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, 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 laboratory exercises

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.

Writing and discussing a short thesis.

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.
9. Regularization: curse of dimensionality, Ridge, Lasso.
10. Discrimination vs generative models, Naive Bayes model.
11. Trees, information gain (entropy, Gini index), ensemble/bagging eìand
random forest. Boosting.