of a geometric random variable with $$p=0.20$$, $$1-p=0.80$$, and $$x=4$$: There is about a 10% chance that the marketing representative would have to select 4 people before he would find one who attended the last home football game. of a geometric random variable with $$1-p=0.80$$, and $$x=6$$: $$P(X >6)=1-P(X \leq 6)=1-[1-0.8^6]=0.8^6=0.262$$. Consider two random variables X and Y defined as: X = Number of sixes after 12 rolls of fair die, Y = Number of rolls until we get 6 on fair die. The variance is 20, as determined by: $$\sigma^2=Var(X)=\dfrac{1-p}{p^2}=\dfrac{0.80}{0.20^2}=20$$. To find the desired probability, we need to find $$P(X=4$$, which can be determined readily using the p.m.f. A representative from the National Football League's Marketing Division randomly selects people on a random street in Kansas City, Kansas until he finds a person who attended the last home football game. What is the probability that the marketing representative must select 4 people before he finds one who attended the last home football game? Trials should be independent of each other. There is about a 26% chance that the marketing representative would have to select more than 6 people before he would find one who attended the last home football game. How many people should we expect (that is, what is the average number) the marketing representative needs to select before he finds one who attended the last home football game? Let’s list down all the properties of random variable Y: Random variable Y is a little bit different than X. 11.3 - Geometric Examples Example 11-1 Continued Section A representative from the National Football League's Marketing Division randomly selects people on a random street in Kansas City, Kansas until he finds a person who attended the last home football game. In order to prove the properties, we need to recall the sum of the geometric series. Probability of success on each trial should be constant. From the above descriptions, we can see that random variable Y does not have a fixed number of trials. That is, we should expect the marketing representative to have to select 5 people before he finds one who attended the last football game. The random variable $$X$$ associated with a geometric probability distribution is discrete and therefore the geometric … 11.2 - Key Properties of a Geometric Random Variable, 1.5 - Summarizing Quantitative Data Graphically, 2.4 - How to Assign Probability to Events, 7.3 - The Cumulative Distribution Function (CDF), Lesson 11: Geometric and Negative Binomial Distributions, 11.5 - Key Properties of a Negative Binomial Random Variable, 12.4 - Approximating the Binomial Distribution, 13.3 - Order Statistics and Sample Percentiles, 14.5 - Piece-wise Distributions and other Examples, Lesson 15: Exponential, Gamma and Chi-Square Distributions, 16.1 - The Distribution and Its Characteristics, 16.3 - Using Normal Probabilities to Find X, 16.5 - The Standard Normal and The Chi-Square, Lesson 17: Distributions of Two Discrete Random Variables, 18.2 - Correlation Coefficient of X and Y. To understand geometric random variables, it is required to understand what is a random variable and what is a binomial random variable. Lesson 20: Distributions of Two Continuous Random Variables, 20.2 - Conditional Distributions for Continuous Random Variables, Lesson 21: Bivariate Normal Distributions, 21.1 - Conditional Distribution of Y Given X, Section 5: Distributions of Functions of Random Variables, Lesson 22: Functions of One Random Variable, Lesson 23: Transformations of Two Random Variables, Lesson 24: Several Independent Random Variables, 24.2 - Expectations of Functions of Independent Random Variables, 24.3 - Mean and Variance of Linear Combinations, Lesson 25: The Moment-Generating Function Technique, 25.3 - Sums of Chi-Square Random Variables, Lesson 26: Random Functions Associated with Normal Distributions, 26.1 - Sums of Independent Normal Random Variables, 26.2 - Sampling Distribution of Sample Mean, 26.3 - Sampling Distribution of Sample Variance, Lesson 28: Approximations for Discrete Distributions, Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris, Duis aute irure dolor in reprehenderit in voluptate, Excepteur sint occaecat cupidatat non proident. And, let $$X$$ denote the number of people he selects until he finds his first success. Excepturi aliquam in iure, repellat, fugiat illum voluptate repellendus blanditiis veritatis ducimus ad ipsa quisquam, commodi vel necessitatibus, harum quos a dignissimos. The geometric probability distribution is used in situations where we need to find the probability $$P(X = x)$$ that the $$x$$th trial is the first success to occur in a repeated set of trials. $$\mu=E(X)=\dfrac{1}{p}=\dfrac{1}{0.20}=5$$. MARKOV PROPERTY =⇒ MEMORYLESS PROPERTY Example: Products are inspected until ﬁrst defective is found. Those are: In this post we understood what are geometric random variable and how these are different than binomial random variable. X = Number of sixes after 12 rolls of fair die. From above example we can list down conditions for geometric random variable. On this page, we state and then prove four properties of a geometric random variable. Enjoy the videos and music you love, upload original content, and share it all with friends, family, and the world on YouTube. What is Y- Number of rolls until we get 6 on fair die. Let’s try to understand geometric random variable with some examples. Before going further let’s recall what are the conditions for Binomial Random Variable: Let’s try to understand geometric random variable with some examples. Monty Hall Problem; Theoretical Vs Experimental probability, The Significance and Applications of Covariance Matrix, Why Clocks in Motion Slow Down According to Relativity Theory, Interesting Properties and Use Cases of the Covariance Matrix, Solving the Monty Hall Problem with Bayes Theorem.