Warning! The directory is not yet complete and will be amended until the beginning of the term.
290170 VU Introduction to Statistical Analysis for Teacher Candidates, Group C (2019S)
Continuous assessment of course work
Labels
Registration/Deregistration
Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).
- Registration is open from Th 07.02.2019 12:00 to Tu 19.02.2019 23:00
- Deregistration possible until Fr 15.03.2019 23:00
Details
max. 50 participants
Language: German
Lecturers
Classes (iCal) - next class is marked with N
Alle Termine sind s.t. zu verstehen!
Anwesenheit in der 1. LV-Einheit zwingend erforderlich! Bei Abwesenheit in der 1. LV-Einheit verfällt der Fixplatz und Studierende der Warteliste rücken auf einen Fixplatz nach.
Monday
04.03.
16:45 - 18:15
Hörsaal II NIG Erdgeschoß
Monday
11.03.
18:00 - 21:00
Class Room 4 ZID UniCampus Hof 7 Eingang 7.1 2H-O1-33
Monday
18.03.
16:45 - 18:15
Hörsaal II NIG Erdgeschoß
Monday
25.03.
18:00 - 21:00
Class Room 4 ZID UniCampus Hof 7 Eingang 7.1 2H-O1-33
Monday
01.04.
16:45 - 18:15
Hörsaal II NIG Erdgeschoß
Monday
08.04.
18:00 - 21:00
Class Room 4 ZID UniCampus Hof 7 Eingang 7.1 2H-O1-33
Monday
29.04.
16:45 - 18:15
Hörsaal II NIG Erdgeschoß
Monday
06.05.
18:00 - 21:00
Class Room 4 ZID UniCampus Hof 7 Eingang 7.1 2H-O1-33
Monday
13.05.
16:45 - 18:15
Hörsaal II NIG Erdgeschoß
Monday
20.05.
18:00 - 21:00
Class Room 4 ZID UniCampus Hof 7 Eingang 7.1 2H-O1-33
Monday
27.05.
16:45 - 18:15
Hörsaal II NIG Erdgeschoß
Monday
03.06.
18:00 - 21:00
Class Room 4 ZID UniCampus Hof 7 Eingang 7.1 2H-O1-33
Monday
17.06.
16:45 - 18:15
Hörsaal II NIG Erdgeschoß
Monday
24.06.
18:00 - 21:00
Class Room 4 ZID UniCampus Hof 7 Eingang 7.1 2H-O1-33
Monday
01.07.
09:00 - 15:00
Class Room 4 ZID UniCampus Hof 7 Eingang 7.1 2H-O1-33
Monday
07.10.
16:45 - 18:00
PC-Raum 1 Schenkenstraße 8-10, 1.UG
Information
Aims, contents and method of the course
Assessment and permitted materials
Active continuous cooperation (6%), individual exercises (42%), term exam (52%)
Minimum requirements and assessment criteria
Grading is based on the achievement of points, the maximum being 100 points.
To pass the course all of the following criteria must be met:
-) Attendence of the course and practical sessions (only 1 practical session can be missed),
-) Handing in of at least 5 individual exercises
-) A minimum of at least 21 points on the individual exercises
-) A minimum of 26 points on the final exam
-) A minimum of 51 points in total
Benotungsschlüssel
> 87,5 points: A
> 75,0 to 87,5 points: B
> 62,5 to 75,0 points: C
> 50,0 to 62,5: points: D
<= 50,0 points: E
To pass the course all of the following criteria must be met:
-) Attendence of the course and practical sessions (only 1 practical session can be missed),
-) Handing in of at least 5 individual exercises
-) A minimum of at least 21 points on the individual exercises
-) A minimum of 26 points on the final exam
-) A minimum of 51 points in total
Benotungsschlüssel
> 87,5 points: A
> 75,0 to 87,5 points: B
> 62,5 to 75,0 points: C
> 50,0 to 62,5: points: D
<= 50,0 points: E
Examination topics
Fragen zum in der Vorlesung behandelten Stoff
Interpretation der Ergebnisse der durchgeführten Analysen
Interpretation der Ergebnisse der durchgeführten Analysen
Reading list
Duller, C. (2013): Einführung in die Statistik mit Excel und SPSS. Ein anwendungsorientiertes Lehr- und Arbeitsbuch. Springer.
Matthäus, W.-G. und Schulze J. (2011): Statistik mit Excel. Beschreibende Statistik für Jedermann. Vieweg+Teubner.
Matthäus, W.-G. und Schulze J. (2011): Statistik mit Excel. Beschreibende Statistik für Jedermann. Vieweg+Teubner.
Association in the course directory
(L1-d3) (BA UF GW 10)
Last modified: Mo 07.09.2020 15:42
Part 1. Introduction: data scales, survey data in Excel, statistical analysis with Excel
Part 2. Univariate Statistics 1: Descriptive statistics, Frequency distribution, Histogram
Part 3. Univariate Statistics 2: Frequency table, Percentages, Graphs
Part 4. Bivariate Statistics 1: Scatterplot, Correlation, Regression
Part 5. Bivariate Statistics 2: Boxplot, Tests, T-Test
Part 6. Bivariate Statistics 3: Crosstabulation, Chi-Square
Aims:
Method: Lecture in class combined with practical exercises in the computer room.