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Module 4 Practice Problems

20 Comprehensive Problems • Covers All Module 4 Topics

How to Use These Practice Problems

Problems 1-5: Random Variables & Probability Distributions

Problem 1 Discrete vs ContinuousEasy

Classify each random variable as discrete (D) or continuous (C):

  1. Number of customers entering a store in one hour
  2. Weight of a randomly selected apple
  3. Number of defective items in a batch of 50
  4. Time until the next phone call
  5. Number of correct answers on a 20-question quiz
Hint: Discrete = counting, Continuous = measuring

Solution:

a) Discrete - counting customers (0, 1, 2, ...)

b) Continuous - measuring weight (any positive value)

c) Discrete - counting defects (0, 1, 2, ..., 50)

d) Continuous - measuring time (any positive value)

e) Discrete - counting correct answers (0, 1, 2, ..., 20)

Problem 2 Valid Distribution?Easy

Is this a valid probability distribution? X: 0, 1, 2, 3 with P(X=x): 0.1, 0.4, 0.3, 0.2

Hint: Check both conditions: 0 ≤ P(X) ≤ 1 and Σ P(X) = 1

Solution:

Yes, this is valid.

Check 1: All probabilities between 0 and 1? (0.1, 0.4, 0.3, 0.2 all satisfy this)

Check 2: Do they sum to 1? 0.1 + 0.4 + 0.3 + 0.2 = 1.0

Both conditions satisfied → valid distribution

Problem 3 Calculate Expected ValueMedium

Given the distribution: X: 1, 2, 3, 4 with P(X=x): 0.2, 0.3, 0.4, 0.1. Find E(X).

Hint: E(X) = Σ [x · P(X=x)]

Solution:

E(X) = Σ [x · P(X=x)]

= 1(0.2) + 2(0.3) + 3(0.4) + 4(0.1)

= 0.2 + 0.6 + 1.2 + 0.4

= 2.4

Interpretation: The expected (average) value is 2.4

Problem 4 Variance CalculationMedium

Using the same distribution from Problem 3, calculate Var(X) and σ.

Hint: Var(X) = Σ [(x - μ)² · P(X=x)], use μ = 2.4 from Problem 3

Solution:

μ = 2.4 (from Problem 3)

Var(X) = (1-2.4)²(0.2) + (2-2.4)²(0.3) + (3-2.4)²(0.4) + (4-2.4)²(0.1)

= 1.96(0.2) + 0.16(0.3) + 0.36(0.4) + 2.56(0.1)

= 0.392 + 0.048 + 0.144 + 0.256

= 0.84

σ = √0.84 = 0.917

Problem 5 Create a DistributionMedium

A game involves rolling a die. You win $x where x is the number shown. Find the probability distribution and E(X).

Hint: Fair die means each outcome has probability 1/6

Solution:

Probability Distribution:

x123456
P(X=x)1/61/61/61/61/61/6

Expected Winnings:

E(X) = 1(1/6) + 2(1/6) + 3(1/6) + 4(1/6) + 5(1/6) + 6(1/6)

= (1+2+3+4+5+6)/6 = 21/6 = $3.50

Problems 6-10: Binomial Conditions

Problem 6 Identify BinomialEasy

Which scenario is binomial? Explain why or why not.

A: Draw 10 cards from a deck without replacement, count aces.

B: Roll a die 15 times, count number of 6's.

Hint: Check FTIC conditions

Solution:

Scenario A: NOT binomial

  • Fixed n = 10
  • Two outcomes (ace/not)
  • NOT independent (cards not replaced)
  • Probability changes (P changes after each draw)

Scenario B: BINOMIAL

  • Fixed n = 15
  • Two outcomes (6 or not 6)
  • Independent rolls
  • Constant p = 1/6

Problem 7 Identify ParametersEasy

A basketball player makes 75% of free throws and shoots 12 times. If X = number made, identify n and p.

Solution:

n = 12 (number of trials/shots)

p = 0.75 (probability of success/make)

This is binomial: Fixed trials, two outcomes (make/miss), independent shots, constant 75% success rate.

Problem 8 Binomial ConditionsMedium

A student guesses on a 20-question true/false test. Verify this is binomial and state n and p.

Solution:

Verification:

  • Fixed: n = 20 questions
  • Two outcomes: correct or incorrect
  • Independent: answering one doesn't affect others
  • Constant: p = 0.5 (guessing gives 50% chance)

Yes, this is binomial with n = 20, p = 0.5

Problem 9 NOT BinomialMedium

Flip a coin until you get heads. Count number of flips. Is this binomial? Why or why not?

Solution:

NOT binomial.

Reason: The number of trials is NOT fixed. You keep flipping until you get heads, which could be 1 flip, 10 flips, or 100 flips. This violates the "fixed n" condition.

Note: This follows a geometric distribution instead.

Problem 10 Quality Control ScenarioMedium

A factory produces items with a 4% defect rate. Inspector randomly selects 30 items for testing. Is X = number of defects binomial? Identify n and p.

Solution:

Yes, binomial (assuming random selection with replacement or very large population).

n = 30 (items inspected)

p = 0.04 (defect rate)

Conditions satisfied: Fixed 30 trials, two outcomes (defect/OK), independent selections, constant 4% rate.

Problems 11-15: Binomial Probability Calculations

Problem 11 Exact ProbabilityMedium

Flip a fair coin 4 times. Find P(X = 2 heads).

Hint: Use P(X=k) = C(n,k) × p^k × (1-p)^(n-k)

Solution:

n = 4, p = 0.5, k = 2

C(4,2) = 4!/(2!2!) = 6

P(X = 2) = 6 × (0.5)² × (0.5)²

= 6 × 0.25 × 0.25

= 6 × 0.0625

= 0.375 or 37.5%

Problem 12 Multiple Choice TestHard

A 10-question multiple choice test has 4 choices per question. If a student guesses randomly, find P(exactly 3 correct).

Hint: p = 1/4 for random guessing on 4-choice questions

Solution:

n = 10, p = 0.25, k = 3

C(10,3) = 10!/(3!7!) = 120

P(X = 3) = 120 × (0.25)³ × (0.75)⁷

= 120 × 0.015625 × 0.1335

= 120 × 0.002086

0.250 or 25.0%

Problem 13 Mean and SDMedium

For a binomial distribution with n = 50 and p = 0.3, find μ and σ.

Hint: μ = np, σ = √(np(1-p))

Solution:

Mean: μ = np = 50 × 0.3 = 15

Standard Deviation:

σ = √(np(1-p)) = √(50 × 0.3 × 0.7) = √10.5 ≈ 3.24

Interpretation: Expect about 15 successes on average, with typical variation of 3.24.

Problem 14 At MostHard

Roll a die 3 times. Find P(at most 1 six). [P(X ≤ 1)]

Hint: P(X ≤ 1) = P(X=0) + P(X=1)

Solution:

n = 3, p = 1/6 ≈ 0.1667

P(X = 0): C(3,0) × (1/6)⁰ × (5/6)³ = 1 × 1 × 125/216 = 125/216

P(X = 1): C(3,1) × (1/6)¹ × (5/6)² = 3 × (1/6) × (25/36) = 75/216

P(X ≤ 1): 125/216 + 75/216 = 200/216 ≈ 0.926 or 92.6%

Problem 15 At LeastHard

A treatment is 80% effective. If 5 patients receive it, find P(at least 4 improve). [P(X ≥ 4)]

Hint: P(X ≥ 4) = P(X=4) + P(X=5)

Solution:

n = 5, p = 0.8

P(X = 4): C(5,4) × (0.8)⁴ × (0.2)¹ = 5 × 0.4096 × 0.2 = 0.4096

P(X = 5): C(5,5) × (0.8)⁵ × (0.2)⁰ = 1 × 0.32768 × 1 = 0.32768

P(X ≥ 4): 0.4096 + 0.32768 ≈ 0.737 or 73.7%

Problems 16-20: Applications

Problem 16 Technology UseMedium

For binomial with n=100, p=0.15, use technology notation to find P(X ≥ 20).

Hint: P(X ≥ 20) = 1 - P(X ≤ 19)

Solution:

TI-84: 1 - binomcdf(100, 0.15, 19)

Excel: =1 - BINOM.DIST(19, 100, 0.15, TRUE)

R: 1 - pbinom(19, 100, 0.15)

Result: ≈ 0.113 or 11.3%

Problem 17 Normal Approximation CheckMedium

Can you use normal approximation for n=25, p=0.6? Check conditions.

Hint: Need np ≥ 10 AND n(1-p) ≥ 10

Solution:

Check: np = 25 × 0.6 = 15 ≥ 10

Check: n(1-p) = 25 × 0.4 = 10 ≥ 10

Yes, normal approximation is appropriate.

Use X ~ N(μ=15, σ=√6 ≈ 2.45)

Problem 18 Expected DefectsEasy

A factory produces 1000 items per day with 2% defect rate. How many defects expected per day?

Solution:

n = 1000, p = 0.02

μ = np = 1000 × 0.02 = 20 defects

σ = √(1000 × 0.02 × 0.98) = √19.6 ≈ 4.43 defects

Expect about 20 defects per day, with typical variation of 4-5 defects.

Problem 19 Probability RangeHard

For binomial n=8, p=0.4, find P(2 ≤ X ≤ 5).

Hint: Sum probabilities from X=2 to X=5

Solution:

P(2 ≤ X ≤ 5) = P(X=2) + P(X=3) + P(X=4) + P(X=5)

Using binomial formula or technology:

P(X=2) = C(8,2)(0.4)²(0.6)⁶ ≈ 0.209

P(X=3) = C(8,3)(0.4)³(0.6)⁵ ≈ 0.279

P(X=4) = C(8,4)(0.4)⁴(0.6)⁴ ≈ 0.232

P(X=5) = C(8,5)(0.4)⁵(0.6)³ ≈ 0.124

Sum: 0.209 + 0.279 + 0.232 + 0.124 ≈ 0.844 or 84.4%

Problem 20 Comprehensive ProblemHard

A pharmaceutical company tests a drug on 60 patients. Historical data shows 70% success rate.

a) Verify binomial conditions and state n and p

b) Find expected number of successes and standard deviation

c) Find P(at least 45 patients improve)

Hint: Part (c) benefits from technology or normal approximation

Solution:

Part a: Binomial conditions verified:

  • Fixed n = 60 patients
  • Two outcomes (improve/don't improve)
  • Independent (assuming each patient responds independently)
  • Constant p = 0.7

Part b: Expected value and SD

μ = np = 60 × 0.7 = 42 patients

σ = √(np(1-p)) = √(60 × 0.7 × 0.3) = √12.6 ≈ 3.55 patients

Part c: P(X ≥ 45)

Check normal approximation: np=42≥10, n(1-p)=18≥10

Using technology: P(X ≥ 45) = 1 - binomcdf(60, 0.7, 44) ≈ 0.200 or 20.0%

Interpretation: About 20% chance that 45 or more patients improve.

Completed All 20 Problems!

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