Let $N\sim\mathrm{Pois}(\lambda_1)$ be the number of movies that will be released next year. Suppose that for each movie the number of tickets sold is $T\sim\mathrm{Pois}(\lambda_2)$, independently. Find the mean and variance of the number of movie tickets that will be sold next year. The expectation is quite easy to find. $E(NT)=E(E(N|T))=\lambda_1×\lambda_2$. But I am stuck at calculating variance. Any help would be appreciated!
2026-03-26 19:20:53.1774552853
Expectation and variance of number of movie tickets
772 Views Asked by Bumbble Comm https://math.techqa.club/user/bumbble-comm/detail At
1
There are 1 best solutions below
Related Questions in PROBABILITY-THEORY
- Is this a commonly known paradox?
- What's $P(A_1\cap A_2\cap A_3\cap A_4) $?
- Another application of the Central Limit Theorem
- proving Kochen-Stone lemma...
- Is there a contradiction in coin toss of expected / actual results?
- Sample each point with flipping coin, what is the average?
- Random variables coincide
- Reference request for a lemma on the expected value of Hermitian polynomials of Gaussian random variables.
- Determine the marginal distributions of $(T_1, T_2)$
- Convergence in distribution of a discretized random variable and generated sigma-algebras
Related Questions in CONDITIONAL-EXPECTATION
- Expectation involving bivariate standard normal distribution
- Show that $\mathbb{E}[Xg(Y)|Y] = g(Y) \mathbb{E}[X|Y]$
- How to prove that $E_P(\frac{dQ}{dP}|\mathcal{G})$ is not equal to $0$
- Inconsistent calculation for conditional expectation
- Obtaining expression for a conditional expectation
- $E\left(\xi\text{|}\xi\eta\right)$ with $\xi$ and $\eta$ iid random variables on $\left(\Omega, \mathscr{F}, P\right)$
- Martingale conditional expectation
- What is $\mathbb{E}[X\wedge Y|X]$, where $X,Y$ are independent and $\mathrm{Exp}(\lambda)$- distributed?
- $E[X|X>c]$ = $\frac{\phi(c)}{1-\Phi(c)}$ , given X is $N(0,1)$ , how to derive this?
- Simple example dependent variables but under some conditions independent
Related Questions in POISSON-DISTRIBUTION
- Given $X$ Poisson, and $f_{Y}(y\mid X = x)$, find $\mathbb{E}[X\mid Y]$
- Mean and variance of a scaled Poisson random variable
- Conditional expectation poisson distribution
- Consistent estimator for Poisson distribution
- Fitting Count Data with Poisson & NBD
- How to prove that $P(X = x-1) \cdot P(X=x+1) \le (P(X=x))^2$ for a Poisson distribution
- Expected value of geometric mean of Poisson random variables
- Show $\mu$ is unbiased and find $\mathsf{Var}(\mu)$
- $E[\min(X,2)]$ for$ X\sim Po(3)$
- High risk probability
Related Questions in VARIANCE
- Proof that $\mathrm{Var}\bigg(\frac{1}{n} \sum_{i=1}^nY_i\bigg) = \frac{1}{n}\mathrm{Var}(Y_1)$
- $\{ X_{i} \}_{i=1}^{n} \thicksim iid N(\theta, 1)$. What is distribution of $X_{2} - X_{1}$?
- Reason generalized linear model
- Variance of $\mathrm{Proj}_{\mathcal{R}(A^T)}(z)$ for $z \sim \mathcal{N}(0, I_m)$.
- Variance of a set of quaternions?
- Is the usage of unbiased estimator appropriate?
- Stochastic proof variance
- Bit of help gaining intuition about conditional expectation and variance
- Variance of $T_n = \min_i \{ X_i \} + \max_i \{ X_i \}$
- Compute the variance of $S = \sum\limits_{i = 1}^N X_i$, what did I do wrong?
Trending Questions
- Induction on the number of equations
- How to convince a math teacher of this simple and obvious fact?
- Find $E[XY|Y+Z=1 ]$
- Refuting the Anti-Cantor Cranks
- What are imaginary numbers?
- Determine the adjoint of $\tilde Q(x)$ for $\tilde Q(x)u:=(Qu)(x)$ where $Q:U→L^2(Ω,ℝ^d$ is a Hilbert-Schmidt operator and $U$ is a Hilbert space
- Why does this innovative method of subtraction from a third grader always work?
- How do we know that the number $1$ is not equal to the number $-1$?
- What are the Implications of having VΩ as a model for a theory?
- Defining a Galois Field based on primitive element versus polynomial?
- Can't find the relationship between two columns of numbers. Please Help
- Is computer science a branch of mathematics?
- Is there a bijection of $\mathbb{R}^n$ with itself such that the forward map is connected but the inverse is not?
- Identification of a quadrilateral as a trapezoid, rectangle, or square
- Generator of inertia group in function field extension
Popular # Hahtags
second-order-logic
numerical-methods
puzzle
logic
probability
number-theory
winding-number
real-analysis
integration
calculus
complex-analysis
sequences-and-series
proof-writing
set-theory
functions
homotopy-theory
elementary-number-theory
ordinary-differential-equations
circles
derivatives
game-theory
definite-integrals
elementary-set-theory
limits
multivariable-calculus
geometry
algebraic-number-theory
proof-verification
partial-derivative
algebra-precalculus
Popular Questions
- What is the integral of 1/x?
- How many squares actually ARE in this picture? Is this a trick question with no right answer?
- Is a matrix multiplied with its transpose something special?
- What is the difference between independent and mutually exclusive events?
- Visually stunning math concepts which are easy to explain
- taylor series of $\ln(1+x)$?
- How to tell if a set of vectors spans a space?
- Calculus question taking derivative to find horizontal tangent line
- How to determine if a function is one-to-one?
- Determine if vectors are linearly independent
- What does it mean to have a determinant equal to zero?
- Is this Batman equation for real?
- How to find perpendicular vector to another vector?
- How to find mean and median from histogram
- How many sides does a circle have?
Given, $N$=Number of movies that will be released next year
and $T$=number of tickets sold for each movie
we are also given that
$$N \sim \mathrm{Pois}({\lambda}_1)\quad and\quad T\sim \mathrm{Pois}({\lambda}_2)\; independently$$ We now need to find out the mean and variance of the number of movie tickets that will be sold next year.
Number of movie tickets that will be sold next year=NT
Thus we have to find E[NT] and Var[NT]
(since N and T are independent Random variables)
$$E[NT]=E[N]E[T]={\lambda_1 \lambda_2}$$
(since $N \sim \mathrm{Pois}({\lambda}_1)\mbox{ and }\quad T\sim \mathrm{Pois}({\lambda}_2)\;\;E[N]=\lambda_1\;,E[T]= \lambda_2) $
Now;
$$\text{Var}[NT]=E[(NT)^2]-(E[NT])^2=E[N^2]E[T^2]-(E[N]E[T])^2$$ [since $N$ and $T$ are independent $Random \;variables$ then $g(N)$ and $f(T)$ are independent.
Thus $N^2$ and $T^2$ are independent Random variables]
Refer:Are functions of independent variables also independent?
Now just evaluate$E[N^2]\;and\;E[T^2]$.Then you are done