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zysi [14]
1 year ago
5

Consider an urn containing 8 white balls, 7 red balls and 5 black balls.

Mathematics
2 answers:
Gnom [1K]1 year ago
8 0
  1. When 2 balls are randomly selected without replacement, the probability of getting two (2) white balls is 0.1474.
  2. When 5 balls are randomly selected without replacement, the probability of getting two (2) white balls is 0.3456.
  3. When 150 balls are randomly selected with replacement, the probability of getting at least seventy two (72) white balls is 0.7948.

<h3>How to determine the probabilities?</h3>

First of all, we would determine the total number of balls in the urn as follows:

Total number of balls = 8 + 7 + 5

Total number of balls = 20 balls.

Next, we would determine the probability of getting two (2) white balls without replacement:

P(2 white balls) = 8/20 × 7/19

P(2 white balls) = 2/5 × 7/19

P(2 white balls) = 0.1474.

<h3>Part 2.</h3>

When 5 balls are selected without replacement, the probability of getting two (2) white balls would be calculated as follows:

P = [⁵C₂ × (8/20 × 7/19) × (12/18 × 11/17 × 10/16)]

P = [5!/(2! × (5 - 2)!) × (2/5 × 7/19) × (2/3 × 11/17 × 5/4)]

P = [5!/(2! × 3!) × (2/5 × 7/19) × (2/3 × 11/17 × 5/4)]

P = [20/2 × (2/5 × 7/19) × (2/3 × 11/17 × 5/4)]

P = [10 × (2/5 × 7/19) × (2/3 × 11/17 × 5/4)]

P = 0.3456.

<h3>Part 3.</h3>

When 150 balls are randomly selected with replacement, the probability of getting at least seventy two (72) white balls would be calculated by applying binomial probability equation. Mathematically, binomial probability is given by this equation:

P =\; ^nC_r (p)^r (q)^{(n-r)}

Substituting the given parameters into the formula, we have;

P = [¹⁵⁰C₇₂ × (8/20)⁷² × (8/20)⁽¹⁵⁰ ⁻ ⁷²⁾]

P = [150!/(72! × (150 - 72)!) × (8/20)⁷² × (8/20)⁽¹⁵⁰ ⁻ ⁷²⁾]

P = [150!/(72! × (78)!) × (4/5)⁷² × (4/5)⁽⁷⁸⁾]

P = 0.7948.

Read more on probability here: brainly.com/question/14805135

#SPJ1

weqwewe [10]1 year ago
5 0

Answer + Step-by-step explanation:

1) The probability of getting 2 white balls is equal to:

=\frac{8}{20} \times \frac{7}{19}\\\\= 0.147368421053

2) the probability of getting 2 white balls is equal to:

=C^{2}_{5}\times (\frac{8}{20} \times \frac{7}{19}) \times (\frac{12}{18} \times \frac{11}{17} \times \frac{10}{16})\\=0.397316821465

3) The probability of getting at least 72 white balls is:

=C^{72}_{150}\times \left( \frac{8}{20} \right)^{72}  \times \left( \frac{7}{20} \right)^{78}  +C^{73}_{150}\times \left( \frac{8}{20} \right)^{73}  \times \left( \frac{7}{20} \right)^{77}  + \cdots +C^{149}_{150}\times \left( \frac{8}{20} \right)^{149}  \times \left( \frac{7}{20} \right)^{1}  +\left( \frac{8}{20} \right)^{150}

=\sum^{150}_{k=72} [C^{k}_{150}\times  \left( \frac{8}{15} \right)^{k}  \times \left( \frac{7}{15} \right)^{150-k}]

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satela [25.4K]

Answer:

a) s = 0.4

b) Z = 2.5

c) 99th percentile.

d) The larger sample size would lead to a smaller margin of error, which would lead to a higher z-score and a increased percentile.

Step-by-step explanation:

To solve this question, we need to understand the normal probability distribution and the central limit theorem.

Normal Probability Distribution:

Problems of normal distributions can be solved using the z-score formula.

In a set with mean \mu and standard deviation \sigma, the z-score of a measure X is given by:

Z = \frac{X - \mu}{\sigma}

The Z-score measures how many standard deviations the measure is from the mean. After finding the Z-score, we look at the z-score table and find the p-value associated with this z-score. This p-value is the probability that the value of the measure is smaller than X, that is, the percentile of X. Subtracting 1 by the p-value, we get the probability that the value of the measure is greater than X.

Central Limit Theorem

The Central Limit Theorem establishes that, for a normally distributed random variable X, with mean \mu and standard deviation \sigma, the sampling distribution of the sample means with size n can be approximated to a normal distribution with mean \mu and standard deviation s = \frac{\sigma}{\sqrt{n}}.

For a skewed variable, the Central Limit Theorem can also be applied, as long as n is at least 30.

Assume the average height for American women is 64 inches with a standard deviation of 2 inches

This means that \mu = 64, \sigma = 2

A) Calculate the standard error for the distribution of means.

Sample of 25 means that n = 25, so s = \frac{2}{\sqrt{25}} = 0.4.

B) Calculate the z statistic for the sorority group.

Sample mean of 65 means that X = 65.

Z = \frac{X - \mu}{\sigma}

By the Central Limit Theorem

Z = \frac{X - \mu}{0.4}

Z = \frac{65 - 64}{0.4}

Z = 2.5

C) What is the approximate percentile for this sample? Enter as a whole number.

Z = 2.5 has a p-value of 0.9938, so 0.99*100 = 99th percentile.

D) If the sorority actually had 36 members (still with an average of 65 inches), would you expect the percentile value to increase or decrease? why?

The larger sample size would lead to a smaller margin of error, which would lead to a higher z-score and a increased percentile.

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3 years ago
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Naddik [55]
The only 'small number' that I know that would affect the answer is an exponent that looks like ³ so the problem would be x³+x

remember that x³=x times x times x
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3 years ago
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The gallons of oil in the tank is 1000

A 6500 gallon storage tank is 2/13 full

The number of oil in the tank can be calculated by multiplying the amount of gallons in the tank which is 6500 by the measurement of the tank which is 2/13

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Answer:

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Step-by-step explanation:

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Knowing this, we can easily calculate the value for any year, counting from the original 5000.

From this formula, we can derive a specific one that will serve for any value of <em>t</em>.

a)

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