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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationFri, 21 Dec 2012 03:31:34 -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/t13560787280r52lq6glnfvok0.htm/, Retrieved Thu, 28 Mar 2024 08:39:49 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=203273, Retrieved Thu, 28 Mar 2024 08:39:49 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact134
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] [d1865ed705b6ad9ba3d459a02c528b22]
- R  D                      [Multiple Regression] [] [2012-12-21 08:31:34] [14d0a7ecb926325afa0eb6a607fbc7a0] [Current]
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Dataseries X:
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 Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time11 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.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 & 11 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ fisher.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203273&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]11 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ fisher.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203273&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203273&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time11 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.wessa.net







Multiple Linear Regression - Estimated Regression Equation
FACEBOOK[t] = + 106.560092453682 -3.82743846730223e-09VOLUME[t] + 0.499709523869405LINKEDIN[t] -0.0401650455929862NASDAQ[t] -735.453385224349INF.CONS.CONF[t] -0.184480024207724FED[t] + 60.8387556425689FUNDS.RATE[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
FACEBOOK[t] =  +  106.560092453682 -3.82743846730223e-09VOLUME[t] +  0.499709523869405LINKEDIN[t] -0.0401650455929862NASDAQ[t] -735.453385224349INF.CONS.CONF[t] -0.184480024207724FED[t] +  60.8387556425689FUNDS.RATE[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203273&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]FACEBOOK[t] =  +  106.560092453682 -3.82743846730223e-09VOLUME[t] +  0.499709523869405LINKEDIN[t] -0.0401650455929862NASDAQ[t] -735.453385224349INF.CONS.CONF[t] -0.184480024207724FED[t] +  60.8387556425689FUNDS.RATE[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203273&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203273&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] = + 106.560092453682 -3.82743846730223e-09VOLUME[t] + 0.499709523869405LINKEDIN[t] -0.0401650455929862NASDAQ[t] -735.453385224349INF.CONS.CONF[t] -0.184480024207724FED[t] + 60.8387556425689FUNDS.RATE[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)106.56009245368210.6867279.971300
VOLUME-3.82743846730223e-090-0.68020.4976750.248838
LINKEDIN0.4997095238694050.0565038.84400
NASDAQ-0.04016504559298620.004704-8.538900
INF.CONS.CONF-735.453385224349142.627352-5.15651e-061e-06
FED-0.1844800242077240.066216-2.7860.0062120.003106
FUNDS.RATE60.838755642568915.3189423.97150.0001236.1e-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) & 106.560092453682 & 10.686727 & 9.9713 & 0 & 0 \tabularnewline
VOLUME & -3.82743846730223e-09 & 0 & -0.6802 & 0.497675 & 0.248838 \tabularnewline
LINKEDIN & 0.499709523869405 & 0.056503 & 8.844 & 0 & 0 \tabularnewline
NASDAQ & -0.0401650455929862 & 0.004704 & -8.5389 & 0 & 0 \tabularnewline
INF.CONS.CONF & -735.453385224349 & 142.627352 & -5.1565 & 1e-06 & 1e-06 \tabularnewline
FED & -0.184480024207724 & 0.066216 & -2.786 & 0.006212 & 0.003106 \tabularnewline
FUNDS.RATE & 60.8387556425689 & 15.318942 & 3.9715 & 0.000123 & 6.1e-05 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203273&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]106.560092453682[/C][C]10.686727[/C][C]9.9713[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]VOLUME[/C][C]-3.82743846730223e-09[/C][C]0[/C][C]-0.6802[/C][C]0.497675[/C][C]0.248838[/C][/ROW]
[ROW][C]LINKEDIN[/C][C]0.499709523869405[/C][C]0.056503[/C][C]8.844[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]NASDAQ[/C][C]-0.0401650455929862[/C][C]0.004704[/C][C]-8.5389[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]INF.CONS.CONF[/C][C]-735.453385224349[/C][C]142.627352[/C][C]-5.1565[/C][C]1e-06[/C][C]1e-06[/C][/ROW]
[ROW][C]FED[/C][C]-0.184480024207724[/C][C]0.066216[/C][C]-2.786[/C][C]0.006212[/C][C]0.003106[/C][/ROW]
[ROW][C]FUNDS.RATE[/C][C]60.8387556425689[/C][C]15.318942[/C][C]3.9715[/C][C]0.000123[/C][C]6.1e-05[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203273&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203273&T=2

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)106.56009245368210.6867279.971300
VOLUME-3.82743846730223e-090-0.68020.4976750.248838
LINKEDIN0.4997095238694050.0565038.84400
NASDAQ-0.04016504559298620.004704-8.538900
INF.CONS.CONF-735.453385224349142.627352-5.15651e-061e-06
FED-0.1844800242077240.066216-2.7860.0062120.003106
FUNDS.RATE60.838755642568915.3189423.97150.0001236.1e-05







Multiple Linear Regression - Regression Statistics
Multiple R0.873842612627142
R-squared0.76360091164303
Adjusted R-squared0.751681629877132
F-TEST (value)64.0643393319029
F-TEST (DF numerator)6
F-TEST (DF denominator)119
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.23244036439577
Sum Squared Residuals593.071007689439

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.873842612627142 \tabularnewline
R-squared & 0.76360091164303 \tabularnewline
Adjusted R-squared & 0.751681629877132 \tabularnewline
F-TEST (value) & 64.0643393319029 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 119 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.23244036439577 \tabularnewline
Sum Squared Residuals & 593.071007689439 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203273&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.873842612627142[/C][/ROW]
[ROW][C]R-squared[/C][C]0.76360091164303[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.751681629877132[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]64.0643393319029[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]119[/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]2.23244036439577[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]593.071007689439[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203273&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203273&T=3

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Regression Statistics
Multiple R0.873842612627142
R-squared0.76360091164303
Adjusted R-squared0.751681629877132
F-TEST (value)64.0643393319029
F-TEST (DF numerator)6
F-TEST (DF denominator)119
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.23244036439577
Sum Squared Residuals593.071007689439







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
127.7228.1757715830042-0.455771583004244
226.928.0963982592893-1.19639825928935
325.8628.29644022118-2.43644022117996
426.8124.97968399704051.83031600295945
526.3126.18973281575350.120267184246546
627.126.84745518843210.252544811567944
72727.7252643832042-0.725264383204229
827.425.91015670144171.48984329855833
927.2728.0306928033366-0.760692803336575
1028.2928.6992176872835-0.409217687283472
1130.0129.59958113415240.410418865847585
1231.4129.66250048084511.74749951915494
1331.9128.41599870443373.49400129556627
1431.627.80338972181253.79661027818753
1531.8429.71476226490642.12523773509355
1633.0530.50728143444222.54271856555781
1732.0631.05597554702941.00402445297063
1833.131.41091240511561.68908759488435
1932.2329.30955740122472.92044259877533
2031.3629.17442579214872.18557420785127
2131.0923.8444733934977.24552660650298
2230.7730.26962466608690.500375333913147
2331.229.13832802625082.06167197374915
2431.4728.98197928912532.48802071087469
2531.7330.84629499609640.883705003903623
2632.1728.1052466457014.06475335429899
2731.4727.57717093678973.89282906321026
2830.9728.93283677254472.03716322745534
2930.8132.2018875582021-1.39188755820209
3030.7231.5990557746801-0.879055774680141
3128.2430.2015120840272-1.96151208402721
3228.0929.3593478469227-1.26934784692266
3329.1127.88002661935181.2299733806482
342927.14984973976641.85015026023361
3528.7627.59319894001941.16680105998062
3628.7528.58196029013810.168039709861948
3728.4529.6799166550263-1.2299166550263
3829.3429.6115464400555-0.271546440055522
3926.8426.58231865999050.257681340009525
4023.725.0399529876776-1.33995298767764
4123.1526.2706059001926-3.12060590019264
4221.7125.0491409037633-3.33914090376329
4320.8821.6713051225068-0.791305122506769
4420.0420.9811746527953-0.941174652795303
4521.0926.0487802731609-4.9587802731609
4621.9226.8863069403358-4.96630694033582
4720.7222.777730169055-2.057730169055
4820.7222.1059046667128-1.38590466671285
4921.0122.0122321093334-1.00223210933344
5021.821.72946666808650.070533331913472
5121.621.50598437722150.0940156227785253
5220.3820.25548455952460.124515440475408
5321.219.89670586664081.30329413335922
5419.8719.09347424337940.776525756620592
5519.0517.31656345421841.73343654578156
5620.0118.21945644640211.79054355359795
5719.1519.8593620261574-0.709362026157445
5819.4319.9110132916207-0.48101329162074
5919.4420.6454336581908-1.20543365819085
6019.419.844306845708-0.444306845707959
6119.1519.522035187406-0.372035187406018
6219.3420.7644171653531-1.42441716535306
6319.121.3820327732817-2.28203277328165
6419.0822.4249557102993-3.3449557102993
6518.0520.9412511647947-2.89125116479474
6617.7217.44019702645940.279802973540633
6718.5821.7873862856612-3.20738628566116
6818.9622.0791053312595-3.11910533125949
6918.9821.4533720844894-2.47337208448938
7018.8122.1793767643029-3.36937676430293
7119.4322.1223991058029-2.69239910580293
7220.9322.2706594367269-1.3406594367269
7320.7120.7792907423805-0.069290742380535
742222.1072497849232-0.107249784923172
7521.5221.4894924650440.0305075349560362
7621.8721.5926179417450.277382058255039
7723.2921.17022273012432.11977726987574
7822.5922.26297766746050.327022332539546
7922.8621.54669356694991.31330643305009
8020.7922.74906444279-1.95906444279001
8120.2822.5911341851633-2.31113418516332
8220.6223.2506920977035-2.63069209770347
8320.3221.1465674997956-0.826567499795629
8421.6619.18224573589982.47775426410017
8521.9919.99933198089941.99066801910064
8622.2720.84490707286411.42509292713587
8721.8321.67875829559220.151241704407787
8821.9420.59282117375061.34717882624937
8920.9119.5749421158971.33505788410303
9020.420.13446977942970.265530220570268
9120.2220.21467645375220.00532354624782431
9219.6420.1552490637983-0.515249063798282
9319.7521.2118189565927-1.46181895659272
9419.5119.7573808559822-0.247380855982205
9519.5219.16574029540250.354259704597533
9619.4817.83858110791541.64141889208462
9719.8816.14723212164883.73276787835121
9818.9717.0967792112131.87322078878697
991919.3980680136815-0.398068013681477
10019.3218.62361526733340.696384732666559
10119.517.96076230630131.53923769369867
10223.2220.64098903268182.57901096731821
10322.5619.52357838177463.0364216182254
10421.9419.11821882620012.82178117379994
10521.1121.6130376966663-0.503037696666303
10621.2122.4222962583145-1.21229625831452
10721.1823.2997959336264-2.11979593362642
10821.2523.4840352821391-2.2340352821391
10921.1720.8191545257290.350845474271013
11020.4722.3363818989551-1.86638189895505
11119.9921.5801920822239-1.59019208222393
11219.2120.8110735966654-1.60107359666537
11320.0722.429049075556-2.35904907555602
11419.8623.3606623501511-3.5006623501511
11522.3624.0020845069808-1.64208450698081
11622.1725.9131274219904-3.74312742199043
11723.5626.3938849270176-2.83388492701763
11822.9225.1499662092401-2.22996620924015
11923.125.8871917686906-2.78719176869061
12024.3225.7401561993856-1.42015619938563
12123.9924.3669102781734-0.376910278173443
12225.9424.20796812203251.73203187796748
12326.1524.09918765391442.05081234608563
12426.3623.40553891354882.95446108645123
12527.3222.07138116207965.24861883792038
1262822.51140790535995.48859209464007

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 27.72 & 28.1757715830042 & -0.455771583004244 \tabularnewline
2 & 26.9 & 28.0963982592893 & -1.19639825928935 \tabularnewline
3 & 25.86 & 28.29644022118 & -2.43644022117996 \tabularnewline
4 & 26.81 & 24.9796839970405 & 1.83031600295945 \tabularnewline
5 & 26.31 & 26.1897328157535 & 0.120267184246546 \tabularnewline
6 & 27.1 & 26.8474551884321 & 0.252544811567944 \tabularnewline
7 & 27 & 27.7252643832042 & -0.725264383204229 \tabularnewline
8 & 27.4 & 25.9101567014417 & 1.48984329855833 \tabularnewline
9 & 27.27 & 28.0306928033366 & -0.760692803336575 \tabularnewline
10 & 28.29 & 28.6992176872835 & -0.409217687283472 \tabularnewline
11 & 30.01 & 29.5995811341524 & 0.410418865847585 \tabularnewline
12 & 31.41 & 29.6625004808451 & 1.74749951915494 \tabularnewline
13 & 31.91 & 28.4159987044337 & 3.49400129556627 \tabularnewline
14 & 31.6 & 27.8033897218125 & 3.79661027818753 \tabularnewline
15 & 31.84 & 29.7147622649064 & 2.12523773509355 \tabularnewline
16 & 33.05 & 30.5072814344422 & 2.54271856555781 \tabularnewline
17 & 32.06 & 31.0559755470294 & 1.00402445297063 \tabularnewline
18 & 33.1 & 31.4109124051156 & 1.68908759488435 \tabularnewline
19 & 32.23 & 29.3095574012247 & 2.92044259877533 \tabularnewline
20 & 31.36 & 29.1744257921487 & 2.18557420785127 \tabularnewline
21 & 31.09 & 23.844473393497 & 7.24552660650298 \tabularnewline
22 & 30.77 & 30.2696246660869 & 0.500375333913147 \tabularnewline
23 & 31.2 & 29.1383280262508 & 2.06167197374915 \tabularnewline
24 & 31.47 & 28.9819792891253 & 2.48802071087469 \tabularnewline
25 & 31.73 & 30.8462949960964 & 0.883705003903623 \tabularnewline
26 & 32.17 & 28.105246645701 & 4.06475335429899 \tabularnewline
27 & 31.47 & 27.5771709367897 & 3.89282906321026 \tabularnewline
28 & 30.97 & 28.9328367725447 & 2.03716322745534 \tabularnewline
29 & 30.81 & 32.2018875582021 & -1.39188755820209 \tabularnewline
30 & 30.72 & 31.5990557746801 & -0.879055774680141 \tabularnewline
31 & 28.24 & 30.2015120840272 & -1.96151208402721 \tabularnewline
32 & 28.09 & 29.3593478469227 & -1.26934784692266 \tabularnewline
33 & 29.11 & 27.8800266193518 & 1.2299733806482 \tabularnewline
34 & 29 & 27.1498497397664 & 1.85015026023361 \tabularnewline
35 & 28.76 & 27.5931989400194 & 1.16680105998062 \tabularnewline
36 & 28.75 & 28.5819602901381 & 0.168039709861948 \tabularnewline
37 & 28.45 & 29.6799166550263 & -1.2299166550263 \tabularnewline
38 & 29.34 & 29.6115464400555 & -0.271546440055522 \tabularnewline
39 & 26.84 & 26.5823186599905 & 0.257681340009525 \tabularnewline
40 & 23.7 & 25.0399529876776 & -1.33995298767764 \tabularnewline
41 & 23.15 & 26.2706059001926 & -3.12060590019264 \tabularnewline
42 & 21.71 & 25.0491409037633 & -3.33914090376329 \tabularnewline
43 & 20.88 & 21.6713051225068 & -0.791305122506769 \tabularnewline
44 & 20.04 & 20.9811746527953 & -0.941174652795303 \tabularnewline
45 & 21.09 & 26.0487802731609 & -4.9587802731609 \tabularnewline
46 & 21.92 & 26.8863069403358 & -4.96630694033582 \tabularnewline
47 & 20.72 & 22.777730169055 & -2.057730169055 \tabularnewline
48 & 20.72 & 22.1059046667128 & -1.38590466671285 \tabularnewline
49 & 21.01 & 22.0122321093334 & -1.00223210933344 \tabularnewline
50 & 21.8 & 21.7294666680865 & 0.070533331913472 \tabularnewline
51 & 21.6 & 21.5059843772215 & 0.0940156227785253 \tabularnewline
52 & 20.38 & 20.2554845595246 & 0.124515440475408 \tabularnewline
53 & 21.2 & 19.8967058666408 & 1.30329413335922 \tabularnewline
54 & 19.87 & 19.0934742433794 & 0.776525756620592 \tabularnewline
55 & 19.05 & 17.3165634542184 & 1.73343654578156 \tabularnewline
56 & 20.01 & 18.2194564464021 & 1.79054355359795 \tabularnewline
57 & 19.15 & 19.8593620261574 & -0.709362026157445 \tabularnewline
58 & 19.43 & 19.9110132916207 & -0.48101329162074 \tabularnewline
59 & 19.44 & 20.6454336581908 & -1.20543365819085 \tabularnewline
60 & 19.4 & 19.844306845708 & -0.444306845707959 \tabularnewline
61 & 19.15 & 19.522035187406 & -0.372035187406018 \tabularnewline
62 & 19.34 & 20.7644171653531 & -1.42441716535306 \tabularnewline
63 & 19.1 & 21.3820327732817 & -2.28203277328165 \tabularnewline
64 & 19.08 & 22.4249557102993 & -3.3449557102993 \tabularnewline
65 & 18.05 & 20.9412511647947 & -2.89125116479474 \tabularnewline
66 & 17.72 & 17.4401970264594 & 0.279802973540633 \tabularnewline
67 & 18.58 & 21.7873862856612 & -3.20738628566116 \tabularnewline
68 & 18.96 & 22.0791053312595 & -3.11910533125949 \tabularnewline
69 & 18.98 & 21.4533720844894 & -2.47337208448938 \tabularnewline
70 & 18.81 & 22.1793767643029 & -3.36937676430293 \tabularnewline
71 & 19.43 & 22.1223991058029 & -2.69239910580293 \tabularnewline
72 & 20.93 & 22.2706594367269 & -1.3406594367269 \tabularnewline
73 & 20.71 & 20.7792907423805 & -0.069290742380535 \tabularnewline
74 & 22 & 22.1072497849232 & -0.107249784923172 \tabularnewline
75 & 21.52 & 21.489492465044 & 0.0305075349560362 \tabularnewline
76 & 21.87 & 21.592617941745 & 0.277382058255039 \tabularnewline
77 & 23.29 & 21.1702227301243 & 2.11977726987574 \tabularnewline
78 & 22.59 & 22.2629776674605 & 0.327022332539546 \tabularnewline
79 & 22.86 & 21.5466935669499 & 1.31330643305009 \tabularnewline
80 & 20.79 & 22.74906444279 & -1.95906444279001 \tabularnewline
81 & 20.28 & 22.5911341851633 & -2.31113418516332 \tabularnewline
82 & 20.62 & 23.2506920977035 & -2.63069209770347 \tabularnewline
83 & 20.32 & 21.1465674997956 & -0.826567499795629 \tabularnewline
84 & 21.66 & 19.1822457358998 & 2.47775426410017 \tabularnewline
85 & 21.99 & 19.9993319808994 & 1.99066801910064 \tabularnewline
86 & 22.27 & 20.8449070728641 & 1.42509292713587 \tabularnewline
87 & 21.83 & 21.6787582955922 & 0.151241704407787 \tabularnewline
88 & 21.94 & 20.5928211737506 & 1.34717882624937 \tabularnewline
89 & 20.91 & 19.574942115897 & 1.33505788410303 \tabularnewline
90 & 20.4 & 20.1344697794297 & 0.265530220570268 \tabularnewline
91 & 20.22 & 20.2146764537522 & 0.00532354624782431 \tabularnewline
92 & 19.64 & 20.1552490637983 & -0.515249063798282 \tabularnewline
93 & 19.75 & 21.2118189565927 & -1.46181895659272 \tabularnewline
94 & 19.51 & 19.7573808559822 & -0.247380855982205 \tabularnewline
95 & 19.52 & 19.1657402954025 & 0.354259704597533 \tabularnewline
96 & 19.48 & 17.8385811079154 & 1.64141889208462 \tabularnewline
97 & 19.88 & 16.1472321216488 & 3.73276787835121 \tabularnewline
98 & 18.97 & 17.096779211213 & 1.87322078878697 \tabularnewline
99 & 19 & 19.3980680136815 & -0.398068013681477 \tabularnewline
100 & 19.32 & 18.6236152673334 & 0.696384732666559 \tabularnewline
101 & 19.5 & 17.9607623063013 & 1.53923769369867 \tabularnewline
102 & 23.22 & 20.6409890326818 & 2.57901096731821 \tabularnewline
103 & 22.56 & 19.5235783817746 & 3.0364216182254 \tabularnewline
104 & 21.94 & 19.1182188262001 & 2.82178117379994 \tabularnewline
105 & 21.11 & 21.6130376966663 & -0.503037696666303 \tabularnewline
106 & 21.21 & 22.4222962583145 & -1.21229625831452 \tabularnewline
107 & 21.18 & 23.2997959336264 & -2.11979593362642 \tabularnewline
108 & 21.25 & 23.4840352821391 & -2.2340352821391 \tabularnewline
109 & 21.17 & 20.819154525729 & 0.350845474271013 \tabularnewline
110 & 20.47 & 22.3363818989551 & -1.86638189895505 \tabularnewline
111 & 19.99 & 21.5801920822239 & -1.59019208222393 \tabularnewline
112 & 19.21 & 20.8110735966654 & -1.60107359666537 \tabularnewline
113 & 20.07 & 22.429049075556 & -2.35904907555602 \tabularnewline
114 & 19.86 & 23.3606623501511 & -3.5006623501511 \tabularnewline
115 & 22.36 & 24.0020845069808 & -1.64208450698081 \tabularnewline
116 & 22.17 & 25.9131274219904 & -3.74312742199043 \tabularnewline
117 & 23.56 & 26.3938849270176 & -2.83388492701763 \tabularnewline
118 & 22.92 & 25.1499662092401 & -2.22996620924015 \tabularnewline
119 & 23.1 & 25.8871917686906 & -2.78719176869061 \tabularnewline
120 & 24.32 & 25.7401561993856 & -1.42015619938563 \tabularnewline
121 & 23.99 & 24.3669102781734 & -0.376910278173443 \tabularnewline
122 & 25.94 & 24.2079681220325 & 1.73203187796748 \tabularnewline
123 & 26.15 & 24.0991876539144 & 2.05081234608563 \tabularnewline
124 & 26.36 & 23.4055389135488 & 2.95446108645123 \tabularnewline
125 & 27.32 & 22.0713811620796 & 5.24861883792038 \tabularnewline
126 & 28 & 22.5114079053599 & 5.48859209464007 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203273&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]28.1757715830042[/C][C]-0.455771583004244[/C][/ROW]
[ROW][C]2[/C][C]26.9[/C][C]28.0963982592893[/C][C]-1.19639825928935[/C][/ROW]
[ROW][C]3[/C][C]25.86[/C][C]28.29644022118[/C][C]-2.43644022117996[/C][/ROW]
[ROW][C]4[/C][C]26.81[/C][C]24.9796839970405[/C][C]1.83031600295945[/C][/ROW]
[ROW][C]5[/C][C]26.31[/C][C]26.1897328157535[/C][C]0.120267184246546[/C][/ROW]
[ROW][C]6[/C][C]27.1[/C][C]26.8474551884321[/C][C]0.252544811567944[/C][/ROW]
[ROW][C]7[/C][C]27[/C][C]27.7252643832042[/C][C]-0.725264383204229[/C][/ROW]
[ROW][C]8[/C][C]27.4[/C][C]25.9101567014417[/C][C]1.48984329855833[/C][/ROW]
[ROW][C]9[/C][C]27.27[/C][C]28.0306928033366[/C][C]-0.760692803336575[/C][/ROW]
[ROW][C]10[/C][C]28.29[/C][C]28.6992176872835[/C][C]-0.409217687283472[/C][/ROW]
[ROW][C]11[/C][C]30.01[/C][C]29.5995811341524[/C][C]0.410418865847585[/C][/ROW]
[ROW][C]12[/C][C]31.41[/C][C]29.6625004808451[/C][C]1.74749951915494[/C][/ROW]
[ROW][C]13[/C][C]31.91[/C][C]28.4159987044337[/C][C]3.49400129556627[/C][/ROW]
[ROW][C]14[/C][C]31.6[/C][C]27.8033897218125[/C][C]3.79661027818753[/C][/ROW]
[ROW][C]15[/C][C]31.84[/C][C]29.7147622649064[/C][C]2.12523773509355[/C][/ROW]
[ROW][C]16[/C][C]33.05[/C][C]30.5072814344422[/C][C]2.54271856555781[/C][/ROW]
[ROW][C]17[/C][C]32.06[/C][C]31.0559755470294[/C][C]1.00402445297063[/C][/ROW]
[ROW][C]18[/C][C]33.1[/C][C]31.4109124051156[/C][C]1.68908759488435[/C][/ROW]
[ROW][C]19[/C][C]32.23[/C][C]29.3095574012247[/C][C]2.92044259877533[/C][/ROW]
[ROW][C]20[/C][C]31.36[/C][C]29.1744257921487[/C][C]2.18557420785127[/C][/ROW]
[ROW][C]21[/C][C]31.09[/C][C]23.844473393497[/C][C]7.24552660650298[/C][/ROW]
[ROW][C]22[/C][C]30.77[/C][C]30.2696246660869[/C][C]0.500375333913147[/C][/ROW]
[ROW][C]23[/C][C]31.2[/C][C]29.1383280262508[/C][C]2.06167197374915[/C][/ROW]
[ROW][C]24[/C][C]31.47[/C][C]28.9819792891253[/C][C]2.48802071087469[/C][/ROW]
[ROW][C]25[/C][C]31.73[/C][C]30.8462949960964[/C][C]0.883705003903623[/C][/ROW]
[ROW][C]26[/C][C]32.17[/C][C]28.105246645701[/C][C]4.06475335429899[/C][/ROW]
[ROW][C]27[/C][C]31.47[/C][C]27.5771709367897[/C][C]3.89282906321026[/C][/ROW]
[ROW][C]28[/C][C]30.97[/C][C]28.9328367725447[/C][C]2.03716322745534[/C][/ROW]
[ROW][C]29[/C][C]30.81[/C][C]32.2018875582021[/C][C]-1.39188755820209[/C][/ROW]
[ROW][C]30[/C][C]30.72[/C][C]31.5990557746801[/C][C]-0.879055774680141[/C][/ROW]
[ROW][C]31[/C][C]28.24[/C][C]30.2015120840272[/C][C]-1.96151208402721[/C][/ROW]
[ROW][C]32[/C][C]28.09[/C][C]29.3593478469227[/C][C]-1.26934784692266[/C][/ROW]
[ROW][C]33[/C][C]29.11[/C][C]27.8800266193518[/C][C]1.2299733806482[/C][/ROW]
[ROW][C]34[/C][C]29[/C][C]27.1498497397664[/C][C]1.85015026023361[/C][/ROW]
[ROW][C]35[/C][C]28.76[/C][C]27.5931989400194[/C][C]1.16680105998062[/C][/ROW]
[ROW][C]36[/C][C]28.75[/C][C]28.5819602901381[/C][C]0.168039709861948[/C][/ROW]
[ROW][C]37[/C][C]28.45[/C][C]29.6799166550263[/C][C]-1.2299166550263[/C][/ROW]
[ROW][C]38[/C][C]29.34[/C][C]29.6115464400555[/C][C]-0.271546440055522[/C][/ROW]
[ROW][C]39[/C][C]26.84[/C][C]26.5823186599905[/C][C]0.257681340009525[/C][/ROW]
[ROW][C]40[/C][C]23.7[/C][C]25.0399529876776[/C][C]-1.33995298767764[/C][/ROW]
[ROW][C]41[/C][C]23.15[/C][C]26.2706059001926[/C][C]-3.12060590019264[/C][/ROW]
[ROW][C]42[/C][C]21.71[/C][C]25.0491409037633[/C][C]-3.33914090376329[/C][/ROW]
[ROW][C]43[/C][C]20.88[/C][C]21.6713051225068[/C][C]-0.791305122506769[/C][/ROW]
[ROW][C]44[/C][C]20.04[/C][C]20.9811746527953[/C][C]-0.941174652795303[/C][/ROW]
[ROW][C]45[/C][C]21.09[/C][C]26.0487802731609[/C][C]-4.9587802731609[/C][/ROW]
[ROW][C]46[/C][C]21.92[/C][C]26.8863069403358[/C][C]-4.96630694033582[/C][/ROW]
[ROW][C]47[/C][C]20.72[/C][C]22.777730169055[/C][C]-2.057730169055[/C][/ROW]
[ROW][C]48[/C][C]20.72[/C][C]22.1059046667128[/C][C]-1.38590466671285[/C][/ROW]
[ROW][C]49[/C][C]21.01[/C][C]22.0122321093334[/C][C]-1.00223210933344[/C][/ROW]
[ROW][C]50[/C][C]21.8[/C][C]21.7294666680865[/C][C]0.070533331913472[/C][/ROW]
[ROW][C]51[/C][C]21.6[/C][C]21.5059843772215[/C][C]0.0940156227785253[/C][/ROW]
[ROW][C]52[/C][C]20.38[/C][C]20.2554845595246[/C][C]0.124515440475408[/C][/ROW]
[ROW][C]53[/C][C]21.2[/C][C]19.8967058666408[/C][C]1.30329413335922[/C][/ROW]
[ROW][C]54[/C][C]19.87[/C][C]19.0934742433794[/C][C]0.776525756620592[/C][/ROW]
[ROW][C]55[/C][C]19.05[/C][C]17.3165634542184[/C][C]1.73343654578156[/C][/ROW]
[ROW][C]56[/C][C]20.01[/C][C]18.2194564464021[/C][C]1.79054355359795[/C][/ROW]
[ROW][C]57[/C][C]19.15[/C][C]19.8593620261574[/C][C]-0.709362026157445[/C][/ROW]
[ROW][C]58[/C][C]19.43[/C][C]19.9110132916207[/C][C]-0.48101329162074[/C][/ROW]
[ROW][C]59[/C][C]19.44[/C][C]20.6454336581908[/C][C]-1.20543365819085[/C][/ROW]
[ROW][C]60[/C][C]19.4[/C][C]19.844306845708[/C][C]-0.444306845707959[/C][/ROW]
[ROW][C]61[/C][C]19.15[/C][C]19.522035187406[/C][C]-0.372035187406018[/C][/ROW]
[ROW][C]62[/C][C]19.34[/C][C]20.7644171653531[/C][C]-1.42441716535306[/C][/ROW]
[ROW][C]63[/C][C]19.1[/C][C]21.3820327732817[/C][C]-2.28203277328165[/C][/ROW]
[ROW][C]64[/C][C]19.08[/C][C]22.4249557102993[/C][C]-3.3449557102993[/C][/ROW]
[ROW][C]65[/C][C]18.05[/C][C]20.9412511647947[/C][C]-2.89125116479474[/C][/ROW]
[ROW][C]66[/C][C]17.72[/C][C]17.4401970264594[/C][C]0.279802973540633[/C][/ROW]
[ROW][C]67[/C][C]18.58[/C][C]21.7873862856612[/C][C]-3.20738628566116[/C][/ROW]
[ROW][C]68[/C][C]18.96[/C][C]22.0791053312595[/C][C]-3.11910533125949[/C][/ROW]
[ROW][C]69[/C][C]18.98[/C][C]21.4533720844894[/C][C]-2.47337208448938[/C][/ROW]
[ROW][C]70[/C][C]18.81[/C][C]22.1793767643029[/C][C]-3.36937676430293[/C][/ROW]
[ROW][C]71[/C][C]19.43[/C][C]22.1223991058029[/C][C]-2.69239910580293[/C][/ROW]
[ROW][C]72[/C][C]20.93[/C][C]22.2706594367269[/C][C]-1.3406594367269[/C][/ROW]
[ROW][C]73[/C][C]20.71[/C][C]20.7792907423805[/C][C]-0.069290742380535[/C][/ROW]
[ROW][C]74[/C][C]22[/C][C]22.1072497849232[/C][C]-0.107249784923172[/C][/ROW]
[ROW][C]75[/C][C]21.52[/C][C]21.489492465044[/C][C]0.0305075349560362[/C][/ROW]
[ROW][C]76[/C][C]21.87[/C][C]21.592617941745[/C][C]0.277382058255039[/C][/ROW]
[ROW][C]77[/C][C]23.29[/C][C]21.1702227301243[/C][C]2.11977726987574[/C][/ROW]
[ROW][C]78[/C][C]22.59[/C][C]22.2629776674605[/C][C]0.327022332539546[/C][/ROW]
[ROW][C]79[/C][C]22.86[/C][C]21.5466935669499[/C][C]1.31330643305009[/C][/ROW]
[ROW][C]80[/C][C]20.79[/C][C]22.74906444279[/C][C]-1.95906444279001[/C][/ROW]
[ROW][C]81[/C][C]20.28[/C][C]22.5911341851633[/C][C]-2.31113418516332[/C][/ROW]
[ROW][C]82[/C][C]20.62[/C][C]23.2506920977035[/C][C]-2.63069209770347[/C][/ROW]
[ROW][C]83[/C][C]20.32[/C][C]21.1465674997956[/C][C]-0.826567499795629[/C][/ROW]
[ROW][C]84[/C][C]21.66[/C][C]19.1822457358998[/C][C]2.47775426410017[/C][/ROW]
[ROW][C]85[/C][C]21.99[/C][C]19.9993319808994[/C][C]1.99066801910064[/C][/ROW]
[ROW][C]86[/C][C]22.27[/C][C]20.8449070728641[/C][C]1.42509292713587[/C][/ROW]
[ROW][C]87[/C][C]21.83[/C][C]21.6787582955922[/C][C]0.151241704407787[/C][/ROW]
[ROW][C]88[/C][C]21.94[/C][C]20.5928211737506[/C][C]1.34717882624937[/C][/ROW]
[ROW][C]89[/C][C]20.91[/C][C]19.574942115897[/C][C]1.33505788410303[/C][/ROW]
[ROW][C]90[/C][C]20.4[/C][C]20.1344697794297[/C][C]0.265530220570268[/C][/ROW]
[ROW][C]91[/C][C]20.22[/C][C]20.2146764537522[/C][C]0.00532354624782431[/C][/ROW]
[ROW][C]92[/C][C]19.64[/C][C]20.1552490637983[/C][C]-0.515249063798282[/C][/ROW]
[ROW][C]93[/C][C]19.75[/C][C]21.2118189565927[/C][C]-1.46181895659272[/C][/ROW]
[ROW][C]94[/C][C]19.51[/C][C]19.7573808559822[/C][C]-0.247380855982205[/C][/ROW]
[ROW][C]95[/C][C]19.52[/C][C]19.1657402954025[/C][C]0.354259704597533[/C][/ROW]
[ROW][C]96[/C][C]19.48[/C][C]17.8385811079154[/C][C]1.64141889208462[/C][/ROW]
[ROW][C]97[/C][C]19.88[/C][C]16.1472321216488[/C][C]3.73276787835121[/C][/ROW]
[ROW][C]98[/C][C]18.97[/C][C]17.096779211213[/C][C]1.87322078878697[/C][/ROW]
[ROW][C]99[/C][C]19[/C][C]19.3980680136815[/C][C]-0.398068013681477[/C][/ROW]
[ROW][C]100[/C][C]19.32[/C][C]18.6236152673334[/C][C]0.696384732666559[/C][/ROW]
[ROW][C]101[/C][C]19.5[/C][C]17.9607623063013[/C][C]1.53923769369867[/C][/ROW]
[ROW][C]102[/C][C]23.22[/C][C]20.6409890326818[/C][C]2.57901096731821[/C][/ROW]
[ROW][C]103[/C][C]22.56[/C][C]19.5235783817746[/C][C]3.0364216182254[/C][/ROW]
[ROW][C]104[/C][C]21.94[/C][C]19.1182188262001[/C][C]2.82178117379994[/C][/ROW]
[ROW][C]105[/C][C]21.11[/C][C]21.6130376966663[/C][C]-0.503037696666303[/C][/ROW]
[ROW][C]106[/C][C]21.21[/C][C]22.4222962583145[/C][C]-1.21229625831452[/C][/ROW]
[ROW][C]107[/C][C]21.18[/C][C]23.2997959336264[/C][C]-2.11979593362642[/C][/ROW]
[ROW][C]108[/C][C]21.25[/C][C]23.4840352821391[/C][C]-2.2340352821391[/C][/ROW]
[ROW][C]109[/C][C]21.17[/C][C]20.819154525729[/C][C]0.350845474271013[/C][/ROW]
[ROW][C]110[/C][C]20.47[/C][C]22.3363818989551[/C][C]-1.86638189895505[/C][/ROW]
[ROW][C]111[/C][C]19.99[/C][C]21.5801920822239[/C][C]-1.59019208222393[/C][/ROW]
[ROW][C]112[/C][C]19.21[/C][C]20.8110735966654[/C][C]-1.60107359666537[/C][/ROW]
[ROW][C]113[/C][C]20.07[/C][C]22.429049075556[/C][C]-2.35904907555602[/C][/ROW]
[ROW][C]114[/C][C]19.86[/C][C]23.3606623501511[/C][C]-3.5006623501511[/C][/ROW]
[ROW][C]115[/C][C]22.36[/C][C]24.0020845069808[/C][C]-1.64208450698081[/C][/ROW]
[ROW][C]116[/C][C]22.17[/C][C]25.9131274219904[/C][C]-3.74312742199043[/C][/ROW]
[ROW][C]117[/C][C]23.56[/C][C]26.3938849270176[/C][C]-2.83388492701763[/C][/ROW]
[ROW][C]118[/C][C]22.92[/C][C]25.1499662092401[/C][C]-2.22996620924015[/C][/ROW]
[ROW][C]119[/C][C]23.1[/C][C]25.8871917686906[/C][C]-2.78719176869061[/C][/ROW]
[ROW][C]120[/C][C]24.32[/C][C]25.7401561993856[/C][C]-1.42015619938563[/C][/ROW]
[ROW][C]121[/C][C]23.99[/C][C]24.3669102781734[/C][C]-0.376910278173443[/C][/ROW]
[ROW][C]122[/C][C]25.94[/C][C]24.2079681220325[/C][C]1.73203187796748[/C][/ROW]
[ROW][C]123[/C][C]26.15[/C][C]24.0991876539144[/C][C]2.05081234608563[/C][/ROW]
[ROW][C]124[/C][C]26.36[/C][C]23.4055389135488[/C][C]2.95446108645123[/C][/ROW]
[ROW][C]125[/C][C]27.32[/C][C]22.0713811620796[/C][C]5.24861883792038[/C][/ROW]
[ROW][C]126[/C][C]28[/C][C]22.5114079053599[/C][C]5.48859209464007[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203273&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203273&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
127.7228.1757715830042-0.455771583004244
226.928.0963982592893-1.19639825928935
325.8628.29644022118-2.43644022117996
426.8124.97968399704051.83031600295945
526.3126.18973281575350.120267184246546
627.126.84745518843210.252544811567944
72727.7252643832042-0.725264383204229
827.425.91015670144171.48984329855833
927.2728.0306928033366-0.760692803336575
1028.2928.6992176872835-0.409217687283472
1130.0129.59958113415240.410418865847585
1231.4129.66250048084511.74749951915494
1331.9128.41599870443373.49400129556627
1431.627.80338972181253.79661027818753
1531.8429.71476226490642.12523773509355
1633.0530.50728143444222.54271856555781
1732.0631.05597554702941.00402445297063
1833.131.41091240511561.68908759488435
1932.2329.30955740122472.92044259877533
2031.3629.17442579214872.18557420785127
2131.0923.8444733934977.24552660650298
2230.7730.26962466608690.500375333913147
2331.229.13832802625082.06167197374915
2431.4728.98197928912532.48802071087469
2531.7330.84629499609640.883705003903623
2632.1728.1052466457014.06475335429899
2731.4727.57717093678973.89282906321026
2830.9728.93283677254472.03716322745534
2930.8132.2018875582021-1.39188755820209
3030.7231.5990557746801-0.879055774680141
3128.2430.2015120840272-1.96151208402721
3228.0929.3593478469227-1.26934784692266
3329.1127.88002661935181.2299733806482
342927.14984973976641.85015026023361
3528.7627.59319894001941.16680105998062
3628.7528.58196029013810.168039709861948
3728.4529.6799166550263-1.2299166550263
3829.3429.6115464400555-0.271546440055522
3926.8426.58231865999050.257681340009525
4023.725.0399529876776-1.33995298767764
4123.1526.2706059001926-3.12060590019264
4221.7125.0491409037633-3.33914090376329
4320.8821.6713051225068-0.791305122506769
4420.0420.9811746527953-0.941174652795303
4521.0926.0487802731609-4.9587802731609
4621.9226.8863069403358-4.96630694033582
4720.7222.777730169055-2.057730169055
4820.7222.1059046667128-1.38590466671285
4921.0122.0122321093334-1.00223210933344
5021.821.72946666808650.070533331913472
5121.621.50598437722150.0940156227785253
5220.3820.25548455952460.124515440475408
5321.219.89670586664081.30329413335922
5419.8719.09347424337940.776525756620592
5519.0517.31656345421841.73343654578156
5620.0118.21945644640211.79054355359795
5719.1519.8593620261574-0.709362026157445
5819.4319.9110132916207-0.48101329162074
5919.4420.6454336581908-1.20543365819085
6019.419.844306845708-0.444306845707959
6119.1519.522035187406-0.372035187406018
6219.3420.7644171653531-1.42441716535306
6319.121.3820327732817-2.28203277328165
6419.0822.4249557102993-3.3449557102993
6518.0520.9412511647947-2.89125116479474
6617.7217.44019702645940.279802973540633
6718.5821.7873862856612-3.20738628566116
6818.9622.0791053312595-3.11910533125949
6918.9821.4533720844894-2.47337208448938
7018.8122.1793767643029-3.36937676430293
7119.4322.1223991058029-2.69239910580293
7220.9322.2706594367269-1.3406594367269
7320.7120.7792907423805-0.069290742380535
742222.1072497849232-0.107249784923172
7521.5221.4894924650440.0305075349560362
7621.8721.5926179417450.277382058255039
7723.2921.17022273012432.11977726987574
7822.5922.26297766746050.327022332539546
7922.8621.54669356694991.31330643305009
8020.7922.74906444279-1.95906444279001
8120.2822.5911341851633-2.31113418516332
8220.6223.2506920977035-2.63069209770347
8320.3221.1465674997956-0.826567499795629
8421.6619.18224573589982.47775426410017
8521.9919.99933198089941.99066801910064
8622.2720.84490707286411.42509292713587
8721.8321.67875829559220.151241704407787
8821.9420.59282117375061.34717882624937
8920.9119.5749421158971.33505788410303
9020.420.13446977942970.265530220570268
9120.2220.21467645375220.00532354624782431
9219.6420.1552490637983-0.515249063798282
9319.7521.2118189565927-1.46181895659272
9419.5119.7573808559822-0.247380855982205
9519.5219.16574029540250.354259704597533
9619.4817.83858110791541.64141889208462
9719.8816.14723212164883.73276787835121
9818.9717.0967792112131.87322078878697
991919.3980680136815-0.398068013681477
10019.3218.62361526733340.696384732666559
10119.517.96076230630131.53923769369867
10223.2220.64098903268182.57901096731821
10322.5619.52357838177463.0364216182254
10421.9419.11821882620012.82178117379994
10521.1121.6130376966663-0.503037696666303
10621.2122.4222962583145-1.21229625831452
10721.1823.2997959336264-2.11979593362642
10821.2523.4840352821391-2.2340352821391
10921.1720.8191545257290.350845474271013
11020.4722.3363818989551-1.86638189895505
11119.9921.5801920822239-1.59019208222393
11219.2120.8110735966654-1.60107359666537
11320.0722.429049075556-2.35904907555602
11419.8623.3606623501511-3.5006623501511
11522.3624.0020845069808-1.64208450698081
11622.1725.9131274219904-3.74312742199043
11723.5626.3938849270176-2.83388492701763
11822.9225.1499662092401-2.22996620924015
11923.125.8871917686906-2.78719176869061
12024.3225.7401561993856-1.42015619938563
12123.9924.3669102781734-0.376910278173443
12225.9424.20796812203251.73203187796748
12326.1524.09918765391442.05081234608563
12426.3623.40553891354882.95446108645123
12527.3222.07138116207965.24861883792038
1262822.51140790535995.48859209464007







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.06762306100865930.1352461220173190.932376938991341
110.03357577498687420.06715154997374840.966424225013126
120.01062512953042290.02125025906084580.989374870469577
130.005891575901789550.01178315180357910.99410842409821
140.001944584584608150.003889169169216290.998055415415392
150.001840704218771730.003681408437543460.998159295781228
160.0006617409888083250.001323481977616650.999338259011192
170.0002200347280787140.0004400694561574280.999779965271921
180.0001227544869534540.0002455089739069090.999877245513047
196.28315110341741e-050.0001256630220683480.999937168488966
202.504632782798e-055.00926556559601e-050.999974953672172
215.78253503715609e-050.0001156507007431220.999942174649628
221.96236995548957e-053.92473991097914e-050.999980376300445
237.6055351612532e-061.52110703225064e-050.999992394464839
243.70953638145278e-067.41907276290556e-060.999996290463619
251.41534600922844e-062.83069201845688e-060.999998584653991
260.0002464266661304860.0004928533322609710.999753573333869
270.003338666802255080.006677333604510160.996661333197745
280.003272551646722660.006545103293445320.996727448353277
290.002867468659965810.005734937319931610.997132531340034
300.002321788031630230.004643576063260460.99767821196837
310.005962266992698940.01192453398539790.994037733007301
320.01326367004943270.02652734009886540.986736329950567
330.01587393016240880.03174786032481760.984126069837591
340.03597066629257270.07194133258514550.964029333707427
350.04481765648512860.08963531297025720.955182343514871
360.0482709674431430.09654193488628610.951729032556857
370.05267735181331510.105354703626630.947322648186685
380.08344581469677250.1668916293935450.916554185303227
390.09734148499410130.1946829699882030.902658515005899
400.1431243526002070.2862487052004150.856875647399793
410.4861471721274990.9722943442549970.513852827872501
420.6798017802918190.6403964394163610.320198219708181
430.6440716879291960.7118566241416090.355928312070804
440.59862984969430.80274030061140.4013701503057
450.7032930094868680.5934139810262630.296706990513132
460.807117743836840.3857645123263210.19288225616316
470.7834759798598790.4330480402802410.21652402014012
480.7517971154401410.4964057691197180.248202884559859
490.7304531196494860.5390937607010280.269546880350514
500.7359234331106260.5281531337787490.264076566889374
510.7474813897857960.5050372204284070.252518610214204
520.7334892137549990.5330215724900020.266510786245001
530.767607101712640.4647857965747190.23239289828736
540.7788241670727470.4423516658545070.221175832927253
550.7531690981399320.4936618037201360.246830901860068
560.715279382874470.5694412342510610.28472061712553
570.6913326408412130.6173347183175740.308667359158787
580.6728296880222540.6543406239554930.327170311977746
590.6661723816504470.6676552366991060.333827618349553
600.655102748091310.689794503817380.34489725190869
610.6521849164374380.6956301671251230.347815083562562
620.6726071363974790.6547857272050410.327392863602521
630.7275897441874230.5448205116251540.272410255812577
640.8144698187055560.3710603625888880.185530181294444
650.9496532048415610.1006935903168780.0503467951584392
660.9995242790054350.0009514419891294180.000475720994564709
670.9994247169906620.00115056601867580.0005752830093379
680.9993124056273480.001375188745304740.000687594372652372
690.9990944173644930.001811165271013080.000905582635506538
700.9987709031712210.002458193657557550.00122909682877878
710.9982487675740750.003502464851850220.00175123242592511
720.9979969847039930.004006030592014650.00200301529600733
730.9974921920578180.005015615884364140.00250780794218207
740.9967177173581210.006564565283758380.00328228264187919
750.9955145324206650.008970935158670220.00448546757933511
760.9940698856474790.0118602287050420.00593011435252102
770.994180455065360.01163908986928090.00581954493464047
780.9922067225233840.01558655495323270.00779327747661636
790.9915568279307260.01688634413854870.00844317206927437
800.9895397589198110.02092048216037890.0104602410801894
810.9860658647923360.02786827041532830.0139341352076641
820.9810359017267710.03792819654645820.0189640982732291
830.9737436327828150.0525127344343710.0262563672171855
840.9690321022796250.06193579544074940.0309678977203747
850.9636724602958530.0726550794082950.0363275397041475
860.9545095023375980.09098099532480490.0454904976624025
870.9412342749104280.1175314501791440.058765725089572
880.9314869765758570.1370260468482860.0685130234241432
890.9257716214738180.1484567570523640.0742283785261818
900.9289240407353580.1421519185292850.0710759592646423
910.9091676518876380.1816646962247230.0908323481123616
920.8960853465116040.2078293069767920.103914653488396
930.8962108591269280.2075782817461440.103789140873072
940.8725835657625720.2548328684748560.127416434237428
950.8521612593151260.2956774813697470.147838740684874
960.8434287363317520.3131425273364960.156571263668248
970.8410655613594170.3178688772811660.158934438640583
980.8855911098365030.2288177803269930.114408890163497
990.8733495522878540.2533008954242920.126650447712146
1000.9113231624566720.1773536750866560.0886768375433278
1010.9716826574760130.05663468504797330.0283173425239866
1020.9738600166472370.05227996670552580.0261399833527629
1030.9705373538951380.05892529220972410.029462646104862
1040.9607840336157320.07843193276853650.0392159663842682
1050.9388603641927930.1222792716144140.0611396358072072
1060.9101524993453370.1796950013093260.0898475006546628
1070.9660393243091310.06792135138173890.0339606756908694
1080.9456440544549890.1087118910900210.0543559455450106
1090.9728378308848670.05432433823026680.0271621691151334
1100.968528566060260.06294286787947910.0314714339397396
1110.9501804012735920.0996391974528150.0498195987264075
1120.9106321428125450.178735714374910.0893678571874551
1130.871965837145220.256068325709560.12803416285478
1140.9136186156182980.1727627687634040.0863813843817018
1150.945470329449240.1090593411015210.0545296705507603
1160.869606915118840.260786169762320.13039308488116

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.0676230610086593 & 0.135246122017319 & 0.932376938991341 \tabularnewline
11 & 0.0335757749868742 & 0.0671515499737484 & 0.966424225013126 \tabularnewline
12 & 0.0106251295304229 & 0.0212502590608458 & 0.989374870469577 \tabularnewline
13 & 0.00589157590178955 & 0.0117831518035791 & 0.99410842409821 \tabularnewline
14 & 0.00194458458460815 & 0.00388916916921629 & 0.998055415415392 \tabularnewline
15 & 0.00184070421877173 & 0.00368140843754346 & 0.998159295781228 \tabularnewline
16 & 0.000661740988808325 & 0.00132348197761665 & 0.999338259011192 \tabularnewline
17 & 0.000220034728078714 & 0.000440069456157428 & 0.999779965271921 \tabularnewline
18 & 0.000122754486953454 & 0.000245508973906909 & 0.999877245513047 \tabularnewline
19 & 6.28315110341741e-05 & 0.000125663022068348 & 0.999937168488966 \tabularnewline
20 & 2.504632782798e-05 & 5.00926556559601e-05 & 0.999974953672172 \tabularnewline
21 & 5.78253503715609e-05 & 0.000115650700743122 & 0.999942174649628 \tabularnewline
22 & 1.96236995548957e-05 & 3.92473991097914e-05 & 0.999980376300445 \tabularnewline
23 & 7.6055351612532e-06 & 1.52110703225064e-05 & 0.999992394464839 \tabularnewline
24 & 3.70953638145278e-06 & 7.41907276290556e-06 & 0.999996290463619 \tabularnewline
25 & 1.41534600922844e-06 & 2.83069201845688e-06 & 0.999998584653991 \tabularnewline
26 & 0.000246426666130486 & 0.000492853332260971 & 0.999753573333869 \tabularnewline
27 & 0.00333866680225508 & 0.00667733360451016 & 0.996661333197745 \tabularnewline
28 & 0.00327255164672266 & 0.00654510329344532 & 0.996727448353277 \tabularnewline
29 & 0.00286746865996581 & 0.00573493731993161 & 0.997132531340034 \tabularnewline
30 & 0.00232178803163023 & 0.00464357606326046 & 0.99767821196837 \tabularnewline
31 & 0.00596226699269894 & 0.0119245339853979 & 0.994037733007301 \tabularnewline
32 & 0.0132636700494327 & 0.0265273400988654 & 0.986736329950567 \tabularnewline
33 & 0.0158739301624088 & 0.0317478603248176 & 0.984126069837591 \tabularnewline
34 & 0.0359706662925727 & 0.0719413325851455 & 0.964029333707427 \tabularnewline
35 & 0.0448176564851286 & 0.0896353129702572 & 0.955182343514871 \tabularnewline
36 & 0.048270967443143 & 0.0965419348862861 & 0.951729032556857 \tabularnewline
37 & 0.0526773518133151 & 0.10535470362663 & 0.947322648186685 \tabularnewline
38 & 0.0834458146967725 & 0.166891629393545 & 0.916554185303227 \tabularnewline
39 & 0.0973414849941013 & 0.194682969988203 & 0.902658515005899 \tabularnewline
40 & 0.143124352600207 & 0.286248705200415 & 0.856875647399793 \tabularnewline
41 & 0.486147172127499 & 0.972294344254997 & 0.513852827872501 \tabularnewline
42 & 0.679801780291819 & 0.640396439416361 & 0.320198219708181 \tabularnewline
43 & 0.644071687929196 & 0.711856624141609 & 0.355928312070804 \tabularnewline
44 & 0.5986298496943 & 0.8027403006114 & 0.4013701503057 \tabularnewline
45 & 0.703293009486868 & 0.593413981026263 & 0.296706990513132 \tabularnewline
46 & 0.80711774383684 & 0.385764512326321 & 0.19288225616316 \tabularnewline
47 & 0.783475979859879 & 0.433048040280241 & 0.21652402014012 \tabularnewline
48 & 0.751797115440141 & 0.496405769119718 & 0.248202884559859 \tabularnewline
49 & 0.730453119649486 & 0.539093760701028 & 0.269546880350514 \tabularnewline
50 & 0.735923433110626 & 0.528153133778749 & 0.264076566889374 \tabularnewline
51 & 0.747481389785796 & 0.505037220428407 & 0.252518610214204 \tabularnewline
52 & 0.733489213754999 & 0.533021572490002 & 0.266510786245001 \tabularnewline
53 & 0.76760710171264 & 0.464785796574719 & 0.23239289828736 \tabularnewline
54 & 0.778824167072747 & 0.442351665854507 & 0.221175832927253 \tabularnewline
55 & 0.753169098139932 & 0.493661803720136 & 0.246830901860068 \tabularnewline
56 & 0.71527938287447 & 0.569441234251061 & 0.28472061712553 \tabularnewline
57 & 0.691332640841213 & 0.617334718317574 & 0.308667359158787 \tabularnewline
58 & 0.672829688022254 & 0.654340623955493 & 0.327170311977746 \tabularnewline
59 & 0.666172381650447 & 0.667655236699106 & 0.333827618349553 \tabularnewline
60 & 0.65510274809131 & 0.68979450381738 & 0.34489725190869 \tabularnewline
61 & 0.652184916437438 & 0.695630167125123 & 0.347815083562562 \tabularnewline
62 & 0.672607136397479 & 0.654785727205041 & 0.327392863602521 \tabularnewline
63 & 0.727589744187423 & 0.544820511625154 & 0.272410255812577 \tabularnewline
64 & 0.814469818705556 & 0.371060362588888 & 0.185530181294444 \tabularnewline
65 & 0.949653204841561 & 0.100693590316878 & 0.0503467951584392 \tabularnewline
66 & 0.999524279005435 & 0.000951441989129418 & 0.000475720994564709 \tabularnewline
67 & 0.999424716990662 & 0.0011505660186758 & 0.0005752830093379 \tabularnewline
68 & 0.999312405627348 & 0.00137518874530474 & 0.000687594372652372 \tabularnewline
69 & 0.999094417364493 & 0.00181116527101308 & 0.000905582635506538 \tabularnewline
70 & 0.998770903171221 & 0.00245819365755755 & 0.00122909682877878 \tabularnewline
71 & 0.998248767574075 & 0.00350246485185022 & 0.00175123242592511 \tabularnewline
72 & 0.997996984703993 & 0.00400603059201465 & 0.00200301529600733 \tabularnewline
73 & 0.997492192057818 & 0.00501561588436414 & 0.00250780794218207 \tabularnewline
74 & 0.996717717358121 & 0.00656456528375838 & 0.00328228264187919 \tabularnewline
75 & 0.995514532420665 & 0.00897093515867022 & 0.00448546757933511 \tabularnewline
76 & 0.994069885647479 & 0.011860228705042 & 0.00593011435252102 \tabularnewline
77 & 0.99418045506536 & 0.0116390898692809 & 0.00581954493464047 \tabularnewline
78 & 0.992206722523384 & 0.0155865549532327 & 0.00779327747661636 \tabularnewline
79 & 0.991556827930726 & 0.0168863441385487 & 0.00844317206927437 \tabularnewline
80 & 0.989539758919811 & 0.0209204821603789 & 0.0104602410801894 \tabularnewline
81 & 0.986065864792336 & 0.0278682704153283 & 0.0139341352076641 \tabularnewline
82 & 0.981035901726771 & 0.0379281965464582 & 0.0189640982732291 \tabularnewline
83 & 0.973743632782815 & 0.052512734434371 & 0.0262563672171855 \tabularnewline
84 & 0.969032102279625 & 0.0619357954407494 & 0.0309678977203747 \tabularnewline
85 & 0.963672460295853 & 0.072655079408295 & 0.0363275397041475 \tabularnewline
86 & 0.954509502337598 & 0.0909809953248049 & 0.0454904976624025 \tabularnewline
87 & 0.941234274910428 & 0.117531450179144 & 0.058765725089572 \tabularnewline
88 & 0.931486976575857 & 0.137026046848286 & 0.0685130234241432 \tabularnewline
89 & 0.925771621473818 & 0.148456757052364 & 0.0742283785261818 \tabularnewline
90 & 0.928924040735358 & 0.142151918529285 & 0.0710759592646423 \tabularnewline
91 & 0.909167651887638 & 0.181664696224723 & 0.0908323481123616 \tabularnewline
92 & 0.896085346511604 & 0.207829306976792 & 0.103914653488396 \tabularnewline
93 & 0.896210859126928 & 0.207578281746144 & 0.103789140873072 \tabularnewline
94 & 0.872583565762572 & 0.254832868474856 & 0.127416434237428 \tabularnewline
95 & 0.852161259315126 & 0.295677481369747 & 0.147838740684874 \tabularnewline
96 & 0.843428736331752 & 0.313142527336496 & 0.156571263668248 \tabularnewline
97 & 0.841065561359417 & 0.317868877281166 & 0.158934438640583 \tabularnewline
98 & 0.885591109836503 & 0.228817780326993 & 0.114408890163497 \tabularnewline
99 & 0.873349552287854 & 0.253300895424292 & 0.126650447712146 \tabularnewline
100 & 0.911323162456672 & 0.177353675086656 & 0.0886768375433278 \tabularnewline
101 & 0.971682657476013 & 0.0566346850479733 & 0.0283173425239866 \tabularnewline
102 & 0.973860016647237 & 0.0522799667055258 & 0.0261399833527629 \tabularnewline
103 & 0.970537353895138 & 0.0589252922097241 & 0.029462646104862 \tabularnewline
104 & 0.960784033615732 & 0.0784319327685365 & 0.0392159663842682 \tabularnewline
105 & 0.938860364192793 & 0.122279271614414 & 0.0611396358072072 \tabularnewline
106 & 0.910152499345337 & 0.179695001309326 & 0.0898475006546628 \tabularnewline
107 & 0.966039324309131 & 0.0679213513817389 & 0.0339606756908694 \tabularnewline
108 & 0.945644054454989 & 0.108711891090021 & 0.0543559455450106 \tabularnewline
109 & 0.972837830884867 & 0.0543243382302668 & 0.0271621691151334 \tabularnewline
110 & 0.96852856606026 & 0.0629428678794791 & 0.0314714339397396 \tabularnewline
111 & 0.950180401273592 & 0.099639197452815 & 0.0498195987264075 \tabularnewline
112 & 0.910632142812545 & 0.17873571437491 & 0.0893678571874551 \tabularnewline
113 & 0.87196583714522 & 0.25606832570956 & 0.12803416285478 \tabularnewline
114 & 0.913618615618298 & 0.172762768763404 & 0.0863813843817018 \tabularnewline
115 & 0.94547032944924 & 0.109059341101521 & 0.0545296705507603 \tabularnewline
116 & 0.86960691511884 & 0.26078616976232 & 0.13039308488116 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203273&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]10[/C][C]0.0676230610086593[/C][C]0.135246122017319[/C][C]0.932376938991341[/C][/ROW]
[ROW][C]11[/C][C]0.0335757749868742[/C][C]0.0671515499737484[/C][C]0.966424225013126[/C][/ROW]
[ROW][C]12[/C][C]0.0106251295304229[/C][C]0.0212502590608458[/C][C]0.989374870469577[/C][/ROW]
[ROW][C]13[/C][C]0.00589157590178955[/C][C]0.0117831518035791[/C][C]0.99410842409821[/C][/ROW]
[ROW][C]14[/C][C]0.00194458458460815[/C][C]0.00388916916921629[/C][C]0.998055415415392[/C][/ROW]
[ROW][C]15[/C][C]0.00184070421877173[/C][C]0.00368140843754346[/C][C]0.998159295781228[/C][/ROW]
[ROW][C]16[/C][C]0.000661740988808325[/C][C]0.00132348197761665[/C][C]0.999338259011192[/C][/ROW]
[ROW][C]17[/C][C]0.000220034728078714[/C][C]0.000440069456157428[/C][C]0.999779965271921[/C][/ROW]
[ROW][C]18[/C][C]0.000122754486953454[/C][C]0.000245508973906909[/C][C]0.999877245513047[/C][/ROW]
[ROW][C]19[/C][C]6.28315110341741e-05[/C][C]0.000125663022068348[/C][C]0.999937168488966[/C][/ROW]
[ROW][C]20[/C][C]2.504632782798e-05[/C][C]5.00926556559601e-05[/C][C]0.999974953672172[/C][/ROW]
[ROW][C]21[/C][C]5.78253503715609e-05[/C][C]0.000115650700743122[/C][C]0.999942174649628[/C][/ROW]
[ROW][C]22[/C][C]1.96236995548957e-05[/C][C]3.92473991097914e-05[/C][C]0.999980376300445[/C][/ROW]
[ROW][C]23[/C][C]7.6055351612532e-06[/C][C]1.52110703225064e-05[/C][C]0.999992394464839[/C][/ROW]
[ROW][C]24[/C][C]3.70953638145278e-06[/C][C]7.41907276290556e-06[/C][C]0.999996290463619[/C][/ROW]
[ROW][C]25[/C][C]1.41534600922844e-06[/C][C]2.83069201845688e-06[/C][C]0.999998584653991[/C][/ROW]
[ROW][C]26[/C][C]0.000246426666130486[/C][C]0.000492853332260971[/C][C]0.999753573333869[/C][/ROW]
[ROW][C]27[/C][C]0.00333866680225508[/C][C]0.00667733360451016[/C][C]0.996661333197745[/C][/ROW]
[ROW][C]28[/C][C]0.00327255164672266[/C][C]0.00654510329344532[/C][C]0.996727448353277[/C][/ROW]
[ROW][C]29[/C][C]0.00286746865996581[/C][C]0.00573493731993161[/C][C]0.997132531340034[/C][/ROW]
[ROW][C]30[/C][C]0.00232178803163023[/C][C]0.00464357606326046[/C][C]0.99767821196837[/C][/ROW]
[ROW][C]31[/C][C]0.00596226699269894[/C][C]0.0119245339853979[/C][C]0.994037733007301[/C][/ROW]
[ROW][C]32[/C][C]0.0132636700494327[/C][C]0.0265273400988654[/C][C]0.986736329950567[/C][/ROW]
[ROW][C]33[/C][C]0.0158739301624088[/C][C]0.0317478603248176[/C][C]0.984126069837591[/C][/ROW]
[ROW][C]34[/C][C]0.0359706662925727[/C][C]0.0719413325851455[/C][C]0.964029333707427[/C][/ROW]
[ROW][C]35[/C][C]0.0448176564851286[/C][C]0.0896353129702572[/C][C]0.955182343514871[/C][/ROW]
[ROW][C]36[/C][C]0.048270967443143[/C][C]0.0965419348862861[/C][C]0.951729032556857[/C][/ROW]
[ROW][C]37[/C][C]0.0526773518133151[/C][C]0.10535470362663[/C][C]0.947322648186685[/C][/ROW]
[ROW][C]38[/C][C]0.0834458146967725[/C][C]0.166891629393545[/C][C]0.916554185303227[/C][/ROW]
[ROW][C]39[/C][C]0.0973414849941013[/C][C]0.194682969988203[/C][C]0.902658515005899[/C][/ROW]
[ROW][C]40[/C][C]0.143124352600207[/C][C]0.286248705200415[/C][C]0.856875647399793[/C][/ROW]
[ROW][C]41[/C][C]0.486147172127499[/C][C]0.972294344254997[/C][C]0.513852827872501[/C][/ROW]
[ROW][C]42[/C][C]0.679801780291819[/C][C]0.640396439416361[/C][C]0.320198219708181[/C][/ROW]
[ROW][C]43[/C][C]0.644071687929196[/C][C]0.711856624141609[/C][C]0.355928312070804[/C][/ROW]
[ROW][C]44[/C][C]0.5986298496943[/C][C]0.8027403006114[/C][C]0.4013701503057[/C][/ROW]
[ROW][C]45[/C][C]0.703293009486868[/C][C]0.593413981026263[/C][C]0.296706990513132[/C][/ROW]
[ROW][C]46[/C][C]0.80711774383684[/C][C]0.385764512326321[/C][C]0.19288225616316[/C][/ROW]
[ROW][C]47[/C][C]0.783475979859879[/C][C]0.433048040280241[/C][C]0.21652402014012[/C][/ROW]
[ROW][C]48[/C][C]0.751797115440141[/C][C]0.496405769119718[/C][C]0.248202884559859[/C][/ROW]
[ROW][C]49[/C][C]0.730453119649486[/C][C]0.539093760701028[/C][C]0.269546880350514[/C][/ROW]
[ROW][C]50[/C][C]0.735923433110626[/C][C]0.528153133778749[/C][C]0.264076566889374[/C][/ROW]
[ROW][C]51[/C][C]0.747481389785796[/C][C]0.505037220428407[/C][C]0.252518610214204[/C][/ROW]
[ROW][C]52[/C][C]0.733489213754999[/C][C]0.533021572490002[/C][C]0.266510786245001[/C][/ROW]
[ROW][C]53[/C][C]0.76760710171264[/C][C]0.464785796574719[/C][C]0.23239289828736[/C][/ROW]
[ROW][C]54[/C][C]0.778824167072747[/C][C]0.442351665854507[/C][C]0.221175832927253[/C][/ROW]
[ROW][C]55[/C][C]0.753169098139932[/C][C]0.493661803720136[/C][C]0.246830901860068[/C][/ROW]
[ROW][C]56[/C][C]0.71527938287447[/C][C]0.569441234251061[/C][C]0.28472061712553[/C][/ROW]
[ROW][C]57[/C][C]0.691332640841213[/C][C]0.617334718317574[/C][C]0.308667359158787[/C][/ROW]
[ROW][C]58[/C][C]0.672829688022254[/C][C]0.654340623955493[/C][C]0.327170311977746[/C][/ROW]
[ROW][C]59[/C][C]0.666172381650447[/C][C]0.667655236699106[/C][C]0.333827618349553[/C][/ROW]
[ROW][C]60[/C][C]0.65510274809131[/C][C]0.68979450381738[/C][C]0.34489725190869[/C][/ROW]
[ROW][C]61[/C][C]0.652184916437438[/C][C]0.695630167125123[/C][C]0.347815083562562[/C][/ROW]
[ROW][C]62[/C][C]0.672607136397479[/C][C]0.654785727205041[/C][C]0.327392863602521[/C][/ROW]
[ROW][C]63[/C][C]0.727589744187423[/C][C]0.544820511625154[/C][C]0.272410255812577[/C][/ROW]
[ROW][C]64[/C][C]0.814469818705556[/C][C]0.371060362588888[/C][C]0.185530181294444[/C][/ROW]
[ROW][C]65[/C][C]0.949653204841561[/C][C]0.100693590316878[/C][C]0.0503467951584392[/C][/ROW]
[ROW][C]66[/C][C]0.999524279005435[/C][C]0.000951441989129418[/C][C]0.000475720994564709[/C][/ROW]
[ROW][C]67[/C][C]0.999424716990662[/C][C]0.0011505660186758[/C][C]0.0005752830093379[/C][/ROW]
[ROW][C]68[/C][C]0.999312405627348[/C][C]0.00137518874530474[/C][C]0.000687594372652372[/C][/ROW]
[ROW][C]69[/C][C]0.999094417364493[/C][C]0.00181116527101308[/C][C]0.000905582635506538[/C][/ROW]
[ROW][C]70[/C][C]0.998770903171221[/C][C]0.00245819365755755[/C][C]0.00122909682877878[/C][/ROW]
[ROW][C]71[/C][C]0.998248767574075[/C][C]0.00350246485185022[/C][C]0.00175123242592511[/C][/ROW]
[ROW][C]72[/C][C]0.997996984703993[/C][C]0.00400603059201465[/C][C]0.00200301529600733[/C][/ROW]
[ROW][C]73[/C][C]0.997492192057818[/C][C]0.00501561588436414[/C][C]0.00250780794218207[/C][/ROW]
[ROW][C]74[/C][C]0.996717717358121[/C][C]0.00656456528375838[/C][C]0.00328228264187919[/C][/ROW]
[ROW][C]75[/C][C]0.995514532420665[/C][C]0.00897093515867022[/C][C]0.00448546757933511[/C][/ROW]
[ROW][C]76[/C][C]0.994069885647479[/C][C]0.011860228705042[/C][C]0.00593011435252102[/C][/ROW]
[ROW][C]77[/C][C]0.99418045506536[/C][C]0.0116390898692809[/C][C]0.00581954493464047[/C][/ROW]
[ROW][C]78[/C][C]0.992206722523384[/C][C]0.0155865549532327[/C][C]0.00779327747661636[/C][/ROW]
[ROW][C]79[/C][C]0.991556827930726[/C][C]0.0168863441385487[/C][C]0.00844317206927437[/C][/ROW]
[ROW][C]80[/C][C]0.989539758919811[/C][C]0.0209204821603789[/C][C]0.0104602410801894[/C][/ROW]
[ROW][C]81[/C][C]0.986065864792336[/C][C]0.0278682704153283[/C][C]0.0139341352076641[/C][/ROW]
[ROW][C]82[/C][C]0.981035901726771[/C][C]0.0379281965464582[/C][C]0.0189640982732291[/C][/ROW]
[ROW][C]83[/C][C]0.973743632782815[/C][C]0.052512734434371[/C][C]0.0262563672171855[/C][/ROW]
[ROW][C]84[/C][C]0.969032102279625[/C][C]0.0619357954407494[/C][C]0.0309678977203747[/C][/ROW]
[ROW][C]85[/C][C]0.963672460295853[/C][C]0.072655079408295[/C][C]0.0363275397041475[/C][/ROW]
[ROW][C]86[/C][C]0.954509502337598[/C][C]0.0909809953248049[/C][C]0.0454904976624025[/C][/ROW]
[ROW][C]87[/C][C]0.941234274910428[/C][C]0.117531450179144[/C][C]0.058765725089572[/C][/ROW]
[ROW][C]88[/C][C]0.931486976575857[/C][C]0.137026046848286[/C][C]0.0685130234241432[/C][/ROW]
[ROW][C]89[/C][C]0.925771621473818[/C][C]0.148456757052364[/C][C]0.0742283785261818[/C][/ROW]
[ROW][C]90[/C][C]0.928924040735358[/C][C]0.142151918529285[/C][C]0.0710759592646423[/C][/ROW]
[ROW][C]91[/C][C]0.909167651887638[/C][C]0.181664696224723[/C][C]0.0908323481123616[/C][/ROW]
[ROW][C]92[/C][C]0.896085346511604[/C][C]0.207829306976792[/C][C]0.103914653488396[/C][/ROW]
[ROW][C]93[/C][C]0.896210859126928[/C][C]0.207578281746144[/C][C]0.103789140873072[/C][/ROW]
[ROW][C]94[/C][C]0.872583565762572[/C][C]0.254832868474856[/C][C]0.127416434237428[/C][/ROW]
[ROW][C]95[/C][C]0.852161259315126[/C][C]0.295677481369747[/C][C]0.147838740684874[/C][/ROW]
[ROW][C]96[/C][C]0.843428736331752[/C][C]0.313142527336496[/C][C]0.156571263668248[/C][/ROW]
[ROW][C]97[/C][C]0.841065561359417[/C][C]0.317868877281166[/C][C]0.158934438640583[/C][/ROW]
[ROW][C]98[/C][C]0.885591109836503[/C][C]0.228817780326993[/C][C]0.114408890163497[/C][/ROW]
[ROW][C]99[/C][C]0.873349552287854[/C][C]0.253300895424292[/C][C]0.126650447712146[/C][/ROW]
[ROW][C]100[/C][C]0.911323162456672[/C][C]0.177353675086656[/C][C]0.0886768375433278[/C][/ROW]
[ROW][C]101[/C][C]0.971682657476013[/C][C]0.0566346850479733[/C][C]0.0283173425239866[/C][/ROW]
[ROW][C]102[/C][C]0.973860016647237[/C][C]0.0522799667055258[/C][C]0.0261399833527629[/C][/ROW]
[ROW][C]103[/C][C]0.970537353895138[/C][C]0.0589252922097241[/C][C]0.029462646104862[/C][/ROW]
[ROW][C]104[/C][C]0.960784033615732[/C][C]0.0784319327685365[/C][C]0.0392159663842682[/C][/ROW]
[ROW][C]105[/C][C]0.938860364192793[/C][C]0.122279271614414[/C][C]0.0611396358072072[/C][/ROW]
[ROW][C]106[/C][C]0.910152499345337[/C][C]0.179695001309326[/C][C]0.0898475006546628[/C][/ROW]
[ROW][C]107[/C][C]0.966039324309131[/C][C]0.0679213513817389[/C][C]0.0339606756908694[/C][/ROW]
[ROW][C]108[/C][C]0.945644054454989[/C][C]0.108711891090021[/C][C]0.0543559455450106[/C][/ROW]
[ROW][C]109[/C][C]0.972837830884867[/C][C]0.0543243382302668[/C][C]0.0271621691151334[/C][/ROW]
[ROW][C]110[/C][C]0.96852856606026[/C][C]0.0629428678794791[/C][C]0.0314714339397396[/C][/ROW]
[ROW][C]111[/C][C]0.950180401273592[/C][C]0.099639197452815[/C][C]0.0498195987264075[/C][/ROW]
[ROW][C]112[/C][C]0.910632142812545[/C][C]0.17873571437491[/C][C]0.0893678571874551[/C][/ROW]
[ROW][C]113[/C][C]0.87196583714522[/C][C]0.25606832570956[/C][C]0.12803416285478[/C][/ROW]
[ROW][C]114[/C][C]0.913618615618298[/C][C]0.172762768763404[/C][C]0.0863813843817018[/C][/ROW]
[ROW][C]115[/C][C]0.94547032944924[/C][C]0.109059341101521[/C][C]0.0545296705507603[/C][/ROW]
[ROW][C]116[/C][C]0.86960691511884[/C][C]0.26078616976232[/C][C]0.13039308488116[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203273&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203273&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.06762306100865930.1352461220173190.932376938991341
110.03357577498687420.06715154997374840.966424225013126
120.01062512953042290.02125025906084580.989374870469577
130.005891575901789550.01178315180357910.99410842409821
140.001944584584608150.003889169169216290.998055415415392
150.001840704218771730.003681408437543460.998159295781228
160.0006617409888083250.001323481977616650.999338259011192
170.0002200347280787140.0004400694561574280.999779965271921
180.0001227544869534540.0002455089739069090.999877245513047
196.28315110341741e-050.0001256630220683480.999937168488966
202.504632782798e-055.00926556559601e-050.999974953672172
215.78253503715609e-050.0001156507007431220.999942174649628
221.96236995548957e-053.92473991097914e-050.999980376300445
237.6055351612532e-061.52110703225064e-050.999992394464839
243.70953638145278e-067.41907276290556e-060.999996290463619
251.41534600922844e-062.83069201845688e-060.999998584653991
260.0002464266661304860.0004928533322609710.999753573333869
270.003338666802255080.006677333604510160.996661333197745
280.003272551646722660.006545103293445320.996727448353277
290.002867468659965810.005734937319931610.997132531340034
300.002321788031630230.004643576063260460.99767821196837
310.005962266992698940.01192453398539790.994037733007301
320.01326367004943270.02652734009886540.986736329950567
330.01587393016240880.03174786032481760.984126069837591
340.03597066629257270.07194133258514550.964029333707427
350.04481765648512860.08963531297025720.955182343514871
360.0482709674431430.09654193488628610.951729032556857
370.05267735181331510.105354703626630.947322648186685
380.08344581469677250.1668916293935450.916554185303227
390.09734148499410130.1946829699882030.902658515005899
400.1431243526002070.2862487052004150.856875647399793
410.4861471721274990.9722943442549970.513852827872501
420.6798017802918190.6403964394163610.320198219708181
430.6440716879291960.7118566241416090.355928312070804
440.59862984969430.80274030061140.4013701503057
450.7032930094868680.5934139810262630.296706990513132
460.807117743836840.3857645123263210.19288225616316
470.7834759798598790.4330480402802410.21652402014012
480.7517971154401410.4964057691197180.248202884559859
490.7304531196494860.5390937607010280.269546880350514
500.7359234331106260.5281531337787490.264076566889374
510.7474813897857960.5050372204284070.252518610214204
520.7334892137549990.5330215724900020.266510786245001
530.767607101712640.4647857965747190.23239289828736
540.7788241670727470.4423516658545070.221175832927253
550.7531690981399320.4936618037201360.246830901860068
560.715279382874470.5694412342510610.28472061712553
570.6913326408412130.6173347183175740.308667359158787
580.6728296880222540.6543406239554930.327170311977746
590.6661723816504470.6676552366991060.333827618349553
600.655102748091310.689794503817380.34489725190869
610.6521849164374380.6956301671251230.347815083562562
620.6726071363974790.6547857272050410.327392863602521
630.7275897441874230.5448205116251540.272410255812577
640.8144698187055560.3710603625888880.185530181294444
650.9496532048415610.1006935903168780.0503467951584392
660.9995242790054350.0009514419891294180.000475720994564709
670.9994247169906620.00115056601867580.0005752830093379
680.9993124056273480.001375188745304740.000687594372652372
690.9990944173644930.001811165271013080.000905582635506538
700.9987709031712210.002458193657557550.00122909682877878
710.9982487675740750.003502464851850220.00175123242592511
720.9979969847039930.004006030592014650.00200301529600733
730.9974921920578180.005015615884364140.00250780794218207
740.9967177173581210.006564565283758380.00328228264187919
750.9955145324206650.008970935158670220.00448546757933511
760.9940698856474790.0118602287050420.00593011435252102
770.994180455065360.01163908986928090.00581954493464047
780.9922067225233840.01558655495323270.00779327747661636
790.9915568279307260.01688634413854870.00844317206927437
800.9895397589198110.02092048216037890.0104602410801894
810.9860658647923360.02786827041532830.0139341352076641
820.9810359017267710.03792819654645820.0189640982732291
830.9737436327828150.0525127344343710.0262563672171855
840.9690321022796250.06193579544074940.0309678977203747
850.9636724602958530.0726550794082950.0363275397041475
860.9545095023375980.09098099532480490.0454904976624025
870.9412342749104280.1175314501791440.058765725089572
880.9314869765758570.1370260468482860.0685130234241432
890.9257716214738180.1484567570523640.0742283785261818
900.9289240407353580.1421519185292850.0710759592646423
910.9091676518876380.1816646962247230.0908323481123616
920.8960853465116040.2078293069767920.103914653488396
930.8962108591269280.2075782817461440.103789140873072
940.8725835657625720.2548328684748560.127416434237428
950.8521612593151260.2956774813697470.147838740684874
960.8434287363317520.3131425273364960.156571263668248
970.8410655613594170.3178688772811660.158934438640583
980.8855911098365030.2288177803269930.114408890163497
990.8733495522878540.2533008954242920.126650447712146
1000.9113231624566720.1773536750866560.0886768375433278
1010.9716826574760130.05663468504797330.0283173425239866
1020.9738600166472370.05227996670552580.0261399833527629
1030.9705373538951380.05892529220972410.029462646104862
1040.9607840336157320.07843193276853650.0392159663842682
1050.9388603641927930.1222792716144140.0611396358072072
1060.9101524993453370.1796950013093260.0898475006546628
1070.9660393243091310.06792135138173890.0339606756908694
1080.9456440544549890.1087118910900210.0543559455450106
1090.9728378308848670.05432433823026680.0271621691151334
1100.968528566060260.06294286787947910.0314714339397396
1110.9501804012735920.0996391974528150.0498195987264075
1120.9106321428125450.178735714374910.0893678571874551
1130.871965837145220.256068325709560.12803416285478
1140.9136186156182980.1727627687634040.0863813843817018
1150.945470329449240.1090593411015210.0545296705507603
1160.869606915118840.260786169762320.13039308488116







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level270.252336448598131NOK
5% type I error level390.364485981308411NOK
10% type I error level550.514018691588785NOK

\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 & 27 & 0.252336448598131 & NOK \tabularnewline
5% type I error level & 39 & 0.364485981308411 & NOK \tabularnewline
10% type I error level & 55 & 0.514018691588785 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203273&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]27[/C][C]0.252336448598131[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]39[/C][C]0.364485981308411[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]55[/C][C]0.514018691588785[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203273&T=6

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Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level270.252336448598131NOK
5% type I error level390.364485981308411NOK
10% type I error level550.514018691588785NOK



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')
}