The process of regression imputation in toxicology involves several steps:
Identifying Missing Data: First, the dataset is examined to identify any missing values. This can be done through descriptive statistics or visual methods such as histograms and scatter plots. Selecting Predictors: Variables that are strongly correlated with the missing data are chosen as predictors. In toxicology, these could include factors like dosage, exposure time, or biological endpoints. Building the Regression Model: A regression model is constructed using the available data. This model is then used to predict the missing values based on the observed relationships between the predictors and the response variable. Imputing the Missing Values: The predicted values from the regression model are used to replace the missing data points, thereby creating a complete dataset.
