66106 - Experimental Methodology And Data Analysis

Learning outcomes

At the end of the course the student is able to statistically interpret the relationships between variables and measured parameters. It is able to independently assess the quality and effectiveness of the results obtained, thanks to the appropriate use of tools and operational techniques, supported by experience in the field of statistics. The student will then have acquired the theory of the main basic statistical methods and their applications to case studies, using the programming language R.

 

 

 

Course contents

Section 1. Programming language R Install R and RStudio, Set up a work session, Create basic objects and functions on R. Recognize and create basic structures, objects and functions, Create vectors, matrices, arrays, lists, dataframe farms, Convert objects to R, Use logical operators, Using conditional statements or control structures, Creating functions, Uploading files to R, Manipulating vectors, matrices, datasets. Handle missing values, Handle duplicate data, Manipulate dates, Use some basic statistics functions, Create simple graphs with basic functions, Create graphs with ggplot2.

 

Section 2. Statistical Applications. Basic statistics, probability, probability distributions, descriptive statistics, linear and non-linear models, inferential statistics, hypothesis tests, analysis of variance, multivariate statistics.

Readings/Bibliography

Class notes and computer codes

Teaching methods

In class lessons and programming exercises

Assessment methods

Written exam

Teaching tools

R language and coding

Office hours

See the website of Marco Bittelli