Fundamentals of Biostatistics - 95012

courses

ID:

95012

Dettaglio:

SSD: Statistics for Experimental and Technological Research Duration: 48 CFU: 6

Located in:

DALMINE

Url:

Course Details:

HEALTH TECNOLOGY AND ENGINEERING - 95-270/PERCORSO COMUNE Year: 2

Approval Status:

Draft

Year:

2025

Date/time interval

Secondo Semestre (23/02/2026 - 06/06/2026)

Course Objectives

At the end of the course, the student knows the statistical vocabulary and has acquired the basic theory of descriptive statistics, probability and inferential statistics, with particular reference to biostatistical applications. Moreover, the student is able to use the Matlab software to solve inferential problems.

Course Prerequisites

Knowledge of the topics taught in the first year courses "Calculus" and "Informatics".

Teaching Methods

Students attend class lessons and computer labs.

Assessment Methods

The exam consists in a computer lab test and in an optional oral test. The oral test can be attended only if the mark of the lab test is equal or higher than 18. The exam vote is equal to the lab test mark. Marks lower than 18 implies fail.

Students attending the class lessons can perform the exam through 2 intermediate computer lab tests. The second test can be attended only if the mark of the first test is equal or higher than 18. If the marks of both the tests are equal or higher than 18, the final mark is given by the average of the marks. Students with a final mark equal or higher than 18 can attend the optional oral test.

The computer lab test consists in exercises solved using the Matlab software and in a multiple choice test related to the theoretical part of the course.

Elements of univariate descriptive statistics: frequency tables, measures of position (mean, mode, median and quantiles), variability indexes (variance, standard deviation and coefficient of variation). Elements of bivariate descriptive statistics: covariance and correlation coefficient. Elements of probability: definition of probability, probability theorems, conditional probability, independency, law of total probability and Bayes' theorem. Random variables: definition, probability distribution, density function, cumulative distribution, expected value and variance. Examples of random variable: Bernoulli, Binomial, Poisson, Uniform, Normal, T-Student, Chi-square. Elements of inferential statistics: random sample and sample distributions. Sample mean and sample variance. Point estimate and estimators. Confidence intervals for the mean, the variance and the proportion. Hypothesis testing, simple regression and analysis of variance. Use of the Matlab software with particular reference to the statistical toolbox and high level programming.