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The “Statistics 1: Introduction to ANOVA, Regression, and Logistic Regression” course is a comprehensive introduction to key statistical concepts and techniques using SAS/STAT software. This 21-hour course is designed for users who perform statistical analyses and want to deepen their understanding of statistical methods such as t-tests, ANOVA (Analysis of Variance), and linear regression. The course also provides a brief introduction to logistic regression, offering learners the foundational skills necessary for more advanced statistical studies.
Throughout this course, learners will be introduced to essential concepts such as generating descriptive statistics, exploring data with graphs, and performing basic statistical tests like t-tests. A significant portion of the course focuses on ANOVA, where participants will learn how to apply multiple comparison techniques and assess data for group differences. Regression analysis is also covered extensively, including simple linear regression and techniques for assessing assumptions in multiple regression models. The course highlights model selection techniques, helping learners choose the best predictor variables for their analyses.
For those interested in categorical data, the course introduces chi-square statistics and basic logistic regression models. Learners will gain hands-on experience fitting multiple logistic regression models, allowing them to score new data and predict outcomes based on their models. The course uses SAS software throughout, emphasizing real-world application and analysis using PROC TTEST, PROC GLM, PROC LOGISTIC, and PROC GLMSELECT.
The course is ideal for statisticians, researchers, and business analysts who need to perform statistical analyses using SAS. A solid understanding of basic statistics and experience with SAS programming is recommended before taking this course, ensuring learners are prepared to engage with more complex topics in later courses, such as “Statistics 2: ANOVA and Regression” and “Categorical Data Analysis Using Logistic Regression.”
