Unit INTRODUCTION TO STATISTICS IN TOURISM
 Course
 Economics of tourism
 Studyunit Code
 20099409
 Curriculum
 In all curricula
 Teacher
 Silvia Pandolfi
 Teachers

 Silvia Pandolfi
 Hours
 63 ore  Silvia Pandolfi
 CFU
 9
 Course Regulation
 Coorte 2021
 Offered
 2022/23
 Learning activities
 Caratterizzante
 Area
 Statisticomatematico
 Academic discipline
 SECSS/01
 Type of studyunit
 Obbligatorio (Required)
 Type of learning activities
 Attività formativa monodisciplinare
 Language of instruction
 italian
 Contents
 The course aims at enabling students to critically understand quantitative reports and provides the first notions to correctly perform a statistical analysis of economic data.
The main knowledge acquired will be: a) the basic elements of descriptive statistics: population, types of characters, frequency distributions, mean, variance, dependence and association between two characters; b) the basic elements of inferential statistics: notions of probability, random sample, point or interval estimate, hypothesis tests.
Students who successfully complete the module will be able to critically understand the statistical reports related to tourism as well as to perform some simple analysis and to construct appropriate syntheses of the data.  Reference texts
 G. Cicchitelli, P. D’Urso, M. Minozzo. Statistica: Principi e metodi, Pearson, 2017.
 Educational objectives
 First notions of statistical data analysis with a particular view on tourism phenomena. The course is aimed at providing the main notions about Descriptive and Inferential Statistics.
Descriptive statistics:
Preliminary concepts; Comparisons between statistical quantities; Statistical distributions; Graphical tools; Means; Variability and concentration; An overview of the characteristic constants; Dependence analysis; Regression analysis; Correlation.
Inferential Statistics:
Probability; Random variables; Some specific probabilistic models; Sample distributions; Point estimation; Interval estimation; Hypothesis testing.  Prerequisites
 Notions acquired in the first module of Principles of Mathematics.
 Teaching methods
 Six hours of lectures and practical exercises every week.
 Other information
 Learning verification modality
 Compulsory written exam; oral exam on an elective basis.
 Extended program
 Descriptive statistics
Preliminary concepts: essential terminology; sources of statistical data relating to tourism; measurement of variables; data collection; data matrix; statistical ratios.
Statistical distributions: disaggregated statistical distributions; frequency distributions; relative frequencies; cumulative frequencies; frequency distributions with data grouped into classes with and without class totals; bivariate and multivariate distributions; time series; spatial series.
Graphical tools: graphics for distributions of quantitative variables: bar chart; histogram; boxplot; graphical representation for nominal variables: pie charts; tridimensional graphics; graphical representation of time series and spatial series.
Means: arithmetic mean; geometric mean; square mean; case of frequency distributions; case of data grouped into classes; weighted mean; median; quartiles and quantiles; central value; mode.
Variability and concentration: variability; average deviations: mean deviation; standard deviation; alternative formula for the standard deviation; range; interquartile range; percentage variability indices; concentration; G and R concentration indices; geometric interpretation of concentration indices.
Asymmetry indices: symmetry and asymmetry; asymmetry indices.
An overview of the characteristic constants: graphics and characteristic constants; box plot.
Index numbers: fixedbase and mobilebase index numbers. Mean percentage change. Index of Laspeyres.
Dependence analysis: Disaggregate and frequency bivariate distributions; marginal and conditional distributions; graphical representations of bivariate distributions; chisquared index of statistical association.
Regression analysis: statistical relationships; simple linear regression; least square method for the regression parameters; fitting of data to regression line; index rsquare and its properties. Time series case; mean error of prediction.
Correlation: notion of correlation; Bravais correlation coefficient and its properties.
Inferential Statistics:
Probability: random experiments; sample space and events; basic set theory operations; probability; interpretation of probability; computing probabilities; conditional probability; independence; Bayes theorem.
Random variables: discrete random variables; mean and standard deviation; continuous random variables; mean and standard deviation; quantiles; standardized random variables; mean and variance of a linear combination of random variables (only introduction).
Some specific probabilistic models: Bernoulli distribution; binomial distribution; Poisson distribution; normal distribution; standardized normal distribution; approximation of the binomial distribution through the normal distribution; chisquare distribution.
Sample distributions: random sample; parameter; statistical inference: parameter estimation and hypothesis testing; sample statistics; sample distribution of the mean for normal populations and with large sample size (central limit theorem); sample distribution of the variance; sample distribution of the mean when the population variance is unknown; tStudent distribution and use of statistical tables.
Point estimation: estimator; properties o estimators; unbiasedness; mean square error; asymptotic properties.
Interval estimation: interval estimator and interval estimate; interval estimation of the mean of a normal population; size of confidence interval; the case of unknown variance; interval estimation of the mean with large sample sizes; confidence interval for the parameter p of a Bernoulli population; confidence interval of the variance of a normal population with unknown variance.
Hypothesis testing: statistical hypotheses; Testing hypotheses on the mean of a normal population; Ztest; pvalue; Ttest; testing hypotheses on the mean in case of large sample size; testing hypotheses on the parameter p of a Bernoulli population; testing hypotheses on the variance of a normal population with unknown mean; errors of first and second type and their probabilities; power of a statistical test.
Chisquare test of independence between two categorical random variables.