Not quite. I explained how the ideas works and did two examples, albeit the first example was problem a), (I did not want to do any complicated examples). My reply is the basic explanation that one would find in any basic statistics text on standardizing a normal random variable. As for the...
Your z-table gives you the probability to the left of z-value of the distribution Z~N(0,1). In other words let Z~N(0,1), μ=mean=0 and σ=std=1. Now for a given z value the table returns P(Z<z*).
Now the question is how to work with X~N(μ,σ). To compute the probability P(X<x*)you transform the...
let n=even, k<=n/2
\begin{pmatrix} n \\ k \end{pmatrix} =\begin{pmatrix} n \\ k-1 \end{pmatrix} *{ (n-k+1)/(k)}
then (n-k+1)/k > (n/2 +1)/k >1
QED
...the symmetry between k and n-k finishes the proof.
Some clarifications:
Can it be view as a marginal distribution?
Let W= {R, ~R} (the weather) and let X= {T,F}^3 (the call answers) and P(X,W) be the joint distribution.
You cannot compute P(T^3, R) or P(T^3, ~R) because you nothing about the joint distribution even under the...
2) To solve the problem you have to show for every ε>0 there exists an N .st. if n>N then |x_n - y_n|<ε. Now since x_n/y_n-->1 , given an ε>0 there exist N .st. if n>N then 1-ε< x_n/y_n <1 +ε. And now given that y_n>0 makes the next step easier. (When proving limits always go back to the basic...
The blurb could be a little bit clearer. When talking about a solution to a differential equation in a given problem the domain of interest is essential. The blurb implies this but it could be explained a bit more explicitly. So to answer your question, it is not a solution to a specific problem...
There is a problem here: these are still the conditional probabilities P(3y|R) = 8/27 and P(3y|~R) = 1/27 and so P(3y) ~= P(3y|R) + P(3y|~R) but P(3y)= P(3y|R)P(R) + P(3y|~R)P(~R). On cannot avoid the fact that there is incomplete information; Ibix is correct in the Bayesian approach...