Statistics - Data Science from Scratch: First Principles with Python (2015)
Data Science from Scratch: First Principles with Python (2015)
Facts are stubborn, but statistics are more pliable.
Statisticsrefers to the mathematics and techniques with which we understand data.It is a rich, enormous field, more suited to a shelf (or room) in a library rather than a chapter in a book, and so our discussion will necessarily not be a deep one. Instead, I’ll try to teach you just enough to be dangerous, and pique your interest just enough that you’ll go off and learn more.
Describing a Single Set of Data
Through a combination of word-of-mouth and luck, DataSciencester has grown to dozens of members, andthe VP of Fundraising asks you for some sort of description of how many friends your members have that he can include in his elevator pitches.
Using techniques fromChapter 1, you are easily able to produce this data. But now you are faced with the problem of how todescribeit.
One obvious description of any data set is simply the data itself:
# ... and lots more
For a small enough data set this might even be the best description. But for a larger data set, this is unwieldy and probably opaque. (Imagine staring at a list of 1 million numbers.) For that reason we use statistics to distill and communicate relevant features of our data.
As a first approach you put the friend counts intoa histogram usingCounterandplt.bar()(Figure 5-1):
xs=range(101)# largest value is 100
ys=[friend_counts[x]forxinxs]# height is just # of friends
plt.title("Histogram of Friend Counts")
plt.xlabel("# of friends")
plt.ylabel("# of people")
Unfortunately, this chart is still too difficult to slip into conversations. So you start generating some statistics. Probably the simplest statistic is simply the number of data points:
You’re probably also interested in the largest and smallest values:
which are just special cases of wanting to know the values in specific positions:
But we’re only getting started.
Usually, we’ll want some notion of where our data is centered.Most commonly we’ll use themean(or average), which is just the sum of the data divided by its count:
# this isn't right if you don't from __future__ import division
If you have two data points, the mean is simply the point halfway between them. As you add more points, the mean shifts around, but it always depends on the value of every point.
We’ll also sometimes beinterested in themedian, which is the middle-most value (if the number of data points is odd) or the average of the two middle-most values (if the number of data points is even).
For instance, if we have five data points in a sorted vectorx, the median isx[5 // 2]orx. If we have six data points, we want the average ofx(the third point) andx(the fourth point).
Notice that — unlike the mean — the median doesn’t depend on every value in your data. For example, if you make the largest point larger (or the smallest point smaller), the middle points remain unchanged, which means so does the median.
Themedianfunction is slightly more complicated than you might expect, mostly because of the “even” case:
"""finds the 'middle-most' value of v"""
# if odd, return the middle value
# if even, return the average of the middle values
Clearly, the mean is simpler to compute, and it varies smoothly as our data changes. If we havendata points and one of them increases by some small amounte, then necessarily the mean will increase bye/n. (This makes the mean amenable to all sorts of calculus tricks.) Whereas in order to find the median, we have to sort our data. And changing one of our data points by a small amountemight increase the median bye, by some number less thane, or not at all (depending on the rest of the data).
There are, in fact, nonobvious tricks to efficientlycompute medianswithout sorting the data. However, they are beyond the scope of this book, sowehave to sort the data.
At the same time, the mean is very sensitive to outliers in our data. If our friendliest user had 200 friends (instead of 100), then the mean would rise to 7.82, while the median would stay the same. If outliers are likely to be bad data (or otherwise unrepresentative of whatever phenomenon we’re trying to understand), then the mean can sometimes give us a misleading picture. For example, the story is often told that in the mid-1980s, the major at the University of North Carolina with the highest average starting salary was geography, mostly on account of NBA star (and outlier) Michael Jordan.
A generalization ofthe median is thequantile, which represents the value less than which a certain percentile of the data lies. (The median represents the value less than which 50% of the data lies.)
"""returns the pth-percentile value in x"""
Less commonly you might want tolook at themode, or most-common value[s]:
"""returns a list, might be more than one mode"""
mode(num_friends)# 1 and 6
But most frequently we’ll just use the mean.
Dispersionrefers to measures of how spread out our data is.Typically they’re statistics for which values near zero signifynot spread out at alland for which large values (whatever that means) signifyvery spread out. For instance, a very simple measureis therange, which is just the difference between the largest and smallest elements:
# "range" already means something in Python, so we'll use a different name
The range is zero precisely when themaxandminare equal, which can only happen if the elements ofxare all the same, which means the data is as undispersed as possible. Conversely, if the range is large, then themaxis much larger than theminand the data is more spread out.
Like the median, the range doesn’t really depend on the whole data set. A data set whose points are all either 0 or 100 has the same range as a data set whose values are 0, 100, and lots of 50s. But it seems like the first data set “should” be more spread out.
A more complex measure ofdispersion is thevariance, which is computed as:
"""translate x by subtracting its mean (so the result has mean 0)"""
"""assumes x has at least two elements"""
This looks like it is almost the average squared deviation from the mean, except that we’re dividing byn-1instead ofn. In fact, when we’re dealing with a sample from a larger population,x_baris only anestimateof the actual mean, which means that on average(x_i - x_bar) ** 2is an underestimate ofx_i’s squared deviation from the mean, which is why we divide byn-1instead ofn. SeeWikipedia.
Now, whatever units our data is in (e.g., “friends”), all of our measures of central tendency are in that same unit. The range will similarly be in that same unit. The variance, on the other hand, has units that are thesquareof the original units (e.g., “friends squared”). As it can be hard to make sense of these, we oftenlook instead at thestandard deviation:
Both the range and the standard deviation have the same outlier problem that we saw earlier for the mean. Using the same example, if our friendliest user had instead 200 friends, the standard deviation would be 14.89, more than 60% higher!
A more robust alternative computes the difference between the 75th percentile value and the 25th percentile value:
which is quite plainly unaffected by a small number of outliers.
DataSciencester’s VP of Growth has a theory that the amount of time people spend on the site is relatedto the number of friends they have on the site (she’s not a VP for nothing), and she’s asked you to verify this.
After digging through traffic logs, you’ve come up with a listdaily_minutesthat shows how many minutes per day each user spends on DataSciencester, and you’ve ordered it so that its elements correspond to the elements of our previousnum_friendslist. We’d like to investigate the relationship between these two metrics.
We’ll first look atcovariance, the paired analogue of variance.Whereas variance measures how a single variable deviates from its mean, covariance measures how two variables vary in tandem from their means:
Recall thatdotsums up the products of corresponding pairs of elements. When corresponding elements ofxandyare either both above their means or both below their means, a positive number enters the sum. When one is above its mean and the other below, a negative number enters the sum. Accordingly, a “large” positive covariance means thatxtends to be large whenyis large and small whenyis small. A “large” negative covariance means the opposite — thatxtends to be small whenyis large and vice versa. A covariance close to zero means that no such relationship exists.
Nonetheless, this number can be hard to interpret, for a couple of reasons:
§ Its units are the product of the inputs’ units (e.g., friend-minutes-per-day), which can be hard to make sense of. (What’s a “friend-minute-per-day”?)
§ If each user had twice as many friends (but the same number of minutes), the covariance would be twice as large. But in a sense the variables would be just as interrelated. Said differently, it’s hard to say what counts as a “large” covariance.
For this reason, it’s morecommon to look at thecorrelation, which divides out the standard deviations of both variables:
return0# if no variation, correlation is zero
Thecorrelationis unitless and always lies between -1 (perfect anti-correlation) and 1 (perfect correlation). A number like 0.25 represents a relatively weak positive correlation.
However, one thing we neglected to do was examine our data. Check outFigure 5-2.
The person with 100 friends (who spends only one minute per day on the site) is a huge outlier, and correlation can be very sensitive to outliers.What happens if we ignore him?
Without the outlier, there is a much stronger correlation (Figure 5-3).
You investigate further and discover that the outlier was actually an internaltestaccount that no one ever bothered to remove. So you feel pretty justified in excluding it.
One not uncommon surprise when analyzing datais Simpson’s Paradox, in which correlations can be misleading whenconfoundingvariables are ignored.
For example, imagine that you can identify all of your members as either East Coast data scientists or West Coast data scientists. You decide to examine which coast’s data scientists are friendlier:
# of members
avg. # of friends
It certainly looks like the West Coast data scientists are friendlier than the East Coast data scientists. Your coworkers advance all sorts of theories as to why this might be: maybe it’s the sun, or the coffee, or the organic produce, or the laid-back Pacific vibe?
When playing with the data you discover something very strange. If you only look at people with PhDs, the East Coast data scientists have more friends on average. And if you only look at people without PhDs, the East Coast data scientists also have more friends on average!
# of members
avg. # of friends
Once you account for the users’ degrees, the correlation goes in the opposite direction! Bucketing the data as East Coast/West Coast disguised the fact that the East Coast data scientists skew much more heavily toward PhD types.
This phenomenon crops up in the real world with some regularity. The key issue is that correlation is measuring the relationship between your two variablesall else being equal. If your data classes are assigned at random, as they might be in a well-designed experiment, “all else being equal” might not be a terrible assumption. But when there is a deeper pattern to class assignments, “all else being equal” can be an awful assumption.
The only real way to avoid this is byknowing your dataand by doing what you can to make sure you’ve checked for possible confounding factors. Obviously, this is not always possible. If you didn’t have the educational attainment of these 200 data scientists, you might simply conclude that there was something inherently more sociable about the West Coast.
Some Other Correlational Caveats
A correlation of zero indicates that thereis no linear relationship between the two variables. However, there may be other sorts of relationships. For example, if:
thenxandyhave zero correlation. But they certainly have a relationship — each element ofyequals the absolute value of the corresponding element ofx. What they don’t have is a relationship in which knowing howx_icompares tomean(x)gives us information about howy_icompares tomean(y). That is the sort of relationship that correlation looks for.
In addition, correlation tells you nothing about how large the relationship is. The variables:
are perfectly correlated, but (depending on what you’re measuring) it’s quite possible that this relationship isn’t all that interesting.
Correlation and Causation
You have probably heard at some point that “correlation is not causation,” most likelyby someone looking at data that posed a challenge to parts of his worldview that he was reluctant to question. Nonetheless, this is an important point — ifxandyare strongly correlated, that might mean thatxcausesy, thatycausesx, that each causes the other, that some third factor causes both, or it might mean nothing.
Consider the relationship betweennum_friendsanddaily_minutes. It’s possible that having more friends on the sitecausesDataSciencester users to spend more time on the site. This might be the case if each friend posts a certain amount of content each day, which means that the more friends you have, the more time it takes to stay current with their updates.
However, it’s also possible that the more time you spend arguing in the DataSciencester forums, the more you encounter and befriend like-minded people. That is, spending more time on the sitecausesusers to have more friends.
A third possibility is that the users who are most passionate about data science spend more time on the site (because they find it more interesting) and more actively collect data science friends (because they don’t want to associate with anyone else).
One way to feel more confident about causality is by conducting randomized trials. If you can randomly split your users into two groups with similar demographics and give one of the groups a slightly different experience, then you can often feel pretty good that the different experiences are causing the different outcomes.
For instance, if you don’t mind being angrily accused ofexperimenting on your users, you could randomly choose a subset of your users and show them content from only a fraction of their friends. If this subset subsequently spent less time on the site, this would give you some confidence that having more friendscausesmore time on the site.
For Further Exploration
§ SciPy,pandas, andStatsModelsall come with a wide variety of statistical functions.
§ Statistics isimportant. (Or maybe statisticsareimportant?) If you want to be a good data scientist it would be a good idea to read a statistics textbook. Many are freely available online. A couple that I like are: