# My Favorite One-Liners: Part 100

In this series, I’m compiling some of the quips and one-liners that I’ll use with my students to hopefully make my lessons more memorable for them.

Today’s quip is one that I’ll use surprisingly often:

If you ever meet a mathematician at a bar, ask him or her, “What is your favorite application of the Cauchy-Schwartz inequality?”

The point is that the Cauchy-Schwartz inequality arises surprisingly often in the undergraduate mathematics curriculum, and so I make a point to highlight it when I use it. For example, off the top of my head:

1. In trigonometry, the Cauchy-Schwartz inequality states that $|{\bf u} \cdot {\bf v}| \le \; \parallel \!\! {\bf u} \!\! \parallel \cdot \parallel \!\! {\bf v} \!\! \parallel$

for all vectors ${\bf u}$ and ${\bf v}$. Consequently, $-1 \le \displaystyle \frac{ {\bf u} \cdot {\bf v} } {\parallel \!\! {\bf u} \!\! \parallel \cdot \parallel \!\! {\bf v} \!\! \parallel} \le 1$,

which means that the angle $\theta = \cos^{-1} \left( \displaystyle \frac{ {\bf u} \cdot {\bf v} } {\parallel \!\! {\bf u} \!\! \parallel \cdot \parallel \!\! {\bf v} \!\! \parallel} \right)$

is defined. This is the measure of the angle between the two vectors ${\bf u}$ and ${\bf v}$.

2. In probability and statistics, the standard deviation of a random variable $X$ is defined as $\hbox{SD}(X) = \sqrt{E(X^2) - [E(X)]^2}$.

The Cauchy-Schwartz inequality assures that the quantity under the square root is nonnegative, so that the standard deviation is actually defined. Also, the Cauchy-Schwartz inequality can be used to show that $\hbox{SD}(X) = 0$ implies that $X$ is a constant almost surely.

3. Also in probability and statistics, the correlation between two random variables $X$ and $Y$ must satisfy $-1 \le \hbox{Corr}(X,Y) \le 1$.

Furthermore, if $\hbox{Corr}(X,Y)=1$, then $Y= aX +b$ for some constants $a$ and $b$, where $a > 0$. On the other hand, if $\hbox{Corr}(X,Y)=-1$, if $\hbox{Corr}(X,Y)=1$, then $Y= aX +b$ for some constants $a$ and $b$, where $a < 0$.

Since I’m a mathematician, I guess my favorite application of the Cauchy-Schwartz inequality appears in my first professional article, where the inequality was used to confirm some new bounds that I derived with my graduate adviser.

# Correlation and Causation: Index

I’m using the Twelve Days of Christmas (and perhaps a few extra days besides) to do something that I should have done a long time ago: collect past series of posts into a single, easy-to-reference post. The following posts formed my series on data sets that (hopefully) persuade students that correlation is not the same as causation.

Part 1: Piracy and global warming. Also, usage of Internet Explorer and murder.

Part 2: An xkcd comic.

Part 3: STEM spending and suicide. Consumption of margarine and divorce. Consumption of mozzarella and earning a doctorate. Marriage rates and deaths by drowning.

Part 4: Donna the Deer Lady.

# Why Not to Trust Statistics

Math with Bad Drawings has an excellent post on how the blind use of descriptive statistics can be deceptive. I’ll definitely be sharing a few these with my students. Here’s one of several examples; I recommend reading the whole thing.  # Correlation is not Causation (Part 4)

One of the standard topics in an undergraduate statistics course is the principle that two things that are highly correlated do not necessarily have a cause-and-effect relationship. Here is a hilarious example of this fallacy.

And, in case you’re wondering, here’s the rest of the story:

# Five Ways to Lie with Charts

This is a cute article about ways that people can lie with charts: (1) Puzzling perspective, (2) Swindling shapes, (3) Trendsetters are tricksters (implying a false correlation), (4) Hiding in plain sight, and (5) Changing the scale of the axis.

http://nautil.us/issue/19/illusions/five-ways-to-lie-with-charts

# Correlation and Causation: Index

I’m using the Twelve Days of Christmas (and perhaps a few extra days besides) to do something that I should have done a long time ago: collect past series of posts into a single, easy-to-reference post. The following posts formed my series on data sets that (hopefully) persuade students that correlation is not the same as causation.

Part 1: Piracy and global warming. Also, usage of Internet Explorer and murder.

Part 2: An xkcd comic.

Part 3: STEM spending and suicide. Consumption of margarine and divorce. Consumption of mozzarella and earning a doctorate. Marriage rates and deaths by drowning.