date_range Starts on January 15, 2020
event_note End date April 1, 2020
list 8 sequences
assignment Level : Introductive
chat_bubble_outline Language : English

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## Key Information

credit_card Free access
verified_user Fee-based Certificate
timer 32 hours in total

## About the content

Data is everywhere, from the media to the health sciences, and from financial forecasting to engineering design. It drives our decisions, and shapes our views and beliefs. But how can we make sense of it?

This course introduces some of the key ideas and concepts of statistics, the discipline that allows us to analyse and interpret the data that underpins modern society.

In this course, you will explore the key principles of statistics for yourself, using interactive applets, and you will learn to interpret and evaluate the data you encounter in everyday life.

No previous knowledge of statistics is required, although familiarity with secondary school mathematics is advisable.

Logo image: (C) The University of Edinburgh 2016 CC BY, derived from Waverley Bridge, by Manuel Farnlack on Flickr, 2010 CC BY

After completing this course, you'll be able to:

• Understand the key principles of statistics;
• Interpret and evaluate the kinds of data found in everyday life;
• Perform, and interpret results from, simple statistical analyses.

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## Prerequisite

Secondary school mathematics (GCSE/Standard Grade/National 5 grade C or above in Mathematics, or equivalent)

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## Syllabus

Week 1: Introducing Data
What is statistics? We begin the course with this question, and see how data lies at the heart of statistics. We look at common techniques for presenting and summarising data.

Week 2: Patterns in Data
We look further into the science of data analysis, focusing on finding and interpreting relationships between different data sets, and on using trends in data to make predictions.

Week 3: Collecting Data
We look at key methods of data collection, seeing how we generally use samples of a population to make predictions about the whole population. We learn about how to choose a representative sample, and how to set up a statistical experiment.

Week 4: Uncertainty in Data
Using samples to make predictions about a population brings uncertainty into our data. As the study of risk and uncertainty, probability is therefore key to understanding statistics. We introduce the ideas here for describing and quantifying uncertainty via probabilities.

Week 5: Distribution
We look again at probability and describe a range of common situations that lead to standard forms for describing the associated probability of different possible outcomes. The ability to describe such probabilities provides the basis for building up the knowledge and understanding needed to study deeper statistical methods.

Week 6: Estimation
We will build on the idea of estimating properties of a population using sample data. Further, as the answer that we provide is only an estimate of the (unknown) true value, we will also describe how we may construct an associated uncertainty interval for the parameter being estimated, using properties of the sampling distribution.

We introduce the testing method that is fundamental to all of science: the hypothesis test. We learn how to set up and perform a hypothesis test, and look at how such tests are used in scientific research.

Week 7: Statistical Testing
We introduce the concepts of the testing method that is fundamental to all of science: the hypothesis test. We learn how to set up and perform simple hypothesis tests.

Week 8: Further Statistical Testing
We build on the ideas of the hypothesis test and look at further tests that are commonly used in scientific research.

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## Instructors

Mairi Walker
Mathematics Engagement Officer for the year 2016/17
The University of Edinburgh

Ruth King
Thomas Bayes’ Chair of Statistics
The University of Edinburgh

Chris Sangwin
Professor of Technology Enhanced Science Education
The University of Edinburgh

Bruce Worton
Lecturer in Statistics
University of Edinburgh

Francesca Iezzi
Mathematics Engagement Officer
University of Edinburgh

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