Syllabus

Title
0330 Statistics for Economics and Social Sciences with R
Instructors
Assoz.Prof PD Dr. Bettina Grün
Contact details
Type
PI
Weekly hours
2
Language of instruction
Englisch
Registration
09/14/21 to 09/21/21
Registration via LPIS
Notes to the course
Subject(s) Bachelor Programs
Dates
Day Date Time Room
Wednesday 10/06/21 02:00 PM - 04:00 PM LC.-1.038
Wednesday 10/13/21 02:00 PM - 04:00 PM LC.-1.038
Wednesday 10/20/21 02:00 PM - 04:00 PM LC.-1.038
Wednesday 11/03/21 12:00 PM - 02:00 PM LC.-1.038
Wednesday 11/10/21 02:00 PM - 04:00 PM LC.-1.038
Wednesday 11/17/21 02:00 PM - 04:00 PM LC.-1.038
Wednesday 11/24/21 02:00 PM - 04:00 PM Online-Einheit
Wednesday 12/01/21 02:00 PM - 04:00 PM Online-Einheit
Wednesday 12/15/21 02:00 PM - 04:00 PM Online-Einheit
Wednesday 12/22/21 02:00 PM - 04:00 PM Online-Einheit
Wednesday 01/12/22 02:00 PM - 04:00 PM LC.-1.038
Wednesday 01/19/22 02:00 PM - 04:00 PM LC.-1.038
Contents

 

  • Introduction to R
  • Descriptive statistics and visualizations:
  1. Measures of location (average, median, ...)
  2. Measures of dispersion (variance, standard deviation, ...)
  3. Histogram, density plot
  4. Bar plot, spine plot, mosaic plot
  5. Box plot
  6. Scatter plot
  7. Association (correlation, Pearson & Spearman)

 

  • Statistical inference:
  1. Chi-squared tests
  2. Confidence intervals
  3. Odds ratios
  4. (Binary) logistic regression
  5. Dummy variables for categorical predictors
  6. (Univariate) simple and multiple linear regression
  7. T-test and simple analysis of variance
  8. Mann-Whitney U-Test and Kruskal-Wallis H test
  9. Analysis of variance (ANOVA)

 

Learning outcomes
In this course, you will learn basic statistical techniques widely used in econometrics and economics. All presented methods are illustrated with practical examples to provide you with the tools to carry out statistical analysis using the software R.
Attendance requirements

For this course participation is obligatory. Students are allowed to miss a maximum of 20% (no matter if excused or not excused).

Teaching/learning method(s)

    The course takes place weekly. The theoretical basics are repeated and worked out in advance by the students in self-study using the literature provided. In the presence units questions regarding this content are discussed and the practical application using the statistical software package R is illustrated and practiced based on exercises. The homework exercises are submitted online. The final exam takes place in the last unit.

      Assessment
      • Active participation in the practical exercises (a maximum 8 bonus points possible).
      • Homework: 10 exercises (50 points, each exercise is worth 5 points).
      • Quizzes with a total of 12 points. A maximum of 10 points will be scored.
      • Final exam (20 points).
      • Of the 80 total points, at least 70% must be achieved for a positive grade.
      • Grading key: 4 – (56 – 61 p.), 3 – (62 – 67 p.), 2 – (68 – 73 p.), 1 – (74+ p.)
      Availability of lecturer(s)

      bettina.gruen@wu.ac.at

      Last edited: 2021-09-27



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