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.