PLEASE HELP 15 POINTS, It is now time to complete the Utilities Discussion. Research the cost of some sort of household bill. Examples are natural gas, electricity, water, etc. Find one that models a linear function and write a function to represent the cost of this utility.

Explain how you found your information and wrote your function. Be sure to define your variables, use function notation.

Step-by-step explanation:

The watt (W) is a unit of electrical power, which is the rate at which we use energy. We pay the electric company for the use of energy. A kilowatt (kW) is equal to 1000 watts: kilo is a prefix that means 1000. (Note that k is the normal prefix for kilo, W is the normal abbreviation for Watts and h is the normal abbreviation for hours). A watt-hour (Wh) and a kilowatt-hour (kWh) are units of energy, where 1000 Wh= 1 kWh. The equation relating energy and power is:

Energy = Power x Time

For example, if a 75 W bulb (power) is turned on for 2 hours (time), it will use 150 Wh or 0.15 kWh of energy (energy = power x time).

The cost for each unit of energy we use is measured in cents (c) or dollars ($) per kWh. The typical cost per unit of energy is 10 c/kWh or $0.10/kWh. Using the equation below, we can determine the cost of our energy use:

Cost of energy use = Energy used x Cost per unit of energy:

Using our example above, the cost of using a 75 W bulb for 2 hours is:

Cost of energy use = 0.15 kWh x 10 c/kWh = 1.5 c.

[Units] [Using the Bill] [Reading a Meter] [The Cost] [The Environmental Cost] [How it is Produced]

the answer   is 200

step-by-step explanation:

this is a problem about poisson probabilities.   i don't know if you've

already studied poisson distributions, or if you have a place to look

to read more about the subject.  

here's how you can see a poisson distribution coming.   when there are

a lot of events that may happen at any time (or any place), and when

you're counting how many of them happen at a particular time (or a

particular place), and each event is independent of the others, then

the result is a poisson distribution.   this is roughly true for the

chocolate chips; one chip being in a cookie has only a small effect on

the probability that another chip will be in the cookie.

once you know you have a poisson distribution, you know a lot.   there

is only one parameter to a poisson distribution, so if you know the

mean, you know everything.   the form of the distribution is

  probability of n chips = {[e^(-x)]*(x^n)} / (n! )

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step-by-step explanation: