# Set up packages for lecture. Don't worry about understanding this code, but
# make sure to run it if you're following along.
import numpy as np
import babypandas as bpd
import pandas as pd
from matplotlib_inline.backend_inline import set_matplotlib_formats
import matplotlib.pyplot as plt
from matplotlib.animation import FuncAnimation, PillowWriter
from scipy import stats
set_matplotlib_formats("svg")
plt.style.use('ggplot')

np.set_printoptions(threshold=20, precision=2, suppress=True)
pd.set_option("display.max_rows", 7)
pd.set_option("display.max_columns", 8)
pd.set_option("display.precision", 2)

# Animations
from IPython.display import display, HTML, IFrame, clear_output
import ipywidgets as widgets

import warnings
warnings.filterwarnings('ignore')

# Demonstration code
def r_scatter(r):
    "Generate a scatter plot with a correlation approximately r"
    x = np.random.normal(0, 1, 1000)
    z = np.random.normal(0, 1, 1000)
    y = r * x + (np.sqrt(1 - r ** 2)) * z
    plt.scatter(x, y)
    plt.xlim(-4, 4)
    plt.ylim(-4, 4)
    
def show_scatter_grid():
    plt.subplots(1, 4, figsize=(10, 2))
    for i, r in enumerate([-1, -2/3, -1/3, 0]):
        plt.subplot(1, 4, i+1)
        r_scatter(r)
        plt.title(f'r = {np.round(r, 2)}')
    plt.show()
    plt.subplots(1, 4, figsize=(10, 2))
    for i, r in enumerate([1, 2/3, 1/3]):
        plt.subplot(1, 4, i+1)
        r_scatter(r)
        plt.title(f'r = {np.round(r, 2)}')
    plt.subplot(1, 4, 4)
    plt.axis('off')
    plt.show()

Lecture 23 – Correlation

DSC 10, Winter 2023

Announcements

• Homework 6 is due tomorrow at 11:59pm.
• Lab 7 is due on Saturday 3/11 at 11:59pm.
• The Final Project is due on Tuesday 3/14 at 11:59pm.

Agenda

• Recap: Statistical inference.
• Association.
• Correlation.
• Regression.

Recap: Statistical inference

Four big ideas in statistical inference

Every statistical test and simulation we've run in the second half of the class is related to one of the following four ideas. To solidify your understanding of what we've done, it's a good idea to review past lectures and assignments and see how what we did in each section relates to one of these four ideas.

• To test whether a sample came from a known population distribution, use "standard" hypothesis testing.

• To test whether two samples came from the same unknown population distribution, use permutation testing.

• To estimate a population parameter given a single sample, construct a confidence interval using bootstrapping (for most statistics) or the CLT (for the sample mean).

• To test whether a population parameter is equal to a particular value, $x$, construct a confidence interval using bootstrapping (for most statistics) or the CLT (for the sample mean), and check whether $x$ is in the interval.

Association

Prediction

• Suppose we have a dataset with at least two numerical variables.

• We're interested in predicting one variable based on another:
  • Given my education level, what is my income?
  • Given my height, how tall will my kid be as an adult?
  • Given my age, how many countries have I visited?

• To do this effectively, we need to first observe a pattern between the two numerical variables.

• To see if a pattern exists, we'll need to draw a scatter plot.

Association

• An association is any relationship or link between two variables in a scatter plot. Associations can be linear or non-linear.🔗."

• If two variables have a positive association ↗️, then as one variable increases, the other tends to increase.
• If two variables have a negative association ↘️, then as one variable increases, the other tends to decrease.

• If two variables are associated, then we can predict the value of one variable based on the value of the other.

Example: Hybrid cars 🚗

hybrid = bpd.read_csv('data/hybrid.csv')
hybrid

	vehicle	year	price	acceleration	mpg	class
0	Prius (1st Gen)	1997	24509.74	7.46	41.26	Compact
1	Tino	2000	35354.97	8.20	54.10	Compact
2	Prius (2nd Gen)	2000	26832.25	7.97	45.23	Compact
...	...	...	...	...	...	...
150	C-Max Energi Plug-in	2013	32950.00	11.76	43.00	Midsize
151	Fusion Energi Plug-in	2013	38700.00	11.76	43.00	Midsize
152	Chevrolet Volt	2013	39145.00	11.11	37.00	Compact

153 rows × 6 columns

'acceleration' and 'price'

Is there an association between these two variables? If so, what kind?

hybrid.plot(kind='scatter', x='acceleration', y='price', figsize=(10, 5));

'mpg' and 'price'

Is there an association between these two variables? If so, what kind?

hybrid.plot(kind='scatter', x='mpg', y='price', figsize=(10, 5));

Observations:

• There is an association – cars with better fuel economy tended to be cheaper.
  • Why do we think that is? 🤔
• The association looks more curved than linear.
  • It may roughly follow $y \approx \frac{1}{x}$.

Linear changes in units

• A linear change in units doesn't change the shape of the plot, it only changes the scale of the plot.
  • Linear change means adding or subtracting a constant, and multiplying or dividing by a constant.

• In other words, instead of plotting price in dollars and fuel economy in miles per gallon, we can plot price in Yen (🇯🇵) and fuel economy in kilometers per liter and the plot would look the same, just with different axes:

hybrid.assign(
    km_per_liter=hybrid.get('mpg') * 0.425144,
    yen=hybrid.get('price') * 140.34 
).plot(kind='scatter', x='km_per_liter', y='yen', figsize=(10, 5));

Converting columns to standard units

• Recall: Suppose $x$ is a numerical variable, and $x_i$ is one value of that variable. To convert $x_i$ to standard units, $$x_{i \: \text{(su)}} = \frac{x_i - \text{mean of $x$}}{\text{SD of $x$}}$$

• Converting columns to standard units makes different scatter plots comparable, by putting the $x$ and $y$ axes on the same scale.
  • Both axes measure the number of standard deviations above the mean.

• Converting columns to standard units doesn't change shape of the scatter plot, because the conversion is linear.

def standard_units(any_numbers):
    "Convert any array of numbers to standard units."
    any_numbers = np.array(any_numbers)
    return (any_numbers - any_numbers.mean()) / np.std(any_numbers)

def standardize(df):
    """Return a DataFrame in which all columns of df are converted to standard units."""
    df_su = bpd.DataFrame()
    for column in df.columns:
        df_su = df_su.assign(**{column + ' (su)': standard_units(df.get(column))})
    return df_su

Standard units for hybrid cars

For a given pair of variables:

• Which cars are average in both variables?
• Which cars are well above or well below average in both variables?

hybrid_su = standardize(hybrid.get(['price', 'acceleration', 'mpg'])).assign(vehicle=hybrid.get('vehicle'))
hybrid_su

	price (su)	acceleration (su)	mpg (su)	vehicle
0	-6.94e-01	-1.54	0.59	Prius (1st Gen)
1	-1.86e-01	-1.28	1.76	Tino
2	-5.85e-01	-1.36	0.95	Prius (2nd Gen)
...	...	...	...	...
150	-2.98e-01	-0.07	0.75	C-Max Energi Plug-in
151	-2.90e-02	-0.07	0.75	Fusion Energi Plug-in
152	-8.17e-03	-0.29	0.20	Chevrolet Volt

153 rows × 4 columns

'acceleration' and 'price'

hybrid_su.plot(kind='scatter', x='acceleration (su)', y='price (su)', figsize=(10, 5));

Which cars have 'acceleration's and 'price's that are more than 2 SDs above average?

hybrid_su[(hybrid_su.get('acceleration (su)') > 2) &
          (hybrid_su.get('price (su)') > 2)]

	price (su)	acceleration (su)	mpg (su)	vehicle
47	2.71	2.05	-1.46	ActiveHybrid X6
60	3.04	2.88	-1.16	ActiveHybrid 7
95	2.96	2.12	-1.35	ActiveHybrid 7i
146	2.11	2.12	-0.90	ActiveHybrid 7L
147	2.66	2.24	-0.90	Panamera S

'mpg' and 'price'

hybrid_su.plot(kind='scatter', x='mpg (su)', y='price (su)', figsize=(10, 5));

Which cars have close to average 'mpg's and close to average 'price's?

hybrid_su[(hybrid_su.get('mpg (su)') <= 0.3) &
          (hybrid_su.get('mpg (su)') >= -0.3) &
          (hybrid_su.get('price (su)') <= 0.3) &
          (hybrid_su.get('price (su)') >= -0.3)]

	price (su)	acceleration (su)	mpg (su)	vehicle
10	-1.24e-01	-0.56	-0.26	Escape
22	-2.13e-01	-1.02	-0.17	Mercury Mariner
57	-8.47e-02	0.72	-0.11	Audi Q5
...	...	...	...	...
70	-2.14e-01	-0.07	0.02	HS 250h
102	-2.69e-03	-0.29	0.20	Chevrolet Volt
152	-8.17e-03	-0.29	0.20	Chevrolet Volt

8 rows × 4 columns

Observation on associations in standard units

• If two variables are positively associated ↗️,
  • their high, positive values in standard units are typically seen together, and
  • their low, negative values are typically seen together as well.

• If two variables are negatively associated ↘️,
  • high, positive values of one are typically coupled with low, negative values of the other.

• If two variables aren't associated, there should be no such pattern.

When there is a positive association, most data points fall in the lower left and upper right quadrants.

hybrid_su.plot(kind='scatter', x='acceleration (su)', y='price (su)', figsize=(10, 5))
plt.axvline(0, color='black');
plt.axhline(0, color='black');

When there is a negative association, most data points fall in the upper left and lower right quadrants.

hybrid_su.plot(kind='scatter', x='mpg (su)', y='price (su)', figsize=(10, 5))
plt.axvline(0, color='black');
plt.axhline(0, color='black');

Correlation

Definition: Correlation coefficient

The correlation coefficient $r$ of two variables $x$ and $y$ is defined as the

• average value of the
• product of $x$ and $y$
• when both are measured in standard units.

If x and y are two Series or arrays,

r = (x_su * y_su).mean()

where x_su and y_su are x and y converted to standard units.

Let's calculate $r$ for 'acceleration' and 'price'.

hybrid_su

	price (su)	acceleration (su)	mpg (su)	vehicle
0	-6.94e-01	-1.54	0.59	Prius (1st Gen)
1	-1.86e-01	-1.28	1.76	Tino
2	-5.85e-01	-1.36	0.95	Prius (2nd Gen)
...	...	...	...	...
150	-2.98e-01	-0.07	0.75	C-Max Energi Plug-in
151	-2.90e-02	-0.07	0.75	Fusion Energi Plug-in
152	-8.17e-03	-0.29	0.20	Chevrolet Volt

153 rows × 4 columns

r_acc_price = (hybrid_su.get('acceleration (su)') * hybrid_su.get('price (su)')).mean()
r_acc_price

0.6955778996913982

hybrid_su.plot(kind='scatter', x='acceleration (su)', y='price (su)', figsize=(10, 5))
plt.axvline(0, color='black');
plt.axhline(0, color='black');

Note that the correlation is positive, and most data points fall in the lower left and upper right quadrants!

Let's now calculate $r$ for 'mpg' and 'price'.

hybrid_su

	price (su)	acceleration (su)	mpg (su)	vehicle
0	-6.94e-01	-1.54	0.59	Prius (1st Gen)
1	-1.86e-01	-1.28	1.76	Tino
2	-5.85e-01	-1.36	0.95	Prius (2nd Gen)
...	...	...	...	...
150	-2.98e-01	-0.07	0.75	C-Max Energi Plug-in
151	-2.90e-02	-0.07	0.75	Fusion Energi Plug-in
152	-8.17e-03	-0.29	0.20	Chevrolet Volt

153 rows × 4 columns

r_mpg_price = (hybrid_su.get('mpg (su)') * hybrid_su.get('price (su)')).mean()
r_mpg_price

-0.5318263633683789

hybrid_su.plot(kind='scatter', x='mpg (su)', y='price (su)', figsize=(10, 5));
plt.axvline(0, color='black');
plt.axhline(0, color='black');

Note that the correlation is negative, and most data points fall in the upper left and lower right quadrants!

The correlation coefficient, $r$

• $r$ measures how clustered points are around a straight line – it measures linear association.
  • If two variables are correlated, it means they are linearly associated.

• $r$ is always between $-1$ and $1$.
  • If $r = 1$, the scatter plot is a line of slope 1.
  • If $r = -1$, the scatter plot is a line of slope -1.
  • If $r = 0$, there is no linear association (uncorrelated).

show_scatter_grid()