How to solve this problem for my pre calculous class.

Mathematics

1 answer:

Vilka [71]3 years ago

4 0

Take the cos-1 of both sides to get 3x=30 then divide by 3 to get ×=10

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If the exponential parent function,

laila [671]

Answer: $f(x) =3^{-x}-4$

Step-by-step explanation:

Some transformations for a function f(x) are shown below:

If $f(x)+k$ , the function is translated up "k" units.

If $f(x)-k$ , the function is translated down "k" units.

If $-f(x)$ , the function is reflected across the x-axis.

If $f(-x)$ , the function is reflected across the y-axis.

Therefore, knowing those transformations and given the exponential parent function:

$f(x) = 3^x$

If it is reflected across the y-axis and the it is translated down 4 units, we can determine that the resulting function is:

$f(x) =3^{-x}-4$

6 0

3 years ago

Find the product of the two binomials below<br> (x-8) (x+4)

LekaFEV [45]

Multiply it out
x^2 + 4x -8x -32
Combine like terms
x^2 -4x - 32

3 0

3 years ago

Read 2 more answers

A manufacturer knows that their items have a normally distributed length, with a mean of 15.4 inches, and standard deviation of

Masteriza [31]

Answer:

0.9452 = 94.52% probability that their mean length is less than 16.8 inches.

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 normal distributions can be solved using the z-score formula.

In a set with mean $\mu$ and standard deviation $\sigma$ , the z-score 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 p-value, we get the probability that the value of the measure is greater than X.

Central Limit Theorem

The Central Limit Theorem establishes 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 $s = \frac{\sigma}{\sqrt{n}}$ .