## STATISTICAL PROCESSING OF EXPERIMENTAL DATA ELABORAZIONE STATISTICA DEI DATI SPERIMENTALI

A.Y. Credits
2019/2020 6
Lecturer Email Office hours for students
Margherita Carletti Each week, after lesson time
Teaching in foreign languages
Course with optional materials in a foreign language English
This course is entirely taught in Italian. Study materials can be provided in the foreign language and the final exam can be taken in the foreign language.

### Assigned to the Degree Course

Biotechnology (L-2)
Curriculum: PERCORSO COMUNE
Date Time Classroom / Location
Date Time Classroom / Location

### Learning Objectives

The course aims at providing students with theoretical and practical tools (using Excel) useful to analyse and process data sets of biological nd biotechnological kind.

### Program

1. Descriptive Statistics

1.1 Statistical data and their graphical representation: cartesian diagrams, bar graphs, histograms, aerograms (pie charts).

1.2 Absolute, relative, percent and cumulative frequences.

1.3 Centrality (position) indexes: mean, mode and median. Quantiles and quartiles.

1.4 Dispersion (variability) indexes: variance, standard deviation, mean absolute difference, range, variation coefficient, interquartile difference.

1.5 Shape indexes: skewness, kurtosis.

1.6 Interpolation and approximation of experimental data.

1.7 Simple linear regression: the mean squares line; index R^2.

1.8 Pearson's linear correlation coefficient.

1.9 Regression and correlation; residual analysis.

1.10  Applications with Excel

2. Probability Theory

2.1 Events and definitions of probability of an event: classical, frequentistic and axiomatic definition by Kolmogorov.

2.2 Disjoint and independent events. Conditional probability.

2.3 Theorem of the sum and of the product; total probability theorem; Bayes' theorem.

2.4 Diagnostic tests. Sensibility, specificity and predictive value of a test.

2.5 Hints of combinatorics: permutations, combinations and dispositions.

2.6 Random variables (r.vs.): discrete and (absolutely) continuous r.vs; cumulative distribution function, distribution or density function of a r.v.

2.7 Centrality and variability indexes for r.vs.

2.8 Probability distribution functions of (discrete) r.vs: Bernoulli, binomial, Poisson r.vs.

2.9 Probability density functions of (coninuous) r.vs: exponential and Gausian r.vs. Use of the tables of the standard Gaussian r.v.

2.10 Central Limit Theorem and corollaries.

2.11 Applications with Excel.

3. Basic Inferential Statistics

3.1 Deductive and inductive inference; direct and inverse inference.

3.2 Population and sample. Distribution of the sample mean.

3.3 Hints of estimation theory and hypotheses testing (Neyman-Pearson and significance theories).

### Bridging Courses

Although not compulsory, having passed the exam of Mathematics (first year, 8 CFU)) is highly recommended, especially for the comprehension of Section 2,  "Probability Theory".

### Learning Achievements (Dublin Descriptors)

Students will have to show:

D1. A detailed knowledge and not mnemonic comprehension of the treated subjects

D2. The ability of applying the known statistical methods to different and new contexts

D3. The capability of using simple computational tools (Excel) for statistical data visualisation and processing

D4. The ability of interpreting the achieved experimental results on the basis of the used statistical methods

D5. The use of specific scientific language while exposing the treated subjects.

### Teaching Material

The teaching material prepared by the lecturer in addition to recommended textbooks (such as for instance slides, lecture notes, exercises, bibliography) and communications from the lecturer specific to the course can be found inside the Moodle platform › blended.uniurb.it

Not available

### Didactics, Attendance, Course Books and Assessment

Didactics

- Frontal lessons

- Examples and exercises using Excel in the computer laboratory

- The whole material uploaded in the Moodle platform http://blended.uniurb.it

Attendance

Not compulsory

Course books

M. Abate, Matematica e Statistica (3rd edition, or preceeding) - Le basi delle scienze della vita, McGraw-Hill, 2017  or any equivalent textbook in English.

Assessment

Oral examination on the entire program, and laboratory test on the use of Excel.

### Additional Information for Non-Attending Students

Didactics

- Use of the textbook

- Weekly check in the Moodle platform http://blended.uniurb.it

Attendance

Not compulsory

Course books

The same as attending students

Assessment

The same as attending students

### Notes

None

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