Understanding the relationship between random variables and their distributions. Being able to use the tools - also advanced - of statistical inference. Analysing a two-way contingency table. Interval estimation of parameters. Test of hypothesis. Build and evaluate a simple regression model and a multiple regression model.
Learning Objectives - Last names L-Z
Understanding the relationship between random variables and their distributions. Being able to use the tools - also advanced - of statistical inference. Build and evaluate a simple regression model. Analysing a two-way and three-way contingency table. Recognize the different types of multivariate relationships. Build and evaluate a multiple regression model. Social Indicator Construction and definion of composite indicators.
Introduction to statistical methodology;Variables and their measurement;Sampling methods; Bivariate descriptive statistics; Probability; Probability distributions;Sampling distributions; Association measures between variables: descriptive and inferential approaches; Functional and statistical relationship between two variables; Introduction to multivariate analysis;Multiple Regression;Analysis of Variance (ANOVA) methods;Quantitative and Categorical Predictors.
Introduction to statistical methodology;Variables and their measurement;Sampling methods; Sampling distributions; Bivariate descriptive statistics;Association measures between variables: descriptive and inferential approaches; Functional and statistical relationship between two variables; Introduction to multivariate analysis;Multiple Regression and Correlation;Social indicators: definition and sysntesis.
Prerequisites - Last names A-K
Knowledge of basic concepts of descriptive and inferential statistics.
Prerequisites - Last names L-Z
Knowledge of basic concepts of descriptive and inferential statistics.
Teaching Methods - Last names A-K
LESSONS AND PRACTICE IN CLASSROOM
Teaching Methods - Last names L-Z
LESSONS AND PRACTICE IN CLASSROOM
Type of Assessment - Last names A-K
WRITTEN EXAMINATION AND ORAL DISCUSSION
Type of Assessment - Last names L-Z
WRITTEN EXAMINATION AND ORAL DEFENSE
Course program - Last names A-K
Sampling and measurement (Agresti, Chapter 2 and teacher’s notes)
1. The variables and their measurement; 2. Randomization; 3. Sampling variability and potential sources of bias; 4. Other methods of probabilistic sampling (teacher’s notes); 5. Basic concepts of probability and sampling distributions (teacher’s notes); 6. Inference for finite populations (teacher’s notes) ; 7. The main survey techniques in social sciences (teacher’s notes)
The analysis of the association between categorical variables (Agresti Chapter 8)
1. Contingency tables 2. Chi-square test of independence; 3. Residuals: identifying the structure of the association; 4. Measurements of the association in contingency tables; 5. Association between ordinal variables (optional); 6. Inference for ordinal measures of association (optional)
Introduction to linear regression and multivariate relations (Agresti Chapter 10 and references to Ch. 9.1)
1. References on simple linear regression model; 2. Association and causality; 3. Controlling for other variables; 4. Types of multivariate relationships; 5. inferential aspects
Multiple regression and correlation (Agresti, Chapter 11)
1. The multiple regression model; 2. Multiple correlation and R-squared; 3. Inference for multiple regression coefficients; 4. Regression models: some comparisons; 5. Partial correlation and standardized regression coefficients
Course program - Last names L-Z
Sampling and measurement (Agresti, Chapter 2 and teacher’s notes)
1. The variables and their measurement; 2. Randomization; 3. Sampling variability and potential sources of bias; 4. Other methods of probabilistic sampling (teacher’s notes); 5. Basic concepts of probability and sampling distributions (teacher’s notes); 6. Inference for finite populations (teacher’s notes) ; 7. The main survey techniques in social sciences (teacher’s notes)
The analysis of the association between categorical variables (Agresti Chapter 8)
1. Contingency tables 2. Chi-square test of independence; 3. Residuals: identifying the structure of the association; 4. Measurements of the association in contingency tables; 5. Association between ordinal variables (optional); 6. Inference for ordinal measures of association (optional)
Introduction to linear regression and multivariate relations (Agresti Chapters 9 and 10)
1. References on simple linear regression model; 2. Association and causality; 3. Controlling for other variables; 4. Types of multivariate relationships; 5. inferential aspects
Multiple regression and correlation (Agresti, Chapter 11)
1. The multiple regression model; 2. Multiple correlation and R-squared; 3. Inference for multiple regression coefficients; 4. Regression models: some comparisons; 5. Partial correlation and standardized regression coefficients
Suggested readings - Last names A-K
Agresti Alan and Finlay Barbara (2012) “Statistical Methods for the Social Sciences ”. Pearson, Prentice Hall.
Lectures notes.
Suggested readings - Last names L-Z
Agresti Alan and Finlay Barbara (2009) “Statistical Methods for the Social Sciences ”. Pearson, Prentice Hall.
Further information - Last names A-K
Please consult the teacher's website for any changes to the program.
Further information - Last names L-Z
Please consult the teacher's website for any changes to the program.