## English translation: mean square residue, degree(s) of freedom = df, goodness of fit index

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 01:24 Jul 9, 2002
Spanish to English translations [Non-PRO]
Science - Mathematics & Statistics / Statistics
 Spanish term or phrase: residuo cuadratico medio, g.d.l., indice de bondad del ajuste, I am working on a statistics project and I need help with some words: 1) residuo cuadratico medio (I think it is average quadratic residue) 2) g.d.l. (for example...El estadistico x es significativo...139,06 con 12 g.d.l.) 3)indice de bondad del ajuste (for example...El residuo cuadratico medio alcanza un valor aceptable de 0,057, de forma similar al indice de bondad del ajuste, que es bastante elevado. Gracias por la ayuda
 Scott Yusman
 English translation:mean square residue, degree(s) of freedom = df, goodness of fit index Explanation: http://www.google.com/search?hl=es&ie=UTF-8&oe=UTF-8&q="mean... MEAN SQUARE RESIDUE http://216.239.37.100/search?q=cache:LPjE422_OdcC:www.iberis... Modelo mallado con elementos sólidos TETRA10 de alto orden y 10 nodos/elemento compuesto por: 9,949 nodos, 5,333 elementos y 29,847 grados de libertad (GDL). http://www.animatedsoftware.com/statglos/statglos.htm Statistians use the terms "degrees of freedom" to describe the number of values in the final calculation of a statistic that are free to vary. Consider, for example the statistic s-square. To calculate the s-square of a random sample, we must first calculate the mean of that sample and then compute the sum of the several squared deviations from that mean. While there will be n such squared deviations only (n - 1) of them are, in fact, free to assume any value whatsoever. This is because the final squared deviation from the mean must include the one value of X such that the sum of all the Xs divided by n will equal the obtained mean of the sample. All of the other (n - 1) squared deviations from the mean can, theoretically, have any values whatsoever. For these reasons, the statistic s-square is said to have only (n - 1) degrees of freedom. http://216.239.37.100/search?q=cache:huPfPANxa48C:www.ssicen... To illustrate all the goodness-of-fit statistics, we consider the following output based on Example 5A of the SIMPLIS manual: CHI-SQUARE WITH 24 DEGREES OF FREEDOM = 52.626 (P = 0.000648) ESTIMATED NON-CENTRALITY PARAMETER (NCP) = 28.626 90 PERCENT CONFIDENCE INTERVAL FOR NCP = (11.415 ; 53.568) MINIMUM FIT FUNCTION VALUE = 0.365 POPULATION DISCREPANCY FUNCTION VALUE (F0) = 0.199 90 PERCENT CONFIDENCE INTERVAL FOR F0 = (0.0793 ; 0.372) ROOT MEAN SQUARE ERROR OF APPROXIMATION (RMSEA) = 0.0910 90 PERCENT CONFIDENCE INTERVAL FOR RMSEA = (0.0575 ; 0.124) P-VALUE FOR TEST OF CLOSE FIT (RMSEA \ < 0.05) = 0.0250 EXPECTED CROSS-VALIDATION INDEX (ECVI) = 0.657 90 PERCENT CONFIDENCE INTERVAL FOR ECVI = (0.538 ; 0.830) ECVI FOR SATURATED MODEL = 0.625 CHI-SQUARE FOR INDEPENDENCE MODEL WITH 36 DEGREES OF FREEDOM = 496.218 INDEPENDENCE AIC = 514.218 MODEL AIC = 94.626 SATURATED AIC = 90.000 INDEPENDENCE CAIC = 550.009 MODEL CAIC = 178.137 SATURATED CAIC = 268.953 ROOT MEAN SQUARE RESIDUAL (RMR) = 0.0755 STANDARDIZED RMR = 0.0755 GOODNESS OF FIT INDEX (GFI) = 0.928 ADJUSTED GOODNESS OF FIT INDEX (AGFI) = 0.866 PARSIMONY GOODNESS OF FIT INDEX (PGFI) = 0.495 NORMED FIT INDEX (NFI) = 0.894 NON-NORMED FIT INDEX (NNFI) = 0.907 PARSIMONY NORMED FIT INDEX (PNFI) = 0.596 COMPARATIVE FIT INDEX (CFI) = 0.938 INCREMENTAL FIT INDEX (IFI) = 0.939 RELATIVE FIT INDEX (RFI) = 0.841 CRITICAL N (CN) = 118.606
Selected response from:

xxxLia Fail
Spain
Local time: 12:02
 4 KudoZ points were awarded for this answer

4 +1mean square residue, degree(s) of freedom = df, goodness of fit indexxxxLia Fail
4average square root remainder
 Maria
4sb
 luskie

21 mins   confidence:
sb

Explanation:
mean square error (most probably)

degrees of freedom (or d.f. - quite sure!)

goodness of fit index (or GFI - most probably)

but memories fade away at such a fast pace (and I'm not yet a candidate for Alzheimer... :) - so please wait for the colleagues' opinion!

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Note added at 2002-07-09 01:50:11 (GMT)
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dimenticavo: i \"degrees of freedom\" sono i \"gradi di libertà\" in italiano (e, btw, è un concetto davvero intrigante :) - in spagnolo sarà l\'equivalente, ma non lo so e adesso ho troppo sonno per fare ricerche (sorry) ...

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well, error or residue...

 luskieLocal time: 12:02Works in fieldNative speaker of: Italian

23 mins   confidence:
average square root remainder

Explanation:
I believe that's the first definition.

Variación GdL(1) F AP LP PFP PSP PFV PSV PFR PSR PFG PSG HG PST PFT MS
Híbrido 2 116,38** 213,67 62,01** 33,95** 13,98* 330,4** 51,16** 1,87** 0,74** 3,14** 0,86 1424,67** 65,35** 441,4** 163,3
Inoculación 1 203,72** 2606,49** 48,93** 222,16** 104,4** 173,91* 0,77 0,63 0,18 0,06 0,19 67,66 144** 1013,57** 352,29**
Hib.xInoc. 2 1,63 233,97 8,25 1,06 0,37 53,46 5,52 0,3 0,17 2,01** 0,76** 562,14 13,11 71,9 25,79
Error 43 9,62 186,42 3,97 6,24 3,28 34,56 3,57 0,16 0,055 0,29 0,12 185,39 11,08 67,37 28,48

(1) GdL = Grados de libertad; F = días a floración; AP = altura de planta; LP = longitud de panícula; PFP = peso fresco de panícula; PSP = peso seco de panícula, PFV = Peso fresco de materia verde; PSV = peso seco de materia verde; PFR = peso fresco de raíz; PSR = peso seco de raíz; PFG = peso fresco del grano, PSG = peso seco del grano; HG = humedad del grano; PST = peso seco total; PFT = peso fresco total; MS = materia seca.
** Significativo al 1%, * Significativo al 5%
http://www.redpav-fpolar.info.ve/fitopato/v092/0902f002.html

I am still looking for the third definition.

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Note added at 2002-07-09 01:52:30 (GMT)
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la tercera es: \"chi square test for goodness of fit\"Subject - Mathematics (=MA)
- \"Statistics (sn: methodological and technical aspects; us: for statistical data in a specific subject field, see that subject field under XX3)\"(=ST)

(1)

Reference Kendall, Dict Stat Terms-1960
(2)

Reference EPILEX EUROFFICE 1993

Definition in general,the goodness of agreement between an observed set of values and a second set which are derived wholly or partly on a hypothetical basis,that is to say,derive from the \"fitting\" of a model to the data

Reference Kendall & Buckland,Dict.of Stat.Terms,1982
(1)
TERM goodness of fit

Reference Kendall & Buckland,Dict.of Stat.Terms,1982;Epilex Euroffice 1993

Note {DOM} statistical methodology:constraints-fitting-forecasts
{GRM} n.p.
{NTE} the term is used especially in relation to the fitting of theoretical distributions to observation and the fitting of regression lines.The excellence of the fit is often measured by some criterion depending on the squares of differences between observed and theoretical value,and if the criterion has a minimum value the corresponding fit is said to be \"best\"

Subject - \"Statistics (sn: methodological and technical aspects; us: for statistical data in a specific subject field, see that subject field under XX3)\"(=ST)

(1)

Reference Dicc.práctico de Estadística y técnicas de investigación científica,Sierra Bravo,1991

Definition tests the hypothesis that the distribution of the population from which nominal data are drawn agrees with a posited distribution

Reference Prophet StatGuide Gloss.,GTE Internetworking,1997
(1)
TERM chi-square test for goodness of fit

Reference Prophet StatGuide Gloss.,GTE Internetworking,1997

Note {DOM} statistical methodology:general aspects
{GRM} n.p.
{NTE} the chi-square goodness-of-fit test compares observed and expected frequencies(counts).The chi-square test statistic is basically the sum of the squares of the differences between the observed and expected frequencies,with each squared difference divided by the corresponding expected frequency

 MariaLocal time: 05:02Native speaker of: Spanish

37 mins   confidence: peer agreement (net): +1
mean square residue, degree(s) of freedom = df, goodness of fit index

Explanation:

MEAN SQUARE RESIDUE

http://216.239.37.100/search?q=cache:LPjE422_OdcC:www.iberis...

Modelo mallado con elementos sólidos TETRA10 de alto orden y 10 nodos/elemento compuesto por:

9,949 nodos,
5,333 elementos y

http://www.animatedsoftware.com/statglos/statglos.htm

Statistians use the terms "degrees of freedom" to describe the number of values in the final calculation of a statistic that are free to vary. Consider, for example the statistic s-square.

To calculate the s-square of a random sample, we must first calculate the mean of that sample and then compute the sum of the several squared deviations from that mean. While there will be n such squared deviations only (n - 1) of them are, in fact, free to assume any value whatsoever. This is because the final squared deviation from the mean must include the one value of X such that the sum of all the Xs divided by n will equal the obtained mean of the sample. All of the other (n - 1) squared deviations from the mean can, theoretically, have any values whatsoever. For these reasons, the statistic s-square is said to have only (n - 1) degrees of freedom.

http://216.239.37.100/search?q=cache:huPfPANxa48C:www.ssicen...

To illustrate all the goodness-of-fit statistics, we consider the following output based on Example 5A of the SIMPLIS manual:

CHI-SQUARE WITH 24 DEGREES OF FREEDOM = 52.626 (P = 0.000648)
ESTIMATED NON-CENTRALITY PARAMETER (NCP) = 28.626
90 PERCENT CONFIDENCE INTERVAL FOR NCP = (11.415 ; 53.568)
MINIMUM FIT FUNCTION VALUE = 0.365
POPULATION DISCREPANCY FUNCTION VALUE (F0) = 0.199
90 PERCENT CONFIDENCE INTERVAL FOR F0 = (0.0793 ; 0.372)
ROOT MEAN SQUARE ERROR OF APPROXIMATION (RMSEA) = 0.0910
90 PERCENT CONFIDENCE INTERVAL FOR RMSEA = (0.0575 ; 0.124)
P-VALUE FOR TEST OF CLOSE FIT (RMSEA \ < 0.05) = 0.0250
EXPECTED CROSS-VALIDATION INDEX (ECVI) = 0.657
90 PERCENT CONFIDENCE INTERVAL FOR ECVI = (0.538 ; 0.830)
ECVI FOR SATURATED MODEL = 0.625
CHI-SQUARE FOR INDEPENDENCE MODEL WITH 36 DEGREES OF FREEDOM = 496.218 INDEPENDENCE AIC = 514.218
MODEL AIC = 94.626
SATURATED AIC = 90.000
INDEPENDENCE CAIC = 550.009
MODEL CAIC = 178.137
SATURATED CAIC = 268.953
ROOT MEAN SQUARE RESIDUAL (RMR) = 0.0755
STANDARDIZED RMR = 0.0755
GOODNESS OF FIT INDEX (GFI) = 0.928
ADJUSTED GOODNESS OF FIT INDEX (AGFI) = 0.866
PARSIMONY GOODNESS OF FIT INDEX (PGFI) = 0.495
NORMED FIT INDEX (NFI) = 0.894
NON-NORMED FIT INDEX (NNFI) = 0.907
PARSIMONY NORMED FIT INDEX (PNFI) = 0.596
COMPARATIVE FIT INDEX (CFI) = 0.938
INCREMENTAL FIT INDEX (IFI) = 0.939
RELATIVE FIT INDEX (RFI) = 0.841
CRITICAL N (CN) = 118.606

 xxxLia FailSpainLocal time: 12:02Native speaker of: EnglishPRO pts in category: 20

agree
 10 hrs

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