Content
Log models, scales and linearization
Logarithmic scales, linearizing, and log-log or semi-log graphs
Want a deeper conceptual understanding? Try our interactive lesson!
Exercises
No exercises available for this concept.
Logarithmic scales, linearizing, and log-log or semi-log graphs
Want a deeper conceptual understanding? Try our interactive lesson!
No exercises available for this concept.
A natural logarithmic model is given by f(x)=a+blnx.
Notice f(1)=a and f(e)=a+b.
Powered by Desmos
When dealing with numbers at very different scales (i.e. 0.000001 and 1,000,000), it can be helpful to express numbers using a logarithmic scale, which converts any number x to logb(x) where b is a common base (often 10).
Exponential models of the form y=Aekx and power models of the form y=Axn can be linearized by taking logs:
Hence, given data in terms of x and y, we can convert the data into lny and lnx.
If lny and x have a linear relationship, then y and x have an exponential relationship.
If lny and lnx have a linear relationship, then y and x have a power relationship.
We can find values for A and k or n by performing a linear regression on lny and x or lnx.
Both axes of a log-log graph have a logarithmic scale. Straight lines on log-log graphs represent power relationships.
One axis (usually y) of a semi-log graph has a logarithmic scale. Straight lines on semi-log graphs represent exponential relationships.