Find the inverse of the following Cube Root Functions

100 PTS help all information in pictures below

SVETLANKA909090 [29]

Answer:

Question 1: 11%

Question 2: 89%

Question 3: 43%

Question 4: 11%

Step-by-step explanation:

Looking at picture 1, we need to find the crossing point between -1.2 and 0.05. That has 0.1056, which is the same as 10.56%. 10.56% rounds to 11%, so C is our answer.

Picture 2 has the same chart, but we just need to find the inverse, since the inequality sign is flipped. 100 - 10.56 is 89.44%, which rounds to 89%, so D is the answer for Picture 2.

Picture 3 has two tables. 0.73 has 76.73% and -0.41 has 34.09%. Subtract 34.09% from 76.73% to get 42.64% That rounds to 43%, so A is the answer.

Picture 4 essentially has the same expression as Picture 2 (only the sign has switched): P(z ≥ 1.25). The meeting point is 89.44%. Now, subtract that from 100 to get 10.56%, which rounds to 11%. C is our answer for Picture 4.

I hope this helps you! ^w^

6 0

2 years ago

Read 2 more answers

The probability density function of the time to failure of an electronic component in a copier (in hours) is f(x) for Determine

salantis [7]

The question is incomplete. Here is the complete question.

The probability density function of the time to failure of an electronic component in a copier (in hours) is

$f(x)=\frac{e^{\frac{-x}{1000} }}{1000}$

for x > 0. Determine the probability that

a. A component lasts more than 3000 hours before failure.

b. A componenet fails in the interval from 1000 to 2000 hours.

c. A component fails before 1000 hours.

d. Determine the number of hours at which 10% of all components have failed.

Answer: a. P(x>3000) = 0.5

b. P(1000<x<2000) = 0.2325

c. P(x<1000) = 0.6321

d. 105.4 hours

Step-by-step explanation: <em>Probability Density Function</em> is a function defining the probability of an outcome for a discrete random variable and is mathematically defined as the derivative of the distribution function.

So, probability function is given by:

P(a<x<b) = $\int\limits^b_a {P(x)} \, dx$

Then, for the electronic component, probability will be:

P(a<x<b) = $\int\limits^b_a {\frac{e^{\frac{-x}{1000} }}{1000} } \, dx$

P(a<x<b) = $\frac{1000}{1000}.e^{\frac{-x}{1000} }$

P(a<x<b) = $e^{\frac{-b}{1000} }-e^\frac{-a}{1000}$

a. For a component to last more than 3000 hours:

P(3000<x<∞) = $e^{\frac{-3000}{1000} }-e^\frac{-a}{1000}$

Exponential equation to the infinity tends to zero, so:

P(3000<x<∞) = $e^{-3}$

P(3000<x<∞) = 0.05

There is a probability of 5% of a component to last more than 3000 hours.

b. Probability between 1000 and 2000 hours:

P(1000<x<2000) = $e^{\frac{-2000}{1000} }-e^\frac{-1000}{1000}$

P(1000<x<2000) = $e^{-2}-e^{-1}$

P(1000<x<2000) = 0.2325

There is a probability of 23.25% of failure in that interval.

c. Probability of failing between 0 and 1000 hours:

P(0<x<1000) = $e^{\frac{-1000}{1000} }-e^\frac{-0}{1000}$

P(0<x<1000) = $e^{-1}-1$

P(0<x<1000) = 0.6321

There is a probability of 63.21% of failing before 1000 hours.

d. P(x) = $e^{\frac{-b}{1000} }-e^\frac{-a}{1000}$

0.1 = $1-e^\frac{-x}{1000}$

$-e^{\frac{-x}{1000} }=-0.9$

${\frac{-x}{1000} }=ln0.9$

-x = -1000.ln(0.9)

x = 105.4

10% of the components will have failed at 105.4 hours.

5 0

3 years ago

Which of the following is an informal proof of the given conjecture? Conjecture: All squares are similar

tester [92]

The 3rd one because of the laws and newton’s ways

7 0

3 years ago

What is the slope 5x+2y=-2

SpyIntel [72]

The slope is -2.5 because you subtract 2y from both sides, then add two to both sides, then divide both sides by 2 to isolate y

6 0

3 years ago

1.) What is a qualitative prediction? * and 2.) What is a quantitative prediction? * THERE IS A PIC THAT COME WITH QUESTION PLEA

frozen [14]

I believe a qualitative prediction requires a prediction with out any numerical data to support it while a quantitative predictions require a prediction supported by numerical data.

A real world example of this is in chemistry during a lab. qualitative data is based off of observation with out numerical data such as a color change. quantitative data is based off of observation with numerical data such as the mass changes.

(quantitative prediction is decision from data based on percentages, probabilities, and so on while qualitative predictions are based off of given information).
I hope this helps and let me know if you need further explaining.

3 0

3 years ago