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3 years ago

80 POINTS AND BRAINLIEST

Mathematics

1 answer:

yawa3891 [41]3 years ago

6 0

Answer: $x=50.6$

Step-by-step explanation:

According to the Side Splitter Theorem, when a line is parallel to a side of a triangle and it intersects the other two sides, these sides are divided proportionally.

Based on this, you can write the following proportion:

$\frac{MA}{AN}=\frac{MB}{BP}$

Observing the figure given in the exercise, you can identify that:

$MA=46.2-14=32.2\\\\AN=14\ cm\\\\MB=x\\\\BP=72.6-x$

Finally, you must substitute values into $\frac{MA}{AN}=\frac{MB}{BP}$ and solve for "x" in order to find its value. This is:

$\frac{32.2}{14}=\frac{x}{72.6-x}\\\\2.3=\frac{x}{72.6-x}\\\\(2.3)(72.6-x)=x\\\\166.98-2.3x=x\\\\166.98=x+2.3x\\\\\frac{166.98}{3.3}=x\\\\x=50.6$

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Answer: y = x/2 + 3

Step-by-step explanation:

The equation of a straight line can be represented in the slope-intercept form, y = mx + c

Where c = intercept

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Change in value of x = x2 -x1

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Looking at the graph,

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3 years ago

Suppose the selling price of homes is skewed right with a mean of 350,000 and a standard deviation of 160000 If we record the se

lys-0071 [83]

Answer:

The distribution will be approximately normal, with mean 350,000 and standard deviation 25,298.

Step-by-step explanation:

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.

Population:

Suppose the selling price of homes is skewed right with a mean of 350,000 and a standard deviation of 160000

Sample of 40

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3 years ago

Read 2 more answers

The bad debt ratio for a financial institution is defined to be the dollar values of loans defaulted divided by the total dollar

Nimfa-mama [501]

Answer:

(a) NULL HYPOTHESIS, $H_0$ : $\mu \leq$ 3.5%

ALTERNATE HYPOTHESIS, $H_1$ : $\mu$ > 3.5%

(b) We conclude that the the mean bad debt ratio for Ohio banks is higher than the mean for all federally insured banks.

Step-by-step explanation:

We are given that a random sample of seven Ohio banks is selected.The bad debt ratios for these banks are 7, 4, 6, 7, 5, 4, and 9%.The mean bad debt ratio for all federally insured banks is 3.5%.

We have to test the claim of Federal banking officials that the mean bad debt ratio for Ohio banks is higher than the mean for all federally insured banks.

(a) Let, NULL HYPOTHESIS, $H_0$ : $\mu \leq$ 3.5% {means that the the mean bad debt ratio for Ohio banks is less than or equal to the mean for all federally insured banks}

ALTERNATE HYPOTHESIS, $H_1$ : $\mu$ > 3.5% {means that the the mean bad debt ratio for Ohio banks is higher than the mean for all federally insured banks}

The test statistics that will be used here is One-sample t-test;

T.S. = $\frac{\bar X - \mu}{\frac{s}{\sqrt{n} } }$ ~ $t_n_-_1$

where, $\bar X$ = sample mean debt ratio of Ohio banks = 6%

s = sample standard deviation = $\sqrt{\frac{\sum (X-\bar X)^{2} }{n-1} }$ = 1.83%

n = sample of banks = 7

So, test statistics = $\frac{6-3.5}{\frac{1.83}{\sqrt{7} } }$ ~ $t_6$

= 3.614

(b) Now, at 1% significance level t table gives critical value of 3.143. Since our test statistics is more than the critical value of t so we have sufficient evidence to reject null hypothesis as it will fall in the rejection region.

Therefore, we conclude that the the mean bad debt ratio for Ohio banks is higher than the mean for all federally insured banks.

Hence, Federal banking officials claim was correct.

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