SAS Statistical Business Analyst E-learning Course Trianing in India
SAS Certification training - SAS Statistical Business Analyst
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.
- 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.
- e-Learning (online, hands-on tutorials)
- Course materials (course notes and data)
- Links to other relevant content such as video tutorials and recommended reading
- Certification Prep Guide
- Quiz to test your knowledge
- 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.
- Get trained by experts.
- Train when and where you want.
- Learn at your own pace.
- Satisfaction guaranteed.
- Content equivalent to instructor-based courses, optimized for self-study.
- Certificate of completion.
- Created by SAS experts.
The learning environment includes the following content:
Learn How To:
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.
Statistics 1: Introduction to ANOVA, Regression, and Logistic Regression
- descriptive statistics
- inferential statistics
- steps for conducting a hypothesis test
- basics of using your SAS software
- examining data distributions
- obtaining and interpreting sample statistics using the UNIVARIATE and MEANS procedures
- examining data distributions graphically in the UNIVARIATE and SGPLOT procedures
- constructing confidence intervals
- performing simple tests of hypothesis
- performing tests of differences between two group means using PROC TTEST
- performing one-way ANOVA with the GLM procedure
- performing post-hoc multiple comparisons tests in PROC GLM
- performing two-way ANOVA with and without interactions
- producing correlations with the CORR procedure
- fitting a simple linear regression model with the REG procedure
- understanding the concepts of multiple regression
- using automated model selection techniques in PROC REG to choose from among several candidate models
- interpreting models
- examining residuals
- investigating influential observations
- assessing collinearity
- producing frequency tables with the FREQ procedure
- examining tests for general and linear association using the FREQ procedure
- understanding exact tests
- understanding the concepts of logistic regression
- fitting univariate and multivariate logistic regression models using the LOGISTIC procedure
Predictive Modeling Using Logistic Regression
- business applications
- analytical challenges
- parameter estimation
- adjustments for oversampling
- missing values
- categorical inputs
- variable clustering
- variable screening
- subset selection
- ROC curves and Lift charts
- optimal cutoffs
- K-S statistic
- c statistic
- evaluating a series of models