Solutions to x squared +10x +16 =0

Mathematics

1 answer:

melisa1 [442]3 years ago

4 0

Answer: x = - 8, x=- 2

Step-by-step explanation:

Factor to get (x+8)(x+2).

For solutions, make that equal to 0.

x+ 8 = 0

x+2 = 0

so x = - 8, x= - 2

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8 to power -2 multiplied by a to power of 7

levacccp [35]

(8^ -2)(4^4)(16^ -1)


a^-x=(1/a^x)


 (4^4)/((8^2)(16))

"NOTE" 16^1=16


4^4=256

8^2=64

256/((64)(16))=1/4

convert 1/4 to a power of two

(4) is 2

1/4=1/(2^2)

1^2=1

(1/2)^2=(1/4)


1/2 to the power of 2 is the answer

It would look like this

1/2²

8 0

3 years ago

Write an equation of the line passing through the point (2,8) that is perpendicular to the line 2x-3y=7

labwork [276]

Answer:

y = (-3/2x) + (35/4)

Step-by-step explanation:

So the perpendicular line will have a slope that is the opposite reciprocal of the other line's slope. Solve for the original line's slope first:

2x - 3y = 7 --> -3y = -2x + 7 --> y = (2x/3) - (7/3)

So, the slope of the perpendicular line will be (-3/2x)

Solve for the y-intercept of the perpendicular like by using the linear equation and substituting in (2,8):

8 = (-3/(2*2)) + b --> (32/4) = -(3/4) + b --> b = (35/4)

So, y = (-3/2x) + (35/4)

Hope that helps! :D

4 0

3 years ago

A racing car consumes a mean of 100 gallons of gas per race with a variance of 64. If 44 racing cars are randomly selected, what

lesantik [10]

Answer:

83.89% probability that the sample mean would be greater than 98.8 gallons

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 $\sigma$ , a large sample size can be approximated to a normal distribution with mean \mu and standard deviation, which is also called standard error $s = \frac{\sigma}{\sqrt{n}}$