BUSINESS ANALYTICS
Syllabus
EN
IT
Learning Objectives
LEARNING OUTCOMES:
The course in Business Statistics provides an introduction to the modelling of economic and management variables using regression and multivariate methods, both in a parametric than a nonparametric framework; the emphasis is on business, marketing and industrial applications. The program will cover models for the analysis of dependence (linear regression, ANOVA, autoregressive model, logit and probit models) and exploratory techniques for data reduction (principal component analysis and clustering analysis).
KNOWLEDGE AND UNDERSTANDING:
Knowledge and understanding of parametric and nonparametric statistical techniques applied to marketing, sales and financial problems. At the end of the course students should be able to understand: (i) how to apply statistical models in a supervised and unsupervised approach; (ii) perfectly know the model’s assumptions and understanding of the tools needed to verify these hypotheses; (iii) understand the model selection techniques and measures of the model prediction capability. In particular, students will manage:
• Multiple Linear regression model
• Logit and Probit model
• Analysis of Variance (ANOVA)
• Autoregressive model AR(1)
• Cluster Analysis
• Principal Component Analysis
APPLYING KNOWLEDGE AND UNDERSTANDING:
Practical evidence of the concepts will be given with examples using statistical software such as STATA and SAS applied on real datasets. The students will have to practice both in class that with homeworks on the use of specific software so to be able to comment and understand the output.
MAKING JUDGEMENTS:
Students will be able to choose the more appropriate statistical techniques and to select the right set of explanatory variables. On the basis of results obtained, they will be able to give an interpretation about the relationship between the variables under study.
COMMUNICATION SKILLS:
Students will be able to prepare statistical reports using graphs, tables, figures and commenting them.
LEARNING SKILLS:
Students will have access to reading and understanding scientific articles using the multivariate methods considered in the course program. They will be able to identify the most appropriate methods to answer specific research questions.
The course in Business Statistics provides an introduction to the modelling of economic and management variables using regression and multivariate methods, both in a parametric than a nonparametric framework; the emphasis is on business, marketing and industrial applications. The program will cover models for the analysis of dependence (linear regression, ANOVA, autoregressive model, logit and probit models) and exploratory techniques for data reduction (principal component analysis and clustering analysis).
KNOWLEDGE AND UNDERSTANDING:
Knowledge and understanding of parametric and nonparametric statistical techniques applied to marketing, sales and financial problems. At the end of the course students should be able to understand: (i) how to apply statistical models in a supervised and unsupervised approach; (ii) perfectly know the model’s assumptions and understanding of the tools needed to verify these hypotheses; (iii) understand the model selection techniques and measures of the model prediction capability. In particular, students will manage:
• Multiple Linear regression model
• Logit and Probit model
• Analysis of Variance (ANOVA)
• Autoregressive model AR(1)
• Cluster Analysis
• Principal Component Analysis
APPLYING KNOWLEDGE AND UNDERSTANDING:
Practical evidence of the concepts will be given with examples using statistical software such as STATA and SAS applied on real datasets. The students will have to practice both in class that with homeworks on the use of specific software so to be able to comment and understand the output.
MAKING JUDGEMENTS:
Students will be able to choose the more appropriate statistical techniques and to select the right set of explanatory variables. On the basis of results obtained, they will be able to give an interpretation about the relationship between the variables under study.
COMMUNICATION SKILLS:
Students will be able to prepare statistical reports using graphs, tables, figures and commenting them.
LEARNING SKILLS:
Students will have access to reading and understanding scientific articles using the multivariate methods considered in the course program. They will be able to identify the most appropriate methods to answer specific research questions.
Prerequisites
Basic knowledge of descriptive statistics, elements of probability, random variables (Probability density function, Cumulative density function, expected value and variance) and statistical inference (point estimation, properties of estimators, estimation methods, statistical test, confidence interval).
Program
Lecture 1-2
linear regression model
• Statistical model
• Linear regression model
• Parameters estimation and measure of goodness of fit
• Diagnostic tools based on residuals
• Applications and Examples with Stata/SAS
Lecture 3-4
Multiple linear regression model
• matrix approach to linear regression
• Estimation of regression coefficients
• Inferences about regression parameters
• Diagnostic tools based on residuals
• Multicollinearity and VIF index
• Variables Selection methods: Backward, forward, stepwise
• Applications and Examples with Stata/SAS
Lecture 5
One-way Analysis of Variance
• Definition
• Effects Estimation
• Applications and Examples with Stata/SAS
Lecture 6
Logit and probit
• Statistical model
Assesment method: AIC and BIC
• Estimation and goodness of fit
• Classification matrix
• ROC curve
Lecture 7
Forecasting
• Autoregressive model
• Estimation and inference
Lecture 8
• Applications with SAS on real data sets
Lecture 9
• Introduction to Multivariate Statistics
• Principal components Analysis (PCA): Introduction and Motivation (Data dimension reduction, linear combination of variables)
• PCA: Presentation of Method (eigenvalues and eigenvectors, loadings, scores)
Lecture 10
• Graphical Methods (Biplots)
• PCA: Applications and Examples with Stata/SAS
Lecture 11
• Non Hierarchical Cluster Analysis: Overview, Partitional clustering: K-means
• Cluster Analysis: Applications and Examples with Stata/SAS
Lecture 12
• Agglomerative Hierarchical clustering (Ward, single linkage, complete linkage, average linkage)
• Cluster evaluation (dendrogram, pseudo-F, pseudo-T2)
• Cluster Analysis: Applications and Examples with Stata/SAS
linear regression model
• Statistical model
• Linear regression model
• Parameters estimation and measure of goodness of fit
• Diagnostic tools based on residuals
• Applications and Examples with Stata/SAS
Lecture 3-4
Multiple linear regression model
• matrix approach to linear regression
• Estimation of regression coefficients
• Inferences about regression parameters
• Diagnostic tools based on residuals
• Multicollinearity and VIF index
• Variables Selection methods: Backward, forward, stepwise
• Applications and Examples with Stata/SAS
Lecture 5
One-way Analysis of Variance
• Definition
• Effects Estimation
• Applications and Examples with Stata/SAS
Lecture 6
Logit and probit
• Statistical model
Assesment method: AIC and BIC
• Estimation and goodness of fit
• Classification matrix
• ROC curve
Lecture 7
Forecasting
• Autoregressive model
• Estimation and inference
Lecture 8
• Applications with SAS on real data sets
Lecture 9
• Introduction to Multivariate Statistics
• Principal components Analysis (PCA): Introduction and Motivation (Data dimension reduction, linear combination of variables)
• PCA: Presentation of Method (eigenvalues and eigenvectors, loadings, scores)
Lecture 10
• Graphical Methods (Biplots)
• PCA: Applications and Examples with Stata/SAS
Lecture 11
• Non Hierarchical Cluster Analysis: Overview, Partitional clustering: K-means
• Cluster Analysis: Applications and Examples with Stata/SAS
Lecture 12
• Agglomerative Hierarchical clustering (Ward, single linkage, complete linkage, average linkage)
• Cluster evaluation (dendrogram, pseudo-F, pseudo-T2)
• Cluster Analysis: Applications and Examples with Stata/SAS
Books
Slides and other teaching material will be available on the course website.
- J. Neter, M. Kutner, C. Nachtsheim, W. Wasserman, 1996, Applied Linear Regression Models, Irwi
- J. Lattin, J. Carroll, P. Green, 2003, Analyzing Multivariate Data, Thomson
- J. Neter, M. Kutner, C. Nachtsheim, W. Wasserman, 1996, Applied Linear Regression Models, Irwi
- J. Lattin, J. Carroll, P. Green, 2003, Analyzing Multivariate Data, Thomson
Bibliography
- J. Neter, M. Kutner, C. Nachtsheim, W. Wasserman, 1996, Applied Linear Regression Models, Irwi
- J. Lattin, J. Carroll, P. Green, 2003, Analyzing Multivariate Data, Thomson
- J. Lattin, J. Carroll, P. Green, 2003, Analyzing Multivariate Data, Thomson
Teaching methods
Classroom teaching, exercises, discussion of case studies, 8 hours of practical exercising (LAB) by using statistical software. Examples of applications are shown using STATA and SAS statistical software. Students can replicate the examples at home using the free SAS University Edition software and download numerous datasets from the course website.
To check the preparation, the course of self-assessment texts and exam simulations can be downloaded from the website. At the end of the LAB, an optional final test will be held which will allow to obtain up to 2 points to be added to the final test grade.
There will be quizzes at the beginning and end of the lecture via Kahoot during the theory classes. the top 20% of students will be given 1 point to add to their final grade. In addition, those who have made at least 4 podiums will be given an additional point.
he exam will be assessed according to the following criteria:
Not suitable: important deficiencies and / or inaccuracies in the knowledge and understanding of the topics; limited capacity for analysis and synthesis, frequent generalizations and limited critical and judgment skills, the arguments are presented in an inconsistent way and with inappropriate language;
18-20: just sufficient knowledge and understanding of the topics with possible generalizations and imperfections; sufficient capacity for analysis, synthesis and autonomy of judgment, the topics are frequently exposed in an inconsistent way and with inappropriate / technical language;
21-23: Routine knowledge and understanding of topics; Ability to correct analysis and synthesis with sufficiently coherent logical argument and appropriate / technical language.
24-26: Fair knowledge and understanding of the topics; good analysis and synthesis skills with rigorously expressed arguments but with a language that is not always appropriate / technical.
27-29: Complete knowledge and understanding of the topics; remarkable abilities of analysis and synthesis. Good autonomy of judgment. Topics exposed rigorously and with appropriate / technical language.
30-30L: Excellent level of knowledge and in-depth understanding of the topics. Excellent skills of analysis, synthesis and autonomy of judgment. Arguments expressed in an original way and with appropriate technical language.
To check the preparation, the course of self-assessment texts and exam simulations can be downloaded from the website. At the end of the LAB, an optional final test will be held which will allow to obtain up to 2 points to be added to the final test grade.
There will be quizzes at the beginning and end of the lecture via Kahoot during the theory classes. the top 20% of students will be given 1 point to add to their final grade. In addition, those who have made at least 4 podiums will be given an additional point.
he exam will be assessed according to the following criteria:
Not suitable: important deficiencies and / or inaccuracies in the knowledge and understanding of the topics; limited capacity for analysis and synthesis, frequent generalizations and limited critical and judgment skills, the arguments are presented in an inconsistent way and with inappropriate language;
18-20: just sufficient knowledge and understanding of the topics with possible generalizations and imperfections; sufficient capacity for analysis, synthesis and autonomy of judgment, the topics are frequently exposed in an inconsistent way and with inappropriate / technical language;
21-23: Routine knowledge and understanding of topics; Ability to correct analysis and synthesis with sufficiently coherent logical argument and appropriate / technical language.
24-26: Fair knowledge and understanding of the topics; good analysis and synthesis skills with rigorously expressed arguments but with a language that is not always appropriate / technical.
27-29: Complete knowledge and understanding of the topics; remarkable abilities of analysis and synthesis. Good autonomy of judgment. Topics exposed rigorously and with appropriate / technical language.
30-30L: Excellent level of knowledge and in-depth understanding of the topics. Excellent skills of analysis, synthesis and autonomy of judgment. Arguments expressed in an original way and with appropriate technical language.
Exam Rules
The preparation will be verified through a final exam consisting of a test with multiple-choice and open-ended questions. The final exam is a 1 hour written exam. It will be a test with multiple-choice questions and open-ended questions. The questions will mainly focus on the hypotheses underlying the statistical models, on the interpretation of the outputs, on the properties of some statistical indexes. For each question with a closed answer it will be given as a score 1 if correct, 0 if incorrect; each open question will be given a score between 0 and 2; the final evaluation will be proportionally reported in thirtieths.
The exam will be assessed according to the following criteria:
Not suitable: important deficiencies and / or inaccuracies in the knowledge and understanding of the topics; limited capacity for analysis and synthesis, frequent generalizations and limited critical and judgment skills, the arguments are presented in an inconsistent way and with inappropriate language;
18-20: just sufficient knowledge and understanding of the topics with possible generalizations and imperfections; sufficient capacity for analysis, synthesis and autonomy of judgment, the topics are frequently exposed in an inconsistent way and with inappropriate / technical language;
21-23: Routine knowledge and understanding of topics; Ability to correct analysis and synthesis with sufficiently coherent logical argument and appropriate / technical language.
24-26: Fair knowledge and understanding of the topics; good analysis and synthesis skills with rigorously expressed arguments but with a language that is not always appropriate / technical.
27-29: Complete knowledge and understanding of the topics; remarkable abilities of analysis and synthesis. Good autonomy of judgment. Topics exposed rigorously and with appropriate / technical language.
30-30L: Excellent level of knowledge and in-depth understanding of the topics. Excellent skills of analysis, synthesis and autonomy of judgment. Arguments expressed in an original way and with appropriate technical language.
During the course you will conduct non- mandatory exercises (LAB) in which the use of SAS statistical software is explained. At the end of the exercises there will be a written test (Lab exam) that awards up to 2 points to be added to the final exam grade. Only those who have taken all the Lab lectures will be allowed to participate in this test. There will be quizzes at the beginning and end of the lecture via Kahoot during the theory classes. the top 20% of students will be given 1 point to add to their final grade. In addition, those who have made at least 4 podiums will be given an additional point.
The exam will be assessed according to the following criteria:
Not suitable: important deficiencies and / or inaccuracies in the knowledge and understanding of the topics; limited capacity for analysis and synthesis, frequent generalizations and limited critical and judgment skills, the arguments are presented in an inconsistent way and with inappropriate language;
18-20: just sufficient knowledge and understanding of the topics with possible generalizations and imperfections; sufficient capacity for analysis, synthesis and autonomy of judgment, the topics are frequently exposed in an inconsistent way and with inappropriate / technical language;
21-23: Routine knowledge and understanding of topics; Ability to correct analysis and synthesis with sufficiently coherent logical argument and appropriate / technical language.
24-26: Fair knowledge and understanding of the topics; good analysis and synthesis skills with rigorously expressed arguments but with a language that is not always appropriate / technical.
27-29: Complete knowledge and understanding of the topics; remarkable abilities of analysis and synthesis. Good autonomy of judgment. Topics exposed rigorously and with appropriate / technical language.
30-30L: Excellent level of knowledge and in-depth understanding of the topics. Excellent skills of analysis, synthesis and autonomy of judgment. Arguments expressed in an original way and with appropriate technical language.
During the course you will conduct non- mandatory exercises (LAB) in which the use of SAS statistical software is explained. At the end of the exercises there will be a written test (Lab exam) that awards up to 2 points to be added to the final exam grade. Only those who have taken all the Lab lectures will be allowed to participate in this test. There will be quizzes at the beginning and end of the lecture via Kahoot during the theory classes. the top 20% of students will be given 1 point to add to their final grade. In addition, those who have made at least 4 podiums will be given an additional point.