Syllabus

Title
4530 Statistics for Economics and Social Sciences with R
Instructors
PD Dr. Thomas Rusch, Bakk.
Contact details
thomas.rusch@wu.ac.at
Type
PI
Weekly hours
2
Language of instruction
Englisch
Registration
02/15/23 to 02/21/23
Registration via LPIS
Notes to the course
Subject(s) Bachelor Programs
Statistics for Economics and Social Sciences
Dates
Day Date Time Room
Thursday 03/02/23 05:00 PM - 07:00 PM LC.-1.038
Thursday 03/09/23 05:00 PM - 07:00 PM LC.-1.038
Thursday 03/16/23 05:00 PM - 07:00 PM LC.-1.038
Thursday 03/23/23 05:00 PM - 07:00 PM LC.2.064 PC Raum
Thursday 03/30/23 05:00 PM - 07:00 PM LC.2.064 PC Raum
Thursday 04/13/23 05:00 PM - 07:00 PM LC.2.064 PC Raum
Thursday 04/20/23 05:00 PM - 07:00 PM LC.2.064 PC Raum
Thursday 04/27/23 05:00 PM - 07:00 PM LC.2.064 PC Raum
Thursday 05/04/23 05:00 PM - 07:00 PM LC.-1.038
Thursday 05/11/23 05:00 PM - 07:00 PM LC.-1.038
Thursday 05/25/23 05:00 PM - 07:00 PM LC.-1.038
Thursday 06/01/23 05:00 PM - 07:00 PM LC.-1.038
Thursday 06/15/23 05:00 PM - 07:00 PM LC.-1.038
Thursday 06/22/23 05:00 PM - 07:00 PM LC.-1.038

On this page:

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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)

 

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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.
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Attendance requirements

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

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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.

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      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.)
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      Readings

      Please log in with your WU account to use all functionalities of read!t. For off-campus access to our licensed electronic resources, remember to activate your VPN connection connection. In case you encounter any technical problems or have questions regarding read!t, please feel free to contact the library at readinglists@wu.ac.at.

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      Availability of lecturer(s)

      thomas.rusch@wu.ac.at

      Last edited: 2022-11-03



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