Unit COMPUTER SCIENCE AND STATISTICS
- Course
- Food science and technology
- Study-unit Code
- GP000935
- Curriculum
- In all curricula
- Teacher
- Alessandra Vinci
- Teachers
-
- Alessandra Vinci
- Hours
- 60 ore - Alessandra Vinci
- CFU
- 6
- Course Regulation
- Coorte 2017
- Offered
- 2017/18
- Learning activities
- Base
- Area
- Matematiche, fisiche, informatiche e statistiche
- Academic discipline
- INF/01
- Type of study-unit
- Obbligatorio (Required)
- Type of learning activities
- Attività formativa monodisciplinare
- Language of instruction
- Italian
- Contents
- Descriptive statistics. The scientific method, measurement of natural phenomena and experimental data variability. Absolute, relative, and cumulated frequency distributions. Measures of location: media, mode and median. Measures of variability: range, deviance, variance, standard deviation, variation coefficient. Quantiles. Contingency tables, co-deviance, correlations, simple linear regression analysis.Probability. Concepts of population and sample. Elements of Combinatorics and probability theory. Discrete and continuous probability distributions: binomial and normal distribution.Inferential statistics. Parameters and estimators. Estimation methods and criteria: General considerations. Sampling from a normal population: mean and standard deviation. Confidence intervals.Test of hypothesis. Introduction to the hypothesis test for the mean and the variance of the population, Student t-test.Data processing: basic and advanced functions of spreadsheet for data analysis (filters, ranks, PivotTables, statistical functions) and for solving logical-mathematical problems.Examples of descriptive analysis data, simple linear regression and test of hypothesis, also by computer applications.
- Reference texts
- Pelosi M. K., Sandifer T. M., Cerchiello P., Giudici P. (2009). Introduzione alla Statistica, McGraw-Hill.
- Educational objectives
- The aim of the study unit is to enable students to operate with the principal statistical techniques and software packages for the processing and interpretation of experimental data for the decision making.The main knowledges will be:basic concepts of statistics (experimental method and concepts of population and sample) and aims of an statistical investigationsummarize the experimental data (using indicators and graphically)difference between descriptive and inferential statisticsrelative and absolute sample frequency measures of variation and central positioncorrelation and regression analysisdiscrete and continuous probability distributions estimation of a normal distribution parameters statistical testdata analysis by computer programsThis knowledge led to the skills:Carry out a sampling design Graphical representations of samples frequenciescalculation of the most important measures of descriptive statisticsdetermination of the equation of the regression functioncalculation of probabilities (for normal distributed variables)use of statistical hypothesis testsuse of the spreadsheet and other specific programs for data statistical analysis use of database management softwareidentify the most suitable techniques in solving problems
- Prerequisites
- Knowledge of basic mathematics and analytic geometry
- Teaching methods
- The course is organized as follows:-theoretical lesson about the topics of the course;-practical training about the use of the spreadsheet for analysis, presentation and interpretation of experimental data. Students will also have the opportunity to carry out self-assessment exercises (multiple choice test) on the platform Unistudium.
- Learning verification modality
- Final exam quiz or the possible progress tests are held in a computer room on the platform Unistudium and cover topics of both lectures and practice. Enrollment for the progress test and the final exam through the University website.
The questions of the progress tests and the final exam are resolution of exercises. The past exam quiz are provided in the learning material on Unistudium.
Progress tests are during the lessons and the registration on Unistudium is mandatory. - Extended program
- The main knowledges will be:basic concepts of statistics (experimental method and concepts of population and sample) and aims of an statistical investigation summarize the experimental data (using indicators and graphically)difference between descriptive and inferential statisticsrelative and absolute sample frequency measures of variation and central positioncorrelation and regression analysisdiscrete and continuous probability distributions estimation of a normal distribution parameters statistical testdata analysis by computer programsThis knowledge led to the skills:Carry out a sampling design Graphical representations of samples frequenciescalculation of the most important measures of descriptive statisticsdetermination of the equation of the regression functioncalculation of probabilities (for normal distributed variables)use of statistical hypothesis testsuse of the spreadsheet and other specific programs for data statistical analysis use of database management softwareidentify the most suitable techniques in solving problems