Introduction to Statistics

Target Audience:
Anyone with no statistical background or a limited understanding of basic statistical concepts. Additionally, the course is an appropriate refresher for those whose main statistical experience was gained many years ago.

Prerequisites:
A basic knowledge of SPSS and of statistics is recommended.

Overview:
The course logically guides attendees through an introduction to the key elements of many statistical applications, including a broad range of techniques for exploring and summarising data, as well as investigating and testing underlying relationships within data. You will gain an understanding of when and why to use these various techniques as well as how to apply them with confidence, and interpret their output, using SPSS. The course provides a solid grounding in statistical analysis. For those who wish to progress into more advanced and powerful applications, this course provides you with the knowledge, ability and confidence required to attend higher-level statistical courses.

Objectives:
By the end of the course you will have a solid grounding in statistical theory and will have learned to:
• Identify different types of data
• Choose the appropriate techniques for exploring, summarising and testing the data
• Interpret your output and draw appropriate conclusions about the data

Duration: 2 days

Course Content:
The course can be broken down into three main areas:

Overview of the design and data collection issues:
• Defining the population and taking samples
• Levels of measurement (types of data)

Exploratory data analysis:
• Matching appropriate exploratory techniques to types of data
• Frequency tables, bar and pie charts, histograms, box-plots and stem- and-leaf plots
• Summaries of central tendency and dispersion
• The 'normal distribution' and its importance
• Assessing normality

Inferential statistics:
• Interpreting cross tabulation tables and testing for significant relationships with chi-square
• Looking for relationships through scatter plots, assessing the strength of relationships using correlations.
• Calculating group means and testing for significant differences between pairs of group means using t-tests