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Linear Correlation

Introduction

• The news is filled with examples of correlations and associations:
  
  • Drinking a glass of red wine per day may decrease your chances of a heart attack.
  • Taking one aspirin per day may decrease your chances of stroke or of a heart attack.
  • Eating lots of certain kinds of fish may improve your health and make you smarter.
  • Driving slower reduces your chances of getting killed in a trafficaccident.
  • Taller people tend to weigh more.
  • Pregnant women that smoke tend to have low birthweight babies.
  • Animals with large brains tend to be more intelligent.
  • The more you study for an exam, the higher the score you are likely to receive.
• The correlation, denoted by r, measures the amount of linear association between two variables.
• r is always between -1 and 1 inclusive.
• The R-squared value, denoted by R², is the square of the correlation. It measures the proportion of variation in the dependent variable that can be attributed to the independent variable.
• The R-squared value R² is always between 0 and 1 inclusive.
• Perfect positive linear association. The points are exactly on the trend line.
  Correlation r = 1; R-squared = 1.00
• Large positive linear association. The points are close to the linear trend line.
  Correlation r = 0.9; R=squared = 0.81.
• Small positive linear association. The points are far from the trend line.
  Correlation r = 0.45; R-squared = 0.2025.
• No association. There is no association between the variables.
  Correlation r = 0.0; R-squared = 0.0.
• Small negative association.
  Correlation r = -0.3. R-squared = 0.09.
• Large negative association.
  Correlation r = -0.95; R-squared = 0.9025
• Perfect negative association.
  Correlation r = -1. R-squared = 1.00.
• How high must a correlation be to be considered meaningful? It depends on the discipline. Here are some rough guidelines:
   
  

  Discipline	r meaningful if	R2 meaningful if
  Physics	r < -0.95 or 0.95 < r	0.9 < R2
  Chemistry	r < -0.9 or 0.9 < r	0.8 < R2
  Biology	r < -0.7 or 0.7 < r	0.5 < R2
  Social Sciences	r < -0.5 or 0.5 < r	0.25 < R2

Calculating the Correlation

• To calculate the correlation, first standardize both the x and y variables:
       z_xi = (x_i - x) / SD_x      z_yi = (y_i - y) / SD_y
• Then compute r = the average of the products z_xi z_yi
• Example:   Compute the correlation r of this dataset:

  x	1	3	4	5	7
  y	5	9	7	1	13

• We calculate:
  x = (1 + 3 + 4 + 5 + 7) / 5 = 4
  y = (5 + 9 + 7 + 1 + 13) / 5 = 7
  SD_x² = [(1-4)² + (3-4)² + (4-4)² + (5-4)² + (7-4)²] / 5 = 4
  SD_x = √4 = 2
  SD_y² = [(5-7)² + (9-7)² + (7-7)² + (1-7)² + (13-7)²] / 5 = 16
  SD_y = √16 = 4
• Now compute the average of the z-scores of the x- and y-variables:
  

  x	y	zx	zy	zxzy
  1	5	-1.5	-0.5	0.75
  3	9	-0.5	0.5	-0.25
  4	7	0.0	0.0	0.00
  5	1	0.5	-1.5	-0.75
  7	13	1.5	1.5	2.25
  Ave. of zxzy: 0.40

• Thus the correlation r is 0.4.
• Remember: the correlation is always between -1 and 1, inclusive.
• Why does this work? Here are three possibilities:
   
  
  
  1. In diagram (a), the x- and y-variables have a positive relationship. Most of the (x,y) points lie in quadrants I and III where the z_xz_y product is positive. Therefore r > 0.
  2. In diagram (b), the x- and y-variables have a negative relationship. Most of the (x,y) points lie in quadrants II and IV where the z_xz_y product is negative. Thereform r < 0.
  3. In diagram (c), the x- and y-variables have no relationship. The positive products in quadrants I and III cancel out the negative products in quadrants II and VI so the average of the products is close to 0; r is also close to 0.

Calculating the Correlation with SD+

• Compute the correlation r of this dataset:
  

  x	1	3	4	5	7
  y	5	9	7	1	13

• Use SPSS to calculate descriptive statistics and z-scores:
  x = 4.00    SD_x+ = 2.236    x = 7.00    SD_x+ = 4.472

x	y	zx	zy	zxzy
1	5	-1.34164	-0.44721	0.60
2	9	-0.44721	0.44721	-0.20
3	7	0.0	0.0	0.00
4	1	0.44721	-1.34164	-0.60
5	13	1.34164	1.34164	1.80
Ave. of zxzy: 0.32

• Multiply by the correction factor n / (n - 1):
       (ave of z_xz_y) * n / (n-1) = 0.32 * 5 / (5-1) = 0.32 * 5 / 4 = 0.4.
  This is the same answer obtained previously using SD_x and SD_y.

Correlation with SPSS

• With the Laundry Dataset, use SPSS to display the scatterplot of price (y-axis) vs. rating (x-axis):
  
  1. Import the Laundry Dataset dataset into SPSS.
  2. Verify that for the variables Rating and Price, Type is set to Numeric and Measure to Scale.
  3. Set the labels for Rating and Price to "Consumer Union Rating" and "Price (Cents) per Load", respectively.
  4. Select Graphs >> Chart Builder. In the Chart Builder Dialog, click CK because you have already set the Measure of both variables to Scale.
     In the next Chart Builder Dialog, select Scatter/Dot, and drag the Simple Scatter from the Gallery into the Chart Preview area.
     Drag the Rating variable into the X-axis box and the Price variable into the Y-axis box.
     Click OK.
• In the Laundry Dataset, use SPSS to compute the correlation of rating and price.
  
  1. Select Analyze >> Correlate >> Bivariate...
     Drag "Consumer Union Rating" and "Price (Cents) per Load" into the Variables box.
     Click OK.
• The output shows that the correlation is 0.671

Cautions

• Caution:   Correlation does not necessarily imply causation.
• If X is correlated with Y, there could be five explanations:
  
  1. X causes Y
  2. Y causes X
  3. X causes Y and Y causes X
  4. Some third variable Z causes X and Y
  5. The correlation is a coincidence; there is no causal relationship between X and Y.
• Here are some examples of correlations with implied causations that have various explanations:
  
  1. The more firemen that are fighting a fire, the bigger the fire is going to be.
     The actual causation is Y → X: The bigger the fire is, the more firemen are necessary to fight it.
  2. For a gas, an increase in pressure causes an increase in temperature.
     This is Charles' Law for an ideal gas. In fact X → Y and Y → X. The causation works in both directions: an increase in either temperature or pressure causes an increase in the other.
  3. Children that sleep with the light on are likely to develop nearsightedness later in life.
     This result was published in a study in May 13, 1999, in the Journal Nature. In fact a follow up study showed that Z → X and Z → Y. There is a strong link between parental nearsightedness and child nearsightedness. Also, nearsighted parents were more likely to leave the light on in a child's room.
  4. Women that take hormone replacement therapy (HRT) are less likely to have coronary heart disease.
     At first glance X → Y, but after controlling for the third variable socio-economic group, the opposite effect was found: women that take HRT were more likely to develop heart disease.
  5. As ice cream sales increase, the rate of drowning deaths increase.
     This is also a case of Z → X and Z → Y. Both events depend on the season of the year. In the summer months, ice cream sales increase; drowning deaths also increase because more people to swimming.
  6. Piracy causes global warming.
     It is true that both piracy and global warming have increased over the past several decades, but this is just a coincidence. There is no causal relationship. Another explanation is that both result from a common third factor: population increase.
• For a correlation between X and Y to imply causation,
  
  1. X must precede Y in time,
  2. the causation must be plausable,
  3. common causes from other variables are controlled for.
• Question:   Does smoking cause lung cancer?
• Caution:   The correlation is misleading if there is a nonlinear relationship between the variables.
  Example 1:   There is a perfect quadratic relationship between x and y, but the correlation is -0.368. A quadratic relationship between x and y means that there is an equation y = ax² + bx + c that allows us to compute y from x. a, b, and c must be determined from the dataset.
• Caution:   Outliers can distort the correlation:
  Example 2:   Without the outlier, the correlation is 1; with the outlier the correlation is 0.514.
  Example 3:   Without the outlier, the correlation is 1; with the outlier the correlation is 0.522.