Learning Objectives

At the end of this course, the students should be able to: From the nature of the research problem and the data (sampling method, measurement scale, i.e.), using various descriptive methods, set up and validate a statistical description of the problem. Translate the research hypotheses into statistical hypotheses on the (population) parameters, estimate the parameters, and assess the sampling error of estimates and its importance for the problem at hand. Discuss the structure, the function and the features of each descriptive statistics seen during the course Identify the most suitable statistical technique (among those seen during the lectures) to study a reallife phenomenon Compute and assess and the main descriptive statistics

Derive conclusions on the computed statistics

Explain and discuss the concepts of probability, random variable and distribution

Discuss the structure of the basic distributions seen during the lectures

Relate the concepts of pvalue, distribution and critical value, and discuss how they can be used to create tests for hypothesis

Draw and perform tests for verifying hypotheses relative to specific parameters (similar to those seen during the lecture)

Understand the concept of correlation

Understand the concepts of regression and its graphical representation

Explain the difference between correlation and regression

Compute correlations and run simple regressions (similar to those seen in classes) using the tools employed in the course.

Interpret the results of a regression (coefficients, standard errors, pvalues, Rsquared and so on…) and understand how it differs from a correlation

Choose between the two techniques according to the setting and the asked questions

Assess the validity of inferences regarding: a) the relation between sample and population to which the inference applies (sample selection), b) nonresponse and missing values in variables, and c) sample size.

Prerequisite

Students not enrolled in BSc in Business Administration & Service Management must document a level in English equal to TOEFL 575, and A level in mathematics equal to Danish level B

Examination

Methods III – Statistics and quantitative methods


Marking Scale

7step scale

Censorship

Internal examiners

Exam Period

October

• Duration of exam: 4 hours
• The written exam takes place on CBS computers
• Graphs can be written by hand
• Aids: Open book, but please note:
• Students have access to their personal files (Sdrive on CBS network)
• Students do NOT have access to Internet, Site Scape/ LEARN, and other services from CBS (except their personal Sdrive on CBS network)
• Students are not allowed to bring personal electronic devices to the exam, except a nonprogrammable calculator.
• Retake examinations and makeup examinations are subject to the same regulations as the ones noted above 

Examination


Prerequisites for Attending the Exam


Course Content

The primary objective of the course is to present central statistical tools required to carry out business research, and to make the students capable of discussing these, with reference to empirical contexts.
The course aims to address statistical methods and problems, and to expose students to a variety of established methods for collecting, analyzing and representing data.
In particular, the purpose of the module is twofold. On the one hand, students will learn how phenomena are represented and measured in statistics, how they are analyzed and what kind of conclusions statistical research can lead to. Students will also learn how to perform those analyses. This in turn means that each student will learn how to use the software tools needed to run those studies (e.g. Statistical Software) and will be provided with the necessary framing to be able to form her or his own opinions on the results in terms of reliability, soundness and interpretation. On the other hand, this first module will also implicitly propose to the students the perspective that one needs to apply when dealing with economic phenomena. The aim is making each student able to use in practice what she or he has learnt in theory during the lectures.
Main modules composing the course:
• Descriptive Statistics • Probability Theory • Random Variables • Normal Distribution • Hypothesis Testing • Correlation Analysis • Regression Analysis 
Teaching Methods

Lecture, exercises, and group discussions.

Further Information

The course is first offered i the fall 2011

Literature

The book “Statistical Methods for Social Science”, by Allan Agresti and Barbara Finlay, is available at SL Books. 