Obiettivi formativi
By the end of this course, students will be able to: 1. Understand and apply basic statistical concepts and principles to business problems. 2. Exploit regression analysis to analyze and interpret data in a business context. 3. Understand, use and interpret simple, multiple, and logistic regression. 4. Use the Python programming language to perform data analysis.
Prerequisiti
Completion of an introductory course in statistics, covering basic concepts of probability, descriptive statistics, hypothesis testing.
Familiarity with basic algebra and calculus concepts, including functions, derivatives, and integrals (I want to stress basic).
Basic knowledge of computer programming, including familiarity with Python or another statistical software package.
These prerequisites are intended to ensure that students have a solid foundation in the concepts and tools necessary to succeed in the course.
At the beginning of the course we will review briefly all these topics.
Risultati di apprendimento attesi
Understand the basic principles of applied business statistics and how they are used to make decisions in a variety of business settings.
Apply techniques of regression analysis to quantify the relationships between variables and make predictions based on data.
Apply logistic regression analysis to make predictions based on binary outcomes, such as yes/no decisions.
Understand the principles of clustering and principle component analysis.
Develop proficiency in using Python or other statistical software packages to analyze and interpret data.
Communicate effectively about statistical analyses and their results, both in writing and in oral presentations.
Think statistically.
By the end of the course, students should be able to use these techniques to analyze real-world data, draw appropriate conclusions, and communicate their findings effectively to others.
Contenuti Del Corso
Topics may undergo changes
1. Introduction and review of basic concepts
2. Simple Linear Regression (recap)
Intro to simple linear regression
Estimating the regression parameters
Evaluating the goodness of fit
Making predictions and inferences
Confidence intervals and hypothesis tests
3. Multiple Linear Regression
Intro to multiple linear regression
Estimating the regression parameters
Evaluating the goodness of fit
Making predictions and inferences
Confidence intervals and hypothesis tests
4. Logistic Regression
Logistic regression to say "yes" or "no"
Examples of logistic regression in business settings
Interpreting logistic regression coefficients
The logistic regression in the decision-making process
5. Instrumental Variables (pills)
6. Cluster Analysis (pills)
Each week, we use the statistical software Python to analyze topics covered in class that week.
Data visualization will play a key role in this course.
Testi Di Riferimento
Stock, J. and Watson, M. (2019). Introduction to Econometrics (4th ed.). Pearson International.
Witten, J. and Tibshirani, H. (2017). An Introduction to Statistical Learning with Applications in Python. Springer Verlag.
Metodologie Didattiche
Students will utilize various teaching methods, including class lectures, computer exercises, and take-home assignments.
Modalità di verifica dell'apprendimento
Continuous assessment
Criteri per l’assegnazione dell’elaborato finale
Interview with candidate
Settimana 1
Introduction and Course Overview
Settimana 2
Simple Linear Regression (recap)
1. Introduction and Definition
2. Parameters Estimation and Interpretation
3. Model Properties and Assumptions
4. Model Validation
5. Testing the Regression Parameters
Settimana 3
Multiple Linear Regression
1. Model Introduction and Definition
2. Parameters Estimation and Interpretation
Settimana 4
Multiple Linear Regression
1. Model Properties and Assumptions
2. Model Validation
Settimana 5
Multiple Linear Regression
1. Testing the Regression Parameters
2. Checking Model Assumptions
Settimana 6
Multiple Linear Regression
Extensions: dummy variables, interactions, polynomial regression, logarithmic transformations
Settimana 7
Multiple Linear Regression
F-test, AIC, BIC, forward and backward selection
Settimana 8
Logistic Regression
1. Model Introduction and Definition
2. Parameters Interpretation
Settimana 9
Logistic Regression
1. Model Validation
2. Likelihood and Maximum Likelihood Estimation (pills)
Settimana 10
Instrumental Variables
Settimana 11
Cluster Analysis
Settimana 12
General recap. Closing remarks.