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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationThu, 15 Dec 2011 02:48:26 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Dec/15/t1323935395emexlo2v6rkqvdn.htm/, Retrieved Mon, 29 Apr 2024 11:43:23 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=155284, Retrieved Mon, 29 Apr 2024 11:43:23 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact147

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
- R  D  [Multiple Regression] [Tutorial 2-1] [2011-11-21 19:39:55] [9e469a83342941fcd5c6dffbf184cd3a]
-   PD    [Multiple Regression] [Tutorial2-1] [2011-11-21 20:30:04] [9e469a83342941fcd5c6dffbf184cd3a]
-   PD      [Multiple Regression] [Statistiek Paper 3.1] [2011-12-15 07:45:19] [9e469a83342941fcd5c6dffbf184cd3a]
-   PD          [Multiple Regression] [Paper statistiek 3.1] [2011-12-15 07:48:26] [5ae3d23a633522d14794d358c652ae9c] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
27,25111111	54	17	18	-3
32,94777778	46	12	19	-2
30,12388889	60	17	19	0
27,26277778	40	17	19	0
23,3625	20	17	19	0
36,27361111	46	29	20	-4
18,1875	18	13	20	1
33,84666667	39	17	20	2
41,82777778	77	22	20	-4
62,31388889	83	39	20	0
94,88055556	168	21	20	0
37,03555556	55	20	20	-3
28,20083333	42	22	20	0
41,42	56	35	21	-4
8,608055556	14	17	21	-2
90,22194444	154	47	21	0
64,15666667	53	30	21	-1
54,59805556	57	29	21	-3
39,93222222	46	34	21	4
42,30527778	53	33	21	2
62,51666667	93	41	21	1
42,35388889	65	32	21	0
27,1775	38	24	21	-1
54,39944444	67	31	21	-2
70,69111111	83	39	21	0
25,69416667	32	18	21	-2
8,826111111	23	17	21	1
58,58527778	56	30	21	2
41,40583333	44	26	21	0
65,8925	84	38	21	0
36,98083333	55	30	21	4
48,44861111	100	31	21	3
81,78444444	77	33	21	4
90,3075	99	36	21	3
29,55777778	30	14	21	1
73,82472222	146	32	21	-2
100,6391667	119	34	21	2
60,81833333	41	29	21	2
31,28083333	41	20	21	3
41,235	91	37	21	3
50,5775	63	33	21	2
36,80194444	41	36	21	3
35,67305556	64	38	21	2
47,915	52	43	21	3
90,16611111	110	37	21	2
50,45361111	70	30	21	6
21,195	31	20	21	2
16,495	49	12	21	1
67,79222222	68	44	22	2
46,52444444	45	28	22	0
95,63805556	75	30	22	1
45,2125	32	28	22	-3
23,77055556	34	21	22	0
88,165	86	31	22	-3
39,79055556	103	27	22	2
53,70527778	78	35	22	2
67,98583333	95	33	22	0
63,67833333	247	31	22	2
65,77361111	119	31	23	0
67,64194444	71	42	23	0
62,12	73	33	23	-1
27,98611111	72	30	23	3
26,45194444	34	32	23	3
50,87972222	66	39	24	-4
67,51666667	63	29	24	-1
42,46416667	58	28	24	2
26,82222222	76	17	25	0
75,51555556	103	37	25	-3
48,53444444	92	34	26	0

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'George Udny Yule' @ yule.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 5 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=155284&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]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=155284&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=155284&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'George Udny Yule' @ yule.wessa.net






Multiple Linear Regression - Estimated Regression Equation
Totale_score[t] = + 0.98034468063275 -0.0207239953818484time_in_rfc[t] + 0.00517328518621255logins[t] + 0.0776257841385451compendiums_reviewed[t] -0.100729529199834`What_is_your_age?`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Totale_score[t] =  +  0.98034468063275 -0.0207239953818484time_in_rfc[t] +  0.00517328518621255logins[t] +  0.0776257841385451compendiums_reviewed[t] -0.100729529199834`What_is_your_age?`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=155284&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Totale_score[t] =  +  0.98034468063275 -0.0207239953818484time_in_rfc[t] +  0.00517328518621255logins[t] +  0.0776257841385451compendiums_reviewed[t] -0.100729529199834`What_is_your_age?`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=155284&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=155284&T=1

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


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

Multiple Linear Regression - Estimated Regression Equation
Totale_score[t] = + 0.98034468063275 -0.0207239953818484time_in_rfc[t] + 0.00517328518621255logins[t] + 0.0776257841385451compendiums_reviewed[t] -0.100729529199834`What_is_your_age?`[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.980344680632754.1678870.23520.8147940.407397
time_in_rfc-0.02072399538184840.019484-1.06370.2914830.145742
logins0.005173285186212550.0097890.52850.5989810.299491
compendiums_reviewed0.07762578413854510.0438631.76970.0815380.040769
`What_is_your_age?`-0.1007295291998340.207086-0.48640.6283340.314167

\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) & 0.98034468063275 & 4.167887 & 0.2352 & 0.814794 & 0.407397 \tabularnewline
time_in_rfc & -0.0207239953818484 & 0.019484 & -1.0637 & 0.291483 & 0.145742 \tabularnewline
logins & 0.00517328518621255 & 0.009789 & 0.5285 & 0.598981 & 0.299491 \tabularnewline
compendiums_reviewed & 0.0776257841385451 & 0.043863 & 1.7697 & 0.081538 & 0.040769 \tabularnewline
`What_is_your_age?` & -0.100729529199834 & 0.207086 & -0.4864 & 0.628334 & 0.314167 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=155284&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]0.98034468063275[/C][C]4.167887[/C][C]0.2352[/C][C]0.814794[/C][C]0.407397[/C][/ROW]
[ROW][C]time_in_rfc[/C][C]-0.0207239953818484[/C][C]0.019484[/C][C]-1.0637[/C][C]0.291483[/C][C]0.145742[/C][/ROW]
[ROW][C]logins[/C][C]0.00517328518621255[/C][C]0.009789[/C][C]0.5285[/C][C]0.598981[/C][C]0.299491[/C][/ROW]
[ROW][C]compendiums_reviewed[/C][C]0.0776257841385451[/C][C]0.043863[/C][C]1.7697[/C][C]0.081538[/C][C]0.040769[/C][/ROW]
[ROW][C]`What_is_your_age?`[/C][C]-0.100729529199834[/C][C]0.207086[/C][C]-0.4864[/C][C]0.628334[/C][C]0.314167[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=155284&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=155284&T=2

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


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.980344680632754.1678870.23520.8147940.407397
time_in_rfc-0.02072399538184840.019484-1.06370.2914830.145742
logins0.005173285186212550.0097890.52850.5989810.299491
compendiums_reviewed0.07762578413854510.0438631.76970.0815380.040769
`What_is_your_age?`-0.1007295291998340.207086-0.48640.6283340.314167






Multiple Linear Regression - Regression Statistics
Multiple R0.225721445480674
R-squared0.0509501709498847
Adjusted R-squared-0.0083654433657474
F-TEST (value)0.85896726414679
F-TEST (DF numerator)4
F-TEST (DF denominator)64
p-value0.493481854593082
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.27374461140365
Sum Squared Residuals330.874531704776

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.225721445480674 \tabularnewline
R-squared & 0.0509501709498847 \tabularnewline
Adjusted R-squared & -0.0083654433657474 \tabularnewline
F-TEST (value) & 0.85896726414679 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 64 \tabularnewline
p-value & 0.493481854593082 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.27374461140365 \tabularnewline
Sum Squared Residuals & 330.874531704776 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=155284&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.225721445480674[/C][/ROW]
[ROW][C]R-squared[/C][C]0.0509501709498847[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]-0.0083654433657474[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]0.85896726414679[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]64[/C][/ROW]
[ROW][C]p-value[/C][C]0.493481854593082[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.27374461140365[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]330.874531704776[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=155284&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=155284&T=3

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


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

Multiple Linear Regression - Regression Statistics
Multiple R0.225721445480674
R-squared0.0509501709498847
Adjusted R-squared-0.0083654433657474
F-TEST (value)0.85896726414679
F-TEST (DF numerator)4
F-TEST (DF denominator)64
p-value0.493481854593082
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.27374461140365
Sum Squared Residuals330.874531704776






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1-30.201456984652596-3.2014569846526
2-2-0.446845440490675-1.55315455950932
300.0722317331242386-0.0722317331242386
400.0280596828305829-0.0280596828305829
500.00542331780697853-0.00542331780697853
6-40.70313880609304-4.70313880609304
71-0.3089092422183961.3089092422184
82-0.2142876145064272.21428761450643
9-40.205025633476716-4.20502563347672
1001.13114960291358-1.13114960291358
110-0.5012967204234390.501296720423439
12-30.0352757822590219-3.03527578225902
1300.306365385809782-0.306365385809782
14-41.01324309399704-5.01324309399704
15-20.0786955868092298-2.07869558680923
1601.44036318070863-1.44036318070863
17-10.138399742677974-1.13839974267797
18-30.279559711784805-3.2795597117848
1940.9147171578383123.08528284216169
2020.8241251775369441.17587482246306
2111.2332021280767-0.233202128076704
2200.807571399213803-0.807571399213803
23-10.361401839347199-1.3614018393472
24-20.490660147857684-2.49066014785768
2500.856810559113752-0.856810559113752
26-2-0.104651983520685-1.89534801647932
2710.1207361711703360.879263828829664
2820.2693810358634541.73061896413655
2900.252825204519245-0.252825204519245
3000.883804454644346-0.883804454644346
3140.7119381576864423.28806184231356
3230.7847036014517692.21529639854823
3340.130117954464983.86988204553502
3430.3001758169126142.69982418308739
351-0.5055711492481891.50557114924819
36-20.574406094703177-2.57440609470318
3720.03427653938217711.96572346061782
3820.06787814102610911.93212185897389
393-0.01861890262944813.01861890262945
4031.353393582937221.64660641706278
4120.7044245343141651.29557546568583
4231.108974162440961.89102583755904
4321.406606377936680.59339362206332
4431.478953876355451.52104612364455
4520.4376378808029021.5623621191971
4660.510327650986245.48967234901376
4720.138667008861441.86133299113856
481-0.2918173526004081.29181735260041
4921.126687232782310.873312767217694
5000.206442455777595-0.206442455777595
511-0.5009376703957191.50093767039572
52-30.166378478872634-3.16637847887263
5300.0777073178276536-0.0777073178276536
54-3-0.211534180295099-2.7884658197049
5520.5684202944903781.43157970550962
5620.7717257989164411.22827420108356
5700.408469911536534-0.408469911536534
5821.128826301671060.871173698328936
5900.322493741599613-0.322493741599613
6000.889340346882734-0.889340346882734
61-10.315491611081636-1.31549161108164
6230.7848315292006772.21516847079932
6330.7752923233857562.22470767661424
64-40.877247255212998-4.877247255213
65-1-0.25931440168096-0.74068559831904
6620.1563812825531911.84361871744681
670-0.3809491542738840.380949154273884
68-30.302124813259791-3.30212481325979
6900.470768216844002-0.470768216844002

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & -3 & 0.201456984652596 & -3.2014569846526 \tabularnewline
2 & -2 & -0.446845440490675 & -1.55315455950932 \tabularnewline
3 & 0 & 0.0722317331242386 & -0.0722317331242386 \tabularnewline
4 & 0 & 0.0280596828305829 & -0.0280596828305829 \tabularnewline
5 & 0 & 0.00542331780697853 & -0.00542331780697853 \tabularnewline
6 & -4 & 0.70313880609304 & -4.70313880609304 \tabularnewline
7 & 1 & -0.308909242218396 & 1.3089092422184 \tabularnewline
8 & 2 & -0.214287614506427 & 2.21428761450643 \tabularnewline
9 & -4 & 0.205025633476716 & -4.20502563347672 \tabularnewline
10 & 0 & 1.13114960291358 & -1.13114960291358 \tabularnewline
11 & 0 & -0.501296720423439 & 0.501296720423439 \tabularnewline
12 & -3 & 0.0352757822590219 & -3.03527578225902 \tabularnewline
13 & 0 & 0.306365385809782 & -0.306365385809782 \tabularnewline
14 & -4 & 1.01324309399704 & -5.01324309399704 \tabularnewline
15 & -2 & 0.0786955868092298 & -2.07869558680923 \tabularnewline
16 & 0 & 1.44036318070863 & -1.44036318070863 \tabularnewline
17 & -1 & 0.138399742677974 & -1.13839974267797 \tabularnewline
18 & -3 & 0.279559711784805 & -3.2795597117848 \tabularnewline
19 & 4 & 0.914717157838312 & 3.08528284216169 \tabularnewline
20 & 2 & 0.824125177536944 & 1.17587482246306 \tabularnewline
21 & 1 & 1.2332021280767 & -0.233202128076704 \tabularnewline
22 & 0 & 0.807571399213803 & -0.807571399213803 \tabularnewline
23 & -1 & 0.361401839347199 & -1.3614018393472 \tabularnewline
24 & -2 & 0.490660147857684 & -2.49066014785768 \tabularnewline
25 & 0 & 0.856810559113752 & -0.856810559113752 \tabularnewline
26 & -2 & -0.104651983520685 & -1.89534801647932 \tabularnewline
27 & 1 & 0.120736171170336 & 0.879263828829664 \tabularnewline
28 & 2 & 0.269381035863454 & 1.73061896413655 \tabularnewline
29 & 0 & 0.252825204519245 & -0.252825204519245 \tabularnewline
30 & 0 & 0.883804454644346 & -0.883804454644346 \tabularnewline
31 & 4 & 0.711938157686442 & 3.28806184231356 \tabularnewline
32 & 3 & 0.784703601451769 & 2.21529639854823 \tabularnewline
33 & 4 & 0.13011795446498 & 3.86988204553502 \tabularnewline
34 & 3 & 0.300175816912614 & 2.69982418308739 \tabularnewline
35 & 1 & -0.505571149248189 & 1.50557114924819 \tabularnewline
36 & -2 & 0.574406094703177 & -2.57440609470318 \tabularnewline
37 & 2 & 0.0342765393821771 & 1.96572346061782 \tabularnewline
38 & 2 & 0.0678781410261091 & 1.93212185897389 \tabularnewline
39 & 3 & -0.0186189026294481 & 3.01861890262945 \tabularnewline
40 & 3 & 1.35339358293722 & 1.64660641706278 \tabularnewline
41 & 2 & 0.704424534314165 & 1.29557546568583 \tabularnewline
42 & 3 & 1.10897416244096 & 1.89102583755904 \tabularnewline
43 & 2 & 1.40660637793668 & 0.59339362206332 \tabularnewline
44 & 3 & 1.47895387635545 & 1.52104612364455 \tabularnewline
45 & 2 & 0.437637880802902 & 1.5623621191971 \tabularnewline
46 & 6 & 0.51032765098624 & 5.48967234901376 \tabularnewline
47 & 2 & 0.13866700886144 & 1.86133299113856 \tabularnewline
48 & 1 & -0.291817352600408 & 1.29181735260041 \tabularnewline
49 & 2 & 1.12668723278231 & 0.873312767217694 \tabularnewline
50 & 0 & 0.206442455777595 & -0.206442455777595 \tabularnewline
51 & 1 & -0.500937670395719 & 1.50093767039572 \tabularnewline
52 & -3 & 0.166378478872634 & -3.16637847887263 \tabularnewline
53 & 0 & 0.0777073178276536 & -0.0777073178276536 \tabularnewline
54 & -3 & -0.211534180295099 & -2.7884658197049 \tabularnewline
55 & 2 & 0.568420294490378 & 1.43157970550962 \tabularnewline
56 & 2 & 0.771725798916441 & 1.22827420108356 \tabularnewline
57 & 0 & 0.408469911536534 & -0.408469911536534 \tabularnewline
58 & 2 & 1.12882630167106 & 0.871173698328936 \tabularnewline
59 & 0 & 0.322493741599613 & -0.322493741599613 \tabularnewline
60 & 0 & 0.889340346882734 & -0.889340346882734 \tabularnewline
61 & -1 & 0.315491611081636 & -1.31549161108164 \tabularnewline
62 & 3 & 0.784831529200677 & 2.21516847079932 \tabularnewline
63 & 3 & 0.775292323385756 & 2.22470767661424 \tabularnewline
64 & -4 & 0.877247255212998 & -4.877247255213 \tabularnewline
65 & -1 & -0.25931440168096 & -0.74068559831904 \tabularnewline
66 & 2 & 0.156381282553191 & 1.84361871744681 \tabularnewline
67 & 0 & -0.380949154273884 & 0.380949154273884 \tabularnewline
68 & -3 & 0.302124813259791 & -3.30212481325979 \tabularnewline
69 & 0 & 0.470768216844002 & -0.470768216844002 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=155284&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]-3[/C][C]0.201456984652596[/C][C]-3.2014569846526[/C][/ROW]
[ROW][C]2[/C][C]-2[/C][C]-0.446845440490675[/C][C]-1.55315455950932[/C][/ROW]
[ROW][C]3[/C][C]0[/C][C]0.0722317331242386[/C][C]-0.0722317331242386[/C][/ROW]
[ROW][C]4[/C][C]0[/C][C]0.0280596828305829[/C][C]-0.0280596828305829[/C][/ROW]
[ROW][C]5[/C][C]0[/C][C]0.00542331780697853[/C][C]-0.00542331780697853[/C][/ROW]
[ROW][C]6[/C][C]-4[/C][C]0.70313880609304[/C][C]-4.70313880609304[/C][/ROW]
[ROW][C]7[/C][C]1[/C][C]-0.308909242218396[/C][C]1.3089092422184[/C][/ROW]
[ROW][C]8[/C][C]2[/C][C]-0.214287614506427[/C][C]2.21428761450643[/C][/ROW]
[ROW][C]9[/C][C]-4[/C][C]0.205025633476716[/C][C]-4.20502563347672[/C][/ROW]
[ROW][C]10[/C][C]0[/C][C]1.13114960291358[/C][C]-1.13114960291358[/C][/ROW]
[ROW][C]11[/C][C]0[/C][C]-0.501296720423439[/C][C]0.501296720423439[/C][/ROW]
[ROW][C]12[/C][C]-3[/C][C]0.0352757822590219[/C][C]-3.03527578225902[/C][/ROW]
[ROW][C]13[/C][C]0[/C][C]0.306365385809782[/C][C]-0.306365385809782[/C][/ROW]
[ROW][C]14[/C][C]-4[/C][C]1.01324309399704[/C][C]-5.01324309399704[/C][/ROW]
[ROW][C]15[/C][C]-2[/C][C]0.0786955868092298[/C][C]-2.07869558680923[/C][/ROW]
[ROW][C]16[/C][C]0[/C][C]1.44036318070863[/C][C]-1.44036318070863[/C][/ROW]
[ROW][C]17[/C][C]-1[/C][C]0.138399742677974[/C][C]-1.13839974267797[/C][/ROW]
[ROW][C]18[/C][C]-3[/C][C]0.279559711784805[/C][C]-3.2795597117848[/C][/ROW]
[ROW][C]19[/C][C]4[/C][C]0.914717157838312[/C][C]3.08528284216169[/C][/ROW]
[ROW][C]20[/C][C]2[/C][C]0.824125177536944[/C][C]1.17587482246306[/C][/ROW]
[ROW][C]21[/C][C]1[/C][C]1.2332021280767[/C][C]-0.233202128076704[/C][/ROW]
[ROW][C]22[/C][C]0[/C][C]0.807571399213803[/C][C]-0.807571399213803[/C][/ROW]
[ROW][C]23[/C][C]-1[/C][C]0.361401839347199[/C][C]-1.3614018393472[/C][/ROW]
[ROW][C]24[/C][C]-2[/C][C]0.490660147857684[/C][C]-2.49066014785768[/C][/ROW]
[ROW][C]25[/C][C]0[/C][C]0.856810559113752[/C][C]-0.856810559113752[/C][/ROW]
[ROW][C]26[/C][C]-2[/C][C]-0.104651983520685[/C][C]-1.89534801647932[/C][/ROW]
[ROW][C]27[/C][C]1[/C][C]0.120736171170336[/C][C]0.879263828829664[/C][/ROW]
[ROW][C]28[/C][C]2[/C][C]0.269381035863454[/C][C]1.73061896413655[/C][/ROW]
[ROW][C]29[/C][C]0[/C][C]0.252825204519245[/C][C]-0.252825204519245[/C][/ROW]
[ROW][C]30[/C][C]0[/C][C]0.883804454644346[/C][C]-0.883804454644346[/C][/ROW]
[ROW][C]31[/C][C]4[/C][C]0.711938157686442[/C][C]3.28806184231356[/C][/ROW]
[ROW][C]32[/C][C]3[/C][C]0.784703601451769[/C][C]2.21529639854823[/C][/ROW]
[ROW][C]33[/C][C]4[/C][C]0.13011795446498[/C][C]3.86988204553502[/C][/ROW]
[ROW][C]34[/C][C]3[/C][C]0.300175816912614[/C][C]2.69982418308739[/C][/ROW]
[ROW][C]35[/C][C]1[/C][C]-0.505571149248189[/C][C]1.50557114924819[/C][/ROW]
[ROW][C]36[/C][C]-2[/C][C]0.574406094703177[/C][C]-2.57440609470318[/C][/ROW]
[ROW][C]37[/C][C]2[/C][C]0.0342765393821771[/C][C]1.96572346061782[/C][/ROW]
[ROW][C]38[/C][C]2[/C][C]0.0678781410261091[/C][C]1.93212185897389[/C][/ROW]
[ROW][C]39[/C][C]3[/C][C]-0.0186189026294481[/C][C]3.01861890262945[/C][/ROW]
[ROW][C]40[/C][C]3[/C][C]1.35339358293722[/C][C]1.64660641706278[/C][/ROW]
[ROW][C]41[/C][C]2[/C][C]0.704424534314165[/C][C]1.29557546568583[/C][/ROW]
[ROW][C]42[/C][C]3[/C][C]1.10897416244096[/C][C]1.89102583755904[/C][/ROW]
[ROW][C]43[/C][C]2[/C][C]1.40660637793668[/C][C]0.59339362206332[/C][/ROW]
[ROW][C]44[/C][C]3[/C][C]1.47895387635545[/C][C]1.52104612364455[/C][/ROW]
[ROW][C]45[/C][C]2[/C][C]0.437637880802902[/C][C]1.5623621191971[/C][/ROW]
[ROW][C]46[/C][C]6[/C][C]0.51032765098624[/C][C]5.48967234901376[/C][/ROW]
[ROW][C]47[/C][C]2[/C][C]0.13866700886144[/C][C]1.86133299113856[/C][/ROW]
[ROW][C]48[/C][C]1[/C][C]-0.291817352600408[/C][C]1.29181735260041[/C][/ROW]
[ROW][C]49[/C][C]2[/C][C]1.12668723278231[/C][C]0.873312767217694[/C][/ROW]
[ROW][C]50[/C][C]0[/C][C]0.206442455777595[/C][C]-0.206442455777595[/C][/ROW]
[ROW][C]51[/C][C]1[/C][C]-0.500937670395719[/C][C]1.50093767039572[/C][/ROW]
[ROW][C]52[/C][C]-3[/C][C]0.166378478872634[/C][C]-3.16637847887263[/C][/ROW]
[ROW][C]53[/C][C]0[/C][C]0.0777073178276536[/C][C]-0.0777073178276536[/C][/ROW]
[ROW][C]54[/C][C]-3[/C][C]-0.211534180295099[/C][C]-2.7884658197049[/C][/ROW]
[ROW][C]55[/C][C]2[/C][C]0.568420294490378[/C][C]1.43157970550962[/C][/ROW]
[ROW][C]56[/C][C]2[/C][C]0.771725798916441[/C][C]1.22827420108356[/C][/ROW]
[ROW][C]57[/C][C]0[/C][C]0.408469911536534[/C][C]-0.408469911536534[/C][/ROW]
[ROW][C]58[/C][C]2[/C][C]1.12882630167106[/C][C]0.871173698328936[/C][/ROW]
[ROW][C]59[/C][C]0[/C][C]0.322493741599613[/C][C]-0.322493741599613[/C][/ROW]
[ROW][C]60[/C][C]0[/C][C]0.889340346882734[/C][C]-0.889340346882734[/C][/ROW]
[ROW][C]61[/C][C]-1[/C][C]0.315491611081636[/C][C]-1.31549161108164[/C][/ROW]
[ROW][C]62[/C][C]3[/C][C]0.784831529200677[/C][C]2.21516847079932[/C][/ROW]
[ROW][C]63[/C][C]3[/C][C]0.775292323385756[/C][C]2.22470767661424[/C][/ROW]
[ROW][C]64[/C][C]-4[/C][C]0.877247255212998[/C][C]-4.877247255213[/C][/ROW]
[ROW][C]65[/C][C]-1[/C][C]-0.25931440168096[/C][C]-0.74068559831904[/C][/ROW]
[ROW][C]66[/C][C]2[/C][C]0.156381282553191[/C][C]1.84361871744681[/C][/ROW]
[ROW][C]67[/C][C]0[/C][C]-0.380949154273884[/C][C]0.380949154273884[/C][/ROW]
[ROW][C]68[/C][C]-3[/C][C]0.302124813259791[/C][C]-3.30212481325979[/C][/ROW]
[ROW][C]69[/C][C]0[/C][C]0.470768216844002[/C][C]-0.470768216844002[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=155284&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=155284&T=4

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


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1-30.201456984652596-3.2014569846526
2-2-0.446845440490675-1.55315455950932
300.0722317331242386-0.0722317331242386
400.0280596828305829-0.0280596828305829
500.00542331780697853-0.00542331780697853
6-40.70313880609304-4.70313880609304
71-0.3089092422183961.3089092422184
82-0.2142876145064272.21428761450643
9-40.205025633476716-4.20502563347672
1001.13114960291358-1.13114960291358
110-0.5012967204234390.501296720423439
12-30.0352757822590219-3.03527578225902
1300.306365385809782-0.306365385809782
14-41.01324309399704-5.01324309399704
15-20.0786955868092298-2.07869558680923
1601.44036318070863-1.44036318070863
17-10.138399742677974-1.13839974267797
18-30.279559711784805-3.2795597117848
1940.9147171578383123.08528284216169
2020.8241251775369441.17587482246306
2111.2332021280767-0.233202128076704
2200.807571399213803-0.807571399213803
23-10.361401839347199-1.3614018393472
24-20.490660147857684-2.49066014785768
2500.856810559113752-0.856810559113752
26-2-0.104651983520685-1.89534801647932
2710.1207361711703360.879263828829664
2820.2693810358634541.73061896413655
2900.252825204519245-0.252825204519245
3000.883804454644346-0.883804454644346
3140.7119381576864423.28806184231356
3230.7847036014517692.21529639854823
3340.130117954464983.86988204553502
3430.3001758169126142.69982418308739
351-0.5055711492481891.50557114924819
36-20.574406094703177-2.57440609470318
3720.03427653938217711.96572346061782
3820.06787814102610911.93212185897389
393-0.01861890262944813.01861890262945
4031.353393582937221.64660641706278
4120.7044245343141651.29557546568583
4231.108974162440961.89102583755904
4321.406606377936680.59339362206332
4431.478953876355451.52104612364455
4520.4376378808029021.5623621191971
4660.510327650986245.48967234901376
4720.138667008861441.86133299113856
481-0.2918173526004081.29181735260041
4921.126687232782310.873312767217694
5000.206442455777595-0.206442455777595
511-0.5009376703957191.50093767039572
52-30.166378478872634-3.16637847887263
5300.0777073178276536-0.0777073178276536
54-3-0.211534180295099-2.7884658197049
5520.5684202944903781.43157970550962
5620.7717257989164411.22827420108356
5700.408469911536534-0.408469911536534
5821.128826301671060.871173698328936
5900.322493741599613-0.322493741599613
6000.889340346882734-0.889340346882734
61-10.315491611081636-1.31549161108164
6230.7848315292006772.21516847079932
6330.7752923233857562.22470767661424
64-40.877247255212998-4.877247255213
65-1-0.25931440168096-0.74068559831904
6620.1563812825531911.84361871744681
670-0.3809491542738840.380949154273884
68-30.302124813259791-3.30212481325979
6900.470768216844002-0.470768216844002






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.5056879568625740.9886240862748530.494312043137426
90.4596898640298660.9193797280597330.540310135970134
100.6274742504647960.7450514990704080.372525749535204
110.4982066583697160.9964133167394330.501793341630284
120.5084418427184750.983116314563050.491558157281525
130.4725651207007690.9451302414015390.527434879299231
140.5077654778144680.9844690443710650.492234522185532
150.4496696943362170.8993393886724330.550330305663783
160.5446656115500160.9106687768999670.455334388449984
170.5353799757554730.9292400484890530.464620024244527
180.6017911955772290.7964176088455420.398208804422771
190.8800425216354030.2399149567291950.119957478364597
200.8821397707650040.2357204584699920.117860229234996
210.8541240665029480.2917518669941040.145875933497052
220.8186168118455470.3627663763089050.181383188154453
230.7953387887125150.409322422574970.204661211287485
240.8207394454466660.3585211091066680.179260554553334
250.7843190500564650.4313618998870710.215680949943535
260.8098789079137520.3802421841724970.190121092086248
270.7949525647655470.4100948704689070.205047435234453
280.7777520055503770.4444959888992470.222247994449623
290.7456509494410540.5086981011178910.254349050558946
300.7156023678801970.5687952642396070.284397632119803
310.8038560604162550.3922878791674910.196143939583745
320.8025773251731310.3948453496537380.197422674826869
330.8729563768902030.2540872462195940.127043623109797
340.873661341576980.2526773168460390.126338658423019
350.8351222933635230.3297554132729530.164877706636477
360.8928094533930830.2143810932138330.107190546606917
370.8696195109071220.2607609781857550.130380489092878
380.8339944743538710.3320110512922570.166005525646129
390.8281997597851040.3436004804297910.171800240214896
400.8243153381772690.3513693236454620.175684661822731
410.777874006189980.444251987620040.22212599381002
420.741937400057730.516125199884540.25806259994227
430.698449369688720.6031012606225590.30155063031128
440.6394509137766480.7210981724467030.360549086223352
450.5881505551775830.8236988896448330.411849444822417
460.8547900935199940.2904198129600120.145209906480006
470.8089468709644470.3821062580711060.191053129035553
480.7547634236790920.4904731526418170.245236576320908
490.7367227298004750.526554540399050.263277270199525
500.6755183130911770.6489633738176450.324481686908823
510.800798413279920.3984031734401590.19920158672008
520.9042798229959070.1914403540081850.0957201770040927
530.9324155289488640.1351689421022730.0675844710511364
540.9254633007492880.1490733985014240.074536699250712
550.8867645433638130.2264709132723740.113235456636187
560.8307530352691020.3384939294617970.169246964730898
570.7461218439054180.5077563121891640.253878156094582
580.6340839909725760.7318320180548480.365916009027424
590.5074395749337320.9851208501325350.492560425066267
600.4145811819803670.8291623639607340.585418818019633
610.273385249014780.5467704980295610.72661475098522

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 0.505687956862574 & 0.988624086274853 & 0.494312043137426 \tabularnewline
9 & 0.459689864029866 & 0.919379728059733 & 0.540310135970134 \tabularnewline
10 & 0.627474250464796 & 0.745051499070408 & 0.372525749535204 \tabularnewline
11 & 0.498206658369716 & 0.996413316739433 & 0.501793341630284 \tabularnewline
12 & 0.508441842718475 & 0.98311631456305 & 0.491558157281525 \tabularnewline
13 & 0.472565120700769 & 0.945130241401539 & 0.527434879299231 \tabularnewline
14 & 0.507765477814468 & 0.984469044371065 & 0.492234522185532 \tabularnewline
15 & 0.449669694336217 & 0.899339388672433 & 0.550330305663783 \tabularnewline
16 & 0.544665611550016 & 0.910668776899967 & 0.455334388449984 \tabularnewline
17 & 0.535379975755473 & 0.929240048489053 & 0.464620024244527 \tabularnewline
18 & 0.601791195577229 & 0.796417608845542 & 0.398208804422771 \tabularnewline
19 & 0.880042521635403 & 0.239914956729195 & 0.119957478364597 \tabularnewline
20 & 0.882139770765004 & 0.235720458469992 & 0.117860229234996 \tabularnewline
21 & 0.854124066502948 & 0.291751866994104 & 0.145875933497052 \tabularnewline
22 & 0.818616811845547 & 0.362766376308905 & 0.181383188154453 \tabularnewline
23 & 0.795338788712515 & 0.40932242257497 & 0.204661211287485 \tabularnewline
24 & 0.820739445446666 & 0.358521109106668 & 0.179260554553334 \tabularnewline
25 & 0.784319050056465 & 0.431361899887071 & 0.215680949943535 \tabularnewline
26 & 0.809878907913752 & 0.380242184172497 & 0.190121092086248 \tabularnewline
27 & 0.794952564765547 & 0.410094870468907 & 0.205047435234453 \tabularnewline
28 & 0.777752005550377 & 0.444495988899247 & 0.222247994449623 \tabularnewline
29 & 0.745650949441054 & 0.508698101117891 & 0.254349050558946 \tabularnewline
30 & 0.715602367880197 & 0.568795264239607 & 0.284397632119803 \tabularnewline
31 & 0.803856060416255 & 0.392287879167491 & 0.196143939583745 \tabularnewline
32 & 0.802577325173131 & 0.394845349653738 & 0.197422674826869 \tabularnewline
33 & 0.872956376890203 & 0.254087246219594 & 0.127043623109797 \tabularnewline
34 & 0.87366134157698 & 0.252677316846039 & 0.126338658423019 \tabularnewline
35 & 0.835122293363523 & 0.329755413272953 & 0.164877706636477 \tabularnewline
36 & 0.892809453393083 & 0.214381093213833 & 0.107190546606917 \tabularnewline
37 & 0.869619510907122 & 0.260760978185755 & 0.130380489092878 \tabularnewline
38 & 0.833994474353871 & 0.332011051292257 & 0.166005525646129 \tabularnewline
39 & 0.828199759785104 & 0.343600480429791 & 0.171800240214896 \tabularnewline
40 & 0.824315338177269 & 0.351369323645462 & 0.175684661822731 \tabularnewline
41 & 0.77787400618998 & 0.44425198762004 & 0.22212599381002 \tabularnewline
42 & 0.74193740005773 & 0.51612519988454 & 0.25806259994227 \tabularnewline
43 & 0.69844936968872 & 0.603101260622559 & 0.30155063031128 \tabularnewline
44 & 0.639450913776648 & 0.721098172446703 & 0.360549086223352 \tabularnewline
45 & 0.588150555177583 & 0.823698889644833 & 0.411849444822417 \tabularnewline
46 & 0.854790093519994 & 0.290419812960012 & 0.145209906480006 \tabularnewline
47 & 0.808946870964447 & 0.382106258071106 & 0.191053129035553 \tabularnewline
48 & 0.754763423679092 & 0.490473152641817 & 0.245236576320908 \tabularnewline
49 & 0.736722729800475 & 0.52655454039905 & 0.263277270199525 \tabularnewline
50 & 0.675518313091177 & 0.648963373817645 & 0.324481686908823 \tabularnewline
51 & 0.80079841327992 & 0.398403173440159 & 0.19920158672008 \tabularnewline
52 & 0.904279822995907 & 0.191440354008185 & 0.0957201770040927 \tabularnewline
53 & 0.932415528948864 & 0.135168942102273 & 0.0675844710511364 \tabularnewline
54 & 0.925463300749288 & 0.149073398501424 & 0.074536699250712 \tabularnewline
55 & 0.886764543363813 & 0.226470913272374 & 0.113235456636187 \tabularnewline
56 & 0.830753035269102 & 0.338493929461797 & 0.169246964730898 \tabularnewline
57 & 0.746121843905418 & 0.507756312189164 & 0.253878156094582 \tabularnewline
58 & 0.634083990972576 & 0.731832018054848 & 0.365916009027424 \tabularnewline
59 & 0.507439574933732 & 0.985120850132535 & 0.492560425066267 \tabularnewline
60 & 0.414581181980367 & 0.829162363960734 & 0.585418818019633 \tabularnewline
61 & 0.27338524901478 & 0.546770498029561 & 0.72661475098522 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=155284&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]0.505687956862574[/C][C]0.988624086274853[/C][C]0.494312043137426[/C][/ROW]
[ROW][C]9[/C][C]0.459689864029866[/C][C]0.919379728059733[/C][C]0.540310135970134[/C][/ROW]
[ROW][C]10[/C][C]0.627474250464796[/C][C]0.745051499070408[/C][C]0.372525749535204[/C][/ROW]
[ROW][C]11[/C][C]0.498206658369716[/C][C]0.996413316739433[/C][C]0.501793341630284[/C][/ROW]
[ROW][C]12[/C][C]0.508441842718475[/C][C]0.98311631456305[/C][C]0.491558157281525[/C][/ROW]
[ROW][C]13[/C][C]0.472565120700769[/C][C]0.945130241401539[/C][C]0.527434879299231[/C][/ROW]
[ROW][C]14[/C][C]0.507765477814468[/C][C]0.984469044371065[/C][C]0.492234522185532[/C][/ROW]
[ROW][C]15[/C][C]0.449669694336217[/C][C]0.899339388672433[/C][C]0.550330305663783[/C][/ROW]
[ROW][C]16[/C][C]0.544665611550016[/C][C]0.910668776899967[/C][C]0.455334388449984[/C][/ROW]
[ROW][C]17[/C][C]0.535379975755473[/C][C]0.929240048489053[/C][C]0.464620024244527[/C][/ROW]
[ROW][C]18[/C][C]0.601791195577229[/C][C]0.796417608845542[/C][C]0.398208804422771[/C][/ROW]
[ROW][C]19[/C][C]0.880042521635403[/C][C]0.239914956729195[/C][C]0.119957478364597[/C][/ROW]
[ROW][C]20[/C][C]0.882139770765004[/C][C]0.235720458469992[/C][C]0.117860229234996[/C][/ROW]
[ROW][C]21[/C][C]0.854124066502948[/C][C]0.291751866994104[/C][C]0.145875933497052[/C][/ROW]
[ROW][C]22[/C][C]0.818616811845547[/C][C]0.362766376308905[/C][C]0.181383188154453[/C][/ROW]
[ROW][C]23[/C][C]0.795338788712515[/C][C]0.40932242257497[/C][C]0.204661211287485[/C][/ROW]
[ROW][C]24[/C][C]0.820739445446666[/C][C]0.358521109106668[/C][C]0.179260554553334[/C][/ROW]
[ROW][C]25[/C][C]0.784319050056465[/C][C]0.431361899887071[/C][C]0.215680949943535[/C][/ROW]
[ROW][C]26[/C][C]0.809878907913752[/C][C]0.380242184172497[/C][C]0.190121092086248[/C][/ROW]
[ROW][C]27[/C][C]0.794952564765547[/C][C]0.410094870468907[/C][C]0.205047435234453[/C][/ROW]
[ROW][C]28[/C][C]0.777752005550377[/C][C]0.444495988899247[/C][C]0.222247994449623[/C][/ROW]
[ROW][C]29[/C][C]0.745650949441054[/C][C]0.508698101117891[/C][C]0.254349050558946[/C][/ROW]
[ROW][C]30[/C][C]0.715602367880197[/C][C]0.568795264239607[/C][C]0.284397632119803[/C][/ROW]
[ROW][C]31[/C][C]0.803856060416255[/C][C]0.392287879167491[/C][C]0.196143939583745[/C][/ROW]
[ROW][C]32[/C][C]0.802577325173131[/C][C]0.394845349653738[/C][C]0.197422674826869[/C][/ROW]
[ROW][C]33[/C][C]0.872956376890203[/C][C]0.254087246219594[/C][C]0.127043623109797[/C][/ROW]
[ROW][C]34[/C][C]0.87366134157698[/C][C]0.252677316846039[/C][C]0.126338658423019[/C][/ROW]
[ROW][C]35[/C][C]0.835122293363523[/C][C]0.329755413272953[/C][C]0.164877706636477[/C][/ROW]
[ROW][C]36[/C][C]0.892809453393083[/C][C]0.214381093213833[/C][C]0.107190546606917[/C][/ROW]
[ROW][C]37[/C][C]0.869619510907122[/C][C]0.260760978185755[/C][C]0.130380489092878[/C][/ROW]
[ROW][C]38[/C][C]0.833994474353871[/C][C]0.332011051292257[/C][C]0.166005525646129[/C][/ROW]
[ROW][C]39[/C][C]0.828199759785104[/C][C]0.343600480429791[/C][C]0.171800240214896[/C][/ROW]
[ROW][C]40[/C][C]0.824315338177269[/C][C]0.351369323645462[/C][C]0.175684661822731[/C][/ROW]
[ROW][C]41[/C][C]0.77787400618998[/C][C]0.44425198762004[/C][C]0.22212599381002[/C][/ROW]
[ROW][C]42[/C][C]0.74193740005773[/C][C]0.51612519988454[/C][C]0.25806259994227[/C][/ROW]
[ROW][C]43[/C][C]0.69844936968872[/C][C]0.603101260622559[/C][C]0.30155063031128[/C][/ROW]
[ROW][C]44[/C][C]0.639450913776648[/C][C]0.721098172446703[/C][C]0.360549086223352[/C][/ROW]
[ROW][C]45[/C][C]0.588150555177583[/C][C]0.823698889644833[/C][C]0.411849444822417[/C][/ROW]
[ROW][C]46[/C][C]0.854790093519994[/C][C]0.290419812960012[/C][C]0.145209906480006[/C][/ROW]
[ROW][C]47[/C][C]0.808946870964447[/C][C]0.382106258071106[/C][C]0.191053129035553[/C][/ROW]
[ROW][C]48[/C][C]0.754763423679092[/C][C]0.490473152641817[/C][C]0.245236576320908[/C][/ROW]
[ROW][C]49[/C][C]0.736722729800475[/C][C]0.52655454039905[/C][C]0.263277270199525[/C][/ROW]
[ROW][C]50[/C][C]0.675518313091177[/C][C]0.648963373817645[/C][C]0.324481686908823[/C][/ROW]
[ROW][C]51[/C][C]0.80079841327992[/C][C]0.398403173440159[/C][C]0.19920158672008[/C][/ROW]
[ROW][C]52[/C][C]0.904279822995907[/C][C]0.191440354008185[/C][C]0.0957201770040927[/C][/ROW]
[ROW][C]53[/C][C]0.932415528948864[/C][C]0.135168942102273[/C][C]0.0675844710511364[/C][/ROW]
[ROW][C]54[/C][C]0.925463300749288[/C][C]0.149073398501424[/C][C]0.074536699250712[/C][/ROW]
[ROW][C]55[/C][C]0.886764543363813[/C][C]0.226470913272374[/C][C]0.113235456636187[/C][/ROW]
[ROW][C]56[/C][C]0.830753035269102[/C][C]0.338493929461797[/C][C]0.169246964730898[/C][/ROW]
[ROW][C]57[/C][C]0.746121843905418[/C][C]0.507756312189164[/C][C]0.253878156094582[/C][/ROW]
[ROW][C]58[/C][C]0.634083990972576[/C][C]0.731832018054848[/C][C]0.365916009027424[/C][/ROW]
[ROW][C]59[/C][C]0.507439574933732[/C][C]0.985120850132535[/C][C]0.492560425066267[/C][/ROW]
[ROW][C]60[/C][C]0.414581181980367[/C][C]0.829162363960734[/C][C]0.585418818019633[/C][/ROW]
[ROW][C]61[/C][C]0.27338524901478[/C][C]0.546770498029561[/C][C]0.72661475098522[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=155284&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=155284&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.5056879568625740.9886240862748530.494312043137426
90.4596898640298660.9193797280597330.540310135970134
100.6274742504647960.7450514990704080.372525749535204
110.4982066583697160.9964133167394330.501793341630284
120.5084418427184750.983116314563050.491558157281525
130.4725651207007690.9451302414015390.527434879299231
140.5077654778144680.9844690443710650.492234522185532
150.4496696943362170.8993393886724330.550330305663783
160.5446656115500160.9106687768999670.455334388449984
170.5353799757554730.9292400484890530.464620024244527
180.6017911955772290.7964176088455420.398208804422771
190.8800425216354030.2399149567291950.119957478364597
200.8821397707650040.2357204584699920.117860229234996
210.8541240665029480.2917518669941040.145875933497052
220.8186168118455470.3627663763089050.181383188154453
230.7953387887125150.409322422574970.204661211287485
240.8207394454466660.3585211091066680.179260554553334
250.7843190500564650.4313618998870710.215680949943535
260.8098789079137520.3802421841724970.190121092086248
270.7949525647655470.4100948704689070.205047435234453
280.7777520055503770.4444959888992470.222247994449623
290.7456509494410540.5086981011178910.254349050558946
300.7156023678801970.5687952642396070.284397632119803
310.8038560604162550.3922878791674910.196143939583745
320.8025773251731310.3948453496537380.197422674826869
330.8729563768902030.2540872462195940.127043623109797
340.873661341576980.2526773168460390.126338658423019
350.8351222933635230.3297554132729530.164877706636477
360.8928094533930830.2143810932138330.107190546606917
370.8696195109071220.2607609781857550.130380489092878
380.8339944743538710.3320110512922570.166005525646129
390.8281997597851040.3436004804297910.171800240214896
400.8243153381772690.3513693236454620.175684661822731
410.777874006189980.444251987620040.22212599381002
420.741937400057730.516125199884540.25806259994227
430.698449369688720.6031012606225590.30155063031128
440.6394509137766480.7210981724467030.360549086223352
450.5881505551775830.8236988896448330.411849444822417
460.8547900935199940.2904198129600120.145209906480006
470.8089468709644470.3821062580711060.191053129035553
480.7547634236790920.4904731526418170.245236576320908
490.7367227298004750.526554540399050.263277270199525
500.6755183130911770.6489633738176450.324481686908823
510.800798413279920.3984031734401590.19920158672008
520.9042798229959070.1914403540081850.0957201770040927
530.9324155289488640.1351689421022730.0675844710511364
540.9254633007492880.1490733985014240.074536699250712
550.8867645433638130.2264709132723740.113235456636187
560.8307530352691020.3384939294617970.169246964730898
570.7461218439054180.5077563121891640.253878156094582
580.6340839909725760.7318320180548480.365916009027424
590.5074395749337320.9851208501325350.492560425066267
600.4145811819803670.8291623639607340.585418818019633
610.273385249014780.5467704980295610.72661475098522






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level00OK

\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 & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=155284&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]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=155284&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=155284&T=6

As an alternative you can also use a QR Code:  
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 testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level00OK


Figures (Output of Computation)

Figure 1
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Figure 6
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Figure 7
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Figure 10
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Input Parameters & R Code

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')
}