Why might a researcher choose to transform data before performing a regression analysis?

To make a non-linear relationship appear more linear, allowing the use of linear regression tools.

In the video, which transformation was applied to the data?

Logarithmic transformation of the y-values

It allows the use of well-established linear regression tools and hypothesis testing methods.

The model can be mathematically 'unwound' to express the relationship in terms of the original variables.

Linear regression has more readily available tools for analysis and hypothesis testing compared to fitting exponential curves directly.

I can analyze how different data transformations affect statistical analyses.

1
When data appears non-linear, we can still apply linear regression tools by transforming the data.
2
In the video's example, plotting x versus log(y) reveals a linear pattern that can be analyzed using linear regression.
3
Transforming data allows us to use existing linear regression tools, and we can mathematically convert the model back to the original relationship.