# COMP6053 lecture: Relationship between two variables: correlation, covariance and r-squared.

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1 COMP6053 lecture: Relationship between two variables: correlation, covariance and r-squared

2 Relationships between variables So far we have looked at ways of characterizing the distribution of a single variable, and testing hypotheses about the population based on a sample. We're now moving on to the ways in which two variables can be examined together. This comes up a lot in research!

3 Relationships between variables You might want to know: To what extent the change in a patient's blood pressure is linked to the dosage level of a drug they've been given. To what degree the number of plant species in an ecosystem is related to the number of animal species. Whether temperature affects the rate of a chemical reaction.

4 Relationships between variables We assume that for each case we have at least two real-valued variables. For example: both height (cm) and weight (kg) recorded for a group of people. The standard way to display this is using a dot plot or scatterplot.

5

6 The basic story

7 The basic story

8 The basic story

9 Measuring relationships? We're going to need a way of measuring the extent to which one variable changes when another one does. Another way of putting it: when we know the value of variable A, how much information do we have about variable B's value?

10 Recap of the one-variable case Perhaps we can borrow some ideas about the way we characterized variation in the single-variable case. With one variable, we start out by finding the mean, which is also the expectation of the distribution.

11 Sum of the squared deviations Then find the sum of all the squared deviations from the mean. This gives us a measure of the total variation: it will be higher for bigger samples.

12 Sum of the squared deviations We divide this total by N, the sample size... (or N-1 if we are using our sample to estimate the value for a wider population) to get...

13 The variance This is a good measure of how much variation exists in the sample, normalized by sample size. It has the nice property of being additive. The only problem is that the variance is measure in units squared. So we take the square root to get...

14 The standard deviation This is another measure of the "average spread" of the distribution. It is now measured in the original units. The sample standard deviation (division by N-1) is a good estimate for the population standard deviation.

15 The standard deviation With a good estimate of the population SD, we can reason about the standard deviation of the distribution of sample means. That's a number that gets smaller as the sample sizes get bigger. To calculate this from the sample standard deviation we divide through by the square root of N, the sample size, to get...

16 The standard error This measures the precision of our estimation of the true population mean. Plus or minus 1.96 standard errors from the sample mean should capture the true population mean 95% of the time. The standard error is itself the standard deviation of the distribution of the sample means.

17 Variation in one variable So, these four measures all describe aspects of the variation in a single variable: a. Sum of the squared deviations b. Variance c. Standard deviation d. Standard error Can we adapt them for thinking about the way in which two variables might vary together?

18 Two variable example Consider a small sample of four records with two variables recorded, X and Y. Values: (1,1) (4,3) (7,5) (8,7). X and Y could be anything. Let's say X is hours spent fishing, Y is number of fish caught.

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20 Two variable example We can see there's a positive relationship but how should we quantify it? We can start by calculating the mean for each variable. Mean of X = 5. Mean of Y = 4.

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22 Two variable example In the one-variable case, the next step would be to find the deviations from the mean and then square them. In the two-variable case, we need to connect the variables. We do this by multiplying each X-deviation by its associated Y-deviation and taking the sum of those values.

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24 Calculating covariance -4 x -3 = 12-1 x -1 = 1 2 x 1 = 2 3 x 3 = 9 Total of the cross-multiplied deviates = 24. Divide by N if this is the population, or divide by N-1 if this is a sample and we're estimating the population.

25 Calculating covariance If this was the population, we get 24 / 4 = 6. If this is a sample and we want to estimate the true population value, we get 24 / 3 = 8. Assuming this is a sample, we have a measure of 8 "fish-hours" for the estimated covariance between X and Y.

26 Calculating covariance Note that if lower values of X had gone with higher values of Y and vice versa, we would have got a negative number. If big values of X and Y were randomly scattered, covariance would be close to zero. Note also the relationship with variance: if we calculated the covariance of X with itself, we'd get the variance of X.

27 Interpreting covariance? Covariance has some of the properties we want: positive, negative, and absent relationships can be recognized. But "fish-hours" is difficult to interpret. Can we scale it in some way? Well, the standard deviation of X is in hours, and the standard deviation of Y is in fish...

28 The correlation coefficient So if we take the covariance and divide through by the product of the two standard deviations, we've scaled it to a dimensionless measure. This is called a correlation coefficient. Or, more technically, a Pearson productmoment correlation coefficient.

29 The correlation coefficient What sort of magnitude will this measure have? Recall that if we calculate the covariance of X with itself, we'd get the variance of X. You can't get any more strongly related than something with itself.

30 The correlation coefficient So in this maximal X-against-X case, when we divide through by the two standard deviations, the denominator is SD of X times SD of X. SD of X, squared, equals the variance of X. Thus the biggest a correlation coefficient can ever be is 1 (or minus one for X-againstnegative-X).

31 The correlation coefficient This measure runs between -1 and 1, and represents negative, absent, and positive relationships. It's often referred to as "r". It's extremely popular as a way of measuring the strength of a linear relationship.

32 The correlation coefficient In our case, the sample standard deviations of X and Y are 3.16 and 2.58 respectively. r = 8 / (3.16 * 2.58) = This is a very strong positive relationship, as we can see from the original scatter plot.

33 Another example Invented data set where X is normally distributed, mean = 100, SD = 10. For each of 500 cases, Y is equal to X plus a normal variate, mean = 100, SD = 10. Y and X are clearly related, but there's also a significant part of the variation in Y that has nothing to do with X.

34

35 Calculating the correlation coefficient In Python, we use pylab.corrcoef(a,b) where a and b are lists (returns a matrix). In R, it's cor(a,b) where a and b are variable names. You can also use cor (data) to get a matrix showing the correlation of everything with everything else in the data frame. For the previous example, r = 0.72.

36 Interpreting correlation coefficients : Weak relationship; may be an artefact of the data set and in fact there is no relationship at all : Moderate relationship; you might be on to something, or you might not : Strong relationship; you can be confident that these two variables are connected in some way : Very strong relationship; variables are almost measuring the same thing.

37 Correlations measure linear relationships only

38 Correlation is not causality Of course, just because X and Y are correlated does not mean that X causes Y. They could both be caused by some other factor Z. Y might cause X instead. Low correlations might result from no causal linkage, just sampling noise.

39 Range effects Two variables can be strongly related across the whole of their range, but with no strong relationship in a limited subset of that range. Consider the relationship between price and top speed in cars: broadly positive. But if we look only at very expensive cars, the two values may be uncorrelated.

40 Range effects Consider the X, Y scatterplot from a few slides back. If we limit the range of X to between 95 and 105, the correlation coefficient is only 0.27.

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42 Confidence intervals Confidence intervals for correlation coefficients can be calculated in much the same way as for means. As an exercise: using the Python code for this lecture, try drawing samples of size 50 repeatedly from the X, Y distribution and look at the range of values for r you get.

43 Information about Y from X If I know the correlation between two things, what does knowing one thing tell me about the value of the other? Consider the X, Y example. X was a random variable, and Y was equal to X plus another random variable from the same distribution. The correlation worked out at about 0.7. Why?

44 R-squared Turns out that if we square the correlation coefficient we get a direct measure of the proportion of the variance explained. In our example case we know that X explains exactly 50% of the variance in Y. The square root of

45 R-squared r = 0.3 explains 9% of the variance. r = 0.6 explains 36% of the variance. r = 0.9 explains 81% of the variance. "R-squared" is a standard way of measuring the proportion of variance we can explain in one variable using one or more other variables. This connects with the next lecture on ANOVA.

46 Python code The Python code used to produce the graphs and correlation coefficients in this lecture is available here.

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