HELP ME PLEASE!!! THANK YOU

Helppppppppppp me i need the answer

Oksi-84 [34.3K]

Answer:

Step-by-step explanation:

i think its embryonic

4 0

3 years ago

Chapters: 1.Integers

SCORPION-xisa [38]

Answer:

a) -12

b) -4

c)-2

Step-by-step explanation:

a) pattern is minus 5

b) pattern is minus 8

c) pattern is plus 4

4 0

3 years ago

Which expressions are completely factored? Select each correct answer. 32y10−24=8(4y10−3) 18y3−6y=3y(6y2−2) 16y5+12y3=4y3(4y2+3)

laila [671]

Answer:

Option A and Option C

Step-by-step explanation:

Option A: $32y^{10}-24 = 8(4y^{10}-3)$ This is completely factored as 8 is the Highest Common Monomial factor

Option B: $18y^{3}-6y = 3y(6y^{2}-2)$ This is not completely factored as 2 is still a common factor of 6y2 and -2.

Option C: $16y^{5}+12y^{3}=4y^{3}(4y^{2}+3)$ This is completely factored as 4y2 is the Highest Common Monomial factor

Option D: $20y^{7}+10y^{2} = 5y(4y^{6}+2y)$ This is not completely factored as 2y is still a common factor of 4y6 and 2y.

So the options that are completely factored are Option A and Option C

3 0

3 years ago

Using quadratic form 6x^2-8x+3=0

LenaWriter [7]

x=8+sqrt-8/12, x=8-sqrt-8/12

I think...

5 0

2 years ago

Based on historical data, your manager believes that 37% of the company's orders come from first-time customers. A random sample

fomenos

Answer:

0.6214 = 62.14% probability that the sample proportion is between 0.26 and 0.38

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 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 $s = \sqrt{\frac{p(1-p)}{n}}$