93064 - Statistics

Learning outcomes

The aim of the course is to introduce the elementary concepts of descriptive statistics, probability, statistical inference, and linear regression. The course will provide students with the basic knowledge to develop applied quantitative analyses of complex social and economic phenomena such as those characterizing the modern tourism sector and the urban economy. Prerequisite is knowledge of basics of Mathematics.

Course contents

part 1) DESCRIPTIVE STATISTICS

Basics of statistics and sampling; empirical distributions and their graphical representation; relevant summaries of a distribution; tools to examine association between two variables; linear regression

part 2) PROBABILITY AND STATISTICAL INFERENCE

Random events and sample space. Axioms of probability. Conditional probability. Density and probability fuctions of a random variable; expectaed value and variance of a random variable. Bernoulli and Binomial distributions. Normal and Normal Standard distribution. Central limit theorem. Sample variability of an estimator; the sample mean estimator and its properties, point estimation and confidence intervals; statistical tests; inference on the parameters of the simple linear regression model.

Readings/Bibliography

Data Analysis for Social Science. A friendly and practical introduction. Alena Llaudet, Kosuke Imai. (Princeton University Press).

Further readings:

Quantitative Social Science Data with R. Brian J. Fogarty. (Sage).

Statistical Methods for the Social Sciences. Alan Agresti (5th edition, Pearson).

Teaching methods

Frontal lectures based on slides and notes at the board. Lectures will be on methods and practicals (tutorials sessions with excersises on blackboard).

Considering the nature of the activities and the teaching methods adopted, the attendance of this training activity requires all students to participate in the safety modules 1 and 2 on studying places [https://elearning-sicurezza.unibo.it/ ] in e-learning mode.

Assessment methods

The exam aims to evaluate 1) students' understanding of the theory behind descriptive and inferential statistical methods; 2) student's ability to communicate the outputs produced by descriptive and inferential tools.

Exam structure. The exam consists of two parts: written and oral. To pass the exam students' have to pass both written and oral parts. The final grade is expressed as numbers between 18 (sufficient) and 30 (excellent) and will result from averaging the two parts using equal weight.

The written part consists of a computer-based quiz with multiple-choice questions (in presence, using a desktop in a university lab). The outcome of this exam is immediately available after the quiz and the oral exam will only take place for students who pass the written part. There will be 15 questions at the quiz, students who provide at least 3 corrects answers out of 15 can access the oral exam; those who do not perform at least 3 correct answers must resit the exam in another examination date. The oral exam will focus on the applied project (AP). The AP is compulsory for every student and consists of a data analysis on a real case study performed using the software rstudio. At the oral exam, the teacher will ask questions primarily on the AP. Therefore, students must submit their AP in form of a slide presentation via moodle before the exam date. The slide presentation must include: 1) description of data and research questions; 2) descriptive analysis (at least one example of the tools studied during the course); 3) inferential analysis (at least one tool . Max 10 slides. Instructions on how to do the AP and prepare an effective slide presentation for the oral exam will be given during lab tutorials. Students will learn the software rstudio during the lab tutorials included in the 60 hours lectures.

The written exam can be split in two partial exams; the first partial exam date will be at the end of first mid-term (usually begin of April), the second one will coincide with the first exam date (usually begin of June). The decision to sit two partial written exams in place of one written exam is up to students. In the former case, the grade for the written exam is the average of the two (written) partial exams. Some answers to possible doubts follows. Only students who pass the first partial exam can sit the second partial exam. Students sitting the first partial exam can sit the second partial exam only once and at the first date set for the full exam (usually begin of June). If students fail or reject the grade obtained at the first partial exam they will have to resit the full exam. If students fail or reject the grade obtained at the second partial exam they will have to resit the full exam. 

General rules (enrollment, registration, rejections). Students are required to enroll via almaesami website at least 3 days before the exam date. After the exam students will be notified via email of the grades being published on almaesami and the date set for registration. Students can reject the grade obtained at the exam once. To this end, they must email a request to the instructor within the date set for registration. The instructor will confirm reception of the request within the same date. Rejection is intended with respect to the whole exam; there is no need to communicate rejection of a grade obtained at a partial (written) exam.

Teaching tools

Slides and blackboard. Some examples may be run using RStudio free software.

Office hours

See the website of Massimo Ventrucci