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Prof. Giovanni Camillo Porzio

Contact information: porzio@unicas.it

Term: First semester

Credits (ECTS): 12

Prerequisites: No formal prerequisites are requested. However, a preliminary knowledge of random variables, basic statistical inference and math is warmly welcome.

Language of Instruction: English

Class hours: 84



LEARNING OBJECTIVES:

Cognitive / Knowledge skills

  • Develop an understanding of the basic concepts of applied statistics, both in terms of statistical inference and data analysis.
  • Evaluate the quality of the data at hand.
  • Understand the different roles each technique may have within a statistical analysis.
  • Be able to understand the main fact (data, methods, results) within an empirical study in Economics and/or Business.

Analytical / Critical Thinking Skills (Oral & Written)

  • Develop the ability to understand to what extent statistical tools may provide answers to empirical questions.
  • Ascertain whether a theoretical model has an empirical foundation, given a proper dataset.  
  • Estimate the parameters of a model, given a proper dataset.
  • Analyzing a relationship between some variables, given a proper dataset.
  • Critical assess the strength of an empirical study (in Economics and /or Business).

 

COURSE DESCRIPTION:

The aim of this course is to provide students with some logical and technical statistical tools which may be exploited to tackle economics and business issues starting from data. The exploratory data analysis and model building perspective is adopted. Room is devoted to applications and case studies.

Course contents: Basics of statistical inference. Simple Linear Regression. Multiple Regression. Weighted regression. Polynomial Regression. Regression with categorical predictors. Dummy variables. Transformations. Regression Diagnostics: Residuals, Outliers and Influence. Nonconstant Variance. Variance Stabilizing Transformations. Graphs for Model Assessment. Variable Selection. Nonlinear Regression. Binary response regression.  Experimental and observational studies/variables.

 

INSTRUCTIONAL FORMAT:

The class will meet for 2 hours (gross of interclass break), four times a week, for a total of 42 sessions. After an introduction aimed at providing the needed background, participants are required to read the materials related to the class and to be prepared prior to coming to class.

Classes will consist of a lecture by the instructor, to be followed by a discussion of the main topics and the assigned case. Main points about the materials and all doubts brought up by the students will be addressed by the instructor during the class.

Time for presentations of homework solutions by students is also allowed within the total amount of class hours (presentations will be approximately scheduled once a week).

 

WORKLOAD EXPECTATIONS:

All students are expected to spend in the average at least 2,5 hours of time on academic studies outside of, and in addition to, each hour of class time. That is, the average student will spend around 18 hours of work at home each week of the term (maybe you are not the average student, though!).

 FORMS OF ASSESSMENT:

The instructor will use many forms of assessment to calculate the final grade you receive for this course. For the record, these are listed and weighted below. The content, criteria and specific requirements for each assessment category will be explained in greater detail in class. Any questions about the requirements should be discussed directly with your faculty well in advance of the due date for each assignment.

 

FORM OF ASSESSMENT

VALUE

Class participation        

10%

Homework solutions

  5%

Assignment presentation(s)

10%

Final exam

75%

 

ASSESSMENT OVERVIEW:

Class Participation:  This grade will be calculated to reflect your participation in class discussions, your capacity to introduce ideas and thoughts dealing with the texts, your ability to use language effectively, and to present your analysis in intellectual, constructive argumentation. If you cannot attend classes your participation can be shown by interacting with your instructor during office hours, i.e. by asking about specific subjects of the syllabus and discussing assignments.

Homework: Homework will be assigned to students weekly. Text available on the MOODLE platform:

http://webuser3.unicas.it/dipeg_didattica_innovativa/moodle/login/index.php?lang=en

Homework includes suggested reading and problems. A printed copy of the solutions to the problems must be provided in class by the due time. Later homework will not be graded.

Solutions can be the result of a joint work of a team of students.

Printed solutions will be evaluated according to two criteria: correctness and neatness.

Correctness means to what extent the solution answer to the main question given by the problem.

Neatness implies the solution is written while considering the reader is not the teacher but a generic person who is interested in the matter, but not really well acquainted with the issue.

Here are some further advices to provide a neat solution:

1) Write comments. Statistics is not about software, but about analysis and interpretation.

Even a simple request as "Find the standard error of ..." requires an answer like "The standard error of ... is equal to ...".

2) Do not report R command lines (especially if not useful for achieving your solution!). If you like, they can be provided in a sort of (very short) Appendix at the end of your solution.

3) Make reader's work the easiest (use bold to stress exercise number, say).

Assignment presentation: Students will be asked to provide a short presentation in class of some of the homework assigned.

Final Exam: Your abilities will be tested in two important areas of competency: data analysis; critical reading of a scientific paper (in the area of Business and/or Economics).

Structure: a combination of a written and oral exam.

The written exam combines open questions (20%) and a data analysis case study (80%), to be tackled by means of a statistical software.

The oral examination is aimed at assessing the significance you ascribe to the facts and ideas you have integrated across your study in this course. It may include a critical reading of a scientific paper, from a statistical point of view.

Prior to the examinations, a comprehensive review will be given during class.

 

CLASS/INSTRUCTOR POLICIES:

Professionalism and communications: As a student, you are expected to maintain a professional, respectful and conscientious manner in the classroom with your instructors and fellow peers.

You are expected to take your academic work seriously and engage actively in your classes. Advance preparation, completing your assignments, showing a focused and respectful attitude is expected of all students. Simply showing up for class or meeting minimum outlined criteria will not earn you a good grade in this course. Utilizing communications, properly addressing your faculty and staff, asking questions and expressing your views respectfully demonstrate your professionalism and cultural sensitivity.

Attendance and Classroom behavior: Although attendance is not compulsory, it is highly recommended. All students must have a respectful attitude towards the professor as well as the classmates.

Arriving late / departing early from Class: Once they have decided to attend, students must behave consistently. Arriving late or leaving class early is disruptive and shows a lack of respect for instructor and fellow students.

Make-up classes: The instructor reserves the right to schedule make-up classes in the event of an unforeseen or unavoidable schedule change. Make-up classes may be scheduled outside of typical class hours, as necessary. 

Missing Examinations: Examinations will not be rescheduled. Pre-arranged travel or anticipated absence does not constitute an emergency and requests for missing or rescheduling exams will not be granted.

Use of Cell Phones, Laptops and Other Electronic Devices: Always check with your instructor about acceptable usage of electronic devices in class. Inappropriate usage of your electronic devices will result in a warning and may lead to a deduction in participation grades. Use of a cellphone for phone calls, text messages, emails, or any other purposes during class is impolite, inappropriate and prohibited. Faculty determines whether laptops will be allowed in class. The use of a laptop, tablets or of cell phones is prohibited during all tests and exams, unless otherwise specified by your instructor. 

 REQUIRED READINGS:

Listed below are the required course textbook and additional readings. These are required materials for the course and you are expected to have constant access to them from the very beginning of the course for reading, highlighting and note-taking. It is required that you have unrestricted access to each. Access to additional sources required for certain class sessions may be provided in paper or electronic format consistently with applicable copyright legislation.

Required text:

Weisberg S. (2014). Applied Linear Regression. Fourth Edition. New York: Wiley

Recommended readings (to be selected and assigned throughout the semester):

The following primary and secondary materials, articles and readings are either available on the web or will be provided in Pdf format by the instructor through the MOODLE platform:

http://webuser3.unicas.it/dipeg_didattica_innovativa/moodle/login/index.php?lang=en

Hand D. (2008) Statistics: A very short introduction, Oxford University Press.   

Jonathan A C Sterne, George Davey Smith (2001). Sifting the evidence—what's wrong with significance tests? British Medical Journal, 322, 226–31.

Charles J. Geyer (2003) Model Selection in R. Manuscript.

Martin A. Koschat and Darius J. Sabavala (1994). The Effects of Television Advertising on Local Telephone Usage, Marketing Science, 13, 374-391.

Gary Gutting (2013) What Do Scientific Studies Show?, The New York Times, April 25.

Dilnot, A. (2012), Numbers and Public Policy: The Power of Official Statistics and Statistical Communication in Public Policymaking. Fiscal Studies, 33: 429–448. doi: 10.1111/j.1475-5890.2012.00167.x

Online References & Research Tools

[1] Textbook website:

http://users.stat.umn.edu/~sandy/alr4ed/

[2] Primer for the textbook:

Weisberg, S. (2014). Computing Primer for Applied Linear Regression, 4th Edition, Using R. Online,

http://users.stat.umn.edu/~sandy/alr4ed/links/alrprimer.pdf

This computer primer supplements Applied Linear Regression, 4th Edition (Weisberg, 2014).

The expectation is that you will read the book and then consult this primer to see how to apply what you have learned using R

[3] How to get the R program:

http://www.r-project.org/

R is a free software environment for statistical computing and graphics.

Some web resources you may find useful

(to strengthen your statistical background):

For beginners: https://www.khanacademy.org/math/probability

Advanced:       http://www.statlect.com/fundamentals_of_statistics.htm

[Ultima modifica: mercoledì 13 settembre 2017]