Individual value plot frequently asked questions

What is an individual value plot?

An Individual Value Plot (also known as a "dot plot" in some contexts) is a type of data visualization that displays each individual data point on a number line. This plot helps show the distribution of data and highlights the frequency and spread of individual values in a dataset.

When should I use an Individual Value Plot?

You should use an Individual Value Plot when:

• You have a small to moderate dataset.
• You want to visualize the specific values of data points.
• You need to assess the distribution and identify any outliers, patterns, or gaps in the data.

How do I interpret an Individual Value Plot?

Clusters: If multiple dots are concentrated in a region, it shows where most data points are located.

Outliers: Dots far from the main cluster of points indicate outliers or unusual values.

Gaps: A lack of dots in a certain area can suggest that no data points fall within that range.

What kind of data works best with an Individual Value Plot?

Discrete Data: Works well with data that consists of distinct, separate values (e.g., counts, survey responses, measurements).

Small to Medium-Sized Datasets: Ideal for datasets where you can plot individual values without the plot becoming too cluttered.

Non-continuous Data: It is especially effective for data that doesn’t have a natural continuous scale (e.g., categorical values, counts, ratings).

Can an Individual Value Plot be used with categorical data?

Yes, Individual Value Plots can be used with categorical data by placing the categories along the number line or as labels on a scatter plot. Each data point would be plotted as a dot under its respective category.

When do we use dot plots vs. individual value plots?

Dot plots are typically plotted with the values on the x-axis and the frequency or count on the y-axis. Individual value plots are usually created with the values on the y-axis and the grouping variable on the x-axis. A jitter value is added to the individual value plot so that we can see multiple dots that may overlap with each other.

A dot plot is typically used when we want to see a data distribution (similar to a histogram), and an individual value plot generally is used to compare groups.

Are bins used for creating the individual value plot?

No, bins are not used to create the individual value plot. However, you can add a small jitter to the data points so they don't overlap. Select the setup options below to add a jitter to the data points.

What is the difference between an Individual Value Plot and a Box Plot?

Individual Value Plot: Shows each individual data point on a number line, providing a clear picture of the data's spread and specific values.

Box Plot: Displays summary statistics (median, quartiles, and outliers) of a dataset but does not show individual data points.

How does an Individual Value Plot differ from a regular dot plot?

While both represent individual data points as dots:

• Dot Plot: Typically used for small datasets with discrete values, and it shows individual data points along a number line. Dots can be stacked if there are repeated values.
• Individual Value Plot: Emphasizes the distinctness of each data point, often using a scatter-style plot or a variation where each value is shown separately without grouping or stacking, focusing more on individual values.

What are the benefits of using an Individual Value Plot?

Detailed View of Data: You can see each individual data point clearly, making it easy to assess the exact values and their distribution.

Simple Interpretation: It is easy to understand, especially for small datasets.

Highlights Outliers: Individual points that fall far from the cluster of data can be easily spotted, helping you identify potential outliers or anomalies.

Are there any limitations to Individual Value Plots?

Clutter with Large Datasets: For larger datasets, individual value plots can become cluttered and harder to interpret. This is especially true when there are many repeated values.

Not Ideal for Continuous Data: It is less effective for visualizing large sets of continuous data, where trends and patterns might be better shown using line graphs, histograms, or boxplots.

Limited Scale: If the dataset includes a large range of values, you might need to adjust the scale to fit all data points on a single axis.

What are the different types of individual value plots we can create?

Let's take an example of a manufacturing process of two suppliers. The dimension of a critical parameter is shown in the table below. The data was collected for two shifts. The following 100 rows of data were collected for these suppliers.

Shift	Supplier 1	Supplier 2
1	9.996122047	21.3612898
1	12.62574265	16.7757814
1	11.59708641	19.2680117
1	12.4210514	21.61882075
1	13.75779483	14.55036061
1	16.27320778	19.52052386
1	14.87549121	23.18510389

We want to depict the supplier data graphically using the individual value plot. The following types of plots can be created:

1. Individual value plot of Supplier 1 vs Supplier 2 (without jitter)
   
2. Individual value plot of Supplier 1 vs Supplier 2 (with Jitter)
   
3. Individual value plot of Supplier 1 and Supplier 2 vs Shift

Note that we can have up to 20 categorical variables for this plot. If you have more than 20 categorical variables, you must split the data into multiple groups and create a plot for each group. You can use the by-variable option for this exercise.

Case 1: Individual value plot of Supplier 1 and Supplier 2 (without Jitter)

For this case, click on Analysis Setup and specify the Setup & Data options as shown below:

The graphical output is shown below.

Case 2: Comparison of Supplier 1 and Supplier 2 with Jitter

For this case, click on Analysis Setup and specify the Setup options as shown below:

The graphical output is shown below.

Case 3: Comparison of Supplier 1 and Supplier 2 by Shift, including a connector between groups

For this case, click on Analysis Setup and specify the Setup and Data options as shown below:

The graphical output is shown below.

How does an Individual Value Plot help with data analysis?

An Individual Value Plot:

• Shows Data Spread: Helps you see the overall spread and distribution of the dataset.
• Identifies Patterns and Trends: Spot clusters of data points, gaps, or any repeating trends.
• Highlights Outliers: By showing every data point, you can easily spot values that fall far outside the norm.

How do we add a horizontal reference line on the individual value plot?

To add a horizontal reference line on a chart, click on Analysis Setup > Charts and specify the value in the Horizontal Ref Lines as shown below. In this example, we plot two horizontal lines at 10 and 25.

The resulting chart will have a reference line added to the chart, as shown below:

You can specify multiple values separated by a semi-colon if you need more than one horizontal line.

 Reference: Some of the text in this article has been generated using AI tools such as ChatGPT and edited for content and accuracy.

 

 

 

 

 

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