The reason for a bimodel distribution is that a bimodal distribution may occasionally result from merging data from two processes or populations.
<h3>What is a bimodel distribution?</h3>
- Two modes comprise a bimodal distribution. In other words, the results of two distinct processes are integrated into a single collection of data.
- The distribution sometimes goes by the name "double-peaked." Consider the distribution of production data over two shifts in a manufacturing facility.
- Bimodal distributions frequently happen as a result of underlying events.
- A bimodal distribution, for instance, can be seen in the amount of patrons who visit a restaurant each hour because people typically eat out for lunch and dinner.
- The bimodal distribution is brought on by the underlying human behavior.
- If a data set has two modes, it is bimodal. This indicates that no particular data value has the highest frequency of occurrence. Instead, the highest frequency is tied between two data values.
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Answer:
$41,500
Explanation:
Calculation to determine What was the initial cost of the machine to be capitalized
Purchase price $35,000
Add Freight $1,500
Add Installation $3,000
Add Testing $2,000
Total Cost $41,500
Therefore the initial cost of the machine is $41,500
Answer:
D. $0.93
Explanation:
Upmove (U) = High price/current price
= 42/40
= 1.05
Down move (D) = Low price/current price
= 37/40
= 0.925
Risk neutral probability for up move
q = (e^(risk free rate*time)-D)/(U-D)
= (e^(0.02*1)-0.925)/(1.05-0.925)
= 0.76161
Put option payoff at high price (payoff H)
= Max(Strike price-High price,0)
= Max(41-42,0)
= Max(-1,0)
= 0
Put option payoff at low price (Payoff L)
= Max(Strike price-low price,0)
= Max(41-37,0)
= Max(4,0)
= 4
Price of Put option = e^(-r*t)*(q*Payoff H+(1-q)*Payoff L)
= e^(-0.02*1)*(0.761611*0+(1-0.761611)*4)
= 0.93
Therefore, The value of each option using a one-period binomial model is 0.93
Answer:
Monte Carlo Simulation
Explanation:
Monte Carlo simulation refers to a methodology used in monetary, program management, expense, and other prediction frameworks to know the impact of financial risks. A Monte Carlo model allows one to see all or most of the possible results in order to get a better understanding of the probability of a judgment.
In other words, Monte Carlo approaches can also be used in theory to address any issue with a deterministic explanation. By using the law of large numbers, by getting the empirical average of individual variable tests, integrals represented by expected value of a certain independent variables can be estimated.
