The following table represents the joint probability distribution of the number of people waiting in line at Food Truck X and Food Truck Y. Y=1 Y=2 Y=3 Y=4 X=0 1/5 1/15 2/45 X=1 2/45 1/15 1/15 X=2 1/15 1/60 1/15 X=3 4/45 1/60 1/15 4/45 a. Determine the value of C (probability of no one waiting in line at Food Truck X and 2 people waiting in line at Food Truck Y) Answer in fraction or in exact decimals b. What is the probability of equal number of people waiting in line at both Food Truck X and Y? Answer in fraction or rounded to four decimal places

The following table represents the joint probability distribution of the number of people waiting in line at Food Truck X and Food Truck Y. Y=1 Y=2 Y=3 Y=4 X=0 1/5 1/15 2/45 X=1 2/45 1/15 1/15 X=2 1/15 1/60 1/15 X=3 4/45 1/60 1/15 4/45 a. Determine the value of C (probability of no one waiting in line at Food Truck X and 2 people waiting in line at Food Truck Y) Answer in fraction or in exact decimals b. What is the probability of equal number of people waiting in line at both Food Truck X and Y? Answer in fraction or rounded to four decimal places

Name: 1. Procedures not needed, give me the answers only 5:10 PM G G67 .The
Uploaded: 2024-03-20T07:07:12.000Z
Channel: Bartleby
Description: 1. Procedures not needed, give me the answers only 5:10 PM G G67 .The following table represents the jointprobability distribution of the number of peoplewaiting in line at Food Truck X and Food Truck Y.Y=1Y=2Y=3Y=4X=01/51/152/45X=12/451/151/15X=21/1

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

See similar textbooks

Similar questions

Let X be a stochastic variable with a N(8,16) distribution. Suppose P(X<b) = 0.719. Calculate the value of b
Let m(t) = 1/10 + (1/10)et + (3/10)e2t + (1/2)e3t be the moment gen- erating function of random variable Y . Find the following a) E(Y), b) E(Y2), c) V(Y), d) the probability distribution of Y .
1. Consider the Gaussian distribution N (m, σ2).(a) Show that the pdf integrates to 1.(b) Show that the mean is m and the variance is σ.
Consider a real random variable X with zero mean and variance σ2X . Suppose that wecannot directly observe X, but instead we can observe Yt := X + Wt, t ∈ [0, T ], where T > 0 and{Wt : t ∈ R} is a WSS process with zero mean and correlation function RW , uncorrelated with X.Further suppose that we use the following linear estimator to estimate X based on {Yt : t ∈ [0, T ]}:ˆXT =Z T0h(T − θ)Yθ dθ,i.e., we pass the process {Yt} through a causal LTI filter with impulse response h and sample theoutput at time T . We wish to design h to minimize the mean-squared error of the estimate.a. Use the orthogonality principle to write down a necessary and sufficient condition for theoptimal h. (The condition involves h, T , X, {Yt : t ∈ [0, T ]}, ˆXT , etc.)b. Use part a to derive a condition involving the optimal h that has the following form: for allτ ∈ [0, T ],a =Z T0h(θ)(b + c(τ − θ)) dθ,where a and b are constants and c is some function. (You must find a, b, and c in terms ofthe information…
The time, X, to infection for Eagle Flu in minutes, after coming into contact with the virus has cumulative distribution F with the following definition:F(x) = 0 for x < 1 and F(x)= (3/2)-(3/2x) for 0 ≤ x ≤ 3 j) What is the variance of X? k) What is the probability that X is more than 0.1 above its expected value?
x 1 2 3 4 5 P(X=x) 0.21 0.197 0.2 0.173 0.22 use the information in Table 3 to determine the probability that,from a sample of 10 randomly selected adults, exactly 3 of them exceeds the required standard for practices towards COFLU-20. (ii) identifying a suitable probability distribution to be used to find the probability that exactly three (3) adults, out of 10, exceeds the required standard for practices towards COFLU-20.State the value(s) of the parameter(s) for this distribution.
The following table represents the joint probability distribution of the number of letters dropped daily in mailbox X and mailbox Y. Y=2 Y=3 Y=4 Y=5 PX(X=x) X=0 0.3 0.03 0.2 0.54 X=1 0.04 0.025 0.04 0.03 X=2 0.07 0.1 0.01 X=3 0.025 0.02 0.02 PY(Y=y) 0.135 a. Determine the probability of 3 letters being dropped in mailbox X and 4 letters being dropped at mailbox Y. Answer in fraction or in exact decimals unanswered b. What is the probability of at least 3 letters being dropped in both mailboxes X and Y. Answer in fraction or in exact decimals unanswered c. If at least 4 letters were dropped in mailbox Y, what is the probability of 2 letters being dropped in mailbox X? Answer in fraction or in exact decimals unanswered
. Determine the value for c and the covariance and correlation for the joint probability mass function fXY (x, y ) = c (x + y) for x = 75, 75+1, 75+2 and y = 75, 75+1, 75+2. (Round up after multiplying) (Sum of probabilities = ?)
The following table represents the joint probability distribution of the number of people waiting in line at Food Truck X and Food Truck Y. Y=0 Y=1 Y=2 Y=3 PX(X=x) X=0 0.32 0.06 0.025 0.01 X=1 0.02 0.05 0.07 X=2 0.025 0.05 0.105 X=3 0.026 0.2 0.05 0.024 PY(Y=y) 0.114 a. Determine the probability of 3 people waiting in line at Food Truck Y and 2 people waiting in line at Food Truck X. b. What is the probability of at most 3 people waiting in line at both Food Truck X and Y combined? c. If at least 2 people are waiting in line at Food Truck X, what is the probability that 3 people are waiting in line at Food Truck Y?
(b) Let Z be a discrete random variable with E(Z) = 0. Does it necessarily follow that E(Z³) = 0? If yes, give a proof; if no, give a counterexample.
A gasoline wholesale distributor has bulk storage tanks that hold xed supplies andare lled every Monday. Of interest to the wholesaler is the proportion of this supplythat is sold during the week. Over many weeks of observation, the distributor foundthat this proportion could be modeled by a beta distribution with = 4 and = 2.Find the probability that the wholesaler will sell at least 90% of her stock in a givenweek.
1. Suppose that the amount X dispensed by a beverage-dispensing machine has a uniform probability distribution on [a, b] (in Ounces). (a) Given a and b, find x0 such that P(X < μ+x0) = 0.90, where μ = E[X]. (b)Given an i.i.d. sample X1, . . . , X200, explain how to estimate θ = b − a. Is the proposed estimator unbiased?