Identify which choices would be considered descriptive statistics and which would be considered inferential statistics.

a. Of 500 randomly selected people in New York City, 210 people had O+ blood.

b. "42 percent of the people in New York City have O+ blood." Is the statement descriptive statistics or inferential statistics?

c. "58 percent of the people of New York City do not have type O+ blood." Is the statement descriptive statistics or inferential statistics?

d. "42 percent of all people living in New York State have type O+ blood." Is the statement descriptive statistics or inferential statistics?

Consider the same assumption in previous question. In addition, we consider a possible vomiting out of coughing as shown in the following network. where V = vomiting [v or -v). We assume that P(v|c) = 0.2 and P(v|-c) = 0.1. Use Bayesian exact inference to obtain the following probabilities: 1. Ptf,a,c,-v) 2. Plflv) Are the following statements true? Justify your answers. 1.F__A|V 2.F__V|A We take a Bayesian approach to inference about A, the mortality rate in an exponential model for survival time. We specify that FGamma (a, ,8) a priori, then the posterior distribution is also Gamma. Posterior distribution is p ([1] Y) "Gamma (a + n, B + n37). | Maximum likelihood estimator: 1m. = % Reverting to likelihood-based inference, obtain an approximate 95% confidence interval for A, using the fact that the information function for the simple exponential model is {MFR/3.2. Now calculate the prior and posterior probabilities that it lies within this approximate 95% confidence interval, under the proposed prior distribution above, and comment on the results. (a)| This question asks you to consider a Bayesian approach to inference about .1, the mortality rate in an exponential model for survival time. In order to take a Bayesian approach, we specify a prior distribution for A which is gamma distribution. I Show that the gamma distribution is a conjugate prior distribution for the exponential model, i.e. if we specify that 1"\"Gamma (a, ,3} a priori, then the posterior distribution is also Gamma, with parameters that depend on a, [3,113. o Provide expressions for the parameters of this Gamma posterior distribution, and for the mean and mode of the posterior distribution