Advanced statistical methods in natural sciences
Informacje ogólne
Kod przedmiotu: | 20-SD-S3-ADV-METH |
Kod Erasmus / ISCED: | (brak danych) / (brak danych) |
Nazwa przedmiotu: | Advanced statistical methods in natural sciences |
Jednostka: | Szkoła Doktorska |
Grupy: | |
Punkty ECTS i inne: |
2.00
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Język prowadzenia: | angielski |
Zajęcia w cyklu "semestr letni 2020/2021" (zakończony)
Okres: | 2021-02-22 - 2021-09-30 |
Przejdź do planu
PN C
WT C
ŚR C
CZ C
PT C
|
Typ zajęć: |
Ćwiczenia, 30 godzin, 6 miejsc
|
|
Koordynatorzy: | Piotr Skubała | |
Prowadzący grup: | Izabella Franiel, Joanna Kohyt, Piotr Skubała | |
Lista studentów: | (nie masz dostępu) | |
Zaliczenie: |
Przedmiot -
Zaliczenie z modułu
Ćwiczenia - Zaliczenie z modułu |
Zajęcia w cyklu "semestr letni 2021/2022" (zakończony)
Okres: | 2022-02-21 - 2022-09-30 |
Przejdź do planu
PN C
WT ŚR CZ PT |
Typ zajęć: |
Ćwiczenia, 15 godzin
|
|
Koordynatorzy: | Piotr Skubała | |
Prowadzący grup: | Joanna Kohyt, Piotr Skubała | |
Lista studentów: | (nie masz dostępu) | |
Zaliczenie: | Zaliczenie z modułu | |
Sposób ustalania oceny końcowej: | (tylko po angielsku) The final grade for the module is weighted on the average of following student activities: - Reports from the realised classes (0.8) - Active participation in discussion (0.2) The final grade is set up on the basis of the percent scale: 91-100%: excellent (5.0) 81-90%: very good (4.5) 71-80%: good (4.0) 61-70%: satisfactory (3.5) 51-60%: with minimum academic criteria (3.0) 0-50%: fail (2.0) To be awarded a final grade, the student must have passed each report. The final date for returning completed reports is one week from the last class (April 25, 2022). Only one absence is possible. Any difficulties with the fulfillment of the presented requirements should be reported immediately to the lecturers. |
|
Pełny opis: |
(tylko po angielsku) The aim of the module is to broaden the students’ knowledge on advanced methods in statistics applied in natural sciences, but also applicable in other disciplines of knowledge, e.g. economics, medicine, politics. Students learn different methods of multivariate analysis, especially ordination and classification (cluster analysis). Indirect gradient analyses, e.g. Polar ordination (PO), Principal Coordinates Analysis (PCoA), Nonmetric Multidimensional Scaling (NMDS), Principal Components Analysis (PCA), Correspondence Analysis (CA), Detrended Correspondence Analysis (DCA) and direct gradient analyses, e.g. Canonical Correspondence Analysis (CCA), Detrended Canonical Correspondence Analysis (DCCA), Redundancy Analysis (RDA) are discussed and practiced on many examples. As regards cluster analysis, hierarchical agglomerative methods is mainly practiced. Practicals are taught using the following statistical programs: Statistica, Multivariate Variate Statistical Package (MVSP) and PAST (PAleontological STatistics). |
Zajęcia w cyklu "semestr letni 2022/2023" (zakończony)
Okres: | 2023-02-27 - 2023-09-30 |
Przejdź do planu
PN WT ŚR CZ K
PT |
Typ zajęć: |
Konwersatorium, 15 godzin
|
|
Koordynatorzy: | Joanna Kohyt, Piotr Skubała | |
Prowadzący grup: | Joanna Kohyt, Piotr Skubała | |
Lista studentów: | (nie masz dostępu) | |
Zaliczenie: | Zaliczenie z modułu | |
Sposób ustalania oceny końcowej: | (tylko po angielsku) The final grade for the module is weighted on the average of following student activities: - Reports from the realised classes (0.8) - Active participation in discussion (0.2) The final grade is set up on the basis of the percent scale: 91-100%: excellent (5.0) 81-90%: very good (4.5) 71-80%: good (4.0) 61-70%: satisfactory (3.5) 51-60%: with minimum academic criteria (3.0) 0-50%: fail (2.0) To be awarded a final grade, the student must have passed each report. The final date for returning completed reports is one week from the last class (May 19, 2023). Only one absence is possible. Any difficulties with the fulfillment of the presented requirements should be reported immediately to the lecturers. |
|
Pełny opis: |
(tylko po angielsku) The aim of the module is to broaden the students’ knowledge on advanced methods in statistics applied in natural sciences, but also applicable in other disciplines of knowledge, e.g. economics, medicine, politics. Students learn different methods of multivariate analysis, especially ordination and classification (cluster analysis). Indirect gradient analyses, e.g. Polar ordination (PO), Principal Coordinates Analysis (PCoA), Nonmetric Multidimensional Scaling (NMDS), Principal Components Analysis (PCA), Correspondence Analysis (CA), Detrended Correspondence Analysis (DCA) and direct gradient analyses, e.g. Canonical Correspondence Analysis (CCA), Detrended Canonical Correspondence Analysis (DCCA), Redundancy Analysis (RDA) are discussed and practiced on many examples. As regards cluster analysis, hierarchical agglomerative methods is mainly practiced. Practicals are taught using the following statistical programs: Statistica, Multivariate Variate Statistical Package (MVSP) and PAST (PAleontological STatistics). |
Właścicielem praw autorskich jest Uniwersytet Ślaski w Katowicach.