Statistics for Sustainable Innovation

The first part of the course focuses on probability theory:

• Sample space, events, probability, axioms.
• Probability rules, conditional probability, independence, Bayes' theorem, random variable.
• Discrete distributions, cumulative distribution function, expectation and variance.
• Continuous distributions, density function, mean, variance, correlations and conditional probability

The second part goes more explicitly into statistics. It covers:

• Estimation theory
• The concept of confidence intervals
• The concept of hypothesis testing, t-tests, p-values, Type I and Type II errors, statistical power and size
• Simple linear regression models

In the assignments, the students gain hands-on experience with the data and apply the concepts discussed in class. To do so, they use the STATA statistical package (R is also accepted but no support is offered) and work with data commonly used in innovation studies (Firm-level data, patent data, survey data)

The aim of the course is to provide an introduction to statistics, by focusing on the basics of probability theory and statistics.

You must meet the following requirements

• Registered for a degree programme other than
• HBO-TOP Applied Physics, Pre-Master
• Completed none of the course modules listed below
• Behavioral research methods 2: dwd (0HV50)

• Statistical compendium
• Lecture slides per week
• D.C.Montgomery, G.C.Runger, Applied Statistics and Probability for Engineers, 7th ed. (ISBN: 978-1-119-58559-6 (printed version) E-book: 978-1-119-40036-3), Wiley.