# 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.