Westonci.ca is the best place to get answers to your questions, provided by a community of experienced and knowledgeable experts. Get detailed and accurate answers to your questions from a dedicated community of experts on our Q&A platform. Get precise and detailed answers to your questions from a knowledgeable community of experts on our Q&A platform.
Sagot :
Sure! Let's go through this step-by-step.
Step 1: Understanding the Binomial Distribution Formula
The binomial distribution formula is given by:
[tex]\[ P(r) = C(n, r) \cdot p^r \cdot (1 - p)^{n - r} \][/tex]
where:
- [tex]\( P(r) \)[/tex] is the probability of having exactly [tex]\( r \)[/tex] successes in [tex]\( n \)[/tex] trials.
- [tex]\( C(n, r) \)[/tex] is the binomial coefficient, which describes the number of ways to choose [tex]\( r \)[/tex] successes out of [tex]\( n \)[/tex] trials, and is calculated as [tex]\( \frac{n!}{r!(n-r)!} \)[/tex]
- [tex]\( p \)[/tex] is the probability of success on a single trial.
- [tex]\( (1 - p) \)[/tex] is the probability of failure on a single trial.
- [tex]\( n \)[/tex] is the total number of trials.
- [tex]\( r \)[/tex] is the number of successes we are interested in.
Step 2: Calculate the Binomial Coefficient [tex]\( C(n, r) \)[/tex]
Given [tex]\( n = 100 \)[/tex] and [tex]\( r = 2 \)[/tex], the binomial coefficient [tex]\( C(n, r) \)[/tex] is:
[tex]\[ C(100, 2) = \frac{100!}{2!(100-2)!} \][/tex]
This can be computed as:
[tex]\[ C(100, 2) = \frac{100 \times 99}{2 \times 1} = 4950 \][/tex]
Step 3: Compute the Probability [tex]\( P(r) \)[/tex]
Next, we need to plug in the values into the binomial distribution formula. Given [tex]\( p = 0.03 \)[/tex], [tex]\( n = 100 \)[/tex], and [tex]\( r = 2 \)[/tex], we get:
[tex]\[ P(2) = C(100, 2) \cdot (0.03)^2 \cdot (1 - 0.03)^{100 - 2} \][/tex]
[tex]\[ P(2) = 4950 \cdot (0.03)^2 \cdot (0.97)^{98} \][/tex]
Let's break it down:
1. Compute [tex]\( (0.03)^2 = 0.0009 \)[/tex]
2. Compute [tex]\( (0.97)^{98} \approx 0.0452 \)[/tex]
Now, multiply these together along with the binomial coefficient:
[tex]\[ P(2) = 4950 \cdot 0.0009 \cdot 0.0452 \approx 0.2252 \][/tex]
Step 4: Rounding the Final Answer
Finally, we round our result to four decimal places:
[tex]\[ P(2) \approx 0.2252 \][/tex]
So, for [tex]\( n = 100 \)[/tex], [tex]\( p = 0.03 \)[/tex], and [tex]\( r = 2 \)[/tex], the probability [tex]\( P(r) \)[/tex] is approximately 0.2252.
Step 1: Understanding the Binomial Distribution Formula
The binomial distribution formula is given by:
[tex]\[ P(r) = C(n, r) \cdot p^r \cdot (1 - p)^{n - r} \][/tex]
where:
- [tex]\( P(r) \)[/tex] is the probability of having exactly [tex]\( r \)[/tex] successes in [tex]\( n \)[/tex] trials.
- [tex]\( C(n, r) \)[/tex] is the binomial coefficient, which describes the number of ways to choose [tex]\( r \)[/tex] successes out of [tex]\( n \)[/tex] trials, and is calculated as [tex]\( \frac{n!}{r!(n-r)!} \)[/tex]
- [tex]\( p \)[/tex] is the probability of success on a single trial.
- [tex]\( (1 - p) \)[/tex] is the probability of failure on a single trial.
- [tex]\( n \)[/tex] is the total number of trials.
- [tex]\( r \)[/tex] is the number of successes we are interested in.
Step 2: Calculate the Binomial Coefficient [tex]\( C(n, r) \)[/tex]
Given [tex]\( n = 100 \)[/tex] and [tex]\( r = 2 \)[/tex], the binomial coefficient [tex]\( C(n, r) \)[/tex] is:
[tex]\[ C(100, 2) = \frac{100!}{2!(100-2)!} \][/tex]
This can be computed as:
[tex]\[ C(100, 2) = \frac{100 \times 99}{2 \times 1} = 4950 \][/tex]
Step 3: Compute the Probability [tex]\( P(r) \)[/tex]
Next, we need to plug in the values into the binomial distribution formula. Given [tex]\( p = 0.03 \)[/tex], [tex]\( n = 100 \)[/tex], and [tex]\( r = 2 \)[/tex], we get:
[tex]\[ P(2) = C(100, 2) \cdot (0.03)^2 \cdot (1 - 0.03)^{100 - 2} \][/tex]
[tex]\[ P(2) = 4950 \cdot (0.03)^2 \cdot (0.97)^{98} \][/tex]
Let's break it down:
1. Compute [tex]\( (0.03)^2 = 0.0009 \)[/tex]
2. Compute [tex]\( (0.97)^{98} \approx 0.0452 \)[/tex]
Now, multiply these together along with the binomial coefficient:
[tex]\[ P(2) = 4950 \cdot 0.0009 \cdot 0.0452 \approx 0.2252 \][/tex]
Step 4: Rounding the Final Answer
Finally, we round our result to four decimal places:
[tex]\[ P(2) \approx 0.2252 \][/tex]
So, for [tex]\( n = 100 \)[/tex], [tex]\( p = 0.03 \)[/tex], and [tex]\( r = 2 \)[/tex], the probability [tex]\( P(r) \)[/tex] is approximately 0.2252.
We appreciate your time. Please come back anytime for the latest information and answers to your questions. Thanks for using our service. We're always here to provide accurate and up-to-date answers to all your queries. Discover more at Westonci.ca. Return for the latest expert answers and updates on various topics.