- Docente: Alberto Roverato
- Credits: 9
- SSD: SECS-S/01
- Language: English
- Teaching Mode: Traditional lectures
- Campus: Bologna
- Corso: First cycle degree programme (L) in Business and Economics (cod. 8965)
Learning outcomes
At the end of the course students have the basic tools for analysing and describing a set of data through numerical indexes, graphical representations and dependence models for both univariate and bivariate data. The students are able to deal with basic tools of probability theory and its applications. The students will be also able to estimate population parameters from sample data by using standard inferential techniques (point estimation, confidence interval and hypothesis testing).
Course contents
The course program is organized in three parts as described below.
1. Exploratory Data Analysis
The data matrix. Types of variables. Frequency tables. Graphical representations. Summary measures of position and dispersion. Linear transformations, standardization and bell-shaped distributions. Association of two quantitative variables, covariance and correlation coefficient. Outline of simple linear regression.
2. Probability Theory
Random experiment, sample space and events, probability measure. Conditional probability, independence, Bayes theorem and total probability law. Random variables, cumulative distribution function and probability density function. Expected value and variance of a random variable. Discrete and continuous uniform distribution. Bernoulli distribution. Exponential distribution. Gaussian distribution. Independent variables. Linear combination of random variables and the central limit theorem.
3. Inferential Statistics
Random sampling. Parametric statistical models. Sampling distributions. Point estimation. Bias and mean squared error. Confidence intervals for the mean of a Gaussian population. Th Student t distribution. Approximate confidence interval for a probability. Hypothesis testing on the mean of a Gaussian population. The p-value. Approximate test on a probability.
Readings/Bibliography
Newbold, P., Carlson W.L. and Thorne, B.
Statistics for Business and Economics
Pearson – Prentice Hall
Teaching methods
Teacher's lectures.
Assessment methods
Written examination.
Teaching tools
Slides of the course, materials for self-assessment.
Links to further information
http://www2.stat.unibo.it/roverato/homepage.html
Office hours
See the website of Alberto Roverato