ITA | ENG
B005457 - STATISTICS
Main information
Teaching Language
Course Content
Suggested readings
Learning Objectives
Prerequisites
Teaching Methods
Type of Assessment
Course program
Academic Year 2015-16
Course year
First year - First Semester
Belonging Department
Agrifoodstuffs Production and Environmental Sciences (DISPAA)
Course Type
Single education field course
Scientific Area
SECS-S/01 - STATISTICS
Credits
6
Teaching Hours
48
Teaching Term
21/09/2015 ⇒ 18/12/2015
Attendance required
No
Type of Evaluation
Final Grade
Course Content
show
Course program
show
Lectureship
Teaching Language
Italian (some papers in English)
Course Content
Descriptive statistics. Probability calculus. Bayesian linear models and logistic regression. Design of experiments.
Suggested readings (Search our library's catalogue)
Stefanini F.M., 2007, Introduzione alla statistica applicata, con esempi in R, Pearson Education, pp 272. http://hpe.pearson.it/stefanini/
Stefanini F.M., 2014, Introduzione ai metodi Bayesiani in statistica applicata: materiale ausiliario (sito web)
Lindley D.V., 2000, The Philosophy of Statistics, The Statistician, 49(3): 293-337.
Stefanini F.M., 2014, Introduzione ai metodi Bayesiani in statistica applicata: materiale ausiliario (sito web)
Lindley D.V., 2000, The Philosophy of Statistics, The Statistician, 49(3): 293-337.
Learning Objectives
Knolewdge acquired:
Basic elements of Bayesian statistics.
Linear and logistic regression models for univariate responses. Foundations of experimental design.
Competence acquired (at the end of the course);
Recognizing the nature of variables investigated during the study of a phenomenon. Evaluation of critical features characterizing a designed experiment. Selection of suitable statistical techniques to perform the analysis of experimental results.
Skills acquired (at the end of the course):
1. Assesment of raw data quality by means of suitable indices; summarizing the key features of the investigated phenomenon.
2. Data analysis using the R software.
3. Fitting of linear and logist regression models. 4. Design of experiments.
Basic elements of Bayesian statistics.
Linear and logistic regression models for univariate responses. Foundations of experimental design.
Competence acquired (at the end of the course);
Recognizing the nature of variables investigated during the study of a phenomenon. Evaluation of critical features characterizing a designed experiment. Selection of suitable statistical techniques to perform the analysis of experimental results.
Skills acquired (at the end of the course):
1. Assesment of raw data quality by means of suitable indices; summarizing the key features of the investigated phenomenon.
2. Data analysis using the R software.
3. Fitting of linear and logist regression models. 4. Design of experiments.
Prerequisites
Courses to be used as requirements (required and/or recommended)
Courses required: ........................................none
Courses recommmended: basic calculus.
Courses required: ........................................none
Courses recommmended: basic calculus.
Teaching Methods
Contact hours for:
Lectures:48
Laboratory/practice:
Seminars/excursions:
Lectures:48
Laboratory/practice:
Seminars/excursions:
Type of Assessment
Vivo examination on subjects of lectures, laboratory assignments and homework.
Course program
How to study for the final exam, the R software. Frequencies distributions, moments, quantiles. Graphical univariate and multivariate summaries.
Probability calculus and common random variables: Bernoulli, Binomiale, Normal, Poisson, Multinomial, Beta and Gamma families. Introduction to Bayesian subjective methods. Linear and logistic regression models: estimation and testing with qualitative and/or quantitative explanatory variables.
Randomized controlled experiments: random sampling, randomization, control, replication, target and baselines variables, random blocks, full factorial designs, Balanced Incomplete Blocks designs.
Probability calculus and common random variables: Bernoulli, Binomiale, Normal, Poisson, Multinomial, Beta and Gamma families. Introduction to Bayesian subjective methods. Linear and logistic regression models: estimation and testing with qualitative and/or quantitative explanatory variables.
Randomized controlled experiments: random sampling, randomization, control, replication, target and baselines variables, random blocks, full factorial designs, Balanced Incomplete Blocks designs.