Prepare data - linear regression and curve fitting
Use linear and non-linear transformations to extract deeper insights from raw data.
8 articles
- When and why to use simple linear model fits to derive responses in SynthaceA basic introduction to fitting simple linear models to your response data
- How to use the simple linear fit transformation in SynthaceLearn how to use the Linear Fit transformation on the data preparation ap
- Improved data reshaping and calculation updatesLearn about some recent changes we have made to data reshaping and transformation calculation
- Understanding data reshaping for processing and analysisUnderstand what reshaping data is, how it works and how to use it
- Performing non-linear curve fits using 4 & 5-parameter logistic regressionLearn how to apply 4 and 5 parameter logistic fits to your data in Synthace
- Fitting bespoke curves to your dataBuild custom curve fits (Hill, Michaelis–Menten, and more) directly in Prepare Data
- Technical Documentation for Logistic Curve FitsDetails of how fits to 4 and 5 parameter logistic fits are implemented; 4PL fits 5PL fits
- Technical Documentation: Generic Curve FittingThis article provides details of how curve fitting for generic nonlinear functions is implemented for reference purposes.