Answer:
Step-by-step explanation:
you have to make a box like this:
║ ---------- ║------------ ║ label one side percent and the other amount
║ x% ║ 96 kg ║
║ _ _ _║_____ _║ Now if the patient originally weighed 102 kg which
║100% ║ 120 kg ║ is 100% place the numbers in the bottom box.
║ _ _ _ ║ _ _ _ ║ and if the patient currently weighs 96 kg then let
Percent Amount percentage of the weight lost be x. Now cross multiply. You should get 100*96=120*x. Simplify that to get 9600=120x, now divide by 120 on both sides and you get 80 so x= 80. But the problem isn't done yet. Now you have to subtract 80% from 100% to find the weight lost, because 80% is the percentage of the current weight. after you have subtracted you get 20
20% of the original weight was lost.
Well what I would do is split the 40% into 20% so from 40% to 20% that is /2. So 32/2=16 so 20% of a number is 16, we know there is 5, 20% in 100% so multiply 16 and 5 which gives you 80. Now 25% of 80 is the same as 80*.25 or 80/4 which is 20
Your Answer 20
<span>The dimensions are 40 inches by 55 inches.
Explanation<span>:
We know that perimeter is the sum of all of the sides. Since this is rectangular, opposite sides are equal. This gives us
y+11/8y+y+11/8y=190.
Combining like terms, we have
2y+22/8y=190.
Writing 22/8 as a mixed number, we have
2y+2 3/4y=190
4 3/4y=190.
Divide both sides by 4 3/4:
(4 3/4y)</span></span>÷<span><span>(4 3/4)=190</span></span>÷<span><span>(4 3/4)
y=190</span></span>÷<span><span>(4 3/4).
Convert the mixed number to an improper fraction:
y=190</span></span>÷<span><span>(19/4).
To divide fractions, flip the second one and multiply:
y=190*(4/19)=760/19=40.
Since y=40, 11/8y=11/8(40)=440/8=55.</span></span>
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.
