# Determining noise spectral density

I am working with a digital sensor (magnetometer) that can operate at multiple frequencies. The application note (http://www.st.com/resource/en/application_note/dm00136626.pdf) gives RMS noise values at each of these frequencies:

I would like to get a sense of the actual power spectral density curve (i.e. mGauss/sqrt(Hz)) so I can better understand what my SNR will be if I apply a bandpass filter on a narrow range of frequenies. Is it valid to 'back-calculate' a rough curve from these values by determining the average noise spectral density of each frequency region as follows?

$$(3.5-0)/sqrt(77.5-0) \approx .398$$

$$(4.0-3.5)/sqrt(150-77.5) \approx .059$$

$$(4.6-4.0)/sqrt(280-150) \approx .053$$

$$(5.3-4.6)/sqrt(500-280) \approx .047$$

(Note: I assume that the internal circuitry of the sensor is applying the appropriate low-pass filter in accordance with the Nyquist theorem; e.g. 1000Hz signal has 500Hz LPF applied.)

Edit: Just to clarify from Dave's answer below, I believe the RMS can be calculated as:

$$noise^2_{RMS} = \int_{f_{min}}^{f_{max}} PSD(f)df$$

Where $PSD(f)$ (given in units $mGauss^2/Hz$) is the square of the plot above. However, it is possible that the underlying PSD changes for each sampling mode, while the plot assumes there is a fixed underlying PSD for all sampling modes.