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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Tue, 14 Dec 2010 08:45:57 +0000

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/14/t1292316410xkhfs02wzrnjnbs.htm/, Retrieved Thu, 02 May 2024 19:39:41 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=109263, Retrieved Thu, 02 May 2024 19:39:41 +0000

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No (this computation is public)

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Estimated Impact

156

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

-     [Multiple Regression] [] [2010-12-05 18:56:24] [b98453cac15ba1066b407e146608df68]
-   PD    [Multiple Regression] [WS 10 - Multiple ...] [2010-12-14 08:45:57] [89d441ae0711e9b79b5d358f420c1317] [Current]

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Dataseries X:

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100.44	1576.23	29.29	710.45
100.88	1546.37	28.99	720
101.42	1545.05	28.91	720
99.97	1552.34	29.29	720
100.56	1594.3	30.96	754.78
99.51	1605.78	30.57	802.73
98.96	1673.21	30.59	845.24
100.85	1612.94	31.39	893.91
100.66	1566.34	31.28	931.43
100.22	1530.17	31.1	940
100.30	1582.54	31.7	947.73
100.73	1702.16	32.57	960
101.46	1701.93	32.49	996.96
101.35	1811.15	32.46	1000
101.14	1924.2	32.3	1000
101.68	2034.25	32.97	1000
101.47	2011.13	32.9	1013.04
100.59	2013.04	32.93	1095.24
101.18	2151.67	33.72	1159.09
100.87	1902.09	33.33	1200
99.79	1944.01	33.44	1200
100.74	1916.67	33.89	1282.61
99.34	1967.31	34.34	1513.64
100.07	2119.88	33.56	1669.05
103.68	2216.38	32.67	1700
103.52	2522.83	32.57	1700
104.68	2647.64	33.23	1700
103.75	2631.23	32.85	1665.91
103.70	2693.41	32.61	1650
102.98	3021.76	32.57	1650
106.30	2953.67	32.98	1619.57
107.21	2796.8	31.33	1599.05
106.83	2672.05	29.8	1572.73
105.60	2251.23	28.06	1470
104.30	2046.08	25.47	1268
104.43	2420.04	24.65	1217.39
104.36	2608.89	23.94	1154.09
106.21	2660.47	23.89	984
107.34	2493.98	23.54	900
106.92	2541.7	24.28	900
104.80	2554.6	25.51	916.67
103.85	2699.61	27.03	957.73
103.39	2805.48	27.09	966.09
103.38	2956.66	27.3	980
103.93	3149.51	27.11	990.91
104.41	3372.5	26.39	1000.91
104.47	3379.33	27.54	1042.38
103.84	3517.54	26.85	1142.61
103.65	3527.34	26.82	1214.29
103.17	3281.06	25.9	1218
103.40	3089.65	24.96	1202.61
112.72	3222.76	25.4	1200
114.77	3165.76	24.38	1228.57
116.18	3232.43	24.73	1195.91
116.93	3229.54	25.43	1180
115.19	3071.74	26.04	1210.91
114.55	2850.17	25.59	1272.27

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	6 seconds
R Server	'RServer@AstonUniversity' @ vre.aston.ac.uk

\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 & 6 seconds \tabularnewline
R Server & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109263&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]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'RServer@AstonUniversity' @ vre.aston.ac.uk[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109263&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109263&T=0

As an alternative you can also use a QR Code:

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

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	6 seconds
R Server	'RServer@AstonUniversity' @ vre.aston.ac.uk

Multiple Linear Regression - Estimated Regression Equation
Chocolade[t] = + 116.668997738777 + 0.00156233966989654Cacao[t] -0.749158502206502Suiker[t] + 0.0048421384532872Grondnoten[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Chocolade[t] =  +  116.668997738777 +  0.00156233966989654Cacao[t] -0.749158502206502Suiker[t] +  0.0048421384532872Grondnoten[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109263&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Chocolade[t] =  +  116.668997738777 +  0.00156233966989654Cacao[t] -0.749158502206502Suiker[t] +  0.0048421384532872Grondnoten[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109263&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109263&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
Chocolade[t] = + 116.668997738777 + 0.00156233966989654Cacao[t] -0.749158502206502Suiker[t] + 0.0048421384532872Grondnoten[t] + e[t]

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	116.668997738777	6.05139	19.2797	0	0
Cacao	0.00156233966989654	0.001032	1.5138	0.136009	0.068004
Suiker	-0.749158502206502	0.182389	-4.1075	0.000139	7e-05
Grondnoten	0.0048421384532872	0.00195	2.4827	0.016241	0.008121

\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) & 116.668997738777 & 6.05139 & 19.2797 & 0 & 0 \tabularnewline
Cacao & 0.00156233966989654 & 0.001032 & 1.5138 & 0.136009 & 0.068004 \tabularnewline
Suiker & -0.749158502206502 & 0.182389 & -4.1075 & 0.000139 & 7e-05 \tabularnewline
Grondnoten & 0.0048421384532872 & 0.00195 & 2.4827 & 0.016241 & 0.008121 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109263&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]116.668997738777[/C][C]6.05139[/C][C]19.2797[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Cacao[/C][C]0.00156233966989654[/C][C]0.001032[/C][C]1.5138[/C][C]0.136009[/C][C]0.068004[/C][/ROW]
[ROW][C]Suiker[/C][C]-0.749158502206502[/C][C]0.182389[/C][C]-4.1075[/C][C]0.000139[/C][C]7e-05[/C][/ROW]
[ROW][C]Grondnoten[/C][C]0.0048421384532872[/C][C]0.00195[/C][C]2.4827[/C][C]0.016241[/C][C]0.008121[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109263&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109263&T=2

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	116.668997738777	6.05139	19.2797	0	0
Cacao	0.00156233966989654	0.001032	1.5138	0.136009	0.068004
Suiker	-0.749158502206502	0.182389	-4.1075	0.000139	7e-05
Grondnoten	0.0048421384532872	0.00195	2.4827	0.016241	0.008121

Multiple Linear Regression - Regression Statistics
Multiple R	0.758359579084536
R-squared	0.575109251189275
Adjusted R-squared	0.551058831445272
F-TEST (value)	23.9126492306927
F-TEST (DF numerator)	3
F-TEST (DF denominator)	53
p-value	6.38400776686865e-10
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.9574051191386
Sum Squared Residuals	463.55098705148

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.758359579084536 \tabularnewline
R-squared & 0.575109251189275 \tabularnewline
Adjusted R-squared & 0.551058831445272 \tabularnewline
F-TEST (value) & 23.9126492306927 \tabularnewline
F-TEST (DF numerator) & 3 \tabularnewline
F-TEST (DF denominator) & 53 \tabularnewline
p-value & 6.38400776686865e-10 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.9574051191386 \tabularnewline
Sum Squared Residuals & 463.55098705148 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109263&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.758359579084536[/C][/ROW]
[ROW][C]R-squared[/C][C]0.575109251189275[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.551058831445272[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]23.9126492306927[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]3[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]53[/C][/ROW]
[ROW][C]p-value[/C][C]6.38400776686865e-10[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.9574051191386[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]463.55098705148[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109263&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109263&T=3

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Regression Statistics
Multiple R	0.758359579084536
R-squared	0.575109251189275
Adjusted R-squared	0.551058831445272
F-TEST (value)	23.9126492306927
F-TEST (DF numerator)	3
F-TEST (DF denominator)	53
p-value	6.38400776686865e-10
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.9574051191386
Sum Squared Residuals	463.55098705148

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	100.44	100.628849131168	-0.188849131167517
2	100.88	100.853187641515	0.0268123584847969
3	101.42	100.911058033327	0.508941966672553
4	99.97	100.637767258683	-0.667767258682527
5	100.56	99.6206379079519	0.939362092048148
6	99.51	100.162925922058	-0.65292592205792
7	98.96	100.459130621604	-1.49913062160416
8	100.85	100.001308486456	0.848691513544215
9	100.66	100.192587927849	0.467412072151346
10	100.22	100.31242375893	-0.0924237589303361
11	100.3	99.9821781163628	0.31782188363717
12	100.73	99.576710329578	1.15328967042198
13	101.46	99.815249108864	1.64475089113603
14	101.35	100.023082703574	1.32691729642574
15	101.14	100.319570563609	0.8204294363909
16	101.68	99.9895698478029	1.69043015219715
17	101.47	100.06903113522	1.40096886477983
18	100.59	100.447564229784	0.14243577021632
19	101.18	100.381486701721	0.798513298279315
20	100.87	100.481821666892	0.388178333107574
21	99.79	100.464907510612	-0.674907510611772
22	100.74	100.48508087567	0.254919124330061
23	99.34	101.345755677424	-2.00575567742351
24	100.07	102.920982209606	-2.85098220960607
25	103.68	103.888363239844	-0.208363239844097
26	103.52	104.442058081905	-0.922058081904553
27	104.68	104.142609084648	0.53739091535196
28	103.75	104.236582821631	-0.486582821630952
29	103.7	104.436488720043	-0.736488720042878
30	102.98	104.979449290742	-1.99944929074166
31	106.3	104.41856832358	1.88143167641978
32	107.21	105.310234947143	1.89976505285717
33	106.83	106.134100497609	0.69589950239134
34	105.6	106.282739628256	-0.682739628255924
35	104.3	106.924434198127	-2.62443419812747
36	104.43	107.87793608577	-3.44793608577044
37	104.36	108.398379104904	-4.03837910490394
38	106.21	107.692823180668	-1.48282318066791
39	107.34	107.288175094723	0.0518249052770176
40	106.92	106.808352652138	0.111647347862366
41	104.8	105.987760324182	-1.1877603241816
42	103.85	105.274412481251	-1.42441248125139
43	103.39	105.43534814944	-2.04534814944043
44	103.38	105.581573521157	-2.20157352115725
45	103.93	106.078038572441	-2.14803857244138
46	104.41	107.014240201553	-2.60424020155318
47	104.47	106.364182185619	-1.89418218561891
48	103.84	107.582360055091	-3.74236005509077
49	103.65	107.967230223254	-4.31723022325357
50	103.17	108.289647365043	-5.11964736504314
51	103.4	108.620288410106	-5.22028841010626
52	112.72	108.485983721232	4.23401627876775
53	114.77	109.299411927909	5.4705880720908
54	116.18	108.983223396045	7.19677660395545
55	116.93	108.377258860062	8.5527411399378
56	115.19	107.823405473398	7.36659452660232
57	114.55	108.111472814225	6.43852718577467

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 100.44 & 100.628849131168 & -0.188849131167517 \tabularnewline
2 & 100.88 & 100.853187641515 & 0.0268123584847969 \tabularnewline
3 & 101.42 & 100.911058033327 & 0.508941966672553 \tabularnewline
4 & 99.97 & 100.637767258683 & -0.667767258682527 \tabularnewline
5 & 100.56 & 99.6206379079519 & 0.939362092048148 \tabularnewline
6 & 99.51 & 100.162925922058 & -0.65292592205792 \tabularnewline
7 & 98.96 & 100.459130621604 & -1.49913062160416 \tabularnewline
8 & 100.85 & 100.001308486456 & 0.848691513544215 \tabularnewline
9 & 100.66 & 100.192587927849 & 0.467412072151346 \tabularnewline
10 & 100.22 & 100.31242375893 & -0.0924237589303361 \tabularnewline
11 & 100.3 & 99.9821781163628 & 0.31782188363717 \tabularnewline
12 & 100.73 & 99.576710329578 & 1.15328967042198 \tabularnewline
13 & 101.46 & 99.815249108864 & 1.64475089113603 \tabularnewline
14 & 101.35 & 100.023082703574 & 1.32691729642574 \tabularnewline
15 & 101.14 & 100.319570563609 & 0.8204294363909 \tabularnewline
16 & 101.68 & 99.9895698478029 & 1.69043015219715 \tabularnewline
17 & 101.47 & 100.06903113522 & 1.40096886477983 \tabularnewline
18 & 100.59 & 100.447564229784 & 0.14243577021632 \tabularnewline
19 & 101.18 & 100.381486701721 & 0.798513298279315 \tabularnewline
20 & 100.87 & 100.481821666892 & 0.388178333107574 \tabularnewline
21 & 99.79 & 100.464907510612 & -0.674907510611772 \tabularnewline
22 & 100.74 & 100.48508087567 & 0.254919124330061 \tabularnewline
23 & 99.34 & 101.345755677424 & -2.00575567742351 \tabularnewline
24 & 100.07 & 102.920982209606 & -2.85098220960607 \tabularnewline
25 & 103.68 & 103.888363239844 & -0.208363239844097 \tabularnewline
26 & 103.52 & 104.442058081905 & -0.922058081904553 \tabularnewline
27 & 104.68 & 104.142609084648 & 0.53739091535196 \tabularnewline
28 & 103.75 & 104.236582821631 & -0.486582821630952 \tabularnewline
29 & 103.7 & 104.436488720043 & -0.736488720042878 \tabularnewline
30 & 102.98 & 104.979449290742 & -1.99944929074166 \tabularnewline
31 & 106.3 & 104.41856832358 & 1.88143167641978 \tabularnewline
32 & 107.21 & 105.310234947143 & 1.89976505285717 \tabularnewline
33 & 106.83 & 106.134100497609 & 0.69589950239134 \tabularnewline
34 & 105.6 & 106.282739628256 & -0.682739628255924 \tabularnewline
35 & 104.3 & 106.924434198127 & -2.62443419812747 \tabularnewline
36 & 104.43 & 107.87793608577 & -3.44793608577044 \tabularnewline
37 & 104.36 & 108.398379104904 & -4.03837910490394 \tabularnewline
38 & 106.21 & 107.692823180668 & -1.48282318066791 \tabularnewline
39 & 107.34 & 107.288175094723 & 0.0518249052770176 \tabularnewline
40 & 106.92 & 106.808352652138 & 0.111647347862366 \tabularnewline
41 & 104.8 & 105.987760324182 & -1.1877603241816 \tabularnewline
42 & 103.85 & 105.274412481251 & -1.42441248125139 \tabularnewline
43 & 103.39 & 105.43534814944 & -2.04534814944043 \tabularnewline
44 & 103.38 & 105.581573521157 & -2.20157352115725 \tabularnewline
45 & 103.93 & 106.078038572441 & -2.14803857244138 \tabularnewline
46 & 104.41 & 107.014240201553 & -2.60424020155318 \tabularnewline
47 & 104.47 & 106.364182185619 & -1.89418218561891 \tabularnewline
48 & 103.84 & 107.582360055091 & -3.74236005509077 \tabularnewline
49 & 103.65 & 107.967230223254 & -4.31723022325357 \tabularnewline
50 & 103.17 & 108.289647365043 & -5.11964736504314 \tabularnewline
51 & 103.4 & 108.620288410106 & -5.22028841010626 \tabularnewline
52 & 112.72 & 108.485983721232 & 4.23401627876775 \tabularnewline
53 & 114.77 & 109.299411927909 & 5.4705880720908 \tabularnewline
54 & 116.18 & 108.983223396045 & 7.19677660395545 \tabularnewline
55 & 116.93 & 108.377258860062 & 8.5527411399378 \tabularnewline
56 & 115.19 & 107.823405473398 & 7.36659452660232 \tabularnewline
57 & 114.55 & 108.111472814225 & 6.43852718577467 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109263&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]100.44[/C][C]100.628849131168[/C][C]-0.188849131167517[/C][/ROW]
[ROW][C]2[/C][C]100.88[/C][C]100.853187641515[/C][C]0.0268123584847969[/C][/ROW]
[ROW][C]3[/C][C]101.42[/C][C]100.911058033327[/C][C]0.508941966672553[/C][/ROW]
[ROW][C]4[/C][C]99.97[/C][C]100.637767258683[/C][C]-0.667767258682527[/C][/ROW]
[ROW][C]5[/C][C]100.56[/C][C]99.6206379079519[/C][C]0.939362092048148[/C][/ROW]
[ROW][C]6[/C][C]99.51[/C][C]100.162925922058[/C][C]-0.65292592205792[/C][/ROW]
[ROW][C]7[/C][C]98.96[/C][C]100.459130621604[/C][C]-1.49913062160416[/C][/ROW]
[ROW][C]8[/C][C]100.85[/C][C]100.001308486456[/C][C]0.848691513544215[/C][/ROW]
[ROW][C]9[/C][C]100.66[/C][C]100.192587927849[/C][C]0.467412072151346[/C][/ROW]
[ROW][C]10[/C][C]100.22[/C][C]100.31242375893[/C][C]-0.0924237589303361[/C][/ROW]
[ROW][C]11[/C][C]100.3[/C][C]99.9821781163628[/C][C]0.31782188363717[/C][/ROW]
[ROW][C]12[/C][C]100.73[/C][C]99.576710329578[/C][C]1.15328967042198[/C][/ROW]
[ROW][C]13[/C][C]101.46[/C][C]99.815249108864[/C][C]1.64475089113603[/C][/ROW]
[ROW][C]14[/C][C]101.35[/C][C]100.023082703574[/C][C]1.32691729642574[/C][/ROW]
[ROW][C]15[/C][C]101.14[/C][C]100.319570563609[/C][C]0.8204294363909[/C][/ROW]
[ROW][C]16[/C][C]101.68[/C][C]99.9895698478029[/C][C]1.69043015219715[/C][/ROW]
[ROW][C]17[/C][C]101.47[/C][C]100.06903113522[/C][C]1.40096886477983[/C][/ROW]
[ROW][C]18[/C][C]100.59[/C][C]100.447564229784[/C][C]0.14243577021632[/C][/ROW]
[ROW][C]19[/C][C]101.18[/C][C]100.381486701721[/C][C]0.798513298279315[/C][/ROW]
[ROW][C]20[/C][C]100.87[/C][C]100.481821666892[/C][C]0.388178333107574[/C][/ROW]
[ROW][C]21[/C][C]99.79[/C][C]100.464907510612[/C][C]-0.674907510611772[/C][/ROW]
[ROW][C]22[/C][C]100.74[/C][C]100.48508087567[/C][C]0.254919124330061[/C][/ROW]
[ROW][C]23[/C][C]99.34[/C][C]101.345755677424[/C][C]-2.00575567742351[/C][/ROW]
[ROW][C]24[/C][C]100.07[/C][C]102.920982209606[/C][C]-2.85098220960607[/C][/ROW]
[ROW][C]25[/C][C]103.68[/C][C]103.888363239844[/C][C]-0.208363239844097[/C][/ROW]
[ROW][C]26[/C][C]103.52[/C][C]104.442058081905[/C][C]-0.922058081904553[/C][/ROW]
[ROW][C]27[/C][C]104.68[/C][C]104.142609084648[/C][C]0.53739091535196[/C][/ROW]
[ROW][C]28[/C][C]103.75[/C][C]104.236582821631[/C][C]-0.486582821630952[/C][/ROW]
[ROW][C]29[/C][C]103.7[/C][C]104.436488720043[/C][C]-0.736488720042878[/C][/ROW]
[ROW][C]30[/C][C]102.98[/C][C]104.979449290742[/C][C]-1.99944929074166[/C][/ROW]
[ROW][C]31[/C][C]106.3[/C][C]104.41856832358[/C][C]1.88143167641978[/C][/ROW]
[ROW][C]32[/C][C]107.21[/C][C]105.310234947143[/C][C]1.89976505285717[/C][/ROW]
[ROW][C]33[/C][C]106.83[/C][C]106.134100497609[/C][C]0.69589950239134[/C][/ROW]
[ROW][C]34[/C][C]105.6[/C][C]106.282739628256[/C][C]-0.682739628255924[/C][/ROW]
[ROW][C]35[/C][C]104.3[/C][C]106.924434198127[/C][C]-2.62443419812747[/C][/ROW]
[ROW][C]36[/C][C]104.43[/C][C]107.87793608577[/C][C]-3.44793608577044[/C][/ROW]
[ROW][C]37[/C][C]104.36[/C][C]108.398379104904[/C][C]-4.03837910490394[/C][/ROW]
[ROW][C]38[/C][C]106.21[/C][C]107.692823180668[/C][C]-1.48282318066791[/C][/ROW]
[ROW][C]39[/C][C]107.34[/C][C]107.288175094723[/C][C]0.0518249052770176[/C][/ROW]
[ROW][C]40[/C][C]106.92[/C][C]106.808352652138[/C][C]0.111647347862366[/C][/ROW]
[ROW][C]41[/C][C]104.8[/C][C]105.987760324182[/C][C]-1.1877603241816[/C][/ROW]
[ROW][C]42[/C][C]103.85[/C][C]105.274412481251[/C][C]-1.42441248125139[/C][/ROW]
[ROW][C]43[/C][C]103.39[/C][C]105.43534814944[/C][C]-2.04534814944043[/C][/ROW]
[ROW][C]44[/C][C]103.38[/C][C]105.581573521157[/C][C]-2.20157352115725[/C][/ROW]
[ROW][C]45[/C][C]103.93[/C][C]106.078038572441[/C][C]-2.14803857244138[/C][/ROW]
[ROW][C]46[/C][C]104.41[/C][C]107.014240201553[/C][C]-2.60424020155318[/C][/ROW]
[ROW][C]47[/C][C]104.47[/C][C]106.364182185619[/C][C]-1.89418218561891[/C][/ROW]
[ROW][C]48[/C][C]103.84[/C][C]107.582360055091[/C][C]-3.74236005509077[/C][/ROW]
[ROW][C]49[/C][C]103.65[/C][C]107.967230223254[/C][C]-4.31723022325357[/C][/ROW]
[ROW][C]50[/C][C]103.17[/C][C]108.289647365043[/C][C]-5.11964736504314[/C][/ROW]
[ROW][C]51[/C][C]103.4[/C][C]108.620288410106[/C][C]-5.22028841010626[/C][/ROW]
[ROW][C]52[/C][C]112.72[/C][C]108.485983721232[/C][C]4.23401627876775[/C][/ROW]
[ROW][C]53[/C][C]114.77[/C][C]109.299411927909[/C][C]5.4705880720908[/C][/ROW]
[ROW][C]54[/C][C]116.18[/C][C]108.983223396045[/C][C]7.19677660395545[/C][/ROW]
[ROW][C]55[/C][C]116.93[/C][C]108.377258860062[/C][C]8.5527411399378[/C][/ROW]
[ROW][C]56[/C][C]115.19[/C][C]107.823405473398[/C][C]7.36659452660232[/C][/ROW]
[ROW][C]57[/C][C]114.55[/C][C]108.111472814225[/C][C]6.43852718577467[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109263&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109263&T=4

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	100.44	100.628849131168	-0.188849131167517
2	100.88	100.853187641515	0.0268123584847969
3	101.42	100.911058033327	0.508941966672553
4	99.97	100.637767258683	-0.667767258682527
5	100.56	99.6206379079519	0.939362092048148
6	99.51	100.162925922058	-0.65292592205792
7	98.96	100.459130621604	-1.49913062160416
8	100.85	100.001308486456	0.848691513544215
9	100.66	100.192587927849	0.467412072151346
10	100.22	100.31242375893	-0.0924237589303361
11	100.3	99.9821781163628	0.31782188363717
12	100.73	99.576710329578	1.15328967042198
13	101.46	99.815249108864	1.64475089113603
14	101.35	100.023082703574	1.32691729642574
15	101.14	100.319570563609	0.8204294363909
16	101.68	99.9895698478029	1.69043015219715
17	101.47	100.06903113522	1.40096886477983
18	100.59	100.447564229784	0.14243577021632
19	101.18	100.381486701721	0.798513298279315
20	100.87	100.481821666892	0.388178333107574
21	99.79	100.464907510612	-0.674907510611772
22	100.74	100.48508087567	0.254919124330061
23	99.34	101.345755677424	-2.00575567742351
24	100.07	102.920982209606	-2.85098220960607
25	103.68	103.888363239844	-0.208363239844097
26	103.52	104.442058081905	-0.922058081904553
27	104.68	104.142609084648	0.53739091535196
28	103.75	104.236582821631	-0.486582821630952
29	103.7	104.436488720043	-0.736488720042878
30	102.98	104.979449290742	-1.99944929074166
31	106.3	104.41856832358	1.88143167641978
32	107.21	105.310234947143	1.89976505285717
33	106.83	106.134100497609	0.69589950239134
34	105.6	106.282739628256	-0.682739628255924
35	104.3	106.924434198127	-2.62443419812747
36	104.43	107.87793608577	-3.44793608577044
37	104.36	108.398379104904	-4.03837910490394
38	106.21	107.692823180668	-1.48282318066791
39	107.34	107.288175094723	0.0518249052770176
40	106.92	106.808352652138	0.111647347862366
41	104.8	105.987760324182	-1.1877603241816
42	103.85	105.274412481251	-1.42441248125139
43	103.39	105.43534814944	-2.04534814944043
44	103.38	105.581573521157	-2.20157352115725
45	103.93	106.078038572441	-2.14803857244138
46	104.41	107.014240201553	-2.60424020155318
47	104.47	106.364182185619	-1.89418218561891
48	103.84	107.582360055091	-3.74236005509077
49	103.65	107.967230223254	-4.31723022325357
50	103.17	108.289647365043	-5.11964736504314
51	103.4	108.620288410106	-5.22028841010626
52	112.72	108.485983721232	4.23401627876775
53	114.77	109.299411927909	5.4705880720908
54	116.18	108.983223396045	7.19677660395545
55	116.93	108.377258860062	8.5527411399378
56	115.19	107.823405473398	7.36659452660232
57	114.55	108.111472814225	6.43852718577467

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
7	0.0110919263314323	0.0221838526628646	0.988908073668568
8	0.00307341589620816	0.00614683179241632	0.996926584103792
9	0.000810860924750158	0.00162172184950032	0.99918913907525
10	0.000333032041551853	0.000666064083103707	0.999666967958448
11	5.94661321646689e-05	0.000118932264329338	0.999940533867835
12	4.89564065201209e-05	9.79128130402419e-05	0.99995104359348
13	4.24256990492723e-05	8.48513980985447e-05	0.99995757430095
14	1.40157723615351e-05	2.80315447230702e-05	0.999985984227639
15	3.03142571801483e-06	6.06285143602967e-06	0.999996968574282
16	7.28735107506242e-07	1.45747021501248e-06	0.999999271264893
17	1.57785564996345e-07	3.1557112999269e-07	0.999999842214435
18	5.74893316062774e-08	1.14978663212555e-07	0.999999942510668
19	1.28931498096247e-08	2.57862996192494e-08	0.99999998710685
20	2.72384101857145e-09	5.44768203714291e-09	0.99999999727616
21	1.6794062612227e-09	3.3588125224454e-09	0.999999998320594
22	4.8523449229751e-10	9.7046898459502e-10	0.999999999514765
23	1.24095322983146e-10	2.48190645966291e-10	0.999999999875905
24	3.43281005865454e-11	6.86562011730907e-11	0.999999999965672
25	2.43002797781207e-09	4.86005595562414e-09	0.999999997569972
26	6.05611122770546e-10	1.21122224554109e-09	0.999999999394389
27	2.32774084593878e-10	4.65548169187756e-10	0.999999999767226
28	5.09279563882809e-11	1.01855912776562e-10	0.999999999949072
29	1.2393172920087e-11	2.4786345840174e-11	0.999999999987607
30	3.06175388300055e-11	6.12350776600111e-11	0.999999999969382
31	4.66852046271487e-11	9.33704092542974e-11	0.999999999953315
32	1.30462374706155e-10	2.6092474941231e-10	0.999999999869538
33	6.34258773306336e-11	1.26851754661267e-10	0.999999999936574
34	1.71678736716874e-11	3.43357473433749e-11	0.999999999982832
35	6.97109256835058e-12	1.39421851367012e-11	0.999999999993029
36	3.4478032232141e-11	6.8956064464282e-11	0.999999999965522
37	9.50461915210832e-10	1.90092383042166e-09	0.999999999049538
38	4.12772994249908e-10	8.25545988499817e-10	0.999999999587227
39	2.6050983495391e-10	5.21019669907819e-10	0.99999999973949
40	1.00898982527744e-10	2.01797965055488e-10	0.9999999998991
41	5.89293304080852e-11	1.1785866081617e-10	0.99999999994107
42	3.6146951492443e-11	7.2293902984886e-11	0.999999999963853
43	4.02151343994447e-11	8.04302687988895e-11	0.999999999959785
44	3.75891694918603e-11	7.51783389837206e-11	0.99999999996241
45	2.0980305991169e-11	4.19606119823379e-11	0.99999999997902
46	1.38080906927148e-11	2.76161813854297e-11	0.999999999986192
47	4.0625829147808e-12	8.1251658295616e-12	0.999999999995937
48	3.2416436180385e-12	6.48328723607701e-12	0.999999999996758
49	1.65136635712565e-12	3.3027327142513e-12	0.999999999998349
50	4.26550611620233e-06	8.53101223240466e-06	0.999995734493884

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
7 & 0.0110919263314323 & 0.0221838526628646 & 0.988908073668568 \tabularnewline
8 & 0.00307341589620816 & 0.00614683179241632 & 0.996926584103792 \tabularnewline
9 & 0.000810860924750158 & 0.00162172184950032 & 0.99918913907525 \tabularnewline
10 & 0.000333032041551853 & 0.000666064083103707 & 0.999666967958448 \tabularnewline
11 & 5.94661321646689e-05 & 0.000118932264329338 & 0.999940533867835 \tabularnewline
12 & 4.89564065201209e-05 & 9.79128130402419e-05 & 0.99995104359348 \tabularnewline
13 & 4.24256990492723e-05 & 8.48513980985447e-05 & 0.99995757430095 \tabularnewline
14 & 1.40157723615351e-05 & 2.80315447230702e-05 & 0.999985984227639 \tabularnewline
15 & 3.03142571801483e-06 & 6.06285143602967e-06 & 0.999996968574282 \tabularnewline
16 & 7.28735107506242e-07 & 1.45747021501248e-06 & 0.999999271264893 \tabularnewline
17 & 1.57785564996345e-07 & 3.1557112999269e-07 & 0.999999842214435 \tabularnewline
18 & 5.74893316062774e-08 & 1.14978663212555e-07 & 0.999999942510668 \tabularnewline
19 & 1.28931498096247e-08 & 2.57862996192494e-08 & 0.99999998710685 \tabularnewline
20 & 2.72384101857145e-09 & 5.44768203714291e-09 & 0.99999999727616 \tabularnewline
21 & 1.6794062612227e-09 & 3.3588125224454e-09 & 0.999999998320594 \tabularnewline
22 & 4.8523449229751e-10 & 9.7046898459502e-10 & 0.999999999514765 \tabularnewline
23 & 1.24095322983146e-10 & 2.48190645966291e-10 & 0.999999999875905 \tabularnewline
24 & 3.43281005865454e-11 & 6.86562011730907e-11 & 0.999999999965672 \tabularnewline
25 & 2.43002797781207e-09 & 4.86005595562414e-09 & 0.999999997569972 \tabularnewline
26 & 6.05611122770546e-10 & 1.21122224554109e-09 & 0.999999999394389 \tabularnewline
27 & 2.32774084593878e-10 & 4.65548169187756e-10 & 0.999999999767226 \tabularnewline
28 & 5.09279563882809e-11 & 1.01855912776562e-10 & 0.999999999949072 \tabularnewline
29 & 1.2393172920087e-11 & 2.4786345840174e-11 & 0.999999999987607 \tabularnewline
30 & 3.06175388300055e-11 & 6.12350776600111e-11 & 0.999999999969382 \tabularnewline
31 & 4.66852046271487e-11 & 9.33704092542974e-11 & 0.999999999953315 \tabularnewline
32 & 1.30462374706155e-10 & 2.6092474941231e-10 & 0.999999999869538 \tabularnewline
33 & 6.34258773306336e-11 & 1.26851754661267e-10 & 0.999999999936574 \tabularnewline
34 & 1.71678736716874e-11 & 3.43357473433749e-11 & 0.999999999982832 \tabularnewline
35 & 6.97109256835058e-12 & 1.39421851367012e-11 & 0.999999999993029 \tabularnewline
36 & 3.4478032232141e-11 & 6.8956064464282e-11 & 0.999999999965522 \tabularnewline
37 & 9.50461915210832e-10 & 1.90092383042166e-09 & 0.999999999049538 \tabularnewline
38 & 4.12772994249908e-10 & 8.25545988499817e-10 & 0.999999999587227 \tabularnewline
39 & 2.6050983495391e-10 & 5.21019669907819e-10 & 0.99999999973949 \tabularnewline
40 & 1.00898982527744e-10 & 2.01797965055488e-10 & 0.9999999998991 \tabularnewline
41 & 5.89293304080852e-11 & 1.1785866081617e-10 & 0.99999999994107 \tabularnewline
42 & 3.6146951492443e-11 & 7.2293902984886e-11 & 0.999999999963853 \tabularnewline
43 & 4.02151343994447e-11 & 8.04302687988895e-11 & 0.999999999959785 \tabularnewline
44 & 3.75891694918603e-11 & 7.51783389837206e-11 & 0.99999999996241 \tabularnewline
45 & 2.0980305991169e-11 & 4.19606119823379e-11 & 0.99999999997902 \tabularnewline
46 & 1.38080906927148e-11 & 2.76161813854297e-11 & 0.999999999986192 \tabularnewline
47 & 4.0625829147808e-12 & 8.1251658295616e-12 & 0.999999999995937 \tabularnewline
48 & 3.2416436180385e-12 & 6.48328723607701e-12 & 0.999999999996758 \tabularnewline
49 & 1.65136635712565e-12 & 3.3027327142513e-12 & 0.999999999998349 \tabularnewline
50 & 4.26550611620233e-06 & 8.53101223240466e-06 & 0.999995734493884 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109263&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]7[/C][C]0.0110919263314323[/C][C]0.0221838526628646[/C][C]0.988908073668568[/C][/ROW]
[ROW][C]8[/C][C]0.00307341589620816[/C][C]0.00614683179241632[/C][C]0.996926584103792[/C][/ROW]
[ROW][C]9[/C][C]0.000810860924750158[/C][C]0.00162172184950032[/C][C]0.99918913907525[/C][/ROW]
[ROW][C]10[/C][C]0.000333032041551853[/C][C]0.000666064083103707[/C][C]0.999666967958448[/C][/ROW]
[ROW][C]11[/C][C]5.94661321646689e-05[/C][C]0.000118932264329338[/C][C]0.999940533867835[/C][/ROW]
[ROW][C]12[/C][C]4.89564065201209e-05[/C][C]9.79128130402419e-05[/C][C]0.99995104359348[/C][/ROW]
[ROW][C]13[/C][C]4.24256990492723e-05[/C][C]8.48513980985447e-05[/C][C]0.99995757430095[/C][/ROW]
[ROW][C]14[/C][C]1.40157723615351e-05[/C][C]2.80315447230702e-05[/C][C]0.999985984227639[/C][/ROW]
[ROW][C]15[/C][C]3.03142571801483e-06[/C][C]6.06285143602967e-06[/C][C]0.999996968574282[/C][/ROW]
[ROW][C]16[/C][C]7.28735107506242e-07[/C][C]1.45747021501248e-06[/C][C]0.999999271264893[/C][/ROW]
[ROW][C]17[/C][C]1.57785564996345e-07[/C][C]3.1557112999269e-07[/C][C]0.999999842214435[/C][/ROW]
[ROW][C]18[/C][C]5.74893316062774e-08[/C][C]1.14978663212555e-07[/C][C]0.999999942510668[/C][/ROW]
[ROW][C]19[/C][C]1.28931498096247e-08[/C][C]2.57862996192494e-08[/C][C]0.99999998710685[/C][/ROW]
[ROW][C]20[/C][C]2.72384101857145e-09[/C][C]5.44768203714291e-09[/C][C]0.99999999727616[/C][/ROW]
[ROW][C]21[/C][C]1.6794062612227e-09[/C][C]3.3588125224454e-09[/C][C]0.999999998320594[/C][/ROW]
[ROW][C]22[/C][C]4.8523449229751e-10[/C][C]9.7046898459502e-10[/C][C]0.999999999514765[/C][/ROW]
[ROW][C]23[/C][C]1.24095322983146e-10[/C][C]2.48190645966291e-10[/C][C]0.999999999875905[/C][/ROW]
[ROW][C]24[/C][C]3.43281005865454e-11[/C][C]6.86562011730907e-11[/C][C]0.999999999965672[/C][/ROW]
[ROW][C]25[/C][C]2.43002797781207e-09[/C][C]4.86005595562414e-09[/C][C]0.999999997569972[/C][/ROW]
[ROW][C]26[/C][C]6.05611122770546e-10[/C][C]1.21122224554109e-09[/C][C]0.999999999394389[/C][/ROW]
[ROW][C]27[/C][C]2.32774084593878e-10[/C][C]4.65548169187756e-10[/C][C]0.999999999767226[/C][/ROW]
[ROW][C]28[/C][C]5.09279563882809e-11[/C][C]1.01855912776562e-10[/C][C]0.999999999949072[/C][/ROW]
[ROW][C]29[/C][C]1.2393172920087e-11[/C][C]2.4786345840174e-11[/C][C]0.999999999987607[/C][/ROW]
[ROW][C]30[/C][C]3.06175388300055e-11[/C][C]6.12350776600111e-11[/C][C]0.999999999969382[/C][/ROW]
[ROW][C]31[/C][C]4.66852046271487e-11[/C][C]9.33704092542974e-11[/C][C]0.999999999953315[/C][/ROW]
[ROW][C]32[/C][C]1.30462374706155e-10[/C][C]2.6092474941231e-10[/C][C]0.999999999869538[/C][/ROW]
[ROW][C]33[/C][C]6.34258773306336e-11[/C][C]1.26851754661267e-10[/C][C]0.999999999936574[/C][/ROW]
[ROW][C]34[/C][C]1.71678736716874e-11[/C][C]3.43357473433749e-11[/C][C]0.999999999982832[/C][/ROW]
[ROW][C]35[/C][C]6.97109256835058e-12[/C][C]1.39421851367012e-11[/C][C]0.999999999993029[/C][/ROW]
[ROW][C]36[/C][C]3.4478032232141e-11[/C][C]6.8956064464282e-11[/C][C]0.999999999965522[/C][/ROW]
[ROW][C]37[/C][C]9.50461915210832e-10[/C][C]1.90092383042166e-09[/C][C]0.999999999049538[/C][/ROW]
[ROW][C]38[/C][C]4.12772994249908e-10[/C][C]8.25545988499817e-10[/C][C]0.999999999587227[/C][/ROW]
[ROW][C]39[/C][C]2.6050983495391e-10[/C][C]5.21019669907819e-10[/C][C]0.99999999973949[/C][/ROW]
[ROW][C]40[/C][C]1.00898982527744e-10[/C][C]2.01797965055488e-10[/C][C]0.9999999998991[/C][/ROW]
[ROW][C]41[/C][C]5.89293304080852e-11[/C][C]1.1785866081617e-10[/C][C]0.99999999994107[/C][/ROW]
[ROW][C]42[/C][C]3.6146951492443e-11[/C][C]7.2293902984886e-11[/C][C]0.999999999963853[/C][/ROW]
[ROW][C]43[/C][C]4.02151343994447e-11[/C][C]8.04302687988895e-11[/C][C]0.999999999959785[/C][/ROW]
[ROW][C]44[/C][C]3.75891694918603e-11[/C][C]7.51783389837206e-11[/C][C]0.99999999996241[/C][/ROW]
[ROW][C]45[/C][C]2.0980305991169e-11[/C][C]4.19606119823379e-11[/C][C]0.99999999997902[/C][/ROW]
[ROW][C]46[/C][C]1.38080906927148e-11[/C][C]2.76161813854297e-11[/C][C]0.999999999986192[/C][/ROW]
[ROW][C]47[/C][C]4.0625829147808e-12[/C][C]8.1251658295616e-12[/C][C]0.999999999995937[/C][/ROW]
[ROW][C]48[/C][C]3.2416436180385e-12[/C][C]6.48328723607701e-12[/C][C]0.999999999996758[/C][/ROW]
[ROW][C]49[/C][C]1.65136635712565e-12[/C][C]3.3027327142513e-12[/C][C]0.999999999998349[/C][/ROW]
[ROW][C]50[/C][C]4.26550611620233e-06[/C][C]8.53101223240466e-06[/C][C]0.999995734493884[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109263&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109263&T=5

As an alternative you can also use a QR Code:

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

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
7	0.0110919263314323	0.0221838526628646	0.988908073668568
8	0.00307341589620816	0.00614683179241632	0.996926584103792
9	0.000810860924750158	0.00162172184950032	0.99918913907525
10	0.000333032041551853	0.000666064083103707	0.999666967958448
11	5.94661321646689e-05	0.000118932264329338	0.999940533867835
12	4.89564065201209e-05	9.79128130402419e-05	0.99995104359348
13	4.24256990492723e-05	8.48513980985447e-05	0.99995757430095
14	1.40157723615351e-05	2.80315447230702e-05	0.999985984227639
15	3.03142571801483e-06	6.06285143602967e-06	0.999996968574282
16	7.28735107506242e-07	1.45747021501248e-06	0.999999271264893
17	1.57785564996345e-07	3.1557112999269e-07	0.999999842214435
18	5.74893316062774e-08	1.14978663212555e-07	0.999999942510668
19	1.28931498096247e-08	2.57862996192494e-08	0.99999998710685
20	2.72384101857145e-09	5.44768203714291e-09	0.99999999727616
21	1.6794062612227e-09	3.3588125224454e-09	0.999999998320594
22	4.8523449229751e-10	9.7046898459502e-10	0.999999999514765
23	1.24095322983146e-10	2.48190645966291e-10	0.999999999875905
24	3.43281005865454e-11	6.86562011730907e-11	0.999999999965672
25	2.43002797781207e-09	4.86005595562414e-09	0.999999997569972
26	6.05611122770546e-10	1.21122224554109e-09	0.999999999394389
27	2.32774084593878e-10	4.65548169187756e-10	0.999999999767226
28	5.09279563882809e-11	1.01855912776562e-10	0.999999999949072
29	1.2393172920087e-11	2.4786345840174e-11	0.999999999987607
30	3.06175388300055e-11	6.12350776600111e-11	0.999999999969382
31	4.66852046271487e-11	9.33704092542974e-11	0.999999999953315
32	1.30462374706155e-10	2.6092474941231e-10	0.999999999869538
33	6.34258773306336e-11	1.26851754661267e-10	0.999999999936574
34	1.71678736716874e-11	3.43357473433749e-11	0.999999999982832
35	6.97109256835058e-12	1.39421851367012e-11	0.999999999993029
36	3.4478032232141e-11	6.8956064464282e-11	0.999999999965522
37	9.50461915210832e-10	1.90092383042166e-09	0.999999999049538
38	4.12772994249908e-10	8.25545988499817e-10	0.999999999587227
39	2.6050983495391e-10	5.21019669907819e-10	0.99999999973949
40	1.00898982527744e-10	2.01797965055488e-10	0.9999999998991
41	5.89293304080852e-11	1.1785866081617e-10	0.99999999994107
42	3.6146951492443e-11	7.2293902984886e-11	0.999999999963853
43	4.02151343994447e-11	8.04302687988895e-11	0.999999999959785
44	3.75891694918603e-11	7.51783389837206e-11	0.99999999996241
45	2.0980305991169e-11	4.19606119823379e-11	0.99999999997902
46	1.38080906927148e-11	2.76161813854297e-11	0.999999999986192
47	4.0625829147808e-12	8.1251658295616e-12	0.999999999995937
48	3.2416436180385e-12	6.48328723607701e-12	0.999999999996758
49	1.65136635712565e-12	3.3027327142513e-12	0.999999999998349
50	4.26550611620233e-06	8.53101223240466e-06	0.999995734493884

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	43	0.977272727272727	NOK
5% type I error level	44	1	NOK
10% type I error level	44	1	NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 43 & 0.977272727272727 & NOK \tabularnewline
5% type I error level & 44 & 1 & NOK \tabularnewline
10% type I error level & 44 & 1 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109263&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]43[/C][C]0.977272727272727[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]44[/C][C]1[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]44[/C][C]1[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109263&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109263&T=6

As an alternative you can also use a QR Code:

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	43	0.977272727272727	NOK
5% type I error level	44	1	NOK
10% type I error level	44	1	NOK

Figure 1

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Figure 3

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Figure 4

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Figure 5

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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Figure 10

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Parameters (Session):

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')
}

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code