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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Tue, 15 Dec 2009 03:41:15 -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/Dec/15/t1260873767upm5swg4yrrfuru.htm/, Retrieved Wed, 08 May 2024 21:02:48 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=67830, Retrieved Wed, 08 May 2024 21:02:48 +0000

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Estimated Impact

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 14:14:11] [b98453cac15ba1066b407e146608df68]
-    D      [Multiple Regression] [] [2009-12-15 10:41:15] [cb3e966d7bf80cd999a0432e97d174a7] [Current]

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Dataseries X:

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Histogram

Boxplots

89.1	0	90.3	94.1
82.6	0	89.1	90.3
102.7	0	82.6	89.1
91.8	0	102.7	82.6
94.1	0	91.8	102.7
103.1	0	94.1	91.8
93.2	0	103.1	94.1
91	0	93.2	103.1
94.3	0	91	93.2
99.4	0	94.3	91
115.7	0	99.4	94.3
116.8	0	115.7	99.4
99.8	0	116.8	115.7
96	0	99.8	116.8
115.9	0	96	99.8
109.1	0	115.9	96
117.3	0	109.1	115.9
109.8	0	117.3	109.1
112.8	0	109.8	117.3
110.7	0	112.8	109.8
100	0	110.7	112.8
113.3	0	100	110.7
122.4	0	113.3	100
112.5	0	122.4	113.3
104.2	0	112.5	122.4
92.5	0	104.2	112.5
117.2	0	92.5	104.2
109.3	0	117.2	92.5
106.1	0	109.3	117.2
118.8	0	106.1	109.3
105.3	0	118.8	106.1
106	0	105.3	118.8
102	0	106	105.3
112.9	0	102	106
116.5	0	112.9	102
114.8	0	116.5	112.9
100.5	0	114.8	116.5
85.4	0	100.5	114.8
114.6	0	85.4	100.5
109.9	0	114.6	85.4
100.7	0	109.9	114.6
115.5	0	100.7	109.9
100.7	1	115.5	100.7
99	1	100.7	115.5
102.3	1	99	100.7
108.8	1	102.3	99
105.9	1	108.8	102.3
113.2	1	105.9	108.8
95.7	1	113.2	105.9
80.9	1	95.7	113.2
113.9	1	80.9	95.7
98.1	1	113.9	80.9
102.8	1	98.1	113.9
104.7	1	102.8	98.1
95.9	1	104.7	102.8
94.6	1	95.9	104.7
101.6	1	94.6	95.9
103.9	1	101.6	94.6
110.3	1	103.9	101.6
114.1	1	110.3	103.9

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=67830&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=67830&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=67830&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] = + 52.256193984698 -4.11718487866997X[t] + 0.220811068130770Y1[t] + 0.336607809560939Y2[t] -16.6367537407777M1[t] -24.0326149655328M2[t] + 7.5024817685998M3[t] -3.88948328986188M4[t] -9.8984806608981M5[t] -0.800380067444774M6[t] -10.3918250786584M7[t] -11.9106931547395M8[t] -8.9388430332159M9[t] -0.887711502151985M10[t] + 3.94199266424966M11[t] + 0.0617692125452959t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  52.256193984698 -4.11718487866997X[t] +  0.220811068130770Y1[t] +  0.336607809560939Y2[t] -16.6367537407777M1[t] -24.0326149655328M2[t] +  7.5024817685998M3[t] -3.88948328986188M4[t] -9.8984806608981M5[t] -0.800380067444774M6[t] -10.3918250786584M7[t] -11.9106931547395M8[t] -8.9388430332159M9[t] -0.887711502151985M10[t] +  3.94199266424966M11[t] +  0.0617692125452959t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=67830&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  52.256193984698 -4.11718487866997X[t] +  0.220811068130770Y1[t] +  0.336607809560939Y2[t] -16.6367537407777M1[t] -24.0326149655328M2[t] +  7.5024817685998M3[t] -3.88948328986188M4[t] -9.8984806608981M5[t] -0.800380067444774M6[t] -10.3918250786584M7[t] -11.9106931547395M8[t] -8.9388430332159M9[t] -0.887711502151985M10[t] +  3.94199266424966M11[t] +  0.0617692125452959t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=67830&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=67830&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] = + 52.256193984698 -4.11718487866997X[t] + 0.220811068130770Y1[t] + 0.336607809560939Y2[t] -16.6367537407777M1[t] -24.0326149655328M2[t] + 7.5024817685998M3[t] -3.88948328986188M4[t] -9.8984806608981M5[t] -0.800380067444774M6[t] -10.3918250786584M7[t] -11.9106931547395M8[t] -8.9388430332159M9[t] -0.887711502151985M10[t] + 3.94199266424966M11[t] + 0.0617692125452959t + 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)	52.256193984698	13.757349	3.7984	0.000443	0.000222
X	-4.11718487866997	2.827576	-1.4561	0.152469	0.076234
Y1	0.220811068130770	0.130152	1.6966	0.096845	0.048423
Y2	0.336607809560939	0.129791	2.5935	0.012854	0.006427
M1	-16.6367537407777	3.207553	-5.1867	5e-06	3e-06
M2	-24.0326149655328	3.799676	-6.3249	0	0
M3	7.5024817685998	4.293143	1.7475	0.087518	0.043759
M4	-3.88948328986188	3.952901	-0.984	0.330517	0.165258
M5	-9.8984806608981	3.556994	-2.7828	0.00791	0.003955
M6	-0.800380067444774	3.275642	-0.2443	0.808101	0.404051
M7	-10.3918250786584	3.064577	-3.3909	0.001481	0.00074
M8	-11.9106931547395	3.550716	-3.3544	0.001645	0.000823
M9	-8.9388430332159	3.383603	-2.6418	0.011375	0.005688
M10	-0.887711502151985	3.400153	-0.2611	0.795249	0.397625
M11	3.94199266424966	3.163533	1.2461	0.219332	0.109666
t	0.0617692125452959	0.074454	0.8296	0.411226	0.205613

\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) & 52.256193984698 & 13.757349 & 3.7984 & 0.000443 & 0.000222 \tabularnewline
X & -4.11718487866997 & 2.827576 & -1.4561 & 0.152469 & 0.076234 \tabularnewline
Y1 & 0.220811068130770 & 0.130152 & 1.6966 & 0.096845 & 0.048423 \tabularnewline
Y2 & 0.336607809560939 & 0.129791 & 2.5935 & 0.012854 & 0.006427 \tabularnewline
M1 & -16.6367537407777 & 3.207553 & -5.1867 & 5e-06 & 3e-06 \tabularnewline
M2 & -24.0326149655328 & 3.799676 & -6.3249 & 0 & 0 \tabularnewline
M3 & 7.5024817685998 & 4.293143 & 1.7475 & 0.087518 & 0.043759 \tabularnewline
M4 & -3.88948328986188 & 3.952901 & -0.984 & 0.330517 & 0.165258 \tabularnewline
M5 & -9.8984806608981 & 3.556994 & -2.7828 & 0.00791 & 0.003955 \tabularnewline
M6 & -0.800380067444774 & 3.275642 & -0.2443 & 0.808101 & 0.404051 \tabularnewline
M7 & -10.3918250786584 & 3.064577 & -3.3909 & 0.001481 & 0.00074 \tabularnewline
M8 & -11.9106931547395 & 3.550716 & -3.3544 & 0.001645 & 0.000823 \tabularnewline
M9 & -8.9388430332159 & 3.383603 & -2.6418 & 0.011375 & 0.005688 \tabularnewline
M10 & -0.887711502151985 & 3.400153 & -0.2611 & 0.795249 & 0.397625 \tabularnewline
M11 & 3.94199266424966 & 3.163533 & 1.2461 & 0.219332 & 0.109666 \tabularnewline
t & 0.0617692125452959 & 0.074454 & 0.8296 & 0.411226 & 0.205613 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=67830&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]52.256193984698[/C][C]13.757349[/C][C]3.7984[/C][C]0.000443[/C][C]0.000222[/C][/ROW]
[ROW][C]X[/C][C]-4.11718487866997[/C][C]2.827576[/C][C]-1.4561[/C][C]0.152469[/C][C]0.076234[/C][/ROW]
[ROW][C]Y1[/C][C]0.220811068130770[/C][C]0.130152[/C][C]1.6966[/C][C]0.096845[/C][C]0.048423[/C][/ROW]
[ROW][C]Y2[/C][C]0.336607809560939[/C][C]0.129791[/C][C]2.5935[/C][C]0.012854[/C][C]0.006427[/C][/ROW]
[ROW][C]M1[/C][C]-16.6367537407777[/C][C]3.207553[/C][C]-5.1867[/C][C]5e-06[/C][C]3e-06[/C][/ROW]
[ROW][C]M2[/C][C]-24.0326149655328[/C][C]3.799676[/C][C]-6.3249[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M3[/C][C]7.5024817685998[/C][C]4.293143[/C][C]1.7475[/C][C]0.087518[/C][C]0.043759[/C][/ROW]
[ROW][C]M4[/C][C]-3.88948328986188[/C][C]3.952901[/C][C]-0.984[/C][C]0.330517[/C][C]0.165258[/C][/ROW]
[ROW][C]M5[/C][C]-9.8984806608981[/C][C]3.556994[/C][C]-2.7828[/C][C]0.00791[/C][C]0.003955[/C][/ROW]
[ROW][C]M6[/C][C]-0.800380067444774[/C][C]3.275642[/C][C]-0.2443[/C][C]0.808101[/C][C]0.404051[/C][/ROW]
[ROW][C]M7[/C][C]-10.3918250786584[/C][C]3.064577[/C][C]-3.3909[/C][C]0.001481[/C][C]0.00074[/C][/ROW]
[ROW][C]M8[/C][C]-11.9106931547395[/C][C]3.550716[/C][C]-3.3544[/C][C]0.001645[/C][C]0.000823[/C][/ROW]
[ROW][C]M9[/C][C]-8.9388430332159[/C][C]3.383603[/C][C]-2.6418[/C][C]0.011375[/C][C]0.005688[/C][/ROW]
[ROW][C]M10[/C][C]-0.887711502151985[/C][C]3.400153[/C][C]-0.2611[/C][C]0.795249[/C][C]0.397625[/C][/ROW]
[ROW][C]M11[/C][C]3.94199266424966[/C][C]3.163533[/C][C]1.2461[/C][C]0.219332[/C][C]0.109666[/C][/ROW]
[ROW][C]t[/C][C]0.0617692125452959[/C][C]0.074454[/C][C]0.8296[/C][C]0.411226[/C][C]0.205613[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=67830&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=67830&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)	52.256193984698	13.757349	3.7984	0.000443	0.000222
X	-4.11718487866997	2.827576	-1.4561	0.152469	0.076234
Y1	0.220811068130770	0.130152	1.6966	0.096845	0.048423
Y2	0.336607809560939	0.129791	2.5935	0.012854	0.006427
M1	-16.6367537407777	3.207553	-5.1867	5e-06	3e-06
M2	-24.0326149655328	3.799676	-6.3249	0	0
M3	7.5024817685998	4.293143	1.7475	0.087518	0.043759
M4	-3.88948328986188	3.952901	-0.984	0.330517	0.165258
M5	-9.8984806608981	3.556994	-2.7828	0.00791	0.003955
M6	-0.800380067444774	3.275642	-0.2443	0.808101	0.404051
M7	-10.3918250786584	3.064577	-3.3909	0.001481	0.00074
M8	-11.9106931547395	3.550716	-3.3544	0.001645	0.000823
M9	-8.9388430332159	3.383603	-2.6418	0.011375	0.005688
M10	-0.887711502151985	3.400153	-0.2611	0.795249	0.397625
M11	3.94199266424966	3.163533	1.2461	0.219332	0.109666
t	0.0617692125452959	0.074454	0.8296	0.411226	0.205613

Multiple Linear Regression - Regression Statistics
Multiple R	0.902332302776116
R-squared	0.814203584633248
Adjusted R-squared	0.7508638975764
F-TEST (value)	12.8545564789813
F-TEST (DF numerator)	15
F-TEST (DF denominator)	44
p-value	1.81019643719083e-11
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	4.80445749036213
Sum Squared Residuals	1015.64371817466

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.902332302776116 \tabularnewline
R-squared & 0.814203584633248 \tabularnewline
Adjusted R-squared & 0.7508638975764 \tabularnewline
F-TEST (value) & 12.8545564789813 \tabularnewline
F-TEST (DF numerator) & 15 \tabularnewline
F-TEST (DF denominator) & 44 \tabularnewline
p-value & 1.81019643719083e-11 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 4.80445749036213 \tabularnewline
Sum Squared Residuals & 1015.64371817466 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=67830&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.902332302776116[/C][/ROW]
[ROW][C]R-squared[/C][C]0.814203584633248[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.7508638975764[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]12.8545564789813[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]15[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]44[/C][/ROW]
[ROW][C]p-value[/C][C]1.81019643719083e-11[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]4.80445749036213[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1015.64371817466[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=67830&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=67830&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	0.902332302776116
R-squared	0.814203584633248
Adjusted R-squared	0.7508638975764
F-TEST (value)	12.8545564789813
F-TEST (DF numerator)	15
F-TEST (DF denominator)	44
p-value	1.81019643719083e-11
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	4.80445749036213
Sum Squared Residuals	1015.64371817466

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	89.1	87.2952437883586	1.80475621164137
2	82.6	78.4170688180601	4.18293118193987
3	102.7	108.174733450415	-5.47473345041491
4	91.8	99.094889311781	-7.2948893117809
5	94.1	97.5066374828394	-3.40663748283945
6	103.1	103.505347621325	-0.405347621324616
7	93.2	96.7371693978234	-3.53716939782339
8	91	96.1235112458414	-5.1235112458414
9	94.3	95.3389289153693	-1.03892891536929
10	99.4	103.439969002776	-4.03996900277598
11	115.7	110.568384600741	5.13161539925906
12	116.8	112.004081388329	4.79591861167107
13	99.8	101.158696330884	-1.35869633088371
14	96	90.4410847509678	5.55891524903223
15	115.9	115.476535876213	0.423464123787176
16	109.1	107.261370609767	1.83862939023281
17	117.3	106.511122598250	10.7888774017503
18	109.8	115.192710057906	-5.39271005790625
19	112.8	106.767135286657	6.0328647133431
20	110.7	103.447911055806	7.25208894419367
21	100	107.027650575483	-7.02765057548341
22	113.3	112.070996490015	1.22900350998458
23	122.4	116.297553512800	6.10244648720046
24	112.5	118.903594648246	-6.40359464824567
25	104.2	103.205711612523	0.994288387476765
26	92.5	90.7064704201747	1.79352957982533
27	117.2	116.926002050367	0.273997949633184
28	109.3	107.111528215417	2.18847178458255
29	106.1	107.734105514849	-1.63410551484863
30	118.8	113.528178207297	5.27182179270263
31	105.3	105.725657983295	-0.425657983294858
32	106	105.562528881418	0.437471118582446
33	102	104.206510534105	-2.20651053410530
34	112.9	111.671792471884	1.22820752811592
35	116.5	117.623675255213	-1.12367525521267
36	114.8	118.207396772993	-3.40739677299331
37	100.5	102.468821543358	-1.96882154335802
38	85.4	91.4048979806245	-6.00489798062453
39	114.6	114.854025121806	-0.254025121806433
40	109.9	104.888734540938	5.01126545906168
41	100.7	107.732642401412	-7.03264240141219
42	115.5	113.278993675671	2.22100632432867
43	100.7	99.8033449587078	0.896655041292176
44	99	100.060037868338	-1.06003786833849
45	102.3	97.7364828050832	4.56351719491683
46	108.8	106.005826797270	2.79417320272967
47	105.9	113.443377890618	-7.54337789061836
48	113.2	111.110753103481	2.08924689651913
49	95.7	95.1715267248764	0.528473275123591
50	80.9	86.4304780301729	-5.5304780301729
51	113.9	108.868703501199	5.03129649880097
52	98.1	99.8434773220961	-1.74347732209615
53	102.8	101.51549200265	1.28450799734997
54	104.7	106.394770437800	-1.69477043780044
55	95.9	98.866692373517	-2.96669237351703
56	94.6	96.1060109485962	-1.50601094859622
57	101.6	95.8904271699588	5.70957283004116
58	103.9	105.111415238054	-1.2114152380542
59	110.3	112.867008740628	-2.56700874062849
60	114.1	111.174174086951	2.92582591304878

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 89.1 & 87.2952437883586 & 1.80475621164137 \tabularnewline
2 & 82.6 & 78.4170688180601 & 4.18293118193987 \tabularnewline
3 & 102.7 & 108.174733450415 & -5.47473345041491 \tabularnewline
4 & 91.8 & 99.094889311781 & -7.2948893117809 \tabularnewline
5 & 94.1 & 97.5066374828394 & -3.40663748283945 \tabularnewline
6 & 103.1 & 103.505347621325 & -0.405347621324616 \tabularnewline
7 & 93.2 & 96.7371693978234 & -3.53716939782339 \tabularnewline
8 & 91 & 96.1235112458414 & -5.1235112458414 \tabularnewline
9 & 94.3 & 95.3389289153693 & -1.03892891536929 \tabularnewline
10 & 99.4 & 103.439969002776 & -4.03996900277598 \tabularnewline
11 & 115.7 & 110.568384600741 & 5.13161539925906 \tabularnewline
12 & 116.8 & 112.004081388329 & 4.79591861167107 \tabularnewline
13 & 99.8 & 101.158696330884 & -1.35869633088371 \tabularnewline
14 & 96 & 90.4410847509678 & 5.55891524903223 \tabularnewline
15 & 115.9 & 115.476535876213 & 0.423464123787176 \tabularnewline
16 & 109.1 & 107.261370609767 & 1.83862939023281 \tabularnewline
17 & 117.3 & 106.511122598250 & 10.7888774017503 \tabularnewline
18 & 109.8 & 115.192710057906 & -5.39271005790625 \tabularnewline
19 & 112.8 & 106.767135286657 & 6.0328647133431 \tabularnewline
20 & 110.7 & 103.447911055806 & 7.25208894419367 \tabularnewline
21 & 100 & 107.027650575483 & -7.02765057548341 \tabularnewline
22 & 113.3 & 112.070996490015 & 1.22900350998458 \tabularnewline
23 & 122.4 & 116.297553512800 & 6.10244648720046 \tabularnewline
24 & 112.5 & 118.903594648246 & -6.40359464824567 \tabularnewline
25 & 104.2 & 103.205711612523 & 0.994288387476765 \tabularnewline
26 & 92.5 & 90.7064704201747 & 1.79352957982533 \tabularnewline
27 & 117.2 & 116.926002050367 & 0.273997949633184 \tabularnewline
28 & 109.3 & 107.111528215417 & 2.18847178458255 \tabularnewline
29 & 106.1 & 107.734105514849 & -1.63410551484863 \tabularnewline
30 & 118.8 & 113.528178207297 & 5.27182179270263 \tabularnewline
31 & 105.3 & 105.725657983295 & -0.425657983294858 \tabularnewline
32 & 106 & 105.562528881418 & 0.437471118582446 \tabularnewline
33 & 102 & 104.206510534105 & -2.20651053410530 \tabularnewline
34 & 112.9 & 111.671792471884 & 1.22820752811592 \tabularnewline
35 & 116.5 & 117.623675255213 & -1.12367525521267 \tabularnewline
36 & 114.8 & 118.207396772993 & -3.40739677299331 \tabularnewline
37 & 100.5 & 102.468821543358 & -1.96882154335802 \tabularnewline
38 & 85.4 & 91.4048979806245 & -6.00489798062453 \tabularnewline
39 & 114.6 & 114.854025121806 & -0.254025121806433 \tabularnewline
40 & 109.9 & 104.888734540938 & 5.01126545906168 \tabularnewline
41 & 100.7 & 107.732642401412 & -7.03264240141219 \tabularnewline
42 & 115.5 & 113.278993675671 & 2.22100632432867 \tabularnewline
43 & 100.7 & 99.8033449587078 & 0.896655041292176 \tabularnewline
44 & 99 & 100.060037868338 & -1.06003786833849 \tabularnewline
45 & 102.3 & 97.7364828050832 & 4.56351719491683 \tabularnewline
46 & 108.8 & 106.005826797270 & 2.79417320272967 \tabularnewline
47 & 105.9 & 113.443377890618 & -7.54337789061836 \tabularnewline
48 & 113.2 & 111.110753103481 & 2.08924689651913 \tabularnewline
49 & 95.7 & 95.1715267248764 & 0.528473275123591 \tabularnewline
50 & 80.9 & 86.4304780301729 & -5.5304780301729 \tabularnewline
51 & 113.9 & 108.868703501199 & 5.03129649880097 \tabularnewline
52 & 98.1 & 99.8434773220961 & -1.74347732209615 \tabularnewline
53 & 102.8 & 101.51549200265 & 1.28450799734997 \tabularnewline
54 & 104.7 & 106.394770437800 & -1.69477043780044 \tabularnewline
55 & 95.9 & 98.866692373517 & -2.96669237351703 \tabularnewline
56 & 94.6 & 96.1060109485962 & -1.50601094859622 \tabularnewline
57 & 101.6 & 95.8904271699588 & 5.70957283004116 \tabularnewline
58 & 103.9 & 105.111415238054 & -1.2114152380542 \tabularnewline
59 & 110.3 & 112.867008740628 & -2.56700874062849 \tabularnewline
60 & 114.1 & 111.174174086951 & 2.92582591304878 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=67830&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]89.1[/C][C]87.2952437883586[/C][C]1.80475621164137[/C][/ROW]
[ROW][C]2[/C][C]82.6[/C][C]78.4170688180601[/C][C]4.18293118193987[/C][/ROW]
[ROW][C]3[/C][C]102.7[/C][C]108.174733450415[/C][C]-5.47473345041491[/C][/ROW]
[ROW][C]4[/C][C]91.8[/C][C]99.094889311781[/C][C]-7.2948893117809[/C][/ROW]
[ROW][C]5[/C][C]94.1[/C][C]97.5066374828394[/C][C]-3.40663748283945[/C][/ROW]
[ROW][C]6[/C][C]103.1[/C][C]103.505347621325[/C][C]-0.405347621324616[/C][/ROW]
[ROW][C]7[/C][C]93.2[/C][C]96.7371693978234[/C][C]-3.53716939782339[/C][/ROW]
[ROW][C]8[/C][C]91[/C][C]96.1235112458414[/C][C]-5.1235112458414[/C][/ROW]
[ROW][C]9[/C][C]94.3[/C][C]95.3389289153693[/C][C]-1.03892891536929[/C][/ROW]
[ROW][C]10[/C][C]99.4[/C][C]103.439969002776[/C][C]-4.03996900277598[/C][/ROW]
[ROW][C]11[/C][C]115.7[/C][C]110.568384600741[/C][C]5.13161539925906[/C][/ROW]
[ROW][C]12[/C][C]116.8[/C][C]112.004081388329[/C][C]4.79591861167107[/C][/ROW]
[ROW][C]13[/C][C]99.8[/C][C]101.158696330884[/C][C]-1.35869633088371[/C][/ROW]
[ROW][C]14[/C][C]96[/C][C]90.4410847509678[/C][C]5.55891524903223[/C][/ROW]
[ROW][C]15[/C][C]115.9[/C][C]115.476535876213[/C][C]0.423464123787176[/C][/ROW]
[ROW][C]16[/C][C]109.1[/C][C]107.261370609767[/C][C]1.83862939023281[/C][/ROW]
[ROW][C]17[/C][C]117.3[/C][C]106.511122598250[/C][C]10.7888774017503[/C][/ROW]
[ROW][C]18[/C][C]109.8[/C][C]115.192710057906[/C][C]-5.39271005790625[/C][/ROW]
[ROW][C]19[/C][C]112.8[/C][C]106.767135286657[/C][C]6.0328647133431[/C][/ROW]
[ROW][C]20[/C][C]110.7[/C][C]103.447911055806[/C][C]7.25208894419367[/C][/ROW]
[ROW][C]21[/C][C]100[/C][C]107.027650575483[/C][C]-7.02765057548341[/C][/ROW]
[ROW][C]22[/C][C]113.3[/C][C]112.070996490015[/C][C]1.22900350998458[/C][/ROW]
[ROW][C]23[/C][C]122.4[/C][C]116.297553512800[/C][C]6.10244648720046[/C][/ROW]
[ROW][C]24[/C][C]112.5[/C][C]118.903594648246[/C][C]-6.40359464824567[/C][/ROW]
[ROW][C]25[/C][C]104.2[/C][C]103.205711612523[/C][C]0.994288387476765[/C][/ROW]
[ROW][C]26[/C][C]92.5[/C][C]90.7064704201747[/C][C]1.79352957982533[/C][/ROW]
[ROW][C]27[/C][C]117.2[/C][C]116.926002050367[/C][C]0.273997949633184[/C][/ROW]
[ROW][C]28[/C][C]109.3[/C][C]107.111528215417[/C][C]2.18847178458255[/C][/ROW]
[ROW][C]29[/C][C]106.1[/C][C]107.734105514849[/C][C]-1.63410551484863[/C][/ROW]
[ROW][C]30[/C][C]118.8[/C][C]113.528178207297[/C][C]5.27182179270263[/C][/ROW]
[ROW][C]31[/C][C]105.3[/C][C]105.725657983295[/C][C]-0.425657983294858[/C][/ROW]
[ROW][C]32[/C][C]106[/C][C]105.562528881418[/C][C]0.437471118582446[/C][/ROW]
[ROW][C]33[/C][C]102[/C][C]104.206510534105[/C][C]-2.20651053410530[/C][/ROW]
[ROW][C]34[/C][C]112.9[/C][C]111.671792471884[/C][C]1.22820752811592[/C][/ROW]
[ROW][C]35[/C][C]116.5[/C][C]117.623675255213[/C][C]-1.12367525521267[/C][/ROW]
[ROW][C]36[/C][C]114.8[/C][C]118.207396772993[/C][C]-3.40739677299331[/C][/ROW]
[ROW][C]37[/C][C]100.5[/C][C]102.468821543358[/C][C]-1.96882154335802[/C][/ROW]
[ROW][C]38[/C][C]85.4[/C][C]91.4048979806245[/C][C]-6.00489798062453[/C][/ROW]
[ROW][C]39[/C][C]114.6[/C][C]114.854025121806[/C][C]-0.254025121806433[/C][/ROW]
[ROW][C]40[/C][C]109.9[/C][C]104.888734540938[/C][C]5.01126545906168[/C][/ROW]
[ROW][C]41[/C][C]100.7[/C][C]107.732642401412[/C][C]-7.03264240141219[/C][/ROW]
[ROW][C]42[/C][C]115.5[/C][C]113.278993675671[/C][C]2.22100632432867[/C][/ROW]
[ROW][C]43[/C][C]100.7[/C][C]99.8033449587078[/C][C]0.896655041292176[/C][/ROW]
[ROW][C]44[/C][C]99[/C][C]100.060037868338[/C][C]-1.06003786833849[/C][/ROW]
[ROW][C]45[/C][C]102.3[/C][C]97.7364828050832[/C][C]4.56351719491683[/C][/ROW]
[ROW][C]46[/C][C]108.8[/C][C]106.005826797270[/C][C]2.79417320272967[/C][/ROW]
[ROW][C]47[/C][C]105.9[/C][C]113.443377890618[/C][C]-7.54337789061836[/C][/ROW]
[ROW][C]48[/C][C]113.2[/C][C]111.110753103481[/C][C]2.08924689651913[/C][/ROW]
[ROW][C]49[/C][C]95.7[/C][C]95.1715267248764[/C][C]0.528473275123591[/C][/ROW]
[ROW][C]50[/C][C]80.9[/C][C]86.4304780301729[/C][C]-5.5304780301729[/C][/ROW]
[ROW][C]51[/C][C]113.9[/C][C]108.868703501199[/C][C]5.03129649880097[/C][/ROW]
[ROW][C]52[/C][C]98.1[/C][C]99.8434773220961[/C][C]-1.74347732209615[/C][/ROW]
[ROW][C]53[/C][C]102.8[/C][C]101.51549200265[/C][C]1.28450799734997[/C][/ROW]
[ROW][C]54[/C][C]104.7[/C][C]106.394770437800[/C][C]-1.69477043780044[/C][/ROW]
[ROW][C]55[/C][C]95.9[/C][C]98.866692373517[/C][C]-2.96669237351703[/C][/ROW]
[ROW][C]56[/C][C]94.6[/C][C]96.1060109485962[/C][C]-1.50601094859622[/C][/ROW]
[ROW][C]57[/C][C]101.6[/C][C]95.8904271699588[/C][C]5.70957283004116[/C][/ROW]
[ROW][C]58[/C][C]103.9[/C][C]105.111415238054[/C][C]-1.2114152380542[/C][/ROW]
[ROW][C]59[/C][C]110.3[/C][C]112.867008740628[/C][C]-2.56700874062849[/C][/ROW]
[ROW][C]60[/C][C]114.1[/C][C]111.174174086951[/C][C]2.92582591304878[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=67830&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=67830&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	89.1	87.2952437883586	1.80475621164137
2	82.6	78.4170688180601	4.18293118193987
3	102.7	108.174733450415	-5.47473345041491
4	91.8	99.094889311781	-7.2948893117809
5	94.1	97.5066374828394	-3.40663748283945
6	103.1	103.505347621325	-0.405347621324616
7	93.2	96.7371693978234	-3.53716939782339
8	91	96.1235112458414	-5.1235112458414
9	94.3	95.3389289153693	-1.03892891536929
10	99.4	103.439969002776	-4.03996900277598
11	115.7	110.568384600741	5.13161539925906
12	116.8	112.004081388329	4.79591861167107
13	99.8	101.158696330884	-1.35869633088371
14	96	90.4410847509678	5.55891524903223
15	115.9	115.476535876213	0.423464123787176
16	109.1	107.261370609767	1.83862939023281
17	117.3	106.511122598250	10.7888774017503
18	109.8	115.192710057906	-5.39271005790625
19	112.8	106.767135286657	6.0328647133431
20	110.7	103.447911055806	7.25208894419367
21	100	107.027650575483	-7.02765057548341
22	113.3	112.070996490015	1.22900350998458
23	122.4	116.297553512800	6.10244648720046
24	112.5	118.903594648246	-6.40359464824567
25	104.2	103.205711612523	0.994288387476765
26	92.5	90.7064704201747	1.79352957982533
27	117.2	116.926002050367	0.273997949633184
28	109.3	107.111528215417	2.18847178458255
29	106.1	107.734105514849	-1.63410551484863
30	118.8	113.528178207297	5.27182179270263
31	105.3	105.725657983295	-0.425657983294858
32	106	105.562528881418	0.437471118582446
33	102	104.206510534105	-2.20651053410530
34	112.9	111.671792471884	1.22820752811592
35	116.5	117.623675255213	-1.12367525521267
36	114.8	118.207396772993	-3.40739677299331
37	100.5	102.468821543358	-1.96882154335802
38	85.4	91.4048979806245	-6.00489798062453
39	114.6	114.854025121806	-0.254025121806433
40	109.9	104.888734540938	5.01126545906168
41	100.7	107.732642401412	-7.03264240141219
42	115.5	113.278993675671	2.22100632432867
43	100.7	99.8033449587078	0.896655041292176
44	99	100.060037868338	-1.06003786833849
45	102.3	97.7364828050832	4.56351719491683
46	108.8	106.005826797270	2.79417320272967
47	105.9	113.443377890618	-7.54337789061836
48	113.2	111.110753103481	2.08924689651913
49	95.7	95.1715267248764	0.528473275123591
50	80.9	86.4304780301729	-5.5304780301729
51	113.9	108.868703501199	5.03129649880097
52	98.1	99.8434773220961	-1.74347732209615
53	102.8	101.51549200265	1.28450799734997
54	104.7	106.394770437800	-1.69477043780044
55	95.9	98.866692373517	-2.96669237351703
56	94.6	96.1060109485962	-1.50601094859622
57	101.6	95.8904271699588	5.70957283004116
58	103.9	105.111415238054	-1.2114152380542
59	110.3	112.867008740628	-2.56700874062849
60	114.1	111.174174086951	2.92582591304878

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
19	0.869075307891764	0.261849384216472	0.130924692108236
20	0.814716826246422	0.370566347507156	0.185283173753578
21	0.85174798323729	0.296504033525421	0.148252016762710
22	0.846989440551556	0.306021118896889	0.153010559448444
23	0.94424845473594	0.111503090528119	0.0557515452640594
24	0.998328137250768	0.00334372549846490	0.00167186274923245
25	0.997274695682457	0.0054506086350856	0.0027253043175428
26	0.997528610878277	0.00494277824344642	0.00247138912172321
27	0.9943443696376	0.0113112607247984	0.00565563036239919
28	0.98822498509394	0.0235500298121193	0.0117750149060597
29	0.984566569245833	0.0308668615083347	0.0154334307541674
30	0.982211590654233	0.0355768186915346	0.0177884093457673
31	0.971811079173001	0.0563778416539975	0.0281889208269987
32	0.965491117540715	0.0690177649185694	0.0345088824592847
33	0.950969035752081	0.0980619284958384	0.0490309642479192
34	0.914274740512324	0.171450518975352	0.0857252594876758
35	0.916394608813004	0.167210782373993	0.0836053911869964
36	0.884027845525573	0.231944308948854	0.115972154474427
37	0.82341976535224	0.353160469295521	0.176580234647761
38	0.770952054052808	0.458095891894384	0.229047945947192
39	0.716746408554063	0.566507182891874	0.283253591445937
40	0.73797755144934	0.52404489710132	0.26202244855066
41	0.797942771577721	0.404114456844557	0.202057228422279

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
19 & 0.869075307891764 & 0.261849384216472 & 0.130924692108236 \tabularnewline
20 & 0.814716826246422 & 0.370566347507156 & 0.185283173753578 \tabularnewline
21 & 0.85174798323729 & 0.296504033525421 & 0.148252016762710 \tabularnewline
22 & 0.846989440551556 & 0.306021118896889 & 0.153010559448444 \tabularnewline
23 & 0.94424845473594 & 0.111503090528119 & 0.0557515452640594 \tabularnewline
24 & 0.998328137250768 & 0.00334372549846490 & 0.00167186274923245 \tabularnewline
25 & 0.997274695682457 & 0.0054506086350856 & 0.0027253043175428 \tabularnewline
26 & 0.997528610878277 & 0.00494277824344642 & 0.00247138912172321 \tabularnewline
27 & 0.9943443696376 & 0.0113112607247984 & 0.00565563036239919 \tabularnewline
28 & 0.98822498509394 & 0.0235500298121193 & 0.0117750149060597 \tabularnewline
29 & 0.984566569245833 & 0.0308668615083347 & 0.0154334307541674 \tabularnewline
30 & 0.982211590654233 & 0.0355768186915346 & 0.0177884093457673 \tabularnewline
31 & 0.971811079173001 & 0.0563778416539975 & 0.0281889208269987 \tabularnewline
32 & 0.965491117540715 & 0.0690177649185694 & 0.0345088824592847 \tabularnewline
33 & 0.950969035752081 & 0.0980619284958384 & 0.0490309642479192 \tabularnewline
34 & 0.914274740512324 & 0.171450518975352 & 0.0857252594876758 \tabularnewline
35 & 0.916394608813004 & 0.167210782373993 & 0.0836053911869964 \tabularnewline
36 & 0.884027845525573 & 0.231944308948854 & 0.115972154474427 \tabularnewline
37 & 0.82341976535224 & 0.353160469295521 & 0.176580234647761 \tabularnewline
38 & 0.770952054052808 & 0.458095891894384 & 0.229047945947192 \tabularnewline
39 & 0.716746408554063 & 0.566507182891874 & 0.283253591445937 \tabularnewline
40 & 0.73797755144934 & 0.52404489710132 & 0.26202244855066 \tabularnewline
41 & 0.797942771577721 & 0.404114456844557 & 0.202057228422279 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=67830&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]19[/C][C]0.869075307891764[/C][C]0.261849384216472[/C][C]0.130924692108236[/C][/ROW]
[ROW][C]20[/C][C]0.814716826246422[/C][C]0.370566347507156[/C][C]0.185283173753578[/C][/ROW]
[ROW][C]21[/C][C]0.85174798323729[/C][C]0.296504033525421[/C][C]0.148252016762710[/C][/ROW]
[ROW][C]22[/C][C]0.846989440551556[/C][C]0.306021118896889[/C][C]0.153010559448444[/C][/ROW]
[ROW][C]23[/C][C]0.94424845473594[/C][C]0.111503090528119[/C][C]0.0557515452640594[/C][/ROW]
[ROW][C]24[/C][C]0.998328137250768[/C][C]0.00334372549846490[/C][C]0.00167186274923245[/C][/ROW]
[ROW][C]25[/C][C]0.997274695682457[/C][C]0.0054506086350856[/C][C]0.0027253043175428[/C][/ROW]
[ROW][C]26[/C][C]0.997528610878277[/C][C]0.00494277824344642[/C][C]0.00247138912172321[/C][/ROW]
[ROW][C]27[/C][C]0.9943443696376[/C][C]0.0113112607247984[/C][C]0.00565563036239919[/C][/ROW]
[ROW][C]28[/C][C]0.98822498509394[/C][C]0.0235500298121193[/C][C]0.0117750149060597[/C][/ROW]
[ROW][C]29[/C][C]0.984566569245833[/C][C]0.0308668615083347[/C][C]0.0154334307541674[/C][/ROW]
[ROW][C]30[/C][C]0.982211590654233[/C][C]0.0355768186915346[/C][C]0.0177884093457673[/C][/ROW]
[ROW][C]31[/C][C]0.971811079173001[/C][C]0.0563778416539975[/C][C]0.0281889208269987[/C][/ROW]
[ROW][C]32[/C][C]0.965491117540715[/C][C]0.0690177649185694[/C][C]0.0345088824592847[/C][/ROW]
[ROW][C]33[/C][C]0.950969035752081[/C][C]0.0980619284958384[/C][C]0.0490309642479192[/C][/ROW]
[ROW][C]34[/C][C]0.914274740512324[/C][C]0.171450518975352[/C][C]0.0857252594876758[/C][/ROW]
[ROW][C]35[/C][C]0.916394608813004[/C][C]0.167210782373993[/C][C]0.0836053911869964[/C][/ROW]
[ROW][C]36[/C][C]0.884027845525573[/C][C]0.231944308948854[/C][C]0.115972154474427[/C][/ROW]
[ROW][C]37[/C][C]0.82341976535224[/C][C]0.353160469295521[/C][C]0.176580234647761[/C][/ROW]
[ROW][C]38[/C][C]0.770952054052808[/C][C]0.458095891894384[/C][C]0.229047945947192[/C][/ROW]
[ROW][C]39[/C][C]0.716746408554063[/C][C]0.566507182891874[/C][C]0.283253591445937[/C][/ROW]
[ROW][C]40[/C][C]0.73797755144934[/C][C]0.52404489710132[/C][C]0.26202244855066[/C][/ROW]
[ROW][C]41[/C][C]0.797942771577721[/C][C]0.404114456844557[/C][C]0.202057228422279[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=67830&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=67830&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
19	0.869075307891764	0.261849384216472	0.130924692108236
20	0.814716826246422	0.370566347507156	0.185283173753578
21	0.85174798323729	0.296504033525421	0.148252016762710
22	0.846989440551556	0.306021118896889	0.153010559448444
23	0.94424845473594	0.111503090528119	0.0557515452640594
24	0.998328137250768	0.00334372549846490	0.00167186274923245
25	0.997274695682457	0.0054506086350856	0.0027253043175428
26	0.997528610878277	0.00494277824344642	0.00247138912172321
27	0.9943443696376	0.0113112607247984	0.00565563036239919
28	0.98822498509394	0.0235500298121193	0.0117750149060597
29	0.984566569245833	0.0308668615083347	0.0154334307541674
30	0.982211590654233	0.0355768186915346	0.0177884093457673
31	0.971811079173001	0.0563778416539975	0.0281889208269987
32	0.965491117540715	0.0690177649185694	0.0345088824592847
33	0.950969035752081	0.0980619284958384	0.0490309642479192
34	0.914274740512324	0.171450518975352	0.0857252594876758
35	0.916394608813004	0.167210782373993	0.0836053911869964
36	0.884027845525573	0.231944308948854	0.115972154474427
37	0.82341976535224	0.353160469295521	0.176580234647761
38	0.770952054052808	0.458095891894384	0.229047945947192
39	0.716746408554063	0.566507182891874	0.283253591445937
40	0.73797755144934	0.52404489710132	0.26202244855066
41	0.797942771577721	0.404114456844557	0.202057228422279

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	3	0.130434782608696	NOK
5% type I error level	7	0.304347826086957	NOK
10% type I error level	10	0.434782608695652	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 & 3 & 0.130434782608696 & NOK \tabularnewline
5% type I error level & 7 & 0.304347826086957 & NOK \tabularnewline
10% type I error level & 10 & 0.434782608695652 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=67830&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]3[/C][C]0.130434782608696[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]7[/C][C]0.304347826086957[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]10[/C][C]0.434782608695652[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=67830&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=67830&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	3	0.130434782608696	NOK
5% type I error level	7	0.304347826086957	NOK
10% type I error level	10	0.434782608695652	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 = 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