BEGIN:VCALENDAR
PRODID:-//Columba Systems Ltd//NONSGML CPNG/SpringViewer/ICal Output/3.3-
M3//EN
VERSION:2.0
CALSCALE:GREGORIAN
METHOD:PUBLISH
BEGIN:VEVENT
DTSTAMP:20140525T090217Z
DTSTART:20140611T110000Z
DTEND:20140611T120000Z
SUMMARY:Research Matters: Pretty pictures and parameters - understanding
statistics using graphics (Research Counts)
UID:{http://www.columbasystems.com/customers/uom/gpp/eventid/}o15h-huzafi
5i-723gdz
DESCRIPTION:Using statistics effectively in education involves applying a
range of\nanalyses to model many different types of data\; linear model
s for \nvariables measured on a continuous scale (eg. OLS regression and
ANOVA)\, \nlogit models for categorical variables (eg. logistic\, propo
rtional-odds \nand multinomial) and models for count variables (eg. Pois
son regression\, \nchi-square and log-linear). \n\nThese models present
a challenge for researchers and a steep learning\ncurve for PhD student
s who often have little or no background in \nstatistics and as a conseq
uence are often daunted by the time and resources \nrequired to become s
tatistically literate. This session will argue that \nit is possible for
a comprehensive range of techniques to be learned \nwithin the time ava
ilable to PhD students if the techniques are applied \nas part of a cohe
rent underlying theory (generalized linear models) and a \nsystematic me
thod of representating these models is adopted (a graphical \nrepresenta
tion based on predictions).\n \nAlthough this presentation deals with a
range of statistical models\, the \nemphasis is on how to teach a compre
hensive system of analysis in a \ntime-frame typically available to a Ph
D student.
STATUS:TENTATIVE
TRANSP:TRANSPARENT
CLASS:PUBLIC
LOCATION:AG3/4\, Ellen Wilkinson Building\, Manchester
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