16:36 Jul 11, 2017 |
English to French translations [PRO] Bus/Financial - Mathematics & Statistics | |||||||
---|---|---|---|---|---|---|---|
|
| ||||||
| Selected response from: Francois Boye United States Local time: 03:06 | ||||||
Grading comment
|
Summary of answers provided | ||||
---|---|---|---|---|
4 | régression polynomiale flexible |
| ||
1 | régression par spline adaptative |
|
Discussion entries: 5 | |
---|---|
flex polynomial regression régression polynomiale flexible Explanation: flex = flexible Polynomial regression extends the linear model by adding extra predictors, obtained by raising each of the original predictors to a power. Cubic regression uses three variables X, X2, and X3 as predictors. This is a simple way to provide a non-linear fit to the data. •Step functions cut the range of a variable into K distinct regions in order to produce a qualitative variable. This has the effect of fitting a piece wise constant function. •Regression splines are more flexible than polynomials and step functions, and are actually an extension of the two. The divide the range of X into K distinct regions. For each region, a polynomial function is fit to the data, however, the polynomials are constrained so that they join smoothly at the region boundaries or knots. These can provide and extremely flexible fit. |
| ||
Grading comment
| |||
Notes to answerer
| |||
Login to enter a peer comment (or grade) |
flex polynomial regression régression par spline adaptative Explanation: C'est probablement ceci (voir lien ci-dessous), mais seul le client pourrait le confirmer Reference: http://fr.wikipedia.org/wiki/R%C3%A9gression_multivari%C3%A9... |
| ||
Notes to answerer
| |||
Login to enter a peer comment (or grade) |
Login or register (free and only takes a few minutes) to participate in this question.
You will also have access to many other tools and opportunities designed for those who have language-related jobs (or are passionate about them). Participation is free and the site has a strict confidentiality policy.