  Machine Learning requires a large amount of training data in order to build
accurate models. Sometimes the data arrives over time, requiring significant
storage space and recalculating the model to account for the new data. On-line
learning addresses these issues by incrementally modifying the model as data is
encountered, and then discarding the data. In this study we introduce a new
online linear regression approach. Our approach combines newly arriving data
with a previously existing model to create a new model. The introduced model,
named OLR-WA (OnLine Regression with Weighted Average) uses user-defined
weights to provide flexibility in the face of changing data to bias the results
in favor of old or new data. We have conducted 2-D and 3-D experiments
comparing OLR-WA to a static model using the entire data set. The results show
that for consistent data, OLR-WA and the static batch model perform similarly
and for varying data, the user can set the OLR-WA to adapt more quickly or to
resist change.
