A rectangular painting measures 1212 inches by 1414 inches and contains a frame of uniform width around the four edges. The perimeter of the rectangle formed by the painting and its frame is 7676 inches. Determine the width of the frame.

The answer to your question is 303 in

Step-by-step explanation:

Data

Painting 1212 in x 1414 in

Perimeter painting + frame = 7676

width of the frame = ?

Process

1.- Calculate the perimeter of the painting

Perimeter = 2 length + 2 width

Perimeter = 2(1212) + 2(1414)

Perimeter = 2424 + 2828

Perimeter = 5252 in

2.- Subtract both perimeters

                7676 - 5252 = 2424 in

3.- Divide the result by 8 because there are 8 borders

                 2424/8 = 303 in

4.- Conclusion

The frame has a width of 303 in

the width of the frame is 303 inches.

Step-by-step explanation:

Let x represent the width of the frame.

The rectangular painting measures 1212 inches by 1414 inches and contains a frame of uniform width around the four edges. This means that the length of the painting and the frame is 1212 + 2x and the width of the painting and the frame is 1414 + 2x

The formula for determining the perimeter of a rectangle is expressed as

Perimeter = 2(length + width)

The perimeter of the rectangle formed by the painting and its frame is 7676 inches. This means that

2(1212 + 2x + 1414 + 2x) = 7676

2(2626 + 4x) = 7676

Dividing through by 2, it becomes

2626 + 4x = 7676/2 = 3838

4x = 3838 - 2626 = 1212

x = 1212/4 = 303 inches

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! )