UEF Statistics Teaching Bulletin, Spring 2018

Transkriptio

1 UEF Statistics Teaching Bulletin, Spring 2018 The minor subject of statistics offers methodological courses to all students of the university. In Spring 2018, we offer the following basic courses in Finnish: Regressiotekniikat, 4 op, Joensuu, kuvaus sivulla 2 Tilastollinen ohjelmistokurssi, 2 op, Kuopio, kuvaus sivulla 2 Tilastotieteen peruskurssi, 5 op, Kuopio, kuvaus sivulla 3 Todennäköisyysmallit, päättely ja epäparametriset menetelmät, 2-5 op, Kuopio ja Joensuu, kuvaus sivulla 3 For students with sufficient basic knowledge, we offer also intermediate and advanced courses. Courses Introduction to Statistical Inference 1 and 2 run every year and the rest intermediate and advanced courses run every second year. These courses are suitable methodological Ph.D. studies in many fields. In spring 2018, we offer the following courses in Joensuu and Kuopio. Regression analysis 1, 4 cr, period 3, description on page 4 Regression analysis 2, 4 cr, period 4, description on page 4 Introduction to Statistical Inference 1, 5 cr, period 4, description on page 5 Statistical experimental design, 5 cr, period 4, description on page 6 Tentative long term schedule of up coming courses can be found at We also provide statistical consultiong for PhD students and researchers of the university. For more details, see UEF statistics teaching bulletin, provides timely information on the available statistics courses to the students of UEF. The bulletin is published at the beginning of each semester and posted to the Yammer-group Statistics-Info 1

2 Regressiotekniikat, 4 op, Joensuu kontaktiopetusta 28 tuntia sisältäen sekä luentoja että harjoituksia Ajoitus: 4. periodi. Opettaja: Esko Valtonen, esko.valtonen@uef.fi Sisältö: Kurssilla tarkastellaan tavallisen lineaarisen yhden ja monen selittäjän regressiomallin ohella yhden ja monen selittäjän logistista regressiomallia. Klassisen yksiuuntaisen ja kaksisuuntaisen varianssianlyysin ohella kurssilla tutustutaan myös ensimainitun epäparametriseen vastineeseen. Esitiedot: Kurssit Kuvaileva tilastotiede ja aineiston hankinta, Tilastolliset mallit ja testaus sekä Todennäköisyysmallit, päättely ja epäparametriset menetelmät Tilastollinen ohjelmistokurssi, 2 op ( 2 op ) 16 t pienryhmäopetusta Ajoitus: 4. periodi Opettaja: Mika Hujo, mika.hujo@uef.fi Sisältö: SPSS-ohjelmiston käyttöympäristö ja peruskäsitteet, havaintoaineiston kuvailu tunnusluvuilla ja kuvioilla, muuttujamuunnoksia, hypoteesin testaus (mm. odotusarvojen t-testit), regressio- ja varianssianalyysi. Esitietovaatimukset: Tilastotieteen johdantokurssi ja tilastotieteen peruskurssi tai vastaavat tiedot. Kurssin suorittaminen ei edellytä aiempaa kokemusta SPSS:n käytöstä. Suoritustapa: Näyttökoe 2

3 Tilastotieteen peruskurssi, 4 op Monimuoto-opetus, ohjauksia noin 14 tuntia. Ajoitus: 3. periodi. Ensimmäinen tapaaminen (kaikille yhteinen). Opettaja: Mika Hujo; mika.hujo@uef.fi Sisältö: Moniulotteinen jakauma, estimointi (piste-estimointi, väliestimointi), epäparametrisia testijä, χ 2 -riippumattomuustesti, yhden ja monen selittäjän regressioanalyysia, 1- suuntainen varianssianalyysi, logistisen regression alkeet. Esitietovaatimukset: Tilastotieteen johdantokurssi , 2-5 op, Todennäköisyysmallit, päättely ja epäparametriset menetelmät, Joensuu ja Kuopio ( 5 op) 28 t luentoja + 14 t harjoituksia ( 2 op) 28 t luentoja + harjoitustehtäviä (vain Joensuu) Ajoitus: 3. periodi. Opettaja: Esko Valtonen, esko.valtonen@uef.fi Sisältö: Kurssilla tutustutaan matemaattisen tilastotieteen keskeisiin käsitteisiin ja tuodaan esille tuloksia, joita sovelletaan toistuvasti tilastollisessa päättelyssä.tavoitteena on sekä antaa perusteita aiemmisssa tilastotieteen perusopinnoissa tarkastelluille päättelymenetelmille (kuten t-testille) että tutustuttaa soveltavilla kursseilla tarvittaviin tekniikoihin (kuten pns- ja SU-estimointiin). Kurssilla tarkastellaan myös muutamia usein käytettäviä epäparametria testejä. Vaikka esitystapa on formaalinen, pääpaino ei ole tiukan eksaktissa ja yksityiskohtaisessa tulosten todistamisessa vaan niiden ensisijassa niiden esittelyssä ja sisällön avaamisessa. Esitiedot: Joensuussa kurssit Kuvaileva tilastotiede ja aineiston hankinta sekä Tilastolliset mallit ja testaus, Kuopiossa kurssit Tilastotieteen johdantokurssi ja Tilastotieteen peruskurssi. 3

4 Regression analysis 1 and Regression analysis 2, 4+4 cr Responsible teacher: Juho Kettunen, juho.kettunen@uef.fi Timing: Regression analysis 1: 3rd period (first lecture on at in TB178 (Joensuu) and F211 (Kuopio)). Regression analysis 2: 4th period (first lecture on at in TB178 in Joensuu and F211 in Kuopio). For complete information on timing and locations, see WebOodi. Teaching language: English. The regression analysis courses are recommended for students that aim to do quantitative research in their graduate thesis or in postgraduate studies. Regression analysis is a general technique to estimate statistical relations between observed quantities, to construct models with causal relationships, and to study the reliability of constructed models and their predictions. Course Regression analysis 1 covers the classical linear regression model (chapter 3 of the book Regression models, methods and applications ). The aim is that after the course, the student understands the basic concepts of linear regression analysis. Student should be able to assess the model fit and interpret the results. Students also learn to use R in model estimation, in assessing the model diagnostics and in presenting the results. Regression analysis 2 (starting at ) will deepen the students knowledge of the regression analysis and how to handle common problems encountered in regression analysis. Course cover topics including the general linear model, regularization techniques (ridge regression and lasso) and the generalized linear models. Lectures follow the book Fahrmeir, L., Kneib, T., Lang, S. and Marx, Brian. Regression models, methods and applications. The recommended background knowledge includes some basic courses in statistics. Completing the self-study R-course prior or parallel to the course is recommended (see and prior knowledge of the courses in statistical inference helps in understanding the material. Regression analysis 1 and 2 contain both 28 hours lectures, demonstrations, and a written exam. Literature Fahrmeir, L., Kneib, T., Lang, S. and Marx, Brian. Regression models, methods and applications. Springer, Lecture material Additional reading that can be helpful Weisberg, S., Applied Linear Regression Fourth Ed., Wiley,

5 Johdatus tilastolliseen päättelyyn 1 (5 op) Introduction to statistical inference 1 (5 credits) Responsible teacher (lectures and course material): Ville Hautamäki, ville.hautamaki@uef.fi Timing: 4th period, for complete information on timing and locations, see WebOodi. Teaching language: English When studying a specific intermediate course/topic in statistics, such as regression analysis, linear mixed models, generalized linear models, spatial statistics, sampling, multivariate analysis, or Bayesian inference, student encounters some amount of theoretical knowledge that should have been mastered before. The courses on statistical inference (Introduction to statistical inference 1 and 2) cover these basics so that further studies are faciliated. Especially, we try to focus on the general understanding of concepts and ideas, not so much on the mathematical proofs and technical details. In general, many of the results will not be formally proven but they will be demonstrated and justified with example calculations and computer simulations using R. Course Introduction to statistical inference 1 will start with univariate random variables, the description of them using probability distributions, and summaries of the essential properties using expected value and variance. Thereafter we will introduce the necessary matrix algebra for treatment of multivariate random variables. The third part will cover multivariate random variables and the related distributions: joint distribution, conditional distribution and marginal distribution, as well as their summaries: mean variance and covariance. The course Introduction to statistical inference 2, which will be given in fall 2017, will continue with the theory on parameter estimation, hypothesis testing, confidence intervals and important large-sample results. The course is highly recommended for all students who are going to study statistics beyond the introductory level, especially if your plan an academic career on a field where statistical methods are used as standard tools. The course should be taken right after, or even parallel to the basic courses. Together with the second part (Introduction to statistical inference 2) it is a mandatory course for intermediate studies in statistics (tilastotieteen aineopinnot). Completing the self-study R-course sections 1-4 (1 credits) prior or parallel to the course is recommended (see view.php?id=3749). The course includes 32 hours of lectures and 16 hours of demonstrations where the solutions of the weekly exercises are presented. As additional reading, one could use e.g.,: G. Casella and R. L. Berger, Statistical inference, 2002 DeGroot and Schervish, Probability and statistics,

6 Tilastollinen koesuunnittelu (5 op) Statistical experimental design (5 credits) Blended learning, approximately 28 hours contact guidance for students. Responsible teacher: Mika Hujo, Lecturer in statistics, Timing: 4th period (First meeting on ). For complete information on timing and locations, see WebOodi. Teaching language: English In an experiment, we deliberately change levels one or more process variables/factors in order to observe the effect of changes on the response variable. The statistical design of experiments is an efficient procedure for planning experiments so that the data obtained can be analyzed to yield valid and objective conclusions. In the real world there often appears some grouping of statistical units. A grouping induces dependencies between statistical units. This kind of dependency can be accounted for using correct methods. One should understand that if there is a grouping of statistical units standard anova procedures do not anymore work. Common reasons for dependence/grouping are Data has a grouped structure, such as many trees on a sample plot, many branches from a tree, many locations from the same tracked animal, several newt captures around the same breeding pond, several growth measurements on the same calendar year,... With such datasets, observations from the same group are often similar to each other, which implies dependence. The data consists of repeated measurements of the same unit, such as growth rings of a tree. The observations from successive years are dependent, but the dependence gets weaker and probably vanishes as time lag gets longer. The data are correlated because the observations are taken at locations close to each other. The dependence is the stronger the smaller the distance between observations. Application areas of the design of experiments is wide including enviromental, industrial, medical, pharmaceutical and so on applications. The aim is that after the course, the student understands the basic concepts of experimental design. He/She should also be able to analyze data from different designs (at least with R-software), interpret and utilize the results, and explore the validity of the inherent assumptions or at least understand the assumptions that have been made. The recommended background knowledge includes some basic courses in statistics: Basic statistics in English or Tilastotieteen johdantokurssi and tilastotieteen peruskurssi Completing the self-study R-course sections 1-4 (1 credits) prior or parallel to the course is recommended (see As additional reading, one could use e.g., the following books: D. Montgomery: Design and Analysis of Experiments J. C. Pinheiro, D. M. Bates: Mixed-Effects Models in S and S-PLUS 6