Ateneo - UPO

The course presents the statistical methodology for the managment and 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, Springer.

Useful references:
P. H. Franses , R. Paap (2010) Quantitative Models in Marketing Research. Cambridge University Press.

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 and of basic Mathematics in economics (see E0252, E0362 and EA007).

Classroom lectures, laboratory exercises and seminar activities.

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.

An assessment is based on a compulsory written test consisting in theoretical questions to test the knowledge of the concepts and the mastery of the language; numerical exercises to test abilities acquired in the use of calculation algorithms and of the software; structured data-based problems and commentary on the results in order to assess autonomy in the statistical analysis.
Please note that after the third attempt to sit the exam in the course of a calendar year, the registration block is automatically applied.

The course is divided in two parts.

PART I - STATISTICAL METHODOLOGY FOR MANAGEMENT AND QUANTITATIVE MARKETING (4 CFU)

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. Supervised classification. Bayesian rule and selection of discriminant variables. Logistic Models. Odds-ratio and its relation with the posterior probability. Diagnostic analysis: confusion matrix and ROC curve.
6. Unsupervised statistical Learning. Clustering: k-means and hierarchical clustering. Dendrograms and their interpretation.

1. Review of probabilities and statistical inference.
2. Survey methods: statistical sampling and techniques for data collection in marketing research.
3. Data cleaning and pre-processing data. Exploratory analysis and data visualization.
4. Matrix representations of multidimensional data: data matrix, covariance matrix and correlation matrix, matrices of dissimilarity.
5. Main techniques of data-mining in management and marketing:
• Hierarchical cluster analysis and k-means method. Segmentation methods;
• Principal component analysis.
6. Multivariate linear regression model and logistic regression.
7. Conjoint analysis: general methodology and applications.
8. Customer satisfaction.

PART II - INTRODUCTION TO DATA SCIENCE (2 ECTS)

1. Methods of dimensionality reduction. Principal component analysis: definition and interpretation. Biplot.
2. CART. Splitting rules (RSS, Entropy, Gini index). Pruning a tree.
3. Generalized additive models. General aspects.
4. Hints to neural networks: basic ideas of neural networks (layer architecture, easy examples of fully connected networks). Model training problem: backpropagation.