Quantitative Methods to Support Healthcare Decisions
A.Y. 2022/2023
Learning objectives
The learning objectives of the course aim to develop the knowledge, the comprehension, and the use of statistical methods for the data analysis. The students will learn how statistics could answer to research questions in healthcare decision.
In the course, the students will study the analysis of single events and the evaluation of possible relations across events or variables, with a focus on the inferential problems.
It will be emphasized the importance of formulate interpretative hypothesis as a starting point of each quantitative data analysis. The different theoretical topics will be studied also from an operational point of view by developing practical examples and real data through statistical software.
In the course, the students will study the analysis of single events and the evaluation of possible relations across events or variables, with a focus on the inferential problems.
It will be emphasized the importance of formulate interpretative hypothesis as a starting point of each quantitative data analysis. The different theoretical topics will be studied also from an operational point of view by developing practical examples and real data through statistical software.
Expected learning outcomes
At the end of the course, the student will be able to know the main statistical methods and the advanced techniques for data analysis. The student will be able to develop an analysis on real data, from the choice of the data and of the most appropriate statistical methods to the comprehension and interpretation of the results.
Lesson period: Third trimester
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course cannot be attended as a single course. Please check our list of single courses to find the ones available for enrolment.
Course syllabus and organization
Single session
Responsible
Course syllabus
The program will present the following topics:
1. Comparison of two groups
2. Analyzing association between categorical variables
3. Linear regression and correlation
4. Introduction to multivariate relationships
5. Multiple regression and correlation
6. Regression with categorical predictors: Analysis of methods (ANOVA)
7. Multiple regression with Quantitative and categorical predictors
8. Model building with multiple regression
Further info concerning the course content will be provided on https://ariel.unimi.it
1. Comparison of two groups
2. Analyzing association between categorical variables
3. Linear regression and correlation
4. Introduction to multivariate relationships
5. Multiple regression and correlation
6. Regression with categorical predictors: Analysis of methods (ANOVA)
7. Multiple regression with Quantitative and categorical predictors
8. Model building with multiple regression
Further info concerning the course content will be provided on https://ariel.unimi.it
Prerequisites for admission
Knowledge of the elements of Statistics provided in an introductory course is required (descriptive statistics, sampling and measurement; probability distributions; statistical inference: significance estimation and tests; linear regression and correlation).
Teaching methods
o Face-to-face lectures
o Lectures through the use of statistical software
o Lectures through the use of statistical software
Teaching Resources
Agresti A., Statistical Methods for the Social Sciences, 5th Edition, Pearson
Instructors will upload the slides and additional materials on https://ariel.unimi.it
Instructors will upload the slides and additional materials on https://ariel.unimi.it
Assessment methods and Criteria
The final exam consists in solving questions and numerical exercises for data analysis and the output analysis of statistical softwares, for understanding the knowledge acquired in the interpretation of the results and the theoretical fundamentals.
SECS-S/01 - STATISTICS
SECS-S/06 - MATHEMATICAL METHODS OF ECONOMICS, FINANCE AND ACTUARIAL SCIENCES
SECS-S/06 - MATHEMATICAL METHODS OF ECONOMICS, FINANCE AND ACTUARIAL SCIENCES
Lessons: 40 hours
Professor:
Spinelli Daniele
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Professor(s)