What is the question?
I'm assuming it is to find the length and width.
+_= plus or minus
(X+36)
____________
| |
(X) | |
|____________|
X^2+36X-2040<0
X<-36+_(36^2-4*-2040)^(1/2)
-----------------------------------
2
X<-18+_2((591)^(1/2))
This is probably not what you wanted, sorry
Let Ted be x.
Ed is 7 years older = x + 7
Ed = (3/4)Ted
(x + 7) = (3/4)x
x + 7 = 3x/4
x - 3x/4 = -7
x/4 = -7
x = -28, Ted = -28 years.
(x + 7) = -28 + 7 = -21, Ed = -21 years
Goodness. We had negative numbers for the ages, well does that make sense? No it doesn't.
Our answer is correct. But the sense in the question is lacking. The question has been wrongly set.
<span>We might assume negative ages to mean before they came into the world, before birth! </span>
Answer:
P(working product) = .99*.99*.96*.96 = .0.903
Step-by-step explanation:
For the product to work, all four probabilities must come to pass, so that
P(Part-1)*P(Part-2)*P(Part-3)*P(Part-4)
where
P(Part-1) = 0.96
P(Part-2) = 0.96
P(Part-3) = 0.99
P(Part-4) = 0.99
As all parts are independent, so the formula is P(A∩B) = P(A)*P(B)
P (Working Product) = P(Part-1)*P(Part-2)*P(Part-3)*P(Part-4)
P (Working Product) = 0.96*0.96*0.96*0.99*0.99
P(Working Product) = 0.903
Answer:
In the long run cost of the refrigerator g(x) will be cheaper.
Step-by-step explanation:
The average annual cost for owning two different refrigerators for x years is given by two functions
f(x) = 
= 
and g(x) = 
= 
If we equate these functions f(x) and g(x), value of x (time in years) will be the time by which the cost of the refrigerators will be equal.
At x = 1 year
f(1) = 850 + 62 = $912
g(1) = 1004 + 51 = $1055
So initially f(x) will be cheaper.
For f(x) = g(x)
=
x = 
Now f(15) = 56.67 + 62 = $118.67
and g(x) = 66.93 + 51 = $117.93
So g(x) will be cheaper than f(x) after 14 years.
This tells below 14 years f(x) will be less g(x) but after 14 years cost g(x) will be cheaper than f(x).
Answer:
Step-by-step explanation:
Given is a paired data which consist of temperatures (X in mm) and growth
We have to find the linear correlation i.e. the measure of association between these two variables.
x y xy x^2 y^2
62 36 2232 3844 1296
76 39 2964 5776 1521
50 50 2500 2500 2500
51 13 663 2601 169
71 33 2343 5041 1089
46 33 1518 2116 1089
51 17 867 2601 289
44 6 264 1936 36
79 16 1264 6241 256
Mean 58.88888889 27 1623.888889 3628.444444 916.1111111
cov 33.88888889
std dev x 13.43916333 14.50861813
sx *sy
r 0.195529176
Hence we find that correlation coefficient 0.1955.
