Università degli studi dell'Insubria

APPLIED STATISTICS

A.A. di erogazione 2017/2018
Insegnamento obbligatorio

 (A.A. 2017/2018)

Docenti

RECLA ALESSANDRO
TONINI DANIELE
Anno di corso: 
1
Tipologia di insegnamento: 
Affine/Integrativa
Settore disciplinare: 
STATISTICA PER LA RICERCA SPERIMENTALE E TECNOLOGICA (SECS-S/02)
Attivita' formativa capogruppo: 
Crediti: 
6
Ciclo: 
Primo Semestre
Ore di attivita' frontale: 
40
Dettaglio ore: 
Lezione (40 ore)

Objective of the module
The main purpose of the module is to supply a general view of the use of statistic methods as a worthwhile support for decisional processes in management, marketing and finance.
At the end of the course, the students will be able to analyze and interpret a set of data, giving also managerial implications. He or she will be able to use in autonomy the software of data analysis.

Prerequisiti: 

Prerequisites
During the course, there will be a review of basic statistical concepts. So, there are not prerequisites for this course. However, in case of difficulty, the recommended basic statistic book is indicated below in the teaching materials and textbooks.

Course introduction. Examination Rules. Presentation of the course. Elements of univariate statistics.
Presentation of MySTAT software; exercises about univariate statistics.
Elements of bivariate statistics: cross tabulation, chi-squared test, linear correlation e analysis of variance. T-test and F-test.
Practical session about bivariate statistics
The simple linear regression model: theoretical elements and inference on the coefficients
Practical session about simple linear regression model
The multiple linear regression: theoretical elements and inference on the parameters, F test.
Practical session about multiple linear regression
The multiple linear regression: the use of categorical variables (dummy); multicollinearity.
Practical session about multiple linear regression

Factor analysis: methodological aspects and practical examples.
Practical session about Factor analysis
Non-hierarchical cluster analysis (K-Means): methodological aspects and practical examples.
Exercises about Non-hierarchical cluster analysis (K-Means).
Hierarchical cluster analysis: methodological aspects and practical examples.
Practical session about Hierarchical cluster analysis.
Logistic regression: definitions, estimation and interpretation of the coefficients, evaluation of the model effectiveness.
Practical session about logistic regression.

Modalities of Teaching
Theoretical and practical sessions focus on applied data analysis:
• Business issues
• Applied statistical methodology
• Interpretation of the results
The practical session are based on the use of real data analyzed through the MYSTAT software with actual data.

Modalita' di verifica dell'apprendimento: 

Examination rules
The exam consists of a written exam, composed by exercises, theory questions and interpretation of MYSTAT outputs. The exam program is constituted by the subjects described in the course syllabus. All the subjects are covered in the material available on the weblearning page of the course.

The exam will be written. At their choice the students can select to take the exam through the two midterms or through a unique final exam.

Partial Exams:
The first midterm shall concern all the material in the syllabus covering the first part of the course.
The second midterm shall concern the material in the syllabus covering the second part of the course.
Each partial exam will last 1 hour.
Each midterm can have a maximum grade of 16 and the final grade will be the sum of the grades in the two midterms. For instance, if the grade in the first midterm is 4 and in the second midterm is 16, one obtains the grade of 20.
The minimum grade to pass the exam is 18. Thus, to access the second midterm a score greater than or equal to 2 in the first midterm is necessary. A total grade higher than 30 (i.e., 31 or 32) is rewarded by “cum laude.”
General Exam:
The exam lasts 1 hour and 30 minutes. There will be exercises covering the entire course material.
A total grade higher than 30 (i.e., 31 or 32) is rewarded by “cum laude.”

Note: the above rules and syllabus apply to all students, without distinctions in terms of attendance of the course.

The final grade of the course depends only on the grade of the exam (general or partial).

Teaching materials and textbooks
- Slides and other digital materials provided by the teachers
- Suggested readings (not mandatory):
1) Klibanoff, Sandroni, Moselle, Saraniti, "Managerial Statistics: A Case-Based Approach" - South-Western Pub, 2005
2) Malhotra, Birks, Wills “Marketing Research – An Applied Approach"- Pearson – Prentice Hall, ed. 2012
Recommended basic statistics textbook: P. Newbold, W.L. Carlson, B. Thorne, "Statistics for Business and Economics", Pearson. (Eighth edition – Global edition, 2013)

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