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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationMon, 10 Dec 2012 15:26:46 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Dec/10/t1355171236raact2fbnomatym.htm/, Retrieved Fri, 29 Mar 2024 11:47:58 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=198329, Retrieved Fri, 29 Mar 2024 11:47:58 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact105
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
-   PD  [Multiple Regression] [] [2012-11-20 19:56:15] [147786ccb76fa00e429d4b9f5f28b291]
-   PD      [Multiple Regression] [] [2012-12-10 20:26:46] [26ce3afa84a4087bb435ca409d5552c3] [Current]
- R PD        [Multiple Regression] [] [2012-12-10 21:28:04] [147786ccb76fa00e429d4b9f5f28b291]
- RMPD        [Recursive Partitioning (Regression Trees)] [] [2012-12-10 21:39:35] [147786ccb76fa00e429d4b9f5f28b291]
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Dataseries X:
6,80	225,00	0,44	0,67	9,20
6,30	180,00	0,44	0,80	11,70
6,40	190,00	0,46	0,76	15,80
6,20	180,00	0,42	0,65	8,60
6,90	205,00	0,45	0,90	23,20
6,40	225,00	0,43	0,78	27,40
6,30	185,00	0,49	0,77	9,30
6,80	235,00	0,47	0,75	16,00
6,90	235,00	0,44	0,82	4,70
6,70	210,00	0,48	0,83	12,50
6,90	245,00	0,52	0,63	20,10
6,90	245,00	0,49	0,76	9,10
6,30	185,00	0,37	0,71	8,10
6,10	185,00	0,42	0,78	8,60
6,20	180,00	0,44	0,78	20,30
6,80	220,00	0,50	0,88	25,00
6,50	194,00	0,50	0,83	19,20
7,60	225,00	0,43	0,57	3,30
6,30	210,00	0,37	0,82	11,20
7,10	240,00	0,50	0,71	10,50
6,80	225,00	0,40	0,77	10,10
7,30	263,00	0,48	0,66	7,20
6,40	210,00	0,48	0,24	13,60
6,80	235,00	0,43	0,73	9,00
7,20	230,00	0,56	0,72	24,60
6,40	190,00	0,44	0,76	12,60
6,60	220,00	0,49	0,75	5,60
6,80	210,00	0,40	0,74	8,70
6,10	180,00	0,42	0,71	7,70
6,50	235,00	0,49	0,74	24,10
6,40	185,00	0,48	0,86	11,70
6,00	175,00	0,39	0,72	7,70
6,00	192,00	0,44	0,79	9,60
7,30	263,00	0,48	0,66	7,20
6,10	180,00	0,34	0,82	12,30
6,70	240,00	0,52	0,73	8,90
6,40	210,00	0,48	0,85	13,60
5,80	160,00	0,41	0,81	11,20
6,90	230,00	0,41	0,60	2,80
7,00	245,00	0,41	0,57	3,20
7,30	228,00	0,45	0,73	9,40
5,90	155,00	0,29	0,71	11,90
6,20	200,00	0,45	0,80	15,40
6,80	235,00	0,55	0,78	7,40
7,00	235,00	0,48	0,74	18,90
5,90	105,00	0,36	0,84	7,90
6,10	180,00	0,53	0,79	12,20
5,70	185,00	0,35	0,70	11,00
7,10	245,00	0,41	0,78	2,80
5,80	180,00	0,43	0,87	11,80
7,40	240,00	0,60	0,71	17,10
6,80	225,00	0,48	0,70	11,60
6,80	215,00	0,46	0,73	5,80
7,00	230,00	0,44	0,76	8,30




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.

\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 & 8 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ fisher.wessa.net \tabularnewline
R Framework error message & 
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
\tabularnewline \hline \end{tabular} %Source: https://freestatistics.org/blog/index.php?pk=198329&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]8 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ fisher.wessa.net[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=198329&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=198329&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 Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.







Multiple Linear Regression - Estimated Regression Equation
X3[t] = + 0.0463207948241594 + 0.0358511465297426X1[t] + 0.000533034143459888X2[t] + 0.0221667216849505X4[t] + 0.00330809125935631`X5\r`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
X3[t] =  +  0.0463207948241594 +  0.0358511465297426X1[t] +  0.000533034143459888X2[t] +  0.0221667216849505X4[t] +  0.00330809125935631`X5\r`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=198329&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]X3[t] =  +  0.0463207948241594 +  0.0358511465297426X1[t] +  0.000533034143459888X2[t] +  0.0221667216849505X4[t] +  0.00330809125935631`X5\r`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=198329&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=198329&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
X3[t] = + 0.0463207948241594 + 0.0358511465297426X1[t] + 0.000533034143459888X2[t] + 0.0221667216849505X4[t] + 0.00330809125935631`X5\r`[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.04632079482415940.1254680.36920.7135810.356791
X10.03585114652974260.0250111.43340.1580910.079046
X20.0005330341434598880.0003831.39020.1707360.085368
X40.02216672168495050.0673110.32930.7433190.371659
`X5\r`0.003308091259356310.0011062.99090.0043440.002172

\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) & 0.0463207948241594 & 0.125468 & 0.3692 & 0.713581 & 0.356791 \tabularnewline
X1 & 0.0358511465297426 & 0.025011 & 1.4334 & 0.158091 & 0.079046 \tabularnewline
X2 & 0.000533034143459888 & 0.000383 & 1.3902 & 0.170736 & 0.085368 \tabularnewline
X4 & 0.0221667216849505 & 0.067311 & 0.3293 & 0.743319 & 0.371659 \tabularnewline
`X5\r` & 0.00330809125935631 & 0.001106 & 2.9909 & 0.004344 & 0.002172 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=198329&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]0.0463207948241594[/C][C]0.125468[/C][C]0.3692[/C][C]0.713581[/C][C]0.356791[/C][/ROW]
[ROW][C]X1[/C][C]0.0358511465297426[/C][C]0.025011[/C][C]1.4334[/C][C]0.158091[/C][C]0.079046[/C][/ROW]
[ROW][C]X2[/C][C]0.000533034143459888[/C][C]0.000383[/C][C]1.3902[/C][C]0.170736[/C][C]0.085368[/C][/ROW]
[ROW][C]X4[/C][C]0.0221667216849505[/C][C]0.067311[/C][C]0.3293[/C][C]0.743319[/C][C]0.371659[/C][/ROW]
[ROW][C]`X5\r`[/C][C]0.00330809125935631[/C][C]0.001106[/C][C]2.9909[/C][C]0.004344[/C][C]0.002172[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=198329&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=198329&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
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.04632079482415940.1254680.36920.7135810.356791
X10.03585114652974260.0250111.43340.1580910.079046
X20.0005330341434598880.0003831.39020.1707360.085368
X40.02216672168495050.0673110.32930.7433190.371659
`X5\r`0.003308091259356310.0011062.99090.0043440.002172







Multiple Linear Regression - Regression Statistics
Multiple R0.632244531880127
R-squared0.399733148092321
Adjusted R-squared0.350731772426388
F-TEST (value)8.15759032598397
F-TEST (DF numerator)4
F-TEST (DF denominator)49
p-value4.00495484393915e-05
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.0457822144318972
Sum Squared Residuals0.102704546756123

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.632244531880127 \tabularnewline
R-squared & 0.399733148092321 \tabularnewline
Adjusted R-squared & 0.350731772426388 \tabularnewline
F-TEST (value) & 8.15759032598397 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 49 \tabularnewline
p-value & 4.00495484393915e-05 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.0457822144318972 \tabularnewline
Sum Squared Residuals & 0.102704546756123 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=198329&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.632244531880127[/C][/ROW]
[ROW][C]R-squared[/C][C]0.399733148092321[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.350731772426388[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]8.15759032598397[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]49[/C][/ROW]
[ROW][C]p-value[/C][C]4.00495484393915e-05[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.0457822144318972[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]0.102704546756123[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=198329&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=198329&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 R0.632244531880127
R-squared0.399733148092321
Adjusted R-squared0.350731772426388
F-TEST (value)8.15759032598397
F-TEST (DF numerator)4
F-TEST (DF denominator)49
p-value4.00495484393915e-05
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.0457822144318972
Sum Squared Residuals0.102704546756123







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
10.440.455327416619878-0.0153274166198784
20.440.4245672088667470.0154327911332533
30.460.4461591702502830.0138408297497173
40.420.4074020030570250.0125979969429747
50.450.499663472022182-0.0496634720221821
60.430.503632558313611-0.073632558313611
70.490.4186279589110420.0713720410889575
80.470.484926116352896-0.0149261163528964
90.440.452681470293091-0.0126814702930909
100.480.4582101664404740.0217898335595261
110.520.5047447400016360.0152552599983636
120.490.471237409967760.0187625900322395
130.370.413328246098718-0.0433282460987178
140.420.4093637329403940.010636267059606
150.440.448988344610538-0.00898834461053772
160.50.509585099354248-0.00958509935424848
170.50.4646756022768550.0353243977231455
180.430.462273923244975-0.0322739232449753
190.370.439347521974564-0.0693475219745642
200.50.4792654602352610.0207345397647391
210.40.460521370921794-0.0605213709217943
220.480.486670437600663-0.00667043760066345
230.480.4380153570727220.0419846429272777
240.430.461326143103703-0.0313261431037032
250.560.524385987427410.0356140125725902
260.440.4355732782203420.00442672177965752
270.490.4353562257977440.0546437742022561
280.40.447229529356249-0.0472295293562486
290.420.4021696095717270.0178303904282726
300.490.50074464437791-0.0107446443779103
310.480.4321474975381170.0478525024618826
320.390.396140991418303-0.00614099141830316
330.440.4130396157678450.0269603842321552
340.480.486670437600663-0.00667043760066345
350.340.419825168750111-0.0798251687501109
360.520.4600753900420930.0599246099579073
370.480.4515370573005420.0284629426994579
380.410.3945485743198490.015451425680151
390.410.438854247412325-0.0288542474123254
400.410.451093109070392-0.0410931090703919
410.450.476843713868098-0.0268437138680978
420.290.395567509968578-0.105567509968578
430.450.4438827147425890.00611728525741147
440.550.4571415331729810.0928584668270194
450.480.501468143094129-0.0214681430941287
460.360.3585651115772020.00143488842279787
470.530.4188293579736270.111170642026373
480.350.401189355616156-0.0511893556161561
490.410.458009998773463-0.0480099987734632
500.430.4085241152457580.0214758847542424
510.60.5118542065059350.0881457934940647
520.480.4639318372928820.0160681627071178
530.460.4400795682045650.0199204317954348
540.440.464180539461351-0.0241805394613514

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 0.44 & 0.455327416619878 & -0.0153274166198784 \tabularnewline
2 & 0.44 & 0.424567208866747 & 0.0154327911332533 \tabularnewline
3 & 0.46 & 0.446159170250283 & 0.0138408297497173 \tabularnewline
4 & 0.42 & 0.407402003057025 & 0.0125979969429747 \tabularnewline
5 & 0.45 & 0.499663472022182 & -0.0496634720221821 \tabularnewline
6 & 0.43 & 0.503632558313611 & -0.073632558313611 \tabularnewline
7 & 0.49 & 0.418627958911042 & 0.0713720410889575 \tabularnewline
8 & 0.47 & 0.484926116352896 & -0.0149261163528964 \tabularnewline
9 & 0.44 & 0.452681470293091 & -0.0126814702930909 \tabularnewline
10 & 0.48 & 0.458210166440474 & 0.0217898335595261 \tabularnewline
11 & 0.52 & 0.504744740001636 & 0.0152552599983636 \tabularnewline
12 & 0.49 & 0.47123740996776 & 0.0187625900322395 \tabularnewline
13 & 0.37 & 0.413328246098718 & -0.0433282460987178 \tabularnewline
14 & 0.42 & 0.409363732940394 & 0.010636267059606 \tabularnewline
15 & 0.44 & 0.448988344610538 & -0.00898834461053772 \tabularnewline
16 & 0.5 & 0.509585099354248 & -0.00958509935424848 \tabularnewline
17 & 0.5 & 0.464675602276855 & 0.0353243977231455 \tabularnewline
18 & 0.43 & 0.462273923244975 & -0.0322739232449753 \tabularnewline
19 & 0.37 & 0.439347521974564 & -0.0693475219745642 \tabularnewline
20 & 0.5 & 0.479265460235261 & 0.0207345397647391 \tabularnewline
21 & 0.4 & 0.460521370921794 & -0.0605213709217943 \tabularnewline
22 & 0.48 & 0.486670437600663 & -0.00667043760066345 \tabularnewline
23 & 0.48 & 0.438015357072722 & 0.0419846429272777 \tabularnewline
24 & 0.43 & 0.461326143103703 & -0.0313261431037032 \tabularnewline
25 & 0.56 & 0.52438598742741 & 0.0356140125725902 \tabularnewline
26 & 0.44 & 0.435573278220342 & 0.00442672177965752 \tabularnewline
27 & 0.49 & 0.435356225797744 & 0.0546437742022561 \tabularnewline
28 & 0.4 & 0.447229529356249 & -0.0472295293562486 \tabularnewline
29 & 0.42 & 0.402169609571727 & 0.0178303904282726 \tabularnewline
30 & 0.49 & 0.50074464437791 & -0.0107446443779103 \tabularnewline
31 & 0.48 & 0.432147497538117 & 0.0478525024618826 \tabularnewline
32 & 0.39 & 0.396140991418303 & -0.00614099141830316 \tabularnewline
33 & 0.44 & 0.413039615767845 & 0.0269603842321552 \tabularnewline
34 & 0.48 & 0.486670437600663 & -0.00667043760066345 \tabularnewline
35 & 0.34 & 0.419825168750111 & -0.0798251687501109 \tabularnewline
36 & 0.52 & 0.460075390042093 & 0.0599246099579073 \tabularnewline
37 & 0.48 & 0.451537057300542 & 0.0284629426994579 \tabularnewline
38 & 0.41 & 0.394548574319849 & 0.015451425680151 \tabularnewline
39 & 0.41 & 0.438854247412325 & -0.0288542474123254 \tabularnewline
40 & 0.41 & 0.451093109070392 & -0.0410931090703919 \tabularnewline
41 & 0.45 & 0.476843713868098 & -0.0268437138680978 \tabularnewline
42 & 0.29 & 0.395567509968578 & -0.105567509968578 \tabularnewline
43 & 0.45 & 0.443882714742589 & 0.00611728525741147 \tabularnewline
44 & 0.55 & 0.457141533172981 & 0.0928584668270194 \tabularnewline
45 & 0.48 & 0.501468143094129 & -0.0214681430941287 \tabularnewline
46 & 0.36 & 0.358565111577202 & 0.00143488842279787 \tabularnewline
47 & 0.53 & 0.418829357973627 & 0.111170642026373 \tabularnewline
48 & 0.35 & 0.401189355616156 & -0.0511893556161561 \tabularnewline
49 & 0.41 & 0.458009998773463 & -0.0480099987734632 \tabularnewline
50 & 0.43 & 0.408524115245758 & 0.0214758847542424 \tabularnewline
51 & 0.6 & 0.511854206505935 & 0.0881457934940647 \tabularnewline
52 & 0.48 & 0.463931837292882 & 0.0160681627071178 \tabularnewline
53 & 0.46 & 0.440079568204565 & 0.0199204317954348 \tabularnewline
54 & 0.44 & 0.464180539461351 & -0.0241805394613514 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=198329&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]0.44[/C][C]0.455327416619878[/C][C]-0.0153274166198784[/C][/ROW]
[ROW][C]2[/C][C]0.44[/C][C]0.424567208866747[/C][C]0.0154327911332533[/C][/ROW]
[ROW][C]3[/C][C]0.46[/C][C]0.446159170250283[/C][C]0.0138408297497173[/C][/ROW]
[ROW][C]4[/C][C]0.42[/C][C]0.407402003057025[/C][C]0.0125979969429747[/C][/ROW]
[ROW][C]5[/C][C]0.45[/C][C]0.499663472022182[/C][C]-0.0496634720221821[/C][/ROW]
[ROW][C]6[/C][C]0.43[/C][C]0.503632558313611[/C][C]-0.073632558313611[/C][/ROW]
[ROW][C]7[/C][C]0.49[/C][C]0.418627958911042[/C][C]0.0713720410889575[/C][/ROW]
[ROW][C]8[/C][C]0.47[/C][C]0.484926116352896[/C][C]-0.0149261163528964[/C][/ROW]
[ROW][C]9[/C][C]0.44[/C][C]0.452681470293091[/C][C]-0.0126814702930909[/C][/ROW]
[ROW][C]10[/C][C]0.48[/C][C]0.458210166440474[/C][C]0.0217898335595261[/C][/ROW]
[ROW][C]11[/C][C]0.52[/C][C]0.504744740001636[/C][C]0.0152552599983636[/C][/ROW]
[ROW][C]12[/C][C]0.49[/C][C]0.47123740996776[/C][C]0.0187625900322395[/C][/ROW]
[ROW][C]13[/C][C]0.37[/C][C]0.413328246098718[/C][C]-0.0433282460987178[/C][/ROW]
[ROW][C]14[/C][C]0.42[/C][C]0.409363732940394[/C][C]0.010636267059606[/C][/ROW]
[ROW][C]15[/C][C]0.44[/C][C]0.448988344610538[/C][C]-0.00898834461053772[/C][/ROW]
[ROW][C]16[/C][C]0.5[/C][C]0.509585099354248[/C][C]-0.00958509935424848[/C][/ROW]
[ROW][C]17[/C][C]0.5[/C][C]0.464675602276855[/C][C]0.0353243977231455[/C][/ROW]
[ROW][C]18[/C][C]0.43[/C][C]0.462273923244975[/C][C]-0.0322739232449753[/C][/ROW]
[ROW][C]19[/C][C]0.37[/C][C]0.439347521974564[/C][C]-0.0693475219745642[/C][/ROW]
[ROW][C]20[/C][C]0.5[/C][C]0.479265460235261[/C][C]0.0207345397647391[/C][/ROW]
[ROW][C]21[/C][C]0.4[/C][C]0.460521370921794[/C][C]-0.0605213709217943[/C][/ROW]
[ROW][C]22[/C][C]0.48[/C][C]0.486670437600663[/C][C]-0.00667043760066345[/C][/ROW]
[ROW][C]23[/C][C]0.48[/C][C]0.438015357072722[/C][C]0.0419846429272777[/C][/ROW]
[ROW][C]24[/C][C]0.43[/C][C]0.461326143103703[/C][C]-0.0313261431037032[/C][/ROW]
[ROW][C]25[/C][C]0.56[/C][C]0.52438598742741[/C][C]0.0356140125725902[/C][/ROW]
[ROW][C]26[/C][C]0.44[/C][C]0.435573278220342[/C][C]0.00442672177965752[/C][/ROW]
[ROW][C]27[/C][C]0.49[/C][C]0.435356225797744[/C][C]0.0546437742022561[/C][/ROW]
[ROW][C]28[/C][C]0.4[/C][C]0.447229529356249[/C][C]-0.0472295293562486[/C][/ROW]
[ROW][C]29[/C][C]0.42[/C][C]0.402169609571727[/C][C]0.0178303904282726[/C][/ROW]
[ROW][C]30[/C][C]0.49[/C][C]0.50074464437791[/C][C]-0.0107446443779103[/C][/ROW]
[ROW][C]31[/C][C]0.48[/C][C]0.432147497538117[/C][C]0.0478525024618826[/C][/ROW]
[ROW][C]32[/C][C]0.39[/C][C]0.396140991418303[/C][C]-0.00614099141830316[/C][/ROW]
[ROW][C]33[/C][C]0.44[/C][C]0.413039615767845[/C][C]0.0269603842321552[/C][/ROW]
[ROW][C]34[/C][C]0.48[/C][C]0.486670437600663[/C][C]-0.00667043760066345[/C][/ROW]
[ROW][C]35[/C][C]0.34[/C][C]0.419825168750111[/C][C]-0.0798251687501109[/C][/ROW]
[ROW][C]36[/C][C]0.52[/C][C]0.460075390042093[/C][C]0.0599246099579073[/C][/ROW]
[ROW][C]37[/C][C]0.48[/C][C]0.451537057300542[/C][C]0.0284629426994579[/C][/ROW]
[ROW][C]38[/C][C]0.41[/C][C]0.394548574319849[/C][C]0.015451425680151[/C][/ROW]
[ROW][C]39[/C][C]0.41[/C][C]0.438854247412325[/C][C]-0.0288542474123254[/C][/ROW]
[ROW][C]40[/C][C]0.41[/C][C]0.451093109070392[/C][C]-0.0410931090703919[/C][/ROW]
[ROW][C]41[/C][C]0.45[/C][C]0.476843713868098[/C][C]-0.0268437138680978[/C][/ROW]
[ROW][C]42[/C][C]0.29[/C][C]0.395567509968578[/C][C]-0.105567509968578[/C][/ROW]
[ROW][C]43[/C][C]0.45[/C][C]0.443882714742589[/C][C]0.00611728525741147[/C][/ROW]
[ROW][C]44[/C][C]0.55[/C][C]0.457141533172981[/C][C]0.0928584668270194[/C][/ROW]
[ROW][C]45[/C][C]0.48[/C][C]0.501468143094129[/C][C]-0.0214681430941287[/C][/ROW]
[ROW][C]46[/C][C]0.36[/C][C]0.358565111577202[/C][C]0.00143488842279787[/C][/ROW]
[ROW][C]47[/C][C]0.53[/C][C]0.418829357973627[/C][C]0.111170642026373[/C][/ROW]
[ROW][C]48[/C][C]0.35[/C][C]0.401189355616156[/C][C]-0.0511893556161561[/C][/ROW]
[ROW][C]49[/C][C]0.41[/C][C]0.458009998773463[/C][C]-0.0480099987734632[/C][/ROW]
[ROW][C]50[/C][C]0.43[/C][C]0.408524115245758[/C][C]0.0214758847542424[/C][/ROW]
[ROW][C]51[/C][C]0.6[/C][C]0.511854206505935[/C][C]0.0881457934940647[/C][/ROW]
[ROW][C]52[/C][C]0.48[/C][C]0.463931837292882[/C][C]0.0160681627071178[/C][/ROW]
[ROW][C]53[/C][C]0.46[/C][C]0.440079568204565[/C][C]0.0199204317954348[/C][/ROW]
[ROW][C]54[/C][C]0.44[/C][C]0.464180539461351[/C][C]-0.0241805394613514[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=198329&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=198329&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 IndexActualsInterpolationForecastResidualsPrediction Error
10.440.455327416619878-0.0153274166198784
20.440.4245672088667470.0154327911332533
30.460.4461591702502830.0138408297497173
40.420.4074020030570250.0125979969429747
50.450.499663472022182-0.0496634720221821
60.430.503632558313611-0.073632558313611
70.490.4186279589110420.0713720410889575
80.470.484926116352896-0.0149261163528964
90.440.452681470293091-0.0126814702930909
100.480.4582101664404740.0217898335595261
110.520.5047447400016360.0152552599983636
120.490.471237409967760.0187625900322395
130.370.413328246098718-0.0433282460987178
140.420.4093637329403940.010636267059606
150.440.448988344610538-0.00898834461053772
160.50.509585099354248-0.00958509935424848
170.50.4646756022768550.0353243977231455
180.430.462273923244975-0.0322739232449753
190.370.439347521974564-0.0693475219745642
200.50.4792654602352610.0207345397647391
210.40.460521370921794-0.0605213709217943
220.480.486670437600663-0.00667043760066345
230.480.4380153570727220.0419846429272777
240.430.461326143103703-0.0313261431037032
250.560.524385987427410.0356140125725902
260.440.4355732782203420.00442672177965752
270.490.4353562257977440.0546437742022561
280.40.447229529356249-0.0472295293562486
290.420.4021696095717270.0178303904282726
300.490.50074464437791-0.0107446443779103
310.480.4321474975381170.0478525024618826
320.390.396140991418303-0.00614099141830316
330.440.4130396157678450.0269603842321552
340.480.486670437600663-0.00667043760066345
350.340.419825168750111-0.0798251687501109
360.520.4600753900420930.0599246099579073
370.480.4515370573005420.0284629426994579
380.410.3945485743198490.015451425680151
390.410.438854247412325-0.0288542474123254
400.410.451093109070392-0.0410931090703919
410.450.476843713868098-0.0268437138680978
420.290.395567509968578-0.105567509968578
430.450.4438827147425890.00611728525741147
440.550.4571415331729810.0928584668270194
450.480.501468143094129-0.0214681430941287
460.360.3585651115772020.00143488842279787
470.530.4188293579736270.111170642026373
480.350.401189355616156-0.0511893556161561
490.410.458009998773463-0.0480099987734632
500.430.4085241152457580.0214758847542424
510.60.5118542065059350.0881457934940647
520.480.4639318372928820.0160681627071178
530.460.4400795682045650.0199204317954348
540.440.464180539461351-0.0241805394613514







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.1593834380053640.3187668760107280.840616561994636
90.1880048073002570.3760096146005140.811995192699743
100.1229194049196720.2458388098393440.877080595080328
110.218327017111890.4366540342237790.78167298288811
120.1546535970802630.3093071941605260.845346402919737
130.2858364739370770.5716729478741540.714163526062923
140.1964505292892460.3929010585784910.803549470710754
150.1304811434322890.2609622868645790.869518856567711
160.0976560379523260.1953120759046520.902343962047674
170.09444564857571960.1888912971514390.90555435142428
180.07589635341655270.1517927068331050.924103646583447
190.1657123237486010.3314246474972030.834287676251399
200.1336324570543750.2672649141087510.866367542945625
210.1660912632488120.3321825264976240.833908736751188
220.1175736804263090.2351473608526190.882426319573691
230.1378888115005820.2757776230011640.862111188499418
240.1115187648794650.223037529758930.888481235120535
250.1050970965606220.2101941931212450.894902903439378
260.07047445304168080.1409489060833620.929525546958319
270.09266229831400270.1853245966280050.907337701685997
280.09909209401075340.1981841880215070.900907905989247
290.07643066540469020.152861330809380.92356933459531
300.05424707256271510.108494145125430.945752927437285
310.04941829240154970.09883658480309950.95058170759845
320.03387734217542940.06775468435085880.966122657824571
330.02391849788847630.04783699577695250.976081502111524
340.01412512048286830.02825024096573670.985874879517132
350.05386367956314350.1077273591262870.946136320436856
360.06890905559577370.1378181111915470.931090944404226
370.04955641279167460.09911282558334910.950443587208325
380.03074470920734240.06148941841468480.969255290792658
390.02163144882441890.04326289764883780.978368551175581
400.01694852002704040.03389704005408080.98305147997296
410.01165600212770070.02331200425540130.988343997872299
420.04480187819975690.08960375639951380.955198121800243
430.02712186715207060.05424373430414120.972878132847929
440.08441510351841960.1688302070368390.91558489648158
450.222260677096170.444521354192340.77773932290383
460.7045285823306810.5909428353386380.295471417669319

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 0.159383438005364 & 0.318766876010728 & 0.840616561994636 \tabularnewline
9 & 0.188004807300257 & 0.376009614600514 & 0.811995192699743 \tabularnewline
10 & 0.122919404919672 & 0.245838809839344 & 0.877080595080328 \tabularnewline
11 & 0.21832701711189 & 0.436654034223779 & 0.78167298288811 \tabularnewline
12 & 0.154653597080263 & 0.309307194160526 & 0.845346402919737 \tabularnewline
13 & 0.285836473937077 & 0.571672947874154 & 0.714163526062923 \tabularnewline
14 & 0.196450529289246 & 0.392901058578491 & 0.803549470710754 \tabularnewline
15 & 0.130481143432289 & 0.260962286864579 & 0.869518856567711 \tabularnewline
16 & 0.097656037952326 & 0.195312075904652 & 0.902343962047674 \tabularnewline
17 & 0.0944456485757196 & 0.188891297151439 & 0.90555435142428 \tabularnewline
18 & 0.0758963534165527 & 0.151792706833105 & 0.924103646583447 \tabularnewline
19 & 0.165712323748601 & 0.331424647497203 & 0.834287676251399 \tabularnewline
20 & 0.133632457054375 & 0.267264914108751 & 0.866367542945625 \tabularnewline
21 & 0.166091263248812 & 0.332182526497624 & 0.833908736751188 \tabularnewline
22 & 0.117573680426309 & 0.235147360852619 & 0.882426319573691 \tabularnewline
23 & 0.137888811500582 & 0.275777623001164 & 0.862111188499418 \tabularnewline
24 & 0.111518764879465 & 0.22303752975893 & 0.888481235120535 \tabularnewline
25 & 0.105097096560622 & 0.210194193121245 & 0.894902903439378 \tabularnewline
26 & 0.0704744530416808 & 0.140948906083362 & 0.929525546958319 \tabularnewline
27 & 0.0926622983140027 & 0.185324596628005 & 0.907337701685997 \tabularnewline
28 & 0.0990920940107534 & 0.198184188021507 & 0.900907905989247 \tabularnewline
29 & 0.0764306654046902 & 0.15286133080938 & 0.92356933459531 \tabularnewline
30 & 0.0542470725627151 & 0.10849414512543 & 0.945752927437285 \tabularnewline
31 & 0.0494182924015497 & 0.0988365848030995 & 0.95058170759845 \tabularnewline
32 & 0.0338773421754294 & 0.0677546843508588 & 0.966122657824571 \tabularnewline
33 & 0.0239184978884763 & 0.0478369957769525 & 0.976081502111524 \tabularnewline
34 & 0.0141251204828683 & 0.0282502409657367 & 0.985874879517132 \tabularnewline
35 & 0.0538636795631435 & 0.107727359126287 & 0.946136320436856 \tabularnewline
36 & 0.0689090555957737 & 0.137818111191547 & 0.931090944404226 \tabularnewline
37 & 0.0495564127916746 & 0.0991128255833491 & 0.950443587208325 \tabularnewline
38 & 0.0307447092073424 & 0.0614894184146848 & 0.969255290792658 \tabularnewline
39 & 0.0216314488244189 & 0.0432628976488378 & 0.978368551175581 \tabularnewline
40 & 0.0169485200270404 & 0.0338970400540808 & 0.98305147997296 \tabularnewline
41 & 0.0116560021277007 & 0.0233120042554013 & 0.988343997872299 \tabularnewline
42 & 0.0448018781997569 & 0.0896037563995138 & 0.955198121800243 \tabularnewline
43 & 0.0271218671520706 & 0.0542437343041412 & 0.972878132847929 \tabularnewline
44 & 0.0844151035184196 & 0.168830207036839 & 0.91558489648158 \tabularnewline
45 & 0.22226067709617 & 0.44452135419234 & 0.77773932290383 \tabularnewline
46 & 0.704528582330681 & 0.590942835338638 & 0.295471417669319 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=198329&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]8[/C][C]0.159383438005364[/C][C]0.318766876010728[/C][C]0.840616561994636[/C][/ROW]
[ROW][C]9[/C][C]0.188004807300257[/C][C]0.376009614600514[/C][C]0.811995192699743[/C][/ROW]
[ROW][C]10[/C][C]0.122919404919672[/C][C]0.245838809839344[/C][C]0.877080595080328[/C][/ROW]
[ROW][C]11[/C][C]0.21832701711189[/C][C]0.436654034223779[/C][C]0.78167298288811[/C][/ROW]
[ROW][C]12[/C][C]0.154653597080263[/C][C]0.309307194160526[/C][C]0.845346402919737[/C][/ROW]
[ROW][C]13[/C][C]0.285836473937077[/C][C]0.571672947874154[/C][C]0.714163526062923[/C][/ROW]
[ROW][C]14[/C][C]0.196450529289246[/C][C]0.392901058578491[/C][C]0.803549470710754[/C][/ROW]
[ROW][C]15[/C][C]0.130481143432289[/C][C]0.260962286864579[/C][C]0.869518856567711[/C][/ROW]
[ROW][C]16[/C][C]0.097656037952326[/C][C]0.195312075904652[/C][C]0.902343962047674[/C][/ROW]
[ROW][C]17[/C][C]0.0944456485757196[/C][C]0.188891297151439[/C][C]0.90555435142428[/C][/ROW]
[ROW][C]18[/C][C]0.0758963534165527[/C][C]0.151792706833105[/C][C]0.924103646583447[/C][/ROW]
[ROW][C]19[/C][C]0.165712323748601[/C][C]0.331424647497203[/C][C]0.834287676251399[/C][/ROW]
[ROW][C]20[/C][C]0.133632457054375[/C][C]0.267264914108751[/C][C]0.866367542945625[/C][/ROW]
[ROW][C]21[/C][C]0.166091263248812[/C][C]0.332182526497624[/C][C]0.833908736751188[/C][/ROW]
[ROW][C]22[/C][C]0.117573680426309[/C][C]0.235147360852619[/C][C]0.882426319573691[/C][/ROW]
[ROW][C]23[/C][C]0.137888811500582[/C][C]0.275777623001164[/C][C]0.862111188499418[/C][/ROW]
[ROW][C]24[/C][C]0.111518764879465[/C][C]0.22303752975893[/C][C]0.888481235120535[/C][/ROW]
[ROW][C]25[/C][C]0.105097096560622[/C][C]0.210194193121245[/C][C]0.894902903439378[/C][/ROW]
[ROW][C]26[/C][C]0.0704744530416808[/C][C]0.140948906083362[/C][C]0.929525546958319[/C][/ROW]
[ROW][C]27[/C][C]0.0926622983140027[/C][C]0.185324596628005[/C][C]0.907337701685997[/C][/ROW]
[ROW][C]28[/C][C]0.0990920940107534[/C][C]0.198184188021507[/C][C]0.900907905989247[/C][/ROW]
[ROW][C]29[/C][C]0.0764306654046902[/C][C]0.15286133080938[/C][C]0.92356933459531[/C][/ROW]
[ROW][C]30[/C][C]0.0542470725627151[/C][C]0.10849414512543[/C][C]0.945752927437285[/C][/ROW]
[ROW][C]31[/C][C]0.0494182924015497[/C][C]0.0988365848030995[/C][C]0.95058170759845[/C][/ROW]
[ROW][C]32[/C][C]0.0338773421754294[/C][C]0.0677546843508588[/C][C]0.966122657824571[/C][/ROW]
[ROW][C]33[/C][C]0.0239184978884763[/C][C]0.0478369957769525[/C][C]0.976081502111524[/C][/ROW]
[ROW][C]34[/C][C]0.0141251204828683[/C][C]0.0282502409657367[/C][C]0.985874879517132[/C][/ROW]
[ROW][C]35[/C][C]0.0538636795631435[/C][C]0.107727359126287[/C][C]0.946136320436856[/C][/ROW]
[ROW][C]36[/C][C]0.0689090555957737[/C][C]0.137818111191547[/C][C]0.931090944404226[/C][/ROW]
[ROW][C]37[/C][C]0.0495564127916746[/C][C]0.0991128255833491[/C][C]0.950443587208325[/C][/ROW]
[ROW][C]38[/C][C]0.0307447092073424[/C][C]0.0614894184146848[/C][C]0.969255290792658[/C][/ROW]
[ROW][C]39[/C][C]0.0216314488244189[/C][C]0.0432628976488378[/C][C]0.978368551175581[/C][/ROW]
[ROW][C]40[/C][C]0.0169485200270404[/C][C]0.0338970400540808[/C][C]0.98305147997296[/C][/ROW]
[ROW][C]41[/C][C]0.0116560021277007[/C][C]0.0233120042554013[/C][C]0.988343997872299[/C][/ROW]
[ROW][C]42[/C][C]0.0448018781997569[/C][C]0.0896037563995138[/C][C]0.955198121800243[/C][/ROW]
[ROW][C]43[/C][C]0.0271218671520706[/C][C]0.0542437343041412[/C][C]0.972878132847929[/C][/ROW]
[ROW][C]44[/C][C]0.0844151035184196[/C][C]0.168830207036839[/C][C]0.91558489648158[/C][/ROW]
[ROW][C]45[/C][C]0.22226067709617[/C][C]0.44452135419234[/C][C]0.77773932290383[/C][/ROW]
[ROW][C]46[/C][C]0.704528582330681[/C][C]0.590942835338638[/C][C]0.295471417669319[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=198329&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=198329&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-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.1593834380053640.3187668760107280.840616561994636
90.1880048073002570.3760096146005140.811995192699743
100.1229194049196720.2458388098393440.877080595080328
110.218327017111890.4366540342237790.78167298288811
120.1546535970802630.3093071941605260.845346402919737
130.2858364739370770.5716729478741540.714163526062923
140.1964505292892460.3929010585784910.803549470710754
150.1304811434322890.2609622868645790.869518856567711
160.0976560379523260.1953120759046520.902343962047674
170.09444564857571960.1888912971514390.90555435142428
180.07589635341655270.1517927068331050.924103646583447
190.1657123237486010.3314246474972030.834287676251399
200.1336324570543750.2672649141087510.866367542945625
210.1660912632488120.3321825264976240.833908736751188
220.1175736804263090.2351473608526190.882426319573691
230.1378888115005820.2757776230011640.862111188499418
240.1115187648794650.223037529758930.888481235120535
250.1050970965606220.2101941931212450.894902903439378
260.07047445304168080.1409489060833620.929525546958319
270.09266229831400270.1853245966280050.907337701685997
280.09909209401075340.1981841880215070.900907905989247
290.07643066540469020.152861330809380.92356933459531
300.05424707256271510.108494145125430.945752927437285
310.04941829240154970.09883658480309950.95058170759845
320.03387734217542940.06775468435085880.966122657824571
330.02391849788847630.04783699577695250.976081502111524
340.01412512048286830.02825024096573670.985874879517132
350.05386367956314350.1077273591262870.946136320436856
360.06890905559577370.1378181111915470.931090944404226
370.04955641279167460.09911282558334910.950443587208325
380.03074470920734240.06148941841468480.969255290792658
390.02163144882441890.04326289764883780.978368551175581
400.01694852002704040.03389704005408080.98305147997296
410.01165600212770070.02331200425540130.988343997872299
420.04480187819975690.08960375639951380.955198121800243
430.02712186715207060.05424373430414120.972878132847929
440.08441510351841960.1688302070368390.91558489648158
450.222260677096170.444521354192340.77773932290383
460.7045285823306810.5909428353386380.295471417669319







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level50.128205128205128NOK
10% type I error level110.282051282051282NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 5 & 0.128205128205128 & NOK \tabularnewline
10% type I error level & 11 & 0.282051282051282 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=198329&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]5[/C][C]0.128205128205128[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]11[/C][C]0.282051282051282[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=198329&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=198329&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 testsOK/NOK
1% type I error level00OK
5% type I error level50.128205128205128NOK
10% type I error level110.282051282051282NOK



Parameters (Session):
par1 = 3 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 3 ; par2 = Do not include Seasonal Dummies ; par3 = No 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')
}