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

1. Introduction to data: Data basics; Sampling principles; Experiments and observational studies
2. Summarizing data: Examining numerical data; Considering categorical data
3. Probability: Defining probability; Conditional probability; Bayes theorem
4. Random variables: Discrete and continuous; Expectation; Linear combination; Central limit theorem
5. Distributions of random variables: Normal; Geometric; Binomial
6. Foundations for inference: Point estimates and sampling variability; Confidence intervals; Hypothesis testing
7. Inference for numerical data: One-sample means; Paired data; Difference of two means
8. Inference for one proportion
9. Introduction to linear regression: Fitting a line, residuals and correlation; Least squares regression; Diagnostics

Readings/Bibliography

David M Diez, Christopher D Barr, Mine C ̧etinkaya-Rundel (2015). OpenIntro Statistics (Fourth Edition), from Chapter 1 to Chapter 8.

This textbook is available under a Creative Commons license. Visit openintro.org for a free PDF

Teaching methods

Lectures and practical sessions in the lab

Assessment methods

Written exam

Teaching tools

Slides, lab material, comprehensive Moodle page

Office hours

See the website of Monica Chiogna