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*Unverified author*

R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Sun, 17 Apr 2011 22:56:49 +0000

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Apr/18/t1303080843dzi683nqwrfvwnf.htm/, Retrieved Wed, 08 May 2024 11:13:51 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=120611, Retrieved Wed, 08 May 2024 11:13:51 +0000

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185

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

-       [Multiple Regression] [earning merck] [2011-04-17 22:56:49] [d41d8cd98f00b204e9800998ecf8427e] [Current]

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Dataseries X:

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0.79	4.372157	0.358171	0.768038
0.9	4.37581	0.402526	0.778505
0.83	4.378543	0.201355	0.756444
0.74	4.38421	0.210216	0.761823
0.88	4.390016	0.201257	0.761285
0.8	4.394889	0.194135	0.771903
0.856	4.4004	0.203109	0.789904
0.9	4.402502	0.215335	0.771369
0.8	4.406353	0.186933	0.771341
0.76	4.409155	0.212281	0.765422
0.82	4.415841	0.300082	0.756006
0.85	4.42116	0.316122	0.741814
0.5	4.427843	0.24835	0.751497
0.51	4.431814	0.24658	0.778627
0.73	4.437513	0.252709	0.799937
0.79	4.439901	0.286081	0.774228
0.64	4.441522	0.288254	0.771279
0.68	4.445791	0.326279	0.787727
0.62	4.449679	0.320449	0.762896
0.62	4.454067	0.27137	0.776688
0.72	4.461934	0.253831	0.770499
0.85	4.46565	0.288447	0.812129
0.81	4.47138	0.303094	0.796086
0.72	4.476353	0.327162	0.781913
0.67	4.482516	0.35257	0.811975
0.83	4.488015	0.275083	0.364749
0.83	4.491572	0.284799	0.368819
0.8	4.495281	0.26807	0.81384
0.81	4.499013	0.277758	0.820645
0.79	4.505014	0.288119	0.352631
0.77	4.512458	0.291262	0.344194
0.72	4.517143	0.298976	0.391427
0.82	4.520863	0.275749	0.405785
0.85	4.526598	0.247584	0.394203
0.79	4.534546	0.24387	0.378895
0.72	4.53802	0.242759	0.427009
0.77	4.547529	0.25426	0.433414
0.8	4.549285	0.270391	0.466915
0.74	4.552936	0.284539	0.453939
0.65	4.553422	0.237426	0.481904
0.68	4.558144	0.249957	0.467294
0.65	4.557134	0.247617	0.454965
0.63	4.558865	0.241152	0.448804
0.55	4.563113	0.25159	0.500405
0.59	4.566379	0.217016	0.481728
0.575	4.569327	0.098371	0.477096
0.55	4.570893	0.140313	0.465967
0.487	4.574841	0.106751	0.50759
0.52	4.578616	0.131664	0.49542
0.495	4.587183	0.134684	0.501743
0.48	4.592299	0.099122	0.499578
0.42	4.598265	0.096562	0.542387
0.435	4.604053	0.116527	0.532769
0.415	4.609274	0.132874	0.534754
0.4	4.615192	0.129996	0.507085
0.35	4.622587	0.116976	0.533487

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' @ www.wessa.org

\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' @ www.wessa.org \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=120611&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' @ www.wessa.org[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=120611&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=120611&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' @ www.wessa.org

Multiple Linear Regression - Estimated Regression Equation
basiceps[t] = -23.9293930240113 + 5.70542661862854gdp[t] + 0.406023757176962`D/E`[t] -0.304011213321105GM[t] -0.0331158042092614t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
basiceps[t] =  -23.9293930240113 +  5.70542661862854gdp[t] +  0.406023757176962`D/E`[t] -0.304011213321105GM[t] -0.0331158042092614t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=120611&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]basiceps[t] =  -23.9293930240113 +  5.70542661862854gdp[t] +  0.406023757176962`D/E`[t] -0.304011213321105GM[t] -0.0331158042092614t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=120611&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=120611&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
basiceps[t] = -23.9293930240113 + 5.70542661862854gdp[t] + 0.406023757176962`D/E`[t] -0.304011213321105GM[t] -0.0331158042092614t + 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)	-23.9293930240113	13.45014	-1.7791	0.08118	0.04059
gdp	5.70542661862854	3.076833	1.8543	0.06948	0.03474
`D/E`	0.406023757176962	0.216751	1.8732	0.066773	0.033387
GM	-0.304011213321105	0.110187	-2.759	0.008031	0.004015
t	-0.0331158042092614	0.013768	-2.4053	0.019825	0.009912

\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) & -23.9293930240113 & 13.45014 & -1.7791 & 0.08118 & 0.04059 \tabularnewline
gdp & 5.70542661862854 & 3.076833 & 1.8543 & 0.06948 & 0.03474 \tabularnewline
`D/E` & 0.406023757176962 & 0.216751 & 1.8732 & 0.066773 & 0.033387 \tabularnewline
GM & -0.304011213321105 & 0.110187 & -2.759 & 0.008031 & 0.004015 \tabularnewline
t & -0.0331158042092614 & 0.013768 & -2.4053 & 0.019825 & 0.009912 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=120611&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]-23.9293930240113[/C][C]13.45014[/C][C]-1.7791[/C][C]0.08118[/C][C]0.04059[/C][/ROW]
[ROW][C]gdp[/C][C]5.70542661862854[/C][C]3.076833[/C][C]1.8543[/C][C]0.06948[/C][C]0.03474[/C][/ROW]
[ROW][C]`D/E`[/C][C]0.406023757176962[/C][C]0.216751[/C][C]1.8732[/C][C]0.066773[/C][C]0.033387[/C][/ROW]
[ROW][C]GM[/C][C]-0.304011213321105[/C][C]0.110187[/C][C]-2.759[/C][C]0.008031[/C][C]0.004015[/C][/ROW]
[ROW][C]t[/C][C]-0.0331158042092614[/C][C]0.013768[/C][C]-2.4053[/C][C]0.019825[/C][C]0.009912[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=120611&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=120611&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)	-23.9293930240113	13.45014	-1.7791	0.08118	0.04059
gdp	5.70542661862854	3.076833	1.8543	0.06948	0.03474
`D/E`	0.406023757176962	0.216751	1.8732	0.066773	0.033387
GM	-0.304011213321105	0.110187	-2.759	0.008031	0.004015
t	-0.0331158042092614	0.013768	-2.4053	0.019825	0.009912

Multiple Linear Regression - Regression Statistics
Multiple R	0.830537538535335
R-squared	0.689792602916333
Adjusted R-squared	0.665462610988203
F-TEST (value)	28.3515343923639
F-TEST (DF numerator)	4
F-TEST (DF denominator)	51
p-value	2.02482475231136e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.0848051288500866
Sum Squared Residuals	0.36678740384327

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.830537538535335 \tabularnewline
R-squared & 0.689792602916333 \tabularnewline
Adjusted R-squared & 0.665462610988203 \tabularnewline
F-TEST (value) & 28.3515343923639 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 51 \tabularnewline
p-value & 2.02482475231136e-12 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.0848051288500866 \tabularnewline
Sum Squared Residuals & 0.36678740384327 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=120611&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.830537538535335[/C][/ROW]
[ROW][C]R-squared[/C][C]0.689792602916333[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.665462610988203[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]28.3515343923639[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]51[/C][/ROW]
[ROW][C]p-value[/C][C]2.02482475231136e-12[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.0848051288500866[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]0.36678740384327[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=120611&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=120611&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	0.830537538535335
R-squared	0.689792602916333
Adjusted R-squared	0.665462610988203
F-TEST (value)	28.3515343923639
F-TEST (DF numerator)	4
F-TEST (DF denominator)	51
p-value	2.02482475231136e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.0848051288500866
Sum Squared Residuals	0.36678740384327

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	0.79	0.894445871277677	-0.104445871277677
2	0.9	0.896999088885986	0.00300091111401407
3	0.83	0.804502801747461	0.0254971982525388
4	0.74	0.805682150381863	-0.0656821503818628
5	0.88	0.802218044312576	0.0777819556874244
6	0.8	0.790785091754233	0.0092149082457671
7	0.856	0.787283044986148	0.0687169550138521
8	0.9	0.776758941823395	0.123241058176605
9	0.8	0.754091361085105	0.0459086389148948
10	0.76	0.74905349482981	0.0109465051701899
11	0.82	0.792596034481226	0.0274039655187736
12	0.85	0.800654542661022	0.0493454573389778
13	0.5	0.775207321894069	-0.275207321894069
14	0.51	0.755781280519776	-0.245781280519776
15	0.73	0.751190743261943	-0.0211907432619431
16	0.79	0.753065146925748	0.0369348530742524
17	0.64	0.730976657957713	-0.0909766579577131
18	0.68	0.732655996913325	-0.0526559969133246
19	0.62	0.726904675330925	-0.106904675330925
20	0.62	0.694704120491596	-0.0747041204915959
21	0.72	0.701233182213203	0.0187668177867968
22	0.85	0.690717674886645	0.159282325113355
23	0.81	0.701118247068805	0.108881752931195
24	0.72	0.710456460148117	0.00954353985188322
25	0.67	0.713680266716961	-0.0436802667169608
26	0.83	0.816438759499908	0.0135612405000921
27	0.83	0.806324758959622	0.0236752410403783
28	0.8	0.652286636481672	0.147713363518328
29	0.81	0.64232824626601	0.16767175373399
30	0.79	0.789939023334514	6.09766654858148e-05
31	0.77	0.803135490149919	-0.0331354901499185
32	0.72	0.785522315273001	-0.0655223152730008
33	0.82	0.759834991276226	0.0601650087237738
34	0.85	0.751525207476593	0.098474792523407
35	0.79	0.766901965451555	0.0230980345484453
36	0.72	0.738528525403457	-0.0185285254034574
37	0.77	0.762388110320703	0.00761188967929737
38	0.8	0.735655924823306	0.0643440751766939
39	0.74	0.73305990681925	0.00694009318075026
40	0.65	0.675086269094241	-0.0250862690942414
41	0.68	0.67844097690595	0.00155902309404949
42	0.65	0.642360750469111	0.00763924953088872
43	0.63	0.61836910923182	0.0116308907681797
44	0.55	0.598040750657327	-0.0480407506573266
45	0.59	0.575199021835068	0.014800978164932
46	0.575	0.512138306567366	0.062861693432634
47	0.55	0.508369989659441	0.0416300103405593
48	0.487	0.471498381670089	0.015501618329911
49	0.52	0.473735649274818	0.0462643507251817
50	0.495	0.488802163752189	0.00619783624781115
51	0.48	0.471094489547945	0.00890551045205517
52	0.42	0.457963423695985	-0.0379634236959848
53	0.435	0.46890087291711	-0.0339008729171104
54	0.415	0.471606909183836	-0.0566069091838363
55	0.4	0.479498969591847	-0.0794989695918467
56	0.35	0.475261861854795	-0.125261861854795

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 0.79 & 0.894445871277677 & -0.104445871277677 \tabularnewline
2 & 0.9 & 0.896999088885986 & 0.00300091111401407 \tabularnewline
3 & 0.83 & 0.804502801747461 & 0.0254971982525388 \tabularnewline
4 & 0.74 & 0.805682150381863 & -0.0656821503818628 \tabularnewline
5 & 0.88 & 0.802218044312576 & 0.0777819556874244 \tabularnewline
6 & 0.8 & 0.790785091754233 & 0.0092149082457671 \tabularnewline
7 & 0.856 & 0.787283044986148 & 0.0687169550138521 \tabularnewline
8 & 0.9 & 0.776758941823395 & 0.123241058176605 \tabularnewline
9 & 0.8 & 0.754091361085105 & 0.0459086389148948 \tabularnewline
10 & 0.76 & 0.74905349482981 & 0.0109465051701899 \tabularnewline
11 & 0.82 & 0.792596034481226 & 0.0274039655187736 \tabularnewline
12 & 0.85 & 0.800654542661022 & 0.0493454573389778 \tabularnewline
13 & 0.5 & 0.775207321894069 & -0.275207321894069 \tabularnewline
14 & 0.51 & 0.755781280519776 & -0.245781280519776 \tabularnewline
15 & 0.73 & 0.751190743261943 & -0.0211907432619431 \tabularnewline
16 & 0.79 & 0.753065146925748 & 0.0369348530742524 \tabularnewline
17 & 0.64 & 0.730976657957713 & -0.0909766579577131 \tabularnewline
18 & 0.68 & 0.732655996913325 & -0.0526559969133246 \tabularnewline
19 & 0.62 & 0.726904675330925 & -0.106904675330925 \tabularnewline
20 & 0.62 & 0.694704120491596 & -0.0747041204915959 \tabularnewline
21 & 0.72 & 0.701233182213203 & 0.0187668177867968 \tabularnewline
22 & 0.85 & 0.690717674886645 & 0.159282325113355 \tabularnewline
23 & 0.81 & 0.701118247068805 & 0.108881752931195 \tabularnewline
24 & 0.72 & 0.710456460148117 & 0.00954353985188322 \tabularnewline
25 & 0.67 & 0.713680266716961 & -0.0436802667169608 \tabularnewline
26 & 0.83 & 0.816438759499908 & 0.0135612405000921 \tabularnewline
27 & 0.83 & 0.806324758959622 & 0.0236752410403783 \tabularnewline
28 & 0.8 & 0.652286636481672 & 0.147713363518328 \tabularnewline
29 & 0.81 & 0.64232824626601 & 0.16767175373399 \tabularnewline
30 & 0.79 & 0.789939023334514 & 6.09766654858148e-05 \tabularnewline
31 & 0.77 & 0.803135490149919 & -0.0331354901499185 \tabularnewline
32 & 0.72 & 0.785522315273001 & -0.0655223152730008 \tabularnewline
33 & 0.82 & 0.759834991276226 & 0.0601650087237738 \tabularnewline
34 & 0.85 & 0.751525207476593 & 0.098474792523407 \tabularnewline
35 & 0.79 & 0.766901965451555 & 0.0230980345484453 \tabularnewline
36 & 0.72 & 0.738528525403457 & -0.0185285254034574 \tabularnewline
37 & 0.77 & 0.762388110320703 & 0.00761188967929737 \tabularnewline
38 & 0.8 & 0.735655924823306 & 0.0643440751766939 \tabularnewline
39 & 0.74 & 0.73305990681925 & 0.00694009318075026 \tabularnewline
40 & 0.65 & 0.675086269094241 & -0.0250862690942414 \tabularnewline
41 & 0.68 & 0.67844097690595 & 0.00155902309404949 \tabularnewline
42 & 0.65 & 0.642360750469111 & 0.00763924953088872 \tabularnewline
43 & 0.63 & 0.61836910923182 & 0.0116308907681797 \tabularnewline
44 & 0.55 & 0.598040750657327 & -0.0480407506573266 \tabularnewline
45 & 0.59 & 0.575199021835068 & 0.014800978164932 \tabularnewline
46 & 0.575 & 0.512138306567366 & 0.062861693432634 \tabularnewline
47 & 0.55 & 0.508369989659441 & 0.0416300103405593 \tabularnewline
48 & 0.487 & 0.471498381670089 & 0.015501618329911 \tabularnewline
49 & 0.52 & 0.473735649274818 & 0.0462643507251817 \tabularnewline
50 & 0.495 & 0.488802163752189 & 0.00619783624781115 \tabularnewline
51 & 0.48 & 0.471094489547945 & 0.00890551045205517 \tabularnewline
52 & 0.42 & 0.457963423695985 & -0.0379634236959848 \tabularnewline
53 & 0.435 & 0.46890087291711 & -0.0339008729171104 \tabularnewline
54 & 0.415 & 0.471606909183836 & -0.0566069091838363 \tabularnewline
55 & 0.4 & 0.479498969591847 & -0.0794989695918467 \tabularnewline
56 & 0.35 & 0.475261861854795 & -0.125261861854795 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=120611&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]0.79[/C][C]0.894445871277677[/C][C]-0.104445871277677[/C][/ROW]
[ROW][C]2[/C][C]0.9[/C][C]0.896999088885986[/C][C]0.00300091111401407[/C][/ROW]
[ROW][C]3[/C][C]0.83[/C][C]0.804502801747461[/C][C]0.0254971982525388[/C][/ROW]
[ROW][C]4[/C][C]0.74[/C][C]0.805682150381863[/C][C]-0.0656821503818628[/C][/ROW]
[ROW][C]5[/C][C]0.88[/C][C]0.802218044312576[/C][C]0.0777819556874244[/C][/ROW]
[ROW][C]6[/C][C]0.8[/C][C]0.790785091754233[/C][C]0.0092149082457671[/C][/ROW]
[ROW][C]7[/C][C]0.856[/C][C]0.787283044986148[/C][C]0.0687169550138521[/C][/ROW]
[ROW][C]8[/C][C]0.9[/C][C]0.776758941823395[/C][C]0.123241058176605[/C][/ROW]
[ROW][C]9[/C][C]0.8[/C][C]0.754091361085105[/C][C]0.0459086389148948[/C][/ROW]
[ROW][C]10[/C][C]0.76[/C][C]0.74905349482981[/C][C]0.0109465051701899[/C][/ROW]
[ROW][C]11[/C][C]0.82[/C][C]0.792596034481226[/C][C]0.0274039655187736[/C][/ROW]
[ROW][C]12[/C][C]0.85[/C][C]0.800654542661022[/C][C]0.0493454573389778[/C][/ROW]
[ROW][C]13[/C][C]0.5[/C][C]0.775207321894069[/C][C]-0.275207321894069[/C][/ROW]
[ROW][C]14[/C][C]0.51[/C][C]0.755781280519776[/C][C]-0.245781280519776[/C][/ROW]
[ROW][C]15[/C][C]0.73[/C][C]0.751190743261943[/C][C]-0.0211907432619431[/C][/ROW]
[ROW][C]16[/C][C]0.79[/C][C]0.753065146925748[/C][C]0.0369348530742524[/C][/ROW]
[ROW][C]17[/C][C]0.64[/C][C]0.730976657957713[/C][C]-0.0909766579577131[/C][/ROW]
[ROW][C]18[/C][C]0.68[/C][C]0.732655996913325[/C][C]-0.0526559969133246[/C][/ROW]
[ROW][C]19[/C][C]0.62[/C][C]0.726904675330925[/C][C]-0.106904675330925[/C][/ROW]
[ROW][C]20[/C][C]0.62[/C][C]0.694704120491596[/C][C]-0.0747041204915959[/C][/ROW]
[ROW][C]21[/C][C]0.72[/C][C]0.701233182213203[/C][C]0.0187668177867968[/C][/ROW]
[ROW][C]22[/C][C]0.85[/C][C]0.690717674886645[/C][C]0.159282325113355[/C][/ROW]
[ROW][C]23[/C][C]0.81[/C][C]0.701118247068805[/C][C]0.108881752931195[/C][/ROW]
[ROW][C]24[/C][C]0.72[/C][C]0.710456460148117[/C][C]0.00954353985188322[/C][/ROW]
[ROW][C]25[/C][C]0.67[/C][C]0.713680266716961[/C][C]-0.0436802667169608[/C][/ROW]
[ROW][C]26[/C][C]0.83[/C][C]0.816438759499908[/C][C]0.0135612405000921[/C][/ROW]
[ROW][C]27[/C][C]0.83[/C][C]0.806324758959622[/C][C]0.0236752410403783[/C][/ROW]
[ROW][C]28[/C][C]0.8[/C][C]0.652286636481672[/C][C]0.147713363518328[/C][/ROW]
[ROW][C]29[/C][C]0.81[/C][C]0.64232824626601[/C][C]0.16767175373399[/C][/ROW]
[ROW][C]30[/C][C]0.79[/C][C]0.789939023334514[/C][C]6.09766654858148e-05[/C][/ROW]
[ROW][C]31[/C][C]0.77[/C][C]0.803135490149919[/C][C]-0.0331354901499185[/C][/ROW]
[ROW][C]32[/C][C]0.72[/C][C]0.785522315273001[/C][C]-0.0655223152730008[/C][/ROW]
[ROW][C]33[/C][C]0.82[/C][C]0.759834991276226[/C][C]0.0601650087237738[/C][/ROW]
[ROW][C]34[/C][C]0.85[/C][C]0.751525207476593[/C][C]0.098474792523407[/C][/ROW]
[ROW][C]35[/C][C]0.79[/C][C]0.766901965451555[/C][C]0.0230980345484453[/C][/ROW]
[ROW][C]36[/C][C]0.72[/C][C]0.738528525403457[/C][C]-0.0185285254034574[/C][/ROW]
[ROW][C]37[/C][C]0.77[/C][C]0.762388110320703[/C][C]0.00761188967929737[/C][/ROW]
[ROW][C]38[/C][C]0.8[/C][C]0.735655924823306[/C][C]0.0643440751766939[/C][/ROW]
[ROW][C]39[/C][C]0.74[/C][C]0.73305990681925[/C][C]0.00694009318075026[/C][/ROW]
[ROW][C]40[/C][C]0.65[/C][C]0.675086269094241[/C][C]-0.0250862690942414[/C][/ROW]
[ROW][C]41[/C][C]0.68[/C][C]0.67844097690595[/C][C]0.00155902309404949[/C][/ROW]
[ROW][C]42[/C][C]0.65[/C][C]0.642360750469111[/C][C]0.00763924953088872[/C][/ROW]
[ROW][C]43[/C][C]0.63[/C][C]0.61836910923182[/C][C]0.0116308907681797[/C][/ROW]
[ROW][C]44[/C][C]0.55[/C][C]0.598040750657327[/C][C]-0.0480407506573266[/C][/ROW]
[ROW][C]45[/C][C]0.59[/C][C]0.575199021835068[/C][C]0.014800978164932[/C][/ROW]
[ROW][C]46[/C][C]0.575[/C][C]0.512138306567366[/C][C]0.062861693432634[/C][/ROW]
[ROW][C]47[/C][C]0.55[/C][C]0.508369989659441[/C][C]0.0416300103405593[/C][/ROW]
[ROW][C]48[/C][C]0.487[/C][C]0.471498381670089[/C][C]0.015501618329911[/C][/ROW]
[ROW][C]49[/C][C]0.52[/C][C]0.473735649274818[/C][C]0.0462643507251817[/C][/ROW]
[ROW][C]50[/C][C]0.495[/C][C]0.488802163752189[/C][C]0.00619783624781115[/C][/ROW]
[ROW][C]51[/C][C]0.48[/C][C]0.471094489547945[/C][C]0.00890551045205517[/C][/ROW]
[ROW][C]52[/C][C]0.42[/C][C]0.457963423695985[/C][C]-0.0379634236959848[/C][/ROW]
[ROW][C]53[/C][C]0.435[/C][C]0.46890087291711[/C][C]-0.0339008729171104[/C][/ROW]
[ROW][C]54[/C][C]0.415[/C][C]0.471606909183836[/C][C]-0.0566069091838363[/C][/ROW]
[ROW][C]55[/C][C]0.4[/C][C]0.479498969591847[/C][C]-0.0794989695918467[/C][/ROW]
[ROW][C]56[/C][C]0.35[/C][C]0.475261861854795[/C][C]-0.125261861854795[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=120611&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=120611&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	0.79	0.894445871277677	-0.104445871277677
2	0.9	0.896999088885986	0.00300091111401407
3	0.83	0.804502801747461	0.0254971982525388
4	0.74	0.805682150381863	-0.0656821503818628
5	0.88	0.802218044312576	0.0777819556874244
6	0.8	0.790785091754233	0.0092149082457671
7	0.856	0.787283044986148	0.0687169550138521
8	0.9	0.776758941823395	0.123241058176605
9	0.8	0.754091361085105	0.0459086389148948
10	0.76	0.74905349482981	0.0109465051701899
11	0.82	0.792596034481226	0.0274039655187736
12	0.85	0.800654542661022	0.0493454573389778
13	0.5	0.775207321894069	-0.275207321894069
14	0.51	0.755781280519776	-0.245781280519776
15	0.73	0.751190743261943	-0.0211907432619431
16	0.79	0.753065146925748	0.0369348530742524
17	0.64	0.730976657957713	-0.0909766579577131
18	0.68	0.732655996913325	-0.0526559969133246
19	0.62	0.726904675330925	-0.106904675330925
20	0.62	0.694704120491596	-0.0747041204915959
21	0.72	0.701233182213203	0.0187668177867968
22	0.85	0.690717674886645	0.159282325113355
23	0.81	0.701118247068805	0.108881752931195
24	0.72	0.710456460148117	0.00954353985188322
25	0.67	0.713680266716961	-0.0436802667169608
26	0.83	0.816438759499908	0.0135612405000921
27	0.83	0.806324758959622	0.0236752410403783
28	0.8	0.652286636481672	0.147713363518328
29	0.81	0.64232824626601	0.16767175373399
30	0.79	0.789939023334514	6.09766654858148e-05
31	0.77	0.803135490149919	-0.0331354901499185
32	0.72	0.785522315273001	-0.0655223152730008
33	0.82	0.759834991276226	0.0601650087237738
34	0.85	0.751525207476593	0.098474792523407
35	0.79	0.766901965451555	0.0230980345484453
36	0.72	0.738528525403457	-0.0185285254034574
37	0.77	0.762388110320703	0.00761188967929737
38	0.8	0.735655924823306	0.0643440751766939
39	0.74	0.73305990681925	0.00694009318075026
40	0.65	0.675086269094241	-0.0250862690942414
41	0.68	0.67844097690595	0.00155902309404949
42	0.65	0.642360750469111	0.00763924953088872
43	0.63	0.61836910923182	0.0116308907681797
44	0.55	0.598040750657327	-0.0480407506573266
45	0.59	0.575199021835068	0.014800978164932
46	0.575	0.512138306567366	0.062861693432634
47	0.55	0.508369989659441	0.0416300103405593
48	0.487	0.471498381670089	0.015501618329911
49	0.52	0.473735649274818	0.0462643507251817
50	0.495	0.488802163752189	0.00619783624781115
51	0.48	0.471094489547945	0.00890551045205517
52	0.42	0.457963423695985	-0.0379634236959848
53	0.435	0.46890087291711	-0.0339008729171104
54	0.415	0.471606909183836	-0.0566069091838363
55	0.4	0.479498969591847	-0.0794989695918467
56	0.35	0.475261861854795	-0.125261861854795

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
8	0.368092839961436	0.736185679922871	0.631907160038564
9	0.382460138951777	0.764920277903555	0.617539861048223
10	0.34122660687997	0.68245321375994	0.65877339312003
11	0.226436220099159	0.452872440198319	0.77356377990084
12	0.148344618132942	0.296689236265884	0.851655381867058
13	0.872472920056497	0.255054159887006	0.127527079943503
14	0.985646474885092	0.0287070502298167	0.0143535251149084
15	0.982290703899982	0.0354185922000358	0.0177092961000179
16	0.977393861633798	0.045212276732404	0.022606138366202
17	0.986570544684846	0.0268589106303088	0.0134294553151544
18	0.984559126421533	0.030881747156935	0.0154408735784675
19	0.993069269878225	0.0138614602435507	0.00693073012177535
20	0.998437660302903	0.00312467939419361	0.0015623396970968
21	0.999525498708933	0.000949002582132943	0.000474501291066471
22	0.999711721297219	0.000576557405562896	0.000288278702781448
23	0.999676318108198	0.000647363783603804	0.000323681891801902
24	0.999680243184748	0.00063951363050319	0.000319756815251595
25	0.99998241795985	3.51640803016937e-05	1.75820401508468e-05
26	0.999993407271242	1.31854575155473e-05	6.59272875777363e-06
27	0.999988856358854	2.22872822919315e-05	1.11436411459658e-05
28	0.999986197975699	2.76040486024493e-05	1.38020243012246e-05
29	0.999981468888422	3.7062223156699e-05	1.85311115783495e-05
30	0.999968580265748	6.28394685033558e-05	3.14197342516779e-05
31	0.999976750472135	4.64990557308652e-05	2.32495278654326e-05
32	0.9999997101245	5.79750998889227e-07	2.89875499444614e-07
33	0.999999107596307	1.78480738657014e-06	8.92403693285068e-07
34	0.999998258064936	3.48387012872599e-06	1.74193506436299e-06
35	0.999995544123983	8.91175203309586e-06	4.45587601654793e-06
36	0.999999228873563	1.54225287434799e-06	7.71126437173993e-07
37	0.999998478897744	3.04220451248575e-06	1.52110225624287e-06
38	0.999999849508878	3.00982243772086e-07	1.50491121886043e-07
39	0.999999758444063	4.83111873014692e-07	2.41555936507346e-07
40	0.99999914811774	1.70376451948906e-06	8.51882259744531e-07
41	0.9999979581732	4.08365360130456e-06	2.04182680065228e-06
42	0.99999128619672	1.74276065597068e-05	8.7138032798534e-06
43	0.999954919957823	9.01600843548167e-05	4.50800421774084e-05
44	0.999925475210096	0.000149049579807533	7.45247899037667e-05
45	0.999602320318834	0.000795359362331551	0.000397679681165775
46	0.999197407416852	0.00160518516629618	0.00080259258314809
47	0.99558959491874	0.00882081016251913	0.00441040508125957
48	0.994105188375519	0.0117896232489629	0.00589481162448144

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 0.368092839961436 & 0.736185679922871 & 0.631907160038564 \tabularnewline
9 & 0.382460138951777 & 0.764920277903555 & 0.617539861048223 \tabularnewline
10 & 0.34122660687997 & 0.68245321375994 & 0.65877339312003 \tabularnewline
11 & 0.226436220099159 & 0.452872440198319 & 0.77356377990084 \tabularnewline
12 & 0.148344618132942 & 0.296689236265884 & 0.851655381867058 \tabularnewline
13 & 0.872472920056497 & 0.255054159887006 & 0.127527079943503 \tabularnewline
14 & 0.985646474885092 & 0.0287070502298167 & 0.0143535251149084 \tabularnewline
15 & 0.982290703899982 & 0.0354185922000358 & 0.0177092961000179 \tabularnewline
16 & 0.977393861633798 & 0.045212276732404 & 0.022606138366202 \tabularnewline
17 & 0.986570544684846 & 0.0268589106303088 & 0.0134294553151544 \tabularnewline
18 & 0.984559126421533 & 0.030881747156935 & 0.0154408735784675 \tabularnewline
19 & 0.993069269878225 & 0.0138614602435507 & 0.00693073012177535 \tabularnewline
20 & 0.998437660302903 & 0.00312467939419361 & 0.0015623396970968 \tabularnewline
21 & 0.999525498708933 & 0.000949002582132943 & 0.000474501291066471 \tabularnewline
22 & 0.999711721297219 & 0.000576557405562896 & 0.000288278702781448 \tabularnewline
23 & 0.999676318108198 & 0.000647363783603804 & 0.000323681891801902 \tabularnewline
24 & 0.999680243184748 & 0.00063951363050319 & 0.000319756815251595 \tabularnewline
25 & 0.99998241795985 & 3.51640803016937e-05 & 1.75820401508468e-05 \tabularnewline
26 & 0.999993407271242 & 1.31854575155473e-05 & 6.59272875777363e-06 \tabularnewline
27 & 0.999988856358854 & 2.22872822919315e-05 & 1.11436411459658e-05 \tabularnewline
28 & 0.999986197975699 & 2.76040486024493e-05 & 1.38020243012246e-05 \tabularnewline
29 & 0.999981468888422 & 3.7062223156699e-05 & 1.85311115783495e-05 \tabularnewline
30 & 0.999968580265748 & 6.28394685033558e-05 & 3.14197342516779e-05 \tabularnewline
31 & 0.999976750472135 & 4.64990557308652e-05 & 2.32495278654326e-05 \tabularnewline
32 & 0.9999997101245 & 5.79750998889227e-07 & 2.89875499444614e-07 \tabularnewline
33 & 0.999999107596307 & 1.78480738657014e-06 & 8.92403693285068e-07 \tabularnewline
34 & 0.999998258064936 & 3.48387012872599e-06 & 1.74193506436299e-06 \tabularnewline
35 & 0.999995544123983 & 8.91175203309586e-06 & 4.45587601654793e-06 \tabularnewline
36 & 0.999999228873563 & 1.54225287434799e-06 & 7.71126437173993e-07 \tabularnewline
37 & 0.999998478897744 & 3.04220451248575e-06 & 1.52110225624287e-06 \tabularnewline
38 & 0.999999849508878 & 3.00982243772086e-07 & 1.50491121886043e-07 \tabularnewline
39 & 0.999999758444063 & 4.83111873014692e-07 & 2.41555936507346e-07 \tabularnewline
40 & 0.99999914811774 & 1.70376451948906e-06 & 8.51882259744531e-07 \tabularnewline
41 & 0.9999979581732 & 4.08365360130456e-06 & 2.04182680065228e-06 \tabularnewline
42 & 0.99999128619672 & 1.74276065597068e-05 & 8.7138032798534e-06 \tabularnewline
43 & 0.999954919957823 & 9.01600843548167e-05 & 4.50800421774084e-05 \tabularnewline
44 & 0.999925475210096 & 0.000149049579807533 & 7.45247899037667e-05 \tabularnewline
45 & 0.999602320318834 & 0.000795359362331551 & 0.000397679681165775 \tabularnewline
46 & 0.999197407416852 & 0.00160518516629618 & 0.00080259258314809 \tabularnewline
47 & 0.99558959491874 & 0.00882081016251913 & 0.00441040508125957 \tabularnewline
48 & 0.994105188375519 & 0.0117896232489629 & 0.00589481162448144 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=120611&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]0.368092839961436[/C][C]0.736185679922871[/C][C]0.631907160038564[/C][/ROW]
[ROW][C]9[/C][C]0.382460138951777[/C][C]0.764920277903555[/C][C]0.617539861048223[/C][/ROW]
[ROW][C]10[/C][C]0.34122660687997[/C][C]0.68245321375994[/C][C]0.65877339312003[/C][/ROW]
[ROW][C]11[/C][C]0.226436220099159[/C][C]0.452872440198319[/C][C]0.77356377990084[/C][/ROW]
[ROW][C]12[/C][C]0.148344618132942[/C][C]0.296689236265884[/C][C]0.851655381867058[/C][/ROW]
[ROW][C]13[/C][C]0.872472920056497[/C][C]0.255054159887006[/C][C]0.127527079943503[/C][/ROW]
[ROW][C]14[/C][C]0.985646474885092[/C][C]0.0287070502298167[/C][C]0.0143535251149084[/C][/ROW]
[ROW][C]15[/C][C]0.982290703899982[/C][C]0.0354185922000358[/C][C]0.0177092961000179[/C][/ROW]
[ROW][C]16[/C][C]0.977393861633798[/C][C]0.045212276732404[/C][C]0.022606138366202[/C][/ROW]
[ROW][C]17[/C][C]0.986570544684846[/C][C]0.0268589106303088[/C][C]0.0134294553151544[/C][/ROW]
[ROW][C]18[/C][C]0.984559126421533[/C][C]0.030881747156935[/C][C]0.0154408735784675[/C][/ROW]
[ROW][C]19[/C][C]0.993069269878225[/C][C]0.0138614602435507[/C][C]0.00693073012177535[/C][/ROW]
[ROW][C]20[/C][C]0.998437660302903[/C][C]0.00312467939419361[/C][C]0.0015623396970968[/C][/ROW]
[ROW][C]21[/C][C]0.999525498708933[/C][C]0.000949002582132943[/C][C]0.000474501291066471[/C][/ROW]
[ROW][C]22[/C][C]0.999711721297219[/C][C]0.000576557405562896[/C][C]0.000288278702781448[/C][/ROW]
[ROW][C]23[/C][C]0.999676318108198[/C][C]0.000647363783603804[/C][C]0.000323681891801902[/C][/ROW]
[ROW][C]24[/C][C]0.999680243184748[/C][C]0.00063951363050319[/C][C]0.000319756815251595[/C][/ROW]
[ROW][C]25[/C][C]0.99998241795985[/C][C]3.51640803016937e-05[/C][C]1.75820401508468e-05[/C][/ROW]
[ROW][C]26[/C][C]0.999993407271242[/C][C]1.31854575155473e-05[/C][C]6.59272875777363e-06[/C][/ROW]
[ROW][C]27[/C][C]0.999988856358854[/C][C]2.22872822919315e-05[/C][C]1.11436411459658e-05[/C][/ROW]
[ROW][C]28[/C][C]0.999986197975699[/C][C]2.76040486024493e-05[/C][C]1.38020243012246e-05[/C][/ROW]
[ROW][C]29[/C][C]0.999981468888422[/C][C]3.7062223156699e-05[/C][C]1.85311115783495e-05[/C][/ROW]
[ROW][C]30[/C][C]0.999968580265748[/C][C]6.28394685033558e-05[/C][C]3.14197342516779e-05[/C][/ROW]
[ROW][C]31[/C][C]0.999976750472135[/C][C]4.64990557308652e-05[/C][C]2.32495278654326e-05[/C][/ROW]
[ROW][C]32[/C][C]0.9999997101245[/C][C]5.79750998889227e-07[/C][C]2.89875499444614e-07[/C][/ROW]
[ROW][C]33[/C][C]0.999999107596307[/C][C]1.78480738657014e-06[/C][C]8.92403693285068e-07[/C][/ROW]
[ROW][C]34[/C][C]0.999998258064936[/C][C]3.48387012872599e-06[/C][C]1.74193506436299e-06[/C][/ROW]
[ROW][C]35[/C][C]0.999995544123983[/C][C]8.91175203309586e-06[/C][C]4.45587601654793e-06[/C][/ROW]
[ROW][C]36[/C][C]0.999999228873563[/C][C]1.54225287434799e-06[/C][C]7.71126437173993e-07[/C][/ROW]
[ROW][C]37[/C][C]0.999998478897744[/C][C]3.04220451248575e-06[/C][C]1.52110225624287e-06[/C][/ROW]
[ROW][C]38[/C][C]0.999999849508878[/C][C]3.00982243772086e-07[/C][C]1.50491121886043e-07[/C][/ROW]
[ROW][C]39[/C][C]0.999999758444063[/C][C]4.83111873014692e-07[/C][C]2.41555936507346e-07[/C][/ROW]
[ROW][C]40[/C][C]0.99999914811774[/C][C]1.70376451948906e-06[/C][C]8.51882259744531e-07[/C][/ROW]
[ROW][C]41[/C][C]0.9999979581732[/C][C]4.08365360130456e-06[/C][C]2.04182680065228e-06[/C][/ROW]
[ROW][C]42[/C][C]0.99999128619672[/C][C]1.74276065597068e-05[/C][C]8.7138032798534e-06[/C][/ROW]
[ROW][C]43[/C][C]0.999954919957823[/C][C]9.01600843548167e-05[/C][C]4.50800421774084e-05[/C][/ROW]
[ROW][C]44[/C][C]0.999925475210096[/C][C]0.000149049579807533[/C][C]7.45247899037667e-05[/C][/ROW]
[ROW][C]45[/C][C]0.999602320318834[/C][C]0.000795359362331551[/C][C]0.000397679681165775[/C][/ROW]
[ROW][C]46[/C][C]0.999197407416852[/C][C]0.00160518516629618[/C][C]0.00080259258314809[/C][/ROW]
[ROW][C]47[/C][C]0.99558959491874[/C][C]0.00882081016251913[/C][C]0.00441040508125957[/C][/ROW]
[ROW][C]48[/C][C]0.994105188375519[/C][C]0.0117896232489629[/C][C]0.00589481162448144[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=120611&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=120611&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	0.368092839961436	0.736185679922871	0.631907160038564
9	0.382460138951777	0.764920277903555	0.617539861048223
10	0.34122660687997	0.68245321375994	0.65877339312003
11	0.226436220099159	0.452872440198319	0.77356377990084
12	0.148344618132942	0.296689236265884	0.851655381867058
13	0.872472920056497	0.255054159887006	0.127527079943503
14	0.985646474885092	0.0287070502298167	0.0143535251149084
15	0.982290703899982	0.0354185922000358	0.0177092961000179
16	0.977393861633798	0.045212276732404	0.022606138366202
17	0.986570544684846	0.0268589106303088	0.0134294553151544
18	0.984559126421533	0.030881747156935	0.0154408735784675
19	0.993069269878225	0.0138614602435507	0.00693073012177535
20	0.998437660302903	0.00312467939419361	0.0015623396970968
21	0.999525498708933	0.000949002582132943	0.000474501291066471
22	0.999711721297219	0.000576557405562896	0.000288278702781448
23	0.999676318108198	0.000647363783603804	0.000323681891801902
24	0.999680243184748	0.00063951363050319	0.000319756815251595
25	0.99998241795985	3.51640803016937e-05	1.75820401508468e-05
26	0.999993407271242	1.31854575155473e-05	6.59272875777363e-06
27	0.999988856358854	2.22872822919315e-05	1.11436411459658e-05
28	0.999986197975699	2.76040486024493e-05	1.38020243012246e-05
29	0.999981468888422	3.7062223156699e-05	1.85311115783495e-05
30	0.999968580265748	6.28394685033558e-05	3.14197342516779e-05
31	0.999976750472135	4.64990557308652e-05	2.32495278654326e-05
32	0.9999997101245	5.79750998889227e-07	2.89875499444614e-07
33	0.999999107596307	1.78480738657014e-06	8.92403693285068e-07
34	0.999998258064936	3.48387012872599e-06	1.74193506436299e-06
35	0.999995544123983	8.91175203309586e-06	4.45587601654793e-06
36	0.999999228873563	1.54225287434799e-06	7.71126437173993e-07
37	0.999998478897744	3.04220451248575e-06	1.52110225624287e-06
38	0.999999849508878	3.00982243772086e-07	1.50491121886043e-07
39	0.999999758444063	4.83111873014692e-07	2.41555936507346e-07
40	0.99999914811774	1.70376451948906e-06	8.51882259744531e-07
41	0.9999979581732	4.08365360130456e-06	2.04182680065228e-06
42	0.99999128619672	1.74276065597068e-05	8.7138032798534e-06
43	0.999954919957823	9.01600843548167e-05	4.50800421774084e-05
44	0.999925475210096	0.000149049579807533	7.45247899037667e-05
45	0.999602320318834	0.000795359362331551	0.000397679681165775
46	0.999197407416852	0.00160518516629618	0.00080259258314809
47	0.99558959491874	0.00882081016251913	0.00441040508125957
48	0.994105188375519	0.0117896232489629	0.00589481162448144

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	28	0.682926829268293	NOK
5% type I error level	35	0.853658536585366	NOK
10% type I error level	35	0.853658536585366	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 & 28 & 0.682926829268293 & NOK \tabularnewline
5% type I error level & 35 & 0.853658536585366 & NOK \tabularnewline
10% type I error level & 35 & 0.853658536585366 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=120611&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]28[/C][C]0.682926829268293[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]35[/C][C]0.853658536585366[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]35[/C][C]0.853658536585366[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=120611&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=120611&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	28	0.682926829268293	NOK
5% type I error level	35	0.853658536585366	NOK
10% type I error level	35	0.853658536585366	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):

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

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