ggplotScatter {ggplot.layers}

.NET clr documentation

ggplotScatter

Description

A scatter plot is a type of data visualization that uses dots to represent the values of two different numerical variables. The position of each dot on the horizontal and vertical axes corresponds to the values of the two variables being plotted. Scatter plots are particularly useful for showing the relationship between two variables and are a key tool in statistics and data analysis.

Here are some key aspects of scatter plots:

Purpose:

• To observe and interpret relationships between two quantitative variables.

• To identify trends, patterns, or clusters in data.

• To help in making predictions or drawing conclusions based on the observed relationships.

Components:

• X-axis (Horizontal axis): Represents one variable, often called the independent variable.

• Y-axis (Vertical axis): Represents the other variable, often called the dependent variable.

• Data Points (Dots): Each point represents an observation with coordinates corresponding to its values on both axes.

Types of Relationships:

• Positive Correlation: As the value of one variable increases, the value of the other variable also increases. The points tend to rise from left to right.

• Negative Correlation: As the value of one variable increases, the value of the other variable decreases. The points tend to fall from left to right.

• No Correlation: There is no apparent relationship between the variables. The points are scattered randomly on the plot.

• Non-linear Relationship: The relationship between variables is not a straight line but might be curved or follow another pattern.

Interpreting the Plot:

• Cluster: A group of points that are close together, indicating a concentration of data points.

• Outlier: A point that is distant from other points, indicating an unusual observation or a data error.

• Trend Line (or Line of Best Fit): A line that best represents the relationship between the variables. It can be used to make predictions.

Uses:

• In fields like economics, biology, psychology, and social sciences to understand the dynamics between variables.

• In machine learning and data science for feature selection and to understand the underlying structure of data.

scatter and bubble

Declare

            
# namespace ggplot.layers
export class ggplotScatter extends ggplotLayer {
   alpha: double;
   colorMap: ggplotColorMap;
   dataset: any kind;
   reader: ggplotReader;
   shape: Nullable`1;
   showLegend: boolean;
   size: ggplotSize;
   stroke: string;
   which: Expression;
   zindex: integer;
}

        

.NET clr type reference tree

1. this class extends from ggplotLayer class: ggplotLayer
2. use by property member colorMap: ggplotColorMap
3. use by property member dataset: any kind
4. use by property member reader: ggplotReader
5. use by property member shape: Nullable`1
6. use by property member size: ggplotSize
7. use by property member which: Expression

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