Please find links to key chapters from this area - Statistics, Descriptive and Applications, of the course below. In addition, here is a growing collection of practice questions for you to use as a review exercise for the whole unit. Currently there are 5 exam style questions worth 38 marks and should be completed in about 40 minutes, before looking at the solutions.
What are the different types of data?
Data is usually described as either qualititative (using words) or quantitative (using numbers). Within the quantitative category, data is either discrete or continuous. Statistical Concepts
What is an outlier?
Outliers are data points that differ significantly from the others Statistical Concepts
What is cumulative frequency?
Cumulative frequency is a running total of all frequencies. It is usually shown by drawing an 'S' shaped graph Cumulative Frequency & Box Plots
How do box plots work?
Box plots display a five number summary of a data set. They are very useful for making comparisons between data sets. Cumulative Frequency & Box Plots
What is Spearman correlation used for?
Spearman's Rank Correlation Coefficient is used to measure the relationship between two variables by ranking them. Find out more here: Spearman's Rank Correlation Coefficient 4.10
100 word minimum introduction to the page with the associated image Key Concepts Here we can put specific references to the syllabus and add and key dfinitions we think are important. text Essentials A few words here to say something about what...
Statistics are so powerful because it allows us to create meaning from data. In this chapter we will learn the differences between types of data, how to choose useful samples, and how to recognise outliers in data.
This topic is all about these two related tools for helping us look at how a data set is spread out. Learn about filling in cumulative frequency tables, plotting the corresponding curves and using the curves to draw box plots and answer questions...
Measures of central tendency, such as the mean, median and mode, are very useful ways of respresenting large amounts of data with just one value. They can be very useful in forming conclusions about data, but they can also misrepresent data and hide infor
This topic is all about looking for relationships between variables. Does life expectancy depend on GDP? By collecting 2 variables about the things we survey we can use scattergraphs to represent the data and then look for correlation and models that we ca
Random variables are a set of possible outcomes from a random experiment. They can either be discrete or continuous. In this chapter we will find out more about discrete random variables, how they can be represented, displayed and analysed.
The normal distribution is a fascinating, naturally occurring phenomenon that has very relevant applications to understanding the world around us. When a data set is normally distributed it has some key properties that allow us to make predictions about th
Description of the concept... The following is a series of slides and videos that will help you understand, learn about and review this sub-topic. Keep track of your progress and practice the exam questions on this ACTIVITY LINK This first section...
The chi squared independence tests is a widely used technique for looking for a relationship between variables that are categorical. We can use a scatter graph to look for a relationship between GDP and life expectancy, but what about GDP and...