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Loc. Folcara  03043 CASSINO (FR)
Centralino 0776 2991
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Lecturer info
PORZIO GIOVANNI CAMILLO  Professore Ordinario
Italian versionDepartment: Dipartimento: Economia e Giurisprudenza
Scientific Sector: SECSS/01
Student reception: Due to the Covid19 pandemic restrictions, please write an email to porzio@unicas.it to get an online appointment.
Contact info:
EMail: porzio@unicas.it

Teaching Applied Statistics (91893)
Primo anno di Economics and Entrepreneurship (LM56), Curriculum unico
Credits (CFU): 9,00Program:
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!).Reference books:
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.
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 Google Classroom website:
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.htm 
Teaching Applied Statistics (91950)
Primo anno di Global economy and business (LM56), Global Economy and Business
Credits (CFU): 12,00Program:
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!).Reference books:
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.
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 Google Classroom website:
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.htm 
Teaching Applied Statistics (91950)
Primo anno di Global economy and business (LM56), Dual Degree Unicas  Epoka University
Credits (CFU): 12,00Program:
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!).Reference books:
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.
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 Google Classroom website:
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.htm 
Teaching Applied Statistics (91950)
Primo anno di Global economy and business (LM56), Dual Degree with Samara State University of Economics
Credits (CFU): 12,00Program:
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!).Reference books:
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.
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 Google Classroom website:
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.htm 
Teaching Statistics (91983)
Primo anno di Economia e commercio (L33), Economics and business
Credits (CFU): 12,00Program:
########################################
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
Organizing and visualizing data.
Summarizing and Displaying Measurement Data. Turning data into information. Picturing data: histograms, pie charts, barplot and scatter.
Numerical descriptive measures.
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.Reference books:
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 
Teaching Applied Statistics (91950)
Secondo anno di Global economy and business (LM56), Dual Degree Epoka University  Unicas
Credits (CFU): 12,00Program:
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!).Reference books:
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.
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 Google Classroom website:
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.htm
Prenotazione appello
E' possibile prenotarsi ad un appello d'esame, collegandosi al portale studenti.
Elenco appelli d'esame disponibili
Al momento non ci sono appelli disponibili.
Full Professor of Statistics, University of Cassino and Southern Lazio, since 2005
Degrees
Ph.D., Computational Statistics, 1998, Università di Napoli Federico II
Master of Science in Statistics, 1998, University of Minnesota, USA
Visiting
Visiting professor, MartinLutherUniversität HalleWittenberg, D, May 2019
Visiting professor, Tunis Business School, Université de Tunis, TN, April 2019
Visiting scholar, Katholieke Universiteit Leuven – KULeuven, Be, December 2018
Visiting scholar, MartinLutherUniversität HalleWittenberg, D, May 2016
Visiting professor, MartinLutherUniversität HalleWittenberg, D, May 2015
Visiting scholar, Katholieke Universiteit Leuven – KULeuven, Be, March 2014
Visiting professor, MartinLutherUniversität HalleWittenberg, D, MayJune 2011
Appointments within the University of Cassino and Southern Lazio
Chair, International Relations, University of Cassino and Southern Lazio, since 1 Nov. 2015
Dean, Department of Economics and Law, University of Cassino and Southern Lazio, 20122014
Member, Academic Senate, University of Cassino and Southern Lazio, 20122015
Member, University Evaluation Board ("Nucleo di Valutazione di Ateneo"), University of Cassino, 20102012
Chair, Master Program in Global Economy and Business, University of Cassino, 20082012
Director, Graduate School in Economics, University of Cassino, 20052008
Director, Ph.D. program in Economics, Enterprises, and Quantitative Methods, University of Cassino, 20052006
His research interests include parametric and nonparametric statistics. Particularly, he has worked on directional data analysis, visualization methods, data depths, structural equation models, statistical process control.
He has published on many national and international journals, including: Applications of Mathematics, 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.
He has refereed for many national and international journals.
He regularly attends national and international scientific conferences and workshops, where he presents and discusses his research findings.
He participated to National Research Projects (PRIN).
He his fellows of the Italian Statistical Society, the European Network for Business and Industrial Statistics, and of the American Statistical Association.
Vencalek O., Demni H., Messaoud A., Porzio G.C. (2020) On the optimality of the maxdepth and maxrank classifiers for spherical data, Applications of Mathematics, Volume 65, Issue 3, Pages 331342. https://doi.org/10.21136/AM.2020.033119
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