GraphPad Prism - Regression

The class covers all methods of regression analysis (linear and nonlinear) which are implemented in GraphPad Prism. Keywords like: linearity, statistical modeling, least squares method, constraints, global fit, model comparisons, dose response curves and enzyme cinetic models will be explained.

Content:

• Understand statistical principles in regression analysis
• Master the PRISM interface with respect to linear and nonlinear regression
• Perform and interpret linear regression
• Perform and interpret nonlinear regression
• Professional and fail save data handling, formatting and presentation of graph types specific to regression analysis

Requirements:

You need to be able to master the GraphPad Prism dialog system and output, thus the introductory training Introduction to Data Analysis with GraphPad Prism is a prerequisite. The class also covers the basic statistical know-how needed to understand the different methods.