Insegnamento a.a. 2023-2024

Topic Reference:

1. Course presentation & simple linear regression (Chapter 9 of the textbook A and notes).
2. Multivariate regression I (Chapter 11 of the textbook A, paragraphs 11.1-11.4 and notes)
3. Multivariate regression II (Chapter 11 and 14 of the textbook A, paragraphs 11.5-11.7, 14.4 and notes).
4. Examples & exercises (Notes).
5. Analysis of the variance I (Chapter 12 of the textbook A, paragraphs 12.1-12.3 and notes).
6. Analysis of the variance II (Chapter 12 of the textbook A, paragraphs 12.4-12.5 and notes).
7. Combining ANOVA and Regression (Chapter 13 of the textbook A, paragraphs 13.1-13.4 and notes).
8. Examples & exercises (Notes).
9. Longitudinal Data I (Chapter 12 of the textbook A, paragraphs 12.6-12.7 and notes).
10. Longitudinal Data II (Chapter 2 of the textbook B, paragraphs 2.1-2.3 and notes).
11. Longitudinal Data III (Chapters 3, 4 of the textbook B, paragraphs 3.1-3-5, 4.1-4.3 and notes).
12. Examples & exercise (Notes).
13. Categorical data analysis I (Chapter 8 of the textbook A, paragraphs 8.1-8.3).
14. Categorical data analysis II (Chapter 8 of the textbook A, paragraphs 8.4 and Notes).
15. Categorical data analysis III (Chapter 8 of the textbook A, paragraphs 8.5-8.6 and Notes).
16. Examples & exercises (Notes).
17. Logistic regression I (Chapter 15 of the textbook A, paragraphs 15.1, 15.3 and notes).
18. Logistic regression II (Chapter 15 of the textbook A, paragraphs 15.2, 15.3 and notes).
19. Logistic regression II (Chapter 15 of the textbook A, paragraphs 15.4, 15.5 and notes).
20. Examples & exercises (Notes).

• Understand the theoretical background of the main statistical techniques.
• Learn how to organize and analyze a dataset.
• Learn how to apply suitable methods to estimate the impact of public interventions.
• Learn the use of the software STATA for data analysis. 

• Become confident with the main statistical techniques for data analysis and select the most appropriate technique to respond to the research questions.
• Independently organize a dataset and define an appropriate strategy for the data analysis process.
• Apply suitable methods to estimate the impact of public interventions.
• Become an independent user of the software STATA for data analysis.

• Face-to-face lectures
• Exercises (exercises, database, software etc.)
• Case studies /Incidents (traditional, online)
• Group assignments

• The open answer questions ascertain the problem-solving ability of students. They need to be able to correctly identify a proper statistical methodology to solve a real-world problem, provide the correct solution of the problem and formulate the correct interpretation of the obtained results.

• During the course there is also an assignment (empirical analysis) that students should perform on their own or in groups and hand in before the end of the course. The assignment ascertains the ability of student to formulate a correct statistical methodology to analyse a real dataset, perform an appropriately statistical analysis of the available data and correctly interpret the outcome.

Attending and non-attending students. The assessment methods have been designed to stimulate the active involvement in the course. The grade breakdown is as follows:

• Group assignment 30%
• First and second partial or final written exam 70%

• At the end of the course there is an exam to test the knowledge acquired. There is a written exam with questions and exercises on the topics taught in class (see the detailed description). The open answer questions ascertain the problem-solving ability of students. They need to be able to correctly identify a proper statistical methodology to solve a real-world problem, provide the correct solution of the problem and formulate the correct interpretation of the obtained results. The maximum grade for the final exam is 22 points. To consider the final written exam successfully passed students need to obtain a grade of 12 points. Alternatively, students can complete two mid-term exams during the course. The maximum grade for the mid-term exams is still 22 points for both. Students can attend the second partial exam if they have obtained a minimum grade of 11 points in the first partial exam. If the second partial exam is not handed in, the student must take the final exam. To consider the the two written partial exams successfully passed students need to obtain an average grade of 12 points between the two exams.
• During the course there is also an assignment (empirical analysis) that students should perform on their own or in groups and hand in before the end of the course. The assignment ascertains the ability of student to formulate a correct statistical methodology to analyse a real dataset, perform an appropriately statistical analysis of the available data and correctly interpret the outcome. The assignment is worth a maximum of 9 points. The grade of the assignment is valid also for the following academic years; hence you do not need to repeat the assignment. To consider the final successfully passed students need to obtain a grade of 18 points out of 31. The examination procedures are the same for students who attend and do not attend the classes. A successful assignment is considered valid for the current academic year and the following one. If a student does not pass the written exam within the following academic year, the assignment must be retaken.

• Textbook A: A. AGRESTI, B. FINLAY, Statistical Methods for the Social Sciences, Prentice Hall, 2009, 4th edition.
• Textbook B: E. W. FREES, Longitudinal and Panel Data, Cambridge University Press, 2004, 1st edition. 
• A selection of notes and other materials, available in the course reserve (e-learning).
• U. KOHLER, F. KREUTER, Data Analysis Using Stata, Stata Press, 2012, 3rd edition.