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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Thu, 19 Nov 2009 13:27:27 -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/19/t1258662525neq9mxojibrlmou.htm/, Retrieved Fri, 26 Apr 2024 08:11:05 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=57937, Retrieved Fri, 26 Apr 2024 08:11:05 +0000

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Ws 7.2 Dummy's

Estimated Impact

153

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 14:03:14] [b98453cac15ba1066b407e146608df68]
-    D      [Multiple Regression] [Ws 7.2 Dummy's] [2009-11-19 20:27:27] [88e98f4c87ea17c4967db8279bda8533] [Current]
-    D        [Multiple Regression] [Ws 7 link 2 verbe...] [2009-11-22 21:56:19] [616e2df490b611f6cb7080068870ecbd] 

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Dataseries X:

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Histogram

Boxplots

1.4	8.2
1.2	8.0
1.0	7.5
1.7	6.8
2.4	6.5
2.0	6.6
2.1	7.6
2.0	8.0
1.8	8.1
2.7	7.7
2.3	7.5
1.9	7.6
2.0	7.8
2.3	7.8
2.8	7.8
2.4	7.5
2.3	7.5
2.7	7.1
2.7	7.5
2.9	7.5
3.0	7.6
2.2	7.7
2.3	7.7
2.8	7.9
2.8	8.1
2.8	8.2
2.2	8.2
2.6	8.2
2.8	7.9
2.5	7.3
2.4	6.9
2.3	6.6
1.9	6.7
1.7	6.9
2.0	7.0
2.1	7.1
1.7	7.2
1.8	7.1
1.8	6.9
1.8	7.0
1.3	6.8
1.3	6.4
1.3	6.7
1.2	6.6
1.4	6.4
2.2	6.3
2.9	6.2
3.1	6.5
3.5	6.8
3.6	6.8
4.4	6.4
4.1	6.1
5.1	5.8
5.8	6.1
5.9	7.2
5.4	7.3
5.5	6.9
4.8	6.1
3.2	5.8
2.7	6.2
2.1	7.1
1.9	7.7
0.6	7.9
0.7	7.7

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	3 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 & 3 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57937&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]3 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=57937&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57937&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	3 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 6.55556335362125 -0.571609540172981X[t] + 0.000561849015211116M1[t] + 0.0553358183600769M2[t] -0.163738945999203M3[t] -0.213781172039566M4[t] + 0.168542473572323M5[t] + 0.134220565537727M6[t] + 0.428593144820758M7[t] + 0.320025335624217M8[t] + 0.245728763213839M9[t] + 0.131406855179243M10[t] -0.105754098838056M11[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  6.55556335362125 -0.571609540172981X[t] +  0.000561849015211116M1[t] +  0.0553358183600769M2[t] -0.163738945999203M3[t] -0.213781172039566M4[t] +  0.168542473572323M5[t] +  0.134220565537727M6[t] +  0.428593144820758M7[t] +  0.320025335624217M8[t] +  0.245728763213839M9[t] +  0.131406855179243M10[t] -0.105754098838056M11[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57937&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  6.55556335362125 -0.571609540172981X[t] +  0.000561849015211116M1[t] +  0.0553358183600769M2[t] -0.163738945999203M3[t] -0.213781172039566M4[t] +  0.168542473572323M5[t] +  0.134220565537727M6[t] +  0.428593144820758M7[t] +  0.320025335624217M8[t] +  0.245728763213839M9[t] +  0.131406855179243M10[t] -0.105754098838056M11[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57937&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57937&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
Y[t] = + 6.55556335362125 -0.571609540172981X[t] + 0.000561849015211116M1[t] + 0.0553358183600769M2[t] -0.163738945999203M3[t] -0.213781172039566M4[t] + 0.168542473572323M5[t] + 0.134220565537727M6[t] + 0.428593144820758M7[t] + 0.320025335624217M8[t] + 0.245728763213839M9[t] + 0.131406855179243M10[t] -0.105754098838056M11[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)	6.55556335362125	1.88745	3.4732	0.001057	0.000529
X	-0.571609540172981	0.255851	-2.2341	0.029885	0.014942
M1	0.000561849015211116	0.751089	7e-04	0.999406	0.499703
M2	0.0553358183600769	0.754027	0.0734	0.941785	0.470893
M3	-0.163738945999203	0.747947	-0.2189	0.827588	0.413794
M4	-0.213781172039566	0.742345	-0.288	0.774528	0.387264
M5	0.168542473572323	0.775304	0.2174	0.828773	0.414386
M6	0.134220565537727	0.779682	0.1721	0.864003	0.432001
M7	0.428593144820758	0.774831	0.5531	0.582582	0.291291
M8	0.320025335624217	0.775051	0.4129	0.681404	0.340702
M9	0.245728763213839	0.774493	0.3173	0.752329	0.376164
M10	0.131406855179243	0.774831	0.1696	0.866	0.433
M11	-0.105754098838056	0.776266	-0.1362	0.892172	0.446086

\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) & 6.55556335362125 & 1.88745 & 3.4732 & 0.001057 & 0.000529 \tabularnewline
X & -0.571609540172981 & 0.255851 & -2.2341 & 0.029885 & 0.014942 \tabularnewline
M1 & 0.000561849015211116 & 0.751089 & 7e-04 & 0.999406 & 0.499703 \tabularnewline
M2 & 0.0553358183600769 & 0.754027 & 0.0734 & 0.941785 & 0.470893 \tabularnewline
M3 & -0.163738945999203 & 0.747947 & -0.2189 & 0.827588 & 0.413794 \tabularnewline
M4 & -0.213781172039566 & 0.742345 & -0.288 & 0.774528 & 0.387264 \tabularnewline
M5 & 0.168542473572323 & 0.775304 & 0.2174 & 0.828773 & 0.414386 \tabularnewline
M6 & 0.134220565537727 & 0.779682 & 0.1721 & 0.864003 & 0.432001 \tabularnewline
M7 & 0.428593144820758 & 0.774831 & 0.5531 & 0.582582 & 0.291291 \tabularnewline
M8 & 0.320025335624217 & 0.775051 & 0.4129 & 0.681404 & 0.340702 \tabularnewline
M9 & 0.245728763213839 & 0.774493 & 0.3173 & 0.752329 & 0.376164 \tabularnewline
M10 & 0.131406855179243 & 0.774831 & 0.1696 & 0.866 & 0.433 \tabularnewline
M11 & -0.105754098838056 & 0.776266 & -0.1362 & 0.892172 & 0.446086 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57937&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]6.55556335362125[/C][C]1.88745[/C][C]3.4732[/C][C]0.001057[/C][C]0.000529[/C][/ROW]
[ROW][C]X[/C][C]-0.571609540172981[/C][C]0.255851[/C][C]-2.2341[/C][C]0.029885[/C][C]0.014942[/C][/ROW]
[ROW][C]M1[/C][C]0.000561849015211116[/C][C]0.751089[/C][C]7e-04[/C][C]0.999406[/C][C]0.499703[/C][/ROW]
[ROW][C]M2[/C][C]0.0553358183600769[/C][C]0.754027[/C][C]0.0734[/C][C]0.941785[/C][C]0.470893[/C][/ROW]
[ROW][C]M3[/C][C]-0.163738945999203[/C][C]0.747947[/C][C]-0.2189[/C][C]0.827588[/C][C]0.413794[/C][/ROW]
[ROW][C]M4[/C][C]-0.213781172039566[/C][C]0.742345[/C][C]-0.288[/C][C]0.774528[/C][C]0.387264[/C][/ROW]
[ROW][C]M5[/C][C]0.168542473572323[/C][C]0.775304[/C][C]0.2174[/C][C]0.828773[/C][C]0.414386[/C][/ROW]
[ROW][C]M6[/C][C]0.134220565537727[/C][C]0.779682[/C][C]0.1721[/C][C]0.864003[/C][C]0.432001[/C][/ROW]
[ROW][C]M7[/C][C]0.428593144820758[/C][C]0.774831[/C][C]0.5531[/C][C]0.582582[/C][C]0.291291[/C][/ROW]
[ROW][C]M8[/C][C]0.320025335624217[/C][C]0.775051[/C][C]0.4129[/C][C]0.681404[/C][C]0.340702[/C][/ROW]
[ROW][C]M9[/C][C]0.245728763213839[/C][C]0.774493[/C][C]0.3173[/C][C]0.752329[/C][C]0.376164[/C][/ROW]
[ROW][C]M10[/C][C]0.131406855179243[/C][C]0.774831[/C][C]0.1696[/C][C]0.866[/C][C]0.433[/C][/ROW]
[ROW][C]M11[/C][C]-0.105754098838056[/C][C]0.776266[/C][C]-0.1362[/C][C]0.892172[/C][C]0.446086[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57937&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57937&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)	6.55556335362125	1.88745	3.4732	0.001057	0.000529
X	-0.571609540172981	0.255851	-2.2341	0.029885	0.014942
M1	0.000561849015211116	0.751089	7e-04	0.999406	0.499703
M2	0.0553358183600769	0.754027	0.0734	0.941785	0.470893
M3	-0.163738945999203	0.747947	-0.2189	0.827588	0.413794
M4	-0.213781172039566	0.742345	-0.288	0.774528	0.387264
M5	0.168542473572323	0.775304	0.2174	0.828773	0.414386
M6	0.134220565537727	0.779682	0.1721	0.864003	0.432001
M7	0.428593144820758	0.774831	0.5531	0.582582	0.291291
M8	0.320025335624217	0.775051	0.4129	0.681404	0.340702
M9	0.245728763213839	0.774493	0.3173	0.752329	0.376164
M10	0.131406855179243	0.774831	0.1696	0.866	0.433
M11	-0.105754098838056	0.776266	-0.1362	0.892172	0.446086

Multiple Linear Regression - Regression Statistics
Multiple R	0.368317136159586
R-squared	0.135657512788799
Adjusted R-squared	-0.0677171900844245
F-TEST (value)	0.667032383439366
F-TEST (DF numerator)	12
F-TEST (DF denominator)	51
p-value	0.774100232451549
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.22415323350237
Sum Squared Residuals	76.4261080938098

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.368317136159586 \tabularnewline
R-squared & 0.135657512788799 \tabularnewline
Adjusted R-squared & -0.0677171900844245 \tabularnewline
F-TEST (value) & 0.667032383439366 \tabularnewline
F-TEST (DF numerator) & 12 \tabularnewline
F-TEST (DF denominator) & 51 \tabularnewline
p-value & 0.774100232451549 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.22415323350237 \tabularnewline
Sum Squared Residuals & 76.4261080938098 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57937&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.368317136159586[/C][/ROW]
[ROW][C]R-squared[/C][C]0.135657512788799[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]-0.0677171900844245[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]0.667032383439366[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]12[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]51[/C][/ROW]
[ROW][C]p-value[/C][C]0.774100232451549[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.22415323350237[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]76.4261080938098[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57937&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57937&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	0.368317136159586
R-squared	0.135657512788799
Adjusted R-squared	-0.0677171900844245
F-TEST (value)	0.667032383439366
F-TEST (DF numerator)	12
F-TEST (DF denominator)	51
p-value	0.774100232451549
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.22415323350237
Sum Squared Residuals	76.4261080938098

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	1.4	1.86892697321801	-0.468926973218012
2	1.2	2.03802285059747	-0.838022850597475
3	1	2.10475285632468	-1.10475285632468
4	1.7	2.45483730840541	-0.75483730840541
5	2.4	3.00864381606919	-0.608643816069193
6	2	2.9171609540173	-0.917160954017298
7	2.1	2.63992399312735	-0.539923993127348
8	2	2.30271236786161	-0.302712367861615
9	1.8	2.17125484143394	-0.371254841433939
10	2.7	2.28557674946853	0.414423250531467
11	2.3	2.16273770348583	0.137262296514168
12	1.9	2.21133084830659	-0.31133084830659
13	2	2.09757078928721	-0.097570789287205
14	2.3	2.15234475863207	0.147655241367929
15	2.8	1.93326999427279	0.86673000572721
16	2.4	2.05471063028432	0.345289369715678
17	2.3	2.43703427589621	-0.137034275896211
18	2.7	2.63135618393081	0.0686438160691928
19	2.7	2.69708494714465	0.00291505285535434
20	2.9	2.58851713794811	0.311482862051894
21	3	2.45705961152043	0.542940388479571
22	2.2	2.28557674946853	-0.085576749468534
23	2.3	2.04841579545124	0.251584204548764
24	2.8	2.03984798625470	0.760152013745305
25	2.8	1.92608792723531	0.873912072764689
26	2.8	1.92370094256288	0.876299057437121
27	2.2	1.70462617820360	0.495373821796402
28	2.6	1.65458395216324	0.945416047836765
29	2.8	2.20839045982702	0.591609540172982
30	2.5	2.51703427589621	-0.017034275896211
31	2.4	3.04005067124843	-0.640050671248435
32	2.3	3.10296572410379	-0.80296572410379
33	1.9	2.97150819767611	-1.07150819767611
34	1.7	2.74286438160692	-1.04286438160692
35	2	2.44854247357232	-0.448542473572323
36	2.1	2.49713561839308	-0.397135618393081
37	1.7	2.44053651339099	-0.740536513390994
38	1.8	2.55247143675316	-0.752471436753157
39	1.8	2.44771858042847	-0.647718580428473
40	1.8	2.34051540037081	-0.540515400370812
41	1.3	2.83716095401730	-1.53716095401730
42	1.3	3.03148286205189	-1.73148286205189
43	1.3	3.15437257928303	-1.85437257928303
44	1.2	3.10296572410379	-1.90296572410379
45	1.4	3.14299105972801	-1.74299105972801
46	2.2	3.08583010571071	-0.885830105710708
47	2.9	2.90583010571071	-0.0058301057107079
48	3.1	2.84010134249687	0.259898657503131
49	3.5	2.66918032946019	0.830819670539814
50	3.6	2.72395429880505	0.876045701194949
51	4.4	2.73352335051496	1.66647664948504
52	4.1	2.85496398652650	1.24503601347350
53	5.1	3.40877049419028	1.69122950580972
54	5.8	3.20296572410379	2.59703427589621
55	5.9	2.86856780919654	3.03143219080346
56	5.4	2.70283904598270	2.69716095401730
57	5.5	2.85718628964151	2.64281371035849
58	4.8	3.20015201374531	1.59984798625469
59	3.2	3.1344739217799	0.0655260782200998
60	2.7	3.01158420454876	-0.311584204548763
61	2.1	2.49769746740829	-0.397697467408292
62	1.9	2.20950571264937	-0.309505712649368
63	0.6	1.87610904025549	-1.27610904025549
64	0.7	1.94038872224973	-1.24038872224973

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 1.4 & 1.86892697321801 & -0.468926973218012 \tabularnewline
2 & 1.2 & 2.03802285059747 & -0.838022850597475 \tabularnewline
3 & 1 & 2.10475285632468 & -1.10475285632468 \tabularnewline
4 & 1.7 & 2.45483730840541 & -0.75483730840541 \tabularnewline
5 & 2.4 & 3.00864381606919 & -0.608643816069193 \tabularnewline
6 & 2 & 2.9171609540173 & -0.917160954017298 \tabularnewline
7 & 2.1 & 2.63992399312735 & -0.539923993127348 \tabularnewline
8 & 2 & 2.30271236786161 & -0.302712367861615 \tabularnewline
9 & 1.8 & 2.17125484143394 & -0.371254841433939 \tabularnewline
10 & 2.7 & 2.28557674946853 & 0.414423250531467 \tabularnewline
11 & 2.3 & 2.16273770348583 & 0.137262296514168 \tabularnewline
12 & 1.9 & 2.21133084830659 & -0.31133084830659 \tabularnewline
13 & 2 & 2.09757078928721 & -0.097570789287205 \tabularnewline
14 & 2.3 & 2.15234475863207 & 0.147655241367929 \tabularnewline
15 & 2.8 & 1.93326999427279 & 0.86673000572721 \tabularnewline
16 & 2.4 & 2.05471063028432 & 0.345289369715678 \tabularnewline
17 & 2.3 & 2.43703427589621 & -0.137034275896211 \tabularnewline
18 & 2.7 & 2.63135618393081 & 0.0686438160691928 \tabularnewline
19 & 2.7 & 2.69708494714465 & 0.00291505285535434 \tabularnewline
20 & 2.9 & 2.58851713794811 & 0.311482862051894 \tabularnewline
21 & 3 & 2.45705961152043 & 0.542940388479571 \tabularnewline
22 & 2.2 & 2.28557674946853 & -0.085576749468534 \tabularnewline
23 & 2.3 & 2.04841579545124 & 0.251584204548764 \tabularnewline
24 & 2.8 & 2.03984798625470 & 0.760152013745305 \tabularnewline
25 & 2.8 & 1.92608792723531 & 0.873912072764689 \tabularnewline
26 & 2.8 & 1.92370094256288 & 0.876299057437121 \tabularnewline
27 & 2.2 & 1.70462617820360 & 0.495373821796402 \tabularnewline
28 & 2.6 & 1.65458395216324 & 0.945416047836765 \tabularnewline
29 & 2.8 & 2.20839045982702 & 0.591609540172982 \tabularnewline
30 & 2.5 & 2.51703427589621 & -0.017034275896211 \tabularnewline
31 & 2.4 & 3.04005067124843 & -0.640050671248435 \tabularnewline
32 & 2.3 & 3.10296572410379 & -0.80296572410379 \tabularnewline
33 & 1.9 & 2.97150819767611 & -1.07150819767611 \tabularnewline
34 & 1.7 & 2.74286438160692 & -1.04286438160692 \tabularnewline
35 & 2 & 2.44854247357232 & -0.448542473572323 \tabularnewline
36 & 2.1 & 2.49713561839308 & -0.397135618393081 \tabularnewline
37 & 1.7 & 2.44053651339099 & -0.740536513390994 \tabularnewline
38 & 1.8 & 2.55247143675316 & -0.752471436753157 \tabularnewline
39 & 1.8 & 2.44771858042847 & -0.647718580428473 \tabularnewline
40 & 1.8 & 2.34051540037081 & -0.540515400370812 \tabularnewline
41 & 1.3 & 2.83716095401730 & -1.53716095401730 \tabularnewline
42 & 1.3 & 3.03148286205189 & -1.73148286205189 \tabularnewline
43 & 1.3 & 3.15437257928303 & -1.85437257928303 \tabularnewline
44 & 1.2 & 3.10296572410379 & -1.90296572410379 \tabularnewline
45 & 1.4 & 3.14299105972801 & -1.74299105972801 \tabularnewline
46 & 2.2 & 3.08583010571071 & -0.885830105710708 \tabularnewline
47 & 2.9 & 2.90583010571071 & -0.0058301057107079 \tabularnewline
48 & 3.1 & 2.84010134249687 & 0.259898657503131 \tabularnewline
49 & 3.5 & 2.66918032946019 & 0.830819670539814 \tabularnewline
50 & 3.6 & 2.72395429880505 & 0.876045701194949 \tabularnewline
51 & 4.4 & 2.73352335051496 & 1.66647664948504 \tabularnewline
52 & 4.1 & 2.85496398652650 & 1.24503601347350 \tabularnewline
53 & 5.1 & 3.40877049419028 & 1.69122950580972 \tabularnewline
54 & 5.8 & 3.20296572410379 & 2.59703427589621 \tabularnewline
55 & 5.9 & 2.86856780919654 & 3.03143219080346 \tabularnewline
56 & 5.4 & 2.70283904598270 & 2.69716095401730 \tabularnewline
57 & 5.5 & 2.85718628964151 & 2.64281371035849 \tabularnewline
58 & 4.8 & 3.20015201374531 & 1.59984798625469 \tabularnewline
59 & 3.2 & 3.1344739217799 & 0.0655260782200998 \tabularnewline
60 & 2.7 & 3.01158420454876 & -0.311584204548763 \tabularnewline
61 & 2.1 & 2.49769746740829 & -0.397697467408292 \tabularnewline
62 & 1.9 & 2.20950571264937 & -0.309505712649368 \tabularnewline
63 & 0.6 & 1.87610904025549 & -1.27610904025549 \tabularnewline
64 & 0.7 & 1.94038872224973 & -1.24038872224973 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57937&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]1.4[/C][C]1.86892697321801[/C][C]-0.468926973218012[/C][/ROW]
[ROW][C]2[/C][C]1.2[/C][C]2.03802285059747[/C][C]-0.838022850597475[/C][/ROW]
[ROW][C]3[/C][C]1[/C][C]2.10475285632468[/C][C]-1.10475285632468[/C][/ROW]
[ROW][C]4[/C][C]1.7[/C][C]2.45483730840541[/C][C]-0.75483730840541[/C][/ROW]
[ROW][C]5[/C][C]2.4[/C][C]3.00864381606919[/C][C]-0.608643816069193[/C][/ROW]
[ROW][C]6[/C][C]2[/C][C]2.9171609540173[/C][C]-0.917160954017298[/C][/ROW]
[ROW][C]7[/C][C]2.1[/C][C]2.63992399312735[/C][C]-0.539923993127348[/C][/ROW]
[ROW][C]8[/C][C]2[/C][C]2.30271236786161[/C][C]-0.302712367861615[/C][/ROW]
[ROW][C]9[/C][C]1.8[/C][C]2.17125484143394[/C][C]-0.371254841433939[/C][/ROW]
[ROW][C]10[/C][C]2.7[/C][C]2.28557674946853[/C][C]0.414423250531467[/C][/ROW]
[ROW][C]11[/C][C]2.3[/C][C]2.16273770348583[/C][C]0.137262296514168[/C][/ROW]
[ROW][C]12[/C][C]1.9[/C][C]2.21133084830659[/C][C]-0.31133084830659[/C][/ROW]
[ROW][C]13[/C][C]2[/C][C]2.09757078928721[/C][C]-0.097570789287205[/C][/ROW]
[ROW][C]14[/C][C]2.3[/C][C]2.15234475863207[/C][C]0.147655241367929[/C][/ROW]
[ROW][C]15[/C][C]2.8[/C][C]1.93326999427279[/C][C]0.86673000572721[/C][/ROW]
[ROW][C]16[/C][C]2.4[/C][C]2.05471063028432[/C][C]0.345289369715678[/C][/ROW]
[ROW][C]17[/C][C]2.3[/C][C]2.43703427589621[/C][C]-0.137034275896211[/C][/ROW]
[ROW][C]18[/C][C]2.7[/C][C]2.63135618393081[/C][C]0.0686438160691928[/C][/ROW]
[ROW][C]19[/C][C]2.7[/C][C]2.69708494714465[/C][C]0.00291505285535434[/C][/ROW]
[ROW][C]20[/C][C]2.9[/C][C]2.58851713794811[/C][C]0.311482862051894[/C][/ROW]
[ROW][C]21[/C][C]3[/C][C]2.45705961152043[/C][C]0.542940388479571[/C][/ROW]
[ROW][C]22[/C][C]2.2[/C][C]2.28557674946853[/C][C]-0.085576749468534[/C][/ROW]
[ROW][C]23[/C][C]2.3[/C][C]2.04841579545124[/C][C]0.251584204548764[/C][/ROW]
[ROW][C]24[/C][C]2.8[/C][C]2.03984798625470[/C][C]0.760152013745305[/C][/ROW]
[ROW][C]25[/C][C]2.8[/C][C]1.92608792723531[/C][C]0.873912072764689[/C][/ROW]
[ROW][C]26[/C][C]2.8[/C][C]1.92370094256288[/C][C]0.876299057437121[/C][/ROW]
[ROW][C]27[/C][C]2.2[/C][C]1.70462617820360[/C][C]0.495373821796402[/C][/ROW]
[ROW][C]28[/C][C]2.6[/C][C]1.65458395216324[/C][C]0.945416047836765[/C][/ROW]
[ROW][C]29[/C][C]2.8[/C][C]2.20839045982702[/C][C]0.591609540172982[/C][/ROW]
[ROW][C]30[/C][C]2.5[/C][C]2.51703427589621[/C][C]-0.017034275896211[/C][/ROW]
[ROW][C]31[/C][C]2.4[/C][C]3.04005067124843[/C][C]-0.640050671248435[/C][/ROW]
[ROW][C]32[/C][C]2.3[/C][C]3.10296572410379[/C][C]-0.80296572410379[/C][/ROW]
[ROW][C]33[/C][C]1.9[/C][C]2.97150819767611[/C][C]-1.07150819767611[/C][/ROW]
[ROW][C]34[/C][C]1.7[/C][C]2.74286438160692[/C][C]-1.04286438160692[/C][/ROW]
[ROW][C]35[/C][C]2[/C][C]2.44854247357232[/C][C]-0.448542473572323[/C][/ROW]
[ROW][C]36[/C][C]2.1[/C][C]2.49713561839308[/C][C]-0.397135618393081[/C][/ROW]
[ROW][C]37[/C][C]1.7[/C][C]2.44053651339099[/C][C]-0.740536513390994[/C][/ROW]
[ROW][C]38[/C][C]1.8[/C][C]2.55247143675316[/C][C]-0.752471436753157[/C][/ROW]
[ROW][C]39[/C][C]1.8[/C][C]2.44771858042847[/C][C]-0.647718580428473[/C][/ROW]
[ROW][C]40[/C][C]1.8[/C][C]2.34051540037081[/C][C]-0.540515400370812[/C][/ROW]
[ROW][C]41[/C][C]1.3[/C][C]2.83716095401730[/C][C]-1.53716095401730[/C][/ROW]
[ROW][C]42[/C][C]1.3[/C][C]3.03148286205189[/C][C]-1.73148286205189[/C][/ROW]
[ROW][C]43[/C][C]1.3[/C][C]3.15437257928303[/C][C]-1.85437257928303[/C][/ROW]
[ROW][C]44[/C][C]1.2[/C][C]3.10296572410379[/C][C]-1.90296572410379[/C][/ROW]
[ROW][C]45[/C][C]1.4[/C][C]3.14299105972801[/C][C]-1.74299105972801[/C][/ROW]
[ROW][C]46[/C][C]2.2[/C][C]3.08583010571071[/C][C]-0.885830105710708[/C][/ROW]
[ROW][C]47[/C][C]2.9[/C][C]2.90583010571071[/C][C]-0.0058301057107079[/C][/ROW]
[ROW][C]48[/C][C]3.1[/C][C]2.84010134249687[/C][C]0.259898657503131[/C][/ROW]
[ROW][C]49[/C][C]3.5[/C][C]2.66918032946019[/C][C]0.830819670539814[/C][/ROW]
[ROW][C]50[/C][C]3.6[/C][C]2.72395429880505[/C][C]0.876045701194949[/C][/ROW]
[ROW][C]51[/C][C]4.4[/C][C]2.73352335051496[/C][C]1.66647664948504[/C][/ROW]
[ROW][C]52[/C][C]4.1[/C][C]2.85496398652650[/C][C]1.24503601347350[/C][/ROW]
[ROW][C]53[/C][C]5.1[/C][C]3.40877049419028[/C][C]1.69122950580972[/C][/ROW]
[ROW][C]54[/C][C]5.8[/C][C]3.20296572410379[/C][C]2.59703427589621[/C][/ROW]
[ROW][C]55[/C][C]5.9[/C][C]2.86856780919654[/C][C]3.03143219080346[/C][/ROW]
[ROW][C]56[/C][C]5.4[/C][C]2.70283904598270[/C][C]2.69716095401730[/C][/ROW]
[ROW][C]57[/C][C]5.5[/C][C]2.85718628964151[/C][C]2.64281371035849[/C][/ROW]
[ROW][C]58[/C][C]4.8[/C][C]3.20015201374531[/C][C]1.59984798625469[/C][/ROW]
[ROW][C]59[/C][C]3.2[/C][C]3.1344739217799[/C][C]0.0655260782200998[/C][/ROW]
[ROW][C]60[/C][C]2.7[/C][C]3.01158420454876[/C][C]-0.311584204548763[/C][/ROW]
[ROW][C]61[/C][C]2.1[/C][C]2.49769746740829[/C][C]-0.397697467408292[/C][/ROW]
[ROW][C]62[/C][C]1.9[/C][C]2.20950571264937[/C][C]-0.309505712649368[/C][/ROW]
[ROW][C]63[/C][C]0.6[/C][C]1.87610904025549[/C][C]-1.27610904025549[/C][/ROW]
[ROW][C]64[/C][C]0.7[/C][C]1.94038872224973[/C][C]-1.24038872224973[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57937&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57937&T=4

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	1.4	1.86892697321801	-0.468926973218012
2	1.2	2.03802285059747	-0.838022850597475
3	1	2.10475285632468	-1.10475285632468
4	1.7	2.45483730840541	-0.75483730840541
5	2.4	3.00864381606919	-0.608643816069193
6	2	2.9171609540173	-0.917160954017298
7	2.1	2.63992399312735	-0.539923993127348
8	2	2.30271236786161	-0.302712367861615
9	1.8	2.17125484143394	-0.371254841433939
10	2.7	2.28557674946853	0.414423250531467
11	2.3	2.16273770348583	0.137262296514168
12	1.9	2.21133084830659	-0.31133084830659
13	2	2.09757078928721	-0.097570789287205
14	2.3	2.15234475863207	0.147655241367929
15	2.8	1.93326999427279	0.86673000572721
16	2.4	2.05471063028432	0.345289369715678
17	2.3	2.43703427589621	-0.137034275896211
18	2.7	2.63135618393081	0.0686438160691928
19	2.7	2.69708494714465	0.00291505285535434
20	2.9	2.58851713794811	0.311482862051894
21	3	2.45705961152043	0.542940388479571
22	2.2	2.28557674946853	-0.085576749468534
23	2.3	2.04841579545124	0.251584204548764
24	2.8	2.03984798625470	0.760152013745305
25	2.8	1.92608792723531	0.873912072764689
26	2.8	1.92370094256288	0.876299057437121
27	2.2	1.70462617820360	0.495373821796402
28	2.6	1.65458395216324	0.945416047836765
29	2.8	2.20839045982702	0.591609540172982
30	2.5	2.51703427589621	-0.017034275896211
31	2.4	3.04005067124843	-0.640050671248435
32	2.3	3.10296572410379	-0.80296572410379
33	1.9	2.97150819767611	-1.07150819767611
34	1.7	2.74286438160692	-1.04286438160692
35	2	2.44854247357232	-0.448542473572323
36	2.1	2.49713561839308	-0.397135618393081
37	1.7	2.44053651339099	-0.740536513390994
38	1.8	2.55247143675316	-0.752471436753157
39	1.8	2.44771858042847	-0.647718580428473
40	1.8	2.34051540037081	-0.540515400370812
41	1.3	2.83716095401730	-1.53716095401730
42	1.3	3.03148286205189	-1.73148286205189
43	1.3	3.15437257928303	-1.85437257928303
44	1.2	3.10296572410379	-1.90296572410379
45	1.4	3.14299105972801	-1.74299105972801
46	2.2	3.08583010571071	-0.885830105710708
47	2.9	2.90583010571071	-0.0058301057107079
48	3.1	2.84010134249687	0.259898657503131
49	3.5	2.66918032946019	0.830819670539814
50	3.6	2.72395429880505	0.876045701194949
51	4.4	2.73352335051496	1.66647664948504
52	4.1	2.85496398652650	1.24503601347350
53	5.1	3.40877049419028	1.69122950580972
54	5.8	3.20296572410379	2.59703427589621
55	5.9	2.86856780919654	3.03143219080346
56	5.4	2.70283904598270	2.69716095401730
57	5.5	2.85718628964151	2.64281371035849
58	4.8	3.20015201374531	1.59984798625469
59	3.2	3.1344739217799	0.0655260782200998
60	2.7	3.01158420454876	-0.311584204548763
61	2.1	2.49769746740829	-0.397697467408292
62	1.9	2.20950571264937	-0.309505712649368
63	0.6	1.87610904025549	-1.27610904025549
64	0.7	1.94038872224973	-1.24038872224973

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
16	0.250999624610369	0.501999249220739	0.749000375389631
17	0.134298690750899	0.268597381501798	0.865701309249101
18	0.0675270237970275	0.135054047594055	0.932472976202973
19	0.0341317582116171	0.0682635164232342	0.965868241788383
20	0.0221540947594259	0.0443081895188518	0.977845905240574
21	0.0167895289030647	0.0335790578061293	0.983210471096935
22	0.00776063731442184	0.0155212746288437	0.992239362685578
23	0.00308720280404967	0.00617440560809934	0.99691279719595
24	0.00183501473543106	0.00367002947086212	0.998164985264569
25	0.00155482626682898	0.00310965253365795	0.998445173733171
26	0.00116432807710713	0.00232865615421426	0.998835671922893
27	0.00047760688548082	0.00095521377096164	0.99952239311452
28	0.000218556234601086	0.000437112469202172	0.999781443765399
29	9.07356060410878e-05	0.000181471212082176	0.999909264393959
30	3.10013810131361e-05	6.20027620262722e-05	0.999968998618987
31	1.18027548394712e-05	2.36055096789423e-05	0.99998819724516
32	4.56410593138198e-06	9.12821186276396e-06	0.999995435894069
33	1.70353454775037e-06	3.40706909550074e-06	0.999998296465452
34	6.46461819126007e-07	1.29292363825201e-06	0.99999935353818
35	1.81045520984456e-07	3.62091041968912e-07	0.999999818954479
36	4.76224967405622e-08	9.52449934811245e-08	0.999999952377503
37	1.26500865572980e-08	2.53001731145959e-08	0.999999987349913
38	3.50441040158472e-09	7.00882080316945e-09	0.99999999649559
39	9.23672890299638e-10	1.84734578059928e-09	0.999999999076327
40	2.17353995256554e-10	4.34707990513107e-10	0.999999999782646
41	3.21153447368962e-10	6.42306894737924e-10	0.999999999678847
42	1.34698530406356e-09	2.69397060812711e-09	0.999999998653015
43	3.81965692725504e-08	7.63931385451007e-08	0.99999996180343
44	1.01779936657762e-05	2.03559873315525e-05	0.999989822006334
45	0.080459088583182	0.160918177166364	0.919540911416818
46	0.526362502411322	0.947274995177356	0.473637497588678
47	0.519724390500221	0.960551218999559	0.480275609499779
48	0.735016324271627	0.529967351456746	0.264983675728373

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
16 & 0.250999624610369 & 0.501999249220739 & 0.749000375389631 \tabularnewline
17 & 0.134298690750899 & 0.268597381501798 & 0.865701309249101 \tabularnewline
18 & 0.0675270237970275 & 0.135054047594055 & 0.932472976202973 \tabularnewline
19 & 0.0341317582116171 & 0.0682635164232342 & 0.965868241788383 \tabularnewline
20 & 0.0221540947594259 & 0.0443081895188518 & 0.977845905240574 \tabularnewline
21 & 0.0167895289030647 & 0.0335790578061293 & 0.983210471096935 \tabularnewline
22 & 0.00776063731442184 & 0.0155212746288437 & 0.992239362685578 \tabularnewline
23 & 0.00308720280404967 & 0.00617440560809934 & 0.99691279719595 \tabularnewline
24 & 0.00183501473543106 & 0.00367002947086212 & 0.998164985264569 \tabularnewline
25 & 0.00155482626682898 & 0.00310965253365795 & 0.998445173733171 \tabularnewline
26 & 0.00116432807710713 & 0.00232865615421426 & 0.998835671922893 \tabularnewline
27 & 0.00047760688548082 & 0.00095521377096164 & 0.99952239311452 \tabularnewline
28 & 0.000218556234601086 & 0.000437112469202172 & 0.999781443765399 \tabularnewline
29 & 9.07356060410878e-05 & 0.000181471212082176 & 0.999909264393959 \tabularnewline
30 & 3.10013810131361e-05 & 6.20027620262722e-05 & 0.999968998618987 \tabularnewline
31 & 1.18027548394712e-05 & 2.36055096789423e-05 & 0.99998819724516 \tabularnewline
32 & 4.56410593138198e-06 & 9.12821186276396e-06 & 0.999995435894069 \tabularnewline
33 & 1.70353454775037e-06 & 3.40706909550074e-06 & 0.999998296465452 \tabularnewline
34 & 6.46461819126007e-07 & 1.29292363825201e-06 & 0.99999935353818 \tabularnewline
35 & 1.81045520984456e-07 & 3.62091041968912e-07 & 0.999999818954479 \tabularnewline
36 & 4.76224967405622e-08 & 9.52449934811245e-08 & 0.999999952377503 \tabularnewline
37 & 1.26500865572980e-08 & 2.53001731145959e-08 & 0.999999987349913 \tabularnewline
38 & 3.50441040158472e-09 & 7.00882080316945e-09 & 0.99999999649559 \tabularnewline
39 & 9.23672890299638e-10 & 1.84734578059928e-09 & 0.999999999076327 \tabularnewline
40 & 2.17353995256554e-10 & 4.34707990513107e-10 & 0.999999999782646 \tabularnewline
41 & 3.21153447368962e-10 & 6.42306894737924e-10 & 0.999999999678847 \tabularnewline
42 & 1.34698530406356e-09 & 2.69397060812711e-09 & 0.999999998653015 \tabularnewline
43 & 3.81965692725504e-08 & 7.63931385451007e-08 & 0.99999996180343 \tabularnewline
44 & 1.01779936657762e-05 & 2.03559873315525e-05 & 0.999989822006334 \tabularnewline
45 & 0.080459088583182 & 0.160918177166364 & 0.919540911416818 \tabularnewline
46 & 0.526362502411322 & 0.947274995177356 & 0.473637497588678 \tabularnewline
47 & 0.519724390500221 & 0.960551218999559 & 0.480275609499779 \tabularnewline
48 & 0.735016324271627 & 0.529967351456746 & 0.264983675728373 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57937&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]16[/C][C]0.250999624610369[/C][C]0.501999249220739[/C][C]0.749000375389631[/C][/ROW]
[ROW][C]17[/C][C]0.134298690750899[/C][C]0.268597381501798[/C][C]0.865701309249101[/C][/ROW]
[ROW][C]18[/C][C]0.0675270237970275[/C][C]0.135054047594055[/C][C]0.932472976202973[/C][/ROW]
[ROW][C]19[/C][C]0.0341317582116171[/C][C]0.0682635164232342[/C][C]0.965868241788383[/C][/ROW]
[ROW][C]20[/C][C]0.0221540947594259[/C][C]0.0443081895188518[/C][C]0.977845905240574[/C][/ROW]
[ROW][C]21[/C][C]0.0167895289030647[/C][C]0.0335790578061293[/C][C]0.983210471096935[/C][/ROW]
[ROW][C]22[/C][C]0.00776063731442184[/C][C]0.0155212746288437[/C][C]0.992239362685578[/C][/ROW]
[ROW][C]23[/C][C]0.00308720280404967[/C][C]0.00617440560809934[/C][C]0.99691279719595[/C][/ROW]
[ROW][C]24[/C][C]0.00183501473543106[/C][C]0.00367002947086212[/C][C]0.998164985264569[/C][/ROW]
[ROW][C]25[/C][C]0.00155482626682898[/C][C]0.00310965253365795[/C][C]0.998445173733171[/C][/ROW]
[ROW][C]26[/C][C]0.00116432807710713[/C][C]0.00232865615421426[/C][C]0.998835671922893[/C][/ROW]
[ROW][C]27[/C][C]0.00047760688548082[/C][C]0.00095521377096164[/C][C]0.99952239311452[/C][/ROW]
[ROW][C]28[/C][C]0.000218556234601086[/C][C]0.000437112469202172[/C][C]0.999781443765399[/C][/ROW]
[ROW][C]29[/C][C]9.07356060410878e-05[/C][C]0.000181471212082176[/C][C]0.999909264393959[/C][/ROW]
[ROW][C]30[/C][C]3.10013810131361e-05[/C][C]6.20027620262722e-05[/C][C]0.999968998618987[/C][/ROW]
[ROW][C]31[/C][C]1.18027548394712e-05[/C][C]2.36055096789423e-05[/C][C]0.99998819724516[/C][/ROW]
[ROW][C]32[/C][C]4.56410593138198e-06[/C][C]9.12821186276396e-06[/C][C]0.999995435894069[/C][/ROW]
[ROW][C]33[/C][C]1.70353454775037e-06[/C][C]3.40706909550074e-06[/C][C]0.999998296465452[/C][/ROW]
[ROW][C]34[/C][C]6.46461819126007e-07[/C][C]1.29292363825201e-06[/C][C]0.99999935353818[/C][/ROW]
[ROW][C]35[/C][C]1.81045520984456e-07[/C][C]3.62091041968912e-07[/C][C]0.999999818954479[/C][/ROW]
[ROW][C]36[/C][C]4.76224967405622e-08[/C][C]9.52449934811245e-08[/C][C]0.999999952377503[/C][/ROW]
[ROW][C]37[/C][C]1.26500865572980e-08[/C][C]2.53001731145959e-08[/C][C]0.999999987349913[/C][/ROW]
[ROW][C]38[/C][C]3.50441040158472e-09[/C][C]7.00882080316945e-09[/C][C]0.99999999649559[/C][/ROW]
[ROW][C]39[/C][C]9.23672890299638e-10[/C][C]1.84734578059928e-09[/C][C]0.999999999076327[/C][/ROW]
[ROW][C]40[/C][C]2.17353995256554e-10[/C][C]4.34707990513107e-10[/C][C]0.999999999782646[/C][/ROW]
[ROW][C]41[/C][C]3.21153447368962e-10[/C][C]6.42306894737924e-10[/C][C]0.999999999678847[/C][/ROW]
[ROW][C]42[/C][C]1.34698530406356e-09[/C][C]2.69397060812711e-09[/C][C]0.999999998653015[/C][/ROW]
[ROW][C]43[/C][C]3.81965692725504e-08[/C][C]7.63931385451007e-08[/C][C]0.99999996180343[/C][/ROW]
[ROW][C]44[/C][C]1.01779936657762e-05[/C][C]2.03559873315525e-05[/C][C]0.999989822006334[/C][/ROW]
[ROW][C]45[/C][C]0.080459088583182[/C][C]0.160918177166364[/C][C]0.919540911416818[/C][/ROW]
[ROW][C]46[/C][C]0.526362502411322[/C][C]0.947274995177356[/C][C]0.473637497588678[/C][/ROW]
[ROW][C]47[/C][C]0.519724390500221[/C][C]0.960551218999559[/C][C]0.480275609499779[/C][/ROW]
[ROW][C]48[/C][C]0.735016324271627[/C][C]0.529967351456746[/C][C]0.264983675728373[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57937&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57937&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
16	0.250999624610369	0.501999249220739	0.749000375389631
17	0.134298690750899	0.268597381501798	0.865701309249101
18	0.0675270237970275	0.135054047594055	0.932472976202973
19	0.0341317582116171	0.0682635164232342	0.965868241788383
20	0.0221540947594259	0.0443081895188518	0.977845905240574
21	0.0167895289030647	0.0335790578061293	0.983210471096935
22	0.00776063731442184	0.0155212746288437	0.992239362685578
23	0.00308720280404967	0.00617440560809934	0.99691279719595
24	0.00183501473543106	0.00367002947086212	0.998164985264569
25	0.00155482626682898	0.00310965253365795	0.998445173733171
26	0.00116432807710713	0.00232865615421426	0.998835671922893
27	0.00047760688548082	0.00095521377096164	0.99952239311452
28	0.000218556234601086	0.000437112469202172	0.999781443765399
29	9.07356060410878e-05	0.000181471212082176	0.999909264393959
30	3.10013810131361e-05	6.20027620262722e-05	0.999968998618987
31	1.18027548394712e-05	2.36055096789423e-05	0.99998819724516
32	4.56410593138198e-06	9.12821186276396e-06	0.999995435894069
33	1.70353454775037e-06	3.40706909550074e-06	0.999998296465452
34	6.46461819126007e-07	1.29292363825201e-06	0.99999935353818
35	1.81045520984456e-07	3.62091041968912e-07	0.999999818954479
36	4.76224967405622e-08	9.52449934811245e-08	0.999999952377503
37	1.26500865572980e-08	2.53001731145959e-08	0.999999987349913
38	3.50441040158472e-09	7.00882080316945e-09	0.99999999649559
39	9.23672890299638e-10	1.84734578059928e-09	0.999999999076327
40	2.17353995256554e-10	4.34707990513107e-10	0.999999999782646
41	3.21153447368962e-10	6.42306894737924e-10	0.999999999678847
42	1.34698530406356e-09	2.69397060812711e-09	0.999999998653015
43	3.81965692725504e-08	7.63931385451007e-08	0.99999996180343
44	1.01779936657762e-05	2.03559873315525e-05	0.999989822006334
45	0.080459088583182	0.160918177166364	0.919540911416818
46	0.526362502411322	0.947274995177356	0.473637497588678
47	0.519724390500221	0.960551218999559	0.480275609499779
48	0.735016324271627	0.529967351456746	0.264983675728373

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	22	0.666666666666667	NOK
5% type I error level	25	0.757575757575758	NOK
10% type I error level	26	0.787878787878788	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 & 22 & 0.666666666666667 & NOK \tabularnewline
5% type I error level & 25 & 0.757575757575758 & NOK \tabularnewline
10% type I error level & 26 & 0.787878787878788 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57937&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]22[/C][C]0.666666666666667[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]25[/C][C]0.757575757575758[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]26[/C][C]0.787878787878788[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57937&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57937&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	22	0.666666666666667	NOK
5% type I error level	25	0.757575757575758	NOK
10% type I error level	26	0.787878787878788	NOK

Figure 1

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Figure 3

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Figure 4

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Figure 5

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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Figure 10

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Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = 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