85290 - INTRODUCTORY STATISTICS

Learning outcomes

The course covers the fundamental aspects of probability theory and the principles of statistical inference. Upon successful completion of this Course, students are able to perform a rigorous data analysis: i) manipulate and summarize data; ii) visualize and understand relationships inside data; iii) apply the appropriate tools of probability theory and inferential statistics to extract useful information, test hypotheses and make predictions.

Course contents

-- Probability Theory

1. Fundamentals of probability
2. Random variables and probability distributions
3. Functions of a random variable
4. Bivariate random variables
5. Convergence of random variables and limit theorems

-- Statistics

1. Fundamentals of statistics
2. Point estimation
3. Interval estimation
4. Hypothesis testing

-- Modern and reproducible data analysis with R and knitr

1. Introduction to R
2. Introduction to knitr

 

Readings/Bibliography

-- Probability and mathematical statistics

• Casella, G., Berger, R.L., Statistical Inference, 2nd ed. 2002, Thomson Learning (Cengage), ISBN: 9780534243128.
• J. Shao, Mathematical Statistics, 2nd ed., 2003, Springer. ISBN 978-1-4419-2978-5.
  
• K. Knight, Mathematical Statistics, 1999, CRC press.

-- R and knitr

• P. Dalgaard, Introductory Statistics with R, 2008, Springer, ISBN 978-0-387-79053-4.
• Y. Xie, Dynamic Documents with R and knitr, 2nd Ed., 2015, Chapman & Hall/CRC. See also https://yihui.name/knitr/.
  
• Y. Xie, R Markdown: The Definitive Guide: Authoring Books and Technical Documents with R Markdown .
  

Teaching methods

• Lectures.
• Classes.
• Computer science lab sessions.
  
  

Assessment methods

A two-hour written examination composed of

• Theoretical questions.
• Exercises.

Teaching tools

The following material will be provided:

• Slides of the lectures.
• Solved exercises.
• Mock exam.

Office hours

See the website of Simone Giannerini