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 open-source 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 coefficient1, multinomial distribution1
1An 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 distribution1, negative binomial distribution1
1An introduction to Mathematical Statistics and its Application, Larsen and Marx, Prentice Hall
Readings:
- Study chapter 7
* Maximum likelihood estimators1
1Introduction 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 log-likelihood function1
* Score function and Fisher information1
1Applied 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 1Computational 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 (on-line 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 (4-th 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 4-th (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.