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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Wed, 18 Nov 2009 11:46:54 -0700

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Nov/18/t1258570092fi8qoix2j7io0tm.htm/, Retrieved Wed, 01 May 2024 20:43:00 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=57587, Retrieved Wed, 01 May 2024 20:43:00 +0000

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IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

236

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

-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RM D  [Multiple Regression] [Seatbelt] [2009-11-12 14:10:54] [b98453cac15ba1066b407e146608df68]
- R  D      [Multiple Regression] [] [2009-11-18 18:46:54] [d5837f25ec8937f9733a894c487f865c] [Current]

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Dataseries X:

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Histogram

Boxplots

3030.29	101.2
2803.47	101.1
2767.63	100.7
2882.6	100.1
2863.36	99.9
2897.06	99.7
3012.61	99.5
3142.95	99.2
3032.93	99
3045.78	99
3110.52	99.3
3013.24	99.5
2987.1	99.7
2995.55	100
2833.18	100.4
2848.96	100.6
2794.83	100.7
2845.26	100.7
2915.02	100.6
2892.63	100.5
2604.42	100.6
2641.65	100.5
2659.81	100.4
2638.53	100.3
2720.25	100.4
2745.88	100.4
2735.7	100.4
2811.7	100.4
2799.43	100.4
2555.28	100.5
2304.98	100.6
2214.95	100.6
2065.81	100.5
1940.49	100.5
2042.00	100.7
1995.37	101.1
1946.81	101.5
1765.9	101.9
1635.25	102.1
1833.42	102.1
1910.43	102.1
1959.67	102.4
1969.6	102.8
2061.41	103.1
2093.48	103.1
2120.88	102.9
2174.56	102.4
2196.72	101.9
2350.44	101.3
2440.25	100.7
2408.64	100.6
2472.81	101
2407.6	101.5
2454.62	101.9
2448.05	102.1
2497.84	102.3
2645.64	102.5
2756.76	102.9
2849.27	103.6
2921.44	104.3

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	3 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 3 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57587&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57587&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57587&T=0

As an alternative you can also use a QR Code:

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

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	3 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation
Gzhidx[t] = + 100.445809918502 -0.0003448988264416Bel20[t] + 0.0483895407575485t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Gzhidx[t] =  +  100.445809918502 -0.0003448988264416Bel20[t] +  0.0483895407575485t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57587&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Gzhidx[t] =  +  100.445809918502 -0.0003448988264416Bel20[t] +  0.0483895407575485t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57587&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57587&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
Gzhidx[t] = + 100.445809918502 -0.0003448988264416Bel20[t] + 0.0483895407575485t + 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)	100.445809918502	0.900743	111.5143	0	0
Bel20	-0.0003448988264416	0.000298	-1.1569	0.252121	0.12606
t	0.0483895407575485	0.006859	7.055	0	0

\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) & 100.445809918502 & 0.900743 & 111.5143 & 0 & 0 \tabularnewline
Bel20 & -0.0003448988264416 & 0.000298 & -1.1569 & 0.252121 & 0.12606 \tabularnewline
t & 0.0483895407575485 & 0.006859 & 7.055 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57587&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]100.445809918502[/C][C]0.900743[/C][C]111.5143[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Bel20[/C][C]-0.0003448988264416[/C][C]0.000298[/C][C]-1.1569[/C][C]0.252121[/C][C]0.12606[/C][/ROW]
[ROW][C]t[/C][C]0.0483895407575485[/C][C]0.006859[/C][C]7.055[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57587&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57587&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)	100.445809918502	0.900743	111.5143	0	0
Bel20	-0.0003448988264416	0.000298	-1.1569	0.252121	0.12606
t	0.0483895407575485	0.006859	7.055	0	0

Multiple Linear Regression - Regression Statistics
Multiple R	0.784739150333494
R-squared	0.615815534066134
Adjusted R-squared	0.6023353773667
F-TEST (value)	45.6831139130597
F-TEST (DF numerator)	2
F-TEST (DF denominator)	57
p-value	1.44340095431517e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.749246965713169
Sum Squared Residuals	31.9981478909323

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.784739150333494 \tabularnewline
R-squared & 0.615815534066134 \tabularnewline
Adjusted R-squared & 0.6023353773667 \tabularnewline
F-TEST (value) & 45.6831139130597 \tabularnewline
F-TEST (DF numerator) & 2 \tabularnewline
F-TEST (DF denominator) & 57 \tabularnewline
p-value & 1.44340095431517e-12 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.749246965713169 \tabularnewline
Sum Squared Residuals & 31.9981478909323 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57587&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.784739150333494[/C][/ROW]
[ROW][C]R-squared[/C][C]0.615815534066134[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.6023353773667[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]45.6831139130597[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]2[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]57[/C][/ROW]
[ROW][C]p-value[/C][C]1.44340095431517e-12[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.749246965713169[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]31.9981478909323[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57587&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57587&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	0.784739150333494
R-squared	0.615815534066134
Adjusted R-squared	0.6023353773667
F-TEST (value)	45.6831139130597
F-TEST (DF numerator)	2
F-TEST (DF denominator)	57
p-value	1.44340095431517e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.749246965713169
Sum Squared Residuals	31.9981478909323

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	101.2	99.449055994482	1.75094400551790
2	101.1	99.5756754870528	1.52432451294723
3	100.7	99.63642620175	1.06357379825002
4	100.1	99.6451627244315	0.454837275568457
5	99.9	99.7001881186098	0.199811881390184
6	99.7	99.7369545689163	-0.036954568916286
7	99.5	99.7454910502785	-0.245491050278510
8	99.2	99.7489264779977	-0.548926477997658
9	99	99.8352617876403	-0.835261787640314
10	99	99.879219378478	-0.879219378478088
11	99.3	99.9052801692118	-0.60528016921181
12	99.5	99.9872214678056	-0.487221467805595
13	99.7	100.044626663886	-0.344626663886324
14	100	100.090101809560	-0.0901018095604436
15	100.4	100.194492572767	0.205507427232691
16	100.6	100.237439610044	0.362560389956380
17	100.7	100.304498524276	0.395501475723556
18	100.7	100.335494817217	0.364505182783457
19	100.6	100.359824215842	0.240175784158466
20	100.5	100.415936041323	0.084063958676896
21	100.6	100.563728872849	0.0362711271506085
22	100.5	100.599277830299	-0.0992778302985136
23	100.4	100.641404008368	-0.241404008367877
24	100.3	100.697132996152	-0.397132996152111
25	100.4	100.717337404813	-0.317337404812844
26	100.4	100.756887188649	-0.356887188648694
27	100.4	100.808787799459	-0.408787799459418
28	100.4	100.830965029407	-0.430965029407405
29	100.4	100.883586478765	-0.483586478765392
30	100.5	101.016183067999	-0.516183067998663
31	100.6	101.150900785015	-0.550900785014549
32	100.6	101.230341567117	-0.630341567116635
33	100.5	101.330169318850	-0.830169318849678
34	100.5	101.421781580537	-0.921781580536887
35	100.7	101.435160441422	-0.735160441422346
36	101.1	101.499632614457	-0.399632614456875
37	101.5	101.564770442226	-0.0647704422264223
38	101.9	101.675555629676	0.224444370324485
39	102.1	101.769006202108	0.330993797892331
40	102.1	101.749047142429	0.350952857570714
41	102.1	101.770876024563	0.329123975437433
42	102.4	101.802282747106	0.59771725289388
43	102.8	101.847247442517	0.952752557482888
44	103.1	101.863971822019	1.23602817798094
45	103.1	101.901300457413	1.19869954258737
46	102.9	101.940239770326	0.959760229674336
47	102.4	101.970115142080	0.429884857920173
48	101.9	102.010861724843	-0.11086172484343
49	101.3	102.006233418000	-0.706233418000384
50	100.7	102.023647595155	-1.32364759515521
51	100.6	102.082939387817	-1.48293938781658
52	101	102.109196770881	-1.10919677088137
53	101.5	102.180077164111	-0.680077164111174
54	101.9	102.212249562049	-0.312249562049432
55	102.1	102.262905088097	-0.162905088096713
56	102.3	102.294122116286	0.00587788371426798
57	102.5	102.291535610495	0.208464389504791
58	102.9	102.301599993659	0.598400006341438
59	103.6	102.318082943982	1.28191705601799
60	104.3	102.341581136435	1.95841886356474

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 101.2 & 99.449055994482 & 1.75094400551790 \tabularnewline
2 & 101.1 & 99.5756754870528 & 1.52432451294723 \tabularnewline
3 & 100.7 & 99.63642620175 & 1.06357379825002 \tabularnewline
4 & 100.1 & 99.6451627244315 & 0.454837275568457 \tabularnewline
5 & 99.9 & 99.7001881186098 & 0.199811881390184 \tabularnewline
6 & 99.7 & 99.7369545689163 & -0.036954568916286 \tabularnewline
7 & 99.5 & 99.7454910502785 & -0.245491050278510 \tabularnewline
8 & 99.2 & 99.7489264779977 & -0.548926477997658 \tabularnewline
9 & 99 & 99.8352617876403 & -0.835261787640314 \tabularnewline
10 & 99 & 99.879219378478 & -0.879219378478088 \tabularnewline
11 & 99.3 & 99.9052801692118 & -0.60528016921181 \tabularnewline
12 & 99.5 & 99.9872214678056 & -0.487221467805595 \tabularnewline
13 & 99.7 & 100.044626663886 & -0.344626663886324 \tabularnewline
14 & 100 & 100.090101809560 & -0.0901018095604436 \tabularnewline
15 & 100.4 & 100.194492572767 & 0.205507427232691 \tabularnewline
16 & 100.6 & 100.237439610044 & 0.362560389956380 \tabularnewline
17 & 100.7 & 100.304498524276 & 0.395501475723556 \tabularnewline
18 & 100.7 & 100.335494817217 & 0.364505182783457 \tabularnewline
19 & 100.6 & 100.359824215842 & 0.240175784158466 \tabularnewline
20 & 100.5 & 100.415936041323 & 0.084063958676896 \tabularnewline
21 & 100.6 & 100.563728872849 & 0.0362711271506085 \tabularnewline
22 & 100.5 & 100.599277830299 & -0.0992778302985136 \tabularnewline
23 & 100.4 & 100.641404008368 & -0.241404008367877 \tabularnewline
24 & 100.3 & 100.697132996152 & -0.397132996152111 \tabularnewline
25 & 100.4 & 100.717337404813 & -0.317337404812844 \tabularnewline
26 & 100.4 & 100.756887188649 & -0.356887188648694 \tabularnewline
27 & 100.4 & 100.808787799459 & -0.408787799459418 \tabularnewline
28 & 100.4 & 100.830965029407 & -0.430965029407405 \tabularnewline
29 & 100.4 & 100.883586478765 & -0.483586478765392 \tabularnewline
30 & 100.5 & 101.016183067999 & -0.516183067998663 \tabularnewline
31 & 100.6 & 101.150900785015 & -0.550900785014549 \tabularnewline
32 & 100.6 & 101.230341567117 & -0.630341567116635 \tabularnewline
33 & 100.5 & 101.330169318850 & -0.830169318849678 \tabularnewline
34 & 100.5 & 101.421781580537 & -0.921781580536887 \tabularnewline
35 & 100.7 & 101.435160441422 & -0.735160441422346 \tabularnewline
36 & 101.1 & 101.499632614457 & -0.399632614456875 \tabularnewline
37 & 101.5 & 101.564770442226 & -0.0647704422264223 \tabularnewline
38 & 101.9 & 101.675555629676 & 0.224444370324485 \tabularnewline
39 & 102.1 & 101.769006202108 & 0.330993797892331 \tabularnewline
40 & 102.1 & 101.749047142429 & 0.350952857570714 \tabularnewline
41 & 102.1 & 101.770876024563 & 0.329123975437433 \tabularnewline
42 & 102.4 & 101.802282747106 & 0.59771725289388 \tabularnewline
43 & 102.8 & 101.847247442517 & 0.952752557482888 \tabularnewline
44 & 103.1 & 101.863971822019 & 1.23602817798094 \tabularnewline
45 & 103.1 & 101.901300457413 & 1.19869954258737 \tabularnewline
46 & 102.9 & 101.940239770326 & 0.959760229674336 \tabularnewline
47 & 102.4 & 101.970115142080 & 0.429884857920173 \tabularnewline
48 & 101.9 & 102.010861724843 & -0.11086172484343 \tabularnewline
49 & 101.3 & 102.006233418000 & -0.706233418000384 \tabularnewline
50 & 100.7 & 102.023647595155 & -1.32364759515521 \tabularnewline
51 & 100.6 & 102.082939387817 & -1.48293938781658 \tabularnewline
52 & 101 & 102.109196770881 & -1.10919677088137 \tabularnewline
53 & 101.5 & 102.180077164111 & -0.680077164111174 \tabularnewline
54 & 101.9 & 102.212249562049 & -0.312249562049432 \tabularnewline
55 & 102.1 & 102.262905088097 & -0.162905088096713 \tabularnewline
56 & 102.3 & 102.294122116286 & 0.00587788371426798 \tabularnewline
57 & 102.5 & 102.291535610495 & 0.208464389504791 \tabularnewline
58 & 102.9 & 102.301599993659 & 0.598400006341438 \tabularnewline
59 & 103.6 & 102.318082943982 & 1.28191705601799 \tabularnewline
60 & 104.3 & 102.341581136435 & 1.95841886356474 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57587&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]101.2[/C][C]99.449055994482[/C][C]1.75094400551790[/C][/ROW]
[ROW][C]2[/C][C]101.1[/C][C]99.5756754870528[/C][C]1.52432451294723[/C][/ROW]
[ROW][C]3[/C][C]100.7[/C][C]99.63642620175[/C][C]1.06357379825002[/C][/ROW]
[ROW][C]4[/C][C]100.1[/C][C]99.6451627244315[/C][C]0.454837275568457[/C][/ROW]
[ROW][C]5[/C][C]99.9[/C][C]99.7001881186098[/C][C]0.199811881390184[/C][/ROW]
[ROW][C]6[/C][C]99.7[/C][C]99.7369545689163[/C][C]-0.036954568916286[/C][/ROW]
[ROW][C]7[/C][C]99.5[/C][C]99.7454910502785[/C][C]-0.245491050278510[/C][/ROW]
[ROW][C]8[/C][C]99.2[/C][C]99.7489264779977[/C][C]-0.548926477997658[/C][/ROW]
[ROW][C]9[/C][C]99[/C][C]99.8352617876403[/C][C]-0.835261787640314[/C][/ROW]
[ROW][C]10[/C][C]99[/C][C]99.879219378478[/C][C]-0.879219378478088[/C][/ROW]
[ROW][C]11[/C][C]99.3[/C][C]99.9052801692118[/C][C]-0.60528016921181[/C][/ROW]
[ROW][C]12[/C][C]99.5[/C][C]99.9872214678056[/C][C]-0.487221467805595[/C][/ROW]
[ROW][C]13[/C][C]99.7[/C][C]100.044626663886[/C][C]-0.344626663886324[/C][/ROW]
[ROW][C]14[/C][C]100[/C][C]100.090101809560[/C][C]-0.0901018095604436[/C][/ROW]
[ROW][C]15[/C][C]100.4[/C][C]100.194492572767[/C][C]0.205507427232691[/C][/ROW]
[ROW][C]16[/C][C]100.6[/C][C]100.237439610044[/C][C]0.362560389956380[/C][/ROW]
[ROW][C]17[/C][C]100.7[/C][C]100.304498524276[/C][C]0.395501475723556[/C][/ROW]
[ROW][C]18[/C][C]100.7[/C][C]100.335494817217[/C][C]0.364505182783457[/C][/ROW]
[ROW][C]19[/C][C]100.6[/C][C]100.359824215842[/C][C]0.240175784158466[/C][/ROW]
[ROW][C]20[/C][C]100.5[/C][C]100.415936041323[/C][C]0.084063958676896[/C][/ROW]
[ROW][C]21[/C][C]100.6[/C][C]100.563728872849[/C][C]0.0362711271506085[/C][/ROW]
[ROW][C]22[/C][C]100.5[/C][C]100.599277830299[/C][C]-0.0992778302985136[/C][/ROW]
[ROW][C]23[/C][C]100.4[/C][C]100.641404008368[/C][C]-0.241404008367877[/C][/ROW]
[ROW][C]24[/C][C]100.3[/C][C]100.697132996152[/C][C]-0.397132996152111[/C][/ROW]
[ROW][C]25[/C][C]100.4[/C][C]100.717337404813[/C][C]-0.317337404812844[/C][/ROW]
[ROW][C]26[/C][C]100.4[/C][C]100.756887188649[/C][C]-0.356887188648694[/C][/ROW]
[ROW][C]27[/C][C]100.4[/C][C]100.808787799459[/C][C]-0.408787799459418[/C][/ROW]
[ROW][C]28[/C][C]100.4[/C][C]100.830965029407[/C][C]-0.430965029407405[/C][/ROW]
[ROW][C]29[/C][C]100.4[/C][C]100.883586478765[/C][C]-0.483586478765392[/C][/ROW]
[ROW][C]30[/C][C]100.5[/C][C]101.016183067999[/C][C]-0.516183067998663[/C][/ROW]
[ROW][C]31[/C][C]100.6[/C][C]101.150900785015[/C][C]-0.550900785014549[/C][/ROW]
[ROW][C]32[/C][C]100.6[/C][C]101.230341567117[/C][C]-0.630341567116635[/C][/ROW]
[ROW][C]33[/C][C]100.5[/C][C]101.330169318850[/C][C]-0.830169318849678[/C][/ROW]
[ROW][C]34[/C][C]100.5[/C][C]101.421781580537[/C][C]-0.921781580536887[/C][/ROW]
[ROW][C]35[/C][C]100.7[/C][C]101.435160441422[/C][C]-0.735160441422346[/C][/ROW]
[ROW][C]36[/C][C]101.1[/C][C]101.499632614457[/C][C]-0.399632614456875[/C][/ROW]
[ROW][C]37[/C][C]101.5[/C][C]101.564770442226[/C][C]-0.0647704422264223[/C][/ROW]
[ROW][C]38[/C][C]101.9[/C][C]101.675555629676[/C][C]0.224444370324485[/C][/ROW]
[ROW][C]39[/C][C]102.1[/C][C]101.769006202108[/C][C]0.330993797892331[/C][/ROW]
[ROW][C]40[/C][C]102.1[/C][C]101.749047142429[/C][C]0.350952857570714[/C][/ROW]
[ROW][C]41[/C][C]102.1[/C][C]101.770876024563[/C][C]0.329123975437433[/C][/ROW]
[ROW][C]42[/C][C]102.4[/C][C]101.802282747106[/C][C]0.59771725289388[/C][/ROW]
[ROW][C]43[/C][C]102.8[/C][C]101.847247442517[/C][C]0.952752557482888[/C][/ROW]
[ROW][C]44[/C][C]103.1[/C][C]101.863971822019[/C][C]1.23602817798094[/C][/ROW]
[ROW][C]45[/C][C]103.1[/C][C]101.901300457413[/C][C]1.19869954258737[/C][/ROW]
[ROW][C]46[/C][C]102.9[/C][C]101.940239770326[/C][C]0.959760229674336[/C][/ROW]
[ROW][C]47[/C][C]102.4[/C][C]101.970115142080[/C][C]0.429884857920173[/C][/ROW]
[ROW][C]48[/C][C]101.9[/C][C]102.010861724843[/C][C]-0.11086172484343[/C][/ROW]
[ROW][C]49[/C][C]101.3[/C][C]102.006233418000[/C][C]-0.706233418000384[/C][/ROW]
[ROW][C]50[/C][C]100.7[/C][C]102.023647595155[/C][C]-1.32364759515521[/C][/ROW]
[ROW][C]51[/C][C]100.6[/C][C]102.082939387817[/C][C]-1.48293938781658[/C][/ROW]
[ROW][C]52[/C][C]101[/C][C]102.109196770881[/C][C]-1.10919677088137[/C][/ROW]
[ROW][C]53[/C][C]101.5[/C][C]102.180077164111[/C][C]-0.680077164111174[/C][/ROW]
[ROW][C]54[/C][C]101.9[/C][C]102.212249562049[/C][C]-0.312249562049432[/C][/ROW]
[ROW][C]55[/C][C]102.1[/C][C]102.262905088097[/C][C]-0.162905088096713[/C][/ROW]
[ROW][C]56[/C][C]102.3[/C][C]102.294122116286[/C][C]0.00587788371426798[/C][/ROW]
[ROW][C]57[/C][C]102.5[/C][C]102.291535610495[/C][C]0.208464389504791[/C][/ROW]
[ROW][C]58[/C][C]102.9[/C][C]102.301599993659[/C][C]0.598400006341438[/C][/ROW]
[ROW][C]59[/C][C]103.6[/C][C]102.318082943982[/C][C]1.28191705601799[/C][/ROW]
[ROW][C]60[/C][C]104.3[/C][C]102.341581136435[/C][C]1.95841886356474[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57587&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57587&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	101.2	99.449055994482	1.75094400551790
2	101.1	99.5756754870528	1.52432451294723
3	100.7	99.63642620175	1.06357379825002
4	100.1	99.6451627244315	0.454837275568457
5	99.9	99.7001881186098	0.199811881390184
6	99.7	99.7369545689163	-0.036954568916286
7	99.5	99.7454910502785	-0.245491050278510
8	99.2	99.7489264779977	-0.548926477997658
9	99	99.8352617876403	-0.835261787640314
10	99	99.879219378478	-0.879219378478088
11	99.3	99.9052801692118	-0.60528016921181
12	99.5	99.9872214678056	-0.487221467805595
13	99.7	100.044626663886	-0.344626663886324
14	100	100.090101809560	-0.0901018095604436
15	100.4	100.194492572767	0.205507427232691
16	100.6	100.237439610044	0.362560389956380
17	100.7	100.304498524276	0.395501475723556
18	100.7	100.335494817217	0.364505182783457
19	100.6	100.359824215842	0.240175784158466
20	100.5	100.415936041323	0.084063958676896
21	100.6	100.563728872849	0.0362711271506085
22	100.5	100.599277830299	-0.0992778302985136
23	100.4	100.641404008368	-0.241404008367877
24	100.3	100.697132996152	-0.397132996152111
25	100.4	100.717337404813	-0.317337404812844
26	100.4	100.756887188649	-0.356887188648694
27	100.4	100.808787799459	-0.408787799459418
28	100.4	100.830965029407	-0.430965029407405
29	100.4	100.883586478765	-0.483586478765392
30	100.5	101.016183067999	-0.516183067998663
31	100.6	101.150900785015	-0.550900785014549
32	100.6	101.230341567117	-0.630341567116635
33	100.5	101.330169318850	-0.830169318849678
34	100.5	101.421781580537	-0.921781580536887
35	100.7	101.435160441422	-0.735160441422346
36	101.1	101.499632614457	-0.399632614456875
37	101.5	101.564770442226	-0.0647704422264223
38	101.9	101.675555629676	0.224444370324485
39	102.1	101.769006202108	0.330993797892331
40	102.1	101.749047142429	0.350952857570714
41	102.1	101.770876024563	0.329123975437433
42	102.4	101.802282747106	0.59771725289388
43	102.8	101.847247442517	0.952752557482888
44	103.1	101.863971822019	1.23602817798094
45	103.1	101.901300457413	1.19869954258737
46	102.9	101.940239770326	0.959760229674336
47	102.4	101.970115142080	0.429884857920173
48	101.9	102.010861724843	-0.11086172484343
49	101.3	102.006233418000	-0.706233418000384
50	100.7	102.023647595155	-1.32364759515521
51	100.6	102.082939387817	-1.48293938781658
52	101	102.109196770881	-1.10919677088137
53	101.5	102.180077164111	-0.680077164111174
54	101.9	102.212249562049	-0.312249562049432
55	102.1	102.262905088097	-0.162905088096713
56	102.3	102.294122116286	0.00587788371426798
57	102.5	102.291535610495	0.208464389504791
58	102.9	102.301599993659	0.598400006341438
59	103.6	102.318082943982	1.28191705601799
60	104.3	102.341581136435	1.95841886356474

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
6	0.0107768402339661	0.0215536804679322	0.989223159766034
7	0.00608838554383647	0.0121767710876729	0.993911614456163
8	0.00155783697921221	0.00311567395842443	0.998442163020788
9	0.00047728704873582	0.00095457409747164	0.999522712951264
10	0.000645273789006602	0.00129054757801320	0.999354726210993
11	0.0077001871799187	0.0154003743598374	0.992299812820081
12	0.0318209203040236	0.0636418406080473	0.968179079695976
13	0.06308804850926	0.12617609701852	0.93691195149074
14	0.113560588840654	0.227121177681307	0.886439411159346
15	0.114409504250112	0.228819008500224	0.885590495749888
16	0.114853774630169	0.229707549260338	0.885146225369831
17	0.0915666895254915	0.183133379050983	0.908433310474508
18	0.0799874164136122	0.159974832827224	0.920012583586388
19	0.0763138749979186	0.152627749995837	0.923686125002081
20	0.0570226933878343	0.114045386775669	0.942977306612166
21	0.0643204448347024	0.128640889669405	0.935679555165298
22	0.0522827633696943	0.104565526739389	0.947717236630306
23	0.0388294860723995	0.077658972144799	0.9611705139276
24	0.0292549182136868	0.0585098364273736	0.970745081786313
25	0.0184310272496815	0.0368620544993630	0.981568972750319
26	0.0113781618247325	0.022756323649465	0.988621838175267
27	0.00678587536427857	0.0135717507285571	0.993214124635721
28	0.00444258486400467	0.00888516972800935	0.995557415135995
29	0.00284826718308700	0.00569653436617399	0.997151732816913
30	0.00204917532356174	0.00409835064712349	0.997950824676438
31	0.00250665816557411	0.00501331633114822	0.997493341834426
32	0.00245574774370203	0.00491149548740407	0.997544252256298
33	0.00254628733287257	0.00509257466574513	0.997453712667127
34	0.00248579190314352	0.00497158380628704	0.997514208096856
35	0.00158596872775184	0.00317193745550369	0.998414031272248
36	0.00097337692017536	0.00194675384035072	0.999026623079825
37	0.000692626398447790	0.00138525279689558	0.999307373601552
38	0.000474225984717692	0.000948451969435385	0.999525774015282
39	0.000299426271252842	0.000598852542505684	0.999700573728747
40	0.00022566125658265	0.0004513225131653	0.999774338743417
41	0.000174450948700117	0.000348901897400234	0.9998255490513
42	0.000226030988709789	0.000452061977419578	0.99977396901129
43	0.000693844913984343	0.00138768982796869	0.999306155086016
44	0.00476168305440548	0.00952336610881097	0.995238316945595
45	0.0312403785594689	0.0624807571189377	0.968759621440531
46	0.174617838117056	0.349235676234112	0.825382161882944
47	0.536226059484385	0.92754788103123	0.463773940515615
48	0.949417733094874	0.101164533810251	0.0505822669051257
49	0.997265510462471	0.00546897907505795	0.00273448953752898
50	0.995290453802295	0.00941909239541017	0.00470954619770509
51	0.98844202976142	0.0231159404771609	0.0115579702385804
52	0.976526586670427	0.0469468266591469	0.0234734133295734
53	0.936412264539578	0.127175470920843	0.0635877354604216
54	0.908701645919244	0.182596708161513	0.0912983540807564

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
6 & 0.0107768402339661 & 0.0215536804679322 & 0.989223159766034 \tabularnewline
7 & 0.00608838554383647 & 0.0121767710876729 & 0.993911614456163 \tabularnewline
8 & 0.00155783697921221 & 0.00311567395842443 & 0.998442163020788 \tabularnewline
9 & 0.00047728704873582 & 0.00095457409747164 & 0.999522712951264 \tabularnewline
10 & 0.000645273789006602 & 0.00129054757801320 & 0.999354726210993 \tabularnewline
11 & 0.0077001871799187 & 0.0154003743598374 & 0.992299812820081 \tabularnewline
12 & 0.0318209203040236 & 0.0636418406080473 & 0.968179079695976 \tabularnewline
13 & 0.06308804850926 & 0.12617609701852 & 0.93691195149074 \tabularnewline
14 & 0.113560588840654 & 0.227121177681307 & 0.886439411159346 \tabularnewline
15 & 0.114409504250112 & 0.228819008500224 & 0.885590495749888 \tabularnewline
16 & 0.114853774630169 & 0.229707549260338 & 0.885146225369831 \tabularnewline
17 & 0.0915666895254915 & 0.183133379050983 & 0.908433310474508 \tabularnewline
18 & 0.0799874164136122 & 0.159974832827224 & 0.920012583586388 \tabularnewline
19 & 0.0763138749979186 & 0.152627749995837 & 0.923686125002081 \tabularnewline
20 & 0.0570226933878343 & 0.114045386775669 & 0.942977306612166 \tabularnewline
21 & 0.0643204448347024 & 0.128640889669405 & 0.935679555165298 \tabularnewline
22 & 0.0522827633696943 & 0.104565526739389 & 0.947717236630306 \tabularnewline
23 & 0.0388294860723995 & 0.077658972144799 & 0.9611705139276 \tabularnewline
24 & 0.0292549182136868 & 0.0585098364273736 & 0.970745081786313 \tabularnewline
25 & 0.0184310272496815 & 0.0368620544993630 & 0.981568972750319 \tabularnewline
26 & 0.0113781618247325 & 0.022756323649465 & 0.988621838175267 \tabularnewline
27 & 0.00678587536427857 & 0.0135717507285571 & 0.993214124635721 \tabularnewline
28 & 0.00444258486400467 & 0.00888516972800935 & 0.995557415135995 \tabularnewline
29 & 0.00284826718308700 & 0.00569653436617399 & 0.997151732816913 \tabularnewline
30 & 0.00204917532356174 & 0.00409835064712349 & 0.997950824676438 \tabularnewline
31 & 0.00250665816557411 & 0.00501331633114822 & 0.997493341834426 \tabularnewline
32 & 0.00245574774370203 & 0.00491149548740407 & 0.997544252256298 \tabularnewline
33 & 0.00254628733287257 & 0.00509257466574513 & 0.997453712667127 \tabularnewline
34 & 0.00248579190314352 & 0.00497158380628704 & 0.997514208096856 \tabularnewline
35 & 0.00158596872775184 & 0.00317193745550369 & 0.998414031272248 \tabularnewline
36 & 0.00097337692017536 & 0.00194675384035072 & 0.999026623079825 \tabularnewline
37 & 0.000692626398447790 & 0.00138525279689558 & 0.999307373601552 \tabularnewline
38 & 0.000474225984717692 & 0.000948451969435385 & 0.999525774015282 \tabularnewline
39 & 0.000299426271252842 & 0.000598852542505684 & 0.999700573728747 \tabularnewline
40 & 0.00022566125658265 & 0.0004513225131653 & 0.999774338743417 \tabularnewline
41 & 0.000174450948700117 & 0.000348901897400234 & 0.9998255490513 \tabularnewline
42 & 0.000226030988709789 & 0.000452061977419578 & 0.99977396901129 \tabularnewline
43 & 0.000693844913984343 & 0.00138768982796869 & 0.999306155086016 \tabularnewline
44 & 0.00476168305440548 & 0.00952336610881097 & 0.995238316945595 \tabularnewline
45 & 0.0312403785594689 & 0.0624807571189377 & 0.968759621440531 \tabularnewline
46 & 0.174617838117056 & 0.349235676234112 & 0.825382161882944 \tabularnewline
47 & 0.536226059484385 & 0.92754788103123 & 0.463773940515615 \tabularnewline
48 & 0.949417733094874 & 0.101164533810251 & 0.0505822669051257 \tabularnewline
49 & 0.997265510462471 & 0.00546897907505795 & 0.00273448953752898 \tabularnewline
50 & 0.995290453802295 & 0.00941909239541017 & 0.00470954619770509 \tabularnewline
51 & 0.98844202976142 & 0.0231159404771609 & 0.0115579702385804 \tabularnewline
52 & 0.976526586670427 & 0.0469468266591469 & 0.0234734133295734 \tabularnewline
53 & 0.936412264539578 & 0.127175470920843 & 0.0635877354604216 \tabularnewline
54 & 0.908701645919244 & 0.182596708161513 & 0.0912983540807564 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57587&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]6[/C][C]0.0107768402339661[/C][C]0.0215536804679322[/C][C]0.989223159766034[/C][/ROW]
[ROW][C]7[/C][C]0.00608838554383647[/C][C]0.0121767710876729[/C][C]0.993911614456163[/C][/ROW]
[ROW][C]8[/C][C]0.00155783697921221[/C][C]0.00311567395842443[/C][C]0.998442163020788[/C][/ROW]
[ROW][C]9[/C][C]0.00047728704873582[/C][C]0.00095457409747164[/C][C]0.999522712951264[/C][/ROW]
[ROW][C]10[/C][C]0.000645273789006602[/C][C]0.00129054757801320[/C][C]0.999354726210993[/C][/ROW]
[ROW][C]11[/C][C]0.0077001871799187[/C][C]0.0154003743598374[/C][C]0.992299812820081[/C][/ROW]
[ROW][C]12[/C][C]0.0318209203040236[/C][C]0.0636418406080473[/C][C]0.968179079695976[/C][/ROW]
[ROW][C]13[/C][C]0.06308804850926[/C][C]0.12617609701852[/C][C]0.93691195149074[/C][/ROW]
[ROW][C]14[/C][C]0.113560588840654[/C][C]0.227121177681307[/C][C]0.886439411159346[/C][/ROW]
[ROW][C]15[/C][C]0.114409504250112[/C][C]0.228819008500224[/C][C]0.885590495749888[/C][/ROW]
[ROW][C]16[/C][C]0.114853774630169[/C][C]0.229707549260338[/C][C]0.885146225369831[/C][/ROW]
[ROW][C]17[/C][C]0.0915666895254915[/C][C]0.183133379050983[/C][C]0.908433310474508[/C][/ROW]
[ROW][C]18[/C][C]0.0799874164136122[/C][C]0.159974832827224[/C][C]0.920012583586388[/C][/ROW]
[ROW][C]19[/C][C]0.0763138749979186[/C][C]0.152627749995837[/C][C]0.923686125002081[/C][/ROW]
[ROW][C]20[/C][C]0.0570226933878343[/C][C]0.114045386775669[/C][C]0.942977306612166[/C][/ROW]
[ROW][C]21[/C][C]0.0643204448347024[/C][C]0.128640889669405[/C][C]0.935679555165298[/C][/ROW]
[ROW][C]22[/C][C]0.0522827633696943[/C][C]0.104565526739389[/C][C]0.947717236630306[/C][/ROW]
[ROW][C]23[/C][C]0.0388294860723995[/C][C]0.077658972144799[/C][C]0.9611705139276[/C][/ROW]
[ROW][C]24[/C][C]0.0292549182136868[/C][C]0.0585098364273736[/C][C]0.970745081786313[/C][/ROW]
[ROW][C]25[/C][C]0.0184310272496815[/C][C]0.0368620544993630[/C][C]0.981568972750319[/C][/ROW]
[ROW][C]26[/C][C]0.0113781618247325[/C][C]0.022756323649465[/C][C]0.988621838175267[/C][/ROW]
[ROW][C]27[/C][C]0.00678587536427857[/C][C]0.0135717507285571[/C][C]0.993214124635721[/C][/ROW]
[ROW][C]28[/C][C]0.00444258486400467[/C][C]0.00888516972800935[/C][C]0.995557415135995[/C][/ROW]
[ROW][C]29[/C][C]0.00284826718308700[/C][C]0.00569653436617399[/C][C]0.997151732816913[/C][/ROW]
[ROW][C]30[/C][C]0.00204917532356174[/C][C]0.00409835064712349[/C][C]0.997950824676438[/C][/ROW]
[ROW][C]31[/C][C]0.00250665816557411[/C][C]0.00501331633114822[/C][C]0.997493341834426[/C][/ROW]
[ROW][C]32[/C][C]0.00245574774370203[/C][C]0.00491149548740407[/C][C]0.997544252256298[/C][/ROW]
[ROW][C]33[/C][C]0.00254628733287257[/C][C]0.00509257466574513[/C][C]0.997453712667127[/C][/ROW]
[ROW][C]34[/C][C]0.00248579190314352[/C][C]0.00497158380628704[/C][C]0.997514208096856[/C][/ROW]
[ROW][C]35[/C][C]0.00158596872775184[/C][C]0.00317193745550369[/C][C]0.998414031272248[/C][/ROW]
[ROW][C]36[/C][C]0.00097337692017536[/C][C]0.00194675384035072[/C][C]0.999026623079825[/C][/ROW]
[ROW][C]37[/C][C]0.000692626398447790[/C][C]0.00138525279689558[/C][C]0.999307373601552[/C][/ROW]
[ROW][C]38[/C][C]0.000474225984717692[/C][C]0.000948451969435385[/C][C]0.999525774015282[/C][/ROW]
[ROW][C]39[/C][C]0.000299426271252842[/C][C]0.000598852542505684[/C][C]0.999700573728747[/C][/ROW]
[ROW][C]40[/C][C]0.00022566125658265[/C][C]0.0004513225131653[/C][C]0.999774338743417[/C][/ROW]
[ROW][C]41[/C][C]0.000174450948700117[/C][C]0.000348901897400234[/C][C]0.9998255490513[/C][/ROW]
[ROW][C]42[/C][C]0.000226030988709789[/C][C]0.000452061977419578[/C][C]0.99977396901129[/C][/ROW]
[ROW][C]43[/C][C]0.000693844913984343[/C][C]0.00138768982796869[/C][C]0.999306155086016[/C][/ROW]
[ROW][C]44[/C][C]0.00476168305440548[/C][C]0.00952336610881097[/C][C]0.995238316945595[/C][/ROW]
[ROW][C]45[/C][C]0.0312403785594689[/C][C]0.0624807571189377[/C][C]0.968759621440531[/C][/ROW]
[ROW][C]46[/C][C]0.174617838117056[/C][C]0.349235676234112[/C][C]0.825382161882944[/C][/ROW]
[ROW][C]47[/C][C]0.536226059484385[/C][C]0.92754788103123[/C][C]0.463773940515615[/C][/ROW]
[ROW][C]48[/C][C]0.949417733094874[/C][C]0.101164533810251[/C][C]0.0505822669051257[/C][/ROW]
[ROW][C]49[/C][C]0.997265510462471[/C][C]0.00546897907505795[/C][C]0.00273448953752898[/C][/ROW]
[ROW][C]50[/C][C]0.995290453802295[/C][C]0.00941909239541017[/C][C]0.00470954619770509[/C][/ROW]
[ROW][C]51[/C][C]0.98844202976142[/C][C]0.0231159404771609[/C][C]0.0115579702385804[/C][/ROW]
[ROW][C]52[/C][C]0.976526586670427[/C][C]0.0469468266591469[/C][C]0.0234734133295734[/C][/ROW]
[ROW][C]53[/C][C]0.936412264539578[/C][C]0.127175470920843[/C][C]0.0635877354604216[/C][/ROW]
[ROW][C]54[/C][C]0.908701645919244[/C][C]0.182596708161513[/C][C]0.0912983540807564[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57587&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57587&T=5

As an alternative you can also use a QR Code:

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

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
6	0.0107768402339661	0.0215536804679322	0.989223159766034
7	0.00608838554383647	0.0121767710876729	0.993911614456163
8	0.00155783697921221	0.00311567395842443	0.998442163020788
9	0.00047728704873582	0.00095457409747164	0.999522712951264
10	0.000645273789006602	0.00129054757801320	0.999354726210993
11	0.0077001871799187	0.0154003743598374	0.992299812820081
12	0.0318209203040236	0.0636418406080473	0.968179079695976
13	0.06308804850926	0.12617609701852	0.93691195149074
14	0.113560588840654	0.227121177681307	0.886439411159346
15	0.114409504250112	0.228819008500224	0.885590495749888
16	0.114853774630169	0.229707549260338	0.885146225369831
17	0.0915666895254915	0.183133379050983	0.908433310474508
18	0.0799874164136122	0.159974832827224	0.920012583586388
19	0.0763138749979186	0.152627749995837	0.923686125002081
20	0.0570226933878343	0.114045386775669	0.942977306612166
21	0.0643204448347024	0.128640889669405	0.935679555165298
22	0.0522827633696943	0.104565526739389	0.947717236630306
23	0.0388294860723995	0.077658972144799	0.9611705139276
24	0.0292549182136868	0.0585098364273736	0.970745081786313
25	0.0184310272496815	0.0368620544993630	0.981568972750319
26	0.0113781618247325	0.022756323649465	0.988621838175267
27	0.00678587536427857	0.0135717507285571	0.993214124635721
28	0.00444258486400467	0.00888516972800935	0.995557415135995
29	0.00284826718308700	0.00569653436617399	0.997151732816913
30	0.00204917532356174	0.00409835064712349	0.997950824676438
31	0.00250665816557411	0.00501331633114822	0.997493341834426
32	0.00245574774370203	0.00491149548740407	0.997544252256298
33	0.00254628733287257	0.00509257466574513	0.997453712667127
34	0.00248579190314352	0.00497158380628704	0.997514208096856
35	0.00158596872775184	0.00317193745550369	0.998414031272248
36	0.00097337692017536	0.00194675384035072	0.999026623079825
37	0.000692626398447790	0.00138525279689558	0.999307373601552
38	0.000474225984717692	0.000948451969435385	0.999525774015282
39	0.000299426271252842	0.000598852542505684	0.999700573728747
40	0.00022566125658265	0.0004513225131653	0.999774338743417
41	0.000174450948700117	0.000348901897400234	0.9998255490513
42	0.000226030988709789	0.000452061977419578	0.99977396901129
43	0.000693844913984343	0.00138768982796869	0.999306155086016
44	0.00476168305440548	0.00952336610881097	0.995238316945595
45	0.0312403785594689	0.0624807571189377	0.968759621440531
46	0.174617838117056	0.349235676234112	0.825382161882944
47	0.536226059484385	0.92754788103123	0.463773940515615
48	0.949417733094874	0.101164533810251	0.0505822669051257
49	0.997265510462471	0.00546897907505795	0.00273448953752898
50	0.995290453802295	0.00941909239541017	0.00470954619770509
51	0.98844202976142	0.0231159404771609	0.0115579702385804
52	0.976526586670427	0.0469468266591469	0.0234734133295734
53	0.936412264539578	0.127175470920843	0.0635877354604216
54	0.908701645919244	0.182596708161513	0.0912983540807564

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	22	0.448979591836735	NOK
5% type I error level	30	0.612244897959184	NOK
10% type I error level	34	0.693877551020408	NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 22 & 0.448979591836735 & NOK \tabularnewline
5% type I error level & 30 & 0.612244897959184 & NOK \tabularnewline
10% type I error level & 34 & 0.693877551020408 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57587&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]22[/C][C]0.448979591836735[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]30[/C][C]0.612244897959184[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]34[/C][C]0.693877551020408[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57587&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57587&T=6

As an alternative you can also use a QR Code:

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	22	0.448979591836735	NOK
5% type I error level	30	0.612244897959184	NOK
10% type I error level	34	0.693877551020408	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 = Include Monthly Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 2 ; 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