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 (the statistical tests) such data can be properly analyzed.
Prerequisites
Elementary mathematics: summation, product, matrices, determinants, limits, derivatives.
Course unit content
Main contents of the course are probability theory and related distributions (normal, binomial);experimental data and their treatment (tables, charts, 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. Probability theory
3. Random variables and their distributions: binomial, Poisson, normal.
5. Hypothesis testing
6. The chi-square test
7. Contingency tables and association between categorical variables
8. Statistical inference: the distribution of an estimate; the confidence interval
9. Inference in a population with normal distribution. Standardized normal distribution and Student's t distribution.
8. The Student's t test and the comparison of two samples
9. Comparing more than two samples: analysis of the variance one way, two ways, interaction
10. Correlation
11. Linear regression
Bibliography
M.C. Whitlock, D. Schluter, Analisi Statistica dei dati biologici. 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 in which the student is asked to solve a problem by applying the statistical test chosen among those covered in the course and answer 3 theoretical questions related to the topics of the course.
To verify the level of learning I will use an evaluation scheme that is organized as follows:
The students will be requested to solve an exercise applying the correct statistical test. The exercise itself is worth 12 points and its aim is to verify the level of knowledge the students have reached during the course and the capability to apply them in concrete cases. The 12 points are decomposed as follows: 5 points if the student shows he/she has understood the problem and formulates correctly the hypotheses to be tested. Identifying the appropriate test for the hypothesis testing is worth 4 points; the correct execution of the test is worth 2 points; order and clarity is worth 1 point.
The open questions are formulated to ascertain the level of knowledge and the skill about the topics treated during the course. Each question is worth 6 ponts, of which 4 are assigned to the correctedness of the answer and the other 2 points to the clarity of the written exposition.
Other information
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