RightChain Scatter Plots

X Y charts (aka scatter plots) are one of many chart types supported by RightChain AI. X Y charts depict observations as a function of two variables. For example, an SKU may be displayed in an X Y chart with its revenue on the X axis, and its margin percentage on the Y axis. In addition, RightChain AI can create a user defined number of clusters of data points, that are helpful in creating supply chain strata, for products, customers, suppliers, etc., RightChain has also incorporated the capability to display multiple statistical computations, and display lines; as well as to customize the presentation of data points and labels.

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Fundamentals of XY Charts

An XY chart, also commonly known as a scatter plot or scatter graph, is a type of data visualization that uses dots to represent the values obtained for two different variables - one plotted along the x-axis and the other plotted along the y-axis. This chart is used to observe relationships, patterns, or trends between the two variables. Here are the main features and uses of an XY chart:

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1. Axes:

   • X-axis: Typically represents the independent variable.

   • Y-axis: Represents the dependent variable, showing the response as the independent variable changes.

2. Data Points: Each dot on the scatter plot represents a single data point. Its position on the horizontal and vertical axes indicates the values of the two variables.

3. Purpose: The main use of an XY chart is to show how much one variable is affected by another. It is a tool commonly used to identify the type of relationship between two variables, such as linear, quadratic, or none.

4. Trend Lines: Often, a line of best fit (or a trend line) is added to a scatter plot to summarize the relationship between the variables. This can be a straight line or a more complex function, fitted to the data points using regression analysis.

5. Correlation: Scatter plots are ideal for spotting correlations between variables. A positive correlation results in the points sloping upwards; a negative correlation results in the points sloping downwards; and no correlation results in a random distribution of points.

6. Outliers: They are also useful for identifying outliers or anomalies in data that do not fit the pattern established by the rest.
    

Scatter plots are widely used in statistics, data analysis, and many types of scientific research. They help in understanding and visualizing the relationships between numerical variables, making them crucial in predictive analytics, trend analysis, and regression modeling.