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					A Declaration of Independence?                                                       Peter Flanagan-Hyde
Random Variables, Simulations, and the Law of Cosines                        Phoenix Country Day School
                                                                           peter.flanagan-hyde@pcds.org
Abstract

Many teachers demonstrate many of the concepts of random variables through simulation. However, the
concept of independence doesn’t lend itself to this approach – randomly generated sets of values will not
be independent, even when the conditions of the simulation include independent random variables. This
leads to interesting connections to other mathematical topics, including the Law of Cosines, vectors, and
projectile motion.

Combining Variation in Random Variables

In an introductory statistics class, students learn the following, which forms the basis of most of
statistical inference:

       If X and Y are two independent random variables, then the sum X + Y (or difference X – Y) has
       variance given by:

                               X  Y   XY (or  X  Y   XY )
                                2    2     2         2    2     2


These bear at least a formal similarity to the more famous Pythagorean Theorem, which forms the basis
of most calculations of length:

       If a and b are legs of a right triangle, then the third side c has length given by

                                               a 2  b 2  c2
Is there anything to the symmetry in these two important theorems? The discussion that follows
explains answers this question in the affirmative.

A Scenario: Life plus Monopoly

The board game Life uses a spinner numbered 1 – 10, and the game Monopoly
uses two dice, numbered 1 – 6. Imagine a hybrid game that borrows from
these elements, played by spinning the Life spinner and throwing one of the
Monopoly dice and subtracting the results. Your token is then advanced by this
difference, making for an interesting game: you generally move forward, but
might move backwards or even not move at all. How does the spaces
advanced relate to the individual outcomes of the spinner and the die?

At the right is a simulation of 20 plays of this game done on a TI calculator.
To follow along with exactly these numbers on your calculator, reset the
random number generator as shown on the top screen; otherwise, you’ll
generate a different simulation.

In the notation of the preceding section, X is the outcome of a spin, with 20
examples stored in L1, and Y is the outcome of 20 throws of a die, stored in L2. Twenty examples of the
difference X – Y are stored in L3.

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Can we use this simulation to show that the variances add, sX       sY  sXY ?
                                                                      2    2          Here is a tabulation of
the variables that we have:

                    Random           Mean          Standard          Variance
                    Variable                       deviation
                      X (L1)          x  5.65      sX  3.4683            2
                                                                         s X  12.0289
                       Y (L2)         y  3.45       sY  2.0641          2
                                                                         sY  4.2605
                     X – Y (L3)      x  y  2.2    sX Y  3.5333      2
                                                                      s X Y  12.4842

The means are quite close to expected (5.5 and 3.5) and the mean of the difference is a demonstration of
the property, true for all random variables, X – Y  X  Y .

The variances, however are another story: the sum of the variances of X and Y are not even close to the
variance of the difference!
                                        2         2       2
                                       sX       sY    sX Y
                                     12.0289  4.2605  12.4842

Let’s explore this discrepancy:
                                 2             2                            2
                                sX           sY                          sX Y
                            12.0289  4.2605             3.8052      12.4842

The missing quantity is about 3.8052. Does this number have any meaning?

The original theorem stated, “If X and Y are independent, then the variances add.” If the variances don’t
add, then this implies that X and Y are not independent – despite the fact that we had set up the
simulation that way.

At the right is a scatterplot of the spin (X) and die (Y) values (there are only 17
points, since some combinations are repeated). This doesn’t show a striking
pattern. But a linear regression reveals that the two lists of values are not
independent, since the correlation between them isn’t zero – the correlation,
0.2658, measures an association.

So now we have a case in which we might start a theorem, “If X and Y are not
independent, then there is a discrepancy between the sum of the variances and the
variance of the difference.” Not so neat.

Can our analogy to the Pythagorean Theorem be of any assistance? That is, does



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the sentence, “If a and b are the sides of a not-right triangle, then there is a discrepancy between the sum
of the squares of the sides and the square of the third side.” make any sense? It does, if your students
know the Law of Cosines!

                                            a 2  b 2  2ab cosC  c2

Line this up with previous equations, replacing the a and b in the Law of Cosines with the standard
deviations, sX and sY – reasonable by analogy, at least:

                             a2              b2          2ab cosC                       c2
                              2                2                                            2
                             sX              sY       2s X sY cosC                     s X Y
                          12.0289          4.2605          3.8052                12.4842

But what is the analogy to the angle C in the Law of Cosines? It’s easy enough to solve for cos C:

                                                     2sx sy cosC  3.8052
                                       2(3.4683)(2.0641)cosC  3.8052
                                                        cosC  0.2658

This is a familiar number – it’s the correlation coefficient! This measured the degree to which the two
lists of values were actually not independent. Of course, had this been zero, it would indicate that the
lists were independent. In that case, the Law of Cosines reduces to the Pythagorean Theorem.

Students who have taken a course in Physics may be familiar with the use of “independent” related to
the idea of “perpendicular” in their study of projectile motion. The independence of horizontal motion
and vertical motion is a classic demonstration that many students have seen.

An explanation

The correlation coefficient has a formula that includes the sum of a product of “standardized values”

                                        n   xi  x   yi  y        n
                                       
                                        
                                       i1     sx   sy 
                                                             
                                                                        z z 
                                                                              x       y
                                  r                                  i1
                                                   n 1                      n 1
The standardized values are calculated as the deviation from the mean, divided by
the standard deviation. These measure the number of standard deviations from the
mean for each spin or throw of the die. This is a linear transformation of the
values in each of the lists. This is easily enough implemented on the calculator, as
shown at the right.

In another connection to work students may have done elsewhere, the indicated
product of the two lists, shown in the second screen, creates a new list in which
each element is the product of the corresponding elements in the original lists. The



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sum then adds these values – but that is exactly how the dot product of two vectors is calculated. So
now it appears that the correlation coefficient can be thought of as the dot product of two vectors,
divided by some quantity, in this case 19.

Why 19? It’s n – 1, which appears in the formula used to calculate the standard deviation of a list.

This is familiar to students as well from their studies of vectors in a precalculus course. They may have
seen a formula similar to the one below, that describes the angle between two vectors, u and v :
                                                     v v
                                                    uv
                                             cos  v v
                                                    u v

So, if the numerator is like a dot product, is the denominator of the correlation
coefficient like the product of the lengths of two vectors? The length of the vector
is the square root of the sum of the squares of its components – for two dimensions
this is the Pythagorean Theorem again, and for higher dimensions the formula is
the same, just longer.

The calculator screen at the right above shows that the “length” of the “vector”
for both standardized lists is the same, 4.3589 in this example. The product of
these “lengths” is indeed 19, or n – 1, the denominator in the formula for the
correlation coefficient.

Thinking about representing a list of random numbers as a vector in 20
dimensions is a little mind-boggling! But that’s just the beauty of this
algebraic approach, where the form doesn’t change as the dimensions increase.
Whatever the number of dimensions, the two “vectors” L1 and L2 determine a
unique plane. These “vectors” can then be sketched in this plane, and the
picture looks just like the illustration in many precalculus books, such as the
picture shown at the right. The angle between the vectors is about 74.59°, which
can be calculated as shown in the screen at the right, using the value for the
correlation coefficient, r, that was produced in the linear regression.


The screens at the right demonstrate that the scatterplot in
the transformed space of the standardized list has exactly the
same look as the original scatterplot. The only thing that
has changed is the position and scale on the x- and y-axes.
Standardizing is an example of a linear transformation of the
plane, which will always preserved this look.

The last screens show that the regression line in the
transformed space has the same look as the previous
regression line. The slope isn’t exactly the same, since the
axes have been rescaled, but under any linear transformation
of x and y the correlation coefficient is an invariant.



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A Declaration of Independence?                                                  Peter Flanagan-Hyde
Random Variables, Simulations, and the Law of Cosines                   Phoenix Country Day School
                                                                      peter.flanagan-hyde@pcds.org
Screenshots




reset random number      random spins and         difference spin – die    scatterplot (x for spin, y
                         throws of the die                                 for die) (ZoomStat)




linear regression        slope in not 0, nor is   scatterplot with line
                         correlation




transformation to        set up graph with        exactly the same as      linear regression
standardize spins and    standardized lists       above, after rescaling
die                                               with ZoomStat




same correlation as      rescaled line is
above, and in the “z-    identical
space” slope = r




correlation calculated   “length” of              angle between vectors,   output of program to
using the formula        standardized vectors =   90° = independent        repeat simulation
                           n 1


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A Declaration of Independence?                                                       Peter Flanagan-Hyde
Random Variables, Simulations, and the Law of Cosines                        Phoenix Country Day School
                                                                           peter.flanagan-hyde@pcds.org
Repeating the Simulation

The lists that have been used in this demonstration, along with a program that allows easy repetition of
the simulation to explore the distribution of correlation coefficients and angles between vectors is
available in the TI group INDEPRV. The lists are stored with the names listed below. You can
reproduce these lists using the command 0→rand, then running the program. To start the random
numbers at an arbitrary point, I have students typically store their birthday or last 4 digits of their phone
number to the random number seed. For example, using the last digits of my cell phone number, I’d
enter 1591→rand. Since the “random” number generator isn’t really random, if you do this with a
group each person should start with a different seed.

The screenshot below shows a typical output of the program. Questions that can be explorations are the
distribution of the correlation coefficient (should have a mean of 0) or the distribution of the angle
(should have a mean of 90).

Group INDEPRV

LISTS:
     L1     =   LSPIN      20 spins of the wheel
     L2     =   LDIE       20 tosses of the die
     L3     =   LDIF       differences, SPIN - DIE
     L4     =   LZSPIN     standardized spins
     L5     =   LZDIE      standardized die
     L6     =   LZDIF      standardized differences

pgrmSPINDIE
     20→N
     randInt(1,10,N)→L₁
     randInt(1,6,N)→L₂
     2-Var Stats L₁ ,L₂
     Sx→A:Sy→B
     L₁ +L₂ →L₃
     1-Var Stats L₃
     Sx→C
         - -
     Disp "CORRELATION",R

       Disp "ANGLE θ",θ
                                                             I
                                                             llustration of the spinner from the game Life



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