At Westonci.ca, we connect you with experts who provide detailed answers to your most pressing questions. Start exploring now! Join our platform to connect with experts ready to provide detailed answers to your questions in various areas. Discover detailed answers to your questions from a wide network of experts on our comprehensive 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 on our site. Don't hesitate to return whenever you have more questions or need further clarification. We hope you found this helpful. Feel free to come back anytime for more accurate answers and updated information. Thank you for trusting Westonci.ca. Don't forget to revisit us for more accurate and insightful answers.