# Use two ordered pairs to write a prediction equation. Then using your prediction equation to predict the number of comic books sold when the price is $4.50.

The table is as follows:

Price-$2.00 # sold was 16

-$2.50 13

-$2.75 12

-$3.00 10

-$3.50 7

I used (2.00,16) & (3.00, 10) to get the prediction equation y=-6x + 4 but do not know how to solve for the # of books sold when the price is $4.50.

## OOPS--prediction equation is y= -6x+24

## The relationship between price and number sold is not quite linear. If you choose prices of = = 2.50 and 3.50 to get a prediction equation, the straight-line fit would be

y = 28 - 6x

Then if x = 4.50, the number sold is

y = 28 - 27 = 1

## To use the prediction equation y = -6x + 4 to predict the number of comic books sold when the price is $4.50, you can substitute x = 4.50 into the equation and solve for y.

Let's plug in the value x = 4.50 into the prediction equation:

y = -6(4.50) + 4

y = -27 + 4

y = -23

Therefore, when the price is $4.50, the predicted number of comic books sold is -23. However, it is important to note that predicting a negative number of comic books sold does not make sense in this context. It is likely that the prediction equation may not accurately reflect the relationship between price and the number of books sold for prices beyond the given data points.

## To solve for the number of books sold when the price is $4.50 using the prediction equation y = -6x + 4, you need to substitute the given price into the equation and calculate the corresponding number of books sold.

Let's start by substituting x = 4.50 into the equation:

y = -6(4.50) + 4

Now, multiply -6 by 4.50:

y = -27 + 4

Next, perform the subtraction:

y = -23

So, when the price is $4.50, the predicted number of books sold would be -23. However, it is not possible to have a negative number of books sold. Therefore, the prediction equation may not be accurate for prices above $3.50.

It is important to note that prediction equations use the pattern in the given data to make predictions, but they are not always perfect. In this case, the prediction equation assumes a linear relationship between price and the number of books sold. However, the actual relationship may not be perfectly linear, resulting in a less accurate prediction when the price is outside the range of the given data.