1. Review of Probability Concepts ECON 6002 Econometrics Memorial University of Newfoundland Adapted from Vera Tabakova’s notes

2.  B.1 Random Variables  B.2 Probability Distributions  B.3 Joint, Marginal and Conditional Probability Distributions  B.4 Properties of Probability Distributions  B.5 Some Important Probability Distributions

3.  A random variable is a variable whose value is unknown until it is observed.  A discrete random variable can take only a limited, or countable, number of values.  A continuous random variable can take any value on an interval.

4.  The probability of an event is its “limiting relative frequency,” or the proportion of time it occurs in the long-run.  The probability density function (pdf) for a discrete random variable indicates the probability of each possible value occurring.

6. Figure B.1 College Employment Probabilities

7.  The cumulative distribution function (cdf) is an alternative way to represent probabilities. The cdf of the random variable X, denoted F(x), gives the probability that X is less than or equal to a specific value x

9.  For example, a binomial random variable X is the number of successes in n independent trials of identical experiments with probability of success p.

10.  For example, if we know that the MUN basketball team has a chance of winning of 70% (p=0.7) and we want to know how likely they are to win at least 2 games in the next 3 weeks

11.  First: for winning only once in three weeks, likelihood is 0.189, see?  Times

12.  For example, if we know that the MUN basketball team has a chance of winning of 70% (p=0.7) and we want to know how likely they are to win at least 2 games in the next 3 weeks…  The likelihood of winning exactly 2 games, no more or less:

13.  For example, if we know that the MUN basketball team has a chance of winning of 70% (p=0.7) and we want to know how likely they are to win at least 2 games in the next 3 weeks  So 3 times 0.147 = 0.441 is the likelihood of winning exactly 2 games

14.  For example, if we know that the MUN basketball team has a chance of winning of 70% (p=0.7) and we want to know how likely they are to win at least 2 games in the next 3 weeks  And 0.343 is the likelihood of winning exactly 3 games

15.  For example, if we know that the MUN basketball team has a chance of winning of 70% (p=0.7) and we want to know how likely they are to win at least 2 games in the next 3 weeks  For winning only once in three weeks: likelihood is 0.189  0.441 is the likelihood of winning exactly 2 games  0.343 is the likelihood of winning exactly 3 games  So 0.784 is how likely they are to win at least 2 games in the next 3 weeks  In STATA di Binomial(3,2,0.7) di Binomial(n,k,p)

16.  For example, if we know that the MUN basketball team has a chance of winning of 70% (p=0.7) and we want to know how likely they are to win at least 2 games in the next 3 weeks  So 0.784 is how likely they are to win at least 2 games in the next 3 weeks  In STATA di binomial(3,2,0.7) di Binomial(n,k,p) is the likelihood of winning 1 or less  So we were looking for 1- binomial(3,2,0.7)

17. Figure B.2 PDF of a continuous random variable

23. y 0.04/.18=.22 1.14/.18=.78

24.  Two random variables are statistically independent if the conditional probability that Y = y given that X = x, is the same as the unconditional probability that Y = y.

25. Y = 1 if shaded Y = 0 if clear X = numerical value (1, 2, 3, or 4)

29.  B.4.1Mean, median and mode  For a discrete random variable the expected value is: Where f is the discrete PDF of x

30.  For a continuous random variable the expected value is: The mean has a flaw as a measure of the center of a probability distribution in that it can be pulled by extreme values.

31.  For a continuous distribution the median of X is the value m such that  In symmetric distributions, like the familiar “bell-shaped curve” of the normal distribution, the mean and median are equal.  The mode is the value of X at which the pdf is highest.

32. Where g is any function of x, in particular;

34.  The variance of a discrete or continuous random variable X is the expected value of The variance

35.  The variance of a random variable is important in characterizing the scale of measurement, and the spread of the probability distribution.  Algebraically, letting E(X) = μ,

36.  The variance of a constant is?

37. Figure B.3 Distributions with different variances

42. Figure B.4 Correlated data

43.  If X and Y are independent random variables then the covariance and correlation between them are zero. The converse of this relationship is not true. Covariance and correlation coefficient

44.  The correlation coefficient is a measure of linear correlation between the variables  Its values range from -1 (perfect negative correlation) and 1 (perfect positive correlation) Covariance and correlation coefficient

45.  If a and b are constants then:

46. So: Why is that? (and of course the same happens for the case of var(X-Y))

47.  If X and Y are independent then:

48. Otherwise this expression would have to include all the doubling of each of the (non-zero) pairwise covariances between variables as summands as well

50.  B.5.1The Normal Distribution  If X is a normally distributed random variable with mean μ and variance σ 2, it can be symbolized as

51. Figure B.5a Normal Probability Density Functions with Means μ and Variance 1

52. Figure B.5b Normal Probability Density Functions with Mean 0 and Variance σ 2

53.  A standard normal random variable is one that has a normal probability density function with mean 0 and variance 1.  The cdf for the standardized normal variable Z is

55.  A weighted sum of normal random variables has a normal distribution.

57. Figure B.6 The chi-square distribution

58.  A “t” random variable (no upper case) is formed by dividing a standard normal random variable by the square root of an independent chi-square random variable,, that has been divided by its degrees of freedom m.

59. Figure B.7 The standard normal and t (3) probability density functions

60.  An F random variable is formed by the ratio of two independent chi- square random variables that have been divided by their degrees of freedom.

61. Figure B.8 The probability density function of an F random variable

62. Slide B-62 Principles of Econometrics, 3rd Edition  binary variable  binomial random variable  cdf  chi-square distribution  conditional pdf  conditional probability  continuous random variable  correlation  covariance  cumulative distribution function  degrees of freedom  discrete random variable  expected value  experiment  F-distribution  joint probability density function  marginal distribution  mean  median  mode  normal distribution  pdf  probability  probability density function  random variable  standard deviation  standard normal distribution  statistical independence  variance