Statistics is called “science of data”, or even “art of data”. Since, a good
judgment a good sense and mathematics are all prerequisite for a good statistician; I
am such a person, with high sensitivity to number, good ability of analysis, logical
thinking, solid foundations of mathematics and high passion in statistics.
I am always on top in mathematics and programming courses from high school
to college. When I was in high school, I could always put forward many new
solutions to difficult problems and almost a straight-A student in all math and physics
courses. In college, I succeeded in courses of Mathematics Analysis, Linear Algebra,
Statistics and Probability Theory and nearly got full marks of most of them. In
Mathematics Analysis class, I used Nested Intervals Theory to prove the Weierstrass
Theory by dividing the bounded set into half unlimitedly. I am really good at
univariate calculus, multivariable calculus and matrix algebra, which serves as the
base of further study in statistics. In addition, my IT background enables me familiar
with many computer technologies, especially programming and modeling which
greatly facilities statistical computing. I ranked top 5 in such courses as Data
Structure and Java Programming and accumulated much experience in
Object-Oriented Programming Language through several projects as well.
With solid mathematical and computing ability, it is the strong desire that
intrigues me to explore statistics in my future studies. I believe it is my hobby of
reading that brings the book The Lady Tasting Tea to me, as well as deep addiction to
statistics. In the book, a fabulous statistics revolution, more and more precise
scientific experiments, understanding of variance, and discovering of different facets
of data bring me into the beautiful world full of meaningful data. Within following
three days, I finished two books-- Statistics Concepts and Controversies and Basic
Econometrics. Through self-study, I got a grip on the basic technologies of statistics,
from sample survey and completely randomized design in data collecting to
confidence interval and significance level in statistical inference. I also read several
books about various regression models, parameter estimation methods and paid much
attention on Logistic regression model—a quite prevalent model in many areas,
including transportation, energy, housing, and marketing – to name only a few. At the
same time, I audited the course Discrete Choice Modeling, supplied for only statistical
graduate students. Through the course, I learned much statistical modeling knowledge
and several basic tools
With my solid mathematics foundation and strong interest in statistics, I began to
do some research in it. Currently, I participate in a multivariate analysis research
project. This project is conducted by Prof. Lo from Dept. Statistics of City University
of Hong Kong. He guides me to read books like An Introduction to Multivariate
Statistical Analysis, Discrete Choice Methods with Simulation and Markov Chain
Monte Carlo, as well as the paper — Modeling Consumer Demand for Variety. Faced
a situation where respondents could perform several activities of different duration in
the morning, I built a multinomial logit model. Based on the model, I used a
log-normal error term in the utility function and derived the Kuhn-Tucker first-order
conditions for the optimal demands that are used to formulate the likelihood function
for the parameters. Then I used GHK simulator and Metropolis- Hastings algorithm to
obtain simulated probabilities and used the Maximum Simulated Likelihood to
estimate the parameters. The research is helpful in urban traffic planning and I shall
continue it in the coming semester.
Besides, many other courses I took and projects I participated help me a lot in the
way to being an excellent statistician. For instance, in Management Economics course,
I used regression analysis to predict short-term and long-term cost functions in an
enterprise; and used Coefficient of Determination to decide which independent
variable can explain the change of dependent variable in regression model; and used
the sale record to predict the future need based on the analysis of time series. Also in
one project studying people’s activities and motives in Yahoo! Answers, I used SPSS
to make Kolmogro-Smirnov test, compared number of answers in different categories
to see whether they have statistically significant difference and took nonparametric
linkage analysis to judge if the number of answers and asker’s rate is correlated.
Besides my academic ability, my effective communication skills and
responsibility sense suggest my success in business. I was a department leader in
Computer Association and Youth Volunteer Association, communicating with people
from other 13 departments and collecting their information and needs every week.
And once, within just one night, I led a team to made 15 pieces of 2 by 2cm manual
drawing presentation board for an exhibition in our university which displayed the
Chinese traditional culture and attracted thousands of people to visit. I also spent one
day shooting the whole process of blood donation in the Beijing Red Cross Center,
interviewing the staff there and then made 30 minutes video to publicize it and attract
people to participate in blood donation. In 2008 summer, with great honor I became a
volunteer in 2008 Olympic Games. During 25 days work, I helped thousands of
people from more than 200 countries to handle the problem with their Accreditation
Passes to Olympic Village and received lots of praise.
Though future is unknown, statistics can give truthful indications of the way
things will be. Though data are meaningless and bored to others, I enjoy interpreting
the numerical results in meaningful terms. However, becoming a statistician is not my
final career goal. I would rather adapt the statistical methods to solve specific
problems in many fields, such as economics and marketing. With my background of
interdiscipline combining Management Information Science and Statistics, I
believe I can do a better job in data collecting (using IT technology), organizing
(using Database technology) and processing (using Statistical technology) than others
to solve practical problems like long-range industry forecasts, reproduction levels of
certain organisms or quantities of steel needed for a given project.