Answer:
The exception is due to vacation.
Explanation:
This is an example of a right answer, while, yes the individual must be registered in Canada, the exception is due to vacation.
Answer:
The average expected rate of return on the market portfolio is 10 percent.
Explanation:
The CAPM (fixed asset pricing) model describes the relationship between systematic risk and expected return on assets, especially stocks. CAPM is widely used throughout the financial community to value high-risk securities and achieve the expected returns on assets when taking into account the risk of those assets and the cost of capital.
The formula for calculating the expected return on an asset taking into account its risk is as follows:
ERi = Rf + βi (ERm - Rf)
where:
ERi = expected return on investment
Rf = risk-free interest rate = 4 percent.
βi = beta inversion =1.0
(ERm −Rf) = market risk premium = 6 percent.
ERi = 4 + 1 ×(6) =10
The average expected rate of return on the market portfolio is 10 percent.
<span>When museums charge a lower admission fee to students and senior citizens, this form of pricing is known as price discrimination.
Price discrimination is a way of changing the prices for something based on time of day, amounts sold, or who they are sold to. This type of discrimination is done to try and maximize profits in the largest way possible. </span>
Answer:
transferred-out 135,000
Explanation:
We solve using the following identity:
beginning WIP + cost added during the period:
total cost to be accounted for.
Then this value can be either ransferred-out r remain at the ending WIP
so we construct as follows:
beginning 0
added 180,000
Total cost 180,000
ending <u> (45,000) </u>
transferred-out 135,000
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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