© 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

C.F. 81006500607 (5xmille)

Coordinate bancarie: SWIFT BIC: POCAIT3CXXX

IBAN: IT75 B053 7274 3700 0001 0409 621

# Scheda Insegnamento

## Applied Statistics (codice 91950)

**Curriculum:**Global Economy and Business del corso di Global economy and business

**Programmazione per l'A.A.:**2020/2021

**Appelli d'esame:** Calendario - Prenotazioni

**Orari del corso di Global economy and business:** apri

**Crediti Formativi Universitari (CFU):**12,00

**Settore Scientifico Disciplinare (SSD):**SECS-S/01

**Ambito disciplinare:**Statistico-matematico

**Attività:**Attività formative caratterizzanti (B)

**Ore aula:**84

**Canale unico**

**Obiettivi:**

Cognitive / Knowledge skills

Develop an understanding of the basic concepts of applied statistics, both in terms of statistical inference and data analysis.

Evaluate the quality of the data at hand.

Understand the different roles each technique may have within a statistical analysis.

Be able to understand the main fact (data, methods, results) within an empirical study in Economics and/or Business.

Analytical / Critical Thinking Skills (Oral & Written)

Develop the ability to understand to what extent statistical tools may provide answers to empirical questions.

Ascertain whether a theoretical model has an empirical foundation, given a proper dataset.

Estimate the parameters of a model, given a proper dataset.

Analyzing a relationship between some variables, given a proper dataset.

Critical assess the strength of an empirical study (in Economics and /or Business).

**Programma:**

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 note-taking. It is required that you have unrestricted access to each. Access to additional sources required for certain class sessions may be provided in paper or electronic format consistently with applicable copyright legislation.

Required text:

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

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

The following primary and secondary materials, articles and readings are either available on the web or will be provided in pdf format by the instructor through the 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, 374-391.

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

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

Online References & Research Tools

[1] Textbook website:

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

[2] Primer for the textbook:

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

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

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

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

[3] How to get the R program:

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

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

Some web resources you may find useful

(to strengthen your statistical background):

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

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

## Applied Statistics (codice 91950)

**Curriculum:**Dual Degree with Samara State University of Economics del corso di Global economy and business

**Programmazione per l'A.A.:**2020/2021

**Appelli d'esame:** Calendario - Prenotazioni

**Orari del corso di Global economy and business:** apri

**Crediti Formativi Universitari (CFU):**12,00

**Settore Scientifico Disciplinare (SSD):**SECS-S/01

**Ambito disciplinare:**Statistico-matematico

**Attività:**Attività formative caratterizzanti (B)

**Ore aula:**84

**Canale unico**

**Obiettivi:**

Cognitive / Knowledge skills

Develop an understanding of the basic concepts of applied statistics, both in terms of statistical inference and data analysis.

Evaluate the quality of the data at hand.

Understand the different roles each technique may have within a statistical analysis.

Be able to understand the main fact (data, methods, results) within an empirical study in Economics and/or Business.

Analytical / Critical Thinking Skills (Oral & Written)

Develop the ability to understand to what extent statistical tools may provide answers to empirical questions.

Ascertain whether a theoretical model has an empirical foundation, given a proper dataset.

Estimate the parameters of a model, given a proper dataset.

Analyzing a relationship between some variables, given a proper dataset.

Critical assess the strength of an empirical study (in Economics and /or Business).

**Programma:**

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 note-taking. It is required that you have unrestricted access to each. Access to additional sources required for certain class sessions may be provided in paper or electronic format consistently with applicable copyright legislation.

Required text:

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

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

The following primary and secondary materials, articles and readings are either available on the web or will be provided in pdf format by the instructor through the 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, 374-391.

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

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

Online References & Research Tools

[1] Textbook website:

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

[2] Primer for the textbook:

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

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

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

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

[3] How to get the R program:

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

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

Some web resources you may find useful

(to strengthen your statistical background):

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

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

## Applied Statistics (codice 91950)

**Curriculum:**Dual Degree Unicas - Epoka University del corso di Global economy and business

**Programmazione per l'A.A.:**2020/2021

**Appelli d'esame:** Calendario - Prenotazioni

**Orari del corso di Global economy and business:** apri

**Crediti Formativi Universitari (CFU):**12,00

**Settore Scientifico Disciplinare (SSD):**SECS-S/01

**Ambito disciplinare:**Statistico-matematico

**Attività:**Attività formative caratterizzanti (B)

**Ore aula:**84

**Canale unico**

**Obiettivi:**

Cognitive / Knowledge skills

Develop an understanding of the basic concepts of applied statistics, both in terms of statistical inference and data analysis.

Evaluate the quality of the data at hand.

Understand the different roles each technique may have within a statistical analysis.

Be able to understand the main fact (data, methods, results) within an empirical study in Economics and/or Business.

Analytical / Critical Thinking Skills (Oral & Written)

Develop the ability to understand to what extent statistical tools may provide answers to empirical questions.

Ascertain whether a theoretical model has an empirical foundation, given a proper dataset.

Estimate the parameters of a model, given a proper dataset.

Analyzing a relationship between some variables, given a proper dataset.

Critical assess the strength of an empirical study (in Economics and /or Business).

**Programma:**

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 note-taking. It is required that you have unrestricted access to each. Access to additional sources required for certain class sessions may be provided in paper or electronic format consistently with applicable copyright legislation.

Required text:

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

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

The following primary and secondary materials, articles and readings are either available on the web or will be provided in pdf format by the instructor through the 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, 374-391.

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

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

Online References & Research Tools

[1] Textbook website:

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

[2] Primer for the textbook:

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

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

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

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

[3] How to get the R program:

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

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

Some web resources you may find useful

(to strengthen your statistical background):

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

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

## Applied Statistics (codice 91950)

**Curriculum:**Dual Degree Epoka University - Unicas del corso di Global economy and business

**Programmazione per l'A.A.:**2020/2021

**Appelli d'esame:** Calendario - Prenotazioni

**Orari del corso di Global economy and business:** apri

**Crediti Formativi Universitari (CFU):**12,00

**Settore Scientifico Disciplinare (SSD):**SECS-S/01

**Ambito disciplinare:**Statistico-matematico

**Attività:**Attività formative caratterizzanti (B)

**Ore aula:**84

**Canale unico**

**Obiettivi:**

Cognitive / Knowledge skills

Develop an understanding of the basic concepts of applied statistics, both in terms of statistical inference and data analysis.

Evaluate the quality of the data at hand.

Understand the different roles each technique may have within a statistical analysis.

Be able to understand the main fact (data, methods, results) within an empirical study in Economics and/or Business.

Analytical / Critical Thinking Skills (Oral & Written)

Develop the ability to understand to what extent statistical tools may provide answers to empirical questions.

Ascertain whether a theoretical model has an empirical foundation, given a proper dataset.

Estimate the parameters of a model, given a proper dataset.

Analyzing a relationship between some variables, given a proper dataset.

Critical assess the strength of an empirical study (in Economics and /or Business).

**Programma:**

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 note-taking. It is required that you have unrestricted access to each. Access to additional sources required for certain class sessions may be provided in paper or electronic format consistently with applicable copyright legislation.

Required text:

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

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

The following primary and secondary materials, articles and readings are either available on the web or will be provided in pdf format by the instructor through the 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, 374-391.

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

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

Online References & Research Tools

[1] Textbook website:

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

[2] Primer for the textbook:

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

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

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

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

[3] How to get the R program:

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

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

Some web resources you may find useful

(to strengthen your statistical background):

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

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

*[Ultima modifica: mercoledì 30 novembre 2016]*