How to interact with a Parisian waiter

un cafe
“A coffee: 7 euros. A coffee, please: 4.25 euros. Good day–a coffee, please: 1.40 euros.”

Probably no one in France strikes more fear into the American heart than the French waiter.  We all know the stereotypes: they’re rude, they’re impatient, and their service sucks.  Actually, none of this is true, with the possible exception of the “impatient” part, and if you see that, it’s probably your fault.

Let’s start with “rude.”  This is actually a pretty typical conception that Americans have of the French.  In fact, if anything, the French are hyper-polite with strangers.  The issue that arises is that every culture has different conceptions of politeness, and unsurprisingly, French and American politeness is different.  The key is to learn to recognize French politeness when you see it, and to respond equally politely.

I would love to know enough about French politeness to explain all of its ins and outs, but I don’t.  I do, however, know enough to explain how to interact with a waiter.

On to the “impatient” thing: The first thing that you have to know about waiters in Paris is that they are covering far more tables than an American waiter would believe possible.  No exaggeration here: if you ever asked an American to cover as many tables as a Parisian waiter covers every single day, they would probably quit.

French waiters, like pretty much everyone else in France, have a métier, and they know how to do their job.  In turn, they expect you to know how to interact with them when they are doing their job.  One thing that this means is that you have to be aware that if everything goes as it should, you will not see that waiter very often, and he will not be making a lot of trips to your table.  So: be ready to order everything at once.  Have your drink order, your food order, and your dessert order ready.  If you are going to want water, ask for it along with everything else.  (If you want to make a French waiter crazy: order drinks.  When he comes back with your drinks, ask for water.  When he brings your water, tell him that you would like a menu.  When he brings your food, tell him that you would like an extra napkin.  Then, wait until he brings you your dessert to tell him that you’re going to share it, and would like another spoon.)

Finally, the service: Service is not supposed to be fast in France.  That is just not a goal.  Meals in France typically take a long time–my friends tell me stories of three-hour Sunday dinners with their families (during which children were expected to sit quietly and listen politely, but more on French kids some other time).  One book that I read about French and American differences in the business world said that in France, a three-hour lunch with your co-workers is like a two-week team-building ropes course camping trip in the US.  In fact, for your waiter to rush you would be rude.  Edmund White points out in his book Inside A Pearl that in France, the more expensive the restaurant, the slower the service.  When you go into a cafe and order yourself a cup of coffee or a glass of wine, you have just bought yourself a table for as long as you want it.  If the waiter is running to your table repeatedly, he is rushing you out the door–and he won’t.  (So, what do you do if you just want a fast cup of coffee?  Go into the cafe and stand at the bar.  Coffee at the bar typically costs about 1 euro less than coffee at the table, and it’s a very quick interaction–you order, you get your coffee, you drink it, you pay, and you leave as soon as you like.)

And, no, your waiter is not judging you: A fear about French waiters that we rarely admit to in America, but that I suspect we all share, is that they are judging our taste when we order.  Really, they don’t care.  If your waiter makes a weird face when you order something, it doesn’t mean that he thinks you have bad taste–it’s his polite way of telling you that the chef isn’t really hitting the target with that particular dish today.  You should thank him.

And finally, tipping: Tips are included in the bill.  However, you should leave something extra as a courtesy.  At a minimum, leave the small change to round up to the next euro.  If you liked the service, leave a euro or two extra.  Leave three euros, and you will be remembered the next time–guaranteed.  And, the more often you go there, the closer you will get to friendly treatment, if that’s important to you—strangers can make French people uncomfortable (which makes the waiter’s job extra-difficult), but they can be really warm if they recognize you.

From a linguistic point of view, the interesting thing about dealing with a waiter is understanding the various culture-specific things that they say to you.  Here are some useful expressions to know:

  • A boire? or A manger?: the waiter is asking you if you’re there just for drinks, or to eat.  This will determine whether you get directed to a table with silverware, or without it.  You can also use these to tell the waiter that you’re just there for drinks, or that you’re there to eat.
  • Vous désirez?: This is roughly “what would you like?”  It’s interesting in that it shows a common form of forming a question in the spoken language: normal word order, i.e. no inversions or question words, with rising intonation.  This wasn’t taught in the US, in my day.
  • Vous avez choisi?: This is roughly “have you decided?”  I.e., have you decided what to order?  Again, we see the normal word order, but with a rising intonation.
  • C’est bon: This is how you tell the waiter to “keep the change.”  (Hopefully you will have added a euro or two–see above.)

There are some things that I still haven’t figured out.  The big one: how do I tell the waiter that I prefer to sit outside?  Feel free to tell me the answer in the comments!

Regression models in French: Part II

A linear mixed effects regression model of some data on intonation from Bodo Winter.
A linear mixed effects regression model of some data on intonation from Bodo Winter.

In a previous post, we talked about the vocabulary of a number of different kinds of regression models. Today, let’s move on to mixed effects models.  You can find an excellent discussion of mixed effects models here on Bodo Winter’s web site.  There is also some coverage of mixed effects models in Harald Baayen’s book Analyzing linguistic data: A practical introduction to statistics using RStefan Gries’s book Statistics for linguistics with R: A practical introduction has a good critique of current applications of mixed effects models in linguistics.

In statistics, an “effect” is anything that might affect the values in a sample. If we are building a statistical model of crop yields, an example of an effect might be the weather. If we are building a statistical model of voice onset times (the time gap between when a consonant is released from the mouth and when the vocal cords start vibrating for a following vowel), an example of an effect might be whether or not the associated syllable is stressed.

We can talk about two kinds of effects: fixed effects, and random effects.
As explained by Bodo Winter, fixed effects are things with a systematic and predictable influence on your results. They exhaust the possible levels, even if “only” defined operationally. As Stefan Gries puts it, fixed effects cover all possible levels (values that a variable could take) in the population. In contrast, random effects (as explained by Bodo Winter) are generally something that can be expected to have a non-systematic, idiosyncratic, unpredictable, or “random” influence on your data. In linguistic experiments, that is often “subject” and “item.” As Stefan Gries puts it, random effects sample the population, rather than exhausting it.  To see if you have this down, figure out if the following described fixed effects, or random effects:

  1. The Kukú language has voiced, voiceless, and implosive consonants. I have some of each in my experiment.
  2. The Kukú language has bilabial, alveolar, palatal, velar, labiovelar, and glottal stops. I have some of each in my experiment.
  3. For the purposes of my experiment, I am defining politeness as having two levels: casual, and formal. I have some of each in my experiment.
  4. There are almost 2,000 verbs in the CRAFT corpus. My student sampled 30 for a study of sentence plausibility.
  5. There are a number of labs in the Pharmacology department. My student recruited volunteers from one of them.
  6. There is an infinite number of sentences in Mousey Banana (a former co-worker’s favorite language name; more properly, it would be Musey Banana or Massa Banana; they are spoken in Chad and Cameroon), as there are in any language. Suppose that I took a sample of them for a study of intonation.

(Yes, these come straight from my statistics lecture notes.)  The first three of those are all fixed effects.  The last three are all random effects.

The beauty of mixed effects regression models is that they let you take random effects into account in building the model.  With a standard regression model, random effects introduce variability that your model will not be able to account for–going back to the voice onset example (the time lag between a consonant and vocal cord vibration for the vowel), the voice onset time will be affected systematically by things like whether or not the syllable is stressed and where in the mouth the consonant is formed, but it will also be affected randomly by things like which speakers I happen to have chosen–even with all other things being equal, my voice onset times will not be exactly the same as yours.  A mixed effects model lets you take these random effects into account.

With that background, we are ready for the French vocabulary that we need for talking about mixed effects models:

  • un effet: effect.
  • un effet aléatoire: random effect (the most common way of saying it).
  • un effet de/du hasard: another way of saying random effect (less common).
  • un effet fixe: fixed effect.
  • les effets mixtes: mixed effects.

Now comes the hard question: how do you link together modèle and effets mixtes?  Here’s the most common way of doing it:

  • le modèle à effets mixtes: mixed effects model.

Here are some examples from the linguee.fr website:

  • Nous proposons un modèle de régression spatial dans un cadre
    général de modèles à effets mixtes pour résoudre le problème de l’estimation pour petits domaines.  “A spatial regression model in a general mixed effects model framework has been proposed for the small area estimation problem.” (from statcan.gc.ca)
  • Ces analyses reposent sur des modèles physiologiques plus ou moins simplifiés et nécessitent des outils statistiques pluscomplexes comme la modélisation non-linéaire à effets mixtes.  “These analyses rely on more or less simple physiological models and require more complex statistical tools such as non-linear mixed effects modelling.” (from digiteo.fr)
  • Développement de méthodes d’estimation des paramètres des modèles non linéaires à effets mixtes par maximisation des vraisemblances approchées.  “Development of parameters estimation methods in the nonlinear mixed effects models with maximisation of approximated likelihood.” (from lemenuel.com))

Having beaten mixed effects models to death, I should point out that although they are very hot in American linguistics right now, they are not used as commonly in France at this time.  I brought up a question about mixed effects models after a talk in France once, and was embarrassed when the speaker asked me to switch to English.  It turned out afterwards that my French was OK–the speaker just wasn’t familiar with mixed effects models, and didn’t recognize the technical terms.

I haven’t tested this, but I suspect that chocolate is an effective tool of seduction everywhere

Old poster advertising a French brand of chocolate.  Photo from http://www.postercorner.com/Culinary-Chocolate-Palhasson-Vintage-Poster-Print-p/00720.htm.
Old poster advertising a French brand of chocolate. Photo from http://www.postercorner.com/Culinary-Chocolate-Palhasson-Vintage-Poster-Print-p/00720.htm.

You can find a list of the great restaurants of Paris anywhere.  However, a list of the great chocolatiers of Paris is harder to find.  Here’s a link to an article that talks about French chocolate and chocolate makers (chocolatiers), and gives the addresses of a number of Parisian chocolate shops.  What a nice theme for a day in Paris–a tour of the arrondissements by way of chocolate.

List of chocolatiers and chocolate shops of Paris from francetoday.com.

Here are some chocolate- and dessert-related terms that you might not have come across elsewhere:

  • la bûche de Noël: Christmas cake. (Picture below.)
  • la crème anglaise: custard.
  • la ganache: a cream-filled chocolate, a truffle.  Other meanings, too: the lower jaw, and an old fool.
  • la gaufre: waffle.  You probably knew this one already, but I include it here anyway, just because I like the word.  Another thing to note: le gaufre is a gopher (the North American mammal).  La gaufre: waffle.  Le gaufre: gopher.  Fun.
Bûche de Noël. Photo from http://blog.lamadeleine.com/2013/12/23/yule-love-it-the-history-of-buche-de-noel/.
Bûche de Noël. Photo from http://blog.lamadeleine.com/2013/12/23/yule-love-it-the-history-of-buche-de-noel/.

The DALF: I find myself in the same position as a 3rd grade immigrant student

DALF C1 diploma. Picture from http://www.delfdalf.fr/dalf-c1.html.

I used to tutor a 3rd grade student.  He had recently arrived in the US from Mexico, and didn’t yet speak English.  I found that what he needed the most was to have the instructions on his homeworks explained to him in Spanish.  The language of those homework instructions was, frankly, sometimes ridiculous–convoluted sentence structures, odd uses of vocabulary, and the like.  It wouldn’t surprise me if some native-speaker parents had trouble with them, and there certainly was no way that his monolingual Spanish-speaking parents were going to be able to give him any help with them.

Fast-forward some years, and I now find myself preparing to take the DALF, or the test for the Diplôme approfondi de langue française.  This is the French implementation of the last two levels of the CEFR.  The Common European Framework of Reference for Languages (CEFR) is a European attempt to define levels of proficiency in a variety of languages.  It has six levels, from A1 (the lowest) to C2 (the highest).  A grande école (a member of the French system of elite schools that operates in parallel with the university system) would probably require a C1 or C2.

The last time I was in France, I picked up a couple of exam prep books for the DALF.  (I think a prep book is called an entrenez-vous, but can’t swear to it.)  Of course, I can’t even understand the instructions for preparing for the test!  This is frightening, as the requirements for C1 look difficult, and if I can’t even understand the instructions…  Here is one part of the description of what your level of ability has to be like for the oral comprehension part of the C1 test:

  • Je peux extraire des détails précis d’une annonce publique émise dans de mauvaises conditions et déformée par la sonorisation (par example, des annonces publiques dans une gare, un stade).  “I can extract precise details of a public announcement produced under poor conditions and distorted by the sound system, for example in a train station or sports venue.”

Can you imagine?  I’m lucky if I can understand that kind of speech in my native language! Let’s look at some of the vocabulary that you will need just to understand the description of the oral comprehension test in the book Réussir le DALF:

  • la colonne: column.  We are allowed to take notes in the right-hand colonne of the exam paper while listening to recordings during the oral comprehension part of the exam.
    • la colonne or la colonne vertébrale: spinal column.
  • le débat: discussion, debate.  At a later point in the exam, we have a débat with the examining committee.
  • repérer: to spot; (shelter, an enemy) to locate.  (You might remember this verb from this post on vocabulary from a French conference–apparently I didn’t.)
  • porter sur: lots of ways to translate this, but it basically means to be about. Here are some examples of its use from linguee.fr:
    • Cette information doit porter sur la mise en place d’une relation et sur un comportement responsable envers l’autre. “This information should be about forming relationships and about how to relate to each other in a responsible manner.”  (europarl.europa.eu)
    • Ces modalités peuvent notamment porter sur les procédures d’élaboration et d’adoption des projets de plans de déploiement commun.  “These rules may cover in particular the procedures for the preparation and adoption of draft joint deployment plans.”  (eur-lex.europa.eu)
  • le support: medium or format; also a support, and (for a book or tool) a stand.  We are encouraged to listen to recordings of lots of different supports in order to prepare for the test.
  • tirer parti de: to take advantage of, to make good use of (WordReference.com).  My test prep book instructs us to tirer parti des caractéristiques de l’oral–“take advantage of the characteristics of the oral,” like knowing the characteristics of different types of discourse–spontaneous speaking; written language read out loud…
  • faire preuve de: to show (e.g. courage).  We are told that vous devrez faire preuve d’un très bon niveau de compréhension en français sur des sujets abstraits ou complexes même hors de votre domaine de spécialité.  “You must show a very high level of comprehension in French of abstract or complex subjects, even outside of your area of specialty.”
    • faire ses preuves: to prove oneself.
  • rédiger: (an article, a letter) to write; (a contract) to draw up.

(Definitions from the Collins French-English Kindle dictionary, unless otherwise noted.)

In case you’re wondering how my 3rd grade student third grade student turned out:  when I explained the instructions to him in Spanish, he was able to do the homeworks, as often as not.  By the end of the school year, his English was great, and he didn’t need me anymore.  I hope I do as well on the DALF…

Resources for learning foreign languages: Foreign Service Institute and Defense Language Institute courses

Linguists frequently get asked about resources for learning foreign languages.  In a recent post, I talked about what to use if you just want to get some basic skills in a language before a trip to someplace where that language is spoken.  In this post, I’ll point you towards the best resources that I know of for getting in-depth knowledge of a language.

As far as I know, the two best producers of foreign language instructional materials are the US Foreign Service and the US military’s Defense Language Institute.  Happily, they make many of their materials available for free on the Web.  Typically, you can download the textbook for a course as a PDF, and the audio materials as mp3 files.

These courses will require serious dedication on your part.  They have written and audio components, and you will need to do both.  However, they are well worth the effort.  These courses will give you in-depth knowledge of the language, as well as reading, writing, speaking, and comprehension skills.

You can find a listing of Foreign Service Institute courses at the following link.  There’s everything from Amharic to Yoruba–pretty impressive: http://fsi-languages.yojik.eu/

You can find a listing of Defense Language Institute courses at the following link.  Albanian to Vietnamese!  https://www.livelingua.com/dli-language-courses.php

There are a ton of other materials available through the Defense Language Institute web site.  Poke around at this link and you’re likely to find something of interest: http://www.dliflc.edu/

Resources for learning foreign languages: Pimsleur

Every linguist probably gets asked at least once a week what the best language-learning materials are.  The answer really depends on what your goals are.  If you are looking to get started with a language, looking to learn just enough of a language to get by on a trip, or are less interested in learning the mechanics of the language than in quickly getting up to speed, my best advice is to get a Pimsleur course.

Pimsleur courses are completely audio.  Some courses do come with a small booklet designed to teach you a little bit about reading the language, but the majority of them do not, and the written materials are always optional.  The structure of a Pimsleur course is that you are taught small phrases, and then drilled on them, over and over.

I have seen it claimed in reviews on Amazon that all Pimsleur courses follow the exact same sequence.  Having done several of them, I can say that this is not true–the Farsi one is the only one that covers haggling, the Turkish one teaches you to ask for tea, rather than beer (a staple of the other Pimsleur courses), and the Swedish one goes on and on about how to say that you would like something to eat and something to drink.  (More on that later.)  Having said that, they do all seem to start the same way.  You are taught a simple dialogue in which you learn to ask someone if they speak English, tell them that you do/don’t speak their language, and explain that you are an American.  You are taught to say complex phrases by starting at the end and working your way back to the beginning.  For reasons that I’m not clear on, that works quite well.

One of the things that amazes me about Pimsleur courses is that when I do them, I am almost always puzzled by why they teach you to say certain things–and then, when I get to wherever it is that the language is spoken, I find a use for them.  For example, in the Japanese course, they drill you over and over on how to say “How much does this cost in dollars?”  I couldn’t think of a single reason for this.  Then I got to Japan for the first time.  After a super-long flight, I was starved, and wanted a bowl of noodles before I left the airport.  Problem: I didn’t have any Japanese currency.  (This was before the advent of ATMs, which has made changing money almost irrelevant.)  Ahah: I asked the lady at the counter of the noodle place “How much does this cost in dollars?”  She told me, and I got my yummy bowl of noodles–mystery solved.  The Italian one drilled me on and on with sentences like “is this Via Veneto? No, it’s that over there.”  I couldn’t imagine why this would be that important, and then I got to Sardinia and discovered that an intersection was more likely to have six streets intersecting at odd angles than two, and figuring out which street you were on was, indeed, a challenge worthy of repeated conversations with strangers.

Pimsleur courses will give you basically no insight into the language that you are studying at all: they are just rote memorization and practice.  However: if you follow the instructions, listen carefully, repeat everything that you are told to repeat, and attempt to respond to every prompt, you will be able to speak what you do know with confidence, to be understood by native speakers, and to understand the answers to your questions.  This is huge–it is easy to learn how to ask where the bathroom is, but difficult to understand “all the way back, down the stairs, and to the left.”  With Pimsleur courses, you will be able to do that.

Pimsleur courses are not all created equal.  So far, I have done Japanese, Czech, Farsi, Turkish, Italian, Mandarin, and Swedish.  The Swedish one was a disappointment–it went on and on about how to say that you would like something to eat and something to drink, long before it taught you things that are relevant and will need repeatedly (to the extent that you ever need to speak Swedish in Sweden, which is not very often, although it’s fun to see the look of surprise on a Swede’s face when you try).  You are also not very likely to retain much if you don’t continue to use the language, but that is true of any instructional method.  However, if you are looking to pick up some of a language before you go to a country, this is the method that I would recommend–you can do it in your car, and–most importantly–if you are just looking to be able to function, it works.  In closing, here is a funny story about a conversation that I was able to have in Turkish, thanks entirely to my Pimsleur course–it’s way outside of what the course was intended for, but you will find that a little bit of a language can go a surprisingly long way, if you are creative:

Click on the picture if you can't read it clearly.
Click on the picture if you can’t see it clearly.

Regression models in French: Part I

One of the hot topics in linguistics right now is mixed effects models.  A mixed effect model is a kind of regression analysis.  Regression analysis is a way of building a statistical model of a phenomenon.  There are all kinds of things that you might want to build a statistical model of in linguistics, including phonetic relationships, sociolinguistics, syntax, and doubtless many others.  I’m going to use this post to put up some links to things that you might find useful in learning about mixed models, and of course we’ll come across some French vocabulary on the way.  (A note on the vocabulary in this post: it is mostly not found in dictionaries.  I induced it from examples on linguee.fr, an excellent source for finding examples of French technical vocabulary in use.)

The absolute best material for learning about mixed effects models so far is this tutorial by Bodo Winter.  If you’re not familiar with simple linear regression (i.e. with fixed effects only), you might want to check out this tutorial of his first.  Besides being really clear, Bodo’s tutorial is especially suitable for linguists, because it works through an extended example on F0 (fundamental frequency–roughly, the pitch of your voice) variation in situations of different politeness levels.

A regression line predicting female first formant frequencies from male formant frequencies, for speakers of several languages. Data from
A regression line predicting female first formant frequencies from male formant frequencies, for speakers of several languages. Data from Johnson (2011).

Let’s build up to the vocabulary of mixed effects models.  First, some basic vocabulary for talking about regression modelling.  Bear in mind that regression modelling–well, simple linear regression modelling–is about finding a formula that can predict the value for something on the basis of the value of something else.  The figure to the left plots F1 (first formant frequencies–part of what makes a vowel sound like what it sounds like) for female speakers of several language over the F1 for male speakers of the same language.  (The data comes from the web site accompanying Keith Johnson’s book Quantitative methods in linguistics.)  The line on the plot reflects a formula that will let you predict the F1 of a female speaker if you know the F1 of a male speaker.  Not surprisingly, the female frequencies are always higher–one of the determinants of overall patterns of F1 is that all other things being equal, the shorter your vocal tract is, the higher your F1 will be, and all other things being equal, women have shorter vocal tracts than men, on average.  What the line says is that you can get pretty close to an accurate prediction of the female F1 if you multiply the male F1 by 1.29.  (Yes, we’re glossing over the y intercept.)  OK, now on to that basic vocabulary:

  • le modèle: model.
  • le modèle de régression: regression model.
  • la régression linéaire: linear regression.
  • la régression logistique: logistic regression.
  • la régression linéaire simple: simple linear regression.

That got us through simple linear regression modelling.  Recall that in simple linear regression, you’re predicting a value for something on the basis of the value of something else.  But, most things don’t have simple one-to-one relationships.  Rather, it’s often the case that you need to predict one thing on the basis of multiple other things.  For example, suppose that you want to know what affects how long it takes a speaker of a language to respond to the question of whether or not a given sentence is grammatical (i.e., could be said in that language.  Colorless green ideas sleep furiously doesn’t mean anything, but you could say it in English.  On the other hand, green sleep colorless ideas furiously is something that you couldn’t say in English).  You might have to include multiple things in the model–how long the sentence is, how frequent the words in the sentence are, how long the words are, etc.  In this case–predicting one thing (response time) from multiple things (sentence length, word frequency, word length)–you need something called multiple linear regression.  This brings up more vocabulary:

  • la régression multiple: multiple regression.
  • la régression linéaire multiple: multiple linear regression.
The relationship between age and the percentage of correctly formed past tense verbs. From https://www.studyblue.com/notes/note/n/cognition/deck/10754142.
The relationship between age and the percentage of correctly formed past tense verbs. From https://www.studyblue.com/notes/note/n/cognition/deck/10754142.

So far, we know how to talk about linear regression.  What both kinds of linear regression have in common is that (a) we’re predicting a value from something else–from one value in the case of simple linear regression, or from multiple values in the case of multiple linear regression–and (b) we can describe the relationship between the value that we’re trying to predict and the value(s) that we’re trying to predict it from on the basis of a (straight) line.  Some relationships can’t be described by a straight line, though.  A classic example in linguistics is the U-shaped curve in language acquisition by children.  This describes a common phenomenon relating age to the percentage of correct productions of some linguistic target–say, irregular plurals, or the past tenses of verbs.  Initially, the child has a high percentage of correct productions.  Then, the child goes through a stage where the percentage of correct productions drops.  (As the figure suggests, this is thought to be because the child has made a transition from “memorizing” the regular and irregular forms to developing a hypothesis about a rule for forming plurals, or past tenses, or whatever.)  Finally, the child’s percentage of production of the correct forms climbs again.  Now we can’t describe the relationship between what we’re trying to predict (the percentage of correct productions and what we’re trying to predict it from (the child’s age) with a straight line.  However, there is another kind of regression that we can use.  It is called non-linear regression:

  • la régression non linéaire: non-linear regression.

We’ve now talked about three kinds of regression modelling.  They all have in common the fact that they are used to predict the value for something from the value(s) for something else.  If we’re trying to predict one value from one other value, that’s simple linear regression (la régression linéaire simple).  If we’re trying to predict one value from multiple other values, that’s multiple linear regression (la régression linéaire multiple).  And, if the relationship between what we’re trying to predict and what we’re trying to predict it from can’t be described by a straight line, then we have non-linear regression (la régression non linéaire).  (Before you ask: yes, there is such a thing as non-linear multiple regression, but I don’t know how to say it in French.  Heck, I’m not even sure how to say it in English–non-linear multiple regression?  Multiple non-linear regression?  It’s pretty rare.)  There’s one more kind of regression modelling that we need to talk about before we can move on to mixed effects regression modelling: logistic regression.

Logistic regression is used to predict the probability of something from something else.  Up ’til now, we’ve been predicting a value; now we’re predicting a probability.  What is the probability that a vowel will be unvoiced (whispered)?  What is the probability that I will pronounce -ing, versus -in’?  These are questions for logistic regression.  I’ll leave out the details, but we need to know the vocabulary:

  • la régression logistique: logistic regression.

OK, we can talk about a variety of types of regression modelling in French now.  But, to talk about mixed effects regression modelling, we also need to be able to talk about effects.  This post is already super-long, so let’s save that for next time.  In the meantime, here’s a shout-out to Bodo Winter, regression-modelling explainer extraordinaire: https://twitter.com/BodoWinter.