Master Program:  Economics & Enterpreneurship 
Credits:  10 ECTS 
Start date:  21 September 2015 
End date:  08 March 2016 
Class calendar:  Monday, 12:00  14:00 Tuesday, 12:00  14:00 
Room:  0.05 / 1.05 
Language: English
Master Program:  Economics & Enterpreneurship 
Credits:  10 ECTS 
Start date:  21 September 2015 
End date:  08 March 2016 
Class calendar:  Monday, 12:00  14:00 Tuesday, 12:00  14:00 
Room:  0.05 / 1.05 
Language: English
AIMS
Aim of the course is to provide students with a broad overview of statistical methods and models which may be exploited to tackle economics and business issues starting from data. Students will learn statistics by doing, exploiting R, a popular opensource software for data analysis. Emphasis on the applications of the techniques and on the interpretation of results will help students to appreciate the relevance of the statistical tools in real life applications.
REQUIRED BACKGROUND
A basic knowledge of elementary calculus is required. The course will start assuming a previous knowledge of statistics at an undergraduate level: the minimal prerequisite is a undergraduate course in probability and basic statistics.
TEACHING
Lectures and lab sessions.
EXAMINATION METHODS
Written + oral final exam.
DETAILED SYLLABUS
(for further details, see the diary of class, weekly updated with the topics covered in the classroom)
CONTENTS
Gathering and Exploring Data: data description, graphical and numerical summaries, association, gathering data. Probability distributions and sampling distributions. Inferential statistics: confidence intervals and significance tests about population central values, for comparing two population central values, about population variances, for comparing two variances. Analzying associations: association between categorical variables and between numerical variables. Statistical models: multiple regression, analysis of variance, analysis of covariance. Nonparametric statistics.
PREREQUISITE (basic requirements)
EXTRACTS FROM THE FOLLOWING BOOKS
ADDITIONAL READINGS (EXTRACTS)
Date  Topics 

26/09/2016  morning 
LECTURE 1 Introduction to the class. Basic jargon: population vs sample, parameter vs statistic, exploratory analysis vs inferential analysis. units, variables and their types. Population probability model. Random variable (r.v.), expectation, variability, skewness. The Bernoulli probability model. 
26/09/2016  afternoon 27/09/2016 28/09/2016 29/09/2016 30/09/2016  morning 30/09/2016  afternoon 03/10/2016 
SHORT COURSE ON R

04/10/2016 
LECTURE 2 
10/10/2016  LECTURE 3 Other special distributions: the hypergeometric distribution, the Poisson distribution, the geometric distribution, the negative binomial distribution. 
11/10/2016 
LECTURE 4 
17/10/2016 
LECTURE 5 
18/10/2016 
LECTURE 6 
24/10/2016 
LECTURE 7

25/10/2016 
LECTURE 8 
07/10/2016  LECTURE 9 Element of frequentist inference: properties of estimators. Frequentist properties of the likelihood. 
08/10/2016  LECTURE 10 The sampling distributions for the main statistics: inference on the mean (case of known and unknown variance), inference on variance, inference on proportion. 
14/11/2016  LECTURE 11 Resampling methods: the bootstrap method for deriving the approximate sampling distribution of a statistic. Point estimate and interval estimate. Confidence interval for the mean (case of known and unknown variance), inference on variance, inference on proportion. 
15/11/2016  LECTURE 12 Confidence interval for the variance of a normal population. Bootstrap confidence intervals: the percentile method. Introduction to hypothesis testing: type of hypothesis, the spaces involved, the decision rule, the two type of errors, test statistic. 
21/11/2016  LECTURE 13 Hypothesis tests for one sample problems. 
22/11/2016  LECTURE 14 Inferences on two samples: comparing means, variances and proportions. 
Lezione del:  Argomenti trattati 

09/01/2017  LECTURE 15 Anova. 
10/01/2017  LECTURE 16 The simple linear regression model: the problem of estimation. 
16/01/2017  LECTURE 17 The simple linear regression model: the problem of inference. 
17/01/2017  LECTURE 18 The simple linear regression model: the problem of prediction. 
23/01/2017  LECTURE 19 The multiple linear regression model. 
24/01/2017  LECTURE 20 The multiple linear regression model: use of a dummy regressor. 
30/01/2017  LECTURE 21 The multiple linear regression model: use of a nominal/ordinal regressor. 
31/01/2017  LECTURE 22 A regression model with a dummy response variable: the linear probability model. 
06/02/2017  LECTURE 23 A regression model with a dummy response variable: the logit and the probit model. 
07/02/2017  LECTURE 24 Regression model for nominal response. 
13/02/2017  LECTURE 25 Regression model for ordinal response. 
14/02/2017  LECTURE 26 Regression model for count data: the Poisson regression model. 
20/02/2017  LECTURE 27 Regression model for count data: the negative binomial regression model. 
21/02/2017  LECTURE 28 
27/02/2017  LECTURE 29 
28/02/2017  LECTURE 30 
06/03/2017  LECTURE 31 
07/03/2017  LECTURE 32 
Readings (Berenson & al.):  study chapter 1, chapter 2, chapter 3, chapter 4  study the R transcript of R short course (available in the Lectures section)
Readings:  Read the Introduction to RStudio (Data & Statistical Services, Princeton University)
Readings (Berenson & al.):  Study chapter 5: sections 5.1, 5.2 (not covered during the lecture) and 5.3
 Study Multinomial distribution:
* wikipedia link, link to the online multimedia course (Rice University),
* multinomial coefficient^{1}, multinomial distribution^{1}
^{1}An introduction to Mathematical Statistics and its Application, Larsen and Marx, Prentice Hall
Readings:  Study chapter 5: sections 5.4, 5.5, 5.6
 Study chapter 6: sections 6.1, 6.2, 6.3 (not covered during the lecture), 6.4, 6.5, 6.6 (partially covered during the lecture)
* geometric distribution^{1}, negative binomial distribution^{1}
^{1}An introduction to Mathematical Statistics and its Application, Larsen and Marx, Prentice Hall
Readings:
 Study chapter 7
* Maximum likelihood estimators^{1}
^{1}Introduction to Probability and Statistics for Engineerings and Scientists, Sheldon Ross, Academic Press
Readings:
 Study (again and in detail) the chapter on Maximum likelihood estimators (see homework #5)
 Study Taylor series: Wikipedia link
* Quadratic approximation of the loglikelihood function^{1}* Score function and Fisher information^{1}
^{1}Applied Statistics and Inference, Held and Sabanés Bové, Springer
Readings:
 Study chapter 7
 Study chapter 12: section 12.5
Readings:
* Bootstrap methods (not considering the Matlab code)^{1}  Study chapter 8
 Study chapter 9: section 9.1 ^{1}Computational Statistics Handbook with Matlab, Martinez and Martinez, Chapman and Hall/CRC
Readings:
 Study chapter 9: sections 9.2, 9.3, 9.4, 9.5 and 9.6 (online topic)
 Study chapter 10
 Study chapter 12: section 12.5
Readings (Gujarati and Porter):
 Chapters 1 and 2
Readings (Gujarati and Porter):
 Chapter 3
Readings (Gujarati and Porter):
 Chapter 4
 download here the dataset for the exercise: Grade Point Average, using the command:
gpa < read.table("http://domenicovistocco.it/teachingMaterials/busStats/datasets/grade_point_data.txt")
Readings (Gujarati and Porter):
 Chapter 5
 download here the dataset for the exercise: Grade Point Average, using the command:
gpa < read.table("http://domenicovistocco.it/teachingMaterials/busStats/datasets/grade_point_data.txt")
Readings (Gujarati and Porter):
 Study again chapters 1, 2, 3, 4 and 5
 Study chapter 6: sections 2 and 3
Readings (Gujarati and Porter):
 Chapter 7 and 8
 download here the dataset for the exercise: Brand Preference, using the command:
brand < read.table("http://domenicovistocco.it/teachingMaterials/busStats/datasets/brand_preference.txt", header = TRUE)
Readings:
 Chapter 9
Readings:
 Chapter 15
Readings:
 Chapter 15
First and second term:
Monday, 3:00  5:00 pm
Tuesday, 3:00  5:00 pm
III trimestre (and during the week when there is no teaching):
see the schedule weekly announced
(see yellow box "Next office hours")
You can make a date at other times writing to the teacher: vistocco@unicas.it
NOTE:
Wednesday 28th June, I will meet students at 9:00  10:30 a.m.
Student ID  Student  Mark  Oral timetable (4th October) 

0047951  Boateng Owsu Ose  16  3:00 p.m. 
0047963 
Dao Khanh Ly 
17  3:00 p.m. 
0047968 
Do Hong Duong 
17  3:00 p.m. 
0006975  Grossi Fabiola  Failed   
The oral exams will be held on October 4th (room 9.24).
I have absolutely no tolerance for cheating in any form. Students who are caught cheating will be given the strongest possible consequences allowed by the university. Students who cheat and are not caught will be haunted by the memory of their misdeeds for the rest of their miserable lives.
(a famous Professor of Philosophy from Ohio State University)
I am sorry but none of the midterm exams were sufficient.
In your interest, I solved the midterm exam so to show the very basic underlying reasoning. See here the solutions
I encourage you in doing your weekly homework and above all in focusing on understading what you are doing (do not just mechanically apply the formulas).
If you think that what you studied was enough to be sufficient, you should work harder and/or differently.
You have time to adequately prepare for the full exam that will be held in March.