This course focuses on designing business and household surveys and analyzing data collected under complex survey designs. The course addresses the SAS procedures POWER, SURVEYSELECT, SURVEYMEANS, SURVEYFREQ, SURVEYREG, SURVEYLOGISTIC, and SURVEYIMPUTE. In addition, the graphing procedures GPLOT, SGPLOT, and SGPANEL are also covered.

Ismerje meg hogyan...

• calculate sample sizes using the POWER procedure
• select complex samples using the SURVEYSELECT procedure
• estimate descriptive statistics using the SURVEYMEANS procedure
• estimate frequencies and percentages using the SURVEYFREQ procedure
• fit general linear models using the SURVEYREG procedure
• fit logistic regression models using the SURVEYLOGISTIC procedure
• handle missing value adjustment using SURVEYIMPUTE procedure
• implement balanced repeated replication, Taylor series and jackknife variance estimation
• perform domain analysis
• assess fitted linear/logistic regression models.

Kinek ajánljuk

Survey statisticians, biostatisticians, epidemiologists, data analysts, and/or social scientists who design, and analyze data from, complex probability surveys

Elérhető képzési formák	Időtartam
Live Web tanfolyam:	6 fél napos

Before attending this course, you should

• be able to execute SAS programs and create SAS data sets. You can gain this experience by completing the SAS Programming 1: Essentials course.
• have a basic understanding of macro variables
• have completed a course in statistics that covers linear regression and logistic regression. You can gain this experience by completing the Statistics 1: Introduction to ANOVA, Regression, and Logistic Regression and Statistics 2: ANOVA and Regression courses.

A tanfolyam SAS/STAT szoftver használatára épül.

Survey Design

• simple random sampling
• stratified random sampling
• probability proportional to size sampling
• Chromy's minimal replacement sampling
• two-stage cluster sampling

Estimating Descriptive Statistics

• expansion (Horvitz-Thompson) estimation
• regression estimation
• ratio estimation

Analytical Uses of Survey Data

• linear regression and ANOVA
• contingency table analysis
• binary logistic regression
• domain analysis
• model assessment