Statistika on-line

- zapiski iz statističnih predavanj -

• Anscombe quartet

Vprašanja in odgovori za Multivariatno analizo(Anuška Ferligoj, Fakulteta za družbene vede):

• Vprašanja za MVA na podiplomskem študiju
• Vprašanja za 1. kolokvij za MVA
• Vprašanja za 2. kolokvij za MVA
• Vprašanja za 3. kolokvij za MVA
• On-line slovarček statističnih pojmov

Analiza socialnih omrežij (Anuška Ferligoj, Valentina Hlebec, Andrej Mrvar, Fakulteta za družbene vede):

• Naloga 1 (stranke - opis problema, tip omrežja)
• Naloga 2 (stranke - opis enot in relacij)
• Naloga 3 (stranke - opis generatorja imen)
• Naloga 4 (stranke - kvaliteta merjenja)
• Naloga 5 (učenci - opis relacije in omrežja)
• Naloga 6 (učenci - premer omrežja, acikličnost, stopnje točk, razdalje med točkami)
• Naloga 7 (učenci - komponente, jedra)
• Naloga 8 (učenci - razvrščanje v skupine)
• Naloga 9 (učenci - mere pomembnosti, vmesnosti, usredinjenost omrežja)
• Naloga 10 (učenci - bločni modeli, strukturna enakovrednost, regularna enakovrednost)

Advanced Regression Analysis (Guy Whitten, University of Essex):

• Exercise 1 (lung cancer problem);
• Exercise 2 (support for Edouard Balladur);
• Exercise 3 (support for Jacques Chirac);
• Exercise 4 (support for Jacques Chirac - logit model);
• Exercise 5 (modelling heteroskedasticity).

Exploratory Data Analysis (Eugene Horber, University of Essex):

• Exercise 1 (simple analysis);
• Exercise 2 (start to learn LISP-STAT, see exploratory tools in action);
• Exercise 3 (some more information on LISP-STAT and the commands we have already studied; additional graphical tools, namely plots, scatter plot matrices and spin plots; study similar tools in SPSS);
• Exercise 4 (a demonstration of the problems of ordinary regression; some information on objects and how to interact with them; how to work with regression tools in LISP-STAT; how to analyse residuals; more on Lisp-Stat - animation);
• Exercise 5 (an exploration of multiple regression and using ARC; some details on re-expressions);
• Exercise 6 (robust statistics and diagnostic plots);
• Exercise 7 (General ideas on group analysis; lowess as an example of smoothing; smoothing time series using running medians);
• Some EDA terms.

Time Series Analysis I. (Kostas Drakos, University of Essex):

• Exercise 1 (simple analysis);
• Exercise 2 (ACF and PACF);
• Exercise 3 (detecting autocorrelation);
• Exercise 4 (Exact Maximun Likelihood, Cochrane-Orcutt method, Gauss-Newton method);
• Exercise 5 (Koyck model);
• Exercise 6 (causality and time series);
• Exercise 7 (cointegration).

Time Series Analysis II. - basic ideas only (Harold D. Clarke, University of Essex):

• Exercise 1 (simple analysis);
• Intervention models.

Correspondence Analysis - basic ideas only (Jorg Blasius, University of Essex):

• Asymetric plot;
• Analysis of PolitBarometer data;
• Analysis of PolitBarometer data with index variable;
• Sample exam questions.

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Naloge za MVA (97/98):

Osnovna statistična analiza

• Naloga 1
• SPSS in Excel programi za 1. nalogo

Razvrščanje v skupine

• Naloga 2
• Excel programi za 2. nalogo
• Predstavitev podatkov z zvezdami

Razvrščanje v skupine s pomočjo metode glavnih komponent

• Naloga 3
• Pascalski program za 3D predstavitev podatkov

Multipla regresija

• Naloga 4

Faktorska analiza

• Naloga 5

Diskriminantna analiza

• Naloga 6

Lisrel model

• Naloga 7

Zanesljivost merjenja

• Naloga 8

(C) Matej Kovačič, 2001