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Assume the lifetime (in months) of batteries is modeled as an exponential random variable with parameter ๐œ† . The probability density function of the random variable is ๐‘ ๐œ† ( ๐‘ฅ ) = ๐œ† ๐‘’ โˆ’ ๐œ† ๐‘ฅ , with ๐‘ฅ โˆˆ [ 0 , โˆž ) . We have collected an i.i.d. sample of 3 batteries whose lifetimes in months are 8, 10, 9, respectively. The standardized log-likelihood of a sample with ๐‘› i.i.d. observations is 1 ๐‘› log ๐ฟ ( ๐‘ฅ , ๐œ† ) = 1 ๐‘› โˆ‘ ๐‘– = 1 ๐‘› ( log ( ๐œ† ) โˆ’ ๐œ† ๐‘ฅ ๐‘– ) . Given this information what is the Maximum Likelihood estimate of ๐œ† ?

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A.๐œ† ฬ‚ = 1 9 .
B.๐œ† ฬ‚ = 9 .
C.๐œ† ฬ‚ = log ( 9 ) .
D.There is not enough information to compute the estimate of ๐œ† .
E.๐œ† ฬ‚ = 1 log ( 9 ) .
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We start by identifying the model and the data given. The lifetimes of batteries follow an exponential distribution with parameter lambda, and we have an i.i.d. sample of lifetimes: 8, 10, and 9 months, with n = 3. The standardized log-likelihood provided is (1/n) โˆ‘ (log(lambda) โˆ’ lambda x_i) = log(lambda) โˆ’ lambda xฬ„, where xฬ„ is the sample mean. F......Login to view full explanation

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