Write the quadratic equation in factored form. be sure to write the entire equation.x 2 + x

Manny wants to cover the windows in his home with either wood blinds or curtains. He has 35 windows that he would like to cover,

stepladder [879]

Answer:

$x + y \leq 35\\\\100x + 60y\leq2100\\\\x\geq 0\\\\y\geq 0$

Step-by-step explanation:

Call x the amount of blinds that Manny buys.

Let's call and the amount of curtains that Manny buys

There are only 35 windows, so the number of curtains and windows can not be greater than 35.

$x + y \leq 35\\x\geq 0\\y\geq 0$

Manny can only spend $ 2100

Then we can represent this by an inequality in the following way.

$100x \leq 2100$

and

$60y\leq 2100$

We can write this as a single inequality:

$100x +60y \leq2100$

Finally, the set of inequalities to model this problem is:

$x + y \leq 35\\\\100x + 60y\leq2100\\\\x\geq 0\\\\y\geq 0$

5 0

4 years ago

What is the range of the function y = x^2 ?

Gelneren [198K]

The range is all of the y values.

The domain of all quadratics is all real numbers, but the range of y=x^2 is y≥0. This is because quadratic functions graph to form a parabola, which, when the a value is positive, opens upward. The lowest y value when graphed is 0 and all other values are more than that.

4 0

4 years ago

Assume that foot lengths of women are normally distributed with a mean of 9.6 in and a standard deviation of 0.5 in.a. Find the

Makovka662 [10]

Answer:

a) 78.81% probability that a randomly selected woman has a foot length less than 10.0 in.

b) 78.74% probability that a randomly selected woman has a foot length between 8.0 in and 10.0 in.

c) 2.28% probability that 25 women have foot lengths with a mean greater than 9.8 in.

Step-by-step explanation:

The Central Limit Theorem estabilishes that, for a random variable X, with mean $\mu$ and standard deviation $\sigma$ , a large sample size can be approximated to a normal distribution with mean $\mu$ and standard deviation $\frac{\sigma}{\sqrt{n}}$ .

Normal probability distribution

Problems of normally distributed samples can be 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.

In this problem, we have that:

$\mu = 9.6, \sigma = 0.5$ .