Commerce Corner : Scatter plot and Interpretations

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.

comic strip