TRANSFORMING HIGHER EDUCATION THROUGH EXCEPTIONAL ONLINE LEARNING

Authors

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Sandro Leidi:
Lead advisor and course author

A senior statistician at the Statistical Services Centre, University of Reading, Sandro has been working in Statistics since 1997, training professionals and providing training to institutions. Along with his colleagues, he has been delivering statistical e-learning since 2004. His consultancy group has many renowned clients, including the National Audit Office, UK, and the United Nations Framework Convention for Climate Change.

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Wilma Alexander:
Accessibility advisor

Wilma Alexander is part of the Learning Services team at the University of Edinburgh, supporting the use of online tools and technologies across the university. She has a special interest in usable and accessible digital practice, tutors on usability and accessibility for the university's Master's in Digital Education, and promotes the use of online activities for inclusive teaching and learning in the context of staff development.

Learning outcomes

By the end of this course you will be able to:

  • Define the terms sample, population, estimate, standard error, Normally distributed and confidence intervals
  • State what is meant by 'the sampling distribution of the mean'
  • Explain why presenting the standard error of an estimate is essential for making a valid generalisation
  • Explain how the Normal distribution is used to compute a confidence interval
  • Interpret a confidence interval.

Course structure

The diagram on the right explains the chronology of the course. Click on each of the sections to view a more detailed breakdown of this course.

Orientation

Introduction to the course, helping you gain a feel for how it will develop.

Course files

The course content. The target knowledge and concepts are introduced during this stage.

Closing

Summarises what you have learned.

Course quiz

A chance to test your knowledge and recall
what you have learned from the course so far.

Highlights

Course highlights include:

  • An animation activity that helps learners to understand the concept of sampling distribution
  • Several 'Interactive model' pods that allow learners to play around with diagrams and datasets to consolidate theory comprehension
  • A worked example on calculating confidence intervals using statistical software
  • A Course quiz to test the concepts learned in the course.

Supporting institutions

The Statistical Methods for Research programme has been developed in conjunction with the following institutions:

  • Brunel University, UK
  • Dublin Institute of Technology, Ireland
  • Edith Cowan University, Australia
  • James Cook University, Australia
  • London Metropolitan University, UK
  • Sheffield Hallam University, UK
  • University College Cork, Ireland
  • University of Birmingham, UK
  • University of Brighton, UK
  • University of Huddersfield, UK
  • University of Reading, UK

Authors

Sandro Leidi: Lead advisor and course author

A senior statistician at the Statistical Services Centre, University of Reading, Sandro has been working in Statistics since 1997, training professionals and providing training to institutions. Along with his colleagues, he has been delivering statistical e-learning since 2004. His consultancy group has many renowned clients, including the National Audit Office, UK, and the United Nations Framework Convention for Climate Change.


Wilma Alexander: Accessibility advisor

Wilma Alexander is part of the Learning Services team at the University of Edinburgh, supporting the use of online tools and technologies across the university. She has a special interest in usable and accessible digital practice, tutors on usability and accessibility for the university's Master's in Digital Education, and promotes the use of online activities for inclusive teaching and learning in the context of staff development.


Learning outcomes

By the end of this course you will be able to:

  • Define the terms sample, population, estimate, standard error, Normally distributed and confidence intervals
  • State what is meant by 'the sampling distribution of the mean'
  • Explain why presenting the standard error of an estimate is essential for making a valid generalisation
  • Explain how the Normal distribution is used to compute a confidence interval
  • Interpret a confidence interval.

Course structure

The bullet points below explain the chronology of the course and give a breakdown of each of the sections you will encounter.

Orientation

The Orientation section introduces you to the content and aims of the course. There is an opportunity to assess your current knowledge, to help you evaluate your learning at the end of the course.

  • Introduction

Course files

The course files contain the core course content. The content is divided into units and screens.

  • Unit 1: Sampling variability and the precision of a sample estimate
  • Unit 2: Using standard errors to build confidence intervals

Closing

The Closing section summarises what you have learned.

  • Course summary

Course quiz

The Course quiz section allows you to assess and consolidate what you have learned in the course.

  • Course quiz resources
  • Course quiz

Highlights

Course highlights include:

  • An animation activity that helps learners to understand the concept of sampling distribution
  • Several 'Interactive model' pods that allow learners to play around with diagrams and datasets to consolidate theory comprehension
  • A worked example on calculating confidence intervals using statistical software
  • A Course quiz to test the concepts learned in the course.

Supporting institutions

The Statistical Methods for Research programme has been developed
in conjunction with the following institutions:

  • Brunel University, UK
  • Dublin Institute of Technology, Ireland
  • Edith Cowan University, Australia
  • James Cook University, Australia
  • London Metropolitan University, UK
  • Sheffield Hallam University, UK
  • University College Cork, Ireland
  • University of Birmingham, UK
  • University of Brighton, UK
  • University of Huddersfield, UK
  • University of Reading, UK