In [8]:
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline


## The Problem¶

Here we're going to look at some census data from http://www.census.gov/hhes/socdemo/education/data/cps/historical/index.html

Specifically we'll look at the percent of adults over the age of 25 with a college degree by year

Is participation growing and at what rate?

A full report is here: http://www.census.gov/prod/2012pubs/p20-566.pdf

In [24]:
d = np.loadtxt('year.txt')
year = d[:,0]
participation_all = d[:,1]


## Plot year versus participation¶

In [29]:
plt.plot(year, participation_all, 'o')
plt.xlabel('year', fontsize=20)
plt.ylabel('% participation', fontsize=20)

Out[29]:
<matplotlib.text.Text at 0x112d35e10>

Here we see the trend looks linear. Let's try to fit the data to make some observations

To do this, let's let t be time and participation b. If we assume the data behaves like: $$b_i = x_0 + x_1 t_i$$ for each year $i$, then we're assuming the growth is linear in time.

What are $x_0$ and $x_1$ in this case?

In [30]:
n = len(perc)
A = np.ones((n,2))
A[:,1] = year
b = participation_all


We have a big system: $$A x = b$$ where $b$ is the participation and $x$ are the parameters that determine the shape of the linear growth. We can solve this with

1. pseudo-inverse (bad idea) $x = (A^T A)^{-1} A^T b$
2. QR factorization (hold on!)
In [31]:
x = np.linalg.solve(A.T.dot(A), A.T.dot(b))
print(x)

[ -8.15531563e+02   4.20432737e-01]


Now let's plot the line to see if it matches up:

In [34]:
plt.plot(year, participation_all, 'o')
t = np.linspace(year.min(), year.max(), 100)
plt.plot(t, x[0] + x[1]*t, 'r-', lw=3)
plt.xlabel('year', fontsize=20)
plt.ylabel('% participation', fontsize=20)

Out[34]:
<matplotlib.text.Text at 0x112e39048>
In [ ]: