Repository of Reproducible Computations

Free Statistics

of Irreproducible Research!

Author's title

Author

*The author of this computation has been verified*

R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Tue, 20 Dec 2011 21:08:24 -0500

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Dec/20/t132443338184joyadj5r9b34a.htm/, Retrieved Mon, 06 May 2024 05:37:18 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=158345, Retrieved Mon, 06 May 2024 05:37:18 +0000

QR Codes:

Paste this QR Code to cite your computation.

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

106

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

-       [Multiple Regression] [paper] [2011-12-21 02:08:24] [6e647d331a8f33aa61a2d78ef323178e] [Current]

Feedback Forum

Post a new message

Dataseries X:

Download CSV

Histogram

Boxplots

2	210907	56	396	79	30	1	0
0	149061	44	656	43	26	1	0
0	237213	84	655	78	38	1	1
4	133131	55	525	44	30	1	1
0	324799	154	1436	158	47	1	1
0	230964	53	612	102	30	1	0
0	236785	119	865	77	31	1	1
1	344297	75	963	80	30	1	1
0	174724	92	966	123	34	1	1
3	174415	100	801	73	31	1	1
0	223632	73	513	105	33	1	1
4	294424	77	992	107	33	1	0
1	106408	30	260	33	14	1	0
0	96560	76	503	42	17	0	0
0	265769	146	927	96	32	1	1
0	149112	56	537	56	35	1	0
2	152871	58	532	59	28	1	0
2	362301	119	1635	76	34	1	1
0	183167	66	557	91	39	1	0
2	218946	41	866	76	29	1	1
2	244052	68	574	101	44	1	1
0	341570	168	1276	94	21	0	1
0	196553	57	503	41	29	1	1
2	143246	103	464	67	27	1	0
0	167488	45	619	69	28	1	0
4	143756	46	479	105	34	1	0
2	152299	53	537	62	33	1	1
2	193339	78	465	100	35	1	1
0	130585	46	299	67	29	1	0
3	112611	41	248	46	20	0	1
3	148446	91	905	135	37	1	1
2	182079	63	512	124	33	1	0
0	243060	63	786	58	29	1	1
0	162765	32	489	68	28	1	1
0	85574	34	351	37	21	0	1
1	225060	93	669	93	41	1	0
0	133328	55	506	56	20	0	1
3	100750	72	407	83	30	1	1
0	101523	42	316	59	22	0	1
0	243511	71	603	133	42	1	1
0	152474	65	577	106	32	1	1
3	132487	41	411	71	36	1	1
0	317394	86	975	116	31	1	0
0	244749	95	964	98	33	1	1
2	128423	64	369	32	38	1	0
0	97839	38	417	25	24	1	0
2	229242	247	719	63	31	1	1
2	324598	110	1402	113	37	1	0
0	195838	67	564	111	31	1	0
0	254488	83	747	120	39	1	0
0	92499	32	319	25	18	0	1
0	224330	83	612	131	39	1	0
6	181633	70	564	47	30	1	1
0	271856	103	824	109	37	1	1
3	95227	34	239	37	32	1	1
0	98146	40	459	15	17	0	0
0	118612	46	454	54	12	0	0
1	65475	18	225	16	13	0	1
0	108446	60	389	22	17	0	0
2	121848	39	339	37	17	0	0
2	76302	31	333	29	20	0	1
0	98104	54	636	55	17	0	0
0	30989	14	93	5	17	0	1
1	31774	23	170	0	17	0	0
0	150580	77	530	27	22	0	1
1	59382	49	227	29	12	0	0
0	84105	20	261	17	17	0	0

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	5 seconds
R Server	'AstonUniversity' @ aston.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 & 'AstonUniversity' @ aston.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=158345&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]'AstonUniversity' @ aston.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=158345&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=158345&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	5 seconds
R Server	'AstonUniversity' @ aston.wessa.net

Multiple Linear Regression - Estimated Regression Equation
Testscore[t] = + 0.525372467394108 -6.03097612635339e-06RFC_time[t] + 0.00133481928169378Logins[t] + 0.000593928768506347Cviews[t] -0.00723585139885787Bcomp[t] + 0.0234202363596706CompRev[t] + 1.09778855478478CourseId[t] + 0.328254992249426Geslacht[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Testscore[t] =  +  0.525372467394108 -6.03097612635339e-06RFC_time[t] +  0.00133481928169378Logins[t] +  0.000593928768506347Cviews[t] -0.00723585139885787Bcomp[t] +  0.0234202363596706CompRev[t] +  1.09778855478478CourseId[t] +  0.328254992249426Geslacht[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=158345&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Testscore[t] =  +  0.525372467394108 -6.03097612635339e-06RFC_time[t] +  0.00133481928169378Logins[t] +  0.000593928768506347Cviews[t] -0.00723585139885787Bcomp[t] +  0.0234202363596706CompRev[t] +  1.09778855478478CourseId[t] +  0.328254992249426Geslacht[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=158345&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=158345&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
Testscore[t] = + 0.525372467394108 -6.03097612635339e-06RFC_time[t] + 0.00133481928169378Logins[t] + 0.000593928768506347Cviews[t] -0.00723585139885787Bcomp[t] + 0.0234202363596706CompRev[t] + 1.09778855478478CourseId[t] + 0.328254992249426Geslacht[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)	0.525372467394108	0.724103	0.7255	0.470983	0.235492
RFC_time	-6.03097612635339e-06	5e-06	-1.2181	0.228021	0.114011
Logins	0.00133481928169378	0.006257	0.2133	0.831805	0.415903
Cviews	0.000593928768506347	0.001169	0.5081	0.613281	0.306641
Bcomp	-0.00723585139885787	0.008307	-0.871	0.387265	0.193632
CompRev	0.0234202363596706	0.044058	0.5316	0.597013	0.298507
CourseId	1.09778855478478	0.653052	1.681	0.098048	0.049024
Geslacht	0.328254992249426	0.354874	0.925	0.358739	0.179369

\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) & 0.525372467394108 & 0.724103 & 0.7255 & 0.470983 & 0.235492 \tabularnewline
RFC_time & -6.03097612635339e-06 & 5e-06 & -1.2181 & 0.228021 & 0.114011 \tabularnewline
Logins & 0.00133481928169378 & 0.006257 & 0.2133 & 0.831805 & 0.415903 \tabularnewline
Cviews & 0.000593928768506347 & 0.001169 & 0.5081 & 0.613281 & 0.306641 \tabularnewline
Bcomp & -0.00723585139885787 & 0.008307 & -0.871 & 0.387265 & 0.193632 \tabularnewline
CompRev & 0.0234202363596706 & 0.044058 & 0.5316 & 0.597013 & 0.298507 \tabularnewline
CourseId & 1.09778855478478 & 0.653052 & 1.681 & 0.098048 & 0.049024 \tabularnewline
Geslacht & 0.328254992249426 & 0.354874 & 0.925 & 0.358739 & 0.179369 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=158345&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]0.525372467394108[/C][C]0.724103[/C][C]0.7255[/C][C]0.470983[/C][C]0.235492[/C][/ROW]
[ROW][C]RFC_time[/C][C]-6.03097612635339e-06[/C][C]5e-06[/C][C]-1.2181[/C][C]0.228021[/C][C]0.114011[/C][/ROW]
[ROW][C]Logins[/C][C]0.00133481928169378[/C][C]0.006257[/C][C]0.2133[/C][C]0.831805[/C][C]0.415903[/C][/ROW]
[ROW][C]Cviews[/C][C]0.000593928768506347[/C][C]0.001169[/C][C]0.5081[/C][C]0.613281[/C][C]0.306641[/C][/ROW]
[ROW][C]Bcomp[/C][C]-0.00723585139885787[/C][C]0.008307[/C][C]-0.871[/C][C]0.387265[/C][C]0.193632[/C][/ROW]
[ROW][C]CompRev[/C][C]0.0234202363596706[/C][C]0.044058[/C][C]0.5316[/C][C]0.597013[/C][C]0.298507[/C][/ROW]
[ROW][C]CourseId[/C][C]1.09778855478478[/C][C]0.653052[/C][C]1.681[/C][C]0.098048[/C][C]0.049024[/C][/ROW]
[ROW][C]Geslacht[/C][C]0.328254992249426[/C][C]0.354874[/C][C]0.925[/C][C]0.358739[/C][C]0.179369[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=158345&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=158345&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)	0.525372467394108	0.724103	0.7255	0.470983	0.235492
RFC_time	-6.03097612635339e-06	5e-06	-1.2181	0.228021	0.114011
Logins	0.00133481928169378	0.006257	0.2133	0.831805	0.415903
Cviews	0.000593928768506347	0.001169	0.5081	0.613281	0.306641
Bcomp	-0.00723585139885787	0.008307	-0.871	0.387265	0.193632
CompRev	0.0234202363596706	0.044058	0.5316	0.597013	0.298507
CourseId	1.09778855478478	0.653052	1.681	0.098048	0.049024
Geslacht	0.328254992249426	0.354874	0.925	0.358739	0.179369

Multiple Linear Regression - Regression Statistics
Multiple R	0.362927402987008
R-squared	0.131716299838894
Adjusted R-squared	0.0286995896502881
F-TEST (value)	1.2785915954581
F-TEST (DF numerator)	7
F-TEST (DF denominator)	59
p-value	0.27663917780112
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.36639693712046
Sum Squared Residuals	110.155394796558

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.362927402987008 \tabularnewline
R-squared & 0.131716299838894 \tabularnewline
Adjusted R-squared & 0.0286995896502881 \tabularnewline
F-TEST (value) & 1.2785915954581 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 59 \tabularnewline
p-value & 0.27663917780112 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.36639693712046 \tabularnewline
Sum Squared Residuals & 110.155394796558 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=158345&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.362927402987008[/C][/ROW]
[ROW][C]R-squared[/C][C]0.131716299838894[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.0286995896502881[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]1.2785915954581[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]59[/C][/ROW]
[ROW][C]p-value[/C][C]0.27663917780112[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.36639693712046[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]110.155394796558[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=158345&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=158345&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	0.362927402987008
R-squared	0.131716299838894
Adjusted R-squared	0.0286995896502881
F-TEST (value)	1.2785915954581
F-TEST (DF numerator)	7
F-TEST (DF denominator)	59
p-value	0.27663917780112
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.36639693712046
Sum Squared Residuals	110.155394796558

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	2	0.792106442681787	1.20789355731821
2	0	1.47031154554377	-1.47031154554377
3	0	1.34751081015815	-1.34751081015815
4	4	1.91796342495012	2.08203657504988
5	0	1.00849146840579	-1.00849146840579
6	0	0.629002728494075	-0.629002728494075
7	0	1.36482998106701	-1.36482998106701
8	1	0.670772856133356	0.329227143866644
9	0	1.50047662018959	-1.50047662018959
10	3	1.70655236012651	1.29344763987349
11	0	1.01792743013611	-1.01792743013611
12	4	0.538087030393473	3.46191296960653
13	1	1.26498318566142	-0.264983185661422
14	0	0.437452107963213	-0.437452107963213
15	0	1.14883093305528	-1.14883093305528
16	0	1.53206033274127	-1.53206033274127
17	2	1.3234406794889	0.6765593205111
18	2	1.14266769381939	0.857332306180606
19	0	1.19232835542403	-1.19232835542403
20	2	1.32928996766094	0.670710032339063
21	2	1.2108964616582	0.789103538341801
22	0	-0.4126153758359	0.4126153758359
23	0	1.52335738055769	-1.52335738055769
24	2	1.31986148857231	0.680138511427694
25	0	1.19724653965945	-1.19724653965945
26	4	1.13858922458001	2.86141077541999
27	2	1.74683456511844	0.253165434881559
28	2	1.26180903516553	0.738190964834473
29	0	1.26897720416731	-1.26897720416731
30	3	0.512050695063719	2.48794930493628
31	3	1.60482462897004	1.39517537102996
32	2	0.788854290701303	1.2111457092987
33	0	1.29595605724625	-1.29595605724625
34	0	1.46665829298465	-1.46665829298465
35	0	0.815484023725625	-0.815484023725625
36	1	1.07460158516278	-0.0746015851627754
37	0	0.486669540883828	-0.486669540883828
38	3	1.78366259144716	1.21633740855284
39	0	0.57341853842704	-0.57341853842704
40	0	0.956999894391528	-0.956999894391528
41	0	1.44375642850749	-1.44375642850749
42	3	1.78060545441092	1.21939454558908
43	0	0.28950895793271	-0.28950895793271
44	0	1.23845016586154	-1.23845016586154
45	2	1.81165486161548	0.188345138384517
46	0	1.71267716578476	-1.71267716578476
47	2	1.59576682142698	0.404233178573019
48	2	0.693934025185918	1.30606597481408
49	0	0.789323258733722	-0.789323258733722
50	0	0.687891810354501	-0.687891810354501
51	0	0.76861366260232	-0.76861366260232
52	0	0.709999239237274	-0.709999239237274
53	6	1.64692697787031	4.35307302212969
54	0	1.01658360271838	-1.01658360271838
55	3	2.04615814384639	0.953841856153614
56	0	0.549068607640723	-0.549068607640723
57	0	0.0313785357325962	-0.0313785357325962
58	1	0.805099468048953	0.194900531951047
59	0	0.421419965585709	-0.421419965585709
60	2	0.174327409216567	1.82567259078343
61	2	0.89117463352217	1.10882536647783
62	0	0.383700714653052	-0.383700714653052
63	0	1.10262114699888	-1.10262114699888
64	1	0.86355698419479	0.136443015805209
65	0	0.682923618679614	-0.682923618679614
66	1	0.438672164062095	0.561327835937905
67	0	0.474983558835003	-0.474983558835003

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 2 & 0.792106442681787 & 1.20789355731821 \tabularnewline
2 & 0 & 1.47031154554377 & -1.47031154554377 \tabularnewline
3 & 0 & 1.34751081015815 & -1.34751081015815 \tabularnewline
4 & 4 & 1.91796342495012 & 2.08203657504988 \tabularnewline
5 & 0 & 1.00849146840579 & -1.00849146840579 \tabularnewline
6 & 0 & 0.629002728494075 & -0.629002728494075 \tabularnewline
7 & 0 & 1.36482998106701 & -1.36482998106701 \tabularnewline
8 & 1 & 0.670772856133356 & 0.329227143866644 \tabularnewline
9 & 0 & 1.50047662018959 & -1.50047662018959 \tabularnewline
10 & 3 & 1.70655236012651 & 1.29344763987349 \tabularnewline
11 & 0 & 1.01792743013611 & -1.01792743013611 \tabularnewline
12 & 4 & 0.538087030393473 & 3.46191296960653 \tabularnewline
13 & 1 & 1.26498318566142 & -0.264983185661422 \tabularnewline
14 & 0 & 0.437452107963213 & -0.437452107963213 \tabularnewline
15 & 0 & 1.14883093305528 & -1.14883093305528 \tabularnewline
16 & 0 & 1.53206033274127 & -1.53206033274127 \tabularnewline
17 & 2 & 1.3234406794889 & 0.6765593205111 \tabularnewline
18 & 2 & 1.14266769381939 & 0.857332306180606 \tabularnewline
19 & 0 & 1.19232835542403 & -1.19232835542403 \tabularnewline
20 & 2 & 1.32928996766094 & 0.670710032339063 \tabularnewline
21 & 2 & 1.2108964616582 & 0.789103538341801 \tabularnewline
22 & 0 & -0.4126153758359 & 0.4126153758359 \tabularnewline
23 & 0 & 1.52335738055769 & -1.52335738055769 \tabularnewline
24 & 2 & 1.31986148857231 & 0.680138511427694 \tabularnewline
25 & 0 & 1.19724653965945 & -1.19724653965945 \tabularnewline
26 & 4 & 1.13858922458001 & 2.86141077541999 \tabularnewline
27 & 2 & 1.74683456511844 & 0.253165434881559 \tabularnewline
28 & 2 & 1.26180903516553 & 0.738190964834473 \tabularnewline
29 & 0 & 1.26897720416731 & -1.26897720416731 \tabularnewline
30 & 3 & 0.512050695063719 & 2.48794930493628 \tabularnewline
31 & 3 & 1.60482462897004 & 1.39517537102996 \tabularnewline
32 & 2 & 0.788854290701303 & 1.2111457092987 \tabularnewline
33 & 0 & 1.29595605724625 & -1.29595605724625 \tabularnewline
34 & 0 & 1.46665829298465 & -1.46665829298465 \tabularnewline
35 & 0 & 0.815484023725625 & -0.815484023725625 \tabularnewline
36 & 1 & 1.07460158516278 & -0.0746015851627754 \tabularnewline
37 & 0 & 0.486669540883828 & -0.486669540883828 \tabularnewline
38 & 3 & 1.78366259144716 & 1.21633740855284 \tabularnewline
39 & 0 & 0.57341853842704 & -0.57341853842704 \tabularnewline
40 & 0 & 0.956999894391528 & -0.956999894391528 \tabularnewline
41 & 0 & 1.44375642850749 & -1.44375642850749 \tabularnewline
42 & 3 & 1.78060545441092 & 1.21939454558908 \tabularnewline
43 & 0 & 0.28950895793271 & -0.28950895793271 \tabularnewline
44 & 0 & 1.23845016586154 & -1.23845016586154 \tabularnewline
45 & 2 & 1.81165486161548 & 0.188345138384517 \tabularnewline
46 & 0 & 1.71267716578476 & -1.71267716578476 \tabularnewline
47 & 2 & 1.59576682142698 & 0.404233178573019 \tabularnewline
48 & 2 & 0.693934025185918 & 1.30606597481408 \tabularnewline
49 & 0 & 0.789323258733722 & -0.789323258733722 \tabularnewline
50 & 0 & 0.687891810354501 & -0.687891810354501 \tabularnewline
51 & 0 & 0.76861366260232 & -0.76861366260232 \tabularnewline
52 & 0 & 0.709999239237274 & -0.709999239237274 \tabularnewline
53 & 6 & 1.64692697787031 & 4.35307302212969 \tabularnewline
54 & 0 & 1.01658360271838 & -1.01658360271838 \tabularnewline
55 & 3 & 2.04615814384639 & 0.953841856153614 \tabularnewline
56 & 0 & 0.549068607640723 & -0.549068607640723 \tabularnewline
57 & 0 & 0.0313785357325962 & -0.0313785357325962 \tabularnewline
58 & 1 & 0.805099468048953 & 0.194900531951047 \tabularnewline
59 & 0 & 0.421419965585709 & -0.421419965585709 \tabularnewline
60 & 2 & 0.174327409216567 & 1.82567259078343 \tabularnewline
61 & 2 & 0.89117463352217 & 1.10882536647783 \tabularnewline
62 & 0 & 0.383700714653052 & -0.383700714653052 \tabularnewline
63 & 0 & 1.10262114699888 & -1.10262114699888 \tabularnewline
64 & 1 & 0.86355698419479 & 0.136443015805209 \tabularnewline
65 & 0 & 0.682923618679614 & -0.682923618679614 \tabularnewline
66 & 1 & 0.438672164062095 & 0.561327835937905 \tabularnewline
67 & 0 & 0.474983558835003 & -0.474983558835003 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=158345&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]2[/C][C]0.792106442681787[/C][C]1.20789355731821[/C][/ROW]
[ROW][C]2[/C][C]0[/C][C]1.47031154554377[/C][C]-1.47031154554377[/C][/ROW]
[ROW][C]3[/C][C]0[/C][C]1.34751081015815[/C][C]-1.34751081015815[/C][/ROW]
[ROW][C]4[/C][C]4[/C][C]1.91796342495012[/C][C]2.08203657504988[/C][/ROW]
[ROW][C]5[/C][C]0[/C][C]1.00849146840579[/C][C]-1.00849146840579[/C][/ROW]
[ROW][C]6[/C][C]0[/C][C]0.629002728494075[/C][C]-0.629002728494075[/C][/ROW]
[ROW][C]7[/C][C]0[/C][C]1.36482998106701[/C][C]-1.36482998106701[/C][/ROW]
[ROW][C]8[/C][C]1[/C][C]0.670772856133356[/C][C]0.329227143866644[/C][/ROW]
[ROW][C]9[/C][C]0[/C][C]1.50047662018959[/C][C]-1.50047662018959[/C][/ROW]
[ROW][C]10[/C][C]3[/C][C]1.70655236012651[/C][C]1.29344763987349[/C][/ROW]
[ROW][C]11[/C][C]0[/C][C]1.01792743013611[/C][C]-1.01792743013611[/C][/ROW]
[ROW][C]12[/C][C]4[/C][C]0.538087030393473[/C][C]3.46191296960653[/C][/ROW]
[ROW][C]13[/C][C]1[/C][C]1.26498318566142[/C][C]-0.264983185661422[/C][/ROW]
[ROW][C]14[/C][C]0[/C][C]0.437452107963213[/C][C]-0.437452107963213[/C][/ROW]
[ROW][C]15[/C][C]0[/C][C]1.14883093305528[/C][C]-1.14883093305528[/C][/ROW]
[ROW][C]16[/C][C]0[/C][C]1.53206033274127[/C][C]-1.53206033274127[/C][/ROW]
[ROW][C]17[/C][C]2[/C][C]1.3234406794889[/C][C]0.6765593205111[/C][/ROW]
[ROW][C]18[/C][C]2[/C][C]1.14266769381939[/C][C]0.857332306180606[/C][/ROW]
[ROW][C]19[/C][C]0[/C][C]1.19232835542403[/C][C]-1.19232835542403[/C][/ROW]
[ROW][C]20[/C][C]2[/C][C]1.32928996766094[/C][C]0.670710032339063[/C][/ROW]
[ROW][C]21[/C][C]2[/C][C]1.2108964616582[/C][C]0.789103538341801[/C][/ROW]
[ROW][C]22[/C][C]0[/C][C]-0.4126153758359[/C][C]0.4126153758359[/C][/ROW]
[ROW][C]23[/C][C]0[/C][C]1.52335738055769[/C][C]-1.52335738055769[/C][/ROW]
[ROW][C]24[/C][C]2[/C][C]1.31986148857231[/C][C]0.680138511427694[/C][/ROW]
[ROW][C]25[/C][C]0[/C][C]1.19724653965945[/C][C]-1.19724653965945[/C][/ROW]
[ROW][C]26[/C][C]4[/C][C]1.13858922458001[/C][C]2.86141077541999[/C][/ROW]
[ROW][C]27[/C][C]2[/C][C]1.74683456511844[/C][C]0.253165434881559[/C][/ROW]
[ROW][C]28[/C][C]2[/C][C]1.26180903516553[/C][C]0.738190964834473[/C][/ROW]
[ROW][C]29[/C][C]0[/C][C]1.26897720416731[/C][C]-1.26897720416731[/C][/ROW]
[ROW][C]30[/C][C]3[/C][C]0.512050695063719[/C][C]2.48794930493628[/C][/ROW]
[ROW][C]31[/C][C]3[/C][C]1.60482462897004[/C][C]1.39517537102996[/C][/ROW]
[ROW][C]32[/C][C]2[/C][C]0.788854290701303[/C][C]1.2111457092987[/C][/ROW]
[ROW][C]33[/C][C]0[/C][C]1.29595605724625[/C][C]-1.29595605724625[/C][/ROW]
[ROW][C]34[/C][C]0[/C][C]1.46665829298465[/C][C]-1.46665829298465[/C][/ROW]
[ROW][C]35[/C][C]0[/C][C]0.815484023725625[/C][C]-0.815484023725625[/C][/ROW]
[ROW][C]36[/C][C]1[/C][C]1.07460158516278[/C][C]-0.0746015851627754[/C][/ROW]
[ROW][C]37[/C][C]0[/C][C]0.486669540883828[/C][C]-0.486669540883828[/C][/ROW]
[ROW][C]38[/C][C]3[/C][C]1.78366259144716[/C][C]1.21633740855284[/C][/ROW]
[ROW][C]39[/C][C]0[/C][C]0.57341853842704[/C][C]-0.57341853842704[/C][/ROW]
[ROW][C]40[/C][C]0[/C][C]0.956999894391528[/C][C]-0.956999894391528[/C][/ROW]
[ROW][C]41[/C][C]0[/C][C]1.44375642850749[/C][C]-1.44375642850749[/C][/ROW]
[ROW][C]42[/C][C]3[/C][C]1.78060545441092[/C][C]1.21939454558908[/C][/ROW]
[ROW][C]43[/C][C]0[/C][C]0.28950895793271[/C][C]-0.28950895793271[/C][/ROW]
[ROW][C]44[/C][C]0[/C][C]1.23845016586154[/C][C]-1.23845016586154[/C][/ROW]
[ROW][C]45[/C][C]2[/C][C]1.81165486161548[/C][C]0.188345138384517[/C][/ROW]
[ROW][C]46[/C][C]0[/C][C]1.71267716578476[/C][C]-1.71267716578476[/C][/ROW]
[ROW][C]47[/C][C]2[/C][C]1.59576682142698[/C][C]0.404233178573019[/C][/ROW]
[ROW][C]48[/C][C]2[/C][C]0.693934025185918[/C][C]1.30606597481408[/C][/ROW]
[ROW][C]49[/C][C]0[/C][C]0.789323258733722[/C][C]-0.789323258733722[/C][/ROW]
[ROW][C]50[/C][C]0[/C][C]0.687891810354501[/C][C]-0.687891810354501[/C][/ROW]
[ROW][C]51[/C][C]0[/C][C]0.76861366260232[/C][C]-0.76861366260232[/C][/ROW]
[ROW][C]52[/C][C]0[/C][C]0.709999239237274[/C][C]-0.709999239237274[/C][/ROW]
[ROW][C]53[/C][C]6[/C][C]1.64692697787031[/C][C]4.35307302212969[/C][/ROW]
[ROW][C]54[/C][C]0[/C][C]1.01658360271838[/C][C]-1.01658360271838[/C][/ROW]
[ROW][C]55[/C][C]3[/C][C]2.04615814384639[/C][C]0.953841856153614[/C][/ROW]
[ROW][C]56[/C][C]0[/C][C]0.549068607640723[/C][C]-0.549068607640723[/C][/ROW]
[ROW][C]57[/C][C]0[/C][C]0.0313785357325962[/C][C]-0.0313785357325962[/C][/ROW]
[ROW][C]58[/C][C]1[/C][C]0.805099468048953[/C][C]0.194900531951047[/C][/ROW]
[ROW][C]59[/C][C]0[/C][C]0.421419965585709[/C][C]-0.421419965585709[/C][/ROW]
[ROW][C]60[/C][C]2[/C][C]0.174327409216567[/C][C]1.82567259078343[/C][/ROW]
[ROW][C]61[/C][C]2[/C][C]0.89117463352217[/C][C]1.10882536647783[/C][/ROW]
[ROW][C]62[/C][C]0[/C][C]0.383700714653052[/C][C]-0.383700714653052[/C][/ROW]
[ROW][C]63[/C][C]0[/C][C]1.10262114699888[/C][C]-1.10262114699888[/C][/ROW]
[ROW][C]64[/C][C]1[/C][C]0.86355698419479[/C][C]0.136443015805209[/C][/ROW]
[ROW][C]65[/C][C]0[/C][C]0.682923618679614[/C][C]-0.682923618679614[/C][/ROW]
[ROW][C]66[/C][C]1[/C][C]0.438672164062095[/C][C]0.561327835937905[/C][/ROW]
[ROW][C]67[/C][C]0[/C][C]0.474983558835003[/C][C]-0.474983558835003[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=158345&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=158345&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	2	0.792106442681787	1.20789355731821
2	0	1.47031154554377	-1.47031154554377
3	0	1.34751081015815	-1.34751081015815
4	4	1.91796342495012	2.08203657504988
5	0	1.00849146840579	-1.00849146840579
6	0	0.629002728494075	-0.629002728494075
7	0	1.36482998106701	-1.36482998106701
8	1	0.670772856133356	0.329227143866644
9	0	1.50047662018959	-1.50047662018959
10	3	1.70655236012651	1.29344763987349
11	0	1.01792743013611	-1.01792743013611
12	4	0.538087030393473	3.46191296960653
13	1	1.26498318566142	-0.264983185661422
14	0	0.437452107963213	-0.437452107963213
15	0	1.14883093305528	-1.14883093305528
16	0	1.53206033274127	-1.53206033274127
17	2	1.3234406794889	0.6765593205111
18	2	1.14266769381939	0.857332306180606
19	0	1.19232835542403	-1.19232835542403
20	2	1.32928996766094	0.670710032339063
21	2	1.2108964616582	0.789103538341801
22	0	-0.4126153758359	0.4126153758359
23	0	1.52335738055769	-1.52335738055769
24	2	1.31986148857231	0.680138511427694
25	0	1.19724653965945	-1.19724653965945
26	4	1.13858922458001	2.86141077541999
27	2	1.74683456511844	0.253165434881559
28	2	1.26180903516553	0.738190964834473
29	0	1.26897720416731	-1.26897720416731
30	3	0.512050695063719	2.48794930493628
31	3	1.60482462897004	1.39517537102996
32	2	0.788854290701303	1.2111457092987
33	0	1.29595605724625	-1.29595605724625
34	0	1.46665829298465	-1.46665829298465
35	0	0.815484023725625	-0.815484023725625
36	1	1.07460158516278	-0.0746015851627754
37	0	0.486669540883828	-0.486669540883828
38	3	1.78366259144716	1.21633740855284
39	0	0.57341853842704	-0.57341853842704
40	0	0.956999894391528	-0.956999894391528
41	0	1.44375642850749	-1.44375642850749
42	3	1.78060545441092	1.21939454558908
43	0	0.28950895793271	-0.28950895793271
44	0	1.23845016586154	-1.23845016586154
45	2	1.81165486161548	0.188345138384517
46	0	1.71267716578476	-1.71267716578476
47	2	1.59576682142698	0.404233178573019
48	2	0.693934025185918	1.30606597481408
49	0	0.789323258733722	-0.789323258733722
50	0	0.687891810354501	-0.687891810354501
51	0	0.76861366260232	-0.76861366260232
52	0	0.709999239237274	-0.709999239237274
53	6	1.64692697787031	4.35307302212969
54	0	1.01658360271838	-1.01658360271838
55	3	2.04615814384639	0.953841856153614
56	0	0.549068607640723	-0.549068607640723
57	0	0.0313785357325962	-0.0313785357325962
58	1	0.805099468048953	0.194900531951047
59	0	0.421419965585709	-0.421419965585709
60	2	0.174327409216567	1.82567259078343
61	2	0.89117463352217	1.10882536647783
62	0	0.383700714653052	-0.383700714653052
63	0	1.10262114699888	-1.10262114699888
64	1	0.86355698419479	0.136443015805209
65	0	0.682923618679614	-0.682923618679614
66	1	0.438672164062095	0.561327835937905
67	0	0.474983558835003	-0.474983558835003

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
11	0.92083051951763	0.158338960964741	0.0791694804823704
12	0.99141257299741	0.01717485400518	0.00858742700259002
13	0.981488801726794	0.0370223965464121	0.0185111982732061
14	0.964077698492536	0.0718446030149276	0.0359223015074638
15	0.941910283726795	0.116179432546409	0.0580897162732046
16	0.942935970452996	0.114128059094007	0.0570640295470037
17	0.9179295523735	0.164140895252998	0.0820704476264992
18	0.879276404971324	0.241447190057351	0.120723595028676
19	0.846635030452554	0.306729939094892	0.153364969547446
20	0.79595578387448	0.40808843225104	0.20404421612552
21	0.758900010173782	0.482199979652436	0.241099989826218
22	0.691858433698159	0.616283132603683	0.308141566301841
23	0.689997629810974	0.620004740378053	0.310002370189026
24	0.670443691388375	0.65911261722325	0.329556308611625
25	0.649152847365324	0.701694305269352	0.350847152634676
26	0.830613943682153	0.338772112635694	0.169386056317847
27	0.780041743812142	0.439916512375716	0.219958256187858
28	0.737784420431727	0.524431159136547	0.262215579568273
29	0.717418779445524	0.565162441108952	0.282581220554476
30	0.810937777307458	0.378124445385083	0.189062222692542
31	0.786224985284307	0.427550029431387	0.213775014715693
32	0.773442204576044	0.453115590847911	0.226557795423956
33	0.777504236860532	0.444991526278935	0.222495763139468
34	0.81328175713934	0.373436485721319	0.186718242860659
35	0.78824468257302	0.42351063485396	0.21175531742698
36	0.725268012802193	0.549463974395613	0.274731987197807
37	0.66790197415208	0.664196051695839	0.332098025847919
38	0.68401341913041	0.631973161739179	0.315986580869589
39	0.619172072536047	0.761655854927905	0.380827927463953
40	0.569730447276485	0.86053910544703	0.430269552723515
41	0.542769208916877	0.914461582166246	0.457230791083123
42	0.531364834706334	0.937270330587331	0.468635165293666
43	0.474066630622225	0.94813326124445	0.525933369377775
44	0.480140521885062	0.960281043770125	0.519859478114938
45	0.399245007361851	0.798490014723703	0.600754992638149
46	0.730334954868991	0.539330090262018	0.269665045131009
47	0.659615544911981	0.680768910176037	0.340384455088019
48	0.578004495912212	0.843991008175576	0.421995504087788
49	0.562210081162902	0.875579837674196	0.437789918837098
50	0.470419197732729	0.940838395465458	0.529580802267271
51	0.378996316271856	0.757992632543712	0.621003683728144
52	0.295332717534855	0.590665435069711	0.704667282465145
53	0.792287182115129	0.415425635769742	0.207712817884871
54	0.836420110846291	0.327159778307417	0.163579889153709
55	0.718166988884464	0.563666022231072	0.281833011115536
56	0.573526754407836	0.852946491184327	0.426473245592164

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 & 0.92083051951763 & 0.158338960964741 & 0.0791694804823704 \tabularnewline
12 & 0.99141257299741 & 0.01717485400518 & 0.00858742700259002 \tabularnewline
13 & 0.981488801726794 & 0.0370223965464121 & 0.0185111982732061 \tabularnewline
14 & 0.964077698492536 & 0.0718446030149276 & 0.0359223015074638 \tabularnewline
15 & 0.941910283726795 & 0.116179432546409 & 0.0580897162732046 \tabularnewline
16 & 0.942935970452996 & 0.114128059094007 & 0.0570640295470037 \tabularnewline
17 & 0.9179295523735 & 0.164140895252998 & 0.0820704476264992 \tabularnewline
18 & 0.879276404971324 & 0.241447190057351 & 0.120723595028676 \tabularnewline
19 & 0.846635030452554 & 0.306729939094892 & 0.153364969547446 \tabularnewline
20 & 0.79595578387448 & 0.40808843225104 & 0.20404421612552 \tabularnewline
21 & 0.758900010173782 & 0.482199979652436 & 0.241099989826218 \tabularnewline
22 & 0.691858433698159 & 0.616283132603683 & 0.308141566301841 \tabularnewline
23 & 0.689997629810974 & 0.620004740378053 & 0.310002370189026 \tabularnewline
24 & 0.670443691388375 & 0.65911261722325 & 0.329556308611625 \tabularnewline
25 & 0.649152847365324 & 0.701694305269352 & 0.350847152634676 \tabularnewline
26 & 0.830613943682153 & 0.338772112635694 & 0.169386056317847 \tabularnewline
27 & 0.780041743812142 & 0.439916512375716 & 0.219958256187858 \tabularnewline
28 & 0.737784420431727 & 0.524431159136547 & 0.262215579568273 \tabularnewline
29 & 0.717418779445524 & 0.565162441108952 & 0.282581220554476 \tabularnewline
30 & 0.810937777307458 & 0.378124445385083 & 0.189062222692542 \tabularnewline
31 & 0.786224985284307 & 0.427550029431387 & 0.213775014715693 \tabularnewline
32 & 0.773442204576044 & 0.453115590847911 & 0.226557795423956 \tabularnewline
33 & 0.777504236860532 & 0.444991526278935 & 0.222495763139468 \tabularnewline
34 & 0.81328175713934 & 0.373436485721319 & 0.186718242860659 \tabularnewline
35 & 0.78824468257302 & 0.42351063485396 & 0.21175531742698 \tabularnewline
36 & 0.725268012802193 & 0.549463974395613 & 0.274731987197807 \tabularnewline
37 & 0.66790197415208 & 0.664196051695839 & 0.332098025847919 \tabularnewline
38 & 0.68401341913041 & 0.631973161739179 & 0.315986580869589 \tabularnewline
39 & 0.619172072536047 & 0.761655854927905 & 0.380827927463953 \tabularnewline
40 & 0.569730447276485 & 0.86053910544703 & 0.430269552723515 \tabularnewline
41 & 0.542769208916877 & 0.914461582166246 & 0.457230791083123 \tabularnewline
42 & 0.531364834706334 & 0.937270330587331 & 0.468635165293666 \tabularnewline
43 & 0.474066630622225 & 0.94813326124445 & 0.525933369377775 \tabularnewline
44 & 0.480140521885062 & 0.960281043770125 & 0.519859478114938 \tabularnewline
45 & 0.399245007361851 & 0.798490014723703 & 0.600754992638149 \tabularnewline
46 & 0.730334954868991 & 0.539330090262018 & 0.269665045131009 \tabularnewline
47 & 0.659615544911981 & 0.680768910176037 & 0.340384455088019 \tabularnewline
48 & 0.578004495912212 & 0.843991008175576 & 0.421995504087788 \tabularnewline
49 & 0.562210081162902 & 0.875579837674196 & 0.437789918837098 \tabularnewline
50 & 0.470419197732729 & 0.940838395465458 & 0.529580802267271 \tabularnewline
51 & 0.378996316271856 & 0.757992632543712 & 0.621003683728144 \tabularnewline
52 & 0.295332717534855 & 0.590665435069711 & 0.704667282465145 \tabularnewline
53 & 0.792287182115129 & 0.415425635769742 & 0.207712817884871 \tabularnewline
54 & 0.836420110846291 & 0.327159778307417 & 0.163579889153709 \tabularnewline
55 & 0.718166988884464 & 0.563666022231072 & 0.281833011115536 \tabularnewline
56 & 0.573526754407836 & 0.852946491184327 & 0.426473245592164 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=158345&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]11[/C][C]0.92083051951763[/C][C]0.158338960964741[/C][C]0.0791694804823704[/C][/ROW]
[ROW][C]12[/C][C]0.99141257299741[/C][C]0.01717485400518[/C][C]0.00858742700259002[/C][/ROW]
[ROW][C]13[/C][C]0.981488801726794[/C][C]0.0370223965464121[/C][C]0.0185111982732061[/C][/ROW]
[ROW][C]14[/C][C]0.964077698492536[/C][C]0.0718446030149276[/C][C]0.0359223015074638[/C][/ROW]
[ROW][C]15[/C][C]0.941910283726795[/C][C]0.116179432546409[/C][C]0.0580897162732046[/C][/ROW]
[ROW][C]16[/C][C]0.942935970452996[/C][C]0.114128059094007[/C][C]0.0570640295470037[/C][/ROW]
[ROW][C]17[/C][C]0.9179295523735[/C][C]0.164140895252998[/C][C]0.0820704476264992[/C][/ROW]
[ROW][C]18[/C][C]0.879276404971324[/C][C]0.241447190057351[/C][C]0.120723595028676[/C][/ROW]
[ROW][C]19[/C][C]0.846635030452554[/C][C]0.306729939094892[/C][C]0.153364969547446[/C][/ROW]
[ROW][C]20[/C][C]0.79595578387448[/C][C]0.40808843225104[/C][C]0.20404421612552[/C][/ROW]
[ROW][C]21[/C][C]0.758900010173782[/C][C]0.482199979652436[/C][C]0.241099989826218[/C][/ROW]
[ROW][C]22[/C][C]0.691858433698159[/C][C]0.616283132603683[/C][C]0.308141566301841[/C][/ROW]
[ROW][C]23[/C][C]0.689997629810974[/C][C]0.620004740378053[/C][C]0.310002370189026[/C][/ROW]
[ROW][C]24[/C][C]0.670443691388375[/C][C]0.65911261722325[/C][C]0.329556308611625[/C][/ROW]
[ROW][C]25[/C][C]0.649152847365324[/C][C]0.701694305269352[/C][C]0.350847152634676[/C][/ROW]
[ROW][C]26[/C][C]0.830613943682153[/C][C]0.338772112635694[/C][C]0.169386056317847[/C][/ROW]
[ROW][C]27[/C][C]0.780041743812142[/C][C]0.439916512375716[/C][C]0.219958256187858[/C][/ROW]
[ROW][C]28[/C][C]0.737784420431727[/C][C]0.524431159136547[/C][C]0.262215579568273[/C][/ROW]
[ROW][C]29[/C][C]0.717418779445524[/C][C]0.565162441108952[/C][C]0.282581220554476[/C][/ROW]
[ROW][C]30[/C][C]0.810937777307458[/C][C]0.378124445385083[/C][C]0.189062222692542[/C][/ROW]
[ROW][C]31[/C][C]0.786224985284307[/C][C]0.427550029431387[/C][C]0.213775014715693[/C][/ROW]
[ROW][C]32[/C][C]0.773442204576044[/C][C]0.453115590847911[/C][C]0.226557795423956[/C][/ROW]
[ROW][C]33[/C][C]0.777504236860532[/C][C]0.444991526278935[/C][C]0.222495763139468[/C][/ROW]
[ROW][C]34[/C][C]0.81328175713934[/C][C]0.373436485721319[/C][C]0.186718242860659[/C][/ROW]
[ROW][C]35[/C][C]0.78824468257302[/C][C]0.42351063485396[/C][C]0.21175531742698[/C][/ROW]
[ROW][C]36[/C][C]0.725268012802193[/C][C]0.549463974395613[/C][C]0.274731987197807[/C][/ROW]
[ROW][C]37[/C][C]0.66790197415208[/C][C]0.664196051695839[/C][C]0.332098025847919[/C][/ROW]
[ROW][C]38[/C][C]0.68401341913041[/C][C]0.631973161739179[/C][C]0.315986580869589[/C][/ROW]
[ROW][C]39[/C][C]0.619172072536047[/C][C]0.761655854927905[/C][C]0.380827927463953[/C][/ROW]
[ROW][C]40[/C][C]0.569730447276485[/C][C]0.86053910544703[/C][C]0.430269552723515[/C][/ROW]
[ROW][C]41[/C][C]0.542769208916877[/C][C]0.914461582166246[/C][C]0.457230791083123[/C][/ROW]
[ROW][C]42[/C][C]0.531364834706334[/C][C]0.937270330587331[/C][C]0.468635165293666[/C][/ROW]
[ROW][C]43[/C][C]0.474066630622225[/C][C]0.94813326124445[/C][C]0.525933369377775[/C][/ROW]
[ROW][C]44[/C][C]0.480140521885062[/C][C]0.960281043770125[/C][C]0.519859478114938[/C][/ROW]
[ROW][C]45[/C][C]0.399245007361851[/C][C]0.798490014723703[/C][C]0.600754992638149[/C][/ROW]
[ROW][C]46[/C][C]0.730334954868991[/C][C]0.539330090262018[/C][C]0.269665045131009[/C][/ROW]
[ROW][C]47[/C][C]0.659615544911981[/C][C]0.680768910176037[/C][C]0.340384455088019[/C][/ROW]
[ROW][C]48[/C][C]0.578004495912212[/C][C]0.843991008175576[/C][C]0.421995504087788[/C][/ROW]
[ROW][C]49[/C][C]0.562210081162902[/C][C]0.875579837674196[/C][C]0.437789918837098[/C][/ROW]
[ROW][C]50[/C][C]0.470419197732729[/C][C]0.940838395465458[/C][C]0.529580802267271[/C][/ROW]
[ROW][C]51[/C][C]0.378996316271856[/C][C]0.757992632543712[/C][C]0.621003683728144[/C][/ROW]
[ROW][C]52[/C][C]0.295332717534855[/C][C]0.590665435069711[/C][C]0.704667282465145[/C][/ROW]
[ROW][C]53[/C][C]0.792287182115129[/C][C]0.415425635769742[/C][C]0.207712817884871[/C][/ROW]
[ROW][C]54[/C][C]0.836420110846291[/C][C]0.327159778307417[/C][C]0.163579889153709[/C][/ROW]
[ROW][C]55[/C][C]0.718166988884464[/C][C]0.563666022231072[/C][C]0.281833011115536[/C][/ROW]
[ROW][C]56[/C][C]0.573526754407836[/C][C]0.852946491184327[/C][C]0.426473245592164[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=158345&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=158345&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
11	0.92083051951763	0.158338960964741	0.0791694804823704
12	0.99141257299741	0.01717485400518	0.00858742700259002
13	0.981488801726794	0.0370223965464121	0.0185111982732061
14	0.964077698492536	0.0718446030149276	0.0359223015074638
15	0.941910283726795	0.116179432546409	0.0580897162732046
16	0.942935970452996	0.114128059094007	0.0570640295470037
17	0.9179295523735	0.164140895252998	0.0820704476264992
18	0.879276404971324	0.241447190057351	0.120723595028676
19	0.846635030452554	0.306729939094892	0.153364969547446
20	0.79595578387448	0.40808843225104	0.20404421612552
21	0.758900010173782	0.482199979652436	0.241099989826218
22	0.691858433698159	0.616283132603683	0.308141566301841
23	0.689997629810974	0.620004740378053	0.310002370189026
24	0.670443691388375	0.65911261722325	0.329556308611625
25	0.649152847365324	0.701694305269352	0.350847152634676
26	0.830613943682153	0.338772112635694	0.169386056317847
27	0.780041743812142	0.439916512375716	0.219958256187858
28	0.737784420431727	0.524431159136547	0.262215579568273
29	0.717418779445524	0.565162441108952	0.282581220554476
30	0.810937777307458	0.378124445385083	0.189062222692542
31	0.786224985284307	0.427550029431387	0.213775014715693
32	0.773442204576044	0.453115590847911	0.226557795423956
33	0.777504236860532	0.444991526278935	0.222495763139468
34	0.81328175713934	0.373436485721319	0.186718242860659
35	0.78824468257302	0.42351063485396	0.21175531742698
36	0.725268012802193	0.549463974395613	0.274731987197807
37	0.66790197415208	0.664196051695839	0.332098025847919
38	0.68401341913041	0.631973161739179	0.315986580869589
39	0.619172072536047	0.761655854927905	0.380827927463953
40	0.569730447276485	0.86053910544703	0.430269552723515
41	0.542769208916877	0.914461582166246	0.457230791083123
42	0.531364834706334	0.937270330587331	0.468635165293666
43	0.474066630622225	0.94813326124445	0.525933369377775
44	0.480140521885062	0.960281043770125	0.519859478114938
45	0.399245007361851	0.798490014723703	0.600754992638149
46	0.730334954868991	0.539330090262018	0.269665045131009
47	0.659615544911981	0.680768910176037	0.340384455088019
48	0.578004495912212	0.843991008175576	0.421995504087788
49	0.562210081162902	0.875579837674196	0.437789918837098
50	0.470419197732729	0.940838395465458	0.529580802267271
51	0.378996316271856	0.757992632543712	0.621003683728144
52	0.295332717534855	0.590665435069711	0.704667282465145
53	0.792287182115129	0.415425635769742	0.207712817884871
54	0.836420110846291	0.327159778307417	0.163579889153709
55	0.718166988884464	0.563666022231072	0.281833011115536
56	0.573526754407836	0.852946491184327	0.426473245592164

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	2	0.0434782608695652	OK
10% type I error level	3	0.0652173913043478	OK

\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 & 2 & 0.0434782608695652 & OK \tabularnewline
10% type I error level & 3 & 0.0652173913043478 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=158345&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]2[/C][C]0.0434782608695652[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]3[/C][C]0.0652173913043478[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=158345&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=158345&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	0	0	OK
5% type I error level	2	0.0434782608695652	OK
10% type I error level	3	0.0652173913043478	OK

Figure 1

PNG link

Postscript link

PDF link

Figure 2

PNG link

Postscript link

PDF link

Figure 3

PNG link

Postscript link

PDF link

Figure 4

PNG link

Postscript link

PDF link

Figure 5

PNG link

Postscript link

PDF link

Figure 6

PNG link

Postscript link

PDF link

Figure 7

PNG link

Postscript link

PDF link

Figure 8

PNG link

Postscript link

PDF link

Figure 9

PNG link

Postscript link

PDF link

Figure 10

PNG link

Postscript link

PDF link

Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

R code (references can be found in the software module):

library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code