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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Sat, 18 Dec 2010 19:32:07 +0000

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/2010/Dec/18/t1292700637rbzljlk8jxl1cxx.htm/, Retrieved Tue, 30 Apr 2024 05:44:17 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=112170, Retrieved Tue, 30 Apr 2024 05:44:17 +0000

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IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

113

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

F     [Univariate Data Series] [HPC Retail Sales] [2008-03-02 15:42:48] [74be16979710d4c4e7c6647856088456]
-  MPD  [Univariate Data Series] [WS8 1] [2010-11-30 15:47:30] [07a238a5afc23eb944f8545182f29d5a]
- RMP     [Classical Decomposition] [WS8 2] [2010-11-30 15:54:02] [07a238a5afc23eb944f8545182f29d5a]
- RMPD      [Univariate Data Series] [Statistiek: Werkl...] [2010-12-12 15:20:09] [07a238a5afc23eb944f8545182f29d5a]
-    D        [Univariate Data Series] [Statistiek: Werkl...] [2010-12-14 09:08:05] [07a238a5afc23eb944f8545182f29d5a]
-               [Univariate Data Series] [Statistiek: Werkl...] [2010-12-14 09:12:36] [07a238a5afc23eb944f8545182f29d5a]
- RMPD            [Univariate Explorative Data Analysis] [Statistiek: U EDA...] [2010-12-17 19:07:44] [07a238a5afc23eb944f8545182f29d5a]
- RMP               [Central Tendency] [Statistiek: centr...] [2010-12-18 09:18:46] [07a238a5afc23eb944f8545182f29d5a]
- RMP                 [Harrell-Davis Quantiles] [Statistiek: betro...] [2010-12-18 10:06:35] [07a238a5afc23eb944f8545182f29d5a]
- RMPD                  [Bivariate Explorative Data Analysis] [statistiek: Bivar...] [2010-12-18 13:22:48] [07a238a5afc23eb944f8545182f29d5a]
- RMPD                    [Pearson Correlation] [statistiek: pears...] [2010-12-18 14:12:28] [07a238a5afc23eb944f8545182f29d5a]
-                           [Pearson Correlation] [statistiek: pears...] [2010-12-18 14:15:15] [07a238a5afc23eb944f8545182f29d5a]
- RMPD                        [Trivariate Scatterplots] [Statistiek trivar...] [2010-12-18 16:57:07] [07a238a5afc23eb944f8545182f29d5a]
- RMPD                          [Multiple Regression] [statistiek Multip...] [2010-12-18 19:03:25] [07a238a5afc23eb944f8545182f29d5a]
-    D                            [Multiple Regression] [statistiek Multip...] [2010-12-18 19:19:51] [07a238a5afc23eb944f8545182f29d5a]
-    D                                [Multiple Regression] [statistiek Multip...] [2010-12-18 19:32:07] [67e3c2d70de1dbb070b545ca6c893d5e] [Current]

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Dataseries X:

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Histogram

Boxplots

6.5	8.9	-0.6	9
6.3	8.4	1.1	11
5.9	8.1	1.4	13
5.5	8.3	1.4	12
5.2	8.1	1.3	13
4.9	8	1.4	15
5.4	8.7	-0.1	13
5.8	9.2	1.8	16
5.7	9	1.5	10
5.6	8.9	1.5	14
5.5	8.5	1.4	14
5.4	8.1	1.6	15
5.4	7.5	1.6	13
5.4	7.1	1.6	8
5.5	6.9	1.4	7
5.8	7.1	1.7	3
5.7	7	1.8	3
5.4	6.7	1.9	4
5.6	7	2.2	4
5.8	7.3	2.1	0
6.2	7.7	2.4	-4
6.8	8.4	2.6	-14
6.7	8.4	2.8	-18
6.7	8.8	2.7	-8
6.4	9.1	2.6	-1
6.3	9	2.9	1
6.3	8.6	2.8	2
6.4	7.9	2.2	0
6.3	7.7	2.2	1
6	7.8	2.2	0
6.3	9.2	2	-1
6.3	9.4	2	-3
6.6	9.2	1.7	-3
7.5	8.7	1.4	-3
7.8	8.4	1.3	-4
7.9	8.6	1.4	-8
7.8	9	1.3	-9
7.6	9.1	1.3	-13
7.5	8.7	1.4	-18
7.6	8.2	2	-11
7.5	7.9	1.7	-9
7.3	7.9	1.8	-10
7.6	9.1	1.7	-13
7.5	9.4	1.6	-11
7.6	9.4	1.7	-5
7.9	9.1	1.9	-15
7.9	9	1.8	-6
8.1	9.3	1.7	-6
8.2	9.9	1.6	-3
8	9.8	1.8	-1
7.5	9.3	1.6	-3
6.8	8.3	1.5	-4
6.5	8	1.5	-6
6.6	8.5	1.3	0
7.6	10.4	1.4	-4
8	11.1	1.4	-2
8.1	10.9	1.3	-2
7.7	10	1.3	-6
7.5	9.2	1.2	-7
7.6	9.2	1.1	-6
7.8	9.5	1.4	-6
7.8	9.6	1.2	-3
7.8	9.5	1.5	-2
7.5	9.1	1.1	-5
7.5	8.9	1.3	-11
7.1	9	1.5	-11
7.5	10.1	1.1	-11
7.5	10.3	1.4	-10
7.6	10.2	1.3	-14
7.7	9.6	1.5	-8
7.7	9.2	1.6	-9
7.9	9.3	1.7	-5
8.1	9.4	1.1	-1
8.2	9.4	1.6	-2
8.2	9.2	1.3	-5
8.2	9	1.7	-4
7.9	9	1.6	-6
7.3	9	1.7	-2
6.9	9.8	1.9	-2
6.6	10	1.8	-2
6.7	9.8	1.9	-2
6.9	9.3	1.6	2
7	9	1.5	1
7.1	9	1.6	-8
7.2	9.1	1.6	-1
7.1	9.1	1.7	1
6.9	9.1	2	-1
7	9.2	2	2
6.8	8.8	1.9	2
6.4	8.3	1.7	1
6.7	8.4	1.8	-1
6.6	8.1	1.9	-2
6.4	7.7	1.7	-2
6.3	7.9	2	-1
6.2	7.9	2.1	-8
6.5	8	2.4	-4
6.8	7.9	2.5	-6
6.8	7.6	2.5	-3
6.4	7.1	2.6	-3
6.1	6.8	2.2	-7
5.8	6.5	2.5	-9
6.1	6.9	2.8	-11
7.2	8.2	2.8	-13
7.3	8.7	2.9	-11
6.9	8.3	3	-9
6.1	7.9	3.1	-17
5.8	7.5	2.9	-22
6.2	7.8	2.7	-25
7.1	8.3	2.2	-20
7.7	8.4	2.5	-24
8	8.2	2.3	-24
7.8	7.6	2.6	-22
7.4	7.2	2.3	-19
7.4	7.5	2.2	-18
7.7	8.7	1.8	-17
7.8	9	1.8	-11
7.8	8.6	2	-11
8	7.9	1.6	-12
8.1	7.8	1.5	-10
8.4	8.2	1.4	-15

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	9 seconds
R Server	'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 & 9 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112170&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]9 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112170&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112170&T=0

As an alternative you can also use a QR Code:

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

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	9 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation
Mannen[t] = + 3.51184532722517 + 0.424759546585032Vrouwen[t] -0.431370175281263Inflatie[t] -0.0540453662063982Consumvertr[t] + 0.00511209506842643t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Mannen[t] =  +  3.51184532722517 +  0.424759546585032Vrouwen[t] -0.431370175281263Inflatie[t] -0.0540453662063982Consumvertr[t] +  0.00511209506842643t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112170&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Mannen[t] =  +  3.51184532722517 +  0.424759546585032Vrouwen[t] -0.431370175281263Inflatie[t] -0.0540453662063982Consumvertr[t] +  0.00511209506842643t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112170&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112170&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
Mannen[t] = + 3.51184532722517 + 0.424759546585032Vrouwen[t] -0.431370175281263Inflatie[t] -0.0540453662063982Consumvertr[t] + 0.00511209506842643t + 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)	3.51184532722517	0.546731	6.4234	0	0
Vrouwen	0.424759546585032	0.052987	8.0163	0	0
Inflatie	-0.431370175281263	0.093452	-4.6159	1e-05	5e-06
Consumvertr	-0.0540453662063982	0.006619	-8.1651	0	0
t	0.00511209506842643	0.001651	3.0959	0.002465	0.001232

\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) & 3.51184532722517 & 0.546731 & 6.4234 & 0 & 0 \tabularnewline
Vrouwen & 0.424759546585032 & 0.052987 & 8.0163 & 0 & 0 \tabularnewline
Inflatie & -0.431370175281263 & 0.093452 & -4.6159 & 1e-05 & 5e-06 \tabularnewline
Consumvertr & -0.0540453662063982 & 0.006619 & -8.1651 & 0 & 0 \tabularnewline
t & 0.00511209506842643 & 0.001651 & 3.0959 & 0.002465 & 0.001232 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112170&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]3.51184532722517[/C][C]0.546731[/C][C]6.4234[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Vrouwen[/C][C]0.424759546585032[/C][C]0.052987[/C][C]8.0163[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Inflatie[/C][C]-0.431370175281263[/C][C]0.093452[/C][C]-4.6159[/C][C]1e-05[/C][C]5e-06[/C][/ROW]
[ROW][C]Consumvertr[/C][C]-0.0540453662063982[/C][C]0.006619[/C][C]-8.1651[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]t[/C][C]0.00511209506842643[/C][C]0.001651[/C][C]3.0959[/C][C]0.002465[/C][C]0.001232[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112170&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112170&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)	3.51184532722517	0.546731	6.4234	0	0
Vrouwen	0.424759546585032	0.052987	8.0163	0	0
Inflatie	-0.431370175281263	0.093452	-4.6159	1e-05	5e-06
Consumvertr	-0.0540453662063982	0.006619	-8.1651	0	0
t	0.00511209506842643	0.001651	3.0959	0.002465	0.001232

Multiple Linear Regression - Regression Statistics
Multiple R	0.842249326263617
R-squared	0.709383927591517
Adjusted R-squared	0.699275542464266
F-TEST (value)	70.1777701048472
F-TEST (DF numerator)	4
F-TEST (DF denominator)	115
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.473901715520587
Sum Squared Residuals	25.8270261369358

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.842249326263617 \tabularnewline
R-squared & 0.709383927591517 \tabularnewline
Adjusted R-squared & 0.699275542464266 \tabularnewline
F-TEST (value) & 70.1777701048472 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 115 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.473901715520587 \tabularnewline
Sum Squared Residuals & 25.8270261369358 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112170&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.842249326263617[/C][/ROW]
[ROW][C]R-squared[/C][C]0.709383927591517[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.699275542464266[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]70.1777701048472[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]115[/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]0.473901715520587[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]25.8270261369358[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112170&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112170&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	0.842249326263617
R-squared	0.709383927591517
Adjusted R-squared	0.699275542464266
F-TEST (value)	70.1777701048472
F-TEST (DF numerator)	4
F-TEST (DF denominator)	115
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.473901715520587
Sum Squared Residuals	25.8270261369358

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	6.5	7.06973119621155	-0.569731196211554
2	6.3	6.02104348759652	0.278956512403478
3	5.9	5.66122593369226	0.238774066307739
4	5.5	5.8053353042841	-0.305335304284093
5	5.2	5.71458714135724	-0.514587141357239
6	4.9	5.52599553182624	-0.62599553182624
7	5.4	6.58358530483888	-1.18358530483888
8	5.8	5.81933774154623	-0.0193377415462284
9	5.7	6.19318117712042	-0.493181177120417
10	5.6	5.93963585270475	-0.339635852704748
11	5.5	5.81798114666729	-0.317981146667287
12	5.4	5.51287002183905	-0.112870021839050
13	5.4	5.37121712136925	0.0287828786307469
14	5.4	5.47665222883566	-0.0766522288356578
15	5.5	5.53713181584973	-0.0371318158497295
16	5.8	5.71396623247638	0.0860337675236239
17	5.7	5.63346535535817	0.0665346446418273
18	5.4	5.41396720271657	-0.0139672027165651
19	5.6	5.41709610917612	0.182903890823877
20	5.8	5.80895455057378	-0.00895455057377787
21	6.2	6.07074087651743	0.129259123482569
22	6.8	6.82736428120311	-0.0273642812031101
23	6.7	6.96238380604088	-0.262383806040876
24	6.7	6.64008307520746	0.0599169247925401
25	6.4	6.43744248833473	-0.037442488334734
26	6.3	6.16257684374748	0.137423156252517
27	6.3	5.98687677150362	0.313123228496376
28	6.4	6.06157002154408	0.338429978455918
29	6.3	5.9276848410891	0.372315158910896
30	6	6.02931825702243	-0.0293182570224317
31	6.3	6.76941311857255	-0.469413118572554
32	6.3	6.96756785537078	-0.667567855370784
33	6.6	7.01713909370658	-0.417139093706582
34	7.5	6.93928246806687	0.560717531933129
35	7.8	6.91414908289431	0.885850917105687
36	7.9	7.17725753457721	0.722742465422788
37	7.8	7.44945583201418	0.350544167985824
38	7.6	7.7132253465667	-0.113225346566699
39	7.5	7.77552343650498	-0.275523436504977
40	7.6	6.93111608966734	0.668883910332658
41	7.5	6.83012064093184	0.669879359068159
42	7.3	6.84614108467854	0.45385891532146
43	7.6	7.56623775179633	0.0337622482036743
44	7.5	7.63382399595559	-0.133823995955591
45	7.6	7.2715268762575	0.328473123742497
46	7.9	7.60339073435815	0.296609265641852
47	7.9	7.12275559643861	0.777244403561386
48	8.1	7.29843257301068	0.801567426989323
49	8.2	7.43940131493905	0.760598685060946
50	8	7.20767268787993	0.792327312120072
51	7.5	7.19476977712489	0.305230222875112
52	6.8	6.8723047093428	-0.0723047093428067
53	6.5	6.85807967284852	-0.358079672848520
54	6.6	6.83757337902732	-0.237573379027325
55	7.6	7.82277305990478	-0.222773059904780
56	8	8.01712610516993	-0.0171261051699311
57	8.1	7.98042330844948	0.119576691550522
58	7.7	7.81943327641697	-0.119433276416968
59	7.5	7.5819201179519	-0.0819201179518932
60	7.6	7.57612386434205	0.0238761356579520
61	7.8	7.5792527708016	0.220747229198395
62	7.8	7.55097875696559	0.249021243034407
63	7.8	7.33015847858474	0.469841521415261
64	7.5	7.50005092375085	-5.09237508521318e-05
65	7.5	7.65820927168441	-0.158209271684409
66	7.1	7.61952328635509	-0.519523286355086
67	7.5	8.26441895277955	-0.764418952779553
68	7.5	8.1710265383742	-0.671026538374209
69	7.6	8.39298116113785	-0.792981161137851
70	7.7	7.73269129596062	-0.0326912959606161
71	7.7	7.5788079210733	0.121192078926699
72	7.9	7.36707748844651	0.532922511553487
73	8.1	7.4573061785166	0.642693821483393
74	8.2	7.3007785521508	0.899221447849199
75	8.2	7.51248588910579	0.687514110894206
76	8.2	7.20605263853831	0.993947361461689
77	7.9	7.36239248354766	0.537607516452341
78	7.3	7.10818609626237	0.191813903737633
79	6.9	7.36683179354257	-0.466831793542566
80	6.6	7.50003281545613	-0.900032815456126
81	6.7	7.37705598367942	-0.67705598367942
82	6.9	7.08301789321412	-0.183017893214115
83	7	7.05788450804156	-0.0578845080415568
84	7.1	7.50626788143944	-0.406267881439441
85	7.2	7.17553836772158	0.0244616322784173
86	7.1	7.02942271284909	0.070577287150913
87	6.9	7.01321448774593	-0.113214487745930
88	7	6.89866643885367	0.101333561146335
89	6.8	6.77701173281621	0.0229882671837939
90	6.4	6.71006345585477	-0.310063455854767
91	6.7	6.82260522046637	-0.122605220466367
92	6.6	6.71119780023756	-0.111197800237556
93	6.4	6.63268011172822	-0.232680111728221
94	6.3	6.53928769732288	-0.239287697322878
95	6.2	6.87958033830797	-0.679580338307965
96	6.5	6.58157587062492	-0.0815758706249231
97	6.8	6.60916572591952	0.190834274080483
98	6.8	6.32471385839324	0.475286141606762
99	6.4	6.07430916264102	0.325690837358978
100	6.1	6.34072292867204	-0.240722928672038
101	5.8	6.19708683959337	-0.397086839593372
102	6.1	6.35078243312423	-0.250782433124230
103	7.2	7.01617267116599	0.183827328834007
104	7.3	7.08243678958601	0.217563210413987
105	6.9	6.7664173160795	0.133582683920496
106	6.1	6.99085150463698	-0.890851504636977
107	5.8	7.18256064715963	-1.38256064715963
108	6.2	7.56351073987902	-1.36351073987902
109	7.1	7.7264608648486	-0.626460864848601
110	7.7	7.86081932681674	-0.160819326816744
111	8	7.86725354762442	0.132746452375583
112	7.8	7.38000812974465	0.419991870255351
113	7.4	7.18249136014425	0.217508639855754
114	7.4	7.30412297050991	0.0958770294900898
115	7.7	7.93744922538648	-0.237449225386482
116	7.8	7.74571698719203	0.0542830128079709
117	7.8	7.49465122857019	0.30534877142981
118	8	7.429025077348	0.570974922652003
119	8.1	7.32670750287325	0.773292497126749
120	8.4	7.81508726513581	0.584912734864194

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 6.5 & 7.06973119621155 & -0.569731196211554 \tabularnewline
2 & 6.3 & 6.02104348759652 & 0.278956512403478 \tabularnewline
3 & 5.9 & 5.66122593369226 & 0.238774066307739 \tabularnewline
4 & 5.5 & 5.8053353042841 & -0.305335304284093 \tabularnewline
5 & 5.2 & 5.71458714135724 & -0.514587141357239 \tabularnewline
6 & 4.9 & 5.52599553182624 & -0.62599553182624 \tabularnewline
7 & 5.4 & 6.58358530483888 & -1.18358530483888 \tabularnewline
8 & 5.8 & 5.81933774154623 & -0.0193377415462284 \tabularnewline
9 & 5.7 & 6.19318117712042 & -0.493181177120417 \tabularnewline
10 & 5.6 & 5.93963585270475 & -0.339635852704748 \tabularnewline
11 & 5.5 & 5.81798114666729 & -0.317981146667287 \tabularnewline
12 & 5.4 & 5.51287002183905 & -0.112870021839050 \tabularnewline
13 & 5.4 & 5.37121712136925 & 0.0287828786307469 \tabularnewline
14 & 5.4 & 5.47665222883566 & -0.0766522288356578 \tabularnewline
15 & 5.5 & 5.53713181584973 & -0.0371318158497295 \tabularnewline
16 & 5.8 & 5.71396623247638 & 0.0860337675236239 \tabularnewline
17 & 5.7 & 5.63346535535817 & 0.0665346446418273 \tabularnewline
18 & 5.4 & 5.41396720271657 & -0.0139672027165651 \tabularnewline
19 & 5.6 & 5.41709610917612 & 0.182903890823877 \tabularnewline
20 & 5.8 & 5.80895455057378 & -0.00895455057377787 \tabularnewline
21 & 6.2 & 6.07074087651743 & 0.129259123482569 \tabularnewline
22 & 6.8 & 6.82736428120311 & -0.0273642812031101 \tabularnewline
23 & 6.7 & 6.96238380604088 & -0.262383806040876 \tabularnewline
24 & 6.7 & 6.64008307520746 & 0.0599169247925401 \tabularnewline
25 & 6.4 & 6.43744248833473 & -0.037442488334734 \tabularnewline
26 & 6.3 & 6.16257684374748 & 0.137423156252517 \tabularnewline
27 & 6.3 & 5.98687677150362 & 0.313123228496376 \tabularnewline
28 & 6.4 & 6.06157002154408 & 0.338429978455918 \tabularnewline
29 & 6.3 & 5.9276848410891 & 0.372315158910896 \tabularnewline
30 & 6 & 6.02931825702243 & -0.0293182570224317 \tabularnewline
31 & 6.3 & 6.76941311857255 & -0.469413118572554 \tabularnewline
32 & 6.3 & 6.96756785537078 & -0.667567855370784 \tabularnewline
33 & 6.6 & 7.01713909370658 & -0.417139093706582 \tabularnewline
34 & 7.5 & 6.93928246806687 & 0.560717531933129 \tabularnewline
35 & 7.8 & 6.91414908289431 & 0.885850917105687 \tabularnewline
36 & 7.9 & 7.17725753457721 & 0.722742465422788 \tabularnewline
37 & 7.8 & 7.44945583201418 & 0.350544167985824 \tabularnewline
38 & 7.6 & 7.7132253465667 & -0.113225346566699 \tabularnewline
39 & 7.5 & 7.77552343650498 & -0.275523436504977 \tabularnewline
40 & 7.6 & 6.93111608966734 & 0.668883910332658 \tabularnewline
41 & 7.5 & 6.83012064093184 & 0.669879359068159 \tabularnewline
42 & 7.3 & 6.84614108467854 & 0.45385891532146 \tabularnewline
43 & 7.6 & 7.56623775179633 & 0.0337622482036743 \tabularnewline
44 & 7.5 & 7.63382399595559 & -0.133823995955591 \tabularnewline
45 & 7.6 & 7.2715268762575 & 0.328473123742497 \tabularnewline
46 & 7.9 & 7.60339073435815 & 0.296609265641852 \tabularnewline
47 & 7.9 & 7.12275559643861 & 0.777244403561386 \tabularnewline
48 & 8.1 & 7.29843257301068 & 0.801567426989323 \tabularnewline
49 & 8.2 & 7.43940131493905 & 0.760598685060946 \tabularnewline
50 & 8 & 7.20767268787993 & 0.792327312120072 \tabularnewline
51 & 7.5 & 7.19476977712489 & 0.305230222875112 \tabularnewline
52 & 6.8 & 6.8723047093428 & -0.0723047093428067 \tabularnewline
53 & 6.5 & 6.85807967284852 & -0.358079672848520 \tabularnewline
54 & 6.6 & 6.83757337902732 & -0.237573379027325 \tabularnewline
55 & 7.6 & 7.82277305990478 & -0.222773059904780 \tabularnewline
56 & 8 & 8.01712610516993 & -0.0171261051699311 \tabularnewline
57 & 8.1 & 7.98042330844948 & 0.119576691550522 \tabularnewline
58 & 7.7 & 7.81943327641697 & -0.119433276416968 \tabularnewline
59 & 7.5 & 7.5819201179519 & -0.0819201179518932 \tabularnewline
60 & 7.6 & 7.57612386434205 & 0.0238761356579520 \tabularnewline
61 & 7.8 & 7.5792527708016 & 0.220747229198395 \tabularnewline
62 & 7.8 & 7.55097875696559 & 0.249021243034407 \tabularnewline
63 & 7.8 & 7.33015847858474 & 0.469841521415261 \tabularnewline
64 & 7.5 & 7.50005092375085 & -5.09237508521318e-05 \tabularnewline
65 & 7.5 & 7.65820927168441 & -0.158209271684409 \tabularnewline
66 & 7.1 & 7.61952328635509 & -0.519523286355086 \tabularnewline
67 & 7.5 & 8.26441895277955 & -0.764418952779553 \tabularnewline
68 & 7.5 & 8.1710265383742 & -0.671026538374209 \tabularnewline
69 & 7.6 & 8.39298116113785 & -0.792981161137851 \tabularnewline
70 & 7.7 & 7.73269129596062 & -0.0326912959606161 \tabularnewline
71 & 7.7 & 7.5788079210733 & 0.121192078926699 \tabularnewline
72 & 7.9 & 7.36707748844651 & 0.532922511553487 \tabularnewline
73 & 8.1 & 7.4573061785166 & 0.642693821483393 \tabularnewline
74 & 8.2 & 7.3007785521508 & 0.899221447849199 \tabularnewline
75 & 8.2 & 7.51248588910579 & 0.687514110894206 \tabularnewline
76 & 8.2 & 7.20605263853831 & 0.993947361461689 \tabularnewline
77 & 7.9 & 7.36239248354766 & 0.537607516452341 \tabularnewline
78 & 7.3 & 7.10818609626237 & 0.191813903737633 \tabularnewline
79 & 6.9 & 7.36683179354257 & -0.466831793542566 \tabularnewline
80 & 6.6 & 7.50003281545613 & -0.900032815456126 \tabularnewline
81 & 6.7 & 7.37705598367942 & -0.67705598367942 \tabularnewline
82 & 6.9 & 7.08301789321412 & -0.183017893214115 \tabularnewline
83 & 7 & 7.05788450804156 & -0.0578845080415568 \tabularnewline
84 & 7.1 & 7.50626788143944 & -0.406267881439441 \tabularnewline
85 & 7.2 & 7.17553836772158 & 0.0244616322784173 \tabularnewline
86 & 7.1 & 7.02942271284909 & 0.070577287150913 \tabularnewline
87 & 6.9 & 7.01321448774593 & -0.113214487745930 \tabularnewline
88 & 7 & 6.89866643885367 & 0.101333561146335 \tabularnewline
89 & 6.8 & 6.77701173281621 & 0.0229882671837939 \tabularnewline
90 & 6.4 & 6.71006345585477 & -0.310063455854767 \tabularnewline
91 & 6.7 & 6.82260522046637 & -0.122605220466367 \tabularnewline
92 & 6.6 & 6.71119780023756 & -0.111197800237556 \tabularnewline
93 & 6.4 & 6.63268011172822 & -0.232680111728221 \tabularnewline
94 & 6.3 & 6.53928769732288 & -0.239287697322878 \tabularnewline
95 & 6.2 & 6.87958033830797 & -0.679580338307965 \tabularnewline
96 & 6.5 & 6.58157587062492 & -0.0815758706249231 \tabularnewline
97 & 6.8 & 6.60916572591952 & 0.190834274080483 \tabularnewline
98 & 6.8 & 6.32471385839324 & 0.475286141606762 \tabularnewline
99 & 6.4 & 6.07430916264102 & 0.325690837358978 \tabularnewline
100 & 6.1 & 6.34072292867204 & -0.240722928672038 \tabularnewline
101 & 5.8 & 6.19708683959337 & -0.397086839593372 \tabularnewline
102 & 6.1 & 6.35078243312423 & -0.250782433124230 \tabularnewline
103 & 7.2 & 7.01617267116599 & 0.183827328834007 \tabularnewline
104 & 7.3 & 7.08243678958601 & 0.217563210413987 \tabularnewline
105 & 6.9 & 6.7664173160795 & 0.133582683920496 \tabularnewline
106 & 6.1 & 6.99085150463698 & -0.890851504636977 \tabularnewline
107 & 5.8 & 7.18256064715963 & -1.38256064715963 \tabularnewline
108 & 6.2 & 7.56351073987902 & -1.36351073987902 \tabularnewline
109 & 7.1 & 7.7264608648486 & -0.626460864848601 \tabularnewline
110 & 7.7 & 7.86081932681674 & -0.160819326816744 \tabularnewline
111 & 8 & 7.86725354762442 & 0.132746452375583 \tabularnewline
112 & 7.8 & 7.38000812974465 & 0.419991870255351 \tabularnewline
113 & 7.4 & 7.18249136014425 & 0.217508639855754 \tabularnewline
114 & 7.4 & 7.30412297050991 & 0.0958770294900898 \tabularnewline
115 & 7.7 & 7.93744922538648 & -0.237449225386482 \tabularnewline
116 & 7.8 & 7.74571698719203 & 0.0542830128079709 \tabularnewline
117 & 7.8 & 7.49465122857019 & 0.30534877142981 \tabularnewline
118 & 8 & 7.429025077348 & 0.570974922652003 \tabularnewline
119 & 8.1 & 7.32670750287325 & 0.773292497126749 \tabularnewline
120 & 8.4 & 7.81508726513581 & 0.584912734864194 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112170&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]6.5[/C][C]7.06973119621155[/C][C]-0.569731196211554[/C][/ROW]
[ROW][C]2[/C][C]6.3[/C][C]6.02104348759652[/C][C]0.278956512403478[/C][/ROW]
[ROW][C]3[/C][C]5.9[/C][C]5.66122593369226[/C][C]0.238774066307739[/C][/ROW]
[ROW][C]4[/C][C]5.5[/C][C]5.8053353042841[/C][C]-0.305335304284093[/C][/ROW]
[ROW][C]5[/C][C]5.2[/C][C]5.71458714135724[/C][C]-0.514587141357239[/C][/ROW]
[ROW][C]6[/C][C]4.9[/C][C]5.52599553182624[/C][C]-0.62599553182624[/C][/ROW]
[ROW][C]7[/C][C]5.4[/C][C]6.58358530483888[/C][C]-1.18358530483888[/C][/ROW]
[ROW][C]8[/C][C]5.8[/C][C]5.81933774154623[/C][C]-0.0193377415462284[/C][/ROW]
[ROW][C]9[/C][C]5.7[/C][C]6.19318117712042[/C][C]-0.493181177120417[/C][/ROW]
[ROW][C]10[/C][C]5.6[/C][C]5.93963585270475[/C][C]-0.339635852704748[/C][/ROW]
[ROW][C]11[/C][C]5.5[/C][C]5.81798114666729[/C][C]-0.317981146667287[/C][/ROW]
[ROW][C]12[/C][C]5.4[/C][C]5.51287002183905[/C][C]-0.112870021839050[/C][/ROW]
[ROW][C]13[/C][C]5.4[/C][C]5.37121712136925[/C][C]0.0287828786307469[/C][/ROW]
[ROW][C]14[/C][C]5.4[/C][C]5.47665222883566[/C][C]-0.0766522288356578[/C][/ROW]
[ROW][C]15[/C][C]5.5[/C][C]5.53713181584973[/C][C]-0.0371318158497295[/C][/ROW]
[ROW][C]16[/C][C]5.8[/C][C]5.71396623247638[/C][C]0.0860337675236239[/C][/ROW]
[ROW][C]17[/C][C]5.7[/C][C]5.63346535535817[/C][C]0.0665346446418273[/C][/ROW]
[ROW][C]18[/C][C]5.4[/C][C]5.41396720271657[/C][C]-0.0139672027165651[/C][/ROW]
[ROW][C]19[/C][C]5.6[/C][C]5.41709610917612[/C][C]0.182903890823877[/C][/ROW]
[ROW][C]20[/C][C]5.8[/C][C]5.80895455057378[/C][C]-0.00895455057377787[/C][/ROW]
[ROW][C]21[/C][C]6.2[/C][C]6.07074087651743[/C][C]0.129259123482569[/C][/ROW]
[ROW][C]22[/C][C]6.8[/C][C]6.82736428120311[/C][C]-0.0273642812031101[/C][/ROW]
[ROW][C]23[/C][C]6.7[/C][C]6.96238380604088[/C][C]-0.262383806040876[/C][/ROW]
[ROW][C]24[/C][C]6.7[/C][C]6.64008307520746[/C][C]0.0599169247925401[/C][/ROW]
[ROW][C]25[/C][C]6.4[/C][C]6.43744248833473[/C][C]-0.037442488334734[/C][/ROW]
[ROW][C]26[/C][C]6.3[/C][C]6.16257684374748[/C][C]0.137423156252517[/C][/ROW]
[ROW][C]27[/C][C]6.3[/C][C]5.98687677150362[/C][C]0.313123228496376[/C][/ROW]
[ROW][C]28[/C][C]6.4[/C][C]6.06157002154408[/C][C]0.338429978455918[/C][/ROW]
[ROW][C]29[/C][C]6.3[/C][C]5.9276848410891[/C][C]0.372315158910896[/C][/ROW]
[ROW][C]30[/C][C]6[/C][C]6.02931825702243[/C][C]-0.0293182570224317[/C][/ROW]
[ROW][C]31[/C][C]6.3[/C][C]6.76941311857255[/C][C]-0.469413118572554[/C][/ROW]
[ROW][C]32[/C][C]6.3[/C][C]6.96756785537078[/C][C]-0.667567855370784[/C][/ROW]
[ROW][C]33[/C][C]6.6[/C][C]7.01713909370658[/C][C]-0.417139093706582[/C][/ROW]
[ROW][C]34[/C][C]7.5[/C][C]6.93928246806687[/C][C]0.560717531933129[/C][/ROW]
[ROW][C]35[/C][C]7.8[/C][C]6.91414908289431[/C][C]0.885850917105687[/C][/ROW]
[ROW][C]36[/C][C]7.9[/C][C]7.17725753457721[/C][C]0.722742465422788[/C][/ROW]
[ROW][C]37[/C][C]7.8[/C][C]7.44945583201418[/C][C]0.350544167985824[/C][/ROW]
[ROW][C]38[/C][C]7.6[/C][C]7.7132253465667[/C][C]-0.113225346566699[/C][/ROW]
[ROW][C]39[/C][C]7.5[/C][C]7.77552343650498[/C][C]-0.275523436504977[/C][/ROW]
[ROW][C]40[/C][C]7.6[/C][C]6.93111608966734[/C][C]0.668883910332658[/C][/ROW]
[ROW][C]41[/C][C]7.5[/C][C]6.83012064093184[/C][C]0.669879359068159[/C][/ROW]
[ROW][C]42[/C][C]7.3[/C][C]6.84614108467854[/C][C]0.45385891532146[/C][/ROW]
[ROW][C]43[/C][C]7.6[/C][C]7.56623775179633[/C][C]0.0337622482036743[/C][/ROW]
[ROW][C]44[/C][C]7.5[/C][C]7.63382399595559[/C][C]-0.133823995955591[/C][/ROW]
[ROW][C]45[/C][C]7.6[/C][C]7.2715268762575[/C][C]0.328473123742497[/C][/ROW]
[ROW][C]46[/C][C]7.9[/C][C]7.60339073435815[/C][C]0.296609265641852[/C][/ROW]
[ROW][C]47[/C][C]7.9[/C][C]7.12275559643861[/C][C]0.777244403561386[/C][/ROW]
[ROW][C]48[/C][C]8.1[/C][C]7.29843257301068[/C][C]0.801567426989323[/C][/ROW]
[ROW][C]49[/C][C]8.2[/C][C]7.43940131493905[/C][C]0.760598685060946[/C][/ROW]
[ROW][C]50[/C][C]8[/C][C]7.20767268787993[/C][C]0.792327312120072[/C][/ROW]
[ROW][C]51[/C][C]7.5[/C][C]7.19476977712489[/C][C]0.305230222875112[/C][/ROW]
[ROW][C]52[/C][C]6.8[/C][C]6.8723047093428[/C][C]-0.0723047093428067[/C][/ROW]
[ROW][C]53[/C][C]6.5[/C][C]6.85807967284852[/C][C]-0.358079672848520[/C][/ROW]
[ROW][C]54[/C][C]6.6[/C][C]6.83757337902732[/C][C]-0.237573379027325[/C][/ROW]
[ROW][C]55[/C][C]7.6[/C][C]7.82277305990478[/C][C]-0.222773059904780[/C][/ROW]
[ROW][C]56[/C][C]8[/C][C]8.01712610516993[/C][C]-0.0171261051699311[/C][/ROW]
[ROW][C]57[/C][C]8.1[/C][C]7.98042330844948[/C][C]0.119576691550522[/C][/ROW]
[ROW][C]58[/C][C]7.7[/C][C]7.81943327641697[/C][C]-0.119433276416968[/C][/ROW]
[ROW][C]59[/C][C]7.5[/C][C]7.5819201179519[/C][C]-0.0819201179518932[/C][/ROW]
[ROW][C]60[/C][C]7.6[/C][C]7.57612386434205[/C][C]0.0238761356579520[/C][/ROW]
[ROW][C]61[/C][C]7.8[/C][C]7.5792527708016[/C][C]0.220747229198395[/C][/ROW]
[ROW][C]62[/C][C]7.8[/C][C]7.55097875696559[/C][C]0.249021243034407[/C][/ROW]
[ROW][C]63[/C][C]7.8[/C][C]7.33015847858474[/C][C]0.469841521415261[/C][/ROW]
[ROW][C]64[/C][C]7.5[/C][C]7.50005092375085[/C][C]-5.09237508521318e-05[/C][/ROW]
[ROW][C]65[/C][C]7.5[/C][C]7.65820927168441[/C][C]-0.158209271684409[/C][/ROW]
[ROW][C]66[/C][C]7.1[/C][C]7.61952328635509[/C][C]-0.519523286355086[/C][/ROW]
[ROW][C]67[/C][C]7.5[/C][C]8.26441895277955[/C][C]-0.764418952779553[/C][/ROW]
[ROW][C]68[/C][C]7.5[/C][C]8.1710265383742[/C][C]-0.671026538374209[/C][/ROW]
[ROW][C]69[/C][C]7.6[/C][C]8.39298116113785[/C][C]-0.792981161137851[/C][/ROW]
[ROW][C]70[/C][C]7.7[/C][C]7.73269129596062[/C][C]-0.0326912959606161[/C][/ROW]
[ROW][C]71[/C][C]7.7[/C][C]7.5788079210733[/C][C]0.121192078926699[/C][/ROW]
[ROW][C]72[/C][C]7.9[/C][C]7.36707748844651[/C][C]0.532922511553487[/C][/ROW]
[ROW][C]73[/C][C]8.1[/C][C]7.4573061785166[/C][C]0.642693821483393[/C][/ROW]
[ROW][C]74[/C][C]8.2[/C][C]7.3007785521508[/C][C]0.899221447849199[/C][/ROW]
[ROW][C]75[/C][C]8.2[/C][C]7.51248588910579[/C][C]0.687514110894206[/C][/ROW]
[ROW][C]76[/C][C]8.2[/C][C]7.20605263853831[/C][C]0.993947361461689[/C][/ROW]
[ROW][C]77[/C][C]7.9[/C][C]7.36239248354766[/C][C]0.537607516452341[/C][/ROW]
[ROW][C]78[/C][C]7.3[/C][C]7.10818609626237[/C][C]0.191813903737633[/C][/ROW]
[ROW][C]79[/C][C]6.9[/C][C]7.36683179354257[/C][C]-0.466831793542566[/C][/ROW]
[ROW][C]80[/C][C]6.6[/C][C]7.50003281545613[/C][C]-0.900032815456126[/C][/ROW]
[ROW][C]81[/C][C]6.7[/C][C]7.37705598367942[/C][C]-0.67705598367942[/C][/ROW]
[ROW][C]82[/C][C]6.9[/C][C]7.08301789321412[/C][C]-0.183017893214115[/C][/ROW]
[ROW][C]83[/C][C]7[/C][C]7.05788450804156[/C][C]-0.0578845080415568[/C][/ROW]
[ROW][C]84[/C][C]7.1[/C][C]7.50626788143944[/C][C]-0.406267881439441[/C][/ROW]
[ROW][C]85[/C][C]7.2[/C][C]7.17553836772158[/C][C]0.0244616322784173[/C][/ROW]
[ROW][C]86[/C][C]7.1[/C][C]7.02942271284909[/C][C]0.070577287150913[/C][/ROW]
[ROW][C]87[/C][C]6.9[/C][C]7.01321448774593[/C][C]-0.113214487745930[/C][/ROW]
[ROW][C]88[/C][C]7[/C][C]6.89866643885367[/C][C]0.101333561146335[/C][/ROW]
[ROW][C]89[/C][C]6.8[/C][C]6.77701173281621[/C][C]0.0229882671837939[/C][/ROW]
[ROW][C]90[/C][C]6.4[/C][C]6.71006345585477[/C][C]-0.310063455854767[/C][/ROW]
[ROW][C]91[/C][C]6.7[/C][C]6.82260522046637[/C][C]-0.122605220466367[/C][/ROW]
[ROW][C]92[/C][C]6.6[/C][C]6.71119780023756[/C][C]-0.111197800237556[/C][/ROW]
[ROW][C]93[/C][C]6.4[/C][C]6.63268011172822[/C][C]-0.232680111728221[/C][/ROW]
[ROW][C]94[/C][C]6.3[/C][C]6.53928769732288[/C][C]-0.239287697322878[/C][/ROW]
[ROW][C]95[/C][C]6.2[/C][C]6.87958033830797[/C][C]-0.679580338307965[/C][/ROW]
[ROW][C]96[/C][C]6.5[/C][C]6.58157587062492[/C][C]-0.0815758706249231[/C][/ROW]
[ROW][C]97[/C][C]6.8[/C][C]6.60916572591952[/C][C]0.190834274080483[/C][/ROW]
[ROW][C]98[/C][C]6.8[/C][C]6.32471385839324[/C][C]0.475286141606762[/C][/ROW]
[ROW][C]99[/C][C]6.4[/C][C]6.07430916264102[/C][C]0.325690837358978[/C][/ROW]
[ROW][C]100[/C][C]6.1[/C][C]6.34072292867204[/C][C]-0.240722928672038[/C][/ROW]
[ROW][C]101[/C][C]5.8[/C][C]6.19708683959337[/C][C]-0.397086839593372[/C][/ROW]
[ROW][C]102[/C][C]6.1[/C][C]6.35078243312423[/C][C]-0.250782433124230[/C][/ROW]
[ROW][C]103[/C][C]7.2[/C][C]7.01617267116599[/C][C]0.183827328834007[/C][/ROW]
[ROW][C]104[/C][C]7.3[/C][C]7.08243678958601[/C][C]0.217563210413987[/C][/ROW]
[ROW][C]105[/C][C]6.9[/C][C]6.7664173160795[/C][C]0.133582683920496[/C][/ROW]
[ROW][C]106[/C][C]6.1[/C][C]6.99085150463698[/C][C]-0.890851504636977[/C][/ROW]
[ROW][C]107[/C][C]5.8[/C][C]7.18256064715963[/C][C]-1.38256064715963[/C][/ROW]
[ROW][C]108[/C][C]6.2[/C][C]7.56351073987902[/C][C]-1.36351073987902[/C][/ROW]
[ROW][C]109[/C][C]7.1[/C][C]7.7264608648486[/C][C]-0.626460864848601[/C][/ROW]
[ROW][C]110[/C][C]7.7[/C][C]7.86081932681674[/C][C]-0.160819326816744[/C][/ROW]
[ROW][C]111[/C][C]8[/C][C]7.86725354762442[/C][C]0.132746452375583[/C][/ROW]
[ROW][C]112[/C][C]7.8[/C][C]7.38000812974465[/C][C]0.419991870255351[/C][/ROW]
[ROW][C]113[/C][C]7.4[/C][C]7.18249136014425[/C][C]0.217508639855754[/C][/ROW]
[ROW][C]114[/C][C]7.4[/C][C]7.30412297050991[/C][C]0.0958770294900898[/C][/ROW]
[ROW][C]115[/C][C]7.7[/C][C]7.93744922538648[/C][C]-0.237449225386482[/C][/ROW]
[ROW][C]116[/C][C]7.8[/C][C]7.74571698719203[/C][C]0.0542830128079709[/C][/ROW]
[ROW][C]117[/C][C]7.8[/C][C]7.49465122857019[/C][C]0.30534877142981[/C][/ROW]
[ROW][C]118[/C][C]8[/C][C]7.429025077348[/C][C]0.570974922652003[/C][/ROW]
[ROW][C]119[/C][C]8.1[/C][C]7.32670750287325[/C][C]0.773292497126749[/C][/ROW]
[ROW][C]120[/C][C]8.4[/C][C]7.81508726513581[/C][C]0.584912734864194[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112170&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112170&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	6.5	7.06973119621155	-0.569731196211554
2	6.3	6.02104348759652	0.278956512403478
3	5.9	5.66122593369226	0.238774066307739
4	5.5	5.8053353042841	-0.305335304284093
5	5.2	5.71458714135724	-0.514587141357239
6	4.9	5.52599553182624	-0.62599553182624
7	5.4	6.58358530483888	-1.18358530483888
8	5.8	5.81933774154623	-0.0193377415462284
9	5.7	6.19318117712042	-0.493181177120417
10	5.6	5.93963585270475	-0.339635852704748
11	5.5	5.81798114666729	-0.317981146667287
12	5.4	5.51287002183905	-0.112870021839050
13	5.4	5.37121712136925	0.0287828786307469
14	5.4	5.47665222883566	-0.0766522288356578
15	5.5	5.53713181584973	-0.0371318158497295
16	5.8	5.71396623247638	0.0860337675236239
17	5.7	5.63346535535817	0.0665346446418273
18	5.4	5.41396720271657	-0.0139672027165651
19	5.6	5.41709610917612	0.182903890823877
20	5.8	5.80895455057378	-0.00895455057377787
21	6.2	6.07074087651743	0.129259123482569
22	6.8	6.82736428120311	-0.0273642812031101
23	6.7	6.96238380604088	-0.262383806040876
24	6.7	6.64008307520746	0.0599169247925401
25	6.4	6.43744248833473	-0.037442488334734
26	6.3	6.16257684374748	0.137423156252517
27	6.3	5.98687677150362	0.313123228496376
28	6.4	6.06157002154408	0.338429978455918
29	6.3	5.9276848410891	0.372315158910896
30	6	6.02931825702243	-0.0293182570224317
31	6.3	6.76941311857255	-0.469413118572554
32	6.3	6.96756785537078	-0.667567855370784
33	6.6	7.01713909370658	-0.417139093706582
34	7.5	6.93928246806687	0.560717531933129
35	7.8	6.91414908289431	0.885850917105687
36	7.9	7.17725753457721	0.722742465422788
37	7.8	7.44945583201418	0.350544167985824
38	7.6	7.7132253465667	-0.113225346566699
39	7.5	7.77552343650498	-0.275523436504977
40	7.6	6.93111608966734	0.668883910332658
41	7.5	6.83012064093184	0.669879359068159
42	7.3	6.84614108467854	0.45385891532146
43	7.6	7.56623775179633	0.0337622482036743
44	7.5	7.63382399595559	-0.133823995955591
45	7.6	7.2715268762575	0.328473123742497
46	7.9	7.60339073435815	0.296609265641852
47	7.9	7.12275559643861	0.777244403561386
48	8.1	7.29843257301068	0.801567426989323
49	8.2	7.43940131493905	0.760598685060946
50	8	7.20767268787993	0.792327312120072
51	7.5	7.19476977712489	0.305230222875112
52	6.8	6.8723047093428	-0.0723047093428067
53	6.5	6.85807967284852	-0.358079672848520
54	6.6	6.83757337902732	-0.237573379027325
55	7.6	7.82277305990478	-0.222773059904780
56	8	8.01712610516993	-0.0171261051699311
57	8.1	7.98042330844948	0.119576691550522
58	7.7	7.81943327641697	-0.119433276416968
59	7.5	7.5819201179519	-0.0819201179518932
60	7.6	7.57612386434205	0.0238761356579520
61	7.8	7.5792527708016	0.220747229198395
62	7.8	7.55097875696559	0.249021243034407
63	7.8	7.33015847858474	0.469841521415261
64	7.5	7.50005092375085	-5.09237508521318e-05
65	7.5	7.65820927168441	-0.158209271684409
66	7.1	7.61952328635509	-0.519523286355086
67	7.5	8.26441895277955	-0.764418952779553
68	7.5	8.1710265383742	-0.671026538374209
69	7.6	8.39298116113785	-0.792981161137851
70	7.7	7.73269129596062	-0.0326912959606161
71	7.7	7.5788079210733	0.121192078926699
72	7.9	7.36707748844651	0.532922511553487
73	8.1	7.4573061785166	0.642693821483393
74	8.2	7.3007785521508	0.899221447849199
75	8.2	7.51248588910579	0.687514110894206
76	8.2	7.20605263853831	0.993947361461689
77	7.9	7.36239248354766	0.537607516452341
78	7.3	7.10818609626237	0.191813903737633
79	6.9	7.36683179354257	-0.466831793542566
80	6.6	7.50003281545613	-0.900032815456126
81	6.7	7.37705598367942	-0.67705598367942
82	6.9	7.08301789321412	-0.183017893214115
83	7	7.05788450804156	-0.0578845080415568
84	7.1	7.50626788143944	-0.406267881439441
85	7.2	7.17553836772158	0.0244616322784173
86	7.1	7.02942271284909	0.070577287150913
87	6.9	7.01321448774593	-0.113214487745930
88	7	6.89866643885367	0.101333561146335
89	6.8	6.77701173281621	0.0229882671837939
90	6.4	6.71006345585477	-0.310063455854767
91	6.7	6.82260522046637	-0.122605220466367
92	6.6	6.71119780023756	-0.111197800237556
93	6.4	6.63268011172822	-0.232680111728221
94	6.3	6.53928769732288	-0.239287697322878
95	6.2	6.87958033830797	-0.679580338307965
96	6.5	6.58157587062492	-0.0815758706249231
97	6.8	6.60916572591952	0.190834274080483
98	6.8	6.32471385839324	0.475286141606762
99	6.4	6.07430916264102	0.325690837358978
100	6.1	6.34072292867204	-0.240722928672038
101	5.8	6.19708683959337	-0.397086839593372
102	6.1	6.35078243312423	-0.250782433124230
103	7.2	7.01617267116599	0.183827328834007
104	7.3	7.08243678958601	0.217563210413987
105	6.9	6.7664173160795	0.133582683920496
106	6.1	6.99085150463698	-0.890851504636977
107	5.8	7.18256064715963	-1.38256064715963
108	6.2	7.56351073987902	-1.36351073987902
109	7.1	7.7264608648486	-0.626460864848601
110	7.7	7.86081932681674	-0.160819326816744
111	8	7.86725354762442	0.132746452375583
112	7.8	7.38000812974465	0.419991870255351
113	7.4	7.18249136014425	0.217508639855754
114	7.4	7.30412297050991	0.0958770294900898
115	7.7	7.93744922538648	-0.237449225386482
116	7.8	7.74571698719203	0.0542830128079709
117	7.8	7.49465122857019	0.30534877142981
118	8	7.429025077348	0.570974922652003
119	8.1	7.32670750287325	0.773292497126749
120	8.4	7.81508726513581	0.584912734864194

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
8	0.107879455448015	0.215758910896029	0.892120544551985
9	0.0635432549429895	0.127086509885979	0.93645674505701
10	0.0641880504319815	0.128376100863963	0.935811949568018
11	0.157160100198054	0.314320200396108	0.842839899801946
12	0.237719457591210	0.475438915182419	0.76228054240879
13	0.227213517896932	0.454427035793864	0.772786482103068
14	0.156382312267814	0.312764624535629	0.843617687732186
15	0.105080934041066	0.210161868082133	0.894919065958934
16	0.0665690861852334	0.133138172370467	0.933430913814767
17	0.0411245832880739	0.0822491665761479	0.958875416711926
18	0.0265328424135454	0.0530656848270908	0.973467157586455
19	0.0151697819157403	0.0303395638314806	0.98483021808426
20	0.0095562436310046	0.0191124872620092	0.990443756368995
21	0.00529401021730387	0.0105880204346077	0.994705989782696
22	0.00357607923910717	0.00715215847821433	0.996423920760893
23	0.00337006593863634	0.00674013187727268	0.996629934061364
24	0.00245676647646566	0.00491353295293131	0.997543233523534
25	0.0016106296878035	0.003221259375607	0.998389370312196
26	0.00103301883327683	0.00206603766655366	0.998966981166723
27	0.00086626311284255	0.0017325262256851	0.999133736887157
28	0.000923581988004788	0.00184716397600958	0.999076418011995
29	0.000736259502936229	0.00147251900587246	0.999263740497064
30	0.000425730664088876	0.000851461328177752	0.999574269335911
31	0.000362958527921033	0.000725917055842067	0.999637041472079
32	0.000423844731382511	0.000847689462765021	0.999576155268618
33	0.000282700623550093	0.000565401247100186	0.99971729937645
34	0.00429017464551027	0.00858034929102054	0.99570982535449
35	0.0278337597774432	0.0556675195548864	0.972166240222557
36	0.0427285468921628	0.0854570937843256	0.957271453107837
37	0.0334267493827452	0.0668534987654904	0.966573250617255
38	0.0260730971294723	0.0521461942589445	0.973926902870528
39	0.0251390003380828	0.0502780006761657	0.974860999661917
40	0.0247203743611432	0.0494407487222863	0.975279625638857
41	0.0214005318509492	0.0428010637018984	0.97859946814905
42	0.0157256327034959	0.0314512654069918	0.984274367296504
43	0.0113896486463379	0.0227792972926759	0.988610351353662
44	0.00899880357962838	0.0179976071592568	0.991001196420372
45	0.00640687633435754	0.0128137526687151	0.993593123665642
46	0.00482690674604272	0.00965381349208544	0.995173093253957
47	0.00658802623578987	0.0131760524715797	0.99341197376421
48	0.0102192376291713	0.0204384752583426	0.989780762370829
49	0.0143075500654408	0.0286151001308816	0.98569244993456
50	0.0208268689522337	0.0416537379044675	0.979173131047766
51	0.0209445054915973	0.0418890109831947	0.979055494508403
52	0.0375663796974661	0.0751327593949321	0.962433620302534
53	0.0840210559219864	0.168042111843973	0.915978944078014
54	0.107421808367256	0.214843616734512	0.892578191632744
55	0.101601194452958	0.203202388905916	0.898398805547042
56	0.0824165060763141	0.164833012152628	0.917583493923686
57	0.0664239157429601	0.132847831485920	0.93357608425704
58	0.0567982969024378	0.113596593804876	0.943201703097562
59	0.0485761121812084	0.0971522243624169	0.951423887818792
60	0.038123331818163	0.076246663636326	0.961876668181837
61	0.0310067291578798	0.0620134583157595	0.96899327084212
62	0.0239252456612378	0.0478504913224756	0.976074754338762
63	0.0232674064296305	0.046534812859261	0.97673259357037
64	0.0183911255084545	0.036782251016909	0.981608874491545
65	0.0170552705912858	0.0341105411825716	0.982944729408714
66	0.0261519928672272	0.0523039857344545	0.973848007132773
67	0.0484660642200261	0.0969321284400522	0.951533935779974
68	0.0668951266039352	0.133790253207870	0.933104873396065
69	0.111063181170264	0.222126362340527	0.888936818829736
70	0.0888213214350519	0.177642642870104	0.911178678564948
71	0.0695412917533678	0.139082583506736	0.930458708246632
72	0.0714198668764713	0.142839733752943	0.928580133123529
73	0.0733384609226863	0.146676921845373	0.926661539077314
74	0.132994231885458	0.265988463770917	0.867005768114542
75	0.177221393462785	0.354442786925569	0.822778606537215
76	0.468656715725288	0.937313431450576	0.531343284274712
77	0.723925989634934	0.552148020730133	0.276074010365066
78	0.817716984129412	0.364566031741175	0.182283015870588
79	0.83786364871154	0.324272702576921	0.162136351288460
80	0.903182651801079	0.193634696397843	0.0968173481989214
81	0.9180739021301	0.163852195739800	0.0819260978698998
82	0.898647782385862	0.202704435228276	0.101352217614138
83	0.874837027078193	0.250325945843615	0.125162972921807
84	0.873529039470195	0.25294192105961	0.126470960529805
85	0.860122880978352	0.279754238043295	0.139877119021648
86	0.83778702900701	0.324425941985981	0.162212970992990
87	0.809777172917662	0.380445654164676	0.190222827082338
88	0.773972759004977	0.452054481990046	0.226027240995023
89	0.729374196934483	0.541251606131034	0.270625803065517
90	0.695955591316622	0.608088817366756	0.304044408683378
91	0.643957677801542	0.712084644396915	0.356042322198458
92	0.591563572971027	0.816872854057947	0.408436427028973
93	0.536054893324444	0.927890213351111	0.463945106675556
94	0.489682497451089	0.979364994902178	0.510317502548911
95	0.50952662834296	0.98094674331408	0.49047337165704
96	0.448102430753173	0.896204861506346	0.551897569246827
97	0.391442467319564	0.782884934639127	0.608557532680436
98	0.372894404472233	0.745788808944466	0.627105595527767
99	0.331536362812071	0.663072725624142	0.668463637187929
100	0.27385746893732	0.54771493787464	0.72614253106268
101	0.242069026213412	0.484138052426824	0.757930973786588
102	0.198763926849258	0.397527853698517	0.801236073150742
103	0.195356475548809	0.390712951097617	0.804643524451191
104	0.259839579675318	0.519679159350636	0.740160420324682
105	0.591730126596658	0.816539746806684	0.408269873403342
106	0.598304448872959	0.803391102254083	0.401695551127041
107	0.708582417140141	0.582835165719717	0.291417582859859
108	0.988240853675928	0.0235182926481441	0.0117591463240720
109	0.973331914639	0.0533361707219993	0.0266680853609996
110	0.93795438053974	0.124091238920522	0.0620456194602608
111	0.955529967150477	0.0889400656990463	0.0444700328495232
112	0.99584140767631	0.00831718464738266	0.00415859232369133

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 0.107879455448015 & 0.215758910896029 & 0.892120544551985 \tabularnewline
9 & 0.0635432549429895 & 0.127086509885979 & 0.93645674505701 \tabularnewline
10 & 0.0641880504319815 & 0.128376100863963 & 0.935811949568018 \tabularnewline
11 & 0.157160100198054 & 0.314320200396108 & 0.842839899801946 \tabularnewline
12 & 0.237719457591210 & 0.475438915182419 & 0.76228054240879 \tabularnewline
13 & 0.227213517896932 & 0.454427035793864 & 0.772786482103068 \tabularnewline
14 & 0.156382312267814 & 0.312764624535629 & 0.843617687732186 \tabularnewline
15 & 0.105080934041066 & 0.210161868082133 & 0.894919065958934 \tabularnewline
16 & 0.0665690861852334 & 0.133138172370467 & 0.933430913814767 \tabularnewline
17 & 0.0411245832880739 & 0.0822491665761479 & 0.958875416711926 \tabularnewline
18 & 0.0265328424135454 & 0.0530656848270908 & 0.973467157586455 \tabularnewline
19 & 0.0151697819157403 & 0.0303395638314806 & 0.98483021808426 \tabularnewline
20 & 0.0095562436310046 & 0.0191124872620092 & 0.990443756368995 \tabularnewline
21 & 0.00529401021730387 & 0.0105880204346077 & 0.994705989782696 \tabularnewline
22 & 0.00357607923910717 & 0.00715215847821433 & 0.996423920760893 \tabularnewline
23 & 0.00337006593863634 & 0.00674013187727268 & 0.996629934061364 \tabularnewline
24 & 0.00245676647646566 & 0.00491353295293131 & 0.997543233523534 \tabularnewline
25 & 0.0016106296878035 & 0.003221259375607 & 0.998389370312196 \tabularnewline
26 & 0.00103301883327683 & 0.00206603766655366 & 0.998966981166723 \tabularnewline
27 & 0.00086626311284255 & 0.0017325262256851 & 0.999133736887157 \tabularnewline
28 & 0.000923581988004788 & 0.00184716397600958 & 0.999076418011995 \tabularnewline
29 & 0.000736259502936229 & 0.00147251900587246 & 0.999263740497064 \tabularnewline
30 & 0.000425730664088876 & 0.000851461328177752 & 0.999574269335911 \tabularnewline
31 & 0.000362958527921033 & 0.000725917055842067 & 0.999637041472079 \tabularnewline
32 & 0.000423844731382511 & 0.000847689462765021 & 0.999576155268618 \tabularnewline
33 & 0.000282700623550093 & 0.000565401247100186 & 0.99971729937645 \tabularnewline
34 & 0.00429017464551027 & 0.00858034929102054 & 0.99570982535449 \tabularnewline
35 & 0.0278337597774432 & 0.0556675195548864 & 0.972166240222557 \tabularnewline
36 & 0.0427285468921628 & 0.0854570937843256 & 0.957271453107837 \tabularnewline
37 & 0.0334267493827452 & 0.0668534987654904 & 0.966573250617255 \tabularnewline
38 & 0.0260730971294723 & 0.0521461942589445 & 0.973926902870528 \tabularnewline
39 & 0.0251390003380828 & 0.0502780006761657 & 0.974860999661917 \tabularnewline
40 & 0.0247203743611432 & 0.0494407487222863 & 0.975279625638857 \tabularnewline
41 & 0.0214005318509492 & 0.0428010637018984 & 0.97859946814905 \tabularnewline
42 & 0.0157256327034959 & 0.0314512654069918 & 0.984274367296504 \tabularnewline
43 & 0.0113896486463379 & 0.0227792972926759 & 0.988610351353662 \tabularnewline
44 & 0.00899880357962838 & 0.0179976071592568 & 0.991001196420372 \tabularnewline
45 & 0.00640687633435754 & 0.0128137526687151 & 0.993593123665642 \tabularnewline
46 & 0.00482690674604272 & 0.00965381349208544 & 0.995173093253957 \tabularnewline
47 & 0.00658802623578987 & 0.0131760524715797 & 0.99341197376421 \tabularnewline
48 & 0.0102192376291713 & 0.0204384752583426 & 0.989780762370829 \tabularnewline
49 & 0.0143075500654408 & 0.0286151001308816 & 0.98569244993456 \tabularnewline
50 & 0.0208268689522337 & 0.0416537379044675 & 0.979173131047766 \tabularnewline
51 & 0.0209445054915973 & 0.0418890109831947 & 0.979055494508403 \tabularnewline
52 & 0.0375663796974661 & 0.0751327593949321 & 0.962433620302534 \tabularnewline
53 & 0.0840210559219864 & 0.168042111843973 & 0.915978944078014 \tabularnewline
54 & 0.107421808367256 & 0.214843616734512 & 0.892578191632744 \tabularnewline
55 & 0.101601194452958 & 0.203202388905916 & 0.898398805547042 \tabularnewline
56 & 0.0824165060763141 & 0.164833012152628 & 0.917583493923686 \tabularnewline
57 & 0.0664239157429601 & 0.132847831485920 & 0.93357608425704 \tabularnewline
58 & 0.0567982969024378 & 0.113596593804876 & 0.943201703097562 \tabularnewline
59 & 0.0485761121812084 & 0.0971522243624169 & 0.951423887818792 \tabularnewline
60 & 0.038123331818163 & 0.076246663636326 & 0.961876668181837 \tabularnewline
61 & 0.0310067291578798 & 0.0620134583157595 & 0.96899327084212 \tabularnewline
62 & 0.0239252456612378 & 0.0478504913224756 & 0.976074754338762 \tabularnewline
63 & 0.0232674064296305 & 0.046534812859261 & 0.97673259357037 \tabularnewline
64 & 0.0183911255084545 & 0.036782251016909 & 0.981608874491545 \tabularnewline
65 & 0.0170552705912858 & 0.0341105411825716 & 0.982944729408714 \tabularnewline
66 & 0.0261519928672272 & 0.0523039857344545 & 0.973848007132773 \tabularnewline
67 & 0.0484660642200261 & 0.0969321284400522 & 0.951533935779974 \tabularnewline
68 & 0.0668951266039352 & 0.133790253207870 & 0.933104873396065 \tabularnewline
69 & 0.111063181170264 & 0.222126362340527 & 0.888936818829736 \tabularnewline
70 & 0.0888213214350519 & 0.177642642870104 & 0.911178678564948 \tabularnewline
71 & 0.0695412917533678 & 0.139082583506736 & 0.930458708246632 \tabularnewline
72 & 0.0714198668764713 & 0.142839733752943 & 0.928580133123529 \tabularnewline
73 & 0.0733384609226863 & 0.146676921845373 & 0.926661539077314 \tabularnewline
74 & 0.132994231885458 & 0.265988463770917 & 0.867005768114542 \tabularnewline
75 & 0.177221393462785 & 0.354442786925569 & 0.822778606537215 \tabularnewline
76 & 0.468656715725288 & 0.937313431450576 & 0.531343284274712 \tabularnewline
77 & 0.723925989634934 & 0.552148020730133 & 0.276074010365066 \tabularnewline
78 & 0.817716984129412 & 0.364566031741175 & 0.182283015870588 \tabularnewline
79 & 0.83786364871154 & 0.324272702576921 & 0.162136351288460 \tabularnewline
80 & 0.903182651801079 & 0.193634696397843 & 0.0968173481989214 \tabularnewline
81 & 0.9180739021301 & 0.163852195739800 & 0.0819260978698998 \tabularnewline
82 & 0.898647782385862 & 0.202704435228276 & 0.101352217614138 \tabularnewline
83 & 0.874837027078193 & 0.250325945843615 & 0.125162972921807 \tabularnewline
84 & 0.873529039470195 & 0.25294192105961 & 0.126470960529805 \tabularnewline
85 & 0.860122880978352 & 0.279754238043295 & 0.139877119021648 \tabularnewline
86 & 0.83778702900701 & 0.324425941985981 & 0.162212970992990 \tabularnewline
87 & 0.809777172917662 & 0.380445654164676 & 0.190222827082338 \tabularnewline
88 & 0.773972759004977 & 0.452054481990046 & 0.226027240995023 \tabularnewline
89 & 0.729374196934483 & 0.541251606131034 & 0.270625803065517 \tabularnewline
90 & 0.695955591316622 & 0.608088817366756 & 0.304044408683378 \tabularnewline
91 & 0.643957677801542 & 0.712084644396915 & 0.356042322198458 \tabularnewline
92 & 0.591563572971027 & 0.816872854057947 & 0.408436427028973 \tabularnewline
93 & 0.536054893324444 & 0.927890213351111 & 0.463945106675556 \tabularnewline
94 & 0.489682497451089 & 0.979364994902178 & 0.510317502548911 \tabularnewline
95 & 0.50952662834296 & 0.98094674331408 & 0.49047337165704 \tabularnewline
96 & 0.448102430753173 & 0.896204861506346 & 0.551897569246827 \tabularnewline
97 & 0.391442467319564 & 0.782884934639127 & 0.608557532680436 \tabularnewline
98 & 0.372894404472233 & 0.745788808944466 & 0.627105595527767 \tabularnewline
99 & 0.331536362812071 & 0.663072725624142 & 0.668463637187929 \tabularnewline
100 & 0.27385746893732 & 0.54771493787464 & 0.72614253106268 \tabularnewline
101 & 0.242069026213412 & 0.484138052426824 & 0.757930973786588 \tabularnewline
102 & 0.198763926849258 & 0.397527853698517 & 0.801236073150742 \tabularnewline
103 & 0.195356475548809 & 0.390712951097617 & 0.804643524451191 \tabularnewline
104 & 0.259839579675318 & 0.519679159350636 & 0.740160420324682 \tabularnewline
105 & 0.591730126596658 & 0.816539746806684 & 0.408269873403342 \tabularnewline
106 & 0.598304448872959 & 0.803391102254083 & 0.401695551127041 \tabularnewline
107 & 0.708582417140141 & 0.582835165719717 & 0.291417582859859 \tabularnewline
108 & 0.988240853675928 & 0.0235182926481441 & 0.0117591463240720 \tabularnewline
109 & 0.973331914639 & 0.0533361707219993 & 0.0266680853609996 \tabularnewline
110 & 0.93795438053974 & 0.124091238920522 & 0.0620456194602608 \tabularnewline
111 & 0.955529967150477 & 0.0889400656990463 & 0.0444700328495232 \tabularnewline
112 & 0.99584140767631 & 0.00831718464738266 & 0.00415859232369133 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112170&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]8[/C][C]0.107879455448015[/C][C]0.215758910896029[/C][C]0.892120544551985[/C][/ROW]
[ROW][C]9[/C][C]0.0635432549429895[/C][C]0.127086509885979[/C][C]0.93645674505701[/C][/ROW]
[ROW][C]10[/C][C]0.0641880504319815[/C][C]0.128376100863963[/C][C]0.935811949568018[/C][/ROW]
[ROW][C]11[/C][C]0.157160100198054[/C][C]0.314320200396108[/C][C]0.842839899801946[/C][/ROW]
[ROW][C]12[/C][C]0.237719457591210[/C][C]0.475438915182419[/C][C]0.76228054240879[/C][/ROW]
[ROW][C]13[/C][C]0.227213517896932[/C][C]0.454427035793864[/C][C]0.772786482103068[/C][/ROW]
[ROW][C]14[/C][C]0.156382312267814[/C][C]0.312764624535629[/C][C]0.843617687732186[/C][/ROW]
[ROW][C]15[/C][C]0.105080934041066[/C][C]0.210161868082133[/C][C]0.894919065958934[/C][/ROW]
[ROW][C]16[/C][C]0.0665690861852334[/C][C]0.133138172370467[/C][C]0.933430913814767[/C][/ROW]
[ROW][C]17[/C][C]0.0411245832880739[/C][C]0.0822491665761479[/C][C]0.958875416711926[/C][/ROW]
[ROW][C]18[/C][C]0.0265328424135454[/C][C]0.0530656848270908[/C][C]0.973467157586455[/C][/ROW]
[ROW][C]19[/C][C]0.0151697819157403[/C][C]0.0303395638314806[/C][C]0.98483021808426[/C][/ROW]
[ROW][C]20[/C][C]0.0095562436310046[/C][C]0.0191124872620092[/C][C]0.990443756368995[/C][/ROW]
[ROW][C]21[/C][C]0.00529401021730387[/C][C]0.0105880204346077[/C][C]0.994705989782696[/C][/ROW]
[ROW][C]22[/C][C]0.00357607923910717[/C][C]0.00715215847821433[/C][C]0.996423920760893[/C][/ROW]
[ROW][C]23[/C][C]0.00337006593863634[/C][C]0.00674013187727268[/C][C]0.996629934061364[/C][/ROW]
[ROW][C]24[/C][C]0.00245676647646566[/C][C]0.00491353295293131[/C][C]0.997543233523534[/C][/ROW]
[ROW][C]25[/C][C]0.0016106296878035[/C][C]0.003221259375607[/C][C]0.998389370312196[/C][/ROW]
[ROW][C]26[/C][C]0.00103301883327683[/C][C]0.00206603766655366[/C][C]0.998966981166723[/C][/ROW]
[ROW][C]27[/C][C]0.00086626311284255[/C][C]0.0017325262256851[/C][C]0.999133736887157[/C][/ROW]
[ROW][C]28[/C][C]0.000923581988004788[/C][C]0.00184716397600958[/C][C]0.999076418011995[/C][/ROW]
[ROW][C]29[/C][C]0.000736259502936229[/C][C]0.00147251900587246[/C][C]0.999263740497064[/C][/ROW]
[ROW][C]30[/C][C]0.000425730664088876[/C][C]0.000851461328177752[/C][C]0.999574269335911[/C][/ROW]
[ROW][C]31[/C][C]0.000362958527921033[/C][C]0.000725917055842067[/C][C]0.999637041472079[/C][/ROW]
[ROW][C]32[/C][C]0.000423844731382511[/C][C]0.000847689462765021[/C][C]0.999576155268618[/C][/ROW]
[ROW][C]33[/C][C]0.000282700623550093[/C][C]0.000565401247100186[/C][C]0.99971729937645[/C][/ROW]
[ROW][C]34[/C][C]0.00429017464551027[/C][C]0.00858034929102054[/C][C]0.99570982535449[/C][/ROW]
[ROW][C]35[/C][C]0.0278337597774432[/C][C]0.0556675195548864[/C][C]0.972166240222557[/C][/ROW]
[ROW][C]36[/C][C]0.0427285468921628[/C][C]0.0854570937843256[/C][C]0.957271453107837[/C][/ROW]
[ROW][C]37[/C][C]0.0334267493827452[/C][C]0.0668534987654904[/C][C]0.966573250617255[/C][/ROW]
[ROW][C]38[/C][C]0.0260730971294723[/C][C]0.0521461942589445[/C][C]0.973926902870528[/C][/ROW]
[ROW][C]39[/C][C]0.0251390003380828[/C][C]0.0502780006761657[/C][C]0.974860999661917[/C][/ROW]
[ROW][C]40[/C][C]0.0247203743611432[/C][C]0.0494407487222863[/C][C]0.975279625638857[/C][/ROW]
[ROW][C]41[/C][C]0.0214005318509492[/C][C]0.0428010637018984[/C][C]0.97859946814905[/C][/ROW]
[ROW][C]42[/C][C]0.0157256327034959[/C][C]0.0314512654069918[/C][C]0.984274367296504[/C][/ROW]
[ROW][C]43[/C][C]0.0113896486463379[/C][C]0.0227792972926759[/C][C]0.988610351353662[/C][/ROW]
[ROW][C]44[/C][C]0.00899880357962838[/C][C]0.0179976071592568[/C][C]0.991001196420372[/C][/ROW]
[ROW][C]45[/C][C]0.00640687633435754[/C][C]0.0128137526687151[/C][C]0.993593123665642[/C][/ROW]
[ROW][C]46[/C][C]0.00482690674604272[/C][C]0.00965381349208544[/C][C]0.995173093253957[/C][/ROW]
[ROW][C]47[/C][C]0.00658802623578987[/C][C]0.0131760524715797[/C][C]0.99341197376421[/C][/ROW]
[ROW][C]48[/C][C]0.0102192376291713[/C][C]0.0204384752583426[/C][C]0.989780762370829[/C][/ROW]
[ROW][C]49[/C][C]0.0143075500654408[/C][C]0.0286151001308816[/C][C]0.98569244993456[/C][/ROW]
[ROW][C]50[/C][C]0.0208268689522337[/C][C]0.0416537379044675[/C][C]0.979173131047766[/C][/ROW]
[ROW][C]51[/C][C]0.0209445054915973[/C][C]0.0418890109831947[/C][C]0.979055494508403[/C][/ROW]
[ROW][C]52[/C][C]0.0375663796974661[/C][C]0.0751327593949321[/C][C]0.962433620302534[/C][/ROW]
[ROW][C]53[/C][C]0.0840210559219864[/C][C]0.168042111843973[/C][C]0.915978944078014[/C][/ROW]
[ROW][C]54[/C][C]0.107421808367256[/C][C]0.214843616734512[/C][C]0.892578191632744[/C][/ROW]
[ROW][C]55[/C][C]0.101601194452958[/C][C]0.203202388905916[/C][C]0.898398805547042[/C][/ROW]
[ROW][C]56[/C][C]0.0824165060763141[/C][C]0.164833012152628[/C][C]0.917583493923686[/C][/ROW]
[ROW][C]57[/C][C]0.0664239157429601[/C][C]0.132847831485920[/C][C]0.93357608425704[/C][/ROW]
[ROW][C]58[/C][C]0.0567982969024378[/C][C]0.113596593804876[/C][C]0.943201703097562[/C][/ROW]
[ROW][C]59[/C][C]0.0485761121812084[/C][C]0.0971522243624169[/C][C]0.951423887818792[/C][/ROW]
[ROW][C]60[/C][C]0.038123331818163[/C][C]0.076246663636326[/C][C]0.961876668181837[/C][/ROW]
[ROW][C]61[/C][C]0.0310067291578798[/C][C]0.0620134583157595[/C][C]0.96899327084212[/C][/ROW]
[ROW][C]62[/C][C]0.0239252456612378[/C][C]0.0478504913224756[/C][C]0.976074754338762[/C][/ROW]
[ROW][C]63[/C][C]0.0232674064296305[/C][C]0.046534812859261[/C][C]0.97673259357037[/C][/ROW]
[ROW][C]64[/C][C]0.0183911255084545[/C][C]0.036782251016909[/C][C]0.981608874491545[/C][/ROW]
[ROW][C]65[/C][C]0.0170552705912858[/C][C]0.0341105411825716[/C][C]0.982944729408714[/C][/ROW]
[ROW][C]66[/C][C]0.0261519928672272[/C][C]0.0523039857344545[/C][C]0.973848007132773[/C][/ROW]
[ROW][C]67[/C][C]0.0484660642200261[/C][C]0.0969321284400522[/C][C]0.951533935779974[/C][/ROW]
[ROW][C]68[/C][C]0.0668951266039352[/C][C]0.133790253207870[/C][C]0.933104873396065[/C][/ROW]
[ROW][C]69[/C][C]0.111063181170264[/C][C]0.222126362340527[/C][C]0.888936818829736[/C][/ROW]
[ROW][C]70[/C][C]0.0888213214350519[/C][C]0.177642642870104[/C][C]0.911178678564948[/C][/ROW]
[ROW][C]71[/C][C]0.0695412917533678[/C][C]0.139082583506736[/C][C]0.930458708246632[/C][/ROW]
[ROW][C]72[/C][C]0.0714198668764713[/C][C]0.142839733752943[/C][C]0.928580133123529[/C][/ROW]
[ROW][C]73[/C][C]0.0733384609226863[/C][C]0.146676921845373[/C][C]0.926661539077314[/C][/ROW]
[ROW][C]74[/C][C]0.132994231885458[/C][C]0.265988463770917[/C][C]0.867005768114542[/C][/ROW]
[ROW][C]75[/C][C]0.177221393462785[/C][C]0.354442786925569[/C][C]0.822778606537215[/C][/ROW]
[ROW][C]76[/C][C]0.468656715725288[/C][C]0.937313431450576[/C][C]0.531343284274712[/C][/ROW]
[ROW][C]77[/C][C]0.723925989634934[/C][C]0.552148020730133[/C][C]0.276074010365066[/C][/ROW]
[ROW][C]78[/C][C]0.817716984129412[/C][C]0.364566031741175[/C][C]0.182283015870588[/C][/ROW]
[ROW][C]79[/C][C]0.83786364871154[/C][C]0.324272702576921[/C][C]0.162136351288460[/C][/ROW]
[ROW][C]80[/C][C]0.903182651801079[/C][C]0.193634696397843[/C][C]0.0968173481989214[/C][/ROW]
[ROW][C]81[/C][C]0.9180739021301[/C][C]0.163852195739800[/C][C]0.0819260978698998[/C][/ROW]
[ROW][C]82[/C][C]0.898647782385862[/C][C]0.202704435228276[/C][C]0.101352217614138[/C][/ROW]
[ROW][C]83[/C][C]0.874837027078193[/C][C]0.250325945843615[/C][C]0.125162972921807[/C][/ROW]
[ROW][C]84[/C][C]0.873529039470195[/C][C]0.25294192105961[/C][C]0.126470960529805[/C][/ROW]
[ROW][C]85[/C][C]0.860122880978352[/C][C]0.279754238043295[/C][C]0.139877119021648[/C][/ROW]
[ROW][C]86[/C][C]0.83778702900701[/C][C]0.324425941985981[/C][C]0.162212970992990[/C][/ROW]
[ROW][C]87[/C][C]0.809777172917662[/C][C]0.380445654164676[/C][C]0.190222827082338[/C][/ROW]
[ROW][C]88[/C][C]0.773972759004977[/C][C]0.452054481990046[/C][C]0.226027240995023[/C][/ROW]
[ROW][C]89[/C][C]0.729374196934483[/C][C]0.541251606131034[/C][C]0.270625803065517[/C][/ROW]
[ROW][C]90[/C][C]0.695955591316622[/C][C]0.608088817366756[/C][C]0.304044408683378[/C][/ROW]
[ROW][C]91[/C][C]0.643957677801542[/C][C]0.712084644396915[/C][C]0.356042322198458[/C][/ROW]
[ROW][C]92[/C][C]0.591563572971027[/C][C]0.816872854057947[/C][C]0.408436427028973[/C][/ROW]
[ROW][C]93[/C][C]0.536054893324444[/C][C]0.927890213351111[/C][C]0.463945106675556[/C][/ROW]
[ROW][C]94[/C][C]0.489682497451089[/C][C]0.979364994902178[/C][C]0.510317502548911[/C][/ROW]
[ROW][C]95[/C][C]0.50952662834296[/C][C]0.98094674331408[/C][C]0.49047337165704[/C][/ROW]
[ROW][C]96[/C][C]0.448102430753173[/C][C]0.896204861506346[/C][C]0.551897569246827[/C][/ROW]
[ROW][C]97[/C][C]0.391442467319564[/C][C]0.782884934639127[/C][C]0.608557532680436[/C][/ROW]
[ROW][C]98[/C][C]0.372894404472233[/C][C]0.745788808944466[/C][C]0.627105595527767[/C][/ROW]
[ROW][C]99[/C][C]0.331536362812071[/C][C]0.663072725624142[/C][C]0.668463637187929[/C][/ROW]
[ROW][C]100[/C][C]0.27385746893732[/C][C]0.54771493787464[/C][C]0.72614253106268[/C][/ROW]
[ROW][C]101[/C][C]0.242069026213412[/C][C]0.484138052426824[/C][C]0.757930973786588[/C][/ROW]
[ROW][C]102[/C][C]0.198763926849258[/C][C]0.397527853698517[/C][C]0.801236073150742[/C][/ROW]
[ROW][C]103[/C][C]0.195356475548809[/C][C]0.390712951097617[/C][C]0.804643524451191[/C][/ROW]
[ROW][C]104[/C][C]0.259839579675318[/C][C]0.519679159350636[/C][C]0.740160420324682[/C][/ROW]
[ROW][C]105[/C][C]0.591730126596658[/C][C]0.816539746806684[/C][C]0.408269873403342[/C][/ROW]
[ROW][C]106[/C][C]0.598304448872959[/C][C]0.803391102254083[/C][C]0.401695551127041[/C][/ROW]
[ROW][C]107[/C][C]0.708582417140141[/C][C]0.582835165719717[/C][C]0.291417582859859[/C][/ROW]
[ROW][C]108[/C][C]0.988240853675928[/C][C]0.0235182926481441[/C][C]0.0117591463240720[/C][/ROW]
[ROW][C]109[/C][C]0.973331914639[/C][C]0.0533361707219993[/C][C]0.0266680853609996[/C][/ROW]
[ROW][C]110[/C][C]0.93795438053974[/C][C]0.124091238920522[/C][C]0.0620456194602608[/C][/ROW]
[ROW][C]111[/C][C]0.955529967150477[/C][C]0.0889400656990463[/C][C]0.0444700328495232[/C][/ROW]
[ROW][C]112[/C][C]0.99584140767631[/C][C]0.00831718464738266[/C][C]0.00415859232369133[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112170&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112170&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
8	0.107879455448015	0.215758910896029	0.892120544551985
9	0.0635432549429895	0.127086509885979	0.93645674505701
10	0.0641880504319815	0.128376100863963	0.935811949568018
11	0.157160100198054	0.314320200396108	0.842839899801946
12	0.237719457591210	0.475438915182419	0.76228054240879
13	0.227213517896932	0.454427035793864	0.772786482103068
14	0.156382312267814	0.312764624535629	0.843617687732186
15	0.105080934041066	0.210161868082133	0.894919065958934
16	0.0665690861852334	0.133138172370467	0.933430913814767
17	0.0411245832880739	0.0822491665761479	0.958875416711926
18	0.0265328424135454	0.0530656848270908	0.973467157586455
19	0.0151697819157403	0.0303395638314806	0.98483021808426
20	0.0095562436310046	0.0191124872620092	0.990443756368995
21	0.00529401021730387	0.0105880204346077	0.994705989782696
22	0.00357607923910717	0.00715215847821433	0.996423920760893
23	0.00337006593863634	0.00674013187727268	0.996629934061364
24	0.00245676647646566	0.00491353295293131	0.997543233523534
25	0.0016106296878035	0.003221259375607	0.998389370312196
26	0.00103301883327683	0.00206603766655366	0.998966981166723
27	0.00086626311284255	0.0017325262256851	0.999133736887157
28	0.000923581988004788	0.00184716397600958	0.999076418011995
29	0.000736259502936229	0.00147251900587246	0.999263740497064
30	0.000425730664088876	0.000851461328177752	0.999574269335911
31	0.000362958527921033	0.000725917055842067	0.999637041472079
32	0.000423844731382511	0.000847689462765021	0.999576155268618
33	0.000282700623550093	0.000565401247100186	0.99971729937645
34	0.00429017464551027	0.00858034929102054	0.99570982535449
35	0.0278337597774432	0.0556675195548864	0.972166240222557
36	0.0427285468921628	0.0854570937843256	0.957271453107837
37	0.0334267493827452	0.0668534987654904	0.966573250617255
38	0.0260730971294723	0.0521461942589445	0.973926902870528
39	0.0251390003380828	0.0502780006761657	0.974860999661917
40	0.0247203743611432	0.0494407487222863	0.975279625638857
41	0.0214005318509492	0.0428010637018984	0.97859946814905
42	0.0157256327034959	0.0314512654069918	0.984274367296504
43	0.0113896486463379	0.0227792972926759	0.988610351353662
44	0.00899880357962838	0.0179976071592568	0.991001196420372
45	0.00640687633435754	0.0128137526687151	0.993593123665642
46	0.00482690674604272	0.00965381349208544	0.995173093253957
47	0.00658802623578987	0.0131760524715797	0.99341197376421
48	0.0102192376291713	0.0204384752583426	0.989780762370829
49	0.0143075500654408	0.0286151001308816	0.98569244993456
50	0.0208268689522337	0.0416537379044675	0.979173131047766
51	0.0209445054915973	0.0418890109831947	0.979055494508403
52	0.0375663796974661	0.0751327593949321	0.962433620302534
53	0.0840210559219864	0.168042111843973	0.915978944078014
54	0.107421808367256	0.214843616734512	0.892578191632744
55	0.101601194452958	0.203202388905916	0.898398805547042
56	0.0824165060763141	0.164833012152628	0.917583493923686
57	0.0664239157429601	0.132847831485920	0.93357608425704
58	0.0567982969024378	0.113596593804876	0.943201703097562
59	0.0485761121812084	0.0971522243624169	0.951423887818792
60	0.038123331818163	0.076246663636326	0.961876668181837
61	0.0310067291578798	0.0620134583157595	0.96899327084212
62	0.0239252456612378	0.0478504913224756	0.976074754338762
63	0.0232674064296305	0.046534812859261	0.97673259357037
64	0.0183911255084545	0.036782251016909	0.981608874491545
65	0.0170552705912858	0.0341105411825716	0.982944729408714
66	0.0261519928672272	0.0523039857344545	0.973848007132773
67	0.0484660642200261	0.0969321284400522	0.951533935779974
68	0.0668951266039352	0.133790253207870	0.933104873396065
69	0.111063181170264	0.222126362340527	0.888936818829736
70	0.0888213214350519	0.177642642870104	0.911178678564948
71	0.0695412917533678	0.139082583506736	0.930458708246632
72	0.0714198668764713	0.142839733752943	0.928580133123529
73	0.0733384609226863	0.146676921845373	0.926661539077314
74	0.132994231885458	0.265988463770917	0.867005768114542
75	0.177221393462785	0.354442786925569	0.822778606537215
76	0.468656715725288	0.937313431450576	0.531343284274712
77	0.723925989634934	0.552148020730133	0.276074010365066
78	0.817716984129412	0.364566031741175	0.182283015870588
79	0.83786364871154	0.324272702576921	0.162136351288460
80	0.903182651801079	0.193634696397843	0.0968173481989214
81	0.9180739021301	0.163852195739800	0.0819260978698998
82	0.898647782385862	0.202704435228276	0.101352217614138
83	0.874837027078193	0.250325945843615	0.125162972921807
84	0.873529039470195	0.25294192105961	0.126470960529805
85	0.860122880978352	0.279754238043295	0.139877119021648
86	0.83778702900701	0.324425941985981	0.162212970992990
87	0.809777172917662	0.380445654164676	0.190222827082338
88	0.773972759004977	0.452054481990046	0.226027240995023
89	0.729374196934483	0.541251606131034	0.270625803065517
90	0.695955591316622	0.608088817366756	0.304044408683378
91	0.643957677801542	0.712084644396915	0.356042322198458
92	0.591563572971027	0.816872854057947	0.408436427028973
93	0.536054893324444	0.927890213351111	0.463945106675556
94	0.489682497451089	0.979364994902178	0.510317502548911
95	0.50952662834296	0.98094674331408	0.49047337165704
96	0.448102430753173	0.896204861506346	0.551897569246827
97	0.391442467319564	0.782884934639127	0.608557532680436
98	0.372894404472233	0.745788808944466	0.627105595527767
99	0.331536362812071	0.663072725624142	0.668463637187929
100	0.27385746893732	0.54771493787464	0.72614253106268
101	0.242069026213412	0.484138052426824	0.757930973786588
102	0.198763926849258	0.397527853698517	0.801236073150742
103	0.195356475548809	0.390712951097617	0.804643524451191
104	0.259839579675318	0.519679159350636	0.740160420324682
105	0.591730126596658	0.816539746806684	0.408269873403342
106	0.598304448872959	0.803391102254083	0.401695551127041
107	0.708582417140141	0.582835165719717	0.291417582859859
108	0.988240853675928	0.0235182926481441	0.0117591463240720
109	0.973331914639	0.0533361707219993	0.0266680853609996
110	0.93795438053974	0.124091238920522	0.0620456194602608
111	0.955529967150477	0.0889400656990463	0.0444700328495232
112	0.99584140767631	0.00831718464738266	0.00415859232369133

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	15	0.142857142857143	NOK
5% type I error level	34	0.323809523809524	NOK
10% type I error level	49	0.466666666666667	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 & 15 & 0.142857142857143 & NOK \tabularnewline
5% type I error level & 34 & 0.323809523809524 & NOK \tabularnewline
10% type I error level & 49 & 0.466666666666667 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112170&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]15[/C][C]0.142857142857143[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]34[/C][C]0.323809523809524[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]49[/C][C]0.466666666666667[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112170&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112170&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	15	0.142857142857143	NOK
5% type I error level	34	0.323809523809524	NOK
10% type I error level	49	0.466666666666667	NOK

Figure 1

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Figure 2

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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):

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;

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

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

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code