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B026436 - STATISTICS
Main information
Teaching Language
Course Content
Suggested readings
Learning Objectives
Prerequisites
Teaching Methods
Type of Assessment
Course program
Academic Year 2016-17
Course year
First year - First Semester
Belonging Department
Agrifoodstuffs Production and Environmental Sciences (DISPAA)
Modulo di sola Frequenza of
Scientific Area
SECS-S/01 - STATISTICS
Credits
6
Teaching Hours
48
Teaching Term
19/09/2016 ⇒ 16/12/2016
Attendance required
No
Type of Evaluation
Giudizio Finale
Course Content
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Course program
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Lectureship
Teaching Language
Italian
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., 2016, Introduzione ai metodi Bayesiani in statistica applicata
Learning Objectives
Knowledge 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
Lectures:48
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.