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

Fri, 20 Nov 2009 01:47:44 -0700

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/2009/Nov/20/t125870704409d1p8kpu8qhekm.htm/, Retrieved Thu, 25 Apr 2024 08:00:33 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=57985, Retrieved Thu, 25 Apr 2024 08:00:33 +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

246

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

-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RMPD  [Multiple Regression] [Seatbelt] [2009-11-12 13:54:52] [b98453cac15ba1066b407e146608df68]
-    D    [Multiple Regression] [model 1] [2009-11-17 14:36:29] [ed603017d2bee8fbd82b6d5ec04e12c3]
-    D      [Multiple Regression] [multiple regression] [2009-11-19 21:38:11] [ed603017d2bee8fbd82b6d5ec04e12c3]
-   P         [Multiple Regression] [monthly dummies] [2009-11-19 22:00:07] [ed603017d2bee8fbd82b6d5ec04e12c3]
-   P             [Multiple Regression] [model3] [2009-11-20 08:47:44] [87085ce7f5378f281469a8b1f0969170] [Current]
-    D              [Multiple Regression] [model 4] [2009-11-20 08:59:37] [ed603017d2bee8fbd82b6d5ec04e12c3] 
-    D                [Multiple Regression] [W7: Model 4] [2009-11-22 13:34:45] [03d5b865e91ca35b5a5d21b8d6da5aba] 
-    D                  [Multiple Regression] [review 7] [2009-11-24 21:51:11] [309ee52d0058ff0a6f7eec15e07b2d9f] 
-    D                [Multiple Regression] [] [2009-11-22 15:02:10] [9f35ad889e41dd0c9322ca60d75b9f47] 
-    D                [Multiple Regression] [Beste model] [2009-12-05 15:17:52] [34b80aeb109c116fd63bf2eb7493a276] 
-    D              [Multiple Regression] [Workshop7] [2009-11-20 13:14:04] [34b80aeb109c116fd63bf2eb7493a276] 
-    D                [Multiple Regression] [workshop7] [2009-11-20 13:34:45] [34b80aeb109c116fd63bf2eb7493a276] 
-   P                   [Multiple Regression] [Workshop 7: verbe...] [2009-11-27 14:49:24] [7c2a5b25a196bd646844b8f5223c9b3e] 
-   PD                [Multiple Regression] [Workshop 7] [2009-11-20 16:57:31] [78762f311bef5a0e45c439762ada383c] 
-   P                   [Multiple Regression] [verb ws 7] [2009-11-21 09:45:52] [134dc66689e3d457a82860db6471d419] 
-    D                [Multiple Regression] [model 3] [2009-12-05 14:58:14] [34b80aeb109c116fd63bf2eb7493a276] 
-    D              [Multiple Regression] [W7: Linear Trend] [2009-11-21 14:22:35] [03d5b865e91ca35b5a5d21b8d6da5aba] 
-    D              [Multiple Regression] [] [2009-11-22 14:13:11] [9f35ad889e41dd0c9322ca60d75b9f47] 

Feedback Forum

Post a new message

Dataseries X:

Download CSV

Histogram

Boxplots

106.1	97.89
106	98.69
105.9	99.01
105.8	99.18
105.7	98.45
105.6	98.13
105.4	98.29
105.4	99.1
105.5	99.26
105.6	98.85
105.7	98.05
105.9	98.53
106.1	99.34
106	100.14
105.8	100.3
105.8	100.22
105.7	99.9
105.5	99.58
105.3	99.9
105.2	100.78
105.2	100.78
105	100.46
105.1	100.06
105.1	100.28
105.2	100.78
104.9	101.58
104.8	102.06
104.5	102.02
104.5	101.68
104.4	101.32
104.4	101.81
104.2	102.3
104.1	102.12
103.9	102.1
103.8	101.75
103.9	101.5
104.2	102.16
104.1	103.47
103.8	104.05
103.6	104.09
103.7	103.55
103.5	102.77
103.4	102.89
103.1	103.6
103.1	103.76
103.1	103.92
103.2	103.35
103.3	103.32
103.5	104.2
103.6	105.44
103.5	105.81
103.3	106.25
103.2	105.94
103.1	105.82
103.2	105.96
103	106.49
103	106.32
103.1	105.88
103.4	105.07

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	4 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 4 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57985&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]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57985&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57985&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	4 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation
Werkl[t] = + 91.491378559978 + 0.153267237582525Infl[t] + 0.0739357297362894M1[t] -0.0975590909259045M2[t] -0.235867431137875M3[t] -0.331874013777070M4[t] -0.22297054679554M5[t] -0.224489251969629M6[t] -0.261953247870372M7[t] -0.446548293832267M8[t] -0.365388945862221M9[t] -0.293576150375666M10[t] -0.0235218046077498M11[t] -0.0802397445445542t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Werkl[t] =  +  91.491378559978 +  0.153267237582525Infl[t] +  0.0739357297362894M1[t] -0.0975590909259045M2[t] -0.235867431137875M3[t] -0.331874013777070M4[t] -0.22297054679554M5[t] -0.224489251969629M6[t] -0.261953247870372M7[t] -0.446548293832267M8[t] -0.365388945862221M9[t] -0.293576150375666M10[t] -0.0235218046077498M11[t] -0.0802397445445542t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57985&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Werkl[t] =  +  91.491378559978 +  0.153267237582525Infl[t] +  0.0739357297362894M1[t] -0.0975590909259045M2[t] -0.235867431137875M3[t] -0.331874013777070M4[t] -0.22297054679554M5[t] -0.224489251969629M6[t] -0.261953247870372M7[t] -0.446548293832267M8[t] -0.365388945862221M9[t] -0.293576150375666M10[t] -0.0235218046077498M11[t] -0.0802397445445542t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57985&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57985&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
Werkl[t] = + 91.491378559978 + 0.153267237582525Infl[t] + 0.0739357297362894M1[t] -0.0975590909259045M2[t] -0.235867431137875M3[t] -0.331874013777070M4[t] -0.22297054679554M5[t] -0.224489251969629M6[t] -0.261953247870372M7[t] -0.446548293832267M8[t] -0.365388945862221M9[t] -0.293576150375666M10[t] -0.0235218046077498M11[t] -0.0802397445445542t + 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)	91.491378559978	12.231892	7.4797	0	0
Infl	0.153267237582525	0.12668	1.2099	0.232645	0.116322
M1	0.0739357297362894	0.215315	0.3434	0.732909	0.366454
M2	-0.0975590909259045	0.276681	-0.3526	0.72603	0.363015
M3	-0.235867431137875	0.298492	-0.7902	0.433557	0.216778
M4	-0.331874013777070	0.294718	-1.1261	0.266104	0.133052
M5	-0.22297054679554	0.243827	-0.9145	0.365349	0.182675
M6	-0.224489251969629	0.211245	-1.0627	0.29359	0.146795
M7	-0.261953247870372	0.216299	-1.2111	0.23219	0.116095
M8	-0.446548293832267	0.252702	-1.7671	0.083994	0.041997
M9	-0.365388945862221	0.241149	-1.5152	0.136716	0.068358
M10	-0.293576150375666	0.218438	-1.344	0.185692	0.092846
M11	-0.0235218046077498	0.196627	-0.1196	0.905311	0.452656
t	-0.0802397445445542	0.018546	-4.3266	8.3e-05	4.2e-05

\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) & 91.491378559978 & 12.231892 & 7.4797 & 0 & 0 \tabularnewline
Infl & 0.153267237582525 & 0.12668 & 1.2099 & 0.232645 & 0.116322 \tabularnewline
M1 & 0.0739357297362894 & 0.215315 & 0.3434 & 0.732909 & 0.366454 \tabularnewline
M2 & -0.0975590909259045 & 0.276681 & -0.3526 & 0.72603 & 0.363015 \tabularnewline
M3 & -0.235867431137875 & 0.298492 & -0.7902 & 0.433557 & 0.216778 \tabularnewline
M4 & -0.331874013777070 & 0.294718 & -1.1261 & 0.266104 & 0.133052 \tabularnewline
M5 & -0.22297054679554 & 0.243827 & -0.9145 & 0.365349 & 0.182675 \tabularnewline
M6 & -0.224489251969629 & 0.211245 & -1.0627 & 0.29359 & 0.146795 \tabularnewline
M7 & -0.261953247870372 & 0.216299 & -1.2111 & 0.23219 & 0.116095 \tabularnewline
M8 & -0.446548293832267 & 0.252702 & -1.7671 & 0.083994 & 0.041997 \tabularnewline
M9 & -0.365388945862221 & 0.241149 & -1.5152 & 0.136716 & 0.068358 \tabularnewline
M10 & -0.293576150375666 & 0.218438 & -1.344 & 0.185692 & 0.092846 \tabularnewline
M11 & -0.0235218046077498 & 0.196627 & -0.1196 & 0.905311 & 0.452656 \tabularnewline
t & -0.0802397445445542 & 0.018546 & -4.3266 & 8.3e-05 & 4.2e-05 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57985&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]91.491378559978[/C][C]12.231892[/C][C]7.4797[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Infl[/C][C]0.153267237582525[/C][C]0.12668[/C][C]1.2099[/C][C]0.232645[/C][C]0.116322[/C][/ROW]
[ROW][C]M1[/C][C]0.0739357297362894[/C][C]0.215315[/C][C]0.3434[/C][C]0.732909[/C][C]0.366454[/C][/ROW]
[ROW][C]M2[/C][C]-0.0975590909259045[/C][C]0.276681[/C][C]-0.3526[/C][C]0.72603[/C][C]0.363015[/C][/ROW]
[ROW][C]M3[/C][C]-0.235867431137875[/C][C]0.298492[/C][C]-0.7902[/C][C]0.433557[/C][C]0.216778[/C][/ROW]
[ROW][C]M4[/C][C]-0.331874013777070[/C][C]0.294718[/C][C]-1.1261[/C][C]0.266104[/C][C]0.133052[/C][/ROW]
[ROW][C]M5[/C][C]-0.22297054679554[/C][C]0.243827[/C][C]-0.9145[/C][C]0.365349[/C][C]0.182675[/C][/ROW]
[ROW][C]M6[/C][C]-0.224489251969629[/C][C]0.211245[/C][C]-1.0627[/C][C]0.29359[/C][C]0.146795[/C][/ROW]
[ROW][C]M7[/C][C]-0.261953247870372[/C][C]0.216299[/C][C]-1.2111[/C][C]0.23219[/C][C]0.116095[/C][/ROW]
[ROW][C]M8[/C][C]-0.446548293832267[/C][C]0.252702[/C][C]-1.7671[/C][C]0.083994[/C][C]0.041997[/C][/ROW]
[ROW][C]M9[/C][C]-0.365388945862221[/C][C]0.241149[/C][C]-1.5152[/C][C]0.136716[/C][C]0.068358[/C][/ROW]
[ROW][C]M10[/C][C]-0.293576150375666[/C][C]0.218438[/C][C]-1.344[/C][C]0.185692[/C][C]0.092846[/C][/ROW]
[ROW][C]M11[/C][C]-0.0235218046077498[/C][C]0.196627[/C][C]-0.1196[/C][C]0.905311[/C][C]0.452656[/C][/ROW]
[ROW][C]t[/C][C]-0.0802397445445542[/C][C]0.018546[/C][C]-4.3266[/C][C]8.3e-05[/C][C]4.2e-05[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57985&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57985&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)	91.491378559978	12.231892	7.4797	0	0
Infl	0.153267237582525	0.12668	1.2099	0.232645	0.116322
M1	0.0739357297362894	0.215315	0.3434	0.732909	0.366454
M2	-0.0975590909259045	0.276681	-0.3526	0.72603	0.363015
M3	-0.235867431137875	0.298492	-0.7902	0.433557	0.216778
M4	-0.331874013777070	0.294718	-1.1261	0.266104	0.133052
M5	-0.22297054679554	0.243827	-0.9145	0.365349	0.182675
M6	-0.224489251969629	0.211245	-1.0627	0.29359	0.146795
M7	-0.261953247870372	0.216299	-1.2111	0.23219	0.116095
M8	-0.446548293832267	0.252702	-1.7671	0.083994	0.041997
M9	-0.365388945862221	0.241149	-1.5152	0.136716	0.068358
M10	-0.293576150375666	0.218438	-1.344	0.185692	0.092846
M11	-0.0235218046077498	0.196627	-0.1196	0.905311	0.452656
t	-0.0802397445445542	0.018546	-4.3266	8.3e-05	4.2e-05

Multiple Linear Regression - Regression Statistics
Multiple R	0.969649892450111
R-squared	0.940220913928512
Adjusted R-squared	0.922951400174526
F-TEST (value)	54.4439714587521
F-TEST (DF numerator)	13
F-TEST (DF denominator)	45
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.292594098675783
Sum Squared Residuals	3.85250879609523

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.969649892450111 \tabularnewline
R-squared & 0.940220913928512 \tabularnewline
Adjusted R-squared & 0.922951400174526 \tabularnewline
F-TEST (value) & 54.4439714587521 \tabularnewline
F-TEST (DF numerator) & 13 \tabularnewline
F-TEST (DF denominator) & 45 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.292594098675783 \tabularnewline
Sum Squared Residuals & 3.85250879609523 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57985&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.969649892450111[/C][/ROW]
[ROW][C]R-squared[/C][C]0.940220913928512[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.922951400174526[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]54.4439714587521[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]13[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]45[/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]0.292594098675783[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]3.85250879609523[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57985&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57985&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	0.969649892450111
R-squared	0.940220913928512
Adjusted R-squared	0.922951400174526
F-TEST (value)	54.4439714587521
F-TEST (DF numerator)	13
F-TEST (DF denominator)	45
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.292594098675783
Sum Squared Residuals	3.85250879609523

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	106.1	106.488404432123	-0.388404432122857
2	106	106.359283656982	-0.359283656982363
3	105.9	106.189781088252	-0.289781088252242
4	105.8	106.039590191458	-0.239590191457530
5	105.7	105.956368830459	-0.256368830459257
6	105.6	105.825564864714	-0.225564864714213
7	105.4	105.732383882282	-0.33238388228211
8	105.4	105.591695554218	-0.191695554217505
9	105.5	105.617137915656	-0.117137915656208
10	105.6	105.545871399189	0.0541286008106235
11	105.7	105.613072210347	0.0869277896532885
12	105.9	105.629922544450	0.270077455550484
13	106.1	105.747764992083	0.352235007916891
14	106	105.618644216942	0.381355783057624
15	105.8	105.424618890199	0.375381109800943
16	105.8	105.236111184009	0.563888815991294
17	105.7	105.215729390419	0.484270609580731
18	105.5	105.084925424674	0.41507457532578
19	105.3	105.016267200255	0.283732799744666
20	105.2	104.886307578822	0.313692421178498
21	105.2	104.887227182247	0.312772817753007
22	105	104.829754717163	0.170245282837413
23	105.1	104.958262423353	0.141737576647053
24	105.1	104.935263275684	0.164736724315702
25	105.2	105.005592879667	0.194407120332713
26	104.9	104.876472104527	0.0235278954734443
27	104.8	104.731492293810	0.0685077061903472
28	104.5	104.549115277123	-0.0491152771225977
29	104.5	104.525668138782	-0.0256681387815173
30	104.4	104.388733483533	0.0112665164668423
31	104.4	104.346130689503	0.0538693104967011
32	104.2	104.156396845412	0.0436031545877104
33	104.1	104.129728346073	-0.0297283460729360
34	103.9	104.118236052263	-0.218236052263273
35	103.8	104.254407120333	-0.454407120332761
36	103.9	104.159372371000	-0.259372371000317
37	104.2	104.254224732997	-0.054224732996521
38	104.1	104.203270249023	-0.10327024902289
39	103.8	104.073617162064	-0.273617162064228
40	103.6	103.903501524384	-0.303501524383782
41	103.7	103.849400938526	-0.149400938526185
42	103.5	103.648094043493	-0.148094043493175
43	103.4	103.548782371558	-0.148782371557775
44	103.1	103.392767319735	-0.292767319734931
45	103.1	103.418209681174	-0.318209681173628
46	103.1	103.434305490129	-0.334305490128832
47	103.2	103.536757765930	-0.336757765930146
48	103.3	103.475441808866	-0.175441808865870
49	103.5	103.604012963130	-0.104012963130227
50	103.6	103.542329772526	0.0576702274741849
51	103.5	103.380490565675	0.11950943432518
52	103.3	103.271681823027	0.0283181769726160
53	103.2	103.252832701814	-0.0528327018137714
54	103.1	103.152682183585	-0.0526821835852335
55	103.2	103.056435856401	0.143564143598519
56	103	102.872832701814	0.127167298186226
57	103	102.847696874850	0.152303125149765
58	103.1	102.771832341256	0.328167658744069
59	103.4	102.837500480037	0.562499519962565

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 106.1 & 106.488404432123 & -0.388404432122857 \tabularnewline
2 & 106 & 106.359283656982 & -0.359283656982363 \tabularnewline
3 & 105.9 & 106.189781088252 & -0.289781088252242 \tabularnewline
4 & 105.8 & 106.039590191458 & -0.239590191457530 \tabularnewline
5 & 105.7 & 105.956368830459 & -0.256368830459257 \tabularnewline
6 & 105.6 & 105.825564864714 & -0.225564864714213 \tabularnewline
7 & 105.4 & 105.732383882282 & -0.33238388228211 \tabularnewline
8 & 105.4 & 105.591695554218 & -0.191695554217505 \tabularnewline
9 & 105.5 & 105.617137915656 & -0.117137915656208 \tabularnewline
10 & 105.6 & 105.545871399189 & 0.0541286008106235 \tabularnewline
11 & 105.7 & 105.613072210347 & 0.0869277896532885 \tabularnewline
12 & 105.9 & 105.629922544450 & 0.270077455550484 \tabularnewline
13 & 106.1 & 105.747764992083 & 0.352235007916891 \tabularnewline
14 & 106 & 105.618644216942 & 0.381355783057624 \tabularnewline
15 & 105.8 & 105.424618890199 & 0.375381109800943 \tabularnewline
16 & 105.8 & 105.236111184009 & 0.563888815991294 \tabularnewline
17 & 105.7 & 105.215729390419 & 0.484270609580731 \tabularnewline
18 & 105.5 & 105.084925424674 & 0.41507457532578 \tabularnewline
19 & 105.3 & 105.016267200255 & 0.283732799744666 \tabularnewline
20 & 105.2 & 104.886307578822 & 0.313692421178498 \tabularnewline
21 & 105.2 & 104.887227182247 & 0.312772817753007 \tabularnewline
22 & 105 & 104.829754717163 & 0.170245282837413 \tabularnewline
23 & 105.1 & 104.958262423353 & 0.141737576647053 \tabularnewline
24 & 105.1 & 104.935263275684 & 0.164736724315702 \tabularnewline
25 & 105.2 & 105.005592879667 & 0.194407120332713 \tabularnewline
26 & 104.9 & 104.876472104527 & 0.0235278954734443 \tabularnewline
27 & 104.8 & 104.731492293810 & 0.0685077061903472 \tabularnewline
28 & 104.5 & 104.549115277123 & -0.0491152771225977 \tabularnewline
29 & 104.5 & 104.525668138782 & -0.0256681387815173 \tabularnewline
30 & 104.4 & 104.388733483533 & 0.0112665164668423 \tabularnewline
31 & 104.4 & 104.346130689503 & 0.0538693104967011 \tabularnewline
32 & 104.2 & 104.156396845412 & 0.0436031545877104 \tabularnewline
33 & 104.1 & 104.129728346073 & -0.0297283460729360 \tabularnewline
34 & 103.9 & 104.118236052263 & -0.218236052263273 \tabularnewline
35 & 103.8 & 104.254407120333 & -0.454407120332761 \tabularnewline
36 & 103.9 & 104.159372371000 & -0.259372371000317 \tabularnewline
37 & 104.2 & 104.254224732997 & -0.054224732996521 \tabularnewline
38 & 104.1 & 104.203270249023 & -0.10327024902289 \tabularnewline
39 & 103.8 & 104.073617162064 & -0.273617162064228 \tabularnewline
40 & 103.6 & 103.903501524384 & -0.303501524383782 \tabularnewline
41 & 103.7 & 103.849400938526 & -0.149400938526185 \tabularnewline
42 & 103.5 & 103.648094043493 & -0.148094043493175 \tabularnewline
43 & 103.4 & 103.548782371558 & -0.148782371557775 \tabularnewline
44 & 103.1 & 103.392767319735 & -0.292767319734931 \tabularnewline
45 & 103.1 & 103.418209681174 & -0.318209681173628 \tabularnewline
46 & 103.1 & 103.434305490129 & -0.334305490128832 \tabularnewline
47 & 103.2 & 103.536757765930 & -0.336757765930146 \tabularnewline
48 & 103.3 & 103.475441808866 & -0.175441808865870 \tabularnewline
49 & 103.5 & 103.604012963130 & -0.104012963130227 \tabularnewline
50 & 103.6 & 103.542329772526 & 0.0576702274741849 \tabularnewline
51 & 103.5 & 103.380490565675 & 0.11950943432518 \tabularnewline
52 & 103.3 & 103.271681823027 & 0.0283181769726160 \tabularnewline
53 & 103.2 & 103.252832701814 & -0.0528327018137714 \tabularnewline
54 & 103.1 & 103.152682183585 & -0.0526821835852335 \tabularnewline
55 & 103.2 & 103.056435856401 & 0.143564143598519 \tabularnewline
56 & 103 & 102.872832701814 & 0.127167298186226 \tabularnewline
57 & 103 & 102.847696874850 & 0.152303125149765 \tabularnewline
58 & 103.1 & 102.771832341256 & 0.328167658744069 \tabularnewline
59 & 103.4 & 102.837500480037 & 0.562499519962565 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57985&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]106.1[/C][C]106.488404432123[/C][C]-0.388404432122857[/C][/ROW]
[ROW][C]2[/C][C]106[/C][C]106.359283656982[/C][C]-0.359283656982363[/C][/ROW]
[ROW][C]3[/C][C]105.9[/C][C]106.189781088252[/C][C]-0.289781088252242[/C][/ROW]
[ROW][C]4[/C][C]105.8[/C][C]106.039590191458[/C][C]-0.239590191457530[/C][/ROW]
[ROW][C]5[/C][C]105.7[/C][C]105.956368830459[/C][C]-0.256368830459257[/C][/ROW]
[ROW][C]6[/C][C]105.6[/C][C]105.825564864714[/C][C]-0.225564864714213[/C][/ROW]
[ROW][C]7[/C][C]105.4[/C][C]105.732383882282[/C][C]-0.33238388228211[/C][/ROW]
[ROW][C]8[/C][C]105.4[/C][C]105.591695554218[/C][C]-0.191695554217505[/C][/ROW]
[ROW][C]9[/C][C]105.5[/C][C]105.617137915656[/C][C]-0.117137915656208[/C][/ROW]
[ROW][C]10[/C][C]105.6[/C][C]105.545871399189[/C][C]0.0541286008106235[/C][/ROW]
[ROW][C]11[/C][C]105.7[/C][C]105.613072210347[/C][C]0.0869277896532885[/C][/ROW]
[ROW][C]12[/C][C]105.9[/C][C]105.629922544450[/C][C]0.270077455550484[/C][/ROW]
[ROW][C]13[/C][C]106.1[/C][C]105.747764992083[/C][C]0.352235007916891[/C][/ROW]
[ROW][C]14[/C][C]106[/C][C]105.618644216942[/C][C]0.381355783057624[/C][/ROW]
[ROW][C]15[/C][C]105.8[/C][C]105.424618890199[/C][C]0.375381109800943[/C][/ROW]
[ROW][C]16[/C][C]105.8[/C][C]105.236111184009[/C][C]0.563888815991294[/C][/ROW]
[ROW][C]17[/C][C]105.7[/C][C]105.215729390419[/C][C]0.484270609580731[/C][/ROW]
[ROW][C]18[/C][C]105.5[/C][C]105.084925424674[/C][C]0.41507457532578[/C][/ROW]
[ROW][C]19[/C][C]105.3[/C][C]105.016267200255[/C][C]0.283732799744666[/C][/ROW]
[ROW][C]20[/C][C]105.2[/C][C]104.886307578822[/C][C]0.313692421178498[/C][/ROW]
[ROW][C]21[/C][C]105.2[/C][C]104.887227182247[/C][C]0.312772817753007[/C][/ROW]
[ROW][C]22[/C][C]105[/C][C]104.829754717163[/C][C]0.170245282837413[/C][/ROW]
[ROW][C]23[/C][C]105.1[/C][C]104.958262423353[/C][C]0.141737576647053[/C][/ROW]
[ROW][C]24[/C][C]105.1[/C][C]104.935263275684[/C][C]0.164736724315702[/C][/ROW]
[ROW][C]25[/C][C]105.2[/C][C]105.005592879667[/C][C]0.194407120332713[/C][/ROW]
[ROW][C]26[/C][C]104.9[/C][C]104.876472104527[/C][C]0.0235278954734443[/C][/ROW]
[ROW][C]27[/C][C]104.8[/C][C]104.731492293810[/C][C]0.0685077061903472[/C][/ROW]
[ROW][C]28[/C][C]104.5[/C][C]104.549115277123[/C][C]-0.0491152771225977[/C][/ROW]
[ROW][C]29[/C][C]104.5[/C][C]104.525668138782[/C][C]-0.0256681387815173[/C][/ROW]
[ROW][C]30[/C][C]104.4[/C][C]104.388733483533[/C][C]0.0112665164668423[/C][/ROW]
[ROW][C]31[/C][C]104.4[/C][C]104.346130689503[/C][C]0.0538693104967011[/C][/ROW]
[ROW][C]32[/C][C]104.2[/C][C]104.156396845412[/C][C]0.0436031545877104[/C][/ROW]
[ROW][C]33[/C][C]104.1[/C][C]104.129728346073[/C][C]-0.0297283460729360[/C][/ROW]
[ROW][C]34[/C][C]103.9[/C][C]104.118236052263[/C][C]-0.218236052263273[/C][/ROW]
[ROW][C]35[/C][C]103.8[/C][C]104.254407120333[/C][C]-0.454407120332761[/C][/ROW]
[ROW][C]36[/C][C]103.9[/C][C]104.159372371000[/C][C]-0.259372371000317[/C][/ROW]
[ROW][C]37[/C][C]104.2[/C][C]104.254224732997[/C][C]-0.054224732996521[/C][/ROW]
[ROW][C]38[/C][C]104.1[/C][C]104.203270249023[/C][C]-0.10327024902289[/C][/ROW]
[ROW][C]39[/C][C]103.8[/C][C]104.073617162064[/C][C]-0.273617162064228[/C][/ROW]
[ROW][C]40[/C][C]103.6[/C][C]103.903501524384[/C][C]-0.303501524383782[/C][/ROW]
[ROW][C]41[/C][C]103.7[/C][C]103.849400938526[/C][C]-0.149400938526185[/C][/ROW]
[ROW][C]42[/C][C]103.5[/C][C]103.648094043493[/C][C]-0.148094043493175[/C][/ROW]
[ROW][C]43[/C][C]103.4[/C][C]103.548782371558[/C][C]-0.148782371557775[/C][/ROW]
[ROW][C]44[/C][C]103.1[/C][C]103.392767319735[/C][C]-0.292767319734931[/C][/ROW]
[ROW][C]45[/C][C]103.1[/C][C]103.418209681174[/C][C]-0.318209681173628[/C][/ROW]
[ROW][C]46[/C][C]103.1[/C][C]103.434305490129[/C][C]-0.334305490128832[/C][/ROW]
[ROW][C]47[/C][C]103.2[/C][C]103.536757765930[/C][C]-0.336757765930146[/C][/ROW]
[ROW][C]48[/C][C]103.3[/C][C]103.475441808866[/C][C]-0.175441808865870[/C][/ROW]
[ROW][C]49[/C][C]103.5[/C][C]103.604012963130[/C][C]-0.104012963130227[/C][/ROW]
[ROW][C]50[/C][C]103.6[/C][C]103.542329772526[/C][C]0.0576702274741849[/C][/ROW]
[ROW][C]51[/C][C]103.5[/C][C]103.380490565675[/C][C]0.11950943432518[/C][/ROW]
[ROW][C]52[/C][C]103.3[/C][C]103.271681823027[/C][C]0.0283181769726160[/C][/ROW]
[ROW][C]53[/C][C]103.2[/C][C]103.252832701814[/C][C]-0.0528327018137714[/C][/ROW]
[ROW][C]54[/C][C]103.1[/C][C]103.152682183585[/C][C]-0.0526821835852335[/C][/ROW]
[ROW][C]55[/C][C]103.2[/C][C]103.056435856401[/C][C]0.143564143598519[/C][/ROW]
[ROW][C]56[/C][C]103[/C][C]102.872832701814[/C][C]0.127167298186226[/C][/ROW]
[ROW][C]57[/C][C]103[/C][C]102.847696874850[/C][C]0.152303125149765[/C][/ROW]
[ROW][C]58[/C][C]103.1[/C][C]102.771832341256[/C][C]0.328167658744069[/C][/ROW]
[ROW][C]59[/C][C]103.4[/C][C]102.837500480037[/C][C]0.562499519962565[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57985&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57985&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	106.1	106.488404432123	-0.388404432122857
2	106	106.359283656982	-0.359283656982363
3	105.9	106.189781088252	-0.289781088252242
4	105.8	106.039590191458	-0.239590191457530
5	105.7	105.956368830459	-0.256368830459257
6	105.6	105.825564864714	-0.225564864714213
7	105.4	105.732383882282	-0.33238388228211
8	105.4	105.591695554218	-0.191695554217505
9	105.5	105.617137915656	-0.117137915656208
10	105.6	105.545871399189	0.0541286008106235
11	105.7	105.613072210347	0.0869277896532885
12	105.9	105.629922544450	0.270077455550484
13	106.1	105.747764992083	0.352235007916891
14	106	105.618644216942	0.381355783057624
15	105.8	105.424618890199	0.375381109800943
16	105.8	105.236111184009	0.563888815991294
17	105.7	105.215729390419	0.484270609580731
18	105.5	105.084925424674	0.41507457532578
19	105.3	105.016267200255	0.283732799744666
20	105.2	104.886307578822	0.313692421178498
21	105.2	104.887227182247	0.312772817753007
22	105	104.829754717163	0.170245282837413
23	105.1	104.958262423353	0.141737576647053
24	105.1	104.935263275684	0.164736724315702
25	105.2	105.005592879667	0.194407120332713
26	104.9	104.876472104527	0.0235278954734443
27	104.8	104.731492293810	0.0685077061903472
28	104.5	104.549115277123	-0.0491152771225977
29	104.5	104.525668138782	-0.0256681387815173
30	104.4	104.388733483533	0.0112665164668423
31	104.4	104.346130689503	0.0538693104967011
32	104.2	104.156396845412	0.0436031545877104
33	104.1	104.129728346073	-0.0297283460729360
34	103.9	104.118236052263	-0.218236052263273
35	103.8	104.254407120333	-0.454407120332761
36	103.9	104.159372371000	-0.259372371000317
37	104.2	104.254224732997	-0.054224732996521
38	104.1	104.203270249023	-0.10327024902289
39	103.8	104.073617162064	-0.273617162064228
40	103.6	103.903501524384	-0.303501524383782
41	103.7	103.849400938526	-0.149400938526185
42	103.5	103.648094043493	-0.148094043493175
43	103.4	103.548782371558	-0.148782371557775
44	103.1	103.392767319735	-0.292767319734931
45	103.1	103.418209681174	-0.318209681173628
46	103.1	103.434305490129	-0.334305490128832
47	103.2	103.536757765930	-0.336757765930146
48	103.3	103.475441808866	-0.175441808865870
49	103.5	103.604012963130	-0.104012963130227
50	103.6	103.542329772526	0.0576702274741849
51	103.5	103.380490565675	0.11950943432518
52	103.3	103.271681823027	0.0283181769726160
53	103.2	103.252832701814	-0.0528327018137714
54	103.1	103.152682183585	-0.0526821835852335
55	103.2	103.056435856401	0.143564143598519
56	103	102.872832701814	0.127167298186226
57	103	102.847696874850	0.152303125149765
58	103.1	102.771832341256	0.328167658744069
59	103.4	102.837500480037	0.562499519962565

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
17	0.00475826936044502	0.00951653872089004	0.995241730639555
18	0.00189385854555843	0.00378771709111686	0.998106141454442
19	0.000426111327040718	0.000852222654081436	0.99957388867296
20	0.000312593041501181	0.000625186083002361	0.999687406958499
21	0.00135732426594403	0.00271464853188806	0.998642675734056
22	0.0303172593554905	0.060634518710981	0.96968274064451
23	0.0177926153806101	0.0355852307612201	0.98220738461939
24	0.0585337692293472	0.117067538458694	0.941466230770653
25	0.287185452589496	0.574370905178991	0.712814547410505
26	0.540667724348018	0.918664551303964	0.459332275651982
27	0.564497545663184	0.871004908673631	0.435502454336816
28	0.69142496012081	0.617150079758379	0.308575039879190
29	0.677448217804985	0.645103564390029	0.322551782195015
30	0.675101136058609	0.649797727882783	0.324898863941392
31	0.660380555172001	0.679238889655998	0.339619444827999
32	0.742145860473345	0.51570827905331	0.257854139526655
33	0.887318666488898	0.225362667022204	0.112681333511102
34	0.90693431141704	0.18613137716592	0.09306568858296
35	0.87452889705197	0.250942205896058	0.125471102948029
36	0.925742891921038	0.148514216157924	0.0742571080789618
37	0.963557214435686	0.072885571128628	0.036442785564314
38	0.967271821113423	0.0654563577731549	0.0327281788865775
39	0.95598716092845	0.0880256781431009	0.0440128390715504
40	0.935336605583656	0.129326788832688	0.0646633944163438
41	0.991021455007308	0.0179570899853831	0.00897854499269157
42	0.993578091727347	0.0128438165453064	0.00642190827265319

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
17 & 0.00475826936044502 & 0.00951653872089004 & 0.995241730639555 \tabularnewline
18 & 0.00189385854555843 & 0.00378771709111686 & 0.998106141454442 \tabularnewline
19 & 0.000426111327040718 & 0.000852222654081436 & 0.99957388867296 \tabularnewline
20 & 0.000312593041501181 & 0.000625186083002361 & 0.999687406958499 \tabularnewline
21 & 0.00135732426594403 & 0.00271464853188806 & 0.998642675734056 \tabularnewline
22 & 0.0303172593554905 & 0.060634518710981 & 0.96968274064451 \tabularnewline
23 & 0.0177926153806101 & 0.0355852307612201 & 0.98220738461939 \tabularnewline
24 & 0.0585337692293472 & 0.117067538458694 & 0.941466230770653 \tabularnewline
25 & 0.287185452589496 & 0.574370905178991 & 0.712814547410505 \tabularnewline
26 & 0.540667724348018 & 0.918664551303964 & 0.459332275651982 \tabularnewline
27 & 0.564497545663184 & 0.871004908673631 & 0.435502454336816 \tabularnewline
28 & 0.69142496012081 & 0.617150079758379 & 0.308575039879190 \tabularnewline
29 & 0.677448217804985 & 0.645103564390029 & 0.322551782195015 \tabularnewline
30 & 0.675101136058609 & 0.649797727882783 & 0.324898863941392 \tabularnewline
31 & 0.660380555172001 & 0.679238889655998 & 0.339619444827999 \tabularnewline
32 & 0.742145860473345 & 0.51570827905331 & 0.257854139526655 \tabularnewline
33 & 0.887318666488898 & 0.225362667022204 & 0.112681333511102 \tabularnewline
34 & 0.90693431141704 & 0.18613137716592 & 0.09306568858296 \tabularnewline
35 & 0.87452889705197 & 0.250942205896058 & 0.125471102948029 \tabularnewline
36 & 0.925742891921038 & 0.148514216157924 & 0.0742571080789618 \tabularnewline
37 & 0.963557214435686 & 0.072885571128628 & 0.036442785564314 \tabularnewline
38 & 0.967271821113423 & 0.0654563577731549 & 0.0327281788865775 \tabularnewline
39 & 0.95598716092845 & 0.0880256781431009 & 0.0440128390715504 \tabularnewline
40 & 0.935336605583656 & 0.129326788832688 & 0.0646633944163438 \tabularnewline
41 & 0.991021455007308 & 0.0179570899853831 & 0.00897854499269157 \tabularnewline
42 & 0.993578091727347 & 0.0128438165453064 & 0.00642190827265319 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57985&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]17[/C][C]0.00475826936044502[/C][C]0.00951653872089004[/C][C]0.995241730639555[/C][/ROW]
[ROW][C]18[/C][C]0.00189385854555843[/C][C]0.00378771709111686[/C][C]0.998106141454442[/C][/ROW]
[ROW][C]19[/C][C]0.000426111327040718[/C][C]0.000852222654081436[/C][C]0.99957388867296[/C][/ROW]
[ROW][C]20[/C][C]0.000312593041501181[/C][C]0.000625186083002361[/C][C]0.999687406958499[/C][/ROW]
[ROW][C]21[/C][C]0.00135732426594403[/C][C]0.00271464853188806[/C][C]0.998642675734056[/C][/ROW]
[ROW][C]22[/C][C]0.0303172593554905[/C][C]0.060634518710981[/C][C]0.96968274064451[/C][/ROW]
[ROW][C]23[/C][C]0.0177926153806101[/C][C]0.0355852307612201[/C][C]0.98220738461939[/C][/ROW]
[ROW][C]24[/C][C]0.0585337692293472[/C][C]0.117067538458694[/C][C]0.941466230770653[/C][/ROW]
[ROW][C]25[/C][C]0.287185452589496[/C][C]0.574370905178991[/C][C]0.712814547410505[/C][/ROW]
[ROW][C]26[/C][C]0.540667724348018[/C][C]0.918664551303964[/C][C]0.459332275651982[/C][/ROW]
[ROW][C]27[/C][C]0.564497545663184[/C][C]0.871004908673631[/C][C]0.435502454336816[/C][/ROW]
[ROW][C]28[/C][C]0.69142496012081[/C][C]0.617150079758379[/C][C]0.308575039879190[/C][/ROW]
[ROW][C]29[/C][C]0.677448217804985[/C][C]0.645103564390029[/C][C]0.322551782195015[/C][/ROW]
[ROW][C]30[/C][C]0.675101136058609[/C][C]0.649797727882783[/C][C]0.324898863941392[/C][/ROW]
[ROW][C]31[/C][C]0.660380555172001[/C][C]0.679238889655998[/C][C]0.339619444827999[/C][/ROW]
[ROW][C]32[/C][C]0.742145860473345[/C][C]0.51570827905331[/C][C]0.257854139526655[/C][/ROW]
[ROW][C]33[/C][C]0.887318666488898[/C][C]0.225362667022204[/C][C]0.112681333511102[/C][/ROW]
[ROW][C]34[/C][C]0.90693431141704[/C][C]0.18613137716592[/C][C]0.09306568858296[/C][/ROW]
[ROW][C]35[/C][C]0.87452889705197[/C][C]0.250942205896058[/C][C]0.125471102948029[/C][/ROW]
[ROW][C]36[/C][C]0.925742891921038[/C][C]0.148514216157924[/C][C]0.0742571080789618[/C][/ROW]
[ROW][C]37[/C][C]0.963557214435686[/C][C]0.072885571128628[/C][C]0.036442785564314[/C][/ROW]
[ROW][C]38[/C][C]0.967271821113423[/C][C]0.0654563577731549[/C][C]0.0327281788865775[/C][/ROW]
[ROW][C]39[/C][C]0.95598716092845[/C][C]0.0880256781431009[/C][C]0.0440128390715504[/C][/ROW]
[ROW][C]40[/C][C]0.935336605583656[/C][C]0.129326788832688[/C][C]0.0646633944163438[/C][/ROW]
[ROW][C]41[/C][C]0.991021455007308[/C][C]0.0179570899853831[/C][C]0.00897854499269157[/C][/ROW]
[ROW][C]42[/C][C]0.993578091727347[/C][C]0.0128438165453064[/C][C]0.00642190827265319[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57985&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57985&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
17	0.00475826936044502	0.00951653872089004	0.995241730639555
18	0.00189385854555843	0.00378771709111686	0.998106141454442
19	0.000426111327040718	0.000852222654081436	0.99957388867296
20	0.000312593041501181	0.000625186083002361	0.999687406958499
21	0.00135732426594403	0.00271464853188806	0.998642675734056
22	0.0303172593554905	0.060634518710981	0.96968274064451
23	0.0177926153806101	0.0355852307612201	0.98220738461939
24	0.0585337692293472	0.117067538458694	0.941466230770653
25	0.287185452589496	0.574370905178991	0.712814547410505
26	0.540667724348018	0.918664551303964	0.459332275651982
27	0.564497545663184	0.871004908673631	0.435502454336816
28	0.69142496012081	0.617150079758379	0.308575039879190
29	0.677448217804985	0.645103564390029	0.322551782195015
30	0.675101136058609	0.649797727882783	0.324898863941392
31	0.660380555172001	0.679238889655998	0.339619444827999
32	0.742145860473345	0.51570827905331	0.257854139526655
33	0.887318666488898	0.225362667022204	0.112681333511102
34	0.90693431141704	0.18613137716592	0.09306568858296
35	0.87452889705197	0.250942205896058	0.125471102948029
36	0.925742891921038	0.148514216157924	0.0742571080789618
37	0.963557214435686	0.072885571128628	0.036442785564314
38	0.967271821113423	0.0654563577731549	0.0327281788865775
39	0.95598716092845	0.0880256781431009	0.0440128390715504
40	0.935336605583656	0.129326788832688	0.0646633944163438
41	0.991021455007308	0.0179570899853831	0.00897854499269157
42	0.993578091727347	0.0128438165453064	0.00642190827265319

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	5	0.192307692307692	NOK
5% type I error level	8	0.307692307692308	NOK
10% type I error level	12	0.461538461538462	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 & 5 & 0.192307692307692 & NOK \tabularnewline
5% type I error level & 8 & 0.307692307692308 & NOK \tabularnewline
10% type I error level & 12 & 0.461538461538462 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57985&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]5[/C][C]0.192307692307692[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]8[/C][C]0.307692307692308[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]12[/C][C]0.461538461538462[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57985&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57985&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	5	0.192307692307692	NOK
5% type I error level	8	0.307692307692308	NOK
10% type I error level	12	0.461538461538462	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 = Include Monthly Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly 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