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The Gaussian distribution arises in many contexts and is widely used for modeling continuous random variables. p(x | µ, σ2) = N(x; µ, σ2) = 1 Z exp ( − (x − µ)2 2σ2 ) . The normalization constant Z is Z = √ 2πσ2.

Normal distribution, also known as the Gaussian distribution, is a probability distribution that is symmetric about the mean, showing that data near the mean are more frequent in occurrence than data far from the mean. In graphical form, the normal distribution appears as a "bell curve".

k) The resolution (Rs) of a column for two analytes (A eluting before B) is given by Rs = [tR(B) - tR(A)]/[(WA + WB)/2], where W is the baseline width of a chromatographic peak in the same units as the analyte retention time.

The Gaussian distribution is also commonly called the "normal distribution" and is often described as a "bell-shaped curve". If the probability of a single event is p = and there are n = events, then the value of the Gaussian distribution function at value x = is x 10^ .

Gaussian distribution (also known as normal distribution) is a bell-shaped curve, and it is assumed that during any measurement values will follow a normal distribution with an equal number of measurements above and below the mean value.

Equation (1) indicates that the resolution is the difference between peak retention times divided by the average peak width. In a peak with Gaussian distribution, the peak width is W = 4 σ (where σ is the standard deviation) and the peak FWHM is W0. 5h = 2.354σ.

That is, the resolution is the difference in retention times divided by the average baseline peak width (thus the factor of 2 in equation 1). The peak width at the baseline for a Gaussian peak is 4σ (4 standard deviations), whereas at the half-height, it is 2.354σ, so the factor in equation 2 is (2 × 2.354/4) = 1.18.

Definition 2: Resolution can be expressed as the total number of pixels. With megapixel cameras, the resolution is generally the total number of pixels, divided by 1,000,000, and rounded off.