This class exists in these courses :
• IÉSEG > MIB > MIB > 3,00 ECTS

Prerequisites

None.

Learning outcomes

At the end of the course, the student should be able to :
Have acquired the techniques on how to collect and analyze data and information in support of business decisions.
Produce and interpret graphical summaries of data;
Describe basic characteristics of the data distribution;
Produce and interpret numerical summary statistics;
Understand properties of the normal curve;
Graphically and numerically describe the relations between two quantitative variables;
Interpret a correlation coefficient, r, and the coefficient of determination;
Formulate and interpret null and alternative hypotheses;
Fit simple linear regression models;
Use simple and multiple linear regression models to predict the value of one variable based on
the value of (an) associated variable(s);
Fit and interpret interactions between independent variables.
Develop a greater awareness about ESRS topics such as conducting research in a rigorous, responsible, and ethical way, collecting and treating data with all necessary caution and interpreting results with all necessary reservations.

Course description

The course is designed to immerse students into the principles of descriptive and inferential statistical
analyses in order to make students acquainted with the techniques on how to collect and analyze data and information in order to provide solutions to business problems and challenges. Through readings, lectures, in-class exercises, a dedicated software (SPSS; to be used in and out of class) and a tailored online environment, this course addresses the collection, description, analysis and critical summary of data, including the concepts of frequency distribution, parameter estimation, hypothesis testing, and regression analyses.

Students are strongly recommended to regularly review and practice the course content in line with the course sessions.

Class type

Class structure

Teaching methods

• E-learning
• Interactive class
• Presentation
• Project work
• Research

Assessment

The instructor expects students to actively participate and behave responsibly in the course sessions. The student is assessed on the course-based (online) MCQs, a group project including analysis exercises with SPSS and being able to explain the meaning of the findings hereon, and a final exam covering statistics exercises and comprehensive theory questions.

Recommended reading

• Recommended supportive readings will be discussed in class -

Internet resources

• IESEG Online

• McGraw-Hill online learning environment

  Online learning environment for practice and (MCQ) assessment. URL and personal access code will be provided by IESEG