Factor completely 3x2 + 9x − 3.

2 answers:

7 0

Factor completely: 3x2 + 9x − 3

3x2 + 9x − 3 (a 3 can be divided from each number)
3(x2+3x-1)

can x2+3x-1 be factored out anymore? no

3(x2+3x-1) is the answer

padilas [110]3 years ago

5 0

Answer:

Option (2) is correct choice.

Step-by-step explanation:

Given polynomial $3x^2+9x-3$

We have factorize the given polynomial and choose the correct from options given.

Factor are the smallest forms a given number is broken such that the product of factors gives back the number we factorized.

Here, $3x^2+9x-3$

First taking 3 common from whole expression.

$3x^2+9x-3=3(x^2+3x-1)$

Thus, option (2) is correct choice.

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According to 2013 report from Population Reference Bureau, the mean travel time to work of workers ages 16 and older who did not

gregori [183]

Answer:

a) 48.80% probability that his travel time to work is less than 30 minutes

b) The mean is 30.7 minutes and the standard deviation is of 3.83 minutes.

c) 13.13% probability that in a random sample of 36 NJ workers commuting to work, the mean travel time to work is above 35 minutes

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