Statistics is a compulsory course aimed at introducing the basic statistical techniques for analysing data. Topics include exploratory data analysis, basic probability theory and statistical inference. Students will :
- learn to analyze and visualize data in R and create reproducible data analysis reports;
- acquire a theoretical understanding of statistical techniques and an appropriate critical sense in choosing the most suitable analysis for each data set;
- develop the ability to analyse real data sets and interpret the results.
There are no prerequisites, but students should have attended or are currently attending lectures on both Introduction to Computer Science and Programming and Mathematics.
- learn to analyze and visualize data in R and create reproducible data analysis reports;
- acquire a theoretical understanding of statistical techniques and an appropriate critical sense in choosing the most suitable analysis for each data set;
- develop the ability to analyse real data sets and interpret the results.
There are no prerequisites, but students should have attended or are currently attending lectures on both Introduction to Computer Science and Programming and Mathematics.
teacher profile teaching materials
All topics will also be illustrated using statistical software R through RStudio. The exercises and part of the lectures will be carried out in the computer room or remotely using the statistical software.
For each statistical methodology, a specific application will be illustrated on data sets on which students can practice. Therefore, the student will be taught not only to apply statistical techniques but also to choose the most appropriate technique and to comment on the output for decision-making purposes.
Exploratory Data Analysis
- Statistical characters and measurement scales. Simple distributions. Graphical display of data. Empirical distribution function.
-Position, variability and shape of statistical distributions.
- Double, marginal and conditional statistical distributions. Moments of double distributions, correlation.
Examples of exploratory data analysis using statistical software.
Elements of Probability Calculus
- Conditional probability. Independence. Bayes' rule. Discrete random variables. Probability function, density function, distribution function. Moments of random variables.
- Discrete probability distributions: binomial, Poisson, uniform.
- Continuous probability distributions: uniform, normal, Student's t, χ^2, exponential.
- Linear combinations of random variables, convergence, law of large numbers and central limit theorem.
- Use of statistical software to represent probability distributions and their properties.
Statistical Inference
- Population and sample: finite and infinite populations; random sample from finite and infinite populations; probability distribution of random sample.
- Sample statistics and their distributions.
- Parameter estimation: properties of estimators.
- Confidence intervals for a mean and a proportion.
- Hypothesis testing: elements of statistical tess, tests for means, proportions, and tests for difference in means and proportions.
- Hypothesis testing of independence and conformity.
- Simple linear regression estimation and hypothesis testing on the parameters of the regression line.
- Application of statistical methodologies through analysis of simple datasets using statistical software.
Other course material is available on Teams, Moodle and OneDrive.
Solutions to some exercises and exam assignments are also available at the website:
https://corsodistatistica.wixsite.com/website
Programme
The Statistics course introduces students to the techniques of collecting, organizing, and analysing statistical data. The course also introduces students to the basic concepts of probability calculus and statistical inference for the analysis of statistical data derived from sample surveys.All topics will also be illustrated using statistical software R through RStudio. The exercises and part of the lectures will be carried out in the computer room or remotely using the statistical software.
For each statistical methodology, a specific application will be illustrated on data sets on which students can practice. Therefore, the student will be taught not only to apply statistical techniques but also to choose the most appropriate technique and to comment on the output for decision-making purposes.
Exploratory Data Analysis
- Statistical characters and measurement scales. Simple distributions. Graphical display of data. Empirical distribution function.
-Position, variability and shape of statistical distributions.
- Double, marginal and conditional statistical distributions. Moments of double distributions, correlation.
Examples of exploratory data analysis using statistical software.
Elements of Probability Calculus
- Conditional probability. Independence. Bayes' rule. Discrete random variables. Probability function, density function, distribution function. Moments of random variables.
- Discrete probability distributions: binomial, Poisson, uniform.
- Continuous probability distributions: uniform, normal, Student's t, χ^2, exponential.
- Linear combinations of random variables, convergence, law of large numbers and central limit theorem.
- Use of statistical software to represent probability distributions and their properties.
Statistical Inference
- Population and sample: finite and infinite populations; random sample from finite and infinite populations; probability distribution of random sample.
- Sample statistics and their distributions.
- Parameter estimation: properties of estimators.
- Confidence intervals for a mean and a proportion.
- Hypothesis testing: elements of statistical tess, tests for means, proportions, and tests for difference in means and proportions.
- Hypothesis testing of independence and conformity.
- Simple linear regression estimation and hypothesis testing on the parameters of the regression line.
- Application of statistical methodologies through analysis of simple datasets using statistical software.
Core Documentation
Newbold P. , Carlson W. & Thorne B. (2021) Statistics for Business and Economics, Pearson ed.Other course material is available on Teams, Moodle and OneDrive.
Solutions to some exercises and exam assignments are also available at the website:
https://corsodistatistica.wixsite.com/website
Type of delivery of the course
6 hours per week of classes by the lecturer and 2 hours per week of tutorials plus 2 hours per week of exercise tutoring plus 2 hours of tutoring per week and 2 hours of student hours per week.Attendance
Study of the lecture notes, the practical exercises and the books.Type of evaluation
Examination procedure - The written test lasts 2 hours and consists of: exercises, multiple choice questions, theoretical questions and comments on output from the analysis of a simple dataset. - It is not allowed to introduce any notes and/or books in the exam room. Students are allowed to bring only the tables of probability distributions available on the course website. - The written test is passed if the student obtains a pass in both the practical and in the theoretical part. - A candidate who has passed the written test may request that the grade obtained in the written exam be recorded, unless an oral exam is requested by the lecturer. - A candidate who has passed the written test may request to perform the oral exam. - Students who are awarded a "seriously inadequate" grade in the written test are not allowed to take the exam in the next exam session.