Semi-log Plot

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## Equations

### Finding the function from the semi-log plot

#### Linear-log plot

#### log-linear plot

## Real-world examples

### Phase diagram of water

### 2009 "swine flu" progression

### Microbial growth

## See also

## References

This article uses material from the Wikipedia page available here. It is released under the Creative Commons Attribution-Share-Alike License 3.0.

Semi-log Plot

In science and engineering, a **semi-log plot**, or **semi-log graph** (or **semi-logarithmic** **plot**/**graph**), has one axis on a logarithmic scale, the other on a linear scale. It is useful for data with exponential relationships, where one variable covers a large range of values^{[1]}, or to zoom in and visualize that - what seems to be a straight line in the beginning - is in fact the slow start of a logarithmic curve that is about to spike and changes are much bigger than thought initially.^{[2]}.

All equations of the form form straight lines when plotted semi-logarithmically, since taking logs of both sides gives

This is a line with slope and vertical intercept. The logarithmic scale is usually labeled in base 10; occasionally in base 2:

A **log-linear** (sometimes log-lin) plot has the logarithmic scale on the *y*-axis, and a linear scale on the *x*-axis; a **linear-log** (sometimes lin-log) is the opposite. The naming is *output-input* (*y*-*x*), the opposite order from (*x*, *y*).

On a semi-log plot the spacing of the scale on the *y*-axis (or *x*-axis) is proportional to the logarithm of the number, not the number itself. It is equivalent to converting the *y* values (or *x* values) to their log, and plotting the data on linear scales. A log-log plot uses the logarithmic scale for both axes, and hence is not a semi-log plot.

The equation of a line on a linear-log plot, where the abscissa axis is scaled logarithmically (with a logarithmic base of *n*), would be

The equation for a line on a log-linear plot, with an ordinate axis logarithmically scaled (with a logarithmic base of *n*), would be:

On a linear-log plot, pick some *fixed point* (*x*_{0}, *F*_{0}), where *F*_{0} is shorthand for *F*(*x*_{0}), somewhere on the straight line in the above graph, and further some other *arbitrary point* (*x*_{1}, *F*_{1}) on the same graph. The slope formula of the plot is:

which leads to

or

which means that

In other words, *F* is proportional to the logarithm of *x* times the slope of the straight line of its lin-log graph, plus a constant. Specifically, a straight line on a lin-log plot containing points (*F*_{0}, *x*_{0}) and (*F*_{1}, *x*_{1}) will have the function:

On a log-linear plot (logarithmic scale on the y-axis), pick some *fixed point* (*x*_{0}, *F*_{0}), where *F*_{0} is shorthand for *F*(*x*_{0}), somewhere on the straight line in the above graph, and further some other *arbitrary point* (*x*_{1}, *F*_{1}) on the same graph. The slope formula of the plot is:

which leads to

Notice that *n*^{logn(F1)} = *F*_{1}. Therefore, the logs can be inverted to find:

or

This can be generalized for any point, instead of just *F _{1}*:

In physics and chemistry, a plot of logarithm of pressure against temperature can be used to illustrate the various phases of a substance, as in the following for water:

While ten is the most common base, there are times when other bases are more appropriate, as in this example:

In biology and biological engineering, the change in numbers of microbes due to asexual reproduction and nutrient exhaustion is commonly illustrated by a semi-log plot. Time is usually the independent axis, with the logarithm of the number or mass of bacteria or other microbe as the dependent variable. This forms a plot with four distinct phases, as shown below.

- Nomograph, more complicated graphs
- Nonlinear regression#Transformation, for converting a nonlinear form to a semi-log form amenable to non-iterative calculation
- Log-log plot

This article uses material from the Wikipedia page available here. It is released under the Creative Commons Attribution-Share-Alike License 3.0.

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