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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Thu, 17 Dec 2009 04:28:53 -0700

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Dec/17/t1261049376zwrcmijtb565bni.htm/, Retrieved Tue, 30 Apr 2024 01:02:22 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=68762, Retrieved Tue, 30 Apr 2024 01:02:22 +0000

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User-defined keywords

Estimated Impact

407

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

-     [Partial Correlation] [CVM Paper: Partia...] [2009-12-17 09:09:51] [03d5b865e91ca35b5a5d21b8d6da5aba]
- RMPD  [Multiple Regression] [CVM Paper: Multip...] [2009-12-17 11:25:28] [03d5b865e91ca35b5a5d21b8d6da5aba]
-   PD      [Multiple Regression] [CVM Paper: Multip...] [2009-12-17 11:28:53] [a5ada8bd39e806b5b90f09589c89554a] [Current]
-   P         [Multiple Regression] [CVM Paper: Multip...] [2009-12-17 13:14:43] [03d5b865e91ca35b5a5d21b8d6da5aba] 
-   P         [Multiple Regression] [vraag3_EliendeHaas] [2010-01-26 08:05:02] [2663058f2a5dda519058ac6b2228468f] 
-   P         [Multiple Regression] [Examenvraag 3] [2010-01-26 08:06:14] [1433a524809eda02c3198b3ae6eebb69] 
-   P         [Multiple Regression] [] [2010-01-26 08:06:15] [1e83ffa964db6f7ea6ccc4e7b5acbbff] 
-   P         [Multiple Regression] [Vraag 3- computer...] [2010-01-26 08:06:02] [12f02da0296cb21dc23d82ae014a8b71] 
-   P         [Multiple Regression] [vraag 3 0900909] [2010-01-26 08:07:43] [5e6d255681a7853beaa91b62357037a7] 
-   P         [Multiple Regression] [examen Kim De Vos] [2010-01-26 08:07:57] [005293453b571dbccb80b45226e44173] 
-   P         [Multiple Regression] [examen statistiek] [2010-01-26 08:08:57] [f1a50df816abcbb519e7637ff6b72fa0] 
-             [Multiple Regression] [examen comp] [2010-01-26 08:08:33] [134dc66689e3d457a82860db6471d419] 
-   P         [Multiple Regression] [examen] [2010-01-26 08:05:05] [eff6c0799cbd248896739259ce57a08e] 
-   P         [Multiple Regression] [examen statistiek] [2010-01-26 08:09:32] [309ee52d0058ff0a6f7eec15e07b2d9f] 
-             [Multiple Regression] [] [2010-01-26 08:10:08] [4fe1472705bb0a32f118ba3ca90ffa8e] 
-   P         [Multiple Regression] [vraag3] [2010-01-26 08:10:26] [74be16979710d4c4e7c6647856088456] 
-   P         [Multiple Regression] [lineair trend] [2010-01-26 08:13:01] [dff692ae32125bdbbfc005d665e23b83] 
-   P         [Multiple Regression] [Examenvraag3 bloggen] [2010-01-26 08:11:35] [f15cf5036ae52d4243ad71d4fb151dbe] 
-   P         [Multiple Regression] [] [2010-01-26 08:17:34] [4d237c8ec53dd43f33fce99b7dc91018] 
-   P         [Multiple Regression] [] [2010-01-26 08:18:46] [3af9fa3d2c04a43d660a9a466bdfbaa0] 
-   P         [Multiple Regression] [vraag 3] [2010-01-26 08:25:14] [eaf42bcf5162b5692bb3c7f9d4636222] 
-   P         [Multiple Regression] [] [2010-01-26 08:25:50] [e0a128c302a1ec9189220a385b8da313] 
-   P         [Multiple Regression] [examen bloggen] [2010-01-26 08:25:42] [03c44f58d7d4de05d4cfabfda8c46d2c] 
-   P         [Multiple Regression] [Trend examen] [2010-01-26 08:26:05] [a94022e7c2399c0f4d62eea578db3411] 
-   P         [Multiple Regression] [examenvraag] [2010-01-26 08:25:55] [b5ed7ca2d893a9c0b68720eb2614783c] 
-   P         [Multiple Regression] [sdws examen] [2010-01-26 08:26:44] [f7fc9270f813d017f9fa5b506fdc7682] 
-   P         [Multiple Regression] [] [2010-01-26 08:27:37] [445b292c553470d9fed8bc2796fd3a00] 
-   P         [Multiple Regression] [0.313] [2010-01-26 08:27:05] [f5d341d4bbba73282fc6e80153a6d315] 
-   P         [Multiple Regression] [] [2010-01-26 08:26:32] [a21bac9c8d3d56fdec8be4e719e2c7ed] 
-   P         [Multiple Regression] [parameter] [2010-01-26 08:27:43] [24c4941ee50deadff4640c9c09cc70cb] 
-   P         [Multiple Regression] [] [2010-01-26 08:28:46] [b5908418e3090fddbd22f5f0f774653d] 
-   P         [Multiple Regression] [Examen testshw1, ...] [2010-01-26 08:37:56] [96e597a9107bfe8c07649cce3d4f6fec] 
-   P         [Multiple Regression] [examan] [2010-01-26 08:40:29] [b8b64ced21f32e31669b267b64eede7f] 
-   P         [Multiple Regression] [Examen Bram Hutten] [2010-01-26 08:42:31] [786e067c4f7cec17385c4742b96b6dfa] 
-   P         [Multiple Regression] [examen statistiek] [2010-01-26 08:44:04] [28d531aeb5ea2ff1b676cbab66947a19] 
-             [Multiple Regression] [] [2010-01-26 08:45:05] [830e13ac5e5ac1e5b21c6af0c149b21d] 
-   P         [Multiple Regression] [] [2010-01-26 08:58:12] [0750c128064677e728c9436fc3f45ae7] 
-   P         [Multiple Regression] [ExamenStatistiek] [2010-01-26 08:59:03] [a66d3a79ef9e5308cd94a469bc5ca464] 
-   P         [Multiple Regression] [Vraag 3] [2010-01-26 08:57:15] [626f1d98f4a7f05bcb9f17666b672c60] 
-   P         [Multiple Regression] [Examen Statistisc...] [2010-01-26 08:59:14] [1646a2766cb8c4a6f9d3b2fffef409b3] 
-   P         [Multiple Regression] [Examenvraag3] [2010-01-26 08:59:45] [2b2cfeea2f5ac2a1bcb842baaf1415ef] 
-   P         [Multiple Regression] [Examenvraag 3] [2010-01-26 09:01:00] [253127ae8da904b75450fbd69fe4eb21] 
-   P         [Multiple Regression] [s0901624] [2010-01-26 08:57:55] [74be16979710d4c4e7c6647856088456] 
-   P         [Multiple Regression] [Examen] [2010-01-26 09:02:59] [b6394cb5c2dcec6d17418d3cdf42d699] 
-   P         [Multiple Regression] [] [2010-01-26 09:00:18] [21a312bc02d33649b9f78fd202ca0963] 
-   P         [Multiple Regression] [Lode Merckx computer] [2010-01-26 09:03:20] [74be16979710d4c4e7c6647856088456] 
-   P         [Multiple Regression] [] [2010-01-26 09:02:32] [ebd107afac1bd6180acb277edd05815b] 
-   P         [Multiple Regression] [3e examenvraag co...] [2010-01-26 09:01:42] [8cf9233b7464ea02e32be3b30fdac052] 
-   P         [Multiple Regression] [Computeropdracht ...] [2010-01-26 09:04:39] [2f17fb7f9ce5412e0690130b6ae01587] 
-   P         [Multiple Regression] [Vraag 3 examen Fa...] [2010-01-26 09:03:13] [9717cb857c153ca3061376906953b329] 
-   P         [Multiple Regression] [Examen Statistiek] [2010-01-26 09:05:04] [4d62210f0915d3a20cbf115865da7cd4] 

[Truncated]

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Dataseries X:

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Histogram

Boxplots

25.6	7.4	1.8
23.7	7.1	2.7
22	6.8	2.3
21.3	6.9	1.9
20.7	7.2	2
20.4	7.4	2.3
20.3	7.3	2.8
20.4	6.9	2.4
19.8	6.9	2.3
19.5	6.8	2.7
23.1	7.1	2.7
23.5	7.2	2.9
23.5	7.1	3
22.9	7	2.2
21.9	6.9	2.3
21.5	7.1	2.8
20.5	7.3	2.8
20.2	7.5	2.8
19.4	7.5	2.2
19.2	7.5	2.6
18.8	7.3	2.8
18.8	7	2.5
22.6	6.7	2.4
23.3	6.5	2.3
23	6.5	1.9
21.4	6.5	1.7
19.9	6.6	2
18.8	6.8	2.1
18.6	6.9	1.7
18.4	6.9	1.8
18.6	6.8	1.8
19.9	6.8	1.8
19.2	6.5	1.3
18.4	6.1	1.3
21.1	6.1	1.3
20.5	5.9	1.2
19.1	5.7	1.4
18.1	5.9	2.2
17	5.9	2.9
17.1	6.1	3.1
17.4	6.3	3.5
16.8	6.2	3.6
15.3	5.9	4.4
14.3	5.7	4.1
13.4	5.4	5.1
15.3	5.6	5.8
22.1	6.2	5.9
23.7	6.3	5.4
22.2	6	5.5
19.5	5.6	4.8
16.6	5.5	3.2
17.3	5.9	2.7
19.8	6.5	2.1
21.2	6.8	1.9
21.5	6.8	0.6
20.6	6.5	0.7
19.1	6.2	-0.2
19.6	6.2	-1
23.5	6.5	-1.7
24	6.7	-0.7

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=68762&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=68762&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=68762&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
W<25j[t] = + 6.0283260818606 + 2.69535910941759`W>25j`[t] -0.271201565433912Inflatie[t] -0.238306399471402M1[t] -1.47486330634128M2[t] -2.94805085936598M3[t] -3.82645389474653M4[t] -4.40827460192685M5[t] -4.71544560113093M6[t] -4.85845387804123M7[t] -4.52413730096343M8[t] -4.76743039081759M9[t] -4.18398729768748M10[t] -0.54712015654339M11[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
W<25j[t] =  +  6.0283260818606 +  2.69535910941759`W>25j`[t] -0.271201565433912Inflatie[t] -0.238306399471402M1[t] -1.47486330634128M2[t] -2.94805085936598M3[t] -3.82645389474653M4[t] -4.40827460192685M5[t] -4.71544560113093M6[t] -4.85845387804123M7[t] -4.52413730096343M8[t] -4.76743039081759M9[t] -4.18398729768748M10[t] -0.54712015654339M11[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=68762&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]W<25j[t] =  +  6.0283260818606 +  2.69535910941759`W>25j`[t] -0.271201565433912Inflatie[t] -0.238306399471402M1[t] -1.47486330634128M2[t] -2.94805085936598M3[t] -3.82645389474653M4[t] -4.40827460192685M5[t] -4.71544560113093M6[t] -4.85845387804123M7[t] -4.52413730096343M8[t] -4.76743039081759M9[t] -4.18398729768748M10[t] -0.54712015654339M11[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=68762&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=68762&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
W<25j[t] = + 6.0283260818606 + 2.69535910941759`W>25j`[t] -0.271201565433912Inflatie[t] -0.238306399471402M1[t] -1.47486330634128M2[t] -2.94805085936598M3[t] -3.82645389474653M4[t] -4.40827460192685M5[t] -4.71544560113093M6[t] -4.85845387804123M7[t] -4.52413730096343M8[t] -4.76743039081759M9[t] -4.18398729768748M10[t] -0.54712015654339M11[t] + e[t]

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	6.0283260818606	2.09838	2.8728	0.006136	0.003068
`W>25j`	2.69535910941759	0.299566	8.9976	0	0
Inflatie	-0.271201565433912	0.107819	-2.5153	0.015448	0.007724
M1	-0.238306399471402	0.740577	-0.3218	0.749073	0.374537
M2	-1.47486330634128	0.740435	-1.9919	0.052339	0.02617
M3	-2.94805085936598	0.740542	-3.9809	0.000242	0.000121
M4	-3.82645389474653	0.739417	-5.175	5e-06	2e-06
M5	-4.40827460192685	0.745741	-5.9113	0	0
M6	-4.71544560113093	0.75202	-6.2704	0	0
M7	-4.85845387804123	0.74618	-6.5111	0	0
M8	-4.52413730096343	0.740292	-6.1113	0	0
M9	-4.76743039081759	0.738669	-6.4541	0	0
M10	-4.18398729768748	0.740359	-5.6513	1e-06	0
M11	-0.54712015654339	0.738546	-0.7408	0.462575	0.231288

\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) & 6.0283260818606 & 2.09838 & 2.8728 & 0.006136 & 0.003068 \tabularnewline
`W>25j` & 2.69535910941759 & 0.299566 & 8.9976 & 0 & 0 \tabularnewline
Inflatie & -0.271201565433912 & 0.107819 & -2.5153 & 0.015448 & 0.007724 \tabularnewline
M1 & -0.238306399471402 & 0.740577 & -0.3218 & 0.749073 & 0.374537 \tabularnewline
M2 & -1.47486330634128 & 0.740435 & -1.9919 & 0.052339 & 0.02617 \tabularnewline
M3 & -2.94805085936598 & 0.740542 & -3.9809 & 0.000242 & 0.000121 \tabularnewline
M4 & -3.82645389474653 & 0.739417 & -5.175 & 5e-06 & 2e-06 \tabularnewline
M5 & -4.40827460192685 & 0.745741 & -5.9113 & 0 & 0 \tabularnewline
M6 & -4.71544560113093 & 0.75202 & -6.2704 & 0 & 0 \tabularnewline
M7 & -4.85845387804123 & 0.74618 & -6.5111 & 0 & 0 \tabularnewline
M8 & -4.52413730096343 & 0.740292 & -6.1113 & 0 & 0 \tabularnewline
M9 & -4.76743039081759 & 0.738669 & -6.4541 & 0 & 0 \tabularnewline
M10 & -4.18398729768748 & 0.740359 & -5.6513 & 1e-06 & 0 \tabularnewline
M11 & -0.54712015654339 & 0.738546 & -0.7408 & 0.462575 & 0.231288 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=68762&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]6.0283260818606[/C][C]2.09838[/C][C]2.8728[/C][C]0.006136[/C][C]0.003068[/C][/ROW]
[ROW][C]`W>25j`[/C][C]2.69535910941759[/C][C]0.299566[/C][C]8.9976[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Inflatie[/C][C]-0.271201565433912[/C][C]0.107819[/C][C]-2.5153[/C][C]0.015448[/C][C]0.007724[/C][/ROW]
[ROW][C]M1[/C][C]-0.238306399471402[/C][C]0.740577[/C][C]-0.3218[/C][C]0.749073[/C][C]0.374537[/C][/ROW]
[ROW][C]M2[/C][C]-1.47486330634128[/C][C]0.740435[/C][C]-1.9919[/C][C]0.052339[/C][C]0.02617[/C][/ROW]
[ROW][C]M3[/C][C]-2.94805085936598[/C][C]0.740542[/C][C]-3.9809[/C][C]0.000242[/C][C]0.000121[/C][/ROW]
[ROW][C]M4[/C][C]-3.82645389474653[/C][C]0.739417[/C][C]-5.175[/C][C]5e-06[/C][C]2e-06[/C][/ROW]
[ROW][C]M5[/C][C]-4.40827460192685[/C][C]0.745741[/C][C]-5.9113[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M6[/C][C]-4.71544560113093[/C][C]0.75202[/C][C]-6.2704[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M7[/C][C]-4.85845387804123[/C][C]0.74618[/C][C]-6.5111[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M8[/C][C]-4.52413730096343[/C][C]0.740292[/C][C]-6.1113[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M9[/C][C]-4.76743039081759[/C][C]0.738669[/C][C]-6.4541[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M10[/C][C]-4.18398729768748[/C][C]0.740359[/C][C]-5.6513[/C][C]1e-06[/C][C]0[/C][/ROW]
[ROW][C]M11[/C][C]-0.54712015654339[/C][C]0.738546[/C][C]-0.7408[/C][C]0.462575[/C][C]0.231288[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=68762&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=68762&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)	6.0283260818606	2.09838	2.8728	0.006136	0.003068
`W>25j`	2.69535910941759	0.299566	8.9976	0	0
Inflatie	-0.271201565433912	0.107819	-2.5153	0.015448	0.007724
M1	-0.238306399471402	0.740577	-0.3218	0.749073	0.374537
M2	-1.47486330634128	0.740435	-1.9919	0.052339	0.02617
M3	-2.94805085936598	0.740542	-3.9809	0.000242	0.000121
M4	-3.82645389474653	0.739417	-5.175	5e-06	2e-06
M5	-4.40827460192685	0.745741	-5.9113	0	0
M6	-4.71544560113093	0.75202	-6.2704	0	0
M7	-4.85845387804123	0.74618	-6.5111	0	0
M8	-4.52413730096343	0.740292	-6.1113	0	0
M9	-4.76743039081759	0.738669	-6.4541	0	0
M10	-4.18398729768748	0.740359	-5.6513	1e-06	0
M11	-0.54712015654339	0.738546	-0.7408	0.462575	0.231288

Multiple Linear Regression - Regression Statistics
Multiple R	0.913680740314576
R-squared	0.834812495221791
Adjusted R-squared	0.788129069958384
F-TEST (value)	17.8824173785758
F-TEST (DF numerator)	13
F-TEST (DF denominator)	46
p-value	8.79296635503124e-14
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.16761859153805
Sum Squared Residuals	62.7133260640438

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.913680740314576 \tabularnewline
R-squared & 0.834812495221791 \tabularnewline
Adjusted R-squared & 0.788129069958384 \tabularnewline
F-TEST (value) & 17.8824173785758 \tabularnewline
F-TEST (DF numerator) & 13 \tabularnewline
F-TEST (DF denominator) & 46 \tabularnewline
p-value & 8.79296635503124e-14 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.16761859153805 \tabularnewline
Sum Squared Residuals & 62.7133260640438 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=68762&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.913680740314576[/C][/ROW]
[ROW][C]R-squared[/C][C]0.834812495221791[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.788129069958384[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]17.8824173785758[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]13[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]46[/C][/ROW]
[ROW][C]p-value[/C][C]8.79296635503124e-14[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.16761859153805[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]62.7133260640438[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=68762&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=68762&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	0.913680740314576
R-squared	0.834812495221791
Adjusted R-squared	0.788129069958384
F-TEST (value)	17.8824173785758
F-TEST (DF numerator)	13
F-TEST (DF denominator)	46
p-value	8.79296635503124e-14
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.16761859153805
Sum Squared Residuals	62.7133260640438

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	25.6	25.2475142742984	0.352485725701641
2	23.7	22.9582682257126	0.741731774287357
3	22	20.7849535660362	1.21504643396377
4	21.3	20.284567067771	1.01543293222899
5	20.7	20.4842339368726	0.215766063127420
6	20.4	20.6347742899218	-0.234774289921848
7	20.3	20.0866293193528	0.213370680647179
8	20.4	19.4512828788372	0.948717121162844
9	19.8	19.2351099455264	0.564890054473616
10	19.5	19.4405365015412	0.0594634984588263
11	23.1	23.8860113755105	-0.78601137551053
12	23.5	24.6484271299089	-1.14842712990890
13	23.5	24.1134646629523	-0.613464662952346
14	22.9	22.8243330974878	0.0756669025121628
15	21.9	21.054489476978	0.845510523022009
16	21.5	20.579557480764	0.920442519235997
17	20.5	20.5368085954672	-0.0368085954672038
18	20.2	20.7687094181466	-0.568709418146646
19	19.4	20.7884220804967	-1.38842208049668
20	19.2	21.0142580314009	-1.81425803140093
21	18.8	20.1776528065765	-1.37765280657646
22	18.8	20.0338486365115	-1.23384863651147
23	22.6	22.8892282013737	-0.289228201373672
24	23.3	22.9243966925769	0.375603307423065
25	23	22.7945709192791	0.205429080720904
26	21.4	21.612254325496	-0.212254325495997
27	19.9	20.3272422137829	-0.427242213782885
28	18.8	19.9607908437425	-1.16079084374246
29	18.6	19.7569866736775	-1.15698667367747
30	18.4	19.42269551793	-1.02269551793001
31	18.6	19.0101513300779	-0.410151330077933
32	19.9	19.3444679071557	0.555532092844254
33	19.2	18.4281678671933	0.771832132806741
34	18.4	17.9334673165563	0.466532683443667
35	21.1	21.5703344577004	-0.470334457700419
36	20.5	21.6055029489037	-1.10550294890369
37	19.1	20.7738844144620	-1.67388441446198
38	18.1	19.8594380771285	-1.75943807712849
39	17	18.1964094283001	-1.19640942830005
40	17.1	17.8028379017162	-0.702837901716238
41	17.4	17.6516083902459	-0.251608390245877
42	16.8	17.0477813235566	-0.247781323556649
43	15.3	15.8792040614739	-0.579204061473933
44	14.3	15.7558092862984	-1.45580928629840
45	13.4	14.4327068981850	-1.03270689818505
46	15.3	15.3653807173949	-0.0653807173949337
47	22.1	20.5923431676462	1.50765683235381
48	23.7	21.5446000178483	2.15539998215171
49	22.2	20.4705657290082	1.72943427099178
50	19.5	18.3457062741750	1.15429372582496
51	16.6	17.0369053149028	-0.436905314902841
52	17.3	17.3722467060063	-0.0722467060062862
53	19.8	18.5703624037369	1.22963759626313
54	21.2	19.1260394504448	2.07396054955515
55	21.5	19.3355932085986	2.16440679140137
56	20.6	18.8341818963078	1.76581810369223
57	19.1	18.0263624825188	1.07363751748115
58	19.6	18.8267668279961	0.773233172003911
59	23.5	23.4620827977692	0.0379172022308077
60	24	24.2770732107622	-0.277073210762190

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 25.6 & 25.2475142742984 & 0.352485725701641 \tabularnewline
2 & 23.7 & 22.9582682257126 & 0.741731774287357 \tabularnewline
3 & 22 & 20.7849535660362 & 1.21504643396377 \tabularnewline
4 & 21.3 & 20.284567067771 & 1.01543293222899 \tabularnewline
5 & 20.7 & 20.4842339368726 & 0.215766063127420 \tabularnewline
6 & 20.4 & 20.6347742899218 & -0.234774289921848 \tabularnewline
7 & 20.3 & 20.0866293193528 & 0.213370680647179 \tabularnewline
8 & 20.4 & 19.4512828788372 & 0.948717121162844 \tabularnewline
9 & 19.8 & 19.2351099455264 & 0.564890054473616 \tabularnewline
10 & 19.5 & 19.4405365015412 & 0.0594634984588263 \tabularnewline
11 & 23.1 & 23.8860113755105 & -0.78601137551053 \tabularnewline
12 & 23.5 & 24.6484271299089 & -1.14842712990890 \tabularnewline
13 & 23.5 & 24.1134646629523 & -0.613464662952346 \tabularnewline
14 & 22.9 & 22.8243330974878 & 0.0756669025121628 \tabularnewline
15 & 21.9 & 21.054489476978 & 0.845510523022009 \tabularnewline
16 & 21.5 & 20.579557480764 & 0.920442519235997 \tabularnewline
17 & 20.5 & 20.5368085954672 & -0.0368085954672038 \tabularnewline
18 & 20.2 & 20.7687094181466 & -0.568709418146646 \tabularnewline
19 & 19.4 & 20.7884220804967 & -1.38842208049668 \tabularnewline
20 & 19.2 & 21.0142580314009 & -1.81425803140093 \tabularnewline
21 & 18.8 & 20.1776528065765 & -1.37765280657646 \tabularnewline
22 & 18.8 & 20.0338486365115 & -1.23384863651147 \tabularnewline
23 & 22.6 & 22.8892282013737 & -0.289228201373672 \tabularnewline
24 & 23.3 & 22.9243966925769 & 0.375603307423065 \tabularnewline
25 & 23 & 22.7945709192791 & 0.205429080720904 \tabularnewline
26 & 21.4 & 21.612254325496 & -0.212254325495997 \tabularnewline
27 & 19.9 & 20.3272422137829 & -0.427242213782885 \tabularnewline
28 & 18.8 & 19.9607908437425 & -1.16079084374246 \tabularnewline
29 & 18.6 & 19.7569866736775 & -1.15698667367747 \tabularnewline
30 & 18.4 & 19.42269551793 & -1.02269551793001 \tabularnewline
31 & 18.6 & 19.0101513300779 & -0.410151330077933 \tabularnewline
32 & 19.9 & 19.3444679071557 & 0.555532092844254 \tabularnewline
33 & 19.2 & 18.4281678671933 & 0.771832132806741 \tabularnewline
34 & 18.4 & 17.9334673165563 & 0.466532683443667 \tabularnewline
35 & 21.1 & 21.5703344577004 & -0.470334457700419 \tabularnewline
36 & 20.5 & 21.6055029489037 & -1.10550294890369 \tabularnewline
37 & 19.1 & 20.7738844144620 & -1.67388441446198 \tabularnewline
38 & 18.1 & 19.8594380771285 & -1.75943807712849 \tabularnewline
39 & 17 & 18.1964094283001 & -1.19640942830005 \tabularnewline
40 & 17.1 & 17.8028379017162 & -0.702837901716238 \tabularnewline
41 & 17.4 & 17.6516083902459 & -0.251608390245877 \tabularnewline
42 & 16.8 & 17.0477813235566 & -0.247781323556649 \tabularnewline
43 & 15.3 & 15.8792040614739 & -0.579204061473933 \tabularnewline
44 & 14.3 & 15.7558092862984 & -1.45580928629840 \tabularnewline
45 & 13.4 & 14.4327068981850 & -1.03270689818505 \tabularnewline
46 & 15.3 & 15.3653807173949 & -0.0653807173949337 \tabularnewline
47 & 22.1 & 20.5923431676462 & 1.50765683235381 \tabularnewline
48 & 23.7 & 21.5446000178483 & 2.15539998215171 \tabularnewline
49 & 22.2 & 20.4705657290082 & 1.72943427099178 \tabularnewline
50 & 19.5 & 18.3457062741750 & 1.15429372582496 \tabularnewline
51 & 16.6 & 17.0369053149028 & -0.436905314902841 \tabularnewline
52 & 17.3 & 17.3722467060063 & -0.0722467060062862 \tabularnewline
53 & 19.8 & 18.5703624037369 & 1.22963759626313 \tabularnewline
54 & 21.2 & 19.1260394504448 & 2.07396054955515 \tabularnewline
55 & 21.5 & 19.3355932085986 & 2.16440679140137 \tabularnewline
56 & 20.6 & 18.8341818963078 & 1.76581810369223 \tabularnewline
57 & 19.1 & 18.0263624825188 & 1.07363751748115 \tabularnewline
58 & 19.6 & 18.8267668279961 & 0.773233172003911 \tabularnewline
59 & 23.5 & 23.4620827977692 & 0.0379172022308077 \tabularnewline
60 & 24 & 24.2770732107622 & -0.277073210762190 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=68762&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]25.6[/C][C]25.2475142742984[/C][C]0.352485725701641[/C][/ROW]
[ROW][C]2[/C][C]23.7[/C][C]22.9582682257126[/C][C]0.741731774287357[/C][/ROW]
[ROW][C]3[/C][C]22[/C][C]20.7849535660362[/C][C]1.21504643396377[/C][/ROW]
[ROW][C]4[/C][C]21.3[/C][C]20.284567067771[/C][C]1.01543293222899[/C][/ROW]
[ROW][C]5[/C][C]20.7[/C][C]20.4842339368726[/C][C]0.215766063127420[/C][/ROW]
[ROW][C]6[/C][C]20.4[/C][C]20.6347742899218[/C][C]-0.234774289921848[/C][/ROW]
[ROW][C]7[/C][C]20.3[/C][C]20.0866293193528[/C][C]0.213370680647179[/C][/ROW]
[ROW][C]8[/C][C]20.4[/C][C]19.4512828788372[/C][C]0.948717121162844[/C][/ROW]
[ROW][C]9[/C][C]19.8[/C][C]19.2351099455264[/C][C]0.564890054473616[/C][/ROW]
[ROW][C]10[/C][C]19.5[/C][C]19.4405365015412[/C][C]0.0594634984588263[/C][/ROW]
[ROW][C]11[/C][C]23.1[/C][C]23.8860113755105[/C][C]-0.78601137551053[/C][/ROW]
[ROW][C]12[/C][C]23.5[/C][C]24.6484271299089[/C][C]-1.14842712990890[/C][/ROW]
[ROW][C]13[/C][C]23.5[/C][C]24.1134646629523[/C][C]-0.613464662952346[/C][/ROW]
[ROW][C]14[/C][C]22.9[/C][C]22.8243330974878[/C][C]0.0756669025121628[/C][/ROW]
[ROW][C]15[/C][C]21.9[/C][C]21.054489476978[/C][C]0.845510523022009[/C][/ROW]
[ROW][C]16[/C][C]21.5[/C][C]20.579557480764[/C][C]0.920442519235997[/C][/ROW]
[ROW][C]17[/C][C]20.5[/C][C]20.5368085954672[/C][C]-0.0368085954672038[/C][/ROW]
[ROW][C]18[/C][C]20.2[/C][C]20.7687094181466[/C][C]-0.568709418146646[/C][/ROW]
[ROW][C]19[/C][C]19.4[/C][C]20.7884220804967[/C][C]-1.38842208049668[/C][/ROW]
[ROW][C]20[/C][C]19.2[/C][C]21.0142580314009[/C][C]-1.81425803140093[/C][/ROW]
[ROW][C]21[/C][C]18.8[/C][C]20.1776528065765[/C][C]-1.37765280657646[/C][/ROW]
[ROW][C]22[/C][C]18.8[/C][C]20.0338486365115[/C][C]-1.23384863651147[/C][/ROW]
[ROW][C]23[/C][C]22.6[/C][C]22.8892282013737[/C][C]-0.289228201373672[/C][/ROW]
[ROW][C]24[/C][C]23.3[/C][C]22.9243966925769[/C][C]0.375603307423065[/C][/ROW]
[ROW][C]25[/C][C]23[/C][C]22.7945709192791[/C][C]0.205429080720904[/C][/ROW]
[ROW][C]26[/C][C]21.4[/C][C]21.612254325496[/C][C]-0.212254325495997[/C][/ROW]
[ROW][C]27[/C][C]19.9[/C][C]20.3272422137829[/C][C]-0.427242213782885[/C][/ROW]
[ROW][C]28[/C][C]18.8[/C][C]19.9607908437425[/C][C]-1.16079084374246[/C][/ROW]
[ROW][C]29[/C][C]18.6[/C][C]19.7569866736775[/C][C]-1.15698667367747[/C][/ROW]
[ROW][C]30[/C][C]18.4[/C][C]19.42269551793[/C][C]-1.02269551793001[/C][/ROW]
[ROW][C]31[/C][C]18.6[/C][C]19.0101513300779[/C][C]-0.410151330077933[/C][/ROW]
[ROW][C]32[/C][C]19.9[/C][C]19.3444679071557[/C][C]0.555532092844254[/C][/ROW]
[ROW][C]33[/C][C]19.2[/C][C]18.4281678671933[/C][C]0.771832132806741[/C][/ROW]
[ROW][C]34[/C][C]18.4[/C][C]17.9334673165563[/C][C]0.466532683443667[/C][/ROW]
[ROW][C]35[/C][C]21.1[/C][C]21.5703344577004[/C][C]-0.470334457700419[/C][/ROW]
[ROW][C]36[/C][C]20.5[/C][C]21.6055029489037[/C][C]-1.10550294890369[/C][/ROW]
[ROW][C]37[/C][C]19.1[/C][C]20.7738844144620[/C][C]-1.67388441446198[/C][/ROW]
[ROW][C]38[/C][C]18.1[/C][C]19.8594380771285[/C][C]-1.75943807712849[/C][/ROW]
[ROW][C]39[/C][C]17[/C][C]18.1964094283001[/C][C]-1.19640942830005[/C][/ROW]
[ROW][C]40[/C][C]17.1[/C][C]17.8028379017162[/C][C]-0.702837901716238[/C][/ROW]
[ROW][C]41[/C][C]17.4[/C][C]17.6516083902459[/C][C]-0.251608390245877[/C][/ROW]
[ROW][C]42[/C][C]16.8[/C][C]17.0477813235566[/C][C]-0.247781323556649[/C][/ROW]
[ROW][C]43[/C][C]15.3[/C][C]15.8792040614739[/C][C]-0.579204061473933[/C][/ROW]
[ROW][C]44[/C][C]14.3[/C][C]15.7558092862984[/C][C]-1.45580928629840[/C][/ROW]
[ROW][C]45[/C][C]13.4[/C][C]14.4327068981850[/C][C]-1.03270689818505[/C][/ROW]
[ROW][C]46[/C][C]15.3[/C][C]15.3653807173949[/C][C]-0.0653807173949337[/C][/ROW]
[ROW][C]47[/C][C]22.1[/C][C]20.5923431676462[/C][C]1.50765683235381[/C][/ROW]
[ROW][C]48[/C][C]23.7[/C][C]21.5446000178483[/C][C]2.15539998215171[/C][/ROW]
[ROW][C]49[/C][C]22.2[/C][C]20.4705657290082[/C][C]1.72943427099178[/C][/ROW]
[ROW][C]50[/C][C]19.5[/C][C]18.3457062741750[/C][C]1.15429372582496[/C][/ROW]
[ROW][C]51[/C][C]16.6[/C][C]17.0369053149028[/C][C]-0.436905314902841[/C][/ROW]
[ROW][C]52[/C][C]17.3[/C][C]17.3722467060063[/C][C]-0.0722467060062862[/C][/ROW]
[ROW][C]53[/C][C]19.8[/C][C]18.5703624037369[/C][C]1.22963759626313[/C][/ROW]
[ROW][C]54[/C][C]21.2[/C][C]19.1260394504448[/C][C]2.07396054955515[/C][/ROW]
[ROW][C]55[/C][C]21.5[/C][C]19.3355932085986[/C][C]2.16440679140137[/C][/ROW]
[ROW][C]56[/C][C]20.6[/C][C]18.8341818963078[/C][C]1.76581810369223[/C][/ROW]
[ROW][C]57[/C][C]19.1[/C][C]18.0263624825188[/C][C]1.07363751748115[/C][/ROW]
[ROW][C]58[/C][C]19.6[/C][C]18.8267668279961[/C][C]0.773233172003911[/C][/ROW]
[ROW][C]59[/C][C]23.5[/C][C]23.4620827977692[/C][C]0.0379172022308077[/C][/ROW]
[ROW][C]60[/C][C]24[/C][C]24.2770732107622[/C][C]-0.277073210762190[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=68762&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=68762&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	25.6	25.2475142742984	0.352485725701641
2	23.7	22.9582682257126	0.741731774287357
3	22	20.7849535660362	1.21504643396377
4	21.3	20.284567067771	1.01543293222899
5	20.7	20.4842339368726	0.215766063127420
6	20.4	20.6347742899218	-0.234774289921848
7	20.3	20.0866293193528	0.213370680647179
8	20.4	19.4512828788372	0.948717121162844
9	19.8	19.2351099455264	0.564890054473616
10	19.5	19.4405365015412	0.0594634984588263
11	23.1	23.8860113755105	-0.78601137551053
12	23.5	24.6484271299089	-1.14842712990890
13	23.5	24.1134646629523	-0.613464662952346
14	22.9	22.8243330974878	0.0756669025121628
15	21.9	21.054489476978	0.845510523022009
16	21.5	20.579557480764	0.920442519235997
17	20.5	20.5368085954672	-0.0368085954672038
18	20.2	20.7687094181466	-0.568709418146646
19	19.4	20.7884220804967	-1.38842208049668
20	19.2	21.0142580314009	-1.81425803140093
21	18.8	20.1776528065765	-1.37765280657646
22	18.8	20.0338486365115	-1.23384863651147
23	22.6	22.8892282013737	-0.289228201373672
24	23.3	22.9243966925769	0.375603307423065
25	23	22.7945709192791	0.205429080720904
26	21.4	21.612254325496	-0.212254325495997
27	19.9	20.3272422137829	-0.427242213782885
28	18.8	19.9607908437425	-1.16079084374246
29	18.6	19.7569866736775	-1.15698667367747
30	18.4	19.42269551793	-1.02269551793001
31	18.6	19.0101513300779	-0.410151330077933
32	19.9	19.3444679071557	0.555532092844254
33	19.2	18.4281678671933	0.771832132806741
34	18.4	17.9334673165563	0.466532683443667
35	21.1	21.5703344577004	-0.470334457700419
36	20.5	21.6055029489037	-1.10550294890369
37	19.1	20.7738844144620	-1.67388441446198
38	18.1	19.8594380771285	-1.75943807712849
39	17	18.1964094283001	-1.19640942830005
40	17.1	17.8028379017162	-0.702837901716238
41	17.4	17.6516083902459	-0.251608390245877
42	16.8	17.0477813235566	-0.247781323556649
43	15.3	15.8792040614739	-0.579204061473933
44	14.3	15.7558092862984	-1.45580928629840
45	13.4	14.4327068981850	-1.03270689818505
46	15.3	15.3653807173949	-0.0653807173949337
47	22.1	20.5923431676462	1.50765683235381
48	23.7	21.5446000178483	2.15539998215171
49	22.2	20.4705657290082	1.72943427099178
50	19.5	18.3457062741750	1.15429372582496
51	16.6	17.0369053149028	-0.436905314902841
52	17.3	17.3722467060063	-0.0722467060062862
53	19.8	18.5703624037369	1.22963759626313
54	21.2	19.1260394504448	2.07396054955515
55	21.5	19.3355932085986	2.16440679140137
56	20.6	18.8341818963078	1.76581810369223
57	19.1	18.0263624825188	1.07363751748115
58	19.6	18.8267668279961	0.773233172003911
59	23.5	23.4620827977692	0.0379172022308077
60	24	24.2770732107622	-0.277073210762190

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
17	0.0273397186282271	0.0546794372564542	0.972660281371773
18	0.00744337222734536	0.0148867444546907	0.992556627772655
19	0.058242086007885	0.11648417201577	0.941757913992115
20	0.0771353581140986	0.154270716228197	0.922864641885901
21	0.0512416691428479	0.102483338285696	0.948758330857152
22	0.0432686461962748	0.0865372923925496	0.956731353803725
23	0.033097563314059	0.066195126628118	0.96690243668594
24	0.0188847697040868	0.0377695394081736	0.981115230295913
25	0.0272949855333516	0.0545899710667032	0.972705014466648
26	0.0385060487557989	0.0770120975115977	0.96149395124420
27	0.0571927763169398	0.114385552633880	0.94280722368306
28	0.140330079191260	0.280660158382520	0.85966992080874
29	0.193195428098178	0.386390856196355	0.806804571901822
30	0.259133342988957	0.518266685977914	0.740866657011043
31	0.297742086947925	0.59548417389585	0.702257913052075
32	0.318040343268515	0.63608068653703	0.681959656731485
33	0.324588245472652	0.649176490945304	0.675411754527348
34	0.245453242412578	0.490906484825156	0.754546757587422
35	0.175622903943095	0.351245807886191	0.824377096056905
36	0.159436044018570	0.318872088037139	0.84056395598143
37	0.175399429796948	0.350798859593896	0.824600570203052
38	0.45440740900961	0.90881481801922	0.54559259099039
39	0.684639584106719	0.630720831786561	0.315360415893281
40	0.805102449894396	0.389795100211208	0.194897550105604
41	0.793797306097249	0.412405387805502	0.206202693902751
42	0.679369740748603	0.641260518502794	0.320630259251397
43	0.549026737485484	0.901946525029031	0.450973262514516

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
17 & 0.0273397186282271 & 0.0546794372564542 & 0.972660281371773 \tabularnewline
18 & 0.00744337222734536 & 0.0148867444546907 & 0.992556627772655 \tabularnewline
19 & 0.058242086007885 & 0.11648417201577 & 0.941757913992115 \tabularnewline
20 & 0.0771353581140986 & 0.154270716228197 & 0.922864641885901 \tabularnewline
21 & 0.0512416691428479 & 0.102483338285696 & 0.948758330857152 \tabularnewline
22 & 0.0432686461962748 & 0.0865372923925496 & 0.956731353803725 \tabularnewline
23 & 0.033097563314059 & 0.066195126628118 & 0.96690243668594 \tabularnewline
24 & 0.0188847697040868 & 0.0377695394081736 & 0.981115230295913 \tabularnewline
25 & 0.0272949855333516 & 0.0545899710667032 & 0.972705014466648 \tabularnewline
26 & 0.0385060487557989 & 0.0770120975115977 & 0.96149395124420 \tabularnewline
27 & 0.0571927763169398 & 0.114385552633880 & 0.94280722368306 \tabularnewline
28 & 0.140330079191260 & 0.280660158382520 & 0.85966992080874 \tabularnewline
29 & 0.193195428098178 & 0.386390856196355 & 0.806804571901822 \tabularnewline
30 & 0.259133342988957 & 0.518266685977914 & 0.740866657011043 \tabularnewline
31 & 0.297742086947925 & 0.59548417389585 & 0.702257913052075 \tabularnewline
32 & 0.318040343268515 & 0.63608068653703 & 0.681959656731485 \tabularnewline
33 & 0.324588245472652 & 0.649176490945304 & 0.675411754527348 \tabularnewline
34 & 0.245453242412578 & 0.490906484825156 & 0.754546757587422 \tabularnewline
35 & 0.175622903943095 & 0.351245807886191 & 0.824377096056905 \tabularnewline
36 & 0.159436044018570 & 0.318872088037139 & 0.84056395598143 \tabularnewline
37 & 0.175399429796948 & 0.350798859593896 & 0.824600570203052 \tabularnewline
38 & 0.45440740900961 & 0.90881481801922 & 0.54559259099039 \tabularnewline
39 & 0.684639584106719 & 0.630720831786561 & 0.315360415893281 \tabularnewline
40 & 0.805102449894396 & 0.389795100211208 & 0.194897550105604 \tabularnewline
41 & 0.793797306097249 & 0.412405387805502 & 0.206202693902751 \tabularnewline
42 & 0.679369740748603 & 0.641260518502794 & 0.320630259251397 \tabularnewline
43 & 0.549026737485484 & 0.901946525029031 & 0.450973262514516 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=68762&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]0.0273397186282271[/C][C]0.0546794372564542[/C][C]0.972660281371773[/C][/ROW]
[ROW][C]18[/C][C]0.00744337222734536[/C][C]0.0148867444546907[/C][C]0.992556627772655[/C][/ROW]
[ROW][C]19[/C][C]0.058242086007885[/C][C]0.11648417201577[/C][C]0.941757913992115[/C][/ROW]
[ROW][C]20[/C][C]0.0771353581140986[/C][C]0.154270716228197[/C][C]0.922864641885901[/C][/ROW]
[ROW][C]21[/C][C]0.0512416691428479[/C][C]0.102483338285696[/C][C]0.948758330857152[/C][/ROW]
[ROW][C]22[/C][C]0.0432686461962748[/C][C]0.0865372923925496[/C][C]0.956731353803725[/C][/ROW]
[ROW][C]23[/C][C]0.033097563314059[/C][C]0.066195126628118[/C][C]0.96690243668594[/C][/ROW]
[ROW][C]24[/C][C]0.0188847697040868[/C][C]0.0377695394081736[/C][C]0.981115230295913[/C][/ROW]
[ROW][C]25[/C][C]0.0272949855333516[/C][C]0.0545899710667032[/C][C]0.972705014466648[/C][/ROW]
[ROW][C]26[/C][C]0.0385060487557989[/C][C]0.0770120975115977[/C][C]0.96149395124420[/C][/ROW]
[ROW][C]27[/C][C]0.0571927763169398[/C][C]0.114385552633880[/C][C]0.94280722368306[/C][/ROW]
[ROW][C]28[/C][C]0.140330079191260[/C][C]0.280660158382520[/C][C]0.85966992080874[/C][/ROW]
[ROW][C]29[/C][C]0.193195428098178[/C][C]0.386390856196355[/C][C]0.806804571901822[/C][/ROW]
[ROW][C]30[/C][C]0.259133342988957[/C][C]0.518266685977914[/C][C]0.740866657011043[/C][/ROW]
[ROW][C]31[/C][C]0.297742086947925[/C][C]0.59548417389585[/C][C]0.702257913052075[/C][/ROW]
[ROW][C]32[/C][C]0.318040343268515[/C][C]0.63608068653703[/C][C]0.681959656731485[/C][/ROW]
[ROW][C]33[/C][C]0.324588245472652[/C][C]0.649176490945304[/C][C]0.675411754527348[/C][/ROW]
[ROW][C]34[/C][C]0.245453242412578[/C][C]0.490906484825156[/C][C]0.754546757587422[/C][/ROW]
[ROW][C]35[/C][C]0.175622903943095[/C][C]0.351245807886191[/C][C]0.824377096056905[/C][/ROW]
[ROW][C]36[/C][C]0.159436044018570[/C][C]0.318872088037139[/C][C]0.84056395598143[/C][/ROW]
[ROW][C]37[/C][C]0.175399429796948[/C][C]0.350798859593896[/C][C]0.824600570203052[/C][/ROW]
[ROW][C]38[/C][C]0.45440740900961[/C][C]0.90881481801922[/C][C]0.54559259099039[/C][/ROW]
[ROW][C]39[/C][C]0.684639584106719[/C][C]0.630720831786561[/C][C]0.315360415893281[/C][/ROW]
[ROW][C]40[/C][C]0.805102449894396[/C][C]0.389795100211208[/C][C]0.194897550105604[/C][/ROW]
[ROW][C]41[/C][C]0.793797306097249[/C][C]0.412405387805502[/C][C]0.206202693902751[/C][/ROW]
[ROW][C]42[/C][C]0.679369740748603[/C][C]0.641260518502794[/C][C]0.320630259251397[/C][/ROW]
[ROW][C]43[/C][C]0.549026737485484[/C][C]0.901946525029031[/C][C]0.450973262514516[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=68762&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=68762&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	0.0273397186282271	0.0546794372564542	0.972660281371773
18	0.00744337222734536	0.0148867444546907	0.992556627772655
19	0.058242086007885	0.11648417201577	0.941757913992115
20	0.0771353581140986	0.154270716228197	0.922864641885901
21	0.0512416691428479	0.102483338285696	0.948758330857152
22	0.0432686461962748	0.0865372923925496	0.956731353803725
23	0.033097563314059	0.066195126628118	0.96690243668594
24	0.0188847697040868	0.0377695394081736	0.981115230295913
25	0.0272949855333516	0.0545899710667032	0.972705014466648
26	0.0385060487557989	0.0770120975115977	0.96149395124420
27	0.0571927763169398	0.114385552633880	0.94280722368306
28	0.140330079191260	0.280660158382520	0.85966992080874
29	0.193195428098178	0.386390856196355	0.806804571901822
30	0.259133342988957	0.518266685977914	0.740866657011043
31	0.297742086947925	0.59548417389585	0.702257913052075
32	0.318040343268515	0.63608068653703	0.681959656731485
33	0.324588245472652	0.649176490945304	0.675411754527348
34	0.245453242412578	0.490906484825156	0.754546757587422
35	0.175622903943095	0.351245807886191	0.824377096056905
36	0.159436044018570	0.318872088037139	0.84056395598143
37	0.175399429796948	0.350798859593896	0.824600570203052
38	0.45440740900961	0.90881481801922	0.54559259099039
39	0.684639584106719	0.630720831786561	0.315360415893281
40	0.805102449894396	0.389795100211208	0.194897550105604
41	0.793797306097249	0.412405387805502	0.206202693902751
42	0.679369740748603	0.641260518502794	0.320630259251397
43	0.549026737485484	0.901946525029031	0.450973262514516

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	2	0.0740740740740741	NOK
10% type I error level	7	0.259259259259259	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 & 0 & 0 & OK \tabularnewline
5% type I error level & 2 & 0.0740740740740741 & NOK \tabularnewline
10% type I error level & 7 & 0.259259259259259 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=68762&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]2[/C][C]0.0740740740740741[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]7[/C][C]0.259259259259259[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=68762&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=68762&T=6

As an alternative you can also use a QR Code:

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	2	0.0740740740740741	NOK
10% type I error level	7	0.259259259259259	NOK

Figure 1

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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 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;

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

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

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code