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cluponka [151]
3 years ago
7

Please solve will give brainliest

Mathematics
2 answers:
ASHA 777 [7]3 years ago
8 0

Answer:

1 5/8

Step-by-step explanation:

13 / 8 = 1 remainder 5

The whole number in the mixed number is how many times the divisor goes into the dividend evenly (1).

The remainder from the division problem (5) is the numerator.

The divisor in the division problem (8) is the denominator.

alina1380 [7]3 years ago
4 0

1 \times \frac{5}{8}

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Step-by-step explanation:

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pleaseeeeeeee HELPPPPPPPPPPPp In a parking lot, 25% of the cars are SUVs, and 40% are pickup trucks. The rest of the cars are se
Arisa [49]

Answer:

60 cars

Step-by-step explanation:

In order to find how many total cars are in the parking lot, you must use a ratio. This is what you know:

25% = SUVs

40% = pickups

X% = cars

100% - (25% + 40%) = 35%. Thus, the remaining 35% of the cars are sedans. Now, we use a ratio to find the TOTAL number vehicles. See below:

X/65% * 21/35% = 35x = (21x65) = 1,365 (Hint: remember cross-multiplying??)

35x = 1,365

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Sanity check:

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A researcher is interested in finding a 95% confidence interval for the mean number minutes students are concentrating on their
Sever21 [200]

Answer:

A. Normal

B. Between 40.08 minutes and 43.92 minutes.

C. About 95 percent of these confidence intervals will contain the true population mean number of minutes of concentration and about 5 percent will not contain the true population mean number of minutes of concentration.

Step-by-step explanation:

Central Limit Theorem

The Central Limit Theorem estabilishes 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}}

x% confidence interval:

A confidence interval is built from a sample, has bounds a and b, and has a confidence level of x%. It means that we are x% confident that the population mean is between a and b.

Question A:

By the Central Limit Theorem, a normal distribution.

Question B:

We have that to find our \alpha level, that is the subtraction of 1 by the confidence interval divided by 2. So:

\alpha = \frac{1 - 0.95}{2} = 0.025

Now, we have to find z in the Ztable as such z has a pvalue of 1 - \alpha.

That is z with a pvalue of 1 - 0.025 = 0.975, so Z = 1.96.

Now, find the margin of error M as such

M = z\frac{\sigma}{\sqrt{n}}

In which \sigma is the standard deviation of the population and n is the size of the sample.

M = 1.96\frac{12}{\sqrt{150}} = 1.92

The lower end of the interval is the sample mean subtracted by M. So it is 42 - 1.92 = 40.08 minutes

The upper end of the interval is the sample mean added to M. So it is 42 + 1.92 = 43.92 minutes

Between 40.08 minutes and 43.92 minutes.

Question C:

x% confidence interval -> x% will contain the true population mean, (100-x)% wont.

So, 95% confidence interval:

About 95 percent of these confidence intervals will contain the true population mean number of minutes of concentration and about 5 percent will not contain the true population mean number of minutes of concentration.

3 0
3 years ago
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