# So, using the new notation, P X,Y (0,1) =.08 This is the value which the joint probability function for X and Y takes when X=0 and Y=1.

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1 Joint probabilit is the probabilit that the RVs & Y take values &. like the PDF of the two events, and. We will denote a joint probabilit function as P,Y (,) = P(= Y=) Marginal probabilit of is the probabilit that RV has the value regardless of the value of Y. That is P ( ) = P( = ) = P (, ), Y Eample: Let represent the number of weekl credit card purchases a person makes, and Y the number of credit cards a person owns. Suppose the bivariate table for the two variables looks as follows: Y So, using the new notation, P,Y (0,1) =.08 This is the value which the joint probabilit function for and Y takes when =0 and Y=1. The marginal probabilit of is the probabilit that a randoml selected person makes a certain number of credit card purchases per week, for eample P (2) = the probabilit that a randoml selected person makes 2 credit card purchases per week, 2

2 P ( 2) = P (2, ) = =.36, Y The complete marginal probabilit function of is: P (0) =.2, P (1) =.19, P (2) =.36, and P (3) =.25. We can also rewrite the definition of conditional probabilit using this new notation: P Y (2,3)=P(Y=2 =3)= P P, ( 23, ). 05 = =.. 2 () Y The interpretation of this is, as we know from previous discussion of conditional probabilit, that the probabilit that someone who makes three weekl credit card purchases owns two credit cards is.2. We can also rewrite the definitions of mutuall eclusive events using this notation, If P,Y (,)=0 for all pairs and, then and Y are mutuall eclusive, otherwise the are not. And the definition of independent events, If P,Y (,)=P ()P Y () for all pairs and then and Y are independent, otherwise the are not. For single variable distributions, we defined cumulative probabilit functions. We ma also be interested in the probabilit over some range for bivariate distributions. For eample, we might want to know what proportion of the population owns 2 or fewer credit cards and makes 2 or 3

3 fewer credit card purchases per week, i.e. we might be interested in the following cells: The joint cumulative probabilit function defines the probabilit that takes a value less than or equal to and Y takes a value less than of equal to, F,Y (a,b) = P,Y ( a, Y b) = a Y b P (, ), Y Eample: In the above eample, F,Y (2,2) = =.63. The interpretation is that 63% own two or fewer cards and make 2 or fewer purchases per week. Or, the probabilit that a randoml selected person owns two or fewer cards and used them to make two or fewer purchases per week is.63. Joint cumulative probabilit is a crucial concept when we are interested in the joint probabilit of two or more continuous RV s. It defines the probabilit that the each of the variables falls into some given interval. Remember that the probabilit that a continuous RV is equal to an particular value is zero. 4

4 B. Covariance and correlation We want to use bivariate probabilit distributions to talk about the relationship between two variables. The test for independence tells us whether or not two variables are independent. We also want to know how two variables are related if the are not independent, e.g. if income and education are not independent, do people with more education tend to have higher incomes or lower incomes? Covariance measures the average product of deviations of two variables from their respective means. It offers a measure of how two variables covar, whether one is above (below) its mean when the other is above (below) its mean. [( µ )( Y µ )] = ( µ )( Cov(, Y) = σ = E µ ) p, Y A simpler formula to use to calculate the covariance is Cov = σ = µ µ = (, ) µ µ (, Y ) Y E( Y ) p, Y Y (, ) Note that covariance is also a descriptive statistic which can be used to describe data: Population: σ = ( µ )( µ Y 1 N 1 s Y ) n 1 Sample: = ( )( The simplified equations for the descriptive statistic are 1 Population: σ Y = µ µ N ) Sample: s Y = n n 1 n 1 5

5 Covariance is a measure of simultaneous movements in and Y. In other words, the value of the covariance answers the question when is above its mean, where do we epect Y to lie with respect to its mean? Eample: consider the credit card eample again Y The easiest equation to use to calculate covariance is: Cov(,Y)= = p, (, ) µµ. What we need to do is, working from top to bottom and left to right, multipl for each cell in the table the specific value of associated with that cell times the specific value for Y associated with that cell times the probabilit of the intersection of those specific values for and Y. For the first column, =0, Y can be either 1, 2, or 3. Since =0, each product of and Y is going to be 0, so we re done. In the net column, = 1. That s another convenient shortcut because for the cells in this column, when we sum up **P Y (,), we can forget about the term. Now, once again Y can be either 1, 2, or 3, so we have: (1*.1) + (2*.05) + (3*.04) = =.32 6

6 In the net column, = 2. No short-cut here, but since our getting the idea I ll just sum up: (2*1*.1) + (2*2*.22) + (2*3*.04) = = We have one more column to do, and that s where = 3: (3*1*.02) + (3*2*.05) + (3*3*.18) = = Summing up the epressions for all four columns, we have: = That s the first half of the covariance term, or p, (, ).. = 362 To complete the calculation of the covariance, all we have to do is subtract the product of the means from Now, before we can do that, we have to calculate the means: ( ) (0*.2) +(1*.19) + (2*.36) + (3*.25) = = µ p 1.66 = = ( ) (1*.3) +(2*.4) + (3*.3) = = 2.0 µ Y p Y = = Now, the product of the means is: µ µ Y = 1.66*2.0 = 3.32, so Cov(,Y) = = 0.3 What does this mean? We want to answer questions like when is above its mean, where do we epect Y to lie with respect to its mean? A positive covariance, like in our eample, means that there is a positive relationship between and Y. What that means is that and Y tend to move in the same direction. When is high (above its mean), Y tends to be high as well. When Y is low (below its mean), tends to be low as well. Some eamples of things with 7

7 positive covariances are: income and education, height and weight, and political power or influence and wealth. When the covariance is negative, we sa that there is a negative relationship between and Y. That means that and Y tend to move in the opposite direction. When is high (above its mean), Y tends to be low. When Y is low (below its mean), tends to be high. Some eamples of things with negative covariances are: education and povert status, household wealth and the number of children in the famil, temperature and the number of winter coats sold. The final possibilit is that the value of the covariance is zero. In that case, we sa that there is no linear relationship between and Y. Note the qualification of the word linear. Let me eplain. Sometimes a zero covariance will mean that there is no relationship between and Y. Notice the lack of a qualification. That means that knowledge about the location of vis-à-vis the mean of tells us nothing about the probable location of Y. This not the onl possible source of a zero covariance, and hence the qualification of linear. Graphicall, if we graphed values of and Y, a positive relationship would look like one of the dotted lines, and a negative relationship would have a downward slope as depicted b the solid lines. What do we mean b the term linear? well, consider the following lines: Y 8

8 Linear means straight, so a linear relationship is a straight line. Now, even the two curved lines are moving basicall in one direction. That s different from the U-shaped curve below: Y This line is non-linear (or curved), and describes a specific non-linear relationship between and Y. When is small, as increases, Y tends to fall, and when is big, as gets bigger, Y tends to get bigger as well. An eample of this tpe of relationship is age () and net ta paments and government transfers (Y). So what should the sign of the covariance be? Well, depending on how negative and how positive these ranges are, we might get a covariance of zero. So we might find that the covariance is zero even though information about one variable relative to its mean does actuall tell us something about the position of the other variable relative to its mean. Covariance might be zero 9

9 simpl because this relationship is non-linear in such a wa that positive covariance over some range eactl cancels out the negative covariance over another range. So how can we tell these two cases apart? Well, with the tools we have now, we can t. We can plot the values and eeball the graph, but that isn t a ver precise tool. We won t discuss non-linear tools in this course, but ou should be aware of the problem and remember to interpret a zero covariance as no linear relationship rather than no relationship. So, to sum up, the covariance tells us the direction of the linear relationship if there is a linear or near-linear relationship, and whether or not there is a linear relationship. If two variables and Y are independent then their covariance is zero. However, if the covariance of and Y is zero, the variables are not necessaril independent ( this follows from the discussion above). Another problem with covariance is that it is not bounded. What that means is that the covariance can take on an value between negative infinit and positive infinit. The actual size of the covariance depends on the units of measure of the underling random variables, and Y. Therefore it tells us nothing about the strength of the relationship between and Y, onl the direction. Eample: Suppose the average credit card purchase is \$100, and we changed from the number of purchases to the amount spend with credit cards. Instead of 0, 1, 2, and 3, would take on the values of 0, 100, 200, and 300. The joint probabilit values would remain the same and 10

10 the number of credit cards owned would be the same. But everwhere we had an in the covariance equation, we would replace it with \$100*. If we repeated the calculations we did before, we would find that now Cov(,Y) = 30. Before, we had a covariance of.3, and now it is 100 times greater. B changing the units of b a factor of 100, we ve increased the size of the covariance b the same magnitude. So, does this represent a stronger positive relationship than before? No. The underling relationship is the same. We have a measure of the relationship between two variables which helps us to correct this problem. Correlation between two variables and Y is the covariance divided b the product of their standard deviations. Population: ρ Y, Cov(, Y) σ σ = Sample: r Y Y Cov(, Y) = ρ, Y = s s Unlike the covariance, the correlation is bounded. The units of the covariance are the units of times the units of Y. If is measured in hours and Y in dollars, the units of the covariance are hour*dollar. The correlation is the covariance b the two standard deviations, which have the same units as and Y. So, the units cancel out and we are left with a pure number that has no units whatsoever. Correlation varies between 1 and -1. The size (in absolute value) of the correlation reflects the strength of the relationship, while the sign (like the covariance) represents the direction. What this means is that whereas the covariance told us onl about the direction, the correlation tells us something about how likel high (low) values of are to be associated with high (low) values of Y. A correlation Y 11

11 Eample: let s calculate the correlation for the credit card eample σ = p ( ) µ = ( 0 *. 2) + ( 1 *. 19) + ( 2 *. 36) + ( 3 *. 25) 166. = = σ = p ( ) µ = ( 1 *.) 3 + ( 2 *.) 4 + ( 3 *.) 3 2 = =. 6 σ Y Y Y = 106., σ =. 77 Y. 3 ( 106. )(. 77) This means that:ρ Y = =. 367 We can sa that this value indicates a stronger relationship than if the correlation were onl.075, and a weaker relationship than if the value were greater in absolute value than.5, sa Like the covariance, the correlation onl captures linear relationships. Neither the covariance nor the correlation tell us anthing about causation. Suppose we observe that ice cream cone sales in a given da and the number of violent crimes committed in a given da are positivel correlated. Is this a causal relationship? Which wa does the causalit go? Or is a third factor, e.g. the temperature outside, driving the correlation? 12

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