Guides and InsightsExcel

Microsoft-Excel
Hello dear readers, the editorial staff of Tecnics.it as we know it is difficult to settle in a country with a different language. Especially when we go abroad for work and daily use Excel, familiarize yourself with the more traditional functions in another language is very difficult. To satisfy this need we have decided to provide this quick and easy translation table among the most spoken languages ​​in the world.

Excel – Match formulas syntax between languages

English French Italian
ABS ABS ASS
ACCRINT INTERET.ACC INT.MATURATO.PER
ACCRINTM INTERET.ACC.MAT INT.MATURATO.SCAD
ACOS ACOS ARCCOS
ACOSH ACOSH ARCCOSH
ADDRESS ADRESSE INDIRIZZO
AMORDEGRC AMORDEGRC AMMORT.DEGR
AMORLINC AMORLINC AMMORT.PER
AND ET E
AREAS ZONES AREE
ASC ASC ASC
ASIN ASIN ARCSEN
ASINH ASINH ARCSENH
ATAN ATAN ARCTAN
ATAN2 ATAN2 ARCTAN.2
ATANH ATANH ARCTANH
AVEDEV ECART.MOYEN MEDIA.DEV
AVERAGE MOYENNE MEDIA
AVERAGEA AVERAGEA MEDIA.VALORI
BAHTTEXT BAHTTEXT BAHTTESTO
BESSELI BESSELI BESSEL.I
BESSELJ BESSELJ BESSEL.J
BESSELK BESSELK BESSEL.K
BESSELY BESSELY BESSEL.Y
BETA.DIST LOI.BETA.N DISTRIB.BETA.N
BETA.INV BETA.INVERSE.N INV.BETA.N
BETADIST LOI.BETA DISTRIB.BETA
BETAINV BETA.INVERSE INV.BETA
BIN2DEC BINDEC BINARIO.DECIMALE
BIN2HEX BINHEX BINARIO.HEX
BIN2OCT BINOCT BINARIO.OCT
BINOM.DIST LOI.BINOMIALE DISTRIB.BINOM.N
BINOM.INV LOI.BINOMIALE.INVERSE.N INV.BINOM
BINOMDIST LOI.BINOMIALE DISTRIB.BINOM
CALL FONCTION.APPELANTE RICHIAMA
CEILING PLAFOND ARROTONDA.ECCESSO
CELL CELLULE CELLA
CHAR CAR CODICE.CARATT
CHIDIST LOI.KHIDEUX DISTRIB.CHI
CHIINV KHIDEUX.INVERSE INV.CHI
CHISQ.DIST.RT LOI.KHIDEUX.DROITE DISTRIB.CHIQUAD.DS
CHISQ.INV.RT LOI.KHIDEUX.INVERSE.DROITE INV.CHIQUAD.DS
CHISQ.TEST CHISQ.TEST TEST.CHIQUAD
CHITEST TEST.KHIDEUX TEST.CHI
CHOOSE CHOISIR SCEGLI
CLEAN EPURAGE LIBERA
CODE CODE CODICE
COLUMN COLONNE RIF.COLONNA
COLUMNS COLONNES COLONNE
COMBIN COMBIN COMBINAZIONE
COMPLEX COMPLEXE COMPLESSO
CONCATENATE CONCATENER CONCATENA
CONFIDENCE INTERVALLE.CONFIANCE CONFIDENZA
CONFIDENCE.NORM INTERVALLE.CONFIANCE.NORMAL CONFIDENZA.NORM
CONVERT CONVERT CONVERTI
CORREL COEFFICIENT.CORRELATION CORRELAZIONE
COS COS COS
COSH COSH COSH
COUNT COMPTE CONTA.NUMERI
COUNTA NBVAL CONTA.VALORI
COUNTBLANK NB.VIDE CONTA.VUOTE
COUNTIF NB.SI CONTA.SE
COUPDAYBS NB.JOURS.COUPON.PREC GIORNI.CED.INIZ.LIQ
COUPDAYS NB.JOURS.COUPONS GIORNI.CED
COUPDAYSNC NB.JOURS.COUPON.SUIV GIORNI.CED.NUOVA
COUPNCD DATE.COUPON.SUIV DATA.CED.SUCC
COUPNUM NB.COUPONS NUM.CED
COUPPCD DATE.COUPON.PREC DATA.CED.PREC
COVAR COVARIANCE COVARIANZA
COVARIANCE.P COVARIANCE.PEARSON COVARIANZA.P
CRITBINOM CRITERE.LOI.BINOMIALE CRIT.BINOM
CUMIPMT CUMUL.INTER INT.CUMUL
CUMPRINC CUMUL.PRINCPER CAP.CUM
DATE DATE DATA
DATEVALUE DATEVAL DATA.VALORE
DAVERAGE BDMOYENNE DB.MEDIA
DAY JOUR GIORNO
DAYS360 JOURS360 GIORNO360
DB DB AMMORT.FISSO
DCOUNT BDNB DB.CONTA.NUMERI
DCOUNTA BDNBVAL DB.CONTA.VALORI
DDB DDB AMMORT
DEC2BIN DECBIN DECIMALE.BINARIO
DEC2HEX DECHEX DECIMALE.HEX
DEC2OCT DECOCT DECIMALE.OCT
DEGREES DEGRES GRADI
DELTA DELTA DELTA
DEVSQ SOMME.CARRES.ECARTS DEV.Q
DGET BDLIRE DB.VALORI
DISC TAUX.ESCOMPTE TASSO.SCONTO
DMAX BDMAX DB.MAX
DMIN BDMIN DB.MIN
DOLLAR EURO VALUTA
DOLLARDE PRIX.DEC VALUTA.DEC
DOLLARFR PRIX.FRAC VALUTA.FRAZ
DPRODUCT BDPRODUIT DB.PRODOTTO
DSTDEV BDECARTYPE DB.DEV.ST
DSTDEVP BDECARTYPEP DB.DEV.ST.POP
DSUM BDSOMME DB.SOMMA
DURATION DUREE DURATA
DVAR BDVAR DB.VAR
DVARP BDVARP DB.VAR.POP
EDATE MOIS.DECALER DATA.MESE
EFFECT TAUX.EFFECTIF EFFETTIVO
EOMONTH FIN.MOIS FINE.MESE
ERF ERF FUNZ.ERRORE
ERF.PRECISE ERF.PRECIS FUNZ.ERRORE.PRECISA
ERFC ERFC FUNZ.ERRORE.COMP
ERFC.PRECISE ERFC.PRECIS FUNZ.ERRORE.COMP.PRECISA
ERROR.TYPE TYPE.ERREUR ERRORE.TIPO
EUROCONVERT EUROCONVERT EUROCONVERT
EVEN PAIR PARI
EXACT EXACT ESATTO
EXP EXP EXP
EXPON.DIST LOI.EXPONENTIELLE.N EXPON.DIST
EXPONDIST LOI.EXPONENTIELLE DISTRIB.EXP
F.DIST.RT LOI.F.DROITE DISTRIB.F.DS
F.INV.RT INVERSE.LOI.F.DROITE INV.F.DS
F.TEST F.TEST TESTF
FACT FACT FATTORIALE
FACTDOUBLE FACTDOUBLE FATT.DOPPIO
FAUX FAUX FALSO
FDIST LOI.F DISTRIB.F
FIND, FINDB TROUVE, TROUVERB TROVA, TROVA.B
FINV INVERSE.LOI.F INV.F
FISHER FISHER FISHER
FISHERINV FISHER.INVERSE INV.FISHER
FIXED CTXT FISSO
FLOOR PLANCHER ARROTONDA.DIFETTO
FORECAST PREVISION PREVISIONE
FREQUENCY FREQUENCE FREQUENZA
FTEST TEST.F TEST.F
FV VC VAL.FUT
FVSCHEDULE VC.PAIEMENTS VAL.FUT.CAPITALE
GAMMA.DIST LOI.GAMMA.N DISTRIB.GAMMA.N
GAMMA.INV LOI.GAMMA.INVERSE.N INV.GAMMA.N
GAMMADIST LOI.GAMMA DISTRIB.GAMMA
GAMMAINV LOI.GAMMA.INVERSE INV.GAMMA
GAMMALN LNGAMMA LN.GAMMA
GAMMALN.PRECISE LNGAMMA.PRECIS LN.GAMMA.PRECISA
GCD PGCD MCD
GEOMEAN MOYENNE.GEOMETRIQUE MEDIA.GEOMETRICA
GESTEP SUP.SEUIL SOGLIA
GETPIVOTDATA LIREDONNEESTABCROISDYNAMIQUE INFO.DATI.TAB.PIVOT
GROWTH CROISSANCE CRESCITA
HARMEAN MOYENNE.HARMONIQUE MEDIA.ARMONICA
HEX2BIN HEXBIN HEX.BINARIO
HEX2DEC HEXDEC HEX.DECIMALE
HEX2OCT HEXOCT HEX.OCT
HLOOKUP RECHERCHEH CERCA.ORIZZ
HOUR HEURE ORA
HYPERLINK LIEN_HYPERTEXTE COLLEG.IPERTESTUALE
HYPGEOM.DIST LOI.HYPERGEOMETRIQUE.N DISTRIB.IPERGEOM.N
HYPGEOMDIST LOI.HYPERGEOMETRIQUE DISTRIB.IPERGEOM
IF SI SE
IMABS COMPLEXE.MODULE COMP.MODULO
IMAGINARY COMPLEXE.IMAGINAIRE COMP.IMMAGINARIO
IMARGUMENT COMPLEXE.ARGUMENT COMP.ARGOMENTO
IMCONJUGATE COMPLEXE.CONJUGUE COMP.CONIUGATO
IMCOS COMPLEXE.COS COMP.COS
IMDIV COMPLEXE.DIV COMP.DIV
IMEXP COMPLEXE.EXP COMP.EXP
IMLN COMPLEXE.LN COMP.LN
IMLOG10 COMPLEXE.LOG10 COMP.LOG10
IMLOG2 COMPLEXE.LOG2 COMP.LOG2
IMPOWER COMPLEXE.PUISSANCE COMP.POTENZA
IMPRODUCT COMPLEXE.PRODUIT COMP.PRODOTTO
IMREAL COMPLEXE.REEL COMP.PARTE.REALE
IMSIN COMPLEXE.SIN COMP.SEN
IMSQRT COMPLEXE.RACINE COMP.RADQ
IMSUB COMPLEXE.DIFFERENCE COMP.DIFF
IMSUM COMPLEXE.SOMME COMP.SOMMA
INDEX INDEX INDICE
INDIRECT INDIRECT INDIRETTO
INFO INFORMATIONS AMBIENTE.INFO
INT ENT INT
INTERCEPT ORDONNEE.ORIGINE INTERCETTA
INTRATE TAUX.INTERET TASSO.INT
IPMT INTPER INTERESSI
IRR TRI TIR.COST
ISODD EST.IMPAIR VAL.DISPARI
ISPMT ISPMT INTERESSE.RATA
ISTEXT ESTVIDE VAL.VUOTO
JIS JIS ORDINAMENTO.JIS
KURT KURTOSIS CURTOSI
LARGE GRANDE.VALEUR GRANDE
LCM PPCM MCM
LEFT, LEFTB GAUCHE, GAUCHEBs SINISTRA, SINISTRAB
LEN, LENB NBCAR, LENB LUNGHEZZA, LUNGB
LINEST DROITEREG REGR.LIN
LN LN LN
LOG LOG LOG
LOG10 LOG10 LOG10
LOGEST LOGREG REGR.LOG
LOGINV LOI.LOGNORMALE.INVERSE INV.LOGNORM
LOGNORM.DIST LOI.LOGNORMALE.N DISTRIB.LOGNORM.N
LOGNORM.INV LOI.LOGNORMALE.INVERSE.N INV.LOGNORM.N
LOGNORMDIST LOI.LOGNORMALE DISTRIB.LOGNORM
LOOKUP RECHERCHE CERCA
LOWER MINUSCULE MINUSC
MATCH EQUIV CONFRONTA
MAX MAX MAX
MAXA MAXA MAX.VALORI
MDETERM DETERMAT MATR.DETERM
MDURATION DUREE.MODIFIEE DURATA.M
MEDIAN MEDIANE MEDIANA
MID, MIDB STXT, MIDB STRINGA.ESTRAI, MEDIA.B
MIN MIN MIN
MINA MINA MIN.VALORI
MINUTE MINUTE MINUTI
MINVERSE INVERSEMAT MATR.INVERSA
MIRR TRIM TIR.VAR
MMULT PRODUITMAT MATR.PRODOTTO
MOD MOD RESTO
MODE MODE MODA
MODE.SNGL MODE.SIMPLE MODA.SNGL
MONTH MOIS MESE
MROUND ARRONDI.AU.MULTIPLE ARROTONDA.MULTIPLO
MULTINOMIAL MULTINOMIALE MULTINOMIALE
N N NUM
NA NA NON.DISP
NEGBINOM.DIST LOI.BINOMIALE.NEG.N DISTRIB.BINOM.NEG.N
NEGBINOMDIST LOI.BINOMIALE.NEG DISTRIB.BINOM.NEG
NETWORKDAYS NB.JOURS.OUVRES GIORNI.LAVORATIVI.TOT
NOMINAL TAUX.NOMINAL NOMINALE
NORM.DIST LOI.NORMALE.N DISTRIB.NORM.N
NORM.INV LOI.NORMALE.INVERSE.N INV.NORM.N
NORM.S.DIST LOI.NORMALE.STANDARD.N DISTRIB.NORM.ST.N
NORM.S.INV LOI.NORMALE.STANDARD.INVERSE.N INV.NORM.S
NORMDIST LOI.NORMALE DISTRIB.NORM
NORMINV LOI.NORMALE.INVERSE INV.NORM
NORMSDIST LOI.NORMALE.STANDARD DISTRIB.NORM.ST
NORMSINV LOI.NORMALE.STANDARD.INVERSE INV.NORM.ST
NOT NON NON
NOW MAINTENANT ADESSO
NPER NPM NUM.RATE
NPV VAN VAN
OCT2BIN OCTBIN OCT.BINARIO
OCT2DEC OCTDEC OCT.DECIMALE
OCT2HEX OCTHEX OCT.HEX
ODD IMPAIR DISPARI
ODDFPRICE PRIX.PCOUPON.IRREG PREZZO.PRIMO.IRR
ODDFYIELD REND.PCOUPON.IRREG REND.PRIMO.IRR
ODDLPRICE PRIX.DCOUPON.IRREG PREZZO.ULTIMO.IRR
ODDLYIELD REND.DCOUPON.IRREG REND.ULTIMO.IRR
OFFSET DECALER SCARTO
OR OU O
PEARSON PEARSON PEARSON
PERCENTILE CENTILE PERCENTILE
PERCENTILE.INC CENTILE.INCLURE INC.PERCENTILE
PERCENTRANK RANG.POURCENTAGE PERCENT.RANGO
PERCENTRANK.INC RANG.POURCENTAGE.INCLURE INC.PERCENT.RANGO
PERMUT PERMUTATION PERMUTAZIONE
PHONETIC PHONÉTIQUE FURIGANA
PI PI PI.GRECO
PMT VPM RATA
POISSON LOI.POISSON POISSON
POISSON.DIST LOI.POISSON.N DISTRIB.POISSON
POWER PUISSANCE POTENZA
PPMT PRINCPER P.RATA
PRICE PRIX.TITRE PREZZO
PRICEDISC VALEUR.ENCAISSEMENT PREZZO.SCONT
PRICEMAT PRIX.TITRE.ECHEANCE PREZZO.SCAD
PROB PROBABILITE PROBABILITÀ
PRODUCT PRODUIT PRODOTTO
PROPER NOMPROPRE MAIUSC.INIZ
PV VA VA
QUARTILE QUARTILE QUARTILE
QUARTILE.INC QUARTILE.INCLURE INC.QUARTILE
QUOTIENT QUOTIENT QUOZIENTE
RADIANS RADIANS RADIANTI
RAND ALEA CASUALE
RANDBETWEEN ALEA.ENTRE.BORNES CASUALE.TRA
RANK RANG RANGO
RANK.EQ EQUATION.RANG RANGO.UG
RATE TAUX TASSO
RECEIVED VALEUR.NOMINALE RICEV.SCAD
REGISTER.ID REGISTRE.NUMERO Funzione IDENTIFICATORE.REGISTRO
REPLACE, REPLACEB REMPLACER, REMPLACERB RIMPIAZZA, SOSTITUISCI.B
REPT REPT RIPETI
RIGHT, RIGHTB DROITE, DROITEB DESTRA, DESTRA.B
ROMAN ROMAIN ROMANO
ROUND ARRONDI Funzione ARROTONDA
ROUNDDOWN ARRONDI.INF ARROTONDA.PER.DIF
ROUNDUP ARRONDI.SUP ARROTONDA.PER.ECC
ROW LIGNE RIF.RIGA
ROWS LIGNES RIGHE
RSQ COEFFICIENT.DETERMINATION RQ
RTD RTD DATITEMPOREALE
SEARCH, SEARCHB CHERCHE, CHERCHERB RICERCA, CERCA.B
SECOND SECONDE SECONDI
SERIESSUM SOMME.SERIES SOMMA.SERIE
SIGN SIGNE SEGNO
SIN SIN SEN
SINH SINH SENH
SKEW COEFFICIENT.ASYMETRIE ASIMMETRIA
SLN AMORLIN AMMORT.COST
SLOPE PENTE PENDENZA
SMALL PETITE.VALEUR PICCOLO
SQL.REQUEST SQL.REQUEST SQL.REQUEST
SQRT RACINE RADQ
SQRTPI RACINE.PI RADQ.PI.GRECO
STANDARDIZE CENTREE.REDUITE NORMALIZZA
STDEV ECARTYPE DEV.ST
STDEV.P ECARTYPE.PEARSON DEV.ST.P
STDEV.S ECARTYPE.STANDARD DEV.ST.C
STDEVA STDEVA DEV.ST.VALORI
STDEVP ECARTYPEP DEV.ST.POP
STDEVPA STDEVPA DEV.ST.POP.VALORI
STEYX ERREUR.TYPE.XY ERR.STD.YX
SUBSTITUTE SUBSTITUE SOSTITUISCI
SUBTOTAL SOUS.TOTAL SUBTOTALE
SUM SOMME SOMMA
SUMIF SOMME.SI SOMMA.SE
SUMPRODUCT SOMMEPROD MATR.SOMMA.PRODOTTO
SUMSQ SOMME.CARRES SOMMA.Q
SUMX2MY2 SOMME.X2MY2 SOMMA.DIFF.Q
SUMX2PY2 SOMME.X2PY2 SOMMA.SOMMA.Q
SUMXMY2 SOMME.XMY2 SOMMA.Q.DIFF
SYD SYD AMMORT.ANNUO
T T T
T.DIST.2T LOI.STUDENT.BILATERALE DISTRIB.T.2T
T.DIST.RT LOI.STUDENT.DROITE DISTRIB.T.DS
T.INV.2T LOI.STUDENT.INVERSE.BILATERALE INV.T.2T
T.TEST T.TEST TESTT
TAN TAN TAN
TANH TANH TANH
TBILLEQ TAUX.ESCOMPTE.R BOT.EQUIV
TBILLPRICE PRIX.BON.TRESOR BOT.PREZZO
TBILLYIELD RENDEMENT.BON.TRESOR BOT.REND
TDIST LOI.STUDENT DISTRIB.T
TEXT TEXTE TESTO
TIME TEMPS ORARIO
TIMEVALUE TEMPSVAL ORARIO.VALORE
TINV LOI.STUDENT.INVERSE INV.T
TODAY AUJOURDHUI OGGI
TRANSPOSE TRANSPOSE MATR.TRASPOSTA
TREND TENDANCE TENDENZA
TRIM SUPPRESPACE ANNULLA.SPAZI
TRIMMEAN MOYENNE.REDUITE MEDIA.TRONCATA
VRAI VRAI VERO
TRUNC TRONQUE TRONCA
TTEST TEST.STUDENT TEST.T
TYPE TYPE TIPO
UPPER MAJUSCULE MAIUSC
VALUE CNUM VALORE
VAR VAR VAR
VAR.P VAR.P VAR.P
VAR.S VAR.S VAR.C
VARA VARA VAR.VALORI
VARP VAR.P VAR.POP
VARPA VARPA VAR.POP.VALORI
VDB VDB AMMORT.VAR
VLOOKUP RECHERCHEV CERCA.VERT
WEEKDAY JOURSEM GIORNO.SETTIMANA
WEEKNUM NO.SEMAINE NUM.SETTIMANA
WEIBULL LOI.WEIBULL WEIBULL
WEIBULL.DIST LOI.WEIBULL.N DISTRIB.WEIBULL
WORKDAY SERIE.JOUR.OUVRE GIORNO.LAVORATIVO
XIRR TRI.PAIEMENTS TIR.X
XNPV VAN.PAIEMENTS VAN.X
YEAR ANNEE ANNO
YEARFRAC FRACTION.ANNEE FRAZIONE.ANNO
YIELD RENDEMENT.TITRE REND
YIELDDISC RENDEMENT.SIMPLE REND.TITOLI.SCONT
YIELDMAT RENDEMENT.TITRE.ECHEANCE REND.SCAD
Z.TEST Z.TEST TESTZ
ZTEST TEST.Z TEST.Z

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3 Responses to “Excel – Italian English French language is no longer a problem”

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  • Fernando says:

    Aris SpanosIn an attempt to avoid fuhetrr misunderstandings and talk passed each other, let me reiterate what are the hypotheses of interest in this empirical example. The primary (substantive) hypothesis of interest is that x(t) is a good predictor of y(t). This is framed, in the first place, as a test of the hypothesis that its coefficient is significantly different from zero. Given that the t-statistic for this coefficient is 79.141 [critical value at .05 around 2], it seems that it is statistically highly significant. However, to ensure that this inference is trustworthy, one needs to ensure that the probabilistic assumptions of the estimated model, that render this t-test reliable, are valid for this particular data. That is where, Mis-Specification (M-S) testing comes into the picture. The basic question posed by M-S testing is whether the particular data constitute a truly typical realization of the stochastic process {[y(t)|X(t)=x(t)], t=1,2, ,n..} underlying the Linear Regression (LR) model, i.e. whether assumptions [1]-[5] are valid for this particular data. Hence, the secondary (statistical) hypotheses of interest relevant for M-S testing are the assumptions [1]-[5] which are predesignated once the LR model is selected as the relevant statistical model.Statistical model validation is all about a modeler doing a thorough job in probing for potential departures from these (predesignated) assumptions; effective probing of potential misspecification errors. When I teach M-S testing I tell my students that there are two crucial problems that can lead a modeler down into the self-delusion cul de sac. The first is to begin M-S testing without knowning what the model assumptions are; have an incomplete list of assumptions hence the list [1]-[5]. The second is to apply some arbitrary battery of M-S tests that had no capacity [low power] to detect the departures from assumptions [1]-[5]. The idea behind using graphical techniques, in this context, is to make the choice of M-S tests less arbitrary and render the selected M-S tests potentially capable of detecting any departures from [1]-[5]. Looking or graphing the data does not affect either the primary or secondary hypotheses of interest because they are all predesignated. If the data exhibit temporal dependence/heterogeneity nothing in looking or graphing the data will alter that in any shape or form. However, graphing and looking at the data renders M-S testing much more effective by enabling the modeler to select M-S tests that have the capacity to detect the existing departures from [1]-[5], and thus avoid the self-delusion cul de sac. A third potential problem that I warn my students against is to confuse M-S testing with Neyman-Pearson (N-P) testing. The latter is testing withing the boundaries of the prespecified statistical model and the latter is testing outside the boundaries. This gives rise to several differences in both the underlying reasoning and what one can infer from the two types of tests. For instance, the most serious error in M-S testing is actually the type II error (not the type I), because it can lead the unaware into the self-delusion cul de sac! For those interested in these issues, I discuss them extensive in various published papers; e.g. Spanos, A. (2000), Revisiting Data Mining: `hunting’ with or without a license, The Journal of Economic Methodology, 7: 231-264.

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