Rapid Surveys (unpublished)‚ © 2008. NOT FOR COMMERCIAL DISTRIBUTION 3 Simple Random Sampling 3.1 INTRODUCTION Everyone mentions simple random sampling‚ but few use this method for population-based surveys. Rapid surveys are no exception‚ since they too use a more complex sampling scheme. So why should we be concerned with simple random sampling? The main reason is to learn the theory of sampling. Simple random sampling is the basic selection process of sampling and is easiest to understand
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WHAT IS A RANDOM VARIABLE? A random variable assigns a number to each outcome of a random circumstance‚ or‚ equivalently‚ a random variable assigns a number to each unit in a population. It is easier to create rules for broad classes of situations and then identify how a specific example fits into a class than it is to create rules for each specific example. We can employ this strategy quite effectively for working with a wide variety of situations Involving probability and random outcomes. We
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Thiss thesis contains work on reinforced random walks‚ the reconstruction of random sceneriess observed along a random walk path‚ and the length of a longest increasing subsequencee in a random permutation. In this introduction‚ I will survey some of the work inn the area and describe my results. Furthermore I will explain how all three subjects fit intoo the framework of random walks in stochastic surroundings. Section 1 is dedicated to reinforcedd random walks. Section 2 describes scenery reconstruction
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Selected Answer: .5006 . Question 5 .5 out of 5 points An automotive center keeps tracks of customer complaints received each week. The probability distribution for complaints can be represented as a table or a graph‚ both shown below. The random variable xi represents the number of complaints‚ and p(xi) is the probability of receiving xi complaints. xi 0 1 2 3 4 5 6 p(xi) .15 .1 .28 .20 .10 .10 .07 What is the expected number of complaints received per week? Round
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continuous random variable because the time is being measured. All possible results for the variable time (t) would be greater than > 0. b) The weight of a T-bone steak is a continuous random variable because the weight of the steak is measured. All the possible results for the weight of the T-bone steak would be positive numbers making the variable weight (w) > greater than 0. c) The number of free throw attempts before the first shot is made is a discrete random variable
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Modelling 2 Week 3: Discrete Random Variables Stephen Bush Department of Mathematical Sciences MM2: Statistics - Week 3 - 1 Random Variables • Reference: Devore § 3.1 – 3.5 • Definitions: • An experiment is any process of obtaining one outcome where the outcome is uncertain. • A random variable is a numerical variable whose value can change from one replicate of the experiment to another. • Sample means and sample standard deviations are random variables • They are different
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Discrete Random Variables: Homework Exercise 1 Complete the PDF and answer the questions. |X |P(X = x) |X(P(X = x) | |0 |0.3 | | |1 |0.2 | | |2 | | | |3 |0.4 | | a. Find the probability that X = 2. b. Find the expected value. Exercise 2 Suppose that you are offered the following “deal.” You roll a die. If you
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a 3-point scale. Let X denote the rating given by expert A and Y denote the rating given by B. The following table gives the joint distribution for X and Y . 4.12 If a dealer’s proﬁt‚ in units of $5000‚ on a new automobile can be looked upon as a random variable X having the density function fx= 21-x‚0<x<10‚elsewhere ﬁnd the average proﬁt per automobile. 4.14 Find the proportion X of individuals who can be expected to respond to a certain mail-order solicitation if X has the density function
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are lifted at a time. Compute the probability of an accident. [0.1587] 3. A soft –drink vending machine is set so that the amount of drink dispensed is a random variable with a mean of 200 milliliters and a standard deviation of 15 milliliters. What is the probability that the mean amount dispensed in a random sample of size 36 is at least 204 milliliters? [0.0548] 4. An automatic machine in a manufacturing process is operating properly if the lengths of an important
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binomial distribution for the random variable X? Give reasons for your answer in each case. (a) An auto manufacturer chooses one car from each hour’s production for a detailed quality inspection. One variable recorded is the count X of finish defects (dimples‚ ripples‚ etc.) in the car’s paint. No: There is no fixed n (i.e.‚ there is no definite upper limit on the number of defects). (b) The pool of potential jurors for a murder case contains 100 persons chosen at random from the adult residents
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