1 / 10

z(H) = Ms – c z(F) = Mn-c Como d’ = Ms – Mn y Ms = z(H) + c y Mn = z(F) +c

z(H) = Ms – c z(F) = Mn-c Como d’ = Ms – Mn y Ms = z(H) + c y Mn = z(F) +c Ms – Mn = (z(H) + c) – (z(F) +c) = z(H) – z(F). C = -1/2 [z(H) + z(F)] Por que el punto medio implica Ms = -Mn. Relación entre c y β : ln ( β ) = cd’.

mbickham
Download Presentation

z(H) = Ms – c z(F) = Mn-c Como d’ = Ms – Mn y Ms = z(H) + c y Mn = z(F) +c

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. z(H) = Ms – c z(F) = Mn-c Como d’ = Ms – Mn y Ms = z(H) + c y Mn = z(F) +c Ms – Mn = (z(H) + c) – (z(F) +c) = z(H) – z(F)

  2. C = -1/2 [z(H) + z(F)] Por que el punto medio implica Ms = -Mn

  3. Relación entre c y β: ln (β) = cd’

  4. Criterion location measures index bias by measuring the distance between the decision criterion and the intersection of the two stimulus distributions. Likelihood ratio measures index bias by measuring the relative height of the two distributions at the decision criterion. The lack of empirical support favoring one family of bias measures is surprising, because the isobias functions predicted by criterion location measures clearly differ from those predicted by likelihood ratio measures.

  5. Isobias functions plot the combinations of hit and false alarm rates that yield the same numerical value for a particular measure of bias.

More Related