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DTSTAMP:20260823T201906Z
UID:6e6a3aa6-fecb-482e-ad93-59685b88daee
DTSTART:20261208T093000Z
DTEND:20261209T130000Z
DESCRIPTION:An applied introduction to linear mixed effects models\, also k
 nown as multi-level or hierarchical models\, for researchers who want to u
 se these methods in their own research or studies.\n\nThis course focuses 
 on the practical skills and key concepts needed to work with mixed effects
  models using R and the lme4 package. The emphasis is on applied examples\
 , real datasets\, interpretation\, and model checking rather than mathemat
 ical derivation.\n\nTopics include random intercepts and slopes\, the synt
 ax of the lme4 package\, model visualisation\, significance testing\, mode
 l comparison\, checking assumptions\, nested random effects\, crossed rand
 om effects\, and complex experimental designs.\n\nBy the end of the course
 \, participants should be able to:\n\n* understand what mixed effects mode
 ls are and when they should be used\n* fit mixed effects models using the 
 lme4 package in R\n* interpret and visualise mixed effects model outputs c
 onfidently\n* assess model assumptions and evaluate model quality\n* under
 stand how mixed models handle grouped and non-independent data\n\nPlease n
 ote that this course builds on knowledge of linear modelling and should no
 t be considered a general introduction to statistical modelling.\n\n\n    
   Book this event or register interest\n      This event is not yet open f
 or booking. Please choose the section that applies to you.\n      \n      
 University of Cambridge member\n      Register interest\n      External pa
 rticipant\n      Register interest\n      \n      \n\n== Intended audience
  ==\n\nThis course is suitable for:\n\n* postgraduate students\, postdocto
 ral researchers\, and other researchers working with experimental or obser
 vational data\n* participants whose data contain repeated measures\, group
 ed observations\, hierarchical structure\, or other forms of non-independe
 nce\n* researchers who already understand linear models and want to extend
  their statistical toolkit\n* participants with a working knowledge of R a
 nd RStudio\n\n== Course fees ==\n\nAll fees are per full training day.\n\n
 {| class="wikitable"\n|-\n! Category\n! Fee\n|-\n| Industry full charge\n|
  £130.00\n|-\n| Academic / Government / charity concessionary\n| £65.00\
 n|-\n| Cambridge University staff members / postdocs / visitors\n| £65.00
 \n|-\n| Cambridge University registered students\n| Free\n|-\n| Cambridge 
 University registered students non-attendance\n| £22.00\n|-\n| Special ev
 ents\n| Per event\n|}\n\nPayment options will be provided in booking confi
 rmation emails sent after registration.\n\n[More details…](https://bioin
 fotraining.bio.cam.ac.uk/postgraduate/eligibility)\n\n== General informati
 on ==\n\nMore detailed information is available on our dedicated [cancella
 tion and non-attendance policy](https://bioinfotraining.bio.cam.ac.uk/canc
 ellation-and-non-attendance)\, [waiting list](https://bioinfotraining.bio.
 cam.ac.uk/waiting-list)\, [accessibility](https://bioinfotraining.bio.cam.
 ac.uk/accessibility-support)\, [privacy policies](https://bioinfotraining.
 bio.cam.ac.uk/privacy-and-cookie-policies) and [terms &amp\; conditions](h
 ttps://bioinfotraining.bio.cam.ac.uk/https%3A/bioinfotraining.bio.cam.ac.u
 k/events/terms-and-conditions) pages.\n\nGuidance on visiting Cambridge an
 d finding accommodation is available [here](https://bioinfotraining.bio.ca
 m.ac.uk/about).
LOCATION:Craik-Marshall Building
SUMMARY:Linear mixed effects models (ONLINE LIVE TRAINING)
URL;VALUE=URI:http://training.csx.cam.ac.uk/bioinformatics/event/6431904
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