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bezimeni [28]
3 years ago
5

A city has 4 new houses for every 7 old houses. If there are 32 new houses in the city, how many old houses are there?

Mathematics
1 answer:
Maslowich3 years ago
3 0

So the ration is 4:7

The total of houses is 32 new houses. What I did was I divided 32 by 4:

32/4 = 8

That means that the ratio (4:7) would be multiplied by 8 (only the :7 part):

7 * 8 = 56

There are 56 old houses (the ratio is now 32:56)

<em>Thank you and can you please give me brainly :3</em>

<em />

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Consider the probability that at least 88 out of 153 registered voters will vote in the presidential election. Assume the probab
Liono4ka [1.6K]

Answer:

0.9319 = 93.19% probability that at least 88 out of 153 registered voters will vote in the presidential election.

Step-by-step explanation:

Binomial probability distribution

Probability of exactly x successes on n repeated trials, with p probability.

Can be approximated to a normal distribution, using the expected value and the standard deviation.

The expected value of the binomial distribution is:

E(X) = np

The standard deviation of the binomial distribution is:

\sqrt{V(X)} = \sqrt{np(1-p)}

Normal probability distribution

Problems of normally distributed distributions can be solved using the z-score formula.

In a set with mean \mu and standard deviation \sigma, the zscore 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 pvalue, we get the probability that the value of the measure is greater than X.

When we are approximating a binomial distribution to a normal one, we have that \mu = E(X), \sigma = \sqrt{V(X)}.

153 voters:

This means that n = 153

Assume the probability that a given registered voter will vote in the presidential election is 63%.

This means that p = 0.63

Mean and standard deviation:

\mu = E(X) = np = 153(0.63) = 96.39

\sigma = \sqrt{V(X)} = \sqrt{np(1-p)} = \sqrt{153*0.63*0.37} = 5.97

Consider the probability that at least 88 out of 153 registered voters will vote in the presidential election.

Using continuity correction, this is: P(X \geq 88 - 0.5) = P(X \geq 87.5), which is 1 subtracted by the p-value of Z when X = 87.5.

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

Z = \frac{87.5 - 96.39}{5.97}

Z = -1.49

Z = -1.49 has a p-value of 0.0681.

1 - 0.0681 = 0.9319

0.9319 = 93.19% probability that at least 88 out of 153 registered voters will vote in the presidential election.

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The following linear equation represents the line of best fit for the following scatter plot
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Answer:

C) About 243 hits

General Formulas and Concepts:

<u>Pre-Algebra </u>

Order of Operations: BPEMDAS

  1. Brackets
  2. Parenthesis
  3. Exponents
  4. Multiplication
  5. Division
  6. Addition
  7. Subtraction
  • Left to Right

Equality Properties

  • Multiplication Property of Equality
  • Division Property of Equality
  • Addition Property of Equality
  • Subtract Property of Equality

Step-by-step explanation:

<u>Step 1: Define </u>

y = home runs

x = hits

[Best Line of Fit] y = 0.15x - 1.5

<em>We can use this to predict the average of the scatter plot. </em>

home runs = y = 35

<u>Step 2: Solve for </u><em><u>x</u></em><u> hits</u>

  1. Substitute [BLF]:                    35 = 0.15x - 1.5
  2. Add 1.5 on both sides:          36.5 = 0.15x
  3. Divide 0.15 on both sides:    243.333 = x
  4. Rewrite:                                  x = 243.333

Remember that this is a <em>prediction</em>. According to the best line of fit, we would need approximately ~243 to get 35 home runs.

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Answer:

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Step-by-step explanation:

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