Scatter
Plot and Interpretation
A
scatter plot is a type of data visualization that displays values for typically
two variables for a set of data. The data points are plotted on a two-dimensional
graph, where each point represents an observation with coordinates determined
by the values of the two variables.
Key Elements of Scatter Plot:
X-axis (Horizontal Axis): Represents the
independent variable.
Y-axis (Vertical Axis): Represents the
dependent variable.
Data Points: Each point represents an
individual observation.
Trend Line (Optional): A line that
indicates the general direction of the data points.
Interpretation of Scatter Plot:
1.
Positive Correlation: As the value of the
independent variable increases, the value of the dependent variable also
increases, and vice versa. The points trend upwards from left to right.
2.
Negative Correlation: As the value of the
independent variable increases, the value of the dependent variable decreases,
and vice versa. The points trend downwards from left to right.
3.
No Correlation: There is no
discernible pattern between the two variables. The points are scattered
randomly.
4.
Outliers: Points that fall far away from the
general pattern of the data. These can indicate anomalies or special cases.
Scatter plots
are particularly useful for identifying relationships between variables and are
often used in exploratory data analysis.
Figure 1
Research
Book References
Here are ten
books that provide in-depth knowledge about scatter plots and data
interpretation:
1.
Bertin, J. (1983). Semiology of graphics: Diagrams, networks, maps. University
of Wisconsin Press.
2.
Chambers, J. M., Cleveland, W. S., Kleiner, B., & Tukey, P. A. (1983).
Graphical methods for data analysis. Wadsworth International Group.
3.
Cleveland, W. S. (1993). Visualizing data. Hobart Press.
4.
Few, S. (2009). Now you see it: Simple visualization techniques for
quantitative analysis. Analytics Press.
5.
Kelleher, C., & Wagener, T. (2011). Ten simple rules for better figures.
PLOS Computational Biology, 7(3), e1001187.
https://doi.org/10.1371/journal.pcbi.1001187
6.
Knaflic, C. N. (2015). Storytelling with data: A data visualization guide for
business professionals. Wiley.
7.
Tukey, J. W. (1977). Exploratory data analysis. Addison-Wesley.
8.
Tufte, E. R. (2001). The visual display of quantitative information (2nd ed.).
Graphics Press.
9.
Ware, C. (2004). Information visualization: Perception for design (2nd ed.).
Morgan Kaufmann.
10.
Wilkinson, L. (2005). *The
These
books cover various aspects of data visualization, including scatter plots, and
provide guidelines for interpreting and presenting data effectively.
