# 96993 - Mathematics and Statistics (9 CFU)

• from Sep 23, 2024 to Dec 20, 2024

## Learning outcomes

At the end of the course, the student possesses the basic knowledge of Mathematics, necessary to tackle the other disciplines of the Degree Course of Natural Sciences. In particular, the student is able to: - understand and use the graph of a function for mathematical models; - understand the use of the tools of differential and integral calculus and linear algebra in applications; - use a simple mathematical software to solve equations, draw graphs and study them, perform calculations with derivatives, integrals and matrices. He also possesses knowledge of basic statistical methods. In particular, the student is able to: - become familiar with the scientific method; - adopt the most suitable basic statistical analysis methods for both field and laboratory experiments.

## Course contents

MODULE 1

Set theory and combinatorics

• Set and functions.
• Fondamental counting principles.

Linear algebra

• The geometric vectors: algebraic structure, scalar and vector product.
• Matrices: vector structure and product of matrices; echelon form; definition of rank and calculation techniques; linear transformation associated with a matrix.
• Square matrices: invertible matrices; definition of determinant and calculation techniques.
• Linear systems: matrix notation; Rouché-Capelli theorem and solution techniques for linear systems; parametric and Cartesian representation of subspaces of R^n; structure theorem for linear systems.

Analysis

• Real functions of a real variable: definition, injectivity, surjectivity, monotony; graph of a function; elementary functions (powers, roots, exponentials, logarithms, functions, circular); limits and continuity.
• Differential calculus for real functions of real variable: derivative, growth and decrease, local extremes, study of the graph of a
function, Taylor's formula.
• Integral calculus for real functions of real variable: primitive, fundamental theorem of integral calculus, integration by substitution and by parts.

MODULE 2

Introduction to the course; introduction to descriptive statistics; starting test; means, median, and mode; quantiles.

Dipsersion indices; data distributions; the normal distribution; the standard normal curve and the test Z.

Normality tests (quantiles and Q-Q plot; Shapiro and Wilk’s test).

Introduction to inferential statistics; one-sample Student’s t-test.

Two-sample t-test (paired/unpaired samples); nonparametric tests: Wilcoxon’s and Mann-Whitney tests.

Qualitative variables: chi squared test.

Linear regression and correlation: Pearson’s method, r and R squared; significance of a correlation.

Analysis of variance; one-way and two-way ANOVA; Tukey’s post-hoc test.

Lab class: introduction to R; create an input file; call and explore data; normality and t tests with R; chi squared, regression and ANOVA with R.

It will be communicated shortly.

## Assessment methods

The exam consists of a written test and an oral exam. More information are available on virtuale.

## Teaching tools

All the material is available on Virtuale. [https://virtuale.unibo.it/]

In addition to the usual lessons, a tutor will be available every week to answer questions and to helps solving the exercises.

## Office hours

See the website of Riccardo Biagioli