Math Problem Statement
Solution
The problem provided can be described as follows:
Let be the number of people who make a pledge during a drive for contributions to a school. There are 500 people contacted in the drive, and each has a probability of 0.15 of making a pledge, independent of one another. The task is to estimate .
Step 1: Model Selection
Since we are dealing with independent trials (500 people) and each trial has a constant probability of success (0.15), , the number of people making pledges, follows a binomial distribution. That is: Where:
- is the number of people contacted,
- is the probability of making a pledge.
Step 2: Approximation with Normal Distribution
For large , the binomial distribution can be approximated by a normal distribution using the Central Limit Theorem. The parameters of the approximating normal distribution are:
Thus, can be approximated by , a normal distribution with mean 75 and standard deviation 7.98.
Step 3: Standardization and Probability Calculation
We are asked to find . To compute this, we standardize the value of 80:
Now, we need to find . Using standard normal distribution tables or a calculator:
Final Answer:
Let me know if you would like more details on the steps or calculations.
Here are 5 related questions to expand your understanding:
- How would the result change if the probability of pledging was 0.20 instead of 0.15?
- Can you explain why the normal approximation works well for large sample sizes in binomial distributions?
- How would you calculate the exact binomial probability without using the normal approximation?
- What would happen to the probability if we wanted ?
- How do we interpret the result of in terms of the school's fundraising campaign?
Tip: For binomial problems with large and moderate , normal approximations can simplify calculations greatly.
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Math Problem Analysis
Mathematical Concepts
Probability
Binomial Distribution
Normal Approximation
Central Limit Theorem
Formulas
Binomial distribution: X ~ Binomial(n, p)
Normal approximation: Z = (X - μ) / σ
Mean: μ = np
Variance: σ^2 = np(1 - p)
Standard deviation: σ = √(np(1 - p))
Theorems
Central Limit Theorem
Suitable Grade Level
Undergraduate Statistics/Probability
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