Date: Sun, 7 Aug 2022 18:20:35 +0300 (EEST) Message-ID: <1490098061.16043.1659885635466@wiki-1.it.helsinki.fi> Subject: Exported From Confluence MIME-Version: 1.0 Content-Type: multipart/related; boundary="----=_Part_16042_242664922.1659885635465" ------=_Part_16042_242664922.1659885635465 Content-Type: text/html; charset=UTF-8 Content-Transfer-Encoding: quoted-printable Content-Location: file:///C:/exported.html Applied logistic regression, spring 2015

# Applied logistic regression, spring 2015

### Lecturer

Juha Alho, professor

This course covers the statistical modelling of binary outcomes using re= gression techniques. Binary outcome refers to YES/NO answers to such questions as is the respondent ill o= r not, did the respondent vote for a particular political party or not, did= the respondent buy a particular product or not etc.

Typical background characteristics on which the probability of the two p= ossible responses may depend include age, sex, socio-economic status, life = style characteristics etc.

In this setting the application of ordinary regression techniques is onl= y partly justified. Among the many models that could be entertained, logist= ic regression is favored becau= se of its mathematical tractability. A price one has to pay for adopting it= (or some other variant) is that the maximum likelihood theory = becomes more complicated and the in= terpretation of the parameters is more complex than in ordinary linear regr= ession.

The course emphasises intuitive understanding rather than mathematical p= recision. It should be accessible for all doctoral students in social scien= ces, for example.

R will be used for computation, but no previous familiarity with the pro= gram is assumed.

In order to pass the course, the students are expected to complete a set= of home work assignments that will be graded, and a small final exam.

Regist= er for the course