Ateneo - UPO

The course presents the statistical methodology for the quantitative marketing with the support of ad hoc software.

Some selected chapters in:
G. James, D. Witten, T. Hastie, R. Tibshirani (2013). An Introduction to Statistical Learning with Applications in R, 2nd edtion, Springer.

The course introduces the main statistical tools for management and marketing with the aid of an ad hoc software. The aim is to increase the knowledge on the main multivariate exploratory and predictive statistical techniques, to develop analytical skills through the use of these techniques and the corresponding IT tools and develop a critical capacity in the use of the same.

Contents of basic Statistics.

Classroom lectures and exercises. The lecture-based and interactive components are integrated into the course, accounting for approximately 2/3 and 1/3 of the total, respectively.

Students with physical disabilities, Learning Disabilities or Special Education Needs can request specific services and tools via the Staff Sviluppo e Coordinamento Carriere e Servizi alle Studentesse e agli Studenti, consulting the University webpage: https://www.uniupo.it/en/services/servicesstudents-physical-or-learning-disabilities. Students with disabilities, learning disabilities or special education needs, once they have contacted the University Staff, can refer to the tutor in charge of the course to define the examination modalities, concerning academic aspects.

Assessment is based on a mandatory oral exam designed to evaluate:
1. knowledge of basic concepts and definitions;
2. command of the subject matter;
3. knowledge of the main computational algorithms;
4. the ability to competently and critically apply the tools learned to real-world problems.

1. Introduction to Statistical Learning: forecasting and inference. Supervised and Unsupervised Statistical Learning.
2. Linear Regression. OLS estimation. Confidence intervals and tests.
3. Multiple Linear Regression: OLS estimation and test, analysis of variance and F test. Variable selection: resampling (CV) and predictive statistics (AIC, BIC, Cp). Leverage and HIP. Dummy variables for categorical variables. Modeles with interactions. Multicollinearity: definition, effects and VIF. Normality of residuals: QQ-plot. Multiplicative models.
4. Classical conjoint analysis. Definition of factors, levels and stimuli. Factorial and complete design. Likert scales.
5. Introduction to semi and non-parametric methods in regression: splines, kernel regression, local polynomials, additive regression.

Students will be avaluated according to the following marking scheme:

Mark Description
< 18 Significant gaps in content, missing answers or inadequate answers.
18–24 Acceptable preparation, but with significant gaps or topics not studied adequately.
25–28 Good knowledge of the content and the ability to make connections between the different parts of the syllabus.
> 28 Comprehensive and in-depth preparation, with a clear and coherent understanding of the topics covered.