How is solving literal equations like solving equations in one variable

Mathematics

2 answers:

Soloha48 [4]3 years ago

5 0

Answer:

When given a literal equation, you will often be asked to solve the equation for a

given variable. The goal is to isolate that given variable. The process is the same

process that you use to solve linear equations; the only difference is that you will

be working with a lot more letters, and you may not be able to simplify as much as

you can with linear equations. This packet will hopefully show you the process in a

simple manner so that you will be able to solve literal equations yourself. See

examples before for the method to solving literal equations for a given variable:

nikitadnepr [17]3 years ago

5 0

Answer:

Step-by-step explanation:

To solve a literal equation means to rewrite the equation so a different variable stands alone on one side of the equals sign. We have to be told for which variable we want to solve.

Linear equations in one variable may take the form a x + b = 0 \displaystyle ax+b=0 ax+b=0 and are solved using basic algebraic operations. We begin by classifying linear equations in one variable as one of three types: identity, conditional, or inconsistent. An identity equation is true for all values of the variable.

I hope this helps. Sorry if I could not answer your question!

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Suppose the daily customer volume at a call center has a normal distribution with mean 5,500 and standard deviation 1,000. What

lakkis [162]

Answer:

0.0665 = 6.65% probability that the call center will get between 4,800 and 5,000 calls in a day.

Step-by-step explanation:

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.

Mean 5,500 and standard deviation 1,000.

This means that $\mu = 5500, \sigma = 1000$