SOP Review: Start new year by doing me a favor!

[CHOCTICK]

Enjoy. 

 

 

 

I am pursuing graduate studies in applied statistics’ because the ability to analyze data and forecast associated trends has wide application. With a MS in applied statistics, I will be well prepared to extract meaningful descriptive data, analyze that data backwards and forwards for trends, and provide stakeholders with the necessary information to complete their task. As a result of the University of Iowa having a solid statistics program to complement their actuarial science program, I will be able to obtain a thorough understanding of the statistics behind the tools of an actuary while simultaneously learning statistical essentials such as probability theory, inference, and modeling. Upon graduation, I will work in a data driven occupation within the private sector or for an agency of the federal government.

 

Statistics’ use in predicting and modeling are the most attractive to me. The ability to model phenomenon and forecast the future with these models has wide and practical application. Statistics also has the power to make processes more efficient. Having studied structural engineering, I have seen the application of statistics first hand. The Load and Resistance Factor (LRFD) design process has its roots in statistics. Prior to the LRFD method, engineers used the Allowable Strength Design (ASD) method exclusively to calculate the weight structural members (beams, columns, base plates, etc.) had to withstand.

 

The ASD method multiplies all load effects—e.g. snow, wind, cars—by the same amplification factor to account for uncertainty in estimating how much weight a structural member must support. The LRFD method takes into account the certainty with which we can estimate certain loads; the weight of a piece of machinery can be estimated with more certainty than the load effects from a group of workers occupying a space for a Christmas party. Probabilistic models were used to come up with larger amplification factors for load magnitudes with high uncertainty and smaller amplification factors for load magnitudes with low uncertainty. Where the ASD method says you would need a column that can support 1500 pounds, the LRFD method would more accurately require a column that can support 1000 pounds. Less required strength means a smaller I beam could be used which results in money saved in steel.

 

The LRFD method has made the design process more efficient; money that would have needlessly been spent on infrastructure—due to overdesign of structural members—now can be allocated for business and economy growing pursuits. The rise in popularity of the LRFD method is a testament to this fact.

 

I am also interested in learning more about data mining. This field of statistics would make good use of my computer programming skills. With my programming experience in C++, Visual Basic Applications in Excel, MATLAB, and Python, I will be able to use my degree in statistics to glean the last modicum of pertinent information from a set of data.

 

Actuarial sciences’ use in fields other than insurance is appealing to me. The application of statistical modeling to finance in the form of asset and liability valuation is very important; fallacies in the assumptions and logic used in these applications can have serious monetary consequences. Insights into the cyclical nature of the process being modeled and factors that might influence the data being modeled are all things that should not be ignored in financial markets. This is why the use of Bayesian statistics is on the rise within financial forecasting.

 

For the non-statistician, frequentist statistics defines the scope of their statistical knowledge; they are familiar with normal distributions, bell curves, and established probabilities. Unlike Bayesian statistics, frequentist statistics’ gives little attention to prior knowledge of the process being modeled. Disregard of prior knowledge of the process being modeled is especially deleterious in the world of finance. For example, securities are connected to world events; world events do repeat themselves and the likelihood to repeat can be studied. If a negative world event is on the horizon, this should be factored into the statistical model. Bayesian statistics addresses this important information through the prior and posterior distribution. I am looking forward to studying Bayesian statistics and seeing how it can be applied in finance and insurance to improve the accuracy of the estimating process.

 

The statistics program at the University of Iowa has everything I need to achieve a thorough understanding of the fundamentals of statistics. Considering the quality of your statistics program—as indicated by your national ranking—and the strength of your actuarial science program—as demonstrated by your CAE designation by the SOA—I feel confident in your statistics’ program ability to prepare me for the actuarial exams and a career in the actuarial sciences or statistics.

 

 

MBS 7107

Edited January 1, 2013 by CHOCTICK

[CHOCTICK]

Does the lack of posts mean my SOP is good as is? Please review. I need to submit my applications by Friday (1/4/13)

[ttgrad]

The ASD method multiplies all load effects—e.g. snow, wind, cars—by the same amplification factor to account for uncertainty in estimating how much weight a structural member must support. The LRFD method takes into account the certainty with which we can estimate certain loads; the weight of a piece of machinery can be estimated with more certainty than the load effects from a group of workers occupying a space for a Christmas party. Probabilistic models were used to come up with larger amplification factors for load magnitudes with high uncertainty and smaller amplification factors for load magnitudes with low uncertainty. Where the ASD method says you would need a column that can support 1500 pounds, the LRFD method would more accurately require a column that can support 1000 pounds. Less required strength means a smaller I beam could be used which results in money saved in steel.

 

The LRFD method has made the design process more efficient; money that would have needlessly been spent on infrastructure—due to overdesign of structural members—now can be allocated for business and economy growing pursuits. The rise in popularity of the LRFD method is a testament to this fact.

 

Hey,

 

It's not bad. I do find that the above quoted area could be reconsidered a bit. You seem to be describing the method, which is a bit redundant given the audience (professors who will be well verse in the method I assume) You can be a bit more personal and provide greater context as to what made you interested in it. 

 

Just my 2 cents.

 

All the best with your application.

[biotechie]

Are they expecting your Statement of Purpose to be specifically limited to statistics? It would be helpful to know what is in the prompt provided to you before you wrote the essay. If you are not required to stick to strictly statistics, my questions are:

We can see a little passion in the essay, but what makes you so passionate?

Can we have a little dose of you in the essay? Where is the human behind the paper?

 

What makes you stand out? You don't have to tell them "I stand out, because..." Include an anecdote that pushes them to see what a complex and amazing person you can be and how that will separate you from the other students who have written essays similar to yours. Obviously science for be is different than for you, but I was able to show I was passionate and what I wanted to work on through a story that personally affected me. You could potentially include portrayal of your field in the public, etc.

[selecttext]

"I am pursuing ... in"

 

change this to "I am interested in..." (...) "I would like to study..." (...) "Graduate studies at...would help me accomplish this by..."

 

Be specific - you won't be held to your SOP but it will show the committee and your potential supervisor that you have direction and vision.

[SeriousSillyPutty]

first off: (A big pet peeve of mine, so forgive me for being over dramatic)

STOP APOSTROPHE ABUSE. Let's review the examples:

 

(1)I am pursuing graduate studies in applied statistics’

...

 

(2)Statistics’ use in predicting and modeling are the most attractive to me.

...

 

 (3) Actuarial sciences’ use in fields other than insurance is appealing to me.

...

(4)Unlike Bayesian statistics, frequentist statistics’ gives

...

(5)I feel confident in your statistics’ program ability to prepare me for the actuarial exams and a career in the actuarial sciences or statistics.

 

Apostrophes are used for contractions or to show possession. That's it. The Oatmeal does a great explanation of this: http://theoatmeal.com/comics/apostrophe .

If you're not sure, try replacing the word you're thinking of that ends in an S (in this case, "statistics") with a plural word that doesn't end in an S (such as "men") and see if you need to add an "apostrophe s" to it.

In sentence (1), you would say, "I am pursuing graduate studies in men" not "I am pursuing graduate studies in men's", so NO APOSTROPHE.

In sentences (2) and (3), "Men's use in..." actually makes sense. The use belongs to the men (or to the statistics) so you can keep the apostrophe. (That being said, this wording is a little awkward, so if you have time you might want to reword it anyway.)

In sentence (4), you wouldn't say "men's give" or even "man's give" so NO APOSTROPHE.

In sentence (5), if you're talking about the ability that belongs to to the whole statics program, then the apostrophe needs to go with program: "I feel confident in your statistics program's ability to...".

In more opinionated comments:

- Cut the detailed explanation of LRFD. It was a really good explanation -- I feel like I understand what the difference is and why it would be valuable, even though I don't know engineering or stats -- but a persona statement is the place for it. If they know about it, they don't need the explanation; if they don't know about it, they don't care about the details. I would shorten it to something like:

"Statistics’ use in predicting and modeling are the most attractive to me. The ability to model phenomenon and forecast the future with these models has wide and practical application. Statistics also has the power to make processes more efficient. Having studied structural engineering, I have seen the application of statistics first hand. The Load and Resistance Factor design(LRFD) process has its roots in statistics. Prior to the LRFD method, engineers used the Allowable Strength Design (ASD) method, which multiplies all load effects by the same amplification factor to account for uncertainty in estimating how much weight a structural member must support. The LRFD method takes into account the certainty with which we can estimate certain loads. The LRFD method has made the design process more efficient; money that would have needlessly been spent on infrastructure—due to overdesign of structural members—now can be allocated for business and economy growing pursuits. The rise in popularity of the LRFD method is a testament to this fact."

... or maybe even shorter.

This will give you more space to talk about you, as others have suggested.

Oh, and personally, I prefer the sound of "after graduating, I plan to", rather than "after graduating, I will". Saying "I will" makes you sound like you are overly confident about predicting the future, and assumes you will get into the program. But nothing is certain in statistics, right? ;-)

Good luck!

[CHOCTICK]

Thanks for all of the feedback guys. The apostrophe abuse was very helpful. I cannot believe I made so many mistakes. I am applying to a MS Statistics: Actuarial Sciences concentration program.

 

The SOP prompt follows:

 

 

Your statement should include your purpose in pursuing graduate study, any research you wish to pursue, and your future career goals. Write your statement on a separate sheet and submit it directly to your department.