Answer:
40 air conditioner and 60 fans yield a 1,900 dollar profit
Explanation:
We apply this constrain on excel solver tool.
Wiring Drilling Profit per unit Total Profit
AC 40 units 120 80 25 1000
Fan 60 units 120 60 15 <u> 900 </u>
240 140 1900 1,900
Using the Gordon Growth Model (a.k.a. Dividend Discount Model), the intrinsic value of a stock can be calculated, exclusive of current market conditions. In this model, the value of the stock is equated to the present value of the stock's future dividends.
<span>Value of stock (P0) = D1 / (k - g)
</span>where
D1<span> = </span><span>expected annual </span>dividend<span> per share in the following year </span>
<span>k = the investor's discount rate or required </span>rate of return
g = the expected dividend growth rate
<u>From the problem:</u>
The value of stock is $10.80
D1 is $0.40
g is 0.08
k is unknown
Solution:
Rearranging the equation for Gordon Growth Model to solve for k:
k = (D1/P0) + g
Substituting the variables with the given values,
k = (0.40/10.80) + 0.08
k = 0.1170
In percent form, this is
0.1170 * 100% = 11.70%.
Thus, the total rate of return on the stock is 11.70%.
Answer:
ello
Explanation:
I'll be your fren if that's what cha asking :^
In addition to prototyping, Powder Bed Fusion (PBF) AM processes have lately been more widely used to manufacture end-use parts. These changes lead to necessity of higher requirements to quality of a final product. Optimization of process parameters is one of the ways to achieve desired quality of a part.
In addition to prototyping, Powder Bed Fusion (PBF) AM processes have lately been more widely used to manufacture end-use parts. These changes lead to necessity of higher requirements to quality of a final product.
Optimization of process parameters is one of the ways to achieve desired quality of a part. Finite Element Method (FEM) and machine learning techniques are applied to evaluate and optimize AM process parameters. While FEM requires specific information, Powder Bed Fusion Machine Learning is based on big amounts of data. This paper provides a conceptual framework on combination of mathematical modelling and Machine Learning to avoid these issues.
Learn more about Powder Bed Fusion here
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