**Course co-ordinator(s):** Dr George Streftaris (Edinburgh).

**Aims:**

The aim of this course is to provide an introduction to the statistical issues associated with the collection, description, and interpretation of data. In addition, this course aims to introduce statistical computing with a view to describing data using various graphical and numerical methods.

**Summary:**

The aim of statistical analysis is to *provide insight by means of numbers *. This process usually involves three stages:

- Collecting data
- Describing and presenting data
- Drawing conclusions from the data (inference).

In this course, we will (primarily) consider the statistical principles and techniques used in the first two stages of statistical analysis. There will be some discussion of inference at the end of the course.

## Detailed Information

**Pre-requisites:** none.

**Location: **Edinburgh.

**Semester: **1.

**Syllabus:**

- Introduction to the concept of statistics

– The use and role of statistics in real life.

– The purpose of statistical science (data collection, data description and inference using data). - Collecting data

– The concept of a statistical population and sample; distinction between parameter and statistic.

– Issues related to sampling (random sampling versus other sampling methods – e.g. stratified or quota sampling).

– Data from experiments and observational studies.

– Types of data/variables (quantitative and qualitative; measurement scales; continuous and discrete). - Describing and understanding data

– Graphical summaries of data (frequency tables, histograms, stem-plots, etc).

– Graphical displays using computers – introduction to Excel

– Numerical summaries of data: sample mean, median, variance, quartiles. - Describing and understanding data from two-dimensional populations

– Graphical exploration of relationships between two variables: cross- tabulations and scatterplots.

– The sample correlation coefficient.

– Introduction to the concepts of association and causation.

– Least-squares regression lines and prediction. - Drawing conclusions from data – an introduction to statistical inference.

**Learning Outcomes: Subject Mastery**

At the end of this course, students should

- Understand the role statistical science in everyday life.
- Be able to distinguish between populations and samples, parameters and statistics.
- Be familiar with various sampling methods and be able to identify sources of bias in sampling.
- Understand the principles of good experimental design and be able to identify sources of bias in poorly designed experiments/observational studies.
- Be able to interpret and describe data using appropriate graphical displays
- Be able to calculate numerical summaries of data and interpret them as measures of centre and variation.
- Be able to explore the relationships between two variables using scatter-plots and cross-tabulation.
- Be able to calculate the sample correlation coefficient and interpret its value.
- Appreciate the issues related to the concepts of association and causation.
- Be able to calculate the least-squares regression line and to use it for prediction.
- Be able to use Excel to produce graphical and numerical summaries of data.
- Be able to use LaTex to produce a structured report.

**Reading list:**

*Statistics: Concepts and Controversies* by David S. Moore and William Notz. 6th edition. W.H. Freeman and Co.

**Assessment Methods:**

2-hour Final exam (70%), continuous assessment consisting of a class test (up to 20%) and a project (minimum 10%).

**SCQF Level: **7.

**Credits:** 15.

## Other Information

**Help:** If you have any problems or questions
regarding the course, you are encouraged to contact the lecturer

**VISION:** further information and course materials
are available on VISION