# Statistics Comments Project

Need help with posting comments and discussions on Perusall.

I attached documents of readings material and each file needs 3 comments. Total 6 comments.

Each comment needs to be thoughtful, (60-100 words).

The second file needs comments on P18, 25,32-34, 71-74 ? You can choose either page on the second file to post comments, but it is better to spread out the comments. For example one comment at the beginning of P18, second on P32-34, third on P71-74)

Chapter 11
How You Will Die: Causes or
Conditions?
Causes of death, colloquially
We now pause our inquiry into when you will die and concentrate for some time on how it might
happen. Let us pay another visit to Nathan Yau’s series of (interactive) visualizations for Flowing
Data. We already discussed Years You Have Left to Live, Probably. Another of them is called
Causes of Death and the last How you will die (links work if you download the PDF but do not
work within Perusall).
These visualizations are really a treasure trove. So much going on. I’ve reproduced an image from
Causes of Death below, but you really should interact with it on the web.
Take a close look at the image in Figure 11.1. How many variables are represented here? (Pause
and think this over.) There are fifteen color bands representing causes of death, but these are not
each variables. Rather, they are categories of a single variable: cause-of-death, as operationalized
by the Centers for Disease Control and Prevention.1 These include cancer, congenital defects, and
external causes (e.g., pianos falling on your head), among others. Along the bottom of the image we
see age, which is another variable. Indeed, the most salient aspect of this visualization is how much
the relative importance of different causes of death changes over the course of one’s life span.
If you die in your 20s, it is likely that you died from external causes. While if you died in your
80s, it is more likely because of a circulatory or respiratory disease. This intuitive sense-making
is an example of conditional probabilities. It may not be a two-way table, but Figure 11.1 is in
many ways analogous to a two-way table. The two variables are age-at-death and cause-of-death.
Although we are not putting numbers to the probabilities we can read this image as indicating that
P(cause = external | age in 20s) > P(cause = circ or resp | age in 20s)
(read, the probability that cause of death is external given that age of death is in ones 20s. . . ).
While the opposite is true in your 80s:
1 Operationalization of a variable is a fancy and more specific word for defining it. It doesn’t seem necessary to
define “cause of death” in the common-sense use of the word define. But when we decide how many and which
categories to include in our use of the variable, we operationalize this definition. For example, we could operationalize
cause of death as “natural causes” or “other”. This would be a dichotomous operationalization. And it leaves a lot to
the imagination. The Center for Disease Control is particularly interested in diseases and not in external causes. So
it makes sense that all external causes are banded together in this figure. However, don’t be fooled. There are many
subcategories, including very specific ones like “Pedal cyclist injured in collision with heavy transport vehicle or bus.”
55
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CHAPTER 11. HOW YOU WILL DIE: CAUSES OR CONDITIONS?
Figure 11.1: Screenshot of Causes of Death visualization

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