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White raven [17]
3 years ago
8

Clarise evaluated this expression. (66.3 – 14.62) ÷ 0.6 – 0.22 (51.68) ÷ 0.6 – 0.22 (51.68) ÷ 0.42 51.68 ÷ 0.16 32.3 Which error

s did Clarise make? Check all that apply. -She subtracted before evaluating the exponents.
-She evaluated inside the parentheses first.

-She divided before she subtracted.

-She subtracted before she divided. She divided incorrectly.
Mathematics
2 answers:
Gennadij [26K]3 years ago
7 0
Let's go through this problem step by step while bearing in mind the concept of PEMDAS.

Step 1: (66.3-14.62)/0.6-0.2^{2}
Declaration of the expression. Nothing wrong here yet.

Step 2: (51.68)/0.6-0.2^{2}
Clarise evaluated what's inside the parenthesis first - which was the right thing to do! There are no mistakes in this step.

Step 3: (51.68)/0.4^{2}
In this step she subtracted first. This should not be the case! PEMDAS tells us that the exponents and division gets higher priority than subtraction. This is therefore the first mistake Clarise makes.

Step 4: (51.68)/0.16
In this step Clarise evaluates the exponent. This does not violate any rules (relative to the previous expression) since PEMDAS tells us that exponents take higher priority than division.

Step 5: 3.23 (Clarise's final answer)
In Clarise's final step, she manages to get the wrong answer! Dividing 51.68 by 0.16 would give us 323. This is another mistake of Clarise.

Looking at the choices, we can now identify what mistakes Clarise made:
-She subtracted before evaluating the exponents
-She subtracted before she divided
-She divided incorrectly
gulaghasi [49]3 years ago
3 0

Answer:1,4,5

Step-by-step explanation:

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Answer:

<u>The cube root parent function:</u>

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<u>Translated 5 units right:</u>

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<u>Translated 3 units up:</u>

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0% probability that the mean of the sample taken is less than 2.2 feet.

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 2.5 feet and a standard deviation of 0.2 feet.

This means that \mu = 2.5, \sigma = 0.2

Sample of 41

This means that n = 41, s = \frac{0.2}{\sqrt{41}}

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This is the p-value of Z when X = 2.2 So

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

By the Central Limit Theorem

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

Z = \frac{2.2 - 2.5}{\frac{0.2}{\sqrt{41}}}

Z = -9.6

Z = -9.6 has a p-value of 0.

0% probability that the mean of the sample taken is less than 2.2 feet.

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Step-by-step explanation:

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