Data Analysis
A.Y. 2018/2019
Learning objectives
Graduate students, researchers in the public and private sector, heads of departments and managers are increasingly dealing with surveys.
This course is intended as an introduction at master/graduate level for those who are new to survey. The aim of the course is to provide an overview of basic probability sampling and estimation methods.
After the course, participants are ready to apply the learned surveys and are able to critically assess existing surveys and survey documentation
This course is intended as an introduction at master/graduate level for those who are new to survey. The aim of the course is to provide an overview of basic probability sampling and estimation methods.
After the course, participants are ready to apply the learned surveys and are able to critically assess existing surveys and survey documentation
Expected learning outcomes
Undefined
Lesson period: First 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
Lesson period
First trimester
ATTENDING STUDENTS
Course syllabus
NON-ATTENDING STUDENTS
Basic concepts in Probability and Statistics: definition of Bernoulli and Normal random variables; point estimation of a mean and a proportion; definition of unbiasedness; definition of variance and mean square error; interval estimation.
The central concept of probability sampling.
The simple random sampling procedure (estimating the mean, the population total and the proportion; variance and standard error of the estimates; confidence intervals and the choice of sample size).
Random sampling with replacement method (estimating the mean, the population total and the proportion; the variance of the estimates; the standard errors; confidence intervals and the choice of sample size).
Non equal probability selection methods (Horvitz-Thompson estimator and Hansen-Hurvitz estimator; variance and standard error of the estimates)
Stratified simple random sampling (estimating the mean, the population total and the proportion; variance and standard error of the estimates; confidence intervals ; optimum choice of stratum sample sizes).
Cluster sampling (estimating the mean, the population total and the proportion; the variance of the estimates; the standard errors; confidence intervals).
Systematic sampling (as a form of cluster sampling).
The central concept of probability sampling.
The simple random sampling procedure (estimating the mean, the population total and the proportion; variance and standard error of the estimates; confidence intervals and the choice of sample size).
Random sampling with replacement method (estimating the mean, the population total and the proportion; the variance of the estimates; the standard errors; confidence intervals and the choice of sample size).
Non equal probability selection methods (Horvitz-Thompson estimator and Hansen-Hurvitz estimator; variance and standard error of the estimates)
Stratified simple random sampling (estimating the mean, the population total and the proportion; variance and standard error of the estimates; confidence intervals ; optimum choice of stratum sample sizes).
Cluster sampling (estimating the mean, the population total and the proportion; the variance of the estimates; the standard errors; confidence intervals).
Systematic sampling (as a form of cluster sampling).
Course syllabus
Basic concepts in Probability and Statistics: definition of Bernoulli and Normal random variables; point estimation of a mean and a proportion; definition of unbiasedness; definition of variance and mean square error; interval estimation.
The central concept of probability sampling.
The simple random sampling procedure (estimating the mean, the population total and the proportion; variance and standard error of the estimates; confidence intervals and the choice of sample size).
Random sampling with replacement method (estimating the mean, the population total and the proportion; the variance of the estimates; the standard errors; confidence intervals and the choice of sample size).
Non equal probability selection methods (Horvitz-Thompson estimator and Hansen-Hurvitz estimator; variance and standard error of the estimates)
Stratified simple random sampling (estimating the mean, the population total and the proportion; variance and standard error of the estimates; confidence intervals ; optimum choice of stratum sample sizes).
Cluster sampling (estimating the mean, the population total and the proportion; the variance of the estimates; the standard errors; confidence intervals).
Systematic sampling (as a form of cluster sampling).
The central concept of probability sampling.
The simple random sampling procedure (estimating the mean, the population total and the proportion; variance and standard error of the estimates; confidence intervals and the choice of sample size).
Random sampling with replacement method (estimating the mean, the population total and the proportion; the variance of the estimates; the standard errors; confidence intervals and the choice of sample size).
Non equal probability selection methods (Horvitz-Thompson estimator and Hansen-Hurvitz estimator; variance and standard error of the estimates)
Stratified simple random sampling (estimating the mean, the population total and the proportion; variance and standard error of the estimates; confidence intervals ; optimum choice of stratum sample sizes).
Cluster sampling (estimating the mean, the population total and the proportion; the variance of the estimates; the standard errors; confidence intervals).
Systematic sampling (as a form of cluster sampling).
SECS-S/01 - STATISTICS - University credits: 9
Lessons: 60 hours
Professors:
Migliorini Elena, Tommasi Chiara
Professor(s)
Reception:
Wednesday from 9:00 to 12:00
Via Conservatorio, III floor, Room n. 35