Histogram allows us to compare large sample of data or data with large value ranges
easily. It also provides consistency due to the equal intervals. It is easy to transform
data from a table to a histogram. Some of the disentangles of histogram including: it will
be difficult to get the exact number of an independent variable if the y-axis is not about
frequency. Histogram is also hard for us to compare data with multiple categories. I
would not choose a different graph to represent the data in this graph I chosen from the
internet. It provided a clear visualization of each % of fat. The y-axis is frequency, so it
is easy to point out the exact number of inputs in each column. There is only one
category in this example, so it is easy to compare.
https://statisticsbyjim.com/basics/histograms/
Scatterplot can also show large quantities of data at the same time. It is visible
to see correlation and relationship between variables due to clustering effects.
However, scatterplot is not helpful for discretized data, and it is not helpful for
categorical data. Also, there is an overplotting problem when a lot of the data
stacked on top of each other. It is also difficult to tell the amount of inputs of
an independent variable on a scatterplot too.
this is a very bad example of scatterplot.
As you can see, it is very difficult to count the number of inputs for a single
independent variable. I would use a bar chart to present these data, and it can
show the relationship between grades better.
https://www.chrisstucchio.com/blog/2012/dont_use_scatterplots.html
