & 0.239488409253275 \tabularnewline
58 & 19.43 & 18.4402132226067 & 0.989786777393278 \tabularnewline
59 & 19.44 & 19.7751395030628 & -0.335139503062833 \tabularnewline
60 & 19.4 & 19.8829297527112 & -0.482929752711182 \tabularnewline
61 & 19.15 & 20.0504189195931 & -0.900418919593117 \tabularnewline
62 & 19.34 & 21.4829590271889 & -2.14295902718892 \tabularnewline
63 & 19.1 & 21.591751127392 & -2.491751127392 \tabularnewline
64 & 19.08 & 22.9517880700121 & -3.87178807001207 \tabularnewline
65 & 18.05 & 21.169239373462 & -3.11923937346204 \tabularnewline
66 & 17.72 & 17.7607815200774 & -0.0407815200773515 \tabularnewline
67 & 18.58 & 21.9121490260412 & -3.33214902604117 \tabularnewline
68 & 18.96 & 21.8305737935665 & -2.8705737935665 \tabularnewline
69 & 18.98 & 20.7516069545223 & -1.77160695452225 \tabularnewline
70 & 18.81 & 20.9706408527037 & -2.16064085270366 \tabularnewline
71 & 19.43 & 21.4251569689556 & -1.99515696895562 \tabularnewline
72 & 20.93 & 22.4408077092004 & -1.51080770920041 \tabularnewline
73 & 20.71 & 21.4421472465046 & -0.732147246504564 \tabularnewline
74 & 22 & 22.8959909847409 & -0.895990984740864 \tabularnewline
75 & 21.52 & 21.7080900883637 & -0.188090088363669 \tabularnewline
76 & 21.87 & 22.1931017347724 & -0.323101734772358 \tabularnewline
77 & 23.29 & 21.5133289398006 & 1.77667106019941 \tabularnewline
78 & 22.59 & 22.7459985291156 & -0.155998529115623 \tabularnewline
79 & 22.86 & 21.7830332155491 & 1.07696678445093 \tabularnewline
80 & 20.79 & 22.5086665002008 & -1.71866650020081 \tabularnewline
81 & 20.28 & 21.9384075439583 & -1.65840754395825 \tabularnewline
82 & 20.62 & 22.0112648740713 & -1.39126487407134 \tabularnewline
83 & 20.32 & 20.498306794838 & -0.178306794837998 \tabularnewline
84 & 21.66 & 19.6837343556432 & 1.97626564435684 \tabularnewline
85 & 21.99 & 20.7391566049903 & 1.25084339500965 \tabularnewline
86 & 22.27 & 21.7109210437843 & 0.559078956215745 \tabularnewline
87 & 21.83 & 22.037989934914 & -0.207989934913971 \tabularnewline
88 & 21.94 & 21.3072033088989 & 0.632796691101102 \tabularnewline
89 & 20.91 & 19.9750303366097 & 0.934969663390306 \tabularnewline
90 & 20.4 & 20.6944100326118 & -0.29441003261182 \tabularnewline
91 & 20.22 & 20.4073699540117 & -0.187369954011728 \tabularnewline
92 & 19.64 & 19.8916515875481 & -0.251651587548112 \tabularnewline
93 & 19.75 & 20.529826665949 & -0.779826665949009 \tabularnewline
94 & 19.51 & 18.4372994767354 & 1.0727005232646 \tabularnewline
95 & 19.52 & 18.3585482479827 & 1.16145175201727 \tabularnewline
96 & 19.48 & 17.91030681172 & 1.56969318828004 \tabularnewline
97 & 19.88 & 16.6623627753808 & 3.21763722461922 \tabularnewline
98 & 18.97 & 17.7677852839928 & 1.20221471600721 \tabularnewline
99 & 19 & 19.5707790162542 & -0.570779016254242 \tabularnewline
100 & 19.32 & 19.1902962453187 & 0.129703754681291 \tabularnewline
101 & 19.5 & 18.1723819173625 & 1.3276180826375 \tabularnewline
102 & 23.22 & 20.7919472576911 & 2.42805274230892 \tabularnewline
103 & 22.56 & 19.5857841645624 & 2.97421583543757 \tabularnewline
104 & 21.94 & 18.8060056469988 & 3.13399435300118 \tabularnewline
105 & 21.11 & 20.6841980050417 & 0.425801994958305 \tabularnewline
106 & 21.21 & 20.8978126654517 & 0.312187334548324 \tabularnewline
107 & 21.18 & 22.4302974840284 & -1.25029748402836 \tabularnewline
108 & 21.25 & 23.5126892942705 & -2.26268929427053 \tabularnewline
109 & 21.17 & 21.267342598223 & -0.0973425982230165 \tabularnewline
110 & 20.47 & 22.9633525622289 & -2.49335256222885 \tabularnewline
111 & 19.99 & 21.5872214965525 & -1.59722149655246 \tabularnewline
112 & 19.21 & 21.1241427596646 & -1.9141427596646 \tabularnewline
113 & 20.07 & 22.5513846562342 & -2.48138465623421 \tabularnewline
114 & 19.86 & 23.6506942381306 & -3.79069423813057 \tabularnewline
115 & 22.36 & 23.8344471637142 & -1.47444716371418 \tabularnewline
116 & 22.17 & 25.6098782917811 & -3.43987829178111 \tabularnewline
117 & 23.56 & 25.5876907737125 & -2.02769077371251 \tabularnewline
118 & 22.92 & 23.7665568980448 & -0.846556898044766 \tabularnewline
119 & 23.1 & 25.1385496082296 & -2.03854960822963 \tabularnewline
120 & 24.32 & 25.8795992049144 & -1.55959920491437 \tabularnewline
121 & 23.99 & 25.0102733009083 & -1.02027330090829 \tabularnewline
122 & 25.94 & 24.8490619711192 & 1.09093802888085 \tabularnewline
123 & 26.15 & 24.218730974398 & 1.93126902560204 \tabularnewline
124 & 26.36 & 23.8964685745329 & 2.46353142546711 \tabularnewline
125 & 27.32 & 22.1846610425704 & 5.13533895742962 \tabularnewline
126 & 28 & 22.727491631026 & 5.27250836897399 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202832&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]28.8031745216087[/C][C]-1.0831745216087[/C][/ROW]
[ROW][C]2[/C][C]26.9[/C][C]28.7778957383713[/C][C]-1.8778957383713[/C][/ROW]
[ROW][C]3[/C][C]25.86[/C][C]28.4287384147402[/C][C]-2.5687384147402[/C][/ROW]
[ROW][C]4[/C][C]26.81[/C][C]25.3471577577755[/C][C]1.46284224222447[/C][/ROW]
[ROW][C]5[/C][C]26.31[/C][C]26.412204738805[/C][C]-0.102204738805046[/C][/ROW]
[ROW][C]6[/C][C]27.1[/C][C]27.1972611034376[/C][C]-0.0972611034376203[/C][/ROW]
[ROW][C]7[/C][C]27[/C][C]27.7926936228178[/C][C]-0.79269362281779[/C][/ROW]
[ROW][C]8[/C][C]27.4[/C][C]25.537922827071[/C][C]1.86207717292901[/C][/ROW]
[ROW][C]9[/C][C]27.27[/C][C]27.1980869717298[/C][C]0.0719130282701972[/C][/ROW]
[ROW][C]10[/C][C]28.29[/C][C]27.3602358791685[/C][C]0.929764120831449[/C][/ROW]
[ROW][C]11[/C][C]30.01[/C][C]28.762900948027[/C][C]1.24709905197303[/C][/ROW]
[ROW][C]12[/C][C]31.41[/C][C]29.859645102238[/C][C]1.55035489776196[/C][/ROW]
[ROW][C]13[/C][C]31.91[/C][C]29.0628316391285[/C][C]2.84716836087152[/C][/ROW]
[ROW][C]14[/C][C]31.6[/C][C]28.6274852367701[/C][C]2.9725147632299[/C][/ROW]
[ROW][C]15[/C][C]31.84[/C][C]29.9864931287219[/C][C]1.85350687127807[/C][/ROW]
[ROW][C]16[/C][C]33.05[/C][C]31.1331126728504[/C][C]1.91688732714964[/C][/ROW]
[ROW][C]17[/C][C]32.06[/C][C]31.4990778159326[/C][C]0.560922184067391[/C][/ROW]
[ROW][C]18[/C][C]33.1[/C][C]32.0801004946217[/C][C]1.0198995053783[/C][/ROW]
[ROW][C]19[/C][C]32.23[/C][C]29.6434366734732[/C][C]2.58656332652681[/C][/ROW]
[ROW][C]20[/C][C]31.36[/C][C]29.0715779548374[/C][C]2.28842204516265[/C][/ROW]
[ROW][C]21[/C][C]31.09[/C][C]23.3949354526231[/C][C]7.69506454737686[/C][/ROW]
[ROW][C]22[/C][C]30.77[/C][C]28.8848316395118[/C][C]1.88516836048818[/C][/ROW]
[ROW][C]23[/C][C]31.2[/C][C]28.3185679579051[/C][C]2.88143204209486[/C][/ROW]
[ROW][C]24[/C][C]31.47[/C][C]29.0982902048501[/C][C]2.3717097951499[/C][/ROW]
[ROW][C]25[/C][C]31.73[/C][C]31.555621290854[/C][C]0.174378709145987[/C][/ROW]
[ROW][C]26[/C][C]32.17[/C][C]28.7606653695924[/C][C]3.40933463040757[/C][/ROW]
[ROW][C]27[/C][C]31.47[/C][C]27.6360071129346[/C][C]3.83399288706544[/C][/ROW]
[ROW][C]28[/C][C]30.97[/C][C]29.4278412790566[/C][C]1.54215872094338[/C][/ROW]
[ROW][C]29[/C][C]30.81[/C][C]32.5560242931394[/C][C]-1.74602429313945[/C][/ROW]
[ROW][C]30[/C][C]30.72[/C][C]31.98679052881[/C][C]-1.26679052881002[/C][/ROW]
[ROW][C]31[/C][C]28.24[/C][C]30.2684398206477[/C][C]-2.02843982064765[/C][/ROW]
[ROW][C]32[/C][C]28.09[/C][C]29.0147991667483[/C][C]-0.924799166748252[/C][/ROW]
[ROW][C]33[/C][C]29.11[/C][C]27.047586587716[/C][C]2.06241341228395[/C][/ROW]
[ROW][C]34[/C][C]29[/C][C]25.955858538739[/C][C]3.04414146126102[/C][/ROW]
[ROW][C]35[/C][C]28.76[/C][C]26.9560978338882[/C][C]1.80390216611177[/C][/ROW]
[ROW][C]36[/C][C]28.75[/C][C]28.863311114622[/C][C]-0.113311114621973[/C][/ROW]
[ROW][C]37[/C][C]28.45[/C][C]30.4406086872232[/C][C]-1.99060868722317[/C][/ROW]
[ROW][C]38[/C][C]29.34[/C][C]30.478866334416[/C][C]-1.13886633441603[/C][/ROW]
[ROW][C]39[/C][C]26.84[/C][C]26.7431991995973[/C][C]0.0968008004027293[/C][/ROW]
[ROW][C]40[/C][C]23.7[/C][C]25.4138073117127[/C][C]-1.71380731171269[/C][/ROW]
[ROW][C]41[/C][C]23.15[/C][C]26.57380910417[/C][C]-3.42380910417[/C][/ROW]
[ROW][C]42[/C][C]21.71[/C][C]25.442844267331[/C][C]-3.73284426733102[/C][/ROW]
[ROW][C]43[/C][C]20.88[/C][C]21.6710123234214[/C][C]-0.791012323421387[/C][/ROW]
[ROW][C]44[/C][C]20.04[/C][C]20.4883793292217[/C][C]-0.448379329221684[/C][/ROW]
[ROW][C]45[/C][C]21.09[/C][C]25.3471494540006[/C][C]-4.25714945400056[/C][/ROW]
[ROW][C]46[/C][C]21.92[/C][C]25.7552859529671[/C][C]-3.83528595296709[/C][/ROW]
[ROW][C]47[/C][C]20.72[/C][C]22.0164346530825[/C][C]-1.29643465308248[/C][/ROW]
[ROW][C]48[/C][C]20.72[/C][C]22.2586864498303[/C][C]-1.53868644983028[/C][/ROW]
[ROW][C]49[/C][C]21.01[/C][C]22.6760624155855[/C][C]-1.66606241558551[/C][/ROW]
[ROW][C]50[/C][C]21.8[/C][C]22.4850164477953[/C][C]-0.685016447795306[/C][/ROW]
[ROW][C]51[/C][C]21.6[/C][C]21.6909995061317[/C][C]-0.0909995061317235[/C][/ROW]
[ROW][C]52[/C][C]20.38[/C][C]20.7050802854053[/C][C]-0.325080285405276[/C][/ROW]
[ROW][C]53[/C][C]21.2[/C][C]20.0628577819135[/C][C]1.13714221808653[/C][/ROW]
[ROW][C]54[/C][C]19.87[/C][C]19.2116803971472[/C][C]0.65831960285282[/C][/ROW]
[ROW][C]55[/C][C]19.05[/C][C]17.0816340357614[/C][C]1.9683659642386[/C][/ROW]
[ROW][C]56[/C][C]20.01[/C][C]17.6405449020264[/C][C]2.36945509797364[/C][/ROW]
[ROW][C]57[/C][C]19.15[/C][C]18.9105115907467[/C][C]0.239488409253275[/C][/ROW]
[ROW][C]58[/C][C]19.43[/C][C]18.4402132226067[/C][C]0.989786777393278[/C][/ROW]
[ROW][C]59[/C][C]19.44[/C][C]19.7751395030628[/C][C]-0.335139503062833[/C][/ROW]
[ROW][C]60[/C][C]19.4[/C][C]19.8829297527112[/C][C]-0.482929752711182[/C][/ROW]
[ROW][C]61[/C][C]19.15[/C][C]20.0504189195931[/C][C]-0.900418919593117[/C][/ROW]
[ROW][C]62[/C][C]19.34[/C][C]21.4829590271889[/C][C]-2.14295902718892[/C][/ROW]
[ROW][C]63[/C][C]19.1[/C][C]21.591751127392[/C][C]-2.491751127392[/C][/ROW]
[ROW][C]64[/C][C]19.08[/C][C]22.9517880700121[/C][C]-3.87178807001207[/C][/ROW]
[ROW][C]65[/C][C]18.05[/C][C]21.169239373462[/C][C]-3.11923937346204[/C][/ROW]
[ROW][C]66[/C][C]17.72[/C][C]17.7607815200774[/C][C]-0.0407815200773515[/C][/ROW]
[ROW][C]67[/C][C]18.58[/C][C]21.9121490260412[/C][C]-3.33214902604117[/C][/ROW]
[ROW][C]68[/C][C]18.96[/C][C]21.8305737935665[/C][C]-2.8705737935665[/C][/ROW]
[ROW][C]69[/C][C]18.98[/C][C]20.7516069545223[/C][C]-1.77160695452225[/C][/ROW]
[ROW][C]70[/C][C]18.81[/C][C]20.9706408527037[/C][C]-2.16064085270366[/C][/ROW]
[ROW][C]71[/C][C]19.43[/C][C]21.4251569689556[/C][C]-1.99515696895562[/C][/ROW]
[ROW][C]72[/C][C]20.93[/C][C]22.4408077092004[/C][C]-1.51080770920041[/C][/ROW]
[ROW][C]73[/C][C]20.71[/C][C]21.4421472465046[/C][C]-0.732147246504564[/C][/ROW]
[ROW][C]74[/C][C]22[/C][C]22.8959909847409[/C][C]-0.895990984740864[/C][/ROW]
[ROW][C]75[/C][C]21.52[/C][C]21.7080900883637[/C][C]-0.188090088363669[/C][/ROW]
[ROW][C]76[/C][C]21.87[/C][C]22.1931017347724[/C][C]-0.323101734772358[/C][/ROW]
[ROW][C]77[/C][C]23.29[/C][C]21.5133289398006[/C][C]1.77667106019941[/C][/ROW]
[ROW][C]78[/C][C]22.59[/C][C]22.7459985291156[/C][C]-0.155998529115623[/C][/ROW]
[ROW][C]79[/C][C]22.86[/C][C]21.7830332155491[/C][C]1.07696678445093[/C][/ROW]
[ROW][C]80[/C][C]20.79[/C][C]22.5086665002008[/C][C]-1.71866650020081[/C][/ROW]
[ROW][C]81[/C][C]20.28[/C][C]21.9384075439583[/C][C]-1.65840754395825[/C][/ROW]
[ROW][C]82[/C][C]20.62[/C][C]22.0112648740713[/C][C]-1.39126487407134[/C][/ROW]
[ROW][C]83[/C][C]20.32[/C][C]20.498306794838[/C][C]-0.178306794837998[/C][/ROW]
[ROW][C]84[/C][C]21.66[/C][C]19.6837343556432[/C][C]1.97626564435684[/C][/ROW]
[ROW][C]85[/C][C]21.99[/C][C]20.7391566049903[/C][C]1.25084339500965[/C][/ROW]
[ROW][C]86[/C][C]22.27[/C][C]21.7109210437843[/C][C]0.559078956215745[/C][/ROW]
[ROW][C]87[/C][C]21.83[/C][C]22.037989934914[/C][C]-0.207989934913971[/C][/ROW]
[ROW][C]88[/C][C]21.94[/C][C]21.3072033088989[/C][C]0.632796691101102[/C][/ROW]
[ROW][C]89[/C][C]20.91[/C][C]19.9750303366097[/C][C]0.934969663390306[/C][/ROW]
[ROW][C]90[/C][C]20.4[/C][C]20.6944100326118[/C][C]-0.29441003261182[/C][/ROW]
[ROW][C]91[/C][C]20.22[/C][C]20.4073699540117[/C][C]-0.187369954011728[/C][/ROW]
[ROW][C]92[/C][C]19.64[/C][C]19.8916515875481[/C][C]-0.251651587548112[/C][/ROW]
[ROW][C]93[/C][C]19.75[/C][C]20.529826665949[/C][C]-0.779826665949009[/C][/ROW]
[ROW][C]94[/C][C]19.51[/C][C]18.4372994767354[/C][C]1.0727005232646[/C][/ROW]
[ROW][C]95[/C][C]19.52[/C][C]18.3585482479827[/C][C]1.16145175201727[/C][/ROW]
[ROW][C]96[/C][C]19.48[/C][C]17.91030681172[/C][C]1.56969318828004[/C][/ROW]
[ROW][C]97[/C][C]19.88[/C][C]16.6623627753808[/C][C]3.21763722461922[/C][/ROW]
[ROW][C]98[/C][C]18.97[/C][C]17.7677852839928[/C][C]1.20221471600721[/C][/ROW]
[ROW][C]99[/C][C]19[/C][C]19.5707790162542[/C][C]-0.570779016254242[/C][/ROW]
[ROW][C]100[/C][C]19.32[/C][C]19.1902962453187[/C][C]0.129703754681291[/C][/ROW]
[ROW][C]101[/C][C]19.5[/C][C]18.1723819173625[/C][C]1.3276180826375[/C][/ROW]
[ROW][C]102[/C][C]23.22[/C][C]20.7919472576911[/C][C]2.42805274230892[/C][/ROW]
[ROW][C]103[/C][C]22.56[/C][C]19.5857841645624[/C][C]2.97421583543757[/C][/ROW]
[ROW][C]104[/C][C]21.94[/C][C]18.8060056469988[/C][C]3.13399435300118[/C][/ROW]
[ROW][C]105[/C][C]21.11[/C][C]20.6841980050417[/C][C]0.425801994958305[/C][/ROW]
[ROW][C]106[/C][C]21.21[/C][C]20.8978126654517[/C][C]0.312187334548324[/C][/ROW]
[ROW][C]107[/C][C]21.18[/C][C]22.4302974840284[/C][C]-1.25029748402836[/C][/ROW]
[ROW][C]108[/C][C]21.25[/C][C]23.5126892942705[/C][C]-2.26268929427053[/C][/ROW]
[ROW][C]109[/C][C]21.17[/C][C]21.267342598223[/C][C]-0.0973425982230165[/C][/ROW]
[ROW][C]110[/C][C]20.47[/C][C]22.9633525622289[/C][C]-2.49335256222885[/C][/ROW]
[ROW][C]111[/C][C]19.99[/C][C]21.5872214965525[/C][C]-1.59722149655246[/C][/ROW]
[ROW][C]112[/C][C]19.21[/C][C]21.1241427596646[/C][C]-1.9141427596646[/C][/ROW]
[ROW][C]113[/C][C]20.07[/C][C]22.5513846562342[/C][C]-2.48138465623421[/C][/ROW]
[ROW][C]114[/C][C]19.86[/C][C]23.6506942381306[/C][C]-3.79069423813057[/C][/ROW]
[ROW][C]115[/C][C]22.36[/C][C]23.8344471637142[/C][C]-1.47444716371418[/C][/ROW]
[ROW][C]116[/C][C]22.17[/C][C]25.6098782917811[/C][C]-3.43987829178111[/C][/ROW]
[ROW][C]117[/C][C]23.56[/C][C]25.5876907737125[/C][C]-2.02769077371251[/C][/ROW]
[ROW][C]118[/C][C]22.92[/C][C]23.7665568980448[/C][C]-0.846556898044766[/C][/ROW]
[ROW][C]119[/C][C]23.1[/C][C]25.1385496082296[/C][C]-2.03854960822963[/C][/ROW]
[ROW][C]120[/C][C]24.32[/C][C]25.8795992049144[/C][C]-1.55959920491437[/C][/ROW]
[ROW][C]121[/C][C]23.99[/C][C]25.0102733009083[/C][C]-1.02027330090829[/C][/ROW]
[ROW][C]122[/C][C]25.94[/C][C]24.8490619711192[/C][C]1.09093802888085[/C][/ROW]
[ROW][C]123[/C][C]26.15[/C][C]24.218730974398[/C][C]1.93126902560204[/C][/ROW]
[ROW][C]124[/C][C]26.36[/C][C]23.8964685745329[/C][C]2.46353142546711[/C][/ROW]
[ROW][C]125[/C][C]27.32[/C][C]22.1846610425704[/C][C]5.13533895742962[/C][/ROW]
[ROW][C]126[/C][C]28[/C][C]22.727491631026[/C][C]5.27250836897399[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202832&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202832&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	28.8031745216087	-1.0831745216087
2	26.9	28.7778957383713	-1.8778957383713
3	25.86	28.4287384147402	-2.5687384147402
4	26.81	25.3471577577755	1.46284224222447
5	26.31	26.412204738805	-0.102204738805046
6	27.1	27.1972611034376	-0.0972611034376203
7	27	27.7926936228178	-0.79269362281779
8	27.4	25.537922827071	1.86207717292901
9	27.27	27.1980869717298	0.0719130282701972
10	28.29	27.3602358791685	0.929764120831449
11	30.01	28.762900948027	1.24709905197303
12	31.41	29.859645102238	1.55035489776196
13	31.91	29.0628316391285	2.84716836087152
14	31.6	28.6274852367701	2.9725147632299
15	31.84	29.9864931287219	1.85350687127807
16	33.05	31.1331126728504	1.91688732714964
17	32.06	31.4990778159326	0.560922184067391
18	33.1	32.0801004946217	1.0198995053783
19	32.23	29.6434366734732	2.58656332652681
20	31.36	29.0715779548374	2.28842204516265
21	31.09	23.3949354526231	7.69506454737686
22	30.77	28.8848316395118	1.88516836048818
23	31.2	28.3185679579051	2.88143204209486
24	31.47	29.0982902048501	2.3717097951499
25	31.73	31.555621290854	0.174378709145987
26	32.17	28.7606653695924	3.40933463040757
27	31.47	27.6360071129346	3.83399288706544
28	30.97	29.4278412790566	1.54215872094338
29	30.81	32.5560242931394	-1.74602429313945
30	30.72	31.98679052881	-1.26679052881002
31	28.24	30.2684398206477	-2.02843982064765
32	28.09	29.0147991667483	-0.924799166748252
33	29.11	27.047586587716	2.06241341228395
34	29	25.955858538739	3.04414146126102
35	28.76	26.9560978338882	1.80390216611177
36	28.75	28.863311114622	-0.113311114621973
37	28.45	30.4406086872232	-1.99060868722317
38	29.34	30.478866334416	-1.13886633441603
39	26.84	26.7431991995973	0.0968008004027293
40	23.7	25.4138073117127	-1.71380731171269
41	23.15	26.57380910417	-3.42380910417
42	21.71	25.442844267331	-3.73284426733102
43	20.88	21.6710123234214	-0.791012323421387
44	20.04	20.4883793292217	-0.448379329221684
45	21.09	25.3471494540006	-4.25714945400056
46	21.92	25.7552859529671	-3.83528595296709
47	20.72	22.0164346530825	-1.29643465308248
48	20.72	22.2586864498303	-1.53868644983028
49	21.01	22.6760624155855	-1.66606241558551
50	21.8	22.4850164477953	-0.685016447795306
51	21.6	21.6909995061317	-0.0909995061317235
52	20.38	20.7050802854053	-0.325080285405276
53	21.2	20.0628577819135	1.13714221808653
54	19.87	19.2116803971472	0.65831960285282
55	19.05	17.0816340357614	1.9683659642386
56	20.01	17.6405449020264	2.36945509797364
57	19.15	18.9105115907467	0.239488409253275
58	19.43	18.4402132226067	0.989786777393278
59	19.44	19.7751395030628	-0.335139503062833
60	19.4	19.8829297527112	-0.482929752711182
61	19.15	20.0504189195931	-0.900418919593117
62	19.34	21.4829590271889	-2.14295902718892
63	19.1	21.591751127392	-2.491751127392
64	19.08	22.9517880700121	-3.87178807001207
65	18.05	21.169239373462	-3.11923937346204
66	17.72	17.7607815200774	-0.0407815200773515
67	18.58	21.9121490260412	-3.33214902604117
68	18.96	21.8305737935665	-2.8705737935665
69	18.98	20.7516069545223	-1.77160695452225
70	18.81	20.9706408527037	-2.16064085270366
71	19.43	21.4251569689556	-1.99515696895562
72	20.93	22.4408077092004	-1.51080770920041
73	20.71	21.4421472465046	-0.732147246504564
74	22	22.8959909847409	-0.895990984740864
75	21.52	21.7080900883637	-0.188090088363669
76	21.87	22.1931017347724	-0.323101734772358
77	23.29	21.5133289398006	1.77667106019941
78	22.59	22.7459985291156	-0.155998529115623
79	22.86	21.7830332155491	1.07696678445093
80	20.79	22.5086665002008	-1.71866650020081
81	20.28	21.9384075439583	-1.65840754395825
82	20.62	22.0112648740713	-1.39126487407134
83	20.32	20.498306794838	-0.178306794837998
84	21.66	19.6837343556432	1.97626564435684
85	21.99	20.7391566049903	1.25084339500965
86	22.27	21.7109210437843	0.559078956215745
87	21.83	22.037989934914	-0.207989934913971
88	21.94	21.3072033088989	0.632796691101102
89	20.91	19.9750303366097	0.934969663390306
90	20.4	20.6944100326118	-0.29441003261182
91	20.22	20.4073699540117	-0.187369954011728
92	19.64	19.8916515875481	-0.251651587548112
93	19.75	20.529826665949	-0.779826665949009
94	19.51	18.4372994767354	1.0727005232646
95	19.52	18.3585482479827	1.16145175201727
96	19.48	17.91030681172	1.56969318828004
97	19.88	16.6623627753808	3.21763722461922
98	18.97	17.7677852839928	1.20221471600721
99	19	19.5707790162542	-0.570779016254242
100	19.32	19.1902962453187	0.129703754681291
101	19.5	18.1723819173625	1.3276180826375
102	23.22	20.7919472576911	2.42805274230892
103	22.56	19.5857841645624	2.97421583543757
104	21.94	18.8060056469988	3.13399435300118
105	21.11	20.6841980050417	0.425801994958305
106	21.21	20.8978126654517	0.312187334548324
107	21.18	22.4302974840284	-1.25029748402836
108	21.25	23.5126892942705	-2.26268929427053
109	21.17	21.267342598223	-0.0973425982230165
110	20.47	22.9633525622289	-2.49335256222885
111	19.99	21.5872214965525	-1.59722149655246
112	19.21	21.1241427596646	-1.9141427596646
113	20.07	22.5513846562342	-2.48138465623421
114	19.86	23.6506942381306	-3.79069423813057
115	22.36	23.8344471637142	-1.47444716371418
116	22.17	25.6098782917811	-3.43987829178111
117	23.56	25.5876907737125	-2.02769077371251
118	22.92	23.7665568980448	-0.846556898044766
119	23.1	25.1385496082296	-2.03854960822963
120	24.32	25.8795992049144	-1.55959920491437
121	23.99	25.0102733009083	-1.02027330090829
122	25.94	24.8490619711192	1.09093802888085
123	26.15	24.218730974398	1.93126902560204
124	26.36	23.8964685745329	2.46353142546711
125	27.32	22.1846610425704	5.13533895742962
126	28	22.727491631026	5.27250836897399

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
21	0.0645414221287039	0.129082844257408	0.935458577871296
22	0.0196117079416961	0.0392234158833922	0.980388292058304
23	0.0117215992877539	0.0234431985755078	0.988278400712246
24	0.00367569876118179	0.00735139752236357	0.996324301238818
25	0.0055243554633096	0.0110487109266192	0.99447564453669
26	0.0279747662797428	0.0559495325594855	0.972025233720257
27	0.0843249179602182	0.168649835920436	0.915675082039782
28	0.0619926568683416	0.123985313736683	0.938007343131658
29	0.0447836403409481	0.0895672806818962	0.955216359659052
30	0.0295395911850445	0.059079182370089	0.970460408814956
31	0.0319761183514184	0.0639522367028369	0.968023881648582
32	0.0323148922169607	0.0646297844339214	0.967685107783039
33	0.0287368909325533	0.0574737818651066	0.971263109067447
34	0.0449940051350498	0.0899880102700996	0.95500599486495
35	0.0613486966310529	0.122697393262106	0.938651303368947
36	0.0633252313548281	0.126650462709656	0.936674768645172
37	0.0701445287552289	0.140289057510458	0.929855471244771
38	0.0788614092438967	0.157722818487793	0.921138590756103
39	0.0720692064722961	0.144138412944592	0.927930793527704
40	0.0768018808933697	0.153603761786739	0.92319811910663
41	0.257888999077411	0.515777998154822	0.742111000922589
42	0.252098956914643	0.504197913829285	0.747901043085357
43	0.211265683358205	0.422531366716409	0.788734316641795
44	0.17429300733329	0.34858601466658	0.82570699266671
45	0.286869078736889	0.573738157473778	0.713130921263111
46	0.505478211967128	0.989043576065744	0.494521788032872
47	0.500053088236544	0.999893823526913	0.499946911763456
48	0.478431268708441	0.956862537416883	0.521568731291559
49	0.466888128322414	0.933776256644828	0.533111871677586
50	0.446033770072452	0.892067540144904	0.553966229927548
51	0.433701418200562	0.867402836401125	0.566298581799438
52	0.396118425234236	0.792236850468473	0.603881574765764
53	0.525773027871128	0.948453944257745	0.474226972128872
54	0.702697482720423	0.594605034559155	0.297302517279577
55	0.65906747327007	0.68186505345986	0.34093252672993
56	0.611154110573633	0.777691778852734	0.388845889426367
57	0.575693235871305	0.84861352825739	0.424306764128695
58	0.542206839042936	0.915586321914128	0.457793160957064
59	0.539701124843114	0.920597750313772	0.460298875156886
60	0.552873912450499	0.894252175099002	0.447126087549501
61	0.561487769230037	0.877024461539927	0.438512230769963
62	0.644330030965197	0.711339938069606	0.355669969034803
63	0.736070296438989	0.527859407122022	0.263929703561011
64	0.801034021910459	0.397931956179081	0.198965978089541
65	0.895067645824874	0.209864708350253	0.104932354175127
66	0.997993414621073	0.00401317075785345	0.00200658537892672
67	0.997807824242725	0.00438435151454951	0.00219217575727475
68	0.99770891748654	0.00458216502692092	0.00229108251346046
69	0.996856468234951	0.00628706353009705	0.00314353176504853
70	0.995165636203892	0.00966872759221596	0.00483436379610798
71	0.992586827036556	0.0148263459268889	0.00741317296344447
72	0.989643091808487	0.0207138163830256	0.0103569081915128
73	0.9863866737875	0.0272266524250009	0.0136133262125005
74	0.980642673413846	0.0387146531723088	0.0193573265861544
75	0.973648750197601	0.0527024996047976	0.0263512498023988
76	0.963976997502624	0.0720460049947518	0.0360230024973759
77	0.964745874587935	0.0705082508241298	0.0352541254120649
78	0.952888273893118	0.0942234522137632	0.0471117261068816
79	0.942907348490021	0.114185303019958	0.0570926515099792
80	0.947990041548729	0.104019916902543	0.0520099584512713
81	0.937212849334721	0.125574301330557	0.0627871506652786
82	0.913306140265902	0.173387719468196	0.0866938597340982
83	0.892681660941714	0.214636678116571	0.107318339058286
84	0.86663998785053	0.266720024298941	0.13336001214947
85	0.839576501596241	0.320846996807517	0.160423498403759
86	0.798271585639551	0.403456828720898	0.201728414360449
87	0.773150924748442	0.453698150503116	0.226849075251558
88	0.775507426348451	0.448985147303098	0.224492573651549
89	0.810427379886832	0.379145240226335	0.189572620113168
90	0.831267655491913	0.337464689016174	0.168732344508087
91	0.8411248496315	0.317750300737	0.1588751503685
92	0.945647749662933	0.108704500674134	0.0543522503370669
93	0.984933322495558	0.030133355008885	0.0150666775044425
94	0.97429770994057	0.0514045801188594	0.0257022900594297
95	0.956953882476154	0.0860922350476924	0.0430461175238462
96	0.931045207223	0.137909585553999	0.0689547927769997
97	0.900864500668895	0.19827099866221	0.0991354993311051
98	0.872797381965312	0.254405236069377	0.127202618034688
99	0.871449470427328	0.257101059145344	0.128550529572672
100	0.934032521649848	0.131934956700304	0.0659674783501521
101	0.970358242847448	0.059283514305103	0.0296417571525515
102	0.953907199057413	0.0921856018851742	0.0460928009425871
103	0.904795779509167	0.190408440981667	0.0952042204908334
104	0.820687875850153	0.358624248299694	0.179312124149847
105	0.672811036370008	0.654377927259983	0.327188963629992

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
21 & 0.0645414221287039 & 0.129082844257408 & 0.935458577871296 \tabularnewline
22 & 0.0196117079416961 & 0.0392234158833922 & 0.980388292058304 \tabularnewline
23 & 0.0117215992877539 & 0.0234431985755078 & 0.988278400712246 \tabularnewline
24 & 0.00367569876118179 & 0.00735139752236357 & 0.996324301238818 \tabularnewline
25 & 0.0055243554633096 & 0.0110487109266192 & 0.99447564453669 \tabularnewline
26 & 0.0279747662797428 & 0.0559495325594855 & 0.972025233720257 \tabularnewline
27 & 0.0843249179602182 & 0.168649835920436 & 0.915675082039782 \tabularnewline
28 & 0.0619926568683416 & 0.123985313736683 & 0.938007343131658 \tabularnewline
29 & 0.0447836403409481 & 0.0895672806818962 & 0.955216359659052 \tabularnewline
30 & 0.0295395911850445 & 0.059079182370089 & 0.970460408814956 \tabularnewline
31 & 0.0319761183514184 & 0.0639522367028369 & 0.968023881648582 \tabularnewline
32 & 0.0323148922169607 & 0.0646297844339214 & 0.967685107783039 \tabularnewline
33 & 0.0287368909325533 & 0.0574737818651066 & 0.971263109067447 \tabularnewline
34 & 0.0449940051350498 & 0.0899880102700996 & 0.95500599486495 \tabularnewline
35 & 0.0613486966310529 & 0.122697393262106 & 0.938651303368947 \tabularnewline
36 & 0.0633252313548281 & 0.126650462709656 & 0.936674768645172 \tabularnewline
37 & 0.0701445287552289 & 0.140289057510458 & 0.929855471244771 \tabularnewline
38 & 0.0788614092438967 & 0.157722818487793 & 0.921138590756103 \tabularnewline
39 & 0.0720692064722961 & 0.144138412944592 & 0.927930793527704 \tabularnewline
40 & 0.0768018808933697 & 0.153603761786739 & 0.92319811910663 \tabularnewline
41 & 0.257888999077411 & 0.515777998154822 & 0.742111000922589 \tabularnewline
42 & 0.252098956914643 & 0.504197913829285 & 0.747901043085357 \tabularnewline
43 & 0.211265683358205 & 0.422531366716409 & 0.788734316641795 \tabularnewline
44 & 0.17429300733329 & 0.34858601466658 & 0.82570699266671 \tabularnewline
45 & 0.286869078736889 & 0.573738157473778 & 0.713130921263111 \tabularnewline
46 & 0.505478211967128 & 0.989043576065744 & 0.494521788032872 \tabularnewline
47 & 0.500053088236544 & 0.999893823526913 & 0.499946911763456 \tabularnewline
48 & 0.478431268708441 & 0.956862537416883 & 0.521568731291559 \tabularnewline
49 & 0.466888128322414 & 0.933776256644828 & 0.533111871677586 \tabularnewline
50 & 0.446033770072452 & 0.892067540144904 & 0.553966229927548 \tabularnewline
51 & 0.433701418200562 & 0.867402836401125 & 0.566298581799438 \tabularnewline
52 & 0.396118425234236 & 0.792236850468473 & 0.603881574765764 \tabularnewline
53 & 0.525773027871128 & 0.948453944257745 & 0.474226972128872 \tabularnewline
54 & 0.702697482720423 & 0.594605034559155 & 0.297302517279577 \tabularnewline
55 & 0.65906747327007 & 0.68186505345986 & 0.34093252672993 \tabularnewline
56 & 0.611154110573633 & 0.777691778852734 & 0.388845889426367 \tabularnewline
57 & 0.575693235871305 & 0.84861352825739 & 0.424306764128695 \tabularnewline
58 & 0.542206839042936 & 0.915586321914128 & 0.457793160957064 \tabularnewline
59 & 0.539701124843114 & 0.920597750313772 & 0.460298875156886 \tabularnewline
60 & 0.552873912450499 & 0.894252175099002 & 0.447126087549501 \tabularnewline
61 & 0.561487769230037 & 0.877024461539927 & 0.438512230769963 \tabularnewline
62 & 0.644330030965197 & 0.711339938069606 & 0.355669969034803 \tabularnewline
63 & 0.736070296438989 & 0.527859407122022 & 0.263929703561011 \tabularnewline
64 & 0.801034021910459 & 0.397931956179081 & 0.198965978089541 \tabularnewline
65 & 0.895067645824874 & 0.209864708350253 & 0.104932354175127 \tabularnewline
66 & 0.997993414621073 & 0.00401317075785345 & 0.00200658537892672 \tabularnewline
67 & 0.997807824242725 & 0.00438435151454951 & 0.00219217575727475 \tabularnewline
68 & 0.99770891748654 & 0.00458216502692092 & 0.00229108251346046 \tabularnewline
69 & 0.996856468234951 & 0.00628706353009705 & 0.00314353176504853 \tabularnewline
70 & 0.995165636203892 & 0.00966872759221596 & 0.00483436379610798 \tabularnewline
71 & 0.992586827036556 & 0.0148263459268889 & 0.00741317296344447 \tabularnewline
72 & 0.989643091808487 & 0.0207138163830256 & 0.0103569081915128 \tabularnewline
73 & 0.9863866737875 & 0.0272266524250009 & 0.0136133262125005 \tabularnewline
74 & 0.980642673413846 & 0.0387146531723088 & 0.0193573265861544 \tabularnewline
75 & 0.973648750197601 & 0.0527024996047976 & 0.0263512498023988 \tabularnewline
76 & 0.963976997502624 & 0.0720460049947518 & 0.0360230024973759 \tabularnewline
77 & 0.964745874587935 & 0.0705082508241298 & 0.0352541254120649 \tabularnewline
78 & 0.952888273893118 & 0.0942234522137632 & 0.0471117261068816 \tabularnewline
79 & 0.942907348490021 & 0.114185303019958 & 0.0570926515099792 \tabularnewline
80 & 0.947990041548729 & 0.104019916902543 & 0.0520099584512713 \tabularnewline
81 & 0.937212849334721 & 0.125574301330557 & 0.0627871506652786 \tabularnewline
82 & 0.913306140265902 & 0.173387719468196 & 0.0866938597340982 \tabularnewline
83 & 0.892681660941714 & 0.214636678116571 & 0.107318339058286 \tabularnewline
84 & 0.86663998785053 & 0.266720024298941 & 0.13336001214947 \tabularnewline
85 & 0.839576501596241 & 0.320846996807517 & 0.160423498403759 \tabularnewline
86 & 0.798271585639551 & 0.403456828720898 & 0.201728414360449 \tabularnewline
87 & 0.773150924748442 & 0.453698150503116 & 0.226849075251558 \tabularnewline
88 & 0.775507426348451 & 0.448985147303098 & 0.224492573651549 \tabularnewline
89 & 0.810427379886832 & 0.379145240226335 & 0.189572620113168 \tabularnewline
90 & 0.831267655491913 & 0.337464689016174 & 0.168732344508087 \tabularnewline
91 & 0.8411248496315 & 0.317750300737 & 0.1588751503685 \tabularnewline
92 & 0.945647749662933 & 0.108704500674134 & 0.0543522503370669 \tabularnewline
93 & 0.984933322495558 & 0.030133355008885 & 0.0150666775044425 \tabularnewline
94 & 0.97429770994057 & 0.0514045801188594 & 0.0257022900594297 \tabularnewline
95 & 0.956953882476154 & 0.0860922350476924 & 0.0430461175238462 \tabularnewline
96 & 0.931045207223 & 0.137909585553999 & 0.0689547927769997 \tabularnewline
97 & 0.900864500668895 & 0.19827099866221 & 0.0991354993311051 \tabularnewline
98 & 0.872797381965312 & 0.254405236069377 & 0.127202618034688 \tabularnewline
99 & 0.871449470427328 & 0.257101059145344 & 0.128550529572672 \tabularnewline
100 & 0.934032521649848 & 0.131934956700304 & 0.0659674783501521 \tabularnewline
101 & 0.970358242847448 & 0.059283514305103 & 0.0296417571525515 \tabularnewline
102 & 0.953907199057413 & 0.0921856018851742 & 0.0460928009425871 \tabularnewline
103 & 0.904795779509167 & 0.190408440981667 & 0.0952042204908334 \tabularnewline
104 & 0.820687875850153 & 0.358624248299694 & 0.179312124149847 \tabularnewline
105 & 0.672811036370008 & 0.654377927259983 & 0.327188963629992 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202832&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]21[/C][C]0.0645414221287039[/C][C]0.129082844257408[/C][C]0.935458577871296[/C][/ROW]
[ROW][C]22[/C][C]0.0196117079416961[/C][C]0.0392234158833922[/C][C]0.980388292058304[/C][/ROW]
[ROW][C]23[/C][C]0.0117215992877539[/C][C]0.0234431985755078[/C][C]0.988278400712246[/C][/ROW]
[ROW][C]24[/C][C]0.00367569876118179[/C][C]0.00735139752236357[/C][C]0.996324301238818[/C][/ROW]
[ROW][C]25[/C][C]0.0055243554633096[/C][C]0.0110487109266192[/C][C]0.99447564453669[/C][/ROW]
[ROW][C]26[/C][C]0.0279747662797428[/C][C]0.0559495325594855[/C][C]0.972025233720257[/C][/ROW]
[ROW][C]27[/C][C]0.0843249179602182[/C][C]0.168649835920436[/C][C]0.915675082039782[/C][/ROW]
[ROW][C]28[/C][C]0.0619926568683416[/C][C]0.123985313736683[/C][C]0.938007343131658[/C][/ROW]
[ROW][C]29[/C][C]0.0447836403409481[/C][C]0.0895672806818962[/C][C]0.955216359659052[/C][/ROW]
[ROW][C]30[/C][C]0.0295395911850445[/C][C]0.059079182370089[/C][C]0.970460408814956[/C][/ROW]
[ROW][C]31[/C][C]0.0319761183514184[/C][C]0.0639522367028369[/C][C]0.968023881648582[/C][/ROW]
[ROW][C]32[/C][C]0.0323148922169607[/C][C]0.0646297844339214[/C][C]0.967685107783039[/C][/ROW]
[ROW][C]33[/C][C]0.0287368909325533[/C][C]0.0574737818651066[/C][C]0.971263109067447[/C][/ROW]
[ROW][C]34[/C][C]0.0449940051350498[/C][C]0.0899880102700996[/C][C]0.95500599486495[/C][/ROW]
[ROW][C]35[/C][C]0.0613486966310529[/C][C]0.122697393262106[/C][C]0.938651303368947[/C][/ROW]
[ROW][C]36[/C][C]0.0633252313548281[/C][C]0.126650462709656[/C][C]0.936674768645172[/C][/ROW]
[ROW][C]37[/C][C]0.0701445287552289[/C][C]0.140289057510458[/C][C]0.929855471244771[/C][/ROW]
[ROW][C]38[/C][C]0.0788614092438967[/C][C]0.157722818487793[/C][C]0.921138590756103[/C][/ROW]
[ROW][C]39[/C][C]0.0720692064722961[/C][C]0.144138412944592[/C][C]0.927930793527704[/C][/ROW]
[ROW][C]40[/C][C]0.0768018808933697[/C][C]0.153603761786739[/C][C]0.92319811910663[/C][/ROW]
[ROW][C]41[/C][C]0.257888999077411[/C][C]0.515777998154822[/C][C]0.742111000922589[/C][/ROW]
[ROW][C]42[/C][C]0.252098956914643[/C][C]0.504197913829285[/C][C]0.747901043085357[/C][/ROW]
[ROW][C]43[/C][C]0.211265683358205[/C][C]0.422531366716409[/C][C]0.788734316641795[/C][/ROW]
[ROW][C]44[/C][C]0.17429300733329[/C][C]0.34858601466658[/C][C]0.82570699266671[/C][/ROW]
[ROW][C]45[/C][C]0.286869078736889[/C][C]0.573738157473778[/C][C]0.713130921263111[/C][/ROW]
[ROW][C]46[/C][C]0.505478211967128[/C][C]0.989043576065744[/C][C]0.494521788032872[/C][/ROW]
[ROW][C]47[/C][C]0.500053088236544[/C][C]0.999893823526913[/C][C]0.499946911763456[/C][/ROW]
[ROW][C]48[/C][C]0.478431268708441[/C][C]0.956862537416883[/C][C]0.521568731291559[/C][/ROW]
[ROW][C]49[/C][C]0.466888128322414[/C][C]0.933776256644828[/C][C]0.533111871677586[/C][/ROW]
[ROW][C]50[/C][C]0.446033770072452[/C][C]0.892067540144904[/C][C]0.553966229927548[/C][/ROW]
[ROW][C]51[/C][C]0.433701418200562[/C][C]0.867402836401125[/C][C]0.566298581799438[/C][/ROW]
[ROW][C]52[/C][C]0.396118425234236[/C][C]0.792236850468473[/C][C]0.603881574765764[/C][/ROW]
[ROW][C]53[/C][C]0.525773027871128[/C][C]0.948453944257745[/C][C]0.474226972128872[/C][/ROW]
[ROW][C]54[/C][C]0.702697482720423[/C][C]0.594605034559155[/C][C]0.297302517279577[/C][/ROW]
[ROW][C]55[/C][C]0.65906747327007[/C][C]0.68186505345986[/C][C]0.34093252672993[/C][/ROW]
[ROW][C]56[/C][C]0.611154110573633[/C][C]0.777691778852734[/C][C]0.388845889426367[/C][/ROW]
[ROW][C]57[/C][C]0.575693235871305[/C][C]0.84861352825739[/C][C]0.424306764128695[/C][/ROW]
[ROW][C]58[/C][C]0.542206839042936[/C][C]0.915586321914128[/C][C]0.457793160957064[/C][/ROW]
[ROW][C]59[/C][C]0.539701124843114[/C][C]0.920597750313772[/C][C]0.460298875156886[/C][/ROW]
[ROW][C]60[/C][C]0.552873912450499[/C][C]0.894252175099002[/C][C]0.447126087549501[/C][/ROW]
[ROW][C]61[/C][C]0.561487769230037[/C][C]0.877024461539927[/C][C]0.438512230769963[/C][/ROW]
[ROW][C]62[/C][C]0.644330030965197[/C][C]0.711339938069606[/C][C]0.355669969034803[/C][/ROW]
[ROW][C]63[/C][C]0.736070296438989[/C][C]0.527859407122022[/C][C]0.263929703561011[/C][/ROW]
[ROW][C]64[/C][C]0.801034021910459[/C][C]0.397931956179081[/C][C]0.198965978089541[/C][/ROW]
[ROW][C]65[/C][C]0.895067645824874[/C][C]0.209864708350253[/C][C]0.104932354175127[/C][/ROW]
[ROW][C]66[/C][C]0.997993414621073[/C][C]0.00401317075785345[/C][C]0.00200658537892672[/C][/ROW]
[ROW][C]67[/C][C]0.997807824242725[/C][C]0.00438435151454951[/C][C]0.00219217575727475[/C][/ROW]
[ROW][C]68[/C][C]0.99770891748654[/C][C]0.00458216502692092[/C][C]0.00229108251346046[/C][/ROW]
[ROW][C]69[/C][C]0.996856468234951[/C][C]0.00628706353009705[/C][C]0.00314353176504853[/C][/ROW]
[ROW][C]70[/C][C]0.995165636203892[/C][C]0.00966872759221596[/C][C]0.00483436379610798[/C][/ROW]
[ROW][C]71[/C][C]0.992586827036556[/C][C]0.0148263459268889[/C][C]0.00741317296344447[/C][/ROW]
[ROW][C]72[/C][C]0.989643091808487[/C][C]0.0207138163830256[/C][C]0.0103569081915128[/C][/ROW]
[ROW][C]73[/C][C]0.9863866737875[/C][C]0.0272266524250009[/C][C]0.0136133262125005[/C][/ROW]
[ROW][C]74[/C][C]0.980642673413846[/C][C]0.0387146531723088[/C][C]0.0193573265861544[/C][/ROW]
[ROW][C]75[/C][C]0.973648750197601[/C][C]0.0527024996047976[/C][C]0.0263512498023988[/C][/ROW]
[ROW][C]76[/C][C]0.963976997502624[/C][C]0.0720460049947518[/C][C]0.0360230024973759[/C][/ROW]
[ROW][C]77[/C][C]0.964745874587935[/C][C]0.0705082508241298[/C][C]0.0352541254120649[/C][/ROW]
[ROW][C]78[/C][C]0.952888273893118[/C][C]0.0942234522137632[/C][C]0.0471117261068816[/C][/ROW]
[ROW][C]79[/C][C]0.942907348490021[/C][C]0.114185303019958[/C][C]0.0570926515099792[/C][/ROW]
[ROW][C]80[/C][C]0.947990041548729[/C][C]0.104019916902543[/C][C]0.0520099584512713[/C][/ROW]
[ROW][C]81[/C][C]0.937212849334721[/C][C]0.125574301330557[/C][C]0.0627871506652786[/C][/ROW]
[ROW][C]82[/C][C]0.913306140265902[/C][C]0.173387719468196[/C][C]0.0866938597340982[/C][/ROW]
[ROW][C]83[/C][C]0.892681660941714[/C][C]0.214636678116571[/C][C]0.107318339058286[/C][/ROW]
[ROW][C]84[/C][C]0.86663998785053[/C][C]0.266720024298941[/C][C]0.13336001214947[/C][/ROW]
[ROW][C]85[/C][C]0.839576501596241[/C][C]0.320846996807517[/C][C]0.160423498403759[/C][/ROW]
[ROW][C]86[/C][C]0.798271585639551[/C][C]0.403456828720898[/C][C]0.201728414360449[/C][/ROW]
[ROW][C]87[/C][C]0.773150924748442[/C][C]0.453698150503116[/C][C]0.226849075251558[/C][/ROW]
[ROW][C]88[/C][C]0.775507426348451[/C][C]0.448985147303098[/C][C]0.224492573651549[/C][/ROW]
[ROW][C]89[/C][C]0.810427379886832[/C][C]0.379145240226335[/C][C]0.189572620113168[/C][/ROW]
[ROW][C]90[/C][C]0.831267655491913[/C][C]0.337464689016174[/C][C]0.168732344508087[/C][/ROW]
[ROW][C]91[/C][C]0.8411248496315[/C][C]0.317750300737[/C][C]0.1588751503685[/C][/ROW]
[ROW][C]92[/C][C]0.945647749662933[/C][C]0.108704500674134[/C][C]0.0543522503370669[/C][/ROW]
[ROW][C]93[/C][C]0.984933322495558[/C][C]0.030133355008885[/C][C]0.0150666775044425[/C][/ROW]
[ROW][C]94[/C][C]0.97429770994057[/C][C]0.0514045801188594[/C][C]0.0257022900594297[/C][/ROW]
[ROW][C]95[/C][C]0.956953882476154[/C][C]0.0860922350476924[/C][C]0.0430461175238462[/C][/ROW]
[ROW][C]96[/C][C]0.931045207223[/C][C]0.137909585553999[/C][C]0.0689547927769997[/C][/ROW]
[ROW][C]97[/C][C]0.900864500668895[/C][C]0.19827099866221[/C][C]0.0991354993311051[/C][/ROW]
[ROW][C]98[/C][C]0.872797381965312[/C][C]0.254405236069377[/C][C]0.127202618034688[/C][/ROW]
[ROW][C]99[/C][C]0.871449470427328[/C][C]0.257101059145344[/C][C]0.128550529572672[/C][/ROW]
[ROW][C]100[/C][C]0.934032521649848[/C][C]0.131934956700304[/C][C]0.0659674783501521[/C][/ROW]
[ROW][C]101[/C][C]0.970358242847448[/C][C]0.059283514305103[/C][C]0.0296417571525515[/C][/ROW]
[ROW][C]102[/C][C]0.953907199057413[/C][C]0.0921856018851742[/C][C]0.0460928009425871[/C][/ROW]
[ROW][C]103[/C][C]0.904795779509167[/C][C]0.190408440981667[/C][C]0.0952042204908334[/C][/ROW]
[ROW][C]104[/C][C]0.820687875850153[/C][C]0.358624248299694[/C][C]0.179312124149847[/C][/ROW]
[ROW][C]105[/C][C]0.672811036370008[/C][C]0.654377927259983[/C][C]0.327188963629992[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202832&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202832&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
21	0.0645414221287039	0.129082844257408	0.935458577871296
22	0.0196117079416961	0.0392234158833922	0.980388292058304
23	0.0117215992877539	0.0234431985755078	0.988278400712246
24	0.00367569876118179	0.00735139752236357	0.996324301238818
25	0.0055243554633096	0.0110487109266192	0.99447564453669
26	0.0279747662797428	0.0559495325594855	0.972025233720257
27	0.0843249179602182	0.168649835920436	0.915675082039782
28	0.0619926568683416	0.123985313736683	0.938007343131658
29	0.0447836403409481	0.0895672806818962	0.955216359659052
30	0.0295395911850445	0.059079182370089	0.970460408814956
31	0.0319761183514184	0.0639522367028369	0.968023881648582
32	0.0323148922169607	0.0646297844339214	0.967685107783039
33	0.0287368909325533	0.0574737818651066	0.971263109067447
34	0.0449940051350498	0.0899880102700996	0.95500599486495
35	0.0613486966310529	0.122697393262106	0.938651303368947
36	0.0633252313548281	0.126650462709656	0.936674768645172
37	0.0701445287552289	0.140289057510458	0.929855471244771
38	0.0788614092438967	0.157722818487793	0.921138590756103
39	0.0720692064722961	0.144138412944592	0.927930793527704
40	0.0768018808933697	0.153603761786739	0.92319811910663
41	0.257888999077411	0.515777998154822	0.742111000922589
42	0.252098956914643	0.504197913829285	0.747901043085357
43	0.211265683358205	0.422531366716409	0.788734316641795
44	0.17429300733329	0.34858601466658	0.82570699266671
45	0.286869078736889	0.573738157473778	0.713130921263111
46	0.505478211967128	0.989043576065744	0.494521788032872
47	0.500053088236544	0.999893823526913	0.499946911763456
48	0.478431268708441	0.956862537416883	0.521568731291559
49	0.466888128322414	0.933776256644828	0.533111871677586
50	0.446033770072452	0.892067540144904	0.553966229927548
51	0.433701418200562	0.867402836401125	0.566298581799438
52	0.396118425234236	0.792236850468473	0.603881574765764
53	0.525773027871128	0.948453944257745	0.474226972128872
54	0.702697482720423	0.594605034559155	0.297302517279577
55	0.65906747327007	0.68186505345986	0.34093252672993
56	0.611154110573633	0.777691778852734	0.388845889426367
57	0.575693235871305	0.84861352825739	0.424306764128695
58	0.542206839042936	0.915586321914128	0.457793160957064
59	0.539701124843114	0.920597750313772	0.460298875156886
60	0.552873912450499	0.894252175099002	0.447126087549501
61	0.561487769230037	0.877024461539927	0.438512230769963
62	0.644330030965197	0.711339938069606	0.355669969034803
63	0.736070296438989	0.527859407122022	0.263929703561011
64	0.801034021910459	0.397931956179081	0.198965978089541
65	0.895067645824874	0.209864708350253	0.104932354175127
66	0.997993414621073	0.00401317075785345	0.00200658537892672
67	0.997807824242725	0.00438435151454951	0.00219217575727475
68	0.99770891748654	0.00458216502692092	0.00229108251346046
69	0.996856468234951	0.00628706353009705	0.00314353176504853
70	0.995165636203892	0.00966872759221596	0.00483436379610798
71	0.992586827036556	0.0148263459268889	0.00741317296344447
72	0.989643091808487	0.0207138163830256	0.0103569081915128
73	0.9863866737875	0.0272266524250009	0.0136133262125005
74	0.980642673413846	0.0387146531723088	0.0193573265861544
75	0.973648750197601	0.0527024996047976	0.0263512498023988
76	0.963976997502624	0.0720460049947518	0.0360230024973759
77	0.964745874587935	0.0705082508241298	0.0352541254120649
78	0.952888273893118	0.0942234522137632	0.0471117261068816
79	0.942907348490021	0.114185303019958	0.0570926515099792
80	0.947990041548729	0.104019916902543	0.0520099584512713
81	0.937212849334721	0.125574301330557	0.0627871506652786
82	0.913306140265902	0.173387719468196	0.0866938597340982
83	0.892681660941714	0.214636678116571	0.107318339058286
84	0.86663998785053	0.266720024298941	0.13336001214947
85	0.839576501596241	0.320846996807517	0.160423498403759
86	0.798271585639551	0.403456828720898	0.201728414360449
87	0.773150924748442	0.453698150503116	0.226849075251558
88	0.775507426348451	0.448985147303098	0.224492573651549
89	0.810427379886832	0.379145240226335	0.189572620113168
90	0.831267655491913	0.337464689016174	0.168732344508087
91	0.8411248496315	0.317750300737	0.1588751503685
92	0.945647749662933	0.108704500674134	0.0543522503370669
93	0.984933322495558	0.030133355008885	0.0150666775044425
94	0.97429770994057	0.0514045801188594	0.0257022900594297
95	0.956953882476154	0.0860922350476924	0.0430461175238462
96	0.931045207223	0.137909585553999	0.0689547927769997
97	0.900864500668895	0.19827099866221	0.0991354993311051
98	0.872797381965312	0.254405236069377	0.127202618034688
99	0.871449470427328	0.257101059145344	0.128550529572672
100	0.934032521649848	0.131934956700304	0.0659674783501521
101	0.970358242847448	0.059283514305103	0.0296417571525515
102	0.953907199057413	0.0921856018851742	0.0460928009425871
103	0.904795779509167	0.190408440981667	0.0952042204908334
104	0.820687875850153	0.358624248299694	0.179312124149847
105	0.672811036370008	0.654377927259983	0.327188963629992

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	6	0.0705882352941176	NOK
5% type I error level	14	0.164705882352941	NOK
10% type I error level	29	0.341176470588235	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 & 6 & 0.0705882352941176 & NOK \tabularnewline
5% type I error level & 14 & 0.164705882352941 & NOK \tabularnewline
10% type I error level & 29 & 0.341176470588235 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202832&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]6[/C][C]0.0705882352941176[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]14[/C][C]0.164705882352941[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]29[/C][C]0.341176470588235[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202832&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202832&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	6	0.0705882352941176	NOK
5% type I error level	14	0.164705882352941	NOK
10% type I error level	29	0.341176470588235	NOK

Figure 1

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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):

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