This program is for those who want to enhance their predictive and statistical modeling skills to drive data-informed business outcomes. If modeling data for business outcomes is relevant in your job role or industry, this certificate is a valuable indication of your proficiency.
Overview
Syllabus
Course 1: Introduction to Statistical Analysis: Hypothesis Testing
- Offered by SAS. This introductory course is for SAS software users who perform statistical analyses using SAS/STAT software. The focus is on ... Enroll for free.
Course 2: Regression Modeling Fundamentals
- Offered by SAS. This introductory course is for SAS software users who perform statistical analyses using SAS/STAT software. The focus is on ... Enroll for free.
Course 3: Predictive Modeling with Logistic Regression using SAS
- Offered by SAS. This course covers predictive modeling using SAS/STAT software with emphasis on the LOGISTIC procedure. This course also ... Enroll for free.
- Offered by SAS. This introductory course is for SAS software users who perform statistical analyses using SAS/STAT software. The focus is on ... Enroll for free.
Course 2: Regression Modeling Fundamentals
- Offered by SAS. This introductory course is for SAS software users who perform statistical analyses using SAS/STAT software. The focus is on ... Enroll for free.
Course 3: Predictive Modeling with Logistic Regression using SAS
- Offered by SAS. This course covers predictive modeling using SAS/STAT software with emphasis on the LOGISTIC procedure. This course also ... Enroll for free.
Courses
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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 includes a brief introduction to logistic regression.
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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 includes a brief introduction to logistic regression.
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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. You learn to use logistic regression to model an individual's behavior as a function of known inputs, create effect plots and odds ratio plots, handle missing data values, and tackle multicollinearity in your predictors. You also learn to assess model performance and compare models.
Taught by
Jordan Bakerman and Michael J Patetta
