BASIC STATISTICS

Course objectives

Knowledge and understanding: students will learn the basics of statistical reasoning and the basic techniques of data analysis for the study and interpretation of phenomena in the socio-economic, business and financial fields. Applying knowledge and understanding: students will be able to apply the main methods of descriptive statistical analysis, the rules of probability calculation, as well as the procedures of statistical inference. The course aims at providing the skills necessary to grasp and describe the core information contained in the data, including the calculation of synthetic indicators, the construction of appropriate graphic representations, as well as the estimation of the parameters of a reference population, evaluating appropriately the margin of uncertainty. Making judgement: students will develop the ability to formalize problems of investigation using statistical-probabilistic language and acquire the necessary tools to solve them independently with a critical judgment based on data processing. Communication skills: students will consolidate the quantitative approach to economic thinking, using statistical evidence to support their decisions. Learning skills: students will be able to continue their training in the economic disciplines, approaching advanced courses of the mathematical-statistical area with a more solid scientific background.

Channel 1
SERGIO PEZZULLI Lecturers' profile

Program - Frequency - Exams

Course program
The syllabus includes the main concepts and methods of probability calculus, descriptive statistics and inferential statistics. It is divided into the following modules. 1. Probability and random variables. Definitions and properties of probability. Total probabilities and joint probabilities. Bayes' theorem. Main discrete and continuous probability distributions. 2. One-dimensional descriptive statistics. Measurements of: position, variability, concentration and shape. Graphical representations. 3. Two-dimensional descriptive statistics. Contingency tables. Independence and measures of association. Scatter diagram, covariance and correlation. Properties of the mean and variance operators. Marginal average law. Marginal variance decomposition and correlation ratio. 4. Simple linear regression. Intercept and slope of the regression line. Least Squares Method. Linear determination index. Graphical analysis of residuals. 5. Point estimation. Sample distribution, estimator and estimate. Estimation of averages and proportions and Central Limit Theorem. Consistency, bias, efficiency and Mean Square Error. Maximum likelihood estimates (short notes). 6. Hypothesis testing and confidence intervals. Chi-square test of independence. Significance, power and p-value. Tests and confidence intervals on an average and on a proportion. Confidence intervals for variance.
Prerequisites
No prerequisites
Books
G. Cicchitelli. Statistica. Principi e Metodi. Pearson ed. 2017
Teaching mode
Front and remote lessons
Frequency
Optional
Exam mode
Questions regarding fundamental points of the course program. The oral exam is at the discretion of the teacher. In the event of a sufficient written test, the student has the right to take the oral test even when given the opportunity to confirm the grade of the written test.
Lesson mode
Front and remote lessons
  • Lesson code1015450
  • Academic year2024/2025
  • CourseManagement and corporate law
  • CurriculumSingle curriculum
  • Year2nd year
  • Semester1st semester
  • SSDSECS-S/01
  • CFU9
  • Subject areaStatistico-matematico