Non proportional I think. Wait for someone else to answer
The total amount of money accrued ( principal and interest ) in 35 years is $570.78.
<h3>What is the total amount accrued?</h3>
The formula for compound interest is expressed as;
A = P( 1 + r/t )^(n×t)
Given the data in the question;
- Principal P = $200
- Rate r = 3% = 3/100 = 0.03
- Compounded monthly n = 12
- Time t = 35
- Amount accrued in 35 years A = ?
Plug the given values into the equation above.
A = P( 1 + r/n )^(n×t)
A = 200( 1 + 0.03/12 )^(12×35)
A = 200( 1 + 0.0025 )^(420)
A = 200( 1.0025 )^(420)
A = 200( 2.85390914 )
A = $570.78
Therefore, the total amount of money accrued ( principal and interest ) in 35 years is $570.78.
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Answer: It should be used 2 for type-A and 3 for type-B to minimize the cost.
Step-by-step explanation: As it is stipulated, <u>x</u> relates to type-A and y to type-B.
Type-A has 60 deluxe cabins and B has 80. It is needed a minimum of 360 deluxe cabins, so:
60x + 80y ≤ 360
For the standard cabin, there are in A 160 and in B 120. The need is for 680, so:
160x + 120y ≤ 680
To calculate how many of each type you need:
60x + 80y ≤ 360
160x + 120y ≤ 680
Isolating x from the first equation:
x = 
Substituing x into the second equation:
160() + 120y = 680
-3200y+1800y = 10200 - 14400
1400y = 4200
y = 3
With y, find x:
x =
x = 
x = 2
To determine the cost:
cost = 42,000x + 51,000y
cost = 42000.2 + 51000.3
cost = 161400
To keep it in a minimun cost, it is needed 2 vessels of Type-A and 3 vessels of Type-B, to a cost of $161400
It takes Owen 2 minutes to eat 1 carrot stick
The equation of the least-squared regression line is: In(Element) = 2.305 - 0.101(Time).
<h3>What is a regression line?</h3>
A regression line displays the connection between scattered data points in any set. It shows the relation between the dependent y variable and independent x variables when there is a linear pattern.
According to the given problem,
From the table we can see,
ln(Element) is the dependent variable and Time is the independent variable.
The constant = 2.305,
Time = -0.101
Hence, we can conclude, our least squared regression line will be
In (Element) = 2.305 - 0.101 (Time).
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