Answer:
1,120,000 dollars
Explanation:
The pension income is 80% out of current salary of 70,000 dollars, therefore 56,000 dollars. For this to be future retirement stream savings must be at a level of 1,120,000 dollars or 56,000/0,05. This strategy would assume 5% return on savings or investments and the lifestyle in retirement equal to pre-retirement period. This strategy would also assume no additional post-retirement income.
Answer: Company Pays $1640
Carol Bryd pays $410
Explanation:
The total bill is $2300 and the deductible needs to be taken out.
$2300-$250
=$2050
Company Payment.
Company Pays 80% which translates to 0.8
0.8*2050
= $1640 is the company Payment.
Carol then pays the difference which is
$2050 - $1640
= $410
Carol pays $410
Answer:
5.09%
Explanation:
The internal rate of return is the discount rate that equates the after tax cash flows from an investment to the amount invested.
IRR can be calculated using a financial calculator.
Cash flow in year 0 = $-600,000
Cash flow each year from year 1 to 29 = $48,000 - $16,000 = $32,000
Cash flow in year 30 = $32,000 + $500,000 = $532,000
IRR = 5.09%
To find the IRR using a financial calacutor:
1. Input the cash flow values by pressing the CF button. After inputting the value, press enter and the arrow facing a downward direction.
2. After inputting all the cash flows, press the IRR button and then press the compute button.
I hope my answer helps you
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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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