Applied Statistics
PROF. RICCARDO BRAMANTE; PROF. DIMITRIS FOUSKAKIS
COURSE AIMS
The course is a natural follow-up to the first Statistics course. It will provide
methods, models and tools to analyze and interpret empirical data, with special
emphasis on economic and business problems. It is split into two modules. Module
I will cover simulation methods, more advanced topics in multiple linear regression
models as well as the analysis of categorical (qualitative) data. The study will be
carried out using the powerful open-source R language, which is rapidly becoming
the most popular environment for statistical computing and graphics. You will be
given an hands-on introduction to R, including practical lab sessions. Module II
will cover hypothesis testing in two-samples, as well as time series and forecasting
models for economic and business analysis. Applications to real data will be
considered throughout the course.
COURSE CONTENT
MODULE I: Prof. Dimitris Fouskakis
Introduction to the R language. Graphs. Probability distribution and simulation.
Multiple linear regression: output interpretation, dummy and categorical as
explanatory variables, regression diagnostics, model selection. Categorical data:
contingency tables, descriptive measures and interpretations, test of independence;
logistic regression. Case studies.
MODULE II: Prof. Riccardo Bramante
Two-sample hypothesis-testing: means, proportions, dependent samples.
Time series models. Introduction: descriptive statistics; trend and seasonality;
correlogram. Probability models for time series: stationarity; Moving average
(MA), Autoregressive (AR), ARMA and ARIMA models. Forecasting:
Exponential smoothing, Forecasting from ARIMA models. Case studies.
READING LIST
MODULE I
slides provided by the teacher.
MODULE II
P. NEWBOLD-W.L. CARLSON-B.M. THORNE, Statistics for Business and Economics, Pearson, 2013 [8th
Global edition. (e-text available within Pearson online platform MyMathLab Global)].
Each student is required to purchase access to MyMathLab Global, which contains the e-text of the
textbook, and a variety of student resources..
TEACHING METHOD
The whole course involves lectures and PC-labs, and requires active participation,
ongoing personal study and self-evaluation through the Pearson online platform
MyMathLab Global.
ASSESSMENT METHOD
Exams will be managed exclusively in the PC lab, through the online platform
MyMathLab Global as well as Blackboard.
There will be a first partial exam in the middle of the term. The second partial exam
will take place together with the first general exam