The area of a cell phone is 12 inches squared. What is the length of the missing side?

Calculate the future value of $51,123.21, earning interest at a rate of that is compounded daily for 20 years and 2 months (use

mina [271]

The compound interest formula is : $A = P(1+ \frac{r}{n})^n ^t$

where, A= Future value including the interest,

P= Principle amount, r= rate of interest in decimal form,

t= number of years and n= number of compounding in a year

Here, in this problem P= $ 51,123.21 , t= 20 years and 2 months

So, t= 20 + (2/12) years

t= 20 + 0.17 = 20.17 years

As the amount is compounded daily, so n= (12×30)= 360 [Using the traditional Banker’s rule of 30 days per month]

Thus, $A = 51,123.21( 1+ \frac{r}{360})^3^6^0^*^2^0^.^1^7$

$A= 51,123.21 (1+\frac{r}{360})^7^2^6^1^.^2$

When the interest rate is given, then we can use this equation for finding the future value.

3 0

2 years ago

Given the function f(x) = 3x3 + 7x2 + 5x, find f(-3).

Julli [10]

Answer:

the ans is b -33

Step-by-step explanation:

by putting the -3 in the place of x and adding,subtracting

4 0

3 years ago

Annual starting salaries in a certain region of the U. S. for college graduates with an engineering major are normally distribut

defon

Answer:

0.8665 = 86.65% probability that the sample mean would be at least $39000

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