LEARNING OUTCOMES

To provide the main ideas and techniques of inferential statistics in order to estimate , under a probability model, the errors involved when inferring from a sample to a population. 

TEACHING METHODS

Combination of traditionals lectures (40 hours) and exercises sessions (24 hours)

SYLLABUS/CONTENT

Sampling and estimation. Populations, samples and point estimators. Properties of point estimators. Some point estimators and their probability distributions. Confidence intervals. Hypothesis tests. How to define and use a statistical test (hypotheses, errors of the first and second type, critical region). Parametric tests. Tests of large samples. Comparative tests. Some non-parametric tests.

Statistics and tests for linear multiple models. Confidence intervals for the parameters, estimated values and residuals, "studentized" residuals, test of hypotheses on single coefficients and on subsets of coefficients. Forecast.

RECOMMENDED READING/BIBLIOGRAPHY

Casella G., Berger R.L. (2002), Statistical Inference, Pacific Grove, CA: Duxbury

Mood A.M., Graybill F.A., Boes D.C. (1991), Introduction to the Theory of Statistics, McGraw-Hill, Inc. 

Rogantin M.P. (2004), Introduzione alla statistica, C.L.U.T., Torino

Ross S.M. (2003), Probabilità e statistica per l’ingegneria e le scienze, Apogeo, Milano

Wasserman L. (2005), All of Statistics, Springer