20354 - STATISTICS AND DATA ANALYSIS - PREPARATORY COURSE
Department of Decision Sciences
Course taught in English
IM (I/II sem. - P) - MM (I/II sem. - P) - ACME (I/II sem. - P) - EMIT (I/II sem. - P) - GIO (I/II sem. - P) - PPA (I/II sem. - P) - TS (I/II sem. - P)
Course Director:
RAFFAELLA PICCARRETA
RAFFAELLA PICCARRETA
Instructors:
Class 1: ALESSANDRO RECLA
Class 1: ALESSANDRO RECLA
Suggested background knowledge
The course has not specific prerequisites
Mission & Content Summary
MISSION
The course aims at providing students with the basic knowledge of statistics and data analysis acquired by students who attended the basic course of Statistics for (most of the) Bachelor programs at Bocconi University, necessary to successfully attend some of the courses taught at Masters of Sciences.
The course focuses on techniques for collecting and analyzing data, and on the main concepts of statistical thinking, both descriptive and inferential. In order to better understand inferential tools, basic concepts of probability theory are presented.
In addition, the course provides an introduction to the software R / R Studio which is used to support statistical analyses in the basic course on Statistics (Bachelor programs) at Bocconi.
CONTENT SUMMARY
The course is articulated as follows:
- Descriptive analysis of a data set.
- Data collection, organizing data in tables, graphical presentation methods.
- Measures of central and non central tendency, measures of variation.
- Shape of a distribution. Outliers and extreme values.
- Tabulating and graphing bivariate data.
- Measures of association and of dependence (association and independence-contingency coefficient mean dependency, linear relationships; covariance, correlation coefficient).
- Simple linear regression. Some basic concepts on multiple regression analysis are also illustrated.
- Probability theory and Random variables.
- Experiments, sample spaces and events. Definition of probability and rules of probability. Conditional probability and independent events.
- Random variables: discrete and continuous.
- Inferential statistics
- Sample and Sampling distribution. Descriptive versus Inferential Statistics.
- Point and confidence interval estimation
- Fundamentals of Hypothesis Testing. Hypothesis test for association (chi-square and correlation). Test for equality of the means (ANOVA). Tests on coefficients in regression models.
- Introduction to R/RStudio
Please note that specific modules might be designed for students enrolled in different Master of Science
Intended Learning Outcomes (ILO)
KNOWLEDGE AND UNDERSTANDING
At the end of the course student will be able to...
- Recognize different types of data.
- Understand the difference between the tools of descriptive and inferential statistics, and identify the most suitable approach for the problem at hand.
- Recognize simple statistical models.
- Understand the structure and the functioning of the statistical software R/RStudio
APPLYING KNOWLEDGE AND UNDERSTANDING
At the end of the course student will be able to...
- Properly summarize a dataset.
- Interpret the results obtained by applying simple statistical models, as regression models, to study the relationships between variables of interest.
- Create simple objects and use basic functions in R/RStudio to describe data
Teaching methods
- Lectures
- Practical Exercises
DETAILS
- Lezioni
- Esercitazioni pratiche
Assessment methods
| Continuous assessment | Partial exams | General exam | |
|---|---|---|---|
|
x |
ATTENDING AND NOT ATTENDING STUDENTS
There is no formal assessment for this course
Teaching materials
ATTENDING AND NOT ATTENDING STUDENTS
The slides available on Bboard are exhaustive and offer a short but complete description of the topics. For a more detailed discussion, students can refer to
- P. NEWBOLD, W.L. CARLSON, B. THORNE, Statistics for Business and Economics, Pearson/Prentice Hall, 9th global edition (2019).
Last change 19/09/2024 10:26