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madam [21]
3 years ago
7

The equation 9 y minus 6 x = 36 is written in standard form. What is the first step when writing an equivalent equation which so

lves for x? Divide both sides of the equation by 36. Multiply both sides of the equation by 6. Add 9y to both sides of the equation. Subtract 9y from both sides of the equation.
Mathematics
1 answer:
enot [183]3 years ago
8 0

Answer:

D. Subtract 9y from both sides of the equation.

Step-by-step explanation:

A. Divide both sides of the equation by 36.

B. Multiply both sides of the equation by 6.

C. Add 9y to both sides of the equation.

D. Subtract 9y from both sides of the equation.

Given:

9y - 6x = 36

To solve for x

Step 1: subtract 9y from both sides

9y - 6x - 9y = 36 - 9y

- 6x = 36 - 9y

Step 2: Divide both sides by -6

- 6x / -6 = (36 - 9y) / - 6

x = (36 - 9y) / - 6

The answer is

x = (36 - 9y) / - 6

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Lady_Fox [76]

Answer:

0.0838 = 8.38% probability of obtaining a sample proportion less than 0.5.

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.

For a proportion p in a sample of size n, the sampling distribution of the sample proportion will be approximately normal with mean \mu = p and standard deviation s = \sqrt{\frac{p(1-p)}{n}}

Proportion of 0.6

This means that p = 0.6

Sample of 46

This means that n = 46

Mean and standard deviation:

\mu = p = 0.6

s = \sqrt{\frac{p(1-p)}{n}} = \sqrt{\frac{0.6*0.4}{46}} = 0.0722

Probability of obtaining a sample proportion less than 0.5.

p-value of Z when X = 0.5. So

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

By the Central Limit Theorem

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

Z = \frac{0.5 - 0.6}{0.0722}

Z = -1.38

Z = -1.38 has a p-value of 0.0838

0.0838 = 8.38% probability of obtaining a sample proportion less than 0.5.

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