I'm taking my exam friday morning and not gonna lie I feel pretty far from confident. I took probability in school and have been using adapt to study but I have a pretty low earned level, like 4. I have a pretty good understanding of a lot of the topics we covered in class and also most of the stuff in the textbook we used (A first course in probability, ross) but they're are also a good amount we didn't get to in class or isn't in the book.
The topics I'm pretty strong in are:
-The general probability ones (combinations, bayes, independence, law of total probability, demorgans etc)
-Most of the univariate distributions like Poisson/binoial , geometric, exponential, uniform, and normal
The one's where I am having a little trouble are:
-conditional expectations/variance (I have a pretty decent understanding of this but some of the harder problems get me)
-moment generating functions (again, I have a pretty decent understanding, some trouble with it)
-ordered stats (barely looked at it, but it looks pretty easy)
-bivariate transformations (probs the one I'm least confident in, but doesn't seem too bad as long as you remember the Jacobian process?)
I heard that ordered stats aren't too big of a topic on the exam, maybe a couple questions, but I don't really get how adapt or others are doing (by using transformations). That being said, I do figure out most of those problems by just using the formula:
(n!/((j-1)!(n-j)!))(F(x))^(j-1)(1-F(x))^(n-j)(f(x)) = density function
where j is the jth ordered variable and n is the total amount of variables.
Is that equation good enough or should I learn how to do it by transformations? Also how well should I know moment generating functions? I get that M'(0)= E(x) and M''(0)= E(x^2) as well as a basic understanding of how the function works. Also how well should I know the gamma, negative binomial, and hypergeomtric distributions? I've figured them out on the adapt practice exams without a problem despite not knowing really how they work (I just use my understanding of the others).
Any tips or advice on anything like stuff I should study more?
The topics I'm pretty strong in are:
-The general probability ones (combinations, bayes, independence, law of total probability, demorgans etc)
-Most of the univariate distributions like Poisson/binoial , geometric, exponential, uniform, and normal
The one's where I am having a little trouble are:
-conditional expectations/variance (I have a pretty decent understanding of this but some of the harder problems get me)
-moment generating functions (again, I have a pretty decent understanding, some trouble with it)
-ordered stats (barely looked at it, but it looks pretty easy)
-bivariate transformations (probs the one I'm least confident in, but doesn't seem too bad as long as you remember the Jacobian process?)
I heard that ordered stats aren't too big of a topic on the exam, maybe a couple questions, but I don't really get how adapt or others are doing (by using transformations). That being said, I do figure out most of those problems by just using the formula:
(n!/((j-1)!(n-j)!))(F(x))^(j-1)(1-F(x))^(n-j)(f(x)) = density function
where j is the jth ordered variable and n is the total amount of variables.
Is that equation good enough or should I learn how to do it by transformations? Also how well should I know moment generating functions? I get that M'(0)= E(x) and M''(0)= E(x^2) as well as a basic understanding of how the function works. Also how well should I know the gamma, negative binomial, and hypergeomtric distributions? I've figured them out on the adapt practice exams without a problem despite not knowing really how they work (I just use my understanding of the others).
Any tips or advice on anything like stuff I should study more?
Any last minute tips/advice before my exam tomorrow?
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