A final reminder for two courses being run by guest lecturer Professor
Dallas Johnson - "Analysis of Messy Data..." and "Design and Analysis of
Crossover Experiments", both scheduled to take place in late June.
Summary information is given below. For more detailed information and
registration either visit our website (http://www.reading.ac.uk/ssc, go to
"Short Courses" and link to Dallas Johnson courses) or contact Kellie
Watkins at the Statistical Services Centre, The University of Reading
(email [log in to unmask]).
Please note a deadline has been set for registration - Monday 5 June.
***Analysis of Messy Data: Experimental Designs used in Medical Research***
Dates: 26 - 28 June
Duration: 3 days
Price: £735 (£1135 for both Dallas Johnson courses)
This course is designed for those involved with analysing certain kinds of
messy experiments that have ordinal and/or continous data. Emphasis is
placed on analysing data where restrictions have been used in assigning
subjects to treatment groups. These include experiments done in many
locations and experiments with repeated measurements on each subject. A
correct analysis of such experiments usually involves using models that
contain more than one error term. This course will show how to use the SAS
procedures GLM and MIXED to obtain correct analyses of these kinds of messy
experiments.
The objectives of the course are to discuss and compare different
alternatives for obtaining a correct analysis of repeated measures and the
other kinds of mixed model experiments, how to write the appropriate SAS
commands, how to choose an appropriate error structure, how to interpret
the resulting outputs, and how to make correct inferences. The advantages
and disadvantages of using SAS mixed and SAS GLM will also be discussed.
***Design and Analysis of Crossover Experiments using SAS GLM and SAS MIXED
Procedures***
Dates: 29 - 30 June
Duration: 2 days
Price: £490 (£1135 for both Dallas Johnson courses)
Crossover designs are a special type of a repeated measures experiment
where the experimental units are given different treatments in different
sequences. Crossover designs offer some real advantages over traditional
designs when one is comparing treatments as they allow each experimental
unit to serve as its own control. However, there are certain pitfalls that
must be avoided if crossover designs are going to be used effectively. A
correct analysis of such experiments usually involves using mixed models
that contain more than one error term.
This course will introduce the important issues and discuss
appropriate statistical analyses of crossover designs using SAS GLM and SAS
MIXED.
Guest Lecturer - Professor Dallas Johnson
Professor Johnson is Head of the Department of Statistics at Kansas State
University and co-author of two books with George A. Milliken entitled
Analysis of Messy Data, Vol 1 - Designed Experiments and Analysis of Messy
Data, Vol 2 - Nonreplicated Experiments. He is also the author of Applied
Mutivariate Methods for Data Analysts.
Kellie Watkins
Statistical Services Centre
The University of Reading
Harry Pitt Building
PO Box 240
Whiteknights Road
Reading RG6 6FN
UK
Tel: +44 (0)118 931 8689
Fax: +44 (0)118 975 3169
Tel: +44 (0)118 931 8689
Fax: +44 (0)118 975 3169
Email: [log in to unmask]
See our website on http://www.reading.ac.uk/ssc
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