Math Stats: Preparing for Grad School?

In summary, the conversation discusses the level of mathematical rigor and usage of calculus in an introductory mathematical statistics course. It is noted that undergraduate statistics courses provide a basis for understanding before moving on to more rigorous and complex analysis. It is also mentioned that graduate level mathematical stats courses can be just as rigorous as graduate level analysis or algebra courses. However, it is acknowledged that statistics is best understood in the context of the real world and is an applied discipline, so excessive rigor may be ignored in favor of practicality. It is important for statisticians to understand the limitations of their results in order to effectively apply them.
  • #1
selig5560
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I was wondering what a introductory (1 year sequence) mathematical statistics would be like in terms of mathematical rigor and usages of calculus 1 - 3. The course I'm thinking about taking requires calc 3. I've heard that graduate level mathematical stats courses can be just as rigerous as a graduate level analysis or algebra course. So would an undergraduate stats course give light on what's to come in graduate school?
 
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  • #2


Hey selig5560.

The stuff in undergraduate is meant to be a basis for giving intuition before you move to a completely symbolic representation where the rigor is introduced.

It's the same for non-statistical math with calculus: you get some intuition first before you go to real and complex analysis.

A probability course with measure theory and analysis will have a good chance of being just as rigorous as a normal real analysis course if the course is using analysis and measure theory as the starting point for analyzing things.

Statistics though (and probability), is best understood in the context of the real world.

Statistics is an applied discipline, and this means looking at things that are real-world and have some basis in reality. Because of this, anything that has more rigor that it needs to have is often ignored due to the focus being an applied pursuit as opposed to a theoretical one.

There is a place for this analysis and I would compare it to the case of the engineer where they use the calculus results to do their job without having to worry about why it works.

The important thing though, is for the statistician (and the engineer in the above example) to understand the limitations of the result. If you are going to use it you need to understand how it holds and when it doesn't and the context in this regard.
 

1. What is the purpose of studying math stats?

The purpose of studying math stats is to gain a deep understanding of the principles and theories behind statistical analysis. This knowledge is essential for conducting research, making data-driven decisions, and solving complex problems in a variety of fields such as science, engineering, economics, and social sciences.

2. How will studying math stats prepare me for grad school?

Studying math stats will prepare you for grad school by providing a strong foundation in statistical methods and techniques. This will not only help you excel in your coursework, but also prepare you for advanced research and data analysis in your field of study.

3. What skills and knowledge are required for success in math stats?

To succeed in math stats, you should have a strong background in mathematics and a solid understanding of concepts such as probability, calculus, and linear algebra. It is also important to have critical thinking skills, attention to detail, and the ability to analyze and interpret data.

4. What are some common career paths for math stats graduates?

Math stats graduates have a wide range of career options, including data analyst, statistician, research scientist, actuary, and financial analyst. They can also pursue careers in fields such as healthcare, marketing, government, and education.

5. Are there any recommended resources for preparing for grad school in math stats?

Yes, some recommended resources for preparing for grad school in math stats include textbooks such as "Mathematical Statistics with Applications" by Wackerly, Mendenhall, and Scheaffer, online courses such as those offered by Coursera or edX, and practice problems and exams from previous graduate level courses.

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