© Università degli Studi di Cassino e del Lazio Meridionale
Viale dell'Università  Rettorato  (Campus Universitario)
Loc. Folcara  03043 CASSINO (FR)
Centralino 0776 2991
Fax 0776 310562
PEC
P.IVA 01730470604
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Coordinate bancarie: SWIFT BIC: POCAIT3CXXX
IBAN: IT75 B053 7274 3700 0001 0409 621
Scheda Docente
PORZIO GIOVANNI CAMILLO  Professore Ordinario
English versionAfferente a: Dipartimento: Economia e Giurisprudenza
Settore Scientifico Disciplinare: SECSS/01
Orari di ricevimento: Martedì 1516, in stanza 9.02, oppure su appuntamento in altri orari/giorni. Per evitare disguidi, è preferibile scrivere un email a porzio@unicas.it prima di presentarsi in ufficio durante l'orario di ricevimento. Office hours: Tuesday 1516 in my office (room 9.02), or on appointment. To avoid misunderstanding, please write always an email to porzio@unicas.it before showing you up at the office.
Recapiti:
EMail: porzio@unicas.it

Insegnamento Business Statistics (91847)
Primo anno di Economia e Imprenditorialità (LM56), Curriculum unico
Crediti Formativi Universitari (CFU): 9,00 
Insegnamento Applied Statistics (91950)
Primo anno di Economia e mercati globali (LM56), Dual Degree Unicas  Epoka University
Crediti Formativi Universitari (CFU): 12,00Programma:
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!).Testi:
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 notetaking. 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.
Much more info are available on the elearning platform at http://webuser3.unicas.it/dipeg_didattica_innovativa/moodle/course/view.php?id=58
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, 374391.
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.14755890.2012.00167.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.rproject.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.htmValutazione:
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.
Makeup classes: The instructor reserves the right to schedule makeup classes in the event of an unforeseen or unavoidable schedule change. Makeup classes may be scheduled outside of typical class hours, as necessary.
Missing Examinations: Examinations will not be rescheduled. Prearranged 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. 
Insegnamento Applied Statistics (91950)
Primo anno di Economia e mercati globali (LM56), Global Economy and Business
Crediti Formativi Universitari (CFU): 12,00Programma:
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!).Testi:
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 notetaking. 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.
Much more info are available on the elearning platform at http://webuser3.unicas.it/dipeg_didattica_innovativa/moodle/course/view.php?id=58
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, 374391.
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.14755890.2012.00167.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.rproject.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.htmValutazione:
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.
Makeup classes: The instructor reserves the right to schedule makeup classes in the event of an unforeseen or unavoidable schedule change. Makeup classes may be scheduled outside of typical class hours, as necessary.
Missing Examinations: Examinations will not be rescheduled. Prearranged 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. 
Insegnamento Applied Statistics (91950)
Primo anno di Economia e mercati globali (LM56), Dual Degree with Samara State University of Economics
Crediti Formativi Universitari (CFU): 12,00Programma:
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!).Testi:
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 notetaking. 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.
Much more info are available on the elearning platform at http://webuser3.unicas.it/dipeg_didattica_innovativa/moodle/course/view.php?id=58
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, 374391.
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.14755890.2012.00167.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.rproject.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.htmValutazione:
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.
Makeup classes: The instructor reserves the right to schedule makeup classes in the event of an unforeseen or unavoidable schedule change. Makeup classes may be scheduled outside of typical class hours, as necessary.
Missing Examinations: Examinations will not be rescheduled. Prearranged 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. 
Insegnamento Statistics (91983)
Primo anno di Economia e commercio (L33), Economics and business
Crediti Formativi Universitari (CFU): 12,00Programma:
########################################
STATISTICS  PART I
########################################
Kolmogorov's axiomatic approach to probability. Random experiments, events, and probabilities. Sample space. Operations between events, relations between events. Three axioms. Some basic theorems. Conditional probability. Independence.
Random variables. Probability mass function. Expected value and variance. Bernoulli random variable. Binomial random variable.
Cumulative distribution function (cdf). Linear combinations of random variables. Expected value of linear combinations of random variables. Variance of linear combinations of independent random variables. Expected value and variance of the Binomial random variable. The Poisson random variable.
Random experiments with an uncountable set of events. Continuous random variables. Density function. The Normal random variable, its shape and its density function. Expected value and variance of continuous random variables. Expected value and variance of the Normal rv. Cumulative distribution function (cdf). Standardization. Expected value and variance of a standardized random variable. Distribution of a standardized random variable. Distribution of a linear combination of a Normal random variable. The standardized Normal. Computing normal probabilities using the standardized Normal and "statistical tables".
Computing the quantiles of a Normal distribution. Sum of random variables. Central Limit Theorem. The Normal Approximation to the Binomial Distribution. The Uniform distribution (discrete and continuous).elements of hypothesis testing. Null and alternative hypothesis. Rejection region. Critical values. First and second type error. Test level. Test for the mean when the variance is known (two tails). Test for the mean when the variance is unknown (two tails). Test for a proportion (two tails).
A random variable describes a population. A statistic as a random variable. Sampling distribution of a statistic. Sampling distribution of the sample mean. Sampling distribution of the sample proportion.
Confidence interval for the population mean. Confidence interval for the population proportion. Determining the sample size to obtain a given sampling error.
Elements of Hypothesis Testing. Null and alternative hypothesis. Rejection region. Critical values. First and second type error. Test level. Test for the mean when the variance is known (two tails). Test for the mean when the variance is uknown (two tails). Test for a proportion (two tails).
Onetail Tests. Fisher and the pvalue approach to testing. Potential HypothesisTesting Pitfalls and Ethical Issues. PooledVariance t Test for the Difference Between Two Means.
Comparing the Means of Two Related Populations. Paired t Test. Ftest for the Ratio of Two Variances. ChiSquare Test of independence. ChiSquare Test for differences among two or more proportions.
The Completely Randomized Design: OneWay Analysis of Variance. OneWay ANOVA F Test for Differences Among More Than Two Means. Multiple Comparisons: The TukeyKramer Procedure. ANOVA Assumptions.
########################################
STATISTICS  PART II
########################################
Introduction to statistical analysis: What is statistics all about? Detecting Patterns and Relationships Improper
uses of statistics. Phases of a statistical survey Survey design and preliminary planning, Pretesting, Final survey design and planning, Data collection, Data cleaning, datafile construction, analysis, and final report.
Reading the News. The educated consumer of data. Four hypothetical examples of bad reports. Planning your study. Measures, Mistakes and Misunderstandings. "Simple" measures don't exist. Defining what is being measured. 3.5 Defining a common language.
How to get a good sample, research strategies. Defining a common
language. Simple random sampling and other sampling methods. Difficulties and disasters in sampling.
Experiments and Observational Studies. Designing a good experiment. Difficulties and disasters in experiments. Designing a good observational sxperiment. Difficulties and disasters in observational studies. Getting the Big Picture
Summarizing and Displaying Measurement Data. Turning data into information. Picturing data: histograms, pie charts, barplot and scatter. Summary statistics. Bellshaped Curves and Other shapes. Populations, frequency curves and proportions. Normal Curves. Percentiles and standardized scores. zscores and familiar intervals. Welldesigned statistical pictures. Pictures of categorical data. Pictures of measurements variables. Pictures of trends across time.
Relationships between Measurement Variables. Statistical relationships. Strength vs. statistical significance. Correlation. Relationships can be deceiving: spurious correlations. Correlation does not imply causation. Relationships between categorical variables. Displaying relationships: Contingency tables. Statistical Significance for 2x2 tables. 13.1 Measuring the strength of a relationship. Steps for assessing statistical significance. The chisquare test.
Probability and longterm expectations. Probability. Relative frequency interpretation. Personalprobability interpretation. Some simple probability rules. When intuition differs from relative frequency. 17.1 Revisiting personal probability. Coincidences. 17.3 The gambler's fallacy. Confusion of the inverse. Using expected values to make wise decisions.
Understanding the Economic news. Cost of living: The Consumer Price Index. Uses of the Consumer Price Index. Criticisms of the Consumer Price Index. Seasonal adjustments: Reporting the Consumer Price Index. Economic Indicators.
Understanding and Reporting Trends over Time: what is a time series. A Time Series Plot, components of a time series, irregular Cycles and Random Fluctuations.Testi:
PART I:
Mark L. Berenson, David M. Levine, Kathryn A. Szabat, David F. Stephan, Basic Business Statistics: Global Edition, 14/E, ISBN10: 1292265035 • ISBN13: 9781292265032
©2019 • Pearson • Published 19 Jun 2019
PART II:
Jessica M. Utts, Seeing Through Statistics  4th Edition, Paperback ISBN10: 1285050886 ISBN13: 9781285050881
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Denominazione insegnamento: 91983 Statistics  Economia e commercio  (2019/2020)
Data e ora appello: 24/02/2020, ore 10:00
Luogo: 0.06
Tipo prova: prova orale
Prenotabile: dal 03/12/2019 al 10/02/2020 (prenota l'appello) 
Denominazione insegnamento: 10416 APPLIED STATISTICS  ECONOMIA, MANAGEMENT E FINANZA D'IMPRESA
10416 APPLIED STATISTICS  GLOBAL ECONOMY AND BUSINESS  ECONOMIA E STRATEGIE D'IMPRESA PER IL MERCATO GLOBALE
10416 APPLIED STATISTICS  ECONOMIA, IMPRESA E ISTITUZIONI
10416 APPLIED STATISTICS  ECONOMIA E DIRITTO D'IMPRESA
10416 APPLIED STATISTICS  Global economy and business
10416 APPLIED STATISTICS  Economia
10416 APPLIED STATISTICS  Global economy and business  Economia e strategie d'impresa per il mercato globale
90414 APPLIED STATISTICS  Global economy and business
90414 APPLIED STATISTICS  Global economy and business  Economia e strategie d'impresa per il mercato globale
90414 APPLIED STATISTICS  Management
90414 APPLIED STATISTICS  Economics and entrepreneurship  Economia e Imprenditorialità
90414 APPLIED STATISTICS  ECONOMIA E DIRITTO D'IMPRESA
90414 APPLIED STATISTICS  Economics and entrepreneurship
91950 Applied Statistics  Economia e mercati globali  (2019/2020)
Data e ora appello: 24/02/2020, ore 10:00
Luogo: 0.06
Tipo prova: prova orale
Prenotabile: dal 07/01/2020 al 10/02/2020 (prenota l'appello) 
Denominazione insegnamento: 90442 STATISTICS FOR BUSINESS AND ECONOMICS  Economics and entrepreneurship  Economia e Imprenditorialità
90442 STATISTICS FOR BUSINESS AND ECONOMICS  Global economy and business
90442 STATISTICS FOR BUSINESS AND ECONOMICS  ECONOMIA E DIRITTO D'IMPRESA
90442 STATISTICS FOR BUSINESS AND ECONOMICS  Global economy and business  Economia e strategie d'impresa per il mercato globale
90442 STATISTICS FOR BUSINESS AND ECONOMICS  Management
90442 STATISTICS FOR BUSINESS AND ECONOMICS  MANAGEMENT
91847 Business Statistics  Economia e Imprenditorialità
91847 Business Statistics  Economia e Imprenditorialita'
10457 ELEMENTI DI INFERENZA STATISTICA  Economia  (2019/2020)
Data e ora appello: 24/02/2020, ore 10:00
Luogo: Aula 0.06
Tipo prova: prova orale
Prenotabile: dal 07/01/2020 al 10/02/2020 (prenota l'appello)
Professore di I fascia, Settore SECSS01  Statistica, Università degli Studi di Cassino e del Lazio Meridionale, dal 2005
Formazione
Dottore di Ricerca in Statistica Computazionale e Applicazioni, 1998, Università di Napoli Federico II;
Master of Science in Statistics, 1998, University of Minnesota, USA.
Visiting
Visiting professor, MartinLutherUniversität HalleWittenberg, D, Maggio 2019
Visiting professor, Tunis Business School, Université de Tunis, TN, Aprile 2019
Visiting scholar, Katholieke Universiteit Leuven – KULeuven, Be, Dicembre 2018
Visiting scholar, MartinLutherUniversität HalleWittenberg, D, Maggio 2016
Visiting professor, MartinLutherUniversität HalleWittenberg, D, Maggio 2015
Visiting scholar, Katholieke Universiteit Leuven – KULeuven, Be, Marzo 2014
Visiting professor, MartinLutherUniversität HalleWittenberg, D, MaggioGiugno 2011
Esperienze gestionali
Presidente, Centro Rapporti Internazionali, Università degli Studi di Cassino e del Lazio Meridionale, dal 1.11.2015
Direttore, Dipartimento di Economia e Giurisprudenza, Università degli Studi di Cassino e del Lazio Meridionale, 20122014;
Componente del Senato Accademico, Università degli Studi di Cassino e del Lazio Meridionale, 20122015;
Componente del Nucleo di Valutazione di Ateneo, Università degli Studi di Cassino, 20102012;
Presidente, Laurea Magistrale in Global Economy and Business, Università degli Studi di Cassino, 20082012;
Direttore, Scuola di Dottorato in Economia, Università degli Studi di Cassino, 20052008;
Coordinatore, Dottorato in Economia, Impresa e Analisi Quantitative, Università degli Studi di Cassino, 20052006.
Gli interessi di ricerca includono l’analisi dei dati direzionali, i metodi non parametrici, i metodi grafici e di visualizzazione, le misure della profondità dei dati, i modelli ad equazioni strutturali, il data mining, il controllo statistico dei processi produttivi.
I risultati delle sue ricerche, svolte in collaborazione con diversti autori, anche stranieri, sono stati pubblicati in libri e su diverse riviste nazionali e internazionali. Tra le riviste, sono da segnalare: Applied Stochastic Models in Business and Industry, Biometrics, Computational Statistics and Data Analysis, Journal of Applied Statistics, Metron, Quality and Reliability Engineering International, Quality and Quantity, Statistics and Probability Letters, Statistics in Medicine, The Canadian Journal of Statistics.
Ha svolto e svolge attività di revisione di lavori per diverse riviste nazionali e internazionali.
Partecipa regolarmente a conferenze nazionali e internazionali, dove presenta e discute i contenuti delle sue ricerche.
Ha partecipato quale componente di unità di ricerca a Progetti di Ricerca di Interesse Nazionale (PRIN).
E' socio della Società Italiana di Statistica, dell'European Network for Business and Industrial Statistics e dell'American Statistical Association.
Kirschstein T., Liebscher S., Pandolfo G., Porzio G.C., Ragozini G. (2019) On finitesample robustness of directional location estimators, Computational Statistics & Data Analysis, Volume 133, Pages 5375. https://doi.org/10.1016/j.csda.2018.08.028
Pandolfo G., Paindaveine D., Porzio G.C. (2018) Distancebased depths for directional data, The Canadian Journal of Statistics, Volume 46, Issue 4, Pages 593609. https://doi.org/10.1002/cjs.11479
Buttarazzi D., Pandolfo G., Porzio G.C. (2018) A boxplot for circular data, Biometrics, Volume 74, Issue 4, Pages 14921501. https://doi.org/10.1111/biom.12889
Pandolfo G., D'Ambrosio A., Porzio G.C. (2018) A note on depthbased classification of circular data, Electronic Journal of Applied Statistical Analysis, Vol. 11, Issue 02, 447462. DOI: 10.1285/i20705948v11n2p447
Pandolfo G., Casale G., Porzio G.C. (2018) Testing Circular Antipodal Symmetry Through Data Depths. In: Mola F., Conversano C., Vichi M. (eds) Classification, (Big) Data Analysis and Statistical Learning. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Cham, pp. 97104. https://doi.org/10.1007/9783319557083_11
La Rocca M., Porzio G.C., Vitale M.P., Doreian P. (2018) Finite Sample Behavior of MLE in Network Autocorrelation Models. In: Mola F., Conversano C., Vichi M. (eds) Classification, (Big) Data Analysis and Statistical Learning. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Cham, pp. 4350. https://doi.org/10.1007/9783319557083_5
Kirschstein T., Liebscher S., Porzio G.C., Ragozini G. (2016). Minimum volume peeling: A robust nonparametric estimator of the multivariate mode. COMPUTATIONAL STATISTICS & DATA ANALYSIS, vol. 93, p. 456468, ISSN: 01679473, doi: 10.1016/j.csda.2015.04.012
Vitale Maria Prosperina, Porzio Giovanni C., Doreian Patrick (2016). Examining the effect of social influence on student performance through network autocorrelation models. JOURNAL OF APPLIED STATISTICS, vol. 23, p. 115127, ISSN: 02664763, doi: 10.1080/02664763.2015.1049517
Kirschstein T., Liebscher S., Pandolfo G., Porzio G.C., Ragozini G. (2016). A robust estimator for the mean direction of the von MisesFisher distribution. In: Book of Abstracts – 48th Scientific Meeting of the Italian Statistical Society, Salerno, Italy, 810 June 2016.
Kirschstein T, Liebscher S, Porzio G C, Ragozini G. (2015). Nonparametric estimates of the mode for directional data. In: Book of Abstracts  CLADAG 2015. ISBN: 9788884679499, Santa Margherita di Pula, October 810, 2015
La Rocca Michele, Porzio Giovanni C., Vitale Maria Prosperina, Doreian Patrick (2015). ON THE SAMPLING DISTRIBUTIONS OF THE ML ESTIMATORS IN NETWORK EFFECT MODELS. In: Book of Abstracts  CLADAG 2015. ISBN: 9788884679499, Santa Margherita di Pula, October 810, 2015
Casale Giovanni, Pandolfo Giuseppe, Porzio Giovanni Camillo (2015). TESTING ANTIPODAL SYMMETRY OF CIRCULAR DATA. In: Book of Abstracts  CLADAG 2015. ISBN: 9788884679499, Santa Margherita di Pula, October 810, 2015
Pandolfo G., Porzio G. C. (2015). Nonparametric classifiers for directional data with an application to subpopulations of compositional data. In: Abstracts of CARME2015 Correspondence Analysis and Related Methods Conference, Naples, Italy, 2023 September 2015.
Buttarazzi D., Porzio G.C. (2015). Exploring environmental data through circular boxplots. In: BOOK OF ABSTRACTS  GRASPA 2015. ISBN: 9788888793771, Bari, 1516 giugno 2015
Vitale M.P., Doreian P., La Rocca M., Porzio G.C. (2014). Resampling regression models in the presence of network effects. In: (a cura di) Angela BlancoFernandez, Gil GonzalezRodriguez and George Loizou, PROGRAMME AND ABSTRACTS  7th International Conference of the ERCIM (European Research Consortium for Informatics and Mathematics) Working Group on Computational and Methodological Statistics (ERCIM 2014). p. 62, Pisa: CMStatistics and CFEnetwork, ISBN: 9788493782245, Pisa, Italy.
G. C. Porzio (2013). Regression analysis by example. JOURNAL OF APPLIED STATISTICS, vol. 40, p. 1, ISSN: 02664763, doi: 10.1080/02664763.2013.817041
G.C. Porzio, G. Pandolfo (2013). On depth functions for directional data. In: Cladag 2013 9th Meeting of the Classification and Data Analysis Group Book of Abstracts . p. 365368, Padova:CLEUP, ISBN: 9788867871179, Modena, September 18  20, 2013
P. Costantini, G. C. Porzio, G. Ragozini, J. Romo (2012). Archetypal Functions. In: Analysis and Modeling of Complex Data in Behavioural and Social Sciences. p. 14, Padova:CLEUP, ISBN: 9788861299160, Anacapri, Italy, 34 September 2012
G. C. Porzio, G. Ragozini, S. Liebscher, T. Kirschstein (2012). Minimum Volume Peeling: a Multivariate Mode Estimator. In: . Proceedings of the XLVI Scientific Meeting of the Italian Statistical Society. Roma, 2022 giugno 2012, p. 14, Padova:CLEUP, ISBN: 9788861298828
Porzio G.C., Vitale M.P. (2012). Discovering Interaction in Structural Equation Models through a Diagnostic Plot. In: Bulletin of the ISI 58th World Statistics Congress of the International Statistical Institute, 2011. p. 5184, The Hague, The Netherlands:International Statistical Institute, ISBN: 9789073592339, Dublin, Ireland, 21st26th August 2011
G. Ragozini, G.C. Porzio, S. Liebscher, T. Kirschstein (2012). Minimum volume peeling: A multivariate mode estimator. In: International Conference on Computing and Statistics (CFEERCIM 2012). p. 9798, Spain: ERCIM WG on Computing and Statistics, ISBN: 9788493782221, Oviedo, Spain, 13 Dicembre 2012
A. Messaoud, G.C. Porzio, H. Abidi, M. Limam (2011). A data depth based EWMA control chart. In: Livret des Resumes  43e Journées de Statistique, 1 6, 23  27 Maggio 2011, Gammarth  Tunisie.
G.C. Porzio, G. Ragozini, S. Liebscher, T. Kirschstein (2011). Multivariate modal regions: applications in robust statistics and related computational issues. In: Book of Abstract  THE INTERNATIONAL CONFERENCE ON ROBUST STATISTICS, 67 68, June 27th  July 1st, 2011, Valladolid  Spain.
COSTANTINI P, LINTING M, G. PORZIO (2010). Mining performance data through nonlinear PCA with optimal scaling. APPLIED STOCHASTIC MODELS IN BUSINESS AND INDUSTRY, vol. 26, p. 85101, ISSN: 15241904, doi: 10.1002/asmb.771
G. PORZIO, RAGOZINI G (2010). Convex hull probability depth: first results. In: . PROCEEDINGS OF THE 45TH SCIENTIFIC MEETING OF THE ITALIAN STATISTICAL SOCIETY. Padova, 1618 giugno 2010, p. 18, Padova:CLEUP, ISBN: 9788861295667
G. PORZIO, RAGOZINI G (2009). On the stochastic ordering of folded binomials. STATISTICS & PROBABILITY LETTERS, vol. 79, p. 12991304, ISSN: 01677152, doi: 10.1016/j.spl.2009.01.021
BALZANO S, G. PORZIO (2009). Robust Hotelling’s T2 Control Charts. In: Proceedings of Eurisbis ’09  European Regional Meeting of the International Society for Business and Industrial Statistics. p. 3840, CAGLIARI:Università di Cagliari, ISBN: 9788889744130, Cagliari, IT, 30 May  3 June
G. PORZIO, G. RAGOZINI, D. VISTOCCO (2008). On the use of archetypes as benchmarks. APPLIED STOCHASTIC MODELS IN BUSINESS AND INDUSTRY, vol. 24, p. 419437, ISSN: 15241904, doi: 10.1002/asmb.727
G. PORZIO, RAGOZINI G (2008). A Nonparametric Multivariate Control Chart based on Data Depth. In: Atti della XLIV Riunione Scientifica della Società Italiana di Statistica. p. 12, PADOVA:cleup, ISBN: 9788861292284, Arcavacata di Rende (CS), 2527 giugno 2008
COSTANTINI P, G. PORZIO (2008). Analyzing teaching evaluation data through nonlinear PCA with optimal scaling. In: Convegno Internazionale: Statistical Modelling for University Evaluation  Abstract book. p. 148151, FOGGIA:CDP Service Edizioni, ISBN: 9788896025024, Foggia and Baia delle Zagare, 56 Settembre 2008
G. PORZIO, VITALE M.P. (2007). Exploring Nonlinearities in Path Models. QUALITY & QUANTITY, vol. 41, p. 937954, ISSN: 00335177, doi: 10.1007/s111350069022x
PORZIO G, S. BALZANO, BAUCO C (2007). Il medico tra scienze umanistiche e statistiche: progettualità integrata centro UVA "dottore Angelico" di Acquino e Dipartimento Scienze Economiche Università di Cassino. GERIATRIA, vol. XIX n.5, supplemento, p. 5759, ISSN: 11225807
CIPOLLONE P, FERRANTE F, PORZIO G (2007). Il reddito dei laureati Almalaurea: analisi e spunti di riflessione. In: CONSORZIO INTERUNIVERSITARIO ALMALAUREA. IX Rapporto sulla condizione occupazionale dei laureati. p. 257274, ISBN: 9788815119759
G. PORZIO, RAGOZINI G (2007). Multivariate Control Charts from a Data Mining Perspective. In: LIAO; T.W.; AND TRIANTAPHYLLOU; E.; EDS.. Recent Advances in Data Mining ofEnterprise Data. vol. 6, p. 413462, SINGAPORE:World Scientific, ISBN: 9789812779854
PORZIO G, RAGOZINI G, VISTOCCO D (2006). Archetypal Analysis for Data Driven Benchmarking. In: S. ZANI, A. CERIOLI, M. RIANI, M. VICHI. Data Analysis, Classification and the Forward Search. p. 309318, ISBN: 354035977X
NATALE L., PORZIO G (2004). Autovalutazione via Web: un'indagine tra gli studenti. In: NATALE L., PORZIO G.C.. Modelli per la valutazione e l'autovalutazione universitaria. p. 103129, PADOVA:CLEUP, ISBN: 8871788079
PORZIO G (2004). Valutare l'efficacia dei test di ingresso. In: NATALE L., PORZIO G.C.. Modelli per la valutazione e l'autovalutazione universitaria. p. 91101, PADOVA:CLEUP, ISBN: 8871788079
NATALE L., PORZIO G (2004). Valutazione e autovalutazione universitaria: necessità di un confronto. In: NATALE L., PORZIO G.C.. Modelli per la valutazione e l'autovalutazione universitaria. p. VVII, PADOVA:CLEUP, ISBN: 8871788079
G. PORZIO, RAGOZINI G. (2003). Visually Mining Offline Data for Quality Improvement. QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL, vol. 19, p. 273283, ISSN: 07488017, doi: 10.1002/qre.588
G. PORZIO (2002). A Simulated Band to Check Binary Regression Models. METRON, vol. LX, p. 8395, ISSN: 00261424
Luisa Natale, Giovanni Porzio (2002). Introduzione Valutazione e autovalutazione universitaria: necessità di un confronto . In: Luisa Natale, Giovanni Porzio (a cura di) . Modelli per la valutazione e l'autovalutazione universitaria. p. 57, Padova:CLEUP, ISBN: 8871788079
G. PORZIO, RAGOZINI G. (2002). Un approccio nonparametrico per il monitoraggio di processi multivariati. In: LAURO N.C.; SCEPI G.. Analisi Multivariata per la Qualità Totale. p. 211223, MILANO:FrancoAngeli, ISBN: 9788846443311
G. PORZIO, SCRUCCA L (2002). Visualizing the correlation coefficient. In: Atti XLI Riunione Scientifica Società Italiana di Statistica. p. 241244, Padova:CLEUP, ISBN: 8871785894, Milano, 57 giugno 2002
PAN W., CONNETT JE., PORZIO G, WEISBERG S. (2001). Graphical Model Checking with Correlated Response Data. STATISTICS IN MEDICINE, vol. 20, p. 29352949, ISSN: 02776715
PORZIO G (2001). A Plot for Submodel Selection in Generalized Linear Models. In: S. BORRA, R. ROCCI, M. VICHI, M. SHADER. Advances in Classification and Data Analysis. p. 217224, Heidelberg:SpringerVerlag
PORZIO G, RAGOZINI G (2001). Testing through Empirical CenterOutward Quantiles. In: PROVASI C.. Modelli Complessi e Metodi Computazionali Intensivi per la Stima e la Previsione. p. 409414, PADOVA:Cleup Editrice
PORZIO G, RAGOZINI G (2000). Peeling multivariate data sets: a new approach. QUADERNI DI STATISTICA, vol. 2, p. 8599, ISSN: 15943739
PORZIO G, RAGOZINI G (2000). Exploring the Periphery of Data Scatters: Are There Outliers ?. In: H.A.L. KIERS, J.P. RASSON, P.J.F. GROENEN, M. SCHADER. Data Analysis, Classification, and Related Methods. p. 235240, Heidelberg:SpringerVerlag