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rwasp_multipleregression.wasp

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Multiple Regression

Date of computation

Thu, 20 Dec 2012 15:03:15 -0500

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Dec/20/t1356033964ioyknq5jmy6s9rt.htm/, Retrieved Tue, 16 Apr 2024 05:49:52 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=203079, Retrieved Tue, 16 Apr 2024 05:49:52 +0000

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Estimated Impact

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

-     [Univariate Data Series] [data set] [2008-12-01 19:54:57] [b98453cac15ba1066b407e146608df68]
- RMP   [Multiple Regression] [Unemployment] [2010-11-30 13:40:15] [b98453cac15ba1066b407e146608df68]
- R  D      [Multiple Regression] [Paper 2012] [2012-12-20 20:03:15] [0ce3a3cc7b36ec2616d0d876d7c7ef2d] [Current]

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Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	9 seconds
R Server	'Sir Ronald Aylmer Fisher' @ fisher.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 9 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ fisher.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203079&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]9 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ fisher.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203079&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203079&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	9 seconds
R Server	'Sir Ronald Aylmer Fisher' @ fisher.wessa.net

Multiple Linear Regression - Estimated Regression Equation
Exchange_rate[t] = + 1.42759060003935 + 0.0314143615974818Dummies[t] -0.0287697257743677M1[t] -0.0484481990075852M2[t] -0.0232694914420618M3[t] -0.00714078387653842M4[t] -0.00738822884560742M5[t] -0.0163119148796643M6[t] -0.0122213278466348M7[t] -0.017309286947778M8[t] -0.0155139127155879M9[t] + 0.00321684022996043M10[t] + 0.00253842011498021M11[t] -0.00147870756552342t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Exchange_rate[t] =  +  1.42759060003935 +  0.0314143615974818Dummies[t] -0.0287697257743677M1[t] -0.0484481990075852M2[t] -0.0232694914420618M3[t] -0.00714078387653842M4[t] -0.00738822884560742M5[t] -0.0163119148796643M6[t] -0.0122213278466348M7[t] -0.017309286947778M8[t] -0.0155139127155879M9[t] +  0.00321684022996043M10[t] +  0.00253842011498021M11[t] -0.00147870756552342t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203079&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Exchange_rate[t] =  +  1.42759060003935 +  0.0314143615974818Dummies[t] -0.0287697257743677M1[t] -0.0484481990075852M2[t] -0.0232694914420618M3[t] -0.00714078387653842M4[t] -0.00738822884560742M5[t] -0.0163119148796643M6[t] -0.0122213278466348M7[t] -0.017309286947778M8[t] -0.0155139127155879M9[t] +  0.00321684022996043M10[t] +  0.00253842011498021M11[t] -0.00147870756552342t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203079&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203079&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
Exchange_rate[t] = + 1.42759060003935 + 0.0314143615974818Dummies[t] -0.0287697257743677M1[t] -0.0484481990075852M2[t] -0.0232694914420618M3[t] -0.00714078387653842M4[t] -0.00738822884560742M5[t] -0.0163119148796643M6[t] -0.0122213278466348M7[t] -0.017309286947778M8[t] -0.0155139127155879M9[t] + 0.00321684022996043M10[t] + 0.00253842011498021M11[t] -0.00147870756552342t + 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)	1.42759060003935	0.043813	32.5834	0	0
Dummies	0.0314143615974818	0.023409	1.342	0.185126	0.092563
M1	-0.0287697257743677	0.052272	-0.5504	0.58428	0.29214
M2	-0.0484481990075852	0.053269	-0.9095	0.367052	0.183526
M3	-0.0232694914420618	0.053273	-0.4368	0.66397	0.331985
M4	-0.00714078387653842	0.053283	-0.134	0.893879	0.44694
M5	-0.00738822884560742	0.052198	-0.1415	0.88796	0.44398
M6	-0.0163119148796643	0.052281	-0.312	0.756219	0.378109
M7	-0.0122213278466348	0.053345	-0.2291	0.819643	0.409821
M8	-0.017309286947778	0.053377	-0.3243	0.746953	0.373477
M9	-0.0155139127155879	0.053414	-0.2904	0.772566	0.386283
M10	0.00321684022996043	0.055279	0.0582	0.953806	0.476903
M11	0.00253842011498021	0.054702	0.0464	0.963156	0.481578
t	-0.00147870756552342	0.000537	-2.7544	0.007955	0.003978

\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) & 1.42759060003935 & 0.043813 & 32.5834 & 0 & 0 \tabularnewline
Dummies & 0.0314143615974818 & 0.023409 & 1.342 & 0.185126 & 0.092563 \tabularnewline
M1 & -0.0287697257743677 & 0.052272 & -0.5504 & 0.58428 & 0.29214 \tabularnewline
M2 & -0.0484481990075852 & 0.053269 & -0.9095 & 0.367052 & 0.183526 \tabularnewline
M3 & -0.0232694914420618 & 0.053273 & -0.4368 & 0.66397 & 0.331985 \tabularnewline
M4 & -0.00714078387653842 & 0.053283 & -0.134 & 0.893879 & 0.44694 \tabularnewline
M5 & -0.00738822884560742 & 0.052198 & -0.1415 & 0.88796 & 0.44398 \tabularnewline
M6 & -0.0163119148796643 & 0.052281 & -0.312 & 0.756219 & 0.378109 \tabularnewline
M7 & -0.0122213278466348 & 0.053345 & -0.2291 & 0.819643 & 0.409821 \tabularnewline
M8 & -0.017309286947778 & 0.053377 & -0.3243 & 0.746953 & 0.373477 \tabularnewline
M9 & -0.0155139127155879 & 0.053414 & -0.2904 & 0.772566 & 0.386283 \tabularnewline
M10 & 0.00321684022996043 & 0.055279 & 0.0582 & 0.953806 & 0.476903 \tabularnewline
M11 & 0.00253842011498021 & 0.054702 & 0.0464 & 0.963156 & 0.481578 \tabularnewline
t & -0.00147870756552342 & 0.000537 & -2.7544 & 0.007955 & 0.003978 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203079&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]1.42759060003935[/C][C]0.043813[/C][C]32.5834[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Dummies[/C][C]0.0314143615974818[/C][C]0.023409[/C][C]1.342[/C][C]0.185126[/C][C]0.092563[/C][/ROW]
[ROW][C]M1[/C][C]-0.0287697257743677[/C][C]0.052272[/C][C]-0.5504[/C][C]0.58428[/C][C]0.29214[/C][/ROW]
[ROW][C]M2[/C][C]-0.0484481990075852[/C][C]0.053269[/C][C]-0.9095[/C][C]0.367052[/C][C]0.183526[/C][/ROW]
[ROW][C]M3[/C][C]-0.0232694914420618[/C][C]0.053273[/C][C]-0.4368[/C][C]0.66397[/C][C]0.331985[/C][/ROW]
[ROW][C]M4[/C][C]-0.00714078387653842[/C][C]0.053283[/C][C]-0.134[/C][C]0.893879[/C][C]0.44694[/C][/ROW]
[ROW][C]M5[/C][C]-0.00738822884560742[/C][C]0.052198[/C][C]-0.1415[/C][C]0.88796[/C][C]0.44398[/C][/ROW]
[ROW][C]M6[/C][C]-0.0163119148796643[/C][C]0.052281[/C][C]-0.312[/C][C]0.756219[/C][C]0.378109[/C][/ROW]
[ROW][C]M7[/C][C]-0.0122213278466348[/C][C]0.053345[/C][C]-0.2291[/C][C]0.819643[/C][C]0.409821[/C][/ROW]
[ROW][C]M8[/C][C]-0.017309286947778[/C][C]0.053377[/C][C]-0.3243[/C][C]0.746953[/C][C]0.373477[/C][/ROW]
[ROW][C]M9[/C][C]-0.0155139127155879[/C][C]0.053414[/C][C]-0.2904[/C][C]0.772566[/C][C]0.386283[/C][/ROW]
[ROW][C]M10[/C][C]0.00321684022996043[/C][C]0.055279[/C][C]0.0582[/C][C]0.953806[/C][C]0.476903[/C][/ROW]
[ROW][C]M11[/C][C]0.00253842011498021[/C][C]0.054702[/C][C]0.0464[/C][C]0.963156[/C][C]0.481578[/C][/ROW]
[ROW][C]t[/C][C]-0.00147870756552342[/C][C]0.000537[/C][C]-2.7544[/C][C]0.007955[/C][C]0.003978[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203079&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203079&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)	1.42759060003935	0.043813	32.5834	0	0
Dummies	0.0314143615974818	0.023409	1.342	0.185126	0.092563
M1	-0.0287697257743677	0.052272	-0.5504	0.58428	0.29214
M2	-0.0484481990075852	0.053269	-0.9095	0.367052	0.183526
M3	-0.0232694914420618	0.053273	-0.4368	0.66397	0.331985
M4	-0.00714078387653842	0.053283	-0.134	0.893879	0.44694
M5	-0.00738822884560742	0.052198	-0.1415	0.88796	0.44398
M6	-0.0163119148796643	0.052281	-0.312	0.756219	0.378109
M7	-0.0122213278466348	0.053345	-0.2291	0.819643	0.409821
M8	-0.017309286947778	0.053377	-0.3243	0.746953	0.373477
M9	-0.0155139127155879	0.053414	-0.2904	0.772566	0.386283
M10	0.00321684022996043	0.055279	0.0582	0.953806	0.476903
M11	0.00253842011498021	0.054702	0.0464	0.963156	0.481578
t	-0.00147870756552342	0.000537	-2.7544	0.007955	0.003978

Multiple Linear Regression - Regression Statistics
Multiple R	0.436814740469516
R-squared	0.190807117491451
Adjusted R-squared	-0.000456654737842621
F-TEST (value)	0.997612434741195
F-TEST (DF numerator)	13
F-TEST (DF denominator)	55
p-value	0.466179335276689
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.0861859072925227
Sum Squared Residuals	0.408540583870942

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.436814740469516 \tabularnewline
R-squared & 0.190807117491451 \tabularnewline
Adjusted R-squared & -0.000456654737842621 \tabularnewline
F-TEST (value) & 0.997612434741195 \tabularnewline
F-TEST (DF numerator) & 13 \tabularnewline
F-TEST (DF denominator) & 55 \tabularnewline
p-value & 0.466179335276689 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.0861859072925227 \tabularnewline
Sum Squared Residuals & 0.408540583870942 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203079&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.436814740469516[/C][/ROW]
[ROW][C]R-squared[/C][C]0.190807117491451[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]-0.000456654737842621[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]0.997612434741195[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]13[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]55[/C][/ROW]
[ROW][C]p-value[/C][C]0.466179335276689[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.0861859072925227[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]0.408540583870942[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203079&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203079&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	0.436814740469516
R-squared	0.190807117491451
Adjusted R-squared	-0.000456654737842621
F-TEST (value)	0.997612434741195
F-TEST (DF numerator)	13
F-TEST (DF denominator)	55
p-value	0.466179335276689
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.0861859072925227
Sum Squared Residuals	0.408540583870942

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	1.2999	1.39734216669946	-0.0974421666994555
2	1.3074	1.4075993474982	-0.100199347498197
3	1.3242	1.4312993474982	-0.107099347498197
4	1.3516	1.4459493474982	-0.0943493474981966
5	1.3511	1.41280883336612	-0.0617088333661224
6	1.3419	1.40240643976654	-0.060506439766542
7	1.3716	1.43643268083153	-0.0648326808315299
8	1.3622	1.39845165256738	-0.0362516525673814
9	1.3896	1.43018268083153	-0.04058268083153
10	1.4227	1.44743472621155	-0.0247347262115548
11	1.4684	1.44527759853105	0.0231224014689487
12	1.457	1.40984610925307	0.0471538907469342
13	1.4718	1.41101203751066	0.0607879624893435
14	1.4748	1.38985485671192	0.0849451432880846
15	1.5527	1.41355485671192	0.139145143288084
16	1.5751	1.42820485671192	0.146895143288084
17	1.5557	1.39506434257984	0.160635657420159
18	1.5553	1.38466194898026	0.170638051019739
19	1.577	1.41868819004525	0.158311809954751
20	1.4975	1.3807071617811	0.1167928382189
21	1.437	1.38102382844777	0.055976171552233
22	1.3322	1.39827587382779	-0.066075873827792
23	1.2732	1.39611874614729	-0.122918746147288
24	1.3449	1.42351598006427	-0.0786159800642666
25	1.3239	1.36185318512689	-0.0379531851268936
26	1.2785	1.34069600432815	-0.0621960043281527
27	1.305	1.39581036592563	-0.0908103659256345
28	1.319	1.41046036592563	-0.0914603659256345
29	1.365	1.40873421339104	-0.043734213391042
30	1.4016	1.39833181979146	0.00326818020853824
31	1.4088	1.40094369925897	0.00785630074103233
32	1.4268	1.3943770325923	0.0324229674076989
33	1.4562	1.39469369925897	0.0615063007410321
34	1.4816	1.41194574463899	0.0696542553610073
35	1.4914	1.40978861695849	0.081611383041511
36	1.4614	1.3743571276805	0.0870428723194964
37	1.4272	1.34410869434061	0.0830913056593874
38	1.3686	1.32295151354187	0.0456484864581284
39	1.3569	1.34665151354187	0.0102484864581284
40	1.3406	1.36130151354187	-0.0207015135418716
41	1.2565	1.35957536100728	-0.103075361007279
42	1.2209	1.3491729674077	-0.128272967407699
43	1.277	1.38319920847269	-0.106199208472687
44	1.2894	1.37663254180602	-0.08723254180602
45	1.3067	1.37694920847269	-0.0702492084726868
46	1.3898	1.39420125385271	-0.00440125385271173
47	1.3661	1.36062976457473	0.00547023542527383
48	1.322	1.35661263689422	-0.0346126368942226
49	1.336	1.32636420355433	0.00963579644566855
50	1.3649	1.33662138435307	0.0282786156469277
51	1.3999	1.36032138435307	0.0395786156469276
52	1.4442	1.37497138435307	0.0692286156469276
53	1.4349	1.341830870221	0.093069129779002
54	1.4388	1.3628428382189	0.0759571617811005
55	1.4264	1.33404035608892	0.0923596439110761
56	1.4343	1.35888805101974	0.0754119489802609
57	1.377	1.32779035608892	0.0492096439110761
58	1.3706	1.34504240146895	0.0255575985310512
59	1.3556	1.34288527378845	0.0127147262115547
60	1.3179	1.33886814610794	-0.0209681461079415
61	1.2905	1.30861971276805	-0.0181197127680505
62	1.3224	1.31887689356679	0.00352310643320877
63	1.3201	1.31116253196931	0.0089374680306906
64	1.3162	1.32581253196931	-0.0096125319693094
65	1.2789	1.32408637943472	-0.0451863794347171
66	1.2526	1.31368398583514	-0.0610839858351368
67	1.2288	1.31629586530264	-0.0874958653026429
68	1.24	1.34114356023346	-0.101143560233458
69	1.2856	1.34146022690012	-0.0558602269001245

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 1.2999 & 1.39734216669946 & -0.0974421666994555 \tabularnewline
2 & 1.3074 & 1.4075993474982 & -0.100199347498197 \tabularnewline
3 & 1.3242 & 1.4312993474982 & -0.107099347498197 \tabularnewline
4 & 1.3516 & 1.4459493474982 & -0.0943493474981966 \tabularnewline
5 & 1.3511 & 1.41280883336612 & -0.0617088333661224 \tabularnewline
6 & 1.3419 & 1.40240643976654 & -0.060506439766542 \tabularnewline
7 & 1.3716 & 1.43643268083153 & -0.0648326808315299 \tabularnewline
8 & 1.3622 & 1.39845165256738 & -0.0362516525673814 \tabularnewline
9 & 1.3896 & 1.43018268083153 & -0.04058268083153 \tabularnewline
10 & 1.4227 & 1.44743472621155 & -0.0247347262115548 \tabularnewline
11 & 1.4684 & 1.44527759853105 & 0.0231224014689487 \tabularnewline
12 & 1.457 & 1.40984610925307 & 0.0471538907469342 \tabularnewline
13 & 1.4718 & 1.41101203751066 & 0.0607879624893435 \tabularnewline
14 & 1.4748 & 1.38985485671192 & 0.0849451432880846 \tabularnewline
15 & 1.5527 & 1.41355485671192 & 0.139145143288084 \tabularnewline
16 & 1.5751 & 1.42820485671192 & 0.146895143288084 \tabularnewline
17 & 1.5557 & 1.39506434257984 & 0.160635657420159 \tabularnewline
18 & 1.5553 & 1.38466194898026 & 0.170638051019739 \tabularnewline
19 & 1.577 & 1.41868819004525 & 0.158311809954751 \tabularnewline
20 & 1.4975 & 1.3807071617811 & 0.1167928382189 \tabularnewline
21 & 1.437 & 1.38102382844777 & 0.055976171552233 \tabularnewline
22 & 1.3322 & 1.39827587382779 & -0.066075873827792 \tabularnewline
23 & 1.2732 & 1.39611874614729 & -0.122918746147288 \tabularnewline
24 & 1.3449 & 1.42351598006427 & -0.0786159800642666 \tabularnewline
25 & 1.3239 & 1.36185318512689 & -0.0379531851268936 \tabularnewline
26 & 1.2785 & 1.34069600432815 & -0.0621960043281527 \tabularnewline
27 & 1.305 & 1.39581036592563 & -0.0908103659256345 \tabularnewline
28 & 1.319 & 1.41046036592563 & -0.0914603659256345 \tabularnewline
29 & 1.365 & 1.40873421339104 & -0.043734213391042 \tabularnewline
30 & 1.4016 & 1.39833181979146 & 0.00326818020853824 \tabularnewline
31 & 1.4088 & 1.40094369925897 & 0.00785630074103233 \tabularnewline
32 & 1.4268 & 1.3943770325923 & 0.0324229674076989 \tabularnewline
33 & 1.4562 & 1.39469369925897 & 0.0615063007410321 \tabularnewline
34 & 1.4816 & 1.41194574463899 & 0.0696542553610073 \tabularnewline
35 & 1.4914 & 1.40978861695849 & 0.081611383041511 \tabularnewline
36 & 1.4614 & 1.3743571276805 & 0.0870428723194964 \tabularnewline
37 & 1.4272 & 1.34410869434061 & 0.0830913056593874 \tabularnewline
38 & 1.3686 & 1.32295151354187 & 0.0456484864581284 \tabularnewline
39 & 1.3569 & 1.34665151354187 & 0.0102484864581284 \tabularnewline
40 & 1.3406 & 1.36130151354187 & -0.0207015135418716 \tabularnewline
41 & 1.2565 & 1.35957536100728 & -0.103075361007279 \tabularnewline
42 & 1.2209 & 1.3491729674077 & -0.128272967407699 \tabularnewline
43 & 1.277 & 1.38319920847269 & -0.106199208472687 \tabularnewline
44 & 1.2894 & 1.37663254180602 & -0.08723254180602 \tabularnewline
45 & 1.3067 & 1.37694920847269 & -0.0702492084726868 \tabularnewline
46 & 1.3898 & 1.39420125385271 & -0.00440125385271173 \tabularnewline
47 & 1.3661 & 1.36062976457473 & 0.00547023542527383 \tabularnewline
48 & 1.322 & 1.35661263689422 & -0.0346126368942226 \tabularnewline
49 & 1.336 & 1.32636420355433 & 0.00963579644566855 \tabularnewline
50 & 1.3649 & 1.33662138435307 & 0.0282786156469277 \tabularnewline
51 & 1.3999 & 1.36032138435307 & 0.0395786156469276 \tabularnewline
52 & 1.4442 & 1.37497138435307 & 0.0692286156469276 \tabularnewline
53 & 1.4349 & 1.341830870221 & 0.093069129779002 \tabularnewline
54 & 1.4388 & 1.3628428382189 & 0.0759571617811005 \tabularnewline
55 & 1.4264 & 1.33404035608892 & 0.0923596439110761 \tabularnewline
56 & 1.4343 & 1.35888805101974 & 0.0754119489802609 \tabularnewline
57 & 1.377 & 1.32779035608892 & 0.0492096439110761 \tabularnewline
58 & 1.3706 & 1.34504240146895 & 0.0255575985310512 \tabularnewline
59 & 1.3556 & 1.34288527378845 & 0.0127147262115547 \tabularnewline
60 & 1.3179 & 1.33886814610794 & -0.0209681461079415 \tabularnewline
61 & 1.2905 & 1.30861971276805 & -0.0181197127680505 \tabularnewline
62 & 1.3224 & 1.31887689356679 & 0.00352310643320877 \tabularnewline
63 & 1.3201 & 1.31116253196931 & 0.0089374680306906 \tabularnewline
64 & 1.3162 & 1.32581253196931 & -0.0096125319693094 \tabularnewline
65 & 1.2789 & 1.32408637943472 & -0.0451863794347171 \tabularnewline
66 & 1.2526 & 1.31368398583514 & -0.0610839858351368 \tabularnewline
67 & 1.2288 & 1.31629586530264 & -0.0874958653026429 \tabularnewline
68 & 1.24 & 1.34114356023346 & -0.101143560233458 \tabularnewline
69 & 1.2856 & 1.34146022690012 & -0.0558602269001245 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203079&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]1.2999[/C][C]1.39734216669946[/C][C]-0.0974421666994555[/C][/ROW]
[ROW][C]2[/C][C]1.3074[/C][C]1.4075993474982[/C][C]-0.100199347498197[/C][/ROW]
[ROW][C]3[/C][C]1.3242[/C][C]1.4312993474982[/C][C]-0.107099347498197[/C][/ROW]
[ROW][C]4[/C][C]1.3516[/C][C]1.4459493474982[/C][C]-0.0943493474981966[/C][/ROW]
[ROW][C]5[/C][C]1.3511[/C][C]1.41280883336612[/C][C]-0.0617088333661224[/C][/ROW]
[ROW][C]6[/C][C]1.3419[/C][C]1.40240643976654[/C][C]-0.060506439766542[/C][/ROW]
[ROW][C]7[/C][C]1.3716[/C][C]1.43643268083153[/C][C]-0.0648326808315299[/C][/ROW]
[ROW][C]8[/C][C]1.3622[/C][C]1.39845165256738[/C][C]-0.0362516525673814[/C][/ROW]
[ROW][C]9[/C][C]1.3896[/C][C]1.43018268083153[/C][C]-0.04058268083153[/C][/ROW]
[ROW][C]10[/C][C]1.4227[/C][C]1.44743472621155[/C][C]-0.0247347262115548[/C][/ROW]
[ROW][C]11[/C][C]1.4684[/C][C]1.44527759853105[/C][C]0.0231224014689487[/C][/ROW]
[ROW][C]12[/C][C]1.457[/C][C]1.40984610925307[/C][C]0.0471538907469342[/C][/ROW]
[ROW][C]13[/C][C]1.4718[/C][C]1.41101203751066[/C][C]0.0607879624893435[/C][/ROW]
[ROW][C]14[/C][C]1.4748[/C][C]1.38985485671192[/C][C]0.0849451432880846[/C][/ROW]
[ROW][C]15[/C][C]1.5527[/C][C]1.41355485671192[/C][C]0.139145143288084[/C][/ROW]
[ROW][C]16[/C][C]1.5751[/C][C]1.42820485671192[/C][C]0.146895143288084[/C][/ROW]
[ROW][C]17[/C][C]1.5557[/C][C]1.39506434257984[/C][C]0.160635657420159[/C][/ROW]
[ROW][C]18[/C][C]1.5553[/C][C]1.38466194898026[/C][C]0.170638051019739[/C][/ROW]
[ROW][C]19[/C][C]1.577[/C][C]1.41868819004525[/C][C]0.158311809954751[/C][/ROW]
[ROW][C]20[/C][C]1.4975[/C][C]1.3807071617811[/C][C]0.1167928382189[/C][/ROW]
[ROW][C]21[/C][C]1.437[/C][C]1.38102382844777[/C][C]0.055976171552233[/C][/ROW]
[ROW][C]22[/C][C]1.3322[/C][C]1.39827587382779[/C][C]-0.066075873827792[/C][/ROW]
[ROW][C]23[/C][C]1.2732[/C][C]1.39611874614729[/C][C]-0.122918746147288[/C][/ROW]
[ROW][C]24[/C][C]1.3449[/C][C]1.42351598006427[/C][C]-0.0786159800642666[/C][/ROW]
[ROW][C]25[/C][C]1.3239[/C][C]1.36185318512689[/C][C]-0.0379531851268936[/C][/ROW]
[ROW][C]26[/C][C]1.2785[/C][C]1.34069600432815[/C][C]-0.0621960043281527[/C][/ROW]
[ROW][C]27[/C][C]1.305[/C][C]1.39581036592563[/C][C]-0.0908103659256345[/C][/ROW]
[ROW][C]28[/C][C]1.319[/C][C]1.41046036592563[/C][C]-0.0914603659256345[/C][/ROW]
[ROW][C]29[/C][C]1.365[/C][C]1.40873421339104[/C][C]-0.043734213391042[/C][/ROW]
[ROW][C]30[/C][C]1.4016[/C][C]1.39833181979146[/C][C]0.00326818020853824[/C][/ROW]
[ROW][C]31[/C][C]1.4088[/C][C]1.40094369925897[/C][C]0.00785630074103233[/C][/ROW]
[ROW][C]32[/C][C]1.4268[/C][C]1.3943770325923[/C][C]0.0324229674076989[/C][/ROW]
[ROW][C]33[/C][C]1.4562[/C][C]1.39469369925897[/C][C]0.0615063007410321[/C][/ROW]
[ROW][C]34[/C][C]1.4816[/C][C]1.41194574463899[/C][C]0.0696542553610073[/C][/ROW]
[ROW][C]35[/C][C]1.4914[/C][C]1.40978861695849[/C][C]0.081611383041511[/C][/ROW]
[ROW][C]36[/C][C]1.4614[/C][C]1.3743571276805[/C][C]0.0870428723194964[/C][/ROW]
[ROW][C]37[/C][C]1.4272[/C][C]1.34410869434061[/C][C]0.0830913056593874[/C][/ROW]
[ROW][C]38[/C][C]1.3686[/C][C]1.32295151354187[/C][C]0.0456484864581284[/C][/ROW]
[ROW][C]39[/C][C]1.3569[/C][C]1.34665151354187[/C][C]0.0102484864581284[/C][/ROW]
[ROW][C]40[/C][C]1.3406[/C][C]1.36130151354187[/C][C]-0.0207015135418716[/C][/ROW]
[ROW][C]41[/C][C]1.2565[/C][C]1.35957536100728[/C][C]-0.103075361007279[/C][/ROW]
[ROW][C]42[/C][C]1.2209[/C][C]1.3491729674077[/C][C]-0.128272967407699[/C][/ROW]
[ROW][C]43[/C][C]1.277[/C][C]1.38319920847269[/C][C]-0.106199208472687[/C][/ROW]
[ROW][C]44[/C][C]1.2894[/C][C]1.37663254180602[/C][C]-0.08723254180602[/C][/ROW]
[ROW][C]45[/C][C]1.3067[/C][C]1.37694920847269[/C][C]-0.0702492084726868[/C][/ROW]
[ROW][C]46[/C][C]1.3898[/C][C]1.39420125385271[/C][C]-0.00440125385271173[/C][/ROW]
[ROW][C]47[/C][C]1.3661[/C][C]1.36062976457473[/C][C]0.00547023542527383[/C][/ROW]
[ROW][C]48[/C][C]1.322[/C][C]1.35661263689422[/C][C]-0.0346126368942226[/C][/ROW]
[ROW][C]49[/C][C]1.336[/C][C]1.32636420355433[/C][C]0.00963579644566855[/C][/ROW]
[ROW][C]50[/C][C]1.3649[/C][C]1.33662138435307[/C][C]0.0282786156469277[/C][/ROW]
[ROW][C]51[/C][C]1.3999[/C][C]1.36032138435307[/C][C]0.0395786156469276[/C][/ROW]
[ROW][C]52[/C][C]1.4442[/C][C]1.37497138435307[/C][C]0.0692286156469276[/C][/ROW]
[ROW][C]53[/C][C]1.4349[/C][C]1.341830870221[/C][C]0.093069129779002[/C][/ROW]
[ROW][C]54[/C][C]1.4388[/C][C]1.3628428382189[/C][C]0.0759571617811005[/C][/ROW]
[ROW][C]55[/C][C]1.4264[/C][C]1.33404035608892[/C][C]0.0923596439110761[/C][/ROW]
[ROW][C]56[/C][C]1.4343[/C][C]1.35888805101974[/C][C]0.0754119489802609[/C][/ROW]
[ROW][C]57[/C][C]1.377[/C][C]1.32779035608892[/C][C]0.0492096439110761[/C][/ROW]
[ROW][C]58[/C][C]1.3706[/C][C]1.34504240146895[/C][C]0.0255575985310512[/C][/ROW]
[ROW][C]59[/C][C]1.3556[/C][C]1.34288527378845[/C][C]0.0127147262115547[/C][/ROW]
[ROW][C]60[/C][C]1.3179[/C][C]1.33886814610794[/C][C]-0.0209681461079415[/C][/ROW]
[ROW][C]61[/C][C]1.2905[/C][C]1.30861971276805[/C][C]-0.0181197127680505[/C][/ROW]
[ROW][C]62[/C][C]1.3224[/C][C]1.31887689356679[/C][C]0.00352310643320877[/C][/ROW]
[ROW][C]63[/C][C]1.3201[/C][C]1.31116253196931[/C][C]0.0089374680306906[/C][/ROW]
[ROW][C]64[/C][C]1.3162[/C][C]1.32581253196931[/C][C]-0.0096125319693094[/C][/ROW]
[ROW][C]65[/C][C]1.2789[/C][C]1.32408637943472[/C][C]-0.0451863794347171[/C][/ROW]
[ROW][C]66[/C][C]1.2526[/C][C]1.31368398583514[/C][C]-0.0610839858351368[/C][/ROW]
[ROW][C]67[/C][C]1.2288[/C][C]1.31629586530264[/C][C]-0.0874958653026429[/C][/ROW]
[ROW][C]68[/C][C]1.24[/C][C]1.34114356023346[/C][C]-0.101143560233458[/C][/ROW]
[ROW][C]69[/C][C]1.2856[/C][C]1.34146022690012[/C][C]-0.0558602269001245[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203079&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203079&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	1.2999	1.39734216669946	-0.0974421666994555
2	1.3074	1.4075993474982	-0.100199347498197
3	1.3242	1.4312993474982	-0.107099347498197
4	1.3516	1.4459493474982	-0.0943493474981966
5	1.3511	1.41280883336612	-0.0617088333661224
6	1.3419	1.40240643976654	-0.060506439766542
7	1.3716	1.43643268083153	-0.0648326808315299
8	1.3622	1.39845165256738	-0.0362516525673814
9	1.3896	1.43018268083153	-0.04058268083153
10	1.4227	1.44743472621155	-0.0247347262115548
11	1.4684	1.44527759853105	0.0231224014689487
12	1.457	1.40984610925307	0.0471538907469342
13	1.4718	1.41101203751066	0.0607879624893435
14	1.4748	1.38985485671192	0.0849451432880846
15	1.5527	1.41355485671192	0.139145143288084
16	1.5751	1.42820485671192	0.146895143288084
17	1.5557	1.39506434257984	0.160635657420159
18	1.5553	1.38466194898026	0.170638051019739
19	1.577	1.41868819004525	0.158311809954751
20	1.4975	1.3807071617811	0.1167928382189
21	1.437	1.38102382844777	0.055976171552233
22	1.3322	1.39827587382779	-0.066075873827792
23	1.2732	1.39611874614729	-0.122918746147288
24	1.3449	1.42351598006427	-0.0786159800642666
25	1.3239	1.36185318512689	-0.0379531851268936
26	1.2785	1.34069600432815	-0.0621960043281527
27	1.305	1.39581036592563	-0.0908103659256345
28	1.319	1.41046036592563	-0.0914603659256345
29	1.365	1.40873421339104	-0.043734213391042
30	1.4016	1.39833181979146	0.00326818020853824
31	1.4088	1.40094369925897	0.00785630074103233
32	1.4268	1.3943770325923	0.0324229674076989
33	1.4562	1.39469369925897	0.0615063007410321
34	1.4816	1.41194574463899	0.0696542553610073
35	1.4914	1.40978861695849	0.081611383041511
36	1.4614	1.3743571276805	0.0870428723194964
37	1.4272	1.34410869434061	0.0830913056593874
38	1.3686	1.32295151354187	0.0456484864581284
39	1.3569	1.34665151354187	0.0102484864581284
40	1.3406	1.36130151354187	-0.0207015135418716
41	1.2565	1.35957536100728	-0.103075361007279
42	1.2209	1.3491729674077	-0.128272967407699
43	1.277	1.38319920847269	-0.106199208472687
44	1.2894	1.37663254180602	-0.08723254180602
45	1.3067	1.37694920847269	-0.0702492084726868
46	1.3898	1.39420125385271	-0.00440125385271173
47	1.3661	1.36062976457473	0.00547023542527383
48	1.322	1.35661263689422	-0.0346126368942226
49	1.336	1.32636420355433	0.00963579644566855
50	1.3649	1.33662138435307	0.0282786156469277
51	1.3999	1.36032138435307	0.0395786156469276
52	1.4442	1.37497138435307	0.0692286156469276
53	1.4349	1.341830870221	0.093069129779002
54	1.4388	1.3628428382189	0.0759571617811005
55	1.4264	1.33404035608892	0.0923596439110761
56	1.4343	1.35888805101974	0.0754119489802609
57	1.377	1.32779035608892	0.0492096439110761
58	1.3706	1.34504240146895	0.0255575985310512
59	1.3556	1.34288527378845	0.0127147262115547
60	1.3179	1.33886814610794	-0.0209681461079415
61	1.2905	1.30861971276805	-0.0181197127680505
62	1.3224	1.31887689356679	0.00352310643320877
63	1.3201	1.31116253196931	0.0089374680306906
64	1.3162	1.32581253196931	-0.0096125319693094
65	1.2789	1.32408637943472	-0.0451863794347171
66	1.2526	1.31368398583514	-0.0610839858351368
67	1.2288	1.31629586530264	-0.0874958653026429
68	1.24	1.34114356023346	-0.101143560233458
69	1.2856	1.34146022690012	-0.0558602269001245

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
17	0.0177212273212443	0.0354424546424886	0.982278772678756
18	0.00423982939953102	0.00847965879906204	0.995760170600469
19	0.00101557657291583	0.00203115314583166	0.998984423427084
20	0.00454364394788262	0.00908728789576523	0.995456356052117
21	0.030039662142255	0.06007932428451	0.969960337857745
22	0.162997183126356	0.325994366252713	0.837002816873644
23	0.46858080155151	0.93716160310302	0.53141919844849
24	0.945900865282278	0.108198269435444	0.0540991347177219
25	0.956582054815657	0.0868358903686861	0.0434179451843431
26	0.958404018957292	0.0831919620854164	0.0415959810427082
27	0.984438719876258	0.0311225602474841	0.0155612801237421
28	0.992978302423507	0.014043395152986	0.00702169757649299
29	0.99397989006495	0.0120402198700992	0.00602010993504962
30	0.990401311846645	0.0191973763067089	0.00959868815335446
31	0.983880363814867	0.0322392723702663	0.0161196361851332
32	0.974219866430899	0.0515602671382018	0.0257801335691009
33	0.962721333093678	0.0745573338126447	0.0372786669063224
34	0.948441548122911	0.103116903754178	0.0515584518770891
35	0.933843481844256	0.132313036311488	0.0661565181557438
36	0.929379269659269	0.141241460681461	0.0706207303407305
37	0.916115498796483	0.167769002407033	0.0838845012035166
38	0.881251949240577	0.237496101518845	0.118748050759423
39	0.829546382080207	0.340907235839586	0.170453617919793
40	0.773438928647173	0.453122142705653	0.226561071352827
41	0.813931368262505	0.37213726347499	0.186068631737495
42	0.901703494605777	0.196593010788447	0.0982965053942233
43	0.928372061164412	0.143255877671176	0.0716279388355882
44	0.956087039187663	0.0878259216246736	0.0439129608123368
45	0.982135714143797	0.0357285717124051	0.0178642858562026
46	0.978396278028358	0.0432074439432838	0.0216037219716419
47	0.974332841336259	0.0513343173274814	0.0256671586637407
48	0.978871271114838	0.0422574577703242	0.0211287288851621
49	0.97316283927413	0.0536743214517406	0.0268371607258703
50	0.978065154234229	0.0438696915315418	0.0219348457657709
51	0.980934515490489	0.038130969019022	0.019065484509511
52	0.97967638547319	0.0406472290536203	0.0203236145268101

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
17 & 0.0177212273212443 & 0.0354424546424886 & 0.982278772678756 \tabularnewline
18 & 0.00423982939953102 & 0.00847965879906204 & 0.995760170600469 \tabularnewline
19 & 0.00101557657291583 & 0.00203115314583166 & 0.998984423427084 \tabularnewline
20 & 0.00454364394788262 & 0.00908728789576523 & 0.995456356052117 \tabularnewline
21 & 0.030039662142255 & 0.06007932428451 & 0.969960337857745 \tabularnewline
22 & 0.162997183126356 & 0.325994366252713 & 0.837002816873644 \tabularnewline
23 & 0.46858080155151 & 0.93716160310302 & 0.53141919844849 \tabularnewline
24 & 0.945900865282278 & 0.108198269435444 & 0.0540991347177219 \tabularnewline
25 & 0.956582054815657 & 0.0868358903686861 & 0.0434179451843431 \tabularnewline
26 & 0.958404018957292 & 0.0831919620854164 & 0.0415959810427082 \tabularnewline
27 & 0.984438719876258 & 0.0311225602474841 & 0.0155612801237421 \tabularnewline
28 & 0.992978302423507 & 0.014043395152986 & 0.00702169757649299 \tabularnewline
29 & 0.99397989006495 & 0.0120402198700992 & 0.00602010993504962 \tabularnewline
30 & 0.990401311846645 & 0.0191973763067089 & 0.00959868815335446 \tabularnewline
31 & 0.983880363814867 & 0.0322392723702663 & 0.0161196361851332 \tabularnewline
32 & 0.974219866430899 & 0.0515602671382018 & 0.0257801335691009 \tabularnewline
33 & 0.962721333093678 & 0.0745573338126447 & 0.0372786669063224 \tabularnewline
34 & 0.948441548122911 & 0.103116903754178 & 0.0515584518770891 \tabularnewline
35 & 0.933843481844256 & 0.132313036311488 & 0.0661565181557438 \tabularnewline
36 & 0.929379269659269 & 0.141241460681461 & 0.0706207303407305 \tabularnewline
37 & 0.916115498796483 & 0.167769002407033 & 0.0838845012035166 \tabularnewline
38 & 0.881251949240577 & 0.237496101518845 & 0.118748050759423 \tabularnewline
39 & 0.829546382080207 & 0.340907235839586 & 0.170453617919793 \tabularnewline
40 & 0.773438928647173 & 0.453122142705653 & 0.226561071352827 \tabularnewline
41 & 0.813931368262505 & 0.37213726347499 & 0.186068631737495 \tabularnewline
42 & 0.901703494605777 & 0.196593010788447 & 0.0982965053942233 \tabularnewline
43 & 0.928372061164412 & 0.143255877671176 & 0.0716279388355882 \tabularnewline
44 & 0.956087039187663 & 0.0878259216246736 & 0.0439129608123368 \tabularnewline
45 & 0.982135714143797 & 0.0357285717124051 & 0.0178642858562026 \tabularnewline
46 & 0.978396278028358 & 0.0432074439432838 & 0.0216037219716419 \tabularnewline
47 & 0.974332841336259 & 0.0513343173274814 & 0.0256671586637407 \tabularnewline
48 & 0.978871271114838 & 0.0422574577703242 & 0.0211287288851621 \tabularnewline
49 & 0.97316283927413 & 0.0536743214517406 & 0.0268371607258703 \tabularnewline
50 & 0.978065154234229 & 0.0438696915315418 & 0.0219348457657709 \tabularnewline
51 & 0.980934515490489 & 0.038130969019022 & 0.019065484509511 \tabularnewline
52 & 0.97967638547319 & 0.0406472290536203 & 0.0203236145268101 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203079&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]0.0177212273212443[/C][C]0.0354424546424886[/C][C]0.982278772678756[/C][/ROW]
[ROW][C]18[/C][C]0.00423982939953102[/C][C]0.00847965879906204[/C][C]0.995760170600469[/C][/ROW]
[ROW][C]19[/C][C]0.00101557657291583[/C][C]0.00203115314583166[/C][C]0.998984423427084[/C][/ROW]
[ROW][C]20[/C][C]0.00454364394788262[/C][C]0.00908728789576523[/C][C]0.995456356052117[/C][/ROW]
[ROW][C]21[/C][C]0.030039662142255[/C][C]0.06007932428451[/C][C]0.969960337857745[/C][/ROW]
[ROW][C]22[/C][C]0.162997183126356[/C][C]0.325994366252713[/C][C]0.837002816873644[/C][/ROW]
[ROW][C]23[/C][C]0.46858080155151[/C][C]0.93716160310302[/C][C]0.53141919844849[/C][/ROW]
[ROW][C]24[/C][C]0.945900865282278[/C][C]0.108198269435444[/C][C]0.0540991347177219[/C][/ROW]
[ROW][C]25[/C][C]0.956582054815657[/C][C]0.0868358903686861[/C][C]0.0434179451843431[/C][/ROW]
[ROW][C]26[/C][C]0.958404018957292[/C][C]0.0831919620854164[/C][C]0.0415959810427082[/C][/ROW]
[ROW][C]27[/C][C]0.984438719876258[/C][C]0.0311225602474841[/C][C]0.0155612801237421[/C][/ROW]
[ROW][C]28[/C][C]0.992978302423507[/C][C]0.014043395152986[/C][C]0.00702169757649299[/C][/ROW]
[ROW][C]29[/C][C]0.99397989006495[/C][C]0.0120402198700992[/C][C]0.00602010993504962[/C][/ROW]
[ROW][C]30[/C][C]0.990401311846645[/C][C]0.0191973763067089[/C][C]0.00959868815335446[/C][/ROW]
[ROW][C]31[/C][C]0.983880363814867[/C][C]0.0322392723702663[/C][C]0.0161196361851332[/C][/ROW]
[ROW][C]32[/C][C]0.974219866430899[/C][C]0.0515602671382018[/C][C]0.0257801335691009[/C][/ROW]
[ROW][C]33[/C][C]0.962721333093678[/C][C]0.0745573338126447[/C][C]0.0372786669063224[/C][/ROW]
[ROW][C]34[/C][C]0.948441548122911[/C][C]0.103116903754178[/C][C]0.0515584518770891[/C][/ROW]
[ROW][C]35[/C][C]0.933843481844256[/C][C]0.132313036311488[/C][C]0.0661565181557438[/C][/ROW]
[ROW][C]36[/C][C]0.929379269659269[/C][C]0.141241460681461[/C][C]0.0706207303407305[/C][/ROW]
[ROW][C]37[/C][C]0.916115498796483[/C][C]0.167769002407033[/C][C]0.0838845012035166[/C][/ROW]
[ROW][C]38[/C][C]0.881251949240577[/C][C]0.237496101518845[/C][C]0.118748050759423[/C][/ROW]
[ROW][C]39[/C][C]0.829546382080207[/C][C]0.340907235839586[/C][C]0.170453617919793[/C][/ROW]
[ROW][C]40[/C][C]0.773438928647173[/C][C]0.453122142705653[/C][C]0.226561071352827[/C][/ROW]
[ROW][C]41[/C][C]0.813931368262505[/C][C]0.37213726347499[/C][C]0.186068631737495[/C][/ROW]
[ROW][C]42[/C][C]0.901703494605777[/C][C]0.196593010788447[/C][C]0.0982965053942233[/C][/ROW]
[ROW][C]43[/C][C]0.928372061164412[/C][C]0.143255877671176[/C][C]0.0716279388355882[/C][/ROW]
[ROW][C]44[/C][C]0.956087039187663[/C][C]0.0878259216246736[/C][C]0.0439129608123368[/C][/ROW]
[ROW][C]45[/C][C]0.982135714143797[/C][C]0.0357285717124051[/C][C]0.0178642858562026[/C][/ROW]
[ROW][C]46[/C][C]0.978396278028358[/C][C]0.0432074439432838[/C][C]0.0216037219716419[/C][/ROW]
[ROW][C]47[/C][C]0.974332841336259[/C][C]0.0513343173274814[/C][C]0.0256671586637407[/C][/ROW]
[ROW][C]48[/C][C]0.978871271114838[/C][C]0.0422574577703242[/C][C]0.0211287288851621[/C][/ROW]
[ROW][C]49[/C][C]0.97316283927413[/C][C]0.0536743214517406[/C][C]0.0268371607258703[/C][/ROW]
[ROW][C]50[/C][C]0.978065154234229[/C][C]0.0438696915315418[/C][C]0.0219348457657709[/C][/ROW]
[ROW][C]51[/C][C]0.980934515490489[/C][C]0.038130969019022[/C][C]0.019065484509511[/C][/ROW]
[ROW][C]52[/C][C]0.97967638547319[/C][C]0.0406472290536203[/C][C]0.0203236145268101[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203079&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203079&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	0.0177212273212443	0.0354424546424886	0.982278772678756
18	0.00423982939953102	0.00847965879906204	0.995760170600469
19	0.00101557657291583	0.00203115314583166	0.998984423427084
20	0.00454364394788262	0.00908728789576523	0.995456356052117
21	0.030039662142255	0.06007932428451	0.969960337857745
22	0.162997183126356	0.325994366252713	0.837002816873644
23	0.46858080155151	0.93716160310302	0.53141919844849
24	0.945900865282278	0.108198269435444	0.0540991347177219
25	0.956582054815657	0.0868358903686861	0.0434179451843431
26	0.958404018957292	0.0831919620854164	0.0415959810427082
27	0.984438719876258	0.0311225602474841	0.0155612801237421
28	0.992978302423507	0.014043395152986	0.00702169757649299
29	0.99397989006495	0.0120402198700992	0.00602010993504962
30	0.990401311846645	0.0191973763067089	0.00959868815335446
31	0.983880363814867	0.0322392723702663	0.0161196361851332
32	0.974219866430899	0.0515602671382018	0.0257801335691009
33	0.962721333093678	0.0745573338126447	0.0372786669063224
34	0.948441548122911	0.103116903754178	0.0515584518770891
35	0.933843481844256	0.132313036311488	0.0661565181557438
36	0.929379269659269	0.141241460681461	0.0706207303407305
37	0.916115498796483	0.167769002407033	0.0838845012035166
38	0.881251949240577	0.237496101518845	0.118748050759423
39	0.829546382080207	0.340907235839586	0.170453617919793
40	0.773438928647173	0.453122142705653	0.226561071352827
41	0.813931368262505	0.37213726347499	0.186068631737495
42	0.901703494605777	0.196593010788447	0.0982965053942233
43	0.928372061164412	0.143255877671176	0.0716279388355882
44	0.956087039187663	0.0878259216246736	0.0439129608123368
45	0.982135714143797	0.0357285717124051	0.0178642858562026
46	0.978396278028358	0.0432074439432838	0.0216037219716419
47	0.974332841336259	0.0513343173274814	0.0256671586637407
48	0.978871271114838	0.0422574577703242	0.0211287288851621
49	0.97316283927413	0.0536743214517406	0.0268371607258703
50	0.978065154234229	0.0438696915315418	0.0219348457657709
51	0.980934515490489	0.038130969019022	0.019065484509511
52	0.97967638547319	0.0406472290536203	0.0203236145268101

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	3	0.0833333333333333	NOK
5% type I error level	15	0.416666666666667	NOK
10% type I error level	23	0.638888888888889	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 & 3 & 0.0833333333333333 & NOK \tabularnewline
5% type I error level & 15 & 0.416666666666667 & NOK \tabularnewline
10% type I error level & 23 & 0.638888888888889 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203079&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]3[/C][C]0.0833333333333333[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]15[/C][C]0.416666666666667[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]23[/C][C]0.638888888888889[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203079&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203079&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	3	0.0833333333333333	NOK
5% type I error level	15	0.416666666666667	NOK
10% type I error level	23	0.638888888888889	NOK

Figure 1

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Figure 3

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Figure 4

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Figure 5

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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Figure 10

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Parameters (Session):

par1 = 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