Unit COMPUTER SCIENCE AND STATISTICS
- Course
- Food science and technology
- Study-unit Code
- GP000935
- Curriculum
- Viticoltura e enologia
- Teacher
- Lorenzo Vergni
- Teachers
-
- Lorenzo Vergni
- Hours
- 54 ore - Lorenzo Vergni
- CFU
- 6
- Course Regulation
- Coorte 2022
- Offered
- 2022/23
- 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 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.- Reference texts
Pelosi M. K., Sandifer T. M., Cerchiello P., Giudici P. (2009). Introduzione alla Statistica, McGraw-Hill.
Notes provided by the lecturer- Educational objectives
The main objective of the course is to provide students with the basis for the statistical processing of data collected during sample surveys. In particular the main knowledge acquired will be: • basic concepts of statistics (experimental method and concept of sample and population) and objectives of a statistical survey • synthesize and describe the data collected (graphically and with the appropriate indicators ) • difference between descriptive and inferential statistics • concepts of relative and absolute frequency sampling • measures of central tendency and dispersion in a sample • correlation and regression analysis • discrete and continuous probability distributions • estimation of the parameters of a normal distribution • statistical hypotheses • functionality of programs useful for data analysis and management. Such knowledge will enable the development of the following skills: 1-perform sampling surveys (number, sampling method); 2-graphical representations of the sample frequency for qualitative and quantitative data; 3-calculate the main measures of central tendency and dispersion of a sample; 4-identify the regression line; 5-calculate the probability of events; 6-verify the statistical hypotheses; 7-use the spreadsheet and other specific programs for statistical data analysis; 8-use computer applications for the management and display of data; 9-knowing how to identify the most suitable techniques for solving data-related problems.- Prerequisites
Knowledge of basic mathematics and computer science- Teaching methods
The course is organized as follows:-theoretical lectures about the topics of the course;-practical training about the use of the spreadsheet for analysis, presentation and interpretation of experimental data.- Other information
For information on lectures and exams visit
http://dsa3.unipg.it/- Learning verification modality
Final exam is written (2 hours) and it is held in a computer room to make it possible the evaluation of both the statistics and computer sciences knowledge. The past exams are available in the learning material on Unistudium.- Extended program
Graphic representations: pie and bar diagrams; frequency histograms. Measures of central tendency (median, mean). Measures of variability (range, variance, standard deviation, coefficient of variation). Percentiles and quartiles. Interquartile distance. Bivariate analysis. Analysis of bivariate qualitative data: contingency table; absolute and relative joint frequencies. Analysis of bivariate quantitative data: dispersion diagram; relationship types between two variables; linear correlation coefficient; simple linear regression using ordinary least squares; interpolation and extrapolation of values. Random experiment. Sample space. Random event. Probability: classical definition. Complementary event. Intersection and union of events. General rule for calculating the probability multiple events. Conditional probability. Independent events. Definition of a random variable. Discrete random variables. Probability distribution and graphic representation. Expected value and variance. Binomial random variable. Continuous random variables. Probability density function. Normal random variable and meaning of its parameters. Standardization of random variables and standard normal random variable Z. Tables of the Z. Calculation of the probabilities of intervals for a normal random variable. Calculation of quantiles of a normal random variable. Sample distributions. Distribution of the sample mean and standard deviation: point estimators and interval estimators. Confidence intervals for mean, variance, coefficients of the linear regression. Hypothesis test. General principles. Phases of hypothesis testing. Test statistic, critical values and rejection region of a test. I and II species error. Various examples of hypothesis tests for one samples and two samples cases.. Comparison of means, variances and sample proportions. Test for the significance of the regression. Computer lab exercises: use of Excel software in data analysis (mathematical and logical functions, functions for descriptive statistics, types of graphs, regression analysis, probability, inferential statistics, test hypotheses).