Learning objectives
Main objective of this introductory course to data analysis is to let the student becoming familiar with the discipline, its logic and its tools. In particular, the student should develop the skill to select data that are most appropriate to solve a question and by which tools such data can be appropriately analyzed.
Prerequisites
Elemantary mathematics: matrices, limits, derivatives.
Course unit content
Main contents of the course are probability theory and related distributions (normal, binomial);experimental data and their treatment (tables, chartsm, hystograms) and the most important elementary statistical tests (Z, T, ANOVA, Regression, Correlation).
Full programme
1.Sampling and data: descriptive statistics
2.Probability theory
3.Random variables and their distributions
4. Sampling
5. Estimation problems: Z and t distributions, and differences between two means
6. Hypothesis testing
7. One-way and two-way ANOVA
8. Regression
9.Correlation
Bibliography
M.C. Whitlock, D. Schulter
Statistical analysis of biological data.
Zanichelli
T.H. Wonnacott, R.J. Wonnacott
Introductory statistics
Franco Angeli
Teaching methods
Concepts are explained in classroom-taught lessons and exercises are assigned to the students to make the concept they have learned theoretically operational.
Assessment methods and criteria
Written examination with questions concerning the concepts illustrated in the classroom and exercises.
Other information
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2030 agenda goals for sustainable development
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