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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationFri, 21 Dec 2012 03:23:32 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Dec/21/t1356078230krgawl00ia2bs70.htm/, Retrieved Sat, 20 Apr 2024 12:29:34 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=203270, Retrieved Sat, 20 Apr 2024 12:29:34 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact199

Tree of Dependent Computations

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] [d1865ed705b6ad9ba3d459a02c528b22]
-   PD                [Multiple Regression] [] [2012-12-21 08:01:57] [74be16979710d4c4e7c6647856088456]
- R PD                    [Multiple Regression] [] [2012-12-21 08:23:32] [14d0a7ecb926325afa0eb6a607fbc7a0] [Current]
- 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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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
27.72	0.02	41837160	91.51	2747.48	0.016	62.7	0.16
26.90	0.02	35204750	91.09	2760.01	0.016	62.7	0.17
25.86	0.02	42367740	93.00	2778.11	0.016	62.7	0.17
26.81	0.02	61427940	93.08	2844.72	0.016	62.7	0.16
26.31	0.02	26132090	94.13	2831.02	0.016	62.7	0.16
27.10	0.02	3799718	96.26	2858.42	0.016	62.7	0.17
27.00	0.02	28202230	94.29	2809.73	0.016	62.7	0.17
27.40	0.02	15809640	94.46	2843.07	0.016	62.7	0.16
27.27	0.02	17110160	95.53	2818.61	0.016	62.7	0.17
28.29	0.02	16835510	98.29	2836.33	0.016	62.7	0.17
30.01	0.02	43517670	102.01	2872.80	0.016	62.7	0.18
31.41	0.02	42958450	105.16	2895.33	0.016	62.7	0.17
31.91	0.02	30826830	105.34	2929.76	0.016	62.7	0.17
31.60	0.01	15549740	105.27	2930.45	0.016	62.7	0.16
31.84	0.01	21843070	102.19	2859.09	0.016	62.7	0.17
33.05	0.01	73424890	106.85	2892.42	0.016	62.7	0.17
32.06	0.01	24330740	103.05	2836.16	0.016	62.7	0.17
33.10	0.01	24785970	106.42	2854.06	0.016	62.7	0.16
32.23	0.01	28553940	105.17	2875.32	0.016	62.7	0.15
31.36	0.01	17659080	102.74	2849.49	0.016	62.7	0.15
31.09	0.01	19508980	106.27	2935.05	0.016	62.7	0.09
30.77	0.01	14110230	107.63	2951.23	0.0141	65.4	0.18
31.20	0.01	8765498	108.54	2976.08	0.0141	65.4	0.17
31.47	0.01	10027250	108.24	2976.12	0.0141	65.4	0.17
31.73	0.01	10943350	108.86	2937.33	0.0141	65.4	0.17
32.17	0.01	17755740	102.98	2931.77	0.0141	65.4	0.17
31.47	0	14238190	99.53	2902.33	0.0141	65.4	0.17
30.97	0	12997760	101.08	2887.98	0.0141	65.4	0.17
30.81	0	11299240	104.64	2866.19	0.0141	65.4	0.18
30.72	0	8102653	105.59	2908.47	0.0141	65.4	0.19
28.24	0	24549800	103.21	2896.94	0.0141	65.4	0.18
28.09	0	30410530	103.84	2910.04	0.0141	65.4	0.17
29.11	0	16807730	104.61	2942.60	0.0141	65.4	0.16
29.00	0	13671200	108.65	2965.90	0.0141	65.4	0.13
28.76	0	11854290	106.26	2925.30	0.0141	65.4	0.13
28.75	0	12383610	104.20	2890.15	0.0141	65.4	0.14
28.45	0	11512350	102.99	2862.99	0.0141	65.4	0.15
29.34	0	16749990	102.19	2854.24	0.0141	65.4	0.15
26.84	0	61009290	100.82	2893.25	0.0141	65.4	0.14
23.70	0	123011300	103.42	2958.09	0.0141	65.4	0.14
23.15	0	29253590	104.18	2945.84	0.0141	65.4	0.14
21.71	0	55998620	102.65	2939.52	0.0141	65.4	0.13
20.88	0	44488370	95.64	2920.21	0.0169	61.3	0.14
20.04	0	56264460	93.51	2909.77	0.0169	61.3	0.14
21.09	0	80626220	108.51	2967.90	0.0169	61.3	0.14
21.92	0	27733830	111.55	2989.91	0.0169	61.3	0.14
20.72	0	36699380	106.70	3015.86	0.0169	61.3	0.13
20.72	0	29514550	104.93	3011.25	0.0169	61.3	0.13
21.01	-0.01	15605960	105.23	3018.64	0.0169	61.3	0.13
21.80	-0.01	25714310	104.92	3020.86	0.0169	61.3	0.13
21.60	-0.01	24904700	104.60	3022.52	0.0169	61.3	0.13
20.38	-0.01	38971320	101.76	3016.98	0.0169	61.3	0.13
21.20	-0.01	47682050	102.23	3030.93	0.0169	61.3	0.13
19.87	-0.01	157188200	103.99	3062.39	0.0169	61.3	0.13
19.05	-0.01	129057400	101.36	3076.59	0.0169	61.3	0.13
20.01	-0.01	100818300	102.92	3076.21	0.0169	61.3	0.13
19.15	-0.01	70483330	105.25	3067.26	0.0169	61.3	0.13
19.43	-0.01	49779450	105.71	3073.67	0.0169	61.3	0.13
19.44	-0.01	32747000	105.42	3053.40	0.0169	61.3	0.13
19.40	-0.01	29588690	105.11	3069.79	0.0169	61.3	0.13
19.15	-0.01	20663220	104.67	3073.19	0.0169	61.3	0.13
19.34	-0.01	25402980	107.51	3077.14	0.0169	61.3	0.13
19.10	-0.01	16071190	109.00	3081.19	0.0169	61.3	0.13
19.08	-0.01	30571430	107.37	3048.71	0.0169	61.3	0.14
18.05	-0.01	58612440	107.30	3066.96	0.0169	61.3	0.13
17.72	-0.01	46177000	107.37	3075.06	0.0199	70.3	0.14
18.58	-0.01	60657900	113.28	3069.27	0.0199	70.3	0.16
18.96	-0.01	46028860	119.10	3135.81	0.0199	70.3	0.16
18.98	-0.01	36325880	119.04	3136.42	0.0199	70.3	0.15
18.81	-0.01	24752340	117.80	3104.02	0.0199	70.3	0.15
19.43	-0.01	47343020	117.90	3104.53	0.0199	70.3	0.15
20.93	-0.01	121399400	119.55	3114.31	0.0199	70.3	0.15
20.71	-0.01	64896660	119.47	3155.83	0.0199	70.3	0.15
22.00	-0.01	72707430	123.23	3183.95	0.0199	70.3	0.16
21.52	-0.02	50593510	121.40	3178.67	0.0199	70.3	0.16
21.87	-0.02	36696330	121.43	3177.80	0.0199	70.3	0.16
23.29	-0.02	78525460	122.51	3182.62	0.0199	70.3	0.15
22.59	-0.02	57115160	122.78	3175.96	0.0199	70.3	0.16
22.86	-0.02	51163120	122.84	3179.96	0.0199	70.3	0.15
20.79	-0.02	78968380	122.70	3160.78	0.0199	70.3	0.16
20.28	-0.02	46169460	119.89	3117.73	0.0199	70.3	0.15
20.62	-0.02	38212360	118.00	3093.70	0.0199	70.3	0.16
20.32	-0.02	30061050	119.61	3136.60	0.0199	70.3	0.14
21.66	-0.02	65415370	120.40	3116.23	0.0199	70.3	0.09
21.99	-0.02	51198150	117.94	3113.53	0.0216	73.1	0.15
22.27	-0.02	29276680	118.77	3120.04	0.0216	73.1	0.16
21.83	-0.02	31940720	121.68	3135.23	0.0216	73.1	0.16
21.94	-0.02	46549400	121.98	3149.46	0.0216	73.1	0.15
20.91	-0.02	40483780	118.83	3136.19	0.0216	73.1	0.15
20.40	-0.02	32190200	117.97	3112.35	0.0216	73.1	0.15
20.22	-0.02	27125670	113.07	3065.02	0.0216	73.1	0.16
19.64	-0.3	39282420	111.98	3051.78	0.0216	73.1	0.16
19.75	-0.3	21803710	113.77	3049.41	0.0216	73.1	0.16
19.51	-0.3	18743920	110.41	3044.11	0.0216	73.1	0.16
19.52	-0.3	20154860	110.85	3064.18	0.0216	73.1	0.16
19.48	-0.3	21816100	111.18	3101.17	0.0216	73.1	0.16
19.88	-0.3	44020450	109.42	3104.12	0.0216	73.1	0.15
18.97	-0.3	52059860	108.87	3072.87	0.0216	73.1	0.15
19.00	-0.3	34769600	106.72	3005.62	0.0216	73.1	0.16
19.32	-0.3	32269470	107.28	3016.96	0.0216	73.1	0.15
19.50	-0.3	72281000	104.13	2990.46	0.0216	73.1	0.15
23.22	-0.3	228364700	107.55	2981.70	0.0216	73.1	0.17
22.56	-0.3	76050080	105.72	2986.12	0.0216	73.1	0.16
21.94	-0.3	9999999	104.55	2987.95	0.0216	73.1	0.16
21.11	-0.02	99311480	106.93	2977.23	0.0216	73.1	0.18
21.21	-0.3	37631000	106.85	3020.06	0.0176	73.1	0.17
21.18	-0.3	38308550	106.78	2982.13	0.0176	73.1	0.16
21.25	-0.3	31752420	107.29	2999.66	0.0176	73.1	0.17
21.17	-0.3	29030780	104.14	3011.93	0.0176	73.1	0.16
20.47	-0.3	33352920	101.21	2937.29	0.0176	73.1	0.16
19.99	-0.3	34106840	96.35	2895.58	0.0176	73.1	0.16
19.21	-0.3	42257790	95.62	2904.87	0.0176	73.1	0.16
20.07	-0.3	67220540	99.00	2904.26	0.0176	73.1	0.16
19.86	-0.3	71524510	99.26	2883.89	0.0176	73.1	0.16
22.36	-0.3	229081600	98.77	2846.81	0.0176	73.1	0.16
22.17	-0.3	78808770	100.65	2836.94	0.0176	73.1	0.16
23.56	-0.3	107091400	103.13	2853.13	0.0176	73.1	0.16
22.92	-0.3	84944370	105.53	2916.07	0.0176	73.1	0.16
23.10	-0.3	46515660	106.76	2916.68	0.0176	73.1	0.16
24.32	-0.3	89720920	107.59	2926.55	0.0176	73.1	0.16
23.99	-0.3	29520310	107.62	2966.85	0.0176	73.1	0.16
25.94	-0.3	123513900	108.82	2976.78	0.0176	73.1	0.16
26.15	-0.3	85687430	107.59	2967.79	0.0176	73.1	0.16
26.36	-0.3	49113040	107.85	2991.78	0.0176	73.1	0.16
27.32	-0.3	88572990	107.11	3012.03	0.0176	73.1	0.16
28.00	-0.3	126867400	108.14	3010.24	0.0176	73.1	0.16

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R Server'Herman Ole Andreas Wold' @ wold.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 & 8 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203270&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]8 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203270&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203270&T=0

As an alternative you can also use a QR Code:  
QR Code


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net






Multiple Linear Regression - Estimated Regression Equation
FACEBOOK[t] = + 106.647468849512 -0.0723980587962752REV.GROWTH[t] -3.83991038496243e-09VOLUME[t] + 0.500580592307162LINKEDIN[t] -0.0401856243339012NASDAQ[t] -735.451578545474INF.CONS.CONF[t] -0.186462683285858FED[t] + 60.8989867115308FUNDS.RATE[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
FACEBOOK[t] =  +  106.647468849512 -0.0723980587962752REV.GROWTH[t] -3.83991038496243e-09VOLUME[t] +  0.500580592307162LINKEDIN[t] -0.0401856243339012NASDAQ[t] -735.451578545474INF.CONS.CONF[t] -0.186462683285858FED[t] +  60.8989867115308FUNDS.RATE[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203270&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]FACEBOOK[t] =  +  106.647468849512 -0.0723980587962752REV.GROWTH[t] -3.83991038496243e-09VOLUME[t] +  0.500580592307162LINKEDIN[t] -0.0401856243339012NASDAQ[t] -735.451578545474INF.CONS.CONF[t] -0.186462683285858FED[t] +  60.8989867115308FUNDS.RATE[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203270&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203270&T=1

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Estimated Regression Equation
FACEBOOK[t] = + 106.647468849512 -0.0723980587962752REV.GROWTH[t] -3.83991038496243e-09VOLUME[t] + 0.500580592307162LINKEDIN[t] -0.0401856243339012NASDAQ[t] -735.451578545474INF.CONS.CONF[t] -0.186462683285858FED[t] + 60.8989867115308FUNDS.RATE[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)106.64746884951211.3483619.397600
REV.GROWTH-0.07239805879627523.056972-0.02370.9811460.490573
VOLUME-3.83991038496243e-090-0.67670.4999520.249976
LINKEDIN0.5005805923071620.0676197.402900
NASDAQ-0.04018562433390120.004803-8.366900
INF.CONS.CONF-735.451578545474143.23011-5.13481e-061e-06
FED-0.1864626832858580.106912-1.74410.0837510.041875
FUNDS.RATE60.898986711530815.5924853.90570.0001577.9e-05

\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) & 106.647468849512 & 11.348361 & 9.3976 & 0 & 0 \tabularnewline
REV.GROWTH & -0.0723980587962752 & 3.056972 & -0.0237 & 0.981146 & 0.490573 \tabularnewline
VOLUME & -3.83991038496243e-09 & 0 & -0.6767 & 0.499952 & 0.249976 \tabularnewline
LINKEDIN & 0.500580592307162 & 0.067619 & 7.4029 & 0 & 0 \tabularnewline
NASDAQ & -0.0401856243339012 & 0.004803 & -8.3669 & 0 & 0 \tabularnewline
INF.CONS.CONF & -735.451578545474 & 143.23011 & -5.1348 & 1e-06 & 1e-06 \tabularnewline
FED & -0.186462683285858 & 0.106912 & -1.7441 & 0.083751 & 0.041875 \tabularnewline
FUNDS.RATE & 60.8989867115308 & 15.592485 & 3.9057 & 0.000157 & 7.9e-05 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203270&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]106.647468849512[/C][C]11.348361[/C][C]9.3976[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]REV.GROWTH[/C][C]-0.0723980587962752[/C][C]3.056972[/C][C]-0.0237[/C][C]0.981146[/C][C]0.490573[/C][/ROW]
[ROW][C]VOLUME[/C][C]-3.83991038496243e-09[/C][C]0[/C][C]-0.6767[/C][C]0.499952[/C][C]0.249976[/C][/ROW]
[ROW][C]LINKEDIN[/C][C]0.500580592307162[/C][C]0.067619[/C][C]7.4029[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]NASDAQ[/C][C]-0.0401856243339012[/C][C]0.004803[/C][C]-8.3669[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]INF.CONS.CONF[/C][C]-735.451578545474[/C][C]143.23011[/C][C]-5.1348[/C][C]1e-06[/C][C]1e-06[/C][/ROW]
[ROW][C]FED[/C][C]-0.186462683285858[/C][C]0.106912[/C][C]-1.7441[/C][C]0.083751[/C][C]0.041875[/C][/ROW]
[ROW][C]FUNDS.RATE[/C][C]60.8989867115308[/C][C]15.592485[/C][C]3.9057[/C][C]0.000157[/C][C]7.9e-05[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203270&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203270&T=2

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)106.64746884951211.3483619.397600
REV.GROWTH-0.07239805879627523.056972-0.02370.9811460.490573
VOLUME-3.83991038496243e-090-0.67670.4999520.249976
LINKEDIN0.5005805923071620.0676197.402900
NASDAQ-0.04018562433390120.004803-8.366900
INF.CONS.CONF-735.451578545474143.23011-5.13481e-061e-06
FED-0.1864626832858580.106912-1.74410.0837510.041875
FUNDS.RATE60.898986711530815.5924853.90570.0001577.9e-05






Multiple Linear Regression - Regression Statistics
Multiple R0.873843255565265
R-squared0.763602035296901
Adjusted R-squared0.749578427221293
F-TEST (value)54.4511819768477
F-TEST (DF numerator)7
F-TEST (DF denominator)118
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.24187457257371
Sum Squared Residuals593.068188700001

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.873843255565265 \tabularnewline
R-squared & 0.763602035296901 \tabularnewline
Adjusted R-squared & 0.749578427221293 \tabularnewline
F-TEST (value) & 54.4511819768477 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 118 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.24187457257371 \tabularnewline
Sum Squared Residuals & 593.068188700001 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203270&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.873843255565265[/C][/ROW]
[ROW][C]R-squared[/C][C]0.763602035296901[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.749578427221293[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]54.4511819768477[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]118[/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]2.24187457257371[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]593.068188700001[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203270&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203270&T=3

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Regression Statistics
Multiple R0.873843255565265
R-squared0.763602035296901
Adjusted R-squared0.749578427221293
F-TEST (value)54.4511819768477
F-TEST (DF numerator)7
F-TEST (DF denominator)118
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.24187457257371
Sum Squared Residuals593.068188700001






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
127.7228.1697031753911-0.449703175391099
226.928.0903911808697-1.19039118086971
325.8628.2916350720444-2.4316350720444
426.8124.97273775551311.83726224448694
526.3126.18442333177110.125576668228913
627.126.84431806091540.255681939084597
72727.72110888364-0.721108883639975
827.425.90501543696221.49498456303781
927.2728.0275730287995-0.757573028799536
1028.2928.6981408317578-0.408140831757817
1130.0129.60126367952110.40873632047885
1231.4129.66586791661611.7441320833839
1331.9128.41896571103963.49103428896042
1431.627.80659375880343.79340624119665
1531.8429.71727573085682.12272426914321
1633.0530.5125248656662.53747513433402
1732.0631.05967897634991.00032102365005
1833.131.41657498732841.6834250126716
1932.2329.31304433935722.91695566064283
2031.3629.17646346265222.18353653734785
2131.0923.84418828257487.24581171742516
2230.7730.27032276098320.499677239016759
2331.229.13877176008172.06122823991834
2431.4728.98214514280812.4878548571919
2531.7330.84778773604690.882212263953098
2632.1728.10164695746994.06835304253013
2731.4727.57193975176283.8980602482372
2830.9728.92926651906922.0407334809308
2930.8132.2024902136208-1.39249021362077
3030.7231.6002580542083-0.88025805420829
3128.2430.2000710554035-1.9600710554035
3228.0929.3575106046771-1.26751060467715
3329.1127.87775739831111.2322426016889
342927.1488523370261.85114766297405
3528.7627.59097784094581.16902215905424
3628.7528.579263841880.170736158120026
3728.4529.6773383095344-1.22733830953438
3829.3429.6083859803816-0.268385980381595
3926.8426.57600775083880.263992249161187
4023.725.0337992469397-1.33379924693974
4123.1526.2665355994828-3.11653559948277
4221.7125.0429320534846-3.33293205348463
4320.8821.6682672844676-0.788267284467572
4420.0420.976349410614-0.936349410613996
4521.0926.0555209774718-4.96552097747178
4621.9226.8959024241429-4.97590242414288
4720.7222.7818528243212-2.06185282432119
4820.7222.108670007448-1.388670007448
4921.0122.0160041410818-1.00600414108177
5021.821.73279691330540.0672030866945611
5121.621.50901181721960.090988182780358
5220.3820.25597673365780.124023266342199
5321.219.89721172999661.30278827000335
5419.8719.09350002831050.776499971689537
5519.0517.31435695605851.73564304394148
5620.0118.21896883065661.79103116934342
5719.1519.8614665148512-0.711466514851196
5819.4319.9136447791532-0.483644779153202
5919.4420.6484420942687-1.20844209426866
6019.419.8467473551887-0.446747355188734
6119.1519.524133776782-0.374133776781987
6219.3420.7688491891692-1.4288491891692
6319.121.3877957304858-2.28779573048584
6419.0822.4303886883451-3.35038868834515
6518.0520.9452955701708-2.8952955701708
6617.7217.42705461163140.292945388368555
6718.5821.7805350529971-3.20053505299707
6818.9622.076136859665-3.116136859665
6918.9821.4498574998347-2.46985749983466
7018.8122.175593150229-3.36559315022896
7119.4322.118410354314-2.68841035431402
7220.9322.2669830630006-1.33698306300056
7320.7120.7753949513772-0.0653949513772438
742222.1065554324606-0.106555432460635
7521.5221.48831249666970.0316875033302913
7621.8721.59165533341310.278344666586895
7723.2921.16897768601922.1210223139808
7822.5922.26297420443640.327025795563625
7922.8621.54613197573161.3138680242684
8020.7922.7490311280175-1.95903112801754
8120.2822.5893458376171-2.30934583761711
8220.6223.2484534889397-2.62845348893972
8320.3221.1437455243193-0.823745524319324
8421.6619.17707860382572.48292139617428
8521.9919.99032038913911.99967961086094
8622.2720.83736021376231.43263978623773
8721.8321.67340042888220.156599571117802
8821.9420.5866472831451.35335271685497
8920.9119.56637308951761.34362691048243
9020.420.12574566822410.274254331775864
9120.2220.20332357409980.0166764259001569
9219.6420.1633390205564-0.523339020556448
9319.7521.2217348905024-1.47173489050237
9419.5119.7645172287168-0.254517228716775
9519.5219.1728293257920.347170674208016
9619.4817.84517566441441.63482433558558
9719.8816.15135364889723.72864635110283
9818.9717.10096446961471.86903553038533
991919.4025823486571-0.40258234865713
10019.3218.62781290843810.692187091561855
10119.517.96226239795371.53773760204627
10223.2220.64490640648652.57509359351354
10322.5619.52710808701283.03289191298719
10421.9419.12151549344192.8184845065581
10521.1121.5984473903717-0.488447390371747
10621.2122.4271860719972-1.2171860719972
10721.1823.3047945631239-2.12479456312392
10821.2523.4898014894148-2.23980148941477
10921.1720.82135599965510.348644000344935
11020.4722.3375132342062-1.86751323420619
11119.9921.577938961323-1.58793896132298
11219.2120.8078917613245-1.5978917613245
11320.0722.4285126712042-2.35851267120415
11419.8623.360717933786-3.50071793378603
11522.3624.0005112877411-1.64051128774111
11622.1725.9152691139489-3.74526911394887
11723.5626.3975009602537-2.83750096025372
11822.9225.1546537967082-2.23465379670824
11923.125.8934174970121-2.79341749701209
12024.3225.7463629498924-1.42636294989242
12123.9924.3730646545255-0.383064654525504
12225.9424.21379115329751.72620884670246
12326.1524.1045960425012.04540395749901
12426.3623.41113624871522.94886375128479
12527.3222.07542504585135.24457495414868
1262822.51590822084045.48409177915961

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 27.72 & 28.1697031753911 & -0.449703175391099 \tabularnewline
2 & 26.9 & 28.0903911808697 & -1.19039118086971 \tabularnewline
3 & 25.86 & 28.2916350720444 & -2.4316350720444 \tabularnewline
4 & 26.81 & 24.9727377555131 & 1.83726224448694 \tabularnewline
5 & 26.31 & 26.1844233317711 & 0.125576668228913 \tabularnewline
6 & 27.1 & 26.8443180609154 & 0.255681939084597 \tabularnewline
7 & 27 & 27.72110888364 & -0.721108883639975 \tabularnewline
8 & 27.4 & 25.9050154369622 & 1.49498456303781 \tabularnewline
9 & 27.27 & 28.0275730287995 & -0.757573028799536 \tabularnewline
10 & 28.29 & 28.6981408317578 & -0.408140831757817 \tabularnewline
11 & 30.01 & 29.6012636795211 & 0.40873632047885 \tabularnewline
12 & 31.41 & 29.6658679166161 & 1.7441320833839 \tabularnewline
13 & 31.91 & 28.4189657110396 & 3.49103428896042 \tabularnewline
14 & 31.6 & 27.8065937588034 & 3.79340624119665 \tabularnewline
15 & 31.84 & 29.7172757308568 & 2.12272426914321 \tabularnewline
16 & 33.05 & 30.512524865666 & 2.53747513433402 \tabularnewline
17 & 32.06 & 31.0596789763499 & 1.00032102365005 \tabularnewline
18 & 33.1 & 31.4165749873284 & 1.6834250126716 \tabularnewline
19 & 32.23 & 29.3130443393572 & 2.91695566064283 \tabularnewline
20 & 31.36 & 29.1764634626522 & 2.18353653734785 \tabularnewline
21 & 31.09 & 23.8441882825748 & 7.24581171742516 \tabularnewline
22 & 30.77 & 30.2703227609832 & 0.499677239016759 \tabularnewline
23 & 31.2 & 29.1387717600817 & 2.06122823991834 \tabularnewline
24 & 31.47 & 28.9821451428081 & 2.4878548571919 \tabularnewline
25 & 31.73 & 30.8477877360469 & 0.882212263953098 \tabularnewline
26 & 32.17 & 28.1016469574699 & 4.06835304253013 \tabularnewline
27 & 31.47 & 27.5719397517628 & 3.8980602482372 \tabularnewline
28 & 30.97 & 28.9292665190692 & 2.0407334809308 \tabularnewline
29 & 30.81 & 32.2024902136208 & -1.39249021362077 \tabularnewline
30 & 30.72 & 31.6002580542083 & -0.88025805420829 \tabularnewline
31 & 28.24 & 30.2000710554035 & -1.9600710554035 \tabularnewline
32 & 28.09 & 29.3575106046771 & -1.26751060467715 \tabularnewline
33 & 29.11 & 27.8777573983111 & 1.2322426016889 \tabularnewline
34 & 29 & 27.148852337026 & 1.85114766297405 \tabularnewline
35 & 28.76 & 27.5909778409458 & 1.16902215905424 \tabularnewline
36 & 28.75 & 28.57926384188 & 0.170736158120026 \tabularnewline
37 & 28.45 & 29.6773383095344 & -1.22733830953438 \tabularnewline
38 & 29.34 & 29.6083859803816 & -0.268385980381595 \tabularnewline
39 & 26.84 & 26.5760077508388 & 0.263992249161187 \tabularnewline
40 & 23.7 & 25.0337992469397 & -1.33379924693974 \tabularnewline
41 & 23.15 & 26.2665355994828 & -3.11653559948277 \tabularnewline
42 & 21.71 & 25.0429320534846 & -3.33293205348463 \tabularnewline
43 & 20.88 & 21.6682672844676 & -0.788267284467572 \tabularnewline
44 & 20.04 & 20.976349410614 & -0.936349410613996 \tabularnewline
45 & 21.09 & 26.0555209774718 & -4.96552097747178 \tabularnewline
46 & 21.92 & 26.8959024241429 & -4.97590242414288 \tabularnewline
47 & 20.72 & 22.7818528243212 & -2.06185282432119 \tabularnewline
48 & 20.72 & 22.108670007448 & -1.388670007448 \tabularnewline
49 & 21.01 & 22.0160041410818 & -1.00600414108177 \tabularnewline
50 & 21.8 & 21.7327969133054 & 0.0672030866945611 \tabularnewline
51 & 21.6 & 21.5090118172196 & 0.090988182780358 \tabularnewline
52 & 20.38 & 20.2559767336578 & 0.124023266342199 \tabularnewline
53 & 21.2 & 19.8972117299966 & 1.30278827000335 \tabularnewline
54 & 19.87 & 19.0935000283105 & 0.776499971689537 \tabularnewline
55 & 19.05 & 17.3143569560585 & 1.73564304394148 \tabularnewline
56 & 20.01 & 18.2189688306566 & 1.79103116934342 \tabularnewline
57 & 19.15 & 19.8614665148512 & -0.711466514851196 \tabularnewline
58 & 19.43 & 19.9136447791532 & -0.483644779153202 \tabularnewline
59 & 19.44 & 20.6484420942687 & -1.20844209426866 \tabularnewline
60 & 19.4 & 19.8467473551887 & -0.446747355188734 \tabularnewline
61 & 19.15 & 19.524133776782 & -0.374133776781987 \tabularnewline
62 & 19.34 & 20.7688491891692 & -1.4288491891692 \tabularnewline
63 & 19.1 & 21.3877957304858 & -2.28779573048584 \tabularnewline
64 & 19.08 & 22.4303886883451 & -3.35038868834515 \tabularnewline
65 & 18.05 & 20.9452955701708 & -2.8952955701708 \tabularnewline
66 & 17.72 & 17.4270546116314 & 0.292945388368555 \tabularnewline
67 & 18.58 & 21.7805350529971 & -3.20053505299707 \tabularnewline
68 & 18.96 & 22.076136859665 & -3.116136859665 \tabularnewline
69 & 18.98 & 21.4498574998347 & -2.46985749983466 \tabularnewline
70 & 18.81 & 22.175593150229 & -3.36559315022896 \tabularnewline
71 & 19.43 & 22.118410354314 & -2.68841035431402 \tabularnewline
72 & 20.93 & 22.2669830630006 & -1.33698306300056 \tabularnewline
73 & 20.71 & 20.7753949513772 & -0.0653949513772438 \tabularnewline
74 & 22 & 22.1065554324606 & -0.106555432460635 \tabularnewline
75 & 21.52 & 21.4883124966697 & 0.0316875033302913 \tabularnewline
76 & 21.87 & 21.5916553334131 & 0.278344666586895 \tabularnewline
77 & 23.29 & 21.1689776860192 & 2.1210223139808 \tabularnewline
78 & 22.59 & 22.2629742044364 & 0.327025795563625 \tabularnewline
79 & 22.86 & 21.5461319757316 & 1.3138680242684 \tabularnewline
80 & 20.79 & 22.7490311280175 & -1.95903112801754 \tabularnewline
81 & 20.28 & 22.5893458376171 & -2.30934583761711 \tabularnewline
82 & 20.62 & 23.2484534889397 & -2.62845348893972 \tabularnewline
83 & 20.32 & 21.1437455243193 & -0.823745524319324 \tabularnewline
84 & 21.66 & 19.1770786038257 & 2.48292139617428 \tabularnewline
85 & 21.99 & 19.9903203891391 & 1.99967961086094 \tabularnewline
86 & 22.27 & 20.8373602137623 & 1.43263978623773 \tabularnewline
87 & 21.83 & 21.6734004288822 & 0.156599571117802 \tabularnewline
88 & 21.94 & 20.586647283145 & 1.35335271685497 \tabularnewline
89 & 20.91 & 19.5663730895176 & 1.34362691048243 \tabularnewline
90 & 20.4 & 20.1257456682241 & 0.274254331775864 \tabularnewline
91 & 20.22 & 20.2033235740998 & 0.0166764259001569 \tabularnewline
92 & 19.64 & 20.1633390205564 & -0.523339020556448 \tabularnewline
93 & 19.75 & 21.2217348905024 & -1.47173489050237 \tabularnewline
94 & 19.51 & 19.7645172287168 & -0.254517228716775 \tabularnewline
95 & 19.52 & 19.172829325792 & 0.347170674208016 \tabularnewline
96 & 19.48 & 17.8451756644144 & 1.63482433558558 \tabularnewline
97 & 19.88 & 16.1513536488972 & 3.72864635110283 \tabularnewline
98 & 18.97 & 17.1009644696147 & 1.86903553038533 \tabularnewline
99 & 19 & 19.4025823486571 & -0.40258234865713 \tabularnewline
100 & 19.32 & 18.6278129084381 & 0.692187091561855 \tabularnewline
101 & 19.5 & 17.9622623979537 & 1.53773760204627 \tabularnewline
102 & 23.22 & 20.6449064064865 & 2.57509359351354 \tabularnewline
103 & 22.56 & 19.5271080870128 & 3.03289191298719 \tabularnewline
104 & 21.94 & 19.1215154934419 & 2.8184845065581 \tabularnewline
105 & 21.11 & 21.5984473903717 & -0.488447390371747 \tabularnewline
106 & 21.21 & 22.4271860719972 & -1.2171860719972 \tabularnewline
107 & 21.18 & 23.3047945631239 & -2.12479456312392 \tabularnewline
108 & 21.25 & 23.4898014894148 & -2.23980148941477 \tabularnewline
109 & 21.17 & 20.8213559996551 & 0.348644000344935 \tabularnewline
110 & 20.47 & 22.3375132342062 & -1.86751323420619 \tabularnewline
111 & 19.99 & 21.577938961323 & -1.58793896132298 \tabularnewline
112 & 19.21 & 20.8078917613245 & -1.5978917613245 \tabularnewline
113 & 20.07 & 22.4285126712042 & -2.35851267120415 \tabularnewline
114 & 19.86 & 23.360717933786 & -3.50071793378603 \tabularnewline
115 & 22.36 & 24.0005112877411 & -1.64051128774111 \tabularnewline
116 & 22.17 & 25.9152691139489 & -3.74526911394887 \tabularnewline
117 & 23.56 & 26.3975009602537 & -2.83750096025372 \tabularnewline
118 & 22.92 & 25.1546537967082 & -2.23465379670824 \tabularnewline
119 & 23.1 & 25.8934174970121 & -2.79341749701209 \tabularnewline
120 & 24.32 & 25.7463629498924 & -1.42636294989242 \tabularnewline
121 & 23.99 & 24.3730646545255 & -0.383064654525504 \tabularnewline
122 & 25.94 & 24.2137911532975 & 1.72620884670246 \tabularnewline
123 & 26.15 & 24.104596042501 & 2.04540395749901 \tabularnewline
124 & 26.36 & 23.4111362487152 & 2.94886375128479 \tabularnewline
125 & 27.32 & 22.0754250458513 & 5.24457495414868 \tabularnewline
126 & 28 & 22.5159082208404 & 5.48409177915961 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203270&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]28.1697031753911[/C][C]-0.449703175391099[/C][/ROW]
[ROW][C]2[/C][C]26.9[/C][C]28.0903911808697[/C][C]-1.19039118086971[/C][/ROW]
[ROW][C]3[/C][C]25.86[/C][C]28.2916350720444[/C][C]-2.4316350720444[/C][/ROW]
[ROW][C]4[/C][C]26.81[/C][C]24.9727377555131[/C][C]1.83726224448694[/C][/ROW]
[ROW][C]5[/C][C]26.31[/C][C]26.1844233317711[/C][C]0.125576668228913[/C][/ROW]
[ROW][C]6[/C][C]27.1[/C][C]26.8443180609154[/C][C]0.255681939084597[/C][/ROW]
[ROW][C]7[/C][C]27[/C][C]27.72110888364[/C][C]-0.721108883639975[/C][/ROW]
[ROW][C]8[/C][C]27.4[/C][C]25.9050154369622[/C][C]1.49498456303781[/C][/ROW]
[ROW][C]9[/C][C]27.27[/C][C]28.0275730287995[/C][C]-0.757573028799536[/C][/ROW]
[ROW][C]10[/C][C]28.29[/C][C]28.6981408317578[/C][C]-0.408140831757817[/C][/ROW]
[ROW][C]11[/C][C]30.01[/C][C]29.6012636795211[/C][C]0.40873632047885[/C][/ROW]
[ROW][C]12[/C][C]31.41[/C][C]29.6658679166161[/C][C]1.7441320833839[/C][/ROW]
[ROW][C]13[/C][C]31.91[/C][C]28.4189657110396[/C][C]3.49103428896042[/C][/ROW]
[ROW][C]14[/C][C]31.6[/C][C]27.8065937588034[/C][C]3.79340624119665[/C][/ROW]
[ROW][C]15[/C][C]31.84[/C][C]29.7172757308568[/C][C]2.12272426914321[/C][/ROW]
[ROW][C]16[/C][C]33.05[/C][C]30.512524865666[/C][C]2.53747513433402[/C][/ROW]
[ROW][C]17[/C][C]32.06[/C][C]31.0596789763499[/C][C]1.00032102365005[/C][/ROW]
[ROW][C]18[/C][C]33.1[/C][C]31.4165749873284[/C][C]1.6834250126716[/C][/ROW]
[ROW][C]19[/C][C]32.23[/C][C]29.3130443393572[/C][C]2.91695566064283[/C][/ROW]
[ROW][C]20[/C][C]31.36[/C][C]29.1764634626522[/C][C]2.18353653734785[/C][/ROW]
[ROW][C]21[/C][C]31.09[/C][C]23.8441882825748[/C][C]7.24581171742516[/C][/ROW]
[ROW][C]22[/C][C]30.77[/C][C]30.2703227609832[/C][C]0.499677239016759[/C][/ROW]
[ROW][C]23[/C][C]31.2[/C][C]29.1387717600817[/C][C]2.06122823991834[/C][/ROW]
[ROW][C]24[/C][C]31.47[/C][C]28.9821451428081[/C][C]2.4878548571919[/C][/ROW]
[ROW][C]25[/C][C]31.73[/C][C]30.8477877360469[/C][C]0.882212263953098[/C][/ROW]
[ROW][C]26[/C][C]32.17[/C][C]28.1016469574699[/C][C]4.06835304253013[/C][/ROW]
[ROW][C]27[/C][C]31.47[/C][C]27.5719397517628[/C][C]3.8980602482372[/C][/ROW]
[ROW][C]28[/C][C]30.97[/C][C]28.9292665190692[/C][C]2.0407334809308[/C][/ROW]
[ROW][C]29[/C][C]30.81[/C][C]32.2024902136208[/C][C]-1.39249021362077[/C][/ROW]
[ROW][C]30[/C][C]30.72[/C][C]31.6002580542083[/C][C]-0.88025805420829[/C][/ROW]
[ROW][C]31[/C][C]28.24[/C][C]30.2000710554035[/C][C]-1.9600710554035[/C][/ROW]
[ROW][C]32[/C][C]28.09[/C][C]29.3575106046771[/C][C]-1.26751060467715[/C][/ROW]
[ROW][C]33[/C][C]29.11[/C][C]27.8777573983111[/C][C]1.2322426016889[/C][/ROW]
[ROW][C]34[/C][C]29[/C][C]27.148852337026[/C][C]1.85114766297405[/C][/ROW]
[ROW][C]35[/C][C]28.76[/C][C]27.5909778409458[/C][C]1.16902215905424[/C][/ROW]
[ROW][C]36[/C][C]28.75[/C][C]28.57926384188[/C][C]0.170736158120026[/C][/ROW]
[ROW][C]37[/C][C]28.45[/C][C]29.6773383095344[/C][C]-1.22733830953438[/C][/ROW]
[ROW][C]38[/C][C]29.34[/C][C]29.6083859803816[/C][C]-0.268385980381595[/C][/ROW]
[ROW][C]39[/C][C]26.84[/C][C]26.5760077508388[/C][C]0.263992249161187[/C][/ROW]
[ROW][C]40[/C][C]23.7[/C][C]25.0337992469397[/C][C]-1.33379924693974[/C][/ROW]
[ROW][C]41[/C][C]23.15[/C][C]26.2665355994828[/C][C]-3.11653559948277[/C][/ROW]
[ROW][C]42[/C][C]21.71[/C][C]25.0429320534846[/C][C]-3.33293205348463[/C][/ROW]
[ROW][C]43[/C][C]20.88[/C][C]21.6682672844676[/C][C]-0.788267284467572[/C][/ROW]
[ROW][C]44[/C][C]20.04[/C][C]20.976349410614[/C][C]-0.936349410613996[/C][/ROW]
[ROW][C]45[/C][C]21.09[/C][C]26.0555209774718[/C][C]-4.96552097747178[/C][/ROW]
[ROW][C]46[/C][C]21.92[/C][C]26.8959024241429[/C][C]-4.97590242414288[/C][/ROW]
[ROW][C]47[/C][C]20.72[/C][C]22.7818528243212[/C][C]-2.06185282432119[/C][/ROW]
[ROW][C]48[/C][C]20.72[/C][C]22.108670007448[/C][C]-1.388670007448[/C][/ROW]
[ROW][C]49[/C][C]21.01[/C][C]22.0160041410818[/C][C]-1.00600414108177[/C][/ROW]
[ROW][C]50[/C][C]21.8[/C][C]21.7327969133054[/C][C]0.0672030866945611[/C][/ROW]
[ROW][C]51[/C][C]21.6[/C][C]21.5090118172196[/C][C]0.090988182780358[/C][/ROW]
[ROW][C]52[/C][C]20.38[/C][C]20.2559767336578[/C][C]0.124023266342199[/C][/ROW]
[ROW][C]53[/C][C]21.2[/C][C]19.8972117299966[/C][C]1.30278827000335[/C][/ROW]
[ROW][C]54[/C][C]19.87[/C][C]19.0935000283105[/C][C]0.776499971689537[/C][/ROW]
[ROW][C]55[/C][C]19.05[/C][C]17.3143569560585[/C][C]1.73564304394148[/C][/ROW]
[ROW][C]56[/C][C]20.01[/C][C]18.2189688306566[/C][C]1.79103116934342[/C][/ROW]
[ROW][C]57[/C][C]19.15[/C][C]19.8614665148512[/C][C]-0.711466514851196[/C][/ROW]
[ROW][C]58[/C][C]19.43[/C][C]19.9136447791532[/C][C]-0.483644779153202[/C][/ROW]
[ROW][C]59[/C][C]19.44[/C][C]20.6484420942687[/C][C]-1.20844209426866[/C][/ROW]
[ROW][C]60[/C][C]19.4[/C][C]19.8467473551887[/C][C]-0.446747355188734[/C][/ROW]
[ROW][C]61[/C][C]19.15[/C][C]19.524133776782[/C][C]-0.374133776781987[/C][/ROW]
[ROW][C]62[/C][C]19.34[/C][C]20.7688491891692[/C][C]-1.4288491891692[/C][/ROW]
[ROW][C]63[/C][C]19.1[/C][C]21.3877957304858[/C][C]-2.28779573048584[/C][/ROW]
[ROW][C]64[/C][C]19.08[/C][C]22.4303886883451[/C][C]-3.35038868834515[/C][/ROW]
[ROW][C]65[/C][C]18.05[/C][C]20.9452955701708[/C][C]-2.8952955701708[/C][/ROW]
[ROW][C]66[/C][C]17.72[/C][C]17.4270546116314[/C][C]0.292945388368555[/C][/ROW]
[ROW][C]67[/C][C]18.58[/C][C]21.7805350529971[/C][C]-3.20053505299707[/C][/ROW]
[ROW][C]68[/C][C]18.96[/C][C]22.076136859665[/C][C]-3.116136859665[/C][/ROW]
[ROW][C]69[/C][C]18.98[/C][C]21.4498574998347[/C][C]-2.46985749983466[/C][/ROW]
[ROW][C]70[/C][C]18.81[/C][C]22.175593150229[/C][C]-3.36559315022896[/C][/ROW]
[ROW][C]71[/C][C]19.43[/C][C]22.118410354314[/C][C]-2.68841035431402[/C][/ROW]
[ROW][C]72[/C][C]20.93[/C][C]22.2669830630006[/C][C]-1.33698306300056[/C][/ROW]
[ROW][C]73[/C][C]20.71[/C][C]20.7753949513772[/C][C]-0.0653949513772438[/C][/ROW]
[ROW][C]74[/C][C]22[/C][C]22.1065554324606[/C][C]-0.106555432460635[/C][/ROW]
[ROW][C]75[/C][C]21.52[/C][C]21.4883124966697[/C][C]0.0316875033302913[/C][/ROW]
[ROW][C]76[/C][C]21.87[/C][C]21.5916553334131[/C][C]0.278344666586895[/C][/ROW]
[ROW][C]77[/C][C]23.29[/C][C]21.1689776860192[/C][C]2.1210223139808[/C][/ROW]
[ROW][C]78[/C][C]22.59[/C][C]22.2629742044364[/C][C]0.327025795563625[/C][/ROW]
[ROW][C]79[/C][C]22.86[/C][C]21.5461319757316[/C][C]1.3138680242684[/C][/ROW]
[ROW][C]80[/C][C]20.79[/C][C]22.7490311280175[/C][C]-1.95903112801754[/C][/ROW]
[ROW][C]81[/C][C]20.28[/C][C]22.5893458376171[/C][C]-2.30934583761711[/C][/ROW]
[ROW][C]82[/C][C]20.62[/C][C]23.2484534889397[/C][C]-2.62845348893972[/C][/ROW]
[ROW][C]83[/C][C]20.32[/C][C]21.1437455243193[/C][C]-0.823745524319324[/C][/ROW]
[ROW][C]84[/C][C]21.66[/C][C]19.1770786038257[/C][C]2.48292139617428[/C][/ROW]
[ROW][C]85[/C][C]21.99[/C][C]19.9903203891391[/C][C]1.99967961086094[/C][/ROW]
[ROW][C]86[/C][C]22.27[/C][C]20.8373602137623[/C][C]1.43263978623773[/C][/ROW]
[ROW][C]87[/C][C]21.83[/C][C]21.6734004288822[/C][C]0.156599571117802[/C][/ROW]
[ROW][C]88[/C][C]21.94[/C][C]20.586647283145[/C][C]1.35335271685497[/C][/ROW]
[ROW][C]89[/C][C]20.91[/C][C]19.5663730895176[/C][C]1.34362691048243[/C][/ROW]
[ROW][C]90[/C][C]20.4[/C][C]20.1257456682241[/C][C]0.274254331775864[/C][/ROW]
[ROW][C]91[/C][C]20.22[/C][C]20.2033235740998[/C][C]0.0166764259001569[/C][/ROW]
[ROW][C]92[/C][C]19.64[/C][C]20.1633390205564[/C][C]-0.523339020556448[/C][/ROW]
[ROW][C]93[/C][C]19.75[/C][C]21.2217348905024[/C][C]-1.47173489050237[/C][/ROW]
[ROW][C]94[/C][C]19.51[/C][C]19.7645172287168[/C][C]-0.254517228716775[/C][/ROW]
[ROW][C]95[/C][C]19.52[/C][C]19.172829325792[/C][C]0.347170674208016[/C][/ROW]
[ROW][C]96[/C][C]19.48[/C][C]17.8451756644144[/C][C]1.63482433558558[/C][/ROW]
[ROW][C]97[/C][C]19.88[/C][C]16.1513536488972[/C][C]3.72864635110283[/C][/ROW]
[ROW][C]98[/C][C]18.97[/C][C]17.1009644696147[/C][C]1.86903553038533[/C][/ROW]
[ROW][C]99[/C][C]19[/C][C]19.4025823486571[/C][C]-0.40258234865713[/C][/ROW]
[ROW][C]100[/C][C]19.32[/C][C]18.6278129084381[/C][C]0.692187091561855[/C][/ROW]
[ROW][C]101[/C][C]19.5[/C][C]17.9622623979537[/C][C]1.53773760204627[/C][/ROW]
[ROW][C]102[/C][C]23.22[/C][C]20.6449064064865[/C][C]2.57509359351354[/C][/ROW]
[ROW][C]103[/C][C]22.56[/C][C]19.5271080870128[/C][C]3.03289191298719[/C][/ROW]
[ROW][C]104[/C][C]21.94[/C][C]19.1215154934419[/C][C]2.8184845065581[/C][/ROW]
[ROW][C]105[/C][C]21.11[/C][C]21.5984473903717[/C][C]-0.488447390371747[/C][/ROW]
[ROW][C]106[/C][C]21.21[/C][C]22.4271860719972[/C][C]-1.2171860719972[/C][/ROW]
[ROW][C]107[/C][C]21.18[/C][C]23.3047945631239[/C][C]-2.12479456312392[/C][/ROW]
[ROW][C]108[/C][C]21.25[/C][C]23.4898014894148[/C][C]-2.23980148941477[/C][/ROW]
[ROW][C]109[/C][C]21.17[/C][C]20.8213559996551[/C][C]0.348644000344935[/C][/ROW]
[ROW][C]110[/C][C]20.47[/C][C]22.3375132342062[/C][C]-1.86751323420619[/C][/ROW]
[ROW][C]111[/C][C]19.99[/C][C]21.577938961323[/C][C]-1.58793896132298[/C][/ROW]
[ROW][C]112[/C][C]19.21[/C][C]20.8078917613245[/C][C]-1.5978917613245[/C][/ROW]
[ROW][C]113[/C][C]20.07[/C][C]22.4285126712042[/C][C]-2.35851267120415[/C][/ROW]
[ROW][C]114[/C][C]19.86[/C][C]23.360717933786[/C][C]-3.50071793378603[/C][/ROW]
[ROW][C]115[/C][C]22.36[/C][C]24.0005112877411[/C][C]-1.64051128774111[/C][/ROW]
[ROW][C]116[/C][C]22.17[/C][C]25.9152691139489[/C][C]-3.74526911394887[/C][/ROW]
[ROW][C]117[/C][C]23.56[/C][C]26.3975009602537[/C][C]-2.83750096025372[/C][/ROW]
[ROW][C]118[/C][C]22.92[/C][C]25.1546537967082[/C][C]-2.23465379670824[/C][/ROW]
[ROW][C]119[/C][C]23.1[/C][C]25.8934174970121[/C][C]-2.79341749701209[/C][/ROW]
[ROW][C]120[/C][C]24.32[/C][C]25.7463629498924[/C][C]-1.42636294989242[/C][/ROW]
[ROW][C]121[/C][C]23.99[/C][C]24.3730646545255[/C][C]-0.383064654525504[/C][/ROW]
[ROW][C]122[/C][C]25.94[/C][C]24.2137911532975[/C][C]1.72620884670246[/C][/ROW]
[ROW][C]123[/C][C]26.15[/C][C]24.104596042501[/C][C]2.04540395749901[/C][/ROW]
[ROW][C]124[/C][C]26.36[/C][C]23.4111362487152[/C][C]2.94886375128479[/C][/ROW]
[ROW][C]125[/C][C]27.32[/C][C]22.0754250458513[/C][C]5.24457495414868[/C][/ROW]
[ROW][C]126[/C][C]28[/C][C]22.5159082208404[/C][C]5.48409177915961[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203270&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203270&T=4

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
127.7228.1697031753911-0.449703175391099
226.928.0903911808697-1.19039118086971
325.8628.2916350720444-2.4316350720444
426.8124.97273775551311.83726224448694
526.3126.18442333177110.125576668228913
627.126.84431806091540.255681939084597
72727.72110888364-0.721108883639975
827.425.90501543696221.49498456303781
927.2728.0275730287995-0.757573028799536
1028.2928.6981408317578-0.408140831757817
1130.0129.60126367952110.40873632047885
1231.4129.66586791661611.7441320833839
1331.9128.41896571103963.49103428896042
1431.627.80659375880343.79340624119665
1531.8429.71727573085682.12272426914321
1633.0530.5125248656662.53747513433402
1732.0631.05967897634991.00032102365005
1833.131.41657498732841.6834250126716
1932.2329.31304433935722.91695566064283
2031.3629.17646346265222.18353653734785
2131.0923.84418828257487.24581171742516
2230.7730.27032276098320.499677239016759
2331.229.13877176008172.06122823991834
2431.4728.98214514280812.4878548571919
2531.7330.84778773604690.882212263953098
2632.1728.10164695746994.06835304253013
2731.4727.57193975176283.8980602482372
2830.9728.92926651906922.0407334809308
2930.8132.2024902136208-1.39249021362077
3030.7231.6002580542083-0.88025805420829
3128.2430.2000710554035-1.9600710554035
3228.0929.3575106046771-1.26751060467715
3329.1127.87775739831111.2322426016889
342927.1488523370261.85114766297405
3528.7627.59097784094581.16902215905424
3628.7528.579263841880.170736158120026
3728.4529.6773383095344-1.22733830953438
3829.3429.6083859803816-0.268385980381595
3926.8426.57600775083880.263992249161187
4023.725.0337992469397-1.33379924693974
4123.1526.2665355994828-3.11653559948277
4221.7125.0429320534846-3.33293205348463
4320.8821.6682672844676-0.788267284467572
4420.0420.976349410614-0.936349410613996
4521.0926.0555209774718-4.96552097747178
4621.9226.8959024241429-4.97590242414288
4720.7222.7818528243212-2.06185282432119
4820.7222.108670007448-1.388670007448
4921.0122.0160041410818-1.00600414108177
5021.821.73279691330540.0672030866945611
5121.621.50901181721960.090988182780358
5220.3820.25597673365780.124023266342199
5321.219.89721172999661.30278827000335
5419.8719.09350002831050.776499971689537
5519.0517.31435695605851.73564304394148
5620.0118.21896883065661.79103116934342
5719.1519.8614665148512-0.711466514851196
5819.4319.9136447791532-0.483644779153202
5919.4420.6484420942687-1.20844209426866
6019.419.8467473551887-0.446747355188734
6119.1519.524133776782-0.374133776781987
6219.3420.7688491891692-1.4288491891692
6319.121.3877957304858-2.28779573048584
6419.0822.4303886883451-3.35038868834515
6518.0520.9452955701708-2.8952955701708
6617.7217.42705461163140.292945388368555
6718.5821.7805350529971-3.20053505299707
6818.9622.076136859665-3.116136859665
6918.9821.4498574998347-2.46985749983466
7018.8122.175593150229-3.36559315022896
7119.4322.118410354314-2.68841035431402
7220.9322.2669830630006-1.33698306300056
7320.7120.7753949513772-0.0653949513772438
742222.1065554324606-0.106555432460635
7521.5221.48831249666970.0316875033302913
7621.8721.59165533341310.278344666586895
7723.2921.16897768601922.1210223139808
7822.5922.26297420443640.327025795563625
7922.8621.54613197573161.3138680242684
8020.7922.7490311280175-1.95903112801754
8120.2822.5893458376171-2.30934583761711
8220.6223.2484534889397-2.62845348893972
8320.3221.1437455243193-0.823745524319324
8421.6619.17707860382572.48292139617428
8521.9919.99032038913911.99967961086094
8622.2720.83736021376231.43263978623773
8721.8321.67340042888220.156599571117802
8821.9420.5866472831451.35335271685497
8920.9119.56637308951761.34362691048243
9020.420.12574566822410.274254331775864
9120.2220.20332357409980.0166764259001569
9219.6420.1633390205564-0.523339020556448
9319.7521.2217348905024-1.47173489050237
9419.5119.7645172287168-0.254517228716775
9519.5219.1728293257920.347170674208016
9619.4817.84517566441441.63482433558558
9719.8816.15135364889723.72864635110283
9818.9717.10096446961471.86903553038533
991919.4025823486571-0.40258234865713
10019.3218.62781290843810.692187091561855
10119.517.96226239795371.53773760204627
10223.2220.64490640648652.57509359351354
10322.5619.52710808701283.03289191298719
10421.9419.12151549344192.8184845065581
10521.1121.5984473903717-0.488447390371747
10621.2122.4271860719972-1.2171860719972
10721.1823.3047945631239-2.12479456312392
10821.2523.4898014894148-2.23980148941477
10921.1720.82135599965510.348644000344935
11020.4722.3375132342062-1.86751323420619
11119.9921.577938961323-1.58793896132298
11219.2120.8078917613245-1.5978917613245
11320.0722.4285126712042-2.35851267120415
11419.8623.360717933786-3.50071793378603
11522.3624.0005112877411-1.64051128774111
11622.1725.9152691139489-3.74526911394887
11723.5626.3975009602537-2.83750096025372
11822.9225.1546537967082-2.23465379670824
11923.125.8934174970121-2.79341749701209
12024.3225.7463629498924-1.42636294989242
12123.9924.3730646545255-0.383064654525504
12225.9424.21379115329751.72620884670246
12326.1524.1045960425012.04540395749901
12426.3623.41113624871522.94886375128479
12527.3222.07542504585135.24457495414868
1262822.51590822084045.48409177915961






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.09480841892492530.1896168378498510.905191581075075
120.03274036589449760.06548073178899520.967259634105502
130.01745390301629080.03490780603258160.982546096983709
140.006057917148753190.01211583429750640.993942082851247
150.003372488010388050.006744976020776110.996627511989612
160.001433513927269710.002867027854539420.99856648607273
170.000457483144704570.000914966289409140.999542516855295
180.0001968110883684610.0003936221767369210.999803188911632
198.67116116659371e-050.0001734232233318740.999913288388334
203.25567443872876e-056.51134887745752e-050.999967443255613
213.75542831774507e-057.51085663549014e-050.999962445716823
221.16401749873882e-052.32803499747764e-050.999988359825013
234.26095832693228e-068.52191665386455e-060.999995739041673
241.97009649500278e-063.94019299000555e-060.999998029903505
256.42801174882117e-071.28560234976423e-060.999999357198825
267.00428207822077e-050.0001400856415644150.999929957179218
276.15064146407661e-050.0001230128292815320.999938493585359
284.4982897102545e-058.99657942050899e-050.999955017102897
290.0001094975391607640.0002189950783215270.999890502460839
300.0002296276602177220.0004592553204354440.999770372339782
310.002983195219229510.005966390438459030.99701680478077
320.01151733393240280.02303466786480560.988482666067597
330.01601890915242010.03203781830484010.98398109084758
340.03544071953709350.0708814390741870.964559280462907
350.04269799334867860.08539598669735720.957302006651321
360.04568497516379910.09136995032759820.954315024836201
370.05051170501077850.1010234100215570.949488294989221
380.08767204333677530.1753440866735510.912327956663225
390.1186786255028340.2373572510056680.881321374497166
400.1822255380694560.3644510761389110.817774461930544
410.5804052694673790.8391894610652420.419594730532621
420.7547013237245210.4905973525509590.245298676275479
430.7371864535580840.5256270928838320.262813546441916
440.7142135413885120.5715729172229770.285786458611488
450.7864976981908670.4270046036182650.213502301809133
460.8577075608567250.2845848782865510.142292439143276
470.8380566615697240.3238866768605520.161943338430276
480.812690056780380.374619886439240.18730994321962
490.8058879734124010.3882240531751980.194112026587599
500.8086623855626330.3826752288747330.191337614437366
510.8190029431284370.3619941137431250.180997056871563
520.8117266981385880.3765466037228230.188273301861412
530.8429094909819490.3141810180361010.157090509018051
540.8424011083921540.3151977832156920.157598891607846
550.8141009815411490.3717980369177030.185899018458851
560.7810977421779910.4378045156440170.218902257822009
570.767902168042840.464195663914320.23209783195716
580.759774986214410.480450027571180.24022501378559
590.7627369300076340.4745261399847320.237263069992366
600.7612897317762990.4774205364474020.238710268223701
610.769266894587960.4614662108240790.23073310541204
620.7877514826455750.424497034708850.212248517354425
630.8264381189883860.3471237620232280.173561881011614
640.8846455102896350.230708979420730.115354489710365
650.9621303208646490.07573935827070260.0378696791353513
660.9982284515339310.003543096932138890.00177154846606945
670.997692100465480.004615799069040010.00230789953452
680.9973843649748570.005231270050286180.00261563502514309
690.9966543270312830.00669134593743440.0033456729687172
700.9955211752329790.008957649534042160.00447882476702108
710.9937306627363490.01253867452730160.00626933726365082
720.9928861137415040.0142277725169910.0071138862584955
730.9906700329152930.01865993416941470.00932996708470737
740.9876869302581780.02462613948364360.0123130697418218
750.9861281686217080.02774366275658320.0138718313782916
760.9843059059875470.03138818802490690.0156940940124535
770.9866546682793340.02669066344133180.0133453317206659
780.9836261451161440.03274770976771150.0163738548838558
790.983207304489260.03358539102147970.0167926955107399
800.9798901538406490.04021969231870120.0201098461593506
810.9730572706119810.05388545877603720.0269427293880186
820.9623660807969020.07526783840619690.0376339192030985
830.9505871738824640.0988256522350710.0494128261175355
840.9431918939179950.1136162121640110.0568081060820055
850.9422068615581710.1155862768836580.057793138441829
860.9363479318517320.1273041362965370.0636520681482685
870.9178504063044950.1642991873910090.0821495936955046
880.9005324051202960.1989351897594080.0994675948797041
890.8820279126608930.2359441746782140.117972087339107
900.8738716593777190.2522566812445620.126128340622281
910.8564751067274150.2870497865451690.143524893272585
920.8812782358997450.2374435282005090.118721764100255
930.8748174922922790.2503650154154430.125182507707721
940.8469706325580920.3060587348838150.153029367441907
950.8233646956706750.353270608658650.176635304329325
960.8138592492925880.3722815014148240.186140750707412
970.8110557660340840.3778884679318310.188944233965916
980.8659247502708390.2681504994583210.134075249729161
990.8467800110072880.3064399779854230.153219988992712
1000.8962631978454940.2074736043090120.103736802154506
1010.9816480947139840.03670381057203170.0183519052860159
1020.9721377785000110.05572444299997740.0278622214999887
1030.9619998254786230.07600034904275450.0380001745213773
1040.941121692813230.1177566143735410.0588783071867705
1050.9096018499523970.1807963000952060.0903981500476028
1060.87211230335920.25577539328160.1278876966408
1070.9456362854928720.1087274290142560.0543637145071282
1080.9153807288240150.1692385423519690.0846192711759846
1090.9524249285473490.09515014290530140.0475750714526507
1100.9434934234943540.1130131530112930.0565065765056464
1110.9120209687898350.175958062420330.0879790312101651
1120.8489241131556680.3021517736886640.151075886844332
1130.7862892791191420.4274214417617170.213710720880858
1140.830977174498160.338045651003680.16902282550184
1150.864481315102140.2710373697957210.13551868489786

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 & 0.0948084189249253 & 0.189616837849851 & 0.905191581075075 \tabularnewline
12 & 0.0327403658944976 & 0.0654807317889952 & 0.967259634105502 \tabularnewline
13 & 0.0174539030162908 & 0.0349078060325816 & 0.982546096983709 \tabularnewline
14 & 0.00605791714875319 & 0.0121158342975064 & 0.993942082851247 \tabularnewline
15 & 0.00337248801038805 & 0.00674497602077611 & 0.996627511989612 \tabularnewline
16 & 0.00143351392726971 & 0.00286702785453942 & 0.99856648607273 \tabularnewline
17 & 0.00045748314470457 & 0.00091496628940914 & 0.999542516855295 \tabularnewline
18 & 0.000196811088368461 & 0.000393622176736921 & 0.999803188911632 \tabularnewline
19 & 8.67116116659371e-05 & 0.000173423223331874 & 0.999913288388334 \tabularnewline
20 & 3.25567443872876e-05 & 6.51134887745752e-05 & 0.999967443255613 \tabularnewline
21 & 3.75542831774507e-05 & 7.51085663549014e-05 & 0.999962445716823 \tabularnewline
22 & 1.16401749873882e-05 & 2.32803499747764e-05 & 0.999988359825013 \tabularnewline
23 & 4.26095832693228e-06 & 8.52191665386455e-06 & 0.999995739041673 \tabularnewline
24 & 1.97009649500278e-06 & 3.94019299000555e-06 & 0.999998029903505 \tabularnewline
25 & 6.42801174882117e-07 & 1.28560234976423e-06 & 0.999999357198825 \tabularnewline
26 & 7.00428207822077e-05 & 0.000140085641564415 & 0.999929957179218 \tabularnewline
27 & 6.15064146407661e-05 & 0.000123012829281532 & 0.999938493585359 \tabularnewline
28 & 4.4982897102545e-05 & 8.99657942050899e-05 & 0.999955017102897 \tabularnewline
29 & 0.000109497539160764 & 0.000218995078321527 & 0.999890502460839 \tabularnewline
30 & 0.000229627660217722 & 0.000459255320435444 & 0.999770372339782 \tabularnewline
31 & 0.00298319521922951 & 0.00596639043845903 & 0.99701680478077 \tabularnewline
32 & 0.0115173339324028 & 0.0230346678648056 & 0.988482666067597 \tabularnewline
33 & 0.0160189091524201 & 0.0320378183048401 & 0.98398109084758 \tabularnewline
34 & 0.0354407195370935 & 0.070881439074187 & 0.964559280462907 \tabularnewline
35 & 0.0426979933486786 & 0.0853959866973572 & 0.957302006651321 \tabularnewline
36 & 0.0456849751637991 & 0.0913699503275982 & 0.954315024836201 \tabularnewline
37 & 0.0505117050107785 & 0.101023410021557 & 0.949488294989221 \tabularnewline
38 & 0.0876720433367753 & 0.175344086673551 & 0.912327956663225 \tabularnewline
39 & 0.118678625502834 & 0.237357251005668 & 0.881321374497166 \tabularnewline
40 & 0.182225538069456 & 0.364451076138911 & 0.817774461930544 \tabularnewline
41 & 0.580405269467379 & 0.839189461065242 & 0.419594730532621 \tabularnewline
42 & 0.754701323724521 & 0.490597352550959 & 0.245298676275479 \tabularnewline
43 & 0.737186453558084 & 0.525627092883832 & 0.262813546441916 \tabularnewline
44 & 0.714213541388512 & 0.571572917222977 & 0.285786458611488 \tabularnewline
45 & 0.786497698190867 & 0.427004603618265 & 0.213502301809133 \tabularnewline
46 & 0.857707560856725 & 0.284584878286551 & 0.142292439143276 \tabularnewline
47 & 0.838056661569724 & 0.323886676860552 & 0.161943338430276 \tabularnewline
48 & 0.81269005678038 & 0.37461988643924 & 0.18730994321962 \tabularnewline
49 & 0.805887973412401 & 0.388224053175198 & 0.194112026587599 \tabularnewline
50 & 0.808662385562633 & 0.382675228874733 & 0.191337614437366 \tabularnewline
51 & 0.819002943128437 & 0.361994113743125 & 0.180997056871563 \tabularnewline
52 & 0.811726698138588 & 0.376546603722823 & 0.188273301861412 \tabularnewline
53 & 0.842909490981949 & 0.314181018036101 & 0.157090509018051 \tabularnewline
54 & 0.842401108392154 & 0.315197783215692 & 0.157598891607846 \tabularnewline
55 & 0.814100981541149 & 0.371798036917703 & 0.185899018458851 \tabularnewline
56 & 0.781097742177991 & 0.437804515644017 & 0.218902257822009 \tabularnewline
57 & 0.76790216804284 & 0.46419566391432 & 0.23209783195716 \tabularnewline
58 & 0.75977498621441 & 0.48045002757118 & 0.24022501378559 \tabularnewline
59 & 0.762736930007634 & 0.474526139984732 & 0.237263069992366 \tabularnewline
60 & 0.761289731776299 & 0.477420536447402 & 0.238710268223701 \tabularnewline
61 & 0.76926689458796 & 0.461466210824079 & 0.23073310541204 \tabularnewline
62 & 0.787751482645575 & 0.42449703470885 & 0.212248517354425 \tabularnewline
63 & 0.826438118988386 & 0.347123762023228 & 0.173561881011614 \tabularnewline
64 & 0.884645510289635 & 0.23070897942073 & 0.115354489710365 \tabularnewline
65 & 0.962130320864649 & 0.0757393582707026 & 0.0378696791353513 \tabularnewline
66 & 0.998228451533931 & 0.00354309693213889 & 0.00177154846606945 \tabularnewline
67 & 0.99769210046548 & 0.00461579906904001 & 0.00230789953452 \tabularnewline
68 & 0.997384364974857 & 0.00523127005028618 & 0.00261563502514309 \tabularnewline
69 & 0.996654327031283 & 0.0066913459374344 & 0.0033456729687172 \tabularnewline
70 & 0.995521175232979 & 0.00895764953404216 & 0.00447882476702108 \tabularnewline
71 & 0.993730662736349 & 0.0125386745273016 & 0.00626933726365082 \tabularnewline
72 & 0.992886113741504 & 0.014227772516991 & 0.0071138862584955 \tabularnewline
73 & 0.990670032915293 & 0.0186599341694147 & 0.00932996708470737 \tabularnewline
74 & 0.987686930258178 & 0.0246261394836436 & 0.0123130697418218 \tabularnewline
75 & 0.986128168621708 & 0.0277436627565832 & 0.0138718313782916 \tabularnewline
76 & 0.984305905987547 & 0.0313881880249069 & 0.0156940940124535 \tabularnewline
77 & 0.986654668279334 & 0.0266906634413318 & 0.0133453317206659 \tabularnewline
78 & 0.983626145116144 & 0.0327477097677115 & 0.0163738548838558 \tabularnewline
79 & 0.98320730448926 & 0.0335853910214797 & 0.0167926955107399 \tabularnewline
80 & 0.979890153840649 & 0.0402196923187012 & 0.0201098461593506 \tabularnewline
81 & 0.973057270611981 & 0.0538854587760372 & 0.0269427293880186 \tabularnewline
82 & 0.962366080796902 & 0.0752678384061969 & 0.0376339192030985 \tabularnewline
83 & 0.950587173882464 & 0.098825652235071 & 0.0494128261175355 \tabularnewline
84 & 0.943191893917995 & 0.113616212164011 & 0.0568081060820055 \tabularnewline
85 & 0.942206861558171 & 0.115586276883658 & 0.057793138441829 \tabularnewline
86 & 0.936347931851732 & 0.127304136296537 & 0.0636520681482685 \tabularnewline
87 & 0.917850406304495 & 0.164299187391009 & 0.0821495936955046 \tabularnewline
88 & 0.900532405120296 & 0.198935189759408 & 0.0994675948797041 \tabularnewline
89 & 0.882027912660893 & 0.235944174678214 & 0.117972087339107 \tabularnewline
90 & 0.873871659377719 & 0.252256681244562 & 0.126128340622281 \tabularnewline
91 & 0.856475106727415 & 0.287049786545169 & 0.143524893272585 \tabularnewline
92 & 0.881278235899745 & 0.237443528200509 & 0.118721764100255 \tabularnewline
93 & 0.874817492292279 & 0.250365015415443 & 0.125182507707721 \tabularnewline
94 & 0.846970632558092 & 0.306058734883815 & 0.153029367441907 \tabularnewline
95 & 0.823364695670675 & 0.35327060865865 & 0.176635304329325 \tabularnewline
96 & 0.813859249292588 & 0.372281501414824 & 0.186140750707412 \tabularnewline
97 & 0.811055766034084 & 0.377888467931831 & 0.188944233965916 \tabularnewline
98 & 0.865924750270839 & 0.268150499458321 & 0.134075249729161 \tabularnewline
99 & 0.846780011007288 & 0.306439977985423 & 0.153219988992712 \tabularnewline
100 & 0.896263197845494 & 0.207473604309012 & 0.103736802154506 \tabularnewline
101 & 0.981648094713984 & 0.0367038105720317 & 0.0183519052860159 \tabularnewline
102 & 0.972137778500011 & 0.0557244429999774 & 0.0278622214999887 \tabularnewline
103 & 0.961999825478623 & 0.0760003490427545 & 0.0380001745213773 \tabularnewline
104 & 0.94112169281323 & 0.117756614373541 & 0.0588783071867705 \tabularnewline
105 & 0.909601849952397 & 0.180796300095206 & 0.0903981500476028 \tabularnewline
106 & 0.8721123033592 & 0.2557753932816 & 0.1278876966408 \tabularnewline
107 & 0.945636285492872 & 0.108727429014256 & 0.0543637145071282 \tabularnewline
108 & 0.915380728824015 & 0.169238542351969 & 0.0846192711759846 \tabularnewline
109 & 0.952424928547349 & 0.0951501429053014 & 0.0475750714526507 \tabularnewline
110 & 0.943493423494354 & 0.113013153011293 & 0.0565065765056464 \tabularnewline
111 & 0.912020968789835 & 0.17595806242033 & 0.0879790312101651 \tabularnewline
112 & 0.848924113155668 & 0.302151773688664 & 0.151075886844332 \tabularnewline
113 & 0.786289279119142 & 0.427421441761717 & 0.213710720880858 \tabularnewline
114 & 0.83097717449816 & 0.33804565100368 & 0.16902282550184 \tabularnewline
115 & 0.86448131510214 & 0.271037369795721 & 0.13551868489786 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203270&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]11[/C][C]0.0948084189249253[/C][C]0.189616837849851[/C][C]0.905191581075075[/C][/ROW]
[ROW][C]12[/C][C]0.0327403658944976[/C][C]0.0654807317889952[/C][C]0.967259634105502[/C][/ROW]
[ROW][C]13[/C][C]0.0174539030162908[/C][C]0.0349078060325816[/C][C]0.982546096983709[/C][/ROW]
[ROW][C]14[/C][C]0.00605791714875319[/C][C]0.0121158342975064[/C][C]0.993942082851247[/C][/ROW]
[ROW][C]15[/C][C]0.00337248801038805[/C][C]0.00674497602077611[/C][C]0.996627511989612[/C][/ROW]
[ROW][C]16[/C][C]0.00143351392726971[/C][C]0.00286702785453942[/C][C]0.99856648607273[/C][/ROW]
[ROW][C]17[/C][C]0.00045748314470457[/C][C]0.00091496628940914[/C][C]0.999542516855295[/C][/ROW]
[ROW][C]18[/C][C]0.000196811088368461[/C][C]0.000393622176736921[/C][C]0.999803188911632[/C][/ROW]
[ROW][C]19[/C][C]8.67116116659371e-05[/C][C]0.000173423223331874[/C][C]0.999913288388334[/C][/ROW]
[ROW][C]20[/C][C]3.25567443872876e-05[/C][C]6.51134887745752e-05[/C][C]0.999967443255613[/C][/ROW]
[ROW][C]21[/C][C]3.75542831774507e-05[/C][C]7.51085663549014e-05[/C][C]0.999962445716823[/C][/ROW]
[ROW][C]22[/C][C]1.16401749873882e-05[/C][C]2.32803499747764e-05[/C][C]0.999988359825013[/C][/ROW]
[ROW][C]23[/C][C]4.26095832693228e-06[/C][C]8.52191665386455e-06[/C][C]0.999995739041673[/C][/ROW]
[ROW][C]24[/C][C]1.97009649500278e-06[/C][C]3.94019299000555e-06[/C][C]0.999998029903505[/C][/ROW]
[ROW][C]25[/C][C]6.42801174882117e-07[/C][C]1.28560234976423e-06[/C][C]0.999999357198825[/C][/ROW]
[ROW][C]26[/C][C]7.00428207822077e-05[/C][C]0.000140085641564415[/C][C]0.999929957179218[/C][/ROW]
[ROW][C]27[/C][C]6.15064146407661e-05[/C][C]0.000123012829281532[/C][C]0.999938493585359[/C][/ROW]
[ROW][C]28[/C][C]4.4982897102545e-05[/C][C]8.99657942050899e-05[/C][C]0.999955017102897[/C][/ROW]
[ROW][C]29[/C][C]0.000109497539160764[/C][C]0.000218995078321527[/C][C]0.999890502460839[/C][/ROW]
[ROW][C]30[/C][C]0.000229627660217722[/C][C]0.000459255320435444[/C][C]0.999770372339782[/C][/ROW]
[ROW][C]31[/C][C]0.00298319521922951[/C][C]0.00596639043845903[/C][C]0.99701680478077[/C][/ROW]
[ROW][C]32[/C][C]0.0115173339324028[/C][C]0.0230346678648056[/C][C]0.988482666067597[/C][/ROW]
[ROW][C]33[/C][C]0.0160189091524201[/C][C]0.0320378183048401[/C][C]0.98398109084758[/C][/ROW]
[ROW][C]34[/C][C]0.0354407195370935[/C][C]0.070881439074187[/C][C]0.964559280462907[/C][/ROW]
[ROW][C]35[/C][C]0.0426979933486786[/C][C]0.0853959866973572[/C][C]0.957302006651321[/C][/ROW]
[ROW][C]36[/C][C]0.0456849751637991[/C][C]0.0913699503275982[/C][C]0.954315024836201[/C][/ROW]
[ROW][C]37[/C][C]0.0505117050107785[/C][C]0.101023410021557[/C][C]0.949488294989221[/C][/ROW]
[ROW][C]38[/C][C]0.0876720433367753[/C][C]0.175344086673551[/C][C]0.912327956663225[/C][/ROW]
[ROW][C]39[/C][C]0.118678625502834[/C][C]0.237357251005668[/C][C]0.881321374497166[/C][/ROW]
[ROW][C]40[/C][C]0.182225538069456[/C][C]0.364451076138911[/C][C]0.817774461930544[/C][/ROW]
[ROW][C]41[/C][C]0.580405269467379[/C][C]0.839189461065242[/C][C]0.419594730532621[/C][/ROW]
[ROW][C]42[/C][C]0.754701323724521[/C][C]0.490597352550959[/C][C]0.245298676275479[/C][/ROW]
[ROW][C]43[/C][C]0.737186453558084[/C][C]0.525627092883832[/C][C]0.262813546441916[/C][/ROW]
[ROW][C]44[/C][C]0.714213541388512[/C][C]0.571572917222977[/C][C]0.285786458611488[/C][/ROW]
[ROW][C]45[/C][C]0.786497698190867[/C][C]0.427004603618265[/C][C]0.213502301809133[/C][/ROW]
[ROW][C]46[/C][C]0.857707560856725[/C][C]0.284584878286551[/C][C]0.142292439143276[/C][/ROW]
[ROW][C]47[/C][C]0.838056661569724[/C][C]0.323886676860552[/C][C]0.161943338430276[/C][/ROW]
[ROW][C]48[/C][C]0.81269005678038[/C][C]0.37461988643924[/C][C]0.18730994321962[/C][/ROW]
[ROW][C]49[/C][C]0.805887973412401[/C][C]0.388224053175198[/C][C]0.194112026587599[/C][/ROW]
[ROW][C]50[/C][C]0.808662385562633[/C][C]0.382675228874733[/C][C]0.191337614437366[/C][/ROW]
[ROW][C]51[/C][C]0.819002943128437[/C][C]0.361994113743125[/C][C]0.180997056871563[/C][/ROW]
[ROW][C]52[/C][C]0.811726698138588[/C][C]0.376546603722823[/C][C]0.188273301861412[/C][/ROW]
[ROW][C]53[/C][C]0.842909490981949[/C][C]0.314181018036101[/C][C]0.157090509018051[/C][/ROW]
[ROW][C]54[/C][C]0.842401108392154[/C][C]0.315197783215692[/C][C]0.157598891607846[/C][/ROW]
[ROW][C]55[/C][C]0.814100981541149[/C][C]0.371798036917703[/C][C]0.185899018458851[/C][/ROW]
[ROW][C]56[/C][C]0.781097742177991[/C][C]0.437804515644017[/C][C]0.218902257822009[/C][/ROW]
[ROW][C]57[/C][C]0.76790216804284[/C][C]0.46419566391432[/C][C]0.23209783195716[/C][/ROW]
[ROW][C]58[/C][C]0.75977498621441[/C][C]0.48045002757118[/C][C]0.24022501378559[/C][/ROW]
[ROW][C]59[/C][C]0.762736930007634[/C][C]0.474526139984732[/C][C]0.237263069992366[/C][/ROW]
[ROW][C]60[/C][C]0.761289731776299[/C][C]0.477420536447402[/C][C]0.238710268223701[/C][/ROW]
[ROW][C]61[/C][C]0.76926689458796[/C][C]0.461466210824079[/C][C]0.23073310541204[/C][/ROW]
[ROW][C]62[/C][C]0.787751482645575[/C][C]0.42449703470885[/C][C]0.212248517354425[/C][/ROW]
[ROW][C]63[/C][C]0.826438118988386[/C][C]0.347123762023228[/C][C]0.173561881011614[/C][/ROW]
[ROW][C]64[/C][C]0.884645510289635[/C][C]0.23070897942073[/C][C]0.115354489710365[/C][/ROW]
[ROW][C]65[/C][C]0.962130320864649[/C][C]0.0757393582707026[/C][C]0.0378696791353513[/C][/ROW]
[ROW][C]66[/C][C]0.998228451533931[/C][C]0.00354309693213889[/C][C]0.00177154846606945[/C][/ROW]
[ROW][C]67[/C][C]0.99769210046548[/C][C]0.00461579906904001[/C][C]0.00230789953452[/C][/ROW]
[ROW][C]68[/C][C]0.997384364974857[/C][C]0.00523127005028618[/C][C]0.00261563502514309[/C][/ROW]
[ROW][C]69[/C][C]0.996654327031283[/C][C]0.0066913459374344[/C][C]0.0033456729687172[/C][/ROW]
[ROW][C]70[/C][C]0.995521175232979[/C][C]0.00895764953404216[/C][C]0.00447882476702108[/C][/ROW]
[ROW][C]71[/C][C]0.993730662736349[/C][C]0.0125386745273016[/C][C]0.00626933726365082[/C][/ROW]
[ROW][C]72[/C][C]0.992886113741504[/C][C]0.014227772516991[/C][C]0.0071138862584955[/C][/ROW]
[ROW][C]73[/C][C]0.990670032915293[/C][C]0.0186599341694147[/C][C]0.00932996708470737[/C][/ROW]
[ROW][C]74[/C][C]0.987686930258178[/C][C]0.0246261394836436[/C][C]0.0123130697418218[/C][/ROW]
[ROW][C]75[/C][C]0.986128168621708[/C][C]0.0277436627565832[/C][C]0.0138718313782916[/C][/ROW]
[ROW][C]76[/C][C]0.984305905987547[/C][C]0.0313881880249069[/C][C]0.0156940940124535[/C][/ROW]
[ROW][C]77[/C][C]0.986654668279334[/C][C]0.0266906634413318[/C][C]0.0133453317206659[/C][/ROW]
[ROW][C]78[/C][C]0.983626145116144[/C][C]0.0327477097677115[/C][C]0.0163738548838558[/C][/ROW]
[ROW][C]79[/C][C]0.98320730448926[/C][C]0.0335853910214797[/C][C]0.0167926955107399[/C][/ROW]
[ROW][C]80[/C][C]0.979890153840649[/C][C]0.0402196923187012[/C][C]0.0201098461593506[/C][/ROW]
[ROW][C]81[/C][C]0.973057270611981[/C][C]0.0538854587760372[/C][C]0.0269427293880186[/C][/ROW]
[ROW][C]82[/C][C]0.962366080796902[/C][C]0.0752678384061969[/C][C]0.0376339192030985[/C][/ROW]
[ROW][C]83[/C][C]0.950587173882464[/C][C]0.098825652235071[/C][C]0.0494128261175355[/C][/ROW]
[ROW][C]84[/C][C]0.943191893917995[/C][C]0.113616212164011[/C][C]0.0568081060820055[/C][/ROW]
[ROW][C]85[/C][C]0.942206861558171[/C][C]0.115586276883658[/C][C]0.057793138441829[/C][/ROW]
[ROW][C]86[/C][C]0.936347931851732[/C][C]0.127304136296537[/C][C]0.0636520681482685[/C][/ROW]
[ROW][C]87[/C][C]0.917850406304495[/C][C]0.164299187391009[/C][C]0.0821495936955046[/C][/ROW]
[ROW][C]88[/C][C]0.900532405120296[/C][C]0.198935189759408[/C][C]0.0994675948797041[/C][/ROW]
[ROW][C]89[/C][C]0.882027912660893[/C][C]0.235944174678214[/C][C]0.117972087339107[/C][/ROW]
[ROW][C]90[/C][C]0.873871659377719[/C][C]0.252256681244562[/C][C]0.126128340622281[/C][/ROW]
[ROW][C]91[/C][C]0.856475106727415[/C][C]0.287049786545169[/C][C]0.143524893272585[/C][/ROW]
[ROW][C]92[/C][C]0.881278235899745[/C][C]0.237443528200509[/C][C]0.118721764100255[/C][/ROW]
[ROW][C]93[/C][C]0.874817492292279[/C][C]0.250365015415443[/C][C]0.125182507707721[/C][/ROW]
[ROW][C]94[/C][C]0.846970632558092[/C][C]0.306058734883815[/C][C]0.153029367441907[/C][/ROW]
[ROW][C]95[/C][C]0.823364695670675[/C][C]0.35327060865865[/C][C]0.176635304329325[/C][/ROW]
[ROW][C]96[/C][C]0.813859249292588[/C][C]0.372281501414824[/C][C]0.186140750707412[/C][/ROW]
[ROW][C]97[/C][C]0.811055766034084[/C][C]0.377888467931831[/C][C]0.188944233965916[/C][/ROW]
[ROW][C]98[/C][C]0.865924750270839[/C][C]0.268150499458321[/C][C]0.134075249729161[/C][/ROW]
[ROW][C]99[/C][C]0.846780011007288[/C][C]0.306439977985423[/C][C]0.153219988992712[/C][/ROW]
[ROW][C]100[/C][C]0.896263197845494[/C][C]0.207473604309012[/C][C]0.103736802154506[/C][/ROW]
[ROW][C]101[/C][C]0.981648094713984[/C][C]0.0367038105720317[/C][C]0.0183519052860159[/C][/ROW]
[ROW][C]102[/C][C]0.972137778500011[/C][C]0.0557244429999774[/C][C]0.0278622214999887[/C][/ROW]
[ROW][C]103[/C][C]0.961999825478623[/C][C]0.0760003490427545[/C][C]0.0380001745213773[/C][/ROW]
[ROW][C]104[/C][C]0.94112169281323[/C][C]0.117756614373541[/C][C]0.0588783071867705[/C][/ROW]
[ROW][C]105[/C][C]0.909601849952397[/C][C]0.180796300095206[/C][C]0.0903981500476028[/C][/ROW]
[ROW][C]106[/C][C]0.8721123033592[/C][C]0.2557753932816[/C][C]0.1278876966408[/C][/ROW]
[ROW][C]107[/C][C]0.945636285492872[/C][C]0.108727429014256[/C][C]0.0543637145071282[/C][/ROW]
[ROW][C]108[/C][C]0.915380728824015[/C][C]0.169238542351969[/C][C]0.0846192711759846[/C][/ROW]
[ROW][C]109[/C][C]0.952424928547349[/C][C]0.0951501429053014[/C][C]0.0475750714526507[/C][/ROW]
[ROW][C]110[/C][C]0.943493423494354[/C][C]0.113013153011293[/C][C]0.0565065765056464[/C][/ROW]
[ROW][C]111[/C][C]0.912020968789835[/C][C]0.17595806242033[/C][C]0.0879790312101651[/C][/ROW]
[ROW][C]112[/C][C]0.848924113155668[/C][C]0.302151773688664[/C][C]0.151075886844332[/C][/ROW]
[ROW][C]113[/C][C]0.786289279119142[/C][C]0.427421441761717[/C][C]0.213710720880858[/C][/ROW]
[ROW][C]114[/C][C]0.83097717449816[/C][C]0.33804565100368[/C][C]0.16902282550184[/C][/ROW]
[ROW][C]115[/C][C]0.86448131510214[/C][C]0.271037369795721[/C][C]0.13551868489786[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203270&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203270&T=5

As an alternative you can also use a QR Code:  
QR Code


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.09480841892492530.1896168378498510.905191581075075
120.03274036589449760.06548073178899520.967259634105502
130.01745390301629080.03490780603258160.982546096983709
140.006057917148753190.01211583429750640.993942082851247
150.003372488010388050.006744976020776110.996627511989612
160.001433513927269710.002867027854539420.99856648607273
170.000457483144704570.000914966289409140.999542516855295
180.0001968110883684610.0003936221767369210.999803188911632
198.67116116659371e-050.0001734232233318740.999913288388334
203.25567443872876e-056.51134887745752e-050.999967443255613
213.75542831774507e-057.51085663549014e-050.999962445716823
221.16401749873882e-052.32803499747764e-050.999988359825013
234.26095832693228e-068.52191665386455e-060.999995739041673
241.97009649500278e-063.94019299000555e-060.999998029903505
256.42801174882117e-071.28560234976423e-060.999999357198825
267.00428207822077e-050.0001400856415644150.999929957179218
276.15064146407661e-050.0001230128292815320.999938493585359
284.4982897102545e-058.99657942050899e-050.999955017102897
290.0001094975391607640.0002189950783215270.999890502460839
300.0002296276602177220.0004592553204354440.999770372339782
310.002983195219229510.005966390438459030.99701680478077
320.01151733393240280.02303466786480560.988482666067597
330.01601890915242010.03203781830484010.98398109084758
340.03544071953709350.0708814390741870.964559280462907
350.04269799334867860.08539598669735720.957302006651321
360.04568497516379910.09136995032759820.954315024836201
370.05051170501077850.1010234100215570.949488294989221
380.08767204333677530.1753440866735510.912327956663225
390.1186786255028340.2373572510056680.881321374497166
400.1822255380694560.3644510761389110.817774461930544
410.5804052694673790.8391894610652420.419594730532621
420.7547013237245210.4905973525509590.245298676275479
430.7371864535580840.5256270928838320.262813546441916
440.7142135413885120.5715729172229770.285786458611488
450.7864976981908670.4270046036182650.213502301809133
460.8577075608567250.2845848782865510.142292439143276
470.8380566615697240.3238866768605520.161943338430276
480.812690056780380.374619886439240.18730994321962
490.8058879734124010.3882240531751980.194112026587599
500.8086623855626330.3826752288747330.191337614437366
510.8190029431284370.3619941137431250.180997056871563
520.8117266981385880.3765466037228230.188273301861412
530.8429094909819490.3141810180361010.157090509018051
540.8424011083921540.3151977832156920.157598891607846
550.8141009815411490.3717980369177030.185899018458851
560.7810977421779910.4378045156440170.218902257822009
570.767902168042840.464195663914320.23209783195716
580.759774986214410.480450027571180.24022501378559
590.7627369300076340.4745261399847320.237263069992366
600.7612897317762990.4774205364474020.238710268223701
610.769266894587960.4614662108240790.23073310541204
620.7877514826455750.424497034708850.212248517354425
630.8264381189883860.3471237620232280.173561881011614
640.8846455102896350.230708979420730.115354489710365
650.9621303208646490.07573935827070260.0378696791353513
660.9982284515339310.003543096932138890.00177154846606945
670.997692100465480.004615799069040010.00230789953452
680.9973843649748570.005231270050286180.00261563502514309
690.9966543270312830.00669134593743440.0033456729687172
700.9955211752329790.008957649534042160.00447882476702108
710.9937306627363490.01253867452730160.00626933726365082
720.9928861137415040.0142277725169910.0071138862584955
730.9906700329152930.01865993416941470.00932996708470737
740.9876869302581780.02462613948364360.0123130697418218
750.9861281686217080.02774366275658320.0138718313782916
760.9843059059875470.03138818802490690.0156940940124535
770.9866546682793340.02669066344133180.0133453317206659
780.9836261451161440.03274770976771150.0163738548838558
790.983207304489260.03358539102147970.0167926955107399
800.9798901538406490.04021969231870120.0201098461593506
810.9730572706119810.05388545877603720.0269427293880186
820.9623660807969020.07526783840619690.0376339192030985
830.9505871738824640.0988256522350710.0494128261175355
840.9431918939179950.1136162121640110.0568081060820055
850.9422068615581710.1155862768836580.057793138441829
860.9363479318517320.1273041362965370.0636520681482685
870.9178504063044950.1642991873910090.0821495936955046
880.9005324051202960.1989351897594080.0994675948797041
890.8820279126608930.2359441746782140.117972087339107
900.8738716593777190.2522566812445620.126128340622281
910.8564751067274150.2870497865451690.143524893272585
920.8812782358997450.2374435282005090.118721764100255
930.8748174922922790.2503650154154430.125182507707721
940.8469706325580920.3060587348838150.153029367441907
950.8233646956706750.353270608658650.176635304329325
960.8138592492925880.3722815014148240.186140750707412
970.8110557660340840.3778884679318310.188944233965916
980.8659247502708390.2681504994583210.134075249729161
990.8467800110072880.3064399779854230.153219988992712
1000.8962631978454940.2074736043090120.103736802154506
1010.9816480947139840.03670381057203170.0183519052860159
1020.9721377785000110.05572444299997740.0278622214999887
1030.9619998254786230.07600034904275450.0380001745213773
1040.941121692813230.1177566143735410.0588783071867705
1050.9096018499523970.1807963000952060.0903981500476028
1060.87211230335920.25577539328160.1278876966408
1070.9456362854928720.1087274290142560.0543637145071282
1080.9153807288240150.1692385423519690.0846192711759846
1090.9524249285473490.09515014290530140.0475750714526507
1100.9434934234943540.1130131530112930.0565065765056464
1110.9120209687898350.175958062420330.0879790312101651
1120.8489241131556680.3021517736886640.151075886844332
1130.7862892791191420.4274214417617170.213710720880858
1140.830977174498160.338045651003680.16902282550184
1150.864481315102140.2710373697957210.13551868489786






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level220.20952380952381NOK
5% type I error level370.352380952380952NOK
10% type I error level480.457142857142857NOK

\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 & 22 & 0.20952380952381 & NOK \tabularnewline
5% type I error level & 37 & 0.352380952380952 & NOK \tabularnewline
10% type I error level & 48 & 0.457142857142857 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203270&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]22[/C][C]0.20952380952381[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]37[/C][C]0.352380952380952[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]48[/C][C]0.457142857142857[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203270&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203270&T=6

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level220.20952380952381NOK
5% type I error level370.352380952380952NOK
10% type I error level480.457142857142857NOK


Figures (Output of Computation)

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Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}