This preview shows pages 1–2. Sign up to view the full content.
This preview has intentionally blurred sections. Sign up to view the full version.View Full Document
Unformatted text preview: ( ) 1( ) ( ) 1( ) v d N v dv N v N v N v C dv ∞ ′ ′ = ⇒ = − + ∫ C a constant (C=0 here). For those who know the sigmoidal is also the Fermi-Dirac distribution. We may actually replace infinity by 100 (maximum) but the curve will not be perfect very near 100 as we will see. ( ) ( )/ 2 ( )/ / ( ) 1 v v dv v v dv N dv N v e C e − − = + + Finally I would like to add that a useful technique when approximating by a Gaussian is to split the dispersion σ (sigma) to σ=N/K where N is the total number and K is a quality factor to be determined after fitting the function. 20 40 60 80 100 1000 2000 3000 4000 5000 6000 candidates with score > v score = v Fig.2 Distribution of N(v)/N%- percentage of candidates who achieved marks between v and v+dv 20 40 60 80 100 1 2 3 distribution N(v)/N total % score v...
View Full Document
- Spring '09