Prof. ssa SCARINZI
This first course in Statistics for undergraduate Students (63 hours, 8 credits) aims to offer an introduction to Statistics and both statistical terminology and methodology providing the Students the main basic tools for analysing and interpreting statistical data, so that they can be able to summarize data, interpret reports and newspaper accounts that use statistics and probability, and review plans for surveys and experiments.
- Descriptive statistics: sources and methods of data collection. The statistical population and the statistical units. Data types, statistical variates and statistical variables. Collecting data and the “matrix” of individual data. Presenting individual data by means of frequencies tables and charts. The frequencies cumulative function of a statistical variable. Measures of position (i.e. the quantiles and the means). Measures of dispersion (i.e. the range, the inter- quartile range and the variance,). Linear transformation of a statistical variable. The Tchebychev’s inequality. Bivariate statistical variates and bivariate statistical variables. Definitions, conjoint frequencies distribution, marginal distributions and conditional distributions. Mean and variance of a conditional variable. The covariance and the linear correlation coefficient. The analysis of depencence. Statistical dependence (and its measures). Dependence in mean (and its measures). The linear regression model and the method of the Least Squares. Measures of goodness of fit of a regression model. Some non linear models useful in economics.
- Elements of probability: random experiments and their nature. The set of all the possible outcomes of a random experiment, the Algebra of events. The axiomatic definition of probability. Conditional probability. Independent and dependent events. Bayes’ rule. Repeated trials of a random experiment. Random variables, definitions and classifications. Expected values of a random variable, the moments and the moments generating function of a random variable. Functions of a random variable. Some outstanding random variables (Bernoulli, discrete Uniform, Binomial, Geometric, continuous Uniform, Gaussian, Exponential, ?). Random samples. Sampling from Gaussian densities, main sample statistics and their distribution, parametric point estimates.
All the basic information about the course will be available on klips from January the 1st. More details will be uploaded (daily or weekly) to klips during the course.
Texts (Revised new versions available from end November 2009)
E. D. ISAIA, Exercises on Descriptive Statistics, published on klips, 2009
E. D. ISAIA, Exercises on Elemetary Probability, published on klips, 2009