Scatter plot - Explanation and definition of Scatter plot.
What is scatter plot
Scatter plot is an analysis tool which graphically represents the relationship between two variables by we can observe the dependence or influence of one variable on the other, allowing the graphical display of the possible correlation. Also known as Scatter diagram that graph is an analysis tool generally used in the field of quality management in order to find the relationships of the causes that produce an effect.
As we have mentioned in the above definition the scatter plot indicates the relationship between two variables, and therefore if these two variables translate to data sets, we can relate this data sets in order to verify or find out that there a relationship between them, as is the relationship approximately.
Scatter plots are used to:
Observe the intensity in the relationship between two variables, this relationship can be between a fact and one of the alleged causes that produce or to find the relationship between two causes of the same effect.
Quickly see abnormal changes.
Analyze certain issues through comparisons.
Scatter Plot steps
The steps to build a scatter plot are:
Select 2 variables that will be related.
Establish a hypothesis of the possible relationship between the two variables defined above
Build a table that relates us the values of both variables in pairs. If you do not have that information it will be necessary to make a data collection.
Draw the diagram by putting a variable in each of the cartesian axes (x, y) with a value scale that fits the data is available.
Represented in the graph each pair of values for a point.
Find the correlation analyzing the trend of the cloud of points and the correlation between variables.
Today thanks to computing science we have software based on spreadsheets like Excel, Numbers or Calc that allow you to quickly make a scatter plot just enter the data of the variables.
Scatter plot interpretation
Once you have made the scatter plot the shape that it takes the point cloud will allow us to analyze the relationship between the 2 variables or groups of data, may obtain the following figures and interpretations:
Positive correlation - It can observe as the cloud of points obtained acquires a crescent-shaped stretch, when the points of the cloud is next to the line is known as a strong, if they are distant from the straight line is known as weak. For example the relationship between the height and weight of a person is positive due higher altitudes greater weight.
Negative correlation - Unlike the previous case it can observe as the cloud of points obtained acquires a form of descending line, when the points of the cloud is next to the line is known as a strong, if they are distant from the straight it is known as weak. For example the ratio for smokers between the number of packets of snuff per month and years is negative because a greater amount of snuff smoking reduced life expectancy.
Complex correlation - The cloud of points obtained acquires a curve, ellipse or other geometric shape.
Zero correlation – It observed a distribution of point cloud with a circular shape, indicating the absence of relationship between the two variables. For example the relationship between the color of your eyes and foot size is zero.
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