Linear
Create a best-fit straight line for simple linear datasets and is useful when the data increases or decreases at a steady rate. Equation: y = a + b * x
Logarithmic
Create a best-fit curved line and is useful when the rate of change in the data increases or decreases quickly and then levels out. A logarithmic trendline can use negative and positive values. Equation: y = a + b * log(x)
Exponential
Create a curved line and is useful when data rises or falls at constantly increasing rates. This option should not be used if your data contains zero or negative values. Equation: y = a + e^(b * x)
Power
Create a curved line and is useful for datasets that compare measurements that increase at a specific rate. This option should not be used if your data contains zero or negative values. Equation: y = a * x^b
Quadratic
Finds the best-fit for a dataset shaped like a parabola (concave up or down). Equation: y = a + b * x + c * x^2
Moving average
Create a smooth trendline based on a set of averages. Also known as a rolling average, a moving average uses a specific number of data points (determined by your Granularity selection), averages them, and uses the average as a point in the line. Examples include a seven day moving average or a four week moving average.
