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, 23 Nov 2010 20:49:21 +0000

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/2010/Nov/23/t12905452758uj4tbqjr0ej8e1.htm/, Retrieved Thu, 25 Apr 2024 04:56:20 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=99646, Retrieved Thu, 25 Apr 2024 04:56:20 +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

171

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

-     [Multiple Regression] [Decreasing Compet...] [2010-11-17 09:04:39] [b98453cac15ba1066b407e146608df68]
-   PD  [Multiple Regression] [WS7 - popularitei...] [2010-11-20 16:26:09] [8ef49741e164ec6343c90c7935194465]
-   P     [Multiple Regression] [WS7 - Determinist...] [2010-11-20 18:14:50] [8ef49741e164ec6343c90c7935194465]
-    D        [Multiple Regression] [ws 7] [2010-11-23 20:49:21] [b47314d83d48c7bf812ec2bcd743b159] [Current]

Feedback Forum

Post a new message

Dataseries X:

Download CSV

Histogram

Boxplots

102.89	167.16	100.70	106.88	97.69
102.64	179.84	99.62	        107.45	101.69
103.33	174.44	99.83	        107.65	102.72
103.56	180.35	100.74	107.72	101.85
103.60	193.17	100.84	108.10	114.94
104.24	195.16	100.85	108.38	106.20
105.31	202.43	99.71	        108.62	106.76
105.40	189.91	100.80	108.79	107.24
105.89	195.98	100.06	109.03	106.50
105.89	212.09	100.57	109.34	106.77
105.54	205.81	99.79	        109.73	108.24
106.15	204.31	99.90	        109.76	104.43
106.14	196.07	100.12	109.96	100.90
105.85	199.98	100.40	110.49	103.91
106.27	199.10	100.51	111.37	103.81
106.51	198.31	100.70	111.56	104.59
106.82	195.72	100.62	111.90	104.94
106.53	223.04	99.70	        111.96	111.64
107.14	238.41	99.48	        112.25	111.27
107.39	259.73	99.36	        112.39	106.82
107.33	326.54	99.39	        112.30	106.07
107.53	335.15	99.45	        112.49	111.35
107.42	321.81	99.28	        112.77	112.59
108.25	368.62	99.40	        113.15	108.59
108.26	369.59	99.10	        113.15	106.83
108.93	425.00	99.48	        113.28	112.51
109.43	439.72	99.74	        113.83	113.61
109.61	362.23	100.42	114.49	114.96
109.74	328.76	100.80	114.76	118.66
110.12	348.55	100.66	114.96	116.84
110.16	328.18	101.03	115.41	121.19
110.44	329.34	101.22	115.84	117.42
111.23	295.55	101.23	116.31	116.88
112.86	237.38	100.10	117.23	115.01
112.77	226.85	99.98	        117.97	111.81
113.04	220.14	99.91	        118.08	110.61
112.79	239.36	99.84	        118.27	110.67
113.87	224.69	99.68	        118.88	113.28
114.28	230.98	99.74	        119.11	112.08
115.51	233.47	99.71	        119.29	111.41
116.76	256.70	99.35	        119.36	113.81
116.91	253.41	99.21	        119.48	109.16
116.47	224.95	99.21	        120.10	105.09
116.94	210.37	99.16	        120.30	102.23
117.24	191.09	99.20	        120.54	101.95
116.82	198.85	99.08	        120.86	104.75
117.48	211.04	98.16	        121.10	107.25
117.11	206.25	98.00	        121.42	105.25
117.31	201.19	97.90	        121.81	102.75
117.77	194.37	97.88	        122.21	107.21
118.37	191.08	97.56	        122.82	107.24
117.91	192.87	96.86	        123.02	106.01
118.12	181.61	96.86	        123.14	121.36
118.02	157.67	96.75	        123.12	120.44
117.77	196.14	97.12	        123.42	109.40
117.85	246.35	97.22	        123.50	111.51
118.68	271.90	97.52	        125.77	111.97
118.90	270.29	97.57	        125.99	114.64

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	7 seconds
R Server	'George Udny Yule' @ 72.249.76.132
R Framework error message	The field 'Names of X columns' contains a hard return which cannot be interpreted. Please, resubmit your request without hard returns in the 'Names of X columns'.

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 7 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
R Framework error message & The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'. \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99646&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]7 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[ROW][C]R Framework error message[/C][C]The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99646&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99646&T=0

As an alternative you can also use a QR Code:

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

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	7 seconds
R Server	'George Udny Yule' @ 72.249.76.132
R Framework error message	The field 'Names of X columns' contains a hard return which cannot be interpreted. Please, resubmit your request without hard returns in the 'Names of X columns'.

Multiple Linear Regression - Estimated Regression Equation
Bier[t] = + 69.8633744517073 -0.00683435744293085Tarwe[t] + 0.297906175405562suiker[t] + 0.0895655954552326minerwater[t] -0.0559117429109895`fruit `[t] + 0.301835836738048t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Bier[t] =  +  69.8633744517073 -0.00683435744293085Tarwe[t] +  0.297906175405562suiker[t] +  0.0895655954552326minerwater[t] -0.0559117429109895`fruit
`[t] +  0.301835836738048t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99646&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Bier[t] =  +  69.8633744517073 -0.00683435744293085Tarwe[t] +  0.297906175405562suiker[t] +  0.0895655954552326minerwater[t] -0.0559117429109895`fruit
`[t] +  0.301835836738048t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99646&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99646&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
Bier[t] = + 69.8633744517073 -0.00683435744293085Tarwe[t] + 0.297906175405562suiker[t] + 0.0895655954552326minerwater[t] -0.0559117429109895`fruit `[t] + 0.301835836738048t + 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)	69.8633744517073	28.616018	2.4414	0.018069	0.009035
Tarwe	-0.00683435744293085	0.00218	-3.1355	0.002821	0.00141
suiker	0.297906175405562	0.133131	2.2377	0.029551	0.014775
minerwater	0.0895655954552326	0.255794	0.3501	0.727642	0.363821
`fruit `	-0.0559117429109895	0.022358	-2.5008	0.015581	0.00779
t	0.301835836738048	0.084159	3.5865	0.00074	0.00037

\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) & 69.8633744517073 & 28.616018 & 2.4414 & 0.018069 & 0.009035 \tabularnewline
Tarwe & -0.00683435744293085 & 0.00218 & -3.1355 & 0.002821 & 0.00141 \tabularnewline
suiker & 0.297906175405562 & 0.133131 & 2.2377 & 0.029551 & 0.014775 \tabularnewline
minerwater & 0.0895655954552326 & 0.255794 & 0.3501 & 0.727642 & 0.363821 \tabularnewline
`fruit
` & -0.0559117429109895 & 0.022358 & -2.5008 & 0.015581 & 0.00779 \tabularnewline
t & 0.301835836738048 & 0.084159 & 3.5865 & 0.00074 & 0.00037 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99646&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]69.8633744517073[/C][C]28.616018[/C][C]2.4414[/C][C]0.018069[/C][C]0.009035[/C][/ROW]
[ROW][C]Tarwe[/C][C]-0.00683435744293085[/C][C]0.00218[/C][C]-3.1355[/C][C]0.002821[/C][C]0.00141[/C][/ROW]
[ROW][C]suiker[/C][C]0.297906175405562[/C][C]0.133131[/C][C]2.2377[/C][C]0.029551[/C][C]0.014775[/C][/ROW]
[ROW][C]minerwater[/C][C]0.0895655954552326[/C][C]0.255794[/C][C]0.3501[/C][C]0.727642[/C][C]0.363821[/C][/ROW]
[ROW][C]`fruit
`[/C][C]-0.0559117429109895[/C][C]0.022358[/C][C]-2.5008[/C][C]0.015581[/C][C]0.00779[/C][/ROW]
[ROW][C]t[/C][C]0.301835836738048[/C][C]0.084159[/C][C]3.5865[/C][C]0.00074[/C][C]0.00037[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99646&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99646&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)	69.8633744517073	28.616018	2.4414	0.018069	0.009035
Tarwe	-0.00683435744293085	0.00218	-3.1355	0.002821	0.00141
suiker	0.297906175405562	0.133131	2.2377	0.029551	0.014775
minerwater	0.0895655954552326	0.255794	0.3501	0.727642	0.363821
`fruit `	-0.0559117429109895	0.022358	-2.5008	0.015581	0.00779
t	0.301835836738048	0.084159	3.5865	0.00074	0.00037

Multiple Linear Regression - Regression Statistics
Multiple R	0.991103313689328
R-squared	0.982285778405966
Adjusted R-squared	0.980582487868078
F-TEST (value)	576.698899310532
F-TEST (DF numerator)	5
F-TEST (DF denominator)	52
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.734513116633436
Sum Squared Residuals	28.0544949623413

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.991103313689328 \tabularnewline
R-squared & 0.982285778405966 \tabularnewline
Adjusted R-squared & 0.980582487868078 \tabularnewline
F-TEST (value) & 576.698899310532 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 52 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.734513116633436 \tabularnewline
Sum Squared Residuals & 28.0544949623413 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99646&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.991103313689328[/C][/ROW]
[ROW][C]R-squared[/C][C]0.982285778405966[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.980582487868078[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]576.698899310532[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]52[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.734513116633436[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]28.0544949623413[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99646&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99646&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	0.991103313689328
R-squared	0.982285778405966
Adjusted R-squared	0.980582487868078
F-TEST (value)	576.698899310532
F-TEST (DF numerator)	5
F-TEST (DF denominator)	52
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.734513116633436
Sum Squared Residuals	28.0544949623413

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	102.89	103.132683638906	-0.242683638906177
2	102.64	102.853526571595	-0.213526571595034
3	103.33	103.215152259253	0.114847740747191
4	103.56	103.802604471137	-0.24260447113661
5	103.6	103.348764674565	0.251235325435013
6	104.24	104.153726201515	0.0862737984848322
7	105.31	104.056448386560	1.25355161344014
8	105.4	104.756956624306	0.643043375694419
9	105.89	104.859727774228	1.03027222577168
10	105.89	105.216063426023	0.673936573977261
11	105.54	105.281192530834	0.258807469165562
12	106.15	105.841760291386	0.308239708613978
13	106.14	106.48073216361	-0.340732163609888
14	105.85	106.718434811289	-0.868434811288836
15	106.27	107.143463460163	-0.873463460162977
16	106.51	107.480706916274	-0.97070691627391
17	106.82	107.787294437193	-0.967294437192651
18	106.53	107.259107205440	-0.729107205440385
19	107.14	107.437020977250	-0.297020977250447
20	107.39	107.818746011574	-0.428746011574177
21	107.33	107.706788516404	-0.376788516404455
22	107.53	107.689458366650	-0.159458366649669
23	107.42	107.987568287375	-0.567568287375306
24	108.25	108.262918491175	-0.0129184911753782
25	108.26	108.567157816095	-0.30715781609545
26	108.93	108.297571081250	0.632428918750427
27	109.43	108.564018942331	0.865981057668588
28	109.61	109.581657776669	0.0283422233314187
29	109.74	110.042753165678	-0.302753165677895
30	110.12	110.287302695253	-0.167302695252599
31	110.16	110.635668114295	-0.475668114295267
32	110.44	111.235478746547	-0.795478746546753
33	111.23	111.843514754071	-0.613514754071378
34	112.86	112.393226492119	0.466773507881204
35	112.77	112.976475489634	-0.206475489634281
36	113.04	113.380262739529	-0.340262739529257
37	112.79	113.543551552498	-0.753551552497622
38	113.87	113.806687789089	0.063312210911412
39	114.28	114.171104066483	0.108895933517175
40	115.51	114.500567842858	1.00943215714189
41	116.76	114.408476741746	2.35152325825364
42	116.91	114.961828225906	1.94817177409439
43	116.47	115.741261338299	0.728738661700558
44	116.94	116.305667501602	0.634332498398377
45	117.24	116.78833702778	0.451662972220061
46	116.82	116.873497620107	-0.0534976201070794
47	117.48	116.699665343874	0.780334656125546
48	117.11	117.127057241067	-0.0170572410669104
49	117.31	117.608394248431	-0.298394248430646
50	117.77	117.737342144220	0.0326578557795465
51	118.37	118.019290701756	0.350709298243679
52	117.91	118.187043278759	-0.277043278759200
53	118.12	117.718336598076	0.401663401924421
54	118.02	118.200664764272	-0.180664764271798
55	117.77	118.993943475454	-1.22394347545425
56	117.85	118.871608312618	-1.02160831261753
57	118.68	119.265792669255	-0.585792669254667
58	118.9	119.463947207674	-0.563947207673924

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 102.89 & 103.132683638906 & -0.242683638906177 \tabularnewline
2 & 102.64 & 102.853526571595 & -0.213526571595034 \tabularnewline
3 & 103.33 & 103.215152259253 & 0.114847740747191 \tabularnewline
4 & 103.56 & 103.802604471137 & -0.24260447113661 \tabularnewline
5 & 103.6 & 103.348764674565 & 0.251235325435013 \tabularnewline
6 & 104.24 & 104.153726201515 & 0.0862737984848322 \tabularnewline
7 & 105.31 & 104.056448386560 & 1.25355161344014 \tabularnewline
8 & 105.4 & 104.756956624306 & 0.643043375694419 \tabularnewline
9 & 105.89 & 104.859727774228 & 1.03027222577168 \tabularnewline
10 & 105.89 & 105.216063426023 & 0.673936573977261 \tabularnewline
11 & 105.54 & 105.281192530834 & 0.258807469165562 \tabularnewline
12 & 106.15 & 105.841760291386 & 0.308239708613978 \tabularnewline
13 & 106.14 & 106.48073216361 & -0.340732163609888 \tabularnewline
14 & 105.85 & 106.718434811289 & -0.868434811288836 \tabularnewline
15 & 106.27 & 107.143463460163 & -0.873463460162977 \tabularnewline
16 & 106.51 & 107.480706916274 & -0.97070691627391 \tabularnewline
17 & 106.82 & 107.787294437193 & -0.967294437192651 \tabularnewline
18 & 106.53 & 107.259107205440 & -0.729107205440385 \tabularnewline
19 & 107.14 & 107.437020977250 & -0.297020977250447 \tabularnewline
20 & 107.39 & 107.818746011574 & -0.428746011574177 \tabularnewline
21 & 107.33 & 107.706788516404 & -0.376788516404455 \tabularnewline
22 & 107.53 & 107.689458366650 & -0.159458366649669 \tabularnewline
23 & 107.42 & 107.987568287375 & -0.567568287375306 \tabularnewline
24 & 108.25 & 108.262918491175 & -0.0129184911753782 \tabularnewline
25 & 108.26 & 108.567157816095 & -0.30715781609545 \tabularnewline
26 & 108.93 & 108.297571081250 & 0.632428918750427 \tabularnewline
27 & 109.43 & 108.564018942331 & 0.865981057668588 \tabularnewline
28 & 109.61 & 109.581657776669 & 0.0283422233314187 \tabularnewline
29 & 109.74 & 110.042753165678 & -0.302753165677895 \tabularnewline
30 & 110.12 & 110.287302695253 & -0.167302695252599 \tabularnewline
31 & 110.16 & 110.635668114295 & -0.475668114295267 \tabularnewline
32 & 110.44 & 111.235478746547 & -0.795478746546753 \tabularnewline
33 & 111.23 & 111.843514754071 & -0.613514754071378 \tabularnewline
34 & 112.86 & 112.393226492119 & 0.466773507881204 \tabularnewline
35 & 112.77 & 112.976475489634 & -0.206475489634281 \tabularnewline
36 & 113.04 & 113.380262739529 & -0.340262739529257 \tabularnewline
37 & 112.79 & 113.543551552498 & -0.753551552497622 \tabularnewline
38 & 113.87 & 113.806687789089 & 0.063312210911412 \tabularnewline
39 & 114.28 & 114.171104066483 & 0.108895933517175 \tabularnewline
40 & 115.51 & 114.500567842858 & 1.00943215714189 \tabularnewline
41 & 116.76 & 114.408476741746 & 2.35152325825364 \tabularnewline
42 & 116.91 & 114.961828225906 & 1.94817177409439 \tabularnewline
43 & 116.47 & 115.741261338299 & 0.728738661700558 \tabularnewline
44 & 116.94 & 116.305667501602 & 0.634332498398377 \tabularnewline
45 & 117.24 & 116.78833702778 & 0.451662972220061 \tabularnewline
46 & 116.82 & 116.873497620107 & -0.0534976201070794 \tabularnewline
47 & 117.48 & 116.699665343874 & 0.780334656125546 \tabularnewline
48 & 117.11 & 117.127057241067 & -0.0170572410669104 \tabularnewline
49 & 117.31 & 117.608394248431 & -0.298394248430646 \tabularnewline
50 & 117.77 & 117.737342144220 & 0.0326578557795465 \tabularnewline
51 & 118.37 & 118.019290701756 & 0.350709298243679 \tabularnewline
52 & 117.91 & 118.187043278759 & -0.277043278759200 \tabularnewline
53 & 118.12 & 117.718336598076 & 0.401663401924421 \tabularnewline
54 & 118.02 & 118.200664764272 & -0.180664764271798 \tabularnewline
55 & 117.77 & 118.993943475454 & -1.22394347545425 \tabularnewline
56 & 117.85 & 118.871608312618 & -1.02160831261753 \tabularnewline
57 & 118.68 & 119.265792669255 & -0.585792669254667 \tabularnewline
58 & 118.9 & 119.463947207674 & -0.563947207673924 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99646&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]102.89[/C][C]103.132683638906[/C][C]-0.242683638906177[/C][/ROW]
[ROW][C]2[/C][C]102.64[/C][C]102.853526571595[/C][C]-0.213526571595034[/C][/ROW]
[ROW][C]3[/C][C]103.33[/C][C]103.215152259253[/C][C]0.114847740747191[/C][/ROW]
[ROW][C]4[/C][C]103.56[/C][C]103.802604471137[/C][C]-0.24260447113661[/C][/ROW]
[ROW][C]5[/C][C]103.6[/C][C]103.348764674565[/C][C]0.251235325435013[/C][/ROW]
[ROW][C]6[/C][C]104.24[/C][C]104.153726201515[/C][C]0.0862737984848322[/C][/ROW]
[ROW][C]7[/C][C]105.31[/C][C]104.056448386560[/C][C]1.25355161344014[/C][/ROW]
[ROW][C]8[/C][C]105.4[/C][C]104.756956624306[/C][C]0.643043375694419[/C][/ROW]
[ROW][C]9[/C][C]105.89[/C][C]104.859727774228[/C][C]1.03027222577168[/C][/ROW]
[ROW][C]10[/C][C]105.89[/C][C]105.216063426023[/C][C]0.673936573977261[/C][/ROW]
[ROW][C]11[/C][C]105.54[/C][C]105.281192530834[/C][C]0.258807469165562[/C][/ROW]
[ROW][C]12[/C][C]106.15[/C][C]105.841760291386[/C][C]0.308239708613978[/C][/ROW]
[ROW][C]13[/C][C]106.14[/C][C]106.48073216361[/C][C]-0.340732163609888[/C][/ROW]
[ROW][C]14[/C][C]105.85[/C][C]106.718434811289[/C][C]-0.868434811288836[/C][/ROW]
[ROW][C]15[/C][C]106.27[/C][C]107.143463460163[/C][C]-0.873463460162977[/C][/ROW]
[ROW][C]16[/C][C]106.51[/C][C]107.480706916274[/C][C]-0.97070691627391[/C][/ROW]
[ROW][C]17[/C][C]106.82[/C][C]107.787294437193[/C][C]-0.967294437192651[/C][/ROW]
[ROW][C]18[/C][C]106.53[/C][C]107.259107205440[/C][C]-0.729107205440385[/C][/ROW]
[ROW][C]19[/C][C]107.14[/C][C]107.437020977250[/C][C]-0.297020977250447[/C][/ROW]
[ROW][C]20[/C][C]107.39[/C][C]107.818746011574[/C][C]-0.428746011574177[/C][/ROW]
[ROW][C]21[/C][C]107.33[/C][C]107.706788516404[/C][C]-0.376788516404455[/C][/ROW]
[ROW][C]22[/C][C]107.53[/C][C]107.689458366650[/C][C]-0.159458366649669[/C][/ROW]
[ROW][C]23[/C][C]107.42[/C][C]107.987568287375[/C][C]-0.567568287375306[/C][/ROW]
[ROW][C]24[/C][C]108.25[/C][C]108.262918491175[/C][C]-0.0129184911753782[/C][/ROW]
[ROW][C]25[/C][C]108.26[/C][C]108.567157816095[/C][C]-0.30715781609545[/C][/ROW]
[ROW][C]26[/C][C]108.93[/C][C]108.297571081250[/C][C]0.632428918750427[/C][/ROW]
[ROW][C]27[/C][C]109.43[/C][C]108.564018942331[/C][C]0.865981057668588[/C][/ROW]
[ROW][C]28[/C][C]109.61[/C][C]109.581657776669[/C][C]0.0283422233314187[/C][/ROW]
[ROW][C]29[/C][C]109.74[/C][C]110.042753165678[/C][C]-0.302753165677895[/C][/ROW]
[ROW][C]30[/C][C]110.12[/C][C]110.287302695253[/C][C]-0.167302695252599[/C][/ROW]
[ROW][C]31[/C][C]110.16[/C][C]110.635668114295[/C][C]-0.475668114295267[/C][/ROW]
[ROW][C]32[/C][C]110.44[/C][C]111.235478746547[/C][C]-0.795478746546753[/C][/ROW]
[ROW][C]33[/C][C]111.23[/C][C]111.843514754071[/C][C]-0.613514754071378[/C][/ROW]
[ROW][C]34[/C][C]112.86[/C][C]112.393226492119[/C][C]0.466773507881204[/C][/ROW]
[ROW][C]35[/C][C]112.77[/C][C]112.976475489634[/C][C]-0.206475489634281[/C][/ROW]
[ROW][C]36[/C][C]113.04[/C][C]113.380262739529[/C][C]-0.340262739529257[/C][/ROW]
[ROW][C]37[/C][C]112.79[/C][C]113.543551552498[/C][C]-0.753551552497622[/C][/ROW]
[ROW][C]38[/C][C]113.87[/C][C]113.806687789089[/C][C]0.063312210911412[/C][/ROW]
[ROW][C]39[/C][C]114.28[/C][C]114.171104066483[/C][C]0.108895933517175[/C][/ROW]
[ROW][C]40[/C][C]115.51[/C][C]114.500567842858[/C][C]1.00943215714189[/C][/ROW]
[ROW][C]41[/C][C]116.76[/C][C]114.408476741746[/C][C]2.35152325825364[/C][/ROW]
[ROW][C]42[/C][C]116.91[/C][C]114.961828225906[/C][C]1.94817177409439[/C][/ROW]
[ROW][C]43[/C][C]116.47[/C][C]115.741261338299[/C][C]0.728738661700558[/C][/ROW]
[ROW][C]44[/C][C]116.94[/C][C]116.305667501602[/C][C]0.634332498398377[/C][/ROW]
[ROW][C]45[/C][C]117.24[/C][C]116.78833702778[/C][C]0.451662972220061[/C][/ROW]
[ROW][C]46[/C][C]116.82[/C][C]116.873497620107[/C][C]-0.0534976201070794[/C][/ROW]
[ROW][C]47[/C][C]117.48[/C][C]116.699665343874[/C][C]0.780334656125546[/C][/ROW]
[ROW][C]48[/C][C]117.11[/C][C]117.127057241067[/C][C]-0.0170572410669104[/C][/ROW]
[ROW][C]49[/C][C]117.31[/C][C]117.608394248431[/C][C]-0.298394248430646[/C][/ROW]
[ROW][C]50[/C][C]117.77[/C][C]117.737342144220[/C][C]0.0326578557795465[/C][/ROW]
[ROW][C]51[/C][C]118.37[/C][C]118.019290701756[/C][C]0.350709298243679[/C][/ROW]
[ROW][C]52[/C][C]117.91[/C][C]118.187043278759[/C][C]-0.277043278759200[/C][/ROW]
[ROW][C]53[/C][C]118.12[/C][C]117.718336598076[/C][C]0.401663401924421[/C][/ROW]
[ROW][C]54[/C][C]118.02[/C][C]118.200664764272[/C][C]-0.180664764271798[/C][/ROW]
[ROW][C]55[/C][C]117.77[/C][C]118.993943475454[/C][C]-1.22394347545425[/C][/ROW]
[ROW][C]56[/C][C]117.85[/C][C]118.871608312618[/C][C]-1.02160831261753[/C][/ROW]
[ROW][C]57[/C][C]118.68[/C][C]119.265792669255[/C][C]-0.585792669254667[/C][/ROW]
[ROW][C]58[/C][C]118.9[/C][C]119.463947207674[/C][C]-0.563947207673924[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99646&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99646&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	102.89	103.132683638906	-0.242683638906177
2	102.64	102.853526571595	-0.213526571595034
3	103.33	103.215152259253	0.114847740747191
4	103.56	103.802604471137	-0.24260447113661
5	103.6	103.348764674565	0.251235325435013
6	104.24	104.153726201515	0.0862737984848322
7	105.31	104.056448386560	1.25355161344014
8	105.4	104.756956624306	0.643043375694419
9	105.89	104.859727774228	1.03027222577168
10	105.89	105.216063426023	0.673936573977261
11	105.54	105.281192530834	0.258807469165562
12	106.15	105.841760291386	0.308239708613978
13	106.14	106.48073216361	-0.340732163609888
14	105.85	106.718434811289	-0.868434811288836
15	106.27	107.143463460163	-0.873463460162977
16	106.51	107.480706916274	-0.97070691627391
17	106.82	107.787294437193	-0.967294437192651
18	106.53	107.259107205440	-0.729107205440385
19	107.14	107.437020977250	-0.297020977250447
20	107.39	107.818746011574	-0.428746011574177
21	107.33	107.706788516404	-0.376788516404455
22	107.53	107.689458366650	-0.159458366649669
23	107.42	107.987568287375	-0.567568287375306
24	108.25	108.262918491175	-0.0129184911753782
25	108.26	108.567157816095	-0.30715781609545
26	108.93	108.297571081250	0.632428918750427
27	109.43	108.564018942331	0.865981057668588
28	109.61	109.581657776669	0.0283422233314187
29	109.74	110.042753165678	-0.302753165677895
30	110.12	110.287302695253	-0.167302695252599
31	110.16	110.635668114295	-0.475668114295267
32	110.44	111.235478746547	-0.795478746546753
33	111.23	111.843514754071	-0.613514754071378
34	112.86	112.393226492119	0.466773507881204
35	112.77	112.976475489634	-0.206475489634281
36	113.04	113.380262739529	-0.340262739529257
37	112.79	113.543551552498	-0.753551552497622
38	113.87	113.806687789089	0.063312210911412
39	114.28	114.171104066483	0.108895933517175
40	115.51	114.500567842858	1.00943215714189
41	116.76	114.408476741746	2.35152325825364
42	116.91	114.961828225906	1.94817177409439
43	116.47	115.741261338299	0.728738661700558
44	116.94	116.305667501602	0.634332498398377
45	117.24	116.78833702778	0.451662972220061
46	116.82	116.873497620107	-0.0534976201070794
47	117.48	116.699665343874	0.780334656125546
48	117.11	117.127057241067	-0.0170572410669104
49	117.31	117.608394248431	-0.298394248430646
50	117.77	117.737342144220	0.0326578557795465
51	118.37	118.019290701756	0.350709298243679
52	117.91	118.187043278759	-0.277043278759200
53	118.12	117.718336598076	0.401663401924421
54	118.02	118.200664764272	-0.180664764271798
55	117.77	118.993943475454	-1.22394347545425
56	117.85	118.871608312618	-1.02160831261753
57	118.68	119.265792669255	-0.585792669254667
58	118.9	119.463947207674	-0.563947207673924

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
9	0.0496990253031775	0.099398050606355	0.950300974696823
10	0.0222756820580731	0.0445513641161461	0.977724317941927
11	0.0564725395864933	0.112945079172987	0.943527460413507
12	0.0941182559770203	0.188236511954041	0.90588174402298
13	0.0766250365206111	0.153250073041222	0.923374963479389
14	0.0399444132636314	0.0798888265272627	0.960055586736369
15	0.0575262014697434	0.115052402939487	0.942473798530256
16	0.0344679143784245	0.0689358287568489	0.965532085621576
17	0.0228079669946299	0.0456159339892597	0.97719203300537
18	0.0382155980381344	0.0764311960762687	0.961784401961866
19	0.0248468396196000	0.0496936792392001	0.9751531603804
20	0.0166701843084037	0.0333403686168075	0.983329815691596
21	0.0114028351402277	0.0228056702804555	0.988597164859772
22	0.00666737999275523	0.0133347599855105	0.993332620007245
23	0.0078581376272185	0.015716275254437	0.992141862372782
24	0.00704218197155988	0.0140843639431198	0.99295781802844
25	0.00570483905101195	0.0114096781020239	0.994295160948988
26	0.00409940757513739	0.00819881515027479	0.995900592424863
27	0.00509648246836754	0.0101929649367351	0.994903517531633
28	0.00295873944358895	0.00591747888717789	0.99704126055641
29	0.00153954833773910	0.00307909667547821	0.99846045166226
30	0.000750297593806008	0.00150059518761202	0.999249702406194
31	0.000383009087513662	0.000766018175027324	0.999616990912486
32	0.000275270399288779	0.000550540798577559	0.999724729600711
33	0.000631697535094076	0.00126339507018815	0.999368302464906
34	0.0119296005931846	0.0238592011863691	0.988070399406815
35	0.0134119722703183	0.0268239445406365	0.986588027729682
36	0.0176538812980499	0.0353077625960999	0.98234611870195
37	0.106031732314648	0.212063464629295	0.893968267685352
38	0.27556054225684	0.55112108451368	0.72443945774316
39	0.88713782419094	0.225724351618119	0.112862175809059
40	0.996938389905658	0.00612322018868453	0.00306161009434226
41	0.998678266203408	0.00264346759318382	0.00132173379659191
42	0.999233083538432	0.00153383292313501	0.000766916461567504
43	0.999036886084385	0.00192622783123081	0.000963113915615406
44	0.997098621419335	0.00580275716133079	0.00290137858066540
45	0.994679421162514	0.0106411576749724	0.0053205788374862
46	0.991797467895607	0.0164050642087860	0.00820253210439301
47	0.98199747757775	0.0360050448445007	0.0180025224222503
48	0.967347507450653	0.0653049850986936	0.0326524925493468
49	0.960372262097357	0.0792554758052866	0.0396277379026433

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.0496990253031775 & 0.099398050606355 & 0.950300974696823 \tabularnewline
10 & 0.0222756820580731 & 0.0445513641161461 & 0.977724317941927 \tabularnewline
11 & 0.0564725395864933 & 0.112945079172987 & 0.943527460413507 \tabularnewline
12 & 0.0941182559770203 & 0.188236511954041 & 0.90588174402298 \tabularnewline
13 & 0.0766250365206111 & 0.153250073041222 & 0.923374963479389 \tabularnewline
14 & 0.0399444132636314 & 0.0798888265272627 & 0.960055586736369 \tabularnewline
15 & 0.0575262014697434 & 0.115052402939487 & 0.942473798530256 \tabularnewline
16 & 0.0344679143784245 & 0.0689358287568489 & 0.965532085621576 \tabularnewline
17 & 0.0228079669946299 & 0.0456159339892597 & 0.97719203300537 \tabularnewline
18 & 0.0382155980381344 & 0.0764311960762687 & 0.961784401961866 \tabularnewline
19 & 0.0248468396196000 & 0.0496936792392001 & 0.9751531603804 \tabularnewline
20 & 0.0166701843084037 & 0.0333403686168075 & 0.983329815691596 \tabularnewline
21 & 0.0114028351402277 & 0.0228056702804555 & 0.988597164859772 \tabularnewline
22 & 0.00666737999275523 & 0.0133347599855105 & 0.993332620007245 \tabularnewline
23 & 0.0078581376272185 & 0.015716275254437 & 0.992141862372782 \tabularnewline
24 & 0.00704218197155988 & 0.0140843639431198 & 0.99295781802844 \tabularnewline
25 & 0.00570483905101195 & 0.0114096781020239 & 0.994295160948988 \tabularnewline
26 & 0.00409940757513739 & 0.00819881515027479 & 0.995900592424863 \tabularnewline
27 & 0.00509648246836754 & 0.0101929649367351 & 0.994903517531633 \tabularnewline
28 & 0.00295873944358895 & 0.00591747888717789 & 0.99704126055641 \tabularnewline
29 & 0.00153954833773910 & 0.00307909667547821 & 0.99846045166226 \tabularnewline
30 & 0.000750297593806008 & 0.00150059518761202 & 0.999249702406194 \tabularnewline
31 & 0.000383009087513662 & 0.000766018175027324 & 0.999616990912486 \tabularnewline
32 & 0.000275270399288779 & 0.000550540798577559 & 0.999724729600711 \tabularnewline
33 & 0.000631697535094076 & 0.00126339507018815 & 0.999368302464906 \tabularnewline
34 & 0.0119296005931846 & 0.0238592011863691 & 0.988070399406815 \tabularnewline
35 & 0.0134119722703183 & 0.0268239445406365 & 0.986588027729682 \tabularnewline
36 & 0.0176538812980499 & 0.0353077625960999 & 0.98234611870195 \tabularnewline
37 & 0.106031732314648 & 0.212063464629295 & 0.893968267685352 \tabularnewline
38 & 0.27556054225684 & 0.55112108451368 & 0.72443945774316 \tabularnewline
39 & 0.88713782419094 & 0.225724351618119 & 0.112862175809059 \tabularnewline
40 & 0.996938389905658 & 0.00612322018868453 & 0.00306161009434226 \tabularnewline
41 & 0.998678266203408 & 0.00264346759318382 & 0.00132173379659191 \tabularnewline
42 & 0.999233083538432 & 0.00153383292313501 & 0.000766916461567504 \tabularnewline
43 & 0.999036886084385 & 0.00192622783123081 & 0.000963113915615406 \tabularnewline
44 & 0.997098621419335 & 0.00580275716133079 & 0.00290137858066540 \tabularnewline
45 & 0.994679421162514 & 0.0106411576749724 & 0.0053205788374862 \tabularnewline
46 & 0.991797467895607 & 0.0164050642087860 & 0.00820253210439301 \tabularnewline
47 & 0.98199747757775 & 0.0360050448445007 & 0.0180025224222503 \tabularnewline
48 & 0.967347507450653 & 0.0653049850986936 & 0.0326524925493468 \tabularnewline
49 & 0.960372262097357 & 0.0792554758052866 & 0.0396277379026433 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99646&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]9[/C][C]0.0496990253031775[/C][C]0.099398050606355[/C][C]0.950300974696823[/C][/ROW]
[ROW][C]10[/C][C]0.0222756820580731[/C][C]0.0445513641161461[/C][C]0.977724317941927[/C][/ROW]
[ROW][C]11[/C][C]0.0564725395864933[/C][C]0.112945079172987[/C][C]0.943527460413507[/C][/ROW]
[ROW][C]12[/C][C]0.0941182559770203[/C][C]0.188236511954041[/C][C]0.90588174402298[/C][/ROW]
[ROW][C]13[/C][C]0.0766250365206111[/C][C]0.153250073041222[/C][C]0.923374963479389[/C][/ROW]
[ROW][C]14[/C][C]0.0399444132636314[/C][C]0.0798888265272627[/C][C]0.960055586736369[/C][/ROW]
[ROW][C]15[/C][C]0.0575262014697434[/C][C]0.115052402939487[/C][C]0.942473798530256[/C][/ROW]
[ROW][C]16[/C][C]0.0344679143784245[/C][C]0.0689358287568489[/C][C]0.965532085621576[/C][/ROW]
[ROW][C]17[/C][C]0.0228079669946299[/C][C]0.0456159339892597[/C][C]0.97719203300537[/C][/ROW]
[ROW][C]18[/C][C]0.0382155980381344[/C][C]0.0764311960762687[/C][C]0.961784401961866[/C][/ROW]
[ROW][C]19[/C][C]0.0248468396196000[/C][C]0.0496936792392001[/C][C]0.9751531603804[/C][/ROW]
[ROW][C]20[/C][C]0.0166701843084037[/C][C]0.0333403686168075[/C][C]0.983329815691596[/C][/ROW]
[ROW][C]21[/C][C]0.0114028351402277[/C][C]0.0228056702804555[/C][C]0.988597164859772[/C][/ROW]
[ROW][C]22[/C][C]0.00666737999275523[/C][C]0.0133347599855105[/C][C]0.993332620007245[/C][/ROW]
[ROW][C]23[/C][C]0.0078581376272185[/C][C]0.015716275254437[/C][C]0.992141862372782[/C][/ROW]
[ROW][C]24[/C][C]0.00704218197155988[/C][C]0.0140843639431198[/C][C]0.99295781802844[/C][/ROW]
[ROW][C]25[/C][C]0.00570483905101195[/C][C]0.0114096781020239[/C][C]0.994295160948988[/C][/ROW]
[ROW][C]26[/C][C]0.00409940757513739[/C][C]0.00819881515027479[/C][C]0.995900592424863[/C][/ROW]
[ROW][C]27[/C][C]0.00509648246836754[/C][C]0.0101929649367351[/C][C]0.994903517531633[/C][/ROW]
[ROW][C]28[/C][C]0.00295873944358895[/C][C]0.00591747888717789[/C][C]0.99704126055641[/C][/ROW]
[ROW][C]29[/C][C]0.00153954833773910[/C][C]0.00307909667547821[/C][C]0.99846045166226[/C][/ROW]
[ROW][C]30[/C][C]0.000750297593806008[/C][C]0.00150059518761202[/C][C]0.999249702406194[/C][/ROW]
[ROW][C]31[/C][C]0.000383009087513662[/C][C]0.000766018175027324[/C][C]0.999616990912486[/C][/ROW]
[ROW][C]32[/C][C]0.000275270399288779[/C][C]0.000550540798577559[/C][C]0.999724729600711[/C][/ROW]
[ROW][C]33[/C][C]0.000631697535094076[/C][C]0.00126339507018815[/C][C]0.999368302464906[/C][/ROW]
[ROW][C]34[/C][C]0.0119296005931846[/C][C]0.0238592011863691[/C][C]0.988070399406815[/C][/ROW]
[ROW][C]35[/C][C]0.0134119722703183[/C][C]0.0268239445406365[/C][C]0.986588027729682[/C][/ROW]
[ROW][C]36[/C][C]0.0176538812980499[/C][C]0.0353077625960999[/C][C]0.98234611870195[/C][/ROW]
[ROW][C]37[/C][C]0.106031732314648[/C][C]0.212063464629295[/C][C]0.893968267685352[/C][/ROW]
[ROW][C]38[/C][C]0.27556054225684[/C][C]0.55112108451368[/C][C]0.72443945774316[/C][/ROW]
[ROW][C]39[/C][C]0.88713782419094[/C][C]0.225724351618119[/C][C]0.112862175809059[/C][/ROW]
[ROW][C]40[/C][C]0.996938389905658[/C][C]0.00612322018868453[/C][C]0.00306161009434226[/C][/ROW]
[ROW][C]41[/C][C]0.998678266203408[/C][C]0.00264346759318382[/C][C]0.00132173379659191[/C][/ROW]
[ROW][C]42[/C][C]0.999233083538432[/C][C]0.00153383292313501[/C][C]0.000766916461567504[/C][/ROW]
[ROW][C]43[/C][C]0.999036886084385[/C][C]0.00192622783123081[/C][C]0.000963113915615406[/C][/ROW]
[ROW][C]44[/C][C]0.997098621419335[/C][C]0.00580275716133079[/C][C]0.00290137858066540[/C][/ROW]
[ROW][C]45[/C][C]0.994679421162514[/C][C]0.0106411576749724[/C][C]0.0053205788374862[/C][/ROW]
[ROW][C]46[/C][C]0.991797467895607[/C][C]0.0164050642087860[/C][C]0.00820253210439301[/C][/ROW]
[ROW][C]47[/C][C]0.98199747757775[/C][C]0.0360050448445007[/C][C]0.0180025224222503[/C][/ROW]
[ROW][C]48[/C][C]0.967347507450653[/C][C]0.0653049850986936[/C][C]0.0326524925493468[/C][/ROW]
[ROW][C]49[/C][C]0.960372262097357[/C][C]0.0792554758052866[/C][C]0.0396277379026433[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99646&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99646&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
9	0.0496990253031775	0.099398050606355	0.950300974696823
10	0.0222756820580731	0.0445513641161461	0.977724317941927
11	0.0564725395864933	0.112945079172987	0.943527460413507
12	0.0941182559770203	0.188236511954041	0.90588174402298
13	0.0766250365206111	0.153250073041222	0.923374963479389
14	0.0399444132636314	0.0798888265272627	0.960055586736369
15	0.0575262014697434	0.115052402939487	0.942473798530256
16	0.0344679143784245	0.0689358287568489	0.965532085621576
17	0.0228079669946299	0.0456159339892597	0.97719203300537
18	0.0382155980381344	0.0764311960762687	0.961784401961866
19	0.0248468396196000	0.0496936792392001	0.9751531603804
20	0.0166701843084037	0.0333403686168075	0.983329815691596
21	0.0114028351402277	0.0228056702804555	0.988597164859772
22	0.00666737999275523	0.0133347599855105	0.993332620007245
23	0.0078581376272185	0.015716275254437	0.992141862372782
24	0.00704218197155988	0.0140843639431198	0.99295781802844
25	0.00570483905101195	0.0114096781020239	0.994295160948988
26	0.00409940757513739	0.00819881515027479	0.995900592424863
27	0.00509648246836754	0.0101929649367351	0.994903517531633
28	0.00295873944358895	0.00591747888717789	0.99704126055641
29	0.00153954833773910	0.00307909667547821	0.99846045166226
30	0.000750297593806008	0.00150059518761202	0.999249702406194
31	0.000383009087513662	0.000766018175027324	0.999616990912486
32	0.000275270399288779	0.000550540798577559	0.999724729600711
33	0.000631697535094076	0.00126339507018815	0.999368302464906
34	0.0119296005931846	0.0238592011863691	0.988070399406815
35	0.0134119722703183	0.0268239445406365	0.986588027729682
36	0.0176538812980499	0.0353077625960999	0.98234611870195
37	0.106031732314648	0.212063464629295	0.893968267685352
38	0.27556054225684	0.55112108451368	0.72443945774316
39	0.88713782419094	0.225724351618119	0.112862175809059
40	0.996938389905658	0.00612322018868453	0.00306161009434226
41	0.998678266203408	0.00264346759318382	0.00132173379659191
42	0.999233083538432	0.00153383292313501	0.000766916461567504
43	0.999036886084385	0.00192622783123081	0.000963113915615406
44	0.997098621419335	0.00580275716133079	0.00290137858066540
45	0.994679421162514	0.0106411576749724	0.0053205788374862
46	0.991797467895607	0.0164050642087860	0.00820253210439301
47	0.98199747757775	0.0360050448445007	0.0180025224222503
48	0.967347507450653	0.0653049850986936	0.0326524925493468
49	0.960372262097357	0.0792554758052866	0.0396277379026433

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	12	0.292682926829268	NOK
5% type I error level	28	0.682926829268293	NOK
10% type I error level	34	0.829268292682927	NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 12 & 0.292682926829268 & NOK \tabularnewline
5% type I error level & 28 & 0.682926829268293 & NOK \tabularnewline
10% type I error level & 34 & 0.829268292682927 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99646&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]12[/C][C]0.292682926829268[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]28[/C][C]0.682926829268293[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]34[/C][C]0.829268292682927[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99646&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99646&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	12	0.292682926829268	NOK
5% type I error level	28	0.682926829268293	NOK
10% type I error level	34	0.829268292682927	NOK

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 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = 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