Michael is planting a garden. The probability that a seed will produce a plant is 9/10

Which of these linear functions has a greater rate of change than a linear function with a slope of 6 and a y–intercept of 3? Se

valentinak56 [21]

Answer: the correct options are B, D and F

Step-by-step explanation:

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

Where c = intercept

m = slope = rate of change

Looking at the equations given, the three that apply are

B) slope = 7

y intercept = 4

It has a greater rate of change than a linear function with a slope of 6 and a y–intercept of 3

D) y = 9x + 1

slope = 9

y intercept = 1

It has a greater rate of change than a linear function with a slope of 6 and a y–intercept of 3

F) y = 8x + 5

slope = 8

y intercept = 5

It has a greater rate of change than a linear function with a slope of 6 and a y–intercept of 3

8 0

3 years ago

I want to how they answer this question. What is the value of x?

mestny [16]

√(15+x) = 2x+3
take sqr for both side

15x+10=(2x+3)^2

15x+10=4x^2+9+2*2x*3
15x+10=4x^2+9+12x
4x^2-3x--1=0

solve it u will x=1,-1/4

4 0

3 years ago

A rectangle with an area of 24 square units has length x+1 and width 4x-6. find the value of x.

astra-53 [7]

Area = length x width.
24 = (x + 2)(x - 3) = x² - x - 6
x² - x - 30 = 0
(x + 5)(x - 6) = 0
We can eliminate the root x = - 5 because dimensions cannot be negative. So, the only valid answer is x = 6

The length = x + 2 = 6 + 2 = 8 in.
The width = x - 3 = 6 - 3 = 3 in.

6 0

3 years ago

Suppose monthly rental prices for a one-bedroom apartment in a large city has a distribution that is skewed to the right with a

omeli [17]

Answer:

a) Nothing, beause the distribution of the monthly rental prices are not normal.

b) 1.43% probability that the sample mean rent price will be greater than $900

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 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 with size n of at least 30 can be approximated to a normal distribution with mean $\mu$ and standard deviation $s = \frac{\sigma}{\sqrt{n}}$

(a) Suppose a one-bedroom rental listing in this large city is selected at random. What can be said about the probability that the listed rent price will be at least $930?

Nothing, beause the distribution of the monthly rental prices are not normal.

(b) Suppose a random sample 30 one-bedroom rental listing in this large city will be selected, the rent price will be recorded for each listing, and the sample mean rent price will be computed. What can be said about the probability that the sample mean rent price will be greater than $900?

Now we can apply the Central Limit Theorem.

$\mu = 880, \sigma = 50, n = 30, s = \frac{50}{\sqrt{30}} = 9.1287$