Magnetic surveying is one technique used by archaeologists to determine anomalies arising from variations in magnetic susceptibility. Unusual changes in magnetic susceptibility might (or might not) indicate an important archaeological discovery. Let x be a random variable that represents a magnetic susceptibility (MS) reading for a randomly chosen site at an archaeological research location. A random sample of 120 sites gave the readings shown in the table below. Magnetic Susceptibility Readings, centimeter-gram-second × 10-6 (cmg × 10-6) Magnetic Susceptibility OsSx< 10 10 sx < 20 20 sx < 30 Number of Readings Estimated Probability 24/120 = 0.20 60/120 = 0.50 24/120 = 0.20 6/120 = 0.05 6/120 = 0.05 archaeological point of view. Let us say you take readings at n = 12 sites chosen at random. Letr Comment "cool" 24 "neutral" "warm" 60 24 "very interesting" "hot spot" 30 sx< 40 40 SX Suppose a reading between 30 and 40 is called "very interesting" from be a binomial random variable that represents the number of "very interesting" readings from these 12 sites. n USE SALT (a) Let us call "very interesting" a binomial success. Use the table above to find p, the probability of success on a single trial, where p = P(success) = P(30 s x < 40). (b) What is the expected value u and standard deviation o for the random variable r? (Round your standard deviation to three decimal places.) (c) What is the probability that you will find at least one "very interesting" reading in the 12 sites? (Round your answer to three decimal places.) (d) What is the probability that you will find fewer than three "very interesting" readings in the 12 sites? (Round your answer to three decimal places.)
Magnetic surveying is one technique used by archaeologists to determine anomalies arising from variations in magnetic susceptibility. Unusual changes in magnetic susceptibility might (or might not) indicate an important archaeological discovery. Let x be a random variable that represents a magnetic susceptibility (MS) reading for a randomly chosen site at an archaeological research location. A random sample of 120 sites gave the readings shown in the table below. Magnetic Susceptibility Readings, centimeter-gram-second × 10-6 (cmg × 10-6) Magnetic Susceptibility OsSx< 10 10 sx < 20 20 sx < 30 Number of Readings Estimated Probability 24/120 = 0.20 60/120 = 0.50 24/120 = 0.20 6/120 = 0.05 6/120 = 0.05 archaeological point of view. Let us say you take readings at n = 12 sites chosen at random. Letr Comment "cool" 24 "neutral" "warm" 60 24 "very interesting" "hot spot" 30 sx< 40 40 SX Suppose a reading between 30 and 40 is called "very interesting" from be a binomial random variable that represents the number of "very interesting" readings from these 12 sites. n USE SALT (a) Let us call "very interesting" a binomial success. Use the table above to find p, the probability of success on a single trial, where p = P(success) = P(30 s x < 40). (b) What is the expected value u and standard deviation o for the random variable r? (Round your standard deviation to three decimal places.) (c) What is the probability that you will find at least one "very interesting" reading in the 12 sites? (Round your answer to three decimal places.) (d) What is the probability that you will find fewer than three "very interesting" readings in the 12 sites? (Round your answer to three decimal places.)
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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Contingency Table
A contingency table can be defined as the visual representation of the relationship between two or more categorical variables that can be evaluated and registered. It is a categorical version of the scatterplot, which is used to investigate the linear relationship between two variables. A contingency table is indeed a type of frequency distribution table that displays two variables at the same time.
Binomial Distribution
Binomial is an algebraic expression of the sum or the difference of two terms. Before knowing about binomial distribution, we must know about the binomial theorem.
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