93264 - 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

-- Main textbook

• Ross, S. Introduction to Probability and Statistics for Engineers and Scientists. 6th Ed. 2020, Academic press, ISBN: 9780128243466.

-- 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.
• Lavine, M., Introduction to Statistical Thought. 2013. http://people.math.umass.edu/~ lavine/Book/book.html
  

-- 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.
  
  

All students must attend Module 1, 2 on Health and Safety online

Assessment methods

A two-hour written examination composed of

• Exercises.
• Theoretical questions.

In case of online exams, the theoretical part will be assessed by means of an oral examination.

Teaching tools

The following material will be provided:

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

Office hours

See the website of Simone Giannerini