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rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Fri, 10 Dec 2010 09:44:18 +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/10/t12919741953uxbfaqgirg8ov6.htm/, Retrieved Mon, 29 Apr 2024 14:19:41 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=107470, Retrieved Mon, 29 Apr 2024 14:19:41 +0000

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Estimated Impact

304

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] [] [2010-12-10 09:44:18] [0bf4568947c4284a0258563e64d5d827] [Current]
-   P       [Multiple Regression] [] [2010-12-21 11:34:30] [504b6ff240ec7a3fcbc007044ae7a0bb] 

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Dataseries X:

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101.76	101.82	107.34	93.63	99.85
102.37	101.68	107.34	93.63	99.91
102.38	101.68	107.34	93.63	99.87
102.86	102.45	107.34	96.13	99.86
102.87	102.45	107.34	96.13	100.10
102.92	102.45	107.34	96.13	100.10
102.95	102.45	107.34	96.13	100.12
103.02	102.45	107.34	96.13	99.95
104.08	102.45	112.60	96.13	99.94
104.16	102.52	112.60	96.13	100.18
104.24	102.52	112.60	96.13	100.31
104.33	102.85	112.60	96.13	100.65
104.73	102.85	112.61	96.13	100.65
104.86	102.85	112.61	96.13	100.69
105.03	103.25	112.61	96.13	101.26
105.62	103.25	112.61	98.73	101.26
105.63	103.25	112.61	98.73	101.38
105.63	103.25	112.61	98.73	101.38
105.94	104.45	112.61	98.73	101.38
106.61	104.45	112.61	98.73	101.44
107.69	104.45	118.65	98.73	101.40
107.78	104.80	118.65	98.73	101.40
107.93	104.80	118.65	98.73	100.58
108.48	105.29	118.65	98.73	100.58
108.14	105.29	114.29	98.73	100.58
108.48	105.29	114.29	98.73	100.59
108.48	105.29	114.29	98.73	100.81
108.89	106.04	114.29	101.67	100.75
108.93	105.94	114.29	101.67	100.75
109.21	105.94	114.29	101.67	100.96
109.47	105.94	114.29	101.67	101.31
109.80	106.28	114.29	101.67	101.64
111.73	106.48	123.33	101.67	101.46
111.85	107.19	123.33	101.67	101.73
112.12	108.14	123.33	101.67	101.73
112.15	108.22	123.33	101.67	101.64
112.17	108.22	123.33	101.67	101.77
112.67	108.61	123.33	101.67	101.74
112.80	108.61	123.33	101.67	101.89
113.44	108.61	123.33	107.94	101.89
113.53	108.61	123.33	107.94	101.93
114.53	109.06	123.33	107.94	101.93
114.51	109.06	123.33	107.94	102.32
115.05	112.93	123.33	107.94	102.41
116.67	115.84	129.03	107.94	103.58
117.07	118.57	128.76	107.94	104.12
116.92	118.57	128.76	107.94	104.10
117.00	118.86	128.76	107.94	104.15
117.02	118.98	128.76	107.94	104.15
117.35	119.27	128.76	107.94	104.16
117.36	119.39	128.76	107.94	102.94
117.82	119.49	128.76	110.30	103.07
117.88	119.59	128.76	110.30	103.04
118.24	120.12	128.76	110.30	103.06
118.50	120.14	128.76	110.30	103.05
118.80	120.14	128.76	110.30	102.95
119.76	120.14	132.63	110.30	102.95
120.09	120.14	132.63	110.30	103.05

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	7 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

\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 & 7 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=107470&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]7 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=107470&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=107470&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	7 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation
HuurDVD[t] = + 83.1351258298301 -0.0934365930966516Cultuur[t] + 0.109135245038658Bioscoop[t] + 0.0869617581299381Schouwburg[t] + 0.0646540079085407EendagsA[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
HuurDVD[t] =  +  83.1351258298301 -0.0934365930966516Cultuur[t] +  0.109135245038658Bioscoop[t] +  0.0869617581299381Schouwburg[t] +  0.0646540079085407EendagsA[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=107470&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]HuurDVD[t] =  +  83.1351258298301 -0.0934365930966516Cultuur[t] +  0.109135245038658Bioscoop[t] +  0.0869617581299381Schouwburg[t] +  0.0646540079085407EendagsA[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=107470&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=107470&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
HuurDVD[t] = + 83.1351258298301 -0.0934365930966516Cultuur[t] + 0.109135245038658Bioscoop[t] + 0.0869617581299381Schouwburg[t] + 0.0646540079085407EendagsA[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)	83.1351258298301	1.838159	45.2274	0	0
Cultuur	-0.0934365930966516	0.09627	-0.9706	0.336171	0.168085
Bioscoop	0.109135245038658	0.028887	3.7781	0.000402	0.000201
Schouwburg	0.0869617581299381	0.039463	2.2036	0.031919	0.015959
EendagsA	0.0646540079085407	0.05414	1.1942	0.237718	0.118859

\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) & 83.1351258298301 & 1.838159 & 45.2274 & 0 & 0 \tabularnewline
Cultuur & -0.0934365930966516 & 0.09627 & -0.9706 & 0.336171 & 0.168085 \tabularnewline
Bioscoop & 0.109135245038658 & 0.028887 & 3.7781 & 0.000402 & 0.000201 \tabularnewline
Schouwburg & 0.0869617581299381 & 0.039463 & 2.2036 & 0.031919 & 0.015959 \tabularnewline
EendagsA & 0.0646540079085407 & 0.05414 & 1.1942 & 0.237718 & 0.118859 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=107470&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]83.1351258298301[/C][C]1.838159[/C][C]45.2274[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Cultuur[/C][C]-0.0934365930966516[/C][C]0.09627[/C][C]-0.9706[/C][C]0.336171[/C][C]0.168085[/C][/ROW]
[ROW][C]Bioscoop[/C][C]0.109135245038658[/C][C]0.028887[/C][C]3.7781[/C][C]0.000402[/C][C]0.000201[/C][/ROW]
[ROW][C]Schouwburg[/C][C]0.0869617581299381[/C][C]0.039463[/C][C]2.2036[/C][C]0.031919[/C][C]0.015959[/C][/ROW]
[ROW][C]EendagsA[/C][C]0.0646540079085407[/C][C]0.05414[/C][C]1.1942[/C][C]0.237718[/C][C]0.118859[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=107470&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=107470&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)	83.1351258298301	1.838159	45.2274	0	0
Cultuur	-0.0934365930966516	0.09627	-0.9706	0.336171	0.168085
Bioscoop	0.109135245038658	0.028887	3.7781	0.000402	0.000201
Schouwburg	0.0869617581299381	0.039463	2.2036	0.031919	0.015959
EendagsA	0.0646540079085407	0.05414	1.1942	0.237718	0.118859

Multiple Linear Regression - Regression Statistics
Multiple R	0.937045913686147
R-squared	0.878055044355907
Adjusted R-squared	0.868851651477107
F-TEST (value)	95.4055809546664
F-TEST (DF numerator)	4
F-TEST (DF denominator)	53
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.458486156322067
Sum Squared Residuals	11.1411064435661

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.937045913686147 \tabularnewline
R-squared & 0.878055044355907 \tabularnewline
Adjusted R-squared & 0.868851651477107 \tabularnewline
F-TEST (value) & 95.4055809546664 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 53 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.458486156322067 \tabularnewline
Sum Squared Residuals & 11.1411064435661 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=107470&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.937045913686147[/C][/ROW]
[ROW][C]R-squared[/C][C]0.878055044355907[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.868851651477107[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]95.4055809546664[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]53[/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]0.458486156322067[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]11.1411064435661[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=107470&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=107470&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	0.937045913686147
R-squared	0.878055044355907
Adjusted R-squared	0.868851651477107
F-TEST (value)	95.4055809546664
F-TEST (DF numerator)	4
F-TEST (DF denominator)	53
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.458486156322067
Sum Squared Residuals	11.1411064435661

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	99.85	100.127198644296	-0.277198644295529
2	99.91	100.054923388201	-0.144923388200880
3	99.87	100.05398902227	-0.183989022269906
4	99.86	100.254808616035	-0.394808616034638
5	100.1	100.253874250104	-0.153874250103675
6	100.1	100.249202420449	-0.149202420448842
7	100.12	100.246399322656	-0.126399322655933
8	99.95	100.239858761139	-0.289858761139169
9	99.94	100.598234820220	-0.658234820220197
10	100.18	100.598399359925	-0.418399359925161
11	100.31	100.590924432477	-0.280924432477434
12	100.65	100.618529769961	0.0314702300385115
13	100.65	100.582024750304	0.0679752496958729
14	100.69	100.569877993202	0.120122006798429
15	101.26	100.597647870391	0.662352129609404
16	101.26	100.710620701026	0.549379298974222
17	101.38	100.709686335095	0.670313664905178
18	101.38	100.709686335095	0.670313664905178
19	101.38	100.811683285281	0.568316714718751
20	101.44	100.749080767906	0.69091923209351
21	101.4	101.173418266467	0.226581733533075
22	101.4	101.203206308852	0.196793691148244
23	100.58	101.189190819887	-0.609190819887265
24	100.58	101.191276963753	-0.61127696375305
25	100.58	100.843892139959	-0.263892139959382
26	100.59	100.812123698307	-0.222123698306515
27	100.81	100.812123698307	-0.00212369830651655
28	100.75	101.045748912167	-0.295748912166995
29	100.75	101.031097923939	-0.281097923939262
30	100.96	101.004935677872	-0.0449356778722066
31	101.31	100.980642163667	0.329357836332932
32	101.64	100.986914071258	0.653085928741681
33	101.46	101.614542789084	-0.154542789084159
34	101.73	101.68081642189	0.0491835781100019
35	101.73	101.759267024541	-0.0292670245406264
36	101.64	101.765194746351	-0.125194746350822
37	101.77	101.763326014489	0.0066739855111056
38	101.74	101.759170463506	-0.0191704635056463
39	101.89	101.747023706403	0.142976293596923
40	101.89	102.092604916408	-0.202604916407770
41	101.93	102.084195623029	-0.154195623029064
42	101.93	102.039869890200	-0.109869890199809
43	102.32	102.041738622062	0.278261377938245
44	102.41	102.413636260089	-0.00363626008916682
45	103.58	103.075534563676	0.50446543632427
46	104.12	103.312619470698	0.807380529302485
47	104.1	103.326634959662	0.773365040337978
48	104.15	103.350809253276	0.79919074672451
49	104.15	103.362036750818	0.787963249181803
50	104.16	103.362851896158	0.79714810384248
51	102.94	103.375013759631	-0.435013759631192
52	103.07	103.495529909975	-0.425529909974759
53	103.04	103.500837238893	-0.460837238892813
54	103.06	103.525041745249	-0.465041745248511
55	103.05	103.502930935944	-0.452930935944159
56	102.95	103.474899958015	-0.524899958015158
57	102.95	103.721742832605	-0.771742832605233
58	103.05	103.690908756883	-0.640908756883344

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 99.85 & 100.127198644296 & -0.277198644295529 \tabularnewline
2 & 99.91 & 100.054923388201 & -0.144923388200880 \tabularnewline
3 & 99.87 & 100.05398902227 & -0.183989022269906 \tabularnewline
4 & 99.86 & 100.254808616035 & -0.394808616034638 \tabularnewline
5 & 100.1 & 100.253874250104 & -0.153874250103675 \tabularnewline
6 & 100.1 & 100.249202420449 & -0.149202420448842 \tabularnewline
7 & 100.12 & 100.246399322656 & -0.126399322655933 \tabularnewline
8 & 99.95 & 100.239858761139 & -0.289858761139169 \tabularnewline
9 & 99.94 & 100.598234820220 & -0.658234820220197 \tabularnewline
10 & 100.18 & 100.598399359925 & -0.418399359925161 \tabularnewline
11 & 100.31 & 100.590924432477 & -0.280924432477434 \tabularnewline
12 & 100.65 & 100.618529769961 & 0.0314702300385115 \tabularnewline
13 & 100.65 & 100.582024750304 & 0.0679752496958729 \tabularnewline
14 & 100.69 & 100.569877993202 & 0.120122006798429 \tabularnewline
15 & 101.26 & 100.597647870391 & 0.662352129609404 \tabularnewline
16 & 101.26 & 100.710620701026 & 0.549379298974222 \tabularnewline
17 & 101.38 & 100.709686335095 & 0.670313664905178 \tabularnewline
18 & 101.38 & 100.709686335095 & 0.670313664905178 \tabularnewline
19 & 101.38 & 100.811683285281 & 0.568316714718751 \tabularnewline
20 & 101.44 & 100.749080767906 & 0.69091923209351 \tabularnewline
21 & 101.4 & 101.173418266467 & 0.226581733533075 \tabularnewline
22 & 101.4 & 101.203206308852 & 0.196793691148244 \tabularnewline
23 & 100.58 & 101.189190819887 & -0.609190819887265 \tabularnewline
24 & 100.58 & 101.191276963753 & -0.61127696375305 \tabularnewline
25 & 100.58 & 100.843892139959 & -0.263892139959382 \tabularnewline
26 & 100.59 & 100.812123698307 & -0.222123698306515 \tabularnewline
27 & 100.81 & 100.812123698307 & -0.00212369830651655 \tabularnewline
28 & 100.75 & 101.045748912167 & -0.295748912166995 \tabularnewline
29 & 100.75 & 101.031097923939 & -0.281097923939262 \tabularnewline
30 & 100.96 & 101.004935677872 & -0.0449356778722066 \tabularnewline
31 & 101.31 & 100.980642163667 & 0.329357836332932 \tabularnewline
32 & 101.64 & 100.986914071258 & 0.653085928741681 \tabularnewline
33 & 101.46 & 101.614542789084 & -0.154542789084159 \tabularnewline
34 & 101.73 & 101.68081642189 & 0.0491835781100019 \tabularnewline
35 & 101.73 & 101.759267024541 & -0.0292670245406264 \tabularnewline
36 & 101.64 & 101.765194746351 & -0.125194746350822 \tabularnewline
37 & 101.77 & 101.763326014489 & 0.0066739855111056 \tabularnewline
38 & 101.74 & 101.759170463506 & -0.0191704635056463 \tabularnewline
39 & 101.89 & 101.747023706403 & 0.142976293596923 \tabularnewline
40 & 101.89 & 102.092604916408 & -0.202604916407770 \tabularnewline
41 & 101.93 & 102.084195623029 & -0.154195623029064 \tabularnewline
42 & 101.93 & 102.039869890200 & -0.109869890199809 \tabularnewline
43 & 102.32 & 102.041738622062 & 0.278261377938245 \tabularnewline
44 & 102.41 & 102.413636260089 & -0.00363626008916682 \tabularnewline
45 & 103.58 & 103.075534563676 & 0.50446543632427 \tabularnewline
46 & 104.12 & 103.312619470698 & 0.807380529302485 \tabularnewline
47 & 104.1 & 103.326634959662 & 0.773365040337978 \tabularnewline
48 & 104.15 & 103.350809253276 & 0.79919074672451 \tabularnewline
49 & 104.15 & 103.362036750818 & 0.787963249181803 \tabularnewline
50 & 104.16 & 103.362851896158 & 0.79714810384248 \tabularnewline
51 & 102.94 & 103.375013759631 & -0.435013759631192 \tabularnewline
52 & 103.07 & 103.495529909975 & -0.425529909974759 \tabularnewline
53 & 103.04 & 103.500837238893 & -0.460837238892813 \tabularnewline
54 & 103.06 & 103.525041745249 & -0.465041745248511 \tabularnewline
55 & 103.05 & 103.502930935944 & -0.452930935944159 \tabularnewline
56 & 102.95 & 103.474899958015 & -0.524899958015158 \tabularnewline
57 & 102.95 & 103.721742832605 & -0.771742832605233 \tabularnewline
58 & 103.05 & 103.690908756883 & -0.640908756883344 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=107470&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]99.85[/C][C]100.127198644296[/C][C]-0.277198644295529[/C][/ROW]
[ROW][C]2[/C][C]99.91[/C][C]100.054923388201[/C][C]-0.144923388200880[/C][/ROW]
[ROW][C]3[/C][C]99.87[/C][C]100.05398902227[/C][C]-0.183989022269906[/C][/ROW]
[ROW][C]4[/C][C]99.86[/C][C]100.254808616035[/C][C]-0.394808616034638[/C][/ROW]
[ROW][C]5[/C][C]100.1[/C][C]100.253874250104[/C][C]-0.153874250103675[/C][/ROW]
[ROW][C]6[/C][C]100.1[/C][C]100.249202420449[/C][C]-0.149202420448842[/C][/ROW]
[ROW][C]7[/C][C]100.12[/C][C]100.246399322656[/C][C]-0.126399322655933[/C][/ROW]
[ROW][C]8[/C][C]99.95[/C][C]100.239858761139[/C][C]-0.289858761139169[/C][/ROW]
[ROW][C]9[/C][C]99.94[/C][C]100.598234820220[/C][C]-0.658234820220197[/C][/ROW]
[ROW][C]10[/C][C]100.18[/C][C]100.598399359925[/C][C]-0.418399359925161[/C][/ROW]
[ROW][C]11[/C][C]100.31[/C][C]100.590924432477[/C][C]-0.280924432477434[/C][/ROW]
[ROW][C]12[/C][C]100.65[/C][C]100.618529769961[/C][C]0.0314702300385115[/C][/ROW]
[ROW][C]13[/C][C]100.65[/C][C]100.582024750304[/C][C]0.0679752496958729[/C][/ROW]
[ROW][C]14[/C][C]100.69[/C][C]100.569877993202[/C][C]0.120122006798429[/C][/ROW]
[ROW][C]15[/C][C]101.26[/C][C]100.597647870391[/C][C]0.662352129609404[/C][/ROW]
[ROW][C]16[/C][C]101.26[/C][C]100.710620701026[/C][C]0.549379298974222[/C][/ROW]
[ROW][C]17[/C][C]101.38[/C][C]100.709686335095[/C][C]0.670313664905178[/C][/ROW]
[ROW][C]18[/C][C]101.38[/C][C]100.709686335095[/C][C]0.670313664905178[/C][/ROW]
[ROW][C]19[/C][C]101.38[/C][C]100.811683285281[/C][C]0.568316714718751[/C][/ROW]
[ROW][C]20[/C][C]101.44[/C][C]100.749080767906[/C][C]0.69091923209351[/C][/ROW]
[ROW][C]21[/C][C]101.4[/C][C]101.173418266467[/C][C]0.226581733533075[/C][/ROW]
[ROW][C]22[/C][C]101.4[/C][C]101.203206308852[/C][C]0.196793691148244[/C][/ROW]
[ROW][C]23[/C][C]100.58[/C][C]101.189190819887[/C][C]-0.609190819887265[/C][/ROW]
[ROW][C]24[/C][C]100.58[/C][C]101.191276963753[/C][C]-0.61127696375305[/C][/ROW]
[ROW][C]25[/C][C]100.58[/C][C]100.843892139959[/C][C]-0.263892139959382[/C][/ROW]
[ROW][C]26[/C][C]100.59[/C][C]100.812123698307[/C][C]-0.222123698306515[/C][/ROW]
[ROW][C]27[/C][C]100.81[/C][C]100.812123698307[/C][C]-0.00212369830651655[/C][/ROW]
[ROW][C]28[/C][C]100.75[/C][C]101.045748912167[/C][C]-0.295748912166995[/C][/ROW]
[ROW][C]29[/C][C]100.75[/C][C]101.031097923939[/C][C]-0.281097923939262[/C][/ROW]
[ROW][C]30[/C][C]100.96[/C][C]101.004935677872[/C][C]-0.0449356778722066[/C][/ROW]
[ROW][C]31[/C][C]101.31[/C][C]100.980642163667[/C][C]0.329357836332932[/C][/ROW]
[ROW][C]32[/C][C]101.64[/C][C]100.986914071258[/C][C]0.653085928741681[/C][/ROW]
[ROW][C]33[/C][C]101.46[/C][C]101.614542789084[/C][C]-0.154542789084159[/C][/ROW]
[ROW][C]34[/C][C]101.73[/C][C]101.68081642189[/C][C]0.0491835781100019[/C][/ROW]
[ROW][C]35[/C][C]101.73[/C][C]101.759267024541[/C][C]-0.0292670245406264[/C][/ROW]
[ROW][C]36[/C][C]101.64[/C][C]101.765194746351[/C][C]-0.125194746350822[/C][/ROW]
[ROW][C]37[/C][C]101.77[/C][C]101.763326014489[/C][C]0.0066739855111056[/C][/ROW]
[ROW][C]38[/C][C]101.74[/C][C]101.759170463506[/C][C]-0.0191704635056463[/C][/ROW]
[ROW][C]39[/C][C]101.89[/C][C]101.747023706403[/C][C]0.142976293596923[/C][/ROW]
[ROW][C]40[/C][C]101.89[/C][C]102.092604916408[/C][C]-0.202604916407770[/C][/ROW]
[ROW][C]41[/C][C]101.93[/C][C]102.084195623029[/C][C]-0.154195623029064[/C][/ROW]
[ROW][C]42[/C][C]101.93[/C][C]102.039869890200[/C][C]-0.109869890199809[/C][/ROW]
[ROW][C]43[/C][C]102.32[/C][C]102.041738622062[/C][C]0.278261377938245[/C][/ROW]
[ROW][C]44[/C][C]102.41[/C][C]102.413636260089[/C][C]-0.00363626008916682[/C][/ROW]
[ROW][C]45[/C][C]103.58[/C][C]103.075534563676[/C][C]0.50446543632427[/C][/ROW]
[ROW][C]46[/C][C]104.12[/C][C]103.312619470698[/C][C]0.807380529302485[/C][/ROW]
[ROW][C]47[/C][C]104.1[/C][C]103.326634959662[/C][C]0.773365040337978[/C][/ROW]
[ROW][C]48[/C][C]104.15[/C][C]103.350809253276[/C][C]0.79919074672451[/C][/ROW]
[ROW][C]49[/C][C]104.15[/C][C]103.362036750818[/C][C]0.787963249181803[/C][/ROW]
[ROW][C]50[/C][C]104.16[/C][C]103.362851896158[/C][C]0.79714810384248[/C][/ROW]
[ROW][C]51[/C][C]102.94[/C][C]103.375013759631[/C][C]-0.435013759631192[/C][/ROW]
[ROW][C]52[/C][C]103.07[/C][C]103.495529909975[/C][C]-0.425529909974759[/C][/ROW]
[ROW][C]53[/C][C]103.04[/C][C]103.500837238893[/C][C]-0.460837238892813[/C][/ROW]
[ROW][C]54[/C][C]103.06[/C][C]103.525041745249[/C][C]-0.465041745248511[/C][/ROW]
[ROW][C]55[/C][C]103.05[/C][C]103.502930935944[/C][C]-0.452930935944159[/C][/ROW]
[ROW][C]56[/C][C]102.95[/C][C]103.474899958015[/C][C]-0.524899958015158[/C][/ROW]
[ROW][C]57[/C][C]102.95[/C][C]103.721742832605[/C][C]-0.771742832605233[/C][/ROW]
[ROW][C]58[/C][C]103.05[/C][C]103.690908756883[/C][C]-0.640908756883344[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=107470&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=107470&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	99.85	100.127198644296	-0.277198644295529
2	99.91	100.054923388201	-0.144923388200880
3	99.87	100.05398902227	-0.183989022269906
4	99.86	100.254808616035	-0.394808616034638
5	100.1	100.253874250104	-0.153874250103675
6	100.1	100.249202420449	-0.149202420448842
7	100.12	100.246399322656	-0.126399322655933
8	99.95	100.239858761139	-0.289858761139169
9	99.94	100.598234820220	-0.658234820220197
10	100.18	100.598399359925	-0.418399359925161
11	100.31	100.590924432477	-0.280924432477434
12	100.65	100.618529769961	0.0314702300385115
13	100.65	100.582024750304	0.0679752496958729
14	100.69	100.569877993202	0.120122006798429
15	101.26	100.597647870391	0.662352129609404
16	101.26	100.710620701026	0.549379298974222
17	101.38	100.709686335095	0.670313664905178
18	101.38	100.709686335095	0.670313664905178
19	101.38	100.811683285281	0.568316714718751
20	101.44	100.749080767906	0.69091923209351
21	101.4	101.173418266467	0.226581733533075
22	101.4	101.203206308852	0.196793691148244
23	100.58	101.189190819887	-0.609190819887265
24	100.58	101.191276963753	-0.61127696375305
25	100.58	100.843892139959	-0.263892139959382
26	100.59	100.812123698307	-0.222123698306515
27	100.81	100.812123698307	-0.00212369830651655
28	100.75	101.045748912167	-0.295748912166995
29	100.75	101.031097923939	-0.281097923939262
30	100.96	101.004935677872	-0.0449356778722066
31	101.31	100.980642163667	0.329357836332932
32	101.64	100.986914071258	0.653085928741681
33	101.46	101.614542789084	-0.154542789084159
34	101.73	101.68081642189	0.0491835781100019
35	101.73	101.759267024541	-0.0292670245406264
36	101.64	101.765194746351	-0.125194746350822
37	101.77	101.763326014489	0.0066739855111056
38	101.74	101.759170463506	-0.0191704635056463
39	101.89	101.747023706403	0.142976293596923
40	101.89	102.092604916408	-0.202604916407770
41	101.93	102.084195623029	-0.154195623029064
42	101.93	102.039869890200	-0.109869890199809
43	102.32	102.041738622062	0.278261377938245
44	102.41	102.413636260089	-0.00363626008916682
45	103.58	103.075534563676	0.50446543632427
46	104.12	103.312619470698	0.807380529302485
47	104.1	103.326634959662	0.773365040337978
48	104.15	103.350809253276	0.79919074672451
49	104.15	103.362036750818	0.787963249181803
50	104.16	103.362851896158	0.79714810384248
51	102.94	103.375013759631	-0.435013759631192
52	103.07	103.495529909975	-0.425529909974759
53	103.04	103.500837238893	-0.460837238892813
54	103.06	103.525041745249	-0.465041745248511
55	103.05	103.502930935944	-0.452930935944159
56	102.95	103.474899958015	-0.524899958015158
57	102.95	103.721742832605	-0.771742832605233
58	103.05	103.690908756883	-0.640908756883344

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
8	0.0289658713591797	0.0579317427183593	0.97103412864082
9	0.00746213064323157	0.0149242612864631	0.992537869356768
10	0.00216179362829080	0.00432358725658161	0.99783820637171
11	0.00063376116478035	0.0012675223295607	0.99936623883522
12	0.000282983811411905	0.000565967622823811	0.999717016188588
13	0.000106513456637936	0.000213026913275872	0.999893486543362
14	2.59918901128813e-05	5.19837802257625e-05	0.999974008109887
15	5.47678210960592e-06	1.09535642192118e-05	0.99999452321789
16	0.000770064060613216	0.00154012812122643	0.999229935939387
17	0.00119871914466579	0.00239743828933158	0.998801280855334
18	0.00087729561782457	0.00175459123564914	0.999122704382175
19	0.000778754016190083	0.00155750803238017	0.99922124598381
20	0.0021863964359172	0.0043727928718344	0.997813603564083
21	0.00230804787171213	0.00461609574342425	0.997691952128288
22	0.00159498883196763	0.00318997766393526	0.998405011168032
23	0.0422248428992797	0.0844496857985594	0.95777515710072
24	0.174225941550468	0.348451883100937	0.825774058449532
25	0.24101990674562	0.48203981349124	0.75898009325438
26	0.227601318898627	0.455202637797254	0.772398681101373
27	0.174439467581799	0.348878935163598	0.8255605324182
28	0.234329145405208	0.468658290810416	0.765670854594792
29	0.297638940656466	0.595277881312933	0.702361059343534
30	0.297691480171988	0.595382960343977	0.702308519828012
31	0.244284925505156	0.488569851010312	0.755715074494844
32	0.228159175735471	0.456318351470941	0.77184082426453
33	0.201929374191878	0.403858748383756	0.798070625808122
34	0.160903059470874	0.321806118941748	0.839096940529126
35	0.144052211664309	0.288104423328618	0.855947788335691
36	0.140024790897970	0.280049581795940	0.85997520910203
37	0.146776591626137	0.293553183252274	0.853223408373863
38	0.162806012368132	0.325612024736265	0.837193987631868
39	0.633276099769395	0.733447800461209	0.366723900230605
40	0.633062812935343	0.733874374129313	0.366937187064656
41	0.648388781821508	0.703222436356984	0.351611218178492
42	0.585919113484092	0.828161773031816	0.414080886515908
43	0.515764917684114	0.968470164631771	0.484235082315886
44	0.449894036454031	0.899788072908061	0.55010596354597
45	0.584384989232597	0.831230021534806	0.415615010767403
46	0.488499046882027	0.976998093764055	0.511500953117973
47	0.382325755874989	0.764651511749978	0.617674244125011
48	0.27816375170928	0.55632750341856	0.72183624829072
49	0.250508298317221	0.501016596634442	0.749491701682779
50	0.992493813993596	0.0150123720128081	0.00750618600640404

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 0.0289658713591797 & 0.0579317427183593 & 0.97103412864082 \tabularnewline
9 & 0.00746213064323157 & 0.0149242612864631 & 0.992537869356768 \tabularnewline
10 & 0.00216179362829080 & 0.00432358725658161 & 0.99783820637171 \tabularnewline
11 & 0.00063376116478035 & 0.0012675223295607 & 0.99936623883522 \tabularnewline
12 & 0.000282983811411905 & 0.000565967622823811 & 0.999717016188588 \tabularnewline
13 & 0.000106513456637936 & 0.000213026913275872 & 0.999893486543362 \tabularnewline
14 & 2.59918901128813e-05 & 5.19837802257625e-05 & 0.999974008109887 \tabularnewline
15 & 5.47678210960592e-06 & 1.09535642192118e-05 & 0.99999452321789 \tabularnewline
16 & 0.000770064060613216 & 0.00154012812122643 & 0.999229935939387 \tabularnewline
17 & 0.00119871914466579 & 0.00239743828933158 & 0.998801280855334 \tabularnewline
18 & 0.00087729561782457 & 0.00175459123564914 & 0.999122704382175 \tabularnewline
19 & 0.000778754016190083 & 0.00155750803238017 & 0.99922124598381 \tabularnewline
20 & 0.0021863964359172 & 0.0043727928718344 & 0.997813603564083 \tabularnewline
21 & 0.00230804787171213 & 0.00461609574342425 & 0.997691952128288 \tabularnewline
22 & 0.00159498883196763 & 0.00318997766393526 & 0.998405011168032 \tabularnewline
23 & 0.0422248428992797 & 0.0844496857985594 & 0.95777515710072 \tabularnewline
24 & 0.174225941550468 & 0.348451883100937 & 0.825774058449532 \tabularnewline
25 & 0.24101990674562 & 0.48203981349124 & 0.75898009325438 \tabularnewline
26 & 0.227601318898627 & 0.455202637797254 & 0.772398681101373 \tabularnewline
27 & 0.174439467581799 & 0.348878935163598 & 0.8255605324182 \tabularnewline
28 & 0.234329145405208 & 0.468658290810416 & 0.765670854594792 \tabularnewline
29 & 0.297638940656466 & 0.595277881312933 & 0.702361059343534 \tabularnewline
30 & 0.297691480171988 & 0.595382960343977 & 0.702308519828012 \tabularnewline
31 & 0.244284925505156 & 0.488569851010312 & 0.755715074494844 \tabularnewline
32 & 0.228159175735471 & 0.456318351470941 & 0.77184082426453 \tabularnewline
33 & 0.201929374191878 & 0.403858748383756 & 0.798070625808122 \tabularnewline
34 & 0.160903059470874 & 0.321806118941748 & 0.839096940529126 \tabularnewline
35 & 0.144052211664309 & 0.288104423328618 & 0.855947788335691 \tabularnewline
36 & 0.140024790897970 & 0.280049581795940 & 0.85997520910203 \tabularnewline
37 & 0.146776591626137 & 0.293553183252274 & 0.853223408373863 \tabularnewline
38 & 0.162806012368132 & 0.325612024736265 & 0.837193987631868 \tabularnewline
39 & 0.633276099769395 & 0.733447800461209 & 0.366723900230605 \tabularnewline
40 & 0.633062812935343 & 0.733874374129313 & 0.366937187064656 \tabularnewline
41 & 0.648388781821508 & 0.703222436356984 & 0.351611218178492 \tabularnewline
42 & 0.585919113484092 & 0.828161773031816 & 0.414080886515908 \tabularnewline
43 & 0.515764917684114 & 0.968470164631771 & 0.484235082315886 \tabularnewline
44 & 0.449894036454031 & 0.899788072908061 & 0.55010596354597 \tabularnewline
45 & 0.584384989232597 & 0.831230021534806 & 0.415615010767403 \tabularnewline
46 & 0.488499046882027 & 0.976998093764055 & 0.511500953117973 \tabularnewline
47 & 0.382325755874989 & 0.764651511749978 & 0.617674244125011 \tabularnewline
48 & 0.27816375170928 & 0.55632750341856 & 0.72183624829072 \tabularnewline
49 & 0.250508298317221 & 0.501016596634442 & 0.749491701682779 \tabularnewline
50 & 0.992493813993596 & 0.0150123720128081 & 0.00750618600640404 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=107470&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]8[/C][C]0.0289658713591797[/C][C]0.0579317427183593[/C][C]0.97103412864082[/C][/ROW]
[ROW][C]9[/C][C]0.00746213064323157[/C][C]0.0149242612864631[/C][C]0.992537869356768[/C][/ROW]
[ROW][C]10[/C][C]0.00216179362829080[/C][C]0.00432358725658161[/C][C]0.99783820637171[/C][/ROW]
[ROW][C]11[/C][C]0.00063376116478035[/C][C]0.0012675223295607[/C][C]0.99936623883522[/C][/ROW]
[ROW][C]12[/C][C]0.000282983811411905[/C][C]0.000565967622823811[/C][C]0.999717016188588[/C][/ROW]
[ROW][C]13[/C][C]0.000106513456637936[/C][C]0.000213026913275872[/C][C]0.999893486543362[/C][/ROW]
[ROW][C]14[/C][C]2.59918901128813e-05[/C][C]5.19837802257625e-05[/C][C]0.999974008109887[/C][/ROW]
[ROW][C]15[/C][C]5.47678210960592e-06[/C][C]1.09535642192118e-05[/C][C]0.99999452321789[/C][/ROW]
[ROW][C]16[/C][C]0.000770064060613216[/C][C]0.00154012812122643[/C][C]0.999229935939387[/C][/ROW]
[ROW][C]17[/C][C]0.00119871914466579[/C][C]0.00239743828933158[/C][C]0.998801280855334[/C][/ROW]
[ROW][C]18[/C][C]0.00087729561782457[/C][C]0.00175459123564914[/C][C]0.999122704382175[/C][/ROW]
[ROW][C]19[/C][C]0.000778754016190083[/C][C]0.00155750803238017[/C][C]0.99922124598381[/C][/ROW]
[ROW][C]20[/C][C]0.0021863964359172[/C][C]0.0043727928718344[/C][C]0.997813603564083[/C][/ROW]
[ROW][C]21[/C][C]0.00230804787171213[/C][C]0.00461609574342425[/C][C]0.997691952128288[/C][/ROW]
[ROW][C]22[/C][C]0.00159498883196763[/C][C]0.00318997766393526[/C][C]0.998405011168032[/C][/ROW]
[ROW][C]23[/C][C]0.0422248428992797[/C][C]0.0844496857985594[/C][C]0.95777515710072[/C][/ROW]
[ROW][C]24[/C][C]0.174225941550468[/C][C]0.348451883100937[/C][C]0.825774058449532[/C][/ROW]
[ROW][C]25[/C][C]0.24101990674562[/C][C]0.48203981349124[/C][C]0.75898009325438[/C][/ROW]
[ROW][C]26[/C][C]0.227601318898627[/C][C]0.455202637797254[/C][C]0.772398681101373[/C][/ROW]
[ROW][C]27[/C][C]0.174439467581799[/C][C]0.348878935163598[/C][C]0.8255605324182[/C][/ROW]
[ROW][C]28[/C][C]0.234329145405208[/C][C]0.468658290810416[/C][C]0.765670854594792[/C][/ROW]
[ROW][C]29[/C][C]0.297638940656466[/C][C]0.595277881312933[/C][C]0.702361059343534[/C][/ROW]
[ROW][C]30[/C][C]0.297691480171988[/C][C]0.595382960343977[/C][C]0.702308519828012[/C][/ROW]
[ROW][C]31[/C][C]0.244284925505156[/C][C]0.488569851010312[/C][C]0.755715074494844[/C][/ROW]
[ROW][C]32[/C][C]0.228159175735471[/C][C]0.456318351470941[/C][C]0.77184082426453[/C][/ROW]
[ROW][C]33[/C][C]0.201929374191878[/C][C]0.403858748383756[/C][C]0.798070625808122[/C][/ROW]
[ROW][C]34[/C][C]0.160903059470874[/C][C]0.321806118941748[/C][C]0.839096940529126[/C][/ROW]
[ROW][C]35[/C][C]0.144052211664309[/C][C]0.288104423328618[/C][C]0.855947788335691[/C][/ROW]
[ROW][C]36[/C][C]0.140024790897970[/C][C]0.280049581795940[/C][C]0.85997520910203[/C][/ROW]
[ROW][C]37[/C][C]0.146776591626137[/C][C]0.293553183252274[/C][C]0.853223408373863[/C][/ROW]
[ROW][C]38[/C][C]0.162806012368132[/C][C]0.325612024736265[/C][C]0.837193987631868[/C][/ROW]
[ROW][C]39[/C][C]0.633276099769395[/C][C]0.733447800461209[/C][C]0.366723900230605[/C][/ROW]
[ROW][C]40[/C][C]0.633062812935343[/C][C]0.733874374129313[/C][C]0.366937187064656[/C][/ROW]
[ROW][C]41[/C][C]0.648388781821508[/C][C]0.703222436356984[/C][C]0.351611218178492[/C][/ROW]
[ROW][C]42[/C][C]0.585919113484092[/C][C]0.828161773031816[/C][C]0.414080886515908[/C][/ROW]
[ROW][C]43[/C][C]0.515764917684114[/C][C]0.968470164631771[/C][C]0.484235082315886[/C][/ROW]
[ROW][C]44[/C][C]0.449894036454031[/C][C]0.899788072908061[/C][C]0.55010596354597[/C][/ROW]
[ROW][C]45[/C][C]0.584384989232597[/C][C]0.831230021534806[/C][C]0.415615010767403[/C][/ROW]
[ROW][C]46[/C][C]0.488499046882027[/C][C]0.976998093764055[/C][C]0.511500953117973[/C][/ROW]
[ROW][C]47[/C][C]0.382325755874989[/C][C]0.764651511749978[/C][C]0.617674244125011[/C][/ROW]
[ROW][C]48[/C][C]0.27816375170928[/C][C]0.55632750341856[/C][C]0.72183624829072[/C][/ROW]
[ROW][C]49[/C][C]0.250508298317221[/C][C]0.501016596634442[/C][C]0.749491701682779[/C][/ROW]
[ROW][C]50[/C][C]0.992493813993596[/C][C]0.0150123720128081[/C][C]0.00750618600640404[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=107470&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=107470&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
8	0.0289658713591797	0.0579317427183593	0.97103412864082
9	0.00746213064323157	0.0149242612864631	0.992537869356768
10	0.00216179362829080	0.00432358725658161	0.99783820637171
11	0.00063376116478035	0.0012675223295607	0.99936623883522
12	0.000282983811411905	0.000565967622823811	0.999717016188588
13	0.000106513456637936	0.000213026913275872	0.999893486543362
14	2.59918901128813e-05	5.19837802257625e-05	0.999974008109887
15	5.47678210960592e-06	1.09535642192118e-05	0.99999452321789
16	0.000770064060613216	0.00154012812122643	0.999229935939387
17	0.00119871914466579	0.00239743828933158	0.998801280855334
18	0.00087729561782457	0.00175459123564914	0.999122704382175
19	0.000778754016190083	0.00155750803238017	0.99922124598381
20	0.0021863964359172	0.0043727928718344	0.997813603564083
21	0.00230804787171213	0.00461609574342425	0.997691952128288
22	0.00159498883196763	0.00318997766393526	0.998405011168032
23	0.0422248428992797	0.0844496857985594	0.95777515710072
24	0.174225941550468	0.348451883100937	0.825774058449532
25	0.24101990674562	0.48203981349124	0.75898009325438
26	0.227601318898627	0.455202637797254	0.772398681101373
27	0.174439467581799	0.348878935163598	0.8255605324182
28	0.234329145405208	0.468658290810416	0.765670854594792
29	0.297638940656466	0.595277881312933	0.702361059343534
30	0.297691480171988	0.595382960343977	0.702308519828012
31	0.244284925505156	0.488569851010312	0.755715074494844
32	0.228159175735471	0.456318351470941	0.77184082426453
33	0.201929374191878	0.403858748383756	0.798070625808122
34	0.160903059470874	0.321806118941748	0.839096940529126
35	0.144052211664309	0.288104423328618	0.855947788335691
36	0.140024790897970	0.280049581795940	0.85997520910203
37	0.146776591626137	0.293553183252274	0.853223408373863
38	0.162806012368132	0.325612024736265	0.837193987631868
39	0.633276099769395	0.733447800461209	0.366723900230605
40	0.633062812935343	0.733874374129313	0.366937187064656
41	0.648388781821508	0.703222436356984	0.351611218178492
42	0.585919113484092	0.828161773031816	0.414080886515908
43	0.515764917684114	0.968470164631771	0.484235082315886
44	0.449894036454031	0.899788072908061	0.55010596354597
45	0.584384989232597	0.831230021534806	0.415615010767403
46	0.488499046882027	0.976998093764055	0.511500953117973
47	0.382325755874989	0.764651511749978	0.617674244125011
48	0.27816375170928	0.55632750341856	0.72183624829072
49	0.250508298317221	0.501016596634442	0.749491701682779
50	0.992493813993596	0.0150123720128081	0.00750618600640404

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	13	0.302325581395349	NOK
5% type I error level	15	0.348837209302326	NOK
10% type I error level	17	0.395348837209302	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 & 13 & 0.302325581395349 & NOK \tabularnewline
5% type I error level & 15 & 0.348837209302326 & NOK \tabularnewline
10% type I error level & 17 & 0.395348837209302 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=107470&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]13[/C][C]0.302325581395349[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]15[/C][C]0.348837209302326[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]17[/C][C]0.395348837209302[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=107470&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=107470&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	13	0.302325581395349	NOK
5% type I error level	15	0.348837209302326	NOK
10% type I error level	17	0.395348837209302	NOK

Figure 1

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Figure 2

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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 = 5 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 5 ; 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