Simple Linear Regression Analysis
Description
Regression models are used when the behavior of one variable is ‘explained’ by changes in other variables. A statistical model takes account of the inherent variability of the data: this will arise because of measuring or observational errors or chance circumstances. A deterministic model contains no element of uncertainty or randomness.
Purpose
Regression analysis is used to examine the relationship between a dependent variable. Regression analysis is a fundamental statistical method used to understand the relationship between variables. There are different types of regression.
Understanding regression analysis is crucial for researchers and analysts to derive meaningful insights and make informed decisions based on data. This training equips you with the competencies to apply common statistical methods for inference and apply descriptive and inferential methodologies according to the type of study design for answering a particular research question.
$195 (Fee covers training materials)
June 29 and July 3, 2026 10 am - 12 pm MT
Expected Learning Outcomes
By the end of this training, you will be able to
identify the assumptions of simple linear regression;
explain linear regression and how it relates to prediction;
differentiate between correlation and simple linear regression; and
access when to apply both correlation and regression.
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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