09464 - Biometry and Statistics

Learning outcomes

At the end of the course, the student will have knowledge of univariate and multivariate statistical methods dedicated to naturalistic experimentation. In particular, the student will be able to: design biological experiments in the field and in the laboratory; plan sampling in nature; process experimental data collected from field and laboratory observations.

 

Course contents

Theory

- Introduction. Statistical surveys. Descriptive statistics vs inferential statistics. Types of statistical variables. Levels of measurements. Data matrices. The summation operator.
- Frequency distributions and cumulative frequency distributions. The empirical distribution function.
- Measures of central tendency and their properties. Measures of variability and their properties. Measures of distribution shape. Boxplots.
- Bivariate frequency distributions. Marginal and conditional distributions. Conditional mean and conditional variance. Variance decomposition. Statistical independence.
- Scatterplots. Covariance and correlation. Linear regression.
- Matrices for multivariate statistical analysis.

- Introduction to statistical inference.
- Sample space, statistics and estimators.
- Finite and asymptotic properties of estimators.
- Methods of estimation.
- Hypothesis testing.
- Statistical tests for mean value, variance and frequency.
- Tests of goodness of fit and independence.
- Simple linear regression model: descriptive and inferential aspects

R Laboratory

Introduction to R – Basic commands and data structures. Data import and cleaning. Functions, conditionals, and iteration. Descriptive statistics and basic graphs.

Statistical analysis with R – Analysis of case studies in R with the aim of applying the methods learned in the theoretical part of the course.

Readings/Bibliography

D. Freedman, R. Pisani, R. Purves, Statistics, W. W. Norton, 1997.

Peter Dalgaard. Introductory Statistics with R. Springer, New York, 2002.

 

Teaching methods

Each topic covered in the lectures will be followed by exercises in practical classes.

Assessment methods

The examination is designed to assess the achievement of the following learning outcomes:

• a thorough understanding of the theoretical concepts covered during the lectures;
• the ability to design and plan sample surveys;
• the ability to apply the above concepts using the R statistical software.

Assessment consists of a compulsory written examination and an optional oral examination. The written examination lasts two hours and comprises both theoretical and applied exercises to be completed using the R statistical software.

During the written examination, students may consult a set of formula sheets prepared by themselves. These formula sheets must not exceed four A4 pages. No other materials may be consulted. The formula sheets must be submitted together with the examination paper at the end of the test.

The final grade will be awarded according to the following scale:

• Below 18: Fail
• 18–23: Satisfactory
• 24–26: Fair
• 27–28: Good
• 29–30: Very Good
• 30 cum laude: Excellent

Regarding assessment, the use of artificial intelligence (AI) is prohibited. Any use of AI constitutes a violation of academic integrity.

Teaching tools

The teaching materials used during the course will be made available on [https://virtuale.unibo.it/] .

Office hours

See the website of Carlo Trivisano