4: Probability. What is probability? Random variables (RVs)


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1 4: Probability b binomial µ expected value [parameter] n number of trials [parameter] N normal p probability of success [parameter] pdf probability density function pmf probability mass function RV random variable σ standard deviation [parameter] x value for random variable X (e.g., observed number of successes for a binomial random variable) X random variable X What is probability? The probability of an event is its relative frequency in the long run. If an event occurs x times out of n, then its probability will converge on X n as n becomes infinitely large. For example, if we flip a coin many times, we expect to see half the flips turn up heads. Note that when n is small, the observed relative frequency of an event will not be a reliable reflection of its probability. However, as the number of observations n increases, the observed frequency becomes a more reliable reflection of the probability. For example, if a coin is flipped 10 times, there is no guarantee that it will turn up as exactly 5 heads every time. However, if the coin is flipped 10,000 times, chances are pretty good that the proportions of heads will be pretty close to 0.5. Random variables (RVs) A random variable (RV) is a quantity take of various values depending on chance. There are two types of random variables: (1) discrete random variables and (2) continuous random variables. Discrete random variables form a countable set of outcomes. We will study binomial random variables as a way to familiarize ourselves with discrete random variable. Continuous random variables form an infinite continuum of possible outcomes. We will study normal (Gaussian) random variables as a way to familiarize ourselves with continuous random variables. Page 1 of probability.docx (5/18/2016)
2 Binomial random variables Definition Binomial random variables are discrete RVs of counts that describe the number of successes (X) in n independent Bernoulli trials, a where each Bernoulli trial has the probability of success p. Binomial random variables have two parameters: n the number of independent Bernoulli trials p the consistent probability of success for each trial EXAMPLE. As an example of a binomial RV, consider the number of successful treatments (X) in 3 patients (n) where the probability of success in each instance p is X can take on the discrete values of 0, 1, 2, or 3. Notation. Let b represent the binomial distribution and ~ represent distributed as. Thus, X~b(n, p) is said random variable X is distributed as a binomial random variable with parameters n and p. The binomial random variable in our example is X~b(3,.25). Let Pr(X = x) represent the probability random variable X takes on the value x. For example, Pr(X = 0) the probability of 0 successes for random variable X. Pr(X x) represent the probability random variable X takes on a value x or less. This is the cumulative probability of the event. DEFINITION. The probability mass function (pmf) of a discrete random variable assigns probabilities for all possible outcomes. EXAMPLE. The pmf for X~b(3,.25) is shown in Table 1. The exact probabilities of events are shown in the second column. The cumulative probability is shown in the third column. TABLE 1. The pmf for X~b(3,.25). X Number of successes Pr(X = x) Probability 0 (event A) (event B) (event C) (event D) Pr(X x) Cumulative Probability How we calculated these probabilities is not currently the issue. Instead, let us focus on meaning. The above pmf states that for X~b(3,.25) we expect to see 0 successes of the time, 1 success of the time, 2 successes of the time, and 3 successes of the time. To calculate binomial probabilities, use this app If you are curious or for some reason you need to calculate binomial probabilities by hand, use the formulas in Chapter 6 of Basic Biostatistics for Public Health Practice. a A Bernoulli trial is a random event that can take on one of two possible outcomes: success or failure. Page 2 of probability.docx (5/18/2016)
3 Rules for working with probabilities Let: A event A (e.g., let A represent 0 success out of 3 in our Example). B event B (e.g., let B represent 1 success out of 3). Pr(A) the probability of event A. (e.g., Pr(A) = ). Ā the complement of event A not A. the union of events (e.g., A B A or B). Rule 1: Probabilities can be no less than 0% and no more than 100%. an event with probability 0 never occurs and an event with probability 1 always occurs Note that an all the events in Table 1 obey this rule. 0 Pr(A) 1 Rule 2: All possible outcomes taken together have probability exactly equal to 1. Pr(all possible outcomes) = 1 Note that in Table 1, Pr(all possible outcomes) = = 1. Rule 3: When two events are disjoint (cannot occur together), the probability of their union is the sum of their individual probabilities. Pr(A B) = Pr(A) + Pr(B), if A and B are disjoint In Table 1 let A 0 successes and A 1 success. Pr(A B) = = Rule 4: The probability of a complement is equal to 1 minus the probability of the event. Pr(Ā) = 1 Pr(A) In Table 1, Ā (1, 2, or 3 successes) and Pr(Ā) = = Page 3 of probability.docx (5/18/2016)
4 The area under the curve (AUC) concept Probability mass functions (pmf s) can be drawn. For our Example (pmf in Table 1): X~BINOMIAL(3,.25) X = 0 X = 1 X = 2 X = 3 Figure 1. X~b(3,.25). On the horizontal axis, the first bar stretches from 0 to 1. Therefore, this rectangle has base = 1. It has height = Thus, the area of this bar = h b = = = Pr(X = 0). The second bar also has a base of 1 (from 1 to 2), height of , and area = h b = = This corresponds to Pr(X = 1). The combined area of the first two bars = Pr(X = 0) or Pr(X = 1) = = In fact the area between any two values is equal to the probability of obtaining an outcome between these two values. This fact is referred to as the area under the curve (AUC) rule. We can also use the rule of complements to find AUCs (probabilities). Let A 0 successes. Therefore Ā one or more successes. By the rule of complements, Pr(Ā) = = If you add up the Pr(X = 1) + Pr(X = 2) + Pr(X = 3) you will see that this AUC is also equal to Page 4 of probability.docx (5/18/2016)
5 Normal (Gaussian) random variables Probability density functions (pdf) We will use normal random variables as a way to introduce continuous random variables. Probability density functions (pdf) assign probabilities for all possible outcomes for continuous random variables. A pdf cannot be easily shown in tabular form. I can however be drawn. While probability functions for discrete random variables (pmfs) are chunky pdfs are smooth curves. Normal pdf are recognized by their smooth, symmetrical bell shape. Normal random variables are a family of random variables. Each family member is are characterized by two parameters, μ ( mu ) and σ ( sigma ). μ the pdf s mean or expected value σ the pdf s standard deviation When μ changes, the location of the normal pdf changes. When σ changes, the spread of the normal pdf changes. Figure 2. σ1 > σ2 μ and σ are the analogues of xx and s for data distributions. However, you cannot calculate μ and σ. They describe probability functions, which are distinct from data. You can get an idea of the size of σ on a normal pdf by identifying the curve s points of inflection. This is where the curve begins to change slope. Trace the normal curve with your finger. You are skiing either the slope. The point at which the slope begins to flatten is a point of inflection. Page 5 of probability.docx (5/18/2016)
6 The left inflection point marks the location μ σ. This is one σunit below the mean. The right point of inflection marks the location of μ σ. This is one σunit below the mean. Page 6 of probability.docx (5/18/2016)
7 Normal probabilities and the area under the curve (AUC) Probabilities for continuous random variables are represented as the area under the curve (AUC) see prior section on AUCs! The rule helps get a grip on AUCs (probaiblities) from normal random variables. This rule applies to normal random variables only and says: 68% of the AUC for normal RVs lies in the region μ ± σ 95% of the AUC for normal RVs lies in the region μ ± 2σ 99.7% of the AUC for normal RVs lies in the region μ ± 3σ Visually, the 95 part of the rule looks like this: Think in terms of these landmarks: Although μ and σ vary from one normal random variable to the next, you can apply the rule to any normal random variable. Keep in mind that (a) AUC = probability (b) the total AUC = 1 (c) the values lie on the horizontal axis EXAMPLE. The Wechsler Intelligence Scale is used to measure intelligence. It is calibrated to produce a Normal distribution with μ = 100 and σ = 15 within each age group. NOTATION. Let X~N(µ, σ) represent a normal random variable with mean µ and standard deviation σ. Page 7 of probability.docx (5/18/2016)
8 Using this notation, Wechsler Intelligence scale scores is represented X~N(100, 15). The rule states that for X~N(100, 15): 68% of the AUC lies in the range μ ± σ = 100 ± 15 = 85 to % of the AUC lies in the range μ ± 2σ = 100 ± (2)(15) = 70 to % of the AUC lies in the range μ ± 3σ = 100 ± (3)(15) = 55 to 145 This next figure shows the AUC for X~N(100, 15). Notice the center of the curve is on µ. Also notice landmarks at ±1σ, ±2σ, ±3σ on the horizontal axis. Finding AUCs with an app for normal random variables The key concept here is the AUC between any two points corresponds to the probability of that observation. This applet will calculate AUCs between any two points on any X~N(μ, σ): Example. Plug in values for X~(100,15). The AUC between 130 and corresponds to the right tail of the pdf. Note that this AUC (probability) is (roughly 2.5%). Page 8 of probability.docx (5/18/2016)
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