Class Applied Statistics in Social Sciences

The course aims to prepare students with necessary knowledge to:

1. Given a data set, apply descriptive statistics techniques, both numerical and graphical, in order to obtain adequate interpretations of the data;
2. Understand the concept of regression;
3. Become familiar with the concepts of probability, conditional probability, and with rules for calculating with probabilities;
4. Understand the concept of random variable; identify whether a random variable is discrete or continuous; regarding probability function, be able to calculate fundamental parameters such as expected value, variance and standard deviation;
5. Understand the normal distribution.

1. Descriptive Statistics
   • Basic concepts
   • Classification of variables
   • Frequency distributions and graphical represention
   • Measures of location: Mean, mode, median and quartiles
   • Measures of dispersion
2. Simple Linear Regression
   • Representation of bivariate data
   • The linear correlation coefficient
   • Linear regression model.
3. Introduction to Probability Theory
   • Fundamental concepts
   • Event algebra
   • Basic probability rules and axioms
   • Conditional probability
4. Random Variables
   • Basic concepts
   • Classification of random variables.
   • Probability function.
   • Expected value and variance.
5. Normal Distribution

Applicable

• Healey, J. (2012). Statistics: A Tool for Social Research, 9th edition, Cengage Learning.
• Reis, E.; Andrade, M.; Calapez, T.; Melo, P. (2015). Estatística aplicada, volume 1. Edições Sílabo. Lisboa. 2007 ISBN 978-972-618-469-0
• Reis, E.; Andrade, M.; Calapez, T.; Melo, P. (2012). Exercícios de Estatística aplicada, volume 1. Edições Sílabo. Lisboa. ISBN 978-972-618-688-5
• Reis, E. (2009). Estatística Descritiva. Edições Sílabo. Lisboa.