79191 - Analysis of Data

Course Unit Page

Academic Year 2019/2020

Learning outcomes

The course gives students further experience of analysing data in a wide variety of contexts, using the R computer package. By the end of the course the students will be able to: - implement simple statistical techniques, such as the normal linear model, in R. - interpret the results from statistical procedures and draw appropriate conclusions. - develop and implement an appropriate modeling approach to answer questions of interest about a given data set. - critically assess the quality of a statistical analysis conducted by someone else. - write up the results of a statistical analysis concisely in the form of a report

Course contents

1) Introduction to R: downloading and installing R, R language essentials, data entry

2) Univariate and bivariate descriptive statistics with R for quantitative and qualitative variables.

3) Probability distributions in R: random sampling, discrete and continuous distributions, densities, cumulative distribution functions and quantiles.

4) Point estimation, confidence intervals and hypotheses testing in R.

5) The linear regression model in R.

6) RMarkdown, ggplot

Readings/Bibliography

P. Dalgaard (2008) Introductory statistics with R - 2 ed. New York: Springer.

Teaching methods

All the lectures will be held in the lab where several applications will be developed by using R.

Assessment methods

The final exam aims at evaluating the achievement of the following educational targets:

- to know the main features of R

- to be able to perform statistical applications in R

The exam consists of a practical test in the computer lab.

Teaching tools

Computer and R scripts.

Office hours

See the website of Marco Novelli