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Applied data analysis and statistics

Informacje ogólne

Kod przedmiotu: 2100-AC-M1-ADA-1 Kod Erasmus / ISCED: (brak danych) / (brak danych)
Nazwa przedmiotu: Applied data analysis and statistics
Jednostka: Wydział Biologii i Ochrony Środowiska (2012-2019)
Grupy:
Punkty ECTS i inne: 2.00
Język prowadzenia: angielski
Całkowity nakład pracy studenta:

Contact hours with teacher:

- participation in lectures - 30 hrs

- consultations - 5 hrs


Self-study hours

- writing projects - 10 hrs

- reading literature - 5 hrs

- preparation for test/ examination- 10 hrs


Altogether: 60 hrs (2 ECTS)


Efekty uczenia się - wiedza:

Student

W1: Obtain a conceptual and applied knowledge of basic concepts and methods of statistical analysis – K_W03

W2: is familiar with the analysis of variance – K_W03

W3: knows how to measure changes over time – K_W03

W4: possesses knowledge to use regression analysis – K_W03


Efekty uczenia się - umiejętności:

Student

U1: is able to use the analysis of variance and interpret and evaluate the results in the given research task – K_U03, K_U04

U2: is capable of apply regression analysis and interpret the results – K_U03, K_U04

U3: has skills in measuring changes over time – K_U03, K_U04

U4: is able to use statistical softwares for statistical computation– K_W03, K_U04


Metody dydaktyczne podające:

- wykład konwersatoryjny

Metody dydaktyczne poszukujące:

- ćwiczeniowa
- projektu
- seminaryjna

Skrócony opis:

The purpose of this course is to teach students how to use the applied data analysis tools in the field of humanities. Students will analyze a number of datasets using different statistical softwares. Emphasis will be placed on generating results and interpreting results appropriately, not statistical theories. Upon completion of this course, students should be able to prepare datasets for analysis, and conduct a wide range of descriptive and inferential analyses of data.

Pełny opis:

This is an intermediate statistics course focused on fundamentals of statistical inference and applied data analysis tools. Emphasis on thinking statistically, evaluating assumptions, and developing practical skills for real-life applications mainly to the field of humanities. Topics include collecting, presenting and interpreting statistical data, ranking data, comparing groups with t-tests and ANOVA analysis, linear regression and causal inference, measuring changes over time.

Topics:

1. What does statistics in humanities deal with?

2. Types of statistical data. Stevens scales of measurement. Collecting, visualising data and interpreting data.

3. Summering data – average, mode, quantiles.

4. Measuring spread – standard deviation, asymmetry, kurtosis.

5. Everything happens somewhere: spatial data.

6. Ranking data.

7. Sampling and sampling frames.

8. Key concepts in statistics (null hypothesis, confidence, significance, critical values, degrees of freedom, one and two tail tests) and using them intelligently.

9. Comparing groups – the Chi-square test.

10. Comparing two groups – the Student’s t-test.

11. Compering means of different samples – the one and two-way ANOVA analysis.

12. Understanding relationships – correlation coefficients.

13. Association causation and effect – regression analysis.

14. Measuring changes and rate of changes over time – time series, chain and constant index measurements.

15. Predicting new observations from known data.

Literatura:

1. John Canning, Statistics for the humanities, Brighton, UK, first edition 2014, download free: http://statisticsforhumanities.net/book/, A website accompanying this book is available at www.statisticsforhumanities.net.

2. Rajesh Ekka, Research methodology and data analysis in humanities & social sciences, Laxmi Book Publication, 2014.

3. Web Centre for Social Research Methods, https://socialresearchmethods.net/kb/index.php.

4. Denniz Dönmez, Social science methods for empirical data collection and analysis, ETH Zurich, 2015, www.timgroup.ethz.ch/download.

5. Donald J. Treiman, Quantitative Data Analysis: Doing Social Research to test Ideas, Jossey-Bass, 2009.

6. Neil J. Salkind, Statistics for People Who (Think They) Hate Statistics, Sage Pubn, 2016.

Metody i kryteria oceniania:

Assessment methods:

– project – U1, U2, U3, U4

– activity – W1, W2, W3, W4, U1, U2, U3

Assessment criteria:

tutorial:

fail - <=50 %

satisfactory – 50-60%

satisfactory plus – 61-70%

good – 71-80%

good plus- 81-90%

very good- 91-100%

Zajęcia w cyklu "Rok akademicki 2018/19" (zakończony)

Okres: 2018-10-01 - 2019-09-30
Wybrany podział planu:


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Typ zajęć: Seminarium, 30 godzin więcej informacji
Koordynatorzy: Mariola Piłatowska, Wojciech Rejchel
Prowadzący grup: Mariola Piłatowska, Wojciech Rejchel
Lista studentów: (nie masz dostępu)
Zaliczenie: Zaliczenie na ocenę
Opisy przedmiotów w USOS i USOSweb są chronione prawem autorskim.
Właścicielem praw autorskich jest Uniwersytet Mikołaja Kopernika w Toruniu.