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Physics, physics lab, lab of mathematical and statistical methodologies

A.Y. 2021/2022

Learning objectives

The aim of the course is to provide students with the physical and statistical background needed for the quantitative understanding of biological phenomena. Furthermore, it will provide the knowledge of the physical principles behind many lab instruments as well as the statistical tools to correctly interpret experiments.

Expected learning outcomes

After following this course, the students will know the fundamental principles of classical physics and statistics. The students will know how to apply them to solve simple problems and how to quantitatively approach the biosciences.

**Lesson period:**
Second semester

**Assessment methods:** Esame

**Assessment result:** voto verbalizzato in trentesimi

Course syllabus and organization

### A - L

Responsible

Lesson period

Second semester

more information will be given, on the basis of the evolving situation of Public Health

**Prerequisites for admission**

Have attended the basic course in calculus of the first semester.

**Assessment methods and Criteria**

The student is required to pass a separate test for each of the 3 modules. The module "Physics" requires a final written test and an oral one; the written test can be substituted by 2 tests taken during the lesson time. The Laboratory module requires a preformatted relation at the end of the laboratory activities, and a written test with exercises on the sujects of the program. The evaluation is the arithmetic mean of the laboratory and written test evaluations. The Statistics module requires a written tests with exercises of probability and statistics and an oral test about the basics concepts treated in the lessons

**Modulo: Fisica**

**Course syllabus**

Introduction: Physical quantities, units, vectors.

Kinematics

Dynamics for the material point

Universal gravity law

Electrical field

Elastic and anelastic collisions

Fluids: Bernoulli

Thermodinamics: Principles zero, first second; trasformations and termical machines

Kinematics

Dynamics for the material point

Universal gravity law

Electrical field

Elastic and anelastic collisions

Fluids: Bernoulli

Thermodinamics: Principles zero, first second; trasformations and termical machines

**Teaching methods**

lessons and exercitations using the blackboard, in addition some lessons with multimedial presentations

**Teaching Resources**

R. A. Serway, J. W. Jewett "Principi di Fisica" EdiSES, additional notes, exercises and exams examples are available on Ariel.

**Modulo: Laboratorio di Fisica**

**Course syllabus**

The course is partly lessons and partly laboratory. Lessons cover a short pratical introduction to applied statistics and physics complements. the physics subjects treated are: electrical circuits (RC included); mechanical waves and electomagnetical waves spectrum, geometrical optics and elements of physical optics, elements of radioactivity.

The final result of the laboratory is the measurement of the Faraday constant, with 2 different methods, of which precision, accuracy and compatibility are evaluated.

The final result of the laboratory is the measurement of the Faraday constant, with 2 different methods, of which precision, accuracy and compatibility are evaluated.

**Teaching methods**

Lessons supported by projected material.

Attendance is strongly recommended for the lessons and is compulsory for the activities in laboratory

Attendance is strongly recommended for the lessons and is compulsory for the activities in laboratory

**Teaching Resources**

R. A. Serway, J. W. Jewett "Principi di Fisica" EdiSES

Analisi degli errori sperimentali di laboratorio; L. Miramonti, L. Perini, I. Veronese; EdiSES

Analisi degli errori sperimentali di laboratorio; L. Miramonti, L. Perini, I. Veronese; EdiSES

**Modulo: Laboratorio di metodi matematici e statistici**

**Course syllabus**

Descriptive statistics.

Population sampling. Types of data and variables. Subdivision of data into classes and creation of frequency tables. Histogram and histogram/bars.Centrality indexes (mean, mode, median, midrange), dispersion indexes (range, standard deviation, variance), percentiles, quartiles. Outliers. Boxplots.

Probability and random variables.

Sample space, events, probability of events. Probability of union and intersection of events. Complementary events. Independent events. Conditional probability. Bayes theorem. Factorial and binomial coefficients. Random variables. Expected value, variance and standard deviation of discrete random variables. Discrete random variables: binomial and Poisson. Continuous random variables: uniform and normal. Standardization and calculations with the normal distribution. Percentiles. Normal approximation of binomial distribution.

Inferential statistics.

Fundamental concepts: population, sample, parameters, statistics, estimators. Sample mean behavior: large number law and central limit theorem. Confidence interval: general concepts. Confidence interval for a proportion. Confidence interval for the mean, both with known and unknown standard deviation. Student's distribution. Hypothesis test. General concepts: null and alternative hypothesis, first and second kind errors, significativity level, power function, p-value, statistics of the test, critical region. Test on the proportion. Test on the mean (both with known and unknown variance). Inference on two samples. Inference on two proportions. Inference on two means, both for independent and paired samples.

Linear regression and non-parametric precedures.

Covariance and correlation. Simple linear regression. Test on regression coefficients and model validation. Chi-square distribution. Test of independence and good adaptation.

Population sampling. Types of data and variables. Subdivision of data into classes and creation of frequency tables. Histogram and histogram/bars.Centrality indexes (mean, mode, median, midrange), dispersion indexes (range, standard deviation, variance), percentiles, quartiles. Outliers. Boxplots.

Probability and random variables.

Sample space, events, probability of events. Probability of union and intersection of events. Complementary events. Independent events. Conditional probability. Bayes theorem. Factorial and binomial coefficients. Random variables. Expected value, variance and standard deviation of discrete random variables. Discrete random variables: binomial and Poisson. Continuous random variables: uniform and normal. Standardization and calculations with the normal distribution. Percentiles. Normal approximation of binomial distribution.

Inferential statistics.

Fundamental concepts: population, sample, parameters, statistics, estimators. Sample mean behavior: large number law and central limit theorem. Confidence interval: general concepts. Confidence interval for a proportion. Confidence interval for the mean, both with known and unknown standard deviation. Student's distribution. Hypothesis test. General concepts: null and alternative hypothesis, first and second kind errors, significativity level, power function, p-value, statistics of the test, critical region. Test on the proportion. Test on the mean (both with known and unknown variance). Inference on two samples. Inference on two proportions. Inference on two means, both for independent and paired samples.

Linear regression and non-parametric precedures.

Covariance and correlation. Simple linear regression. Test on regression coefficients and model validation. Chi-square distribution. Test of independence and good adaptation.

**Teaching methods**

The course is held through lectures mainly on the blackboard.

The student is encouraged to ask questions, to participate actively in the class discussion to improve his / her logical-analytical reasoning skills, to take a scientific and critical attitude to problems with uncertainty aspects, to use the precise technical language of the matter as well as its specific methodologies for solving statistical problems.

The student is encouraged to ask questions, to participate actively in the class discussion to improve his / her logical-analytical reasoning skills, to take a scientific and critical attitude to problems with uncertainty aspects, to use the precise technical language of the matter as well as its specific methodologies for solving statistical problems.

**Teaching Resources**

Sheldon Ross, Probabilità e statistica per l'ingegneria e le scienze (terza edizione), Maggioli Editore (2015)

Modulo: Fisica

FIS/07 - APPLIED PHYSICS - University credits: 6

Practicals: 16 hours

Lessons: 40 hours

Lessons: 40 hours

Modulo: Laboratorio di Fisica

FIS/06 - PHYSICS OF THE EARTH AND OF THE CIRCUMTERRESTRIAL MEDIUM - University credits: 3

Practicals: 32 hours

Laboratories: 16 hours

Laboratories: 16 hours

Professors:
Gallo Salvatore, Perini Laura

Shifts:

Professor:
Perini Laura

Turno 1

Professor:
Perini LauraTurno 2

Professor:
Perini LauraTurno 3

Professor:
Gallo Salvatore
Modulo: Laboratorio di metodi matematici e statistici

MAT/06 - PROBABILITY AND STATISTICS - University credits: 0

SECS-S/02 - STATISTICS FOR EXPERIMENTAL AND TECHNOLOGICAL RESEARCH - University credits: 0

SECS-S/02 - STATISTICS FOR EXPERIMENTAL AND TECHNOLOGICAL RESEARCH - University credits: 0

Laboratories: 32 hours

Lessons: 8 hours

Lessons: 8 hours

Professors:
Campi Luciano, Villa Elena

### M - Z

Responsible

Lesson period

Second semester

**Prerequisites for admission**

Have attended the basic course in calculus of the first semester.

**Modulo: Fisica**

**Teaching Resources**

R. A. Serway, J. W. Jewett "Principi di Fisica" EdiSES, additional notes, exercises and exams examples are available on Ariel.

**Modulo: Laboratorio di Fisica**

**Teaching Resources**

R. A. Serway, J. W. Jewett "Principi di Fisica" EdiSES

Analisi degli errori sperimentali di laboratorio; L. Miramonti, L. Perini, I. Veronese; EdiSES

Analisi degli errori sperimentali di laboratorio; L. Miramonti, L. Perini, I. Veronese; EdiSES

**Modulo: Laboratorio di metodi matematici e statistici**

**Course syllabus**

Descriptive statistics.

Population sampling. Types of data and variables. Subdivision of data into classes and creation of frequency tables. Histogram and histogram/bars.Centrality indexes (mean, mode, median, midrange), dispersion indexes (range, standard deviation, variance), percentiles, quartiles. Outliers. Boxplots.

Probability and random variables.

Sample space, events, probability of events. Probability of union and intersection of events. Complementary events. Independent events. Conditional probability. Bayes theorem. Factorial and binomial coefficients. Random variables. Expected value, variance and standard deviation of discrete random variables. Discrete random variables: binomial and Poisson. Continuous random variables: uniform and normal. Standardization and calculations with the normal distribution. Percentiles. Normal approximation of binomial distribution.

Inferential statistics.

Fundamental concepts: population, sample, parameters, statistics, estimators. Sample mean behavior: large number law and central limit theorem. Confidence interval: general concepts. Confidence interval for a proportion. Confidence interval for the mean, both with known and unknown standard deviation. Student's distribution. Hypothesis test. General concepts: null and alternative hypothesis, first and second kind errors, significativity level, power function, p-value, statistics of the test, critical region. Test on the proportion. Test on the mean (both with known and unknown variance). Inference on two samples. Inference on two proportions. Inference on two means, both for independent and paired samples.

Linear regression and non-parametric precedures.

Covariance and correlation. Simple linear regression. Test on regression coefficients and model validation. Chi-square distribution. Test of independence and good adaptation.

Population sampling. Types of data and variables. Subdivision of data into classes and creation of frequency tables. Histogram and histogram/bars.Centrality indexes (mean, mode, median, midrange), dispersion indexes (range, standard deviation, variance), percentiles, quartiles. Outliers. Boxplots.

Probability and random variables.

Sample space, events, probability of events. Probability of union and intersection of events. Complementary events. Independent events. Conditional probability. Bayes theorem. Factorial and binomial coefficients. Random variables. Expected value, variance and standard deviation of discrete random variables. Discrete random variables: binomial and Poisson. Continuous random variables: uniform and normal. Standardization and calculations with the normal distribution. Percentiles. Normal approximation of binomial distribution.

Inferential statistics.

Fundamental concepts: population, sample, parameters, statistics, estimators. Sample mean behavior: large number law and central limit theorem. Confidence interval: general concepts. Confidence interval for a proportion. Confidence interval for the mean, both with known and unknown standard deviation. Student's distribution. Hypothesis test. General concepts: null and alternative hypothesis, first and second kind errors, significativity level, power function, p-value, statistics of the test, critical region. Test on the proportion. Test on the mean (both with known and unknown variance). Inference on two samples. Inference on two proportions. Inference on two means, both for independent and paired samples.

Linear regression and non-parametric precedures.

Covariance and correlation. Simple linear regression. Test on regression coefficients and model validation. Chi-square distribution. Test of independence and good adaptation.

**Teaching methods**

The course is held through lectures and practicals.

**Teaching Resources**

Sheldon Ross, Probabilità e statistica per l'ingegneria e le scienze (terza edizione), Maggioli Editore (2015)

Modulo: Fisica

FIS/07 - APPLIED PHYSICS - University credits: 6

Practicals: 16 hours

Lessons: 40 hours

Lessons: 40 hours

Professor:
Camilloni Carlo

Modulo: Laboratorio di Fisica

FIS/06 - PHYSICS OF THE EARTH AND OF THE CIRCUMTERRESTRIAL MEDIUM - University credits: 3

Practicals: 32 hours

Laboratories: 16 hours

Laboratories: 16 hours

Professor:
Miramonti Lino

Shifts:

Professor:
Miramonti Lino

Turno 1

Professor:
Miramonti LinoTurno 2

Professor:
Miramonti Lino
Modulo: Laboratorio di metodi matematici e statistici

MAT/06 - PROBABILITY AND STATISTICS - University credits: 0

SECS-S/02 - STATISTICS FOR EXPERIMENTAL AND TECHNOLOGICAL RESEARCH - University credits: 0

SECS-S/02 - STATISTICS FOR EXPERIMENTAL AND TECHNOLOGICAL RESEARCH - University credits: 0

Laboratories: 32 hours

Lessons: 8 hours

Lessons: 8 hours

Professors:
Maurelli Mario, Ugolini Stefania

Professor(s)

Reception:

Monday 14-16 by appointment by email; other days by appointment by email

online meeting

Reception:

Monday 14-15 and 17.30 18.30

Office at Physics Department

Reception:

Please write an email

Room of the teacher or online room