|Unit||Credits||Disciplinary sector||Period||Academic staff|
|1 - lezione||8||SECS-S/01-STATISTICS||Secondo semestre||
|2 - esercitazione||2||SECS-S/01-STATISTICS||Secondo semestre||
The course provides to students in economic and business sciences an introduction to probability and to descriptive and inferential statistics.
Prerequisite to the course is the mastering of a few basic mathematical concepts such as limit, derivative and integration at the level of an undergraduate first year introductory course in calculus.
Descriptive Statistics: data collection and classification; data types; frequency distributions; histograms and charts; measures of central tendency; arithmetic mean, geometric mean and harmonic mean; median; quartiles and percentiles; fixed and varying base indices; Laspayres and Paasche indices; variability and measures of dispersion; variance and standard deviation; coefficient of variation; moments; Pearson’s and Fisher’s indices of skewness and kurtosis; multivariate distributions; scatterplots; covariance; variance of the sum of more variables; method of least squares; least-squares regression line; Pearson’s coefficient of linear correlation r; Cauchy-Schwarz inequality; R-square coefficiente; deviance residual and deviance explained; multivariate frequency distributions; conditional distributions; measures of association; chi-squared index of dependence; index of association C; Simpson’s paradox.
Probability: events, probability spaces and event trees; combinatorics; conditional probability; independence; Bayes theorem; discrete and continuous random variables; distribution function; expectation and variance; Markov and Tchebycheff inequalities; discrete uniform distribution; Bernoulli distribution; binomial distribution; Poisson distribution; geometric distribution; continuous uniform distribution; normal distribution; exponential distribution; multivariate discrete random variables; joint probability distribution; marginal and conditional probability distributions; independence; covariance; correlation coefficient; linear combinations of random variables; average of random variables; sum of normal random variables; weak law of large numbers; Bernoulli’s law of large numbers for relative frequencies; central limit theorem.
Inferential Statistics: sample statistics and sampling distributions; chi-square distribution; Student-t distribution; Snedecors-F distribution; point estimates and estimators; unbiasedness; efficiency; consistency; estimate of the mean, of a proportion and of a variance; confidence intervals for a mean for a proportion (large samples) and for a variance; hypothesis testing; one and two tails tests for a mean, for a proportion (large samples) and for a variance; hypothesis testing for differences in two means, two proportions (large samples) and two variances.
The course consists of a series of lectures (64 hours) and of ten exercise classes (20 hours).
Some of the exercise classes are Excel laboratory sessions.
All classes are essential to a proper understanding of the topics of the course.
For the official examination both written and oral sessions are mandatory.
The course is considered completed if the candidate has done the written test and passed the oral exam.
Students that has received at least 15 out of 30 in the written exam are allowed to attend the oral exam.
|Outcomes Exams||Outcomes Percentages||Average||Standard Deviation|
|18||19||20||21||22||23||24||25||26||27||28||29||30||30 e Lode|
Data from AA 2008/2009 based on 397 students. I valori in percentuale sono arrotondati al numero intero più vicino.