STATISTICS

Degree course: 
Corso di First cycle degree in ECONOMICS AND MANAGEMENT
Academic year when starting the degree: 
2016/2017
Year: 
2
Academic year in which the course will be held: 
2017/2018
Course type: 
Compulsory subjects, characteristic of the class
Credits: 
9
Period: 
Second semester
Standard lectures hours: 
74
Detail of lecture’s hours: 
Lesson (60 hours), Exercise (14 hours)
Requirements: 

The topics covered in the courses of Mathematics I and II.

Final Examination: 
Orale

Students will be evaluated in their knowledge and understanding through the means of a written exam concerning theory and exercises.

Assessment: 
Voto Finale

Students will acquire a good understanding of the statistical tools and techniques related to descriptive statistics, probability and inferential statistics covered in the course, as well as the ability to employ them in economic and business applications.

DESCRIPTIVE STATISTICS (about 30 hours)
−Classification of the variables
−Absolute, relative and percentage frequencies.
−Graphical representation of the variables:
1. Pie charts, bar charts, histograms.
2. Cumulative distribution function.

−Numerical description of the variables:
−Measures of central tendency: analytic means (arithmetic and geometric mean); median and quartiles, mode.
−Measures of variability.

−Bivariate analysis:
−Scatter plot and contingency table. Joint, marginal, conditional distributions. Conditional mean.
−Independence.
−Chi-square measure of association
−Linear dependence between two variables: covariance and correlation coefficient.

- Simple linear Regression:
- Ordinarily least square estimators
- Prediction
- R2 coefficient of determination.

PROBABILITY (about 10 hours)
- Basic notions and rules
- Assiomatic probability
- Bayes theorem

INFERENTIAL STATISTICS (about 20 hours)
Random variables (r.v.):
−Bernoulli, Binomial, Norma distributions.

Point estimation
−Random sample, estimator and estimate: definition.
−Sample mean and sample variance: definition and properties.
−Central Limit Theorem
−Properties of estimators: unbiasedness, asymptotic unbiasedness, efficiency, consistency.

Confidence Intervals
−Definition of Confidence Intervals (C.I.)
−C.I. for the mean of the normal population: variance known and unknown; Student T distribution.
−C.I. for the mean in case of large samples: variance known and unknown.

Test of Hypothesis
−Introduction
−Test for the mean of a normal population: variance known and unknown
−Test for the mean in case of large samples: variance known and unknown

- G. CICCHITELLI, P. D’URSO, M. MINOZZO (2017), Statistica: principi e metodi, Terza edizione, Pearson Italia, Milano (Chapters 1-6; 9-11; 13 - 14; 16 - 20)
- Notes and additional exercises will be available on e-learning page.

The theoretical lessons will be accompanied by weekly exercises classes.