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Detaljni izvedbeni plan
Akademska godina 2023. / 2024. Semestar Zimski
Studij:

Sveučilišni prijediplomski studij povijesti, Sveučilišni prijediplomski studij psihologije, Sveučilišni prijediplomski studij sociologije, Sveučilišni prijediplomski studij komunikologije		 Godina studija:

Sveučilišni prijediplomski studij povijesti: 1., 2., 3.;
Sveučilišni prijediplomski studij psihologije: 1., 2., 3.;
Sveučilišni prijediplomski studij sociologije: 1., 2., 3.;
Sveučilišni prijediplomski studij komunikologije: 1., 2., 3.;

I. OSNOVNI PODACI O PREDMETU
Naziv predmeta Introduction to Statistics
Kratica predmeta IZBP233 Šifra predmeta 252568
Status predmeta Izborni ECTS bodovi 6
Preduvjeti za upis predmeta Nema
Ukupno opterećenje predmeta
Vrsta nastave Ukupno sati
Predavanja 30
Seminari 30
Mjesto i vrijeme održavanja nastave HKS – prema objavljenom rasporedu

II. NASTAVNO OSOBLJE
Nositelj predmeta
Ime i prezime Luka Šikić
Akademski stupanj/naziv Doktor znanosti Izbor Docent
Kontakt e-mail luka.sikic@unicath.hr Telefon +385 (1)
Konzultacije Prema objavljenom rasporedu
Suradnici na predmetu
Ime i prezime Petra Palić
Akademski stupanj/naziv Doktorica znanosti Izbor Docent
Kontakt e-mail petra.palic@unicath.hr Telefon +385 (1)
Konzultacije Prema objavljenom rasporedu

III. DETALJNI PODACI O PREDMETU
Jezik na kojem se nastava održava Engleski
Opis
predmeta

 

Course Objectives:

This course introduces the fundamental statistics principles, focusing on developing research questions, hypothesis formation, research design, and data analysis. Students will gain practical experience using statistical software and learn the proper application of statistical tests. Moreover, the course highlights the importance of effectively communicating research results to various audiences, giving students the skills to present their findings. Students must pass two-semester and final oral exams to complete the course successfully.

 

Course Content:

Foundations: Introduction to the key concepts and principles of descriptive statistics.

Statistical Programming Essentials: Familiarization with a widely-used programming language for statistical analysis, including basic syntax and functionality.

Descriptive Statistics: Measures of central tendency, Measures of variability, Measures of association, and Data visualization.

Statistical Theory: Probability distributions, Population, Sample, Hypothesis testing. 

Data Analysis: Inferential statistical techniques, such as testing of the mean, categorical analysis, ANOVA, and regression.

 
Očekivani ishodi
učenja na razini
predmeta		 1. Demonstrate a solid understanding of fundamental statistical concepts, including probability theory, descriptive statistics, hypothesis testing, and basic inferential techniques. 2. Formulate research questions and generate testable hypotheses relevant to real-world problems in social science research. 3. Design and execute simple experiments, collect data, and apply appropriate statistical techniques to analyze and interpret the results. 4. Develop proficiency in using statistical software for data management, visualization, and analysis, as well as interpreting the output generated by the software. 5. Critically evaluate and assess the validity of statistical analyses and conclusions in scientific research papers and reports. 6. Collaborate effectively in group tasks and discussions, contributing to the collective understanding of statistical concepts and their applications. 7. Demonstrate a solid statistical foundation, paving the way for further studies in more advanced statistical techniques and methodologies.
Literatura
Obvezna

Navarro, D. J. (2019). Learning Statistics with R: A tutorial for psychology students and other beginners. Adelaide, Australia: University of Adelaide Press. Available online: https://learningstatisticswithr.com/ Peck, R., Olsen, C., & Devore, J. L. (2011). Introduction to Statistics and Data Analysis. Boston: Cengage Learning.                                                       Weiss, N. A. (2015). Introductory Statistics. Boston: Pearson.
Dopunska

Moore, D. S., Notz, W. I., & Flinger, M. A. (2018). The Basic Practice of Statistics. New York: W. H. Freeman and Company.

    Triola, M. F. (2017). Elementary Statistics. Boston: Pearson.

    De Veaux, R. D., Velleman, P. F., & Bock, D. E. (2016). Intro Stats. Boston: Pearson.

    Diez, D. M., Barr, C. D., & Çetinkaya-Rundel, M. (2014). OpenIntro Statistics. CreateSpace Independent Publishing Platform.

    Peck, R., Olsen, C., & Devore, J. L. (2011). Introduction to Statistics and Data Analysis. Boston: Cengage Learning.

    Johnson, R. A., & Kuby, P. (2016). Just the Essentials of Elementary Statistics. Boston: Cengage Learning.

    Agresti, A., & Franklin, C. (2013). Statistics: The Art and Science of Learning from Data. Boston: Pearson.
Način ispitivanja i ocjenjivanja
Polaže seDa Isključivo kontinuirano praćenje nastaveNe Ulazi u prosjekDa
Preduvjeti za dobivanje
potpisa i polaganje
završnog ispita

Attendance is crucial for success in this course, and students are expected to attend at least 70% of lectures and seminar sessions.
Način polaganja ispita

Class activities: Two-semester exams and a final oral exam.
Način ocjenjivanja

Grading Manner

 

The final course grade is based on 100 points earned through the student’s continuous involvement in-class activities:

Fair (2) – 50 to 64 points

Good (3) – 65 to 79 points

Very good (4) – 80 to 89 points

Excellent (5) – 90 to 100 points

 

Earning credits:

Class activities contribute to 70% of the grade:

Exam 1 – maximum 35 points

Exam 2 – maximum 35 points

The final (oral) exam contributes to 30% of the grade:

Final exam – maximum of 30 points
Detaljan prikaz ocjenjivanja unutar Europskoga sustava za prijenos bodova
VRSTA AKTIVNOSTI ECTS bodovi - koeficijent
opterećenja studenata		 UDIO
OCJENE
(%)
Pohađanje nastave 1.5 0
Kolokvij-međuispit 1.575 35
Kolokvij-međuispit 1.575 35
Ukupno tijekom nastave 4.65 70
Završni ispit 1.35 30
UKUPNO BODOVA (nastava+zav.ispit) 6 100
Datumi kolokvija The first exam is in the 7th week of the course, and the second is in the 15th week.
Datumi ispitnih rokova Prema objavljenom rasporedu

IV. TJEDNI PLAN NASTAVE
Predavanja
Tjedan Tema
1. Introduction to the course.
2. Introduction to the R programming language.
3. Descriptive statistics.
4. Graphs and visualization.
5. Basics of probability theory.
6. Estimating population parameters.
7. Testing statistical hypotheses.
8. Midterm exam.
9. Categorical data analysis.
10. Comparing means.
11. Linear regression.
12. ANOVA.
13. Factorial ANOVA.
14. Multivariate statistical models.
15. Final exam.
Seminari
Tjedan Tema
1. Introduction to the course.
2. Introduction to the R programming language.
3. Descriptive statistics.
4. Graphs and visualization.
5. Basics of probability theory.
6. Estimating population parameters.
7. Testing statistical hypotheses.
8. Midterm exam.
9. Categorical data analysis.
10. Comparing means.
11. Linear regression.
12. ANOVA.
13. Factorial ANOVA.
14. Multivariate statistical models.
15. Final exam.