- Basic knowledge of Mathematics
- Basic knowledge of Descriptive Statistics and Inferential Analysis
The course is structured into two parts (MODULO I – professor M.Esposito / MODULO II – professor G. Gonano). Provided that statistical analysis based on sample data has an important role in supporting economic and financial decisions this course has three main learning outcomes common on both parts: (1) improve knowledge on Inferential Analysis previously acquired in the course of Statistics; (2) provide ability to apply basic statistical tools for the study of quantitative economic and business phenomena; (3) learn how to use the spreadsheet for statistical analysis.
At the end of the course the student will have acquired:
1. KNOWLEDGE AND ABILITY TO UNDERSTAND: knowledge on the main elements of Inferential Analysis, ability to understand the appropriate statistical tools for the analysis of the economic phenomenon in hands, practical ability to use the spreadsheet for the statistical analysis.
2. ABILITY TO APPLY KNOWLEDGE AND UNDERSTANDING: ability to recognize and apply the basic tools to carry out the study of the phenomenon, to analyse and interpret the results of the statistical analysis, ability on how to use the spreadsheet for statistical analysis.
3. SELF JUDGEMENT: ability to recognize whether the statistical method applied is used appropriately for the study of the phenomenon.
4. COMMUNICATION SKILLS: ability to present methods and results of a statistical analysis in a precise and rigorous way.
5. LEARNING SKILLS: together with a course in Statistics it encourages in-depth study of the statistical methodologies proposed in the context of advanced specialized courses.
1. Random Variables. The Probability Density Function. The Cumulative Distribution Function. Expectation and Variance of a Random Variables. The Normal Distribution. The Standard Normal Distribution. The Normal Probability Plot. The Uniform Random Variable. The Exponential Random Variable. The Bivariate Probability Distribution Function.
2. A random sample and sampling distribution functions. Distribution of the sample mean. Distribution of the sample variance.
3. Estimators, estimates. Properties of estimators.
4. Confidence Intervals for the mean with Normal probability distribution. Confidence Intervals for the difference of means with Normal probability distribution (indipendent random sample or dependent sample).
5. Statistical Hypotheses for mean. Statistical Hypotheses for the difference of means. Test for the variance with Normal distribution function. Test for equal variances with Normal distribution functions.
6. Classical linear regression model. Correlation analysis. Least squares Method. Inferential analysis on the coefficients of the linear regression model. Errors distribution analysis.
7. Multiple regression model. Ordinary least squares method. Goodness of Fit. Inferential analysis: parameters, least squares estimates and estimators, standard assumptions for the linear regression model, hypothesis tests on the coefficients.
8. Non linear regression model. Linear regression model with dummy variables.
9. Multicollinearity. Specification Analysis. Heteroscedasticity.
P. Newbold, W.L. Carlson, B. Thorne, Statistics for Business and Economics, Pearson University, 8th ed., 2013
D. Giuliani e al., Analisi statistica con Excel, Maggioli Editore, 2015.
Didattic materials distributed on the on line platform