sample x1=2, x2=3, x4=5 from iid Uniform(1,b). Find the maximum likelihood estimation of b
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sample x1=2, x2=3, x4=5 from iid Uniform(1,b). Find the maximum likelihood estimation of b
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- This column contains data x1, . . . , xn, which is assumed to be a sample from a normal distribution N(µ, σ 2 ). For this sample, find the maximum likelihood estimates µˆ and σˆ.You are hired by a bank to help determine its lending standards. You decide to start by estimating a simple linear regression model to help predict whether a loan will become past-due (i.e. if any of the payments are tate). You ask the bank to give you data on the following two random variables: PD is an indicator variable equal to 1 if the loan is past-due, and zero if all payments have been made on time. credit score is the client's credit score at the time the loan was originated. The table below shows sample descriptive statistics for these variables: Variable PD credit_score Sample Mean 0.1 Sample Standard Deviation 0.08 550 25 The sample covariance between PD and credit score is -0.9 Use OLS to estimate the following simple linear regression model: PD = Bo + Bicredit_score + u Round your answers to four decimals. What should be the average value of the credit score among the bank's clients if the banks wants to have a predicted share of past due loans equal to 0.05 (i.e: 5%)?…I am lost with the way im supposed to solve this question please help?
- please include all the steps. thank youA team of engineers at the research center of a car manufacturer performs crash tests to determine the proportion of times the cars' airbags fail to operate in a crash. With the airbag system's new modified design, the team expected to reduce the failed proportion to be below last year's proportion of 0.08. They decided to test HO: p = 0.08 versus Ha: p < 0.08, where p = the proportion of failed airbags during crash tests. If 300 crashes performed in the lab resulted in 18 failures, which of the following is the test statistic for this test?7
- please helpLet yit denote the outcome of a random variable for the i-th individual at time and let Xit denote a covariate measure on the i-th individual at time t, t, i = 1, ..., n, t = 1,..., T. The data could be modeled using the simple linear regression model: Yit = Bo + B₁xt + Eit, Eit~N(0, t) a) Explain why using this approach may be inappropriate. b) Write a mathematical expression of the random intercept model and the random coefficients models as alternatives to this model. c) Provide an expression for the proportion of variance attributable to each of the variance components in the random coefficients models. d) Write out an expression for the likelihood, priors and posterior for the model in part (c) e) Write out a WinBUGS code for this problem.Heteroskedasticity arises because of non-constant variance of the error terms. We said proportional heteroskedasticity exists when the error variance takes the following structure: Var(et)=σt^2=σ^2 xt. But as we know, that is only one of many forms of heteroskedasticity. To get rid of that specific form of heteroskedasticity using Generalized Least Squares, we employed a specific correction – we divided by the square root of our independent variable x. And the reason why that specific correction worked, and yielded a variance of our GLS estimates that was sigma-squared, was because of the following math: (Picture 1) Where var(et)=σ^2 according to our LS assumptions. In other words, dividing everything by the square root of x made this correction work to give us sigma squared at the end of the expression. But if we have a different form of heteroskedasticity (i.e. a difference variance structure), we have to do a different correction to get rid of it. (a) what correction would you use…
