The number of customers, X, arriving in an ATM in the afternoon can be modelled using a Poisson distribution with mean 6.5. Compute P(X>4) and SD of X. O P(X>4) = 0.2236, SD = 2.55 O P(X>4) = 0.1180, SD = 6.50 O P(X>4) = 0.7764, SD = 6.50 O P(X>4) = 0.2236, SD = 6.50 %3D O P(X>4) = 0.7764, SD = 2.55 %3D

The number of customers, X, arriving in an ATM in the afternoon can be modelled using a Poisson distribution with mean 6.5. Compute P(X>4) and SD of X. O P(X>4) = 0.2236, SD = 2.55 O P(X>4) = 0.1180, SD = 6.50 O P(X>4) = 0.7764, SD = 6.50 O P(X>4) = 0.2236, SD = 6.50 %3D O P(X>4) = 0.7764, SD = 2.55 %3D

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Description: +A portal.bartleby.com17/ 20>>>85 %O8 inin 11 secsThe number of customers, X, arriving in an ATM in the afternoon can be modelled using a Poissondistribution with mean 6.5. Compute P(X>4) and SD of X.O P(X>4) = 0.2236, SD = 2.55O P(X>4) = 0.1180, SD

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Chapter1: Combinatorial Analysis

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Concept explainers

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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O8 inin 11 secs
The number of customers, X, arriving in an ATM in the afternoon can be modelled using a Poisson
distribution with mean 6.5. Compute P(X>4) and SD of X.
O P(X>4) = 0.2236, SD = 2.55
O P(X>4) = 0.1180, SD = 6.50
O P(X>4) = 0.7764, SD = 6.50
O P(X>4) = 0.2236, SD = 6.50
O P(X>4) = 0.7764, SD = 2.55
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