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R Software Module

rwasp_multipleregression.wasp

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Multiple Regression

Date of computation

Fri, 19 Dec 2008 03:16:07 -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/2008/Dec/19/t12296819027407tvfl6z2t2n7.htm/, Retrieved Sun, 19 May 2024 07:11:21 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=35022, Retrieved Sun, 19 May 2024 07:11:21 +0000

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197

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-       [Multiple Regression] [Paper1GeoffreyMoos] [2008-12-19 10:16:07] [c04cf09058bb6459559bb5d5f71f8469] [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	5 seconds
R Server	'George Udny Yule' @ 72.249.76.132

\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 & 5 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=35022&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]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=35022&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=35022&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	5 seconds
R Server	'George Udny Yule' @ 72.249.76.132

Multiple Linear Regression - Estimated Regression Equation
y[t] = + 0.893237154150196 + 0.105662055335968x[t] -0.00188669301712875M1[t] -0.00739591567852424M2[t] -0.00787549407114603M3[t] -0.00922266139657425M4[t] -0.0105698287220025M5[t] -0.0119169960474307M6[t] -0.0132641633728589M7[t] -0.00661133069828712M8[t] -0.00195849802371534M9[t] -0.00130566534914356M10[t] + 0.00134716732542821M11[t] + 0.00134716732542822t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
y[t] =  +  0.893237154150196 +  0.105662055335968x[t] -0.00188669301712875M1[t] -0.00739591567852424M2[t] -0.00787549407114603M3[t] -0.00922266139657425M4[t] -0.0105698287220025M5[t] -0.0119169960474307M6[t] -0.0132641633728589M7[t] -0.00661133069828712M8[t] -0.00195849802371534M9[t] -0.00130566534914356M10[t] +  0.00134716732542821M11[t] +  0.00134716732542822t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=35022&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]y[t] =  +  0.893237154150196 +  0.105662055335968x[t] -0.00188669301712875M1[t] -0.00739591567852424M2[t] -0.00787549407114603M3[t] -0.00922266139657425M4[t] -0.0105698287220025M5[t] -0.0119169960474307M6[t] -0.0132641633728589M7[t] -0.00661133069828712M8[t] -0.00195849802371534M9[t] -0.00130566534914356M10[t] +  0.00134716732542821M11[t] +  0.00134716732542822t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=35022&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=35022&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] = + 0.893237154150196 + 0.105662055335968x[t] -0.00188669301712875M1[t] -0.00739591567852424M2[t] -0.00787549407114603M3[t] -0.00922266139657425M4[t] -0.0105698287220025M5[t] -0.0119169960474307M6[t] -0.0132641633728589M7[t] -0.00661133069828712M8[t] -0.00195849802371534M9[t] -0.00130566534914356M10[t] + 0.00134716732542821M11[t] + 0.00134716732542822t + 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)	0.893237154150196	0.00987	90.5006	0	0
x	0.105662055335968	0.008745	12.0821	0	0
M1	-0.00188669301712875	0.010579	-0.1784	0.859213	0.429607
M2	-0.00739591567852424	0.011098	-0.6664	0.50839	0.254195
M3	-0.00787549407114603	0.011233	-0.7011	0.486693	0.243347
M4	-0.00922266139657425	0.011188	-0.8243	0.413926	0.206963
M5	-0.0105698287220025	0.011149	-0.9481	0.347943	0.173971
M6	-0.0119169960474307	0.011114	-1.0722	0.289099	0.14455
M7	-0.0132641633728589	0.011085	-1.1966	0.237478	0.118739
M8	-0.00661133069828712	0.011061	-0.5977	0.55291	0.276455
M9	-0.00195849802371534	0.011043	-0.1774	0.85999	0.429995
M10	-0.00130566534914356	0.011029	-0.1184	0.90627	0.453135
M11	0.00134716732542821	0.011021	0.1222	0.903236	0.451618
t	0.00134716732542822	0.000243	5.5521	1e-06	1e-06

\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) & 0.893237154150196 & 0.00987 & 90.5006 & 0 & 0 \tabularnewline
x & 0.105662055335968 & 0.008745 & 12.0821 & 0 & 0 \tabularnewline
M1 & -0.00188669301712875 & 0.010579 & -0.1784 & 0.859213 & 0.429607 \tabularnewline
M2 & -0.00739591567852424 & 0.011098 & -0.6664 & 0.50839 & 0.254195 \tabularnewline
M3 & -0.00787549407114603 & 0.011233 & -0.7011 & 0.486693 & 0.243347 \tabularnewline
M4 & -0.00922266139657425 & 0.011188 & -0.8243 & 0.413926 & 0.206963 \tabularnewline
M5 & -0.0105698287220025 & 0.011149 & -0.9481 & 0.347943 & 0.173971 \tabularnewline
M6 & -0.0119169960474307 & 0.011114 & -1.0722 & 0.289099 & 0.14455 \tabularnewline
M7 & -0.0132641633728589 & 0.011085 & -1.1966 & 0.237478 & 0.118739 \tabularnewline
M8 & -0.00661133069828712 & 0.011061 & -0.5977 & 0.55291 & 0.276455 \tabularnewline
M9 & -0.00195849802371534 & 0.011043 & -0.1774 & 0.85999 & 0.429995 \tabularnewline
M10 & -0.00130566534914356 & 0.011029 & -0.1184 & 0.90627 & 0.453135 \tabularnewline
M11 & 0.00134716732542821 & 0.011021 & 0.1222 & 0.903236 & 0.451618 \tabularnewline
t & 0.00134716732542822 & 0.000243 & 5.5521 & 1e-06 & 1e-06 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=35022&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]0.893237154150196[/C][C]0.00987[/C][C]90.5006[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]x[/C][C]0.105662055335968[/C][C]0.008745[/C][C]12.0821[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M1[/C][C]-0.00188669301712875[/C][C]0.010579[/C][C]-0.1784[/C][C]0.859213[/C][C]0.429607[/C][/ROW]
[ROW][C]M2[/C][C]-0.00739591567852424[/C][C]0.011098[/C][C]-0.6664[/C][C]0.50839[/C][C]0.254195[/C][/ROW]
[ROW][C]M3[/C][C]-0.00787549407114603[/C][C]0.011233[/C][C]-0.7011[/C][C]0.486693[/C][C]0.243347[/C][/ROW]
[ROW][C]M4[/C][C]-0.00922266139657425[/C][C]0.011188[/C][C]-0.8243[/C][C]0.413926[/C][C]0.206963[/C][/ROW]
[ROW][C]M5[/C][C]-0.0105698287220025[/C][C]0.011149[/C][C]-0.9481[/C][C]0.347943[/C][C]0.173971[/C][/ROW]
[ROW][C]M6[/C][C]-0.0119169960474307[/C][C]0.011114[/C][C]-1.0722[/C][C]0.289099[/C][C]0.14455[/C][/ROW]
[ROW][C]M7[/C][C]-0.0132641633728589[/C][C]0.011085[/C][C]-1.1966[/C][C]0.237478[/C][C]0.118739[/C][/ROW]
[ROW][C]M8[/C][C]-0.00661133069828712[/C][C]0.011061[/C][C]-0.5977[/C][C]0.55291[/C][C]0.276455[/C][/ROW]
[ROW][C]M9[/C][C]-0.00195849802371534[/C][C]0.011043[/C][C]-0.1774[/C][C]0.85999[/C][C]0.429995[/C][/ROW]
[ROW][C]M10[/C][C]-0.00130566534914356[/C][C]0.011029[/C][C]-0.1184[/C][C]0.90627[/C][C]0.453135[/C][/ROW]
[ROW][C]M11[/C][C]0.00134716732542821[/C][C]0.011021[/C][C]0.1222[/C][C]0.903236[/C][C]0.451618[/C][/ROW]
[ROW][C]t[/C][C]0.00134716732542822[/C][C]0.000243[/C][C]5.5521[/C][C]1e-06[/C][C]1e-06[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=35022&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=35022&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)	0.893237154150196	0.00987	90.5006	0	0
x	0.105662055335968	0.008745	12.0821	0	0
M1	-0.00188669301712875	0.010579	-0.1784	0.859213	0.429607
M2	-0.00739591567852424	0.011098	-0.6664	0.50839	0.254195
M3	-0.00787549407114603	0.011233	-0.7011	0.486693	0.243347
M4	-0.00922266139657425	0.011188	-0.8243	0.413926	0.206963
M5	-0.0105698287220025	0.011149	-0.9481	0.347943	0.173971
M6	-0.0119169960474307	0.011114	-1.0722	0.289099	0.14455
M7	-0.0132641633728589	0.011085	-1.1966	0.237478	0.118739
M8	-0.00661133069828712	0.011061	-0.5977	0.55291	0.276455
M9	-0.00195849802371534	0.011043	-0.1774	0.85999	0.429995
M10	-0.00130566534914356	0.011029	-0.1184	0.90627	0.453135
M11	0.00134716732542821	0.011021	0.1222	0.903236	0.451618
t	0.00134716732542822	0.000243	5.5521	1e-06	1e-06

Multiple Linear Regression - Regression Statistics
Multiple R	0.978537207620351
R-squared	0.957535066697435
Adjusted R-squared	0.94578944684779
F-TEST (value)	81.5227360458389
F-TEST (DF numerator)	13
F-TEST (DF denominator)	47
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.0174219911547682
Sum Squared Residuals	0.0142657114624507

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.978537207620351 \tabularnewline
R-squared & 0.957535066697435 \tabularnewline
Adjusted R-squared & 0.94578944684779 \tabularnewline
F-TEST (value) & 81.5227360458389 \tabularnewline
F-TEST (DF numerator) & 13 \tabularnewline
F-TEST (DF denominator) & 47 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.0174219911547682 \tabularnewline
Sum Squared Residuals & 0.0142657114624507 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=35022&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.978537207620351[/C][/ROW]
[ROW][C]R-squared[/C][C]0.957535066697435[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.94578944684779[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]81.5227360458389[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]13[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]47[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.0174219911547682[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]0.0142657114624507[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=35022&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=35022&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	0.978537207620351
R-squared	0.957535066697435
Adjusted R-squared	0.94578944684779
F-TEST (value)	81.5227360458389
F-TEST (DF numerator)	13
F-TEST (DF denominator)	47
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.0174219911547682
Sum Squared Residuals	0.0142657114624507

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	0.89	0.892697628458503	-0.00269762845850255
2	0.89	0.88853557312253	0.00146442687747086
3	0.89	0.889403162055336	0.000596837944664412
4	0.89	0.889403162055336	0.000596837944664324
5	0.89	0.889403162055336	0.000596837944664384
6	0.89	0.889403162055336	0.000596837944664333
7	0.89	0.889403162055336	0.000596837944664376
8	0.9	0.897403162055336	0.00259683794466436
9	0.91	0.903403162055336	0.00659683794466438
10	0.92	0.905403162055336	0.0145968379446644
11	0.92	0.909403162055336	0.0105968379446644
12	0.92	0.909403162055336	0.0105968379446644
13	0.92	0.908863636363635	0.0111363636363649
14	0.92	0.904701581027668	0.0152984189723321
15	0.92	0.905569169960474	0.0144308300395257
16	0.92	0.905569169960474	0.0144308300395258
17	0.92	0.905569169960474	0.0144308300395258
18	0.92	0.905569169960474	0.0144308300395258
19	0.92	0.905569169960474	0.0144308300395258
20	0.92	0.913569169960474	0.00643083003952575
21	0.92	0.919569169960474	0.000430830039525739
22	0.92	0.921569169960474	-0.00156916996047426
23	0.92	0.925569169960474	-0.00556916996047426
24	0.92	0.925569169960474	-0.00556916996047426
25	0.92	0.925029644268774	-0.00502964426877375
26	0.92	0.920867588932807	-0.000867588932806436
27	0.92	0.921735177865613	-0.00173517786561289
28	0.92	0.921735177865613	-0.00173517786561287
29	0.92	0.921735177865613	-0.00173517786561288
30	0.92	0.921735177865613	-0.00173517786561286
31	0.92	0.921735177865613	-0.00173517786561288
32	0.92	0.929735177865613	-0.00973517786561288
33	0.92	0.935735177865613	-0.0157351778656129
34	0.92	0.937735177865613	-0.0177351778656129
35	0.92	0.941735177865613	-0.0217351778656129
36	0.92	0.941735177865613	-0.0217351778656129
37	0.92	0.941195652173912	-0.0211956521739124
38	0.92	0.937033596837945	-0.0170335968379451
39	1.01	1.04356324110672	-0.0335632411067193
40	1.01	1.04356324110672	-0.0335632411067193
41	1.01	1.04356324110672	-0.0335632411067194
42	1.01	1.04356324110672	-0.0335632411067193
43	1.01	1.04356324110672	-0.0335632411067194
44	1.04	1.05156324110672	-0.0115632411067193
45	1.05	1.05756324110672	-0.00756324110671932
46	1.05	1.05956324110672	-0.00956324110671931
47	1.06	1.06356324110672	-0.00356324110671931
48	1.06	1.06356324110672	-0.00356324110671930
49	1.06	1.06302371541502	-0.0030237154150188
50	1.06	1.05886166007905	0.00113833992094852
51	1.08	1.05972924901186	0.0202707509881421
52	1.08	1.05972924901186	0.0202707509881421
53	1.08	1.05972924901186	0.0202707509881421
54	1.08	1.05972924901186	0.0202707509881421
55	1.08	1.05972924901186	0.0202707509881421
56	1.08	1.06772924901186	0.0122707509881421
57	1.09	1.07372924901186	0.0162707509881421
58	1.09	1.07572924901186	0.0142707509881421
59	1.1	1.07972924901186	0.0202707509881421
60	1.1	1.07972924901186	0.0202707509881421
61	1.1	1.07918972332016	0.0208102766798426

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 0.89 & 0.892697628458503 & -0.00269762845850255 \tabularnewline
2 & 0.89 & 0.88853557312253 & 0.00146442687747086 \tabularnewline
3 & 0.89 & 0.889403162055336 & 0.000596837944664412 \tabularnewline
4 & 0.89 & 0.889403162055336 & 0.000596837944664324 \tabularnewline
5 & 0.89 & 0.889403162055336 & 0.000596837944664384 \tabularnewline
6 & 0.89 & 0.889403162055336 & 0.000596837944664333 \tabularnewline
7 & 0.89 & 0.889403162055336 & 0.000596837944664376 \tabularnewline
8 & 0.9 & 0.897403162055336 & 0.00259683794466436 \tabularnewline
9 & 0.91 & 0.903403162055336 & 0.00659683794466438 \tabularnewline
10 & 0.92 & 0.905403162055336 & 0.0145968379446644 \tabularnewline
11 & 0.92 & 0.909403162055336 & 0.0105968379446644 \tabularnewline
12 & 0.92 & 0.909403162055336 & 0.0105968379446644 \tabularnewline
13 & 0.92 & 0.908863636363635 & 0.0111363636363649 \tabularnewline
14 & 0.92 & 0.904701581027668 & 0.0152984189723321 \tabularnewline
15 & 0.92 & 0.905569169960474 & 0.0144308300395257 \tabularnewline
16 & 0.92 & 0.905569169960474 & 0.0144308300395258 \tabularnewline
17 & 0.92 & 0.905569169960474 & 0.0144308300395258 \tabularnewline
18 & 0.92 & 0.905569169960474 & 0.0144308300395258 \tabularnewline
19 & 0.92 & 0.905569169960474 & 0.0144308300395258 \tabularnewline
20 & 0.92 & 0.913569169960474 & 0.00643083003952575 \tabularnewline
21 & 0.92 & 0.919569169960474 & 0.000430830039525739 \tabularnewline
22 & 0.92 & 0.921569169960474 & -0.00156916996047426 \tabularnewline
23 & 0.92 & 0.925569169960474 & -0.00556916996047426 \tabularnewline
24 & 0.92 & 0.925569169960474 & -0.00556916996047426 \tabularnewline
25 & 0.92 & 0.925029644268774 & -0.00502964426877375 \tabularnewline
26 & 0.92 & 0.920867588932807 & -0.000867588932806436 \tabularnewline
27 & 0.92 & 0.921735177865613 & -0.00173517786561289 \tabularnewline
28 & 0.92 & 0.921735177865613 & -0.00173517786561287 \tabularnewline
29 & 0.92 & 0.921735177865613 & -0.00173517786561288 \tabularnewline
30 & 0.92 & 0.921735177865613 & -0.00173517786561286 \tabularnewline
31 & 0.92 & 0.921735177865613 & -0.00173517786561288 \tabularnewline
32 & 0.92 & 0.929735177865613 & -0.00973517786561288 \tabularnewline
33 & 0.92 & 0.935735177865613 & -0.0157351778656129 \tabularnewline
34 & 0.92 & 0.937735177865613 & -0.0177351778656129 \tabularnewline
35 & 0.92 & 0.941735177865613 & -0.0217351778656129 \tabularnewline
36 & 0.92 & 0.941735177865613 & -0.0217351778656129 \tabularnewline
37 & 0.92 & 0.941195652173912 & -0.0211956521739124 \tabularnewline
38 & 0.92 & 0.937033596837945 & -0.0170335968379451 \tabularnewline
39 & 1.01 & 1.04356324110672 & -0.0335632411067193 \tabularnewline
40 & 1.01 & 1.04356324110672 & -0.0335632411067193 \tabularnewline
41 & 1.01 & 1.04356324110672 & -0.0335632411067194 \tabularnewline
42 & 1.01 & 1.04356324110672 & -0.0335632411067193 \tabularnewline
43 & 1.01 & 1.04356324110672 & -0.0335632411067194 \tabularnewline
44 & 1.04 & 1.05156324110672 & -0.0115632411067193 \tabularnewline
45 & 1.05 & 1.05756324110672 & -0.00756324110671932 \tabularnewline
46 & 1.05 & 1.05956324110672 & -0.00956324110671931 \tabularnewline
47 & 1.06 & 1.06356324110672 & -0.00356324110671931 \tabularnewline
48 & 1.06 & 1.06356324110672 & -0.00356324110671930 \tabularnewline
49 & 1.06 & 1.06302371541502 & -0.0030237154150188 \tabularnewline
50 & 1.06 & 1.05886166007905 & 0.00113833992094852 \tabularnewline
51 & 1.08 & 1.05972924901186 & 0.0202707509881421 \tabularnewline
52 & 1.08 & 1.05972924901186 & 0.0202707509881421 \tabularnewline
53 & 1.08 & 1.05972924901186 & 0.0202707509881421 \tabularnewline
54 & 1.08 & 1.05972924901186 & 0.0202707509881421 \tabularnewline
55 & 1.08 & 1.05972924901186 & 0.0202707509881421 \tabularnewline
56 & 1.08 & 1.06772924901186 & 0.0122707509881421 \tabularnewline
57 & 1.09 & 1.07372924901186 & 0.0162707509881421 \tabularnewline
58 & 1.09 & 1.07572924901186 & 0.0142707509881421 \tabularnewline
59 & 1.1 & 1.07972924901186 & 0.0202707509881421 \tabularnewline
60 & 1.1 & 1.07972924901186 & 0.0202707509881421 \tabularnewline
61 & 1.1 & 1.07918972332016 & 0.0208102766798426 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=35022&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]0.89[/C][C]0.892697628458503[/C][C]-0.00269762845850255[/C][/ROW]
[ROW][C]2[/C][C]0.89[/C][C]0.88853557312253[/C][C]0.00146442687747086[/C][/ROW]
[ROW][C]3[/C][C]0.89[/C][C]0.889403162055336[/C][C]0.000596837944664412[/C][/ROW]
[ROW][C]4[/C][C]0.89[/C][C]0.889403162055336[/C][C]0.000596837944664324[/C][/ROW]
[ROW][C]5[/C][C]0.89[/C][C]0.889403162055336[/C][C]0.000596837944664384[/C][/ROW]
[ROW][C]6[/C][C]0.89[/C][C]0.889403162055336[/C][C]0.000596837944664333[/C][/ROW]
[ROW][C]7[/C][C]0.89[/C][C]0.889403162055336[/C][C]0.000596837944664376[/C][/ROW]
[ROW][C]8[/C][C]0.9[/C][C]0.897403162055336[/C][C]0.00259683794466436[/C][/ROW]
[ROW][C]9[/C][C]0.91[/C][C]0.903403162055336[/C][C]0.00659683794466438[/C][/ROW]
[ROW][C]10[/C][C]0.92[/C][C]0.905403162055336[/C][C]0.0145968379446644[/C][/ROW]
[ROW][C]11[/C][C]0.92[/C][C]0.909403162055336[/C][C]0.0105968379446644[/C][/ROW]
[ROW][C]12[/C][C]0.92[/C][C]0.909403162055336[/C][C]0.0105968379446644[/C][/ROW]
[ROW][C]13[/C][C]0.92[/C][C]0.908863636363635[/C][C]0.0111363636363649[/C][/ROW]
[ROW][C]14[/C][C]0.92[/C][C]0.904701581027668[/C][C]0.0152984189723321[/C][/ROW]
[ROW][C]15[/C][C]0.92[/C][C]0.905569169960474[/C][C]0.0144308300395257[/C][/ROW]
[ROW][C]16[/C][C]0.92[/C][C]0.905569169960474[/C][C]0.0144308300395258[/C][/ROW]
[ROW][C]17[/C][C]0.92[/C][C]0.905569169960474[/C][C]0.0144308300395258[/C][/ROW]
[ROW][C]18[/C][C]0.92[/C][C]0.905569169960474[/C][C]0.0144308300395258[/C][/ROW]
[ROW][C]19[/C][C]0.92[/C][C]0.905569169960474[/C][C]0.0144308300395258[/C][/ROW]
[ROW][C]20[/C][C]0.92[/C][C]0.913569169960474[/C][C]0.00643083003952575[/C][/ROW]
[ROW][C]21[/C][C]0.92[/C][C]0.919569169960474[/C][C]0.000430830039525739[/C][/ROW]
[ROW][C]22[/C][C]0.92[/C][C]0.921569169960474[/C][C]-0.00156916996047426[/C][/ROW]
[ROW][C]23[/C][C]0.92[/C][C]0.925569169960474[/C][C]-0.00556916996047426[/C][/ROW]
[ROW][C]24[/C][C]0.92[/C][C]0.925569169960474[/C][C]-0.00556916996047426[/C][/ROW]
[ROW][C]25[/C][C]0.92[/C][C]0.925029644268774[/C][C]-0.00502964426877375[/C][/ROW]
[ROW][C]26[/C][C]0.92[/C][C]0.920867588932807[/C][C]-0.000867588932806436[/C][/ROW]
[ROW][C]27[/C][C]0.92[/C][C]0.921735177865613[/C][C]-0.00173517786561289[/C][/ROW]
[ROW][C]28[/C][C]0.92[/C][C]0.921735177865613[/C][C]-0.00173517786561287[/C][/ROW]
[ROW][C]29[/C][C]0.92[/C][C]0.921735177865613[/C][C]-0.00173517786561288[/C][/ROW]
[ROW][C]30[/C][C]0.92[/C][C]0.921735177865613[/C][C]-0.00173517786561286[/C][/ROW]
[ROW][C]31[/C][C]0.92[/C][C]0.921735177865613[/C][C]-0.00173517786561288[/C][/ROW]
[ROW][C]32[/C][C]0.92[/C][C]0.929735177865613[/C][C]-0.00973517786561288[/C][/ROW]
[ROW][C]33[/C][C]0.92[/C][C]0.935735177865613[/C][C]-0.0157351778656129[/C][/ROW]
[ROW][C]34[/C][C]0.92[/C][C]0.937735177865613[/C][C]-0.0177351778656129[/C][/ROW]
[ROW][C]35[/C][C]0.92[/C][C]0.941735177865613[/C][C]-0.0217351778656129[/C][/ROW]
[ROW][C]36[/C][C]0.92[/C][C]0.941735177865613[/C][C]-0.0217351778656129[/C][/ROW]
[ROW][C]37[/C][C]0.92[/C][C]0.941195652173912[/C][C]-0.0211956521739124[/C][/ROW]
[ROW][C]38[/C][C]0.92[/C][C]0.937033596837945[/C][C]-0.0170335968379451[/C][/ROW]
[ROW][C]39[/C][C]1.01[/C][C]1.04356324110672[/C][C]-0.0335632411067193[/C][/ROW]
[ROW][C]40[/C][C]1.01[/C][C]1.04356324110672[/C][C]-0.0335632411067193[/C][/ROW]
[ROW][C]41[/C][C]1.01[/C][C]1.04356324110672[/C][C]-0.0335632411067194[/C][/ROW]
[ROW][C]42[/C][C]1.01[/C][C]1.04356324110672[/C][C]-0.0335632411067193[/C][/ROW]
[ROW][C]43[/C][C]1.01[/C][C]1.04356324110672[/C][C]-0.0335632411067194[/C][/ROW]
[ROW][C]44[/C][C]1.04[/C][C]1.05156324110672[/C][C]-0.0115632411067193[/C][/ROW]
[ROW][C]45[/C][C]1.05[/C][C]1.05756324110672[/C][C]-0.00756324110671932[/C][/ROW]
[ROW][C]46[/C][C]1.05[/C][C]1.05956324110672[/C][C]-0.00956324110671931[/C][/ROW]
[ROW][C]47[/C][C]1.06[/C][C]1.06356324110672[/C][C]-0.00356324110671931[/C][/ROW]
[ROW][C]48[/C][C]1.06[/C][C]1.06356324110672[/C][C]-0.00356324110671930[/C][/ROW]
[ROW][C]49[/C][C]1.06[/C][C]1.06302371541502[/C][C]-0.0030237154150188[/C][/ROW]
[ROW][C]50[/C][C]1.06[/C][C]1.05886166007905[/C][C]0.00113833992094852[/C][/ROW]
[ROW][C]51[/C][C]1.08[/C][C]1.05972924901186[/C][C]0.0202707509881421[/C][/ROW]
[ROW][C]52[/C][C]1.08[/C][C]1.05972924901186[/C][C]0.0202707509881421[/C][/ROW]
[ROW][C]53[/C][C]1.08[/C][C]1.05972924901186[/C][C]0.0202707509881421[/C][/ROW]
[ROW][C]54[/C][C]1.08[/C][C]1.05972924901186[/C][C]0.0202707509881421[/C][/ROW]
[ROW][C]55[/C][C]1.08[/C][C]1.05972924901186[/C][C]0.0202707509881421[/C][/ROW]
[ROW][C]56[/C][C]1.08[/C][C]1.06772924901186[/C][C]0.0122707509881421[/C][/ROW]
[ROW][C]57[/C][C]1.09[/C][C]1.07372924901186[/C][C]0.0162707509881421[/C][/ROW]
[ROW][C]58[/C][C]1.09[/C][C]1.07572924901186[/C][C]0.0142707509881421[/C][/ROW]
[ROW][C]59[/C][C]1.1[/C][C]1.07972924901186[/C][C]0.0202707509881421[/C][/ROW]
[ROW][C]60[/C][C]1.1[/C][C]1.07972924901186[/C][C]0.0202707509881421[/C][/ROW]
[ROW][C]61[/C][C]1.1[/C][C]1.07918972332016[/C][C]0.0208102766798426[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=35022&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=35022&T=4

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	0.89	0.892697628458503	-0.00269762845850255
2	0.89	0.88853557312253	0.00146442687747086
3	0.89	0.889403162055336	0.000596837944664412
4	0.89	0.889403162055336	0.000596837944664324
5	0.89	0.889403162055336	0.000596837944664384
6	0.89	0.889403162055336	0.000596837944664333
7	0.89	0.889403162055336	0.000596837944664376
8	0.9	0.897403162055336	0.00259683794466436
9	0.91	0.903403162055336	0.00659683794466438
10	0.92	0.905403162055336	0.0145968379446644
11	0.92	0.909403162055336	0.0105968379446644
12	0.92	0.909403162055336	0.0105968379446644
13	0.92	0.908863636363635	0.0111363636363649
14	0.92	0.904701581027668	0.0152984189723321
15	0.92	0.905569169960474	0.0144308300395257
16	0.92	0.905569169960474	0.0144308300395258
17	0.92	0.905569169960474	0.0144308300395258
18	0.92	0.905569169960474	0.0144308300395258
19	0.92	0.905569169960474	0.0144308300395258
20	0.92	0.913569169960474	0.00643083003952575
21	0.92	0.919569169960474	0.000430830039525739
22	0.92	0.921569169960474	-0.00156916996047426
23	0.92	0.925569169960474	-0.00556916996047426
24	0.92	0.925569169960474	-0.00556916996047426
25	0.92	0.925029644268774	-0.00502964426877375
26	0.92	0.920867588932807	-0.000867588932806436
27	0.92	0.921735177865613	-0.00173517786561289
28	0.92	0.921735177865613	-0.00173517786561287
29	0.92	0.921735177865613	-0.00173517786561288
30	0.92	0.921735177865613	-0.00173517786561286
31	0.92	0.921735177865613	-0.00173517786561288
32	0.92	0.929735177865613	-0.00973517786561288
33	0.92	0.935735177865613	-0.0157351778656129
34	0.92	0.937735177865613	-0.0177351778656129
35	0.92	0.941735177865613	-0.0217351778656129
36	0.92	0.941735177865613	-0.0217351778656129
37	0.92	0.941195652173912	-0.0211956521739124
38	0.92	0.937033596837945	-0.0170335968379451
39	1.01	1.04356324110672	-0.0335632411067193
40	1.01	1.04356324110672	-0.0335632411067193
41	1.01	1.04356324110672	-0.0335632411067194
42	1.01	1.04356324110672	-0.0335632411067193
43	1.01	1.04356324110672	-0.0335632411067194
44	1.04	1.05156324110672	-0.0115632411067193
45	1.05	1.05756324110672	-0.00756324110671932
46	1.05	1.05956324110672	-0.00956324110671931
47	1.06	1.06356324110672	-0.00356324110671931
48	1.06	1.06356324110672	-0.00356324110671930
49	1.06	1.06302371541502	-0.0030237154150188
50	1.06	1.05886166007905	0.00113833992094852
51	1.08	1.05972924901186	0.0202707509881421
52	1.08	1.05972924901186	0.0202707509881421
53	1.08	1.05972924901186	0.0202707509881421
54	1.08	1.05972924901186	0.0202707509881421
55	1.08	1.05972924901186	0.0202707509881421
56	1.08	1.06772924901186	0.0122707509881421
57	1.09	1.07372924901186	0.0162707509881421
58	1.09	1.07572924901186	0.0142707509881421
59	1.1	1.07972924901186	0.0202707509881421
60	1.1	1.07972924901186	0.0202707509881421
61	1.1	1.07918972332016	0.0208102766798426

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
17	1.32661052953698e-42	2.65322105907396e-42	1
18	2.62975256265686e-55	5.25950512531372e-55	1
19	2.26340034435676e-72	4.52680068871352e-72	1
20	9.93647598646616e-05	0.000198729519729323	0.999900635240135
21	0.00278147062124583	0.00556294124249166	0.997218529378754
22	0.027701048863691	0.055402097727382	0.972298951136309
23	0.0598041405223617	0.119608281044723	0.940195859477638
24	0.0975215336620576	0.195043067324115	0.902478466337942
25	0.137751755995303	0.275503511990606	0.862248244004697
26	0.223181299776170	0.446362599552339	0.77681870022383
27	0.207533843060371	0.415067686120741	0.79246615693963
28	0.199206857440226	0.398413714880452	0.800793142559774
29	0.206485136317861	0.412970272635722	0.793514863682139
30	0.246425353981852	0.492850707963704	0.753574646018148
31	0.371328845939342	0.742657691878683	0.628671154060658
32	0.401770891691380	0.803541783382761	0.598229108308620
33	0.395630328084145	0.791260656168289	0.604369671915856
34	0.432948368804974	0.865896737609948	0.567051631195026
35	0.406509282829102	0.813018565658205	0.593490717170898
36	0.364987744346943	0.729975488693887	0.635012255653057
37	0.296054542699971	0.592109085399941	0.70394545730003
38	0.221844829950187	0.443689659900374	0.778155170049813
39	0.207985974335174	0.415971948670348	0.792014025664826
40	0.216696179415554	0.433392358831107	0.783303820584446
41	0.270907797429717	0.541815594859434	0.729092202570283
42	0.459623313379127	0.919246626758255	0.540376686620873
43	1	6.76959227119712e-54	3.38479613559856e-54
44	1	3.57754690268425e-40	1.78877345134212e-40

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
17 & 1.32661052953698e-42 & 2.65322105907396e-42 & 1 \tabularnewline
18 & 2.62975256265686e-55 & 5.25950512531372e-55 & 1 \tabularnewline
19 & 2.26340034435676e-72 & 4.52680068871352e-72 & 1 \tabularnewline
20 & 9.93647598646616e-05 & 0.000198729519729323 & 0.999900635240135 \tabularnewline
21 & 0.00278147062124583 & 0.00556294124249166 & 0.997218529378754 \tabularnewline
22 & 0.027701048863691 & 0.055402097727382 & 0.972298951136309 \tabularnewline
23 & 0.0598041405223617 & 0.119608281044723 & 0.940195859477638 \tabularnewline
24 & 0.0975215336620576 & 0.195043067324115 & 0.902478466337942 \tabularnewline
25 & 0.137751755995303 & 0.275503511990606 & 0.862248244004697 \tabularnewline
26 & 0.223181299776170 & 0.446362599552339 & 0.77681870022383 \tabularnewline
27 & 0.207533843060371 & 0.415067686120741 & 0.79246615693963 \tabularnewline
28 & 0.199206857440226 & 0.398413714880452 & 0.800793142559774 \tabularnewline
29 & 0.206485136317861 & 0.412970272635722 & 0.793514863682139 \tabularnewline
30 & 0.246425353981852 & 0.492850707963704 & 0.753574646018148 \tabularnewline
31 & 0.371328845939342 & 0.742657691878683 & 0.628671154060658 \tabularnewline
32 & 0.401770891691380 & 0.803541783382761 & 0.598229108308620 \tabularnewline
33 & 0.395630328084145 & 0.791260656168289 & 0.604369671915856 \tabularnewline
34 & 0.432948368804974 & 0.865896737609948 & 0.567051631195026 \tabularnewline
35 & 0.406509282829102 & 0.813018565658205 & 0.593490717170898 \tabularnewline
36 & 0.364987744346943 & 0.729975488693887 & 0.635012255653057 \tabularnewline
37 & 0.296054542699971 & 0.592109085399941 & 0.70394545730003 \tabularnewline
38 & 0.221844829950187 & 0.443689659900374 & 0.778155170049813 \tabularnewline
39 & 0.207985974335174 & 0.415971948670348 & 0.792014025664826 \tabularnewline
40 & 0.216696179415554 & 0.433392358831107 & 0.783303820584446 \tabularnewline
41 & 0.270907797429717 & 0.541815594859434 & 0.729092202570283 \tabularnewline
42 & 0.459623313379127 & 0.919246626758255 & 0.540376686620873 \tabularnewline
43 & 1 & 6.76959227119712e-54 & 3.38479613559856e-54 \tabularnewline
44 & 1 & 3.57754690268425e-40 & 1.78877345134212e-40 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=35022&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]1.32661052953698e-42[/C][C]2.65322105907396e-42[/C][C]1[/C][/ROW]
[ROW][C]18[/C][C]2.62975256265686e-55[/C][C]5.25950512531372e-55[/C][C]1[/C][/ROW]
[ROW][C]19[/C][C]2.26340034435676e-72[/C][C]4.52680068871352e-72[/C][C]1[/C][/ROW]
[ROW][C]20[/C][C]9.93647598646616e-05[/C][C]0.000198729519729323[/C][C]0.999900635240135[/C][/ROW]
[ROW][C]21[/C][C]0.00278147062124583[/C][C]0.00556294124249166[/C][C]0.997218529378754[/C][/ROW]
[ROW][C]22[/C][C]0.027701048863691[/C][C]0.055402097727382[/C][C]0.972298951136309[/C][/ROW]
[ROW][C]23[/C][C]0.0598041405223617[/C][C]0.119608281044723[/C][C]0.940195859477638[/C][/ROW]
[ROW][C]24[/C][C]0.0975215336620576[/C][C]0.195043067324115[/C][C]0.902478466337942[/C][/ROW]
[ROW][C]25[/C][C]0.137751755995303[/C][C]0.275503511990606[/C][C]0.862248244004697[/C][/ROW]
[ROW][C]26[/C][C]0.223181299776170[/C][C]0.446362599552339[/C][C]0.77681870022383[/C][/ROW]
[ROW][C]27[/C][C]0.207533843060371[/C][C]0.415067686120741[/C][C]0.79246615693963[/C][/ROW]
[ROW][C]28[/C][C]0.199206857440226[/C][C]0.398413714880452[/C][C]0.800793142559774[/C][/ROW]
[ROW][C]29[/C][C]0.206485136317861[/C][C]0.412970272635722[/C][C]0.793514863682139[/C][/ROW]
[ROW][C]30[/C][C]0.246425353981852[/C][C]0.492850707963704[/C][C]0.753574646018148[/C][/ROW]
[ROW][C]31[/C][C]0.371328845939342[/C][C]0.742657691878683[/C][C]0.628671154060658[/C][/ROW]
[ROW][C]32[/C][C]0.401770891691380[/C][C]0.803541783382761[/C][C]0.598229108308620[/C][/ROW]
[ROW][C]33[/C][C]0.395630328084145[/C][C]0.791260656168289[/C][C]0.604369671915856[/C][/ROW]
[ROW][C]34[/C][C]0.432948368804974[/C][C]0.865896737609948[/C][C]0.567051631195026[/C][/ROW]
[ROW][C]35[/C][C]0.406509282829102[/C][C]0.813018565658205[/C][C]0.593490717170898[/C][/ROW]
[ROW][C]36[/C][C]0.364987744346943[/C][C]0.729975488693887[/C][C]0.635012255653057[/C][/ROW]
[ROW][C]37[/C][C]0.296054542699971[/C][C]0.592109085399941[/C][C]0.70394545730003[/C][/ROW]
[ROW][C]38[/C][C]0.221844829950187[/C][C]0.443689659900374[/C][C]0.778155170049813[/C][/ROW]
[ROW][C]39[/C][C]0.207985974335174[/C][C]0.415971948670348[/C][C]0.792014025664826[/C][/ROW]
[ROW][C]40[/C][C]0.216696179415554[/C][C]0.433392358831107[/C][C]0.783303820584446[/C][/ROW]
[ROW][C]41[/C][C]0.270907797429717[/C][C]0.541815594859434[/C][C]0.729092202570283[/C][/ROW]
[ROW][C]42[/C][C]0.459623313379127[/C][C]0.919246626758255[/C][C]0.540376686620873[/C][/ROW]
[ROW][C]43[/C][C]1[/C][C]6.76959227119712e-54[/C][C]3.38479613559856e-54[/C][/ROW]
[ROW][C]44[/C][C]1[/C][C]3.57754690268425e-40[/C][C]1.78877345134212e-40[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=35022&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=35022&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	1.32661052953698e-42	2.65322105907396e-42	1
18	2.62975256265686e-55	5.25950512531372e-55	1
19	2.26340034435676e-72	4.52680068871352e-72	1
20	9.93647598646616e-05	0.000198729519729323	0.999900635240135
21	0.00278147062124583	0.00556294124249166	0.997218529378754
22	0.027701048863691	0.055402097727382	0.972298951136309
23	0.0598041405223617	0.119608281044723	0.940195859477638
24	0.0975215336620576	0.195043067324115	0.902478466337942
25	0.137751755995303	0.275503511990606	0.862248244004697
26	0.223181299776170	0.446362599552339	0.77681870022383
27	0.207533843060371	0.415067686120741	0.79246615693963
28	0.199206857440226	0.398413714880452	0.800793142559774
29	0.206485136317861	0.412970272635722	0.793514863682139
30	0.246425353981852	0.492850707963704	0.753574646018148
31	0.371328845939342	0.742657691878683	0.628671154060658
32	0.401770891691380	0.803541783382761	0.598229108308620
33	0.395630328084145	0.791260656168289	0.604369671915856
34	0.432948368804974	0.865896737609948	0.567051631195026
35	0.406509282829102	0.813018565658205	0.593490717170898
36	0.364987744346943	0.729975488693887	0.635012255653057
37	0.296054542699971	0.592109085399941	0.70394545730003
38	0.221844829950187	0.443689659900374	0.778155170049813
39	0.207985974335174	0.415971948670348	0.792014025664826
40	0.216696179415554	0.433392358831107	0.783303820584446
41	0.270907797429717	0.541815594859434	0.729092202570283
42	0.459623313379127	0.919246626758255	0.540376686620873
43	1	6.76959227119712e-54	3.38479613559856e-54
44	1	3.57754690268425e-40	1.78877345134212e-40

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	7	0.25	NOK
5% type I error level	7	0.25	NOK
10% type I error level	8	0.285714285714286	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 & 7 & 0.25 & NOK \tabularnewline
5% type I error level & 7 & 0.25 & NOK \tabularnewline
10% type I error level & 8 & 0.285714285714286 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=35022&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]7[/C][C]0.25[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]7[/C][C]0.25[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]8[/C][C]0.285714285714286[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=35022&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=35022&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	7	0.25	NOK
5% type I error level	7	0.25	NOK
10% type I error level	8	0.285714285714286	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