Syllabus

Title
0409 Statistics for Economics and Social Sciences with R
Instructors
Annalisa Cadonna, Ph.D.
Contact details
  • Type
    PI
  • Weekly hours
    2
  • Language of instruction
    Englisch
Registration
09/17/19 to 09/24/19
Registration via LPIS
Notes to the course
Subject(s) Bachelor Programs
Dates
Day Date Time Room
Wednesday 10/02/19 02:00 PM - 04:00 PM TC.3.02
Wednesday 10/09/19 02:00 PM - 04:00 PM TC.-1.61
Wednesday 10/16/19 02:00 PM - 04:00 PM LC.2.064 Raiffeisen Kurslabor
Wednesday 10/23/19 02:00 PM - 04:00 PM LC.2.064 Raiffeisen Kurslabor
Wednesday 10/30/19 02:00 PM - 04:00 PM TC.-1.61
Wednesday 11/06/19 02:30 PM - 04:30 PM TC.-1.61
Wednesday 11/13/19 02:00 PM - 04:00 PM LC.-1.038
Wednesday 11/20/19 02:00 PM - 04:00 PM LC.2.064 Raiffeisen Kurslabor
Wednesday 11/27/19 02:00 PM - 04:00 PM LC.-1.038
Wednesday 12/04/19 03:00 PM - 05:00 PM LC.-1.038
Wednesday 12/11/19 02:00 PM - 04:00 PM LC.-1.038
Wednesday 12/18/19 02:00 PM - 04:00 PM LC.-1.038

Contents

 

  • Introduction to R
  • Descriptive statistics and graphs:
  1. Measures of center(average, median, ...)
  2. Measures ofdispersion (variance, standard deviation, ...)
  3. Histogram, density plot
  4. Bar plot, spineplot, mosaic plot
  5. Box plot
  6. Scatterplot
  7. Association (correlation,Pearson & Spearman)

 

  • Statistical inference:
  1. Chi-squared tests
  2. Confidenceintervals
  3. Odds Ratios
  4. (Binary) logistic regression
  5. Dummyvariabels for categorial 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 lecture 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 is held weekly. The subject is presented by the lecturer in theoretical units and illustrated with data sets. The performance of the students is assessed through various exercises and a test (see Assesment).

      Assessment

      • No more than 2 absences.
      • Quizzes: 5 best out 6  (10 points total, 2 points per quiz)
      • Homework: 10 exercises (50 points, each exercise is worth 5 points).
      • Multiple choice final exam  (20 points).
      • Of the 80 total points, at least 70% must be achieved for a positive score.
      • Active participation in the computer exercises (8 bonus points for cooperation possible).

      Availability of lecturer(s)

      annalisa.cadonna@wu.ac.at

      Last edited: 2019-03-20



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