You do not need to flip the inequality sign when solving the following -5 ≤ 4r + 3.

2 answers:

7 0

Yes.

For inequality equation, you only flip the sign when you divide a negative number into both sides, here you will divide 4 on each side, so you won't flip the sign.

-Dominant- [34]3 years ago

7 0

the answer is true

do i get a prize?

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In triangle rst, rs=x+7, rt=2x-3, and st=x+7. If the perimeter is 32, find the value of x and classify the triangle by its sides

lana66690 [7]

Answer:

x = 5.25 and it is an isosceles triangle.

Step-by-step explanation:

The perimeter of a figure it the measure of all its sides added up.

Therefore RS+RT+ST=32

By substitution:

(x+7)+(2x-3)+(x+7)=32

Combine like terms and add:

4x+11=32

Subtract 11

4x=21

Divide 4

x=5.25

After substituting the value back in, we get that RS=12.25, RT=7.5, and ST=12.25

In an isosceles triangle, two sides have the same measure.

x = 5.25 and it is an isosceles triangle.

5 0

3 years ago

Read 2 more answers

Judy’s measured potassium level varies according to the Normal distribution with μ = 3.8 and σ = 0.2 mmol/l. Let us consider wha

Basile [38]

Answer:

The blood potassium level L such that the probability is only 0.05 that the average of four measurements is less than L is 3.64.

Step-by-step explanation:

To solve this question, we have to understand the normal probability distribution and the central limit theorem.

Normal probability distribution:

Problems of normally distributed samples are 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.

Central limit theorem:

The Central Limit Theorem estabilishes that, for a random variable X, with mean $\mu$ and standard deviation $\sigma$ , the sample means of size n can be approximated to a normal distribution with mean $\mu$ and standard deviation, which is also called standard error $s = \frac{\sigma}{\sqrt{n}}$