How is Logistic Regression Applied in Nanotechnology?
Logistic regression is applied in nanotechnology through several steps. Initially, a dataset is collected that includes various predictor variables such as particle size, coating materials, and environmental conditions. The outcome variable is binary, indicating the presence or absence of a property of interest. The logistic regression model is then trained on this dataset to find the best-fit parameters. Once trained, the model can be used to predict the probability of the outcome for new data points.
