Data Collection, Representation and Analysis
A.Y. 2018/2019
Learning objectives
To professionalize students in data analysis concerning:
1) The evaluation of the impact of experimental variables and accuracy of model fitting through regression models and ANOVA
2) The evaluation of association patterns among several variables through multivariate analysis techniques
3) The use of R software , freely available , having the advantage to provide several libraries useful for statistical analysis of ecological end environmental data
1) The evaluation of the impact of experimental variables and accuracy of model fitting through regression models and ANOVA
2) The evaluation of association patterns among several variables through multivariate analysis techniques
3) The use of R software , freely available , having the advantage to provide several libraries useful for statistical analysis of ecological end environmental data
Expected learning outcomes
To evaluate statistical analysis which could be adequate according to study design and to the measurement scale of the variables recorded during sampling phase.
Lesson period: Second semester
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course cannot be attended as a single course. Please check our list of single courses to find the ones available for enrolment.
Course syllabus and organization
Single session
Responsible
Lesson period
Second semester
Course syllabus
Course Program
Introduction to linear algebra: vectors and matrices
Basic R software: Introduction to programming
General Linear model
Simple and multiple linear regression model
Ordinary least squares and maximum likelihood estimation methods
Inference
Residual analysis and outliers identification
Explained variation and coefficient of determination
Analysis of variance
The use of Dummy variables in regression analysis
One -Way analysis of variance
Multi-Way analysis of variance
Multiple comparisons
Interactions between factors
Analysis of covariance
Models with fixed and random factors
Introduction to generalized linear models
Poisson Regression
Logistic Regression
Survival analysis
Right and left censoring
Distribution functions for failure time
Kaplan-Meier estimator of the survival function
Introduction to regression models
Multivariate analysis
Principal components analysis
Factorial analysis
Course prerequisites
Descriptive statistic and probability distributions (Gaussian, Binomial, Poisson).
Sampling distributions, confidence intervals, hypothesis testing for means and proportions
Course exam
A test of data analysis followed by an oral presentation
Introduction to linear algebra: vectors and matrices
Basic R software: Introduction to programming
General Linear model
Simple and multiple linear regression model
Ordinary least squares and maximum likelihood estimation methods
Inference
Residual analysis and outliers identification
Explained variation and coefficient of determination
Analysis of variance
The use of Dummy variables in regression analysis
One -Way analysis of variance
Multi-Way analysis of variance
Multiple comparisons
Interactions between factors
Analysis of covariance
Models with fixed and random factors
Introduction to generalized linear models
Poisson Regression
Logistic Regression
Survival analysis
Right and left censoring
Distribution functions for failure time
Kaplan-Meier estimator of the survival function
Introduction to regression models
Multivariate analysis
Principal components analysis
Factorial analysis
Course prerequisites
Descriptive statistic and probability distributions (Gaussian, Binomial, Poisson).
Sampling distributions, confidence intervals, hypothesis testing for means and proportions
Course exam
A test of data analysis followed by an oral presentation
SECS-S/01 - STATISTICS - University credits: 6
Practicals with elements of theory: 12 hours
Lessons: 40 hours
Lessons: 40 hours
Professors:
Biganzoli Elia, Boracchi Patrizia
Professor(s)
Reception:
On appointment (email)