# Understanding Residual Plots and Table of Values

## What is a Residual Plot?

A residual plot is a type of graph that shows the difference between observed and predicted values. It is most commonly used in the fields of statistics, machine learning, and data science. The residual plot is used to determine if a linear regression model is appropriate for a given set of data points. If the points on the graph form a straight line, then the linear regression model is a good fit. If the points on the graph form a non-linear pattern, then the model is not a good fit.

## Table of Values Representing Residual Plots

A table of values is a great way to visualize a residual plot. Tables of values provide more information than a simple graph. The table contains the observed and predicted values as well as the residuals. The residual is the difference between the observed and predicted value. This helps to identify any patterns in the data that may not be visible in a graph.

## How to Read a Table of Values

Reading a table of values representing a residual plot is relatively simple. The first column of the table contains the observed values, the second column contains the predicted values, and the third column contains the residuals. The residuals represent the difference between the observed and predicted values. If the residuals form a pattern, then the linear regression model is not a good fit for the data points.

## Interpreting the Residual Plot

Once the table of values is created, it’s time to interpret the results. If the residuals form a linear pattern, then the linear regression model is a good fit for the data points. If the residuals form a non-linear pattern, then the linear regression model is not a good fit for the data points. In either case, the table of values helps to provide more insight into the data points.

## Conclusion

Table of values is an effective way to visualize residual plots. They provide more information than a simple graph and help to identify patterns in the data. Reading a table of values is relatively easy and understanding the results of the residual plot can be done quickly. Knowing how to read and interpret a table of values is useful for data analysis and machine learning.