Statistics

647 Statistics

  • Study programme and level: University degree programme Public sector governance 1st Cycle
  • 1st year
  • 8 ECTS
  • Course type: Core
  • Lectures: 60
  • Tutorial: 30
  • Other forms of study: 30
  • Individual work: 120
  • Lecturer: Lan Umek, PhD

 

1. Objectives and competences

Student:

  • defines problem, asks empirical research questions, prepares, and processes statistical research,
  • uses computational technology and statistical tools for gathering, analysing and presenting statistical data and results of empirical research
  • uses descriptive statistical techniques for single variable analysis,
  • prepares sampling process, calculates the values of sample parameters and estimates the values of population parameters,
  • forms hypothesis, chooses and uses suitable hypothesis testing technique,
  • analyses and reports relationships between variables, predicts and controls values of variables in relationship,
  • relates the discovered statistical phenomena to the practical situation in public sector.

Student is qualified:

  • to examine numerical data to grasp issues, draw conclusions, and solve problems;
  • to use statistical techniques to analyse data and solve practical problems;
  • to perceive, understand and resolve challenges, solvable with statistical approach;
  • to identify, collect, and organize data for statistical research and value based decision-making;
  • to use IT equipment and statistical tools to proceed statistical research and create reports and presentation;
  • to grasp the meaning of statistical information, and apply it to situations at hand;
  • to convey the results of statistical research using presentation of statistical information

2. Content

Vital role of statistics in empirical research

  • Problem solving, asking research questions, inductive, deductive researching
  • Entities and their properties – variables
  • How and where to acquire data for empirical researching, how to present statistical data

Analysis of relationships between variables

  • Relationships between variables as a key to statistical prediction, how to detect and illustrate relationship between variables
  • How to predict and control the values of variables

Techniques for studying relationships between variables

  • An overview of the techniques for studying relationships between variables
  • Techniques for illustrating relationships between variables
  • The analysis of single variable
  • Sampling and estimation
  • Hypothesis testing
  • Techniques for detecting relationships between variables
  • Techniques for predicting and controlling the values of variables

3. Readings

  • BENČINA, Jože, DEVJAK, Srečko (2010). Statistika v upravi. Fakulteta za upravo, Ljubljana: e-studij.fu.uni-lj.si/course/, 130 str.
  • ROSENBERG, Kenneth .M. (2007). The Excel Statistics Companion. Thomson Higher Education, Belmont. Poglavja 1 - 7, 154 strani.
  • SCHMULLER, Joseph (2009). Statistical Analysis with Excel for Dummies. Wiley Publishing, Inc., Indianapolis. Poglavja 1 – 16, 335 str.
  • ARON Arthur, ARON Elaine N., COUPS Elliot (2008). Statistics for the Behavioral and Social Sciences, A Brief Course, 4th ed. Pearson Education, Inc., . New Jersey. Poglavja 1 – 10, 358 str.
  • SELJAK, Janko (2000). Statistika v javni upravi. Visoka upravna šola, Ljubljana. 318 str.

4. Intended learning outcomes

Students will:

  • understand the vital role of statistics in public sector data analysis;
  • be able: to detect problem situation, to define problem statement and ask research question; to formulate problem and define hypothesis in terms of statistics; to proceed basic hypothesis testing; understand some basic statistical techniques for processing statistical data; be able to identify and use appropriate statistical techniques for analysing relationships between variables;
  • be able: to predict and control statistical phenomena; to interpret the results of statistical analysis in terms what the solution means for the problem at hand; develop commitment to the practical application of statistics in data analyses in public sector.

5. Learning and teaching methods

  • lecture
  • practical work
  • e-learning

6. Assessment

 

  • exam part:written or oral exam or 2 mid-term exams (80 %)

written part of the exam can be done by 2-mid term exams (both exceeding 50 %)

  • active participation (e-learning): quizzes (20 %)

Student can get a positive grade if the combined result from the exam and quizzes (0.8*exam+0.2*quizzes) exceeds 50%.