STATISTICAL PROCESSING OF EXPERIMENTAL DATA
ELABORAZIONE STATISTICA DEI DATI SPERIMENTALI
| A.Y. | Credits |
|---|---|
| 2022/2023 | 6 |
| Lecturer | Office hours for students | |
|---|---|---|
| Giovanni Stabile | By appointment Wednesday 13-14 and Tuesday 8-9. Otherwise online by appointment |
| 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
| Date | Time | Classroom / Location |
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| Date | Time | Classroom / Location |
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Learning Objectives
The course aims at providing students with theoretical and practical tools (using Python) useful to analyse and process data sets of biological and 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.
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.
3. Basic Inferential Statistics
3.1 Deductive and inductive inference; direct and inverse inference.
3.2 Population and sample. Distribution of the sample mean for a normal population and of the sample proportion of a Bernoulli population (large samples).
3.3 Hypotheses testing (Neyman-Pearson) for the mean of a normal population and the proportion of a Bernoulli population (large samples): p-value.
4. Examples of applications using Python
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 to apply the known statistical methods to different and new contexts
D3. The capability of using simple computational tools (Python) for statistical data visualization and processing
D4. The ability to interpret 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
Supporting Activities
Not available
Teaching, Attendance, Course Books and Assessment
- Teaching
- Frontal lessons
- Examples and exercises using Python in the computer laboratory
- The whole material uploaded in the Moodle platform http://blended.uniurb.it
- Attendance
Not compulsory
- Course books
M. Carletti, Elaborazione Statistica dei dati Sperimentali, McGraw-Hill, 2019 (credit M. Abate, McGraw-Hill) or any other equivalent book in English.
- Assessment
Laboratory project in google Colab to be prepared at home and delivered before the exam.
Written exam with 11 closed-ended questions. For each question: 3pt correct, 0pt not answered, -1pt wrong.
The final mark is the weighted average (25% project, 75% written exam) of the project and the written examination.
Possibility to increase the mark with an oral examination.
- Disabilità e DSA
Le studentesse e gli studenti che hanno registrato la certificazione di disabilità o la certificazione di DSA presso l'Ufficio Inclusione e diritto allo studio, possono chiedere di utilizzare le mappe concettuali (per parole chiave) durante la prova di esame.
A tal fine, è necessario inviare le mappe, due settimane prima dell’appello di esame, alla o al docente del corso, che ne verificherà la coerenza con le indicazioni delle linee guida di ateneo e potrà chiederne la modifica.
Additional Information for Non-Attending Students
- Teaching
- 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
- Disabilità e DSA
Le studentesse e gli studenti che hanno registrato la certificazione di disabilità o la certificazione di DSA presso l'Ufficio Inclusione e diritto allo studio, possono chiedere di utilizzare le mappe concettuali (per parole chiave) durante la prova di esame.
A tal fine, è necessario inviare le mappe, due settimane prima dell’appello di esame, alla o al docente del corso, che ne verificherà la coerenza con le indicazioni delle linee guida di ateneo e potrà chiederne la modifica.
Notes
None
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