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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Mon, 23 Nov 2009 08:31:21 -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/23/t1258990363rm4w5q7wvoc0yqo.htm/, Retrieved Sat, 27 Apr 2024 07:12:35 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58795, Retrieved Sat, 27 Apr 2024 07:12:35 +0000

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Original text written by user:

0=lichten niet aan 1= lichten aan

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

159

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

-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RMPD  [Multiple Regression] [Seatbelt] [2009-11-12 13:54:52] [b98453cac15ba1066b407e146608df68]
-    D    [Multiple Regression] [Eco. Crisis] [2009-11-19 19:59:19] [36becc366f59efff5c3495030cea7527]
-   P       [Multiple Regression] [2de model] [2009-11-20 15:08:27] [36becc366f59efff5c3495030cea7527]
-   P         [Multiple Regression] [3de model] [2009-11-20 15:37:22] [36becc366f59efff5c3495030cea7527]
-    D            [Multiple Regression] [relatie lichten-o...] [2009-11-23 15:31:21] [d41d8cd98f00b204e9800998ecf8427e] [Current]

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Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	4 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 4 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58795&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58795&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58795&T=0

As an alternative you can also use a QR Code:

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

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	4 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 99.8161370716511 -1.16314641744547X[t] -0.720998442367575M1[t] + 1.25069262720664M2[t] + 1.95905036344756M3[t] + 2.10250882658359M4[t] + 5.1958665628245M5[t] + 3.48322429906542M6[t] + 0.65321131879543M7[t] + 1.60456905503635M8[t] + 4.05792679127726M9[t] + 3.99528452751817M10[t] + 1.73664226375908M11[t] -0.0433577362409143t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  99.8161370716511 -1.16314641744547X[t] -0.720998442367575M1[t] +  1.25069262720664M2[t] +  1.95905036344756M3[t] +  2.10250882658359M4[t] +  5.1958665628245M5[t] +  3.48322429906542M6[t] +  0.65321131879543M7[t] +  1.60456905503635M8[t] +  4.05792679127726M9[t] +  3.99528452751817M10[t] +  1.73664226375908M11[t] -0.0433577362409143t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58795&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  99.8161370716511 -1.16314641744547X[t] -0.720998442367575M1[t] +  1.25069262720664M2[t] +  1.95905036344756M3[t] +  2.10250882658359M4[t] +  5.1958665628245M5[t] +  3.48322429906542M6[t] +  0.65321131879543M7[t] +  1.60456905503635M8[t] +  4.05792679127726M9[t] +  3.99528452751817M10[t] +  1.73664226375908M11[t] -0.0433577362409143t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58795&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58795&T=1

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 99.8161370716511 -1.16314641744547X[t] -0.720998442367575M1[t] + 1.25069262720664M2[t] + 1.95905036344756M3[t] + 2.10250882658359M4[t] + 5.1958665628245M5[t] + 3.48322429906542M6[t] + 0.65321131879543M7[t] + 1.60456905503635M8[t] + 4.05792679127726M9[t] + 3.99528452751817M10[t] + 1.73664226375908M11[t] -0.0433577362409143t + 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)	99.8161370716511	1.695379	58.8754	0	0
X	-1.16314641744547	1.479181	-0.7863	0.435452	0.217726
M1	-0.720998442367575	1.905165	-0.3784	0.706735	0.353368
M2	1.25069262720664	1.903351	0.6571	0.514191	0.257096
M3	1.95905036344756	1.902297	1.0298	0.308145	0.154072
M4	2.10250882658359	1.993964	1.0544	0.296855	0.148427
M5	5.1958665628245	1.993129	2.6069	0.012072	0.006036
M6	3.48322429906542	1.993022	1.7477	0.086779	0.043389
M7	0.65321131879543	1.994798	0.3275	0.744717	0.372358
M8	1.60456905503635	1.991524	0.8057	0.424309	0.212155
M9	4.05792679127726	1.988974	2.0402	0.046738	0.023369
M10	3.99528452751817	1.98715	2.0106	0.049892	0.024946
M11	1.73664226375908	1.986055	0.8744	0.386156	0.193078
t	-0.0433577362409143	0.03808	-1.1386	0.260414	0.130207

\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) & 99.8161370716511 & 1.695379 & 58.8754 & 0 & 0 \tabularnewline
X & -1.16314641744547 & 1.479181 & -0.7863 & 0.435452 & 0.217726 \tabularnewline
M1 & -0.720998442367575 & 1.905165 & -0.3784 & 0.706735 & 0.353368 \tabularnewline
M2 & 1.25069262720664 & 1.903351 & 0.6571 & 0.514191 & 0.257096 \tabularnewline
M3 & 1.95905036344756 & 1.902297 & 1.0298 & 0.308145 & 0.154072 \tabularnewline
M4 & 2.10250882658359 & 1.993964 & 1.0544 & 0.296855 & 0.148427 \tabularnewline
M5 & 5.1958665628245 & 1.993129 & 2.6069 & 0.012072 & 0.006036 \tabularnewline
M6 & 3.48322429906542 & 1.993022 & 1.7477 & 0.086779 & 0.043389 \tabularnewline
M7 & 0.65321131879543 & 1.994798 & 0.3275 & 0.744717 & 0.372358 \tabularnewline
M8 & 1.60456905503635 & 1.991524 & 0.8057 & 0.424309 & 0.212155 \tabularnewline
M9 & 4.05792679127726 & 1.988974 & 2.0402 & 0.046738 & 0.023369 \tabularnewline
M10 & 3.99528452751817 & 1.98715 & 2.0106 & 0.049892 & 0.024946 \tabularnewline
M11 & 1.73664226375908 & 1.986055 & 0.8744 & 0.386156 & 0.193078 \tabularnewline
t & -0.0433577362409143 & 0.03808 & -1.1386 & 0.260414 & 0.130207 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58795&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]99.8161370716511[/C][C]1.695379[/C][C]58.8754[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]X[/C][C]-1.16314641744547[/C][C]1.479181[/C][C]-0.7863[/C][C]0.435452[/C][C]0.217726[/C][/ROW]
[ROW][C]M1[/C][C]-0.720998442367575[/C][C]1.905165[/C][C]-0.3784[/C][C]0.706735[/C][C]0.353368[/C][/ROW]
[ROW][C]M2[/C][C]1.25069262720664[/C][C]1.903351[/C][C]0.6571[/C][C]0.514191[/C][C]0.257096[/C][/ROW]
[ROW][C]M3[/C][C]1.95905036344756[/C][C]1.902297[/C][C]1.0298[/C][C]0.308145[/C][C]0.154072[/C][/ROW]
[ROW][C]M4[/C][C]2.10250882658359[/C][C]1.993964[/C][C]1.0544[/C][C]0.296855[/C][C]0.148427[/C][/ROW]
[ROW][C]M5[/C][C]5.1958665628245[/C][C]1.993129[/C][C]2.6069[/C][C]0.012072[/C][C]0.006036[/C][/ROW]
[ROW][C]M6[/C][C]3.48322429906542[/C][C]1.993022[/C][C]1.7477[/C][C]0.086779[/C][C]0.043389[/C][/ROW]
[ROW][C]M7[/C][C]0.65321131879543[/C][C]1.994798[/C][C]0.3275[/C][C]0.744717[/C][C]0.372358[/C][/ROW]
[ROW][C]M8[/C][C]1.60456905503635[/C][C]1.991524[/C][C]0.8057[/C][C]0.424309[/C][C]0.212155[/C][/ROW]
[ROW][C]M9[/C][C]4.05792679127726[/C][C]1.988974[/C][C]2.0402[/C][C]0.046738[/C][C]0.023369[/C][/ROW]
[ROW][C]M10[/C][C]3.99528452751817[/C][C]1.98715[/C][C]2.0106[/C][C]0.049892[/C][C]0.024946[/C][/ROW]
[ROW][C]M11[/C][C]1.73664226375908[/C][C]1.986055[/C][C]0.8744[/C][C]0.386156[/C][C]0.193078[/C][/ROW]
[ROW][C]t[/C][C]-0.0433577362409143[/C][C]0.03808[/C][C]-1.1386[/C][C]0.260414[/C][C]0.130207[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58795&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58795&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)	99.8161370716511	1.695379	58.8754	0	0
X	-1.16314641744547	1.479181	-0.7863	0.435452	0.217726
M1	-0.720998442367575	1.905165	-0.3784	0.706735	0.353368
M2	1.25069262720664	1.903351	0.6571	0.514191	0.257096
M3	1.95905036344756	1.902297	1.0298	0.308145	0.154072
M4	2.10250882658359	1.993964	1.0544	0.296855	0.148427
M5	5.1958665628245	1.993129	2.6069	0.012072	0.006036
M6	3.48322429906542	1.993022	1.7477	0.086779	0.043389
M7	0.65321131879543	1.994798	0.3275	0.744717	0.372358
M8	1.60456905503635	1.991524	0.8057	0.424309	0.212155
M9	4.05792679127726	1.988974	2.0402	0.046738	0.023369
M10	3.99528452751817	1.98715	2.0106	0.049892	0.024946
M11	1.73664226375908	1.986055	0.8744	0.386156	0.193078
t	-0.0433577362409143	0.03808	-1.1386	0.260414	0.130207

Multiple Linear Regression - Regression Statistics
Multiple R	0.616314324614588
R-squared	0.379843346725136
Adjusted R-squared	0.215311989733846
F-TEST (value)	2.30863802299548
F-TEST (DF numerator)	13
F-TEST (DF denominator)	49
p-value	0.0176730361287295
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	3.13965149329566
Sum Squared Residuals	483.01316346833

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.616314324614588 \tabularnewline
R-squared & 0.379843346725136 \tabularnewline
Adjusted R-squared & 0.215311989733846 \tabularnewline
F-TEST (value) & 2.30863802299548 \tabularnewline
F-TEST (DF numerator) & 13 \tabularnewline
F-TEST (DF denominator) & 49 \tabularnewline
p-value & 0.0176730361287295 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 3.13965149329566 \tabularnewline
Sum Squared Residuals & 483.01316346833 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58795&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.616314324614588[/C][/ROW]
[ROW][C]R-squared[/C][C]0.379843346725136[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.215311989733846[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]2.30863802299548[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]13[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]49[/C][/ROW]
[ROW][C]p-value[/C][C]0.0176730361287295[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]3.13965149329566[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]483.01316346833[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58795&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58795&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	0.616314324614588
R-squared	0.379843346725136
Adjusted R-squared	0.215311989733846
F-TEST (value)	2.30863802299548
F-TEST (DF numerator)	13
F-TEST (DF denominator)	49
p-value	0.0176730361287295
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	3.13965149329566
Sum Squared Residuals	483.01316346833

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	105.29	99.0517808930424	6.23821910695756
2	101.23	100.980114226376	0.249885773624083
3	102.33	101.645114226376	0.68488577362408
4	100.26	101.745214953271	-1.48521495327104
5	104.13	104.795214953271	-0.665214953271052
6	103.54	103.039214953271	0.500785046728965
7	100.02	100.165844236760	-0.145844236760132
8	98.66	101.073844236760	-2.41384423676013
9	108.64	103.483844236760	5.15615576323987
10	105.67	103.377844236760	2.29215576323986
11	102.66	101.075844236760	1.58415576323986
12	100.3	99.2958442367601	1.00415576323987
13	95.13	98.5314880581516	-3.40148805815165
14	93.2	100.459821391485	-7.25982139148495
15	102.84	101.124821391485	1.71517860851506
16	101.36	101.22492211838	0.135077881619931
17	102.55	104.27492211838	-1.72492211838007
18	103.12	102.51892211838	0.601077881619936
19	96.3	99.6455514018692	-3.34555140186916
20	99.13	100.553551401869	-1.42355140186916
21	102.23	102.963551401869	-0.733551401869156
22	104.3	102.857551401869	1.44244859813084
23	99.58	100.555551401869	-0.97555140186916
24	98.45	98.7755514018692	-0.325551401869159
25	96.23	98.0111952232607	-1.78119522326067
26	97.62	99.939528556594	-2.31952855659397
27	102.32	100.604528556594	1.71547144340602
28	105.23	100.704629283489	4.52537071651091
29	100.05	103.754629283489	-3.70462928348909
30	102.66	101.998629283489	0.6613707165109
31	100.98	99.1252585669782	1.85474143302182
32	99.2	100.033258566978	-0.833258566978186
33	98.36	102.443258566978	-4.08325856697819
34	102.56	102.337258566978	0.222741433021815
35	97.33	100.035258566978	-2.70525856697819
36	96.22	98.2552585669782	-2.03525856697819
37	99.22	97.4909023883697	1.7290976116303
38	102.32	99.419235721703	2.90076427829699
39	104.22	100.084235721703	4.135764278297
40	100.06	100.184336448598	-0.124336448598122
41	107.23	103.234336448598	3.99566355140189
42	99.62	101.478336448598	-1.85833644859812
43	98.32	97.4418193146417	0.878180685358247
44	101.23	98.3498193146417	2.88018068535826
45	102.33	100.759819314642	1.57018068535825
46	100.6	100.653819314642	-0.0538193146417504
47	95.63	98.3518193146417	-2.72181931464175
48	94.63	96.5718193146417	-1.94181931464175
49	95.66	95.8074631360333	-0.147463136033261
50	100.78	97.7357964693666	3.04420353063344
51	90.36	98.4007964693666	-8.04079646936656
52	95.45	98.5008971962617	-3.05089719626168
53	103.65	101.550897196262	2.09910280373833
54	99.89	99.7948971962617	0.0951028037383176
55	97.68	96.9215264797508	0.758473520249232
56	99.62	97.8295264797508	1.79047352024923
57	98.33	100.239526479751	-1.90952647975078
58	96.23	100.133526479751	-3.90352647975077
59	102.65	97.8315264797508	4.81847352024923
60	99.35	96.0515264797508	3.29847352024922
61	92.65	95.2871703011423	-2.63717030114228
62	100.6	97.2155036344756	3.38449636552440
63	97.67	97.8805036344756	-0.210503634475587

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 105.29 & 99.0517808930424 & 6.23821910695756 \tabularnewline
2 & 101.23 & 100.980114226376 & 0.249885773624083 \tabularnewline
3 & 102.33 & 101.645114226376 & 0.68488577362408 \tabularnewline
4 & 100.26 & 101.745214953271 & -1.48521495327104 \tabularnewline
5 & 104.13 & 104.795214953271 & -0.665214953271052 \tabularnewline
6 & 103.54 & 103.039214953271 & 0.500785046728965 \tabularnewline
7 & 100.02 & 100.165844236760 & -0.145844236760132 \tabularnewline
8 & 98.66 & 101.073844236760 & -2.41384423676013 \tabularnewline
9 & 108.64 & 103.483844236760 & 5.15615576323987 \tabularnewline
10 & 105.67 & 103.377844236760 & 2.29215576323986 \tabularnewline
11 & 102.66 & 101.075844236760 & 1.58415576323986 \tabularnewline
12 & 100.3 & 99.2958442367601 & 1.00415576323987 \tabularnewline
13 & 95.13 & 98.5314880581516 & -3.40148805815165 \tabularnewline
14 & 93.2 & 100.459821391485 & -7.25982139148495 \tabularnewline
15 & 102.84 & 101.124821391485 & 1.71517860851506 \tabularnewline
16 & 101.36 & 101.22492211838 & 0.135077881619931 \tabularnewline
17 & 102.55 & 104.27492211838 & -1.72492211838007 \tabularnewline
18 & 103.12 & 102.51892211838 & 0.601077881619936 \tabularnewline
19 & 96.3 & 99.6455514018692 & -3.34555140186916 \tabularnewline
20 & 99.13 & 100.553551401869 & -1.42355140186916 \tabularnewline
21 & 102.23 & 102.963551401869 & -0.733551401869156 \tabularnewline
22 & 104.3 & 102.857551401869 & 1.44244859813084 \tabularnewline
23 & 99.58 & 100.555551401869 & -0.97555140186916 \tabularnewline
24 & 98.45 & 98.7755514018692 & -0.325551401869159 \tabularnewline
25 & 96.23 & 98.0111952232607 & -1.78119522326067 \tabularnewline
26 & 97.62 & 99.939528556594 & -2.31952855659397 \tabularnewline
27 & 102.32 & 100.604528556594 & 1.71547144340602 \tabularnewline
28 & 105.23 & 100.704629283489 & 4.52537071651091 \tabularnewline
29 & 100.05 & 103.754629283489 & -3.70462928348909 \tabularnewline
30 & 102.66 & 101.998629283489 & 0.6613707165109 \tabularnewline
31 & 100.98 & 99.1252585669782 & 1.85474143302182 \tabularnewline
32 & 99.2 & 100.033258566978 & -0.833258566978186 \tabularnewline
33 & 98.36 & 102.443258566978 & -4.08325856697819 \tabularnewline
34 & 102.56 & 102.337258566978 & 0.222741433021815 \tabularnewline
35 & 97.33 & 100.035258566978 & -2.70525856697819 \tabularnewline
36 & 96.22 & 98.2552585669782 & -2.03525856697819 \tabularnewline
37 & 99.22 & 97.4909023883697 & 1.7290976116303 \tabularnewline
38 & 102.32 & 99.419235721703 & 2.90076427829699 \tabularnewline
39 & 104.22 & 100.084235721703 & 4.135764278297 \tabularnewline
40 & 100.06 & 100.184336448598 & -0.124336448598122 \tabularnewline
41 & 107.23 & 103.234336448598 & 3.99566355140189 \tabularnewline
42 & 99.62 & 101.478336448598 & -1.85833644859812 \tabularnewline
43 & 98.32 & 97.4418193146417 & 0.878180685358247 \tabularnewline
44 & 101.23 & 98.3498193146417 & 2.88018068535826 \tabularnewline
45 & 102.33 & 100.759819314642 & 1.57018068535825 \tabularnewline
46 & 100.6 & 100.653819314642 & -0.0538193146417504 \tabularnewline
47 & 95.63 & 98.3518193146417 & -2.72181931464175 \tabularnewline
48 & 94.63 & 96.5718193146417 & -1.94181931464175 \tabularnewline
49 & 95.66 & 95.8074631360333 & -0.147463136033261 \tabularnewline
50 & 100.78 & 97.7357964693666 & 3.04420353063344 \tabularnewline
51 & 90.36 & 98.4007964693666 & -8.04079646936656 \tabularnewline
52 & 95.45 & 98.5008971962617 & -3.05089719626168 \tabularnewline
53 & 103.65 & 101.550897196262 & 2.09910280373833 \tabularnewline
54 & 99.89 & 99.7948971962617 & 0.0951028037383176 \tabularnewline
55 & 97.68 & 96.9215264797508 & 0.758473520249232 \tabularnewline
56 & 99.62 & 97.8295264797508 & 1.79047352024923 \tabularnewline
57 & 98.33 & 100.239526479751 & -1.90952647975078 \tabularnewline
58 & 96.23 & 100.133526479751 & -3.90352647975077 \tabularnewline
59 & 102.65 & 97.8315264797508 & 4.81847352024923 \tabularnewline
60 & 99.35 & 96.0515264797508 & 3.29847352024922 \tabularnewline
61 & 92.65 & 95.2871703011423 & -2.63717030114228 \tabularnewline
62 & 100.6 & 97.2155036344756 & 3.38449636552440 \tabularnewline
63 & 97.67 & 97.8805036344756 & -0.210503634475587 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58795&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]105.29[/C][C]99.0517808930424[/C][C]6.23821910695756[/C][/ROW]
[ROW][C]2[/C][C]101.23[/C][C]100.980114226376[/C][C]0.249885773624083[/C][/ROW]
[ROW][C]3[/C][C]102.33[/C][C]101.645114226376[/C][C]0.68488577362408[/C][/ROW]
[ROW][C]4[/C][C]100.26[/C][C]101.745214953271[/C][C]-1.48521495327104[/C][/ROW]
[ROW][C]5[/C][C]104.13[/C][C]104.795214953271[/C][C]-0.665214953271052[/C][/ROW]
[ROW][C]6[/C][C]103.54[/C][C]103.039214953271[/C][C]0.500785046728965[/C][/ROW]
[ROW][C]7[/C][C]100.02[/C][C]100.165844236760[/C][C]-0.145844236760132[/C][/ROW]
[ROW][C]8[/C][C]98.66[/C][C]101.073844236760[/C][C]-2.41384423676013[/C][/ROW]
[ROW][C]9[/C][C]108.64[/C][C]103.483844236760[/C][C]5.15615576323987[/C][/ROW]
[ROW][C]10[/C][C]105.67[/C][C]103.377844236760[/C][C]2.29215576323986[/C][/ROW]
[ROW][C]11[/C][C]102.66[/C][C]101.075844236760[/C][C]1.58415576323986[/C][/ROW]
[ROW][C]12[/C][C]100.3[/C][C]99.2958442367601[/C][C]1.00415576323987[/C][/ROW]
[ROW][C]13[/C][C]95.13[/C][C]98.5314880581516[/C][C]-3.40148805815165[/C][/ROW]
[ROW][C]14[/C][C]93.2[/C][C]100.459821391485[/C][C]-7.25982139148495[/C][/ROW]
[ROW][C]15[/C][C]102.84[/C][C]101.124821391485[/C][C]1.71517860851506[/C][/ROW]
[ROW][C]16[/C][C]101.36[/C][C]101.22492211838[/C][C]0.135077881619931[/C][/ROW]
[ROW][C]17[/C][C]102.55[/C][C]104.27492211838[/C][C]-1.72492211838007[/C][/ROW]
[ROW][C]18[/C][C]103.12[/C][C]102.51892211838[/C][C]0.601077881619936[/C][/ROW]
[ROW][C]19[/C][C]96.3[/C][C]99.6455514018692[/C][C]-3.34555140186916[/C][/ROW]
[ROW][C]20[/C][C]99.13[/C][C]100.553551401869[/C][C]-1.42355140186916[/C][/ROW]
[ROW][C]21[/C][C]102.23[/C][C]102.963551401869[/C][C]-0.733551401869156[/C][/ROW]
[ROW][C]22[/C][C]104.3[/C][C]102.857551401869[/C][C]1.44244859813084[/C][/ROW]
[ROW][C]23[/C][C]99.58[/C][C]100.555551401869[/C][C]-0.97555140186916[/C][/ROW]
[ROW][C]24[/C][C]98.45[/C][C]98.7755514018692[/C][C]-0.325551401869159[/C][/ROW]
[ROW][C]25[/C][C]96.23[/C][C]98.0111952232607[/C][C]-1.78119522326067[/C][/ROW]
[ROW][C]26[/C][C]97.62[/C][C]99.939528556594[/C][C]-2.31952855659397[/C][/ROW]
[ROW][C]27[/C][C]102.32[/C][C]100.604528556594[/C][C]1.71547144340602[/C][/ROW]
[ROW][C]28[/C][C]105.23[/C][C]100.704629283489[/C][C]4.52537071651091[/C][/ROW]
[ROW][C]29[/C][C]100.05[/C][C]103.754629283489[/C][C]-3.70462928348909[/C][/ROW]
[ROW][C]30[/C][C]102.66[/C][C]101.998629283489[/C][C]0.6613707165109[/C][/ROW]
[ROW][C]31[/C][C]100.98[/C][C]99.1252585669782[/C][C]1.85474143302182[/C][/ROW]
[ROW][C]32[/C][C]99.2[/C][C]100.033258566978[/C][C]-0.833258566978186[/C][/ROW]
[ROW][C]33[/C][C]98.36[/C][C]102.443258566978[/C][C]-4.08325856697819[/C][/ROW]
[ROW][C]34[/C][C]102.56[/C][C]102.337258566978[/C][C]0.222741433021815[/C][/ROW]
[ROW][C]35[/C][C]97.33[/C][C]100.035258566978[/C][C]-2.70525856697819[/C][/ROW]
[ROW][C]36[/C][C]96.22[/C][C]98.2552585669782[/C][C]-2.03525856697819[/C][/ROW]
[ROW][C]37[/C][C]99.22[/C][C]97.4909023883697[/C][C]1.7290976116303[/C][/ROW]
[ROW][C]38[/C][C]102.32[/C][C]99.419235721703[/C][C]2.90076427829699[/C][/ROW]
[ROW][C]39[/C][C]104.22[/C][C]100.084235721703[/C][C]4.135764278297[/C][/ROW]
[ROW][C]40[/C][C]100.06[/C][C]100.184336448598[/C][C]-0.124336448598122[/C][/ROW]
[ROW][C]41[/C][C]107.23[/C][C]103.234336448598[/C][C]3.99566355140189[/C][/ROW]
[ROW][C]42[/C][C]99.62[/C][C]101.478336448598[/C][C]-1.85833644859812[/C][/ROW]
[ROW][C]43[/C][C]98.32[/C][C]97.4418193146417[/C][C]0.878180685358247[/C][/ROW]
[ROW][C]44[/C][C]101.23[/C][C]98.3498193146417[/C][C]2.88018068535826[/C][/ROW]
[ROW][C]45[/C][C]102.33[/C][C]100.759819314642[/C][C]1.57018068535825[/C][/ROW]
[ROW][C]46[/C][C]100.6[/C][C]100.653819314642[/C][C]-0.0538193146417504[/C][/ROW]
[ROW][C]47[/C][C]95.63[/C][C]98.3518193146417[/C][C]-2.72181931464175[/C][/ROW]
[ROW][C]48[/C][C]94.63[/C][C]96.5718193146417[/C][C]-1.94181931464175[/C][/ROW]
[ROW][C]49[/C][C]95.66[/C][C]95.8074631360333[/C][C]-0.147463136033261[/C][/ROW]
[ROW][C]50[/C][C]100.78[/C][C]97.7357964693666[/C][C]3.04420353063344[/C][/ROW]
[ROW][C]51[/C][C]90.36[/C][C]98.4007964693666[/C][C]-8.04079646936656[/C][/ROW]
[ROW][C]52[/C][C]95.45[/C][C]98.5008971962617[/C][C]-3.05089719626168[/C][/ROW]
[ROW][C]53[/C][C]103.65[/C][C]101.550897196262[/C][C]2.09910280373833[/C][/ROW]
[ROW][C]54[/C][C]99.89[/C][C]99.7948971962617[/C][C]0.0951028037383176[/C][/ROW]
[ROW][C]55[/C][C]97.68[/C][C]96.9215264797508[/C][C]0.758473520249232[/C][/ROW]
[ROW][C]56[/C][C]99.62[/C][C]97.8295264797508[/C][C]1.79047352024923[/C][/ROW]
[ROW][C]57[/C][C]98.33[/C][C]100.239526479751[/C][C]-1.90952647975078[/C][/ROW]
[ROW][C]58[/C][C]96.23[/C][C]100.133526479751[/C][C]-3.90352647975077[/C][/ROW]
[ROW][C]59[/C][C]102.65[/C][C]97.8315264797508[/C][C]4.81847352024923[/C][/ROW]
[ROW][C]60[/C][C]99.35[/C][C]96.0515264797508[/C][C]3.29847352024922[/C][/ROW]
[ROW][C]61[/C][C]92.65[/C][C]95.2871703011423[/C][C]-2.63717030114228[/C][/ROW]
[ROW][C]62[/C][C]100.6[/C][C]97.2155036344756[/C][C]3.38449636552440[/C][/ROW]
[ROW][C]63[/C][C]97.67[/C][C]97.8805036344756[/C][C]-0.210503634475587[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58795&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58795&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	105.29	99.0517808930424	6.23821910695756
2	101.23	100.980114226376	0.249885773624083
3	102.33	101.645114226376	0.68488577362408
4	100.26	101.745214953271	-1.48521495327104
5	104.13	104.795214953271	-0.665214953271052
6	103.54	103.039214953271	0.500785046728965
7	100.02	100.165844236760	-0.145844236760132
8	98.66	101.073844236760	-2.41384423676013
9	108.64	103.483844236760	5.15615576323987
10	105.67	103.377844236760	2.29215576323986
11	102.66	101.075844236760	1.58415576323986
12	100.3	99.2958442367601	1.00415576323987
13	95.13	98.5314880581516	-3.40148805815165
14	93.2	100.459821391485	-7.25982139148495
15	102.84	101.124821391485	1.71517860851506
16	101.36	101.22492211838	0.135077881619931
17	102.55	104.27492211838	-1.72492211838007
18	103.12	102.51892211838	0.601077881619936
19	96.3	99.6455514018692	-3.34555140186916
20	99.13	100.553551401869	-1.42355140186916
21	102.23	102.963551401869	-0.733551401869156
22	104.3	102.857551401869	1.44244859813084
23	99.58	100.555551401869	-0.97555140186916
24	98.45	98.7755514018692	-0.325551401869159
25	96.23	98.0111952232607	-1.78119522326067
26	97.62	99.939528556594	-2.31952855659397
27	102.32	100.604528556594	1.71547144340602
28	105.23	100.704629283489	4.52537071651091
29	100.05	103.754629283489	-3.70462928348909
30	102.66	101.998629283489	0.6613707165109
31	100.98	99.1252585669782	1.85474143302182
32	99.2	100.033258566978	-0.833258566978186
33	98.36	102.443258566978	-4.08325856697819
34	102.56	102.337258566978	0.222741433021815
35	97.33	100.035258566978	-2.70525856697819
36	96.22	98.2552585669782	-2.03525856697819
37	99.22	97.4909023883697	1.7290976116303
38	102.32	99.419235721703	2.90076427829699
39	104.22	100.084235721703	4.135764278297
40	100.06	100.184336448598	-0.124336448598122
41	107.23	103.234336448598	3.99566355140189
42	99.62	101.478336448598	-1.85833644859812
43	98.32	97.4418193146417	0.878180685358247
44	101.23	98.3498193146417	2.88018068535826
45	102.33	100.759819314642	1.57018068535825
46	100.6	100.653819314642	-0.0538193146417504
47	95.63	98.3518193146417	-2.72181931464175
48	94.63	96.5718193146417	-1.94181931464175
49	95.66	95.8074631360333	-0.147463136033261
50	100.78	97.7357964693666	3.04420353063344
51	90.36	98.4007964693666	-8.04079646936656
52	95.45	98.5008971962617	-3.05089719626168
53	103.65	101.550897196262	2.09910280373833
54	99.89	99.7948971962617	0.0951028037383176
55	97.68	96.9215264797508	0.758473520249232
56	99.62	97.8295264797508	1.79047352024923
57	98.33	100.239526479751	-1.90952647975078
58	96.23	100.133526479751	-3.90352647975077
59	102.65	97.8315264797508	4.81847352024923
60	99.35	96.0515264797508	3.29847352024922
61	92.65	95.2871703011423	-2.63717030114228
62	100.6	97.2155036344756	3.38449636552440
63	97.67	97.8805036344756	-0.210503634475587

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
17	0.85481076170617	0.290378476587659	0.145189238293829
18	0.782941095816412	0.434117808367177	0.217058904183588
19	0.683200509314376	0.633598981371248	0.316799490685624
20	0.620578760852334	0.758842478295332	0.379421239147666
21	0.555973204074385	0.888053591851229	0.444026795925615
22	0.468746825121946	0.937493650243893	0.531253174878054
23	0.354976815969053	0.709953631938105	0.645023184030947
24	0.260971443166907	0.521942886333814	0.739028556833093
25	0.180020701393697	0.360041402787394	0.819979298606303
26	0.219954414720282	0.439908829440564	0.780045585279718
27	0.201605211083524	0.403210422167048	0.798394788916476
28	0.418651356887086	0.837302713774173	0.581348643112914
29	0.421673628564609	0.843347257129218	0.578326371435391
30	0.350312242069452	0.700624484138905	0.649687757930548
31	0.345486664819384	0.690973329638768	0.654513335180616
32	0.302126106109771	0.604252212219542	0.697873893890229
33	0.374599278695154	0.749198557390308	0.625400721304846
34	0.292051448304320	0.584102896608641	0.70794855169568
35	0.292736000671649	0.585472001343299	0.707263999328351
36	0.285915957190497	0.571831914380994	0.714084042809503
37	0.233020360254323	0.466040720508646	0.766979639745677
38	0.307855757040834	0.615711514081667	0.692144242959166
39	0.427141340433714	0.854282680867429	0.572858659566286
40	0.346489901798661	0.692979803597321	0.653510098201339
41	0.35432978892375	0.7086595778475	0.64567021107625
42	0.261280543164749	0.522561086329499	0.738719456835251
43	0.175910000784693	0.351820001569385	0.824089999215307
44	0.121208181950737	0.242416363901475	0.878791818049263
45	0.118267606168703	0.236535212337406	0.881732393831297
46	0.185158199670356	0.370316399340712	0.814841800329644

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
17 & 0.85481076170617 & 0.290378476587659 & 0.145189238293829 \tabularnewline
18 & 0.782941095816412 & 0.434117808367177 & 0.217058904183588 \tabularnewline
19 & 0.683200509314376 & 0.633598981371248 & 0.316799490685624 \tabularnewline
20 & 0.620578760852334 & 0.758842478295332 & 0.379421239147666 \tabularnewline
21 & 0.555973204074385 & 0.888053591851229 & 0.444026795925615 \tabularnewline
22 & 0.468746825121946 & 0.937493650243893 & 0.531253174878054 \tabularnewline
23 & 0.354976815969053 & 0.709953631938105 & 0.645023184030947 \tabularnewline
24 & 0.260971443166907 & 0.521942886333814 & 0.739028556833093 \tabularnewline
25 & 0.180020701393697 & 0.360041402787394 & 0.819979298606303 \tabularnewline
26 & 0.219954414720282 & 0.439908829440564 & 0.780045585279718 \tabularnewline
27 & 0.201605211083524 & 0.403210422167048 & 0.798394788916476 \tabularnewline
28 & 0.418651356887086 & 0.837302713774173 & 0.581348643112914 \tabularnewline
29 & 0.421673628564609 & 0.843347257129218 & 0.578326371435391 \tabularnewline
30 & 0.350312242069452 & 0.700624484138905 & 0.649687757930548 \tabularnewline
31 & 0.345486664819384 & 0.690973329638768 & 0.654513335180616 \tabularnewline
32 & 0.302126106109771 & 0.604252212219542 & 0.697873893890229 \tabularnewline
33 & 0.374599278695154 & 0.749198557390308 & 0.625400721304846 \tabularnewline
34 & 0.292051448304320 & 0.584102896608641 & 0.70794855169568 \tabularnewline
35 & 0.292736000671649 & 0.585472001343299 & 0.707263999328351 \tabularnewline
36 & 0.285915957190497 & 0.571831914380994 & 0.714084042809503 \tabularnewline
37 & 0.233020360254323 & 0.466040720508646 & 0.766979639745677 \tabularnewline
38 & 0.307855757040834 & 0.615711514081667 & 0.692144242959166 \tabularnewline
39 & 0.427141340433714 & 0.854282680867429 & 0.572858659566286 \tabularnewline
40 & 0.346489901798661 & 0.692979803597321 & 0.653510098201339 \tabularnewline
41 & 0.35432978892375 & 0.7086595778475 & 0.64567021107625 \tabularnewline
42 & 0.261280543164749 & 0.522561086329499 & 0.738719456835251 \tabularnewline
43 & 0.175910000784693 & 0.351820001569385 & 0.824089999215307 \tabularnewline
44 & 0.121208181950737 & 0.242416363901475 & 0.878791818049263 \tabularnewline
45 & 0.118267606168703 & 0.236535212337406 & 0.881732393831297 \tabularnewline
46 & 0.185158199670356 & 0.370316399340712 & 0.814841800329644 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58795&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]17[/C][C]0.85481076170617[/C][C]0.290378476587659[/C][C]0.145189238293829[/C][/ROW]
[ROW][C]18[/C][C]0.782941095816412[/C][C]0.434117808367177[/C][C]0.217058904183588[/C][/ROW]
[ROW][C]19[/C][C]0.683200509314376[/C][C]0.633598981371248[/C][C]0.316799490685624[/C][/ROW]
[ROW][C]20[/C][C]0.620578760852334[/C][C]0.758842478295332[/C][C]0.379421239147666[/C][/ROW]
[ROW][C]21[/C][C]0.555973204074385[/C][C]0.888053591851229[/C][C]0.444026795925615[/C][/ROW]
[ROW][C]22[/C][C]0.468746825121946[/C][C]0.937493650243893[/C][C]0.531253174878054[/C][/ROW]
[ROW][C]23[/C][C]0.354976815969053[/C][C]0.709953631938105[/C][C]0.645023184030947[/C][/ROW]
[ROW][C]24[/C][C]0.260971443166907[/C][C]0.521942886333814[/C][C]0.739028556833093[/C][/ROW]
[ROW][C]25[/C][C]0.180020701393697[/C][C]0.360041402787394[/C][C]0.819979298606303[/C][/ROW]
[ROW][C]26[/C][C]0.219954414720282[/C][C]0.439908829440564[/C][C]0.780045585279718[/C][/ROW]
[ROW][C]27[/C][C]0.201605211083524[/C][C]0.403210422167048[/C][C]0.798394788916476[/C][/ROW]
[ROW][C]28[/C][C]0.418651356887086[/C][C]0.837302713774173[/C][C]0.581348643112914[/C][/ROW]
[ROW][C]29[/C][C]0.421673628564609[/C][C]0.843347257129218[/C][C]0.578326371435391[/C][/ROW]
[ROW][C]30[/C][C]0.350312242069452[/C][C]0.700624484138905[/C][C]0.649687757930548[/C][/ROW]
[ROW][C]31[/C][C]0.345486664819384[/C][C]0.690973329638768[/C][C]0.654513335180616[/C][/ROW]
[ROW][C]32[/C][C]0.302126106109771[/C][C]0.604252212219542[/C][C]0.697873893890229[/C][/ROW]
[ROW][C]33[/C][C]0.374599278695154[/C][C]0.749198557390308[/C][C]0.625400721304846[/C][/ROW]
[ROW][C]34[/C][C]0.292051448304320[/C][C]0.584102896608641[/C][C]0.70794855169568[/C][/ROW]
[ROW][C]35[/C][C]0.292736000671649[/C][C]0.585472001343299[/C][C]0.707263999328351[/C][/ROW]
[ROW][C]36[/C][C]0.285915957190497[/C][C]0.571831914380994[/C][C]0.714084042809503[/C][/ROW]
[ROW][C]37[/C][C]0.233020360254323[/C][C]0.466040720508646[/C][C]0.766979639745677[/C][/ROW]
[ROW][C]38[/C][C]0.307855757040834[/C][C]0.615711514081667[/C][C]0.692144242959166[/C][/ROW]
[ROW][C]39[/C][C]0.427141340433714[/C][C]0.854282680867429[/C][C]0.572858659566286[/C][/ROW]
[ROW][C]40[/C][C]0.346489901798661[/C][C]0.692979803597321[/C][C]0.653510098201339[/C][/ROW]
[ROW][C]41[/C][C]0.35432978892375[/C][C]0.7086595778475[/C][C]0.64567021107625[/C][/ROW]
[ROW][C]42[/C][C]0.261280543164749[/C][C]0.522561086329499[/C][C]0.738719456835251[/C][/ROW]
[ROW][C]43[/C][C]0.175910000784693[/C][C]0.351820001569385[/C][C]0.824089999215307[/C][/ROW]
[ROW][C]44[/C][C]0.121208181950737[/C][C]0.242416363901475[/C][C]0.878791818049263[/C][/ROW]
[ROW][C]45[/C][C]0.118267606168703[/C][C]0.236535212337406[/C][C]0.881732393831297[/C][/ROW]
[ROW][C]46[/C][C]0.185158199670356[/C][C]0.370316399340712[/C][C]0.814841800329644[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58795&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58795&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
17	0.85481076170617	0.290378476587659	0.145189238293829
18	0.782941095816412	0.434117808367177	0.217058904183588
19	0.683200509314376	0.633598981371248	0.316799490685624
20	0.620578760852334	0.758842478295332	0.379421239147666
21	0.555973204074385	0.888053591851229	0.444026795925615
22	0.468746825121946	0.937493650243893	0.531253174878054
23	0.354976815969053	0.709953631938105	0.645023184030947
24	0.260971443166907	0.521942886333814	0.739028556833093
25	0.180020701393697	0.360041402787394	0.819979298606303
26	0.219954414720282	0.439908829440564	0.780045585279718
27	0.201605211083524	0.403210422167048	0.798394788916476
28	0.418651356887086	0.837302713774173	0.581348643112914
29	0.421673628564609	0.843347257129218	0.578326371435391
30	0.350312242069452	0.700624484138905	0.649687757930548
31	0.345486664819384	0.690973329638768	0.654513335180616
32	0.302126106109771	0.604252212219542	0.697873893890229
33	0.374599278695154	0.749198557390308	0.625400721304846
34	0.292051448304320	0.584102896608641	0.70794855169568
35	0.292736000671649	0.585472001343299	0.707263999328351
36	0.285915957190497	0.571831914380994	0.714084042809503
37	0.233020360254323	0.466040720508646	0.766979639745677
38	0.307855757040834	0.615711514081667	0.692144242959166
39	0.427141340433714	0.854282680867429	0.572858659566286
40	0.346489901798661	0.692979803597321	0.653510098201339
41	0.35432978892375	0.7086595778475	0.64567021107625
42	0.261280543164749	0.522561086329499	0.738719456835251
43	0.175910000784693	0.351820001569385	0.824089999215307
44	0.121208181950737	0.242416363901475	0.878791818049263
45	0.118267606168703	0.236535212337406	0.881732393831297
46	0.185158199670356	0.370316399340712	0.814841800329644

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	0	0	OK
10% type I error level	0	0	OK

\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 & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58795&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]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58795&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58795&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	0	0	OK
5% type I error level	0	0	OK
10% type I error level	0	0	OK

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 = 1 ; par2 = Include Monthly 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