# A Simple Scatterplot using SPSS Statistics

## Introduction

A **simple scatterplot** can be used to (a) determine whether a relationship is linear, (b) detect outliers and (c) graphically present a relationship between two continuous variables. For example, determining whether a relationship is linear (or not) is an important assumption if you are analysing your data using Pearson's product-moment correlation, Spearman's rank-order correlation, simple linear regression, multiple regression, amongst other statistical tests.

Note: If you are analysing your data using an ANCOVA (analysis of covariance) or two-way ANOVA, for example, you will need to consider a **grouped scatterplot** instead (N.B., if you need help creating a grouped scatterplot using SPSS Statistics, we show you how in our enhanced content).

For example, a simple scatterplot could be used to determine if there is a linear relationship between lawyers' salaries and the number of years they have practiced law (i.e., your dependent variable would be "salary" and your independent variable would be "years practicing law"). A simple scatterplot could also be used to determine if there is a linear relationship between the distance women can run in 30 minutes and their VO_{2}max, which is a measure of fitness (i.e., your dependent variable would be "distance run" and your independent variable would be "VO_{2}max").

The purpose of this guide is to show you how to create a simple scatterplot using SPSS Statistics. First, we introduce the example we have used in this guide. Next, we show how to use the **Chart Builder** in SPSS Statistics to create a simple scatterplot based on whether you have SPSS Statistics **versions 27** or **28** (or the **subscription version** of SPSS Statistics), **versions 25** or **26**, or **version 24** or an **earlier version** of SPSS Statistics. If you are unsure which version of SPSS Statistics you are using, see our guide: Identifying your version of SPSS Statistics.

###### SPSS Statistics

## Example

This guide will use the example from the linear regression guide, where researchers wanted to determine if there was a linear relationship between cholesterol concentration (a type of fat in the blood) and the time spent watching TV in otherwise healthy 45 to 65 year old men (an at-risk category of people for heart disease). They believed that there would be a positive relationship: the more time people spent watching TV, the greater their cholesterol concentration.

Daily time spent watching TV was recorded in the variable time_tv and cholesterol concentration recorded in the variable cholesterol. Therefore, to determine whether a linear relationship exists between the two continuous variables, which is one of the assumptions that must be met when running a linear regression, the researchers generated a simple scatterplot by plotting the dependent variable, cholesterol, against the independent variable, time_tv.