# Data Presentation for Single Variable

Data Presentation for Single Variable

Understanding Variables

• All data collected and analysed under statistical investigations comes in the form of variables, essentially values that can change or differ.
• A single variable typically represents a specific characteristic or attribute of interest.
• Data for that characteristic is gathered across a sample or population.

Types of Variables

• Understand the difference between qualitative and quantitative variables.
• Qualitative variables, also known as categorical variables, represent categories or groups. They can’t be quantified.
• Quantitative variables, often referred to as numerical variables, represent measurable quantities. These can be further classified into discrete or continuous variables.

Methods of Data Presentation for Single Variable

• Bar charts: Useful for representing frequency distribution of categorical (qualitative) variables.
• Histograms: Best suited for showing frequency distribution of continuous (quantitative) variables.
• Pie charts: Good for showing proportions of different categories in a dataset.
• Box plots: Useful for comparing the distribution and spread of numerical data.
• Stem and Leaf plots: Helpful for representing quantitative data while preserving the original data points.

Features of Good Graphs and Charts

• Title: Always have a clear title that summarises what the graph or chart depicts.
• Scale: Choose a reasonable scale for the axes.
• Labels: Both axes should be clearly labelled with the variable names and units.
• Legend: If using multiple data sets or categories in one graph, include a legend for clarity.
• Accuracy: Ensure accuracy in representing data points. Misleading graphs can distort the understanding of the data.

Interpretation of Graphs and Charts

• Be able to read and interpret the above charts and graphs.
• Understand the mean (average), median (middle value) and mode (most frequent value). These are measures of central tendency that give you a ‘typical’ value for your data.
• Understand range (difference between max and min values), interquartile range (the range of the middle 50% of the data) and standard deviation (average distance of data points from the mean). These are measures of spread that tell you how much your data varies.
• Always support interpretations with data values or calculations whenever possible.