Jim has started a new exercise program. He has monthly checkups where his percentage of body fat is measured. Jim records his body fat percentage and the number of months he has been on the exercise program. He collects data for 10 months and finds a linear model to give the relationship between the time spent exercising and his percentage of body fat. The equation of the line is ŷ = 17 – 1.25x, where ŷ is his percentage of body fat and x is the time spent exercising (in months). The residual plot is shown. Based on the residual plot, is the linear model appropriate?

No, there is no clear pattern in the residual plot.
Yes, there is no clear pattern in the residual plot.
No, there is a clear pattern in the residual plot, indicating that the linear model is not appropriate.
Yes, his percentage of body fat clearly decreases for the first four months and then increases after that.

i pretty sure it is

answer: 48

just remember to use pemdas:

parentheses

expressions

multiplication

division

addition

subtraction

a) 40 more; b) old factory:   10%, new factory:   16%; c) week 4

explanation:

a) at week 0, we see in the graph that the new factory produces 190 specialty items.  

using the function, substituting 0 in for w,

this is a difference of 230-190 = 40.

b) the function for the old factory is written in the form f(x) = a(1+r)ˣ, where a is the original amount, x is the amount of time and r is the growth rate.   the equation for the old factory is ; 230 is in place of a and is the original amount; w is the amount of time; which means 1+r = 1.1.   this means that r must be 0.1 or 10%.

for the new factory, we find the percent of increase to find the growth rate; the formula for this is

between the first two points, the amount of change is 220-190 = 30; this gives us

30/190 = 0.158 ≈ 0.16 = 16%

c) at week 0, the old factory has 230 while the new factory has 190.

at week 1, the old factory has 230(1.1) = 253 while the new factory has 220.

at week 2, the old factory has 230(1.1²) = 278.3 while the new factory has 252.

at week 3, the old factory has 230(1.1³) = 306.13 while the new factory has 290.

at week 4, the old factory has 230(1.1⁴) = 336.743 while the new factory has 337.   this is the first week that the new factory surpasses the old.