# Excel graph to show relationship between two variables

### How to Use Excel to Determine the Relationship Between Two Sets of Data | It Still Works

Use a scatter chart (XY chart) in Excel to show scientific XY data. Note: we added a trendline to clearly see the relationship between these two variables. How many items (data points) will you display for each variable? Scatter plot charts are good for relationships and distributions, but pie charts . Good for showing the relationship between two different variables where one. You can create a line graph in Excel to view the relationship between data. has a variety of graphs that convert your values to data points and allow you to see.

Stacked Area Stacked area charts are best used to show changes in composition over time. A good example would be the changes of market share among top players or revenue shares by product line over a period of time.

### Using Excel to Calculate and Graph Correlation Data | Educational Research Basics by Del Siegle

Stacked area charts might be colorful and fun, but you should use them with caution, because they can quickly become a mess. Not in data visualization, though. These charts are among the most frequently used and also misused charts. The one on the right is a good example of a terrible, useless pie chart - too many components, very similar values. A pie chart typically represents numbers in percentages, used to visualize a part to whole relationship or a composition.

## Data Visualization – How to Pick the Right Chart Type?

Pie charts are not meant to compare individual sections to each other or to represent exact values you should use a bar chart for that. When possible, avoid pie charts and donuts. I mean, like, never! You might think that you could use a stacked donut to present composition, while allowing some comparison with an emphasis on compositionbut it would perform badly for both.

Use stacked column charts instead. Make sure that the total sum of all segments equals percent.

Ideally, there should be only two categories, like men and women visiting your website, or only one category, like a market share of your company, compared to the whole market.

Scatter Charts Scatter charts are primarily used for correlation and distribution analysis. Scatter charts can also show the data distribution or clustering trends and help you spot anomalies or outliers. A good example of scatter charts would be a chart showing marketing spending vs. Bubble Charts A bubble chart is a great option if you need to add another dimension to a scatter plot chart.

Scatter plots compare two values, but you can add bubble size as the third variable and thus enable comparison. If the bubbles are very similar in size, use labels. A good example of a bubble chart would be a graph showing marketing expenditures vs. A standard scatter plot might show a positive correlation for marketing costs and revenue obviouslywhen a bubble chart could reveal that an increase in marketing costs is chewing on profits. Use Scatter and Bubble charts to: Present patterns in large sets of data, linear or non-linear trends, correlations, clusters, or outliers.

Compare large number of data points without regard to time. The more data you include in a scatter chart, the better comparisons you can make. Present relationships, but not exact values for comparisons.

Map Charts Map charts are good for giving your numbers a geographical context to quickly spot best and worst performing areas, trends, and outliers. If you have any kind of location data like coordinates, country names, state names or abbreviations, or addresses, you can plot related data on a map. A good example would be website visitors by country, state, or city, or product sales by state, region or city.

When to use map charts? If you want to display quantitative information on a map. To present spatial relationships and patterns. When a regional context for your data is important.

To get an overview of the distribution across geographic locations. Only if your data is standardized that is, it has the same data format and scale for the whole set. Gantt Charts Gantt charts were adapted by Karol Adamiecki in But the name comes from Henry Gantt who independently adapted this bar chart type much later, in the s. Gantt charts are essentially project maps, illustrating what needs to be done, in what order, and by what deadline.

You can visualize the total time a project should take, the resources involved, as well as the order and dependencies of tasks. But project planning is not the only application for a Gantt chart.

It can also be used in rental businesses, displaying a list of items for rent cars, rooms, apartments and their rental periods. To display a Gantt chart, you would typically need, at least, a start date and an end date. Gauges are a great choice to: Show progress toward a goal. Represent a percentile measure, like a KPI. Show an exact value and meaning of a single measure. Display a single bit of information that can be quickly scanned and understood. The bad side of gauge charts is that they take up a lot of space and typically only show a single point of data.

## Popular Topics

If there are many gauge charts compared against a single performance scale, a column chart with threshold indicators would be a more effective and compact option. Multi Axes Charts There are times when a simple chart just cannot tell the whole story. If you want to show relationships and compare variables on vastly different scales, the best option might be to have multiple axes.

But it comes at a cost. Scatter Plots Scatter plots are similar to line graphs in that they use horizontal and vertical axes to plot data points. However, they have a very specific purpose. Scatter plots show how much one variable is affected by another.

- Using Excel to Calculate and Graph Correlation Data
- How to Use Excel to Determine the Relationship Between Two Sets of Data
- Scatter Plots

The relationship between two variables is called their correlation. Scatter plots usually consist of a large body of data.

**How To... Plot Multiple Data Sets on the Same Chart in Excel 2010**

The closer the data points come when plotted to making a straight line, the higher the correlation between the two variables, or the stronger the relationship.

If the data points make a straight line going from the origin out to high x- and y-values, then the variables are said to have a positive correlation. If the line goes from a high-value on the y-axis down to a high-value on the x-axis, the variables have a negative correlation.

These two variables one plotted on the X axis, one on the Y are totally random, and are not closely related. The two variables below, however, are correlated: In general, as one variable rises, so does the other. Understanding Correlation Coefficient The correlation coefficient tells you how related two variables are. The coefficient is between -1 and 1. This is what you should get when you have two sets of random numbers.

A coefficient of -1 means you have a perfect negative correlation: A coefficient of 1 is a perfect positive correlation: Any number between those represents a scale. As you can see in the graphic below, correlation only looks for a linear relationship. Two variables can be strongly related in another way and still have a correlation coefficient of zero: In this spreadsheet, we have a list of cars, with model and year, and their values.

But not by very much.