Synopsis of Correlation Analysis
Description
Correlation analysis is a statistical technique used to measure and evaluate the strength and direction of the relationship between two or more variables. It helps to determine whether and how strongly pairs of variables are related.
Purpose
Correlation analysis is a valuable tool for understanding how variables are related in a dataset. It helps you identify patterns and dependencies, which can inform further investigation or modeling. However, it’s essential to interpret correlation results cautiously and consider other factors before drawing conclusions about cause and effect. Enhance your understanding of correlation analysis and learn how changes in one variable are associated with changes in another variable.
$195 (Fee covers training materials)
July 17 and 18, 2026 10 am - 12 pm MT
Expected Learning Outcomes
By the end of this training, you will be able to
distinguish between correlation, linear and multiple regression, and logistic regression;
explain the purpose and methods of linear (simple and multiple) and logistic regression;
recognize when to use linear and logistic regression;
Identify regression models; and
interpret regression results.
Target Learners
This training program is designed for individuals or groups of people who work in clinical settings, academia, pharmaceutical industries, Government, corporations, nonprofit organizations, and organizations that work on biological data.
Learning Approach
Programs are delivered through live online interactive sessions with seasoned instructors having vast experiences in academia. Contents are well structured and manageable with self-paced learning activities and exercises. Our approach provides learners opportunities for hands-on practice, asking questions, investigating problems, and applying their knowledge to solve complex scenarios or real-world problems. In the end, learners develop deeper understanding, critical thinking, and problem-solving skills.
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