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- Let X1,...,Xn be iid exponential(θ) random variables. Derive the LRT of H0 : θ = θ0 versus Ha : θ 6= θ0. Determine an approximate critical value for a size-α test using the large sample approximation.The use of the interval p̂ ± (z critical value) p̂(1 − p̂) n requires a large sample. For each of the following combinations of n and p̂, indicate whether the sample size is large enough for use of this interval to be appropriate. a. n = 51 and p̂ = 0.30 Yes or No b. n = 51 and p̂ = 0.05 Yes or No c. n = 11 and p̂ = 0.45 Yes or NoFind the critical value, z0, from the z table for the given parameters. a=0.20 two tailed test
- Find the critical value, z0, from the z table for the given parameters. a=0.10 left tailed testIf you let X1, X2, X3, X4 equal the cholesterol level of a woman under the age of 50, a man under 50, a woman 50 or older, and a man 50 or older, respectively. Assuming the distribution of Xi is N(μi, σ2), i = 1, 2, 3, 4 and you test the null hypothesis H0: μ1 = μ2 = μ3 = μ4, using seven observations of each Xi, what would be the critical region for an alpha = 0.05 significance level?If X1 and X2 constitute a random sample of size n = 2from an exponential population, find the efficiency of 2Y1relative to X, where Y1 is the first order statistic and 2Y1and X are both unbiased estimators of the parameter
- Consider a real random variable X with zero mean and variance σ2X . Suppose that wecannot directly observe X, but instead we can observe Yt := X + Wt, t ∈ [0, T ], where T > 0 and{Wt : t ∈ R} is a WSS process with zero mean and correlation function RW , uncorrelated with X.Further suppose that we use the following linear estimator to estimate X based on {Yt : t ∈ [0, T ]}:ˆXT =Z T0h(T − θ)Yθ dθ,i.e., we pass the process {Yt} through a causal LTI filter with impulse response h and sample theoutput at time T . We wish to design h to minimize the mean-squared error of the estimate.a. Use the orthogonality principle to write down a necessary and sufficient condition for theoptimal h. (The condition involves h, T , X, {Yt : t ∈ [0, T ]}, ˆXT , etc.)b. Use part a to derive a condition involving the optimal h that has the following form: for allτ ∈ [0, T ],a =Z T0h(θ)(b + c(τ − θ)) dθ,where a and b are constants and c is some function. (You must find a, b, and c in terms ofthe information….A sample of 9 measurements, randomly selected from a normally distributed population, resulted in x= 2.6, and s= 0.9 Conduct a hypothesis test to verify the claim that the population mean is greater than 2.5 . Use a=.05. In general, a ____ t-statistic and a _____ p-value (relative to a chosen critical value and alpha) indicate that our estimated statistic is ____ to occur by chance if the null is true. a. Large; large; likely b. Small; large; likely c. Small; small; unlikely d. Large; large; likely
- A random sample of 50 students was asked to estimate how much money they spent on textbooks in a year. The sample skewness of these amounts was found to be 0.83 and the sample kurtosis was 3.98. Test at the 10% level the null hypothesis that the population dis- tribution of amounts spent is normal.A researcher is interested in testing whether annual house hold income in Philadelphia is normal. So she took a sample of 50 house holds and found that skewness (s)= 2.3190 and Kurtosis (k) = 6.7322. Use the Jarque- Bera Test to test , at alpha 0.05, whether income follows normal distribution. -Yes, population is normal because Chi-Square test is higher than critical value. -Yes, population is normal because Chi-Square test is less than critical value. -No, population is not normal because Chi-Square test is higher than critical value. -No, population is not normal because Chi-Square test is less than critical value.Q2C. Considering the following MA(3) process: y_t = u_t − 0.7u_(t-1) − 0.2u_(t-2) + 0.4u_(t-3) Where u_t is a white noise process with variance equal to 1. What is the value epsilon_k = cov(y_t,y_(t-k)) for k = 0? Provide the correct answer along with the working steps and underlying assumptions used to calculate the value of epsilon_k = cov(y_t,y_(t-k)) for k = 0.
