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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Tue, 17 Nov 2009 07:36:29 -0700

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Nov/17/t1258468648hnp3qef5ppr2hlt.htm/, Retrieved Mon, 29 Apr 2024 18:47:16 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=57369, Retrieved Mon, 29 Apr 2024 18:47:16 +0000

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Original text written by user:

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No (this computation is public)

User-defined keywords

Estimated Impact

240

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

-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RMPD  [Multiple Regression] [Seatbelt] [2009-11-12 13:54:52] [b98453cac15ba1066b407e146608df68]
-    D      [Multiple Regression] [model 1] [2009-11-17 14:36:29] [87085ce7f5378f281469a8b1f0969170] [Current]
-    D        [Multiple Regression] [miltiple regression] [2009-11-19 18:03:37] [ed603017d2bee8fbd82b6d5ec04e12c3] 
-    D        [Multiple Regression] [multiple regression] [2009-11-19 21:38:11] [ed603017d2bee8fbd82b6d5ec04e12c3] 
-   P           [Multiple Regression] [monthly dummies] [2009-11-19 22:00:07] [ed603017d2bee8fbd82b6d5ec04e12c3] 
-   P             [Multiple Regression] [model3] [2009-11-20 08:47:44] [ed603017d2bee8fbd82b6d5ec04e12c3] 
-    D              [Multiple Regression] [model 4] [2009-11-20 08:59:37] [ed603017d2bee8fbd82b6d5ec04e12c3] 
-    D                [Multiple Regression] [W7: Model 4] [2009-11-22 13:34:45] [03d5b865e91ca35b5a5d21b8d6da5aba] 
-    D                  [Multiple Regression] [review 7] [2009-11-24 21:51:11] [309ee52d0058ff0a6f7eec15e07b2d9f] 
-    D                [Multiple Regression] [] [2009-11-22 15:02:10] [9f35ad889e41dd0c9322ca60d75b9f47] 
-    D                [Multiple Regression] [Beste model] [2009-12-05 15:17:52] [34b80aeb109c116fd63bf2eb7493a276] 
-    D              [Multiple Regression] [Workshop7] [2009-11-20 13:14:04] [34b80aeb109c116fd63bf2eb7493a276] 
-    D                [Multiple Regression] [workshop7] [2009-11-20 13:34:45] [34b80aeb109c116fd63bf2eb7493a276] 
-   P                   [Multiple Regression] [Workshop 7: verbe...] [2009-11-27 14:49:24] [7c2a5b25a196bd646844b8f5223c9b3e] 
-   PD                [Multiple Regression] [Workshop 7] [2009-11-20 16:57:31] [78762f311bef5a0e45c439762ada383c] 
-   P                   [Multiple Regression] [verb ws 7] [2009-11-21 09:45:52] [134dc66689e3d457a82860db6471d419] 
-    D                [Multiple Regression] [model 3] [2009-12-05 14:58:14] [34b80aeb109c116fd63bf2eb7493a276] 
-    D              [Multiple Regression] [W7: Linear Trend] [2009-11-21 14:22:35] [03d5b865e91ca35b5a5d21b8d6da5aba] 
-    D              [Multiple Regression] [] [2009-11-22 14:13:11] [9f35ad889e41dd0c9322ca60d75b9f47] 
-    D            [Multiple Regression] [W7: Monthly Dummies] [2009-11-21 14:07:55] [03d5b865e91ca35b5a5d21b8d6da5aba] 
-    D            [Multiple Regression] [WS7] [2009-11-21 15:04:45] [9f35ad889e41dd0c9322ca60d75b9f47] 
-    D          [Multiple Regression] [WS7] [2009-11-20 22:38:21] [9f35ad889e41dd0c9322ca60d75b9f47] 
-    D          [Multiple Regression] [W7: Multiple regr...] [2009-11-21 13:40:01] [03d5b865e91ca35b5a5d21b8d6da5aba] 
-    D          [Multiple Regression] [model 1 multiple ...] [2009-12-06 11:43:28] [ed603017d2bee8fbd82b6d5ec04e12c3] 
-   PD          [Multiple Regression] [multiple regressi...] [2009-12-06 12:47:31] [ed603017d2bee8fbd82b6d5ec04e12c3] 
-   PD            [Multiple Regression] [multiple regressi...] [2009-12-08 20:02:18] [ed603017d2bee8fbd82b6d5ec04e12c3] 
-   PD          [Multiple Regression] [multiple regressi...] [2009-12-06 13:06:41] [ed603017d2bee8fbd82b6d5ec04e12c3] 

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Dataseries X:

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Histogram

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5.7	97.33	91.4
6.1	97.89	91.1
6	98.69	104.4
5.9	99.01	97.6
5.8	99.18	93.7
5.7	98.45	104.5
5.6	98.13	95.4
5.4	98.29	86.5
5.4	99.1	102.9
5.5	99.26	101.9
5.6	98.85	103.7
5.7	98.05	100.7
5.9	98.53	94.2
6.1	99.34	93.6
6	100.14	104.7
5.8	100.3	101
5.8	100.22	97.6
5.7	99.9	105.8
5.5	99.58	93.7
5.3	99.9	91.2
5.2	100.78	106.3
5.2	100.78	103.4
5	100.46	107.4
5.1	100.06	101.2
5.1	100.28	96.9
5.2	100.78	96.3
4.9	101.58	109.8
4.8	102.06	97.9
4.5	102.02	105.1
4.5	101.68	107.9
4.4	101.32	95
4.4	101.81	95.2
4.2	102.3	105.8
4.1	102.12	110.1
3.9	102.1	112.2
3.8	101.75	102.5
3.9	101.5	103.7
4.2	102.16	102
4.1	103.47	112.3
3.8	104.05	103.3
3.6	104.09	106.9
3.7	103.55	104.6
3.5	102.77	100.7
3.4	102.89	99
3.1	103.6	106.5
3.1	103.76	114.9
3.1	103.92	114.1
3.2	103.35	102.2
3.3	103.32	107
3.5	104.2	107.4
3.6	105.44	107.4
3.5	105.81	110.1
3.3	106.25	105.6
3.2	105.94	110.9
3.1	105.82	101.9
3.2	105.96	93.2
3	106.49	110.5
3	106.32	113.1
3.1	105.88	101.7
3.4	105.07	96.7

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=57369&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=57369&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57369&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
manwerk[t] = + 42.9053953972478 -0.371174663740103infl[t] -0.00596100651594128indprod[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
manwerk[t] =  +  42.9053953972478 -0.371174663740103infl[t] -0.00596100651594128indprod[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57369&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]manwerk[t] =  +  42.9053953972478 -0.371174663740103infl[t] -0.00596100651594128indprod[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57369&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57369&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
manwerk[t] = + 42.9053953972478 -0.371174663740103infl[t] -0.00596100651594128indprod[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)	42.9053953972478	2.145333	19.9994	0	0
infl	-0.371174663740103	0.024574	-15.1046	0	0
indprod	-0.00596100651594128	0.009699	-0.6146	0.541276	0.270638

\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) & 42.9053953972478 & 2.145333 & 19.9994 & 0 & 0 \tabularnewline
infl & -0.371174663740103 & 0.024574 & -15.1046 & 0 & 0 \tabularnewline
indprod & -0.00596100651594128 & 0.009699 & -0.6146 & 0.541276 & 0.270638 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57369&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]42.9053953972478[/C][C]2.145333[/C][C]19.9994[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]infl[/C][C]-0.371174663740103[/C][C]0.024574[/C][C]-15.1046[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]indprod[/C][C]-0.00596100651594128[/C][C]0.009699[/C][C]-0.6146[/C][C]0.541276[/C][C]0.270638[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57369&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57369&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)	42.9053953972478	2.145333	19.9994	0	0
infl	-0.371174663740103	0.024574	-15.1046	0	0
indprod	-0.00596100651594128	0.009699	-0.6146	0.541276	0.270638

Multiple Linear Regression - Regression Statistics
Multiple R	0.923950647602837
R-squared	0.853684799205702
Adjusted R-squared	0.848550932511165
F-TEST (value)	166.284956349597
F-TEST (DF numerator)	2
F-TEST (DF denominator)	57
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.41123506047334
Sum Squared Residuals	9.63951367286317

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.923950647602837 \tabularnewline
R-squared & 0.853684799205702 \tabularnewline
Adjusted R-squared & 0.848550932511165 \tabularnewline
F-TEST (value) & 166.284956349597 \tabularnewline
F-TEST (DF numerator) & 2 \tabularnewline
F-TEST (DF denominator) & 57 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.41123506047334 \tabularnewline
Sum Squared Residuals & 9.63951367286317 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57369&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.923950647602837[/C][/ROW]
[ROW][C]R-squared[/C][C]0.853684799205702[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.848550932511165[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]166.284956349597[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]2[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]57[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.41123506047334[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]9.63951367286317[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57369&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57369&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	0.923950647602837
R-squared	0.853684799205702
Adjusted R-squared	0.848550932511165
F-TEST (value)	166.284956349597
F-TEST (DF numerator)	2
F-TEST (DF denominator)	57
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.41123506047334
Sum Squared Residuals	9.63951367286317

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	5.7	6.23412937986647	-0.53412937986647
2	6.1	6.02805987012679	0.0719401298732074
3	6	5.65183875247269	0.348161247527311
4	5.9	5.57359770438425	0.326402295615746
5	5.8	5.53374593696061	0.266254063039393
6	5.7	5.74032457111872	-0.0403245711187177
7	5.6	5.91334562281062	-0.313345622810620
8	5.4	5.90701063460408	-0.507010634604076
9	5.4	5.50859865011316	-0.108598650113159
10	5.5	5.45517171043068	0.0448282895693196
11	5.6	5.59662351083543	0.00337648916456707
12	5.7	5.91144626137534	-0.211446261375338
13	5.9	5.7720289651337	0.127971034866295
14	6.1	5.47495409141379	0.625045908586214
15	6	5.11184718809475	0.888152811905245
16	5.8	5.07451496600532	0.725485033994677
17	5.8	5.12447636125873	0.675523638741269
18	5.7	5.19437200022484	0.505627999775157
19	5.5	5.38527607146457	0.114723928535432
20	5.3	5.28140269535759	0.0185973046424141
21	5.2	4.86475779287558	0.335242207124417
22	5.2	4.88204471177181	0.317955288228188
23	5	4.97697657810488	0.0230234218951166
24	5.1	5.16240468399976	-0.0624046839997579
25	5.1	5.10637858599548	-0.00637858599548303
26	5.2	4.924367858035	0.275632141965004
27	4.9	4.54695453907771	0.353045460922294
28	4.8	4.43972667802216	0.360273321977843
29	4.5	4.41165441765699	0.088345582343014
30	4.5	4.52116298508398	-0.0211629850839815
31	4.4	4.73168284808607	-0.331682848086066
32	4.4	4.54861506155022	-0.148615061550224
33	4.2	4.3035528072486	-0.103552807248597
34	4.1	4.34473191870327	-0.244731918703266
35	3.9	4.33963729829459	-0.439637298294595
36	3.8	4.52737019380826	-0.72737019380826
37	3.9	4.61301065192416	-0.713010651924156
38	4.2	4.37816908493279	-0.178169084932789
39	4.1	3.83053190831906	0.269468091680942
40	3.8	3.66889966199327	0.131100338006730
41	3.6	3.63259305198627	-0.0325930519862744
42	3.7	3.8467376853926	-0.146737685392598
43	3.5	4.15950184852205	-0.65950184852205
44	3.4	4.12509459995034	-0.725094599950336
45	3.1	3.81685303982531	-0.716853039825305
46	3.1	3.70739263889298	-0.607392638892978
47	3.1	3.65277349790732	-0.552773497907315
48	3.2	3.93527903377888	-0.735279033778878
49	3.3	3.91780144241456	-0.617801442414564
50	3.5	3.58878333571689	-0.0887833357168928
51	3.6	3.12852675267917	0.471473247320834
52	3.5	2.97509740950228	0.524902590497715
53	3.3	2.83860508677838	0.461394913221624
54	3.2	2.92207589800332	0.27792410199668
55	3.1	3.02026591629561	0.0797340837043944
56	3.2	3.02016222006068	0.17983777993932
57	3	2.72031423555264	0.279685764447359
58	3	2.76791531144701	0.232084688552988
59	3.1	2.99918763777439	0.100812362225613
60	3.4	3.32964414798358	0.0703558520164221

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 5.7 & 6.23412937986647 & -0.53412937986647 \tabularnewline
2 & 6.1 & 6.02805987012679 & 0.0719401298732074 \tabularnewline
3 & 6 & 5.65183875247269 & 0.348161247527311 \tabularnewline
4 & 5.9 & 5.57359770438425 & 0.326402295615746 \tabularnewline
5 & 5.8 & 5.53374593696061 & 0.266254063039393 \tabularnewline
6 & 5.7 & 5.74032457111872 & -0.0403245711187177 \tabularnewline
7 & 5.6 & 5.91334562281062 & -0.313345622810620 \tabularnewline
8 & 5.4 & 5.90701063460408 & -0.507010634604076 \tabularnewline
9 & 5.4 & 5.50859865011316 & -0.108598650113159 \tabularnewline
10 & 5.5 & 5.45517171043068 & 0.0448282895693196 \tabularnewline
11 & 5.6 & 5.59662351083543 & 0.00337648916456707 \tabularnewline
12 & 5.7 & 5.91144626137534 & -0.211446261375338 \tabularnewline
13 & 5.9 & 5.7720289651337 & 0.127971034866295 \tabularnewline
14 & 6.1 & 5.47495409141379 & 0.625045908586214 \tabularnewline
15 & 6 & 5.11184718809475 & 0.888152811905245 \tabularnewline
16 & 5.8 & 5.07451496600532 & 0.725485033994677 \tabularnewline
17 & 5.8 & 5.12447636125873 & 0.675523638741269 \tabularnewline
18 & 5.7 & 5.19437200022484 & 0.505627999775157 \tabularnewline
19 & 5.5 & 5.38527607146457 & 0.114723928535432 \tabularnewline
20 & 5.3 & 5.28140269535759 & 0.0185973046424141 \tabularnewline
21 & 5.2 & 4.86475779287558 & 0.335242207124417 \tabularnewline
22 & 5.2 & 4.88204471177181 & 0.317955288228188 \tabularnewline
23 & 5 & 4.97697657810488 & 0.0230234218951166 \tabularnewline
24 & 5.1 & 5.16240468399976 & -0.0624046839997579 \tabularnewline
25 & 5.1 & 5.10637858599548 & -0.00637858599548303 \tabularnewline
26 & 5.2 & 4.924367858035 & 0.275632141965004 \tabularnewline
27 & 4.9 & 4.54695453907771 & 0.353045460922294 \tabularnewline
28 & 4.8 & 4.43972667802216 & 0.360273321977843 \tabularnewline
29 & 4.5 & 4.41165441765699 & 0.088345582343014 \tabularnewline
30 & 4.5 & 4.52116298508398 & -0.0211629850839815 \tabularnewline
31 & 4.4 & 4.73168284808607 & -0.331682848086066 \tabularnewline
32 & 4.4 & 4.54861506155022 & -0.148615061550224 \tabularnewline
33 & 4.2 & 4.3035528072486 & -0.103552807248597 \tabularnewline
34 & 4.1 & 4.34473191870327 & -0.244731918703266 \tabularnewline
35 & 3.9 & 4.33963729829459 & -0.439637298294595 \tabularnewline
36 & 3.8 & 4.52737019380826 & -0.72737019380826 \tabularnewline
37 & 3.9 & 4.61301065192416 & -0.713010651924156 \tabularnewline
38 & 4.2 & 4.37816908493279 & -0.178169084932789 \tabularnewline
39 & 4.1 & 3.83053190831906 & 0.269468091680942 \tabularnewline
40 & 3.8 & 3.66889966199327 & 0.131100338006730 \tabularnewline
41 & 3.6 & 3.63259305198627 & -0.0325930519862744 \tabularnewline
42 & 3.7 & 3.8467376853926 & -0.146737685392598 \tabularnewline
43 & 3.5 & 4.15950184852205 & -0.65950184852205 \tabularnewline
44 & 3.4 & 4.12509459995034 & -0.725094599950336 \tabularnewline
45 & 3.1 & 3.81685303982531 & -0.716853039825305 \tabularnewline
46 & 3.1 & 3.70739263889298 & -0.607392638892978 \tabularnewline
47 & 3.1 & 3.65277349790732 & -0.552773497907315 \tabularnewline
48 & 3.2 & 3.93527903377888 & -0.735279033778878 \tabularnewline
49 & 3.3 & 3.91780144241456 & -0.617801442414564 \tabularnewline
50 & 3.5 & 3.58878333571689 & -0.0887833357168928 \tabularnewline
51 & 3.6 & 3.12852675267917 & 0.471473247320834 \tabularnewline
52 & 3.5 & 2.97509740950228 & 0.524902590497715 \tabularnewline
53 & 3.3 & 2.83860508677838 & 0.461394913221624 \tabularnewline
54 & 3.2 & 2.92207589800332 & 0.27792410199668 \tabularnewline
55 & 3.1 & 3.02026591629561 & 0.0797340837043944 \tabularnewline
56 & 3.2 & 3.02016222006068 & 0.17983777993932 \tabularnewline
57 & 3 & 2.72031423555264 & 0.279685764447359 \tabularnewline
58 & 3 & 2.76791531144701 & 0.232084688552988 \tabularnewline
59 & 3.1 & 2.99918763777439 & 0.100812362225613 \tabularnewline
60 & 3.4 & 3.32964414798358 & 0.0703558520164221 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57369&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]5.7[/C][C]6.23412937986647[/C][C]-0.53412937986647[/C][/ROW]
[ROW][C]2[/C][C]6.1[/C][C]6.02805987012679[/C][C]0.0719401298732074[/C][/ROW]
[ROW][C]3[/C][C]6[/C][C]5.65183875247269[/C][C]0.348161247527311[/C][/ROW]
[ROW][C]4[/C][C]5.9[/C][C]5.57359770438425[/C][C]0.326402295615746[/C][/ROW]
[ROW][C]5[/C][C]5.8[/C][C]5.53374593696061[/C][C]0.266254063039393[/C][/ROW]
[ROW][C]6[/C][C]5.7[/C][C]5.74032457111872[/C][C]-0.0403245711187177[/C][/ROW]
[ROW][C]7[/C][C]5.6[/C][C]5.91334562281062[/C][C]-0.313345622810620[/C][/ROW]
[ROW][C]8[/C][C]5.4[/C][C]5.90701063460408[/C][C]-0.507010634604076[/C][/ROW]
[ROW][C]9[/C][C]5.4[/C][C]5.50859865011316[/C][C]-0.108598650113159[/C][/ROW]
[ROW][C]10[/C][C]5.5[/C][C]5.45517171043068[/C][C]0.0448282895693196[/C][/ROW]
[ROW][C]11[/C][C]5.6[/C][C]5.59662351083543[/C][C]0.00337648916456707[/C][/ROW]
[ROW][C]12[/C][C]5.7[/C][C]5.91144626137534[/C][C]-0.211446261375338[/C][/ROW]
[ROW][C]13[/C][C]5.9[/C][C]5.7720289651337[/C][C]0.127971034866295[/C][/ROW]
[ROW][C]14[/C][C]6.1[/C][C]5.47495409141379[/C][C]0.625045908586214[/C][/ROW]
[ROW][C]15[/C][C]6[/C][C]5.11184718809475[/C][C]0.888152811905245[/C][/ROW]
[ROW][C]16[/C][C]5.8[/C][C]5.07451496600532[/C][C]0.725485033994677[/C][/ROW]
[ROW][C]17[/C][C]5.8[/C][C]5.12447636125873[/C][C]0.675523638741269[/C][/ROW]
[ROW][C]18[/C][C]5.7[/C][C]5.19437200022484[/C][C]0.505627999775157[/C][/ROW]
[ROW][C]19[/C][C]5.5[/C][C]5.38527607146457[/C][C]0.114723928535432[/C][/ROW]
[ROW][C]20[/C][C]5.3[/C][C]5.28140269535759[/C][C]0.0185973046424141[/C][/ROW]
[ROW][C]21[/C][C]5.2[/C][C]4.86475779287558[/C][C]0.335242207124417[/C][/ROW]
[ROW][C]22[/C][C]5.2[/C][C]4.88204471177181[/C][C]0.317955288228188[/C][/ROW]
[ROW][C]23[/C][C]5[/C][C]4.97697657810488[/C][C]0.0230234218951166[/C][/ROW]
[ROW][C]24[/C][C]5.1[/C][C]5.16240468399976[/C][C]-0.0624046839997579[/C][/ROW]
[ROW][C]25[/C][C]5.1[/C][C]5.10637858599548[/C][C]-0.00637858599548303[/C][/ROW]
[ROW][C]26[/C][C]5.2[/C][C]4.924367858035[/C][C]0.275632141965004[/C][/ROW]
[ROW][C]27[/C][C]4.9[/C][C]4.54695453907771[/C][C]0.353045460922294[/C][/ROW]
[ROW][C]28[/C][C]4.8[/C][C]4.43972667802216[/C][C]0.360273321977843[/C][/ROW]
[ROW][C]29[/C][C]4.5[/C][C]4.41165441765699[/C][C]0.088345582343014[/C][/ROW]
[ROW][C]30[/C][C]4.5[/C][C]4.52116298508398[/C][C]-0.0211629850839815[/C][/ROW]
[ROW][C]31[/C][C]4.4[/C][C]4.73168284808607[/C][C]-0.331682848086066[/C][/ROW]
[ROW][C]32[/C][C]4.4[/C][C]4.54861506155022[/C][C]-0.148615061550224[/C][/ROW]
[ROW][C]33[/C][C]4.2[/C][C]4.3035528072486[/C][C]-0.103552807248597[/C][/ROW]
[ROW][C]34[/C][C]4.1[/C][C]4.34473191870327[/C][C]-0.244731918703266[/C][/ROW]
[ROW][C]35[/C][C]3.9[/C][C]4.33963729829459[/C][C]-0.439637298294595[/C][/ROW]
[ROW][C]36[/C][C]3.8[/C][C]4.52737019380826[/C][C]-0.72737019380826[/C][/ROW]
[ROW][C]37[/C][C]3.9[/C][C]4.61301065192416[/C][C]-0.713010651924156[/C][/ROW]
[ROW][C]38[/C][C]4.2[/C][C]4.37816908493279[/C][C]-0.178169084932789[/C][/ROW]
[ROW][C]39[/C][C]4.1[/C][C]3.83053190831906[/C][C]0.269468091680942[/C][/ROW]
[ROW][C]40[/C][C]3.8[/C][C]3.66889966199327[/C][C]0.131100338006730[/C][/ROW]
[ROW][C]41[/C][C]3.6[/C][C]3.63259305198627[/C][C]-0.0325930519862744[/C][/ROW]
[ROW][C]42[/C][C]3.7[/C][C]3.8467376853926[/C][C]-0.146737685392598[/C][/ROW]
[ROW][C]43[/C][C]3.5[/C][C]4.15950184852205[/C][C]-0.65950184852205[/C][/ROW]
[ROW][C]44[/C][C]3.4[/C][C]4.12509459995034[/C][C]-0.725094599950336[/C][/ROW]
[ROW][C]45[/C][C]3.1[/C][C]3.81685303982531[/C][C]-0.716853039825305[/C][/ROW]
[ROW][C]46[/C][C]3.1[/C][C]3.70739263889298[/C][C]-0.607392638892978[/C][/ROW]
[ROW][C]47[/C][C]3.1[/C][C]3.65277349790732[/C][C]-0.552773497907315[/C][/ROW]
[ROW][C]48[/C][C]3.2[/C][C]3.93527903377888[/C][C]-0.735279033778878[/C][/ROW]
[ROW][C]49[/C][C]3.3[/C][C]3.91780144241456[/C][C]-0.617801442414564[/C][/ROW]
[ROW][C]50[/C][C]3.5[/C][C]3.58878333571689[/C][C]-0.0887833357168928[/C][/ROW]
[ROW][C]51[/C][C]3.6[/C][C]3.12852675267917[/C][C]0.471473247320834[/C][/ROW]
[ROW][C]52[/C][C]3.5[/C][C]2.97509740950228[/C][C]0.524902590497715[/C][/ROW]
[ROW][C]53[/C][C]3.3[/C][C]2.83860508677838[/C][C]0.461394913221624[/C][/ROW]
[ROW][C]54[/C][C]3.2[/C][C]2.92207589800332[/C][C]0.27792410199668[/C][/ROW]
[ROW][C]55[/C][C]3.1[/C][C]3.02026591629561[/C][C]0.0797340837043944[/C][/ROW]
[ROW][C]56[/C][C]3.2[/C][C]3.02016222006068[/C][C]0.17983777993932[/C][/ROW]
[ROW][C]57[/C][C]3[/C][C]2.72031423555264[/C][C]0.279685764447359[/C][/ROW]
[ROW][C]58[/C][C]3[/C][C]2.76791531144701[/C][C]0.232084688552988[/C][/ROW]
[ROW][C]59[/C][C]3.1[/C][C]2.99918763777439[/C][C]0.100812362225613[/C][/ROW]
[ROW][C]60[/C][C]3.4[/C][C]3.32964414798358[/C][C]0.0703558520164221[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57369&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57369&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	5.7	6.23412937986647	-0.53412937986647
2	6.1	6.02805987012679	0.0719401298732074
3	6	5.65183875247269	0.348161247527311
4	5.9	5.57359770438425	0.326402295615746
5	5.8	5.53374593696061	0.266254063039393
6	5.7	5.74032457111872	-0.0403245711187177
7	5.6	5.91334562281062	-0.313345622810620
8	5.4	5.90701063460408	-0.507010634604076
9	5.4	5.50859865011316	-0.108598650113159
10	5.5	5.45517171043068	0.0448282895693196
11	5.6	5.59662351083543	0.00337648916456707
12	5.7	5.91144626137534	-0.211446261375338
13	5.9	5.7720289651337	0.127971034866295
14	6.1	5.47495409141379	0.625045908586214
15	6	5.11184718809475	0.888152811905245
16	5.8	5.07451496600532	0.725485033994677
17	5.8	5.12447636125873	0.675523638741269
18	5.7	5.19437200022484	0.505627999775157
19	5.5	5.38527607146457	0.114723928535432
20	5.3	5.28140269535759	0.0185973046424141
21	5.2	4.86475779287558	0.335242207124417
22	5.2	4.88204471177181	0.317955288228188
23	5	4.97697657810488	0.0230234218951166
24	5.1	5.16240468399976	-0.0624046839997579
25	5.1	5.10637858599548	-0.00637858599548303
26	5.2	4.924367858035	0.275632141965004
27	4.9	4.54695453907771	0.353045460922294
28	4.8	4.43972667802216	0.360273321977843
29	4.5	4.41165441765699	0.088345582343014
30	4.5	4.52116298508398	-0.0211629850839815
31	4.4	4.73168284808607	-0.331682848086066
32	4.4	4.54861506155022	-0.148615061550224
33	4.2	4.3035528072486	-0.103552807248597
34	4.1	4.34473191870327	-0.244731918703266
35	3.9	4.33963729829459	-0.439637298294595
36	3.8	4.52737019380826	-0.72737019380826
37	3.9	4.61301065192416	-0.713010651924156
38	4.2	4.37816908493279	-0.178169084932789
39	4.1	3.83053190831906	0.269468091680942
40	3.8	3.66889966199327	0.131100338006730
41	3.6	3.63259305198627	-0.0325930519862744
42	3.7	3.8467376853926	-0.146737685392598
43	3.5	4.15950184852205	-0.65950184852205
44	3.4	4.12509459995034	-0.725094599950336
45	3.1	3.81685303982531	-0.716853039825305
46	3.1	3.70739263889298	-0.607392638892978
47	3.1	3.65277349790732	-0.552773497907315
48	3.2	3.93527903377888	-0.735279033778878
49	3.3	3.91780144241456	-0.617801442414564
50	3.5	3.58878333571689	-0.0887833357168928
51	3.6	3.12852675267917	0.471473247320834
52	3.5	2.97509740950228	0.524902590497715
53	3.3	2.83860508677838	0.461394913221624
54	3.2	2.92207589800332	0.27792410199668
55	3.1	3.02026591629561	0.0797340837043944
56	3.2	3.02016222006068	0.17983777993932
57	3	2.72031423555264	0.279685764447359
58	3	2.76791531144701	0.232084688552988
59	3.1	2.99918763777439	0.100812362225613
60	3.4	3.32964414798358	0.0703558520164221

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
6	0.133654654729225	0.267309309458449	0.866345345270775
7	0.0979029324828419	0.195805864965684	0.902097067517158
8	0.117675259472100	0.235350518944201	0.8823247405279
9	0.147082653542542	0.294165307085084	0.852917346457458
10	0.097912471093227	0.195824942186454	0.902087528906773
11	0.0561045026838678	0.112209005367736	0.943895497316132
12	0.030162622879887	0.060325245759774	0.969837377120113
13	0.0193878970320463	0.0387757940640925	0.980612102967954
14	0.0261584552537144	0.0523169105074287	0.973841544746286
15	0.0268962203271467	0.0537924406542933	0.973103779672853
16	0.0212995745305414	0.0425991490610827	0.978700425469459
17	0.0175592379635252	0.0351184759270504	0.982440762036475
18	0.0145143555366644	0.0290287110733288	0.985485644463336
19	0.0148091564933966	0.0296183129867932	0.985190843506603
20	0.0226163761169921	0.0452327522339841	0.977383623883008
21	0.0457191101840913	0.0914382203681827	0.954280889815909
22	0.0624831999165149	0.124966399833030	0.937516800083485
23	0.103569397511921	0.207138795023843	0.896430602488079
24	0.121614930147984	0.243229860295967	0.878385069852016
25	0.130545926812266	0.261091853624533	0.869454073187734
26	0.152427924500632	0.304855849001265	0.847572075499368
27	0.232261531137293	0.464523062274585	0.767738468862707
28	0.329100884488003	0.658201768976005	0.670899115511997
29	0.446635330251446	0.893270660502892	0.553364669748554
30	0.574327428930753	0.851345142138495	0.425672571069247
31	0.6600179970986	0.6799640058028	0.3399820029014
32	0.720244471385001	0.559511057229998	0.279755528614999
33	0.780693605916276	0.438612788167449	0.219306394083724
34	0.833383783387174	0.333232433225652	0.166616216612826
35	0.868438136568065	0.26312372686387	0.131561863431935
36	0.902818202314845	0.19436359537031	0.097181797685155
37	0.917267182206942	0.165465635586116	0.0827328177930578
38	0.947619543910288	0.104760912179423	0.0523804560897117
39	0.990342097425197	0.0193158051496058	0.0096579025748029
40	0.994954705716354	0.0100905885672920	0.00504529428364601
41	0.99488399715649	0.0102320056870192	0.0051160028435096
42	0.99714706196525	0.00570587606949953	0.00285293803474976
43	0.996654209346526	0.00669158130694823	0.00334579065347411
44	0.99500733042256	0.00998533915488111	0.00499266957744056
45	0.993820153176877	0.0123596936462463	0.00617984682312316
46	0.991195772399331	0.0176084552013381	0.00880422760066904
47	0.988802631930184	0.0223947361396326	0.0111973680698163
48	0.987571672618272	0.0248566547634566	0.0124283273817283
49	0.992389897525642	0.0152202049487156	0.0076101024743578
50	0.991995108609736	0.0160097827805277	0.00800489139026383
51	0.990281155930347	0.0194376881393068	0.0097188440696534
52	0.995791508123941	0.00841698375211731	0.00420849187605866
53	0.998832492925788	0.00233501414842308	0.00116750707421154
54	0.996691481770487	0.00661703645902615	0.00330851822951308

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
6 & 0.133654654729225 & 0.267309309458449 & 0.866345345270775 \tabularnewline
7 & 0.0979029324828419 & 0.195805864965684 & 0.902097067517158 \tabularnewline
8 & 0.117675259472100 & 0.235350518944201 & 0.8823247405279 \tabularnewline
9 & 0.147082653542542 & 0.294165307085084 & 0.852917346457458 \tabularnewline
10 & 0.097912471093227 & 0.195824942186454 & 0.902087528906773 \tabularnewline
11 & 0.0561045026838678 & 0.112209005367736 & 0.943895497316132 \tabularnewline
12 & 0.030162622879887 & 0.060325245759774 & 0.969837377120113 \tabularnewline
13 & 0.0193878970320463 & 0.0387757940640925 & 0.980612102967954 \tabularnewline
14 & 0.0261584552537144 & 0.0523169105074287 & 0.973841544746286 \tabularnewline
15 & 0.0268962203271467 & 0.0537924406542933 & 0.973103779672853 \tabularnewline
16 & 0.0212995745305414 & 0.0425991490610827 & 0.978700425469459 \tabularnewline
17 & 0.0175592379635252 & 0.0351184759270504 & 0.982440762036475 \tabularnewline
18 & 0.0145143555366644 & 0.0290287110733288 & 0.985485644463336 \tabularnewline
19 & 0.0148091564933966 & 0.0296183129867932 & 0.985190843506603 \tabularnewline
20 & 0.0226163761169921 & 0.0452327522339841 & 0.977383623883008 \tabularnewline
21 & 0.0457191101840913 & 0.0914382203681827 & 0.954280889815909 \tabularnewline
22 & 0.0624831999165149 & 0.124966399833030 & 0.937516800083485 \tabularnewline
23 & 0.103569397511921 & 0.207138795023843 & 0.896430602488079 \tabularnewline
24 & 0.121614930147984 & 0.243229860295967 & 0.878385069852016 \tabularnewline
25 & 0.130545926812266 & 0.261091853624533 & 0.869454073187734 \tabularnewline
26 & 0.152427924500632 & 0.304855849001265 & 0.847572075499368 \tabularnewline
27 & 0.232261531137293 & 0.464523062274585 & 0.767738468862707 \tabularnewline
28 & 0.329100884488003 & 0.658201768976005 & 0.670899115511997 \tabularnewline
29 & 0.446635330251446 & 0.893270660502892 & 0.553364669748554 \tabularnewline
30 & 0.574327428930753 & 0.851345142138495 & 0.425672571069247 \tabularnewline
31 & 0.6600179970986 & 0.6799640058028 & 0.3399820029014 \tabularnewline
32 & 0.720244471385001 & 0.559511057229998 & 0.279755528614999 \tabularnewline
33 & 0.780693605916276 & 0.438612788167449 & 0.219306394083724 \tabularnewline
34 & 0.833383783387174 & 0.333232433225652 & 0.166616216612826 \tabularnewline
35 & 0.868438136568065 & 0.26312372686387 & 0.131561863431935 \tabularnewline
36 & 0.902818202314845 & 0.19436359537031 & 0.097181797685155 \tabularnewline
37 & 0.917267182206942 & 0.165465635586116 & 0.0827328177930578 \tabularnewline
38 & 0.947619543910288 & 0.104760912179423 & 0.0523804560897117 \tabularnewline
39 & 0.990342097425197 & 0.0193158051496058 & 0.0096579025748029 \tabularnewline
40 & 0.994954705716354 & 0.0100905885672920 & 0.00504529428364601 \tabularnewline
41 & 0.99488399715649 & 0.0102320056870192 & 0.0051160028435096 \tabularnewline
42 & 0.99714706196525 & 0.00570587606949953 & 0.00285293803474976 \tabularnewline
43 & 0.996654209346526 & 0.00669158130694823 & 0.00334579065347411 \tabularnewline
44 & 0.99500733042256 & 0.00998533915488111 & 0.00499266957744056 \tabularnewline
45 & 0.993820153176877 & 0.0123596936462463 & 0.00617984682312316 \tabularnewline
46 & 0.991195772399331 & 0.0176084552013381 & 0.00880422760066904 \tabularnewline
47 & 0.988802631930184 & 0.0223947361396326 & 0.0111973680698163 \tabularnewline
48 & 0.987571672618272 & 0.0248566547634566 & 0.0124283273817283 \tabularnewline
49 & 0.992389897525642 & 0.0152202049487156 & 0.0076101024743578 \tabularnewline
50 & 0.991995108609736 & 0.0160097827805277 & 0.00800489139026383 \tabularnewline
51 & 0.990281155930347 & 0.0194376881393068 & 0.0097188440696534 \tabularnewline
52 & 0.995791508123941 & 0.00841698375211731 & 0.00420849187605866 \tabularnewline
53 & 0.998832492925788 & 0.00233501414842308 & 0.00116750707421154 \tabularnewline
54 & 0.996691481770487 & 0.00661703645902615 & 0.00330851822951308 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57369&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]6[/C][C]0.133654654729225[/C][C]0.267309309458449[/C][C]0.866345345270775[/C][/ROW]
[ROW][C]7[/C][C]0.0979029324828419[/C][C]0.195805864965684[/C][C]0.902097067517158[/C][/ROW]
[ROW][C]8[/C][C]0.117675259472100[/C][C]0.235350518944201[/C][C]0.8823247405279[/C][/ROW]
[ROW][C]9[/C][C]0.147082653542542[/C][C]0.294165307085084[/C][C]0.852917346457458[/C][/ROW]
[ROW][C]10[/C][C]0.097912471093227[/C][C]0.195824942186454[/C][C]0.902087528906773[/C][/ROW]
[ROW][C]11[/C][C]0.0561045026838678[/C][C]0.112209005367736[/C][C]0.943895497316132[/C][/ROW]
[ROW][C]12[/C][C]0.030162622879887[/C][C]0.060325245759774[/C][C]0.969837377120113[/C][/ROW]
[ROW][C]13[/C][C]0.0193878970320463[/C][C]0.0387757940640925[/C][C]0.980612102967954[/C][/ROW]
[ROW][C]14[/C][C]0.0261584552537144[/C][C]0.0523169105074287[/C][C]0.973841544746286[/C][/ROW]
[ROW][C]15[/C][C]0.0268962203271467[/C][C]0.0537924406542933[/C][C]0.973103779672853[/C][/ROW]
[ROW][C]16[/C][C]0.0212995745305414[/C][C]0.0425991490610827[/C][C]0.978700425469459[/C][/ROW]
[ROW][C]17[/C][C]0.0175592379635252[/C][C]0.0351184759270504[/C][C]0.982440762036475[/C][/ROW]
[ROW][C]18[/C][C]0.0145143555366644[/C][C]0.0290287110733288[/C][C]0.985485644463336[/C][/ROW]
[ROW][C]19[/C][C]0.0148091564933966[/C][C]0.0296183129867932[/C][C]0.985190843506603[/C][/ROW]
[ROW][C]20[/C][C]0.0226163761169921[/C][C]0.0452327522339841[/C][C]0.977383623883008[/C][/ROW]
[ROW][C]21[/C][C]0.0457191101840913[/C][C]0.0914382203681827[/C][C]0.954280889815909[/C][/ROW]
[ROW][C]22[/C][C]0.0624831999165149[/C][C]0.124966399833030[/C][C]0.937516800083485[/C][/ROW]
[ROW][C]23[/C][C]0.103569397511921[/C][C]0.207138795023843[/C][C]0.896430602488079[/C][/ROW]
[ROW][C]24[/C][C]0.121614930147984[/C][C]0.243229860295967[/C][C]0.878385069852016[/C][/ROW]
[ROW][C]25[/C][C]0.130545926812266[/C][C]0.261091853624533[/C][C]0.869454073187734[/C][/ROW]
[ROW][C]26[/C][C]0.152427924500632[/C][C]0.304855849001265[/C][C]0.847572075499368[/C][/ROW]
[ROW][C]27[/C][C]0.232261531137293[/C][C]0.464523062274585[/C][C]0.767738468862707[/C][/ROW]
[ROW][C]28[/C][C]0.329100884488003[/C][C]0.658201768976005[/C][C]0.670899115511997[/C][/ROW]
[ROW][C]29[/C][C]0.446635330251446[/C][C]0.893270660502892[/C][C]0.553364669748554[/C][/ROW]
[ROW][C]30[/C][C]0.574327428930753[/C][C]0.851345142138495[/C][C]0.425672571069247[/C][/ROW]
[ROW][C]31[/C][C]0.6600179970986[/C][C]0.6799640058028[/C][C]0.3399820029014[/C][/ROW]
[ROW][C]32[/C][C]0.720244471385001[/C][C]0.559511057229998[/C][C]0.279755528614999[/C][/ROW]
[ROW][C]33[/C][C]0.780693605916276[/C][C]0.438612788167449[/C][C]0.219306394083724[/C][/ROW]
[ROW][C]34[/C][C]0.833383783387174[/C][C]0.333232433225652[/C][C]0.166616216612826[/C][/ROW]
[ROW][C]35[/C][C]0.868438136568065[/C][C]0.26312372686387[/C][C]0.131561863431935[/C][/ROW]
[ROW][C]36[/C][C]0.902818202314845[/C][C]0.19436359537031[/C][C]0.097181797685155[/C][/ROW]
[ROW][C]37[/C][C]0.917267182206942[/C][C]0.165465635586116[/C][C]0.0827328177930578[/C][/ROW]
[ROW][C]38[/C][C]0.947619543910288[/C][C]0.104760912179423[/C][C]0.0523804560897117[/C][/ROW]
[ROW][C]39[/C][C]0.990342097425197[/C][C]0.0193158051496058[/C][C]0.0096579025748029[/C][/ROW]
[ROW][C]40[/C][C]0.994954705716354[/C][C]0.0100905885672920[/C][C]0.00504529428364601[/C][/ROW]
[ROW][C]41[/C][C]0.99488399715649[/C][C]0.0102320056870192[/C][C]0.0051160028435096[/C][/ROW]
[ROW][C]42[/C][C]0.99714706196525[/C][C]0.00570587606949953[/C][C]0.00285293803474976[/C][/ROW]
[ROW][C]43[/C][C]0.996654209346526[/C][C]0.00669158130694823[/C][C]0.00334579065347411[/C][/ROW]
[ROW][C]44[/C][C]0.99500733042256[/C][C]0.00998533915488111[/C][C]0.00499266957744056[/C][/ROW]
[ROW][C]45[/C][C]0.993820153176877[/C][C]0.0123596936462463[/C][C]0.00617984682312316[/C][/ROW]
[ROW][C]46[/C][C]0.991195772399331[/C][C]0.0176084552013381[/C][C]0.00880422760066904[/C][/ROW]
[ROW][C]47[/C][C]0.988802631930184[/C][C]0.0223947361396326[/C][C]0.0111973680698163[/C][/ROW]
[ROW][C]48[/C][C]0.987571672618272[/C][C]0.0248566547634566[/C][C]0.0124283273817283[/C][/ROW]
[ROW][C]49[/C][C]0.992389897525642[/C][C]0.0152202049487156[/C][C]0.0076101024743578[/C][/ROW]
[ROW][C]50[/C][C]0.991995108609736[/C][C]0.0160097827805277[/C][C]0.00800489139026383[/C][/ROW]
[ROW][C]51[/C][C]0.990281155930347[/C][C]0.0194376881393068[/C][C]0.0097188440696534[/C][/ROW]
[ROW][C]52[/C][C]0.995791508123941[/C][C]0.00841698375211731[/C][C]0.00420849187605866[/C][/ROW]
[ROW][C]53[/C][C]0.998832492925788[/C][C]0.00233501414842308[/C][C]0.00116750707421154[/C][/ROW]
[ROW][C]54[/C][C]0.996691481770487[/C][C]0.00661703645902615[/C][C]0.00330851822951308[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57369&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57369&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
6	0.133654654729225	0.267309309458449	0.866345345270775
7	0.0979029324828419	0.195805864965684	0.902097067517158
8	0.117675259472100	0.235350518944201	0.8823247405279
9	0.147082653542542	0.294165307085084	0.852917346457458
10	0.097912471093227	0.195824942186454	0.902087528906773
11	0.0561045026838678	0.112209005367736	0.943895497316132
12	0.030162622879887	0.060325245759774	0.969837377120113
13	0.0193878970320463	0.0387757940640925	0.980612102967954
14	0.0261584552537144	0.0523169105074287	0.973841544746286
15	0.0268962203271467	0.0537924406542933	0.973103779672853
16	0.0212995745305414	0.0425991490610827	0.978700425469459
17	0.0175592379635252	0.0351184759270504	0.982440762036475
18	0.0145143555366644	0.0290287110733288	0.985485644463336
19	0.0148091564933966	0.0296183129867932	0.985190843506603
20	0.0226163761169921	0.0452327522339841	0.977383623883008
21	0.0457191101840913	0.0914382203681827	0.954280889815909
22	0.0624831999165149	0.124966399833030	0.937516800083485
23	0.103569397511921	0.207138795023843	0.896430602488079
24	0.121614930147984	0.243229860295967	0.878385069852016
25	0.130545926812266	0.261091853624533	0.869454073187734
26	0.152427924500632	0.304855849001265	0.847572075499368
27	0.232261531137293	0.464523062274585	0.767738468862707
28	0.329100884488003	0.658201768976005	0.670899115511997
29	0.446635330251446	0.893270660502892	0.553364669748554
30	0.574327428930753	0.851345142138495	0.425672571069247
31	0.6600179970986	0.6799640058028	0.3399820029014
32	0.720244471385001	0.559511057229998	0.279755528614999
33	0.780693605916276	0.438612788167449	0.219306394083724
34	0.833383783387174	0.333232433225652	0.166616216612826
35	0.868438136568065	0.26312372686387	0.131561863431935
36	0.902818202314845	0.19436359537031	0.097181797685155
37	0.917267182206942	0.165465635586116	0.0827328177930578
38	0.947619543910288	0.104760912179423	0.0523804560897117
39	0.990342097425197	0.0193158051496058	0.0096579025748029
40	0.994954705716354	0.0100905885672920	0.00504529428364601
41	0.99488399715649	0.0102320056870192	0.0051160028435096
42	0.99714706196525	0.00570587606949953	0.00285293803474976
43	0.996654209346526	0.00669158130694823	0.00334579065347411
44	0.99500733042256	0.00998533915488111	0.00499266957744056
45	0.993820153176877	0.0123596936462463	0.00617984682312316
46	0.991195772399331	0.0176084552013381	0.00880422760066904
47	0.988802631930184	0.0223947361396326	0.0111973680698163
48	0.987571672618272	0.0248566547634566	0.0124283273817283
49	0.992389897525642	0.0152202049487156	0.0076101024743578
50	0.991995108609736	0.0160097827805277	0.00800489139026383
51	0.990281155930347	0.0194376881393068	0.0097188440696534
52	0.995791508123941	0.00841698375211731	0.00420849187605866
53	0.998832492925788	0.00233501414842308	0.00116750707421154
54	0.996691481770487	0.00661703645902615	0.00330851822951308

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	6	0.122448979591837	NOK
5% type I error level	22	0.448979591836735	NOK
10% type I error level	26	0.530612244897959	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 & 6 & 0.122448979591837 & NOK \tabularnewline
5% type I error level & 22 & 0.448979591836735 & NOK \tabularnewline
10% type I error level & 26 & 0.530612244897959 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57369&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]6[/C][C]0.122448979591837[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]22[/C][C]0.448979591836735[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]26[/C][C]0.530612244897959[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57369&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57369&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	6	0.122448979591837	NOK
5% type I error level	22	0.448979591836735	NOK
10% type I error level	26	0.530612244897959	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 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

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

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

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

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code