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# FM Reciprocal Transformation

## 3.5 Introduction to Data Transformations

### Linearization

• So far we have only looked at methods of analysing linear associations and not non-linear associations. Luckily, linearization provides a convenient way of transforming non-linear associations into linear ones so that they can be analysed using the same methods.
• Linearization works by applying a transformation of some form to either the explanatory and/or response variables datasets. In Further Maths, you will only deal with situations requiring one of the datasets to be transformed at a time.
• Keep in mind that the formula for the linearised model must include the transformation (e.g. the formula for a model which has undergone a square transformation to the explanatory variable will be of the form y=a+bx^2).

### Square Transformation

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