In this lecture, we introduce linear regression
a simple but very powerful method to analyze data
and make predictions and apply it
in a very unexpected context-- predicting
the quality of wines.
Bordeaux is a region in France popular for producing wine.
While this wine has been produced
in much the same way for hundreds of years,
there are differences in price and quality
from year to year that are sometimes very significant.
Bordeaux wines are widely believed
to taste better when they are order,
so there's an incentive to store young wines
until they are mature.
The main problem is that it is hard to determine
the quality of the wine when it is so young just by tasting it,
since the taste will change so significantly by the time it
will actually be consumed.
This is why wine tasters and experts are helpful.
They taste the wines and then predict
which ones will be the best wines later.
The question we'll address in this lecture--
can analytics model this process better
and make stronger predictions?
On March 4, 1990, the New York Times
announced that Princeton economics professor Orley
Ashenfelter can predict the quality of Bordeaux wine
without tasting a single drop.
Ashenfelter's predictions have nothing
to do with assessing the aroma of the wine,
looking at the legs, or declaring that the wine tastes
citrusy, oaky, or nutty.
They are the results of a mathematical model.
Ashenfelter used a method called linear regression.
The methods predicts an outcome variable or dependent variable.
And in doing so, it uses a set of what
is called independent variables.
For the dependent variable, Ashenfelter
chose a typical price in 1990-1991 for Bordeaux wine
in an auction.
This approximates quality.
As independent variables, he used
age of the wine-- so the older wines are more expensive--
and weather-related information, specifically
the average growing season temperature, the harvest
rain, and winter rain.
In these figures, we depict the data
during the period from 1952 to 1978.
There are four independent variables--
the age of the wine, the average growing season temperature,
the harvest rain, and winter rain.
And on the vertical axis, you observe the logarithm
of the price, the realization in an auction.
So these are the primitive data that Ashenfelter used.
So Ashenfelter believed that his predictions
are more accurate than those of the world's
most influential wine critic.
His name is Robert Parker.
In response, Parker called Ashenfelter
to be "an absolute total sham," and he adds that,
"rather like a movie critic who never goes to see the movie
but tells you how good it is based
on the actors and the director."