SAS Certified Statistical Business Analyst Using SAS 9
This self-paced learning is designed for individuals who need a solid foundation in using SAS software to conduct and interpret complex statistical data analysis and who can learn autonomously. The included content helps you prepare for the SAS Certified Statistical Business Analyst Using SAS(R)9: Regression and Modeling credential.
What is a modern analytics platform ?
- SAS Statistics1: Introduction to ANOVA, Regression, and Logistic Regression: This introductory course is for SAS software users who perform statistical analyses using SAS/STAT software. The focus is on t tests, ANOVA, and linear regression, and the course includes a brief introduction to logistic regression.
- Predictive Modeling Using Logistic Regression: This course covers predictive modeling using SAS/STAT software with emphasis on the LOGISTIC procedure. This course also discusses selecting variables and interactions, recoding categorical variables based on the smooth weight of evidence, assessing models, treating missing values, and using efficiency techniques for massive data sets.
The learning environment includes the following content:
- e-Learning (online, hands-on tutorials)
- Course materials (course notes and data)
- Quiz to test your knowledge
Learn How To:
- generate descriptive statistics and explore data with graphs
- perform analysis of variance and apply multiple comparison techniques
- perform linear regression and assess the assumptions
- use regression model selection techniques to aid in the choice of predictor variables in multiple regression
- use diagnostic statistics to assess statistical assumptions and identify potential outliers in multiple regression
- use chi-square statistics to detect associations among categorical variables
- fit a multiple logistic regression model
- score new data using developed models
- use logistic regression to model an individual’s behavior as a function of known inputs
- create effect plots and odds ratio plots using ODS Statistical Graphics
- handle missing data values
- tackle multicollinearity in your predictors
- assess model performance and compare models.
- have completed the equivalent of an undergraduate course in statistics covering p-values, hypothesis testing, analysis of variance, and regression
- be able to execute SAS programs and create SAS data sets. You can gain this experience by completing the SAS Programming 1: Essentials course.
This course addresses SAS/STAT software. This course also addresses Base SAS software and touches on SAS/GRAPH software. You can benefit from this course even if SAS/GRAPH software is not installed at your location. Note: Access to software is not included.