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Royal Statistical Society Courses at The University of Manchester - September 2019 In partnership with methods@manchester methods@manchester is delighted to be hosting three courses delivered by the Royal Statistical Society during September 2019. These cover a range of topics including: basic statistics, R, and spatial data analysis in R. Booking is available through the following course links. 09-10 September 2019 - Basic Statistics: Understanding & Analysing Data<https://events.rss.org.uk/rss/302/home> 25-26 September 2019 - Introduction to R & Statistical Modelling in R<https://events.rss.org.uk/rss/303/home> 27 September 2019 - Spatial data analysis in R<https://events.rss.org.uk/rss/304/home> Each course is briefly outlined below. Basic Statistics: Understanding and Analysing Data 09-10 September 2019 Course Outline The purpose of this course is to help participants understand some basic statistical concepts and develop a strategy for approaching simple data analysis. The course will introduce basic concepts such as hypothesis and confidence interval estimation. It will provide the tools to undertake simple analysis of a data set and will include some helpful hints and tips for reading and understanding reported statistics. Learning Outcomes By the end of the course, participants will understand basic approaches to statistical inference. They will be equipped with the skills necessary to undertake simple analyses and to understand some of the basic terms often used to report statistical results. The course will mainly use calculations by hand to aid understanding, but will include Excel for some statistical results. Topics Covered Day 1: The normal distribution, basic study design, data summary, confidence intervals, introduction to hypothesis tests, analysis of contingency tables -- the chi-squared test. Day 2: T-tests, non-parametric tests, (Wilcoxon signed rank test, Mann-Whitney U test), correlation and regression, basic presentation of data and results. Target Audience This course is aimed at those who have either never undertaken a formal statistics course, or those who have studied some statistics in the past but wish to undertake a refresher. It is ideal for statistical novices who have never had any formal training but are starting to encounter statistics in their work and wish to gain some insight. Assumed Knowledge No prior knowledge is assumed. Fees (Registration before 12 August 2019) Non Member £596+vat RSS Fellow £507+vat RSS CStat: also MIS, FIS & GradStat £478+vat Fees (Registration on/after 12 August 2019) Non Member £663+vat RSS Fellow £563+vat RSS CStat: also MIS, FIS & GradStat £530+vat Multiple booking discounts available for bookings of 3 or more places - please contact for further information Invoice and card payments accepted. Introduction to R & Statistical Modelling in R 25-26 September 2019 Course Outline The purpose of this course is to introduce participants to the R environment for statistical computing. Day 1 of the course focuses on entering, working with and visualising data in R. Day 2 focuses on regression modelling in R, including linear, general linear, logistic and survival models. Learning Outcomes By the end of Day 1, participants will be able to use R to: * Perform data entry from a variety of sources (e.g. Excel and SPSS spreadsheets). * Produce simple variable summaries (e.g. means, variances, quartiles) and graphical displays (e.g. histograms, box plots, scatter plots). * Find further information using the help system and online resources. * Perform simple hypothesis tests on one or two variables; appropriately interpreting results and checking validity of assumptions. By the end of Day 2, participants will be able to: * Fit regression models in R between a response variable (including continuous, binary, categorical and survival responses) and a set of possible predictor variables * Make appropriate assumptions about the structure of the data in a regression model and check the validity of these assumptions in R. Topics Covered Topics covered in Day 1 include: entering data and obtaining help in R; working with data in R; summarising data graphically and numerically in R; basic hypothesis tests in R. Topics covered in Day 2 include: the linear model in R; the general linear model in R; logistic regression in R; survival models in R. Target Audience This course is ideally suited to anyone who: * is familiar with basic statistical methods (e.g. t-tests, boxplots) and who want to implement these methods using R. * has used menu-driven statistical software (e.g. SPSS, Minitab) and who want to investigate the flexibility offered by a command line package such as R. * is already familiar with basic statistical methods in R and who wish to extend their knowledge to regression involving multiple predictor variables, binary, categorical and survival response variables. * is familiar with regression methods in menu-driven software (e.g. SPSS, Minitab) and who wish to migrate to using R for their analyses. Assumed Knowledge The course requires familiarity with basic statistical methods (e.g. t-tests, box plots) but assumes no previous knowledge of statistical computing. Each participant will need to bring their own laptop installed with the R software (which can be downloaded free for Linux, MacOS X or windows from http://www.stats.bris.ac.uk/R/) Fees (Registration before 28 August 2019) Non Member £596+vat RSS Fellow £507+vat RSS CStat: also MIS, FIS & GradStat £478+vat Fees (Registration on/after 28 August 2019) Non Member £663+vat RSS Fellow £563+vat RSS CStat: also MIS, FIS & GradStat £530+vat Multiple booking discounts available for bookings of 3 or more places - please contact for further information Invoice and card payments accepted. Spatial Data Analysis in R 27 September 2019 Course Outline As spatial datasets get larger and more sophisticated software needs to be harnessed for their analysis. R is now a widely used open source software platform for working with spatial data thanks to its powerful analysis and visualisation packages. Learning Outcomes The focus of this course is providing participants with the understanding needed to apply R's powerful suite of geographical tools to their own problems. Topics Covered Introducing R as a GIS The structure of spatial objects in R Loading and interrogating spatial data Visualising spatial datasets Acquiring external data with R Point pattern analysis and spatial interpolation Geographical models in R Webmaps Target Audience Participants with spatial data problems who are not making use of R and are falling behind in the ever changing world of data science. Assumed Knowledge A basic understanding of the R software is assumed. Each participant will need to bring their own laptop installed with the latest versions of Rstudio and R software. Fees (Registration before 30 August 2019) Non Member £382+vat RSS Fellow £324+vat RSS CStat: also MIS, FIS & GradStat £306+vat Fees (Registration on/after 30 August 2019) Non Member £424+vat RSS Fellow £361+vat RSS CStat: also MIS, FIS & GradStat £340+vat Multiple booking discounts available for bookings of 3 or more places - please contact for further information Invoice and card payments accepted. methods@manchester https://www.methods.manchester.ac.uk/ You may leave the list at any time by sending the command SIGNOFF allstat to [log in to unmask], leaving the subject line blank.