Statistical Services Centre

Applied Statistics at the University of Reading
...making sense of statistics

Our 2015 Short Course Programme

The courses offered here are open to the public. We offer discounts on some courses for academic staff and students, and also for group bookings. Please see the individual course pages, or contact us by email or phone (+44 (0)118 378 8689) for details of available discounts.

If you would like a topic or software added to our public short course programme, please contact us.

If you wish to be added to our mailing list to receive our annual short course brochure and upcoming course via email, please click here

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e-SMS: Statistics Made Simple


Dates: 9 September 2015;

Duration: 11 weeks

Full Course Details


Overview:

New course dates! Based on recent feedback, we have decided to run e-SMS with topics openning on Wednesdays instead of Mondays. Therefore, we have revised the course dates accordingly.

Statistics Made Simple is an innovative online course designed specially for those who are starting their research, or who are involved in development projects involving data collection and analysis. It is designed to give confidence to those who found previous statistics courses inaccessible or uninteresting. It will provide you with the key concepts and the skills necessary to be able to start analysing your data with confidence and understanding.


Intended Audience:

The e-SMS course is designed for anyone who requires a foundation course in statistics for research, and who wants to study online. If your previous statistics course was (a) a long time ago; (b) more on theory and formulas than ideas and interpretation; or (c) taught without integrating the use of computers for statistical work, then this course is for you. Applicants must be competent in the use of the internet (e-mail, web browsing etc) and have some familiarity with the MS Excel spreadsheet (including some use of functions and formulas).

How You Will Benefit

The course will help prepare you for your research work, and will give you confidence in your use of statistics. You will gain key transferable skills in statistics, specifically in understanding, describing and quantifying variability. You will also learn to build and interpret simple statistical models using real datasets.

For more information, click here.

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e-CSPRO: Data Management using CSPro


Dates: 4 November 2015;

Duration: 5 weeks

Full Course Details


Overview:

This 5-week e-learning course will take you through the process of designing and implementing a data entry application for a typical survey. Participants will learn how to create the data dictionary, data entry screen and how to program checks and skips. The course then considers the data validation tools available including data comparisons and simple frequency tables. Finally, we show how data is stored in the system and how it can easily be exported for transfer to statistics packages such as SAS and STATA as well as to Excel.


Intended Audience:

This is an introductory course aimed at researchers, demographers, data managers and others who are considering using CSPro to implement survey data management systems. No previous experience of CSPro is required but participants are assumed to be comfortable with Windows based software. Participants should install CSPro prior to the course.

How You Will Benefit

You will gain an understanding of data structures, allowing you to design better data entry applications. You will also get an introduction to logic programming and an appreciation of the steps involved in ensuring data quality. Finally, you will leave the course with a comprehensive set of course materials including practical exercies, example data sets and video demonstrations, all of which can be accessed even after the course has ended.

For more information, click here.

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Bayesian Analysis Made Easy


Dates: 3 November 2015;

Duration: 2 days

Full Course Details


Overview:

This course is aimed at those who are new to Bayesian statistics and want to develop an understanding and application of the methods. Emphasis will be on practical data analysis and interpretation. Only essential theory will be outlined.


Intended Audience:

Scientists and technologists who want an introduction to Bayesian methods for data analysis. No prior knowledge of Bayesian statistics is required. A working knowledge of linear and generalised linear models and statistical distributions is required.

No previous experience of using OpenBUGS or R is required.

How You Will Benefit

By the end of the course, you will have a firm understanding of Bayesian methods and their flexibility. You will also have acquired a working knowledge of specialised software for Bayesian data analysis and will be able to fit and interpret linear and generalised linear models.

For more information, click here.

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Advanced Excel for Students Records Administrators


Dates: 16 September 2015;

Duration: 1 day

Full Course Details


Overview:

Do you have to replicate faculty descriptors at the school level, or department descriptors at the student level? Do you have to aggregate module results at the student level? Do you have to compute average marks per student for taught modules only? This one day course provides participants with hands-on experience of performing all these tasks within Excel.


Intended Audience:

Administrators in Educational establishments dealing with student records data; those involved in linking tables of students data at different levels and anyone involved in producing returns to the HESA will likely benefit from this course.

Participants are assumed to be able to use Excel formulae, obtain summary figures (counts, totals, percentages) using Excel, and be familiar with the storage of data in lists to enable the use of filters and Pivot Tables.

How You Will Benefit

By the end of the course you will be familiar with basic methods for fast, efficient and dynamic data crunching.

For more information, click here.

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Advanced Graphics using R


Dates: Next run to be announced.

Duration: 1 day

Full Course Details


Overview:

This course seeks to move your thinking from traditional understanding of graphics (based on graphics you know about - histograms, boxplots, scatterplots) to creating the ideal graphic to meet your objectives. It will use standard R graphics as well as those from the ggplot2 and lattice packages.


Intended Audience:

Any R users who need to present their data graphically will benefit from this course. You should be familiar with basic statistical concepts such as regression and simple analysis of variance. A rudimentary knowledge of R will be assumed including the ability to produce graphics such as a histogram with a title and axis labels.

How You Will Benefit

You will learn how to customise graphics in R, how to overlay various graphics, how to integrate statistical analyses in graphics and how to use more complex graphing packages. Hands-on computer practicals will ensure ample opportunity to practise coding graphics, using both the standard R graphics and the specialised packages.

For more information, click here.

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Advanced Topics in Survival Analysis


Dates: 15 July 2015;

Duration: 3 days

Full Course Details


Overview:

This course is concerned with models for different types of data structure, or with different underlying assumptions. During lectures and practical sessions the statistical package SAS is used to illustrate the methodologies.


Intended Audience:

Statisticians in medical research in public sector institutions and in the pharmaceutical and related industries, who already have some familiarity with modelling survival data. In particular, some experience in using proportional hazards models will be advantageous.

How You Will Benefit

If you deal regularly with survival data and need more tools for their analysis, then this course will introduce you to a range of different survival analysis models.

For more information, click here.

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Analysis of Binary and Categorical Data


Dates: Next run to be announced.

Duration: 2 days

Full Course Details


Overview:

This course will explain statistical techniques for studying associations between outcomes like those mentioned, and possible explanatory factors. The emphasis will be on practical application and interpretation rather than theory. A large component of the course will be PC-based practical work on user-friendly statistics packages such as Minitab, R, SAS and Stata.


Intended Audience:

Scientists and technologists who already have some statistical training but whose knowledge is lacking in the area of statistical methods specifically for binary and categorical data. Prior attendance on A Review of Basic Statistics and Regression Analysis: A Hands-on Approach, or equivalent knowledge, is required for this course.

How You Will Benefit

You will be introduced to some of the necessary tools for summarising and analysing binary and categorical data, and learn how to fit and interpret models for exploring associations in such data.

For more information, click here.

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Analysis of Generalised Linear Mixed Models


Dates: 9 November 2015;

Duration: 2 days

Full Course Details


Overview:

This course emphasises practical understanding, although an outline of the theory will be presented. Practical examples will be used to illustrate the methods, and participants will have the opportunity to fit and interpret models themselves in hands-on computer practicals. The GLIMMIX and NLMIXED procedures of the statistical package SAS will be used, as appropriate, throughout the course.


Intended Audience:

Statisticians who are already familiar with linear mixed models. It will be assumed that participants are regular SAS users, and have a working knowledge of generalised linear models.

How You Will Benefit

You will learn how to formulate generalised linear models with fixed and random effects for a range of situations, and how to fit and interpret them in the SAS software.

For more information, click here.

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Analysis of Mixed Models


Dates: Next run to be announced.

Duration: 3 days

Full Course Details


Overview:

How to fit linear mixed models and interpret the results for a range of common situations is the subject of this course. The MIXED procedure of the statistical package SAS will be used to illustrate ideas in the lectures and for hands-on computer practical sessions.


Intended Audience:

Statisticians who are already familiar with General Linear Models. It will be assumed that participants are regular SAS users.

How You Will Benefit

The course will give you the skills to formulate, fit and interpret mixed models for a range of practical situations, as well as an appreciation of some of the benefits of mixed modelling.

For more information, click here.

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A Review of Basic Statistics


Dates: 20 July 2015; 28 September 2015; 30 November 2015;

Duration: 2 days

Full Course Details


Overview:

This course builds up the basic ideas of statistics systematically and quickly. It provides a review of the methods of summarising and presenting data, estimation, confidence intervals and hypothesis testing. Mathematical details are kept to a minimum.


Intended Audience:

Scientists and technologists who have had some previous training in, or exposure to, statistics but who now wish to understand basic statistical ideas more thoroughly.

How You Will Benefit

If you deal regularly with survival data and need more tools for their analysis, then this course will introduce you to a range of different survival analysis models.

For more information, click here.

Apply Now

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Data Management using CSPro: A Hands-On Approach


Dates: Next run to be announced.

Duration: 6 days

Full Course Details


Overview:

This 6-day workshop goes in detail through the process of designing and implementing a data entry application for a typical survey. Participants will learn how to create the data dictionary, data entry screen and how to program checks and skips. The workshop then considers the data validation tools available including data comparisons and simple frequency tables. Finally we show how data is stored in the system and how it can easily be exported for transfer to statistics packages such as SAS and STATA as well as to Excel.


Intended Audience:

This is an introductory course aimed at researchers, demographers, data managers, and others who wish to use CSPro to implement survey data management systems. No previous experience of CSPro is required but participants are assumed to be comfortable with Windows-based software.

How You Will Benefit

You will gain an understanding of data structures and how to design a suitable data entry application to match different structures. You will also gain core experience with logic programming and an appreciation of the steps involved in ensuring data quality throughout a project.

For more information, click here.

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Design and Analysis of Experiments


Dates: 16 November 2015;

Duration: 3 days

Full Course Details


Overview:

Conducting cost-effective experiments based on good design principles is fundamental to many areas of scientific research. In this course participants will learn to use resources effectively and to plan well-designed experiments, so as to obtain clear results and a high degree of precision.


Intended Audience:

Scientists, researchers, psychologists and industrial experimenters who design their own experiments and/or analyse experimental data. Participants should be familiar with the analysis of variance (ANOVA).

How You Will Benefit

Participants will gain intuition and confidence in the statistical aspects of sound experimental design and learn how to analyse data arising from a variety of different designs.

For more information, click here.

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General Linear Models


Dates: Next run to be announced.

Duration: 2 days

Full Course Details


Overview:

TThis course briefly presents the theory of and discuss their application and interpretation in problems of biological and medical sciences and in pharmaceutical work. Many examples are used to illustrate a wide range of GLMs. Practical sessions based on SAS help participants understand the ideas involved.


Intended Audience:

Statisticians who have experience with multiple regression and analysis of variance, and have had some previous exposure to the analysis of variance for unbalanced data. Familiarity with SAS software is assumed.

How You Will Benefit

Participants will gain confidence in correctly using SAS for analysing different types of problems and in output produced by GLM fitting. In particular, identifying the correct type of sums of squares to use will be of benefit in analysing real-life problems.

For more information, click here.

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Introduction to Analysis of Variance (ANOVA)


Dates: Next run to be announced.

Duration: 2 days

Full Course Details


Overview:

In this beginners' course, the theory, practice and interpretation of ANOVA in studies involving treatment (grouping) factors and in regression analysis will be explained. The General Linear Model (GLM) on which the ANOVA is based will be discussed in order to demonstrate how ANOVA can be used to deal with more complex situations.


Intended Audience:

Scientists and technologists. Participants will be assumed to have working knowledge of the basic principles of statistical methodology such as estimation, confidence intervals and hypothesis testing.

How You Will Benefit

This two-day course will give you a firm grounding in the principles underlying analysis of variance, and an appreciation of how the basic techniques can be extended to more complex real life situations.

For more information, click here.

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Introduction to Capture-Recapture Techniques


Dates: 24 September 2015;

Duration: 2 days

Full Course Details


Overview:

This course will introduce capture-recapture techniques and models, and highlight their importance in the application fields of medicine and public health, and the life and social sciences.


Intended Audience:

The course is aimed at statisticians, biostatisticians, biometricians, epidemiologists, public health statisticians and social science researchers who have little or no knowledge of capture-recapture techniques.

How You Will Benefit

The course will develop your understanding of the huge potential of capture-recapture techniques.

For more information, click here.

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Introduction to Epidemiological Methods


Dates: Next run to be announced.

Duration: 3 days

Full Course Details


Overview:

This course will explain the main types of epidemiological investigations upon which assessments are made, such as cohort and case-control studies. The relative merits and disadvantages of such designs will be discussed from a practical viewpoint.


Intended Audience:

Medical or public health professionals who already have some statistical training, but whose knowledge is lacking in the area of statistical methods specifically for epidemiology. Prior attendance on A Review of Basic Statistics, or equivalent knowledge, is required for this course. No prior knowledge of the Stata statistical package is assumed.

How You Will Benefit

You will be introduced to the main types of study design used in epidemiology, and learn how to perform and interpret simple statistical analyses of data from such studies.

For more information, click here.

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Introduction to Logistic Regression


Dates: Next run to be announced.

Duration: 2 days

Full Course Details


Overview:

This course will explain the use of logistic regression for studying associations between binary outcomes and possible explanatory factors. The emphasis will be on practical application and interpretation rather than theory. A large component of the course will be PC-based practical work on user-friendly statistics packages such as Minitab, R, SAS and Stata.


Intended Audience:

Scientists and technologists who already have some statistical training but whose knowledge is lacking in the area of regression methods for binary response data. Prior attendance on A Review of Basic Statistics and Regression Analysis: A Hands-on Approach, or equivalent knowledge, is required for this course.

How You Will Benefit

You will be introduced to the increasingly widely used modelling technique of logistic regression for analysing binary response data, and learn how to fit and interpret such models.

For more information, click here.

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Introduction to Multiple Comparison Methods


Dates: 13 July 2015;

Duration: 1 day

Full Course Details


Overview:

The course consists of a mixture of presentations and practicals. It begins with an overview of the multiplicity problem, basic definitions and some historical methods. We then move onto parametric multiple inference in the linear model, and introduce the max T method which forms the basis for procedures such Tukey and Dunnett's multiple comparison procedures.


Intended Audience:

Medical statisticians, and related, engaged in clinical trials and medical related research, who have little or no experience of dealing with multiplicity issues. Experience of SAS and R would be advantageous but is not essential.

How You Will Benefit

This course gives a practical introduction to multiple comparison methods from basic methods through to more recent sophisticated approaches.

For more information, click here.

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Introduction to Survival Analysis


Dates: 7 October 2015;

Duration: 3 days

Full Course Details


Overview:

This course emphasises the practical aspects of analysing survival data and interpreting models, but the underlying theory is explained as appropriate. In practical sessions participants apply the methods covered to a simulated clinical trial and to report on the results. The statistical package SAS is used to illustrate the methodologies in the presentations and for practical work. R and Stata may also be used for practical work.


Intended Audience:

Statisticians engaged in medical research in public sector industries and in the pharmaceutical and related industries, who have little or no experience of dealing with survival data. No previous experience of R, SAS or Stata is required.

How You Will Benefit

This course will give a thorough Introduction to Survival Analysis from basic methods through to commonly-used modelling approaches.

For more information, click here.

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Microsoft Access for Data Management: A Hands-on Approach


Dates: 11 November 2015;

Duration: 3 days

Full Course Details


Overview:

The course takes participants through the process of design and implementation of databases using MS-Access 2010. We aim to disprove misconceptions that databases are mysterious entities and make an effort to clarify the jargon often associated with them. The course is a combination of demonstrations and practical work where the skills are acquired by 'doing'.


Intended Audience:

Most of us regularly deal with data of one type or another; you might be in business and need to keep names and addresses of customers; you might be a supplier needing to manage stock levels and customer orders; those in education would need to keep student records; you might be a technician needing to keep track of equipment and supplies; or a researcher managing data from surveys or experiments; or you may simply wish to keep a catalogue of your ever-increasing collection of DVDs. Access can be used in all these situations. Even if you believe you don't have much data at the moment it is worthwhile considering using a database system as you are likely to find the amount of data you have increases exponentially with time and this increase is easier to deal with if you are already using a well-designed database.

How You Will Benefit

You will gain hands-on experience in designing databases and knowledge of key VBA programming techniques for ensuring data quality. You will also gain experience of developing a secure data entry system within your database for end-users.

For more information, click here.

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Microsoft Excel for Statistics - What you can and cannot do


Dates: Next run to be announced.

Duration: 2 days

Full Course Details


Overview:

This course gives practical experience in the use of Microsoft® Excel® for data summary, presentation and for other basic statistical work. It concentrates on areas where Excel really can excel, such as array functions. We look at data entry and management, pivot tables, and graphics.


Intended Audience:

Participants are assumed to have some experience in Excel. This course is intended for anyone who currently uses or is thinking of using Excel for handling data and for their statistical work. It is also for statisticians, if they need to advise and support others on the efficient use of Excel.

How You Will Benefit

This course is designed to turn your general Excel capabilities into skills in the use of Excel for data processing and basic statistical applications. The course will make use of our Excel add-in called SSC-Stat, which provides some statistical features that are not available in standard Excel. A copy of this add-in will be given to all participants.

For more information, click here.

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Microsoft Excel for Statistics


Dates: Next run to be announced.

Duration: 1 day

Full Course Details


Overview:

This course gives practical experience in the use of Excel for summarising data and for other statistical work, concentrating on its strengths such as array functions. We look at data entry and management and pivot tables and pivot charts. We review Excel's facilities for statistical analysis, advising on their use and limitations, and we show how add-ins can be used to enhance Excel's capabilities.


Intended Audience:

This course is intended for anyone who currently uses Excel for handling data and for their statistical work. It is also for statisticians, if they need to advise and support others on the efficient use of Excel.

How You Will Benefit

This course is designed to turn your general Excel capabilities into skills in the use of Excel for data processing and basic statistical applications. The course makes use of our add-in called SSC-Stat, which provides statistical features that are not available in standard Excel.

For more information, click here.

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Modelling Binary and Categorical Repeated Measurement Data


Dates: Next run to be announced.

Duration: 2 days

Full Course Details


Overview:

Just as with normal repeated measurement data, there are different ways to approach the analysis of repeated binary, categorical and count data. Two popular approaches are marginal models and subject effects models, and the assumptions underlying each of these are different. This course concentrates on these two approaches. The theory underlying the methods, what the differences are, and how to fit and interpret the models are all covered.


Intended Audience:

Statisticians. No previous experience of analysing repeated non-normal data is required, but some knowledge of general linear modelling and generalised linear models will be assumed. Familiarity with methods for normal repeated measurement data is useful but not essential.

How You Will Benefit

This course will provide you with practical skills for tackling the analysis of repeated binary, categorical and count data.

For more information, click here.

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Multilevel Generalised Linear Models


Dates: Next run to be announced.

Duration: 2 days

Full Course Details


Overview:

This course extends the traditional multilevel model with normally distributed errors to those with discrete responses. Common discrete variables are binary and counts. Typically these types of responses are analysed using generalised linear models such as logistic regression and Poisson regression.


Intended Audience:

Statisticians and experienced data analysts faced with the need to analyse hierarchical or multilevel data, with discrete responses. Some familiarity with normal multilevel models would be an advantage. This can be gained by attending our course on Multilevel Modelling.

How You Will Benefit

You will learn how to extend generalised linear models to analyse common discrete response data in a multilevel framework. You will also extend your knowledge of Multilevel Modelling for normal data to encompass a wider class of models.

For more information, click here.

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Multilevel Modelling


Dates: Next run to be announced.

Duration: 2 days

Full Course Details


Overview:

Although the theory of Multilevel Modelling will be explained, the emphasis in this course is on its practical implementation and interpretation of results. The course will focus on models with normally distributed errors. The MLwiN package will be used in the presentations as well as other software as appropriate.


Intended Audience:

Statisticians and other data analysts faced with the need to analyse multilevel data. Some familiarity with standard statistical modelling techniques, such as multiple regression, will be assumed. No previous experience with the MLwiN software is required.

How You Will Benefit

You will update your data analysis skills by learning how to fit and interpret models for analysing hierarchical data. You will also learn to use specialised software for Multilevel Modelling.

For more information, click here.

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Multiple Imputation: A Practical Introduction


Dates: 19 October 2015;

Duration: 1 day

Full Course Details


Overview:

Incomplete dataset arise often: for example in clinical trials because of patient non-compliance and in observational studies because of unobserved explanatory variables. This course explores imputation - methods of replacing missing data with a probable value based on other available information, so as to preserve all cases.


Intended Audience:

Scientists and analysts engaged in statistical analysis of data, who have no experience of multiple imputation. A working knowledge of statistical modelling (e.g. logistic regression) is required, as well as of one of the statistics packages listed above.

How You Will Benefit

This course will give a thorough introduction to Multiple Imputation by chained equations for multiple incomplete variables, both numerical and categorical. Various imputation models will be explored, as well as the presentation of results from a Multiple Imputation analysis.

For more information, click here.

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Multivariate Analysis


Dates: 23 July 2015;

Duration: 2 days

Full Course Details


Overview:

During this course, commonly used multivariate techniques will be introduced and developed, and relationships between them examined. The emphasis of the course will be practical application and interpretation of results using a range of scientific related examples. Mathematical details are kept to a minimum.


Intended Audience:

Scientists and technologists involved in analysing multivariate data. A working knowledge of basic statistics and linear regression will be assumed, such as standard deviations and correlations, and simple and multiple linear regression.

How You Will Benefit

Participants will gain a sound practical understanding of commonly used multivariate techniques.

For more information, click here.

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Practical Bayesian Data Analysis


Dates: Next run to be announced.

Duration: 3 days

Full Course Details


Overview:

Beyesian statistical methods utilise prior information about model perameters in the inference process. The emphasis of this course is on practical data analysis, but also explains the essential theory behind these methods. It includes a practical introduction to WinBUGS and explores the flexibility of Bayesian methods.


Intended Audience:

Statisticians and data analysts who wish to use a Bayesian approach in analysing their data. No prior knowledge of WinBUGS or R will be assumed.

How You Will Benefit

You will extend your data analysis skills to cover a very wide class of modelling, including the use of prior information. You will learn how to use specialised software for Bayesian data analysis.

For more information, click here.

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Regression Analysis: A Hands-on Approach


Dates: 30 September 2015; 2 December 2015;

Duration: 2 days

Full Course Details


Overview:

The course introduces commonly-used linear regression techniques using a combination of presentations and computer-based practicals, whereby theory is firmly placed into practice. You have a choice of packages from Genstat, Minitab, R, SAS and Stata.


Intended Audience:

Scientists and technologists who need to be conversant with the concepts of regression and the process of choosing a model. Attendance on A Review of Basic Statistics, or equivalent knowledge, is assumed.

How You Will Benefit

Participants will acquire an appreciation of how basic regression concepts can be extended easily to investigate more complex situations. You will also learn how to interpret computer output and to report results in non-statistical language, as well as a guide to the analysis of variance table.

For more information, click here.

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Repeated Measurements Analysis


Dates: 12 October 2015;

Duration: 2 days

Full Course Details


Overview:

During the course the main approaches to dealing with repeated normal data are covered, from simple methods to more complex modelling, especially mixed modelling. The practicalities associated with choosing, fitting and interpreting models will also be addressed. The SAS software package will be used in lectures and hands-on computer practicals to illustrate the different techniques.


Intended Audience:

Statisticians who need to analyse data from repeated measurements experiments. No previous experience of repeated measurements is required, but knowledge of linear modelling and analysis of variance is assumed.

How You Will Benefit

The course will give you the skills to apply a range of repeated measurements analysis methods for normal data and an appreciation of their relative advantages and disadvantages.

For more information, click here.

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Statistical Analysis of Examination and Other Numerical Data


Dates: 15 September 2015;

Duration: 1 day

Full Course Details


Overview:

The course provides participants with hands-on experience of analysing numerical data, with a focus on examination data, and how to report and interpret results to committees. Starting with the notion of summarising numerical data using summary statistics and exploratory graphics, the course continues with a number of basic statistical methods orientated around the use of means, finishing with the use of simple linear regression for exploring trends.


Intended Audience:

Those involved in exploring and interpreting examination performance, and in reporting and presenting to committees. Also, those involved in Policy and Planning units who are required to draw and report conclusions from numerical data.

Participants are assumed to have knowledge of basic formulae and functions in Excel. Ideally, participants will have previously attended our short course Statistics for University Administrators, or have equivalent knowledge.

How You Will Benefit

By the end of the course you will be familiar with basic statistical methods for making sound assessments of numerical examination, or other data, using Excel.

For more information, click here.

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Statistics for Policy Makers


Dates: 18 September 2015;

Duration: 0.5 days

Full Course Details


Overview:

The course covers key statistical concepts crucial to the effective statistical results constantly used to make decisions in business and government.


Intended Audience:

The course is aimed at people who are interested in using statistical results for decision making, who need to get the best of the statistical information available and would like to make sure that the they can interpret statistics and statistical results effectively.

How You Will Benefit

Decision makers in government and the private sector and members of teams that use statistics to build policies or brief policy makers.

For more information, click here.

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Statistical Modelling and Graphics using R


Dates: 3 June 2015;

Duration: 2 days

Full Course Details


Overview:

This course encourages an interactive approach to statistical analysis based on the use of R's publication-quality graphics, as well as its extensive data exploration and statistical modelling capabilities. To find out more about R, look at http://www.r-project.org/.


Intended Audience:

Statisticians and others who analyse data will benefit from this course. The course is not intended as a first course in statistics. You should be familiar with basic statistical concepts up to regression and analysis of variance, and preferably you will have already some experience with a statistics package.

How You Will Benefit

You will learn to use R for basic statistical analysis and graphics. You will begin to see why it's so popular. All optional practical exercises are included with the course materials and can be followed after the course.

For more information, click here.

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Statistical Modelling in Epidemiology


Dates: Next run to be announced.

Duration: 2 days

Full Course Details


Overview:

Non-model-based statistical analysis methods have been popular in epidemiology. Among these have been stratified analyses to adjust for a potential confounding variable, in particular Mantel-Haenszel type methods for analysing binary outcomes. This course will introduce commonly-used statistical modelling techniques. The emphasis will be on practical application, rather than theoretical results. Presentations and hands-on computer practicals will use the statistical package Stata.


Intended Audience:

Medical or public health professionals working in epidemiology who already have some statistical knowledge, but wish to be conversant with the concepts and applications of statistical modelling. Prior attendance on Introduction to Epidemiological Methods, or equivalent knowledge, is required for this course. Participants will be assumed to have an elementary knowledge of Stata.

How You Will Benefit

You will be introduced to common statistical modelling techniques that are applicable in epidemiology, learn about the flexibility of a statistical model and how to perform and interpret basic analyses.

For more information, click here.

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Statistics for University Administrators


Dates: 18 June 2015; 14 September 2015;

Duration: 1 day

Full Course Details


Overview:

Ever been asked for the margin of error on a percentage? Or if your percentage is different from the benchmark? Or if the percentage of student retention for this year is different from that for last year? This one-day course provides participants with hands-on experience of analysing their own records and confidently interpreting numerical results for reports to committees, or for returns to the Higher Education Statistics Agency.


Intended Audience:

Administrators in educational establishments working in Policy and Planning units; those involved in interpreting student records and in reporting to committees; anyone involved in producing returns to the HESA - anyone in these positions will benefit greatly from this course.

Participants are assumed to have basic Excel skills such as obtaining totals and percentages.

How You Will Benefit

By the end of the course you will be familiar with basic statistical methods for making sound assessments. You will also be able to appreciate HESA guidelines on the statistical importance of your results. To date, 163 out of 190 attendees (86.8% ± 5.0%) said they would definitely recommend this course.

For more information, click here.

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Survival Analysis for Medical and Health Professionals


Dates: Next run to be announced.

Duration: 2 days

Full Course Details


Overview:

The course is a basic practical introduction to some of the commonly-used tools for analysing survival data. Statistical theory underlying the different approaches is kept to a minimum, and emphasis is placed on how to summarise data and how to interpret common hypothesis tests. The course also introduces and explains the concept of modelling survival data based on the widely-used Cox regression model.


Intended Audience:

Medical and health professionals who need analytical tools for making inferences from survival data. Participants will be assumed to have some knowledge of elementary statistical techniques (as are covered in A Review of Basic Statistics) and regression modelling (see our course Regression Analysis: A Hands-on Approach).

How You Will Benefit

You will acquire practical experience in the use of commonly-used techniques for the analysis of survival data, and an appreciation of more complex methods.

For more information, click here.

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Survival Analysis using R


Dates: Next run to be announced.

Duration: 1 day

Full Course Details


Overview:

The course is a practical introduction to some of the commonly-used tools for analysing survival data. Statistical theory underlying the different approaches is kept to a minimum, and emphasis is placed on the application of mainstream techniques in the statistics package R.


Intended Audience:

Medical statisticians, epidemiologists and other health-related professionals who are already familiar with applying survival techniques in a standard statistics package and wish to begin using R for the same task.

Participants will be assumed to have a basic working knowledge of R, covering data manipulation, an understanding of generic functions, extractor functions and concepts of object oriented programming such as methods and classes.

How You Will Benefit

You will acquire practical experience in the use of R for the analysis of survival data, and an appreciation of how R compares to other statistical packages

For more information, click here.

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Taking Microsoft Excel Further: Macros for Data Management and Statistics


Dates: 17 September 2015;

Duration: 1 day

Full Course Details


Overview:

This one-day course introduces participants to ways of extending Excel using macros and other features of Visual Basic for Applications (VBA). Examples include simple programs to reorganise data and to perform statistical calculations. Practical sessions enable participants to develop and run simple macros..


Intended Audience:

Those who need to extend their use of Excel beyond the standard facilities. The examples are mainly for those who will use Excel for statistical applications, but the concepts are equally relevant for any area of application. Participants will be assumed to have some experience in Excel. Programming experience in any language would be useful, but is not assumed.

How You Will Benefit

You will be able to use Excel more efficiently, even for non-standard problems. Where applications need to be written that require more professional programming skills, you will be able to understand the issues involved, so that you can discuss the requirements and time-scales more effectively.

For more information, click here.

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The Incomplete and Utter Guide to Statistics


Dates: Next run to be announced.

Duration: 2 days

Full Course Details


Overview:

This course seeks to establish a framework that will help you to understand data analyses and address the complications that may lie within. It will not detail the specifics of how each technique applies in a given situation but will provide you with a general approach to tackle a variety of problems. It will also allow you to be more discerning when reading statistical reports and summaries.


Intended Audience:

If you have little awareness of statistics but encounter data, reports or analysis results regularly, this course is for you. If you do your own analysis but always use the same few techniques, this course will expand your awareness and allow you to choose the right tool for the job.

How You Will Benefit

You will benefit by learning how to approach problems from a statistical point of view, as well as how to apply key principles to help you choose appropriately from among the available methods and techniques.

For more information, click here.

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Validation of Diagnostic Tests


Dates: Next run to be announced.

Duration: 1 day

Full Course Details


Overview:

Diagnostic tests are developed to assist with the diagnosis of a disease or medical problem, but, be quicker, cheaper or less invasive than a reference test (or 'gold standard'), against which they are evaluated. The evaluation process is often known as validation of a diagnostic test. Long established in diagnostic medicine, screening and epidemiology, these techniques have been widely applied in medicine and health related areas such as pregnancy testing, addiction and mental health diagnosis, and more general medical screening.


Intended Audience:

Medical scientists and related who wish to be conversant with the process of evaluation of diagnostic tests. Familiarity with standard errors and confidence intervals for proportions is required. Knowledge equivalent to topics in our course A Review of Basic Statistics would be ideal.

How You Will Benefit

Participants will learn about standard measures and techniques that are used to evaluate the performance of diagnostic tests that yield a yes/no outcome. You will also learn how to extend these techniques to diagnostic tests that yield categorical or continuous outcomes.

For more information, click here.

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What Sample Size Do I Need?


Dates: 22 July 2015;

Duration: 1 day

Full Course Details


Overview:

This course aims to give a practical introduction to sample size determination in the context of some commonly used significance tests.


Intended Audience:

Scientists and researchers who need to address the problem of sample size determination in planning a study. Participants will be assumed to have a working knowledge of sampling distributions, confidence intervals and significance tests for both means and proportions. This material is covered in our course A Review of Basic Statistics.

How You Will Benefit

This course will give you a sound introduction to sample size determination. Opportunity will be given to participants to discuss general issues related to sample size.

For more information, click here.

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Bayesian Modelling, Inference, Prediction and Decision-Making


Dates: 23 November 2015;

Duration: 2 days

Full Course Details


Overview:

Bayesian methods offer an approach to inference, prediction and decision-making that allow you to synthesize all relevant sources of information in drawing conclusions and making decisions in the presence of uncertainty. This course offers an introduction to Bayesian modelling.


Intended Audience:

Statisticians, biostatisticians, epidemiologists, data analysts, data-miners, and machine-learning specialists who wish to broaden and deepen:

  • Their understanding of Bayesian methods and
  • Their toolkits for using Bayesian models to find meaningful patterns, arrive at statistically sound inferences and make better decisions.

Some graduate coursework in statistics (or an allied field such as biostatistics, epidemiology or machine learning) will provide sufficient mathematical background for participants. To get the most out of the course, participants should be comfortable with hearing the course presenter discuss:

  • Differentiation and integration of functions of several variables and
  • Discrete and continuous probability distributions (joint, marginal, and conditional) for several variables at a time, but all necessary concepts will be approashed in a sufficiently intuitive manner that rustiness on these topics will not prevent understanding of the key ideas.

This course assumes no previous exposure to Bayesian ideas or methods.

How You Will Benefit

You will:

  • Gain a deeper understanding of maximum-likelihood-based methods and when they can be expected to behave in a sub-optimal manner;
  • Broaden and deepen your facility in the fitting and interpretation of Bayesian models to solve important problems in science, public policy and business; and
  • Learn how to write your own programs in WinBUGS and R to fit Bayesian models in your own work.

For more information, click here.

Apply Now

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Bayesian Hierarchical Modelling


Dates: 25 November 2015;

Duration: 1 day

Full Course Details


Overview:

An intermediate-level course on Bayesian hierarchical modelling.


Intended Audience:

Statisticians, biostatisticians, epidemiologists, data analysts, data-miners, and machine-learning specialists who wish to broaden and deepen:

  • Their understanding of Bayesian methods and
  • Their toolkits for using Bayesian models to find meaningful patterns, arrive at statistically sound inferences and make better decisions.

Some graduate coursework in statistics (or an allied field such as biostatistics, epidemiology or machine learning) will provide sufficient mathematical background for participants. To get the most out of the course, participants should be comfortable with hearing the course presenter discuss:

  • Differentiation and integration of functions of several variables and
  • Discrete and continuous probability distributions (joint, marginal, and conditional) for several variables at a time, but all necessary concepts will be approashed in a sufficiently intuitive manner that rustiness on these topics will not prevent understanding of the key ideas.

Participants interested in attending this course should ideally have had exposure to the ideas covered in the course Bayesian Modelling, Inference, Prediction and Decision-Making

How You Will Benefit

You will:

  • Gain a deeper understanding of maximum-likelihood-based methods and when they can be expected to behave in a sub-optimal manner;
  • Broaden and deepen your facility in the fitting and interpretation of Bayesian models to solve important problems in science, public policy and business; and
  • Learn how to write your own programs in WinBUGS and R to fit Bayesian models in your own work.

For more information, click here.

Apply Now

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Bayesian Model Specification


Dates: 26 November 2015;

Duration: 1 day

Full Course Details


Overview:

An intermediate-level course on Bayesian model specification.


Intended Audience:

Statisticians, biostatisticians, epidemiologists, data analysts, data-miners, and machine-learning specialists who wish to broaden and deepen:

  • Their understanding of Bayesian methods and
  • Their toolkits for using Bayesian models to find meaningful patterns, arrive at statistically sound inferences and make better decisions.

Some graduate coursework in statistics (or an allied field such as biostatistics, epidemiology or machine learning) will provide sufficient mathematical background for participants. To get the most out of the course, participants should be comfortable with hearing the course presenter discuss:

  • Differentiation and integration of functions of several variables and
  • Discrete and continuous probability distributions (joint, marginal, and conditional) for several variables at a time, but all necessary concepts will be approashed in a sufficiently intuitive manner that rustiness on these topics will not prevent understanding of the key ideas.

Participants interested in attending this course should ideally have had exposure to the ideas covered in the two previous courses in this series: Bayesian Modelling, Inference, Prediction and Decision-Making and Bayesian Hierarchical Modelling.

How You Will Benefit

You will:

  • Gain a deeper understanding of maximum-likelihood-based methods and when they can be expected to behave in a sub-optimal manner;
  • Broaden and deepen your facility in the fitting and interpretation of Bayesian models to solve important problems in science, public policy and business; and
  • Learn how to write your own programs in WinBUGS and R to fit Bayesian models in your own work.

For more information, click here.

Apply Now

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Case Studies in Bayesian Small-to-Very-Large-Scale Data Science


Dates: 27 November 2015;

Duration: 1 day

Full Course Details


Overview:

TBC


Intended Audience:

Statisticians, biostatisticians, epidemiologists, data analysts, data-miners, and machine-learning specialists who wish to broaden and deepen:

  • Their understanding of Bayesian methods and
  • Their toolkits for using Bayesian models to find meaningful patterns, arrive at statistically sound inferences and make better decisions.

Some graduate coursework in statistics (or an allied field such as biostatistics, epidemiology or machine learning) will provide sufficient mathematical background for participants. To get the most out of the course, participants should be comfortable with hearing the course presenter discuss:

  • Differentiation and integration of functions of several variables and
  • Discrete and continuous probability distributions (joint, marginal, and conditional) for several variables at a time, but all necessary concepts will be approashed in a sufficiently intuitive manner that rustiness on these topics will not prevent understanding of the key ideas.

Participants interested in attending this course should ideally have had exposure to the ideas covered in the three previous courses in this series: Bayesian Modelling, Inference, Prediction and Decision-Making, Bayesian Hierarchical Modelling and Bayesian Model Specification.

How You Will Benefit

You will:

  • Gain a deeper understanding of maximum-likelihood-based methods and when they can be expected to behave in a sub-optimal manner;
  • Broaden and deepen your facility in the fitting and interpretation of Bayesian models to solve important problems in science, public policy and business; and
  • Learn how to write your own programs in WinBUGS and R to fit Bayesian models in your own work.

For more information, click here.

Apply Now

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Multilevel Modelling with MPlus


Dates: 21 October 2015;

Duration: 3 days

Full Course Details


Overview:

In this 3-day course we introduce multilevel modelling using the MPlus statistical software. The basic multilevel model is introduced along with its assumptions and an analysis strategy. Estimation methods and the intraclass correlation coefficient are considered. An application to longitudinal data shows how to deal with repeated measures within-subjects.


Intended Audience:

The target group is PhD students and other post-graduate level researchers, and allied professionals, in the social and behavioural sciences, and related disciplines. Participants will be assumed to have a working knowledge of standard regression modelling, in particular linear regression models. Participants should also have some knowledge of logistic regression models for modelling dichotomous outcomes – this can be obtained from Chapter 19 in Field (2013).

How You Will Benefit

At the end of the course you will have a good working knowledge of multilevel modelling, and be able to fit and interpret models using the MPlus statistical software.

For more information, click here.

Apply Now

Page last updated: May 22 2015 16:16:49.