FOI Nastava - Odabrana poglavlja statističkih metoda u informacijskim znanostima

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Odabrana poglavlja statističkih metoda u informacijskim znanostima

Selected Topics in Statistical Methods for Information Science
2020/2021

7 ECTSa

Doktorski studij Informacijske znanosti 1.1 (PDDSIZ)

Katedra za kvantitativne metode

1. semestar

Osnovne informacijemdi-information-variant

Izvođači nastavemdi-account-group

Nastavni plan i programmdi-clipboard-text-outline

Model praćenjamdi-human-male-board

Ispitni rokovimdi-clipboard-check-outline

Rasporedmdi-calendar-clock

Konzultacijemdi-account-voice

Izvođenje kolegija

Studij	Studijski program	Semestar	Obavezan
Doktorski studij Informacijske znanosti 1.1 (PDDSIZ)		1	obavezan

Cilj kolegija

Cilj je upoznati studente s načinom uzorkovanja podataka, njihovog grafičkog i tablealrnog prikazivanja te metodama analize podataka za potrebe znanstveno-istraživačkog rada. Studenti će biti upoznati s načinom postavljanja i dokazivanja hipoteza, s uvjetima za primjenu pojednih metoda te s tehnikama eksplorativnih statističkih metoda u cilju otkrivanja uzoraka u podacima.

Preduvjeti

Kolegij nema definirane preduvjete

Norma kolegija

Predavanja

30 sati

Nastavnik	Uloga na kolegiju	Oblik nastave	Tjedana	Sati	Grupa
Dobša Jasminka	Nositelj	Predavanja doktorski studij	1	15	1
Šimić Diana	Nositelj	Predavanja doktorski studij	1	15	1

Sadržaj predavanja

1. Podaci
Mjerne skale podataka, varijable, observacije.
2. Kvalitativni podaci
Deskriptivne statistike, tablica frekvencija, stupčasti dijagram, kontingencijska tablica, nezavisnost kvalitativnih varijabli.
3. Kvantitativni podaci
Histogram, deskriptivne statistike, box-plot, nezavisnost kvantitativnih varijabli, dijagram rasipanja, korelacija.
4. Nezavisnost kvantitativne i kvalitativne varijable
Paralelni box-plotovi, točkasti dijagrami.
5. Uzorkovanje
Koncepti populacije i uzorka, vjerojatnosne razdiobe, parametri razdioba, razdioba uzorkovanja.
6. Procjena parametara
Paradigma, metode, nepristranost, konzistentnost, učinkovitost.
7. Pripasavanje razdiobe
Hi-kvadrat test, qq dijagram, Kolmogorov-Smirnov test.
8. Modeli za kvalitativne varijable
Kontingencijska tablica i hi-kvadrat test, log-linearni model.
9. Opći linearni model
T-test, linearna regresija, ANOVA, ANCOVA, zajednički okvir, dizajn eksperimenta, fiksni i slučajni efekti.
10. Generalizirani linearni model
Logistička i Poissonova regresija.
11. Multivarijatne metode
Korelacijska matrica, MANOVA, kanonička korelacija, kanonička diskriminacijska analiza.
12. Redukcija dimenzionalnosti
Analiza glavnih komponenti, zajednička faktorska analiza. Primjer u rudarenju tekstualnih dokumenata: latentno semantičko indeksiranje.
13. Udaljenosti / sličnosti
Matrica udaljenosti / sličnosti, klaster analiza, multidimenzionalno skaliranje.
14. Klasifikacija podataka
K-najbližih susjeda, stable odlučivanja, metoda potpornih vektora (engl. Support vector machines).

Sadržaj seminara/vježbi

Ishodi učenja kolegija

Ishodi učenja programa

Osnovna literatura

1. de Veaux RD, Velleman PF, Bock DE (2009) Intro Stats (3. izdanje). Boston: Pearson Education Inc. ISBN-13: 978-0-321-50045-8
2. Kalton G (ur.) (1983) Introduction to Survey Sampling. Sage Publications, Inc. ISBN-13: 978-0803921269
3. Milliken GA, Johnson DE (1984) Analysis of Messy Data, Volume I: Designed Experiments. Chapman & Hall. ISBN-13: 978-0-412-99081-6
4. Milliken GA, Johnson DE (1989) Analysis of Messy Data, Volume II: Nonreplicated Experiments. Chapman & Hall. ISBN-13: 978-0-412-06371-8
5. Milliken GA, Johnson DE (2001) Analysis of Messy Data, Volume III: Analysis of Covariance. Chapman & Hall. ISBN-13: 978-1-584-88083-7
6. McCulloch CE, Searle SR, Neuhaus JM (2008) Generalized, Linear, and Mixed Models (2. izdanje, Wiley Series in Probability and Statistics). Wiley-Interscience.
7. Izenman AJ (2008) Modern Multivariate Statistical Techniques: Regression, Classification, and Manifold Learning. Springer. ISBN-13: 978-0387781884
8. I.H. Witten, E. Frank (2000) Data Mining,Practical Machine Learning Tools and Techniques with Java Implementations, Morgan Kaufmann, ISBN 1-55860-552-5
9. T.M. Mitchell (1997), Machine Learning, McGraw-Hill

Dopunska literatura

1. Converse JM, Presser S (1986) Survey Questions: Handcrafting the Standardized Questionnaire. Sage Publications, Inc. ISBN-13: 978-0803927438
2. Skrondal A, Rabe-Hesketh S (2004) Generalized Latent Variable Modeling: Multilevel, Longitudinal, and Structural Equation Models. Chapman and Hall/CRC. ISBN-13: 978-1584880004
3. Cowpertwait PSP, Metcalfe AV (2009) Introductory Time Series with R. Springer. ISBN-13: 978-0387886978
4. Ostini R, Nering ML (ur.) (2005) Polytomous Item Response Theory Models (Quantitative Applications in the Social Sciences). Sage Publications, Inc. ISBN-13: 978-0-761-93068-6
5. Simon JL (1997) Resampling: The New Statistics (2nd edition). Resampling Stats. ISBN-13: 978-0534217204 http://www.resample.com/content/text/index.shtml
6. Hoaglin DC, Mosteller F, Tukey JW (ur.) (2000) Understanding Robust and Exploratory Data Analysis. Wiley-Interscience. ISBN-13: 978-0471384915
7. Aldenderfer MS, Blashfield RK (1984) Cluster Analysis. Sage Publications, Inc. ISBN-13: 978-0803923768
8. Lynch SM (2009) Introduction to Applied Bayesian Statistics and Estimation for Social Scientists. New York: Springer. ISBN-13: 978-1441924346
9. Gwet KL (2010) Handbook of Inter-Rater Reliability (2. izdanje) Advanced Analytics, LLC. ISBN-13: 978-0970806222
10. Duda RO, Hart PE, Stork DG (2001) Pattern Classification, 2. izdanje, John Wiley and Sons, ISBN 0-471-05669-3
11. Berry MJA, Linoff G (2000) Mastering Data Mininbg: The Art and Science of Customer Relationship Management, 2. izdanje, John Wiley and Sons, ISBN 0-471-33123-6

Slični kolegiji

University of Pittsburgh School of Information Science Graduate programme Informations Science and Technology. Intermediate analytical course in applied statistical methods: vector and matrix notation, multiple correlation and regression, T and F distributions, analysis of variance, planned comparisons and post hoc comparisons, analysis of covariance, and nonparametric techniques. Utilizes SPSS statistical programming package.
University of Pittsburgh School of Information Science Graduate programme Informations Science and Technology. Introduction to data-mining techniques, including data preprocessing, data-mining primitives, association rules, decision trees, cluster analysis, classification and machine learning, data visualization, and data warehousing. Detailed applications from a wide variety of domains.
University of Hawaii The Department of Information and Computer Sciences and the Library and Information Science Program in the College of Natural Sciences, the School of Communications in the College of Social Sciences, and the Department of Information Technology Management in the Shidler College of Business. Interdisciplinary Ph.D. program in Information and Communication Sciences. Introductory statistics in education and social sciences. Topics include probability distributions; sampling distributions; hypothesis testing using t-tests, correlation, simple regression, ANOVA; and applications in research. (Meets PhD common inquiry methods requirement or elective.)
University of Hawaii The Department of Information and Computer Sciences and the Library and Information Science Program in the College of Natural Sciences, the School of Communications in the College of Social Sciences, and the Department of Information Technology Management in the Shidler College of Business. Interdisciplinary Ph.D. program in Information and Communication Sciences. The analysis and interpretation of behavioral science data using common statistical software packages. (Crosslisted as SW 652).
University of Hawaii The Department of Information and Computer Sciences and the Library and Information Science Program in the College of Natural Sciences, the School of Communications in the College of Social Sciences, and the Department of Information Technology Management in the Shidler College of Business. Interdisciplinary Ph.D. program in Information and Communication Sciences. Advanced application of general linear model to complex problems of data analysis. Relation of analysis of variance and covariance to regression analysis. Non-linearity and treatment of missing data. Class requires basic statistics.
University of Hawaii The Department of Information and Computer Sciences and the Library and Information Science Program in the College of Natural Sciences, the School of Communications in the College of Social Sciences, and the Department of Information Technology Management in the Shidler College of Business. Interdisciplinary Ph.D. program in Information and Communication Sciences. Theory and method of factor analysis and related methods of multivariate analysis.
University of Hawaii The Department of Information and Computer Sciences and the Library and Information Science Program in the College of Natural Sciences, the School of Communications in the College of Social Sciences, and the Department of Information Technology Management in the Shidler College of Business. Interdisciplinary Ph.D. program in Information and Communication Sciences. Multivariate forms of multiple linear regression, analysis of variance and co-variance. Multiple discriminant analysis, canonical correlation, and principal-components analysis are discussed.

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