regressioimputointi

Regressioimputointi, or regression imputation in English, is a statistical technique used to fill in missing data points in a dataset. It involves building a regression model based on the observed values of one or more variables to predict the missing values of another variable. For example, if a dataset has missing values for income, a regression model could be built using other variables like education level, age, and employment status to estimate the missing income figures.

The process typically involves selecting predictor variables that are known to be correlated with the variable containing missing values. A regression equation is then estimated using the complete cases in the dataset. This equation is subsequently used to predict the missing values based on the values of the predictor variables for those cases. While it can be more sophisticated than simple imputation methods like mean imputation, regression imputation can still introduce bias if the model is misspecified or if there are unobserved factors influencing the missingness. It's important to consider the assumptions of the regression model and the nature of the missing data when applying this technique.