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padilas [110]
3 years ago
8

You are given an unfair coin (i.e. a coin where the probability of it landing on either side is NOT 1/21/2) and told that the pr

obability of getting at least one tails in three tosses of said coin is 26/2726/27. The probability of getting heads in one toss of the unfair coin can be expressed in the form M/NM/N, where MM and NN are relatively prime positive integers (i.e. their greatest common divisor is 1). Compute M + NM+N
Mathematics
1 answer:
andrey2020 [161]3 years ago
6 0

Here we have a problem of probability, we will find that the probability of landing in heads is M/N = 1/3, then we have:

M + N = 1 + 3 = 4.

Let's see how we got that:

Let's define:

p = probability of landing on tails

q = probability of landing on heads.

The probability of getting at least one tails in 3 tosses is 26/27

This means that the probability of not getting tails in the 3 tosses is:

P = 1 - 26/27 = 1/27

And the case where you do not get any tails in the 3 tosses, means that in all the 3 tosses you got heads.

The probability of getting 3 heads in a row is:

P = q^3 = 1/27

Solving for q, we get:

q = ∛(1/27) = 1/3

Now we want to express q = M/N = 1/3

then we have:

M = 1

N = 3

Now we want to compute M + N = 1 + 3 = 4

If you want to learn more about probability, you can read:

brainly.com/question/24369877

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I assume that 0.53 with a bar means the decimal fraction in which 3 is repeating.

24/45, 8/15, and 48/90 all result in the same decimal value. Thus, we are simply looking for the one that is fully simplified.

The correct answer is 8/15.

Hope this helps!

5 0
2 years ago
Consider the following hypothesis test: H0: μ1 - μ2 = 0 Ha: μ1 - μ2 ≠ 0 There are two independent samples taken from the two pop
nlexa [21]

Answer:

The value of the test statistic is z = 1.78

Step-by-step explanation:

Before finding the test statistic, we need to understand the central limit theorem and subtraction of normal variables.

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.

Subtraction between normal variables:

When two normal variables are subtracted, the mean is the difference of the means, while the standard deviation is the square root of the sum of the variances.

Sample 1:

\mu_1 = 110, s_1 = \frac{7.2}{\sqrt{81}} = 0.8

Sample 2:

\mu_2 = 108, s_2 = \frac{6.3}{\sqrt{64}} = 0.7875

The test statistic is:

z = \frac{X - \mu}{s}

In which X is the sample mean, \mu is the value tested at the null hypothesis, and s is the standard error.

0 is tested at the null hypothesis:

This means that \mu = 0

Distribution of the difference:

X = \mu_1 - \mu_2 = 110 - 108 = 2

s = \sqrt{s_1^2+s_2^2} = \sqrt{0.8^2+0.7875^2} = 1.1226

What is the value of the test statistic?

z = \frac{X - \mu}{s}

z = \frac{2 - 0}{1.1226}

z = 1.78

The value of the test statistic is z = 1.78

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