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ENPR300 Probability & Random Process Assignment 04, 2025-26 | University of Dubai

Build a creative direct and digital marketing plan that balances customer acquisition against retention, then justify every tactic you select and the schedule you propose.

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March 24, 2026
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Assignment brief

Course Code: ENPR 300           Course Title: Probability & Random ProcessSemester: Fall           Academic year : 2025/2026Assessment Instrument: Assignment04       Date: November 27, 2025Course Instructor: Dr. Diana Dawoud     College/Department: CEIT/ Electrical Engineering
Assignment University of Dubai Campaign schedule

Marks are carried by the justification, not the list. A plan that names ten channels without defending any of them sits at the bottom of the band; one that defends four sits well above it.

ENPR300 Assignment 04

Course Code: ENPR 300           
Course Title: Probability & Random Process
Semester: Fall           
Academic year : 2025/2026
Assessment Instrument: Assignment04       
Date: November 27, 2025
Course Instructor: Dr. Diana Dawoud     
College/Department: CEIT/ Electrical Engineering

Learning Outcomes

This instrument assesses the following Course Learning Outcomes (CLO):

Course Learning Outcomes (CLO)* Question Mark
4    Demonstrate understanding the law of large numbers, the central limit theorem. ?? /2
5    Apply the statistical concepts to solve relevant engineering problems. 2 /2

* Linkages to Program Outcomes and Concentration Outcomes are provided in the Syllabus.

Question Q1 Q2 Total
Grade      
Out of 22/2 17/2 /2

Exam Instructions

  • Answer all questions
  • Time allowed is One Week.

Question 1 [22 points]

(1) Let {Xi}i=1 be a sequence of i.i.d. Bernoulli random variables with

P(Xi = 1) = 0.3,    P(Xi = 0) = 0.7.

Define the sample mean

n = (1/n) Σi=1n Xi.

A. [3 points] Find E[Xi] and Var(Xi).

B. [3 points] According to the Law of Large Numbers, what happens to X̄n as n → ∞? Be precise.

C. [4 points] Suppose you observe X̄50 = 0.46 for n = 50 trials. Comment on whether this contradicts the LLN. Justify your answer.

(2) The lifetime (in hours) of a certain type of electronic component has mean μ = 1200 hours and standard deviation σ = 300 hours. Assume lifetimes of different components are independent and identically distributed.

A. [4 points] Let X̄25 be the sample mean lifetime of a random sample of n = 25 components. Using the Central Limit Theorem, approximate

P(X̄25 > 1300).

B. [4 points] For a sample of n = 100 components, approximate

P(1150 < X̄100 < 1250).

C. [4 points] Comment on how increasing the sample size from n = 25 to n = 100 affects the variability of the sample mean and the accuracy of the CLT approximation.

Question 2 [17 points]

(1) In a baseband communication receiver, the input noise is modeled as an ideal white Gaussian noise process with two–sided PSD

SX(f) = N₀/2    for all f ∈ ℝ.

The receiver front–end includes an ideal lowpass filter with bandwidth B Hz and unity gain in the passband.

A. [3 points] Derive the output noise PSD SY(f) at the filter output.

B. [3 points] Compute the output noise power (i.e., variance) σY2 in terms of N₀ and B.

C. [3 points] Explain qualitatively how changing the bandwidth B affects the noise power at the receiver input, and discuss the engineering trade–off between noise reduction and signal distortion.

(2) Consider the following three random processes used to model different types of signals in a communication system:

1. X₁(t) with

E[X₁(t)] = 0,    RX₁(t₁, t₂) = σ² e−|t₁ − t₂|.

2. X₂(t) with

E[X₂(t)] = A cos(2πf₀t),    RX₂(t₁, t₂) = σ² e−|t₁ − t₂|.

3. X₃(t) with

E[X₃(t)] = 0,    RX₃(t₁, t₂) = σ² e−|t₁| e−|t₂|.

A. [6 points] For each process, decide whether it is wide-sense stationary (WSS). Justify your answer in terms of the mean and the autocorrelation function.

B. [2 points] From an engineering perspective, which of these processes is most appropriate to model stationary background noise? Explain briefly.

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