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vlada-n [284]
3 years ago
7

20 is 50% of what base

Mathematics
1 answer:
Tresset [83]3 years ago
5 0
The base would be 40
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Jana finished 1/4 of her homework at school and 2/3 of it before dinner how much of her homework has she finished so far
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11/12

Step-by-step explanation:

1/4+2/3

1/4×3/3+2/3×4/4

3/12+8/12

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Which y values are represented on the graph below?
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Kelly made 6 batches of cookies. Her friend asked her to make an extra batch . She used 17 1/4 cups of sugar and needs another 7
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Answer:

Kelly needs 24 3/4 cups of sugar altogether.

Step-by-step explanation:

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3 years ago
For each logarithmic equation, write an equivalent equation in exponential form
Montano1993 [528]

Answer:

  • loga b = c             ⇔   a^c = b

<u>Apply the property above:</u>

  • 1.  ln 618 = p   ⇔ e^p = 618
  • 2. ln q = 2       ⇔ e^2 = q
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8 0
2 years ago
The mean height of women in a country (ages 20-29) is 64 4 inches A random sample of 50 women in this age group is selected What
Simora [160]

Answer:

0.0721 = 7.21% probability that the mean height for the sample is greater than 65 inches.

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.

Mean of 64.4 inches, standard deviation of 2.91

This means that \mu = 64.4, \sigma = 2.91

Sample of 50 women

This means that n = 50, s = \frac{2.91}{\sqrt{50}}

What is the probability that the mean height for the sample is greater than 65 inches?

This is 1 subtracted by the p-value of Z when X = 65. So

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

By the Central Limit Theorem

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

Z = \frac{65 - 64.4}{\frac{2.91}{\sqrt{50}}}

Z = 1.46

Z = 1.46 has a p-value of 0.9279

1 - 0.9279 = 0.0721

0.0721 = 7.21% probability that the mean height for the sample is greater than 65 inches.

8 0
2 years ago
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