In probability theory and statistics, the gamma distribution is a two-parameter family of continuous probability distributions. The exponential distribution, Erlang distribution, and chi-squared distribution are special cases of the gamma distribution. The two parameters of the gamma distribution are alpha, a shape parameter, and beta, a scale parameter. In Excel, a random value can be simulated from a gamma distribution using GAMMA.INV(RAND(), alpha, beta). Consider a gamma distribution characterized by shape parameter alpha = 4 and scale parameter beta = 3. Estimate the 40th percentile of this distribution using Monte Carlo simulation. Use GAMMA.INV to randomly generate n = 2000 values of the distribution, then find the 40th percentile of the simulated gamma data values. Understanding that answers from simulations are subject to sampling variability, choose the answer choice that is closest to your simulated value. Hint: After completing this simulation problem, you should close the Excel file. Open Excel simulation spreadsheets involving the RAND() function can slow down your computer's performance as the RAND() function repeatedly re-evaluates.
In probability theory and statistics, the gamma distribution is a two-parameter family of continuous probability distributions. The exponential distribution, Erlang distribution, and chi-squared distribution are special cases of the gamma distribution. The two parameters of the gamma distribution are alpha, a shape parameter, and beta, a scale parameter. In Excel, a random value can be simulated from a gamma distribution using GAMMA.INV(RAND(), alpha, beta). Consider a gamma distribution characterized by shape parameter alpha = 4 and scale parameter beta = 3. Estimate the 40th percentile of this distribution using Monte Carlo simulation. Use GAMMA.INV to randomly generate n = 2000 values of the distribution, then find the 40th percentile of the simulated gamma data values. Understanding that answers from simulations are subject to sampling variability, choose the answer choice that is closest to your simulated value. Hint: After completing this simulation problem, you should close the Excel file. Open Excel simulation spreadsheets involving the RAND() function can slow down your computer's performance as the RAND() function repeatedly re-evaluates.
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![In probability theory and statistics, the gamma distribution is a two-parameter family
of continuous probability distributions. The exponential distribution, Erlang
distribution, and chi-squared distribution are special cases of the gamma distribution.
The two parameters of the gamma distribution are alpha, a shape parameter, and
beta, a scale parameter. In Excel, a random value can be simulated from a gamma
distribution using GAMMA.INV(RAND(), alpha, beta).
Consider a gamma distribution characterized by shape parameter alpha = 4 and scale
parameter beta = 3. Estimate the 40th percentile of this distribution using Monte
Carlo simulation. Use GAMMA.INV to randomly generate n = 2000 values of the
distribution, then find the 40th percentile of the simulated gamma data values.
Understanding that answers from simulations are subject to sampling variability,
choose the answer choice that is closest to your simulated value.
Hint: After completing this simulation problem, you should close the Excel file. Open
Excel simulation spreadsheets involving the RAND() function can slow down your
computer's performance as the RAND() function repeatedly re-evaluates.](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F0a08c678-100e-4106-b818-092f6da34e38%2Fdcd7164c-6d44-44b5-b032-4fde4e333b06%2Fcdrdp1n_processed.jpeg&w=3840&q=75)
Transcribed Image Text:In probability theory and statistics, the gamma distribution is a two-parameter family
of continuous probability distributions. The exponential distribution, Erlang
distribution, and chi-squared distribution are special cases of the gamma distribution.
The two parameters of the gamma distribution are alpha, a shape parameter, and
beta, a scale parameter. In Excel, a random value can be simulated from a gamma
distribution using GAMMA.INV(RAND(), alpha, beta).
Consider a gamma distribution characterized by shape parameter alpha = 4 and scale
parameter beta = 3. Estimate the 40th percentile of this distribution using Monte
Carlo simulation. Use GAMMA.INV to randomly generate n = 2000 values of the
distribution, then find the 40th percentile of the simulated gamma data values.
Understanding that answers from simulations are subject to sampling variability,
choose the answer choice that is closest to your simulated value.
Hint: After completing this simulation problem, you should close the Excel file. Open
Excel simulation spreadsheets involving the RAND() function can slow down your
computer's performance as the RAND() function repeatedly re-evaluates.
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