Give an example of reciprocals?

Evgesh-ka [11]

What?

explanation: ..

5 0

3 years ago

Paige earned $15.50 per hour. She received a 10% raise. Paige worked 10 hours at her new salary. How much did Paige earn?

PSYCHO15rus [73]

Answer:

C- $170.50

Step-by-step explanation:

6 0

2 years ago

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Jon drew a triangle with coordinates (5,1), (7,-2) and (3,-1) he drew an image of the triangle with the coordinates of (5,-1), (

stira [4]

Jon reflected over the x axis.

Reflecting over the x axis turns the y corrdinate into the opposite sign of the y coordinate. Here is the formula for that, if you want to take notes.

(x,y)—>(x,-y)

I hope this helped. Your answer to this question is D

7 0

2 years ago

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a class has 60 students.Given that 85% passed a science test. Find the number of students who failed and then convert o %

Vanyuwa [196]

Given:

Total students = 60

Student passed = 85%

To find:

The number of students who failed and then convert it in %.

Solution:

We have,

Number of passed student = 85% of Total students

= $\dfrac{85}{100}\times 60$

= $51$

So, number of passes students is 51.

Number of students who failed = Total students - Passes students

= $60-51$

= $9$

So, number of students who failed is 9.

$\text{Fail}\%=\dfrac{\text{Failed students}}{\text{Total students}}\times 100$

$\text{Fail}\%=\dfrac{9}{60}\times 100$

$\text{Fail}\%=15$

Therefore, 15% students fail in the test.

7 0

2 years ago

Suppose a research firm conducted a survey to determine the mean amount steady smokers spend on cigarettes during a week. A samp

shutvik [7]

Answer:

0.9910 = 99.10% probability that a sample of 170 steady smokers spend between $19 and $21

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 $s = \frac{\sigma}{\sqrt{n}}$ .