DESCRIPTION

Please keep in mind the OMSI rules.

Save your files often.

Submit each answer individually and make sure you receive the notification of successful submission.

Don't wait till the last moment to submit all answers, as there might be network traffic in the last 5 minutes causing unexpected delay and failure.  

Remember that clicking CopyQtoA will copy the entire question box to the answer box.

In questions involving code which will PARTIALLY be given to you in the question specs, you may need to add new lines. 
There may not be information given as to where the lines should be inserted. 

MAKE SURE TO RUN THE R CODE ANSWER IN PROBLEMS THAT INVOLVE R CODE!


QUESTION

(text answer - 25 pts) A courtroom phrase that we discussed as relevent to
hypothesis testing is ____________________.


QUESTION

(text answer - 25 pts) Say we have a random sample, X_1, X_2, ... ,
X_n from some population in which X has unknown density f_X.  We will
form a histogram, but with freq = TRUE so we get actual bin counts.  Say
our bins are (i-1,i] for i = 1,2,...,25, with an extra bin
(25,infinity).  Suppose, unknown to us, f_X is exponential with mean
2.0.  Let N_i be the count for bin i, i = 1,2,...,25.  Which one(s) of
the following is/are true?

(i) N_i is not a random variable
(ii) N_i has a binomial distribution
(iii) N_i has a negative binomial distribution
(iv) N_i has a geometric distribution
(v) N_i has an exponential distribution
(vi) N_i has an approximate normal distribution
(vii) EN_i = i
(viii) EN_i = 0
(ix) EN_i = sigma/sqrt(n)

QUESTION -ext .R -run 'Rscript ./omsi_answer3.R'

(R code answer - 25 pts) A random variable X has density 
f_X(t) = c t^2 and support [2,6], where c is the normalizing constant.  
Write a function to compute the cdf F_X(t).

cdf <- function(t) 
{


}

print(cdf(3.5))

QUESTION -ext .R -run 'Rscript ./omsi_answer4.R'

(R code answer - 25 pts)  Using the dataset PimaIndiansDiabetes2 in the mlbench
package, find an approximate 99% confidence interval for the mean age in
the sampled population.

library(mlbench)


print(c(  ,  ))