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

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

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 will focus on:

- students' understanding of the basic statistics to produce empirical summaries and investigate relationships between two or more variables;

- students' understanding of the output of a statistical analysis.

EXAM STRUCTURE. The exam consists of two partial exams, each one consisting of a computer-based quiz administrated in a university lab, with both multiple choice questions and questions where it is required a numerical input. More details will be given during the course. To pass the whole exam students have to pass each single partial exam.

Students taking the exam are required to register first, following the procedure in almaesami webpage. During the exam, students are allowed to use their hand-written personal notes, a copy of the table of the standard Normal cumulative distribution and the table of the T-student (both can be found in the textbook). It is forbidden to use any extra material that is not hand-written personal notes like books, tablet, smartphone during the exam.

REGISTRATION. Grades are normally published a couple of days after the exam date on almaesami. The teacher will register the grades on the office hours date right after the exam; in the same office hours students can ask an appointment to discuss with the teacher the mistakes they have done in the exam.

REJECTING THE GRADE. Students can reject the grade obtained at the exam once. To this end, he/she 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, whose grade is the average of the grades obtained in the two mid-terms. If the grade is rejected, the student must retake the full exam (consisting of both parts). The only grade that can be rejected without any communication from the student is the one of the first mid-term.

Students sitting the first mid-term can take the second mid-term on the first examination date set for the full exam, right at the end of the integrated course. A student can sit the second mid-term only once; if he/she fails or rejects the grade obtained, he/she will have to resit the full exam and will lose the grade obtained in the first mid-term.

Teaching tools

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

Office hours

See the website of Massimo Ventrucci