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Korvikt [17]
3 years ago
11

Diana made a recipe that yields 6 and 1 over 2 cups. If each serving is 1 over 4 cup, which expression will help Diana determine

the number of servings her recipe will yield? 6 and 1 over 2 ⋅ 1 over 4 6 and 1 over 2 ÷ 1 over 4 6 and 1 over 2 + 1 over 4 6 and 1 over 2 − 1 over 4
Mathematics
2 answers:
Misha Larkins [42]3 years ago
6 0

Answer:

6 and 1 over 2 ÷ 1 over 4

Step-by-step explanation:

Given,

The total cups in a recipe =  6 and 1 over 2 cups

=6\frac{1}{2}\text{ cups}

Also, the cups in each serving =  1 over 4 cup

=\frac{1}{4}\text{ cups}

Hence, the number of servings = \frac{\text{total cups in a recipe}}{\text{cups in each serving}}

=\frac{6\frac{1}{2}}{\frac{1}{4}}

= 6 and 1 over 2 ÷ 1 over 4

Second option is correct.

Degger [83]3 years ago
5 0
Six and a half divided by one over four
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<u>Alg I</u>

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Read 2 more answers
The results of a common standardized test used in psychology research is designed so that the population mean is 155 and the sta
galina1969 [7]

Answer:

The value <em>155</em> is zero standard deviations from the [population] mean, because \\ x = \mu, and therefore \\ z = 0.

Step-by-step explanation:

The key concept we need to manage here is the z-scores (or standardized values), and we can obtain a z-score using the next formula:

\\ z = \frac{x - \mu}{\sigma} [1]

Where

  • z is the <em>z-score</em>.
  • x is the <em>raw score</em>: an observation from the normally distributed data that we want <em>standardize</em> using [1].
  • \\ \mu is the <em>population mean</em>.
  • \\ \sigma is the <em>population standard deviation</em>.

Carefully looking at [1], we can interpret it as <em>the distance from the mean of a raw value in standard deviations units. </em>When the z-score is <em>negative </em>indicates that the raw score, <em>x</em>, is <em>below</em> the population mean, \\ \mu. Conversely, a <em>positive</em> z-score is telling us that <em>x</em> is <em>above</em> the population mean. A z-score is also fundamental when determining probabilities using the <em>standard normal distribution</em>.

For example, think about a z-score = 1. In this case, the raw score is, after being standardized using [1], <em>one standard deviation above</em> from the population mean. A z-score = -1 is also one standard deviation from the mean but <em>below</em> it.

These standardized values have always the same probability in the <em>standard normal distribution</em>, and this is the advantage of using it for calculating probabilities for normally distributed data.

A subject earns a score of 155. How many standard deviations from the mean is the value 155?

From the question, we know that:

  • x = 155.
  • \\ \mu = 155.
  • \\ \sigma = 50.

Having into account all the previous information, we can say that the raw score, <em>x = 155</em>, is <u><em>zero standard deviations units from the mean.</em></u> <u><em>The subject   earned a score that equals the population mean.</em></u> Then, using [1]:

\\ z = \frac{x - \mu}{\sigma}

\\ z = \frac{155 - 155}{50}

\\ z = \frac{0}{50}

\\ z = 0

As we say before, the z-score "tells us" the distance from the population mean, and in this case this value equals zero:  

\\ x = \mu

Therefore

\\ z = 0

So, the value 155 is zero standard deviations <em>from the [population] mean</em>.

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