Syllabus

Title
5906 Statistics for Economics and Social Sciences with R
Instructors
Annalisa Cadonna, Ph.D.
Contact details
annalisa.cadonna@wu.ac.at
Type
PI
Weekly hours
2
Language of instruction
Englisch
Registration
02/21/18 to 03/01/18
Registration via LPIS
Notes to the course
Subject(s) Bachelor Programs
Statistics for Economics and Social Sciences
Dates
Day Date Time Room
Wednesday 03/14/18 03:30 PM - 05:45 PM D2.-1.019 Workstation-Raum
Wednesday 03/21/18 02:30 PM - 04:45 PM TC.-1.61
Wednesday 04/11/18 02:30 PM - 04:45 PM TC.-1.61
Wednesday 04/18/18 01:00 PM - 03:15 PM TC.-1.61
Wednesday 04/25/18 01:00 PM - 03:15 PM TC.-1.61
Wednesday 05/09/18 01:00 PM - 03:15 PM LC.-1.038
Wednesday 05/16/18 01:00 PM - 03:15 PM TC.-1.61
Wednesday 05/23/18 03:30 PM - 05:45 PM D2.-1.019 Workstation-Raum
Wednesday 05/30/18 01:00 PM - 03:15 PM LC.-1.038
Wednesday 06/06/18 03:30 PM - 05:45 PM D2.-1.019 Workstation-Raum

On this page:

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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)
  10. PrincipalComponent Analysis (PCA)


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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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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).
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      Assessment
      • No more than 2 absences.
      • Active participation in the computer exercises (8 bonus points for cooperation possible).
      • 3 lab sessions in groups with 3, 3 and 4 exercises. (50 points, each exercise is worth 5 points).
      • Multiple choice final exam  (30 points).
      • Of the 80 total points, at least 70% must be achieved for a positive score.
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      Availability of lecturer(s)

      annalisa.cadonna@wu.ac.at

      Last edited: 2017-10-24



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