satat-discustion 3-1

Discussion 3.1 (one page)

In this module, you have learned about two of the most frequently used probability distributions in biostatistics: the binomial and normal distributions. For your initial post, discuss one or two aspects of each distribution that are particularly complex or confusing to you and what about that aspect remains confusing or unclear. When responding to your peers, consider what a clear explanation would be of at least one of the confusing topics they post.

Response one

I would have to say that whole understanding of this is complex and confusing for me to tell you the truth. It's  like I'll get the concept of what the meaning of the word is and, then as I continue to read that's when it will get confusing. So binomial is something that focused on an certain type of characteristics within an study with only two outcomes. Now having that somewhat of an understand it then put this into a formula which then become complex because I now have to break this down to a problem to be solve to figure out what is the number of the two outcomes. Which for a binomial I would probably want the good outcomes of the study but, understanding how to get to the number is a little difficult still. The understanding of normal distributions was just as hard as the binomial but the formula was more difficult. This definition was more complex to read because the meaning of the word was symbols that mean normal distributions are often described as “bell-shaped” and symmetrical, and their mean, median, and mode match. Having the understanding of this is confusing and I will hope as I continue to learn that this gets a little bit more understanding.

Response Two

The chapters on Binomial Distributions and Normal Distributions are both very difficult to understand. In the Binomial Distribution chapter all the formulas just completely throw you off just when you thought there was a slight chance of it starting to make a little bit of sense. It seems to me personally that I was a little closer to understanding the Normal Distribution chapter after examining the chapter few times over but not really quite there yet. The videos that are under this module do help a lot. Although it seems that the chapter is much more in depth than the videos. The videos I can learn to understand and with practice be able to analyze how it is suppose to make sense. I find the calculations and formulas found in the textbook for the Binomial chapter still confusing, however with the StatCrunch video about calculating the binomial distributions I can learn to understand for those particular kinds of problems. For the Normal Distribution chapter, the last section of the chapter labeled 7.4 Assessing Departures From Normality, I will have to look at a little closer to try to figure out how the points on the diagonal line are defined in order to visualize what is normal and abnormal.

Response three

 I understand the difference between discrete (countable) and continuous (unbroken/infinite) random variables, but I'm finding the LANGUAGE of math difficult to comprehend since it has been so long since I've taken a math course. For instance, when the probability of an event is Pr(A) or P(A), I had to look up what does P mean?  What does PR mean?  It's a lot of vocabulary to memorize, so I have started a vocab list for things like "Compliment of probability"=1-probability, and independence= events (A and B) are independent if and only if P(A and B) = P(A) x P(B).  The specificity required to write equations that make sense is something that I struggle with, and foresee needing a lot of practice to perfect. I knew that | meant something, but I had to look up the definition before I could even try to comprehend what P(B|A) = P(A and B)/P(A) meant.  

          For binomial distributions, a confusing aspect is that I have to look up each word that I don't know- like "parameters" and "coefficient" and all of the symbols. I find the Khan and stat-crunch videos very helpful because I can play them and keep rewinding and watching until everything makes sense visually with the graphs.  I don't think there is anything that remains unclear now, but I will have to continuously re-read my vocab list to refresh my memory of what everything means.  

          The normal distribution for continuous variables makes sense to me as far as including all values and having a normal distribution.  The confusing part would be if I would have to memorize the formulas.  I like that stat-crunch is pretty easy to navigate, and the videos definitely help.  I don't think there is anything that remains unclear, except maybe the example in our class notes where height was listed as a continuous random variable, where I would assume that height has a max, so I would have assumed that this was not continuous.  

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