Analysis Review for Statistics

In summary: Additionally, it is often helpful to have knowledge of differential equations, which is something that can usually be picked up in an introductory engineering course.
  • #1
klausas
6
0
I'm making the jump from math ed to pure math. I've done a bit more than the usual math-for-teachers stuff and my advisor is convinced I can pull it off, but if I sound nervous, this is why.

I know what to review and study up on before fall semester for two of my classes but I'm at a loss for the third, Mathematical Statistics. The official prerequisite is first semester analysis; the textbook is Hogg, McKean, and Craig. I do not know the professor and am not sure if asking him about this would make the wrong first impression.

It's been a while since I had prob and stat but I think I remember the ideas and will get a reference for formulas and re-memorize the ones that come up. Analysis, though, was supposedly mashed into my calculus coursework; I know (or at least think I know?) that the difference between intro calc and analysis is rigorous reasoning from first principles about the number system, but what I am not sure about is how analysis applies to mathematical statistics as a prereq. If it's just to make sure the students have met axiomatic reasoning and proofwriting before, I'll be OK; I like that sort of thing (and as a result am getting an A in the summer class my advisor told me to take to test my proofwriting skills). On the other hand, if there are specific topics I need from analysis, I'm at a bit of a loss as to which ones.

I have about two weeks relatively clear and would like to be as prepared as possible by the time the semester begins. If anyone can suggest optimal topics to study, I will owe you cookies.
 
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  • #2
I'm interested in this too, my school also requires Real Analysis 1 for Math. Stats.
 
  • #3
Over 24 hours and no suggestions...should I post this question somewhere else?
 
  • #4
Well, IMO analysis is a little too much of a pre-req for math stats. I used the same book for my math stat class and I don't think that analysis is required - though it would certainly help. The thing about math stats is that you are going to be doing statistics from the point of view of a mathematician. So, while your intro to prob stat course might have had you memorising a lot of distributions and mgfs and random facts and stuff, you will be deducing these facts for yourself in math stats. Because of this, there is a lot of stuff that deals with series and sequences and so it is a good idea to be solid on those sorts of things. Also, brushing up on multiple integrals will really help a lot, too. Other than reviewing series/sequences and multiple integrals, I can't really think of anything else.
 
  • #5
As was said earlier, in mathematical statistics there will be a great deal of emphasis first on establishing a basic understanding of probability theory, which does require an understanding of first year analysis or calculus (I have always though of first year analysis as a slightly more rigorous version of calculus, with an emphasis on proving from first principles).

Mathematical statistics will also place a great deal of emphasis on asymptotics, so a good background in linear algebra will be required for those.
 

1. What is the purpose of an analysis review for statistics?

An analysis review for statistics is a process of examining and evaluating statistical data in order to gain insights, identify patterns and trends, and make informed decisions based on the findings. It aims to provide a comprehensive understanding of the data and its significance.

2. What are the steps involved in conducting an analysis review for statistics?

The steps involved in conducting an analysis review for statistics include defining the research question, collecting and organizing data, performing descriptive and inferential statistical analyses, interpreting the results, and drawing conclusions or making recommendations.

3. What are the common statistical techniques used in an analysis review?

The common statistical techniques used in an analysis review include measures of central tendency (mean, median, mode), measures of variability (standard deviation, range, variance), correlation analysis, regression analysis, hypothesis testing, and data visualization.

4. How do you ensure the accuracy and reliability of the results in an analysis review?

To ensure the accuracy and reliability of the results in an analysis review, it is important to use appropriate statistical methods, carefully collect and organize data, conduct thorough data cleaning and validation, and use reliable software or tools for analysis. It is also important to clearly document all the steps and assumptions made during the analysis.

5. What are the potential limitations of an analysis review for statistics?

Potential limitations of an analysis review for statistics include biased or incomplete data, faulty assumptions, and limitations of the statistical methods used. It is important to acknowledge and address these limitations in order to avoid drawing incorrect or misleading conclusions from the analysis.

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